From 7fe34bbd0d675e3abd2df0c4587ef13e1babc3a8 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 18:12:41 -0400 Subject: [PATCH 01/75] Clear the tree for the 1.0 rewrite Remove the 0.x tests, notebooks, in-package samplers, drivers and the declaration classes. The eight modules the rewrite harvests stay in place for one commit so that git mv can carry their history into the new layout. The 0.x package remains at v0.1.0 and on legacy/0.x. --- docs/api.rst | 197 - docs/bugs_found.md | 209 - docs/design.md | 245 - docs/examples | 1 - docs/examples.rst | 43 - .../30s_optical_potential_calibration.ipynb | 497 -- .../calibration_config_emcee_dynesty.ipynb | 661 -- examples/correlated_observations.ipynb | 785 -- examples/gp_discrepancy.ipynb | 972 --- examples/linear_calibration_demo.ipynb | 1851 ----- examples/measurement_to_calibration.ipynb | 618 -- examples/normalization_inference.ipynb | 2196 ------ examples/overconfidence.ipynb | 1489 ---- examples/robust_likelihoods.ipynb | 481 -- examples/sampling_algos.ipynb | 891 --- examples/systematic_err_demo.ipynb | 6398 ----------------- src/rxmc/adaptive_metropolis.py | 109 - src/rxmc/config.py | 581 -- src/rxmc/constraint.py | 483 -- src/rxmc/elastic_diffxs_model.py | 233 - src/rxmc/evidence.py | 173 - src/rxmc/ias_pn_model.py | 177 - src/rxmc/metropolis_hastings.py | 73 - src/rxmc/observation.py | 354 - src/rxmc/param_sampling.py | 407 -- src/rxmc/params.py | 66 - src/rxmc/physical_model.py | 153 - src/rxmc/priors.py | 304 - src/rxmc/proposal.py | 93 - src/rxmc/walker.py | 272 - test/conftest.py | 6 - test/helpers.py | 22 - test/test_config.py | 446 -- test/test_constraint.py | 720 -- test/test_covariance.py | 812 --- test/test_evidence.py | 135 - test/test_likelihood_model.py | 171 - test/test_model_comparison.py | 213 - test/test_observation.py | 293 - test/test_params.py | 52 - test/test_predictive.py | 200 - test/test_priors.py | 179 - test/test_proposal.py | 64 - test/test_reaction_models.py | 158 - test/test_reaction_observation.py | 422 -- test/test_regression.py | 141 - test/test_sampler.py | 409 -- test/test_transforms.py | 128 - 48 files changed, 25583 deletions(-) delete mode 100644 docs/api.rst delete mode 100644 docs/bugs_found.md delete mode 100644 docs/design.md delete mode 120000 docs/examples delete mode 100644 docs/examples.rst delete mode 100644 examples/30s_optical_potential_calibration.ipynb delete mode 100644 examples/calibration_config_emcee_dynesty.ipynb delete mode 100644 examples/correlated_observations.ipynb delete mode 100644 examples/gp_discrepancy.ipynb delete mode 100644 examples/linear_calibration_demo.ipynb delete mode 100644 examples/measurement_to_calibration.ipynb delete mode 100644 examples/normalization_inference.ipynb delete mode 100644 examples/overconfidence.ipynb delete mode 100644 examples/robust_likelihoods.ipynb delete mode 100644 examples/sampling_algos.ipynb delete mode 100644 examples/systematic_err_demo.ipynb delete mode 100644 src/rxmc/adaptive_metropolis.py delete mode 100644 src/rxmc/config.py delete mode 100644 src/rxmc/constraint.py delete mode 100644 src/rxmc/elastic_diffxs_model.py delete mode 100644 src/rxmc/evidence.py delete mode 100644 src/rxmc/ias_pn_model.py delete mode 100644 src/rxmc/metropolis_hastings.py delete mode 100644 src/rxmc/observation.py delete mode 100644 src/rxmc/param_sampling.py delete mode 100644 src/rxmc/params.py delete mode 100644 src/rxmc/physical_model.py delete mode 100644 src/rxmc/priors.py delete mode 100644 src/rxmc/proposal.py delete mode 100644 src/rxmc/walker.py delete mode 100644 test/conftest.py delete mode 100644 test/helpers.py delete mode 100644 test/test_config.py delete mode 100644 test/test_constraint.py delete mode 100644 test/test_covariance.py delete mode 100644 test/test_evidence.py delete mode 100644 test/test_likelihood_model.py delete mode 100644 test/test_model_comparison.py delete mode 100644 test/test_observation.py delete mode 100644 test/test_params.py delete mode 100644 test/test_predictive.py delete mode 100644 test/test_priors.py delete mode 100644 test/test_proposal.py delete mode 100644 test/test_reaction_models.py delete mode 100644 test/test_reaction_observation.py delete mode 100644 test/test_regression.py delete mode 100644 test/test_sampler.py delete mode 100644 test/test_transforms.py diff --git a/docs/api.rst b/docs/api.rst deleted file mode 100644 index c1d869c..0000000 --- a/docs/api.rst +++ /dev/null @@ -1,197 +0,0 @@ -API Reference -============= - -Configuration -------------- - -High-level configuration objects for assembling a calibration problem and -handing it to an external sampler (emcee, dynesty, etc.). - -.. autosummary:: - :toctree: generated/ - :nosignatures: - - rxmc.config.CalibrationConfig - rxmc.config.ParameterConfig - -Priors ------- - -Prior distribution classes that satisfy the generic prior protocol required by -:class:`~rxmc.config.ParameterConfig`. Any user-defined class with ``logpdf``, -``rvs``, and (optionally) ``prior_transform`` methods can be used directly. - -.. autosummary:: - :toctree: generated/ - :nosignatures: - - rxmc.priors.IndependentPrior - rxmc.priors.TruncatedNormalPrior - -Core building blocks --------------------- - -.. autosummary:: - :toctree: generated/ - :nosignatures: - - rxmc.constraint.Constraint - rxmc.evidence.Evidence - rxmc.observation.Observation - rxmc.params.Parameter - rxmc.physical_model.PhysicalModel - rxmc.physical_model.Polynomial - -Transforms ----------- - -One low-level, numpy-style transform type shared by observations (the -comparison space, e.g. ``transform=log``), models (parametric transforms such -as a latent normalisation) and covariance terms (coordinate transforms). - -.. autosummary:: - :toctree: generated/ - :nosignatures: - - rxmc.transforms.Transform - rxmc.transforms.as_transform - rxmc.transforms.identity - rxmc.transforms.log - rxmc.transforms.exp - rxmc.transforms.scale - rxmc.transforms.per_observation_scaling - -Covariance terms ----------------- - -The stacked covariance of a :class:`~rxmc.constraint.Constraint` is assembled -additively from :class:`~rxmc.covariance.Term` objects — a single generic type: -a numpy-style callable of a :class:`~rxmc.covariance.TermContext` (the term's -local ``x``/``y``/``ym``) and its parameters, plus a ``kind`` -(``"diag"``/``"mode"``/``"matrix"``). The factory helpers build the common -terms in one line; anything else is a direct ``Term(fn, params, kind=...)``. -A :class:`~rxmc.covariance.StackContext` bundles the stacked ``x``/``y``/``ym`` -that a :class:`~rxmc.covariance.ConstraintCovariance` is evaluated on. - -.. autosummary:: - :toctree: generated/ - :nosignatures: - - rxmc.covariance.Term - rxmc.covariance.TermContext - rxmc.covariance.statistical_term - rxmc.covariance.normalization_term - rxmc.covariance.offset_term - rxmc.covariance.noise_term - rxmc.covariance.noise_fraction_term - rxmc.covariance.model_error_term - rxmc.covariance.systematic_term - rxmc.covariance.kernel_term - rxmc.covariance.ones - rxmc.covariance.ym - rxmc.covariance.averaging - rxmc.covariance.x_basis - rxmc.covariance.exp_growth - rxmc.covariance.constant_amplitude - rxmc.covariance.exp_growth_amplitude - rxmc.covariance.stacked_supports - rxmc.covariance.ConstraintCovariance - rxmc.covariance.StackContext - -Likelihood functionals ----------------------- - -Thin functionals of the pre-computed Mahalanobis statistics -``(d2, logdet, n)``; all covariance modeling lives on the -:class:`~rxmc.covariance.ConstraintCovariance`. - -.. autosummary:: - :toctree: generated/ - :nosignatures: - - rxmc.likelihood_model.Likelihood - rxmc.likelihood_model.GaussianLikelihood - rxmc.likelihood_model.StudentT - rxmc.likelihood_model.Chi2 - rxmc.likelihood_model.mahalanobis_distance_sqr_cholesky - rxmc.likelihood_model.log_likelihood - -Predictive utilities --------------------- - -Posterior-predictive helpers, including Gaussian-process discrepancy -propagation. - -.. autosummary:: - :toctree: generated/ - :nosignatures: - - rxmc.predictive.predictive_band - rxmc.predictive.gp_posterior_predictive - rxmc.predictive.total_predictive_band - -Model comparison ----------------- - -Sampler-agnostic posterior-predictive checks, held-out scoring, and -nested-sampling evidence bookkeeping. - -.. autosummary:: - :toctree: generated/ - :nosignatures: - - rxmc.model_comparison.predictive_draws - rxmc.model_comparison.coverage_curve - rxmc.model_comparison.coverage_error - rxmc.model_comparison.sharpness - rxmc.model_comparison.heldout_log_predictive - rxmc.model_comparison.log_posterior_predictive - rxmc.model_comparison.logz_summary - rxmc.model_comparison.compare_logz - rxmc.model_comparison.log_jacobian - rxmc.model_comparison.split_samples - -Sampling --------- - -.. autosummary:: - :toctree: generated/ - :nosignatures: - - rxmc.walker.Walker - rxmc.param_sampling.Sampler - rxmc.param_sampling.MetropolisHastingsSampler - rxmc.param_sampling.AdaptiveMetropolisSampler - rxmc.param_sampling.BatchedAdaptiveMetropolisSampler - rxmc.proposal.ProposalDistribution - rxmc.proposal.NormalProposalDistribution - rxmc.proposal.HalfNormalProposalDistribution - rxmc.proposal.LogspaceNormalProposalDistribution - -Sampling algorithms -------------------- - -Low-level sampling functions used internally by the sampler classes. - -.. autosummary:: - :toctree: generated/ - :nosignatures: - - rxmc.metropolis_hastings.metropolis_hastings - rxmc.adaptive_metropolis.adaptive_metropolis - -Domain-specific models ----------------------- - -Reaction-physics observation and model classes for elastic differential -cross sections and isobaric-analog (p,n) cross sections. - -.. autosummary:: - :toctree: generated/ - :nosignatures: - - rxmc.elastic_diffxs_observation.ElasticDifferentialXSObservation - rxmc.elastic_diffxs_observation.momentum_transfer - rxmc.elastic_diffxs_model.ElasticDifferentialXSModel - rxmc.ias_pn_observation.IsobaricAnalogPNObservation - rxmc.ias_pn_model.IsobaricAnalogPNXSModel diff --git a/docs/bugs_found.md b/docs/bugs_found.md deleted file mode 100644 index f9949fb..0000000 --- a/docs/bugs_found.md +++ /dev/null @@ -1,209 +0,0 @@ -# Bugs and inconsistencies found during the architecture review - -Found while reading the `api_generalisation` branch (head `b8bc7d8`) for the -ground-up design comparison in `groundup_design.md`. Every item was verified -by reading the code at the cited lines (line numbers refer to `b8bc7d8`). -Items are grouped by how sure I am that they are wrong rather than merely -fragile. - -**Status:** every item except 11 is fixed on this branch; each carries a -**Resolution** line. Item 11 is a design-level change and is deferred to -`groundup_design.md`. - -## Confirmed bugs - -### 1. `IsobaricAnalogPNObservation` silently drops two solver arguments - -- **Where:** `src/rxmc/ias_pn_observation.py:52-53` (constructor signature) - and `:113-120` (the `set_up_solver` call). -- **What:** the constructor accepts `wavelengths_beyond_range` and - `zeros_per_node`, documents them (`:92-94`), and `set_up_solver` takes them - (`:209-210`), but the call inside `__init__` never forwards them. The - defaults are always used. The elastic observation forwards both - (`src/rxmc/elastic_diffxs_observation.py:149-150`). -- **Why it matters:** a user tuning the Lagrange basis size for the (p,n) IAS - channel gets no effect and no error. -- **Fix:** add the two keyword arguments to the `set_up_solver` call. -- **Resolution:** forwarded; `test_reaction_observation.py::TestSolverSettingsForwarding` - asserts the kwargs reach `set_up_solver` for both reaction observations. - -### 2. `BatchedAdaptiveMetropolisSampler` adapts its proposal during burn-in - -- **Where:** `src/rxmc/param_sampling.py:376-388`. -- **What:** the class docstring (`:304`), the `sample` docstring (`:354`) and - the `burn` parameter docstring (`:366-368`) all say the proposal covariance - is replaced only after *non-burn* batches. The code recomputes - `self.proposal_cov` and `self.args` unconditionally; only `record_batch` is - guarded by `if not burn`. -- **Why it matters:** burn-in batches feed the adaptation, so the behaviour - differs from what the docstring promises and from `AdaptiveMetropolisSampler`. - Either the doc or the code is wrong. Adapting during burn-in is arguably - the *better* behaviour, so the likely fix is to the docstrings. -- **Fix:** decide, then make the docstrings and the `if not burn:` guard agree. -- **Resolution:** kept the code behaviour (adapt after every batch, burn-in - included) and fixed the three docstrings. `sample` now also refreshes - `self.proposal` so it never goes stale; pinned by - `test_sampler.py::TestSamplerPriors::test_batched_adaptive_updates_proposal_after_burn_batch`. - -### 3. `Parameter` defines `__eq__` without `__hash__` - -- **Where:** `src/rxmc/params.py:39-48`. -- **What:** defining `__eq__` sets `__hash__ = None`, so `Parameter` objects - are unhashable. Every uniqueness check in the package keys on `id(p)` or - `p.name` for this reason (`covariance.py`, `constraint.py`, `evidence.py`), - and nothing documents it. -- **Why it matters:** `set(model.params)` or `{p: value}` raises `TypeError` - at runtime. It is a trap for anyone extending the package, and it is the - root reason the by-identity routing needs `id()` bookkeeping. -- **Fix:** either drop `__eq__` (identity is the sharing semantics anyway) or - add `__hash__ = object.__hash__`. See `groundup_design.md` §2.1. -- **Resolution:** value-based `__hash__` consistent with the existing value - `__eq__`; `bounds` is coerced to a 2-tuple of floats so the hash is stable; - `__repr__` added. Identity routing in `covariance.py` / `evidence.py` is - untouched. New `test/test_params.py`. - -### 4. Two independent pint `UnitRegistry` instances - -- **Where:** `src/rxmc/elastic_diffxs_observation.py:24` and - `src/rxmc/ias_pn_observation.py:12`. -- **What:** each module builds its own registry. pint refuses to combine - quantities from different registries. -- **Why it matters:** latent today because no code path mixes the two, but any - helper that takes a quantity from one module into the other will raise. - `DEFAULT_LMAX = 20` is likewise duplicated (`:27` and `:14`). -- **Fix:** one `ureg` in a shared module (`observation_from_measurement.py` - is the natural home; its docstring already says it holds what the two share). -- **Resolution:** `ureg`, `DEFAULT_LMAX`, `XS_UNIT`, `RUTHERFORD_UNIT` and - `MB_PER_B` live in `observation_from_measurement.py` (now exported from - `rxmc`); both observation modules import and re-export them. - `test_reaction_observation.py::TestSharedUnits`. - -## Inconsistencies between the two sampler front ends - -`CalibrationConfig` and `Walker` are meant to be two drivers over the same -posterior. They are not. - -### 5. `Walker.log_posterior` evaluates the likelihood when the prior is `-inf` - -- **Where:** `src/rxmc/walker.py:140-143` versus - `src/rxmc/config.py:405-411`. -- **What:** the config path short-circuits on a non-finite prior and skips - the forward model. The walker path always calls `Evidence.log_likelihood` - first. The Gibbs conditional has the same split: - `config.conditional_posterior` (`config.py:579-583`) short-circuits, - the inline closure at `walker.py:130-132` does not. -- **Why it matters:** out-of-bounds proposals cost a full reaction-model - solve in the walker. With bounds also enforced inside the kernels this is - a performance bug, not a correctness bug, but it is a silent divergence. -- **Resolution:** `Walker.log_posterior` and the Gibbs closure evaluate the - prior first and return `-inf` without touching the likelihood. - `test_sampler.py::TestWalkerPosterior::test_*_skips_likelihood_when_prior_neg_inf`. - -### 6. Tempering exists only on the config path - -- **Where:** `config.py:245-252, 384, 583`; no counterpart in `walker.py`. -- **What:** `likelihood_scaling` scales the likelihood (and the Gibbs - conditionals) in `CalibrationConfig`. `Walker` has no such knob; the only - way to temper is `Evidence(weights=...)`. `examples/overconfidence.ipynb` - demonstrates both and prints a check that they agree. -- **Why it matters:** two names for one concept, with one of them reachable - from only one driver. -- **Resolution:** `Walker(..., likelihood_scaling=)` added with the same - semantics as the config (scales the likelihood in the model block and the - Gibbs conditionals, never the prior). - `test_sampler.py::TestWalkerPosterior::test_*_applies_likelihood_scaling*`. - -### 7. List-of-scipy priors are accepted by one driver and rejected by the other - -- **Where:** `config.py:133-135, 153-155, 184-185` (list branches in - `ParameterConfig`) versus `param_sampling.py:66-70` (`_validate_object` - requires `prior.logpdf`) and `walker.py:131, 160` (calls - `sampler.prior.logpdf` directly). -- **What:** `ParameterConfig` special-cases a plain list of frozen scipy - distributions in three places. A `Sampler` built with the same list fails - at construction because a list has no `logpdf`. -- **Why it matters:** the prior protocol is documented as one thing and - implemented as two. `IndependentPrior` already exists to wrap a list; - `ParameterConfig` could wrap in `__init__` and delete all three branches. -- **Resolution:** `rxmc.priors.as_prior` wraps a list/tuple in - `IndependentPrior`; both `ParameterConfig.__init__` and `Sampler.__init__` - call it, and the four list branches in `ParameterConfig` are gone. - Behaviour change: `x0` for a list prior is now seeded (`IndependentPrior` - default seed) instead of drawing from numpy's global state, and - `config.prior` returns the wrapper. Tests in `test_config.py`, - `test_sampler.py`, `test_priors.py`. - -### 8. `ParameterConfig._infer_dim` misreads priors whose `mean` is a method - -- **Where:** `src/rxmc/config.py:92-98`. -- **What:** if the prior has no integer `dim`, the fallback is - `int(np.size(dist.mean))`. For any object whose `mean` is a *method* this - is `1`, regardless of the true dimension. -- **Why it matters:** a custom multi-dimensional prior exposing `mean()` is - reported as one-dimensional and rejected at `config.py:109-113` with a - misleading message. Frozen scipy multivariate distributions happen to - expose `mean` as an array, which is why the tests pass. -- **Fix:** call `mean` if callable, or require `dim` and drop the guess. -- **Resolution:** an integer `dim` wins; otherwise `mean` is called when it - is a method. `test_config.py::test_infer_dim_calls_mean_method`. - -## Fragile, not wrong - -These are not bugs today but each is one refactor away from becoming one. - -### 9. Unit conventions split across model and observation with no shared constant - -- `elastic_diffxs_model.py:166` and `ias_pn_model.py:124, 170` divide by a - bare `1000` (mb/sr to b/sr). The matching assumption lives in the - observation as `ureg.millibarn / ureg.steradian` - (`elastic_diffxs_observation.py:204`). Nothing ties them together. -- **Resolution:** both models divide by `MB_PER_B`, derived from the shared - registry next to `XS_UNIT` / `RUTHERFORD_UNIT`, which the observations now - use. `test_reaction_observation.py::TestSharedUnits::test_unit_constants_agree`. - -### 10. Model and observation compatibility is checked by string, or not at all - -- `elastic_diffxs_model.py:137, 174` compare `observation.quantity` to - `self.quantity`. `ias_pn_model.py:135, 159` reach straight for - `observation.constraint_workspace` with no check. Pairing an elastic model - with an IAS observation fails inside jitr with a shape error. -- **Resolution:** each model checks `isinstance` against its observation class - first in `evaluate` and `visualizable_model_prediction` and raises a named - `ValueError` (a string check cannot work: both observations report - `quantity == "dXS/dA"`). `test_reaction_models.py::TestObservationTypeChecks`. - -### 11. Masked views share solver workspaces by reference, routed by `id()` - -- `observation.py:204` uses `copy.copy`, so a masked view of a reaction - observation shares both jitr workspaces and every array with its root. - `transforms.py:281, 301` route `per_observation_scaling` by - `id(obs.identity)`, and `test_holds_observation_references` exists only to - stop id recycling. Any deep copy, pickle, or reconstruction of an - observation breaks the routing with a `KeyError` at evaluation time. -- **Deferred:** design-level; see `groundup_design.md` §2.3–2.4 (bind-time - predictors, blocks without identity keys). - -### 12. The burn-in loop in `Walker.walk` duplicates the main loop - -- `walker.py:207-221` versus `:229-241`: identical bodies apart from - `burn=True` and the progress string. Any change to one must be mirrored. -- **Resolution:** one `_run_batch(steps, burn)` sweep plus `_batch_message`; - the burn-in line prints no acceptance fraction because nothing is recorded - during burn-in. `test_sampler.py::TestWalkerPosterior::test_burn_message_has_no_acceptance_fraction`. - -### 13. `prior_transform` clips the unit cube only at the top level - -- `config.py:492-506` clips `u` to `[eps, 1-eps]`; `ParameterConfig.prior_transform` - and `IndependentPrior.prior_transform` (`priors.py:273-277`) do not. - Calling either directly with an exact `0.0` or `1.0` returns `±inf`. - (`TruncatedNormalPrior` is finite at the boundary by construction.) -- **Resolution:** `rxmc.priors.clip_unit_cube` is applied in all four - transforms. `test_priors.py::TestUnitCubeClipping`, - `test_config.py::test_prior_transform_boundary_finite`. - -## Already fixed on this branch - -- The `_rows` row-count check in `model_comparison.py` was reported by an - earlier read as unable to fire. At `b8bc7d8` it is called without `n` for - the model samples and with `n` for the covariance samples (`:138, 143`), - which is correct. diff --git a/docs/design.md b/docs/design.md deleted file mode 100644 index fe892ce..0000000 --- a/docs/design.md +++ /dev/null @@ -1,245 +0,0 @@ -# Design: the stacked covariance model - -This page records the architecture of `rxmc`'s covariance layer — the design -that replaced the pre-0.1 "likelihood model zoo" — and the decisions locked in -during that refactor. - -## The two mechanisms - -Two different things hide under "share a covariance," and they live on -different axes: - -- **(A) Correlating observations** is a statement about **covariance - structure** — off-diagonal blocks coupling observation *i* and *j*. -- **(B) Two covariance terms sharing a parameter** is a statement about - **parameter wiring** — the observations stay independent; one θ component - feeds two different terms. - -**A couples the data; B couples the parameters.** They get distinct -mechanisms: - -- **A — the covariance owns the stacked block.** A - {class}`~rxmc.constraint.Constraint` is the maximal block of - mutually-correlated data: it owns one multivariate likelihood over the - stacked vector `y = [y1; y2; ...]` of its observations. A *coupling* term is - simply one whose `support` spans more than one observation block. -- **B — parameter routing by identity.** - {class}`~rxmc.covariance.ConstraintCovariance` deduplicates the - {class}`~rxmc.params.Parameter` objects its terms reference **by identity** - (gather, not slice): referencing the *same* object in two terms yields one - entry in the sampled vector, gathered into both. - -The normalization example shows why both are needed: two datasets with -*independent* flux measurements of the same *magnitude* are case B (two -block-local `normalization_term`s sharing one `Parameter`); two datasets -normalized by the *same* uncertain flux are case A (one `normalization_term` -whose support spans both blocks). This is the D'Agostini / Barlow -correlated-systematics distinction. - -## Terms and the assembled covariance - -There is exactly one term type. A {class}`~rxmc.covariance.Term` is a -numpy-style callable `fn(c, *values) -> array` of a -{class}`~rxmc.covariance.TermContext` `c` — the term's local view of the -stacked `x`, `y` and `ym` on its `support` — and the sampled values of the -`Parameter`s it declares, plus a `kind` saying how the array enters the -covariance: - -| `kind` | `fn` returns | contribution | -|------------|-------------------------------|---------------------------------| -| `"diag"` | standard-deviation vector `v` | `Σ_ii += v_i²` | -| `"mode"` | mode vector `v` | `Σ += v vᵀ` (one correlated mode) | -| `"matrix"` | symmetric block `M` | `Σ_block += M` | - -A plain array instead of `fn` is a fixed contribution (factored once and -cached). `support=None` (the default) means *the whole constraint* and is -bound when the term is added to a `ConstraintCovariance`; an explicit support -places a term on a subset of a multi-observation constraint. A `coords` -transform (see below) is applied to `x[support]` before `fn` sees it, so a -kernel can live in momentum transfer rather than angle without the term -knowing. - -The factory helpers are one-line conveniences over this single type: -`statistical_term`, `normalization_term`, `offset_term`, `noise_term`, -`noise_fraction_term`, `model_error_term`, `systematic_term` (a mode with a -user basis), and `kernel_term` (sklearn kernels; one parameter per free -hyperparameter *element*, plus an optional parametric `amplitude` so that -`Σ += a aᵀ ∘ K`). Bases are ordinary callables of the `TermContext` -(`ones`, `ym`, `averaging`, `x_basis(scale)`, or parametric ones like -`exp_growth(scale)` whose extra parameters are passed as `basis_params`). -Anything the helpers cannot say is a direct `Term`: - -```python -# noise growing with angle: sigma(theta) = eps * exp(l * theta / pi) -Term(lambda c, e, l: np.exp(e) * np.exp(l * c.x / np.pi), (log_eps, slope), kind="diag") -# the same thing through the helper -noise_term(log_eps, basis=exp_growth(np.pi), basis_params=(slope,)) -``` - -{class}`~rxmc.covariance.ConstraintCovariance` assembles the terms. It is -constructed with the true observation block boundaries -(`blocks=stacked_supports(observations)`), from which two structural facts are -decided **once, conservatively**: - -- `block_diagonal` — true only if every off-diagonal-capable term - (`couples_offdiagonal`, i.e. `kind != "diag"`) provably sits inside a single - block. With no blocks supplied, any coupling-capable term forces the dense - path; there is no guessing from support shape. -- `is_constant` — true when no term depends on parameters or context; the - Cholesky factors (dense and per-block) are then computed once and cached - read-only. - -`ConstraintCovariance.stacked_distance(ctx, params)` owns the dispatch between -the block-diagonal fast path (factor each block separately, `O(Σ nᵢ³)`) and a -single dense Cholesky — and is the seam where a future low-rank (Woodbury) -path would slot in. - -## Transforms are one low-level type - -{class}`~rxmc.transforms.Transform` is a numpy-style callable -`fn(a, *values)` with an optional tuple of `Parameter`s, an optional analytic -derivative and inverse, and composition via `|`. Anything callable is accepted -wherever a transform is expected. The same type serves three roles, so there -is no wrapper class per use: - -- **Comparison space, on the observation.** `Observation(x, y, - transform=log)` takes *raw* `y`, stores `y_raw`, `y = log(y_raw)`, and the - delta-method statistical error `|t′(y_raw)|·σ`; the `Constraint` applies the - same transform to the model prediction, so the model is written once, in - physical space, and can never be double-transformed. `obs.log_jacobian` - (`Σ log|t′|`) is the constant needed to compare evidences across comparison - spaces. Parametric transforms are rejected here. -- **Parametric model transforms, on the model.** `PhysicalModel(params, - transform=scale())` appends the transform's parameters to the model's and - applies it after `evaluate` (unlike the former `ScaledModel`, which prepended a - `log normalization` parameter, the scale parameters come *last* and default to - `log_rho` / `log_rho_i`). This is the Kennedy–O'Hagan latent scale ρ (it - changes the *mean*, so it is not a covariance term); - `per_observation_scaling(observations)` gives one ρᵢ per dataset, routed by - observation identity. -- **Coordinates, on a term.** `Term(..., coords=q)` / `kernel_term(kernel, - coords=q)` evaluate the term in transformed coordinates; a parametric - `coords` contributes its parameters to the term. - -## Masks: hold-out as part of support - -Which points *enter* a likelihood is a property of the support machinery, not -of the data: `Observation(..., mask=)` (or `obs.masked(mask)`, -`obs.masked_where(lambda x: x < cut)`) marks points active at the point level -without rebuilding anything — a reaction observation keeps its solver -workspace — and `Constraint(..., mask=)` selects observations at the -constraint level. The two combine into `constraint.active`, the stacked -indices the residual and the factorisation are restricted to; `n_data_pts` is -the active count. Terms are always authored over the full stack, so the same -term list describes the fit and the held-out views: `constraint.complement()` -is the held-out counterpart (every inactive point becomes active), sharing the -`Term`/`Parameter` objects so a posterior sample of the fit scores it directly -(see {mod}`rxmc.model_comparison`). - -## Observations are leaves - -An {class}`~rxmc.observation.Observation` is pure data — `x`, `y`, -`y_stat_err` — plus the measurement's reported systematic magnitudes retained -as **inert metadata** (`y_sys_err_normalization` fractional, -`y_sys_err_offset` absolute in internal units). It emits only its statistical -diagonal automatically. Every correlated mode is an explicit term: -`obs.systematic_terms()` converts the metadata on request (propagated to the -comparison space by the delta method when the observation has a transform), and -**nothing is ever folded into a covariance silently** — a deliberate behavior -change from pre-0.1 versions, pinned by regression tests. - -The reaction observation classes' `from_measurement` constructors keep this -contract across unit conversion: dimensionful errors (statistical, offset) are -divided by the unit normalization (retained as `obs.norm`; a per-angle array -in the Rutherford-conversion cases), the fractional normalization error passes -through untouched. - -## Constraints, likelihood functionals, and parameters - -`Constraint(observations, physical_model, likelihood=GaussianLikelihood(), -extra_terms=(), include_statistical_term=True, mask=None)` builds the stacked covariance -from each observation's statistical term plus the explicit `extra_terms` -(`include_statistical_term=False` composes the entire covariance from -`extra_terms`, e.g. to let a `noise_term` *replace* reported statistics). - -A likelihood ({class}`~rxmc.likelihood_model.Likelihood`: -`GaussianLikelihood`, `StudentT`, `Chi2`) is a thin functional of the -pre-computed `(d2, logdet, n)` statistics. The constraint's parameter vector -is the **full tuple** — covariance parameters followed by likelihood -parameters (e.g. Student-t `nu`) — and every method (`log_likelihood`, `chi2`, -`covariance_matrix`, `marginal_log_likelihood`) takes it in that order, -validating the count. - -Mean renormalization (a Kennedy–O'Hagan latent scale ρ) is **not** a -covariance term: it changes the mean, so it lives on the model side as a -parametric transform (`PhysicalModel(..., transform=rxmc.transforms.scale())` -or `per_observation_scaling(observations)`), flowing through the ordinary -model-parameter machinery. - -## Model comparison lives outside the sampler - -{mod}`rxmc.model_comparison` consumes a constraint plus posterior *samples* and -never touches a sampler: posterior-predictive draws from `N(ym(θ), Σ(θ))` on -the active points (or the model-only predictive), empirical coverage curves and -sharpness, held-out log predictive scores on `constraint.complement()`, and -nested-sampling evidence bookkeeping (`logz_summary` with replicate-based -errors — the sampler's own error is a lower bound — and `compare_logz` with a -conservative tie verdict). `log_jacobian` supplies the comparison-space -constant for comparing evidences of, say, a log-space and a linear-space fit -of the same data. - -## Error-model recipes - -The motivating study — comparing error models for α+Ca elastic scattering data -without reported uncertainties — becomes one term list per model: - -| error model | `extra_terms` | -|----------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------| -| constant noise (log space) | `[noise_term(log_err)]` with `Observation(..., transform=log)` | -| fractional noise (linear space) | `[noise_fraction_term(log_err)]` | -| noise growing with angle | `[noise_term(log_err, basis=exp_growth(np.pi), basis_params=(slope,))]` | -| + rank-one mode ∝ θ | `[..., systematic_term(log_sys, basis=x_basis(np.pi))]` | -| + rank-one offset / normalisation | `[..., offset_term(parameter=log_sys)]` / `[..., normalization_term(parameter=log_sys)]` | -| GP discrepancy in angle, constant amplitude | `[..., kernel_term(Matern(1.0, nu=2.5), coords=lambda x: x/np.pi, amplitude=constant_amplitude, amplitude_params=(log_A,))]` | -| GP with angle-growing amplitude | `[..., kernel_term(..., amplitude=exp_growth_amplitude(1.0), amplitude_params=(log_A, slope))]` | -| GP in momentum transfer with amplitude `A q^{r/2}` | `[..., kernel_term(RBF(1.0), coords=lambda x: 2*k*np.sin(x/2), amplitude=lambda c, lA, r: np.exp(lA)*c.x**(r/2), amplitude_params=(log_A, r))]` | -| heavy tails | any of the above with `likelihood=StudentT()` | - -Fit/held-out splits are `obs.masked_where(lambda x: x < cut)` and -`constraint.complement()`; evidences are compared with -`compare_logz(logz_summary(...), logz_summary(...))` after adding -`log_jacobian` to the log-space fits. - -## Scope decisions (locked) - -- **Constraint = maximal correlated block.** {class}`~rxmc.evidence.Evidence` - stays a weighted sum over *independent* constraints, so factorization cost - is bounded at the block level. -- **Covariance/likelihood parameters are constraint-scoped.** Case-A and - case-B sharing both happen *within* a constraint. Sharing a `Parameter` - object across constraints, or duplicating a parameter name anywhere in an - `Evidence`, is a hard error — the sanctioned model for a systematic shared - between datasets is one constraint with a cross-block coupling term. -- **Tempering is consistent**: `Evidence` weights and - `CalibrationConfig.likelihood_scaling` apply to the likelihood only (never - the prior), including inside the Gibbs conditionals. -- **Fail fast**: constant covariances are factored eagerly at `Constraint` - construction, so a singular covariance (e.g. an EXFOR subentry with no - statistical error and no covering term) raises a named, actionable error - instead of a `LinAlgError` mid-chain. - -## Known limitations - -- **No low-rank fast path yet.** Cross-block couplings are typically low rank, - and the design anticipates a Woodbury / matrix-determinant-lemma update on - top of the block-diagonal base; today they take the dense `O(N³)` path. - `ConstraintCovariance.stacked_distance` is the seam. -- **Non-constant block-diagonal covariances still assemble the dense `N×N` - matrix** before factoring its blocks (per-term `add_to` writes into the full - matrix by design). -- **Masked rows are still assembled.** The full `N×N` covariance is built and - then restricted to the active rows; a held-out view pays for the inactive - rows' terms (cheap next to the forward model, but not free for large dense - kernels). -- Multi-mode systematics on `Observation` are deferred; the factory helpers - accept a `mask=` argument directly for masked (partial-support) terms. diff --git a/docs/examples b/docs/examples deleted file mode 120000 index a6573af..0000000 --- a/docs/examples +++ /dev/null @@ -1 +0,0 @@ -../examples \ No newline at end of file diff --git a/docs/examples.rst b/docs/examples.rst deleted file mode 100644 index abeccd8..0000000 --- a/docs/examples.rst +++ /dev/null @@ -1,43 +0,0 @@ -Examples -======== - -The following notebooks demonstrate the main workflows and features of ``rxmc``. -They are rendered with their pre-computed outputs; to run them locally, install -the example dependencies first: - -.. code-block:: bash - - pip install -ve '.[examples]' - jupyter lab - -Basic calibration ------------------ - -.. toctree:: - :maxdepth: 1 - - examples/linear_calibration_demo.ipynb - examples/systematic_err_demo.ipynb - examples/robust_likelihoods.ipynb - -Realistic nuclear physics calibration --------------------------------------- - -.. toctree:: - :maxdepth: 1 - - examples/30s_optical_potential_calibration.ipynb - examples/measurement_to_calibration.ipynb - examples/calibration_config_emcee_dynesty.ipynb - -Advanced topics ---------------- - -.. toctree:: - :maxdepth: 1 - - examples/correlated_observations.ipynb - examples/gp_discrepancy.ipynb - examples/normalization_inference.ipynb - examples/sampling_algos.ipynb - examples/overconfidence.ipynb diff --git a/examples/30s_optical_potential_calibration.ipynb b/examples/30s_optical_potential_calibration.ipynb deleted file mode 100644 index ddd5060..0000000 --- a/examples/30s_optical_potential_calibration.ipynb +++ /dev/null @@ -1,497 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "title", - "metadata": {}, - "source": [ - "# 30s optical potential calibration\n", - "\n", - "This notebook demonstrates a small end-to-end adaptive Metropolis calibration\n", - "of an elastic differential cross section model using **mock data** .\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "imports", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using database version X4-2025-12-31 located in: /mnt/home/beyerkyl/x4db/unpack_exfor-2025/X4-2025-12-31\n" - ] - } - ], - "source": [ - "import corner\n", - "import jitr\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from jitr.optical_potentials.potential_forms import (\n", - " thomas_safe,\n", - " woods_saxon_prime_safe,\n", - " woods_saxon_safe,\n", - ")\n", - "from scipy import stats\n", - "\n", - "import rxmc\n", - "from rxmc.params import Parameter" - ] - }, - { - "cell_type": "markdown", - "id": "model-heading", - "metadata": {}, - "source": [ - "## Reaction and optical model\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "model-setup", - "metadata": {}, - "outputs": [], - "source": [ - "Ca40 = (40, 20)\n", - "neutron = (1, 0)\n", - "E_lab = 14.1\n", - "\n", - "rxn = jitr.reactions.ElasticReaction(target=Ca40, projectile=neutron)\n", - "\n", - "mso = 1.0 / jitr.utils.constants.WAVENUMBER_PION\n", - "\n", - "\n", - "def central_potential(r, Vv, Wv, Rv, av, Wd, Rd, ad):\n", - " return -(Vv + 1j * Wv) * woods_saxon_safe(r, Rv, av) + (\n", - " 4j * ad * Wd\n", - " ) * woods_saxon_prime_safe(r, Rd, ad)\n", - "\n", - "\n", - "def spin_orbit_potential(r, Vso, Wso, Rso, aso):\n", - " return (Vso + 1j * Wso) * mso**2 * thomas_safe(r, Rso, aso)\n", - "\n", - "\n", - "R = 1.2 * 40 ** (1 / 3)\n", - "fixed_spin_orbit = (6.0, -3, R, 0.45)\n", - "\n", - "\n", - "def extract_params(ws, *x):\n", - " Vv, Wv, Rv, av, Wd, Rd, ad = x\n", - " central_params = (Vv, Wv, Rv, av, Wd, Rd, ad)\n", - " return central_params, fixed_spin_orbit\n", - "\n", - "\n", - "params = [\n", - " Parameter(\"Vv\", unit=\"MeV\"),\n", - " Parameter(\"Wv\", unit=\"MeV\"),\n", - " Parameter(\"Rv\", unit=\"fm\"),\n", - " Parameter(\"av\", unit=\"fm\"),\n", - " Parameter(\"Wd\", unit=\"MeV\"),\n", - " Parameter(\"Rd\", unit=\"fm\"),\n", - " Parameter(\"ad\", unit=\"fm\"),\n", - "]\n", - "\n", - "omp = rxmc.elastic_diffxs_model.ElasticDifferentialXSModel(\n", - " \"dXS/dA\",\n", - " interaction_central=central_potential,\n", - " interaction_spin_orbit=spin_orbit_potential,\n", - " calculate_interaction_from_params=extract_params,\n", - " params=params,\n", - " model_name=\"minimal_elastic_demo\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "data-heading", - "metadata": {}, - "source": [ - "## Generate mock data and construct the observation directly\n", - "\n", - "The key API change demonstrated here is that we can create an\n", - "`ElasticDifferentialXSObservation` directly from arrays and metadata, without\n", - "building an `exfor_tools.Distribution` object first.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "mock-data", - "metadata": {}, - "outputs": [], - "source": [ - "angles_deg = np.linspace(2.0, 160.0, 28)\n", - "true_params = np.array(\n", - " [48.0, 3.5, 1.1 * 40 ** (1 / 3), 0.7, 21, 1.2 * 40 ** (1 / 3), 0.5]\n", - ")\n", - "\n", - "template_obs = rxmc.elastic_diffxs_observation.ElasticDifferentialXSObservation(\n", - " x=angles_deg,\n", - " y=np.ones_like(angles_deg, dtype=float),\n", - " Elab=E_lab,\n", - " reaction=rxn,\n", - " quantity=\"dXS/dA\",\n", - " measurement_quantity=\"dXS/dA\",\n", - " y_units=\"barn / steradian\",\n", - " dataset_label=\"template\",\n", - ")\n", - "\n", - "y_true = omp.evaluate(template_obs, *true_params)\n", - "\n", - "rng = np.random.default_rng(42)\n", - "y_stat_err = 0.2 * np.maximum(y_true, 1e-4)\n", - "y_mock = np.clip(y_true + rng.normal(scale=y_stat_err * 1.3), 1e-6, None)\n", - "\n", - "obs = rxmc.elastic_diffxs_observation.ElasticDifferentialXSObservation(\n", - " x=angles_deg,\n", - " y=y_mock,\n", - " Elab=E_lab,\n", - " reaction=rxn,\n", - " quantity=\"dXS/dA\",\n", - " measurement_quantity=\"dXS/dA\",\n", - " y_units=\"barn / steradian\",\n", - " y_stat_err=y_stat_err,\n", - " dataset_label=\"mock elastic dataset\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "3aefb01e-0a81-460d-8b2f-5965d57485e4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Mock differential cross section data for n + $^{40}$Ca')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.errorbar(np.rad2deg(obs.x), obs.y, obs.y_stat_err, linestyle=\"none\", marker=\".\")\n", - "plt.xlabel(r\"$\\theta$ [deg]\")\n", - "plt.ylabel(r\"$d\\sigma/d\\Omega$ [mb/Sr]\")\n", - "plt.yscale(\"log\")\n", - "plt.title(\"Mock differential cross section data for n + $^{40}$Ca\")" - ] - }, - { - "cell_type": "markdown", - "id": "inference-heading", - "metadata": {}, - "source": [ - "## Set up the inference problem\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "run-inference", - "metadata": {}, - "outputs": [], - "source": [ - "constraint = rxmc.constraint.Constraint(\n", - " observations=[obs],\n", - " physical_model=omp,\n", - " likelihood=rxmc.likelihood_model.GaussianLikelihood(),\n", - ")\n", - "evidence = rxmc.evidence.Evidence(constraints=[constraint])\n", - "\n", - "prior_mean = np.array(\n", - " [50.0, 3, 1.2 * 40 ** (1 / 3), 0.65, 18, 1.2 * 40 ** (1 / 3), 0.65]\n", - ")\n", - "prior_cov = np.diag([7, 7, 0.2, 0.2, 10, 0.2, 0.2]) ** 2\n", - "prior = stats.multivariate_normal(mean=prior_mean, cov=prior_cov)\n", - "\n", - "walker = rxmc.walker.Walker(\n", - " model_sampler=rxmc.param_sampling.BatchedAdaptiveMetropolisSampler(\n", - " params=omp.params,\n", - " prior=prior,\n", - " starting_location=prior_mean,\n", - " initial_proposal_cov=prior_cov / 100,\n", - " ),\n", - " evidence=evidence,\n", - " rng=np.random.default_rng(7),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "5c055e38-e74b-4840-ab72-8d8fe76f611d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/1 completed, 1000 steps.\n", - "Batch: 1/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.172\n", - "Batch: 2/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.181\n", - "Batch: 3/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.199\n", - "Batch: 4/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.202\n", - "Batch: 5/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.212\n", - "Batch: 6/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.269\n", - "Batch: 7/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.250\n", - "Batch: 8/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.210\n", - "Batch: 9/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.203\n", - "Batch: 10/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.181\n", - "Batch: 11/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.225\n", - "Batch: 12/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.220\n", - "Batch: 13/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.172\n", - "Batch: 14/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.243\n", - "Batch: 15/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.147\n", - "Batch: 16/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.222\n", - "Batch: 17/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.211\n", - "Batch: 18/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.161\n", - "Batch: 19/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.218\n", - "Batch: 20/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.209\n", - "CPU times: user 36.4 s, sys: 15 ms, total: 36.4 s\n", - "Wall time: 36.4 s\n" - ] - }, - { - "data": { - "text/plain": [ - "0.20535" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%time\n", - "walker.walk(n_steps=20000, burnin=1000, batch_size=1000, verbose=True)\n", - "samples = walker.model_sampler.chain[40:]\n", - "acceptance_fraction = walker.model_sampler.overall_acceptance_fraction()\n", - "acceptance_fraction" - ] - }, - { - "cell_type": "markdown", - "id": "summary-heading", - "metadata": {}, - "source": [ - "## Posterior summary and predictive band\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "posterior-summary", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vv: truth=48.00 mean=43.94, std=2.62\n", - "Wv: truth=3.50 mean=4.73, std=2.03\n", - "Rv: truth=3.76 mean=3.93, std=0.15\n", - "av: truth=0.70 mean=0.64, std=0.05\n", - "Wd: truth=21.00 mean=18.74, std=2.93\n", - "Rd: truth=4.10 mean=4.10, std=0.08\n", - "ad: truth=0.50 mean=0.50, std=0.05\n" - ] - } - ], - "source": [ - "posterior_mean = np.mean(samples, axis=0)\n", - "posterior_std = np.std(samples, axis=0)\n", - "draw_indices = np.linspace(\n", - " 0, samples.shape[0] - 1, min(60, samples.shape[0]), dtype=int\n", - ")\n", - "posterior_draws = samples[draw_indices]\n", - "y_draws = np.array(\n", - " [omp.visualizable_model_prediction(obs, *draw) for draw in posterior_draws]\n", - ")\n", - "y_mean = np.mean(y_draws, axis=0)\n", - "y_low, y_high = np.percentile(y_draws, [5, 95], axis=0)\n", - "y_true = omp.visualizable_model_prediction(obs, *true_params)\n", - "for i in range(len(params)):\n", - " print(\n", - " f\"{params[i].name}: truth={true_params[i]:1.2f} mean={posterior_mean[i]:1.2f}, std={posterior_std[i]:1.2f}\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f3bb6f33-2d53-49f2-af1f-7b729c8f33c8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0, '$i$')" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(samples.shape[1] + 1, 1, figsize=(8, 8), sharex=True)\n", - "logp = walker.model_sampler.logp_chain\n", - "for i in range(samples.shape[1]):\n", - " axes[i].plot(samples[:, i])\n", - " axes[i].set_ylabel(f\"${omp.params[i].latex_name}$ [{omp.params[i].unit}]\")\n", - " true_value = true_params[i]\n", - " axes[i].hlines(true_value, 0, len(samples), \"r\", linestyle=\"--\")\n", - "axes[-1].plot(logp)\n", - "axes[-1].set_ylabel(r\"$\\log{\\mathcal{L}(\\alpha_i | \\mathcal{O})}$\")\n", - "\n", - "axes[-1].set_xlabel(r\"$i$\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "c7258dd0-20aa-4aaa-b1ce-2f1b217e8275", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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mhmQyufm1eDxOVVVVbqP1S+Cnh0I62cUROonGw8T89mzsjBAWFBKk3Hh+2M5gQZIkSZIkSdI2yGBBgy6RSJBMJpk/fz41NTUAVFVVdexpkFwTQoXTroaqmT0fcEcpDTR2xo7xOSRJkiRJkiSNaAYLGjI1NTXMnj27542qZsLUWVtlPJIkSZIkSZKkgSsa7gFIkiRJkiRJkqTth8GCJEmSJEmSJEkqmMGCJEmSJEmSJEkqmMGCJEmSJEmSJEkqmM2bJaC2tnbz46qqKqqrq4dxNEBiUe/bxCfA2BlDPxZJkiRJkiRJymOwoBGtqqqKeDzOvHnzNr8Wj8epra0dnnAhPgGicbjx/N63jcbhgscNFyRJkiRJkiRtVQYLGnTR5CoOnFJE+fpFsLybaluFXJG/FVRXV1NbW0sikQDCyoV58+aRSCSGJ1gYOyOEBck1PW+XWBTCh+QagwVJkiRJkiRJW5XBggbX+iXss+BcFn5sFNz/Mbi/h22j8XCF/jCrrq4e/tJH+cbOMCyQJEmSJEmStM0yWNDgSq6huLWJD9yY5Ms/+D01e+/d/bb2CJAkSZIkSZKk7Y7BgoZE7eo2GsfOhKmzhnsokiRJkiRJkqRB1E0BfEmSJEmSJEmSpC25YkHanhXSBNuSU5IkSZIkSZIGkcGCCrd+CSTX9LxNIRPd24Ha2loAqqqqtq3GzlnxCaH59Y3n975tNA4XPG64IEmSJEmSJGlQGCyoMOuXwE8PhXSy101bIjESycxWGNTgq6qqIh6PM2/ePADi8Ti1tbXbXrgwdkYICwoJem48P2xnsCBJkiRJkiRpEBgsqDDJNSFUOO1qqJrZ4a0VK1fy3ve8h8amJgASyQxrWsqpqqoajpEOSHV1NbW1tSQSCWpra5k3bx6JRGLbCxYgBAWGBZIkSZIkSZK2MoMF9U3VTJg6q8NLK1Yu5OHXksyfP5+ampqw2bZaQqgA1dXV2+3Yu2UvBkmSJEmSJEmDxGBBg9Y7oaamhtmzZw/SoDQo7MUgSZIkSZIkaZAZLIx0feidQDQeJqrb1dXVbS4ZpG1UX3sx1D3S+7aubJAkSZIkSZJGNIOFHVmhKxG66Z2whbwJ5bq6OmpqakgmQyARj8e3y54KhegpONkuSj4V0ovBlQ2SJEmSJEmSCmSwMMQymQwA9S89AqMqtt6Jk2vgxo9CS2Pv25aUw9h9YdT0HjdbuXIlK5+5H4AXX3yRZDLJL3/5S/baay8mTJjA2LFjqa+vH4zRD6qmVAstTSEAqa+vJxUr7M++tLSU8vJy5s2b1+025eXlzJ8/n6qqKqZMmcKUKVMGZcxbXdEYOPtuaFzb83ZrXoZbPwXP3w0T9hjwaes3NQC5fyeSJEmSJEmStn2RjDN6Q2rp0qXMmOGV3VJPlixZwvTpPQdbkiRJkiRJkrYNBgtDrK2tjeXLlzN69GgikchWPXd9fT0zZsxgyZIlVFZWbtVzbw078ufbkT8b5D5fXV0dkUiEqVOnUlRUNNzDkiRJkiRJklQASyENsaKiomG/EruysnKHnJzO2pE/34782QDGjBmzQ38+SZIkSZIkaUfkJcKSJEmSJEmSJKlgBguSJEmSJEmSJKlgBgs7sNLSUr72ta9RWlo63EMZEjvy59uRPxvs+J9PkiRJkiRJ2pHZvFmSJEmSJEmSJBXMFQuSJEmSJEmSJKlgBguSJEmSJEmSJKlgJcM9gB1dW1sby5cvZ/To0UQikeEejrRNyWQybNy4kalTp1JU1H3O6b8jqXuF/juSJEmSJEkaLAYLQ2z58uXMmDFjuIchbdOWLFnC9OnTu33ff0dS73r7dyRJkiRJkjRYDBaG2OjRo4Ew4VNZWTnMo9nBde5Dnk5CqpEmYpz500cAuPaiYymLFfhn374/sXKIxrvfzivo+62+vp4ZM2Zs/nfSHf8dbTuaUi2c+f27Abj208cV/u9JQ6bQf0eSJEmSJEmDxRmhIZYt21JZWemE6FDrHCwQvu9YqoWSshAMVFZWFj4R2pAKoUJRCVT08NsZLAxYb+WN/He07ej3vycNOcuESZIkSZKkrcVizFJ3YvEQKsR6WK0gSZIkSZIkSSOMl5pK3YnGey6BJEmSJEmSJEkjkCsWJEmSJEmSJElSwQwWJAiNmhsS4V6SJEmSJEmS1C2DBQkglYS2lnAvSZIkSZIkSeqWwYIENmqWJEmSJEmSpALZvFkCGzVLkiRJkiRJUoEMFjR8MpnBPV4k0vvrkUj32/VXoZ9jsM8rSZIkSZIkScPAUkiSJEmSJEmSJKlgBguSJEmSJEmSJKlgBguSJEmSJEmSJKlgBguSJEmSJEmSJKlgBguSJEmSJEmSJKlgBgva9qST0JAI9zuC7OdJNQz3SCRJkiRJkiRpwEqGewDSFlJJaGsJ99F4mJhPJSEWD8+3N/mfJ1Yx3KORhkRdXR2JRAKAqqoqqqurh3lEkiRJkiRJGioGC9r2xOK5IAG2DBq2N50/j7SDqauro6amhmQyrDKKx+PU1tYaLkiSJEmSJO2gLIWkbU80DhVVuRAhFoeiku13Yj77eVytoB1UIpEgmUwyf/585s+fTzKZ3Lx6QZIkSZIkSTseVyxo29ZbGaRCyiRlMh0f5z/fmobrvJHI8JxXI05NTc1wD0GSJEmSJElbgSsWtG3LL4PUn/clSZIkSZIkSYPKYEHbtt7KIG3vZZIkSZIkSZIkaTtjKSRt26I9lDgq5P2hUEj5peE4liRJkiRJkiRtBa5YkPpqoOWX0kloSORCBUs5SZIkSZIkSdqOGCxIfTXQ8kv5YcJglHLKDyokSZIkSZIkaYhZCkk7vnTj4B5voOWXYvGO5Y8GWgIpP6iwnJIkSZIkSZKkIWawoG3HUPUbGOor+fs67sHuC5EfVEhb0ZIlS9m4YS21tbXDPRRJkiRJkiRtRQYL2nb058r7Qib1u3t9sIKM4V4xMBwNrCVg9kGz2bRhHQDxeJyqqioSicQwj0qSJEmSJElDzWBB247+XHlfyKR+tHzL19JJWLcESqLt2wxSaaP+6EvAMVSrOqR+aEwmmT9/PjU1NVRVVVFdXW2wIEmSJEmSNAIYLKhwmUxh20Ui/Tt+f66878+kfiQS9imJQksaRse7HnPzptCfIVo+tGWG+rLioUPj54rCjj/Uv5tGtJqaGmbPnj3cw5AkSZIkSdJWVDTcA9B2Lp2EhsTQ9DEo5NjReC5c6MsYYnEorYRxM7qfoE83QvNGWL8sHL87+ZP9/RGLQ1FJYeFFX7aVJEmSJEmSpCHgigUNzFD2F+jt2NmyQKkklMT6NoZYRe9X/EfLoWFtWNmQbux+Mn+gpZD6slLDfgqSJEmSJEmShpnBggrXVX3/gU6q96S3Y2eDhwiFX8WfboTSaOHnHzstVw6pO4VO9mdXYGSAUVXDHxDYr0GSJEmSJElSPxgsqHBdrSDIn1TPn6gutP5/T3qbsM8GD2WVhU+Mp5NAZeFjiMUHLzRJJUNpJYDUNjCZP5SrTSRJkiRJkiTtsAwWVLjuVhB0VZJoMIKF3mQnw7O9DQqZHC90Aj3V0D7h3qlxcyrZ/4bOsTiUjg4rFnrbd2usJhjK1SaSJEmSJEmSdlgGCypcdysI+lOSaLD09ar7nkoadXXczr0V0o1dv95ZV8FANA5jq/t2/qFcTWC/BkmSJEmSJEn9UDTcA9AOIBYPgUJFVbhtjdUKnc8di+d6GKSTHR/397gtqVxAkBUt7/r1zrLBQEOif+PI/1ySJEmSJEmStA1xxYIGbjiufM9fEVBRFV5rSOSu8k8nYeNqKC6GMbv3/fixCojlrVqAvPsmKInmVi301NS6JdW/lQddfac2W5YkSZIkSZK0DXDFgrYNnVcY9Lb6IL9UUFb+Vf4ZoK0Z2lr7v2ohWh6OFy3PlUBqWBtChZZ0rqxSV2OJtgceo6p6XnnQl5UVXZ1HkiRJkiRJkrYyVywIMplhOl7edh16CpRvOYme/x7kJvuj5bnjRMvDubNX9VdOh5bG3EoDgPVLYezELcs1pRrymjK3vxer6LhduhEqxofHo/K366EJcm+rOQrppZBfdqk/5ZEikb5tL0mSJEmSJEk9MFhQ17Z22Z0OQUEXz/MfQxhXVxPs2ZUFRSUwbgY0rIHm5tz7yQSQgrHTOoYGDWsgtQlio3ruEZEfPGx+bQDfUU+hRFY2fMj2sZAkSZIkSZKkYWSwoK4VciX9QGSDi2h5LiTIn1zv6nkhouWQXBN6G0TL2wOK1tz7kZK8/gjtAUGqAZrWhbJJpaO6GW9jrt9Cf5pTdxXUFBreFBI+SJIkSZIkSdJWYo8FdS2/X8FgyvYU2JTo2Bi5s1SyfRVBH/sJZCfpS2K55srx8bn3J+wGpaM7rn5IN0JpJURHQXxC18fN77fQ22frql9CV/0RCu2ZkO3XAIX3Y5AkSZIkSZKkIeKKBXVtIOV9epKdTI8QVhVEUtCQPWd5LsjosEKgD+NItTd7zgAVo8Pz5Kbc+7FyiFV23CcbFozqoszR5v0qcuPKf97VZ+tqlUdXqw76uhJhqFeRSJIkSZIkSVIBDBa0dWUn08sqcxPlDWuhvLJjiNC5x0JWKrll74Ws7D7Fsdxqi4Y14Ryb92+EWLTTmAoIDbLv55dDyjZ8zq6S6GsT576GN5ZEkiRJkiRJkrQNMFjQ4Oupd0D+ZHomEybmK9pLFfVUZigruSaUA4oUQ3xcCBHW1kGmNZQxGjutiybQeT0WWhqByi2P21MPhWyAALlySKkGWL8s9GvI/1xDuZJgqI8vSZIkSZIkSQUwWNDAdQ4SNiUgtQlio2Bcde/755dA2nzMxo49GLL3yfXQuAHKx4RyR0Xtf8KZlvaeDPWwvg7WLYbVL0JxKcVTD2ZqppXlTIGS8lxQ0HnVQ3c9FLJjKSoJvQ6yoUKmBVqA0fHcd5DVW0NmSZIkSZIkSdpOGSxo4DrX/o+0vx7ptF3nAKKnPgr5pZCy2zXWQ9noMKGfaoB1r4UAYcUzsPoFWPc6tDZvMbzov+dzDbCWMURvewtMnQVTD4RJb4Ki4o6hwebP1JArq9S8Eda/Dk0bIFIUXm9NQ+komFQDrSlINwOZXOjR1z4IPa3ykCRJkiRJkqRtiMGCgv5MbOdfpZ/taQBhgj7bcyBf5wAiPzzoqndC52bOFeMhuRZuuQDeqO16TCVlUDUTJu0NVXtBahNtrz1My9KnGM8GeOHWcINQ8mjaITBtNlRODcfetAo2roANS8PjhkQos1SIkvbmz7EKmLwvHPMFmLJfYfvamFmSJEmSJEnSdsJgQRCJdJzY7q55cSbT8Xl2n2yokA0ZouV5JYXy9unQkLnTsTqXPspfyVAcDbfWFNz6yRAqFJfCxJkhRJi4N0zcCybsAWNmhFUI+cN8cyvvvfJu9mIxlx/RRHT5k7DsibAS4dV7w63nLwgqJsLoyeG8qU3tt4ZwjGxz6JbGcEsmwgqHF2+DmlPg6M/AhN0hWtb9KTKZsCIjPq6XsTD8qxsinZeidKPz30t/t5EkSZIkSZK0TTFYUJANBjqvMih0nw5X3HfRpyB/RUL+KoRsgNAhdKDjYwiT+Ld9BpY8DqWj4dy/hzAhX3aCvwvpSIxn2ZvWNx9NtDQGba0hoFjyKCx5LBx/1GQYPQVGTQkhwqhJ4bWKibleDp1lMiFMSDVAc3vg0LQe/vUHqL0Zam+BF26D/d8fVjCMmd7991k2upvBdwoS+rK6YbhDCO1wlixZOtxDkCRJkiRJ0jAzWFCQLeHTF9FOk9VdBRPZQCGVhJLYlv0UNr4RyheNnQYVE/LG0+k4j/0CnvtL6HHw7l9uGSr0VVExTNk33A75SNfbZNp6P04kEsZfUgbxvPHvfCQc/gm4/zvw8l3w9LXw7F9gn3fBYR+Hqj3DdtkApXOwkpVOwhuLQjmmiomhGXZfQiBLLGkQ1dXVMfug2Rxxye8BKI/Hqaqq6mUvSZIkSZIk7WiKhnsA2s6lk6EPAeR6K6SS0LAmFyq0tYRGzkUlW06cF5eEZszZEkhdeenOMEEPcPzXYY/jhuSjDLrJ+8L7fgtn3ww7HxFKOf3nevjt2+Geb8DGlXkln8pDDwkI32e6vaxUKhlChdbmXDPsaDz3XefL/hbZfSGED/n9L6QBSCQSNCZzf18Ln1pIdXX1MI5IkiRJkiRJw8FgQQOTf0V8VucSR0Ul4Wr+igkdJ7ij5RAbBaWjcg2cs4FE1oal8NePhdUDs+bBoR/dep9tsEw/GM66AebdCNMOChP/j/8CfvdOaFjdMWzJfnf530GkuP376+XK8K5+i+5CCGkQzJjRQ2kvSZIkSZIk7bAshZTnxRdfJJFIcOSRR/b7GM3NzTQ3N29+Xl9fPxhD23Z1VZYnW9YHtuyr0Hnf/NfXLQk9CmKjcq/f883w2rSD4G2XF944eFu02xzY9S2w6Hb4+2dg4wp49QEYvxs0rM2VRGpp6vi9VE5pX+3RSzjQnz4ZkiRJkiRJktRHrlho9+9//5vZs2ezcOHCAR3nsssuY8yYMZtvM2bMGKQRbqO6uiI+Fs/1S2jrpcxRvkin+9UvhtJBACf8PyiODcaIh1ckAnu9DfZ7X3i+7ElIroXk6nAfK+/4ffallFFfSiT15f3+GqrjSpIkSZIkSRpWBgvA008/zZFHHsnHP/5xLrzwwgEd69JLL2XDhg2bb0uWLBmkUW5nUslwa01t2VehO/EJMGpSrgnygm+GEkh7vS2UE9qR7HxUuF/yGDSuC4+7WowxGKWMuiqR1Jf3h+q8kiRJkiRJkrZLI74U0ksvvcRhhx3GZz7zGb7xjW+QTqf529/+xrJly5g4cSJz585l0qRJBR+vtLSU0tLSIRzxdiLdCCWxrq+2TyUhuQYy5FY2ZEsmVUwI7798D9TeCpEiOObSrT78IVd9GBRFYeNyWLsYph8Uvo9UY+g50V/pZK4cUv6qh55KJBVSQin/uLGKwsZiaSZJkiRJkiRphzSig4WWlhZ+8pOfMGrUKGbNmgXAqaeeyvLly2loaOD111/npJNO4uKLL+aYY44Z1rFud6LloRFzJAWp8twkc7oxTFI3bwrbZSedsyWTYvFw/8B3w+sHnAET9x6ezzCUSkfDzBPhhdvgtQdhl6Ny38FAgoX8VQLZYCEa73nFQ2/vdz5uocFCIceVJEmSJEmStN0Z0cFCSUkJn/zkJ2lqauJ73/seF198Mfvvvz/XX389u+++O7W1tZxxxhn85Cc/2bGDhUym69e3uPq9m+22OF4bRMtCuNDWEgKGbKhQHA2HKR0V7kvK2s/VGB5n2mDpE1D3SOip8JbPQfPGws7bmurmc7TmHjdvAApcUdLSVNh2kQIripV0Wvmy/xkhWHjhFjj2i5BKtQcxDQVe5d9F7aShWiWQf9zu/l4kSZIkSZIkjQgjssdCa2tuonnPPffkc5/7HHvuuScHHHAA3//+95k5cybFxcXsu+++/PCHP+TGG2/kP//5zzCOeJgUUiM/lcwFB51Fy0MppAiQ2ghN66A1DfHxMGYajJ0WtkuuhZb2Bs+ZDNx3eXh88Hkwdgdufr3HcRCvgobVIUiJloXgJd3Y8/fak0J6MvSnqfJg9HqQJEmSJEmStEMYcSsWFi1axK233spZZ53FTjvtBMDuu+/ON77xDWpra9lll10AyLRfld3U1MTMmTOZPHnycA15+BRUe7+xYxmjbNPmaDnE2m+pRtiwHNpaYe3rkG6CMVPDe+lGSLeXRSoph5fvhBVPh3I7R31663zO3qQbYfG9sOh22LQKSkqhuDTcl5Tl7otjuec7HQA7H9HzcYujsN974bFfwFO/g2mHQKQ4fHedv9fB1FW5pM2ftYseDZIkSZIkSZKUZ0QFCy+//DKHH34469atY82aNVx88cVUVVUBUF1dzYwZM4hEQnmZ7P3999/P9OnTR2ZD5t5q5KeSYSI6A1SMDq91mBAvD6/FykOQsOp5aEtD4xqIj8u939LSPjEfhQXfCK+9+YJwhfxAZNr6v29rGl59AF78O7xyd/g8fREpgrOuh0k1PW93wBkhWFi8ABrXQtUe0JLa8nvNypaUipZDJNK/EKCnwKin0EGSJEmSJEmSGEHBQkNDA5dddhmnnHIKBx98MBdeeCEtLS187nOf2xwu5Hv22Wf505/+xC9+8QsefPBBxowZMwyj3kak81ch5E02pxvDVfpFJXnNmdsnxEs7TYjHymH0FEi/3h5YlOfeGzUxHOPRn8GaVyA+AQ7/eN/GmMnAhqWw8hlY+Sys/A+8EYKM2Jhqvto6jqWRnSh+/mWYNBPG7QLl48PkfFZTPbx2P7xyD7x6PzRtyL03ZjrsdTJM2S+EDi1N0NIMrc0hCNj8uCmsuFj5H7jra/D++VAS637ck98UjrnyP3DvZfD27wGRjt9rh98iL7iB/oUAPQVGQ9WjQZIkSZIkSdIOY8QEC0VFRRx00EFMmDCB008/nYkTJ3LGGWcAbA4XsqsUXnvtNT772c+yaNEi7rvvPvbbb7/hHPrwyQYKqWSYHO9clidbsicbEmSDhpZUbuI7Vt5x+4l7tE+Yl3c8xhNXw/3fDq/N/SKUVvY8toZECBFWPAMr2yfyG9d1uWnR2lc4EkLgcfetuTdKK2H8rjBuV9i4EpY9GSbqs8rHw8y3hkBhpwM6hhD5Ojdv3vQG/O5UWPUc3PP/4IT/7X5fgMM/CX/9GNTeAomX4J0/hNKxUDF+y23zv/NIBDYlQsPn9Da8wsDySpIkSZIkSdIOZcQEC+Xl5ZxzzjlUVFQA8P73v59MJsOZZ55JJpPhC1/4AhMmTKC1tZWKigp+/vOfU1RURHV19TCPfBhly+JECGFAtLxjKZ5YvOugIZLashxS9v3GteGK/4a1udcWXpMXKnwJDjq3+zE1JODmT8Cyp7Z8rygKE/eCKfvClP1h8r4QLSe1ejG/uvUhprOCt0/fRPH612HjcmiuD6sLVjydO8b43WH3ubD7saGMUVE//omMmgQnfwdu+jg8dyNM2htmfaD77fc9DWKj4NZPwepa+P27wvew3/vD++uWQP0KqNwJxs3I+84j4fFgli7qbymknsIDyytJkiRJkiRJO5QREywAm0OF1tZWioqKOP3008lkMpx11llEIhEuuugivvvd7/Lqq69y7bXXUlZWNswj3so6Tw5ny+KUVbZPCGegYc2WzZo7Bw3NZR1XMkBo4JxuDPe0htUFoybBA9+Dh74ftpn7JTj64u7Ht3ElXH82rF0MRGDC7iFAmLI/7LR/CAW6KDvUFt+Jm4tCaaYT37U/xaWlYSzrX4e1r8K610Kz6N3mwNidczu2NPX/u9zlqNB8+oHvwb1XwIQ9oeYd3W8/80T46L1w0yfgtQfgn1+CJY/DKT8MoUJrY7gfN6Pjfl2VLhrICoH+lkLqKTywvJIkSZIkSZK0QxlRwUJWcXExmUyGtrY2zjjjDCKRCGeffTa33HILr7zyCo8//vjICxVgy8nhrmrxd1X+qHljWIEwdlp7uFDecaVCdru2FmhaD7SxZsNGIvf9jPGLrgvv9xYqbFgK150NG+pg9E7w/t+HMkb5WlOFf9ZoOUzcO9yGykEfgjdqQwPo2y4O5ZTGzuh++9FT4AM3hF4T93wTam+GFf+CE74JFZPCioUtPkcPKwQ2JXJhT6EBQ28Nu7vTU3jQ32NKkiRJkiRJ2iYV9b7JjikSiRCJRMhkMpx++ukcffTRrF69moULF3LggQcO9/CGRyzedcPgzttUTMhtEy2HljSURNtXJCRDyJBq7LhftBza0qHJcbqJoqf/WHiosPZVuPb0ECqMqYYzr9syVNgWRSKhv8KkmrBC44ZzwmqCnhQVh5UO5/0DxlbD+jr487nw6oItVyt0J/s7RsgFRUMtGoeKKgMEbVZbW8vChQupq6sb7qFIkiRJkiRpkI3IFQtZkUiE1tZWPvvZz7JgwQL+/e9/bx+NmjOZoTluV1eW55fVKeliFUe0HMZODWFCuj1UoCX0DIhNax9uBqJlkC6H0lGw8LeMe+WmsP/RF8PBH+q28TKrF8F1Hwjvj6mGky4L50m8tOW2xdGuj5HO+77qV0JJgd9fbyFAVm+T6XO/DDdfEBpM3/QJePuVPTdzLhsLO82Cj9wDf/8MPH8T3Hc5xMd37D9RvGXZp83jicYh1ZC3uqSHz5wtZ1Xoyoaext5Xg3ksbROqqqqIx+PMmzcPgHg8Tm1t7cjuVyNJkiRJkrSDGbErFvK96U1vYuHChey///7DPZRtT7aszvo6WP1y6LHQWTQeJq+Lo7m+BN3MYz937VeILPxteHL0xfDmT3R/7pXP5kKF8bvByd+G+ISBfZ7hMGoSHPuVsIrghdvgiV8Vtl9ZJbz7KnjLZ8Pz2y+FxfcO/viyZaq2xsoG7fCqq6upra3lqaeeYv78+SSTSRKJxHAPS5IkSZIkSYNoxAcLxcXFnHfeecyaNWu4h7JtypbVaVgLyTdCA+EutyuHSAmUjwVKOjZuznr0Z+zb8FB4PPdLPYcKyxbCdfNCqFC1F5x0RbiSf3u10/7hMwPc/1149f7C9otE4OjPwH7vg0wr/OUjkFhU2L7ZwCDd2PN20fLeS2BJfVBdXc3s2bOpqakZ7qFIkiRJkiRpCIz4YAFCSaQRJ52EhkTv5X6ytfPjEyBS3HU5pM3bTQiT1GWjt3z/gSuJPPT98Li3ngqvPwI3nAupTTD9EHjrt6C0i2Nub2adBfu9F8jA3y6Gda8Vtl8kEsonTT8UmuvhTx+AZN7KkVRD+C1TDR33ywYGXYU8+bJ9M+yPIEmSJEmSJKkABgsjVbbEUaHlb8bNgAl7QHxcKIfUOZBIJ3NlkjpPZj/1GyL3fis87i1USLwEN34kHG+Xo+C9v4ZYReGfa1sWicBx/wNTDwwBwa0XFb5vSSm87zcwdmdY/zr84X2w5pXwXncrE2IVIRSC8Ntkb4X85oUGT5IkSZIkSZJGHIOFkSpb4qjQ8jfZFQkAmRZItU9iN6wJvRfWL4fURti4vOME9/JniNz5tfB4zud7DhUAnvg/aGmG6sNDf4Herrbf3pTE4JQfhcdvPA/JtYXvW1EFZ/wBysfDqmfhl3PgyWuguQFaU91/V9ngoWFtYaWRoO/BkyRJkiRJkqQRw2BhpMqWOOpr+ZtsL4VY+yR2ch20NYemzS0t0NYK6U1h8jrVAH89H9INsMvR8JbP9Hzs5BqovSU8PurT4Sr9raF5E2xcCZveCFfpJ9eE3g5NG6CpPryfaghX72e66UrdF6MmQ+W08HjtK33bt2omnH8PVB8RSkXd9mn45xfD997dyo5sSaSK8YWVRoK+B0+SJEmSJEmSRoyS4R6AtjPReMcwIj4uhAujxoWwIbmWZcuXsyLxOjWL/4+KNS+HifTTfgmRXnKsp/8Urryfsn8oFzTIxmTqiaxcARtfD+WE1teF+76sGhg9FY64ECbvM7DBjN8N6pfB2sWhj0RfVE6FeX+BR34K914GL94GK/4VGlzvfuyWAUMsHm6pZMcyVunGsIqhYnxuNUo6GbaLxXNllCRJkiRJkiQpj8HCjiZ/YnhrNOOtmLA5UCDdCBXjWVq/lImv3UbF63eFhs/v+RWMmtTLuBvhX/PD44PODf0I+qKtFZo3hpUGjWvb79dB/XJia1/n+tZXGcMmuKOb/YtjYTVCpg3I3ndh43L455fgTafBAWdAcbRv48yasDu89kCuT0JfFRWHFSC7viX0pFhfFxpeH/0ZmHtp+wqLxrA6IbvqIN0YVpNA+NtoWBtWmzSsDdulG8PfT3Es/A1l/3629t+UJEmSJEmSpG2awcKOJr82/lBOAqeToc9CrDzcp9onrFPlVMfWs1Pd9eH5cV+BnY/o/XiPXQUNq6FyKo9vnMyzv/sjNXvtyX5vqmFU521fvR/qHoGm9ZsDhExTPRG6LlNUBIwB2ojAqMkUjdsZxla339ofd1VGKNWQFzRkwsT7U9fAK/fAs3+G5f8KJZsm7lXQV9bB+N3C/drFfd8334xD4WMPwG2XhDHdfwWMmQZ7ndyxn0L2PjoKIoQgoWJ8bsVCtg9Dhi1LIG2tvylJkiRJkiRJ2wWDhe1RT1fzZ0veDEVt/Pzzppsg0xruY3EyLaPCpHRriin3fz6UNNrzrXDwh7tvFtxUH+43LCPz+C+JAF++P8M3L3ln3ikj7DG9igP3nM7svaZz/K7FHNRwz5ZDA1rbMqxOZli1KUN9uphdZuzEjGlTSZVO4KLXjmEpO3H9PgspW/4orHst3HoyZf8tXxu3ayg39NpDoT/Crf8N+58eXuttlUW2rwLA2F3C/ZrF0NrScbt0gQ2T29r3K47CO38I43aGB74XQoaysTDtoPBeNjQoKoFx1bn9YxW5ckfZVQmjKrdcqQAdw4bB6DORNZjHkiRJkiRJkrRVGCzsaGIVuavvh3rStrGehkgZyZYG4iUR4pXj4MbziWxYElYBvOPKLifbU6kUJ779XSxe/ApvOfwQvjFrObu0prh7cQvfvPlFYrEobz5oFi8tfp0Vq97gpSWreWnJam65/1+85+OjYHwRN9amufP1IhKN8OLKRlY1ZGgrLuUjJx3IHxY8x5LV9UA9v7hwV845aS9eeX2Xwfvc43aFikmhlFH9Mnj6j7DymVDCqXxcYcfIrlioXxZWXRS6X3ciETjqYli9CF64NTR1PvsmYHx7Q+3GXB+FfPnhQWfZlQpFJX3vt2D5JEmSJEmSJGmH1Us3XY1o6SQ0JLq/gr68ksbGRtqaGmhKbmLp9Z8nsuh2KC6Fd18FZWO63O3Xv/099z3wIEuWrWDVwtvYJb2IlrYMl9zVwuc/9VFefepe7rv1WpY/9zCrah/jjis/zuUffyd/OLuaPcYXsbS+jXNvauQXjzXw52caqF0D8945h0X/919cdt4x1F59Ph86Maw2+PODL5DJC1g+9qN/sLGppctx9UmsIqzIqD4ciqKw6lm462uw7MnC9o+Ph0k1QAZe/PvAxwMhXHjH92HymyC5Bv58Hqx+ETYsg/Kuf4vN4UHDmvb7RO43j8W3LItUqPzySZIkSZIkSZJ2KK5YGOl6urK8c239VHJzQ+BMe7Pf8soJLHzsQSKL7+Go1deF/U74OkzZr8vTNTU18Y3LvwPAxR/7IF+YtADYwC//leG8Cz/Ppz56ToftJ02cwImH7c2J+4yDRx+ADFTMejeL/jyTtfVJ1mxoYJedxjNj8jjYuBKAirIYHzt5Ftf88xnu+tdrnPTl6yg7LfR5+MM9z/H408Xc+In9qdmpi74KfRGJwKR9Qj+DJ64ODZQf/TnsfCQccGboY9CTfd4Fb9TCczfBrA8MbCxZsTi89xq45m2w5iV48HtwzJe6DwiypbOyqxlaUrnfPLtKIRsO9GXlwVCW5JIkSZIkSZI0rFyxMNL1dGV55yvWs7X628MFouWQTrLq1eeYnbgx9FzY//QeJ8l/+X/XsGzZcmZMn87l792TiUUboHwcn7jqAT710XPIZDL8+g838J5zL+Btp5/H3FM/wJvPv5KH/vwzyLRxy6IWxp/ze478rx9w9S0P05bJsNOEyi3OUzMjV7rnoeeWbn48ZfwoXliZ5NBvPsFzyzb1/3vLVzkV5n4pBAxE4PWH4J7/B80be95v73eE7/eN52DZU4MzFoAx0+E9vworKV5/CFYsDCFBV8FANB7ey95GVXX8zbv6++htJUv+cS2DJEmSJEmSJO1wDBZGup7K3eRNDr+xejUvvrqExLoN4b0NSyHxCk31q5maeIh426bQe+Dt3+u2iXFrays/+PHPAfji5y8huurp8MYBZ0LpaJYuX8FJ7/8QH/7vS7nxb3dw+933c+9Dj/HY86+za2UoX/St+5sBWLx8DT+4/j6O+eSP2fOMb7DgqZc6nKuyopSD9pwCwPvfsvfm1x++8oPEY0Vsam7l5dXdNJXuj6IS2Pc9MOfzoV/CplXwyE+gNd39PuXjQrgAoeFyw+rBG8+0g2D0lPaxRQvfr3Mg0NXfh2WOJEmSJEmSpBHNUkgjSVdlj6J5V6bnP++039qli0hnSkgky6iaAJnUJsi0UFbUxr4t/4YIcPTFofdAqoG2tjau//ONVM+YwRGHHwbAbf+4g1dfe43x48fxwQ+cCX+8FoDm0btw3XV/5VOX/i8b6jdSVlbK5z55PrvuPIO16zbw4lP3UlnxEtDMjZefT8WkXbjnqUXc/MCz3PrQs7y2Yi3Hfuon/Pe7DuayDx1DeWmYSL/78jN5fVU9M3eewqntCwJW1ydJptooLSni2L0H2DC5K1V7hibK934L1rwMT10Dh5zfbdjC3C+Fxs9rF8PfPg3v/TUwCFf5P3sjbFgSwot939P/40Tb/1YaErBuKVSMt8yRJEmSJEmSNML1e8VCU1PTYI5DW0N3V5r3dgV6KknVuDGUFbcxYUIVRMuJxEYRiVcRX/1vKiNJGD0V9nvf5l0+/6WvcuYHz+PIuScw5/i3cdfdC/jBj38GwPnnnUu8vJyWNYsBOPDdF3DOBZ9lQ/1GDjlwf/614BZm7bcPl//wKi756rf45c0Ps25jWF0QKylmzKhy3j3nAH7z5Q/w2p+/xkdPDf0TfnjTkxx4wa9ZuroegDEVZey/26QOH+XWR8LKhuNrxjG6bIhytcqp8OZPQKQYljwGtTd3v22sAk75CZSOhuUL4d7LB37+thZ46Afh8f5n9FyyqFANa6G1Kdxb5kiSJEmSJEka0fodLEyePJkPf/jD3HvvvYM4HA2p7soe9VQOqf39qolT2LNmfyZOrArbjZkOlTvReO+VYZvDPwElpZt3+edd92x+fP+DD3HC209lwX33U1JSwsfP/zCbVi2mpC1Fa1uGl9e0MrFqPP/v0k/z8D+u5y+33sFp53yCF19eTCwW5cj9dyXTfqz3feUarr7lYTKZ8MroijKu+tzp/P27H2PKuApeXLqWi395d7dfwd+feAWAdx04sW/fXV9N2gcOPDs8rr0V6h7pfttxu8BJ3wYi8Myf4Ok/Dezcz94I616D0srQ9yHT6x69qxgPxWXhXpIkSZIkSdKINqBg4ZprruG4446jurqaSy+9lOeee24wx6bB1t2V5r1dgd7+fiZaTiaTyd2e/QvxxhVQNhYOOqfDLuedMw+A4uJizj/vXMrKygD4wmc+zc47V/PHn30bgKWbinn16QdZVfsYX77kAha98ipfvfwHAFzyiQ+z+sXHefDnFzF5fGjQ3JBs4qNXXMfbP3MVb6zLNUd+2+H7cPs3TwfgxodepO6NDV1+lIUvrwTg5P2qunx/UO16NMx8W3j81G9g6ZPdb7vbHDjiwvD4zq/CsoX9O2f+aoXZ50B8QmjInE7C+jpYV9e/FQwVVTBpZriXJEmSJEmSNKL1uxbMokWLePzxx/n973/P9ddfzxVXXMG3v/1tZs2axbx58zjzzDOZMmXKYI51x5cZjEvLh0CqAdKNUByDaBkvv/wySxcv4ogXrqAU4JCPhKvZW0ODZSJFnP/h87jyRz+lrm4JkyZN5NUXn+OZ/zzL8cfNZf2GehYuuAlOjlI+eXcmTRgN6SSZTIbPfOWbtLW18e63Hct3v/ypcLySMkpjMWiEi844lvN+ci//eLSW/T/4bf7wv+dx3CE1ABwwYwxz95/BgmeW8L0bHuaHH50b9m/tmJ8duPMYpk6e0PvnLrSZ8iM/7f69TCYEL03r4eZPwEnfgtE7db3tLkfCkkdhyePw1/+Cs2+EUZO63hZC+aTOnm5frVA2Ft70XqiYCJk22LAcUpsgWgapcigp23LfSIE5Y3f9IjrbVv+eJUmSJEmSJA1Iv1csABx66KH8+Mc/Zvny5dxyyy28733v44UXXuCSSy5hxowZnHTSSfzhD38YrLGqO+lkaK7b1yvRs/ulGrZ8L9WQey/dCM0boX4FpJtY+urLTKv7K6X1r4bVDId8ZIvd4/E4l372EgBu/+ddTJkymRNPOA6AT3zq0+w2pg2Aibvvv3mf7/3id/zjngcpKSnhii9d1PGA7XPZpx59AFf+9/uo2XUnVq2t58RP/ZBv/ebvtLWF4110yoEA/OjWf3PO92+noSm9xdjKY8WbSykNuUgExu4SvqfUJlhwWfguu9y2CA7/JEzYAxregFs+Ba2pws/V1gL3fy88PuhcGN0eSqQbIdMCzQ2h70O0vOfj9PfvSZIkSZIkSdKIMKBgIau4uJh3vOMd/OlPf2LVqlVcc801zJkzh7vuuotzzjmn9wNoYHprvpwvf9I4u1+6sYvtGnPvRcuhJQ2ZVqhfwe6jG5i55s6w3XFfDeV2uvDCi4sAOHDWAZtfu/TLX+Pa625gr6piACJjZgBw213387lvfB+AK792CXvutnOno4U/1Qu+80cu+M61RIuLec/cA2lry/Cln9/MqZ/9Ges2NfHOQ3fjWx88kqKiCL+7p5ZDLv4jr6zMlUUqjRXz8Etrue6xZb1/V4OlqAjG7w7xKti4Au7/LrRuGXgA4bt+189yzZzv+Ubh5/nPDbDuVSgfD4d9PPTNiJaHW6QExk6H+Pjemy735e9poAwvJEmSJEmSpO3OoAQL+SKRCJFIhKKicOitdmX4SJZtvgy919HPnzTO7tfVFezR8tx7sQoYO41IpJhIURHVtf8Xjr/zkXDQh7od1u3/DOHDSSceD8BPfvYLvv29EB7M2Xda2GjMDP78tzt5xwcvJJPJ8LGz38snzztzi2O1tf8dvbzkDQCeeXkp/3jkOT76rqMojZXwtwf/w0EX/YHaJWu59H2HsuCb72Xq+Apql6zl2C/9efNxPvvugwG45NrnWN/QzeT+QGTaoHkTtDR1fL04CnMvhZJyeON5ePyq7ksFjdsF3v49IBIaOT9zXe/nzV+tcMBZ4TerGA+x8hAkjJkKpaO6/q3TSUiuyf3N9NbMezClugi1JEmSJEmSJG3TBiVYaG1t5bbbbuOss85i8uTJfOhDH+Kee+7h2GOP5Te/+c1gnGLkKqQsTbb5MoQyO6mN3V9tnj9pnN0vVtHFdhUd34tVQPlYWHQHLHsy7PuOH3Rbl7+pqYkXF70EwKz9Q7mjr3/zMgC+9sXPMLZtHQCZMdOZd+EXN+/34298gUinGv4PP/MKr65YA0BFWYzbrvwkB+41g2RTipvue5qHr/4c0yaO5dVV9XzrhscBeMu+03nqBx/goD0msaY+N3l90amz2X1SBcvXNXHw1+7jlVVdlIHqr9YUrHoW1iwK4UFLc8f3x1bD0ReH72zxffD6w90fa7dj4KiLwuO7/jcERj1ZdHtYrVAUhQPO2HIVSjQeQoV045Z/S+lGaGvN/c301sx7MMV6KcskSZIkSZIkaZszoGDhscce48ILL2Tq1Kmccsop/OlPf2KPPfbgiiuuYMmSJfzzn//k7LPPHqyxjkx9KUsTi4cSOrHR3V9t3tOkcX5fhfzHHbZp7w9Q/eZwZX03ysrKOOTggwC46Za/ATB71iwA9i9fFSbhy8cRGTWZ7//PZzfvd8Otd3Y4zrV//Qdv+a/v0tbaCsCVF72fvXaewitLQ2Plc99xODOrJ9OcDo2j33pgroRSUcV4fv3Vj3DEmw/Z/NrC1VF+8MmT2XPvfVjWHOe/fvM0mXRTCGQybd1+noJ1WIXQxYqEqbNgelg1wcaVPR/rsP+CqpnQlobVLxR2/kgEiktzKxNSjbnVCNkAYYvQoRyKintfodBTT47+2hrhhSRJkiRJkqRBVdLfHWfOnMkrr7xCJpNh6tSpXHLJJZx99tnst99+gzk+xeK5skW9icbDVfH9ld9XAcLjhjW5PgvRcpjWPkm/9MkwSV1U3O3hPvKhc3jiyaf41TW/4eKLLuS0d53CP++6m0nL74JJwMy3QVExHz/n/Sxf9Qbf+MHVnP/Zr3PAm2bypr32YP5f/sY5//0V2traGFtRCrSw6/RJHPW5q6hvaOKI/XfnG//1Ln74p7tJrN/E7juN4cw5ewPwRqqU854/nHSmmPITjtg8pisSc4A57HoW7J7exDFPfpzM648QiRBWEpSOgtLKcBtdFL7TTisoulUcg0lvgmQilD4qKet6u/VLwv2E3Xo+XiQC5ePC496aOM88KYQQiUWw4Btw6k/D6+nG0Bsj+xtm7/NF21evdLP6ZLMOZbS6WOWiHVJdXR2JRAKA2traYR6NJEmSJEmStgX9XrGwYsUKzj77bO68806WLFnCt7/9bUOFobA1y9Lk91XIPo4QruZf397seJcjw6qI5vpQ9qcHZ7z/vcTjcV54cRHf/t73OfWd72BaZRGHV20KG+z99s3b/s8lH2fX6mkkG5v41o/+j3Q6zcc+9/9oa2vjtGMOpGpM+PzvufRq/vXiEiaMqeC6b5xPJpPhe38Mqxy++L5DKSkOf9IbWmKkM92HHvtnnufqoq/y+UNbKYoAkeKwYqGpHjYsDaWMXrkbXroD6h6F1S/CplVhYr0nxSUwekq3Da1p3hQaOANM2KPnY0EIKwBaegkWikrgnT8EIlB7M7z0z/B6djVCtL3XQnzCln9LnXssdGdr9l7QNqGuro6amhoOOuggDjroIObNm0d5vO+/f21tLQsXLqSurpeSXpIkSZIkSdou9GnFwne/+13mzp3L7NmzeeONNygvtz56oZpSLcRSvUxKD3uj61KIluaeRkshE4MNy6EkCslNUD6e6NRDKH71HtKLH6R1wr5bHqYtlC2KlVXwta9+lS9+5Wt86X++SXnFaC4+birFRfWsHb038YrpkA7lh771o19TtyJBcayMU08+gVaKOfnEY/nr3+/mwWfrWPfmVsaXwvL1TVRPm8Kvv/xBqiaM59q7n2R1fTNTp0zivW/Zl6bWECykWrteZTA6s4kPZ/7I2zL3ArC8oYhnm6bwloP2IdKSJNJcT1HzBoqaNxBJbSLSmoKGN8INyJTEad75GCjJ+54y0cK+3pYMRatfIQa0jZpMqngUtHTxm6dbcz9BUYxiIJ1qojXv9fAhO/09TZ5NyUEfpuSpX5G586thnLEKKImFqkwNG6GlMTSQbmmE5HqIjw37trWG3yIe6+EDxCAaAyJbnrsr+X/P2d4O2V4P7ZoKOY6GTSKRIJlMMn/+fGpqagAYPWY8n/zjcwXtX1VVRTweZ968eQDE43Fqa2uprh7AyipJkiRJkiQNu0gmU/hsdlFREZFIhMrKSubMmcPcuXM55phjOOCAA4ZyjNu1+vp6xowZw7FfvJ6Ssh3jSu/3tv2N8zN/5BFm8z/Fnyl4v0imjWvaPs1OrOY7kf/irqK3FLzvn1s/wmiSfLjouyyNTO37oDMZ5mQe5eOZ3zKOegBujRzPryNnkIx0/btEM2l243X2yrzCXrzCQZlnGEc990XezLeKPtX3MQBntt3EuZnrWRA5nMuLLux1+y+2/Yg5mUf5WeSD3Fx0Uq/bl2WauKrtc0whwV8jJ/GLog/2a5xbS0tTknu+9X42bNhAZWVlt9tl/x31tp0G18KFCznooIN46qmnmD17NhDCoFOvuAOAmz//VspiPefT2VJKtbW1zJs3r8OxNDj89yFJkiRJkra2Pq1Y+PrXv859993HI488wi233MKtt94KwLhx4zjmmGOYO3cuc+fOZZ999hmSwWrb8HRkH8jAfrxAUaaNtt5q87fbn1p2YjUNlPNA5LA+nbOEcLV+C92XN+rOxEyCC9uu4TD+BcDrTOMHRR/h+chePe6XjkR5kT14MRJKFu2RWcyP2r7KnMyj3J85jAf7+BkAZmZeAeBFdi9o+xRhNUSUdEHbN0XK+FHRh/lW2xWcmrmD+zJvpjYys8/jlAZLdXW1KxQkSZIkSZJ2MH0KFr7yla/wla98hXQ6zWOPPcaCBQtYsGABjz32GDfeeCN//etfAZg4cWKHoGHmTCc2r/30cb1fSVro4pFCmwl3VYomK1tnP53MlcZpawk19OPjOx4n1Rjez7RBcgO0TSPz+wpGpRu46ezJZKbs33H7pnUdnr6xejUf/OinOHDik/CmYq59upG/33YWJx51MB8/6x18/zc3cv/jz2zefvLEcZxw5EGMHzOaivIyMs0NFE1qhmK474efIBUdw0E1u/DQ0y9Rvyl8pn9f9y32WHELABsaW/jukyU8W3MJp2Tu4NzM9ZTTTIoS/hQ5lesjp5COhAn7K1v/h915vevvL7plg+IMMWhr4sttP6K5ZExoeHzgB7r7BTra43hKb1wMTfCh4/fl3Emjut4ur/dCyd1T4Dk497Aqzj50VsftYqO7OdHRtP7jFYqf+zNXjv09qTNvCg2ci6Ph983+3ptP0l6aqKVxi1JFA1LA32l9fT2TvzU4p5MkSZIkSZK0dfQpWMiKRqMcddRRHHXUUZuDhkcffZR77rmHe++9l8cee4wbbriBG264gUgkQkuLddTLYiW9lgwZ2mChGYozYWK5oipvo9FABlLFIXyIlgOpvMdAciXQAk2boGwUNDXDtNnw2gOULn8UqjuVNWktbj99hj/9+a9c+Jkv0ZZcy7svCRPhe7/nC0T+9lX+seAh/rHgISaOH8Pb3zKbtrY2Hlr4HMuXr+S3N9zW4ZDf+FoIZb52/rs4811v5YXXVnD4uf9DazpcyX/7/U/y37s3863bl/H9e5bDuJ1ZsNdlHEioBf8sM/lB0fksiUzrcNyStmbKipq7/v4iXfROKC6GTBGRTBtlbZugpAyK27rev7PU2hC6RIoonbQ7lHTzO0bzVmXEysJLmTTRaKfVGrEeVm+c+HV49V6K1r5C2eM/hsM/CbHS8JumG8PfwqZVUBQFRsHYGUBe8JVOQioZGjX3t3F4AX+nqd7+TUiSJEmSJEna5gzKrF40GuXoo4/m6KOP5oILLuCOO+7giiuu4Nlnnx2Mw2swxOK5ieLupJK5VQ3FsTABDaFx88b1UD4KKIHxu8DOR8BrD8BrD8GbP9Hl4X79uz/ykU9eDMCXT6iirCQFE/fmqFPP465ba5h/zS+58Z8PsnrtBm65+xEAznvvSbzr+CNY+PzLNCSbaGhspLF+HRBWNHzo3XMhFuPcr11FU3OuPNBtD/ybpx9OMP/x1QB8Zk4bB/IcTZTyy8gH+HvkWDJdlGy6ZVELn9274G8xTJYXl+ZWeLT1ITRLLAr343YJgUQhitvDjdZUHwYJlI+Dt34T/voxeOrXsP/7YNzBud84AxSXhZUMXeVZqWT4bKlk/4MFSZIkSZIkSTukAQcLa9eu5b777mPBggXce++9PPdcuEK8tLSUY445hjlz5gx4kMqTfyV5bMtSPd2K9nLleboR0pvatx0VVjZkzxcphqrdQimkaHk4zq5vgfuugLqHoa0Vira8er5qwniKi4tpbW3l1F0agWIuu3Ml3/nK3qxbt569d5vBozf8iMVLVnDdbfdxzV/u4Jq/3MGXP3EW7zzu8NyB1i+Bv10UHkfCeXaZOpGnF9Vt3uTV5W9w74o1FEXgR+/flfP3TUEL/Dnydm4rOr7Lj9yaTjE1uqnQbzCnqDhc6d+Whpbmbj//Flb+p/2L2bPwcxXHwn1LH4MFgL3fATNPgkW3w91fh3NubV+tEGtfuTIBkmtCsJBKdvx7KiSIkiRJkiRJkjQi9TlYWL9+Pffddx/33nsvCxYs4Nlnn6WtrY14PM7hhx/O6aefzpw5czj00EOJxWJDMeaRLf9K8kKChUJL2kTLQ6AQAeITwvYNazpOQrfllfwZt1vYp7ke1rwME7dshHzqO97Gon89zOVX/pipo28G4M+PL2XdunCcFxYv4a0fupQHrv0+b738EJatSvDPB5/ip3+4he9+4WMA1G9s4MG7H+RkIBMphuIYEeCv37uIJ59fzDW33M9tD/yb11esobQkwnUf3otTDxgPaxcD8NbyZ0ivWMWfxl8AwEt/+AKHTk7z5mlFHD81yb67b+j9O+xKUXEIFrq83L8LDavhxX+Ex9MPLfw82XJULU19Gh4QVlccfXEIFrKhRrYUUrQ8/Mbp9pUX6caOf0/Zv5WGRPiIo6pcuSBJkiRJkiQJ6GOwcNBBB/H000+TyWSoqKjgyCOP5IwzzmDOnDkccsghlJRYL33I9fVK8mwQ0ZAIE8OdA4Zs8BAth3EzOu6bPwmdSkKqIbdioXQUTKyB5Qthxb+7DBYAdtt1F3754++RuvpZ2LCYn1/2RSr2OYFYNMbbTn0Pr9Qt59gPfpY7f3MFF579Lv754FNcfd3fGT9mNA899Rz3PPpvjpjayskfrODF1SmOOv4Cdp8+mcamFOs3JVmzfhPJpmYqR5Vz6/m78ZY9x4QTl42FdJKK+sXccU8tRe/5OG2RIp742FgmbHimy7EWLJPJlSYqiva+WqGtFV66K5Qd2vlImH5I4efKBgv5jbf7Ivtbt7bkrXTJ+/3zf+POUklobl/RkRpArwVJkiRJkiRJO5Q+JQH/+te/KCoq4rTTTuOSSy7h0EMPpahoy7r1GkLRPpZAygYRLamua+Zng4d0Y8cJ51Sy45XtDWvCBHm6MVdWaadZ7cHC07D/6T0Po3IibFjMofvsAjWhqcHdv/s2bznrYha9upQjT7+IX192MbPftAcLn3uZL115zeZ9j9ijCkixeF2GNesbWLO+Y/miaZPG8bcffoZZ63INnzdFRtPSHGFsaRt/eneU5W0Xc2PkZCoGYxFNpjWUhYJcqaKeLHkcGteGsOPNHy+8+Tbkfqtsv4u+ai8dRaZ1y98YtgwaOr9XOiqsWIh1EUYNpLGzJEmSJEmSpO1Wn4KF888/n/vuu4+//OUv3HjjjYwePZojjzySOXPmcMwxx3DwwQcbNGxrsiFALNn1Sods8ND5ivVsiZyGNblJ7aLijttNau96vOLp3sdRPi7cN67f/NLO0ybz4J++z1s/dCm1r9Rx4ocuZe/dqznpLeGK/mMOO4C3H3Mob1p7B7x8J4cddhhX77Ybr69M8Ob99mDiuErGjo6z805VlMai8Cg0p9u46sFVfPP2pWRaW7jwsBifPryMqdE3+GTmN2QSBfRC6Enn1Qq9hQQbV8Lyf4XHh38Cysb07Xybg4WGvu2XlV1Nke2P0ddzj63e8nUbO0uSJEmSJEkjWp+ChauuugqAlStXsmDBAhYsWMB9993HP/7xDyKRyObySNmg4ZBDDqG4eIATuRoc3TVvjsZzE875qxRSSahfCdFSKGnvsxCf0HHfaQeF+5X/6b2BcfnYcN+4rsPLM3aaxAPXfp+v/vA3/Pavd/LCK3W88Epoynz7/U/w5e9fw3XvKeO0vYv53z8+xI8eWwDApPGVfO2j7+Zj7zmO4uIi1tU3cM3dy/nhguXUrQ0T/7tPLGOPXWZQvNMkfrLmIE7L/J2pmTf69LVtocNqhWjP27am4eW7gQxU7QXVh/e8fVcGvGKhPejLtA1eI2YbO0uSJEmSJEkjWr+aIkyZMoUzzzyTM888E4AVK1Z0CBruuOMOIpEI8XicI488kttvv31QB73DKrRETqaHhsH5ZWoKLZnU1hrus1eiZzKQboLScmjLhHI6JWXQeTXKTgdASXm4mn7d4m77LAC5FQtN63OvVUwCYELFJH76nW/wra98nt9cfws/ueZPvPxqCBdaWlrZtf0i/yUbI+w5fQKbGlOsWFPPBZf/lt/89S5m7bETf7j7aZJNaQCmjq/ga6cfzIeO3YtoSTFNbcXcuu5Ebsscz80zrib2xjMdA46KSeHK/LKxIVTJ/g75Y81a8UxopDx6J6jaM7w2/eCuP/O//xiOUT4OjrwwF0j0Jn+7/NCn8/6FHg/af+M+lGDqSayib+W4JEmSJEmSJO1QBqXb8k477cRZZ53FWWedRWtrKzfddBNf//rXefbZZ7nzzjsH4xRbTUtLC5lMhmi0l6vRt1X5ZWr6Ovmb38g3DiTXwahxudcjkY5XqRcVw5R9YekTodxPT8FCWbYU0tpuNxlTOZr//sgH+NSHz2Ltug20tLbSkk6z09/Ogkyav1zxCSIVVaRbWrnq1sf50q/v5IkXl/HEi8sA2G/n8Vx48n7MmzOT8tIt/7TbIkW0jdkZ4mMguRbWvhxKFTW8EW4QVmaUVoZbcUluEr2oBJrq28OGCIyZscXxO3ijFl5q/9s/+EP9n4gfaPPmzatIMiEw6kt/B0mSJEmSJEnqwoCDhba2Np566ikWLFjAvffey4MPPkhDQwOZ9qvqq6qqBjzIreX555/n61//OsuXL2ePPfbgxBNP3LwqY7sxkDI1sewkdnu4MHF8eN6wtusGzwBT9g/Bwoqn4YAzuj92Fz0WuhOJRJgwfmz79usgE1YiRNrLKUVLivnkuw/nvXP25Su/votkc5qPveMQjt65hEihE+fx8RA/FFKbYO2rkFwDzRvD52xcu2UAUlJG6GIMjJoE0bLuj51uhCd+HR7vNgem7FfYmLqyuRRSP4OFSF55qkwrpJvzmnJXQKqh43NJkiRJkiRJ6kWfg4VMJsPChQu59957WbBgAQ8++CAbN27cHCSMGzeOU045hblz5zJ37lz2228Ak6pb0aJFizjiiCN45zvfyQknnMDdd9/Nd77zHf75z39yzTXXFHyc5uZmmpubNz+vr68fiuF2r7teCt1JNUBzQ/vEcnmuaXO6MTxPNYbH6aYwmR4t7xguTMw2cP53z+fppsdCrzaE1QiUjg6rBvJMGT+aqz/z7rxtl/bt2ACxUbmJ/0wbNG+C5vpwS64J309rKpQ/yhrby2qFZ66DZALiVbD/6X0fU76BrliI5JWv2rgqhAvFsVxT7nQyPE83GixIkiRJkiRJKkifgoVTTz2V+++/f/NkeSaTYfTo0bztbW9j7ty5HHvsscyaNavwq8a3EZlMht/97neccMIJ/P73vwfgM5/5DNdccw1XXXUVp59+Otddd11Bx7rsssv4+te/PpTDHVzZ0knZICG/HBKEx8XRECyU5E1IZwOGbH+BFf+BlmYoKe36PNkVCxuWwvrXYezOhY1v4/JwXza23x+xYJEiKKsMN8j1WGhNh4Ah1R7A9BTc1D0Ci+8Ljw/9cO577K/W5twYWtO9N4zuLNOad6xUWMFQVAKRVHs/DcLzgY5TkiRJkiRJ0ojRp2Dh1ltvpaKighNOOGHzioSDDz6Yos5NfbczkUiEZcuWsXLlys2vxeNxzjvvPMrKyvjpT3/KpZdeymWXXdbrsS699FIuvvjizc/r6+uZMaOXK9yHUywOmxLQlg6rE2LluZJIqUZoaQz3RKAlFfr/5pdFmrIfjJoMm1bBf26AA+d1fZ6qmTB6Suhp8Pt3w0mXhzJKvY5vVLhvXDd8PQKKo2HFRXbVRXcW3QFP/yk83v3Y3GqO/mpcB7dcGB5P3HuLFRsFefD74X5sdfgus029syWQKkbnVirkN/7uy6oXSZIkSZIkSSNKnxKBBx54gHXr1nH77bfz+c9/nkMPPXS7DxWyJZxmz55Na2srL7zwwub3ysvLed/73scJJ5zAggULeOONN3o9XmlpKZWVlR1u27RYRbhavSgaJprzpRvD60SgbHSYcI5PyF3hnkqGngkHfShs/9CPQjmhrkTL4azrYers0Mvg5gvgkZ9Aa0vP45t+MBSXhl4I/Sl1tDVk2kKgkA0V9jgeDvzAwI7ZvAluPB/WvBKCm1N+3PdQZcnj8NRvw+N3/BBGTcyFCLEKqKjKhQwNifaAqb3xtyRJkiRJkiR1o0+pwJFHHklJyYD7PW9TsmWbTj75ZF566SW+/e1vs3Hjxs3vV1ZWctFFF/HEE0/w8MMPD9cwh072yvW29JblcKLlIUSIj8uFCbE4VEwI769fFkKC/U+H0kpY8xK88PfuzzV6Jzh9Phx0Xnj+zHVw64WwqYfApjgGE2eGx6tr+/85h0qmDR77ZVitALD/+2HWWR17G/RVuhFu+jisei6UkHrP/8GY6X07RksT/P2zQCb8PuN3h/V14ffu6nxtLWE1SlFJ/xp/S5IkSZIkSRoxtu/lBoNo99135/rrr+ePf/wjl156KYlEYvN7sViMAw88kLFjxw7fAIdKKhlK/WQIE8ypxi23iZZDxfiOE87pRiiJQks6NFbe7/3h9Yd+EEoWdac4CnMvhVN/Gq6WX/Us/OW8cHV9dybWhPvVL0Bba/fbbW1tLbDyP7DksdC74NDzYa+3DaxcU2sabv1vWPZkKF102tUwYfe+H+fBH8DaV6BiIhx8fmgm3bxpy1UpkAuQKqrCzTJIkiRJkiRJknpgsJBn7ty53HDDDfzqV7/iox/9KNdeey3PPfcc3/nOd1i6dCm7796PCd5tXSze3syXXO+ErOyV7F1NRgM0ta/sSCXhoHNDyaJlT/UcEmTteSKc9n9QtSc0bYC/fwae/HXXwcG4ncNkd7oxNH7eFrQ0w4qnw9hLyuDoT8PORwzsmG2toSfCaw+EY777FzD5TX0/zspn4dGfh8fHfCn8xk2boKi46ybN2bJIhQYKqQbYtLrr1Q+SJEmSJEmSdng7Vl2jQfDOd76Thx9+mIsvvpgvfOELlJSUEI1G+cc//rHtNGEu9Ir4nlYOZEXjuX4J2QAhuS5X9ijdGB53Vdon0wLJNaHvwugpoeTOv34HT/0aZr6193PvfDic8ze46+vw9B/hqWtCuZ73/Krj+cZWw7SDwoT7+jrY9S1dH2/GoV2/3pKB51Lh8T6nwKt39z42CCWNurJhGdx7WZhYLxsLx34Jxu/W+/Em7Nn9e5kM3PVVeP2h0NfivdfA7nN7Pl5xdMvXWtPwj89CphX2OAH2ODb8nuNmhJUlsThheUon2d+/kMbNqWSuF0NP2w5Ho21JkiRJkiRJQ84VC12YPXs2t9xyC/fddx8333wzDz74IAceeOBwD2topJPQsCY8zvZOyK5SyPZTiMXDJHLDmlxj32g5RNpXOrQ0kYmWM//1SSEQeOWeUOKoECVlcNJl8I7vQ0kpvHI3vHTnlttNmx3uVzwdJs9709YaehSsWVzYOAq1+oUQACQToWfECf9bWKjQk0wG7v82PPuX8P29++e9hwrdeeQnoTxT2Vg45oshpCgfF1YrJNfAuiVdN2fOrk4ppHFzdpWLvRgkSZIkSZKkEckVC92orKyksrJyuIcxNNLJMIGcDQyaN0LD2tBHIZUMYUHp6E77NHYMHGJxqNo9NHAuiUJyDU8s/Bd7jh3HYWPXwMM/hndfVfiY3nQaJF6GR38Kj/w4lErKv+J9/G5hsrxpPbzxPOx0QNfHWb8EXr0XXr0fkmshUkTx7POAY/v0FXVpyWNh4r41HVYfzPlsaFo9UI/9HBb+Njw+4f/B3u/o33ESi+C+74THx3wxlJlKN0JZZXv/jE2Q3gQN5Fai5IcDjRty4VJPogWsapAkSZIkSZK0w3LFwkiUX8oGYOMboaxRw1ooiYVJ466uRm+s7/g8Foex00IIkYHDZ+/Ho427hPeevwnWvda3cR3y4TDZvfI/sPjeju9FimBq+6qRZQs7vte8MWz/98/B3y6C524KoUJxDDJtRJ/6Fee3/YFIpq1v49l8/E3w6M9C/4PWdCjLdOyXBydUWPj7EFYAHHMpvOnd/TvO+iVw08ehtRl2OhB2mQOx8vB9phvDb92aBoq77qcBUD5mIJ9EkiRJkiRJ0gjhioWRKLtSIXs/ehK0pMOKBei6wS9Aed5EerYmf7Q8XOUeLef0eedB5DwyN3+SyGv3w+NXwVsvK3xc8Qkwax48cTU88J3QDLmkNPf+tINg8QJY9Z8QWqx7HVY+A2tehmxoECkOZZN2mxO2f/4WePpa3pu5jZ0yqyD93336qlj1XFh90bQeiMBeJ4feC0XFfTtOV174G9zX/v0cfiEceHb/jrP6Rfi/E8OKhNLRcOj5ITxINYbfKNMK6SYYNTmUMMqGDfm/c/Y1yxtJkiRJkiRJ6oXBwkiXnUgeVd6xl0K2KTPkrmzPTkpnX8srjRTJHifdCAecAa/dH3ot9NWhH4VnrgsT+nd+BU66Ivfe2GoYMwM2LIEHvtdxv7HVoVnxLkdCWd6V9/u9l1R8MjzyE47kSdru+iLscnTHbbqSaQs9D579c+iBUDkVDvsvqJrZ98/UlTWvwD+/Eh7PPiccu78WfCuEClP2h5Muh8rpobFzNjxY+wrUr4LWyTBhj1wpq3yxeCg9tbmHhgGDJEmSJEmSpK4ZLIxE+aWQKqo6XrmercUPucnltpYQKuTX3+/qqvds2DBhz/B87WLY9AaMmlT42EZNglN+An8+NwQMk/aBePtKikgEDj4PHrgyjHH8rmEyfaf9oWJimPzvQtvOR/H5x8bx1bbvM2HDkhAY7HkijJne9RjSSXj5LqhfFp7vNhcOOrfj6omBaE3B7Z8LZYt2Pgre8tmO/ST6YsUzYeUDkdD0eeLeuZUK0fJQDinVDNFYuO9pRUL+34XBgiRJkiRJkqRu2GNhJIrFQ1DQ1SRztBxio6B0VHgcLe+4UiH/GNmgoWFN+2R0+7Zjq2FC+5X9S5/o+/h2mwNzvhAe3/2/odRPVkUVHPcVOOkyOOrTsMdxIVToxQuRPflk0TdpG79HmNB/4W+w4umwGiHfhmXwnxtCqFBcCm/+BBz2scELFQAe+Sm8URuaUZ/4zdA/or/uvTzc7/eerkMFgMopUBwP9z3p6e9CkiRJkiRJktoZLIxE0Xj7SoUuJpBjcRg3A8bOyJXMqZjQ/WRzp5JIVEwIKwxmHBreX/JY/8Z46EdDI+NMKzz5a2hI5I2/HGIVfT7k2sg4Usf9L1TtBWSg7uHQs6GtJZQ+WvokvHBrWLFQPh7e+i3Y9S39G393lj4JT/wqPD7+6zCq91Ckx2O9dEfoK3H4p0Lz7ca1WzZmHjcDdj4k3HclW/4qWwZJkiRJkiRJknpgsKAt9WWiubsVDZP2Cff9DRYiEXjr5aHUUToJj18NLc39O1a+4lgobVR9BBCBxIvw/M3wwm2w7AkgE678f9NpMGbawM+Xr3kT3PGFcI43vRv2PGFgx8uuVtj3vVBcBqmNkKHr36Mn2XCoYU2uFJIkSZIkSZIkdcMeC1tLJrNl2Z3OCq2z39txupNOhknjWDysVuj8fPN2+asQelkZEKvIbZNqCPuUxGDqrPDaymegpbGbmv29fI5oKZz2S/jtO2Djclh0O7zzh92XDtqwtOvXizJA+4qHWCXse1q4LVsIC74JDW+E90pK4Yj/hj2OD8/H7dzz+LKyPSl6c+8VUL88lIo66YpQbqorhZRGqns0rLYoKgmNn0ui0JKGsZO7WF3Szd9V9veHjj00eiuFNJh/p/39W5YkSZIkSZI0bFyxMJLkN+ft6nlWf2vtbw4kmmDym2DU5PB82cL+j3n0FHjnj6A4GhoqP/rz/h+rs2mz4ZQfw8SasErhlJ/mQoXB9vLd8J/rQ2jwzh91HyoU6r4rwv2B82DiTCgdDWPbV1h0Xm2SToZSUulOv3P294dQGit7g663lyRJkiRJkiQMFraebWGSNj8wSCfDrSW1ZYAQbe+tkEr2fdwbV0LjunBVe/Xh4bX+lkPKmnogHPe18PiRn8BLdw7sePkqp4ZVEO/8UVhJMBQ2rQ4rIwAO/2Su/0R/vfoAvP5QKOt09CUd+2DkrzbJ6muA1N32kiRJkiRJkoTBwtaTaux9m8HS3RXq+U2bU8kwMd25DFJWb5PLqYZwjlRDx9eLY9DWGlYtVL85vDbQYAFg3/fAgWeHx7d/AVYvGvgx+2rVs7D4vi0/c08ybXD3/0DTBpiyHxx98cDGkMnAfe29FfY/A0rKO/5G2Z4XkFu50F2A0F0T7/6uWJEkSZIkSZI0IthjYWuJ9aGZ7kDlhwJd9jYgtyKhu8nj3t7vqg9DtBxio4AMRMtC42WAJY9D88ZQrmcg3vJZSLwESx6FWz4JZ/+19x4Qg6X2Vvj7Z8LjklLY80R403ug+rDu98lk4LGrQj+E4lI45ScheBmIf/0elj4BJWWht8Lm36DT75RuD47SjV2HBz2JdhM2SZIkSZIkSRKuWNh6tuZEbSFXnHd3tXpv729eqZCExvpO562AcdUwZlrYb9yuMGZGaG58xxcH9pkg9Fl4x5WhfNGGJXDv5QM/ZqHWvZZ73NIcgoY/nwu/Oj6EB/XLO26fycDDP4InfhWev+USqNpzYGN4+Me5cOPNnwjNpYtKQqCTlW4MIU5yPbSmOr4nSZIkSZIkSYPAYGFH1FtoMBDJNbDpDWhaC+WVXW+TTkJyLRQVw0mXAxF4+lp47cGBn798HLz1svD42T/D4nsHfsxCjNs13E+dDWfdAAecEVZn1C+Dx38Jv30n/PW/4MW/h8n9+y6Hhb8L+xx9SSjl1F+ZDNz1P3DP/wvPD78A5ny+Y2+FrGg5tKShbHSuV4a0DamtrWXhwoXU1dUN91AkSZIkSZLUT5ZCUt9k2u+Ly7e8Wj4r3RRK9BSVwMy3wkHnwlPXhFUL59+T6wHQXzMOhdkfDBP3d34FPnhLCByG0oTdw/3axbDT/uE251J4+U545npY+ngoUbT0CSiKQlsaiMDcL8K+p/X/vMk1cMeX4Lkbw/MjL4KjPh2aY3clFoex00K4sa2sVkgnc2W1LLG0XairqyORSFBbWztox6yqqiIejzNv3jwA4vE4tbW1VFcPUdN0SZIkSZIkDRmDBfUs2wg6Q7g6PnuFfLS8+/4G0bIQLkTLwvOjLoLn/gpvPB/CgIPPG/i4jvx0WAGxdnG4kv/tVw78mD0ZtwsQgab10LAaKiaGz1fzTth9biiFVPs3qL0FNq6ASDGc8HXY6239O19rOoQx938nNH6OFMPhn4SaU6BxXfj+U8lcgJC/MiE2CCsVBjMMKKTnh7YZdXV11NTUkEyGpuDxeJyqqqoBH7e6upra2trNgcW8efNIJBIGC5IkSZIkSdshgwX1LJWEjauhtSk8H1fde8Pkzs1/S8rDpPiCb8C9l8E+74L4+IGNK1oGJ10B154BL/4D9jg+1yx6KETLQ7iw7lVY/UIIFvJVToXDPgqHfgSWPx2adU/cu3/neuUe+OdXYM1L4fmkfeDwT4XvvqU5t2qkQwPtzr0w8kOHfjS4zoYBDYlcSaX+hgK9NQLXNiWRSJBMJpk/fz41NTVUVVUN2uR/dXW1QYIkSZIkSdIOwGBha4lEui9f059jFSKT6X2b3sTiUFwEkVIodPhFxZ2OUQFvOg2euS5Mlt93Bbz9uyFwKERxrOvXdzkSjvwUPPh9uOcbcMafYNTELbdLtwGJ8Dg+HmIF/tm3tXR8PnHvECyseg6qD8+9Pnpqx+0qp3d9vJJuPkdW4qUQKLx8Z/tYq+DYr8CB86ClCRrWhN8gPiF8J/kT9unGjisMOoQOecFCoSsRssduSfV/tUH27zRW0X24MVj/JjToampqmD179nAPQ5IkSZIkSdsgmzerZ9E4TNwrXK1f0c9yKLE4lFXC3C+F509dA6ueHZzxHXkRTNkvlAe6538HJ0zpzsS9wv3qFwb3uE0b4I4vwy+OCqFCUQkc/GH42AOw99tDqADhe4xPyE3S5zfpzi83lN22qKTrlQz523Une+xRVV0fR5IkSZIkSdKIZbCg3uVPYPdVKhmutAfY5agwUZ5pg9svHZwQoDgK7/wRFJfC6w+FXg5DZVJ7aaPVLw7O8dpa4anfwE8Ogcd+Hib893wrfOJRePv3wmfLrjrIX4HQlc5BQne/WXeBQ3cG8ttLkiRJkiRJ2iEZLIwk2UbM6V6uVh/M/ZNroOGNsE/FBDj2q1BSFhovv3Br/8bR2cS9YM7nwuMHr4QNywbnuFucpz1YWPtq/7/DrE2r4NdvhdsuCd/RhD3hrBvgzD/C6J3C9wwhBIiWh1v2cVcKDQAMCiRJkiRJkiQNkMHCSFJoGZxC9+8taEglIbkeGjeE+1QylPI5+MPh/Tu/FibVB8OhH4WpB4ax3HohLH1ycI6blU6GckvFUSADa17u/7EW3we/OgGW/wvKxsAxX4Kzb4Y9jm0/VyM0b4SGtbnmy7GKEAj0pxFzfw00iJIkSZIkSZK0Q7J580iS3+x3MPbPDxq6ugI+3QhlozveR8vhsP+C52+C+mXwlw/DWdd336C5UEXFcPzX4YZzYN1r8NePwu7Hwf6nE2kr4k2ZOiJAZEUDlBS1l2HKQIZwn2qAxjWhbFOy/da4NkysJxMdSxBF46Gxcl81b4S7/ieUPwIYvxu873cwanLHlQjR8hAqlES3bL6caug4ls3NmgtsytwXvf2+kiRJkiRJkkYkg4WRJDrASedo3iR2fqme/KAiO8GdnShvSUPlTu0T3uXhPhaH066GP74f6h6B278AJ38PIpH+jw1gzHT4wF/gsV/As3+GV+6GV+6mFLgyu83NAzh+SRnsNAuO/ypUTu3bvq8+AH+/BDYsCc8P/hAc9zUoHR2epxpzKxRKR8HYabkgJhsmRMtzvRYa66G8MjfpPxQhwECDKEmSJEmSJEk7JIMF9V12EruoJJTn6eq97FX15ZXt203ouN3Oh8O7r4Lrz4Z//yH0STj0YwMfW/lYOOYLsO974NGfwdpXaMtEWF6fJkOEaWNjFEUiQPstQriPloVVCPEJuduoSeHzxatyfQn6Gn40b4J7/h/863fheeU0OPlKmHl8x+3ymzOXjsqVP4IQ4mTfy4YLFePDe9lJ/8EIATqvehhoECVJkiRJkiRph2SwoL7LTmJDmPTOL7+TfS+7YiE7Gd6VXY6Et3wW7rsilAgavwfscdzgjLFqT3jH9wFIpdv48NWLALj5jJmUxQr8sy+ODmwMrz0It30aNiwNzw/8IJz4v7lVCp01bYT4uC1fz4YJ+f0WsrKrR2LxLUOe7PuFlkiy9JEkSZIkSZKkAhgsqO+yV7Jnr6TPn4iOxjsGCT1dQZ9uglnzYM0roXTRTR+Dc24Lqxe2Z12tUnjHD3LNmVONHQOXdCMk1wKtHfsnZHUOE/L1FgZ0brjdU8hg6SNJkiRJkiRJBTBYUP91OxFdYLmg0lFhIv3kb8P612HpE/C7d8LMt8Feb4Pdj22fUC/wz3TcLl2/nmoBwooFxlZDtLiw49FW2GZF7eNra4Gn/wT3Xgb1y8NrB5wJcz4P43YO31W6MdyXxKClKWyTaYXWprBCoq9tJnoLA/Lf7y2E6CnAkCRJkiRJkqR2Bgvqv/wa/H0puZPdPv+q/ZO/C9d/MAQMz/wp3IpLYbc5IWSY+VYYNXnoPstAZDLwwm1wzzcg8WJ4bWw1vO07MO2g3GdMroHUJqAk18wawvdQOS087q5sVGfZhs696dwnoSEBLSmIWe5IkiRJkiRJUv8YLGhw9LU+fyoJbXmlf0ZNhlN+AW88Ayv+Da8/HEKGl/4ZbkTCJP1eb4O9T4aqmWFCv7UZWrK3VFgF0NKcez1aDvGdwrZ9bbxciNceCoHCsifD8/JxcPQlcMiHoaSs47aZ9vtoWcdm1oWUHko1QMOasKIhPiHX7LlxA5SPKex7z4YM9lGQJEmSJEmSNAAGCxocfa3PH4uHyfL8q/aLimDMLjDjzXDKT2B1Lbz4D3j+Flj1nzB5v+zJ0L+gOAatqYJOVQbcTIw3qCJ63V4wrhrGTIcxM3K30VOgqNASScCKZ2DBN+GVe8LzaBze/Ak44pNQFIVNq0OQUDEh951UTIB0vPtVCalkxybN+dKNYbVDSxOkmqBifCjBlA0o+vS920dBkiRJkiRJUv8ZLCinr+WMBnr8/Cv6o3GIroDURmish5ZGmLRPuB32cVj7Krz2ALxyF7z6QNehQnFpWA1QXBr6FRRHw/ka3qCMFNUsh1eXw6tdjK10NFQfATsfCbscCZP3g6JuVjg8+IMQKkCY3J81D47873CMomguBABIkhcWxHtpZt2+CiHduGWwEC2H2ChoS0NJNHyX0Xjff6vOpZEkSZIkSZIkqY8MFkayzhP9fS1nlK+QfXvbZsxOkG6C8vamztltYuUwftewqmD2B0OfgGQiTLRXToamBogUhQn3+ITcZ2uf0G9KZ/j4lTcwiQTfmFtOtGFl6HewYSmsfw3qV0DzRnjpjnCD9qDh8LygYd+wouGBK0NzZoDd5sJRl8CuR4YyRdlQIBsCZAi3zWFBF585f5VCtLxj34l82cbKqQm5BtCWNJIkSZIkSZI0DAwWRrLOk9MDKZNTyL69bROfADt1M7ne+Wr+llRYndDSAiWlYZv8ffKvzM+0sDwyheVMoXXf2UTLK3LvJdfAppWw/BlY9yosXxjKLTVvzOvvQAgaJtXAksfD8zd/Ag48J4Qh2XMn17SPqxzGzgifNftaxeiuP3O6MZyrYS2MndZe2qiHXhDZgCHa0H1YIUmSJEmSJElDyGBhJOs80T+QMjmF7Nt5m3T+1fq9jCH/av50I5SNhqaNodxRUUnoOdDThHy3YyqHojLY5QiYeWIIN9paQ++ExffCyqdh5X/C5P/mUOGTcOj5EB/XMcxINbWXKWqf8E83hl4QLanwONuoOlsWKfu4YS3QAuuXhXABuu+1kJUNGAox1CWuJEmSJEmSJI0oBgsj2XDX2083hkn8/LJH3YmVh1v+vvFx4XF3zZC7kj0fQHJdyCKiZaEEU6x9m6LiUP5o6oHhnMUxWPUsvP4IFMVg16NDiaNop/GURKElDaPKc+NKN0IkFVZbNNZDeWXHlQaxeAgT1i/LhRKpZOjREBvVfXiQylux0Nt3Z9kk9UE0uYoDpxRRvn4RLC/a4v1ISyt7ZF5lA92swpEkSZIkSdIOz2BBQ6unq+V76imQ3TfdCCWdQoXOIUN3+2WP27gp915Rce686ezrJVBSAsn1HVdPpJugpSkEGDsdEG4ra2H90tDvgalhu1QynDNSDGMndQwNYnFItZdJam2EjY0wZlrH8WbDhc0rMpLh9Z4WYGRLQ+WHBd191wMpcaWRZf0S9llwLgs/Ngru/xjcv+UmpcBPgSZKYcMcmLjLVh6kJEmSJEmShpvBwo4s0o/SQD3JZPq+T09Xy8dGhVt30k1hhUFLE5TmbdfdBHqmrf2cjZBpDfcQjrF5G6ClOTR7phham6BsVDhXSTT33oYVIXiIlkFLReixkD1ArDzcby5xlOxY8gg6rjSIVbT3UsgLODqnBrGK8Hvlf0+Z9s/a1SqDrsKC7r7rvpRN0siWXENxaxMfuDHJl3/we2r23nuLTZpbWvn+NX/mC5mf0dy4Bthlqw9TkiRJkiRJw8tgQUOru6vlC6n739W+6SSsWxpCAOhm0r29b0GEsNohUpx7L/EiVE4MQUXFhDARX1QSztGwNlyOvbmsUXEIPvJXVIyZBg3loafD+qXQuDaMYcw0SNeHEkalXZQwipaH1zN0v3IgGwwUlYRjtrVAQyLXWDv/s3ZVxsqVCRoktavbaBw7E6bO2uK9TKqFJZFHIAORxCIoKd7yAPniE0Izc0mSJEmSJO0wDBbUf4WEA931cSik7n9X+6aSuV4Go3var33VAkB8fN6Ym8N9tkdCfkPo8srwmTKEkklVe+Qm6VN55ZUm7Rleq18WPkMEqKjK9UboamFHIasGOgcDqWRYBdHT99T5N7CHgrqzfkkoydWTxKKCDrWB0TRRStkt/9X7xtE4XPB4l+FCbW3t5sdVVVVUV1cXdH5JkiRJkiQNL4MF9d9AmgLnT6IXElBssV9x+0R+N/vFykMppFh5x4n+qplQ0b4KIdsDISvdGCbyS2K5VQz577W1dGy8XDkNittXL6QaQsAQa18J0VkhzZY7BwPROMSSPa9CyP8Nss8L+R41sqxfAj89NNe/owetxWUkkht73GZ1pIqPFH2H/zt3X0p7WrGQWAQ3nh8Cjbxgoaqqing8zrx58za/Fo/Hqa2tNVyQJEmSJEnaDhgsqP8GUnonfxK9IVF4QJEtC9TWEkoXlVd2vV/+8VMtudfHTIXSaNefJRbvvqF0V69XTGgPESLhMxRnA4kuViZ01Wy5w/vdhCS9rULI/w0GEvRox5ZcE/7GTrs6hGs9eH7xCpZ89eReD7k6UkVmygEQ6/v/Rqqrq6mtrSWRSABh5cK8efNIJBIGC5IkSZIkSdsBgwX132CV3slOikOYoO/tivvs9hXjc8+hfXK+fZVCd/sn10JkdPdhSOdVDPmvQ15z5s5BRjeBROf3C1l50JfvtPNvYI8F9aSq674J+dIr27bKUKqrqw0RJEmSJEmStlMGCxp+2cnxvqxcgC0DiFR7X4VUY+71dBKSm/K2aQhXWPdn8j3dCM0bw0qJsdM6HiO/h0K27FG0PPdabz0WBqPxsj0WtK3qpXdD+fpFzKiMbKXBSJIkSZIkaaAMFrTtKHRyvbur+/P7KmzeNq+JM0BruvtVBfnH3xwM5JclKg+hQkm059UHWwQQvTRtBkMB7ZjiE8Lf9Y3n97hZDVB7wSgWJ1dtnXFJkiRJkiRpQAwWNDy66ilQ6OR6dwFEd/s35TWirdyp9+Ciu5UJsXh43lPJIwjvbVgOmRZoWFNYsLDFGPrQ0FraVo2dARc8Hno89ODVJ+9g14XfoiS1YSsNTJIkSZIkSQNhsNBJa2srxcXFwz2MHV93qw4KmVDvy9X9LY1hgn/zvoWsVkiGMKJs9JYrE3rrtQAhSIiPheZN0N/qLjZi1o5i7Ixw60HTqBe20mAkSZIkSZI0GIqGewDbkueff56rrrqKxsbGfh+jubmZ+vr6DrcRJxLp/RaLQ1EXvQ7yJ9T7fN6iLW8ZIJPpuA2R7m/pRiiOhWCgNNvkuYtt2lraw4VukoOKKhg1Kdz3R3ffT4fPW8D3HLFuvSRJkiRJkqTBZbDQ7umnn2bfffelvr6e8vJwVXsmf0K6QJdddhljxozZfJsxo+crdUesWEWYdO98NX4hE+p9Map9gr/gccWhJZV7HI2HFQwNiXBf6Bij8a4/X6EGur8kSZIkSZIkDRFLIQHPPPMMhx9+OJ/73Of4whe+sPn1TCZDJBLZfF+ISy+9lIsvvnjz8/r6esOFvhjsJsbROMRjfds+Fu9YhqhzWaLBGKM9FDRY1i/ptYcBiUW9Hqauro5EIkFtbe0gDUySJEmSJEk7qhEfLLz44ovMmjWLCy64gMsvvxyAH/3oRzz77LOUlJRwyimncNJJJxV8vNLSUkpLS4dquOqrdBKSG3vfLl/n5tDdNYsu9PxdBQj2UNBgWL8EfnpobjVNT6JxiE/o8q26ujpqampIJsNx4vE4VVX9LOMlSZIkSZKkHd6IDxZWrlwJQFVVFStWrOD9738/kUiE0tJSotEoJ598Mj/+8Y+54IILhnmk6lJvV/5nJ/D7cozOKxIGskKhuwBhIGGFlJVcE/5+T7saqmb2vG18QrdNlBOJBMlkkvnz51NTU0NVVRXV1dVDMGBJkiRJkiTtCEZ0sJDJZJgzZw433XQT73rXu/jhD3/I8ccfz5VXXsn06dNpbm7mBz/4ARdffDGHHXYYBx988HAPefhtayV8ervyPxaHdOvAjjEQ3QUIg13ySSNb1UyYOmvAh6mpqWH27NkDH48kSZIkSZJ2aCO2eXNra5hszmQynHLKKfztb39j+vTpXHjhhUyfPh0IZY3e+973MmbMGF5++eXhHO62I38SflvQWyPlHsq/FHyMnnRu7NzV+W3CLBWkbNPrsPzfPd/WLxm+AUqSJEmSJAkYgSsWVq1axeTJkykuLqa1tZXi4mLa2to4+eST2XfffZk4cSKQa9xcVFTE1KlTmTJlyjCPfBuxrZXwGYwr/4ei1FFfbGurQKStrCU2hoZUhl0XfgsWfqvnjaNxuODxbss6SZIkSZIkaeiNqGDhhRdeYP/99+ekk07illtu6RAuAB1qikciEQCuvvpqWltb2XvvvYdlzNscS/jkZAOBCFBW2f/j9CecyA8jYhX9P7e0DUjHJ1Pz003c8dc/UtPTf2sTi+DG80NvCYMFSZL0/9m77/C26quB419NW/Ie2YkTZycQEjIoIaQQ9p4tlDYUKKMtLW1pGQXSUuBtKS3QAi0tq9AWyt4bQkgIBAhkkQRnDzs73kOyNd8/jq41LMnyduLzeR4/UuSrqyvpXvNwzu+co5RSSimlekyfSSzs3r2byy+/nGnTprF27VrOP/98XnzxxRbJBcPSpUt54okneOaZZ1iwYIFWLKiWPC6w2qWNUryEQKqVCO2pAolMRmhiQR0EymqDuHM7Z1aEUkoppZRSSimllOpafSaxsHDhQgYOHMjPfvYzysvLue6666KSCz6fD6tVPo7S0lIWLFjAqlWrWLhwIYcddlgPH30vEari6Pb9BYOd95peN6SlWF3Q2uu2lhBItRIhtgoklc+lt7WkUp2vukxW5idTvqF7jkUppZRSSimllFJKqQh9JrFw9tlnk5GRwTHHHIPP5yMQCHDDDTc0JxesVmtz5UJRURGXXnopV111Ffn5+T196KozZxBU7wD78M5Z5Z+oLZRxvND+odCtsWdopcLBrLoM/n5E4qHgkVIZUK6UUkoppZRSSimlVCfqE4mFQCCA0+nkrLPOAsBqtXLmmWdiMpm4/vrroyoXHnroIU444QRGjRrVw0etmnXGgGSD1db17YOM4zVbIaOw615HHbxcFZJUOO8RKBybfFtngc4bUEoppZRSSimllFLdqk8kFsxmc4vH0tPTOf300zGZTFx33XV861vfYujQodx///1s3ry5B45SJdSZbX/SsjqvgiBRJYW2KVKdpVBnDiillFJKKaWUUkqp3qdPJBaAqBkKBofDwemnn47P5+Oiiy4iLy+PL7/8kuLi4h46ShVXopZD7eEsALutc/aVqJIi3vF2ZjsnpZRSSimllFJKKaWU6kF9IrHg9/uxWq1s27aNt99+myuuuAKbTYLL6enpzJ8/H6fTyeLFi5k4cWIPH61KSW8I1MdWJiQ7ps5s56TUQaqkpASAwsJCioqKEm+YytBqbRGllFJKKaWUUkop1WUO+sSCUamwbds2xo0bx0UXXcSPfvSj5t+/9dZbLFq0iEWLFmlS4UDSGwL1sZUJyY5J2yMpQ3WZzFBIJpXA+UGksLAQp9PJ3LlzAXA6nZSUlLRMLjgL5Np66crWd2pzwk+WanJBKaWUUkoppZRSqgsc1ImFyKTC1KlTmTt3Lg899BAmk6l5mzlz5vDRRx8xcODAHjxS1Wa9MVCf7Jg6s52TOnBVl8Hfj5DqltbYnBJI7wOKioooKSmhvLyckpIS5s6dS3l5ecvEQu4wSRakkph56UrZThMLSimllFJKKaWUUp3uoE0sxCYVzjrrLB566KGoOQuBQACHw4HD4ejBI1Xt0hsD9b3xmFTv4qqQpMJ5j8hg5mT6WCufoqKi5O2PDLnD+tTnopRSSimllFJKKdUbHZSJhciZCkZS4dFHH20xvNlsNvfQESql+rTCsTB4Sk8fBaWlpc1VAkoppZRSSimllFJKpeqgTCxYLBa2b9/OIYccwoUXXsgjjzyCxWLp6cM68AWDrW8TOcDYntH1xxQposVVwscTbdOW/cVK5XNp62sr1cVKS0uZMGECLpe0ZXI6nRQWFvbwUXUyHfKslFJKKaWUUkop1SUOysSC3+/nrrvu4qKLLuKf//ynJhW6U+QA4+5OLCilUlZeXo7L5eLJJ59kwoQJFBYWptaKqJsYVRTtOi4d8qyUUkoppZRSSinVpQ7KxILFYuEPf/gD2dnZ2u6ou/XGocpKqYQmTJjA1KlTe/owmhUWFuJ0Opk7dy4glRQlJSVtSy7okGellFJKKaWUUkqpLnVQJhYAcnNze/oQ+iZbD7RAUqo3qC5LLZCtkioqKqKkpKR59sPcuXMpLy9ve9WCDnlWSimllFJKKaWU6jIHbWJBKaW6TXUZ/P0ImTHSGptTWvX0kANhYHNRUVGvasuklFJKKaWUUkoppaJpYkEppTrKVSFJhfMegcKxybftwWHBB+rA5g7NW1BKKaWUUkoppZRSnU4TC0oplUxbWhwVjoXBU7r8kNqrtw9sjtUp8xZSkUqLqh5MCCmllFJKKaWUUkr1NppYUAcOrys8GNqmw6FVNziAWhwlE9v+qLcNbE4k2bwF4z1BByoZnAXyvb10Zevb2pxw4X/B2UqFhyYglFJKKaWUUkop1QdoYkEdODwuCPjkVhMLqqNSrUQ4AFocxWME3vfv38955513wLU/MsTOWygpKYn7ntpVyZA7DH6ytPXzwFUOz14MT57f+j41AaGUUkoppZRSSqk+QBMLXSwYDAJQW1vbw0fSCULvpVUmU9e8rtcHHjfYHeCP83kmeN1Gjw9fowQga2tr8dg7+bQPBkPVFKFjS5T06OzP5SBgXBfBVs6t5uto46eQmdHxF3ZVwEtXgc/d+rZWB+QeCplDW9+2l1znZWVlzJgxA7db3p/D4eDFF1+ksLCQgoICcnNz2/03qcuvpwTS0tJwOBzNbZGM91RRUcFVV13Fu+++y7hx4xg4cCADBw5MfcfmHMjMSb5N5ki4+ANwVybfzjivHjuv9de1OuC8hzulyqW2vgFo/TpSSimllFJKKaWU6iymoEYiutSOHTsYNkxXpSqVTFlZGUOHJg7c63WkVOtau46UUkoppZRSSimlOosmFrpYIBBg165dZGVlYermFeu1tbUMGzaMsrIysrOzu/W1u8PB/P4O5vcG4fdXWlqKyWRi8ODBmM3mhNv3xHV0sH8H0DfeIxz87zMYDFJXV9fqdaSUUkoppZRSSinVWbQVUhczm809voI0Ozv7oAymGQ7m93cwvzeAnJyclN5fT15HB/t3AH3jPcLB/T5zclpp56SUUkoppZRSSinViXRpo1JKKaWUUkoppZRSSimlUqaJBaWUUkoppZRSSimllFJKpUwTCwextLQ0br31VtLS0nr6ULrEwfz+Dub3BgfG+zsQjrGj+sJ7hL7zPpVSSimllFJKKaW6iw5vVkoppZRSSimllFJKKaVUyrRiQSmllFJKKaWUUkoppZRSKdPEQgJayKGUUkoppZRSSimllFJKtWTt6QPoTfx+PxaLBQCTyUQgEMBs7ljuJRAIsGvXLrKysjCZTJ1xmEodNILBIHV1dQwePDjptabXkVKJ6XWkVMeleh0ppZRSSimllBKaWAjZsGEDf/7zn6mrq6OgoIC///3v7fofy6amJpqampr/vXPnTiZOnNiZh6rUQaesrIyhQ4c2/1uvI6XaLvY6irVr1y6GDRvWjUek1IGntetIKaWUUkoppZTQxAKwZs0ajj32WE499VTS0tJYuHAhN910E3feeScgq9hSXd155513ctttt7V4vKysjOzs7E49bpWaRo+Pi/7yAQBPX3s86TZL8id4XeBxg90BNmfqL9RQAQEfmK2QUdDy97pCuIXa2lqGDRtGVlZW1ON6HfVeLa4neyv/GfE0tO96MsReV3odtZDoOopl/P5Av47afA4qlYJUryOllFJKKaWUUqLP/994TU0Nl19+OZdccgn33HMPjY2N/OpXvyI9Pb15m7a0jLjpppv45S9/2fxv439Us7OzD+hAzoHM7vFhTZeAZnZ2duuJBdr5PTms4HGB3Rk/gKoB0YRirzG9jnqvFtdTa0HdYAeDdLHXlV5HCbX23yrj9wf6ddTmc1CpNtA2YUoppZRSSimVmj7/f+P79++nrq6O733vewCkp6fj9/t57733+Oyzz7Db7fzjH/9g8ODBKVUupKWlkZaW1h2HrnobW4KEgmozvY5UM72ulFJKKaWUUkoppXqdPj+dLicnh8bGRh588EEqKiq49dZbeeKJJzjllFM46aST2Lt3LyeccAJer1dXsSmllFJKKaWUUkoppZTq8/p8xUJubi433HADf/jDH9ixYwcfffQR//73v7nwwgsBOOecc5g6dSovv/wyF1xwQQ8frVJKKaWUUkoppZRSSinVs/p8xYLNZuOKK65g1apV3HvvvYwcOZKjjjoKkKHNXq+XQYMG0b9//x4+UqWUUkoppZRSSimllFKq5/XJxEIwGIz6t9VqJS8vj0GDBmG321m0aBEgA/yefvppTCYTY8eO7YlDVb2N1wUN5XKrlOo6eq0ppZRSSimllFJK9Vp9phXS7t27qaqqYuLEiQlnJVgsFkaPHs3f//53Hn74YYYMGcL777/P/PnzGTx4cDcfseqVPC4I+ORWB8oq1XX0WlNKKaWUUkoppZTqtfpExcLOnTuZNGkS8+bN48svv4y7TTAYJDs7mz//+c+cf/75DBo0iOHDh/PJJ58wZcqU7j1g1XvZnWC2yq1SquvotaaUUkoppZRSSinVa/WJioUNGzZQU1NDTU0NDzzwAD//+c+ZOnUqAIFAAL/fj81mIxgMMnz4cH7xi19gtVoJBAKYzX0i99IzYlpStYvXJSuaDXZny9XNka8TDEKCipWU2DPkJ9H+k0l1u44cn+p7evK8CgbB0yDXYLxrLx7jmo3dPvb44l1rbT22VOj1ppRSSimllFJKKdVmfSJqPnnyZE477TQuvPBC1qxZw7333svatWubf2+z2QB44oknKC0txWqVfEuilkmqFzHapTRUhNumKKW6T2TLoq7YXimllFJKKaWUUkr1Ogd9YsHv9+P3+1m3bh2nn3468+bNY8OGDdx3333MmjWLCy64AIDFixdz5513csstt+D3+wFNLBwQjHYpGQXaNkWpntDWlkXa4kgppZRSSimllFLqgHfQt0Iym83069ePGTNmsGbNGs4991zS0tK45JJLaGpq4sorrwRg9uzZXHfddZx44olYLJYePmqVMluK7VeUUl2jrdegXrNKKaWUUkoppZRSB7yDvmLBqDqwWCwsXLgQgJdeegm/38+wYcNYvHgxS5YsAeCqq66iuLi4pw5VKaWUUkoppZRSSimllOr1DvqKhWAwiMlk4rjjjmPLli1cffXVvPXWWyxbtoyVK1dy/fXXY7fbmTp1Kmlpadr+SCmllFJKKaWUUkoppZRK4qBPLBiJguLiYi677DIGDBjAG2+8QXFxMcXFxZhMJiZPnkx6enoPH6lSSimllFJKKaWUUkop1fsd9IkFw8yZM3n00UeZPn06hx12WHMlwznnnNPTh6aUUkqpHlJaWkp5eXnc3xUWFlJUVNTNR6SUUkoppZRSSvV+fSaxYLPZuPTSSzGbZayEtjxSPcbrAo8L7DrEVnUBPb+USllpaSkTJkzA5XLF/b3T6aSkpESTC0oppZRSSimlVIw+k1gAmpMKqhN5GsJBTHtG5+yzswOjXnfH99Gu103wPjwuCPjkVgO/qrNFnl/Gvzvz+kzE64L6cjABGYW949zWJItqRXl5OS6XiyeffJIJEyZE/a6kpIS5c+dSXl6uiQWllFJKKaWUUipGn0osqA4KBls+1hVB8th9plpdEu/4ABoqUtuusyV6H3ZnONjZkcqZVN9HRz+/9u6vr+mp7yNW5PnVFddn5HG5KsCUJfv2uMBTL4/b2hHI74rzT5N4KkUTJkxg6tSpPX0YSimllFJKKaXUAUMTC6pjIoOYvXWfnREHT7TyOdmK6ETvw57R9avH1cGlLSvvY4P6kedgZ6/gjwza251gz5TrrTP/HnREV/x9UkoppZRSSimllFKaWFAd1J6VyW3ZZ2QgtL3BeEd+x48p0crnZCuiu+KzUX1T7HmWaoIgXpKhM1fwm63hoL3NCXm9rF2MXoNKKaWUUkoppZRSXUKHDqjeLbZffMrPa4CGcrntjNXKdmd0ELW1x5WK5XWFz8m2ij3P2ntddPb56izQwL1SSimllFJKKaVUH6QVC6rnpLLquq2tTDwNMqzZ6wKLXe7b0tr+urESrXzWFdEqVZHJgLZW38SeZ+1t8RO7HyPZEQQyUxi47HWBq65tr6mUUkoppZRSSimlDjqaWFA9J5W2LLY2tkDyumWfQWRlts3RvtftbJ3R0kkd2Dqz339nJbQ8LmgKJQo8KezTuHY6Q2fPe1BKKaWUUkoppZRS3UYTC6rndGag1dMQbgtjtkJGVjiA74kIhHpcEtA0AenZ8pgR4Iw8rs4KdBr79rjAam/fanV1cDCSAaYUpokb53NXB93tTkjLkkRcvOswNvhvd4LX3/p+U0kaVJWBqxychdB/XIfehlJKKaWUUkoppZTqXppYUD2nM9sIGSupzVbIKEy8nasSgi6wZ4Zf23iuuxYc2Z1byWDs20TXzWKIDeJ2xkpwXU3es7qrqsbmhNw4A5fjJcSM69WWQmIhlWHTPjcEfXIbj56DSimllFJKKaWUUr2WJhZU9+qqYKFR/RCv9VEkE7I629soveXtzojnpktyISO/848rPbvrKhVig7idEZTuiXZRKqwzq3nayuuCqh1gtYHZ0jIh5o2o7vG6wdvU8nqOPf5451POUGhwRl9vkX8f9BxUvURJSUmLxwoLCykqipOUU0oppZRSSiml+ghNLKj2a0+SoKuChfaM6P0ZQ5xtDiBieLMjH0yZ0ceRYQytLYegH6p3yHvLSGGYbWu6Y7hzbBC3M4LSPRnYVi3P5/Zq7zVqtYHPC3kDws+LbRlmPGYJhh+PfK3Whk1nFLasLooacN0N56DOPlFJFBYW4nQ6mTt3bovfOZ1OSkpKNLmglFJKKaWUUqrP0sSCar/2JAnaEywMBsP3EwVKvS6oL5eKBGdBeIiz1w02e8TrO8CeJQmH5sRDaP82BzRUAgHw1LchKRBsfZM2bBaVFEn0OXlcsk28IG7sv4MpvnBk7397hgZa26o3tu6JvEat6ak9x5Yu50ymQ54TDMjjTQ3SuigYcZ543GA3S1WDUeUA0ZVDQULnYFBuE52PJlP034dE11+q53MqohIZrZzvwWDnfsepzNpQPaqoqIiSkhLKy8ujHi8pKWHu3LmUl5drYkEppZRSSimlVJ+liQXVfu1JErRnBX8q7VE8LkkGGK8BydsaGS2QYh/LHQINFZKgsDligvztDLTHJgFaE5kUiTzGhgpJfBjvSdvE9C69sXVPVAItgtcVmjeCnE/xElNel5xzkUzW6ESd1QY2oyLIB3VVMgw6lqtCrk97JuQk+Wy6o8InUlv/hvXG71h1qaKiIk0eKKWUUkoppZRScWhiQbVfVwQB4wXyY9ujNJSDzwP2iOCe3SlBSyMh4HXLIOY2vXYoAZBREAo0muS1moP87UwsGImCVIORscFg47hqdoLZLMmF3CEtEw+qZ/XG9lGRlSdG5QFIpYGRiPM4Wp6XXhdU7wKrFRrr5daWCblDweMLb2e2RiQtrJDVP/5xBGNuY3nbmHwzntPR6gFbG1sg9cbvWPWYeLMXQOcvKKWUUkoppZTqGzSxoHqXqNX6oYBfvPYosYF6mxPyhrXcF8jK7Da/dkTgMLbyoT1VDG0ZCh1bTWEcly1d2tBk5GtP+N6ou1fbt5fXBT43mCyhf7vlsagKIDfgg/pqsIYqEuJ17nHmgz30nxEj2RVvgHpGAXjjJDAijyngb1slQCrVA53dnupA+Y5Vl0o2ewF0/oJSSimllFJKqb5BEwuqe8UOgDWC80awHmJWQZPaINhYRnC+oUKCjwZXJXgtEa/tDFcExHttiK588DRA9c5wL/lUg/ttrZ6IZFQw5Axt30rp3tj7X3WtRMmvhgqo3ArpmVLh42mUuQked8w15oBaH1isMmfBmS+PJROvvVjUMbnlmJy0PA9tzvZV4LhrJGmR8DW1dZHqfIlmL4DOX1BKKaWUUkop1XdoYkF1LyPQ564BR064MsFVAU31kJYJuUUSGG0oj18VENUDvjwcjPS4Wg48tjnAVR/+d8AHDTUS6DcCmUZFgNnaMkgZ25bI65akgs8rw22N143dJvI4jH20t31KawHb1mhwtW+ITM41VMZPfrmqZGhwY71ULHgbwGeBzJgWRjYnOHNDcxEcyYP3rfG6JBnnrZeqG1tE5YLXFa6WyCiMfk4qyTBHTvLX7qnWRZrMO+jp7AWllFJKKaWUUn2dJhZU93PXSHAxsjogtge71w1NdRIgzRsaPzjncYW3saWD1d4ygG93RlcgmK3hlkQ2Rygp4JLV1HaH9JuPen5GdGDW2FdmRMLDSEw0VIC3MSKg64xOOnRXu5ZY2he+e/VUUNk4D+v2gacO/H4YODF6G1s6NFZD1kD5d9AvCQZPKCERebzOfLA6WlYqGMmLSMmSa15XKBlnkSqJyNcwWiB5XdHXWSrJsFTO655qXaTJPKWUUkoppZRSSh3kNLGgup8jJxTgj1ihbAxMjkwC1O2D9KzEwTm7M7wy24TsEyTAH1kxYI3YZ2RPeI8roq2RCSxxEhMtXjMm0QDhigSTp2U1Q6oJklSDkJEB3FTbMGlf+O7VU0Fl4zy0pkt1QHpm/G3yhoHJGk6ked3x2yElEplY8LgBS3R7MIieVWLsMy3e8ThbzncwEjMmID1JC7HefF5rMq/XKS0tTdi6SCmllFJKKaWUUm2niQXVdh1ZkZ0o4BYvYJ/VX4L0iYJzNqdUHzRUQkaWJCeMmQqRCYJEveEj2xpl5IeG2noiZi8kGdIc9TtjngOhY8kPv0+PC5pqIS07eYKktSCkkVDwuiISIDq8uVfqiqByKteccQ3ZHPJjIlSVY5yr6aFkQqg6xwjMV++AqrJwFYPBE0o4NDVIcs4Umt4cmfxzV4LLA/jAR3RCzdjOeB1XubQ7a6iCjLzw75wRCUaQ9xn0gdcHB+op3puTHn1QaWkpEyZMwOVyxf290+mksLAw7u+UUkoppZRSSikVnyYWVNt1ZEV2qgG3yJZD8VYzGwFWIwjpcUliwVi1DeHKBewpvEY6uIhOShitZYwgfmQyIep3oeOo2SmtZbzpQEEocWGH9LzkcxIiPxPj/cXOijBeL0h0Cynt5d77dEVQ2UhQNVSE5x0k+s5jk3RVZeCtA1sm5MapmvE2SqKhqQH2roWdy6FiE1Rvh/KNULUFCsbAD94NJy0MQULJOSB3SMtzFsKvZ3NKUsFqg5pdcmvLhNzYJGOoEinog6odiSt9lEpReXk5LpeLJ598kgkTJrT4fWFhoc5LUEoppZRSSiml2kgTCwqCwda3iQxgR67INlYxt3V/smHiX0UG4oP+8ONNDRJgb2oAa5qsyjZ+gkSvjjZ6t1sjEgtBwO+T+xa7BE5f+ZEEUrMHQ9ZgyBkqP9lD5LZglLRkaqqXY/E0gK8R6veD3w3uKqjYCpWboWGfbDd0Ghx7sxyDM1dezxNaLZssSGokbeK1ZHLXSjVE5CBd7eXefimfpz30up76iOqC9HDbr4ZKcGSFroH01vdnCoZvgwG5H/DB9iVQ+hns/goqN0FNGfia4u9j7xr49AGY/avwPkBai5k8oZkjcRJhRqWExy33naFKBb8nNN+BcMLOuOZtDklS1OwEiy2caAu/odQ+v0RiK5F66jxQ3W7ChAlMnTq1pw9DKaWUUkoppZQ6KGhiIcLevXvJz8/HZrO1vnFfExnAzuzXtW14jLY/Bmt6uJ2RzQGuSgh4JVjpCA2YjQo8Et273ROxL3cFWEKByfVvwXu3yHYA1aXyUxbnmCxpkmSAUPKgLvl72PklFB0F408L7XuX9L23Z0FekpWxRtIm9v14XPL8Gnd0NUOqbXe0sqHjuvszNFoRedySTModHEo0hH6fqMWXcayuKonBW9Plx2KDLYvg61dg3ZvSxiiWNR0Kx0DhOOgX+qndCe/cBJ/cD4ecB2n9wtvbHXJONx9zxLVrVNZ43KEKBatsm5EfrvoxrmdvPXgz5TFXpSQscoZEt1SKfX/t/S5iK5GUUkoppZRSSimlVJtpYiFkxYoVTJs2jQ8++IA5c+a0ez9NTU00NYVX/dbW1nbG4fW87hxGagT+3LXgyA4HKo0go80RDgxm5If7xrvKwxULke1oXHvD+w74obEGvnwElj0uj408Fk76PTSUy6ptI8FQUwrVZRJY9TdJRUIkqwOyBkhv+syBkD1IbveugdXPwwe3w9iTwO+FpmqplEjLIimbM34g1YRUSVjTo6sZjPfpdcnxJwq0HmCVDb3yOursz7C14LhxXoO0QLI7oqtVvK5Qu6/0OO2N3BKsD/igYjNs/hDWvwmuivA2zgI59/tNCCURxkNuEZjN0fsKBmHNS7DjC3jvNzDr10neU2hYud8riQG7E+rLJang80GGI/zejPfXWCXXpT1Tkgr+Jrnmsoe0bAlm6FA7NkfihIVSSimllFJKKaWUSokmFoBVq1ZxzDHHcO2118ZNKgSDQUzxWv7Eceedd3Lbbbd19iH2nMjgZ2a/1rfvKGPgsQlJGoAENiu2yOptZ6GsZo4NDHrdofkGEYFGrytcjWCoL4cFN8P2T+TfR10Dx94CZgsUjo3e1vjK/V6o3SXJhqAfsgZJMiEtO34rqMZa2DRfEhErnoLxZ0JaZiiw2s4Boc4CaeNkDOSN1VqgtTsTQ52gV15Hnf0ZtvadGUmjhopw5ULkdtW7pALHUQCFo0Lneygpt+1jqUrY9pG06jI4C2D86TDxHBh+lFQVtBDTGshkgpN/D4+dBBvfxTT2TCA//vvxuqB+H5htksQwWhv53IA3VCWRH6628Lol2ebzybEZFQsEW7YEa6gIVzMY3wUkTqglGr4eb1C8UkoppZRSSimllGqTPp9YWLNmDUcddRS//OUvueOOOwgGg6xfv56dO3dSXFzMyJEjMZlMBAIBzLEreeO46aab+OUvf9n879raWoYNG9aVb6FzJFo9HRn87IpgnCe06tpEOLBotUvA01idXV8ugVVfk2xXvQ3q98KwI8K920H6tvsAm9HaaKf0pDdLEHNycC1pL1wHdbvkPZ55nwRYW2OxQd5w+UlFejbM+gXMvxUW/hGKjpbe8llZ7V/pnmz4s/H7ZEHvrhgo3IV65XXU2meYSnueRLNKkjFW9se2PvI1SqLL1xjat1v298x3YP+68HbOAhh/Bkw8O0kyoRWDD4fDLoCvnsO2+C7GBq9ig2lUzHtzy9wSq0MqFIz8hN0BXkdoZkR9aFtH9CDoDEf4M8koCLdUikyiGdUMDZWSoLM5JamQKDnT3pZHHW15pW3HlFJKKaWUUkop1Qf06cRCU1MTN910E42Njdxxxx0AnHHGGezdu5fly5dz2GGHMX36dB599FHMZnNKlQtpaWmkpaV1x+F3rkSrp7t6pbvXHQ42Gm2AIgOKRoDRZAWzHz7+Kyz7l/zOYodBk2WWwYjZMPDQ0D5DiQWrDXxesKZxTeAxzgh+AHVA7gj49hMw4JCueU8AM34AXzwCNTvgs7/BKX9oX5DR45KV30Ek4HqQJA5a0+PXUXuCw6m054ncxgiOtybRd5s9CFzp4eHgNgeseFKSCvZMOPT8UDJhViiZ0MEhxXPmweYPMdeW8Vd+yyumU8AzE+wRr+91Q87g8L8juWqkndL2j6VCaMLZcOg5YI9T/RAvkebMD1csRG6X7O+TMfA8UqJKhubfJ/geUz0nDrC2Y0oppZRSSimllFLt0acTCzabjZtvvpmNGzcya9YsMjIySEtL45577qFfv368++67PPbYY9xwww386U9/Srkd0gEpUYCuswPWUcG50Iple6a0SvGGhhZH9ZF3S8VA7S5Y8LvwSuzMAVK1sOML+VlyH6TnSBVD8bFya8uGyvWkvXMLZwRlIrNv6mVYT/itvGZXsqbDCb+DF6+ANS/AtMtg6LTWn2d8PkZvea8bmkKJl9aqFlTnaU9wOJUkXOQ2yQLVXhc0Nchq/0Svn1EQWt3fIMknk0mSWQDH/xamX57acacqexD8cBH+t2/BUvIy5wffJvjIbDjxNhh7Svzzs3Y3lLwh8x1KP5WqIsOGt+H9eTDp2zDlIhg4Kfq5kVULRiVDRgHhHmW0/vfJkd3yMaOSoaEy3GoplWRqqufEAdZ2TCmllFJKKaWUUqo9+nRiwWw2M3PmTJ566im+//3v09TUxGuvvcbgwbLiduTIkWzatIklS5ZQX19PZmYXB6N7UmcmEJIFTI3gXIMxaNkBecOkHVJsT3WQAP2KJ2HRH6Xli7MAzrwfxp4MlVtgy0LYugi2LpahzBvflx+AjP7QsA8TsId+3Gu+ittP+gXW7jrrJ5wlq8W3fwIf3w3febr15xifj6si3OIpLVMWm7d32Ky2Zmm79gSHU7mGIrdJ1sbH44o/VyGS1xVuBRb0wcqnoW63zACZMjf1426LjH54z/oHv10/hmsC/2Jg7Q548XI510+/GzL6yUyHdW9ByauweSEEvOHnF4yWbX2NMuC8YT98/g/5GXgYTPkuTPqWXOcNFTKA2pbZviB9oiHNNodUPrgrIRhKPBgD0I3vPN4slFTPiYOsekgppZRSSimllFIqnj6dWDBMnTqVp556ir1799K/f38AAoEA6enpDB8+nMWLF6c0X0GFJFvZ63FJ9YEtHbLs0YkEo22JsVLZ0wBvXw8b35PfjzoOzv6bVCsAFIySnxmXy+vtXi2Jhi0LYcdSaNgHSJXCD1fOptGU3i1vv5nJBCf/AR6ZA+vflgTI0BnJV6kbg6uDyHsyWyF3WPgzgbYHWbU1S9t1R3DY7pT5ISZP9NBx43dGxUIsI6HgqgJ8Mvi4sRaWPyG/P/pasHZtG6kvTVP4oflPvDhtKdYvH4aS12DbYhg0RW4DvvDG/cZLMmHi2dBvXHjg+fG/hU0fwKqnYcO7sOcreOcreO83MPoEGHcKDDoc7BEvbFwHxjUQeS3FJtASDWm2Z8g+0rOlVVpWxL5SGaatlFJKKaWUUkoppfpmYiF2VoLJZOKwww4DaE4gGLcbN25k8uTJ2Gy27j/QA1Wylb3eRnk8EAgPkW2okJ+gX57nccGm9+HD38uqYksanHArHHElmBIkeMxWGDJNfmb/SpISO76AzP74csfSuOr9rnu/yQyYCNMvgy8eg7dugO+/Ko8nSroYg6tjV1tHDaJtY3CztZXWUQOFu2BAtwprEfx2xg9m25xSrRPv+dW7ZDiyr1FuCULpYln935XVCjEaTen4TrgD6+Rvw+s/h71rYMuH8ssBh8gMhYlnQeGY+Duw2CR5MO4UqdBZ8zKs+h/sXiVtkja8Dfkj4TvPhJ9jXAee0ByVyM/O44KmOmlxlDc0fhLAmK8AkJYFmRGtprSFkVJKKaWUUkoppVTK+kxiYffu3VRVVTFx4sS4sxJiKxIqKyu5++67efXVV1m4cGH3JBaCKQ5X7alZD6kenzE7QZ4U/buMfCjfHPpVIFyZ0FQngb7GSvjkflj7kmzTbwKceR/0Hx8OCCZiiVjabLHC8Jly3xPR193vkbYxqQikuF28AHCkWdfCmpegfD2sfg4Ou0gqNoKB8DZel/wEgYyslqutbQ4Jvvo80UNnU2lz1NpK68iV2gdDYqGzr6NU95dsOHJzFU4oedTUIOeNLT3U7ijmfEh0fN5GGUru94aHJHsaYNkTcn/Wz8FiiX+OB/ypvY3qsuh/b/tYEnrDjwodQ8Rx+rww4FC47B1pbdRYDWNOlkoig99Lq9KyYfql8rPva/jqOfmp3AL/+zZc/BLkDJXztLFWBjhHJd+C4RZHVlviqgMjMWG2tmx3lEpFQirX28E8i0cppZRSSimllFIqpE/099m5cyeTJk1i3rx5fPnll61u/84773Dttdfyn//8h3fffZeJEyd2w1H2ERkF4MiDNGeolUuIIwdc5fDC5eGkwhE/hCvel6TCgcyRB8fcKPcX34MEQSOCksYq9IA/OmlgMFZZB5GgdGSCJTIp0F52pwRa+8pKba9L5ht4Grr2dTwuCdJXlUlSKOCTNlcmi/y+oUJuMwpSa7HjCSWfTBbIGSLnisclrYTq94aqFb7becfvbYQFt8Nbv4I3r4VP/94y+WGw2OS1j7w6OqnQHv0nyuDzy9+H3OFQvR3+ex7U7JDfp4fmIhgDnY1KA69bEg5pWYnnkdgc4Yqg9uiM600ppZRSSimllFLqINAnEgsbNmygpqaGmpoaHnjgAZYvX978u0AggNcbvaL20EMPZfbs2Xz00Uccfvjh3X24B7+MfDCngTNPfjIKZZXy/y6Eys0ydPmiZ+Hk37deDXCgmPxdGDhJBkx/cFv07zxuWWXt8yboqe8OB6Vjg6KdkRSwhYbVHgzVCqnoruCw1y0zFGp3yndstspQ4owC+X3QL483b+8KDSxOcFxet1Tl2EMtlLxucFfAl4/J72f9vPNmK1Rtgxd/AOveDLcfW/EfeG+etGDqDjlD4OKXI5IL50LlVqk6ik0MNLcKi/nsPA3RSSR7RuJz3Ug4Jfr8oe8l4ZRSSimllFJKKaUS6BOJhcmTJ3Paaadx4YUXsmbNGu69917Wrl3b/HujzdHjjz/O9u3bGTp0KD/4wQ8YOXJkTx3ygcPrgqpSqC5NHpAzGMHc3CGhdiZOWPc6vHgF+Nwwcg788CMYfXzXHnd3M1vglLvk/sr/Qdnn4SCy3QH2TMgdnGBorCMiKB0TFDWSAvEGQbcWJO2ruis4bHNIWyJrmnzHRuse4zs3WaITSR53y2RD8+9CLXgig+o2hwT+m2crdFK1wsb34IXLJMnnyIezHoDjb5XPbPMH2N+8hpxgbee8Vmuikgul8PJV4K5u+d0Z14gx9Nyo6omcTdKaVBJOxvUGen0ppZRSSimllFKqTzvoEwt+vx+/38+6des4/fTTmTdvHhs2bOC+++5j1qxZXHDBBQAsXryYO++8k1tuuQWfzxd3DoOKo74candIcDOVFeBet8xTqNgENTvh83/Cm7+SFitTvgcXPd2y93lvEAxCxWao29P+fRQdCYeeDwThlaulSsPjDgUrk7TDSbbKGuInEbRlS2LdVaFhd0LBaMgtkqRQ8+DhmO/cqFSAlskGQ0MFeOslcG4E1Ss2wrLH5X5nVSt89Gd4/zdyTIOnwoVPwpDpMO40OPN+SMvCvG8N9wZ+R1awvuOvl4rI5EJNGTz/fWiKSWzYnZJcMBGeQwJta33UloSTXl9KKaWUUkoppZTq4w764c1ms5l+/foxY8YM1qxZw7nnnktaWhqXXHIJTU1NXHnllQDMnj2b6667jhNPPBGr9aD/WDosGBpi+unHC9my7ivGjj+EGSdPYMmSJaxbV8KE8RM46ihjeLIrPGTV5oCGSpknsONLmB9qC3T0L+HYm3rX4NOAD3Yug43zYdN8qCmVyoJLXpdgcXuc8DvYsgiqtsKz35PBziOPg9yh4RY5bRUZ5DSSE0bfeW3Z0rOMtkUGr1uSVA0VoSoGZ7hSwWSRc8BINEQ+17gsTMjzlz4C7/8W/E1QOLZzqhWWPAAf/0XuT70UjrhSAu2GIdPgvEcJvv5zhtbv4deBByBwKmDp+Gu3xkgu/PssadP0zk1w9t+jtzFaRRnJAWM2SbK5JZG/S2V4s0GvL6WUUkoppZRSSvVxB33FglF5YLFYWLhwIQAvvfQSfr+fYcOGsXjxYpYsWQLAVVddRXFxcU8d6oHF42LjhnW89s57rC6t4YvNFVzzqxu49NJLeeaZZ1i7dk3zdlTvlCoFr1sCcblDZAjx/N9CwAvjz+w9SQVPA6x/C964Fv7+DXjmu7DsX5JUAPDUw9s3Jh5i25rsIXDVh1A0U/b14R3w71Ph5R/C16+GW7bE9oZP9BjEX2mdqEVSX9MTLaE8ocRA7Gp2Y9gwRLc8im2LZCQaItv3OAtk9ojNCa9eDe/cKEmFcadKoquj1QpfPAYL75T7R/4EjvxxdFLBkDcCz0l/ppE0prMa6+I7O/a6bZEzBM75u8x8WPk/KHldHjeGZLsqwq2ijL87DfvkNvaaiW2R1NbzRK8vpZRSSimllFJK9XEH/dL8YDCIyWTiuOOOY8uWLVx99dW89dZbLFu2jJUrV3L99ddjt9uZOnUqaWlpvb8FUqhSQFYz9+CwXbuT7aU7yOw3nNLd5ZwyeRqPP/kMwWCQ3bt2ccghh4aON2IwcWZEe5JFd8lQ27wRcOZ9PZNUCPikX7urEnZ+ARvfh+1LJGBrSM+FUXNg9IlyrE99C3YshS8fhxmXt+91s4fA91+Fzx6E5f+VXvZbFsiPPVPazow+HoaFKj6M7zkyGBo7Z0EDnPHFq+boalHfU4LXdNeCLT3cAimS3RFOOjRUhFbVO6FuNzz3fWmhZTLDcb+BWT+T1+qIVU9L+yOAWb+AyRcl3TxYMJp7TVdyc/BvWJc+CEOmwMSzO3YMqSo6Uto+ffwXeP0XMOBQ8PskSWdLl+/Y7pTPzWqDuipwZElyIXdIRHWCI1yxAD1zniillFJKKaWUUkodwA76xIKRKCguLuayyy5jwIABvPHGGxQXF1NcXIzJZGLy5Mmkp6f38JGmKDIA1hWJhcjERbIAm9XB0LGT8diyOb+4mHGjhnP8UVP5ZOkyph85O9wGyQjcZTrC7UM+f0hWG1vscP6jkJ6d+vH5GuGje2DPagkkWtIibh2h++lya00DvxdrXTm/CpSQHazD/uTd0FgJ7iporIn/GrnDYcyJMOp4GDI1euX2nJvhvXmw+G4oni1taNrDbIWjfgYzr4E9X8Hal2HtS1CzA1Y/Jz/2LBh7srS5Kf6mPM9dCxn5cj/V7ypSe55zIOvqljVRn6cjPGTZBKRlhbeLbAcG4MiG+r0yN8HvhbRscFWFB3jbnJLwaqqDun2w4S34+F55vYx+cP5jcv511NpX4M3r5P4RV8E3r5dzsBWLzEcxOrCNC4JvwBs/l1kSAw7p+PGk4thfw+YFsHuVJBfO+KsMR7dlRs9WAPkOGiolydBQGd3+KPLvZ3vPk752PSmllFJKKaWUUkqFHPSJBcPMmTN59NFHmT59OocddlhzJcM555zT04fWNl0dKE22cjcyiGZ1MH78eMaPH8/yFct59cVnmHvRBfzxzj+AMz/6eCOPtWxpuOXKibfDoCmpH5urAl68QuYetIEVOMn4x+7Y35ogPQfyi6UqYcyJUDBGKijirQQ/7DtS2bB1Ebx1HXzvRbB2ICllMsGgyfJz/K2w88tQkuEVqN8Da16Qn9wiOOUuGHx4+Ln15bJS254JeSnOfOhrK7O7upoj6vMMrYK32lu2poqqYghVJFjSAb/c+rwS/DYGO4Psb/OH8NFdMuwcYMTRcN7DkDWo/cfsdUu1UNnnMquAIEyZK+dfGyqHHjd9h/OLarFs/wheuBR+8B448pI/KeCXAcwZ/dqfGLXY4dyH4OFjYdtHUPKqJEWMzzsyiWPPCM91aawErw2wRlcvdERDOTTVQ1pm++euqE5TWlpKeXl5i8dLSkp64GiUUkoppZRSSqmDW59JLNhsNi699FLMZhkr0StbHqVyTLErbZMJBlvfxkgWGO1WolqEBKODdEZw1OMCqwN8bvC42FyyGneTl41bt3HotKNk/kDQFN6/8Xy/F17/eXiuwmEXSmC8NY01ULkVXvuprKZOy5LV/tY08DWFf7wNcutvAp9H7lus+Ow5/Ht1gBqy+OkJI7HnDJQAqCNXVoqbY4bPNoUqGXye+MdzzI2wawXsXQuL74Hjf9v6ewBoqm19m37jZMDzcb+RwO/Xr0DJG1BdKm1wjr4WvvEjqdwwEf4JBkOftatlMD30PTUneRIlplI5X9qip66xVF+3M95v1Odpirl+Io4j8nFrulw/tvToa8tVDSa3BKt9jbDwj7Dqf/J8R54E/iddIO/P2xjed11MtszvleB93W6o3Q21u6BuV+h2tyToIo09FY76qSQbAPauTv6efQBOAiYz3uNuw/LiXDk/X7gMzvqbXE8BP9TugIotUL4BKrfIwPKq7XJ92pzyfkYeG95v9uDUPnOTGQpGwQm3SmJk0V0w5iToF6oeaqqXGRXBYPjvmNcNFhs01kN6Zqg1Uqg6xGROnHBLNgAa2TUEQ7dtlOr51xv/W9ULlZaWMmHCBFyu+HMynE4nhYWF3XxUSimllFJKKaXUwavPJBaA5qSCimAE1Ix+8JEVBh4XlG+SIJ2zEDILo4PSoeeOG1VEyfZ9jBgbCuy5KkNtiZzS3sVbD9YMeP8WGYKcNwLOvD/1gFnZUnjzl9IWJmeoBC/z4wzZjhx2G8HnDfDc2m0AXD1iBDg7uFI5o58kF967Bb78l/SXHzS5Y/uMZbbA8KPkZ84tMkx6/Vvw0Z8koXHGvTLQ1/icQZIKAX84uWAkGrxuWeXtcXXdwNm+2hImNomTKPEXOScjGAg/r/m7c4PVKsH/sk9hyQPgrpTfTfkuHHtLdCVQIuUb5Fqp3dX6cWcNkiHiM6+W4Hp7pOfAGX+RpFfpp/DSFXIeVG2LnlUSyWSW8+WdG2HqpVJtEJvcS8X0y2Hje1LV8fwlcM6DUtETO6PCF/psfcjfDVeV/Du2OsRVEUomlocfi6w0MZlanuOZheBxdl0FmUpZeXk5LpeLJ598kgkTJrT4fWFhIUVFWlWilFJKKaWUUkp1lj6VWFBxGCuuI4eYGgH6hkqpAgBZfB0RCA0GAvLc+nImjR/NIVOPxGxNJ+iuJBjw01RbhQsXTl8T6RYTphX/wbTujdBchcdkroKnrvXjW/28rEoO+KRt0hl/SS3A2tXGnARbPoRN8yXof+mb4c+ws6XnyGf25WMw/zZY/6b0lz/vYQkMG4xkgnFbvVPa65gs4PdI0NTeRS2Q+lqLpfZoDlIHWn5GNgeULYOP/gh7Vslj/SbAKX+EYUektv/ST+GtG+SatTkge6hUAWQPliRC5gDIHiT307I7byV84Vipsnnn11LJY7CkSSA/d4Tc5o+U28wB8Onf4atnYPkTsH+dtEVLtWLBYDJJgvKR46RV1GMnwaHnSyVE9iCpAAn6pJrAniVzXmzOcOLBHnG92jPC309DpczAiKwmMeZnxJ7jOji915kwYQJTp07t6cNQSimllFJKKaUOeppY6OtszuiAuBFcq98rq9+xSJDOWRD1NJO/MTyk1mLH7HVhMtopNdXj8nnYtG0z+3du4/D+PoYuuV+emOpchWBA2gx9+nf599hT5LnWtM54153DaIlUsVEqCY6/tetey2SCGVfAkGnw8g+l9cx/zoaT/g9mXCm/jwxyusolqeDzQt6Arg/8d/XsjwOdpyF8vcTOIfA0wEd/hs//Id+RLUPOrWmXSvueVHz9Ciz4vVQXDZkOp93dcih6vJkhnWXsKVKJULMjlEAYCdlDpBLBF6dy4ehrof8EWPgHKPtMZjSc/zgMmNi2180aCFcugAV3wFfPwZoXYf3b0ipt8vfk75kzDzIi/n4lSgYYSQRjOHrskOfIigWllFJKKaWUUkqpPk4TCyqaEVyzpIM51AIpb1jL7TwuaU3UWAtWBzv3VbKj6iu27thDfW0NI0eOpK6uDouvkYIld4bnKky/IrXjWHQXfP6Q3D/iSjiyA+1aukp6LsyZB29eC188JquiZ17dsWHOrRl8OFz+frg10js3SuD2qGuit7M5QyutLaEqFJes3M7MjrvbDuvLK7e9LpmLEEQC2PFaIXndEvT3esER87z/XSDJKYBxp8HJd0rLL3+CGR+xNr4PH9wefv7xv5XKoO425qTWt4k09hRJQLxzo7Ru+u/ZMPdlGHho2/aTNRDO/rsk3t69BXZ8AYv+CCWvwXefjd7W6wpXK8Ser63Nr+nL57hSSimllFJKKaVUjF4WqVXdzuuCqjKoLguvxs0ogNyhkNk/eqVvJLtTVsOHVkXv3bsTanbwycL5bN9eyldffcWhhx7KIa7PcDTtlzYnZ96XWvuV2l3w5eNy/5Q/wsyf9r6kgmHE0dInniB88hd47ETYuqhrX9NojXTkT+TfmxdIhYIxU8EV0SPeapfWLhZ74vkHRoDb+PHGH37aYvvWtjsQtee9eVwyMNhTn3DOBwB1+6Q1j7GN1w0f3ytJhcwBcOFTcMF/JanQFu4quc0tghPv6JmkQnsVjoVvPQGDp8rA6nduaH9lxeDDpSXZuQ/LgPd9X8OOZdEtjzxu+Q6MGQzG9+1p6PBbUUoppZRSSimllOpLemm0VnUbj0sCok0xQVEjwZCo7YfVAXlDJYCXkc/AwgJsjgwOHVtMdrqVMQMyGZ3lY9T+92T7Y2+WgHgqPvuHrNYuOhImf6dj7687nHg7nP2gJGKqtsGzc+HFK2Dnsq57TZMJhoT6iPvc4aHNsQOczRawpYO7Nvzc2OC50SapoSLUBquVBENkW6WDTbz31lqywe6EtEywZyafs5HVH0zW8Db718GKJ+X+WX+XFfztMWqOJN6qS6F2Z/v20ZPSc6SlV1oO7FkdTiq2h8kEh54LE8+Rf2/5MGa4tkO+A7sjNIdkl1ReJUsIeRoO3kSaUkoppZRSSimlVDtpYqGvszslIJrWSlA0HquD7eUNfLJyPf6MgUw78hiO/ObxTJo4lt2lm5h/57ehsVqG0E66ILV91u6Cr0LtS2b9vG3H01NMJphwJly5EKZfLkHeje/Cf8+B/5wlLVm6or+9sTI94AslEEIVCX5PeEW2s1C+Y0dEC6TY4LndCWarJJLMVpkDkCxxYGx/MPaaN94bhIPJnZFIsTkkCZc7JPwdvX09EITDLpTkQHtl9JO5CgAb3m3/fnqSswDm3Cz3F/9ZZjV0xMSz5LbkNWgKJdVi2yB53GC1SuVVsr99xtyZgzGRppRSSimllFJKKdVOmljo62xOmaGQO6xdgeIdO3bQ2Ohmx75KcBbg8kJe4QBKN67lxAGhFi3H/UYC36mIrFYomtnm4+lRaVlwwu/gB+9JIsVil+HOr/4E/jkLPv8nNNZ03usZiQW/VxIIxspsT2Oon39M4sD4fmP/bXNCRqH8GI/5PInPB2P7g7HfvPHeIBxMbi2RkkorJHtGeL+uClh8N+xfL9/bif/X8eMeF6p22HiAJhZAqpOGHiGf4XvzIBhs/75GHA2OfGkTtXmhPBbbBsnukLkoGfnymonaIdkcLb//zmoHdjC3FVNKKaWUUkoppdRBTRMLqkOGDh1KWdkOvlq1inm/+Q333HMPny3/iu+N90rP9KKZqQ91PRCrFeLpNw5Ovweu/gxmXSursWt3wcI/woMz4f3fQuXWjr+OJU1uPQ3hwKSrItTaqjYc+I9NBCRLDHhcyecx9BWRyYTWEimptkICaTe17RNY8oD8++Q7wZnf8eMddTyYbVCxCco3dnx/hoBfztu3fy2zPPzeztt3LJNZZqpY7LD5A1j3Rvv3ZbbChDPk/qb3Q23C3HL89pjvyBuqSGktKRT5/XdWO7CDua2YUkoppZRSSimlDmrWnj4A1Q4dWclrtHaJChwnGagctX1kQE6OYfigQvLsPuqxsOqLJQzKdZBdtZoJthWy2ZxbZJVwvEP2NUX/e8nfpFph2BGhYa6h3zfWtnxuPL7GBO8h4sWb6sGX4v5SrbJo2J/4d2NOhOJjYOtCWP8WlG+A5f+B5f+FkcfCzJ/AwEnRzzESBq2x2OQ24AvPVAgC9nSwZbYvMWB3hr/vjkrlPO3IudyefUaez/aMxNvZM8K/b+0YbU4ZnNx8kifbPgAf/UnO89EnwPjTwN+UYNMU22cZLbCKj4HN82HTfBg0ueV2u1aktj9/xOvuK5FkBcDK/8G6t7CMPgVb8FS8Jhvs+LL1/4qYU8xf2zNkTsmMK+CzB+H938j7iJ3NYk1xOPWEs+Ra2/AOzJkn14vfKxULHje4qqQVkskaaiXmSPxdG4kJ4++m3SmzSEye8LXXViZT9PWWymB7pZRSSimllFJKqV5CKxb6mraukG1te6+bUcXFZDtsHP2NaRTmpHNG9gYIBmDcaTB0emqv46qANc/L/Zk/Te05BwqrXao2vv8afOsJSSgQlMGyz86F+n3t3G8oARHwhQObGQXScz+jILxdqu1WvC4JlnZ2W5be1O6lu1eIe1xSpWC83ro3YX+JVDeccmfnBpONFfrr3+y8hI0xgDxvBKTnQmM1tjXP8HjgWs4IvN81FQzTr4C8Yvmb8PFf27+f4tnSDslVAbtXRs8PMZIKPp9cK85WWnt5Y84bI7lgsXfsXLJnQGa/5EkupZRSSimllFJKqV5IEwt9TVsH77a2vc3BoYdN5oxzLuCaX97IjT84jwGuEmlrcuxNqR/X2pclSDlwEgz7RurPO5CYTDB8Jpz7EFz2tgy19jXKqur2iBzebHOGVlWHgpxeF7jK5ae+PLVguscFnjpoqku+bVsTBb2p3Ut3D542Bv963VBTBgvvlMfn3AzZQzr3tYqPlfOgdhfsTrE6IZmAXwLyABPOhhPvgMO+QzA9l35Uck3wcdIW3AJbFnZugsFqh+Nvlftrng8nN9oqsh3SmhflO7CG5iU482S+Qs7g8LVTUwbVZfHPa1uc8+ZgHmKulFJKKaWUUkop1QpNLPQ1bR2828r2Lh+Uu4K4/eDyBODTv8kvJn8XCsek9hrBAHz1nNw/7MLUnnOgyx8JR10j91c9LS2a2qp5eLNHbr2hIcLlW+W2oUqCwybiB0BjEwR2pwRb07KSB0vbmijoTQHY7h48bQz+DQbh1Z/J/IuhM2Dq97vgtdJh9Ilyv6QD8wkM5RvkeO2ZUDhWWgmNPJamOXfwgOky9pOPqbEaVv0P3uvkBMPQ6XDot+T+/N/JMPH2mHCW3G6aD03Vcj8jP/xjzFtwVUD1TknEJUosxJ43B/MQc6WUUkoppZRSSqlW6IyFg03cGQpd8TpuaKjAXVXJgiUrWL66hGMGNXJabYmsCp79q9T3tXM5VG+XdiDjTuu6Y+5tRs2B/FFQuRlW/BeO/HHbnm+0QvK6ZYCzzSnJBEcm+LyQkSe/CxL/fIhMEBh9+vOKWn/dts5hsHXxudgbGNedzdFyVbvdCcv+I3M2LHY47W6p6Ols9XslSQew8T04/rdt34ffA3vXyo9R9TD48Oh5IxYbb5hP5N3gsbx8yLvYNr4FoQRDcNXTmOyZkJ4Nadlym54NaTnRj2UNDCfGEjn6WmkXVrUVlj8OR/yw7e/HaIfkroTVL8lME5AZC163fF8mE7iqJQFnsh7856rqFiUlJXEfLywspKgohb+zSimllFJKKaVUL6eJhYNNbLC4q16jfBO4K6ndW8OSRR9Q1Rjk8LRPwAIccQVkD2rD/kKr9TMHdm+v8YAfandA5VZZUZ43HHKGth7w7CwmM3zjh/D2DfD5PySpkjc89edn9g+3cdn+MYw5GXKHRK+4DiKtZeKdD+0d1NwXEgVtZVx3xoDfWJvel9sZV6ReyZOKYFAGKK98Sl7DGPicPTj1fdTtlZZHu1fCvq/DFTAgVQrDZ8V9mtdkwz/iWGzFs2D7J9Quf4lsS6O00/LUATsTv6YzX1orWZMMKk/PgW/eCO/cAF88Bod+W57XFmYrTL8MFt8DS/4KQT8ccyPU7JIZCyAJhfQssKbL9aPntuqAwsJCnE4nc+fOjft7p9NJSUmJJheUUkoppZRSSh3wNLFwsGlLsNgY1msieUsPr4v9u8uorHWRX1BAoaWRoKcBEyZq6+sZMWYiU3cvZJClWga8tnX4cv8Jclu1VY6pCwJ7zqCLYkqxrKuA6s1SJVC1PTqIChLszx4iAf68kTK0Nm+4PBa5ajsYgMYaaSVUv1duXRXhn5xhEkSOfE48E86S+RKln8L78+DbbZi3YHPCxHOkFc3alyWxYAT9XeXJ2yAZz0/ls+6uKpgDmXHdgQxrjqxcCAZh55dyf8xJnfN6Hhd8/Qos+7cMgzYMmQZTvgejT0j+/GAQ1r0u7Yvqdkf/Lj0XBhwCAw6FfuPlvSRjsVE/8EiK7/8vNr+LoblWXvrDlRTl2aCxNjS3o1aul8ZaaNgPrkrY+hGMOTH5vsedAsufkITH0ofh2F8n3z6eY38t7/fje+HTB2DkHMgfIYOb7UiVj68RHLnhpJye56qdioqKKCkpoby8vMXvSkpKmDt3LuXl5ZpYUEoppZRSSil1wNPEwsGmLavJjWG9mMLPiRdA9rioKt+Hzx+gal8jhYNyMAV8mLIHkT9qIDMdQ5j+2b/BhbRASs9p2zFnDoCMfhJw3FciwdGO8nthwzuwcxn2ii28HAgFT5fGbGdNh7xiaYdSXSrVEzVl8rPt4/B2ZhvkDpPtG8qltYqxOjyeXctln0dclfw4TSZZuf3vM6FsKax+XhISqTp8riQWSt6AU+tkPgKEKxnSCjpeBdIdVTAHmthki80pAfiqMvDWgy0znFio3Ax1e6QSZtgRHXvdqm0y7Pur5yRYD3JOTjhTEgr9xqe2n7UvwZoX5L7JIjMUBk+BQVOkXZHJ1KbDeuzlBVTWNgCwt8HL9/76AYseuxWz2QzmmLZPWxZJ66+N78HIY6UqIhGTGWb9Al6+ClY/C4dfDJn92nRsmMxw3C1SnfTVc/DZg3DOPyDDIRUm+KR6wdso1T3xkpteV7gaRa8B1YqioiJNHCillFJKKaWUOuhpYqEvM4b1mgivuE4QQM7PsFLZCHn9h7Bl29eU7dzL4JE2xk4+kqK974Frr6zqPyJxUDwYDLJ69RrmL/iQhYs+YtTwIdx7522YTCZZHb3lQ+nt3tHEQtnn8Pk/oWYHEJ5Qvp988oeOwZJfDAWjpCIhe1C4330wGBriuk2qGapLJZBbvV1WNFduiXkhEzjypD2LsxCcBfIT8Emw/+tXpLVSa3MjcofB0b+AhXfCortg/OmQlWIrqWFHQsFoqNgEX78qiQbo3HZF7W2ZdDBLdK2YYm5BVuaDJBVsRjC7jRrKZUDy+rfDj+UOh6mXyPnSlmTe1kXhpMLki6S6IfI9NNa06dC8Xh/3PvkWALdccS5/feotPl6xnkdfWsBV34pTOTH8KCh5HdxVUqlT/M3kLzD8KDnPyz6Dz/4O5z/SpuNrNvs6WP0CbJ4PNdtldgTIcOog4UHOxmfhdYWTDF4XNNVJNUreME0uKKWUUkoppZRSqs/TxEJfFm9Yr9HOpbpUgm2ZhQAUDi6mwGwFZz7Lly3H4wmwtWwvQ4btImPxvfKcOTfL6mlfU4uX8vl8XHzp5Tzz3PNRjx9/zGzOOPVEab1iJBbaq3o7fP4Q7PhC/p2eC4eejydvLN/9IJc6UxavHleIxRKI/3yTSVpCZRTCkOnhNkbBgPShr94mlRDO0DbOfGkz5Pe23JfZCiv+I6ujswaFg5iJHH4xrHsT9nwF794E5z+e2qpxk0lWqn9wm6wCPzx+X+82i7ciX4UlSrbYnLLyPfLz2rpYbke0EkBPZNsn8MbPoX4fYJKh31MvgZHHSFLMuGZTsWc1LA0F5iecJT8d9PKCLyjdXU7//BzmXXku/fKy+MWf/8MNf/0f3z3taDIzYz4jiw3GniTVA+vflhkOrbUMO/oX8PR3JCGx72voP7HtB1owCiZ9G756Fhb9Gc5/TJI8zoL457fXJe3EjORCQyVYbVq5o5RSSimllFJKKYUmFhQgGQRkNbXNIatyjYHKHmd4lbU1DQJ+iseMY/v27QwfPhzTx3+FxmppwXLot0KBuJYrsm+4cR7PPPc8VquVE+Ycw3sffEggEODF19/ijNNPhYGHyYb71kpQPlLtruSH76mXlcgb35UkgMkiwcpRc8CaTsAfpM4UahG0aznsW5HaxzLw0Oh/m0yhQci18lMVqmAIxElU5I+EgZNhzypYcIe0Nzrk3OSvd9TP4JUfwYZ3pdph/OnJtzdayEz6Niz4P9ixVAbwDpwUc9ztuMw7o/1RqomRztYV+4wVm2zxNMh1U7dbvhdruiSegoFwS60RR0tVjDfFREDtLvjyXzJHgaC07DrxNqlQMX4P0mYpFV+/Ii2Qgn4oGCPVNNs/brldnMRgFL8ZOFbuL76bN57fDMClU+ykf3oPPx0U5O5cGzuqXSz73x0cc8GPW+4jf2Tob81+aVk2cJLMJUnEkQcjZsO2xfDB/8G3H2/9/cbLVcz6hbQb2/iuVEv0nwBeU/x2YTan/C2zOeT3ecNaJpO6Y/5IMNg52yillFJKKaWUUkp1InPrm6g+x+YArDLcFCRollEgwVJgzOjRnHD88YwdkIFz9X9lm+N+k3DV8dcl67j/Hw8D8Ox/HuXtV57j/ddfBODt9z4gEAiEg/gVm1sPbBoCfgkQvv4L2PC2BHH7T5Tg4bhTm4+3R5hMMPEsyC2SNkorn2q9xUx+sbSmAXj/t+CuTu21sgaGh/WueCr6d163tNFJNZhtsDsTD31WLTVUQN1Oae/jbwq3Qtq3TgZp2zJgyNTU91e7C167BpY9AQRh/Jmywt5IKrT5+Mph3RtSXZM9GEYf3+4EzM6K+ub7wWCQBRvl3yeNl+SdxWziiCI5bxZsrMfvj5N4s9hh2Dfk/rZPUguMT7tUKjQ2fyDzSNojfyQcer7c//yfco4nG07tdYUSrQ2SOIgdch+ZgFNKKaWUUkoppZTqQzSxoFqyOyEjH7L6J99u0V0SNC86EsaclHCz626+Fb/fz9lnnMp5Z58JwNFHHUlmZgZ79+1n5VerIaO/BBuDAVnF3Jo9X8E7N8qKbk+drL6efrm0FMoobMu77TpmKxz2HVlt7a6SZIHfk/w5U74rq8ld5VLpkKop35PbtS9Fr2D3uqU3fNWOtiUX4gVRVTRPA1SVStswbyOY08CRD1lDpFrB65KEF8g1YrGntt9N8+HfZ8HuVfL5n/A7mHMT2NqZKPO44MPfy/E68iTp1lrroQT8/gCn/e7l5n9/sKGenTVe0qwmjhoRXvU/dagE629/dy/5Z/yWU657hM+/3h69s6FHyGfSsA/KN7T+4jlDYewpcn/RH9u/Sn/WteEERdX2xMPNvW5oqpeKqERzMTQBp5RSSimllFJKqT5KEwsqPpsDfJ7wit2GCgmOV2yFPWvh/d/IKnyA429NuPr53fcX8PZ787HZbPz597c1P2632znh2GMAeOvd+fL8zFAio35f4uMKBmVuwYe/h5oyGbw6/Qdwyp/av5q7K9kzJOhvTYe9a+Dje5MHRC12OPUuwCQDdo3Bv60Zc6JULrgr4auIORY2B/i84d7wXle4giHyvmo7r1uCzk31QFDOxfxiyB0qCQGPW9rtABTPbn1/viZpafXyj8Ltxb79RNKkXasCPlj8Z5k/YnPA+DM6VMnz5pdb2by7uvnf5zy2DYCjijNwWHzNibNLj8jnjEOyyUozU9vQyLtL13P2zU/Q5PGFd2ZLh6Ez5P62xaklCg6fK8e/40tJwLRHwahw1cLiuxNvZ3NAWqZ8r8mqGpRSSimllFJKKaX6IE0s9GVGYNkT6hPeUBFu6WF3yo/FDrU7oWEfprrdmNwVmL58FNPn/5TtvnlDODgYw+fz8cub5gHw0x9ewZjRo6J+f9rJ0r7nrfc+kAec/eQ2WcXCvq9h6yK5P/ZUOOM+GHNyu1dgd4uMfjDpAlklvfF9WP9W8u2HTINpl8n9926RKo7WWGww4yq5v/rZ8OM2B+QNhbSs8MBho3VLe9u4aEJC2BwSdE7LBLsDHFnh3zVUQM2OcMueESkkFt6+MdT6CPn+z31IVum3V8APSx6QygdLmiQV0rPbvz/ggdfjzyf5xZHp8neidif4GhmSa+P1K4upuvNQVjx2LUP65bC3so5nPoh5/rBvyIr/2l1ynK1xFkgiEeCjuztetbDpfWnF5GmIv50t1AYuUVVDT7dC0mtRKaWUUkoppZRSPUQTCyG7du3igw8+4JlnnmHbtm3t3k9TUxO1tbVRP72OEYyqL5egmNctP8Z9g80hQT9LaIWzJR0cBTQ3kM/oDzOvTvgyjzz+H74uWU9BQT6/+fV1LX5/6knHA/D5F8uo2L9PKhAgefBz3etyO+p46bmelpnim+5hBaNgyly5X/pZ69t/8zrAJG12XJWpvcbUi+U5+9fJ92uIbGsU2bqlvW1cuiGYekBcR/YMCTobg5xNVkkwALiqpAWVMQS937jW92ckzE6/F467JTycuz38Xvj4HqkEMFlg9q/CFUHt9HVpBfNXlmIyh6uT7jprEKdPzOK4kRFtniLafVnMJqaMGcJPzj0KgL88v5hgZDIgLRMGTZH7Ja+ldiBH/gjMNthfIpUY7VEwCgaHZl5UbIzf6ije38RYPd0KqflaTHKMSimllFJKKaWUUl1AEwvA6tWrmT17Nr/5zW+4+OKL+cEPfsCbb77Zrn3deeed5OTkNP8MGzask4+2ExjBKBPRw0vdtdHbeN3yu9yhkkQwerzPvhayh0pv9Pm3tdi94c67/wrA726+gby83Ba/HzpkMJMOmUggEGDtC3fKXAFHvqzYj6dqO+xaIW2TJpzV9vfd0wYfLreVm1rf1p4B6Tly351iYsGRB4Vj5P7OZdG/i6xOMcTOUUh19XM3BFMPiOsIwsFnCCcZAJx5UjFgMFuT7yfgk0QEwIijO3ZMviaZf1L6mbzuMTfA0Okd2ydw90tfAnDGjJHNj/3k6ELeuGokmemRFUMt26L98KyZONJsrNq0ixUbdkb/0ji20k/BVdH6gaTnhv9GpNoqLB7jOzFZ4rc6MhKrydog9fQskuZrUVs1KaWUUkoppZRSqnv1+cTC5s2bOf3007nooot4/fXX2bx5M01NTTz77LOtPzmOm266iZqamuafsrKyTj7iTmAEozIK5ccIEDsiKgUiV+vaQ+1AvE0QaAKfD07+vWy37ImEMxHqG6S9yJxvJm4D8/3vXkCaBUbvDVUiHPnjxENuS16V22FHyjyBA01BKCBbvy8cRE7GmS+3qVYsAAwJBWm3fSzPM1ZbG8mkhsrE1QYeV+JBzw3lsG+D3HZDMPWAuI6gZfDZmElid0C/MeHtTK38qW2sCd/vSLuiYECSCruWy3U05+aErcraYtWW/TzxwVoArj378JYbRL6/OO81P9tJXpZ8Rj5/TGuvzP6QM0yOfcM7qR1Q8Tflduvi1LZPxmpv2erI0xBOrCZqg9Qb9HRiQymllFJKKaWUUn1Wn04sNDU18c9//pNvfvOb3HzzzeTm5lJUVMSvf/1rFixYQEVFCqtnY6SlpZGdnR31021aW3Fu/B7CwSgjEArRAdJ4q3WduWBOk9tRx0H/Q4AgbP4w7ssN6C8zE/btTzwz4eorL+PXx+UzOCNInSlbBh3HU78PSpfI/QlnJ9xfr2bPhKxBcr9ic+vbO0KJhVQrFgCGhNq77FwmK+aNxEJzMik/frWBNzRzobEmPOg5UkMl+Bvlthv06HXUFvaMUHIuFHz2uCEYak1jTQtvl2piIS2r9eqGZDZ9ALtXyoDj434TbjPUQdc//hHBIFwweyxHjB3UcoOoxELLigWvz8/uCkmmFQ3Ibfl8o2ph/VvRlR6JGMOwS5dI26eO8HkkkdBQHp61EK8NktcFVaXSnkxnGiillFJKKaWUUqqP69OJhWAwiM1m47jjjsPpdGKxSDuPwsJC3G43Ho+nlT30Mq31vo/3e+MxCA0pdUa3QYoMQDvzoXCk3NocMPZkeXzz/LgvN6C/9HTfuy9xYsFp8XPjLOklf8v79VTWJjj2dW/IoNaBh0F+ccL99Xr5oQHWlSkkFjpSsbDvawnwNieKnOEKlXgrnD0uWbntyA8Peo6UkS8zNjLyUz+WvsjuCM9aiBq63TLYHsVdLbfpue1/7aY6WPmk3J98EQw4pP37irBodRnvr9iOzWrmj5ckqD6KSpy0fK+7ymvk763VQv+8OHNR+k+Q1l8N5bBjaesHNeBQaf3VVCeJlI6w2lsmEuIlVj0umZnRVJ/4b6zXBfX7Ew+DVkoppZRSSimllDpIdGBp7IEvPT2da6+9ln79ZGV9IBDAbDYzZMgQ+vfvj8MRDip98cUXzJjR8ZYinSLOimAgnBSwO2WbyCGpsb+PfczmoDkgGNUGKaINSOyq69EnwMf3SsVCMAjmUJ/1UCuj/kZiobwyfnsjVzl89g8cQTdbai08uKQK/u/33P/H30Vvt+0j2PyB3O8/AXYta7ErIGHlBAErcI3c//pVsFvibxdr39epbZeR4lDcopmQPxK2fwzlGxOvzDYC0o48uXVVxASpje2CLR/rN06SBp56CdI6C8K/M6oSjO/fuG8Mdfa4IDM7flsVIyEBic+/VI6vPdvEbt/aczrz+FLhaZDkTzAoiZeM0GdutLsymaOPKd73biSP0nPCv0/W2z/ShnfldttieU1HnswNMB43LH0otf0NnBT1z9seXwPAFUf1p9i7jsadNuBY+aXZCqagDFM2mG3RVReuCkpLpZXVsH5ZmBur4r/u8FlSsbDmJblOEskbIbdFM2X7TQsk0RDLktbysXis6fJZG0mFhnL5t3G+G2wOsGfJn8lE301k8jbyOkp27bUmlfM51XNeKaWUUkoppZRSqpN0qGLhk08+6azj6DFGUiEYDGI2y8fh8Xiorq7G7ZZA07x587jqqqsoLy/vseNMiT0DMvsl7gkerx+38Vjkc1IZWgrSviQ9BxqrYeeXLX5tVCzsS1Sx4KqAFf8BoH7SZfiD8OBjT7K2ZEP0djuXS7AuaxDkFiU/pt6uoA0VC+1phWS2htvflC4JzVkIra6ODHrGVq90Ra/2VIdB9/Q+O8rrBk8deOulBZLBSBC01gYJwq2Q2lux0FAO+0rk/vCjw0m+DvpoYw0fbqjBZjFx40lDEm/YSiuksv0yGH5YYZKWViPnyO2e1bLqvzXDZ8nt9k7475DR0gqiKxdiWyTZHeFKosjHDcbfztiKn2TXnlJKKaWUUkoppdQBqEOJhdmzZzN69Gh+97vfsXHjxs46pi4XjLNS2RQRDHO73dTW1mKz2bj99tv505/+xCOPPEJhYWGL5x1UjCAahINsUX3HQ/MYjKCu2RoOBm5q2Q4p3Aop/nBnvnhMAnj9D+Gws3/KOaefhN/v5xc33x7+jprqZRAtwLBvdP/K3GAAfI0S+K3fH/o8XPErCFJhtEKqLm29N3xzK6QEK7wTGTJNbks/D/X7jwhguiXA2zxzITYA2pm6IoDqcfd8UDY22GysZLdlSuDZYJwjKSUWquU2PaftxxMMShUMQTm/sge3fR+RvI3Nd297UyoN+mfaOPmBrymtbIr/nFQTC/2yEr9u1oBQ5UEQtiSoPopkJBb2fBU9/LojWgzjjqje8rqlIqR6Z3gIeuQMBggnKGITdJHXW+y11xuTZUoppZRSSimllFKt6FBi4fLLL6eyspLbb7+d8ePHM3PmTB588MF2DT3uart37+brr6W1jamV4HRGRgbjxo3j5ptv5g9/+AOffvop06dP747D7HrJgliuChmS7Ap9f1GBtHKo3gWu/XJrPH/0CXK76YMWuxs0cAAAny39Aq83JoheswNWPyf3Z/0MTCbuvv1m7HY78xd9wo2/+6MkF9a8CL4mWb1fOCb19xnwQdVW2Pc1tooSfhX4J1cGnsJStxPq9sj7aayRALXfI8FUdxXU7pLBynvWwP51sHettC2qLoX6PVC3W6oN9n4t29Xuhvq9knxIRUY/GeIc8Mk+kzFaIbnbeD0ZiYW9q8Hnle/K+L4coRXjXVGhEKurkhddnRBJxEgouGKCyvYMyB0GuUOjB6Ibn7kpheoBI7HgyG37cVVslHPQbIWiI9v+/Hj7q9jE7t17+HxLDRYz7KzxsH6vm/97uyxq0+2VTdz53m6ueXFH82Nvrq1rsct91fJZ5GS00p5o1HFyu3UR+H3Jt80eJImUYABKP2v9fSUTm1Q1KrgiEw02h1xPVpt890aSrq1irz2tYFBKKaWUUkoppdQBqEOJhUceeYQ9e/bw/PPPc9ZZZ7FixQp++tOfMnjwYM4++2xeeOEFmpoSrHDtRjt37mTSpEnMmzePL79s2bInltfrZeXKlTz33HN8+umnTJs2rRuOspskC2J53BLg9EQMMDUCaQ2VcuuuD/+7Zgf0myjb7l4ZTkiEnHXGaeTn57H26xLuuvsv0a+16C5ZsT/0iOZg6Kji4fz1D78B4M8PPMwLL78GK/4r2w87og298wPSFia0qtzSVM1JwY/4VvBNbHWlULFJkgZ7Vks1RNlSaeW0dy1UbpHkQWN1uKLAZAarQ1aTp2WFAsVBCRy7ymHvGmnH0lqiAOQ9GD3iW2uHZMxHaMvwZggnFio2yWdhsYd7urcWlO/M1dNdlbzoyoRIsvdvrF4PkrxVmMctlSJNoYqGVCoWjNY/ba1YCPig7HO5P2SaJK06g9fNINN+tv0ik7mHh9ukjemXDkBmsJ5DgyU8Mn89haZKrp8RTgLc+tZOat3RcyRmjB0EwNtfbolbMdZs8OHhocyJZqlEGtFJ7ZBihzcbjAoEe0YogTRE/gbYHOEknacBqsugqizx0OZkf3e7o3roIFJaWsrKlSub/71y5UqWL19OSUlJzx2UUkoppZRSSinVB3UosQBgt9s5//zzefnll9m9ezcPPvggM2bM4I033uDCCy9k4MCBXHnllZ1xrO22YcMGampqqKmp4YEHHmD58uXNvwsEAi1W0+fk5HDuuefy2Wefcfjhh3f34XatZEEsu0N6vBvtXIxAmtkiA1pNFigoluClCRkQ7EsQSEPmV/ztL/cAcPsf/shXq2UILFsWwtqXABPM+jl+v59Ply6nsbGJH/9gLvN+9VMA3nz0D9CwX4LIAyam/h7r9kgFgdkGecV4c0bymOk7PG86HZ+jnwQu07JkaGvzkFmTvI4zH7KHQMFoGSDbbzz0nwiFo2W+Q94IGSBdOFaqDyKl0h7JXSUBfwhXJCTiCyXlLLbk28XKGijzKIIBqNoS/r6TBfqNgHp9ee9ePW1vZe5HRyULABur1zPyW84liT1Gk1WuD2g9YFy3F9a9KfcHHNK2422qDydB+rfxuYn0Gw9ZAyl3myh0mvnHKRZ+9g07d8xJ4yeTPZj2fMWLgau4J3AHd59g5cqpdopywv8pufxwC79/b3fULs88cjQZ6Ta27K7ms5KdiV/bbJGWZxCeGZGMMTi9o4mwVOfKGImGjPzw9s0zNupaJiaan5fk7253VA8dJEpLS5kwYQJHH31082NHH30006ZNY+7cuTidzoO/ZaFSSimllFJKKdVLdDixECkvL48f/ehHfPzxx2zevJlf//rXuFwu/vWvf3Xmy7TZ5MmTOe2007jwwgtZs2YN9957L2vXrm3+vc0mgdvHH3+cbdu2MXDgQJ5++mnGjx/fU4fcdZIFsZwFkNk/vFIeJJBmc8pKapsDMgrkx5kvCYadK2W7QVOinxfynQu+xTlnnYHX6+Wyq36Mt6Ea3rlRfjnlIrZ7c5lz1kUcdcr5zDz5PHbv2cdN115N0dDBnDw41FKl/4SIBEAr/B6pOABpT5NRiD9jAM+Zz+JR8/fw5Y2W4O2gyTJ8uuhI6dU+/CgYMlWSCPnFEpy3Z0hQP16lRGNNuHWK2SrPyx3e+vGtekaSHoVjYUgr7bWMCoicdgysHjhJbss3SDDT4wq3RIq3It8IqJvo3aunuzr4mqj/vachevV6a8eYURCu4Mnsn3z7j+8Bn1tW6486vm3HGxkIb+/cj1gWG2T049I3giza5sNhhftOSWfeN9NwBhtIM0l1wl4KKW1II5ieB5kDIVNan/14up0V2yrYuC/cHiwj3c65R40F4KkP17Z8zUgFo+W2ckvrx1q7S26zkwyWTkWy7zZ2pkbs9s0zNrISJyY0edApysvLcblcPPrYY82Pffzxxyxbtoxly5ZRUlJCUVE7/l4qpZRSSimllFKqzTo1sWBYsGABt99+O3//+9/xer2tzjToSn6/H7/fz7p16zj99NOZN28eGzZs4L777mPWrFlccMEFACxevJg777yTefPm4fP5mpMNivBq3sjV4jYn5AyFnUvl36PjB0RNJhP/uP+v5OXlsXzFSjY8eoW0UMoZyvP7RjJ59mks/vQLAFau/pojTzqX7WU7+evvfsk54yWZsNs6LPVjrdkpAVZ7hsxlSIXJlHqbJW+jzFao3wsEpfJh2JFSIdDaPur3wro35P60y1rfvibUzz63De/fMCCUWNizOnoVfqIV+UZAPaOwbwRAjWBxbIIlUf/7RCvRo/YXMVfB64Kq7XI/trIlUlMdfP2a3J9zc9uHk1vs4fuBVmYStEEwGOSz7S5O+5+LSn8oeO7I490yB3P+6+E88yN833I//YeNwpRRKNdBWjbBNGkP9PAZ6cx7Q85fr8/Phh0VfHu2JGqf/agEr8+f8LXJHym31WXg8yQ/0LpOSCy0Nh8lXpukyGSDMWMjb1jrSSfVKcaPG9d8f8qUKUydOpWpU6dqUkEppZRSSimllOpGnZZYWLVqFddffz1Dhw7lxBNP5IknnmDQoEH83//9H1u2pLDytIuYzWb69evHjBkzWLNmDeeeey6/+93vePnll1m9ejVnnHEGALNnz+a6667jjjvuwGq19mgypFvEW7neWp/x2GBzUy1sXij3jSHOcQwcOIDrr/0ZRw61MKHhUwAeLB3LBT+8gZraOo6cfjjzX36SMaNGULpjF7NO/RYTraU4bCbWlfv5+b9T6LUOEuRzhaoIcoraHqRtTcN+aWPkc0vf/JyhUqVgbWUgrWHFfyHgleqOwVNb374mNBA3Z2jbj3VgRGIhchV+opYs8VZUe11SNVFV2jkzF3qTVAfmGp+XsRI93up1kGsm6AvPJ/G4oSZUcZKRpGKhfCMQlBX/Aw9r+/swmaTlF3RqYqG0somKBh9NfnD2HwEFo6mwDOCC/5WzuCxIgyl+AN2U0Q9v0MKIXDPHDPZw2B+/JvPcexh3xcM88vYq+uc6Ka9x8/7yrYlf3FkAadkQ9EP19uQHWhNqq5Q9uH1vFGReTKLZCIbYQc2J/lYqpZRSSimllFJK9REdSizs2LGDu+66i0mTJjF16lTuuecefD4f11xzDUuXLqWkpISbb765R1cRGgkCi8XCwoULAXjppZfw+/0MGzaMxYsXs2TJEgCuuuoqiouLe+pQO4+nQYbBJgsGxwusptpn3LD+fekt7shrta3P+WeewmNnpWM2Qe3wU/jJX18B4LfX/4zFbz3H8cfMYsk7LzJzxlSqqmvY+aG0unhytY/nF61l4cpWklPBoKxwBqlUSOukIbYGV6XMbjCqFArHyvtONXnhqYdN8+V+KtUKEK5YyGlHxYIRpN6/TmZjGEmDtrRk8bhkRb2nrvfOXGivVAfmGp8XSELBVRk/oGwLzVVonk/ikHkVkLwVUkVogLfR/qc9jDZhnZhYeL+kGoAjRmSRbpP/TPzx3R3UNvo5dEiSVfkmM7acgQBcPcNOod2DxyvVCW8s3cTJU+Xva9J2SCZTuGohWTukYBBqjcRCByoWLJbE1QgGY1Czoa1/K5VSSimllFJKKaUOMik2ro9v+HDpKZ+ens4FF1zAxRdfzMknn4zFYumUg+sMwWAQk8nEcccdx5YtW7j66qt56623WLZsGStXruT666/HbrczdepU0tLSerZSIRjsnP1EJg0SDer1uKSffnpEwMyekbyVR2QPd68bNr4j90ceK8FA4/fG0NoIY/e8Cv0s7K0P8PJeCaIOKMzntqsvgIa9ABRaYf6//8QPfvpLjh2+Nbzvj97l5ofeYMkfzk58bDU7JAAO4K6EHZURv0wD45TctRz6pTALAaAwNGPDVQF7QoOnC8dC4bjoxMCh57a+ryUPyOczYjYMnpx8W7NFzgUjsZA3XB5rIcn5kjNEBnE3VsP+Epkp0aqYc9/ulCRKkN47c6G9jPPcSJgkTbQE5XxvqoPGWkkoZWTR8vMPyvcWDILVIdUpIK2QIq9tf8Sw+PL1cps/MvpxCM/waI0tXaponPmQm6S6Zewp4fsBH2xfIn8LbA4YekQ4SL7hHd5dIZ/LycM8sGc1dU1BHlwk19dtswI8aOynehvQsl1RuQsKnfDMtxy48g7hyic3Mr+kkkKzrPx/bcl6vFs/w2aNyG0b7btAkmm7V0pFx4jZ0Ts3KoTcVeHkaf6I1CuHYllD77uhPDyM2UgeGTMUvO7oayDe30rj76oxKL01qf63xjh3ku2/s/7boZRSSimllFJKKZWiDlUsHHPMMTz22GPs3buXp59+mtNOO61XJRUgXLFQXFzMHXfcwcsvv8zrr79OcXEx5557LnfffTc33HAD6enpB0/7o3irsSNbH3lcYLWHV7Ank2jYr9cNe1bJ/VGJ2yABsmp+6cMA/OStRur9ks+qqK7B54teZe10pHP/96diNpn4aLuP4086DZvVzKfr9/H5hn3x9x/ww+5VyY+hvbwu2PEFEISswS2TCqmo3AI7l0v7pBmXp/achn3ga5LntGc1tskUboe05lWoKku+vSfB92xzQuZBOnMh1XZIIMFlv1cScUZLKY9L5ip4XKFgtD965XtTqH1OshkLFZvktmBU+9+HJdQKyd/KPIJIZisMnREOmu9Y2nzsXn+Q9zbLdXnqGLlWn1vrxeWFcQVmThrVej76qdVetlUH6J9hZoStnDEDJHiflW6hINNGfZOfL7fXJd5B3gi5rdqWeBujVVhGP7Cmt3pMCRnJVSOZEFuNYLSCg/jXiKEt51N7JNv/wdaqTCmllFJKKaWUUr1ehxILCxYs4NJLLyUzM3Hbma1bt3LppZd25GU6xcyZM3n00Ud59913mTZtGsHQCs9zzjnn4Gh/FCleu5vIoFRs4iFR8iD2eZEa9oWDoqPmJD6WYADemwcBH8vq+/NiiQ+Px4vDkY7P52fbjj0tntK/ejkAT37l5dFn3+CioyXoet+ba+K/RsXGuFUSHRbwQdlSCdimZcPgKe2b27D6Rbkdc2I4YNoao61T1uBw4LitBh4qt/u/hn3rJQhuBMJjGau0I3/X1YHSnpZqOyRj25whUsFhBJwjV7bbHFJVEtkax5j3kWzGgtHqJ78DiQVzaIBzbMVDayIrFSKSC0vK/NQ2QT+niemDzQSDQf61UvZ92RRb0gSsyyN/VyvdQS5/LZRkadjP4f3l8bpGP8eOzQVgwbrKeLsQeaGqooZ9iYcrd2QGSazIZIKRSIitSGjtemjL+dQeyfbv0VkPSimllFJKKaWU6l6dNrw5VmlpKVdeeSXjx4/nv//9b1e9TMpsNhuXXnophx0mvecPmuqEVEUGpSITD14XVO0I9dKPEzBLFMz66nm5HTkneQ/59W+HBghn8rb/aADKKyoYN0baIX29cVvU5sGKLVC1DY8fnv/ayzOvf8B53xgBwPOfbmFnRcyQVV8j7P061U8hdcEg7Fopq84tdhh2RLiXfVvsXQv7vpbnTrs09ecZbZBy2zFfwWDMWajcDBar9KNPNHDWCKxGtXvp4kBpZzMSZK0N4jW0Zd4EyOeQURD+PKKC0U4ZOhy5L1eF3Ca6PhproV7agHV7xYIhTnLhk1KpVjh5tBWzycTzX/tYUubHaoa5h9lYV+6Pu6tbF7jI/EMVz65pos4TZMFWP//4Uo7p26NcZNqhttHPcePzAViwrirxcdkzw1UI7ur429R0wnyF5teLSCZEzliIvG9cD5C4uqe5kqU8eXVDeyQ7X+0660EppZRSSimllFLdq12JhY8//pg5c+aQnZ1Nfn4+Z599NuvXS69wl8vFL3/5S8aOHctjjz1Gv379uP/++zv1oNvLbO6yPErvFxn0igx2NZSDtw4aa+IHkGODWV63rHpf9T/59+Fzk7/uZ6Fu7NMuJb1Qhnjv21/OhLFjACjZvC1q8xVvPArAgq0+cgsHEggEeHNZGbMnDMTnD/LgOzFJhP3rpZe9vZOGNQcD8llUl0HdLsAUahnTzuD62pflduSxkDUo9ecZFQsdWY094BC5rdkBWf1lJIC7tuV2Riuf2N7tbQ2897SuqLDwuqKrPCLbH8UmGiIFfDLoGRK3QqraGv59Wlb7j9FILNTubF+v/Zjkwg+m2jl9jJXTRlupcAX46VtSMXDz0XYGZJo495nw5+sPjVV56qsmbl/USDAIb27wUu+RiR3vbJIkRa49wK9mplHr9nHc+DwAPtlUQ6M3fpKCYBDMoffVWB1/m1qjYqETEguRIitRoqpSIgZ5JzrPjHOwoTJ526K2JMBScaBco0oppZRSSimllDpotDnSvmzZMk444QQWLVpEfX091dXVvP7668yZM4dNmzZxxBFH8Ne//pXCwkLuu+8+Nm/ezE9+8pOuOHbVVvECr0FkeKkjP/Gg58iVt0agzdh20/zkr2kMVN3yIQMLcgBJLEwcH0osbNrevOkXq0p4bv4XAJww0sL0HFnR/MTCDVx8jFQ4PPR+Ce6miLkMxgrijrRCCgblM6nbA+WboW5vqP2KSYYeOwvat9+G/VCxWfYz4Yy2Pddow1K3u32vDbD9E7l1FoA9SyK91jhtleK1QepMyVptdaauqLAwrhmjyiMy0GwkGYyfyPfnawy3RXInWJnvkAA7DeUypLi9CuTaYPsSWP0C+NpfueA1pzMw08wb33Vy3gQrty1qYr8ryCH9zNzyzTTe2OCjrDacvLjkpXo+KfVyxavhIPmOGj/HDjez/qcZvPqd8HdR1RikrsnPuIFOBubYafIF+GxLnEQXwOYPZBi7yZz4+ksLzUaoKm37+4Xw36b6feEKA2NgtlGJEjtvAZKfZ8bvMvKTtC06yFuMKaWUUkoppZRSqk9oc2LhT3/6Ex6PhzvvvJN9+/axb98+br/9dvbs2cPs2bNZt24d8+bNY9OmTVxzzTWkpaV1xXGr9ogXEMsslFYtmYUtt28oh12rJfBmBMFsDlkhffq9EvRb9TSUvJH4Nc+8D9JzYe9avhGU2Qn7yiuYMM5ILGwDoK7exbd/8hv+9LGbN0udWM0mnjzHzoXT8vH6AqzeXsmI/plU1DXx30URQdj+E+WnPfweCQhXbZNV/Y21QFDeX0YhjD4Bcovat2+AnSvktt84SM9p23NHHiu3pZ9DUzuSJr5G+CRUKXTk1fKdp2WDzxsdJIX4bZA6U3cFUo0V5bG98RNJJeFhXDPGZxYZaDaSDK5KaSVWszu8L18TFI6V+4kGi+cWwZiTgCB8/s/UjjmeoiNh3GlyPe75SgalG22Y2sLm4L9lQ/jTJ034AkHSrCZ+f1w6vzjSzr/OdmC3mHh8pYeBGTA4uIeRwe3srfNx24cuThlt5dZj0njmfAfvzHXy3Uk2xhRYqG0K8o8vPfxqoY37P/dQ6/ZhMpk4bpwkVeLOWdizGtaE5pIccl7iio8xJ8rt1oXgTTCHIRlHrtzW7IquMoDweRRv3kKySh7jd8ZP3LZFB1iLMaWUUkoppZRSSqk42pxY+OSTTzjuuOO48cYbKSwspLCwkHnz5nHMMcewb98+/vSnP3H77beTnp7eFcfb93Tmau94AbFkQbKGSjCboLEuuq+8Mx9GHgMzrpTH3vylrPaPJ2cYHPcbAEbte5vxhWb2RyQW1m0pJRgM8vGXq9i+cw8D+hVw9hN7eOFrL2lWE4+d5OWoYRYefLeEs2fIQNdbnv6SPVWhz8NkgoGTpJVLSoLSs72qFCq3SQDW75X9pGfLTIO8EfIeYwPwbbVLEikMObztz80fCbnDpc3Tto/b/vyVT0m1Q+YAGH2yBPXNFsgd0jKgabT06ap2Kr01kJpKwsMW0+4osv2RkWRw5ss5ZLWGKxtsDigcL/d3r0y8/2/8WG43vgv717XvfZhMMHwmTL9MWoLV74XP/iHzPdroi6213Di/iQe/8LClyk9Wmom/nJzOEUMsALz6nQx2/DyNxwO/5B+Bm1h8iZ33Ls7g5Qud/O7YNC481IbdYmLtPj+Xv+Zm8D11XP1mI6v3S5VDtUsC9ydMlDkLLy3fTzCyfVPtLvjyMSAIw2fBqOMTH+yAQ2W+gtcN2xa3+b3ikGMg4I2uMujodd+atibAlFJKKaWUUkoppXqhNicW9u3bx7Rp01o8PmPGDAAuueSSjh+VCuvJthkZ+ZCWC4WhwbKuyujBvyfcKgOC3ZXw+s8T93cffzqMPBZz0McjZ6ZTU1ND8XCpBKita6CyuhazSU7FQf0L8Afhey+5+WA7ZNhNvDM3g0n9TbyxrIxJw/Mpr23kt88si36N/OIkbyTIzOCX/Mb/F9LwSAWGL7TC2eaErIEyPDdroAQVO2Owd1Md7N8g9wdPbfvzTSYYdZzc37Kgbc+NrFY4/GKZG2G1h+dsxOOJSWB1dUKrN2hrwiNyvoIxl8LmkESDMx98ES26bE4YfqTcT1SxAFA4BsaeKvc//Vv73ochbwQc+WOphPA1wapnYN2b4VX4KQiEruGaRvjPSi9Xve6mwRNssY2LdCrJodEHe+oDePwAJj7a7mfKP+u57LVG/r3KS1GumbvOH83vz5G/IeX10qbpvMP7k24zs3ZXA0u3htohNdXLTBZfIxSMgckXJb8WTaZw1cLG91J+j83Sc+XW3xRdZWAE/CMHN7dFi/Zx3dQKTCmllFJKKaWUUqobtTmx4PP5yMhoudLSeKygoJ396FV8PbnaO6MQ+o+VW48LAv7oxILFDuf8E6zp0hP9y3/F34/JBMffStDq5OgiK9+f6MNisTB4gLRf2lK6i/Q0OwDuxiYAPH7407oRfLTdR5Yd5n8/A5uvnuGFMqT58Q/Xs3Vvgv7sMdLw8LvAvRzNF5iM484oDFUFDJVKBVMnD/bevQoISpDXGPjaVkZiYfOHbRvKG1mtMPl7YEtvfSV27JyFjia0jKBsbw6mtjXhkWioryE9ZgDzUEm2sme1XDuJzPwJmCywZWHyJEQq0rNh+g9gxGz5d+lnsPSR8CDpNijKNfPIci8XvSjf4Zq9fhy/r2Xo/V7OtfyLiyz/ALuTgdl27DYrmC0MybHy1b4AX+z0s/JH2az9SQ43nDKcIXnSEq+iwUcgECTHaeVb0/oD8NjHuyT5tfQhcJWDsxCO+GF4fkoyY06S243vt/1cNVohuRN8NvG+41TEXjs6U0EppZRSSimllFIHoRQiN6rbxFuda/T5bo/IYLQ3tMra7mzfynG7E+rLpW2Irymc6BgwAU64Dd65EebfKi2S8ke1fL4jn+Bxv8H03k3cdUI6NeV7GDliOLv2lrNlXz0jhg0BoNEbXl1dMLiIi9/axotn+5k+2ML7FzuZ/XgZkyeMYlXJZv7vw1oe++OPwq+RFgrset0SaGzYB8jM4gpy+dA0izPHpZNWNCW19xwIpPjZZLZ8bPdXcjvsyPDvnSkmGIxqisFTZLB2/V7YtQIGxMySsMU5LyKrFaZfLu2PjNY9xop7myP8/Rkr7yE6gWVs396EVmQwNdn529bqEJOp9ee0JQmTCmN/tnRpD2byyPdisshjngZ5nyYkuWAcX+FY+Y68DVC5WWZtQMt5G4Mmw6Rvw1fPyIr9i56Rx13lqR1fvHZBY06SYc4f3C7thZY+LEmmIa1Uz+SVA1ug6EiGTxwKrz3FBlcuHPVjdi3bTKPvafr1iziPZ14DlvB1Mgo484uneG3JOv6xYzx/P/9MyCik0OsDPsYfCFI94jTyczK4/OLDePKzv/DMskoe/F4Qa8VGuVbO/ack/RKJHIZdOAayB8t7XHgnzP5l+HeZrcz3aU4sJBiubczRaGtrpNhrp6PXUirXSGdUWSmllFJKKaWUUkq1QbuWaT/55JOcdtppUT9PPfUUQIvHTzvtNE4//fROPWjVDp2xatbb2LJqAeCIK2R1va8RXv6hDEWOwzzjB3y+C7LSTDgW3sqoEcMA2LK9jPTQkO/GxvBzm5o8XPCtb3HKky621tkYmm1m/sUZuCtlnsO/X3qXTdt2tnwhmwPGnwYDDoHCMXhGn8Zc8994xPw9gs5uqKjxNYVXng+b0f79WNOkdz7A1kWpPWfFk6Fqhf4w9iQJdhsBzXgrsI3HIHr1vrGaH9pXedBb5yq0R0MF7N8EnlCQ2SLVNc1zKTxuaTVldUQn7fxN0M+Ys9BKJcKsX8h+t3/Svpka8Qw/Ci74t8wg8TTAFw/DV8/KPIgETIQD1MP7SwKkdH8NwWCQfdVyDvTPSZ7o/Pl5cs4+8d4KqurkXLPbrGRnyNyd/dV1ABwzdQxD++dxxWEBrLuXS9XQyXcmTyrEMlvh2F/L/ZVPwb6S1J+bLgOkqdsbv91RvMHNqYithOmtrcCUUkoppZRSSimlOqBdiYVNmzbxzjvvRP1s2rSJYDDY4nHjR/WwjgZ6PS6w2sDnbbmC19sIJ94BjjwJoH705/j7MJmZ90UOTb4gmbuXcOZICWhv3lZKeroEaxubmpo3r29w8dPL51LvtzLr4UpqyGJUvpmXzoNJIwrx+wPc8bf/xH8tsxWKZkLxMQSyhxLo7FZHyexeJcmVjH4ygLkjRh4rt1sWtr6trxE++avcn3qJBKqNxfvGXAC/J/r7MwYQJ1qV3d6ElD0DMvv1vgG17el376qCQJPc2kOVCnZHeH9et3yukY81VEj7oQGHyGPJBjgD5AyBKXPl/kd/6ryqi8wBcNbf4fDQvrd8CIv/DA37W33qsH7ZmEzgbvKxv8bF3ioJvvfLCZ8r+6rr2b63mkBEdc+cKcVMKh6Aq9HLw2980fx4Ya5U7pRXy35MJhPXnTSUP58Yqiw4+loYMavt73HEbBh7srRTWnBH6jMljIqFxqq2tztSSimllFJKKaWU6uPaHG3dunVrm3+2bNnSFceu2qLN/eRjArB2p7Qayh3SMjnhdYOzQJILAEvuh7LP4+62ggL+b7EkD05P+5J8h4lNW0txpMtq5ujEQgPDhw3hjzdcxe76IDMfqsBtzuSQ/haeOMlN/wwTT746nw1by1L/HLrDjqVyO+yIjrcoKT5GbnetlO8jGaNawVkIQ2dKANwZWpXtdbc+wDmeg6nyANqXKHHmgTlNbm3OcKUCSGskV7kkHZpfww1BvyR1CkPtj3atbP11jvqpJHh2rYBN76d+fK2xWOHIq+HIn0hrpupS+PD3sHN5i03r3Y3N99PsVgblS3ux7Xtr2BdKCOyrCQfhR37vXkZ89x4sJ97K/S99CkjC4JffOgqAO59ezP4qqVDoF0osGBUL1O3h6lE7sZhNPLfeApO/2/73+M3r5e/TvhIZWp0KR+jaaAwdT0N5+KetA5uVUkoppZRSSiml+pg2JxaGDx/erh91gEkWgDV69Rur4F0V0k5k7CnSKz4YgLeui7vbUaNGctfHHnZ6c0gPuvjjCWl8/PkyqqprsVgsNDY2UZCXC8C0yYcC8LNLzmP2jMMo2dPIX8smUe2xMHWQhWU/zOaooSYeevr1rvoU2i4YgB3L5P7QDrRBMmQPkpZIBGVAdsLXDUpCB2DiOTLEN2iKaMfikFX1xndmaG1AbWRCykg2HchB1/YkSjIKoN9ouY3kdclq9/r9MgC4ITQE2KhqyMiHIYfLY3tWy7mR9HX6yeBlkKqf1rZvq4GTYM4t0mrI1yitkXaEKwrK9lTw0gL597B+2WzbU83+Gvmu02wWzGZJkm3dUx139x+sCCeQLz5xCocW96emoZE3PlkNwID8bAB27a+RdkzL/4MNHwu3+bjmnaaOJeGcBdJOCuDTv4M7/jFGMappPHXQVAv1+6B2R8vrIZVB5IkqYdpTIaOUUkoppZRSSil1AOjG/jDqgBIbgDUSDV53dDDaG1qdbbXJdtMvk9u6PXHbuVzzk6vxBuB7T8tg5UunpFGQHuBPf3uEY46SQPz5Z5zMvF/+mDtvkUGsZrOZW67+HgB/fOZjFudfxKbKAEOzgiy61EnRnnepq63twg+jDQJ+8NTL/bwRHd/f8v/KzAaA9NzE2wUDUq0AMOkCSMuWwcKuiuih3UE/VO8MJxdaa4UUqTPmdPSEyOBuZ/a797hDn7NDBjIbcfHIqob+E0LH0ACNKZyj3/iR7HP/OtiS4lyNtnDmw9G/grxi+XdEFcxTb32Cu9HDN8YPYe7xk7j9qcV4fQGOnzKCw0YO4OhDZCYKEZe1KZRsmDOlmH9dd27z4xaLmbNmynyJhcs3ADBikCRmtu+phK0fgauCWn8a5z/nZtbksR1/b5n9Q8cXTC1JsfQRuc0rBiyhA49zPRh/7+rLEycJEl0bB+o1o7pUSUkJy5cvb/FTWlra04emlFJKKaWUUkqlTBMLKjVGosHmiA5G2xxgzwRbptzf9olsP3xW3ODe7KOPZtZRR7FoaxPbvPnYzEGummbj6ZffZMaUSQCUbNzMHb/+BRkZ4eDviUdPZ8yIodTWN7C93spJz1l4fIUHs8nEz6dDzbM/BndVi9frdmYrzRHmBEOsU7bmJekZD9LKZuzJSV7XIiveQVrfFI6SxELksG2bQ2ZkWG3ymCc0H8DmSG0F/4HaFqmrgrv20LmfN0LOf6sjPF/BCD6n54AlNEPASDglk54LM66U+8uekO+vs5kt0Fgj9/OLmx9+9j1pZXTFKVNYuXkP/57/FQC/v2wOALMPLcJiNrF9X3Xzc3Y9ewOlT1/Hgnt+QEFO9Hlx7GTZ98LlGwkGg82JhdrKvc0zQ+5dbqfSHeSUmRM79p6CAVjyN7k/+UL53JPZuwaWPiz3T70LMgslMZE7tOXAZpsDfB5wV0hlQ7zzKNG1caBeM6pLFBYW4nQ6mTt3LtOmTWvxM2HCBE0uKKWUUkoppZQ6YGhiQcUXG4w1VmHbQz36I+87C8KBM2PI8Mhj4u7WZDJxy003AvC7d6Rq4dqjs7GagqzfJK1UPlm6nH37K6KeZzabuXru2QA89PTrnHv6SfzgtUZu+qKAcleAoWn1BNa8DHvWdN7g2/YwmWRoMkgwsr3Wvw3v3iz3p14Cs37e+nOyBspt3R65tTmk5UxzsNst7XnSsuR3rbVBimWs9u9tA5lb01XBXZtTkgveRsAncxZclZJAKN8M1Tvkszc+r6a61PY7/QcSGK/eLsOWO1tjjbRuwtQ8XHzD9t2sXL8dq9XCqTNG8f0/v0YgEOQ7x07kG+OHAJCdkcb0sYOjdpXlTGNY//hB/KMOKcJqMVO6p5JtuysYPjAfgDP77wS/hybnYP7vXamyOfkbHUwsbJwP5etlhsS0y5JvG/DD2zdI9c6402DMSfIdxZ7bRjreJq8AAQAASURBVAskkHMnPUcSc/HOo0SVMJ1ZIaMOeEVFRZSUlLBs2bIWP08++SQul4vy8lZm6SillFJKKaWUUr2EJhZUfJHB2Obe+qH+/FVlUF0WTjoYAeqGCigLDS4u/mbCXZ9y8snMmD6d/610UeNPJ9/m4fyJVj5Y/BmHHzqBQCDAG++3DKheev4pOB3prNmwlUnjRgLw53e3c9O6Sby10YuZAJR9Bhve7tk5ANZQYqG9FQubP4Q3fyWrsCd9C+bcnFprl8xQYqF+r9zanFK1YLFL//+ATx43kkJtaYN0IOvo4PJkPG6pAHHXy20QCT4H/eCtD7VLygxtm0LFAsh8jCN+KPeX/Tv8vXWWyq1ymz2o+bt/7r3PADjhiEO575UvKCktZ0BeBn/7ySlRTz1ucurzcjIcdo4YPxSARcs3Mrq/k1cudHDacLkuHlqfi98f4Ljp4xg+qCDZrpIL+OCzB+X+1IvBkZt8++X/kQHZaVnwzRsSbxeZeLM5pEVV3jBNEqgOKSoqYurUqS1+JkyY0NOHppRSSimllFJKtYkmFlR8kcHY2PkK3noJkka22DFbYc9XEPBC9lDIG5lw1yaTid/fcRveAPz1E1nF/aujM6mrb2D82FEAvPL2fACCwSB3P/IsH366gtzsTOaefQIAH3y6nCMPn4jfH2BI8VguecfGj9904w2YoHYXrHkRKjZ34QeUhKUDiYWypfDaNfJ5jz8dTrwj9aG2RsVCTZnMVvC6wt9NRn7LJEJk5YkKS6V1klEFAtIOqaA41BLMAfZ0Wfluywy3S4K2JbumXSaB7Joy2DS//e8lnqpQYiHiGn02lFiYOn44d78gLZEe/vnpFGRHnxtzJo9o00sdc5hsX7F5GYfufpazx9to8gWpHX4Sv3l2JQC/+u4JbX8Pkb5+Tao7HHlw+Nzk29btgYV/kPuzfgF5RYm3jUy8GRUNmlRQSimllFJKKaWUAsDa0wfQZwSDrbfoSTWA3N2tfuyh5II1Xf5ty5QxAtZ0WVVvS5efUglIMvIYMCfPWZ1wwgkcO3sWDy5dwi2z7UwfEGD6YDM1DY0AvLdwCfV+G+8vWMT1f/wnAP5NH3LZt0/j4Wfe4MV3PuL/fnU5n634mqdfX8BfbrmGi3/1ez7e4ebLGw8lrXqTtJGxpcOsG2BVaLDx9Mtg/cupve/cEaltFwhE/9tILHgbo39nsSXfz66V8NpPJSEx9hQ479FWnhNzHhjDa6tLZcV9WgZkD5GV2T0hlfO0K87ljl5HxvnenHCJsz+j5ZE9E3KGRTxeLt+/zQHOwtD+IhMLKR5bWiYccSUsvgdWPAWTv5v4XKjckto+fXJtUbFJbnOGgq+RDdv3sGZTGVaLmRfmLyUYhO+fPYezLmgZpJ+aNR54MfxAXnHS/4ocd3QNg6qXcs3YMvBCSXmQ77zQwBnnO6htaGTC6OGccvYFrf69iJKWHb5fvz88K+HIn0DWoPDvrGktnzv/VvneBk2Go34m8yZiryOD0erNE6pgMQagt6az/46nuj+llFJKKaWUUkqpbqQVC31VW9q92EIBNqNCIXcI5AyR1diRti6S2+L48xUimUwm7ph3I/sagjy7Vlq9XHOEnSWff8HIEcNpamrinfkfUrJ+Y/NzAoEA35gykSkTR+NubGLpqnVkZ2awYWsZg/oXMG3yIazZ4+FF+7dh+uVgssD6t7G/eQ05wdqUPpZO0Z6KhX0l8NKV8n0UfxPOby2pEEfzjIVQK6RUY/ZtORfaqzteo7PEtk7yuiRhEHnswYjbyN/bnBKsjgxAG4mFphRbIRmmfFfml9SUwdevtvfdRAv4ZX8gQ6eRaxEkzl3nkmt82sTR4ef4msDroq7Bxbd+cVfqr1VdxnENr3LNEXI9VAw6lovez+OrvQHWb5UBtafPmYm5LUmFSE118MJl0vorb4R8XslseBdKXpO/CyfcEUoqpMBoidTZw7+VUkoppZRSSimlDmCaWOhLIoO7qbR7MTSUw67V4NrfctCvxw37N8OWj2HvGnksyXyFSLNmfoMZ0w7nr5/KPi881I7dW8uMaYcD8Mobb3HOmacCMHr4EKxWKyaTiYd+fx1ms5ln31jArGmHAvDEi28z9TAZALtucyl84yo4+wFIy8K8dw1/DfyWYcGdKR1Xh7U1sVCxGV74ATTVwuDD4YL/hqtD2iJzgNy6yiUw7sxP7XltORfaqzteo6t4XRKQj0ws2B2ANTS8OeL3NqdUKkQmFppnLKQ4vNlgc8KMK+T+5/9o/8yOSHW7ZT/W9OYKlzFFAzh6yhj8gQBTx8kMhT88/Dzu3Rtg6SPwyo8JvvRDyv77E07K2sDp49OxB5McSzAAG9+DRX/E3LCXyiYrJz/ZwCNbBpGdkweAKVS50eBubN/78DXByz+E/SXyeZ//r/gVCs3bN8rAZoDDvyfzSCK/T0+otVW889NoiaQtw5RSSimllFJKKaWaaWLhYJNsZXhkcDdyOHNrGirBbILG+paDfr1ucFfA9o/l3wMnSVA7BSaTiV9cfRVf7grw5V4TaRa4apoNm1V6q7zxzvuMGTWS1W8/zucv/7P5eUdMnsAvLvsWAMvXbgDghbcXMXzoEADWrAtVOQyZBuc/SiBrCIPZx18Cv8O8a3lKx9YhRmLBl0IguKZMVl27K6H/IXDuw9LPvT2MioWG/fI9pTqUOfZc6Irqgracb71NZBVCc3WCGxzZkljzuuW7TlTh0N6KBYDDviPXU+0uWJtiC69kqrfLbe5wMIX//P/028cDsGr9Nn72zQJeOMuD46PbpQrJ78FEkIl5Xm46Oo1XzwvyYuBK/uj/PZaNb8vMhoBfduSqhE/ug7UvyQDrQZN5LeMi3tvs5+2Fn1GYnwNI9RFATV07hqwH/PDGtVD2uXy23/oX5LUyVPrLf8m1ljkQpl4OVqt8dwajKsFV0TLBYMwi0fkKSimllFJKKaWUUs00sXCwSbYyPDK4G9vuJVJsYDkjH9JyIb843P7I44bqneCqkgDl7q/kcV8TbHgH/N6UDvdb55zJ4EEDuedjCTD+eLqdkpK19CsspKamloWLP+HQcSPJz82Oet7t1/6AkUWD2VteRV5OFo1NHqpragD4ZOkKgkb/8rwReM55hK8ZQxYN2N76OVRuS+nY2iwYhPIN4Z73ra0wb6yFFy6XVi4Fo6X9UXp28uck4yiQW3dl24PYHlfLapaG8vBjHU02JDvfervIKgSjOiGIJBtMSCLJuC5c5RKYNioYvK5wyx1POxILtnQ44ody/5P7Ycuijr2XqlBiwZEX9fC5syfyuxOy+eziAPfN8XJ0kRWvP4hn0HRqv3EdQ++t4/svu6nMm0LQkYcdL4ezFtv6V2HRXfD6z+C9eTLDoHy9fCZT5sIRP2L6jCMAWFmyicI8SSz4/UZioY2fya4V8NzFsPFdeY1z/wkDDkn+nIAfPv6L3D/qZ9BvFNizWrZyc9eGEwyxlVlKKaWUUkoppZRSKoomFg5G7pr4j7cW3DWCxw3l0cmJjELoP1YSDM3busFbHwqWmmDABHm8fAM8OxfuOwze/w3sXZv0UO12Oz+6/BJe/NrH/iYbg7PMHGHfxAlzZgPwyhtvx31ehtPBI3+4HoDqGmkx89GnX5Kensa+8grWb9oa3tiRx43mW/iYGZgCPih5HRoTfEbt4aqEr1+BN38p79nnlmRLwajEz/F74Z1fywry7CGy6jrV1kXxNNbAZw/K/axBbat68Ljke2yqj65mCSJ97Kt2tDwn+iqjeiGjQJINzoLoaoaAX5INkY8ZQfyG/e17zUnflsSTuxJe+RF8+IfUqmHiMY6l7DPY+L585+vfwv7R77l1FgzJNlPeaOZvq9Io+ms9d6ws5OuadHbWBVmwN5v8k66j6dT7ucL8Z/5muhT/gMlgdUh1gqscAl68WUUw5xYYcTSYTIwtHobVaqG2rgG7TeaGeLySeKypTbFiYdcKePlH8OLlUqlgtsHpf4Gima0/1++RSgSAcaeE/g7GqUBwZMt7MVsTV/t0RTXPgTR/RCmllFJKKaWUUipEEwvdpTuDRo6c9j3PWKkeJH5rnMjWITYH2DIlIO2uguLj4XsvwOHfl2Brw3747B/w8DHwyBxY92bClz35+Dl4A/DnT2WI882z0xiQJyv3X3nj7ea2KbGOO2oqxx81jSDSVmnpitVMmjAGkCRD1Fsz2fmj+ScECsdL4H/tK/Je28vvhZ1fwqd/g9euhlVPQ90uWUU9YjaccDvkFsV/blOd9Iff/IEESM+8LzwfIRXBIFSXwern4a3r4KFvwp9HwxePyO+Pv7VtLYfsTmkpk5YZXc2SWQg+L1htLc+Jvip2hoLxb5Drw++JDkrbnJAR+m6rS9v3mtY0+O6zcPjF8u8V/4VnLoKqbW3f19iTYdRxcn/9mzD/tzIPwevGl17Aj9/yMuTuavYOnMOe+iAPPPUmJZt3ADC+eKg8z2SizDSE180n4Z3xYzj9HuqO/g3ffs3ClH/W88PF+VHns91uY/woaVXU5JWEiLtJbqtbq1iITCjsWCrn4GHfgSs+kCRBKmwOyBkm96u2SkVJcxVOqO2RzSHJGhPh7y/ezIXYirDOSAocyPNHlFJKKaWUUkop1WdZe/oA+gxPN7XWsDvDq87b+9zM7OjVvEbgy+sOtw+xO8A+BNzVYDZDIAj9DoHCsXDMjbDjc1jzsrRF2rManr8UfvoF5I1o8bKHT56Ew+Hg/o9ruGn2AIZmuxm570syMzPYtXsPn6/8mplTD417yJd961Q+WLIMR3oaLncjNqusiP7wk8+56vsXRG3rNdnxnPgH0p//nrQf2vg+jDs19c8nGJRgbtlnklSIbJfSbzwUHwtF30je7qd2lyQVyjfIdmf8ReZSJBPwS+XHjqVQ+pms2K7b3XK7/JGyur34GDm2FvMwXOFzw+OS2RkZ+ZJEyIuTBLE5IW9o+DkHYhujtoj8fFp7r0Zg2uYMVyZY7eG2R0YrJGchFBTLY+1NLIC8xpybYfhMeOdm2Pc1PHk+nHAbDJiY+n5MZphwllQurHlJBi3nDIVRJ2AddBgZa57H88W7LF62FovFTE1dAytKpLXXqGEDE+7zV/94ixdWVAGw6tWPeODGS8lwhAeQTxw9gjXrt1BfL9dMg0tuq2oSDLR2V0vSY1todovZKsc9/QfQf0Lq79dQOFZmLOwtgf4Tw3+Pg6G/axkF0W2QPC6pyLJlRv8tjf37Gtk2zOYMJenaOCOlI3+zlVJKKaWUUkoppXqIJhYi+P1+LBZL1GPBYBCTyZTyPpqammhqamr+d21trdyJ7efdVWwdCAAneq4R+LKmt/xd9iBwpUsveLMV0rLk/ugTYPA0mH0dvH0d7PgCVjwJx81r+bI2G6eeeBwvvfYmL+wczJVFmzm3/3Y+/OZMXn5rPk+8+E7CxMJpc44EwOVuBGDPvnIA3nx/EY2NTaSnp0U/IWsQTDgTVj8nCY/swTBocuLPJBiQvvS7V8HuldCwL/w7Rz4M+waMPSU8ODmZvWslqdCwHzL6wbkPtd4f3tcI/zkbdi6LftxslWTG4MPlGIYfJRUSrspQFUkN5AyKTi5EroxuqAR/Yyi5kGTYdkfOpw5IeB11pcjPJ5XEQsAvLXZsoZXmRvsjkNXuptB2BaPlsZod4SHH7TVyDlz8Mrx1vSS33r4eRp8I3/iRXHepGjFbBjj7vZKQCv2NO3nmodzz1LtUVNczZvhg1m3ZgZSrwOJlX4dml0T/Paysqec/b3wc9diu/dUsL9mKzWph3CQfazdKa7IB/aTdVyAYxGw2s3PPfsp27WXY4IiKnfKN0lasdmd0QiF7cNs+q0iFY6RCqHIzuOvAmSd/kz0RCTibI5yQMyoQYv/0x14Pxt9Gnyd87rQ1sdBD15hSSimllFJKKaVUR2grpJCSkhJ+9rOfceqpp3LzzTfz2muvAbQpqQBw5513kpOT0/wzbFioBUdvDByl2sbDWInrdbesvMjIl2GouUPkvpFA8TaCpwEsNmldArDy/9m77/i2yuvx45+rZUvytpxpO3s4CVmEQNgQwl6FMBtGoLSlQGnpl7a0tPwoUCiUtrQUyiij0IayR9lhhR3IICRx9rATJ7HlbUm2hvX749G1ZFmS5b3O+/VSHctXV1dX95HKOc9zzn/iNnW+8fprALhh2XoqmszkZxg4fXQNAMtee69lhnO07Mx0sjLSWn7fsbuUkcPzqG9w8db7H8d+PTljVWAVYOtyqIua/d8cgKqdqrzRO7+Gj++Bbe+opILRDPnzVRPYRb9TSYpkkgp7V6neE64KNXv64ufaTyoArPm3eqwpFcYcCUf8BC55GX66AZa8CCf9HmZd2LrsUoMzPBNbp8/GD3jVe2nPAWNq674Z/UjccdSTIpubt0fvteD1qPfU6wmXSDLb1DgwWtR5zx6v9tvsg4b9XT/O9BFw3uNw2DWABtvehf/9pOOlkbIKVR+QiM+46lDPg+wMO3OLVI+QrHQ7dmsqxTtKef2jr9vs5olXP6LJ62PO1LFkpatz9+qHq7jwl3/j3P/7CzNOupQNW3ZiNps45tDZAHi9Pg6ZORWAdz7+Kryz7R/A85erpELGaLjg3yoZ2ZWkAqgxB6qxujVd/VvvtaC/35aI3225YB+mfnrd4fJJ0VqVDfOGxlmSfSOEEEIIIYQQQgghBjBJLAAbN27kiCOOIBgMMnnyZHbu3MkFF1zAfffd17KNmqnbvptuuona2tqWW2lpqfqDprV/6wmJkgeRM7TbOzavWzVo9XtQl03o5mtUs+T1m69RBVwt9lDN/jQYf6wKurrKVT13g1HdTJaW22GHH84xRx1BQ6OP5/epWu4nZ2xh2qSx1De4ee6jDZA9LuZt/LixAEwcr+q4zzpIBez/+9q76vkjZxBb7GqG9/E3Q+Hh6jVteQuGHwTGFNizCr78h+pfsOtj1RTZbFMrME74HVz2uipfNPMCtVpg5CwYfXDiW3kxfPxH9R6MOwYu+x8MmwqpmbFvmkHdAl749C/quI//DSx+HI7+Pxh3tFrxkJYH/iZw7giVpDKqx6WmQWOD+rfBqJ63ulQlG/TAd0tD7gSrFboqmWs+znUfdxwlIxhM7hYtXnNz/Tj9HrVCwe9R15E9TyUQgsHQzPYg+FzhfiSeulBvCkM4MF5T0uq6T3iz5sS/2YfBwt/CkhfUv2tL1Sz/srWQV6TK/bS5FbV7q0b1NsnOymDODNWvZOPOMq5dciYAv71/GUGjOXyqJ53AP179AoAfXvNjrHb1+P3BLAAyMtLJzFD3Xbz4bIaNUckEtzfASScuAuCdLzdAZiGs+y+88TOVEBt7JFy5XH12ZIxufbOkJXcLNodv+qqRqh2gGdXqDv1vaG1vFru6Fiz2cImk9nogRG6XzPXXhfEhhBBCCCGEEEII0deGfGKhsbGR3/3ud1x22WU88MAD3Hffffz+97+nsLCQn/70p9x2222AWrmQTHIhJSWFjIyMVrc+lagxaEdmaFtsrUu96PSSMK7qcF15ffusAsguUDOsp52l7l/9ZNyn+OX//RSAX7+4lbIGGJUGv1ikArL3/eMx/P7YzZbHjVGz2ccWqp9er2oM+9pby3HHWemAZoBjfq4Cla4KePYSeP82VS7F61I16IvOglPvhctfV7XsJy7sWJmTYDN8eCe89//Uv2ddDBc8DalJXhNrnla9FNJHqlIw9ty2599VBc1N6ieEyrqYID1P/e6NSCr4fQOmjnu/G0cQeyzZHapRsZ6g8XpUssrXCNaIY06PSCx0pzFHwNI3VADe36RW2Lx6LTR2rnSU3vMgOyOdudNUYmHNxu3835WLSbNbWbNxG//7YGXL9h+u+JSt27aTnp7GxRcsbik95vWqlUlnnnYK1bu+pXrXtzz+93ux2dSKJk9jIycdfwwAn368gubXfwYf/UHtdN6VcOEzYOvG1TQO9Vqo3aPGYnT/kUSS+Zz0ulWT8wE0xoQQQgghhBBCCCG6YsgnFjRNY+vWrS2lVgKBAOPGjeOEE07g3HPP5dZbb+XJJ59s2XbASRQU02doJxMst9jDpV6i9xHwAkH1M16PhtnfVf/e/r7qWRDDSYsWMmvmDKrr3fxn9zAATs3cQl52Bmu/3cDv/3R/zMeNH6PKAA1zqODuis9Wkj9qJC6XmzfefT/xa1p4SzjImD5KNT8+/S9w4TKVeCg8TJW06ShfI7x6HXz9T/X7kTfAafeqUkrJ8DfCJ38OP9YU6hXhiyrLYs8BQ0q4pJHFpspSpYTKvdTsVUkFzaSSPP2xJFdvSrb8V6zHQNuxFL3KwWJVM+Jt2eqnJVSvXy+X1d2JBVDles79Jxx7kzq+zW/Av86MO84SaUksZKYxu2g8ADtK92E0Grj+0rMBuOOB/7Rs/9R//gvApRedT9q2V/nFIV5Om2TC6FX7MRgMaJpGVmYmmqaRnqbKllXX1HLI3FnMLMzktXMCGLa9AwYznPpHVd4r2XGSLHueSiIShJ0rOvbYeCtZIllskJIhY0wIIYQQQgghhBBDxpBPLPj9fvLz89m9ezcVFRUYjUZ27tzJc889x+LFi1m6dCnLli2jsbEx6XJI/UoyQbGu7t9sVaVbvKqBcktNcv3mdUPeFChYoP4eZ9WCpmn88mdq1cI975ezp64Zh8XLi784EYDf3X0fX369ps3jxoUSC/UNDRy1YD5+v5+C0SMB+O+LryU+/pxxcM6jcO6jcN4TMP/7qveBwZj4cYm4KuHZJbD1bRUgPe3PcNiPOlbWpGW1wgiYfIoKTvvcUFOmmjPrvS7suTBskvqp02vFg5pFLUmFsEQreNp7DLQ/lvS6/frNbFPvVWaoR0RtDyQWQK3Amf99+O5zKoBesxv+sxjKN3ZoN+EVC2nkZGUwNl81VV5bvJ0blp5DRpqN9Vt3tWz//kcryE6F303bCm//ih9Mc/G/i238afTrrPuhncvyNsCmN9S1DIwpGI3ZbMbjaaTy2/f4+FIzc0YaqQtYVFmnOZd0z/mIpeBQ9XPPV6GybXFWM3VGT3/OCiGEEEIIIYQQQvQzQz6xYLfbOfHEE3nrrbdYsmQJ119/PTNmzGDx4sVccMEFnHzyyaxevZqGhoaBuWKhN5htqgSIyRwKgIdqjdfuVX0VXJUq2D0ntGph9VNQVxZzV4vPOYtxY8dQXlXHQxtUkO5gzwoevnIeWjDA93/yyzYJnvxRajZ4yZ4yrv/hlQAUb9kGwOvvvEdtKFgaV9owyBrTPfXMq3epgO6+tZCaBef9SzV47ojmQHi1wuxLVdNfb6hxdku5lSRLufh9sUsoRerMLP6BqiPlv7ryGAivLoFwYqHkC6jvhgbO8YycrZp6501V7+myCzs0Q7+mLlwKCWD6RNW3ZMPW3eRkZfCTy77Tavs0XyUrr0ojp/obMKXy5p40NjsDABw03MjxmbvhrZ/DPxfBC1dirtzE8DwHZ00xMezjX5Bh9LJmX4AjnvQSzJ/fDScggREHqZ8H1ofKtnVjYkEIIYQQQgghhBBiiBnSiQU9QH3NNddw8803U1hYSFlZGXfddRcPPPAAAEajkdGjR2O1dqAm91BjtoE9WwWxvR41wzvgVQ1SIdTYFph6ugrgu53w1DmqEW4Uk8nExRecB8B7VSP4Zn8AK41clb+F4mvTma5t5d33P2r1mPXFmwEYW5jPWaedSP7okdTU1pE/agQeTyN/e/jxHnvprexbC/85TzXSzSyAi5+F/EM6vp8DG9QMb0saHHRuOJGgl9mxpCa/L2uM3gTRiYTOzOIfqDozs7yzs9H1fgsAU09R72flNnjkeNj6bsf21RFpw+Ci/6prr6kenl8KXz8Wu2F1FF+oj4nFYqK5uZk1G7cDMKFQ9Yj47pnHt2xbGNzDx5enMjHHQJnbxMqimzn7KSdT/+5i6bpDOee/br5onqlWAGlGKP0SnrmIR46r5b+LrRiCAQLjF3LUE27Wl9ZRUVHRAycD9bq/fQ4+vU/9nj0m1C8mzmf6UEq0CSGEEEIIIYQQQnTSkE4saJpGc3MzAJdccgmPPPIIzzzzDNddd13LNu+++y6OUO1+kYDXA/jBU6PKIpltkJkP9mGqBjyANRMue5Vg+khwbob/XgKNtW12depJiwAo3rabM16xcv1bjTQa7EzM1vjPuTYmffF/sO5ZFaisLeWjjz8GYOExR2IymfjeJRcBtDT8ve+hx3r85bP9ffjvEvBUw/CD4OLnIGd85/ZV8rn6OeIgMFkha1So5FSo7JTREi6FlIjZGnumfXQiQZ+RD0MvoNqdQeTo/hctiSCragb+3RdUkN1dqa6Vd25WDZd7QmoGnP8UzFismhW/fxt88qdQP5T49NyDhsbX326hrLySNLuV4xfMAmBc/ghMZhOTgjv4Y/PvGGkPsqEiyMEPVnPo4h/j9XoZN3YM63c7eWmTnz1Tr1RJjqVvwrSzaQ5qnDzRRIpJg4mLMJ5+L1kOVW5p1+6O94RoV/0+da5fvhoaa2DETDj+FtUYOl5iYSgl2vqpkpISVq9e3eZWXFzc14cmhBBCCCGEEEKIEFNfH0Bvqaqqory8HKPRyJgxY7BYVENeg6F1bsVoVLX1V69ezSOPPMKyZctYsWIFdnsSDY77is+tgmAWW+/X+NZr//tC/RUIgqdONRO22NoGtbMK4ZKX4InTVUmS5y6HC/7dartD588jOzuL6uoazrviMv762JN86cngjV8vxrD6CcbZG+H937Vs/8bRQfbOTiM35XV4cxM/nZ3B3rkWdtZsp+DQKSxfvatnz8E3z8Dy36oA7rhj4Iy/JtcQOx49sTBsevi8+tzhZIIerI7FGypFZbZGnP+oEk8WW/h6gXDSwuUMB1SHSq34yCByV1+zu0r1v/CnQaYtfF713hhpw+DC/8KKu2HNv2DlI7D7czjnIcid2D2vJ5IpBU65G4YVwQd3wNZ3oHYPLPytSnTEoK/i0jR45T11HZ569CGkhD4vzWYT352Xxx+a78COh+YRs8i94G5OqvsrTz69DIBTTjyBJ//9DADTi6aqHWeMghNv59w/f8Z3RuzhkPmHUnTyH8BgYmxBPnvL9rNz5y7mH9KJFT6xXwis/Y9K3jTVqcbQ866Ameer1Qo+T3ic6I3OddHjoyP68rN4kCgpKaGoqAi3O3Zix2azkZvrAA707oEJIYQQQgghhBCilSGRWFi/fj2XXnopfr+fLVu2cPPNN3PTTTe1JBGiuVwuDhw4wNatW/noo4+YOXNmLx9xB3VncDRZbie4qoGgWolgTlXBbq9HrVhIxDGZ4JIX0Z48HfaugheuhPMeB5Mq8WM0GjnphIU889wLpKenk5Zm58t1m/ms+Re8smM7o8qXc/TYVBYUjSSl0YkRHwWZGlQXQ3UxGcDDZ+jlgvZRdoSFnYE/sUmbiGGPG9LzOhc0jBYMwtePwqon1O8zFsOi21TD5q7ss+QL9e+ciYA/nFAIBlRSwZ4bnh2vJxB0rkrwNYA5Lf5rNMcJenYloDpQJfuakwkYB6N+6txV6j0JGCF3Apz4Oxg9D96/VSXXHj1BJQBmnt/VV9OWpqmAeu4EeOUa1cz51Wth4f8Dx6S2LyGiXNLLyz8D4KwTFoQ32LuKh46rxUSANUwn+8h7GDt2Mk88/Hd++L3LeeOtdznnrDN44OF/YjabmThhPDSrVRmbtmzj5S+28T+TiQN/+lvL58S4MQV8+uXX3bdioXYPvPF/sOMD9fuoOWpc6gkFdw3YUH0WPFVq1Ujk+xprfCSbMOiLz+JBxul04na7efrppykqKmrzd4fDwbARo4ANvX9wQgghhBBCCCGEaDHoEwsbN27k2GOPZenSpSxdupQ333yTG2+8kcsuu4yCAtVQNRgMtmrMbLfbOf744znqqKNIS0vrq0NPXlcDwl5X68fr/040695VrQKGwWYw5IRn0FusgBa/zAiqBBUjD4KzH4CXfgC7P4WXfwTfeaglKH/KSSfwzHMv8MFHK/jRFZdw91//wR1/up/Xlj3GGRft53dPrsZq9bLgkDmsX/UFPzz3OG695iKo2wt1eynf8S3Ond8yOdfIqHSNUXzNEcGv4eVnQDOolRN5UyGvSM3ozipUgcdkBfxq5vnm19XvC66Fw6/vegPoqh2q4bUxBcYeBpjC51ZPMLgqwe9RJZF8ntbvuxb1U1/BkMwM6ngJh8Es2decTMDYngNea9vVJEFU4s2cph7b7IPZF0DhofDa9VD6Bbx6nVphMO2sLr+kmPSVNMtvUYH3138GR/0Mxh/T+lBDiYXaBjcbt5VgNBo49ZhQU+Vdn8Bn92MiwOcczB2G68i+62889cjfADhs/iEcNv8QXn/zHQCmTJ6I2WyGJpVY+O9LrwFw4nFHk5Od1fKcYwvV5/DOXV1MLASDsOYpeO9WtXLEmAKH/xjmLVWlvtzVEGgEW5b6fPJUgbsWUkN9MLojYTAUk3M9pKioiLlz58b8W6PX38tHI4QQQgghhBBCiGiDOrHgdDq5+uqrWbJkCffccw+gghXLly9nz549VFZW4nA4yM/PB+APf/gDTU1N/Pa3vyUlJYWUlJS+PPz4ooPXFnvsJEASzVqBtjXF4wXQIp/XnqMC3PZc1dhWL6NjMKn7Ej2XXoJk/HFw9kPw4vdg23I1y/icR8Bg5OTTzwauZs3adTzx2D/568NP8MVXq/l25wGWL1/OBRd9l9ffeJP3V6hyLWOPvhBmXdDyNLmBAIdOm82Bvbu59/8uwZRqZ0pwG4eYt2P1VUL1LnXb8pZ6QGoWjD8WJp6gjsvuAJ8rzmtwqVnfOz5SSYqT7oTZFydxopNIOpR+qX4OnwZGG2SNDgcpLXZ1zpv9Kljd0j8h8n0JNRnW7/d5kp9B3dWkSLRkrr9kr9GOSPZ1RD93olnpyQSMLWnqFi3NAd7Qe6IZVEIIVO+Rc/8JH94Ja5+GV3+s+nKMPlj9Pdn+C8mW3Zp8EhQeplYu7PgQPvy96nWSNwXSRkDGSEakG0k1QTWqP8mokaPImr4Q1jwNn/4VCBKYeha3bTmXgGZi2bPPc81VSzns0HAJow2hZurTi4pCK2wcBINBnnnpDQAuuPAidZ2GjJusZqXv3LUrudcRa0VQ9W6VpNm1Qv0+co4qMWXNUde/ZoDsgtbvr79JJRX8PkhvZ2xEvv+Jrq94n8VCCCGEEEIIIYQQg8ygbt6saRonn3wy11xzTct9t99+O2+//TY/+tGPOOOMM/je977HJ598gsvlYs2aNbz11ltUVVX14VH3Ab15r16TP1bD32h2BwyborZzOdV98R7nDZXt0ZMKzf7wLPqi0+D8J1UN9A0vwYtXQdUOhg0bxiGheuuvvvY6V15xOQC33HobNpuNl154jssvu7TlKRYef1yrpzQajfzq5z/D44c/vbiS5w2nc4fxJ5z/5Qz48TpY/Dgcfh2MOUIFAhtrYOPLKmHwlxnw+Mnw6X2w7xu1KkPnqoBlF6mkgilVrbJIKqmQJD2xUHBY66SCTm/IrCd0ogPgZlvr+5N9P0Xipr3R57Uj4j3WbFWrFE76PUw6EfyNsOxiqC3t3PEnIzUTznsS5v9A/b7+Bfjg9/Daj+Hf5/HIrLV4fp3BTeYnWfsDO/862aNWFb39KyAIcy/Fd8qfCGjhnPTNt97W6imWf/AhADOmT2u5r7a2lk2bVcLhyCMOb7X9mMJCAEpKOvm6Vz0B/zhSJRVMVlhwHZz5ADgmt77+YzUtT8lQCYdkVvOk5UnSQAghhBBCCCGEECJkUK9YyM3N5dprryU9XTXnfOaZZ7jllltYtmwZixYtYv369dx4440sX76cI488kjvvvJPU1FRycnL6+Mh7WXQ5mI4ET/VgncEUnoXsdbVuIByZTDBbwz91k06Es+6Hl69WyYUNL8HEE7j7e8dz/Fdf8fu7/sAHy9/mn489wceffMIbb7zJaaedymOPPswh8w7GZDJRWFjYZob3iSccD8Desn2MD923a9duSB8OU05VN1DHVrZGrZrY9h4c+Fb9XrZGJRdsuaqUTMF8+OIBqClRM6HPfRRGze7YuW6P3l9h+EFtz7N+3hKtCIk2FMsbdVZvl7GJbG5+7qPw+ClwYINKXC19M7yyobsZjLDwNzD2SFWGqW4fNOyHun14q0ux4CfN6GXWCCPgg81vqsctuBaOvhF84USbZjDw3gcfsfz9Dzjh+ONY8cmnvPveB5hMJi46/9yW7TIzM5kzexZr1n7DQ4/8kz/ceXvE39TqiAZXQ8dfy8f3qsbUAAWHwqI7VLLAFmpOHX39x2paHkmaLwshhBBCCCGEEEIkZVAnFoCWpALAggUL+Prrr1vqNh999NEMHz6cVatWEQwGGTduXF8dZv8XL+AWKxirJxLclerfoBIPeqIhVuB2wkI470mCXz2KtnMFbFvOsSyn9OcO/vRxHf/46x/58bXXcPcf7+WXv76Zk08+CaPRyI+u/mHcQ87LU4kOb1M44bBl23YaGhpa984wmCD/EHU79iao3w/b34etb6u68u5K2PCiuoHqx7D4ccjp5uulfj/U7FZlWxyTWp8/VxUE/aCZYq9kEF3XG0kYfRxFN91OSYeLlsEjJ6jkwgvfU9dYR/p+dNSE49QtwvbiTSw4bD7jci2ccfQc9m5ezeITDuWUJT+GySe22cVZp5/KCy+8wKLTzubq71/JN+vWA/C9pZcyYfz4lu00TeO2//dbTj/7XP72wIP89PprGTFiBADWVJVk9HgaO3b8EUmF4IJr0RZch9tgx+12Y9OstHknk3l/pfmyEEIIIYQQQgghRFIGfWIh0pgxYxgzZgygmpR6vV7S0tKYMWNGq+bNIoZ4ATc9WOdzh0sied2q7H+Q1n0X9JJI0UFVUPcVLqB53LHUlG6i9r0/MWzvO4y2ern3xFRqGj9gzczjeTgri/XrN/DwI49y9Q9/kPCQG/wGhk+YQVNEo0+bo4BX3v+S+fMPIdNqYVhmatsHpo9Q5Y2mnwUBL+xdrWrS7/xIrV44/c9gz+vMWUxs+/vqZ/Z4NetaP3+eOjCZob4a0oe1bdgcj8y+7jvxzr0+jmK9h5kFcNF/4InTYes7agXPqX+E1IxeO+ypU6cwvHAia7du46TRC3jsmS95efcGyn59DLV1jdR6/Hh9gZbtf/6b27Hn5fPSK//j8f++SmNtBampqdz8ixvb7PvUU07msEPn88WXK/n9H+7hr3++FwCbTZ0Hj8eT3EH6m1QT6pUPAxA85hdszz6WXR9/QVpOHuNGj8DjqcZmSaLEUTRpviz6WHFxccz7HQ6HWpknhBBCCCGEEEL0E0MqsRBJ0zTuuOMOPv30U2699da+Ppz+r72Amx4w9dSCNTO8QkEv3+N1Q81eFSDXRZZECv07aLCQOW42Hw1bTFXwMA7StjC65GXyU13kfnE7t9x8Ez/9v1/wsxt/wTFHH820aUUxD6e8rpErHvmKWZfd1er++Vf9kX9thH9t/Aqz0cBj318QO7mgM1pUw9vCw+DYX3bkjHVMQzl8ECoPM+E4lYjRz589VJorJbT6JrKMVCLtzb72uaHBqZJAdofUj+9O+rlvcIZX6ehNtfUVC7GMPhi+8w94/koofhX2rYWzHww3dO5hmqbxnTNP5w/3/oVdu0sZPWoUe8vKePrFN3lpXx6+QOuG1795dTuMWsSCqxfR7PfxyX3f5ydXLWH06FEx9337rbdwwsmn8eBDj/C9pZczc+ZBWK1q/Hk8HoLBYOIkr3MrvHI17P8WgOCRP4PDr2PvWy/j93hwNjUxaVQutlRz23MfKV7iR0qHiT7icDiw2WwsWbIk5t9tNhvFxcWSXBBCCCGEEEII0W8M6ubN8Tz//PNce+21PPDAA7z88stMmjSprw+p/2uvea3eJNWe27rskT033Gch6If6crV9ZN8Fnwea6qFmL8amWoxNtUwoGEla9nBSF1yF9/z/UO+FmTleZvvXcOKiE/B4PFy05BIaG2OXT6l1+9oEQaP5As3UerxdOSvdIxiEN36mSh/lTgw31o3sqRB5S3Y2dXuNmxucqlFwQ0XshsWi8yw28HvBU6Wubf386uOnZq9avQOtm5t73TDmSFjyolrBUFMCT54Jn/wFmgMxn6q7nX3GaQC8+c67XLbkQgCeefmNdseTwWTmqX89xR23/jbuNguPP47vnHUmfr+fK39wNX6/H6tVJVn0VWQxBYOw9j/w2CKVVLDlwnceRjvqp2heD4UjckhP0SgaO4ycdCtWi0klzOI1407UqFuIPlBYWEhxcTGrVq1qc3v66adxu904nc6+PkwhhBBCCCGEEKLFkFyxUFRUxHPPPceKFSuYNm1arzxno9ePJaIkT68IJg4EqqB+aDZ7srPg47KAWW82G3reyNcbtIDfAKkO8AXAZIVAALTQYzxNYDITrHGCyczkUTlMLppBs9GCFmzmrcBhnMMXzK15g3v+37Os27CJDcVbuPGm33DPH0KrEvzhwKvXH24wm4jX10yjN07A1p9kIFdLMj9njL0/47r/YN76DkGjBe+JdxPMGAPuqlDiJQDEa+IbmtntrgRXNdizVcC1Reg9CdL6vdB5XOBxg1kDUmJv0xXtXX+ocdER3TqOkji+zo8RC2gpYExX13aKQ51fnwf2bYNmH9RVwQgL+D0R7zXq3zlT4bJ3ML97E8bil+Gju2je8RG+k/9EMCtqxrKWxOsANd6SMHP2bEbmF1B+oJwJE6dgtKTy9Zp1zJ/X/mOLpk3D6w8CEc9lbD0+7v3jH/nwky9Ys24Df/rbg1z9g6swmlMAqKqtJzsr6mupqQ7zOz/HuOlV9TLGHIXvjAcgbXjooybAiHHTGDF2Gvg8NLrr0MxpKgnnc4Mh1rWdos5HzL/F0Y/L5XV0HIleUFOqPpvbY8uFrAJAJRdkRYIQQgghhBBCiIFCCwaTia4NPj6fD7PZ3P6GXVRXV0dmZibH/+pZTKlSYqOzDMFm/tx8C1PZzgrtUO4wXN/Xh9QtRgQP8GDzTdho5BHtIp43nNHXh9Sr/I1u3v/9+dTW1pKREb+XwJAdR8EgJwQ/5trg41hRTcgryWIHY9ipFbKDQnZoYyhlJM1aDzZ67iNTg1u5qfl+RlBBAANPaOfznHY6wWSTeUNER8dRe9v1pdWrV3PwwQezatUq5s6dG3ObRq+fs/7wNgCv/OIkUi39bI5ETSn8fb5KbLXHbINrVrYkF2JJ5pyIrhsI40MIIYQQQggh+pN+9l/jvac3kgqi+zRrBu4zXMn9zTdzdPBL3g2uYaU2p68Pq0sMwWZubP4HNhr5lqm8qJ3W14ck+htNY7l2NBuDk/hp8yPMZBO51JBLDYcEv1HbBMGLmd2MDiUbxvC1NpNSbXTfHnuSDMFmMqkjmxqyqSU7WEs2tYzkACcHP8RIM/vI4y7DtWzSpGydGADclSqpcM4j4JgcfzvnFnjxKij5POHqBmvNFgoy+u+KGSGEEEIIIYQQQ9OQTSz0tmU/Xdh/Z8B1ZNFKV8sntXp8RNNkrwf8HoJeD/gaVVkfk5Vmn5v777+f995+g4ZR+Txrz+aisZV8v+ZuHNk/4vGXlrN+/XoALCkpLD73HGbNPAh/Sg7vV+W1ezh/uuQQJgxPj/3HpEufJDt7OgheF5rrADSUY9zyOqavNhO02Jl80f28NHJm6Hnj7M9dqcrkGEyqfEas9yJuWaQk9EGplzrnfob/Pvntu3UcRV738a7r9q73yPfE7oj9PF53eB968+ZY71vkexup5RjOp9FVgbb7UwxVO9Dq92Co3olWsRGLz80kdjEpuEu9NIMF3zlP0DzhhPB+ku3RoBn4+4MP8otf3sS555zLPx95mKNPP4+8E65p96F/+u5cJgxPp7aulgcfeoS/3v8gdXW1LX9//bVXOT5lHSufuYd0g5vJI7OwBOowJLj0/EXfIeuke7grVb3vmqapUmHuarBlgy2H6IV3qgG0ps6dq1JVDbPlJv+ZFev9SHZ8JPt52o3jra6urkPjSPQSx2QYNTv+32256nPgxasS7qYIKL4mjR3uA916eEIIIYQQQgghRFdIYqGXpFpM/a9cg65DiYUmMAaBJrDECcgn/fi0iPu9YAwStBhCTaKt4PPQbIT/+8mPSbNZKNldys6U4exueJExGQFOd/6debc9zO7qAPf97a+sXPkVTz35BE8B6SMnsODq+9o9HIvZQKolTgmZpMuthLYLNkP1LjiwHqp3Q8MBaNgP9frP/eBtaPs0x91MimMM+GrBYlXnxRdq5GuxhRv+aukR95lC5z/qPfAZISMLDEbo6PXWB4kFb1rHkgTdOo70697nBlcZmMyAsfV1HescR4p8T+IdlyUDiHid3tAY8FWjxoENzEZw1YSac0ftp9UxNMHURa0TGfUHYPcX0FAGdWVQ+iXanpVYXrwczn8KJi1S2yXXdgQ0I3k5WQR8TdRUVZBmS+Ffjz3Kz/6zpt2Hul113H77X7j/wYeorVUJhRkzZlCQn89bb71F/b8vxzzexREjAAwQqAMNmoNBanxmjGl5WB0F7K1ys78hiHnisRx81k3s27WNvRVVjCyYwJgxheBuImhqVufDbCTo86Dpzc7N1nBiwZKutmn20+ozK9b4anUOosYa9OvEgre/freIxLIKVBmkdnox7Pz6bcat/j0mb23C7YQQQgghhBBCiN4k0QgRX6zgmz7j2tLJOvf640HNJDZbQ/tXiQTN71VBwNAsY0MQsOfwo+tuIOj1oHmquOmab/lhfjETsg0Yl1/NpKuXc/Hnn/PF55/x72XLqKhwUtecSjLzsxsaGmgV9E1WUz2UF6skwoENsH8DlG8En6v9x5pSwT4M0oZB0ekwZwnU7gsFtlGJBa9bnQevO3zuzXGCoJGi35/2AqhCnR+TGfw+SO/gOUrmPYmmv0f6ta6PB2tm8o+NHH+aAUbNBH8RZI0Cgxle/B4UvwbPXtI6uZCk7KxsAKqrqwFISU1NtHmL0846l4qdGwCYVlTEb371S86/8GJqqqt4/UeTWTzeRSAIv3qvkabsKZyy+BIuveYXuIKpHHvskZx75qmMysvmP2/8kxRbBhNNAeb5Gzmwbw9+r5+9ZXtVYkGP3Yd+aj4PWnOoOTaon5HXvKcO7DnhA401viJ15n0VojOyChL2VwBoTNvUSwcjhBBCCCGEEEIkTxILIr7OBrcT0R/vcqp96wFA/QbgqgItCKmZama2ycqXK7+keGMxs8Y7OOLUxTy4/E1+GFzFuKwgex89mZqr3+Owww7lsMMOBaC81sMV//gMXyD+NO2Az8ubr77EnBvaKfHiqYa9a6BsNexfpxIJ1btib2swQ+4EGD4d0kZC+nCwZqvXYh+mZkwbDOq12nJC56IyHNhOC5Vq6WwCJ/r9aS+AKsLnOL2Xgsn6e2Rxt36Pk3m/Y40/S+iaSbOG/3bOo/D8Utj8Rji5MOG4pA8xKysLgJrQqoNMqxmz0ZBwPDX7vdRXlTN3zmxuvukXnHXmGRgMBtAg57PbuGR6kEBzkEte8rBsvZ+bf30mT769mnJXkNGjsqmurmHKQXN455138JuzwF3DnIOKABiR5+DAgf3kDQslB+y54FMJyc+/+IIt69cyffI4DllwtPpMiUzYuKrCSbuWcxZjfEkSTgghhBBCCCGEECJpklgQ8XV1dUJ7+25wguYFr7X1c1gzIOBVSYVQTfSNGzfSVF/N9m21LD73fMZNPZhHH/0r3+N9xmX52P/AMRw44bcMX/gj0AwMy7Ty2A8Pp9bjw+tr5oanvgJUTwWL2cCLL77M//vNr9iebeMX1/8QozFGOaS6Mnj/Nlj3HOEp0hHsw2DkTBg+Q9XSTs2G9GGQVQi2WPX2gyqJEAyoGdTeMlUjPjIwDCrpYrHFr9nfET35Hg4WfTU7Pfp5O3oMeiA8VumdZh8cczP4m2D7eyq5cOgPYMF1YM1qd9e56RbOKTJx3owDNN8zkWHWTP5z9I85MOZMvIFgy3gqalzLVVdcysWXLGXLxnUccfAM3n791fB48jfBG9fBt88R1Az8ZrWDZeu3AXDUUUfyt/vvByAnJ5v6hnoWHHYYlU5VFmbBrMmcsPB4APLHTiR/5HAI+CKC/6HPhg0bqG9oYu2WPRxyjL5SxxO+9mOtRon1nksSTgghhBBCCCGEECJpklgQ8XUm4JrsrF+9kW3kqgVoKYlESrr6WbkNjKnMmDKB7Zu+ZfzECWC2Mn7CBI485Rxue7SEX03eysQcA3x+K+x8CRb9DsYewbBMK8MyrTR6w0WRJgxPJ9Vi5MdLz+N3v7yeXbsq2Lp1K1OnTm39Gj5/ED77q/o3QGYBjJgJo+ephEJ2IdiHQ+Yo8HuSO1c+t9o2CPgbwaCpBrR6YgFUE+tgoP3gZqzzHOu+WMclM7MHB5dTleRq9kPacHXt6O+n1wMpVjjuVypBt/Vt+OxvsPopOOJ6OORKVZIrUv0B2PIWbH2Hop0f8cL5oX01VkNjNRnLbyAj72G8R/8KgkbQNP73/FMUjc5g3efvMWLEcP795GPhpILLCc9fAXtWgmYkcOb9HHtYLve8fTr5+fm88+671NbWYrOmkpGR0VJu6bQTj+P04xa0PjafGxrrwWSC2r1qtQ+A2cq06dPZuGED06ZPDzXG1nstRFzbyaxG6e4knIwzIYQQQgghhBBCDGKSWBDdqyOzfvUkgjkisB5ZEqlmLzTWgdHNIdMmMG/6BLQgYLFiBU486jDGjbyNu/9yHzmrP+JXR6WQsX8dPHU2TDoJTrgFHJNiPrXVaqWwsIBNmzZTVrZPJRaCQdjwErz3O6jbqzbMnw8n3QGj54YfXLNXNWI2p6rjDwZUELG91+v1gNECmlG9Zne1WrHQkkzwqCSDr7H94Gas85zsuZeZ2YODvlDBmKquqcgElf5vzQin/wV2roBP/wwVm+C9W+GrR+Hon8OoWbDlHZVQKFvd8nAN2NeUyksbvTz7TT3zRhn57XF2Mio2YXnhUu5lCv80XMR7O3dxw42/BOBnP7me4cOHqx1UbFarJGpKICUD/zn/JDjuGOaMrmXFRx+xf/8+zr/gQgDmzJmDwWDAmprK5198QUFuGvkjh4FmUn0R3FVq3Niy1NgwmtV9qRng87DgsMNYcNhhqmGzqzKcrNS0jgX2u3vliowz0c1SG3ZD2drEG9ly2+3ZIIQQQgghhBBCdAdJLIju1ZFZv5FJhFjsORDwhAKnqICipnouqCC9iUljC5kxcw53fbqOx9aU88oNC1hgKlYztLcth0mLMKWN4sLmBqrIwrA9CFkjIG0Yo0eMYNOmzezbvx/2roZ3boY9qsSL1zoc55xrGbXwahWgjGS2qsCNXqrJ71HHU7dOHWtWfuxAosUaTh6YbapOPKikhKtKvUaLNbkSSLHOc+R9iWZLS3mkwSHNAV6bSnBFv8f67+5q8NTAhONh2pkE1z0LK+5Gq9sL/7u+zS69uUWYp5/J3z8o4eNt5VgzbXiGbebBbzby6Opq/nr+OC6Z1MCMwGb+3Pz/OOZcEzct97CrOZUrLr9U7WTHR/DiVdBURzCrkMZz/oVpxLSW55g4cQLXXHMNfr+fMWPGYLPZ8Pl8lJWV0ehp5LmX38dV7WTGnEM4+7yL1BgLNXNvWanQ8jpDyUl3pUq0WGzhcdnXgX0ZZ6Kb+C2ZuLxBxq3+Paz+feKNzTa4ZqUkF4QQQgghhBBC9DhJLIju1R2zfiPLmYw4SK0k0IP3+kxsi5WguwYtxc6UKVM47cQTePTJf/Odh7fx7QcvYfvsj9j3rIAtb2EClur7fv7hlqd552goPzgN047fwXZny/G7Zyxh37hzyBwxniAqntn6NUastLBYwWIH11ZoqgPNHbrfnfy5MNvAHLlqIS3Jx8QIJuv36c2xYwVV+6qngOhe+vsYjNNQ2esB/Kp8EIDBCLMuhOlnE/z6cbRP/wI+D5XpU9mXOZfGwmMgbThzD55H87r7ycryk5mZQVpaGikmjXUbt3DZUzt5/ZSjufzQbE4MfsjZU4ycMcnOp66x5JiaYNWT8PavIBggMGoenjMepNGUhc3nAyAQ8POPhx5izdq1ZGZm8tvf/Ia1a9dQVlbGlKlTSbWmUlJWQSAQ4OtvNnDW4mBoPIXKpgGanpDTe0u4q8DboP5tiUjYRa5Y6AsyzkQ38dmGU/T3Bt5+6T8URZbti+bcopJ67kpJLAghhBBCCCGE6HGSWBD9j8/TUs5Ea5l970GzRNRNN9swZo0Er4eJE8dxysKjeO6lVzlQXs6Tr3/Jj374NL7yb9D2rSVQt58Pv1pPdrCGuQ4vBrcTzVOJgWZGpBkgqJIKpdlHMPq8O7HaHYzXjGrFBKgArc8TPj6zNfw3nT279eqK5hjlkSJLHrVZRWBtnTjpKpktLSxWcJtUP48QTdPU9TtvKRx0PqvXb8ATMFFXX4/D7mDk8BFo/kbmz5xEmsXInvIqVq1aTW29m+HDHPj9fl5Y/iU1C17gheCp/Nr8JOOa1nN0+m74+3zVNBpomnIWwVPuwWRKIaVZwxRKbnz++efcddcfADjhuKM544wzWLjweFJSUjAaTTgcuezds4cvV65k1qxZ6nghZtm0Vn+zpKkVC5Fl1SSw36+VlJTgdDrb3F9cXNwHR9MBNaUqcJ+Ic0u3P21pXRBP1mQYNbvb9y2EEEIIIYQQQnSGJBZE8qJLAsWjzyRuf4ex725V0scDNWVgMqvnj5zNb7aD2Y7XlMHY8ROYMH4sq9d+y40//wWPP/EkP7/xRs4770r8zRp/WvUBAE9ftAACfmypKTz617t4/P67OWTKKHInHsxv/u8pKusqsTX5sWVmgmZQz+NvVAmBhgOqHBOh2u/6TGo0sOepm88NDU4IeCHV0fqcpdjDr0vfd8upMKhto++H+GWNEr0fFru6QQfej0Ei2deb7PXcX3hdEStl7OH7Y70Or1v1IzCngjVDbWO0hP/eWAcGA45cByWVbqZNmUzBSIdK5Hld5OXmcPAMAx/+9e+MyM0kb9gw9u/fT8HwXD7/ZjMApdposn70Fk0VazC8dyvmfV8D0HTkz2mY9X1sKeoYg81+0DQeffRRrrvuxzQ3N3PaouN45slHMaY5cHvsuFwuNm3ezO53djN90lgWn/Bj9fHgaww3Ym659qNeb+S13h8MtOuqD5SUlFBUVITb7Y75d5vNhsORREm43lZTqhJovtjH3YrZpvoddKN4SReHw0FhYWG3PpcQQgghhBBCCNEeSSyInpWo1n880SV9TGbw+yC97eM3bdrE6m+LqaiowGA0M2L4cFxuNxs3buTypUu5/Y47eO31N9s+h8FI+siJfHOgmQPBRs4aHWDLqg9JzRlFTX0jEydacThCs5/12dLGVCCgEg3NflX33edp/dq8bjBZQnXe49S997pb/67fF690UVdrxXfmPRD9j88DTfWqH0fW6NbB9MjyYXpCrtmvYvB6z4FIoeRdwYQpFEyxgrtSJQAanKBBXU0l7y3/AL/Xi9EUZPKM2cycOZOdWzfh8YcTN/UN9aSOnkfDOc9gLf8aDGYC+fOhydvq6f7xj4e4/nrV08GRnUWqKcif/vYAN/7fjaz/6iM2btnJzj37GTVqNNu3bGD2mGz1QLOt5djdHg8ulwu73Y7dJtfxQOZ0OnG73Tz99NMUFRW1+Xu/DZS7K9Xn6TmPgGNy4m27sYmyw+HAZrOxZMmS2E9ls1FcXEyh/D86IYQQQgghhBC9SP4zVPSsrgbF9VI+6bGD4tt37MBsMrNmzVpMZjMjRozAkmKhurqanTt3sW3bNn7161/DzEvbPLaxUZWICQaDNFQdYNfGNQwbXU2KYwyVlapER2Wlk9xcB3l5jnCCQad527629koQ6eejwRluXq0/1lPXtsRSMvtsT183sRXdw2xVSQWTOZTQikgsRJQPA9R7raGCm7GuGz1553ODu5KKSifVlVXkZFpx5I2ktsFHjVdj5KjRjCwcz6JTz2TixIlYLBY++uQz7vlUJceuvvpHPLvsadA0AoVHxDzs8vJybr31VgAKCwtJSUlhd0UDB95cTuHIPJa/8zb7Dhygzm/hoosuYsLkmWwp3U9pSSmjJs+kaLqa9e1yufD7/Sq5YGunQbkYEIqKipg7d25fH0bHOXq3JFFhYSHFxcVxS0ctWbIEp9NJ4YgYK96EEEIIIYQQQogeIokF0bN6uNb/hPHj2b5jBwaDgXFjx5JihNHDc9i6s5TUlBQ2Fm/i1VdeYWGMxMKq1asBSEtLo6q+EZvdjinFSqbNTFaug8pKJ01NXiornSqxoCcCdC5UoDcyGZBMXXdPnfppsrQO9lszYm/f1Vrx0m9hcAShLXa1UiGq1wDQugeBqxJ8DWBOa/c9d+4rpdpZTnVdPSlZo9i6p5Ka+kbS09OYOq6AGs8IJkyZTmZmJm63B4vFwp7SPYC65t9+6y1+97vb+PmNN7bZdyAQ4LHHHue3v/0ttbW1pKSkMHv2bDRNo7z8AFnZ2ewo3Ud5hZM6T4BmYzPHHnMMU6dO5fU33sDjt+LZtZei6bMBsNvtLSsWAPV+NtWpMZidP3DfVyGSUFhY2D9XcQghhBBCCCGEGLIksSB6VleD4u3Mtp86dSpTJ47B5Ktj3cYtWLQAa1avIsVoZOrYkZgCHtZt2xtz16v1xEJ6JhVNJsr9aWQYU5lYNLMlcKuvWIjJ5wb8ydXbjmTNAL9XlaixRMy81oDUOMmFrpAmtgNz1Ua8ngqxRCa99May0aX+YyRX9ux30lBTTVl5JYGyGiwpKaRlOcjMMvDdiy+ksroOV7OFzVs209TUhMVs4Y033oAZ4ZIsd999N5999hmXX345p59+Gtu3b+fDDz7khRdfbBljudlZjB03HhN+MixGMsfmk5U3kvSc4Rxx4lkUFxczdUIheXYD+BtbEobr1q3j+eefZ9asWZx80kk48vLCZZAsttAKDlO4vNhATx6JvtNHTZmFEEIIIYQQQoiBShILQunpGd2d3X8ys+1dThYeNpsTjj6CN97/jM8//4LMPAcjc7zMmTKWrY8827Kp3x8g6PfS0BBg/fr1AMydO4f9+/YzbFQhRquNoNcNwSC5uTnk5GRjMBgIRjQE1vTmrF4PeGqBDpSf0F9PWkZUH4k4fRlE9xiIqzaiyxv5POEeHtGlkCLZclv1JmgRI7mSkp5Dg9uNKehj+PCReHxBUlJSyMzIIoCRlPRsmj1uUrzVmJqbWbN2F4HmQMsuL7/sEh775z/55JNP+OSTT9A0rdVYSU9P57LvXsCsWbN49/0V5A/PpvjbtQQ1A5htLF26FJvVSjAYRGushmY/zT63ShhOncorr7xCbW0tK1as4LjjjsNZUcGwvDy1c7NNrVTQ39fuSB4NhpUtouP6uCmzEEIIIYQQQggxEEliQSg9PaO7s/tPMNu+JcAfBLMWhICHsfnDSE3PYmfJXgJ56VSW76dgWDgIVF5ezqTxY/j666/x+/2YTCbKy8upq6/jueeeY9r4Ufzvhf8wZ+4hnHXR5Wzbto0dO3cybuxYpkyZ0voALFYgM3GwOjpQGev1dDToHbnP9mayC2UgrtqILG/UXjPmSNElu/TGzhBeJRMyZswY8uwGhmek8uf77ufrjTs44YSFLF26lJWrv2V4Tgb7tn3LN199QrZjBN6AmUAgnFiYXDic67/3XT787Cs2bd9NY2MjqampjBo1EtAYOWIEEyZP48xzzsfV6Oej999hx+69NBuMmFPTsQc9bN64lW82bCY91UT53p2s27SduYceyXcvvpgFCxawYsUKZs2aRYrFgkNPKrSco6j3tbPjKLKx+kBb2SK6ro+aMgshhBBCCCGEEAOZJBaE0tMzuhPtvyOzhGNtm+YAr9r/zOnTeOAvf2RPlZs33niDFStWYM0KByOPO+54CkaPaAmO2u02TGYz1TU1fPXVVzj3jwKvi3WbdvD+l2sZNmwY04qmsXPXrraJBX1meKJzlkygsqNB78h9SmJh8LLY2zZoTkkP39eqVFKi5FYoKWEwgb11WS+7zYZ9ZAF5ubls3lGC1+flq6++YtGiRdTU1PDN5x+wZ/d2DD4X1Y0ai05fjNecyWehx7/13keMzk3n5JNO5PcLT6a8vJyyfWU4ch18+uknNDQ08MUXX1DhdLJnzx527z3A5l17SU1N5dA5s/j222/YvnMXxVv3YDab2bBhA06nky9WrWPYsGFcsXQpV15xRXLnqyvjKPJxnlqwy4z0IamXmzILIYQQQgghhBADmSQWhNKdM7pdznBTYz2QmWj/HZklHGvblpUAKtBqz0wnByujRo+msHAMowvGsTX08Lq6WtZVlrfsrra2jldeebXl95yMNCpr68nMDFL/zSrco4dx6JyDKBw7tu2xtMwMjy5mTzgBAm1miXfZQCzrI9rqSEItOskAsUsl6SKTDfqKh3jXS2j8zJ6/gHfeeReLxcKBAwfIysriy8o6NIudkv1OjigqxOVxYzaZwK8e2hjQ2L57L5MmT+bbNSt5/e33SUtP4/AFhzN58hS2bNlMoDlIQ0M9Ho+b2tpa9bjGRsoqa9i+cxe2DAfp6TXYbHaOOuooli1bRl5eHmvWrGHRCSd0/py1J944smZ2bb9C9LVkekHIygshhBBCCCGEEF0kiQXR/VxVEGgMJRfiND6O1JFAeaJtI0rAWG1B3G43s6ZPobJiP1tDZd+nTJ5Clt1MXU0lVpOB7XsOUFXnatmFUWtG0zQMBgNmvFhTUlh07JFgy0nmlYfpCZAYs8S7TE+kaDESGmLg6GrZnVilkjx1qjl4ZCJBHxc+j0r6xQnK3/vHe/n9nXfS6HFT69zP+WcswlNXzdr1m/A2NfL1l1+wqXgT5e4gzD8IgPS0NPIcNjLsVr5c+TnuukpwV7FzWw71Hi/5+fmMGjWKsr1lTJo0mW+//bbl+TSjhRff+oisrCwmTZzI/PnzmTtnDhMnTmT16tXMmTOn+89Zq/PXDWXJRLtKSkpwOp2t7isuLu6joxnk9FV0L17V/rZmG1yzUpILQgghhBBCCCE6TRILUQKBAEajsa8Po3d1d4DangOuSvUz0b71Jq8dWS2RcNvwc9ltdmbNmk1J8Wq2FldAqLKJ2dDMlPwcMmeMJ1BTytz8VKrIZNWWPWRaU/AHDRiMJlJSrUwYO5GDiiZRUevCubscR14eeQ4HMVcoROuNAGVEk9yEJAHRO5J9P3RdvUYiVzFomtqXPSe87+jrNFZQPnIFgMnK7Nmz+fqrr5g8fgw0Bzj68PlUu5r43ysvkmoMYGvw0uA3kB3a5cKFJ7B3RzG5w0agpVjJzbCTajHR3OTiwN691B/YjbOskHFTpnHUUUezfv16Kiur8Pv9HH74Alas+JjVq1fR1NTEnLlzcXs8zJo1i8MPPxybzdr2GDtyzjpz3cdaGSI6raSkhKKiItzutk2RbTYbDkc3J12HuqwClSxwVybezrlFJR/clZJYEEIIIYQQQgjRaZJYiLBt2zbeffddzjzzTEaPHt2pfTQ1NdHU1NTye11dXXcd3sCRlqduHdGdJU5C5syezZzpk1mzbmPLfVlpKWTlOpg9fTKrPnPipYHcrHRyHMNwudykBqChoQGL2UzeqAUMHzeNvz/8GGPHjuPQ+fNDiYUkDMRmwf3IkBhH3XmNJLOvWEH5yGSDycqpp5zCqaeeQnOjC/werrvhRjZtL6GytgGLxcLEEekcNWsO60MPP/PMM6mpXIDRaMCY5qDqwF5GOrL5eu0Ggl4X9c79NFSU8nF5FSeeeBJHHnlkS4Nnr9dHY2Mj6ekZuFwu8vPzcblc+P1+XC5XOLEQeYx2h4yrAcTpdOJ2u3n66acpKipq9TeHw0FhYWEfHdkgllUgyQIhhBBCCCGEEL1CEgsh69at47jjjuPKK6/E41G1ypubmzEYDASDQbQkZ7/eeeed3HrrrT15qINTd5Y4iWS2MffIhXyxVv06eswExk+egW1YAWNnBanZv5sFx5/G1vsfwmwyk52Tg9fnpbGpiTfffIt9+/YzengOB3ZtxHHicTQHmzFgaNl9steF6BgZRz2gA6V/gmYrmsXGNxu3YrVaMZlMLFy4kJycHPbuOwAZarsXX3iePds3suDQeSw66XSMqWnk5ORwyNF7qdy1nofv/zPltS4IwKGHzmf27NkMHz6crVu3omlwyCGH0NjYyIQJE3C73QwbNgyXy4Xdbm9ZAKJJeaIBr6ioiLlz5/b1YQghhBBCCCGEEKIbGdrfZPDbt28fixcv5oorruDuu+9m4sSJAC3lGzoSPL7pppuora1tuZWWlvbIMQ86Flv7TY59blUj3te2rEY0TdPQ/B40dyUnHXdUy/17D1Tx3icreffDj9m8ez85E+bgM6RQU1PNnr172L17Ny6Xm8pKJ6ZmL9V7tzLWYWfh4fPIMzWiBZoSPGs36cDrHKxkHPWgyOvLbGtZBaBpmrqhYTQaMBg0CgsLKSsrY/LkySxevJht27ZhSUlp2dX6rz/mwK7NfPTecvx1B9i85lP+8dd7+d/Lz2ExGRiWP4ZGUhk3bhwGzUBxcTG7du6ktrYWl8vN6NGjSU1NJTc3l23btrU6TE0LVTOKOMaEr0UIIYQQQgghhBBC9BpZsQBs3LiR3Nxc7rrrLgKBADfccAObNm3C4/Fw3nnncd111wEktXIhJSWFlIjAm0iSPptaDxTGKonU0VUNLic0NYAhvG1zczNGoxENjVSrlR07dnDgwAFGjRrFlClTsdmsbFi/AU3TGJ6bzkmLTuCSJd9VjW9NZtZ99Rnf7thPUVERB0fOwO3OUk49tXpjABkU46gHynt1yzF04Pp6++23CAZVgP+dd95lxIgRaEZLy9+HORyUuWrIGz6cVKuVivID7Nm1DZvNxrZdGZx+9mJGTtnNQVMngLuSD999gz179+Lz+cjJyWHTpk00NTby5ptvctZZZ7WUQiovL8dut6ubLcExylgRovOcW9rfxpYrpZWEEEIIIYQQQsQkiQWgtLQUo9GI0WjkuOOOw2q1Mn/+fDweDz/5yU/YvXs3f/zjH6XsTW9IFCjsaEmUYNRPYPr0aVhMBmpqavD5/Zx++ml8/fUqrFbVSNRsNgMwevRojj3heK644nuQEg7IFm/bTYPbR3FxcevEQmcCnPECz91V+qVV01tpSNvr+kPQO9YxdPL6Wr16NdnZ2ZSW7YdQC5Ull15Oaekexk89CGvOaDIyc0hNz8aans74CROZMeMgDj54HppPHYcZHwaDgbKyMgqG5+LBg8Vqxp6WRl1dHXa7HZfLRVNTU0uvhYSJBSmTJETH2XLV58GLV7W/rdmmGkJLckEIIYQQQgghRBRJLABTpkxhw4YN3HvvvdhsNh5++OGW5s1HHHEEixcv5rjjjuO0007r4yMdAhIFCqNrxEcF5l1ud0t9drvNBmkO8NqA8Mz3/Px89u0pIT09nSnj8jn72IPJMTZiM8Pw0WNJz8igpqaa1FQr02YfAvZc3B5Py34nTZ9NcXFxm0aknQpwxgs8d1dT38j9S2Kh98W7JjqykiHZbTuSpOrA9eV2h6/9uXPn8s033+DfHS5LNf3gw5k6y8v2bdv5cs06tLRcvnP+d7FYzEyZMgXcVTS66nDV1WHLzOGo40/CsW0306ZNo7m+gukTD6fR62fbvhoyMlQTZ4fD0ZJgsNvbuW6lSbpoT00puCsTb5PMzP1+rri4uM19cRtkZxWoZEEy5+XFq9R2klgQQgghhBBCCBFlyCcWgsEgRUVFnHnmmfz73/8mEAi0JBWCwSCLFi1i1qxZ7Ny5s4+PdJDxumIHQjsSKIwKzLtcLgKRs5z1fXn9LQ+ZOXUShfmj+OSTj6ncv4c1Kz+jcHg2Z516En6M2FOMpKeeyt4DleTk5AC0lGdxuVwUFRVRWFjYNuDZmQBnT8+2ltncfSveNREvodSVskUdTVIlmbCIvPZPPHERixYt4qVX/sfD68PbVFVWsWXLFpqbm8nIyGDHju1UVVXjaWxkdtFkPv5sOd8Wb2HyjDmcftppzJ63AICguxLNVQX2HIpLKmhsbKSiogKHw4HNZsVms8Z/vUIko6YU/j4/uR4cZpuayT/AqPFiY8mSJW3+ZrPZKC4ujp9ckGSBEEIIIYQQQoguGPKJBU3TyMrK4owzzuDzzz9n27ZtvPPOO5x44olomkZaWhrZ2dmkpqb29aEOLt1RJiYqcJ7MLOepk8YwoWg2Hrebxvoq3lz+EZrJxIGaJjLtZlIsFhqbmhg18SA2b95Efn4+JSUl2KxWxowd2yrQarNGBT47WlO/p2db6/sfyiW8+kOfg2jxEj5dKVvU0SRSkuPvww8/5Msvv2T+/Pmcf/55AJx66sk8vP7dlm127NxBaWkpqakpHHbYYXzxxRcYDAa2bN7C7Fmz2FRSTq3Hz/bt29ldUkJZWRmpqamkG73kZKaTa1fB0YqKCvLy8lr2++67y1m1ahVz587lpBNPTO51CRHJXak+A855BByTE287QHsJFBYWUlxcjNPpbHV/cXExS5Yswel0xk4sCCGEEEIIIYQQXTTkEwt6Q+bFixfT3NzM7373O6688kpuu+02xo8fz5tvvklxcTELFy7s60MdXLpjNn1UYN5usyWuxw6UVdRgTithxkEzWP/tenbsOYCnzo2zopK9gSZGj3Dg8QWhrIzyA+UYDAYmT54C0JJIiJu80IO1DU7QUL0d0hz9J6A9FPWHPgfR4iWUOlO2KDJxYnckfwxJjr/ib1eT4q9n07erIZRYiOTyeNizZw9ZWVnk5GRTUJBPVfVkPvzgQ7bv2I73317y8/Mxm80cdNBBlJWV4fF4WL9+PaOH5ZCXZSNn1Dhyc1PIy1PHHwz1RFm1ahX19fWsWrVKEguiaxyTYdTsvj6KHlNYWCjJAyGEEEIIIYQQvW7IJBaqqqooLy/HaDQyZswYLBYLoFYsNDc3YzAYOP/883E4HDz77LNcffXVTJo0CU3TeP311xk3blwfvwLRrngz8yPur/f4KCkp5aijjmLBgsN55513eOuttygoVAHW3bt3k5+fT1nZPjIyMli/fgMzZhyEIy8PzWBQ/Ruikwp6JFQP1mpeaKpX93kjAsNDeeVAb4o8z5EB9P5y/uMdh8XeuhdGMBh7u0id7aMR/VzRh4g6xvmzprN29dfMnj0DTTOgaaBphpbt3LVV2Gx2SkpKmThpEprBQFZWNlXV1ezbt5+mJi9nnHEG555zDgA7d+2itLSUrKwsPH5oMqajWayhBuvqOdVzaMydO5dVq1Zx8MEHtz5nyZwXIYQQQgghhBBCCNGjhkRiYf369Vx66aX4/X62bNnCzTffzE033YTRaATAYDDg9/sxmUwcf/zxHH/88fz2t7/FbDZjMpnIzs7u41cwCPXRTPLU0KqDjz/+mIKCAk488UQWLVqEFgpcvvbaa5SXl6smtX4/Y8aMYfr06R17Ev31BJH+Bn2tnQD6gNfDfTROOfMcTjn55Lj7t5s1HA4HxxxzDFlZWQCUlpYyYsQIampqyMnJYd++fezctYtxY8cybNgw7HY7BQUFuN3uVqWPop100kmcdNJJ7R9kfyx3JYQQQgghhBBCCDHIDfrEwsaNGzn22GNZunQpS5cu5c033+TGG2/ksssuo6BA1VMOBoOYTK1PxYgRIzAYDLF2KbpDVwKiXQgkHnH44Xz15ed4PB5WrlxJaWkpBQUFjB07FoCDDjqIrVu3kpGRQXp6esLyEi6Xq6Uskt1mCydLDCZVlsabRMNQMXj1RsDbbOvZxEk7iRlbZi4FBSqZYAuVISsoKEDTNE499VT27NmDx+OhtLSUcaEeJXV1dTQ1NTF27FhsNiu7du1mz5495Ofnt4zDDvG6oakOXJWQXSDJBSGEEEIIIYQQQoheMKgTC06nk6uvvpolS5Zwzz33AFBUVMTy5cvZs2cPlZWVOBwO8vPzAfjDH/6A1+vlN7/5jSQVeprF3vkAYGQvA4utw4HbgoICSktLqampaQl6Op1ONm7cyPTp07FarezcuZPU1FTGhoKhLQmEiDJIkY2c7TZb62RJf6ztL3rXULgGzKnY7XYmTpzYkpwdO3YMY8aMQdPA7Xazfft2Ro8eDagG6xUVFaSkpKgG6DYrpaWlNDY28vHHH/PSSy/h9XpZuHAhhxxySOLn1hM3AH4fmMyD+1wLIYQQQgghhBBC9CODOrGgaRonn3wyixcvbrnv9ttv5+2332b//v04nU6mT5/OzTffzJw5c1izZg0lJSVcc8015OTk9OGRi4Qiexl0InA7ZswYxo4dy65QvfeCggJWrFhBQ0MDxcXFpKSk0NTURHl5ORCVQIhILNjt9tb3RTfa7cESNWIA6OEyRf1Fm3EQIS8vj+zs7Jakg81mbUnW6dsXFBSwZ88empqa2LhxI4FAgJycnPYTC5ErhLILhsS5FkIIIYQQQgghhOgvBnViITc3l2uvvZb09HQAnnnmGW655RaWLVvGokWLWL9+PTfeeCPLly/nyCOP5M477yQ1NVWSCv2dHsCPLDXTCWPHjmXs2LEEg0GmTZvGxo0bKSoqwmq10tTUREFBQUsANFbgtGUFQ6xmstFJBjH0DJFrwGazYrNZY/4t1tiJ3l5f4aD3Y2hsbGTMmDHtP3Fk4maInGsRlhd0kkk92v5vwGSMvZFzS+8eVD9UXFwc836Hw5Gw1F8ryZxHWy5kFXTgyIQQQgghhBBCDHSDOrEAtCQVABYsWMDXX3/N3LlzATj66KMZPnw4q1atIhgMMm7cuL46TNEZ3RhMnDdvHvPmzSMYDKJpWptZ1bpgRBKhoqKCiooK8vLyyHM4AFqaQAsxGAWDwVZjIBgM59X0S9/t9rRKyFVUVAAqoRAMhreLNm3atLjjLiZJJgxdtXt4tPlGUmmCx9rZ1mxTQe8hxuFwYLPZWLJkScy/22w2iouLEycXbLnq/L14VftPaLbBNSsluSCEEEIIIYQQQ8igTyxEGjNmTMtM2GAwiNfrJS0tjRkzZkhAeIjQNK1N/4zI917/d2Q/hfLy8pZSSHqDWlCJhaamJpwVFS2JBSGGEk0LJwo01D8iS4fpY0Ql4Bxqe9p+1mqa+p/oPiZCxKJ5Kkmhibu0H/HTpYtJibdiAYbsTPrCwkKKi4txOp1t/lZcXMySJUtwOp2JEwtZBSpZ4K5M/GTOLSr54K4ckudaCCGEEEIIIYaqIZVYiKRpGnfccQeffvopt956a18fjuisyHJIlp4JSEaWc4nsy5CXl0dFRQWOvLweeV4helu8RuUdEV3+SF+xsHFjMXl5eQyLHi+9MIbF4FSqjSY4YhZYhuz/lUmosLAw+XJH8WQVSLJACCGEEEIIIURMQ/K/xp9//nk+/PBDnnnmGd59910mTZrU14ckOktv4Op1d09Q0utqE+SMDLKWlpbi8XhYuXIlI0eOVAkGWa0gekNkAL6HSgDFa1Qez+6SUsr37aWgoIBxY8cCYLfZWnoo2GxW8vIcbNxY3LJyoU1iobvHsOh1JSUlcWfGCyGEEEIIIYQQYnAakomFoqIinnvuOVasWMG0adP6+nD6Xqzmw7F0d7moZPeX6PgiG7hGbhcZhA1aWu8r0f4ig5wxgrcFBQWUlpZSU1ODx+OhtLSUsWPHsHFjMdu2bWPixIlMj3dN9dV5Fl3TX963eNdmMseXZFKi1WqDGPvduHEjxVu2oX917Nmzh4C3kdLS0tY9aqIeqq/uycvLa3ue4o3heGR89CslJSUUFRXhdrtj/t1ms+GQ5KsQQgghhBBCCDHoDMnEwvTp03n66acxm819fSiiq+I1cG0VhLW0/Xs8kUHOGMaOHcPYsWPYtWt3S0kkgG3btuFyudi2bVs4sdALM8zFENLOtRmTfg363GC0xE2Y6ew2G3Zb/L/r1zlkApCfn9+yYiGRvLw8lVSIxWJXt2QTOKJfcTqduN1unn76aYqKitr83eFwdKwcT01puzX9NeeWjh6m6A3JvC9DtOeFEEIIIYQQQgxGQzKxAEhSYbBrNQu6A4+Ll6gIcbs9uFwuhg0bxpjRw9C8bvB5mDhxYsuKhRbtrH4Qg0Bv9gdo59qMSb8Gg4DB1LGkRAwTJ04MrVhQxhQWMmXi+E7tqzv6OYj+o6ioiLlz53ZtJzWl8Pf5alwlYAEaSaGW9K49n+getlz12fTiVe1va7aphtCSXBBCCCGEEEKIAW/IJhZEBw20BquRQVivv9t226oGvU1rSRxMnzatbQmkzswwFwNLf+8PoF+DaRndktyaPm0aEyZO5pH17wCgaRpajNJEse6LlnQ/B1n5M3S4K9X7fc4j4Jgcd7Mmf4DvPbGeCk1KLPULWQUqWdDOShOcW1TywV0piQUhhBBCCCGEGAQksSCS098DqL2kVQ16M4kTB52ZYS4Glv6ePOrH12CrsZSIrPwZehyTYdTsuH8Oev1UaPt773hE+7IKJFkghBBCCCGEEEOMJBZEcvp7ALWXtKlB352BzoG2KkT068A90K9n+yddAkk+ewaHJHonJFWjX3Sb4uLimPd3uC9GR0kvBiGEEEIIIYQYFCSxIJJjlmB3j5NVIaK7DYbZ/v09eTNElJSU4HQ629wfLzjdSpK9EwD1XttyO3GEIlkOhwObzcaSJUti/t1ms/Hiiy/GbLjepaSD9GIQQgghhBBCiEFFEgtC9BcyM1t0N7mmRDcoKSmhqKgIt7ttYqAgQ+PwsTZGUg5la2PvwLklqd4JgMxU7wWFhYUUFxfHTBRVVFRwzjnncPLJJ8d8rM1mo7i4uHPJhY72Yij5vP1t5XoRQgghhBBCiD4jiQUh+gt9ZnYSjW+FSIrM9hcdkGhVgtvt5umnn6aoqKjlfrP7ANM+uBxjoBFevSDxzs02KFwgQeB+orCwMG5yIF7Sobi4mCVLluB0Oju/aiGZXgyyskEIIYQQQgghBgRJLPSwYDAIQF1dXR8fSQKhY2xXXwW84x2fzw1eD1isCYOnjV4//kY107aurg6vpZ9f9kMosaCPi2A712CfjqO+Gh/JPm9PijHGemw89ffPoX6sw+No6+eQ1rrc2v4DB7jkkktobGyM+dj5hVYOKUhlhD1i1ULjAVxuD5zxV8idmPggrTlgyIRuGMMD7jN9gMnKyiIrK6vN/Q0NDS0/e/Sz2JAJl7wHnqrE21Vug9d+DBvfa//6S0JdgwtofxwJIYQQQgghhFC0oPwXVI/as2cPBQUyk06IREpLS8nPz4/7dxlHQrRPxpEQXdfeOBJCCCGEEEIIoUhioYc1NzdTVlZGeno6Wi/PtK2rq6OgoIDS0lIyMjJ69bl7w2B+fYP5tUH49ZWUlKBpGqNGjcJgMMTdvi/G0WB/D2BovEYY/K8zGAxSX1/f78bRQDrvcqw9Y6Acq36cGzduZMqUKQnHkRBCCCGEEEIIReoH9DCDwdDnM98yMjL69X/Qd9Vgfn2D+bUBZGZmJvX6+nIcDfb3AIbGa4TB/TozMzPb3aavxtFAOu9yrD1joBzr6NGjJakghBBCCCGEEEmS/3oSQgghhBBCCCGEEEIIIUTSJLEghBBCCCGEEEIIIYQQQoikSWJhEEtJSeGWW24hJSWlrw+lRwzm1zeYXxsMjNc3EI6xq4bCa4Sh8zr7m4F03uVYe8ZAOdaBcpxCCCGEEEII0Z9I82YhhBBCCCGEEEIIIYQQQiRNViwIIYQQQgghhBBCCCGEECJpklgQQgghhBBCCCGEEEIIIUTSTH19AINdc3MzZWVlpKeno2laXx+OEP1KMBikvr6eUaNGYTDEz3PKOBIiPhlHQnSdjCMhui7ZcSSEEEIIIQYHSSz0sLKyMgoKCvr6MITo10pLS8nPz4/7dxlHQrRPxpEQXSfjSIiua28cCSGEEEKIwUESCz0sPT0dUP8HOyMjo4+PZpCL04e80evnor+8D8CyaxaQihcsVjDbEu8v2ZmIyfY/7+6ZjX31vN2orq6OgoKClnESz6AaR/39fWvn+FqNp58cT6qlna8Rnxu8nuTGXDL68fXcV4baOGr0+rnoz+8BSV6D3am7vxcS6czYkfHRaUNtHA01rT43frqwdz83hpBkx5EQQgghhBgc5P9V9zB9mXxGRob8B2hPixPIsXj9mFJVUCYjd3jy/zEpiYVe0145iUE1jvr7+9bO8bUaTxkZSYynbn6/BsD13FeGyjjq+DXYjXozsdCZsSPjo8uGyjgaavr0c2MIkjJhQgghhBBDgxS/FEIIIYQQQgghhBBCCCFE0mS6jhi8fG7wuoGUvj4SIZKjX7MWG1jsfX00QgiQcSmEEEIIIYQQQsQgKxbE4OV1Q7NfBYV6m88NLmffPLcYuPRr1juArxu59sVg01/GpYwtIYQQQgghhBD9iCQWxOBlsYHBlHzTy+4M2vSXQJQYWPRr1tINTY77Sk9d+/r49Lq6d79CtKej49Lr6pkEgHyvCCGEEEIIIYToR6QUkhi8zDZ18/qT2z4yaJNsMiIeiy1cOqOnRZbpiD5uKeExsJhjvId9JfLagYh/WxI/rqeu/cjxKdey0LkrQUvv3LhJ9NkZqaPjsju/SyL15veKEEIIIYQQQgjRDkksCKHrzqBNbwaIEwWxJBgrOit6dnTLNdZOYqGnrn0JqopYuhLAH2gJgP6UeBRCCCGEEEIIMeRJYkH0LK9r4MyYH6hBm0RBLAnGDi3dOd6irx3938GuH2an6ONT0/roAES/1JXSYT31+Wixd+93SbIrK4QQQgghhBBCiF4kiQXR/YIRkceemhEaS7yAY+T9HQlKBjsRQU0UAEp2f8keo76dxR4/iBwZ4Grv+SVgOzD11HiLTrTp/3bVd22/A1V3j18RWzLnOXIbuwMsCf6vTGc+x/ubjozr7n69yV7PMj6EEEIIIYQQYsiR5s2iZw2GZrQdIc01RV/qjfHW3Q1phegrA+Xzeqh9jwohhBBCCCGEGBBkxYLoWQO1vFBnSekh0Zd6Y7wNpfEsBreB8nk91L5HhRBCCCGEEEIMCJJYEP3fQKovLQEgMZB0ZmyZrT17TEL0loHweT2Qvv+EEEIIIYQQQgwpUgpJ9H/9sVyFzw0u58AuCzMYXoNIXqz3uz+OrYFExtDQ4nNDQ4Vqkt5bEo1Ruf6EEEIIIYQQQvQhWbHQzZqammhqamr5va6urg+PZpDoj+UqerMpdU/px69BxlEPiPV+98exNZD04zEEMo66XeT7bbH3znMmGqP9/PoTQgghhBBCCDG4yYqFkG3btnH77bdz0UUX8fjjj7N169ZO7efOO+8kMzOz5VZQUNDNRzoEmW1gd/SvwElXm2n2h5mm/bghqIyjHhDr/e6JsdUfru3e0o/HEMg46jb6NQ29/34nGqP9/PoTQgghhBBCCDG4SWIBWL9+PUceeSSrV6+mvLycO+64gwceeICmpiaCwWCH9nXTTTdRW1vbcistLe2hoxbdqqPB0GQCson22R9K0PTHhE3IoB1HXlffBd2j3++eSgD0h2u7t/TjMQSDeBz1Nv2aBkjL677VCl0dg/35+htKCUbRL5SUlLB69WpWr15NSUlJXx+OEEIIIYQQQ8KQL4W0Z88eLrjgApYuXcqdd94JwBNPPMENN9zA9ddfz9ixYzu0v5SUFFJSUnrgSEVMkY0tuxLsiVVSoqNNM6O3T1SmQkrQJDRox1F7pUu663rujmPprMhruyNjSJrUdrtBO456W099Xkcn4brz+u/qeOrqZ5GUaRK9qKSkhKKiItxuNZZsNhvFxcUUFhb28ZEJIYQQQggxuA3pFQvBYJD333+foqIifvCDH9Dc3AzAxRdfzOjRo9m9e3cfH+EQl8yMRz144XJ2ralmZEkJ/XkbnGrfDc7kZl5GB4kSlanozzNNRdd5XbGvx/ZKl3TnbP/2xk9Hy6i07M+TeLvIa7sjryfetjLzeejqjfc+mefoqc/ryDHY3ljp6Lno6GdJ9P67+lkkZZpEL3I6nbjdbp5++mmefvpp3G43Tqezrw9LCCGEEEKIQW9Ir1jQNI0RI0Zw5JFHtlqZEAwGaWhoYN++fX13cL2pg+Week28GY9elwpumq3q5vOA3xd/dmTk63M5wZ7edhtzxKzOmhJoagDNqIIi/hrw1oMlHbITzH6LntVq7uRM0WTfD03r3u26+3kHi86Mj3jXrrmd2b+R15D+vJ2dedzejOGOXp9eNzTVgydGsCbeMeqvx2xtZ98utY8gkJaR+HX01XU61K777tad4yh6v135DuvozPro5+rM+Ix8jN0R3q/+vdbeceq/J3rOjq6yiD4PXV2loX++6AkL/Vjle0b0oKKior4+BCGEEEIIIYaUIZ1YADjxxBM58cQTAZVQ0DQNi8VCdnY2ZrO5Zbtly5YxadIk5s2b11eH2j90Z3mF9h4fLyjp86gAiM+jgjIWO5hdsYMyXhe4G8K/JxNA0uMeZqvavy8UUG0vzhEvUCslXoaezgblLPZw4kEPwHU08KlfbxB7xnDk3/VjTbRfPZEHKoFnMsfYJkEiJalj9oDRoo5XyoYJXW+893pyujPP4XND9Z7wmEj28z3WeNHHvc8THm9ma/j+yHMRuVJPTwIkSpYnI15ivKsBfimJJIQQQgghhBCD1pAuhRRNC/0HtKZp2O12UlNTAdUA8+qrryY7O7svD69/6Gp5hI48Xi8/ASqAopeVMVtDAUiruq+mFFxVrYMwOj0JoUumNEOaA9KGqZ+gjiFtePhYOmooNbMVSneWTkm2pEh0CS+IfQwtQcmq5K7LyDGUNRpS0sJ/c1eq57XYwO9V/+5M2Rp9TEvZMBGpp9/7VqvfOvEcXrdKKvh9sRN48UoXxRvT+ljTx2Zk2bHIc6E/Pkj3fbf0xLnWk5gBryQGhRBCCCGEEGIQGvIrFmLx+XxUVVXh9Xq5/fbbue+++1ixYgUTJkzo60Pre12dQdqRx7ucULsHfE2QPiz0eHvrWd0upypTBOCLkVgwW8EXCP9uSiKAFD3Ts7MljdqbOS4Glr5aeZLs9acnDDQSX2/6GLTnhH+Pu8+IEkX2dDW+vBHjSQ9q2h1qP709M1lWA4n2RCYPYiWem+pVID87v+PXkD520iPK/ujXY4MTvA1gSWtbQi/emNZXT+hjU0+e6ysq9Mfoj7e4u/Z93NO8bjDFWYkkhBBCCCGEEGLAGzKJhUAggNFoTGpbTdPIysriV7/6Fbt372bFihVSAknX2SB7osfHCw66qqCpDgJ+NSM0LUbtabNV9T4IErs2tcUOpIR/93uAjLbb9QQ90GswdX61g+g/+ltJj+hxoycMUjMSH19HxnBkiSI9KOuPmEUdmcDoStJRn6nd0XPb394TkVhfJIIiS+fFSjy7qtSqg85cQ9FjKfJ61CsIdaSSUGTSXOdyxr/Gu/p93NOkjJkQQgghhBBCDGpDIrGwZcsWXnvtNS6++GJGjhwZcxu9vwKoFQuapuF0Ovniiy+YOXNmbx7u0BOvKaU9B/xutcoga3TsprexAjGJmNppItudJKgyuPS39zM6qB4dZOyOIK4+gzoyaRc5hmy5YA59jXQlyNnZOvf97T0RifVFIijWNayz2NV3S2d7LLTZX8T1GJnw64qu9IDoisjPj458x0bq74kPIYQQQgghhBBdMugTC9u2bWPBggVUV1dTWVnJDTfcgMPRevZ4ZFIBwGq18v3vf5/DDjuMyZMn9/YhD26xGsfGakqpl1fp7pn+yQRnumtWrQRVBpf+9n62F1RPJojb3rXe0cRdZ8Qq9ZKs/vaeiMT6IhEU7xqOLJHUXd8zscroJSNRuabeGIMxjyni86M7nl/KlgkhhBBCCCHEoDOoEwsul4s777yTM888k3nz5nHdddfh9/v5+c9/3iq5oCcV7rnnHtxuN7fccguXXnppXx324KYHKzy1YM0MJxCiy0n0dODJ51Y1sDViP39TPbgqIbtAgiCif2ovqB4viOtyqvIveh33js4gjyyF1B06UwZJgpQDU1cTQS2J6ZR2N21/XwlKJHVqf124Jrv7WLrj2Lo7CSRly4QQQgghhBBi0BnUiQWDwcDBBx9Mbm4uF1xwAXl5eVx44YUAbZILVVVVrFq1il27dnHttdeSm5vbV4fdd3ojWNfSODY3/Hukjgae9Jme0WLN/Gz1OLdqrBnrOS22rtXdTlZHzndvB1IlcNt1XT2HXQpUJnisqwoCjeGGtckEDyNnVCcqJ5boeSP3AeF/d6bUiwQphyb9fQ8EWt/fmbGSqERSLO09R1evSU9dONnXWS6nSojbc1uvwujMsenfi1pHmkQkoH/3A1SXxE7qCyGEEEIIIYQYUAZ1YsFqtXLZZZdht6sA8/nnn08wGOSiiy4iGAzyy1/+ktzcXAKBAAaDgQceeICmpqahmVSA3gnWdWfpEq8LnNsgGADNCOnDof4AGM1gSVOJBa8L3A0Rj3GDJSNUgilNBTdiBTQtqaohdHeunIgOTHXkfPd2IFUCt13X1XPYlccneqw9J7xiQf9bZHkyr7ttYq7VjOqsjj+vywnO7ZCaDmnD1H36/uyO8HPFC4xGi55NLYmwoUF/3w1RKxZ64/OqvedIZoVQvGva64agXgKwE8emX/+1e6GxBip3QuG88PP1h14k+ne/ywne+tb3CSGEEEIIIYQYkAZ1YgFoSSroyYMLLriAYDDIxRdfjKZp/OQnP+Gee+5h165dPPPMM+TkdHHG4EDWH4IPHeGqBHcloEHmKNAMYE6F5gAQDAWBXBDwhh/jroS0DBXMyC6MvV+vG4wW9TivG4LBcJIisg62/rvRktys1zbNdjswYzbWexMMtv846NyM0/56LQSD7b/uZF9vd5+/6P3FO4fJPm9n3oPIFTwGU+trKxia5W3LVjf9viaXui6bXOH7vG4gCF4PWKxqXHk96thdzojnc4MlPfx7q2s64nW6q8BghMYGyB6j7ou3XbNX/bRHJXi97ta9GKLLl0UHfXtyfIiO645xqb/vXn/r+zszVtqUH2rneol3bUf+PRgMf2eYU9X9ripoblI/9XHn9bResaMRvjXVR/zNppIG7fUgaXKpcWuygvcApIRW3emJhVgB/FbfZ4nOWzePD4tNfWbES+oLIYQQQgghhBgwBn1iQWc0GgkGgzQ3N3PhhReiaRqXXHIJr776Ktu3b2flypWkpHRD3eaBbKDNHtQAa5ZarZCZrwIrvlAwx2xVwUlQAVadvxFqSlVcyJ4Tu1ySHkDye1sHnqIDUS214V3grg4dT3b8REF0IqEjTTl7+73pr9eCzw1k9PVRJKe9c5ioYWsyj4+1H/2aNJg61pC2sT4cCNUAW04okRBQP+25odnGleEEBYT6LUQkFvRm7NFsOeGfkX/XkyD6fbYcNW5tMRK8iXox9NdEmOgdnfm86ug1E+/ajhT5HaEn4wglG6yWcKKu1XZW9b1hSg2PYW8DuGsgc2Ro29A4jHyNPrdKHmiohIJmhKxRrVckJXus+u/tJhm6QaKkvhBCCCGEEEKIAWXIJBYg3KQ5GAxywQUX8PDDD7N27VpWr17NQQcd1MdHJ9rQyztEBl4jg6i23PDMf4sdGspVGSQ9kRDwqX9bs8P7NKWGyzD44gR09YC/2aVWOPi96t/RiQH9d18N1O9XyQprFax8SAWCDvsRWHPULFX9GHuiMedQ4u3mxsF9qbsatur7cVeqa1ADUtLbbud1g69RBTz14KHXrZJiJpP6m8kEPj/YUAFQPRCqs1jBF5FYiNdvQV9doAcq7bltVyC0ev2h44m1XctxVqljzBzV9u/9NREmek9Hy2HFu2ZclRGlizpYFjH6O8LngdQMQFNj0hcxnhrrwysYoo/FXRMaixEJQ0vUWPN6wO0EfxNkjlbJdVAl/pJJKkYeq8+jVkq4q9S+uiu5ICXKRB8qLi7G4XBQWCiJLCGEEEIIIXrKkEosgEouBAIBbrzxRj744APWrl0rSYX+Si9vEhl4jQxGRtZm1zVUqMCpzwMvXgWAdubD4b9bsyBgUgHY9koQRa9SiH4+/e8NFSoo49wCXz4QKs8E7Hgfjv01TD0t+QahIrHo4NpA5XWp6ztWEkBPniUdII1YYWOyqGRarESFrzF0LTdG9CYIJRP8fhXkdFeHmpZHrFKAUIAwFNyMXE0Q7/2IlTSId9zJjA2fR9WgD/pV0DcyMSJBy6HNXQlaevf1Wagtg6Zatbqto4kFfVWDN7SaANR4bEkgRFzrqTGSf3og3pqpSkfp5ZBiNVG2WEEzqf8XF6+KU6JVUdErMNxVKjHf0UbqiUivHtEHHA4HNpuNJUuWYLPZKC4uluSCEEIIIYQQPWTIJRZ006dPZ/Xq1cycObOvD6V/SmamYU/PRtQDNJHBmPaCkUazamD5v+uhtlTtZtl3OL/5ZF7TFqnHpWfHfmwsZquawap5VZAmOjhTuxc8VbDqcdj5obrPMQUIqkTDGzdAoAlmf1cdW7T2yuF01GCfIdpbrynyPPbEKhOfJ34SIFHJn1giV9i0F6jXSx65qtRPvfyRPbSKwZwaSjxE7MPnhpoylXAAMFvCf3NXAWmtA/0+j/rpbwwnIfT7dfpKhujHxSvFYraqmdh+vzoOlxO8jRHHNAivdZEcfawkKm3Ukc9FU6oqRWRKTbydqzJctiveahyDKXZJIv17zGiB0i+h5Euo3gkH1kPFJsgYBZe+prZ1V7b9ftATfRkj1e8Wa0TyL+IzqyOrokyp4XYKrsr4YzEZ+vmG0GecjE/RewoLCykuLubjjz9myZIlOJ1OSSwIIYQQQgjRQ4ZkYsFoNHLFFVe0lEYSMSQz07AnZiPGCgDpAQo9gBovQGJOhaY6eO06qNsLZrWt5irnSp5hcfB/GD/dDEdeF3+WeHQAJ1ZvBV0wCLs+gY//CI3V6r5DvgfH/1b9+8HDoW6PCoK2lMSIfr3dVA6n5XXIDNFuEXkeeyKxkChB1lL6JGK1QINTBf3sjsRNWNsrgZKarpILRnO4LFLLftzhpEKrxsgeFcD3+8BCuHcJgKsCzMaIYw1dz/7G1te7fn99uVohYU5rHWxsb4WDnoSw5YYTF/oxpct1PqT43OCuD/+uB64TlcOK9bkY/V2jJ7fsOeqmj814SS93lUoax2o0rjdyjhzfzX5wboN9a2HfN+q2fz34XG2Pt3Ib/Oc8OP2vYMtS4ylybOr9TwLe0GsJJe1MltbfJcmuCopMdOrH6qoMj8eOfgbq57ujvV6E6CaFhYUUFRX19WEIIYQQQggx6A3JxAIgSYX2JNPYsrtmiEaKDgDFKocU83EuqNwBL31fJRWyx8IlL0P6CLzfPE/5/24nn/3wyd2w6lE49Acw8wKw5yVOHkDs4EzNbnj9/2Dbu+p3x2Q49Y9QuED9XlemkgoAk05KHNjx1MWe1dqZcyhNbLtHT5/H6ARZdGLLYg81Z3Wq4/A2qO1iBU+TTU7pqxFayrKkti6P5PdAUwP40yAz4jn01Qtp1nBAU+dxQZ619YoEvxcIqqCnnsDTV/40VkOwGTKMbY+vsS520+ZW5y0U5LSExkb6IF2ZI+LTvxN0tlzAq8ZKohJZ0Z+z0d81rVYZRCQKopNe+rWur2iw5ajAf/1+9R1Us1slG/TvAFeF+ltDeeuxozPbYcRBMHIWjJytViG8eJVavfDGDXDK3ZA2ItxI3WxTY9JdpXoxBP2hvilpqoFz5HdNsn19YvWG0LydT67K95AQQgghhBBCDAlDNrEg2pFMM9RkZog2OMOzjSO3jRc0jxWQiBd4j1S5HZ69RJU/0pMKoWaWzdMXc9UbmRwb/Jwbs97CUL0DPrwTvngQ5l4G85aqoIzBFDsBEBmcafbDl/+AD36vXoPRAkf8FA6/Dkwp4cdsCpWxGD0PRs9RgRqXM3bJI2uMlQyR5zDe6oNY57CrTWwHeymlZHVXM+Bkz6erUiUPLGnh60N//zVCQUOiZvlHlRsxW+OvvPG62jZu1rmrodkXTgxE12uPPBdeD3giZovbQtduy0oFrwqyGi2hAGjocZZQ4DYlHXxNscdZ9IqeROWRYiZY5NodtFqVJrO1biAOrT8rIfbnpv45G69MT7zZ/bGC7s1+9bimGvjP+VC2OrnXoScRRs0OJRJmQc5EMEQl2i76L/zrTLW64aO74dxHIeALJ/nMNjB5IDWgEnLmNJXgMNtAM7R9Xv1zQV+VFN2YOrrfgsUG3qiVUx0hzdSFEEIIIYQQYkiQxILoGXqCIHLWY6vyKnGC5rECEnpAKF7QtLY0blJB16wZeV87kuu/9ytSt70OK+5R5SY+u0/1R5i4CNKHgzFFJQj0W+TvaPD531QJC4Axh8Ppf4HscW1f/8ZX1M9JJ6mfPg8QDM8q97pUMNkfej3RpZkiz2G8wE5PlD2SUkrdK9nzqUX9hPD7n5oR+7Gxyo1Ul7RNUEDErOuIxs3BZrWqwFMD3noVoAwG1G3vynCD6caaUDkkCzQHMDYHOb95P0E0jBtHg9msxkmzX42TjNGQmg2541snBcxWsDnUa7Tlhl+DntCITOx53ap/id6XJJngZqJzLUmHgS3yvbU7wGZp/ffoz8roz83Iv0ePG31lUDzRQXezVa3s+fIh+PQvKikHagVDxih1Sx+pbhmj1AqE9NBP+7C2SYRYjZdHzoLzn4JlF8D25fDe/4NT723bwNnvU8/RXpNpffzX7YMUq+qx0t5j9NcdK1HRXWRcCiGEEEIIIcSAJokF0b0iAwV60CY6yONzq1sQSIszW18X2cDZ54GmehUUyRqtAqclX8Lzl0P9vrhJhVYMJlUCaca5sP6FcIJhwwvJv8bUTFh0G8xZooIuAV/rv9eVwZ6v1L+nnRW+X1954XVBzV7whRqEmuPUsNaTLHrgK5nVHV0lJSy6V6LzGZko0wPtQcJNwtub9Rs5NvR9+RrV36IrvZlDzV0b61SiYMtb8OWD6lrtIDNwpf7Lp4k2tEFWIeROhMwCFQB1TIJhRSoBEZk8SEkPl0uq3RNa9WCGAO3Xh9dFn+vIzx5JmA1sCcvuecCe3jZBHf25GZ3YjkxCNPvV57M1o23Jo8iVCmYruMrh5auh5At1/5RTVLmijNGtA//B5q695nFHw9kPwQtXwKonwJoDx9/cehuTWa040nsv+EIrEqKT7/r3Z8bI8IoF/bXriT39O9me23uf/zIuhRBCCCGEEGJAk8SC6F7RgYLoBsx63wSjRdWDNqVCQ4UKjtiy286iNKWqbUHNpHZ5VTDF61K1q5+7DBr2J5dU0PlCx1J0Bkw5FTa/ARWb1extf1PoZ2Pb3/1N4JgCR98IaXnqPmhdAglg8+vqZ8Gh4Jig/q0ZIDVNBZu8LtXA1m9UwR+TJTSbPBQUsthbB3ZandOIIGtPlJvo7yUsNK3trN2eFow1pThJiZJDkbXb7Y62vRJi1WOPpK+kCQZVvfVmP5hTwJyl/t5QHnEcqSpo+O1/Yc1TauzoWsoW2cMJDYsNvfk5ZhsYTep5gs34AwHeW78PjSALi4ZhNKj7aW4GTxVU71QJC58bKjapW7SUDCiYr8bI6Hkq2eDcBp5qdZzWDDCmQsaI8PjQGcyJz7WuVQPuBIHp6Pe3t2ZR92Wfn2BQ3RK91mSPryvjI1mJPpd8bvAZ276OuJ+b1ojfg+Gguy1L3WVKUdey1wXNgXCCIOCHdf+FD38fTv4tug0OOl+dKz1ArzPGuU6j6Q2YY5m8CE66A97+FXzyJ5Wcm7c0/Drc1eq7xBtKeng90OxVx220qM8FT416TWYrWDNVcgFC30VutVqpwanGuDlVlUCKbOqe6P31udVzWqxqlVSkeCsMI0kiWwghhBBCCCEGNEksiOTEC0BFr0iItRIhOtlgsalAhhba3l0NzU3qZ3vlGfSAh7tSlYnoaFIhmsGoEgxFZ3T8sfFseFn9jFytYE4FX7B1wMaSpgIv7mqwEdFIN6qudbza3z1NylR0n1gzc/WgGoRLsUTWfI+7L09EwC7immisV8k5W0440dBYrwLza56GlQ+r8QKqTMsR18OsC9u+t+0kNPzeAH/auAKAo089GqMlRiNmf5MqTVa9G2p2QfUuqNoV+vduaKqDbcvVDVQ5sZGzYdh0yJ+nkhqmiLHi84QDmClJBmxbPme84RVUyRhKs6i787X21eeFnqyONb70998bo0+HTi/50xxjhYGrXH2fuavg43ug5DN1f8GhqgxeVmFPvKLW5l6qEoGf/BneuFGtbpp2pvqbFlRj3BLRJN3brL5nqnfBZ39TiXOCMP07cNCFkDdJfddohvAqwNQ0VVbJnJb4eyYykWC20dLM3etpm1hIpql8f09kCyGEEEIIIYRISBILIjnxAlDRTTONllCt9AQle/TkQrNPBSRs2eEVC4n4QuVRGspV+aOakq4lFXpCTSnsWan+Peao1g1Ho1cbuCrVrE4IzaJNDZeycVWG69O3JG1CM2KlTMXAE2tmrh5Ucznb9kqIRU8o+D1q1r7PE04s+EIBPv0aMaeCp1atnln5EDQcUPfrCYXZ32270qY7mVJUCaTciW1rtDf7VVPaHR/Bjg9g72q1yqF6J2x8Sb22UXNgzBEw9XQYNbN1ADOlnfJpOv2abWpQweHsJIPAQ2kWdXe+1s40m+8OZqta/RZrfFlssZO1yTKaYfOb8MWD4Haq77ejb4T5P2jbK6EnHXmDWs2z6gl46QdgssHouWpMmCISbQEfbHkb1j8Huz5uvY+vHlW3MUfAQYth1KFgz1b9UCJXcbirwIMqvWSJSjJEjkNQn0VBIkorRaxS6KuEuBBCCCGEEEKIXiOJBZGceAGo9ppmQuxZiRYbNLnCMx/tuaFyMZXh+yL53ODcCt8+C8WvqiBLf0sqAHz7nPpZcGiovE2oWW5zQB3/vm9Ct7Ww/1tIHwEn/A6y8sPnyVPTNhjWavZnjHPTEwG7oRRg7WmxxoDPrWZU6w28U9sJmOvXQJDWjY4j+T1QuQM2vASrH4f6qBUKPZ1QSIbBpMofjZ4HR/0MGmth16ew80OVaKgpUcm5PSvh60fhByvULG2vp22gs71rP1ZT7PZ0xyzqgbLapztnjHd3s/mOnMN4ryOyD0k8rkoVTLdmhXudgEpIvXcbbHlT/T5sGpz9D8id0P6xdzdNU30c3JVQ/Bq8eCVc/LwqjaShVlZ88Xe1MsmtN6LWYPyxasUDwOp/wY4PYfen6mZzqF5Dcy9VPYtA9S/yNah/m6xtx5vFGh6HXk+4pGG88m7xVioIIYQQQgghhBgUJLEg4osO7MQK3ETfn2yQymxTpU4ikwleDwT9oXrREaWVvG74/H5V1oFQveecCbDkhf6VVACV9AAVsPHUwK4VsHMF7P5EBVCjVe1QM1Cvek/NENVLJemNnnWJZn/21MoCKVPRs7xu8IaCeMmca7NVBUD1IHnk6gRbaNZxUz3897vhvgbpI+HIn6qEQrI133tbaiZMPRWmna7quVfvVAHQrx4D52Z49xZY/M/E177LGdEbIuI82h3h+3vTUFztE3kNx0oKtJd4iH5Md5xDc2ilmNfdehVYJHcVBJrAXRNOLJRvgmcuVOW80GDBtXDsTSopp/fW6W0GI3znH2p13+5P4JkLYOxRaqxXbgtvlzYMZl0Ms5dA9pjw/VNPV2XI1j4N3/xHrfxb+ZAqkXbeY1B0ZmilQZr6jIkeb9FlkCD8u94fCOInPYUQQgghhBBCDDqSWBDxJRvY6crsXHeVCq7W+9TvxtTw7ElQgYvP74fP/qp+H38czL0MJp/UPwOlKZnq50d3g/vnrf9mtsOIg2DkLHWr2wsf3KECVT4PpPjDta+tUbPX9ZJIrQJk9vDfZGXBwGOxqbrkGglmeUf1U/BZ1Zh0V0NqeuhvqeGg7t6vVKDRkgYLf9t6hUIwRg35/kbTIGe8uhUcBg8fo2ZoV25Xs7Nd1WDQWicMvG7we2N/VvVUcqy9z7yhPiZjfXe0917E6sUT6xz63OCu79jxJFrxZcuB2jIwhPa96xN48Sr1vZRZAGc/CIULOvZ8PcWUChc+BU99B8rWwqb/hf82/jg4+DKYeGL878bsMXDcr+Hon8O2d+Dje9XKuV2fqMSCxQqW0bEfq5dBclUBoQSnLSdc1i8YUOOwo9f8QFndI4QQQgghhBCiDUksiPiSDY51NgHhc0PdvtD9LlWKwhKajq2vYvjqkXBS4bhfq9nX/dkJt8BjJ4XLUYycBRNPgImLVN14vS63vxEeCAWrDjoPMkLBHHOqCs7Ul6sgUvQMW73ZpqsKsiPKJ0lApv+JbmweHTwz2+LX/fe5Vamw6H4KLStXUsPNmvXt3dWwdpn6fcY5MO+Knn19PW34NJi0CLa+q2ZVH/0L8LnCf9eDz3YHWNy9G8hv7zNvqI/JziRWYvXiiXUO9XPfEYlWfNlDqxT8Xvj87/DRH4Cg6kWw+PHW5ZH6g5QMuPx1+PJh1Yy98DAoPDzcnyVWE+poRjMUnaF6Au3/Vn3fQvzG8KB+d1VBY7Uq7WdOVeWSQD1GQ93aa9gcbSiu7hHdpqSkBKfTSXFxcV8fihBCCCGEEEOSJBZEfMkGxzqbgPB6VIAjmKrqNGtGFUzXSyKtuBc+/bN67EBIKoBqqLnkRRXwGXeMKkuhCwbD/17zb1Vmw5YLcy5R99lCpY/8XvX6PZUqeNOqIWkouGMySyCmr7U30za6sXlHgmf6Y4OohqzNvtBMYcLB0WBo5YI5Ra10aayFnR+pvxWd1eWX1y8cerVKLHzzDBzxE7Xqx6Cp8xJ5Pns7kD/UVyS0J9n3I1a5PZ9blbZKtBrEF+jccfkiA+cR+zYY4Z1fq+bhAHMuhVP+oHoI9EemVDjix13fjzW0ws4d+mxptbIjKrFgtoE5tGqhsU6tigKoKVPfR5b0zjVslrEkOqmkpISioiLcbvUda7PZcDgcfXxUQgghhBBCDC2SWBDJixdI7WwCwmJVDSQ1wjMf9WDGQEwq6MYdlfjv/kb49C/q3/N/oPpEmFPDdao1QyjJktK26azFpkpFxSrpIXpXdKIsenzEamwOsYOmsR7b5IKU9HCwr1X5I6ta7aMnF2zZUF6sVrPYcmHsEb1/PnrC2CNh+Aw4sB7W/ReO/EloBUcnVyh0V9mVob4iobvEmq2ezGoQWwcD/voYaqxTTdIjPz9dTnj2Uij5XH32LrpNfS5rHen4PUBZQyuevPUqoe/3qKRdSnrofk/Eailb+Ps5LS9cAgk/VOxW59UxIbxyIlkylkQnOZ1O3G43Tz/9NEVFRTgcDgoL46wCFEIIIYQQQvQISSyI9unBOK8bTJbOz5SP1eg5K0b97Pduh5X/UL8fd7MKJg4ma/4N9fsgbTjMWxoO7rirVPDLaAHHxHAA2esGd6UK+Nhzw/0WorMOLqcK9Ojb6AHUZEtSiNjluuIFomMlDhLNojfb1HsUK2ga67F6bwRQ14ItW/1srFczhwmCv0klo8w21dAVYOppqoHqYKBpcNjV8Mo18NWjsOBHKrEQa3Y7xH+vfG5ocIKnSjWLBglm9gexZqtH3pdMIshdCZoeCI+zrT6TXl8Vps+o3/U5vHil+jxOSYdzHlWl64aK1Cz101Ot+kyYTGrlgSWixFEwoBIMsT7TLFZwm9TjmpvUqqqOJhaE6KKioiLmzp3b14chhBBCCCHEkDRIok9DTGRJnUS6a8alHvTUUAFLsxUV5Q79rU1piSSfN1Yz2Q/vikgq/FolFXye5PYXL5gaiCibEfCrAEgyNEOS2xmT285obrtaIfJ5LLbW57JlRm2laiQK4LO1naXtdanH1e5VZWL0kjmRZXj0n/rzDOagalfGR2SA32JPPHu6TZAtiZIe+koEc2r4+ve51S0I2NPUe+2NnCUcdS34GtQNTZUyMaVAwAub31DbTD0j9tiq2hH+t6ca3vqlunYOOh+mnBwu+2JrJzAYWYam2dd+TXdjSuK/t+y3Mfb9k09WSbiGA/DNsyoZB+rYa/aqMiwtxxPnvfK61XltDoDfB+kRf0/2czLZ6ypZvf053tdivQ6LvW3iM3JcxUvERWqv3FhzQI0RU9R1uPlNePH76rrIHgvnPQF5U6DZ284LSfJ9a6xJbrvIz+ZEUjOS2y7Z8WawhpPanhqVHPD7Ic1Ky3e4xRpKXFpbv3+R3/tZo1FJTg/YcxI/p54oivz/CzH/PwRJn+ZBMz6EEEIIIYQQYgBKMnIqhjSLTQXtbbnhRpc1pVBdCrV7wFUebv7YWT43fHAnfH6/+n2glT9K1pqn1ezY9BEw83x1n7tSvX6zLdwotKYUyreonwCEgj7R9OBqU70KVhtSVXDHbA0ngfTguKsqHHjTZ3r7kgxqDRX6td5Srivq90TMNjVbN1HSxmxTY6hltn2lqlHubVCxPL33SNCvfrY5PmuotnmoH4k5Tb3Hm99W11EyZZA81fDKtbDvG6jcBh/+Hp4+V12bXlfix/YFowUODiUTvvwHNOlJNo9KKvh96hxEXvPRLDZ13tLywk3Pu6Kj40fGW+ckM/70vyfa1udWK8J8bpXQ+eIBeOa7atyNORyueEclFfpSwKeSg89fAS/9sHUisKfoKxaa6tVnSdao8NjwuUMrFaxtz2mrXgw2GDYZ8iaHHxeP/l0UOVkgcl8d5XNDQ0X//NwSQgghhBBCiCFAViyI9rXMIA1NIfR5VEDG16gCEinp4UaO+uzDRLPi9VmLJkt4m+qSgdtTIVn+Rvgk9BqP/BlkjFLB4OZAaMamHtDxqCBkQ7lq/pxVqJIFzTESC5HB1az81rN/9X9rmjrf+mxSS6gkT1MDpKSp/Qslega1xd4zqzt8bnDuUAmEgB9S08IzdC3W8IqFaGYbZIZqmwf9KhjpqYHiV9Xfp5yauAySnlSo2q5WJkz/Dmx8GVwVKqm36gmY/V04+HKw53XrS+6S2RfDZ/eBczNsfx+mnRlOIKQQnvEcrwyL2QbZoeu8vebAyWivD0BXtxdKMvX3bblgNoW3j8XXGDr/Hnj3t+o6B5hzSbhJc7Ov2w67Q1xOWP88fPs8uJ3h+/9zARzyPZh3hVrt1hOsWepnMABmS+vz565S3/P+tPBqLH2cxWrS3N41rn/v+xpVElxPWHSm4XOs55SSf0IIIYQQQgjR6ySxIDrOHJo17ferGcD6THo98NBeAK1l1mJzeJtdK1T5ltEHD86kAoRXK9jzYMopKqkAYDC2DqqYreq+lIzWf4sVfNF/T7PGD6xEBuda+mWEZod2c3WXIaezzYD1VQn+JvU4vw80T3jlSnv70pMPQR8EGlWwHaDozPiPiU4qnPV3yB4Dcy+BLW+r67NmN6x8CFY9DtPPUYHN7LHJv66eYs1WJZtWPwmrn1CJBT0JVF2iAqCWtOSCi90R5E+m7FWy23dXQ+nBJN450e8nyXI/+mP02fClH6ukgmaARb/ruybNwSDs/xbW/As2vRFOGtvzYMZi1ax818fw5YOw7V1YeEvPNGQ3WVXppECTSlCmRJRb0r8bvJ5QOb7QZACfJ9zHJ1J7Y6Ll8Y3h/egrTTrahD3Z5xQCKC4uBiA9s51SXUIIIYQQQogOk8SC6Dg9EGDLVcEBn1vN+NQD33rAINHj9SADqFUP3z6v/j35lJ4//r7y2d/Uz7FHQ0MlmM2h8hP5rbez2CA3onlzZFmeaLFqlEfS+y/oATo9qBpZv190XmeD1BarCu5rqPfHXdW6SWo0vSyJ/r7pN5cTPnoImmoTl0EK+OB/P2mbVAA1dovOUE2fd30C3yyDsjWw7hn49lk46U6YcW5Hz0z3O+RKWP0vlUTZuxpGh5p16nHhZOPD3RGMTCb5k+z2spqhrZbybc7w51Tk51dk35z2+BrVjP+qnbD8FnXfET+BQ3/YI4cek6sC1j2nkgm1e6BuT+vyPSNmqlU5ExaqYw0GYctb8NHdqlzZs5eq0nlH3qCS+d1F09SqhYYDUPK5WvGjf87Yc8ANuKvVd7TBGL/UGLQ/JvRxZ49qoB2vx0IyzDZZqSDicjgc2Gw2lixZAkBaZjaH/+ypPj4qIYQQQgghBhdJLIj4Ys0ajQ4CWGzgtUbd185/6OsBCH2W5nu3QtlqVTN++tk9+pJaNNaqGvP7voF9a1VZIGsWWHPUz9QssOWomdL6Tb8v2abO0Ww5UFsKG15QM8OP/IkKKMXSlVmckaJXkOjBndSMoRnEjLymuyMgpZ9P6Fh5HbMNskLb1exRjWU1U+zyRz636sOAH+r8YMuC5lDg8bP7oL5MbXfI9+KXQVr5MFRshtTM1kmFSJoBxh0N074De1epOvS7VsC7v1H154fPaP919aTscWqlwsZX4PnLYclLqleJLdSzItlSKpGfZdD3gcn+OOu6u8dJR+nnxO+N/flliFix4POAJT3+vsypsPkteP0GtbIld6L67O0N+9erVTab31DJvUhGM0w5DQ46D4ZPb/03TVOr2goPg4/vhU3/g3X/heL/wWE/hHlXqtfVHSYcr5KJL18NBzaq/XtRqxKCoYRnYx1kju7a80SXVNRF92sQopsUFhZSXFyM0+mkuLiYy5Ze2deHJIQQQgghxKAjiQURm88N1XvAZIpILlhjBwE6GwT3uWHVk+F612f9vWfKrjT7GR/czdTgNkxvvgz7VqmZ252RNgLmXwWzLup4TejLX4dP74NP/wp7v4ZnL4N5S+H436g+FfEkO6NTX51gjiiLFL2CpKMzrQeb7q7JHblyoDOzzvVGp3aHSjy1KfviCfXRMEFDDRhNUF4MK+5R1xBARj6cdHv81T51ZWp7gAXXxU4qRNI0yJ8H5z4KL/9QrRB4+lwVxM8YhTltJJc3a5TjwLCzCXILIGN07wSgT7xdraaoKYE3fw5n/0PN4E72uVtKgYVWTPWH2uz9cUx25zjpTJJCPycWd+uki36/N6LfjM8NxPn8DAbVd8zyW1SpvTGHw+LHe/Z8N/tVIuOrR1SCTjdyNhSdDlljIDNfjRlzajjBFYs1W13zM85V/Xn2r1OJhrX/gWN+rlYadTbRrTv9z+p9+epR+PxvULkVTrhNJTkjVwNFlkJK9D3U0dJeXemxIEQ7CgsLKSyUPlJCCCGEEEL0FEksDEbdMdvU61bBTL8fLKZwMkEPAoCquxwr2J1MYMHnho2vqdUKAMf8EqadldyxbXodSr9UQSNCt2AzoIXuaw5NigxC9S5SytbyYHPomDdE7CerEEbOgVGzVHmYxlpVh16/tfxeA42h3xv2w/u3qdnc865QzT+TDVKZbXDsTTDrYnjnZtj8ugrmbHkLTr0XJi2Kc66SnNHZarvQ+95eqaSWxw6ROu89NTs81n6TOafuKtj/jSp3kjFKrdoxW1Xtc3+TasytmdRqGaNZrTzY8AIEvKqE0YLr4IjrEr9n7/5GHcuIg2Dqqcm/Js0Ap9wDz10KBzZA3V6o24sRuEjf5qV/hrdPzVRJjsx8Nbs5M1/9njNe/Ts1q+v17FOz4Lx/wWMnwvb3VI36o36W/OP1gLmGWt0hM6Rj685x0pUkRXTSJVaPhXjXfsALr/8M1oRKn8xZAqfcrcZNT/BUq4D/6idVMg/UNTblFJh7KYyc1fl9j5oDl7wIxa+p8kj1++B/P1V9UU7/S9dWExgt6ryMnKX2ueUtVRrprAdVmT5vo/oc8ntUWSl7O3XqO1raq7tW5wkhhBBCCCGE6HWSWEigubkZg6GLswH7QnfMNtX/Qz89omxIZBKhdq8KdMaaTZ9MYOHARnjn12q7GecmHxz86tFwMiJJGuDCymYmMPOwhZhGHQQjZ6oSKgkfGPXe+5tgw4vwxYOqpNGKe+DLf8AhV6mbLcnGgNlj4IKnYNtyeOP/1OzrZReoxMoxN0FWgdpOP6/JJHOg/ZmfiQLdQ6XOe2dnh7eXJIi132TO6a7P4NVrVLmRjpiwEE6+C3InhEuKxbL9fRWM1Ixw9M87Prs5NVOVHGooV8HS+jJ81Xt487NvGBZ0Mt/hwVBfBk11KvHWWAvlG2Lvy2KHzAKVZMidBPmHwOh5kD68Y8c0arbq+/DGz+CD36tZ6IULknvsUC8FlqzurF3fE0mKyB4LZmvb8emuhGcvgd2f9XyjZucW+Pox1SfI36jus+bArAth9kWQ1sHrOx7NoL4jJp0EX/8TvviHWhHxxGlw6h9h0gld2//s70JmoUoklq2Bfy+Gcx4Fx0T1d72XUnuSaeIcawWe162+3zTUd3NfryQSQgghhBBCCNEuSSyE7Nq1i08//ZSamhqKioo4/vjjMRgMBINBtA4EI5qammhqamr5va6uricON7GeaE4aWRvZ51FJhYAPzMbYpZESzd72e+Gl70NjDYw+GM64L7mAz/oXwkmF6eeoACWaeqxmUMFTjVDwNHS/PY+mvNmc+68ygpqBV448GpPWlOBJEjClqBJIB52ngrVfPKBmmn/yJ5VgmHsZHHp18oHSiSfADz+FD+9Uj9/4CuxcAcf+CqadrYLNPo+qdW2xqaCLfq6hbdmj9iQKdPezOu/9YhxF8rpVA1NXFWTnJz8TVz+nkde/KVQbvWITvHWjep9HzFRlT3weFZz0h0og+RrV7z63WpWTNUaVRplyavtjxt8Ib92k/n3I98AxqXOvXTOoMkjpI4C5BHwB/v6FKvHyyqULSLUY1bmpK1OrGmr3hG/6764KVaqrYpO6bVsOXz6o9p9ZoJIMo+aoRMOwqfF7RejmXaGaza5/Hp6/An6wAuxxmtpGB507m1AYgKt6+sU46kpPi8hzru9DA8zZrbeL/Gyr2KKSCrWlkJIB5zysPmu7UzAIJZ/Blw+p5J1u2DTVZHzaWaFVdD3AnAoLrlHP8ep1qk/QS99XfReOuRGMKe3vI55xR8EVb8OyC6F6FzxzAZx5vyrh5G0ncd1yfO2MD58n/FmaNTr83vo8qgeGvg9JLAghhBBCCCFEvyeJBeDbb79l4cKFLFiwgA0bNpCWlsawYcN46aWXsNvtHUou3Hnnndx6a8dm1HdYe8eil78JBkOlgbqZHljQf0bPpjenqiC8q0r1abCFgkDNfnDXqCBI1Q5V+uWcRwFNrQaIp7YEdn0Cb/1K/T7zAjj8urbnobE+5sODviaC+kzt/d9CRlZyr7M5wSzygvmqDv3OFaocRcUmFSj9+p8w/Ttw8BWQMbL1Y6zZsfd17E2qXMYbP4eKYnjzRtj4Mhz5fzBiugrC+EIzYTVDqDa8Sx1fMBiu0127T5Wv0u/TaUb1Mzp54HKq98ieo2r8d3ewNJlrL842vTKOOkJP7JjMCVYgRL0WfbUJqMc21avSR3aHev+WXajuy58PFz+rxkykyMs7GIRmHxjM6rpv9ob/1hgnWPz536F6pwq4zw8l8pJhNCf+uy8iYFq9E8yG8OOyx7btk+JvUjdXuVr5UL9f9Tg5sFEFL2tLQ03NX1Tbm60w/CBVomzSSapxdKvjC31tnXKXarxeuQ3+dZYKtI6cBY7JKnmjGdU48DWqz57mQOyEReQs7Fh9SvT7a/aq9x+6Z6z0xOz5KHHHkc8NZCR+cHd+d3RoRVTE80Y+DtRnn8HU+vOtsQ4CDephpSvhjVCT5qyxcMHTkDMu8fdLy35q2t8m4IP1L8K6Z9RKBQA0GHukSjiPnK3e17p9ahVDy+twQekX6pzmz1MlvXQzL2j/eUF9VrSiwWl/hpUPqcbOX/9TJdvO/Jvq39CeeKukMkbBJS/BC99TKyKeXwon/wEOvlwlS7wu9TP6fYxe3RlvZYLFpr53TOaosn02sKSpz72eSHB34ftI9D8lJSUUFxf39WEIIYQQQggx5A35xEJlZSWXXHIJV1xxBXfddRe1tbW89NJLXHHFFZx66qk899xzDBs2LOmySDfddBM33HBDy+91dXUUFBT05EvofXpNZK9blZwIooIBxlBjRz3o466G5ib1M3OkakT7/i2w+xMw2+G8JyFtWPvPV7YW3vmNCoRMPhkOv7ZXgnLt0gww/liYeoZKfHz1iAp0rvuvWn1w5E9V0CiZ8jMjZ6vmzisfgk/+Ars/VeUoDv+xqs8NoRUielInVSVpmn2hAE6jSio01aua/LFWH0TPJHVVQaAxlFxwdPl0dKd+N47MNsgu6NiqjsjAmtmqkgpGswqEvnSVKoGVNQYWP9Y2qRBN0zpWG76mVF1LoBJXKWnJJxZ6giklVAYp6j30uqBiM5RvVMnGfevAWw97VqrbqifgwmVtkwugApLnPgqPn6oeX75R3W8wq+31UktjFqjPm8hgdDx6nxJXZesEg88TqjPvC5eHGwDijiOvp/M77czKjc6uiIou76b/O/L4G2ug2Q0bXoJP/6yC3oWHqybNthzVa6GrmhrU6pg1T6nEGKhrevKpMPP80Oq5OGp2w65PIRBKbtTtVYmzkTPbX5nTHqMZFlyrEmof/F6Ngae+o8qkTTi+8/u15cJFy1SPiuLXVNmxur0qQRkMht6Hdt7LmGMpdM3Yc9RKpoC39UqibGmyK9pXUlJCUVERbrcbm82Gw9G//v+LEEIIIYQQQ8mQTyzs3buXQCDAVVddBUBmZiaLFi1i6tSpbN68mdNPP52VK1cm3WshJSWFlJQulCIYCPSAqc+tZob6GtXsYGs22NPD29myVVLBlq2CB2uegm+WAZoqTzFsWvvPdWDD/2fvrMOjONc+fM9ashtXICQQCO7FpRQoUqGUUveeenvq7j31fpVTO3UX6got0hYtlOLukABRiNvuJmvz/fHsZjchspEWSue+rr1mdnfk3Zl5Z5LneZ/fD+bdIwGIzmMkSNpcnfg/G0URCYnU4yFnrYwUz10vEkfpi2DSY4dXL9SHL0jUd4YEdA78Dkv/D3b+JPJIMakSiDWFiVmnsdorjVTlD5oqBtmOs8ofzHNV1x8EDIv1VywcZRyV/ai5Mjo+yQ9bsYygd1ZJUG7p05C9RmRazv24aa+P5qKqsPgJOe+dRols0tGKKQw6DpZXZJIEhYszJJm4/QeReZl3D1z4hV9CKpCEnnDNEjGczVotL2s+HNoqr3UfyHIxqZA0BHpPhV6nNdweXyBbcdQ2QvcFtiNaIIN0BCWUGuxHpibkbBqjJX4s9fWdYI5LXWNf37wt3/9ZVQUsfhD2L5P3x10igfW2MGmuOCgVaVu+9sv0mGPEF6j3dDBHN7yuxyXXY+EueW+JE1P28mwxbC/OCN4bpClSx0qCcsF/IH8HzPq3VBgcf3vLj4MhVKof4rqL3N/yF6TKaMrjIovmDEyc1uc74+1LrjI5dqbw2udSb5IqomPd10ejzSksLMRmszFz5kzGjh1Lp05aQkpDQ0NDQ0NDQ0PjSHGURWiPDOXl5WzZsqXmfWVlJSaTiZdeeomCggKef/75I9i6oxDfSEQVr3SBXsxQ6waBwmIhIU2mexbALw/K55MegZ4nN72fkv3w5UUSlOgwUAIa+qM4F6YoMkr67PclAWIIhaxV8OlZUsEQrMxCXDe45Ac47SUJPh/aCl9eCO9Nhjm3w/qPpJpBb5SKBZ8UVVisBHMqCySh46tkCJQSCSQsHhJ7eKUpCr3yKBotwuk1Hg08zkazVJnojVC8HxxlsOp1CZgrejjzHZHtaWvSF0LGEhm5P/Hho6O6J1gUnVz//c+Gaa+AJV5kk5a/2PA60Skw/Go46x24ZSP8eyWc/ooEmBN6yTIl+2Hbt+LH8ONtYjJdH6Yw6ReWOK/kjrn259D8vlJXzudooDWBXJNFjk1r5Wpac1wMcl56qOmYvrlQkgp6k5h6n/rf1icVXNWw4FF4/yRJTjkqIbYrTHoULvxa/HQaSyoUpctIf19SoV0/6DlVvB66TpDj76iAvb9IpYGtqHXtBYjoAKe/JgkFkGqfLy+RSoOWouhgwgOSYNAZYM98+P5a/+ACj9v//AG/AbMvYRQW500wVEFVSYDPhkWqqOomG3w4bdozSaNJevfurSUVNDQ0NDQ0NDQ0NI4w//jEQlJSEmlpaXz88cc899xzzJ07l5EjR3LiiSdy/vnnM3ToUHbu3Hmkm3l0YTRLkCEsToJ68d1Ap5egQX1Bovyd8O2VMhp50EViPNkUripJKlgLJNB4yjNNy8UcLSg6MXm+8GtJiDgq4deHYOkzzdiGAsddDNevgP7nQkgEOL063ctfkCTDuyeKlrfqEn8F37HXG+QzXyWDT26joUDN0Rj4/LvhM3cuy6kdPIvqKOfOHAVF+yQpBHDSU9DlhLZvR3kuLHxc5oddKcHQvyuWWEkmAmz8DLLXNr2OokBMZ9G7n/pfMXW+Yxec96k/4LrpU/jx1toBUZBqIGuhvyooLP5wA9mW9JW2CsT/VTQV1DVa2saTpTXHxRjKdM98XvA8gq4iRyTFLpsjJuWtTaS5HfDDdbDlKznXycNg+mtw6SxJeDX1HCrZJz5CVaWSAOk+Rbah03uvz1ToeyYk9gUU2P8bfHe1+CO0Fr0Rxt0rCYaQSKmM+ORMKNzTuu0OvBAu+gZCo6Qa78NT5Tjp9LXNnH2DDgL7liVOEgghkf7PjRaI7iTSR/VVspRkQ3W59kzS0NDQ0NDQ0NDQ0NA4yvlHJxZUVSU+Pp5XXnkFgHfffZdbb72VG264gRdeeAGAxMREcnJaMeLvaKKtRgH6RiL6AkI+KQuD6fBgndMOX14swfVOo2Hq88EFftZ/IqOMw9vDaS9IcPbvRkxnOPtDGHWTvF/9lvym5hDRHs54He7cC1ctkBGz3adIoKY4A767Usxr7UXyHuQ7nR5cvkoG70jrhgKif7fA59GIyeKvTgjsA76+Et+NGlPahF5+34y2pDwXvroUKg9K8HLEdW2/j7+a1ONFGgxg0xct24Y5BrpPloBrRHv5THX7DdF9BAZFA5MMgbSkr7RVIP6v4q9KNLb0uFSVYfzucv6tfowRN+4eU+GqhZA0qPVtUlVY9IRIGJnCpKronA/FSydYCb5d8yXoHpYghuL1GSnrjZAyHHpPg/ie8kxe+Chs/FQS8K2l20S4+Dto1xeqy+C7qyBjaeu2mToWLp8vv6c4HebfC+bY2ufPN+jAWEdqy+QzUw9Cgsth8/uZaM8kDQ0NDQ0NDQ0NDQ2No5p/dGJBURQ8Hg/9+vXjww8/ZPXq1fz666888cQTgCQeDh48yIABA45wS9uIwIBRW0sNNBRQWPWmjOCMTIJzPwxOosJhhT/+J/Nj72h7Dfq/Ep1eZFo6j5Zjv+y/Ld9Oh0FS7XH+Z3Dd7xI8LsuGn24XXXmnze9pYY4VKRxf8NQXEIXDz/ufHfj8J8haGAOqE3x9oK4sSOoY+bx4X9sYygbiSyqUZYlB8tkfBhfE+zsw8HyZZixqWMKoKVRVRpFXHJTjM+5er09MQBIo8B5W38hr+PslCVrC0ZxozFkPb52AfvccnOh5TbkM5/R3ZSR9W7BhJmz9VpIIp/5X7tvN5cDvMk3s23QftMRJZU3v0737/0QSG21xr4xKhjPfledE5SH44Vr46VaozG9qzYaJ7yGJFp1RvH9Wvl77+7qDDkD6kN50uFRiQ5gsch+NSflr+5lPzk5DQ0NDQ0NDQ0NDQ0MjaP4xiQWXy4XT6az1mcfjqTFljoyMJCoqis6dOwOwd+9eHnzwQRYvXszll1/+l7f3TyEwYNTWo1LrCyhYC2D5SzI/8eHgEwRr3hXN6ZhUkTRpCaoK+5fDlq/R5awhTi0O3uPgz2D0LTLd/oMYUreWqI5w6Ww5RuXZ8PMDYmJqMPiTCVUVMg00SYW/XvbonyK1ZLL4g9I1GuQBwenYrhIwc1dDwa6222/dpMK5nwRnFl64G/b8CvuWigxLzno4uFXMX4szRNbJWiAST20xirqlJPSWgKbbCbvmtmwbB36Hfb+JNM1Z78k0sGrBYfUb0frMmutLlP5TkmRHW/JEVeH3/8H7J0NpJp7oztyme5TZupPazkNk/zL47VmZH3sHdBnb/G2UZkLpAUkER9VTqVAfOgOM/DeMuU0C9pkr4PvrpEKntd4L5hipXBhyhVQM7J4vMkabPm95n04aDJMflflFj8u9w1bU8P29ob7UEEfq+vM9pzQ0NDQ0NDQ0NDQ0NDSC5ih2wm07tm/fzqOPPkpubi7dunVjypQpXHDBBeh0OtxuN3q9vtby+fn5fPrpp3zyyScsXLiQXr16HaGWtzFGS+1/1gODzYH4gmcqEN7IP/i+4KnRXP92lj7rN17ud1ZwbbSXSpUDwNg7RTKiubiqYcXLkL4IABPwGVBGOMYFadCut/g2xHeXwHxrjT6DIbG3yGJsnyVeC+d+3Ppt+pILH58uEkvz7oQZ70CkQc5dqFc+KjCw70sqBTsaOTAp0dJAT3P32Za0Rfubtb+AZIIvyeALqCmK9IX9yyFvI7Tv3/r9lWU3P6lQVQar3oI9Pwe/n9AokYPpOgE6DGtVk5uNoogc0tJnYNsPMPqm5m9jzTsy7XuG6PE7KsHtDSJaC+UaUd1gLYbojpJcqOuvALX7UuD1VPc6+6uvuyPJn/1by/NkpP2eX+R972k4Tn6RPa+sbrt9FO2FOXdKsL3vmWLO3BIOrJBph+Oa/1zpcZLI5y16QirQ1n8IGz4Wf4buJ0v/07fgTzajBcbdLbJLvz4Mh7aI7NKOH+Hk/4PEFvx9M+wa2P877JojfhQXfiu+SPX9PdBYpUJD186R6D8mC+gq/pp9aWhoaGhoaGhoaGhoHCMc84mF3bt3M3r0aKZNm8bkyZNZuHAhzz33HL/88gsffPABer0eh8OByeQPAkRHR3P55Zdz9dVXk5SUdARb/ydSN8kQiM+IFsBRJ1DmCxo4bFCaA7gAgzcYF7C9gl2wzmtUO/mx4PWpV78ppo0JvSQQ31wqD8HCx0QDWtFBygg8ZTmoZdlEUSlmlgc3+ZfX6SE6VWQjzNFiMBkaJa+QCP98aJTfu6CljL0Dds6BjMUyyrPTqJZvy0fd5MKs6+GS2aB4wOOUUd6tkTZpKJDaHBq71v5s2qL9zSEwmVBfQK3jUG9iYRMcd0nr9lWWDTPPDj6poKqQvlDkS6rKAMWb3FDB5RB5Jne1BNzd1ZKgc1XL91VlkhTbPosQYxj3egayXBkOzslgrCcA39b0mioyYvnb4dB2aNcn+HVLM2G3N4ky9AoxNHfZZQS3yw6GUFAAp1O03Z32+pMK0HCSrLJQkhWmcDGk/auvu7aiJQHdP/O3WgtlhH3JfhnNf9ITMOxqcLrbbh/2Evj6Mjl/SYPhxIdaXgVxYLlMU8dAVXnz10/oBWe9K/eI3fPh0FbIWiWvlf+DHqdIX4hKaf62E3vDBV/Aps9g+Yt+E+bh18CYW5onnaYocPr/4J0t0r8WPAQnP+dPqvr6h2/wgQ9fRZCPhq6dI9F/jN6qSw0NDQ0NDQ0NDQ0NDY2gOaYTC6qq8vHHHzN58mQ++eQTAO68804++OAD3nrrLc477zy+/PLLmqTCBx98wMSJE+nUqROdOnU6kk1vnD9b0sdolgC7LyjgkwmpCRqEydRghIoSiEisHYxzOeCXh2QEcPeTIHm4fFaX6jqBF1sRrHlP5kfdIIEeCF4Tevd80ch2WKUtx10CcWk4XArnLIinE9m81HszxspsGQVbkSejLIvT5dUkCsSlSXAyNNL/cbt+wbUvMgkGnAsbP4PFT8GFX9YfwDKEBLc9n5JZZBJcMgveGisSNvnboPMokXnxSTv4AibWooCATd1AUj1tOZLVBi0l8JgGtr/FkikB/a1ulU5g4ExVwWQGS6z/vdMGDrt8brT4DWbzNjWdpLKXNPxdoPxRWCKccBeU58irPirz4feXRKYFRJYs7US5dupStDfgp6syitteIv4ElQdRnFYmsIIJ6grUz14Tg9ik46Bd//qvk+ggg6CN+U6YwqDLCVKFtOFTmPhQ09szR8t0/UfyG7pOgOSh3n05pR+4HJJ0C4kAC7UrTOrSWNBdAVD9XSiYftOW9/G22JbTBiVZcl+H4AO6bX2PcFXL1F4Kn58vSYXIjiJhlTRIrhNXQGJB9YDaSN92Whv+zu0UY+OS/WK2POqGpp8FdZ9bNZ9XwsEtMh/ZEaKD/Bti+Qv1fx6ZJAnt8hzp77ZiMXfe+CnEdJFKhnb9Dq/qSxvf+P56nybG0b+/4k807pgNEx6s31NCaeA+ZQiB6W/AJ2dINUnq8TDgAkncOaxy33N5/RXs5WD2/k0RmFjwJWJBkkiBUmSBCYrGOJIShxoaGhoaGhoaGhoaGv9wjunEgqIo5OTkcPDgwZrPLBYLV1xxBaGhobz22mvcd999PP3006xYsYKnnnqKRYsW8eGHHx4mj3RMEOxoVJ8MiLWwYVkX3zQkovZ7EImE9IUSkDjxweDbt/ptaWO7/pA2Mfj1VFWMJNd9KEGmyI4w5DLRl/biUEzspSvujqEYdYP961WVQUWuTB02CUL5zK1d1ZLccFglAYEqQddlL8DoGyQQ1VxG3QBbvxcpnL0LoPvk5m+jPqI6SqB4+w+QtRq6eY+fs0p+j61YAt5GsyRwAhM9DUlZwZGtNmgLGpK0aSm1EmwW//uKfPG3KHfJCHhLjCRzHHZQXTI1WmRENEDBzsaD2I1R11PhhLsa9i/xuCXhtvUbCcgqegkqdhwSXPWNosg6YfHyUvtSbStndnYUx6ur6eApkGs5b6MslzQI+p3jD+q3Jb1Pl8TC9h9g/D3BSc04bbBeksoMv9r/ue9+FhZR+/owhUl/Dwxy+qhvFHVNJZe3n/j60d+x3zhsklRwOSGiGW3/M36rwwqzbpTrKjRKRtrHd2/bfQAsfhIyV0rVzbh7W2cCfXCLPH+ikqWvNJYYDBZTmPiLxHWD8HaQvVa8UUr2yWvvryJL1Nz+Fp4Ip70I6YthyVNS/fTDddBnBkz6T/AVhkmDpMJjwcOw8HHoPEaSjNYiGVigIvcFi/dZbKpzv6vvbw3fZ2153w6WY9k7RUNDQ0NDQ0NDQ0ND40/gmE0sqKqKoigMHjyYXbt2sXPnzhqvBLPZzDnnnMPu3btZvHgxJSUljB49mrvvvptJkyYdm0kFODwwVkveyPtPfKCJaS1Zl7DDA3D1/eOvemDhIzI/+FIJiARD9hrY+LnMj7kl+NHlrmpY+YYY0AJ0HAz9zg7Om0FRJCDTUFAmJNw/73GJzNKqd8BWKMmFkf8OfjS2j7AEGPovafOyFyQZ0Bp5pUC6TZKga/oCmHCffGYrgYLdEvC0lUKHvnLuPS5JNoRGBj8yVKPOCFuvsarLAVUl4FFFfiqyvRz3sDgJpPkqFkCSXpZ4uYYObfOPoA+W+oyaG6pSKM6QCqDS/d59J0O3E2sl3JqNoqCaY3lXdz7vqhcye8x2QvI3QO4GqQDKWSfmz/3PhZQRbWesC1KFExYvQcj0xaJJ3xSbv4aqUgn2dj4+uP3Urc7yUd/IfN89VWeQtrWE1ujJ+9Z1toHprO93RTSjHX+WFv7SZ+U+pjPC2e//OUmFDZ9K9RgKTH2+ZYniQPI2yLTDoNa27HAUnVQntOsnlRy56ySBbC+Bde/D8GtbFohPmyCJxj9eFUPn7d9L0mHUDcFvY+gVIu23ex58czlcs9R/3wuLDf66sJfL8g1R37XW1tefw970MhoaGhoaGhoaGhoaGho1BDks7e+H4g1onXrqqezZs4dnn32Wigq/MV9kZCS33nora9asYdEiMfm9+uqr6dKlyxFp719CXa19X1AsUAO5blDNF0h11CMn4RvZG/jdlq9FF9oUDsffHly7ijNg1k0SlO1xcv1yDPVRmQ8/3y9JBUUnngwDzm+Z4XNT6AwSFB57u0yrK+D3l2X0aHMZdhWERovkxrYf2q6NaSfKNG8T5G0WuaeDW8FZKcEoc7g/UaQzSAWDzhD8qHmfqXd918I/BVOADrdPZspkAVMEKCroQyTB4Buh69Pt9gW9FMVv2py3iWZRX1KhPk8FZ5VIgi14WJIKpjAZ1dzvzNYlFeqiKKhRKVJJMPE/MP5+MUZ22sR4duXrct21FToD9Jwq81u+bnp5VYXVb8l83zMlwWAt8stX1b33+fD1D6O59j3OaJHkQWAAszX+JT4CE74tXrcNAqL1/b6g99+CtvvuJ3VHiW/63H/epj4vo+DbmgMrxCQZxPumWzMq5OrD7ZR7Lfjlzv4szNFS0TfieqmwsBZItZ5PQqq5mMJg3D0w+XF5v/otyFga/PqKAlP/K9JPpZkw+yYwmP3JhWCrAMyRjX9f37XWmuuvPupWVGhoaGhoaGhoaGhoaGg0yjGbWPCRlpbGV199xWeffcZ9991HYWFhzXcmk4njjjuOuLh/iGFf3cCRySKjrW1FUJrlDZ6ZawebGwvA1f2uqhwWPCrzo28ObgSv2wE/XA/VZdBhIJz0dHCjnKvKYd49kpQIiYRJj4rGc1uOkK6P0Eg4/lapxHBVwR+vw75lzdtGSASMuFbmV7zs95JoLeGJ0H6AzO/+WQLMIRYwhkPKMNHi98kehcX5X8EGRds6iPN3JrCfGM0QHg/mOIhJlmB/YyagCb1l2pzEgsMqo4GDMWpe9KiMHlZVMQg/5TnoOu7P7xvRneCEu6HPGXJsDm0RU9cFj8LOuRIAbS29p8k0YwlUNrG97DVSPWG0iLeJSm1pt4aSaqYwuXf5vGQauv/B4ffUhoLljdGa5ETNukcgIOobLe52gMmC1WYjv6AAqy3I324tlOSw1f9MpjgD5t4l82NuhQHntXmzqTwEP94iUj19pteWyGoph7bK8yA0CmJSW7+9YDBHw5Ar5Norz4YtX7Vue72nSWIeJGHfkJdEfYRGwVnvS4XJzp/Es8Fhl2dbaW7T/aGx/uijvn7SFom9Wu3QKvc0NDQ0NDQ0NDQ0NDSawzGfWACYMGECX3/9Ne+++y7XXHMNn3/+Odu2beO5554jOzubtLS0I93EI4NPE1x1g6PCX6XgC6qB/x9+OHy0et1gQPZq8SsIjZJR+cGQvkhGOVriYPrrYvwYDHt+lhHIER1kVGv7IA2U2wKjWaQi2g+QoOOC/0gwpTkcd7FI01QchKXPt0273A6o9PqJmGMk8ByRBIk9aicRHDb/yO3m0Jwgjm+k97GqWe1Lzpi8fSg6BeK7SuVCfUFep/eYO21+c9jQJkboBvL7S2IwG9Gh8aSCqvoDtYMvg1E3tk43vrno9FJ1NP4+qV5wO+W+sPI1+PpfMOsGGV1dcqBl24/tIj4Vqhu2fdv4sm6nTA2hkNBTzpfL4T8XtuKGEwaB2JsRYG1J8q0llQJtsW4zUFW15lWTQCgtRDWYUL0eC1arFZfLhdV6eEWT1WajoG7SQa0zrcGbALMVtb0xr6rCoifFU6ddP5jyROsTbk67V1IJSBkZvD9BWxCeCEP+JT4G+dtbVkEXyAl3yTYdFVC4p3nrdhwMvU6V+aJ0uQ+6nOLbUV9FTWA1UGPVkb5kHRx+rf9F17+GhoaGhoaGhoaGhoZG/RyzHgt1mTZtGitWrOD222/n3nvvxWAwYDQamTdvHikpzdTJP9pojc6wyQKOCInlBI4WDPRa8Oma19UdD/RZsBbCrnky33lM8PI6m76Uaf9zRJonGNxO/74GnNd6beyWoDfCsCth0xeiL/3b8yL7Muii4AJVhhA4+Un46jLY9Bn0PElGl7eG7bNlBHBYPPQ+zX88VU9tH4W6BsQ+aRgfDV1HPpPWYH5ffUa3xxK+YxZY2WMMbbhSwVosklTFlWLaDXKtBEPeJr8B8ZQnGk4qgJybdn1ltP6RlKyK7ChmuOU5UJYp7SnYJcmRkv0iATbliZYlBPufA7nrRQ5p+LUNX4+dRkjC0lYE+5dD1/H+a784E0LMklxoqLLKYZXzZmiGtFp9PgzHGL4EwoGCAnSuKmLbJ5MYFk9YWBhWq5WwsMO1/q1WK26XC5vVSpjFe2zC48ERYHjttEn12SnPwdzbYcMnMip/wgNt03CPG359WMzMFR2c9ITch+tDVWHXXCjaK8sGvjxO77wC6KRawFYk3il9p7dNW5tDVIo8Ow4slzYPu8I/GKC56I2S8K5bSRIsZdkyjUuT+350Um2PmUDqyi467SIxaC2G6I4Bvk/H+LNEQ0NDQ0NDQ0NDQ0Pjb8w/JrEAMHjwYGbPnk1xcTGVlZW0b9+e+PgWGm4eTbT2H2+TubaBMzTstdBQwqAwHQ78LvNdTghuv8X7IGulBGn6nxN8ew+sENNKc2zwfgx/Bjo9DLoQYtNg40xY8660a9QNwY1a7TQKBl4gmuLzH4B//dgyA04fa96Vaa/TJYjmtEmw21YKigMcXhmkuufSd67tZWCOapsAzrEaYPUlFBw2MJj8CRmfzn1Dx01BpKnWfySj7TuNgsTeTe/P7YBfHgRUkRhKDcKAuF0/CeQf2gp9ZwT5w/4EFMVrmjxKEoBV5WLuvHu+tG3RYxJEjuncvO32Ok3WLc6Q7TVkgK0zyLLrP5JERtfx/ms/sr1I1/iSb4GJVF8fdNolqeByQniQiVJjC5K7RxFqAxUCqqrW+Bb5Egg2h4pOb8FdaiWhg4rFbMZiluOrWgvBaEE1hKIoCmFhYdisVubMncvGDRsYMXIEZ591NkpItOzAXoGtYB/VDhfmuL6EjrwFwx8vwor/iR9Nc8yE68PlgLl3+pMKU56AxD4NL79vKaz7oHn7GHq5VMccCdJOFEPnykOw48fW9Xtfor650mWqKokYgKiOUhVkMvuTrU6bN8lgOfxvCodVvq+qgNCIOgMYjtFniYaGhoaGhoaGhoaGxjHAPyqxAGLaHBnZDAmSvwOt+cc70MA5MKhdN/gcWJ1QH067yB8AdBkX3L59mtBdxkFkUnDrqKpfdqjnKX+OUXNzUBQYfpWMrP3jVdj6rchsjLsnuLaNuwv2/SajXpc+B5MfaVk7Kg9BzlpAEZklvVEC2ZZYMDlqVyiYLLWvFd+59gWAAr9raTWMKexvHWBtEF8SRsE/KthpEzmXsICKm5ogmlmOg8FrZLp7rnw/9Irg9rf6HZE3McfC+HuDW8dnDl20p/Fk4F9NaCSkTZBk4C8PiP/Brw+L8Wswfiw+QsLFxHnrN1K10FBiAaDvGZJY2PmTSKb5rv2Gkmt174Mup5zXuve+1lSJHeVs37GD9PR00tLS6NPbn/yy2e01FQkJ3oR8QUEBCQkSiN5/4AB5memkROoorahk174cuvQbzuDjjqtJOmzcsIHcvFxmz5rNqFGjWbJkCV9++hFRipURI4Zz1llnYfN4iE87Bb3bjrL6TVj0uCQXjguywqcuDivMulES33ojTH0BepzU8PLlOdLvQJJRUSlS9aV65PnjsvvnfZ/HdgkuUfhnYTRD2iTY+aO0vdtk6SctwdcXm5tYsBXJsw9AFwq2AnCFQ5RF+ktpriTqFMX/90RgxaPeBJZob3LOXLuPNef+oKGhoaGhoaGhoaGhofGX8Y/wWDjmaa3OcEW+yA8ESqfU9VoI1EOuD1uBSEREdw7OvNJph63fy3xzDDoL98ioSL0Juk8Jfr0/m/5ni2SHohepm18elEBlU5jC4eSnZH7TZyKr1BIObpVpbBcZJa4z1ParqGpEJ77GzDn+8OtIM2yujc9XxBLnT8ToTYcHmB12UF21tcUPbZaKlvBE6Hlq0/sqSodVb8j8iQ+Kb0YwhLeTUcceNxTsCG6dvxJDCJz4sFyntkJJLlQ308B8wLky3flT4+umHl9bDsnnL2Itqm3K3JB5rLmBJHTdftES0+ajlPT0dKxWK+np6bU+t1qt/P7777z66qss//13EuLj6dO7N/Fx0g9ycnKoKM5nd/o+liz8lUW/r+bXX3/1b8Bp54Rh/YgyuemZEs+mNX8wZ85PlBXkUVBcxm+//ca1dz3GN3MXU2WMhPH34Bzs9eqZfw+UtsCXw14CX18uSQWjBWa83XhSwe2A5S+Cu1oqf0ZcLwbPfWdAv7PkPl/z/kx5P+BcSB7W/La1NSkjpN/bS2D9xy3fTksrFnzVChEBgwR8BTCOgOqf+hKdte6r3r87Gnv2HEP9TUNDQ0NDQ0NDQ0ND4+/MP65i4Yihqk0bUbbWRLKl6A0SBK07WjcQp10SBw41YDRhQNA0a6VMu46nHjfOw9n5I1SXQXh7MVZtLIgRGBTfMVumycMP/84nxVQXjx44y9vOPyCpb9PtA/9vagq3N4EQliCm1es+gKxV8PODkjTxndeG9Lzje0igatv3MO9e+W3BSCIFLnPIl1joKhIv5lhv0NSbO2yOUXCtffwNZSiCNXxtSX9rqNrDaKbmunfY5TMFMEf45ZO2fC3fD7xQlnU7GvkNHpFucTslYNiun5ic10d44uGfJQ+HXXMkOdHjZPmsPDe43xjdqfHv3QH56N+eBcXZ+PINSR1FtBct99ID8N3VcNHXkqRpClO4GKfHpIpfw47ZEuCti8cNKJLE2fCJ9K/Evt6EnyqSOAZTHRkkCzXn8bBzq/iXhdqJCIfNrw8fk9w6SbOW0NxrWVEaXCctLY29e/eSlpZW46MMIme0c+dOHNXVbNq0ieOPHyNfeQ9XcnIyu0oLiU1MpGxLOqrOSX7+IV597VX69evPgK4dGdS/L3EWHZt2ZKBTHSREhUG7WFwuN1a3EZ3OwOI/NjB49Ils+3EBg3ucwKhO21EyV8CK1+D0V2RnAUnbq6++inZxYlBeVFJCcUkpRcUlROqq+HhKJfFqEYREwMn/J4nXhvrB9lmQtVquKUOIPJd81XGBpAwP7hgvfcY/r6pyX1ZV2bZO7/8u2KTE7nlNLxOeKM/STZ/LPaC+e4OP+O71f272Vl5ZC4JLjvvwmdLHdoEob3LBaPZWKHi9ecLr9DEfgffVun2svmdP3aRDMNVDwfSRI/U3mIaGhoaGhoaGhoaGxt8ULbHwV+G0AUehBJPJIoG6uubNdTGaJangM2H0jci2V0jwdN9S+bzr+OD26xtR2Xta7SBLY1RXSuAHRFO6HgrLq4gOM2HQ66A8TwK04W1kzu3xAGrj7U3sDYMvE7+DrJUS2GmgrbUYdYNUK1TkweKn4KQnm9c2X2IhKsWrDR8g7aIoYC/3VzDUBFDroa7Ey99cM/5PJzAgpnpk6rSL5IrOIP3FWgwl+8RwWNEHJ+my/mPI3y7navTNzQ94dRwiiYXc9c1brzX4ZGEUfZAG5qHQrg/kbYHqcljyNJz4UHD+JIoiI8iX/VckkepLLPjoc4YkFnbOgUmPyGc+mRhnlQR8Pe4A01jv+aybRAK/ZJLOUFuexWj2Gz07bH99YqGNUBSFvn370rdv31oJOkWBMIuFMWPGsHr1alJSUrDZ7IRZLCg6OdepnTuTkBCPzWpjolsh78BeVm/aTk52DocOHqJ8YG9iIszkldrpkJxMubWK40cMJj+1A4ntOrBk1WbSd21lyJCB7Ny6gXUbtvLbwl/QzRjFSFagbvqMeZW9mfP7VpYs+53kc6XS6/NvvsftrK71O7rGKHx8SRjxqo4K1UzEtJcl6doYpZlQuEvmO41p2/ue0+av9nNUSgLNaA4ukdYczLGShDy0VSrgxtza/G3USCE107zZV7GQ2PtwE/v6+lJDBPYxX2Ib/OfD94xSkIS5Zu6soaGhoaGhoaGhoaFxxNASC38VgaP7jyaMFohpYoQy+M0Wa957NeMtMVBx0BtUUIIzbj60TUxXdXroFYQkjI/9v/nlluoEiVRV5clvN/PQlxsJDzVw4/ExPD1Ggk3l7CNMdwpWJQyPJ8jR7LU3LkGnQ9sk6NlppHgqNES7vt4KhO/ESNMSBx0GNr4PU5hIKc2+SYKgPU+F1DHBt/HgFpkm9JLBoC67TEMivBUMkeLD4KyURFJDQZ6/OkhzLEhZ+OR1FPyjfasqpG+A9JvNX8h8r6kQ0aHx7ZXlSJAdYOiVjY86boikQXKtlmbKyGOfvEkrKamo4vtV+yEq4NpUPVBdgVpdLh7VHgUMIRiNIVIR4AsS1ocpXAKRh7ZBxhKwxAdv1NvnDFj+EuRukMqMuLT6l0sd45dDytkAXb0eMGU5EuzV6UFVJCng8yFpiIZM7E1hkpRoav2/OSeMHUuvXr1wuVxYrVa2b9/Onj276dWzF4MHDybMEkaYJYwEywAY1IdQs5lVm/ei0+s5kFvAqkOHOK5fLxJiwknokMLBQ/kcyD7I4pUbKCwuJSomjt37c1i/dQ8btu4AVWXjtl28N8HE2A4Ocr68g9d/rEJvDCG5gTb2S9Txy8UWOkToSC/xYB1/HwNiu1JRaWXF2k0sX72RQ4VFnDBiMFPGjSIxPlbujT4ZusQ+Yj7cVjgCkgo6vSSx3A55KTrxGolKab4ngi/xE5jEUxQ47lKRjsr8A3qcIpUXzcF3r7C1MLEQ10AlRLD4+pgvqVD3eeSwyX2lrsdN+FE4cENDQ0NDQ0NDQ0NDQ+MYR0ss/FWYGqkG+DviG8nutEHmCvks6TjRgXdVNb7uhpkyTT1BAn7BoHogfTEAzs7jyMw6SNbBIrIPFXIgt4DVW/cwe/FGAAy4uLG/XwYokUJe89zPU7qbGXrfXO6/pJozju9HuKUBaaJAqisgd6MEJX3sXy7JhcYMJbucIAHd/cvk95pjoNOIxveVPBT6nikJiXl3w5W/BDfy2WH1G2e3GyDXmi5gxLyieEfRh4LTKjrXlrjaAVCfXA80LD/xZ3C0Jtyag7VIDF8NIWLSDBAaIckda7HIGW33Sng1ZdqsqvDzfXJOE/tIRU9LCIkQia2CnZCzvnFd+SawVjn4aMFWZq/cy8KNB1B1RiY+dDWK6uHZefu4eahKdIjkVTyqilEHeKqgugqqQbUWoBgt0ibfy2jxB0TN0SLLUrhbqg/CEvweCo0Rnij9LGOxmKaPu7v+5XSGADmkb/2JBadd5NhCY/1JgaaMrhszsfd9dwzKqagB0jVhYWFs376dkpIStmzZQmxMDAsWLGD37t307NmTQYMGST9w2Uns2IXwjIOoqkrGvn0cOniQ2d9/g9VeTXbuQVyupqV2DuXnc49dz4orw/jXIBM5Xc6jz9jTeS/APqRfn14kxsfTQc3l1TGFRIfA5kNu3iw9AcfXS9jwnzfZuG03Ho+nZp13PhWPn+EDe/HZyXbSQh2UqhHkq13oYHcQYW6DagKnXSoUQK75kHBvJZm3Ukb1QHGGvMwxkmCI7FA7EeeqlvuBrUi25/Teq33B9w6DalfRxXQWk/T0RbDhY5j8eHBVQD58z7Wqssbl2uriewZFJcs90VcZ53u2BBqlB1bNBX7vq2wwWajR4fJVLFgL/d/5quocNqn60Bm0agUNDQ0NDQ0NDQ0NDY0jgJZY+Kv4q/7prStlE8yyRrPXLDFQazwgeFbzeag/meALCNtKJIABwckguaokeAjQ5/QmF9+flcMTz7yGpTydV8YUUValknTeq9gakHV/5fJhXJBWSbwzh1ybkUc2xvPQwAJSwgp40fMIP44z8uH85dzxyTom9EviiYtG0i25ToJAVSXQk7VapIlUVeRdEnrKyFZbERxYIRJHjY0y7XOGBEMKdsCa96Dv9KalL0b9W/ZbliUa3ZMfa/IYUbATUCE0BuK7yTlxlUqACvzBGKMZSnNqj8z2BXWcAQGasHi/OWYw11Fr+Lsk3OoGvwJRAL1XN90XPLMXg60UDJWw9QdJ6CT0FDPhxhJve3+V/qQ3wdjbmxcQrEvSYLk28ja2KLGQX2rl1dnree2n9RRXVBEalYA5sQvdU2RE883qe5w6RgLO+8p13LdUx7oD5XSLhd5xOkZ01DMyWU/naJ03GGqT/gNyf+kwyB+E90mGrXpTDKsTe0P7/k03st/ZkljYPgvG3tGwTJlPDmnXPJhU4h3trUBIlFyDzZFq+YdjsZix2+X+HxkZSUZGBuvXr6dHz55UVlZiNpuJi4sjPj6ezNx8KioqACgrK2XV6tXs2+83YY6NjWHggIH079+PhIQEVFXFo4LL5UKv16OqKg6Hg759+lBV9RmhOSu4ZWwMucPGwQ7xwIlo34UDJU4M7ixmn1VCmBFWZLmY+pmN0qravgRGgwG9XodHVdEpClXVDoaZ0kkLNVNWpTL4rVz2lX4OQHiokQ6xFpJiwugQG0ZSjIUBqfGcNr0PcdFBVBe4nZKYhtrPVJ1BnhumMAncG0LEa8ReIq/87WLA7naIh5C7uuF9OKziGRFdR+6v/3niOVS0F7LXiE9LsIREyv3H7ZD+GhWElKAvQQJgDPPL8JksfmkjX+K6ugJsxVIVUvf7un3QN4DBWuivXAiLr/1M+rt5AGloaGhoaGhoaGhoaBxDaImFY43mSNk4bKJt7tMWr/UPfkBiwVokoy5N4RBtqe2vUFUigT2Anqc03b6qMr8sRNJxDS7mdDp58e2ZPPrCW9jsVVw12AiYWZbpwuYEnU7x+mH7R9LqFHhu9jZGnBNKfDuYE3Y2B8ZO4xbVym2edxjLamZ0cTOjSxV5mJjnSWX6Ax9xzwXjuOSkISiOSqlOyN0AVaX+xoS3gw4D5JjEpsLOuRJIacrYUqeXEdUFO5qu4vBhCoNTn4EvLoL1n8Cgi5qWsghvJ4kP37lo3w8M9XRtk+Xwkdm+c65Su1Lhr5JE+ruMMm0s+GWJk99hCPUnSpxmCHVBZSEUeyVCek1rejS7tyqHQReK5FdrsHkN0fVBVOYEoKoqnyzcxo2v/0qFXUYs9+yeRpeL/osnYCS1BQkU5pLITdHPUX2aSvbL15Kxt4DNhwzMy4CKaicul5sRyXrGdzFyUvdQesW40Tmsch8ITMz1PxfyNksF1MHNwSUWuo6T+5LNm8Br16/+5VLHiARVRZ5UEA04z5sorZNE/Yej1jI+9897PCpKwLWbkpLCmjVriIqKQm8w0D01GdVWRFJiDE6Xk5KCPGLNCr3SUrHa5H6fmbmf/Hy5Ju+6604uuuACUjp1qrVdQWHjxo0sXrKEn3/+mbVr10przmvHJT1gxbb9vOJNKgAMv/p5AG71vE2YuoQVOQqTP7HVm3x2ulw469y2u8dJ8u6jTQ72lfp/c2WVkz25ZezJLau1vP61xYwf0oszJwxlxoQhdEiIbuho+mdN4Yf3fUUkw0geJhUM5dlQmiUJuPKc2ssazLKs0ZsgNprFt8Vhrf85ZI6GbpPFfDp3Q/MSC4oiknoHN8OOn6Dj0CDW0UFMF/GoWPUGnP6q/xlTVz7MViweNL57aUPyYoEEVikEonkAaWhoaGhoaGhoaGhoHFG0xMKxRkP/gNdXyWCySNLAYPL/Y1/fP/hKnanPX8EYCn/MlJGNKSPFMLYpAoMAbicYQnA6nfwwfzF5hwpxOJ1YbXa+/ulXtu0SaQWdTsHtVbDwxb18XgmR4RZio8LJOVSM0+Uiq8jGgt1uhrcLoQP5AFiVMJ7Q30pXdT+nqIuZqC6nAwVcofuOSy+Gn3bPYu+8VXQPLfbvwBACkUkQkwqhUf42+ySRDObanzdE1iqZJg8N3qgzdaxoY++eBwsfg/NmNh6QjkqGkdfDH6/K8pd8J7JHgecxcMR9oLGm75yHRdQOsDZ0Hf1TaahvBPorGEJrLw8QGyGjkCE43fZD22QaTDCvMarLIcNrqN7MaoXnv13N3e8tAWBwt3bcf94oZq4rw1bHJ+Ej5VxGq+tIIp/R6hqWGkZjtERSVVZAbrmL3HJZbsbgdqzMKGX2LjvMtzP7fDPTehqZuXg7F0wZht5r/ouiyMjrTCQBGQx6o/StjCVS6dNQYkFnECPb+ffA6reg3zn1BzIbq0ypW9HVUIXX3xSr1YrVaiUsLAyLpfZvVxSl1i0oMTGRkBBJWMXFxeIozSU0JIpQvYrRYCQ2TIficdGtSzI9+g7g7nvuZtWK5VhtNiIjI7n/3nsxGAzo9YdXmLz0yv+4//77a32mV+CUFJHEWa4MP2wdi2pjorocgEc3xNGtZ0d6dU+jfbtE4sNNxMdG13oZDHqycg+xPyuXDgXLgI0M7GghOc5IdlFlvccnJT4cBcgsrGTh6u0sXL2dG575mN5dkhjWpwsTh/dh2gnHERPpq0wwSsBd9UjVQeD9oS7GUIjrJt5BxfvkeR0SKebEIRFy/fqqH8Arj+SVCApvwD8lobckFgr3NLzfhjjuEph3F2z+EsbeGZz/w/T/wQenwp6fYeNMGH6NfF63GiiqHtkx398nvuV9/dD394qWQNDQ0NDQ0NDQ0NDQ0Dgq0RILfxFVDhcmRxMj3IPV5q41qrQuJjCaZLBk4P5sFd4R126wmPzLhiV5ZUrcYuxrMAMhtdc1RMtnhlDv5959lB4kZMcPKIBj+A14fOu43I203YQvvFJlt+E26rj0loeYNX/xYYsaQsyoXk3sUEsooNIuPpqrzp3A4D5dGd6/O727JKPT6fB4POSv/pqM/AoqctOBXFLV7Frby1BSeU25nHfVCxmrruIUdRH9dLs5o5cRKAIVPJEpuJIG40noK6MvATwBbSo7iAFwRSThUg1eo+QGfmt1OSEHN6MA1R1Hozo9DSxYB4cb5fh7MKUvRNm/DMeuBXi6nljPggE7HnU7IVu/QynPxrl1Fu5RN4OjSkatGy2i9+87/wQmOLznEmqf84auI2ij61SoaqpP1LN8k/0oiP0CzfgdHho8TrZKKMsHlwOiPCJH5bJ7+5EJXHZMlfnoAEdogvQRZwP9w+MmJH+HXC+xvVEbWi4I9Dt/xeh24InpiiO6JzhV8DQgE1SHQ+Uu9MYQdDqF128+jcRoC1d9vJPho2svl6u053NlOpep33CtOpO16kCW3NSdWIeZ7BIn328p5+VlxazOtLPrmcms2lfGjxsPsaX4ENNQidbbufObDJ4+21uR4/SgN0ZiBNy2UpwN9Zc6x0WfNBRjxhLcB1bhHHiZ/4u610nf8whZ/hJKRR7OTV/i7n0mqG4oK4awGAlk2kq8wdy6/QQ51zV9KOTw9z6OgMdCW/SjotJy3C4XVU43OoOJgsJCCgsLsVgsWCwWSTgYAKeNojIbMfGJ7N2zhw7JnSkvyCEmFJb9vpLypWswGxQiLUbKq9z06NOfbVu3kJtfjN4YwpVXXYPBZKaqugp9Pae4sLgUvdF/PBMTE3n6qknE8y2lRLKRvoet05+dmHCRSzue+2g2PdoHBMJ98lt16JKaygmALrsj/LqRsd0i2PPOGZRWVrNpfyEbMgpYn1HI+vQC9ueXk1smJRB6YwiKTiHEaKCq2snu7CJ2Zxfx6S9r0es/ZcKwXpwxbjBT9QY6GC0YnJW4nQ6c+kiqXSrrc6pYsb+K5fur2FPg4KTBm7np5B6kJoQBeojpcXhjPYDH/+eaoXQfBlQ8IdE4DNG1nlG4vPe/6DR51pZnU2WrPDz51djzKOUETFGd0JVl4lw3E/fQqxte1kficejH3Y9x8WOoS5/F0WEYarsB/vthjexdnXuprRKs3iyky+NP4uqNdf5eaSVB9Mvm9iMNDQ0NDQ0NDQ0NDY1/OoqqBhuF02gJ5eXlREVFceL9X2EIPbZG3J3t+ZGr1c85QEeu1T2DGqQe/I/uSzHh4mLdKxQojRggBzDJ8xt3qW+yhoE8qL+n0WW7qJm86bmXCiycrXun0YBCJzW7ZqTrQuV4MpXkBpfVqW6+8FxPFJXcpXuQzUqfRttxgecH/qV+xXa6c5v+0UaXrY8rPZ9zrvojWXTgOt0zuJTG84ATPcu4W30DK2Yu171ImRLZ7H3+1biqbCx66lzKysqIjGy4vX/nfvSZ+9/EUcoNuifZq3RpcLkUNYd3PXdRRQgzdO/haam/gqryjucuOpHLK8oVzNFNamHLm8aoOnnDcy8p5DFbmcxrusubXKebmsFrngexYuZs3Tu1fucUzxLuUN9mFYN4WN+AGXMduqsZvNrA9uoyzfMLN6ofUkAsl+texKkYg9rH0c6x3o/u8LzJFPW3Bq+xaz2fcKY6jznKibyiu6pZ2+6gHuJDz21UY2S67oOgn2PB0FnN4m3PPTjRc4HudSqUiFZvM0ot52PPzYTi4D7dvaxXBjS47IfuW+hAAffp7mO9EoS0WAAneRZzu/oOhcTwL91LQfUVRfXwhOcZhrKF/SRzk+4JHEobJQb+Aprbj5paTuPPYf369QwZMoR169YxePDgoNcZPnI0Ex/6FoBZ95xEqEkbW/VnoPUPDQ0NDQ0NDY1/Fm33H7TGPwqT6mCGOh+Ar5XTmhWMqfaO7g2lEVPKOni8l6pC06P+s+mAGx0R2IijpNFlM5VkPtCdzwe68xtNKoCMio2ikjLC2Urjvgc61cMpqpha/6S0LLD7uTKdEiJJIY9p6q9NLr9IGcMeUgnDzsXqty3ap0bbolM9RCOyPkXENLpsN3U/ABl0anlSAblOO5GLjVAWKWNavJ1gcCpG/qe7AoDT1AX0VPc2uU4GqVgxE4adruyv9V2FIiPNo6ioZ836Sadzg9ury3xlPAXEkkAxJ6lLgt6HxpHDqDoYo64BYIkyqt5lBqlbAdhIA1JYjXCIeFzoCcFJPMUtb2g9HFBS2EtnjLgZp65seoUgmKHOIxQHu+jKehpPFuxUugPQS22+HNIi5XgKiSGeEiaovwe1jqroeE73b4qJIpVs/q1+GHwFmYaGhoaGhoaGhoaGhsbfDm24zl/E57dN/OtH7jT2D73TLhJIPjNIHw5bgHSBxf8ewOOWz41m9Ktexbi0BDWiIzde8wA3BvoHuB2NNsv4ZjhYrWx+9xbWH1R54bF7ueaScxky+Wx27c3g5qsv4darLyUxwesFULCDNXO2gxNK921iydfnEhVmQa/T4XK7KSkXTewZo7rxyjXjiAkLwbk9Gr2jmC5kUURsg225ueghJkZn1//lkCtqvTVkLoMCCItL5vvUFf4vuo47bFVd3kZMywtRTeHcfNoobtbrgtPYh1reDfptt8Lix7jW+AP/uvAyCE/0L2eOO2xVXeaz8OW5TGMRJ51xOWq7vuKHYTCCyynmzY2ZZIL4SHhcouttOXwfbSmFVF5eTrungtscwOe3ntj6ftTU72tqXWuJSOf41vVtz14hptnVFSKJZI6BsgPo31NRFR0f3nmGGHrbS+vdtGH5QlgHPfoPZdaJQ/zeDE2Ru7HWW+P6eZALpk7D+GKAE/C62DYgCeNj4do9nPnQB7jd/uRdWpyRZyboUI2hzE57ilyl/WHrbVL6skA5nknqcq4qeIxhb1s5fVgKr14+nMxCK9Of+Zn8ShWLEWadb2FUigGjt03DNt7PWz85ibco/DbnKzobOsE86BFuY9ZZiYftC6hXU97401A4sIyXRhbjPm6afFhP/wDQb7gdFj7IjZY5XH3+FaKFH5kkX5bnigSLT1Pe6jXADkuQ8+2wifmsAphjW+dB0oaSSc3uRw08j3wFjAcyM9m7Zw8xMbHExcdhs9mIj49n9erVNT4MU089FZx2VKcNxad/732m/LFuEzu2b8dWlMvWrVtYt2kbO9MPMHLUKOb8+BM6nY5qZzUPPvgQ777zDgBRUdFsWL+O2OgoqhxV6HUi3WXcO5+wOXZyrHq2RxwuExSlltGVLAA2KX3Y9/3TZGxdS1RkJBERYeQdKsDtalziZu+1RnrF6cifeR2L9vvvW2FmE4O7JzG0R0eG9+5I947xxERFEh1uJqTOaOes/FJmLd/Om7NXsD+vBKNex8mDOrA4xUq3PnBO9RecELufHh3CURSFDftKePCrzSzdIV5AOh3cPCKUJyaayStXOWj1MLCdHp0vxzj0SnBVE7JpHrihS7dezIqtJ1mR0LtmVr+vI2yHixN2ct6w0trLBeELot9zEmz9gtuiF3DDpXfJ/ashAjwkdPuj4esLOEVdwpRuFpynvuLvKwZv5YPDDvZikduzeJ/RtmKoKhF/iZDw4O7PR7AfaWhoaGhoaGhoaGho/NPREgt/EaEmw19fdt1YYNcUAXhlGQJNSMMjgYCAk8n7vjQL7AUS/QiNgZWvAKBMuI9Qc51Atbvh4MPHMz9jZNYhesSCWefi9ace4ppLzyUn7xDbd+xAURQOHsyj9+iT6NAugZSk9tgqiuni2c/Ysy2YTXosBpX/u/kcrpgxEbfbzXMfzOah177gm9+2sXL7AeY+PJ1OxhhCHcWkqlmsVQY22J4e0e6GKycCBcBVFcr2AWCITcUQ+J2hnsBGxgI5Pl3GEeo1OcUY5Ch0Y8DxG3A2bP8W5dA2Qpc+DtNf8wdSTPUc524nQPcpKHt+IWTDu7K8xRtAMjVigOm0QWWhBEyNFkDvXb6ea7YNEwuOZvaJNulHSkSAkXkzt+XUQ2S0BNh87ajZnkk0+vUqYAOXDqy5skh4IqGh3uvA1UD/KNwJgKFDXwxGvawfDIE/oTwX8jbIx13HYgj8Tt/w+Sgqs3LV05/gqLIzLMXMKb3DGdnZzKj2DqJ1NqCKqZ57+FyZztfKtMNkUd5WLmKEuoEBcVZuHw7/t3wv6/ccZP59J7LtaiPnfmNjQYab6TOr+eUSCyOTpWEX91N4+Q8nGw96OOfqO1j9yeMA6KrLCTU2cJ3V1486DYcDyzDmrcM43CuVU1//ABhyEax+DaUij9B9v8CI6/xBT1NKHWPZam/gM1zOtynSe49sA9owINpW/cjj9bTpkdaFHmki27Vjxw5wO6koLaZ3j27s3buXbt26yfqmCDxuC4qiYC/Kxm6rxGypJsSgIyYqnF5dh1NeVkJefhHbdu7m99+WEB0VzsKFCxg8eAgvPv8Mjz78AIsWLaJXr14kxMegeFzoPC4Mejk+IbtnA5CbcAJq9eHnfpAqhucZdKJMieTgwYN069yR+V+9T2qnZDwOKwVFxeTk5ZNzMJ+cvHyKS8vQ6RT0Oj06nQ6dew6QQ9dINwtcTnp1SiC7oJzy8gqWrNvFknW76j2GMREWYiLMxESYmTSkOzefdTxXnzKIy//vK75ftpUfVmbQLiSZq/rYaW+y0z5RRdW5ufuzjTz/k/R3ox4uGxjCfWNDmbfHifk/fuPyIUl6vj8/nJQovTyP8jZJ4t4ciyk+FZR6KvgCT2u8nEN9cTr6iv0Qk1r/cg3RbTzsmYuuJIPQAwuh5ykNL2sI6G89ToSpL8K8u9HvmYv+i0w48y1J9hkipL85HVBdLMbWRp23D9pAp4LODWERwd2fj2A/0tDQ0NDQ0NDQ0NDQ+KejSSH9k3HawFoI1mKvCand/53DKt85rPJeBVzV4PbA7y9BdTm06wv9zm7WLh9/+lkqq8V89fkHbuKaS88F4L4nXwCgc0oSn383B5vdTvr+TJasWM3qLXvBKw1j0OmYPmEYz30gwSa9Xs+9V83gj5lP0i7aQnZhJR8t2kGlQWRnOtNANUJzqSqTY6Hom648sBZArgR36dZKfXudHqY8KaOo9y2FHT82vc6JD0k7MxZBwXYIi5dX4Khia6FMfThs4KiEaqn+qLV8a6lvf0dyW0ZLy3+fySKVDoEj1Wu2ZwZTqIwENoZLksbulVYJb9f4dlUV8rfLfOLh5rRBoXpgwydiSNx+AER3CnrVVdsPcLBY5IdmXtSRR09O5JRuBm9SAbaXGDHh5DL1G97w3MtAbzDXR5kSxZvKJQA8OTGUcWkWMvIrufrtlUSHSqXChFQ9FQ6Y8omNTQdlBLlBp/D2aRLE37xjD9uzvcfLaZV7UrAkD5NpzjqprGoMQyiMuknm17xbexS2yQJhcTI1WSA6BWJSWleZ8DdCUZTDXgkJCYSEhJCQkEDfvn2ZPn06ffv2rfl+ydIlvPTyyyz4bQVuD9jsdkpz9uKwlbNp2y5c6MlIry2R9dabbxESYsKgNxAbE8vZZ51Nv779MOgN6PUGQkPNhISEEJq/AWPGQgA+2Vp/YmwQci1uUKTfWMxmlv30OamdRNpOp9PRLiGewQP6MG3KeK677Fzuv+Vq7r3pKu664XLuuP4yXGEdAOgep0NVweX2UDLrAba8dxPv3nkGV506hP5d2xEdHoriDWRXOVzkFZWzff8hft+yn0c//JXO5z7FUzMX8fptM3j+okHodQpvLc3ml3S5lotzM6iwu3hhjj9R8cbUMN4+3ULXWD33Lwx4BgPrct18t91bceR2SmIBoOPg4ALqUclgiZeqw9+ehfUfNVgxVS9GMwz2GqKveqN5skaDLoSLvpGqg0Nb4aPTIX2RVHj5tq3XgyHEm9A2y30zPF6esW31/NHQ0NDQ0NDQ0NDQ0ND402hxYuHqq69m2bJlbdkWjb8ah02CdwoSLA2UyHHaaycbwmIhIkkCFBs/lc9OfLhxaYR6GDd2DFaHBCeG9euG0+nk7see45OvZ6MoCrl5Igtx+3X/YukPHzPztWd55N/n8viN5wPQo3M7MrIP8f5j/6613Z6pHbFWSwDmtGFdCIsSCYVUNavhxrgdwWu5Vx6UaXiiHKvGSF8IqNCun19ipTXEd4eR3t+75GmozG96+cES5GXBoxJwDsR33h0BwXmTBUzhIj/R1kHU+vbX4m3Z225bLaGxpITTLrI65liRnDLH+oNoER0a325FnkgfKXqIP1zuJSjSF0HJPgmcD7ygWatOGtqDYb1SAPj3t3mobicuq7T95VXVbKrqyLPK9RQTRQp5POt5krs8rxOl+kdXL9WNptrSHh0q8/4Vh8WkZ+mOfD7b4sJiVPjxAgvjvcmF8R/ZcHvkPjBnj1cWaWAfBg4cVJNEpKo8+B+Q0FOuX0clFOxsevlBF8g5qciTZIxGgyQkJNCnTx8SEhJQVfWw17x581m1aiVLlq9CF56A2WymR8/uuKsq2blzB1999Q3pmVK5M378eG688UaeefYZqqqqcbqch71cLhclJSVUbv8V3ZcXgruan3Y7eWf2CtzOw2X2fBULG5V+eFwOyooO8fGX3zfrN/YcMhaAHrE6zCFGPrrnLPR6Hf26tOPKU4fyzp0z2PzuTZTMfhDXov+j5KfH2PfFfax/51YWvXgtb995NoO6JVFpr+bZz5fQ9YL/I6vIxg93jGVCn0Q+3iTtLsvbzy0freG/F0vSAeCq2VZGvVvOnN0Ofrk0ggHt9ER4i5tuHB7CNUO9b/K3g6tKZILiuwf3w3QGOP52SBkh77NWwcJHYNfcJiULaxh6udxT8jZB5oqmlw+k00i44md5FtqK4MdbYIvX/8dkgbhukgC1eJN5MSmSzPuHJPI0jhxZWW006ERDQ0NDQ0NDQ0PjH06LEwvvvfce48ePJzU1lQcffJCdO4MI5mgcfdjLJKEQFl9bc9lorp1sMIWBJQbWfywBiU6joeuEZu9u3NjjsXsHInscNk6/9Aaee+19ABLiYnE4nUydNI7nH7mbE0YNY+2mrTzy+lc8+e4PAMREWPjto8cZNai2efLnc5dTaXfSs2MM4/p1JDpCfksn1z6MP99bs9wL3VYzZftd/PHGLdi+vJ5EipputMcNRV7zy/DDNeYPY99vMm1ttUIgQ6+QkezV5bD8xaaXH3uHBFoPboadP9X+rqFR9zGdJMjT1iNF69tfi7dlbrtttQU1VT+F3mScM6DPmMVvAZquWPBVK8R1kxG8zaW6AnbMkvl+Z4m/QzMwGQ18/p9LCDEZWLjHSklxIQZFZU2Om2W5Js7r4WL+958z8EMDH+8Ox4PCJHU57ztvpvTbu7jA/gnvd5lLSFI/UPSYXWVsuSmeSV313PZzFb9nuggzKcy50MLxnfSUVsEf2VJZUFolbXjlsTvR6fXi4wJQVRr8D9AZZBQ3QM7appcPrFpY/uLhyTeQ5JW16MglsY4A9VUsKHVGxu/fv59ly5axf/9+ANwuFw6HJIfi4uKwRMURHR3Hj/MW8O2335GRKQG8q666inlz5/L8c8/RoX0HTCYjRsPhL4PBgLGqgPAfr0bvrmb+Xhdnf2XnrKmTeWJ6F169YgQvXCIVKglqEUnkoyo6LpsxhWlRe6kqK+D+J//LgqXBGQ4D6GM7AzBtUAK2ef9hVF+p9rFXO5mzchfXvvADyec+S/wZT3HCzW9w1xs/8d1vW8grLie1QyxXTh3G+ndv5cenL2dEn07Yq528PH83Z724nPNHd+bGMyVx0SVGR4Irj9tnbuCEXgmcPLADIXpYle3mtE8ruWqWleuGhpBzRwzu/8Twv6lhmI2KVAr4vFQ6DvYn34LBHC1VByfcDbFd5fm98ydY9ERw61viYMB5Mr/mveD36yMqBS6dDb1Pl/vjokdh0xfyXWCFkIbGn0h8fDxmi/86GzxkMJmZmUewRRoaGhoaGhoaGhrHBi1OLCxfvpxrrrkGq9XKU089Rd++fRk+fDj/+9//KCgoaMs2avyZmKPq/9wUdniywWkXSR6QgPWhLc3a1cczP+OeO29ldIpUOWzOKmf+omWEhJi45pJzyC8sIiEulg9e9rsnrtsso1ELyrzBvQbkURav2QrAReN6oigKqw/Y2HTQTagBvhyXyQh1HQBqWRbPzN5JRV46vS3FTTfaVQ275oh2vaKTwExT6L2B4bJGqiWai60IrN5KhWCCSmEJ0GuqzBfU0QevO+q+LaWK6qM10kPN2daf/Tvqw1eNUZYNTq+MlCmg8scXsNY1cc5y1su0ff+WtaM8R6RSLPHQ+fgWbSKtYzwzxvana4xCrMmF26Ny/rc2bj0hjpnryli8KZOSvAP07BDJrbpHqSKEcL0Tx6FdXPvc16zbukfOi/c3dA238+slYSy+zMLMzU5eX1NNiB4sRhiZrGdER7kPZJZ5OLO3gZGD+8POOSKDZAiVa7g5GL33KrczuOUHeoOl5bmQvwNKsmonEepWbWkAkJWVhd1uJytL7m+nn346EyaM5/TTTwdg0/bdfD5rPvuy82rMoAHeffdd+vbrxzXXXsuHH33Enj17an0fiOKqxuQ1+L58lh1Vb+LUU05maL8edG8fSVo78Qeqxoiq06OoHrrpcrjp0hnMmDoFh8PJKeddyciTzuKaOx/h1fc+Y9nKdZSWNVAFE99D7qv2Ukpy9/Hh/PXMeOhT4mc8xWn3f8LbP60lp7CconIbv2/Zz7tzVnPH6z8x9Z736Xr+04Sf/CAPvDOf00b34Y/Xb+TX/17NCb0ScLg8XPvuGjZt3V6zq94do1BVWLw9n/mb8ggzKQxN0mM2wtZ8N/+eYyPp+RJumGNj88GA552jElAgoVfLTlxMKnQd739+qO7gpY06jZSprbBl+zaFSSLPt2+305+4872aSuAdifu7xjFDp06dWL9ufc17u81GYWELr2cNDQ0NDQ0NDQ0NjRpanFgYPXo0b7zxBnl5eXz//ffMmDGDrVu3csstt9CxY0dOO+00vvzyS6qqqtqyvRptiW8keaAEUkM4rKjOKtTRN6N2GiVBjs/Ph+J9ja7mdrv55NPPOWHiSVx21bWc39NFuEnBFZ3GE1+uBODCGVOZ/fNiAB687TpuuO9xQjsNZMjks1i2UhICPvkkXPUbLa/fIe0Y3qM9+w+VM+2JH5n4sY1NhQbCjPCI5wXO9cxm+hM/UmBTGdJBx3+nhDb+m32jwCvyROKmxykS1G4K3+jO7bObli0KBqcdZt0k3g2xaTD+vuDWU/S1p/Vu2wYl2fJb6wZ2/m6BnPokl1r7Gxpb32mTl8vhH2XvsItnicMbjA7xGqTbmkhiZa2SqU+ypLk4vEkNc0yrzEyT4iI5p48kD1fmeMgoUSm1u7n7p0MAPDQ5noEdQ9mldKMKSaCZQ4w4XB5mvLCU+Rtzpeol7USI6YKq6OmbqOeN08yc19fIskw3+VaVr88xY9Qr/JLu5PudLs7oaZC+svptacjQK/3HLlgKd8s0WCkpo0X6NXjPcWXtJEJg1ZbDVttzpi51PWmOYVJSUjCbzaSkpGCz2ejXrx8XnH8B7Tt0YPOWzXz77besWb2G+Ph4zjrrLK688kpGjRpFSEgIGRkZfPzxx1x33XUcd9xgUjqlcM655/LiSy+yY+cOXG4XbrcLa0g7HNHdAJjeNwyHw8FFl1xGt559eOV/r+J2S7VLuRKJu4c3gbr2fRRF4fO3X+SCM6fhcrlYtW4T78z8lpseeJoTzvgXMT3HkDr0JC647m72HQiQQgmNqtnft19+yuXPfscPv+/AVuUkOSGSf08fwc/PXMaGt2/gs4cu5KFLJ3H2uAH07dIOk1GPvdrJM58vJiu/FEVRmDS0B0sensjDZ/bjqsFGrusn19U7G9xst8fy28MTeeCMvnSMNVNsV1mb68buhK4xOjpEKFQ64M211Qx8o5zj3xOZJMB7zTZPfhCQBOe272Dt+zKf0FueI8HeK0oOyDSmS/P3DeLr8N2Vsu/uU+C4i/yJO1s9Hk+BBFaFHUkZPI2/PSkpyUe6CRoaGhoaGhoaGhrHHE2IxQexAYOB6dOnM336dMrLy/nqq6+YOXMm8+bNY968eURERFBaWtoGTdVoc4yWxkeRO6wSJFUQ82aDEYyhZAx9lMSia4iw7ocvLoBrl/krGQJQVZXrb7qVd97/UHZn0PPQlASggo2mYXw/910AunftzAdffE9qSkf69uzGLQ9KxcL6zf5RnpUNJBY8Hg/Pvj+L3ftFw7tjXBgnPfI9+WV2BqTG0/X4s3AVbMBQtJMr1S8wj3Tz5FKFuRdZiAhpIqiy/QcJahgt0PNUkYQIhpQRIluUvw02fAxj7wxuvfpQVfjlQdlWaDRMf018EIJa1ztavrEKh8pCcFaA3SkJCJPNf00EBur/DkaaJou0NVBWozm/wWnzrx/MMXDYQG+S4HN4PFRbxYPE4xLTZqfZX71ibURyq7pCzE2hFYkFb0DbFOS10QBxUWGcEi19eckB6XOfrCvjUIWMnL51XBy+Mc56JLh788m92PLRNiqrXFz19kqyXpuBYgqH9v1REnrB7nkUWD0khOkYn6pjw7XSRo+qctZXEkyc2NUgpvBOGyT2gT7Tm9dwlwNK9st8czwqTBYxZlcUuf6dNnCY/ebNvmvJWiSJj6oKiE+TZEMts3sbGEzyWWCV1zFCoBxSly5d6NJFAsz5+fm4XS5KS0spLCzE7XaTk5OLzW6jZ8+ePPrIowDM//lnKioqWLN6NRvWr+HgwUOUlleSn1/ArFmzmDVrFvfccy8TTzyRa6+5mkkTJ8g1sOK/vHLtBFKmDuXl/73KgQMHuOW2OyitsAHHAeAedBmGnT+KBF3hbkLie/DZ2y/yn7tuYuPWHWzeupXN23ezafsusnIOciA7lwPZuXw/byE3X3kR9918JTHRUby+xsGt3eDC/kbu+rWqRqIr3BzCqh1ZbMk4iEdVsVa5sFY5sFY5qLQ7cDilH3g8KrOWb+PGM8fUHLNHJ0fBQUk6vrnOxfU/2YBdfLc6h10vTOWRs/sxf+4c3lknPhIZJXLPvmqwibJq+H6Hg98zXby5FqZ2t7D9oJ2P31/MpEGpjO2XQogpiD/hHFZY+57fe6TbFOhzevPuFcUZMm1JYkH1wKwboDQTojvDjLfkmeTrQ5ZYWa6+AQ6+xLfBKP3zaJLB09DQ0NDQ0NDQ0NDQ0Gh5xUJ9REZGctVVVzF37lyeeOIJDAYDFRVBmuNqHFnqG3HrtEvQ2VEhyQVFD0Yz+w8W80fqrTgM4VCcDvuW1rvJF195lXfe/xCdTscjD95P7i+vEaNUUKWYGX/bu3g8Hq655FxSOoqxbZdOyfTr1Z0wy+GBg1FDvBIxAYmF/Tn5nHbD09z3sphJXzm5L+c/N5/dOaV0Sojgp4dOJyIsFFfKGP6nXI4bHRf207Pu2nASw4K49J02GQHeZ0bwSQWQIOWQf0nwJHsN5G0Oft26rH0fds+XgMq0l8XYMmi8IWDfyPp624qMtjeGSmC0rqHz3ymQU59MkskiQefGjoGPhkytGzoGvu986xrNYtisM8ih97j8QbPGJERy1ossSVRKy82+q30yTK0LavdrZ+S4DnpcHhWXTip69hT4TV7XZPqD6TokCPrgV5uprHKhUxSuGJ9WW5Nfb8ThlgTC9C9s/HZAEhQuj4qqgkNishh1SNWGziiJuOaOyi7OkGMYEtG0n0UgPvkknQlQxE+lvpHTRrMkFXQ6Sbb6RltbvaOtFfzVDX+3Sp9WEBYWhsFgID4+nvj4eCKjIpl22mkMGdiXk04YRtGhXCoqK1m1aiWff/YZWdlZTJpyMjdcdzUvPPUfThx3PJ07dyYmOhqdTsfCRYs49/wLGDBoCG8vlyoZU9YKHrjtOg5k7OGB+8Qv55NPP61pgxrd2e9ns+a9Gnmfnt27ct6MqTx53838+MmrZK77leKdy/n1q7cZP3oY1dUOnnv9A9JGnsoLb35Ejz4D2FagYjEqXDbQVLP9nZkFrNudy7ItB/h9ayYb9+ayJ7uQ3MJyyq3+isw+qe2YMiwgqVWaCQe9coExXbji3Kn8cMdYYsNNZORXsmjbIQx6Haf1NDHrwggyb4/mztHS5z7e5OCxCWYyb4/msQlmBneQ59Xewmqe+Wolk+//gthzXmLaf77mtdnryMgrqf8EqR7xECnYKUnQoVdC3zOa59EAYgoPENuCxMLylyB9gcibnfW+eD6AN4HrTSYYzf7EcKAsksMmSQWXU5K3bSWpp6GhoaGhoaGhoaGhodEmtLpiwYeqqvz666/MnDmTH374AatVAtQTJjTf4FfjT6C+0di1vrfLyGlrMUR3lACl0QzGCAmaWWJrqhI6depEZibYUidj2vs9bP32MKPijZs2c+e9DwDw32ee4tabboBPzwbgxWWlWB0eLj//TF5/5mH+WLsRgP1ZObRLjOerd15g/qLljBg8gHGjh5FsKBK5hLl3glsSC//7dC73vDQTe5WDEJORe684gzc+/4n8MjtJsWEseuJMUhJESmXZ9lx+0l1ClprE4+5nCDXU79NwGBFJItvQEjPd6E7Q/STYPQ/WfwCnPNf8bRz4Q0ZxA0x4AJKHNm/9GkNateER+/UF4n34KlqcNgmSqUhw5+80ItvoHXUeTNVCfRUPjVX1+L7zSXQ47RAWKx4LDru8j06VZRuTQmqtDBL4pZCCrWZpgMFRpVAJvx3wcPnIOB5fWMyGnCpO6xPOT9sruXBmDqvvjAT8FQsuD0wfmszk/h2YtzGXlXsKGNld/BE+XJLOg59WklOhEmaEbflu9DqFj84IZWSygZRIhfJqiAr1JiOOuwRiOje/4YEySM2RgvIFNu3FENHemzzQgzXge1/lQnya3B/DYv2jrY2hYC+Xz3wyaaWZci8NiZD7wDFMWFgY5lAJhsfFSfJ1//4DOCtLqbZZiYuy8O2cn1nx+wrcHg8Jeg8dY8MYf/wI5vy8gPYxEThcycT17Um1vYIDB7I5kJdPdk4ONz3+OuOvD6dfopPCFTOJn3gj995zFy+89DL7MvZRy+1m6JWw91fY+yuZ7x/i5l9cHCy188Bt/2bapNE1i8VERzHphFFMHDuSuQuWcc8TL7Jt117ueOR5OreLZvFdw6BiLc9OS+Sqq8/AVu3CVu3AVuXEWuVEp1MIs1gICzURbg4hzGwiLFReUeGh/qRazvpaSQUSe2NSFKYPTea8TXm8sWAvny7fz5QBHWra1jFSx7NTzGwvcDN3j5MrZllZfkUED403S8ILld5dO3HZJBO/rN9HXnElP63ay0+r9gLQo2Ms1592HLde29d/XEozoSJXgvrH3w5RLZSC8UkeBuMxFEj6YvjtWZmf+LD0oUACfUxMljrvw7yf2cDYAvknDQ0NDQ0NDQ0NDQ0NjT+dVicW1q1bx8yZM/niiy/Iz89HVVX69OnDJZdcwkUXXURysqZpGjTBGim2hKYkYUzhOB5GAAEAAElEQVQWCZoZjAH/1IfVBJHzCwooLiokLi6e7j17061HL0g3wN7vYddccFf5pRWqSli/dlWNMeeXX32NqWg7/w75A5dH5fU1Du6+9Qb+776bACguFUPNsopKUHScOnkCp04OSEjll/rb7HHx6qdzuPn/PgBg3NC+vP7Qtfzntc/JL7PTr3Mc3z9wOmlJMTWr/7R2PxwHm5S+0O9s2PermCErekkcJPaWY5+9Gg78Lisl9IROoyXIqLobP7Y7Ztf/uSlckhLlubDsv3D2e41vx0dotJgBz7tLkgMDzodhVzdfO993Pbkc3vNuPTwp0JQcFsi61d7KI0cQy/sIpr3N/U2K0vx1AhMGgevW7W/BSIM57d5gc5j/M4fNm3yL848EDgmTl8c72t/mlUJSdIcbkGeJ1wjJw2p/1xy9fl9ioTF5k6I9TW7GWLgNQkWC5aGEXCJNUFIFJ3d2sj1PIaPEyaw1B2CIP7Gw+oXzSUxI4L6PfqfKo2NZeiWD+3TjhreW8O4vYr7eISGG1+6/kuufeIdDRWUYJz9MSXI7sp66mg+nGTHpFYjrBoMvA30TjyWfL0Kt35Yu04Re/u+DuUx857E0SxIBIVEysttaDOZIfyUKSOIg0GPFFAYUSqVPIGqdaV0Ck7zBJOmCeS78Gc+OILfpC6bb7HasViu/LV2Ks8qKumMnZ110Odt3bKe4pAS9TkdKnJkpE8cRYjJQWV5JudVOv379CNdXsz89nQsmDaJ73+NYvXkX38xdxJfbyuiXGMqWr59m6IjLiYiI5LSpU/lu1o/+BpijIXIEnPgQLH6KTrbNvNDXw4Xf2nnqlXeYNvWUw9sMTD1tGiefcirP/+8t7n30/zhwqJRX1sKLfUMxOcvp1zlOZLm8FJaUo9PpiM1ZAAT6uHhfvuLMwEoFkNH+3hH/VofKjr2y7ra9mbCzuFYiTQHevNBBn//bwx9ZLnq8ZuXSodH8e7CBeGMV3VM78uHxw1BVlc37Cpi/LoN5azP4fXsOu3OKufu9xdxy883+BIev0qBdP2jn/y1A8B4mDqu/4iqum79Kqy515RBLs2DW9YAqz92u4yUJFxrlX6YmQWdu4L33ntzQ3y5NDZgIpBXeMxoaGhoaGhoaGhoaGhr102IppCeffJI+ffowfPhwXn75ZRRF4dZbb2X9+vVs3bqVe+65R0sqHE00JWtjtEBMsgQb6lmmsLCQ6moHRUUSYFBVFTVlJA5Le/nnfufcWsuffupJjB4xDICVa9YRuv0rAL7d7iKnAsaNHc2/brqXpP5jmX7pvwEYPey4htsfUDXw0EuSVPjP9eex+IPH6d01mRUbdwHw2vUT6RaQVADolhRdM6+GREqgPrarJAx2z4P9y0SqwZdUSB4GPU5tmUlmrTaboMMgmT+4WUa2B4PTDt9dBfYSaD8QTnqyZUGRmooFxa//3hJMFu91Uf+1cdRjCoPwhNZXWgSOpvVhLZKgvkr9x8YnoaW6Rcv/sG1WwSEJvpM8rOVtq/FYaPlvXL5+B+1CHTjcKsOSdFz6QxUlVdArXsdlg0yc1VsChxsPSkJB75VCSoyWfd56+nGcPbobV0/px70f/867v2xDUeDxG89n37zXmDiiP4eK5Bh0S2nP/733Ayd1gQv6G1EVHYy/v+mkQkMUSv8nvmfz1vMdr6oyb1LI67VgifF6jngDnA3JG9V3Xw2PFzmm8AaM3uuT3AqGIyixpKpqgy+Px4OqqlitVlwuF/0HDMASGUe/ERPYnZFJaWkZERERdO7ciSmnziDEHE7moXKGjZvCuZdczmOPPUZSh2Q6JyVwqNJJlUtlwoTJ3H39pcQOOg2A49tXc9tN1wNw+b8uq7+RQ6+Ai77mUHUIXWN0LL/CwtW9KwPug4dTWlbOZ9/MAiApIYYbL54OnUbJlxlL8Hg8zP99I9NveY52J15DuxOv4cwXljFvYy5uTz3brZtUCKDSoTL1MxtL9rsJN8HLJ4dSYlepqHLXJOEBUmJMPH96exQF0gsd/Gd+Pn9kSNbi8z8yWbBhPy63h4FdE7nnnJEseeZC3r3lZAD6pybUliLz3VvaBVQxNBdftUJYYvDJCLcTvrlcnmEdBsLwa+VeF/gYc9j891Kn3Z8gCIur3Z8a+9ulpX1JQ0NDQ0NDQ0NDQ0NDo01occXCQw89hMVi4cILL+Tiiy9m8uTJ6HRtatnwl5OXl0dWVhbDhw8/0k1pexoajV13xF8Do/7i4+NrKhbAO0rVZCEnYhBdbPNhy9cw4NyA5eP4fcGPZGZls3zhXM46KFJAL61yEBkZwfTzLsPlqj1yO7lDO3an76N719TawRHAWuUiRFUwKCphRoVLzjyV//z7PBRFYf32dHLzi9HrFOzVTjLzy+mUGFmzbpjJP5JSVVVJUvQ+XRIJ2WvkJb8Kuk6ApEHBHtWmiU2Dwj0itbLifzD50caXV1X4+X4JTplj4cx3RMaiJfgCaiHhfv33lmC0HPOSLkFRdzQt+ANlDeV9DCESjKuukKoFc+2kF5V5knQwWiCiQ/3bCIZgKhYaQVVVNm9cz/GDYVuBhz3FKj+nSwKhvFpl1HtW8UEAlh5w00n1oPMOyXe4VExGaBdj4bpT+jNrZTovzd4IwNf3nMpZF54FwOfzlgPQLi6KKoeTD7+Zy/qr5NpWup8kVUItJVAKqTn47neKTs5TRAcJbNbFYZfzVHfUdH33zKYqX+qT3AqGo9BMff+BA2RnZ5OcnExiYiJWq5Xzzz8fi1n6yJy5c6iuriYtLY3+/foyZeo0ouLiSTJG4M7OJj4+nt9+W0pJaRH5RUW0i49l2/5C+ltiKS4uJUwfSlG1nrgQN9sXf8PZ5zi55uqrSE7xe81ce9PtlBQVUlxSQlFxMWX5Nl440cP5/YxckZrLwTdnEHXOq5jjD/enOffy69m8bQcA/bt3Yv7vm2hvMnMW4M7ZwKDTrmZrTmXN8h6Pm+/XZPP9mmwSIkMY3zuRcX0SGd+7HanGIsJKZVuV5mRKDmbiVqGsSmXWLhcfb3KQXqISboLrhpi4e0E1K7LcwHa6xBmZfVUq/TpIf7h2TBwzBkTx07ZyZm0tJzlS7uWfLt/PnA/20jEunA2vXk5ClFwHy7ZmA3DiwAAZMZdDnj3QysSC17i5OTJIK1+H3PVSfXfaKxAaAa4AzxnwJ2rt5VId5JNDqktj/amlfUlDQ0NDQ0NDQ0NDQ0OjTWhxYuGjjz7izDPPJCzsb6S33gibN2/mrLPO4pprrqFjx4507NixRduprq6mutpvMFxeXt5WTRSaU/ofDI0FqwL2lZiQQGLC4SNwld6nwaH5oqVsLYCwhFrfpyR3pHPJ74QYFFbnuFl7EFyucozGw+VM3vzoC9786AtSOnbg+BFDSGqXSLuEOEIcxTz/4Sw2XeohxqxwzYyxPHjnFbjdHrbsOcAp1z0OSGXCyf/5HoABqfGcNrwrecVWPl66l4kPXSft9SUsFB2kjgVLPOz5RSoCep4qUg/B4qqWY2Qv8Rpde18uuwRU2vWT7SYPgz0/i2RS/7Ohff+Gt7nla9jylbTvjDcgqmXXoeAdBRsSUVvC5W/An96PWkKANFgNRgs4qurvi74RueZYr39J4eHXV8VBmUZ0aJ1URyvNm1WPysneuOFHGx1klvlHUOdWqORWyHuDDnYXeUjFP1q7w7/e5/j+qVwxqQ/VLjcX/fcXVBWuPbkfZ43uRn5RGY+++TWvf/kzAKedMISte7J4fJyejpE61PB2KH1Ob1G7AfGvsBbIfHz35q3rO14h4Y33S5NZRlz7ApituQ/7AqWtkfQKkj+7H2VnZ2O328nOzia1c2dJKLjskkQzWejatSv9e3ejsvgQwwcPYP/+/SxavJiIiAjCwsKosleRn38IfWgEvbp3JSc7G0PVAYqKY0nu2p2oqCjyd6whjkMMSdLx6nff8e1339E+yV8N+ekXX+N2Vtdq1wXfws/pLl49JZT2pFP49sm8XTaMwdOvZ8zIYTWDIFwuv8zdzys28fOKTQAsvszC+FQD53R3klVu4V+nj+O6cybjcrl59913+HjZfgrKq/l6VRZfr8riqsFG3pkmyZQXV1Zz+8/b6z1eRh3YHPD8H45an+8rcnLGewdYc3s3YixSKZcYYeCKkbFcMTIWT2kWqB5MlgighIIyOw6nv+1bD8j1P6JnQHKycDd4nJLMbE3Sssa4OcjEQnEGLH1G5k96UnwVHHYIN9dJypn9vjS+980lGCk/DQ0NDQ0NDQ0NDQ0NjT+NFicWLrnkkpp5t9vtlcqprnfZTp2O7tHO6enpTJo0iYsvvpjbb78dvb62BI7H4wm6GuPpp5/m0UebGJXeGpoatdrcgFdjwSqftr61WGSS6vnHv/OoGag73kQ5tBW2/QDDr675zuVyceMd9/FQ1O8QqWOHeRgfvnkOF191A06nk+knT2TEkIHEREVyML+QpX+sZsWaDWTl5PH5dz8dti+7O5IYYNvOPSSMvYziMv9I0t5dk8nIygUkXrd5fyGb94v0kN7YiPlyYm+/oWWwMg8AeZvFk6FBMiWYH9FBpjFdoSQD/ngdZrxR/yoVefCb1+R5wgOQenzw7akPt9M/tRbW9gY4yvnT+1FDtCRgbI6s/3PfiFxzNJQe8PssBFKRJ9OIdi1qrn9frZNC0jnK6Rot859sdlFsV+kWozA+1cDxnfQ4PXDtT1W4PBAWArH4ZZ0cbpXZq/cxe/W+ms8uHNeTm6YO5MpXFvDpb29Q7ZBr8a5/nc6TN13AioU/cc0Q8SZQhvxLfA1aSr5X7iWqU/N/v+8c+6SknDYJgprqBkEtkuzzSa4cieqBxoKoDcgj/dn9KDk5uaZiwSfnk5W+i4M5WbTvmELPHgO4/85bweOiuKSMT76di81mZdvWbYwePRq73UZKp06Eh4ezf68LlysTt8eD2+Vh7fYMsrOyubV/Ir05xKPXnIF7cCwzP/2MgoKCmjZ07tyJPt27MmnCCQzo15e42Fji4mKw2ey8+8XbTCr6hr5xHm6xrOXsOy/gMlsHpp86hciIcCacMJpB/fpQUFRMQXY6BwvLcKseVtvCGM9B7jsxlnv+70lCzP7r6qXLhvDsRYNYvbeYpTvyOXQwj1cmyN8+b61zcPvP1eh1CkqAyYZHlZfTm48bmqTjgn5GzutrRB+TzKiX0kkvdHD+R5m8cmYSafEmDHpJPFXYnYR7PKDAqgzpd69cN4mO8f7n1f58SRh1bR/tPzmHtsq0Xd/WJS2LxBw66MTCwsfAVSUeRb1PD+hjXtmjw5Kz5pZVHLT1IAsNDQ0NDQ0NDQ0NDQ2NZtMq8+Z169Zx//3389tvv+FwOOpdRlGUwyRvjjY+//xzTjjhBF544QU8Hg9vvPEGBQUFGAwG7rnnnsMSDY1x3333cfvtt9e8Ly8vJyXlcAmGFtPUqNXmBryakhnwGToHmpjWWt9MgbkbiWz1azF7+eKbH3j7/U+47QYJJFx6nAXlrDPIyc3jnoefYNb8hcxZsJSiXauIjBAJF6vVxrJVa9myfTeHCoo4mF9A4cFsUpPiiTCtAmDrvkMUl0mERlEUThjSh7joCHZkZDNxYCe+uHsq89fv48fVGdiqXNx+9ij+r/6cl9CchIKPYOI0gean5mgooXF99LIcOXcR7UWTujVYC6SKBCA6xe8NEBjUqc+M+CjhT+9HDdHs/mP264Rbi2oHyXzfhXuTBpX5h68f4k1KlGbL9dLSAKDBLBU09lL//pqDziCeAqqbxZdaGPO+lb0lKntLnNx7fAhpsToKbSr3Lawm0gSPeZ4FoMoQyR/Pn8ZHi3fy8aId5JfZOXt0GkmxYQy740vsDrn3D+2bxhM3ns9JYwYB0K1iJehgfk4EJzdXvqguG2bKNKUFEnaV3ooRc7RMHXawFUBRJcR1qS2LFHhtHG0SLL6gbR3aqh/Vlabz0SU1ldTOnWt9ln2oCGe1k+xDRXTqqQOThcz0XezPziM2LhaTyUhySgoGg54hQ4YwaNAgAD6ZOZP0PbupLCkg2a2yfdt2XG4Xm/cVcmIviA0z8Pprr/LsM//HzM+/4ntvdzpwIJOMvXtY/NtyyvL21Xpe3/LgM3gq76b8o7OJtO6nd/tQvl2cxUtvvNfo731wP5zfMZxOUVZmf/wCzrQpjBvSm/gY6a8mg57jeyVwfK8EsHdEPfA7CnDRgBC2FOp5beXhxuu94iWZcEE/A93jAv6miDTy/RWdGf1yOr/sqqTX07sJMSikRBvJr3TxfxNNXD/URHqxh7wKD5ec2JdrThlYs7rHo1JYJs+Vcpv37zC3S7yDAJIa8S5qCrcT9i2V+Q4DglunTGSZ6DvDW+UT5pUSc8nfE84q/72x5rnUgn50FEqDaWhoaGhoaGhoaGho/NNocWJh48aNjB07FoPBwJQpU/jxxx8ZOHAg7du3Z/369RQUFDB+/Hg61wk6HI1kZWXRvbtIaIwaNYrQ0FCqq6s5ePAgH374Ib/++iudO3cOqnIhJCSEkJBGRsi3lrbW725s1J/P0LmB7e3atYuMjH10yM0lEUQCI4DRI4bRpUtnzvk6iz+uDCPswHKqFjzNXbc+wD0PPwHA0IH9iAj3B7UdTic6RUeYxUxifCwWcyh6ZwXfzl3Im3dK8uHmay7j+GGDSIiJJC46gvJKOx0mXAHAM5ePJT7KzMUT+nDxhD4AVLl1sCm4wxE07QeInnt1pfgnOKz+l9MmUkg++QlV9Y/67DW14W1GJsnUVozIGLVilOnK1+V8tB8A7QeJjjWqBHaMoRIwrRXYaUZi4S8YKfqn96P6cNrkpQLhkf7PfEm1+o6RTx6pJBOclWD0ehz4EjZhcRDplbPyVScE0mmUeGiUZUoFTMqIlrU9rhvkrJXrrCVeBSHh0GUs7PuNAe3h0F0RJL9QQUkVLD3gIi3WxN1jTKzMdnHXGCNdyaKIaP5vVThljk2YDHrOG9uD3bklfL8yA7dHkmoT+ifzxF3XMWpgD39wumAnHXUFVLtUdluGcnLLfrFQniOBT0UHw65q3rqqCrkbZT5R7hWYzJJU0OnAVlI7sRB4bz3aJFhM9cvIHIl+lNS5G9nZ2SQleyvBDGYyC624daF0796dUSNHAuLrEZiwGD96OLqqEvLyi3GoBqKjoyksKiRU8Ur+hEYDEB4ezr8uu5Tvn1tYa78Xn39OvYMAdKZQIk0KWOGBp/9H780VrNmwCafThcvlwuV243Q6cZUfxOXyYKuqZl9OPg8sOsQnM8yMjzxA2kMvUmhTiYkMQ3U7UFW5fDyqiqpC/0R4a2oIA9rpefUkPSd3tfD5FgcxoQpdonWc2MXAoPa6BhM0g5LNfH9FZx6ed4itB6uwOVT2Fjo4sYue64dKNc+Tq01cdfJA/nvVBMptDiLMJnQ6BZ1O4czRPfh6+S4ufPZH1oyYQkf7TjEkN8dAxyEtP5k5a2U7Ee0hZWRw67TrI/4KPgklq7dSSzGA4vQ/c+rzrGkOR1tyT0NDQ0NDQ0NDQ0ND4x9IixMLjz8uuvarVq2id+/e6HQ6ZsyYwcMPP4zdbueOO+7gm2++4f3332+zxv5ZqKrKpk2b+PLLL4mNjeXLL7/EYDBQUVHBmWeeyVlnncXatWv/HubUzQ14NTXqr0bGwCaRFO8/8aqqkpGxD5vNhkPvDbi6qmqt2rVLZ7asXMyjT/+Xq356h8/PDCV088fkmvx6z4P69eL2h5/mYH4h6zdvY3f6/nqbOTRJjr3bFMm15/j12FVV5Y0v51PtcNI/NZ7BaYnB//bWYgiVV1NBeWsBVJfLsj1PaXi58EQZNe52yOj2iPYta5e1ANZ+KPN9ZkB5tpj66o0yot0c7Q2OtjCwc6yOFHXYRJJHZ6h93fvkwKI7NnyuA02cfQkbW5EE1Xzr+PwUAjGFycjeTZ/Dug/bJrHQUiI7QucxsH8ZFqPC3IssjHrPxm8H3FxxHOgUhS/OthBqUKjEwgO6e3n359sO07cHGNevI7dPP47ThnVB1zUg0aGqVG/8hhDgnfVOpt00ueXtBdi7QKYdh0BMavPWLdkHVaVyzsO8VR5Gi1Qq2ErAUsdo+2hLJgTyZ7SrJQlEl53UhHBSOw6WKhovycnJHMrOoGOspdY9Rw2o6kpuH8/4sWP4/Y9VhCWkkJCQQFFRIZ3yvwOKahILAM//90VARtDHxMTw2vNPcvLkE5k9Zx6/LlrCpi3bOHgon5SOSQzp05VnEvejAKHJA7mwZzwXnnNGzbbKyspZv3kraxf/yMZd+4mPjuDy6eMZ0juVkjUvEhNSxCunx3LhF0WUlB9eiQCwMgsmfarw1hmxTO9SxWndDZzWvXl/Xp3UO4KTekfg8ajsK3aQV+pgRGwp4MGKhTEjBzF3TQapl79JcUUVOp1CTHgoseGhGA3yfMwrruTkax5h/XWRGAG6TZL7WUvJWCLTvmeCLsjKzXb9ZOozfVZdklQIi5NrqjRX/laISqrfKD1Yjub+qKGhoaGhoaGhoaGh8Q+hxf9xLl++nNNPP53evXvXfOYLEpjNZl599VVWrFjB/fffz2effdb6lv4J+EZMXnzxxdx33328/PLL9OvXj8jISDweDxaLhRdffJGzzz6btWvXMnTo0CPd5LYnmFF/lYXgqJTgtHc5xVVFt6QY9mVX07n3cbB+sV8SJgCLxcIzjz/Eymmn8uzTZ3L3mBAS171I/0QdW/I9vPnRF4etk5baif69exAZEU54mIVwxc4lg0KhaA76SAm2Z+UV8sOiVXw5fzm/b9gJwJWT+zU4IvSIUpQu0x4nNS67pDNIcqEiD8qyWp5Y8FUrxPeQl70cLAlSqaAYZOobgd8SCaRjdaRofb8rUA6sscoOS5w30OUNpjrtMtLXVuwPsNZXsQAw+FJJLGQskWB3TJfmt91nCl2SAR538EHAukT7/XBGJht4bnIId/1aTecohUfGhxBqUPCo8LD+TvYpnXj0wpGormqcbg8Opxu9XseZo9IaTPC5s9YRUr6PapfKotIUbkxupbfEnl9l2q0FCYrcDTKN6yb3N9/IapO5eQHPY1XrvbEEYsBvVgK/a2CdLqmpdEkIb1D6JjMzi7ysdDomxDL6hInkFZaQkpzM3r176ReZDEUZNXJVHo+HF196maE3i5zROWeezqtvvctFV1x7mOzinr3pVOz5HeXqcA5Vepgw8VxOGDWC1E7JbNq6g3WbtrAnvbaEH8CHs5diCQ3hzqndebRfEef3cnPST09wyGlGSV+ETgcKSq1pxxiLBPirKyBvk/T/FqDTKaTGmohwl2JUPORUqPT830Gszvm1lvN4VIrK7RSV137uWmzZGMvD5HnSdXyL2gDI78heK/N9zwx+vcS+Mj20TfqSz7PEh71EKoKsxa1LLGhoaGhoaGhoaGhoaGgccVqcWCgrK6NrV7+Zn9FopLLSb6ar0+kYP348n3/+eeta+CfiC0L36dOH7t2789lnnxERIYFfX3WC2WwmPDwci+UYChgFEsyov8DR2A6bBIYcNrqndeHQoYNs3riH9lBvYsGHy+XivoXVDEsJYUInB0uubc+N69IIjUqg2uGkuKSUSSeM5l/nzyAu1j9SuKy8goce/g/rVm+kXxos2JLHPW/cwfrtGTXLmIwG7vzXdG447S/Q4G8uLocY90JwwZnIjhKALs2C5GHN319gtcKwqyWREdEOohoZbd9cjpWRooEBYag/OGy0SKVCcyo7TBZ5OW1gCIHwBPm8ocRCTBcJAGYsgfWfwMSHm/9bIpOkrU6baJzHtI0E3Z2jQ7A5ISJE7pduj8qv+xW2de8FwB1nDCZU725w/dLKan78cSk/Ll1H+v4DfHdqOZ2jdDy3wsF1l53XusbZiiBnncx3m9j89X2JhXYDIDRSqhTMkRIIbcuqr78rDSUQnTYoyZJkG/h/s68/KcjxrJtwaaRCKj09nbLyMmwOmDD+BJI6qygKxEaEEfHzu7KQN7Gg0+l4/rln+CJLPn77vY9qqma6pXVlysTxjB4xnKQO7TmQmYVx21fAJjYd8rBj11527Dq8qie1UwpDeiQxuHcXCksq+G7Rag7kFvDYt1sZYjRzek8ju+e+xhznKKrz0qlyeqh2ualyuKl2eah2elBR0esU9IqCQQ+ntXdwdh8jJr1CtUtlXa6bajd0jdGRHKmg18mD1eVW2XGomnVZdtZl21mfbSfa4GDOhXJcL/nOhtUJw3t0YOrwNKYMTqV/agIVNgdFFXZyiio584nvsVY5GdytHbcMFCPnnJAedAyNatm5BziwQq7rxN7yCpZ23sRCWZZ4NNTyKbFDaARUVUBYrPczW4DXj8X//lhL1GloaGhoaGhoaGhoaByDtDixkJiYSElJSc379u3bs2fPnlrLVFVVYbM1YlZ7FKCqKvHx8Tz66KOUl5czb948rr/+et544w2Ki4v54YcfCA0NJSEh4c9vTLCj7QNNgduShsx8faOxDaEyqtdZKZI9RjO7d+0iyek1jHTaQfXUkqzwMWfhMjwqXPOLkVVXhBKrq+Cp4SWc9Ekmu9Ml8D5/0TJWbdzGf594mOSOHfhp/gLu/s+T7Ny9l3emhQImlu0qYP12B4qiMGZAGjPGH8c5k4aS0i4WIpPr/10uFTaJ2TNpJ0J5enDHI2D0dqOERjb83d6FoLolYdC+f9PbikyCHCQZ4WnC9NwYevhnq97yeyv0ni4jRVtiTt0YR2NVSEsIDAhDw8HhYCo7AuWPfH3I129Ub+DdJ4VUX6XHiOsksbDtOxh/rwRRm5PIAIjvLiOlSw9Au3oCgT6JkqYYeL5MC3ZD7noeHufX6L/shyq+2Klj4kPy/oz/LuHEQV3p3aUD3ZIT6RAfxe7MQ2zek83SDbv5asFaHE65jp84MYTOUSFklqlUpp3GlMmTau/X7Qiufb7rfv8yQJV+FZcW3LqB+PwVuh4vfcT3M00W8Wzw0VRFgi8YCmAtbPuAaDD97c/okw1d9w6bJBVcToioU61gCJASsxbW7lO+hFtNk/1tjomJxmazEhsRilKWy9w5P/HHhp0MGzyAM3znJSRa/E+AC847jy+eF4+FaadN5aSJ45gyaSJdu9ZT7TNrHWzdxNip5/HDpEH89sc6cg7mM6BPD4YO7Mvg/r2Jj4sRA3Uv/31AZf22vXz7y++8sWopp3a3MjLByr0fzmLpgYYTaYF8DjyyxMF7p4cytrOB0Z0MpBd72HzIzaxdKiVOEyWuEJbu3snOAhdV3tt9uAm2XC9eLfMzQ7jklP58flwy7brVfn6ERUN74IeZS7BWOQE4sV8Hzu4tCf7bZ+Xx5b8aqX4yNfFcyFwp074z/PewxvD9XRIaBVHJkuA8sAK6nODvD8ZQqcoL91Y0Oe2He/343lsL/deNlmDQ0NDQ0NDQ0NDQ0NA4KmlxYqFPnz7s2rWr5v2YMWP44YcfWLlyJSNHjmTHjh189dVX9OrVq00a2lpcLheqqmI0Gms+85kxezweOnfuzCuvvMIrr7zCzJkz+eKLL+jatSs5OTnMmzfvr0ksHGnq/oMfmGgIi5fgQrlX4sUsow0Hd4mlYqcBbBzmsRBIpxQJ0u/NKWL8Wzr+uDKMVFMmV3Sp5sn8cEYNG8KCJcv4+oefmPPLQnp178b6TVsAiIsKZ3L/OKCESePG0mdKGuMH96RdXCMB/aMBVRVjWZDgSjDBP5+Bc1lW8/dnLYA1Ig/C2DvaNtgYGFxtq8qHI43JIjJfznJQVJEsCm/GNVXrmHiDyy6Hvw+FxXmD1N7zYCsC1+F+BICYOCf2hfxtsHEmjLqx+b8nvqckFgp3Qc9WWSILCT3ktxzcDMCSknZEdTRyotFfmbRg1XZ+Xr6h0c306Z7KNacM5qaYxYCb5Gn38X+po1vfvt1eaZjuJzV/XbcDDm6V+U5j5P4G/nMKtX02mvKhqS+QfqziSw5EeO8FvoBy3QqHxiTTHDZJUiuAJY4eXTqREheG2Qg4K9m1dQPVtmrWrN/MGWleSSFvxUJdPv/kQ0JNjUh/FUqFgrljX6b3OJHpJ5/Y5E9UFIUh/bozpF93VPUyihe+RFzBSj4+vx3/y+5HSIiJEKOBEJPROzWgKAoej4rb48Ht9uCpzMftUVnmcpGXmc20pELSYnWkxQZ6NVXDeDk+BTYod5uIMhuIN9hQjRZOnjIJ9I3/mearfAAIK9mBSR/C75kuMh2tqFYozxEDZhToc0bz10/sK4mF/B1SeVeflJbe5L8+6rtufPfSJqS4jum+pqGhoaGhoaGhoaGhcZTT4sTC1KlTue2228jLy6NDhw7cc889fP/994wZM4bY2FhKSkrweDzcf//9bdneFrF9+3YeffRRcnNz6datG1OmTOGCCy5Ap9PhdrvR6/V4PB6Sk5N55JFHuO2225g7dy5JSUn06dOHzp3bRlbkqKeuVIXTXtu41qfNr0ahGs1gK6FDh47E2JJhJ40mFq6/7hqmTJ7Ib8uWs2z57zy4ZjEvjinjnjEh3PLw04QOmM6mLdu58e4HWf7HatZv2oLZHMrN117B3VN7ELvuRbDD2NEjILYFGvRHgpJ9ElzRGaHzqODWqUksZDe+XH2seFWqFToMhLhe4KiQz9uiYiEwuHqsJBZ8o2EdlTISur4gldMmyQcFCT43pCcfFi8BVqc3IBYW4Zf0MIRKEM3tgMpDYK5jCgySfBh+Ffx0G6z7CIZf0/zfk+A1SS7cLW1pi8RSuz4QEg4ojB+YwvjxUOXRM327fP30DWexZssu9mYXsCfrEJW2atrFRjKgezIDuiVzzsQhjDhhCix8BHLdkDQYXbB9oTGqy2U0NECPFiRRDm0Hd7WMrvaNnob6kwgmb9LA5QAK/Z8dVtkSsJzpGE4uNFTJUFcirTHJNKddKt+8y4UZISwmSvqICvFJndmZu4WBvbtCtVQm0BJZH9XjNzSP69789ZEkQ9zxV8CPm+gUWslzZ3eD5CD8lopqV3DirAJrPh57GdbSQqorSzA4K4hQ7Ohxk2CBBByAVO4oSYObTCoA3HvhOM4e14+V2/YzTbcEcGMZfglLHmnBPcTHzjkyTRneMq+fdn1hz89yLwqsvPJVNapIZUuNTFY9143J1nhi6p+QxNPQ0NDQ0NDQ0NDQ0DjKaXFi4brrruPcc88lJkaCZAMHDmThwoU8+eSTZGRkMGTIEG666SamTp3aZo1tCbt372b06NFMmzaNyZMns3DhQp577jl++eUXPvjgA/R6PQ6HA5PJBIjZsMVi4corrzyi7W4RrR3FVzdgZDRLUgEXlOZAVJJUKhjMYrvgtJNfUoqij/XvvxHS0tJIS0vj8n9dJh/MuxfWvU/o0segQy8G9u/Db3O/5cvvZrNtx26uv/ISkjq0h50/gr1U1rE00+zxz5KNCoZ9v8k0eagYXwdDSxML1gJY+77Mj7wRjEZwuSCsHrmklnCsGTY7rP6R6SHh/sTCYcvZJPEAhwfA6h4Tp10SCL6AmbVIgl+uKgnOlWaKz0J9iQWA3tNgydOSfNjxE3Qa2bzfFNMF9EZJBlbk+a+l1tKIJNhN555I6IUTAJGVs9qrCbfUueayVoqfgc4gCZO2SHikL5YgdGxXiO/W/PV9Mkjt+tU25a7vOvedd49Lzqk5qv6AZuByjQU8j8Xqn+ZiNIMxXBJ2gYnskAgwhJLUcyind+5LaKgZ1pfK9/VI7DVJaZYcb50xeGm7+giNgj7TYfMXsPMn6DBA+npzMIZCdCd00RDRAWrSvWW5ci07rOC0yjQkonbCqwm6dYyjm+Eg7HRDWDzHnX5dUEmJelFVf2Kh12kt24bPwLlor9/7xefBpDNIZVhTf6P4+pPTdrjE2LH2PNLQ0NDQ0NDQ0NDQ0Pib0uLEgtFopF27drU+Gz16NHPmzGl1o9oKVVX5+OOPmTx5Mp988gkAd955Jx988AFvvfUW5513Hl9++WVNUuGDDz5g0qRJpKQchSbAwdDWo/hMYVKpUJojmtq2Ym9AyFvZEBJJbOe+lO21yvLVlc0L5B9/GxTshMwV8OPNcOHXKKGRnH/W9NrL2UsBVXTPm9KdV1UozhD99QMrxP9g3MPAXxzAq8iDzFUy32Vc8OuFeYNJ5TkysjNQ670x/nhNAjAdBkKPk0Ryx4SMktXpW389HCuGzT58fUVnaDzgaLJIUkjh8CDWYSNtAyp+HDbpL84qkXCxxEtioTQLEvvUvy+9CQb/C357Fla8Ip4JzUmk6Q0QmyZ9aut3MOhCsMQGv34rURTl8KSCqxrWeM13+57ZNskO1QObv5b5llQrAOR6TZ87DKw/iVAXXyDTZ0TbUEAzmIDnsVj901zq+C3U4B3Rnpramf37D5DaqSPKSqt/neaSv0OmUcktD7T76DkV9v4qkmbrP4F+ZzUoz9QsFEVM3g0hQAv7q8cFWatlvu+ZrfutBTuk2k4fAmktMEUHSPBKYObvkGeyvUQStKZwud/WlyxoiPr+rjnWnkcaGhoaGhoaGhoaGhp/U4KMWv49URSFnJwcDh48WPOZxWLhiiuu4JZbbmHPnj3cd999AKxYsYKnnnqK+++/H7c7OHPGow6TxT9aus226U0u6Aze4ECFP3iqM9Cxczf6DD9R/smvPCSjOYNFp4epz4uxcVkW/PpQ/YmJ0EiRk1E9MmJU9Ry+jK0Ytn0Pc2+HX+6H3fOgugzKsjCufBldMOaTbUV5Hix9VqRWYrpIgDhYdsyWaWRHJJodBE47bJgp86NvlmCuz+A20Jy4wfW9QZ4mKk6OKYLtK0YLxHSSYFhTgSyTxZ9csBWB6pJX+UFI9Jopr/+o8W0cdzGEJUhy7Ltrmu+10XmMTLNXw5zbYNkLUi3gOUL3tL0LpZomLAH6n9M221zyDBxYLqPQ+85o/voeF6Qvkvm0CU2f18AKg7D4wyWxAjFaGv8e/pz79N8Rn8+C7/4U4PHTrVs3oqKiWLlqDVVmrxSPryKrOUR1lGnJPsha1br2Gkww+DJAgUNbpLooffGR61uB+PqZIQT6nN66be1bJtPU470yaC3A93dAWLzI8/ke675pYLKgKbT+ouElMzOT9evXs2PHjjbZ3o4dO8jMzGyTbWloaGhoaGhoaGj8UzlmEwuqN0A9ePBg3G43O3furPnObDZzzjnnMHnyZBYvXkxJSQmjR4/m7rvv5rHHHkOvb8QI8mgmmKBWSzCFScDUYBS9eUAxmVHCYmUa0R4GnC/LLnwc3M7gtx0aDVNfkMDB3l9h02eHL6M3wZDLZPR+7kbY5TVtdVZJsOiP12Hxk5J0KM8VOZhOo2H4dWAIQZ+/lavVerbbFqgeGYWesRTWfgC/PAS/PCgG11HJcPwtwUu/lBwQfX2ACfcHv96O2ZL0iUoRI1qPS46NMTS4gExzgjzHCqaw4PtKcxIvvuCoirfSwQDmcOh/vlzH+5fDgT8aXt8cDRd/K4mM8hxJLuQ3I4jSdRyMuUVGDKsq5G2E5S/CnNthz69ynfxVVBbAviUyP+wq74jsVrJ9Nqx+S+ZPfbZ5STsfB1ZIItIcK4bXTZ3XxvpHS5Jyvvv0P7VawUdAIgGoSVZjNKMoCjt37qCispJVYZPl+z9ehaL05u2jfX+pLABY/BRUlbeuzSkjYOztEN1ZEsc7ZsFvzzW/XW1J3mYxbQfoNa1hqbVgyfR6l/iSlM2lPBdWvCzzI/8NZXneLwz+hHdzkgWt/bvG10cd1patr3FUkJmZSe/evRkyZAgXX3wxFouF+Pj4Fm/PbLFw8cUX07t3by25oKGhoaGhoaGhodEKjtnEguINyp566qns2bOHZ599loqKiprvIyMjufXWW1mzZg2LFsno1auvvpouXf4mxsB/Fg7r4f+EO6wS/HG5IDxe5q3F4PAGhExmcrqcjdMYCcXpsOGT5u2zfX844S6ZX/oMHNx6+DLxPWDAuTKfvhBWvQkL/gObv5J9gug6j7gOZrwtwdW0CeI3AJypzmOyZ2nz2lUf9lIJ4uyYDSv+B/PvlXas/0jkl8pzABXiusEJd0JIZHDbVVWpcvA4RTqp+0nBt2ntBzIdfKlUgVRVNL58XeoL8vwTqxgaorIQKvNrkmqNHhtfcDQsDqJTID5N5K2SB8Nxl8gyv7/U+P5iUuGS78WM2V4CP/zbL6sVDB2HSGLq5P+DHqeIXru9BDIWSRB03QdwaOufO9JaVWH797KPpMGQ0ky/iPrIWuUPWB5/W8uqFUCSEwDdJgJK0wm1xoKgwSbltP50OEaz11vAVu/x69WrN+Hh4UT0n4ra+XhZ9ud7m++bM/LfkqizFsr131rfnagUeb4MvECSQ5UHYeVrft+Ov5LyPEkYglQYxHVt3faqKyVRAc33ePGx+Am5zjsOhS4ngsEgie7QCP8ywSYL2qLf/BMT58cghYWF2Gw2Zs6cybp169ixYwedOrXcN2X9uvXMnDkTm81GYWFhG7ZUQ0NDQ0NDQ0ND459FK0WHj37S0tL46quvOOWUU7BYLDzyyCM1o5xMJhPHHXcccXHNNAQ+mqlr4NxcQ2enXcxfrcUigQR+jwVLNBjMIm0QONLUaSevuJzq5DPouu9j+O156H9u82QUBl0M2Wtg7wIZXX3RNyKBFEjyMBlpvOcXKNwjn4W3k0Bqx8HQrv/h200ZjqvPWRi2f8vN6nuoRclgbEE+rTwXNn1evzSNIUQkj2K7+l/N1d7OWCwjRXVGmPRo8NUKB7fKcdMZ4LiL5DNfAMdZFZznRqBete96cdpkhH1b+XX8nVHqTBvzMqmrHR/4/vhbJemW+YdULXQe1fA+wxJg+uuSuMpeIxJfEx6EnqcE3+7IJBh0AfQ/G3LWwe6fJQlXuFtepnAJ+ncYKG6ybWGq7OPQVijYJYmutjBsLtoLCx+VCqF+Z4nkV0vwuEQmDcSfobqyad+WxvTcgzWRbWv/m2MBk8VftWAtEpNzvRFUD4rRwojhwxkxfLjIirW/VeS90hfBrjnQtRneGsZQmPgwfH+trJ86FnpMaV3bFZ1UL7TrD1u/FrmxDR/Ld0mDWrftYHFYYfsPoLolkd2pkftJsGSvke1Fd5aKu+aSux42fynzJz8N0Ulec3Tv9031NR9t+RzSjJ6PKXr37s3gwYNbvZ2UlGQqynq3QYs0NDQ0NDQ0NDQ0/tkc84kFgAkTJvD1119zzjnnkJubyznnnMOAAQP45JNPyM7OJi0t7Ug3se2oG8BqbkDLaJakgsHoTxwYjOByQlQCmMxSqWArltH1LjvojHSIi+agZwrOwsUYK7JEtmL8vcG3W1Fg8hMi+1KeDb8+DKe9eHhAsvsUCT5VV0LH4yAyucmgpavPWazafoAxrEVd8QKMuObwpEVDqB7Y9xvs/NE7wluRIGxMZwm+RHeGhO7BmyzXh9MuI2lBJJ9im1E1s85brdBrKpgiwFYiAXBfUsdZ1byAiu96UdF0rX34Rtb6jkVjgSqHze9BUvf7qGSpWlj7nlQtNJZYABkNPfUFWPS4JNMWPgq2QknCNSdQrzfK6OOolP9n77zj2yqv//+WLcmWPOLEziQJIwPCCgkbyt57lDAKFAqle1C6oKVfSgcUun5QoIVCoWWVUVr23ntvQhIgkB1iO4ljSbZkW78/jh7fq+sr6UqWbNk+79dLL62re59773Puo3vOcz5HnLTLXpVAQ7wdPn1GHjXjYOK2MGG29/VmoqsTPrhbXm+2T/8LNrevgYfPk+M6aY44LAsNVBgZpPAYCVR6rWOSCa9FZNW56Y6pSeKLSz/tTrg7n5s2k2D1WzfBIz+HM/fMbzvjZsH2X5FC4s/+ESbNlqB0fwmGxaYr/GJX/Q0uJHukyHvHennd++iWTLmeLnnd0y0TAOLtIum1xWHFCQwaGaRCghTJJDz8c3m99XEix2auhfkGBYo5DhkbLWbgVFEURVEURVEURQFGSGAB4IgjjuCFF17gnHPO4dxzz8Xv9xMIBHjwwQeZMmXKYDcvP7LdIDsdWPk6tII1MHpy39/U2RxoVbUyu7SnC7rEITRp4iQmTdkE6s+Fe74tUkU7fAXqJmTelnNWf6gBjv07/Oso+OgRkVLZ4QyYtm/6ctP3c19fZwYJoEQPv6/4Jhv1XMAmHcvg3TtFKqYy6L68obpenLrLXpP3m3wB9j5PnJJ2/NXZ12PIpKn+8tWwYRXUTYLdvpu7XYZ4VPYFYLuTxaHrD4ojpiqVtVBVl/n3mYitFymfmsL1iwFvkiP9lSXpzzq9ZvM4ncf290YmLBCSRzwqjr941N1BuvPXrayF5W/AJll0zM2M4S9eC0/8Bl65Bl68Ugo7H/oHqB4l33vtf6NTAastj4LuLvjsealrsuRFiHwu2UIfPQaNM6RWwyZ7uPeBRBI+SGnV147vO4q8c7tIL4UbYfaX5NkLbteKeLtIQUWaZVb2F/8hmRZeqAj0/Wx+qqjsFofL9SafwJtXx6Rb/3MLQCST3vrgsHGIuuxHsEYe9uPg7M/BMETXifTQR4/A+mX4X7oc2FG+r/BLZkwm6lKBrb1+KtfylW/BkxdLX7Jn1VV4vO4au7Oz3y+llsnHj0lwYdRGYkdemHOK9IVPnoaXrsqvYHuwRiTPGmySMH6PWQGVLvaxJFX/ZdM9rO+9rm/+/ySrJBAWqbKuTgmKRNdCsNM92JoJ87+ltj53LZJSjCGKoiiKoiiKoiiKJ0ZMYAGkkPM999xDa2sr7e3tTJgwoV/F38oS46gBueH2OqPWjpsjtc92TMBCupDPOAy2OhreuEFmbz59iWQd5MOk7WC/X0jGwuO/EpmjbMEJj8R8IX5Z8UOu9/8fvtaPZab/jlkkWpa+LHUTOjeIo2v374tDtthOvtbFMoMdYN+feZeKAHj3DnG8NmwC9RtLJkkiBjW2wIfd8Z3JQWOWMecw5OI4Gy7YHZjFkKexF6ANhKyMnmCG81g9WqR83r5VslSyBRYMvgqRcmmYCo9dCAsehNXvi1RSoTOjK/0SPNhsL8n++fRZCTIsfwNaFsnj1etg/NayzNRdvBWF3bBSpGpAZnL3p2BzTxfc8z1YM1+CE1+8znumUab1LXhAXm+WClaGx4hjerBQiSTBPuYke1y+r4bwWNjrPLj/B1S+fCWTkhezwjfR+zYq/JIB9M/DZXy6+Tg49pp0p3yhVFSKMx0kuPDUxTDny97sc9W7Uq9n1bvyPjRaZLoCYXHu+4MSbO7ukveVQQmaVQYkeNEfm7CzbqlkS1T4YfJO+f02EYNHL5DXc06VgKM9G8VcI70GFgJhLW6uKIqiKIqiKIoyBBhRgQWQos319UW6ER9q5FtvIRvGERRpFqdBhd+a3bz3uXDTF+GtW2Cnr8O4LfJb9w5nigb9wgdFF/uEmwqbee9gpW88iX0uIPjIj+HT58QhP9NRJDkRgzf+BZ89J+/HzYL9f1kc55OTZFIyIroTMjt8+gH5/fa1f8jrLY+BQFDaHnL0bbuGebRFpCVqGtOdNmYZu8N9uEq22B25xdhX4zwzAaFcgbxgSGzivf9IxsCnz3sLLgBsfzpM3A7u/pY4AG88RoJwWxzRv4BXVa3Ubtj8EMk0WPAgLH4aPv8AVr8rjxevEGdh0+ZUjpnBlsnJfMwmWOLpSJ98419yfCdsK8VbCyXZI47KxU9LYO/Yvxem+W7n0+fFBqpHwfgtJVvBU90Z23Wz2M7O4W5vhZKIWgG6aCsk2iVTZe6X4cN78X38BN/mn/y84qf5rXfMpjKe/Pcb0LxAsuOO/Itko/UXZ3AhlyxSZI1k0Kx6W977qyQrY84p7v0str7/bcyGkUGaODu/+kggmRZtyyU7ZOdvymemTyeiktlYU2e9L9b/EEVRFEVRFEVRFGVQ6YcwvDLksDtVi0mszXqdiELdZNFWT/aI4zxffD44/E+iCb9uiczSLpLcQc9GO4g8C8Dbt0iRWcOaBfDIzySo4POJI/eYIs1odWPRIxLgqAyIgzgf5/Dy16Xt/mrY6UwJvNSkZl/bsx4CIXHqdKyVIFC8XRzh8Ygc27VLZDmjYx0IWzUFhiPBcHH3NVgj63A6AhNRCeYkounvASZsZRXaNrU1vDJpO/jKgxIE6ElIZs8DP4LOtpw/9URoNMw6HA79PRx3vejSj0nVoGlfDZ8+Q+CN6/hzz4X8t+dMgo/9TDIbPnlKMh5WvSPHd+5phQc74lG4+zvwzr8Bn8wyn1iE2g8f/E+ep+0HPUmZBW9IRMU+Ei7XxlJdN2H421uhxGMpSbGYBENBnn0+OPgSkpVBduAdduO1/Nc9aQ58+R7pUx3r4I7TJEhbjDHGBBem7S/j35v/ghVvpS8Tb5fA4lMXp4IKPpHm+tLtIpU2WDP1P0sFFjb2GOg0tK2EFy6X13v9FLrj1neJmGRY2IvYl9KeFEVRFEVRFEVRlAFFAwsjCbtT1Y1szrVs2GfJtzdDIgLbnwG+SnGeL3w4/7ZWj4JjrhbJh48fhw/vz38dmZhxkMxQTfbITOx1S2T2+FO/lf2vGQv7nC9OnsoSJfV0tMGTF8nrHb8KozfJ7/cmW2Hzw2Qmd02T9bA7poI1cr6r6sXpFay1Ztl3touTC0aOc7NYjtx4JBWoiWT4PuUYjbSmskVaLUcpwG7fF4fbZ8/3dTzmonqUBLwO+JVlH7d/OXNbCqV2HGxzHBx5uTg9D/wtzD2N7sm70EIDlfRQsf4z+OQJePXvIh0GUki2UPmyni74zxmw6GE5Pof+oW9WUSEsfsYqKD3zYBg1Mb0POJ2d9mthruumkj+5xppgSMaPYEgCpuGxct2KtED9RLrnfAWA7/dcS+Ub/5CaP/lQNx5Ouk1kyUwA3DjH+4sJLkze0QoutHws33Wsh6d+J9JjyR4YOwtO+Bfs+3Oxt8Fiw0pbYGG3/H775G/kPE7ZCTY/NF2aKRDqG+weaHsyfa3Y10dFURRFURRFURRFAwsjilxO1UJmEjqdBD5kFv3EbWHHM+Wz/34DPv8w//ZO2g52+Ya8NtJExcDnk5nYjdPF2fDUxeIYTSZh6m7iQG2aWbztOUkm4eHzxJkzagrs/I38fr/8dSlsDTD31NzLB0KS0dA4HUZPlWBDICRyFybQoORHIib1N1oWSdaH02llHKM+JHgTXSczeYOpoE5FwAoA5RvIA+nDO5wBp/5XAmFrP4Xn/1//9ikbwRqxx22PJ7HX+Xyp4kpOrvgL8V3OltnW42aJ3TdsDLOOKnw7r14r+vfBWjj+RqnZ0h86N0hGx03HSlZH4wwJKjqvgc7rmLP2gVtWipKZXIGDXGNNICySbUZaLBiSIsDxdojH6NrtbBYzhQY2EHj0Z3DFTpI5k0+AwV8Fh/xegsgAb90E792V335moqJSZI0mzZEAwrt3QE+3ZKjF2yHcBLt8S4LXjdOLs81C2bASbj9N2jVmM6mr4pXlb8C7t8vrgy627MiMKcHUebTb1UDLIGmGhKIoiqIoiqIoSskYcTUWlAwY3WMf+RWDdGrK1zRBZZU4gva/QApSLnkRbj8Vznoyf+3mKbsAf4E1C/P7XS4qg7DHj6TA9NrF8tnkHcXR4ytxvO3NG2HRo+JcPuL/5efYT/bAIz+X11sfJ23OhSnobWbZm0LO6ijNjJsOuL0QdiAk2Qg9PVbWhyneDJY+fDAks5V7uq3PWxfDyreknkG4UWb6Fsqk7eDA30gtknfvkDod/VmfV3w+mmmkZ6MxsHGqDyaT/av10LwQnr9MXu/3C5jcjxoNIFkK934f1i+V91sdC7t+1/0a5LyO2WsflLLGwnAlV1Fqr7UlElGxs461IhnXBdSGIBDkuxW/4aDkU3yn5iF8G1bCQ+dKcOH0e8WuvODzSQC8KwbP/hGeuRRGbQRTds57l/uuuwK2nif9un0VfPIkLH1Jvtvi8NIGr71iggrrl0qQ+9i/S1DEC8kkPJoKymz9Rdhorry2BxF6i9qbeguxdCf/QAQZtI6JoiiKoiiKoihKydCMBUWIR8EfzF18NhNmhiqIdEUgJM77edeLVM/aT+HxX+W/3vFbyvO6z8QpUUyCNaIJvdH2Umh2pwEIKqx8B566RF7v/VOYsE1+v3/737DybclAOOii/BydxqmT6TgWKoU1HHGb5Wo/fsEaaNgoNZO9VgJyRurIrg8fCEP1aPk+ug5aPoH4BilMDiIbVNHP+O6UnWGbefL68V8O3szc/gQVerrgwZ9IVsdm+8BWXyx8XZ3t8OC5kqWwfqkUnT7ictjzp9A0zdv1zZ7dpTOec+O8duSSu/EqSRaPSeHmnm7ADw2Ten+T8AW4r+IAOr/+EhxyiWTutCyCO76SrvPvhV2+LTI+yW4JULQuzu/3mQiGpbg6iJxf5waRpcv3ul8KnEGFeTdA/STvv//gf5JdFAjDfr/s+73beGOkkYIZ7KoUY5BmHCmKoiiKoiiKopQMDSwoQn91jzM538KNcERqFvLr18PiZ/Nbb804CI2RmfpGp7qYBGtg97Nh9++LNEYp6WiDe8+WorszDoQ5HmSM0n6/Hp5K1WXY44d9Nblz6f676V2n/V4dqL242YPz+AVrpLD36KnSz40mvF0fHlKBtlqorktJhQVFQgRgy37IBtnZ/WyomwhtK+D5PxdnnQPJWzdLdlNVPRz028KDFJ8+B9fuK7r2IE7dL/4DJu8M4dEip7N+ef6OS3uBeq+MpECd89pRzKLUXYmUrU1yX5+/SmTBTrlLgnxLXoQHf5pfMWafD/b5uRR0jrfD/T+A2Lr+tx0kg6hhY3qrUE/dxXtWQKnYsKp/QYVEFB6/UF7v9j33miqBkAR4jPwRpKSRUv3C7RqrY5CiKIqiKIqiKMqQQgMLitBfR1C2wMSme8KcU+T1fWdb0jFe8Plg7ObyunlBYW0rB0xdhbZlksFx0EX5O0+f/QNEW0QnfvszrM9NQCHamj0jIViTfeZmsYtqlqNj1Wub3OzBfvwizfD5QitLJ1iTrglvXieiqcyFanGQ1k2E9jXQuV6CEfkWSs1EsAb2/6W8fvcOWPpKcdY7ELR+YhUj3+//JMMgX0yWwi3Hw/plUDcJ5v0TDrhQpI8C1RBrhQ0rxIYSHXJuoq3e+mcoD3k4QzwqdR3WLi0vGygFJbl2tIhEUd14CS5nG5sSMQlCH/onyTp78yZ45er8tlkZlJoL9RtB23J48Mf5Zz644auQIuj45PXUXfu/zv6wYRXc9bXCgwoAL/1VjlH9ZKkV4YaROPIH3cck12usFkpXFEVRFEVRFEUZSmiNhXLCbYalm9Z7fyRHCsWtHV6+Mxx0MXz0BKxbAo//Bg77o6U7n4txs2QWavMicf5kwm3WJECiG1iRWmY8BDzG07y2z1+de5k3bHUVjrk6c1tBHDFOPp8Pr10vr/f+WXofMJITyWRhThn7+atpyu+32cilsz4YFKtNkVbo7pBnc8zc7DLRIfIqXR3inK7ww8ePy3ebHyLPPV3u2/B5nNVsaqLMOAC2+xK8dQs8dgGcfEf+DkMQma1sJHqA1Az++om57Smb3n13Ap7+nRyD6fuLVnuu65vT3j59Du7+tlVLYdr+sONZMG4LSHTKo6dFtlVZJcc1kCqi3dOduy/kq9Fu2h8Mi3PcH0j9voykWJLJ9PGmP+OMz5e7Zks84l1P31dh2U0SK0vILlNnf13hF7kkkjBxO9j9B/DcH+HRC6BpC9hsL2/7keyB8Bg47h9w83FSB+WZ38Ohvy9MIq9uYvrrfc6XYzVuy/TlujPYvxOvdSOy1ezZsFLqsbQtkyyKL90hNSWyUeHY97YV8MLl8nqfn0FV6ry7/X8JVFuBVfN92tjl6Hd2KUazfK7/F177qZcMlnyyXBRFURRFURRFURTNWCh7ii0NUOgs8mztyNXGeAS6OuHg38n7166DxU973/bYLeR5zRDNWFj1rjioQIrSTpyd3++TSXjoPHG0TT9ApDQSHdb3ZpZnbZPlBM3n/Jrz195c3AyDcpx9Wqw21YyBymp5Njbl1v+NHEgiBu2rRQ7rw/vlu1lH9K8Nbux1rjgMN6yEf39JnIDlzKvXwur3oWpUYVk8q94VJ7CZfX3Yn0WapX6C2EvHOkh2WVJGNY0QaoD1K+WcVFTm7guFarQHa2D0FJF3KicbcKPUEjT5rt/IjtU09j328YhknRjM6wq/2OPWx8OWR0ug4D9n5j9uNE6HI6+QANQH/4NHfi7r6i8TtobxW/V/PYXirKngJajgxhO/lmvexDkwdXeRFlu3XIJokRb5zmScxHPURfLSL1QeSVEURVEURVEUpWzRwEK5U2znbCE36WbGYHfcvR32NvY6WW06/2ZG/ca7WRI+93w3cy0AJyaw0LygOA6egaSjDe77gdRV2PwQ2P4r+a9j/r3w6bMyU3v/C1MzeG2ztnMVms0VTDLnz0dxHTjF1FkvFsVqU00TjJspz+aYu8l99MqBBKAyAJ89L87u8JjiySDZqaqFE2+S2g/rlpR3cGHNAnjpKnm97/l9a4bkIhGDu86yCj5/+V7Ydh5s+gVonAZdXSkZpBqoGQ21Y8EfkgyGzjbYsFoc2KXsn8Ea2W45ZSu4UeogYL7rzybbZsYTg3ld0ySPMVMkSDVlFznPt52SHojwwiZfgEP/IJkK79xevODCYOFWqLmQoMLaT+V4gNhshU/Glg3LRWYs2SXyYutWSJH66Fr5LFOAwUu/KMcAtaIoiqIoiqIoigJoYKH8KbZztpCb9HhU5HlMG5xOalOIMZ5yYDudrPaitwdcKBrW65bA47/ytv3Rm4hTPRGDdUu9tzvZM7jSBsmkOKTalktdhUP/kP+M7Ng6eOR8eb3b92Dc5jIrN9P5K6QgpuljNU19f1uOdRLKDXPM7RIk8aj01bWp/uqrFCmeT56S95sfKr8pBfUbwYk3l3dwoTshNUd6EiJdtMXh+a/jsV9C80KpyXDQ7yBQle7ADFSJTEvjZiJF05VyQIcb5NpQVZu5HslIo9RBQBMo6O/6jaRSd8L6rE+R9bBIgJ14s2TvrPsM7jwz/3oJWx45PIILbkGFQiTSAN5IFUXfdC+YtrfUvvBVisQYQGyD2JTfL/YWHg0+v1XI3omXflfMvqnjmaIoiqIoiqIoSlHRwMJIwx4E8HpzbXdWZ3JSm8+TiNMnHrMyEuyzT6vqpL4CwBv/hAUP5t5+RaXIU4AUpnVz7nTFYdV74gB67Jdw8/Fw2Ry4ek8qX/kro5Jt3va1mLz8V/joMamrcNifoXpUfr/vTsiM7Lbl4hDa8avWd/EMDpL+FMR0/jYRhbXLZNbvSJKh8OJ8MssYCaRg2BGQiUmR8kSqUHm4URzgnzwh70tdwNUZXLjxGHjzxvJwpC97Vfr15x+ITex/Qf4Bt7f/Da/+XV4feQU0bJTuwIzHpB6LP/U+uk6cnSDZIuNnpfq6zeGpTsfywxSmN2NJIiYSV9F11jLhxvRrljmH4UY46d8QrIUlL8DDP89/+1selR5cuOlYkUcq9+thT5dkRz36f2L7xQgqfP6h1AoC2Gae2FgwJOsL1Io0nN8vGUEVfmiYlJKyaixtVlA+qKySoiiKoiiKoihKUdHizSORfAvY2gsqmt87ndQm6FBbny4N4yZlMfNgkQR6/Xr47zfEAbrJF7K3YfNDYPV78ps1C2DHM6HlY3FOrpkvr92K4LbHCLx4OTcR4HHf7viafwC1o3Pvc395+Wp44S/yeu+fir52PiR74O7vwMdPiHP0sD8jWkXI8V2/0nKU5jqHzvPnlXjUmnlaVyaOoYHAi32YZWJtUpQ5Hk13UgdC4tBMYn0ej0LNOIithYd/JoGGTXYv3X6Y4MJtX4a1i+HZP8Kr10mB521PtIo+DxSffwDP/gk+e07e+yrhgF9Dzdj81vPqdfDgj+X1zt+E6fv1DQYEQ9Y5sc+grslS2LYci42PdIzskRlLAiEpmJ7MUPDYeQ7HzYJjr4F/nyyB7I13g62Oya8NWx4F+OChn0pNj1XvwnN/hhkHSi2HcVvmHxgrBT1dsPx1qV/00WNynTGM3hS+eG3hQYWFj0gwMN4OozeDCduK5FEwJadUXSfZR/EOsbV4B2QyoVzFmEtJvoXYFUVRFEVRFEVRlKxoYGGkYW7qfeTvWMzmELA7r5NJa2Z0pDnlZHUEGPb/Jaz9TGZw33GaFJLcaG7mbc89TbIdnrxIZp8ueaHvMqEGcfL0PraAz+fT89oNBFe/yyHJp+Dmp2DSXNjmOJk1XlGZ3zHIRecGKUj7yjXyfvfvw3Yn57cOU6z5vTtl5ucxV0vBZ1NXIZFy3nQlSuvwN86XukFwAA0mXpxPZpmaMdZ75/dun51+H9x6Eix/DW49UWZDzz6huO23U78RfOUBeP9/Us9g/VJ5fv0G2PZ4kSAKjZYCw5WB0rRh7WfwwmWw4AF5X+GHrY+DHc6Ehin5reulq+DpS+X1Tl+DA38tr+MxS8vdXIv8tjokiRgEK9Pf93TLc1WdfKZOx/LDBIZMcC5YI9kpBIHP+y5vziHI2BMMw/T9YedvSAbZ/eeIU7xxWn7t2PJICUq8fxe8fatkAX3wP3k0zoCtvyi25K8qfF8LwQQTPnpMZNY61lnfhUbD9ANg5kEwZafCpNeSSXjxKsl8IAkb7w77XCAZJJUp+zLnqLpOggnrVqSCCzH3cWMwA3iFBtkVRVEURVEURVEUVzSwMNIw9RIq/PnfYHt1CARr5GGvt+AMLHQn4NDfw93fhqUvwW0nw6n/tQo1O/H5xHkzcTupzdC2HMZungogzJLfNUztO3O0cTrxaYdw7hW3c0zyIfbkVXwr3oAVb4jTdetjRe++v8VV1yyEt2+RQstm9vTu3xeHVr4883sJTuCDw/8Msw5L/94EGOpqSuskGU5OmHxmyXrZb9dlXOp5xKOWYzSYkpo65Q64+7vw4X1w39nQ+olktfhKpEznr5LgxYwDxAH56nXQskiCC6/fYFuuWqSJqurlUV1nvR41GTbaPj+HbLRFpFM+vM/KJtricNjt+/kHFJJJsQtT7Hnnb8KBvxF7T6SOsQ8I1/X9bSAkDxNIMO/twU/TL4ZLfx8q5LJLM5Y4P2uoAj7ou7w5h2bsiUeh0i9BrCUvwMq34a6vwukPpGcYeaGmSYJZW30RVr4pwbqPnxBbevp3Is213Smw1bGlDU4le2DFm7DwYfjkyfRgQnWDZFL0J5hg6I5LgPudf8v72SfB4X+CDWtE4i1QLcGDXptK7XPDJEsmyQ0N4CmKoiiKoiiKogwbNLAw0ujPTb3X38YjltPOWdTWTucGOPC38MAPYeVbcMuJcNrdUnAzE43T4Ph/un+XSY7C52O+bybzfTPZ+bQJVL9xrQQA2paLXNGr18G0fWHitjB+G5GL8CJt0Z2Axc/A/Htk1qhhzDTY6asik5Evr10PT18ir3c4S4pkOjGz4X1FzrYwlEKqYjDlL2BgZ8nagwlpUi6p7fqr4dir4alL4IXL5fHxE1A3wXJ6+6tFAsv+PhCCjXaQQFohVPhFhmzGQfDpsyIN07wQOtuBJHR1QHsHtK/OvI7qBgIT53B0z2Te8c2C5Dj6lOqJt0sNhHfvlHUCbLInfOEHhbU92SN1U0zh2D1+LA7eaKu8j66VGdKBunStfSOFZA8kmGuR+SzaKuenvdmyK2f/sPfd/gYglXRKZZf2saqjTYJOO30bHv0ZrH5fHOaH/6mwYJ7PJ1lvk+bCHj+STJy3bob2VZKZ88YNMPtLMHnH4vWXZFJsdeHDsOjhdButboDN9hFJsI22l8y9/hJtFemjpS/JMTrg17Dz12Xfw2MgEcp8fcsWoEtEJeNusMYBRVEURVEURVEUpahoYGGkke+sXKdD2MtvjbOhwi+zPDNRGZDH0X+FO78Caz6EG44QB+R2XyqNrETdRMkimHsaLHpEikGvWyKzqj+8T5YJj5EAw4RtYcI20DRTZr0a2tfA/LslOBFtkc98lSK5sV3KoVSI5vb7/4VHfyGvtz0JtjjEdRJ83uTr1C+Fs2+w9esHcpZsIiZBs2ir9CW34JqvAvY5D8ZsCg/8WOqHrH4v97p9lXDM32Tmf6H4fLDpnvIAmckfj0DHeinU3dkmztje5/WSkbPyLehYR+XiJ/kmQBKSt9SKTNfE2WIvq96GN2+W34JkFO11rsyeLoSeLnjwJ/DeXYBPrg1zT03VTQhAbIMEFTo3SPHmRKp/xWN9MxTcApyBEHR1gi+euX/a+64GFoqL0y7tAaH+HGv7WNUdh9g6GDsddj8HHv25ZJdF1sBRV/bPEV9dD7NPlGy6hQ9KBtD6pSK79MYNEsjb/FDJ+imEDSth0aOSabR2sfV5sAY221cyEzbavn+ZCU7WLIA7T5dxMVgLX7wOZuwv39kzFExGgj1g5yQRleugPYOhMqh1TBTPLFmyhObmZubPnz/YTVEURVEURVEUxQUNLJQTxS4A6XV9SRfvtXHwJKKFOQJibZb+fCZ6ukSnuWGyyCD980iRlXj4PMkk2OMHMOdUKXLrBbfizQC+but1sB5GjZPXk3eAvc+VrIOPn4SlL8OKt8QRsvhpeYDMFp+4HUzeHloXy6zRZGqdNeMkmDDnZAlaZMOu+e5k0aNw3w/k9Q5niFwMPqht6n9mglenvukvdmefWx+KR/oGKtyCF/bfZlpnKYqeuq3TTVLFK2724b7h1LbCEGkRxzf0Da6ZfhCPwhZHwKTtoXmBOOgizXIsuzpTMj8pR15XTBx9y16VgufH/UN03/OhzuP+J7vdP+9OwKp3SHzyAm89+wBbsYBwvB0+e14edppmwl4/FVkWr7UbKoPp77s6pYD5h/eJDex7PkzZRTIigrVSY8SXFKeqv1rkWaJAQ42cAzOLOpvTtapOHvb+a8dZk8ZzX/DAYBb89fkGfvtu1whnsDoelSBRpFVqKWQMBOZxHqpHQUVA+sPYzaFmtFxrP3oUrjsAjv+XZKt5rTmUKRAxbnPY7btSd+HZP0mg/P3/wsKHRCJp56+5j2U9XalZ/1FxwMfbRbbpA0cmXGVQaiZsfYwEsTONJ17HC7f6QgsfEamoeLvInx3/Lxn7DImYXB+MbdlrybhlfyQ6JCOqYx0kG8RuK/yDI4Pkpb+XQxFupZclS5Ywa9YsolGRlwyHwzQ1ZZmsoiiKoiiKoijKgKOBBcUd44xOktsR4OYwCnlw0tSOl9mkiZjM6PzGs/DmjfDsH2HDCpnJ/eyf4QtnSwHkUmQw+Cpgs73lAdKWFW9JkOGzF6XIbsc6kYRY+pL1uyk7S9bDzIOlZkV/WPoy3HmGHO+tj5OCvl4lOrxkI3iZre+Ue8nmhHcLVOQKXvTHsT/UCIRh9BQPxzyV2VM/CSZuI585azLY6emWmiTv3Cb9pZDgQn+oDMBG29M9djvOf2EbKpLd/O+kUVSteBmWvCRBj9Bo2P170o/7Uxi9eSHc/yP47DlxqB5+mWQ/+JDMhCQpZ/8ouT4lkcCC8TebAJYp5BsI9w0eODOxMhWaLbQmjZKOlwBnMCxBBX8gXV6nz7piubdnzrfB2NSsI6FuEtz7XVj3GfzjoOIVUa+olOyFrY6RGgwvXA6r3pX6C2/8CzaaK/JgRi4wHoFERIJorvhg6i5i51sdK/29FHR1SJHmJ38LJGHyTnDEZRB2OHGDoez1E5wEw1BVK8GOYEiCrIXa0WDL6SkDTnNzM9FolJtuuolZs2bR1NTE1KlTB7tZiqIoiqIoiqLY0MCC4o5xRtfWZ9dLjjSLxrmZ7RkIe3Nkm2W64pZGc02jaFPPPFRmfb54pQQYHvwJPPfnlERSiQIMhkAINt5VHjt9TfTdWz6WAMPy12XG5bYnFK5z72T1+/DvL8mM9BkHwJF/yU/324uzzouEVT5yL27nVwtypuOpAHRKozweFWdqzRixgUzHsKJSpFsgPbgw48Ditt0jPb5KkhNmw9S5sMs3ZTZ/f2f8RtbA05eKpEyyW47hCTeJNFmyWxyUPp/01a64JTMVCEEinC7J4rQN+2x4krK+YC2MnprZaan9unh4OZaBsGQqZJPXAble5sKc/442CYpFW2FUat0NU+HYf8DTF8PHj8M934Flr8BBFxVnfPFViEzRjAMl8+2Fy2HZa7DkxRy/q5TjM3oTmHWESJ7VTZDvSuFM7+qAN2+S8XXDSvlszqlwwIXQ3W2TO4paAYWaRsnIWrcCwqPlfSYCYTnWxWCw5fSUQWPWrFnMnTt3sJuhKIqiKIqiKIoLGlhQCiceFZmDZJfIktSFvc8qDITTi6qCOCvMDOFtT4Adz0xlMPxJnB6lCDAkYuLcD9bA2C36OkZ9FdA0Qx6zT+r/9uy0LoZb5om2/aS5omWdSzbGeXyL5fjMZz1uTvN8a3coloxIpBV6OlPBBRcnXTwqtTySyPdHXSkOtvf+I8GF/tZcKBb9CSokYhJIfO7PIsMCMPMQ2P+X4gyOtEqGQngMkJKIqqnrG9yy4+zT9tnwHeslE8I0OZPTUvt18fB6LO3yOpnwe5gxb85/eIwEFSoDVsAimrK1o/8qRcyfvEgyCla9K8G6UZOzr7urA9pWQNtKCX7XTYKNd3MZP3ySDbfpXrDiTcmQCNakrt2pZ3+1dT2vDA6MHE9XB7x9a3pAoXa8SPBtfZy0o9ouUZWSQIrHpJ3RtXLNiq7NHlgoJhrkUxRFURRFURRFKTs0sKC441W2oqpWZv3WpiQOIs35zSrsda62yO98WLOQ/VWw41cli+Gtmy0nyIM/gTduhLOeKNwJk4iK9MPzl4sUBYh+9VFXZC84XSxWvCFO4fbPYewsOPmO7PUKDM7z4uasK0Qywji6vKCSFMWlZoyVseBGImY52xMpeznmarGTd26TmguH/xm2mTdwbS4my14Viad1S+T9uC0loDA9VTA20iLBRl+l9Ddj85EWCbiEM2R5OG0jEIbRk6XvVtXJZ/agQzGclmobpSce9Zax0Hv+k1YA28ghjbJlRez5I5g0B+46S5z/f99PgnXT9klfX2wdLHgA5t8LnzzVt6bP5ofAcde7B4d9PpFB2shl1nWm2kClIJmEBffDI7+AtmXyWd1E+MI5MOeU1EQBWwDB4JRACo+WoEJ4tLVMIX0/12+c36tNKYqiKIqiKIqilBUaWMhCMpnEN1KL+XmWrXDIHATD0N4Mvrg4BbxI8JiMhQp/eqHVSIs4XMMNUtB4u5NlVvNTF8Hq90SmKN/CxsmkzPR+7EJoWy6fVY8Sh8pHj8LVe4rTdtM98ltvPtt/9e/w6AXQk4BRU+DIK2SGaNsKSzu+MihBGpOVELA5QHMd31JLRqgkRfEwGvAmqOBmc4GQBO+SWPIwFZVwyO+loPL7d8G935P+s9v3hk4B0p4uCRY+92ex5ZpxsMOZMOswqLEVuXXTdU/EpKYCWDbihUzOSfvn/QkOqG3kT67jbR8jQMaEeDy/bZgAdjwq40oglD7Tfvp+cPr9cNfXZGy55QQpPr7DV6T48vx74JOn04MAgZBkKlTVy28WPCjFxo/5a35ydgNFy8fw8HnwyZPy3h5QMNl/yW4rO8g+vjjtpqaxb6ZCIX0/12/UnhRFURRFURRFUcoaDSykWLx4Mffddx/r169nq6224phjjikoqNDZ2Ulnp1WIsa2trZjNLC3FmB1onHxenQGmgG2F39JuNjUXjERMdJ3IWfirpGgySFZBnsVhN09+RPCWP8KK1+WDukmw+9kiI9P6Cdz/A2hZBDceI7NYd/lWeqCjv3Ssh3u+JzNGATbbF3b+JgTrZH87U47SYK1s19SfiDscPLmOb6klIwZAkmJI21E+mP4fa5OC59GW9JnVxqnqNiu/Ow4H/gZCY+C1a+Hp30mw7KCLittvS8G6z+B/35baJQCbHyZF2js7wBdMDyK4Sm+FIFArDtBsWvyF0B9nZpnJtQwJO8p1vJ020tUBFOi4N+tyKwzdNAPOeBAe/rnIIj39O3nYGTMNNt4dNj8YJu0AtY0yTpk6De/dCaEGONjxu8EkHoHn/x+8dJVcMyqDsNPXYe+fZpC0s8kexWNWZkIuyaNC+n6u35SZPSmKoiiKoiiKoijplLn3aWB49913OfDAA9l+++1ZsGABY8eOpaKigqOOOirvdV188cVceOGFJWjlAODFoeZ1Nm+sDQLVQHP2ZZ01FsCSQjISMeGG1LZjomcNUDPW+35tWMmPe65i/+RzsAJpy+7fh12/LRkE0bVSwPW0++CxC+Cdf8Mzv5cZqkdeAfUTvW8rEyvegru/JXIvFQE48Ncw4xCRYQpUyzI9CaistmSlglF3p0omZ4v93JRSzmkAJCnK0o5KIXNj+r/JWLAXMwdYt1xqAkB6oMHUJwHY52cwZjN45OdSk2TDKjj6b+XnjOtOwJoF8NnzYl/xdpEk2ud8KXLblYBwkwTWch1fs2/mODnrUPRn3/vjzCwzuZaytCMnuY6300ZGTYJEN7DE+zacmXGZglH+ajjsj7DR9vDgTyWIMWYzmLwzTNsPpuwo1+yOdvku0iq/m7YfHPZnuPe78Oq1Egjc9dve21cKkj3w7p3w5G+tcXPavhJ4HDMtc2DeniHU/AnE22RfcwUWsvX9TNfOXPZSZvakKIqiKIqiKIqipDPiAwsLFy7k4IMP5swzz+TXv/41a9asYf/992fVqlVpy/X09FBRkXuW5Hnnncc555zT+76trY0pU6YUvd0lwYtDLVPwIeFwgofqJbjgD+au02CvsWAyFww1qUyFRAzWr4T1KQdJ7bjc+5OIwkt/o+rFv7B/UpxK3VseS+VBvxIZiN7lOlL1HSrggF/DRjvA47+EZa/A9QfBYX+y9N7zJZmE166DJ36bkj6aLE7fqbvIPsV84gzt6hApmIrKzPIThkyf28+NeT9Etd7L0o68zmT3Ku1ishLs9mYPtK1bDnRBF1Abkt/ZAw3GgZ7oEMmWugkSvProUbjpaOlnYzYr0s7nSU+3ZAGtfFsK4q58S4qkd3VYy0zeCQ68SAIJ/mqoHesieRS1PjPH0hw/U+i9N7jQLseiq0M09L3WDHGSyb6GYP2EsrQjJ27HO5uNAMQddQlyavU7MuPskkjme3/Qsr8ZB0PjFuDrAX9Y6ib4KuVa3Z2Q4G+iQz6v8Ms4NedLEnR46Fx4+hLpf9ufXqyjlB+fPS9B8pVvy/v6yXDQb6UgujMT02ljvXYWA5IS8KuIyfHqiqWCd2O82YA5L4loZnk/RVEURVEURVEUZcgyogMLnZ2d/O1vf+Oggw7iggsuAGDcuHFsu+22vPPOO5xzzjlMmjSJH/3oR1RUVHiquVBVVUVVVdVANL/4eJkdmCn4YHe6mmXMDFMvM3/txTUN0RZxFhrHo98vxZshe8ZCMgkf/A+e+A20LccHvM9M/lZxKr/f81AqTVAhHhXnEKRmsaYyB7Y8SrIUHr8QmhfCnV8R7fd9fibOEa/E1sEDP4SFD8v7zfaFgy4WWYlESmKiY61kMHR3yT4Gqr3VpnDDfm6GuDZ1WdqR15nsuYJvdoe4WzaKCbT5AxJUaNjI8VlCAg1ONtsbvvgPuOfb4sy/bn/Y/1dSm2Qg6i4sfRkWPWIFE+KRvssEwjBxO5h5EGz3JWhvEbs23/VxMMf6FpM1TmJ7oXeQ60RXQpy9iVjhgQWD01k9BG2qLO3IC9kki9ww5yaT49o5vtjXD6nz2i3fmz5XP0Guyb1t6hD7841OBTwQR3t3wnLM73QWxNZKYOHR/4PqBtjq6CIdFA+0fCTj3sKH5H2wViT9dv2Oe5ZGIgrrVljBSntQIdkNodHgD8n30bXIBQmIp4IQkRZLKqnWZUw25yVJZnm/fBiCwT1FURRFURRFUZThzIgOLPj9fo4//niqq6sJBOTG+re//S233norZ555JqtXr+bBBx/k5Zdf5o477hi5hZztZAo+2J2uyWRq2VAO557teAZr+i6bNI+k5fRP9sizW2DBXwXLX4eHzoNlr6aWG0d8z/M45+Fx4lztbEs5SID1y8V5Em6C0bZZvFW1Usvhi/+QbIPXr5fn5a/Dob+H+kmio+0PyTrN/tpZ/gb85wxL+mjv82DOqbJ8IATRVmhbltpBP9SNE6dorgyPbDjPjTkfXvut2364MdLswOyvWx+1Y45fruCbcYgHw6TZgB3z21pbrYV4VDJaGsb1zXIAceRvNBdOvVtkkT59Fh78sRRrPeJyqK73tr/JXOfXbrdhCFTCi1eII9WOvxomzoYJ20iR8tHTYOpOVrZRpBkCVdLv6+rdC95W1aSKlSOZCMZpHI9a+2OOdcNUkQDLV8YoU39OC5bWpJ/XkWYDA4mvwnGsXfqF/TOfz1re7rg2ttrTY0mHJaLSnxIxCRSER8sysTYJhFcE5PqfiEH1KGsd8Qh0dcq6w6mAeSI1e98Essxn258JLZ/Ae3fA/edIJtqMA7Lsr9e+lOX6HGmGZy6F126QMc1XCbOOgL3Og4bJma9biQ4J7HXFoa7GOq5VNXIMa+tlzI3HwBeToEmg2soqiq5N1UFa6x5YMOeltj67vJ9XhmBwT1EURVEURVEUZTgzogMLlZWV7LDDDvhTM2YXLVrEVVddxd13383hhx8OwN///ncuueQSFi5cyMyZMwezueWN3akdabbNNu3HrOHaJoiHxdlu6EgFBZyBhbaV8ORv4J3brPbMORW2P52e4Bh4JFWwOYm0raNNHDAd68VZG3fostc0yuPwP8HMA+F/3xI5l+tsDqLKoDifqhvkOZR6rgyKtnVPQpydx10Pk+akO8MireJ49VWKbAuAmVhcDG181aYePHIF36rrc5+bQDh9hrGRaukNSDiIR8RpmgQap8GX74YXr4THfwULHpCg2BGXicZ6MenphgfPE115kPVP3V2kviZsnXL+Z3Ccmv2rzRKAdBYrh/QZy+ZaYxyNxez3zgBRrsCSUjzyPdZm+Xgku+M6HoNklzjUQ3XW5/bXbttOs7+QXL+dWTMmC6JjgxRG7myDRQ9Lxtsp/5FgdbHp6oCXr4Hn/gSdG+SzzfaBnb4BozeB0Kjs47A5TnXODA/b+2SPlZ3gD4DPL+8TUSApxzsUdM+0c9pjf+1TizkriqIoiqIoiqKUFSM6sAD0BhUAZsyYwdtvv01TU1NvTYWmpiaCwSCjRo0axFYOMdxkjSLNkiEQqJY6A16cRsYJ0dMlM/x7uiSAAFA7Xp4TUXjxKnjh8pSjA9j8MJEtGr1JSk4pZq1z1CQJJITHSFt8lVBVb0lu2It8mn2ZeTCc8Qg8+BOReelYL0GJ7jhE1sjDjWn7wSGXwphN+35XMyalaZ0rq0MZFuQj4WGWNfZj+nAwJMWOXX8Tk4BWhd9ysM4+CabsBPd+H9Z8CLeeCDt+Ffb9RebitXlQlewgcNfpsCglu7Lf/8F2p7pr4kMq+BGz+rxX53E2ia9SOho1kFA6SiVpk+ucmcLEJlPBzLyPxzLbhD1oV5OyPzPGVdU5aoLY1n30X+G/X4OPHoN/nwRfvhfGb5W9/Z0bYP0yGWO6OmWMcXt0xUUm8M2bYP1S+e34rWHPn8D0/axrgBnLIs3uY00+jv5oK6xdKtl9NY1yzEKpwGF13cBkEWjAXFEURVEURVEUpawY8YEFg6mf0NgohYNNoeYXXniBzTbbjJoadTB5xs25E2mVGZyJCIRTxZmNM79XO90hk2BeVwZTtQc6xMkKInny3l1SB6FtuXy20Q6w/4UwdnOr6GciJlINhvAYCJpu3yhtcdPe7miT2damjaHR4iiq8IvkTftqCXL4gO5OKSrduQ46U86cxmmw6d5QN97bMXM6XcsN+7kpx/aVO/lIeJhl7UWJwZYR5Cg62yvzYuvHkVR9ktqJcNaTUsj1lWsks2Dxs3D0VSJRVCANyfX8quf3VC76BCqr4MjLYdvjs/8o2gKd7SI1k08fyiTx5fadnUizHIeaRrleKOXDYEnaBBzjTbTVKkbs7JPmmmxqolSkxo1Iq9iZqSFkCIbSgwxJJFvt5uOk/sgt8+D4GyXLZ/1SCSC0LZOAe9syK6CQLzXjYOdvSgA81GBlKJhHpgzCfIM7HeshEJDnRCoA78MWpFGHv6IoiqIoiqIoykhjxAQWurq6SCaTvbUUgN6sBKC3foJ5bm1t5Q9/+APXX389Tz75JLW1tQPf6IEkXydDvsvXjJFCl4HqdNkIu2PJOJvam61CmSAyC8YhFEtJId1ic2LWTYI9fgizjhS9anvRz0AIEt2Z2+WcXW3aZnS07bNYO9pS8kVIlsO4egl6dLTBuK1gwxqoGysZEcbZC+JQDYSkkGbv8YulO3sirZDYAIG6zM4fc4wGo3ClU29e8U4ias14rvVQ58DMwrcXJU6SbjfRFojbbKSmKf28dMWgY53VFw+5FKbuJsXEmxfAPw6GI/8CWx+b9+74Vr/HZT3/xwTWkKweje+oKyUzIhdGIj4eyzyDOtd1JZ8Zy5EW6O5IBRc0sFBWDKSkTSKanrUWTBUejsesoF3ccX2GvkXCAdYtt4qNm2UCjoCCnUAYTrgF/nmYZA3948Dc7a0eJQHvioDI51UGZJzxVcprfzAlR1QNDZtKhkJ4NPTE04uZ97bBJYMQ8g/u1E2ADavkOR6zZJFMFoiiKIqiKIqiKIoy4hgRgYUPPviACy+8kBUrVjB9+nQOPPBATjrpJCoqKuju7qaysjJt+UcffZQ777yTRx99lMcee4xttil8Zu+QIV8nQ77L1zT1de4Z57/BOJt8cXFadCVE+9nO2JnwWbO8DtTArt+CHc9KFeNMrc/uXAmGAVuNhngMghnkZMzymZxd1fVWJoNdV9vM2DRFP40TOBAWB3BPd6ptNseV09mTiEF0HYQdJmk/zjB4hStV27pw4lFLpsjLeet1ntuKQdtJxEQKxdhIrYtTMxCS2h9Jn+XEn7QdHHmFyIYteRH+901oWQRbHAZjZ0kQIhdv/5vgQ+cygU5WMI7Gk26nqnGyrYB01OZsdbS7JlXHJBF1n0Ftfu+lj3sJbNY0WhkLSnkxkJI2pq7ChjUSFEjUQkNYHOJdqaCdW2AgTe4oNfPf2FxNyBEcziItFmqAk/8DNx4NaxfLOFg3CeoniSzg6E1gzGbyefVoKYJsz05KdokEUTAEPUkJbvv90NUlEkQdqdoK4dESwPdVpmfAJWKpLItqhxRZntf0hsnyACtYY4IKSS2orCiKoiiKoiiKMhIZ9oGFhQsXsttuu3HEEUdwwAEH8Pjjj/P73/+eRx55hOuvv57Kykri8TjBoOV83mqrrVi9ejXnnXcem2yyyeA1fiCxOxm8OO0KcTRHmsXBUTOm7wxrO2abpqBkZ5tkMASq4ai/wbN/gNg62OlrEmgIVEN0rVXkOZsjsSsG8crMzs9MuGUymCBEMmktZw8kmAK8brNFnXJRgRCEG1yWcxznwXLuq7Z1/hg7Aqvocn+15U2f63V6Ykkm2ftTuDE1K9vmxE8itUkO/C288U95PPtHeVTVw+QdpcDs5B2k2Li/2lpfVwc8cj68dTM+4GXmcGnFN7m5fmK6HaU5Wx37Zy+w62YTZv+89HEvAQi3YKYy8jDOb3810GXVEg+EYVSWfuZ2jQaoqZZ1RlutGfyGeMwhT5YKtFXXwzdflELI3Z22ZcKSZQcp+bINsG4FNExK1RBJtb1ugoyBtaMt2zd/WUxgO5BatqvDkm8yQYWeDmhbC2M2tgJ6/bmm95Enc7F3RVEURVEURVEUZdgzrAMLyWSSf/3rXxxwwAHceOONAPzoRz/i+uuv5+qrr+aEE07gtttu6w0qXH/99ey7775svPHGnHzyyb2ySCMCu6PAaDJnc9oV4pSItEq9g0iru8PPOAsr/OnfJzpSzsoOcb7ve76tHdWWbFLHBpl1Hc8SMPA7Z5pmWM458zpbJoOdQMrh1AUEbMcvEQN8mYMpNWMgEerrbHUeZ7d6FPbvS1UUVckft/7sxba8YPpjpMWSRnIrjBywOfErAylnZrVIIzXOgA/+B6vfk+Ddx4/LAyTDYuJsmLyTPL94Bax6B/CR2P1HXPDibJK+ir52ZJyeYNWCcPb5bAV2nRr4mfpyrgCE2oFisPep6FoZAwrB9FkTLEx0iOO/Yy1EbHVOjE30vrcFmrscQYW09YdS7fPLGOJPZQT0BsqT6csaIq3pNVmS3ZZ8k6kFEWmF+gZ3qSSv2LMUnGNSICyyTYoyWKxbatXgyoCvq5uxyWbW+DTgrCiKoiiKoijFYlgHFnw+H8uXL2fVqlW9n4XDYc444wyqq6u58sorOe+887j44ot54YUXuOiii3j88ce54YYb+sgjjShKJXtjHBzOopeZtuuc8R2ozhzQSMTAl7TJDmXKtAgBtowFgzOQ4Aw+ZJN4sRMIQ8DhTLI7lzI5VLM5W93INGN7sIqiKn1xs6N8M4NyYZyXXXF3eSF7v+qOp/f5uafCrMOloPL6pbBmAax4A5a8BJHPYdmr8jCERsPBl9C98T4kX0p93p2AQEP6/tkDHq5yRx4LlWfry7kCm2oHQ4uBKg4fyiKD5wX7uBAeLYEAkpJpEF0rn9md926Sd/axwU4gLJkKJush2SWvc/Vf5za6Oiz5JpDnYmTuxPNokx0NgiulZt1SuHIn6VNZqAKupYqvVvx+YNqlKIqiKIqiKCOAYRtYSCaT+Hw+5s6dy4IFC/jwww/ZYostAAiFQsybN4+FCxfy5JNPsnbtWnbbbTd+8pOfsP/+++P3D9vD4o1Syd7YpUmMc9E4FRJRmc2dJF32p6dLZkIaCaJE1JJFMm0MhCHQITITmTTn7dizDyItMjuUpBTNTCv6bHPWRFog2izFKkdNSm03ZDmI7bNQ3eSP2ldDZXVuZ6pXMgV/tBZC+RBw9G3T1/PJDHLDBLl6txNy73O9y6dszWQs2PGHJGAwdqYUgU3EJNAQWQMr34YlL8Dy10UPft9fwJhpEItYvzeTqHuzE9wyF5rTHat2mRa7NJL9WuCUkMoXtYOhRamLwyeiIoOXBGrqrM/MDHxIjSu267NbACwQEuk7U7C5ZoysY/0Kq6izCZwnsew6tlYCD4Fqq6C6aYPJfEjLAjA1gpwFl21SS0HHMtFWyVQIN5bmGBpZJrd6Er374fLfQYPgSj+YUu8jtG4hrMiSEdO8UPrgsX+HppkZF4uvmk/1Pd9g6+SH+Fa9TWjdIuZMqHBff7gRGqYUaS8URVEURVEUZfgybD3oRsbo0EMP5Ve/+hWXXnopl112GXV14lSor6/n7LPP5g9/+ANPPPEEX/ziFznrrLMGs8mDR7Eln+w1BzJhZn4ap0I8Kg5NgHjKOWF3DlakumpXPBVAiIsuvKGqVgIQtSEXHfcqa7l4DBJxywlq5JmSPVCRkpwwTlL7LE8fKcmlKli3LCU3UQnjZqakmrpTjpVa62H/cYVfZntmy1pIOz45ZnMGMszsdct88HI+oPj9YLgQj/RvZq3TgWaOs71/ux37TKfN2E5sPYRSwbBsNUvM8iDBBbPNjvVic1W1ElQDaFsF8XYIjYHtT5NH73qiViFqgw+xl0Q7BGotR75b5gKkAoWkBwzcrgVukmj5oDVBhhZ2W/ByvbIvk0xm/o3psyaYVeG3rs2JDrmOR1qlD/oDqbak7Cgta802+99pZ1W1MHqyFQyLrk0PjidsY1swnN6nuzqlDWbsMG2y932zD4GQZCMku1NZCbZ2dHWI3ULmscGJ5+t9yuHaZ1yzYeQK7UGCXAFCDf4pOQhEVzP/27XUPPN1eCbXwmGYumvWYEBPoIEOqjg3eRX84ypmAW98vRbc1h8Iw7df0eCCoiiKoiiKouRg2AYWDNOmTeP222/nkEMOIRwO88tf/pKmJrlhDwaDzJkzh8bGLMV+ldJgnAogM5pBHDT2jAU352A2KZk0mQW71rUtsNAVg8qkNUPaLs9U05hZwsU4eZKpdURboLLK2n42B0kwLNIUZt+8SEDobM7yob/nImN2SZ7yV871Ge11t36XJi0TTu+nZl+ML9buk/XZnp391PwWm0MyNAa61qX/1k5voVmbo7W6Pv04Oo/PQDocB0qCR8lOqQJBvVlv9HVum37mi0tQoSsBdVn6ZTZM++1ZSCCvk1hjG6RnLwVTv+mKQzDDWGIvwJ4pMykQEqe/j4F31CdSwZMkUGsL9ucKEGrwT8nCkiVLWLrgLbYJ+lg892dsusNB2X/gJcNg1GS+WvF7RrGBP52+K598tIiTTz6Zm2++mVmpjGZAMiDuOkv+52lgQVEURVEURVGyMuwDCwD77LMPd9xxB/PmzWPFihXMmzePbbfdlhtvvJFly5Yxbdq0wW7iyMPpiKnwQ8NU77+D7FIymRww/pA4kgIhy/nTsFFfCRe3IsqmfYnU7FETKMjlILH/Nle7DTqbs3zo77kotgPNy/rsAYSapr4SJL0Bh3D6fpllnUEI+2cVtkBdMATBSksCzInTYZ9JGsqtGOxAUGoJHmVwMX3WGcyCdKmyeFSCCv3th85rRTwqzvZMY5Z5ZLJVQ6xNgt/BsPt1KO3zAc48M1lMFf7sAUNF8ciSJUuYNWsWm9d3cOjXa6mePBsmbVeUda/xNbGGJpITZhNr9vHmqh5iDTOLtn5FURRFURRFGWmMiMACwBFHHMELL7zAOeecw7nnnovf7ycQCPDggw8yZYrOSBo0+uN86OPEcdHETsREOsL+m2BKYzstOyGcX4Fme6Ag3yKUXvbZ7tTSIpeDy1CcWZupj7k58TPVgID0dZjv4l19t2W256y14Ibdme/cRj79vBh2oc7P4Y0X2822TL59zM2+7OQq6J6JUL375/YxC9JrlRSLXMfA67Wmv9tRRgzNzc1Eo1F+c/lFsPR3TJwwYbCbpCiKoiiKoihKBkZMYAFg7ty53HPPPbS2ttLe3s6ECRN6ZZGUQaI/Ttts8kfBGut9d3eG34f6OmXsgQYvJKKwdqmlz+3cFzdnSb77rLJISr7k08dM/2pvtoIEbpkEufBqP5mkmcBbPzc2lUjNlO6PXXjVo1dGJv259nq99ueyM2MnbhlBxuaiLRDvyDwO9Ydcx6BYgVcd50YW65ZKv3UhtG4hcyZUMGtsBSwd4HYpiqIoiqIoipIXIyqwAFK0ub4+w+w/ZWBxc7z0Z9aiM1Bg3vuC7ss7ZSUCIZlx7YtDPJTZ4WhvYzzqrs9tKIazpBizqnU26MgiEZVAgY/M8ioGu9Z8f/pqICSOoq549qwFuwSN0WYP+t112t0wNpXEvSisMvwoxvWrkHX059pr+mmk2ZISK8iuzO9cClSbMa4rnn0cMhRSU2Sgxh/NHho5rFsKV+4k/cKF3qLKb1wk/SWsddAURVEURVEUpVwZcYEFpcTEI96dN25O92LOWjTFcZ3SLRmXD/fNesjVbuMEcepz29fZX2dJMWaE6mzQkUU8CvF26cfxDhg9OfN5d2rN2/tqPs7YPvbjwf6NNju467Rn2o5Tu14Z3hTj+lXIOgq99hq78SHxgGzb7U/QpDfDKBVgqA1lX0chNUUGavwZipJzSmFEW6TfH/t3aJrZ5+v5H35oFVWeu/vgFVBuXph7GS9FoxVFURRFURRlGKOBBaW45OO8KURvOpsTJtIiztRgbeHyJpmKN2dqdy5nSLk4S3Q26MgiGBY7MDOZvdijW1+NNENnO1TVeiyu7sF+7G10q6+QcxtFsqlCZm8r/WegMweKuQ6vxKPgD1oZNdm2W6ysNi/7NRDHwO386vijuNE007VocmxVj1VUeTCc9uFG6bt3nZV72UAYvv2KBhcURVEURVGUEYsGFpTiko8DoRC96UxOmEQUOtZCT7c4QQvFi4OmXIIF+TAU26wUTiAMo6dCbVP/HHpJx7OTaCtQa63fq4PTtDFbodtSU8jsbaX/DGTmQLHX4ZV8gs8D6XQfiGPgdn7z2a7K9imDTcMUCRZkqAHRS/NCCT5EWzSwoCiKoiiKooxYNLCgFJdgTf+dAdkcC5mcMPEoVNWLxnS4USSZemdOV7lvJx61ljHrTcTSt1VMx4Y6TJSBphBHor2f1jZBPPX7SKq4M7aaJc0fQcAPTdOgZojpYOss6sFhJBz3fOzOLkVmbMyLjKB97CoEu2xhfwJrbuNarA1qxhTYrqh7MXlFKRHz588HoKmpialTU5l5DVM0WKAoiqIoiqIoHtDAwnAmmWmasQOfr7jb9bq+TO1zzni0r8/UTXDirHUQaba03gPOwEJqfc56CuZ9bD2ERvVPmsLN2TJYdQ6KfX5HGv3tz4UuN1jY+6kp/GzsKR6FgC2wsG4ZjNtUZMhqmga2nf0N1Nmdv17OidpR/zDHL9M13DCQ9lFOwd6c44Ot/3mpBdTv7ZnN5uj3bpk/oXpvv3XDrZi8ZhQpJaCpqYlwOMwpp5wCQDgcZv78+VZwQVEURVEURVGUnFQMdgMUpQ/BsKVN7fk3NZYT1DiLuuN912GKBrptx7yvacx/+07szpb+7JcyNIhHxPmeiOZettxx66eZ+m7DFNGSH4xsBTcbU0Y2Zta/VzsczD7kbGs+40MxxpJijUeZxtFC1xusgdqxMp7reKmUkKlTpzJ//nxef/11brrpJqLRKM3NzYPdLEVRFEVRFEUZUmjGglJ+9FcH2l44MxCGeJf1nX2Gpj2ToBjbdWLkIOyzYgd6VrcyMAxWNoqhmDOv87GDMRtDVZX064Ge/T0SJHWU/Mhlh84+Oph9yNnWYo4/XmyxWNtzZqDkykgpdL2KUgKmTp2qGQqKoiiKoiiK0g80Y0EZfmSbMen83O7cyXe2ayYSUYi0gj/QdxvK8GQgslGy9c9S9zG77rm9oKV9nwe6nwfCVpaSokBfO3TajPN6P5gySP25ZuSyNR1zFEVRFEVRFEVRlAFAMxZGIoPtUMkHe1u9zl7MNhMz3CjFZg32GavFmnUej0pQoSshNR/MZzqzevhSjKLlucjWP03/Be8FYPPBqXtusNtTMWZ/D6Vrk1J+OK/9TpspxfW+WG3Nl9j6zBJk2WxRbUwpJeuWpgef3WheODBtURRFURRFURSl5GhgYSQy2A6VfHArDFlM3BxRhTpGjcMGoKrOKiRttpPvetTxo9jJ5iw0/TityHIR+o5TxisRhcgG92X74yg12zEyZkPh2qSUP06bKeb13k6prtnZ1hsalfl32WxxKI3/ytBi3VK4cidvWZ+BsASmhwNeAiXhRqlJpCiKoiiKoijDDA0sjESGkjb5QLa1GLUderpSBaBttRS8OJ3syzgdPxpoGJ7ke1699M9C7CVbO9x04MNB7+v2sg37dnxowValeGSzGS/25NVGsznr+3P9zrTe/oyLbr/VMUYpBtEW6UvH/h2aZmZfdjg42sONYi93nZV72UAYvv3K0N9nRVEURVEURXGggYWRSLGLFJeSQB4SSKXCq9Mlk7PHywzRtMwMx3p0hunwpBTntRDb9iKx1F9Hf659Nduprh+8Pq7O1fJlsM6NVxvNZif9sfNM6+3PGO72Wx1jlGLSNBMmbTfYrSg9DVMkWOBF+umus2Q5DSwoiqIoiqIowwwNLCjlQ7k69rw6XTI5e7w4Z+3LONczlDJMFO+Uy3n1IrFUym0UczvZ8Jo1oc7V8mOwzk2mfuvsS9n6b3/sfKAmAZTLtUhRhhoNUzRYoCiKoiiKooxoNLCglA/l6tjrr9PFi3PIi2RHIlqawrzK4FAumUOlbke5BAy9Zk2oc7X8GKxzk8k28hmrBtLOC7W1crkWKeWLFmVWFEVRFEVRFMUFDSwMZ3y+wdluMlnY7zI5j7yuL9P+2j8v5JjkcroU+zhn2t9yDbwowlCzt4HCa7/1evwKvR7Yry9u2wrWDL7smuLt3Div6QNte7n6EgyOXeYzRgzW9UoZeozUosyKoiiKoiiKouREAwtK+VBOsybtMz+hPGZc64xqpZQUOts51+/Kpd9q4EApFoPVl/K1tXLJFlKGNiOtKLOiKIqiKIqiKJ7RwIKiuGGf+QnlkSlQToEXZfhRaEZMrt9pv1WU4pCvrWmWm1JMRkpRZkVRFEVRFEVRPFMx2A1QlLIkGIYKvzzbXyvKcKXQfq72oSgDQ762prapKIqiKIqiKIqilBDNWFDKm8GScnDO/NTZnspwp9Ai4ZqRoCi5KcZYlq+tqW0qiqIoiqIoiqIoJUQzFpTyxilJBJbj00shQUUZ6eRrL242pyhK/xgsu9LxUlEURVEURVEURSkRmrGglDduhV9VN1pRvJOvvZRLsWVFGU4Mll3peKkonpk/fz4ATU1NTJ06tbgrb16Yexktfq0oiqIoiqIMMTSwoJQ3blIO6vhUFO/kay8qn6IoxWew7ErHS0XJSVNTE+FwmFNOOQWAcDjM/PnzixNcCDeK7d91Vu5lA2H49isaXFAURVEURVGGDBpYUAaH/uhNq+NTUbwzXO1lsOqvKMpA0t9+PlztX1GKyNSpU5k/fz7Nzc3Mnz+fU045hebm5uIEFhqmSLAg2pJ9ueaFEnyItmhgQVEURVEURRkyaGAhCz09PVRUaBmKkqDyDIqi9Ae9higjAe3nSilZt9Sbw3sAWLJkSa9jfzCYOnVqWiBh/vz5xZNEapiiwQJFURRFURRlWKKBhRSLFy/m8ccfp729nS233JIDDzyQiooKkskkPp/P83o6Ozvp7Ozsfd/W1laK5g597PIMeRzfrCSTuT9PJou3vVzbdeJ1u8Vu3xBE7SgPBqs/DxZmf0txDRlmqB0NAXL13XyljMp9fFPKh3VL4cqdvBX2DoRF0qdELFmyhFmzZhGNSlvC4TBNTU0l21427LJIRZVE8orWYlAURVEURVGGEBpYAN577z322msv5syZw/z582loaGD8+PHce++91NTU5BVcuPjii7nwwgtL3OJhQCAMwZrBboVSpqgdKTkJ1ug1JAdqR8MA08/LPeCnDD2iLRJUOPbv0DQz+7IldmQ3NzcTjUa56aabmDVrVmmKJ3vEyCI9++yzxZVEyoXWYlAURVEURVGGICM+sBCNRvnGN77BCSecwFVXXcW6det46aWX+PGPf8zOO+/M448/zvjx4z3LIp133nmcc845ve/b2tqYMkX/+CtKPqgdKUr/UTtSFCUnTTNh0naDsmmn/NGsWbOYO3fuoLTFztSpU5k1a9bAblRrMSiKoiiKoihDkBEfWOjs7KStrY0DDjgAgIaGBg466CCmTZvGiSeeyMEHH8ybb77pWRapqqqKqqqqgWh6eaOFVZV+oHY0iKjtDhvUjgaJeERtSFFyUE7yR2WD1mJQFEVRFEVRhhgjPrBQX19PT08PTz75JMcccwwAPp+PGTNmcP311zNv3jy+853vcMUVV+RVa2HEowUnFWVoorarKP1DbUgZbMqoKHPGzZeR/NGQRGsxKIqiKIqiKGXAiA4sJJNJKisrmTdvHg8//DAPPPAAhx56aO/322yzDSeddBJPPPEE0WiUcFgdBJ7Jt+CkoijlgdquovQPtSFlMCmjosxeKBf5oyGD1mJQFEVRFEVRyogRHVgwGQinnnoqDz30EFdeeSXhcJi999679/stt9ySm266iUgkooGFfAiUqQREIgaJdeLw0cKvitKXYtuuSispI41gTen6utqTkosyKsqciSVLlvTWVVDyRGsxKIqiKIqiKGXEiA4sgGQtbLbZZlxzzTV86Utf4tJLL+XTTz/l9NNPp7Ozk1deeYVJkyYRCoUGu6lKMUhEoTKZcsxoYEFRSo7KwihK8VB7GtnkI3E0iEWZs2GvraB1FQokn1oMKpmkKIqiKIqilJARE1jo6uoimUwSCAR6P+vp6aGiooKenh622WYbbrvtNs4//3x++9vfcv755zN9+nTeeecdnnjiCWprawex9UrRCISBTpWoUJSBQmVhFKV4qD0NT7wEDKLNcNupQ0LiaMmSJTQ3N6fVTTCfzZ8/v7e2wh577FH2dRVMZsWQqwGRr2TSCTdCOEeQRwMQiqIoiqIoioMREVj44IMPuPDCC1mxYgXTp0/nwAMP5KSTTqKiooLu7m4qKyvp6elhyy235JprruHTTz/lwQcfZPLkyeyxxx5Mnz694G0nk0kA2trairU75U9qn3NS7GLYGbbbEe+iq0NuxNtiCeJVVdDRDR1FOifO7SaiEI9BMJQ+o1SLf/fB2EUyR58ZkXY0WOQ4F2n2tOpT4jW1HmZOByHWBbEs50/to2BGmh2l9cG2NuLBMvwrU+xxMG19WeypFONqpjGtlNsdBPK2o0UvQm0RMh+jLXDX16ArlntZfwiO/VfuoEFoDFSMgkGw9aVLl7LjjjsSi8UIhULcdNNNAJxyyinEYrKPoVCI7bbbjoaGhgG7HuV73aiqqiIUCnHKKacA0uZXX32VKVOGiGO9YhSc+jjEWrMvZ/rfdcfmXqc/BMdek7X/tbVHgNx2pCiKoiiKogwPfMlh/s9v4cKF7LTTThxxxBHMmDGDxx9/nA0bNjB79myuv/56AOLxOMFgsCTbX7Zs2dC5CVGUQWLp0qVMnjw54/dqR4qSG7UjRek/akeK0n9y2ZGiKIqiKIoyPBjWgYVkMskvfvELFixYwB133AFANBrl+uuv5+qrr2bWrFncdtttvctff/317L///kW9Yezp6WHFihXU1dX1FoseKNra2pgyZQpLly6lvr5+QLc9EAzn/RvO+wbW/i1ZsgSfz8ekSZOoqKjIuPxg2NFwPwcwMvYRhv9+JpNJNmzYUHZ2NJSOu7a1NAyVtpp2fvDBB2y++eb9tqOhst+GodTeodRWGHnt9ToeKYqiKIqiKMODMtQPKB4+n4/ly5ezatWq3s/C4TBnnHEG1dXVXHnllZx33nlcfPHFvPDCC1x00UU88cQT3HDDDVRWVhalDRUVFYM+Y6e+vn5I3MwUynDev+G8bwCjRo3ytH+DaUfD/RzAyNhHGN77OWrUqJzLDJYdDaXjrm0tDUOlrRtttFFOZ2g+djRU9tswlNo7lNoKI6u9XsYjRVEURVEUZXgwbAMLyWQSn8/H3LlzWbBgAR9++CFbbLEFIDqp8+bNY+HChTz55JOsXbuW3XbbjZ/85Cfsv//+RQsqKIqiKIqiKIqiKIqiKIqiKMpwY9jmqJr09EMPPZRFixZx6aWXsmHDht7v6+vrOfvss3n11Vd54oknADjrrLPYdNNNB6W9iqIoiqIoiqIoiqIoiqIoijIUGLaBBcO0adO4/fbbueWWWzjvvPNobm7u/S4YDDJnzhwaGxsHsYWlo6qqigsuuICqqqrBbkpJGM77N5z3DYbG/g2FNvaXkbCPMHL2s9wYSsdd21oahkpbi93OobLfhqHU3qHUVtD2KoqiKIqiKMObYV282c69997LvHnzOPTQQ5k3bx7bbrstN954IzfccAOvvvpqUQs2K4qiKIqiKIqiKIqiKIqiKMpwZcQEFgDeeOMNzjnnHBYvXozf7ycQCHDrrbcyZ86cwW6aoiiKoiiKoiiKoiiKoiiKogwJRlRgAaCtrY3W1lba29uZMGECTU1Ng90kRVEURVEURVEURVEURVEURRkyjLjAwkDT09PDihUrqKur6y0orSiKkEwm2bBhA5MmTaKiInPJF7UjRcmM2pGi9B+1I0XpP2pHitJ/vNqRoiiKopQD/sFuwHBnxYoVWr9BUXKwdOlSJk+enPF7tSNFyY3akaL0H7UjRek/akeK0n9y2ZGiKIqilAMaWCgxdXV1gPwxqK+vH+TWDBBek2AGaIZSR7yLk/78OAC3/mA/qoOD1O1zHZdEFOIxCIYgWDMwbRpk2tramDJlSq+dZGJE2lGZkrc9ebke5NP3y+z6Ug6MNDsqyTXd2a/sfTIQtj4fQf0qL4aBXY40OxpUBqG/lOS6EY+4XyeclHG/LzZqR0OXsrlfUjzbkaIoiqKUA/qPocSY9N76+vqR88e5zBwMwXgX/mq54auvry/fwAKp/jGCbkANudLgR6QdlSl525On60G9935fZteXcmKk2FFJrul9+lWG4zMC+5UnhpFdjhQ7GlQGob+U5rrh0ek3BPp9sVE7GnqUzf2S0ovKhCmKoihDARXtUxRFURRFURRFURRFURRFURTFMxpYUBRFURRFURRFURRFURRFURTFMxpYUAaWRBQizfKsKIpSKHotUZTyQe1RKSamP8Ujg90SRVEURVEURVGyoIEFZWCJR6GnS54VRVEKRa8lilI+qD0qxUT7k6IoiqIoiqIMCTSwoAwswTBU+OVZURSlUPRaoijlg9qjUky0PymKoiiKoijKkMA/2A1QRhiBsDwURVH6g15LFKV8UHtUionpTz7fYLdEURRFURRFUZQsaMaCoiiKoiiKoiiKoiiKoiiKoiie0YwFBZJJb8t5nTlW7jPMvO6vV4p9XIp9PpT+MdLOR679tX+fTOZevtjHZbgcZ2VgKLb9Dtb1YLDGrcFa30hjpI0z5W5vXhksOx8u/UBRFEVRFEUZ8mjGgqIoiqIoiqIoiqIoiqIoiqIontHAgqIoiqIoiqIoiqIoiqIoiqIonlEpJEUxJKIQj0JQi1AqiiuJKEQ3DHYrFKX/6PVeKXe0jyqKoiiKoiiKUuZoxoKSmUQUIs3yPBKIR6GnS54VRRHs1wFjI4oy1BkO1/uRNkaPNIZDH1X6h9q4oiiKoiiKUuZoYEHJzEi7qQ2GocIvz4qiCPbrgLERRRnqDIfr/Ugbo0caw6GPKv1DbVxRFEVRFEUpc9RDpGQmGLaciSOBgMoNKEof7NeBQBjCwcFukaL0n+FwvR9pY/RIYzj0UaV/qI0riqIoiqIoZY4GFpTM6E2tMJg6x6qxXN7Yz0+wZrBbUxryvQ5on1VGGoPV5/OxTbVLZSjj1n91/FWUvFiyZAnNzc2975uampg6deogtkhRFEVRlOGABhYUJRf2VPSBvsEbzG0ruUmTCRqmjo180T6rjDSGQp8fCm1UlEy49V8dfxXFM0uWLGHWrFlEo5asVjgcZv78+RpcUBRFURSlX2iNBUXJxWDqHKvGcnmj56cvekyUkcZQ6PNDoY2Kkgm3/qt9WlE809zcTDQa5aabbuL111/npptuIhqNpmUwKIqiKIqiFIJmLAxnkklvy/l8pW1HJry2zyul2o9MqegDIS2RLQ3e7fi5tWmwzu9IoJQyBcW2j1KQiEGiM72/OY+J2Y9i2Yv2ZyUfvPaX/tibfQa1eV8q+y3UjjLZZbEotl2W+/+XYlPu/8MGwo6y4TbW2j/zsl37MsmkPPKxp4H4zzdc+rNStsyaNYu5c+cOdjMURVEURRlGaMaCUn4kohBpludy3p49Db9U28iXfNqkKIWQiNlee+xviSisXQadG6xlB9rOFSUb/e2PA3XttW9npNjQSNlPxTvF6hPxKHS2wdqlfdfl3Ib+v1IURVEURVGUPmhgYSQxFG7OE1G5wetsG7ibt0JvFvNJwx+oG1KVBlBKjf36EfDY3+JRSHbBhtXpn5VbYE4ZufT3Gp3r2htphs8XyHN/sG9npDg6R8p+KpkplZM/GIauBPgDfdfl3EYuG9dxSlEURVEURRmBaGBhJDEUbs7jUbnB60oMnHO8UGd8IAw1TfI6183kQDn8TZu0OKdSChLR9IyFQMhbfwuGweeHuvHpn5VbYE4ZufT3Gp3r2htpge4Oee4P9u2UelwpF0epBsxLT7mc60zk6+T3SiAMo6dAVX3fdTm3kcvGdZxSFEVRFEVRRiBaY2EkYWY4lvPNuWlbXRYN22Lr3PZXJ99+M5lpPW4a3Ioy1IhHoTKQ/+8CYagZA5FWqLJ95tXGh8K1SxnalLJeCkBNowQVahqLt85822y3K8g9jnoZ2waCUp8bZWDOdX/+uznHgGL2Cee67O00k0cKaaOiKIqiKIqijAA0sOBCMpnENxwLqA2Fm3MvbSz1DXC+N79ebya9tnsgCgQqxcN+voI1g92a/Cikrye6C99eqN7bck5bGQrXLmVk4tWGapryc1KWAueM6mzjkdkvH1Dt0W6VoctAOMX789+t2GNAIgaJTne7LbSdhbZxKP+HUBRFURRFUUY8KoVkY/Vq0f8elkGF4USpZRHyTWf3Kj/ktd2aTj+0GMrnq5C+Hi5wxnU+dqvSJ8pQYSjZv92uctlYPAr+oAb1RgoDIaNYTtf1RBa7Heh2DqVriKIoiqIoiqI40IyFFG+//TYHH3wwN998M/vuu2/B6+ns7KSzs7P3fVtbWzGap9jpj6xQPApxxyy1gZJd8eqgyXf7wzDDYUjZ0WDIHzjPeaF9IBiG9mbwxWUdmWyiGOTjoFRnZlEYUnY0FHCzi/7a/0Bev512lW17zv0ajHGmTMY2taMikc91vZBzn89vAmGgc3CCHCr1pyiKoiiKogwjNGMBCSrssssufPnLX+4TVEgmk3mt6+KLL2bUqFG9jylTphSzqYqh0BlebrPUnOsa7ALI+W5/GM52G1J2ZM7XQEoYOM95oX3AODUqg9ltQhmSDCk7Ggq42UV/x4tytTXnfg1GO8vk2KgdDQKFnPt8fhMIZbbbUve7cvvPqSiKoiiKoij9YMQHFj744AN22WUXzj33XC655BKSySRLlizh1VdfJZFI5L2+8847j/Xr1/c+li5dWoJWD2HiEWhfI8/ZSEQh0izPbhSaqh5w+d1ApL3n2p/+UE7yAkVC7SgHznPenz5gfhuPwucLpJ9mW18i1r+2KwOG2lGRKcTOnNd+5/ustlbCcSNfBmOcKZOxTe1oEHA795Fma4zy+htDIgrRlsK3XUzKpF8riqIoiqIoSjEY0VJI69ev56yzzmLs2LFccMEFAJx44ol88MEHfPTRR0yZMoWf/vSnHHfccYwaNcrTOquqqqiqqipls73jtVaE16yMYqzPa1G8XMu5pdRn2q7980AIgrXmC+uzQCi13Yg4TgMh66YvHu37WRoejks+xQDzPc7DUDZmWNuRk0IkH9wkTQrtA+a3kYXQ0wmR1uyzJwfCyZntmBR6nDMxjGvqlJUdlYpksrA+UQy7y94weUq79of6vs+2Tuey2TDjVKlkg3Lte7H/R/h8kgVWBsVsh7UdlXp8K5Te/pak15YirdATT41Rzlo/Pm+2lIuBkN9ytrOU2xzG45uiKIqiKIpSHozojIVRo0Zx1FFHMWPGDE477TR22GEHotEov/rVr3j77bfZbbfd+N3vfsejjz4K5C+LpLjgdabWYM3oSsTk5tM+K9vtsz6/K1GGhTL8KRO5D2rGQEW1PGfDn8XBWawZ1uVyTJThy0D1sUBIrv0mMOB8n418xo1oC0Q+zzybW1GGOjVjoKIq9xjlhrG7XLhdF0qdOaTjnaIoiqIoijKEGZEZCytXrqS5uZltttmGn/zkJ1RVVXHNNdcwdepUrrnmGiZOnAjADTfcwMEHH8xll13Gcccdh09n/vQfrzM+SzmjK1sGQiAEkRYpZhtPfR8IWctnW2e+GRZ27PtXBjM0lQFkoAs3pmXl1KS/Hzezn+vOYgfO7WZDi1kqpWag+phZf7RFxpaaRpfZ1hlIGzdsGRBuWXXRtZDshkCt66oUZdDoz/83e3/Px3bS1pEae7IFxQ1u14V8Mk4LoVTXIv1fqSiKoiiKogwAIy6wsHz5cmbPns2ee+7JT3/6U3beeWe+//3v09DQQFNTExMmTACgq6sLv9/PnDlzeO655wa51UrRbuwSMYisBH9A3jtv5ILh9AyFYNh6ZKO/N4b2/dMbwJHFQEtZpfXvmr7vc9GVJXMnmx2Y7URbcgcYhqG8l1JmDGQfS8Qg3p567WE8yeaIdY5P5rPqeuhOQG1T8duvKP2hP//f3Pp7vph1dHfnXtbtupDr/11/J76U6lqk/ysVRVEURVGUAWDEBRYWLlzYW4DvqquuwufzsdNOO3HaaafR0dHRm5Xg98uhWb58OVtttRU9PT34fD7NWhgInDdp5r0PcZ70d93+AHQloDbD7DUvGQp9fpNHRoLbcjpDWxkonP073/6ebdZnNjsw2+mKuwcYiq0zPRBa2Ur5Ug7n38y2Bqntk8SbnWVzxLrZq92W89nXcjhGyvDHNQvAYwZbIf/H7MQj0s+TQKChsHXk+n9X6owGJ17tVv9XKoqiKIqiKAPAiKuxMHv2bA499FBOOOEE3nvvPS6//HLee+89AILBYO9yHR0dnH/++Tz00EP84Ac/oKKiQoMKA4VTbzYeBX+wOLO6AmGoqoOGjTLfbAXDkm5vbsoiLeltsb/3Si4N3UA4e8FcZehRal3mfNsRj8j7YI30NePMcb7PRaFOCrOdmkbRuk6SXruk2DrTqls9shmM8++0eVP3IBGFhikweoo3+8lWW8E+PmX7zAtqI8pA4Pb/xkvtKvDWt+OOMc5OIgaVQW+Zp4VSihpa2f4/eLXbQBhqx2q2gqIoiqIoilJSRlRgobu7m+7ubj788EMOO+wwzj//fBYuXMjll1/O7rvvzvHHHw/Afffdx+GHH84NN9zAww8/zOabbz7ILR9EBss5GmuzXhfzps3o9Nq1qbMFCpw3v873cY/Hx74PpTym5eLMVsrHaWfa4ebAiUekv5iH0zFjvndz2BSKPcDQFRfn67ol8l2xnTOx9cVblzK0KIWzLxd9guIx6YPxHM5TO7lmI0daYM0iebZvt5CAd7ZjpGOJUkoiLbDinVS/7cc4E4/CuuXQucF9jMunWHo5ke3/w2Bc2xRFURRFURQlAyNKCqmiooKxY8ey44478t5773HMMcdQVVXFaaedRmdnJ2eddRYA++67L2+99RZXXnnlyA4qwOBptIZskkel1MJ26vc6C2Pmko0xv3emwDudQ/Z9iDSXLm1+oFPylcwEw9DeLIXAE4N4Pky/dnOsmP4baxObs9dZiEfEYdNbj6TI9m/qO8TbobPdmtVaTEKjirs+ZegwGHU6nNIjwRAwKvXskXhUnKSRVhg9ue8+RFuhu1OeTSHbfOukGLIdIy9jiUopjSyKeb5j6yQTNbZO+nEh/RdSRZmzyFsGa2xjWleOdRW4f6X435VNxkhrEClFZP78+QA0NTUxbsKkQW6NoiiKoihDkRGVsWCkjCorK3nqqacAuOuuu+ju7mbKlCk8++yzvPjii4TDYc4//3wNKkDhM6P6M9vRbBNKP2MyEILuuNzAmaCCfXa3PbhgbvLsMknxqPzeeXwGa7aZzmQrHwIp6YXK4OBkLRgbBHepI6M93RWHmjF9Z3XaHTaFzvaMNMPnC612OAmERHu+qrb42TxqC8pA45R8CTdCzTh59kowLDbnD/S9bpj3nbH09/nMyvZqY17sx22cc1u/Zj8MD4qZhVc/Ser11E9y77+9WQy2bWXKzOlKyBhmD4rnmwGRiMLapdDZVtzMn0JReUylxDQ1NREOhznllFPYfvvtmTVrFkuXLhvsZimKoiiKMgQZURkLyWQSn8/HvvvuyyeffMK3vvUtHnjgAV5//XXeeustfvzjHxMMBpkzZw5VVVVaUwG8zYxqXyM3ezWNoucK7nUSvM4CM9ssdGa/mXXWS5XtuxgkOq2MhGA4PZjgVijQfJ9WbDb1u2Q3JBLgnGQ3WLPNdCZbeZGtHxQ6O9Lr73LNojTa0xX+vpkCJujgq4SGcfJZpDllF1Xpy0aaLft3rifSIrOrI63Wd86infaAR742n+1YqC0oheLWrwqxVzPGGIeoGTsMJphtl+YLhiVTwe26kYhBdSqbr7o+fUyq8Ri8cF4XnOOlW5Zdtv3rU5TX5bozWJmPSnEpZjHg0amaI73rdvSLSCskNkCg1tqe/b9aIibLQHqGq1lu7RLJGBy/BYyemrs98agVSK8r43FDs4SUIjF16lTmz59Pc3Mz8+fP55RTTqGlJcMkEEVRFEVRlCyMqMCCCRRsuummfOUrX2H8+PHcd999bLrppmy66ab4fD5mz55NdXX1ILc0B8mkt+WKHRhJ9rh/HmmFnnjKedgI+NJvQDM6OHPsR6+Tvzrztt3ojECyC2IbIFQHXbb093gEKpOyPn+1tV+xNgg3yGfm83jE0sb2VVrFZo2sTKBa9tnvl236bf3GrCcRlcBLMDUz2wvm/DodsMrg4NXe3PAiNRJptrIbvNhHmj2FMptRIJSSYAql9sGxYKBa+rfTvnq6RZqowi/9/vP54hSdsLX0yWTA1sSedPsPj0nfRngMRNdCzWjrOCaisg2nPJQPy+bBCmT0cWL5rPWsXWpJNTmPc9Gvf/3oB24MZuA6mcy9P+V8/PJaVwHbjTSLRFeiVoouQ/5yJz3d1ut4RN4ne8BfZTlGu2JQWWU59s36axptwe1U+5NJy2ZDo62vOtvE9hpSEhaJjuxOxzRptKS1Xx1tEqzoI5uWpR/Yr2+mv9jHfrfPBuv/y3Ch2McvH1sqZrDWjDmJqPTpYMgxHqTa5cOyJX8VJFI2tH4l9HRCTxK6qyXzzr9B+pi/SqTCKoENq6Bho9Q2bTbZ2Q6RNll/aLT8JlkHNdXyOh8GUoIy3yCd2puShalTpzJ1qofAm6IoiqIoShZGVGDBsOuuu3Lttdeyww47sO222/ZmMhx99NGD3bShSc2YVFDB5lR03oAWMsvNzPa0OyTdbkD7/C4ky4VTzpeELR0+EAI6+0pG+P0QXZcKGJhZlqmMBF+lOHrs2zf72DDJCj5EWvq2LR6TIEc8BvjyCxQUqputDA2Ms60r7t0pEY9KP0wCNXXW54mo5ag368jlBMr2fSAEnzwFL14By16Rz8ZMg63nwRZHpS8bHi3BA2NvdtyyGOIxaFsF9RMg7Gi7sflIS3rtk0zHYijMMFWGFvGojAXJLpktbbBns9mDvpD7up5Mwj3fkT590MViEz3dYsd2CRhntpwT+9gUDMlj3QoZv8w4lOzOfi3ptfuktV/RFnnfHYeqOvffecWZheT8rNgBOqW8yHdChP0/kr3P+kMQ70ifsBEIWfYRbhA7rW1Iraddgg2jJsqYMXamBBXqJqS+j0LU9l+wq0N+Y7YV6MeEIrdMVyfFyjQoZtaIoiiKoiiKohSBERlYCAQCnH766VRUSImJYSV5ZL95GShndE1jugxDpsLFRmfZy42VkY7wIbOeA2HL0e+8AbXjFnxY/Un6Mk6960BIblD9/tTNYep3JkBhDyT0KdAcs5az3xyb7wB8flkm30CBl5tVZehi+lMw6t1RYJcvCjr6oj0LwC3QkIjKLM4kVp+y24npsyvegOcvgyUvyOdme60fwzO/o+rFy/luz67c4zswVUC2NrsMi7MtiQ6oCknRTmM7/qCjgLpL8ATSpWPM/tepJITighmHTNaL6Tum70dbUn3MVgDZXHOr66A7YQW57NJ5+CzJLpNdk+263tMN930fPn5c3l9/EOz3S9h4D3GO2m3HTSapj3ySbaypabSC22acMhkLXh2ZRtbP7bpSKlTOpTwx/9GSWP0gb6k+x/+cTIEGt/9IzvXQJf05EZOC5ivehIUPQcd6GLuFZNF1byLBsI52qK61+n/DRlamQjwqQYekraxcogMqKq3s00RHqt0dBUyCcQmmOSlWVoMZR4fTfYuiKIqiKIoypBmRgQWgN6gw7CillnEmR0e2NtidlmuXWbIlYN3AmmLIdhIxSKRmkyWBgE2SqNeB4hJEyDT7zdDyCXQ1Suq7fWb3qIl9nfjZAgnOQAeI9JKZtW3a4fPbHEe+vtvIhpebVWXw6a+TLB95iTSpoBYrw8YEE+z25pQbisdS8i4d0N1l1UMx33/8FLxwGSx7Vd5XBmHOKbDb98Vx885t8Pr1+JoXcjiPc3jycXruvB3mnArbnSjLm22bAEbNmJSjydaWmtEQWQsVyOc+LAcPwLrl0LY8tawjYGGcVvGoe2HLwQisKuWJGYdMIMru8ARrtnIi1YfsdXbAsrU+9XVq+gZ9M13Xk0l4+GfiDK2sgnGzYOVb8MAPYcZBsMdPU9d5Y6O2MTbNQevo57EN4gw1WXJpwYlUloWXeiVme+C9+HN/iEeswKEpaq+BhfIhHhWJIJBxIlSf/zlyZvasW27977MXVzaZNsE698C0D+m/S16UoNxHj0JsrfX9/Hus15VVMGZTaJwhNjZxGxi1MVTVSztia8WGuyqoSnbIb9YvhXCdZEQYmbFEhzzHo9DVSZ9gSNqxyjMzw55poIE1RVEURVEUZRgxYgMLw5ZSpEmbmyDjDMgmT5KpDU7Zkng03bHjtr6ODdDdARUboG6cOEzsN6CRVgk+JGqhIUOWAcCYjYEWeZ3slu36qyX9PdYKaxcDPhizWfabPGfGhH1b8ZjUc+g9Bi7tKEagQOsulB/F1ld2zrS24yYVZH7jlEFy1jAIhqCrVmywulq02X2V8Mkz8Myl6RkKJqBQP8n6/Y5fhR3OJP7RM7x826XsxmtUrngdVrwOT18MO38Dtj9dghbGtuMhy5Fi2hJukofJZKhqTM3ATG2nq8OSL3Nea4zTKps8khaJzU0iCtTnXGxIEwyLc70rbkmo2AMAwdr07B234ID5jZErM7OwndfyTH3trZvhtesAHxz9V9jiUHj2T/DsH2DRw7D0JZi2H2x9LEzbx5qZHU3VK3E6+xNRkR3z+8UJ6g+kB9ETUWvGtZf/AiZ4UeH3Xvy5Pxj7NBJQKudSXgTDUFXbN2PBC/Zxy8jfRZqt/3219n4cS9Wm2iCTL5xjFcDnH8D9P4K2ZdZn4UbY4jAYNQWaF8KaD6F5kYwZaz6Ux4f3ZmxiNdAbjrjF9kX9ZDj0DzDzACu7wRkM6bO/HjNQzX+2YDj9uAxUTQZFURRFURRFKTEaWBhuFLO4nsHpDMg1q7F3hnTUeu8mWxJNOUfs27E7d6rrINaTfmNqzxowjshER3p9A/v+J3sgEWV28n0mJj/H/0YE2pdDZI3MWOvckN720Gho2Fj05MfOlGDDqMlQO0EcUb5KWS7Skv47t0CCF/INFGjdhfKj2ME850xr85k9W8juJDXZCZEWkVWyy48Zos3ikAyNhqYx0lebF8Ezv4cP75NlKoMw98uw63fTAwp2fD56Nv4Cv6mM05hs5V+7Lsb/9k3QvhoevxBeuBz2+Alsti+9GvAg20vEpKaCWw2IhG3/RqW27a9Ov9bYZZByFaZVh2V2TJZVueE2k9fL7F63ZeJRmRUdrpfgtb0v2aW07J9BetDOZNNlqq+Q7RqciMJTF8nrfX4GWx4pr/f6CWy2N9z9LVj7Kbx/lzz8Idhkd5i6m8i8tFZAZaXsx4bV4oxMRKG7U9qQ7JZxasczrW32Br8zZPS40dHWt+h67/pcbM4ul1PrcRsGY5+19epQLQbFnvkeCEODrZCrWb/5L5dtO2njVsoujM3VOmwlEJIsAZ/fCo6B1X+fvgRev14+C42BWYfDlkfDxrvJf1A7Pd2w7jMr0LDmQ1izUN53deCJtmXw7xMlML7b9yXoYQ+GRJqtOmImOOBVqtKeZWc/drG29LpkfX5XhHNrMoQ0M0JRFEVRFEUpIRpYUHJjnAE1dd4dds5Z3E5HZyAszoyeLuszu9M8ELIc+UkgmCqsZ88aCI8RZ0y0BZoXSLAgskZuKtcvlUyEtZ9S3R3nUrONd13aWjcRSEqhv9haeax8q+9yoTGw8e6w93my7fbVUBmQWeCjJqfvXzwG8Q3izG2YlMUh5hIocHNcmc8gP8kKlYYpPcUO5jlnWoNDTsLmFO3tE5Up6YhuKxMgslZkhwJhWP6m9KG6CfLda9eJPEuyR36/zfHi/GyYKuvwQItvDF17HI9/7x/De/+RugzNC+GR8ySwcNRfZNvRFkvWzEg39am7YKSbUs5c47QxD7s0jKfCtEpW8g2ADhRu2T9eMoLclom0QkUFNH8C7c0QGgW14y1HeSZpv1wOQ5NVEGkVDXfnddVcq1+5Bto/F5va9dvpy0zZCb71Eix5Sezww/tlzProMXnkw9u3wr6/gJkHynmNtIIv7j4L3HnMoq0yhmXCzebscjkRrIkDnuRgatQ+i0mxs+UyrT/WJs72SCuMnmxds+1Oa2NTAOuWWlJ4xhFvx8iJRVrkP1J4tPxn+uxZeOQXIoUHsMMZ0rezFRSvqJQJII3TYPNDrM97uvsEFjriXRz//54C4Paz96Y66JdlnroYXrseXr8BFj0G+10AU3exxp/1K6DClwouNFn7ANYy4B5szJRlF8qSMZaIwtql1phfcGChxP1DURRFURRFUdDAguKFXmdd0vtvvMwcdjpw7O/tztP2ZiuYEAzBx0/CZy/AhpWpmWqLLOkVF5IVAZb3NLKS8cyZuwP+ps1g9CbyaNjY2n48Aq2LofUTaP0o9Zx6RNaIbNKH90qK/jHXpLSsu+WwOOsvBEOWZIVdqsIeIDD0kbtwCTaYz7rj+d0gqjTM0MPYm13qKJOcRE+XOFbs0kKBMDR/DPH14jQZNVFkxT56BD57Pl1aYtq+4riZOLvw9lYGYfZJsPVx8Pz/g2f+AJ88AX/dDQ78rcw0DdRK4MM+67mzXYIfDRulHFUOzXrj0ArVpwcXyjkbYahoZ5dr29zGDS9jidsyZjZwbD2QlGLh9Rv17WPOPuUM2pn6DHaJoq6E2KRbQDgRlaDgy3+Tz/f8sVV/xE6FHzb5Amy2Fxx0Eax+TwIMCx6U8cY44Y3TPhCGQI31ujIAb90iAfT/nAEzD4ZDfy/Lm2s+ZO6PiZisozthGwNdgi3OmdV2uZwkOr4MJqXO0OqdVDJGnOp+v+WkzjR5JNIskyoAEi6O9t6JE6nJIqE6yV547g8SjAOROjryL7DpnlbwO18qKl36ZBedvtR2gzUQ9MvzoX+ALY6Ae78vAb7/ngXbngg7fl3GT3/qN84MA7dxyq1gtbMdwbD8r80UAHRKhxY6rmgGn6IoiqIoijIAaGBByQ97scdENL34slM2IdcNkFOOwk2ewqzTh2QnvHKtSK70CXL4ZGZo4zRonC4SEY3TYcxmdIYmcOYfnwTg7gP3xx+ozNCeGpiwtTwAfD7ru442WP463PMdaP0YbpkHx14DE2ZbMkjODIWGSX3lkZw3ot1xKzBh2uAmuwHiiHJqfedCbyyHJua82QNOtSHr80gLvYUmjTa63eb81dJ31i+Fl6+E+ffa+lgtbPclmQ3aNLN4ba4MiBN188Pg3u/CijfFXt7/Lxz+/ySrJxEVWSawOWejEhhxmzVuHDlpwUafc8uDQyb5HZ0hWjhu44aXscRtmZqmlBxQNbStgvoJYivGfqBvUNeMN856QoFQ+uz+ho36ZjWYa3sSePNfUsOkcTpsMy/3fvt8MGEbeex9rvsySZfA/u7fg6d+B6/8XTIfFj8Lu30X5p5mXSuc/dEub9OdkECJWXb9cmsfjT06Z1bb5XLsNqAMPKXO0LKv3/lfItN/i0AIgnXp9Uvs9GbB2OpTLX7SCirscIZkDGTLUigFm+0F33gWHvulZC6882/49Fk4+HcSgDdtj0fS/3ttWC3PXdWS1WuWy/Y/zQQMMwUAndKhhdZk0AwhRVEURVEUZQDQwIKSH3bHeDIlY2Qcfl6kSgrZnj8IXZ2iu/vatfL5jIMkVb1xmgQRxmxqzSpzEu9y/zwfquuluOaZj8Ltp4rT9NaT4LA/wJxTZRmToRBthfgKmS0eaki1IWYFKir8lsO0K25lWwTC1uw2czNqbihNpoK54XTesGea0WbWqQw++cw6DITTz7HdeZOIWX3GbV3dcVjxGrz2D1j+mvX52C2kAPM280rrtBm/JZzxMLx4pTg9P35cshcO+JU4aBIRCW40bJRdrsUeuARxBmfTtR/obAE3p60G8sqP0VPkYXAWLDaBBhPM6twggeTQaMtRGAzDKFswwTkL2WiZ+5Dr/Gspffi9zu2rCV9Mquol22H2l+CBH8GyV+Dp36WCeX+CSXP69kfTb7s6ZFyzH5d4O7S3wLjN0yXJMvVnlR4bGbhdWzOde7cZ+nZ6J05Uy++TSXj8V/LdF86Bfc8vfvu9UlUHh/0RZh1pZS/cfipsf4YE8fzVfYMF9kCcyVYw14IKv/yHcxu37GNFLunQ/owrQyWLTlEURVEURRmyaGChnHCbkeiGz+OMXa/r84wvfSaxcfwFQunfedH/7y0qF8p+sxOolhT8J34N790pn+3xQ7kBdR6HTHJIcZtmfDwCyYrc7QORX3ISDMMJN8EDP4EF98vN56p34cCLJGU+EZOZ5G0roCsKHeuhabpI0YTqU84sm+ZwICLPPtIlmSItlnSMPVMhzRnWbN0sZpspnasf5FuHodj9NB+SydzbL7Z9FGt9+c5md67OSB2BOOZ9qYUia6TvBEKwfhnc9EXJqgGpUbLF4bDjGTB5J2tfuuOZt5urxkK37fuuOFRkWH7nb8C0/eD+cyTAcf8PYKMdYMezoHaS7F94tDhrnHIX8ahlz/GUXfR0WcfYTRs/7fiG+h6/TEV3C+2nbs6eUgTyvPTTfK/1Pl/u/S62fQzWuNVpP+8hyznor5bv1q+QgHAymdJ9b4XqOrGveFQ+D4ZkLEom0yWSDCYAXuGH5/4k2WvjthLN9+5E9vZ5LTDrJqdkGLcFnHYPvP1vGSubF8ANh0ntlP1/CZUNVj0jfxASPRI4ia6V7Zt97+4Sm0x0iE0Gqm37mTov8ajUTbFnK9q+zko+Ts7BHGfKmWLbUaZzYv88HoWehNiLv7qv9KP9N8bW+khvRWXSham9YK77z/5Jxq1Rk+ELZ/cdC7xKIXV7mEDSZRururugO8Mx2ng3+Pozkh37+g3w+j9g0SOw98/kO3/KFuMRGWN7rw89loyRL3VNiZt6LJPSj4m/2nYsU4GIqlpcDcmM72B97/Uy6fV/h9qboiiKoiiKUiAaWFDyw202WjxizcoyTvNMTjxDIiYZD/b6A73fRVMFKAEq4IEfwkePAj44+GKY++US7FgeBEJw5OXwwnQpWPvqtVKH4ZA/QO1Y+X6DkaSpgsjn8tqeKm9wO572GemBsDVzNNIimrxxl0K2/ZnRpnUYBoZ8zlEiKjZkn7lo6hJ0Jay6BNFm6SvRdUCPSHStXwo1Y2H708VW6ibK73s8OF6KTdMMOP0Bkbl48rcSYPh8vswInbE/TN3V3dlhirfb5TRireKk7Yqly9TYf2MPbDqvQbnkKfIh0iz2WNOos0DLCbdxJ+28h6wHyDgTa4b1q2HCljB2OoyaZBVktf/OrCvZnd73jKO9q0Oclb3ZCj8Fn8cgdjHwVYjE2RaHwRO/gTf+Be/eLhJJc78MG+8uWX4mWym2DtpXp2Se1sLYMZL5Z4rpZiIRSxWX75T3bjKImVCpsPIj0zlJ+18QlqCCsYO44/+bKcIM4lSPrhU7Sgs62P7XJFIBvpaP4KUr5bMDf1tefaKqVmqWzDoC7j1bxtV7viWZf9t/BTY/VLIVzH80g5F3MseqbYVcM6KtMMo2ltuloPxBCVCAFG32AeHUOk0g0wQfwo35/c/TLDpFURRFURSlxGhgQek/2YoNuxW3NLOv4sn0+gOGeAwS7eJEfeg8WPG6OBKPvAJmHT5w+5UNnw92/z40zoAHfgwfPyFO3WOuFvmJ8bNkX7s60mVrnPIZ9kLOxhlmnKr2LAa71JRbIdv+SFLojefA4OUcmdmLvRrvNmdPICzFjk1dArO+9Stgwwrph23LpCj5qXeLdEs5UFEJu3wTZhwI930flrwI79wqj6o6CS5svDtssrtozFf4U85fR/sToZQsWKKvNj7YajCY5R3XoHwyqnIRaYHujlRwoSn38kppsRdOrgyKo99kfflD7v0F5LPOKFTXSHYZWIGHeMy6Pkda5dnU+7FLiZlgQ2UAXrxCAl+Td4Tp+5dyjzMTGi1yLtudLPJIq96Rdr14BQRqYKPtYfIOUksoPFZmWZtAQs2YvgVqnQRCYtOVVVa5E68yiDrWlB/mnEB6FqT9XAXC6VKTTud5dC30dEJPUvpEZaqwecDhSPf5ZT2BkMyQf+g8yZ6btp846suRTfeU7IWnfwev/wvWfAgP/RSe+yPMPllqQtgDBXYCYbHHRHt6hkE8dc2wBxd6rzm2SSVgSY/SJZlE8Q6ZWKCTQBRFURRFUZQyQQMLijeMwzNTBkKsLd0h4ebEszv6apoc39luzIIhWNcB935PbuKCtXDc9eJ4LDe2OFRmsN1+Sqqo8/Ew75+SLh8Ii4MruhbwpY6frfCfs5CzcYC6FbGG9GPqVbbIC8ZBrSnuxcVZ2NSLBIiZJZpEnHdOmYmuGPiS6cGGrphk9bStKL+ggp3GafDle+DtW+HD+2HJC6Jnv+gReYBLoGFbOQ5g9f/qem+OyUAoNZM8btmdW2CvEFuqabQyFpTBJR6VTC5TEJYOIGlJ2flDfZ3lJmgQCMHYmbBhFdRNSF8mmRSne3cnVAREzq66Tpym9ln6AIFaCe69/z95v+/5g3893Wiu1AR6598pe3tR7O3TZ+QB0u+n7CLOU6e9ZSIYlqLU9vE9Vy0GUK33ciBbrQRngeBsgXDnd4Fq2LAOqkelJIHo+9/PH5T/dmYm/gf3wCdPSiDwoIsG316yUVULB/5GZDhf/6dkqbavhuf/DC//DbY6BuaeAmNmyPL2bI6aMZJlag862IMJzmOZcEwqMdKj8ahkK/oD+WXdmf8UkWYrYKT2pyiKoiiKohQRDSwo3jA3J5luaEL16e/dJH6yzRiOtMpMrUStpNDflwoq1IyDE26UmczlyqQ5cEaqqPPKt+CmY2DX78KeP5bva8eJU8ofTD9+9noV5n02MgUclPLELiUB+c3ora0Xh6idyFqoqICkz1rH5/PhrrPKP6hg8FXITOrtTpZaDqvfhU+fh8+edw80jJ0Fp98nRdBN0NHIyxjHrrPWgp14R2ZHjH2WtVcnjd0xp5kK5UG0FRrGWpJFlUGZBZ3EmlEfaU131HXFJFiQiMns3/CYlMRPKjMh0gy3fQnaV0mthElzYOJsmDAbGjez1mPq3tSNgwd/JDr0m+4pj4TH2gmlpKIyi72lAg0fPy4PEHs7/kYJsrhlExqcY5GX4JzKIA0+2c5Bf7JJAiEpkB7bIPbnDIrb//u1rZC6Ba9eK9/t+h0JOg8FQqOlDsSu35Ig4kt/hdXvwdu3yGPj3WGrY2GzvaFuvPwmV6ais17FaJcizyBB7JrG/LPuzHk1dbrM/xEN8imKoiiKoihFQgMLijfMzYnzhsYUYXbKQ7iuIxVsMDUZTLG4YMhyAPmQ71a/J2++8iDUTyzuvpSC+klw2r1SzPn9u2Qm26KH4bA/i251uEEcTb647L85FprOPnxxOmq8OG2MEyIRFU1nUyA1EIaa0bB+JVQg33/6HPznq1IccvSmcOr/yjuo4KSiEiZuJ49dvy0zzFe/Zzk+P30W1syH/30TTry5r169PQPKOIVrxlhZBImYBBW6ElCbQQYn1yxrJ+ocLT98pI8tiZiMReba2r7G6idgywjypwcberokAFxRAXd8GdYulu+WvyYPQ90kmLy9zO4ftyWM3xr++zWRw6sMwn4XDNSe50cfe+txt7f7z4Zjr8seWLBjr69g3rs5LFUGafCxnwNn9kI2B3giatVYCITFzm47BVo+hjGbQMPGUL8RNEyF0RtD0+bpv/eHYNlr8Np18OEDVjbRuK3EUT/UqAzCtsfDNvNgyUvw0lVSy+SzlC2N3QK++ljm39sn0gRClixSsccU+zm2n3sdx0YUS5Ysobm5mfnz5w92UxRFURRFGaZoYEHxRqabTpPiXuHPY9ZvTGZKtq+B2tSs3/AYufkMhqD109RnjeKU7yqDmZ9eCIThmGtgy6Pg/h/C5x/APw+Hvc+VDAZfW/a6E5mOn5eZ2ZCf1ER/ZClU0sIbfeQi8jhWkWZYtxwCVZYzJ5yyle4uePFK0XxO9ojs1nHXWxITQ5WKSpkVPnG2zAhd+Q5cf4hkLzz7J9jzR+J86eqw6rQYu1i3XDS+I61WYME4jWsz2JZxRucjwTGSnKNDxc5DNpkjL5lyvYEHWyHarpg8d3fBfT+ElkVib8deDeuXwfLX5fH5fJE8mr8C5t/r2E4NnHiTSBANBTLZ26fPyWzyfc51/51zPLJn/kBmh2V/6gApxcF+DpzSRwY3u49HRd4n2gqVUXjgHFj8tHzXtgx4ru+2asdLwHv0JrDiDWheaH03dTepTbDFYSItNlTx+WCTL8hj7afwxo3w+j8k2/b5y2Dv8/r+JhGFjrXQ3S0yn8GQjFu+1He9/w1d/ve51S7Lhj2AUNOUfp5Hyjg2wlmyZAmzZs0iGpXrczgcpqlJsy0VRVEURSkuGlhQcpOtvkKugqhOx7nJcOhog+pa6OoSx599pnbLx/LboSg14vPBFofDlJ3h/nNgwQPwxK9h4cNw+J/lZjtT3Qm3G8V4VJym/tTNd7YbwXxmofVnxtpwm+1Wbg7URFQcDZ3rIdolDk7TrAo/PPwzePcOeT/ny3DI72QGZalY86E4XCdsXbptuDFxWzj0Urjne/DU76To7ITZMrsz0pI6XymnS80YK2MB5HvzvphZQeXmHO0t9t1V/HUPlp1ns0fzHVXWZ7mcY0ZCC1I1GJzbS8kiJaPw9EWw9CUJEnzpNpi0nSwz55TU7yNSDHlZKtCw/DWRdgmNluUn71jIHltE1sC9Z0vgfZt5EqSurs/5s6Jgt7dn/wBTdoLp+6bXkgBbYfmYZYNuGQuF4Hbuy+36PFxwC5ImorB2mfV/wxzvYFgyFpIJeOkK+PA+sZkjL5fgdutiyfAxz7G1UoOgfbXYE4gTfdvjJaAwbkv53XBi9Caw3y9g4jZw5xnw/OUw+0uSwWEn2iqyZD0Jq6BzIATxdli/QrIO7QE7u23l+r/tJFNh7kLHsbQMCM22HQo0NzcTjUa56aabmDVrFk1NTUydOnWwm6UoiqIoyjBDAwtDjWL+sfd6w+5WX8EeMMgWAHA6zk2GQ3i0laEAKUdhqqBddI18VjO2f/vnhWQSWj6SY1BfRBmZmrEw719SOPOhc2HZK3DdgVIAcPvTZZlsMlLGmZOIZpdzsZPJUeB2jvsz83owZ20nohCJF9fJVC6Bkl4ncRRCoySjp67J0mqPtsDtX5ZaBL4KOOA3sPM3gBI6aD64W4qo93TB9ANgr59Cw8zSbc/JdieLhMYb/5JaEqc/KNebRJtISeCX4EEgBONmWL+LtEJPRyq4MAQDlF7p7bsuDvP+Mlh2nskeE1FYu1SuhxUFjH3xmDju/Km/PWbsCYRkW69eDQvul+Dd8f+0ggp2gjWio77x7tZnG1bJOqpH5d8mOy0fS52edUvk/Yo34IlfwRZHwHZfkqLmpS5wa7e3/34NznpSHMKdG2DD51JLwleZLiPVW28h1bZcs9+z4Xbuy+X6PNTxIn0Ut/3fqHNk2/mr4f3/wguXy2eH/h62PcF9W7F1qUDDJzKTP9wIWx8LVQMUJBtMZh0Fm+wh0mKP/gKO/5f1XSIK0XXy2l8t/4VNfYXYWsnecAbs7EGEfOtsZSrMXSh2W9TAwpBi1qxZzJ2bO5vuwwULgIaSt0dRFEVRlOGFBhaGGsX8Y+/1ht2tvoIzYJBJ0sd5c2TeV9UBSbmpMlro9hssKJ1DsKcLlr0Kix4VmZX1SwGf3PjufrboBBcDnw9mnwQbfwHu/Y5ITNz/A3FeHfmX1CzZbkgkrBnpkJ6l4KuUY1WbQwYJMjsK3M5xf2ZeD+asbdNHiulkGmx5m2QPrPsMVrwDHeskgBBbCxtWitOyY504BiKfy7LBOvjidTDjgNTvS9Sut26BB39ibeCjR+GjxwhseQzjk3uy2jeuRBt2cPDFsPJtefz363DmQ6kATLvIIjlndUJ6BoMXubGhSm/fzaOYp1cGy84z2aPd6Vnj/C4iwemu1HkON6ZnyJlxx++XLLkau7MuBC//DV6/Xt4fdQVM28d7e+smFLafdpa+IrOcO9aJXv3sk+C9/4gk07u3y6Npc5hzMmxzvEgHlgq7vd15Opz8HznmoTp5bhjn/VqZb1AgGIb2ZqlFlLD9JrbekjhTCsPLuTDntc6WQWps8fMPpIYUwI5nwdwvZ95WqAFCc6To+UjD5xMbunovyexY+BBstKM1caa6DroTkuUEltThqEmSzdCbHZxnEMEQaZb/tP4QjJ4q6y7Wf5zB/q+klIympibC4TBfPfNM9vvFfwBYunQZM6ZtMrgNUxRFURRlSKCBhXLCy2xE+x/7ZD89il5vEtwcTM7fuqVtm+V63yfT37c3i26vD3FY+BLy+YbV8mwyFno8ynxE1rh/nuihKtnBDrxD4L6b4bNnZAamoTIgN3rv/UdmaG9+qDhwxs7ytl0TCMnG4X+GBQ/BE7+Bjx6Dq3aFA38N47eVm/euGNSNl2W745YDrWFc/5yh+dwIGidcrtmlXmfNeumf+fbhYChVzyPc/9m75ve5imj3187sJFIyYIFqOcbtn8OtJ8Dq9739vnE6HHcDjNvCKoDp1T68Ltf+Obx1Mzz7R3m/9XHi6Hz5b/DRo1R+cBfXcTf3+/aH9ZtAw/js6/N8njIs5/NLIOW6A2DV2/DguXDQbyFucw4HqtPPU3iMVXNi/VKRmQjWDs0Zz9mOn+m7PW0Du107xbQPyBzQsDs9kzbpr2S32FWiHTrWQ3KUONUC1ZY2fCJmBV+M9J7hndvgyd/I6/0uEPmh7njm9nndX6/29va/4ZHzZZvjt4Ej/p/0322PF+f++/+VAHjzAnj0/0Rab/oBIpU0Zee+Rc2Nw7JQ7Pa24i14/Fcpe0sdw0DI/Ri49Rcv449zJn0w3NcBHupnNshQwGu/KnTcyySjY5ck8lfLw3wej4hsz/plcPtpch3d5Asi+dPd6b4dr/thl9fKhr8q9zIg/SjnMt3W6+4EdGfJ9utY7227zmxTgDGbSWbqa9fBwz+Hk++U81aVqunjWty8VrIT3aQx8wmOR1rl/20iZtVV8BIk9nT/UWOtJ9t5LvaYoJScqVOnMn/+fFas+pwLHpZ7sJaWZg0sKIqiKIriCQ0sDDXsf+z7SzFnrWdzILgVoYunnOlJUrOKY6nigGshkgoshPuZsRBbB588SeDjp7iz52WCJGBh6ruqepi6ixS+3Wh7kZ947R+imz3/HqmJsN2XYPuvyOy7/uKrgJ2/DpvtDXd/W3S67/kuzDwEdv4m+LulvVV1KVkTX2H68F7kDjIxFCQngjVQO4TlFOJRcdQkOuTm+7ZTJKjgq4DacVKDo3aCBJlqJ8hs6LoJ8nndBAm2OR2JxSSZhFeukSACwJxTJYvH54NDLoHPT6P7+SsILHuJo5MPk7zhWdHM3vGr7g6WYjFqChz9N7j1RHjjnxL0m2t072PWIxjq23eTjudMqJZ7eWOXxqHb8V0IArWS4WUKe5vPjQPTrX989Lhch0Guz7t8o6S7kIaxtSd/K+832xsOushqu88nckyTtpPC5R89IZkLn38gtXsWPCBa9Uf/Va4PxWTUFDjqr/Dvk8TeJu8gMkmJqGSGGNzszY6X8cc57jj/S+gs6eLgDKD3Oqur3c9RIvW/rSsB950Nbcslm+aYq4d2weWBYs8fSVBw7WJ482bY60e5f+OWHQzeijb31kKrtiSnEtH0zB9FycLUqVMZN2ESPPzwYDdFURRFUZQhhgYWlOKQ5kBwePDcshmiLX1nEcdjUnuhI5VN0B8ppDUfwv++BR3rqQQqgRWMY9y2++OfsgOM3woqKq3lx24ujtMVb8Fr18Pn74s0xrt3yMyz7U725uTv6pAZ3zVj3Qvsjd0cvvKgzAZ//jJY+KDc+O3zC5kpWFEpzhpTjDdfGZf+BAfUgVMYXh3S5ia/pwcCVSLrs/ItCI2B0++Hxmnpy5cygOBGMikzt1++Wt7v8i3Y4cz0mYzjtiRx+BWcd81/ObPnVjZPfAIvXiEZDjt/U4rcep1hmi+b7Q17/gSeuQQe/yWM2RRGb2plHFUGJHto1CRH0DNkq1PRnNnZORQCa8MRu/1Adlsy56jbEVgIhiDoUiMnmApmR1okcB2PWetd/gbccbqsb+vjYL//K+puZaWnGx67EN64Qd7PPhH2+FH6mGSnqk6WmX2iBCLfuxPm3ytBhttOgeOuh1GTi9vGaftY9vbAj6FxBoyZJhkiGz4XWalELTTYzlMhwTnnuOO0z3IrmD5YFLtwrv1/mWtgISb/Q575PXz2gmxz3g2lleEqJmbWfLHqkkSaZV1hj5Jc1aNgn/PEdl69Bnb5ugQ+s40xmfq6l6LN5toYDIsEUrHqKiiKoiiKoihKDjSwoAilnK1rnzUaabHed6yXGy1Iv6GKt8tzocWb13wI//0mdLZBw8YkZhzCt1/flM+YzN27zcTflSXFfdJ2IkWx9GV440aRnzCO0x2/CtP2E8klo3+/YRVsWCHP7astWaSqOjj4d5IV4aQyAHufC1N2EnmBxU+LY3T/C2HsDPmtuYn0MlPNTn+CA+rAKYx4VPpapAVGT8l8DHuLoHeKs27RIxJMOuHGvkGFgaanGx75Obx5k7zf44cSTMvA276t+F7Fr7n30FaCL/w/aP0YnrpIipWfeGvp6qN84WxY+aYcu/vPgRNukc9Do63il8Z5bAI5iRiE6iHWJsUyM83g1MBa/ylkHLEHdCC7M8ycowpb8CreAYl4KviawfFm9M3N9y0fS/ZLIiIBq6OuoHTFShwkYnD3d6RmCcAXzpGAnFcH6Pit5LHDmXDnV0Tq699fguP+IVJpxcRub3eeAV++R5zNiQ5I+mVigB3nuXTrC/3JqhvJFFpfyz45Aaz/YgmTMVrr/rtACF6/Ad68Ud4feYVI8A0FPp8vWRbJpMh4Td6x8HW1fASvXiuBvAo/7P59mHta5iCgndknyZi68m14/EI49A+FjTG55BpBM30URVEURVGUQUMDCyMV5yzRtUtF1x/6Fu3r702/qasQabGc5Aa7LycRhcXPS8FKyN85meyB9/8nmQDxdpiwDRz5F7oravjsjUXe1+PzSUBgi8PFofLilVJc95nfyyMXFZWicXvP98TZM2Fr9+Wm7QvH/1OCC0tekBvPY66GURul30RGWqSYZTyS++ZSnTQDTzAsusb+gLtD1NgSiKzEoofg7VsBHxzzt/45PYpBx3px0i98WNq07y9gq6Nz/87no2f6QbD5gSL58OyfoPUTuOEQCcBttg9ssntxCyb7KkQS6e/7iE3e+105hjWN4jSOtsr1xQQUOtulpkVoNNSktOcz2Yeb7ag8Un4UkvXhdIBlc4aZcxS31S9oWwmhVIaXCSDYA7PRtZIJZ4r/vvFPePJiyV6ZOBvm/VOc5Zk044tBogM+flyklxY/LcHpyioJYrsFn70wajKccBP856syZt5xGnzlof7XWLDjtLcnfi3O0VAtxNqlloUd+7nM1Bc0M6gwCnUU2ycnJGIQ3yATGWrHi6M80zloWylZNQB7/ww2P7h/7c9FPApty0QCM9TgPWMvEZNAwvLXJJtn9XvQ/JFVg+jfp8h/wU33wDd5dyqS3fT4cgQF1i6WQMKiRyWr0NDTBU9fAp89L3aRSxLKVwEH/gb+eQS8dYtIX846wtt+uZEtg1UzfRRFURRFUZRBQgMLIxXnzEJTLLgu3Pd7c3OSiEp6tZn5aQrDecWezh0ISZFNM4N0xZsSEJh/j7yvGQdNm3tf96p34cGfyk0liMPoiMtSs/+zFOjLhq8CZh4M0/eHD+4RPez21Zbefd0EqJsox6FugqWJH6iWG8lIc+aC0obe4MKX4bPnZNb3kVekFxbON2vBK+o0zY2XgtaBMIyenL3GSE+XOHFGTxG5LIBJc6RQ+GCy4i24+1tSY6QikHJ07prfOir8Ukh28k5w+ynQtkIkxN69Qxy2G+8mNjTjANHo7i+hBnEG//NwqVVy3YFw6O9h2xPAHxOHUjwluxZZC9X1cp3JVrMlky3kcoKqDQn24Jkpru4VN4dYrm1FN1jvYy3Q2WPNprZfL6NroacTomuJ+kIkn/szNS+lipI3zYCTbitdbZCeLvj0efjgfxK0M5l4IH3x2KslqBhtybiKnNSOh+P/BTcdK1l0y1+Dhqn9bnoaoQYZS288WgLgwRBE/VDnklHoPJdu10SdSV0Y5tjmK+1j/98VT03qqKxOBRVMFkPU9t8sdV6WvAg9CRmn9vgh9MSLty9OVr4Dd55uFUyu8IvkUE2T/BesGQu1YyGceo6tk2v/qndgzUIriGBn071kHe/f1btsFVdyO2He9G1D5XvLYMbe8h8umZRM14UPwYIHYc1824p8Mn7tdBY0L5KaKJ8+B0/9TopY52Kj7aVW0Zs3ivTa0VfJWGXH/LdOxMAfzlxfq1T/BRVFURRFURSlH2hgYaTidnNfZ3MKuH0fj6ZmAK+DZEP+M6JM5oKhqwPevxvev1NSxQ1zToH9LvBWNLmjDZ79A7zxL8lYCNRIAc5tj5eb02JQ4Yetj4WtjpFtOFPgexLp7zs3WAUuJ2yTe/3T9pUCnP/5qkgujZoiUkl2Ym1ys5mNfJ2cOnM0N16PUTZbsNtSICxOhacvkWDa+uWSoTLQrP1Msgyev0z6b8NU6YMTZ0uNkEIYvTF89XFY9ip8/KTM0F63BD55Sh6PnA9NMyXAsNWxIudSKBO3ha8/K0GRJS/C/74J1Q2w6R7pkjfBasmKcstEMJJIwZrM5zmTE9TYWiIqwZORbkP24FmpZLCc2+rFB1U2J5vdkRqmN2Oh54UrqDVBhT1/LBJExa4HkkzCijfEtj64R7IiDPWTYYtDRXpp8o7F23ZotGQ9vP9fcdBudWxx1mtn0nbyHFkD3V3QMCndzpxkG4t0JvXA4pTRSYSsGe/xSKreVSwlE2ertxBJjQMNGxevToEbK9+BO74s/5v81fK/sKdLJnG0rwbez72OcBOM3xLGby0ZouO3lkkeIHJenz4Hnz5L8tMXqOtcz57Jl+GRl+ERpO5VVyes/dRan68SpuwsY9X0/azi6BNny//Su78Nb90kv93xzNztO+i3sk9v3wr//YYc9x3OsL63/7cOdcs5cgsceKm1oCiKoiiKoigDjAYWRiq5Zom63fwHw1BVKzddpihmNuIyCy7pr06XuWhegO/dO+Dtf8uNFIhzbsujYeevywy5XCSTMhP0id9YWQEzDoIv/EBmtJUCn8+qCZGNVe8CSajfyLuTbdYRcMjvJOvi6d9B7bj0G89Qfe515Bso0JmjuSnGMbLbknFmT9kJlr4CH/wXdv1OcdqajWSPFKv96FFY+IjUDjFsfqjM+K8e1f/tVAZh493lsc/PRVLi02dFUmLpK9C8UB4v/Q32PR92/kbhTqvRG4vm+31nS0Du3u/DN56zbG79sr4F4g2JqNSVSNj0yt2Cd7kKPSfJf4b+cGQgryXBMCRsM5RHTQJ/heVsC9pqLQRDIoH05k3UvvA7+WyPH8He52XfRjIpwbDWxTLmBWut50CN9TpYI4HmNQtTwYT/SjDNEBoDsw6HLY+SmculKsg+YbZsf9XbuZcthKp6ca62rxbN+Y3mWpKJkZbUMbdp9bc3W7Y3usgZFErhmCBDPGLNkK8MgA/px3aHdWcqw6aY0lpO7EGFjXYQ6cjKgEjaRdZIYC62Tl7bH4EwjN9GAswTtpGsg66Y+zbqJsA2x8E2x9HZ2cm5V/6H7ZPvcPL4T6hY9TasSY2F/irYZE/Y/BDJUMgU+Ju+v9RZeP4yePxXEsiYsnP2/azww1FXij28+neRHuzcIOsB6791RaVkLLgFDuKRlNRfVJbx+bLXLvFCsQuCK4qiKIqiKCMSDSwMJ0otyxEIp+SPbNvItk2Ttt3VITdpix7lg+u/x1Zh2yzOUVNg+69IloJXJ3zzIpn9vORFeT9mGhz4a5k9Vg6sfEeeJ87O73fbfwXa10gGxv0/lDT+LY/KPUvNKUMC4jTI1Q+cTlOVdelLsKa4xyIeFYfbxnuIo/29u0oXWEjEYPGzsOhhqRVil+XyVcos563F4VKSGak+H4zZTBw/u3xLHESfPAXv/UcCHI9fKFkbh/+5cKdGRSUccolIwKxZAPd+D064WbZt6rdkqskbaxPZMprlvHgJ3hmMI722Xm0FBm4WurlG2bflr4Zwbd/6Cia48N5/JOgEsPM3cwcVlr4Cj/9a+pQXzCxrQyAszslZR8ImX8itw14MTGbcqvckiFiKAEbj9PTAAsjx7pUeswUWfI5nL+j4M3CY/2ZJ5BpaNabvMY9H5DmUI1OyUNyCCmYcMFKTUNysogo/830zme+bybwv7UZ1d5vUS/BVwqZ7OjI7opnXs/M3ZbxZ+JBkmn7lQal5kg1fBRxyqQTpnvsjPHaBZIvse74c+1wSZtFWWLdUrjX1EyAeSj9nhWSgFloQXG1VURRFURRFsaGBheHEQEjbOLcRj6akf1pFZz4tCyIks97euhlevRbf+qVsZb6evh/s8FWZ/eWUFsq27Rcuh1f+LvIt/irY7Xuw09fkda56BgPFqgIDCyASHdEWeP16uOssOYaT5roX6zOYc9IVtxyefoc8i5cbwVL1n+FyE1qM/QiGJeizyRekL69+TxwUxQqKta2ET56UQMInT6fP4qyqk4LKMw6QZy9SY8Uk1CBFobc8Cl77hzhW5t8jetbHXQ+NMwpbbyAMx14L1+4vjp6XroJtT7RmrjuDZ0ZPPFQvwQV/MDVjN4/MA5VzGRzMNarDVq+gV3M85NAgD4le+v++CSRh+9OlkGqmIJrRT1/4sLz3V8Nme4kjrzMiAcF4uzhcOzdYckxdHVKfZNo+Ipc34wDpG2lyTSWmaaYUg+5skwLqjdOLv43GaeKEbfnI+swEc5ySSIEwxDvcxx/QeiYDiVvBXzNZoaYu9TqaXusjELKkkEoxTqx4K3NQYSAJjYYtDs//dz4fHHyxSAqumQ93fgVOvTv3+OHzSV2Gqjp4/JfyH2DBAyL9OX2/7P06iQTCKyogkKoLY59AUkjWWKGZZiqjqSiKoiiKotjQwEIOkskkvlLqyxaTUstRJHvkxiYek2fzPtIKfr/IHwRCUJkqChhZA/d8B9+nz8nvQ6Nh9okw9zQYvYncyJqb12ysXy7ZCS9eaS0/dReZBV030dLGzTQz1F68uXMDfO5RLqIy6G25aftar3u6U1JIpHS0q9O37YWDLpZj9+F9cOcZMO8GcRR1tsvxNjdyplv6gxDvBpLQuV6Wq2qAmrAEYCoCcgPa2S7p9plmxhW7/xi7yTYrbjBtK5lpKruDXHr6vbOoQ9Yx7HXkOI5lMimf1U2SosaLnxYJk31/3ne7Xoq6didg6asiN/Tpc+kSRyD2sdk+ouveNMOykY51lgyZk2SOYud2e9qwCgI5ZkdHW/t+NmEbOPC3UmuieRFcd4BkLsw8OPu6wL1QZ+M0mfn56C9EHm3sLNGFd2ZBJWJio0bCyEgfVWfJPMjXoem1X3mlFDaSTOZuZzmOe8a+7E3v6YJgA+BLd3R//JRcP3u6JJi1988gtrbvOts/Fwffu3dI3/JViLNxxzOlaKyTQFiOXXc85bSNioyYKQLd2S6P9+5M/13rYmlL47T0jAKvxdLrc9RiGTcLVr4lmRaNm+VeX4+LHblhrhkmWLH6A0vSLVuALVRvK0pru3aCY3JCyvEN6QWFnfTXmenFLottu6Ug3za6Ffy1112IR2DdCvAHoCuROm8d1nU73CjXgkTMff35ts+eqTBxOzj0j1LjoKvTffkVb3jbbtPM3MvYx67mhRDIco0zGROZqPBLUfN/nwir34d7vwtHXJ75umk/LjudJXKXj/6fBOpuP1WuOfucL8fbGRAHqBkt///Md9HWdHsoJNhdyG98vvT/im77W45jh6IoiqIoilIyNLCQYsGCBVx99dWsWLGC7bbbjgMPPJC5c+fi8/nyCi50dnbS2WndILW1tZWqyX0ZjFm0gbBVyDG6FhLt4A+Jo/PesyHWKo6CfX8Bs0/Kv+jc+mXw6AWw9CV5XzteAgob71b0XSkK65elnChVhc8araiUQrq3rpUZov/7JnzxH1A/SeRk6ie4n2d/tXxflZKkiKccAVWB3LIwULr+U0DAYsDtKJvzOJeevvk+EUvpv6ccOeuXQdInzuuaxtR2YhLoCYdE/mvx0yLVss/PvN+Mt68WiaPFz0j/SAtY+cRpv8keMoO6aXNrvZkCCYPFuFkSTHj6Evj8Aylqucs3YY8fFlZ4fYcz5Hh+9Bg89FM44+G+y9hn6nqdIWt3aJr3Q2TW9KCOR6Wgt7CsLWPBbwu2mmvYZy/CbaeI83/GQXDwJX3lgeLt8Oq18Op1VmbPJl8QmZMxm2Zvh88nWXL+KiCHVEx3QmqMGAdpdQNM2VGcqsWUeZmwjQQWVrwF2x5fvPUaxqTGs9aPU5MLsvR/c83vive9diaT6dJ+kRZv9RgGsSbQkLYjp5SiM4MhEbOCCjVjZCzsist/CYBwEWssOIMKh182tOvT1E+Eo66C206V7Kixf4Vdv+Xtt1seJQH/py+BN/4lE0kWPwO7fw9mn2KTGo1ZwQS7zdntYaCzeZwFwRVFURRFUZQRjQYWgA8++IDddtuNPfbYg4aGBi677DIeeOABjj76aM4555y8ggsXX3wxF1544QC0ugR4ksuJQbIr3bFgbngSMehohef/Au/cKt+N3xqO+Vv+TvauOLz+D3j5GujuFKfENvNguy+lZwGUGy2L5LlxuneJJzf81XD8jfCvI0Uu58ajITwGaidIgGH0JjKbNtwomSCjNpLvRk2Uc9PVYWlfV9VDbRPEw4NzE1/ATeiA21G22bC59PTN9yBOMpD+mugQ2YJIqxVYCITEsRYIweYHSyHYdZ/JLOPJO2ZuXzIJb94ks6A/n5/+XWi0BNo22UOKJpey2GaxCY0WeZrX/wnz74aX/irOpyMvl76dDz4fHPZnuHY/cX4+eREc/qe+jrR8HSJ2B84Qk4Ao6/Go0GKja5dBPG59ZqRcjANuxVtw64kSLJi+vwSv7IGq7gS8cxu8+BcrK2jibNjp65LlUkwizVJHxWTa+aslwLfoUZEqm7SdXMvrJvZ/W+O3lmevM7zzpSk1hq/7DAI5AiLmP0Ew6n7ttNug13oMgyg/NqB2VGwnsbNgczwVSDAZDCbgUJvKsIuQkhtbJ58Xazxx1lQ49I/5/x9p/1ykynriMGUXCU6XqiC6VybvAPtfIHW/nvuTZAbOOMDbb6tHwUEXwTbHw8PniYzmE7+BD+6Bw/4EDRv3/b9tsNvDuiW5M1IVRVEURVEUpUSM+MBCIpHgkksu4bjjjuPaa68FYMmSJVx88cXcfPPNxGIxfv7zn3sOLpx33nmcc845ve/b2tqYMmVKSfehaDidZm43uJk0lUGCCnd9A5o/lPc7fQ32+Xn+szI/e0FurtZ9Ju8nbge7fbd0N0zJJJAszg1qcyqw0FRgtoKdqjo46d9wywnw+fsijxRZA6vfzfybjXeDY6+RWYb28zTENOEH3I6yzYbNdezMd2uXicOmKqVb7Q9JgKfONqM5GE4VDEbOz/T9xaH+7p3ZAwuv/UNmNgK9WQmb7ika8I0z+xfEGmwq/CI7M30/yTT47Hm44Qg45q/51ympaYQjL4NbT5LA5LR9RGrGKQXiJJszz3n+vcyaLhM9+LIejwotNuoPQJtNXisQgsgqyZZbuQLu+mqqQPoXYN4/0yVcIi1w52mwJjVGjd4E9viRZDVEi1yjZ+XbsOAhkaML1MCWR8oYtupdKRAdbZbnZa/BlJ1hy6Nh/FaFy4hMSAUWVn8g151iB+AbNhZb7eqAjvXpxZoz4WXcCTemlsszm3EAGVA7Klmto1Qmnaknk+l4mwyHjvXyvhjFm90KNWeSPnKjsz01o/9pS6pv6SvSdzb5ggTUB5PZJ0qtpDdvhPt/BCffAWM9SDMZJm0Hp90nv3/6EgkwXHcAzDlV6ohVj5Jrl5tEEnjLSHVSJmOUoiiKoiiKMvQZ8YGFQCDAypUre28Sk8kkU6dO5f/+7/+49NJLue+++9hkk004+eSTPWUsVFVVUVVVRHmDUuK8sXA6V91ucJ2OAjNTNNoM1x8GiYjMrD/8Mu+ztuy8ewc89kt5HW6CvX4igYVSabbG2+HFq8RhX1Uns/urR8kjPCb1ukGeQw25nTUtH8tzZ7sELPrb7trxcNYTMlOvbQVsWAEbVkqR3g0rUp+tlOfuuARlbjoOTvkvVNX031kzSDefA25H/Q28GIdnxwZx8idS7yHzOUjEJEAw/26ZoXjIJe7LrXwbnr5UXu/yLZj7Zembhq64+++GGlseKUWs//sNWLsYbp4Hpz+Qf5Bu073+P3tnHd/E/f/x58XaJHWllOLuLsMZgzE2GBvbGFO2MXc35vadu7ExY78xmKBjuLu7QwVoS12StrH7/fHJNWlJ26QtY3JPHuUi57nPfe7zltdbSCpt/Azm3ge3LRPtt7q24E8NEvD/OvmbZDb8rfujQKVtlMLbkhZimgFu54K9RBhLC07CnHtFpHViT5jwo8dIqvDn48KpYIyECx6AztdUXZunLuz+GQ7ME68jmwrZE6UGQ2J3aNhNFFlO2ySmqRvEX7PBbimwWjgKQxsKI3BJLuyeKe4T9YlWL44l5yhk7K1Y88FuFRlatekn6iKr4t0/nUNplr+0HdWH5JN33R+oWMMiqJIMXIUaDO7nQJddFAKHumUsOMpEceKlL4hnLe9Czf46FnKPw/qPxDGByMwxx4i2Y82B/XPg4AK46CX/65WcC4Y+LTJWUzfC7Dth0sLAgmo0WlFgvuMVQv5zz0zY8T2cWAk3/QFabdUSZL4yUmt6dvub9FEqKioqKioqKir/fP7zjgWbzUajRo3Izc2ltLSUoKAgXC4XCQkJPPTQQ9x5553MnDmT66677nzvav1TeWBRnYZrletwSyMVnBJOBa0Bbltec+G7qsg+LKaN+wkJi6BQYTQ/F7gcQoKlOFO8Ly0QfwVpVS8T0Rh63Vb1YDvcbWw5tgxm3Sz0chN71G0/JY04n6ENgO5en3vNI8vi3E0dJgxnmfuE0bpcJqSWzgF18OkfShvRaEUbcNiEZrUiOQEeOR59sDinGz+F9R+I70Liql63y4EIRZREgUdTPUSQ/l2JbQM3zRGa1em74Njy2mX/DH4Cji4Xxaz3z4GBnmjjs6SRoHYRn9VxHvXg64XaGGy9l4Ga7zmBOvNsVk/bqrBddx+04BEhORTfESbO9BjyvVEyFS55WxjxzxVKnxLeSMj3Vc6GkyRRxDm6hTDQ758jaoOcWCXOd9+7A3dKS5KQV9v5f7DwCZE9d+Fz9es4aTpQOBaWvSgixcsLAJe4pfeq6Ceq6oN8tcVA8O6f/i2a7/WRXehd9wfEa43u7GL2NouQ6nOUQnhD8ZksCwcdCEnHQPsbR5nIOju4QFzTNnc9lEa94cqvAvudMvbAug/E/oU1gk7jhfwRQIcr4dQ2YXjPSxbSd1dMrf2zZ13R6mHMR/DtpUKaaM8sUUcpUELihXxoq4vgt8ni2brgpMhCDU/0vYyva6amZ7d/eh+loqKioqKioqLyt+E8i5OeH3Jzczl48CBHjhzBYDBw++23M2/ePL788kskSUKj0eByuWjcuDEvvvgi8+bNY+fOned7t+sfg+nsYrR2q4jetbsHI+aYGoo0GkHSQcMuIpLUWcfoaZN74Bua4NswVF/IMuz5VUTD6YKh373Q/0HoMQk6XAEtLhQD4ZjWYqCnZCrkp8Kad6t2dlxwv3A8aA1CN3/mjfD7HcLQfy6RJGGUjXQXHc0+7JFBqlx8Fir+ztXh6xpRqYhyLpUBvEYnJHkiEoWjwWYVMgaKoSc/VUj9rHtPyDp0ugpunlf1+hN7iOsRGZa/7Jbu+hcTFAYthonX2Ydqtw6tQRRzBlGXwvuceUfoKoTECOdOSCXDW1XU1H78uXf+nfF1zwhkmdosXxPKvUjC7Wxz47KL+2xBmoiov26WyC7zhZKNUnqOi+82vkBMlRoO1RHRWEj9DXoMkESE984fa7fdAY9A/wfE6y1fCRm94nqUeBr6NJjjhIPmt9uh2N0GDMbq+wlf14PNAvmnhDyOd1sMBLV/8o1yXvRG8VeV9JG9BHCAzh1jZLMKKZ6D80GjF7KK/khEOm1wfCX88Rh82kcYxPfPFk6FkAbiuejq7wJ3Ksy7TzgVYlqL7FXFqQCgM0CTfqLdRDYT21r+kqihcr4wRkKfO8XrjZ8FJvdUGZtFTKNbiuxTWfJIlFpyPBlcVfVDNbWNf3ofpaKioqKioqKi8rfhP5exsHfvXm688UYcDgcHDx7kmWee4fnnn+eNN97goYcewmg0MnnyZDQaMZgKCQmhffv2mEz/wofv2kQ5+VoHiIjFqGYimjF9d+2jxpSCrdbs2i3vLydWw8nNgCSiyqLcBnm8dJO1horLWLJh0xdQnAFr34fek0WhPm80WhjwkNDc3fQF7PsdkteKv1YjYcCDYpB8rohuIYyxNovHkOArMs3f3/kfVpvhvGCzChkdCXGOlULN4DFiO21ikH9oISx+WhjTgkJF5HSn8TVvY+hTkLxGyKWcWAXNh5yroxFYsuDUDlEINn2XaJcDHhLX119BTBsxVTKYakPnq2HJc3BmvzgGpTCvIo/jbWjzvs79KmL/L8/kqU00qy8pvfo0+Cq/kd0KliLxkWxDP/dOUeDeHAfX/SocwVURngQntwjn3rkkoYuQQbGXCCd0eKOal2k6wC3h9Sns+kk4HbvdEFjmgkYLQ56EBp1h3gPifjFtpIgUT+xe8/I1YYqGq76F78fC4YWwqTV0nghIntoxcHab8HU92d1FhB12UTi4NujrWQKpJkf7+cbf7MOzsk99nCObRTy3oRO/nd4oioqveUd8P+IVIdlVFS4HpGwQmQlHFnlqMoBoi21GQZtLxHUXaP0qxalgs4jnpX73Vi0rpNGJZ7EVr8GZA7Blqsj4OV90vho2fS6ylmqbtQCinweReaoJEhkL4MkSVpx0VfVD6rObioqKioqKiorKX8R/KmNh//79DBkyhAsvvJAZM2bw+uuv8+KLL5KWlsaDDz7Ic889xx133MHTTz/Ntm3byM7O5vvvv6ekpITw8PDzvfv+4280ui+qinLyjpJSsORA1lGRqi07IK69+Dxjd+33XUnVt/gR6Vlbzhzw6F+3u6xiFFx1mGOEYyCqOThKhAHo9A7f84YmwPAX4KZ50PYyQBKD728ugfkPi9T9c4Fi+M1LFhIH9pKKkWnKtQFqpGd9YTAJbX59yNlRoUq0qEYPi54SxpKyIlGs9Y7V/jkVQEQ1d7tBvF79Dric9XsMJflwbAWsfht+vh6mXwkrXhHR04WnRJv+bbKIpK7vbfsi1suxUNuod2OkaN8AW7/2fG4wi/ZQlUHSn2j7f1ukdOU+Q2+CkNjaG23PZTSs3gSmaDSyiyddn6BNXScKCU+c6eUgrgIlY6E6ubv6QKuHKPe9OPuI/8u1HS2y5kDUadg8tXYZSm0vgUl/iP6gKB2+GwPLXqqfDJLGfeGSN8XrdR/AsaXgKoOC01W3m6quB4cdzFFekkoWd/aXpe77WRtstcyc+KuwZAuDtaUOgRfKObbkCseOOQoikyD3mKhJ43KIfqn35KrX4SiDH66AWTeJWgClBeL37XYDTPgJ7lwrZLga9aybU6Fh9+qdCgqmaOFQA2HMT14b2DbrE11Q3bMWZJcIfgFoORwi3DJVSnaQpPPUw/g39UMqKioqKioqKir/SP4zjoXs7Gzuuusurr/+et566y3at2/Pww8/zIgRIzh58iT79u1j0qRJzJkzh2+//ZZLL72UgQMH8sMPP/DLL78QH19NFOTfjbrIUCgGAKhoaLJZ3VFSXgPvwtNgPSOMkpLOU0ugLo4FRQrpHGUsSHknYMcPgCyMu80GBbYCgxn63SMiQl0O2PoN7KhGtiKiMYx6A26cDa0vFts9MAe+HgGLnhZGn/pEcSzkHHFrXlcylCjXBqhp8PWF3iR+58gk3wP8U9th2ghh8JC0MPhJuGlu9UWCfdHnDiETpNQNqCtnDoiimLNuhu8uhSVThHxFfiogQWxb6HItjHwdmvQX182mLzAsfJB4+Uzdt18dEY1FlLejTBgva4vijNn3u/+OVn+MNfVlOK+LE7g+qW2foRgplUjq+pY/qgpZ5l75GwawBVlrgGt+hITONS+ntLn8lHO7fyAkTEBk8QVCp/HQ5y7x+sAcWP9h7Zx5Ma1EAdn2l4u+YONn8OVgOLos8HVVpvtN0MtteF75OuSdFBr9gRo5jWFiWm7szjlbpuyvxFDLzIm/ivqoBaNk0UmI3wsgdTNMHw+2IpE5M+bj6jNldv8ssoQMIaKPuOZHuGsDXPQiJPWuXfFxONupcOn7/hdAbtJfyAqCkHMqyqhyVkl2IRWkiQyNDZ+I/qE+6Xy1yJxSshYCJXOfkFHTm6DpIOEEsmaJqd6dFalkJKjPcSoqKioqKioqKueZ/4wUkiRJXHzxxYwf74kQfuWVV1i8eDEZGRnk5ubSrl07PvvsM7Zt28aRI0coKyujffv2JCZWUTDtfFGTNEIgMhaKQUhvrFoqR5bFVAJMoe5lSsR7SSv0rM3R0KiXWDZjj39Rar40f5XidNYcMaCUNP5HfFZV38EBEEeYXIh+0TPCWBnRROjWe6fvV6a6ughRLUTked4JWPkapG2ANqOr/20uetEjkZS6QQzODy+CCdPBHOuZz9+BtMZHUc5It2Oh4KQ4d/pK6/orC/b5E2kbaDSuLNe8TKBFT6ujKukJX5+7nMJoU5IrDD/BYcLAsflLEYEY0UQUZWzYTRxDTVrQQWFnv+//gKizsP4jcS3pgqHEz+jrU9s9r7MOwabPxH4phCYI2QmNVuhiK9dh9mGIdkt+pW1Ck7mHz3mSz6UboPBC0NfQ1jV+djOV2++wKaI+yZavRAS2klkk+Wm0kp3Q9AJx3vNThPGoywTxna3EI4cUFFJxub9KQkKSzl/xWUmq2E687wvK5/60zQr7X8t7y1kFfH1s12b1mseEbu2bjJaX4ULCcfHbGBI6QWm+7/V7Gzkjm4ppftrZxk9f/ZEvSvL8m6/1KJEZV5wJ5viqi+Ce2nb2Z2EJ0GEc7JsNRxYLJ/5FL/nXlhylFd8PeVIUXV7zlugXfr5e9H0jXoFQPwImqtrmhW6ZsZR1sPgJmLxC3I+UZ4aqrgPlnqMLdsshBXuuI+WZQhfse1mf6/OzD/GnXzgXbbA+a+KExIDNVLf+W5GCCwoVx5u5D2bfIQqfx7SBcV+427Sj4nJK0XRHmXBSAQx4WBQnB9H/uZxCRs8fzuyv+D73uKdQc0xr6HEzZB3wP+Oh8JR4Bju1XWRfLH5W9CFanWizucfRZR3jdedxWnOcoDmVHKCSRkiYKQRH+LfdqvrxHjeL/n/DpyKoxN9AAknyyCA16iVqSdjdv4WEp9+wWb3qaJWI1776LX/PX322IxUVFRUVFRUVlf8U/5mMhejoaO69915atRIGshkzZvD888/z008/sXz5cn744Qdyc3P57rvvSEhIYNCgQVx00UV/P6eCPxjMQsbCH+OYMqCvHCGoN4qBpCKBpDMIe4+tRERNyU4hNRKWCHojst2KHN8BkEQUfnEtI5qVGgsuR/VG/wDRyQ6muN5HU5IDwZHQcXzto+pADK4Sunrkn46tELrYNUWWxrWDy94XRREjmwlj2Mo36s/4ENVcTItOC+OMrrI0TxURbn+XyOm/G1VFYVf1ub0Eyizi2p0xUWgtKwWab1/pcb7Vll63ijZXeAq2TqvdOooyxLKySxiSut8sjIxDnoKOVwpDfGXnliQJY0/7y3GZ4zFRysPyVPTr3q7XdlqBFkOFQUZ2iloJtYnaljTQ7Trxeus3ngweXwWcvbFZIC9VZG+cS1mWv4uUhdJnBGpY9d7/2kbP1vRbVJ5ny9fo1r4FwMfSJFytR/m/LcWxUJwh6qKcS4LDPPfj9J2BL9+wm4h8lrQiOnzlG1U7z2uiaX8hUdP1OrG+Y8vg64tE9l5tpc20erhiqjCY5qfCzBtEUexq25VVPD+Uy/S5+ydHiTDQGqPc0kh/88yB80Vt+29viSlvKThrrpA/yksWEfbX/ADBNch+7v1FOMtC4sVzVH1weqeoW6U4FfyRP/KFVicKoetNwrmw7EVRa2TufbD2PXSH5tKdvYRgRdboRVaRUs9n1091K7ZcmfaXi1oTljPCQRgIx92OhaTenvoJ5jjP87G9xJNBbCs5O5tYRUVFRUVFRUVF5S/kP+NYAAgNDS1/3a9fP7Zu3co111xDVFQUgwcPJiEhgR07qtDM/7eiGIYqR2sq+q1agydlXkIY+ZSoQlOUWE5rcEdMhSAr8g8Ze2q3P7ogT6SYv5FvNSHL3CtPozMHkbVBwlhTH4Y8yS0X0/kaYcA8tU1EV1eOGPVFQhcY+arIOkheKwph1gemaI9hIPe4/8v9lTIm/yR8GX6VbAWn7ezrSG+EIDMcmCuyUgxmuPxzuPwzESFaV3TBMOgx8Xrdh0KGLBDKimDzF8KQF9kcet8uimtWzo6oiqBQbC0v4SvpWmzo0J7eCgseEQVxzwXDnhHnMH0X7JpRu3V0udbdPreKYqO2Ek/tC+/7nrfxzV4CtmJhfD6Xsiy1Nej/XaitM6Gyln5JYc3LlBaKmh9/iOv/e+lKFmiGB7ZdY6Qo4Ay+MwXqG6X4bW0cCwDxHcT1q9HByc2w7OXaX496ozC6XvWtcIjbimHJ8/DjVUIWrTaYouCq78T1m7JeZO/5ep5QUBxE1jz3NNdTm0FvVB0KgTj4veet3H8r3ynty5fzTnbBvPvh9HYhaXT1DxDWsPptOspEBh5A7ztEwEldkGVR/HnTZ+Asg9h2tXcqKJhjhGwgiIxSaw4gQXgjHE0H8750K3dpXqPskg9h8BOiTZiixXwH59XteLzRBUFPd72UrdP8d1o4SkVbAmg21HOOzdGe5w29UmfB6FVz4T/edlRUVFRUVFRUVM4b/ynHgjdNmjShe/fuAMiyTFlZGSEhIfTt2/c879lfSFUySAqK8c0ULQY1piiPQ0HReDUYxWfuQU2mpoFYtk4FnN2yQPXkWNCmrGKUvBInEvauN0FIXL2st5yk3tDzFuFgyT4MGz4TBtyaiG4BvW4Tr9e8Wz/HK0meoqEZu4UEgCW74p8vo8XfJXL674Yvw6nNKgb7viRz9EaRJbLlK/H+wuf9L9DsL52uEpkvpflCf91fnHaxX9Yc0aZ73SqijgNF0jBLcxn3a17BFd4Yygph9VtCHqO+M15C4mHgI+L1mndEpGyghDUUBTABDs0XxkzFwGYv8W180xuFsU2RSVKzearH28jpj3G0sqHTGOZlDBXLZWVlc2jvTrJTD4vvzuyHPx4FZBzdbuZH6Yra7Wtjdx+ftrF2ywdCQlcxzdxbs/RZVcS2FrVCdEbI2AVLnxdOgdoS00pkGgx/QVzj6TtFcecVr9fOsRzXDsZ8Il7v+AEOLRCvLTlnr6/coRcMxVnuGkMOcDgqOflKArue/i0E4uCvLEPm3X9XzkT15Uhd+iIcnC8+v2IqxLeveZv1ma3gKIMtU4UTHqD5UGHkr4tTQSGxB1xwv3DKDX1GZIhe/AaOHpNZqLmQ41JTj8SXLkjMB3BkqZALqy+8sxZ2/eTfMmlbhOPfHCfqxlQV+FOhzkK0WmdBRUVFRUVFRUXlvPGfdSx4I0kSr776KuvWreOqq64637vz16EMPi05Ff+UQa0yeAHISxMGOSUqypIjBvruQY2sDUaWZY5Z3JG3tc1YABFVCsKYUFtkGY6vhLXvojsoCt1Ola7DFevH4Lk2xLWDvneJyM3Ck6IgoD+GpO7Xi2XLimD12/WzL0oB54w9woicn+b5nasyWqhFAP2nJifMpi+E0T+uA3S/sf63r9HCoMfF6y1f+y9FtGemiODUGaH3nXXOoDghNcY2/DVoNwaQ4PgK+G0yLHxcODz2/SYyGYrSay+3AsLo06CzMKauerN261DkkA7Mg9KiilHTvoxvBjNENvboYldn7PsvGT6rwtvIabMKZ1PeyarPife5Vl7LlP8esixz5OgRThzeR/LxI8j5aULOxGmD9mNxjHij9lrfSX3ENG1z7ZYPBFOUyEpzlAmnc22JaiZqLBjMkHVQZBrURbZFoxX3pluXQJtRIhNxy1T4foz/NSS8aXMxDHlavF7wqMjW8iWJZDC5Nf5LhXyLVg/oROFn74hrxfGkXE9VtT9LNpw5VLdnhb8TgTj4q5Mhq5yJ6i1/BKLezPoPxOtL3oZmA2venssJm6eK1/WRrbD5S5E1JGmF46zLhLrJU1YmqTe0HS2er3xk0Py4YC3LNrmfUxO6uGsfOUVGX35q/eyDd9bC+o8q1jSqihOrPfsfZK6YqVAdimzpf7kfUlFRUVFRUVFROS/85x0Lv/zyC/feey+ffvops2fPLq/B8J9AGXxKeGQJfBkD7CVgLxaGvXJNV2cFTVdJkpAkidYGd0SxogXrB7Iss2fvftLTM5CLMkVUJtSclg84HE5KSivpTssu2DsL9s+G/FQkZH6XRvK7FIAWd22IaCwi7oJCRfZBqh8RsRqdKDAoaYQjJKsOxicFxWCsGHMNbsOdOfr8ZCX82wyvlY04yvFZc8VfmVvWJaq5/4WLA8FRCtu+ce+Ln0VOjy6FtE2AJLJr/Cja6nLJpGYVsnLPSb5evJcfVx4ku7DSvUGrh27Xw/DnRRSr0y70upPXCumi1W/BnHvg5+uEZNLa92Hvr4FFhWq0ovA5CNmMvGT/l1VoNVLsX0kupKxxZ2JFVm98U1AM31W1G1VGrKKR02ASEeg6XdXnxPtcK68BclMqLOPQGJAlrcgusBUJCZ9xn9fNAFnqbp+1ydYJhPRdsOhpcNnFuamrFFpsGxj5mpAtyzkCa9/zz1BZHaHxMPYTuPIr0T5yj8Oce2uXXTHoUWg1Qhzv1mmetmWzVgxYsJeIa0PSit/d26lgK4H8U8Lp5/CSmiupwnlqyQFnqZj+GwjEwV/dvMp33vcyb/mxLW4HwQUP+J9RV3hK1CbRGuqntoLynNmkPzQdUPf1+cH/zV9Z/vqOl7/kojteZfmmveKDzhNEGy3KgBWvueWZalnTxJv2l4u2UJTuztCpgRB3xu7hP+HAfN/3UJsPJ4Jaa0FFRUVFRUVFReU8cQ6sXv8s2rVrx6xZs1i9ejXt25+jaPa/E4r8kRLlpjd5NMVNUWIexRigSILojaAPEQ4IbwNApQjDeZ8+zZjCHcJg0Pcuv3bH5XJx/6NP8ckXXwPw2dhw7uwqk+yMIySoGTFVLFdaZuPTWUt59eu5FBRb6dW+OUN7tOHCHq0ZatiJ5vQ2QIK2l1Ia353P17ap1ekKGHOsMK7s/VUUyGzcRwzEqyO6BbS8CI4sgm3TRNHauqAMREPj3TUXws5vJoK34fXfmBGhHF9JIQSHigj+LVOFHnxhOoQl1N+2nDb4dbKIatSb4Jofay62WZwJK18Xr1tdJAyUNZCaVciI52Zz6FTF6GWNRqJf+8aYr76w4gJx7eHSD4TkQ8FJKEhz/50SBimnTWRL5J0Q8++ZBZe+D6EN/Dvu+A7QbJA47i1TYcSr/i2noNVDj0mw6g3hdOx2vZfkUQ31DRTjd5Xfmzz3VLsVirPFvfK/lAFUWRYsslHFc1IuuVfNebSXQpCxvEZNq5atyImMJDo6Bpb9IOZpf7moM2Jz1Go3ZVmmbPfvBAObC2NZ88EnZGVnk5Wdw5nMdLKycwkODmJg354M6d+Hfr26YzJVrV0uyzKn0s+w7/AxMrNzKSgsxlKUzwB5MwPM4lpPKTbwv33xpC7/Bq1Gi1arQavRoNVqCA7SM6htHJf170hsZEjNBxDZTGQGLHkWUtfD9h9E5kFtszcUWgyDq76B6eNFtsGK14SzMBAkCYY+DUcWw+FFwslqihK/v8sp3ocnehx55rizdeHtJWDNFnr7+kT384kVjFXc48zRwsBq9j+Q4bxS+fnrXGHJFgWyzVEep529RGRGZh5wa/hL0Huy/+vMdd+7I5vWPVsBoPVI2PgpnNwE7cd6ZOfOAS6Xi2fe/4G3vpvPhVOGlX8uyzKPvPMD22a8jsYUBRe+ALv+T2RSbJ0Gx1bAsOegQcfab1wXJPq5gpOQlwphidXP3+s2EZSy73eYey9cO1Nsv7y/Mrkl/IqEAy6ioedaKikSDvOa+KuuQxUVFRUVFRUVlf8E/3nHQocOHZg+fTp6/TmOXvy74MvIq0SZKs4EqKiBraRiy3L5amQoz17Yu28faSeO0PTUXAhFFEeOal7jrrhcLu6472G++nY6AA3DtNzUwQVI3PbjCbZ93IeXpzzFnQMT0OlEhKrN7mDGog1M+fQXUjM8UYob9xxl276jdCtejqaDHhcSUtfrkRr1gNrZoGpPUm8xIC3JheR1/jkKet4sDDLHVgipi9i2td++or8dHCEi2hVHw/kaQHobXuuK3Qr4WWj4r6SkEPRBIpK6YVeh4566EbZ/B0OerJ9tuBwi+v/oEmFcveYHaNRTSF1VheyCZS+KLIrwxtC65qyd3KJSLn5hDodO5aHXaWgaF0bz+HAy863sPJHFxkMZKG6F6x99k7uuGcGwvl2RNBphQAltIParHEk4HPLdzobktZCfAtu/h8GP+3/8vW4TjoXdM2HAwwFlRQHQ4yZRp+HUNrGeht3E/a2yQUtxtPoyhPsyyHgb1S3ZwuBT+fP/Gu5jX7Z8OYd2rqdT+7YMGDikwvmQZRlJMYjbrIAELiA0ClmWiY6OIjo6Slz3KevEfAE6XfPzC9iybQebtm5j89YdFB7fysprbLhkmXHP/8TpItnncqvWbeKVdz7BYDDQp0cXhvTvw5D+fQgODmLPrp3sOXCUPQfFX16Bp/B0l3gNP15hpEOc6K8+3FTGE0sLKXVULdXzHcJhN6Bzc8YN6sTlAzvRNKGaazu+vShwu+592PcrlOZB37trdmDXRGxbuPRd+P1Ocd+KbQtdrglsHQldPA7ATZ+L9i0jMiC0ene2QjVZVnp3zSZtkHDOQfX9hznGYzj/J3AunOyV70l2q6emh7PEc370RuFsOLZUvG9yAYQ38r9eR95xMfXj2c4vGnQWhdQL0kSf1mFcnVcpyzJ2uwODwfM8X1hs5c4XP+WnBavR6j31G1IWfUr7Mfey81AyPy/awLWj+osgjD53ij5it1s68LdboctEURC6umu3OsIaCcdCfgo06Vf9vJJGZBEVnBRSgr9PhomzRACBvcQjJ2bJdWeFlXiuJZ1OOBsUDEaxvsp9VuUsO9XJoKKioqKioqKiUgf+844F4L/jVIDqB+mVi5cq07Pms0L+adDpwWEn+fhRzHn76RyaL1K+Bzzk167c89DjfPXtdDQaDd9+8THXRu9Ft2s6mfom5ITK5J/Yy32PPMnHTRMY3L0tKenZrNt1hGKriGhNjIvkpTvHM7RXO9Zu3Uen/D/pGlFEmUPmml+sFC5Yxu9vtSMo+C8eLGl0IjJ8989Cd75Jv5oHpFHNRdTosWWw7kO4/NPab18xFOgMUJjhiS48X4PG+jSw/lVp/oFG9BnDPJrn9hLodI1wLOz4HgY9VnftaLtVaJcfmCc028dPExISNbFrhjBO6IJFZHMN+yHLMle8Pp8DabkkRoew4c2rSYr1SLikZhXy+5aT/OF+P3vpen5duIJrLx3C249PpmG8D4OoRguhCeIvqbdwOix4RMjbZOz1Pxo0qQ/EdxRGs23fwcCH/VtOISQOOlwusiV2/R806lWpYKzboaBEWFtyKvcdPAABAABJREFUISKxkpxINYZB5ZrRaN2F7VUjzc6dOygpLGHP/oMMGFqNU8tRAsZQ0ESKKHdrLpIS8Z57DKm0QBjWGnbza7u/zp7HMy++yqHDRyt8PnO8EdCz5KSJ9j27MyQ2ltiYaGJjo4mLMBMbHUV2bh6r1m1mxdqNnErPYM2GLazZsIWX3/7Y57a0Wi1tmifxYB8dk5plodPIFDqDmG/tCt2a8tlAExqNBofDidPlwul0uadOsvMLmb9sHTsOn2T1zmOs3nmMhz6cTbfWjfjisavp1a6x7wNsMUxca1u/gmPLhRFyyFOBO9sq02qE6L/XvgdLnoPo5tBscGDr6HevcCzsmQldJwqNe/A8T3gXFq6csWAwigw+e4nQl4d/l4OuPp3sCpXvSTar6CNK8sFWKu5rStZVRKIIYADoFGA9sfKMhWb1s9+SBO0uE1kLx1eIrM06ZC1YS8q46qH/sWT9Tq6+uD+XDOrJwRMn+fjHBeQVFqPTafn8hbuZ6VaIjA4P4fGbx/Dsxz/z7Mc/M354H/R695AosQd0vBLWvAuHF8LO6eKaHvYMNOwe+M6FJ0Ia/tdu0Bthwv/B1xcJ6b9fb4PRb0OUu36WwSQyFSpnDhecFM+fzhLR39lKxHn2FUykXIf/9oxSFRUVFRUVFRWVc47fjoXmzWsXpSRJEseOHavVsirngMqDdG+ZCm9ngqEaw5itBHR6Thw7QkpmAfqgYDpa3BGlXSaIVHk/+L+ZvwLw6vPPcEPvaJj9MwDx415m6/39mPrN9zzz4mscSk7nULJHmzY+OpyHrxvFfRNGYAw2gKOMZrF7gCJkjY7/y23Lnyc2U2Y7yLLNB7hkUI9Az1LdSewhnASWbBGh3XJ4zcv0nCSWOTBPGHdialnvw2YRUxmwW4TEiL+SM393Khui6kpVDoRABtvK4FwqEwbp0iJh+AsKheIzomZIbYwRCqd3CN3z3GMimnfcF2L9NZFzFDa4jaH9HxKGhho4cSqLVXtPodNqWPj82ApOBYC4cBMr9pwEd/mT264axdQZc/lp/kpmL93AI5Ou4Kk7rsFkrMaRFtFEON4Ou6W/Rr3ln+NFkkTWwvwHYfu3Qm7Nl+OzOnrfLhwLRxYLPWpfBWNlwGEXjlN7SUXHQnWGQZtVOPEMpn9WFPU5pGvXbuzcuYMmbduRXWhl4Z+/snPnTvr07sNVV1UybJYUgdkt42HN48TxQ5xKz6atcx+xAM2H+O2g+3La9+VOhRbNm9G7R3dGdU/iqtJpAIx87ndGVpYEK80vf3nbDdcgyzLHk1NZuW4TK9duYtX6TTidLjq3bUGnti3FX7uWtEswE7T+XUjfIRZu0p+wgY8y0c+aMi9d14/k9BzmrNnL76t3s2b3cXYcPsn1L/3Avh+eLM/WO4t2l4qI89VvisLQCx4R2VF+tPNq6XcvZB0SUm6z74ZbFont+EvL4cKZcOYA7J8Djdz9r9JmZPnsoAVbydm1nc53pt254Fw4SSrfkwwmiEgCSSfuYZZccQ+zWUTdj6wDwvHQfmxg28mt54wFqLesBafTxcTH3uWP1VsB+HH+Kn6cv6r8+zbNEvnsubvo16MzM2d7lnvwukt4Y9ocjp/M5GDyaTq18nLkBUeIgumtRggpwYJU+P0OaHMJ9LsvsHu8In8USFFocwxM/Bm+HgGZe2DpC3DlNM/35VlyuSDlin5LHwKuMtAGi9+/csaC97KVn3dAPK+qmQsqKioqKioqKioB4rdjweVyeWQL3NhsNtLThcFXp9MRHR1NTk4ODofQnklISMBgqAct1n8DslxBSsgn/uok17SeQPCOHvRVuLTCdt2FIvXBgExyTinFTh0NXKeJsx52Zys84ndq/YTLLuLLH3/DeXA+zP5E7EeLCyE8CW3RKe4cfyFXDevBe+++jbXURsukOGx2B6kZuSTFhlBiKcQoa2HLVyJlXdIgxbRmUqNQZq6J4M+92RQcXAVRyYC75sPeX8Hi5+CuQRf/5ju8yPfnIQlioHZkiTDu12RcDkuEpgMheY2IlLvkzernr2pgqzgWtHqhbW+MEhHrNRX6lPys5W6z+BfNfy50fA3mmttJde2j8j5V5UAIJLK0vFaJUaxfMY426S8KMJ5YA4m9PPM7LDWvE0Shx63fiOtbdor6HRc+DwmdKxY/zj589rJOGyyZIgqpNuwmahSs/6DGTUpZwrin12ro1CRSbFfZbaeLCW8tZP7201w4Unz29t2XcvPwDjzy/s+s332UVz77ibWbdzD/vQcwG92yE76uq6aD4fhqEY2591chy6HUeKmOBh09hqg9M0UWRlX4ug4adoPEnnBqK2z8HAY9IhxANitYc0Q7DU8U8m/lxk9lPVL1hsFAo5Hr8z4e6Lr86Y/qgWFDhzJs6FCysrNxOh1s2bwZm83G1q1bGT/+SkDi7bff5sC2dfTq0YM777kHXE5cweFk5RRRWmpDn+aWbmk22FOQ3uW5LtEYQFvR+D78wmEsXraCfn17s37JXPHh3PvhMEIOLLq5aBveZFVsRxLQ1OikQ3wQKbFGDkeHUWSx4igpJOv0CSxSGsacWeg0JwEnskaP3OlqNE0HiLomJ1b6d5KiW9I0LpQHruzHA1f2Iz2nkM6T3uNwWhYvf7OQF28ZIebLPnT2sgYj9LsHtn0rtrnoKeh9h3+SUdUVaO7/IGQfEUWiZ94E1/9W83XtXai+790w9z7YPUM4KrxrIASFnB2Z7igV95qSQpH9pUzrM4ran+v9XLSJc/1c5+uepDeBKUK0E0kWtRUK0mHvL+L7lhcK2R/ZKZwM/qDUxwmJr/4ZLz/Fv/WFxItptxtg5WtwfKWQHAquJHV4aGG1q5Flmfs+Xcqc5TsJ0mt5+foLOFNgZfmuNCLMQVwzqA35xWUk2o7BiXxgCABzfv6OKKNEWLCGYis4UjaB1isIyvs4etwIR5fD6e3C4XZ0qZD8atRLZNLVhM7dFxakVmwnvlAyH0E44a/+Af7vGlH75M8nYdxnok/VBYv+yl4MJXngcIAxUjwnmKMqPstX14b0bpnT/FPCEaV8ptZhUFFRUVFRUVFR8RO/HQvJyckV3ufn5zN8+HBatWrFq6++Sr9+/dBoNLhcLtavX8+zzz6LxWJh6dKl9b3PKoGgDA4UKg8SFGMYCCO4P8VM9SYkvYlGzVqRnJxCh/R54vMuEyGiMZQV+LVrD9w2kVOb5/JYi6PgkkRE6tCnWLN5B6s3bqdzu1a0aJrEo9eP5Nfl23j7x8UcTM4oXz7aJLHmtkjaRTpwaYPQxLQSRkIgzCgu7aLSv7rAghehDYQusc3i/2C72/XCsXBoAfS7q3ayA8qg3xgJQeGBR3XXuH4/o/n9mc/PqN56o/I+VWUMDiSy1G4VbUdGGNCU5ZoPdTsWVouaAIGQe0IUbszcK963vEhEI9dUqFlh90zhfAgKg16T/TZuBeuFE6DM4azwucslc8uHy5iz6QRGk+e8fDhjKU/fNIK1Xz3Fbyu2MemlaazcdpBLH/qgonOhMkEh0Ppi2P87HFzgt8QNGp3IOlgyBTZ/CV2vC1xmqvdk+H2rkEPqdgM4bOIakJ0ew0p1GVtV8U+UbDnHxiMlGMGslygpOsOQri3ZtO8o3Xt0R6MR19qaNWtwlBSyfuNG7nrgUQA0pkiiWnbHtOYjIkrcjuDmQ3xu4/Gnp9CyWRIh5hCMxmCCg4MpKxPGuX37D+JwONDlHRNtEYQhvhqcTiezl27g96Xr+XPNNnLyPTUUTHroH57B3Q0MdIvzXHdrUx1MmlNMatFUGsb+QqP4KBqZHSRGmWkcG8I1/ZsTH+Hf+U2IDuOtu0Yz6Y2ZvPTdUhrHR3Dr6N5VL2COgQvuFbJnmfuEtExesl/SZ1WiN8LI1+C3yXBmH/zxCIz91H8jeYcrYNWbwgG45m24+PWalykpFEELGp0wjIIqJ1YblD5ObwSdUZxTa56o/XLQLWLXaXxg6ywtEI5XgMgmge+P7Kpa5qhRL5EFkXscDswR9+QA+PLPXXz2x04kCX58dBRX9m8NCIfDt0v38cjXq8grLuO1WZuZ+fQV5ctd994ynPYyEqPENeaqzrGjC4a2l4j6SYcXQeEp4Vw4vVN8l1BDEIriRAkkY0EhqTdcORVm3SzOjzEcBj0ppnoj6EOhKEs4GXXBEJlUcfnqagYp2EvKpU0J9VGH4Z/Wr6moqKioqKioqPyl1LrGwhNPPEFpaSmbNm1C6xUtqNFoGDBgAEuXLqVLly488cQTfP755/Wysyp+UFVEdlURgIoxzJLtpXvsY/Bhs4g/vbF8+datWtPakA0bt4qotwCNp1EF+/jtGiMGrcQhuRlthj1LRnY+l970IIVFviPiwkOMXD64K8nJyXw4sJB2kQ6yrS7eOBjL25d7ZFsiTeLSXnskj9uGnPvIXJ9IktDEzdgtHAuKTnh1xLQWBrTjK2HTl/4ZZCpT5j53YQlCczvI7L4u3Hq8dR0k+huZ7c98f1XNBG9KCjwRtHU1BtutkHdSGG0MRrB7nV/FEJq6sep6JZWRZWHwXv6yuy2GwOAnofVI/416GXuFLjQII7y/zgigzCGyWlwuGZdLRqORKLU5uOn9pcxcdxStRmL6QxfxhXv+l6fNY1TfNnRr04Qrh/UkMTaSEfe9w8ptB3nqk1/48NHrqt5Ys4GiKK/ljJDBaOxH5CeI4vBr3xVGmsMLoe2lfh8fIHS9lzwnIryPLYX2l4vwdEkrIkDryvmI9Kytg+4vMh6Z9WAO0XPlZSO4csL1YI4mJSWV06dP07FjR/bu3UuHXheINuTORghJXU6rUzOEUbLrdcJp7cbbBvjpF1Nx2ssqbxKAwsJCUtNO0XzPJ+KD1qMgtrXPeWVZ5pdFa3nuwx84eNxTED081MwtwzswuZuGVq5j6GSxLZtLYs2ZEL7fK7HoYCFn8mRk2UHy6SyST2dVWPeewylMvdQgznV0C+Ew1lYdKX7zqJ4cOZnNa9OXc+c7v9G2cRz9e1VjvFRqqBxZKtrSoT+EUX/AQ+XO9oAJbQAjXhXSYwfnC3mjC+73b1ldEFz8Bvx8HWydJhzm8R3Ed1UZOo1hbqeCKiNWJ8r7XbOn7y3JE9J4xRni89YjA1tnjrtWiTm25sATb6w54l5bViTq4zS5QGSM6b2k8iQJOk8QWQsHF0DH8X4HQ+QWlfD4NCF59MZNA8udCvnFpdzywWJ+3yD2O8Sop8BSxo3vLqTLAxX7JOVWYnfUkNEJENYQetwsJKWOLQdrNix9XhxXj0ni/PgixC1FWXxG3KsDvde2HA6XfQhz7oHt34MhVLRtJcvY4C7mbPaR9eddO62q304536Fefda5qAeioqKioqKioqLyr6TWjoU5c+Zw8803V3AqVFixTsell17K999/rzoW/kqqisiuKQKwumLN4B6cON3zeK1j1f/EtNt1QtfXTw4s+IwWu9/BoJWYd0xLhztfAI2Ox1/5oNyp0KV9a06knaawqJik+Cjuv2YYt18+iDBtGWz6HCwWihx6Bn+bx+HcQzw+LI64MBEhfdMFiXy/q4w/T4XQ+5PTJLoD9I7ZYzA4RRZDuGQhTuNfdkWtCYkXg0BbkYgi7z255mX63CUcCwfmCR15L4OaXyhSSKZo8bs7yjzFtsHz+1XnbKjOOOqvMd6f+eq7ZoI/GKsxtAdqFLZZ3Vr8WqFv7H1ujZHCoFCcAWmboXkNRVCLz8DCx0UhSxCRnBc+H1h9jOIzsOkz8brFcP8zAdz8vkMYRPu1aYBGI5FdWMLYVxew/mAGIVFxvHHbCNq0bwpuxYjg6CRuench01++nQahOvp2asGs1+/m4vvf5dNfVnD3+GG0bZboe2ManSimvPlLccw9J3m0qKtDbxRG1HUfwKYvoM1o/50uAFqDMA6t+p+ot9DmEmFdkiX3b2kF6lAEN1BjfX04ImrroKtn45HFasVisWA2mzGbTJ73egmz23FjsVqxWmWOHz9GQUEh3bp14+abbwbgo48+4tiRwwxraGGsfZ7IIul0NYx+F4AzBSUUlNix2T0ZNaENmjFs8ABKS0spKy6gKPs0yampFBUV07ZNa3IObaB5DdkK6zZs5pHHHmPTLiE3FBkewh3jR3BjzzDaOg8gZR0AZZPmWGg6CEOTflxoCOFC98d2u4P07HxOZuZyKiuXkwc2cyKziH0HDvJG30Kwuq/RU9shfTdENkOObklVV+4rt43k2Okcfl6+i6tfmM7275sRHx1WxdwIeZTWI0T09IaPIGMP/PkUDH488D5EIaEzjHgF/nwCVr8FMW38N0q3HgntxsCBubDgYbj5D5FB4cvQWdXzR+W2ocqz+KbyedGbKsjYYYwUmZAAbUcHfu4Ux4Kf9bMA8by48VMRUAEiwCJjN2iDRA2qlsMgoavoBxr1gtCGUHRaOOL9kfIC3vltK4VWG52bxfLoFT0B2H40k/Gvz+dEZgF6nYZXb+jP+Iv6MOi5BRSXeTySoQ2a4XTYSUoMJ9eZSma+n85ZSRKZC7FtRDbiqW2Qsh5ObhOZIO0vP9tpGBQisgfLCoVDPLatf9vypuMVwkG0+BnY+Ilwcij3M3NM1Q65mp7tFUdf5TZVVT02gykw55KKioqKioqKisq/nlo7FgoLCykoqN4oW1BQUOM8KvVMZUORv0Zgg7n6wYLeKCJHvQcnaZshZa0w1PX3P1vhyPoFtNj9JgatxJKTwfR44FsaNmzA6o3b+OHXBUiSxKb539OrawdkWabg0FrCzMFCOsNRBms/ExkWwRGE9r2TkNlTcWSdYPqGdB4e2RSAFk0bMfjBJ3FV0g9+ONtTMFSPnWkh75xb54IkiQjV9J2w7zdR3LqmCNKEzp5aC9u+EcZlf3HawemO3nXYxYDRXuZJczcAlhxh0LeVCOODreTsa+SvSoP/q41DNRlSAz1uZT2h8UJ2wm4VUYy2ElHIN7EnHJov2kl1jgV7CfxwuZBY0AbB4CdEcVZ/614orP9AFKENawRdJ1Y9nywLzXZZFgYS93ambxRSY9cPacOR0/lc8tI8jqYXENcgkR53f8JcdMz1kqHuPfltAB7bBnqNzLRBOYzs15HLBnZl3pqdPPjOTyz86JGz6vOUE9dBGCuzD8HWb2HYM/4dZ4+bhXMxfZfQnm5ygX/LKXS/SdQxObVNyE7FtoGSfNDpPCGs/uDL0Bmosb4+2lptHXTVGY9qsS8WiwWHwyGcCSaT5z06TKZocDkoySvEadATHRPDqVOn0Wg07D9wgMZJSezYsYO2oRbG2pd7nApjPgKNljMFJdzy+TrszoqRxb0nv42S36aTXRz/8gGyzwgH2Y6duzg56yl6tdOzsywJW0oxvWLk8uvxyNHjPPXCq/w6ZwEAZlMwj90ynoduvJSw9a9DhltXXpJEW245QtTgcLcXh8PJ4ZR0dh5OoV2zRLq1bUrjBLeBr2UpFGXgOJqGTgPZDhMxjdsII21ZEeQcQco5wq6NKwhu1pc23S+o0N4lSeKrx8az+1g6B1LOMOHZqSz56MGqizkrNO4jnJGr33TXXXgGRr9T+6LOXSeKQszbv4X5D8Ada6qOzK7MyFdFZPepbbBjOvS4SXxeUlgxurqq54/KbUOVZ/FNTedFb/RIgbW+OPD118axcGSJKAKuC3bX7DgsMtSKMyF1vfjTG4WRPa69kFgqOi2ee/xwLGQVWHl/zjYAXryuPxqNxHfL9nHHx0spsztpGh/GrCcvpXFSIlP2t+D6G24gXs4m1jWTeLKJuyWMOLKJ5TgpckPmZslsOXIGvU5D12Z+ZM3ojcJ51vU64Rw/sx92/ijqMIz0kWka2UQ4+/KSa+dYAOh1q3AurHkbFj8rZJIa9ap+mZqe7S05Qj7TEAKR1Tggva8x1bGgoqKioqKioqLiRa0dCx06dGDGjBk8+uijtGjR4qzvjxw5wowZM+jYsWOddlAlQOoq7eJLpsD7M2Xd1hxPUb3WFwtjix9s3rqdQ1MfolU7ic3ZJi54fCbmkBAcDgf3PiuyH2679nIMej3vfjGdpWs3sWbjViJDzQzo0pKnepXSSZeNKygcTb97wBTFLZf1Z/O+E7y16AQdE0MY0TGGApfxLKdCZezoKZDNxHGOnV/mWE+0ZfouaDqg5mU6XC4G2NlHAtuW3StyWWcQBm5JI5wKJndRYVux0Fw2RQr5F8UoWSEi7V+aBl9T+/B13P5mb8iymNfldEvraETxU6jZmZR9WDgVgsLgul+EoduSVf0ylZFdnqLOfe70FIz0hdMmjJsApYVgjODoGSvbU4vQaTVc1qspfR//hdO5FprGhfLFk5fzTmH13YXdJfHCN0t48oquvPPgNSzauJdFG/fy7by1TBoz0PdCkiQkkbIPCYOLv5iihXzG9u9E5kKgjoWQOGjaX2QGpe8ShdaNEe7fOAAjvS+DXqDG+vpoa/VlZK1jtoXZbC7PWAAwmUxYrVZMJhPoZHCUEmwMprSkiAYxEezXaUlPzyAhoQEZGRlERkZygW4XyE7kViP4v+J+fDRgMLIs07B1F+xtLq92d2RJQ5lLQ9cunbn3ztsp2jmbcQ0243DJXPf1Afa/cQlJjRLp0rE9e/YfICVVtBeNRsOtV47gxftuICEuSlwXeSfEOWg9Crn5UPaeKmbHpmMcPLyOQ8npHEw+zdG0TGx2Tx2fQd3bcv+1FzN2SA90sgzpu9BpYNY+O4+uKOC6wQXcNOQCnK4MDu/Zypg2OrpElULBSqwrN2Nq3ldEQ7vPfYgpiN9evpFed3zIyu2HaTV+CqP7d+KygZ25sGfbqp0MkU2EFNGyl0S7Sl4HHcfV/HtWxYXPCaNwzhFI2+S//FhogigmvfYdSF7tcSwYvTIvqtOAr9w2/q39Ul3x57yYooTTe9lLwknm5zMb4KmvEIhMlVJsvN0YaNBR/HUYJ2oppKyD1E2iHtfpHeKv/FhqNlrnFFgZ+eQPNAtzMqB1DGNayixcMJf0w2l8fomeTokRdGkUgq54KewpLZfuq4rm0klCsrLp/dkZAJa9eBnDhjT37zgjmwrJsAPzYNs0yE/zPV98R+FYOLGmds4dhYGPiDaYvEZkd0Q2q/p53R8HgFRpWhVq21NRUVFRUVFRUamCWjsWnn32WcaNG0e3bt249dZbGTBgAHFxcZw5c4Y1a9Ywbdo0LBYLzz77bH3u7z+WUksRhuAaHsj9lfOorshcXbEWu2UKnEBQxc9sdjAaxGcFuejyUtABjojmOGxeRZK9ZCo8uyzz+Vff8sJLr5B8lwRINBv7BNogE6V2F1N/nMP+o2lER8eSkp5Nj9E3VVi+JNfC6aO76dRPbP/ib7LY9vYbJMZG0Lt9M1o0aUjy6Rwu+Xgvdw9NYvKYeL8O1ybrKJWrcEC4/Cx86aq5GemCo9HZrTjS9+FIrMEIanei0YViAFylhRVkPypg8/G51UIwioFNDxjcGQtmz++SewaM7vemKBGhbSmCArdcUpBTGG71BvGd929bz5QGuO5SmwNDTcvUqX0Yzj5ua5GnTZgMnlntJR69ZL3R7VhwgiUfzBFoUrdhyD6EbDBT1uFaz/p8/G6aolzxe4clYgtvKeax+6H5DFB+OjQYzPFoik5hsxbhqqya4nWdSnaL0rpxWnKwB8WQW6pBqw8iMjSYlFwbmUUOYqLCWPW/aygyREIhNfLT0m1M/3UeU269jMcnjeH1bxZw55s/kdAgliHdfUdpalxacexI2Pw5ZuX89bidoF0/IaVuwHZsHa6kvhXnk6u/TnSmeHSAPT8dp84Mkg70bpksn9eYr/tzEDidoAmqup1Uvn4qXze+rrkAqb92VPPxWEpKPHJHsrXCsWl1BsLCDeXb0OoMhIYZSElL48+Ff5CRkYFJKqPUaqFBwyRyrE5csovwqGg2bV3FieRkunQ4BcDt3+7mm1Xzyrd7OKOI3jU4FgBeeuE5br/mUjTOUoIsn0ARHIseTuehOtIWL+V0ZjanM1cDoNUHcdGFg3llypN0CBf1NUrtLgwH5qEBduu78+EvWSxa9wTpZ3J9bE1LSKiJpg1jOJSSzro9J1i35zMS46N4a3QMVzfIwyVpeXWLllOFVt6cd4A35x2gY+NIjqRLNFhm487uWm7poiXaZIVjy5FPrMaZ0ANHi+EgaWjaKJ5pT1/HLW/+Qlp2MZ/P2cDnczYQFxXGhOE9mTiyNx1beBmJHe77nzYEbfOL0G+biittC7a2lc6dv/cXmxPQoEvogS7nCI70/Tiajzp7viramya2g2jfOcex2RwgG8Q1JhnENebrmaOcym3D/d5WApZMz2z6AB2CvrCXUJp/JqBF/OqP/MVXv3XWvaIqvM6TpUgsow2qmMl01U8YfroCTV4yru8uwzbxdyE956zi+cILva0ULWCXDDhrum7cp8NQUoAGsJka4Co/RRKEt4DOLaD77UgFqWiy9qM5sx9N9kFkUwy2Hnd4rmEAp1fWntOO7vhiIvJT2T4JIASwwYkVjIqDUXHK9WOHsrzyxawEc4YYMonhjBTDGdx/UgyR5POc631u61DKzzuDWZEiE2w0Uur083ncva8aU5y4zkPisTnO/i01LS7GsOsn5IMLKBvyYtXP+3LN7VIfHCl+D1sZzrIysFpAXyyyJh0l1bQnH+gixHx6Uw39j/saQ6qxnwq0P1JRUVFRUVFRUflnI8ly7a1w33//Pffddx9FRUUVpC5kWSYsLIwPP/yQG2+8sV529J9KYWEh4eHhDHt6JrqaHAv/MF51vkFPdvOOdDuLNUP8WmaQayPPyB+SSQw3ad5H9lPqJUgu4zPXkySSyR/SUD7Q+FGr4G/ECNdKHpG/ZDdteUz7XI3zt5GP8qHrOTKI4Sbth35vJ14+w/euBykliLHab+qyy38JjlIry1+7moKCAsLCqtYP/6e1o3C5kJddb9KG4/wijWaqppoixsAQ13qekj9mJ+15Qlt7Z+wU53sMYAufSDcxV1O1FvpFrlU8Kos4TjtaJmo+oVCqRr/9HNJH3s5Lrrc5RHPu174S0LL3uqZxmbxUtCvNlIBqLUxyzWCCPJffpYv5XPPP7qf+Le2om7yHN1yvk08o12o+xSX56dz1wW2uH7lKXkAGMdyueZMyKbjmhYCe8i5edf0PC0au13yEVarFeZJlPnRNoQ3HmSldxteaa6ud3SDbGCKvZ6y8iJakALBAGsaH0q2B1Q+pRJScx08uocN+hWYqFqn2EiZjXIu4R/6ODXTnBe2jfi+XJJ/iK9djlBDEOM3Xfvf554N/Szuqilg5mzddr9CQM5wmjsc1U8iSaq4n84zzfQaxmY+kSczXXOTXtr5xPkRDMnlE8xx7pVpK/3ghyS6edX3AALaUf1ZICHmEiz8pnHzltft9DpGcIYZizNW2I6UfySCGOzX/o0QK3El1pWsBt8s/skrqy2uas4uc62Ubs1x3YKSMezWvcETyMyPCB0+5PmSIvLHGfv58EWg7qmk+ldqzfft2evTowbZt2+jevXuN85faHIz93yIAXhwZT9/ePc/1LqpUgdo+VFRUVFT+SdQ6YwHgxhtvZNy4ccyePZtdu3ZRUFBAeHg4Xbp0YezYsWpH+C+nASK6L0PyX7t5uCwiRZdJAwIyMNwg/0IimWQRxVSpeiNtIJhkK5PknwmmjMXSYPbQtk6GnKo4ILUCGVpzHK3swClV3/SsiIGtGT8LCroxYAegjOploFTODXrZzlh5EdfKswnBSikGfpUuqXG5EETB7SJC6rT9U1IDkCGJ09XOF49HZkmPkyHyBuZK58tAIbv/D7zd/SyNZaS8ks4c5Gp5HjOlMX4vm4/ITogkP+DtqpwbBskbAVgn9aqTU6G5nMwVspDq+1gzyW+nAsB413wA/pSG1s6pAPRmJ204TilB/CKNrnF+m2RgsTSExfJghsrreUz+lNHycooI4RtpQq32ASBXiiSLKGLJpQkn2U+bWq/ruNQYZGhGakDLnSYeO1qMlBFLDmfwsz6DSr2TJcXwuObZcufCm66X/XIuKM8VtgCeK5T7aq77PltXbpB/ZQBbsKHjOc1j7KEdjhqeo/zla+laesk7aUA2t8o/8bF0S8DrSEBk0KTj+3nYLhnYInVlkLyJAfLmOjkWVFRUVFRUVFRUVP5OBJSx8PbbbzN06FC6d+9edTFOlQooEQeZZ7JrdrT8lVJI1hyRLl1S5CnsG97wbP1Um1VI5MgOt1RIMGh1yPYSgqf2Q3I5KL1ru0ipL1/Go5ly6RUTWblmLX379GLqG0/Rdul1SLKLdl/Y6D14FMbgYBxOB8eS01izeSfIMk/cM4kpD95Wvg5p/xwMmz5EQsbW/TZcse0r7GJ6dj5drn8Ja0kZb4xvTYeGIdz4awHdb3mz2lPQXj7M/+TXMchl5Z+5NHqchjCchlBI6ObfuXSU1TyPLBN0ci2SrYiySz9FjqnGwBMaD5YsgqcNFZJG9+zyfW2YztY7ljJ2E/TdCGRzHGXXzgFkMIaLWgqmqLPXoVB4GsqKISgEwhvVfDz1RGFhIfFxMX5HtmVm5dTcjrzbh3Kda3RC3qm21CRJYbOiOTgH3dq30BQIw5srvhP2Ef9DTqxUXFGpbeCFdtPH6Ne/i6PjBBwXvebe92z/9s2rDoc2eRX6bV/ijOuIfeBTFefb8HH5S33eUbQlWchaA5LThktvxhbbmZHv72Td0XxG9WzCwq0p9GoVx/JXxtHjxfUkXflCjbsSsnMac+YtoHmjWAw6HQeT07l0YFey84vZuOcooWYj66c9Q7OGHuOiJnM3hu1f4Yptj+3Sj6tZu5vIZhXeanf/H/qlzyAjYR/3Da5m7iLZNUijaPbPxjDvTlyJvbGN/8H3TDqjl5yIFIA0yV9LwO2oiv5IlmUO797EyZQTNGrclNZdPfJSUmXHj/tcyHqTKNDqxe//9zW24lzCQowMvnQiqRnZbN+2lby8PLLS0+iQFEX7Ni149Z2P2XH4JHmFFkotxZx6wECMSSLjoi+I6D62wjqPZRbx8A9bqIl3J3Sk3bIb0WTsxNl6NPZRb5d/9/iUl/n0S08ml06vx2Q0YjQGYzJo6B6v4ecRObhkiSNdp9Cktehrvv1tCfe+/CkAt185jHcfuaHqHZBlDGvfQFOQQoqmCY8vszNnSwoOh5A4uXV4G16a0Itws4ETmYXsyNKx8+hpdh1NZ+eR0+QUWri1q4YvLhFGXHvzC3EmXQBBvg20OYXFvPt/S/lgxjIAzGYjqSt/IMgglteveg1txk7sPW7D2XKEZ0Gzf3KBRDQR09ICgj/tIl7evQuCK+2PvmrnjeGrQWhyDmMb9y0uX/UZbFYh4aIznlcN93PSH/mL0m/Vpc/yvj/pDGd/bysBRylYczD8MhFNfgqu8CbYJvxS8RmuEvqZ16BNWYNt2Eu4WtVQHyD7sKilMkc8w5WO+bKK2jJ+Zs8cXowm9yiGE0sAuGWene/3uGjZMJK1b13LgdQcxr4ym0JLGS0bRvLVfcPp2apB+eJ7C4w8k9ynxs0Ezb+PuaNELQlb8+G4kvr5t38NRRS4fsWLop31vgdni+Fnz5fUF83+3zHMvxtXVEtst63xvT5/pJDm3YX24Fzs/R7G2eU68dymyCBV14YqX1u+xgFmdy0u5Ter3N/5MU4pLCwkPjZazVj4G6BmLPxzUduHioqKiso/iYDCfR5//HEkSSIsLIxBgwYxdOhQhg4dSpcuXc7V/v1rCA7SExxUQ7TXuaydUBkpVAzmg9warPqqBiNaMJmgNA85yCgGFTo9ctpaJJcDTNEERyeKQrUKNlv5y1ZNE1i2vIyBvbrQrmw7yC42pDlIateL716+i5RTmbz62f+xat0mZFnmromX8spd45BsQs9adthwbp2KhAwhDTAUnICCExX2cO+OdIoKCzEbJG5qXUxsSAmtIqu+tDWyk4ny70yUf0dLxXOucdnRlOagL82BwhT3IEqqfjBVydBZJdEtIH0nQac3Q2g1BoOoRDBHACL9Pxgb6H0Mwg0+InolEVko6QwEu4rFQNMUItZnqOL6s1nBXgCSA3RhYKjhtlCPTkVbTW2iEsEGHcE17Z93O1Kuc4MJ9LWMbnQ5hUFG4wK5BHTuQtxKgcTso7DkWVGQEkRh4CHPoOl8NUEaLVS6xpB9ZOrYRAFxnTkcncH9fZmf94NgLx3lqMYAaC2ZaIMr6SuXeGUx2IXzTzKYocSGxm4huCiZnvFOVh8oIypIRoeDjfvTmL5kB1f0bEhNJt0GciaPtTnA3NllHDlxkjkTjIw7Wsac5Zv446PHePXrOazbeZhJz3/Omq+fQ6/8Hu4CtBoJgrV+6L67LBXfdxgDGduR9v6K4Y/74doZENEYND4Ma96426DGmkWwXOYpWl5d0WJ7GWhloAwMNRTkrom/YTuSkUk7mU5ZiZW0Uxl07i3mkWXP7lqtosZCbtoh8nOzSUhsTFLbbqSkpHDy5EkaNWpEm/YdOXZgD807dSM0IooWwSaWL17Ils2bCA/SsOjEUZ587UPSMz2ZM3eNaEOMKR1M0TToPQY0Fe9vBr1/GW6xR2egydgJhhC0Fz6L1uXJ+ppy38189/10CouKefqhu3j1mYc9Cx5cAHt+gYwcNAmdaBOng/zDANw+JAlt8eWcysrnyRtHEmz36Lev2nGEJz6djd3hpHf7JlzR3shFuhRkWaaJK4Wfh8KZ3lqeXubg6x12vly4m/lr9/DxqGDGtdPTLrIZE3sCPbXIciMenZPOuyuzGN0mmDEtnOiPL0OfsQv6ny2vAnDg4BG+m7sKp72M2IgQZr73COF6J8hu7fzwhpCxE31BMnq51LNgiJ+R5Dp3mwwJhbCGUHia4LwDkNS70nzVXM+xbSDnMIaiFN/9iyEMqKXh5Hy2I8oI1geYWVNTMfdA+qzK67KXgcsKlgKISDz7mS7IAIRDZDzcPA++vQxNfgrBM6+CG+dWXdDZJZ4rDBrX2fffyuiAUtGfoTUQHGz0XZ7m1Prq16Nw6DfkYpEN8Pb6MuYfduC0O7mipYO8HfO44sMT5FmctIiUyMvOZNCj05k2Npibu4r7fzhNQVuzY2FlmoYNGQb6NbBhSFkNMc1AW0MfAuL5CsAqMnj1Mc3Qm3xkHspWaH4BaPRoco8SnL0boluePZ8/DheNOKH6kEj0IeHiOcReIvomyeZuTz6ofG1VHgcoBZpdDqDMXaw78H7OFmibUFFRUVFRUVFR+UcTkNjtiy++yNChQ7HZbMybN49HHnmE7t27ExMTw/jx4/nkk0/Yv3//udpXlfpEbxKDBnMMmKOrjnCyl4jIN10wOBxiaopESl4lvm87uqJToRId24nI/L37DyHvnwPAd7vsXDnmEu6Y8j6tRkxi6syFyLLMfTeM5ZPn7+N4WjqPvP4Fw296gncenojOaSWnBApDzjbgy7LMC78fBODe/uHEhogBTbNI3waCePkMb7te5gb5N7TIFFLdIE4W0WOy0z2to+MnprWYZh+ueV5dsMgyAJFJ4C+OUs/yJQVgcA9w7SVgyRF/tkryStYccYySFkLOzoL4R6Nc59UZi2vCbgV7ablxBWsOWPMg7wT8djt8NUw4FXTBMOBhuHsjdL32LMNotZTmi2lwRO33EyAsQUwt2eC0Vz2f011cURfkiUS0FdOrsXh94GQ+r04UmRYPf7ORgR0SqlxVlJzHw64v+Mb1MG10p7i9hzCGJee7uLeXMMzc/+b3TJ1yK+EhJjbtOcYLX/zmtQalXdXSQChJMOQZaNAFygph3gNnX+O+CHFLVlhzxKZdjpqX0xtFtOffKFuhPpGQaNamA7rwBJq16YCk/PP6aSwWCw6Hg6Mp6ZSU2fllzgKef/55fpvxA87CDE6nHKV7n/5cdfOddOjSE0mSOHToEDNmzGDhn4uZOn0W//frPNIzswgPD+eWSZNYsngRH9wxVGyg7WhxjmtBlJxH9PYPxJvBj4nsLy9iY6L56n2REfTptB8pLPLKHirJR87YI1436V9hOY1Gw+Qx/Xjh1lHlAQKyLPPM53MZeu8HbNqXzPZDaXz++1rCM4XB9INNNoZ+Z+HJpaXIMnw1xsjyG020jNJwukjmipklXPGzlXyrp3iuJEno3N3pqpM6T5aCNRsy9511vB/MXMXIRz4np8BCz7ZJbPv6EYb07lRxJiUDreCkX+ewWmLbiWnWwcCWi2klpqd3ivvpvwVbSS2WsVZ/rwmkz6q8LoNJRJ3r9KLPr47wJOFciGgMecnw/RgoOOV7Xqf7ucIfQzt4HAvB4XVz/JQVI1uykIA/jtj5YoeLaJPoV9vGGRj1ZSrZFifBOoljeTJnLDIycMf8UjakBV5A+M80ozhGpw1SNvi/oMsJRe5i4qFV95UEhUKTC8TrI4sD3r+zcNrdQUFm//qmytdWhXGA+3ODSaznPGYOqaioqKioqKio/LMIyLEwZcoUli5dSn5+PqtXr+aFF15g8ODBlJSU8Ntvv3H//ffTqVMnGjRowIQJE/jiiy84fNgPI6rKX4fdKoyOvgb3NqvH+KwYoPVGcNiEwVonjD1HDx/Cvk/oUNOuak3zn36ZzZsffA6AsegEUvZhSh0yc49pyc7O5cuf/8BudzD8gm6s/ek9PpxyD5IkcfcLH/HuN7+Sc2IXD/YSRsfb51np/dI69p0srLCNIxnFbE/OR6+VeGRQZPnnbcNLcdptFeYd6lrLZ66n6MBhLBh51XUnS1OrM2B5DYgVB4OrDk4GxbGQc6T6+UAMxoPcTgHb2dI5VWJXDABBIuJOb/YYTa25YlrZ4CAjZCyMURWNGdVdK/8l7CWg1XvkXmwW2PIl/HAFHF4IyNBxPNy1HoY86XHmBEK5ISaibvsaFOp29Mlgq8IhJcueaGaNzrO/Ngt9mgijxPbj2fRvG0//tvEUl9r5Y/1+NK6KhpoguZTrXb/wjethRsqr0CCj18Cbw410a6Dhg002HrnAQEJMBEfTMnnw7el88eytALw+bS7/t3CdZ3+gbgYonQEufU/Ig+UcERkkNbVRs1uOqaxQtDV/jCkGszDA+Cvj8Q+kQ/v2jB0zhg7t2/v83mw2o9PpaNamA9qwBqSkZ5Ofn092ehrBQXoaxVWUXNuzZw833HADW7dtp6ioCJ1Ox6WXXsr3333H8WNH+eijD7mgb290R0RNhKr6lPm//3LWPb0y98jfo7VbkBO6sM/QnYWLlvLVDzN58c2PuOPhZ7n02sm88q6QNMovKGTVus3ly6Zu+QMJmeUnnGxMrVnebu/x07z2/SJkWebyQV2Y+cqtfD25J70TtVhsMq+tsbEy2cn/1tmYskKsb2gzHWsnmWgSLq713w86+Hx9RckzjbsdZBTZwRjpyYzZ8wvkHC2fr9haxqOfzMHlkmkcH8nyD+8hKT6Ssyh3LKTV3Tke5y7Ce6aWjoXcY9U77/5pfY6hFg7GuhhuK5+fyuvSmyCykegHKhuYlec77/MfngQ3zYPIptU7FxS5x9o4FmqLywH7ZyPJLg5kOblrQRk/TGzEoSxxD1h1zMqxHBuhQRpKHTI6Dbw3MohxbXXYnPDYErHP4RShk6u/b+C0YbcWir5Tkf7K3Ou/M85yRvSpWr07yr8aWrnlyA4v8m/d1SE7xXOdxX0P8dU3+WpTVbWzmrJpVFRUVFRUVFRUVHwQkGNBQa/XM2DAAKZMmcLy5cvJz89n1apVPPfccwwaNIjCwkJmzZrF3XffTfsqjBMq54nqouXsJWcboBV5kKAwkbFgMFK0ZyEGR5HQaG3a/+z1ANN//o2Jt91H6slTJDZswBvXiOtg7iEH48ZfTeeOIvIxMT6Gxd+8Qf8eHcqX7d+9AzoNfHu5GZ1GIlsOZ3OmnkPpxfR+YSU/bUgrnzcqRAx07U4Zk8FjmLyjs51j39zNhs8eIH7NMzzh+pgn5U8xU4JFDmLSYjOvfPgNV3+bxmdbqhh0arQiir9CRkYdshiiWwrjqSVLnOOaUAy+tclYkCRAB+EJ4nfS6IRhvLSiYwabVTge9CFnZyt4Xyv/NINPZbz3P9Bj0RvFtaA3igjBH8eLegV2CyT2hEl/wrgv6laboiRfTI0RtV8HiGtVMQjYqpCsKHcQSO75jWIqu2gW6uSqC5rjdMnc+OFKRvcQ0krZmenc7JjOhs8eoOCPlxnhWsk3roe5Qf6NYMoodBp4Y2swK5MdmA0Si28wodNITPilhC+evQVjsIHFG/aQmZPPfRNGIMsyN075nDkrtlKesVBXSZOQOOFc0OhENOjGT6qfPyjMYygrK657ZguIc/5Pbic1oMggmc1m2rdvx8CBAxg+fDgREREMGDqCPn3707hl2/L5X375ZQYOHMihQ4cICgri9ddeIzUlmd9+/YXLLx9LcLBw1mlS1yOV5PnsUxwOB/c98CAP33cX6z68g/DjC3j9ak9U/rsT2vPx9Z34ZnAuA+RNyJKGG2bm0bH3YC65YgKTH3qGF978kC+//5kFS1aye58wil899hKGD+6PLMt8OXUqkYUHAPjfulKuffF78ouq/w3bNmlAnw5NAdh55CQnz+TRJ/g4AJ9ssZFlldFKMKKFlof7GbA5ZT7cVEaHTy2kFIhrfmBjLdf3rOgMGNNRyJjM2lnA/P1FOIOjhINYdsKuGZAv+r8QUxCPTBBZHqmZeYx8+HNSM/I4i9CGom3ZLB6Db20pz1g4ENhySk2hvOTqI6priub/u1Gb+0VdsuiU82PJrmhM9l6Xsn6DSTgSzhwR0/Lnu5yKASTGKLhxTvXOhUAdC0p/VlvHgiwLw3tROrklMpf9ZOXzqxtxPMeGLEP7eANz9omAi/5NxfX0RH8DD/YN4t2R4p6y8aSTojKZOHL4yvEwaT8+yOapj5ZvYvPUR+mw+w2e4EvWfHAHpQVZXN41RjhllPpVx5aL7IWaKEwX05AG1WbwAtByOCAJx0Xh6ernrQlJK7pPlwMsbgdD5X7fV5uqqp3909qfioqKioqKiorK34JaORYqo9frGThwIM8//zyzZs1i6tSpdOjQAVmWCaA2tIov6tugWznCzW6tmJ2g0Ykiv94p1XqjGGyFJ4AumBY2t9xVFZIV6RmZ3P/EcwDcO/lmDm9ZRaOiHQBM3+vk8fvvYtig/gQZ9JzKzGbfkeQKyz948zgeHRhKl3iJMoKIadqJ7S8NZXiHWKw2JxM/28pbC0QmTExoEDGhYrB76IxnABgXouP/xmqxZR3j8VbHGCavx4mEXR+GWSrj+2FZjG+Sj1OGu/8o5f6FpThcPq5VyW181ejc0kQ+shiqk5zxRm+EcGGo9StrIcgdqeqj2G+VlEshBYksBG8jjgQEV9LetZcIY4GvCDWDSWSr2K1QnP3PHnB6D5gDHTzrTcLgqTfBmrdF1C3AuC+FU6FRr+qX94f6iPBU8JI28oniWNBo3cZ8GTRu6TCbhS/uHEijaDNH0gtYtFMYMfMsZQQ7CilKP8brHY7wiPwl0eSTUiBz1SwrLd/N4aet2Yz5ycrBbCcxJg2Lrjex9bSTh975kcduHA3AEx/O4I7xw7jx0oE4nS6ufuIjdh9Ode9YPWilN+wGQ54Wr1e+AYq0jS8kyZO1UJRe871WcRpU5bABj/HuH9pOZPe/E8nJrF6zhhPJyeITWTgVkpOTKSwsxGKxiMQXGUaPHi2yFy8aRZZVxmKT2bZtO9deO5HXX38dq9VK8+bNWbzoT26/fTJBQUEUFRVRWFiIzWbDZrMhHVwgdqDtpRX6lD179nLxJZfy8SefAfDcY/fz89T3aN/YU/y7RZyZVnEm4re8AcDb60r5cflejEYjXTp15JLhg5l8wzU8/9h9fPnuKyz4aSpHNi/l568/QKvVcOcjU9j7+/uEBkmkWQwcLQknOT2Xyf/7udrnF71Oy7w376RFYgzJ6TnMnTuHDlEOSh0yG4ob8fnoYNIfCWH+tSZ2Zbho90kxD/xZRk6JTLsYDXMnGFl1s4lGERWNtf2amRnaKgS7U+ayqSdo8tJBHlxUhsXcWBg5d0wvdxC/cddl/PLKJMJDgtmwN5muk97i85//ZOGabazfcZATJzNFNk+Iu5htQWrlwwiMWLfTKOuQX0Vmy4lqDkgiOyg/reo2psqwVI9yfhRjck33GUsuuMqg8JSY12mraIh2OUSWY3gjd42FRsK58PN1FYMmHAFKIZW5AxiCalk348x+OLMfhwuummVlRLsILm4bwk87RD/ZNMpAjsVJYriOTg2FI6HY/fjXNEJD0wgJpwxrU0Vfl6DJ5bYmaRRleOpzFWWc4NDB/SzbsJOS/Cwu6RhFt8buZ66wRPH8VVYEp7bVvL9FbsdCWMOa5zXHQCN3Qdy6yiFJGjC7n9e9M1O9+ylfbcpgEteCEjTi/bna/lRUVFRUVFRUVAKkzo6F3Nxcfv/9d+6//346d+5MfHw8N954I0ePHmXIkCE899xz9bGf/13qO4KocrScsn4lO8Ec7flTCrm5i9RKeiOSJBFe6k4PbzbY5yZeeOM98vIL6NqpA++9/jwmUzCS2xBSqI+nSeNGmM0mhvXtCsBDr31O+pmc8uXDQszcOEAY4P+3xsrL809QWGJn7kP9eGK0kFR4/Od9bD4mov7bJojB4Lpkr8KUQL+mRn66JpKOsRoKCeFRzfM43UXtgnUS318ezKP9xED5o802XltTQ2SaJPnOYvDXwCLLnkGcPxriYe4I+ECiQ03uotBFmR6ZBsXYKXO2Bm91uryK3q7WIAat/+QBp/eAuS6DZ28nkjXHUxuhrriLU5ZHStYWmwVK3FHLVRl1lGtPiV4tOAXOsvLvIkOC+P5+EQm9en8GAMcyCimzO+mdqGVoIwcOtHwpXcfBXC1bTzvJssrsznRRZIOJvwqpraYRGka11HIsLZNv5q5mRL9OlJbZue9/3/P185O5YlgvbHYHT3/pllWz5lAvNO7rkYOqJN90FvHuTKn9c6u+1yoOB2vO2VJilZ0NSnv6p7YTN2lpaZSUlJCW5skOs1gsBAUFUVZWhtlsJjs7mwMHDpCTk8POnTuZPn063377Le+88w7PPfccP//8Mw6Hg5YtW7Js2TK6dOmCVqst/wsyGAgKCiIoKAit5DZiesmIbdu2nW49e7Ns+Qp0Oh2zf5vFU08+juQjs0WWZTIyxLVa5pC59qorOLpnMzs3rmTBjK/48r1XeOGJ+5l84zVcctEQWjYXcidTXn+fL7//mRiTWGejlh2Y8eLN6LQaflm5i4Ubq7/3xkaGsvDde+jUoiGtGgtt9WCdxC+PDqdFlIZHFpcR9WYRE34t4XieTIMQiS8uDWb3XWYua6P3eSwul8xtfT1yUqcK7HywJpdGLxygLDgOHCVwdGn591cO6cL2rx+lV7vG5BVZueulz7nkzpfpf/2TNB95Bz/OXwXRLcTM6dU42vxBkVCrKSq7MnqjCFgAca+rj/oC/yWUexCI8xMS4999xhwFLhfYSsVvp3c/42l0HoO03i3xd2a/J9Og8vONsm1/HQWKpF/2odrJb7mfqax2mTKnlrcua8DCA8XM31+MRoLkXNEP3zcgiliz6M/mHLIz/7CdtAIXRnch8XVpnvol49qeXXdre2oxv+8Ux3ZdH696LBotJHQRr60+soAqY3EXoTfH+Xd8zYeIqT9OC1/EufutDR+La8Mc4wkIUhxHSj/lq03p3YEkOkPFtqi2v38NqampbN++nQMHAswuU1FRUVFRUVGpBQE7FvLz85kzZw4PPfQQXbt2JS4ujiuvvJJp06YRHx/PSy+9xKpVq8jPz2f58uU8//zz52K//zuc6wgiZf1VyRMoRmllkGIvgXx31GNEY5+LNGoooiNPpWeQm5cPkoYMmxi8RlCAwyGMfQ9NugKDXs/S9TvoMPp2/ly9pXwdrd3OgkNZZTz32wFaPrYE8+S5fLdWbFunlcplkC7vIQw6UxblkpZfMXtgcJKY/iENY7/UuvxzWZb5bpedaTvF/BoJWkX52RwqG4N0Qf4tV5AmBqBaPcR3rHn+xn3ENHWjf+sHSOotHB+FJz2G2uqMnYozyfs7xYihRG87bBWL+/0T8R4w13bwbLdC1xs8UYmLnoL3OogCzidWBxbB601ZkSfC05+Ix+pIWS/aa1ijqotIavVe2TDFYr81ehG975ZiGtqpIV2aRiPLEKzXcvh0AZuPnOHpQaIdL5f686tmNIOb6Tl8bwjfXx5M2xjRfjrFe4pWD20mjD5xkWF89vQtAKzadgBraRn/9/o99OnUgq1pbqdG4WmP5EZdWP+ROKbmQ0UGQ3V0vV5Md/5YtfPM2/laUklKzPv+aLN4HLT/1HbiJilJ3DiNRiNWq7j3m81mwsLCaNq0KVmnUli54BdSjx4gOzubgwcP4nA42LZtG6WlpZhMJoxG0accPXqUwYMH88WXX1Jc7DuLxtXYXdD0xKryz6KiIsvXcUG/fowdU3U9n7UbNnHffPHbPD3YzP+9+xQNE6opoqps1yXarL6huB9LxRn0ateYUX2F5E9KRs2Sda2S4tj9wzN8+cZTZEV0B8C67SfuWlDKD7vtFNsgyijx4pAgjtwXwu09DOg0ZzsUTubbeHlRJs1fPsB1P5ydWdCsUQKlzUeKN+k7K8jVNE+MYe2n9/P8pJEM7tWBxPjo8u9iIsIgsYd4c2pr3eosZO4V0/j2gTsXdO5nDJfTd3/jjwb8f4XKx1/Z6elvH2aOhvBECI0TRZ31xrMDSPRG2PApzJgostya9IfrZnmec4oyRHS7pPEUvK+Jxv3Ec1HhaTgTuGHTGt6KLaddhAVJ/H5tKGaDxIIDInPz1j4RHM4S/cSEbuGMbBtCpFFLcr7MZT+V0OyDYg5ku9BpoFdD0RelF7kYM+Psa6lZTDAH0q0Y9Rou6VixNgwut1PCnywNxZlv8lHjxBfxbim3jL3+zV+ZPndAXHtR1H3eA6JNK/V/yh1G1UiOKbUUnLa6jS3+6+30b0pqairt2rWjR48eXH/99ZhMJmJi6hi0oqKioqKioqJSDQGNDHv06EFMTAxXXHEFX3/9NQkJCbzyyiusW7eO/Px8lixZwjPPPMOAAQMwGPxMmVapnvMdQVQ5or2kwBOdFZ7oc5HHH7iLzh3akZWdwwUjLufVtz8kLEnUWIg3lPDFN9MBuKh/D7b9/jHdO7Qkr6CIcfe8yKrNuwHQumVcJt8wgZ7NIgjWa5BlyCgQA8pnLmtDy3gR3frAiBb0aRFJQamLW2ee8chXuJxEBYnB4R/SMADyS53lOteT5pSSWyLTrYGGzbeZubbT2RFtPpFljxE5EOOKEp0W38k/Z0Tjfp7lHH7o/IIwGCuRdinrxVQxJICXEdRHIUeFch3n3Kplkv6L2EvAGArjp8GwKcI55CyDfb/C9HHwSW9Y827gusmKPnNwuKdgd205tlxMWw6rvmaBMUL8thqdyHIJSxC/s9cyEweKKOcmccIJMXvNXi5rJQw1v0hC2mjZcQd6rcQNXQzsvtPMrjvNfDM2uHwdwToJY7CB71++k+aN4miRFI/LJbN2x2GCDHpeuftqMi0yWVYZkCE/pW7Hf2a/u6A2opB2TTQfIop1lubDoYW+77Xe8iNypYwG7/vjP1wGyZumTZvQrVs3EhMTsVhENobJZCQ2NgaTyUhG2jGCDTr279rG+vXr0Wq1xMTEMHz4cOLi4rjlllu4+eab6devHwaDgdTUVJ544klatGzFZWPG8uZbb7Fu/QasVisOhwNbI/e9LnNvef/SrFkzZs34PwA2bNxYnpHgi6+/+5HfDzrYY41FIztg2Yt+GdAvv+QiAH5cJaT1KD4DLgc2u+g3zME1P8ekZebx7k/L6HbT6yQ8vJLVKQ5CDfD7NUbu761n/S0msh4L4bnBQYQYKrZJu1Pm990FjP7yOE1ePMBzCzNIybMTYdRyW98o3hyTwBdXN+LbiUncPKoXr805wNos0R7T1v7E4k0HyM4XfaVBr+OFW0ex8PPnCDGJNjhx9CBGDujm6XOsOZB3glqjSIsphtFAUPo8fVDFNhaIBvw/kdoYXysfv68Ak+rWW1niMigUIhLPNiI7bTDnHlgyRTzTdLsBrv/Fk/kIkOe+J4fEi0h+fzCYoMkA8froEv+W8WLexiPcPNuKzQmxwXawW1l7XBxnpwbBOFxgMkgkRejp3DCYY8+04vELDATrwCnDBUladtxhZmxbPcn5LgZ8Y2HvGRfxIZ72lxgRRHyYaN/X9YknwlTp+U/JTtT68VyoOBaMfjoWGrgDSwpSPYE6gaALEnWdNHo49AfsnO75TnEwVC7i7I3NKrIV9HV8tvs3tdN/EdnZ2VitVqZPn862bds4cOAAjRv7DgRTUVFRUVFRUakP/NBj8bBjxw40Gg1XXHEFjzzyCL1790ajqZcyDSp1QYk+qo0BuLIUUmXc0jFZWdnk56YSQw6RyKIQcBWyLUFBQfzwxQcMufQqjp1I4dlX3sI0MoiH+gbRMkrDc6+9w4QrxxINdGzdjI0zP2T8/S8zd9kGLrvjOdbNeI9O7gjuIYMGsqWBBVmWySqykZJtxVLmYHBbz7Z1Wg3fTu5Bx6eXsuRICd9tLeLmXmGUWC0YgcXHXWS2EpF2rd5IodgqnBMhBpgyKIiH+/mOIK0ab4NVAMud2iqmSvRoTUS1EFHklixI3wFJffxbrkl/OL0dUtZB56s9nyvGT28jqK/fXZHAMkd53qsIyopAloQB5oIHIHk1bJ8ORxcLA8GqN2D1m9DiQug6EVqNqNkwUeiOPK5rtkJBmqjdIWmg6aDq55U0ENqgWufDtQNb8uT0zRw6lc/k4W3pakhGI8GaUxpSGouI9st/tvLRRRJ39jSg10p0jtficMmsOOHgwuY6uidoee+RibRvIWS9hvRox7G0TFZs3c8lA7tyYZ8O9O3Ukr2Zp0V2Q24yxLSucp9qZN0HYtrmEv+ygjRa6HkLLH0etnwlMhgqnxPF+GK3igwP768N5ooGnKruo/8E7FYkr37EbDaXF2rWVDonDZJaIMsyJ05n43A4cDgcXHfdxPLvly5dxpEjR3A6nTRr1oz09HRKrFbKyspYs2YNa9asAcBkMvHE44/z5JNP4IrrgObMPjixFjqOA+Dii0fSr29fNmzcyNSvpjHl2afP2u3iYguzfp8LwAJ7PzpqFiKdWC30y1uPrPaQ+/XqRnxcDPtOZVNoiyDM4OTnOYvYc1w4+0w+HAvWUhvr9xxn+bZDLNt6iM37Pc4wvU7Ltyeb0bvJGTrGlfHBKN9Rw8U2mY8323h/o41Mi6eGzpCWZvo0NrE51crXm3Ir+UaELNX0UInD94aQZCjk0WlfM3Kfg8bxkQzs0pxh3VuxcHsqh06cIiE2ko+eniwW1RmEwzltM5zc4q55UAsUx0KD2jgW3A5H76wku1vjXQZCvGR2lD7on9qWvPE2vvr7fFb5+H0ZgKtbr/dzXeWMRAVrDsy8UQQgSBq46GURCV/5/qc4e0Mb+LfvCi2Hw7FlIjDCmuuRwvKDn5bvZn+Wi9X5DRgenYGrJJ+MQhFcEW4UY47WsUFo3M9thaVOXh4WxP19DBzIdjGsmRaNJLE/y8klP1pJKZBpFiHRt7EWRXDvw2tactVnovbXXYN99L0uxbEQQMaC0c9jDA4XjpeUtbB7Jgx6tOZlKtOgEwx7VvRdfz4FTQeKAtz+UF/t69/UTv+FtGvXju7du5/v3VBRUVFRUVH5DxCQV2Dy5Mm0bNmSX3/9lf79+xMVFcXo0aN588032bx5c7msgMpfTF2ihmqSQnKTk5ONzWbDkpksPghLrNYo2bljO07sWs+0j9/mwsEDOJorro2ODYLJzcvnyRdeB4QUxdzlGwgxBtOjYyuKLFYmPPgKsiIN4y5mK0kScWFB9GoeyZB2sRW0qbccz2PS1G043ZffiVw7LqcLp03UXFiU7Jm3xC7TJFzik0uCOfVwKI/3DwrQqUDFbIXqosK9cTkgx130t6GfD/qS5HEmBCKH1NQtK6JkLCjZCeBxKkD1tRUU6aN/svxRbakuEjQoFILdMkKSBKGNYOBDcOMfMOw5YbyTXSJK85dJ8M2omuV9itwZDmG+M4D85tgKMU3sUS5pVC01XLtJMSGM6CIcAlsPneT2niLi+IUVnlomsgx3LSil19RiXlldxv/tsdPlcwt3zBfXWNcGGm4f56nF0qeTyILYd+ykexckbhs3lD1n3LITdYmmTtssnGkaHfS71//lul0P2iBI3yWW94XdCrYSYWyqqj0okaL/1PZSqR8xm0zExcZiNp19PE1btaPfhZdxwZCLcLlcJCcn8+abbzF//gIOHDjI3r17adOmDSaTieHDh/PwQw/x8ccf8dijj3DZqBHodCLy2Wq1smzZMgBkxRnmJYcE0K+vuAcmp/jOZikr82RzPfX+dF5fJSL4C2Y/zsZ1aygpKfW5HIBGo2HiFZcBsDFNrGfJ8pWczi5AkiRaNorB6XSxeX8KL33zJ4Pv/YjIkY9x0QMf8fr3i9m8PwVJkhjYpQWfPHIN6fNeY9pLdxPc83qf2yu2yfxvbRlN3y/mqWVlZFpk4kN1PDY0lnfGJpBf4uR/y7NYcVQUxzZoJZpGGbigmYmrhnblgasG8cD1l7Ld1hSAdy82YdJDamYePy7exq1vzOCXxeuRJImvX76XqIhQz8aVIvMnaymH5HKKjCDwRFwHgpKxUHzG41DISxPXXOWgiPOdqVlbfPUdtZGzrOn4lfPnqEbKpjiz6qzEMwdg6oXiGSEoFCb8BH3v9N0nKBH1VUnrVUVEEsS2Ef3h8RUBLXrklHhecSX2Bo0eDS4+uDgYjUS5k7Oo1IUsy7y6JIumrxyh8XvFfLrFRpRRYk2Kk0+32OjzlYWUApnmkRLNIjXM3O+pufPGojTsTpkBLcPp3jj07J1wuu8r/mQsWN2Saf5mLAB0uVZM98yqWLspEPrdKzJbbcXw22RRW8u75k9V1Ff7+qe2UxUVFRUVFRUVlXoloIyFL774AoCMjAxWrFjBihUrWLVqFQsXLkSSJMxmM/3792fw4MEMGTKEXr16odX6mTqt4r+RujLeUUPe6/DHeKA3Ve9UcBdvjg43kV8oEyG5oyvDkyoG7nvjjkwMjw5m0s03Menmm1g09QXI/4HmEWL/vvr+J5qES8xYuIZ9R0Uk5sUDupEQG0nG6ZNIshjo7Tt2koh8GwUldlKzraTmWDmZW0KuxUaexUZGfikrDmQhy2A2SNzV24zTYWfIZ6msvtlEqUOmdZwRbyXbjvE67u7jx0Ao2EehQqfDo4cfHCacC61H1LyuM/uF0TS6JYTXEJnubZRI7A4H50Pqeug9ueJ8rgjfyyf2FHUW8pLh9G4IiRVRd4pDweVwF2+M9jgd9MbqU+eV/aptZkxVBGrgkuW6aYT7cwyVI0EdpeLcSRpRXFZGnCuNVmR1WHIhNAq63whtLoWyAjg0H7ZOg4zdsPdX6HGTZ/1llRywilZ6aENRaFOhBmdfOfkpwuinyCA16OhbUqjnrf6tb9/s8pffTkxicHoOY1vY0UkajudL5DiNKGUur+1sZPq2MraedrH1tMeBEm2UKHNAkE4S0kRug0ucXRxrzpl0SF4LgC3zIHvPuI87Pw0MPow83vj63WQZ1ruzFTpPgNi2nkKzNWGMhA7jYPcM2PI1NLng7HnKLEIGSSl8Xd292vv6rI82o6yvunUF2iYk6exjMJiQvaJPs7KyyMrKKv86NjaW2NjYCov07NmTkJAQZsyYQXZ2NgUFBWRnZ5OSkkJGRgZJSUk4nU4GDR5Mu3bt2L59OzNn/YLDIX6bcePG8fbbb2N3OHE1HoB282dwfJVoB+79W7hoEQDDhw31HKfX8UYnNGbnti3M+Hkmc+bN49U127muk54mEUUsfX0CA1Y76NiuNb26daZXt8707t6FDm1bodcLg+GbLz7FxcOHYN74DpDM2K6xxHfrhdkYxFsz17Now25y8ivWhmgUE8qwLk0Y2qUxI7o3pWG0cs3mQkGuyBBI6gNpm0DSYm1zOR8vTeatXzaS7Zbza5UYxePj+2JN3sL7a/I54S5IazZITO4Tzn0DImgW5VXguZGXMd/VEY6fITGkhNw3urAuL5YV+zJYsT8Tc3RDptx+JQO6t6tosIxrL6RTijNFX+SvXr7ZnR2Yd9id8WaCmDZny+LUlL2qFAkuOAVZR4XzU2cQ2v8hRs5+oPDzecjfa7+2z1eB4CuLoK5yM1VtR5Gyq7xuu1X0SU4naJzCyKw4dewlQjpnwSPCGB3ZFK78SmSJVWWMVgIiopr5ZzgvLfC8bnsZZB2C4yuhy8SKRvoqanQBxMVEsz8li2xnCLS8EA4vYkJHPT/ttWOLbEmwIYNjOTbuWQKf/XkGgEyLzGtrbby2tqJsZL/mYeRa7Cw/UYLZ5Mk+2JJcSIRJx7e3dfFcm94oz3k1ORa0QUJKD0RWh87HuuBs50GzQSLr15otsqtauZ8jXY6zl/WFklEx5iP4crDIRFr/EfS7W/RX+mIwGP0vuK2ioqKioqKioqJSSwJyLCg0aNCAa6+9lmuvFRE36enpFRwNixYtQpIkTCYT/fv3588//6zXnVapRGU5jqoIxMjldigoWqyxwRKxIdFwsvr6ClXRf9Q18NMPNA51Mn7saH6Zs4ApHwn9bL1Oh93h4FDyaX7830Pc88SLAOSVyHS80r8U8at6J1JSkMPba4UBaFRLcWnnlcJ1Xcz85jXv2lQHTpeMNtBMBRBGZhCD+kDqK+Qmi6kSNeovjXqLacZeIcMTVIPRFdx1FjrD6R1wcpMwdntnJ1hyQLKBrbIkUg3XUG0kHf5u+HMMldP7lXOk0Z1tDNEbhXPBuygmQFJPoUm96GlY9z50u85jlK5MQT1IIZ05IAxFQaEQ267266lEg/Aglj/YCWOeKMD5xBILe09pyx0Li4+WsWZyNAezHMw9WMq+Mw6u6mjkyUEhBOnKhHHfmltukIoOFUaXnCJPFHlecRl7FMdC7rHa7eiRxSLjQG+EfvcFvnyvW4Vj4dAfQiLEW2MchIHGHipsnYFEHtdnmznX7c9grrDerKwsysrKSEtLIykpiaysLFJSUjhw4ADt2rWjZ8+eJCcnc+LECUwmE2VlZTRu3JgjR44QGRnJ1q1badGiBSUlJezYsoFrrhpPVo6nGPJzzz3HDTdcT0iIGY1GQtusv7ivFp4Uhu+o5hw5cpQDBw6i0WgYdXHVskatWrViyrPP8OwzT/PRx5/wxDfPMGOchsf6B/HdLju79h5g194DfPXDz4DIlAkxmwkLDSE0REwvbVRE/27Q1FzKmI/nVFh/WIiRi/p04qK+nbiwuZYWCREVsuZ8ktgTa3YqppJ00jf8wis/FFFkg5YNI7lvTE/yLWU8/e1KsgqEIznGrOX+ARHc0z+CKFMNARkarahzcHIzQYXJDGvVhGEdu4rv2o/1vYwuGOI7iMLPp7b5L62nkC7qHxHf0X+t/crbB2HAthWDJENwJETE/XukVAKVhqmt47G67disoNOBUysc4Uq/L8uw9gNY+w4gCwfq+G9qdmAXiMCPWmXUJfUR9/6SPEheAy2G+bVYQkwEAOnZ+dAhCuLawZn9fDY6mGe3pzK6RxN+3XCcz/4UGTRPXtGNXrE2Pl99mi0pRcSG6EkIN9A1KYTftmdxMt9GwwgD393agbfc2wg16lh4XzdaxFVx3sszFmqQQrIVe+b1VwoJhMOi4xWw+UvY/bPHsRAokU1g5Ksw70HY/DnEtBK1g2SnyLI7146FcxFwoqKioqKioqKi8o+iVo6FyiQkJDBx4kQmTpyI0+lk9uzZvPjii+zdu5clSwIv3KZyjgjEMGXNcQ/+3Sn8kk1E7Cpp8eFJAW06pGErHLKEUQ9xRhctmjclJzuLh264jOtGD6LlJXdz4mQmsZFhvP/geLAtJN+mISYyjNz8QsKMehrHmGgcbaJRlJGYEAORZgNRIQa6Nong7QWHmXVIGCsvahnEK8ONgIuEMD2lWgm8gpcLSmUyimUSw2ohgaQMIKuKSvOF0+EZnCuOAn8Jayj0sHOPi4F5m0v8W67JBcKxkOZ2LCiGb6joTPCuu1ATf3c9XX8GuP4cQ+UIU1/nyGYRn9mtnmwQZZ2KU67jVbDmHZE5sve3ivUuvCmXQqqDY+HkZjFN7FE7o181JAZZwChhcepoEBdLXKYnejvH6uLhPwrZcGcMt/Wq5JhyaIRxQ9GfxuNQKLV7GuTR9EL2KVJIliwR8eqWQPMLlwPWvC1e97hFZOgESsPukNBVGF13/Aj976/4vd4EUX449SpTn23mL25/sbGxZGVl0bBhQ4qLi4mMjGTbtm0UFxdz4MABevbsSVpaGsHBwfTs2ZOEhARKSkpISkpi+/bthIeHU1BQQLdu3fj2u+8qOBUAXnrpJV566aXy9xMmTGD6RT3Rpq4XckhRzVm/YQMgJPNCQmoubF5YWMgDDz0MwM2dJC5uqWP3K4NZaBzPlp172LJjN1t37qGgsIii4mKKir2u5RMSU7qF0jLMjlaCLm2bcvEFXbj4gi707dQSvd79uJSxq8rtu1wyWw6nM3fjUeZuOkp6ZjY77jDTIlLDz9eE8WdhK46lF/Dwl0txukSkfdNIHY8OiWJSrzBMhgCc1aENIKQBFGcIo3+T/jVH5Sf28DgWAkWpr5DQOfBlwdNnBrkDIYLCyms4laPcO/3JoPs7Emh2gvJMZskWy/lrnK1uO8r5NMdWdCosfAy2fSved7sBRv3P3XfVIKFZ7lhoVPN+VUarF1kLO76H/bOh+VC/MkfiooQxPCuvCIiABh2xZJ6gYWgJF8dlo2nSl183HAfgtuFtee363khZh7iiu+fevzOtiJEf7OZMkZ22DUy8dnkz7ptxlMZ3iO/n3Nedvi2qucbKizfX4FhQ+jeD2ZMZ4i8drxKOhZR1UHASwmtxjkFkg5xYC3t/gUVPwcSfIbq1cIifa/4NAScqKioqKioqKip1os6OBZfLxbZt21ixYgUrV65k7dq1WCyi2C5ATIzvAr8q54FADFOKuoAu2C2ZYxSFS4syxOeBRq9pdNhNDdCVpLN/42K++eJ3+sXbyrW2r7yoH78u2cCUj37i98cvhA3QrHlLstY8i7xjerXRoSdzrPy8SQx+l98aw9AWwW7juctnVkHraA0NQ2uTrVBWfixoA2g6hSfFwMsULeQEAqX5UOFYOLbCf8dC4wtgwydwcptItS/NEzJMym9fUigi7SsbdqrjXEg61Cf+DHBrcwy+MoIU54zM2bUqvDMc+twJK16Fte9Bp/G+s1wK3Y6F0Fo6FkoLRcYCBO64qglZFtIpgDkqgY+ujeXx0TK3uVW1TAaJLafsvLO2mMcHVTK8K8dakutelczLs4RBc8KAlmLXbQ5+23icIhuU6qMItueKa71hN//3cd9vItPBGHm2XJi/SJIo4jzvftj2DfS7RzholNoKBqN/2UKVqc828xe3P0X6KCsrC4fDgU6no127duUZCwBJSUls2rSJ0tJSgoODadasGSkpKZw+fZrY2FgaNmyIwWCgb+/eZJ7JJje/EJ1Oh9PpRCNJOF3OclmkGTNmcGPTYYwKAk6shh6TuOzS0URHR5OTk8PnX0zl3nvuqnafw8PDmfrF50y+407uW1jKwfsjMGZu44rR13HFmMcB8cySlZ1DUbGFwqLi8mnhie2U2X7GqHeQ8ftzxDRp69d5crlkFm07zm/rDzNv0zEy8zySMlqNxAubzXxxYQmjmsOff+5hwWbhnO7XLpG7Rnfn2tiD6LS1lOlp0BGOZYk2VpBWrbwMIJxnklbccwI1ZGa4MxZqU7gZPEZXvQliWnocCN7OhEAy6P4NKM9kDlv9GWeV+4S3pM7BBcKpIGngopegt48izb5wlEGhKGQeaJZqOa1HwJ6ZIijl9A4h71gDyvhBKc6MRsevp2K5PjGFq9tJOJsGc//ojpiC9Lw8sddZz4frjxVwyUe7KShx0inRTN9moVz5xT40uiCUFtK7WThQjeyQvzUWrO76VYFkKyhEJAmHYMo6cY4GPBz4OkD8lmM+EI75E6tg1iSYtEA8451r/u4BJyoqKioqKioqKuecgB0Lsiyzfft2Vq5cyYoVK1i7di1FRUXlA4HIyEjGjBnD0KFDGTp0KJ061XIQeh44dOgQ2dnZ9O/fv9brKCsro6zMozVeWFhYH7tWPwRimDJHg92r/oLBJCK3ityDzJyjHuOpnxgbtIYT6bSO1vDoMy+wYdoz5d+9fO+1/L5sE7OXbyJ5fGOaKtuEGiUnPl5yDIdTZkizIOFUAE+BZacNnVzM1fJccojE1UTi6nZBVas3l+s1y2AvFRHXLpeYKusMJCpNlisafWuj89x8KGz9Wgw+nXb/igkm9XYbkE4KA1JYo4pR9ca/WHc3wHT5WrWj+hjgVt5P5b2vCFrFOaNokCveOO8C2R2uhPUfQtZBOLIEWleSc3E5Pc46fzXPK5O+U1ybEU0gNL7G2QOirNBjYNHowG4l1mwGqwySRLtYHZst8PSSIgY2DaJfY4O7DoYLp9OBFkREpywze9MJth3Lwhys44lxwnEwb0sKBVYbjWNDCEpoK2qJ5B7z37HgKIN17toKfe6qm+xDh3Gw8HFRn+LAfGh9kXAqyA63pEQtHAvnmfroj8xmMxaLBbPZTM+ePenZsydWq5WsrCzi4uKw2+2cOXMGh8NBq1atOHbsGEajEYvFgh4ntvxMDIYgLhs7Do1Gw/bt23E6nXRs15qmDePIKy5h49adbNu2jRd+WMWo28yi/oajlKioKF558QXuuvc+przwIhOuuarGYIXbbr2F2269RbxZ/iqse09kDrmjpTUaDfFxscTHeaKbZVlm+6JMUncaaWUqYtOapQRnOOjftTXBQb4jlvOLS/l+2V4+nLONY+n55Z+HGg0M6NAIc7CB/anZTFufSYjTwAcXB/PeyCCu6xNPZNtBtGreRCyw91DAv0k5epNwGGcfEnV8anIU6AziHlF4GlI3QKer/NuOywGZ+8TrBrXMWFD6LUdJRYe2Jad2GXR/IefsuU55JjNYq+67/Ok7q62/4oKlz4nX/R8Uzm5/KUoHZOGQqK3DwxAiZH4OzIW9s8S9vYbnILvb2ajXebLvpm3MoaCxnft6G9Ce3MgH47qIbM5KzvqlB3IZ++lerDYXCeEGjmRa2XNKOPuu7xtPek3763KKAuOlSo2FGjIWlGfi2jgWADpdLZ7t9v1ee8cCiP286lv4fqxwAs64Dm6cD8Gh4tnFki1qb1R4ZqkH/u4BJyoqKioqKioqKuecgBwLY8eOZfXq1eWDKlmWCQ0NZdSoUQwdOpRhw4bRtWvXmrWH/4bs3LmT/v3788Ybb9TJsfD666/z4osv1uOenSd8SRSUFYsif7nHRKHUo0vg4v9B477+rTO+A5xYxSWtg/nyp+1c/cjbXDG8L707taJt80bcOGYI385ezhsz1vP5UCBzvxjg+cDlkrGUOdh/qpBPlwpd9ocHeEllaLSieCGgc5VyKzMAePw69yBRdnrVipTwWYnal0SApK15oOlN2kYRgY0ELYb6v5w3xggxdTmoumJ2JYJCRXHpkjwxsA+J8RhrvA3f5YWbAxgY1kZTN8B0+Vq1o/oY4FbeT+W9rwjaqpwzStux5Ihp86HCqJJ18GzHgqQR2T8FqbWPWFQiU89FhK/3vTzvBADBwGxuIYtoDg8Ca18TOzKczNxVSLTBSMMQmRADKCaho3nwfzO38dEfQkrlwUs7ExtuRJZlPlwgoqCvG9QKyduB4S/7ZwvDTki8kPeoLbILlkxx11CRQB8kjDCIt5j+eU4FCLwd+eq7zWYzZnPFa8tiseBwOLBYhMGutLSUsrIysrKyaNq0KS6Xi4svvpiMY3s5eTKF0ymHCU9owY4dOygrK6OwsIDcgoZo9MG4XC4MBj2NGjVi26mTpBa4aEwBu6Y9TLub32fybbfw+ZdT2bV7N+PGX82vM2cQFhF91n76pN/dwrFQeEr8tpUM1plnsvjxlzl8+9Ov7Nl/iCmDDLw0NJgGhbvoeed6Qs1GRg/oyhUX9mJU/67YHQ7mLNnDrDUHWbIjGbtDOJsjQoK4dnB7EqPNrNpzkoVbj5dvQ6uROE4Sx+RgWkhp9I4sgDN/gKazX9Hb1VKSL6TWQLSb6goZW7Jh8xeeDKlADKGpG8V93xgpjLmB4rSJotwgHKDeeDsTvJ87/kba7ef8ua66vsufvrOqeewlooaPImHZ/4HA9iukgZClKy0QUkqj3/MvsKEy7S6Dw3+KIIujS6HVRdXO7lCe3bSiF7E7nKw/lMnWwy5u7hVBqMMKaZvJOLyN5VkxnHZFUZqXSWpuGd+sz8DhktFIkF4g+pRuSSG8Pq45g9vHM9b3I6UIJClKF8+csluaT2/yPH/5wmmDrd+I1/Ed/D0bFVEyQRyl/hcir4qgEBj6NPw0QRxH3nHQm0WGkCUXXKVu54KXY0GRdfwbtDMVFRUVFRUVFZV/JgE5FubNm4fZbOaiiy4qz0jo2bMnGk0AusB/Q3bt2kX//v256667uO++WhT+9OKpp57i4Yc9xsHCwkKSkgKrR3De8S7c7CgFk9sAITvgkrdFYbiVr4sIyelXwM1/QMOuNa+39cWw8VNGt9Zj1sOvSzbw6xKhoR0dEcpTt11BXFQ4X6w+xfWtwxmQaGftN8/x7ConJXYnhSUOikocFJXaKSp1VBiDDWgdzeg2nroH2zMlvtpchgYXSRF6uvcfSoycS6OS/cQaKw/eqhjMKQWaNRrhUJA07j8/HWfpu+DUdvG6+RCIbePfcpU5IwoUEtPKf6eGy+HR/k3oKPSWFbwN3+VG81roQldn6KhsFAowm+C8taPK+6m81xs9A3C90b/IWmUepV6A4szxRpJgyJMw526htdxhHEQGKJcV0xpYILKInI7AZLpqIigMwhsLw5LTBi4buJwYKaMxp2ncTANoGNNG2aZoS/mlMr8esDNjr50VJ5w4ZWGk79I0mkcv7wrAst2nWHsgA6NBx+0XtYXM5WIV8X5mubmc4pwB9Lw1cH1rBdkFfzzq1h+X4NL3ILGXiKzW6kWdmboaXM6TkTSQdmSxWMozEyo7EirjncUwYMAAmjZtSklJCTabMOQ1bNiQqKgokhKGEH/0IMbwOFJOn6Fv375otVp69OhBQkICs2bNQqfTER/fgNDQMGIiI3hh5SGmjTXSKHkm7VrP5coJN/LKSy8w8YabWLtuPT37XsDPP/1c5b45nU62bNnKtu3bMelgkvvzZavXERwSgdlk4nhyKt/O+JU/lqzE6TZiBhn0ZIR2wSYfpUdDuK5HBD9uy2fGog3MWLSBIIMep8tVLt0E0KFJDOMHtMFo0PHNkj0cOimuc0mCi7o14+pB7bj8gtZEh7l/86IMkZVTeBrSd8CZfeKebjD5lkmr8iDtntoKslM4kpP6Vl1f5eQW2P6d557V42ZoeaH/2zv4h5i2vrh2NVz2zRaSaiHx0O5Sz+cVaipUahd/I+32attRXdp2fdUFqmoee4knot4QErjzWR8M46eJ6PfktbD0eRjxSmDXKojnjy4TYfu3QmousUe1s4eHiOPILSwG4tBpNfRrHc/q/enEvprBXb0MPD3AQAMzTGycyfb00zyzv4w/jwonu1YDThdc0CKMl8c0Y2gbUWy9tPKjnssp+rbiM+WSfYCQAE3qLZ7ZqnvmSlkv2mFoAvS4KbBzonByq5g27F67jNbKJK8V08QeUFosjsVeIjIVlIwFOLtOlM0dSPM3ceapqKioqKioqKj8cwjIArVmzRr69OmDTlePhqvzzJEjR+jTpw+PPvoor7zyCna7nfnz53Pq1CliY2MZOnQocXH+S5QEBQURFFRLA9ffBUXnuDADgoxgzRVRVS4nGIKFFnnrkTD3fkheDb/dBrctq7ngalx7iGiMLj+VjV8/ydRFe9i89wg7DpwgJ7+Ix975nncevYk3vv6d22cXsetOMwMalBBst7DqmNPnKrUaiSHtYpl1f180OdvJtjh5ZnEhU7dakGUI0sHItiaWa4Q295zw+8GRX1HyqByvQZ0kieiv2pJ1yDPAS+oD8e1rv67MvWIaF0BEnFUpmCuBS/ZtdPBXdqI2ToLKRqEAswnOWzs6q3Cz13tLtscRY46p3kjjbTBT5Hl8ORYAWo2EpoNEW1r2Elw5LTADQ1iiMCyWFYkIxZjW/i/rD+aYChGOpS4Nd525llhyGXT0DbakWmgYqqFxmES2VWZ7hkzz6CBGtw2jZZyTgcXN2X48i35tGvDQZZ0x6IVh8o3fhNNt8kXtaBrqgvQSYfzytw7JkcUiiyI4HLpMqN2x+XIqtBktaioYjJ76CnXlPBlJA2lH3lkIlR0LcqVIWpPJhMkkjqNx48Y0adKE7OxssrKyyM7ORpIkDh48yNVXX02rdp25EMjJyeH06dMYjUaWL1/Ob7/9hsvlwul00rlzZ6Kjo7FarThLizlt+4aGpmImtS5iyjvv8tW0b3j15Rf5+NPPOHz4CMNHXcKAx34s35+srCwWLV7MHwv/ZPGSpeTkCN1zkx4mPS3a35jrbsdqP/u4+/Toys3XXsk1veKJDAsRBVCPLuWHG5py3/3j+HX5Fn5dtpnjJ0Woc5tGUbRtFIXTJbPtaCYv/riufF1hJgO3juzMPZd2p0WiD7mR0AbQfpyIIE/dIOrfOG3iHmsIEfcLX21fdgkDsTVH/JUWeL4zx0JiT9+R5I4y2P2z0F0HiGoBvW8PTArF5YTDC8Vrf2v8VNh3GTZ+Jl53vb6ioda7poLy3jtzwaex/K930lXbjurStuurLlBV8+iNnmeB6iLvq6NRLxj3Ofx6Gxz6Q9xvBz0euBG83WVC8ifnCGz6HLpdX+U6kuKF8TstMxdojiRJzHn6YoZOmcvOEzlM36+hLDSBa9o46BORS/cELQuvM7E/W+LeBRZWJDsY3i6Sefd0JFjv5QiTXXSSD9BV3ochcyfYiqjwDBgcAWEJYlpTLZGCNI882OAnan8tKo6FRr1qt3xlDi8S06aDILKJJxDCYK7Y7ivXiVLam7/X8t8oo0hFRUVFRUVFReX8EpCHoC4SQX9HHA4HH3/8MSEhIXTt2hUQck+nT5/GYrGQkpLCxRdfzMMPP8yQIUPO676eM7w15MEzwNfoIKyBJ2PBYHIbAErF9zojjPof/DheaJLPfwCu/Kb6waYkQetRsPkLOuqS+eCp2wCw2e08+MY0Pvv5T554bzqfPns7YSEmjpeup419LzOvjWS1tSUZBTae+3Uf6fllhBl1vHVtZyYPbYYkSciyzLeLLTz8Rz55JWKgOLGLkf9dHE5MmImxlf0S5ft5DmS78lLgmDv6ukHnGqPzakQZvAaSam/NFlNjBKJehI+sBH8LN9fGSfBvLOjn7Yjxzl7w5WBQBu1KYUflM19IEgx/Hr4ZJQyAR5cITWp/kSSIbSuiks8crH/Hwlnb03JaSuA0CbzUJpxW0XrunJPPyhO28lkahtopssnc3svE4NE9z1rFxkMZLNt9Cq1G4uExXcDilmdp0Mm/aFhZhs2fi9fdbhRG2UCRXbDoGdj5IyDBkKcg6QK3sYn6cSgo/APag3cWQiBIkiQuwdgYYmNjWL9+PcuWLaN9+/bk5eURExPDrFmzWLHi/9k77/C2qvOPfyVLsiV529mxs4dDFgkEMtiEvSmEEUaZXfyglFLoAkoLLbS0lLJLWWGWUfYmrISVRRLibBI7i0TelmRr/v547/E9ur6SrpYl2+/nefRo3XHuvee95553LsbECRNw8MEH46OPFqOz0wdvawMmTxiD7+u3YcmSJWhubsbUSROw9+gLMXTrfbjhsCJ81jkR7yz9Bv93zbW4cOH5GDliBD746NOu/R91zHH4fMmnEcaPkpISHDJvLuyWMABS/E+pGQ9XWyc8Hi8KCvJx1inH46JzzsSkieNopbov6H3sfGDrRzA1bcVBk0KYdPnpOPnQ/fHaxyvxwgdfYsOOfV2RCQCQb83D3EnDccbc8bjo6MkotMeJKjOZSOFXWg24NtJYEQ5SPROfmwzaeTaKSgj6lEihQPe0gFYHGRUHTIhSFH4nKXBbdwEwAROOByadmliqMYDuK+59pFAemcQz4PYlZBy3Orp7dcv31IjCzTrjjHhW8XmoVkQORDIASE22ja6brBLXageEI1BBaeLtE4w5koo+v/Nr4JtnaFsHXZnYNsx5wOyfAW/+AtjxFTlbRHk2EoaFTXXfIxwOw2QyodSZjyW3n4aNu1owpboceXlKnw90oHnrKthbt2BSJfDhRQ58sROYtt8YFFjMJFOdbYCvDfmdbvwVX9N6YrjKywccZWT0M3pugz5gy2L6POm05J/xwiFgp9KedBgWGreS4cZsoftYyZDodYesdvX5xGpXj91oX86hiCImM6zfsAE2C8lZZWUlqqur46zBMAzDMEx/pe+EHiSBxWLBz372M3R0dOBvf/sbrr32WkydOhXPP/88xowZg9raWpxzzjn417/+1XcNC3IOeUAtyOysoJeMv4PSIfk7KEQe5cBJ/wCePQ9Y/waw7N/AgZfH3t8EMixg62KakFnyYbNacc+vL0NDcxuef2cJLr/5Ply98CQcd+UlwKe3oLizBbMHuDH5kU3Y00IFFFu9AVz5nxXwBUI4/YBhuOKR5XjzG/LMmzbEintOKsUho8jDsFv4e6YIBYGdy+kVDlHqopHzUgtvDwXUVEiDJhtfT0wY7eVqkWEtsdJQyIqMZBQnuVTQL12edTanakSQoxe0hgWfh1IOBDooV79FUTRGMywAlP7owMuAL+4DPvwDMPKQxNomDAv71gM4JbF1U2TCAAs+vLQCT67y4u9L2rHm+wB2tYXwm/fa8Lv323D45Fdx5uzROOXAkRheWQhPpx8/vIeUMgsPG48RA4uAbftoY/G8RAV1S4E9ayjNw4wLE2+01qhw5E3AxOPp/tbeDJQXqYWb3Y2A1QvkO3WutVuSkxgK+VyShygYSYG0bt06bN68GWPHjsWkSfpRWD6fD5WVldi3bx927NiBxYsX469//SsAYMOGDdjncmHgwEFoa2vDjIkjkG/NQ3OrG16vB2azGR0dHQjXnIKm799BmXsL3rj+UNz46VG482934YlFT2HgwIE46aQTIaTpyy+/RDgcxvTp03D8scfihOOPx8EHK5GVPjfwF8rp/8W7L8eNzmpuc2PZt3Wo8A7F/vl1WPHy3Th5USt2tYW6lrHkmTFrwhAcOa0aR04bgdk1w1BgS+JRymQio8Ce1co9yk0GBjkaQcZWCDgq1Fe0YwmHge8+IQVwyE8GgQMuSz5qboOSBmns/MTqCwlEtMK0BeQNLsYdgTz+xIqgE88qJqge1kaIGMsyUIcmFdk2uq5RJa58rHmacSdeRGk8JpxABY0//jPw5f3JRYqVjQAmn0lRNGteICN4fvf6NZPHUhHy1Zvq8cN7FuO+Kw9BU3sn/vzSKqyta8QZB4/CxUdOgM2Sh7dX7Ma1j25Fp6cdtx5ZgAunWXHwMADNG4EWM93rFUwAGlGCb0z7YV7pPljtRcqzbIJsX0rGivwi4OAfJ76+wLWJ5N3qoIjeVNn4Lr0P2Z+ijX3e6IYFm1NjzHNG9sd4z029wFjOpMZll16KoJ/mXA6HA7W1tWxcYBiGYRhGl35pWAgGg8hTisKNGzcO119/PW655RZUVlbirrvuwrhx5ME4efJk3H333Zg/fz7WrFmDKVMMKr0yTaoF3mS0k4Ouz6buE4uCosjveVagZAQw7xrgkzuB924CqmcDAydG31/VLKCkisLIW3YAo0iBmgfgqfvvwKg7H8Rf7n8Cdy96HU+89gke/dFBOLXgS+S3boc51ImpY4fi/X/8FH9e9B7uevYjXPXEKtzw33Vwe32wWS34w2Un4hfnHgWLRQp/D5oAxbkMh14HNG0wdm4qDBapHFBDBTQ//5daSLN6DjDnqsj0FNEmeFrkXPHbl9Okz15GSig5v3Usz25Po7pPi9IGrSFBTCrdDd2972VFhrOS+pzPQ++ppIjSkqjRxWRKfB35WGTFklaOEjFAxJpU+70AAuQpapE8AQOKgscSRTk39xpg3SvkafzVQ1R41giFg4ER84BViyiPucmin+qkfY+x7Y2YHfv/oBkQztMX/g+wmmECcKHy8no78NKbH+DvDz2J5avX4cM1O/Hhmp346UOfYvKY4SgvcWL9zmYMqSzF335zFVBaqCovB00mZX4s8ouAr/9Nn/dfCJSN1F8uWi54kf5IGBVOu58UZH4P0LQDKBxAfUxcY38b4GunHNyOisj+oafwM9o/jd7HjWwvHbm5tUjtE9EAmzdvhsfjwebNm1FTUwMAWLx4Mb5ZtQrTpk/HEYcfjpEjR2LbpnUYOWwgnAUWvPfmqyi0BNAZBEpLS5BnNmO//SbhzDPOREd7M9as+Aotnk4UlA/F9u3bMH7sWBSVVuD7KT9G2RfXIe+bp3DHDV/ipJNOwpU//gnWr1+P119/A0ftfwkA4Obf/QazZkxDU3MzatdvwD3/+hd+etVV2L1nD6bXjMO7R9Ax1G6pw/ia/brG/nA4jC1bt2HJl19jyRfLsPTzpfh2IxUoH1FiwoafFWLGwBC+u9qJlzZZ8KlnDCbOnIeLTpqH4kIDCmr33vjLAMCJd9G730tysOFNMkqWVNHYMnAiGQ7tZfG35XMDK59UUx8NP5AicfTWdRgogB0OARuUNEg1p8SOdtAbj1ybKGUZTMBBP6JlxLjjbQXsxapS06ZjuJMR8lhQnJgiP9r9Px0kMx5FI9b9wKgSVz5WETkjDFXR+o/RcxIOkxI90EkF0T/+CxmKJp0WuVy8GhyH/YqcLxo2A7WvUb/QMG5cMf72y4vxy789gccXb8QnGxqwy9WCTh+NDR+t3YXfPrMcZrMJzW00ro4eUo7jFl6OPHsA2P4ZFYoOhygiobQaKK1GZ/EonPvNEYDJhFcO64A11gxIx+ABgAzaIop03s+B4qGxj1egV4tu13J6H7o/OSEAZCw3gl6/26SkQRp9KBAIAs4UnmPiGbN6gbGcSY3PPvsMNosZtbW1WLhwIVwuFxsWGIZhGIbRpd8ZFjZu3IjXXnsN5513HoYMGQIAGDNmDP74xz+itrYWI0eOBKAqUjo6OjB+/HgMGjQoW03OLHo55QXxUuD4PKS0nvQDKlS86V3gvxcDl74T3TvOZKJcu1/cRznlR6me2RaLBX++8aeYd+A0XH/bPajdvA2n/+U9LLnEgdlVFtx7cjEOWvAjDCgrxF9/dhrs+Tb86fF34fb6MGvSCDz6mwswadSQ9J6feISCwOrngbUvkreprZA8z0fMTY/CQeTLHXt0YkUzRSqkgiJKsVE4UCfVhJJ+wuTrnucaICWSnCJLLJNOw0JPkIxSJtmc1oBioClUz2O8GgtyO4++CXjpCvIInXhCdKW5lvxCoHwspUHYs5pSV2SDcBh2ewHOP/NEnH/midi6fQdeeuZxvLR4Ob5cuwVrt+zoWvT+Gy7E3c++C39bA26f2ERKx0E18fexaxUpTk15wEEGjS9d7dPUVJj/R9Xr1uoAyoZHGpesSt/xtZP3Z4Gmf/QTr01xKxs7diy++uwjmDsaUbd5PUaMm4hVq1aipaUVK1euwJFHHIFRo0Zh0BknY/u277D522/Q2rAH8HdgUHEhZk8dD0dJIQ466GC89/77+OKLLzBr1iwcd/wpKPn6a7jd7XDkm7F22RIs+XIVFqAUB5Q2Ax/dhkPPeBirVizHX+64A7ffeVdX227+420I+jp0272k2QUcQfJ3wOHHA1Y7pk/ZDxXlZfhy2Urs3efqts7o6qGYOWUiXgiW4ATnRpR11uOciUGcg41AVQXgHQkUTkj3Kab7xuQzgYknU6RBokpw1ybg83sobZEpD5h1OTDlrMQL7crsWUO1HWyFpKxMlK8epPfxxwIVY+mzkBkRRRdLdrTG3mQUmX1BRsWx+z0ULZeIF7m3md5TjVgQzL2ajKwrHgde/zm154BLjT/v5FmB+bdSpGvd51R0fOj0botde9GpmFEzBudcdwe+20URmPOmjsZJc/fDo298iQ11ZLgbNqAEC46agV+cMQtDKpSxtuYUivoLdNKzjyID4aA5tecyv1eNwBl3jPEIu2jUK2mQqmalth2ADEgindv44ykNUrw+H0um+oLcMCkxffr05KLxGIZhGIbpd/SrJ4bNmzdj9uzZaGpqQkNDA6699lpUVpJnb3V1NaqqqmBSJh3i/ZNPPsHw4cN7f0HmZIg3sRC/FzmAMx4CHjxUqbdwLXDmv6NP4CaeRIaFui9o4meJPLcnHT0Pxx8xGy+88SH+cPcj+Nlb2/H15U6cNg5AXhOAEphMJvzxihMxaeQgeDr8uPiEWbBYe/gate0G1r4EtCme4FWzgAOvSL5IopZQANjyAX0ef1xi67oVhZmtiIrzuRvIC9ZSADgVT7yuNEc6ea7NGq93o4Wec5F4nrBdy6VxIm110HkPByl9BBDfsACQQmDUYaQ4//CPwBkPG1eEDJlGhoXd3/SsYSEcIgPY1w/T+RtzBBWkHjIVo0cMx3UXHI/rLjgeDc3teP+rb/Hh17WYWTMSJpMJk8cMxzsvLgYmmkjxaERx+Pk/6X3ymUBpVWLtlI0KR/ya0rvI6ClaCisBn9I3tGnF+pnX5qRJNWjdvQWdXg92121G2GqH1WpDKBTC9On7AwEvCk2daAsG4fMFYCrIw4T9pmG/Sfth/OgqHDX/OORZbXC1evDRmy/B7QvixRfrsGLFCrS0NGPYsOFo2rsLk8aOQt2WDXgkbxgOmN5MhtvJP0D++GPx+9/9Dqed8QP88n/bqVHhMEpLS1EzcYL0mohBgwZiw5pVwM7fAACcTgf2NXuw9MtlXcdjs9lwwP5TMfegAzF38gjMnjEZAys1nt3ff0v7r/tcfVVOIOXlsBmpKe71sNgAJJByKBwC1r9OBu5wECgaAhz5O2CgASNdPDa/R+/j5hv3pBZ4GoFVz9LnmZcoKcXsqmHaSHRYOvK4CxnNRFRPT6N3PvSML34PnX9rAdDRTMul67nEZAKOvpnqDHzzDPDhrWTUOuZW46myBk+mZ5qNbwErHgMG3KZ7fQ+fNRkrH70e9730GQ6dPgZHHzgBJpMJvzzvKCxZ/R1MJmD25FFUb6GzLXJlIxE+ibLySTLcOQdQtFwqhMNUawIAhnevQ5QwWz6kvlE5Hhi2v/p70vU5+tfYxjAMwzAMwyRPvzEsuN1u3H777TjllFNwwAEH4KqrrkIgEMD111/fZVyQWbt2LZ599lk88MAD+Oyzz1BSkiZvr95EvImF/L/VAfzgMeA/x1Iqh2X/AQ68VH+9odOp6GTrTmDHMt2CkHl5eVhwynycddJR+OjVRfBgFQob1lAth4N/3KXMOe+YNEzIEiUUJMXv1o9JqZPuKAVB/deAt4kmyIlOPEWNhcKBlFjY307f9Qo2d/2mtF3PgGBUOd+bSddEWhhnTAD8fjUdVsCAYcFkIgXNv48Gtn0KbP4AGHe0sf0OmQasfYEKpQYDQF7mb++mvd8CX/wT2Fer/lj7Gr0KB5FXZ8lwoGgIKkoLseCYg7DgmIMAAIFAEHc99Q6uOLwaQD0pm+LRXAfUvk6fZ//MWCNDATJiLnuEUk2JmgpjjqSinfEQfcLdqBZC7ccMqR6L3XWbMaR6LOrq6zFp0iTMnDEDc+bMATwNcBRYMWLECASsJWhuacHpCxaS0T7ghcnvhb24AvWrvkF11TB89902BKz5aGlphtfbAafTgeox1XB7O1BcPgjb9zZgGabiAKwGXv0ZcOUnQNEQjB83DgAZFn59w/W45be/6nIGkDlgykTgTjIs7N6wApvqvsfyVavR0NiEA/afipnTp6pOAyKNnZZB+9GruZ4MDFveB1wbgE/vpHGs5mRKRSanvespOpqBz+8Dvl9D36tnk8EsmWLmWsJhYNP79HniSYmvv/wxuucNnAQMma4Us1dqzxgtwGzE2JuuGjq5Srx6R3rGhq7aWR2qk0EqxZu1mMzAsbeTMXjxn4DVzwJN3wGnPWB83NnvdGDXCkrf982zwAGX6C42pLIEt15xYsRvZrMZh0wfk+pRJMaeNcAmxdB28I9Td7Jo+o6e0/JswOBpqbdv49v0Pv7YyN+zUWTZ7+mZ/TAMwzAMwzA5Qb/RkpjNZsycORMVFRVYsGABBgwYgHPOoRQYwrggFBPbtm3DL3/5S2zcuBEff/xx7tRWyHWGzSCl6Ns3AO/fQvmdh0ztvpzJBNScBHz5IHmsjZgTVSFvNptx5MzxgG8Y8Nlmig7YsSw9oePJ4NoEbHhbzZ89sAaY94v0eQMKOlqBrx+iz2OPjp3bWg8RRVEynJRfPg8puuNNhvuDASHTiOiO/CLAAcrZD9A1MEL5aOCgK4Gl9wAf3QaMPsyY0rJsJKVd6mylXMvjj0283yTAnPDXyH/t7/TF6gBmXER1QDa/B2z9iBRGK5+k/6sPBqYsiEjnZbHk4frzjwI+WQG4AQzWuVdoWf08gDBFRBjxyA50Ao+dSAosAF1GhekLlFoUSp75eMpJn4eMCoEARWj1Y0aMm4gR46iOTti6Hbt27sSwYcMofaDNgYbmFmyt24MR42pw0EEHwWw2kzLZbwKsFYDFDm8gDGdhEc485wIEzTYsWbIEo8eMxrRp09HU2IA1a9Zg6IjRGDisGv5D5yFceytM+2qB934PnPFwV6pCALjtz3dg+JCBuOIyfcWkIC/gxcTxYzFx/NjkDry0iuoJTTqNxq3N75Fx/MsHyAB49B/086inStCn1sfxu5W0XG4yOq9/g+Q9zwbMuBgYfXh6jAoAsHMZ0LaL7mdjjkhsXV+7Wgdl5iVAsIMiuExIrgBzzH1lQXHak2jrHWmPUc/YYHMA7W7qG2276bd01uYC6JntwMtovHr1KqD+S+CxE6iO1dj58Z0sLPlkTPjodnLUGHWomi4r13C7gM/vpc/pSIEUDgGrnqbPQ6ZHr7tkFE+jagTURrdmwzjnM+BEwTAMwzAMw/QZ+o1hwW6346KLLoLTSUrTs88+G+FwGOeeey7C4TBuuOEGVFRUIBgMwul04v7774fZbO6/haoSmWjIy866kpSKG9+myeal7+inUJi6APjqYZqMrvsfea/FwuYExh5JipTNH5LBItHUDKkQ6ADWvAjsW0/frXby4hw8Jf1GhT1rgDeuA1p3kEJ5ytmJre/aBGxWJpnDZtC7syL6xDKiqHMUo4LPrS7T22osZJIIOVGMNtqoEKGEaN5uXLkz+ypSPLTupBQHWi9EPUxmqlmy/g3gm6eB2leAoTPIwDco/cbRorASBWO2AguepAgFgKJr5l0LbF9CdVe2LVHTnu1/oWpcCHRSLQn3Xjp3RqJy9iqREaIuQjzWPE9GBaty/xh/IjD2KPrPJhnZ4ikn5bRvev9HeBX3PcOcCfpKwlEjR2LkiBHqDxY79rlDsDiK0djYALOZUpfA71FrtFgdaOsIomjQCLjd7TjrtJNw8nFHAVYHXC4XXl/3LcxmM9auXYuxI4dh7epvcPC8K4HXr+mKKqhdvz6iHT/+2dU45aQTMHjw4MgGWu2k+GzcCrxzI3D6Q4nVqtHDUQ5MPx+YdDpFL6z5L6Uga95G+0qFcIi8v3etUu65biDoj71OSRUw5//IiJwuOluB935HnyednriycfHtZFgsqQKqZtO1txXR9Qh0qAWY49UMMGI06Ou54OMdn16knVUZj3xuUtZv+xT49G9k4B0xJ73tG3MksPBl4KXLaIx74xdA1UHA4TfENxQMmEjL1n8J7FyRm4YFtwt4/2aKLiganHoKpL21tL1dK+l7TRLRQDJBP/DCDyl6qWxU93HUSCRmuo1zthSjORiGYRiGYZheRQbc63IXYVQIBoMIh8NYsGABnn76afztb3/DX/7yF+zatQu//OUvccUVV2Dw4MF9z6ggJvEiTFn7XUaeaMRDXtZkAk64kzzrXBuBj/6iv87AGjJCABS5sGdN/P0MPxBwVJCy5bvP4i+fLjpaga/+TUYFUx5NzOddQ8aNdKY+Cocp1cZzC8moUDwcOHsRMGhSYtv54A+Ua3v8cUDpKFLctuygOgt611MuzOxzU5/wuaMv01eJJQ/Rlm+qJyWcOK8+T/fzPGACKd87WoCWemPbtjmohgCgeOkbZMo5lPs9v5iu4bZPgc/uAl75EbDiCVJm+PUL3SbKR6Y5CBeUUaHZHcsj/7Tkk8LpuD+TV6opj/a97BFShAijQuNWwGIHDv6JsZzY7n30XmKgtkIoSFEfAHDItcD8PwEjoyjVbI7YHtRWh763sCCR+6WWRPtdjjN82HDY7QUYPkxSdIvzqxjfJk6cCKezEBPGVAOhADatW40333oLjTs347ApwzFz4iicdepJmFxVhinjRwH7NtB2BtK9sKoq8vqffuopqKio6N4Ykwk48S7qY9uXAp/9PX0HanOQrA1SUnh9/23q29z4NhkGW3eSorDLqGAig5VzICkPB00mhey084Bj/pReo0I4TBGHrbso2u3omxNbf+cK4Csl2m7edUCeifLfexvJs1o2IsSTm3hyCcSXzd5OrOOLdu/wK84CQT8w/QJg1OFAsBN4/gI1giGdDBgPXPIOMOdqip6p/xJY9AN6/hM1hqIhItX2rY+9XDYQRoX278lwftTvk0+B5GsHFt8GPHkGjYVWJ21v6oLU2vjub4Btn9H9YcGi5KIUjciZTLwxq6/KIsMwDMMwDKNLv4lYkMnLy0M4HEYoFMI555wDk8mECy64AK+++iq2bNmCr776CgUFPegN31NovZJieSkl4gWoXdbmBI66BXj1p2Q0GDdf30tuv9MpN/vWj6gA4Kn3RRYM1mLOo2198ywpTO2l6Sl6F4u2PcCKJ0l5bCsEZiwkZUu66WwHPrmDjgugVALz/0CenYmw7TNKg2PKo0mrsHsEOlTDgPaayoWZZQOCzalGKgARisE+SaJeez4PYLECAb+aHifi/DnUaJDy0ZSXfc9aSm1khClnk4Ju+2dkkDCiTDebSVEx+Sza345lwM6vydty9zf0MudRgcfBUyiSIUlP305TPgJTzoH16/uBlU8oqZd0vMGHTAMOvJyMCt+vpWMK+VWjwuyfAKUjuq+nJajkaAeA4qHxl9/wJtCwGSgooZQdHS2k3PE2Ue0RnzeyRkwqypBUvKb7WCqXESNGYIQSxRAOhyltUV4BYFfG1HAY+0+fjunTpsEc7AD8Xmyt34NOrwcNGzdj9uw5WPfteqz5Zh0OnLYfZs2cAbz3Iq2rKPGLCtWoqeZ9u1HoyI/eoAETgGP/BLxxLfXBQfsBE45P3wEPnERRMXvXUb2FZGmuB1Y/R58n/4BSpIjUdFZ7RtOaRbD2BUrzZLYAx9+R2BgU9AOv/5wiLyaeDIw+hNKHFRRSnR9/OxVCF/08GW98RiXavcPnoWhHSwFQUAQcfQvwyk+Avd8Cb/0KOOvx9BezttrJgDvhOOCTOynSbtUiug8ffiP9rscASquGpu/06ztlC61R4eibqGhzooTDFEH68V9oWwA5fRzxa4qASIXljwNfP0KfT38AKK2mWkAAnUsT1GfqWBHIicpZHxuzGIZhGIZhmNToVxELMiaTCSaTqSty4ZBDDsG+ffuwYsUK7L///tluXmbQeiUl6qUUDa1Hnb+D8uVOPhNAGHjtGvJY1GIyUcqUspGk7Pvw1vhpHwZOokiBcIgKsda+Tp7JmaBhK+WJ7myl4zvoiswYFfatB16+gowKZgtNOE/6e+JGhXCIJsIAKaaKh5LxxTmQ2h3NMGBzKKmSJAWWWE4oygE6B30lzYvPTYWAm+pUr7tE5cHmoMiAsipJQR3l/A2YQN/3rDbextIqKggOAKv/a3w9gAwMA2uAGRcAJ90NzL8VGHMUKUZCQUrHsPp5YPEfge8+SVqGghNPpXPQsoMUSdEYNInqRuTZyOCRqFEBADwuAGGK/nDoeKfLhMPAkrvp8wGXUb0LkYUqr4DqK8RK15BoFEEqXtPpug9nCTGWxnrV1dXh888/R119fddvZrMZJpsTJmclRo4aifI8DwYNHQZTKIQlK9diX1M7vlq1FqbSoWpx8EH7ddu/xUhB7QnHAwdcSp/f+Y0aAZEOlCgK7KsFQqHkthH0A1/eR/eKIdOpjkP5KDKA2ZyU5qwncG0EPr6DPs+9OvFc8l/cR8ZDexlwxG8o/VHJUEofZcojI6xMX482yDTR7h3id0cZvRcPAY67nT5veBOofS1zbSoZDpx8N3D6gxRd420E3rqe7vl6OCtpXAqHKKVYLuBpTI9RoaWeDDpvXKumBjvjYeCUf6ZuVNj+OZ1XAJj7c2DCCfTsHQoAniYyone2k/I/lYg6PXr5mMUwDMMwDMOkl35rWABIIRIKhXDttddi8eLFWLx4ce8v1BxLIaadxMea1MsTkYSVbAXkuXzU72ki1VIPvHdzlGXtwFE3kfJk7zrgqwdjb9tkIqW5yJNe/yV5baVrwiRo2EKpYwKdpPicdbmxVC2JIFIfvXoVpScoGgKc8i/K352MN+HaF0lxbSskhbK/Q821DCg1FOJMBG3OSAOCVlHeV/B5lEl3u9p3ElVy6S3fZaTRGBqGzaTvRlJ+yYg0CWtfjG90i4bJRBETE0+gvNeHXg+MPx4oHEz9e90rpIRv2p74tm0OYKpSA2TFE6QcikbleEp5ZLXTOUvEqADQPQggOYmnaN2+hLzILQVkEASUPmwhJaezIvZ1Fvc/t6v7vc/I/dDnBtr3dU8ppkc/UK7u2LkDHZ0d2Lljh+7/E0dV4fAjjsaY8VOAIZOw3wGHoKCoDJNmzgMCPphalPV0DAuGmfdzMtQFvMCrP1OjX1KlbKQa7dW8LbltrH2RDJ35RRTdk25vciP4PaSoDHYCI+YB+1+Q2PqNWyWjxDVk/HOWkwHPZgfs5WQo0e6zD6UB63Gi3TusDrrPya8Rc4CDf0r/v30DOXNkkhFzgIUvKsbxsOpZr4eIWtibA+mQPI1qdEGyRoWAj6J1nzidokjNFhr7Ln7DeMRiLJrrgf9eTGPUmKOBg35Mv1sLVIOSrZDqYYmaT+k0BPSDMYthGIZhGIYxTr9MhaRlv/32w4oVKzB16tRsN8UYsZQO6QpRllMUJLrNvHzy3CwoAU67H3j8ZComO+VMyrsuqByvvp98D/DiJaTknHBiZLFaET4uUz6a0rh8+QCF0H/9byqqp+cFFpLOl8kMDJ4cve3hMHleb/uEvo85Ejjyt5QzPhp1X0T/T2bItMj9vHkdUPsqfR93DHDs7RSlYLQYtFCuhsPA+teB926i79POA0qqaWJpzqMcx9HSIAFquh45TF4uRhsrPVVvxeagSXdY+RxLpuIVXBbnSq/4tUhlIhsWjKY0MVuBcccCjkry1t/6sX4Kl6oDjW2vs139PO0cMgJ8+zLVIWjdSe+TzwAmnkh9JxqBMLBciXBo2Ewe1hY7yeHq59RjFQyfGfl54gn0WXuu4t1bOlrovXhI7OUAYOk/6X36QrWotMkE2IvpPZ5hQiiKA77u975498Nw2Ng9MxvK40Qw2j4DBcmHDRuO+vp67Ny1C/fedx+mTp2KQw6Zpy4gF8e25OP8s07H+acdR9dBFDktqVKiuMJQw09A8hRLpvKL1M+nPwA8dhIVmX3tauC8Z8n4BBivUxDY0v23gfsBO5dRNM7AGmPbEXQ00/0bAA7/NTAsSsSk0fuG0ft1nk393LgV+OjP9F44CDjtXjUqyEix63AYeOM6SrlXdRAw9jjlvpqnptMzmSg9j2ykTsfzShr7qaFlchF5vBbnUdtf/B5g1hXU1xq3AO/fROknAwbrJgU7jS2nVcIf+Vvg0eOpdsgRvwHKFGNyQYm6zNAZFLG5dx1dA/kZSF4uFnnW+Mv4w8DnSs2H/ELAouk7bhelcXLvoxpXZzxIhuxYyPcXgJxd3r5RjdAYMYciBodON3QYcfugz021MjwuoHwMcMIdFKFoyqO2yO3xe4B2l5oSKZaM6fUhPYzIW66PbX2Uuro6uFwu1NbWZrspDMMwDMP0M/p1xAJA9RYuueQSTJ8+PdtNSQ+ZSm/kjVOAT0bOMz9yLk1mAUqJ5GvXX2fsUWox57d/BbTpGBO0DD+Aoh2cA2gi+Pm9qRXQDAXJc3TDm/R92rnA/FtiGxWSpW4pGRXMVuDI31GkQqKpjwBS7D59NnmveVzkAT77KvJQ9HuVPPIxog58HqB5J6WqkqM+0h06n2vYnKTgKB9hLL2TkULn7gb9wtcAGcIAit5JxFM0zwpM+QF9Xv2s8fWMYDJTurKFL1E+dCgRNK9dQ3VPjCrZbA41gmjdK/EjK4SxJVGEgTGeomfPamDrYjq+OT9Tf7fayVDg98SOJBCKUKudIhu0smPkHmtzqPvqx97YIu3RqJEjceghh+D7779HS0sLvvnmG5ggKZ80443J74UpFIDJ0whT/Ve0TCrRCgJ7GXD246Ss3LkMeP0XsaNsjCIKOO9Zm9h6fi+lAESYDHrp8GY2insfeZE/diLw4KHKuGeiNC3xUo1pWf0s8N3HZGA89jagbDgd296NlCYtWjo9vbHJ5+YohljojUVGxmufh4xEh90IwASsejp2+rp0MXR/YPQRQDgIfP4v/WWEs0fTd8ALFwMvXU7RL9++RMb4nngO0dZUMGJU0LJ7NfDsQjIqOCspJdSCp4CKMelpYzgMvPp/lG7MOQA44S5KRRhtLPJ5AF9b9+c7gdyX+vozXx+nrq4ONTU1mDlzJhYuXAiHw4HKyj7oFMQwDMMwTE7CEQsg5UefQVbapdP7zl6sTDhc0T3b5TzzchG+o35LXnIt9cCHf6Jcv3oc8gsqVPv9t+TNf/bj8T2LS6uoyPHSe8jbbcUT5OU95ojEvKYCncDKRbQNmIBJp0YqJdPN1/+m92nnADMuTHx9n5vS13x+LxD0kffp5B9QtILfC/gLVONOYaUaeeJuiEyJ5Pd2L0AMpFaMti9ipNC5yRdZ+LprXTfQ0U51Llp3Uv8eOQ+GmXoOReZs+5RSIJQaKOKcCI5yMqBNOgX46HZSinxxHynnD7jM2P7GzQc2f0BKxC8foLQPRjydE0EYFuIVbl6qKK/2O4PS1MiGAptD/xrJyIZRvboiRgpdWh3qvrjAZRfTpk3DN998g2nTpsVesKuQvJ+UaEB6DAsAUDEWOOMh4NnzgdpXqI8c9svUtikUo3vXkYHaaN9f+yLQtocUmHOvSa0NRvC5gU3vAev+B2xbQspegLydRx1CdShEXRejuPep0XJzrqK0NjYnGayDHWS4cVREqe8jPa8IOfV7aDxjudEn2ljkbaXUU9EQ49SYwynN1congNevAS7/MHFDb9AHPHMuABNw7jPxowXmXUPjyTfP0mdtrSrnAHp22fYJ0LKT+pR7HzlgALSfkmFAxTiSX3uZarQV6ToDHWof8rnpN6ud5Lt8FFA2GiiKknpPr1BzokaFjhbglZ/SPWv0EcDJ/zAebWGUz+6ie5bZSqndqg+MLSM2B9U5MSF6tKroS/zM16txuVzweDxYtGgRampqUFlZierq6mw3i2EYhmGYfgIbFpj4aBWn8dKCiJyuXesXAsffCTx3HuWd3e90oGpW9/1Y8qnQ7OMn0gRz2aPAgZfGb19+EXDY9cBXDwHblwKb3qGaBVPOAiy22OuGw+TNtew/ZPgwWyilUqJFKxNhby3l3TWZgZk/TGzdcJhSCrz7W1JSA1SU96ib6Pz5OyjPrt9Lk8mIsHhvZEokMSE35QGlAyMnqEaUp/2JWJNuca5kBbbA5yYFm78dKB2ZnGGhtJpynm//jDyDD01RCRqNYTOBc54Blt4NrHmR+ulb1wM1J1Fkg0gZo0d+ERniPvs7pa5Z9h+S3XQWnW3fS++xFD5N20jxAgBz/4/e5X6vNXpGQyjpIowSCSrfWFHTjUMPOQSHHnJI/AVFYe2AF9irRKGly7AAkPL8+DuoqOrSuyl6adz85LdXNoruAX4PeV1XjI2/zq5VlDYFJopaSyaKxwhBP403614BNr9H/VkwdH8aj2tOST7l3Tu/pSis8jE05gqjnbMccDfSu9h2LHkSchpG/Igg2aEhU+ctV4l2X7HHiXiUx/TjbgO2fEB1PT76M3DMrYm1ofZ1tV7QpvfU9HbRqDqIUgJtXwp8fh9w7J+6LzP1bHr5PJSqqWET4NpM7+59ZLRu2UEGikRwbez6WADgCVTiO1TBsmYUUD6SDOtL70mtpkI4DLz1K3qGLKkyblQIh407wKx/kwz/AHDIL4EpZxszcJfFUC7LfYmf+foENTU1mDFjRrabwTAMwzBMP4MNC0x8xIRDnswLjCrPJh5PqYW+eQZ45zfAZe/pL1c5jpQs7/4W+PjPpPgwgtlCUQZFg4FvX6F0KM3bgbIRsAYC+HPQhHx0wva5Gwj5yOMu6Kd3kQrD6iBlaNlIY/tMlmVKEcPxxyXuff7p36iwIEAK1iN+B0w7G/A0kVLGrngshgKKcsauRioIo4NTMTb4veQZms6ifn0VI5NuvRQ/IiLE6weGTAXqlgDfJ1jAGSDZ2f4ZsOJxUuJNOB6onm0877pR8qzApNNI8br8UWDHMlJIbnibDBzlo5BXMhJjw9XYDk3fHVgDHPxjUtLUfa4oa49JT7vCIVLYArENC8v+Q8tWzwYGKzVzZGOC0TRMQkkXYYxLUIHZnxQ1RvNzJ7RNL2CyqEXFv/4PKRZHzAXKE6xjoMfUs6lPLb2HlIKWfGDUoclty5wHDJxE6ZVWLgJmXR57+bY9amqz/Rcaz7+eKOEQ8OTpSiSeQukIYL/TyFgoUrQly7cvU7oak5ny54fCqtHOWdndWCHLk/gu5FLIqbMovqz5PEBnKxkuyob3L+OC3n0lUSOmrRA4+Z/AU2dSOqxJp1JqSaOsXKR+XvPf+IYFAJj7czIsrHoKOOLXMdrmIMcO2bnD20xpHxs20btWGW5z0DHZnJG/dbYCjd+RnDd+B7j3YRBcGAQXsG5l5H6TNSoAwPLHgI1vUyTBqffGNyqEQ8Djp5CTQfVsShk6Yi4ds54x3rUJeEUp0HzgZcCh16Zn7O9PYxTDMAzDMAyTMdiwwKjEUw7pTUISmZjUnESGBY8r9nLTFwLffQJsehd44YfAETd2D52PRtVBgHMgTV47WoDdq5EHoMs8EaXEAwoHUfRAYRKTykRo3U0RBwClnUiEUAD46mH6POsKYOalZDjwd5BSpmUXpQOwl6rKHXejkhpAOXA5msSo9zZDJKI8FZ65gFpQccAE+t6yM/F9jz2aiiTvXkUKydXPkhFp3DFU6Lz6oPQaGZwDgEOvJ8PC8kfJY7RhE9CwCVYA9wIIIA/mD4eSR2TZCKBoKKVDEqQzWmHZo6RcybN1Lw4tI4pO139FBpFJp+qnW4kVgaCVC5aR+Gjzc6fDyCDO+cAa6vfbP6MXgPz8ItwSHIvVpkkw7akAhk9PLvXWob8kr+11r1BamJP+kbxxYdwxZFjYvQp49SqKyht/PHlEy7TtoeLiPjcZ6w68LLn9GaFtNxkVTHnAjAtIHgZPVQopx4nmi8d3nwKvKOkCZ11B5y1eZI8sW6kY7WwOGtssFqWv9WHDgpFxR/scZmSdsUdRKsZvngXe+AVw6bvG60nJkZD5BmtDCTkIBROva2IvJcNHLONHtHRMI9UIqQ53G37z4jqMDm/HlaN2wNKyneTfWUlFppMxKuz+Blh8G30+8jfkQBCPpu1kfAcownbTO/Q5v4gMDSPmkrFh0BRKrfTS5XS/qJ4NHH4j4Gmk4tNahx82EjAMwzAMwzBZgA0LjEqsPPIyyU5khNJxzJGxlzOZKCXSs+eSkubjOyjVj1ZBE43yUcBhv6JULh0t8Jvy8bf1g9Bhysevp7fBll9AShWLjd7zbJTmpSdqbax4nM7x8FnGJqAy2z4DvI2Ur3r+LZS3vysXvHJuzCYyNJQOI8VLKKDk1y1EV61Uuc4CRyoYx6h8AKrSzGxRvXbLRtG7tzHxfedZgfOeB+q+ADa8RYoIb6NkZCijVC7jj0+vkWH4AcDQGaSgbCLPz2DDd3Dv3YZitAOt9fTavkRqqw2Yfn5i6Z5iUf811XwASK5Lhkdf9pBfUKTSmueBFy4BfvAfUqYKjCgztVENNifl4G7eQcWck00Z05eRPaYTkZOY27TT64JXgF0rgC0fAbtIcW/qbMPBWImDwyuBx59SFHIHk1F64onG92EykzEh6KfixakYF6pmASf+HfjmaWDH1ySrO5aRknDcMUBBsWpU6GwjY/lBP46fnz4VmurovbQaOOr36dvu7tXA8xdSxF/NycDRN6up0mIZ77SypTXgGTU0WB0UqZBoqrHeqIRNRp6MrnPMbcDm9yld0JJ/Gq81ctq9wEdK5GSs6AOZlU/S+4TjIg0TPYmtEGtNE7HWNBGXHmiBxWJSjBym5J7/OtvUugrjjwNmXGRsPdcmei8dQenDdnxNKQQ7W8mhZtO79H9+EVA8HNhXS899J98NtLsoCtJkVlIwpul+yzAMwzAMwzBJwoaF/ki0ybVeOL3esol6p/qUbWx8m74bSY9icwA/eJRC9Ru3Ap/ckVge6jxrl+I+GDBh8UZSRoYqdgC2NBeVNUpnm5r+wkjtCC3rXqX3iSeS4ljrWe0oo5RI1gIyKgC0XEGxek3dDZF1FhjjJJJuwmqnc23ykaLN5lQNY54kDAsAXcuR8+g1/xbJyPA2pUda/Ty97GWUzmjuVaoHfyqYzVQ4s2QYMHIe/IEwznohgAFowH8O3ARbq+L12VJPyo8ZF8YvsGyU9r3A2zeQ8qfmFMoHH7OteZTiA1CNC6fdT8ZMqz35KB13IxWidTewYUEPrcd0Kgpfrdd00EcFgcvHANafAXk2dO7diMcefwxTw7U42LYRps42yvW+6T3g4jcocs0oeUr6kpeuoBoEqRgXykdRSqB9GyjlnWsj8N3H5J08Yi5FNAijwuyfkddxJmmRDAvponEr8Mw5FAU3ch7JlxwtEivdkYzWyKCVTbere40GGdHnElEI90YlrDzuaJ/HEnmW08NRDhx7G/X9pf+kqNKBBtKM2QoTq8vgc1OhcgDY/0Lj6/UEyUbWhcNU8LllB9VVOP4vxvtig2JYGLgfMPdquk6hIBWq/+5Titit/5LuFftqadlT7gHy8oHOBiBgobSfANfyYRiGYRiGYbIOGxb6I9Em13ItBbcruvdpot6pfi9Q/wXQuguw2I0rbBzlwNlPAI+dRJO3z+4CDv1V/ILMucrq52mCXT4GGH14YuuGAmoKpVFHqF6dNklB6ixXC2Z2ecuXR06cOf1R8iSS9svm7O6B66ig/zyNiRVt1EM2Mhz9O1JCbHibPB29TZS+aNO7VKSzenby+4mGyYR9qERoSCFQZbAOSqIE/WRU8DZS7ZXDbzB2zoRxwWQCVj8H/O/HwIl3kUHOWZlc2hRnuWJUqEh83f5GMnm75XFEa1gQ96z8oq77XXjwVLxkPhEv4US8cvVhKGhaD3x6F7DhDeDNXwKXfZBYJECelRSDbyF14wJAac/mXEUGhvWvU1FxUXS2p4wKANBcT++J1vKJRsMW4OmzKTXa4Ck0Plvy6foEWrob7xKJQtAaGrqMeY3pM+b1RiWsLE9uV+TzVrxnOSNMPInS6W18B3j9WuDi15NLKxaLda+QkrxsJKX46QusfpZk2mhdBRlRULp0OBnqCsrIMWTINEpVNu1cINBJBojvv6UovXHHUBpFS4FaS4JhGIZhGIZhcoA0JsFmchphLPArk+pYBXvlyareslYHTfRFgbyAj7br9+hvz2oHNiqh3aMOSUypXVIFHHY97WvfBuCLe8mzq7cRCgIrHqPPB16auJecSINkLwOGzwR83ujLWu1qRIOMzyN5j/KkNONor4NIFRIOUsqDdGG2kDf0MbcCP14CnP4ApU9o3Qk8fxHw3k1qjY3eQjgMLPkHpYewFQIn/DWx+4Y5Dzj1PmDqAjrfb1xLEVNuFxn3EsVZCQycwNEKmSLWmGSzk2HHFuX6m/NIIXfS36nuyN51wFcPJt4GYVwYO5+MWq9fQ57DqTBgAjDvWqpBUDoCqBjXc0YFgCKJAKAkDRELu1aRkb+5jowj5z6rprTxeUlx3KzUjxEGvGhjkRGc5UBegZrmLx3Izy69Ea2cxHuWM4LJBBz3Z6qVsHsV8PXDaWlqBKLY8/Tz01t7J1t8/y3w2d/ps9G6CjIiFVJxNRAMUt2xlt1qFLC1gAx2ww8ADv4xpRsDyPjgrKR3gTaCmGEYhmEYhmF6GI5Y6MvI3r3y5KNwQKRnYDgcuZ7s1Rfh+aZZDlCV1F1ec3ZSMPg7aHIE0Ofdq+jz2KNVw4DRPPBTF5Cn238vBnYup/Qvx/yxu/eye6/++v4QgO30eeRcwGZwv51txpYzEn2w4U3Kr+2oVCaJOueyCx2vbJEGadwxpMRxFNB59UsGBnEtTCbpZVa3F8t7NJHc09r+EvUweqBmhR5G25f+HUd+jahhEaZ0PiIiyNtERqJYGFUWhQKR3yeeBIw6nApKrnyCCqZv+xQ49s/AiDnxtzdov9j/+4MA1tLnIdMBaxxFkdGCoAWl9N7RArz+c2DjW/T95LupJokgkcKfJ91N/WHN88BrVwNH30I5reXCtXlW4/2/N+ZoT5aelKNYHtZ659wktc1soVfhQGD+H4BXfwZ8fCcw+UwyTJsNRi4IeTzjIcqbvuFN8uBe8AQZ7gTlo41tr3Cg+nn4AcDMi/WXM6p018p5NGTFbYsSsVBW3V2hKwyd8TBbqD7S8xcCfjdQORE46zEyLgjyCwFvC+V+l8cXbRSCEeTr7ayMjKDU6yPpHI+yNWYZRSsnRiITot2zxO/WAjJ6zf8DGdM+ugOoOZWeuWTyDRoEvE2R3/etpxopZisw4QTA20y/D5pkbHsWg/LRuDX+MqYQAMWoX1IVe+wqiFKUuqMVeOdGksdxxwL7X2BwTFL6VjisRixUzwIKKymFpcUKBDrIWJdfrF8UW/zuc5NMAHQNTUi9bkWu930m69TWUlquyspKVFenMb0ewzAMwzC9nj7gOsQYIhHPNj2vPr+HUoHIXlE+D6Va8DSQMlDUUvB3KArsDnq5XcDeb2kdI/UV9Kg6iApjwkQh6B/dHqlUz2XCYWCF4rE34wLjilZBKEBKLgAYMY88sP1e1VDQspOMKu4GWkab41oQqw+k6vUmR8T0J7TH7dORE4HVrqZL8DRktl35hZQG6dznKI1Cyw7g+YXAu7/N7eiFXauA/xxDRgWzlYwhE05IfnsiLdKUs0luPrgF2LG8+3JG+3+05fpr/+8JErk3TToVGLo/KcDfviG5/YmaC+OPBYKdVKdj18rktpVtulIhpaAEWv0c8MwCOqejDwcuew8YOFH9XxRrdpaTclPPUCIUoUaihXweiuhqqleV3+yRbQy9+1C08yd+93tp+QknAFUHAwEv8MrPkovs0mP18/Q+9ig1HWAqZM1xQNn32zeodRWOuz1xhbynQTG+mICiIZTys3IMjdmy7MSSGfGM526k1KBWR3Ip/hjGAJWVlXA4HFi4cCFmzpyJmpoa1NXVZbtZDMMwDMPkEGxY6C/YnN0jFRJBnoQK/F5SUvrcgKdFVXhbC5T0B4pH5Kb3yKNr4CRScibLhOPI4xigwpgPHgosvae7h1yuseNrwLWBPO+mnZP4+tuW0GS0oITqMwQ6VK8/+TyL+W209BOx0kAIowOQnIK0vyp/tMcdzagj0lA5B9D3ZAs4J8rIucCl76sFM795Gnj0eGD70p7Zv1HCYeDrfwNPnEKpVkqrgYteBWZelPq2hXFh/PFAyA+8dR0ZQv1eug5G0sMJoi3XX/t/IiSiXAZUJSlg3Cge6KTUQ2YL1aTZ8FZybRXGhZHzqL3PXUCp+HoTnW2UPg9ILhVSOAx8/i/g5Supb085Czjv+e6e0eKep9f3xTV3N+jfF/WwOYBAgDy4o6VjZPTRuw9FO3/id1ELIxwCDlXSTtYtBR47gRToqeDaCKz7H32ecnZy2/B56Bnos38Az50P/H0S8O+jsmPsW/EE1S4yW4FT/hk9qiEWIg1SyTAlWs5L59xRoXHm0TxLyPdP8YznLO/+rJfofZZh4lBdXY3a2losX74cixYtgsfjgcvlynazGIZhGIbJIdiwwBhDnoQCanSCyUIveyHlpbbalUmSkhfZ0wTUf06fx85PvR37nw8cfycZKLyNlIf9wUOAD2+lVEO5yIon6X3KmfHT3+ghJuYjD6HisVYnUKAod5wVdC6cA+m8i6gFZ0ViihhhdACSU5D2VeVPPE907XFHM+oIJYG4/j1lWADU6IWzF1H6ktaduRW90NEKvPZ/wHu/I8X/hBOBS96lNEvpwpwHnHIPKVjbdlHKD5+HjKGi8KmR3OuxlvO2pK+9fRFZUdalcHZFV4IJJSmgnvMueZQU1J4GdTsAMHgapScBgLeuT17BZikAznyEIiA6moFnz1NrFvQGRFsdFYnXdAiHSB7fv5m+z7oCOOZPNMYD3ZWcQR8Z+DvbujsfhAJk9NbeF6PdW60OoGw4pXwR6XtSqYsg9tMfFK2xamIBkedbPq9Wu1KrZAr1eUclsGcN8PCRQN0XybWluR548VLa39AZQPXBxtZz76NC0otvAxadSYaEZ88BPvsb1TzxtZPBYtEZwBf3J5YaLxV2rwY++jN9PuJGKl6eDC7FQFkxjs55rFRoXqkWkzaVpbNSfckOQ9GcGxgmBaqrqzFjxgzU1NRkuykMwzAMw+QgbFhgiHgKVKsjUlnt91IItgjjdg4kDyx5QuvvIK9D4R1dMqz7dpNh8hnAZR8AJ/2DoiD8XmD5Y8BTPwCWPZobxZ19bvKWfeMX5P1nMkfPsR2L3auBWlFf4VigeAhQMoQK2ooJqU1RDHgaVcVOrJQ8sUjWQNDbi2JGI5Ynul7uapuju1FHGOGCPjViYe+6zLddy4g5wA/fogKaAEUvPHQY8M6vqQaD0Rzu6SIcphRfT54GbH6fvECP+RNwxsNqyqh0UlACnPlv2s/614Et7wOmvOh9PdH0RvYMtLkvoI08EB7SIpVHNCWY3r2oK3JOuibydgCqJ3DMH5UUYPXA4j8m33abEzj7CSrC3P498NRZlK4r1+looTERoJQtiRAKAq9eBXz5AH0/8nfAzMvUlEdAdyWn1UHe2wHF8CAbHQI++s1qj1SAipRHezeSEUS+pukcT/pSNJGh57Qo583tAtr3qrKoXc9RQddo+AHAxW+Q4tzjAp44FVjz38Tb+vrVZCQoGgKcel/8os3hMPDJX4H751L/W/4YGTfCQdpGzSnA/FuBC1+lOkKhAPDRbcDLP6KxNVMEOqn49EuXk+Fb1FVIhnAY2PoRfa4YqxOl4JEMro30/CwwWgg9lYLpDMMwDMMwDJMEXLyZIeTJt5HJvFAOiYLBeso5kaKneBiwr5W8R/fWAkffRIrxVDBbqAjyxJOAbZ8BXz1AnnVfPUSph46+ObJ4ZqYJh4Gm74D6L6kd9V9FTnanLkg8z/XKRcBbv6I83+VjKEexuDZWB3m7CfxeCqsXUSOxCjXHwkgxyP6EXMhci1ZhJYqXi2XdDaQcMIVJqW22AGOOBNa+AHz1IPWHWZf33LEAJHfzb6W0QO/cSIrX1c/Sy15ONVDGHg1UHWi8uHoyfP8t1UnZqdQ7KKmiornpjFLQo7mOlEMA1TpxlJPc6JHIPTFWP9EiG6T6Yl5srcFNnEezRfWcBtS8/IC+Eky+F4ltAkqxZqlOjUgJot3OkTcBL18OLPsPMKAGmHFhcsdjLwPOeRpY9AO6x7/wQ/Lgn3V5ZmUkGYJ+4OtHgKX/JOMCQGOkUYRRYc3zZHQ78W+0fmcbGQ0KlfMrj//iO0D/exqAznaKkiitjj4W2Rx0fwwHaflMjT2JyGauEK3gsvaelEgxeVGawO+NXgzb76U+4CgHLn4T+N+PyQj78pVA627g4B8ba384rBZNb9sNvHCx+sxittJ7nvIuiq97m8nADZAhb9hMelXP7e6Ucup9FMH53u+pHs8rPwUOuS76vTwZAp1kUPnifjIqAkDFmOTqKgB0Xt/4ueooMuqQ7suI8+9tVYo5SzKnLYQuDH1ag10yBdMZhmEYhmEYJgVybFbMZA0jk2+RJz6WMUFGKAp++CYVTV3+GLD8UWCLEm1QdVDq7TaZaII2ch6w4jHgk79R7t3nFgJH/JqKTSrYw16YWuoBfzMpfT0N5JHnaaDJbtlIUoSUjQSKBsfft6cR2LkC+H4NGRLc+yL/Lx1BStpx88k7zSh+LxUIXPUUfR99JHDsn2IrsWQFjxxVIn43ooBIREnRW0j1mGIpu2SZkWuQdBkWGoFQJxAKqd6g086hNA5L/gG8+xtarqeNCwBFL1z2ARnBNrwFbHqHUot1GRnKgDFHAeOPS5+RoaMV2P4ZRSdseBtAmNLNzLocmHmJMZlLhbrPSQEFAAdeBtScGnv5RBSSiShFZeVgb1MA6clTNEOCUH6K8ygr/RNVfmmNEz5NdI1Wueb30rgw+2dUJ+DtG4CyEcCow5I77sJB5MX97m+Bb18CvryfZOe428hwnm3CYWDLYuDze9S8+JXjaQw0esxao8JxtwNVs+m//CJScIpzrL1+8neRjk8osrVGCIFIedTuolRJmVL8C9lMRhmcLaIZNbX3pGjL6clpYSXg05FPGflaWR3AWY+R8v6L+4APbgba9wBH3RQ/+sBkAs56FPj8Xnru27fe+LEffQsw/Vz1u0XH6GgyAdPPowjOFy8HNr5NivajbkrduBD0AetepXoKwqBQOIiMKlPOIoN0wtv0Ay9fAXz7Mp274/5MTgZarHaK9HWWAz4vYIsx7vq9ZPBzNwKlwxK7n/bFZz2GYRiGYRgma7BhgSGiKcbkCUiE52ECk5H8IuCEvwKDpgEf305ew4vOoIKyR/42Pco1kwmYeCIweCrw3k3Avlrg7RuBARNg62zH/4Iu2NEJPG9we3k2oHgoGRpKquhVOpyUozuX06tpW/d1hu4PVM0CqucAleMSV2Y01wGv/ZyMFSYzMPcaCrs3mdUIEICuS8AXaeSxaRQQeilEYnlfJxq10huIdUzpNDr4PORlKDynAfrsbgSKyik9kuDwG+k928YFs4UMciPnAfNvkYwMb1O+9LUv0MteBoyYBwyqodRjpeOMbT8cJhmpWwps/ZhkJiylKZt4EnDItZTmIhOEwyRHm96jgpu7VtDv448H5v8xvmyyB3V39OQpmiGhq+5IIkYXtzq+xFKoyuh5wwsF6WE3kHJwzX+BFy8j40Dl+OSOvaCYCrYOPxBY/Cdg90pKjXTEb2jsyRZ71lBh2z2r6btzAHDIL4ApPzBuEGzdRUaT2lfJqHDqv4AhM9RULHKkSTxEKjhrFE9rGauDUlcxkUTr71pZiracnpyKdW2e6LLUZSz0qt+P+SMZfd/7PaXHav8eOPmf9LwTC6sDOPSXFK3ZsJnaEwoq7wEaC0JBiiATvw+ZRs9PRhl9BHDmw2Rc2PYpObAka1wQBoXlj6lOIqkaFACKfHjlZ+RQY7ZSe2tO0V/W6lCjeX31gK+NDHR68mO1q+mSxP0vWhSDlr74rMcwDMMwDMNkDTYsMISh0PsonofysqL2gt5kpeZEmjguexj45hlg5RPA1sVkdBg5Lz3HUVoFnPEgpURauQjYtwFmAKLFYasdJkcFFSd0VKivYAfQVAc0byflftBHSlGt8SACE0UijJhD0RdDp5EHdrJs+RB485fkheaopNQwgyarnroRHokdVLRQa+TxeSgCI4zIXP9GlJm9WeEZDaOpjNIxubYXR353VkQaFAQmU3fjgs8NHPyj1PpPKshGhiNuAOq/piiGze+TkWH9a/QCUADgUQzEZoxE3vIDgcE1wMAakqOAD9i1nIpsbvuMUi3JlI8hD+oJxwODJ6f/OPwe2u+m94DN71EaDpkxR1G0VNN2INBBCrOiQelvRyx6owe1QE+ekjEkRFOACeO1XqqXaMptvXzisjL7hL8Bjd8BO5dRJNsP36K+miwTlXHsnV8Du1dRSrF1/6N7dflo6uPlI5PfvlFadwJL/0UyCpDic/8LgHk/N26sb9tDaZOWP04p90x5VONk0il07v0ddG7l+gpAbMUlp2JJHdloJ3/XW07vv1jjXjz5FOl4/F51udk/I5l57Wryuvc0AGc+aqwoeGm1fhpIc5pKvHUZFy5LzrigZ1BwDgBm/zQ1gwJAcvPylVRnzFIAnHAXMGIunT+BiA7RYtK8a7E5KVIhIjLVYArMvvisxzAMwzAMw2QNNiwwhJHQeznNjh5dodk+wFFGv1kL1O05yul1yj00YXvt/0jx+MyC9EYv5FlpUjjuGKC5Dp355fjRGx1oRCmeu3gCCmKFlwM0qW7/nupBtNQDzfVkbGjZQZPMofsDw2bQe0EJeXSnQigALLkb+PJB+j5sJnDa/UBBKX03WyKjFQD6LiIWZPxewNeufHYkpuzri/UVjKYySgfaiIVYaI0LH91GxrAZFwIzf9jzym4ZswUYMZteR/4W2LGM0ovtraVIoNZdGIq9GIq9wFdfqes5BygKSI+0LSt5oI46DBh9WOJ1RozQvpdSYWx6h4wKgQ71P6sdGHYgKXNGzAOGTCEZ8TbSu78TKCjqe/0+U+jJUzL3jWhpPKx2SqVj8qlGhXjGP0dF9JQhoj+e8SDw5BlkOH7hh8B5/01NYVgyHPjBf4CvHqZ6KfVf0kumaAil1SsbRYaGslFkdNDeyxPF30EpBVcuUur4mICak4CDfkR1hYyMoW4X8PFfgGWPqvIyfBbVPxo8BWjZSb85ymh7bhddB28rGVATrd3DJE6ihm+tc0iy9zTZgcQvpb+c/ANyeHjhh2Q4XnQ6sOCpnq1lFY3RR1Bkxbu/jWtccIQ9qMEmWJa9D+xdC3y/Vh2znAOAmReTYS3VSLqOVuCFSyhSzuoETrgDGDod8DRR9FNHK73LBhy/h/4PA7DZFaNDjCLMWiNePOefruX64LMewzAMwzAMkzXYsNCXCYfjLwOQklNWssqetPLEJdr25MKaAT9FLHibadLkaQZsUsoeEdVQdRDwoyXAm9dRmoqVTwDffQScfE/3onZGPdvyhkd+L6bvYV8Qu0xL6beiYYDdgEKpfBRQPdvYfuUiyjGX0xE39z7g5R/RRB0gA8thv6I0AR4XhcU7lJQ6pmb6LMLl88107pvqyavNWamkrCpUJqYORHd3Q/KpgHLd09po+9I9ue6KWFD2H+38ysqOo35PhSk/+wd5IH92F7D0HmDy6cCsK8l4FY88gwpSvVzVeoQ1Xqg1J9NLoaNlH2669ymMDW/DJRPcyNv3LdCwRfL2HEi1RcYeDYw6lPqsEUKB+MsAkedv4zuUBsPXpv5WMpxqQ4w/jor2BjuB1r1A8UBSxJrzgLCJ7lVFhemJWOlPOavTcaw2BxkQ5DQeAL3LUQs2Bym1Az5K4aK3P08D4M9Ttysv424A/O2AtRA47zngkWPIAPDCxcDkMymtUcUYSjUXNujhXCSlgJmv5IPftgRwbQD2baQaKh4XRcu07abaHoL8YqrvceDlqlx0tsEQBcVA7WvA+zepdRRGHUo56eXon1jpjzwNdH/56mFVmTp8FqVzGnUo3TvdLjLIBDrI8FaapyosYxXbjkemZCTXx6NkSdTwnWwEnrZegq1QTcfjdinRCx2A0wmMPwa4+HXgqbMp9dYDc4ExR5Bif8wRZDg2amjwuY0tl2dwmjLtXIpAE2mRPv0rcOq9NK7uoPSVth3L8GJoA8wIAyukdYuGAnP+j+o2CIOjJU6qp1i4XcDzF1CasoJS4OwnqcaLz0PP0QGfKv82h3oN/B3qWGazJ5aCDOBoIYZhGIZhGCYrsGGhP6I3wTc6IdFbVy6sWTZcNTIA5HkaCijKHy+tn2ejz45y4KS/AxNOAt65Qam9cDow/w/AQT/uuwoDgM7Jd59Q7t223YpH252UiiYUBAJK2gm/F2jepSjHCkg5rE1VJSIUrEq6EKNe4ZxnN33oKYGMnF+TiRSNMy8GVj1D6Rh2rQBWP0+v4bOAg64kxX46CiinA3s5VpkmY5VpMi446VDk2fJISbS3lpQyg/aLX9wzVcJhKij67u8AhIGKceS1PeZoirQwmQCY1PvVAMXoI65TxUgyMoiaAKnSn2QpHcdqdQBlVfqK04goOeUVa3+hAOBuIcOedhk5nciACVSM9qmzKO3clg/pv4JSYPgBwLADyNAwbCYp8Y0yYCK9ZNwuMjDs20gGB9dGivZxu8h4+OUDwIyLaJwz0v9cm6g+kTBAlwwH5t9KaZmMjJPeJiqk++WD6ngxZDpFJI05KnIbVjuluAn6VcOPszJ1hWV/kpFskIn0NmKbABnprHYydl/6DvD0OUDDJmDdK/QC6D485ghg9JHAyDmqgaKnkGsubHwb+MfkiBReYlTahYEYNOlQ5FXPItkfMNG4g0g0wmHq3227yfDi2kAREKfeD4yaB8CkRv6IIvRabA7AX0SOIYka7/qTcZthGIZhGIbJKXJEU8X0KKlM8LXrismMCaSMsToiJ0Q+xdMx4CPPzI42wFFKEyt3A3llTTqZCmouvhXY8CaFs+9bT7UX4hUI7E00baeaElsWA999DHS00O+VE4CzHyfFl7tB9dwWXqLhIBCwUP7rgGKc6TIIKdELJiSuUOA8u+lDL/ohQinjij3hN1uACScAww8G2nYBq58B1r4E7PiKXsVDyct51KGkJPQ2kbHO00hpfTwNlELBq/xWUKym/xkxN3HPx0SxOUlB0xMEfcDrvwBWLaLv+19InuMdbeTx2bxDqS/ijB6V4vOo96t00J9kKV3HGu3aGC1QKzBbVE967TLOysgxacwRwHnP0zizZzV5FHc0U50CUasAJroXDz8AGHcsyWWiRm6nUsNHjnoLh2i/n/2Diop/eT+w7D8UOTHrCjIWaOlsAz7/F7DicRoX8vKBOVcBc68y1ncbtgCrnga+fliNjBg4ibYx5migcID+elYHUCJHfrjiF4SNR3+SkXSQ6HNaJtLbiG0Khbio6VQ+GvjJ58DWj4CtHwN1S4Fdq8jQ0LCJ0vqZrRSZOuZIMvpWjE1v26IhGxf8XnqGHDwVGDYTvkH748I3QmgyleKVEw8no3g8wmEqvPzlA2TgCwaUgtOByM/aiDvnAKWmgnQPiCcDVgdQmuQ1ZMMdwzAMwzAMkyXYsNAfSWWCr13X56GQcW1xYXl5UZ+hZWdkPvNwEPB5Ve/VE/9BHqMf3kr5oxu2ksLdSIHAXKSzjXK+b11ME/DG7yL/LygB9jsdOObWSM8+byvltnZWkKHB0wQUK/l+5XMGKOcuybz1nGc3s2iVMvEm/H4PEA4AFaOB0x8AZv8f8M1TwDfPAq27KG+0UVp3UgTB1/+m7wMmSoaGOZk3NEQjFCR58LWTh7ieMjUWngbgxUupGKbJTHm1D/oRKX5DQYrs8bcr9UWiKEFj9XvZ6xMw7gEatyBqH/ImTcd9I5HzYWR/8vUS63Stq/H8rT6Yii8D5JXf9B2wcwWlSNqzmuow7FtPr5WLKGro2Nuork4qmMzAxJOACSdStMSSf5DR8JungTXPAzWnUl8uH0XKzHX/Az65Q00xNv44GivKRsbej7eZCuyufhao/0r9fdB+VNdl1OGU5kh7XkSBZnlMd1ZGKpUB40WctaTab/qSDBkh3YYYvwdod6lpExOp2wB0L5JuziPDWcV4YNblpGSv/wLY9gnw3WdA6w5g+2f0Wvwn4OAfU5rHnkjVM/oI4NL3yOg+aD8qnAwg5AuiSaTFjEegE1j7Ihn29tYmtv8BNVQnq3Jc5PXL5DMXG+4YhmEYhmGYLMGGhf5ISoX9EvQmjVi3IDLE2+ehiAV5u3OvptoIb/ycvOAemU/pKwZMSK692WLXSmDRmZH5s80WYNBkYNx8YPyxlIpCL/zeXqz/3WYno4JNFFXs6D9Klt6MURkJa97LRwBzfw4cci3VEvj6EUqz4CinXO0FJYC9nKIZHBVURNxRQcrS79cBe78lj+zGzaqidNkj1A9PfwiYdGomj7o79V8Cr18D7Nug/lZ1MOW2Hn9M/PV3r6ZimC11QH4RcOp9pNgSXrRWOxno5HtMospI2esTSJ8HKHuTRpLO85Ho9RLGbncDGaQqx5NXcfgn9NuetSQzro3AqqdI+f/I0cD+C4Gjbk4sTZIeJhMw9ijy5K77nHLBb18CfPsi8O1LVDi2dRew42tavmwkcMTvgP1Oib/tfRuAx05Uo+FMZpKRmRcDk89QU5TlO9Gt/o6obWGCmnbN7aJ3oVQWy3hbKU2Stvh2JulvMpRuJbQ2bWIi90OzhRwdtNgcZOz1tVPNmjFH0vONvZwMdls+pCidrR+Rgn7Dm8D5L1K9gUxTPgrAqOTWbdkBPLMAaNhM322FwJSzSTbtpWRE6Wyn+g+OSnKYybPSeTJblChSSb5EbbFUnteE4S+aMY8dRRiGYRiGYZgswYYFJjWMTmZESLrZEpkiput/DynNAaD6IOCsJ4A3rgGatgGPnUQe3GOPSnfrM8f7N5NRoXgYebwOP5AMCcVDSCmqN0kHaNIoG1xkY4J8rpt30GTeVph8xAITnXR5x8bajlA2iMLmzgrV+NBcTwpyp1Kse9q59IrYrugXGiWD30PFXMOg/11bSYG5dy2wczkpfN79DXlAi0KVmcTXDnz4RyoYizApRZyDgOZt5OH63Bek3D34p6T81LYpHAZWPgm88xsqxFw2Ejj3GcAxgGRMKDcB1UNd9mBPRBmpF5GVDg9Q9iaNJNnz0eVBLfURMaYAiW1TKMrFPdfnAVp3A4WVdN+efi6l2Vp6N7D2BYpeWP8mcNRNwLRzUq8BZDJR9NDgx4Hd31DNkC0fqPnqLXZg9k+BmT80LqdL/klGhZIqMiaMP54MkWaLft0TWVkpzkd+EclotHzw7gZ672glI4tcfDuTUQX9RYYydQ6TSZtocyhRDj5Ka6mtIeT3qs9t1gLFAFVAfbtoMEVkTj+PIhjevBZo3Ao8dgKw8EXqo7lIyw5g0RkUueQcCMz+CRkV8mzq2OJuoLEn4FcMazpju6dBGcMrIgvSG4ls0zMeiG3I8maU/hbtw2Sc2lqK4qmsrER1Nc9BGIZhGKa/w4YFJnkSSmchKXEAtXaASOvj85ICsm0fUDQAGDwFuOwD4LmF5On8/IXAUb+nXNS5XtR52xJK1ZJnAxY8BRSUkXdnKKCfmkPGpvk/muHGpHkH0q8Mj2hTP5uMJqqQjnbuY21HKArcDapsiPRXne10bX12nfVko4LOf34PRS6I/4Y4gJIhQPgMWueBeRT5sGoRcMClCZ+ahNi6GHjrV0BLPX0fOx+YdSWlogn5gC8fApb/h7zDX7+aCtTOuoIK2+YX0bG8eT2ligHIGHLa/eQ16vOQUUEUmAUi84ADkcpII/Khlbd09Xv2Jo0k2fMh5CkYVH9zVABWi7pdo4jIBZ+HPJODQYogMxdQKjp3I5CXBxz5e+CAS4A3f0EpUV6/mtIXHX8HMLAm8WPQY8g04PQHKVriy/uons5hv0osVVj795QCCQCO+wsw8fhI46VA/s3vjTTOyQYE7ZgNkELT76VUSYGC7uNVJqMK+roMifuT30PPDsmew2j3uWTSJori9qEAKcrdDRQpaS2g8SnPJjk9KEr3sBJy5/cq6em8VLz49H8Dr10FNG4hZ5Gzn1BTkuUKslGheDhwyVskg6L2lRwdJ489WsOC36tGh/gdqizpGXTk6yXkR894YLVT/aSAH7C6EzMu9LdoHyZjVFZWwuFwYOHChQAAh8OB2tpaNi4wDMMwTD9Hx4WtfxMIBOD3+7PdjN6BNm2I3v/uBnXSJHtkW+2kPJE98wN+wGIh4wJACqMLXwGmnUeFL9+/GXjjOiremk6atgN716sT4mQJh8kj/L3f0/fJPwAKB9IE3JRHxyOfA4F8noziKKdty4qgeNfDKGI7YjKd6vZ6IzaH6gnt95D3rj/GeYh27uXtaLHaFU9iROYwB6ggJCT5AJR2KMWZwwHVU1TG71GUOVI7rA7yDi0ZRrmmhTHhs79THulY+L3kZdqyMzH58DYBr10NPHseGRWKhgKnPQCc8DdgxMEkB0VDgKNvAn7yJXDo9UDhIKBtD/DBH4B/Tid5f/R4MiqYzMCRv6W0aPZS2ofNQcrQ/CLV41qbB9zqUPOJp0s+AGN9oj+TjvOjtw0hT+lUjnkaSEnY2ULpxAaMJbnraAJ8HSSf1QcDl38EHH0L7bv+S+DfR1Ff9bnT15bBkynF1yn3JF5/ZMWTdN8YtB+l3AO6j7tApOezVYy71sj7T6y0K0LOnBUkWzYnLS+nTerrUQWZQNyfwkjtHKbzPgeoMhcG1a/xNNC7aGe05xqrnQx1QqlePISMwoMmU+2DZ86hSLpcQTYqFA0BTr2fooZEBIE8tmjHHoF4lgMoOsRaKEUkRqlrIV+vrvubjvOJzUm/a2XVCLGeQxgmAaqrq1FbW4vly5dj0aJF8Hg8cLlc2W4WwzAMwzBZhiMWJNatW4dbbrkFu3btwtixY3HMMcfg3HPPjb+iRGdnJzo7VWVda2trupuZO8RLTRARuu3onvpFqwAsHQo07wLsVvX3PBtw4l+BAeNJifPN00DTVuDMf9OENlXWvQq8+n+UYqViDFBzCuWeT6SmQ/teYM1/gVXPAC4lf7zFrkRX5AGOQnViqj0HgOIF2E6TUCMTP+Gxnu9Mvt5FLMR2RLqmLExGsy5HsneskeLL0c59LC/bLo9pHc/gwkHUd8S6fg/JhsUCmJQXQH1H9ky1KoaQqMWJvUDNyVTUuW038OX9VNS5ZQcVfG7ZQUU3W3ZSnnePNGF0VAJDpsEycCoODpuxEaO7bz8cBta/Drzza2VdExWlnXs1pWyy6kRZFA0GDr8BmHMVsPYl4PN7yIP8i/vof3s5yfvIefrnUByXiPgA9HNapzOVilAGtbvU65iDnqBZk6N0eMjqbUPIky+QvraGAdhLSKac5cq+vVTHJOAnI67fQwa9SacD444G3r8F2PQu5Y3/9n/AcbdRNE22CPqA5Y/S5wMui0y1pzfuykaD0mHd7z/y2C2+i+XFS0Ysr02bFItelJ6lR+RI3J8Ki1M7H4lGacVDyJzVDbgBOJR0R3rGBL31BD4v3esXPAm8/GNKg/f8BcBpD2Y/zaU2UuHUe8lxQzbAyZGc4p4vp2Dze9VoE7MFKDWY6km+Xkaicryt6n3KKH092ofpUaqrqzlCgWEYhmGYCNiwoLBx40bMmTMHJ598MubPn48PPvgAd955J9599108+uijhrdz++2345ZbbslgS3OIeJMVvfRHEalfCiIVpz4vpaAAIj21Ax2U47agHHj310DdF8CjJ1AofbJFncNh4MsHyFgBADABDVvIi/uzv9N2a04lI0PFmO7rB/3A5g+Ab54BNr9PaZ0A8ggfdThw4GXk9SYmgH4vEPDRBDIcJCVuQRlNzvXSGsXCJ9JIaZR26Zo85sAkNKfkyIhCOt45c7vIK9pZ3r2+hlZB4fNQX3BIygOfl4wKgQBQOpD25W6gyAW/juI1ajvtpISceg7w+T+BD2+Neehd2wz6yFCw5QNYtnwAcWXCD/yBaocMmQoM3A9Y/Syw8W36s2wUGQsGTKT+XjiYfo9WX8SSD+x3GtUkqf+CZMvmpLQu5SP11/F5gOad5MUJRBoaoiml04HoEyZfTqeYyJocpcOI01M59YWCVB6PxPhTaFdlTaQ2sVcAZzxE9RYW/4kMcc9fSIaFY29LPNIgHdS+TgbuwoHA/ueRclMgG66B7pEIsqFARCoA3Qs2x8rtHivVSzR6UXqWHpGjTIzfRoziRpHHqWTaZFNSI5nzgAteIpnZ/D7w0mXASX8HJp2WWvuSRTYqlAwHTrmXnvnCoUgZkFMbaR1lhIyIKA5Rt0XrRKJHtOuuFzXk9wLQRDcyDMMwDMMwTJZhwwKAcDiMJ554AvPnz8eTTz4JALjuuuvw6KOP4sEHH8SCBQvw3HPPGdrWjTfeiGuvvbbre2trK6qqslSkzmgtglRTAHXfMb1pPRu1yjh/hzRh6iBluUixIPB5yCs4z0rpKM55Bvjfj4DmOuCxk4Ef/BsYd4xYWL85eVI+7jwbYLYBb98AfP0w/XbAJcDBPwPqlpC39eYPgX0bgH13AJ/cQfUeJp1GSk9/B7DqaUrP4t6nbnfo/uTNWj2Pin+K1CyiYKa/kyamHa10eoJB+m5zqPnwhQFGr8imTL4zMYVb1vpB8vSIHBk9L7EUPm4XKe5EWpBouBuAYIeybGV0T1KRv9xsoVQKgnynUhBTWl70A2sBSKMRB5OJ5K2gDJh8OrDhDSqO7qykVEllI0mxUlKlpLeoJONA8VCK6Pn+W2DXSgS+W4r62q9RjR3Ia/8e2PQOvQRmCzD7KopCaP+ejAo2JylqhOFQjzwbyYe1AKieC0w9O7oiUyhdfB4yKgT8pAQW955kFJ3xkPuLuLf53Op1zMHaLynJUSrHo+fVnuj9JZbcJaRYM+kr6eTfnJXUP6Pt22YHvHl0/zdbgGAAGHMkMGIe1Qj5/F9kUPvuE+CwG4CDf0z1GYwQzdCmRW6flq+UsWzquUCeptCzCXR7MCG+kUAv8sBkij/e2JxAfmH0/3XXceS07Mjk1HhklHA4dvSC/N1Ivn5fjPVlWRHHof3f5oz8fs7TwCs/pWjPV/+PnDUO+GH3/YYCFC3kcSnjrfI8WDYSKBtBkUUAkGfg/hLqxIjwDowLb4Vl8WLg+9XAnjXU1uJhwIKnabwDuhsEbIUkR1pHGZ+H7g8+LzkOdBl1FMO/L6w8C8a4vnrnUk9Ww2H1xTAMwzAMwzA5AhsWAJhMJuzcuRN79uzp+s3hcOCSSy5BQUEB7r33Xtx44424/fbb424rPz8f+fn5cZfrl1g1E1JAzUfr99CkzVlE34W3l89Dk8igkpLCUU4T0jd+AexcBjxzLjD/VlLkGMHnAV69FNjwFgATcMwfgYN/QpO+itHA/hdQCph1rwLrX6Nc2nvW0Evr3e0cSMrP6eeSV7a7UVXKCMUmQJNOqx3wNAEFxZTiRih7xOQ1EQVoDkQUZJpeI0dag0E0RFFmOVWP7Ekqy4ReLmTtNfcrBjcTFMOCgXZ6mkixb7UDfidw5iMkV3n5QOVotR3+DkVhrxg4TCaKxBk2Exg2E4Gas/GjjZ8hP9yB/54/CPl7VwO7VwI7lgNFg4DDfwOMnEv7dVSoSk0RgSEilvwd1B5ZsSqUNoU6ud1lhNLFBMBW1H15PcV2Juip/SRJzsmRrEADkk/TkmjtBj0lnRFPfIHVQSm5ChQjtbi/O4uAo28Gpi4A3riWcsa//3uK3Dnhr0D1QYm1E1CVhkYVzTtX0FiYZ6NIJC1aw7U27ZFMNEW00fRGiZDjsiOTc3JklFjRCxG5/Q0aFqKtH692QFc6JWm5PCtw+gNUp2DZf0h+vvuEHEzcLqX2yT7A24yYhnNHBRkZSkeoxobSkUD5KHLe2LUK2L0K2P0N8veswUMh5d6xTNpGxVh6rqwcp78PbVSheG41W9RIJ+05EMYGm10dV6Pd6/TOpV7EVmEl4EshcgToVSnImDTSXE8yFQ9racabwjAMwzBM36PfGxbC4TBMJhNmzJiBDRs2YP369Zg4cSIAwG6346yzzsLGjRuxePFi7N27FwMHDsxyi/sAYhLmdgGdbVSsuWgATTC1qUyE4lD2HrPaKQ3Sh7dSupR3fwPsW09pKOQUEBpKwy2wPfcDYM8qUqae8RClOgIiJ1t5NopOGH0k0NEAbP2IXju+pmiCUYcBk88CJp9Gk2OZjjZSlkYorez00uayTkc4O08Ss4vWYBB1ucpI5ZxWaSAUC0ZzlPs8amoGi6LA8DZR8Vm9tnibgFCnalywFwGBAkWupLoHnkags53yzWvroMhtB9BpKkB4+EHAaMWIIHuAy8j7EF6cbXvoN09TZHvlSAC3K9LDXLtNv1e5ZxhUThqRFZanzKItLJtsmpaEl9epY9ItVZ9HVQRqU/SJe7hPU2tAMLAGuPhNqgH07u+AveuAx04A9l8IHHVTZFozLYFOYNcKYPvnQN1SoP4r+r3qIKB6NjBiNjB0RvexRiAi78bOp3uHNjqjW+RgDHnJVBodJvtox5xEU40lun6s/8V91monA1yejVJTrvtflJ2bAHspUKC8wiGgpY7GNU8DvXYuj3sIJgBe5GMLRmLijHmwDK4Bhh1ARdPNBiOMIqJ6KsgQqHfviJClBjV9JdB9jNE7V3rGinQ4lfSiFGSMARSDgb15I/YfbIa9eSOwSxP17HEBz11gyCCfb3VgQPjP2GfKgDGZYRiGYZg+S783LJgUr8ATTjgBf/jDH3DHHXfg7rvvRlERec4XFxfjmmuuwV//+lcsXboUp512WhZb28ew2kmRaS8kz2k9pY9sbJAJBijlROV4MjCsfJIKvp75iG5R5+HhXfhj6C8w79lHnqfnPE2plQQR3nvKJM9RRt5o08+n1C7BDkpplGej9glFj8+r1FBQ8uAL5W3IT8fVpZiSPM2a6pVUSAYLNgu0ik+eJGYXrcHAKN1SrSSg5BF9wJynKu1bdlEaofY9gH9k95QM3ib6XD6Ylnc3KkY7TTFlvxfoaFZTISWCVoHpcyv1DyzqMQvlbNFgNWJB9xjjeJPH8nb2uemYTaBjEMdnRFZYnjKLnkEtGe/baB73sYgovqqjcO+qXeNVo3ead6n1O5yK53+0VHUmE40V448H3r8ZWPkEsHIR1WKYfzMw7TxaprMN2LGMohu2f04K0WBn9+1tfp9eABnCh80kI0P1bGD4ATQ2tn8PrH2Zlpl+Po1BniYy3uUXxk8lFsuA1lM1LpieQTvmiO/JpgSMW88nxv/iPiv64CHXAWWjyVhQWgU4BwBmK1BQQv28aAgp8t2NJI/5irPJvvVA4zaSH48LaNoONG4BGrdS5KnZCgzajwwHQ/dHZ8V+OOOJPQiZzHjl4EmwdDbS8QcU+TNUE0FjkHQ3AI3fAQWFAAYox6cxMtjsasRCrKL0PQHLdd+huR64dxbg96AGwIorC4FPrgQ+0VnW6gAWvgg4YjzXuTbC9NLlKDG3YR/YsMAwDMMwjHH6vWFBMGbMGDz//PM4/vjj4XA4cPPNN6Oykh6sbDYb9t9/f5SWlma3kT1BT3jsyt5qjnIyLlgKpNRBDvUlws7lyZ7fS8UoAUpdNGg/4IVLSVHz2AnAwpcoX66CqbkOd4VuRgnaESqugvncp6lugow82RKTPFGQuQs7oKfPkgv3BQIAAqTcsRWQ8UFELcgkWrBZoJ2UpnuSyB7b2SGaYkHveog0RSJFSThEkQreZqrd0baTlnOCFBytu2hZc74aHWD1KvmfvZH7tdjJI9Si6a8R0QhSShBPA4AoCky/V61/YAPJsfD8BiKNH3p4W9Xi50aQ6y742gGEI8+rnqx0ywPOSpeMoqeczDTCwOVvp3EmWk55m2LoDkP1QI6o3wE1pUm0SBqAipAfch0VNf/8HqDpO8ohv/5NYOQhZAgPdESu46igqITqg6lug7WAxrO6z4HtSyklTN1SegFkaDj0OlKshvzkdT38QMDXBjR/B5isFCGndy66apPY9A1o0VIg8djACFLtC+I+Cyip8TzApFMia2zJ6TAFctSnuwEI+CjlUWl1ZFSQ30N93WqHXKso7AsgZNqrbN9LBglTvvrsKRs7ohVf1qau9DSRcaKjndrh0xlbhUFSL91hrHMZ7zwn838/SKXZb/AosnPGw6h1hXD++efjqaeeQo0SdR+Bo4KMdgzDMAzDMBmADQsSRxxxBP773//irLPOwq5du3DWWWdh6tSpePLJJ7Fjxw6MGTMm203MPD3hsevzAJ2tpMSxFlDdgY5WUnRoC65GeC6LiZEdsBYq3pkNVN/goleB5y+kYrRPng5c8DIZF0IBWN/4KfLRjk0YiaqFr6JgQHX3NhmZbInIhK51RIoXKboCIO9xi4UUVKLegs9Lx2sCPeDLea+jTWD10Co+0z1JZI/t3MJo7mVnBfXFhi1AKEj9THhgWwsURaaSssHqoP7WtA2wl0R6VjrL9VMgyXJolQwLsaIKuuolFJCnqb8d8BfS70LxAkgRPVLxV59HWcZDBhLdc6NJ+RKRPq1QeY/jYRsvDzjTu4hWBNViBYJm6hfRoh2sDsAiRS0IA1ihJB9CpsQ4oC0ILX7ztVN6pFPuBb77CFj6TyruvPFtWqakChgxh6IPhs4gj2ytwnLIVOCgKynVyp61wHefAvVfUD2Ftt3A4j8pC5ooGsLvJcO2rVCpi6JznLKM6NVyAbqnq5Kj+HhsYADjfSGa4ls8+7gbVKeMrmcl6XnIWREZISDLiKeBjHgmJXLPL61n5D5uAj0jmqDeE7SpKrXPnnrnASBDX/EQJYrUC3jb1Gg8Ud8IAOzF3dMdxjqXidaxSPR/pleze88eDAFQ6wphxe4gVu4JwVs6Hhg6PdtNYxiGYRimn8GGBQ0nn3wyli5dimuvvRY33HADLBYLrFYr3nrrLVRV9QNvj57w2LU5aLJlsakTSuFtppefXZsbW0wu3Q2Aey8pcRwVwFmPAy9cDDRvV40Lq56BeddyuGHHreaf46FUilB6FOVowA8UDoysnyBHJJQMVY0IwvjgbkRXTnyrQ1EEK+dYTK6bdwAwkXJXr5094THKHtvZxYgHfTSlidUBFA2ltEcWKSd8ybDuXpT+DsBspn6aZwdKh6rbtRUiosYBQG0wQTWeCQJ+oLBU/a6X3z0cAkyN9L9IvyQUt3K7ZMOCXkRPNEOCMGwkU3eB+3vfQjfNiDAQDFT7hbYvCbRpwnTrlSiRNNHSdQkv6ZCf+uKBlwNVs4DnFpLR75hbgQMvU1PQREut5G6gtjjLgSFTgMH7AVPPpvR6G94AFv+RlKpzrgaqDlRTiwW9QF6BftuNyIgsE8IJQLQjmjGC6V8YvW8KeXS7IscsYbyWi6CLbTVLaSIBNa2d9tlQds6wOZSaIIrRz8jzkb8DqKzuHo0g0Hv21NKyA+hopPSasrzZpXHSo9Q3CoUAc3n3cxbrXKZSx8LI/0yvpa6uDueeeSaWXGTB+eefj5V7QnA4HF2R9qlQFaaoV9OebwBLlNojOilnGYZhGIbpv7BhQYcZM2bg1VdfRWNjI9rb2zF48OC0PKz1ClLx2DWq+LY6gLIqNR1SrEmP1oszYjtK5IIJildqPnDaA8D/fkzGhcdPJs9OAPeYLsH3pgHJHZdAKDmtBZGRCF0KKmUCarMDfntk8WahbNKbIIsJbLCDlEzuBn3DQk94n7HHdnYx4kEfT84Kiuldu67shS0UkP5Oiq7RpkSSlaaA4qXtB7S782tSukRTtjrKVWNHrHZ1LS8pjaJtW6v4iVV3IRrc3/sWeoo0vX4R1SjgAKUJC5Iy3SpFqIk+ahfyFUXxKPbXWabKx+BpwBUf07IlOimK9HA3kkLS3SilMCug92nnAKMOo0LreXmU5kx4SBcOUgwACdYmiTgH0vlzN6p1UlIxzDN9B6P3TSGPAR/Jm7eV5Ed+9tM+34WldzntpVWzrHZdI4YAmYLi2NEIRvB3KMYMaRyUDecAyWXrbmX80zlvsc6l9j/t2J9KnQumV+NyueDt6ABQiKeeegre0vGorKxEdbVORLZRHBUIWx24wX8fyd9/YixrdcB6WKwFGIZhGIbpT7BhIQrFxcUoLi7OdjN6F4kovrVKw2SQJ5bN9VQUs2gI1VhYdAYZFwAEx52AxVvnxt+ebj57yXBgL++eXsLbqHrX2TQKow4pHN5mB/KjRLzIxyE8Q6Mtx95nfRsj1ziWnMlKDZEnXk+Z76ygl7yMTLcClY0UWdC8E3AMVpezWCOVs9GUO7GiLKwOaoeIkJCVn111VzSGhGge50z/JZGILr1+KmQBoEgAE8jA0KUMFXLVEdlP5egevb4Y8FIRWXt5YjVDnOWKcaNAifDLjxwriocobSlQf/N5qBBtqmOrwOoAyobzuMMkR1cUnCKbov/H6kvOCsAvPR9aozhkRNuXFjlFEmzq7/lSRISeg4vfS8+U7kaq7aDX5pJhqow271BSXWqiF8TncDB1pxBObcToUDNxYnrSH5VWofOKz/GL+18HANx18Wzk60UsuDYCL10Oi68l9X0yDMMwDNMnYMMCkz5SVXwnUmtAu7yjAsiz0QTPYifjwn8vBsxm+I+8Cdi6hdbpaAKsUJWZcmFAvYKWEbnlpdy7oihzGKTcCQRIcSQraAs0aWPiHadQ9kar6MzeZ30fI9dYK2fRDAjuBlJmeBopd7xcS8HdALTtopQppcN0DBQaz+bSYWRUsFhJUSrIl3LWx1P2y4pbILI9cu562agge5XLbXK7Ytd36NpfhlOHMblDIko3Pc990QdNeZFGN9k4LFKFyXRTQIr+2wH42oD2fVQ0unhoZFuFUSC/MHJMENu02oGB49RUeV5PpCFBz9vb5uieriziGJMwyMn3FJYpxghGPOsj+qIjUgZkw19ZiilI/V6KKnA3AibJsOAoB/wt+nUUfEr7O1rpnrD7W6BydPfC0lY7MGCsUkdhH/1nseuMp4pBMlXjHDuXMJmmZDg2m0YBAMKDpwE2VhMwDMMwDBMffmJg1FzP8QiHY/+fsOJbs99o6SmiIS/vrFQnXZ1tQNFg4MqPacLn86nreJsAcxAorVIVUR2tFBYvClpa7aBCt0q0gglkJBD54ANeNZ81ALTtBQoHUIFc4QWX71QngCbF48fnpvZ0NAH5SjSM7GkqJqr5GsVVNOJdj0SXM9oPmMxhRHFnlfoMQH0xrKRjkJWeNodS0NUn/a/IlaeJ+r1ZpKTogL7XptSesuFAu0uNIgDIoCcmntHkV+SQF+30tlDRaLm9srzI7df+5vdQGwKKrJgtZGSQ15HPTzq9O1mO0k86FdWpKt0i7tlKoWeTWad9Sj8Q92yAao10Re841PZ4msgInacY5DxN1G8DnYosNNFnv0dVfloLyMAtti3qm4RB6wQ6yXiQzLlLdIzVEkumhHzEaxfLR8+Qjue6ZOXTyL1XWxzZ06BGf1od3R064qE1sAusdjKue5uAPM2zlTZyyeehuglt39MzpaMMaNlN9YjcjZIBxEOOLH4vPa/lO9VnxXwnYNZ4eecXdTdI6rZfOddR658kke7P6LjFMAzDMAzDMEnChgUmd0hUMaSbzkIzWbU5gEBI+r8D8ASVHO7K+qJwdH5R5L59XlIWBf1SigzF8JBnVbfnKCfFkl6BXZHiRSh5fe2kQAr4gcIo7TZqWGByl0wqZLotFyfXu9Xd/X9nuVrk1QR9r03tfoTxLtyp3ybRDkA/NYyQb+H1Ga8gta6XqwcRRdCB7rm7xXIAF5vNddJp/Ek2okuWVW0NgVjtE/dss4UiFbQyZnPS754GxSiASGWpMPqFAvSfME7IBm6/l4wMQcU4HvSpNVSSOXeJjrFGislr4XQtfYdkr2W0fhLRnzTjllxbwUi9BDnFkdWhRhxp6wVZHRSxVxAEOjRjlzbqx+8lI0TQR6ksy0fS+iJFpTgfYUSOLTYnUJ5iSj75XPd0ej8jRg2mZ2mup7EjCvbmjagZYO7BBjEMwzAMw0SHDQtM7mBUMSSH0GsVQd0KujoQkVfXVggU5CtRDoqCU5uWSPZENVmAsJ8iFUyWKHnpw/S7Xtu1CmBbIRkOHBWR4f8iNUYiObiZ3CXdCplYy1ljKAN8blWxCUhyU9E9rQNAETWyMUTbHpsD8AejtEkxZIg0Re4G1VhhpNCkEWwOdBVBl+VVzt0tzr3ZwsVmc510pPbwewF/Z3wjXjRjn1ZWI5RsMdonjzV6aYnEOOWoQFctBjkNkWz0cxapx6I1yAkDhMWmGByiyKYREpVBt4siAPOLgNLq5FK1Mb2XZK9ltH4SYaiuiNyuXFtBlqdwuLsRAVBTHImxy9tIz1ByyrGu47BTxJClQP3N0wigUGPgtpOjiNVB9RPEcYhxRNwbCovTbzTLptxk06jBdKe5Hrh3FvW3KNQAeOoMB4J5BchzVERdjmEYhmEYpidgwwKTWRLx3DayrM+t5noHYk+ChMJUVoQWDwVMivenuwFddRVkb22RMzvgB0qHoqsgrljHZleVslYHpUGKhpgsiu1qJ9NdkQod5HHN9E6S8ezVw2g+c6MKQr8X6FQ8/EUf04tMAGhfYSUdS9lwfWOA1QE4bDrrSgYMm4MUoCZf4saVWMfcpdSp7N4mvWNJRkFjZP+cXz55jOReT2abeeHo/UzsU6+GjsDbqhqntFE60donjxfy965xR0mXIgxsWkO4Xr0D7Xgmvvs8kdEKQOLnLpn+G9a8G0GvXewR3TtJd12naOOiu0GNCtC7b4txzNNExoFAB73blHoMLTuBxm3R61qJqIWQT/1NG6Un5LZkuPKbNlVnhu//4lynM1WYz22szWwMzC08DdTfzngYqByvu0jt+vU4//zz8fh/n8WU0hTrkCRJQft27D/YDHvzRmBXlOgJRwWlnmUYhmEYpk/DhgUmsyTiuR1tWVkB41fqHLQ16ReplFMKAfTZ06z+b7MDtiK1IGagQ52kAugqHOhpBCwWMiiIaAR3A+1bG2ofDXkiKo5Nq9QVxySUWkbzCTO5hbbvpkMhk66UIiEfkGfX72M+j2qoC/oATwtQUNh9n3JfliOAuv6XDBgipYyQ20SUFbGOWfzndqnGG73zksq5N7J/TvGSPJk4h1YHgM7o/UzsU6QYsjk0/RmRRt1ElGzdCjc71DFIpEsRBjZtXQO9egd6xgaRDkmOVkiGeOdepO0LQzXeFVYCPkfqCkf2iGYAzb1Zsla5G4FQp2Jc0PG+ttrVeiWte4B8ZQwrHU5K2N3fAgE3ALMaSaqtuWCzRzqZeNuA/Hz1uzaNppbeeP832uZYUY9M9qgcDwydrvuXd08IK/eE4HcM6tk2AUoqWQdGrbgNK64sBD65EvgkyrJWB/DTr9i4wDAMwzB9HDYsMJklESWNvKys+JEnfFY7pSQqGqi/DW0qJL9XLbKst5xFKZTZ0apGLjgrKAze55HSHUGNXLAZVP5HKFMcajok7TGzl1jvJxMef+naZuFgJS2QjsLGr9QRCfipXxfZutcLATQKCh3Dgig6LvJjywrSdKWCEv+JvPSZUPAY2T/La/Jk4hxaFWNxvH0WSOlLRLou+d4s2pSIYcpqJ2WoXLi5y1hc1D0FEqB+B9RaCgI9Y4PYXqrnLN6593lU46AvjRElRvbN9E7S5cXvLFcjFvSwOoCSoSQHpjxyBhHPdZ4mwJZP0Xbl1Wo7tDUXtNF2ds09I15dh97Yh3tjm5ncp7QK+OlXqF2xBOeffz6eeuop1Eyc2H0510bgpcvJ+MeGBYZhGIbp07BhgcksCSlpHPqKn4hc1k61UKZYTvwuQtnlegk2B+ALdN+XUOjLBoaOVrWQs/hPi99LuXzlXLzR0ObBz1Y0AqdwyTzJKODiXZd0KfXkNC/d9qH0yULJEFeks1+5L+ulRRE54wVCfrVe2oJoxx7rmMV/No9xZYnfAzTVU37tkuHxay7ES+PCNRtSI92pVZLdZ7d7c5JtsjlUpajJE2UZjWzIRZ/j1QjSW98oiaadsjlU46BeVEcqKQ2zcd2ZzCMbnMX3ZJ4zrEpEXaxnpC7jgOZ3RxngrwKsBWR8cDcAXiVlktWuRCoo0QtyxIJs1NN7doy2/96Ezdn72sz0Dkqr4C0dj5V7QvCWRo+sYBiGYRimf8CGBSZ5Mqmw1ip+ZMWKtkisUF7GC2WPth+AUh/lWWMv6/MC/nag7Xs1dVJJjP3kykS0N4bw9wfScV2EDGoLvsrYi1XFj1Zxoo2Y0cstDUT2ZT1DXbfltZFDmntFKseeiFz5PIBHuU9YkjQMsPzkBvHScSWCkT6UyPhmLyYDnsVG7121THRkUs94IEc1pMt4lWi/tTqoQLNANu4Dqac0ZPoe8nNaKtdd++wmK/rjOWQ4KpQoOVHEuQkIdtLn0uH07m6g6AVvs7ReOWCz6O+/q13slMGkmeZ68uCPhWtjz7SFYRiGYRgmTbBhgUmedHmr6WFE8aNV0IjvAE0kY3mfyfi9ZFQI+iM92EQqJNEOmx3wFwLWNlo+kYKWmcDopJfD4XOTdFyXiNodMZSYEXneHZHrx/PUjIWnAUBh933LXtYiakAUXBf1EXqiT9ocgKOSIha0URssP72LeOm40kW0Ys8RRjw5ykZTJydevRy9CAS9FEjx2if3W73fUu232vWTSWnI9G20z2nJXHefh/pvGJQ+DKBxxdcO2Aop0iwefintkb0MaNsFIEzbFfUVfF7AXhrlOKKkQUqnkYyNFExzPXDvLOoL8bA6yGjWFzBiKOEizwzDMAzTq2HDApM86fJWS3r/GgWN8L4WhZm7vN+8sbcjGybEpFgUiZYLNVsdQKmS+iKRWguZIpHCfDyRzT0SvS6xlIfxlJjaPO9d20wiykfGiDLU51HrOBT1cGoUqwMYOCF6u1h+eg/x0nGlC71iz/LvWlmJWidHJ/InGvHyu+u1T+63er8l0m+NpC9KJqUh039I9rr7vUCeLVLWhHwblXMRsSA7gsj1FeTl9Igmw+k0knEkD+NpoHvtGQ9TYeZYRFG019XVweVyoba2NkONTCNKkWe8dHn8ZbnIM8MwDMP0atiwwBjHpFGUaBX7WfdSVNoXMRk0kbdyxDKa4xDHZTLpb8Nkpt9k5UvhgO7rx8Ono7zRI2xwNh3RxhhtMLo9JjOky1MxFeVhtDztog8BZJBLtI2yMigWgQAVj+5JhUo8uTQqP0x6MHqOo92voqXjMpmSu/9Fk0u9Ys/y79HShRkh6j4TqKOgp+yMpwCNdw+KpfRM9box6cXIeZavdzL1OfRI9z1SHIaeXBVWAj5H5PNXLGyF9BLkOyOf3/wdZGgIdkgr6TwLajEyvsa7HuJaAPrjpVY2jcqR0evBY1vuUTk+qZoEdXV1qKmpgcdD/cnhcKCyMofrPilFng2lfuIizwzDMAzTq2HDApMeZMVIuhQMRhSy6fC0FEqVdpfquaY3oUzV40xv/VSUzkaVURyCn11Syj0tK4hkI4ArPddT9POIYukJbNNRAVgNDCPhALBvM33WyyGfjT6abFFcpm8QTS619/50Fe/WSwkW0R63msoPQMy6KVriKUDj3YM4fVHfQr7eRvuQrAAHoqfaSvc9U6/vphr1ol1f9G9zfuRyqYw7RtcV10KvWLv8P0cyMHFwuVzweDxYtGgRampqUFlZierq6vgrZpPSKjYWMAzDMEw/wIArEMNkCW0NBy1CUdPZGn0ZIH56CZuDJn0mxN6fWE5b3K99HymF4qG3frxjTAc9sQ8mOnrX3ShapYNQTKT7eqbSRoHfQwYKbf5gm4MK2oZ8ZFzQyy/MfZTpSYRSMOiL3+fT1TfllGB6+/R7gc42wLWF3v1RUvgl05548i3uLazY7Bskcz8X/crd2L1/5dL9Odo4E4uu/h2jhkKi+zS6brxrkY6xl+lX1NTUYMaMGblvVGAYhmEYpt/AEQtM7hLPi1Ivd3syCO822QMt1nLaNhj1DNRbvyc8RdkbNbuk4n2ZTNqTZEhHXvRYHuADxpJRwV6s75nJfZTpSXweKsxstsTv9+nqm2L9oiiyZrWTUregiMa0whj54BNtD9c96F9Yk4gsEP1KFB/P9JiTLOn08Dd6XHr7NLpuPNlj2ewbNNcbS/nDMAzDMAzTB2HDAtMzJBNyHm/CFU9Rkyjx9hereG6yE+5UCvimK00Gk7ukI01ET6UZsjkonZjJR/uU9+WsjC0ruXpMTN8kkft2uu6fccczJ1A6TC3irC26nkwKGpYTxihy/5TTIonfcyVtXDqNHEZkW5wLE6juSjLrsvz1bZrrgXtnGYuisToohWQfQi4k3SvSMzEMwzAMk3bYsMD0DNG8zFKZePW0wjxa8dxEJtypTjSTyZ3M9E9EX/N7gDxbemVPD6uDthXNmzSd8sp5qZlUSGdfTIccyQYEOQ+730v93O9V7/eJ9H2WEyYZcrXfZENRn0h0k966qZ5HNk7kPp4Guk5nPEyFmWPhqOgzNQcqKyvhcDiwcOHCrt8cDgdqa2vZuMAwDMMw/Qw2LDA9QzQvs56ewPq9gK0ogeXlCIE0eMpp8/ImOmHMpZQETG5P+kVfC0M/h7NR2UvkGDPVP7VtYDno30SrP5ANunLTu9S+mXAhWMmAIL5b7fQSnwWJ9H2WEyYZeqrfJDp+6o1ZmRyD/YphPgygsDju4t1I9zNjrj1jMJFUjgeGTs92K3qM6upq1NbWwuVyAaDIhYULF8LlciVnWDCSKqoPGWYYhmEYpi/BhgWmZ4jmIdrTig+/B0Acw4I8UZUndekobikfbzITRk5rlFtkc9IfT6Ei+lhhcWqyl8gxZqp/atvActC/SaRwa6YRchTwJX8vkA0IspHBWdk9Mi2Rvp9tOcllwysTnZ7qNz4P0NkKuBuAsqrkDNeZHIN9SrRfMtEKQHrOIxsHmRymuro69egERwXJyUuXx1/W6gB++hUbFxiGYRgmx2DDApNdelrxoS3SrLfviHRDaZ7UaY+XJ4y9m3T2j3R4b8qkq2hkLig2cqENTO6QS0rqrhz0nuT7qM0ZaUDQRimkSrYU/OxtzcTC5iCjgsWamuHa26oWnU53+7I97mTbOMgwmaa0iowFRopfv3Q5LceGBYZhGIbJKdiwwPRt/B7A06Z+t9oBX3NsZYc8mczkpI4njL2fnqwbkK10QLnQT3OhDUxm8bmNK7/TqXRPF+nqo1ojQzrIloI/FxSzTO5idVCkgpE+Ess4Zk8iTZHR9vG4wzCZp7SKjQUMwzAM04thwwKTPUym9G4vHO7+m1CoyMjKDr02CMWO3vaM7jeXSPd57m/01PnT9kttv4qWDqgn5CgaJlP8/ee6fDA9Q7x+kEhh+kzIpNFtGu3P2ZRLLXoK/nS3L9ZYyqSPnrhuPYHYb7w+Ivp9NONYvOc5vX2mE36+YjJMXV0dXC4Xamtrs90UhmEYhmEYXdiwwPRtbA7AH9T8xsoOJgeJ1y/Z+5fpy3D/zhzsec30dqLdHzJlYGcYAGiuN5aiJ0PU1dWhpqYGHg/VFXI4HKisrMzY/noFXOSZYRiGYXIONiwwfRurA3DYst2KxOGil4yWbCsHRZ9EfvbawPRdWEGYHnjsYBiGSZ3meuDeWXRPjYfVQcrsNONyueDxeLBo0SLU1NSgsrIy9WLJvRUu8swwDMMwOQsbFhgmkySr5OGil0yuIfpkMBh/2UzAClMmF8j1fshjB9MXyVS/znV5ZrKHp4H6xxkPA5XjYy+bYQ/5mpoazJgxI2Pb7xVwkWeGYRiGyVnYsMBklv4+aUt2MsxpQZhsoie3ok+asxSxwApTJhfI9X4Yb+yQZZtTAjK9hUw9EyUrzyxH/YfK8cDQ6dluBQMkVuSZUyYxDMMwTI/BhgUms+S6EibTJDsZznbaG6Z/oye3ok/6ArHXzRRsbGNygVzvh/HGjkSKZDNMrpCpZ6Jk5ZnlqHeT5doJfRlRZDpraZs4ZRLDMAzD9DhsWGAyS64rYTINGwiY3kguyi3LEpML9PZ+mIuyzTDZIll5ZjnKTYwYDDwu4LkLslo7IRZ1dXVwuVwAVCV9b6CyshIOhwMLFy4EQIWma2tre964wCmTGIZhGKbHYcMCk1l6uxKmJ+jv6aKY3CMTcsv9nGGSJ13yw0WyGSZxtPLHcpR7JFpseeGLgKMy9nI9nCqnrq4ONTU18HjUY3A4HKisjNPOHKC6uhq1tbVwuVyora3FwoUL4XK54hoWZENK2qIcOGUSwzAMw/QobFhgmGzT39NFMf0D7ucMkzwsPwyTPVj+sovR1EU5Umw5WVwuFzweDxYtWoSamhoAWUwplATV1dURbY0WcSGOSWtI6dEoh0RTJi14MucMUQzDMAyTK7BhIcOEw2EAQGtra5Zb0oMoxxyXdHt6Rdlvhy+AQAc9tLa2tsKXb018e34P4PMCNnsGPLkD6raDCfSTPuApJ+QiHKfPsBzFoIfkSNBNnmxxhhGxvWT7uVG0MtoH5MMovUKO0tifE+qDRu/jRvtLtuTSl2b56UfyYZReIUf9jWzdN7SI8QsAWtv65ThjlITlaNPnQGGMOhWeBuClK4CAN/7OLXagdDJQONxIQ+Mv08O0t7cDAKqqqjB27Niu33vqXpOSjEjk5+fDbrd3pUXSYrfbsWjRIjQ0NMDj8eChhx4CAFxxxRV45513MGHCBADA4MGDMXjw4KTaEBdzCXDBB4C3MfZyov89ckb8bVrswBkPpSV9Vmu7G0B8OWIYhmGYXMAU5hEro+zYsQNVVey9wDCxqK+vx/Dh0SeCLEcMEx+WI4ZJHZYjhkkdliOGSZ14csQwDMMwuQAbFjJMKBTCrl27UFRUBFMPeza1traiqqoK9fX1KC4u7tF99wR9+fj68rEB6vHV1dXBZDJh6NChMJvNUZfPhhz19WsA9I9jBPr+cYbDYbS1teWcHPWm885tzQy9pa2inevWrcOECRNSlqPectyC3tTe3tRWoP+1N1fHo2Tob9euJ+lNbQV6vr1G5YhhGIZhcgFOhZRhzGZz1j0NiouLe8VDW7L05ePry8cGACUlJYaOL5ty1NevAdA/jhHo28dZUlISd5lsyVFvOu/c1szQW9o6bNiwuEqcROSotxy3oDe1tze1Fehf7c3l8SgZ+tO162l6U1uBnm2vETliGIZhmFyATeAMwzAMwzAMwzAMwzAMwzAMwxiGDQsMwzAMwzAMwzAMwzAMwzAMwxiGDQt9mPz8fNx0003Iz8/PdlMyQl8+vr58bEDvOL7e0MZU6Q/HCPSf48w1etN557Zmht7S1nS3s7cct6A3tbc3tRXg9vZmetu56E3t7U1tBXpfexmGYRimJ+HizQzDMAzDMAzDMAzDMAzDMAzDGIYjFhiGYRiGYRiGYRiGYRiGYRiGMQwbFhiGYRiGYRiGYRiGYRiGYRiGMYwl2w3o64RCIezatQtFRUUwmUzZbg7D5BThcBhtbW0YOnQozObodk6WI4aJDssRw6QOyxHDpA7LEcOkDssRw6SOUTliGCZ12LCQYXbt2oWqqqpsN4Nhcpr6+noMHz486v8sRwwTH5YjhkkdliOGSR2WI4ZJHZYjhkmdeHLEMEzqsGEhwxQVFQGgG1pxcXGWW9PH0atD7vegw92Ocx9aBQB45pojUZBvTX57erCHSNK0traiqqqqS06iwXIUg3T3U7E9vwfweQGbHbA6uv7u8AVw7j8+BAA88/OjUGDjYSTb9Ck5MtCf+2Uf1J6XKPLJ41Hy9Ck5ynXk/hytLwOJj1sx6Jf3jSzQp+QoVr+S+63Nmfr2ZLJ0H+/wBXDu3z8AwDKSbfqUHPUzWI5yB6NyxDBM6vCdLsOIsMTi4mIe8DON7gN7MWxFAVgKNtK34mI2LOQg8cJ3WY5ikCnDAvTPs80XgKWAlD/FxcX8wJxD9Ak5MtCf+2Uf7HZeolw/Ho9Spk/IUa4T0Z9jnMM0Ghb65X0ji/QJOYrZr6Q2p7GfJrS9NMMyknv0CTnqZ7Ac5R6cJoxhMg8nG2MYhmEYhmEYhmEYhmEYhmEYxjBsWGAYhmEYhmEYhmEYhmEYhmEYxjBsWIhC2Gi4KsMwDMMwDMMwDMMwDMMwDMP0I9iwIBEMBrs+m0wmhEKhLLaGyVn8HsDtoneGyWW4rzJ9Db8H8DRkuxUMw/Q2/N5st4BhIuFnNIZhGIZh+gBcTUZh48aNuPPOO9HW1oaKigrce++9MJsTt7t0dnais7Oz63tra2s6m8nkAj4PEArQu9WR7db0SViO0oTcV23ObLeG6WH6pByJPs0wPUSflKP+CCtvswrLkQ48n2AYhmEYpg/AEQsA1q5dizlz5qCjowP5+fn46KOPcOONN3b9n0hapNtvvx0lJSVdr6qqqkw0mckmNgdgttA7kxFYjtIE99V+TZ+UI9GnGaaH6JNy1B9hxW1WYTnSgZ/RGIZhGIbpA/R7w0JLSwsuvfRSXHTRRXjyySfx4IMP4vDDD0dBQUHXMiaTyfD2brzxRrS0tHS96uvrM9FsJptYHYCzkiepGYTlKE1wX+3X9Ek5sjoAR0W2W8H0I/qkHPVHrPZst6Bfw3KkAz+jMQzDMAzTB+j3bn/79u1DW1sbzj//fABAQUEBgsEg3n33XXzxxRew2Wy4//77MXToUITD4bhGhvz8fOTn5/dE05lkMRqBYtSglO5C3wkYsvoqOSVH2eov6e4H6dov989eQ07JkVGM9K/e0Ad7u5xnanu9kF4pR9kil/ufvEw4nJ5ntz7c79NNTslRf7uvpXue0lfGN4ZhGIZh0kK/j1goKSlBR0cH7rvvPjQ0NOCmm27CY489huOOOw7HHHMMvv/+exx99NHw+/0JRS4wTDe4SBuTy4j+6XNnuyUM03fg+z7D5B6eBpZJhskEPOYxDMMwTL+j30cslJaW4vrrr8dtt92GHTt24JNPPsHjjz+OBQsWAABOO+00zJgxAy+//DLOPvvsLLeW6dVwkTYml+FCzwyTfvi+zzC5B8skw2QGfpZkGIZhmH5Hv49YsFqtuOyyy/DNN9/grrvuwujRozFnzhwAVLTZ7/djyJAhGDhwYJZbyvR6uEgbk8tw/2SY9MNyxTC5B8skw2QGHvMYhmEYpt/RLw0LYU2ORovFgrKyMgwZMgQ2mw0ff/wxACra/Mwzz8BkMmH8+PHZaCrTl+AibUwuI/one5gxTPrg+z7D5B6OCpZJhskEPOYxDMMwTL+j36RC2r17N5qamjBp0qSotRLy8vIwduxY3HvvvXjooYcwbNgwvPfee3j//fcxdOjQHm4xwzAMwzAMwzAMwzAMwzAMw+Qe/SJiYefOnZgyZQp++9vfYtmyZbrLhMNhFBcX484778SZZ56JIUOGYMSIEViyZAmmT5/esw1mGIZhGIZhGIZhGIZhGIZhmBylX0QsbNy4ES0tLWhpacE999yDq6++GjNmzAAAhEIhBINBWK1WhMNhjBgxAtdccw0sFgtCoRDM5n5he2EYhmEYhmEYhmEYhmFSZNWqVbBZSJdUWVmJ6urqLLeIYRgmM/QLrfm0adNwwgknYMGCBVi7di3uuusufPvtt13/W61WAMBjjz2Guro6WCxkb4mWMonpRfg9gKehZ/bldgF7N9A70z/we+h6+z3Z3U662sEwTO9AyLx4ZUr2+d7C5BqZ7pM9JVtM7iL6gM+d2e1z30ocPndML2LevHmYOXMmZs6ciZqaGtTV1WW7SQzDMBmhzxsWgsEggsEg1q9fjxNPPBG//e1vsXHjRtx9992YO3cuzj77bADAp59+ittvvx2/+c1vEAwGAbBhIW343ED7vsw9oMfctwcIBYwtm+rDqrsBCHbQO9M/EP3Ll+IEJ9XtGFlf7t88MWOY+GRz7IqHkHl3g7F7R7Iyn657HNP/SHWc8bn11890n0xUtpi+R32GvBcAAQAASURBVE/1sWz3Lb8HaKoDmut6z/Ngrpw7hjHAvx95BMuXL8eiRYvg8XjgcrHzIcMwfZM+nwrJbDZjwIABOPDAA7F27VqcfvrpyM/Px0UXXYTOzk5cfvnlAIBDDjkE1113HebPn4+8vLwst7qXEw5HfpcfAq2Onm2LzQH4g9H/93uoXTZH93ZqjyMezgqaiDor9P9Pt6HKaPvYQJYasc6f6De2FPt1qtsxsr52MhZNJuP1K/n/cDhxOckksjzbnNluDdMTROuDcl9IdtxJZewyet9N9j4uZF6MNzYHLRNte8mOb/K9hccSJh5yH5H7nPZ+bKT/RZM/vfEu2vbk+0DYZuwY9GQrVfh5LbeId57T9WwXbb/x7qva/pKO8UwPnwfwtdNnqwOwSjLSk893iewnU9eGYTLAxAkTutJvMwzD9GX6vGFBRB3k5eXho48+wrHHHouXXnoJwWAQVVVV+PTTTzF+/HjMmTMHV1xxRZZb20fJ5kOg1QE4YkwmIya+KbbTWUkvpv9gc6ZHgW1NcbJoZH1t/+6LE7NYiiymf5EOg3YuKzASvfckeyzpuscx/Y9MGcwTGS8j7gMGDQupjsdM7yfT971Et58pBy2bA7AVAiblcw75iUSF5ZNhGIZhco4+b1gIh8MwmUw48sgjsXXrVvzkJz/Bm2++ieXLl2PVqlX45S9/CZvNhhkzZiA/P5/TH2WCbD8E+r3R/5MnrtluJ9O/yJQHWjS0/bsv9vVcVgQzPUsifSGaLPalMaEvHQvTO0hVOZuOPivfB2Slqd8L+Dt7bvxlmFTI1LON1QGUScVkfQZTxwp6+jmWYRiGYZicpM8bFoShYNSoUfjhD3+IQYMG4fXXX8eoUaMwatQomEwmTJs2DQUFBVluKROVVB9cY+UNZWULky3S7YHGEzyWZ0Ylaa/mPt5/+D7B9Cfk+4CsNPV7gLywMZlnmWGyTa4+26Rr7GQZYxiGYZheTZ83LAhmz56Nf//73zjggAMwderUrkiG0047LdtNY+KR6oNrOh9S+eGXSRfp9kDLhnLU7wVsRT2zL4bJFLkY6ZLJnNr9xYjCMNGwOgAoEQvxZI1lhslVsj0nSdfYyTLGMAzDML2afmNYsFqtuPjii2E2mwGAUx71JlJ9cLXa09cWvYffbD/YM72TdHugZUM56vcAKGIZYLJHOoxbuegNmsmc2rlmRGGYnsZqV+8bbldsWUu3zPB4yaSLbCvk0zV2JitjLEsMwzAMkxP0G8MCgC6jAtPLyLbSR35w1Xv4zfaDfTQi2s3FN/s8PSEnfg/gaYvcJ5B5GeDJIxONvmrcymRObavipe129Z3zxfQ+ckVm9WRN2zaOGmJyEbnvZlOeUt13sjLGssQwDMMwOQFr2hlGRihb5LoM2gdXZ2XkA6zNAZgtuecBKreb6dvo9dtMIPqUQEQDZVoGuC8z0dAzbmWanpA3vbEmnbBMMdkmlT6YThnUk7VMykeuPjMyvQ+576a7z/q9xmUsW+MJyxLDMAzD5ARsWGByi55SkEZD7+E43oNrNAVQto+FH7j7Dz01qRN9SkumlKBChgB1v9mUKSb36CnjlkxPyVsyY4jRdXh8YLJNKn0wERkUMuFp6Jm2xSPTRkOmbxLv3m6kzyYypvgTkLFsjScsSwzDMAyTE/SrVEhMLyBeWGumQ331QuITCdGV25ftEF3Rbq4n0vcxmjYlHeHqDltybUymTUKGzBaaPMbLhc30XzKZCkzbRzNZpyDaGGI0nZ3RcSfbKQYZRo42kr8bIREZFDLhbkq8jQyTK8S7txu5p3fJgksdy6KtIxc4ZxiGYRiGiQEbFhggHDa2XE8oqHUni1L75AfrcJhCda12ZXmj7YtxvFZ78sWe/R6gaQdgUcRKeyzpPs9sMNAnl/pzKiRyHDZnfMVjOESTSV874C8ESqvod58nUo7i7TaiXWHEXyHOeY41WdbKEBee7b0Y7c89va1oRDUS2zXjRJS2JNvECGOC1N/DIePb8LYCzvIkG6Chr9xP+xs9ISPpIFkHjEQMY0KOnOUAvovTHjeNhz4PEA4C7kagbHj0faX7PKd7e/1NLrN1v+oJeYs3P4qJKXIbAZ++3MnjjCVffa4Uv/uV50V5e9o0TBHb4/GDYRiGYfoDbFhgcot4k0WrXVWC+r30IOv35oai0ecBwgGgtREYUMweoUzuEda8A+mTI62BwiixjAVaGWKZYnqKaAr+TCPvK6K/h9V2xZMze3Hm28kw6aAnZEvIkU+qD+TzAH6fIkeSUV6MhyYAfj9gsXKEHJMbpOP5R2zD5klO7vxeIBQE2vYAeTYgvxAorWanD4ZhGIbp57BhgUkvPnfmUxXJD65CwZIqySpFtW0zWYDigVL7Mpy6iek7ZLqviHQTtkLAUaH+LhvrUiFZA4XeZJnlhkmUdPeZeAp+LekYQ4D4yqNocib2D3DtBCZ3iCeX2TIWB7xAnhL1KhsWxHhYUAw4EV1ZavR+w2MZk0lijTsR/2kiWpORO78H8HnJ6GaxU0SPGA7Z6YNhGIZh+jVcvJlJLz1V1BKgh2inoiB1N5BRo6sdbiXti1t/XYDa6G5QH76FsiYZxOTRWQ7kF6sP+D15PpjehbaIXqb7it8LWGxqXl2BVo48LnoZKe7nk+TFaieFZroMfSw3TCLE6jPJFEFOtChkqmOIEXweOoaA4mmtN4YBCbY7iXPDMEbJ1Xu5RTNeiWdGQJWfWPcAo8elXS5ZeUu06C7LdP8g1rjj9wKdbUDLTrUvJNI3GrZGFjzveoa0A2VVQOFAoLAyPcfBMAzDMEyvhiMWmPSSznBYoayP5gEqlCl+D4Xkyp5n8sO2NT/K9pWHbk8j4CiPrRQVeXf1PH9EW+Qis4JkiuoaLdLJ9G60RfSAzHoax4tMEDLjbaVUKvJEVChZ/B7A26auE/ACKEreWzuaNyeH1TOJEqvPJJvHPRp6/T1dkT8CPdnwe2msMyuPbs07KVWLvP9YMiO2KehWP4LljUkz6bqXy33X7yFP6cIEDGh67UKeqpAV45+7UZWjWNs2elza5ZKVt0TWS6boe38hU8/a2vu1/B2IHbWSSlRLrHHHaqf5TZ6UziuRfhTsBDxNaoSrvC+OUGAYhmEYRoINC0x6sTnT97ApHoA9DZEPzno1FvwdZByQiVfAUn7oBlSvbT2EEcLdCJQO6z4hiTbJNPrwzRPB/oe2iJ7WKJWJ/cVSggiZcpYp35XJcShICsygF/B1AAVSG31KkUtPAxWFthUmpkCKNsnlSSujJUJRY+v+f6w+k25DlRh75LEpnnzFUjwZ8YgW78IA7veSUSHgBwrl/ccogim2KYyHPV0/gsltMpG2J+mUK5p2yH03rETm+AxsO9Z9I8IBRZEpk9+Y4jXWcWnbH8tobvScJyKnLNPRydSztvZZRnv/Fk4kYdAtWo6EScW4G2vcsTmAkmGqoUz0NRMo1Vc88vIBR5n6XTZwm8yJyR4/zzEMAKC2trbrc2VlJaqrq7PYGoZhmPTBhgWmZ0hUqQJ0V7wKz7JQgNI/mEAP6VY7eXFqEQUsY4WqWwqAQEf3yAg5X7XPCwQ8ZLwoKO6ekxdIXRHKE8HMkMsTG70iekbam+5jcjeoUTvOCjVnrpxPN9AB+FqBYJAUmQKLleRBLgot0rOYQJ5uNidHJjCRJNOHI5QvOvf7WKRyf9Zrq1BCymOTduzQHpue4qmzlYzVZcP12+dtBawFtIzFCuQXdTeAFyoG96b67gorLULehMFdT/GZyHlgeh+xrmMsBWemagrEMiLI7ZD7rohYMFL7oEvOGgBbaeSyet7e4nNELa8EjynWedTKm1GlciL3MDbMRydTzxza7epFqgR85IABRPZ1wHi0qttF44GzPLojijx/AdQIA7eLUhmZLTGeL6X1iocAjqLu/4eC3furSLEkRxJxNBzDdFFZWQmHw4GFCxd2/eZwOFBbW8vGBYZh+gRsWGDik47Q4WjePCINjN6ETUyOrG50i1gw+SJTQngayUgAl/IQLU0YPe36bRL55DvbgdKqSOWQnBYGineco1zddrrhiWBm6A0TG/nau13x25vuY/I0KiHvjZFKS79HmYTmAXYAnc2AvQxwlKrLyOnD/B2KccQL+KXJs81pPDIhkfQBeuvk6jVmIkmmD8uKmij1k9OO3wM07QAsyjgjKzltDn3FZFdKFc3Ypqd4EgYDvfPg85BntqcRsJeokQm67dTIXLRzKqc0i2Vw16PdBfjaAFsRUMaT4F6BUaW9IJY3vRGZ9XvIwNWVnitBJbz4DnRXtCYT/Snkzt1AbXI3ao7XqT7TirFXL3Kw3aVG5BVWGneK4eiC3EP0I1OMyK5Utqv3XaSVFNfYhEiZMhKtKmSxeQdgNivGBTl61NM9mrujNdIZKlZfE9uX50veJsDfojiJlCvHFCXlns9DcylAjSTivs0wXVRXV6O2thYuF9Xyqa2txcKFC+FyudiwwDBMn4ANC0x80hE6HM2bR3h8xpqs6oX6+jxA0EcenH4vPTx7W0kR6vfSA7fNia7aCAJPAwAlXYsJQKATMFuBoL+715rfS15B7gYg2BFpsBAYycXLZI9cntjoKX2MtDfVY9JGKDjKVcOcuwER+XP9klxWjicjgz+obstRDtgsJAciQshqB6yFJF+y96dQGLldsdO/CC9uawHJczzlc28wHjGRJNOHZUWNL5CZdmkRynS/BSgarOzbQ+NIGCQ/2jFBpJoII7Jf6imeyoZ3Pw/ivuDvoO82O41zhXbNcpo0LkLmjJzTZGTGpHlncp9Ynv96/SSaN327S+3ThTFSqPg8anquojj9SjZuCSNCPEVrIpGu4visDio06/MA5iCA7/TXi5WrXu772nOqG9GUxDOhjw3kfRa5X2uNsvEU/XLkTShA40HY1D3Nq3Y8EGn6Al6geBgtE8uRQ2w/0KH+H4ZksLZHjmMmc+T+bQ4gvzAykkh+joz13Mcw/YTq6mo2IjAM02dhwwITn3QoZ6N588hpYIzi96rhvHKEgXjQ1nqPihoKQGTaCqsDsDpJgVkyLLIN2uiMUEDdnjYtU6YVmj536hEj/ZVcjgTRU/oYaW+yxyQ82lp2ksebiFAQL3eD0rejKELF72HJsOBpBFAYqZTRMwSKbcWLyOjy4raQEsdIeoBcNh4x+uSyXMp0KdGlEAm/V/XMFH1OHhPksSlev9Q7D+K+YC2QIiMc6BamEU3m/N74SpxkZEakWGI56z3oXedEZE+sL0eIxlpX7KfIwD6iGRFi9UvZ0BEhGxJ6xyd+i2WQlKMXBELx2vW8Kgybmqi6VJ4Dxfpy3ZPecG9kjBPtfhtLFvUib3weiq7WW0c2cIv0lBZr9+Wi7UNs31Kg/u8oA/xm1UnEL0VF2Aq7H0tpFIUpO38wDMMwTJ+HDQu5RNhgfgejIbzp2p6YcIXDxrdplEQVTMKrywTy4gT0FZlCCWO1R3pYmy00Qe5oI0VqvpMekPNsQFAz6RSnxWIDfEHKKxroUDxVxaQySAqgcEjNSW+z6x+T1sMnGtpzHO2hvKf7Qaa211dIVDbSpRAPhyLD4MV25e8AGahCor+aAHspEApFbsfbSqmOwqHIfVgK1AlnSJKToJ+26ygHLPnK/8HIdU156mcxObXau58vMWmVDYRd50ZeVtOvot1DstVPWT56F9qoNllZ6awEfB1k6OpSMBaonpnWAlq3o5WUMF3K/oJImdHKk0w3WbMB/hDJoWhHKEDjh3aMEUZ0uc8ZUeIkY9iJtw6PMz1DIuNMrGtmxPtfmzor2lgl/6+NNPBJqSxlxb21gPqyeH4C9GVGHm/yrMr4EqJo01BQTYdpjqFE9XuUugz50ZfREg4CnW7J+CF5iIuxLhyMHelgBDFey3VPmMyT7rmMmAN0RZBJ11Hu10aJ6FdhRKZhldou5i15Nnp5GhXnjzBgcdLjUp5NfwySZVC0MWSJ/N9qpui5cJhSI3W20/in5+Sk99wJk7Fn3XRfD6P0t/GDYRiGYTIEGxYYY8he89HCv5Mh3na6PMaUB1W9aIWIdmoebG0OADb1f0c5kAd6+M6zUJoLXxvQthsoGkL/axEP9O5Gmtia8tQHfKudvvs9QPOuxHILGz0f7JHde4nVv9PptS1H0gg50RaVFRNVexkZyDxN9G4vU9thsQCe5u4TY4GnAWiR8lR3tAP5OjLj9+p7tulFQYhICNFms6V7gVqGiYbPA9hipWfRqdEjKznlejpaj2G9dEVWB1Ai92ElKs7TBJTYqWC536MUi9XIkbuBlDP2suh9vCsvd4f63e8B/J2U1iLPpiiDoimDU1R0pgLXO+k9JOJFHG+sirWtiLFIkTmfO9JApueUIX6z2EgW2naTorNoiGLQ66D3rv1IY0m3NnjICBH0GDs3Xcdtp2fFkF+tz2C1Uzt9XnV8jZcfP+Y+WFZ6nEzdp0RhY3+M+3Mq6CruNXQ95xXGHgP8HurTJlB/Fr/JqZAAkjNfG41vwqARzQYgZN3ToKYLFIWcM9nHedxhGIZhmKxj0IWa6ffoFdiTv6dru9H+Fx6lVntkwVgt2lRF0RAKz/wymiD62gFvc+R2mncBLbvo4VtMKoN+etDWbt/nJaNCRyspf/xJnhc51N/tUifKIg0F07tIl5xoETlrRT+z2qleiWyI08qJ1UFKT4D6e0cjvcuyFQiQcSGa/HiagVCn1I52/WXlCXa84wgF1SgGuc0+RTmb6LnTnhumbxOI18ekMUFvfBD9zlmub7COd/+12mlcyLNqUlEEaVyQad0NuPfSe0QbPaTAFH3W00R9uGU3pS5r2EayGgYZsm06458wZgBqjaGeJlP3Oyb92BzGUs0lsi2A+q3Prf6nNxZp5cOnIy/iN38HPZt5GoB9m4Ev7gdWPkkyJyOPJeK7R3qGCvoo+iih41KcSsyK4VC+dxh91kwGHsMyS6buU1Y71aEClBoHCWxfe831+le0Puf3KuOHF9i1AtizOr5h2eel5zdfe6Qc+tojl7MWqM+FNjvgHEDRedHoaKV2+NqV7Sd5jhORAR53GIZhGCbrcMQCYww9r3lva/cCYunYrt7/chHYmDmrDXhryt7UVjsQ8AAte4COZjIkWAvIq7SjCTDnk4KmoIgUOpYCdQIs70Moekx58T1KYyHnNOacpL2fdEebCMOBzyMVNpaiecSk01kRvSBgoIP6aUG52m89StHmkiGx5cdRSmlaBAGffjFXrRxG8yYV3qoih7BecVp3Q3wvPZl2F01qbYXdCyUyfQ9PI+BQomJ0U65o+6LyWY5kSNXjuGRo5D6sdtUDO2LZAsDvjvS0Bkh52lwHbHgD2PQepXkZMQcYMx+oGA2EA6RILRkQfTzoUsR6I48/WiqaTMDRdb2HdHoRa2voyH1Qr3aB1a70E0kmW/cAxUqBdL+HDIZhAPYSIFQEbP0Y+OZpMhYAwPLHgaNvAcYdrT7TiYLqYhvC0OCoBKweIOSLfyxCXiwFqmHB71WVqbKMZyoyKF40CXtop0am7lNCDjwNxiIX9Iond6Xck2omuBvU+Ypen/M00biyaxXw6k8BhIHJPwCO+wtQECWaz2YH/IVqxIKQuQ6NYcHmUJ4LlQgh7TmToygA2l9QkTO5kHOiJPIcx+MOwzAMw2QdNiwwxrA5uz8g22OknzBKvMmtnEdaRi+nvAi9jaZUFQhvub21wIbXgdXP0YO7o4ImoGXVQH4xfS8aDFROpPD7siryRtJVGCn7C3hJ2SoMLiKcH6BcnvEmgkZzGjPGyeYkPB3KG73Jp15hY6NGNbMVsBeo0QvyJFj81hXFoPXetgOOEvV7yRDAHiN9mHwMoaBiwJAMDLHOjzgeYWST0zrFQhg6jBSxZXIXo3IrR8ZolZpAd8Wm+KynBE2mjSKdhKNcvXf7NWldBCVDKQ2SzU77r1sKbPsM2PYpsG9D5LINm4AVjwMlw4HBU4GqWUDVQfRZT8ZFehbtf3qpaBI9RqP3T07r0jdIdszUOoJEXc6pGR86gHw7utJ/+ZSUXx2twLIXSQ6attF/RUPJON6wCXjuPKDmZGD/i0j+TFIaPdlo3fVdU/9H99hFarRGwG/vrtDVixZKN/EUpVwMNzXS/Vym95wk+ku0ugtA5HWUr7m8TiyHEZ+Hah607ADWPAd8+xK68hStfQHYuhiYdBoZGapmRdYTsDqAUmlb7gaKunOUAtgV2c5YDl1y+0R7Rf27VKJ5TJr3WPC4wzAMwzBZhw0LTHIY9RDRFsZMl9ekXk55EcLrj/MQvPEtYNXTwI6vIv9z76XXvnX665rygEGTgGnnAlN+0H3yLCbDolit8LS22CLzdwPdJyRikiJINW8vo9LbJ+F6k8+CYlVxIrzZ4kXzAJET3nAY6GihNBPeJiru59oEtO+hyWrQpxaMzbMCzoGAJR8maykcYQ88sAMFZd1D9bVGBUBtawC0bXcTUDos9vUQx+MGKW+Fl3c8ZZdIW5OI12c20sYwsTEqtzYpl3QiHsTp8Dj2K+kkxPa6aoWIqDYHyZl7L6U0atwC7FgGbP+MDNtaRswFpp5Nn1c/D2xfQkqjlh3Ahjfpd7MFGDgJGDYDmHACMPZodf2At7s8inWiHafbRfLlLNcfc3r7/ZNJnExdczl6Ru6PjjLyunaUkbzsWwcsexTY8BYQVFLvFZQC864BDriE2vXhrcCqp4Da1+hVdTAw4yIydusZra0OwG6L30ZxXwhIBm2gexqkgGKk1xrstPIkjjkRI008RSl7aGefWDIiOwi5tpAjk7MysjYPEP06yilgY41TdV8Ayx4BNr+vRglUHQQcdgPw+s+B5m30/7JHgLJRNG+ZchZQMbb7toRhuoCeDUvQCtPulYBnNz3njZitpjuLOFbZ6CY9g7obEr+HaIvAWx3cxxmGYRiml8CGhf5Gury3jXqIaAtjpuIdCqiRCgA9SAd80iTVQp5s2jybYRtGh7fh+PBi5N/zI6CzlX43mYExRwH7LwSGTKfigK27gNadyuedyvddQNsemhzsWUOv928BJhwHTDodGHskKbdsdqrLEOigMHp7MXngiRBi4WHudpFyNb8QKK1WjytaAVEmNXrbJNztokmZ8E7zechrSxgTugwKLvovHKQUXo5SijiImSpMWd+1EfjvRfSeIPkAXgbQARtsD1cChQMo0sc5gCaDhQOB0pFA+SigfHT3fty2jwoL+g30cZ/iEW6xkhx1pX/SrKe9r1kdgC1O1E+EwSaDhgVOW6FPvPNiVG5LhwM25VFGex1jpQHSS9ESD7eLUi+JNClBH7B7LSlwAl6gs43qJ7R/T+NMRwsZ7YTSR8vAGjImjJgLVM+OVOxPP5/W3bWK8mbvWgnsXEFGij2r6bX8MWDuz4FDfk6pZESaGBPonHYVQ5e2q1V0uhuBYIeiDNUxLPS2+yeTOolec22KPlkBr1coXfbm7iranA+seQFY/ig9YwkGTwFm/hDY73R1O5YC4KS/A7MuBz69C6h9Faj/gl4TTwIOvxEoG6mOMQlFXSj3BUu7fgo1T6NiTMwDHDpGdHcj1SFq3al+RwDwmGmcTMc4wB7ayWF0LDayXDwZ8XvouczvBmDSL3gsX0cRQSc7kOg5jITDwNaPgM/vBbZ8oP4+fBZw8E+A8cdRZPWPlwLffQysfZGM0k3fAZ/cSa9Bk4FJp9JzWruSBrZtD9CyA7bmerwaqkc+/MDjUlsdFSRbk04BRs4jJ6pYCIODiMDQOk7pnVf5mYzryjEMwzBMr4INC7lOupVS6fZEi9c+8XApUgN1FWZNwosLUCemZouqOOkKES4AEKDaCBYb0LwD2P4ZbGv+i/tD39L6nQBKqoHp51HkQfFQddvFQ4FhM9XvcghuKEgP3hvfAlYuAr7/Flj3Cr2Kh5ESaOpZ5P0d8gHeFmqPtYDaYspTlTZigiFPNMREQpynaPnxc0FBmSvtMEpvm4S7G1RFH0D9R/R3gZwSKeCnHOy+dikCRlOTwN1A23OUAvvWA89fQEpPQN22zQFYnarntaykD/qA9r2Aex/C7d/D5GtHAXxA267/Z+8sw9u4ti78jsiWzBh0yGFuoIE2aaBpm7Rp0zZlZsbb3tItf2VmZoa0gaZN2oYbZubEsUNOzLYkW/j92DOWLEu2DMFqPY8sS5oZjWbmzDlnr73XkkdIKEI4xDWRdhKVIERESkfodk7tx8JpF1LB5ay5bQS7rx0tWZ+RjO/gqO24BJ6/KvJwYWQeQ3gyQMH6olD3OGuB9C8758H+VRK4qSgNb1+MFkhqDa1PhjYnQavB0id4PaHXMSdB5nB5KDoJKpXsEaJh29/SF81/TUiGUU+D2yWEtZfQma7aMdGOe0wyFO0BPcHJvvreP4+1fiICHwIJ7NrOocOmEmklYE6BmLiqbQ+qV884bZKIYTBKO/rzESHkQIiDbucKodCsV1UJF3+kd4XzP4YDG+Cf12HDRNj0mzw6jpYxXlonSO1Y+28OJCFNZhnL7V2uJpSskUBs70uF5NbpqxOWDivglTatj/ZJKqG28ejE8PqBmtpOpF3VH+H2xeEsV9t90WlXvXHcMv7x96MLdg79xyPBJGDdDlg7ARa9I/MPkD6h81lCKPjPXUCqTNufKg9HGWz9U6rgdsyG3HXyCAIdkjziQUGJbYKS0AIKdoqM5Yov5KGRDN3GQVoX3+/1H0tVEiKKjzQJljhVpXK0kcZkkTYSQQQRRBBBBIcdEWLhaEdjB6UaO5gWbP+CZYkGe11R6isZ1/bNP3unspw8xfd9wYw4AQ5uE9mIgm2Qvx3yNkPBDkAGyk70LFD6M+ii+zBlDpEBeV2g00NCC+h/PfS7TiaZK7+WbKCSPTD3RZj3MrQaCBknQdexYDD7yov9dXljU8ERkIUUTvCmsa6Fhg66I4HSQ4uYFLViITl0e/WXRPJ6ZXkFX/sIND22qVmUq76HWU+Dxwkt+sGFX0mFQR1Q4XBx4Uu/k0gxH13anqiKArDmCvFQus8n3VK8R7K4NYkx/yxUgJlPQqczRSO77VBfNYI/KVJp8qn3vYdS/RrWjgf4AmFQC+mpvh8qaNVYiGR8B0c4xyWkuWUAsRCqMqEmGQltHS3LukofpgZKrQWQ1FL6qs3TYP2v4ovgX4EQky761TFpovNuSQnySG6ce6WiiOdCQkvocraQE1PukszVkj0w9i1fFVxNhs32koB+1wwGQ/3u6aH6E//zpb1uiOxYJFh0+BG2HJlF2kp0vIx1Asd9/gkhWoJF2UHADbNegnU/yXuJraH/ddDrUmkzIOvVhvSuMP4zyL1PxmEbJkoSyJY/5HNzEiS3x5jUjos8sEdphpIdB827+nyF9q2GXQuk77IdlOCt5uvgj+Ld0PMSiE1R9eRjfZ857WI2rUvx/W5LklQVoqvuixQKodpOOBJ/EYRGuH1xvSp2rL7XWhW1YpCKuqjY0PfGcM7h9pmqtFG2uv0YqbQeeIskbNQGU6xIIPW4QMZp638VPwZ7kcxrEjIq+xWHpRk3/LCLPFKYcPvpRJsMsq9Z/0gi1cbfqpIM5iSp/G5/GnQcBebEIN9vCZ04VZYHJbvB6YRm3eT+EC6hGQqRNhJBBBFEEEEEhx0RYuFoR2MHpRo7ezvY/tnyfVI/ppjgWaNGs0/ixFpQPYulijxDStXvM0QJcbBnJexZDruXivGlx1l9/9I64+x5GZfOSqNEiWdS21PCMwOrCYoiWXTNesGoJ2HjVCEZshfIxHTXAljxqWTc9b5cSvkDTdPqcw7qei04rLUHfAL3w3+dUAGgf2OgNJzj0liISa0qSRIsc7TKNeStfi4CTY/NSTD7eVj+iXzeeSyMey94wDUMVChR5JKOt0U/nwxNILxeuReU7JOJY1GOSjhkCwloy4eVX8ojOkFK+DMGQ/OekvWqZbAF+qlA9WtYe/jLCcDRMbk81ipmDhfqSqb633cCZSVCVSbUJHekrRPMCN1kgX1rIWs27JgjQUf/L03OhM5nisdBy351J6obC70uFsmXH69QK5Euhwu+gOhk6Vsh+O83x/v+d9iEVHC5IK4Rgzj+56sxZMciwaLwUB8CJpRUSai+Ppj0XFJLn3wLVG97gQTfgQ3w290i0QJw4o0w8vF690kANOnmIxjmvy7SSMW7RVJsz1L0e5ZyLUhT/uZ1QBEizu0QUjwYEjJkDBfXRHwfVn8LUfHQ99rq+1pFb179/dp7dUGotlPTOYmgdoTbFwdbrqZ25bBJVXN5iZBrlT4i0aoPj71qNViwcxjs/la8B/56HNZPkNexTWHAjVLJowXw3WEQb/6ITYcBN8kjCDwOJ/uV8qpv6gzQbpg8xrykkgyTpTLIli9G0et+lmUNZjFvjooTQsNkkfGdORFMcdJ2NH8gRZH+11YMeoNPjq+h9/pIG4kggggiiCCCw44IsXA0IVjmbH10oOsKbzDxzzARbAAeKPUTLGvUFAPxTdUJVzmU5kr5u94ky5kTwO4VEqF4rwzQyw6IWd/q731a0v6wJEPTntC0FzTrIf9bUnA73ZTMXibL2AshOjq83xZOsEjRSzlwt3FSKbHmeyk3th6EpR/LI60zdD9fMrOjE+W3BJOn0LwYTHEBn/llcZuT5D2PW6ooaoI2OC/L8wVpa5qYer3hDegPxzXZEDR2Bnq4x6Ux4J95rfO7/uxFcs7dDvHw0K4Vo1kMJP1hiFIfJllOUeCvxyQgAhLAGfagKp8URMbFHYSg84fTXXVZdw1SLtHxsp8pbat/R85iMefcOl0IgTU/yMMQ7TOxbX+qXOdupzy7KuQe4fVK1rUlqaqUjDFaKoSM0dJ+w5lcNuT+FwyHugLiaENjHz9/VJOH0IglV+jlAlFRWjWwp2WTGqLA6ZF7qtEsxLTTCttmis779plVt9P8BCG/Opwm5pfaeXYFBGE0hHtcwr1eQrXL9C5w+S8w4TrI3wZfnwenPgltToGYqOoZ34YosNmACrmnOMulH0tIlWVLcyXrXOuj/O8vwSpD/LW0/RE4NqgkhQKOS6iAXeBx8T/Hx2Mba6x2VJ++qiwfnKUquZRenbB1WGX8pcnkVZTJtVNRBvoo2YY+CswiokJFia9S02iWvsigVhm5K2DhuzDrWWlzMWkw5hVod4rsd6C0WDgVC1BV8z2ptXgwgFybBTuhcAfOA9uYs3AxGd69dDQdRHGUQtEudSUFUjKFnGjaA5r0lP+1ygmA+AyY+RQseR/SOorUktb/VPGLMPv1S145t067HIMKq/RxgdDWj4oJ3XbgyBPV4Vynh7JPqC/qW/GknUeHVa55R5DzpxEIFnV8rt0T9eoYTG9S75sWX39kNMv7/v2TopP7s8cFa3+CP/8ngXsU6HsVDL3fd9/VpCzDhVLLfEGDw29857AjGnkBaHmiVMud9n+SSLVxivg42AukSlWrVK0J7U+FM16QuV5Sa/nNxmi1kkndX2N0danAwHlZsPNalzYSGf9FEEEEEUQQQaMgQiz8G1HbALuhkgOa6WyozDUNWoCnZK98p9ftG3BbkuVRmgtbpskgO2eRb11TjBAHzXuL8XKz3lLSfiQHdSmZMPwROOUByXJd+xNsmS6ZpLOekYl0q0HQdRz0HF9VLqAgSyYtphiwOHwTeO1zj9s3MQkX2uRGcaiyHvmQlFHzoDuS6RMch+u4+EuExalVC/YiuT7+elQCnx1OF/3otI7VCSpbAdgKZUIGsq3pD0qGmaKTgGOfKw7tbwgHeqMYALY5GUY9JZVHW/+SyWnJHpF22T5D2rQWdLIVgVIkHg0A5rjq2/W/thVd1es8IqVy5BDOsQ8VIAB5X8uoDvRYsOWDsYagiXb/LC+F6Dghl/37GZD3Vn4tZsj+gcY2J0tlQofTIL6ZvO1xB/uWI4vEVnD5BJh0O2TNgz/uh5P/AyfdJfcEo7nqvcJZLlUKZfuFOFD0EkRylUvbdPglAviTCcEqQ8Ihmmvqc8KW2znKCe3GRn3vV7X1VcG2qw2bDNE+D4Qq6wSMQfyTRfwTHyrbq13Gcw6777VJHetNvhN2L5H3O5wOZzxfNXjf2DCaoUlXaNIVdzs3Ly0WLfpJtw8m2lUokpmKXgg6/+srWCB2wM3SZpZ8CH/8F+KaQcfT/X6zS207/qSA+r6ibtMUeGxVQsGlBqJrageRPqz+aGhySLAEKf9rX5PU8kcg2QByP3ZY5VpLaOFrWzq9tIOyXJh4K2yaIssnZ8LoF6Fl3+rbP9LQGUTGsu1QGP2CkB0VZZKwUlEq/5cXq6/L1LHtQalu2PY3fDwChv9PCHtLkjp+PSiVDgktw9uHhp7XSJuKIIIIIogggkZBhFj4N6K2gViDy1DrEABw2iVwilcNBKqD78IsWPkVrP5OBqIAKBJg7H05dBglAZDAbR0N0Bmg/Uh5lBdLufDanyVDO3uBPGY8AZ3OgMxRkNpB1exWwGWQSUbxXnltSaxaSm/LrzqBrwnaebDmiQeFOa72c3qks+GOVoR7XEJJSvh/Fkr3HKgiEeZUM6H3roY/7lMz14AlH8gjvQt0Hy+PBFVn11YoXgpFufL/jMelLRlj4Jy3IHNEvX7+IYVOLxr1bYcKybB3pRAMGyZBcQ78fDV0OQdOuEKyy02xBPUugapZo6bYGjT6I9f4YUU4xz7UMtr7VYwf/YgFjwusxdXl9DRUap0nymu30xckzV0vbWnzNAnsgcg29LoY+lwFyQGVNkczouJh/Kcw5wWplPvnFSjcASOekM/9fYkMBjFEdzmhcKvI0djzIa4l9Di/arvyJxP8+yJrXs33snADNhEyOzjqe7+qra8K3K7DKtWlpjgJbIZKAvF6fNeQ/3fY8oMnPpSXivyJRjKsnSDjnvJiWW7kY9Dz4iOXDKIoUi0Rk1a3dUY+LlnVG36VKqGrpohBtMsuxzEmgPDW+ipLXMBYwO77zOuWdRV99XYQ6LcQ6cPqh4beZwLbldMm43RtHhL4WSDZpkEJeNbuqV6v3LdnPw/lRTKPGHAzDL49eIXL0QZFJ+MzrapaQ2DFAYiPym/3wv7VQoJvnAKnPyt9GFSVOqxSCRTk3DX0vEbGhRFEEEEEEUTQKIgQC/9G1DYQa4yJfhXz5dTqnzusksGiZUg6DWCMlSz/wOqEmHQJ9PS+1GdMeawgOgF6XyaP4t1i9rzmRwnkrPtFHtEJ0GYIdDoL2p6sSr+Uy0DdVgSpKTLgDTWB90coE9P4dAkiRYI3hxZBg6DqMbfmqd4jcTUTb0azasQcLdnHU+4AR5kELwbcIvJB22fCgY0w82l5tBokcltthoIuCkr2w/QHJBM7tglc/K1U1BztUBTR323RR8r+57wAi96DjZOEmBv2EKR3Ch08888aNcVWlQNTkAlrbHz19SI4tAinT6lpGXuJtAfNC8E/8KAzBDeF1BCYrV9eLETv6u9h30rf++ndoN810P3cYzfAoDNIBnhKB5j+sJh0FuXAiEel/ynMErIuf5v8H0xSMGs2nPVqVVmXQN14zcsk0NPCHw5b9Uq5YIiQ2cFxqAiXwO067UJk6wxVz2XgWKKm4GZ5qRB3TptkHduLIVo1NfY4Yfojcg8HkRQb955k+x+LUHQw9nUZj2XNhW8vgou/h8QWYtbrTx7YClSyQTVvr1ad4EcuaMtoUi/+SQr+HjAREq5+aOz7jNMucxe3s/YKH38YzDKud5b72pc1D6bcKeM9kGrsM54TKa7jEWmd4arJMrb751VpR1+cKRW1HcdAVJCKn8BKIA0NPa8RYjuCCCKIIIIIGgURYuHfiNoGYsEyc2rLtA5EFfPlIMSCMzBLywzfXwwF29QFFDEK63OlZFkHVicci0hqDUPuhZPvgT3LYM1PPvOzTb/JI6kNnP2OGAY6y31ZthC8FDsQWnaptcAvKKCey7hIAOeQQ5ukBAt0BnqPBIN/kGfdLzDtQTmfrQdLNnJ0AvS6SAIWm6ZK4DB7oe+h6IVAKMySKpi0LnDJd1JW7ig7RD/6EMEQJVmtHUaJfEZxjph9RsVBx9OqX8tOW/WsUe18KA4J5OgMkTZwJBDO5N9/Gf9sdxCSTmfw9SX+HguWFEIaiIPPPNNolnvt1+dDwXb5TGcQ75t+1wmZdbzoI/e/TqotJlwnfc1X54ReNraJeEYktID1k0Si4usLxLfBYRfT2sB+P5y+yGQRUsFgjGSD1gcNCZjVVC0SbLv2El+fpREKWuC0JvII1Mo6l6/CrmSvVJ3lbYX8zWJ87rBKwPzke2DIf1S5LVvobR7t0Jvg/E/gm/Ng/1oZu/a9WohJDQ67BIzdFfI60RKkOsFcA0mukuL+9z2ItKP6oDHlbrRqBJAkoGCSWbXdH/VGH/FQsAM+P1PGZ/ookVIddGto757jBToDDL5Dxnda9cJvd8PwPJkn+cNe6pOUamxEiO0IIogggggiaBREiIUIaoc2waltgumPmGRfxUIwGM0ysLbEynb//J+QCuYkkaDofbkE4kEy3o4nKAq07C+PM56FrPlSCrxpqgSEv7sIznxVMtD94e+5ABJgDYQmpVNeAF41MzsmtW4yPhGt0fqjpklKbCo4LDUH44xmOQdLPoS5L8p73c4VI0pDlG85S7J4JfS5AkoPwHq1+iV3LeRtkWXaDZfgR/QxnqHfahDcMBMm3QZb/5SqnzZD5DP/EnlbgUqeGOR9zWOhMlM0kpV2zMBfniBYRmFdZO+07NH87SKrVZgl8id9rxHiOq6pLHe89TOZI+CaafDLjVKhkNxOZPdS2ssjtb3od/vfH5r1kuzy8kI1SzrEvSpcX4WkjEi7OxIoyxNdc1McJIVR5Wn2uwa05AQvVf0W/AOqIO/ro6S/2b1EvKRy10mgNBBJbeCctyFjQEN+1dGFqFi48Bv49nwhUea9DIvfF8m+gbdIH+12gaPCR7oEVidAaKmXcDKpj+SY7VgaLzaG3I12/WumzDq9bx7jX5ng307824vTLlU/1oOQs1RIt4IsaTuOMmjeB855R+7R/yZo1QtzX4aFb8H6CdWJBXOcJI0U71arTlMJmoRwtF+HEUQQQQQRRHAcI0Is/NtQn0GYNsGpKSCqwb98Pr1j6M9BMnKc5ZCzENb9JO+d+5H4KBxOuB2ib+ofuD1c0Bnk97Y7BYY/BD9fJ5nnE2+GsgOisaooall9oQR8NEIhGLFgilGrQeJF+ig2jHOmIaI1emihBbkdVslkrHzfrxLIECWBvRWfy+tBt8t1ockjBENCC8n8GnyHyJ7sWiDZXe1HNSwDu7xEJnlrJ8h10flM6Ho2xIRpqteYiIqDftcKsbB3hZ82tV+JvBe5n5RbAZe0l6SWoa/lQ21iH0H9ofU5EPwcOP2ynZ12cFYEP09Om1wTBVkw+VYo2S1Bzism+XxJDheKssWY05wsBHp0ggSnDiXSOsFNc0Tnuto9JEjp1Na/5LnXJdWTAhxWqUBQUKtEwkgyqIlojbSv4GiM4xKo414TArOrtdcxAZJ9GkFXlA0eByx6H3bOlusiECntoWU/SZ5o0U+uw0N9rR8JxKbBTfOE7F74tsgTLn5P/MHOfR+a9ARnmci4+cNh95EJGiHuioUEv/MdTib1kRyzHUvjRZNFlUN0SPsKd3+dNrm+K43r1WoT7Vp2qhWSJrPP/0w7nxVl4h+XvwUObIKDG+Hg5uCVowkZcPE3dfP7aAg8Lp9HTnTC4fnOmqAzwIAbhVg4uEXmhpr0WuVYzyltyYuaoKOew0qJ0dhDL5Ub6bMiiCCCCCKIICQixEIQeL1elONFEiEQ9ZkMBJvghNLy9zd61F77L6N9bi+RoKCrAmY9I5/1vuzwkQpeD2QvEj+Hzb8LsdD+VOhyNmQOOzJmaZYUuOxHmPawTEz/+p9kAY56EsryZVAN4HJBjLkqSQO+46wFCGLrIF0FEa3RwwX/NmCO91UCOazw87WwdTqgwOnPVJVWCAeJGZB4UcP278BGWPUNbJjsM7QFWPAmLHgTU7M+jPGcwFzlMGeftugnwdHSfeIdYU6iioFzTLIQbwa9TDYTMvwMSoPc9xpiYh+ZYDYMtR0/rc/RtPwDz4H//7Z8iI4Ofp4cNskQnXwLlOw5/KSC1wM7ZsPyz+S5ChQxtzUnS3azZnxpSYaEVtCyL6Q2UkC2JmJSQ2GWbx9PuEKenXZwFfsCa1pQzGhpuGnzsRSYPJyw5kFFqZCp9Q2UaVWK4fTl/mMz7XUoA2enXciKxR/A5qnq8rGqL45ahdmyT3UD18bE1j+lMiIq3veIjpfxU3S8BEpNcYePyNCbhIjreTFsnwFzXhT5sR8uF5+gXpfLMdMqE7xuPw8mu4/fc9iFuIuKqXvSz5EYsx1L40WtLYR7v9HuYQ4bGExSBax5NGnVJrZ8X+UC+CoXnA5Y/QMs/xwqiqtvW2cUoq1JN/XRHVr0rdtYvcZ9V+WVyg4IkV358HttzfOZKqd2hJYn+qqozUfI+ySuufSF9gKRUmvWy3fcLckqeYNK5ARUL5YX1a2913f8FumzIogggggiiCAkIsSCH2w2GxaLBUVRjl9yobEmA5Va/vlVg9sONUM0Jjk4yQA+s02HFZZ8JNrpsU3EuOsQQynKhi1TJMOsZHfVDzWfA1MsdDwdup4jkiuH099Bb4IzX5Py4L8ehVVfQ95mMeRUjOK5oE3ai/aIfrXLWTVAHY5MRTBEtEaDozGCyFW2oWWEqhnBRrNM+r67SCZUhmg4/2NoP7LxfkNtcDvEFHrFVxIU0ZDaUQg/UwxsmAi7FqDbt4K7WMEt3i/QTxoFvS5QfVBMh3Yfo2JlEr5/DeycC62HyKRTIxaMFohOUturXjXJVs9XsPteQ0zsIxPMhiHc4xfqHPhXz2mSLUFNnwtE/uhwkwrlJbDmR6k8KsxS31TE66S8WEyN8QpBZi/0eT4EIipOJDJa9BXT22a9pR0cCqz4Sp7bDpXArFPNqnbafLKGpljVWN7v+GsEt3Z/bOi5/bcjHC+emhBuf+XvneU/Vgs1dtDGB2t+kHESSIVp13MCgnqeeu54LfB6xeh10bvhLR8VJwbRaZ1kPJXWBSWxg2znUIztFUWSU9qeItKeSz+CuS/B/vVw+tPShkANlKpjOJNZzcg2q/5Abl+7Cec8Hskx27E2XqzL/Ua7hymoPgr4yR+p6/tX+jjtUp2w6x+Y/7pI9oCQXU17yLhFe6RkHpo5hdcDq78XYqu8qPblFZ2sk7dFHqu+BiAqtikPedqwnk4oBxKgebfDQ9IpihyrnXNgz0pI6ehLpjKYISZFKnoCSXKDGaIT5TkYnDa1WgUf4Vrf8Vukz4oggggiiCCCkIgQCyrWr1/PpZdeymOPPcb5559fb3KhoqKCioqKytclJSWNvasNQ2NMBhxWGax5kcFalezrcp9eb+DAu6JUMniik2WQeHATrPxSlh390qEryXWUcZpnNqd55xL1+Sbf+1Fx0Pks6HGBDPQ3/gabpoj54LoJ8ohO9EnAZAw8fAPsATeLnMCE62D3UphwPVz9u3x/8T5ZTiMV/APUjYUjnJF9VLWjxggia9uw5qltUNOttov2+09XSgDSnAyXfA8ZJ1atFjhUKNkHq76FNd/55Jl0BuhwmhAKLfv7gjBdz4GyXJzrJpHzzw+0Ixu2/iEPc5JU+2ScKIEbj0t9uMW8Uvtfe27aXczZ64qMAUIsZC2QQCvIhFI7LzHJEqQxmSUIqiHYfS9cE3unTY6Nf1sIqvvv12YaK/uwgTiq2pE/wp2ghzpH/mR2KB+A4hz49kJ5PlykQv42WPYZrPvZJxETFQ89L4K+V0Gi6hvkdkrwx1YgVTa2Ah/JYMsXyYy9K6TP3DlHHiBBlbTOaoZ4PyG+LSF8jOoCjwtWfyf/dxgNTnXfTWawF0tfA9X1+h1WH8ENvszghpzbwO0fBW3qsLYjzYunLsEr/3tPuP2Vv3dWOGbcIDKNM56S/4c9DN3PC38fGwKvB2b+H6z4Ql53PktMcytKfI9y9Vm7N1SUyiNvi3hYAVHAL5jJIgPDX/2haRdI7wLpXYNLS9YHeiOMfgGadIXf/wtbfofCnXDW6+Ibpuhl/OuPSiLB7jvvh1Pi5TDhiPZHdZn7aO0oOl4SPTTfBP/2oW3P65Xqzn9eg/yt8llcMzFh7n2pjKf84T0ExNv+dUJm7Vslr6MTpa+LbaI+0mWfKl83EdLDXijzC+2Ruw6lbD/D2M8wFsHXX0i7yBwJwx6C+OaNv+/+aNpT+rr8rdL3OGOrJo8EQ6j7pX/ViX+lXV36qEAca2RaBBFEEEEEERxGRIgFFV988QXbtm3j6aefxu12c+GFF9aLXHjuued48slDn3l/RFFpXmbwDdC04LZ/kNsUMNizFYDHI1ko5Qb47R7J0Op8JrQZHLxsGKQqoiZ4vRKEddpkIqnpm1aUQPYiorbP5D9eMc/zoqCkd5YAZbOe8jtK98p2mvWApt2gYCfsWQ7718rAe9U38rCkQHo3CbQqCjLiVa8NRf2jvZ/SHnqMr5rFHe6gXAuIthsmZMJ3F8vEdOZT0P9m0CFJgZYkkTvyH+gGm7SEI4MRiCOckd2gduStb6pnCDRGlpIWuHE5ZFsudXKdtxV+uU6CiYmt4cKvJKPNYZXjHw4q8mpfBny69G6XBIg2/QY750kbBLCkilF0896+qhj/6gUV7qYncIv+BNp5d/FW5xUYsmbK/q/4whf4CQc9L4JBt/nIutimta/TrLc8710BRm3SaaGyHRpj5AFq0EytoDJGh76OAyf+gQjWFoLKw9WxzRyGiriQ7cjrrb2dHMr9q3GCHkb79fdY8HrA7ZF7vna/Ld4NX40Tsi6xNVw+AeKa1Nymyg6Et++Bx83rgV3zYdV3kL3A9358C6l8az1Err+iHHkEIjpeMjETArxLPG7Z/wMbffrcpfvgwAZ5rPxaKnQu/FIk0DTUdj1r8A/U71spklGmOGg/QuT2YlVJlsQWfkG1gN/utAup4Hb6PteOj9frW97hF5gLlyDweg9/PxSiTdTYjvzJj2D7WNd2VJ/gVTCz89pIgkoyIVr+15b3eoIbChdsh5+uEpPzLmPl3u2uqL5dV5D3gkHrd2pD/g6pVNgyTV4PvlNI7mrfqxoku53qWLBM2kthVuXDW5xDjMdON7bAmi2wRl1X0YnsWPMT1MqgXuKfEA5CnatOYyAmHSbfIe33h8tg7BvQdoiMUSuPr3rcDVHy0BmQdoRPfifoPTHM6yrc8VC412k4y4VY5qga19XUz/i3B7dLTMr1qgebx++6zV4Es/4PchbLa1McDLxZ/KCM5qr9lAZdmNUKFUG8GKotUwpznhfizOuRa7HPlZLkEZgEVbBT2mZRtjz80aKvPFwVOFxuvpu+gG7ezfQ1bEOpKJVq1a3Toe+1QibqDEJOhINAf5GQiJY5GEj/ZjRL31MJrV9xq5V0Wn9i9vnj+d9T/KtOTP5jRQ4tQdDY7S2CCCKIIIIIjhFEiAUVFouFE044gT59+vDEE0/g9Xq56KKLUBQFt9uNXh9epvpDDz3EvffeW/m6pKSEjIyMGtY4BuEfaA06QAsysDJZJIOmeLcE+pZ8KFkp5iQY+Vj43112AJZ+DHmbVPKgVCb2NUxSFSCb5vylDOWySy8luiw75LIoOgnspmTCqKdh7yrY9reYFNryIWtuePu57S/RAh7xPyEZ6ou0TnD2m/DNeMkobXWSmCLGJkrWm9MmxIvJ3DgDZS3LB0LLixwGHFXtqDEmIZo8ldEqJfPOcpG4mnKnXFdNusNFXx9a8768rbDpd9jyhxBmGpr3ERKs7SmgN0gFRRjYobTG1b8/hoE3SqB/+wwozZXrRqdXJQT0olGs6H3vOe2wfaZIahRli4dIuMHGlv3k+eAmyaQzmHyT96CZ7eUqwVBe/3MYLrF0FJbJH1XtqDERKHtQdlAyS41mKN4D317gq1S4fIIE+esKV7kEY2wFIqlkD6ws0P73lwNUpB31vkSCTA0JHuj0vr6oy1nyG8pyYd9qeeyYI3J+U++BC75s2HWnVUQ07y3HUKuCsObLdgMzrDX4G/5q319FVifYezGhPZoCcZS0qRrb0dEgi1brmCzYOupxdwS5fzrs4oHlsKvSIVb4+RpfX3Xma4cnMOZ2wuxnxftD0cGQ+4Ssqwl6I+gTpAI2oYWvzwAqKpzc+cMW2nqzub9nEYbCHUKYlOWq5N1GWP2t9FfpXWRdTYasPhUzLfvJ/WfirbLtn66WaoZOZ0pVkL0IEpoFJ4FMFum3DsW1f4QqUo+p/kgjQ/WmqufHaReyd8GbPp8RvUkC+oNuO7T+Ihq8XvnuuS9JmwRoO0wMkC0h7tXhwBCFJ60T3+okyWPSZelEF22GRe+Jr8mid4RgGHJf+MRCXdCkuzznrhcCJ1SFeJX+JASB6l910tBrPOKpFUEEEUQQQQS1IkIsqBg+fDiFhYXcdtttFBUV8dRTT5GcnMySJUsYPnw4gwYNCqtyISoqiqioqMOwx0cQ/uan/q9rg8kig94DG2HhO/LeiEfDGwi7HaIfuuzT0BIxOr1kpkTFqc+xkNSGiszR3DA5GhSFy2KbQE3EQpXtGWRi2LIfDPmPZG+X7pfPKrMxvZIppGWVeb0SkFr7o0xYf7kB+l4jJdH1RZsh0Odq0eue8xxcOdlnLq2ZAWoBgPrAn0ywFkgGalSc6JEeIRyydnSoJwi1BcxMMXJ95CyG6Q+BLU98DC7+rnEkTQJhLxQyYcMkCWxoMCdBp9HQeSyktGvYd+j0PuO/YDAGOY/bh4u0RfZC+OVGGPNieBULMalC1OVvE/Ku1UCZ1FvzwWSrHlQzRktwBiQQrGXP1VbFUGX/w7xWjsIy+eOiP6qS7a4GCwL7AL1BAqH52+HHK6rKH8XVIwBSsFPu3daD4S1vioVu50HPC32VBwc21rxOfRDbRGTKOpwGfa6C7y+VtvD3YyInWN9g7w6VWMgYIH2ZlhHqdck9JKFF8ABnYFUiBJfVCXwvHE1/OGraVI3t6GggP8I9ToH9k3aOA8cPJrMvo97rhcm3S7DPkgrjPz08v9VVIdn+O2bLWGz4w0LaNQQ6A7uUluxSWnJPvwwMRrWasyxXkkj2rpBHyV4JpOauExNeRS9ySa0HQ+cxdZMmim8Ol3wLfz0OGyfD1HthzwoZFyoK2AohIURwVJMVrS9CjXeOEBl2VPdH/v0MCEGtN1aVrvR6YMaTUpnpcQGKSKme8qCvWvtQo2C7jJ12L5XX8S2F0GjRp/G/S6eXYP/Zb8Hm32Hx+9LfTLwFel0MJ90jgfvGQkp7nyxYwQ5I7RB8uXCk28K5J4Y7H9DaS1mer887CvqlCI4+ZGdnk5eXh8N1iLyGIoggggiOYkSIBRVGo5G//vqLF154gQceeID33nuPiy66iKKiInJzc49vQ2cNdQm61jYxCQwGadCb4M+HpZw+c7iU1NeGXQukFL5YlZFo0gNOuEICsaYYH5FgsgQNrHidHlDCy8IOCb0RWg0K/lkw+aEuYyWbKGuemPhl/SNl8CmZ9fv+EY9KRnhxjkwqRj4mA3r/AEB9oZ1Le4nPtyHuOB00H4oJdTgmmP4BnZJ98PcTEsxIbN34pILbKdfbhkmSiazJv+gMIsPQ+SzxC9Efwdt/5gjR/J32oMhUTLgBxr0vHg21IWOgTG6z5kJqZzE0V5AKJmuhlM9rk1ONQNAqF2yF8r+/JnwERy8ctqoa/iaLr41pMJgBJ97SffDrDSiBngrhSoppKN4Nv94opEJUHMQ1FyLOkizP5kTxQjEn+d6PbSbVM7WhMY1jY9NhzEtCgGz7WwKg/a6p+3YcVp+UR8uBqvyKKiHhdMmx968+CLqNgP6+Uic+32f87F/1EK6m/7GAw0V+BBuf1ZUoD+yfjGZweKuPHyqTR+ww/w3pS3RGOP+j6pJdhwIOK/x6i0iL6U1w6hNCeh0qxDaRSgitGqJ0v8ix7FkGu5eJ+XvuWnks+UCkkrqMFYIvXNL5/I9Fh3/2c2KUe3CTVMW67NJGtHNgK1IN0i3SZ1cxSg9DUixcvw17yeELhh8p1NY+/I+nLV8kqkyxsqzeKHKq6HzLzH4eln0i67Y/FYb/D5p0U7/Leuh/y+IPfKSGPgoG3AQdRlWVXD0UUHQybmx9kpinb/1TKqi3/gXDHpQKnMbo13QqgbdnGexaCGb/PsMvKcQfDrskjShIvxxqLhTsWnDY5BxbCyCpZei2rLUlxXHkq9MiOGqRnZ1Nly5dsNls6I1RjHx0AgApKUcuSS+CCCKI4HAiQiyo6Nq1K0lJSRiNRrp160ZWVhYOh4M2bdqwYMECzjnnnOObVIC6BV1rytJz2HzZPv7LOu0if7J/rQRsRj1d+2B00Xsw7xX535wMg+6ATmcE9w04ms6POQlOe0YG4PNfl2zxL8eKMW/TnnXfXlQsnPWaSCJt+BXaDIVeFzSSTI+lqk9GXAMCF0c7DkV2aU0mmBqh4LCpsj12CaYXZ0NcU7ke4sLI1K/Lvvx4ucgeaUjvKprUmcMPnUF6fZDeBc77WI7HwU3w45Vw2U/QtEfN67UaJIGZHXNhwK2qHrLFF4B2qvevKjJIKrngdsoyZQfBoGoYx6aHv8/HW3s4nAh17Go6ppqGv8spfjKgtjE/6TtjNJjNuL8ch6Eou2FGzU67BDTLDkByO8nODpS2qI/Wt8cDM54QP5/0btIHNO0Zvo57KDQ/QUxCZz0j0hwpmeKTUhfkLBGiX2cAR5H8Xmc5WOLAgtp+1GNvzZcgjiWAKAgmfwQSsKkokqoH/yQDTRouGLR7ZqSNVUWwIFhdifLA/kkjF4J+n12SGea+JK/PePbQBvc1uCpgwvWSkW2MgVFPiUTX4URcU6nk63KWvC7ZKwTD1umiq6/Jkc17Fca9J5VztUFRYMi90u9NvEWCp7/eBOM/E4N3a6FUMzqtvmq6QAkya754lBlja2g/ftdETeOdhlZDHAuorX047SKnaivAp+GPr00oeknAcNqlkmX+a/L+ma/BCZcfjl8gyFkCfz0qBBeI/9opDwrJp713OGBOguGPSLXr/DfF++33+6Sa4fTnGmd82bS7tI09K6DjGTJW0xtUwscMjlIo3APRMVJBZTRLmwBJMghFLAS7FkwWtUrbUPM9VJtn+Y9VIoggAHl5edhsNr7++msyO3Ti8em5AGRkHAYyPoIIIojgKEA9XF2PTyQmJhIVFcXSpUu5+uqrWblyJR9//DGnn346N910E5MmTTrSu3joYbIE19V32sCaV9WIzGgRWZJQg3W9v6EjvuDqgjfl9YhHaw+olu6HhW/L/z0ulKBj5zH1MyMOBbdDsoAWvit6t40JRZEMuAu+ENNZV4WUwwercAgHbYbIcQDYNBmK9vrOiea1EOp1TdDOpfYIVTJ/PKCm67a+0NqNlkUYk+qb9GsBNwVZRtH5DF4v+LJukgrhYMFbQipExYnm72U/w6U/iBTX0UQqaIhNg3PeERkljxPW/lz7Ou1Pld9XnA2/3a0GAixSqRAV61etYIfyUp/+vSVZDM9NsSIl5q6Agqzw2oiG4609HE6EOnY1HVOjWZVlS/b1ISZL1QofeyHsXoqhaIec2/qSCgDLPpPrKrYJnPtB4+llF2wTTyGHFXYvgWUfw293ij/CondFEqy+11T38SLDhBemPSDEfV0Q10TajMclAd1vL5TKOC2YogU3rfkSxHJXqIE4PxjN1bOrQc6bxwvRcX4+FLVAu2dG2lhVmCxCsBmMvmMTasymwWGVsZtDzabWiASnXTWdtgYfJzhtEtj/8xF53f966H3Zofld/vB64c//yXebYuHCLw4/qRAM8c2h69nSV107DU6+R4hHp03kmsL0JQIkKHvNH7LN4mxY8IYEsBXk3Cp+QdRAKAHPgXDa5OFy+Ii5YOOdwOvGaZMAruMQZ903JoLNSwJRW/swmmWeojfK/zHpcr/TfGViUlQz8HKRAgPJ2j9cpIKrHOa8ABOulXtvXDMY+xac/fbhqRwKheZ9pJ8dfJccu+0z4ZvzhXxrqMl2eld5Lt3rMzIvL/YliLhcYDTIfEpBTSyJDd1m/GEvqf6eMTp8P7lDMX+I4LhDly5d6N2795HejQgiiCCCw45/ZcVCoKSRx+NBp9ORmprK6NGjSUxMZOrUqfTu3ZuOHTtiNBrp3r37Edzjw4RQ2e/1yYrTnk0WvF6v/L/me5SyXJGW6HOllB3XhAVvyuCxWW84+d5DU5Gw5kdfIGbuyzDw5sYfsMemSyn/D5fD/jWwboLostYHfa4U/4acxSLV4bIDigy649TsV6OlqveCohNtUG1gHhswMK4pY/ho0I8+2lFT1YgWxImKk6DOwc1CLJlifROoxsK+NbDqG/l/9IvQ5uTG3f6hgjFatOl3LxXfhFMfr5k8jE2HyyaoE9kleH++Gs7/XF1FEYKwMEukDUr3C2FhK5C20rwPdBsn2zmwSTLVCnN8wYSGVGpFUDNCHbsg73v9JSosKXIuK6uCAtb3uGHnPPm/9eD6kwrFu2H5Z/L/0PvrVslSG/aulOcm3SGti/QD+Vvl+tw0VR6KTj5r3ltI5MQwTU0VBYY9JIGnnMXw89VwxeTwj0N6V7hjOSz5SCoEsxfCxyOg58Uw9D8QneSruPIiRtbmxKrnLJjXAki7qpQZ8U9M8Es4CJR10e6ZkTZWFUYLJGXU7f4TTJrP/z0I7rFQXgaznpXz1rSHVJfSwIBhOFj8Pqz/VQLtZ78lbaFw16H/3rogJk3GYT0vkoqDfatFOu3Sn8KXNEzvIsTll+eI70K3c6H9SJHqMyfKPS/YObakUEXzPxAOm8ji6Aw192WBY5YqVQ71MKk+EghnXlJbRa/JIvfJYLKt2udlSBWJJssTru9OQ5G7DqY9JFUBAN3Ph6H/bZzzU5gl1dR6oyQr1WebBhMMvEXkNX+7W/rPHy+XKoLWJ6mPQeH3YxqSVc+v4hxfQoHeKONEkwUSmiNkQ5F8ZkaSSkDuY0V7hHAIRgCY49V+KM837jCE0V4iiCCCCCKIIIJa8a8hFvbt20dhYSFdu3atJmmk00kQ65ZbbmHXrl288847lWxznz596Nat29FrOHYo4W/qG25GBwQPMuiNsPRj+X/wHWCIqplYyN/my14edPuhIRV2LZCydhSZENrypbS93zW1y7HUFZYUGHwnzH4W5r4IHU6vn+lZi34i9VGYBXsWQ7P+oFcki0eTC7Hmi46/IRoSm8s5dJRJwNWcCI4aJpXBJJCOoInzMY9AyY8CNbMxuW3jXtNup0it4BUt3MYgFTwuyF6sTkBN0i7imzd8u8HQsp+QLWW5QpDUlqXasn8luaDsmo/37ROk+sjtqHk9RQfNe0FSW0hqrcqulFe9/msi2hpDesx/+0dbEKeu++aw1i4NVdu9JNgx9Q9+aoE0W75k4hrNgMm3rE4v0gkgwYz6Yt6rcv207C9VMY2Jfavluc0QaDsUup8nxyV3A+Rt9hnGHlgvjzU/CLkRbjvWG2HMy/DT1XKP+fkquOyX8PuY2CYw4n/Sxmc8LeT1mu8l6NnvWvE0Mlmk4kfzuwg3+O/vi2EtkOCOFtQO5kdTk0xSQ3GsS5nVFBAO9nvCMdIO5rGw4nM4sE62ed7HMl5zBWibNwa8XhmblB0QuRdN9nLkoxKwPJphiIIzX5U2V5wDk26V6lRDmHOFVgNlLDz/DZj6H7hpjozZ8PralkawajCaq8sj+aO+xPexSJjXtM918SPR5isOtcpXg9Y+ygtlXbNa8VmYJWS2Tn9IfhZup1wTC96U5CBLKox6suHG5SCGyJun+qovQKptTryx/tWzTbrLWGzWszJWtOXBxknyACHR2gyV9tyyn3qN1wCNWNCOsyXJdy400sBoBtxyfJx+HnNOu08WKfBeGcwn4Vi87iOIIIIIIojgKMW/gljYs2cPvXr1YujQoTz88MP069ev2jIej4fhw4cza9YsLBYZZGiVDccFqVCfCbU2adUZqgaE6rOt9RNRCrMkwN7nitqXn/uKZHZ3OK3xg/wARTlSrQCio9tmiBAfeVtg8YcS+Gnau3GDv32ukIBRwXaZNIz4X923oSjQ/QKY9xJsmwHdxgthYFFJBID8naBTZABvtEgw1RQrJfZa1o8/gg2uD4XJ8bGCulzftZoDWqsGcfatkeekto27z8s/EwkkcxKc8t+GbcthldL2LdMlO1nD9Ieh1yXQdhSNrqKnN0G7UyRze9tf4clfVJIL41Eq/ErcFb0QaNGJqtFukshA5SyWrLrNf0Dfq2VZUwzEqkFM7fo/1Nf+0ZwdWtd9C+dYhbNMlXZkrhb8XLhsNdvXLqZzxw70690TFL8+OSpe5IUA2tSTWNg5V/TkFb14FjTmfd9e5Ms6bebnr2O0SKCl/Qh5XbpfKht2zBGT2NnPw8BbRf4vHETFwTlvw49XSWXUxJsl0Kl5HYWD+BZw7vtw4g0iSZOzWOQI1/4EQ+4Trxanve5BZu18aj4+/hULIJIm/pULh8pnoaZrUZMPPJZQW2AskKSx5vnMtLX3A7Pfdy/1+SqMfgFSOzRsH/O3yzatByTbu+ygPGuvXRVVl+9zZWipGS3oWpwjFUUx6SKnF5Mu9/qYNHWsc5j8tizJUnnw3cXSdqc9CGe+Er5c5yn/he2zpIJpyl3it+AvIap5KrhcEJdeO5lXX+LbeBSS3LWhpt8arJ0HSu45bFWrFDTys7xECFnt3hQVB04HJGfKXMjtEBK4rpn44eDgZvjtLl8VdcczxCDanNiw7e5fC0s/Ea83kOuzRT8hGmx50t57XSIVf/VBdILcK0Y9LST5rvnyOLABDmyUx5IPhHTLGAC9LhWvvGBIaCnjwcDjnL8T7PnyXTGp0s5Lc2W+Zk6Q+ZTRDOjBHaR/CuaT0BiJIsFwrBPYEUQQQQQRRFAP/CuIhS1btlBcXExxcTFvvfUWd911F3369AGEUHC73RiNMvm2WCyV0kjHlVlzpfFfvpTTa+/VNPAJNWn1H6BrWT4xKaEz270eXybagJtrn8DsWSHBRUUnZneNDYdNSASPS4zCOoyS7xp0mwT+dy0QuSKPB06+W9X5bAToTTDyMfjpKljxpci/pHas+3Z6jBdiYdcC0OmgmZ9MlzUfzLFgL5NMH1AlFGrIRgo2uP43Z/LUJbAcrjlg8W55na+aKjcmsVCwQyQkQAIV9dWFL94jWcobJvmCPdEJkr19cLOU5q/4AuPu5aR5b+KgUkPmZH3QfpQQC1v/lGztcNCyP9y9VrwSzInyMMVJYMnrrrrsP6+Jye2O2SLzojcGJ02dNlH9iA3I9m6syeLR3Lbqum/hLB/OMlXakbnaPWnTupV4SovYtnkD/U7oKcuqUA6skzYWFQdN6kFCu50SRAf1ntzAQGog9qvVCklthewKhbimor/e4TSRJNryByx6Rzwkel8a3nfFNYMLPodvxkPWPCEDR79Y90Bri75w9e+wYSLMeBKKsuGP+6XfGv5IcOLPX76qJk1z8C0XkyrB7lByPbXdg615tY8//FHTtahdg8cS/INl1jzfvcmfQNCOi8MKedtlzAC+9502kRAxmaV6a8L1cu/sfr4EABuCzb/DlLtFjq4mRMUJOdB2CAx7MPgyXo8EQLfPkNe5IbZliFb9otKFuOty9qElGlIyRbZpwnXyexNbiQdDONCbxPz545FC5q/4EgbfLp85bJIt73GLhnwwDxMN/tXFEAloBmvn/u9pbd2fqNHIT0O0kAuWZHnP44bkWFkmvgUU7ZIxV2MSCx43LP0I5rwoHjbRiT6T5PrC65W+Z+U3QlyBzHNaDRLCIiZN7gnLP5Ox3covhQAfUo+EJw0Gk1TitBoIQ/4jVYZ7lolUYdY8qUjdOVcel/0k47dA6PRSTZq3VZKwEjPkvuR1gVcBt0ter/1R/Ik0jw2jBYbeJ+etpvv4oSIT/PFvTsyKIIIIIojgX4t/BbHQq1cvxowZw5lnnskHH3zAq6++ykMPPUS3bt0AKkmFzz77jFNPPZWMjEOQiRIOGmp6VRNMFpmA+xv/1VWfVNs/o1kGc0azlKZ6HHitBZX6sl5XhY+UcZbj3TQVXd5m1VD2KpGzgOBmjl6vZGqC6M7Gt4Cdc8L7jZa04O+7/I5r/naY/ZwEawxRMmHTKhcAUHxSQxt+FTmA3peFntCBeECEg4oSyVhtNxx2zBIj53Pfrz7pra1UOKEltDxRsnTX/izl9JpZLUjpdEKGnDuvN7TRX01ojMF3ONfzobjmww0ihPruugRX/ZcNtj2jWQI8mmG3phed1IZaNavDMfn2eiR46HZKMLBJT6nGCYZ9K4O/X7hLsstyN/j2KSZNrrEm3SSgkdJRZJC2z0R/YB3v8wDvKFdDfk8wNlLQpu1QCfYX7JDKoZTM4Mv5V1FoSFR9UTwuCcaAZLT5o/0oIRay5svxcjuACplcV2ZK20JrVIc7Wazt+juUUi8NhX+7D9U+QmT8WW02rFYrMTExxFj8jk8495LAdhTw3T07Z7Jzs50O7VrjNZjxOn2kkW7XAvmn1aDg8hT+1SzBsPwLud6iE6BvGN4/YZsQq/3snuXy3KRbcKNRLYvUHy37ia73zrmw+lvZv2EPhSe/kZIpZMKUO4UojE2XCoRAlO6tfVtNu8PN82HxBzD/damk+P5iCUyd8RJE+wVwquj5B1YDBOj6+xMJtQUBa4I1X7JTrfm1EwuKUnPbM1lAV1rzNoJt82hIQAm8NxXvBkcpuO1SJQqqnEucJB3EJPnaWEWZrGuvgEm3STVAYms4/fmqAbpwr/vS/fK8aSrMeV76qPSukvFtSZbxiSUZzMnq65Sq8kFlARr221QiYfsMqaBRdDKG8nqlbVeUSCDYUSYPV7n8/uLdkj1dug86ni7bcAGoVTMOqwQqQ0GrMqoNTbpDs14w4lH4+3Eh+eOaSXWPP9whyJXktpJt/ef/pH9qPxLSOsm5jIqTaoWE5j65F21c4F8VoZ1/e4lPR75yzHoUXJ+1Icg9v0EI1ucEkxEzmqk8Ptp9wVrgk3szxVS9NhNbC7FQtBOUYfXbt5LdVV8X74EZT8G+VfK69WAhFSpKJeBfGw5urvra65Ux1M7ZQgiDVOI16yVSgeZEtRpgj3zWdZy0wZ1zIGsepsIbSPf+hwNKmnx/qChBuElRTXvKY+CtMq9a/pmQDFPvg/M+8lXU+VemJ7VViYUdIgFljPJVX++cC4vf9d1nUjtK/zj7ebnHtDlZvSd7pSJKS1QIBf921FjVykdz8kgEEUQQQQQRHCIc98SC2+3G7XazadMm3n33XdLS0njuued44403WL9+Pc2aNePnn39m3rx5PPfcc8ycOZPPP/8cvf4Q6WceKQQz/qvvwMd/gB6TBNZCeQZfhrYhWmR3yotRFr0jn/W9RoI3NWHnHJkM6qPEGKyRod80WUgFFDHK1Ac0AUWRwL3BDAXbZHC76F3Z93CN+WrDkP9ItcHuJTJZro+ed4/xsv6a78WDwmmXrCe3U457fRE4WP43lvTWhVSpLRBrtIixnC1fYvZaMC+5kSoWVn8vermG6Lp5kXi9EtDcMVcCSRpSO0Bz1cfDf1uKovp7tMWzcQqxpXt5wPsu7uV94IRLJQDSUETFSnB451ypWApFLNQXaZ19gYGcxTKB9w9wOqySuasQ/PdEJouCEASL1WrF5XIJuWCp4zGq0o481aoF+w08mX7tm4PXhddpB3OTys90OSqxUB9/BWu+yNIBDLhJZJUaEx63T8+6LpJ+igLthsl1ufkPyfqc94pkQocjbdTuFMn8nvWs/D6PS4JHrgp5uMvleq987ZCAbFoXkaHx7xcN0XDSXSKVMecFWPU1bJkm65/9rshQaPJV/j4Y/u3EX9pKUWpvR+Heg2NSfBULDYUpBmKPUT+hwHuTIVo1PfUbCxgtQrYaY6sf24oyyZjeOUfO0fjP6ucBpWHNj7DgDfm/y9kioxVIioVDnGvIWSL3bIBOZwZvSwnN5Tq2F8kYL2+LyPltnCJjt2DZ0Y2JrudIX7r0Y2l37YbVPt7V0Pca2Pa3VNNNvAWuneYjBmLM1T0wHKocmdbOQkmNRRAc/vcXf7lKp11IhfISCcYbrVWJhbSOkDVXAt6NgR1zpCLMYZX9OekuuY4UReZRdYU1T3xxitQEFkUvCSdtTg5NqCqKJHXEt4D1v6IrzuYdHuE53e1Ai3r/tKDfk9xW7gX718o+rvkhuOyZ5rNwcIsQLyC+e/Ne9hEw8S3Fi6XLOfD7fdIv/XE/XPazkH0VJdL/aj5N4EvACkU2NFa18uGoiogggggiiCCCowzHPbGg0+lIS0ujf//+rFu3jnPPPZeoqCiuuuoqKioquOEGyeQbMmQI9913H6NGjTr+SAUNgYOdxhj4WFLloWU6O9XBlqsc3A4Ofnge6eXb5btOvLHmbXnc8M/r8v8Jl4uhZCOit3cdhnU/yIuUTAlkhkJMikgkLf9cjAUXvi3VFkmtG74jCS2g71Ww5EMJFrU+qe4Twc5nwZ+PwsFNMkhPba+SC87qg+lQCEYaBA6WG6uk91ggKOq7j9p6oSYrRgskWOSclDQisVCa66vuOeHK8NtLRQlsmCL6tyCTz+a9RZ8+tglUWEOva0nB0esqvp+/ncu9EzDsWyEE3AmXS0ZcQ9H+VCEWtv4lsmmNCUWRTOslH8Cm36D5CfK+2Y8UNajySMEm4ZHJoiAEwRITE1NZsRAU4bQvp02uP5O5el9lSZSMZD/OQef1oMtZJC/q46/wz6sSwEnvKvfUxkbBDrW6z+ILltQFLfuDMQbW/wrZC2BGqVQuhENu9bpEsrZXfCnkeDjYMVuI/dOfrd4/xqaLfnyXs+HHyyXrdMrtMPZtX4AzVNWCZpAKgFI9c7i+fUxMangSSMc7Au9NSa1kDBPYhryqZr8VSPT7LGseLPtE/j/rDamuqQ+8Xlj2qW9bvS6Bgbc1rKojdz1s/1v+bzusZoJOb5LrNDZdsv7dTpEYWvkNRCdBYvv670c4GHirBIvztwo53n18eOspihhBfzxSjN7nvChVDBqh4LD7yYyZoXivkHgmi4wnazN1PhbRWGPG2rYTWE3lUpNzDCYhLY1qkpTR4svSz28gseBxw5KPJHsfpJL51CelKrQ+8Hoge5EkKmm+eC36ydxCIwhrk3lLyYT+1+PZMIn44mye8byAe+sY6Hxa+J4h4SA6HgbdKuTbyq+k+ighgMDQ+sqCHSJzueQ9IbNBrvXBd8KAW3zzptOfkXnQ/tXw2z1w1ZSqZLYGLQEr1PyovtXKEUQQQQQRRBBBYztwHn3QMh/1ej2zZ88G4JdffsHtdpORkcG8efNYsEAyHm+88Ubatm1kU9XGhqalG0xS4VB/r63m7120aBFffPsT69ZtkIyfr1VSwRQLF34l8io1YeNk0dSMiod+1zbq7qd4C3jI8zYKXpl0xjWtfaWEFqJ3G99cMoqWfCgGfY2BvlfLPpTuF/KirohO8OmvrvhCDSyYhd/xOAmLqAg0swNVDkLlG6158qwzNHzwHOy7jjbUdx8r9XoD1vNvM04bbFclvaLiRQaiofj7cZG7SO0IXcbWvrzXC3tWwj9vCqmg6CRT7ZT/ill5uMSEouM73Tju0j2FJ7aZEBWL3pUAZkWpLwu6pkco2YP2pwKKTBK1UvfGREfVMHDb36Jl7G8YazSH1rF2WH333SN1Dz7KEWOxkJ6WJtUKwY6Rw4bNWsrBfTlYbSGOncMm+u6OILIrBrMY0Bt856cdWSiOevor7F8rUnIgmY/hyAzVFbmqCWeTbvUPzjTpKh4LRrNs789H1Kq7MDDkP+Id1PokyBwhfUbXcdDzItlmv2uEwDvpLqkQNERLZvgvN/hI0EC0OwUu+kaW3TkHpt7l82QJbEOaB1NN91Stz4kEaBoPRoskfFQzqC4HW7E8g7TPvavg78fk9aDbq0v4hAuvV4KFGqnQ/4aGkwp7lsOmKfJ/i35S0VYXdBsnhLfHBUs+RCkLZcwQBsKR61F0PrP1zX/UbftxTUXCDMSgOlut0HDYhUhwlPoFv1XNeXtx1aB4MGh9lyNIwkBNnx1p1Hc8Ftj31LadwHuWOd73noKsaysEW4Ev8B+uTFYwlBfD1P/4SIWeF8E579WfVLDmCZm3dbrsa3KmyKN2Gl33qiNzIo6BdzNVGYEOL8atU2Xe09jj9sxTpZLC7YD5r1VvVxqxsHsJ/HCJj1ToeTHcsghOvrfqOM0QDeM/kSSR/Wvgt3vlfAUm+xjN0s+Hmh8ZLUJUh0Nk1WXZCCKIIIIIIvgX4LivWPB6vSiKwogRI9ixYwe33norv//+O8uXL2fVqlXcf//9mEwm+vTpQ1RU1NFv2FyrWWwDs65DreewqZkeobMK169fT5m1gi07dtBjxYMoBTuETLj4u9plICpKpSoApLKhIWX4gXDaeczzGomU4ElojS6hDgP46AQJvKz+XoKxq7+HA5tk0G5OrP8+Gc0yOP7jv0IsdBsnurx1Qc+LxFxz/S/Q50oZ5DrLAAMY1IlmTddAsIwbLfNRM9QMNLetL46F7J767qO2XuBkxWkTiQlrIZiixa8DRD+2ofeZLdNlIqkzwOC7wguKbv1TqgFAJrHdzoP4Ol5zftimtMUx9BGit0ySQP2u+fIIB4mt4JQHq+93TCq0OEEM3DdOCa4N3xBknCiTT3sh7FkFmcPkWtcIBv9KhUCJBP/gxPFizFef/iKcDPNgy5gsWPPzcSvG0HJJJouvYiEY9Ea89kK8BvFY6O9VjZFD+SvUhLkvAV7JwG/RV/SYGwul+8UEfYdKJjbt2bDtJbeFUf8HM5+SoNafj8LY130kcCgoutCVP8GIsVYDRfu6YAf8ciNc+n3wddsOFXLhh8skI3zqvXDWa9XNmwOzgbVKhsBM+mO9HR1phNuOjdFgSfDJJTrKYPpDcl7aDYNhD9fv+71euTZXfCmvT7obelxQv21p2L9G/IO8HpGxa39q3ftNRSeVpgvehMIsTAte4WbPUP5WhoA3iRr9B8pLRH5FexTsELJmxP9qrnbtOFqIgT3LRe4lsQ5Vrp3PhF4XyzhzwnVw8TcQnwEGg3gtxJiFaNB8uMyJvqB4KPN0TZ60ZA9EJ4tcUqBJutNe8286EqjveCyw76ltO4H9vtMuRLUmj2jNE3ktf0+Owiy5LutKFh/cAj9fI+fCECXtrdMZdduGBkcZbJgshuEel8jHdjwNmvdt2PhSb+RN3fVs8rTnXuUzlNz10lcOuq3xqsMURe4RE66VdrJ1ulRtaEhSE/w0OaiWJ8Lp/wfN+8g8NBgSMoSg+f4SWD8BMvr5Kob85Y8ac94cQQQRRBBBBBFU4l9TsdC2bVuefvppfv31V6ZMmULbtm0599xzefnll/nvf/9LdHT00U8qQO3ZfQ3Nug61nsmiZnoE/14FhW7duhEbG8uAqCwfqXDVb9VIhYKCAk4ZOYq+w85k8u9/yZuznhHJofgWMrFqLLjKMf3zAp3ZTikxOAffU/cAlCEK+lwhJbsoovE592XY+rdqAFtPtPfL2ln2ad3XbzdcJtwVJfDZaMm+9Xolm81RBsX7as6qrinjprGzSI+F7B7/SWhdstFD/TajBcpLwZ4Pxfth1z/yfvPeDdtPp12COCAkXDiyShWl4usBkDlSgo0NIBUqoTeK38eQeyEmPfz1irJFAzsYup0rzwvehoIGZAYGg87gux9ppr6hMj79gy5aBqMm6XK8ZFjXp7/w//1adqg1Dwqz5bw6bcGPkdGCJTUDXXScTy7JP7u0cnKvkgrW/Krt0GQGtxNFb0SxF5HiLeAC72/yWV1ljLxeydQG8VZoDLjKpU/4/X744XKpwHOUSbtoDJmwlEw443mpeCrZDbuXNnybgUjrDOM/FZLbetBHRAZD26FwgRpIXvezBM0C25LRLJ4LtgLI2y73oaO5au1YhdaOy/KqtqfAKtOYFBmXabI59kJf9vU579W/amfZJyqpoEiwtKGkQvZiIaucNiGhO59d/4ofg0naeGxTFKeNc73TeMfzCKYpN4nGu/WgHLu8LUIGznkBfroaJt0qUmkbJ0tSiatc2t2id8BTgz9EXFOf38vs5+tuSnzGc5DSXkynPx0tiSPGWPGQcNohdwOgGjonZci5DJQh84fRLPI+Hjc4S6u3z1BVekca/uOqmqoEAz8L7HvqMvY0xciyGtGgPXvd0h9pklzxzet+PW6fBV+fJ6RCfHM4/+P6kQpuhyRdTLlLrk2tSmHQrVLV00jz2D91w3AMukf8SWx5MP91uSYbCwktRcIThPjzH+slZkCz3kLKnfcRXD1VSIXakDnc159v+dMvKcRdS1VPI1dUR6paI4gggggi+BfiuK9Y0DBo0CA+/vhj+vXrR8+ePSsrGcaNG3ekd61uqC27r6FZ16HWM1qqSFAA6sRVNaWNSWHQwEEM6tMD7/sny+cDbxUT2AC88NIrzJ0nQdZxl9/A1m/+S+bmqaL1fsbzVc3SALfbg15fj0mlywFzX0KXtwkrZh7UPcQrsWFIIAWDopNJQNMeMpgvzBIN3d1L5He2PaUe2XSKyE9MuB7WT4R+14Un0aRBb4SrpsKk22DrNAk2H9wEo56WEnmDwZd9VRsCM3b8PRfg6CYEGhON5SkBPl34Iis4CiFLJRb6XNGw7S56R2RK4puLdEVhVu3rbP1TfldcU5l8NTaJmtoRRj0VHtG25gepbNizHNK7VP+8xwWwaapoBk++E0a/IHIwjQWdSZ61SZ+9xGd66Q9/jd7ArMZqEiN+7SeUSeLRiPr0F/73B62yyV4s0hza5yECOTEWS9VKBYdNgs3WAp+utSaD5HXL+0qB2sckSzDNYQfFzK2eL4jBjqd5P3R1JaMrSiVQCJLpWF+4HBJ03D5T/AkqAwmKXNutTxIvj4A+TYPH46lbdkdcM2g/SjIyt0yvuzRMOIhNl8zppR/Dtpkw8PbQy2YOl0z3HbNFkm/grRKI0qAFPJ1lcn24neFda1p78t/Ov6UPCkQ42bRaO1YcfgEyr6/KVNuOJpGkoVwlV02xVc9bXbD5d5j9nPw//GHoNKZ+29GwfZaMZTwuyBgAbYZWNROvI5xOFxPnbqRts4vo3qmcRStWM9i7HFNxtuoH8ZmMpYL1XfEtJMif0l6qVxe+o0qo/VhzAswp/4VvLoDshXJ8Op8Z/g5HxcO102Vct2UaTHtAqjfOelUkkRQFysuqV0IE05TXqhhiksGpSmX6fx7Yrx2tqGlcFvjZoaiAMkRLJd0a1aOtLpU9Xi8s/QRmPytVDs37CHlU14pnj0eMo9f+JPMukL6rzRAZfx2CxDhvQoZUVi98R0iF+W/IPT6xVeN8Qa9LxJg9dy1MvFkS0fRGmW9dpyad1fV3db8AFr8v40eU4O0iEOGOg8KtbGjMeUQEEUQQQQQRHCP41xALRqORq6++Gp1OpvHHRHVCuAgVFK4LtPW0TItwJvLWfF9Jr2a2uWkqStEumRydEDyA+uffMyr/b50ATde9D0ZgwE2UJ3fm+edf47fpM8jLLyS/sJCyMisn9e7Aew9dTY8OYQSBvF7IXadmUW7Ca4jmYc+DbFPqYZ4ZiIQWku29fw1s+h3Ki2RSvWGykASpHeq2vRZ9JctozzKZ5A6voxSBOREu+koyfmY9IxPeVoMki7y2wbQ/gg2E/y2D4ypB4YAJRrgTCadNSuUD259FzQxdOFECpa0GS1ZwfVGwU0z/AEY+Ht759bjFrBhE6uQQ3ftKbeX8+OcituXsJ2vvQYpKbdxywamcPaxfleWsKT2I2TVf/Ep6XVI9S1bRwRkvwJfnQN5m+OpcqWI4+e66EW+hoH2fFlg2+8mu+csfaUEXTQ6hpmvAmieyV1Gxx0agRkNDgzBae4lJUQOb1I2kMFmEPDAYZV2dAZvTi81uw2JQsBiRrH9nuZwvcxI47ei2/M3JLMWFHvcZLxHllz3q9XqZP38Ba9et48wxo2mVFkTio+yAPEf7ZGE8Hg9/zl5A9p59dOvUnp5dOxIXG3AuneUi07V/jZisHtgknjYa4ppBx9Oh/Sgc9lLWbMlm5cR57DlQwP78InLzi8jNL1YfRQA8e8MZ3Dl+SPjHrINKLOxbJQbucWF6o9QFmSOEWMheJCRMTeT0gFuFWFj3swSdAmE0S+DaGOszFA52X9XGHl7kWtCbhLAyJxz/fVBNqEs/7Cc7htcrYzQFuafpTdWlLDVvjOjE+u3b7mWiFw8iOdT3GmiIj8Gm30R2xeuBdiNEdmjb3/XenMfj4crHPuT76WLwnpqSTJ+7vsTitZE65Squ7mXgpJYIqWCKFTIwvYv00ZaU6vfyE6+XIOum3yRpptXA4F+c2FqqCRe+LdWtrU8Swi5caOO6+eq4btU3cu5Oe1Y+tyT5VVcGVCAEyiBpkpaJDSBQjzRqCvwG+yzw/lJfqRvNe8IUA/+8IttpeaKvqrI2uB0w/X8yLgfxCBh8uwTP6wJXOcx8RkzBQa7NHhcKqaC9VwO2ZB/g7jd/JT0phhvOGsDg7m3CnwNHJ4gHz6L3RNprwVtybWtG1g2BTi9znl+ul0STuS/55kD1Hac26SaEYMke2PoXtFGrh/zbib80EoQ/Dgr3XnwsSL9GEEEEEUQQQSPjX0MsAJWkwnGHxgwAh7MtrczeWQ6GKDZt28aWnMV0aN2czhs/ENXavlcFDUbk5eWxarXoYq+YNZmKby4jxujlgKkVm929uWHoGDZv3V5tvfmrttLnssd44Koz+d/1Z1PhcDFv5WbmrthMrCWaG88bRtNmcZLRs2W6lKwD6KNwnPwAm+bVMeBfExRFpC3Su4p55c65cGA9TL4DOpwmxsx1yf4bcBP8ssxXtVAXSRmQQOxJd4POKCaMf/1PKiiS/DLaaptYBRsI/1sGx/7XfGCmdbhty2Hz+Skktqg6YYk1iBcGQP/r67+fXi/MeEImq21PkczlcLBnmQR7DObGkWQJgu05uYy96yU27thT5f0/F67hm2dv5+IzBuP1evlwwgzuf+Vrdt4RRQplOPdvxNi8e/UNxjeDKyfCvFek7H/9L2KG2f86OPGmhl2TlZN6RTIRPS5Vo9oKRXskyA3Vdahruga8Ac/HGbyh5DwMZjCYJUgRhE8JuZ76mWIwQ1JL333GYMaWdxCXYsKGHrNW3eByynmzF+KpKEE/+/8A+FEZy3lpUvWyY8cOvvr6G778+ht27Nghu2cwcPlF5/PAPbfTuZNfH6AFP2PSKS4u4fNvfuCt9z5ke1ZOlX3sntmC8f2bMqKtia5xJSQ59qLzVtV49piToXlfsmN6MifLxZKfN7J09QOs3rAFh9MV8vdruOvNSSTGmrnyjH61LgsIudaslxAb2/6CEy4Pb726ILmdBE4Ls6Taqfv5oZdtN0z6wgMbYPW30PdaH+GpBXACg5qhiOyKMvnfFKv6+6jEbEPa+7FaTaQhnH5YO57+nkheL5hsQiy7HMGlLDVyNTqh7vtVsAN+vUn6o/ajYPgj9Q8Ger2w5ntY9K687jxWjMcbaKj+4Js/8v30RRgMeizRJgpLJFBsUyx8uV7h42WljDmxI7++9ySmlNZV9z+Y50qrQdImNk0Vgj++OTQJ0n+BEC1bpok/wz+vwrnv123nFZ2Q6Wkd4adrYf2vQrSNehpcdh+p4HFJ1Z05vqqXidEcOltbI9A1GM1Hn8eCP2oK/Aarrg28v9RY8RCQTOAPp136ndz1sG6CvHf6/4V3ndsKJAs/Z4mcy+GPQL9rwXog/N+tYfnnQiAYLeKN1fE0IQrDwK9z13D1s99RYpW2/sW05XRpnc7ZJ3XDZNCjKBATbeLC4b1o0yzEvMUUI4TIkg8hb6uQDDGpUlHUUMQ1hZP/I1VK/7wG7U7xSYnVB4oCHU6H5Z/KuDuto1TKxTaRc+Isr0rsOMvDJ5xC3YuDJff9W4nwfxmys7PJy8sDYOPGjUd4byKIIIIIjiz+VcTCcYvGDACHsy3NlNZVDjGprN+7DsVWQsmmzSj7VsqAt19w09VZs8XMsnu3bpxgnw8tFIrLvZz4+np2FUtpedMmaTz76H/p0rE9KclJeHMW88CbPzJx1nKe+WQy70+YSWGJFY9HglbtkhQSdk3jhr5RWPRq0McQJcHXTmPwmpsCNWjiasGvuk6K9UbxSehzlVQbbJ8pJmRZ84Rc6HpOeNtp2U8qF/Ysh+WfwZmv1m0/NAy4Gbb8IeX3k2+HKyf5dGD9NZi1zPzaDDSPRzkkh7W6xEZds+ECoRFt5aUQHVc9K3TTbxLIjG0ikgj+Gc51gWa+rDdJtUK41+vGKfLcsm/Yk9G64M8Fa7jkobcoKC6jeVoS543sT5vmaSzfsJPvpi3gsoffZuWmLNZty+H3f1YBMGGDwo19TUz44j3SRtzCyAHByIXmcOYrYkw+6znYu0IyQNf8KEGnbufWL/CkGd7qjRJM0GRqrHlCKricEOsXiNGCMzVdA7Gp4LAc/yRcI2BX9i727NlLixbNad2qdSU5sWLlCrZtWENmq2a06dAFi9GiGpWa2Vu0j9wD2aQ3yyBl5buYywvYQxO+U8bRduVqHnjgfub980/ld8TGxtK5cyeWLVvO59/8wBff/sh5Z4/h2ccfpmOHzEpiYdO+Evp3PoGyMgk4JibE0b93dzZv3cYzA0q5uHsJBp1qIKkmd+cUe5izy8WcXW5mZ7nZVlACZAETqv3WpPgY+ndvT9sW6TRJTqBJSiJNUhJokpJA09RE3v/xL175cgrXvvAj6UmxnDEgzGqmDqcLsbBhklT1pHaSDNLGyCIFubdkDheZmI2TayYWFEX6nil3wurvJHimBS01zffAdhGKyI6KFXIuthH9ePyDiscisRBOkCpUP2W0+KoW/KtStT6qvEiWq6ssi8Mq5sLlRaKFftZr9ScBXBUSUNw8VV73ugQG3NKgyjqv18vr30znpS9/B+DTx67nkjMG8s/aLJ5Tue91f37DgLOu5PclW3jovcm88uid4W28x0VQuEuqYv95TYKgwYgZvRFGPCp+DRsnCwHY5uS6/5hOY+CCT4Vc2DET/nTBaf+nnk+zyOIoXiGPYuKqei3EBKm6gOCExNFMLNSG2gybaxrH+R+vwGNlNAsxN0utFOl5UXha/xWl8M14Id9MsXD2W3I/rQ92zpOKMEWRcU+TbmGt5vV6efTjP3jmS5EUOrlHWzpmpPL9zFVs3HWAjbuqEhwPfzyNC07pydBB/SBqXPUNGqKlXS77VK79v5+EoffX/3f5I3O4EHCrv4WJt8CNc6Q6sb7oqBILO+fC0Ptk33V6cCGSfKVFcr1r/gvhJuWFuhf/W6q7I6iC7OxsunTpgs3mm1NaLBZSUxvJ5DyCCCKI4BhDhFg4mlDfiVQondRwTeP8l1ODPCJLcDBgIOVVMzPsYnwaHQcGMx06tGfPpiK6Fq2UxbqOE2kkzRzVD9OnyUTvupEdYMkHADy+KJpdxRK8ue6is3jp4dtIShB5kq07c3jx00kM6dGGS0aewN2v/8S+vGIU4JqBKdw9MJru8aXoFAA3O4o8ENeSdm0yJYC7cRJ4DMA1sgPLPvFp3dcGj7v2ZUD0d+NbQMczxFDTli+Zd3uWVw30DLg59Db6XCXLr/tF5CTCkXzxBtm/MS/Cp2dA9gLJ9h50q1SOaMFRfw3majI6Qa6/YAPmupoRHm7UtH/a7/GX2KjJ2E+bvMuGg2+veI8EEcwJsqzXq7adaFl/qSpddMIVMrmxF4T3O7T24/VKZczs5+V178sgKsaX+VYTUVG8WwLyKOK/kb+t9u8t3V/LAlGg/wyAsVfdxd/rZT/6t45l4s2ZNE+0AlY8mRBdnM5nCw/w4udCbkSZDDx367k0iy4CFjMyw0G7u15k6y/P0DQuRLaP3gQjH5NrevkXEhSe9oC05SH/CZ01F0q+RctWUxR5eFXS0RgtxzpWzfSsfN8MeGsm2Pzvk0eifdT1O7XfHu42a1rUS1UZm1BBYfV4Ll2yhOzsbFq3bk3rDMlkz8/PZ/4//2D22thn8OB0ONi4cSNdOnekV+tU9m5ajcMQQ3H5QVpu/hWAN3TX41BMXHnN1WzbshlFURg1chhXXnox5559JhaLhcVLlvHcy68x6bc/mDBpKlP++IsH7rmDy9ocoBOwcH0OZWXldOnUkTvH9uKKM/oTY46SPmLLdABK3NGsLYzi7+0V/LWtgi0H3dgr3JQ73bjcPsLaEmWgb2Ya/dun079DE/q3jqVdk7gAyQkXkC8PG7w4JpkD2Z34avZmHn5vImd0uCj0cfbXVE/JFPPL4t2i+b5/re8zS4osm9QGEtuIfJ8uyFCvaS0BqoxBQixsmyH3s5oCj51Gw6wm0jY3/gZ9VVNOh12uo6Icn0+GKcaXTQ1U3lc1aQprQd1IgNqu/WOl8i7c8V+w3xss4KXJkmmBU1s+GNVMd4NJ3teIheiE4OMJf1jzfP//85oYtcc2hdOfUwl7VTYmyLgvKLbPlASVDRNVU1hFspXjW8CmKb7lwjVA3SmVrlv2FnHXpwuZtlKqVp+5tB9XdPNAzgIGxutBJRYytn/NJ1d15rw3l/L25z/w0slWdDq/c5A5IvR3dR0rUivWg0KoDXsoOLFiskh2+ZbpYup+9e+V0mshEWzc2XYYjHtPgq4750pQd8xLEjA1mKVaVWeQbTtsQhhYkuSeG8xkOBghEW4fciikFOvaHwWiShv3Vr+/BLvfeD1yf3La5C1LbPU24PVKYsi+lbL+sIdDzwuKd/v+n/WskAox6XDmy1IBpn2uyY/VhvW/igfKNtVrIL2rGIznbam63I5ZQVf/Znkpz3wvBPq9QxN5/ppeGA06XhvXlO8W5rBudwler/oT95Uyc8NBvp+5ip/mbWTko+MA2LdmNm1Tq16ve03tcLn300qfh3fO8ygF22uuMAhXHnbQ7bDrH7mv/HozjHk5+DURE0bQNqO/EDr2AijIkqo6kxkMdigo9PnPmZMlQU4b49WIGq5P/+uvIffx+iwTwRFDXl4eNpuNr7/+mi5dpHI2NTWVVq1aUe6ovVo1gggiiOB4Q4RY+LfDXxtee61ltWnGf1WkYeyS1RudpJYOm+nZuQM9Uj0oXzwGKCJXEgRut5vJv08nPgpuarIaHB7IHMGtp1xE7C/TOPWkfgwbJNlAJaVW/u/tz3n90x9xqnISvTq05JsnryHNto1OznUYKwoACarmm1ry8KRsPlpSRlLMdlY8nUHr1MN8ecekQcfRor+9f42UQJtiJfO6NjTvI5l/+1ZJif3IR+u3D4mtJUtu2oMy+e9wqlRDaKiU6AnTeyEc/dpjCf6a8NrrhsBp92VApbQRCSxbgZoBVy6TmuxFYkze56q6b78oWwiibNGIJqmtVMOEi81C5NGyHxTn1LxsGPB6vdz3VzmMltezthQDcOvQprx8fhvMJl9wRadT+Pjy9vQeNJzv/lxCm2apPHLNaLpntgCPG+/fa0mLsTGgmYd3fprN0//rH/qLFUUmry1PhF0L1JL8LSLF0WaoEAz+0l81QacSC56AgX9NmcEOuwQd/uUZacuWLZeAf5cu9OvXl2XLlrNp0ya6t23KCe2byUKOmu8Lq1atYuOGDRQWFnL+2aPBaSf/wB6aN2/OgT1ZtG3XjiWLl1JeVsSWjRvo1PRE4uNjKSyx03brB4AXV4+LWb1BAuM7d2YRFRXFljVLaZXRssp3DTixHxO//5L1GzZx//+e4I8/Z/D0C6+QPjqaTieasBvimTbxC04bORxlzXeykq0A77YZKMAaUz/+3h/Plt0H2HLgIF6zi36ZbuItJhIsJuLMJixRBiwmA6ed0Io+mWm+L9cCrTVAp1N44cpBfDV7M6uz8imxOYi3hFFVpDNI1VLJXijYLkGs/B1QulcChrZ8lVBUl03rDF3OlqqpcJHc1qdVvX0mdD079LKGKJF5m/UMrPxK5P0UxZcx71AljhxB5Eb8UbJHgmluO8SmhV6uLtDa9fHkq1UXaEkFLjWpQPUywWgGe5EsUxcppNwNPs34YQ/W3/S5ZI9U3DisEiDvfJZccw3Au9M2cPdnC3G6PBgNOh4dfwIPndcbl9vD7yty+Hj2ThhzVuXyw7pIkNLh8uD2eKsSCzXBaIHel8LiD6VyaPV3oSXJel8KOUtFn37R2zDkvvr9uI6n+8iF7TNg+iNS3RoVV13uKFol1R12X+A0sDpI893QGY5+0i0Y/MehtUGTjdLagrVAiBfwkTKmIGNiWyHMe0n+H3xneAk/O+ep4y4FTn1cSIX6wOOSZAqPSwiK9K5hr7ogy871P0vCx6OnJvHU6SlgEIIp3mxkfP8WtG8Si8vtxe3xMrZPU+48LZN/NucxZ2d55Xa63/0DwzqnUu5wU2p3srfQyp58Kwrw9phobu1vEjNpZzm0H1m/36nBZIEznocfr5Tre/2v0P28+m1Lb5Jq8g0TRbYsY4CcX5MZ9NFg2wtulxAL2lzAHw6br9LLklL7NRaRPvpXo0uXLvTpE0YlUwQRRBDBcY4IsfBvhzVPJv2mWF/Zrz+5EDhYMpnBYccbI9UKc+bOYdOaFZxnnEs6iNRLiIH0wsXLOJiXzw8XxWN25Eu228Bb6WiK4f/uuxGAvbkH+X7KDF58/xty8ySze3jfjqzeuofVW3dz4X/fIPc/cVKhoI+SgGnrQaTEpPF6k6msOPgPy3YWMf6tpcx75GSiTUGyyA4lFAWa9pRMvMId4vnQcXTtE3dFEQmJKXfC2h/EHK2+ppy9LpEMuR2zYMrdcMPMqiZ+mtGqNT+I2V8YWqHHctlvuBOAKsehBhLGaAbFAHF+QTBjtEy0ABa/J8+dxohvQLhw2kTHduU3UpGgM0Kfy6HP1bVnPPpvY/tM+b/zmbA4hMaz1ytBc7erVpmmbQUeSuweLF4bNsXCk2NbcVGvONqnVz9GBVYnZqOOOy8awZ0XBWR/6vQozXpAzmIu6Gbk4QlzeOg/d2Ax1/Lb9EaRRuoyVsiFtT9JG9u/Bq6aUrPJbOU21G7PHaYkldMmmtZeIDa+1sWPR3i9YLfbWbp0KS6Xi82bN9O3b182b96M3W5n0/Zd9O7WUY6RZhwLeLyeaiaRbpeLuPh4oqOj8VTYmDVzJitWrSStVScuuPQqzNHRFBSVsGvndjp3aI8xoSnxTg9Ni37HUpYFlhRcI56ADSsqtznilCHVSAWPx1Ppq9Sta2emTviOXyb9xl3/fYQWcUUAXHvrvUQPlGuzwuHi0Y9+49yYFQxq7mXGDhenfjUz7GP00NeLGDegLY9d1J8T2oUfFG+WHEPbJvHszC1h0eb9nHZCq/BWVHRStZDQUmT/QO5ZBzZIELMwSyRbnFaRrjiwQTKfO54ugdxat69A26ESNN08tWZiAcS4d96rcHCjGO427+ML5rhUiSP/4J1/sE/rg/TRoLPKcwQNg//xjUnxBVWj4nzHu1yI4bCJBbcLZj8j2d4dThfPgfpgyzRY/b1sx5IK3cY1TPoEmLV2L3d8sgCPx8voEzJ4/dqBdGyeyKItuYx/aQZ7CqzojVGMHCPLfzwniysH+sZYLo+XOlnqxjWF7ueKNN+GSUL6twmSuW20SLLN3JckaaTzWEjrVL8f6U8ubPlD7gFjXvb1e1a1GlIjjpwqIR5Mjkz7HGQseKwliviPQ6GGSlyqyh0V74WKYiFcUtr52ojDXpWgcdph+SdSwRnfQmSAaoO9EOa8IP/3uqRhnlb7Vkn71EeJSXiwypMg2JHv4Lwv9lHh8nJOtxgeH+Uj/rxeL98uyOG2L1dTbKs+/ok3G+jRzpcIVeFw8/vy7CrL6HQK3Vsl88RCB0XlFTw8JEoqjFx26HRmwwjc9K5SuTD/dZj7IjQ/of5kY6czhFjIWSgEvDaG07y19GrVQlRMdSLBafeR4Zq0VgQRRBBBBBFEUCMixMK/Hf6mo6HIBH9ogVk1cLRm+RIo3El61EL5fPAdIVf95scJXNrDwIWdkQzuU/5bmb04b8kqnnj9U2YtXFFp+NmhbQavPXonZ3aN5WBhKfe8/jOFO1agUyCrGLa3OZ+RXX0Dd7NJz8939KfvY3NYtrOIaz5eyUfX9sZgOsyXuaLIRMBRJnI122eKVERtaN5HSIn9a0Q+Z8T/6v/9o1+AT0ZJQGn285LR7T+J9JdD8B80h0MaHCvSErUhmHyLRig4bZL1FGqi6g9jtNp+1GCYdtwObIUNk+X//teGt09er5S+z35elYdAgjdD7oXEMAOOGrbPkvMb37z6BNerypppE+4w0SFFz5fn6HF4bmE5Pel7UjSmaMmwzi9zsn6fjcU7S5mxuZjpG4oAaJqygXYt0njwytMZO8RvP5r2hJzFjO9q4rapJXw+YRq3Xj4uvB0xJ8EpD4jm8eQ7pBpj01TodXHt6wZWLGjybiZz9Wvemg/5O8EcK1mDRzLwcgQrhRQFrFYrbdu2ZePGjaSnp2O32+ncuTObN2+mfceOkJARZD0Fu70cq9VKrMWM2Wxm2PDhrFy5kv79+4PBzLqNGym2OVg97TdmTP0VryEag8nEmtWr2Z97gNIyKz1bWJh1kQv08Mbmpiy7+2FoP77ye84afToggZOly1bw1vsfMWHiFEYOG0q3Lh3JbNuWdm1a06d3TzYun4/xh4uhcBPbD1hZ8NlXbN66jb9/n0TfuAMMOtuMzenlrr/c9O7Qgo4Z6XTISKNjRhqx5iiK92dRbHVQYndQbK2g2OZgT4GV6Suzmbh4JxMX7+TsE9vw0NldGdgxPazje1LnpuzMLeGfjfvCJxaCwWSR6oQ01avB65X7yOap0hfsmClm7l3PgSZdaw8CacTC9lm13/MtyZKdvewT8UI57yNpVzEpkFCbtrn6eWJLXxA8gvrDaQswordU9RPSkgo0eaPoxPC2u/obkdOLioeT7q77frmdQghv+k1ep3aUsVEDvX9W78jlktdn4vF4uXp4Rz69bSiKorB1bzFnPTud/NIK0uKjuWJEN9ap69z99Tp27ivy7ZqnHrIjTXtIEsHGyZIIkNAyeOVcxonQ4TTxSZr+CFz2Y9iB4mrwJxc2TxVy5sxXhUyoTB7xqyKplHoJgHY9WPOPzUSRwHFoTfcnfxNrQ7SvSkYjPkEkLa15MjcxJ4mMzpIP5bMRj9Z+T/J6hVQoL4LkTDjx+vr/th1zIF+kvWg1ILz7oddLud1GkqeMrbfH8NdOD6O7J6NXb/EHisu546vV/LhYtMAyUsykxJrQ6xQqnB427yulxO5i0ZZctNqDaY+dxfY9B4kzG4kzm0iOjaJXm1RizUYmbFEY/8C7lDkVnh1hEjLZaRc/nvpe2yCJI9kLIWcxTP0PnP4spIfpPeSP9qPkXBZmSSWfdi80xoKphW/uq82H/IkEo1mS7RRCH3ttPKbhWCPmIogggggiiKCRESEW/u2ITRV5gsDsdFueVC9Y88GkTTi8vgCcQQZbHdu1pOm63yXTudUgaNlfZBgC8PQLr/Lll1+w917JrLKfcB1Ocyvigb/mLeG0K+6pXPakfj25fNxpXHvhWZhMRti/hrSkOL5+8hp2zNRDxXpm7XBw/Zsf8Mqd53PXRSMqs2Jbp1r47ta+nP7SQr5ftIf5Wwp4fHwPaFltl2pHYHl5XaDTi17w5j9kwLprQe0TcUWBftfBb3fBmh/Ea6G+MgNxTeG0Z8TEef4bEvBJyZTzWVEqchOWpOoZ3uGQBsdL2a/DJpUl4JNv0YgVLzVLBDhsquRIkcgORMX5GWOWy7Wzc5ZkC6d2hNZhmjYu+UDOF0hFz5B7JbhX1ywwr8cng9TpzOoTPbdDroNKKPJ7tSyuILjgRxs9mui5oa+JZrFOBrEcSqDkoJdTvixn1d7gFQD780vYn1/CRY98xIbvn6BNc1UjNyUTjBZSsDEq08DydVuCrl8jktrItT3nBQnuhEMsVFYsOORZkzmyFYgGrz/BYCsEnQL2MvmuI4kjXCkUExND586dcTqdFBQUMH/+fFq0aMGoUaNIq8Gszmq14nK5sNlsmM1mTj/tNE4/7TTcHg8oCnpLMl++8yl79u6tJJUD8fiJClF6A9O3ubj7m4XojSsY+agQCzq9nq5dOvHE/z3PNz/8zLbtOyrX++2P6fz2x/Qq29Lr9ey6y0KLOIVr7nuGpXvFJyFKD7Pvk/uhs/Uw1v48slq1BQAHg/cJG3MKePrHZXz/z1YmL8li8pIsTunahAfO6cEZvVsE35aK4T1a8PWcLbw/fT13ndWLlPhGythXFCEW+98Aueth/S/St6/8Sq7/E2+oef2U9kJoFmWLgWjnMTUvP+BmMffMmgdFu6FJ59DEnX+wT4MW7IygYXDYABeUFlbv4/0JHW2sFk7FgvUgLP1E/j/p7vqNTRa/75OJaXMSZAxssETVxAVbuOCZX3G5PfRolcw7N5yEoijklZQz5plp5JdW0C8zldlPnYXeFM05qpVP20RIde5m9z2x7Cr2sOuglW4ZdZCE0tD7Ugle7l8j8pNnvRb8N418XMaB+1aKl1aP8dWXCRcdT/cZOm/5Q8Z7o54O0p7MkpFdE7R2eKy1u8BxqL8XXCD87yuJLYR4Cazactqh7KCM5yxJQio4bTKn6Tqu9v3JmifePDqjSJnWlyyz5on8JUBaF4gLs9K1vIRoTwXRZrn2zuusB1cx6GJYedDAqI//Jr/MgV6n8Pi5nXlobCcMet+40OkScuHlmQdRU1ro1z6NU7sHJ8fPG96Xkf278Ny8jQw8oRtnJ22DXfNl7NmzBq+g2qDo5Fr+7mKp+v7hUhj3vpBzdYE5EVoPknOye5n0g/pouW9pZJK1wCcPpxEJ4Gd8XkObqPRrUw3QjzViLoIIIogggggaGRFiIYLg0IKkCj55JK081BUrpcHA0OGnE7XjGSgjZJDir5lzePyZF2kVr5AQreBFR7vrPsFo+ortc37km4l/AjBm+CDeffo+CotLWLVha/UN2Yto55Lgoy2+HR7PBu55/WcWrN3B7eOHcbJZNHJHdU9n2n2DuPnz1ew8aOOmz1ZXWhaEClwdEhiixTRs029SiltRVrMBJogfQnI70czev0bWry9SMuXZ65YAaZyafW8tgOj44CW+xwtpEA5MFjkfmnyL9p7DJpI3NU1UtbbgdUkWZmX5fLlMDK15Po31+ObhB08Kd8lz21Ng1FPhyx4FYtdCMQs0xkDm8Oqfa9n6il4m0Yret49BiIWV+9z8vNHF5C0uzulm5vH45xjqXczF7gnERylY9BKcbZlkon/rWAa1jWNsz2TSYo3sTBvJva//xLxV27j7tR+Z+NKtslGdHlr0gax/eHSoieUtM+v3W/PUe4UhTBJQ05kv3CnPJrOQCrYiiFaNGbX7H16p7IlrXg8JrUZuR0e4UshiMbN7927mz59PVFQUiYmJxMbGsnjxYg4cOECfPn0YdeqpACxbvpwZM2YQFRXFySefTOvWrbFY1PPjsrN80T+s3biNRSvW8NNPP1NYVARAfHw8PXr0oHv3bvTs0YPMzEwyo4toP/tmPOgpHfIwbw+0UFJmY7bKY7Vv15bhZ/hkesxmM+PGjqF71y6YzdHs2LGTHVm72LFzFzt3ZWPwVNAiTq51T1wLzhjVgU4d2nNCzH4So2cDkNDllDoHPLtkJPPtf07j0Qv78eKvK/l6zmbmbMhlzoZcOjaL5/ZRmVw6sDkpKSnViL7LTunEq5NXsz67gHs+/Ycv7z61jmcnDDTpJiTn3Belkq4KsRgCmrRfUbavgqompGRKJUTueigvqOqxYC+EhOY+j4WaSARNxsdkCd/EOQIfTBawGiAuRMWMZu67f428DsefJnsRuCtExqdTLQRTKGgm44PvEF+OBmJ91kEuf3EyLreH03u3pH/7VM578S+sFU6yDpSxO99Km/RYfnv4dGKijZS7PZzsXcIYzwz63ur7/hbxOr7esotuGT3rvhMaSQM13zPimsoY+Z/XYOv0hhELIOfgxBth0TvSPu0FauVkHZJhtAQJ/zFQTfDv346ldhkou2aySADcHwaz+LooBtHeL90r7/e6JLy+oDBLntudIoRsfbHiS0lIMSdD0+5hr+Z0uzEC07a5MEWbGdFS9Z+zujjto1zyrR56ZiTw6Q196NvWJzu2u8DGrA15jOvbjO4ZCbxxfUcuVHM8Pp2xiXvP7BL0+xRF4cJT+zNj6UZem2/l7MeugBVfSbVByxMb5pcSmw6XfA9/PQY5i4QQryuxACKzBnLf0hurVqiAXA+u8qrycFoFT21kW6VfW7LvdWMjXOP6CCKIIIIIIjgKECEW/u0oywNnmZSHxuCTQtIeTlvle16HOnnyin61oihEV+SiK9sn2c6ZI6ptft/+XC677ha8Xi+jz7sMr+4PFI8Lo6OUnDwvO7L3MGuRaGXfdc0FbM3KYex1/6W8wsErH3/Ppy8+TH9NBnfzH+B2Yjc345ZbbsWZOpv/vDmBn2as4KcZK8hINnPJoBbcOKwNp/VIZ/1zI3j7rx28MN2nEXr+W0v5dbSXKEMYE4XGkGMwJ4EpDhylkLu2dk1iRZEspYIdYk7bEGJhrmo613E0JGb4JlbG6JpLfI9nBAZ+A+WFajTw9ZucaqXSxlif+ZtN1TeuKJNAfYK67V0La5cY05DeRXRhdYb6kwpeD6z5Xv7vMjZEAEC9/vUG+a5a8MlKye4/p5OeqyZW0OSmDCaSyOW6XwD47+h2/La+mFlbSigtd5NndbF6t5XTuybRr0sb3n/gMnpd/jST5q5m6j9rOfPkHgC4Wg/FsXUegzMMNO1QJ4VrQWmuHC+AQbeFt06LfvK8e5nIFxgtUqkQ7QaXE2LVduGwgzkBlBAGf8FwKKsKjgLSb//+/cTHx1NaWkqXLl1wu93s37+fnTt3snr1apxOJ23btmXlypXk5uYSHR1Nbm4uffv2Aa9XKDpbAbu3rkbn1jP9zz8pLCoiPj6eKZMn0bdvX3SKQnl5OSaTZHxGTRJJCV2vCxl/zn0sW7aMt977ENpJ0GXL1m3o9XpGjRjGZRdfwLixY4iN9SNwPW7fvx4PuxZPgX/uxabEcv4lV7Jvfy579u5j2Y61XDQWog3w++xFnDH8pEqPhrqgS0Yyn905kqcv6MFrUzfw4d+b2ZNXwvjUraTkbse5D8oVMzEJyeiiEyAmjajoRD65bRiDHvyFr2Zv5uKT2zOmX5v6nqbQKM4WUkHRQc8Lw1tHO37hZuBWSo2pgTuTWUgFvVHaFEpV8lIL9gUarGrtqKYApsN6xOTBjihqIzCNFsnMDlV1aY6HvM1QlisJEK0G1v6de1fKcyNUGZDQUiogGoCisnLOfXoC1nInLVLjmLdxP9NX7a6yTFJsFL8/cgZNzB7Yt4aowp086hH/I68Xpm93kRJjoH8zKC/MZU+BnRbJdRgXeT2w4C3I3yrX6dD7aj42bU8RYiFnibQrXQM9wDRyvEl3n5SLMyB4WhP8teQdYbQh//7tWCIWAmXXHDafzKV2rCzJVf0VolQ/Je341AbteNRBWrIaCndJBQpA8951khR6d6mTu/orDG2tJzpKAQ84vTpO+rCAPKuHvm0TmfHgEBIscn+2Vbh4aepWXpi6BbvDTfeW8Uy+ZxDN2viuyZcnrebmU9ujUxTemrqWGWv30KddKqP7tGJgUxdD+4hXyKJ126lIvYeojAFCBKz7WeRXGyKJFJsOA26S7e1a4JP5qgs04rSJjDfxUtVLI1hFT7BKOg3BPOgOJRz22peJIIIIIogggqMEEWLheEdtE1AFybLWjEwNptBB0JhksMo6iqcCxWDGuX4qJoCMAb6BuB+uu+0eDubl07N7V1594Rn2vzaTZiYX7ZN15JS4+XPeErL35GIw6IkymTjjqnspr3Cg1+tZt3kHA8+9kfsuPZXTOkYx0r0Kj9fLSa9vZdDSH3n7vosZ3COT93+dy4RZK8kpsPPi1G28/Ps27jkjk5cu7sb9Z3bgilPac5UqJfzn2gNcaHUz8SJzjdIUjYrYdCgohb2rwjM7TO0AmxEt4/oiZ4l4Oyh6GP6Ib0Cu6S6b4g5Nhs3RjoYEfv0np4Fl0lqWk84AyW2gIEuy3GPSJICSNR86jKr9O9K7yvOBDXXbN3/sWigZjMYY6Do2+DLatR9GBc/8bBcfLpf7Q+sEPb9s9dAEyESqK/aUwrgPfRVG2w+W8/cmMQWNidLx3TN9GTukF/dcciovff0nd7zyPaee2Jkok5FV2cXMWOLggZOiaHvwb/CeVbfJ6KbfJLjTvK9UP4SDZj3lPJXlQskekThy2qH0IMQ3lWWsqkSIovcFHsKpRginquAIeiXUF4pKRHXt2hWTyUTz5s1p3bo1MTExOBwO1qxZQ3JyMsuXL6dFixakpqbSsmVLzGYz7dq1Q0Fhy9Yt7Nq1C2f+LpavXMvUOcvYtSsbg8HAd99+w4kn+jIS9QY9eoMBJXcthm3TAYWSHtdw7qhRzJw5S0xYHz0PgJeee4rLxo8jPb1mw+QNGzfz1vsfo1v/E++cruefbUU8/PUzVZbZflIM3dL1vPb5rzzw+XzuGD+Uy07rS4y5ena1x+Nl1c48pq3YxbyN++jaMomHxvclNV6ul5YpMbxyZX+euKA3K1auoFmcaFsbdWDELtdeyZ7K7Q1QdOTcl8rsbVZmzp7DsO7NsERX/d4VG3bw2te/sSe3gE+fupU2LcLzcACkrW+aKv9nDJTgbjjQDN31YRJ/WvstL1KlFM1SqaDJIWn3UU1CQgv2+d9fw5VnOcLyYEcMDpvcv+ylkNYeYoJIkYWqCNGObbbqi9X6pPDMvPepxELzMO+zNaLhlaPXvDqVrXsKMUcZ2JMn1Tc9Wydzw6mdaZZkwWJSGNzMRemeBVAgwWEFyCeRP5VT6GbIYvQ38xjbycjki82c2V7HLV+sZuI9YZAsGrb8KVrwOgMM/W9lJW9IpHeVhARHmfTxTXvU89erqCR7BsgYXh/tC4hqAdTa5MW09UAqLRvavx2N8DeqLsqRCt5oVSJM69+1bHZNHsekfm4vCu87tOXDqQQLhWUfy3im9WAZO4aJPzZZufv3UgY3j6F/Cz14HHi9cOGPNjbnuenWxMT0+0+qJBUmLtvLnV+vJidfjonJoGPd7hL6Pz6LHx/0SacdKLLR8+4fKXe62ZMvFbjTV+bw3ISVxMf8yXsPXkFaUhwHC0tZtjGLkzqfBftXS7+2awG0CVMCNBSa9pBqgooS8Qhq1jv8dctLJEELZN6lJQRVIZn8yINAs/tgONz9TbgEYQQRRBBBBBEcBYgQC8c7ahsIxaSCo1yCzQqSQeW/nNPmk0Iyqxk9XpdMWgxmyjf/LcRC++DSDes3bgagf5/emM1mVhR5aZYOmck6ZmW5ee7drwAYMagv7339K+UVDkYPG8jHLzzI/c++w7eT/uLlb/7kkhtjoKmej1Y4Wbnfw8oJc2mRlsjDV4/mxG5teOe+i5n689d8MncX09Yc4JU/ttMkPor7z+xAosUXEIky6pi8uYKXFzi4/6SGl+KHhdgmULDdNzGvDakd5Dlva83LhYLXC3NelP97jJcSfG1AbTBWzco+HDiaAqkNmRj7ZzJpkxB/aFUGRos852dJwHvrNNg+I0xiQS07L90nWb7mpJqXD4SrAlZ/K/93GStBjKAIj1iwOb1cNdGO0wP9mut4Y7ED9NJuMr1CLCzMcaLXwbheKVw5II0DpU5W5Fj5cXke+VYXj304hbFDevHYdWfy5e+L2Lk3jz8Xb2DskF7Yy528ON/BLf1MxBftEj3ctkPD+61eL2ya4vut4cJoEVmYfatl8pvURsjVKLM8a54Lir7qBDOcSWU4WWzHSjA0SLvt1rUr3bp2rbLY8OHDiIqKYv369bRs2bKy0gCgc+dOdOksxotZWVksXTyfr7/8is07fFVkb77xBiNGVK92AzDMeQ4AV5dxnHXtfcyb9w9Go5HxF1yA5uRzy/XXEm0KnvnrdruZ+vs03nr/Y/6eNUe+b3Q0oKckqjlXXdaXZk2b0KxJE5pVbKWpbgk4dtOtSRR/L9zHTS/+wP3vTOLqMSdyy7knkxIfw19ztjBtZTbTV2ZzoNh3D5i2IptP/t7I/y7sxx1n9kTrXeLMRk5pVgE22G9qywdLbGzckUPreA/d0nT0bmakW7oevddDC4uDy3oauQwoWDsFS+cheGLS+WPFLl555glmLV3vO+7XPcHcz58io2lof4sqOLhR+iGdATqcHt464Es6CDdbVMvCztsuJB5pqiGzdq2rFQuahIQWCPW/v2oydbXBZJGqS8URflXY8QCTRUgFnU7Im2DEgoZgMjAmC+yU9hBWVWTJHpFzVPQND4Y3Auav383EBVvQ6xTsFS70OoUJ95/K2f1boygKxSWlsGMWCXl2EqLA4/UyY6eHricM4IbSO3ErBia2/JQWSdFM21pOhSeGZnGQn59Hqd1JnDkMEi17sWjKg/hhNela8/IgbSPjREn6yFncsGNZul/GCYpO+jO9SfWF8su6Dybp4n89gIxPXQ65jioNbmvo346lSoVAOGxSoe11g8sFMUHGwUZVGlHLZA+XWNCW1yQw64r962Q8ouig3/UilxUG5my3c/6X+wFYmqunv8ptvbfcxcRNDjJTjPx1Y3NS4qLwer08PXETj/8iRiOtUy28eHF3BndIZtzri1i+s4gzn5nK0Ieurtz+9v0lADRPjuH2Md1Zl13A78t3UWS1M3nuKob17cRPfy/jr8XrOalXB/H0WvezGIvXhQgIBp1BSIGtf0pyTl22l6vatCe0lOotfwSrSAjVXvxR0/zhUMiE/Vv6swgiiCCCCI4LNKBOMYJjAiZLzSa0RgvEJEmw2WgWTcpAQzR/ssFkZvuOLGbOW8jWTWuJL1AHb+2DB00/eEPkeD758lt+njiFfsNFC/vikSfQLD2FfQfyyWzdgnf/7z9MmSGTtCfvuZ7mTdL45o0nmPTR8zx5ehq9m+qxufV0GHEFr90t2rSPvD+Zr/9YDIA52sT4E5vzx32DeONykch44McNTF5RVRf69cvkswdnVLA5z81hgabpfnBTSGPcKkhRiYWC7VVkPMLGjtmwe4kEgHtfKdIXWga2yykBnYZknDltktkWrv6nfyD1SMNokSBMXQbs1nw4uLVqpYI2CbEV+ErfLcm+7RotIqPT7hR5vX1GeN9livGZBB/cHP4+guzHnBfEW8EUF7paAfzkGmomFh6fVcH2Qi+pFlib68Hpgb7NpNvIJAsAly6KbU/15caTm/DVkoP0aRXLu5dksv6xEwBYtSWH3PwSYi3RXDxKZIh+miHyZ4N7ZhITn8hLCyrkC1d9G76UQO460TU2RIckNkOiUg5pqTwbo6HCLs8mc9VKBQ3+99K6toFQ24GGbetQooZ2uzMri7nz5rEzKwuAwYMHccMN1xMdHc0HH3zAO2+/zaJFi/joo4956eWX+fiTT/jvAw/w6BPPsHlHNoqicPbZY5k1aybXXHN10K/XZS9Av3MWXp2BO37Zx7x5/xAfH8/SxYv49JOPa9z1vLx8Xn3zHTr06Mc5F13B37PmoNPpOHfsmVwxUnTUx99wH59/8DbPPfkod956IxeMOIGUppLF//xVJ/PqHeNo3zKVEms5b/40ly6XPkuTsf/jstf+4qvZmzlQbCc22si4AW15+erB9G6bSrHNwf2fL6DL7d/y08Is8fSpKBPDZKBpm048ftlJfP7AePoPGMzzS6Po9V4plmdK+HxfJ7xtT+GfwlQK7F6SDXbY9id//PINN7/5O7OWrkev13HpmJNp36opWXsPMvy6J9iTmx/yOFTC65HqHoA2Q8TYMlzUt2JB8UK5KiNizfdd3yaL3Ee1h7+/TW2GmRocVjFa1dbTm46O/uVwwWiRSoXohNpl2vwDZhoqSsUzAYJKWFZDtoyzSOt0VASWH/9qHgCdM+S3nz2wA+ec2IbJS3fR77+/MOOPSSTo7OTZPLyyyMVpP+g47Ssr13y9E7ciBJmiKAzITMLpgW2lkrF/fhcDy7OKa9+Bg5t91T+9Loa2Q8Lf+YwB8pyzOPx1gkGrVkjrBJYUNbEhQD9ee89hU71ObNUrg3QGyTPQkk6OtWqEcKD9ZgXAID4KxmhVCipgTG5SM9u1pI7ywvC+Q0viqAhTOskfXi8s+UD+7zg6PM8TYHd+GWd9the708uYzhauG5SK12Dmz51ebp9qI9Gs44/rmtEs3kCF082VHyyrJBXuPr09G18YxYUDWpISG8XcR4Yyrm8znC6f98Rndwzn+SsG8vv/xrD13Ut46Pw+fHPPqYzuIzKfPdq3YMxg6U9/+2e1rNR6sFTuOO1CLjQUWtWDRuKFi32qDFLTAN8Uk1mdAwWM7/zbSyAc6hgNQs8fjqZ5TgSHFNnZ2axYsYKNGzce6V2JIIIIIjiqEKlYON4Rlg6kIpIEKNX1YU0xvomkqwJ0RnbvO4hiK8C1eim47BDXTLLstYBgtE8S6Yyx43jg/v/wwkuvcN1t97Lzm/9IJmdRNvsO5NMiPZm/332AJYsWYLOX07ZFOv1axUBRDuUVDnqnweiBXvDCFl0HcvYdoGWckXvOH8BrExZz7TNfYvZa6duhGQlZ64iL0nFHL9i4PYH3FxZzwVtLGZS5hajLrwHgvNZWFg1M4dNF+Qz/qoJd9yRgRA2cKHpIbO3LuAw3sBuo0x8IvdHnV7F9Zu1Zas1PkGCpq9wn1RIMwcxqvR6ft0L/6yCxpZjzuZ1yrpxWKLaLcWLgdRGu3m9dM64bs3ze6w2dZX+oKiOsBeCpkGeLlmUbrWrwJ/leB0OLfqI5XrBTyrKT29VehdCstwTM96zwkUw1YccsOSZbpsk1qzNA59G+oLmGKteLEuJ9KjOTl+5x8eoi8VawOqDCDX2a6Vm+z8NIfBULF53cgTfnl3LvD5vweGHutlKWPDKQ1qnJnNAqjpXZpfz1+0QuH9KOC7sZeQOYMHMpL45rSdNEM9cOyeCVyWv4z0kWEkv3wabfa86i1Y6fltHX8QzxEAmE2xF6G816yfPeVXLPM1kguZUa8I8JHjzzv5da8+pfdRB4T66tPYUj2VZXWbea2pEGtd1anWA9eJCYmBgsFtm/nJwc7HY7OTk5tGqVgYJCdk4Of/zxBzt37kRvMFBWVkbr1q35/PPPKydgZrOZyy+/nNtvv40m6U2Iio6mvKLqedJ5nJQUF2Oe8SQAs4pb8f6PfxIVFcXkX36mV/eulDv8yCdFD4oeq9XKpCm/8+2PPzH9rxm4XLJMUmIC119xMbdefwVtMlrA+2oQMKkNXoeVAwfz2Lo9i61LN5Kan8fYJLCWFHLXtddw17UX8feitbz7459MmbMcj8dLry7tOeOUAYw+ZQCD+nTHYNCTs/cAN98fz49TZ/HIyx+xMzefC1+bzeAMPT+MN9MyXofL4yV/xe/YnFIJ1MoJH4zy8tYKCz+tsXHNh0uZtjiGZ85IouvbZTw+LIqb+ho5MxM23RbL3NwYunXtQqvUGHJGduOU50vYnpPLiCv+w+wHB9EsUb0HDQziNZK9UPoSgxl6XyqyheF6JvhXLAQangaDovYjbo9Uy4FkCDvscn0HZtCHgnZ9BruvV9F6P8rlWcKQm6sXYlJrrlTQvlfrq4zRvvd2zJHjl9xOzlFtCQ9ZEsinWa/aid+apOwq71OKjCfDgbvq981Zm8OMVVkY9ArZBySb+ubRPZkz4y/Gv5/Fzf2MnNfFjMPt5bvNJq4dks7YE1z0ermEmWv3MnKcuqGdc+kXX8IvwNR1hXQ7KYrzuxj5bukyhpmioP+NwfenYAes+QnwQtth0DNMc1/tGGtZ1zlLJAgdOO4K19R67yp5btpTxoqBkkdRMbJfTru0Ib2pqseA1p5MMb42FhfG2Cmc67mu13w4/VFNcFhrvqdo1VBRcWD0k2LTG9VKp4DxmzFalgWpHA1HnlFb3lFW8/Wwb1X19/avFWksnRFa9pVlwpCqe/XbaZRVeOnfNp4Jd59IlFHPm3/v4q7vctEp8MPNJ9ChUyqldhdjnvyDf3Y50Ovg8/MSuLxHASXb5zL+ZxvTtlbwyLA4fhoXx9Od+qFSjjzxw3K+vnMogzo3q/K9y7dLkL1fip0T2sSgKLB84y42L/yDTi0Shewq2SP9zv414ZlZ20IQ5GlqFe+BDVLFrfUptUHzV0jvSpXxbjWon4Ua90FVz5/Gkgk7FOO6CA45srOz6dKlCzabEEgWi4XU1DCrRiOIIIIIjnNEKhb+zXD6ZWGEzNSwyjIOK4qioCgKrZqnEWP00tKhSvVkjqxxAPT0k4/TtWsXSkpKmL1WJDBSdaWkJcXz9weP0KZFOj//LdlbF542EEVReP3r3zEPuJLfPnwCo9fBmlw3/Z5ewtUvTeKCp3+mwuHmomHdcLo8jH/qJ9pe8SbJj23H8vBWPl9awpvj0jmrSwwOt5d/dvomzUPfzeH1czPo2dzMvhIXt031yy6JSWu4mV4wKIpPL/VAGBkOOj2kZMr/eVvq9l0bJ0smtykWBt8pQVhjrEx6nOXgrpAgT2AgIVjWtP97/v/XVgUTiPpUCdQHhypjyJIEuih5dtp81R+VGbdq6XzxHt9xtRVICX10PLTsL+9tC7NqQSOe8reHt7zXK1UqBzfLBLjzmRDfvJaVapZCcrq9XDvRiscLSdEKdhf0bqpn1X6poDF5HWSwF4CPFhZy9/dCKiRaDBwocXDh+6vxer2c3l0G3FOWi6nmoI5pDGifiq3CzbO/rgXg2uHtsTrhyVnqeVv/a82kAEggZaMqg9R9fC2/NQha9JXngxslwFVTtlow1LR8XSsQ6tqeDhdMMRCbhtXpxeVyYbX6JB4yMjIwm81kZGSQn1/Aps2bWbduHenp6ZhMRrp160a/fv347LPP2LhxIzqdjmuvvZZ169bx2muvkpmZicFoxGSq/jAajbDlD4wH1mBzwqUfrEZRFL798nNOGRo8M/jHn3+hebvOXHbN9Uz9Yzoul4s+J/Tiw3feYPf6xbz41MO0aZUBGyZBRQl2t56TLr2PxNY9aNqpH0PGjOfax9/nw2kSjMjauoHh1z+FTqfjtMG9mPj6/eyf8SH7ZrzPrx88Q59uHZk6ayFnXH0fib1G02bIBfQcfQ0XjBnO1lnf8cTd12IxwqLd7somdtkvdpq+Uka7N8vo/p6VAR9bGfq5jcRoPa+fnYJBBz+stnL+lwcYlmnh1qnlnPldObtLIcakMDrDRiv7erDmk5Eczcz/DqRVipkt+62MeXUJHk+IwJzHDWt+kP+7nBXUC6lG1FUKSQvCxaWrHk52ac/BJFr84Z9VHfh+4H3dv80crv7lWIXREiBFha+CLnNkeNvQSOqGSps0Ap78dgEAQ7plUGp30K5pAt1bp3He5zn0aqLjtdPlOnttsZs/tzlINOvomB7FU2dU9yPp30LGe5+vclDh8pKRoKOwvIYAtzVPEjfcFWKY3P+6ugf/0jr7fBYObqrbuv7QKhYSW0sFSjByqDIgaoeSXEmOgOrtJVQbOlqr6QIR6p4SDFrfbUlUq7H9+nCn6n1jK/DdJ8OWQlIrFhxldSNJPG5f9Uu7U6QKKQzs3FfIh1OXA/D0uA5EG/Wsyi7h3h8kIeqlCzpxmjr+uv6L9fyzy0FCtMKiG5O5rBvgcRFvdNMjzYvHC0/PKuWleWU8NL5f5XfsOlDC4Acm8MVM37yl2FrBlr1FAPTNTCM9wcyZagXDx3+ryVgxab6EqKUfh0dIh4Il2ZdgE5gsUxP2qRUU2rra2L0xqkyDQWtDR0FFVwSHDnl5edhsNr7++muWL1/Oxo0badWqluTCCCKIIIJ/CepELGRnZ9f7EcFRCG3CDqEHROqAPW//brZtXk9B9nraNU1iwIBBxBWpg832NZfS79mzl927xaSywiJal5nJOqa/+yCd28rrwhIJWnVoJdkoBwqK6dVEx019RX7hzj/KGdClJXqdTOKWbd3L5/efwxWn9qRJUgzRJgl6ON3w+yYrRr3C5Gubs/Y/rXnnXN+EcvNBB2v32Zl0QyYZSUY+Wmbni7XgiU4OrUfv9Uogs7wIyovrl1mlEQsHwyydTOkoz3UhFjwumP28/H/ijVIebzRDQjP5bfHNIDpFpC1shZIhZFMH2v4ZORr8AzpleVB2QJ6P1kBOQwK0NU2gY1JUg8wUVX/fVbV83mGXyaSWNQdgK5KAXGmuaCpD+HJIWul2QZjm3TmLfZOojqdBctva16nMwKt+LZc7vVzwYxnrDrixGKGw3IvFCIMzDHi8MKaDkTbkoMeDV2fktVkiN/bo2HYs/Z8YXy7ZWUyFy8P5fUUG7KdFu1i2PR9FUbj/bNGi/meTSJl4vV4UFN5b6sBlSgB7gVT21IRtM6QtxjaV0vu6IrGVtA+3QzLb6iLD4g+nvZJ4rURdCa6jtT2piImJwWAwEBPj6x/atGnDkCFDaNOmDXl5eTgcDqKiojh5QB9eefp/vPr0w3gdNhwOBz179mTBggW8+eYbNGlSPbjnj9Wr13DrbbdROuVhAF5ZWIHbnMIXn3zMeeeOC7rOY08/w0VXXENJSQlt27Tmfw/ez8ZVS1m+YC43XHs1FosEjuwF+ymb9n8APDrDyoLl6ygpLUVRFFpntCQxLoYdhRIAaZuko6jUd04P5Bdzz0tf0O28+2g39CIuuuNxvvzme7q41jJhHHx6djQH8iRoF2Mx8/hd17D1jlgePMkkwUq7l0mbgmd6l1R4uOvkBGbf3Jz0WD2r9zmIjVJYeFtzJl/bkpZNUiRopTdJP3RgPeSuo00CzHpgIFEGHauyS9hxMMT1tnO2aLFHxYn+dV2hjRHClULSyHm9Uc2WVqVBtKo9W77cFwMRKjh4tBJvxyKcal++9S95HY6EXNlB1QRVUT0zGgP1y07fsa+IWWuy0ekUnG4hua8d1YOnvluA2+1m4sUxGHQwfbubB/+y8duGMnYWCDF2zykpNE+oSo5VuGQ/nG6YuVOu85FtQiSXOGww9wUZByZkwEn3hE+2+UOn9xHbdQmU+sPrhb0iJ0jTnj7yL5CYM5qlj3Oq4xZvHfolpw0Kc8Q492iXd6ktMSBQPz8mRQLWlmRfNYOtQB4VVunPtQB/2ObN6vzB46o9McIfOUvAelDmX+2Gh7VKYamdMY98jbXcwcB2CZzWTSTB/tqQj9vjZVTXFO45TeSUdhy08ePS/SjA6lsT6dvEhYKXPSXS1z05LIqOKTIeLLAHJwA042aA5dtl3NY6LZbUeKn0GNBR5je5xX7XSdOeck7yt8P2WWEejBDQxtC7l4S3vNPmmzs16SbtwloAtoOwf4MvOSiCCOqJLl260KdPn0YhFTZu3MiKFStYsWJFJF4WQQQRHNOo06i4TZs2KPUozVMUpVKWIIKjCFrppv9gvEpJcUzloDuvxIZSUUqpzUqypRUc2IhStEt0/NueEvIrXC4Xl15xFSUlJQwaOIDBoy/E/fVrxJoUerX2ScJ0y2zJ34vXsm5bDgD/d9uF3N1qPXrvAX5Y52TOLjfDeul56JKT+b9v5tE0KZZduUWkxJu5cUwfHr/iFN557wPumnQQlXvgoDeJqCYxnJxu5Be16j6uaVt+3Z3Aja3S+Oz6WM58bTFX/1LC7opYHjk94Nr2eiUzzWmnykTYVR5+ZrOGWDWglr9NJh21yVBUGjiHGVwGWP2d+DJEJ0Dvy3zvV5PDcgFuKNknurqmWKlscFVUDd74l/ZqAfejuTI3LNmvEAhX3slkFiLBX5/VZAZXrFwv2nVhSZQAhN4gBs4g5nOu8tqDEVrFQul+yUTUSuyDYeMUn1Z2u+GSDRkOtPt4QCZZabmbc74pZdZOF1EGIRF+3uBkTAcjWc5k4ppZ6N8jjjhVBqmQBPaQSHxzuGJkd9pYfBNLa4Wbfm0SuHxIW76et5NbP1nMov8bTcdmkgmYrU5W35m+GY/Xy8n9e2LoORKWfSKVN21PCd3O1v0sz93Pq1+VkaJIcGfrn5Cz1FdVUhu0+6PDBgaTyCmY49WAhRp4N1mEbHA5wHRsGcp6/e9z6r8WiwWzWc6DN4BUzcvLY+3aNaxatZq4uDjGjhiEgodVC2exZatUtJ111lm0b9+e8vKq0icFBQWYzdF4vV6mTZvGxx9/wpIlS7imt5Eu55gprNDR9uIXyJ54ZeX3Hygup9juwOH0Xbcf/TCVuGaZXHnZxTx8z200T65OkK9Zt5FtH17Fee0crM11U95lPL/eNoyOmW1p16YVG7dsY8RZF7JTJRaSzQqLPn0EgD/+WcnVj77HgYJimsQo3DEwmqv7xtI7xVHZ1wC06jOE2BjfuW4ep+Pq3hKM/2uHk8XXx+DyiAxSmcNLqQP0CozuJUGZk9pE8+0l6Yz6eB+fLC3jrC4xGPXqFxiioWkvKMoWDxV7IexZTrvEDLq3jGV5Vglrckpo3yTgt3vcsG6C/N/13Lr3W+BXsRCux4KftITBrJqlqp9pxuiaSazDJr/HVQ7RSWoFQqD2dZD7+rFigF4bDpV8X5Xtq/2V0SL/52+VrGx9FLQ5qXZJoj1q8Dsls+7VLo2Mr2dtAODkri34Z4Mkqwzo1IzHv5nPD+ebaRmvkFPi5aKffIHQeTtsxCalU4yF9h18MkPbac3kvDLimjlJjivloxUFjO5gZFjbIP2z1wsL35RrNToRhv63YURXy/5inr17KfS9uu7rF2XLPUBnFOlMc2Jw81nND8rslr4qKjb8/XbYfL4LcYexjdWnTVSRsQlCWvkbwwf9TrvcK+1FkrBgTpBKEJBEh3BgNCMDZK+M28KRtCov8Uk6th8VWlLTDxUOF+c98T2bsvNomRbPz7f2rpyXbz8g468B7RIq3/twzm4So+GPy2NoHSdk3GcrHdw9vZx1t8WTEafw8dhobp0Tx/lD2vDgpGxQC6bjmrbF7XJyxoiTOOBQSDeVs3iLGEUP6OBLEtiQUwRAtww/mU+jWap69q2CVd8IOVDTWLYmtDxRtrFnuVzjtY2hD2yUsW1Mujy8qr9GeZkk1dgKa/em8cfx0t9EcFQhNTUVi8XC5ZdfXvmexWKJVEFEEEEExyzqRCxceeWV1YiFHTt2MG/ePBITE+nduzdNmjQhNzeXVatWUVRUxJAhQ2jXrl2j7nQEjYRgE/Yqk5OYykdKEy/FB3cTZ44DYwwseleW739tjYPFJ59+hoWLFhMfH8+3X31BcUkJlHhpnaigs+WJvAzQo4NopK9ViQXdwQ2kew/g1RtR2g4kJnoWs1fvYvZqCWhOXriFyQt92fwFpeV0UCcUiqJwwJPIteUP4aRqMOTEG15mDXB7GRjjnLx0yZPc+dUqHv19L/1bxXBaF3Xi7PVIppZXM09WJAPT7RCvAkN03UrgTbEyIS0vEnIhvWvNy1cSC3Uw8J3/hjx3Gy/mdDZ1QqQoMsg3qkEbU6zMvRQ1yONFPgs8j/7Xh5ZRfbxmjYarjxqszRgtYLDLZEWbvGqZcPYCiGsuFSvWg5KdVluGvTlRfEtK94k3Q6gs0dx1sPg9+b/VQGjeK6yfKvC7dl0VYIjCWuFm5LvbWZrtItYEky6J5VZVKuy0Xs34scPrDDKY+Ae41fM5APO9vRl0y7UA3FPi4lPzV0QbdZQ7PZSVu0mJhZcu78vkZbtZuj2fT2Zt48JBMmHPL63gQLGdj2YKeXbX5WOhTR+RBSjbL94RncZU33V7oWiEA3Q9pw6/OQDN+wixsGdZ+Ov4m0DqDGICCAGmmeo1coxNRK02G1arVfVSqB6ACZZUcPDgQfLzCzhw4ADl5eWs2biN9GS5j2TnSOCvVatWKjFQNeATHR3Ftm3beeCBB1iyRDIRDQYD/3dGMmAnccxjXD7wpsrlDxSXc+2HC3G6q5JhJ97wMgBbgRs/X8mn1/cjXc2k9Hq9vPXBZ3z77vMsukbIXPvg+3l77DWV669eu4FTx11GUamVwb074jFZ0TlK0dnzuPOdX3nru2kAXD24GZ+MsqPDA0hG6vpCExv2Wrmgm5ER0eslOOVHdLVNkkzQJXs8XNgtBJls8hWOjuxg5r6hCbw0p5jrfz7IQaubs7pYaBZvkO3+P3tnHd/k2b3x7xNtUhcKtJTi7jpgyGDIgG1szN1d3gmz39zlnbsyd2djwDYGw93dKVrXpI09vz9OniZtkzQpZWN7c/HpJyF5PLec+1znXCeltWhMF+yQsaV4L9cPsHDZ7lLW5JRyer+aWtgU7pQMAWMstB8d+Pz1obp4c7hSSN77V93SL/yJ+GoS1rttyX4oPSDHNlggOUCtlEA41usqhIK/4/RoO6w0IkfLsHPaZVwFmYeM1vqJhX3e8bExZZAakLDw26rdPP6lkOitmyby5/p99O/QjE/mbOLsrnomdzHidKuc9nkFsWYD4zpb+WJ1KbP3x/BV59txYsR4ku94t+ofhKEwaCi4nQ7mvHYVLk8VBl0Au678sC8rcNgdoWtbhAMtY2H/SiEtIg3YOiQygjTtAokZ3rolNnmusQHscZcTkjIju26tb4VTd6ExEapP+Ac+Qc0gqFCoXX+iNjTiAcUnfWnxrgWqSsNzZis6b5/2ZjyE86zXfi4kRFxTyB5U7+azV+3k+pd/ZvPefOKtZn5+7Hwy9Turv9/uJRbapcu9Olwelm09wIqr4miTrEMFHpxTxcNzq7h9aBz7Kowkmx0MzTZw48WTeEB3cjWpAL659e59YFTcvNdpHku2SrbZwPZNqrdbv1cKXHdrmVLzgtPay/xTkiNBML3Oq/+ZBEJ6Z/mNq8qkKHNmn9Db+/cPexG4K8UOT2ktdrq1njpntfFPnm+iOGbRsmVLNm3aRH6+SFJv2rSJCy64gPz8/CixEEUUUfwjEZEU0vvvv8/UqVOr/26//XbWrFnDPffcQ05ODrNnz+azzz5j9uzZ5OTkcNddd7F27VqmTJlytK4/isZGkJTiJk2a0K5jV1JadpWFTFWZfNFiQNBDrVu3nseeeAqAt15/hVatsrHvW0tWoncR5acl2r2dTKJrt+6VqFjv8ZWELM4aexyrXr+Kfh3q6sYP7S77vfrjMmZvl8WzApQQW4dUqA0nRsb1bsGlA1NRVbjw41043d4Vr73QSyoosriJSRLnjIZINUMVxVfArDiMVMfqbfeIs6o+qKqQFgCZveW96pao94IdsH+118FkhYRMSMwUHX5rWl0jO5As0JHItRxrOr1H4/4Kd0FFLhTu9kl5GC3yrBVFouDAp8MbCltmCKmg6EJHVdllMUdMEmQNjOyaFZ0va6bsIFSV8faiQpbttZMYo/DHJQk0i9OzJd+DSQ/ds1LQGXyO0X2IA3OMOpeOqhADTgxsK/ZNKeVV0m6bJVm4b7JkYdzz2SqMegWTQbZ767dtFFc4aNs0ngnD+8nYoxVW1u4v0LVrjoS1X0Z23/5weCVaaku8BNV6r5Df2uUQGSWteKq/jJxWkwbCk285hvpGRUVFnVoKgWCz2cjLy8Nms9GkSROys7Pp0KEDWVlZNGvZhh0Hi3nv8x/YuUdI4oyMuuM2iLTiSSedVE0qZGRkMP3nn2maJs4JpZbWdIndUYdUqA2nW6XE7suOfPm1N7n5rgcZ3Ur2K2kygAFeUmH/gUPcdu8jDDlpMoVFxQzs3o4Zz12Pztsu7n79x2pS4ZQR/Xjj3ivQ+TkAK5wKv20tY4u3zStq3XHaoFPwqCrbCuted6VL5bN1Tgoqau736NgU+mSaKLB5uOqbfNo+lcP2fKdvA6MFmnWrno8Mqny3fl9Z3QdS4p1rUtuFX6y5NrT5RwkzM0grcF+wU/qwf/8yWqXQukYg6I1gsEokvLaf1v9C9QltvIZjpv+EjdqFp0ONE9r4oP2Fc5816iH5SeHYCiXLavd82S4cGSTwzYllB8PbPhjsRRLxD+JIjQC5xRVMevR7qpxuJg1qx25v0eaTB7TlkzkbGdpSnL4vLXWwPk/lt2tbcWZPGT/WFBjrtQX1RhP92ySjD+bf15uoJuM1O+tIoJE71pSGFWjVbLb8bRKw4LTLNQZzoFsakGkSyiY6mvNWqD7hH/hUX12FigLI3eaby/3ndIdN+oN2HFuhBAxZEr31tJJ896YzElaqbtEemf8VvQSH1AfV48tI7nV+vcTFvHV7GDXlAzbvzSc9KZbvHzqHHm1qFjLecsiPWFBVNm7dyc/nmLykgsK8/QYenltFUozCXcPjufbHUu74tRKAS9Uvaa/urHNeDU5Vz/4ymL9RxoGBHSRjwV7lYou35kK3rFprCUXns+eqSut9JEFRtNv328WE0Za1bBF7EeD2zUEmi/QdU93AiZA4xuUqo/jnomXLlvTp04c+ffrQuXPnv/tyoogiiiiOCA0QCPXhjjvuYMCAATz66KN1vouNjeXxxx9n+fLl3HnnnUybNu1ITvWPR6XDhcnxF8tBhaoF4LSLoa0AlhQ/I94MRq9Rpl2vwyZGt85YTTgYWgzEULAN144/cbUZV/vEAHw7bTo6g4kxY0Zz6qTTqXS46Zg/A52i8P0WD/2GWkjzZv62b90Sa1wcheVVrNq2n65N22EG1JK9VDncZDVvwu/PXs6AG99h+35favKm/aXovdf78zYXeqOZER2ScKjhyTYcqoCFOU70RjPNky04MeJxOTDZi1EAhzERj96XnmxGQUGlSjWiqt7u4wnP4WKIa44BcJUcwOUK9du4ISYds96I4nZSVbgPNbFF3e10NR1SxqzB6Lf9gjNnJe6ULlBWAi43lJdIRGhJIRj8nXUmcTSoyG+t816Trcy7YHODtYHOKH+EOF5lhH2iUfpRpPenkUjaItRo8UW42YuknoJHB3Y7JCSBrRwsvvZnmPc8BrcDd8vBONN6gCMEUWQrxDz9dhTA1fk0XJYMcAZpK8mdiAHUyhKqnO76HYe1+0RsFkbbIfSuCrDlc3xzE9YYM6M7mOiWaeX5hZXojWZOaGvEElPz2D8po+mnrmUgq3jQ8yw36R4hT0njnHc24cRIq6YxZKUnYHcrbNhTxk9r8tAbzTRLTeTOzzfgVoykp5jZeMiO3mjmnGEdcHh04AFjRQF6wGlpgjvQT62LQzf2GUw/Xg3L38XRtDeeDgEyG+qhzU3bZ6MDHM374ykv8y02beW+9oHffdvKpa/rDIDZNz76Q9tXZxDyQetbwRBOWwzD8dQY/UhviqHSWcGuHbv5c8EikpKSSElJIS0tjbTU1OqMhYLiUtwuF5VONzabDWt8IpNOP5OsFpnYKiuZt3AxtopSKh0yruqMZiqdbmqHKeuNZpplZpHj1ZQ9nFfASRNP4cmJzbi9B5T//jSuNicTE+ONvnSGR+Y6nG4qvX2sWUYmZmscQ7zBXw98uohN319G0/QmfPn9T7ic4pQ/fshgvnrsCmJyFwAqnuS2WNNboTeuBuDnBes461kjLRO6kF62lou6QXYi3DzQJ3fh7HQabrcONHIaE0aPA71OoWmimUpqSmNc+Yudz9Z5yJqXzxfnp9Ojufd7PUy7Mpu3l5Tx/LxiyqtUthXraOHxmWuqqwqlqpIYHUxdaUNvNDOgfTqVHgP4zS2G0lyZc2LT6845YT5Pk8eDDqhygxpq7FKkPemT22ME3IfW4+x0GlQWQYpF+oHTm9ml4A1isEJqZ1/fc7hq9r/6xudIxvK/qB/VQR07zAxutzgwVRMYTcHHCe3+7GVgiY/smThc4rhWTRJkYCuFij2Y9y9HAapan4jq0Ma54FA6TcK85HXUvYuoKjpYPykQZL7Sb5uDUfXgSe2Iw9IM3GHGNbl1zNl4mEq3jo7ZzXjqitF0ufY99EYznVo1x6MYaZNmBlS2F+tISbCyPk/l2Xll6I1mWqRacdZ7ErioTywuxYARN250OP37iykZQ7txGLb/grrkTarGPOWThdKFsOX84e1vSvEeTMunin056BY8tfuhPoxAkmYDMTXrhe7QalzzXsJ1/B3SphRTgHbk194aaw2itTFbec1sWC+OrB+F6BOqyXef4HtfXurNJLb4xpLifPBUgTMfYpNrzum2cpF409p+hZeUjU2FBGnfuv1rMQGexJbeywj9uxg2z8AAuFsMwKmPDz6+urzjUFUFMaobFYUqS3Pf5xpq9Q+LxYopxoLb7WHCkG4M6t6WSrcCfvNCrh3vWiYWd8FOesXlAQrz9+vp1yqRYlcVeqMZk1nH91tU1ufr2FOm8Mj4BFKVUv7P8yLX6x6nQgmcAfLQV6sprlRpnZFGt9bNqPTo+WTBLtyKkdYZcaSnJlLpqXkfpsMbxM5K64on5Lon+HfGZe+iR8WdPQxnfOvQNjRA1jCxjQ+upcpe6SWtTVBRLrJI/uOoElF8Zf1oCFEYBJH2oyiiiCKKKKL4O6GotUWTI0BSUhI33HBDQGJBw//93//x6quvUlxc3NDT/KNRWlpKYmIiI+/5EkPMvyfaYZC6nAc9z7GPZlyufy6sfTLUg7zjuR09KtfrHmO7EqLIrKryiecG0ihiiu5e1ir1SAc1FlSVxzxP0Y+1LKMn9+ruqGEofuG+miTKuEr3NHuUAM7+EBjjmcNt6luspBt36++pd/u33FPIZj936+5ipVJ/0cSJnl+5UZ3KGrpwh/7eiK7t74Kr0sbsx8+ipKSEhITgkUj/1H7URC3gPc8tmHCF1Y7v8bzEcHUxe8jket1jOJXQTqQP3TfRlHzu1N3DaqVbxNenqB7OUX/gIvVrdKhsoQ2P6m4mV2lS774W1c5zngdpQw47acmtugewKw3QcK+FV91304493K+7nSVK8JT3yzyfcbY6DRsx3Kh7lH1K4Mj4QEhVC/nUcwMeFM7WvU6p8vdqhx8p/k39yKra+MhzE3HYeEh3CwuVMOtfBIGievjKcxXx2Lhe9yjbleDSjFrbe1m5lJ90waWDdKqHPqxjnOcPBrGCQpK4WvcUNqXmM9X65826h9istD+i+9AwQF3FrZ43SaaUIhK4SPcSjiDjxF2eVzhBXcjbyrl8rTu5Qef7wH0TzcjnJt3DbFHa1bu9ZhtsoxU36B9v0Dn/Lvyb+lEgDPcs4h71ZfaQyVX6Z8Le70n3Y/RmA58op/Gh7swGnfsl9710ZCevKhfzo25sg44RDC+676MTO3hAdxuLlb4NPk5XdTPPeR6mkETO1b1Ww/Yzqg5e8dxLK/axkL48pLs1cidiPfZlJBigruIRzzNUYuYi3QuUKIn17/QX4d/Qj07yzOY/6jsspSf36e8Mua2ienjf8x+akc/jyo3M1dUva9RB3cHLnvvIJ5nz9a821mWTqR7kXs8LtCEHNwrvK2fzlTIRNYQDPVat4DXPPTQjj/n05xHdfxrFQR6vlvOl52p0qJyre5VCJUIJIqCbuolnPY/gRseVumfYrzSvfyfgFfc9tGc3TyvX8rtuaMTnPRYQaT+qb7sojhwrV66kb9++rFixgj59gq9PKh0uTn1K6qf8cOdYYkz1x/GGe+woIkO0f0QRxV+HI6LqPR4P27eHLi67bdu2OgUfo/jnYw3iIG3BIeLU8rD2OVf9AT0qS+gdmlQAUBTWKVKItru6+YiuNRIMZjn9WIsDA6/pLqpjXKvelGiFCKWQoNrx2YLwJAUOIGnOmeqhsLZf5XUsd2YrZrUy4uuLovFxtvoDJlysoUu9pMIwz2KGq4txo+Np3bX1kgoAG5UOAHRVt9azZWCoio7PdKdxr+4OSokTx4/n/+ijrq13X7ti4X7dFApJpA17udvzCrpIJcICoBl5ABwiPeR27ytnsYYuWKnkPs8LEbX5/qroZW+m7T+eVPi3waZYmaaIU/9sz4+hM+/CQAsOEo+NSkzsIrhubbaaQzv24ETPXOW4kMf0KDqWKz15VP8fztK9wVW6p+uQCgD5iLxPEwqO6B4AzGoVN3je4xHPMyRTyi6yuEt3T1BSAaCpmgvAISV0XwoFnTfTxBOmubiHTABasr9RxoMoGg8DWQkQkrANhOnKKADGqX+gVyOPos1QD9KRnbjR1du3GoIUigEo4sic61tpgwMDKZTQnNwa3zkVE0/prseBgcGsYKw6J+Lj12dfRoKl9GILbYihijPUnxt8nCgCozlSS+CgUr9sVze20Ix8KrCwKExiKw3JvM4jgiLC9cCoOnjG8whtyKGQRO7S/R9f6k4JSSoAVCixPKa7CSd6jmcZp6izGuV6urMJHSp7yGwQqYCqcrnnMwB+UU4Im1QAWKb0AmAAqyI/bxRRRBFFFFFEETGOKGNh4sSJzJw5k48++ohzzjmnzvefffYZF154ISeddNL/rBSSxpQeziv465lS/5/WaRfNUP+UZe0zg8VXnEpLJ3bZRbsVpPCbxy2pxPHp3noDFswvdUGpLKLq0tmoTfy1AVXmL1zIuPESIXkgZxeJ7mJMbw9BUd3kjH2P1sPPB+Db525lzCDR4Ny8+wD9zr0bk8nIod/ewLJ/PsZ1n+JObImzyxkBb7HHVW+w82ARo/q04atJOkwGhT932jj7J1N14bFQ2PDhnXx7up4emVZQPZhLdqB4nLhi0nAFUAozV+ai4KHKlIqq80rLtBtZ73kAaD2cmGlSjLRy0ntSADoQvIXJDPOewrByKq5eF+EaHiDDwZxU8/+qivmNfihl+3Gc8haetqPltw60eHXafb+1JuvjrqqT0n60UVpaStP0tLAjcg7vzyEhNTJ95iOC0y66uZr8Ue3vCnaKzIQ1DRJq6t1SuBPzu8NRPE4c536Dp+UQ+TxQzYyKPMxTT0CxF+Ea9B9cx0+BA6vrvTz9lp8wLn8dd2I2zi6TQ2+86aeQXx8stpF/YC/9M3SoKjz4p4u3V3vYeUsSO2jJrcr9AffrqG7nGc8jmHGyz9iajo9vpcrpoVuLeB46pS3D+rTHbNAz5IGZrNtTxPAuTflhygg+W7Cba99ZQresZBY/Ng66nAaOCmJ+ugaAylPe8Wnl+iN7iO99RR7mT05GqcjF1fUMXGOe9n2nCy5RZvzhKvTbZ+I87ibcA6/3/s7lYC8VSSmdXrR4LSkireDw6y+1tZ/9x03tu9rjra2gpkRSJAjD+VRaWkrTJqnh96Pc/LrbOe2o1dccg6YrnZefT35+PmlpaaQ3qZvJsmjxYiorK6mqrCK7VTapqak0SU2lRVZLSkqKueaaa3n4kYdrqFQ7nA4efPAh3n7rrTrH0xsMbFg4k7bTTkNxV/GuezJ3vDEdjyU5rDF93ScP8MN7L9C9W1c53qqpGP94CE9GXxynvRd0P8PMOzBsn4m7WS+cA64PfoL4+p0bBcWlvH3XefRKdzOpo56ZO2F4G2nLy/a7OOEDG3odrLgyltaZTbh/VhEvzpc6LP2yzLw6KQ0FGPTqftwe2H57U1paHehcojP9/nqFnr360jkzwG894Krqt+Yfr0apKqHqxMdRk2tlasTXGq+CwPzlOSjlh6g67wfUpiGyoszeorEeN+bnWqO4Kqm6erGc119Grr66I1pf85+LjqT/aDga/Sgcu64hJratQApbGyyQ1KKuzRbOPF2b1Kkqw/xaH2kP5/+ImuV18IdTM8DtwPz2UJTKIhyjHsPTaljwbR11a7QY1nyEYe0nuDP64xz1iHy4dUb95wWqKivocs1UDhdV8NmdJ3PeMz+helQ2vnEZr09fzcs/rqTyLjMGHditWZQ49SRZdHg8KuPe3ss6e9Owxo3n3A/Slj2YcKBDxdH/WjzZdSOd9VumYVz3GarejGP0k6gpwTOgaiCuGeZPJ6GUH8TV90pcx90YeLuYMMkRkxXd9lmYvjof1Wil6or5UuvHYPHWDygHY5zI+wSz/Y+SvRexXRdoPooE2hzstIuevtsp96VJI9kKfeOHwQKFO3w1TpK9QU6VRSK/ZEkGlx3jzzej3zEL58iHcfe7KuTpDb/cimHdp7g6TMA17K7Q15qzDAD9nj8xbv4ed9OeOHtdXHe7EDVNZi7bxqX//YHSikpGdU7hhxv7sK+oklveW8i0s42oOiNVTXuj6kw0vXUOtio3y65Lp3O6kT3FLrq+eBhUeHJcIjccF8cOtQW3eu7gVM8MrlM/xImeW3QPsa1Wdl/Bz48yd0p/9DoduKu49JW5fLVwJ+ce35a3r6s7JhgOLMNQuA1Xu3G4+l4W+rnEpNT5SLd3AaY/HkA1xFB12gcy9qeHp0OvHFqN+aMJqKY4qs74EtK896K6QDH4avoEI10i6SP+89ORFnb3Q6TzUTQi++gjmrHwz0O0f0QRxV+HI6qx8PTTTzNv3jzOP/98nnrqKY4//njS09PJzc1l/vz5rF27lvj4eJ566qnGut5/LGJMhrAmlrDQkIWqKR6ID/2Z0wF6FRQHWONAdfi0kCu8CxW1EnQuMBulAHBlEWb7YTD7SfUU76VzMyvxVjPFJaVMOnkCf/ynixS3bHkcWR16cfYpY/n0mx856/bn+PHTdxkzchg926jEmO6loqKcg/pMOvY6C9Z9ir50H/rc1QENwAR9JW5nFef2sJLQqiMARTl7cLvCi/Lv11xhQHolOCulYLTHCYoOg0GPwe0IsIc8e7PiBMX7O4QbKW1QZOHnqCCmZCckBYme1WonJMv3hpLdGHQBnNF1ykgo0GYYrPkMU+4q6DQanEVS7MxUSzM1xgwk+f7vLgN7CRj13nZxhHDafAu3EAXPHBH2iRgc9fejcKMAw+lHpniIDfI8XKUQnyjEW3I66Pzap9MOC5+V9pQ9BFNbPwdFUU7d6/hlitRrSOuAoedZGGwHIDaMQs8t+8Jy0JcfQh+XGFovtnXoAs8tXG66Pf4mz401c3VfEw8NN9C/uZPdzlRMTTKgJPB+W5R2/Fe5lv9TX6KFcxdDmjlYl6uy6GIjFmMOxHTgx+V7WL39EPEWI59dP4A4o8rMlbtxO6uY1CedGJ1b7rdSogSJSSImMYgDsahWBtPQ22Dm3Rg2fI0huaWvKGmLfoH3dztgrxQwNbYdhlGvgqNK+p01VggFFak/ojikDZjigSAGqf+4afJu46ySz6iSfZV4v/7QSPOAHxzGMAvrehFwPjLFo6p+bc7bjcqKC8HtpKy4kJYZfs5obx9v3TyV/bmFZHZoR3bLluTl57Np43qOG9CX6dOn8+rLL2AxG3jooQdRFIWcnBwuvPCi6qLNXTp1pHvPnmzduo01q1fjcFZR7onB3eMcDKs+4PIOxUzesp7x51we1r2VlxZz2llns3ju77RokQmHJEpb17QrMWqQQp8eN+xdAIA+JRt9cYgMoLw1Qb/afaiY579byjsz1mCrcvLMaDOgp1+7NGKyJLvotV9W43YWceGQTLr16Q7mBJ69Avp12cs1761gyc5SBr1YToLFgMvh4JVTksk2laC4VA5XqFzwrY27L5lI7+5BpPgs3vHWVQVV0mnNqdlgrjUOm8OrQ6Q5qM1GAxhDjC/V3+kgtT0cXoe5aBskZ4LtMBgM8p2pVj9y2MQxoyJOUMXbn5xFSP+xyhh8pP0njHkh4vkoHLuuIfaaUw8pGV5Hld/8E8iOA29heXtNx7HeVNMhtn+9tAdLMubs/qDzPg9rmPN910mwYiqmbT9B1wA1bTSU7ar5f1WFnb8CoM/qi77KmwkQH14E8wNvfMuB3EKaJ1toaizHVVVJZoqVVIp4d/pyEg0uDDoh7SyxcVi8v/Ob8w4zb2sR6S3DI6JMOjcxuNGSUU17/wRDgPYemwQJGSilBzDPfwrOfD8kiV2NDZ9D+UFIaIHhxHsxBLOLXGHak3qgw2ho3gvl4Gpilr0qc6FqB6MCqk5eqTUXQd35qZERcT8yG4mpbzwK1Y9MCUCCz+40JtUiMON8/cNph7gkcMVCUqZsV1EAMRZvf0sEhxHKpNC4MbE5RjVEkWpnJWz5EQBDuxMw1LseqJKXSsli08fEodc+80fzTkGPcOopnbgjz8XdL36KuTKPmH3z+HWZg/Ft5XvF4yTmoMyvvVOr+HOPmxU5Nno3j6djqo5HTrCwKdfJDQNiiNF7MHk84IEflLH0UDdxPMsC1lu4pIeB2ANiO60z9+eLPzehqnDLGYOJSQwgn7lD7tGQPRBDXD39vXYdOY8Lpn0o99PnYmKyvEWgw6lBApDdHywpKPZCYqoOgrG9BEw4K+XV5O2zSpC+G2ysDQR/+y7cth/GvBCpXRdFFFFEEUUUfyeOSAqpS5cuLFiwgKFDh7JmzRpeffVVHnjgAV599VXWrFnD0KFDWbBgAV26/EX6+FE0DI4KqMiX9zqDN6owFpKzIClLIjCSMiVaxBzrW6wmeDXNyw7UOWRm82b8/u2HJCclsm/LanSbvRkr/a8EYOrLT3Lq+DFUVVVx6vmX8+sff6IoCu1atwJgx649kNJGsiNUD1XlxQEvfXxPcXT9stZHJFgiMMbWH/KSBx6XL9LOnNCoBbhqIM7rmCsPg/hIzpbXoj3hH7+1N2po1zwxoFW3LKTqQ0WhOB3yd0hbqMgPGHkYNhw27zMNsSBrCEyNH10XEE6bPANniOs3WaWtJGfVJU9shaC1+aG3hz7X1l9g11zpe6MelIi7cJHSBvRmyTYpPxz+fgFgNOjJSEvgmp8q2WRLosoNEzsYSarMwewqC7nvn7rj+F4R3ey3TraQGa/DYpQ+pKoqj367DoDrx3QgPdGC0+Xh17USkXdSL7/aCNo9xEUg3dKsO/S6QN4vfhWK94befu8SadvWVEjv4i3GrHgLQSKfx6aCy+FzVEDwNmGyClnhsPm+M1nluJpzw2iVcTQEyXbMwGUHWwG24gLy8vKxWq2YzWaa1M5W8Pbxls3SGHTccbTMykJVVRYuXMgv077jwsnjOe+cswD473//y91338Mvv/zCcccNYunSpSQmJnLu5FOYeMopdOzQkSuvvKK6vLPBaMDW6zIhynb8TlLlPjKah1dDo3WrbPbvP8D4SWeQk7MPDq2XL5qFiLbfv0IyVgwx0qcixNqduZz/1A+0u+x1XvphObYqJ71axjOyq4z3qTFyZ/uLKvl6hbTxy4Zm8sKs3ezMFSnBcwe3ZOPTYzm1bwZOtwerzsmiKxK4tpcbBRV3fAu6vlbObzvddM8Ow1mq9SVjLJjCICmDwutpjWRObCIkCgdWCWGquqA83/e9wybOvIoCKN7vnW/Kfc4/nUGIBm0O+Sf1n8ZA7fGjPjjt3uK0dpl7KnLluftjy3R5bXuid8yLEN1OBxTYuwhK9oW/X+FOKM+V7LPMyOof/Dh/PU//KP33lcuO41CR2DKtmsTx32kbKLM7GdHBG+Gv6Kvb6LZcO3d8L/NA7xaBC9EGhdbOy4LYaIpOyGu9Sey4NZ/Vf8ySfTI3AYx9rPHasaLAsCnyfs1ncs3OShnHrKkSfR+oLQVrX+HYPZFs91fDaJW5u/Z9maw+UgEku0ojFcA35mhrG6MFSrwBIMECgDTs+F3Grth0sUXCRaU3UiOcDJXCnbDgBVj+XrVDetn6HQAMyZL1zmfrnZzcoW6/Hp4t38/Z6SM87j4hiQ/PbkJMbaJYUXhOdxWHaEJz8rjV81YNB/iQNr555L7356CqcOawzvRpHyCLr6oc7IWAAs171n+PtbHxB8ksiUmE/ldEvr9O78sm37PQ99vHpvgy9CsKG6cNH+n8dKz2pyiiiCKKKKKIAEdELAB069aNOXPmsGfPHn788Uc++ugjfvzxR/bs2cOcOXPo2rVrY1xnFOFAM060v3CNFG1RCmIc1Y5uB/ksMRMSMn3Gt+b8K6lLLAD06dmN37/9kPtHxmPUwYr8GErjpfijyWTiy6mvccpJo6msrOKU8y5nzvxFtGsjzvTtu3azZv1mFh+SJvrC98tZn1M3ZFojFmauy8XlFgeIxaTHaStFdQXKOPDB7XSwP7eInGIHVJbKh3pTYPkVDUdKOGjyE2VhOIGTvMRCyT5J7Q4HrY6X18PrxcGg6MNLdY9NAY8HYuLF2NacFA1FpI6RcPFXOZZqEyOBDH9tMQHyna1I/px2WRC5HfJ7txpS9/gaCrbDn175nv5XQVqEBV51epHKACiqx6EeBto3l0Xu/P16Duky2FnkoVksZJYsZ4z795D7fqycToVLR6skHbcOEt13t0flto9WsGxHARaTnlsmSBr7oq15lNqdpMWb6dfWz0laTSxEKHfV4yzI6C1R2nMeF+dKMOz4TV6zjxcnUWyqRLC5qyRy3Wn3Rp5ZZTzQ+kEwsszozcoxmHzf/ZMdod77tJXk43K5iI2NpUuXznVlkLx9fMbsP3n4kYf5efp0FEXh4MGD4KigMP8wg3p3oU/v3gC89NJLTJ58BkVFRaQ3SePNF5+h/4AB2CpsNElvwoknjq6ux5SUmISpSTvcHb0Fhxe+hBEnbmfoMd2oV5j66nM0a9aUdes30LdPTyjyRlCHcvposixNOkbkdHW5PTz++QJ63/Aun/6xAbdH5cTerZj1+LmsfGAwfdp6n5k3++212XtxuVWGdUjmjTk53PL5ZtreMp3Xf5NaVZkpVr67ZQhLpnRh840JDMhAxvCm3dig70aBTSU5zkxaQhAZPX/4k3RHMm9pjqV6dLproIk32jZ/mxAEigHi/CQiNJujohAMRhnHTHE+qaTYVNn+aMwh/wREOn74O0Y1P6B/QKyqwjaRZaDDmIZdU2ILaOktSrv+m/D327NQXjP6hratamH3wUIufuxTAG4e35nTB2azJ1+CHTKSrbz8yyYAbhvnLSju7bdllW5OfXMrpZVu+rWMZeGmA/WPG6qDRLRaYd6+YiuQ+SQQzPG+II5VH0HeluAHV1VY9KqMAW1HQoeTQt94pGgnWQs4bbD6E+lPIFIvRkvgtlT7M82+Kc8PLyDkaAWOhIuGOGJrrHNS6xIt/p/ZCyWDGSAxK/RxN3wrr+1GRTZGVhMLScG3sRfBsnfgj8fg0DrYswCKdqGqKgtWS+bmkJZ6cko8lDtUWiTo8NSKhB/RSvrFnJ2VQesdJioVGJE1RrB6C6rbQaalEofLwz3TDvDDoq3odAoPXzQi8LVr5GNaO59MXrgoPwwLXpT3A66OfH8N7aRWE7vmCrntbxc6K8Nrw4HaWmMTAX93f4oiiiiiiCKKRsAREwsasrKymDhxIueffz4TJ04kK0uMsV27dnHJJZc01mmiCAXNOKkoiMxIqR2tA74sBi1qvSIf8neKs1pDrNdpEiBjQUPvNAeX9ZKImSk/FzD1U9+C1GQy8dX7r3PqiYPpkuzkt7fuY3g7kUm4+a4H6T38JD5fLMfulurm+g/qSlD0zk7EoFcosTnZckDIAYNeR2VJHgc+v41Xsn7huRa+QmTPtZjFKzHP8krMs+z76HoqS/LIK7JVO36IOYrZCuBzmIYTXR6XLpFnqhtKgz/jGohtAuleMi9nkbxqEaGBoP3ORgs07ybnjE3xOdfCzVyobWgfLcfqXxXZU5sYCWX4a9/Zi72vRbDfWzCuWYhIrfLD8NPN8nyb94Y+FzbsWrWIuvoi9cNA82QhFfcWOshumsTTy838tNWJXlG5jXc5Z/1l9Nvkk7Z7TnmSV3SP8oruUV7TP8asHULundNNDx43z8wr5/mfxQH0+Dm9SU+UMeaLRbsBGNszQ/R6NZR7ZTIiJRYUHQydIjURivfCkteDb7vdS5BkDxbZCYdNnAoJmd426xe16D8uhiLLjhaRVh8a2h/qy8TRGbAmpmEwGIiNDRLx6+3jS1etpbi4hBUrlgMwYMAA2nXqxpDjh9Gj70CyW7WiVatW1bu1zMqiTZu2LF62go5tWjJy6CAmT57M8mWiPa0oSvU5XQOvk502fEdHSxELXrqaXd8+zultfUTrc+f15JWLevPKRb1578r+9OnSht+m/8jwocfTJ13a47YCN2MunsK0mX/gdteSUnDaYccf8j49/ECImct30uu6d/i/9+fi8aicPqQjK16+jF+fOI/RfVqjKIrPkep1UH69XKKgB7dL4oulvojo66auZOu+fCjZj3JgJQOs+7AaVInWzB4MiS3YVyDjcILFFJ66jjbHxB9hTRpNqz8iYkFkCSncIdJiic19xAH4+lZsijiNUttJ5pcphPMziuAwxfqCQmJTJHra6pUecdph7ZdQtFuy29qEWRMqELp7a11t/CG8YAe3C3IWy/vswRGd6tnP/6C43M7A9mk8fYHI2i3dLnW/7A4XFVUuOjRPYFAbr9NRZ8DjUbnow+1sOmQnI9FI03gjZUV56KffzvOxb7J66h3Vx39OfZhX1Pt4Rb2P97iTdK3Aur/9FyxrASCtg7Rb1QNznwweiLFnAexfLiT12Mcb376snbVQcrCmJFY484NmwyiEN4/9XfOdhvrsMS0bqqLAj+wPsM4JhkIvGR2f4a03FAQV+bDrT3mvSTCGC622SaCMBbcTNk2DmfdIhpD/dvtXUFxWweECISaaxemYsd3FKR2FUNLVal+DWugx6WF/qZtVBwITbOlKEe8ZH2bctgdY+vbtbFXa8rYidfCucn9A5VfXceLWB0jXlTB1SSFP/Cpzy42n9qdTyyA1BbSMj+a9w3gYfrAXwTeXS9ZVUjb0Oj+y/f3RdqTMWwXbJRvbZfdrDzHhteFAba2xiYC/uz9FEUUUUUQRRSOg0YiF2ti7dy9XXnklnTp14qOPPjpap4nCH5pxEpsavpHitPkVVfRzIPmn1oM3ir0KbMW+bRK9Ds3S/UGObYfpt6NT3SwpTuOP3W6JHMvbCuu+hpn3YPr0NL4fsp4VV8XxaJ9DXJ/4G03jhYhQVRVTaykweHxLA8M61NXo/GbZAVxulSbxZto0lTTdrQeFYMiyVNI+poi2Zp8kQFtzEe11+/Hk72LL7oPoFOiRqhnbimQuVJZIGq+rSgx8j1uizrS/I4HFWzAsHGJB0YmzEyJzGrf26vnvWy51E9yV8vsFgv/vrDkntD+o2QY0EiLQIvWvirhx2P+a89R2aoXjVDaYvenfxXBAdN1pGsJROev/xJGe3ArGP9MweQrwZbYU7Q5cGDpMqKrK3A3Sl3tmWnG5VX7dWs4pn9lZeDgGVYVLO1dyY8ZqrvO8T091A23Job2yl/bKXnS2PK74oYxvNznRKQr2ykoe+F2i/l6/YiD/8WYrHCq28+4fEqF96Yi2NS9Cc+RESiwAWJJg+J3Sb7b/Ctt+rbtNcQ7kb5Uo8DYjQGf0ZSho0m8mv9/cP4rxWCTLqvtdhNlFobY3WNidV86KdZuoqKjAag3tiOnbtx9JSYn07SvOvz7dOnHWaSfTo+8gBg0byZVXXsm4ceM4e/IkHrn3Drp2bItOpyPvYA5Wi5l2rVqg1+l58MEHAZgwfjxmszjk1abdcHecAKqH2zsfJCPZwrbVC7n+knOqz982PY72zeJp3yyedG8kf9cunZnz63Te+r9LAVi0z8OvcxdyykXX027gOF586yM8Hq/DfM8CcTjozWE54TfuyWP8fV8w7t7P2bAnn+S4GN69ZQLf3DeZPu1rFUTWS+YObgeoKhcMEjmnZ2bsZnhH33z2wbkZtLcth8ProEKcp6S0hayB1fPygA5NibcY2ZNXxtcLt9d7nQ3O/vGHqvqifCMhFtL8iIUqbyS4FkENvr6l/UUdKsER6figzePas3bYxPENcNx1EjzRULQeKsEL9iLYOaf+7fcv90V+h5oLA8DhkrlsfO8WmAx65m06zNeL96AosDNXjnnpCe2qSbsDxXYmvbGJ79cUYTIoPDs5m+kbiwF4dlwcjrzdFOzbUX38tuylPXtozx4fqQA1bbxQkpWKAm2GgzVNorPXfF53G1WFpW/J+0E3SJ8+Gmg3Wn4Xpw3WfFKTWAjHXqpeO6SFN8f93aRfKHtMu+eS/WJf2Qp8+wQbazQywuGVPtTk81Jahb6OrTMk8CexZf2ZDf5QPb5+UVumLncjzH8ONn4v80ZqOzjhXuh5rny/fwVJcVYG9RS5uRt/saMoKuPaBZaAtRgVTu8stuV9s4pxugOvYdKVIn5bupGyQ0KqTFdGUuTQY9TB60MLucJbRq9Tui/r6JGLRwS+P1elT8I1o1fQxxAQv9wpREBsOkx+N6IspzqwpkCmt9ZW7nqvvefNWjBZhYStrw1HIiXWUPzd/SmKKKKIIoooGgENIhbmz5/PCSecQEJCAikpKZx66qls2SKpwDabjVtvvZUOHTrw7rvv0qRJE1566aVGvegogkAzTsJdHIDPMRUo2spe6nsfmwI6M1iTAHFCqtUGexD9ZkWpdkqobge/XWjlRvU9+PwckSvZ9CMUi/GZVyFOHp3HyauPTOG8Myax9Pdp9BpxGntLPCTGKNwzvOb97Cu08cgPkg5805i2WLxFs9buLQagR8ukoLf9wjwhGyZ0isWg1wxyVQx5p92r91wBjjKpPVBZ5I0wUgElMieLBqfdp7sfjrMnd5Noq0LDo04tiaCPkd8vEEJFcdX+LtQi9a+KuDFZ/p7InlCGf/V3FjB7+4KWzbNlui/itza059hhfHg6u8GQ5K3xUFXqi25rAJZuO8yOQ6XEGBXGdUniy5UF7MyvIjXOQM8O2TjiWpBvU2kWp3CqOounPY9hchVzoLCC5+cV0/mFXC7oYazW+d2U58LhlhoKV5/ok3h67qeNVDk9HNc+jZHd/ByxbgfkeQsz17egDwZLsq8YnxqAZMn3SlaktpM+paslFebvYIgEgYi1cB2CR0LKVfe7CGuP1LN9Tk4OdrudnByJOlRVUIP8Gz/+JO6/734mjB/vnRfyUctzUW35eDwqo0aN4oEHHuA/11/FiOMHMbBvL1JSUujSoz8tWrYGo4V333uPrdu2odPpePSRhykrL6O8vILy8gpKBtwOpljiijaw6fWLuGPK7egNPhLu7anvB5V4aJklxOxpE0Yz5frLSE5KYHfOfv5z3xM8/+YHslFMIlJjowrWfuFzhNdCXnEF1784jR7XvsMvy3Zg0Ou45bQBbJ96LZeNDZKZpDf55gqnnXtPbsuFgzJwe1SW7izhvUu7Uf7aKC7qUCECLOZ4cT5mDxFZNL95Ji3Bwu2TJPrz3k+X4HTVQyJqcntxzUJvFwr528QBpjdFVvckuZU4uF1VIhPTUFm9/xXt6VD3eaSkvdESfA6KFDqDLzL7YPAi5tXQiDUAW5CsySBITRBCraCsCqfLw3XvyNx2ar8sNu4rwWzUccXIDszeXoFHVcmI9fD2OA+TOxt445xWzNhQjKrCpJ7JdMuwsjInjMxLVa05b5jrmZedlb5iywEzEVQfUag5ho8GKvJ8NTW0Gk1Ou1yf21G/vfRPc2yGtMc0u9WbaVB7agg0xzvtMs4V7/cWkg8zQMPqtatL9sLcp3yZz/VCEVsFhNgGCUhZ9ZH8VRbLsQdcDcPvgpTWIuWnM0BFHkr5Yd57+DrMJiMztrtZc8hDgS140NNdQ8zodTB9i52J7x+mtLLueLC/xMWMrXbMejjZM4upnltJNslzSLIaSLGKXTWsXRwdvOTCF3M31j2Z2wkbf5T1kykusroTqurLcDrl5boFnRuCWK/UpqLzy8QujCCjPwwpsSiiiCKKKKKIInJiYcWKFZx44onMnTuX8vJyiouLmTZtGieccALbt29nwIABvPDCC6SlpfHiiy+yY8cOrr/++qNx7VE0BjTHVCDHssUvsi02DdLa+AxpEN1KgFZDAx/bECPyJMBxqaWMamPAiAOMVnJj2vLIn1WM/8RG6tNlnP+tVlQtgckXXsUnb79Ev949ePKlN7nvD4lIs1TkgNvB4ZJKXpy5nW53/87mA2WkxJm47kQpuLk7r5z5W2QhF4xY+HVrBW8skjTi/wxLlnuKbSLZBDGJPskGnaEWgeA13E1xkRMLqgor35coUkuyaMLXt/3cp+ScHU6StPtwsWuevDbrKXII6R3kfmrLGjkqAmeraPCXVwDfMwm0SP2rDO1j2aA3xohT0umA7mdKO8nb7EuVr42e3qjr9V+FX0MjEPQmn2729tmyQI0Qqqoy5X3Rwj6zdypWk44nZkn2wn9OaE6sWY85NoGPdyQx6UsnM5QRlBCHDpWMOJVbBhrYd0scL46LwahX2FEEw6ZWkGBWeOuq40QWBsgvreS1WVsBuPf07tWfA+KoclVK0cmGRHaqHlj4MnicoufdfmzdbTTd34QMaUPaeFacA0U54lSIpL6I5hCEun0jXIfgkZByDe0PQbZXFAVFUcjKysJisZCVlYWCElK9Q76XDJX8ggLsNhsKKiuWL+P555/ntddeY9WqVeQczOXQ4VxSmzYnMzMTQ4yFnQcK2bJjD++++y4Al156KV27dsVkNGI0yZ8+tRW2kY8DYFr8Mk9dOYYF8+ZWn/+W2+9i4mlnsnt3gCL3zXsBEF+xm6fvv519q/7ggdtFXum+p17mz0XLUTP7wYRnJTKy9ACs+rBGcdricjuvfL+YDhe/wGs/LsXtUZk0uAMb37qK564+kZT4ECSNovjGT0c5iqLwzqXdGNU5lfIqN0/9vA1z3jpAFbK55WAhFIJoSt9ySk/iLUa2HSjhwc+XBT8vQJkUR6+u69MQaAV/242OTOdap4eMPvL+0Nrw5Ec0OGze/rg3fM33fzpCjRV1ZPn8ZCprS1ZWH69CZCu1bKzBN8nnC1+SgI4jQap3bC7aXf+2mX18jsUtMyI7TaLcb0FZFS9O38j6nGLS4s2YDOLgPHtQa9ISYnhhbgFD3rOxuwSaxun4+iwro5uX8fsmCXy5e4yQi6v2hSAWVNWbmernUO40EdI7Bd/H7YAtv0gQSnoX6HVe3W0UnUReQ/hylpFC9cCP10v7SWkDfS+Tz512qbeg1QD6X4GWmZDYwivtWavQfe1sbJDxyeWU56UixChA4e7Q5+o4HsY9Kf1zx+8iXaRlIoSCokDnU+T93kUie7TgOclWUHTQejiMfhSyBvgIK0OMbx1waC2dWmfy0HWyjvhknZP/m12F2xOYXOjZTM/3F6ZjNSrM2lbJ8W8cZGlOFR6/7acuK+WafkZ23WjmBvV9mlDIvlKVO393kNqsZfV1KIrC5cfJM/2yNrHgccPmn2Xu0Zuhy6ly3eHCUe4jZyJZ84TCYe81JnhJitKDItNkKwq+TxRRRBFFFFFEETEiJhaefvppHA4HTzzxBLm5ueTm5vLwww9z6NAhhg4dyubNm7n33nvZvn07N954Y7WkQRTHKDTHVG3Hcu1odf+FKoC9GGWfaGoHJRYAOk2A467jy61Grv7JzgfKWXiu+IPWj2zm/j+q+GW7i0K7yhV9JLKtsOmQ6qiSAwcPs27jZj5e62RDngc8Ll7+4nea3TCd/3y8lhKbkwFtkllw33BS4ky8M3s7bW+eVp2x0LtV4Cj9B2bKgvO6wYmMbG8Vg1mnl+KrWtR5TCKYE6SwWkyyRK6Z4uVViwiLBAXb4MAqiaYecLUcOxR2zIZ9y8RpPPTW8M9TkQe5G+R9ywG+z/0jsjQnRKAFVjD4kxD/S4vUSFAt8xEj2T3awnH1J4G37zBOCK2KPF/x2IYio7dIInmcPmdgBCgqr2LeRnFE3jM2k5wiB+sPSLvon+0bG64Y0oyFB3U8r1zJObrXGfWxg7dXOCi0q5gNCirgwMgJH1RQ4YQHR8XTIlX2L7E5OOWZOVRUuejRMpnxvTNrXsTeJfKaNbBhOtTbZomUjMEsshOBjqE5d2LTveOZzS9DqVycCuHqMIPPIQh1HfzhEgaNQZY1ckR361atGDZ0KK39aiPUxu7de5g3bz579opMW25uLocPHyKvQpzkq7ftZ9q0abzyyiu8+eabNMnIJqFpNhu27GT//v3s2bMHk9lMXn4eO3aIRMn+/fvZv7+utJ6z0ylUdT8PUOG7a+jW0ucsMpnNTJ8xi449+vKf2+8kL88vMlqTYSjaA1XlWK0WHrj9ekYMHoDdXsnwSRfRffipvDBjO4XtzhBZE0cF6tovWL9gBmc/8jnNzniaG1/+meLySnq2bcbsp87ju/vPoH1mkCyw2tAy+hwVeDwqs9bnU+kUB+Zjw3UYPJXS3pp2C9nuHU43n8zdSpldSMj3ft8U/Jyq6pNxiW8e3nXWhtspfQqEKI0ULfrLa96mWprv9WQEVRQIsVORF77meyho5F84tYL+LoQaK+oU2vWbt4PN4U67OMm1z7tMgo4T5LNvrzoyckFzuoZDLIDYgAC7/xSpyTBRVCbXXul088Fckf66ZUJX/twkmTjZTeJ4f842fl61j8X73JRbs3HFpOL2QAurg3XXxvHLxckMaGKnOP8Q6foSbhuo53TPz5zqmYFedYpTXvV4CQXNyaqInZacHfziVNVL4hdKQMrI+2pmZ/hDI/YaoQZSQCx6VWSpDBYYdofIumm10LTsvf+FrJ/aCCZ9FChL12SFpEwhT2NTffNG2YGaBX8DoftZcPq70j8ProGf/hO6NoeGtA4SAIEKexfKeJvcSkjADuMCSwA18+oRHRapplsvnEhyDBRVwprDHl5fHjxjYmInC39e3Yxm8XrWHXIy8NWDNHssh4u/yOODJflc0t3Dq+MtpFshjxReVi6lw+sOXMZ4zCZpR/uKHDwx6xCPzJT7O1DgR6KoHpkvivfI8+1yqk9SNVxoWfBGa2RkdDBUlVZnxZOQIaSCvUCy8Y9iKb0ooogiiiii+F9ExMTCggULGDlyJHfeeSdpaWmkpaVx7733Mnz4cHJzc3n66ad5+OGHiYmJIEohimMPWrQ6yKKkolCiUbSF6oLnZQHTpLOvUGMgKAr0v4IVaZN5a4WTaSty0BmMfPjqM2RlZtC/T08uOHkEkzqJs378Iz/x7bRfAMjMaMZd/7kOjwq3zZTzXtXHQHaiQv82ybx8UU8W3D+cThnxzFp3mGveXYZHVRncIY1XL+1HlxZ109jzyqpYvFcWCnePDNMxpBEPeqO8Rory3OqFAD3PkaiyUHA7YN5/5X2fi8UgDhe758trk85SeM5WJA4V/4gs7TeMpJhdJCTE/xI0p1VFvjcCSgVHpWT79Dhbttk83bdg8ofeBD288gjL3xXZkYZC0UHnk+U1dxPkbo5o96RYM+0zpL/M31FGZpKJId6imJPf3srsLZLhExej5+sr2oOi4EHHa6elcsVxyaQkxIHBgmK08sjcKnJK3GQn6bm6vy/qdNQjv7Joax7JsSbeu3ZQzWwFVRUiDYRYiBS2Qlj+nrzvfVFw6TAtEt2SJL+Jvdjb/g3gcoXWYQ6ESByCRxOR1lhoBGhySbWJANVoAWsqnXv0paxUHIkHDhxg6PHHM2rUKNLS0khKSiIlJYU9e/aQ3iSdU8ePwWAwMGPGDHr06s1770311UDwonL4fbjTOoMtH9OP16LzRhfPnz2LE0eegMPh4MVXXqdtl5488vhTlJeXS/ZLQgagSjQoEnH5+Zv/5eKzJ2GxxLBhy3Zuuf9Jml/0FhfPjGFVSQKK6qGbex0nJe7AgIuurdJ56YYJLH/9Gk7o2SqyB+Ul7Q/mFtD5/+Zx8ksrWbC9mJsGmpjcxYiKIpkVQQhrt8fDx/P30PmGT7n2DcnUaJEax+tXDw9+TnuRyBApusgkjPyxZ4HUG4prCq2Oj3z/Fl5t631+mRXhzCMKEuWq6L1BD1YhIhrqGP2r6v8cCSIZK/znbaNFHJIOe13ipNLP6RebKnrl3c86cnIhubW8lh7wyQCFQpPOso/bCdt/C+sUJeV2XvtOJGLOOC6bcb2EhP5t3QHuOKUbAI9+u4bLXl+AqsLN4zuTaDUw6PVcer1ZztL9bpJiFMa1ckNFHkmeQu4bauKpUQauVj/hOvVDjP7EggZFJzZefcT2gZVSP0TRwaj7QztQq4mFABlVR4p9y0VWFGDIf7yOZ0WI8uq5jcZp/8eKLFlFPuRu8WUK1ofa0kfB5nj/zy0pvgwtrQhxKLQeChOfl0CR4r3w440iAVcfOo6XOcoYC93OgP5XhZZJ1bJ/8raAqxKj0cAJrSUQq0Oqjv+bXcWuoiCyZ5XF9G1SxZabknl5Qhz9MvSc2UXPYyMULu6ho0WCjqJKlSpLOpfqnucn3WjiYwxMGdWUPYUOLvhwN9kPbuCenw5SXuXBajYy5cxBcmxVFXIrf6v0iU4TIaEBhLZmJ1tTQ28XLjQ7OC5d6tUZYiRoLDZZMscd9bTpY6XNRxFFFFFEEcU/ABETC7m5ufTt27fO5/37S3TaxRdffORXFcWxA80RoOBzqh/eiKI58UbeG1Z08ekTRarl9z8X4XK5mHzyOPauX8zS36fx0c0jMelhU4mFJbsrmHzR1dxx/2MUFBbxxAN3se7xUdxx5nGUEo/ZoLDlrrYsfegEbhjdFoNex/qcEs54aQluj8qFQ1sx/8HRXDcmcBrtb+sOoarQM8NMi6QGZB5ECqfN51hpOQhaDat/n9WfyuLEmgoDrozsfJrsTtsR8qq6xVh32qXOgjneL7I+NnC2SiBEQkL82xBsceG0iXxOVaksXD0uQJHIN0uyRL017SZZBOu+CnzsrqfJIrZ0P3x5gchfVRY37Drjm0L2YHm/+afqopbhQKdTuHqMFNecujgXg15hxvWdGNUxgfIqD+Nf28xHS/Ior3QzoJV3wa0oZCcahCDwkm87Cj08PU806p8fn4DFqPD7uoMMf3AmK3YW0iTBzB/3j6Zvm1oLR1uBPEdTHDTtEvm9L3lDnCmp7X2ZIoHgn7EAEqSqFfGLb4AD9liR5oq0xkIjQJNLyswUp196ejpNmzYjPV2e45DBgzn5lFNo2bIlQ4cOpaCgAJvdxsSJExk9ZjS9evUiISEBe6WdEf26MHlkP1IT4ygtLeX2O+7kpJMmsGHDRtwut/xhpGzsS2CKRZezkAvUbwDo0qkjv07/gVk/fU+f3r0oKyvj/ocfo3Wn7px+9vmsK/Rmw21ZVF2LoWl6Gu+/9DgH187ltafup0+PLjicbj6cvZE+L+xjyq+VuD0ql/QykX9fJuuem8y1p/SnqKyS7QcKWbntEH+s2c0Pi7by0W/rePXH5Tz/7VKe/WYJz3y1mKe+XMQTP+/gsWk7ePK3XAAS9FVsz7WRaDHw4uQsXhgnv5nSpFPAGiuqqvL98v30vHsWF76+hJ2HS2maZOHlK4ey/Y0LmHRcTYK6oLSSG9/6k8vvepqyQ94itbFNGl4UfrM386nraQ0j1LWimQXbffrvRgu4HDJ2aoVSa2cwWFMhqaVXws965MTAX1X/56+C/7xtipVn6h8woCGmlnSVTg8nvwRdTz8ycsGS7G2vqq9Aaygoii9rYfuvYc1L//38D4rKbHTObsqZg1px00ld0OsU/thwiOFdmnLN6I5SDkGFq0/swKn9suj75DqW763gYIWOfGMmuaQxZ5+et1c6eWuFg4/XuShREvhdGcIfyiDc6BHD1vun6MOTuCzaA3u9OvCth9ZflPpoZSxUlsB3V0vf6HASdDlF7iEmSbJTnRXS7xqr/R8rBF1FgdTDqQgQrBEIDQmKURRfMeZwf7eUNtK/UtqIDTf9dtizMPQ+JqsQQifcA5l9619LxTUV20V1SwAJMDxbxubmcWDUKXR+tZzj36vg7t8qWZTjwlUtd6SCx0WCwcEN/XQsuzKWV8dbaJGgo9Cusq3ESHJSCrNzTDgVWRv9d1ImL8zJpeOjG/lkeREeFYa1jeO981py6ItbuHiMt7ZQ7gY4tE7etx8bOtsnFDSyqLGIhcPe7O1mPcQuT2wuv2tKG3n2zsrQbfpYafNRRBFFFFFE8Q9AxCtOl8tFbGxdR6QNO85wAAEAAElEQVT2WWpqIxkExwA8Hg86XYPqWx9dhCsTEqSgZYANg39ltIgBrwB6gxhi856VSLWsgdDmhLr68AEibvodfyJJSUkUFxezdFMOgwcdJ6n0qlrtdO0w4Tpute/guTc/5JmX3+SFN97ljImjeeOuy+kWZ5Xtf70fc2UuS8ub0+u4ERSXVTDhjimUVbro2SKWeRsPMmXqPJ6Z3MYXEa36CISflkpK/YTje0KneqIwtaJ79UFz5taG2wVLXpNFUEIGjH+6fr1RezFMv03eD72tZk2L2lBqOXtU1UcstB4hTgd7EdiKweIS+aVQkXWh2pU5zleYuDERqeSN5sT+K8/tqJBsHUeFyA34f24wSSaI0eLLDDGY5dVph+5nSLbKqk988jz+v6k1BS76AeY8IRJG67+WqM7jbxFHQThOwSadfe+TW0s0W0UeHFgNfS/xfVe8r/aeNTBxUGduf38hq/fZ8cQ1Jy5B4ec7Mzj3tWV8t/wAF324A52yg26tmtDsUu9O3U4HvUSOq6rKLY/9hMMNo3tl0XHwMAa++BvLtkmUakZaIr+9dAOdWwXQfN/8s7y26B9cUqL6fmtlSG37FfbMl/4w8XlI7xx4P4BS7zNIzAJFI8oUX1S0yUrIHHlNEkxDsPok4aCx2jE0/BrChBLgmbRu1Yr09HQqKiqw2e3EWq3EWjW5FnEaP3r/PWC08PY773DnXXcxYMAArrn6agYMGMCevXvZsGED8fHN0dvbUlbyMx3bZFKlWNmydRtLli5lxAkjuemmm/jPzTfRtGlTSOhJ6fBHSPj1Vs5Vf2C92gkMI8FgYPS48YwaM46vvv6ae+9/kO3bt/PdD9PIPmzi+bExzPvxQy68+mN6dOnIGSeP45pLziUxKZlrL7uAay+7gJUbtvP+p19RUlrG8EkTUDtaUX+7j5jyQ7hXf8KTCz08+Hs57nCnVS90CtzQNZ44k8IHl3Ti1IGtic9bCS5VHP8dxtVoC2W2KqYt3sKL3y1h6WbJBkmKi+HOi8Zx45kjiLXUlcj4c9U2hl/7qvd/60gvWsETQwhdX8EUYjyvKPA5T3ucU3+fBOrYEdZkceAU7oTtf0D306QOjcla09GnvdccnyZrzX5Yo28GQH39SHPAa57okLcQ4Y8bzjEbw17TiBhTAL38QM/HZIUqVQhHf1JI5x0jVQ9s/F7IhdPfCk3GVu/rF4iR3BoOrhana9NuNbdLC5DBmtIONnwv42/uJsnmC1J7KLfYxvNfzAHg0fMHoE/KJCsJzhrWmc/mbOS5mbt4//ZJJCX/SbzVRPOUWEY/+gtuj0qvNul8d+8kflq6g0mP/YHTJdHbvdum8/p1ozG3b8HTq0W6c9C4QvShptf4AJmiZQdhxfvyvt1oOO7a+knlJK+TtWinkN/1IZzgDVWF6TdK0eCkljD2cW9WralWAWEleI2FUG0qEOrrh0eCcPqRdr3a86ldO6Eatfpb0Lm9nvOltJYst7IDvkLQodCko/xd/BP8cJ1E8M9+BM7/SkgDDeEGjniCFJDO6A3bZkr9rlbHM+KU82DGVJYf1rPouQt4ZdpKvlmwlQUL7Dy5wMH4/m346YHTUPbMr36GjsoK9C47OoMRJb4pKZlppCg65m4p4vIvdtPdW5LlsdlFbNkr66ATujXjmQv707etdw3R61R5LdwJi1+X930u9tUO0xAoWzcQUttJcAlIhkEgKSgIv2aDqvqIhfQu8n8ty0v73hgjRGewNn0023w480Jj2ohRRBFFFFFEcZTRwFC2fx927drF77//Tnl5OV26dGHMmDHodDpUVa0p2VEPqqqqqKryRWSVloavKfu3w2HzK+brt8i3FciCyOMCWz5snibfnRBetgKAXq9n3OgT+fyrr7nu5luZP3sWcSBRLsV7wBCDvsNYnn0wlsH9evH4S2+zct0mPvvuF84Y3oPTRw+izNSUOTsNnNzGRdnCqTS5/T2ap6ew92AeHVpl0Ca5iu9WFfDsr/u4f0I2CZa6zfuH1WLknjqwAcVhI8WmH2ThbbSIwR2OQbz0TYk2S+8M3SZHdr7CnRL5rjdBy+N8zm1LvDi/4/85EZvHVD8yWmVBVnvxrf3fDFQUSdSolk4P8uxbDZPt8rfAvqWBZX4SMuCUl2Hv+fD7w7Ltbw/A6o9h+N2Q1T/8azXEwICr4I/HhKjIHiLFYMNA2+ZJWM1GbFVOZq0/zLgezTAb9Xx5wwDu+XIDny3ex75COxv2laK5K29990/OHpzNut0F/LBkJ7+tycGg1/HSVcN59vtVLNt2GIvZyBUnD+auC0eT0aRuZDaqWh19R8vjwr9XkAyEX++X9wOuDE0qqB7R2AVIyqpJlIVb4FKLfrSXyu9dUSiRcEfZsd9QHO1+VFFRgdvloqI4j1g11uek0iL9vFk+O9YtxVFRwurVq7HZ7VRU+GRb9HodPYeMZH9JJfPmzWPE8GEkJiby7HPPsXHjJp599lnefvttHnzgAa655mqq2p9Mxf6lxG78nDs9r0HZOZAqhRl1Oh1nn3UWp592GvPnL2D1mjVUrJ8OrGBgCwNl5WUsWLqSBUtXcs/jzzF0YD9OHD6YE4cNplfv3rzUS6Qmdu3ey73vf8I3X+Xz8EAn53Y3cu8QHSNbWrlmhodCl5WEOAsJsRYS46wkxFowmwzodDp03gLYuuLd6HQKBp1Cpc5GHHYu6JcMpVtFRtBgEYeHolBur+KnxVv5cu4Gpi/dRpW3BoM1xsgtpx/H7WcOJql5q6C/w9s/zK/xf6u7GDA3vL7Clp8kKrZZ97DHj4Bo0U/mpZxFQiyAb17yr7sAQmb42x4atL6pZY6F6whtRITdjyJ11oYL/8jZQPNQoM+C2RsuB4x5TN5XkwuERy5oSGkjxELhrvC21+mh1/nw51OSkdk1uG2zYON+KiqdtM9I4rRB7ao/v23yAD6bs5HP5m7kqvG9eOKyEazblUu/m97H7VE5b0Rn3r5xLEu2HOTGN34HYEyfVtx95kCGd89CURQqg/hqw4LTDnOeFHI5rSP0vyK8/bQ+WA+xHxFWfyK/nc4AZ0wVJ7hGerscklHicootEqxNhmpTgRBJEegg0jFHNB9p12uyQnLL0Of2v9+GFq9O9ba9cGuJaDDHiezYDzfA1l/g++vg0l9CBwlFgoxeQiwcWAWqSrfsJqQlWMgvtbO/oJw3bxrHK9eN5pv5W7jgmZ+YvmwnT369jLuHCGG74GAxJ7+8hyKbi3bpFs4bUEHPLIUXf8/hz60l6I0+h/72g6Wkxpt5//qhTOjbou5a2FEBfzwuZFZmP+hx1pHdW3XGQoS1GYLBK39IajuRy9SIBa0+jdESOtjqf63weRRRRBFFFFEcARpELHz88ccsXry4xmfbt0sk+Pjx4+tsrygKP//8c0NO9Zdg/fr1DB8+nN69e7Np0yaSkpJo2rQp06ZNIzY2NiJy4YknnuChhx46yld8lOCfMuy/uPfLpOXP/4qDrsNJPg3lMPHkow8xe+5c1qxdx7kXXcr3bz2JXiMp2o6qds5NnjiayRNHM+Ckc1m2ej0GvR5VVbn8vpdZuriIMTfGM6qNgSkDqrjvD4nofPfhG3j4iWeqzxUf44vS83hqRiY9ftHxDOjYQIdLuNi3DPYuAhToeV54qb2FO2H9d/L+hHsil5/YJfrbZPSmOjLL6I1YjA9hIPsvxI4RB+kx1Y/8FxdOm49k0P5s+eJkdjvrRhsmZEhf2fANrPwodP2AlsfBxT/Cms9h/vNSc+Gby6D9GMleCbfWRkZvKai+ex4seR1OejqszAeDXsfVJ/Xg+e9X8OC3mxjbvSmKomDQ63j63O48fW53DhTZ+W51PpqAxlsz1/P6TytqHOeBcwbQqUUKa3bJIvH9ey/grFF9gp+47KBk1uhN3rYbJmyF8NXFUJErEkiDbgy9fXmeyFIpOmjSQTTIPc7ApFEwaIvS2BQhFTQJkmOk39TGUetH3jEj1qhQgQHFXkp+fjkWazyxTVr6Iv28DuGsFs3ZtWsnLXv2paKiApfLxf79+7FaLRw8eAij0cRFF13Ebbfeytw/57Jp4yYmnXoqo0eP5uuvv2b//gPcetttTH3/fR577DH6Dr6HQxsX0ZY9eKZdCxd9V6ONG41GTjhhBCecMALyxsJbw2kW52bTzKl8MnMZ737yNQcP5zJrznxmzRGnfGpKMqOGD6GsvIIZv82plk2asrgZ5k4DODVmCYOz7Ky91iwR1y0HhybW/eXP8raIVnfhDnBVoqKwVW3FqqWH+Gbdl/y8dCv2Klf15u0zUzhnRDeuP3UATZPrzxR7+obT8XhUPp0lsnsT+7UCDjaMWFBV2Ojt4V1OjXx/f7ToD2u/lACC2kEL1Y6dVJ+UXG3bwx+ROkIbEWH3o6N1jY0VOeu0CamlAie/KFHBa7+InFxI8dZZKAqTWADoNB6WvSVFxXf8HnSzpskyllY53TXs7r7tm3PWsM58+ecmxt/3Jd/cezp3vvcHDqebkwe24+PbJ1Bqc3Dx8yLhddno7rxz89iIAoOComgPrPxA+nBMEgybErQmSh1o83b5YYmODhaJHS7yNsOse+X9oBt90fDV0lhegiHOm01XvBeqysXhneTnkD+a0dhBav4c0XwU7vWG2we1QCoN/mOTwybZZNAwCSu9ESb8V4JECnfCtJvgzA8aJilXG+ld5fg2KXKv0ymcM7wzr0xbyXuz1jKuXxuMBj3njOhCUXkV1706i3ve/5MOMV3RKQrnvbORSqdk8mzPtfPwT7urD20yKFwyqgO7/U53z+k9mNgvq+51qCosfEmCmWKbSJ8IR0osFGyNKIXkn7FgSpD6WtZUSMz0rnW9NQO1WhoaIiGHjxaRHEUUUUQRRRT/QDSIWNi+fXs1kVAbM2bMqPNZoxj2Rwk2m41rrrmGs88+m9dee43i4mIWL17MlClTGDhwIL///jtNmzYNWxbp7rvv5tZbb63+f2lpKVlZAYyyYxG1Iwk1xKaC0wo5S2DbLDEeR9wd8eGzs1vy49dfMGLMeH6aPoNLr6vi3d5rMQKVrcdQO76uyiEp3ZYYEy99/BNfzVyIwaAnJ2Mi7Q7/xL3DzHTv3Z/8pJ4c37cL5VW+cDStzbk9Kld/shXGyudvnN+BK84YEPG1R4SS/bBe9L9pPzp0FLU/5r8gUaLtTpR6DJFCkxbI7CeR2QnN5fe0F8vnwQxf/4XYMeIgPWb7kdPmXZD4LVr9X/0XvVp/6nOhEAsbvoNxj4c+vs4AvS8QTeo/nxHn5LZZsHMu9L9cpI3CkUroe4lPqmLjD2Fnv9x5xkDemL6aJTuKmLH2MCf1rCmlkpFs4fLhrfnRK609tk82yzbn0Kt1GsO7tWBkjxYM6tQct9vDhr2SHdSzXWbok2pRZZl9wne6OMrhm8tl0R7fHM54r365glJvkeG4piJTpiDyHoEcEP4LRvBJMGjOGwg+Xh5DOGr9yDtmxBoNWBPTyDtow1NVToVTxaqqEo1vsAgpD2RktmTsxHRi4lOIjY2loqKCzMxMbDYbbrcHp9NBTk4OaWmp5ObsJsWqw9iiOa07dOaqK6/kjTfe5JNPP2XdunWccsopnHXO+cR2volXPP+HNWcRzH0GTggyJxlihHjK20ynBDuP3P0fHr7rZjZt3cGvcxbw258LmbNgKQWFRXz53U/Vu40+YSjXXX4RE8ediMFgkOyYGXfD/uWw8kPcB9byW1VPPvltNb8sWM2E43sz9eFratg7e/NtPPnTFlqZS7mjP9XFbm/6xcYrS5fUuMy2GcmcPbwbZw3vSo82TSOym5qnJfLJw5fx7M2TcSa0IWvVU1BAaCmkYMjdJHUR9CboMDby/f2h1VnI3QiV5TWzFPyJhHD60tF0hNaDsPvR0brGxoqcddjld3U55G/UQyJvuemHyMiFFG99j8Kd4Z/bECPFo5e+Cas+gtbDAhJzrZsmALCvoByny43Rb5Uy9dYJFJdXMmvlLsbd+wUAqQkW3rr5JDbuLeCaV2eRk1dGm2aJvHj1yCNbe7idYvNuneGbo3QGGH5HZNHnMUkyHrrs4txMPYJsWadN6ipoUqTHXVt3G/85CmoGBvmjsdpUIOdqkJo/YfejQMcM93rD7YP+2YeWhJqkptMOCZIFF3HGggZzPEx6Az48RWzzhS+JxOWRwmAScuHgaslasCRy+dgevDJtJd8v2kZ+iY20RLmPayf2ZlNOAS//uILz39mEw+1BVeGUnmm8c3FHft1YxCdLDrF2XwWn92nClDFZpLXuyqnemscmo56LR7QLfB2bf5b7UvSyHoxJOPJ702STQmURhIuSHLETdQYh1eylvmziUPNNJOTw30h2RxFFFFFEEcWxhoiJhV27IohQ+gegqqqK0tJSRo8eDUBSUhJjx46lbdu2nHPOOYwbN45Vq1aFLYtkNpsxm48wIumvQm3N0toRhRo0zeNFr8j/u53RYHmEgQP68+G7b3HW+Reh2zMfY28LG/Pc9Dj+Cp6452amXHdp9bY2uzhhXv1sOtPmLAfg2SmX0m74RFing00/cmriBhh2MgAVDh+x8M3KPEZ2TOKGz7fzxaoSRnn9IxccV7f+Q6OiZB+smCrGZpNOQhKEg51zJcNBZ4ARd0V+3pylsGuOvM/o662H4SUV3JUSERrMWP8bHTbBcEz2I6dNotTdlTWzB7QFb+2hQes3rYeJznLxHtGZ7jSx/nNZkqUwevczRX5h/3JY/JrUXzj/6/olyGISoe+lsphd97VoYoeBpsmxXDeqDc/+so0Hv93EuB6hHZzf3DWBGH1dfYn5mw5gq3IRY9LTrkWT4Cd0VMCBNfI+VDaHPzwukRk4tFae05kfhJfNoREL8c3kGCrBi1rWLtpX7QT1c9ho77Wox2OElPPHUetHtcaM2KQmVFRYpdaSy179nWKwoJisZHfuTeW6VXTOTifWCLFNmlRnBKiqyt69e9HrK7DbK+napik5O7fRrVcHuhw3EoD77ruP884+jaeffIrvp//KN998zaj7zuVF5QruVl+B+c9Bp5Ogea/A19u0q0T6HloP7U5EURS6dGxHl47tuPnqi3E6nSxdu4Xf5szH7XZz4TmTad+2Vp9JyBAC69cH8Gz8Hv2h1XQuWcnceRXkl6h8MO1Pzh47iJOO74Xb7eG9P3Zxx+frKbY56Zym447+knnwzUYnry1z0jTRTEZSDGOOEzKhd7vmRxyE0Sw1EYyVMg9BwzIWVn8sr21HSl2eI0F6Z+kXjgoozYF471igOXbAJ4EUTDPd38nYGA6nBiDsfnQsS2f4ZysoSBBDZSmMeVSkTLb+IuTCxAroeW7oY2nzSdFu+UtuFd41dJsMKz+Egm3SnwLYkc2SY4kx6al0uNmbV0bbLN/ztMYY+eHBMzjrse+YtkSCm165bgwvfr+cZ75ejNujEmMy8NFtE4izhFMXJAhKD8LsxySbDiSYJmsgdJkUue2rKDLnFO06cmLhjyck+8maCmOfDG/OiUsDR62gh8aMtA7kXA1yzLD7UX0O21DXH0g6TTtmDaLCL/tQ+3/1MSy+Nl68V/6SQsgvBUN6Jxj3BPx0iwQOpbRpGNlbGxm9fMRC2xH0atuUPu2asnL7YT75YyM3T/Jlkz931Ui2HShkxnJZu18zPIOXz22PQa/jvIFNObNvEzYcqKBFspm0eBMVbk/1vqf1b0lqfICAjfLDsOEHed//8vCDp0LB7YCCHfK+Mcb5fbJmJLm1BAMpCMHnL4MUiFiIZD10DK6doogiiiiiiOLvQsTEQnZ29tG4jr8NCQkJeDwe/vjjD047TTSAFUWhffv2TJ06lTPPPJMbbriBV1555ZjOvGgQqnWwgxRRrL2tVix45xxJDU9uWFs4c/JpzEz4ngMzngXWkGBW0OPmjkeew+PxcOcNlwPQJDWZ7bv28sPspQCcfdLx3Hj+BDlIt9MlpT5nKSx6FecJD7B2n0+3+4w3N1a/N8eEWezrSKCqsHchbPpRjNjYJtDrvPBSg8vzpMgbyD7hLtJBfrN5z8H8Z0WiKq0jtD1BjHTNaLYXhyh0h59j/F/WvhsbThs4K6CyRJwzkejytx4Oqz4Ux2Y4xIKGJh3FmbnlF5hxJ+RvFedQOOdtNVTIqn3LYO6T4hhp2r3e3/mOCe15adZ2lu4sYneejdbp4TvM80vtPPLFUl6bvg6AgR2aodcH6QOVpbB8KtgLpZBsVhiZRKoqdSh2z5P2Pfm98B01mqRBYpYQCqHkoWovGDUCVtOx1rIXakRex/q+/7enxvu3fVXFarFgtch48+fM79i4fi1duvVg2FiZU/v26UvfjtkBnUWtWrUCwO12s3v3blq1bkvndq1Q/JxmGzasZ926dYwYMYIRw4fy3GtTAVii9EbVW1Bcdom8DobMvpJFtmsODLm5Th8wGo0MOa4/Q46rp56JTg8dx3HHxyu5KnsnHVL1zLzAyvHv2Siwq2zPOcRvi9dx67MfsW5bDgAD2iRz3ajWFLkPYNbD0KE9cEyMRa/zXkOzHqHPGQkOrJbMKFclJGTKXyRY+6W3kLoick9HCp1eisvvXy5jX2wzcXRqpKsmgVSyD1RFnHy156poVGhdNMQx7PDKf7icUlgb5FX1wPhnJVts4/ci21J6QOT3giGxhTjY87fBF+dLjaCMEHJ3GmISpbjriqkS7awz+GSVvFAUhS5ZqazckcuMFbu5PqtmMEiMycDX957OE18sJDkuhsWb9/Pi9+JEnDSoHc9efgJtmieF90wC4dB6KdDsqpTrbT9WMk8bKs/iqJACwCBEeENRuEueG8DYJyAzTNnAQHZKoD6ltSkNf2dR5/qOGc6YECg4wH/76iLxQc7fvJuMXXmb4MNT4dzPxR6LFN0mw/6VkqXz401SC6r96MiP44/MfrDiA7m2zD4Qk8DFJ3Zj5fbDTFuyvQaxYNDr+PyuU7nzpS/pmRXHNcMzaqxlH/5pN4/+LCmoGUkmkpI20OKy0wG4ekyA+y09CHsXy7iRPUTItiOFxyXPpmCbZDYFCxAIF1XlvjVVZj8Z27SMCqddMhlsxZDYvK4UUiTk8LFMJEcRRRRRRBHFX4z/6eLNqqqi1+s588wzmTlzJtOnT69RI6J79+6ce+65zJ49G5vNhtX6LzMgqnWwa2UsBILTDic+BN9dJTrRn50NF/0IcekNOvWY0aOgdxZ8ciYtyOfTm0dwxnNzuOuxFygoKuGpe2/h+6kv8sJbH/Pep18zpHcn3nnoep5651t+nLOU/95+KYP7XSGyQ6X72f/jI+gVcKtwZt8mrNtfweZDsqB49dz2fNmgqwwTrkqJCj+4Wv7ftJs4ZcKRSPG4pfhsZQmkdYCBV4d/3qI98MO14jgG6DhB9PS1orQaYZSc5YvgiuqBNhxGKygGIRXUWnJITlvwjB+n3UfCaRGQkUBRoO2Ihu13/K2w7B3RtN67SGoZZB8fUus3PTGGfq2TWbS9kD+35IdFLOw+XMqbM9bx+ox1lFSIhNmEfq145eog120r9JEK5gTod5mQC/Vh889SuBIFJjwPzSNwyhZ79ZtS2opDzVYUfkFUo1UcRMX7pa4CePWsAxSi/V92grrsbFm3CltZOavXb2GYv5JObWeRy46tOJ8Kp0qTJk3Iy8vDbDZjw4DFKI6PnJ1bOZBXxJ9/zkOv11FycCetWrXixOGDyAFGqAuFVEhpW53xsnPnTmbMnMWq1atJiE8gLVZHs5RYLlSMGEr2sXPZLFypHdiTc4DdOfvYvXc/u/buY/e+g+zem0OMOYbJp5zEeWdMolePrgEDCh6+5za+/+VXmnl+o1OanaXXp/N5cW9mLFzLTU99AEByrJEHTuvMdaPaYDTogFZCwhwNAtfjkr6xZ6H8v1kPGHp7+DrwICT9nCfl/eAbxWHVGEjIgP0IsWcvEJ35pMyaEkjOStDpJCMsEAluL/VFFkdRv2O1Il+epTXZ9zxNFiEMVDfYVJHzMVmgqkIKC/e/BuKawdI3hIjucY7okQeCooPT3oYfb4DD6+GbK8TZHY501oCrJOJ5y3TY+Yf87kk1g1QuPbErK3fk8sIPK7lgdE8SY2sGh5iMeh64YCif/rGBm9/4DYAPbxvPhSO71n/+YFBV2DFbsgpRRW5m2JQjl3jZMVvafEobKYbeUPz5tPzmWQOgqd+8V5vsDgeBHPdam7KXgCXx6BR1Dhf1HTMcMsN/G4ct9BjisNWdx40WuPBb+Og0yXb7+HQ4++PI6kBpGON1cq/6CJa+JefregT1a2LToFk3qV2TtxmyBjCyp/ShxZsP8MJ3yygqr6TS4eaEni0Z1SubNy6sSxKoqsrHiw9X//9AsYPDFSV4RaAY0L4J4JeRWrJf5MFUVeRah99x5AFJHg8sek0Cs/QmOO3N8OuJBcPvD4kUUkILGHS9nMOa6Pt9bcVgMASusRBFFFFEEUUUUTQI/9PEguYwuPDCC5kxYwavvvoqVquVESNGVH/fpUsXPv74YyoqKv59xEK18e4VX61tpPsb20aLaPaf8wl8erY46T4/Fy74VqK6GgK9SVLuF73M5MzDPPvArdz20HM889pU9h08xMVnnsqtV1/E49dMZOP2HEZedj/L1m8D4OTrH2PRp0/SYfCNuGfdTytjIY+MNJPdqjXnDZAItz0FlZRVummXkcSXuQ18RvWhshQWvAgVebLY7jghqHZwQKz8UKI5DTEw9jF5JuFg4w/w24NQVSaG8cgHoO/FNbfRInPsxUIuRCM/jwxGK6S1BVuSL2NBQ3XthQAZP0aLT5Kk7NBfdbUCvREGXiMRpis/lEVoZQm0Gx2S+BraMY1F2wuZtyWfi4fWdPpoEjYAr89Yxx+rdvDTsl1oH/dsncazlw1lVM8gWv7lubD8PWm7lhTof1l4kZz7V8Lyd+X9sCmRR/0VeYmFhOZCKoAUb3a4wRRGn3DahVRwOaUwJtTVs9Ycpf+rqfEOG5269WTdho107FUr+r+Ws0h12LBXlLI35yAHS6qorKwkLy+PPr17kdK9PXhc5B3cTJXbjMVqobCgAMUcS1KTdIwxB0BVmaBKEdhN1oG8fsutzJg5k23bAtd/sky2cE43I9++cDtTfq0KeRv/fflN/vvym3Tq0I7zzjiVc884lXZtfJHVZpORQQMHsG5HHH3zv6FNrJ0L3fP5epsNg0HP9WeN5v6hBlLiao3nR4NUsBcL2aZl5HQ/E3qeF1mh0OIcmH67OJ07jheir7GgOaftRZL1WF3w3FozariisK7jT5OfMwQgSPyJ8mNQhuyooj7HakUBeBwyzlXriluFaHCUi5yf6pYsBnsx2PMBReTzDqyEfUth28zQ7SA2TbLpfrlTCILptwlx3u2M0O1c0cEJ94qkT+EO2P67jOWJvvniolFdeOSLJWw/WMxJ937JzMfOJt5aU0Jn7c5crnhhOgD/d87gIyMVPC5Y84WQ7wDZg2HwzZERc8GwRa6R7mc2vP8fWieZSAC9LqlZi6h21lww1A4sqT3faW3Kn9jTpISONXsxHDLDfxuHTcaQisKa96OtcRw26Q8VhWCKETvcaRfZogu+gS8ulH7x6VlwxlRodXxk16voRHIsJgEWvSpyc06v5FhD20SbE3zEQot+dGmZRnJcDEXlldzy1uzqzZ7+egmpCRbO6JXMzSe2oHNzv0zAAxXsLqjEbNCx56lB7Myzs6k8iS8Cna84B3KWAarYkyfcEzrrMxyoHqm5snehHOvUV2X9dCTY9acUWgcY/18Jfqste5TY3Le2bWimThRRRBFFFFFEUQP/k8SCf60EVVVp06YNb731Fueddx5PP/00u3fv5pJLLqGqqoqlS5eSkZGBxXLsFug8arAVyCLUFOdLA49Lh3O/kIJkuRvhy4vgvC/DL7paG11OFYmY0v3cOqoFSYkPc+XtD/LZd7/w2Xe/1Nk8KSGWjCYpbNyRw6jL7+fuKyazfYGe54bB3cebUZN915GdKlFulbWL1tWGs1IWZuYwIqb9UbwXDq6VBUlMIvS+MDIZo4NrYcmb8n7YlPD2rSqDXx8QySWQNN9xTwXW/TVaxGlgMNaM3PpfdXo2BoxWSAzw/IzW4Bk/Jis06SDvG5KxcKRQFOh8sjj0t/8m17Dxe+gwLqhDf1inVJ7+GaavOcyWg2V0bC5RXct3FnHJe+vIvFxIrCnvzcPtFCftiT2zuG58D04Z0Dqw/JGqilTL5p8kyycuHfpdGp6Oe/FeKWatesRxNeCqyJ+DVoQxrrnXueYCo1mKIdYm2wIWj/T+tnEhIkNrEw3/FBxBNlONiH6TlWHDRjDsxPH1HkcxWbHEJpBbuAX0ZtatW0fz5s3Zu3cvA3r3YNG835m/eAWJac3o2KEDyXFm1q9ejs3uJsYaRwd20p7dVLpUjr/pTQrtMtAbDAaGDB7M0OOHUFVVRf6BPRQUFbO6KodzOMS53c08vNBAVovmtMrKpHXLFrRq2YJWrVvTqmULcvYd4NOvf2DajN/YvHU79z/+LPc//iwXnj2ZV//7KDc/8AafTF+Aw+kCoH2Kjh/PtdApTc+iK+IpaDuZjN6jpfD60Ub+Nlj9mTipDBbocVbYhdqrUVUu8jeVJVKL4sQHGpcAifdGn1aVQmprKRpstNQMXIhNDZypoDkEXU6IDyHn0lh9zmmrf5tjAfU5VmNTfRkL/rCmSDvRYLKAmgQWr6a5JUnmhX1LpWhxfQST0QoTX5AMhzWfwbz/StDJkFtCE1s6PbQZLrZT0W7Y9ht0GFMt3ZVgNTPz4dMZec9XLNq0n7Me/55fHj2bgwXlvPD9MpZsPsDcdUKkje3bmocuHAp4gp8vFKrKJauvYDugiMxmmxGNQyqU7JNaQIruyCRj/nhcXjtNhLbDfLKkELoQrT/qCyyp3aYq8v9ZgSih5jCT1UdQ1pB/8pIyCuB0yvda7SXteVqS4cz3pf5IzmIhGU57XfpJJFAUGH6njwje8J1km/S/PDzJ1Npo0V/WZo5yKM5Bl5zNS9eeyKd/bCTBaiI5Lgan28O0JdvJLbbx5p92vlyey9wpveneQtY6Xy6XiKtRnZNommCiaYKJ3olt+GJzrXMV7fHVLEhqCS36NgKpoErW6q65cv8nvxR+TbpgqCqXehYg67H2QY7nTzTYi2sW8f6ntPcooogiiiiiOMbwP0MsHDx4kKKiIrp06VLDEaIoCh6Ph+7du/PFF19w77338thjj3HvvffSrl071q5dy+zZs4mLi9Dp/HfBUXHkcjfagl+T0lHxGuBuccInZ8OZU+Gzc2UBOuteGP9Mw85ltED3s2DZ27DqQy475wNaZjbj3c++Y8XajWzbuad607FDevPuIzdg0OsZcsFd7Mg5xPWPvgVAp4QEruoFSvFeOWY4RIejQhyd+VvEYWlOkEicxBahi156XBIppEWIpnWU2giRODc8LpFAUt3Qfow4fuvD/pXw862yWFV0cNz1cPwtwVN5tSwTtyN4lFoURxdaXzJ7s3rKD0tba8hC8kiR1FKcG9tmiJNvw3cSHZbcqs4icVjHNJolmjlYXEnP//udh07vjF5RuOerDXh0JjSBjIkDWtM9M54LRnSiY4sQWQeVpUJm5G3xXUvvC8MjuSpLYPaj4jhI7yKyApE6PjUdd4C0drIAVd2AKovzQNladTSZ/6GkQThorGymSMYYgwVrWhade3rYt28fx/XpQUVxHklxMSxbvY7Zi1YTo9eh2gpoldGLsuI8hg0ZjMMDhwrL6OeSbIWvN3mIa5LFmePGMm7sGEaecAIJCX5klUYouR3w7hgy48spXfheXUkLr6OhX++enHbySZSWlvH9zzP59Ovv+fWPeXz0xTfMnD2X3Lx8QLIW2rZIp13LZnxUnsqtmQdJrdpDxu6vwWwTx8nRyFBQPSJpc3A17JoPqCId0fuCyHXgPW6YeTcU7pS6QBNfkOy5xkSCdy4t3S/PWJuvtPoKoTJ8tM/jQ0RYNyZR7rA33rGOBsIlAGPThESos69dyAT/ffXmmtu2GQGzH4Y9C2ScrC/gQqeHEfcIKTDvv0KolR+GEx8BY4i2pOgk6nrH72JLbftVbCGvDEqvNum8ft2JnPP0z6zZmYvb7WH0PZ+xYU9+9SG6ZqfxyZ2nCJHtbgCxUFkiRXUr8qTd97tUyLXGgpat0GJAw4v27lnglYwyiLyZ2wmqU+zXYPNRIHmkSPvLPy0QJdQcZrRCcou696ORMuZ4sBJczrKqHIbfDQueE0f4d9fAVXMiCyTS0HWS/CZL34Zts+Scg66L3FGvN0odra2/SNZCcjYXjOzKBbUyd1xuD3PW7uX2135mzb5yRj67mt9v60WxzcUTv8ga5pz+TQOdQeawgh2yTgK538w+Rz6vqaoElG3/FVDguOug40lHdkzwk0DKhBP+TyQ3a2crOO0iwanilYPztnNjTFRyL4oooogiiiiOAP8TxML+/fvp2bMnw4YN45577qFfv341vtfpdHg8Hrp06cJbb73F7t27+eWXX2jRogVDhw6lXbt2R34RqgpqPaHz4RproY7TIAdRrfNqUTxG7wJUM74ddohNlgVYWgcp+PfdVZLWm9kX+l7i3b+izhkCQlvM9r8C1nwqzo1Dazlx3AROHCdFmmf9Ppdxk8+nQ6sMvnv5HiwxQhis+vYF3vn6V176eBoxZhMnXHkXbH9LnEj2YtGI1iLOXAr87j1n97MhMQ02fCsFcd0O7yPQibM1d6P86QyQ2k4Wmc26iVanooiUzeLXxUmCIhkXncaH5yg2+REAB9fIMczxMOaxmuSApZZh63HBn/+VRbvqET3icU9DRk9xjgZakDhtvhoAOguU58vPbE399zpHNdTXzyJFOP2yhhyBte7npjhAkfeVpeL4aSyEE/EP0FUK6dL9DHHUH14nTh29UYo6Z/aVv9bDiFcUFn9zGlc98DKzFqziri82VB/m9NHHUe59//kL9xETahZRVSnIt/QtcXboDELCdT29bkRraoBx1uWAby4TR1ViFkx+J3zpNf8+eWCVyB7FJEmx59KDcj1GS+DfItwo0H8qFKVmu/Z3ImmfN2Y/0hyiNZw2cvxW2S2pqrTjKDpAhepm3uxZuEyJFBYW0CzRROvMziTGmWme2Yu0GNixN4f0mCpGlEk9gYHXvsruYWfUzJzwOH3v/TXS24+RsX/nHxIx7Q9/OQQgITaGi846lYvOOpUnnn+Nex79L7l5+WQ2b8ZHbzzHsMED0Ov92rDHLbJ4Kz8Qh2JmPxh4bWjnKoQ3HleVQ/5mqYOQsxQqi33fdTgJBt/gI9Njkuo/Hsg8M+cJkY7Qm0XWpkmnutuFm42oBnHsahkLpQe8bcr7OxktkhWpZTBAXWdoKKJK+y5cuymc7UxHob83Zj+qbd+FIhpq2yROr/yRs7JmPRtdrediSZXgipJ9sPtP6HyKfF5fOxh4tRDG02+XNjXtBpE2CUZ2adkQfS+GX+6SaPAt00W7vdtkaHkcOxeK9N3gfj34cqOLDXvySUqI5dkpl9GvWzu6tG2JweDtg7bC0NcH4PK/z2T47RkhFeLSYeT9cv0awtVeDzZHqB7YOlPedzk1/IxY7XdzO0UmavbD8v++F0Pz7hJ57yiTej+JGT4bQ1V9Y43WTlS1/r4UrA0F2z6cfnQ0CNX6UB8REkz+yX/7QPsaLZLd6HHCcf+RPrR7PvzxmNRc0FB2uO6+gdBykPw16wE//Qd2z5NzTHqtJqmbs6T+Y7UfLcRC0R6wpgVsYwbgxJFtmNO3H6Nvfpnlm/cy8oWN6HU6XG6Vs0f15YLLL/X9Zum9YXMhndRtmPI2QN5G+bzTyUKARBoU02JAzf+rqqxltngz0sc9IcFl4Y6Twc7vL4E0/B6ZVwxG73jnN8c6K8X2A3BaIa6Jt13kS/ZqFFFEEUUUUUTRIPxPEAtbt26lpKSEkpISXn75ZW6++Wb69JHChB6PB7fbjdEoTui0tDTS0tLqkA//GEQaZVTD4aMtQPyieLTPnHZJE9VgtELLgdD/Slj2FvxyB6R3ri6gGRFiEkQTetnbIg3U5oRqI3fMqOGs/f4lmqUlV5MKAPGxVm65+FRuufhUn7RV/IUw/zkoOyDR2D3OqnGaWLUCw/ZfYO98kWIBcaT0Ol8K6x1aJw7IA6ugIleigPI2w/pvxJnZpKPIF7kq5dkMuEruuSHQtHyzjgu9gHVUwGfnSNQaSPr1aW+Ig9ZpD+50qdb890Yplu4DfYxs/28nFv4O1C6ErkFznqmIA7siT5zkjUksRIqYRBj7uBQS3DEbbPmiH3xgpfRBSwo060528x7MeGUKH/yylFuefJsqh4sX776SC04by6TvwziPvRhWfyoEBghxMOQ/vkLW9UFV4bcHYP8KcYRNei28WgyBsN3LLGYPkesC6Qe1pUI0hJud8G8piH60szEq8n2yet65SVVVGUuddnJ2bsVW5WbFqjXoY+LYvXsX/fv3JzXRSv+BA6hy60hLTEVV7SRaDQx0ryAGB7vIIuu40wIWWA6IThOEWNj8M/Q8J7AjPQBuu/4KNm7ZjqIoPPvo/9EkLYCzVKeHobdKO5/9sNTO+f1BKaAelx7mg/JCVaFol2SpHVghmT7+jnujBTL6QLtRDdekXv8tLH5N3o9/Bpr3athx6oNWiLPskBDclkRfe/MnZCE8rfijiWN9bqxt30USSBKubahTREN+zefi/NOIhXDQ8SSRU/rxRrGnPj8fJr0OKa2D76M3iXPxj8dEqm/vIvlLyCAtRyU5Bob07sxDr30OwO2XnMZlkyOsr1MLZrUK05ynRLbJkgyjH214RkEw5CyD8kNi37UZEf5+hzfIs1/3ldgLIHP20NvlvdEitTO04rOBSPBIiPFjofaW0wYVjiObRxs7I9dfqk0jcGLi4fjbYM8imUN2zq1LUIeLrpNkPvz+aslc+PpyCZyIJJghOVsCLkpyxH4LMRckxVv59cUbGfOfV1i2STLBe7bP5N17zq+ZxV9+iLs9bzBCXQx5CKHY8zzocXbjZCosfBGWvC7/H/WgkApHiqoymHazvO80QdZlzkr502w8bS0E8txVahLJ//ZgkiiiiCKKKKI4yvifIBZ69uzJ+PHjmTBhAm+++SbPPfccd999N127SsqoRipMnTqVE088kaysIEVH/wkIx7j2d4ZpiwptMR8ohRp8Ra6q9catYLRJYdiC7bBzNnx1MVz5R80I0XDR92IpLntorUg8+MlUdGsf2hFZbRTHJEKvCyQ6et9SMbqzBoKrirM9P3Cm+hOGnd5IlZQ2Qihk+KX1tjxO/lRVyInd82SRl7dFUuZzlsp2aR2EVLAkRX6fGvZItC3Zg4Nv43GLruueBWII97sc2o4KL2XaaPVGgyLGtSEGdLqo0RwKkTiJAxVC1J6twyYyH1qGiNEqfSy2iY9YaEy5hYZAb5SI0b6XQslecd7vXyHOIHuhpPvvmouCwiUpbTj3lQk4YjOJb9GJSvyijgNBVSXabt2XMpaEylIIhWXviHySooOJzwfOaAgX23+T12bdofQQxKV5dZTr+Z2DjYfV3x8DTpl/AtRar3jHba9juVXTRPYeqKJH74HklpTRpmNX4mJjycpqiTU1C2w2cvbuJdWqkOouJd4jklrTlZFcHomzI3sQpLaXLJpPzhaHZqfx9e5mMpn46I3ntAsPvXGXU0Qy4scbxeHz2/1SCDYUCe12QOFuua6CbUJg22tFYCdlQ9YA+Wva7cg04A+vl2AAEEk9LZvpaCCuqWjCq24o2CrXXjuIoXbGQhSBUdu+C0UW1LbztOLY9Y1T1lTR8l/zuTg8Pa7IZFoy+8LZn4hcTEkOfHE+nPKyfB4MBjOMfljsqg3fwqafofQAV7aB82+NZ2Hhb8TYDpKSGM+NF0wM/1oCQK+6uNfzArr8zdIOT3yo8UkFgM3eOlgdxtWf7WErkACWNZ/LHKzBmioO3X6X+shJk5+j22iR/2sOUofdr25JmJIuJqsQvy4HmP6meay8AEypR38erWG3+dlrGrEJvqw6f9IzNtU3VpnipOjy6o9FRu7qPxteb6D9iXDWh/DVJWJzfXmR1HOIhOBs0V/6Wc4ykUYKMT8lxVuZ9cINnHXvu+w+VMh3T1xFrMXbNp2VsPMPTHsXMUJ14kHB03Y0hn4XN04gTGWJFHrfPkv+P+Ie6HPRkR8X/CSQMmDgdfK7uSqFfLMVSf9w2EF1gWKQjCwN/jae/33WZ/tFEUUUUUQRRRQ18K8nFtxuN263m82bN/Paa6/RpEkTnnjiCV588UU2bNhA8+bN+frrr5k3bx5PPPEEs2fP5v33368pcfBvQ42ih1afBERFvrdYoikw0WBNE8Pcll9zgXvaG6gfnYqSuwm+vBjO+zzyYs7WVF806apP6upfh4u09rKQ2/qLZC3YizDvXcxlqoi3eGKbout3iWQKBDPAFUU0OtudKH9up5AnuZskuq31sMgcpLXhtElWBEhKdCCoqixatv4iMhWnvwMxKWCJ8y0eneVgjIOkQOnbXuLH45b/x6aEcI5W+CR8/m0GdCRkQSRO4mCSFCAOHGe5yBL4S4nFNxeZrXBT5v8KKIo4LJOyxfHvqpJo0UNrxcFRkgOFOzAX7sAMsBbMejPPu7PYqWSj39NUtIsTMmXcqJ2lkJTtLUweZpaChhXvS/YRyAK01fENv8fyw3I/AMltANXnCLAVho7Wr/ArYB9om3+aDvXRQDh9LC4NHNa6TmNv32jXtg3tOnSS38Wa4stCA3Da2XZgO063jqIqD6nkE1N5mErM/KYM5fJIrlVngHM+kVo1u+eLFMXhDZJp0Jho3kMcpfOfl8yDOU9An4slw8DjERm8wp3yV7RLIqc9rprH0JvlOJl9hQBPbdM411aeCz/fDu4qmd+G39E4xw0GnV7IhbIDkLcD4jKkvcSm1u17/0tZQo2BUIEk/nNURSG4K73kQj1OQqMV2o8VSS17oQRUhAqACISU1nDup/D9dUJifXsVnDFV2nMoJLaAwTdJJuy2WWz94Vk6JLk4sWkRq6+JY787lYTCNWDpD/oGLF9UD7erbzCANah6E8rI+xumk18fqspgxx/yvlOQ+lma1FE1geOVb9MZJSCmy2lSSFpv9EmRagSCP5kAfjah3bd9uLJeWhv6OwlyBRmbG3MeDTRO1LDb/MjMqjIoyxP7WjFAYmZd0lOTTTJaJPNyy8+yJljxvsi5NhTZQ4SI+/Iisb0+P1/IhnDRvCdsniZZ1sV767W1kuKtzHrxRt8c63FLINb238FZgQKsoitv6c7n+cF9MBgbQdbq4BqYdqPIq+mMMPJeqbHVGNj1p/wGIBkQ1jRAFXu0PF9sD5u3poICWGtliNeQMY2t//MooogiiiiiiCIg/vXEgk6no0mTJvTv35/169dz2mmnYTabufjii6mqquLKK68EYOjQodx+++2MHj36300qQE1nmLaoqMgXI0oz8P2Nbs240hyomsSONc1nsJ/1MbwzEvYvg5n3wPj/Rp422/t8IRa2zYS9Z4rUUkPQ9gQo3i1G//bfUIADpPORcgY3D25NTLP2kR1Pb5Ro04bKHtXGll9kEZmQIY7XQNjwrURso4j0UYcxNYsvurwRVvU9Yq0YWShngvYbO2z/PgP6aEhFBNpWO4+9VHRdXQZxRmvRbyarL0oqf8uR3dPu+dBu9NHRMTZ4nZmaA8hWKE75wh2i41ucg+Kuogvb6aJuBy24UtFJ1GdFgTgsdQYhCtuNjpxUWP8NzH1K3ve/UsaFhsJWCL8+IO+TsqVwsyVJ6rvYCutfOCq1XmsjWhC9/j4WsL6CF1phRWctJ45XIklz7qTGmSguyCchLRtlmeiuz1f6Y1Ma8OwtSXD62zDvOZH+Wva2OD5G3V8zkvFIYU0V3fZlb4nDaMV7ksXjcQqBVxvmRGmfqe2gSWdo2kVkYhoTRbslU8GWL5l3J7989AvJV5b4iHiPQ6JGHeUNJ7L/TVlC4ZAkobYJ9V3tOUrLWAgHOoPUI1n3pQQ3REosgLT/M9+HaTdJhuYP18G5X4jTtj4YLdDlVNIzRnD7bVfQL2Y3Z3QxkqkvgEWvyvdpHUSeMq1TaKklEBmxwxswbZ/NSHUHLvR4ht6FqbFsutpY/amMYcmtA2cnluyDT86UgBUNzXtKbYm0LjJGmRNEfrTCf54KQBY47FByAHBBZblkpyY095EN5tjwbJ/yfFAcNTOT/yrEpja+PGSgcSKQjWe0iC1giQN7OcQ38WUpBLIFTVZIawsj7pax9I/HJYPhSJA1AM79DL64QCT0vr8WhtwU3r7GGKnXsH8FrP8a2oyUunD1ZLQpiiJBF2s+F8kugNgmOAbcwF1z2zWOfelxi9zeghdl3kvMkuylZvUQjOHi4Fr44Xp53+McaNZT5KrcTvnNU7wkgtMudrkSIEtVI5BA1sFaAFZUGimKKKKIIoooIsK/nljQoh71ej1z5sxh7NixfPvtt7jdbrKyspg3bx4dOnRg8ODBXHXVVX/z1f5FCOQMC1RXwf9zzbjSyAX//Z02lJgEmPAcfHslrP1CnHjhGsYa0rtA6+GSEvztFTDyvjp1EsKCohNDf/Eb4LLjbD2KKzafglsxcLNyMPLjNSZ2zoHfHpT3nU8Jbrzbi+S1eQ8p/Ac1fzcrVBfXDgSnDSqKxJgOBi1TARo/WuxYQSRkQSRO4kCSFBX54LYL6ZOYKQtTf7Q6XorLrXhfotTaR6ATbbBINk7+Nvj5NuknI+5qXEdoIFhTRB9a04j2eKgqOcSzs/bQhj2cmbYdfek+KXxeekC2ScqWFHdNWz0SVJZKoXKQYqCDb27YdXtcsPIjiRTXCt12P9v3vLRCnxqJGiztXSs8qiJ9Chrm5Ps3o74+VkNuz7dNjboItYpoKjUIbQup8fGkJqeAOR7V268GqivJUvc37Jp1ehg+RZx+M++RiM1Pz5T21uOsxnO2G0wiNZSYJU5ah7fsuSFGnI6pbWTeS20HselHt+jp5p9h7pPyTK2pUqw53IKyDcWBVfDNlZL5pDdBRk85p0rDHTb/piyhcEiSUNuE+s5/jjJaAztunTZvEfsAY1aHsdJmd8yG0Y807P6MFpj4gki95G6UqOWzPwn7t09KSuSpJx9n0eotuNqmYtg3X66nskTIwINrZEO9Uead1HaQ0layEAxmkffZt1TsropcdIATPc8o13JrRghppoaiqkxIhaVvyv97nVe3T5fsg49OE5IvJhl6nCmynE27CIlQul9ezfHyCjWDfWrDaRfJl/JiyeJQXSL/YvRmL4RDwBm94+/fRdgdjfMFJBH8sxf8s7G9gQYJzWrKJIVCv0th4csytu2cG1rqKxxk9Bbi7f3xsgbqeU74tXlaD5UM0/JcWPs5rEXajCFG+oHBLBlwhhiZkwwxMsftWy4Of6NVstdaDMDTrB8oYRRCrw8lOWLL5W2W/7cfC+OeaphUbiCs/hSmT5FAlqRsGH6nz3Yzx1GjQH1FgfSJQDW1tKw5LbhOCzQ52nWnoogiiiiiiOJfhn89saCle44cOZKdO3dy3XXXMX36dFasWMHq1auZMmUKJpOJPn36YDabwy8C+W+DvxFV28mmfa6qgRegFQUi/ZLSHobdCXOfgD+fhqSsyLWbJ74As+6VNOPfHhCpiLajpD5AJDBa4fhbQFFwuxTcW46Bpr5ngUTveZzQcTwMujH4th1Pkmiog2vEYVvbSWu0AorP2VmL6KF4P+CSGgtxIRakmn5ybNrRdWr9XfirIsq182iOw0DoNlnawIr3xcFy/teiNx4OFAXO/hiWvi3775oLexfDwKtkwdbYkc3BoNOhxmcwV5fBXAYxaWCJKFLYCoT0MFolWq6hjtklbwoRkNIWBt3QsDa5ewH8ej/kbZL/N+kMJ9wjRRaNVl+mgld2B52h7qJSg3+BWU3qyuPyRrZZa5II/6ZI6khQXx8LVtg85DH9CG2TFZIyff8f9QDufSuI37+chz3PgG0MmMJ0wNRGp/HSXmd4yYW5T0o9jhMfbDzSTlGEHE5tJ4RxSmuIa+6b046288Jhk/l480/y/8x+4igOli3XGFBVWP6e9EMtUnXyu5DRS6JJ/TXNI8WxlCXktAFH4CgLhyQJtU04+9cmPGvXXqgqlyCEpEzf904btBgg++dulMjmhso/mmLhlJfg07OlXtWv98FJz4S9u16v5/i+XeQ/SZNFIqhkrzgs87bIX1WpRP9rGQCKTtpcRT44vXW1DBZcWYO5ZM8k8pVUGk38zOOCvUtEjmbnXHF0gsjjdD295rb+pEJyK7jwewlCcNp9JIIxDtKSwOUCR5m8JmYElj4CCWRQDDKuuOxgK/YWdvYWr42k1sK/hbCD0OOE0wYl+2tG9cckeG3h1MD71IbOILKry94WKasjJRZA5qKsAWLb7VsmWZ/hIL65ONZzFsu+VWVeW6Q8tE0KMi91P0uIrMaA6oEN38OKqVI7yBwvmXtdT2+cNYarEmbcDas+kv9nD4GxT8p602mTNh8IljDuT8vw9oe/VOyxMu9EEUUUUUQRxTGIY8DbenShEQWtW7fm0ksvpWnTpvz000+0bt2a1q1boygKPXv2JCYm5m++0mMIkWpLaraiIUYWfcV7YM2nElkd31wKIocLYwyMf0YiORe+LBHeB1fDoOsjN+qOJUd53ha5H7dD5GHGPRV6oZ6QKTq7OUtg0zToc6FPBkl7Dv6yVJpDoKIAKou8CyaDOAuC/Yb/S6m+jR1NHuh4Ros4BRQCP1OnHfpeBQfXwYEV8PXlcNEP4ReONFphyM3Q+WSY/Zg4Qhe+DOu/Eyd8Zp8jv6+GwpoKLcNckAdD8V7fYnH4HZEXRCzZB78/7HOgWpJFrqDPRTWPZYyRxacxRn5Hl0M+DxYVWl200Y9AcDmCyyyAOLSiC1FBtYNHrfudVjiztkxSrQyGGv9XVRynvE3R62PJIBfPd5fDhV/J/NMQJLWEsz+E5e/DghdEiuJoZC/81QXb3S7YMh2WvytR0IpOpMX6XXZkNYLqg8ctwQHLRbKKtqPE8ZOSLb916WFfJt0/PSLUcQQECYRHkoTaJtB3teem2oRn7RpbFYXye/jbER63jI9Nu0mNhF1zoe3Iht9nfHOY8Dx8c5lIQTbpDN3PaNixdDpxyie3EseuqkLZQZGCKdghf5VFYocCWLxZdy2Pw0UM+XuPcJ7SULBdHMo7ZguxriGljWTM1nak+pMKSS19pAJ4C8u6pci5VpjZhERZGwx1ZZCcdiEdNA15U7zPIWpJke9dhYDbW88kjPs5lgi7hiDSelp6o1cy5wgKx3cY4yUWfoXhdzXOmqP9OCEHcpaGTyyAROm3O1H6qatS5PaqX6u8baKq5nfxzaR+T2PNcaUHYf6zvgLkmX3h5Jek/zcGSvbDtBu8mUoKHHedEHgaGeSs9K5dK30ZC06bEG4qEFu7voKXiNB+d0sAktg/sOSf3D+iiCKKKKKI4ijjX08saBg0aBDvvPMO/fr1o0ePHtWZDJMmTfq7L+3YgH+WQiQOZ6cNVFAxyELUZIERd6HYCqRWwjeXw0U/Qmrb8K9F8RqMya0lMuXgGnEYDr0NYps0/B7/LhTsgIUviTHfejhMeLZe/VNAolxzlojmfMcJksLssNeUN/CXpdIKa9tLITYZUrOjhrCGxo4mD1S8OZQuL8j3tkMi8fPHo+JI/+YyOO/ryKIEU9rA5HfEafjnM1C6D2beJW1r4DU++Z5/GuY9K9HN2UOg1dDw93PaYNFrouXrqpRFcrczpEBgQoZ8X1mKr5i2n+PBVigRdlrWjqOips4u1E2JN1rBZKsb3Vm7Xs2/cSHaWARdRYEvQteSUEcmSVUDkBBerFq9mu3rVzJLN4XnPQ8Qf2A5TPsPTHqt4Q4SRSfSE9lD4PcHxUl5NLIX/gq4XUKurZgqhAKIzNKYRxonqjYUnDb47hrYOgNQYMBVon0dkyBOUpdX69rlBDN1+1owSbJjFeEWyP0rUXtuqh2JXrvGVlJmTTvC365oO1KIhZ1zjoxYAGjRF0bcA7O9Rc0TW0QWdBIMiiLjvCFG+i/IuF64Q9pRk84+Is0V/DBhwVYgRW63zZRsWg0xSZJl2mmCnK+2g7nsoGjB185U0GCy+AJHtD+HXeSMAgUqGC3Sn2LiJKMh1lLzWFodLkd5/XW4/i2ItJ4W1CS0I7HBNEI8o4+cq+yAZNA0Rs2ODmNkDsrdKJkHkWYSKLrAJJHHc+TXFgwet8w5y98TG8wQI2N/x/GNRyrs+hN++o9ktFpSYNQDIieq86uboAWNgBB5WsFmfZD6Chqh51fPKWBfq2WfBIS/bfRPmLuiiCKKKKKIopFxlKv2HTswGo1ccskl9OghRaP+cskjTbLmrzhPRX7k56udpRCbFp5x5LSJwxt8r5ZkOPUVPM16ihbulxfJNUWKjifB2R/Joq1knxRhzdsa+XH+ThTthvkvCKnQcpBE74QrW9P5ZHndvxwqDkuUtMvukyrQfmPtvdEqxrMlQbR761tc+f/m/3aYrI1fR8Je6nvvryMfDEYLGKxS9Hz0Y0IAHN4AP90sC7NIoCjixLh4mhBQik6iSr+5XAp/R3q8vxuH1kv0p6KTbIVwx+fyw/DuGJj/nCxoswYKUTPuCZ98mH8UW20YY4IXq68N/7FV0ywP1MeORls7VuDvvKkPweYihw3yd0BVifxmQTJFFi1azLvvvsuiRYtrfL5z83qqbMXkKJk8orsFVWeAjT/A3KeP5M4ESVlS2Hn4XeIc0bIX1nwuBNSxDLdTnDsfnybEZel+ccAMvhku+ObokwoVefDR6UIq6M1w0tOSLWTx6lp7XOLoMceLM1v7zL+v+fc/rf1of3+VDRUJ/mriMBz7rvb4419fQbPDAo1dWmACyBxltEKbE+T/O+dIZsCRosdZQvqiij1Xsu/IjxkI1hRo0V8yLhojO+fwBpj1f/DpWbDkdSEVdEZoPQwmPg+XzYRhU6ReSiBS4durg5MKteGwi5RlwQ6pl2Cw1CWwTBbJbLA2qSuTpMGSIt83dlHkYxWRzrtahm+guczh950jwHbaOKWqPjJr19wjvweQbJb0LjLf7F/ROMc8WlBV2LsIvrvaF9jRrDtMekPs08ZYZ6seycz9+lIhFdK7Sp2W5r0kY0eDvwxS2UHI3ynjmdMO9jLfNsX7ZNxx2qTfaPZHsHWv9nk4WTDh2kZRRBFFFFFE8S/E/0zGAoAuUp3+xoTD/tdI8zQ0MjscfdVA12+K9aZap/iOozeDKZaicW+Q+M2ZGIr3SPG+cz6pGb2ihLHgazUULvlJFma5GySKdOzjgWs3+EeQ+cOpAl4DtFl3SGtZ/3lBjORwYDAH/jxvi8hBuezi1Dnzg/AWPR6nvMali87xvqWSat31dCnOV+WnmWovlQhQTSM5ra0v6qa+5na0NXXDcUQ0hrOiNoK109oLhnDPHSyK1j9tukZUk9/5tSgmDWnt5LwuO5zyMnx1qTjU5z4JYx6r6QTRiLpQiE2TuiT9rxT5kYOrYckbsO4rXxHL5GzRU09sAcktaxa1CwRbYujvnR7AKzOR0QuMRziuqh6RPAMpdpnZL/T2muSNvRg+P1/6fXxzGRdaDgE8Ep2njS+mWD/irdaYo/1fUXwEnQrEBUiJD3ds9Y8W/CcXdA7Yj/zGDO37YP2ozvPybu+0S6StvVR0wYM4vtatX0dZaRnr1q9jUL+eEi2sQscWaWzcIX1qjdIV14lPYJw5BRa8CKntpQ35I8jwXAc6vyyygVf76tzkLJH+uXMOnPRU+I665j3DPG+Yjk9dkCw3V5VktS15QyJnQTL7Bl0vjv1g7c4Tbvh2GONk/nb47ByRn7GkwDmfQvMeNecXh02KZ2rXoyh15x//9qW1H3sJWBID1zX5u6Eo9dt1kcxx9Y0X9Y1BNfa3AKovutppk6CG2vVOnHYhossPy6uil3nKFCu/oSFGHHWl+yGtQ/33EMwe0jDuCSjaJU7TmfdI7aBQQSz1zQcawrHXnB7AW0w2uXXoucscD3vmw8JXxHlafT19odvp0OlksCSFzuwt2SeZCqX7vPJHP/pIBS3LEXx1ERRFyHVHudfuqIS0hMC2sjkudPH1+r4/ltAY/SgSKSettggEjjDXiANbgfwG/hnGTrtXDrFSCKys42D7r1I/a8TdYZy7ov5t2p0oGQuHN4h0XSikhJkNHu56RrEC3mzChIzgfaQ4R7KPtL5hSRFyre8lNTMH3Y7wzhtoDLAXwffXyvMFyX4b+7jYjOW53uvVyRzqcsjn9lLvOKZKoJXBIra0onjrjnh/d6NVMozDzQipr/35z13hjPnHklRvFFFEEUUUUTQC/qeIhb8Vf1XKfEOdxQ3VV9X2U1Vef+lZViyez5DhI7ns6huwpGVRNvEdkr8/Vxyeb42A09+B1hHInIA4Dc/9HKbfLino02+H/K1SnPmvKlobKQp2wNeXScZGsx5w2psNc+B3OVmIhS2/QIexYHdBvMEXaR2bAiUHJLKtogCSs8I/zz9dU/evQrCaI/6F3gIRF+BdwJZBWS7Ep8siJjZVnAqZ/UUW68cbYOlbsG+5ZLQ07RL5NTbtKlHJa7+QyG1bgfztW1p3W2uaEAxJ2UJcdZ10dDXX68OWGSIjYIqDoVPC28dpE0dm7kaRebnkZyFRnDafpISG+oo4etw+8kdvqpla74+GjK3/toLOwdo51HWKBnteJivENRVSIcQz6da1G+s3rKdb127S97yOoG7dutG+e1/ee0WiOd29L8ZYcVCyVqbdLM60zqcc+b0mtRQH+aqPYO5TUhhz6kky7/Q8p/F0qRsKVxX/z955xzlS1///NWm7SbbvXi8c7Y6l39F7k96kSD1FUER+WBH9flEUOypY+Co2VBAPRKmKKF167+UOjjs49jqX7ZvJbtr8/njPZ+eTyUwySSZt834+HnnsJplM/bw/5V3xxt+B538HjG6iz8LTgf0/Dyw+t3Ltre9ZikqMDZIMnvEXw4FANsKYz0fIpfDCF+1G3i6uGvmzreqaTDWc9BexYfsCs2MRyr0faKW5ACB5VyM7OkgYHTQA3mYg3k+GBDHWaRrV7vngaeD9J5wZFvLhawJO+S1ww3FUp+D+bwAn/Kz68iTQ0mREfPFPNG8FyKi382nAPp+hYrdOkGsqtM4GTvkDGdYEwqAzPkpGBZHSSPHQWJiK03gUHShvarB6Nn4XSyBE44Sm/283diXjmbUYhCwlxym9GwAsOJD+rn+JUnCFHBbLzsV2R1H61A+err06aEN9ZMR/9356L9Ie7fd54564wcbXgdvOI2O1rxk47hpyGhAGafH8xL0RzyfcBSSaaZ4b7tZTkOrPJV8dtFLg9RTDMAzT4LBhoVJUKudiFSc3Lz37JNSxEbz24rPARZ9DKBRCaPu9oZ1/PxK3nI3A0GpK03DYFcABXyhs54EQcPKvSHn07G9IEbvmKSr0PG1R8Set9pPSaGQ9GQBmL6ZXKbUcBj+gsN3YAOVcPfX64p//DicCD3wL2PwGsP5lUgQL76nJgmVqY+XSrTR2uVeDbYZSzG7hL4pjNrdSXvFWKT2FP0QLpXQSePBbwIaXgesPBQ74EtUTKfR5Kh4qGrnjR8nwNvgBMLSG/g6uoVdsgMLD1Qi1p7fuIsXpR66kdl9pEjEKoQeA/b/gzBs8laAIqHXPA83tVBS0ZTp9Jysr1Uj+/tAfyi7MbGc4KKZvLXdUUC1hVora3S+H93H//ffH/vvvT28ShiJBC3dD0wyD8nMvvICDD70cGFkP5fW/UVH00//ojnFB8QBLzgO2PhS4738oeuG/P6AooyO/R6mTKkE6BQy8Tx6sm9/UX8sp8gkgQ83eFwG7nEHFXCvF8n8Ad18CpCYoQuPc2wEohRkA7JTp5nZiVddkquGkvwjmiChTTH8BSeHWmr3fRIzmE8JJIRDKVtZtdRApONc8Duz1qSIuyoKWGRRpd/sngdUP03xun8/m/52m0ZzN4zVSbLlFOklpvF74Axk8AFJo7nY2RTCJ1HpOkI0KbXOA435KUVoJuUaW/lxCHfQ+HKTvPF49/Uo3pUTy+bOdGtxAKNMzIlnqQLbcMIT4Q2Q4FpjrIomX6HPkWgyJmGE88AeBmTsDPYuAyDvAe4/oqb5KZHovGWdHN5LhdtvDSt9nqcQGged+B7x+qx7tpgC7nkmpK9typPYqhldvBu69jMaV9nlUU2zuXsb3gZC1w8LkZ5LhNREzDB5Wv2MYhmEYxhXYsMDY43QCn1ChxFXstddeePa5Z7HLkn0yPIeVnu0QuPgx4J+fJ0XmI98lpeDxP6dQcqcoHuCgyyj/6APfpNRIN50MHPBFYO8LC7++9S9TMbCoHlK7/iUqeAnQZHbmrqQsmbUbeanJXt0TY7R4HO4jb9GhPmB4Lf0d3QRAo7Qcp/2xNC+etlnA3p8Gnr+e0nGcdSt5wMv3LdStL4RqyKsJmDqecFZe2kIpILxo7Rb+/hDQOTfzPphzte9yOqV7ePBbtDB94hpgxT8pmmGuwzQQ5nMT7VYmnaC2M9RHxobIu2RU+HA5cMuZ5JF50GWVNVC9cjPlZm+d5UxppaUptcSqhyjE/cgfUoSHXNQcoPfxMUoRFtYVUFYKbX8o87kV2k7ztXE7r+ypiItGlKwaSLIMahoQN9L4vPP22zh4/32BE39JXvzL73bXuABQOjERvfDoj4F1L5CR/MBL3Y9eSKcoVYwwHmx+C/hwhWFEkGmdaRgU8qWgcRNNI4PgQ9+m99scBpx2PXmJJgo0ADhtN1PBI9Rpf2G3fb57JXKBm9NL2RpLJaO51TgXCAM7HAc8/mNKeZJKZKaFKYVZuwKHf5PGvWeuI+WsUKBOjlNrKOXK4BrdSL7GSCUTnkaFkqctIkVs59akLC40+i4ZB1b8A3jxj0bNh0ALsMd5wJ6fKrxGgWxU6FxA0ba+IADFeo4m1/cBjNQsgbAekbqOohfciFqQ25Mw6IlIlnpRuJYjCtBOrvRo7MlnYydLi44lw8Kqh9wxLCgKycKrt+iF06toWEiO0zzthT9QNBRAdSWO/B5Fyrp9rPv+F3j5Jv04BwJHXAG0zJLWlAUaB/xBiuZW4kA8mPlbEbFVasHlXP16QqVIMgXOajUwNUdfXx8iEUqnvGLFirIfTz5GT08P5s93mL6ZYRimyrBhoZEoVNHrdAKvb/fZiy7CZy/9Bin/ov2Unkco/Pwh4OgfAz29wJM/Jc+wzW8BJ1+X6YnihEXHURqZB75B3m5PXEP72+ciYNoO+X8/1EeK+uV30fV1bUteqVtWkLEhspKMBMNrgXfupd8EWug8/c1UaHZ4be5jzNiJCpi54VV3+DeB9x6j83rq58CxVxvfiYmxP1jcwlBuE257xE21NDAyQgnjl+ov2JFPIRaP0YLjqKvIsPDkNfSs/3wSpV05+CvuKS6bWqltigXh4qUkP2/eQa+V9wELj6Ec0l3buHNMOza8SopaANjvc0bthFw8fg3w5u2kCPnYjcCCA7JTHwH0Xh0EkAQi7wOtegSSUPKLmgultssC+8gpKQuCYu5n3pzy0czivv5g1naLdtD7fI8XOPFaus9v/4uMC8ddDex+doEXYoOIXpi3DylD171A0Quv/ZXSYYR6yJNV/A3qL6taKcIwPbIWGNmgG6XX0d+RDUadHRl/iIzqM3emorQzdiZlajXSmD3xU4r0A4DdPw4cf7Uhv4W2g6lgMHBKof2A0yggQcb3Wv75gRj341HryLt4lIy+oW6KFFh5H9B7YkGXnJOdTiGj2Wt/Be69lOZvoxvpWLYodG3RLfRa87jxVVMryef8/YGt9ifjgxoBRjcDY5vhHd6Ez6RXoAcDCNwRA6KbgbEtgJai3wc7aUzc9WygY27h15OIActOsynU7MneNp3SHRTGyRAeaMnO+e4NGNtZzdEKUVzK7Uko01va6kv+CjVgO1n35EyXKKfCtNlm4VHAU78gw8LG18loVirb6IaFdx8gudzheOrzK5CbP6Sp2AOvw//QLcAHT1BKV4Dk88BLDdlyk3SSHFzWPAFAoXoVu55Bz08BzfM0UfeigDVPIGT/DMXncbX49U9CBQbXUmQRkN2O4qphkGmksW6K0NfXh97eXqiqOvlZKBRCT4/7kaE9PT0IhUJYunRpxrFWrFjBxgWGYeoCNiw0EsUot+Q88nZYTfQDwWyFn6IAe3wc2uxdgX9fBmV4LXl2HfhlijrwFNAcW6ZR3YK37gQe+R55d/7zC+Q5tOcFlCLFxDxtPfxP/gl4/7/GQnLRceR5I08qJ8aATa8Ba58HNr4GbHqDFn3v/Tdzh8FOimzo3Apon0/ech3zKHd9sNOdBUA6SYVt+1fTe8VDXoNN+vnKE2bx3jzhzrWwylhoumxYaIQ0MAHpOShK8YuG2AjpHXY+hQr3PfUzUrY8+TPK83zyr9xP/QBQuoVjrqIF3EPfoegFYWSYvZgMDFsf4p6XqmDVw+TtnIqTwW67I/L/ZsvbwLPX0f8n/RLY/khq21b4Q1TMfGg9EGyhNFQt+rMRdRUSUj9YbHSN0zbeCLKQD6t7nLcYrd6/xUb01GMxI32Vzt47b0spvgDq+06/AbjrMxQdd++lFIl29A/c8+jvmA+c/ifgtVtJPgfeo5cdTW26waGbvKOH1wLjQ7mP4Q+RF/aMnYDpuiGwe/vq1kIRrLzfMCoc/DVgjwso739zunSlyVSJcrOj0H6g1H7DTqGWYXAI29cRSsTIUaT3ROClGynCs2U6Ke/d4uCvUgRd39M0jxOEemhu1bkNKek7t6K/7fPI8BZZCWx5hwwTW94hj/GJUVLurnqI9qF4jbkeAD+A08SbTdI5tMwAlnwS2OW00trdc7+jvqBlhsmoAKNYs2wcHR+lVEii1qsGw5jqD9LLnEte3l9cza24zBXxUq9KzkLPu1Sjvl0qTJm5e5GD0sBq4M8n0Hiz+9LS1gCzd6eo042vUe2s1/9m1IDy+GgsmPzf9FmwC+jZnmqidG9HBuh8jG4C1jwJ/5qn8ff0c/AjBbyjf9c6i2r37HBC+WqhPPFTMioEWoCjfgjsciqtIRNxivgRa0otkd/QY8X4SGb9CyE7CkqLKo+rZFSQU53KBEJAoJWO08jzvzolEolAVVUsW7YMvb29AMoXRTB//nysWLEiIzpi6dKliEQibFhgGKYuYMNCI5FvkWq1qA86mHDJSiIgy/NY0/RVkz4xTC84EMqFj2P1Lz+K7SZeJ4/p9x8DTvpVZt7TfCgKpW9ZcDCF6r91F/DOv2lyuucFwMJjAY8XyuB7+Eb6BhyoPQ/Pe/q5LDgI2OdiKkxopqmFQn3n7EHv0ymKZlj7AnnMCEWPMF6UIwVFOkXX88RPDaXVjicDh3+boibMBcvE34lRyunfoS9oE7HcOXQDId3bLU4LWjeNC/W6cC0U2eup2Ov1+Wnh4wsC7bP0SJ49qbDl6keAPx1Dyky3Q88FsxcDH7+L0l28dCMV6tzwCr1C3YYsaGn4Uyl8MzUGD9Lw39cMIE3KJyHnYnHrNS969VdCBd7WI4G2PpgMe/kW4FoauO9yus+LjqfcvoC+0Exlp0ICDONCtB9IjJNHeEBX1ni8ptRJRSofnLbxRpEFM1apN+R7nG9MkgsyivdAZlqgdApQR0g5oOqFTo/5MSl6nvgp5Wve8g6l6mmd6c51KR6qj9J7Iikx+1cbBdPVfjqP2ABd78QIvQbXZO5DGKbb55F3tPi/fS4pJs1GhGoaFTSNFL/PXw+88x/6bPelwP6f05U/USA2RAWDS4lameqRPTk9oy3mX6X2G0J+AOoHhdOB2ZBgpzwVnx9xJdX4eP8xKqh6zm3AzF2KPy8Zrx845TfkyKEOkAGhYyuahwFUODmLJmD2EnoJ4lGKgu17mmpCbH6TxgbFS8aQlhlIhafjrtV+bEEXPnX07gi0z6I+IdxTusI0uoWK2gLA/l+ksTw6YIw5coSCeKbNenRCqJMKzsoFghN6JKPdnEzIigJ7xWWhES9TkVKNc+J3aj/JULg7e18eH6XKu/crwAdPAv++jCLajvlR8ffb46P6bGufoz539SMU2ZKKO/t939OZ++rYCujelgwN4qUO0HppzRNkqAPg1V/rMBMzdz8avu0PJwNHIY5fhbLuReBxPRL74P8Btj5QH1diAEQ/pfdN4t5bFaHPFcVgNh4kYhRN6PGVtu4Rx2u1kS1/COhkpXC909vbiyVLLPQFLjN//nw2IjAMU7ewYaGRyJuSRVqEiPdJobQuINWHr5kWVGoEUHxAzzZ6jllCAaD4A9j2/92K9Lv3wyPSSvzxI8BRP6AcpYV4+rRMA467hgwMz/ySFPFPXUuT8VA3mvqewcH6pql5+8F70KWFLYo9XiP1RLnR0sCKe4DHriYPPIAUUPteQjnokxNkWBCY841GB4xif0D+HLrCi61cUQv5sPM4r0VkT8KMvPxBWnQm40DAgUJMqj9C919P29PclunNuNMpQLAHeOBySt91o+4J53ZOd4GiUIj7tEXA2IdUmHX5P+ja3n1gcjMvgAPFm74SjrfLxyhayYnC9I3bqC6LP0he0uIejmwgeZA9Q2VEAcbEGHlVx5vII65jTuZzKsboyuTHKvWGfI/zjUkWed8VAIo8noyPQPF7SOGSiBkpRQ77OjBvb+DOC4H1LwJ/PBo4/Q+Fp97LRVMryakV6RQZetUIKXBE0dn2eUDbXElxWgNRCHYkYiR7z/+BahoJtj8aOPgyox+LDZHRfXCttXHBcdTcFIzscdp3lCt3fCBEClHZyzcj73g0x+8l+Tvup8AdFwCbXgduPQdYeid5RbuB4gHm71vaPjxeSkEza1cqBD0xqhfa7Z6UsUQijevffxsAcP62OwN+F8fRx6+mvmfaDsCOJ5FMNLfqRm/duBAbpIhTtZ/G/NQ4eYPL/aDiye8hDxiy0pwjlVE50gbVGoXWLSlmf2JcAYBEyPp+di4Azr0NeOZXwKNXUYTBuhcpGnunU4uL+vT6Kc3eggPJqKDqxup0KvOvlgJS0t+xTVRDK/Iu0L+SZGFgNb2kuVwGigeYuQsSCw7HZ5+bg3XKbPzjgJ3hc1NGrBgfAe75gh4ZdTI5UCmgcUU4EMipkBQ/GXcEcdUocg7YrHNsDKfmKIZiaERjHcMwDMNYwIaFRiLfBFxehMRV8uZIjNt7u5t/KzzfE800IRzbYiysfMHJieFjD96HN954HXvtvgv22f9oaLOXIPrXT6FldBVNMJf/gzx92gvMcTtzZ+DkX1Ph25dunPTA0aDgMWUf3Kp8FL84bAm8PTXoDaBpwKoHKYf8h8vps+Z2YPHHgT0+6ayAYED30JYn0IkY5dAFDIORVdRCtZQ58Vj+bcpFoYtou3QRcioJJ0ohs5e9PwR0zM5OHZYYp8LEp/4JeOwq8ha99yvA878npd6i48oXlt4ynQqi73E+eeANrwc8HkDxIpFW8JtnhpCGgksOmg6/zzf5HbS0vuCVXil5Iay/pvcC2x7uzICoDlC6MwDY41NA6wy6V+ogHTcxbrQjObVRQrq/gRYAXlLk+PyFp0Ga6p7U5aJcqTcy+iqF+kqPl555bJjaIkBpxc6+lQp+D7wH/OU0Ms4t+bg755ELxUPn1dxO0RP1xMgGSsH3ys1G2iZ/iIph7/UpMj5OGkf1KKvhjSRbVjKSS36mQnqWXDjtO8R9ANwv9G5WrJmjFgDrsU0m1Ekp6O6+mOYofz2LotwKiTKtJE2t2fUKysWWlcBLesHZAy+jKItgB70XY7pI4ZZOAeoQgJShbJbTJDW1OnPwcCIrlU4bVA3cPmcrByuA5hAiAthcBFigeID9v0CRNHd/lhT593yBUsftezGw2znFn5evGWibbfFFvmhPDRjbTNE8/auM11Afre3m70sR3FvtDwQ7kFJCWPf8m7n36SYPXkHpATu2osgon58c0vwhIAQjDRJgGOnkCIVEzEhF1GJjjBMG1rhqRG4BxadAqkcDHMMwDMOUGTYsNBL5JuDmRUhcNVJQ5FM6Z3i+6xO+lmmk4JkMYQ3iL3/8PW6+7S60tbWiJRjAPoccAfRsj/Alj0B7/Booz/2WCjJffyhw6NdJqV4IHi95kG59CPDKMkBLIb7DqbjqXzU6+dM0Uhg/fjXlUgVoAbPTKcAuZwCzdjEmzwNrSCEd6rb3ZjNHMAgFwuA6wKeLu1XKGH+oIkXhsjAX3a0khS5IrbyeRBRDXCUFt1yPxG7xYVV/xPJ4emRKuBs462bgud8CT/+S0rrccSFF0Bx8GUXqlOvZeX3ANodmfJRKpHHvcx8AAD67w9bwl9uj7dEfkqdn17bAXp+myBAFdH8SALxadjokYbxJqJSru102OKjZ/Vy+djAVPakrgVPFlmOvbl3etIDxWahDT22lK7mD7ZkyOm0RcPpNwCPfprRF//kapfza73NkuGMITaOooBf/BLxzn5GbvnUWsPPHgN4TyNivSPca0Pu/mOFFalck2E5+pqIxQcZp3yHuQzRSuqJUyJNIDWKeFwDZ45nd2CYi9HzNlD7l7L8BN59Gjht/OprGh20OA7Y9zP2CrvXCw98ledn6EGDrg4wxHpCM3lLKqVAHzRfkl9dP41xyIjsqslLU4zhXarRhrjoUYm7g8VH9NHPkT9a+YhRZMGcP4OJnaL726jJgZD3wwBXAEz8DlnwCWPwJw/BUbhSF0n0FO4AFBxifp+IAPDTHqxZv3UUvxQuc8lugdboxN06o5FSSiAGJIM2tw910PfJzEH1WS440SALZmCpksZi2Xo8GOIZhGIYpM2xYqEeKVSLKE2Z5HyI3uoy82BcTb/F5vv37g3oh2xaaDPr1BZI/jEeeeg4+LYENGzZi5vZLgKZ22jYRA/b8NNJz9sS7f7oIi1qjwAPfAFb8Azj+51SELB+zds98v+3hdHnxFPCvR+izmbsCynj+fQHOJ/7COzYfHkkBm04Cqx+l4p9rn6PP/CHdKHIY0D4bCHYDzfo5jI+SN3hyArQYsKnrYFcg1a64WDGeN07bn5PtqrF4njx2gYtoi7QsGQsVX4BSUQmlgN3iI9Cie9DL+xnXFeHjxnfBTkC2PRz0FVKsP34N8NKfKYf0bZ+kdn/o/wLbHpH7nqcTzq6zOU+R6HgKABkW0LEACORp/1ra2XGtcviufY6KWAPASf9HuehjQ8Ziv3N+ZhsWERxN4cy+SCAX2xbv7TyFzbLhtnxUE02z7vdl3JRzcUw7nC7UhbyljIKsCPUAAb3tKB5DOSTaQrCTXqffCDz1c1LuCIXG3L2oHs9OH3XuvdjUkn8bgAxgTnCau9rj0IBnHo+0NOV/H9sCRD+kNh79kD4T/49tIa/Wsc3G77Y6AFi8FOheSEpPLU0erv5gZt775DAmq8/aRdaZ6zAVEi1U71iNGzm3t5mnCcxylKsoutovpT8y3V+xb0XRZSacuT9Rm0lEMYhxMhACPnE3cOOJ5JG94p/0AigX+7ZHUKTQ3D1yt23zGGhHcsLZdmjPv0k8BYBSIaFlutFvWJFO2X8ns+Zp4N37Se4OuVwfe2IANKno/AQVcTdHUUQjlKYmNkxzZQ35I0cKpZDxqNC2WgsUkuLVajurCAWzLAVCABTTnNF0X31N9CwB2h9AxrZtDqX0Qyv+QZFgT/2CatXscR5FMbTZpHF0uv5IOlzPOI1uTUjztXBPjvmdw/3ZOUENfgDc/3X6/6CvGKnQRPuL9lP6qdgw3dvkOKW8lOdsk8Ygh23W7nf55kKAaR6Zp49mGIZhmAaEDQuNRDGLhoRKeZNF/spcE/gMY0RssvCzIk28DtxrCV7EBE7Zaz8cd9yx0nHIw1gJz8C/PcfgoXeewiW7qFTQ7/rDgAO+DBzwBVro1ivpJBXFXfFPKl6r9tPn3ibyYtrjAlqcJvUFvezR7g+SAk0BRSzYYVcgFbAuLtbInjdOlMVO04eFuzPrW+QqiGmFEyNHXKW0Qgd8CVhyHqUqefEGYOOrlJZizp5kYNj6kKmx2EklgHsvo/93OwuYtw/9b75XVs8xZ5FUc+FSG0/hRpYNud1XQtHk1MgntvNIhtVEDAi05s/jP7yBCg7P24eMVSvuodo+614gBcdu55Cyp7vO0hbZkRynqL2nfkEKLSf4gsBuZ1IatOk70n2LvAuMbATaZth7UsdGMqO1rLCSp0aWMTtyzdPiUXsjgnnMj6tk3JoYpbGpc27+SK2Eqkc3+mkuYlebqakdOOd24MM3KY/86ofJ0L3xNXo9+TNyitjmUGC7I8iDO9hBn/lsnCLqES0NPPQt+n/P84H5exvfxWOUPioZI4OBVfo9gBw+gu3Z3vLF0gjGukLId0+tIhSETJjnEfnmjPKcb2g9OVWl08A+FwO7fxxY/wLw/O8oldizvyYDw65nALueRYaucA+lzitXistaIZ2kVFHxMRqPD7o0ext/kIyPipcMc14/MLwO0BQ9esFBetisfbokE/VogGMYhmGYMsOGBSY3ubzdc2GjVP30xZ/Dpz/9KWtl6+iHANL48mVfBcJX04Ls3q+Qt8/jPzaiF+buWdo1VZJ0EljzJBkT3vm3YUwAyDN8h2OBXc7U84jqymhFV9DI90hMZEV6AvGZmUILpNZj6HslKSR9mGjzACmpLfcXtfYglZXbg2vJUBE2GZASMVISJRNUS+Oo71FhwKd+AbzwJypQe/Pp5Pl18Ncob249Gxie+y2wZQUZ0j7yXePzYhaH8SiF1Wsgg53Hl90HmWWhkWVDbveVWEA7faZiu3jS+CyhAmg1zjkaMZR0sqFbjGNbHQBsfyR557+yjKJ/htcCz15Hr60PJiXhwmOLK7hZbYRB4cmfA6Mb9Q8VUsSEuki529xBiqz2ueQx62smxW/XtkDXAmNfiRgZuZtbAU/A3pM6KEV72KaAM8mTSEumwagDxOQmlxHBaswPSIYCs0e21fMYXEcFUpMAOmdkGyIyUogEgfn7UYqsI78NjH0IrH6E5murH6HaHMvvppeMT09XJtqhMDgE24GeRVS8NZgnaq5WePNOMqQEWoD9P09e1oGgNJ73A8ObgGALELcwBnl8ZPApJjLOTs7YWJefXNGIhY75GWnHpHk6QH1n1xxDCT5zR2DnU4GVDwAvXg/0PQu8ejO9BIoHCHZRXx3S++xQN/0/fQdgh+Opv65nnvgpGfSbWqmuwuhm4x5Npl8LGc9B1EYY2wT49M/MhoViDWpsiGMYhmEYV2DDApObXN7uOX9n8ugQCtVoP6UyaZudPXlvnU4pfwItmMzhfNJ1wMr7gYe/DWx5G7jxOPKmPPArzjzfqjFRzGVMCHZSAcwdTwIWHEiKrs3LSQE8uglo6QGSSVIcA7qSTPIStSsgLMjpqW0xgZ7q+a1LpRDlsmjzwvN9Mg1CLPv52S36o/1UYDjan21YAMj4FmzNNE7s/0WKdnn216RQ7HsWWHYqFRA84IvAomNL94DTtMoZKeJjwGM/AZ77Hb0/6Ku0uAaoDSfjRSweY0BcT1MQCFp7uxXqnTiVqSejipWXtlnGrPIwB1qBJZ8E9voM8N5/gZduBN57FHj/cXq1zKB+esbOVGx82g617aWYHKdiy09daxgUWmcC+30e6D2Z8rrHY4C6BRgfJq/zlunUzwyupagEcxolf5BSP3m8pBC2cggw56q2U2xaKfC8Ad3IZ9HOWOGTTaGOA/5QpuJafjbhnuzn4fPrRoW52fu0yk8ut4eW6RRZtusZNE69cQfw4RsUUTe8weh/kzFgNEbzHSvu+19g4THAbmdPprMsG9EIGUFSEzReTtuB2roTEjGq/wMAB15KCk+51k9CBfrfB7Q4EBsDOhcYvzWnZCmmfdumWqyjvrsSmO/TZFSORc2xYp7FWITadqA1c54XbAM8Fp71iXFgwf5kwB5YTQbgD1cAsX5aJ2lpQI3QCyuzj9fcQTK2+BP1WSNo7XNkWACojl5zB123uHdWNSxErbjwNCA2ah0dV6hBzZzyjQ1xDMMwDFMSbFhgcuN0op23QJq+KB38gHLRj2ygyaFYnE4qfqYbE8poPy3Utj+SXg9+E3jj78Dzv6eXE1pnw7vP5+DXZiOhBJz9plg0jfJ2P/I9YGSd8XlzJzBvb2Cbw8mLfMYi+jweo+tvnUmLDX8z/fX56drF/4Ax6S4kvY4Z9mQrnGIWmuIZhS0iT/IVjAt3G0YF2UN00sA3nYxRcsHNdIoK8B3yP8A+F5FC/tWbgQ0vA7edR4rRE34GzNylsOsYH6Z0Sy/8EZgYIcXL3D3hmbEErVoUo0pr/n0UwpaVwGs3A6//nfK/A8Ci44Hdz9avVQWGN1L/ART2XPxBWvhryC87rNBEQXmLq414nrKXtlmxJntoD62ldqDASK2340nkeb3xDUqT9OYdFNHw/PXGPjx6IfPeE4FFx1Wu+GY+tDTwxu3Af39IRUIBIDwd2PNTwO7nkgFeKDwDQSCpp5fQFMPTNjEONIXor4yTVFhmJ4JC01rZbcfjVTYidVshOPXIzudEUsjcw+MDtj+c5j3N7VS/RB0CPAoZsJJReq+laWxJqvT5mieB/neNug3d2wFn/CVTKe8Cc7SN8P/rc8DyOzNrAIWnkdJ2t3Nzp0SbGAX+fRnJW9scGnehGTIG0P/BFmC0H2jtyPy9m2kYzc+ykQ3iVpjv06QBLV5YFLYdiukvkFtWRGHvQJDk4/Q/AkMbgCF9bdTUQuOS2k8G4pFNVAdHjZDhO7rFWAMt/jhw9A+c1+mpJOkUMLSGirxveYccw7asBCLvkMzt8jFg8bmAOkjb2xWRF/iDNK51LihMHuwQ44sG+5RvDMMwDMM4pgZnI0xdkjdljD7R7tzKiFgQxga1X18MBTMnd+I3omDkydcBO5xIRZ2H+5yd1+gG+B/6Om5AF25VTgaSBwDlyG6x/mXgwSsovBegyITtjiKF1baHA+MjFMbr8RqLCnH9CigXr5hMqwPA+AClnkiCvGwFpeT2ZE+2wilGyWwVrSNHnuR6fuEew8NNRD5MRicEKVd2cjzzMy0NTIwDE1EqHHnsj4GDLyOl6At/oNzXfzqGCj8f8rX87WdoLeUBfnWZEdIPAGseB9Y8jgCA2wGsxSz4/30QMG8vSk9WiLenYGIMWP4PUuauf9H4vHUW8JHvAIuOkdLZ6Ia22AgdJ+FQ4SiipUJdzmQnrlrnJWeDg7uUkrpAHc2/XVxOUSGeYYzaHKB7549nej92b0fFnHf+GLDpdTLO9b8L9K8mxc6qh+h172XANgdT9Fk1jQxrngQevJLOFSC5OfBSSreRThtKrPERGpP8IaBdvxfqgNG/hLuoveerk+AEOd2L/N5qu1zPncer4rGTrXzRDfmeh+z0YfYsFoZwXzPJXGga0D5fP58YRWMCRo0okTJIPufoALDxdeCde4GV/wH6VwE3nQSceXPhhnELlIH38NX0r3GY9hS8b+kFW6fvSF7TG18lpe0z19Frzp7A7udQtI9csL3vGeCfn6f0aVCAI76lz1VVisZIxICwfn2YRhG4Zo9oJ32flae9OcqBxyEDp22+2ChsO0Tkj1XqUiusIiMBighNjAOdWwMdczN/k1DJ+LD/F4B1LwOr7gdW3ge88hcygH/0N8U5G7lFPEqRCBtf0w0Jb5Ps2hWWntYL7P1ZMiqE9HFJzB2t+nvZycbcZ1jVnAFyP1vxOwXUL7EcMQzDMEzJsGGBsaYYpU9s2Dp9C2BMtOWwYKHwE6krrMJfAyEqWJvQF2zbHAJ87kXyFFMHSGECkEdcqMvIzZsYp5Qqb94J7cU/YFr0Q3xeuwHa7+8D9vt/5JXmRhHBkY3Ao1dRJAVA92qP8ym9hlwAMdRF6WgmrzNoGE5SCd3AMkCfKaA0FSKXfrGGhFwLUfk7gBWmdrjhNZsvfZUdZq838aw8iq4E7KbPfE16qp8oeV8B5Hl52NcpbdgDV5AH9vO/p9Rcx/7YOsXExteprsHyf5KHM0CL3L0volQwH74FbHwN6XUvwTOwCvOwEXjz7/QCSIHSs5COHe6hnMDhbiM/sPgs1EUK21dvIa9UUZdC8VJEz65nAAuPJk/XjPuh5xVOJUg5rAHonJ//Ptrdf7t6F4GQUYibC82Wj2Lvp/hdvu8zUpHp+xepfTSQEnAyLYyk7ExMUDqyOXsCe56n7zMGbH6LjArv3EvFN1c9TK97L6MCtftcRPUbyp0yLBohpetbd1PKJoBkb79LgF3PAVq6TR7qMRofzcj9SyBkRElF+7ON/LmwkiM3ZIUVp8VT6v3PN//zB6mdKHEgHjRSlciGqoCsaLVSeirSsaQ5Uc82QMsngMXnAP/+Kikpl50KnH4jsOCAwq8FIMPgU79A4K078RE9QiG1zRHw7vf/yGs8EQNig2Tkfu9RUpKuf5FeD1xBBsRdzyT5f/bXADSKVDj6h8D8/ekY8RjNOQG6J+FuySBgmtvmMxoA1p72PP7YI+7PWMRYO1i2XZf7lVL2J9qGv5nmTf5ma+OuPwR0zKZtdz0N2OPjwDv/AW6/gGqa3HoO8LEbs+dM5SIVB9a/Cqx5gsag9S8B6UT2dt4AGeunLaK55IwdgfatSM7jozR2mI0FVljN4eRC8wDtQ5YRwL4Pi6sUHWKXho9hGIZhmIJhwwJjTTGLmGCBk9rJyb9F2LDsoeIN0P9aKtNrxTMMhDsBb5C85GSv/0CY9r3bmZhYeDKu/8NvcZb2D0wb20gplZ65TlfEFGlgSKjAs78FnvmloRhddBwpYVtnkNJqYhBomWX8Rr5OkQbJLxkY0gkjNLepVc8HbqEI9QedKahzPUPzBDzXs25kT203vGadppAw32fZQ1Rg51kc6jLaUsb204BTfkdh5/++DBheB9x6LrDTKcCR3yWl/+pHyKCw5knjd7N2BxYeB8zdi6KL2mYBsxcDO52GuBbA0msfwA5YhW/vugW+LW+SojU+RgaDQuncmhQ32x0JdMwzPNjMiAV8cpwMC4lxUrLmM47Z3X+7ehfmvOTiuQAcMu8mhcqW+Tnk269VKjJZpobWUjsy2wF8zUAimlkgMxEDWqZRJMNen6ac2O8+ALz3CCk+V95Hrxk7A/teDOz0UWfX5JSRjcC7/wFW/Is8pkX6FsUL7HEecPBXAXhojFQHAF8s8/eKN1O5m1D19Hv6Z8KYkKHAcfpcLORIfrbycxPfNdo4UmlKHbfsFN9yUVVzWxHtx1xYVp7rWBkY4jFSMgrv5UALAL29nngdcN9lwIZXgL+dQzW3ek9wfh0D7wFP/YIM61oaCoBnsRjLPKfhmo99Gt6AqR/Z45P0mhghI8MrfwEG1wCv30ovwc6nA0d8m1IQimsSacY0/X/ZYBLuzqxz5MRoYOVpzxE89oj7o+iOStGIMY+q1f4mHqNi6f6gEaWQ0I27ArkYuHwdi44Flt4B3Ho2GcKWnQacdQulk3UbLU2G9fefoHli3zN0njLt84C5e1OB6bY5ZMzu2BronEeyLeoohDrpvcenz1kdFKK2WyP6/OSA1Wox7uRa/4jvAWMOWatthGEYhmHqBDYsMNYUuogpOL+lrCS3UKDKi1aRCsmqWGAqYYTey98lYqSM8fqA5lb8y3Mk7tcOxV37vgH/a3+mtEQZBoaPZS+IrdA0YPndwCPfN/JZz90LOOoHlAomESPFZzxKqYwy7pG0sI4OZHv3iQV4c5u14aBQz/dcz8RqYcu5rrNxw7tNROuIlEh2ixir+2yusxDuto4KsjIqyGz3EeAzj1JR5Bf+QLVA3nuUZGjLO7SN4gUWHgUsPg/oWqB7Lw+QknVkI22jpYCkglGlFS9gMZI7dcHn14CmNkBLUA2VqF54cHgd/R8bpBQTsUFgfIgWqf4QsMPxlCN47l7k5T0+orfv8dz3PNxDyht1AJiQ8prZLiKlgory+1z1LuTnLlJSeXzWRZ+Z4ihUtoR8eHxGOhVH+9WstwvpHsVmuWmfnWmkFoxFgOYWSuU3bSFF8Rz1HaDveVJALv8HpR37xyXAw9+lyLXFS40oukIZ+gB4+17g7X+RR6jM9J0oSmKHE4E5u9Nn0QFgfJQMJekUMLaFjCGBluw+Iy4Z6oHcRXnzYSVHVvIjIkhKiv5qYCN3PqwiFAv9jcBO8W2O/jFH1AVCmTULAD2aTjcctM/ONi4EgvSdKKbbbkoDc+w1VCR59cPAXZ8B1KvImCZIJWg+N7ye5mQj6+n/oQ/Io1qcz4KDMLHnxbjyLov7YB4/Q13Afp8j4/raZ8k7/IMn6foO+hqluBRy7ZU8ptuleyhqhIlizrnuuZP5s9UzZXkwEPdH3BMRCV2JeWuG4c1mbj655mmWnrtUb2FyO93YEBsFgq3Ur/tFRI/pOrbaHzjrr8DfP0HOHTedDJz9V/uaJOMjwFAfvYb7KMo8NUERCKk4kIzDn5jAN1Lr4UcS/r//jhyetrwNxAYy9xXqBhYcCCw4mCJNOxdYR+spCoBBWhelEjS2ts9xcFMlrNaIVimtzDJiJ1NiOzE2NeLahmEYhmFchg0LjDXlTkOQzzPSvGg1L/zE94kRPZw2DLRL36uDNBFOJoFOKsKXUPxIbX8M/NsfTovF535jGBge/CZFCTR3UL7s5g6aOAc7qPhysIO+f/UWIxd8eDqlv9jptEyP8UTMKA5qp6CxUuAIw4NiylMvFiSA4eXjhELyKXOua/cxR5jYecgLrO5zMR7EMiKFmDiHI79D0Qr3XkapjWKD9PmOpwC7nQ3M6AUCrUZqiKQe3p4cpxQyw5sBRTqPYBvg02gfbbMpX/XksXWvaF8T/Y0NkxJmeC3gbaKi5e2z9HMcpxy7Hp/hwTbpVd2c3VYDId3IIXmr5WqjZqOc/Gzy9XPc/iuDk2Kl0QgpjFBElJlI86OA+nYrZQWQaQCWaemh8URE5oltZu0CdH8HOOxyKjz+wh8o7/VjPyJv6V3OoCgHu2KwE6MkE8Pr9Nda8gzd/EbmdrN2J9nd4XiS0diQUdshOkBGvaYWwN9C8trcQoocq2sJBLMjFpQ4vbdLZ2iHnE88l9JURJCUIkeNbOTORzH3xu43dt7y5ugfQDLY2s01JMNBbABIBA1DuTiW8GIOINPTP9RFY8IpvwUe/h7wyk3A/f8LvHsfRRuNrCdZMxszZBYcBOx7CdCzEFoiDWCls3sD0HnN34+8w/1BkjmndYSE0hgg+WrSC287iU4Q5OsT8z1z+ffFptSsN8S9DKiVG7czDG829zkRM2o3dcy2jkIAjHYT0g1XSoIMDWYDlaB1FnDCL4D7vkbGtJtOBj7ybWB8WDcirCUjwtBacuzIgxfAweLNGukLXxPQswMwbx9g2yOAWbvStTpZj4Q6MVkjy85xxOxEkw/ZkGTlsONkDZtrbsdGO4ZhGIYpCDYsMM5zQms2np9A4YvajAmdxfGFsiKhkrd0MgZoCikZQ120SGsKk7dNfIzyzqcmjAWpR99nczgzBB2gkOMFl1LO3Jf+DLx6EzC6iSb9QsmTC38YOOCLwI4fBWL95DkdCBsF/ppaMov9CeTzsNvGCqEULdZjutSc36UUjK5l3Gj3uTArs/MpqLMWQorJAGVzvrnSwiQnSOkSG6KInECQimCecxuwXHfd3OYQ8nT2NQGpFEX5eFvJg61VjwoK6UrBdBKIS7l0w11AfISuz2Oqit7UTp+JKJy2maRgmRjW89jrtRISKkUlKZ7M9p2M07kn4xQRISN7qwH5F4Dme28X/WO1mBTPpdy58yuJotTe9eQbQ8RzSCcz0zCo/YDSmmPcEbncpRzofgtFW2Kc2mdSl7dQt7FNQH/+LRZKj6ZWMgYnvMC+/w844AvAm3dSLvZNb5Ay9JWbqG7IVgcYxoOhtfR3fNjmtD2kxNn6EFKOzt7dOPbgWt2wNkHyHxvWo35GKdpI3E9bI00LveQiliLlYCl9fSFK03xYtU9Zjmut/VYKu+su5t44NZqKZxiPSjU4wtb9qNkxoqnFSC0XV/V2O05tTvbWDup9vIimiceMOgUAcMLPyRj9+E8o2k7G6wda55ACM9hJUQ9z9qD0fV3bGsaP8YjxG8VrnGtCpZfcVn0+Ol7rTHofjZCc+Zqp9pVVCs0MRb4uY2av6EKigvL1ifmen/z7ep/DFSrv+eatTud1To7rRI4CIT3FUQoY3kDRO7nGObFekNuUeS0TjVBR5/A04KRfA/dfDkTeoag5O0LdlLaoZQatZZr0+9TcAfgCSIwN4XfPjyAJH/7fYVsh0NoNBLtpbpZOUhRcMkn3JTWRfx2jeGj+1ukzrsNqzpoY0mvN9Uvp1RyMG3Yy4ui5he2jgMS8lI3YDMMwDOMINiww7lCoV69TZUNcBRJjpHzRAKCdPN4mF6NdNAlVBzFZh2FgTPeGnkO/yYh0aDGUl6FOYJ/PAAd+gRQ0sUFgZAMQWaUvBhP6JHOM3o8PAR1bUVHc1lnkLRdvIg82y+KEoN+rA3ru4SB5vRY6SXV6b9nDprYwP7diUhnIaXyEckTgpNaGUGIkE4ayBgA8HvKkDncDQ+tIITnWT/Ihe3ILo4LwYvYHAXUMQJ9xjKBJ6W++vokx3Utvjq6w7SGlkgajpohYaEYjxnXlUsBYpVrJtQB0kq86oZLCVi4GyFQOp8qZuAp4JKWeU4O2P0iKPqFvkNsaYBgexodIySKMD1m1bSwUUhkK1m5gt7P0Yq8PU2Tc6v8CK++nlxXN7ZSXumtrUvrM2Im8pH2SDMj3RVxDYpzabHKclCBdczIV+hnnaOpr5Pau+NypH1LusWqqGrndoJh7k0uxZvVszIYEp0pyKy/yiSjJm6itIOqAxGOUNkt4bE+eUwzY60KgfT4w+D5FyHUuALq3IZnxeElxK2oQyZE3Qqnrk4zfsX4gMMPYfzplGBeAbLmPDhgOK7bpbiyUnIEQFZlNjtP7cI/z51SwM0KBv2fcQZYjO/nxh6jewNA6MoSJ+Zw6QENKuCu3ocGK6ADJTDpNkXNn3QL89/tAZCXJSed8WrN0bAV0bgWEppGcqAOZ0XvtcybbSOrDD3DPi8sBAJ/dYUcgGMZkGiaxnkGCHE+aLApNi3sQHaD9h3vsozMy7mGouBRWbrdxIcNiXsqywzAMwzCOYMMC4w7lSp0UCAHxFlp0iogFc/ogTSFFaWKcDATpOEUgzNxR97JLSttLdRTEBDI6QMdpmw10b0fpXNJJmuDKx5MVuwmVPON8zcbEM9qfOQEXNROEYcSje4R2zst9r6xyJcvb2y1cOE1EeSlUGeZEJpw8M6HMETnKYyOkIBHK+lxKikllU4S2D3UiK6+vPwjAS6lTEmNAZDUQ6qCFVbOeE11sFwgB8ZSxf18IUCZyK/Sjg3oIvO6BKuftTsSAcKuh+MmI8JAUZbkKlxezsPRL5yDex9XsYoCM++RSvOQ1Doi+dtT4LNfiP6FSbYSkSTkfVwGfyUNfGB48XmSk3nNS20ZWsIp2DZDH9Bl/ATYvB15dRt6YHVvRfoIdQMd8StXXMi3beCAImPr4REzyINcN314f1UvJlcbI3NfI7b1zpjtjRiEOAzxW1Sa5nk1WXQWLPjrX+Ci3Dy2tp0jSlf1iXErGso3Vcd3oN7SWDAkzdwF6tjXmWsMbDCXp9O0zr2Uy5V0QaJLGrugQ0D7DOC+haBXGhYzUNXNI8SuMFnZYjUViX/ERYBiFRZ2WOq+2isxiykveyLtmw+EpOkAOSr4mZNRQEGkgzbIk0v5oICelcBe1WVEfrnUGcNofrc8rEaPaI1oSGI2Q3PnDGUaFyfObvJYYoKlAogXo0M/FFyPDiMeXe86akKID8xld5O0KTWHl9tpTyHBLG49NDMMwDFMAbFhgaht/iBZ1gKGol4nHAEUjj5222fT9hyuBlpB1TlKvz5jwiwmkYvKQkT+PjwHqEOWCD3WTYiitF70UBUTFYjidpIVyx2xMFmBToHvJeukznz+/MiXfwsTue/ZOKy/lUIY5KtqoK3OEQkMoOISy3onyPREDkKR2KqeXELRO0wu/avo1julKVp8hH0JplJRk0B8E1WRQqWCzvIAUC76OOdlKV6ui7Vber+KaEjHDy898bcUuLM3P06oYIOM+xcqRUErEVUCT0q2EugG/zVRGKN5FFEJ8nJSWCrLr1dh5fPuD1O8n44A/aq/8lw3M6STlfvf6SY66tqY87yKl2MQoMDFC9XuEMcAuV70sMxlp8bqN889Vz0c+R7mvqWZ757Gqdsn1bHJFReSrIWTGHzLmSgCldEFST8vXrM/nVOP7ZILmb1D04urCSGhSYopzkeVFGLURMI4f7sg8l4ApakEofieN4j32RgFZYWq1jT8IJKKGApipbUqpTZFLfqIRY27lDwHJ9Xokiyez/1YHKNKzqYWMz4K4HgEKAHG9rYW6MtcluZT9Xj8wOgS09lCUmtmoAFg4bqmZWTiFATDfeJNMkJHCbBgvJLq0GlHY5XKSYxiGYZgpDhsWGPdxezIoDArJmJGzXXhbJ2KUvzOoh75H+4Fp21AOUKv0RIrk3SomkMILKBmnxaX4fOxDYMNrANKkcO3eLlv5KdIvaSBlUSIKbBokb1S/XnxQ3IPkuDNlSj6li933PCEuL4Uqw5zIQSHPTBgM4lH636xMzOVZrZn+ygg5aZlGf0UIuyhwGze1eZ90zFg/kPTqi9YAJnPfT4xRpELHHGulq1UxarPSKh4FhtZnpmoBrAv1yTjtf5ykqmLcp1ilslBKKKD2YEeGUihkRCFoev6tVDyzfsLk/m0Mc1m55PMYhUXNAl+zMTYApNRRB/QaQa0UYeBELoTn9PB6TEbQeXxGrvvOec7un7l9V6O951PAMtXHrl3kMlwDxtyoGGNRQne6GB2ivyKKQR0wFLEds4GA7u0dkqIGAkHyqBbjoSyruVI1+UIUASHGOnPUgti3OZ2mFfkUpp1z9VQ3DuoqTN6TAubRnAbTHazy6xdqWMjVr5rnYf4gRRkIQ4PddvK5Kb5shb2TlGTiu6ZW472VrMr7CHYCCFuvpcQ5RQdobeNrNtI5CScqXzDzugIhiiBU4plpx+yodGQbyxHDMAzDFA0bFhj3cXsyKBaKIudlRs0Ec2i+UJIGrY8d6gL8gczPxELA6py9fjJoxIbIK0gU9ZSJjRr5gBO6d2lza+ak2i602Yp8Ch9WgFaHQu+7W3JgNhgIJYzHl7nozbW4DHcB8WbJIKdmpkOa3EcI6LRINSQvQOXfRIeAtg5dNr3GdU6mPzKnhcizAJaVV+I6kwmgYzpdq5N6Ck7vO8tRdSj2vguDRHMboAXst5Off7iH8kwDRtsxy40gl2HOMprGFH0j9uEL0DFEhJtQDqUSNJ4A2SmLctYTCep5uFPklSrunRNDx+S11YjChFMg1S9W8mE2NjhVwiZUqrGQ1A3SCgB/C9DdQp9p0A0GerSCBmov7VaK0BClaZGLtdpFxckkVSnSQa/9I0ctQDFSvuS7Lie1EDRNikpycJ8KkRWWK3cod379lh5y1BD9fEg3Npn7/XBXdtsV6fusjLKyk5RYp2SsPfRIhlyp8qwQEakyIhpbRJKOrKe1TdsMIKEfV4iiuX6yiAx12lYrHdnGcsQwDMMwRcOGBcZ97CaDxSo3hGJF9tCODuiT3mCmojOf0ioRA5Ij2ecgzhmQPKKDVKQ5pXvj2IUa+3xGAcK2OYbCVj6vuB7ZII5RC0oepry4tSgyKx3tlJCJmJEDWijhJwtZ9piUoqI9CsVNyjp12OT2khLVnE7C481Or9Qxh7w/EzH6K847l6JHXIPsbQqQkVDOg59PmcppVqYmct8u180xYxvRlceolet7q1zyGXUapGPFRgzFkGjPAKWdsNp/hmzBiESQUxa1zzG8qeVoOace0HGVUi9FB8h7upRUE6UYKVg2aw+nz9NKPpzUH7EirhrRPIo3M7JTJgxSxNp5TJv3KQx5VrV8zGOXLwRocV2m9G2Fcla8Vwf0lC7R7Gg62aDixFgaHQASo4C/1dm9KkRWWK7codz59f0hZ+mw/CGKYhAk9CgKDXRudiRiFC2qDgLts+mz4Q26QVtxp33IqZDiMaoPoXjIMGg2mADGegow1j9OjTaFOCG4YTxnOWIYhmGYomHDAuM+tqH0RXqDBILkCSMWsMmYER4fmFPYuQ1vBIJNxnmaz1n2iA52Aj3b0fdqP9VaCHXQe+EFBFDaJZ8uSu1zc1zDuDFxLeQ+1Iq3KZMfq8LbpSKUELK3o5ViIjoApCd0Y0JP9vuMfQYzIxasohcyrktXIEX7gUTa+NwXAkKt2dvLOaujA5TX3ol3tay8srpOJ16xHInQ2Jifv6wEzJV+x6nHtZAFqzoNALV1oUCBtI2dUU1WzgLWkQhWv7Xal1CiWhnOowM0TsnjTjFj0eBaI1WN2IfTsYlls/Zw2gas5MNJChbLfYUoYiFsY1CY3L+pvciRdubPhzZQBIK/GUhI8pJhrJYMC+oA0DHN5NmtGsdUFHvDSTEGFTsvbqfX7ta2jD2Vvo+JmL3DkjyXjOupmXIVTAZ0J6xBox6VMDR4vZQCyWy0zoc6ACT0ekZC5uR7FBafm+RRbKP2G30LINUHKkMaPDeiDViOGIZhGKZo2LDAFEcxym4n3iB2+5UXsHJ4vKNzjRn/+3zkgdZqcw65zrFZUqCKBYHHS5EK6mCGM1wWZg+kQrxiODy3fijXs3KiyBBFnc1FnsNdmdtZKWfypufS5U6JG+kjAD2dhDfTy1NWziRU4/i5lE+yVynnXmfcpFivajtEMWeRe90q1ZcoeC6KLOfbX7SffuMPAeMjZIQGCk9dYVdA1x+iSAXzuFOoh2ZcNdKTtRZhJGdqj1K8dAtJfyRj9ty2MxiYkSPtMoyHetq8WDy7/opthN8YkAhnjlty8eZcvy3GoCLqQmggY2euCAim/ihmTZSrHcl9q1MZ9YcoUkEdIJlIxoBAM0UTAJlGa6tUfma0JKCOAMFW64jWvGlbJYcqcU2lRgPY3WeONmBcoq+vD5FIBACwYsWKKp8NwzBM/cCGBaY4ilEoOPEGsdqvKN4sJuGxIVqUhrpy7goATUKHNxrvAy25w5zlc0ynpM9zpKNJjNPE2ymFesXwhLl+KNezcqLICPdkKuVF+qNEDJMFnwF75Yyj8wgBflPEgqyQAYz/Qz2GcUE27llh9tpmJQtTDFZKB7PsuKHEi4/rNURsIgvMBc9zYU6ZBAAehZRDxeTEtosMshp3ihmLAMMwLwpVN+dIz8HUNoW0gXIpwO3GJLPBwRxpJwgEKcLA4yXP7ozvpOgFOYWa31Qw15wGCbA3nDgxqIi+aHL/ume3WxEQTG3h9ppInksWHL2iOz4poDY1PmjsU06ll69Oj+Iz6sc5SUdmdS6yQ5Ubxmdxn6MRff+m+yNkjuWIKYK+vj709vZCVY2+OxQKoadHX1sNrSXHkjwoyRS2096n/ze9Bvi81huGuoGOeSWfN8MwTC3AhgXGOXJxPHnSqziN7TahWYQcWClmk+O06EyOU2SAOFxTS/bvzSTGM9M2hHsAv8Nm75EmAk2t9D6u0t+mVnqJ7SbvhcfZvnNhvp/FegUKrO6zk+MyhNP7omnOFoCF7E9QiCJDVqpaKSyEItMfdN5exX48PqBjLoDlxr4UX6bMmvsFJ0oTWe7FwrFSShZu95WhmHZfKFbKHbPslKrES8T0yLe4rmC3uK5C+2zzuBcd0A3nDu6ZLPeljhX5kPu3aMQoVM3RCvVLIXJpJTvFpmqUx56msPUcKqHP/RLjpKAULzPic/lcLGVH+qxjLhCQ5oJuy47oi2LDQLA901PcKq1ZqfNppro4dSpxOudyakyw2l9Al6fmNkpdB4VkqHO+zflKbU7eX7gnU0bKjZO+RJx3Mm7hhCZHeThNUcbyxhhEIhGoqoply5aht7cXANDT04P58+eTUeG6vQ0nqhw0AbhOvPlTjg39IeCS59m4wDDMlIANC0xxlEuBYelVKXl/2qV3MReqlX/blIIrmJVWdsdkmGpgqVQNAWMRSrMi0i9YFaLN54GaK2JCllmrCbeTBXdWXvwyRH1wrZKpj5O2FggBg31kqAYK77tlr3232pGsGAmECjunUtIRcSFmphCsnrnc/sR7u/Zkm+rS5G0sj1+FtLFy50gvRF7EuYuoo1ye55zbvXq4NS+opWeYUQehm1LtmddMTs5X7QeU1spdl5OxTJx3QHpucnRQMp4532WYIujt7cWSJUsyP1T7qa2dej3QszDn7yeSKVx64zMAgJ99cj80WUUsRFYCd14I9D1jGQURHFqJxTM9CA6tBIamsfGBYZiahw0LJlKpFLxem5A1pjrIxWv9QWC6xYAeHQBS49mFagNhkO+AG+dh4VWaGtcn7WxYYKqMlRJGhIrbeWg79d7OMEYkM78zFxk0LwwLXXCXa4HO+eCnPk7ajl/30FSU4vput9unkJ+ESmlcCm2fpSj4S5GJWlKkMZXB6plbRZvZtadc37sxdriNWelciLxU+9wZZ9TbvKBQQ4g5RWYhVPq+FDKWmaPnREStmO8WErXAMIXQsxCYvXvOTbR4EquUTfT/zN2sI39C3dSG77zQch+9AF6+qAV4/CLgGY5sYBim9mHDgsSqVavw4IMP4qSTTsKcOXOK2sfExAQmJiYm34+MjLh1elOPjLB15PF0y6MAtYtkKOXY5gm8eaFY6DEZx0wZOaqkl7ydIkMs1iyjDaRi6NGIdeSCiGoQaAHT93L4eYFKzkrenwb0sJ4yciSY9Ews0Vg86cVZYA0DJxTapoX8aDAUI4VQigJTyARA8l+IHJqvcwpHBE05OXKTQqLN7PrghG5Y00A1sEppS7naJQJ5f56BWelcrjHEnM5silKTclRP84KECgyuo1R8QLZsFCo3+bb36McpdGxwgmU9pCKPYX6G9fI8mcamYx4ZC2xqNqx4+22ce+65uON3P8TWL/+QtmPDAsMwNYwLCeGnBq+//jr22WcfvP/++4jFSImWTlORUq2AnM9XXXUV2tvbJ1/z5vEgYIu8aDOH05vxB/VczjYFxMI9RiTDhytpIuzk2NEITdQnRrKPne+cAiEyKuSawCZUOoZIEWN+XwmqccwSmTJylK8NuY3Vs/aH9Fy5UvqjaMQIFRfebHLx5Ix9xoCJUSCymv4mY5nfBUKGQlQcSyh08rW7Uu6P2L945Wvf8rk1CFNGjgST9TdMz7rQPi7cA0xflN+Ts5i+06pN59qPkJ+WHmey42Z/LmQCKEwOEyrl7pbHzbhK7wfX1tVY44QpJ0flwq6PFW0WsP4+rkfriDFkLAKMfUh/Eyow1Efpy5y0K6vUTFZtOxGzliX5M3lsy3V9pVLpeUKVqEk5cvOZutVv220bV/X6PknrdUch7UgYKazWPubtCtmnOO981+tmm5efoXm+yzC1TMc8in6weMU6FuKVTWms2EK6qI2bNlXvPBmGYRzAhgUAGzduxOmnn44LLrgAP/nJT7DddtsBAFSVJjxKAcWdLr/8cgwPD0++1q5dW5ZznhLIizbzAi5r27C9clRGTokkthteZ39sDfYT9Xzn5GRi7HSRW07qcNE6ZeQoXxsqFjuFyOA6Uv7nXCjGso0IuQx3/iCQTADNrfTXJ22TUO0X5k7aXb77k2txOmkc7K+79l0ppowcCUR7KaatFUOhihqhPDW36Vz7sZIf8/ayHJTjWgvtp+Iq4PNTfyB+Ewjp/YO/MANFHRi9p5wcVRonThpy+xNTbkX/7cQYEB9z1q7M+xLvgUzPUDtZMkcp2CmdS2m75t+Wa55QY0x5OcrVzgvpt+22DYSApjagc659dKpVO7Jqq/mMFICzSDrz2DQxQvNQkZ7IqcwzDJNBT08PQqEQrrjiCgDA6aedhr6+viqfFcMwjD2cCgnA8uXL0d3djR/96EdIpVK49NJL8fbbbyMWi+FjH/sYPv/5zwOgyIV8RoampiY0NbmU03+qYw57LcRjyC41kjk9USJGi1K7Y4sCYFaFOPOF5TotFCpvU42w63oK9daZMnJUyXoBsrKvNccxrQox5yrGHggDHXOk30jPpVT5yHd/cuU/tiqOyWQwZeRIINqLuc5Hufq4QvYr2qrHlx0JUej5WaV2KDbdmBMK7afEseVx0x8COucVdm655LuGmHJyVGnytdmsNJM9RuohAGhqIQVnobnX5fdC0Sl/HvBmn5dT+Sql7Zp/2yD1GKa8HOVqO4X023bb5msndt9btVWrPtyMxweEO53P0QIhWn/5fPkNEg3S5pk6YWgtoPZnFkzeYPK9jays6CnNnz8fK1asQHTV08DjFyE2Po5IJIL58+dX9DwYhmGcwoYFAGvXroXX64XX68Vhhx2GYDCIvffeG7FYDF/60pfwwQcf4JprrikocoEpI/EoTWIVAE2tmd+ZC5X5g7QoFaj9gNLqTnFAp4VCrRa5lYQn8FMPq4Xn5GdlKD5vV7zZLjUZ4E67y7UY53bNCMrRFgrNV+20rTrZr/l65H3XQru3O4diDBR1ZvRmiqDQdiG2F7KSK2rAqYwGQkAiJR0jCPgtop+cnmspbZfbfW1SzjoxhciA2328VXtzcoxQN8mI0337QxRNUa57yDDlYGgtcN3eQELNLJj8uMW2/hDJRYWYP38+4BuwPheGYZgagw0LABYtWoS33noLP/3pTxEKhfD73/9+snjzAQccgNNPPx2HHXYYjj/++CqfKQOAPKd9Ad0bJk8ezUAYaG8CsJzem9MSlWPyO4WLWDI1gtWiUHh45vOiNKdCEpEITnPSVjLlUC0oUZnGxIlHsrmvd9JWi/F0dkMOanFcYvlmcpFPVkqNeClFJsrtlMJUnlLbk1sRWG731XJUq/zeDWrBeYphSkHtJ5k79XqsiKRx7rnn4uabb0bvDjtkbxvq5gLKDMMwNjS8YUHTNPT29uKkk07CzTffjFQqNWlU0DQNRx55JHbbbTe8//77VT7TBsDpZNoqlYtTRGhuOVMw1El6B2YK4ij9kCQ/xRgZkhYFnhlmquFElorp6516K7utXOJxiSk3brfZfLJSTKoyq89YJhig9EgStyJRcrXLYmWM2zrD5KZnIWLJNF7ZlEasYyEVUWYYhmEc0/CGBUVR0NHRgRNPPBHPPPMMVq1ahQceeABHHXUUFEVBS0sLOjs70dzcXO1TnXqYU0tl5OqUFJualrmdXT54u1RV8ufhHiDgM44XCNn/znxcO8y/lxcX5UyfVez5MeXB7ftczP5y1UoQ7UXeRlEy87YLI4OcPsyMvChVlPznWa126vS4TG3hpB0U0laKbVd2siS3q1yKJLvj5pJRGbvxsFgqNS4xjYvrbTaPrMjf5+vvzamQFIVlopGxet5O+2Y73PLWz9Uui5Uxp0YP+XhO5ncMwzAMwzBgw8JkQebTTz8d6XQa3/3ud/GpT30K3/ve97DNNtvgP//5D1asWIEjjjii2qc69alk3tlSFxDV2jfDuIl5IexE/jgvNMMQ5Uz74PZ4yOMSU25quXaAPwSEApmfsUwwtYg/ZN8ui5UxTlHEMHVN7zSbotIynKqJYZgq0jCGhYGBAXz44Yfwer3YaqutEAjQAkNRFKTTaXg8Hpxxxhno6enB3//+d1x88cXYfvvtoSgK7r33Xmy99dZVvoIGgBd5DFM9xMKTPdQYpvrweMjUG7kUogzDlA6PCwzTWIS6kfI24+ZTYV9UWuAPAZc8z8YFhmGqQkMYFt5880184hOfQDKZxMqVK3HFFVfg8ssvh9frBQB4PB4kk0n4fD4cfvjhOPzww/Gtb30Lfr8fPp8PnZ2dVb4ChmEYhmEYhmEYhmEYJidDa6k4cy4iKytzLsXSMQ/LD7sR533sBPui0gBdx50X0vWyYYFhmCow5Q0Ly5cvx6GHHorzzz8f559/Pv7zn//gq1/9Ks477zzMm0cdr6Zp8Pkyb8XMmTPh8eQIN2OYWsLtoonM1ITbCcOUH1nO2LuUYaoDj3dMvcFjB8O4w9Ba4Lq9Saby4Q9RGiFsKftpFUMiNIOLSjMMU/NMacNCJBLBxRdfjKVLl+Lqq68GAPT29uKhhx7CunXr0N/fj56eHsydOxcA8OMf/xjxeBzf/OY32ajQ6MSj9bUglQu61cP5MtWhWu2EFTxMIyDaeVwFfAH3itgyTLWpxz6c50VMtShWXtwugM4wjYraT3J46vVAz8Lc207WJqhNwwLDMEw9MKUNC4qi4JhjjsHpp58++dn3v/993H///di0aRMikQh22mknXHHFFVi8eDFeeeUV9PX14ZJLLkFXV1cVz5ypOvW2IK3loolM7VCtdlJv8sQwxSDauQLA4+P+mJk61GMfzvMiploUKy/cZhnGXXpye/n39fUh8t4WAFuwYsWKip0WwzDMVGNKGxa6u7vxuc99Dq2trQCAW2+9FVdeeSX++te/4sgjj8Sbb76Jr371q3jooYdw4IEH4qqrrkJzczMbFZj6m9z768iLkKke1Won9SZPDFMMop03t7G3KTO1qMc+nOdFTLUoVl5Em1WU8pwXwzCT9PX1obe3F6pqpEsKhULo6emp4lkxDMPUJ1PasABg0qgAAPvttx9efPFFLFmyBABw8MEHY8aMGXjppZegaRq23nrrap0mUyjlDssPhHlByjBuUQsKnnpM5cEUTzVyVddCO2eYclDLbbveUlcyU59alhcrxHiJpmqfCcNUjEgkAlVVsWzZMvT29gIAenp6MH/+/CqfGcMwTP0x5Q0LMltttRW22morAFSwOR6Po6WlBTvvvDMU9g6pL+oxLJ9hmOrBfUZjwbmqGaYx4L6dYUpDyFAqVe0zYZiK09vbO+l0WvdEVubfZrKmBMMwjHs0lGFBRlEU/OAHP8BTTz2F73znO9U+ncZE04r/rVWYsd3+5M81zflxa93Y5Pb5TZX7Ui2qdf9KkaNi9uf28cqF+T7LfQa34amDolg/z0JTUbgtv9WSE27bTDVxu907ac/lSNNU6+M5y3ltUevzunwIGfJIEQu51kvc7hmmtgh1k2H9zgvzb+sPAZc8z8YFhmFcpSENC7fffjseffRR3HrrrXjwwQex/fbbV/uUmFxYpTCptzBjhqklGjEtUCDMnuuNRL0870aURaYxKVdb59SVDFMaYk0VHa32mTAMUwwd88hYoPbn3i6ykowPaj8bFhiGcZWGNCz09vbitttuw+OPP44dd9yx2qfD5CNfmHshi9VEDAi05t6GYaY6QqbGInoO+gorNVmZytQjhdRtcNrGOY0LMxVw0t6nQlvnsYupBuVud2L/0TH3980wTGXomMfGAoZhqkZDGhZ22mknLFu2DH6/v9qnwjghX5h7IYvVhAqADQtMgyNkSolXR9EzFRRMTONRSN0Gp228HGlcGKbSOGnvU6Gt89jFVINytzuxf840xNQDQ2udeeYzDMMwFaMhDQsA2KhQT+RLe1TIYpUXggxjyJTsBVdJpoKCiWk8Cmm3TrfltH7MVMBJe58KbZ3HLqYalLvdif2Husuzf4Zxi6G1wHV7646CefCHstp0X18fIpEIAGDFihXlOEOGYZiGpGENC8wUopDFqj9Y3nNhmHqiWoqeqaBgYhoPv4MUSPK23MaZRqFR2nujXCdTW5S73Yn9x5PlOwbDuIHaT0aFU68Hehbm3jbUnZEaqK+vD729vVBVwygRCoXQ09NTrrNlGIZpGNiwwDQ2nC+XYcoLyxjD5IZlhGEYO7h/YBqdQuobMY1Bz0Jg9u4F/SQSiUBVVSxbtgy9vb20m54ezJ8/vwwnyDAM01iwYYGpH8qxuOJ8uQyTidtyxjLGMLmRZUS8ZyUi0wiw0jw/PIYyhTLV5KqQ+kYMk4fe3l4sWbKk2qfBMAwzpWDDAlM/lGNxxflyGSYTt+WMZYxhciPLCCsRmUaC23t+eAxlCmWqyRXLAMNk1IQoOdLCSXFrUyophmGYXLBhgakd8nnYlGNiyflyDaaah1OtUO776vb+3ZYzljGGMbCSV7OM5JI/7qeZesFJW62EwlCcB5rKd4xywmMoUyhuyFUiBiQmqjPWmPsO8VKUyp4Hw9QAPT09CIVCWLp06eRnoVAIK1asKNy4EOomWbrzwvzb+kPAJc+zcYFhGEewYYGpHuYJYr5Q10C4uBBY+TiKUvsTU01ztp3b18GhxqVh9zzKfV/d3r/bSoxalzeGAdxvp8X2B/nGuXy/r9b4wUxtimkvudqqaKdujTe5zk+cRyrlzv7KCcslUwh27cWN9VJCBbxaZdYEha4HGaaBmD9/PlasWIFIJAKAIheWLl2KSCRSuGGhYx4ZC9T+3NtFVpLxQe1nwwLDMI5gwwJTO3Coa3Xh+18eyn1f+bkxTP1QqryyvDP1Qq20VXEenjqNWGCYauAPAZiojvzWSt/BVJahtc4U3g3I/Pnz3Ssy3TGPjQUMw7gOGxaY2qFYDxvGHfj+l4dy31d+bgxTP5QqryzvTL1QK21VnEc8We0zYZj6IRACAm1VOnaN9B1M5RhaC1y3N0XK5MMfopQ+Dujr68vw9GcYhmHKAxsWGIZhGIZhGIZhGIZhmMqi9pNR4dTrgZ6FubfNUVRYNiRs2bIFp556KlTVMFaEQiH09PS4dtoMwzAMwYYFhmEYhmEYhmEYhmEYpjr0LARm717UT/v6+tDb25tlSLjvvvswbdo02n1Pj3sphaqMHIFRtutyknoqh6GHYZjGgQ0LDMMwDMMwDMMwDMMwjHtUqHZCJBKBqqpYtmwZent7AUwtQ4Kgp6cHoVAIS5cunfwsFAphxYoV7l1rqJtSTt15Yf5t/SEqCM3GBYZpaNiwwDAMwzAMwzAMwzAMw7hDGWonyOmOZIQHf29vL5YsWVLwqdYL8+fPx4oVKzJqRyxduhSRSMTdAs+XPO/MIHTnhbQdGxYYpqFhwwLDMAzDMAzDMAzDMAyTH6eRCC7UThBYpTvK2EWD1FCYP39++SMxOuY5NxZwyiSGaXjYsMAwDMMwDMMwDMMwDMPkptBIhPn7uaJUtkp3JDMVUx85pSI1F8wUmjLpzL8AoTyGHzZAMExdwoaFMqNpGgBgZGSkymfSuIzHk0iO08RnZGQE8UCNN3tNo4laPAYEgjQQW6EolT2vMiDkQsiJHSxHFSQezdn26k6eGoBGkyNugzmQ20CucWQKjB9u02hyVFXM99iurbrYTrnfqAwsRy5QBfkAWEZqiYLl6N1ngJZw2c9rkv5VwFgUOPH/gO7tcm8b7AI87YALsj42NgYAmDdvHrbbzvq41e5TKi1HTU1NCAaDGTUXgsEgli1bNhm9MXPmTMycOdP9g3vagY8/DMQGcm+n9gN3fgb446n59+kLAqf+3lFarHyMjEUB5JcjhmFKR9FY0srKunXrMG8eW10ZJhdr167F3Llzbb9nOWKY/LAcMUzpsBwxTOmwHDFM6bAcMUzp5JMjhmFKhw0LZSadTmPDhg1obW2FUmEPwZGREcybNw9r165FW1tbRY9dCaby9U3lawOM6+vr64OiKJg9ezY8Ho/t9tWQo6n+DIDGuEZg6l+npmkYHR2tOTmqp/vO51oe6uVcxXkuX74cixYtKlmO6uW6BfV0vvV0rkDjnW+tjkfF0GjPrpLU07kClT/fepWjenuu+eDrqW3yXY9TOWIYpnQ4xrHMeDyeqltI29rapsTgYcdUvr6pfG0A0N7e7uj6qilHU/0ZAI1xjcDUvs729va821RLjurpvvO5lod6Odc5c+bkXXwWIkf1ct2CejrfejpXoLHOt5bHo2JopGdXaerpXIHKnm89y1G9Pdd88PXUNrmux4kcMQxTOmy6YxiGYRiGYRiGYRiGYRiGYRjGMWxYYBiGYRiGYRiGYRiGYRiGYRjGMWxYmMI0NTXhyiuvRFNTU7VPpSxM5eubytcG1Mf11cM5lkojXCPQONdZa9TTfedzLQ/1cq5un2e9XLegns63ns4V4POtZ+rtXtTT+dbTuQL1d77VYqrdJ76e2maqXQ/D1DNcvJlhGIZhGIZhGIZhGIZhGIZhGMdwxALDMAzDMAzDMAzDMAzDMAzDMI5hwwLDMAzDMAzDMAzDMAzDMAzDMI7xVfsEpjrpdBobNmxAa2srFEWp9ukwTE2haRpGR0cxe/ZseDz2dk6WI4axh+WIYUqH5YhhSofliGFKh+WIYUqH5YhhSsepHLFhocxs2LAB8+bNq/ZpMExNs3btWsydO9f2e5YjhskPyxHDlA7LEcOUDssRw5QOyxHDlA7LEcOUTj45YsNCmWltbQVAD6Ktra3KZ1MimgYkVCAeAwJBwB+y3q7GLL3j8STO/vnDAIC/fvkINPu9mRtE+4F0EvD4gHB34QeosevNIl99dvmZBsKVOSedkZERzJs3b1JO7KgLORqLOGtHtd5e8pAhT186HM0BaRix6h+cXm++dmomX19U5/e5EOpCjpw+XwfPLatPD/BUZhJxnyspH/Fo9rHsxtUalsu6kCO3qdN5XRZy/2JzPePxJM7+xSMAHPQbVv2VVZuu9ftSBRpSjpySaxyU21dLT+WOK8nLuBYwZMQ8v5Nxu907kN+yHLeGYTlyAat272S8s9vOYftzPEd1Oi92qqdoIPlwCstR5eE12tTDqRzxky4zIpyqra2t/jsqTQOicRpkPT4gbHM9NTawBeJJ+JppUtDW1pZtWAj6gLgKBEK5Jxl21Nj1ZpF34qI/xypeR76ww7qQo2avs3ZU6+0lD1nylDFhsHg25TIs5OuL6vw+F0NNy5GLhoXcbbDBEfe5ovLRlv187cbVOpDLmpYjt6nTeV0WGe3P+hoK6jes+iurNl3r96WKNJQcOSXXOCi3L7edfHKOv8a9dywj5TQs5OqPGlDeWI5KwLLdO7xHVu3QYftzLEdO58VO9RQNKB9OYTmqHLxGm7rkkyN+0kxhBELG4DZV8BdpUGAYGW5HlWUq9kUM4xbVlg/uD+uHareVeoHbNFNOuH0R3B8xtUAttUPuGxiGqQPYsMAUBg9uDMPUAtwXMYw9LB+MU7itMAxTK3B/xNQC3A4ZhmEKwr6sM8MwDMMwDMMwDMMwDMMwDMMwjAk2LDAMwzAMwzAMwzAMwzAMwzAM4xg2LDBMsSRUIBoB4tFqnwnD1D9CnhJqtc+EqQT8vBmGqSe4z2KmOolYtc+AYRoLHlcYhpkisGGBYYolrgLpJP1lGKY0WJ4aC37eDMPUE9xnMVMdVm4yTGXhcYVhmCkCGxYYplgCIcDjo78Mw5QGy1Njwc+bYZh6gvssZqrDxWoZprLwuMIwzBTBV+0TYOoIRan2GeRG0/J/brdNMfhDxiTcyX6rdf9q/blNFdy+z07baq0/X6fXEQjTi5mamNupW89b03K3sUaTy1o/bq3fv0ajWvLhNk6uo9Rrled8DFMMtS5vsnJTUUo/32r1B8zUxu15hNP9ud2eFcXdtQ/PrxiGqSIcscAwbsA5EplGhds+UytwW2SYxoBlnWFKg2WIYaYuLN8Mw1QYNiwwjBtwjkSmUeG2z9QK3BYZpjFgWWeY0mAZYpipC8s3wzAVhg0LDOMGnCORaVS47TO1ArdFhmkMWNYZpjRYhhhm6sLyzTBMheEaCwxTKAmVPAACUr5dzr3LNCqi3QuvGJYDplpUux+WxwauF8I0GvFo9tyoXFRb1hmm3qmUDFmtmRimlpkKbZbHSIZhKgxHLDBTi0rkFOTwQqZS1EuOTJYJppGwk0uWA6aRaZT2Xy/jMtM41HKbbJR+gZk6lNJmhSwyDMM0GGxYYKYWlZjAcnghUynqZUHGMsE0EnZyyXLANDKN0v7rZVxmGodabpON0i8wU4dS2qyQRYZhmAaDUyExU4tAyAhfdEqhIY8cXshUimLaczXIJxNTIayYYQR2csljQ+Fw3zB1CITdfYa12jbqZVxmGodabpNujIucZpCxolxjRCltNhACEin3zoVh6oS+vj5EIhHEk+lqnwpTJdiwwEwtipkMyJ4+tbR4ZZipoqhkGWOmElNFLmsB7hsYO2q1bbD8M7XGVG+Tcl/AhgVGUItjhD8EhJuqfRYMU1H6+vrQ29sLVVXh9TfhiG/eUe1TYqoAGxYYppY9fRhmKsAyxjCMFdw3MHZw22AYBuC+gLGG2wXD1ASRSASqqmLZsmVIpIG/rqn2GTHVgA0LjPtomrPtFMXd/RVLpTx9yn0ddji9z0xlqDf5cON4TmTM7fvC2KNp+e93te6zk3ZQSBt0u13VqlyWC7fbgXl/gTB7oALV6//cbqduykchc7NC+418fWC15JzHt8pQ6/1zLYy/TuYJbpHvenmcYKzIN0ZUS86rJUdO4fUWUyZ6e3spFdKazdU+FaYKcPFmpvFIqEA0Qn8r+VuGqRa12G5r8ZyY+qWR2lMjXStT+0yV9jhVroNhyo0bssLyxpTCVGs/U+16GAbA2rXrqn0KTAVhwwJTHRIqMLYFiEcrf2yRk3EsUvggLudzrHXEJKUa95ipLcrVbhMxZ5Nhq23qSZaY2qdW25Obi0Wxr7GI9bXywpRxm4QKDPUBg3327arcslepdl2rfQhTG1SqHVZzfeSUsQgwtpn+FgvLG1MK9d5+zP1Jvuup9PyuHvohpibp7u6Z/H/JHkvQ19dXxbNhKgkbFpjqUM0JQSAEeHyAgsLPQfy2HvI51vuki3GPcrXbhOqsnVltU0+yxNQ+tdqe3OyHxb4UWF8r9/mM28RVYGIUiI/at6tyy16l2nWt9iFMbVCpdlgP/bhi+lsMLG9MKdR7+zHLeb7rqXS/UA/9EFOTzJs3d/L/mKoiEinBAM3UFVxjgakO5S64JBSegRCAQOZ3IidjxjYOqVQ9BjfgolZMufGHgIA3fzuzaov1JEuNhNwv1lNO41ptT272w2JfzW3W18p9PuM2gRDQ1AposG9X5Za9SrVrN65D7j9rsT9iiqdS7bCa/Xgi5my7cI8+/yvhHGt1zGbqg1prP7n0DlaY5Tzf9VS6X+D5JMMwBcKGBZeZmJjAxMTE5PuRkZEqnk0N4y+z0kq2tPttBvhqTkoqsfgU11eHxZdYjlwmQx6KaG92E2Z/EPD78u+z1hYADUJRciS3lVowLNS7os7Ntp9vXyxnZaGhxyN/COiYX/1zcLtdq/2A0ur+fksda6cwdS9HpbTDQsaxcq+PcuE0zQqPNVWj7uWo3rGTZSd6B5lCZajSMlfNfohhmLqEUyHprFq1Ct///vdx9tln44YbbsC7775b1H6uuuoqtLe3T77mzZvn8pkyjqhkiGQxOeY5xDAnLEcuU6o8lNpei8kNyvniS6YoOaq18PJC2l6jtplGve4KweNRHSBkQLzyyUK55l+11n/WEDUrR5XoP2t5zi9fPxsLap6alaN6oVR5t5PlWur7eU7IMEwVYMMCgDfffBMHHnggXn75ZXz44Yf4wQ9+gF//+teYmJiApmkF7evyyy/H8PDw5Gvt2rVlOusGpJCB0h8yQnXLTTE55mtpAlKDsBy5TKnyUA3DRFwFJkaAwbU8OS6SouRItJVa8VQqpO3VsvKmnJivmxeVrsLjUR0gZCDa76wP8OgB227LSSXnnnVGzcpRseNGIf1sLc/5Mzytg9U+GyYPNStH9UKpzip2slxLfX+jzoUZhqkqDZ8Kad26dTjzzDNx/vnn46qrrgIA3Hjjjbj00kvxxS9+EQsWLChof01NTWhqairDmTJ5Q8yrlTLDSR7CQnMpytR7KpAimPJyVEvP1Mm5yO01niz8GMXk6gyESEnk83NaiSKZEnJUSF9ZTDur15oSMubrLjQdSy31RzVIReWIn0VxCBkIdxvvcxHqBhJD1U9b1EDPu2bHo2JziRfSz9Zy6iD5+gvxpSul7TZQu3ebmpWjeqEQebeS8XLKslty4eQaWQYZhnGZho5Y0DQNjzzyCHp7e3HRRRchnU4DAM455xzMmTMHH3zwQZXPsMGIR4GxLfTXCuElAFh7CZXDQu/EI8mJl0IpngzseTD1qIVnKtr2WCT3ubjh/Vxs+w8EAaVGvfyY6mFXYLKYdlZOWSxFdkqJ0CvUO7YW+iOGcPtZ5JtXVRu3omuEDIiXkz4gn5xU4t6x7BVGOZ5J0fOTGohCcCPNZLHXX0rb5XbPFIpbsl9IezfLuFl23I4OdUsunFwjyyDDMC7T0BELiqJg5syZOPDAAzMiEzRNw9jYGDZu3Fi9k3MDp2mc3C7u63R/5vPL5/0jvASiEevt/EFSOOUN5XXqkqPURiG+Yr2pBNVqB4w9Tp5pgWnY8mMjbwpo4uwP6tso1tsVLQMFyJv5uN6Afm7Scd2+L43Y7hWlvq/bzdQlGd6abrYtzWGKCZvnUIrcFepRl6s/YnmrDOK+yM+ilHslnlul5zBO24tGjjyIR4F0iv76mrO3U8ro/5RLTjSz/Jbp3pllj+XNmmq151z3LxA2otzcfm5O9+f4fkj7cyv1USlrk3K3e6dMFfmoFm6vL3PtT263TqNLS21X5jHCLDsly5Lp/DL0GPJ3ZWinbq5DWY4YhkGDGxYA4KijjsJRRx0FgAwKiqIgEAigs7MTfr9/cru//vWv2H777bHnnntW61SLo55C3ZxOUu22kyf5+Yj2A9EBINxlhM4Xc07i/vqD5UujUcsh1Exx1MIzFRPYptbcHpsJlea3AR8w1AfE0zbbxdyVg1INaszUpdqy4xSzsTuuSnJS5DgH1Ne4zhRGIfMYR/ur0X40oRqRRx5vplImoQLqAI074W7jfpRjnMlFJe5dLcwF6olabc+VwKrfL+Z+OHbCMiHkTxDQvaKLwY12z+NgbVKu51Kq7LtxXmbZKVaWZOJqplw5mR+6AY89DMO4TMMbFmQU3eKqKArC4TCam8l76vLLL8dvfvMbvPTSS9U8veKoBY97pzgd5Oy2kxedgPS/RS7K6ACQngCGN+j7tBnI852TuL+JWOZCt5AFsDzZEfvkiTLjFvEoGdIUUF7pgLQgFRP1aL+1DCRiRtQAAEyMAQkLw0IiZi0HGedho1QVn5vbfD7Z40Vl4+JmgclcY2SpbUzImCDaDyTGAH+Lvn+Ldi/I1f7dqjcktourgC+QvT+WsfrH6byq0DZjt534XmC7XYwiFTxeGpfM38XH9P9DxpjiZJxxE7cUL8XIEcueNfWgDCvX83ZrPWcel/Ih1jPRAZLLiVGgcx59V81nUU/r20aiXM+lVNnPd15W63bzZ4XKjhWyrGsaMLSeasklE0CwzZgXFrIfbv8Mw9QADV1jwY5EIoGBgQHE43F8//vfx7XXXouHHnoI2267bbVPrXBqIQdoPorNUZiV6zBGE96h9aTAEQtQK8JdgKeJQu9zbZfv/MT9NSu65AVwPuTJDuc8ZNwm2g+MrKNaClbtMVdb9QcNo0Jc1esdtNhvl0vha3cc8XmhbV7Iylik9BzDTH1TyvPMNUa61R8L411inN4rKL7dAw5yw9ucd0KlqKPBPmNRKqdDM++Px6PGwfys7WQqX5sQ34s5mN12/mBmpEJCBdR++usP0jgjDHDRCCl4nIwztYjVPcvXZ7HslY9yj//FPDsnv7Hq950eS4xBYjvz+1yIsSo5DowPAYkoEBup/rqyHta3jYaYV6Ti7j4XN2Q2X3uxWqOYPzPLTSFrfYEss4kYGRUmRgBFA5Jx5+MbjxEMw9QYDROxkEql4PV6HW2rKAo6Ojrw9a9/HR988AEef/zx+kuBJKgH755ivRviKhkSogNA51wajKMDNEhn5I2XUPuBhL6Ynb59phe1ed/CayDX+fl1jwMxqRBeDoWER5rDOysd5i17PVTCC5BxjiMvtjzRMQrIgGZONyEQbRWQIhf0/Yi0HNEIeTQHQoC/A8DyzH3IHqXy+8lzVI2USuFW6+MX2uaF3Chxw8AgvIny9SPF5Gplape4SguzaD95UhYyjuQaIwMhavvJOBDIMT5ZRZ3JkTli8elv1tunFFVXTF+fb1y3SxkQVynqCADiUsSSx2YqWEjqAfaeqy+snldshJwuAPt5T742Ib4XKSbttjO3YRHBkIhRBEN7SDeEbaA5HUBpV6z6azEGWrW9DNkM5LwlrmG+t1b3LN+8t5FT/hRKoX2PkzVHKfPiYp9dbITGCESsr8Wq33d6rIxon1D2+1yIOVr7bHqvJYFQF51LOfp9p/ush/VtoyGiH8210dzYb6lREPnai926XR4XzXLjD5JeIRnP3I+cKUHtB5RW49iyzIoaBorXuG+55DGjX6rSGMFzPYZhbGgIw8LKlStxzz334JxzzsGsWbMstxH1FQCKWFAUBZFIBM8++yx23XXXSp5u41Hs4BgIGYaEuEqLzo45FqGMSeM3IxuApibyvA530XbmGgtx1QhNdHJ+0X5AjdDEoGc7QxnrdDEiBmZxjGJzlhYLK1lrC3nS5mgBHDM8RDNSHunPMtStT6iDxj7llETiJUf5mNuBE0OZOU2FbPCQUyqZ5WgytFjJvn4ni0qxvTAwOFl4sNJmaiHarxgLXF3Qjuffr9lzzEphIxan4e5MGSxHui+7BXQgBDS16DVTpG0G1xnjXbH3jtNS1BdWzyvYZnxv10fmVc6YDQa6p2ne/lwaY0T9BeHNmUwALaJOiYUhXY7+MR9DjmxTLNJiOqFUxXUxCuFClaaNrOwp2FDgYPwvZV5crMI72EbjhEhLJ85DPFOrZ+w0ZSRgGJCj/cZ7R85P0nrG7AjiZr+fLzUfU/uUa25dzTm7PC76g/o6Kw5E9c/i4wCSQGwQaJ2hj0/SWGOWjwyZ1QwDhRNnxMmIwIhhtK50ylie6zEMY8OUNyysWrUK++23HwYHB9Hf349LL70UPT2ZilvZqAAAwWAQn/nMZ7Dvvvti4cKFlT7lxqPYSbg/RJEK8mQjn0Lf2wwkY0BsLNNwICMvZlulc5PDDeWBWgGFCPuarZWyTihloHYjDzgrWWuHjAWtg2cjJrrjg+Tx6WvWJ5ymqAPonjF2nmq5JrayXMmGOvN5TEY+RIzFoWxcyDdpTqi6klMfmpzkezcbGJy0Y/Z0qx+c9G/+EEUquN2PxdXMscAOq6gzc1uXF6dOc8WbDRZ296EQY1zHfOfXaDUu2R2Lx5H6wvy8zO/d6iOdzm3k46n9NJZpAJpayagg5CTaD8THgECLvbLT6jqF4TkfVu07GqFIn6aWbPmx+i2Q3/PU7TGokZU9hRoKwj2153wgouNEOpSWtuxnWswzFr/x+MioLRxIxPt8v03EM414Yi4oDIaAe+mIxLnapeYrlkY2ulWacs2tKzFnt5qXmdcucrRPdIDmdclxWrP4m62NdU7asvg+2k/jn1wPTziiyEbRZNxZna1yGOl4rscwjA1T2rAQjUZx1VVX4aSTTsKee+6Jz3/+80gmk/ja176WYVwQRoWrr74aqqriyiuvxCc+8YlqnXbj4qQoYDRCi80W/fmZt7csviQZBNrnAOomUr7KXnAyYlJgNiqYPVLl/Qa7gUDQmfePFaUM1KUuKMWETTKuMVVEbgtOJtOBMLV5LQWMbabImVxYKfllD2pA94YxpVYSsqUFsvdpjk6QF4dyZERctS8ULc7D5wOSSWdKTmbqU4xiMh9WqYvM/wtvMICi2kV/b3UM87H9IUwa8gRyOL1TQ5vcF+S6D6XIRq5rtBqX7JSsbKyrLyzbLHK382LIN7dJSGOPMBAnYnravC5k1fRRTH+B3A4lsuE5Omp/DnZyllAp0jWuAp45ua81Q4nrYuSpE8VoIyt7HM2TCrw/pc6LC1Vmi+OlU5isxWNn/ANyRwFZRWeIsaaQNK3JGODVrA3gpbT1aITmgeHuzN+Kc21uK67/sbvnPHdknCCnNfLr4QhWaWaFDIn5nEgRZkeoG/BL6raMdirJodoPjKwHfE2Z8085Ik8YRQN5HKlEmxeRf/mMiFbkc+ZiGIYxMaUNCx6PB3vssQe6u7tx5plnYtq0aTjrrLMAIMu4MDAwgJdeeglr1qzB5z73OXR3F9EJM6WRb/IXVzPzQwOmGguSJ4E8EU5KRZUCIUDpot+Eu7I9AmQlqLxytfJInVyEpmhbOf1MoeRb1OdapDTygnIqUsykTUxORZ7OXARCmW0lrlLqLy2pF2duprRF5sWkkK1UKnufstyJybkmndfgWhInDUYUg/idbGQQf1uLaOe8eJyalKN/y2UotkphEo048w6z6p/jKi0WUymSLXRny2CufZkVJJOLTQvlUSBUvELL6hqt+iLN9JeZOpSjD7VqQ7KTSFI3ivtbgI6QlDZPN5ALr2iAxgo5tV+h5xGyGRvl67aa63n85I3qK8AQ6CZOnot8n7luVjaVVoaJZ1ZI7adAKDO9q7kdOR2PzMZff4icP4bW6gY7C29oK2cPX5AifSyjWEto69F+IDWuGxckw0Kpz8hOTniNVH9UMspEHEsYt+VizFaRpXbzNzmdrJwKKREDEhPGtYh2OriOvhcpMjWQUUEx1cOzisjLJyuyQTHfmtDufpQj2oFhmCnNlDYsBINBnHfeeQiHaUA444wzoGkazj77bGiahv/93/9Fd3c3UqkUPB4Pfv3rX2NiYoKNCtUi3+QvEMrMDw1kTsLFYlNMkuNRPaxQNfYRV0nBKaelAOzTwwjM6VbEvgBg9EMgEQWgAdMXZf82V2HBjO2K9Ehl7wFGeGz6o0b+z3jU2svMvIgUqb9GB4HW6SRfVuG8QrYUi0mqLHcigmJilAwWigaoAzRhDvcYeX7NdUwA+7ZstciQZVHgZvg8Uz1yKdcTKqDaeB47xc5QbP7fbnvzeSZUUoaa++exD4G3/gGs/A+w7gWgZQZw/r+Blun21z241rreQYbndSTzmHJqD1npFFcNI3o+r1KnypeWHkwWfnYKKzrrg0op4OIqENedRFIJwOs3/Dgyai3ESEE6+iGNTYB9AWcrohFn7T9XlGAgBLRMy4yUlTGPQ1ZjlBPPcidKonzPxUohxPJWPWLDNP9xqpzzhzLTuxarKLcy/iZihswNxygqwt9McjbpSGIx7gVMayUZ0d5a8qSWMrdzkZJJeFG7pUS2uy+8Rqo/4iowMZLpOOgm5ig14aQY7spc/8jRPRkZEaz0BDbRQNF+INicqTtQ9H17Pfpxuw2Dn1U9PHn8cpoe1ElkgxWi33E7JRnDMFOeKW1YADBpVBDGgzPPPBOapuGcc86Boij40pe+hKuvvhpr1qzBrbfeiq6uriqfsYu4ndpGc9lFUUtnvvc163UKVGBsCyi1UAiTK05No4myov/vD1Gx5mi/PtCOZXqyRfuBxBgA6TgJ1chdKkclZExI9c+s8vFOjNFxxiI0mQ60kBeqliAPHM3Cm9upF2BGyLLpXmdMWMR3Dp8vpzgqDTfbvewZU44Fv1DqW3nZaGmjLWoaLSoBkjlNA7pbdO9RGAtOWUb9eqovucaClgI0Rd++2fjM16wb/XzA+CjgbSIP1FAXyYCqF9pNJeh3mpa7OWfIUDDzs9gIGQozcgaLjgKZiwEg0wDi1OgHsByVSiFy5MTIKlD7AbQUtvgRbVl+n/W/Zr29fGwxHoyPAcF2oDkAfPAUsOphYNVDwKbXM487MAbcciZw1l8BxUP7bG7NvDZfABgfofYcjxrXJU4nHjXyz3t0zzbNdK7C2y6yGvB46P98ofAZ90R+VqZ2X4ySphhP+Kkib07bvdPrdXseJmPn9e62UicQAhK6k0hYGJWDJBOBFhjpjxQgOkhjxdgW6rPt6iRY3Zfh9UB8lMa15vbMaDstbYxvYu4pPpfxNQPtczN/JzMxRvI4PkqyLN4nYpR72+Onz2yjcB0oz5zKXL0qhDTNvXZdS+ueYDuQipscNaT9WTl6iH5Y0/Q5W0yfI0ntTm6v6UTmMRMxOoY/DIQ6jO99AZKvxASQHKXPkyrgm02f+wL6uCIdR0vT+BaPSesx6dzlCHK79qmlgIkotcuJKDmYhDrpJX+vpYrzjLZzQpgq40etU65xKxCiNbzsOFjM/tJJPb2eMKTp+5kY09tkmj6PxulYWprWKQJfk7GfybmXtH6SEWuZuAokJOcXBXo0eTO1da+f+oT2IKAOGimVzJEQdg6PVushO0RfElfpr3lMsOuDEjGqb9TUkr1PK9ye5zBTihUrVgAAenp6MH9+jjpVTN0z5Q0LAq/XC03TkE6ncdZZZ0FRFHz84x/HP//5T6xevRrPP/88mpqa8u+IcY5ZoSfSowgFR1zNnqwCNImNj9GA2zGbJsiAPjnQJ7IiZFFY5NMpfQJu2lcyCUDysE6OA2grPCdoQqXzSY7TBKWphWo0hJuB1tmAd9CYKItrE3lShbI22J77GJOTYosBOlfqDKZ+EBNCu6KtIspGgXVqLasaImbyebONj+oT6QF9P/orHqM2jiQwPE55Qy29LGPZn2WdQ5B+LyangCGz4n/zZ7mw8gQy5zm1qxmh9pMh0OsBmruMVEyyESbfgpY9ritLvjYcGzH+zxVpBlgvnHKlf8j3Wxl/M9D/PrDuWaDvWaDvGWB8KHObWbsB2x4BzNoduPfLwMZXgTsvBE6+jr6XDQtySjM7L1Jx/fLCNqFHTch5eUVEUFI33ol0ZHJRwErBqShqB6cGAztjkBiDBMUayYUTSL4xrn0WMLwRCOrLFZEzWj6+P5itPBrbrCvZvYYS1oy5voP5O6FQBayVq+L4iRgpceX3ZuOf1bHjqmEYdyPdhJCzYnPUM+4QjxpeySHhla+3WXkMGl5PSkb5c5l8BqWESopKQSBIhrhElAzKKoDEBgC684cvCDS1kTErMQH4dSW/3THUASAxQg4i4nwmzy1oRJA7UW7mquvgD9LaqpgadeVI38aUF6ce953zcs8bnI5liXFDp9A+i96PbDKMXIlxY/1uZTCYPKdmXW7ktUZMGq/0dh4fBQa3GNs0dwJ+r9EHTNaeC2bWi7RT8MvOVOZ9OCFXRgar70rRN3ChdMZEMBTC0qVLAQChUAgrVqxg48IUpmEMC4BRpFnTNJx55pn4/e9/j1dffRUvv/wydtlllyqf3RREHrAAw7tl0gPTZAwQCzmAlJ9akpSfHfrC0x8C/C00WTcPWLGRTMW+oHUakJQs5F5fbi9Y8V1C98YRi9S4Cjx1LTDUB3QuAGbsDMxZTJ7YUPTJuazoGafJxdA68r6zCxV2exDmQb06FJrSwG5CKIer+y2U2HbRCObzAIy/4nxkw8HoJmNCK4wKwxvou/ExSv1gZagT5+AEoZBJWihurNLbJMZzK3rlaxMTX9ttpfukgaKJlObMgtKAMXHPN4mW+wc2LJSffAoVOZVdrgVWXM2sH9IxR/eWzpP6TpBrO7UfeObXwAvXU9sUNHcA2x5GxoRtDstMe9QyHfjLKcAHTwIPfxc4/meZ+5zMxS1FHch5scU9CbbRmOcLGMdOp+h/cX0+P4AWoKOLZFuN0Ln5c8hNIRSa5oXHo9qg0AhKO0WEUIjnG4us2odsYBf7lNPopZN6xGmMPEgDzYYCUyjtRYRAIkZzyfeeADa+Amx6kyKFxjaRQWHXM4H9LqHjxAYsrkWPLrBycBFzVCB7viqYdJwZ1Oem+vvxUTI2hGyioEV0UrjLPZlkOasO5kjUaD/N4wIt+vtI9jiSiJFRIZXIYXiyMGYlVFL4i/ok6RTto2UaGRUmhugzxQuk08DEMG2rtdO5eHxAm53TiJqZOlYdAoIBctBqMZ1jroLpZsRccNK4Yt5X0N4zOp8zDRut6w+nY1C+/sxq3W6VMjURk8aLcWB0I63PFd2pQxQht+qrhVMhoMupyeM+Y46oG7nVQaA5DKCftgk0A7HNRp/QPsfiOA4U/GIbMee1MlZaOcLkMuzZGS/yOd3YwYY+xsTLL72M0eEBrFixAkuXLkUkEmHDwhSmoQwLABkXUqkUvvrVr+K///0vXn31VTYqlAvzgGX2bhERCwKxkFO8tCCLj2WnRxGTbL9psEUys0iz2DYeI+8dgWITHm6VczudpEmILwg8+iPg5Ruzf+dtAjq3Bnq2A3oWAtN3BDq2AkI9eh7sGJ1XU9h60uP2IMyDenVwa6LsD9LEU1ZQmL+3yvkp2pQ5PZBQxI9FgJieUmJ8DGhuocViWF7w6UaF1pmGNw2Q7bkpInFs70WMFr7mYs05PYr0BbLabz2plQ0uCQdKGHnSrQAIdtP5m6NAnC6OefFaOwRCQEJKaRLqAhAnZY45N21CzawfIi/aZK9/q0VUXI8C0AAEfMb+vQHgrTuBB68k5SVA/f7CY4HtjgDmLKGFn1Vo+Jw9gFOvB277BPDWHUDX1sDh37C+RpEKYLLdS2NfQgXCwsNOWkCbI4JadDnQkpSyzGvjPV3MYjJfn8djUW3itC+zG6tyRYrJ5Hr+4hwAU60dvS8WEUm+gG5A8+nRpyD5W/sUsP4FYOB9IPIOEFmZnaIIIC/ol/8MvP43YMl5wHYnWF+L1TWI+aMY96L6mJYwXU8iRhEViTEyZHibKN2DHIlkRSHRBewwUtvIOdo75hjrFqu6IZA+i6vkySzvZ7JGXMzamBWX5kLJhBHxMD5KBrXX/kqK03APEOyk+WRzO71apgMd8yjKOtRF8yHFY+w7MU5jhSDUQefXEjTGHWHUCJvmUnYGAOHRLQwpCUmmnJDPmYaNafWHW/Npu3W7POYkxqnd+Zv16E0NSGvkOJhKkVwAJAfxaOb+fU26k2DU2FdzW2YbNst2IEjHGh3M3JdV3ROBPNcMm1JjyhHs8rGsDBGqmC/6dIO1g/mc0/RLTuG1EmNi3ry5aN52QbVPg6kQDWdYEOy00054+eWXseuuu1b7VOoDEdpbyMJGKO3EhFMOCfYHs3M+i4Wc2CaZAELSBFREEUQHafIue2LHhgF4je3EgjDcDQz3G/vwSxNkszelfF2BEKCOA6sfAl65mfJmQwH2vpAm1pF3gMi7tHCNvE0vmdbZwCH/Q0aHphYyToxFDI+FzvnGOSigyUouzBMMq0E7IU1OWvLsj3GXXJMpua0BmZ5tAjm9xGTxLivvLEmmzAVchddjXM3M2RlXqd1pKQBBUmYCmcYDfxCAT4+sCWbKotlzUyiBAPLMCUyTriMGDKzWPXb0lGMiFYRd2gkhr8IQYTepFSldnIT/ipRF0Qjdn3CIzkXcN6v7a+4TzO85UqE28IcAv6mWjVgMDa3DpDYn2Ebtqak1Mx2XIMPr3yZE3BvQax2o5OG2/hXgxT9QOiOAcq8fcSWw40ed545deDRw7NXAv78CPHENsPlNYM8LgG0PJ1nJuFbd0JiI6QomNdO4IEdKiG2A7MViqIfavzx2RPuNYoWAwwgOSSbyLSB5gVlbyM9OpIIsRWEt16kZ6stWNuZ6/qINRyNGuq4Wk2yODxvGv8Q4Ke6f/Bnw6i2U5shM62wy6s3ZA5i9hFKQrX0eeOJqKpz+/O/Q9NKNuCy9N/6lfATQjsh93fL8UniimhW90QjVUtm8HBh8DxheS/PCnu2Bs24F/BZe2MU8h2KMdJy+r3IEQnpNKb+x1hGpvnL9xqzEE3Xh/C30WzkSW47oDuhOWqEgsPktYMU/gTWPAf2rjf1vyTpiNu3zaJ2y3ZGGQjQhGehCXYASz5wLZjh4SO3KHKEuK0Djo1QjxddMxg45DaeMlXFCpLNMxgFEjHvHxoT6xS1jkNW63Tzm+JtpPRIbpqnhbR8nQ7TjY4RpLdPcDgQ7KOoz1E3vvQHAF6LPw9P0SFoPMLwBitKE6VoUEwhQu25uR2YdKwkx10zFqa2LwubRfmBkPRmr/SGjuLP8O8BweknouhDFI0UTWvQzMmanknxpy/LeL5ZNhmlkGtKw4PV6ccEFF0ymRmIcUIr3oXnCKf43D1yTi81+vQCsrsCUFSjJhD55l89D0b0A9OcZ7QdW/ht46x9AcgK+aTvhQK0by7FQP4f23Ncz9AHw0k3AK38Boh/qh/AAx/yIFECCdIoW1JF3gC1vA1veoVdkJTC6AfjXF4HtjgL2/6JRpCk2RFET4p76dMVVhkeShfdoVoocm8Wn12J/TPnJNZmKq0aBRn9zZo5/gfBMTiUlz+ocygAhUxoyizTanUeghQxcwS5jMaelqUhsaoIWbek4MBoBPFvo8+S4HlKv6PVFVCA2BN/oFlyafg+t2hgCf0tQ4fLYEL0CIaB7e0oXNmsxsM0hhpwnYmQYVAcz6zckYpIcSJNaWQ4AmtwnE4b32+S9z+GVY54k5/J+M/cJ7HFdu8gKdcB4zqlx6qvTGuDpIu8vS8WmA69rodBITACrHwZe+D318wAtNg/4ArDv/ytuAbbkE0B0C/DYj4CV99GrfR6lbVn8cTI8i3MIdQKRQVJarh+hopvqADC8DmiZAex4Ml3jWIRkGci8ZruUYdEBID1BaZJCHdneclbIMhG2Se8n4AVmbWHVnxWlsDb1oaJfB3SPfimCLt8+5cgaWZGYiJEzhi8ADH4AvLoMeON2Iyo1EAZmLyYDwpw9yKDQMjN7/9seBmxzKPD+Y8DjP4Gy7gUciSdwpPYE0jfeDux1AbDoeBrrNBhpiQQTUV0p+iH1OR8uJzkcWA1seA0YfB+WLqib3iBjRu8J2d8V8xycGOnMxol6Tt9XbxEa/hA5O8lKcTule64UJXKkQ9IUiS1HdCdUkocV/8xUknoDwK5nkJF6fJjGidgAjWPqoPF/bJBew2uBf36O5mqHf4NkJWTKMR8dIGNHooXkI2mOPJecYjz62kaOQhKpYbx+PWJ2kPYXaAECppQwVvMzObVUbBBoncFzMiYTK2OtwB8C/OOUBnnVw4UZFQAgEaXX6MaCftYE4C/ijch4GWihNGTi1Tqb0iIFu8mIF2ijqFJFIcOdAoqaEM5ZZt2AMEYKmfEFaftkgtZ0YcmhxmyEkHULcrRVrhSzDMMweWhIwwIANioUSqHeh7LniZVyb/J/KbpATBQDQZq8xmM0qU4Kz1EvDXxqv8l7s5ms85F3gOd+Bbx5p6GEB+Db+Aq+qf+v3TgXmL8PFdKcsQvVSQBIgfruA8BLNwDvPojJxWLLdGD3jwOLl1IIsYzHS97f3dsAi47NvPbHrgae/TWw6gFg7TPA3hcDO32UPBtE+o18OYzlRYjwXE3G9Htm4XHNHqK1yaQ3my8zx78sI6I78jXnL8oVj0phsxYFns34Q6SoTCeAD98G1jwBvP848MHTZPAoEB+Ao8Ubs+NoMkbyufZZ4PVb6bPWWcD0XmDmrkDLbGDuYiAhFQyU+wd5UitPmP1BwyMwOmB4m8teOVaYF/g5U1+Y5IflqXaxSq0y2W4GgNauzMWU+RnmWjyJxZuvGeh7Dnj6WooqEMfd8wLK2R6eZv17pxz0FaD3JDJgv3YLKXmeuIZq+XRtQ7mx1UHDWGDHkz8Ddjkd2OZwSmPmdGoT7qLiockYkNajOuQ0UlYGO9sxq84UgY2I1bMrpo8z96H+oJHmUkScOVX8WaWiC4TptxteBp79DfDef43vZu4C7KPPpbyBzN9ZpR4DSEmzzaHA1odg4oMX8fjNV+EQ7VkENr8B/OvLwIPfovEpMU4eo8kJPUJXVyjlo2UGndeMnen1wZPASzcCL/3J2rCQ7zlYyZITI43ZOFHP41ehBq9aiM7IlVbRbt5hHof8IWqHwjAEGHar6Gbg9duAd+8jByaBx08OHDueROn4gh02J2jODa+SfD11LdUmufl0Mmwf9HXrn4nadu0hI/oUMNYrHh8pdc1RSP5mciQR6THjMQB6YXO58G1Ti/19mpwfBzMdaZjGI1dK4bGIIVNyv+FvpiiD1Q/T+/0+Dxx8Wf5jJcfJmDU+bBjjxofJiWp80HCoio9hMu1xYhxIxqDFY0hMqAggYewvPkaGjVzGDW8TMHcv4MRr9SgH3aBnNiJMztVUwynEH6TjB/2GkR+wNkLIugU52qpY2eI5YEPT19eHFStWVPs0mBqgYQ0LTIGIxZ4T4tFMj5VwT+aEW/yvpWGZR1RMXoWXiiiC1yTCi1WjUOXEKPDG30kxM/CecYzOBcDu5wLt85Dsew5rXn4EW6MP3pF1wJvrgDfvMI41ezF5no1sMH6/zaGUk3fhMUYO00Lu1ZHfJmXPvZcC618CnvgJsPI/wNE/BLoWGMfOSgmjLybMymXLiUE483dW3hpM9fGHgM652ZMuuaCfHDovpzqyTNkjpWgR+7GazGkatev3HzdeasT6HD1+8g71BmhiO/m//gqEKYS9uR3JQDtueqEfYwjjsyfti0BrN4UHBzvIC3vDK/Ra/zLQv4o8fUY3Aqsf0Q+mAAsOoMK1bbOtz2cyBLgZ6JhL1yeMiojrObg1UgSJNDeCDMWohdLKaaFR9riuXeS+UR0A0KL3f91Gij2rRZQVZkV6IgYMrgHu+x/quwFqB3t9Ctj/8xT14xY92wPH/BA44grgzbvIsL3+JTKSy3gDlM5I5MUOdgHBduC9x0jGX/gD8PJNQO+JFEVhNoJbIe7TxKihBIqrppz3pvtmJxMc3eOcai3ArZ5dIX2cbAiX5xlynyrX/DHj5LpTcXIMefY68voHACiUPmyfzwJbHeA85ZgZRYE2ewmu8VyM32lL8ddDNsL/6p9JftY+n+e3HnLsaJ1BDinCkDBzl+w51+zdybDw/uMUbdG5Veb3+Z6DmBcUKktmQ4LYZz06URXsyFTF6Axz4WYrMuYdNgYwgPajiYiwLnIgGt0A3PV94M3bjToiHj+tUXY8mWTD1piQA3+IjNu7nwM88gNyBHn9b2hacQ/OSR+H2xXdKBbqIoW+VT08Od2RbGgEjCgkLWV87g8CQRhGBnWA1lexAVLimvsWgXl+zDQuVnMN0V8oceu+0x8C0h+SwxMA7PFJZ+0oEDZSOAsc9qcT8SROvvphKFoad39pfzR7krR+GdlIMj2iv0Y36v9vpPVZaoKM0zccC5zwC4rGm7wOCydNsRYUfaWInBL3yc4IMXmNocxoq2LhOWDD0tfXh97eXqiqilAohJ4e1kM1MmxYYNxHFBBKecnjPx8ij2hWkdgYpWRpbkNGbYTxEeD9J4GV91Joo5i4+oI00d79XGCr/SaLkiV3OBWXvPYQgloMfz+jHYFNLwPrnqdQ9fFhvX4CSFmz+7k06ejelgbJUpi5M3D+f2iR+cj3gM1vAH/5KCl+Dv3f7ImNGJjllDCAMSmI9uveTM3ZBZ54QC8fbiiCLJUJwUwFuDl0PiM81cbrXniLyc/+3QepuOz7TwAj67LPY/6+wIKDgK0PAqbtQKGzcgG/PC7PyXgSf3vpIQDAZ3qPAALSMNK5gLxtBOMjwMbXdGPDy/R3eB2w5kng1nOBj91AHp/plN6+pegExUPnIkcQRPsBpEgR2twGxMcB8yPJV+zPCpah6lGqfFnldXaSK1YYFERKOrGPFfcCD3+LxgbZoCAiFNIp6/2Vgj8ELD6XXpvfosXmpCGhC/CHYSmX6RQZrJ/5FbD2OTKYv3kHsO0RFFWxzaG5F8IZSiBdvqxy3uejnr2jK025+5pyGS6c9Ku5vLZzXffG10lx+vrfjPoJ/hCw2znA3p8Gurdz7zoAjCqtSO1zCvwHfo7GouF1usd0mP4GWvRr0f/6mu3lyFw0unMBsM1hFGnxyl+Aw68o7OSKlSW59oqds0G9UKhRv5r9j5zq0jxXy/Ubq6gwOTqgfzXwxM+At+8x2tg2hwE7nwYsOs4wJmgljkets4CTf0Vpwe6/Asq653Eebsex2n/hWT4B7Ha6RYSg7hkNZDtxmOuA+ZqNviM2QOu0xDiAJAB9rZNKWPctZmOm2Ke4Z+wh3VjY1SWU+z67fuBlPTHRNodlG3vLiKZ4dJnwkZGiZ2HmBvLaKzkObFkJ3H0xpd37+8eBQy8nozpgXFciRnO12KAeETTX2MdkxEYOIwQADK2lzzSQPNvVbnQKzwEblkgkAlVVsWzZMhx00EGYP38+xuMl6s+YuoUNC4z7aKDBTizK8hHUJwhZRWIDZHCQi732rwJu+6Se21Zn7l7A7kuBnU4GmuyLFseUINILDgYWfUQ/zzSFJK59nryeFx1Lk2A38XhJMbXDccD9VwDL7wae+SX9Pf6nwPZHGduKgVlMCIRi2d9M3gxIkWe4OXyaB/TyUi5FUL7Q+egAea8Nrc9csFoZIQIhWpw9+E3gud8a33kDJB9bHwwsOJC8X8wpJMpJcxsde+uDjc82vwXc/DGg/10yLpz9V1LaKjAWl+GuzMKycZUMlqMbyVuvKUyL+VSKPje3/diI8VsnsAxVj1LlSzbEysa4fIiIOI+XnrvHCzxwBUUNAMCcPYHT/wh0zC/8nEphxk70MmPl6OrxAjucQK91LwDPXEeKqNUP02vGTsB+nwN2PtVa7uWxRHi0KV6gY3phslCIIrDRQ+bL3deUa7wqtagjkNkvD64B3robeOsOKToBpOjc+zPk4BHsLN3BIxeKJ3Nscosln9ANC8uA/b9gKMGctP1SI+VKef7xqPOUQoVsW26qHV04spkiyJw6M4ixSh0gwzFAchXqBkY3AY9fQ2sEYVBYeCxwyNeoIHm5mL0E+OS9iL9xFwb/8XXMQAT450XAS9cDu51F5+fxG+levH5ShKYSABT6vntb3ZjtyTRETtZA0g0IyXFKDepv1iMzYkbEgvk+meu5iLYNsDNIo2GuS2juT+36gVScshoA1DfXKr5mYNauwKceAO75IjmJ/Pf7QORtil6QZWBkI9AUAjTFei6RlWbN5JA2MQaMD9F3iWZy0hI1FvJh1fdXuw9mqk5vby/mz6/weompOdiwMJWxyzlbbsLdhsXcrA2RPXVEHlhRiDMe0/N4Bo3/Q92GQmRwDVnwh/ro893OppfwAEiO04BnJi559MRVIDUqnes0YIfj9e+imb93amRIxfNv4w8Dp/4e2OVjwH3/S/m0bzkD2P5oKprWsRVdd3ObkXpJ5D0c2Qh4PEB8wshDmrHvPAO62+3AaWi9k+OWo426vU+3FUFOFCW+JprkibQksje/2aikpYCB94F7vmCkblm8lNJ4zd3LaC8eP4ljMk97leqTWJKQ5Gl8mCKT8l2LTNfWwNI7gFvPoULpfzmFjAs9C/WUaM3UnsWCW9Po/sfHSDb8LUDbLMo/nxjT83qnjby/yXGg2ZweKUr3UOQJBjK97NycFDttf7UuR04p9Xpl+VKUwq8l1GVEzci5YgFJ8SD1mSIyLjEujTdjwO3nU0QNAOx9EXDY5TT2yIXCAVN0Tw6sxiIrPHnkxynTFgInXUse0s//Hnj1ZjLi3X0x8OiPgJN+RdFKk8c1Tf8SqlHLyB8yhm7F6fNw2J6dKj6rNW65jfm4uQzKbuDmeCUUN2LeFuwgxc7EiBFdap6P2D22dApIjAIv3AW8ez9FsAk8Poqi2+lUqksg5nzxKNUGcgt57IpHAbgge1bzxO2PorlldAvw9r9IMQvN1PbtDDQltlPz8y9EjgoxSuTath7GIzdpm0nzKl8g//xOS+tK+TgVlI3207w+8i7w4h8p2lQYFLb7CHDAlyj6GbCuiWWOmLHFWbtKLzoRn/YEcJp2L87z/QvK+heB9S86PAaoPtDiT9C5+0NAS48+p2sGEk007rbN1MdVjeTQHyJnEbogY1+yMlQoMxXQ/QKM+aLlPXA4TrtNPaYeqxcy+jaH/amWBt75D/XF4WnUN1u1l5TDcSa6xdl2HqlPzLVOstMfHPk9WhM9fjUVat+8HPjYjVRHK5GmsXh8GGhty4yitWt/cZXWTMJZs6mF5noadEOfTY0Fq/60EhHebq+jGIapCGxYYNwnV2FMkSYp0EJ1FATxmO6JoA++WgpIJIw0J8PrSAE51Ad0LAA+fjfQ7sArtdbY/khgq/2pSOezv6UF9qbXgRP/jwqwAYZ3NjS6H22zKOSxrc0IVxQL/Ub1+qwkbimdhZLGFzC8bUQ4OWAo1MW2ybjuzaVSoTAtZR1Cv/5l4M7PANEPSa5O+Dnl3a1lOrfSjQvnUj75ZacBJ/0a2OYg63stCrr7W/SJcYj6hkRzZqhvWi8K6PGalMkxw2ASH5eKPnO+3qrjRNEqp0SwTcMSMiJdFN1YrcAwUE3uK0aRQH697ax5nGrhjA9TRFg9yE8uOhdQLZ+Dvwa8/Gfghet1A97JVLRw/88DE1HAoxi5qwWxESDcmf8YuQo858IulYHVNjy2FYc8XpV6L4UCYeA9UoKmxg0P43QSGN0C+P3AeJQ8kL0BIJWk77Q0eUXGo8Dm14EV91BUjUDxAPP3owLmi47LltN6xuunNE5PX0sGvh1P1o3iOaI+3JrTlTJfKcQoxVF+hD+oR3ppuuIOmfdfzPPk+Z0/qNek6geG1lB9g7f/JUUoHEORZsKgUGHiSgB/VU7BWZ/+Opqfv5aKRSteUuJ5vEaaSo/+mVDif/AU9RUPfxt48meUtmn/LwAdoez7oA7QnGx4I9A+i6LGzeOKnPolrmauC63aeULVx39Q6iQeP6YOcg3CuEqKZ6dRdC/fRH93O6fwmonVQlEoumL6jsC/vkypkf7wEeDEX9CYmYhZp0GTC6LLdVHUQSA9QX+ndQFN8wx5E/iD1CeJaPFwt/Xc243oRYZhpiRsWGDKi3miKIzQ4m9CJWWgAsNTEgCig7oFXQXGPtSNCmvq26ggCISBI66kycGdF5Kx5NZzKHJhn4sNJWhiHIBihCfKhZjMIcHyAo+VMvZUU2k1WUwrrRsWxun9+Ch52CfGJYXQOC0yRVEuTa8rEPBm5pR/8Qbgke+S10v3dhQV43I+6rLROhM493aKQtr4KnDXhcCx1wDbHUHfB6QUaKKgO2B4svilRSdgTHabWug7YaQUnm7C2CAv7gX1qswU552ocj7Lct8/OSUCAKg5omqCbaQgD7YZIfMyAT0irrkJeOxq4Lnf0OezlwCn/DYzX209E+wADvgisOcFwP1fJ+XV09cCK+8D9roImN6bKQ+AkZbQCnksNz8PpwpRcyoDu204xYU7lHovhfI42EEpULx69FkyBvxNrwdSKPP2AXb8KClPndTgqlcWn0vytvY5YOObwNb7595eyNRYRMqTXeH2X4hRolypLyoxFrt5jECYHIHUQcCnZvepYp43soHGpcH3gS1vA33PkFORiNgGSCYOuozSocifV4vWmcCxP7H+zqrWUDwKvP534MU/kIHhpRvIuL39UcCuZ1Gxc3WQDAkAtfWmFrpHTa2GDKj92WONApoD+3xklPDFMueIgK5U1ecGudpnvc73GhkxlsWGjbRjYb2wcrSf2oxVnYDBNcD7j9H/i5dW9JRdYe6e5IT178vIMH/7BcD+XyRjtYZsR5uM9GHSGifUSbLnbybDgaxH8PiMezm0HpgYBmJD2XM9uzoqXFSdYRgdNiww5UUMSiKVi1xYCDA8t5tMnpPhTjIujL0H3PlpSh00FYwKMrMXA59+GPjXl4C37wUe+jZNHD7yPVI2x4aBzubMCfbkfQsa91NWHgCslMlFNZRWsqeNx0eKNUAPDR/XC5fr+WcT+nn5myliQVZ+i2KqiRh5jP3ry8Abf6fPFh5LNTtEiHi9EOoEzr4VuOMC4IOngXu/BBz1Q6pJEo/lf0YiSkFMmM2LerGNL6B7tPdY76delZmT5x3Lv21FzqNM908uZj+0HtBMYeWy55XHZ+RxF96kcZMSYngdcO+XyaAFAPteAhzy1crWH6kUTa3ASb8k5c6/v0IRQg9+ndJVHPEtYzsxFtsqYyxyZtsVkLfDiZcze0K7R6n3UlbORfvJwJCKU4RcllFBIdnz+Ch1o/hf8VCqx96T6CXmb05SSNYznQuArQ8hpdbyO8iwkKGkkRxB5L5LidfnWOQWlRiL3T6GBkr5qOh1AwCqobDpDWDDq8DGV4C1LwAj67J/G2ihaOUDvkwGhXomEAb2PB/Y4zxg9SPA89dTRODK++g1rZcMbjt+lMbalh4yFoh7JsaTZDx7rGnS01sKedGS2XPEQBBQvRRZlQunz1+euwNsiKgmYiwTCnCxNlL7gZH1JH8ivbLMK8vo7zaHVrRos6u0TAc+8Q/ggW9SurSnryVdwRHfpvugDhiRQHbRBOEuI6LXah4n8DcDgx8ALd322wisjA4MwzQ0bFhgCkf29gCsPT/Mi6XECHmSBFqAjnn0uYZsr5KEislc6f4m4JYpalQQNLcDp/2JJgsPXkkGhg2vAof8DzB9ZwC+7LBgILtgntXzELB3jkG5lVZW91osYjw+UmyLHLz+EIWX//kEqqOxy+kUqtsxlxTlimL97H0BYNnpuheOQgrRfS+p31yTTS3AGTcB//gcLT7v+xrJxSIH6WgmFZsJMjDEBvV7B917KewsbLca7cINJs+7yiHJ5bh/5hDsQJi8o3x+YGwwc1srzysxBiV0+VMHgY7ZwPtPAnd/lgrXNXcAJ18HLDqW8syWkw2vkBdnywx6tc6k8bBS9J5INVfu+QIVln3h95Q7+7BvANsclt8DWZYjc59klZvXch8O2j8XAXQPN++lqFvz6I+Adc+Tou+8eygP9GR6FNjXWGhElnyCxum37gb2+X9kmBHF5gWikG8qQfPbRjesVeL6izlGrnR84W5M1hHwhyja9MYTgA8tInq6twPm7EmeyHP3BHoWuVdnp1ZQPFRjYbuPAP2rgOd/B7x+G7BlBfDAFcBLNwJn3kLRpyE99V60n+5tuDv3WGN2FpDxh/SIxFTuiA8nzz+hAoPrKEIimaRovmjEUF7zGOUu+ebIWWOZZvzxNdFaCjDaUSBECndRtHnxx90/51QCuONTdJw9LwB2PCm7dpVbeAPAsT8G5uxBqTv7ngZuOhHomA90bg10bwPM2AWYtj3Qvb0hG+bUSFbroYS0jQKSQRHhbZWqVPSFQPZ4xjBMQ8OGBaZwnHjImxU9cZUMC/EYDfxiP0IBCNDnsUHKvax4ydNgeC0pYdw2KkQjFIa88XVg8xtA21zgiG+Wb1KQC0UB9vo0TRhEaqR7vgDseCpw8Ffz/9484TJPyurVG7sclENpJU+Ire61eREj55p9806jYOwzv6KiqwsOIiXgvL2tPXAe+wkpKwItVMxr3t7uXk818DUDH/szFZp9607g35cCnXcA03eg780e51a/B/SoDykUXkyK83nTlFuZWS4ZFOedsijqWEnKIlcW3lBiAZNUAGySjm+zWEon9eJ0SQBJYPXjlHIrEaXUR6ffYBi6y0VyAnjsR0aeXxl/EGiZCbTqxgbxf9ts8nju2MrdRVvrTODsv5Eh++HvUgHdm08jg8MBXwZm7ARA0ZU7Jo9qv7QoBYx+aVLxY2HYtFIWsKG7tsj1PGSPXcVLY9Ybt9H7436qt5caJJ0iT9ZyF8rOx8JjSI4H1wD/+iI5kbSa0j8Jj1OvXjyzlNzwTpx+ap1KpLApZrwyj0dmQ4Ovydh2eB0ZFRQPzedkQ0Jze/HnXY9M7wVO+AVw2BU0Bj7/ezI2/OuLwDE/AWIDgKoASFHEhzCuAdljjSDX8zOn27XCyfOPq4ZRwd9MEeSKRgpeXku5T745sp3sC0cSBUaqRRGlfMenaa3fuYD6Yrd5/zHSIwCUkvblPwP7/j+qpVgudj2DZOqOT5GjSv+79Fpl2i48jYz+7XOBrQ8lI5/ZECf6NJE6NBHT0ys1A/4We8Nbhn7HJgqcYZiGhA0LjUyxk3SzotTK88Os6Al3A4mQ4T0aGwGQNLYFSCEocsm3tNEgDQAHXlqaUWFiFLtpb2GRthr+e/4CbH6N8p2a8Xio9kGpfLicFhTTdijsd7MXAxc+CjzyPcpNuvxOYO0zwEFfA7Y/wjp/pBmrZ9roHnDlRp4QW93rrKiccSOX8lPX0mc7n0bhp+tfBFY/TK+ZuwJ7XwTserqRouX9J4DHr6H/T/g51SOYGK3MdZYbr5+8x4f66D7cfTFwzm20aNdSRrqfuIrJlEciFVIqYXgMArTIqCUvGpbBwpGV2HIe10AY0Ewpi8xejYLYCHlEhruATcuB+75KRoUFBwFn/7X8yoHB94F/ftHwXJ2zJ+WvHd0MTIzQ9Q2+Ty87WmbQwrhzAYXyd4i/WxWX+kwYsnf8KPD0/9FYs+4F4G/nkPfsomOBXc80xq9oP43NYqFpTuWSCytlARu6a4tczyMj2q4L2PQmMLqJttvh2OqcrzpAismxD4HoFnpN/q//VQf0GkV+YP6+lAZsu48AzdPz799NvH4yXt54PBW2ffpa4OgfZG4TCNH8ttRimJMe1n7dGDhO/wNTR86q2XeY1zQi0iQ6QHXQZMPCwHv0t3s7yo8uIwo0lwO1H7j3KxT17PWTMdDjlf73URF2xUefz9oNOPhrlZmXhHuAgy4FFh4N3HAs1Zh4/rfAXp+haKjRYaB1mjG2WKUNA6xTG2YcpwtQQQrSXKn98iGO2RoyFNbJuFH3jHGXQIjWRErc+rnZyb7cVlJxw4P+4e9SZKY/RH1wOYo2v3E7/Z2zB9D/HjD0AXDf/wAv3QDP/l8BtFB5Isln7gJc/Aw5XvavAiLv0l/xGttsjI0A8PrfgBN/CSw+xzqlpZw61B8kXY08FpmNqE6LN7MTCcM0HGxYaGSKnaQLRanszSb+ysrsrNQ9ISAuDWQiYmGymGoLeWGHuoFXb6ZFYutsYPdzC7+2wfepsO2aJ9DUvxo/Ee4r74oNFKB7Wxqgw9MpVPflm4DObYAlJYRMrnoIuOuzNIm58FHyPi2EphbghJ9RYaZ/fp4mDv/+MqXJOfIHzkN3Aem+1sCgPpUnGIEQKT6TceTM4y8QtRXe+y+10+Z24LhrKLXEhpeB5/8AvHUXecL88xLyhNnzAqD3BMptDY3Cenc5vfRzFxNM8XySMb1wnkgjo7+6twPmLCn9ePnwNQFn/Bm4/nDywrn1LPLeTqdoIZxO0UvTAL++kE+naIG8z8W0jTBCery50xfIlLt91oIM1hty+qOs4nEO76XPrxfV9AMPX0m5eDsX0EKz3M9juZ4TNxEFgp3AsVcD2x5mfJ+I0QJQjZCydnQzvR/bRB6vgx9QuqYx/fO1z2UfY86ewEd/XVwR3NaZwNE/BPb/gmFgiLxDr6d/CWx9MLDdkcD0HSmKMNBCchTtp8V/PJjpXGBXwDk2YixcAXeNbFN5XKkUuZ6H/F10gAqzAmSYE1FilUDTSBH56s3AyvuNdII5UShF3pon6PXgNxGYvjPOSe+Ap5U9AW3fsp82AIrqOOEXwF2fAZ79NSmhdj7V+F6OCCpFJuIq9XPJBBDwGv+3TiG5qLaBPq4aKXb8QZIJnx5pYmVY6Ny6cuc2uhn4+8dJseiUja+SUfmU35FncyWYsRNw4rUUnf3qzTSG7XA80NNG35vryJmVl3Gb+goCf4gKO2up0gxQ5jlbXCWHNx5nyoOIzrbTSeSSfXMdjjduozkMQG2tHJF1w+uAtc/S/x/5LqW5e2UZ6RG2vI3APy7ENdgBf/KcCWBP94/v8RoOJ9t9xPhcUYDxEd3I8C7w7oO0nvz3V4CuBcCsxfZpxgTmz8zRWk4jAdmJhGEaDjYsNDJmDwEg/yLdKu2LCKOz83gze/mIAU3kygTodx36b5PjwJO/oP8P/HLmhD0fG14Bnvst8M5/IGJhKWlGD1ZiG+x30OHwz96FJhpiEgKQ4uTxqylaoHMBsPVBzo8p6HsW+McletRFCnjpT8Chlxe+H4CKuV38FNVdeOkG8ozQNFoA2OVjlUN3S11Muq2wqfUJRinXKxYgTq/PHwJ8QSMiZ89PGW1x9hJSFH7kSpqkvngDKRofvYpeAHkVH/ujws4xFQe2rCTP6c3LgQ9X0P9qv/N99J4IHPoNINBR2LELpXUWpXj680nAlrfplY/3HyMl6OzdMz+XJ8TivZWRwZzejZWVtYNZwRCPAupY7t8IBdDoFuoT//1TUkz6w8CZy4BQV+7fl0I8Ctz/DeD1W+n9vH2A439GinwZf5DGmu5t7fcVG6I0KkMf0N/BD4z3aj9F9tx8Ol1Tx/zizlcYGA78MqVme+M2YMNLZPgUHn8LDgJ2OQNYcAANqN5ApkwlVPsUEWJugIjxWaOMK9Wg0LFMVqCZfyt/N7iWvO4BipSrBLFBkqNXbzGUtQC19ZaZlO6hZRr9DU8jA5v4P9RNsrLqQVKurH8Zng/fxHl4E+dptyP9p+sokmH7o8jBRNSIyIU/WJwH6k4fpZSbT/+SHEamLQRm7Ezf2Xlmy9g9U/lz2cMaoM/F/9FI/vZQD0a6Yg30Tq4tX6HeRAyIS2kWwz0UqZCVgk+lqGWA8p5XgqG1ZFQY6qP504m/IIeVVFJPB5jS/0+RUj6VpBREj15F5/qXjwIf/Q2lxCsGYfjz+p3tY6dTgI2vUfrPB75BY6B5zSW3afM6MjZKkYh20QuBIDnvZClIi2zj7BhSGXIaD3I8A7mtvP8kcJ++7t7/C+SkVw7evJP+zt/PcGba5yJKVfTiDdBevxW7pN7Gz9PfQeruR4GDLwNm7FieczHT3EaOYHOWALt8jPqAFfcAf/s4cMF9QM/2zsY7gdMIBTOlGILrYTxiGCYLNiw0MmYPASD/Ij2uGuG/4S7y0gbob0ub9fav/42MBTudaoQtBkJ6WqRUdtjjK38BRjdQtIKTgktamiIFnv6/TK/ObQ8HdjsH49N3x3nXrwQA/GOv/eH3prL3sfdFZOF/6y7gn58Dzr2dFptO2fga5XNMxUnxG3kHeOVmyrdYbE7VplaKXtj6IOCOC4E376Awz5Ovo7zdZg9ReWFZ6kDstsKmmp5mTiYopV5vvjBe+VwmxigyYdPr5PW594XZ27XMAA75GnDgl4Dl/6S8tOteIIPE6X/MfY7xKLD+Jd14oBsR+lfl8fJUjPvja9In8UE6nsdDRrMV9wDvPwHPQf8DaPPLWyx63j5kWFv7vMWXpuS5r95Ci9q37wGmLZLy8kohuwAQWU2L6lBPtmFBfn4i7F20hQzlTRXzdTcqZu8ooYjLRSJGiu5AE/Du/cAbf6PPT/kNeeCXiw9XkGdy5F1auO33OXoVW5wz2AEEd882mAHAwPvAbeeRMmnZaVQAfXpv8ecenkYL430uAvpXk4HhzdtIofvu/fQKdgHbH01RfbN2M9IkwQuELbzfRL+vxA0nBJ+P5g+dc7P7sYSqyyGc55qPDRt5lpnSxrJcv03Ggc16Sq9yGhY0jcavV/4CvP0vo6h6IEzpuxaf61yGu7el1z6fBaJbkHj7Qbz00G1YgjcRGF5LtUZe/KPzc5uxM/CxG2h8LpQjrqRUUu/9F7j1XODcO4G2WRZRQHJfp4895jFJID8vs7yI/0XEV772MJWNdE6uLRrRi2jHSTkvOxmIFCGBlsw0i/LYJMakxDgZgIHKRCz0ryaF4dgmMriducwm+sBivjZ/X4qC/XA5tckjvwPsdn5hx9/yDvDQleTcAQU49ifA4qX5f3f4N4HNbwLvPUpR3p96gObDVpE7ZgcRn48iERMxPfWXHr0Q7afP/c3Wa42p3ManAoUacMzRXuoARXknY7T+P+Jb5Uk9lkoAy++m/3c+LfO7YCdw0KWYWPJpPPynb+MY7VF41zwGrHmMjG6ts8kALozgoS7DGF6O9YXiIaPhyAYaV285k+psdcwz5md2zp9AZtQ3AAz2GbUxy1m7jmWVYeoSNixMZZwo/sTAAhipiZotDASAPsCoFGYntgmEaMEj8hpCo0EXIKPBw9+mcFcAeOrnwMLjgN2XAnN2p0mkOqgXXRqjfSXHgSd/Rtsf+CWaNI5utj6fZBx451/Ai38yvNk8PvJA2/0coEv3FkqMG78ZWQc02eRaPOhS2s/G14A7LgDOu8coLJ2LdS8Cd36aUl7M3RM48f+Av3+ClLnPXw/s9SnaTo6QyIWYPAsWHkMTg7svJiPNxBjdm5CeckcOHZ70KJCVr0pu5bqV4lQ2BBSrQJZ/Zxc6Wei+NY1ehWA3Qck4vxzXKx/P7j76Q9SOJ3SPNquCsHEVGFoPeBTg6V/RZ7udRcrDdCJ7eyFHO5xArw9XkMx0bZ3Zpsc2Z/5/2wWURsxMIKwXhF1Af7sW0OTW10y5qMV1Wy1KN78F/PcHQORdBB68HD/CTrjW82kycCl5lKaahSHPCvN+2ufRy4y5wHpzBxkW3rwD2PcS8nL1BqgvEZPnaD8ZFRIT+vraJB+ykVXkaZW95ao5wXUiI+WQI7cNR4XKrR2BEJDI0aaEIk4B5V4XNUkO/Aqw7RHZ/evk+TlcgMYtCmVrGhkv/nsVkJqg9HpHfJM8xtQtufdndz5mzGNRsAM47Q/A3ZdQyPstZ9DYs82hzvaXKxKwfTZw4BeBA75Ayp83biejuxoBXv8rsPwu4Pz/kKFBFPubVO5Lz1mMSXIKxGi/kQfeKsIxrteMcbooDeqG+3IaOusJMZb5mzPbdEJSIJjHQaFcADLncjIbXqS+vGcRjRF28pJ22N+PD2e+j0fJeP3G30hZKehZSGn/Fh5jzCGsIu0c1BpKzdsXV3oXoEkbx+1HDyGw5lFSiJrPxY7NbwK3nkOp1ITDSLPDdudvBk79HfCHIyni6J7PAcf+jAxsmkbXn9aATinF1ESU5GFiFGjuBEItxniqeExGiSh9blYMOXXqcGPOJyj094qS/zeljB+W98C0P00/D3/IkAHhSKUOkFdy5zz734v3mkZGX0Bfg5i2cxolKupq5WLzWxSpMD5Mc7rjriG5HOrL3tZOLo/9CUVrv/8YcP/X4dv0Frza0UgpPmBiCEjbzO+2vEtR2W/drbdJBYAG/OerlCZGpOrMFZF38nVUb2Goj4rRnvpHY44tGwd8TUBCo7+Kx0hvqIDmriKSSB2kGkaDHwDTtjOcfMR83dwOCvGKdtqmnbbTWh+v3JqvFYrsNJJQaa3jb85+PppG6y1NT4+qeIDbP0VtqWMB8NHf0jbjQ86O6/R6U3Fg9SMU8RPsAubvT59lXUcK/+f5NG7XjscfFtwL73uPkHNYLvxhmkctOAjY//OZDinmdY/tPmyiCk77I3DTSTT23HkhcOYtRh8n0oYB9L/ar8uFHvkjG/XkqK1iDCFO77Ob4xHDMBWDDQsMkVAlA4Gdt7XuBRrslFIZxbJzKAOk1LvzM8DK+wAopAwdeI88IN+6k3ICHvAlyt2eThphq68so3zTrbPsvV4mRinX7ys3GcWJhDfbLmdQaHwxeAM0yb79fLLu3/VZ8v7JpYAZ6gP+cTFN7GfsDBz/c1LU7nE+hfi+dgt52JWaj7j3BPp798XAO/fSfT/yB0Zu7Xx5enMpR+XvJg0LDnMo1gNOFtZu5IzUTH/NCO+qdS8AHzxJE+F9PuvkCoh83sipBPCfy8moEOqiXJo92+uvhfR9sRO0GTsBH7sJePVmaM9fj8Wpt/C79NfgeekSYK/zy1MYzSnbHkFGNjVCBobZuwPpdOZz8AdpG+FpkyEvJmNasymPrmx8tUspwWG7hVHo/TKnqciZ0iNGY9nQOkpNl4oDC48lY2w5GB8BHvqWPtYB2PoQ4OirAKUCC/PwNDIu3PNFypd998VkhHZqXMiHolBO+Dl7AEd+l5RPT/6cIgNv+yTwyXsM+cnVx8qpCvLl9A+0kpw6iW6rds71WkQYZMyK/0SMFDEjG0hJHe6SUiDFpCLNNs4UHzxJf7c73P1zHlpL0Tdjm+i9t8koIt6zveuKhQmlGeltjgAWHUn3KTme/0ejm0hxFXmX0hmd8rvCU0MEO6mG0A3HURTgM9cCh15hOJMrIO/rZADK5AABAABJREFUSQNQkJSlTa36WGVxvOQ4jb9C8WNOq+TUQDeV5nzFEO4GojAiEsT9G95ATgnRfmf9TDoFjK6n/4VzUzlY/zI5QE2M0vzuuJ+Qk0Wh+IMUPfDqtsBLf4LvtZvxQ7yC73u+aL19Ogm8diulq53QjewLDqLo7OX/oNRlT19LBvZ89fGCncDpfwL+fCKNKQ9/FzjoK2QskFMZTdboUw0DtTh3+ZmEu8ixpKWb6ov4vZnzdau6CewV7YxKzHFFRLcwJAiltjoI+C0MDP4gtf/+lbTWfv9R+v5jN1LbKhdv3UV/e0+iYug52KDMQuKQb8K75yfJYK7265E1/UBMj7BRt+gpJaPAUJScMYMdwB6fdO+cwz0U1XrTyZSW7/6v0RiWjNFaSciUGH/iY0CihfpFudBzKmlEFlnhtK5dPhp9PGKYOoUNC42OmFhpyPTStUIMLOFWyeMjRjmUZcZHaJH4wVOkrP/ob8jj+r3/As9cR4X0RGqFrQ4kJfz2R5JX0BMiWsGmtsLIBuBv5xpe2uHplJZhlzOce0bnItgBHP9TSmu07gXg/q+TF5DVwnZ0M/C3paRs7N4OOOmXxkC4/ZHAs9fR+S7/J+VdLBXZuPDeI8BjPwA+8j1dYayRB51dnl6nBRqnIm7mR811r8LdhsFNICuwxedv3kF/dzwZ6NzKnfMCgKeupQljUyt5p5gjD4bXlbZ/rw/Y4zzEtzoMy2/9FhbjLeCZXwCr/kPtcObOpe2/6PPyk3fcc7+lhe2i4/RCbi2Z24lnIyIYzAWB7dqJ+DxXSgleoBZGoffLXM8nlaOv9wepNsG9XyIj27QdqHhqITllndL3LPDA16mf9/gp6m3JeXSsWAH1S0qhuY3qsvz7qzTm3nkhcNxPgR1Pcvc4Xj85BMzZA/jDR8jz7d6vACf+kpSzuXLEy+Tqj/0hoFOvFZFQ8+eGL9ZrrhERSup41DDSibpW+XIoa2ng/cfp/21dToM0toUiPsc2AW1zgMWfoLYrIgKSDiN6ikXxOOuDurYBTvktcNv5VMvr3q9QYdBCmbET5cC/8zMUgbr6EVLALjqejN5y2pdwF0UPmZ9NPEbz5fFBkssUslNZMAZOx5vkuB7ZOE4RCoEQEOogJZsCawce8ZkvQJ+PbqQ5uTcAtM3Of26pBADNWYSC4IOnyPEpoVLK1mOuojRNxaIo5ADVtQDaoz/C7onl+GX6CiiRPwCzJIeWD54CHvkhRcgBJBP7fZ4itQEyLviagZdvpPlYYhw4+ge5DYPTd6QaRHdfDLz5d0qxt92RVEPBTGKc1nnCABpXaS4nnke42+jL5OeTy+A9ldc+buLWHDeXgWIsosuaV6+hEaNnnhwnRbzipaih/tXAmqeoPa59NjNarVzFmgXD642UyzsVUL+hYyt6mZFTDkUjNM4+/X/As7+luVYhKZnz0b0drQ1vPYfqUD74HeCQr5LzjahVEu6mMVf0ebKCPxEDWqfruiKbeZe50DPDMA1FGVbaTF0R0MN+W3ry5zQWE7eMCbVKKYlESN2Hb1O43QdP0UT37Fup6KuiUM7DpXcAn3qI6i0oXvKCu/NTwA1Hk2JkbLN9tMLEGE0+xzYDbXOBo35AeTnl4rdu0LUNFbNUPKQEfu432dvEBo1iaW1zKaRXrqXg8dECGaDIinw5wZ3SewIZahQv5R/+96VUj0IdklIY6IhJtwgHtnu+4jueBOTGakIcjQAfrjQUYLJ8ANmFGSdGqJgkQIswt1h5PykpAOAj37bJsesOWvtc/K/n67hGuQhaUzt54dx6FvDYj410DJVm17Po76qH6F6HuuyfA0ByIkKLoxFMFpHPhegr7Yxz+QyzjEGh90tsH+7KHRIu0iA9/G1KWxLsBE77U7aRqVTiY8BD3wZu/yQZFdrnAWfdQkbychgw8uEPAif8nLy800ngX18CXr6pPMcKdlIqGF8zFcV94frssScfwmhgJ3cJlYoFT4xkRqowxSHSIIU6aZz3NWWmXLcau2Q2vk7RoYEwMH8f985rfIiMCsNrSYbOvIUcRYqtS1Vupi0CPnodtf01TwAPfrO4HN47nQKc9Cua60a3AE/9Arj1bPJUVTwkT4kYsGW1YWCQoxUSMaptkk4Bis9Qgud7jo1MzCbdlZgnR/vJSBMby4wACXWTA5PZ6CN+O7yeFJtx/bMhUV9hQf7aOsPrKRXQr/Yig9XzvydZy7VeWPUwOT4lVIoUOPYnpRkVZLY6APEz/ooNmI5Z2ILA38+mOdVQH629bjufjArNHcCBl5KSUhgVAFrn7fUpqlkHkIHhv9/Pn/5kx5PJQAEAj/6A1jRW+JszxxrxPIQXuJifh7sNA3audW2+74sl3/hWj7g1x5UNFADdo6E+yt0vUrymxo1MBh4PsP5Fms/c9z/AL3YF/ngkzfFWPUiy19RKKZBPvZ4yF5QTUVth3j5kCHeLQJgczRYvpWvRUsADVxjpdd1i/r7kLAkAL/2RUkknYno6JNHndQEhvf6DjFg3Wc31hGEE0A0VavXWgwzDVA2OWGh0AuHiJ1UiNZKYbIxFyIN/YDWlaDj7Vmtr+6xdyfPrI1eSVf6Vm/RCsyvoe6toBS0N/OcymtSGp1E4eeus4s7bCfP3JQXtg98CHvsJeajt8jGawK+8H/+fveuOc6Lc2s+kbcr2xsKy7NJ7R0C6FEURG1bs9eq193at14561e+q194bdlCKKIL03lk6yy7be0nZtPn+OPPuTLKTZCbJAot5+PGbbDKZTCbzvu855znnOdi3kPS74ztQoN8iI7/U7xxg/TsUeNr6BZUbRwM+lQt/ki7jhAd8JQyYnr9OkKiJZVFHDrmMHWsNGcHWGnkJCWnzYGs1sOoNupe7nRa9DH9bDfU/ACiw2XVCdI4bDByHJdxE3Hb5ZTCungPs+RXY/An915uAuEQy9uMSKaOa/c0ep/UA8saF39TWHx3601xTtgNY+19g8uOt95FKtrEgDKtckMvC8ieSQmVax8aYcqi9Xv771wXou+Oyk+O341siXy/8KHpVQTxPckPbvwH2LiByGQAGzyb5huNNzGr1wOnP0Fq0+VNqqNlQAky4P3rjjKHjIApozb+Dqgx7nQFk9vfNHg2GUNmPThutXW4XkBAbV6og1Sj3kTryUBKIKYUqVP2DBnJw2sjOWfY8/d11grrM6lDn+dMtok0364PwZSyPJToNparW+XfSuqe/D5j+ono5wMGXAP3PI9tw5WuU6b70aZIKvWwuVSMwPXl/qVG9CdDHkz2qN4s2RoxQCAxTALLKVi1k5+poTHBaX2ksqQwP693DEplcdqpwaKwDUnPJtjv0J70WqnGzvQ747jqgQZBNKlpL/wGqfkvqTGtXci41ZU7KoWzmbV9RELDn6VSJJ9dPKwLw6b1wh+bfeNT7Boa6dgE/SRJgOC1VNpx6G/XXCoShVxD5tvp1YO3bZPONDSCtxDDlMaq4PbQM+OkfNKb6zRRfd9pozeUhBjaZfe12tpYAO544GStYldpsoSST/KtEnDYxeG6Ip3Wr5TNNlLRUsdv3GHGJQJdTgdyxQO4YkiGOto3jD1s1SYDt+I7+7n9+23wOxwGTHgbKdooKDd2nUIJmx8HRSVwZcAFJ+y17DljxMs0xnUcI40kiG8YIMga9iYg4OUjlFPVm9VULMTnZGGI4KRAjFmIIjlba1n5BA2mwTm+k5sgAOYmhSviSOgNnPEOleJs+ATZ+QA6mXLXCmjfJqNYZgXPfaltSgWHYVaT9u+E9yszcv8T39fgO1IMhUINnnZGqKVa8TDI1CZ3ERs6Rou/ZlMnx/Q3Agd+oyaYUTM/f7QLiEFpSIobQkCubtqQSqaA3ygfV2OO6YnJAmQzSaBW9FUKhYjf93kldgFH/iN5xlcCcBpw5B+gzk8iN+iIhm9Lu21RaDgkdSUN74IVir5BIMPJmYN6twOr/I532iff7vm5KFOcy9jv5l81LEalz2B4NZblG7sfjs1sCogGuYUCCx0TyRAAw8iag28TIM95rDpGm7q4fxWxUgNavac8QCX2igNMAU54ETGnAqv8QsV2xm5o6m5Kj+1lDhHL6fQuBPQsAQzLNcZwOSM4OHuAJJUHBnk9oR2PnRIHTRpUethqS0WHXr7GcAqbxmWIFj60aLU1N5XBoGbDwASH4yQXue6UW7mZg3h1A6TYKEl3wfptW2UUdXSdQxezih4nEbCyjZqHGxNDvlUIXB4y4jqSQtn4BLH2WKq0O/g5kDRakJ2QC4gYTEJcjZsxLSY1gvbZOZgRbb4PNNyyZXidpQK+TIc+Y3JE0a1tvovkuIYP6rP34D9J5B4A+ZwY+V6+HKrTrCynr+cyXgMp84Mga4Oh6ysKuPUz/5dD/fGD6C8obuqpEI5eARzUPYt6gJdBt/YyezBtPAc/0HsJOIey7gRfSerTqP+T/DLwweIa3Rkv9gj6cDlQfoGrAAbNINozp7TcL8iwus0j4SO04VjnPbDoWHD3WdtjfWWKpqYoazhsSRFlDKfwJCoOZ1iMepJwgJa5X/ofsF72Z7r+8sUQmdBhw7CpDK/IpUWPPL2KT5ow+QF4bJnDFxZOE2LzbiVzY8hn9t2QAPaaQVFiX0ZGR/OPvBSr3EJG99Qug8yl0PKdd/H2cQiUDkyD1Jwqk/RT85RTVSvKdjGRcDDH8DREjFmIIjhZt63rK+LFW+y4YrL+C00bOYmo3Wogddco/w5hEgXH/4DjDoWWiHNHUJ9tWP9Efkx+l/gg7vqVMWI+b+if0mk6ZEro4cuADYfBllFHEyjiB6JELA2YBGz8i2amtX9J18dfzjzeJmQTSBbs9Bj2PN+QydiyChJi1ipxBa03roBojeXb+QgZ3ci5l60cLDULZeFq3ts/aCYSuE+g7ORooqNXcJGwbqSFZcwPgaBS2dSQj0VhKEhBr/ksZOSOuo2OE6zAMupiIzWXPUyaORkPGMyAavZxfZluwALq/c6h2zLRHQ1mukfvx+OwWx0bmOZctsMSAwUxNvIHQzc5DYdePRHaXbhOf05uA7lOpaV/uqW0W2IkIHEdraWo3YNEDNNY+O5ckodK6R/ezuk0iYqF4E/WWaKwlDV5btbyTqbShbKwCKHywSiydQbjuwnXU6SlI4HGJ86HXI8qIsMb2BjMREH88A6x9CwBPGdMz36B7PlJ4PUQqFK6m8zj/HWrQ3N7Q5yyaIxfeT2PsiwuACz8hWSK1YARD3VFgzf9RAkK3SZS5LjfHOO30G7lsQo8F6W+qInP7eBLJ0Uaw9TbYfGJJo0C1fxa8f1BM7nmDmX7voxuBebdRYoXOCJw1Bxh8aeBzXfNfkoHVGYFz/0uByuxhRDB5PdRvpKGUiOy6QqBW2MZnEmF+DMhsD6eDe+Ij0I24mq5rOI2o+59P47xoHbDsReCcN4Lvb0oBblwKLJ9Dc8/O74Gaw8BlX1JlNgtA+/82zJYDSELPVgVAA2T2Oj522N95/eL8tkBw21lvpvWFgcmA2esooRAAznyRfGkpQslrKQXP07i215Fv4qijx/YaYO8iIvoYsgaSckH3KSGbNkeMrAHANb8AhWuAg8vIz7dWUtXEtq+pumPABcDEh9RXywFkJ572KMU1ClcDtQVAZh8aX6xqwWAiQk/aLF0Kn35AfnLKgQgIpf5WDDHE0C5xAnrFMZxQYJM9y+SRBuakMi91xQDcQHwWEQuVeyn4HilqDokB+SGzKaBzrJHeEzjtEfqvFhwHjBFKgNuCXJj0EPDJTGDbl3RMS4awyEt0djmu9YLNshqtNUBK5/ZjBLtsAFRmBR4LGMx0LXX61k49M8b2L6LtkNnRzbZpLKNtQlb0jhkOOA1lRftnRssZ4O5mkhTb9hUFJVkz95SuwNAriSQIVAkUDOPuoe2y54Glz9DjUbf4Em62aiJKXTYhkBbA0PV3DtU6qO3RUG7Lc1ZbIh/oOfY7tPxtB1xOMXDNpCESI6hq2/ABafgCJP/QdQIFSXqeATHFNULwPBGNtYVU3dNUQf+tFfRdOg2jYFO4evN9zyYi4cd/UFDqi1kUxM2JokZ+9jDalu2g3yCxo/ibed1CIoJDmBf9MkljaBvozdTg0mn1DZYa4kk+x5LqK5HkBun1MxxeAfz2KElaAtQr6oxnWstThgOeBxY9BOyZTwHzmf9H8g7tFd0mApd/T9nVlXuBz84BLvw4/IabQy8nYuHwXxRQNSaLDYCddl+7m/fQVBSX4EvaSaUXgwVyWB8TJpfZ3omFcNcuaVA6EOSaNjPs+hFYcB8RPSl5wIUfBv/9D/xBjY0BYOpTRCpIodFSZn9KV0peOt5Ilsk6VwqOo2Swj84Edn5HPkqo8W6wANOeBHpPJ2ndks3AB2cAs78hXyyQ7cx+I5eD7EutQbwf5O4Ll42y6zm0TZ+FvyvYtZSz2dSQO6vfoCB/Rh9g4MXROTfeS/LLR1aLBIKjTqxEkAOnJanHYVcBHYeQ33ysYLAAPabSf70ZKFxHfSUO/E4kw+ZPqXLo7P+ERy6kdqX+Jrt+JEnq058jW8AKIFmw04L1b/EnW51W8q94CDEISdPnUNJIf2cyLoYYTiLEiIUYgsNnsucBp4RMkDo5Oj3pjKb3Io3Ryj2Rf7bTCsy9mrKeOw6lPgLtEYxciEukTKVokgt546g09MgqCoaNv8cvaGqWX7BbAuG6dpZRbVe3v38ws60y9PRmqlTwMbIkzqi9mjJfOC0FzaOJxlLaHgt5sGhBF0c9SPqdA1TuA7Z/Dez+mUr/lz4NLH8B6HM2STtlDVJ3bH9ywWmjLD9GuLnsVGFiryEDOFjgRXrvqA1ctEdDuS3POZRzKffZgeYul0f8220HnNXUdDO9m0i0sYCcWuz4ViQVTrmRmqxLdWXDaUjn9ZCUXtl2cgSbyohMcAWZz/YuBMBRMCV7BND9NArKqHEgM/sCV/wA/HAj9YeYexXJbvQ7V/13kEOH/hTAcdQBznogPk1onCmpEOLdVMlgShaDPe1tXLQ36M0URGlpVm8GkgKNrRoKsOiNwKHlFCT1NFPD2nNeFxNEggVflGLZc2KD4jPnnBiB00iRNRC4ch7w3dVELnxxITVm7jlN/bHSupNNV7CSerkMu5LmN6B142BHA/UEkPZ28u8dFCiQw0gF3k3E0snQxyRaa5dc1Yfcc24HsPAh6isFUM+Dc98MLjlXdUC0/4deSfbPyY6Og4H+F5Dkyu9PAVd8Tz5RKHQZDVy3CPjyEsqm/uhMkp5lc4Y/2cN+I/BU3aAzBvZ/2PudTcL+7dBWO1ERyGYLZjtLbW2tgaqw179Lr01+LDqV2DwP/PkcBePloNERkWtMojFsTAYyegGDLj3+SVsAXZeu4+n/1CcpMWvBvZSQ9cvd4ZMLY+4kYmHvArJ1Tcm+1SZykFYgSG1jl53IOo/Qo4atPYGqwGKIIYaTDjFiIQZ1kHNc2GIRlwB0GkKPq/ZF9jk8D/xyD+krmtOERTMCPcHjDY4DpjxBj6NNLvhXLWi0ZDAH007Wm6lSod1lVKs0TPyDmW0p9eKf+SZ1RjcLOrW9To++kdpSsdCOiAUpMnoBUx4HpjxFZblbPiX5mV0/APnzyZlU25B63D0UvPrzWWDlq/ScVBaJ0xK5EcyA9r93Ys6ncshVJ8g5l+FIsunNgFmyFuhMQE0VBT6rD4lNlRPCIBb2LSLta4D640x+TFkQJBhKtpLkV9V++deNySRxwf5bBCmhoxuJaKvaR/+3fUn3bvYICrp0OZUyZEOdnyUduPQr4Ne76fv9chdJdoy7J/LvpjVQYLV4E2nDM5nCloanJsDpIHkkjdZXnzyGyBFo/LhsQH2JGGhgpHpL5ZZfNqlOBxxcCiz5F815PacB570dXtVYIKx5UyJt8RJlgZ4sSMoGLv8B+OlmkkX6/jrqRzHpUbGfhVIMu4qIhf2/kfyHrQ4wpfoGZ1z24P0cQgVyWpqjg6pbwl3XnFb5RI1wyNdIoXQtaWlubhLXJGmAOtC1czRQsBqgxISfbiayFhxVM4+9I3glanMT8O01dG2yhwMT7g+878mGSQ9R4LJoLa1BvYP0n5AirQeRC1/NpsqFzy4Azv8fNT33J3ta+mWZSLaXNZEFWvcKbJHBjCcbMLYmtS1C2c5SW9tkAJa/SMRdl9FE2EUD6/4nkgrj7yNbxZQsEgk6c+T20LECp6ExpDcBP//Tl1xQG7zvOAjodholhG7/BjjtX6H97EAVCHoToNUCnNHXtzJY2n9FXAwxxKAIMWKhPSJa2oKBEMhAl36s3iTo8Jl8jYbsU2hbtZ/OM1imAQsAyWHzp1Q6y2mpUsFlDdzIjKG5Uf55t+TE64sou1UJ3M3K9uswQNl+ADD8WiqlZLJI9trWGewpecqOxeRAuoymLJ4jq6lJ2ik3kmGtM9A+GkkWg7/Woc/v28b3lRzUfqYSB1hqHEqDmRzn+3e0v6/X6xu80cUBLi997ravaJ8hVyB0OkjLAZXtxnosmNMBd5Cs0pJtvn+7bNRwkOk4a3X02LUs+Lj1aAAIEihH1oReRaoDBFT90WMaBSnPnEOB1M2fAEc3AN9eTc9l9Kb9lEpnjL2LsnaXPU/kgkZHzrzWQCXAoTJo1FYosLEVk3qRr06Qcy7l9uMV3PfSffRGIKEDVaBYq8TPiktUN8a3fQks/hdJjPQ8neblqr2t93PUKzve4ZVUtl6xi/7WGSmgZE4jZ9aYBMQlUS8Qf1gySAKguZHkAGsOUdams4kClwUraL/EbGD41b5ySd0my5/P1Kfos7d+Aax4heSRpv07dJZbqD4SnYYJxMIuMZuajRmDXyWXwQzl858fAtolUZzH1R6L59t23ZT9zpLP8xk/Jt/nW7T3hbXP5aDKGZdDdPKdNtIj3/kdsPZtOnb/C4AZr9L7pcE4p0QuKRjkxu/2uUTyAjQH95kBFG9Wdjyln6ukosINAAJZUrAq+NrFZL5CwSupnprxKo2t7V8DWz4n2ZupTwE5I2k+UoI+M0hr3lZFGe7dJwrVdqm+AR97jbjeO22+PTJCyftE0hydD3T/SckqlZWl0YBSuRV/GVBpkIzjWq/hHie9brDQfFy0VrTdTalEwHU/LXhfOZ4H5t9J64klHTjjOcDrov+BcGi5su+tVyhR5pL4MzxP18DdTOunziSuA4VbAAikfP6vgC7A/Ka0ktReS9du2FWUhf7HUzS2/BPFAv1m2jhKLpl/J62n311La9fwa2iu0RloDPIe4W+B/NHFiT5ScxPt42gEjAn0tzmVmtuz9c1aRfeEJbV1hWKkdt2x6mkXzfVIaZBd8ef57Scl9KRkXsUu0V867VGqqpL7CKXkZfUhqv5c/Tr9PepmkhZicDuBxgqq0lMCpTK2ZfsBCElkJVsCjyO3Q9nxzBnyz4+8mXpQ7l9M0mGXfK6sckE6/sbcTsTCtq+AvjOJ0HM5aG7wJwT0JnreZactW+85De2b3jM6CYvH066LIYYYwkaMWIiBIDV8lBjoLEjksonNMgHKuNQZabGsOxJew6+jG6lkEaBMlywVgfsTHf49F5a/QEGhSLP3JjwAfHYeZXx3GgbkBmgO7F9O799g82QLivpnSkj/VmpsKGk85bQK+znIqHPZycnXm4H8eZSBHN9BMGijaOS4HRRgANRVQjQ3AeveQsAmuODI4WKEgzEF6NAPSOsJIEKtba+bjNBgBnp6L6rwWfIYGeVL/kVBm6QcdZ8llUX66yUypsfe6XvPa3Xy97zaCgW5Jul/V4TKmJVmEEaaxW6roQCPKRWoFwLuCR3VZZ8VbwaWPEGBnrxxYsVLOHA30zy860chsMERodDtNPn5g/e0fo4hLoGItI6D6Ro1lhFpX7WPiIaGYmDNW1SpFp8Z/Lw4DZFtidnAipdJ8qmhlJqHxiWE910BoNNQ2pZslm9gHyjIKZdFqjSr8e8yvlqCoNWBM8uZJI4ULPGD0/o956e/77IDu74XmjSDGnCf/kzkPYB4L615TRVUsbNMsOdOuZGaFJ+s0OrJZu1+GvD7kyRT+ONN1PBz8mPB9aoZdEZg0CWUYbt/EfVKadWs1gTYQLZ3TRNgjKcgj5y2uZxdF63qu0Dku9rK0miAnQtAQeJgvXxYPyx/DX7/OcZlo/EFULJFQzE1afY4SW991gdAsgKbZMMHlKmv0VOShDRw3dbwOCn5pPow0HAUqC+mv51+iVgaHaAzwqCNw5ueeFhhhn4LR8RFYifqzROJlMkpN1Az5rpCYJsg86UUehMROEufJr/p9ycoSez0Z8UkGJdDSKTS0XzospNtIA1cm5PF4/nDWkMSLtaa1vIuodadUMTB33HtCgVrNc1fnA5I7y7+RkufpfWj95lA5xGRf07BSupbA5CsUb/zIj9mOHA20XrocdG94HXTY5dwb3jcAtkokJgpeWTHhpKByhpIfeTWvU02WDiySLljgd5nUVXRDzcC575FlbR8IhEvxkSaB42Jom8L+EotAr42HdvG7vcYYvhbIUYsxEDwkYjxcxakpcP+i4TLRpkg9gbAlAigGUjtTlkHlXvVEwtNFcC82+lc+pxNWSmRyiqFgsdJARHeK/znKdjjagbgpUx03guAp0wyU0pkn8fIBY+LMgSWPEbHzBkpv3/hWnJQe04L7JjmjSWDafdPlJ2d2pUIC72dSvHltA79SYaY4SsPJY2nXHYaBxzIEJQ6Lls+p+3g2eT0BMtSU4v6YtrqTMozIgEKWLhsQrWCVjBopQFOXsyoc4PGR30hoF0KXXpf9OKTsA8KxzbPU9ZazSFqCFp/lAL8eeMoYBooiKU1ELmw8AGaAxY9DJz9mvLvyCAlF/58hsZRZj+0NMBUc88HI5laSvFj48enaZs0i51dZ7beaHTqgywuG2CTZDE7rYDbQ9mHDqEKTk1/hco9wNwriaTrNAyY9HB4mr48T1VjWz4TyfbkXKDXmdGRP+M09L0SO1HTWHstsOF9CqKtfZsCtkqCXAMvovP57V/AkZXAlxdTgCzcnhQss7t8F9BsB+IUjgN2DzDbIdQ4bI/N0ENBSUNza7UYBJXbx5hIx7FCrAqRao6z38JfxtJpoyxOJk805nZgwoPKCbm6QpKsayyTNB8vp//WSt8G6wA14Bx3t6rLExGYLRcNfW61yBkFXP4dSaDt+Jb+H1kNTH9RWV+JYVcSsXDgd8DrBAyprffhQDaqTg+43YBGxq5oa7I7EEFxLGxI/7HD/lurQvfykcqA+n8H6RzDdPg9bpJz+/MZ8he6TQIu/lTMjg+GI6uJyAWAyY+S9Eg4qNpHthM4cYxyGkqM4Djf5+11tG/9UUEqUy6ZhaNsf1aZ7XUDziZo0IQeEKq6K4VdS7ZQ9U3eOCBvfHhzsMECnHobVSyseo2u64hrlV1DgMbxlCfJv/z9CVr7ao8AF34g/IZGMcsaEG1yl52IhlCkiCVVrFiQQoldF4o4OBnXrkjB5i89RBvx6HqSgOM0RNBGisK1JKvEe6lX0LCrIz+mWvA8tEWrgb3z1Pcp0sURwZDSlWzTpBz59UxKLoTTc4HjgPPeIqmxks3UZ+n893wT8cxC3EOalMXGl79dr9SmiyGGGE46xIiFvyv8swUBMXNUztD2ugXpI5lFwt5AxpxGS6+ndhOJBaVamgAF2ufdQU5pek/gjGfbTvPQaSNN6JKtQEW+imAvB2T2IYY/o3f4mX0cR5rv1kpyHhfcB8x6nzK1pWgoBj49l85Pb6asgoEXkWPjL6Ex8zWg+gB9ryWPk2GARGrmnJwtGgnSgKhcxYIcpE4coE7Ltr1UQQQ6X6WNpxyNlBEl1aWuP0ra1QAw5PKonzIajtI2oYPysVK1j+4RcCTNxQKKvFcgCWtoy7JqvC5y4Mp3Ac310JVvw/9hGw4hB9pDpwK5o1pnPHtcwufsoGxwf5kAt4Pu+9KtQPcpQHIX+XPVm4FpzwC/3kPfdfHDQPZi9eTeuHuAsh3Anl+IYDj/3fCIAFs1VXtIyTqGmI6oL1hQS87JMJipyRvnDLyuBILT7pvl31QFJCTQ82r7jdQeAb6eTfdnRl9qihdOL5+aQ8DGj4DKfPrbnE4VCpn92m4NM6WQM7npI5pn1r9DDUEDSSFJkTceuOwr4PsbaZx+PguY9Z46WT+GlK6U3eaoAxoK6Tv7z5U+OubCGGHBFhbIUTIOnTbf+6W9rC2BoKSheUqOuC7568MDQkY1T/ettVpeXoJdf4CyeJ02YMO7JP8FkOTE6H8qO2evG1j3DlU5+JMHUnAaWgvjs4C8McCpt0dnLPBeIrrtdSRL5hC29lrhb+F/cyMAngKXQlKMXmfGY55kNHEW6HZrgTiBbEnpGngNChcGC13XHlOpeqGhGJh7BSUYTHooePVCRh+g80gKtK1+kwgZSyrNcew3lWaNuh3yiQUnM9kdaOwESoySzhXBCBFWqcBk9TxuCt5v/IgC7AYLMOM/ygLi9cXU04b3ksTY4MuUS3sxuOxETBWuVvc+KQzxVKmWlA0kdqZtQkeaM3gvkQvCfdTcVIOnN1pg4W24v28p9B4r2XBN5WSzHV4B5J4KJHUJ3qhaDgMuAA7+IWSR/5ck2MbfS+S7EnAccOqtQFJn4Kd/kjTSR2cRyZPcRd5ud9mBqkOCbS6sNS7JOGK2qyVdPslBiV0XijiI9eciSNchHkJVtzA/8zywQuiHNvjS1r6wWpTvAn78B/kvXaK4/qhACl+Hu73vQr97Kz1hTKbvqmXys3paJzWCDK1WRxUc9mqqSHU3Uxylci9VPOmMVDWe0ZuuT3IXMQbByIX1/wuPXNCbgUs+oybpdYVUJX7lD+K64pL8doC8XxyOTRdDDDGcVIgRCwHA8zy49tLIRwn8jWt/ZjlY5ihbLFoC0H7OvSmRSAWz8P6MXsAeyOtTB8PK/wDFG8kIPu/tqAfp9LwTmqNbgcNLyeiQBqc0OpIN4DRkfGi0aMkA4rRiRpCtioiIinzKRh1+TfgnxGlI49pWTQ7L/LuAK3/03Wfn92QYcVq65ju/o/+dTwGuXeC7r8FChsH700jXfv07ZGg4GwG3nZwJwJdI8An0BLne1ioxoKo3K9eybU9VEAEdVYUBY6PgoEj7LGz+BABPgbzUrtE9X54H9v9Oj5UGUnkvlbsC1JtDmqXMaQCtRp5ASehIWZj1RfCU7YK76gC6oQjYVQTs/p7kUPLG0TgqXEOa61LtUE5D2Tap3eg61BWSc2mtooZhfWZQjwU5mJKB6c+Tg153BPj5NuDSL5R9Xykm3E/Ewr5FRKx0PkU08uUgF5BgiX8xyc7QYI6HnJPBJDvUzA8uGwVFXXb4yHHFpwOch+Q36groOSXjweOiQF9TOQXyzngmPKmHwrVUJcZ7KVDT/3yg7zmBmzVHE3HxwMibqEqiaj+w6WOg+1Qi5kOhwwDgiu+B768ncuGry4AL3qN5QQ04jsb/oT8pg73ziNYOpcsuyiSldG4dCFTy+7PsYQBwOk6cjLhICHQlmazSoBTLxpYGi42JlAmp0RFRx163pIn7sCoFRwNpVm/9TCAVOGD6C9RoWCkWPUzyfgDprKfkkcxffAciuC0Z9NiSHro/hxrYaymoWbCC5KGUwu0QJANroQUwDqD526dlFwcMmAV0V0DKqQWrXljzJl3zbV9SVug1C4K/b9iVRCzs+p56M1lSfatQ4tPFCpTmRqGfhr9kkvnkJbsDjZ1AiVGqKhOF97idFMyzVdEcDwCT/yXa0sFw4A/qK2Kvo0axU59UH9jkvfS59UUAOAoeavWCjCdP9zHH0WPwosZ+XDwF35NyqIotLjFw/y1OIxKRAHhdIjZynQAOuKdzCfQ6nhKZynYSsdBYChxaRpK1576prhpPowPO+x+wbyH1ImksAxbcT8ea/oLy4/Q7l+aYuVeSLffNlcBNy3yvL/tOVYcAbzM1QZcSCyzbOhIZwJbPOkGIA5cNgIrK5WMN/2QTg5nWKYD8geKNNN7G3xfZ5zgagO9voGrWrIHAxIeObeWaxwVtwXK86/0DiWgCr9GB6zODfCT/ZMRAPRZ4L0lV1h4WemwdoX3Ld9B/gNbeiQ+L933WQJIxYg2dlz0PTHlc+XlbMoDLvgY+nkFr1K/3Axe8Q6/5Vyj4yGFXo6Vn44kwDvwRUPI3hkhQWFiIqqoq5OfnH+9TieEEQoxYkMDj8UCrpcWH4zh4vV5o5Bortkf4G9dKmWWp06yTEAvNTYC1Viw/tQgZxLwX2L+EHserMDhrC4CNH9LjM19S3sA4FHgvNBX5uMu7AhP4dTCskTDuCR2BTkNIKzWxU2ujX655c1MlZQ4VraMg56rXyMBN6x7e+eniKPvpi1lAUxkRDKxJbThI6gxMewr4+VagfLvQQLiJAjJVB6mcmyGUvI8U0oCq0rLe9lb+K3e+PhmiMteJSeMAouFaXwrodOQ0rfsfPRdtXWlHA2Wk7FtIf+eOVfY+Ww1leGp0lFGtBhwHJHeBKyEPs2t6YxK/Brcl/gFN/RFyCIo3+u5vTKbgpSWdSDidJBPcnEbB3P2LBc34EEHY+A5UufDzLcChpaLepxpk9iMH+dAycmI6nxJ8f7mABAvWqQlAt7fKnWghFCEXaLw1S5ryGSTye7YaygDVxcGnYaXBAljigUN/UWYnQNJwobD/N3LYzGnApV+Sc6QW1kpat3gvBdRHXE/O2bGELo4I7vXvkxNatE4ZsQDQujd7LmV/Fq4GfrgJuHqe+vU3ezgRCyWbAf1trV/Xm3x1zaXJDUoDfgYzJR1w8K1YON6IhEBX64j7J3kw8i4uQXgtQHVdi8Z4qqjpDBAhpIZUcDmoESYAnPE8kWj+dpOS5utKwfMkVXZoGZFWLcfmhAboibTOGJOBOIvYEJ29xhIyBLLf6bDhnW0cEmDFFXnV0HlsJOFUc5ASNpxNQJ+Z0c9sNViod0X2cGDBvUSs83zwz+l/HlU6NJUDB34D4jOETGuHKEsB+N4LJ8J4OFZQM3bsDa0lboKBVdTZayhI31hKtp4xmao8Q6FqP9ngvJd635zzf6JEjxrUHSFSQRcHjL5Nfl5X2rw5EnAaknDKGkhJVfnziWw5uBQYMlvlsTiqvO4+mdbONW8CO74jKbZEBYQNQ5fRwFXzgHfGU4V8Q7FIHNgESUS9CeB4krONTxbf659traSPWntAJE3T28JOtVYR8WlJo//+ySbSdcpeR9vkLkCiwkSpQNjyGdlmKV1JPkvqe7QleB44sgrY9An0TWXQAziAXOSMvhhxKSrlMDkNEZhJ2URImNJoLqjcR/d7xW6KmxC7KL6v2ySKKcy7Ddj5AyVUqfFX0nqQNObnF5C08vTn6P22Glp7kjr57s8IB6n9E417KZr3YyTjIgZZFBYWom/fvrDZiLQxm81ITz+GvYNiOGERIxYE7Nu3D3PmzEFjYyPS0tLw5ptvhkUqNDc3o7lZDEg3NKjIrIoGAk3G/kEcKdvsL4kkhdRpZsSC3gzUl1DGv9vhG6zeMRco204BgFNvVX7eq16nz+k6Aeh1uvL3BTxvK2VmH/oTBlsNWKEtb04Hlz2MgorhGC/xGZQtkzuWGrJZK0imZeKDyrRz5WBMpPfmz6ds78GXiq8NmEXNrJgUUp8ZwIALyXgIBGZce9yU/ZHYmbTHNXrA7QLi/SoW/CF3D8WnA06zvFRWIESQvXBcxpHc+bbIgAUgYFgGjkZHAUpbNZEKbjew8hW6lp1HUrAiWqjcS1nGNYeEZsR3UYa0EjQJUjHxHcKTfBFg5Sz4lZuKmyaMhrGpiLJIi9bSi9kjgC6nEtnGaah6Rg56E5DZn5zw5kb5faRI605kYGMpVUR0V0mMAFQSfWgZvT8UDGZyjNxOwMAI2TDkjo5j5c5xX4+kCKSJLX299igFnz0umheddklWVB2RvR43wEnmLXMqUHcQ+Olm+vuUG4Um6SHAep8MmU3jQS2xYK2kwJ+9hu7LsXdTAOh4QKOjSqDaw4IOtwrEJZAM37dXA0c3UGXQ7Lnq5ofcMcAKAGXb5NcUg5nk+KSZ9tJmq3WF5B/HpwceI3ozkBJluRp/BMhsCzqOok2gB3Oo9RJS00deSnJN/c/DWk1BAXOqkB2aBky4j/o7lWwmDXiltkvZNrJF4jPlSYVowWUj8vfAEmo0y5DeC+g6kRJC/KshAulXx4mSQ1438MsOypC9tF8ddDoIlX+LqQpj3yIiFwZdKn+sSMELGRpKJNL0ZpJA+u1fJDvVfTKgNZJ9Yav17ZvRTgiF47YemVQmIbDkK95Daw6TXtRolcmfrvwPkQp544FzXg8uexUMlULVd0Yf5WRxW4LjgA79KDll1480f6glFhh0RpJfO7oBKFpP6/HEB9UdQypf5nVT4BMg/w8gDX9jIv1+0gb30qptTuNbCcSeP4FJhoDjKJKm6W1hp1qrhYbY1WJSTqC5KvdUABxVTzZV0BoTDpw2qtwEgLF3HLvfsGI3EWXCmOUNCXjddQkWcxPxY0IlIi5z5jSUoJWcSxVQfzxJv5PcfNTzdPL7G46Sv6NGkhogIiNrIMnHbvsK6DldHFO2Wvj025ST25O7l5i8XCg7L9gxwkUk4yIGWVRVVcFms+Hzzz9H3759kZ6eji5d2tg+j6Fd4CRJx48MO3fuxJgxY+BwOBAXF4dly5bh4Ycfbnmd55UvCM8//zySkpJa/ufkKGimGE1IJ2Mp9GbKHPafoJuqhKZ7VfLHM5jF3guAmP2lM1EGjs5IgTeXnQIzfzxF+427W3lTzvJdFFQHIi+BdDuA3T9Tr4ad3wO2GvB6MxZyp+E+zWNoPut1oN85kWdEWNLJaEnvRZ/5x9PA9rmi46gWXU6lbdE63+cTs4GrfgYu/BC4dw9w/jvUfDaYbiILCPFeqiRJ6UJZCPGZvr0WLOkBsvBl7qFA908bIaxxxAwXpzX0vkrB7v9AGR96U+vX3W5quLhbkIuIZq+QXT8BH59FpEJiJ5LO6neu8uMzDXo11UShkJxDzuXZr1GmzLCryAlW4oCzMnSl0hYd+tPWf5woRc4o2pZsDk6oAmLgW2dova+1CqjYB9QWhr7n/OfQY4iA4yjapcFOq+918P8bCLw2SV/n3VQVBl7Qjmev2QXCzgXEp/mON0cD8MONdA9ljwBO/3fo8609AhT8BYAjzXO1YKRCUzmRElMeP36kAgPTXG9QSSwAQuXcq5TpXbaDAmNqkD2MiOvGMsqic9oomBBsjLE1BaAKSGdT6DHZ1giQ2RZ0PYr22hhqnDBYqymxwSpDiEmvv60G8DTTliGpM/XjAESJFyU4KlSlZY9oG1KhroiCM/NuIxnBhhK6N7tOpDE2/l6qDIqmxBLHUWPPwbMBcERobPxAfaNNJSjfSdsshb1MRlxLzWqtFUScaoWkBZ3OV+9aCZh9dBxlIdrMPwr23cJdfw1msk8Ss8SgdLCeIgwlW0kyiNMAkx4On1QAqFoHIGLhRALTvy/fHfn9xOahbV+rv6elaKoCagqAmsNC8oFWqFZwisFPW43v/xZpF4kd708ynIAIOI4iWYPawk61pBEZakkT1yT23399M6cRaQVQolK42P41yeYld1EfUJei/ihJ7x1ZBRxZAxSuIwLs6AZKTCreTGO9eDPw53PAwgeJVNDFAYNno3n8I1iomQwv1wYSTMyuDjS3cBzQ5yx6vOfX8D6DkevbvxF6QmhpTGn1fr0WzEKvDL9EVv97ySkoXTgbldl50bwf/06V4scYffv2xbBhw2KkQgwt+NsTC/X19bj++utx9dVX47PPPsM777yDSZMmwWgUy1bV9Fp4+OGHUV9f3/K/qKioLU47OOz1gV/zN8DZVwv0FaWySbYqoK6YFge9kSQfkrOJDdYagHVvU2AhMZt0n5VixSu07XuOaFiohccF7FtMfQq2fQW4rOQ8j7kDzTPfxmuaG7GD6xt+s2U56E3AKTcI2eg8OaMrXwnPIe08krYsW0OKLqOBfucpd1BanG5eDMAxIgE4oQOhDGGNI6UBGTVgQaNAWS/S62qrJpLJmEAZkOBJ+qDTsMjPg/cSaffzP8moyxsHXLuYpCzUoKmctgkdIj8nf3Aa9TqmjFhwWpU57ay57NEwiYVOQykAaq1UpoEfaCxYaygTq6E0tBN6jEk5KQKOo2iXBvs747ZqmsekVQCh5hWDmRrXxWeApE4kWaYGE72Wkk1bSfNu3YoXKWBnTKLMeyWZ9qxpbdeJ6pu2+pMKU5889vJHcmDEQn2YNkdiJ5K3AYD17wIFq5S/V28mcgGgDLm6YqpCko4Ldo/4z88Gs9AMPb61LNaxDoQGyGw7pnad0vU3mO0mHY/mVEAb55uxCwDj7qKxUriGqhaUoHgzbbOHK9tfKXgvNXNd/BA1d3U3kx057Gpg+otEXKuRSQkHeeOAU66na1+yhSpRo5mkAIja2B0GKdtfZwRmf0PzS8VuWv8d9UQuSCtXQpF4bD+58dcWCRkBEJVxJDcvBLP9/NdfpfMKe5/LQRXaQGgbhedFf6bfeUB6D0VfSRYeF1B9kB6faMSCJZ2q9HgPULo9smN1nShUVdeR9Eq44D1ETDqbKLhrTqHeEjqTSBg4m4h8cDZRNYNDqJQ1WMR9gODJRCcA2mQ9ags71ZIOZPYWxpGwJllraGuTIRi6TqLt4TCJBXczKQkAwKh/hEdA8zxV48y7HVjxMrD8JWD5C8Cy56hnyh9Pk/33+xPAksdoW7iG/J+eZwDnvwsMuaxtE01Yr6m4IDGBPjNoe3gZVDeMB6jRukZPiZ8NR8k+NKcQuRBqbMjdSy12nuDzhZqDj6PfFEMMMYSPv70UUmVlJRobG3H55ZcDAIxGIzweD3777TesXbsWBoMBb7/9Njp16qSooXNcXBzi4o5z5qIpKfBr/uVlbOIO5sQ2VVEwpdkKJKQDbgDJmb4Tfl0RsO5denzao8qNssK1wOG/yAAYe6ey90jh9ZC289YvxaCpJRMYeCGQOw7QaAC30NisLaDRAmNuo6ztdf+j/hINpZRdZwzyO/jDnErOQ+Ue4PByYOBF4Z8Tq2bwd4KcVgr46ITXAwXLT4AGTGGNo2PZ08Ffk5U5LU2VFAw48Dvt1yuCjBkGngd+ewzY9BH9PeZ2YMID4TUja2qDioVIoDeTocp7yNGID0F4MGKheAs53sEqd2Q/zwR0HEz9IMp3AJ0Gt95HTrLHH5ZUcpASk09oJzTgOIp2abC/ZjGbbp12ciDYOAk2r+jN1NiX9Vhw2Slo6hLWqqROdDyDCXB6AADD+B3QbXqP9p/5ujKSwOOk7EgAGHq5uu/ZVHFikgqASCw0loU3NgCg1xnA4MuInF9wL3DNr8qbW+aOoUqigpXUv4XJ7jltNLZddgryxPvJkujN8r9btErh1Wj1Bnj9mNp1StdfliUoN/dIx6O0QSYDk7YcfBlVBqx8NbQcktdNlV4AVSxEE2U7xeBM9giqyszoS5mX4QRFwkWnYXRN171D1RnzbqdKHlNy5Mf2eogcAJRXLAAkcTZ7LvDJTJIbXPYMMPkJX9kW/4becghkH0nHmcFCtg3bL8oyIlEZR3LzghrbT+28Yq8Te3t4Q/QPObKK5kCtnuy0SFBzkGTHjEknjr0mRfZwYM8vlL3dZXT4x9FoKRD71xxg0yeUKR1ONRSno3mO01IAlOnCO5rFudDtpuCm2y1UM3jE4KZU1lRptf1xQputR9K1MtoSQnqTWF3HGon7z1s9pwJr3yRiIVQPGjns+lG0zfqdp/4cnTZg9Ru0FgFULaaLo/HP8wCEbcvfwj2UnEMVbym56j8zHDQLa2KwZMOMvkBqN6puP/AHVbargTmNZKn3/Ep9lTL6UCKCxymuPWrsMqmdZ606bvKwMcQQQ9vib08sJCUlweFw4K233sKLL76IN954Ax9//DEeffRRxMfH45tvvsHUqVOxbds26PVhOOoMPB9aJicapeXBDGwmY8RDdO6lTmygBlYchCwpJwUsUrtQxQIEI1tvJA1YZyMF7fqfT4ttKDRVAsuEDMl+59FnWmUkmVjWjhQ8T70c9vwiEgp6M/VOyOxHDmqhkIXn0QAQDN8ja4B4hQF/WwB5KH+47MTmD72SyjDLdwI/3QKMutnX6Q+l+915BBELB/4A+irQ5A+UjaER7lO3k64TMxadNkFOxAkkhFjMgwVjoimBoPZYHBf4PUziKdRYi0pjKb8MbaeNJFm0eiq9Zhr+vU6noLkUSuWySrfTvhveB3Z9D4AjibGep1MWScvxFDbLtFWKvQziEqiqRw5lITLQvDoAQnbyqlcBzhV8/+5Tgr9uMNN5NVWEDtImZlO2ur0WKN5A800g6AM4Rp1PIWLh6PrW+sD+5Fug+8OSHr7z2ZbOm1IYLNEdx/69J1hPFtYoljmPcrrwUuhN4pxprRZ1k9nx2WfYKpDM1+N+71v097CrSM5ErlrMv3/HvsU0t5vTgZzR4uuOIJV+gG+lgjmdegjxHpGs88fuH4Mfj6F0m7L9zns7+OuGBJp/PC4KYCaFmeE96p9E+NcepqztS75Qdq/kjiUJpeKNNL/EC7+xtVrIEHUAvIp7jgUiOCeNy0BjJdS5tWWPk2DrkVoollEUPi9Yv5dQ58TWrxHXE4lUuIYkHvLGBX5P0QYaz4Z4mvvstfL7KV2P6grEx2ysdBktZlrWH6FtbaGy41UfCL2PVwfgauEzfwY0ATLQswbRGKrYTb1HRt0MmAI0/+2osPrAUS/MfWaS9AuUGOCWmcMy+gAXfgR8czklnqx6DZj0II0zc6rQi8Yofw+xeyEQYSX1GXg+9HhRcr+3Ve8NILwEEp81V3i/3oQWBjzYumRKEauIg1Us1B4Blr1Aj/ueQ+OgTiaT3O1Qds4V+bRN703nGWh+2PaNsuMp7pkTB+B8eli0DtAE8OU6DhGJhWDXhZFpwdChP91rVXuJPAunX11qLvl9eqOQsMLRmuOyAi63INmSQhrx5hShEXqTmAQRrcSktrz3lXx2qM8Pts74k4zRhMEsJBcYfGWn9CZx/HUYSNV1jSUkqZgWpOLH317zuoG1go009AoaZ26Hcj9+zy/A3kVUOcNpqD9Kh/6tr2cgcrF8l69fVnEAgNDofd/CwGtNzqnKzk9KLroFMiwuofU6Il2Xu00mYmH3T9S/Ugol63Sfs4lY2P4N2QpeN/U60+nJBkgO8x6JZhLg8V6PYoghBh/87aWQkpOT8cADD+C3337D5Zdfjjlz5uCTTz7BY489hrvvvhtfffUVSktL8eOPCoMExxvBysecNjIuAwVUWwVLBZ1spqGXkAkYU1pnu1buBbZ+QY+nPa1cbujwclqIdUbSklWK6oNUbrzxAwry6M1A33MpsJ81MLxM7mggvScw8h+UYWSrJsdVTc8Fpv9+eHn4vRoAkXDwCgFfq9BHw2Wn0uCUHHWNk042ROO7+Wuy6gyAMZmc/JJtFOBMyROcwgiw+ROBVABlwPU8PfxjNVXS1phM53uigGXdWCtD78txogzH0Q3hfV6OIDtWtL71ay47Gc1uV9tVIZzMY4uBrUOWdL8ePSr0i6VjzF/uQxuH+71vIRX18Kb3AaY+pfzcdgiBmAEXKM/q9ycVxt7RWlrmeIPjRGKusTT84+iNwOnPEEFdsJLmICXIGUnZonWFgFuShag30RjndDS2lN73BgsdQ2uITO9aqbSQyybfr6A9gs0xga4bG1tp3SkIA1DGcDCUbqVt1qDo2ljWKpKABEdE34kAYyJV0BqTacyvel3sTxQuGIEYro2aNw44500AHLBnHvWi4CHMtWmtbTp2PyuV/JFm/x9nKcygkPNxlPTv8a/U9pFdC7Iu6Y3KeiwcXkH3sd4UXt8ef1Sxxs0R2pBthY6DyNdrKAYayyM7lsECDJhFj1llrlroTfQ7Se+LlrVHKxAJdpIqBWhfcwYlQQAx6RUgOmM/mNSY1KZjlXSMcGhupCrgzkI13OHl6j53/xK6F43JlNyoBoVrgR3fEalgiKf3Zw04cQPSLBkmlDxyTyGZsWAVqTqojSvkjSeb0lZNfS9sdWJD+0hEIGJjLYYYTlr87YkFvV6PG264Adu2bcOrr76Kbt26YcwYypbgeR4ulwsdO3ZEZmbmcT7TKCCU0eDfiJYZ2wBphSbl+AZTnHYyBJY8QYtNr+nBM96k8LpJOgigcnwm4xAKjeWkxVtfSAGHnmcAU54AekwJT/oh2ojPFEp5NVS5ULxR+XuzBlG2RlO54GiHCZaZxH47ZgDoTcoX8xPduVQLqbEbje/m3/za3kDXN7GTqP/fa3pkhunWL6kCBqAs4t5nhX8sgBpAAoKG/QkEdg3lqpXkwGQ4jqoYW1KwfiY1B0nmQAq9ibKAWJNzBkayRkPv/WQbW3Jg4w3wnXP815hg+uAGs0jctVQvUOBHu+VDjMAOOGCAa+bbykmgPb8KevIcMPBiZe/x76kQDVKB58k5lMtojQTRIBYAas455g56vPRZMXs2GOISxAoif81+JmUVl6Duvve/X8KBwUJznlIy/WSBT/Nzv3EmHVsjbyKb4cgqIpICgRELwarEwkHhWtpm9Got2XQ8Ed8BGHsXbR11JJFRdyT847FKwCyFFQ5y6Hu22Adl4wfAXr/GnFIywWknu1xubvUP/kn/bo9BHyX9e5T4PkDr9cglrTDg5TN9vW5g04f0eMBFkUtnOa1UAQGcuMSCwSL2fijdEvnxhguVRPt/J3JaLfxJIadAJJhS6b9WT8VeLYFtE0laSu/zQEFxtc+3V0Rj7Acj+aRkgnRN0psomUenF8nlw38p/0yeFwmpIbOV2wteN7D5M4opeN3Ul3HQxUDCCSg9JoWSHgsASSENvAgAT1Vuy56jilal0OpFO3nXz4ApgcZ9UrakGvUkufdjiCGGqOBvTywAgE6nQ0pKCjp27AiDwYDly4kp5zgOX331FTiOQ69evSL7kBPB8AhlNLjsxPjXHRWaudko85pJI1lSfasVXHZyRg8soYyQqU8qP5ftc6nU0ZgEDFGoc83zwM5vyVlK70WEQp8ZJ56+eXIO0GMaPd7zqyjVFAq6OLEZr9psDSmYg8RkQeLTifBgmTlK0B6dy2Bgxi67r9XKILHAcqAGhyZBWszrjk5/hbVvAVs+pcen3Aj0Oyf8YzGwigVLlElS3ktzgb2WypOVSmEwtBALCioWADGjqXhTeJU9lnTSTgWoP4v0N/UnjABRHqm5QVmQJhRCNQQ/GRDIuZQGNFn5u7+kmNRZaXndQeQdABzdCN3y5wAAb3NXg09XuDZX7gF+e5Qen3IDOZGhINeoOVxSQUomVOyiEnV7TXjHCgRGLDSURH6sQZdQnyJPMzDvNmVVA0zC4uDS1r8hIJ9ZHQzSpppt3VyWBR/bK/znIWOiGGi2VbfOyLZVE9msM4WuWuB5MeNeri9NuHA3iw2huyiUhDiWMKUQwZbUhSRV1vxX7DOhFqzJrVLppEAYfjUw7l56vPgRqoxt+Z1rBDJB6EXDCZURoZodt/cqulD2aqjX2bpkrfFtOu+0kV0jlReRk3nd9jVQf5QqggdeGNl3AYCyHQB4stUCSXCdCGD+SnGYY0KKtB6CXAsPbP5U/fsbS+n+9++ZwOR2NDq6lq38WMm8GWgcqH3+7wwpiReo0bp0TbIK/ZcsqZR40EmQWS1YpZzoL1pHSgZ6CxEDSmCvBf58Dti/mP7OHkYSxCdaPEEOSnosMIy/jwhyTkNrxbzbWidUBcOQS2l7ZCWREqYUMRHU36aIFtF2shF2McTwN8Lfhlhwu91wuXyZWq+fVp5Wq0WPHj3w5ptvYsKECbjsssvwf//3f/jiiy/QqVOnyE7AGUEp/7GCtYYCg/ZqeqwzkCEuFwBj2SCrXqe/R1xDUkBK4HUDK16mx8OuDs26M5TtoEx+jZ6qHJQ2lTweyBsLpPWk77rje+XvyzmFtnsXhS+HxCo3WGbCyUYShANm7PIIzxEIWirPsmqNFOy211I5Lvst1WLfImDpv+nxsKvFEvFI4KgH6oXs6PjoEAuJfAN0tgqgpoACmbYaMgbrjqq7d9VIIQGke6qLI8dESSa1HFrkkNaFDpgyeSR7o9inRoqYc9ka/hmiLPDlTyYEqpJraQ4nGVs6PWX9zr0anNeNZdxoLOImKTsfdzMw7w7S3M0dS45WKNhro9eo2eMkyUBGJng9JIcSbTkldn7hZHz6g+OAKY/TMav2U0PbUGDEwpFVvr0tGCnkDCPzU418ViRgkjLtFT762H7rHY/WlR88BMKujog2gH43uSBh9QEaD1oD9bCKFo5uIOLKkhFcT1sNnDY634KV9F0ikZUEaH069VbKzvY4SRpMrQwf76UKViCyigWG8fcCw68BwAM//gOoOkiEAg8iEwwmwXaXBMul65P//HwyVtGpDU7JySC6hKoPae8er1/GL88DK16lx0Muj07CQIlQAXCiViswsCBw6Va/yo4wMVyQxN3+jRg8VYqaI2IvH8C3CqWlf4ZM0Fhu3vQPigcaH+153LRV8Fbqb8rZxmw9Z2sSB5FA0JsAYyrNuc0NIhkbCju+o23fs5XFBdzNwO9PidJl4+4mYlupjPPxBqtY0Clo4M1xVMVx1itEvJRsAX7+p/IEsA4DqErR66L+jzpDANvdBtQeFdeaSO6vmE8VQwztFu1kFo0Mu3fvxuWXX47Jkyfj2muvxVdffQUA0Gg08Hgo+4TneSQmJmLOnDmYNWsWOnbsiNzcXKxatQpDhgyJ/CTkDJoTDZZUqiAwpdHjYBIELjsFsFkDvqFXKv+cwjWCFmKSuqApKyXPG6tcOika4Hmgvpiafh78U1ljak5DGt4ANaJSanR3nUTOe9FaMq7DATM2PE4K8ipZ2E/WDAF/SZb4dGWOgNz1aCyjYLZ/5izLcmcyOgAZxTWHwjvn3fNo2+tMItAiRWM5sOB+Oie9CUhUkKkdDB4nrvV+jU+9d0LnqAHAUzCDzRVeF1QJcOqMtFWakayLA7pOpMdL/62+QgIAuk+m7e6fQu/LfldLCo1NuSz89upcRgNyY8WfzJRqvgfS2QVaOyssk5QDVY38ei/QWAJvSle8zt2gXGps53ckn2fJJAcrlMa510ONOJvKiYiLlFSoPkgBVE4gE1K7AZn9SV4wmmCVOCWbiciIFKYUsZmuksq7vLG0rtcfpabALccRKrpYUEHqeFqrBIdUpiLIaaX93M7oZBKerOsc4DsPMZLEkkbPWdJ8K4UAYdzF0/7LX6TnOK28pOQeQXKn4yAVTWBDwF5LgQqA7ptIZAO9blqfizcBRWtIssjTDDhqlTfLDQadETjlJiB3PP29d4EyO1AKFrSKhtwWxwFnzqH5w+MkWT9OS3a7tCrIKQmWy61PTj/76GRKPgkUnHLZiHitLfJ9TSqDCIj9VlwOYOGD9DizL1X4SOF2AA1H6XEkPbCk531wKT3OGhj58doS6T2JdHdaxT57kaDbRFobmxuBrZ+H3l9vpup1AFj1KtBQRs2Z64vpOYtA3DsbgfqS1gl+TrtYmd8ybwqV3dIgqfR56fohtXPY2tLWlXXRwrEI3srZxszGY+uTOY2uv9NG/pU5kWwIQPm1ZNLBTCkgFKoPUlPnuARg2r/Fvm3tBQkdaatmHcobC1z4Aa3f1QfUyXD2F2IZBSvEJBF/291p811rIrm//u4+VQwxtGOc9MTCvn37MGbMGBgMBkybNg2HDh3CnDlzcO21lBmh1WrhdDrBcRx4nkdubi7uuusufPPNN3juuefQu3eUMkba0mBX6iyH2s+STsx0Zm+x8aaPJIjQU8EpBIc8Lomevwona88C2uZNUMa4M9QcpC3T9Wxr8F5agHd+D+TPo0Bx5R7K6lOCuATxd3fUKXtPUjYw4X56vOSx8DJPpQGYpjKgYj8dRzYbVLgnmqpOzgwBf+NGafWGnFGkNdB9HixzNrMf9VbgvVRmGw5YjwYWPI8E1QeBBfeQ4xuXSESF3hjesTxOoOoA4gqX41J+HkxohlcbR3qkKbmi48Vp1GX+sCCvNCswFCb/i+7zonUkq6YWvc+ioIy9lsqDmdQV+y8nj+TfiJjh714RpMSBYI4CIxP0Jgrc1PkFd+SclbpiwNEILHkYqD0MxGfBdem3sHEKr7e7GVj/Lj0edbMy/esDS2i9McQDkx+LAqngpPkjoxfdd3EJ6gOpSqpzkrKB7lPo8fp3I8/WBoBCYT5SIlUTlwhMe5oe/zWH1h0pWcQeSx1Paw2g0wFud+ux5bILc24UgsPAyZ0JJzcPyTXIrC8Ws3ONScCSx2kO5LTArPflg5m7fqSt0sBNKPA8HdPjBFLywg/s2KrJHtvyOcmLMTvLnCbaptGqdNFoSZLQEE9VR2okkTiNKOFXtC5658PWTJMg7+IU+tGwPgsGIVie0lm+2bG1pn2OByU+T6DglNNG2fCuJl/Jo5asdrNIgDrqgUUPATUHaGxd+HHredvHh4nCfLt/MX2v+A7RrQ5qC2i0tKYClKRRfTCy43Eaqg4CgPXvhx67HAfM+oBstLLtwMb3BCkwq2/Vo9tNa0x9CVB5kO57QKhSESrz/ceHHCEXbP1ob2vLsQjehlqT2N96M/0OPGguY9XLyV1CfwbPi/sr7Y3AEiLTe5/4/RTkMGAW3fP1R4Gj65W/L7UbrbcAxTaUousE2h7dAMQFqMgymH3Xmkjur5Cy3SdxgkgMMbRznNTEAs/z+PTTTzFt2jR89tlnePzxx7Fw4UJcf/312LRpEy655BIAgMFADsjHH3+MwsJC6HRUPslFkkF1LKHUoInU8JFKEhiELFKW3eZuVnYM3is2nOt+mvLPtteSEwkOSOmq4HN4au56cCm0lbvRga9QHmTxOGkB3folsH8JGS2cVswGLd8FlO1UdiwWxJJqtIbCqJuBnFFkHM+/U31mnDSjqqmGHKjmpuDGMGtqdrJlCIRr3Pi/T28iyS5DfPDMWZedeiJwGsomUSuZUF9EskIaXeQEWulWYNEDdO+l5FE5bDiVPgKhgIJVQF0BON6D/cjD45p74UzMIwOX48RMzFDZ4P7gwiAWkjoD4+6hx8uep6oMNdDoRId4zf/R+JAGWmQlr/7mBEIgKBljej9n0mWnecnZFDx44LID8AK/3EVSeKYU4MofwCfnKj+/nd+JckYDFOhfO60iWTX4UjE7TC38SYW07pFle69/T1lT5uHXULCrYndkvXoAWnMrBUIjb6yy9wy9giSRXDZg/l00N7Df3iAZQ+y+saQSIeEf/AR8mzpGI0B8smbCKXG0W5JB9CJhs+hBYN9CkVToO7P1+yr3UtBeowO6TYrO+e75hcaGRgf0P18dEe1xEcm260ca2+U7qTpBZyTbMHcs0HEI3VNAdIMPWoN4DQ4sUVct11mQRixSEQgKBt4rVhskZ5MUoa2SKmRZn4VAa5Z07LFsYUaqt4dgjRJfJth3j4sH9BJbTk6Cj9MAv/0LKN5Ax7j0KyBVxvfgNK37moULr5tIPgDoMbV9SLPkjATyxtH9uPI/QFNFZMfrew7Zd7Yq6l0RCpl9gfPepse7fiQpUdabByBfNakTYEig6pLmBqDmsJggF0jiSI6QC7Z+tLe15XjZs/59tADfKgavm8YRpwESO4U+XnODOO6UShnWCgl7KSrsyBMJxiSg5xn0OH++uhgBi5+oIRayBtJ4cDaRLKacD8xsOlaN2pbkQHsj8WKI4W+EdmC1hA+O41BcXIyysrKW58xmM6677jrceeed2L9/Px5++GEAwOrVq/Hcc8/h0UcfbZFHajfEglKDJlLDx1+mggegFbJ1lJabF2+iII8hXszgUgImK5PUWZkkQvUBkhOq2A394d/xqfcufOa9A/qDi0lj0FbdmmhwWoFDy4DVb5Bx2txIzmr2CGDYlUCv04Gc0bRvwUrKFggFYwpt1TRL0miBma+TcVy0DlivQNva//3sd7EkkwMVFx/cGGYZ2Sdb0DRc49n/fQYLZc+kdAmuoWurpff0nE5///FvdZ/LshmzBoZfWQAAh5ZTJqrLDnQYCEx/SX1PEp6njPIjqynDh/cAcQlwdhiG2zTPYh033Dd7jxm3ahugtlQsuILv54/h15BedXMjVfeoJeAGX0aB6toCauLMAi3BJOBiaA01VUDMoWRBzqbK4M6B1gD8/jgFdwzxwOXfUiBBKdwOsVph5D8oKy4Udv5Azmpi5/AztG010SUVAMBtB9a8FVp72pIODLyYHm/8UDnpL4ejG2mb2V85KclpgLP/Q9/30J/Ae1OA0h2t92P3TbC1x2ChMeloaC1Dx6qM1EhPtEdyUIkDrrRqKCmb1gGtAfj5VsoyDkYqAMCuH2ibM0qUqIgETZXA6v+jxz2mipVuSmCrBrZ/DRT8RY2nOQ2Q0g3odRbQZQxlZDJpPWnQOJrIHUfJG03lYt8EJcgZRdtoEQuOelqTAZqrmDmrM9K847IHvmekY49lC1ur20+wJlxfhjXotaQDKTmBJfj0JmDVa0S6aXTAhR9RNXcgsLldrQ3jj8MrKJHJmAx0HhnZsY4lRv6D5pXawzSv7F8SWY+40bfQ43X/U+Y79T4LmES+PJY9Twk6cvLDpmQiQIzxweeFQOtEoOfZfWUwR6fHxvHAscoCl+uZJK1iqDtCzyVmy8vy+YNVKxiTldtYrGKBZe+3R3SfTPdaU7n6qgUAqN6v/D0ardg/q3yHSCD4E0RSOyTU/RSTSoohhpMSJy2xwAtGzbBhw+DxeLBnz56W10wmEy666CJMmzYNf/75J2prazFmzBg88MADePrpp6HVqsy4Pd5Q6ixH6lQbTOTks5JrDqIhrjR4sVeQQeo5TV2ghbHrad1D7+tuJi1AAEjuAm98FtzQIhPV0FbvpWy5tW+R47Dzewqe7F1AhELBCjJ4jClA3njKvsw5RfyenYYImp48sP83cu6CgVUsOFRULAjn3SIpsfwlyj5VA7bgchogsycdT+53D6Qd+neFnDEUKoDFXncJY0IXR0FvjZ7ILab/qQQs6MCCEOFg90/AXy+S0ZY7jjRElTZIZ3DUk7FatZeOY4gnx7rzSHgtmfIyLuFWLISb7afRAtOfp/cf+B346Z/qgkgGCzD8Onq87n9CSXWN+JoSxEpyWyPQNfGpeDMDWhP1Gwgkc8PzRI7t/42I0ku/UC+ZsvVLddUKjeViRd2wq9TfywCRCqveiC6pAADmdMriXP+/0AGsAReQfJO1koiScMGIzrxx6t6X3guY/Q2dc2U+8PFZwMaPww9aavWtZeik/RraK5xWgVwT1hbZ9UeBs64mucSUDCy8n3o4hSIVmGQRQDZbpOB5kshyNlGSiNIqGIDGcf58QeIngZI8hlxB55Wc03pNYvZO1JuTmsTxoCaA2mkoXe/GEmVJKaHAAmpxiWRzWFIBcwaRRzoTkQX+mvJyaEkuSWs/wRo5bXslv3OLBFSVb1DMX55l5X/IRwCAmW+Erq5u6WsWAbHA8yKJ13emsqBqJJ/l9URHKg+gdXzGK1Rl67KRb7X037QWhoMBFwLJuTTmlVZtj7sH6Hsu9fj69hqgdKd4/7O1Qm8COvSjccJ6zkRjDWmrDOpj2a/hWGWB+5N4/mDyv0r7T7F5UClB7XZSVTigTGrpRIXeFF7VQguxoFK2LFdY846spG0ggshf6jLQ/dSWUkkxxBDDcYPK1NL2A1ZtcNZZZ+Hpp5/GSy+9hNdffx0JCZS1m5iYiLvuugsvv/wyli5dilmzZuHGG29syxOKrDmd/7GUQLHRqHQ/joxGWw1gqwOMCWJ2mKeZXgcoszLQ+eTPp8c9T6dMYyVobqDyO4BKI5sb5Per2EXbyr1CKZ4JSO0KJ2/AxbbL0A/78Uzi19A115EWr7OJAvbSoH1cEpU7x3cgrVNGUMjB3UyBqwkPBO4VwbLEbdXKDTab0DSuxzQquz+0jIKml37lm3EbH0QbUmcCUEsBLf/Fl+mP6k1iAFVqBIRarKPljIRzLJ4P/Z5Ixof/dXDZSONdp6P9WRWB0y5eQ5sgN6WPF4wdC2mp544hKZK9C321cgPdvwA1NgeCZ8f5gwVCAaB0G1XTAFT10HEgcHAJ/X1IgSyK1yNKn0jhbKJjAwDiAO1V9FBnAjhhGeEF8kxr9JXj0oWovGAOhqcZcClwpKSl9uY0YOpTwB9PkTTFFxcBZ71Mjq4SR2PwxcDaN0k26sASIK0XObOpuXQM6VhgsgTS7DQ148YfJ/o4CheBrglz5PUmABwFwqw1QoNFyZhl1W9r3wY2f0yvnftfItsYge2WOFFeDyCnSOJ2EFkMUAa/NYRclrUS2PgBnXtmXwp8MqdVivXvBT6GVP5IoyMZJY8zOGmW0Sv4eTHY6wFwdPxFD1G1jdxcN11oxDvoEmDNfymA3PkUup+liO8Q/PN4XpRy635a6HHsn1zQ5VTght+Bn26hee3Xu2luOucNZRVBbLwx6RIevk4oG38Gs7J7Wcm60BZVqsHOzX+syI0d6fcMOLbMrecfuUCD1wPMux3YMVckFbqfRvO7HMp2ULWozijqLIdCsF4gResoMMFpqRK0+lDo4236qPVzzkYi7YvWis/5nx+zlVxWCjYCgFfyW1jSAE2QYMyBJcHPi9NQQ/ijG4DsYcH3BYikzBpA6+iRVUC/c+X3U0pC2gQZJEsG3bcGi4QQ58S5lveSTWmrBcwprSuPdEb6r1R2R8lYO5brkf+Y4P1+U6mtpjPSYx40J3s9ravYtn9LQXGAbIt+54ROetAIJIA+DjAmBt+3ukr++bLtNNa0cUDXSWKvrVAo3+X7d3MDzcU8D4CXbL3iYwaOE39/RqLHS4KtNQcBLgBZwmxVKQZcABSuJVuzaB19p1NuVJYo47/WTn8O+O46sqGXPSdKVwariD1rDvXDKN8F/HgTcN67QHo3sttdDtoaTJJqBp6uVaRkmtq1SCmUEIPRgvQ7AG1nHzJJxECoF5oKhwr6MxKvQVCkMKfLE3v+fndtAY0FQzz5Kux1pcl7B/9Utp82EQD18kThOoALMIe0+FUhMP7e1s9lDaCqqqZyWq86DQEaQ/g9LMms5iDNW6GSZ5gd0UVQaziymsgZnQFweWnr9dA4kvasASQEkvReEmwsOZslhhhiaPc4aSsWGLp37465c+fiyy+/xMMPP4yqKtGoMxgMGDp0KNLSwtAdP5khLXHzL3dz2skJ5d2kfcwWBiUVCxW7yWjQxalrTOu0Utk7QFksweCoF7MRMnq3OEvNnBFbuIFwp/QgI7f7VAq2pPUgRyu+A/3dZTQ1c1ITYNj1Q2CtXSYdYA9R2SAHjgOmPknNrKr2UZBIKVjgprG8ddYLyzSoPwpU7KPsrUBaoycz5DLd/LMomqqINHA0+QbDXHYKbtQcJN1zl0Oo4BEy3/RmoM8M2nePJPAfDPZasTInnGaWZTtFUiF7BFXcqAkW2GuBahXVFf5gwRu1mXYtPRbCzPbrMYWClXGJNMf8cCNQe0TZe81pwJDL6fG2r4UghJXGRn0J3Rsum9gME2idiWMPQhS1J6jJ/Ay2b6BMJNYEGxDnnMxe8gRQ/jwiiwBg6hOBM6qDYdvXQGMZBd56Tw+9f/VBoSErB/SfpT7I7N9TITEb0EYxd0OrFzWEXbbQxHzOaCCtJxF2279R/3l1hUBTGX0X5lSqRUIWyVeNvRsAB+z6HvjwjND6vtL7S28WZeikGcpA+89a8x8rcmNHmp0nbYIeDE6rQHjbxGSQ5kYieaSkQqhxxRJBuk+J/Do7GigRA6DPNaVEdrxQYOS2yxH9IJkuTgx8bftK+fvYuq6295IcrEICipyuuH8Gvq2WbOOqw4Hnd/85PdgcfyJV6gVs0mwn4tou6ZtkMEkSGVyt5XL2LgLm30GPR98CnPpPZefAyIlIKhbyf6Ft99PUV5gyuBz0O7sdNO97nGSXed2Cj+I3Dnierou9lnyUFkIiTHAaSqg55XryqVx2IvfXvBlaws8f6b2A0x6hxxs+UNYvyGAGZn1Idl3VXmDZM62D804b3RcsiB6NNaStMqjl5JzaCtH+DuHMEU6bmESY3EYVC6wiQq7KLYrQeptxpfc73Oz9BHqvXUgwkYzFaKxJujjy8wAiPJT0/InvQDadu1kkcZSA9VlobiQ5JL3ZN/mKxRVYnzpATCZSUwVzIq0tMcQQg2qc9MQCAJx22mn49ttv8f777+Omm27CV199hV27dmHOnDk4evQoundXIK/zd4K0xM2/3M1gIqbfnE7ZH2wBUdJjYd9i2nadqC6AzYKElszgEiW8l6oVAApoBHNcNVoyPtN6EKHQaSj9HY6hUX0AOLhU/jVjMm0ddeqPCwgZ2U/Q400fU48KJWjRF7a2lodhJILLAXgdZAiwoF8wo/JkW/DlSjX9jWsOdJ+bk0UJMCaX43YL5eQewOP2zdIHKCOR01A2fJ0CA479tmndW2cXh0JFvuh4dRpGDfWU3stuB2njNhSr71MghYdJIakkFqLR+LDjEOCC96iiqaGYyIWjCsfK6H/S71TwF+BtpnnG66ZArtNO/3mPr2yCNJBhCpGheCwQjTGpphRebl+lAV+5EuqW41qB/b8DP99Of4+4nvSb1cLtoKbcAPXSCJUFzHupESxA8ixJ2eo+T7ZRcxsUhOqMorxec0Pw34rjgKFX0uMjq+SrL4KBySB1HhlZsEGjAyY9CFz2Fa1n5TuB9yYD694F6o4ClQfEICmDM4hERXtr3Bds3fRfb0IFd9jroaTabNX0mfWl9L+5Efj1XkqCUEoq8F6RWAiH2PPHti8FCaQcoPeZyt6jRkbQH7o40ALOR9ZnJBBSu9PxS7cGr9KQgjVwZr1LIkFLQC1DXu9aCnMKVWuYgmjL+4+rYOPsRBqD/mOG2Wg2IcDFA3A0Ag2l9LzLTmu7zuhL0BWuBX78B71nwCxK6lGKlh4LYdowtUeAsm00Z/eeEd4xeF70M/QmSkgyp5GvZsmgJCdTMvlFplSyMeMSxEQQrwtobkRc1Q5c6/0aWXyICr9giM8ERlxHkmGcBihcTY3iS7eqO07vs6jyDgCWPKEsYSQpm2xBjZ6yuNe8QXOgs5F8HpdDsD8U9gWUQyhfKFq+UnslzV02oPZoaBvFaSPfqLaI5q+6YrHHglKZIpvE5lQCFkxPalsZJD3fjCv4H3A+vxha3iWQCR6xOTUj/lx2IhzCJRq6jBYq56uA0u2h99doKekFEEkcJdDoxASTg8toy5IWmEKEf586WzVVmNuqAx62FU6ktSWGGGJQDcWe79NPPx3WB3Ach8ceeyys90YTM2fOxOrVq3HPPffgoYcegk6ng16vx8KFC5GTo5AZ/7vAR7IC4mOXjYx2xlJXHpDIgygw0vYupK1Sp5KhtoC2oRot1RVRYEqjV9aLIZooXEPGu7+EDatYcDvIqTaEkYnUfTLQ7zzSzl/8KHDF96EDC+y3c/pl2gO+JfPWGgqcW6vEQCnL5GkloRSB7EsgHE+Swr/0Vw6WdCIMpGPB6ybDKakTZbU3W4H4ZJn3ZlDWcOFqkitipdyBwIIN2SPUfY+qfSKxlTWIjD8lpALvFYw+VsXFkUPYFKZD2VKxEG7zZicZ1+FmEaXkAhe8Dyy4jyoX5l4JzHiZnNNgSO0K9D2Hxte2L0UpGUDMGHPaJY0e/cZGqHsoFKTSSuGOq2iUzCsZD8H2VTo/+K8vAKw2K2z11bBUbYV54Z3kdPWaTn1mwrkfWLVCQkeg1xmh9z+0jNYZXRzQR2UQVZZUMERGlAWDIZ5IPGcTZeNqtIGl+NK6U+Plil3A4b8oYKYUjFhQKoETCt0mATf8Qc09j6wCFj9EUoPj7qbXpZnXBhPZFHL3opr79ESAknER7hwgJ2sIiInJ7mZqVLrrZ3WkAkBEd2Mp3W/dJylrohrwWJspS5/TAMOvDS5nwlC2AyiOIADPcYIEip3kOZmMYbSgN5FcWn0RzTfTngr9HlaxULmXMssjaYbNgjWGBArK6Qxi/xp/mNOAjq3nXR/4j6tg4+xYj0E144PZaG47zSHmFNpqOKrcSOokXAdhzqw+SBn1O76j93SbRBWQSqs9gcibN+8RqhVyRpMNFg5cNsEG46hHXCuJE5nApS6O/ns95KO4HeC8blyKebiUnwdPbQJdv7hE9euwRkvXssfp1MOqsYT6q3Q7japElcjhAcDYu8jGLdlCvWGuWRi6oiNnJHDWS8Avd1Old3wHIG8i2eSAKIsULkLN6W3hKx1PqF2fnDaSj3W7gYRgyWqCAoLLATS4ifisL6bXlAb+rSqJBZbkpbQiIkx4OB0WYBKsMGMWFkPPCT1NpJVDvNeX6NXo6L/OoGyNBMSqhf2/UdXC4MtCyxsl51AyWdU+db2TcsdSX6Ejq4Bxd/mSdPEZrf0BWx2pW6iJe7C1BRDjEifDGIohhr8JFEeAnnzyyVbPcRJDg5ewrex5nudPGGIBoEbO8+bNQ01NDZqampCVlYX0dIWL0d8BTpvoeFjSfP82mEkWxtlExnlyJzI4WUAjUF8FhtojQPV+Wiy7T1Z3XnUFtA1GLDitIgERrWaZSpE7lhbaPb/SOUqdRV0cZUa5HSRLlBZmifPEB8kxbygmJ2jiA8H3Zwuxx00BB7kAhCWd/lurfDMEAhnEbeFMHksNUX8o0XjUm32DdtKgqMFEYwBuGhucVii1lxyzzwyBWFgYmlhgjZs7qyAWag5ThjcAdOgvZIgpdABrDomVRnEJFITVGsIjFnhe0rw5zIoFgIItLCM7HJhTgXPfAn5/nAKp8+4ALkkhvfdgGHMHEQu7fwbG3yfKrjDozfKBBnYPsQy1cIzgaDih0SiZDzIeeP9sKladI8wZHJNp8ZsfWr1P8jk8z4MTXrdZbfDaG2D44zGSZeg0FDjvrfCaJ/NeUTZuzO3K1oJNH9O21/TQGtk+n8XTGGQkQnLusVl7jElC8MxBAcaErMCBsK4TiFg4sko5scDzYlA3XBkkOSRkAVf+CCx/EVjxKhGujgbgrFdE2aNQkN6n0SDl2hpK1k015IN0DbdVk8RIXLwvsWAw0zplstD9uPp1en7Sw8qrD/YsoG2v00P31wgGnifCFqDxlRJCzhKgTMpISAUGnUmsvG0L6aXU7iSbV7iaSJiEjsH3j88kG7G2gMiWUE2Bg4FVLJhSqDCDZYn62+0uW+AqBSn85/9g9tGx1sdWs0ZKbTSvW5BGFSpX4lNEjX3eS/f4t9egJdCXOwa48CP1c7hWsBHDIZObG2luBoA+Z6t/PyBUKwhyq8ZE9eumRkvzh94Mpz4R2+vjMQI7oHU2Uqa/3hx+slZad+D0Z0mCbd8i4NCfZHue8Zyy92t1tO/cq2itXfGysmqSYVdRr4UN7wPLX6CkG5cwPvVGcT4OZw0JNadLX28Pa1QoBBp/gb4buy4JIb6zXlBAcLsBkxHwchLZY6XEgjAPmjNC78t7gYaj9Fhpc+gw4dKY8V9cBwA4D8ugl/ZYkPY94Tih2lvwoZhdZ0xSPo67jKYkDVsV+ZG5Ifwddm3VVgTmjqFt8Uax52Agks5lpz6cbpe8XF8gsLVFGpdor+Mmhhj+hlCckvHnn3+2+j9jxgzo9Xpcd911+Pjjj7Fw4UJ8/PHHuPbaa6HX6zFz5kwsXRpAIuY4ITExEXl5eRgwYMDfl1TwL9NkZdS2al+JCjkZJLcL0AkSIZY08bVQmVdM+4/TKGfiW85XCH4GC/hIm7+xhsnHCqzMXi6ea60SgrdcZNlpcfFitpsSJ5Hpb7PPdNnpuaqDpDEp7bvAShgBuhc8TnmDuS10RI+lhqgc1JYsG0xU6slkkVgZa1y8IJfj99swAy6UHnr5bmp0x2lF400tDPHqssoYEWDJjDwgyqoVwKkf31qDGPRZ8UpkOsUAGbkjbxL/VnI85pxotDRXqSW8IinfDaQRrQbHw/D2/85K5geXjdYZv/Fm1nPQ8k5oWIAnoWNraTGlaCilIJ/WAAy6NPT+rL8IAHQYGMYHSsZc9QFhfm3jSixOsp6E0tVN7UZbFnhSenyLkDm7+RP15xcMGh3Q/3zx/LV6wJTgO+aCSSFJ0R7K5pWMC7k5QM5O85cQ4/220ve6rFRlwHHiet9LQa8RBkY6h2ryHQq8R1z/QhG8DC7hfBM6RfbZbQ2tQQz+KJUR7NCfttUHI/tsaaauOU3sqSC12502oapSSAjyegSZoNZz8AkNNWsks9FMqRSw5D2UPZ3YiZ6XomovAJ7myKt+Aq78Kbz+BmbBfmmqUP9ej0toOM2FJqaCgTWtjsQW4Dh4jSl4VPswrtb8B16t0ffY4UJnAIZeAQy8iP5uVtmXypIu9sJiCWRKcPozQEpXmv/qC4Ux4CeDFM4aolSyTm8+sdYopzU8iaaAfUwCfDelvqLBTNnz6d3J3ohPF/0Sk0J/mdk1ihKSODHxSe09GE1wnBgLiUugczcm+V4vJf0SGHRx4tyh5Hup6Y0pBasm0ZuD+5pOG91jnBZIzg7Pt4mGXxRDDDEccygmFiZOnOjzf//+/Vi2bBk2bdqE9957D1dddRXOOOMMXHXVVXj//fexYcMGLF26FAcOHGjL848hHEiNAZeNdA2bG8k5lTYG1JsoyMyaOOvNVKlgiBcyfnigUtCWzRoU/DNT8ijI6nGKDWaVQtocKBDi4sVSSNa8+ViBZdb1O681ecB089N7Ki/VlIPTSnqhANDvnND7M53KjF601QukEO8mJ1N6LaVNVXUGug/YvdHWON6ZCJEY/Uyr15giGNHxrYmSGiF4ECrba/OntO09XV0gJ7Ur0FVo3lW0DqhUkYESx2S6oqA/zYL3Wr36knmOI7kivYkkjNa/F1ljM94LLBPkjLqfJjY3CwSvG/hd6GMy9Eq6/moJL2YEA+oct/aczWYwo/BoCVZt2IrDBQVBdy04cgSrV6/Grz9/h88/+xjrV/s2YjTrgbTMTuCmPk2O394FwMYPwjsvVuGW1DmwRJAUHEdNjgF1zezYe9N70NqmNwNgTdD3UyZzc2Poe5nnafw0NwJNlVSqX1dEFW62Wpr7Pe7Wx2HjVmsILtvRksmarO67Tf4XOYY7vwd2fKvuvcFQsRv49FzSA+84WJAd0fmOOYMJcDvR0nw4EAxmZfud6JALxPivTdLvyogCSxplwftnBDrtwjxkpXuj20R6nkmuKAGTdSzZGtZXaoFGJ1ab1hxS9h4mWRJp8IdV0iqVXVGLhmL6jdJ60nyjBCxTlmXOhgtGhqfk+TX7Nol2vMtONl9jJVWdaLTEg3o9yhJU/HG8+mwFC1QGOieDiWSPzOnks8jdA4x0NaVS5XG4Moxs/VAT9GYwpwpBQZ7mxnDAceoJrhDogCpoPEIAPloEX9kO2nadqP69bLyk91L+Hq1EFtfRSASQRtc6w9peLyTYtcG9fSIFSMP1dwKNPzXfLVAfGKeQbNLqnBSORUZaxymoNOU4oKMQq4h0XYsmOE6oXGC+mMoELd5LaxEgzkXBwGxCs4pKAkBMMhl8GW0D9Stx2ckuNZjDv+/bqiF6DDHE0KYIu3nz66+/jksvvRT9+/eXfX3gwIG49NJL8dprr4X7ETG0FaTGgNNGFQisXI1lPbFyah6idisgTPZptG0opiAKpwUy+wb/TI4TJZAO/qn+fIHQBh9rSNRYJmY9HCv0mAZk9vN9zu0ESrfRY7W6+f7Yt5h+g5Q8alQbDM2Nov5usiA7YLBQ5oA5nUgYOSeL3RccghufJ1MT50iMfubAm4XsOLlryrISU4MQC9ZqIH8ePR52tfrzyBooBoEO/qGcWJM2gY3UGWUSAKwRoFqY00hrndPQd1ATAPPHzu+B8h10z099KnSwYOuXQOUeCryOukVSjSIEK60y2Z3+Y4AZwQBdz9qjysbHiZTNphY6E45UWWFz8Sgqkg/I88K/oqIi2B0OrNu4DQ2NNuzcSwFGnufB8zwKS6uwftNmFOlycTBHyGr8/anwnL+6QtqyuU8JWLCCEbJqwGmo4ia9Jzl1phQAHAX+mypIBpA1EgWE39tKzzWUAtWHSOKhoZTWUybd4qin4GF9MQVkG0qEnig1NMez+yuUTE1LRp/KirmOg4FRQuPshQ/SOUaKinwiFWzVZDNc8SNlEhtM9L0qBUIGoDlZawg+NpgEV6j92iP81ybpd22pIpUkBVirRMLBYBKccgsF0fqdS8+zdUYJOg2hbdn2yNcHNr6USi8wYsHZFP5n8rwY8Ai3+ikU2L3a83Tl72EERH2kxAKrWPBr3mwwi6QCQKRdQoYg3ZhG9opGGx7ZciKuV8HOyWAiuzdJ8A2sNeJ/l120y6oPRJbMkM6IhTDnSJaYxQLv4YATApFR8H1MvB33eN8R/kgNr4rDH/VHKRFN6guqAbOlM3qrex/L5LZVU8UKk2+R3i+mJLHSJ9r39okUII02yaHmu/krIEifdzYBrqbWr4UC7xXXCKVKBcx/Lt0a2ZiPJrwekoT0CpVLcSqrz61VZG9q9MqkBh0CYW9ODb6fFLUF1IsMoAQsgMaSP1HHElE9zrYj9GNo18jPz0dhYeHxPo0Y2ghhEwsHDhxAWlpwtjMtLQ0HD0ZY7htD9CE1BgxmWpD9y9WYESDVbvUHM4IzeinT4O0m6Mke+lPdgt7SiDiEwWdKpu/Ee4hcOFbofAqQM6r18+U7KAPBlCpmRoWLXT/Stv/5oQ0OljVlTqPfjgUcDBbSjk/uAtnmz+y+sKQHNz6VOJdtRT5E+7hqjX6n3dcxZb0WXHZfKSQWkGaBlLQegY+57SsywrIGkbZ8OMgdS/cY0w1m0i7BoDMK2sB85JmhrGJBE4GcUvZw0sUFqIKjeLP6YzSWAWvfpscT7qegZTA4bcCyF+jx8Guo4sgpODr1JRTslJO4CjYGGipIl1gJuXAiZbMFAcdxsv9zcnJgNBqRkyOvV8sJ/3JycmDRA6OGD0JcQgr6DBjqc5yj5dVo9MShqNoGnHI98j1dSF7rx5tCSvh4/IOejBxQqtELiJVddREauwYzfW5mX0H7XEv3j72WCIbaAto2lgkkgk2UmdDF0f0X34F6EZhTaZ5uIet4midcNromjMwL1YTSUUdbtRULADXa7XIqjYefbolMpqwiH/hiFgV4MvoCs78VyU025hw1tGXN0pWMjXYyhlRDbm1i31VqjzmtYtUpC8yY0yiAndiRgmjdJhIhUbEbKFE4r6b1FPszVe+P7LuwLMoqhcfRGtAICmb+ftCFH/JdsLlUBoHcDlBmDKesckktHA1Ac73QN0xFr4SoEQuC7I5BaHgq/f1bgng2sVKhpRJZIB5sNUDdUVGermKfSFYEwok41pSeE7smDaWArRKw1gJpTCauDrDXhH8O6RFULACUHAJERixookcs3MB/iSxUwasx0FoUDRz8g7adhqnPlOZ5CbEQIonNH4xYYBUPDSVAzQExAYfdP5a0yO7t9pBwxYjo40FySCupGKSSOfoACW/B4LKLkkFKya/MfmRT2aojrxqLBjwuWkt4LyWpGBPVJ2ixtSSxk7K+DM1hEAubP6Ntt9NE8kJvpmMwyS9rNf3XGcT4UgwxCEhPT4fZbMYVV1yBvn37xsiFkxRhEwsZGRlYuHChfHNGAF6vFwsXLvz79jFoL2AVCP4LQEs2tmAA1hXTgsHgsgEFQsOxUDJIDF1G0XGbyqmZpOJztIifGQR2lxcHHEJWZn3xsctG6Hl662A/z1OzZYCa8YZbYg1QtmrpVjK++qqQQUrJE3sr1BX79lUIBmn2tWyJuQJHrq0y25Rqb7cVmHPKtFptEpLBIQnOOwWigWX5BiKWPC7KmAeA4VeHf59wHNBjKgUlPc1A/i+hg4AcJ2Yx2+vC+1yGSCsWGPqcDXSfQkb2ylfVBXp5Hlj+Et2vWYOAIbNDv2fjB0BTGVU7DadGazCY6Npp9UJcSttaGsl/DEidysRMoReNLvR9eiJls4WBrnl5GD9uHPJyc1uqDw4XFGDFypU4XFAAXhB/z8vNxejhgzHjzOm4/srLMGbMGB/bIbtzZ5QUF2Prtm0oLy7Ega5XoYFLpN//13t85nKv14sFixa1/J2V2wPjJ5+Ou+9/CF989Q0aCoW1RQ2x0FKxUBQdKQmtnhyulFySqmGBTXZsrZ4IfUu6oDHcg/ZN6EBj0phIryVlk9xZeg8a2+ZUKvnXmyg7TW8O3Sw9EmJBo6Vm6MYkCkj/NUf9MQAJqVBDjv3Fn/mSfgahiaMxVZRaVDo22vkYUgX2XaWJAS67WHXKAjMGixg08LoBrRHofSb9vf59ZZ+l0Yq2XaSyEWk9AAiNORX0+ti4vxxfbqRA75ZyL2bNtePUD6yotKrQnWY9IvTGyOyvQGDBnC6j1fXPkhIL4dqoHpeYPOBxExnXWCm+zux3VnGsN/k1XfXLErbVAh4H2TLB0FZjTUlQNtA+gc6JJYFIkwIckl5XnPBeVukcSc8LljjSWBaebZrZj+7RxpLQ5E4gREkKSdPcgLN5IgHcSbnqG0HLweWg5rIA2alqYa2iYCinVd9EOlEgFpoq6H6w1wHNNqCxQpT4ZQlVwfyeUDgRq3mOB9QQLEwyx5wGpOSolyBlAXKtQVlyI0C2GFMXON5ySF7Wf4incRZO43VAlPBUKsfXUsWqkODzOIFtgp864AKy41hFd30x9W6sPkDfJVgyagx/a3Tp0gX5+fn4/PPPYbPZUFUV5loXwwkNFSJuvpg9ezZeeuklzJw5E88++ywGDx7c8trWrVvx6KOPYufOnXjggQeicqIxHGNItfHqioHmOgpQsOzI+lKgTJD56TDAt9FQsIZWXScC+xYBh1eQrrrScwHICAniIE54YT32FVWg5J4EWGAjYyMhC/BIFurEzsB2hc0ox97Z+jmnFdg+lwya+A7AgFmifrAUlXtJxkJrAHrPEL6DQqfYv5pgz6+07X6ab5ljoGvRkrGbS5/bVAZ4PJShEcwh9D+e1FCWvk9vVtaUi5XlRxMGkxjQbYtggRw4yf3DmiOyJoBeofGeywEYEwAITcT1Rvr9WVZhajcxiCAlII6spn2MyUDuON/XlGaTSB21LqcCC+4HbFXk5I64VnxNrgJAayCHy2UVjPO40AFI3gCwpDiNFuC8YpYcy46UQmmW81GhH0n2CKr0qC8C/ngaOOVGX73RoQEy1vb8ChSupqDtGc+F/h5N5dTPAQAmPEC/ocFMgc3kzsF7H/iPATZWeFDgN0PQew12/0fz/o30WFHs9bBz505UVFSgrq4OuV264NeFC7Bp40b07pqDHl2zkZmdh+qGIhw6eAg9crMxqG8PWDgHnHWliAOHbVs2ITsnB/MN5+Jy19f0u275tKUh93333Is33nwbUx77HgBgt9mwcvUarFy9BgCw9noLRnXW4vl3v0OXA/E4b+pYWCwhvpMphe5dt4MaPwdz0MbcruxCbPrY92+nle4Rg8X3fjYozLRT6nD6k3tMg9ic6vtaRb6y42UPJ0mxX+4CVr1OVVVyFXrxmfLvr9gNfHEhOaNZA4FZH1Egtv4ojReLkPFmBqCzi6SCUhyrdeBEBZtjEoSttYqe08XRdWSVdUOvAnb/DOz+CZj2lPh7BQsAdB4BFK4hOaQuo5Wdj1w2sjmNbJfaAhpfqd2CNoR99LNVyOI9+McI4KxecXhpjQfby90Y+ZEL/5mVh3MHp4Lz/939CTY3q+ixSF6TjDt7HaAJkt0drNG11yPKQnSfLBLrwcDWyHghA9zZRDaZf9NRJQEZFnzmNJQY1FRNckct9odJ+G8U1zVpHxaDRZQ6NVhApE916+bGxwpOYdz725o++wSwR/3B7DW3g+4xt0OUFjElAg4e4Dn67tYaIm4biikwljNS5ngK8u/MafTfVk2Z8KxBtxzkMqvj4oG0XtRMuno/kJqnPEjIAqp6J9DcAK/XC41ckFVh/yB9HNlr87mpmDZgJKANQH4pJdNMyUDRIrrfEjoCeePkr2mw35Sde0qukPChwJdi9jbrWdZUTvePMRktcjMuO62rUttH6X3mD6nPE601qS3Wtmgf058cDXT9nDbq9+IU5idmc3Kg34Ln/Y7FIWifBY9LTIgyxAf2M+TGW85okiku2w4MEfoF5I4L/j0ZmN1kq6UkLoNFXoaJNwCM0zQlA5xkjeB5+v6M/DYmAcldAU2QuSZYD4mGUtoq7T/CCJn4jNDJYBotsGcJ+bOWDCBrOI0/Wx2tLa4mkmHSxVFyQ3KmWOlorRaq+mUUEqIFJeT8iSJ5FQO6dOmCvn1VVp3F0K4QdsXCk08+iSlTpmDBggUYNmwYEhIS0L17dyQkJGD48OFYuHAhpkyZgieeeCKa5xvD8YAllRYQJungslM2LsvwYWW8StBjGm0P/K78PS3Nm4NnQCSZDWhoBn4+IBgjlXuVf4YSeFxA/nxalI1JVD0QaFE+JPSRyBkdWXDd4wJ2/UCPB16s7D21ArGQlC1kpjkoeG0LIo/DskykVQ1ypatKIc0ii2aJsMFCxlBbGiqB4LSKQRqW3cTkpix+usV6s2j0WjICO2H7F9O257TIs/0BMmBH30yP9/wSOgtPFycarOFmygHwuKjp2G3fFuLDVWXweiMw5DRaoP8schbstUC5guomey2w7Dl6POoWZdJjrLqh42AiOU0Sw11tZiarYIhP981+ay9Z1HLZdmGO2z179uC3xYuxZ88eAMCmjRtRV1+PlRs2o8ljQHF5Dfbv3w+bzYYNq5dj8aKF2LL8V+jdDTi8YzWOlhIZN+GCG8BPfowO+ttjLcEFk8l3Ptq4Yik+fe8t3HHLTRgzeiS6p5JZ89WSDbji+luQ1e9UXH3r/fh9+Sp4PAGCmZxGrHBg82e0YbDQPKCmKV80wOYhU0r4x+g1HRhwEQAeWPiAMqk1QGzUbKumcXblz6L8CMuYZhnFrMrLX3bsZEJbyGVI5yqnTezxwoK1llTaZg0iW83jJKk5JcgeTttwZOn8wbJEy3eH3NUUp8PqIhqr/dI5rLx3AHJT41BQ3Yzz392LC97di9L6EMF8af+baKNyD92/hnjxGimF3ki9roDwpThY42ZjMtBsBRKz5HtnBVrH9Gaa71K6iPtk9hYzto81pAkjAfdRKcPkb79K/zYlisRDkjDvR1KxAIg2h9IG5f5gPU1Kt4b19vwKIq6OVFvx4ZqqgGoCocA116MMGXifU1DxqQQ8LyZH9T5LGVHjj+oDtFXSmNYfTAqpsYzmvqROQIe+wrgwtbZ9lNxnLhtQW0gVlf59to6Hb3IiIdD1M5ioDwirSnAKlXY6lYkEUrQ0blbZAyR7KBEsdUeApsrQ+8uBJVm6HOoC1zxP583O3ZIBpHQLTioEg8cpNm6WS3KUQ0uPBYUVCyxJpvcMwBgv9mJ0NgmfrRGqb7PF+z8ctYQYYoih3SNsYsFoNOK3337Dhx9+iIkTJyIuLg6FhYWIi4vDpEmT8OGHH2Lx4sUwGhWWp8VwYkGqlwdQ1odRaHDlchBr31QGgFNJLAjZ1cWbQ8qvuFwuvPnxN7jzrQUAgP2HjuDXVTthbyaHkud5uNyeFgN60ZPn4K/nLsC5ZwmZZnWFrcpSb3rrz/AMbq8H2LuQsl50QjPEQIanvRYo2UKP1WjvyuHA7xSIsGSQTrISsIqF+I503t5mIM4SNPkDLlvrxlpMjzNSQ/lkKRGWaz7GrhEjGaQkGJPCCiiD5BQbmfc8I3rnmT2cssJ4L7D2rdCl8S3l35WqMzsKqx248bO98ArZoT/vasT1n+3HqS9tQ0GVI5yzJxjMQGche7BwTejzWv4ijbu0nsCI60Ifv3IvsPULejz1SRofinSaVcoxtBfIOYMKxq3VZkNlVRVsdnuLFFJ9fT3MFgvq6+vh8Xow+tRTcejQIezYvgNvvvkW1q1fB5MG6JhmgSUhAQ6nGzaPBpb4RGiNiahtaEST1YZOWR1Q3Xk6CrlsGivLSYbn6aefwvnnn99yDv95821cdtEFeH3Oc1j169dIN9NEd/G1t6Jb1zw0Wa349JsfMW3W1egyeAJufeAJvP/ZNygqLvH9MqzZczgNnE9kMBIgHCkkKU57GEjpSmvgksdDj0kpqZA1kBo1s+xsFmRwSzLGDSbKNnbZgaoDERGdJyyUroXhEhAGM11Tna51I0y9CRgmVLBt/FBZlj0LmlfujTw4wIiFitDEwlOXjcLBWi8qrV5w4JGodWLXY0PwyBnZ0Gk4/LStBv2e3oLH5hfi641V2FLU1NquY9+/LSQZitbTNnt4eEQhayZcXxze57OxYUqh39pgEhs2y90zJ7r+u5L1U8k+0gQZqf0qTQphySA6I0kjsWBcpMRCSlfahk0sCP21SraqssNqrG7c99NRnPFOAQCgcwKHm74+gks/PozKxvB64ryquQkOLkq+e+VeuiYafXgySIDYqyw9SK+yQGDEgr0GcDTRY71J0IY30ZzpcYp6/0ruM6eN+mg1N7Z/vyYakM4vTFufXU8GJrvMrqs/0RAOWHDeoLBxM0NcIpDehx4Xb1T11vJGN/61sArnflYNlxdETnoVjjOeJ+UHdl3iEkhFIZIqkvqj5OsZEpQlj/BeSY8FBcRCbYGQKMkBfc+m5yxChZbHTRV4ppTWvRv1JkESVq++KXcMMcTQbhFR6hzHcbjmmmtwzTXXROl0YjhhwIKo9gbK7mGLv62OjPK/Xqa/B16kTl82sSNJJ5XvBIrWBiw3X7h0Je556lXsOXAYk7tqAVjQbGvA2fe/A42GFmGWFZ2blYrHrp2Oq09Jxvj+negAVZkk8VK1D+ggZpR9uXwvnh0Yh5wkFQu5100NcWsPC30OZgZfwA//RYt3Wk/l5cyBsO0r2g68SHlGO2sgl9aDsr+T84gMsgQ5Z72ZSCR3PRk95rToZd60lSzSsQZz3P0bZ7LnpOXnLrvY8DI1gCbskdWU8RHfQcxWixZGXE8Oas0hqrLpf17gfc1pdG+77SSJFAIeSTVC/6c3IcvohH5qAtxeHlec2glvLi/D+oJGvLuyDM+dlxf+d+g0FDiyihzCqn1ARm/5/QpWAPnzAHDA6f8mSadQWPpvGqNdJwIdBgaX92Il2wBJJ+iEcdheSQQ5yH1/BePWarXC7XbDarXCarWiqrISeXl5cLlcyMvLg8PRjLq6WuTn5yMuzoCmpiYMHjIYnNeBwf1PQT5cKK2sRXlFPVauXIXC4nIkJMYjJdGCG665EtsPlmBERw3eORXwbP4c57+0FEu3FcLhdGPKgCsAAF98/S3+ed2VGHXKcGpGCgDmNPzr/ifw6L8ex+q/luLzb3/GNz/9ipKycrz14Rct5z94QF/MPGMyzj79NJyS3IUyLSJt4HyigRELjjoivO11gKOWpInsdaS36xC2idnApIflg7F6MzDjFeDLS4ADS4Ad3wCDLpX/TLcD+OIioVFzH5I/kkq+6M20hho9vs/pzUQqeJoFaZaTrEeX0rUwXDkOvRlIEWTc/OVQDCZg6GXAXy8SOZQ/n6QcgyE+kwIfDUcpQJg9TPm5+IMRC7UFIUmKwV3T8e0D07G+aAVm9AS2F1ThjBHJePbcXFw6Ih3XfXYAGwuteGahmPF/1uByfHvbSJjjBLemrSoWmpvEHmGdTwnvGEmdSYoj3AbONoFYsGSITU9ddkoicNl9ExxcDuF+MKi/n05EBJPtY+PGZfe1X63VYoVJShfxOZ2O7m8AqDnOFQsZfaiC1FGniNx2uT1484cVePK9nai3e8ABcHsBvZZDThKHuVtqsWRvA2b0S8LoPAtGpXgwJEsDnSa47+PuOAzbKoJIOanFXkoMQ9cJpCEfDqqEigWlUi9SmNPIJvQ4AWsZSYZJ1ze27qiZbw1mCuRyaP9+TTTgv14pWb+USOqG/FyBKFJbsQCQzF9lPlC8Sew/JIHb7YHL44WG46DRcKisa8LLXy/H/346DLuLfKCvexlx5WADmpqsiE/0C6fxXsTxlFxVb3OhyduMdE2TSEIYk6PT/4f5+ym5yo7ltIpSYkqIiE2CdHTuqdQXi32EwQykdyOfSE5Cz2ChCgZbtXAfCGSvEtnVcKRZoyjnGkMMMYSPqNTkezweVFVVobm5Wfb1Ll1UNFGM4cQAc1TYghGXQAuSMR44tAIo3UKTN5OqUIMe04hYKFwD9JqOZas3Ys7bn6C0ogr1jU2ob2hCdW0dACA9NRlXnzMcwAbkpMShc6YeRyvqfA53pKwGNzz/JV7qlIynZ4/C0G7pMHOd0RkV8FTsAZcg9iW48fT+6JyowoHwOKmMt66QgvR9zvZtOukPr4f6RwCRVytU7gEKVgLggEGXKHuP00oNSAGxH4M0S4Q1XJL+zQw8g50cVlalInXMpAF0OcIh2KIeDQPyWCHY9zBYWl+TumIx0CwN5OhNQEMZPQ5UsXBwKW17TAmvPDwYTMnUDHrNm0ROdZ8ceF+NDjClkgFYVyg0LOaweJ8dHIApPYzQajjwPI+Fex14YEkNsgW1pQQD8MYMyhbS6ePw/PndUG/34u2/SmHQkgVaXNWIm/+7GPXWZiSYDUg0xyEl3oj0RBPSEkwY1qMDxg/IaX1eujig4yCq/CjeIE8sNJQAvwtye0OvVNZI/uhGYP9vRBKecqMYjA5kkDInyd5AGrGNNfKaqicbJOOWNWH2h8VigdVKQcL9+/dDq9Vi6NChGDNmDHbt3Im3334bWzauRW5mEly8BqkdOiErKwupWdlY9Puf2LV1Izw8UFFZjeIaK/TmRGjhwe6DxVi9bguqauqweYcXM9NMOLuXHjPTDmO+1QFdnBgYeO2lZzFyhBDwZFrMplSA58FxHMaOGo5RwwfjsgvOxqdzf8Sa9VtQWl6B2voGbNuZj2078/HMK29iRF4iNlwNeOqKsOSvDUhJSUFqYjxMRj0Meh0Meh20Gg2gdYPjuBb/ihMczziDvrXu+/FG/VExO23tW6H3rzkI7PgWGHaV/OuZ/YDx91CF0J/PA91OEzWspdi/hLT0LZnABe+TVIs/DCZRrkcKUwqRIUp7zLQnKF0L1ZLx/uuW3izKStlqKBhgEiSRhlwBrHwF2PBBaGIBIDKh4Sg1746EWDCnUs+rxjJFVQtTB+fglbUezOipxcRsN2Cvg9eQgDidBnMuyMPcTdUobXCiqsmFXSVNqK6qwts/rsDtEzvA4LFKiIUoVywUb6LATGLn4LZgMCQI72MSFmrBpJASsqgfEIN/4gMjG1hDTSUVeSd6cCZY0JKNG//fnPPbMjlXR5N4TWoOATWHqedCOGB2HpPtUQutnpKuijdR/44gleC/b9iLO17/AfkF5QCAQZ1MeG5mNnS6WsDrwqKbcnHJF+XYVmzH5xtr8LnQCD3VxGFmLx3O76PDtO46mPWt1yt318lARXhfoRUaSinRCgD6nBXmMUoik0LiOCJI64+SfJHcdWX3DSD2qAlVPROfHqtWYPBfr5SsXy4brU1Ou9BrgFNPPDULxEI4SXDZw4EtnxG5IFSA8TyP5VsP4cMF6/Hd8h2wN8tXIozMMWJgRwPm7rbhysEGxGuaKZFRAiOAefCrnvYCACdUmilIgAoFRz35M4AKGSShcXNcAvlYwcDzYnLj4MvJnpPOrZY0+h8IrJePlOz1n7/l1pxwEivCTcaIIYYYooqIiIVNmzbhkUcewV9//QWnU76smuM4uKXl7jGceHDaJEFjyYTssgnNFdPoecZyxwmLeFx88EUlELpNAlb9Byjbgc++nY+r7nq81S5arQaTx47E6GGDsK9yD5AMJOndWPDc1XBpTDhcWoX9RZXIP1KOytomrNtdgH0ldbj0ZdKs12uALf+woH+mE96983G2Nh2/cFPxn+vHg9uuMKPIZQd2zyPJJ42OKhWSQ5BkdQVAcz0tbKy0ORy4m4Ff7qbHPacByTKBVzmsfI0yIhI7UyWJs4mqTJKzxUXcKzRydjp8s6/1ghSF3ohWcUR/48AfJ8uizr6HtUos6Q2YVSFohLpdQLyMtnFCJj0O1DSPGdGF64jAUpJprwbdpwDbviGyqKEk+L4JHcnId9QDnAZ/HNFg+kfkjGbGa3B+PzP2V7uw9GAztPo45PFOnMMvwa/3mqGDkHUsBNsbm+nvH7ZWYfHuWqxlRFsQfP3gObhk+hjfJxvLgLKd9FgrYwA3lADfXUv7JecCY+8I+TkAxKbzXSdQVQ/HBb93mZNkSaXsnASZQOrfCAUFR1BUVIScnBzk5eXCZDSiqqwIJt6GiopGWF08iouLceDAAXi9XnAuGxL0XnTKSsegsRMwftw4lJSUYP7iZWhuqoW1vhpcnAW1NTXweHkUFBT42BNDhw4B19EIYDdmTJ2AvXNeR0ZWJ1z6Bt1X3bp2hdvthl6vp6AQp6FqoU0fASOuQ3FpGWbOvglbdsgHM03GOLjcbmwsaMCh2nh0SwF++ORNvLdZnYxEx4wUjB/WB+OH9cX4YX0w0Mu3VNcdF5RtB1a8qv59oWQ4uk8hYsHTHHjfkq20zR0rEg+MsAbEBs0tPXiqxeeYsxptsvVkRqD112WnPhYAaVkbTCS7t/IVImyd1tDBhV5nUEXY9rlAz9ND20DB0GkYZTDv/w3IDN7A7z/ztuK7XU48NFYPs94NNBxFvQP4ZLUDm0u9GNxBgwk9tJjWIw6pcQlCsqYXsJW2HMMGM8xyjWzDhccFHF5Gj5U2s/YHz1NSDUAVB+GAVXz4kwi2WmqCzu4BvYkqFoyJfvdFAAKhPdhxwYKWerPQGFWQ3mC2KpOplPZb8Hro9zQkUp+F+kLg03OAm5Yp1x6XIrMv+Qn1RVRhGU52fbfTiFjY8S35Wx0Ht9pl0dp8nHnfOwCA9GQLnp+ejmtHp0Gr4YAmO9DsQm9LIzb/Mx35VV6sLXLhlz12/LGvETV2Hp9sc+GTbS4kG4HPzifC3gfRskMbSoD175I/kjUQSA9QcRrqGN9dR2tNavfwKsAr9wqVQRzZ5LY6tDQ3Z2BrkbVK+f3fHsbKsYI/Ye7/t8tGvWDYGg+QLd1YTPOT20lxBa8kVqSoabqQfFB9kMa9msSO+EyqEqrcA6x/B+h3Pp77fCn+9f6igG8ZOzAPj492YVovMziOQ365A/lVNeibrsxW2VnhQZMmEaO7RmGMFW+iNdllI5kxmblCFkxWTEkfOvCi3JQ5o3WMSIpAyYf+lf7+87fcOApH5eBkUUaIIYZ2jrCJha1bt2L8+PHQ6XQ4/fTTMX/+fAwePBhZWVnYvHkzKisrMWnSJOTm5oY+WAzHF/7a8UynlZUZuliAVTDG+pxFi3JThbJyen90HEQBbFs1nnn1eQDAVReejZnTJuDXP1bim3mLYXc0Y8lfa7Hkr7UAgMtusaB/phb/mvM25u1tTVRdOnUYeqVp8e5vu2B3uqHTaHDe917MOc2L8/rocTv/EQYiH/AEkKXxB88DO74jGRbWUyFBJuvSH+X5tM3oE1mTzmXPk0FsTiN5FyWoPgCsfZMeT3qYjLWmSsqEslYDFtBvy2JBLCgeBwo+682k/Wutab04y8kASXGyLOrse7idCkp5hWsRLxhSXo9QESKMo9zxpGW9dwFw5hyqeJFixPXUt6PmIGWPjr4lut+F4+j72BBaTzsuAejQH6jaD3iaMSnHi/vHGPDyaicqmrx4Zz3NBSYd8PKZRlzkvQcZIOJhV4UHTy53YlVxMTokVmBQJws4ADuKxWyu7LR43Hr2MHRIsaDB1oyKOhuen0tjW6fVID3J775qKAG2f02ZTInZROr5v/7dteTEJ+UAF36o3LmT9o1JzCJHOti9K3WSQt3n7SHjM0IUFRXBbrejqKgIeXm5OFJYiKK9W8DxHvTu3hU/LFoOnudhNpuxbds2lBUVYGiPDhgzbiI8Wg5Wmw3btm9HekY6iux2JHbIhdvtxtRJfZGfvxv79u1Dbm4ubrn5ZsyceTa6dOkC06fTgUqgMmkI3nz5FcxfsAiDbv4fAOCci2YjMd6EmWeegQvOmYEzx94Hw8qXgGXP4XCjFhP+MQdHS8qQmBCP8aNPwchhg2A2mfDTwiVYs2EL7A6x0vKNdU68Nt2Iu0cb8P5mV4A6DXmUVtZi7uI1mLuYgoZJJh3G9kzBhF6pmDkkE/2yj2GVy4HfgQ3vU3AqvTdwyvUURLNVE4Foq6Igiq2a1jh7Le1rSgEGXBD82Du/o23u2MBrYu1h2loyxMoE3g3YG0la0SmRa5E2bZYbM3+DMdUKgQjuQNci0LykN5FUjstB65LTTnOeOY1++9JtQM7I4OcyYBYFMA4vB5a9AJzzRvi2TZ8ZtB4e3UjzeoAM1YOl9fj33I3wennMK8nApf01cDZWI8XowbOT/YkCSnhxQYcVBU6sO+rC1jIPtpZ5cai2Ac9cuB/3n9UzOiRf0Toi341JYg8g1cdYSxW7ujhg0MXhHYMF4KSkia2WArC2WjEwrjeT/I8/AgVFg61vJ8o4DFX1I5cE419tysBzgDkZmP4i8NvDJCvy631kT6itPjMmkbTiwT+IiBt/n7r3A1TlXHMQ2PUj/TdYKPlBggSzSAS+fOu5uLrDXgBAUa0T93xXj3fP5JBickHjrEf/RKB/f+D6/hxKG+Oxp8qLkkYvtpZ5sLuKR89UCojyQnVf1MBIBaeVqgxOe1T99WSkArPxLnintQ2tBKv/j7bdT6PgKOs/I+fLqPFjThafJ9oIlIHOu2n9AYQqBbuQMKShajpwvveIEunfnNEUVK87QhVHaQr9ewBNVjvQ/wrEr3gaKN+FZlNHzPl6GQDgytOH4Z/njcGArlnweL3w8jw4cEhOMMG++h0crHahzu7FgCwDdGlZeGVlLV5fUYtqm1dy+nGY9CDJbl5XeC++3liFb7ZZ8eBpBozuGoEt6LQCO+ZSn0qAxsawK5XFJwAiJAAiMUOB05BfeHQDUHsI6DwsiO8jzLu26uDqBv7zt9w4CkfloD0pI8QQw0mMsCOf//43BTvXrVuHvn37QqPR4Pzzz8fjjz8Ou92Oe++9F9999x0+/PDDqJ1sDFGAdNEHxFJOjU6i0+oWgs86CuyxfXRxtI/WAAy/lrIW17+nnlgwWODN6ANNxS4MTHWhU7cRuOCsKbjpgX+jooqClf16dUOPvBykpSQjVdOISuwHUI+pPUyYt7cRXTulYWC3TuicmYx3fl6Fr3/fjNkTeqHko2tbDOSqBjvmrT+Ef6/ZiodPBSbxa1G+cRMMcV5oNEEyDHgeAE8BF0M86dMrzWBi2rsdItAoPfAHsPlTenzWy8qz2hY/SgHk7lOoSRrvpeoDrZ6MOCbbExcvBiniJVUMLCBuknH2AzlmDCfLot4iC2UL7TTIXROXICdVV0JGblwiyRYUraVAnBSmFGDig8Cih4iA6HJq9HstsOwzJY064xKI9CvZCq3XhZemGXF2Lz28WhO+2enAgHQe5/TUICeJB1CDCqThofll+GSLHaztQmm9E1uLrOiZYcSMgakYnpuA0WMnonvH5JZxyfM8Zr80HwBg0Gnx02PnY8qQPPE86grJcPY4Kfg06BLfAIo/qXDRx2KDPiWw0xxDzegdgtap3+8s7asA+EqMhGrqF6rM9wRGIMkjaTJYTk5OS8UCAGzcsAElRYeQkWxBRuee6N+/P0pLSzF27FiUlJSgubkZ5Q4OHbv2hDE+GW6XCxazGRPGjwcA1NfXw9HcjPqyI9h3gKrJLp89G1deST0USvdsQM/KXfB4eUy5ZQ6q7Ty0ejG4kp6WhvKyEnz65Tf49MtvYDabMe+6zpiSVobUVU8iwWlFn57dseDr99E1V6z8uu+2G9DUZMWmbTuxbtM2rFv5Jxbt3Y96hwd9M7Q4o4cOiw4oq7bMTE1Er9xOOGVAd+w6eBSrt+5Fvc2BBdsrsWB7JR76bi8G5STg2nGdcd34zkg0KeyXoxY8T2vHnl/o77xxwKhbJE66RErCI6nI8HqFSjtTa21+KdxOYOcP9DiYPB/T/e001JdAMAuavkz+yGWjtYmHvE4v8PfMDA1EcAe6FoHmJYMJMAjJAizYakoFOgwCDv9JQYZQxALHAWf/B/jfeJIw2v4NMOTy8L5XYjb1JTi6AajIB7qMkt3tncU74fXymNC/Ey4dSQGTcz6sQKrWhicnW9AxQYuFe+3YXu7FhhI3tpV50atzKvpnk63Uv1ccGrW12Fddjofm7sKaAzWYe9tIIJIiGI+LgsYA0H0qoA3TfVr/Pm0HXBheZjwg6nRLx6o5hUgFnZEytF12qlKJl2lEG5CICrJOnWjjMNDaGioJBhASbDx0rUzJgCkNmPwE8OONNHfu/B4YeKH6c+p3Dt0je34FxtypvC+aFCOuI/L38HJg29fka7Fm3wDGDuqGey89Da98/SfufP0HjLu7B2psbpz5v/2otnqwuoDD9B469MvQoF+6Fv0yNMhN1qBjAv0HgMslipEFdV48/IcDz002omtKFKrEpKRCUmdKjFIrHem0At9fL7HxPlJn4zHUHxWJ8FG3AKl59DjQvaHGjzlZfJ5oI1AGOqtYYEkGepMoz8f2q9wnHodTQCLFxQM5o4AjK6nBsAJiwdHsxB3PvoP3viV1gw+v7otr84qhPbwUHeKcSEtMxbv3zcITHy3B818shdXhhNXuRKO9GaXVjahpEP0Ck57DxG4mjMgx4rVzO2B0rhGHa9zYX+VEk0eP+UID9Ol9ElBaa8c326zYeLQ5fCKvIh/Y+kVLZTl6nkEVhUoJN6+H+mwByqWaOwwQiYVgcyqbd5nNwojdUGoHsXEUQwwnFcImFlauXIlzzjkHffuK5cy8UBZvMpnw3//+F6tXr8YjjzyCL7/8MvIzjSE6kC76AD3W6HwljVx2wJLg28BZZwDsEsmkYVcDf80BijeSlrJKo297tR5DAEzsbsYFTz6LS255EBVVNeiW2xlz/nUXzj9zsrjwHllJ7PzO73H71Dzc8uD10OnEhXTy8F649PGP8OVf+zBxQCdcPrE3XvlpC176cQtszS5889A5uE8zCY9630AHI8lttEg4+C/ujFQAKODY/3zluo8uu9i4LUSZf0BYq4CFD9Dj4dcC3SYqe1/VfnJoNDrgjOfIkHPZAWMKBU95SGR7ZAKkLptoNARrUByths4nOpQaO3LNm+tKAA1H173HFMo82/NLa2IBoH4jPf8E9i8mguGyr5Q11FKKFmJBoayLRge3xoQPNzlw5QANJuRqAbgwIE2LdBONi2o7j+/MV2A+Nw2PTX8Bc2Zr0OjwoNbmxl/76/HvBYXYX+nAD1ur8diMLkjt5Pt9ahod+Ho5VfZ8cu9ZOPMUiTNQc4gcQa+b5I0GXOSrReqoj4xUAMhpBwR5hABBVGlfBVOifDDFZaPxykMM3igp820nsNnsLU2ZbTYbMjIyUFlZiQMHDqBHjx7Iy6NqRJvdDpuTR42dQ2JaJvQV1Th69Ch++OEH1NTUoL6+Dmlp6Xj2lbdgsZhx6HABmhobkZ3dCSNHjkRcnBHFJcVYvHAhKqqommTixIkwGum3KV7/DQBgdZEHusRM3HL1+Zhxzrl4YyNJbh3cuRGbN23CD/N+wY/zF+BIYRHOfGsfllxpxsQ8HZZcmwLL1e8hOau1nFx8vAUTx47CxLGjgPOplNy7/Vvg0FL8+M+BKMibjSabA012BxqtDpRW1eFQSRUOHS3HoaMVOHS0HLUNVlTUNKCipgGVtQ1Y98VzsJjisPWn/8OKfbX4I78Kv+2swvaiRtz9VT4e/3E/rhvfGbdPzUX3zCjOp143SRA1ke42Bl5MpL8SB1ajUTbv7PyOiNP4rOAZb6xioeNAIQBo9w0gMDjtoiQJ6wfASAaWaf13zAwNRHCHey2kwVaDiRoxMmJBCZKygTG3UTLJxo+JBFckpSCDvudQoKL6ABHpMlJM6/fTPVzbRNVEbg+PZfsb0ezm0bNLAtYcasSSPZQIMa57AsqtjSjfW40Ve2n+yE4x4qVLBuC8YVm47bPt+HlzKT786wiumRSGRjuDtFohR54QCYnKvRQw5jQUQA4XXkF+UBq4NqeJlSjWSsBeD5iSAKeMLaPEvvEP3Ae6944XeR6w6iJEEgwgSiFZJI2vE7PJ3tj+FbDwQSB3jPoeGrnjaB61VQNHViu336XgNMC4u8nGqTkEbPkcGHmDDwn1/M1nY/XOw1izswAz3z2AMV0tqLZ6wHHAV9f2RHaSAXV2N5qavbCadag36ZBgO4z91V4cqqWs6p5pWuyr9uC2BQ4cruMxb28Tnp8ShxvGqqnT84M/qTDyxvD6Ua14hRJMErLCJxUAYO3bdI/kjlUeSG1nySAnBKTXLNQaxcgFc4J4fV02oL5Y9J85rfIKl26TBGJhGVWBh3jfrgOFLaQCAFz/ST7OfqY/MlxF+PQ8E/7UjMSB4hq89NWygMcw6zmY9ByqbV4s2mvDor0UTzk114hPLs3C2K5JcPAGzLeL7xnZhezZP/bbcMEnJXjvoiykW5QSAl6qGiwQZGUtmdQHi/VRVIqqfTQ2DfEkS6gEHQUWsvpA69/UR0ZbmHf1Vt8YghKiN4YYYjhpEHZ6Qn19Pbp1Ex0LvV6PpqYm8cAaDSZNmoQ//vgjsjOMQT2cVpLAYc3rpDCYxSZu7LF0wjeYxZ4KepNAOqTSloeY2R6fCWQIwXNWkqcQVVVV+N+v9J6LR2Yju2Mmhg3sAwA4Y+KpuOCsKa3Z/GRhAW0ohk7ja/jOOm0IXrr1XADAfR+tQs+bP8MTX62H1eECzwPPfrsB+Vwv/FPzHBYe8JAWKQQCQaoRLSUVAGDQReqaSVXupSqB+MzwtHN5L7D8JZKlyOwHTHxA+Xt3/0zb3LGUtaE3SZpfugHwlN3Bei1IoTcD5nTRMLCk+zpm/lJZJwOcVmrkVlcoP06UQu7aJHcCDAmUHTVAyHrb84vYo0QKjgMm/wtIzgOsFcBvj8rvFy5Y4MHTHHw/AS63F5d9U41/zLPi1A+sqG8GAB7pJh5WJ4/3N7tQ4EjGD5oZcHEGDM6JR1q8HnnpRgztEo87p2Rj39Mj0CPDiMKaZlz18T54PL7fJy3RhMHdMgEADTZJJUXlXtIW9rpJT3fgxa1Jha1fREYqAOT0A8EDqWxuZHOfnJPktFHzOGeTSNTqzTR+pJlaTDbEWiU2T7dWiTrZJwisNhsqK6tgs9lRUHAEf/zxBw4dOoSioiI0Nze3kApWqxUHDhwAL0yfI0eOxJAhQzB27FjodDqUlpaisrgQ9WWH0bNrLrp1647a6gr8sXQp5s3/BTt37kTBkSNYtXoN/vPa63jhxRfx2Wefo6KqGgaDAS+++AJ69+6N5uZmNDc3Y4C+EAAwb58bXbt2xeuvv4Ypk8Vm5FqtFuPHnor/vPgsDu/ajE0r/8Dsyy7FLX9aUONNQLbZheRVTwck1+x2B9Zu3IKvF63Bix/Nx48FCeDBwVh/EH1SvRjRvxsmjeiHpHgTvF4vnrj5Isx9+R5s/PoF1Kz8CDUrP8SyD59EdmYq9haUYPZDr4PjOIzomoy7z+iKX+46BWWvTcFbV/ZH307xaHS48fqSAvR8aDnOfX0jFm8vg8cb4Zh3OYAja4hU0OgpW3bgherlJ4LB7SQtYgAY9Y/A2bj2WrFBYHwHMUPRKbN+GEz0u/BuapzaVEU9AaT7+o+pvxOi9d0NJoGsEWw9VimglFgAgF7TiVDwuqhxt1QPWw06DABSulLGONN79sPTs0dBp9Vgx5FqLNhZC52Ww4vnkQ349IKjGCWRknhlVh72PjEUb18zBA+d3QtdM8wornXg8v9txC/byvHsRf0AAA9+sxMHK5pkPy8kyncT+Q9EVq2wQahW6HWG+qCQFGwuk5Ok0psocGQwUyJJuPBPQpLei2wNYwFF6X7HClJfRvV7LTQepMGvlFwK4Gf2o4b38+5Qb4tp9UCfs+lx/s/qz0t6nMGXkX3jslIVWrN47+p1Wnz3zLXIzkhCfrkDu8oc6JCgA88DD84rRka8DsO7WDCxZwL6dTQhyayH1QmkmTmc2VOPUZ11uH2hHTO+tONwHY8uiRxsLuDORc2Ydl+YKgNypEI481bhWqqKAihJKlxSwVZNzXkBImoYXAKBHcifOV73c3uGP8nnv2ZJpZBYHyWf1+1USex20N9qKn1yx/rKIYXAsH7d8d7Tt7ckJ7784PVIO+M+1DfzGNVZi9k9bejftQPuumh8y3uG9+6M316+ETs+uhc1T3dH03M9UPlUd+y4NxevnZuB60cmItGowZojDgx59Qj+u7IWTrdvnGJ4ZyNePScDei3w004rhv3nCHaWKvDLnFZqMM1Iha4TqNI9nPWDrfedhiqvcmDNzku3t+6pJef/+scQ5GIKMcQQw0mLsCsWMjMzUVtb2/J3VlYW9u/f77OPw+GAzRZbnBUjVNNEQFk2RbAsWbkmS7LHsJIWOQfKlDFYAH2TmHnIaYDsYST9U7zZVwM9iOzKH0v/xAOPPAZ7kQ1APDK5GhRW2VFS1QAA+GvDdp+gfNHREvwyfyP2HSnFk7laJBncmH3PC4jr0AtTR/XHlJH9kJWejDuuPBffrtiLNVt2o9HuQtecjnjs1itw1zNvYeeRGnQA0MAl4py5bjxwqgf/nmSAhgMF3/udR1mY+b+Qs5ucS5UKSjULWcZd5R7aZg2Sb4iYGKLx2Ib3gZLN5ORc8K6EGAgAaXMr5sT0mErGGftdGYcSZ5InOwJVI6jJQAl4fgqCWtEMfKmB0wY4haZUkZRiSq8N62nAZVCDS72ZsqQM8eR0lW4FsofL31cXfgh8fJboVI27mwJtSsCaa8mB3SPNjTReQ+DO/y7CdzvtMOg0ePDCkZjx/R6clWOHQctBk5aHOy4aADdnAIRTK+50NkrKK5GdkYycrFTodTqkAvgmswBjb3gOv+6owdA7P8ddF03A5dOGI85AS87s00di2/9+wTerDuGmi84AynYAu3+i8dfzdODMl3ybCDL5I0cdXdvLvlafTcjAiAVjEmCvAzxuGTkFBfeEwUxkHY/Q/RmkTQEB8fEJZGxbrVa43W5YrVYUFRWhtrYWhYWFGDRoEJqampCSkoIePXq0VCwcOXIE27dvx759+2CzWjF23DgMGTwYffr0QcmBHdgbp0dtZTEKy+uxK58CiBMnToTJaERtXR1VM9TVobauDi6XC4MHD8a777yDQQP6wdHsgMFggObgHzCWrgcAzNvrxr7qtfj4o49w5VVXiyduTASE+4oDMOzUCfj41Alobm6Goe4Q8NEZNK+ueAU4/dmWOWfvvgP434ef4OMvvkZdXb3PtfhpdhLO7QlUbpqPtLOfwM4DhZh4wzMAgF/X7Mb8/z3Vsm9KRiImZnTCz2+nYdzse7FgxRZ0mnozzhyQhrOHd8a0QR2R2sGAW2Z2ws1nD8OS7aV4bUE+Fm4pxrytFZi3tQJZaYm4ZPIQXDZ1GEb2y5Uvk+98ivwPV30A+P1JCoYZk2jsZA0I/YNzCs0/do/u+JaIi4SOwNArZdY44ZxrCmhrSQestYAlhT5Lbxb3YdBbKBu+rhgwxlPAVB/vO55C2TxK7Cbg+K01Sj+3ldMeQKtaaQWUv5SEdK3PHk5rQ0MJZYoqsXVSuwIzXwc+OJ2ao+fP9w3YMbj3hj7WwAup4rX6IAVQ/QLkEzoOwV27bXj588V4eEkTzrr+Idw5CthoewefL1iNQ3xn3HlZP6QkmjHykvMBADnFu/HsVyvRJcuBqSPS8OHirZi/pQwVdg36dUnH7sIqjH56BcY8cCt9SJ+zg3tAcQlEkm35jCosAAqY9pnhG/hK6Rr6+wKU7JNPEoAYdXPgPhVK7mdG6vA8BUl9mmpy4r0RSSPeYHaf9D6MRkURx4UeJ/7XJeAaHeD6+WTXmikYKUVcPNn+570NfDidKks2fQoMu0LZd2DVj4Muonvm0HKSefNPYFC65nc+hfoTLHqQ5t2d39H6JZAhnbKA+e9nYfwlt2FtgRVXT+mLb1fux9oCK+5Y3IyP75rWcqj3f9uJW9+xw+lyIy3Jgr55WRg9sit6DnHh/75bjsIGHtkZSaiut2JdfiGmCO+zlh+G0Rwg/zBvnPjYaQWWPiv2VJDKHyn1o+ISiDxZ8gT9Pewq8mf8ofSeXvMmzaEZvemc3EIQt6GUxi+nod/cH3/HCrlIEbJKQXhdb5If5wYzySLZhJiSRhua1GP+sTmV7sVDf5LkbI6fneRHgHMAbrjuGtxw3TUtz1VU1eCeXx34/AITcmzbweFsvPrIP9GxUw4efP1LbNp7FBsKGvDI1CkAJ65vAzKAAULc/fFqO659fyuW5lfj9p8qcdevDZjyGL3W6dki9M1Owv/uvhTrZnhxyb+/xv6j1Rj2+lFcfGY33H7FTIwa3Kf1ebNeK7yX4jATHgA6jwh+XYIlQ5btoG2PKcrkcQFKIOU0FCPxV6fQG4XEEZ58HCVVPsfDDjtetl8MQZGfT8oF6enp6NKly3E+mxiihbCJhX79+mHvXnGCHTt2LH766SesXbsWo0ePRn5+PubOnYs+ffoEOUoMqqHEsYyGYWStBhqOkryL3iwQC5JFw2UD0oXfVkHW29Zt2/HgI4/ht9+pgiXeYkEzDIhz23He9AnYUkqL/6Rxo1FcUorP5/6AH+YvxPpNW1uOMeFiE87vq0cnbS1emb8CH88nBv+0EX3x5XO34PNXHsH9L76DM4Z1xXXD4uA68COGXh2Hf/4lGrZnzDgH/7duMzY3OfH56VakoRrY/IlQWs4D6b2IJAmnOWH5Ttp2UBDU8UfZDmDVa/T49GeAdBUl+1X7SHtRowd6TvNtgqkzkbHmsgslkBbfAEMg/UPpfXYyZosazFRVwCGycSIdE7xXMj7s4uvdJ1Mzv/z5FNCRQ2YfYPoLwC93AX+9DGSPoCBGpGipWHCGLEWdt2Y/3v6F9Dc/ufkUvLpgHzYcqsPesjh8d+do9O3RFYfdcVi+t44agQMYcdu78AhZkW5HIzqYvJh9xmjcdvEUfPbUjbj6yfex41Aprn/xG7z45VJ88shlGN0/D+eM7Y8H//cLVu88DHfhBuj2/0oH7HsuOaXS8effUyESUgEQeyyY04UqrCDzabCgpt5MJIcSyEkknWCOq8VigdVqhcViQU5ODgoKCpCXlwcA6NGjB3Q6HfLyctGvH1WqrVixEuXl5di8eTNSUlKwZcsW1NfXY926dRg+sA8uy8zA7v2FMKdWYs+ePWhubsZtt96Ks8+mjE6nsxl6vQE8z8PhcMBoNFJAnSeZD83BP2D4+SaA9+DLHS7sq/YiKbMzEjt2x8FykUw7WN4Ag04HnveiYP8e/PX7AsybPx87duyEXq/HRUMS8el0QLtjLj74dT1WufqjoLAIf/61suUYmRnp6J3XCQkWM1Zt2oHnlzfh3J4WZDRsR/UHs1Df3BEXDU3GD1vrMLy/bzNNhuEDeuKLlx/A9Y++horqOnyyvA6fLD8InZbDgJxkDM5NxaDcFAzOTcGnt45FVWMz3lq8F5+vKkRZdQNe//YvvP7tX+jWKQ2XTR2GS6cOQ/+uWcG1eAvXAstfoKBJchdg6lNARq9IbgN5uB3A2jfp8am3yhPnDHUFtE3IJvk9gMaay0bOKRtH1moKvOlNRD4A8sHCdiwnFhECaVX7zx1KpTuka70lHcjoQz0TSrYAvc9Udk7xmcC0p4H5d1DgrsdUMaNRDbpOBNa9Q2RYyVbZYMl9l5+Olz9fjO37i2B3OGEyGjC8bx4+X7AaVrsTh4oPoqKmAY1WB4b3zcMTb83F/mKa21fsLMSs8X3x0+q9WLenBMakDGTm9obTK46l+VvLMKQnadcnGbzINEmrV73A/iXU68ftoMBKr+mUcBKObj4AbPqIrn/OKKDj4PCOwcCkkBwNYmKBQWJ7sD5p0mo5tVKWwch16X3YHjSynYLUCvvtZJtTC9ctMRuYcD+w9GlgyeMkG6aiKSwy+lDVQ8VuSlYadmX4521KBqY8ASx6mLKxl78ITHm8JWFkaP+eePSiEXjkszUoq7HiknG98NEfu9HsovvD6+Xx0CerMOdHsaq8ut6KldsOYuW2g+iQmoBbZ03AZ4vWo9ppgD4tHclm0U485dl1uGlKT5w/sitSDC5kGhzy57nrB0r6SOwUXk8FhmXPkd+ZlANMeji8YwDk46x/lx73u4CqPlwWYa5sosB1fKb8e9vD/XwiQcn6ozcH9z8MZrJbmK2j1v/udQYRC/sWAaP/qbrJtzHOgC92uHBeHx0u7KcHNn4I7rRH8cC158BiisNtL3yEFz6ah5tmTUF6gGNkpxjx0fWD8dKyOny3oxHNXvEc+ISO2FHjwuSnF+OhWcOx+o1/YPazc7Fk0wF8Mf9PfDH/T4wf0R/P3X0Nxg3vDzQU0/rKKjC6jKE5SY2Cgj+sVWLyY5fRyt+nN1E8onIvULZTqKKSyFta0siW+zvaaDGEhfT0dJjNZlxxBZH2ZrMZ+fn5MXLhJEHYxMKMGTNw9913o7S0FB07dsSDDz6IH3/8EWPHjkVqaipqa2vh9XrxyCOPRPN8/35QqnMqRTQMIw6A1kgLtN5EhprTJuok22qAFGESKN1Kjo7MYs7zPO6853789+13wPM89Ho9zj9nJjZv3YoVh8owtZsOp3TikNxjDB6593aUlJaj14gJsNnE0jqjQQ+LKQ6l3iQAjbhvRm9oe3XB7+t2YcveI/hzYz4mXPskVr56Db4/nwNKfwZ28tABGJIO/HlBHd72/oGF3GS4hl2NU4ddDS+Af7gq8YLjKfQwCoHGjoMpW5oLkKETDE0VlFXEadX3V3A2Ab/eSwtzzzPUN0ZkMkhdx1MTOoAWeiZ7oNXT71dXTFJI0gBDIP1Ddp8ByjMR2hMMFiAlit/HZaNrzK5ncxMROkmdqJl2/jySQ5ryROBjDLqYMm62fQ38fCsw6/3wJLWkYFl5IXosFJTV4aqXKZvyruk98fFfR7DhUA3S4g1Y+shEdOiYjesKz4ULuhZSAQBG3vhyy2OPy4lVb/wDz330C+Z8thA3nDsBhfNfxoc//IZXv1mOfUWVGHvr/2HOLTNx98UTkZpoxhV93SKpMOhSkoWSjj+5Rs2RkAoswxMgqSpzCsm2uJ2kZ95WQU25SrETLIvGYjbDbKJ5IC83FxaLBZWVlTCbzdDpdLBYLJQ06raj6MAeVBQXACAj8ciRI+jWrRu2bdsGp9OJjdvzkZ2djd4DBqPPQA5NTU345ZdfcNHFFwMAxo0bh48//giZGXR/6/U6eDwsC9eLhk3fw7LyYWg4L77b7cLVP9lx+bU3obb7ufhwmxPYtqHlvO/59P/ZO+swqervj7/u5M5sJ93d3YiUQauIImKL3T+742t3INgBgogKKiEtIN3d3WzXzO7U/f1x5u7Mzs7uzi4LIu77eeaZunPnxifO57zPeR/fa7fTwbKxX5KXmQyA0+lk0ppUquhNvHdZGDfXPMDPk7ezaK8LnU7HoMsu4e7bb+ay/n04tGERD//vE1o1rk/jejWZcGgfAxJPkGBVuMhygouGQuolUazNWMq3n54mom4HWjZpQMM61QvS6q+6tCdD+nTl73XbmDn1O2asP8qu41lsPJjOxoO+DE+A6rEWOtSPZ8nY+zlwPJXJ89fz299b2X88lVe/n8er38+jTtVYBnRtxtAeLek7tA1mszda0+OSAqPrvgNUSW3v88zZy4DZ9CNkn5To09YjS95Wy7SKry8Rof7BCB63T3rOmePTgTdaipIPBan0/9EI0mDnfSbES+BcX6Nj2YkFkMCL3XNg10yY8QjcMC145G9J0BslynTXLIkMr9GhyHiYFBdFdISFzBw7+46epmXDmhw6kQJAvtPJyi37AHh34p8Fv1EUGeI9KkxdsoM29avQsEF9Mro+ht5YONL5y5NN4aT30uhUvu6TJeRC5hHYONlHkMU1gI63+OQ4y4P8bNg4SV53uav8+9FQIIWkCLmg04PD4pMw1eqkgdh9GsFX3PigBZuEaued787XwOwEW6rY2S6nBFk4bb5MbA3adQNocRXsmSv22O8PwE2/lc3Z2fIqWLhdHO5nQiyA2Dv9noM5z8haa888caR60bBaDADZdifJ3sKyXZtURVVVRr37J1OWipLAi7cN5NFR/dlz5DSf/LKY6Ys3cyotm7G/LKF/ry7o+z6FGnCOjUb+j0XAogNgwM03Tf4qSi7Y02HbdHnd4ZbykwoH/5Z6EgAD3z6zuWzjD3Jc4UlQtY2sy5xamwjzZcVp7STUdl9Zf6EoKpL417ILykos1O8j/Tf9oGTC9X6yTD+PioygQa0q3D3zNIOaR2LJPi7r6lZXc/c1l/DltEVs3HWQdyfM5PVesOdkDk//vIvjGXmk5TpJz3WSmuvAEJFAjwc+o03nwnON/zppttPBXx+/xUf3DybHns/Hf+7mx1mLWbp2G5fc9BjjRjXipgZpKB6nZHR2uk3qvZwJqQBweIU8JzYrVK8lJFRtLcTCnnm+2gyqW9ZSBu+YWV5Zukr851C7dm127NhBSkoKO3bsYPTo0aSkpFQSCxcIyl1j4a677uLYsWPEx8sA1aZNGxYsWMDll19OQkIC/fv3548//uDKK6+ssIP9T6IkndOzCWu8EAfx3ujMjGMiHaNpH6tAbF05DkcOpARPf8/KyuLjT8ejqirDhgxmx6Z17Ny9m7379rP+lCwk37xrMAv/+ImWzZpw58NPYLPZ6dKxHc8//jB6vZ48h5PUzBwmrpRVYLTjJK/eM5x1k17mwMSH+P6aOFZe7yRp+5dieKNii2vJu8odrKU1Zpw8pH7FM+pHhKs+XfNsYyJvHmotZEKjy+RRHlIBfNkK8Q1LjQwvgoX/g8zDEFldogHL6nDcPl2eW1zlK8Kt6VmavHq7Or0sLrXFlma42VJ9xrWmmQu+dgaVeqOhwKE5zbzX1+0U54nTDk0ul8yftP3iyCkJl74q0W62FJE3Ka+OtYYQizf/sGgbmbn5dG5SjYcHNGbOZulrvz3Sk1a1YziaqxNSoaS/Mpr44Inb6NGmEU6Xm3G/LGLfsWQeu64v2yc8wehLO+DxqDw+fga2PAcfDonnw8u98gG1u0Pf50onFcqrt6vBkeNLAdaiqIxWqeUQrI376zj760pfoFAUpeCRlJhIi+bNqVe3LomJCVitFlGscNg4ceww8ZFhNG3alMTERKKioli+fDlWqxWLxUJ8fDxHjx5l//79REREcPHFhYtY/v333+zffwCz2VzkEXZkKbVWPIvRSypc94udL7/8khdeeQ2Xp2SZEL3RRLtOXZn4/XecOHaEwwf3s2HZAgY/M4n9kV3Q6xSmj4rmq5fv5cDWtfz+0wQGXNqPZStW0/mqu/ht/jL+XreFr3+ezY3f7mbwnGq8faA5f56IJdmmEm+By6plcHPMKi4+9glLPr2foVcOp/Pwe3n4tc9YsmYLOp1Cn65teOfGjuz84AoOjr2KaY/25sURbbiqc20aVBHHy/F0O3+sO8rslTsY0rMlk168kdN/vMLkF29kaM+WmIx6Dp1MZ/z05Qx87HMS2g/k6ruf5q8fP8b98xhY9y2gQpOBMm+cLVKhULbCfSVnK4As7sEnB6fVFTFavYEKXtvF5ZKFs6kE8gHOnc3zTyNwfCnpvP23LU5r3r+OkJatqDlPHbm+LIHjG8p2nIoiUcnhiSKJNOMhXwR9WVC7mxDfWccgbV+Qv1Fo1UCy9j6YJPUN0jLFfsuxiXOzTePajLysC/VqJNK8dkIRtZxN+0+xYHtyEVIhEE6PQpbdKY7gxW8KqWC0QPuboO/zZ0YqgBTedORIRmz93me2LxD7DkT6zBIr1z/zmM/pqdVJc9rF7nM5i9qljlxfrR8t2OTfZOeVNB877ULmaNdExetQtohDzJHjIxG06wA+m9cYJpJy5iiR0Vv+SdmOrelgaduntvmihM8E8Q2hnTfgaMP3IuHoRZ0kmU+W7zzB+n1CqM9ed5CUTHsBqfDctZ154bZBhFvM6PU6vpm5ktSsXLo0r4vJaGDVnpQipEIgXOhZdzRIXYLNU2SOSGgsfbo8cOTCrMfldfsbxZFaXrgdsHK8vG45HOLryXrW4/ZmH+klcE5b95TW7s+HeiLnMwLnn5L6pcMmQW8OW+HX2neZx+R1WYkFcwRcIlKVbJgAm6aU+TR6d2xOik3lmskSeKTunQspu9HpdDxw3eUALF0vfXna+pP8vPYEy/ems/NEDqey8nG5VcIiokuda/RGE4t3JtNmzMcs2XyQb15/mAN/juWXu1tx8MEIbq53SkiFqq2FYDuTvuCPQ8vluU45+qiWlZi8Qwg7k0X6kUpBhnHB2HmBr5EqUTGoXbs27du3p1mzMgbiVuK8R7kzFoxGI1WqVCn0Wffu3Zk5c+YZH9SFiDyHC5OjFCdhUG1VM7jdoDNDab8vCU67b3EfkuPbDEavA8GWCg4HZKdDnBUcLlRjNGDClNgc/fG1OA+uwh3jlWBw+RaZZksE3Xr0ZPXqNXTp0o3Dx0+ybecewsIjueHhl2HlC0RnbCMvK5U3P/oCpxu6du3C/Ok/MnTkLaAz0Kt9c+4e0Y93v/udPFcyFkM+37/zKL3rh1EnLIcbmgEoHM2G6HZXYGp2GVszo5m7DOapvRiuzuQW9Sd6qatopO7nFd3D7FPqAtC7Y1PyqnhlGAIlHd0hOPhd+eh3L0S/ewY6wJnYAndxt8lZdPGtO7gU0/bpqIoOxyVvoOojwBHiIl2noDu4BNPpHag6I/m1ekHGae+XChhNsi/FDBavHJRqouDe2lIhR2pbYLOBJVKO0epvGJ1B+wuBIMkr4z5D6kehIlRt7pBgFq1+xSTX2FJFZD5UExgtGOv1Rr/nT1xbp+Pq9lAJ+zGhDB6HaeIglBMbca36AlfHO0v+6xIuh0ExYgDc6YdxWqsVS5ztO21DbzQzoEszEmOjSIiNJD3HgWIwcSrHw+1fbyJxWDEksarSgzXc7plMojuVMTfVYPa2GszcksbBnZtoXbMHFms4Xzw1mr+2HOFESibHN8xjdEPRtX9/nZE7evZH7/LrgKoH07S70GUewRNVE8cVX0NYkrcPhdg/1KLbKWnHMAOqIYx8p8fb10pq4ybpRyrSXzwuOQaju4zjaRBUYMZCRfUjVVXZsWsXB/YfoF79ejRr2iTIr81YouI5ceQkdWrFUr9RExYtXorRaMSe7+SygYOZ+tNU8vLziIyA32fMYtas2eiNZiIiI2natCk3jB5N/QaNOJVSOIrfkHmIqtNuQ/E42R/WmjsXbkfVe7jtznvp3GcmkT1vK/Xc9h44TGpmFtGx8SiKQmKU9/507YRn2q2Yj63h5piVOKo8Q57TzYTJU7n/sadxOZ20a92CEZdfzLxla1m6dgtrdxxi7Y5DAHRp05iBTay0tZ6ge3wWieFwRwcTd3SAXamnGDJlFh9PmkVCbDSDendmWAMnfVtWpUp8NJfHR3N5R9Fid7k9DHlzIUt3nKZeUiSjB3QjzzvX6E1hXNGnA1f06UCuPZ+lm/Yze9UOZi7fQXpGJtdHr6W3XbIjclUzdLwVfdPBMle5veOZPsSCo0oI26kqhjXvYcg+iRpRlfymI0qYn1zgtGE6uhYd4IhugMeWLQ4dW6446wxWGROd2RAWL0SDJV76l8MFqhny0rwy6WGF+1aZbZjA8z3P56PcbN/4Yi3BOeG0+6LQzW5xmmljlP+x2HIg1zu/Y5aHLafgP5SktjIWHt9Ifk526dGGbr/7bohGGTIO09RRKHvn41r4Oq5eXukSZ4jzqi4CQ43OGI4sw7PlF1zNr8YTV1hm7IW7hnP5gx/w7axVDOjVgRrVqqA3momNiUFvNLPzSAov3XMN37x8DzNm/cnI134FoHHNOHYfTRMHYilOqjrqUQap86m7coW0S8BdrT3Otjd7nfYUtQ39EcSuKwSPC/OGCSiAs/1tuJ2l9LvSSBpVxZh1Gj3gNMfiNsYICa83yv3Fr+2oJtBZwWoBzEXbh9beDJaKWWd4cVb6UaC9Ziuuv6hy3nn5vmtijBE72IQE3CjINg5X4euAWT53uyGyLrpL38D0xz2oS9/FUb0Las3OxR+ffzswRmOsdzH6ffNxr/wC52Vv+saf0u6/hoDirzQYiGnfX+jS9+NeMRZnzydAp6dV/eo8eW133v51HaeyXRjNYczfeooHvl7JLZe14fuFO/l51WH+z+7CZDTwyNgZKHoTej2s3XOCF24bwqK9ucGPIQB3fb6C14fU4Mqu9VCcKrqTmzHumi1++jY3e+NgAmvFlH6+hhXjMWQdFVuv++Mlr4H0JfcP/abJGLOPo4Ynkd/xHiHfVLztIQKcTtDr5b6H0u7925nRWvr256Fdd1ahmgrPPyX1y4xkyVQ0RohzuqDfmcCWg5KbKXOS20G+3V5yPY3Acbf+Jei73Idx1Seoi17FlZOCu8OtIfe3/z0wGp3BzLd/LOarjW5ua6sn869PCOv7OB6dAb3RTHhkJHkeAyO71+ONP4+QZXdxffca3NevHnHhBnItNXgspfT/iouO5GiKgce/XMi+Q0f5qHsqVyXlADoOZqjoWo8gqfNV0pa0cSDUeTWY/ZeXifnwKhQgv0Z31IK1TwhQXOjim2ECPGkHcBiifPcckzeYzlT43ttyfEFbgbbaP5CpXdZ+VIlKVKL8UFS1Qr1rlQhAVlYW0dHR9H36JwxhF17E3fWeX7hR/YU1tOFZ/RNl+q1VtfG15xFiyeIgNXlK9xRpSmyJv3nG/QG9WF3w3oGBFUpH/lR6s5GWeIpxnDZR9/K052Oqkkw+Rj5SbmO+rleZjtcf9dVDDFLn01ddhhWJnssigvt1r3BSqVLKr3240zOBq9TZzFL68KFuTJmOoaW6g1c9bxFGPvOVnrytu6dMvz8f4MqzsfC1a8jMzCQqqvhUz397P+rnWcrj6jgOU50xurdLNa56eVbyjPoRHhSe1D3NJqVFuf63i7qOlz3vArCOVryuu59spYyyFSWghnqCezzf0ZHNxW7jQeEYVdmn1GEfdbETxt3qd+hR+VK5jqm6IUV+E6emM9lzL24UbtJ9RLJSxtTdYvCg5wsGqovYTFMe0z9fIfs8H3Ch9KPq6gm+9DyGHg9vKXezQHdRhe//G8//4UFhkG5CsfNFadCpbtqwnYvUVVykriaKHNKI5hndE+z3ktYVCbOazwue9+jAFhwY+VkZxFRlMDblLN5DVeU2dTLXqDMAeEe5k3m6i4vd3KraeNnzDq3YSR5mbtO9Q0oF9dtzhQulH4UCnerhG89DVCWFH5WhfKMrReIqCPp4lvGkKtks7yljmKPrU6bfV1VP8YnnWSIRx+ZxqqACejzocaPzPstDXhtws5WmvKx7iJwznMsaqAd53/MCZiSj7ySJfKK7mTVKuzParz/aqNt4y/MqmUQwSvcprlCLphcDzeb2oPCw7kV2KmWox3WOcMH0I1XlafVjLlZXkkUED+le4pgSWtZka3U7b3peRYfKZGUY3+quPePDaaTu5z3Pi5hwMVvpzQfKmH9MUvFiz3IeU8dhxM0yOvKy/pFy7cegupjsuYcocnhe9yirlPblPqZoNZOvPI8SSS7jldFM0w0s977OB1ww/ShEhKu5TPA8QDj28rVvVeVh9QsuV/8C4AjV+Fh3a5nXT1bVxkee56jFCeyYeVN3DyuUTqX/sIxopO7ndc9rRGLjCNWYrFzBX0o33Gc4R/hDp7r5n+ctOrCFo1RljO6dMtu9wzx/co/6Pbupx/36Vyvs2M4VytqPStuuErB+/Xo6dOjAunXraN++6Jid53Ax7E3JNP3ticsIMwVv06XtpxLnD0LtH+WWQqpEJQAWKZKm154tRKuZZfqtTbHyuO5ZUoilLkd51/MSVdTTJf7mQ93t7KI+x0niK2Uko3Wf8JruAdYrrUucLHcpDblX9yqraIsZJ4+p47nf8xVGtWSJGH+Y1Xz6e5bwvvsFxnmeYrC6ACt5HKEa45UbuEX3XplIBYBa6nEAdlOG4nAUJhXW0ooPldvL9PtKnFusUDrgwEhtjlOfw6Vuv0TXldlKb3SoPOYZR6SaXepvgmGV0oHXlPvJw0wHtvCx5xnqqaX/f2kwq3nc7JnCeM8TdGQzDgxMUq7gDt2bvKbcz4/KUNbQhlRi0KFSixP0Vldym/oj96nfokflT6V3UFIBIA6JZE8npsJIhXrqYS7zLjjK40SrxNnHcaUaPyiSGXOv+m2p80FZYfI6ELMJLzepAOBR9GxQWvGR7nbu1L3JPuoQRybveF6hlbqjog4XkEXuq5436cAW7Jh5Vvc43+muOaukgqJ6uE/9poBUGK/cUCKpEKVm8abnVVqxkxysPKV76l9HKvzX4FF0jNfdCMBwdSa11GNl3sciXY+C/vqA+nWZ2/5JpQq3697hd+USXOipzilqcIqqJJNIGvFkEEM2kdiwkocZJ3o8tGE7z3k+wKCWPxIxQs3hOc/7mHGylcY8o3ucW3XvViipANBDlRowK5SOZ0wqDPXM4Ub1FwDGKjefl6TCBQVF4V3lTnbSgChy+J/nzZDXOZuV5nys3ArAdepvjPD8ccaHs0epzxu6+3CjMED9izHqpArOvA0Bqspwz0yeVj/BiJvFSlde191f7t21Z0sBMb+Gtmd0aHerE4gkl33U4Xfl0jPaVyXOPXKVcF73a99XqrPLtgNF4X1lDK8r95FGNLU4wVueV3nM82mZ/BM2xcrDuhfZSHMs5POi532u9/yKooaYbRQC/EmFbTTmft3/WKC7qEJJBYBb1J/owBbyMPOK7qGy272qyiB1AQBzleJtwEpUohKVgMqMhbMOjeE5lZxaOgP6T98Kh03kWwwWX1q832dqEG3ldauX0Xr1/xGVewBn/9dwd7wdXPmFtnnsyWcY9/kXXHzRRSxeupSmTZqwZvkSlHyZ6JWso2R/M4IEfQ6pLgvtPrdzPCOfxx68h6ED+jNjytdk5djR6xT0ej3WMBOdW9SnW5tGRFjkmMb9vIDH3v+Rof17MOn9Z9iX7uGReflFjldRPYxSpzFa/RUdKmuTjSx3t+WOq/v7NlI9KHnpKE47Ss5JdLknUXJOodhSCgwLVdHjSWqBu2ZXPLV7hBZVUaNDkY9M312KLusY+Vd9j6p9byk5a0M5shLTz9ejOO24a3XDOWScaPiDpB2m7RedQ4MJqrWteO1tTRJGZyi5CFQI1yQrK4sqifEhRxKE1I9CRaj9Ldj5Bn7mtIMzV/qOlvqZeRwyD4EpCuLrY/zjHvS7Z+LqfC+uXiEUFzu9A9Nvt6HLPIK7zkU4+70a/JqmHyh1V0rmYYx/v4UuPwtVZ8BZrx+e+MaFtvl27mbu+3QuKPDe9W15ZOJGGlWNZOzN7Rj+7REpQOaVPbrLM4EkUgFYQxs+1d3IcaUaq794lPy0Ixyb+T72fAfPjZ/Gn4v+pk2SwsCWsdSx5tEg2klitdpEdr2ZrYdS6HrXh5hMBg5uXk1UlESg6g4sxjTzPjyJzXFcG6CXGqrWuv92rjyMU69Hf2Q57saDcF72thSjdeaCqkBio+Lbcqjt3R+hyLZUYKRhmfvR6RTZLuDcCpkDfoenqnK4P/44hRMnTmCxWrn44ovZuXMnGRkZtG3TFp07j0M7NxIXF4caFsOJ1ExOnjiBwWhk/bp1LFq0iNOnhSho1Kgx/fr1o2PHDrRt24aoSN8x6zxOEmbcSET6NpYf8dBnohO3Cp37DiKyx62lXotXr2nFZT07k5mZwYMP3M/j991BdLToUOuOrMD068144hrguGEWAG63mwV/LeWLz8fz55LVqB7f4tFoMpIYG01SfCxJ8bEkxkeTFBtFUnw0SXEx3udYqkQbqbb1M/QpIkvn7PYg9lN7+WXlYd6ZsZ3dx2Wu694kkd8f78uLUzfyyZ9Sl+i2IV1RVcix5ZNlyyM5PYc9x5JRUPjsoSFcFbEOXdZRVKMVR9+XURObe++Jyo59B5n391rmLV3N32s343K6Co77iXtv5cn7S5COKm4B63FhXPIa+j2zUVFw9X8Nd+vrit9PzilMv4xGl7oH1RKP44ovUBOaeOv6WMFlk2dN39/mlaixxIE9TfTOTREQVUO+17bXiq4G63ta/yq4USHII10o8xEUM//4jTfae39brpj/NP50Hfr988WWuGZK8ddJq01TZB8ejDMfRL9nFmpYDI7L3kONql76OZwOICHyMlBsKV4NdB3odKDoyXWq9LhN6qusmvAyk6fN4sbY9USaFXa4a9PqzT00qJXEtGeuoPVdnwNQv1os+08IOf3MXdeyMvGaQn+lqB5e8rxDFzZygkTu073Kwi9fxGhPZtX3L1EtISb0IrTV2xb/nerB/O0lKLmncQz7Ak/9vqXvT7PlAqDbPg3TzPsAcLa7GXfHuyDBG5BiS4Nc0dcnPBGscb4fOuw+u95lL9tcVk75saysLKokJVRsPyqpfxQ6zuDXrwhsaaFdi7x0yDyGaep16LJP4KnWFse1U4PUrAge/KFf+yXGZZI16uz7Eu46IWbh5RXvCNXvm49xtWQKOat1xF2lDQBuj4e2D07mwKlM2jVIZMO+ZJrVimXHEekL6755kmWb9/HA+1PR6XU8cm1fsmx5TF55rFCB2eLwXr1l1Do5h6icvQB8sNpN59vfp32rpsX/KLLkYCvjgufR75mNq+1NuPq+WOoxFCePo9u/ENMvN4is7OgZqDVDiDDX+saZSFoG4p+06ypyPgoVDr8aSk5vDQqXU6SOCl3XIP0345jUk1MMEF/fu79s9Jt+wLjkNVRFh/Oq74OPm/ZSNIfyszCs/Bj9lp9QUFFNkbg6jsHdaEBwSdjUwnV+VFVl8qylONdP5M628tnvexVumJaH2WzkrWuaMbxDNTYdzeKd2fuYsfE0kVXrhdSPYhc/w/hOB4gxwx5bJAN/dHHgZAYA9WpVJSYqgg3bpI9VS4pjyntP0r5TiLURwnzzlnJyM+bfRAnB0f9VPA38/BzWhJB2pyRvxzxxCKrRSv6o3yFa6h6J1JFFxlHVXfo46j9Gn+Miz2XtR5UZC6WjMmPhv4dQ+0fFUqOVKBZhJkOxHasA54pY0IpQmayFnW+mKCCgsXg/S05OJi3tGPFx8QUFuxVFoW6VODKq9SRq7wGM23/G2P0u0PmiyDIyMvhxymTcznysYQbcznw6tmuFxWwAj2hF70pzMejzDGZdo9A43s6n/Z0MmZzPoYP76dahDd1ih5V4OqfTsvhi6jzcznwsRoUwg4JJH9y4UxUdPyjD2aU24EnPWDom5tIwfy1hR0yQlyEOeW3RHgyWWKjZGaVmR/TmSPQAxhANSaO+8HunXbRxAXNSQ9/3poDt/HFoBfx8vfy2Zif0vZ9GH2aWRb/BAjgAJ5gMEFkVwiPLX5AagrcVJdLvsxLadAgGtqO0PhGAkPpRqAi1vwU738DPnPmgBxQHmLyGneKAyDjQm+U+tB4Ou2di2PU7hn5Pl359rOHQ/yWYdgf6Q0vR7/kdWgSpcxDK5YivDa1GwJ65KJlHMO2bA/bTULtrQfu4a0BLZqzYzszV+zienInO42TnkRS+XLCTWEtEEdmjUyQwTncDK+hYcC5ulxN7bi64HVSPC+erp0czOiedH+atZ87ek3i8xXeTfx9JmMnAsZMpuJ352J35pKUmkxQfLcebJ4sGXWQVwgL7TUn9wx/a7zKOwC+3wolNoDejv+RF9NEJ8n32SXHkhEcW35ZDbe+FjjESKMUxVYEL0HL3o+LOLaDfq95FYasWzXA786lduzYeZz779+zC7XKxzxrGVVddRbVYK4rqYsnfK7B5zBw9fBC73U6fiy/i+quH8OnYT1i1YRs7t29h5/Yt3sug0LxZM9p36ECbNq1p27oVrq7PkzT9WrrX0vFEV5VXljg4sG8vrXuUfm7RERHcefvNvP7Gm7z37juMH/cp1119JXfffjMdLOKs0VnjfO3KqGfo5f0Y2r4aR0+c5uCxk14iIYboyAiUwPvkygv+x1WegWUfohxdg2n5u5iqteHWi+syukcNLv3ffBZvP8XSrUc5cCKVN0e2YsnWo2w4kMbnvy4OurtEq0KL5N/QeQCjBeXyNzAn+EcoK7RvVp/2zerzxJhryMm1MWfJKkY//Ap5uTl88f2PvPhICfJ6weYFtxMWvgD7FoKiR+n3AsaOozEWt4+Mw/DTdfIcWRXlht8wR9f0c/JZKdQPXPmAt5iqEg7RieAM925rKNxvFKX49hlK/ypyvhfIfARFr0vg9SjL9bn8f/D53+iPrEC/5zcpeBoM7uLGXT0MeRcmHRVHxl8vwOAPpKBmSTAEnG9EtDwCYNIZSHUYSU7P5oEPp/PJ4zcw5uENTByi0kx/mBd76XluwREeGTcTt1OCSfYcPolBr+PWy9pw/cVNWbm98D6vU6fThY3kY+QV3cPkKBFUi4tgx6adLFu3ldGDeoa+Ogqcn/xxchvkngajFVPDi8AQwtwVbJu982H2Q/K69UiMne/AGJkAOLwa13oIj5J+ZY0Q+0+Dv13v0Hu3txTepjg480GvAvk+uyYEnJV+VCKx4H+cxbQ7h01sexUpbm2NCH4ttGLWRosE5eQ5ITIeLnsDZj6E7sRGwmbeC1d9XriQvVrMve12J7hyYNVnGBe+iLHvs9CwX8nnCuAqYbxqcgm4bbDuG4wn1mI0GiCxKejh/4a04O5xi9iw+xgmg56t+08SZtST53TT6843eeueK7i2TysmzV3Lm9/NJMJqJqZ66ZkvRtVB4uEZRDmk5tDEY7V5ePZWeiZ/wpKfPik6Txb8sIT1h9MOB2X+M7S6AkMotp0+yDaOXJgnwTpKpzGYqzUDZ6b3HpbgxHQ6im/fxa2TS8P5YNedSxj95hqnXq5ZZGyQa+bXf7XC2dp61aCT9RLI/ehwA2QeRNk0CdOMu+GmmZAYUO/LVUpbMcZC/+ehfi/4+32U1L0Yl7+Hcd8c6PEwxAeoBBS5bAq3DuvFvvaNee3zt3m0TRZDG8LSGw0MnZzLLZ+t4bFIE9d2rsYTl9XmpSF1eXFxPqWVLW6k7ueVTgeJMcOywy6GTT3N6mnjmfj7It795lf27j/ENZdfxBM3/x+Pvf0VB4+d4Ol3vuSvX0Ms4qz1N48blr0tr5sNwdQ0IIMn1HXU5olyNer3Jsyog7xTEFEF8K51lQgh6MzhJa+PNJvkH5BuK2s/qkQlKlF+VPa2/yIc3qgCh803+Qca0wFISU3B5XCQmpZaQCyoqkqNeg0h5h7YNwmOrxfWP7pGwe9efeNtUlJSada0CffedSczZv3JjFmzcTqdGIGcnFyuumEM+07beGtve75IOszgxvD3a5fS+oY3Sz2V1Ixs+t/1JjsOHKdm1UReeegWAJzOklPk1yptuVf3Ks9kPEmTqDw4tKzwBjoDRFSVaJuIKt7XVSWKraImxvSDgAphMYUjzIrDoRXw43Vyn+r0gEtfB0uMRHuoTlBdYM+GiARwxUBMCFGDpSFYWzGW0dD+tyPY+QZ+ZrJKHzJaZEHitPsiD62x8lmNjmAMh8wjcHwD1AiBnU9sAl3uhhUfw/KPJAMlrl45zyMMmg2Gwyvl/49vkOwWc5Qcq9HCk92N1PIYSdSlM/v+FjwyeTtLthzltWG1Gel5AhMuHBiYqgxmijKMfKVoFtPdw/sQGxWO2+3h0Q+n8MO89QD0btuAhev30rVFHRJiIvh18WZG/+8HAO67qidNG/sV7sz1SuCEJ5bvXDXsWwjT7wZ7upCCV34GsXXkO2usPEoqEAcXdnsv7twC+r2qgt1uJz4+nn79+pGUlERubi5NmjQhJSWFpk2bYrGEUathU3DY0K/dwpZV64mMjKR58+Y4HA4yTh+nVZMG5GZmcCgiAoPRQHJyMhkZmWzbvp1t27czYYL8vU6n4/6Lk/igl43nLw5jVbKVFRmhFZgEePmlF6lZswafjvuMbdu28dX3P/DV9z/w1vD6PNYS7Eo4JrcbfYCToma1JGpWSyrftdSboOcjsOYLaXfHN6K68nn9LzuLt58C4LGhLWhbNw5FUZj+WG/em7EDQ1RVIq1mIq1mosLDiI200iDBRNiWH2gaDw4lDFPHWyEhuPMn12ZnzpJVbNt9gL9WbiAvLx+j0cCX775YtuN35cGfT8Hh5aAzwqWvQv0SUt9T9sh8lHMKYmrDjX/IM/iiF20phduY0SqFGxV8n5fUtyqi7/k7iCo6e++fQlmvi799B4VtvZja0OMhWPwGzH8JGvaXgqdlOh4LDP8Svhsq89uiV+HS/0nWyhlCURS+fXEMwx75kB9mryA2Kpz77rufh794j48vN/JsTwN7U818t3Y/Leok4HB6uGdIB266pDWxkRb2BAwbHdRN3OCVE/pYuZV93pooBoMsh6omxJzxMRdg73x5rter2EyEUnFkNfwyRsbj5sOg12OAzpeZokXdx9QqfV+m8LL1AZPV13fOZ4RynE67ZEcBOK0QnhD8WmjX1GmX741h4MyDWp1gwDvw2z2wZy5MvQmGfx3aten5CORlw6ZJsOg1+U3tECOQi0PzYXI+W6aKTac3QVx9xlzagmU7jjPxr104XG5iI8yk5+SjUxSycvO46+0fuahNAz56eARfzVjOpj3HUHLtJf5VhJrDC573SHIcwoOCrk53el92O5aJo/h77WZ+m7eUKy4tR826Q8tkfI6sBtXPIFp18ZuQdUyy3i5+XO6XonjvYUnzi6X4bYKtfSpRMkKdl7Q+ZgyDmBrCOWj3Ij9H1rUD3pRs7MMrYNI1cNnr0GRA2dffSc3hivGw7VdY+zWc2ga/jpExud1oiG9Y4s8b1KrK4y+8xe+/TuGi/L9oXUXPmjHhXPOznb8OOhi74BBjFxyiYRUrj1zbh+kl7KtA/sissuywi1G/qaTmOJk04y+ev3cUV/bvRuth9zJ1zt+8cN/1LJn4FnX63sziNVs4cPgY9WrXKGHvAdj6C6TsFp9F93JKldkzYJv3jNqMkvul6OVh8toS2j0/kwDGSlSiEhcMKomF8wmhTpjFRe6EGmERzAgPNKYDEB+fQGpqCnFxCezcvZv9+/dTv359wskn5fBO6kY1Jy5zC2z9WYxoID8/n4/GjgNg0MAB9O/fnypVqnDq1Cl++HEqN99wPR9//CXbd+4G4K7nP0RRtsCfT9LDuRRcKWCpBbWDM/VrN2zh1gdfZcveI1RNSmThlE+pX682GZnZ3PfcW+h63IfeWLzTMFlJIOKmSXDgF8kciK0DsfUgtq6QCKU5HDWUIl1UgMBFtiZhE9+g8He6IPGhh5bDj6O8pEJPGDWlcOqu0yZRA1bvsURYiraB8kTgBGsrZW2nJf3vPy3/VRqcNsliURHCRjv+wHPyT/nNTZVoEZMVYmv5PtObxJjdPRt2/gG1Opf831HeIoE9H4QTG+HgUpj3LFz2mmQaaAhVskHr1w37w8FlsGKspNv7pdz3jIaegyyAHTjEpru0sUBS6f1ljwLhdjoY0KUJ7z9zI5iMvP7Zz3wweS4ArzzxAPOXrADgiiuHcyyqHde8+Bhut5vBl/TmvY8+LNyucr1pzhFJRdtbKI4R1QN/fwB/vQ6oUKWVOL4SGwfbWJ60KKpio9xCbPf/UDHFMkFRSj7OgH6vKJCbm0t2djY2mw2bzUbdunW5+urhqB7VF61osIDBgktnplWrVqQkJ6Nz55EYE05YWASJSVWxRsUSYffQq1cvRo4cycaNG1mzZg3LF8wgKzuH7HwVe76TDxedpGNMGKNbm/i0v4u+M/WobieKvtj4eYx6HdFmMODhnjG3cfftt7Js8QLGffUdP0+fgT31GGDmm1/ncv8dNUiIj6NqlSSqJCVSNSGWqlUS6dOzKwP69y7+2gQbn/0x+ENY/iGs+RLl9A4iMvNRgFceuZWn776+4FrVBj64lKISDNkncfzxf5gSdBzO9DDB3plnmg0tNs18xG23M3vBEt/h6XRM+vJTBgweXPJx+keUH98I81+AY+vEATr8S6jfW74L1t9ObIJJIyQCOLEp3PArmKPFqez0yq4oyJjnDHDMmKzFBjCUC6X1N38HUUUTC6pa+hz2T48HjlyRnDD4tVvN1gOZm1qNENstdS8seRsGvl10P8VlcWqIrApXfw0Th0uQycYfoP+LxW8filwSQG4KA4e35DtdHKMfeI5Ppswnpmodrnj4Pd775v94pKuBL4eEcTjTzaIDKdx/TR8euvXqgp9Hm/UYFA8uVUcVNZknPWPRoTJT6VtQN0RR3Rw8JJHYDVp2gLjqPpKsvFBVH7HQsH/xkfSB0GzBzKMyh22YIBmptbrBRU9A9mlv39L7ZLCMFkKen/zhtEFOivw0mKO9rESEhrPR5kucr/yOs7j+aLTIPShwYhaXuR1g81piQRuiW10lNvaU0bB/MUweCaN+Ekdoadd/0DtC3m77Fea/CFd9WbINGIpdd/ETkJ8Fu+eITVenO/oaHfh23EDcj73F5Bl/kZ6TT7MGtdix74hcBoOepZv2sXrHYe4Y3o/7Rg1mw8F0Dnr7SCAS1VRe9bxBHY6RmQ9X/2zn+x8foGad+jx81y289sF4Hnvjcy4bMBiLJQh5VhKhtmeePDcZKNKtoSBw/j+2HtZ8Ka8Hvy9zTk6q9IuYGgS9L9q9LwmB7aCi23Qoa5+zsT76x9ZcftdPu7YRUb4gBE1SyRIjY6ApHK75Hr4ZCKl74JdboEE/uPwNWTNHhBgAotlXvR6FtqOE2Ns1Cw4slkfD/tDtvsJrqgAYgKse7UFeyiFOT7yJpPA05o628sCfeXy50YNOUdh7ysYj363mooduL2KjGlQXzdnN8573iMRGelhtLv9hKzneJI2xk2bw6K3DadWkHsMv7cEvc5dx///G8ecXr9CvWxvmL9/Is2+PY+JHrxSfGaTBEitz+qrx8v6i/4O4+kFOqmhQWBHs+B3c+ZDYDGp1kT5Q1gyeQISsFPAvWEdVohKVKIJKYuFCQqgRFsGiCkqJ+ElKTCQxQTT5Vq9ZjS3Xxv79+6lmyMaZl8upyFZCLGyZKmmGioLRaKRvn978OWcu77z3AcePn+CeO+/ghZdf4aH/e4z+fXrTo1s3rFYrNpuN4SNHsWDWDBo2HQw7Z8D0u+DWeUWOJTUtnWdefY/Pv/8RVVVJiI9lwbTvaVQrjsPHTjLwpofZtns/cdv3cMv1I3B5FHbE9pFj/+JRnrrnBob0v4j4CCNJEXqIv6U8V/vMkbZfnuNKKdx8aLlEbDhtQiqM+KaoM0q7pxrBEAzlicCpiIjRf3Pkj8MmETQADr9roZ1TbopPM1K7J1rkjf890j5rfY0QC9t/E5mjUKI8FB0MfAe+GyJZLj+OEgO795NF03lDRd0eUKU5pB2QhWleFuRn48hJZdbcBSRYoHPTmpjcuZCfzb40DzPyO7LJVZ/p894t0BHdMvFZcrKzCbeYuXFAJ15/+RZ0Oh05NjvvfSfFCse//QLtWjbjuTc/wmAwMPKKAcRGRxEXE01yahp9enbGaAxYLJ5JxoI9wxtV6B072lwn0dca6VYcCpGr/7J2WtEwhRfpq+Hh4URGRpKfn4/ZbCY3NxerNYgusctOq/pV2bH3EJ0GDqRZ7URSk0+h6gycyHRQp1E6vfrX4dJLL+W336azd+8+qlWrRvvO3Tl5ZD89+1zK0KuuYdPGTaxat5zL82bQIA66hO3njw/uwGiNwmwNp/1NrwEyprtdUpS5bvUkpkVfx+hRIwkPD0dRFHp270LP7l344I2XOfLdHcBWUmwqHo+H08kpnE4urNX79sdfMH3CeIYNvKR8105RJALc44F1X/N/3cz0bFGbLneNLLpYUlWR4zq9A5J3op7egSdlDyY87EvzMOgnF+++3rvEvzt2UrIhLu97EX16duWS/v1p16ZVaMeavEscybv/lPemCBjxbYmLbA6vlPkoP1syqEb/LA7O3BSJoLVngiVaMhN0+sLtyJYq46lGavhHzpeSNVlunMuo6/LKZ5yNY9Cuo9MupILLKQEH4LvOTrtklbjzodcTMG0MrP1GHDDVy1HEuGpLGPQu/HavEAtxDaD9DRVyWqOuHEBGVjb3PvMm//vwK8LMZmpc+ihTV7zNiBZGZt8YQ+tP0vj4p0WkZeXy4SPXEh8dQZLFTeTqj1iwYT/LR+YRFZ7DLupT79J78LzyMas2bqd+1ViyU07QsXVT6tYsSpqXC2n7ZL7WG6FuGaK5AwkFgDrdoe+LEiVqS5H+o1J+x78Gh80XxW+8gDJ6giHwWuWmBLdL/W3eQn3J+1mDPnDDNJh0LRxdA98OgtG/lB5gpOhgyIeSPbl/kaxvRnwHVUMcq4PuU4GOt8rYefBvWPou9H0OfVIzJrz9OFUT4nj/21/Zse8Iw/t34Zf5q3C53bRpXIdNuw/x8eQ/0ekUhvfvyphum2netAnO8Oo8MiuNRhxgSNqX9E84jd5jI98US89xR9h62sPmHXuoVqc+Tzwwhm9//JW9Bw7xwlsf8dYLj4d+7Pk5cMBLiDcZUL7zd+TCHw9KIEmTgZIF4syT7G1DMQEAThsk75bgH51eCNFga5MLOVP1n5yngv23tp5S8doMfmup0b/AqnGw5ivYtwDG94Bu90PPB8p+7FHVYdgnkLwTVnwKO2cK+bt3vqgAdL6zxJo5YQl1CBv5Ea5lYzEeXcG4QRbaVHHw4J95NK0eSZIpkxqzb6NBgommSUYa1alONU6ToKai85p+q07q+CGzGjmOrYRbwoiKtHLidBoTflvImGsu55UHb2D20rUsXLmJe14ey9N3XMuiVZuZNH0OzRvV55kHSq8zxpK3xT5Lai7rn/JAVWUOAgk8UBQhnytRiUpUogRUEgsXEs5kAR3MiCrG+Khfv36RjIXIjiPg1HRxlntlXnQ6HTOm/8rrb77Ncy++xKQfpzBq5LV06dyJVavXcO3oG/n804/ZtGYlg64Yzu49e7io/6XM/WUCrU5sEt3mWf/Hqdb3s3HbDrZs382WHbuYMXcRaekZAIwaPpS3X3yC2Jhovpo8mefe+YwTp1OomhiP1eTm3XffQ2800+85IRZ6NqvO46PL6TCqaGjFooJFE2jIz4Yp18u9qN9HdF1VfIUrHXZfkSztveqW58DFEUjKfHHt42wZm/+WdPpgMFn9FvFBotpcDr/FqcX3nfZ9bqpvUWqyygLKHCXOxMOroE6I6fARiXDzDJFD2jhZDOz9f0Hb66DjbaEXFfaHJRZqFF4Mm4CPvzzOwuXruPGq+rz55N18PeV3nvn4K4b2d3HriEZ8P+GHgu1PH9iB25lPeEIMHesnoNPpcLvdPP3BJNKzcmhctzrXXzWYK26WVNzrhw/m9Y++YNfegySnpgHw4ttjGTHkMmo18JPfyPESC6FGJWk4sRl+vhUyDkmkXJ9nxChWPaX/NhghVIkCWK0W6tatQ1JMBLbMFKzF1ZZx2GjTqiVt2rRFtcSBO4+aVmn/G8Z+TU5ONnv37GHo0KEcPnSY/fv3s2nTJiIiIkiq3pD0bDt169aladOmXDvyWgxLa8C6Txl/a3sS93XgjxkzOH74aMHf5acfp17tGhw8eIgtJ/Zx17oVPPXcCwwZNICsrGwOHTpIWno66RmZfHWZi/bNjZzK9dC2TUtGXzOcFs2acOp0MqdOHGPpyrXMmLOQ2x96mp9//5Oa1asWelSrkkRSlaoFsinBcPLUadZu2MyaZWmcWO1h7KUKXaJOSfZMt/slqj95l6Spp+wqlDGkIKVa1h1389behsz59QXqlOLoDDNL5Nndt4xi6OX9QouMzjgCq8fD1l8BVZxeLYdLtmFJkir7Fkm0rtMGNTsJqWD2q8vkcorcmzFCdMyLZKj5PTvtMr9lHhUpQBWJWi0ma7LcOFPna1lwPpDo2jEUSLl4x7NAUkG7Jm4nOPKgahtodTVs+Rlm/h/cOqdodHAoaHSJREkufQcW/U8yQOuFWLC2FNxz0zXY7Hk89r8PefatT3nv+Ud4coGemlEuutWCVXcn0uSDZH74czVzV+2gd/vGZNvymLNyO+MHmWkUbiKLCP6ne5AvEq0c3b2Z7BOH2HQCzCYT3737bOkRoaFCy1ao3a30ehMAmcdg2QdCyGiEQq0uMmZUbS3R7m6HyHMaLb45/0wIOZNVxguFf6d9FgyhXo9Q7FKNLDVFFN6uVme4dhL8fBOc3g7fDIDrpvhkFouD3ijkwq9j4Mgq+OV2GPlDqXIsJULRQbd75ZyPrZN5pt8L6OMb8O5Td2DPz2f85Jn8tmgNAy9qz6yl6zl6OpUPn7iFWUvXM2f5JqbOXcHUuStoXS+BF4Y1YVx0JvU5DDGAC4hviPmSV6j/15tsnbeEvQePcBkQFRnB+HdeYujou3n7k6+oWa0qD9xxY2jHvW+BtOe4+pL1VlZ43PDrHXL9rfHQ+2khFYxhwW12DQ6b/NadD6b4ktdEFyr+yXkq2H/7ZzD4Z7g47ZKB0v4WaDYM5j4jbfzvd2HLFJHbazKw7NHtiU1h6EcSALLyUwn0OrRMHjU7Q5c7Rbo22H4NZgy9Hobt9VE3TuKujiZub2/CoAMIQzqMlt3nte0UsLng7+MGhv+QRo5Dgp4evHEYer2OVz6dzHMfTWD4pT1o1qA2P777JFfc9wpfTp1D68b1+PjZu7nnpbE8+/Y4mjeqx5UD+hR/bsfWS/YhSMZgeeUIj64WuUujVWr7/df6SCUqUYlyoZJYuJDgz/77vy8OpTmSAwwAbcHVrGkzmjVtVrBZ7aZtUXNTUBv0Rdk5QyY1r368Xq/nmaeeYPrvf7Bu/XqW/P0382fPon2XbixfsZKW7Tpx1RXD+OSDd3nk8SfZum07XS+5grFP3sBo9UcMO2fwwjtT+Wyds9ChtWrehE/eeIHaNavz0Rff8cWEnwrIhhaN6zN8QG9e/vBrrJYwhg++jFPe300Z+78yXdKzCi1joaSo850zxPEUVcNHKqguX1aCP4ngtIE9TYxrf5mB3BRflGhJ6f1ny9j8N0f+GK3Br1nBOaVAbhqEB9TIcNggZZ/cK2O418nmJRiaDoJNk2H7tNCJBZBokUtehvY3iabs3vmwYSJsmybkQrvR5ddx9sP1wy5h4fJ1fP/rn8z6ayUP3zoCgN/n/01evoPwcJ/j3WoxY7AYOJmSwcjH3mfv4ZP8tmg1qzbvAeCSrq1p1fsKDh45hsFg4N5brqPLgJGofumw2Tm57Nx7gFoN/BaXucnecy5DxsLGSTD7CXDl4Ymoim7AG1CtvSwcNedaYAFHk9+YWaIM0n8bil8ae7gRwuNiQOf9Rhew+PJ32ChKgTwSQNeuXVmxYgUXdWlPoxrxXNyzC6tWr0av1+N0OomJiaZTx47Ex8cxderPrFm+hG5NqzJC0ROXvZOxz3/JJx9/xLoNm3hm9kkAHPn5bN++A4BmTZuQkZnJiRMn+X7ipCLnkRQu9zY5V2Xj9q1s3LSVzh3b8fqLz3DTiMHcP+Ym2vcZys49+5g4dXrwa6EoxMfFUiUxgapVEqmSmEhiQhyHjhxjzYZNHDt+stD2ue4YJg4zoBxbBz/fXHSHOgPENeC0vioPfjqb1cdVbr/9diZ9dUORGhDB4PGEQJppyDkt0X9bpvqkbZoMFJmAhGAyYX7YMQN+uU2cQXV6SLStP6kA4vTU6cFaTGSb1ucKZOPShPTT5ietr2rOwX8y8r88OFskellIf+0YCohuP2JFi9LWSAdTuOibO3MAFXo8IpleJzbBX29Av+fKd7yd75CI/W3T4I8H4PqpZ+Y89cOjd92IPS+f598Zz7Nvf8r1Vw5g2I/TWXl7OPVj7ay6pwqXT8pn19E0pi5YB8DNbY3c0cGEisLruvs4rSSyauN2du47VLDfkUP60bxROesXBYNGLDToX/J2GqGwYSJ4vHZu7W7Q9V6o2VH6BAjplpcFYVFeZ6j3nmoEXW6aV/qFshENWtDDhZKtEHg9ijuvUOxSNeDZ306o3kbIhJ9vkcyU74fCdT9CUrNiduaFwQxXfApTb4aTW+T3IydBdAg1MoqDzgA9H5baJqd3wJ9PQmQ1lITGjB3VGHPucT7+YwNJcVG0a1qPDTsP8OCb33BJt9aMe/Z27Ec3U9O+nSEN8wkzbJFTxUhuYntyavZi1QkDm8b9wrylqwCwhPnszH4X+WzYGfP+Cp1Y2DVbnhuXQzdfVWHOU5JppzfDVV9ILTxjmK89KyXMnTq9kAqxtXxtINfPlr9QI7MduaEFmZ1NBJsj/fuifxCQFuyj1UMbORn2zhM5o8yj0oca9JH6C+XJ3o6vLxJl3e+H5R+L9M/R1fKo3k7G4GByZYoCLa5AiakFyz7E4LSjAsl5BnacymdfmpuDWTqsvR/khJLELx88zbH0wkoCJqOBb36dy4lkkZk9lZLOrgNH6dauGUP6duGl+6/nuQ8nMGX2Ev6e9A47Dqfw8TdTGDfhl+KJBY9bJC1BggRCqeNXHNZ7sxWaDipfXb/zIXuzEpWoxDlHJbFwoSFU57DTBulHCqeLBk4CJSySVe/CPyXLxvQZczhxeB9DGtenHchistu9EFUDh8PBmLvuYd16KeD6vxdfpEmTxqz6ewkvvvI/fpk2nV+n/8av03/jqccfpVrVqsxbsJBbnv+MLd1MvHtpGB9cHsbmrGhMifXp2aUDzZs05OTpVJ54+S1Wrt1YcEx1a1Xj3huv5s7rr6TfdfcB8MaT9zJm9AiG/SByFzrdeVBgyJUvUYEFUkglZCxs/VWemw/zRuBYfKRCIIlgS5NaESqFZV8CF0clwZ4pzp//MspqEBmMsiDxd0o77d5i2hmyyNX0Y01WuZebJsOOP6Regq6Mw3B8AyGZDq8UA/vUVslk2PwT9HjAG8FT/nZ+y4hBWC1h/O+T79i2+wAfffsLo4Zdwo9/LGDu0tXojT5tTps9H7czn7ZN67Jx50Ge/lCyGSKsYdRIimPsjyKxUrN6Vca/9QKd2rXixmuG8d2U6cTHxfDUA2OoV7sml1zcXXaYlwWbpxSusRAKNv4AfzwEgFr3InQXPwHRtYuOXYEFHAs+swlJV+CMOMdZPf8mlOY8LbRILDzoXD9qFNePGgW2VNxuF6NHXMXx0+msWLGCbt268fhjj4HqYdXqVfw4eTIxYSqb9G7axdejoboXVo5DGfoxLVu2BC+xMHfWH3zyySdM//0PduzcBUD9enVp2KA+LVu0pFXT+jSoX5eqSUnUW3QnZB7i26eupdnWBN795AtWr91Av8FX0//iHjx2/xi+/vgNZs5dxJFjJzh5KpnTqWmkpqWTnplFbq4NVVVJSU0jJTWNbd76QP5QFIVmTRrSvG51rBYzYWYTE3ONXGtZhknNE7IsoTEkNJGaH0ktwGDixWfe5setLq4d0p+n7r05pFuRmpbOkWNyHWpUq1Lyxqe2wU83+LLd6l0shS6rtSn9jw6vkEW86pbxZcQ3RWsQaeQ2FC3a7L+Nv6MvpoZkdSlI1GmgA/zfJp93tkj0spD+JR1DsKwsaxw4LeCyS/2Qvs/BrEdh2YdSvLteOYqyKgpc8opk5xxdK/J9za+ElldBYpOy7y8Azz54Ox9+NZnU9EyGD+xHrs3OFT8t4K8bw6hryeXby1w8PEePUxfG4xeFc2X9fEDF1eBS1h9sDcD9L7wHQLsWjdmwbTeWsBA0p0PFqe0SSa3oxPEVDB6XOLOWvO3LUKjeHrrcLb8JvFdOu9wr8CPKvY7C/EypcaLVzSihXlohnM36I/8UjBaZxw3Gsmc/Bc7vEQleCUyLfO5fqyQ8XtryyMlCuCbvkPoid/3tu0/FwRQhzvApN4h+/MSrodkQsQ2rtCyfvrjBLPKYS96Fk5sh+wRkn0B3YDEftIOXmkWy5NAqrhvYki8i85i27iSO49volryPNnX0eCMF2JFuYGHc9SxUejDz8dvItxWWox3YtwfXX3l5wfuPvxQHZPWqSXz65guhHeuhZTKnQNllkFRV7N41XwEKXDlexikNDpusi8xF5RwLEFnVS2L7fZ+bBu48L7lwoRILNl+x93/qHAPnp5Js6mBzWfNhUPciWPMFrPpMsig/7w23zy//3BJbR6L7O98J676WOijHN8Cvt8OQj3z1pgJRo4OsxezpKNYEkvRGwm15HJ77NxM+mUzjvj0AOJkjZEmY2URevoz1DqergFSoXT2RO68ZSNe2vuCqhrVlXW8yyvrwxuED+fibKWzYtgtVVYtm1jlssHKsEIvmKOj1WPmuBcDpnRJIAtDh5vLt43zI3qxEJSpxzlFJLFxoCDVizmHz6e6agfSjoMk7aJNASQtUrx55+qljbN68GZy5LDYnUZtY4u1pMOFKuGEan0/8ne8nirOxY4f2KIrCL79OIyIigg/ffZuXnn+GV157kylTf+b1t97h0v79uOP2W9m5cxer3C4WHtxG37o6bm2aw5g/VrNkxZpC0c7gdeQ0asC1g/vQqF4tRtz9NGs2bUev13PtkFKixc4lNEJh1TjI8eZQJDSB6JrF/0Zz1MTW80keGa2Soph2UBzWBot8pwIooKiFSQRtcRRKdIoluvRtLnSUxSAyWQMWsVrfsUjUrk4v6e95WRJBZbSIUWyJlcj5o2tL1jMvCbW7wo3ThVBY9hHknIQ5T4suZo+HfcW2yghFURg5pD+XXtSZ3iPvZ8uufcxcuILH7xzF1t37+fPvDQXbGo0G3M58Nu48SM0q8Rw9lUrdGkkcPpHMroPHMRkNPHbvbTz1wBjCvdHi995yHd9NmU5qWga9u3emQ5sWsghe6Y2k1tp8QpPQJZ52eqPf2t2A0vtpWVSaI72FtP0KlTpsgEEi2zTHjcclC1F7OoRFllxjoaS24b9AulCcNMFQBudpsbIiJisG77V66sknC3+nKmxau5LqMWFkZyTTpW1zssLqw643JSul3Wio0qFg8+5du9K3Vw/27t3H+x99wjffT2D/gYPsP3CQufMXFmwXExPNxGEmBtUF694/uMcYRZtHBvDt6gxmL1jM/MXLmL94WUjnZTAYaNW8Cc2aNKJG1SqoQEJ8LGaTmYOHjrBw6XJ+/nNxwfafA/ebITbCTLfudbjhil5c0rgjBoOePUdO8fKHXzPpt7kA3D5yKPOWrmbGgmXY8/JxuVw4XW6cqg6n04XT5SItPYNd+w6Qkppe8B81q1Ut/oDdTpERcNqgSgvR02/YN6RzBeDv94VUaDYUrv7KR4b6R/Bq7cKWIpFzWj/WijcHazPFyRRpfbMye0hQUZkQwa63yeILWHDaofVIcUyu/170y+9aGpq8ViAMZhg6VkiFtH3isFn3tbS/FleJZFJpuvTFQFEUundswx/zlvDoK+/z8SuP0+qeS1g4dzaDXGvoWtPAitsMeFQVnZIHwM/bnYz/cwf6QbKP7XsOEBMVQVREeME+KwQ5p+D3e+V1g77B5zCnHX64WuRwQGoo9HpcIkxdDt82/sEKgffevyh3WFzhTKBQJf3+zTKVxcEULoRleWQNA+d3o392Vapc63w/W04je66dKPcz/YDUT2g5vPT/ssTKWDr1Zgky2jBBHnH1xXnasJ9kE5UFRqtkGeVnyxohZTek7MZ1aifRYU6GNDECu7h0IGT3iyDSLG0+M09l0lYnX653sCnFSL/nLgPA5XRhtYTRonF9WjVtSOe2Lbh5xGDMZiGVN23dyf/eGwfAG8/9Hw3rlyIFZc+AtV9JHRdUqNuzbNHQbqfItG30ZiP2fkqK7/rDmQeObLClQ6x3beXvuC6uzYfHBc8+vpBQlv5+roJocvzkxmJLyKgvOC6vPFLX+6Dt9SIndmqrZMCcKWkdVU3kUzuNga+87cqWWvJvDGGF+mm4NYwbrujP0P49uWZG4U01UkGv19GxRSMu6tiCwRd3plu7ZiiKwt5Dx9l35AQbtu9j/JRZsj9vQfSWTRqg1+tJScvg5OlUqlXxI4aOrRfpQc2v0Ofp8snjgpAKPwwXqbAaHaSOVlkQqvRyJSpRiQsSlcRCEARlg/8tCNXpow32kV5Dw2AAl0tIhtyUUo0J1WiB3FTioixUjY/i6MHTxMTGsaPqEzTb/CrxGYdgwpX0af8qtWvX4vDhI6xdt56bbrvdd6hGI//30AN8OX4szZs15ZXX3mDu/AUAREdHk5mZyTNH9Ky4LZyb2pr5ak8sK3eekN8aDDhdIuWgqirbd+/lhff2FuzbYNDzv0fvIikhjjxnKKH6ZxEuh8jerP3aN/FHVJHItFZXlxxdXq21RPak7Q2I8sgTp7UjRxY7DrsYxApCKnjvjzgNIkJvExfaIrM8KMt1MFpl8eIvPaHtw2QFR7wYwrlpIjdhA8KipQj3zj9gz5zyEwsgbafpYFlcbfgB1n4pESvT7pCigB1vl2iucmQwxMVEMW/C+wwd8wSrN+3gjXETAQgL9zmZDs0dz69zlvLQm99w9JQY4AePnQZg4EXt+eip22jQQSI2Dxw6ytQ/5jD+uykA1KtdkyYxDpj3POxdII5LgPhGUpCwaRkyL7Tsn+bDhEhz2qX9g09exWn3/Ye/DJLTLkSDIcmrEV+CM6KktnEhRn+eLQQrkOk357Ru3hgzTlo2qk2nnv1A0aOGHUbZNBlmPAw3zy+yy4YNGzD2o/d5+YVnGf/FV0z//Q9OnDzJ6dPJOJ1OMjIyGfY9PNTVxFM9TVSxZnEV82hS3435CPyyHVAUdDodlrAwoiLDiY6KIiEulqTEeOLjYknPyGTdpq3sP3iYDZu3sWHzNgDq1KpJano6OTm5BcejKAodWzWmU6umZGTnsHrTTvYeOsbBPxYy+Y+FJMXH0rl1E2YvWYPbLe3ysou78Py7n7Ni/daQL2XtmtW5cuAlVEkqIQJx3bdSsDAsGq78vGyLztR93kLoCmq/50HR+4Sx/Aue+wck2FJFZthh99ZNKGPUmuYAd9pCskcuKARz6JxJJkTImvPecc+eBp3vhj3zpc7V/Bdh4Dvl+29rHNz0BxxcItmX+xZJ5sypbaIFX7+3jNl1epS5nsPT993KsjWb2LprH32uuZOWDapzcbvGnK7eg0b2DfSrloNOUdiQZuWnfVbennMQdHvo5yUWrhnclyNHj7N41QZMJiPXX3Fp+c7RH1nHYPo9YufF1YdLipHenPuskArmKLj8DV9xzIKMn1SZqwK1/f3hX5Q7JqnwvQ11/vk3y1SeDZQ0v2t2gaL31YLR7IrIqtDoUlj9GRxYGhqxALIOuPF3ieDf/pvUHUjbL0Tu3++LY6/pICEZykLumSOFpPJKobjz83jmueepa0qjSzUPzSJtRJrduD0q49Y6eWulSvWatWjUOonuNeuh5eFZLGEoHieD+/fkoduuIyrSz/Y7cowBI28nJ9dG7x6dGTV8SPHHk3EE1n8n2ewuIfpodY1kWIQKRy78MkaIG0UP/V+WDChbuq+2gskqz7Z077rI25+KkEVB7m94woWbqaDBVEIWRyAqKuK8NIJCCXgubh+2NF/AnD1DiOvo6jJ/nNoqJFqFQyk+W6EUmE0GtAN+8MYr+HTitAJiwe32sGrzLlZt3sU7X/+KyWjA5fYUkbVMiI3ijmskoycszIzVEkZ2Ti65Nr9AqdWfy1oSIKIqDHy7bBK7/tBIBVuaFH4e/KGMb4oSOslUUlZMYFuozACvRCUuOFQSC36w2WxYrVJL4F9NLoSCYMaVRjKEYEwoRiuY7MQnmogMM2IOjyY7x06PIfeyNRSSlusAAKyKSURBVLE6LLiX+IxDtFj/DHtXzePH2cuYMWsWWdnZ5OTkkJKSys5du3jj7XeZMGkyjz70IFMnTWTCpEn8Ov13MjMzadSwAbf938Pk5E8kIn07T15k5doDJvLzHThdLmrXrM7LTz5Ej87tWbBkBXMXLGD91l307tqe5x64jfp1apzli1gKNEJhzVeQK87WQoSCIYTUey1a4NiGwp9HVZdMBS3yWstmiPbes9xUIR20qJ1QJu3KRaagrNehYHs/Ass/kjc83vs+x1u0NE9kSHb+AbvnQr8Q08dLgiEMOt0GLa6A1V9InZOTW2DGgxDXADreAgmNyiy7VCUxjmU/j2PcxOlMn7eUtVt2kpvnq3fSdOgD3HHlxbz/xM18O30Rq7fspWHtqlRLjGH7/qP0ve0FzJa3UFWVvQcOF/xudOdEPh9dG8vMu31/Vrur1Iqo06NsmRYetxRrBp+smMMOqF7j1s+JGphdoj00B0FECTUWSjOCK4m58iHInNOjV196dPVbHJksIq2yZy4k70S/aizQKuju4uPjeebJx3nmyccBUPOzSE/PkALNycmcOp3Mj8cPUy9tMb3MO2iRBFNHWNl8ys2Lf+UzfaeLnNxccnJzOX7ydMF+I8LDadSgLs0aN6R9m5ZkZmZz5Nhx9h44xKEjUkw6LjaGnl070atbZy5qUZ2E2GgMBj3xMVFYLWGs3bKLCdPn8eOMRZxOTWfGopUAtG3eCI9HZc5iiWA2m03cNHwANasmYTQaMBoMGK3RBa8jIqw0aVCfxg3qFmQCFYv0Q7DiE3l98ZNlj2Rb/YVcxwZ9ISxG+pZZc3gHkdYxWsFo8xbIdAgBXm6n+H8wnf5Mz1kbpzQiwZ/8CXQ4O3J9UlSWOJ9UnAL0fwmmjZHo4mZDxclZHuiN0KCfPGxpUjdq269CLuydLw9rHDQZJA7ZELWyu3Zoxe6lv/L8O+MZP+EXtu47ztZ9xwu+rxmloFfgUGYWPVo3YNOE5/hq5mq2eb/ff/g4q9ZtwmoJY/rnr9OjY+vynZ+GQ8tFQiovQ/rYFeOlHkIgtv4iTlYUIR5aX+P7zpknGuPOPJmnSoqJ8S/KXRyR8G+tVXKmKKnNl4TibL9CmVl+Y55mP4AUs1/9GRxcKnI9odoweqM4L+v3lkyD3XNgx29wZLUUqj22Dv56UwJEWl8j2u9lhNkcxpsvP+v7QPWgZhxh3tq9jN35B0fSj3EkfS+rtuxFb1xHv+euA8Buz8PtzOeFdz/nw6+m8H93XE+dmlXZtms/k36fx4lTybRs1php340NXg/o1DbY6NXF17Tzk5pDl7uELAkVOckwZbRkUhksUqOiYX/pJx6nL+tUux/R1SRD3L+GVqW8ZdlQUfZsafOZ0QqOvJKvvb+EqWKQQEitvmCsN+MleeeZHac/johdRpUW5Y/898OrD9/IwzcM4oWPJ/L1L3MLPtfpFHSKDodTgiQtYWYa1KpKwzrVGdy7M6MG98YSZkZVVf5etQGHU9ZeKqpINS152xes2Gyo1DZKKkchdChMKiQ2laLyerOvHZQliz+U4CtjGfdbiUpU4l8BRQ3UlfmPYtu2bYwaNYrnn3+e4cMl2qQ85EJ+fj75+fkF77OysqhVqxaZmZlERQVZZJQHZ/OWOW2SmqggbHMxg72qquzatpGj+3ezdOnf5OfnkVSjDl179WXbpvXEGez0PPAeVUx2iKkDN0yTAsR+v//jjxk89OjjHDh4sNC+DQYDYWFh5Obmoqoq/erpmX9jODanSt0PcjDHVOHRe2/nrpuvw2z2c84Xk7KY51QLaiz8dn0CYaYQo6BLKv7lj8B0/mCEQngSdL0nNELB35BJ3gnjussi5t51YPG2IYfdRyYEg9MGGcdlcWqOLDkSpzjDurjPQ+0TIbTTrKwsomNiivSPc9qPKmxh4Xe+mgSP5tjOTfWlWeuNkH0axneTbe5ZGbzOhmZIl4b8INvlpsLGiVKvQNtPeCI0HyoOnpLaoKH4iH2Px8O2fUd5dKXcgwWvDMftzC92e3/odDqeHNqMh9vlkeDxFmdW9LJAbDsqeJG0YAhsyxmH4eMOcl3v3ySOTPA6NINIQwRKTIRauLnQPS2hP/0DhHRWVlZBlleF9aOyzjNl6Uf+20Lh3zlyih/fNkyEPx5A1Zu5TX2NY0o1fnu4F2GmEsZqR3bx3+VliZNv7TcF/SQrrAZLPR2Ze0hh976D7N53kIOHj5atSHIQRIRbqJoQR9XEOJLionE4XZxMTmP3oWNkZUumg9ls4o7rhvHkPTdQvUpA4fJQF7j+ka1p+6XI5YlNULs7DP/K1z7NIUTA5mfDJ53Bkc2xHq/x57YM8m05dOp+EZ269SrcXxw2X8S1w+6rmxB4D8vSP0JpUxXY38rcjzIySu9HZZ0vzzSqT6tPoY1TgRkL/u+ddhk/3fkQWUMc/HZvZKg1DmY/DpsmQUxtuHVOaE5aV2jzASc2SbHMnTPEmQGAIplqXe+FmFq+8ykFyanpLJk1lcUbdnPsdAbdWtXn4vaN+XPFNl7+agYut4eYSCtX9uvMsfaPAjJ3hYcZmPn12/TsFKTOSEwIshwg12rt17DsfXGeVmkJgz/w1b7SEJEEqXvhy35yD9qMgouf8Dqrw3z3WpNCgqJzUqG5KoR7EdgWSsI/2Y8q2q4rKUunrGSLv+SUObL4GmSZx+GjtuLovmclxNYNvl1eZmjnkbpH5F12zhKJJZAMzsvfKOyU17I1S0NeVtCPXS43S9fvYNGarfy1Zjtrdh2h1xM/AvBcqxSOnzjOC+99zs69B4v8tka1KqyYPYVaNfwkm1SPkGzrJ8Dx9b7P6/aEDrcUL9FZXF2K1H0ip5ZxWLYZORli60vQlDVW+o5/xoKGUNdvofaRirbr/O2rYsb4s7I+Otcun9Lmr8Drrwaxs7SMBU3SFCSYyhoLxzbBhCHy/omDPvtfQ34J9l+h7fzWUXOelnmp0+3Q/YHC26XsCml3eQ4Xw36Va/3bVQphBmk/W3Yd4PmPJ7JgxUayc+1Ffme1mLGYzVgtZqxhZsItYWTn2thzSEhzk8lI2pQHCN/0lVyriKrQ61Ef4RhbihyZBm1cdNphzZcip+vIEQnaAW+LLGxEFZnHQvQLlYqy2DbnYD4q73b/dRw+fJilS5cyevRo1q1bR/v2RYuE5zlcDHtzDgC/PXEZYabgQY3r16+nQ4cOxe6nEucPQu0flRkLXnz33Xfs3buXV155BbfbzTXXXFOuzIXXX3+dl1566Swe6VmGpkGZnyV1F7Ro98AoOGD/kRPYHHpq16pJUpUqTP35Zz77ZgK9Ordj8GV9WVbrXnodG0dCxiGYeBXcPKvAOaIoCkOHDOLSS/rx3YQfmDBpMvsPHODkyVO4XC5ycnyT/JoUCzsyw2gWncfWT0aTOOT58y+bRFXF4NjxhywG7N6FcniSRJI3v7JwQeVQEd9ItPptKRLFVLsHWGPEieqwFz/BG60QU93roAsxdTEwauAfjCY4p/3obJxnsEhejRQyWiCuDtTqCof+FpmRLndWzP9qCI+HHg+KpNDmKeKQzU0WsmvzVGg2GBpfXmbJHp1OR6O6tWClLI6//t+9TJ21mJSMbNIyc7DZpZBzrw7N6dqmMWaTEUd4DaxpO2iZtQBT6g7wINe5+TBofa3ICJwJtIV1dC1ZRIRFFtZAzk0DXOJwjalVtD847fK7zGNCEvrLJGnQnKYqEPHvMTj/kX6Um+KbR4rrT/59LnCh4rD7otG09xrJ0Ohy1NrdUQ4v50G+5HHds8H3HyrCoqD7/dD+RikEuHESUXnHGMQxBrVpBiMkDd3lzCctNZX09DRsNhv23Bzstlzy7Lnk223sTs5n0nY4nePB7XbLw+XC7XHjcLpwOl3k5NrZm3uMvYeOFTmMWtWrcPvIIdw+cmhRQqE8SN4l0bO7ZgOqkIX9Xyz7Ym3zT+DIRo1rwFuTF7Brxx6iY6KJi4umU7MGhQudO22yOM9OhsjE0CX4/BHoEDxPsujOaT8KPOeyzk9axKA29/jXVfB3koJso+glMlHBK53ol+nZ93k4sFicen+9DpcWI+1THiQ2gcTHoMdDIgWz9RfY/xfsnClR2y2uknkxBDm8xPhYhvdtz/C+hRemnZrXpVaVWG555Tsysm18P3MF/byb3Dh8AM/dez31alUPsscQ4ciFOc/CXm8Uaosr5ZoFI+6ddtEDd+RCjY5SJNvt8Ep9hUu2qdEqzuviUFr2SaAz/TyrVXLO+pG/00xzRgZm8ITan/wlpyJKkEkMj5esniMrRQ6pOGIhVERWE/utwy0SYLT2G5FKmvM0GN4VR30FwGDQ06dzS/p0bgmAPbw2V/wggR8d2zQnrGMLhg/sy8sffMmEX2dRs2oVWjSuT8uWLbhq0CVUq5okO3I7YNefEtCS5iVCdHrJROp4CyQ0LvvBHVsnBa7t6RKgdvXXEF0bsk6AThFyIbGBEAuZJ3wZ3Mawwv2oJAfm+ZBpWsYx/l/lZyhtDi/p+jv9iNTomhI86HFLu7LEyDbRNWQ97MqTzOVgQVplgarCYW/GQu1ySgqVgFZN6jHtk+fweDzsPniM76cvYNyPs8jIEp+HzZ6PzZ5Pakbh31ktYVx/xaW81ttA+EbJIqXRpUJ8lLWWDAgpsW26FELP8tqk1doJcam6xZbTh8l11iTF9CZfG9VkKlVEfrY8qgjniX1XidBx+PBhmjVrVqDwkpBwgcvHVaLMqMxY8OLFF19k/vz5tG/fnvnz5/PCCy9w7bXXAuB2u4OneQbBOYnIqWgENgGnDdKPeCN0osTxEySqY/uO7ezbt4+GdWrQtF5tWnfqxun0bOpXi+bj99+lQ6euTJ7wDZcde594g00m6Rum+xZd/tEHXjgcDk6cOEl2TjYJ8fHExcVhwiHp8tPvFmfFHYuDp5gXI/OS53Qz7KPVAPz2QGfC9CFGnoYiG5NzWhbCW6YWTsWMrArd7oc2I33nG6qGcOBC+s8nxSFcp6cUZYqsIYZzWGTFTMpnK2MhBPzjkW0QQkSod7viItu1z0typPr/h94kn60cB/Oek2LON0wL/ptQoAuhXTntsGkyrBoPmUfkM3Ok1GDo8YDvmECiKktAntPDsO+l1slv97YlzFjC2HhqKyz7GA78Je8NYRIF1OWuopk+BlORnwdHQPtb+7VE1jboB4Pek0WmNVb6kccl2SEGAxgjILZW0d3lpkLyXpGqMlqEfAiMVi1L5GeoON8zf8qbseCwyb0s6VqV1OdyU3zZPQ4bhQr7OXLh8GrUKaNQXHbeVe7g3sdfLjYapkzn4fIW8F71Gaz7JvT+p0FvEj3sdjdK9Ji3noeqquTk5HLy1ClOnjpd6JGVnc3l/fty+SV9S7cxQmkvp7bCyk/FMauh0SXQ82GRoPBHaZrdqgc+7QJpB1AveoLBL/1CelY2To+Bz999gXbtO/kieB02WfTbMqSvYShMOviPnSVlSuQk+/pZRAUQLGXEeTEfBaLcGXVB9pd+RIIUFAMkNPA5dWypsnl4fFGH9e45MFlsYUb/CvUuKvlvg2XQBUMw5/vJzSL7sn+Rd5sw6U9d7iy90LNWPyfwY1Vl3YZN7Ni1h72HjrLSLA7Z3+5oSpixBNKiNNI9bb/UU0jbJ3PwZa8JQVlcP539hIwr4Ykw/Duo0Uaue36O9AltzgkWsashN1XGKEts0f5xpnPU+Z75Eyq089DGEnsmWKJ9Y4ojt2z9yZ+wgcLrFn9b0OOCxW/Dkreg+RUw/Ivg+3O7ynYeGjxu+O1e2D5diMCRPwi5oNlzpSFEGcw8zAwbuxEoxb7T+m/GYdj0I2z5yZddZIqQLNQOt0BCw9COz3/do6oSpDX9HnDZoWobkRYzh8s8m31K7LyoamIXnN4NnjzIt0vQTmAfKK5vnA2psPL0I/826Tfu/KPzUUWjLPakdl+cNrnfwbLvVLcQS648mHQtZByEayZAkwGF9+VxBv2LItDIsNR98NMNMvfc+mfhtRFInaoQkOfRMewTkTD+7b52xfejnGTcbjc5uTbsefnY8/Kw2fOw2/Ox2e3Y7Hm43G569r2cmCXPwq5Z8ruLnxRSIbC9BR5vMBxeIbXttIyiqOpSCL3RZdIPc1Ol30V7i597XEIc+gcM5aaI/wMkG6+i65NUZiycl9AyDCZOnMhFF11E7drBMzsrMxYuPFRmLJQRffr0IT09nXvvvZeMjAxefvll4uLiWL16NX369KFbt24hRcmbzebC8jz/Rhit4oDzjyIIiCpQVZVmTZtx9Ogx/vhzAcc7dKB161bs3b6Ftm3b0aFTVzBamLtqO7t0rXmp0XaZzGY8BMM+LXbSMJlM1KkTMFA5HNCgr0Tvp+6RiJiu95zFC1AKnHlCdGz7RSKTtIWg3gyNL4WWV4seahn17ItFy6uFWDi+XtLkw2PFsLJnSlZJeNyZTerFRQ38g9EE57QfhXqehaIF/RxmGcdAdUnkZ2ytkq+lltJrDIPGlwmxcHiFpKkHI8sqCkYLdLgZ2l4v8hMrxkqGzbL3Yd9CGPbJmUf6+CN1Hyz/0Ofg1Bnkv3s8KOm1FQktYyGxsZyn3ltDAUU4iGhvRKrmGAhGEEUlQXqeLCaCrX+Ki6o6z/V5/5F+ZLKFdq1Kul5adk9gYT9TONTujKvTXRhXvM8d6g+Qez+YqgXbS9lhiZWCkp3HCGGVtl/ak6KXZ51eXusM8tAb5OAOLBGn6LZp8qjSEtqNhqZDUIxhREZGEBkZQaOGQfTjKyK24/gG6dOaQxYFmgyE7vcWJRRCxd75QiqYIlHbXU/3rkfYuHYF4TGJ5LhNQir4O9v0JsmmM1qLkq+2VB9BVBKxcD5EjwbBP2rXGf3mGv/35YGCjHFmv6LApdVhqNEB2lwnxPSMh+DOxaWTUuVF1dbiMD28UjIkjq6BNZ+LHFOnMRL5XMb/VhSFju3b0rF9Wwky+WR96T8qDXvmwezHxNEVWRWGfw01Oxa//dZfhVTQ6irUaOOXVWANHnHqTyJo8jtOu9gaQYJy/g1z1DntR9r10K6df3s3hcu4G8q10XT7HTbJatQbJaNbb6RIce16PYVYOPi3rAtCyLYJGTo9DP1YHKm7/4SfboRRU84847M8cDvFbtw4SWpKaIioIlkWba+ToLQy79ch8+fKcVIjDKB+Hxj0vhAZbqfMu5ZYiDT51ljhcRKMEBXj+yz9sJ8cn18GcaAU3Pmg8e6fVRYCLgg/Q0nQ7ouK3M/A+2YKF6e2QyQkSWwqxELyrqLEQllxVIIQqdY2NCd9cXA7qKaeIokU9NsPQu5J6avNhhQh1fV6PdFRkURHFZOtZk+HmbeLD0BvEqm9FleW/ZhS98GCl2DXTHlvipSgk653e/uDdzzUArA0m0NnkPWpfx8xWcWOUDnv7LVKnH00a9asWFKhEv9tVBILXhiNRubNm8ebb77JE088wbhx47j22mvJyMjg1KlT/42Czv7wd4TmpsjEEcR5vXHjRjIzM1m3bh0/fP15EW3fPl1asWJJChMz2zPaslQWpwlNoOdDZTseRSdRa7MehXXfQvubg09mrnxJh0w7ILqkOgO62MZEq3YylXI6blVVjJjkneIo3TWzsG5jjY5SO6HpYF96ZkWiRgdJq04/CKe3Q70e8nluGrjzvNG9leloZx3B5I20NPnsdJH/KG2BkpPidbCFi5EZV1+cl/sWlM9QLCv0RimQ2eJKiQib8zSc3ARfXwaXvgqtRpzZ/rOOS6HYbdO8hJsi/3XRo6Frf5YVWqRRdM0ADVYt0ilAEzmQINLGkaoiBRB0XCmpqOP5sDA9n3Cm18q/nxktRR1vpnDcvZ7g0Ipfacgh3Aueh6uLiQ4tL6zx0Oux4N8Fiyju9Rgc3wgbvocdMyRz4M8nxTna8moh1c5G+z+yWvrboWXyXtFJEb9u90qx9jOBt2gz7W9EianJ08+/zA+ff0BOro3t27dx0WXDfNtq98zl8GWbgF90oXe70jiUMjpY/jMoi8xYSftQkWwFlcJO6GBzmz863gH7Fkl08oKXYcBbZ3I2paN2V8lu3bdQpBqSdwgJvv47cYK0HV16raqzAY9bCPOV4+R9zU5w9TcSsVkcUvfBrP+T113uFn18rY2X1N6zj4ud6bL75rBAotUf/+Y5ymkDKjiwIpSxpLRr4y8B67SL/eT2RkDbM4vq+Cc1F9k5W4oUQ61STlK3OOiNcOVnMPUmkQ37cRQMGwtJzSr2f4pDxhHJTNj6q5wjAIpkMbUZJW071Mxsf9jSYP33ovWuFaM1WqHjbdD3GcnycNqFzDZHgDHXZ+vlpsj9SfKTWspN8dUWM3rXrlpb0LIX/MmFSsfo+QXtvoRHBr9vpnAJztK+q9IM9vxZMQWcj6yR51qdSt9WVSWg5NQ2Wftkn5RxO+sE5txkvtUMnnl+v1n2oWRstxoRWpZ2+iHxe2Qfh7AYmW9qdy3bOdnTpdDz2q8lg0PRiQ+l95OF5y7/8RBKzoAzWkOvQ1SJSlTiP4NKYsGL5s2bExsbi9FopEWLFhw8eBCHw0HdunVZvnw5w4YN+++QCv5w2CQ6Jzc1aDR227Zt2bhxI23bti2q7eu0c9P1o2jRqD42p8pfG4z0dsyDBS9CfH1oeEnZjqXpIJmUM4/Ami+genshD9IP+p4zjxHovTABPwEpxGKc1hqqNJUIh8Sm4rD3L/aUlwUpu+WRulciIFJ2Fy24FlVDHKYtrhSt4LMJRRHn1NJ3YPdscQybvDqitjSIjjmvotIuWPg7oTVobV3Rexc+pezDfwhx5kG93kIs7Jl7boiFguPQSY2Dmp3gjwcka2Lmw6Kn3XlM2SNSs0/KonDzj76Fd4N+kqFQo0PFH78/CooX6iQ92hQhfcPlCO4oC3SinUlmjjbmgY+Arex/wRFKNLq/pq6/8y0QOgPv68bwkec59Dumwd5rpQj4P4nqbeXR51lxvmyYAJlHYe1XsqCr10v6hMkqziejWZ4NYfIwep8NFonctKWBPVWebalSt8eW5n1OFbI745D8t84g40eXOyE+ROmJkpC8W7IfFB20u6GguG79Zq3ZuWMnDVq1K7y9NjbmpoEn30cuaI6A8HhwFhOZXYnSofUdl6P8TmKnXRwZzjzvs59DrTQnrDUGej0Bsx4WrfemQ0qXRDpTKIo4Kmt3E/mHv98TG2/Rq0LYWRPEKRKRBOFVxPlR8D7JJ89QUVHj9gyY+YgvQrvDzdDr8ZKDOpx2+NVbV6F2N5kPi+sDBRG5Yb57m58rMku5qWLzWeNkfDCVoR+dp1lAhaDV0wkVFWXvlnZtNEdbIXvBIvdDb5R7kesdo8OipW9Vaw1HVsHBJRVPLIAQald/LaTC4ZXw2/1w1fiKGfeDQctO2DzFR2CDSHq1vgZaj/QVWi8rUveJ9OCmH4VAA+m77W6U+ltR1eQ6642F27w2XuWmCPmWm+aV3vOrLWKKEHs7MNrd3/6rJLLPTwS7L8Hsdisih2TxjsEhFlYuFm4HnBD5Imp2Ln47W5pkfm/9pdji6QqQj5HTJFC9dj30UdUkkynnlGQNrBovBEP93sVnRhzfCHOelDYeUweunVi2fu7IkUDMv9/z+TAa9hcZJa0GjP+6xX88dNjAnuULEgkFlX6ISlTiP49KYsGLmJgYzGYza9asYfz48WzYsIEvv/ySxYsXc+eddwIwbNiwUvZyAcJkFcPZYAy6mL2kf38u6e916ChK4e+9hkBinaYcPZ2Ks/NdHNytUPf0XPj1TrhxuqS+hwqdATrfIfIxKz4p4ZgjIa6eTJweJ55T29FlHCKBdDi4WB4azFFQp7s4dE5slIVrMCg6iK0HNdqLA6dW54pNcy4NjS8VYuHQcnE24S0wG19Xrsu/ISrtQkGglI62KA0l+iQ8wedEBHGcrPtaHCebJkOrawoTXWcbUdXhuinSn5a+K/q9R1ZB/5dCIsyU5B2weaJEaWtaprW6QM9HxMl6JnDlQcoeKTqdkyzPucniUM1NLvwACIuXAovhXseMOVLuVW5qYckWo6VoFkN5oZESWjTV2ex/54PRfibHUByB479Pf0dOKQv+vUp9pisDGK7OEomWa3+Aam3KdkxnA9Y4WTB2uEWIug0TRCrpwGJ5VCT0JiGdu9zp08M9U2SfEJkXQG3QV+5Zfja4nHTrO4hul11dOMjCfzzUJCnC4wIcOEFI2UqEjtJkxkKFPUvmHk1eIhDFFQDOPAbV28j8tOUn6W93/FVyoeGKgqKTbNDGl4sjZ/lH0kZzT8vjVAm/jawKne/0RoeWM8PBlQ9bfoZV48QpZAiDy16VzKDSsOg1iWYNT5BI08gSZACddulntjSR7wuL9RYldfqK2YfHV9y4ez6hLEQJVJy9G2ph2WBjmEaUZh4Dd74QT8YwqQdwZBXsmi1t72wEpBmtcM1EmDRCZPCm3wsD3jxzm8sLq2qjA5sxzpkMh5YUDqyq0xPaXCv9sTzZCSCZPwtf9a7jvIFgiU1lzmw62FskNhsyj0tfMFmk/TvtvuupkQS5abI+9bcZAh3T/tHu/tkLlTi3OBP7MRjZ4MyTOUuLnD+9U2SD210v43RZcWqbjPd6U2GJMdUja5Ejq+DoWvEVeLy1UgwWqN0FomoKERZVHSKrkRdRk2FfHQJF4bcr26E36iUwYMtUIRU0gmHFWKnF2Gxo4fPbM1fmD48TqrSAkT+WTZlg1ywhwrX1UVILuOQlkRazpXrrz2SJ7Kg2jvqPhw6bT5I0GILdy1DG5fNhHVOJSlTirKGSWAA8Hg86nY6EhAQGDBhATEwMM2fOpG3btjRu3Bij0UjLli3/6cP8ZxCs3gKUnL3hvzgNT6BOwwTqaCR7n4ul0NK+BfDTTXDLLIgsgz52iyvFAZu6Vxj8uLri8I+tK0x+bF2JZPM7PofTzcgPF1OPI7zVW8GYsl1SJpN3SzbG7j8L/0dkNZGRSGwqztWEJhDf4J9JvQeJBv/9AXltiQGdSQxtjVDw1/uvdN6cPWhO6rx0iSJU/AqUhppSbbR6Fzh5sght2A+qthI92d/vF4mFvs+d2+hrnV4iKev0gN/vk4yg3+6VwmDNhhRdGKseuqrruMozG/Pk7b7Pa3aS2ie1u535Yjr7JHw72BeRXRriG0H1VhBdrbCx6q/tXnDtz0Lq+7mICj0fyMOzcQz++wzMeisF3ytXc2XMDnTpB+CbgeLwa3/T2XHmlBU6vdQGatBXCOvNUyS6zZkn0ZmuPFnEOr2vnXnyrLoBRcZ6S5wQFdZ4eVi0197n+IYVJ4OnqrD5J5j7tDiS9CaUTndI6J3TCbiknox/UWYoLC0WHl+YtCtr8eZKlIwzdRJbooLLw2kIVmvBFC6a5o4c6P6QOFcyDomM3tCPy38sZYXOIFHMrUbIPJx7SkjmnNPipCl4n+wlHZJlHimIDr0bml4V+v8FEgogDqxhY8U2LA2Zx7x1FYAB73jttdzinZr+TlKH3Vu8PmAc1DIXLjSHTJmdjBU435bk6Cqu/oU/yWCN82UsmCySib3uGwkCWv6RyNJVVL01f5gjYORkmDBMMqun3yUBHa1GlG/+yz4B+5dgPLCUnzwrMeIGLQDcmiCZ0q1HQLQ3O6G8pII9HX69Q6ScQLL4ut8P1TvI/Ac+ks0c6R2L/Ooh5GXLOKaRBDE1gtdP8O9npUm9VeLcIFBqpzQHc3H30x9up6zPE5vKuv7PJ2DZB1I7oO31oCtDAGBYjKzt3A5po22vg2MbZM6zpxXetkoLCepoMjC4TePRgXK48GcGk5AerUYUJhhWfALrvoOWV8l323+X2kIgGQ19nw/dzsvPgjnPSF0iELndrvcWPk6jReY3LRsh2DhaWjZ2sLVAKOPy+bCOqUQlKnHW8J8hFlwuF6qqYjT6jCGNUNB5J567776bQ4cOMXbsWJH2Adq3b0+LFi0u7EJJpSHk4rZeA91h80u1Dy9qHFz9tWi6J++EqTfDDdNCn2AMZtHeRS2aMVCC8W5XLGynMe42nTHqvRrZHrc4dA8tEw3P6m0k4tUSW+r+zjryMmHvQola2DtP3ocnwshJEFdHrmegUVY5SZ9dOO3iXHG7wZEHkUmFdfqLRNMUs2B15vmRC1a4ZbbICP39gdTQ+PE6yaK5+MkKi0ALCTU7wi1/wrQ7pE/8/a7oh170fxQU9tr1J6YtP/OS5xgAqqJHaXy5yEJUK0P2UUlwO+DnW8V5ZYqQSGxrvBj91jgwR8siXmcASzxUbQEJDYrKNzlsYMuQYpfGiLO7uDwXUaHng6TF2TgG/30GXsdSFpZ5ShiOwZ8R9vf/JCtg1qNSO2TQu75U7/MBsXXh4ieCfxdYvNntkLktcP45m2RJ8m6Y/7wUbAaJbhvwJiQ0lutuRUiFgshQv3sUSr/yd1hXEgv/DIwWIVr9JeKCZScEu5fh8b7Aisteg59uEPmSRpdBs8Hn7hxA+kZEojz8EwBUd+HtXPmw9Wdx3mSfhAUvYl41jsGegcxRehe/f5cDtk8T+TKNUIioIsREq6tDDzBZ/pH05ZqdRRM7kLDxH9vA6yiNk/FAi+B32SWgOzxOyAYtc6Gkuea/EBFakfNtMEdXoWtYir2gkan52XJvqrUWiay/XoWF/5NgkSYDRXqybo+KOWYNlhi46gtY+IoUFF/yttiQvZ8sPWJbVaV2yf7F8vDKyOi9j6NUpWr7ARga9Rc7tCLWQ0fXwrQ7pVaL0SpBNPX7SkCIyQpmq5BreiPow8DlEi1b8I1NmjNUuy/+keyBOvwVjf9C3zqbCJTaCeZgDlZcW6unEcwWjEiUtnn7Alj9uWQAZJ+A2Y/D3+9LgFTb60Ibt+PqCXE852mRV170mu87gwVqdpCI/zo9yi//BYUJho0/yCPjsNQQ2jDRN5e1uU6CtUJVRzi8QrIJMw4DimTO9n1O1q2OHCHltOCr0rINS8rGdtrkoQIRUUV/UxLOh3VMJSpRibOG/wSxsH37dl566SWOHz9Ow4YNufTSS7nuuuvQ6XS43W70ej0ej4c+ffqwaNEirFYZ8LRizf9pUiEUaMaW0yYphAqFU+0Do+DCouG6H+GLvuK4/P0BuOrz0CdPRSF49boyQqf36WKfD0g7AHvmyALh8EpfqiVIVsaQj8DkjdTJTQdnrjhTY0MsoKTdJw2VxnHZoOm2miP8yJwSFp0lRWbkZYPVS2AZwqDbfRKt8tdbEslyaDl8P1QWpBc/IQbvuYAlRoo4b/5RirfunQepe8Q5smsWOHLQAdlYma30Y+jNDxEWV0ESLBrmPgdH14hM2e3zwBwjhnaet2C6NdZL8nijP6NrBN+P0w5hkV55pGIidLVI6orsC2dr8Xk+SFoEHkNFnGtJ5xUsgjoQJitc/LRkyiz7UOSGPusFvZ8S6bxzKVlXEShOb/dsIOcULH7Lt5jVm6DDrdB2lEgBWON8WRX+WruavFgBqWotLDumvQ+ma12Js4eSHKIma1FHTTCnTbDISFO4tI/ME5DUFDqNgdWfwbS74OAomb/Ot0KOBrNErLa8uiDzQMk5xf18w0j1N/Rb74BWV/kkDDVCYc1XkvEA5SMUQLIVNv4gry96RK6rVntIKzYb7F74Z5Pkpsq27nx5r5ELxckGBQb2VEaEFg9Hrq+fBHN0FbLdSrHxCqLlvcSPooeeD8p9W/eNkHkbJsjDGi8SQs2GSvBIRTjrjRax2RKbSeTzzpmwZ76sb1QPoAqJoKp+r72f+0PRQbU2OOv15a4V1TiqVOe3nm0xGCtAmlP1wLKP4K835BpF14LhX4ptZzB423UAuWmySPS4BpNFHoEFswOvRbB5RsuACKzFUFZURlufGYoEjgRxMPuPi/73M5gtaAzzZX8bzJL50m602DOrxgvBMOcpIXlDJRiqtoSrvxJSMC/DRwxXbS2EV1h0xV0Pg0mkv5oMlNo9GyYKMajooMfDsiYMBa58WPymnDOqqC70fBSaD5HzVd0SZGUwSMaQFuARShsubnzUm7x+nsD7V8qa4HxYx1SiEpU4a7jgiYXdu3fTvXt3hgwZwiWXXMKCBQt4++23mTt3Lt988w16vZ78/PwC8sBqtRZkMvwnizWHimC62Coy0ZgjS09DDU+EIR/CL7fBrpkyKfZ+6pyfxj8KjwuOrJGshD1eB64/YutKcd9Gl0C1dmIYa2nyWtMsSxPV7pM9EyzRpRvHldE5hVFWnfCSIjPCvCnetjQKijVGVofLXxMd67VfSN2CXbOkfbQdJXJF4YkVdz7FQVEkUiapOcx/0Vsc/aB8F10TZ7OrGL26LXlKGEPLImMWCjZNkUhRgAFviYHssAupoKhCNmiOGFO4j5wJBm28ifA6OjOO+GSRtHuiLVZC6Qs5KdLfwhNK3va/tPg8F+fqX0BOi2ZT/ZzvikGyhzreBk0Hia7s0bVSi2fbNBj8ASQ1OzvH9m+FwyaRtMs/EYIavJIUD4i8oErRwADN6ZmbGlxSrNDCP8CJHR5fGaF2LlCSQ1RzOiv4ohVLctoU2bddHBMuF1z8GKTthb0LpJjzuu+h1XBpP1HFEL3/FAxmb3To1Tg3/kTGX5+QSBosfQPWfw2dbpPt/AmF8CSJFC0roaBBy1ao00MIT1u6SHKo+LJ5A51mgYUyTRavc9jsLUJbih2m3XstsOff3t/Opv3p30+CzeehRtUW6jcWH/GjKND7CakZsG+hyP7sXygkgxahbImDJgMqhmRQFGh/g5B+c54R56G79J9htEDt7lD/YqjbEyyxuDFzdOXG8h9LILJPirTmgSXyvskgqeGl92a/KYbCZJlGIGg1FUCy5RTkmpWU8VZcMebAWgxQusRO0P1XRltXGIobz0oqrl0ow8vmIxXAt5bSCOUmg2Hn75LFUFaCQfNPnCsoOqh3MdTtBae3idM+oXFovz21TWR0k3fI++ZXSvFzc4SQCVZvXZ7oanL9nHmi1hCqzR7sPpXUD/5L659KVKISRXBBEwuqqvL9999zySWXMGHCBAAeffRRvvnmGz777DOuvfZapkyZUkAqfPPNN/Tv359atc4gxe1CRGlFerRJJiIq+EQStOiSHaq1FQNTm/DjG0pq4H8BR9aIse2vIa8zSGRE48sgoTlEJorkTkxNidKJruZbuJg0oyvEids/dVGLiCvtt/81A6GiF7LFGs7eKBu3s7AkkobYOtDnWXHur/pMIrDXfy9Rlx1ughZXhVRY+YxRrY1Elf39vkTEtLgCanfD7YK8NScq/v9ObBYpGxBN0Hq9pL2DL/NA0QNOWZQGRsv4LzSMfpHUGtSAZ/CLjguhLzi8GROhFnz8Lyw+z8W5+heQ0xw5bj+vSVRVUBxyLyOqwtXfwbZfYclbcHw9fNlPSLkeD/1zdXLOF3hcsHkq/P2er6hftbbQ91mJyNOyC/xhtAiZoDhE8724qNDAzyuzFM49Cuki+2WPgNwLg6mw09lkpSA6obR7pTn/wr1tYORk2DUHVo8X6bzNP8mj0WXilK8oabyKgsGMu80oblnSgMvUv7jPOhMl9zT89bpvm/AkIRqaX1kyaV0SCmUrPOp15uQCijh7tGxef7vYaS9aKNNoFe3wkrIU/KHd+7Bi7PB/G86m/VnavFVojleDbwOFxziDqTAZ67BLxkn1NhL1fMUnQjJsmy6EnD3NRzJEVoNu93sdnuUoOquhZie46Q/JRFN0vuxuTVpPy97TvjNHlb9WQijYt0hsOluqXM/LXxe71pYun4Gcb2kyes4c77aW8knpmcIL12IIhUgNhspo67OP4sihQMmrUztArxci22jxraVAXhtM0O4GCcpa9x2s/cpHMKz4RGye5sPOj3pcGhQFqoRYz9Pjlvo/i9+SAs/WeOj/IjS/whu0lgHWGN/2Wtt12kJb85SEkvrBf2n9U4lKVKIILmhiQVEUjh07xsmTJws+s1qt3HrrrYSFhTF27FieeuopXn/9dZYvX85rr73GwoUL+fbbb9HrKyD9859GoHZzeVFakZ7yGFvapNP+RokS+/t9MUCjqkGtzmCK9E34xhANbVd+8M/9tXcNFjCHaEjrQmwDqifE/RnkOi55R7RQVY/oxje+VHQba3WFiCQxnnJTxDEQGSPRC9p1NvstPsviI/NPXQy1CNSFbiAEGpSFyDI/w7aCulEBzJHy0NLoAewZYhy7HOL8dOZD9fYw/CuJRJn/IhxbJ1HGK8dJNkHLqyVVNjpEIjRQg7o4JDUv/L7uRYXfO9yAl1gIiwVTBYyV9nSpq+DKk+KHvZ+U/qwtAnMzJZozPMHbN9LkvX+Un8shfcrl8GY2BBBFEQngsBZ2phWJiApCLmmf6fQ+wqIkFLcwKi9CWficT4uj4hDqMfrPW8HGIHsWmPzS0XV6QCfP5gjZvtNtMp4ufFlSzJe+Azv/gMEfyvwSDKGSDiFLK4V4vv6SdyVBk2wpD1RVMp/mvyi1jUDGjT5PQ+uRJd8bk16cQPle505MreB9QNuHonidQcVIjGn3tzQi99/QpgskRkpAqOdREeerjT05yUGKMPv3JSX470qCNm/5o2FfqNNNAiXWfS3SeXvmyKNBXykmW6d78HPzhDgfVaQ0mN6FUzEyQ7mEMbc/RdiOqRLRCiKb1makbxwIZQx35YMtRWpg5WVKNuimSZKtUKsLxDWA7FOynSVGnD8FfUf12QAOm8x9mlNZ6xOmCIrUDtIQeE0ret45l1CUIOfj114VpeLWMyDXKeQ1S0ljo981918HOPMBVdYuqiIkld4MNTpLkeJLXhG7btt02D5dHJ5znxaHZ8+HJPsg1AyG2CAymcGKi4e6TnH6bWeJK8G+K+G6uPKl7sOq8fI+oQlc850vCtscLlJRKjKf2jLkM/974srz6sJnyrrNZJFtygv/e6UoF/ba5lwiWL8sT5BWWbKCc9Ok/TjcEK34MiTBl9Ef7o3UVxSvRNINQuKt+QKyjsH0u0Wq7LLXoUZ7SGgU4gmHaP/l2X2vTZHF96OwmBD/1m88SD8kx39kpbyv2wuGfiRyhIqXwDaFyzXxL4hu9BJz5ggqRE7aH9r4/W+ehypRiUqcMS5YYkGrj9C+fXt27drFzp07adpUjC2LxcKIESPYvXs3ixYtIj09ne7du/P444/Tv3//C4NUqEgEc+6caeSG0SoGidMui89jG+HAIvjBm7GgN8kizBrnfQ54VG8r0Tn/JqQfksK4R1bL+9bXQP+XxWAILHgdnlCYAHDaxKEaiqEWrOBpeUiC/1p0zrkmUrSIen9pkcACdSarOPZvmy+SYRsmSrTb6e3iNF34MtToKARD8ytEE/rfBo9bjOSMQxBTR+qt+DuxnHZJYc9N80W2WaJ8fUJr54ER0jkpPumj2NqhtWeHzafFG1tTttf6pskaOin3X8HZzmoKds8sUYVlHjKOgsXrDPRfkFZtAcO/hn0LRBYpeRd8MwA6j5FoteIcdv8WnNgEKV75vILIVL/6Q9prjwvWfSsEC4AlFjreLrJRlrjQHNqB2T7+2uLaeOnfFqD0dvFfy4g7lyjNZjvT7Dz/+x8eD7U6SXR9z0ckMnTbNInO3rdQ7LTOd4jUSmTVijm/ioDBLBmAHW4qfdv8LPjrTTi2VsiDvEzR39acNsHQ7maJtHbaRHrSGlfUttAip115kmmQl1VZH0HDuXRQVXS2qiblY4nzvdeenXnSb+r3lkenMVK0dds0yD7uLTr7gWTZtbv+zDIY/gmk7oVf75D6eQDNhsFF/wcGK2QelX5gtEK09zrnpoLqN2/4177ITRNNe3Nk4WysstyjYPe2Itc2lZKxRVGeud1hE3sdSr8/4XFS2N4YJu1Jgy3NV+fEXzJVa0/NrhDCe8tUqXlyZLVktLYZCb2flsDG8xmqChsnityZtrbp9Rh0vDW4KkSwekqVZFolKlGJs4gLlljQ6iMMHDiQl19+mbfeeosPP/yQyEhxVkVFRfHQQw/xzjvvsHDhQoYPH86YMWP+yUM+f3G2jLCCRRWiZ/j7fXB0tSyy3A6J4skuQXKlYT/o+/z5l3IfDFt+lgVDfrYYyYPeFdknzalcpOB1gLFaFkMtaJGrSqO3VPxTkRaFNEX9I+L9HH6KIkW+mg6W6P6tv8DWXyVi5dhaecx9VjSdWw6HpkMkQvLfgCVviwPKEAbXThDHpz8C9XG16+VyFG7ngfevpDokxS0GtcWswejra8GcdJWLScE/QcY5bKDzyy4wGEUmKzLgGLQxr/U1ULcHzHtBJJJWfw77F8Gt88SZ92/DkVXi5Ny/qGy/05uhyx3i/M3PlgyE0jgFjaTWxqXAugv+C9XAtlBau7jQM+L+SZQWkR2KPVHSGKfdf1uq95EhcnUJjeDyN6DLXRINuuVnOLpGHiDR1XW6Sd2Bmp0hrv75n5lyaBlMvwcyDhezgSIOHmusRJ+aIoRMadRf5G4UnTi6/OVe/LMVdQaZ+/Ky5PlCqI/wb4LTBulHZB6B8s/nTptPtkqbe3JTxdHpsPs+C7Qx4+oJudDpdtg126sJfxz+fAKWfSASfv8GgkFVRQpt9uMi/WWJg34vQIM+8n1uii+rPNrvGhfUpghY54QnSHBHYC2/spJuZ5vAriTIfdDmDPBF2IcaEGeyytipUPr4F54QQCikSoCSM8+7TvDWEQiUutICtno9Bj0ekKyaTT/KY/vvUn+h2z3np3xjzmmY8RDs/lPe1+goWQrBspNA7kP6QZmToqsX9i9UohKVqMRZwgVLLGho0KABP/30EwMGDMBqtfLiiy+SkCBRpyaTiXbt2hEfH/8PH+V/CIHFBbXFFcCwT7wagF4ZhezjYpTkZQo7n3NaIsdsKVLseO8CebQcDn2egajqZ/fYVRVQyyCFgSwWZz8BW3+W9zU6wNCxkOTVyNeugRaRoyHQWC2rEyb7FAWp9hGlpJVW4p9FqEWh/VOFG17qIxn2LxKS4dhaiUo+uFRIhh4PiYPnfDYmd88RvXeQYs1VA0hCzbHpX9RSIxf8CZlgWTpa9HpZCowZrYUXs9pngf2ncjEpCLw2Z5tw0f7P4ScfZI6E8MiS/08fBpe9Kn1mzlMS6T/7cbhyfMUf49lCIKGgM4gDU9HJIlpRfK9VN6BIHRfVBfGNoP8LkioPkhFotJY+NjjtQkK4nKJRXdAngtRPCGwLpd3/SrL7n0Mo9kRgBopms2kEk0YoqN6+6Hb62kNsXamf1e0+kQba/xec3ArpB+SxcZJsF54kdaVqdZHn+AaEJNFgMJ9ZsdtQ4MqHRa9KgXNU6Tt9nobI6hJFbYmBPC9BF1Vd+kcwmKOKkgX+BdGNFom0PZta9/8F+M89ZQkQcdiKJ6dD/T+jNyDBmQPOCIjxkgrZx2XuKa5tgPw+2qv/33kMdL1LoqmXvieBVf4EQ4srpJ+58sR56rJLO3Xl+WRPXHlgDJc+GFevqHxZRcDjhqzjkLbf9zi1FQ7+Ld/X6gJDP/YShzq5VrYM8TgEKudo84C2rirJ9ioPEX22CWyT1UuaOMBk+2/LwGhzhia5m5sSup1stEpmMZSeoe+0ic1vtPjsGKddHOiF9mnxHZdmryT5SR5dMU5IvTlPSfbC4jek7/V7QfpaIOntcYv/wZYG9lTp4/ZUUVGo3+fs9DWAXbNg5qPy33qTZFd0u7d4uWaHDbJOSoCmM1c+Cz9DP5c/aQSVQVWVqEQlguKCJxYA+vTpw9SpUxkxYgTHjx9nxIgRtG7dmgkTJnD06FEaNGjwTx/iucH5EGVbqDaDxScFoxXnMlqlWDH4jO1AuQWnXWRT/noT9s6V6O3tv8Pg96HV1RV/zHlZkqq8+nMpOpbYWHTok5pDUjN5RCQV/o2qwo4/YN7zoueo6GWR3fVun/SR5lAOZvAGGsNldcLojWJYOL2GgGawVaLiUFx/Olv9zD9VWDHIwjI8ETrfKY8TG2HHDNGST90Li/4HK8dKMc2mg6DeReeXIXhsHfx2j7xue70QhIHITS0sZ6QtVJx2Wbj4F3PzT/vVIqmLky4qabHpX+SsuMVNZbR1cPwThIs1HoxBTBmtHxrDfFGRjS8Vp8uUUbDlJ1l49XwYqrY6N8daHrid4lzSCsLqDFLAvefDkOSNVvNfOPs7LZ02X/+J8avFUprzTSPqQBxuWraQf3ZCqGToP21zVKIoyjrGaf3aP3vFaYcwtwR7hMWK48K/feSmSvZZ9wfh4ich7aBkLpzaAie3yHyVexp2/C6PMkGRfh+eKLZXeCJEJMpzeBXf67j65XP02VJh6s1wfIO8b3eDEJP+tZGMFhlLrE4fyeGw+9msIRY6d9rFXnM7waiX65yb4iPFK/tNaCiuRlZp0NpsZBmudbAsh8AMSVu611mu+Pqawx587Az8rNPt0GaUjPl/+xEMfz4R+nlpB9NsiBQRTyomsrkkqCqc3imye6l7IXUPpO6DtAOicV/k7/Syxrno0cJFlo1WSKjvy+jwz+4AeV1a/YTyEtHl/V1Z5i6H3Zfl+l8mFgLt4vLaycXZkdo9cdrEwe60F76/BeNywP8Fy7DUUKM93PInrP0Wlr4l6/Vpd4jkULf7pN2f3g6ntklfcNkJCmO4ZEJ0uaviSOLkXfDX60IsgNTsufxNqW1UEpx2sERAfgYh1YQIpa1r98SeKdJ+//WgqkpUohJB8Z8gFgCGDBnC8uXLeeSRR3jyyScxGAwYjUZmz55NrVohFj/9t+NcOH1Km6AKGXne0BWjBdCD20+vNpiGM/gMhJg6MOIbOLoWlr4LB5fA7/fLNhVFLmSfgFWfiT51frbv8xOb5OEPa4KPZEhsIkXZNE3rqBrQ7yWo2UEKj1qiChs4wc71TKI5jRZxJJnCxdhVqHTwnA0U159C7We5qb4ixKFEk/inCqsULeaa0FgWdV3uFFJr2fsSVbb5R3kYLNCgNzQeAI0uLZxKfK5xYAn8dKO0y2rtJGrIllp0URa4WA/VUVPa9S+tfzltkH60eHmEymjr4DifCJeCfmj3FfIDWUj2fEjmje3T5dGgrywi6/SoOGkWpx1Sdsmi9PQOKZpsioBmQ6X/heKAyMuCX2+T/qLopNByyxEQV7dwdJz/wtm/L/hH9JUFWrFmc4RE2+ameucQS+n31n+uudAze5w24F8op6WhpPsTLFq4UIaK93VEku9e56YWzmrQyKnMY+KIrN0Zmg2W/ujIFcf94ZWSjXNkVWE7q0So3sjRFCmCWxxMEeKk7Xq3EBGhIPskTL5OnKiWOCEUmg31tfvAvqaqvmtRyIFl8T2KHFeAI9lhE1JBu96azN+F2m/OBso795RnLg/McnDaxCYzRYidlZsqhLYTec5NFYek5gwN5RgNZtFObzwQtkyBNV96pWEV+c5gFnk7Y5i3uHG473V+ljj/bSk+4q7ZEKl1kNS85P915dFB3URXdT3m8Y9D1tHg2+mNEF1bsiISGonMWZWW8ux2+DLPtTWH/3UuqKvgHR/8ayycL+0+1LmrvBkvFyKCZS0WOP3LsAYtri9r90RFovX95yOn3Sd/VGQu8/ZJZ5ZXZiu+8L4VBVpfDQ36wbqvpcDzgSXyCIawGJlPwqKlz6fvl/ohC16EzVNE6jjxDCSaM46IROyWn6TouqKDjrdJ3RVN4tZpK7wW8s/YNlokG9AYUXSdGAyhtHXtnmhr1fPBxq9EJSpx3uE/QywAtG/fnt9//520tDRycnKoWrVqgSzSfwIV7fQJZigEm6BKMyhMVpmsPH7yFsVFGATq0dfvBfV6woxHJKugIsiFlD2w8lMp8OR2yGfxjWRir9paHETpB+H0NkjZLQaFLcUnQ6PBEAYtr4bGl0uqvMsphr89q6i0S0UWVtK05sPjffu80B08/wSK60+l9TONSMo8DjrFSy6E4PQITBUO/A9NRiz9sGjKNx0kbXnH76Ldm31cnnfNFkO1djdJWQ9P9EZ/JniLo3v1SytCasLjAnuGr9hlXqZEAC14WfpWnZ4S3WZLD+78tMYXdowWVwcj8HP/vlQeUq1ysVg+nE+Ei9YPVVUWlf7ofBfUuUgWkLtn+wrNVm8P3e+DJgNDl7zzuEXe5fQOL4mwU57TD8iiMBA7fpdr1OgyaHEl1L84uH525lHJrEjeJdtf/RXU6OSTJooIcPIGktOBkmHaNQlEMBkx/2LNJquPaChuH4X25x85fB4RTWcDmmPs34pQ708wcirQOa5lnhojAoJBbOKEVAzihDFavNl3NqjWVuYhkL5iSxcHrMEiz7ZMMPo5T41hiMRjLuQki9Mz67hE+GeflAyI3BSZa3KTZb5Z9oFkm3a4GTrcVfJ5ZhwWUiHjkEgeXf0NxNYpbEflponcTHR175zi13eLI75Lgpb9odm/4QlgCjK/V6JkFEjqnIOaHYFZDrkp4sBTvNIkqlvaQExNX9alVkPDZCmdoHXYvFIvYRKI1O4GIch0Bm9dunyp3+GwSb+Krxe8zR3fIJJKu2dLsMmOP4ITDDmnRFZ2zxzM+xfzmsfr5M9CyIvEpkIeVGkuUdPWBIisJplBgc7dQgEebukvRi/RptkGJot8rjilXyt+UmHFtfvyBkeV93ehjo3lyXj5L6Isa9Di7EjtnkREFbWZtFpsCmKvaJ9pxZuddpmfbCmS7e0v7ahtG2OBXv8ngSaLXpX5JaGJtP+aHSC+oUjbKQb5H48L8nIkgn/XbFjylhDd3w7E0GokkWofspUQ5ZFceXBouWSda9m0AI0vgz7PQpUWhbf3J1LA77VSeA7SgtdKgz2z5HXo+WTbV6ISlThv8Z8iFkCKNkdF/YsjzM4EFT0xBDMUghljgdsVSFT4R+gHWZDlnBJ9Uv/tgqURKzoY/J4sTDdMKD+5cGQ1rPjEVxwJpEBSh1ulULTJ6jVIaoLOKPq6YZFi5GcdgRNbxKGUulccol3vlUWOPUMm9xpep7DB5JUgoLDjp7jCzaUhmGMIijpbtYigUItpVaJkFNefSo2G9zoRDGHgtlNQC6MsTgT///C4fZ/bMqQ/5OVAdC2Jzk5qBt0ekAjM/YvgwF+S1ntomTyCQhEZC41w8FuwGz0qL7vFcDf+/JWQIyAOXFeeOHXsXiJBk24KhqaD4NLXxdB35csCE4q251AlWyD4b8pDqlUuFv9ZBBJnga8xl74PrY/kJEtEpD0bLJFeJ4ZFFmpXfSa1e/7+QBZzx9fDz7fKArLrvVJ4MjdZnJY5p3zP/q9zk4MTCCCRbQkNIbYB1GgntW+2TxfSYfs0eZgjhXxufgXU6yVRrSc2w5TrxVFqTYAR3wpZqI3hEQGOqcB50Z+shpKJ68BtgxVr9icaAqERpdqc4m8DXOiL0WDR6P8mVOT9UQBHnkj6OOJ97UpF2riW2QDibC2ILvVeQ0UnWQ0Om7w2WiHSLJGpgdl1il5sMK2uljMP9AZvzYIwea96xFm6apzov6/8FPPar7nbczFTlcFFjz9lD0weKX06qgbcMkv6XmBUqOqS//KHvwRSISKvlPah9R0oXFwz2H2pzDo9fxB4f0xWyM+lkLQP+AhtlxPCouS5uKyFQDk7R44QbdHVAtq/Wewbpx08Hq8spt3XjvzbamJTuHYCnNoOS9/xZi/4EQyJzWDvPJ/kF9KNU4lhldKO/hdfjKlmO1nnGMK89mCct55ELmSekOMraLeWIFHkTl92gnbNjFYhGxzZco4x1Qt/FwzlCVor7nehINSx8UKf4yoKFRFk4H+tA20uoxVMXme7LV36m3/xZi2TPy8byPFmSPvZlPk5FEhmxtSCAW8LqW2w+EgMk4WCwtBuJ7hcEOYly1teBbU6w7IPYcdvGLb8yFfM4EtlFKg9gSD1EHJTYN8i2DNHnjXpYoBaXUVGsEY7mdNsqYX7NhTO2gjM2Na2sYTo77JEh7ZdJSpRiUqUgP8csVCJCkQwQyGYkRW4XaBer7ZNoMGhNwJun9FckmyMooMBb8rr0sgFVZVoH80xlHkUNv8ER1f7tmnQXyIXqrQUQ8IYVjgS1OMEa6y8D/emHsY1kEgHFJ8hkpcBbpfvmLWJX0t1d9qLpmU6bJLKnJsKsbVKNloduZBxzCfZUpwTVrsvZSmmVYmzA60NxNQ482wVp9cg1iRPrDHyufaceQIMBjGCo2tC9/uliGv6ITFmT2ySiE+tQHpephBhePuIPU0ICT/ogS7am/2hnrNViIqwKDBFQp1ucPETEpXjsvmiw3NTvNkCpsKLgmLP33v9CkmMhfuujeaIDSyeGcrxVvaPfw7+zgAo+trtLv63gXDaIe0QGE0yjodHyuJRdcmcYoqEy1+DHvfD6i9FNix1L8x8OPT/MISJE0eruxNfHyKqgcEKnnxxrJoixDHT82HYuxD2L4C980XeYstUeYTFCIm9a7a039h6MPIHmVsg+Dzpf838naBaZlxpUdT+jlBtLjFH+tUnUX1zVLB9BEqP/Zf6zoVwniU55wp9V4qT3GgVW0dBpI+0rLfwyKJt1mjxOYYyj8tvDGHiEFLdQhyoyOf+Wu0Fx+V1IOVly/yWny19xxzha39OGzS6RKSX9i+CJe+gnNrKFcxhoLoA3byV0OM+cSKd3Ao/jpL5LqYOXDFeijU7bIX/12iR/1ApfD1s3sK96OWgDd6lVWnEgn89lNKyFiuzTs9fGK2FI6iNVq/cj7ctx1SXOceEBGiUNI5qY3FOirQ1WzoYvWsQ//uurT/sGeAwSDCJ3kCBFIrHLe3SaBG5ouFfSuDTX29I8JRGMGio1hbq9ya/Zk9GTc0HRaFv/eqg965n8jJ9GRnOvP9v787Dm6ry/4G/b9t0SVdoEQQBBUFZBUYQiqAgm6iMG26AO+PuiOOOC4iOjujvq+My4zLiNorKoOK4jCKbioqiIGBZZbWotOxNS9Lm8/vj9qY3aZabNsm9N3m/nscHm9wk5ybn3HPu+ZxFLXNZ+aHrFl+HZ40a1Hdkq2XSFxSoBnb9DOS1rP9ugpRzvcD7yGD7XBh5HZlDf13WD24LrH+iCaAGLgfk2q3mt5wWjZdK0tpOgvpl9Pao7THfbIYqtSzlFKkBQKmrn/Gq1C8964BvjxRPdUO95BuQUaO2mf74FHDCVfDOuxGFuzfiL/IcvG+sAMb+TV0muWI9sOFTYMMn6lLO+tEaea2BTierG0d3GNTQ9+Cp8f8sb119wL244Xvwfc+BAxUNzKBjGSGTlZWVoaSkBB06cD9Qu2NggZquqSM6tEosXGWnjS7QbuD2/qJ20IRbNkYLLtQcAMreVYMLv/6IDI8H99StRkvsQdaz1eoIVa+n8evTM4Ejh6jBiMP7Aa2ObkivpnqP2gGblad22mhT+gG10e3IUQMi2myG/DbqNGW3bpkK36i9EFMUM+tvSrTNwPTfXaNGWHXDki15ERoP2ntH04DgKLnmCfb9aXlAyzu19XnDyPrlgdwu/+mwQP0IMqc6wiUjQx1Vk5nTcFMIqMs7DPiTGlRw7VZH2xUerpa57EI1n1dVqMuwVKwDPIeANHUUqae2Dk8s3QcA+PPITnB4a9TyVOdRpwnnHabOlsgpUvO9u0o9L210qdYoTqsfYZpdBGTWqWnIyFQ7k/SjN8PRGtFaOdK/RuuIScsIvYkzWVOwYLT+/9MMzFjQeGrUzhHxNgTgADVvKrrRlEUdgVEzgAGTgZVvqkvruSrUpcLyD1fLhSNXXf6h+GiguLP6WHaRuhZ7mtbpUl8WFUUdVV1XvwSeljdra4C2vdUgxKgHgC1fAuv+C6z/n1pmV/9HPa5tP+C8l9TNaA2dZ33HVFWles6+YHO4YIRudlC4uiTce2iv5U2pPYXrsPZ7zsD1OL8VcGAXkFOf90K11TKd6hrxrt0Ns9rcNfUzQGuBzGy17GidQ/olVvSzApxF6qbQblf97FZ9W6mmftkYAMeeDnQaBveP72LtR/9Ab6wFfvw3sPpN9blNC9TBHMVdgNMeV5d9ARoH/oOVA3c1ULOnoV7Lqj+HXIOzWWr2G9vviJ0/9qKNftaWANLyZqilmgL3icorUa/FQH0erPHP3w4ngPpAXM0BdWS11Af2fCOX0dA+zMhS65xTpgG9zwe+f03tTO04GOg+Tg2IAxCPF1AWqJ+RlgXkt6yfiVo/SwFQy6lS1HiGAuBfVl31r6mtVWcL6mcteGrU8ltXa2zmV+B9pNHlKlMp0G0HgfVNpL/DCVwOSOrUPJGZE3pPndzihvaRq7LhtbWehjZT4eFA1R5182Ntn0IlvXFZBhrutdK1+soJtD8B7onz8OqTMzBJ5iL7l2XAv0aq90B7t/mnp2UndUnYY8ao+82Jt+F9tM9x7QUO/A4UtFHvdVy71XrNoa0Cofsecksa6gr9LFQgTBuOZYTMUVJSAqfTiYkTJ8LpdKKsrIzBBZtjYCGV6Ts9jWwkKUHWQWhKx7ORSixwORNHNrBvN1BYiIadXANk1Hc2jZimTqlf/xGw7DlkADhRO2a/7vicFuoGR/lt1GUxjr9C7SjSOvyDrbEtUBvZdR614VJ7SH1Q67zUvg/9BkcOp9rhGsg3RTHgfBxOdaZCqM2r9LMZ9Eu2hPoN9b9buO8+2A2P33rZBvJIMjK6Zm+w8hGskazlK091w8h8bXq8fnSmSOTRpNqmgdq0X22d6sxD6udkFTR0EmqdI/r1z/MOazxS7NBB9f3y26jBgeLOakM3uwVQW4O6jAJ8+vX3AIDr/zASjswMNT8eOqB2kuS0aMiXTvinUftOMp3qOaP+cberofxm5RvPa+GWSjLaEZOINZmDCZZfgjErfWZ9LhD85k3//27dfjyuSkDJD31dy23Z0NGRlauWi8w89T99+dLyY0FbdQP0E29SR2r6AgYudVM9Tw1Q2M4/WKXNBDv4mxogc2Sry6ikO+pn6QQsUaek1S9nlKvusdB+AHDircAv36rLVeS0AE6+Cyg4PPJ3pV8OLC0DUNz1AUWtwyXM76jvOPVtxpsTUKaCvD6w7RBYBvWzhQD/65eZ+SrWFMW652M0XeGuk37PGXi/Wo/a+eHrgA/zmrQMtb7z1NR3hmqfleEfhE9LV9tZ4lX/VdLUf7W1s/f/qg468dT47wukLQmYWz9zIrsQ3h5n49ZPWqGnlOGRDkuQvmWxujQZALTtC4x7CmhxpP/yGb6yEax9VKXOzkjLUAMbWrsvmiX0tHrRUx28rteXtVQOkDenHZYIgfcLWh1j+PVKw79aWczLUdtWe7ardUGjfa+U+qB1XsOeatoAJiWt4V4l3aEuT6l1uLbqAQy9TR0s4pX6Nl79e6fplphp1UUtj3u2AWmZANIa8rhWX2qDRDR+ZVUb7V0fKNTS5XEBEHWGReER0X1PmlgtV2nV67ddRFsuA+ubSH+H/Nw09TpdVaF2smc6GwYjam2sYDJzgRZHNAzs0gYfFR2hKz+56r2Tu8T/MXeVOuhKvzyz26WWM+0eJ63+c3NbYU7aGVgsg/By+zlI37pEDSqkZwJH9Ac6DAaOHqHObtXOW3s/fXsUUMtSVv3Mhcy8hqC59rcWXNDXU1rbbv+v6n1mVl587+Fjfd1luUwJHTp0QFlZGT7//HNMnDgRFRUVDCzYHAMLqSwWncbae1RVwLe+spFGXlWFbmkjAzdLmU6g+MjQG8r6LXuSBox+COhYCuxai1pnK/zzq72oVFrgjomjkZXtVDtMHU61kVxVWT+yza2+PnBTJ+08tY5ZT546ikE/ukg73mjkP1LjKdT7BM5myGsV38YCR8k1j5FOG30QKlCk0aTpmf7L/Hiqgdp96khrJQMo6dzwOcHeU9s/QSs/nvr31EaWOVs27H0A1Of3IMvQaJ2Saenq66t2N4wma3FE6E5i7e94jJbhKJzk4rfvgu6mK9LottySxnVMuM66cOubA2onjX7qubtKfU5B/ajpOrXs5RY3XJvD7Rui/e3IUZfSO/a06K7pfsup6ILbRjpc/DZ9DhIg0NIdWK9HGlWoPV+9T127l8u3WFe462S019CcgoZ2lNHPbqG/iazw38QY8C9f2tIr+iVjtM2TC9sGT4/f56n11GqlGzznTEb67z8AXz+jdgid/njkchdYjn2zfBB62cpwgwP07YNQZYpLIMVfc2fm+u0bl9s4nxgRrF0GqLO1tbXdA+UWAx5nQ0envnOycqO6ZGBefTmSOrVdl1vccN/j2qMuqaTfs8BV1fhz8koa3l87NtSyqvo8rR0frP7IKQTSipseLDN6beKsa2sJNmgk3N9G3kurM4qCdEgG+/2112UGPKeV3aqK4G2iYEvXagPDApdbrd9nZZdSAs+JdyC970Xq452GNczECxZ8D3buucX+KxwELncUrO2W6awPulSrZT8weBftklMsQxQnHTp0QLdu3YI/uXd7w2bs4TiL1WUtyXQMLKSyWHUaV+8DFKnvkDR481NVqc4qqNodvtNHP3JN69gJtvmwryOjfq11RwZQej0AoPbgAbz/zRcAAGlxFOCoH4Gq33DMUw149qvT8rW1C7WKv6oSqPi5fv3SPP/KXT/bI5rKN1LjKdR7hZvNEElTGgeRNs+l8ML9zka+WyOBCV/jsv4GzuNSy5UD9cui1I+EzMj078TIDFJ+BOq+BwL41sN11JcB90G1kvfqllTa9wtQ2Krhs6tQ/9nZDdONowk66m/IgcY356E6Ou2MjXZj/DrYdIGFaPfPaPReBjoDffXLbuBQNZDlrF+fV+tcrL+xdBYH71DyVKszeqp2q4Fr7TH9cb56pNr/70BVFcD+HUB6jjrKLvAm02jdotFep7+Z1qc7UudRsPfXZimFC5qS/RjpJFfc/nlGW1NboHZQhsqb+nyTpwsI6MuXvjPHkdNQLynpjfNYpPbtvnLgiOOBi970n/Xj61yqVJc40mab+jbM1TqWchvKTrgg3sEKwH1AF2wMcWyo9HJwR9NEU682N3ijvV7LF4H5xHdcmPZL4O/srlI7VKp3A3U1QItOjT9Xm7WjpcFV2bA3ibdOXcolLQModDYshVlVqQYqtAADUP9d1S9hJLr2nX42YKT0asLNNoz02nh0djIwZz4tOByPNq4+LwXLE1UV6izsrLzGgYegg0jClF19gFtP289KG4DoyFE79H1pzAGc3dSZroB/YDzwehDsHAIHx+jvHbXrhNbe8s22q399fht1gJcWaAhs0xopF/EoQ7zvoUj2bgeeHuA/kCsUhxO4bhmDCxbAwEIq0yqe5k45yylUR6lpswmCdfwHyi1WR+Kk1x8PhK/wfB2culEygP/ImIP171PrVjcM1FTvbvh/by2AdP+1f32dopXqZstIU5dE0ir8qt1AmlLfIM9Wb0i1zXH1jYJYVr7h3qupvxsb2PZjaDRpwBRUrXPTU6M2bvX7Fmh5NbARC+35DHX9znQH/NbDzcxRpwCnZ6iNaE1VBeDM879BzClo2IxSC2roy2y4TiatUe+7ofbWj5arbyy7Khs2DcwtSY6GKculMfobSH2WdxYDcKvTzIPlq3CB6lCddYG/iXZ8dktAKhuWLtM6F/Ud+9rMH0B3k5fTMIvHtwFfkJvXSB1S2vvU7AfSXPWdQyVoFITwuNT6UIH/6Ff9uVXvV9Oz7xcAotZ5+jRr6Q62f0K465J+JlUqL92SjIy0S/xmFkG93u/doS51lxnkGO19tHxT5/Zv3+0rVwPVWqeMngJ1mYiMrMZp0r+v/m+NVhb1HcH6QLz7oNo5q99zxMhI0WBpBNT6TOr8O7hCjVLXsPOl6aKpV5sTvNECZ3WH1OUiUX8fEmyfqFCBWqDxNdVTrXaIwqsu2RopbZ5q+PYrceSp+alW20PIpQah9+4APAcBV7r/vh6uPVCn3WQARmcDRjubyYh4dHYyMGe+eLZx9fkw2Cwara0YbqUe/YwjtwvYv1Ndekx/aQ8McGtBBI+rYWZc4Gw6TUFbIL0+AYF7qQSmN/B+yW+AS33AW4H/wJZD9eVeP9BR+xtQA9+h2rQRryshAv5GharDeN9Dkbgq1fxz9vNASdfQx1WsB+ZOVo9nYMF0DCxQ82iVU15B447/sIGF+g4Hb63aUZJTYKzCCzV1XNuwKHBkG+DfoNAa+/qRBdqxmTmAFKkjy/Q3BdoMhbzWamet1KgNeHeN8XQHCrz5jrbjqynYwE4NmU610emtVTtp0jJC71uglSGtE7CqQg0q1Hngt6Gew6kuNZGRDTiqAGxWH0/TjYLTb6qpn5ofWGa1RrA7yI2p9vpaN+D1qiP19OVXf5MQrgEeyModNCyXxuhvIPV7LAD1+epA/f8buIGJ1CkSbIacVsc4cho2yAT8OxerKnSdO7qbvMxcdaZC4Gwc7TXaTIHAjkuN/oY1tyVQ51JnLITaUFfrGA12rtq55bZUZz5oZTfDGWTT5ibMWGN+Ti5++2kY+G0D85tA7fhXdO2yYGVSe+9ad8Nz2qAO7XnAvyw4ixtGaAdLU7jOCyW9ofxo5U6bbaEAyMxTgwDOYv9ybKQ8BC615nCqday3zr8+izbAScZFcx1qTie526UGjNIc9bM064NjQTvjQwRqgy7XkqPmP9+msRE2OHbkwLfGvDYzBlDrAaX+M6rrN3tOS1f/1WZ419ao+/I4sgElq+E9mzIb0IhQ+Tqa38zosfEIgFB0EtUmCPY5eSVqmzDcZ2v5URuQFWzpscC2mTYLteaAOtBQm8Xg27h8V8NrM3LUuiVwyTLt+MwQZSCwnLgq1TZbRnZD+1K7TggaXyMC877+uzFaLvQDRZozm6s5ZZ2ST8ASRzl716NvmzTk7F0PlNfvU1KxXv23pCvQtk/i00hNwsACNU+wTouDFWol6olwM6Tv4ND+DvaeQOMbNY1fRZmj66ypbKjE9SNznC3VUdnavgr6St1ZXP/ZOf6VnX7KsNvVMGJAf0McbcdlYKdosA7SWI/2ZAM7dWhlIdSeJJrAxl2ms2ETLre2hrWuXBY5AWctfIGFlp0B917/PR30s4G01+lHqSr1N7BBA4faevNVahnTL6cCNNwwBwbkInXAWLmDhuWy+TKd6o2doHG+ijRVPphgM+S0x1p0CPO6+s4dfeAhaHrr83lVhf8SSaE6LgP3QvCrA4Os553pbEhHuNkGbpcawFPSGj4/WoHfKfNzcgk1qt5oWQq2PnuwTgUt3+jXvQbUGXQZ2eGD1wCCbq4cdpBKsGXI6gMb+jZl0IB8hDX0g31n4c45FHa+NF2irkOZTiAz3/+eQAuOBc48C3V9DxpoizKoqw+8afT3NG6XupdIrUfdV6G2GoCoQZCCw+vfI8d/j4VYfYeB14pQ+Tqaz2M9Yx+ZudH/Vk0ZDNTUPKHlR0eO2h+h38/Ad4xWHnUzD6p2A9n5DXlaey8gIICco14jgn1uYBkPFwzQNkLXguJaPeTUljSrVlcyiPUMI22pp6aIRVmn5BJkiaNuAL6/Kg9YchWwRHesw9mQv8kWGFig2NIqWCOdeI2m/rpCL6NkZNSpdpMZGDTIDNHBE1hZBo4kCKTNctCPBgIAKMZnaug/K9iMBSt3gJL9uF3qCLrAG1wgxCg5Z0MnjrbZX9iOjxygdq86useRHXw0XmBagMgdJaFuqgMfD9UAb/Q6dtAkNYcz+MZ92nPhpsqHE22+CdcZFDhNXvu31uO/LAvQuONS+y9wY8HA99WvrxsuAKLxrfkexSajgVhnJbdQZUCbJVS1G2hxROjfPlgHQrhOBUdAfaRtNOubsRPY/gpDH9Tesw2o9TY8lxFkVpA2MhMI3jGsPzbc800JIoRLP8VWLGcw6q+1+j0UgPDBZb1YtU8CN5f1u6ep7+nMy6kPmtcv6acELA0LXWDB4wIQpEM0WoF1RHPztZVnoFJsxKpdYeR99PkxcOBGKJnOxrNQ9ZxRdsSH22RaE9he09qz+uU1I31fTSk7OWGWQIr0fqzDKFCQJY7K1q7FhAkT8O9//xvdjj224Vhuymw7DCxQ7DW1kRyuARDVtOYQy0kAakepx63eDEttfcPZYDS0qlJdn9SR1zgd0Z5zYGVrtIOUrMtKNzvaDaa2t0LQshCivHnqA2gKjDWOtdEEwabi6mllJHBzT6PCbXwYqfHKxm1qa7SXSBOv083hyFEb1LVuNT1aJ2awm9NQHZdBgwhh6rtIggVCIo3GbvQeDNoltVBlINPZsG9ILINKoZaFCZfHtUEfoepebWkwjy6w0GgEZZClLkJ9nrZxdKiZuaxvrC1ewVDt+hzt/jLh8ks01+Nw5aTRQKwc9X4ocOBVqPuS5ghWRzSnvaz9fgcrGoInLG/JJVbtili8j18Z1Jeh+v/3VDd07vtm0kXZHjNyTWo0sCpInRXpPKO99kX6/jiwhJpKt8RR9a9e/PCrF9VFXbnskc0xsECx19zpiMEqsGjeM9zMg9pqdROl2hp1I9pwGzoFUgL+bWr6wuENqX1ZqYGlNTiz8qMfxe/WNnxON97wNBTwczaMHm1KQz/cxodE4Whl08wNhfWbxNa6GwJ+wTr3Q3UQBXu8KfsghBNpNHYg1lmpyeFUZyrEOqgUdFmYCHVMpLpBWxoszdv4Od8xAeUoXN7Xl2XWR/YTr2Boc4K8oURzPTbaFgNCX7f1aY/VeQT7rOa0l5s7SIWsz0r31IEzgYI9p+1XYqRzP5imXJOiqbOa+jmRvj8OLCGr0PZkCIczIOKOgQWyhsDRK6FGszR3VLi2iVJhW/XvaBrO2nqlgP8eDkRAbBtY7qqm5XNt5KYjp/FeB4FCjkQNMZINUMuf62Doz4wmwBCtUBsfEkVi1s1P4GhTLQ/nhtpMXXe8fs+gaDePDRRNvRmPzjFKTvpgcahlLKMVrqyGqmsi1Q3acjXuWgA/NS99Rj+zucKVWb8NtWMYVEwV8QqGxjrICzT9ehyqrHhcDe07Mzvjm1Mna+l2u9Q9IrLrl2qx0qxhspdwM4P0ZTCwXPnadC0bjm303vUrJYS6R0pkvo31tY8DS8hsWt/c3MmRj3U4geuWMbgQRwwsJDMl2NB6iwocvRJqNIv2eFWF+rhfRRzkfD0uwHWg4e9MJ5BZ0PgYbZkM7ZhgwQ39hpvRjOiMFzE43cJO+cDOjN5UGvndwo3mChzx7xeQi3K0cTCZeep/wT7PU6PuvaBx7QbkUOh9HIwy0rDWvt9Y52ezylGqlctYfs/6YzzVgGuPOvssryR4/kn0zY+WnwP3ONHyr9vVcA6hyi8Qu3ommtGhTe0cS7X8TA0i5a+oAluBx+jyVVWluqRRZl7jkZr6DZjj2Umjf//cktjne21QgXbtCPad6r9vBhb8mXUdinU7Qnu/iNfjEO8X2BZUFDXv7i3X7elT07icuAMHjkS5NF6j5IVIX+B5Gfn+9GVPKx9pGQ3vw3JhXWaXy0j1Qrh7J31eDbz/D8zH7iq1nkJWw2PaSgn699bSowUmQtWf4ZaADSWWdSDbdWR1Re3VYIGrMvxxFevV4IOrkoGFOGJggawhcPSK9jfgPxJOe7zW4BTYgxVA1b7wn601Rqv3ATmFkYMboUbaNLcy52gb0kQatanlS8A/j2Y61Twfau3npqiqAA4dBLLy1I4Ujy6w4K0F0hB6Hwej9GvmKgjfSUzJIxbXPI9L3UQWANwmXDuDnYOWn7U9TvTlOFL5DRY0jIXqffVlNEYjy4k0TV2HOdryr1+OMtRrjQbRXJXA3n3qSNNolkeL95KH4a4dGi4/kVqC5fVIZSdYHnG71KBCrQfITA+ej2urAz5b1+HqqVb3VYm2zMSKX+AgyPmxXFAoka7bRvNOpBlEWnmp090nOZwADgVvB2rvpX9Pd5VaPwnUeiA9IMAcaYUHKy3LS5QIRe0ZLLAIBhYotmLVOa6NWtNGB2gVpPZfpstYI8BIsF1rUGhLxwQGNwI/Qz+iLnAETXMqczYGSBM4atNv6YMwnY9a52Qs8pFv1HX9jabUv78zs+GYtAzAWdT80WH6NXNDdRJz+YfkE4trnsMJIF/Nn7HqUDC6DEmomzgtP2cXNH59pPKrPz7Ud9KUejanUA0uhBoFTamrue02fUBN/7cm5H4+UZb/3BL/AFykgR/hzqtqD5Dmru8ojaKTNN6dl+GuHRptBi2lhmB5/WBFw+ydFh0avyZYmdTybH6YwHVGkA7T6v1qMKFqN1BXE32ZiRV92Qs2kyMeS1FRcoh03Ta6rF+kPObIUWcs1Hr9H8vMD56eYPtsearVwVyAWr7DDU4J1gY1UkdxICMRxQEDCxRbTe0oinZ2gNGlLXJLoE5J3B76mFDvZeQzIo2giUaoWRqB2CBIPfp8lhswkt9oJ46ekTykfaa2Fm+w93MWAzhUvzRZE6fKa+egNeqB4J3EnOaefGLRSefIUfcsiKXA+ihcADnYOYSrO4wGD6JJXyShgufBsH5JPVp+CrrEpE64vBEuT4YqD9GW/8D3idQ2DByYopfbAnDva1gb26h4L6uW6GXbyPqClRMl4F89rZx6XP6jnYPlrcBgYLCymFO/fKwWXIi2zMQKA2rUVEavq80d7JKZq842OHQg9DGR2liOHHWGuEBtszUKoAVcDwL/jrbvgvUNxcre7YCrEkptHY6WzQAA5deVQEZ6wzFGNlkm22JggWKrqR1FzQ0gBONbwzCOlaY+3c29IQw1SyMQGwSpJ5pyFatGpZGRk0DwtUmbui6vwwkUBRl9p08Pp7knD6t2ogXmtYMVgPuAOuosryT6m7h4py+SaNLI+iX1GF1iMlzeaMr1OZp8GdgZY+S14dLkLAaKWhtPK5FZguV1/ewdTeDePoLQy2kBxq71+mVaMnPNmalAlChGZ7yFE2m7kEjlLuKsiCCDU6Jtq/F+imJt73bg6QGAx4UsAE9rj78Y5FiHs35gIiUbBhYotoxWcE25SYyWVnnr1zpsrkSkO1KFzwZB6ol1PjOSh4x+ZrA1R4MFG8JtOm2Elh5uJkbNFXHEWMDj+tGhsQh2x2qpwHhg/ZJ6tPwUaYnJcHkjUp5sbt5vSsCrKeWkKZtlEiVaqNkH+v058iIMCjFyrQ/XyZmI2W2cQUfNEW3+MTrjLZzcYkAJs1JCotpY4c7dqoN6yL5clWqeO/t5HCrqjJtf+goA8P8uHYQs/YwFoH5gB/dESEYMLJA5EjEqUqu807Ji956JSHekCp8NAmquWOahYDeewYIN+rIDcFQ0mSfa63iw0aGJ+FwzsH5JXfFsezR7iYlEdcZUW7+MEgVjdJapprnX+kTUZ3aoM8m6mpN/mrwCQy7UJZhDSFQbi2WHzFDSFVLSExuVXwEA0uY4INNC3c1GlmJi4KPJLPRLU0ppSoXd1JEH7trYvaeVRnNyJA9FEu884nYBmQWNHw+6sV5A2bFKOQJYllJNPJcSaurnGs2DZuVVlhFqjua2nUKVQSP5UjsmXGePnrZZLZGdNKWeiva6rp/Rk+lUR3XXutXZTvGoF6x0z0X205z8E6k8NXXJ16a+n537K4jM5ixWy83cyZGPdTiB65YxuNAEDCyQObRKMXDjsHDiEX2P9j2tNJqToxEoEqN5pKmdhrXVAIIEFoIJti6oVbAspRazruPhPtdoHtSOi7TRbqyxjFBzxKvMGcmX0SyL6XEBUqv+S5Tsor2uazN6tPpHoO7nEK96wUr3XGQ/8cw/wZZ8jef7BSurXO6IyJii9mqwwFUZ/riK9WrwwVXJwEITMLBA5om20xMIvxFZqNe6DoR+Pt4R/XiO8uRoBPuL9yhgo3mkqZ2GGfqljmI8egdI3ChpliVqrqoKoGq3OtK5KRtcGs2D2ijRqj1ATv1svETcPLKMUDSsdO0OtiymqxJAXuO6SgL+DUV/frGq78j+4p3vm/L+4V4T9ey9+mUutY3etf0cWC+Quyq1romBS77q74GMzo4LFG62XLCyygEfRMYVtWewIM4YWCDjYr1pqr6SDPfeWsWZlhF9h432Wj39Z4XbmEwi3VkGeb9Qn+92xb6hFS7tZF36/BKL/BEun+pHrITLp0bLYuD76Bu4sR69AySm0awoLEuJEK9NtxUlsRt6hypvVZVAXY36b1MCC5FGl2nnmJmrloecWqDWA+QHlNlY1FvBsIwkp3iVnUh1W6zyqZF8qR2jXxYzVF2VVwK4ndEF42NZLhJ5LUtlsb5OasfFs80f+P4OA+21YK/Ri3ZUs1aWtI3eswvCn6fR75nsSf/7hstnyXJd059HVp76n8bvHijb/zVGzz+noPHnaCItMWuH7zhe7VOKu23btqGsrMzsZJDFMbBA5jHaUWFkRE2oETmZTsBjYPp7vHCUJ4VjlfwRi07Dpp5LLEfTEZklt7g+qFAc+phYjWbVykN+iowMJPsxeu02a++OUKOsjXa0sm6iYOKdL7T3B9SZa0ZGh8cjTQw0UyArXxMTUc809/yb8nqWQ0qAbdu2oVu3bnC5XHA6nSgpacLgKUoJDCyQ9Rm50Qs1UsLhBJyZ8U1fOKz0KZxkyh9NXc8zlqPpiMySWxJ5pkKsZuAk03WDkpPRPGrWUg7OYsDRjFsgrW7iyErSi/e1Wct3VRXGZ0Zk5rIdRfFn5fZ6IuoZfdl314Y/NtLriSykoqICLpcLr732GoYMGYIOHTrE5XO0GRElJSVx+wyKLwYWKDnEeqSEfq1IqzaUiKykqSOCrDzKiSiWtP0Rat3qUhKsWyjVhbv+cx8DouDs3G4ya5YSpa7mlBfWQ5TiHK7f0LdNGvodno4OGbuB8t2ND6pY3+T3LykpgdPpxMSJEwEATqcTZWVlDC7YEAMLlBxiPVKCGyIRRaepZcbKo5yIYknL66xbiFThrv/xXq+eyK7sPGOG91eUaM25z2A9RKls73Z0X3gpvr8qD1hyFbAkzLEOpzobNEodOnRAWVkZKioqUFZWhokTJ6KiooKBBRtiYIEoGP06pnu3AQJ1Uz82gomCMzry1GgZ4qg2SkbRjpyLZrQcZ9pRMon3qOxgdQzrndTA3zk4T3X8P8POsy3IvjwudcZotPfzzK+UylyVSK+rwYS5Ltz9+KvoduyxoY91FgNF7Zu0FFiHDh0YSEgCDCwQBaOtSVpVARw6oD7m5g0IUUhGR54aLUMc1UbJKNqRc9GMlmOZoWTiiPPSE8HKC8tQauDvHJzHFf/P4CxVMoPb1bT7eeZXIpTt8qK6qCvQto/ZSSELSzM7AUSWlukEsvKBzHyOViBqqkwnkJYRXRlqymuIkk005YBlhsi4YOWFZSg18HcOjh2olKx4P09EFFecsUDWZ+aUZYcTKOLULLIBK0/tb8qIH44SIopuLW1tpl043IiQSBWsjgn2mJXrVmoati+Cc+SY87ksYxRvvJ+3JpZ9siIjm1FrSz+RDwMLYXi9XqSlcVKH6ThlmSgylhMiioQbERJFh3UrUXyxjBGlJpZ9shJnsZoP506OfKzDCVy3jMEFHQYW6m3evBmfffYZDh48iO7du2PUqFFIS0uDiEAxMlKw3qFDh3Do0CHf3/v3749HcpODiLHj9BsnRfFb+OhfoyjGP9eocO/H0aFNwnIUBS1/hysnsdzUVZ/fFSVymTRa3ppStikslqM4MJpPY53vRWIzsiteGxEmcTlnOULsf1+z84GRuksTrm61evvPQuXSUuXI7PxnJfp8iszYvncsyhj5YTmKA7POI9bXZ6P3emadb7DPZdknKylqrwYLXJXhj6tYrwYfXJUMLOgwsABg9erVOOmkk9C3b1+UlZWhqKgIrVu3xvvvv4/c3NyoggsPPfQQpk+fHucUp5jMXPt2ynN0aJOwHDVBuHLCESEpieUoycSiHEeztBIBYDlKeU1tg7L954flyKL86pUYBxaMsvN9XoKxHFFIdrzXY9mnIMrKylBSUoIOHUxYvqyoPYMFTZTy6/y4XC5cffXVOP/88zF//nysWbMGjz32GHbt2oUTTjgBv/32GxRFgdfrNfR+d955J/bt2+f7b/v27XE+A7I0bhDXJCxHMcZ8mJJYjpIMy7EpWI6oSVhe/bAcWRTzqa2wHFFILMsUjb3bgfIV4f8zstdADJWUlMDpdGLixIno1q0btm3bltDPp+ZJ+RkLhw4dwv79+zFy5EgAQFFREUaPHo3OnTvjggsuwJgxY/DDDz8YXhYpKysLWVlZiUh68kjmjXu4QVyTsBzFWLBNXZO53BEAlqOkE219wjIeEyxHFFK4Msb2nx+WI4vS51N3bcPjrkpAyWcethiWo2ZK5nYR6xwyau924OkBanmIoC49GxWuAwlIFNChQweUlZXh888/x8SJE1FRUWHOrAVqkpQPLBQUFMDr9WLhwoU466yzAACKoqBLly6YNWsWxo8fj+uvvx5PPfVUVHstUBSsPnUvmRshlLqaUu48LiBBjQsiaiYr1K2sPymZWaGMNYcV9oEga/LWAgcr6vMGr9+UJPTXbO1vXv8o1bgq1fr/7OeBkq5hD/3p553Yfu/YBCVMDS5069YtYZ9HsZPSgQURQXp6OsaPH4///e9/+PDDDzF2bEPB6dWrFy688EIsWLAALpcLTicbVXERr80kQ4m2o8PuN45EwTSl3GlloanYyUipyoy8n+i6NRjWn8mF13B/VihjzcF9IKzPrDKXlgGgjtdvSi76a7ZVr3+sZylRSroCbfuEPcTzq7Hl4FOSkaWinMUps2dDSgcWtBkIkyZNwscff4ynn34aTqcTJ598su/57t2747XXXkNVVRUDC/GS6Kl70XZ02P3GkSiYppS7TCfgqWv6Z7KTkVKVGXnfCtPiWX8mF17D/VmhjDUHy6f1mVXmnMWA4mb+oOQSeM22Yv5mPUtkbc5itWzOnRz5WIcTuG5ZSgQXUjqwAKizFjp16oTnnnsOF110ER555BFs2bIFl156KQ4dOoRly5ahbdu2yMnJMTupFCvR3kjZ/caRKFYcTsCZ2fTXsxODUlWq5n3Wn8klVfNxstLKJ5d6tS4zyxyv35TMrHr9Yz1LZG1F7dVggasy/HEV69Xgg6uSgYVkUltbCxGBw+HwPeb1epGWlgav14tevXrhzTffxN13340HH3wQd999N44++mj8+OOPWLBgAfLy8kxMPcUUG8pE5mDZo1TFvE/JgPmYKLFY5ohSC8s8kfUVtU+JYEE0UiKw8NNPP2H69OkoLy/H0UcfjVGjRuHCCy9EWloa6urqkJ6eDq/Xi+7du+O5557Dli1b8NFHH+GII47AkCFDcPTRR5t9CkRERERERERERERxsW3bNlRUVKCsrMzspJBNJH1gYf369SgtLcUZZ5yBkSNH4rPPPsPMmTPxySefYNasWUhPT4fb7UZmprq8R0lJCUpKSnD88cebnHIbEzF2nFlTD2P9uUbPlygeEl3e9O8jEvnzrTbFmCgRjOb7WNcfyVKvkrWkWn42iukjIPbtsFj/bk35XEUJ/Tqr3+cRAebd7+uPC3efZFY5J4pg27Zt6NatG1wuFwDA6XSipKTE5FSR1SV1YEFE8Morr2DkyJF49dVXAQC33HILZs2ahWeffRbnn38+3nzzTV9QYdasWRgxYgTat+e0FiIiIiIiIiIiIrK4vduNrf0f7umKCrhcLrz22mvo1q0bSkpK0KFDhxgm0hhttoRZn0/RSerAgqIo+OWXX/Drr7/6HnM6nbj88suRnZ2Np59+GnfeeSceeughLF26FH/961+xYMECvPTSS0hPTzcx5URERERERERERERh7N0OPD0A8LgiH+twAs7isId069YN/fr1i1HijCspKYHT6cTEiRMBqP23ZWVlDC5YXNIGFkQEiqKgX79+WLduHdauXYtjjz0WAJCTk4Px48dj/fr1WLhwIfbs2YPS0lLcdtttGDFiBIMKFHseF+B2AZlOIDPX7NQQxZY+f3PDMaLYYvkiu2BeJQqO9wFEqYt1IyWCq1LNa2c/D5R0DX+ss9iymw936NABZWVlvj0eJk6ciIqKCvsGFiLMEAFg6d/DqKQNLCj168yNHTsW999/Px555BE88cQTyM/PBwAUFBTgpptuwqOPPooFCxbgnHPOweTJk81MMiUztwvw1tY3KnhDQUlGn7/ZYCaKLZYvsgvmVaLgeB9AlLpYN1IilXQF2vYxOxXN0qFDB79AQllZmf2WRHIWq+V9roE+ZocTuG6ZrYMLSRtY0HTu3BlvvfUWTj31VDidTkybNs23+UhmZib69u2L4uLw04CImi3T2TBSgSjZMH8TxQ/LF9kF8ypRcCwbRKmL5Z+oSfTLItluSaSi9mqwwMieF3Mnq8cxsGBtw4YNw9tvv43x48ejvLwc48ePR+/evfHqq69ix44d6Ny5s9lJpGTn4NRnSmIOTu0lihuWL7IL5lWi4HgfQJS6WDdSc8VgU2Y70pZF+vzzzzFx4kR8/vnnpm4oHbWi9saDBTZfMiklAgsAcMYZZ2Dp0qW4+eabcccddyAjIwMOhwMfffQR2re35o9DREREREREREREScRIwMBVAbw5KWabMoezbds2lJWVNfn18dChQwcMGTIkeTd0TpIlk1ImsAAA/fr1w7x587B7924cPHgQbdq08S2LRERERERERERERBQ3e7cDTw8wHjCY+B/AGaHvshkj2rdt24Zu3brB5XLB6XRaqp802IbO2uyFQLaZzaCJdsmkbV9FPtaEmQ0pFVgA1E2bCwoKEvZ5IgIA2L9/f8I+03T159yIxwW4q4HMHPXiWL/BdrzVuGtRW6NesPfv3w93ZoyzfajzDZSg87UTrVxIhO8wJcuREe4q/zIVTozyX9Tlifk+7lKtHMX9mp5IRuuPwPozFJa3Jku1chQXRvNzKHFsJybVdcPCUrIcJcl9gOEyYrTtafHztbKULEdWp/8twrTJDJcjlo+4i7ocbfgKyEvgknWVG4GDVcAZfweKjw5/bE5LoOgIY+/bxOvBli1b4HK58Nxzz6G0tBRFRUVRX1vi2dYqKipCUVERsrKykJOT45u9ECgnJwfffvutvValSSsE8grDH1ObCdRlA29cGfn9MnKAs59r1uwVzf6DVQAilyNFIh1BzbJjxw57ZWoiE2zfvh1HHBG6smQ5IoqM5Yio+ViOiJqP5Yio+ViOiJqP5Yio+SKVIwYW4szr9aK8vBz5+flQEhyZ3r9/P9q3b4/t27cndJZGoiTz+SXzuQEN57dt2zYoioK2bdsiLS0t5PFmlKNk/w2A1DhHIPnPU0Rw4MABy5UjO33vTGt82CWtWjp/+uknHHPMMc0uR3Y5b42d0muntAKpl16r1kdNkWq/XSLZKa1A4tNr13Jkt981Ep6PtUU6HzPKUap9x3aTTOeTqHMxWo44DzjO0tLSwkZ2EiHRyz8lWjKfXzKfGwAUFhYaOj8zy1Gy/wZAapwjkNznWVgYYfokzCtHdvremdb4sEta27VrF7bRDERXjuxy3ho7pddOaQVSK71Wro+aIpV+u0SzU1qBxKbXzuXIbr9rJDwfawt3PmaVo1T6ju0omc4nEediqBzFNQVERERERERERERERJRUGFggIiIiIiIiIiIiIiLDGFhIYllZWbjvvvuQlZVldlLiIpnPL5nPDbDH+dkhjc2VCucIpM55Wo2dvnemNT7sktZYp9Mu562xU3rtlFaA6bUzu30XdkqvndIK2C+9Zkm274nnY21WPB8rpqk5eD7WZbVz4ebNRERERERERERERERkGGcsEBERERERERERERGRYQwsEBERERERERERERGRYQwsEBERERERERERERGRYQwsEBERERERERERERGRYQwsEBERERERERERERGRYQwskO2JiNlJICIioiTCtgUREemxXqBkkoz5ORnPyUqS7ftNtvMxEwMLZFu//fYbAEBRFJNTQqmirq7O7CTERG1tLTwej9nJoBSRLOWGmua3336zzfVGn1cVRYHX643p+8f6/eLNyjdcO3fuxLJly8xORpPZLS+kCraPKBjec0Yn2dt9dr9+u1wuAGp+tnI9b1S8226xYMU0RSOZ8owd8osdMbCQYux+IdCsXLkSffr0wYIFC8xOSlxs27YNH3zwAV544QXs3LkTVVVVZicpptatW4cvv/zS7GRE5aeffsKzzz6L6upqs5PSLD/99BMmTJiA4cOH47LLLsMbb7xhdpLizo75LVls3LgRzz33HH755RezkxJRsBthq9aZ5eXl+OyzzzB79mxs2bLF7OSE9MMPP+Dwww/HF198YXZSIlq/fj2uvvpqXHDBBbjuuusAAGlpTW8mb968GS+88AIef/xxfPLJJ773s2qe2rx5M5588kk88MADeOeddwBYtxPtxx9/xNChQ7F48WJbXFu2bNmCf//733j66ad97VYr54VUZff2kd3bOlYtD8l+zxlrdmr3GWG3ujySNWvWYNCgQfjPf/4DwP4dxbFuu8UC84x1WTG/JAt+i0lq3bp1uPnmm3HBBRfg4Ycfxvfffw/A3hcCzcqVKzFw4EBcfPHFGD58uN9zdj83QL1hHjBgAO655x7ceuutOOGEEzBt2jRs3rzZ7KTFxIoVK9CvXz9fnrSDlStXomfPnti/fz9ycnIA2DOvrV+/HqWlpcjMzMTIkSPx888/Y+bMmbjsssvMTlrc2DG/JYsff/wRJ5xwAjZv3uwLyGmjQqxWfsrKynDjjTfi1FNPxV133YV58+YBsGbH6qpVqzBkyBDcc889mDRpEi6//HJ88MEHZierkZUrV+Kkk07ClClTMGzYsEbPWykPrF69GqWlpaipqUFWVhYWLVqEO++80/d8tGldvXo1jj/+eMyePRszZ87ElClTMHz4cFRVVVmyHbZq1SqUlpbif//7H15++WXMnDkT7733ntnJCmrTpk0YMWIEzjjjDNx8881o166d3/NWG3m2atUqDBgwAG+99Rb+7//+DzfffDNGjRpl2bywceNGPPDAA7jwwgsxa9YsbNiwwewkJYTd20d2auvY6R412e85Y81O7T4j7FaXG/Hyyy9j48aNmDFjBt566y0A1ix7RsS67RarNDHPWJMV80tzWarNJpR01qxZI4WFhXL66afLxIkTpU2bNjJkyBB57LHHfMd4vV4TU9h0a9askezsbJk2bZqIqOexdetWWbZsmbjdbtuel2bPnj3yhz/8QW699VbZvXu3iIhMnz5dhgwZIuPGjZMNGzaYnMLmWbFihTidTvnLX/4S8hir/YYrV66UnJwcuf322/0er6urExHrpTcUr9crU6dOlXPPPdf3WFVVlTz11FPSq1cvOe+880xMXXwYyW8UH+Xl5dKlSxe55ZZb/B4/cOCASSkKbc2aNdKiRQu55ppr5MYbb5QLLrhAsrOz5fHHH/cdY5VyvnHjRmnfvr1MnTpVKioqZOvWrVJaWiqTJk0yO2l+Vq1aJU6nU+6++24RUb+/srIymT9/vmzatMl3nHYdNdPevXtlwIABcvPNN4uISHV1tVx77bW+dka0qqqqZPDgwXLNNdeIiFqvf/TRR9KzZ0/p0aOH/PrrryJijXMXEVm3bp20bdtWpk6dKl6vV3777Tfp1auX/POf//Q7zirpnTFjhpxzzjkioqbpmWeekenTp8uDDz4otbW1JqfOX0VFhRx33HG+9sPevXtl1qxZoiiKDB06VH777TcRsc53u2rVKmndurWcddZZMnz4cOncubPcdNNNUlNTY5lrYDzYvX1kp7aOne5Rk/2eM9bs1O4zwm51uVH33XefDB48WG644Qbp1q2bzJ492/ec1erQcGLddosF5hnrsmJ+aS6rtdkYWEgybrdbLr74Yrniiit8j23dulWuvvpq6devnzzwwAO+x+3WINq7d6+UlpZK+/btfY+dd9550rNnT8nOzpYuXbrICy+8IHv37jUxlc2zdetW6dixo/zvf//ze/zll1+WoUOHykUXXSTl5eUmpa551q1bJ1lZWTJ16lQRUfPq3Llz5cknn5TZs2f7brCtZO3ataIoilx//fW+x5544gmZPHmyXHPNNfLRRx+ZmLroXXrppXLiiSf6PeZyueSFF16Qvn37yh133GFSymJv/fr1tstvyWT+/PkycOBAqa2tldraWrnxxhtl1KhRMmTIEPn73//uO87seqi6ulrOP/98uemmm3yP/fzzz9K1a1dRFEXuv/9+3+Nmp7WmpkZuueUWmTBhglRVVfka8/PmzZN27dpJRUWFqenT1NTUyOmnny5paWm+x8aOHSt/+MMfRFEUOe644/zaKGZ/rxs2bJBu3brJ8uXLfY9dddVVUlpaKmPGjJFx48bJL7/8IiLG0rp7927p1auXzJ071/eY1+uV9evXS79+/aRPnz5+j5uppqZGpkyZIpdddplfR9mECRPk2muvlSlTpsjMmTN9x5udXhGRP/3pT766asCAATJ06FA54YQTpGPHjtKlSxfZsmWLiFjjxn3lypXSs2dP2bhxo++xHTt2SLdu3aR169bSv39/E1Pnb/v27dK9e3e/dsCsWbOkRYsWsnnzZvMSliB2bR/Zqa1jp3vUVLjnjDW7tPuMslNdHo1FixbJjTfeKOvWrZNJkyZJ9+7d5ZNPPpEHHnhAvvzyS9ucS6zbbrHAPGNdVswvzWHFNhuXQkoyDocDO3fu9E3lERF06NAB9957L4YOHYr//ve/+Pe//w3Amks8hFNYWIg//vGP6NKlCy655BIcf/zxcLlcuP/++7Fy5UqUlpbi4YcfxqeffgrAntOZ0tLSkJOTg/LycgDqJnIAcPHFF2PChAlYvXq1Lc9PRPDMM8/A6XSiT58+AIA//vGPmD59Op544glMmjQJV155JRYtWmRqOgP9+uuvAICSkhLs3LkTQ4YMwZw5c7B582Zs2bIFY8eOxdNPP21yKiPT8kq/fv1QV1eHtWvX+p7LycnB+PHjMXLkSCxcuBC///67WcmMmdraWjz11FPIy8uzVX5LJtu3b0d6ejrS09MxYsQIbNiwAQMGDMCAAQNw00034ZZbbgFgfj2kKAo2bNiA9u3bA1D3WTjqqKMwYsQInHPOOZg+fTpefvll37FmEhE4HA4MHz4cTqcT6enpANTrU3V1Ndxut6np0zgcDtx1113o0qULBg8ejFGjRiEtLQ2PPfYYVq9ejUsuuQRff/01brvtNgDmf6+FhYWoqanBM888g8rKStx333146aWXMGbMGIwaNQq//fYbRowYAY/HYyitBQUF8Hq9WLhwoe8xRVHQpUsXzJo1Cy6XC9dff73vcTNlZGTgvPPOw4033giHwwFFUfDggw/ijTfegMfjwbZt2/Cvf/0L48ePt0R6AbUcrFy5Em+++SZatmyJ999/HwsWLMA333yDVq1a4ZxzzgFgnTVz9+/fj1WrVvn+PnjwIDIzM/H4449j165dePTRR01MnUpEsGDBAnTr1g1XXXWVb+mSiy66CO3atcPWrVtNTmH82Ll9ZLe2jp3uUVPhnjPW7NLuM8pOdXk0HA4HPv30U3To0AG33347hg0bhvPPPx/33HMPunTpYpslbmLddosF5hnrsmJ+aSrLttkSH8ugeDp06JBcdtllcuaZZ0p1dbV4vV7fqK2tW7fKqaeeKuPGjTM5ldEpLy+XH3/80ff3448/Lt27d5cxY8Y0Gr0/evToRiOOrK6qqkrcbrfv73HjxkmfPn1kz549IiLi8Xh8z5177rkyaNCgRCexWbZv3y4bN26Ubdu2yZ/+9CcZOHCgtG/fXk477TRZt26d1NbWyqpVq6RHjx6+5Q2sQItWv/fee6IoirRo0ULGjx8v27dvFxF1pOfDDz8smZmZ8u2335qZVMM2btwoJSUlctlll8n+/fv9nisvL5e0tDR55513zElcjK1fv95W+S3ZLF26VIqKiuTRRx+VsWPHyo4dO3zPzZ07V9LS0uS///2viSlUHTx4UMaNGyc33nij/P777yKizlho1aqVzJ49W6688koZPXq0rz41m5ZGEfGr24899lhfnSEismzZskQnrZHvvvtOunfvLn/4wx98o4BEGqYfDx482BJLJLjdbvnHP/4h7du3l9GjR0tOTo7fNO+ff/5ZioqK5M0334z4XloemTZtmgwaNEg++OCDRs/fd999MmTIEKmqqortiTSRvo2xfv16adu2rbz//vu+x5577jnp3LmzrFu3zozk+Wjf7eLFi6W0tFQGDRokkydPFpGGsvDNN99I+/btLVMn79q1S4YNGyZnnXWWPPLII/LBBx9IUVGRTJkyRUTUNp1+9LaZ/ve//8n//d//+T1WU1MjRx55pLzxxhvmJCqB7No+slNbxw73qMl+zxlPdmn3GWHHutyoPXv2SGlpqW/W62mnnSa5ubly1FFHybvvvmty6oyLZdstFphnrM1q+aW5rNhmY2AhCVRWVkpZWZmsX79eRES++uorSU9PlyeeeMJ3jNZwW7ZsmSiKIj/88IMZSY3ajh07pLi4WM466yz5+uuvfY+/9NJL8t///td3EddujO+44w5bNfJWrVol48aNkyVLlsjBgwdFRL0RPeqoo2TkyJFy6NAhv+Off/55GThwYKPHrWr16tVyxBFH+JYZKSsrk0mTJsnpp5/uy6+a+fPni6Iofg16s9TW1orX6/Xlrw8++EB69eolS5Ys8Ttu48aN0qpVK1vddC9YsECysrLkuuuuk127dvker6iokD/84Q+ycOFC8xLXTIHrPG7cuNEW+S3ZeL1e2bNnj1x88cXSt29f6d27t99zBw4ckL59+8qTTz5pYiobPPXUU9K1a1cZNWqU3HjjjeJ0On3ro86ZM0datWrlV1asQB/k2LBhg7Rp08bX6TF16lTp06eP6Wn2er3yww8/yMcff+yro7W2yN/+9jfp1auXZW6uPB6P7N69W9asWSM9evSQbdu2iYh6DuvWrZNu3bpFdW3ctGmTDBw4UMaOHdvodW+++aZ07tzZL0hkJVq+0X6ruXPnSrdu3XxrA5tt165dcskll4jD4ZBRo0b5Pffjjz9Kt27dZM2aNSalroFWRletWiVnnXWWdO3aVbp06eJbskZE5Nprr5UxY8aYlcSQtLR7vV7p27evzJkzx/fc66+/bpnATazZpX1kp7aOne5Rk/2eM57s1u4zys51eTjDhg2Tr776Si655BJp27atvPHGG3L11VdL69atbdNRLBL7tlssMM9YlxXzSyxYpc2WYc48CYqV1atX4+KLL0ZtbS3Wrl2LqVOn4r777sPDDz+MKVOmICcnB5MnT/ZNCc/Ly0P37t3hdDpNTrkx69evx759+7Bv3z4888wzUBQFAwYMwCWXXIKamhrfVKWMDDUr//LLL+jRowe8Xi8URbH0VKY1a9Zg6NChuPDCC9GpUyfk5uYCUJe1eP3113HOOedg1KhRePbZZ9GxY0dkZ2dj2bJlyM/Pt/x0MwC+qcKtW7fGG2+8gdtvvx3HHnssZsyYgbVr1+LII48E0DB9uKamBl27dkXr1q1NS/Nvv/2G1q1bIz09HXV1dUhPT4fX68XYsWPRs2dPtGrVypdmRVGQlpaGtm3bok2bNqalOVrDhg3D22+/jfHjx6O8vBzjx49H79698eqrr2LHjh3o3Lmz2UlskvXr1+P999/HRRddhMMPPxwA0LlzZzzwwAMoKyuzZH5LVoqioKioCGeccQa++uorbNy4EZ988glGjRoFRVGQl5eHFi1aIDs729R0auX4uuuuQ0FBAZYsWYLy8nI8/PDDuOGGGwAA6enpaNeuHXJyckxPp57+7+rqauzfvx8OhwP3338/HnnkESxduhQlJSWmplNRFPTu3RtAw7I02r8bNmzAcccdB4fDkdA0agLTmpGRgRYtWgAAMjMzsXjxYkycOBGKouCNN96Aoijo2rWr4ffu1KkTnnvuOVx00UV45JFHsGXLFlx66aU4dOgQli1bhrZt25qap4LRvpPi4mIADb/V0qVL/dooZhIRlJSUYPr06di/fz8++ugjXHPNNfjHP/6B3bt3491330V2dravrjaToijwer3o2bMnXnrpJYgI9u7di44dOwJQz+XXX3/1lREr0cqGoijIzc31XavvvPNO/OMf/8Dy5cvNTF7c2KF9ZKe2jt3uUZP5njPe7NLui0TfNrBrXa4X2Nbxer1IS0tDSUkJTj31VBQVFeGDDz5Anz590LVrVzgcDvTs2dPEFIcXz7ZbrNJn9zwTyO55Rs9q+SVWLNNmS0j4guJizZo1UlxcLLfccousWbNGHn30UVEURbZt2yYej0emTZsmiqLInXfeKd99953s2rVL7rjjDunUqZNlRp5FUllZKePGjZNnn31W+vXrJxMmTJBVq1aJiP/GfNXV1TJ16lRp1aqVrF271qzkGnbw4EEZNWqUb1SsiDqa/4cffvBNHV29erV0795dOnfuLMcff7ycccYZkp+fLytWrDAr2YatWLFCcnJy5K677pJdu3ZJjx49ZMaMGb5RVsGWFLntttvklFNOMW0jtLKyMnE4HHLGGWf4HgscFRbozjvvlJ49e8rOnTvjnbyYW758uZx00knSoUMH6dSpkxxzzDHy/fffm52sJtmwYYO0bNnSd70LHKltxfyWzPTf95tvvik9evSQI444QmbNmiWLFy+WO+64Qw4//HD5+eefTUylKnCD18Ayf+2118qIESN8M8oSpby83PCo602bNknfvn1l8uTJkpWVJd99912cU9cgmnSKqHX6nXfeKa1atUr4qHIjad23b5+MHz9eBg4cKEOGDJELLrhAiouLg46g9Xg8fssYijTkJ+3fNWvWyFlnnSVHH320tGvXTk466SRp0aKFKSNyw6U3GO23Ki4uTvhoZyPf7fbt2+XWW2+Vww8/XIqKiqRfv37SunVrU+qxaL/bDRs2yF133SUtWrSQsrKyeCevydxut3Tv3l3mzp0rM2bMkJycnKSdraBn1faRndo6drxHTdZ7zkSwU7svULi2gRXr8kiMtHUWLFggAwYMaHQ9r6mpiWfSmiTWbbdYsFv7LxIjbRi75Bkj55Lo/JIoZrfZGFiwqV27dsnQoUPlz3/+s+8xr9cro0ePlqVLl8qKFStk69atMm/ePDn88MOlTZs2cuyxx0q7du0s0Tg2ora2Vn7//Xfp2rWr7NixQ+bOnSv9+/eXyZMnS2lpqW/N0Pfff19OOeUUW51bTU2NnHjiifL9999LbW2tjB49Wvr37y/5+flywgknyAsvvOA79sknn5Q77rhDpk+fbosG7MqVKyUrK0vuuusuEVEv5ueee67079/fd4y+Abp69WqZOnWqFBQUmDZVu7y8XEpLS2XgwIHSqVMnOfvss33PBQsufPPNN3LNNddYtoFg1L59+2Tz5s2yatUq05dNaaqDBw/K5ZdfLpdeeqk89dRToiiK3HrrrX7no89vq1atMj2/JQv9EgeBy7PpG3GfffaZXHXVVZKdnS29evWS3r17J/xaHS6twSxfvlyuvvpqKSwslJUrVyYghQ30yzEYaRCuXbtWFEWRwsLChH6v0abzo48+kosvvtiUutpIWrXrxJYtW2TmzJly3nnnye233x603l2zZo2cd955cuKJJ8qll14qr7/+uu85rc7QysCuXbvk22+/lfvvv19efPFF2bBhQ6xPLyIj6dX75JNP5E9/+pMcddRRCa/jovluq6qqpLy8XF544QX58MMPZcuWLQlNq9H06v32228ybdo0ad++vSlt1kgDJvQ8Ho+UlpbKsccemzJBBY3V2kd2auvY8R41me85Y8lO7T4jjLQNrFSXRxLN+eiXorTCHmLBxLrtFgt2a/9FEm2bS2PFPGPkXBKdX5rLTm02BhZsqqKiQv7617/6raV5//33i6Ioctxxx0n79u1l1KhRsmnTJikvL5fFixfLJ5984reRktVpBX/ChAny8ccfi4i61n1JSYnk5+fLrFmzRES9yM2YMcOyF4Rgfv31V2nVqpV88sknMmXKFBk9erSsWLFCPvroI7n11lulTZs2fhdDO1m2bJncc889ItJQEa1du1YKCwvlmWee8Tt28+bNMnr0aDn66KNN7aB//fXX5eyzz5ZFixbJnDlz5Mgjj/QLLug3t9y6das89NBDUlpamvAOR2rM5XLJ008/7duA6c033wx6wy2i5rcxY8ZIp06dbB0QsoJVq1ZJ3759pVevXpKVleU3I0mjLzciIr/88ov8/vvvsnv37kQm1VBa9Q4ePCgffvihnHLKKabMEFuwYIFkZGTI8OHD5eKLL5bly5f7nqurq2s0Emfnzp1y9tlnJ3zkc7Tp3L59uzz//POyadOmhKZTxHhaA9fQDjbqfN26dVJYWCgTJ06U6dOny9ChQ6Vv375y6aWX+o6x0j5ITUnvL7/8Iq+++qps3rzZ8mk1U1PSe+jQIdm6davfhuaJsm7dOnn00UcbbUKrp+8scLlcMnjwYCkpKWF7x2R2auvY8R41me85Y8VO7T6jom3HWF205xNuZp0VxLLtFgt2a6NEYuR8AmckWDXPRPPbJCq/NJfd2mwMLNjY/v37ff//xhtviKIoMnv2bKmsrJRFixbJ8ccfL/fee6+JKYyNiy++WO644w4REbniiiukRYsW0r17d7n88stl6dKlJqeuabxer1xwwQVy/fXXy+mnn+5rxIqonS8TJ06Uq6++Wjwej+9iZ8XIsBFer1f27t0rZ555ppx33nm+jZE1K1eulK1bt5qYQvVG4b333hMRtZJ56623GgUX9I3nnTt3SmVlZcLTScEFLlMze/ZsURRFbrnlFqmoqBCRhtFomzdvNj2/2V24JQ40wa5XZjTcmprWmpoaOXDgQCKT6hNsOYbVq1eLiP93+OKLL/o6fs24kYkmndpIcrMa79GkVX99CMwbXq9Xpk6dKueee67vsaqqKnnqqaekV69ect555/kd/+KLL/rltURrSnq13yrRbY5U+G7NrHsiLaMjEvw3f/nll2XdunWJSCJFYKe2jl3vUZPxnjMW7NTui0Y0bQMz6xujUvl8wrXdYsFubZRIkul8mtses2Ifmx3bbAwsJIktW7b4RXFFRM444wy/9eLtRissL730ktx7771yzTXX+NZnnDt3rnTu3Fmuvvpqqa6utuQFIZJvv/1WcnNzRVEUmTdvnt9zf/nLX2To0KG2PK9Q/vOf/4iiKPLFF1+YnRQ/wRq91dXV8vbbbzcKLvzzn/+UjRs3JjJ5FAV90Eq7kb311lvll19+kSlTpsiZZ54p1dXVJqfS3kItcTBmzBhZunSp/PDDD7J9+3bfcw8//LDcf//9JqS0aWmdPn26CSltYHQ5hiVLlkiXLl1kwoQJ4vF4El5X2CWdTUnrxIkTw85oufTSS+XEE0/0e8zlcskLL7wgffv29XVKffnll3L00UdHfL94iza9Zv5Wyf7dmpVeI8vo6D3yyCMybdq0BKeSjLJbW8cO96jJfs/ZHHZq90Uj1m0Ds/F84n8+dqnzjUqm80mmc7Frm42BhSTk9XqlpqZGLrzwQnnwwQfNTk6zLV68WBRFkTZt2vhtSvnOO+9YchOoaCxZskQURZHTTz/dF4EXEbnxxhvlyiuvtN0UzHAOHToko0aNkgkTJojL5TI7ORG5XC7fskjnnHOO/PnPfxZFUWyf55Kd1+v1BYtmz54tDodDjjnmGMnIyLDk+q52E26Jgz59+sgRRxwho0ePls8//1wOHjwo559/vgwaNMiUGT52SqvG6HIMIiLPPvusadcju6RTJHZp1d7n73//uwwaNKjR0lP79u2T2267TU444QTfsg/PPfec6b+RHdJrp7SK2C+90SyjU1lZKeeff76ccMIJvlHwZD12betY/R41me85m8qObSkj7NSOMYLnE7/zsVudH0kynU8ynYvGrm02BhaS1D333CMdOnTwawTYldvtln/961++tcKSbaTI4sWLpW3btjJgwAC54oorZNKkSVJYWCirVq0yO2kx99BDD0lBQYHs3LnT7KT4CVwTVFNdXe2bat6yZctGI67Imrxer+86MXz4cGnZsiU3ao6hcEscLF68WAYMGCD33XefiIj8/PPPYdeGjDc7pVUv3HIMX375pcmpa2CXdIrELq0bN26UkpISueyyy/zyl4hIeXm5pKWlyZw5c2Ka9uawU3rtlFYRe6XX6DI6e/bskcrKSstcCyk0u7Z1rHyPmuz3nE1l17aUEXZqxxjB84kfO9X5RiTT+STTuYjYs82WAUoqc+bMwaJFizB79mx8+umn6NKli9lJajaHw4FLL70UaWlpAABFUUxOUWwNHToUCxYswGuvvYavv/4aXbp0wRdffIGePXuanbSYEREoioKrrroKc+bMQU1NjdlJ8qmrq0NGRga2bNmCjz76CFdeeSUcDgcAIDs7G/Pnz4fT6cTnn3+O7t27m5xaMkJRFNTV1eHWW2/FwoULsWLFCvTq1cvsZCWN/Px83/8PGjQI3333Hfr16wdAvZ61bt0ay5cvh4jgqKOOMiuZAOyVVqDhWjl8+HD8/PPPuPbaa/Hhhx9i+fLlWLFiBW699VZkZmaiX79+yMrKMq0+tEs645HWzp0746233sKpp54Kp9OJadOmoaSkBACQmZmJvn37ori4OBGnZoid0muntAL2Sm9ubi4Atc2TlpaG888/HyKCiy66CIqi4KabbsLMmTOxZcsWzJ49Gy1btjQ5xRSJ3do6drhHTfZ7zqayW1vKCDu1Y4zg+cT/fOxU5xuRTOeTTOcC2LPNxsBCkunWrRvefvttLFmyJKk6QbUGXrI65phjMGPGDHi9XgDJd75aZV9UVITFixf7LpZmq62t9QUVjjnmGFx44YW4+uqrfc9/+OGHWLx4MRYvXpxU5SlV9OjRA99//z169+5tdlKSVseOHdGxY0cA6k2A2+1GXl4eevbsabmbFjukVUvHUUcdhcsuuwytW7fGf//7Xxx11FE46qijoCgKjjvuOGRnZzOdBsUjrcOGDcPbb7+N8ePHo7y8HOPHj0fv3r3x6quvYseOHejcuXO8TqdJ7JReO6UVsF9609PTISLwer244IILoCgKJk2ahHnz5mHTpk1YtmwZsrKyzE4mRcEubR273KMm2z1YrNmhLWWEndoxRvB8EsNudX4kyXQ+yXQuGju12RQREbMTQbHl8Xh8I66JKDh9UKFfv34466yz8OyzzyIjoyHeWl1djX379qFNmzYmppSaShvtQolz77334uWXX8b8+fMtORpRz8pp9Xg8ePXVV3H88cejd+/els3LdkknEJ+0fv/997j55puxefNmZGRkwOFw4I033kDfvn1jlOrYslN67ZRWwH7p1W7/FEXBKaecghUrVmDRokWWHvFOwVn5uhuI96jJx8ptKSPs1I4xgueTGHar8yNJpvNJpnPR2KHNxsACEaWcwKDCuHHj8MILL/gFFbxeL0ctERkUuMSBlRtvdkmrXa5BdkknEJ+07t+/H7t378bBgwfRpk0b39Rrq7JTeu2UVsB+6dWW0Xn88cexYsUKy494JyLrsEtbygg7tWOM4Pkkht3q/EiS6XyS6Vw0Vm+zcSkkIkop+j0VQgUVAE6FJoqGXZY4AOyTVrtcg+ySTiA+aS0oKEBBQUHM3zde7JReO6UVsF96Afsso0NE1mKXtpQRdmrHGMHzSQw71vnhJNP5JNO56Fm5zcYZC0SUcrZu3Yru3bvj/PPPx/PPP4/09HSzk0Rke3Za4sBOaSUiiherLCtBRPbDthQRUeJYuc3GwAIRpZS6ujrccMMNcLvd+Oc//9lopgIRERERERERERGFx8ACEaWcvXv3oqCgwLJTK4mIiIiIiIiIiKyMgQUiIiIiIiIiIiIiIjKMw3WJiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIiIiIiMgwBhaIiIiIiIiIyJAjjzwSRx55pNnJILKdSy+9FIqiYMuWLWYnhch2Fi1aBEVRMG3aNLOTQjoMLJDtXXjhhVAUBbNnzw57XGVlJbKyslBSUgK3252g1BFZ1xdffAFFUXDGGWcEff6qq66Coijo27dv0OdnzJgBRVHwyCOPxDOZRLayZcsWKIri95/D4UC7du1w3nnn4bvvvjM7iURElITYriOKPbbriIjCyzA7AUTNdcUVV2D27NmYNWsWLrjggpDHvfbaa3C73Zg0aRIyMzMTmEIiazrhhBOQm5uLJUuWoK6uDunp6X7PayMCVq5cid27d6Nly5aNngeAYcOGJSrJRLbRuXNnTJw4EQBQVVWF5cuX4+2338a7776L+fPnY+jQoSankIiIkgnbdUTxw3YdEVFwnLFAtnfKKafgyCOPxPz587F9+/aQx82aNQuAGoggIsDhcGDw4MHYv38/vv/+e7/ndu7cifXr1+Oss86CiGDx4sV+z7vdbnz11VcoKChAv379EplsIls4+uijMW3aNEybNg0zZ87EggUL8NBDD8Hj8eCee+4xO3lERJRk2K4jih+264iIgmNggWxPURRcdtll8Hq9ePnll4Mes3z5cqxcuRIDBgxAp06dkJ+fj6OPPjrke3bt2hX5+flwuVzxSjaRJWij0rRRahrt77/85S/Iy8tr9Pw333yD6upqDB06FIcOHWKZIjJAC2wvX77c99jw4cORlpaGbdu2BX3N5MmToSgKPv/884SkkcgK3G43nnzySYwePRrt27dHVlYWDjvsMJx99tn44YcffMe98sorUBQFM2bMCPo+X375JRRF4aASShmxaNfpZzq899576N+/P3JyctC6dWtMnjwZe/bsiecpENlGsHadZs2aNTj99NORn5+PwsJCjB07FqtXr050EokswWi7TlNdXY077rgD7du3R3Z2Nnr27Innn3/ehJSTEQwsUFK47LLLkJaWhpdeegki0uh5/WwFp9OJs88+G5s2bcJXX33V6NhvvvkGGzZswDnnnAOn0xn3tBOZSbsBXbhwod/jCxcuRH5+PgYMGIDBgwcHfV57PcsUUXQyMhpWopw0aRJEBP/+978bHXfo0CHMmTMHRx55JE488cREJpHIVLt378ZNN92EQ4cOYezYsZgyZQpOPvlkfPjhhygtLcW3334LADj77LPhdDqDlh9AXQYTUMsZUSqIRbtO88orr+DMM8/E+vXrMWnSJFxyySX48ssvMWLECO5XR6Sjb9cBwOrVq1FaWoqPPvoIY8aMwXXXXQe3243Bgwfj559/NimVROYx2q4DAK/Xi3HjxuFvf/sbWrRogT//+c8YOHAgpkyZgscee8zEs6CQhChJjB49WgDIokWL/B6vqamRFi1aiNPplH379omIyKeffioA5Nprr230Ptdff70AkPnz5yck3URmqq2tlfz8fMnPzxePx+N7vEuXLjJmzBgREfnrX/8qiqLIrl27fM8PGzZMAMj3338vIixTRJrNmzcLABk9enSj52bMmCEA5LTTTvM9tn//fsnJyZHu3bs3On7OnDkCQO6+++64ppnIampqamTHjh2NHl+9erXk5eXJiBEjfI9NmDBBAMiyZcv8jnW73VJcXCzt27cXr9cb9zQTWUGs2nX79u2TgoICyc3NlXXr1vmOc7vdMnToUAEgHTt2TMxJEZko2nadiMhJJ50kAOS1117ze/zOO+8UAAJANm/eHM9kE1lKNO26WbNmCQAZM2aM1NbW+h7/8ccfJTMzUwDIfffdl4hkk0GcsUBJ4/LLLwcAvPjii36Pv/POO9izZw/Gjx+PgoICAOrSE23btsVbb70Fj8fjO7a2thZvvvkm2rVrx43LKCWkp6djyJAhOHDggG8ab3l5OTZs2ICTTjoJAHDSSSf5rcfrdrvx9ddfo0WLFjjuuOMAsEwRBdq4caNvLd5bb70VJ598Mu655x4cdthhmDlzpu+4/Px8jBs3Dj/99FOjqcDaaGtts0CiVJGVlYV27do1erxHjx4YNmwYlixZ4qtrtPKhlRfNhx9+iMrKSkyYMAGKosQ/0UQWEKt23bvvvov9+/fj8ssvR9euXX3v73A48OCDDyb4rIjMZ7Rdt23bNixevBi9e/fGhAkT/N7jrrvuQlFRUYJTTmS+aNp1r7zyCgDgwQcf9Fuar1evXpyBalEMLFDSOPPMM1FcXIw5c+bgwIEDvse1QIMWeACAtLQ0XHTRRaioqMDHH3/se/zjjz/Grl27cNFFFyEtjcWDUkPgtHlt3d2TTz4ZANC/f384nU7f819//TWqq6tx8skn+8oJyxSRv02bNmH69OmYPn06Hn30USxevBiHHXYYPv/8c3Tr1s3vWK2RrO8Y3b17Nz788EP0798fxxxzTELTTmQFK1aswEUXXYQOHTogMzMTiqJAURS8//77cLvdqKioAACMHDkSbdq0wezZs1FXV+d7/auvvgqAyyBR6olFu27lypUAgCFDhjR6/0GDBjVa+oUo2Rlt12llJ9gSlnl5eejTp0+ikkxkKUbbdStXroTT6US/fv0avUewOonMx14eShqZmZmYOHEiXC4X3nrrLQDA9u3b8dlnn6FLly4YOnSo3/HajaZ+XV6uxUupaPjw4QAabjwXLlyI3NxcHH/88QDU0WmDBg3yex5AoxkILFNEDUaPHg0RgYjg999/x8yZM1FRUYEzzzwTBw8ebHTsYYcdhjfeeANerxcA8NZbb8HtdrPsUEpaunQpBg4ciLlz56JPnz644YYbcO+99+K+++7zjag+dOgQAHWE9oUXXojff/8dn376KQBg3759+OCDD9CvXz90797dtPMgMkMs2nX79u0DABx22GGN3j89PR3FxcXxSj6RJRlt14UrOwDQunXrhKSXyEqiadft27eP5cdmGFigpHLFFVcAaJil8NJLL8Hr9frNVtD07t0bvXv3xrx583DgwAEcOHAA8+bNw3HHHYdevXolNN1EZurTpw9atGiBL774ArW1tVi0aBEGDx7sNxrt5JNPxpo1a/D777/7bkQDAwssU0TBtWrVCrfccgvuuusulJWV4e677/Z7PiMjAxdccAF27tyJBQsWAFCDctrjRKnmwQcfxKFDh/DZZ59h3rx5eOyxxzB9+nRMmzYNbdq0aXR84Kyft99+GzU1NQzMUUqKRbuusLAQAPD77783ev+6ujpUVlbG9ySILCxcuy5c2QGA3377LSFpJLKSaNp1hYWFLD82w8ACJZVevXqhf//+WLp0KdauXYuXXnoJ6enpuOSSS4IeP3HiRFRXV+M///kP/vOf/6C6upprWVPKSUtLw9ChQ1FVVYV3330XGzdu9K3Dq9H+/uSTT/D111+jVatW6NGjR6P3YpkiCu2uu+5C27Zt8cwzz2DLli1+z+nXid+8eTOWLl2K0aNHo1WrViaklMhcmzZtQsuWLTF48GC/x10uF77//vtGx/ft2xfdu3fHu+++i6qqKrz22mu+mQxEqSYW7TptBOnnn3/e6P2/+uor1NbWxvEMiOwhWLtOKztffPFFo+MPHjyIFStWJDCFRNYQTbvuuOOOC9neC1YnkfkYWKCko81auPLKK/Hzzz9j7NixOPzww4MeO2HCBKSlpeG1117Dq6++6lsnnijVaKPUpk+fDqBhHV7NgAEDkJ2djb/97W+oqanBySefHHQzTJYpotBycnJw++23w+PxYMaMGX7PaXspzJ07F88//zxEhKOtKWV17NgRe/bswZo1a3yP1dXV4ZZbbsGuXbuCvmbSpEmoqqrCE088gSVLlmDkyJGcMk8pq7ntuj/+8Y8oKCjAiy++iPXr1/se93g8jWbdEaWqYO26Dh06YOjQofjxxx/9locFgL/+9a/Yu3evCSklMlc07Trt/mfq1Kl+e2etWrXKt38WWQsDC5R0LrzwQjidTnz55ZcAGgINwbRt2xbDhw/HwoULsWjRIpxyyilo27ZtopJKZBnaDejq1avhdDrRv39/v+ezsrIwcOBArF692u/4QCxTROH96U9/Qtu2bfHKK69g06ZNfs9NmjQJBw4cwKOPPoqCggKMGzfOpFQSmeuGG26AiODEE0/EVVddhT//+c/o168f3nrrrUYdpJoJEyZAURRMmzaNgTlKec1t1xUWFuLvf/87qqqq0L9/f1x11VW47bbbcNxxx+HAgQMhB20RpZpg7bqnn34aBQUFuPjiizF+/HjcddddGDlyJJ5++mluPkspKZp23SWXXIIRI0bg448/Rt++fXH77bdj8uTJGDRoEEaNGmXOCVBYDCxQ0ikoKMC5554LQN3c5bTTTgt7/KRJk+D1euH1erlkC6WsXr16oaSkBABQWloKh8PR6Bj9NPpQgQWAZYoonOzsbNx5552ora31jSTVTJw4EYqiwOPx4JxzzkFOTo5JqSQy1+mnn445c+agU6dOeO211/D666/j2GOPxbJly9CxY8egr2nfvj1OPvlkeDwe5OXl4cwzz0xsooksJBbtuksuuQTvvPMOunTpgpdffhkvv/wyBg8ejPnz5yMzMzN+iSeykWDtup49e+LLL7/EmDFj8PHHH+Opp56Cw+HAl19+iU6dOpmcYqLEi6Zdl5aWhvfeew+33XYbdu/ejSeeeAJLly7F//t//w9/+ctfTDoDCkcRETE7EUREREREREREREREZA+csUBERERERERERERERIYxsEBERERERERERERERIYxsEBERERERERERERERIYxsEBERERERERERERERIYxsEBERERERERERERERIYxsEBERERERERERERERIYxsEBERERERERERERERIYxsEBERERERERERERERIYxsEBERERERERERERERIYxsEBERERERERERERERIYxsEBERERERERERERERIYxsEBERERERERERERERIb9f2mhKBRohT6vAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = corner.corner(\n", - " samples,\n", - " labels=[p.name for p in omp.params],\n", - " truths=true_params,\n", - " label_kwargs={\"fontsize\": 14},\n", - ")\n", - "_ = corner.corner(prior.rvs(5000), fig=fig, color=\"tab:orange\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "visualize-results", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n", - "\n", - "angles_plot = np.rad2deg(obs.visualization_workspace.angles)\n", - "ax.errorbar(np.rad2deg(obs.x), obs.y, yerr=obs.y_stat_err, fmt=\"o\", label=\"Mock data\")\n", - "ax.plot(angles_plot, y_true, label=\"Truth\", linewidth=2, alpha=0.8)\n", - "ax.fill_between(\n", - " angles_plot, y_low, y_high, alpha=0.4, label=\"90% predictive posterior band\"\n", - ")\n", - "ax.set_xlabel(\"Angle (deg)\")\n", - "ax.set_ylabel(\"d$\\\\sigma$/d$\\\\Omega$ (b/sr)\")\n", - "ax.legend()\n", - "ax.set_yscale(\"log\")\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "af2b08c0-ab30-4be7-9111-ef051d822066", - "metadata": {}, - "source": [ - "Note that the predictive posterior has excellent coverage of the training data, even though the parameter inference was not perfect. One must be wary of infering parameters from optical potentials, as multiple different sets of parameter values can produce the same cross section! " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d3325c9b-5ce5-4dd8-899a-be008fd5b86b", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/examples/calibration_config_emcee_dynesty.ipynb b/examples/calibration_config_emcee_dynesty.ipynb deleted file mode 100644 index c0e6d15..0000000 --- a/examples/calibration_config_emcee_dynesty.ipynb +++ /dev/null @@ -1,661 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "59aaecd72125b689", - "metadata": {}, - "source": [ - "# Calibrating an optical potential with `CalibrationConfig`: emcee vs dynesty\n", - "\n", - "This notebook shows how `CalibrationConfig` acts as a **uniform interface** to\n", - "external inference libraries. We calibrate a nuclear optical-model potential\n", - "to mock $n + {}^{40}$Ca elastic differential cross-section data using both\n", - "[emcee](https://emcee.readthedocs.io/) (MCMC using ensemble sampling) and\n", - "[dynesty](https://dynesty.readthedocs.io/) (nested sampling), then compare the\n", - "resulting posteriors and predictive posterior bands.\n", - "\n", - "The key interface points provided by `CalibrationConfig`:\n", - "\n", - "| sampler | method used |\n", - "|---------|-------------|\n", - "| emcee | `config.log_posterior`, `config.starting_location` |\n", - "| dynesty | `config.log_likelihood`, `config.prior_transform`, `config.ndim` |" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a4e74c882f17c512", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using database version X4-2025-12-31 located in: /mnt/home/beyerkyl/x4db/unpack_exfor-2025/X4-2025-12-31\n" - ] - } - ], - "source": [ - "import corner\n", - "import dynesty\n", - "import emcee\n", - "import jitr\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from jitr.optical_potentials.potential_forms import (\n", - " thomas_safe,\n", - " woods_saxon_prime_safe,\n", - " woods_saxon_safe,\n", - ")\n", - "\n", - "import rxmc\n", - "from rxmc.config import CalibrationConfig, ParameterConfig\n", - "from rxmc.params import Parameter\n", - "from rxmc.priors import TruncatedNormalPrior" - ] - }, - { - "cell_type": "markdown", - "id": "e5ea9b3edf23cefa", - "metadata": {}, - "source": [ - "## Reaction, optical-model, and mock data\n", - "\n", - "We use the same minimal optical-model potential setup as in\n", - "`30s_optical_potential_calibration.ipynb`." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "e86da6406a1b5c37", - "metadata": {}, - "outputs": [], - "source": [ - "Ca40 = (40, 20)\n", - "neutron = (1, 0)\n", - "E_lab = 14.1\n", - "\n", - "rxn = jitr.reactions.ElasticReaction(target=Ca40, projectile=neutron)\n", - "\n", - "mso = 1.0 / jitr.utils.constants.WAVENUMBER_PION\n", - "\n", - "\n", - "def central_potential(r, Vv, Wv, Rv, av, Wd, Rd, ad):\n", - " return -(Vv + 1j * Wv) * woods_saxon_safe(r, Rv, av) + (\n", - " 4j * ad * Wd\n", - " ) * woods_saxon_prime_safe(r, Rd, ad)\n", - "\n", - "\n", - "def spin_orbit_potential(r, Vso, Wso, Rso, aso):\n", - " return (Vso + 1j * Wso) * mso**2 * thomas_safe(r, Rso, aso)\n", - "\n", - "\n", - "R = 1.2 * 40 ** (1 / 3)\n", - "fixed_spin_orbit = (6.0, -3, R, 0.45)\n", - "\n", - "\n", - "def extract_params(ws, *x):\n", - " Vv, Wv, Rv, av, Wd, Rd, ad = x\n", - " return (Vv, Wv, Rv, av, Wd, Rd, ad), fixed_spin_orbit\n", - "\n", - "\n", - "params = [\n", - " Parameter(\"Vv\", unit=\"MeV\"),\n", - " Parameter(\"Wv\", unit=\"MeV\"),\n", - " Parameter(\"Rv\", unit=\"fm\"),\n", - " Parameter(\"av\", unit=\"fm\"),\n", - " Parameter(\"Wd\", unit=\"MeV\"),\n", - " Parameter(\"Rd\", unit=\"fm\"),\n", - " Parameter(\"ad\", unit=\"fm\"),\n", - "]\n", - "\n", - "omp = rxmc.elastic_diffxs_model.ElasticDifferentialXSModel(\n", - " \"dXS/dA\",\n", - " interaction_central=central_potential,\n", - " interaction_spin_orbit=spin_orbit_potential,\n", - " calculate_interaction_from_params=extract_params,\n", - " params=params,\n", - " model_name=\"minimal_elastic_demo\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "62252d74eb52a9d4", - "metadata": {}, - "outputs": [], - "source": [ - "angles_deg = np.linspace(2.0, 160.0, 28)\n", - "true_params = np.array(\n", - " [48.0, 3.5, 1.1 * 40 ** (1 / 3), 0.7, 21, 1.2 * 40 ** (1 / 3), 0.5]\n", - ")\n", - "\n", - "template_obs = rxmc.elastic_diffxs_observation.ElasticDifferentialXSObservation(\n", - " x=angles_deg,\n", - " y=np.ones_like(angles_deg, dtype=float),\n", - " Elab=E_lab,\n", - " reaction=rxn,\n", - " quantity=\"dXS/dA\",\n", - " measurement_quantity=\"dXS/dA\",\n", - " y_units=\"barn / steradian\",\n", - " dataset_label=\"template\",\n", - ")\n", - "\n", - "y_true = omp.evaluate(template_obs, *true_params)\n", - "\n", - "rng = np.random.default_rng(42)\n", - "y_stat_err = 0.2 * np.maximum(y_true, 1e-4)\n", - "y_mock = np.clip(y_true + rng.normal(scale=y_stat_err * 1.3), 1e-6, None)\n", - "\n", - "obs = rxmc.elastic_diffxs_observation.ElasticDifferentialXSObservation(\n", - " x=angles_deg,\n", - " y=y_mock,\n", - " Elab=E_lab,\n", - " reaction=rxn,\n", - " quantity=\"dXS/dA\",\n", - " measurement_quantity=\"dXS/dA\",\n", - " y_units=\"barn / steradian\",\n", - " y_stat_err=y_stat_err,\n", - " dataset_label=\"mock elastic dataset\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7ae38c666c0d856d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Mock $n + {}^{40}$Ca differential cross-section data')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.errorbar(np.rad2deg(obs.x), obs.y, obs.y_stat_err, linestyle=\"none\", marker=\".\")\n", - "plt.xlabel(r\"$\\theta$ [deg]\")\n", - "plt.ylabel(r\"$d\\sigma/d\\Omega$ [b/Sr]\")\n", - "plt.yscale(\"log\")\n", - "plt.title(r\"Mock $n + {}^{40}$Ca differential cross-section data\")" - ] - }, - { - "cell_type": "markdown", - "id": "9758436cf4777841", - "metadata": {}, - "source": [ - "## Build `CalibrationConfig`\n", - "\n", - "We use `TruncatedNormalPrior` here to enforce physical constraints." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "82a298155fe56dd4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ndim: 7\n", - "parameters: ['Vv', 'Wv', 'Rv', 'av', 'Wd', 'Rd', 'ad']\n", - "prior objects: ['TruncatedNormalPrior']\n" - ] - } - ], - "source": [ - "prior_mean = np.array(\n", - " [50.0, 3.0, 1.2 * 40 ** (1 / 3), 0.65, 18.0, 1.2 * 40 ** (1 / 3), 0.65]\n", - ")\n", - "prior_std = np.array([7.0, 7.0, 0.5, 0.20, 10.0, 0.5, 0.20])\n", - "lower = np.zeros_like(prior_mean)\n", - "upper = prior_mean + 10 * prior_std\n", - "\n", - "prior = TruncatedNormalPrior(\n", - " mu=prior_mean,\n", - " sigma=prior_std,\n", - " lower=lower,\n", - " upper=upper,\n", - " seed=0,\n", - ")\n", - "\n", - "model_config = ParameterConfig(\n", - " params=omp.params,\n", - " prior=prior,\n", - " initial_proposal_distribution=prior,\n", - ")\n", - "\n", - "constraint = rxmc.constraint.Constraint(\n", - " observations=[obs],\n", - " physical_model=omp,\n", - " likelihood=rxmc.likelihood_model.GaussianLikelihood(),\n", - ")\n", - "evidence = rxmc.evidence.Evidence(constraints=[constraint])\n", - "\n", - "config = CalibrationConfig(evidence=evidence, model_config=model_config)\n", - "\n", - "print(\"ndim:\", config.ndim)\n", - "print(\"parameters:\", config.parameter_names)\n", - "print(\"prior objects:\", [type(p).__name__ for p in config.prior])" - ] - }, - { - "cell_type": "markdown", - "id": "c95fbc706233ff5d", - "metadata": {}, - "source": [ - "## emcee: ensemble MCMC\n", - "\n", - "`CalibrationConfig.log_posterior` is passed directly as the log-probability\n", - "function; `CalibrationConfig.starting_location` generates the initial walker\n", - "positions by drawing from the proposal distribution." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b11457245e87a591", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5000/5000 [05:03<00:00, 16.47it/s]\n" - ] - } - ], - "source": [ - "nwalkers = 32\n", - "nsteps = 5000\n", - "\n", - "p0 = config.starting_location(nwalkers)\n", - "\n", - "sampler_emcee = emcee.EnsembleSampler(\n", - " nwalkers,\n", - " config.ndim,\n", - " config.log_posterior,\n", - ")\n", - "state = sampler_emcee.run_mcmc(p0, nsteps, progress=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "f7d84c28fb8ddc5d", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The chain is shorter than 50 times the integrated autocorrelation time for 7 parameter(s). Use this estimate with caution and run a longer chain!\n", - "N/50 = 100;\n", - "tau: [200.51739056 200.00513277 183.65262029 143.22836146 211.13930814\n", - " 166.72607996 197.35966807]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Autocorrelation times: [200.5 200. 183.7 143.2 211.1 166.7 197.4]\n", - "Burn-in: 422 thin: 71\n", - "Posterior samples: 2048\n", - "Mean acceptance fraction: 0.320\n" - ] - } - ], - "source": [ - "tau = sampler_emcee.get_autocorr_time(quiet=True)\n", - "burnin = int(2 * np.max(tau))\n", - "thin = max(1, int(0.5 * np.min(tau)))\n", - "flat_emcee = sampler_emcee.get_chain(discard=burnin, thin=thin, flat=True)\n", - "\n", - "print(f\"Autocorrelation times: {np.round(tau, 1)}\")\n", - "print(f\"Burn-in: {burnin} thin: {thin}\")\n", - "print(f\"Posterior samples: {len(flat_emcee)}\")\n", - "print(f\"Mean acceptance fraction: {sampler_emcee.acceptance_fraction.mean():.3f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "73d1640d733a5e7d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "chain = sampler_emcee.get_chain()\n", - "logp_chain = sampler_emcee.get_log_prob()\n", - "\n", - "fig, axes = plt.subplots(config.ndim + 1, 1, figsize=(10, 9), sharex=True)\n", - "for i, (ax, name) in enumerate(zip(axes[:-1], config.parameter_names)):\n", - " ax.plot(chain[:, :, i], alpha=0.3, lw=0.6, color=\"tab:blue\")\n", - " ax.axhline(true_params[i], color=\"r\", lw=1.5, linestyle=\"--\")\n", - " ax.set_ylabel(name)\n", - "axes[-1].plot(logp_chain, alpha=0.3, lw=0.6, color=\"tab:gray\")\n", - "axes[-1].set_ylabel(r\"$\\log p$\")\n", - "axes[-1].set_xlabel(\"step\")\n", - "fig.suptitle(\"emcee chains (red dashes = truth)\")\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "3d504061cb491b17", - "metadata": {}, - "source": [ - "## dynesty: nested sampling\n", - "\n", - "`CalibrationConfig.log_likelihood` and `CalibrationConfig.prior_transform`\n", - "provide the two functions dynesty needs. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "af9341b781df8d82", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2813it [03:14, 14.46it/s, +200 | bound: 12 | nc: 1 | ncall: 64251 | eff(%): 4.704 | loglstar: -inf < 132.820 < inf | logz: 119.434 +/- 0.254 | dlogz: 0.003 > 0.500]\n" - ] - } - ], - "source": [ - "sampler_dyn = dynesty.NestedSampler(\n", - " config.log_likelihood,\n", - " config.prior_transform,\n", - " config.ndim,\n", - " nlive=200,\n", - " sample=\"rwalk\",\n", - ")\n", - "sampler_dyn.run_nested(dlogz=0.5, print_progress=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "b3ba5da881a8bd00", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Posterior samples: 3013\n", - "log Z = 119.43 \u00b1 0.38\n", - "Efficiency: 4.70 %\n" - ] - } - ], - "source": [ - "results_dyn = sampler_dyn.results\n", - "flat_dynesty = results_dyn.samples_equal()\n", - "\n", - "print(f\"Posterior samples: {len(flat_dynesty)}\")\n", - "print(f\"log Z = {results_dyn.logz[-1]:.2f} \u00b1 {results_dyn.logzerr[-1]:.2f}\")\n", - "print(f\"Efficiency: {results_dyn.eff:.2f} %\")" - ] - }, - { - "cell_type": "markdown", - "id": "dcf890213bea8821", - "metadata": {}, - "source": [ - "## Comparing posteriors\n", - "\n", - "Corner plot overlaying the prior (orange), emcee posterior (blue), and dynesty\n", - "posterior (green). Red dashed lines mark the true parameter values used to\n", - "generate the mock data." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "e05d6c94f3dc1ed0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.01, 'Posterior comparison: n + $^{40}$Ca OMP calibration')" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "n_corner = 2000\n", - "rng_plot = np.random.default_rng(1)\n", - "prior_samples = prior.rvs(n_corner)\n", - "idx_e = rng_plot.choice(len(flat_emcee), min(n_corner, len(flat_emcee)), replace=False)\n", - "idx_d = rng_plot.choice(\n", - " len(flat_dynesty), min(n_corner, len(flat_dynesty)), replace=False\n", - ")\n", - "\n", - "fig = plt.figure(figsize=(8, 8))\n", - "\n", - "\n", - "def corner_kwargs(color, alpha=0.4):\n", - " return dict(\n", - " labels=config.parameter_names,\n", - " truths=true_params,\n", - " truth_color=\"red\",\n", - " label_kwargs={\"fontsize\": 11},\n", - " show_titles=True,\n", - " plot_datapoints=False,\n", - " plot_density=False,\n", - " plot_contours=True,\n", - " fill_contours=True,\n", - " no_fill_contours=False,\n", - " contour_kwargs={\n", - " \"colors\": color,\n", - " \"linewidths\": 1.5,\n", - " \"alpha\": alpha,\n", - " },\n", - " hist_kwargs={\n", - " \"density\": True,\n", - " \"histtype\": \"stepfilled\",\n", - " \"alpha\": alpha,\n", - " \"color\": color,\n", - " \"edgecolor\": \"k\",\n", - " },\n", - " labelpad=0.4,\n", - " )\n", - "\n", - "\n", - "corner.corner(\n", - " flat_emcee[idx_e],\n", - " fig=fig,\n", - " **corner_kwargs(\"tab:green\"),\n", - ")\n", - "corner.corner(\n", - " flat_dynesty[idx_d],\n", - " fig=fig,\n", - " **corner_kwargs(\"tab:orange\"),\n", - ")\n", - "corner.corner(\n", - " prior_samples,\n", - " fig=fig,\n", - " **corner_kwargs(\"tab:blue\", alpha=0.2),\n", - ")\n", - "\n", - "# Legend\n", - "from matplotlib.lines import Line2D\n", - "\n", - "handles = [\n", - " Line2D([0], [0], color=\"tab:green\", label=\"emcee\"),\n", - " Line2D([0], [0], color=\"tab:orange\", label=\"dynesty\"),\n", - " Line2D([0], [0], color=\"tab:blue\", label=\"prior\"),\n", - "]\n", - "fig.legend(handles=handles, loc=\"upper right\", fontsize=12)\n", - "fig.suptitle(r\"Posterior comparison: n + $^{40}$Ca OMP calibration\", y=1.01)" - ] - }, - { - "cell_type": "markdown", - "id": "3f2e6d58-11bc-4837-9b1a-51a5bf53c49a", - "metadata": {}, - "source": [ - "While Markov-chain based samplers like the one in emcee are typically great, nested sampling is often better suited when the posterior has multi-modality. Here, we can see in the emcee posterior, that different modes appear, which indicates that convergence was poor." - ] - }, - { - "cell_type": "markdown", - "id": "0c456000a3c13726", - "metadata": {}, - "source": [ - "## Comparing predictive posteriors\n", - "\n", - "We propagate 200 posterior samples from each method through the model and show\n", - "the 5th\u201395th percentile predictive band." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "696cddd8e740f83c", - "metadata": {}, - "outputs": [], - "source": [ - "n_pred = 200\n", - "rng_pred = np.random.default_rng(2)\n", - "\n", - "idx_e_pred = rng_pred.choice(len(flat_emcee), n_pred, replace=False)\n", - "idx_d_pred = rng_pred.choice(len(flat_dynesty), n_pred, replace=False)\n", - "\n", - "y_pred_emcee = np.array(\n", - " [omp.visualizable_model_prediction(obs, *s) for s in flat_emcee[idx_e_pred]]\n", - ")\n", - "y_pred_dynesty = np.array(\n", - " [omp.visualizable_model_prediction(obs, *s) for s in flat_dynesty[idx_d_pred]]\n", - ")\n", - "y_true_vis = omp.visualizable_model_prediction(obs, *true_params)\n", - "\n", - "angles_plot = np.rad2deg(obs.visualization_workspace.angles)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "f489936f103b25a8", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(8, 4))\n", - "\n", - "ax.errorbar(\n", - " np.rad2deg(obs.x),\n", - " obs.y,\n", - " yerr=obs.y_stat_err,\n", - " fmt=\"ko\",\n", - " label=\"Mock data\",\n", - " zorder=5,\n", - ")\n", - "ax.plot(angles_plot, y_true_vis, \"k--\", lw=2, label=\"Truth\")\n", - "\n", - "for y_pred, label, color in [\n", - " (y_pred_emcee, \"emcee 90%\", \"tab:green\"),\n", - " (y_pred_dynesty, \"dynesty 90%\", \"tab:orange\"),\n", - "]:\n", - " lo, hi = np.percentile(y_pred, [5, 95], axis=0)\n", - " ax.fill_between(angles_plot, lo, hi, alpha=0.4, color=color, label=label)\n", - "\n", - "ax.set_yscale(\"log\")\n", - "ax.set_xlabel(r\"$\\theta$ (deg)\")\n", - "ax.set_ylabel(r\"$d\\sigma/d\\Omega$ (b/sr)\")\n", - "ax.legend()\n", - "ax.set_title(r\"Predictive posterior: $n + {}^{40}$Ca\")\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7bce006a-eef3-4749-aaf3-271026e4bbad", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/examples/correlated_observations.ipynb b/examples/correlated_observations.ipynb deleted file mode 100644 index 193b69e..0000000 --- a/examples/correlated_observations.ipynb +++ /dev/null @@ -1,785 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c00", - "metadata": {}, - "source": [ - "# Correlated observations\n", - "\n", - "Two datasets that are *individually* independent can still be **coupled** by a\n", - "shared systematic — e.g. a common detector calibration, flux normalisation, or\n", - "energy scale applied to both. The classic D'Agostini / Barlow point is that\n", - "treating such datasets independently is **overconfident**: the shared systematic\n", - "cannot average down the way independent errors do.\n", - "\n", - "The covariance API expresses this directly. A `Constraint` owns one multivariate\n", - "distribution over the *stacked* vector $y = [y_1; y_2]$, and a covariance `Term`\n", - "whose `support` spans **both** blocks writes off-diagonal blocks that couple the\n", - "data. This is \"case A\" of the refactor:\n", - "\n", - "- **case A — coupling the data**: a cross-block `Term` (off-diagonal $\\Sigma$).\n", - "- **case B — coupling the parameters**: the *same* `Parameter` object shared by two\n", - " block-local terms (one sampled value feeds both; $\\Sigma$ stays block-diagonal).\n", - "\n", - "We demonstrate both." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c01", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:18:30.768095Z", - "iopub.status.busy": "2026-08-11T03:18:30.767914Z", - "iopub.status.idle": "2026-08-11T03:18:32.802335Z", - "shell.execute_reply": "2026-08-11T03:18:32.801332Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using database version X4-2024-12-31 located in: /home/kyle/db/exfor/unpack_exfor-2024/X4-2024-12-31\n" - ] - } - ], - "source": [ - "import corner\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "from scipy import stats\n", - "\n", - "import rxmc\n", - "\n", - "rng = np.random.default_rng(3)" - ] - }, - { - "cell_type": "markdown", - "id": "c02", - "metadata": {}, - "source": [ - "## Two datasets with a shared calibration\n", - "\n", - "A straight-line signal is measured by two instruments over different $x$ ranges.\n", - "Both share **one** unknown multiplicative calibration factor $c \\sim\n", - "\\mathcal N(1, \\sigma_c)$ drawn once — so both datasets are biased by the *same*\n", - "amount. The reported statistical errors are small and independent." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c03", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:18:32.803854Z", - "iopub.status.busy": "2026-08-11T03:18:32.803610Z", - "iopub.status.idle": "2026-08-11T03:18:32.992778Z", - "shell.execute_reply": "2026-08-11T03:18:32.991831Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "class LinearModel(rxmc.physical_model.PhysicalModel):\n", - " def __init__(self):\n", - " super().__init__(\n", - " [rxmc.params.Parameter(\"m\", float), rxmc.params.Parameter(\"b\", float)]\n", - " )\n", - "\n", - " def evaluate(self, observation, m, b):\n", - " return self.y(observation.x, m, b)\n", - "\n", - " def y(self, x, m, b):\n", - " return m * x + b\n", - "\n", - "\n", - "model = LinearModel()\n", - "m_true, b_true = 1.0, 0.5\n", - "sigma_c = 0.10 # shared calibration uncertainty\n", - "noise = 0.02 # independent statistical error\n", - "\n", - "c = rng.normal(1.0, sigma_c) # ONE common factor, applied to both datasets\n", - "x1 = np.linspace(0.0, 2.0, 8)\n", - "x2 = np.linspace(3.0, 5.0, 8)\n", - "y1 = c * model.y(x1, m_true, b_true) + rng.normal(0.0, noise, x1.size)\n", - "y2 = c * model.y(x2, m_true, b_true) + rng.normal(0.0, noise, x2.size)\n", - "\n", - "obs1 = rxmc.observation.Observation(x=x1, y=y1, y_stat_err=noise * np.ones_like(y1))\n", - "obs2 = rxmc.observation.Observation(x=x2, y=y2, y_stat_err=noise * np.ones_like(y2))\n", - "\n", - "xg = np.linspace(0, 5, 100)\n", - "plt.plot(xg, model.y(xg, m_true, b_true), \"k:\", label=\"true signal\")\n", - "plt.errorbar(x1, y1, noise, ls=\"none\", marker=\".\", label=\"dataset 1\")\n", - "plt.errorbar(x2, y2, noise, ls=\"none\", marker=\".\", label=\"dataset 2\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.legend()\n", - "plt.title(f\"both datasets share one calibration c = {c:.3f}\");" - ] - }, - { - "cell_type": "markdown", - "id": "c04", - "metadata": {}, - "source": [ - "## The covariance structure\n", - "\n", - "We build the **same** shared-systematic two ways and compare the assembled\n", - "$\\Sigma$ (via `Constraint.covariance_matrix`):\n", - "\n", - "- **correlated (case A)**: one `normalization_term` over the *full* stacked\n", - " support — its rank-one mode spans both blocks.\n", - "- **independent**: a `normalization_term` per dataset (block-local) — no coupling." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c05", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:18:32.994269Z", - "iopub.status.busy": "2026-08-11T03:18:32.994136Z", - "iopub.status.idle": "2026-08-11T03:18:33.810366Z", - "shell.execute_reply": "2026-08-11T03:18:33.809598Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "correlated block_diagonal = False\n", - "independent block_diagonal = True\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "s1, s2 = rxmc.covariance.stacked_supports([obs1, obs2])\n", - "full = np.concatenate([s1, s2])\n", - "N1 = len(s1)\n", - "\n", - "constraint_corr = rxmc.constraint.Constraint(\n", - " [obs1, obs2],\n", - " model,\n", - " extra_terms=[rxmc.covariance.normalization_term(magnitude=sigma_c, support=full)],\n", - ")\n", - "constraint_indep = rxmc.constraint.Constraint(\n", - " [obs1, obs2],\n", - " model,\n", - " extra_terms=[\n", - " rxmc.covariance.normalization_term(magnitude=sigma_c, support=s1),\n", - " rxmc.covariance.normalization_term(magnitude=sigma_c, support=s2),\n", - " ],\n", - ")\n", - "\n", - "mp = (m_true, b_true)\n", - "S_corr = constraint_corr.covariance_matrix(mp)\n", - "S_indep = constraint_indep.covariance_matrix(mp)\n", - "\n", - "print(\"correlated block_diagonal =\", constraint_corr.covariance.block_diagonal)\n", - "print(\"independent block_diagonal =\", constraint_indep.covariance.block_diagonal)\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(9, 4))\n", - "for ax, S, t in [\n", - " (axes[0], S_corr, \"correlated (case A)\"),\n", - " (axes[1], S_indep, \"independent\"),\n", - "]:\n", - " im = ax.imshow(S, cmap=\"viridis\")\n", - " ax.axhline(N1 - 0.5, color=\"w\", lw=0.8)\n", - " ax.axvline(N1 - 0.5, color=\"w\", lw=0.8)\n", - " ax.set_title(t)\n", - " fig.colorbar(im, ax=ax, fraction=0.046)\n", - "fig.suptitle(r\"stacked covariance $\\Sigma$ — off-diagonal blocks couple the data\");" - ] - }, - { - "cell_type": "markdown", - "id": "c06", - "metadata": {}, - "source": [ - "The correlated $\\Sigma$ has **non-zero off-diagonal blocks** (top-right /\n", - "bottom-left): every point in dataset 1 is correlated with every point in dataset 2\n", - "through the shared calibration. The independent $\\Sigma$ is block-diagonal." - ] - }, - { - "cell_type": "markdown", - "id": "c07", - "metadata": {}, - "source": [ - "## Effect on inference\n", - "\n", - "Fitting the line under each treatment: the correlated model is appropriately\n", - "**less certain** (the shared systematic is a common mode that cannot average\n", - "down), while the independent model is **overconfident**." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c08", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:18:33.812158Z", - "iopub.status.busy": "2026-08-11T03:18:33.811974Z", - "iopub.status.idle": "2026-08-11T03:18:39.649142Z", - "shell.execute_reply": "2026-08-11T03:18:39.648248Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "correlated m = 1.201 ± 0.105 b = 0.582 ± 0.051\n", - "independent m = 1.196 ± 0.079 b = 0.590 ± 0.041\n" - ] - } - ], - "source": [ - "def fit(constraint, seed):\n", - " evidence = rxmc.evidence.Evidence([constraint])\n", - " prior = stats.multivariate_normal(mean=[1.0, 0.5], cov=np.diag([0.3, 0.3]) ** 2)\n", - " sampler = rxmc.param_sampling.BatchedAdaptiveMetropolisSampler(\n", - " params=model.params,\n", - " starting_location=prior.mean,\n", - " prior=prior,\n", - " initial_proposal_cov=prior.cov / 100,\n", - " )\n", - " walker = rxmc.walker.Walker(sampler, evidence, rng=np.random.default_rng(seed))\n", - " walker.walk(n_steps=6000, burnin=2000, batch_size=1000, verbose=False)\n", - " return walker.model_sampler.chain\n", - "\n", - "\n", - "chain_corr = fit(constraint_corr, 5)\n", - "chain_indep = fit(constraint_indep, 5)\n", - "\n", - "for name, ch in [(\"correlated\", chain_corr), (\"independent\", chain_indep)]:\n", - " print(\n", - " f\"{name:12s} m = {ch[:,0].mean():.3f} ± {ch[:,0].std():.3f} \"\n", - " f\"b = {ch[:,1].mean():.3f} ± {ch[:,1].std():.3f}\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c09", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:18:39.650554Z", - "iopub.status.busy": "2026-08-11T03:18:39.650423Z", - "iopub.status.idle": "2026-08-11T03:18:39.814309Z", - "shell.execute_reply": "2026-08-11T03:18:39.813633Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = corner.corner(\n", - " chain_corr,\n", - " labels=[\"m\", \"b\"],\n", - " color=\"tab:blue\",\n", - " truths=[m_true, b_true],\n", - " truth_color=\"k\",\n", - ")\n", - "corner.corner(chain_indep, fig=fig, color=\"tab:red\")\n", - "plt.plot([], [], color=\"tab:blue\", label=\"correlated (case A)\")\n", - "plt.plot([], [], color=\"tab:red\", label=\"independent (overconfident)\")\n", - "fig.legend(loc=\"upper right\");" - ] - }, - { - "cell_type": "markdown", - "id": "c10", - "metadata": {}, - "source": [ - "## Case A vs case B with a *free* shared nuisance\n", - "\n", - "If the calibration magnitude is itself unknown, it becomes a sampled `Parameter`.\n", - "The same `Parameter` object can be wired two ways:\n", - "\n", - "- **case A** — one cross-block term: $\\Sigma$ couples the data (off-diagonal).\n", - "- **case B** — two block-local terms sharing the *same* `Parameter`: one sampled\n", - " value feeds both, but $\\Sigma$ stays **block-diagonal** (the datasets remain\n", - " independent; they only share the uncertainty *magnitude*).\n", - "\n", - "Both have exactly **one** free parameter (gather-by-identity)." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "c11", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:18:39.815796Z", - "iopub.status.busy": "2026-08-11T03:18:39.815612Z", - "iopub.status.idle": "2026-08-11T03:18:39.820536Z", - "shell.execute_reply": "2026-08-11T03:18:39.819858Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "case A (cross-block) n_params=1 block_diagonal=False\n", - "case B (shared param) n_params=1 block_diagonal=True\n", - "case A cross-block max |Sigma[s1,s2]| = 0.1375\n", - "case B cross-block max |Sigma[s1,s2]| = 0.0\n" - ] - } - ], - "source": [ - "eta = rxmc.params.Parameter(\"log eta\", float, latex_name=r\"\\log{\\eta}\")\n", - "\n", - "case_A = rxmc.constraint.Constraint(\n", - " [obs1, obs2],\n", - " model,\n", - " extra_terms=[rxmc.covariance.normalization_term(parameter=eta, support=full)],\n", - ")\n", - "case_B = rxmc.constraint.Constraint(\n", - " [obs1, obs2],\n", - " model,\n", - " extra_terms=[\n", - " rxmc.covariance.normalization_term(parameter=eta, support=s1),\n", - " rxmc.covariance.normalization_term(parameter=eta, support=s2),\n", - " ],\n", - ")\n", - "\n", - "for name, c in [(\"case A (cross-block)\", case_A), (\"case B (shared param)\", case_B)]:\n", - " print(\n", - " f\"{name:24s} n_params={c.n_params} \"\n", - " f\"block_diagonal={c.covariance.block_diagonal}\"\n", - " )\n", - "\n", - "# same single sampled value, different Sigma structure\n", - "val = (np.log(sigma_c),)\n", - "SA = case_A.covariance_matrix(mp, val)\n", - "SB = case_B.covariance_matrix(mp, val)\n", - "print(\"case A cross-block max |Sigma[s1,s2]| =\", np.abs(SA[:N1, N1:]).max().round(4))\n", - "print(\"case B cross-block max |Sigma[s1,s2]| =\", np.abs(SB[:N1, N1:]).max().round(4))" - ] - }, - { - "cell_type": "markdown", - "id": "c12", - "metadata": {}, - "source": [ - "## Takeaways\n", - "\n", - "- **Correlated observations are just a covariance `Term` whose `support` spans\n", - " blocks.** No special machinery — a cross-block `normalization_term` (or any\n", - " a mode or kernel `Term`) writes the off-diagonal $\\Sigma$ blocks.\n", - "- Treating shared-systematic datasets **independently is overconfident**: the\n", - " common mode cannot average down.\n", - "- **A couples the data** (cross-block, off-diagonal $\\Sigma$); **B couples the\n", - " parameters** (same `Parameter` shared by block-local terms, $\\Sigma$\n", - " block-diagonal). Both are expressed by *where* you put the support and *which*\n", - " `Parameter` object you reuse." - ] - }, - { - "cell_type": "markdown", - "id": "2e389f4a", - "metadata": {}, - "source": [ - "# Shared systematics between cross-section datasets\n", - "\n", - "The toy example above carries over to real reaction data unchanged. Here two\n", - "mock *experiments* measure the same $n + {}^{40}$Ca elastic differential cross\n", - "section — one at forward angles, one at backward angles — and both were\n", - "normalized against the **same uncertain flux measurement**. That is case A:\n", - "the shared normalization couples the two datasets, so they belong in **one**\n", - "`Constraint` with a normalization mode spanning both blocks.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "7cd8e173", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:18:39.821956Z", - "iopub.status.busy": "2026-08-11T03:18:39.821803Z", - "iopub.status.idle": "2026-08-11T03:18:39.920680Z", - "shell.execute_reply": "2026-08-11T03:18:39.920050Z" - } - }, - "outputs": [], - "source": [ - "import jitr\n", - "from jitr.optical_potentials.potential_forms import (\n", - " thomas_safe,\n", - " woods_saxon_prime_safe,\n", - " woods_saxon_safe,\n", - ")\n", - "\n", - "from rxmc.params import Parameter\n", - "\n", - "Ca40 = (40, 20)\n", - "neutron = (1, 0)\n", - "E_lab = 14.1\n", - "rxn = jitr.reactions.ElasticReaction(target=Ca40, projectile=neutron)\n", - "mso = 1.0 / jitr.utils.constants.WAVENUMBER_PION\n", - "\n", - "\n", - "def central_potential(r, Vv, Wv, Rv, av, Wd, Rd, ad):\n", - " return -(Vv + 1j * Wv) * woods_saxon_safe(r, Rv, av) + (\n", - " 4j * ad * Wd\n", - " ) * woods_saxon_prime_safe(r, Rd, ad)\n", - "\n", - "\n", - "def spin_orbit_potential(r, Vso, Wso, Rso, aso):\n", - " return (Vso + 1j * Wso) * mso**2 * thomas_safe(r, Rso, aso)\n", - "\n", - "\n", - "R40 = 1.2 * 40 ** (1 / 3)\n", - "fixed_spin_orbit = (6.0, -3, R40, 0.45)\n", - "\n", - "\n", - "def extract_params(ws, *x):\n", - " return tuple(x), fixed_spin_orbit\n", - "\n", - "\n", - "omp = rxmc.elastic_diffxs_model.ElasticDifferentialXSModel(\n", - " \"dXS/dA\",\n", - " interaction_central=central_potential,\n", - " interaction_spin_orbit=spin_orbit_potential,\n", - " calculate_interaction_from_params=extract_params,\n", - " params=[\n", - " Parameter(\"Vv\", unit=\"MeV\"),\n", - " Parameter(\"Wv\", unit=\"MeV\"),\n", - " Parameter(\"Rv\", unit=\"fm\"),\n", - " Parameter(\"av\", unit=\"fm\"),\n", - " Parameter(\"Wd\", unit=\"MeV\"),\n", - " Parameter(\"Rd\", unit=\"fm\"),\n", - " Parameter(\"ad\", unit=\"fm\"),\n", - " ],\n", - " model_name=\"shared_flux_demo\",\n", - ")\n", - "omp_true_params = np.array(\n", - " [48.0, 3.5, 1.1 * 40 ** (1 / 3), 0.7, 21, 1.2 * 40 ** (1 / 3), 0.5]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "d5480648", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:18:39.922385Z", - "iopub.status.busy": "2026-08-11T03:18:39.922240Z", - "iopub.status.idle": "2026-08-11T03:18:51.337995Z", - "shell.execute_reply": "2026-08-11T03:18:51.337238Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def make_xs_observation(angles_deg, label):\n", - " return rxmc.elastic_diffxs_observation.ElasticDifferentialXSObservation(\n", - " x=angles_deg,\n", - " y=np.ones_like(angles_deg, dtype=float),\n", - " Elab=E_lab,\n", - " reaction=rxn,\n", - " quantity=\"dXS/dA\",\n", - " measurement_quantity=\"dXS/dA\",\n", - " y_units=\"barn / steradian\",\n", - " dataset_label=label,\n", - " )\n", - "\n", - "\n", - "sigma_flux = 0.05 # one flux calibration, shared by both experiments\n", - "flux = rng.normal(1.0, sigma_flux)\n", - "\n", - "angles_fwd = np.linspace(5.0, 90.0, 12)\n", - "angles_bwd = np.linspace(60.0, 160.0, 12)\n", - "\n", - "obs_fwd = make_xs_observation(angles_fwd, \"forward experiment\")\n", - "obs_bwd = make_xs_observation(angles_bwd, \"backward experiment\")\n", - "\n", - "for o in (obs_fwd, obs_bwd):\n", - " y_true = omp.evaluate(o, *omp_true_params)\n", - " stat = 0.05 * np.maximum(y_true, 1e-4)\n", - " o.y = np.clip(flux * y_true + rng.normal(scale=stat), 1e-6, None)\n", - " o.y_stat_err = stat\n", - "\n", - "for o in (obs_fwd, obs_bwd):\n", - " plt.errorbar(\n", - " np.rad2deg(o.x), o.y, o.y_stat_err, ls=\"none\", marker=\".\", label=o.subentry\n", - " )\n", - "plt.xlabel(r\"$\\theta$ [deg]\")\n", - "plt.ylabel(r\"$d\\sigma/d\\Omega$ [b/sr]\")\n", - "plt.yscale(\"log\")\n", - "plt.legend()\n", - "plt.title(f\"both experiments share one flux calibration = {flux:.3f}\");" - ] - }, - { - "cell_type": "markdown", - "id": "f248a5cc", - "metadata": {}, - "source": [ - "## One constraint, one cross-block normalization mode\n", - "\n", - "Exactly as in the toy: the coupled covariance gets a single\n", - "`normalization_term` whose support spans **both** blocks; the independent\n", - "alternative gives each experiment its own block-local mode.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "cd5da3bc", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:18:51.339630Z", - "iopub.status.busy": "2026-08-11T03:18:51.339493Z", - "iopub.status.idle": "2026-08-11T03:18:51.622477Z", - "shell.execute_reply": "2026-08-11T03:18:51.621873Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "coupled block-diagonal? False\n", - "independent block-diagonal? True\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "s_fwd, s_bwd = rxmc.covariance.stacked_supports([obs_fwd, obs_bwd])\n", - "s_all = np.concatenate([s_fwd, s_bwd])\n", - "\n", - "xs_coupled = rxmc.constraint.Constraint(\n", - " [obs_fwd, obs_bwd],\n", - " omp,\n", - " extra_terms=[\n", - " rxmc.covariance.normalization_term(magnitude=sigma_flux, support=s_all)\n", - " ],\n", - ")\n", - "xs_indep = rxmc.constraint.Constraint(\n", - " [obs_fwd, obs_bwd],\n", - " omp,\n", - " extra_terms=[\n", - " rxmc.covariance.normalization_term(magnitude=sigma_flux, support=s_fwd),\n", - " rxmc.covariance.normalization_term(magnitude=sigma_flux, support=s_bwd),\n", - " ],\n", - ")\n", - "print(\"coupled block-diagonal?\", xs_coupled.covariance.block_diagonal)\n", - "print(\"independent block-diagonal?\", xs_indep.covariance.block_diagonal)\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(9, 4))\n", - "for a, (c, title) in zip(\n", - " axes,\n", - " [(xs_coupled, \"coupled (case A)\"), (xs_indep, \"independent\")],\n", - "):\n", - " im = a.imshow(c.covariance_matrix(omp_true_params), cmap=\"viridis\")\n", - " a.set_title(title)\n", - " fig.colorbar(im, ax=a, fraction=0.046)\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "a5cda1bf", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:18:51.624199Z", - "iopub.status.busy": "2026-08-11T03:18:51.624047Z", - "iopub.status.idle": "2026-08-11T03:19:37.355527Z", - "shell.execute_reply": "2026-08-11T03:19:37.354746Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 45.8 s, sys: 2.09 ms, total: 45.8 s\n", - "Wall time: 45.7 s\n" - ] - } - ], - "source": [ - "%%time\n", - "xs_prior = stats.multivariate_normal(\n", - " mean=np.array([50.0, 3, 1.2 * 40 ** (1 / 3), 0.65, 18, 1.2 * 40 ** (1 / 3), 0.65]),\n", - " cov=np.diag([7, 7, 0.2, 0.2, 10, 0.2, 0.2]) ** 2,\n", - ")\n", - "\n", - "\n", - "def fit_xs(constraint, seed):\n", - " walker = rxmc.walker.Walker(\n", - " rxmc.param_sampling.BatchedAdaptiveMetropolisSampler(\n", - " params=omp.params,\n", - " starting_location=xs_prior.mean,\n", - " prior=xs_prior,\n", - " initial_proposal_cov=xs_prior.cov / 100,\n", - " ),\n", - " rxmc.evidence.Evidence([constraint]),\n", - " rng=np.random.default_rng(seed),\n", - " )\n", - " walker.walk(n_steps=6000, burnin=1500, batch_size=1000, verbose=False)\n", - " return walker.model_sampler.chain\n", - "\n", - "\n", - "xs_chain_coupled = fit_xs(xs_coupled, 8)\n", - "xs_chain_indep = fit_xs(xs_indep, 8)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "8d155f69", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:19:37.357572Z", - "iopub.status.busy": "2026-08-11T03:19:37.357306Z", - "iopub.status.idle": "2026-08-11T03:19:39.348068Z", - "shell.execute_reply": "2026-08-11T03:19:39.347258Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "coupled Vv=45.63±2.31 Wv=4.19±0.82 Rv=3.90±0.14 av=0.67±0.02 Wd=20.38±1.44 Rd=4.09±0.03 ad=0.50±0.02\n", - "independent Vv=46.78±1.82 Wv=4.16±0.87 Rv=3.84±0.11 av=0.68±0.02 Wd=20.40±1.40 Rd=4.11±0.03 ad=0.50±0.02\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for name, ch in [(\"coupled\", xs_chain_coupled), (\"independent\", xs_chain_indep)]:\n", - " print(\n", - " f\"{name:12s} \"\n", - " + \" \".join(\n", - " f\"{p.name}={ch[:, i].mean():.2f}±{ch[:, i].std():.2f}\"\n", - " for i, p in enumerate(omp.params)\n", - " )\n", - " )\n", - "\n", - "fig = corner.corner(\n", - " xs_chain_coupled,\n", - " labels=[p.name for p in omp.params],\n", - " truths=omp_true_params,\n", - " truth_color=\"k\",\n", - " color=\"tab:blue\",\n", - ")\n", - "corner.corner(xs_chain_indep, fig=fig, color=\"tab:red\")\n", - "plt.plot([], [], color=\"tab:blue\", label=\"coupled (case A)\")\n", - "plt.plot([], [], color=\"tab:red\", label=\"independent\")\n", - "fig.legend(loc=\"upper right\");" - ] - }, - { - "cell_type": "markdown", - "id": "d8d2706f", - "metadata": {}, - "source": [ - "## Takeaways, continued\n", - "\n", - "- **Real reaction data changes nothing structurally**: the shared-flux coupling\n", - " is the same three lines as the toy — put both experiments in one `Constraint`\n", - " and give the `normalization_term` a support spanning both blocks.\n", - "- The independent spelling silently claims the two flux calibrations could\n", - " fluctuate separately — a stronger (and here wrong) assumption, visible as the\n", - " covariance heatmap's missing off-diagonal blocks.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/gp_discrepancy.ipynb b/examples/gp_discrepancy.ipynb deleted file mode 100644 index 236a733..0000000 --- a/examples/gp_discrepancy.ipynb +++ /dev/null @@ -1,972 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c00", - "metadata": {}, - "source": [ - "# Model discrepancy with a Gaussian-process term\n", - "\n", - "A linear model is fit to data drawn from a *mildly non-linear* truth. The model\n", - "is structurally wrong, so a plain fit leaves **correlated** residuals. We absorb\n", - "that structure with a Gaussian-process (GP) **discrepancy** term added to the\n", - "constraint covariance — a `rxmc.covariance.kernel_term` built from a scikit-learn\n", - "kernel — and then **propagate the total uncertainty** (model parameters + GP\n", - "discrepancy + observation noise) to a fine prediction grid using\n", - "`rxmc.predictive.total_predictive_band`.\n", - "\n", - "This is the Kennedy & O'Hagan picture: the discrepancy is a latent correlated\n", - "function, marginalised over its GP prior. The kernel term only inflates the\n", - "covariance **at the data points**; predicting the discrepancy at *new* points is\n", - "the GP posterior-predictive provided by `rxmc.predictive.gp_posterior_predictive`." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c01", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:51.145746Z", - "iopub.status.busy": "2026-08-11T03:07:51.145608Z", - "iopub.status.idle": "2026-08-11T03:07:53.817459Z", - "shell.execute_reply": "2026-08-11T03:07:53.816779Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using database version X4-2024-12-31 located in: /home/kyle/db/exfor/unpack_exfor-2024/X4-2024-12-31\n" - ] - } - ], - "source": [ - "import corner\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "from scipy import stats\n", - "from sklearn.gaussian_process.kernels import ConstantKernel, Matern, WhiteKernel\n", - "\n", - "import rxmc\n", - "\n", - "rng = np.random.default_rng(7)" - ] - }, - { - "cell_type": "markdown", - "id": "c02", - "metadata": {}, - "source": [ - "## A linear model and a non-linear truth\n", - "\n", - "The model is a straight line $y_m(x; m, b) = m x + b$. We give it the usual\n", - "`.y(x, *params)` helper so the same model can be evaluated on a raw grid for\n", - "plotting." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c03", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:53.819066Z", - "iopub.status.busy": "2026-08-11T03:07:53.818832Z", - "iopub.status.idle": "2026-08-11T03:07:53.822492Z", - "shell.execute_reply": "2026-08-11T03:07:53.821615Z" - } - }, - "outputs": [], - "source": [ - "class LinearModel(rxmc.physical_model.PhysicalModel):\n", - " def __init__(self):\n", - " super().__init__(\n", - " [rxmc.params.Parameter(\"m\", float), rxmc.params.Parameter(\"b\", float)]\n", - " )\n", - "\n", - " def evaluate(self, observation, m, b):\n", - " return self.y(observation.x, m, b)\n", - "\n", - " def y(self, x, m, b):\n", - " return m * x + b\n", - "\n", - "\n", - "model = LinearModel()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c04", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:53.823735Z", - "iopub.status.busy": "2026-08-11T03:07:53.823595Z", - "iopub.status.idle": "2026-08-11T03:07:54.037874Z", - "shell.execute_reply": "2026-08-11T03:07:54.037056Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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/AEBKSgpCQ0MBACdPnqz29YiIiOwNh6WcQGBgIDQaDf773//i/PnzpR7dvlfTpk3RuHFjzJgxA8eOHUNsbCxeeeWVal+PiIjI3jDcOAGVSoX169dj3759GD58OBYuXAi1Wo2IiIhSQ2YqlQo///wzBEHApEmTEBsbi1WrViEiIkJ64svc6wmCgIiICG40SkRUBfnFeoTN2YawOduQX8xpAdbAYSknERUVhaioKJO2+xfrKxEREYHvv/++wmPNuZ5SqSz3HkRE5Bpyc3Nx8uRJhIeHo379+nKXA4A9N0RERGSm7OxsHD9+3KQtOjoa3bp1w+bNm2WqqjSGGyIiIiolJyfHZLPk+Ph4aDQadOnSxeSBk4cffhj169eHwWCQo8wyMdwQERG5uIKCAoiiKL1+++23odFosGTJEqmtQYMG0Gg0qFevnsnGyvPnz0dSUhKmTZtm05orwnBDRETkooxGIx599FH4+voiPj5eam/QoAFEUTRpEwQBN2/eRHx8vLQ1D4BSD67YA4YbIiIiB5OqLazyOfv27UPXrl3xzDPPSG0KhQIGgwEGgwF//fWX1D5kyBCkpqYiNjbW5Bp+fn7VL9qGGG6IiIgcQOzxv9cc6/PR79h49Ga5x37++ed48sknceDAAalNpVLhwIED2LNnj8kQ1Ndff42bN29i8ODBUpufnx/q1atn2W/AhhhuiIiI7FyKtgDzt56TXhtF4I0fzuLSzTR8+eWXePPNN02O//3337Ft2zbs27dPamvXrh1Wr16NvXv3mhzbtm1bhIaGlrk9j6Oyv4EyIiIiMnEjMw9G0bTNIIq4mqbFSy+9BEEQMGfOHPj6+gIAxo4diy5duqBv377S8Z6ennjuuedsWbZsGG6sJL9Yj+ZvbQcAnF/0BLzc+VYTETkTa/4/bzAYcO7cObRo0QJKpRIP1vUGRBG4p3dFKQh4uGkoRo0ahUaNGqG4uFj6Wv/+/dG/f3+L1eNoOCzlpG7fvo2wsDBcvnxZ7lKIiKrFlbYpMBqN0ueiKKJBgwZo06YNLly4AAAI0nhiRLgRovHuWjIKAVg8tCWCNJ5Yt24dFi1ahDp16shSuz1iuHFSBoMB8fHxJkm+MiWP93GjTCJyVI4WiLbt3o+HH37YpJdFEAQ89NBD8PHxwY0bN6T2BS9EQVAoAQA7Z3ZHdGQDm9frKDhWQhKdTof4+Hjo9fb/HwIROQ4O09+1adMm/Pjjj2jYe4zUNntvPjINdXH5wAHo9XppzZj169ejTp06UCqVZV4rUONhk5odFXtubKA66xFU1ZUrVzB48GA0b94cgwYNwpEjR0y+XlRUhLCwMISFhaFx48bo06cP1q1bJ309OzsbPXr0AAB07doVYWFheP755ys9j4iITBkMBhw9ehQrVqwwaf/111+xYeuvWHfx718gRQB1+0/D/uNnTBbDCwgIKDfYUOVcMz7bwP3rESwZ2spqXYgFBQXo3bs3OnXqhLVr1yI+Ph7jxo0zOcbd3V16/E+n0+HYsWOYOnUqVCoVoqOj4ePjg3Xr1qFHjx7YsGEDQkJC4OnpWel5RESuTtQXIzU1FeENQgDc3SW7Q4cOEEURTz31lLRT9ogRI6AIaobfdKaPXIsQUKhyjMXxHAXDjRWUtx7BY039EaTxtPj91qxZg8LCQqxevRoeHh5o3749srKyMHHiROkYQRBMlstu0qQJrl+/jlWrViE6OhoKhQLBwcEAgJCQEJNjKzqPiMiV5Z7ZiaztyzE7fjA2ff8dAECj0aBXr17w8vJCTk6OdGzfvn3RqkM37Fy62+SxbqUgIKyul61Ld2oMN1ZQ3noEcZn5Vgk3J0+eRGRkJDw8/h6Dfeyxx0od99NPP+HLL79EXFwc8vLykJuba9bs+uqeR0TkTD788ENs3boVy5YtQ2RkJABAVSsQMOhw+fIlk2N37txZ5jWCNJ5YOLAF5m25+wvwvU89keVwzo0VPFjXG4r7Fnq0ZjIvKiqCu7u7Sdu9QQcAdu3ahejoaERFRWH9+vXYs2cPpk+fjqKiogqvXd3ziIgcVXFxMX7//Xd89dVXJu0HDhzA/v37sXv3bqlNXT8C9Sd8iUNHjpl9/WHtQ6TP+dSTdbDnxgpsncybNGmCb775xqTt9OnTJq+3b9+Onj17YsqUKVJbcnKyyTElk9fu3XPEnPOIiByZXq9Hbm4uatWqBeDu/3E9evSASqXCM888Ax8fHwDA5MmTMWDAAJNVfwWlG9zqhFR76wI+9WQd7LmxElsm8+effx4JCQmIiYmBKIrIyMjAW2+9ZXJMcHAwTp8+jbS0NADAjh078J///MfkmMDAQCgUCly5cqVK5xEROaoVK1agdu3aJnszhYWFoVOnTnj66aeh1Wql9ieeeAITJkxAgwbsabF3DDc2YO1kXr9+faxduxbvvPMOatWqhWbNmuHJJ580OWbixIlo3bo1GjRogDp16mDy5MkYPny4yTGenp54/fXXMXDgQDRo0ADPP/+8WecRETmCdevWYfTo0SYL49WrVw85OTk4fvy4ybEHDx7Ef//7X+lBC3IsHJZyEk8//TQGDx6MrKws1KtXD0ajEePHj5f+YXp6emLbtm3IyclBQUEBAgICkJubizt37phcZ/HixZg/fz7S09Ph7u5u9nlERPbEWJSPY0eP4LEunaW2FStWYP/+/ejevbv0NGmfPn1w7NgxtG3btkb3S9UWItzfp0bXIMthuHEiKpUK9erVAwAoFAqTR7hL+Pr6SrvG+vj4SGPJ91Kr1QgNDa3yeURE1lKV8FCceRMpq6biqVU+yMrKkhbHGzduHLp3747Onf8OPD4+Pmjfvn21arLlemZUNQw3VuLlrkLc0gFyl0FE5LDMCQ+7d+/GF198gY4dO+LVV18FALjVDoZno0dQ18OAxMRE6Re9sWPHWqw2W69nBvDnSlVwzg0REdmMudvRlBUe5v5wBht/+g3Z2dlS+40bN7Bp0yZs2rQJwN1AJCiUCBj2FsQnF+JwhnV+zFW0nhnJj+GGiIis6v4emI1Hb1Z6TlnhwSgCz0+ZhR07dkht/fv3x4IFCxATE1Nub0qKtqDm38R9bL2eGVWNrOHGYDDgxx9/xDPPPINZs2ZV6Vyj0YiXXnpJmkRLRET2pzqBIyMjA2s++xAQjSbtgiiijtpgsqVB/fr1MX/+fHTo0MGmvSkl65mV4ErD9kW2cKPT6dCoUSOsWrUK6enp0uaM5nr77bexfft2bNmyBQUFlk3l9y5iR9XH95GIzAkcSUlJuHDhgvTay8sL33z+MbJ+/VT6f0QhAIsGNkPi5bPlzp2xdW8KVxq2X7KFG6VSiT/++ANbt25Fq1atqnTu/v37sXr1arz//vsWrcnNzQ0AkJ/PMVNLKHkfS95XIrIf+cV6hM3ZhrA525BfrLfafSoLHCtXrkRISAhmz54tfd3b2xuLFy/GyjdfhKi7u9XLzpnd8WyXxhWuBCxnb0pV1jMrmRgct3QAvNz5XI81yPauKhQKhISEVH7gfW7duoUxY8Zg9erV0Ost+w9SqVSiVq1aSE9PB3D3t4fqLqntykRRRH5+PtLT01GrVi1pWwcicj33b0cD0YgXH/aTAkfHjh0hCAIKCgogiqL0f+6rr76K/GI95p7YDsD88DCsfYh0r50zu3PtGRflcJHxhRdewDPPPIMePXqUu+vqvYqKikw2ebx3ln1ZAgMDAUAKOFR9tWrVkt5PInI9WVlZqFOnjhQ47hzcgNyT/8OdlycAI7oDAFq1aoW0tDT4+/tb/P7ct8l1OVS4+eSTT5CYmCg98meOJUuWYOHChWYfLwgCgoKCEBAQAJ1OV50yCXeHothjQ+Sa8vPz0aNHD5w4cQIpKSnw1jwAAFDXa4zRz4/C4MGDpWMFQbBKsCHX5lDhJiYmBrVr15b2NsrIyAAATJgwAcOHD8f48eNLnTN37lzMnDlTep2dnV1q9d2yKJVK/nAmIqqEwWDAwYMHkZaWhqeffhrA3SF9nU4Ho9GIQ4cOoU+/KACAZ6NH8MGiJzjPhKzOof6GffnllyaTfU+fPo2DBw9i+PDhePTRR8s8R61WQ61W26pEIiKXsnPnTvTr1w+BgYEYOnQoFIq7z6l8/fXXCA4ORr169aw6YZmoLHYdbvLy8jB69GhMmzYNvXr1Qt++fU2+XrK/0RNPPFGtyclERGS+vXv3YuXKlejVqxcmTJgAAOjZsyfCwsLQrVs3ZGdno1atWgCAhx9+WMZKydXJGm6mT5+O+Ph4nDlzBrdu3ZLGYTds2AAPDw/odDps2bLFZHyWiIhsQ6vVwtvbW9p48q+//sJ///tfJCcnS+HG3d0d169f55OlZFdkDTeDBg0q8+mlknVRvL29sXnz5nJ/A2jTpg02b96MunXrWrVOIiJXM27cOKxbtw4//fST1Gs+bNgwJCcnS3NrSjDYkL2RNdz06tWrwq+7ublV2Gvj7+/PXh0iohq6c+cOdv55EAMHDpTa3N3dUVxcjH379knhpkGDBvjggw/kKpPIbHY954aIiKzLWJSHRg1DUVhYiLi4ODRs2BAAMGvWLEybNg3NmzeXuUKiqmO4ISJyEfn5+fj555+Rnp6OcRMnAwAUam882qEDsjIzkZycLIWbxo0bl32NYj2av3V31eDzVn6su2SbAntl7/W5MoYbIiIXcfz4cURHR8PPzw+jnhsrtX+2eiNaNawnX2FEFibbxplERGQ9J06cwMSJE7F8+XKprUuXLujWrRumTp2K74/elNoHfXkMG+957ci4KSUB7LkhInIKoihCFEVpEb0TJ07gq6++QuvWrTFlyhQAdzcs3rdvH1K0BeiydLd0rlEE3vjhLB5r6m+VHbQdYfjGlsNtZH3suSEicnArV65Ey5YtsWXLFqlt2LBhmDBhAmJiYiCKosnxNzLzYDRtgkEUEZeZDyJnwHBDRORgcnNzTV5fuXIF58+fx4YNG6S2Bx54AF999RV69uxZah2aB+t6Q3Hf0jRKQUBYXS+r1UxkSww3REQOQhRFjB07FgEBATh37pzUPmHCBPz73//GihUrzLpOkMYTCwe2kF4rBGDx0JZWGZIikgPDDRGRHUtNTZU+FwQBt2/fRkFBAX7++WepPSIiAuPHj5f2dTLHsPZ/78e3c2Z3REc2sEi9RPaA4YaIyA5lZWWhQ4cOCA8Ph1arldoXLFiAI0eOYPbs2Ra7V6DGw2LXIrIHDDdE5HDyi/UIm7MNYXO2Ib9YL3c5FmE0GhEfHy+9rl27NnJycqDT6fDnn39K7e3atUNkZCT3cyKqAJ91IyKS2ZkzZ/DUU09BoVDg6tWrUCgUEAQBa9asQYMGDRAQECB3iUQOhT03REQ2ptPpkJycLL1u1KgRbt++jVu3buHatWtS+yOPPMJgQ1QNDDdERDa0fft2hISE4IUXXpDavLy8sGPHDqSkpKBJkyYyVlc1qdpCuUsgKhPDDRFRBWo6v6e4uBi3b9+WXjdu3Bjp6ek4e/asyXo1jz76KDw97f9R7NjjidLnfT763Wm2bSDnwnBDRGQl69evR0hICN566y2prVGjRti3bx/i4uLg4+MjY3VVl6ItwPytf6+vU7JtQ4q2QMaqiEpjuCEishCDwYCioiLptb+/PzIyMrBr1y6TLRC6desGNze3Uufb+1NgrrJtA4fbHB/DDRGRBXzzzTcIDw/HypUrpbbevXvjp59+wqlTp5zi0W1n3raBw23OheGGiKgaSnbhLpGfn4+bN2/iu+++k9oUCgWefPLJMntpHJGzbtvA4Tbnw3BDRFRFGzduRGRkJHbs2CG1Pfvss/jPf/5j0mYrthzOcsZtG1xluM2VcBE/IqIqOnjwII4fP44vvvgCffv2BQD4+fnh+eefl7ky83m5qxC3dECNruEs2zaUDLfdG3CcZbjNVbHnhoioAof/PISMLe9Bd+fvDSynTp2KJUuW4KuvvpKxMrIUZx1uc2UMN0Tk0Kz9ZMu7b7+N/Iv7kXPi7124mzRpgjlz5qBu3bpWvTfZjjMOt7kyhhsicjjWerLl9u3bWLZsGfLz/55rMe2VGfBu1Qc+LXtZ5B5k/5xluM2Vcc4NETmU8p5seaypf42GEURRRPfu3XHmzBn4+Phg0qRJAIA+jz+OuvuNNa6biGyHPTdE5FAs+WTLqVOnpMe5BUHA+PHj0apVKwQGBlqiVCKSCcMNETkUSywkJ4oi+vXrh7Zt2+L333+X2l9++WWcOnUKgwYNKvM8rlxL5BgYbohINtVZn6W6T7YYjX8PLQmCgPDwcLi5ueHUqVNSu5ubW6mVhLlyLZHjYbghIodTlSdbjEYjPv74YzRp0gQJCQlS+7x583Djxg1Mnz693HPlXLmWvURE1cdwQ0QOrbInWxQKBbZu3Yrr16/jyy+/lNqDgoIQHBxc4bm2XrmWvURElsFwQ0ROJTU1FfPnz0dh4d89H2+//TZWrFiBefPmVela1Z3fU53hNu5vRGQ5fBSciJyGKIro1q0brl69ivr160uPc3ft2hVdu3at8vVK5vfM23I3dFhz5dqKeomqcr9UbSHC/X0sXN3fLLFtA5G1seeGiBxaSnKy9LkgCPjHP/6BTp06oWnTpha5vq1Wrq3JU2AcziIyxXBDRA5JFEVkbf8MEY3DsX//fql9ypQpOHDgAHr27Gnxe1pz5drqPgXG4Syi0hhuiMghCYIAiIDBYMD27duldqVSWepxbqB682BsrTq9RLae9OysSobb4pYOgJc7Z2w4OoYbInII165dw4QJE5CSkiK1aTpHY+/+P/DOO+/IWJl1mNtLZIlFDYmcDcMNETmEF154AV9//TXee+89qU3l54/IRzvIWJX8qjucReTMGG6IyC4lJydDp9NJr+fPn49+/fph1KhRMlZln2w16ZnIUTDcEJHdeffdd9GoUSOsWbNGauvduzf+97//4dFHH5WxMvtnzUnPRI6C4YaI7MK92w14eXmhsLAQu3btkrEiInJUnBJORLKJPZ4IURQhCAJ6L/sdS4e1QnRkA7z00kto0aIFHn/88TLP40JyRFQR9twQkQlbPTJdsj5LyWPbIv5en8XDwwN9+/Yt85FuIqLKMNwQkU2JogidTucy67Nwd28i22O4ISKbOXnyJHr16oV58+Y59fos3A6BSF4MN0RkM0lJSdi7dy9WrFgBX6XBIdZnqerKtdwOgUh+DDdEZDVarRanTp2SXkdFRWHx4sU4deoUfHx8ZFufxZpDRTUZbuMWAESWwXBD5KTk3kvp8OHDaNy4MYYNG4bi4mIAd/eDmjt3Lho0KB1irL0+i62Gipx5uI3IUTDcEJFVtGjRAiqVCm5ubkhISJC1FlsOFXE7BCL5MdwQkUWcOnseD/R8Qeop8vHxwe7du3HmzBk0atRI1tps/WQWt0MgkhcHdYmoxtLT09Exsj0KCwuhrt8MwBMAgIceekjewv5fyVDRvQHHVkNFttgOgYsaEplizw0R1VhAQADGPPscPBtFQulTW+5ySuFQEZFrYbghoio7evQo+vXrh4yMDKntw49jEPD0fLg9UF/GysrHoSIi18FwQ0RVIooiXnrpJWzfvh3z58+X2t3c3GSsqmq4czaRc2O4IaJylawHYzQaIYp3J6wIgoBPPvkEY8aMwbx58+Qsj4ioTAw3RA7AlmvW3L8ezPub/kCnTp2wZs0aqb1z585Yu3YtgoKCrFoLEVF1MNwQkaSs9WC+OHYHxy9cwzvvvAODwSBjdURE5mG4ISJJWevBiBAwetIM7Nu3D0qlUp7CiIiqgOvcEJHEvUgLiEZA+Pv3HqUgYOk/Z1rlsWmuz0JE1sCeGyIXYO5GkbXcRWh3fQnReHf4ievBEJEjYrghclLmbhSZlZUlfd64cWPEvDIKem06AK4HQ0SOieGGyAmZs1GkTqfD66+/jrCwMFy7dk1qHz4iGm4P3H0KqrrrwZjbU+SsSobb4pYOgJc7R/+JbI3hhsgJmbNRpEqlwtGjR5Gbm4tNmzbV+J7m9hQREVmbXfxKER8fDwBo2LChWceLooikpCTUqlULPj4+1iyNyCGVt1FkXQ8RBoMBSqUSgiDgq6++wrlz5zBw4MAa3a+8nqLHmvpzvg4R2ZxsPTeiKGL16tXo2LEjIiIiMGzYsErPKSgowPz58xEQEIAuXbrA398f3bt3x5UrV2xQMZHjKGujyGcfUqFP5/b4+OOPpfZGjRrVONgA5vUUyY1DRUSuQ7Zwo9PpsHv3bnz00UeYPHmyWeckJiZCo9Hg2rVriI+PR3p6Otzd3TFmzBgrV0vkeO7fKLKBPhE3b97EqlWroNPpLHqvkp6ieykFAWF1vSx6HyIic8gWbtzd3bF69Wp07tzZ7HOaNGmCmTNnws/PDwDg6+uL/v374+rVq9Yqk8ihifpiAHcnBr/wwgv47LPPcOTIEYtvcllWTxEfISciuTjkhOKEhAScP38eP/74Iz799FPMmjVL7pKIbMacJ5Hy8vIwc/o0pK57HaLh7l5UgiBgypQpVpundn9PER8hJyK5OGS4mT9/Pp566ilER0cjIiIC48aNK/fYoqIiZGdnm3wQOZqqPomUnZ2N7zZuQHHqFRTcOGHt8kqp7iPkRESW4JDhZtWqVbh27RrS0tLg5uaG3r17l7uh35IlS6DRaKSP0NBQG1dLVDPmrFkD3J2kXyIoKAhfrvw3AkYsglfjR21WKxGRPXDIcFOiVq1aeOONN3Du3DlcvHixzGPmzp0LrVYrfSQkJNi4SqKaMedJpAsXLqBTp044fvy41PbkwIHwfPBhW5VJRGQ37DrcGI1GXLx4EVqtFgDKfMIjOTkZAKRJxvdTq9Xw8/Mz+SByJOY8ifTOO+/g8OHDeOWVV2xbHBGRHZJ1sYcbN26gqKgIt2/fRmFhodT7EhERAUEQkJ2djYceegjffPMNxo4di5iYGCQmJmLAgAGoW7cuTp48iX/+85+Ijo7mcBM5rZInkeZtuTs0VdaTSJ988glUKhWWLFlS4/txp24icnSyhpvp06fj8uXL0uvBgwcDAE6cOAEvLy8olUpERERAo9EAAGbOnInVq1fjX//6F1JTUxESEoIlS5Zg9OjRcpRPZDPD2odI4WbnzO64eHQf3nvvv3j99dcBAHXq1MHq1avlLJGIyG7IGm62bt1a4dd9fX1N5tIolUqMGzeuwqejiJxdevwlDBgwAIIgoGvXrujSpYvcJRER2RWuQU7kYFq3boOJEyfCw8MD7du3l7scIiK7w3BDLi+/WI/mb20HAJxf9ITd7TskiiJiN30PY7E7FO5314/54osvoFDY9fMARESysa//xYmolNdeew3Lli2DT+u+qNN/GgCYFWw4MZiIXBV/9SOyc1FRUXBzc4NKU89koT4iIiobe26I7IwoisjIyEBAQAAAoFevXjh74RL6fnVe5soqxp4iIrIX7LkhsiP5+fl44YUX0L59e2RmZkrtIVzHiYjIbAw3RHZEr9fj4MGDSE5Oxp49e+Quh4jIIXFYisiGKnsyy8/PD7GxscjKykKPHj1kqJCIyPGx54ZIRnq9HrNnzzZZ0LJVq1YMNkRENcBwQySjL774Ah988AGee+453Lp1S+5yiIicAoeliGQ0adIk/PLLLxg/fjxq165d7nF8EomIyHwMN0Q2Vph4Durg5gAAd3d3/PLLLxAEQeaqiIicB4eliGzo9ddmIW3d68j9a5vUxmBDRGRZDDdE90jVFlr1+kFB9QEAhsJcq96HiMiVMdyQy4s9nih93uej37Hx6E2r3Wv6jBkIfHYZanUeabV7EBG5OoYbcmkp2gLM33pOem0UgTd+OIsUbYFFrr9lyxYMHToUer0ewN0hKHX9CItcm4iIysZwQy7tRmYejPftRWkQRcRl5ld6bn6xHmFztiFszjbkF+tLfT0zMxNjxozB5s2bsWLFCkuVTEREleDTUuTSHqzrDYUAk4CjFASE1fWq8bXr1q2L1atXY/fu3Zg4cWKNr0dEROZhzw25tCCNJxYObCG9VgjA4qEtEaTxrNb1MjMzER8fL70eOnQoPvvsM7i5udW4ViIiMg/DDbm8Ye1DpM93zuyO6MgG1brO5cuX0aFDB0RFRUGr1VZ6vLWfzCIiclUMN0T3CNR4VPtcLy8vFBYWoqCgABkZGWUeY8sns4iIXBXn3BBZSEhICH777TcEBATA39+/1NfLezLrsab+1R4GIyKi0thzQ1RNoihCe+g7FCVdkNpatGhRZrABavZkFhERmY/hhqiaVn75Be7sW4P02LfN2tG75Mmse1nqySwiIvobww1RNY1+9jm4B0WgVrcxFe7oXcLST2YREVHZGG6IqqCoqEj63MfHB4Fj3odvuyizz7fUk1lERFQ+hhsiM12/fh2tWrXC+vXrpTZBoaz29WryZBYREZWP4YbITN988w2uXLmChQsXori4WO5yiIioHHwUnMhMCxcuhCiKmDJlCtzd3aG/Zz+pVG0hwv19ZKyOiIhKsOeGqAJ//fUXRPHu89sKhQLvvPMOgoKCAHBBPiIie8VwQ1SOtWvXIjIyEv/85z9Lfa28BflStAW2LJGIiMrAYSlyeV7uKsQtHVCqPT8/HwaDAUlJSTAajVAo/v5doKIF+fhoNxGRvBhuiMoxadIkNG7cGD179jQJNsDfC/LdG3C4IB8RkX3gsBTR/xNFEV9//TUKC//erbt3796lgg3ABfmIiOwZww05lfxiPcLmbEPYnG3Iv+dpJnPMmTMHEyZMQHR0tDSJuCJckI+IyD4x3BD9v379+sHLywtRUVEQBKHyE+7BBfmIiOwH59wQ/b+ePXvi2rVrCAwMlLsUIiKqAfbckMvS6/V4/fXXkZqaKrVZO9iUPJkVt3QAvNz5uwURkTUw3JDLmjlzJt5//33069cPBoNB7nKIiMhCGG7IZU2bNg0PPvggFi1aBKWy+htgEhGRfWG/OLmsxo0b4+LFi3B3d5e7FCIisiD23JDL0Ov1mDhxIv766y+pjcGGiMj5MNyQy3j33Xfx1VdfISoqCvn5+XKXQ0REVsJwQy7jlVdeQffu3bFy5Up4eXGbBCIiZ8U5N+QyNBoN9uzZU+UF+oiIyLFUuefml19+wZo1a9itT3ZPFEXMmDEDmzdvltoYbIiInF+Vw01xcTGmTZuGoKAgTJo0CYcPH7ZGXUQ19u9vNyImJgbR0dGIj4+3+PW5IB8RkX2qcrgZPHgwUlJSsHz5cly5cgWdOnVCixYtsGzZMqSnp1ujRiKzxR5PlD7/19UH8MRLC7F8+XI0bNhQxqqIiMiWqjWh2NPTE2PGjMHu3btx7do1PP300/j0008REhKCoUOHYvv27Zauk6hSKdoCzN96TnptFIErmvZ4csQYGasiIiJbq/HTUoIgSPMYPDw8kJeXh0GDBqFLly64detWjQskMteNzDwYRdM2gwjEZXJ+GBGRK6lWuCkoKMC6devQu3dvhIeHY/v27Zg3bx5SUlKwfft2JCQkQKFQYNWqVZaul6hchjupEI1GkzalICCsLh/7JiJyJVUONz/++COCgoIwffp0tGrVCmfOnMGhQ4cwfvx4eHt7AwD8/f0xcOBApKWlWbxgovJ0a98SPf3SIIp3A45CABYPbYkgjafMlRERkS0JoiiKlR/2t//973+4c+cOhg4dCrVaXe5xBoMBoihCpbKvp0iys7Oh0Wig1Wrh5+cndzlkYfnFejw071cIgoDdr3ZHuL+P3CUREZEFVOXnd5V7bvr3749nnnmmwmADAEql0u6CDTmfq1evYurUqSguLpbaSuaABWo85CqLiIhkxPRBDkun0+HJJ5/EpUuXoFKpEBMTI3dJRERkB7i3FDksNzc3xMTEoHXr1pg7d67c5RARkZ1gzw05tH79+uHxxx+HUqmUuxQiIrIT7LkhhyKKIpYuXYrMzEypjcGGiIjuxXBDDmXx4sWYO3cuunfvbjKJmIiIqATDDTmUIUOGoGHDhpgxYwbc3d3lLoeIiOwQ59yQQ2nevDnOnj0LHx+uX0NERGVjzw3ZvbNnz+LSpUvSawYbIiKqCMMN2bXU1FRERUWhY8eOOHLkiNzlEBGRA5B1WEoURezatQsbN25EUFAQFi1aVOk5cXFx2LhxI65fv47Q0FA899xzaNCggQ2qJTkoFAqEhIQgKysLjRs3lrscIiJyAFXeW8pSdDodWrZsieDgYOj1euTn5+PYsWMVnrN+/XosXLgQQ4YMwYMPPoj9+/dj06ZN2LFjB7p162bWfbm3lOMpLCxERkYGQkND5S6FiIhkUpWf37KFG6PRiKtXr6Jp06Z45ZVX8Mcff1QabhISEhAUFGSyZ9WgQYOQk5OD3bt3m3VfhhvHcOHyVfRfdXeezflFT8DLnXPfiYhcmVU3zrQUhUKBpk2bVumc0NDQUptxhoeHmyzoRo5v48aNaNOyOXJO/SZ3KURE5IAc+tfh27dvY8OGDRg1alS5xxQVFaGoqEh6nZ2dbYvSqAZ+/fVX6HQ66G8lyl0KERE5IId9Wqq4uBgjRoyARqPB/Pnzyz1uyZIl0Gg00gfnbdi/VatW4Zs1a1Gr+/Nyl0JERA7IIcONTqfDiBEjcP36dezYsaPCsbe5c+dCq9VKHwkJCTaslMxlNBqlzwVBwIjokRAU3DOKiIiqzuGGpfR6PaKjo3H69Gns3bu30p4YtVoNtVpto+qoOkRRxIQJE+Dv74/FixdzI0wiIqoRu+65yc/Px5gxY7B//34Ad4PNyJEjcfLkSezdu5fr2ziJP//8E9988w0+/PBDnDhxQu5yiIjIwcnaczN37lwkJCTg+PHjSE9Px5gxYwAAX3/9NdRqNYqLi7Fu3Tr06dMH3bp1w4cffojY2Fh0794db7zxhnQdb29vrFixQq5vg2qoU6dO2LBhA9LS0hAZGSl3OURE5OBkDTddunTBnTt30K9fP5P2kmEJb29vrF27Fp07dwYA9OvXDyEhIaWuw92hHV90dLTcJRARkZOQNdw8+eSTFX7dzc1N6s0BgLZt26Jt27ZWroosKb9Yj+ZvbQdguhhfUVERFi9ejNdee40bYRIRkUXZ9Zwbcl4zZ87EokWLMGDAAMi0SDYRETkphhuSxciRIxEcHIy5c+dCEAS5yyEiIificI+Ck3Po1q0brly5Ak9Pz0qPTdUWItyfQ1dERGQe9tyQzWi1WqSlpUmvKwo2scf/3nqhz0e/Y+PRm1atjYiInAfDDdmEKIqY/OIEtGvXDocOHarw2BRtAeZvPSe9NorAGz+cRYq2wNplEhGRE2C4IZswFmTj8qVLyMrKKrWz+/1uZObBeN8cY4MoIi4z34oVEhGRs+CcG7IJpZcG+w4ewukTxypdqO/But5QCDAJOEpBQFhdLytXSUREzoA9N2Qz3t7e6N27d6XHBWk8sXBgC+m1QgAWD22JIE3lk4+JiIgYbshqRFHEpBfHI+/iH1U+d1j7v1ei3jmzO6IjuY8YERGZh8NSZDUbN27Et2vWAEoV1PUjqn2dQI2HBasiIiJnx3BDVjNs2DC8+tpsrPpLC5Wfv9zlEBGRi+CwFFmNm5sbFr3zLvzaPwXg7mJ8RERE1sZwQxa3Z88eab8oLsZHRES2xnBDFrVx40b06tULw4cPR9LtPC7GR0RENsdwQxZ1+/ZtuLm5oVmzZoi/VcDF+IiIyOY4oZgsavLkyejatSsiIiKQma/nYnxERGRz7Lkhi2vZsiXc3Ny4GB8REcmC4YZq7NixY+jbty9u3iw9WZiL8RERka0x3FCNiKKIyZMnY8eOHZg/f36Fx3IxPiIisgWGG6oRQRCwYcMGDBkyBMuWLZO7HCIiIk4opppr3LgxfvjhB4te08tdhbilAyx6TSIicg3suaFquX37Nq5cuSJ3GURERKUw3FC1/OMf/0CbNm2wbt06uUshIiIywXBDVVZYWIi0tDQUFRWhcePGcpdDRERkgnNuqMo8PDzw22+/4ciRI+jQoYPc5RAREZlgzw2ZLb9Yj7A52xA2ZxsKdAYGGyIisksMN2S2H2I3QXvoO4hGg9ylEBERlYvDUmSW27dvY8a0f+BOZiYUnr4AouQuiYiIqEzsuSGz1KpVC+8ufQ/q0JbwadVH7nKIiIjKxZ4bMosgCBjz7HN493IABEEw+zwuxkdERLbGnhuqUG5uLnQ6nfS6KsGGiIhIDgw3VKHp06cjMjISp06dkrsUIiIis3BYisqVnp6OH3/8Ebdv30Zubq7c5RAREZmF4YbKFRAQgAsXLmD79u3o0qUL8ov1cpdERERUKQ5LUYUCAgLw7LPPyl0GERGR2RhuqJTz58/j2LFjcpdBRERULQw3ZMJgMGDcuHHo0KED1qxZI3c5REREVcZwQyYKCgrQqFEjeHt7o3fv3uUel6ottGFVRERE5mO4cUH3boB5/yRhHx8frFu3DhcuXEBwcLDJ12KPJ0qf9/nod2w8etMm9RIREVUFww2V6f5gk6ItwPyt56TXRhF444ezSNEW2Lo0IiKiCjHcEABg165dmDVrFnJycsr8+o3MPBhF0zaDKCIuM98G1REREZmP69wQ9Ho9Xn75ZVy+fBlqtRrvvvtuqWMerOsNhQCTgKMUBITV9bJhpURERJVjzw1BpVLh448/RpcuXTB79uwyjwnSeGLhwBbSa4UALB7aEkEaT1uVSUREZBaGGwIAREVF4Y8//oBGoyn3mGHtQ6TPd87sjujIBrYojYiIqEoYblxccXFxtc4L1HhYuBIiIiLLYLhxYQXXjqJdq5bYunWr3KUQERFZDMONC9Me2Yy4uBvYv3+/3KUQERFZDJ+WcmEBw+ZhrN85zJo5Q+5SiIiILIbhxoUp3D0x54034eXOvwZEROQ8OCzlgi5cOC99zj2iiIjI2TDcuJiDBw+ix3OzIIpGANwjioiInA/DjYvZceAYaj/xDwjC3T967hFFRETOhuHGxTw+dBQEhekfO/eIIiIiZ8Jw42JK9oi6F/eIIiIiZ8Jw4+Dyi/UIm7MNYXO2Ib9YX+5xP//8M9LS0rhHFBEROT0+A+wCkpKSMGLECKhUKhw7dgzD2odj3pZzAO7uERXu72PWdbzcVYhbOsCapRIREdUYw40LuHPnDlq0aAG1Wo0mTZqgQGeQvsY9ooiIyNkw3LiAFi1a4PDhw8jKyoIgCJWfQERE5MA458ZFKBQK+Pv7y10GERGR1THcOLFdu3Zh1apVMBgMlR9MRETkJDgs5aQMBgOmT5+Oc+fOITMzE7Nnz5a7JCIiIptgz42TMhqNGDduHBo1aoQXX3xR7nKIiIhsxi7CTWZmJtLT061+jitxc3PDzJkzcenSJTzwwANyl0NERGQzsoab2NhY9OnTB0FBQYiKirLaOa5MqVTKXQIREZFNyRZuioqKsH79esyePRsvv/yy1c5xNZmZmRgyZAj++usvuUshIiKShWwTitVqNWJjYwEAv/zyi9XOcTXvvfcefvzxR9y8eRPHjh3jujZERORy+LSUE0nVFmLy5MlITU3F6NGjGWyIiMglOX24KSoqQlFRkfQ6OztbxmosL/Z4ovR5n49+x5KhrbB27doKz+EeUURE5Mzs4mkpa1qyZAk0Go30ERoaKndJFpOiLcD8reek10YReOOHs0jRFshYFRERkbycPtzMnTsXWq1W+khISJC7JIu5kZkHo2jaZhBFxGXmy1MQERGRHbDrYSlRFJGUlITatWvDy8urWtdQq9VQq9UWrsw+PFjXGwoBJgFHIQBhdav3XhERETkDWXtu0tLSkJiYiNzcXOh0OiQmJiIxMRGiePentVarRWhoKL777juzz3ElQRpPLBzYQvreBYhYMrQVgjSeMldGREQkH1l7bkaPHo2LFy9Krzt27AgAuHz5Mry8vKBQKBAcHAxvb2+zz3E1w9qHYN6Wu/Nudr3aA+H+PjJXREREJC9Zw83OnTsr/Lqfnx8SExNN2io7x9UYjUbp80CNh4yVEBER2Qenn1DszA4dOoSHW7dC3vnf5S6FiIjIbjDcOLCPP/4YV65cRkHcSblLISIisht2/bQUVezrr79Gi1atsTIpSO5SiIiI7AZ7bhyYr68vXnt9DtxqBcpdChERkd1guHFA924nQURERKYYbhzQgAEDMHjwYFy/fl3uUoiIiOwOw42DuXz5Mnbv3o1ffvkFKhWnTBEREd2PPx0dTNOmTXH+/HkcOnQIDRo0QH6xXu6SiIiI7ArDjQNq1qwZmjVrJncZREREdonDUg4kP5+7fRMREVWG4cZBnD17FvXr18e8efNMNgn1clchbukAxC0dAC93dsQREREx3DiINWvWQKvV4tKlSxAEQe5yiIiI7BZ/1XcQS5cuRefOndGkSRO5SyEiIrJrDDd2Ir9Yj+ZvbQcAnF/0RKkhJoVCgcGDB8tQGRERkWPhsJSdKygogNFolLsMIiIih8FwY+fmzZuH1q1bY/v27XKXQkRE5BA4LGXHdDodNmzYgKSkJPbeEBERmYnhxo65ubnh3Llz2LBhA/r16yd3OURERA6B4cbOaTQaTJo0Se4yiIiIHAbn3NgprkZMRERUPQw3dkiv16NNmzYYNmwYkpOT5S6HiIjIoXBYyg4dPHAAV69exe3bt6HRaOQuh4iIyKEw3Nihx7p3x7lz53Dt2jV4e3vLXQ4REZFDYbixU82bN0fz5s3lLoOIiMjhcM6NnRENeqRqC+Uug4iIyGEx3NiJ2OOJEEURglKFXh/uxX+PxMtdEhERkUNiuLEDKdoCzN96DoIg3G0QBPxz8zmkaAvkLYyIiMgBMdzYgRuZeTCKpm0GUURcJte6ISIiqiqGGzvwYF1vKATTNqUgIKyulzwFEREROTCGGztQz1eNBU/9/WSUQgAWD22JII2njFURERE5JoYbO7Bx40Z8MHkQ8q8cBgDsnNkd0ZENZK6KiIjIMTHc2IGVK1fi7JkzKE6/DgAI1HjIXBEREZHj4iJ+diA2NhZfrPgKXyYFy10KERGRw2PPjR2oXbs2Zrz6KpQ+D8hdChERkcNjuCEiIiKnwnAjo8WLF2PEiBE4ceKE3KUQERE5DYYbmej1enz22Wf4/vvvcenSJbnLISIichqcUCwTlUqFX3/9FatWrcKwYcOgl7sgIiIiJ8FwI6PWrVsjJiYGAKAvZrwhIiKyBA5LERERkVNhz40M3nzzTQiCgJdffhn169eXuxwiIiKnwnBjY1qtFjExMcjPz0ffvn0ZboiIiCyM4cbGvLy8sHr1avz666/o1q3b3+3uKsQtHSBjZURERM5BEEVRlLsIW8rOzoZGo4FWq4Wfn5/c5RAREZEZqvLzmxOKiYiIyKkw3NjQokWLsGbNGhQWFspdChERkdPinBsbSUtLwzvvvAOdTodWrVqhXbt2cpdERETklBhubMTd3R0LFizAyZMnGWyIiIisiBOKiYiIyO5xQjERERG5LIYbG/jyyy9x6NAhuFgnGRERkSw458bKMjMzMW3aNOh0Opw5cwYtW7aUuyQiIiKnxnBjZQUFBRg1ahSuX7/OYENERGQDnFBsI6IoQhAEm92PiIjImXBCsR1isCEiIrINhhsr2rZtG1JSUuQug4iIyKUw3FiJVqvF8OHDERoaiosXL8pdDhERkcvghGIrSU1NRbt27aDVahERESF3OURERC6D4cZKIiIicODAAWRnZ3O+DRERkQ1xWMrKuMUDERGRbTHcWMGFCxeg0+nkLoOIiMglMdxYmF6vR+/evREcHIwzZ87IXQ4REZHLYbixsOvXr8NoNEIURU4kJiIikoHsE4qPHTuGjRs3IiAgAK+99ppZ5/z222/YtWsXPDw8MGzYMLRu3drKVZqvadOmSExMxOXLl+Hu7i53OURERC5Htp4bvV6PyMhITJo0Cfv27cPGjRvNOm/u3LmIjo6GIAhITU3FI488gh9++MHK1VaNSqVC8+bN5S6DiIjIJcnWcyMIAj7//HNERkbilVdewR9//FHpOZcvX8b777+PzZs3Y+DAgQAAjUaDqVOnYuDAgVCp5O2Iys3NhY+Pj6w1EBERuTrZem6USiUiIyOrdM7PP/8MPz8/DBgwQGp79tlnkZKSgqNHj1q6xCrr3bs3Hn30UZw6dUruUoiIiFyW7HNuquLKlSto0KABlEql1BYeHi59rVOnTqXOKSoqQlFRkfQ6OzvbKrUlJSXhxIkTAICgoCCr3IOIiIgq51BPSxUUFMDX19ekzdvbG0qlEvn5+WWes2TJEmg0GukjNDTUKrUFBwcjKSkJ33//PQICAqxyDyIiIqqcQ4UbHx8f3Llzx6QtOzsbBoOh3JWA586dC61WK30kJCRYrb6AgAAMHjzYatcnIiKiyjlUuGnRogXi4uJQWFgotV24cAEAyn06Sa1Ww8/Pz+SDiIiInJddh5uCggJMnToVhw8fBgAMGjQIer0eq1evlo5Zvnw5IiIi0KZNG7nKJCIiIjsi64TixYsXIzk5GX/88QeSk5MxdepUAMBHH30Ed3d3FBUVYfny5XjkkUfQoUMH1K9fH59++immT5+OX3/9Fbdu3cLp06fxyy+/cOdtIiIiAiBzuHnwwQfh5+eHZs2ambSXBBUvLy98+umn6Nixo/S1F198Eb169cK+ffugVqvxxBNPoE6dOjatm4iIiOyXIIqiKHcRtpSdnQ2NRgOtVsv5N0RERA6iKj+/7XrODREREVFVMdwQERGRU2G4ISIiIqfCcENEREROheGGiIiInArDDRERETkVhhsiIiJyKgw3RERE5FQYboiIiMipyLr9ghxKFmTOzs6WuRIiIiIyV8nPbXM2VnC5cJOTkwMACA0NlbkSIiIiqqqcnBxoNJoKj3G5vaWMRiOSk5Ph6+tr8Z3Es7OzERoaioSEBO5bVQN8Hy2D76Nl8H20DL6PluHK76MoisjJyUH9+vWhUFQ8q8blem4UCgVCQkKseg8/Pz+X+0tnDXwfLYPvo2XwfbQMvo+W4arvY2U9NiU4oZiIiIicCsMNERERORWGGwtSq9WYP38+1Gq13KU4NL6PlsH30TL4PloG30fL4PtoHpebUExERETOjT03RERE5FQYboiIiMipMNwQERGRU2G4sYDU1FRMmzYNPXr0wIgRI7B37165S3JISUlJWLBgATp37oyYmBi5y3FIer0eq1evxsiRI9G3b1/MmDEDN2/elLssh/Tbb79h7Nix6NWrF8aOHYt9+/bJXZJDO3/+PDp37oznnntO7lIczvbt29GxY8dSH7m5uXKXZrdcbhE/S8vNzUXXrl3RqFEjvPbaazh8+DAef/xx/Prrr+jdu7fc5TmMP//8EyNHjsTzzz+PrKwsxMXFyV2SQxo1ahR8fHwwePBg1K5dGytXrkTbtm1x7NgxhIeHy12ew1i+fDl2796NIUOGoH79+tizZw969eqF2NhYDBo0SO7yHE5BQQFGjhwJo9GI06dPy12Ow8nIyMCVK1ewbds2k3ZPT0+ZKrJ/fFqqhj7++GPMnz8fqamp8PLyAgCMHDkS8fHxOHTokMzVOY68vDx4eHhAqVTikUceQdeuXdl7Uw25ubnw8fGRXhsMBoSFheGFF17AokWLZKzMseTn50v/nkv07t0bQUFB+Pbbb2WqynFNmjQJKpUKbm5u2Lt3L06ePCl3SQ7l22+/xaxZs5Camip3KQ6Dw1I1tGvXLvTu3dvkP8JBgwbh8OHD0iadVDlvb28olUq5y3B49wYbAFAqlfD09ERxcbFMFTmm+4NNWloaLl26hDZt2shUkePatGkT9u/fjw8//FDuUhxadnY2+vfvj/79+2POnDnIyMiQuyS7xnBTQ/Hx8ahfv75JW/369SGKIhISEmSqiuiu77//HlevXsXAgQPlLsXhGAwGdOzYEe3atUOjRo0wbtw4zJo1S+6yHEpcXBymTJmCdevWcQilBgRBwPDhw/HSSy/hxRdfxOHDh9GiRQskJyfLXZrd4pybGtLpdKVWiiz5R6zT6eQoiQgAcPz4cYwfPx5z585F586d5S7H4SiVSsTExCAvLw+7du3CsmXL0KFDBwwYMEDu0hyCXq/HqFGjMGvWLLRr107uchza008/jdGjR0uvBwwYgObNm2Px4sX47LPPZKzMfjHc1FDt2rVx69Ytk7asrCwAQJ06deQoiQgnT55E3759MXbsWLz77rtyl+OwOnbsCODufJuMjAy88cYbDDdmunTpEg4dOgS9Xo/Y2FgAd3u6s7Oz0bFjR3z66aeIjIyUuUrHcP8v0Gq1Gt26dcOpU6dkqsj+MdzU0MMPP4w9e/aYtB0+fBj+/v4IDg6WqSpyZadPn0afPn3wzDPP4JNPPpG7HKcRFBSEzMxMuctwGOHh4aUeqoiJicGJEycQExODiIgImSpzDqmpqfD29pa7DLvFOTc1NG7cOFy4cAGbNm0CACQnJ+Pf//43xo0bB0EQZK6OXM3Zs2fRu3dvjBw5kt3VNfDZZ5+ZTNi8evUqVq9ejf79+8tYlWPx9PQstS5LYGAgvLy80LFjR/j5+cldosP417/+ZfL3cePGjfjtt98wcuRIGauyb+y5qaGHH34YX3zxBZ5//nnMnTsXiYmJiIqKwoIFC+QuzaHk5uaiT58+AICLFy8iKSkJf/75J5o2bYo1a9bIXJ3jmDhxIm7duoVjx45JQyoA0KtXLyxevFjGyhxLvXr10KFDB6hUKigUCty8eROjR4/Gxx9/LHdp5IICAgIQGRkJtVqNwsJCaLVaLFu2DGPHjpW7NLvFdW4sJDc3F1euXEFAQACHo6rBYDDg6NGjpdq9vb3RqlUrGSpyTGfPni1z1dI6deqgSZMmMlTkuERRxI0bN1BcXIyGDRvyaR8LKJlzw3/TVSeKIq5duwZBEBAWFsalMyrBcENEREROhXNuiIiIyKkw3BAREZFTYbghIiIip8JwQ0RERE6F4YaIiIicCsMNERERORWGGyIiInIqDDdERETkVBhuiIiIyKkw3BCRQ9PpdNi4cSMuX75s0r5nzx7s3btXnqKISFYMN0Tk0Nzc3HDgwAFERUUhJycHALB//3707dsXer1e5uqISA7cW4qIHF5hYSEiIyPRvn17xMTEoE2bNhg+fDg+/PBDuUsjIhkw3BCRUzhz5gweffRRNGnSBEqlEocPH4a7u7vcZRGRDDgsRUROoVWrVhgxYgTOnDmD999/n8GGyIWx54aInMKxY8fQuXNnNG7cGP7+/tizZw8UCv7+RuSK+C+fiBxeXl4eRo8ejRdffBG7du3C+fPn8d5778ldFhHJhD03ROTwJk6ciP379+PEiRPw9PTEli1bMHz4cBw6dAjt27eXuzwisjH23BCRQ4uLi0NOTg7Wr18PT09PAMCgQYOwaNEi/PTTTzJXR0RyYM8NERERORX23BAREZFTYbghIiIip8JwQ0RERE6F4YaIiIicCsMNERERORWGGyIiInIqDDdERETkVBhuiIiIyKkw3BAREZFTYbghIiIip8JwQ0RERE6F4YaIiIicyv8BHxQufcX/FtwAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# truth: a saturating (non-linear) curve the straight line cannot capture\n", - "K = 2.0\n", - "\n", - "\n", - "def truth(x):\n", - " return (0.8 * x + 1.0) / (1.0 + x / K)\n", - "\n", - "\n", - "x = np.linspace(0.2, 5.0, 30)\n", - "noise = 0.02\n", - "y = truth(x) + rng.normal(0.0, noise, size=x.size)\n", - "observation = rxmc.observation.Observation(x=x, y=y, y_stat_err=noise * np.ones_like(y))\n", - "\n", - "xg = np.linspace(0.0, 5.5, 120) # fine grid for predictions\n", - "\n", - "plt.errorbar(x, y, noise, ls=\"none\", marker=\".\", label=\"data\")\n", - "plt.plot(xg, truth(xg), \"k:\", label=\"truth\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.legend()\n", - "plt.title(\"data from a non-linear truth\");" - ] - }, - { - "cell_type": "markdown", - "id": "c05", - "metadata": {}, - "source": [ - "## Building the constraint with a GP discrepancy term\n", - "\n", - "`rxmc.covariance.kernel_term(kernel)` wraps a scikit-learn kernel as\n", - "a kernel `Term`. It **auto-derives one `Parameter` per free kernel hyperparameter**\n", - "(sampled in sklearn's log-theta space). The constraint then carries those\n", - "hyperparameters as its covariance parameters (`constraint.params`), so it is\n", - "auto-detected as a parametric constraint." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c06", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:54.039335Z", - "iopub.status.busy": "2026-08-11T03:07:54.039172Z", - "iopub.status.idle": "2026-08-11T03:07:54.043021Z", - "shell.execute_reply": "2026-08-11T03:07:54.042513Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GP hyperparameters (sampled in log-theta space):\n", - " discrepancy_k1__k1__constant_value\n", - " discrepancy_k1__k2__length_scale\n", - " discrepancy_k2__noise_level\n" - ] - } - ], - "source": [ - "kernel = ConstantKernel(1.0) * Matern(length_scale=2.0, nu=2.5) + WhiteKernel(1e-6)\n", - "\n", - "constraint_gp = rxmc.constraint.Constraint(\n", - " [observation],\n", - " model,\n", - " extra_terms=[rxmc.covariance.kernel_term(kernel)],\n", - ")\n", - "evidence_gp = rxmc.evidence.Evidence([constraint_gp])\n", - "\n", - "print(\"GP hyperparameters (sampled in log-theta space):\")\n", - "for p in constraint_gp.params:\n", - " print(\" \", p.name)" - ] - }, - { - "cell_type": "markdown", - "id": "c07", - "metadata": {}, - "source": [ - "For comparison we also build two reference constraints over the *same* data:\n", - "a plain line (statistical errors only) and a line with an uncorrelated\n", - "**model-error** term (`model_error_term`, the diagonal $\\gamma^2$ inflation).\n", - "Neither can represent the *correlated* curvature the GP captures." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c08", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:54.044477Z", - "iopub.status.busy": "2026-08-11T03:07:54.044335Z", - "iopub.status.idle": "2026-08-11T03:07:54.048000Z", - "shell.execute_reply": "2026-08-11T03:07:54.047249Z" - } - }, - "outputs": [], - "source": [ - "constraint_plain = rxmc.constraint.Constraint([observation], model)\n", - "evidence_plain = rxmc.evidence.Evidence([constraint_plain])\n", - "\n", - "gamma = rxmc.params.Parameter(\n", - " \"log fractional err\", float, latex_name=r\"\\gamma\", unit=\"dimensionless\"\n", - ")\n", - "constraint_me = rxmc.constraint.Constraint(\n", - " [observation],\n", - " model,\n", - " extra_terms=[rxmc.covariance.model_error_term(gamma, averaging=True)],\n", - ")\n", - "evidence_me = rxmc.evidence.Evidence([constraint_me])" - ] - }, - { - "cell_type": "markdown", - "id": "c09", - "metadata": {}, - "source": [ - "## Sampling\n", - "\n", - "The physical parameters $(m, b)$ are sampled in the model block; each parametric\n", - "constraint's covariance parameters (GP log-theta, or $\\gamma$) are sampled in a\n", - "Gibbs block by a `likelihood_sampler`. GP hyperparameters get a broad\n", - "$\\mathcal N(0, 2)$ prior **per log-hyperparameter**." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "c10", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:54.049326Z", - "iopub.status.busy": "2026-08-11T03:07:54.049168Z", - "iopub.status.idle": "2026-08-11T03:08:09.762020Z", - "shell.execute_reply": "2026-08-11T03:08:09.761234Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "line only: model acceptance 0.45\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "line + model error: model acceptance 0.35\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "line + GP discrepancy: model acceptance 0.38\n" - ] - } - ], - "source": [ - "model_prior = stats.multivariate_normal(mean=[0.8, 1.0], cov=np.diag([0.5, 0.5]) ** 2)\n", - "\n", - "\n", - "def make_model_sampler():\n", - " return rxmc.param_sampling.BatchedAdaptiveMetropolisSampler(\n", - " params=model.params,\n", - " starting_location=model_prior.mean,\n", - " prior=model_prior,\n", - " initial_proposal_cov=model_prior.cov / 100,\n", - " )\n", - "\n", - "\n", - "def make_nuisance_sampler(params, prior, cov):\n", - " return rxmc.param_sampling.BatchedAdaptiveMetropolisSampler(\n", - " params=list(params),\n", - " starting_location=prior.mean,\n", - " prior=prior,\n", - " initial_proposal_cov=cov,\n", - " )\n", - "\n", - "\n", - "theta_prior = stats.multivariate_normal(\n", - " mean=np.zeros(constraint_gp.n_params), cov=4.0 * np.eye(constraint_gp.n_params)\n", - ")\n", - "gamma_prior = stats.multivariate_normal(mean=[np.log(0.05)], cov=[[1.0]])\n", - "\n", - "walkers = {}\n", - "walkers[\"line only\"] = rxmc.walker.Walker(make_model_sampler(), evidence_plain, rng=rng)\n", - "walkers[\"line + model error\"] = rxmc.walker.Walker(\n", - " make_model_sampler(),\n", - " evidence_me,\n", - " likelihood_samplers=[\n", - " make_nuisance_sampler(constraint_me.params, gamma_prior, np.array([[0.04]]))\n", - " ],\n", - " rng=rng,\n", - ")\n", - "walkers[\"line + GP discrepancy\"] = rxmc.walker.Walker(\n", - " make_model_sampler(),\n", - " evidence_gp,\n", - " likelihood_samplers=[\n", - " make_nuisance_sampler(\n", - " constraint_gp.params, theta_prior, 0.04 * np.eye(constraint_gp.n_params)\n", - " )\n", - " ],\n", - " rng=rng,\n", - ")\n", - "\n", - "for key, walker in walkers.items():\n", - " walker.walk(n_steps=4000, burnin=1500, batch_size=500, verbose=False)\n", - " print(\n", - " f\"{key}: model acceptance \"\n", - " f\"{walker.model_sampler.overall_acceptance_fraction():.2f}\"\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "c11", - "metadata": {}, - "source": [ - "## Posterior of the GP hyperparameters" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "c12", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:09.763535Z", - "iopub.status.busy": "2026-08-11T03:08:09.763375Z", - "iopub.status.idle": "2026-08-11T03:08:11.031094Z", - "shell.execute_reply": "2026-08-11T03:08:11.030356Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "gp_walker = walkers[\"line + GP discrepancy\"]\n", - "theta_chain = gp_walker.likelihood_samplers[0].chain\n", - "fig = corner.corner(\n", - " theta_chain,\n", - " labels=[p.latex_name for p in constraint_gp.params],\n", - " show_titles=True,\n", - ")\n", - "fig.suptitle(\"GP hyperparameters (log-theta)\");" - ] - }, - { - "cell_type": "markdown", - "id": "c13", - "metadata": {}, - "source": [ - "## Propagating the total uncertainty\n", - "\n", - "`rxmc.predictive.total_predictive_band` takes the posterior draws\n", - "$[m, b \\mid \\log\\theta]$ and, for each draw, conditions the GP discrepancy on the\n", - "residuals and samples\n", - "$y_* = y_m(x_*) + \\bar f_*(x_*) + \\mathcal N(0,\\ \\mathrm{var}_* + \\sigma^2)$ on the\n", - "fine grid. The line-only and model-error bands are credible intervals on the mean\n", - "line (they cannot bend); the GP band tracks the non-linear truth." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c14", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:11.032555Z", - "iopub.status.busy": "2026-08-11T03:08:11.032395Z", - "iopub.status.idle": "2026-08-11T03:08:12.461318Z", - "shell.execute_reply": "2026-08-11T03:08:12.460505Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "draws_gp = np.column_stack([gp_walker.model_sampler.chain, theta_chain])\n", - "\n", - "band_gp = rxmc.predictive.total_predictive_band(\n", - " model.y,\n", - " kernel,\n", - " x,\n", - " y,\n", - " xg,\n", - " draws_gp,\n", - " n_model_params=model.n_params,\n", - " noise_std=noise,\n", - " levels=(16, 84),\n", - " n_draws=300,\n", - " rng=rng,\n", - ")\n", - "\n", - "\n", - "def mean_line_band(walker, levels=(16, 84)):\n", - " chain = walker.model_sampler.chain[:, : model.n_params]\n", - " return rxmc.predictive.predictive_band(\n", - " np.array([model.y(xg, *p) for p in chain]), levels=levels\n", - " )\n", - "\n", - "\n", - "band_plain = mean_line_band(walkers[\"line only\"])\n", - "band_me = mean_line_band(walkers[\"line + model error\"])\n", - "\n", - "fig, ax = plt.subplots(figsize=(8, 5))\n", - "ax.plot(xg, truth(xg), \"k:\", lw=2, label=\"truth\")\n", - "ax.errorbar(x, y, noise, ls=\"none\", marker=\".\", color=\"k\", alpha=0.6, label=\"data\")\n", - "for (lo, hi), c, lab in [\n", - " (band_plain, \"tab:blue\", \"line only (68% mean band)\"),\n", - " (band_me, \"tab:orange\", \"line + model error (68% mean band)\"),\n", - " (band_gp, \"tab:green\", \"line + GP discrepancy (68% total predictive)\"),\n", - "]:\n", - " ax.fill_between(xg, lo, hi, color=c, alpha=0.3, label=lab)\n", - "ax.set_xlabel(\"x\")\n", - "ax.set_ylabel(\"y\")\n", - "ax.legend()\n", - "ax.set_title(\"Only the GP discrepancy band follows the non-linear structure\");" - ] - }, - { - "cell_type": "markdown", - "id": "c15", - "metadata": {}, - "source": [ - "## What the GP actually captured: the correlated residuals\n", - "\n", - "The smoking gun for model discrepancy is **structure in the residuals** $y -\n", - "y_m(x)$ — a good (well-specified) model leaves white noise. Conditioning the GP on\n", - "those residuals (`rxmc.predictive.gp_posterior_predictive`) recovers a smooth\n", - "discrepancy that matches the true mismatch `truth(x) - line(x)`." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "c16", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:12.463263Z", - "iopub.status.busy": "2026-08-11T03:08:12.463090Z", - "iopub.status.idle": "2026-08-11T03:08:12.747816Z", - "shell.execute_reply": "2026-08-11T03:08:12.747122Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mp_mean = gp_walker.model_sampler.chain.mean(axis=0)\n", - "theta_mean = theta_chain.mean(axis=0)\n", - "residuals = y - model.y(x, *mp_mean)\n", - "\n", - "disc_mean, disc_cov = rxmc.predictive.gp_posterior_predictive(\n", - " kernel, theta_mean, x, residuals, xg, train_noise_var=noise**2\n", - ")\n", - "disc_sd = np.sqrt(np.clip(np.diag(disc_cov), 0.0, np.inf))\n", - "\n", - "plt.figure(figsize=(8, 4))\n", - "plt.axhline(0.0, color=\"0.7\", lw=1)\n", - "plt.plot(x, residuals, \"k.\", label=\"residuals $y - y_m(x)$\")\n", - "plt.plot(xg, truth(xg) - model.y(xg, *mp_mean), \"k:\", label=\"true discrepancy\")\n", - "plt.fill_between(\n", - " xg,\n", - " disc_mean - disc_sd,\n", - " disc_mean + disc_sd,\n", - " color=\"tab:green\",\n", - " alpha=0.3,\n", - " label=\"GP discrepancy (68%)\",\n", - ")\n", - "plt.plot(xg, disc_mean, color=\"tab:green\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"discrepancy\")\n", - "plt.legend()\n", - "plt.title(\"the GP recovers the correlated residual structure\");" - ] - }, - { - "cell_type": "markdown", - "id": "c17", - "metadata": {}, - "source": [ - "## Takeaways\n", - "\n", - "- A GP discrepancy is **just another covariance `Term`** — `kernel_term`\n", - " (a `kernel_term`) added to the constraint. Its hyperparameters become\n", - " constraint parameters and are sampled like any other nuisance.\n", - "- The kernel term only inflates the covariance at the data points. To predict the\n", - " discrepancy at new $x$, use `rxmc.predictive.gp_posterior_predictive`; to get a\n", - " full data-space band that propagates model, discrepancy, and noise uncertainty,\n", - " use `rxmc.predictive.total_predictive_band`.\n", - "- Uncorrelated model error (`model_error_term`) inflates the variance but cannot\n", - " represent correlated mis-modelling; the GP can." - ] - }, - { - "cell_type": "markdown", - "id": "6e491de1", - "metadata": {}, - "source": [ - "# GP discrepancy on a differential cross section\n", - "\n", - "The same machinery on real reaction physics: we generate mock\n", - "$n + {}^{40}$Ca elastic scattering data from a **full** optical potential\n", - "(volume + surface absorption), then fit it with a **deficient** potential that\n", - "has no surface term. The missing physics leaves a smooth, *angle-correlated*\n", - "residual — exactly what a `kernel_term` over the angle grid absorbs.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "a2cefc08", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:12.749919Z", - "iopub.status.busy": "2026-08-11T03:08:12.749754Z", - "iopub.status.idle": "2026-08-11T03:08:24.846032Z", - "shell.execute_reply": "2026-08-11T03:08:24.845346Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import jitr\n", - "from jitr.optical_potentials.potential_forms import (\n", - " thomas_safe,\n", - " woods_saxon_prime_safe,\n", - " woods_saxon_safe,\n", - ")\n", - "\n", - "from rxmc.params import Parameter\n", - "\n", - "Ca40 = (40, 20)\n", - "E_lab = 14.1\n", - "rxn = jitr.reactions.ElasticReaction(target=Ca40, projectile=(1, 0))\n", - "mso = 1.0 / jitr.utils.constants.WAVENUMBER_PION\n", - "R40 = 1.2 * 40 ** (1 / 3)\n", - "fixed_spin_orbit = (6.0, -3, R40, 0.45)\n", - "\n", - "\n", - "def full_central(r, Vv, Wv, Rv, av, Wd, Rd, ad):\n", - " return -(Vv + 1j * Wv) * woods_saxon_safe(r, Rv, av) + (\n", - " 4j * ad * Wd\n", - " ) * woods_saxon_prime_safe(r, Rd, ad)\n", - "\n", - "\n", - "def volume_central(r, Vv, Wv, Rv, av):\n", - " return -(Vv + 1j * Wv) * woods_saxon_safe(r, Rv, av)\n", - "\n", - "\n", - "def spin_orbit_potential(r, Vso, Wso, Rso, aso):\n", - " return (Vso + 1j * Wso) * mso**2 * thomas_safe(r, Rso, aso)\n", - "\n", - "\n", - "omp_full = rxmc.elastic_diffxs_model.ElasticDifferentialXSModel(\n", - " \"dXS/dA\",\n", - " interaction_central=full_central,\n", - " interaction_spin_orbit=spin_orbit_potential,\n", - " calculate_interaction_from_params=lambda ws, *x: (tuple(x), fixed_spin_orbit),\n", - " params=[\n", - " Parameter(n, unit=u)\n", - " for n, u in [\n", - " (\"Vv\", \"MeV\"),\n", - " (\"Wv\", \"MeV\"),\n", - " (\"Rv\", \"fm\"),\n", - " (\"av\", \"fm\"),\n", - " (\"Wd\", \"MeV\"),\n", - " (\"Rd\", \"fm\"),\n", - " (\"ad\", \"fm\"),\n", - " ]\n", - " ],\n", - " model_name=\"full_potential\",\n", - ")\n", - "omp_vol = rxmc.elastic_diffxs_model.ElasticDifferentialXSModel(\n", - " \"dXS/dA\",\n", - " interaction_central=volume_central,\n", - " interaction_spin_orbit=spin_orbit_potential,\n", - " calculate_interaction_from_params=lambda ws, *x: (tuple(x), fixed_spin_orbit),\n", - " params=[\n", - " Parameter(n, unit=u)\n", - " for n, u in [(\"Vv\", \"MeV\"), (\"Wv\", \"MeV\"), (\"Rv\", \"fm\"), (\"av\", \"fm\")]\n", - " ],\n", - " model_name=\"volume_only_potential\",\n", - ")\n", - "\n", - "full_truth = np.array([48.0, 3.5, 1.1 * 40 ** (1 / 3), 0.7, 21, R40, 0.5])\n", - "volume_truth = full_truth[:4] # the deficient model's share of the truth\n", - "\n", - "obs_xs = rxmc.elastic_diffxs_observation.ElasticDifferentialXSObservation(\n", - " x=np.linspace(5.0, 160.0, 25),\n", - " y=np.ones(25),\n", - " Elab=E_lab,\n", - " reaction=rxn,\n", - " quantity=\"dXS/dA\",\n", - " measurement_quantity=\"dXS/dA\",\n", - " y_units=\"barn / steradian\",\n", - " dataset_label=\"mock elastic dataset\",\n", - ")\n", - "y_true_xs = omp_full.evaluate(obs_xs, *full_truth)\n", - "stat_xs = 0.04 * np.maximum(y_true_xs, 1e-4)\n", - "obs_xs.y = np.clip(y_true_xs + rng.normal(scale=stat_xs), 1e-6, None)\n", - "obs_xs.y_stat_err = stat_xs\n", - "\n", - "plt.errorbar(\n", - " np.rad2deg(obs_xs.x), obs_xs.y, stat_xs, ls=\"none\", marker=\".\", label=\"data\"\n", - ")\n", - "plt.plot(\n", - " np.rad2deg(obs_xs.x),\n", - " omp_vol.evaluate(obs_xs, *volume_truth),\n", - " \"tab:red\",\n", - " label=\"deficient model at the true volume params\",\n", - ")\n", - "plt.xlabel(r\"$\\theta$ [deg]\")\n", - "plt.ylabel(r\"$d\\sigma/d\\Omega$ [b/sr]\")\n", - "plt.yscale(\"log\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "id": "f1652855", - "metadata": {}, - "source": [ - "## The discrepancy term acts on the angle grid\n", - "\n", - "The kernel's input coordinate is `obs.x` — the scattering angle **in radians**\n", - "— so the `length_scale` is an angular correlation length. Everything else is\n", - "identical to the toy section.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "1f0df948", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:24.847679Z", - "iopub.status.busy": "2026-08-11T03:08:24.847514Z", - "iopub.status.idle": "2026-08-11T03:08:24.851771Z", - "shell.execute_reply": "2026-08-11T03:08:24.851045Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GP hyperparameters: ['discrepancy_k1__k1__constant_value', 'discrepancy_k1__k2__length_scale', 'discrepancy_k2__noise_level']\n" - ] - } - ], - "source": [ - "kernel_xs = ConstantKernel(1.0) * Matern(length_scale=0.5, nu=2.5) + WhiteKernel(1e-6)\n", - "\n", - "c_vol_plain = rxmc.constraint.Constraint([obs_xs], omp_vol)\n", - "c_vol_gp = rxmc.constraint.Constraint(\n", - " [obs_xs],\n", - " omp_vol,\n", - " extra_terms=[rxmc.covariance.kernel_term(kernel_xs)],\n", - ")\n", - "print(\"GP hyperparameters:\", [p.name for p in c_vol_gp.params])" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "1059d23b", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:24.853287Z", - "iopub.status.busy": "2026-08-11T03:08:24.853128Z", - "iopub.status.idle": "2026-08-11T03:08:58.711360Z", - "shell.execute_reply": "2026-08-11T03:08:58.710543Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 33.9 s, sys: 2.23 ms, total: 33.9 s\n", - "Wall time: 33.9 s\n" - ] - } - ], - "source": [ - "%%time\n", - "vol_prior = stats.multivariate_normal(\n", - " mean=np.array([50.0, 3.0, 1.2 * 40 ** (1 / 3), 0.65]),\n", - " cov=np.diag([7.0, 7.0, 0.2, 0.2]) ** 2,\n", - ")\n", - "\n", - "\n", - "def make_vol_sampler():\n", - " return rxmc.param_sampling.BatchedAdaptiveMetropolisSampler(\n", - " params=omp_vol.params,\n", - " starting_location=vol_prior.mean,\n", - " prior=vol_prior,\n", - " initial_proposal_cov=vol_prior.cov / 100,\n", - " )\n", - "\n", - "\n", - "theta_prior_xs = stats.multivariate_normal(\n", - " mean=np.zeros(c_vol_gp.n_params), cov=4.0 * np.eye(c_vol_gp.n_params)\n", - ")\n", - "\n", - "walker_vol_plain = rxmc.walker.Walker(\n", - " make_vol_sampler(), rxmc.evidence.Evidence([c_vol_plain]), rng=rng\n", - ")\n", - "walker_vol_gp = rxmc.walker.Walker(\n", - " make_vol_sampler(),\n", - " rxmc.evidence.Evidence([c_vol_gp]),\n", - " likelihood_samplers=[\n", - " make_nuisance_sampler(\n", - " c_vol_gp.params, theta_prior_xs, 0.04 * np.eye(c_vol_gp.n_params)\n", - " )\n", - " ],\n", - " rng=rng,\n", - ")\n", - "for w in (walker_vol_plain, walker_vol_gp):\n", - " w.walk(n_steps=5000, burnin=1500, batch_size=500, verbose=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "0fcd16d6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:58.713058Z", - "iopub.status.busy": "2026-08-11T03:08:58.712866Z", - "iopub.status.idle": "2026-08-11T03:08:59.631340Z", - "shell.execute_reply": "2026-08-11T03:08:59.630582Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "no discrepancy Vv=32.20±1.17 Wv=22.95±0.67 Rv=4.28±0.05 av=0.66±0.01\n", - "GP discrepancy Vv=44.56±2.89 Wv=17.74±1.27 Rv=4.17±0.14 av=0.60±0.05\n", - "truth Vv=48.00 Wv=3.50 Rv=3.76 av=0.70\n" - ] - }, - { - "data": { - "image/png": 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KZG9T4BURERERkT5hxowZvPHGG7HHkydP5r333uvBimRvU+AVEREREZE+adiwYaSnp/d0GbIXKfCKiIiIiEiPWbVqFR6PB4Cqqiq8Xm+n51ZWVrJ58+Zu3zsSiVBeXk4gEIh7/IUXXuCQQw5p95zP52PTpk1EIpG41zQ0NFBRUdHh+bb3EQ6HKSsro7a2tt3xTZs24Xa7O1zn8XhYvXo1AMFgkPLyckKhULtzAoEAK1euZOXKlaxfv77D8Z3vEwqF2Lx5M+FwOO57gOhn2dDQEHscCoVYuXIlra2t7c6LRCKsXLky9nfU2yjwioiIiIhIjxk5ciT/8z//w8CBAznooIPIyMjgtttua3fOwoULGT16NMOHD2fUqFEMGzaMzz77rMv7fvHFFwwaNIixY8eSn5/PFVdcQTAYbHfOjlOaI5EIV155JdnZ2UybNo2cnBx+//vfx84tLy/nyCOPJC8vj8mTJzNixAi+/vrrdu/j1ltvZcCAARx55JG89NJLAMyePZuSkhImT57M4MGDmThxIkuXLo1dN3fuXEaPHs1NN91Ebm4u48ePZ8CAAcyZMyd2TkVFBaeddhqnnXYaRxxxBGlpadx4440YhtHhPrfddhv9+vVj0qRJ5OXldZiy/dZbb1FaWsrw4cMZMmQIZ5xxBg0NDZjNZo477jgef/zxdue/9957jBkzpstfROzPFHhFRERERBKQYRhEvN4e+doxiHXHBx98wOeff86mTZv44IMPuPfee/nmm2+A6Kjn2WefzYQJE3C73dTX13P00Udz1llndRrCgsEg559/PieccAL19fVs3bqVlpYWampqOq3hjTfe4NVXX2Xt2rWUlZWxadOmWEOrYDDIsccei8PhYOvWrVRWVvL666+zbNmydvd49913+eKLL1i3bh0//elPWbx4Meeddx6PPfYY1dXV1NbWctxxx3HmmWe2G3VuG8Gtrq6mrq6OSy65hPPPP5+WlhYABg0aFBvhLSsrY+nSpbzyyivMmjWr3esHAgFqa2upqqqiurqaSy65hCuuuCJ2/JtvvuH000/npz/9KU1NTWzdupULL7yQdevWYTabufTSS3n22Wfb3fOZZ57h5JNPJjc3d1d/jfslk7G7/xpFRERERGS/0trayoYNGxg0aBDJyckARLxeVk2Y2CP1DP9mAeaUlG6da7Va+ctf/sJll10We66kpIQ77riDyy67jLfffptTTjmFLVu2kJ2dDUBLSwv5+fk89dRTnH/++R3uOXv2bM444wxqamrIyMgAoqOkAwYMYNasWVxwwQUAZGZm8tRTT3HWWWfx/PPPc/vtt7Ny5UpSU1Pb3e/NN9/k1FNPpbKykn79+nX6Pv7whz9w7bXXxp676qqrKC8v509/+hOGYWAYBj6fj/Hjx7NgwQLGjx/PW2+9xcknn8zixYsZPXo0AH6/n379+vHwww9z0UUXxe4XCASorKyktbWVe+65h0gkEgu9bfepra2NfU4LFy5k/Pjxsecuu+wyli9f3uno+ObNmykuLuaLL75g0qRJ1NXVUVBQwGuvvcaJJ57Yxd9iz4r377+NtYdqEhERERERAaCwsLDd49TU1Njo6tq1aykoKIiFuLbjpaWlrFmzJu791q5dS2FhYSzstr2Gy+XqtIYzzzyTp556iuLiYo477jiOOuoozjrrLNLS0li5ciUFBQWdht02paWl7R4vW7aMFStWcNJJJ7V7ftiwYe3W85rNZg444IDY46SkJIYOHcratWsB8Hq9XHbZZbz22mvk5OSQlpZGbW0tY8aMaXffpKSkDp8TQHNzM9nZ2axcuZKJEzv/JciAAQM47rjjePbZZ5k0aRIvvPACOTk5HHfccV2+7/2ZAq+IiIiISAIyORwM/2ZBj732nuJwOPD5fB2e9/l8pHQyiuxwODo0XwLiPtfG6XTy4Ycfsnz5cj744AOeeOIJ7rzzTr799luSkpK6tYbVYrG0e2yz2TjxxBN5/vnnu7wuEokQCASwWrfHM6/Xi2Pb53jPPfewdOlSNm7cSH5+PgDXX389ixYt2mVNO+rO+7j88su59NJLefDBB3n22We5+OKLO7yv3kSBdw+KRCJUVlaSlpaGyWTq6XJE9iuGYdDc3ExBQQFms9oHiIiI7G0mkwlTN6cV78/apuQuWbIkNuV3/fr1lJWVMX78+E6vqa6uZvXq1QwbNgyAzz//PG5wbhMMBrHZbBx44IEceOCBXHnllTidTj755BOmTp2K2+3m888/Z8qUKbFrQqFQu5C6s8MOO4wnn3yS5uZm0tLSurxu/vz5HH/88UC0g/Lq1asZN24cACtWrGDGjBmxsBuJRJg3b16XI9bxHHroocyaNYtAIIDdbo9bz8knn0xSUhJ33HEHCxcu5B//+Mduvcb+RoF3D6qsrKSoqKinyxDZr23atIkBAwb0dBkiIiLSS0yaNIlTTjmF888/n9///vdYrVZuueUWDjvsMI488si410yePJmjjz6a8847j/vvv59QKMT111/f5UjlI488wrJlyzjrrLMoKCjgrbfewmazMWbMGEpKSjjvvPM455xzuPfeexkyZAhz586lqamJu+++u9N73nDDDbzwwgscd9xx/OIXvyAnJ4eFCxfyyCOPsGjRolg9JpOJn/zkJzz00ENkZmZy++23M3bs2NhU4kMOOYT77ruPY489lpycHB599FGWLVvGYYcdtluf5fXXX8/zzz/PKaecws9//nNsNhvPPPMMl156KTNmzACia5F/9KMfce+99zJt2jSGDh26W6+xv1Hg3YPafmuzadMmbWAt35vH46GgoACI/jLF6XT2cEXfT1NTE0VFRe1+uykiIiIyYsSIDk2iSktLycrKij1+8cUX+b//+z9+8YtfYBgGhx9+OL/85S+7vO/LL7/ML37xC2666SaKiop44IEHeOCBB9qt6x02bFjs5/af/exnzJo1iz//+c9UV1czdOhQ5s6dS0lJCQCzZs3ij3/8I4899hh+v58jjjiCO+64o8v3kZ2dzZdffsn999/Pb37zGwzDYMKECfz73/9uF77tdjtPP/00Dz/8MGVlZUyYMIF77rknNmv0+uuvp6Wlhbvvvjv2/u+66y5WrVoVu0dqairDhw9v9/p2u53hw4djs9kAyM3N5YsvvuD//u//uPXWW3G5XPzoRz+Khd02F110Effee2+7RmK9lbo070FNTU1kZGTQ2NiowCvfm8fjif1Ps6WlJSECr74/RERE9o6uutTK/u2tt97irLPO6nJ98b7297//nauvvrrXDLqoS7OIiIiIiIh0qb6+nnXr1nH33XdzzTXX9IqwuyvqHCMiIiIiItJD4k1F7imvv/46l1xyCVOmTOFXv/pVT5ezR2iEV0REREREpIfMnDlzt7cX2lsuueQSLrnkkp4uY4/SCK+IiIiIiIgkJAVeERERERERSUgKvCIiIiIiCUIbsEhf1NW/ewVeEREREZFerm2fVa/X28OViOx7bf/u274PdqSmVSIiIiIivZzFYiEzM5OamhoAUlJSMJlMPVyVyN5lGAZer5eamhoyMzOxWCwdzlHgFRERERFJAP369QOIhV6RviIzMzP2739nCrwiIiIiIgnAZDLRv39/8vLyCAaDPV2OyD5hs9nijuy2UeAVEREREUkgFoulywAg0peoaZWIiIiIiIgkJAVeERERERERSUgKvCIiIiIiIpKQtIZ3PxesrCTkdgNgdbmwFRT0cEUiIiIiIiK9gwLvfixYWcm6E0/C8PkAMDkcDJ79lkKviIiIiIhIN2hK834s5HZj+HwU3H8fBfffh+HzxUZ7RUREREREpGsa4e0F7KWlPV2CiIiIiIhIr6MRXhEREREREUlICrwiIiIiIiKSkBR4RUREREREJCEp8IqIiIiIiEhCUuAVERERERGRhKTAKyIiIiIiIglJgVdEREREREQSkgKviIiIiIiIJCQFXhEREREREUlICrwiIiIiIiKSkBR4RUREREREJCEp8IqIiIiIiEhCUuAVERERERGRhKTAKyIiIiIiIglJgVdEREREREQSkgKviIiIiIiIJCQFXhEREREREUlICrwiIiIiIiKSkBR4RUREREREJCFZe7oAkd6gosGH2xPo8LzLaacw09EDFYmIiIiIyK4o8IrsQkWDj6MenI8vGO5wzGGzMOemGQq9IiIiIiL7IQVekV1wewL4gmEeOnccQ/JSY8+vrWnhhpcX4vYEFHhFRERERPZDCrwi3TQkL5VRhRk9XYaIiIiIiHSTAu9+KFhZScjtJrB+fU+XIiIiIiIi0msp8O5ngpWVrDvxJAyfDwCTw4HV5SLkdvdwZSIiIiIiIr2LAu9+JuR2Y/h8FNx/H/bSUqwuF7aCAgVeERERERGR3aTAu5+yl5biGDmyp8sQERERERHptcw9XYCIiIiIiIjI3qDAKyIiIiIiIglJgVdEREREREQSkgKviIiIiIiIJCQ1repl2vbmbeveLCIiIiIiIvEp8O4ngpWVhNzuWKDdmdXlwuRwUHnzLUB0f97Bs99S6BUREREREemEAu9+IFhZyboTT8Lw+YBomLW6XO3OsRUUMHj2W7FQXHnzLYTcbgVeERERERGRTijw7gdCbjeGz0fB/fdhLy3tdLqyraBAAVdERERERKSbFHj3I/bSUhwjR/Z0GSIiIiIiIglBXZpFREREREQkISnwioiIiIiISEJS4BUREREREZGEpDW80qdUNPhwewJxj7mcdgozHfu4IhERERER2VsUeKXPqGjwcdSD8/EFw3GPO2wW5tw0Q6FXRERERCRBKPBKn+H2BPAFwzx07jiG5KW2O7a2poUbXl6I2xNQ4BURERERSRAKvNLnDMlLZVRhRk+XISIiIiIie5maVomIiIiIiEhCUuAVERERERGRhKTAKyIiIiIiIglJa3hFvqe1NS1xn9c2RyIiIiIiPUuBV+Q7cjntOGwWbnh5Ydzj2uZIRERERKRnKfCKfEeFmQ7m3DQDtyfQ4Zi2ORIRERER6XkKvCLfQ2GmQ4FWRERERGQ/paZVIiIiIiIikpA0wiuyg3gNqDprSiUiIiIiIvs3BV4RuteAyuW079uiRERERETke1HgFaHrBlSgLYZERERERHojBV6RbdSASkREREQksahplYiIiIiIiCQkBV4RERERERFJSAq8IiIiIiIikpAUeEVERERERCQhKfCKiIiIiIhIQlLgFRERERERkYSkwCsiIiIiIiIJSYFXREREREREEpICr4iIiIiIiCQkBV4RERERERFJSAq8IiIiIiIikpAUeEVERERERCQhWXu6APnuAuvXA2B1ubAVFPRwNSIiIiIiIvsXBd5eyOpyYXI4qLz5FgBMDgeDZ7+l0CsiIiIiIrIDBd5eyFZQwODZbxFyuwmsX0/lzbcQcrsVeEVERERERHagwNtL2QoKFHBFRERERES6oKZVIiIiIiIikpAUeEVERERERCQhKfCKiIiIiIhIQlLgFRERERERkYSkwCsiIiIiIiIJSYFXREREREREEpICr4iIiIiIiCQkBV4RERERERFJSAq8IiIiIiIikpAUeEVERERERCQhKfCKiIiIiIhIQlLgFRERERERkYSkwCsiIiIiIiIJSYFXREREREREEpICr4iIiIiIiCQka08XIJLI1ta0xH3e5bRTmOnYx9WIiIiIiPQtCrw9LFhZSWD9+p4uQ/Ywl9OOw2bhhpcXxj3usFmYc9MMhV4RERERkb1IgbcHBSsrWXfiSRg+HyaHA6vL1dMlyR5SmOlgzk0zcHsCHY6trWnhhpcX4vYEFHhFRERERPYiBd4eFHK7MXw+Cu6/j5SJE7EVFPR0SbIHFWY6FGhFRERERHqQmlbtB+ylpQq7IiIiIiIie5gCr4iIiIiIiCQkBV4RERERERFJSFrDK71SRYMvbkMo0JY/IiIiIiISpcArvU5Fg4+jHpyPLxiOe1xb/oiIiIiICCjwSi/k9gTwBcM8dO44huSltjumLX9ERERERKSNAq/0WkPyUhlVmNHTZYiIiIiIyH5KTatEREREREQkISnwioiIiIiISEJS4BUREREREZGEpMArIiIiIiIiCUmBV0RERERERBKSAq+IiIiIiIgkJAVeERERERERSUgKvCIiIiIiIpKQFHhFREREREQkISnwioiIiIiISEKy9nQBIn3V2pqWuM+7nHYKMx37uBoRERERkcSjwCuyj7mcdhw2Cze8vDDucYfNwpybZpBp27d1iYiIiIgkGgVekX2sMNPBnJtm4PYEOhxbW9PCDS8vxO0JkJmpb08RERERke9DP1GL9IDCTIemLYuIiIiI7GUKvJKQ4q2P7WzNrIiIiIiIJCYFXkko3Vkf63La921RIiIiIiLSIxR4JaF0tT4W1AFZRERERKQvUeCVhKP1sSIiIiIiAmDu6QJERERERERE9gYFXhEREREREUlImtIssh9aW9OC12uKPV5W2UhKSkhrkEVEREREdoMCr8h+ZMcu05FAa+z5sx77DLM9GYfNwpybZij0ioiIiIh0gwKvyH5kxy7TXq+Hg/8Qff6fVx9CZYvBDS8vxO0JKPCKiIiIiHSDAq/Ifqaty7THs/3bc2RBBikNoR6sSkRERESk91HTKhEREREREUlICrwiIiIiIiKSkDSlOUEE1q8HwOpyYSso6OFqREREREREep4Cby9ndbkwORxU3nwLACaHg8Gz31LoFRERERGRPk+Bt5ezFRQwePZbhNxuAuvXU3nzLYTcbgVeERERERHp8xR4E4CtoEABV0REREREZCdqWiUiIiIiIiIJSSO8PSBYWRmbgiwiIiIiIiJ7hwLvPhasrGTdiSdh+HxAtMmU1eXq4apEREREREQSjwLvPhZyuzF8Pgruvw97aam2ERIREREREdlLFHh7iL20FMfIkT1dhoiIiIiISMJS0yoRERERERFJSAq8IiIiIiIikpAUeEVERERERCQhKfCKiIiIiIhIQlLgFRERERERkYSkLs0ivczampa4z7ucdgozHfu4GhERERGR/ZcCbwIKrF8P0Ov3+K1o8OH2BDo831ngS3Qupx2HzcINLy+Me9xhszDnphkKvSIiIiIi2yjwJhCry4XJ4aDy5lsAMDkcDJ79Vq8MvRUNPo56cD6+YDjucYfNgstp38dV9azCTAdzbprR6S8Bbnh5IW5PQIFXRERERGQbBd4EYisoYPDstwi53QTWr6fy5lsIud29MvC6PQF8wTAPnTuOIXmpHY731em7hZmOLt+3pjuLiIiIiGynwJtgbAUFvTLgdmZIXiqjCjN6uoz9nqY7i4iIiIh0pMArkgA03VlEREREpCMFXpEEsavpziIiIiIifY0Cr/QodWIWEREREZG9RYG3jwhWVhJyu4H9Z7sidWIWEREREZG9SYG3DwhWVrLuxJMwfD5g/9muSJ2YRURERERkb1Lg7QNCbjeGz0fB/fcBUHnzLXgXLMDudu8Xo73qxCwiIiIiInuDAm+CC6xfH/uzvbQUq8uFyeGg8uZbgP1ntFdERERERGRPU+BNUPGCbdto7uDZbxFyuwmsX0/lzbcQcrsVeEVEREREJOEo8CaoHYMttG9UZSsoUMAVEREREZGEp8CbwBRsRURERESkLzP3dAEiIiIiIiIie4MCr4iIiIiIiCQkBV4RERERERFJSAq8IiIiIiIikpAUeEVERERERCQhqUvzPhKsrIztfSsiIiIiIiJ7nwLvPhCsrGTdiSdh+HwAmBwOrC5XD1fVUVsoh/b79oqIiIiIiPRGCrz7QMjtxvD5KLj/PuylpftdmAysX0+43s3m665rF8oHz35rv6pTRERERERkdyjw7kP20lIcI0f2dBkxVpcLk8NB5c23ANGQW/Tkk4Qb3FTefAsht1uBV0REREREei0F3j7MVlDA4NlvdZjG7Fu2rIcrExERERER+f4UePs4W0GBRnFFRERERCQhaVsiERERERERSUga4RXpI9bWtHR4zuW0U5jp6IFqRERERET2PgVekQTnctpx2Czc8PLCDsccNguPXziRbKc97nUKwyIiIiLSmynwiiS4wkwHc26agdsTaPd8nSfAj2ct4OJnvox7ncNmYc5NMxR6RURERKTXUuAV6QMKMx1xg2u8IAzR6c83vLwQtyegwCsiIiIivZYCr+wTFQ2+DsEq3ppS2bc6C8IiIiIiIolAgVf2uooGH0c9OB9fMNzhmMNmwRVn/aiIiIiIiMj3pcAre53bE8AXDPPQueMYkpfa7pgaI+3fOhuF19+biIiIiPQGCryyzwzJS2VUYUZPlyHd0FVnZ1BDKxERERHpHRR4RaSDzjo7gxpaiYiIiEjvocArInGpoZWIiIiI9HYKvNKpwPr1WF0ubAUF3To/XidmUDdmERERERHpGQq80oHV5cLkcFB58y2YHA4Gz35rl6G3q07MoG7MiWh3f5HR0ty0lyoREREREYlPgXcv8K1YgS11ezfiwPr1PVjN7rMVFDB49lt4Fyyg8uZb8C5YgN3t7nK0t6tOzKCuvolkVw2tOhPxe/dOQSIiIiIinVDg3Qs2XnAhqRZLu+dMDgdWl6uHKtp9toICUiA20su2P+9qtFedmBNfVw2turJ4fRU/fGjv1CQiIiIiEo8C7x5kGAYAzttvI+vAA9sds2Zm4ktNxdfUi6Z1pqaS+9KLhBoaCJSVUf3LO6j+6CPsJSVYc3Kw5ebGTm1pbiLi99LS3ERTk6kHi04cHo8n9uempibC4fjTxXtCmhnS0nbv73mLIwJs/z4REREREdnbTIZ++txjNm/eTFFRUU+XIbJf27RpEwMGDOjpMkRERESkD1Dg3YMikQiVlZWkpaVhMm0f/WpqaqKoqIhNmzaRnp7egxV+N725/t5cOyRW/WlpaTQ3N1NQUIDZbO7p0kRERESkD9CU5j3IbDZ3OXKVnp7eK0NLm95cf2+uHRKn/owMre8WERERkX1HwywiIiIiIiKSkBR4RUREREREJCEp8O4DSUlJ3HnnnSQlJfV0Kd9Jb66/N9cOql9ERERE5PtQ0yoRERERERFJSBrhFRERERERkYSkwCsiIiIiIiIJSdsS7UGd7cMrImAYRrf24dX3kUjnuvt9JCIiIlEKvHtQZWUlRUVFPV2GyH5t06ZNXe5Xre8jkV3b1feRiIiIRCnw7kFpaWlA9AeR9PT0Hq5m74l4vURaWzEnJ2NOSenpchKWx+OhoKAAiIZAp9PZwxV9P01NTRQVFcW+TzrTV76PRL6L7n4fiYiISJQC7x7UNv0yPT09IX5Qj3i9BCorCTU0YM3MxF5QEA24CfDeegOLxRL7c3p6eq8PvG12NU050b6PRPYGTfcXERHpHgVeAaLrwnZ4ACYTEZ+Plm++JbByJfZhw8g82qURXRERERER6TUUeKWd5k8+pXX1Kqz9CzADni+/AKsVa1MjZoejp8sTERERERHpNgVeiQlWVOB+6SVat9ZgeH0kDxqENT0dszOF1EMOwaTAKyIiIiIivYgCbx/WNo05XF+Pd9Uqav/8KMHqaiy5uYSCAQyfD+uQwWQefzxJQ4b0cLUiIiIiIiK7R4G3j/OvW4dnwQJqn3yKyObN0SeDQSwF/UkqLSXrjDOwa+sLERERERHphRR4+yAjECDc3AyA+7XXaJr/ISa/P3bclJxE5lFHkzxiOPbCwp4qU0RERERE5HtR4O0DduzAHNi8mYqfXot/1SosOTkYhoE5KQn78OH4zWYALMNHYB9UQlJJSQ9VLCIiIiIi8v0p8PYh/vXr2XT5FYSqqgAI19YCEAGMcJhIfT0MHEj2jOlkHHlkD1YqIiIiIiLy/Zl7ugDZNzxffkn5+T8gVFWFrbCQ4hdewOx0AmCy2cBuxzzyQNKmHIxjxIgerlZEREREROT70whvotphGrPn44/ZfN31GD4fyaNHUfDgg9Q++igRjwesVkyFhWT96GIyjz4as8Oh7YdERERERCQhKPAmuLDHQ+Utt2L4fKQcdBC5t9zM1j88RPM774DZjLmkmIL/+R+SSkqwZmf3dLkiIiIiIiJ7jAJvgvNv2ECkpQWAcEsLG8//AUYwCIDZ5aL4D38geejQnixRRERERERkr9Aa3gRiGEbsKxKJEPZ68Xz9NfZx48Bkwr98OUYwiMlux5KXh/PooxV2RUREREQkYfXZEd733nuPsrIyrrrqqp4uZY8LVlTgr6igdfUaGt97j0jNVti25RBOJ2knn0z/m27EnJLSs4WKiIiIiIjsRX0y8NbV1XHaaafh9/sxDIMf//jHPV3SHhOsqKDu5ZcJbd1Ka/lGjJqt4PdDairOGdPJufBCkgcPVtgV6cUqGny4PYFOj7ucdgoz1XxOREREpE8G3szMTJKSkvjpT3/KNddcA/CdQq/f78fv98ceNzU17bEad4exrSOz4fPR9MmntK5YQSQUBp8Pk9WKKSMDk8NB5nHHY0lLJ+LzKfCK9FIVDT6OenA+vmC403McNgtzbpqh0CsiIiJ9Xp8MvBaLhcLCQq688kosFkss9F5yySW8/vrrnHPOOd26zz333MNdd921N0vdLU1ffknzf/6Dv7ISk8WCOScbIzUVDAOby4V3xXK8y5aSMm486dMO6+lyReQ7cHsC+IJhHjp3HEPyUjscX1vTwg0vL8TtCSjwioiISJ/XJwMvwIgRI1i6dCm/+93vALjmmmv485//zNixYzn77LMxmUy7vMdtt93GjTfeGHvc1NREUVHRXqu5M82ffErr8mUEqreAyYQ1PZ2kwaUklZQAJlrXr8PwemldtBjH2DEEystAgVekVxuSl8qowoyeLkNERERkv9ZnuzSPGDGCZcuWAfDrX/+akpISli5dyiGHHNKtsAuQlJREenp6u6+e0LpiOZEWD2ank5TJk0mddhiph02jeeEiGt98k0BlFSZnKiZHMsGaGsyurB6pU0REREREZF/q04F36dKl+P1+TjvtNA499FBuueUWrr32Wh5//PGeLm+3JI84ACMcwpaXh+vEE0g/4ggiHg/hhQsx6uqIVFaSNuVgkocOxep0Etq8idY1a3q6bBERERERkb0qoac0r1ixgnvuuYfVq1czc+ZMfv3rX5OcnAxEA+/dd9/NqaeeSk5ODn/961+xWCyYTCYee+wxLr30Uux2ew+/g+5JO2wqFkcyvnXrqXniCfwVldDSsv0Emw3Dbge7nXC9G2teEoGNG7UHr4iIiIiIJLSEHeGdP38+U6dOJS0tjSlTpvDII4/w05/+NHb8wAMPpKmpqV3YBfjd737HJ598st+H3bDHQ6iujsDmzfjXryfQ0EDr6tW0rlmLUVuLJT0Nc1oa5rw8HJMmESorJ3ngQNKmTsVeVIR94MCefgsiIiIiIiJ7VUKO8AaDQS6++GKee+45TjnlFACGDh3Ktddey+9+9ztycnJwOp188cUXFBYWxsJum9TUjp1P9zdht5u6V16h5ZNPIBQmXFmJKSkJwmFMViuhUIiw3Y6tsBDD6yFcX4dRPBDnuLFYs7N7unwREREREZG9LiFHeN955x3GjRsXC7sAP/jBDzAMg1WrVsWeGzhwYIew21u433iTxr/9jdDKVYRWrsRoaiKydSuEQhjhEKSlYsnPJ7hpI2GvD8NkIvXggxV2RURERESkz0jIwJudnc0VV1zR7jmXy4XT6cTv9/dQVXuOd9ky3M88g+HxQihE0ogR5Pz0J9iKi4k0NWH4fOD1EV6/HurrCWwsJ3XadIVdERERERHpUxJySvPUqVPjPp+UlEQkEok9fuqppzjnnHN6bDuh7jAMo92fwzU1VP/yDoyWFkypqRQ+8gjJg0sxJSVhLSyk+rbbwQCLI5lwbS0kJWF2ZZGufXdFRERERKSPScjA2xmTyRQLvHfddRevvvoqp556ag9X1T2NH8zF88nHNL/xJsa2DsxGSwuNL79M8i9ux2Sx4DzkEMypqURaWgiUlRPp3x9Hfj7OTn4BICKJa21NS6fHXE47hZmOfViNiIiISM/oc4HXMIxY2H3//ffJzc3t6bK6xfvZpzS9+hrsNCW7+b33SB41isyzz6L6zl8TaWnBnJGBecgQ+v/4xxrZFeljXE47DpuFG15e2Ok5DpuFOTfNUOgVERGRhNfnAu99993H1q1be0/YNQyaP/6Epn+/Hgu75tRUUg6bitlmo+nNt9j68MO0LlmCZ/58THY7puwsSh5+CFtOTg8XLyL7WmGmgzk3zcDtCcQ9vramhRteXojbE1DgFRERkYTXpwKv3W7vNWE34vUS8fkI1ddTeeONsWnM1rw8Ch58kMDGjVgyM/GvXYd/xQqa//MfAIxwmKQDDlTYFenDCjMdCrMiIiIi9LHA++yzzzJu3Lj9PuwCRHw+AhWVbLriiljYNaWlYSsuxr96NabkZIhECNfXx64xZWSQdtyxZF9wQU+VLSIiIiIist/oU4H36KOP7ukSusUwDCLNzWy+6ioijY3RJ81mjOZmfF99ReuiRWT+8IeEG9yEtmyJHk9Oxnn00eRccimWtNSeK15ERERERGQ/0acCb28Rdjew8fIrCLvd25+MRDClpEQ7TXs8+L76isD69bHD1v796f+zGwAwOzSVUURERERERIF3PxMoK6P8oosJ1dRsf9JsJvOsMwnV1dPywQcA+Nevx/B6ATClpuKYOAFrVhaYTD1RtoiIiIiIyH5HgXc/YYRCeBcsYNM1P8HweNods/Xvj3/NWgLl5WAYmFJStofdlBRsg0vJ7CX7CYuIiIiIiOwrCrz7Ce8337DxssshFIo+YbXG/hysqCBYURF93mSKhV3MZsy5uTinTiV18uQeqFpERERERGT/pcC7H/Cv38Cmq6+JBVxTcjJGaysWVyZhd0P7kw0j9kfb4MEMfOzR6FRmERERERERacfc0wX0ZUY4THDLFjb+6EexacympCSM1lZMSUkkjxvf6bXW4mIyf/hD7IWFfbJJlWEYcb8ikfjPR49F4j6/urqJ/y6vZnV1E4ZB3C8REREREel9NMLbg4xgkM3XXLO9QZXNhuH3Y7LbSR4zBt/XX8e9zpyXR8H99+EcM2YfVtu7+YJhvIEwKXYLDpul3bHyei/eQIiyei9D89N7qEIREREREdnTFHh7UO2jj9G6bDkQ7bRstLRgzkjHXlyCf80aIs3N7S/IzSX1kClkX3QRKSNH9kDFvZc3ECYcMfAGwh0Cb3FWCuX1XoqzUnqoOhERERER2RsUePeFHebEGoYBJhMN//43dU88sf2cSAQA+6BS/KtXb29MtY1lwAByb7oRS2oa9v7990nZvcWOo7fJVkvcc1Lsltg5Oxuan8bQ/DRAWzqJiIiIiCQSBd4eECgrY8vdv409Th47ltZFiwDwr1qF4fO1v8BqxT50KI6hQ0keMmRfltor7Dh621ngddgsJFvNmLRPsYh0IVhZScjtjj22ulzYCgp6sCIRERH5PhR497GIx8Pmn98ca1Jl7d+f1OnTY4G3Q9jNSCf1qKNImTwZq8u1r8vtFboavd1RayjS6TrezviCodg1KXZ9u4gksmBlJetOPKnd/4dNDgeDZ7+l0CsiItJL6Sf4fcAgupduoLqa+qefIbBsWfSA2UzO1T/GVlKCY9IkfIsWQTAYu86UlkbBvfeRMnoUFm091CmHbXuA7aqjclfreLtzjQKvSGILud0YPh8F99+HvbSUwPr1VN58CyG3W4FXRESkl9K2RPtIcMsW6p96Cs/cubHnXD84n5QJEwhv2UJgw4b2YTc3l+zrr8OankbI7cZkMmk6bic2u718VVbPZre3y/NS7BYsZtMuR4K/7zUi0rvZS0txjByJvbS0p0sRERGR70lDVvuIr3wj3k8/a/ecJSubxtffoO6559qF3aRJkxj48ENAdMRBU5m7VtXYSmsgTFVjK4WZnXdaTrKYSU6J/o7HiDMUHO8XCg6bFYfNuu2aPVRw7PX27P2+i3ifw470SxYRERER6c0UePeFYBDfJx9jBAJYCwsJVVQAUPvII+3PS00l56c/wXXyyZgdDswOB9asrP0jGe1ndgxi/TOSqWxspX9GMmZz55+VP9z5Xrxd2XEdb1v4FRERERGR/Z9+et8HDMD72ecApJ94AuakJBr//W9CW2owAoHoScnJZF12GRlHHYU1O7vniu2FirKcFGU5d3le23rcuhY/Dru128G3/dpffcuIiIiIiPQW+ul9H/B88w2R+noA6p94Emu/foSqq6MHTSasg0sZ+Oc/k1Rc3INVJr62bs5g2q3mVd3tAi0iIiIiIvsXBd69ZYe1kcGtWzEPGkSkvBwikVjYteTnk3bSiWQcfzz2gQN7qtI+o62bs88e3q0Au+M6XhERERER6T30U/xeFKqvj3ZYttpImTCBlsoKiBgQDGIqLsb1wx+Se9GFPV1mn7PjNkay3ZotzZTXeynOSmFoflpPlyMiIiIi8r0p8O4FEa+XUDCId8UKjBYPmCB5yGCMk04m0tBA6vRpZJ18MuaUzjsKy97jC3635lVd33PXja329+ZX5fVevIEQ5fVeBV4RERERSQj730/dCaDlyy+x5uURrq0Fs5mw3481JxdnWhppU6ZgHzCgp0vs09o3oeo88O5OQO1OY6v9vflVcVZKbIRXRERERCQR7H8/dScAw4BgdTX2kkEQCmKYTBAOY83KwlZY2NPl9RpdbRFrGEanuzV1tnds2/12bELV9pyBwY47Gq3Z0sySykYK05MZ0i+902BsGNFdo9o3ttqx8O033d+bXw3NT9PIroiIiIgkFAXevcCakY4pFCZQtoGkwYNxDB5MxOfD7HB0GsZk72v76OOu4d0pXJfXewlHIlQ0tTJmoGuX9+5sXfCOf90pdisp9v3rW07/HvuutTUtnR5zOe0UZjr2YTUiIiIie8f+9dN3gnCOHw9ff03EYiVUU4N51Cit1+1l2qb1FmelkGy1UOfxU+8JkuW0ke1M6uHqRL47l9OOw2bhhpcXdnqOw2Zhzk0zFHpFRESk11Pg3UvsAwcS2LhR2w3tYzsG05zU5A7Hu9uwasfpvYYB9Z4ggVCEek9QgVd6tcJMB3NumoHbE4h7fG1NCze8vBC3J6DAKyIiIr2eAu9ekjxkCMlDh/Z0GX3OjsE0XuDduWHV2i3NlLu9FLtSGNLF+tUspy0WpEV6u8JMh8KsiIiI9Anmni4gEXmXL+/pEvqsLKcNu9XcaTBNsVuwmE2xxlHlbi8+f5iNbm+X9812JjE0L1WjuyIiIiIivYgC717g37Chp0tIOL5giDqPH18w1OV52c4khuWnxR3dhejaxOxtaxgBil0pOJIsDHR9vzXWm91eviyrZ/MugrOIiIiIiOw7mtK8FyQNGkSne+bId7In9rCt8/hxe4K4dmg8NSQ/jSH5aRhd7YHUDZWNrbQGwlQ2tjLge4ZnERERERHZM/rkCO9///tffvGLX+y1+6eMHLnX7p1oDMPo9GvHvYJ2nopsEG0mFe+rM+5t63vdnuDu1YjR6WtFth0ryEjGYbdQkJG8/Zixq/e368+kTW1LK5FI5/eKRCLf8Vjn9/yuvwTY0/cTEREREfmu+twIb0tLCz/4wQ+oq6sjGAxy3333fed7+f1+/H5/7HFTU9OeKFHicNis7UZ2Tez+ILprW+Mpl9PWYf/Zrvaj7eq3QmYjWscAV0q7kd3Nbi9Vja30z0jeIyO+bk+QotzvfRsRERERkT6lzwXe559/ntNPP51Jkybx4x//GOA7h9577rmHu+66a0+WJ3tRTmpyp2t7v4944bZqD09xdqk7tIiIiIjIbutzgXfOnDn8+te/ZsyYMQDfK/Tedttt3HjjjbHHTU1NFBUV7ZlCpVdoDUVYWd0cm0vdFm77ZyTHQvCeoO7QIiIiIiK7r88F3lmzZuF0OgG48sorgfihNxKJYDZ3vcQ5KSmJpCQFkb7MGwiT60xiq8ffLtwOcKVQmJmyR3uXVTR4qWxspWA3p0kvKK9nRVULI/qnMrkke88VJCIiIiKyn+tzgbct7LaJF3rvvvtuGhoaeOCBB/Z5fdKRLxjCGwiTYrd85w7Ne0uK3cKA7BSG9U+LbXW0t+zuNOklmxtYu7WFtTUtpCZbWVnV0mXgrfP4qfcEydqhi/WeNH/llj1+T5F9IbB+PQBWlwtbQUEPVyMiIiK7Y/9KD3vYqlWruPfee1m9ejUzZ87kjjvuiDsiu2Po/eyzz6ivr2fu3Ln7utw+L962QbBntiTaW5Kt5j0edNvWBGdYw+2e75+RHBvh7Y61W1vw+EPYLCZS7TZG9E/t8vz6bV2s6z3BvRJ4l1aqqZv0LlaXC5PDQeXNtwBgcjgYPPsthV4REZFeZP9KD3vQRx99xKmnnsrZZ5/NuHHjeOCBB6ipqeGJJ56Ie/6VV17J7NmzWbt2LXPnziUvL28fVyxuT5BAOLpt0I6BK8VuiY3wdmbH0ck93ZiqLYBmOmxkpSZtG2nee6O5VY2ttAbD+Lyt7Z4vzEzZranMQ3JTWbu1hXEDMhk1ILPT83zB8LZfJkSn8GftpQZZowrS98p9Rb6vYGUlIbc7NpLbxlZQwODZb8WOVd58CyG3W4FXRESkF0nIwBsKhbjooot4+umnOf300wEYPnw4119/Pf/3f/9HTk5Oh2vuvvtuhd0e5nLaeHdJJRvrfRw8KIvDR+RjMoHDtmPAjL+Xa/vRyfiB1zCMLtfUdrY1UdtU4nJfkIwUO95AmGRrNBxGDDB3UlP0WPx7Gmy/buepxG0NrzIc7d9HBAOTEf9+bXW8/GU5325sYPzATM49qJiRhRld7/nL9hF0h91KYaY9+rzR9pl0emnn9+zk9aYP1/eV7H+ClZWsO/EkDJ8PiI7iWl2u2HFbQYECroiISC/WdVemXuqdd95hzJgxsbAL8MMf/hDDMFi1alXcayKRiMJuDzCZTLGvnNRkKhr8eANhvi5v6HJvXJOp/bVZTht2qznu6KQvGKLO48cXDMW9V53Hz9qaFmpbWuMe75+RTJLdQnFWChaziRS7Jfa65h1q2Pkreoz4Xzvcf8ewDlCU5eSgQdkMyNq+3nyX99v2eXxV5qbBF+CrMvf2OszmLmtMsVs6vK+2r++is9f6rvcT2ZtCbjeGz0fB/fdR8uo/NWVZREQkwSTkCG9mZiaXX355u+eysrJISUnB7/fHveZXv/rVvihNdmF8UQbfbmpkfFHGbl3XtsduvMHFthFMXzBCir3j8bap1PWeYNzp0ANcuzeVeHdlOW2xEd7d9c+vy1lQ3siU0kxOHT+QicUuFpS7mVjs2vXF2zhs1v1ubbTIvmYvLcUxcmRPlyEiIiJ7WEL+lDtt2rS4zycnJxOJRGKPn3vuOc4880zS0tL2VWmyC8eMKuCYUdHRlR2nxratM/0u62fb1gC3rVFt4w2E8AUjJG973rUX1q76gmHqWvyAiexUe9zas51J37lJ1ILyRlr8QRZuaubU8fCDKSX8YErJ9ytaRERERCRBJOSU5s6YTKZY4L377ru5//778W1btyX7t7qWAFub/dS1BHb7WofNSrYzqcMopi8Yia1dHZKX2mWzqzqPnzU1LdR54s8Q6Iw3EKbFH6bFH91aaU+bWJxBapKNcUXf/Zc2u5ryLSIiIiLSWyXkCG9nTCYThmFw99138+KLL2rNbq9i7PTf3ecLRkd0HTYzKXYrDps59nhXdnfLnrZGVA6bmdQkC2Dq0GX6+4xatzlrUjFnTYJIF42pdmVvbPu0Zksz5W4vxa4UhuZrBoWIiIiI9Iw+F3gffPBBKioqFHZ7mezUJBz2rrcm2hVfMMLyikY2ur2MHZDBqMLMuGt649nddbZtARlgaF78/W/bB829t83RrnRn26fdVe724vOHKHd7FXhFREREpMf0qcBrs9kUdnup9lsTfdd7mNno9hIKh1lZ2Uj/zJTYaO+u7O462+4E5L0RNL+LvdG0qtiVEhvhbRvJDgX3/JRuEREREZGu9KnA+/TTTzNhwgSF3f1Il3vEGp3vAxuOGMSb3mwYBhZLdIqyL7B9CrPDbiXJambcgAzW13rIdibhaQ3R4InQPzMZu8VMWa2HTQ0+ijIdlOZtH5X0B8OYzdFCtjS1YjGbyEndHn7tFjOtoe3Tk5OtFrJSknClJGGCuJ2jI4ZBktVCktWy7fH2Y2378+742RiGgREx6GRbX4yIgdHJzGzDMDCZu5q2vee3CxqanxYb2a3zBKJdsvfCGmYRERERka70qcB73HHH9XQJshva9paNr+s1q3NXVPPFBjcj+qUyfXg+Dnv0XiMLMxlZmIkvGKbC7cNmMeELRkiyWtjU4KM1EGZTg69d4AVYV9PC4/PXMXtJFQ67hScvnMSEbVv/mEydTE/uIrCbOs+unTJt24c3Hn8kgrc1tG3UOBQbXc52JrGkopG1W1sYkpvK6AGZce67m4V0o84dxbpk9/BItoiIiIj0PX2qS7MkrqpGHws3NVDd1ArAkoomAqEwK6tb4jalctgsFLocpCbbYlOKizIdJNstFGU6Yuetqm7ixn8s4qQ/fcybi6uIGODxh7n8+a/5qqw+dl6K3YLF3LEx1Z622e3ly7J6Nru97Z7fMXDv2GALYN3WFjz+EGu3tuzV2jqTYreSk5rUranjIiIiIiJ7kgKv9Gp/fH81U+55n1v/uZhGb3TrIoDRhelkptg5eJALRydBy2GzkO3cvjduaV4aM4blUZqXxtqaFq55YQEnPPIx7y6rxjDg6APy+fsVB3Po4Gy8gTBXPr+ALzbUxb3X3lLZ2EprIExlY2u753cM3FlOG3arObZ+eHBuKs4kK0Ny4zfPEhERERFJVBpykf1S27Y+WU5bp/vjBsMRnvq4DE8gTG1LPSuqmzluZD/c3gBTBudw+AH9OlzjC4a37eVrkJ2ahDcQwu0Jku6wUtcS5NtNbr4qc/PWokpCEQOTCY45MJ9rZg5heL/oNOdHfziBn/79Wz5eW8u1f/+WV685lCG5qd9pm6FQOMLfvyzHH4wwsjCdQwfndnl+QUYylY2tFGS0/0ySrdtfMxq+t68xHlWYEXcqs4iIiIhIolPglf3SjtNyOwu8c1fW4NnWCCkn1U5tS4AXv9rE8qomBuemMTA7+s/bFwhR7wnQtmrW4w/hbvHzydo6Fm9qYOWWZlZtacbjb99U6cgRedx87HAGZqXEmlYBJNss/PkH47nwmS9ZvLmRy//6NY//YDy5GY4utxnaMRAnWcxUuH3c8PJCFmx0U+RycMHBA3cZeAe4UhjgSun25ygiIiIi0pcp8Mp+qTvb+rz41SYArppeytUzB3P/e6t44YuNLNrcyLlPfMbdp43iyAPy8QUjbGnys6yykRVVTSza1MjqmpZtnZ63c9gsjC3KZPzATKYNyeHg0mwg2qV5Z0k2C78/ZyynP/op5XVeHp67lt+fM67LbYZ2XGf7/tot3P7vpTS3hgDY5PYxf81WZo7IpbzeR0lWCgWpWnEgIiIiIvJ9KPDKfqlt39vOujRvrPfy0ZpaAM6dXERqkpW7ThnJCaP6cfu/l1Je5+WK5xcwfWgONc1+Vm1p7rA90ACXg4nFLiYMzGRcUSYj+qVjtXQ/ZA5wpfDb00Zx3UsLeXfpFs6dVMfM4fG3vPIFw9FtkgIRnvhoHf9cUAHA+KJMzp1cxC//vZRP19Xzh/+u4bhR/Sir91KQ2n7Nbb3X3677soiIiIiIdE2BV3pUF9vwYmB02lXthc/LAZg6OJvctCR820ZhDyhI5/WfHMofP1jLs5+U8eG2UAxQ5HIwodjFuG2juPnp26dKtwbD0XvEGc31BUKkJMUfaZ46JIcLDh7I377YyI3/WMTrP5kau2+9x0+zP0yGwwqY2LDVw6/eWEZZnRcTcOnUEq6cXorNYiYQivCrN5bx7rIttAbD/OLEA9rtzRsxwO3dPs07K6V94I1gYDLi/3LAMMDyHbYe2nkEvN096fqenW8nJSIiIiKy7+x24H399dfZtGkT5513Hjk5OXujJulD2nLRzg2fOmsAVefxU9Pk59VvoyOk504uwrLD+tqwYcJhs3DLcSM4fnR/5q2sYXBeKpNKXBgG2LatnX3gvdUsr2pibFEG04bkMKHY1emoqc1iwW6NH71NJrj1uBF8s7GB5VVNHPn7+eSlJZOXlkRqshVXio3ctGSsZhPPfLKBYNggLy2Ju04ZyZRtU6YBTh5bQG2Ln0c+WMu81bWcPqGZ/MEZseO+YAiwEIlEyHIm0Rpq//l0ta9vdP9eBVARERER6Xt2O/BaLBZ++9vf8rOf/Yxjjz2WCy64gFNPPRWHw7Hri0U6seP6VofN0uFxm8qGVv67rJoGb5C8tCRmDO+8ydPowgxGF24PjZvdXl74YiPPflJGIBQBYO7KrcxduRWzCSYMdDFzeC6HD8+jKKv7jaGSbBYePm8cP3r2KyoafLGveI4ckcdvTx+NxWzCMAz+9sVGnv+sjIsPKeFHh5ZQ0+znpa82cfMri/nzOQfGrqtu8NEvx4XLaSfbaWfx5kaqG330y3AwZkBGu9f4pryeVTUtDM9LpSTXidsTxNVFt2sRERERkUS124H3pJNOYvPmzbz//vu88MILXHnllVx55ZWcccYZXHDBBRxxxBGYzWq2I7snxW5p1/Bp58dtHDYzH6zaCsCZEwqxdXPN7arqZq598VvK67wATCx28cODB7KkopGP1tSytqaFr8vdfF3u5oH/rGZ4fhrHjepHaY6TgkwHw/qldTrKC1Cc7eQ/P5tOTZOfmuZWapr9VDX4qPUE2NLUSoM3yJEH5HHupCJMJhN1LX5ue30pc1bUAPDgf1eT4bDx82OG0+gL8s7Saq5/aWHs/q3hCGYTsc+jyesnGI7Q5PV3fK81LaypbmRBWT0zhuUwND+9y27Xe8Jmtze2XZK6SIuIiIjI/uI7reG1WCwcc8wxHHPMMTz++OO88cYbvPDCCxx//PH8/Oc/55577tnTdUqCc9jaT1122CwkW80dpuIm2Swsq2zCZIIzJw7o9v3vfms55XVeXCk2rj9yKMeMzMdkMnHI4GyunF5KWa2HhZsamLdqKwvK3azatlVRG7MJCjIdlGQ7GZTrZGheKsPy0yjJTiHZGq3bZjFT6HJQ6IrOdgiGI50G8gXl7ljYTUu20twa4plPNnDy2AIePHss9Z4An62qjJ1f2+SnIDtMTmp02nVJbipVja30z+gYYofnpbKgrJ50u4WaZj8HFphxddHtek+obGylNRCmsrFVgVdERERE9hvfu2mVz+ejsbGRxsZGwuEwdrt9T9QlQkWDj6qmVvqnbx81XFUdDaFtI69fl7m5841lXH/kEI4Z2a/Te62paQHg9+eMZUT/9A7H+2Uk88OCYn54cDFNviBvLKpkSUUj5XVeyus8eAJhNrt9bHb7+HhtbbtrB7gcDMtPY1h+GqeOK2BwbmqH++9sbFEmB/ZPZ3lVU2xrouNH9Qeiof43p47i6Pu2B95KtxervTH2OXS1H++E4qzoZ7VtWvPg3NR2+wjvDQUZybERXhERERGR/cV3Crytra28+eabvPDCC7zzzjtkZWVx7rnn8uCDDzJp0qQ9XaP0UVVNrfgDYaqato8aLt7cCMCobWtzH5qzmg21Hn7x76UcWJBOXnrHwOXxh3B7gwDdGn1Md9i4YEpx7LE/GMYXilBW56Gs1sO6rS2s2dLC6i0t1Lb4Y0H4g5U1vPBFOa//ZOouXyfZZuHRH07giue/Zk1NC8Pz07hkakns+OBcJ0VZDjZte2zQeVOqeCYUZ8WC757w5w9W8XV5A5OKM/nJEcM7HO8qgIuIiIiI9JTdDryvvvoql156KZFIhNNPP5033niDo446CovFsuuLRXZD//Rkyuo8OCxmfMFo86pY4C1IZ3llE99sbADA4w9z22tLeeKiiR3us9kdbSCVlmwlNXn3f8djMpnITUsiNy2JySXtQ2RNUysb67ysrmnhla83sbK6mRv/sYgXLj94l/fNcNj4y4UTeXtJFUcfmN9u+rPJZOLE0f35dNvjuatruePA7k/h3tO+Lm/AGwjydXlDj9UgsicFKysJud0E1q/v6VJERERkL9rtn/7T0tJ4/PHHOfXUU0lJ0YiORBldbahL5/uydnaZLxjGYbdSkp2K3WrGGwiTZDGxaHMDAP0zknnio+gPquMHZrKyqpkF5W4em7uO648a2u5e5XUeAHJS7Wyq98Z9vc1uL8m2+N8Ojd4A6Y74a2AjEYOB2SlMKs6kyJXMz/6xiIWbGvjV60v50dRihuZ1nD4NEAyHAQtpyTbOnTwQgNC2fW+D2zpIXzZ1EL/Ydv4na+u49K8LeOCsMRy8w3ZGsToMgy56atHZ+PDq6ibK670UZ6UwND+t3THDMGJ/b5OKM2MjvACLN9azorqZYfmpTCyO1uMLhvAFIzhsZpyd7Fsssj8IVlay7sSTMHzRX4aZHA6sLlcPVyUiIiJ7Q7cDb1VVFampqRxzzDF7sx7pYzrbHtYXjG5LBAYWs4kUu4XKxmi3Y5vFRG5aMnNXRps+XTmtlA11Hu57dxXPfVrGyWP7t1unW93UCkTX6TqTOgm1vhAWc/xZCtVNfpJs8Y95/CEGbAvtuWnJ/GTmEO57bxWvflPBmAEZHNg/k3pPgBv/sZDDhuRw2WGDMJlMZDmT2u0fvKO2XwLYd3jNoiwHFY2tXPjMl1xx2CAG5zhYWtXCuAHpnDq+CIvJhPk77LVbXu/FGwhRXu/tEHgt5u3791531Ih2x5ZXN9PiD/HFejeYTPRLT8ZhtxKOGPiCETrZ0lhkvxByuzF8Pgruvw97aSlWlwtbQUFPlyUiIiJ7Qbf3D3rhhRfIysrikEMO4fbbb2fOnDn4fPH3GhX5vhw2CxaziezUJLKddhw2C4u2TWcenp/Ge8uqCYYNDuifxoEF6Zwwqh+HDckmFDG4+Z+L8QfDsXu1TWnOj7O+N561Nc28v2IL/1lWzbtLq/hsXR2zF1fxxqJK/v1tBR+tqcUbCMW99pDB2Rw7Mh+AP/x3DbXNfj5YWcNHa2q5552V/OzlhbTuUFt3vXzlIZw1sRDDgCc+2sDv3ltDZYOXhZubdvteOyrOSiHFbqV4N/YdBhien0pqkpXMFCv+QITqplYcNjMWswmHTduSSe9gLy3FMXKkwq6IiEgC6/YI78UXX0xeXh5z587l73//O/fccw92u50pU6ZwxBFHcPjhhzNlyhR1aZbvZUlFA+u2ehic62RUQUa7qdA7rt99fWG0g/GZE6LrWk0mEz8/ZjjLKr9i9ZYWHnp/DbceFx2VbAu8eWldDzuW13l4c1FlrKNzV95cXMn4okyOPiC/w7FLppawoqqZjfVefv7PRUwYmLnDdVWU13t54sKJZKd2fxjUmWTlf08dxZEj8rn9X0twe4N8vLaey6em7friLgzNT+swstsdk0qymVSSzWa3l+qmVvqlJ5Nit5Kib38RERER2Y90eygmNzeXiy66iGeffZaysjI2bNjA448/TnFxMU899RQzZszA5XLxyCOP7M16JcGt2+rB6w+xaFMj67d6qPP4AQiFI/x3+ZbonyMGdZ4AWU47M4fnxq51Oe38/NhhADz/aTnNrdHOzBvrout2+3WyZU5NUyuvfbOZ3/93NWtqWrCYTYwrymRKaTaHDs5m7IAMpg/NYebwXA4fnkv/jGRCYYOvytx8s9Hd4X5JVgs3HTMMu9XMR2tqWVrZfhR28eZG3lu25Tt9PkcfmM/saw8jNcmKPxSh3N36ne6zpwxwpTCpOEsdmkVERERkv/Sd9+EtKSnhkksu4dhjj2XevHk8+eSTzJs3j40bN+7J+qSPGZzrZN1WD0kWE4FwBLcnSLYzidcXVrKh1kOmw8on6+oAOG9yUbvOxgCHDs6hJDuFsjovX5e5OWRwNutro02rirPbhzKPP8QrX2/m3wsrCIajC2cnl7g4YXQBWc7tQ5Xrt7a0u/aE0Qa/emMZvkCY/hmOuO9jYFYKJ43pz2vfVFDV2IrdasZqNjGqIIOJxS5OGN35nsG7kpeezMCsFJZXNXHwoD239ZDsPyoafLg9gbjH1nZjBoKIiIiIRO124N2yZQvz5s1j3rx5zJ07l3Xr1jFx4kRmzJjBzTffzLRp0/ZGndJHjC7MZHRhJnUePw2eIJlOG4FQhIfeXwPA5EHZ/Hf5FnJS7Zw2Lv66u4NLsymr8/LZ+jpy0pIIRwxcKTaydwixX26o56E5a2j0RUeBS7JTOGtiEUXdWMtaVufBFwjjsFs6hOgdnTS6H28srGR5ZRP/uHIK4wa6Yo2qguFoJ2aPP8SizQ2ML3LhsHdva6+tzX6WV0VHjddvbeH95VUceWD/bl0r+7+KBh9HPTgfXxdrvR02Cy6n5o+LiIiI7Eq3A+/s2bO55ZZbWLt2LZMmTWLmzJk88sgjTJ06FafTuTdrlF6szuOn3hMky2kjJ7V7TaMAsp1JZG1bEHrti9+y2e0jJ9XOt9umEF84pbjTzslTSrN4+atNfL6+niG5qQAc0D89th7Y4w/x4H9W0+IPMcDl4NKpJdHX7GZ9Syuia4lH9k/vtNNy9H5JnDSmP/9eWMmsz8uZuG0P31A4woert/Lm4krmLK/BFwwzKMfJH84Zy8jCjF2+/odrtgKQm5qEYcDiimaOPLB/u886W22Sey23J4AvGOahc8cxJC817jkup53CzPizC0RERERku24H3pUrV7JixQpOPPFEjj/+eGbOnMmBBx64N2uTXmTn/XQrGrxUNbbSGgqTnZJEvSdIit2KNxAmxW7BsS2sRiKRTvfobW4N8sB/1jB7STUm4JDSbN5cXEVBRjIOm4U5K7awekszby6q4ugD8xkzIINMh53x25pErapu5qM1tQAMy0vlw9W1+INhvtxQT4s/hCvFxslj+lPvCeD2Bimvi79HbzBiYN0WbA3D4NuNDQAUZCZT0+xnZXVz3OuSbGbOmjiAfy+s5O0l1Rw+fDNfltXzn+VbqPcEY+dZzSY21Ho46/HPuGL6IC4/rBSzyYTHv70TtMcfImwOke6wMW9VNPBOGJiBy2FlZGE64Uh0+ncwFP3v+q0trKpqYXj/VCYVZ3e6/VNXQhEDE/E3SjYMA6tF3Zj3piF5qYzqxi9A9pbOpk4rbIuIiEhv0u3Ae8MNNzBt2jTmzp3LG2+8wa233kpKSgozZsxg5syZCsB93M6BqqqxldZgmEA4jN1qxuW04Q1E99b1BsKxwGsymaj3BnB7grh2Gpl8dN46Zn1eDsDtJ4zgz3PXAfDjmYNxewKE/AZvLqrCGwjz7tJqBrgcWM1m+qUnMyw/ldVbWvjPtkZXIwszWLS5gVAowrebGgAYU5iBNxCdNlrd2EppbvyZCjaI7eu7qd5LU2sIu8XMMSP7sdntI91hwxcIU1bnoV9GMq5tI9NmU3RkeVKxi6/L3dz4yuLYPTMcNo48II+jDsijf6aDB/+zmo/X1PLYvPUs3tTI3aeNxrFDoLRazNit0c/sk7XREH/2pIGMG5hJltOOYRi4nLbY5/jZujpaAkFWVrUwuSS7i7+3zpNwZ2G37bqurpXey7VtG7AbXl4Y97jDZmHOTTMUekVERKRX6HbgtVgsHHTQQRx00EHceuuthEIhvvrqK+bOncu//vUvbr75ZlJTU7n77ru54oor9mbN0gv0z0imqrGVQTlOCjNTMJnAFwzHRnh39OX6OsrqvJRkp3D86Oi63Mfnr+MvH24A4LbjR9DoC9HgC1KSncLJY/rz/GflfLy2NhZY/aEIn6yt4/TxhQAcPCiL1Vu2j1CN6JfGos0NLKtqIhiOruntav1tZ9rC8pC8VNbVePh8fR2VjT421nsxjOjWR3ecfCDmHcLgpVNLWFDuxm41c/jwPE4Y3Y8il4OMHdZg3nXKgby1uIpH567jk3V1nPn4p/zymNK4r9/UGiLDYWX0gIx2e95mO5NivzAY0T+VlVUtjOgff0rs7jAMg6UVTZTkpJCWbPve95P9W2Gmgzk3zYjbNGttTQs3vLwQtyegwCsiIiK9wnfu0my1Whk/fjytra20trbS1NTEl19+yapVq/ZkfdJLDXClxLaqaZvu7LBtn8q8o3pvkEAoQr03Os33+c/KuPfd6L+jG44ayolj+nP8Qx8BcM3MwVgtZrY0tbJw29TiQwZn89m6OhZtauDQwdHRzCml2cz6fGPsdUuynbQGw6yoik4/HleU+Z1GKNvWEC+vaoo1jmpjAmqa/azf6mm39vLg0mzevPYwMhxWUuzRb7nKBl/7a00mTh5bwOjCDO55eyVrtgWLnc3fNp15Smk2uV3sKzy5JLvLkd3dcf4Tn/L1xibSkizM/flMMrXZbsIrzHQo0IqIiEhC2K1FeH6/nw8//JC77rqLww8/nMzMTI444ghmzZrFyJEjmTVrFrfccsveqlV6qdZQmLptjXjiGTsgg5IcJ2MHZPDlhnruenM5AFdNH8Rlhw3i9/9ZTbM/xNC8VI4dGd3OZ91WDwaQYrcwpjADh82CQXTdLsCkYlfs/hkOGxaziZpmP6GIgdNuoci1+z/MG4ZBVcP2fW9dKTZGFaZz4SHF3H3aKA4ujTalmvVZOQ3e9qNj/TOSY2G3KyU5Tl68YgqndtKBevWW6Pvz+EOEI51POd6TFldEX7PZH+aTtXX75DVFRERERPaEbo/wzpo1i6uuugqfz8eAAQM4/PDDueiiizj88MMpKSnZiyVKb7XZHW1c5bCayctwtFu7u6NRhZmMKsykwRvghEc+JmLAaeMKuGbmYD5cvZXXvq3ARHQdr3lb86hRheksrWjEEwjz9McbokE2ycK4bQ2r0pJtnD1xAK8s2My1RwwBiG1L5AmEaQ1Gur0NUBuTycS1Rw6hriXA8H5p5KUlsb7WE9vK6OSxBaypaaG2xc/Dc9Zw49HDvtPnlmSzdGgC1ubHMwbz4ZpaPl5bx29nr+D2E0bs9eZRkwZm8OmGBlwpNmYMz92rryUiIiIisid1O/AWFBTw0EMPcfjhhzN06NC9WdM+4Xa7+de//oXVauX888/HZtPaxD2trXFVayhMf1dKh7W7OzIMg1tfXUJVYysl2Sn872mjcHv83PnGMgAuPKSYSdu29QFIT7Zx5sQBvPjlRoLhaBflM8YPIH2HNaZ3nHgAF0wpZui26cXOJCs5qXZqWwKU13sZ0S9tt9/TyILOu+Zmpti5/sih/OG/a6hp9vPQ+2uYWOwiazf3S11Z1cSbiyrjHptQ7OKBs8dw3YsLee7TMl79ZjPTh+YyY3gOhw/PIyd1z29H9Pzlh7Cp3kt+ejJ2qzozS98WWL8eAKvLha0g/kwMERER2X90O/AeeeSRHHnkkXuzln3m008/5cwzzyQ9PZ2ysjJefPFF3nnnnZ4uK+G0Na7qn5EcG12NJxwx+NnLC/nP8i3YLWb+eP54UpOs3PzPRdS2BBiU44yN0u4oPz2ZM8YP4NN1tUwelEW/jPb76Fot5ljYbVOcnRINvHXfLfDuSnZqEjccNZQ//Hc11Y2tXP3CNzx+wYRY5+bu+P1/V2MAxxyYz9Nxjp80poBGX5Df/2c1bm+Q2UuqmL2kCpMpuv3ScaP6ccLo/gzL33Pvr20UW6SvsrpcmBwOKm+OLtsxORwMnv2WQq+IiMh+7js3reqtysrKOP3003n66ac56aST+PTTT5k6dSo+nw+HY/fWdfr9fvx+f+xxU1NTF2cnNiPOHNzs1CQcdisOm5nOlps2+YL86vVlvLm4CrMp2q24NMfJaws28+7SLZhNcMuxw2hpDdHSun1f2tZghGSie+GeNXEAAKFwhEZfgOWV8f8e6j1BMh3R4Fnd1Ep1UytJ27b6aQ2GO92H12o289Tm9e3eZ1vDq0KXI+7rTSh28fn6OtbWtHDxM19xzczBOJN2/HYzGJzbsYPyNxvdfLa+DqvZxFUzSmOBNxyJ4Alsf/+nji3kpNEFLK1s5KPVtXy4Zisrq5tZtaWFVVvW8scP1vLgOeM4blQ/DMOIO5V8u/jNuwyj43ZTIn2VraCAwbPfIuR2E1i/nsqbbyHkdivwioiI7Of6XOB98MEHOeGEEzjppJMAGD16NAMHDuTFF1+kvr6ec845h4EDB3brXvfccw933XXX3iy314jX8bht311fMIIjzozxQCjCLf9czH9X1GA1m7j3zNEcP6o/W5v9/O/bKwC4+JASxha5Olx77uSiuNN3F25q6HT32ENKs+mXkczarS2U13nJS0tm+rDomtT3V2whu5PpwH+eu7aTO0K/uiRKcuLv3zup2MU3GxuoaPDx2Px1XDmtNLZu2GYxU7BTF9xwxOCFL6KdpS88pLjdCG1qkhXDamn3OVstJsYVuRhX5OKqGYNp8Qf5aE0tby2u4vP1ddzyz0U4kywcNiSn3eus3dLMRrePgS4HQ7oYBbZazAq8IjuwFRQo4IqIiPQyfW5B3qZNm6isrMQwDMLhMDfccAM+n493332XRx55hDFjxrBo0aJu3eu2226jsbEx9rVp06a9XH3v4AtGuzKDgcVsardXbBt/MMzVLyzgvytqsFlMPHTuOI4f1R/DMPj1m8to8AYZkpvKj6aW7PH6Jg6MBugF5e7vfa+tLQE21XvxBTp2oE5NtnLV9FKcdgub3T6e/WQDwXCk03u9s7SK1VtaSEu2cs3MwbtdS156MmdOHMBTF0/i+FH9CIYNrnvxW74ua/8+N7p9eAMhNrp9ndxJRERERCQx9LnAe8011/DBBx8wfvx4Ro8ezbx581i6dCn/+Mc/WLZsGUVFRdx2223duldSUhLp6entvmT7yC6YyHbaSd5pOm04YnDZ818zd9VWkq1mHv3BBI4YkQfAG4sqmbtqK1aLiduOH46tiw7E/mCY+99bycXPfMnbS6q6Xd+EbVsWraxuajdNOp5AqPOA2vZeKhpaWbS5kSWbG2nYtpdwm34ZyVw5vZRkm5kNdV5e+Xpz3Onf/lCYR+etA+DqGYN3a83vzixmE787cwzTh+XSGoxwzQvf8N9l1Xywsoa1W5oZ6HKQYrcy8DtszSQiIiIi0pv0ucB7zDHHsHTpUn79619z0EEHcdNNN5GXFw1baWlpXHDBBRqp/Z5S7BYsZlOnXZmXVjTy6bo67FYzj10wgak7TLl9Z2k1AEcMz6M0zhrXHS2rauKvn5WzYKM71s25OzJTbNit0XXFNc2tXZ5bXufp+l4OG5nb5mt7AmHWbGnusD9uQaaDiw8pwWyCbzc1UBZnrfDGOi9bmqLrwV/7toJ3l1YT+R777NqtZh4+bxzjijJp8Yd46etNsVHdIflpHDEir8vpzCIiIiIiiSChA+8XX3xBS0tLh+eHDx/OaaedxsaNG9m8eXO7Y59//jnHHHPMvioxYazd0sz7K7ewdkszDpuFbKe9Q6MkXzBEvTfAO0sqAEi2mpm8w1ZDEF2bC/DByhrWb+34d7ejMYUZpG1rBDWpuOM6387MW7mVQChCbmoSA7O77j688zrbnTnsFkb0T2NicSZJVjNhA+o9gQ7nDclLZeK2Gj9fXxf3+I1HDyU92cramhZ++uK3nPvE591+T/Ek2ywcWBCddZDpsGlUV0RERET6nIQNvEuWLOGII47ghBNOiBt6IbrV0oMPPsgf//hHFi1axE9/+lPWrFnDnXfeuY+r7f3K3V68/hDl7vidjgF8wQjhiMG6rdFR06adOi8DHD48j6MPyCMUMXjwv6uJxJn+28ZqMfOPqw7hrAkDuPnY4d2qszUY5t1l0VHkk8b2x2ru+lugfWflztksZvLSok2vapr9cc85pDQbgMWbG/H4279vk8nEDw8u5o2fTuWnhw/Babewsro5dvy7jvbWbBs1PrAgo9NRXV8wRJ3Hjy/Y9fRuEREREZHeJmED7x/+8AcuueQSli5d2mnovfXWW7nooou4/vrrGT9+PJWVlcybN09rcb+DYlcKKUlWil2dj5g6bGYsZhNTSrOwWaLtf6saOzZOuv2EA3AmWVhe1czrCyu7fN2irBR+fcrIXU5/bvP+yhpa/CHy05M4eFB2t67prpxtgbe5NURrsGMTqwEuB4WZDkIRgy831Me9R1qyjRuOGsrcn8/kokO2dwv/9ZvLO0yV7o62KdtFXYzstq259sZpvCUiIiIi0psl5LZEDQ0NvPfee6xevZpLL72Uo446ihNOOIG3336b1NTtwchqtfLkk09y7733YhgG2dl7NgD1JYPzUhmcF/1sd27KFApHMJvN2K0W7FYLPzpsMK9+W8XK6mY2uX0Mzms/6pidmsR1RwzlnndW8ui8tUwudnXYMqi5NUi8fla1Hj9J1vi/x1m9pTnW3Gp4vzS+2LB9anGDLxj3mu6obty+DthhM+MLRthQ6yHTYefjtbXtzs1Js1PR4GP+6q1srPdijrPvz4ba6Aj4sSP78dttz/3r2wrq/PDA2WPjNvLyBcLE22q3bYQ3Ly0pbrMsiK659gbCna65FhERERHprRIy8Kanp/Pcc8/hdDqZMGECc+bM6TT0AmRlZXVyJ+muePvw+oJh1mxpwdMaoCjbSUHm9tHfzJRoo6d6TyC2N+2OLj60hDcWVbKssomnP9nA/WeNbXfcYjaRFCfhFbkcpCXH2fQX+OMHawmEImQ4bBRnpbSbJtw/I5lDB+fEvW7hpkZqmlup9wTIctoZmLX9fWxtbiVjh02GDQPK6714A2EqG3zYdgrfWU47FrOJBl+QVdXNjB/Yce1x2+jwjiO6FpOJj9fW8Yt/LeX3547DvtN9rRYzSTtt/xSJGNS2RANvvwxH3L8jgBS7lRR7Qv6vQERERET6uISc0mw2mzn66KNjj9tC787Tmx9//HEqKip6qsyE5w2EqW700ewPxzoQQ3TNqH3bKGVlY/wuyRaziV+eeAAWk4n3lm3hozVbv1ctTa1BVm+JrokdV5QRd2S1K1nO6DZBDd5Al1OL0x1WLGYTwXD8KcJWs5n+GckAzN6NrZSuP2oINouJ91fWcM0LC+Lu+7uzem+AUMTABOSkfvdtjkREREREequEDLzx7Bx677rrLh5++GEsFk3j3FtS7Bb6ZThIS7KQn759SrIvEGZgVnRN6Tcb3Z1u/TO8Xxo/nBJdx/rb2SvwBr57U6VXF2wmFDZwpURHd7vydVk9/11e3a5hltNuwW6JbmVU1+LHHwrTGgwTDEfwBsJ4/CFa/CEafUHs29YnN3YyTXrAtvW0n66ro84Tv8HVzsYVZXLb8SNIsVv4ZG0dVzz/NcFw13sEt01nzklNwtrFfsYiIiIiIomqT81jbAu906ZNY+vWrcydO5d+/fr1dFkJy2GzMGZABuGI0WE67dD8aGOwr8vcHPX7Dxmc6+SoA/I5/6CBFO7QYOmamYOZs3wLlY2tXP7811w2dRDTh+Xudi0LNzUA0W2GOpvaC/Desmr+uSC6VVWyzcK0odHXMplMZDntVDe1UtnY2m5kuqa54zZEAP5OAmlbEA5HDDZs9ZDtTIp73s5GF2ZwzeFDeOC9VSwod7O2poUD+nfeYM1ijr7POo+fVxds5syJA7r1OiIiIiIiiaLPDfv85z//YeDAgQq7PeyoA/K5aMpAhualYjWbWLfVw18+XM85f/kMt3d7gEyxW7n9hOjU5qUVTfzsH4s44sH5PDRnNUsrGjttxLSzE0f3B2BVdXOHLYHa/GeHsAvw2jcV7c7NTUuK7S1sMoF525fNYsJuMZFkNeO0W3Cl2MhPS6Jg29TlNoZhsGZLMyuqolOrjz4wP+4a3s6sqm7mTx+sib2fEf06bjG0o2H5qZw7uYiIATe/upiXvtrU7dcSEREREUkEfWqEd/ny5fztb39T2N3HVlQ1sqHWy6CcFA4syNzWpCrMLcePwGo24/GH+HDNVh6as4byOi+/eXM5fzh3XOz66cNy+efVh/DGwkpmL6miptnPvxdW8u+FlQzMSuGE0f04ZWxBbFugeI46MJ9nPimj3hNgwUY304e2HyVeWtHIV2VuIBomF25qoKLBx7+/3b7G22I2MXynkLm1uZWc1Piv2xrcPsIbiRgsq2yiuik6Mjx2QAY3HT2sy9HmHW12+7hnzgZagxGmDc3h/84YvctrTSYTd51yIDaLib99vpHbXltCMBThwkOKu/WaIiIiIiK9XZ8a4T3wwAP59ttvFXb3sbI6L95AiA21XgAcNitZziQctujvW9IdNk4aU8CDZ4/FYjbx1uIq3tmpodPg3FR+dvQw3rthOn+5YCJHHZBHktXMxnovj89fzyl/+oRfv7EstqXPzswmExMGZgKwodbLlqbtU5KXVTbFwu5JY/pz6rgCzj8ounZ4/uqtcffU3RXDMAiGI9S2+Cmv8/B1uZvqplZMwMiCdMYWZXY77ALc994qPP4wY4syeOi8cUQMg9Vbmnc5wm0ymfjFCQdw+bRBAPzqjWU8+8mG3X4/IiIiIiK9UZ8a4QWw2eJvWSPfj2FAaygc28812bq9GVi200Ztc4CBWbYOAc2IGBjbct+YARlcNb2UR+et41dvLOPvlx9EfrqDnU0qcVGa5+R/joN5q7by+qJKllQ08vaSat5eUs3owgxOH1/A5EFZ7boxJ9ssFGenUF7n5dN1dRw+PJd1Wz0srWwC4IjhuUwdnE29J0BOqp2xAzJYtLmRqkYfNospbkA1jOha3HDEoMEbwBsM4w9F8Ici7JxFLWYTowszyHLaiRgQjkTwBcIsKHeTmmylyOUgy2kntK0LdGiHbtAN3iAD81J57IcT8PpDXPrc16ypaWHCwExuOGoYowrTCYXjB2h/KMyNRw3DjIknPlrPb95awaCcVKaURvedjrctlIiIiIhIIuhzgVf2DpPJhDcQJhyJbsfTNnoLkJPmIMORhM1qpsEXoN4TJMtpI9uZhNlmYcccef2RQ5m3aivLq5q4/73VPHbBhLhBM9luIcli5kc5Tn40tYRFmxp49tMy3llSzZKKRpZUNFKcncLFh5Zw7qQizGYTZ04cwAmj+vHzfy6mqTXE+lpvLOweOzKfa48Y0u61+mcO48ezvsEXDDOpxMXkko77NYfCESoaWnn+szLc3vZdmS1mEwOzUhiU46Qk28nJY/tTnO0EIBCK8MHKLfzhv2vY2rK9U3NaspXSHCeluU4KU7dPwOifkcTfLj8Iq9nMxc98wZqa6NZa32xs4KJnvmT60BxuPHoYowozOtQYMaJNw244aihub4BXFmzm9n8t4d/XTCXdoV8AiYiIiEjiUuCVPSbFbomN8O4oy2mLhdx6T5BgKEK9J0i2Myk2KgwGYCLFbuHBc8Zw6p8+Ze6qrfzr2wrOmLDr7sJjizJ56NxxXH+kh5e+2sQ/vt4UWw/s9gS45vAhQHT69NkTB/DXz8pZUtEIwClj+3PUAXkdgnVOahLnH1TEM5+U8eaiKkYVZMRGQyMRg282NfDu0mrqPdEmWwUZyZw4pj8l2U6Ks524HFb6uzpugfR1WT0P/GcVq7dEQ2v/jOTY9Ozm1hCLNjeyaHMjkcD2adePXTgRs9nE+U9+zrqtHvqlJ/PQeeN4c1ElL3+1iQ/X1PLhmlqOG9WPG44cypC81A6vazKZuPW4EXyxoZ6N9V7ufnsF9505ZpefrYiIiIhIb6XAK3uMw2ZtN7LbJislqd3WO23hF4iNCjf6gmQ4bHgDYUb0S+dnRw/l3ndX8dvZKzloUBYD4gTHePpnJPPzY4dz9czBPPdJGX+au5anPtrQLjQfMSKP+au3Ulbn5eSx/Tl74gAaOtkz95RxBby+qJK6lgD/+raCsycNoLLBxz++3kzVtq2Jspx2fnRICSeO6Y9th/1ufTvtG9zcGuQ3by7n/ZU1QHQ09yeHD+GCKQNJslrwh8KU13lZU9PCupoWVm3aypPbrnWl2Lno6S8pq/NSkJHM788Zi8UMP55RyhXTBvGHOWt4Y1El7y6t5j/Lqjl1XCFXTBvEsPz2TbacSVbuPXMMP3zqc95cVMlRB+Rx6rjCbn22IiIiIiK9jQKv7FPZzu3h1xcM49s2uuuwmWn0bQ/CV0wr5b1lW1i4qYGr//YNf7/iYNKSuz/91plk5ZrDB/Ppulq+2djAQ3NWc9r4aLCzmE388sQDqGpspSQ7pcvmUTaLmeNG9uOFLzbydbmb9bUemnxBQhGDZJuZI0bkcf0Rw3a5DtYXDHP9SwtZuKkBi8nE6RMK+fmxw9r9IiDJamFYflpsdNbj6c+Tl0ePvbe0mrI6L/0zkvndGaNYtLmRTEf0NScVZ/HA2WO5anopt7+2hIWbG/nXtxX869sKpg3N4cIpxRw2JCf2PofnpzHAlcLGei/zV21V4BURERGRhNWnujTL/sUbCGO3mnHYrTjsVjIcNiAayixmE384dwy5qXZWb2nh+hcX4g+GCYUjHb7Ckfidik0mE/9z/AEAvL6wsl0H52SbhUE5zm51Si7NdXLRIcWkO6zUewKEIgajCtL5xQkHcNQB+bsMu4FQhJ+/soiFmxpITbLy7CWTufW4Ee3C7q6sr41Ofz55bAFBAxw2Ew2+MP3St+/1Oyw/jScvnsQzF0/iiBG5mE3w0Zparpy1gNMf/ZR/L6zAHwxz62uL2VjvJcNh44rppd2uQURERESkt9EIr/SYndf87rz+t3+Gg/vPHsPVf/uWT9bVMeau/8a9j8kEI/qlcfCgbCYOzGRyafa28AyjB2Rw8tiC6FrXLzfxyxNH7NZ2QG3GFWVyQL80Pl5bi81i5rChOe06QHcmEIpw8z8X8dm6OpJtZv54/nhGFWZ0GtI7U75tS6fBuU4GZkY7Vw/MdHSY6p1sszB6QAYPDxrP1mY/z31axitfb2bVlmZue20J//vWcryBMDaLiT//YAIl25poifQFwcpKQm43gfXre7oUERER2UcUeKXHOGzR7YvacqPD1nGkdNxAF785bSS/eXM5za2hDschujXQiqpmVlQ189yn0QB8QP90DirJYtqwHH529FD+s6yaVVua+brcHbfb8o6aW4N4A2Hydxg9BUiyWTjygPxuv79AKMIt/1zER2tqSbKa+cO54xhblNnt63dUVucBTJTmOBmSn8aQndbmxs6rbWFDnZdB2Skc0D+DX510IFdOL+W1BRXM+qKcrc3RjtD3nD6aicWu71SLSG8UrKxk3YknYfh8AJgcDqwufQ+IiIgkOgVe6VEGBrGNeHfSGorQGgxzUImLv18xBRMR7FYLyTYLZkwkWaMz8r2BEN9sbOCLDfV8vr6OsjovyyubWF7ZxHOflvGrkw7gkqklPD5/PX//chPD+6VhtbSfzR8IG0QiBnNW1PDUR+vxBsIcN6ofFx1STChsEAhF4tdvGHgDHYN4IBThf2cv56syN0lWM78/ZyyTil2EI9H7BMMRguH4U6HbRn93fM2qRj9mezKluc4OexkDGJEImM2U13vxB8KU13sZ0T+6RVHb1OWLDy1hzootOJOszBiWG/e1RRJVyO3G8PkouP8+7KWlWF0ubAUFPV2WiIiI7GUKvLJHdDW7t6spxF0tIg+FDUyYqG7y47RbsVutDN3W0CkSMWL3zSaJoiwnp44rxDAMtrb4+WJ9PX//spwvNrh5dN463rluGq8s2MzWZj9rtrRwwZTidq9V09TKve+tYt6qrbHn3llazefr67j2iCEcMzI/7hRmjz9EalL7ZlrBcISbXlkUC7tPXDiRqUNyOpyzc+huYzFHA23Q2v54ZoqNrE7W/YYM8PqC5KUlsTnkY6ArBas5Wm9qkhWTyYTDbuHMibve4kkkkdlLS3GMHNnTZYiIiMg+oqZVst9KsVuwmE0UZCRjt5pjHZx3JT89mVPGFXDC6P5kOGzUtgT418IKrp4xGIAnP9qA2xvdO9cwDN5YWMnZf/mceau2YreYOGlMf86bPIAhuU7c3iC/eWsFFz/zFSuqmnb52sFwtEHV/NVbSbKa+UucsPtdleZ0vt62bXun0tw0Th8/gNEDMvfIa4qIiIiI9GYa4ZX9VrLVEl3X67THPf7puq18U95AMBwm3WFnfFEm4wduX5N3YP80jhiRy7++reQv89fz4pVTGPb1JlZvaeHRuev46RFD+NXry/h4bS0AIwvScdgsvLW4CoD8tCTOn1zE64sqWVLRyA+e/ILzDx7Iz44aGttv1zAMguEIgVCEek+Ae99byUdrarFbzPzh7HEctofCLkChy8GamhaynLYOHZ53bgAmsjetrWnp9JjLaadwW2M1ERERkZ6mwCv7jTqPn3pPMG6gi2dlVTP1Hj/ldR5G9E9ndU1Lu8A7qSSbcUUuvt3YQFmdl5+88A2XTxvEra8u4bVvK3h/ZQ2NviA2i4nzJhcxf/VWllU2YbeacdotbGn28/LXm7hqeikbaj28u2wLf/9iI28uqgQgGIrgD0XYeUWt3WLm4fPGMWVQNgD+YJiP1tZit5opzkqh4DuGgY9W1zJzWB6jB2R0+HySrea4Tb9E9iSX047DZuGGlxd2eo7DZmHOTTMUekVERGS/oMAr+416T3DbSGmwW4F3RP80vIEwaclW0h12hm1b37sjqyU6rfiCp79kTU0Lf/pgLZcfVsJTH5fR6AuSn57EVdNKefiDtTT6ghRmOrjz5ANoDYT418Iq5q7aymPz13PmhEJ+f85Y7vj30k67RQOML8rk5mOHM7Igg+oGH098VMlLX23E7Q3GzrGYTRRmJjMwy8nArBRKc52cMaGQtOTOp2yPLkxn2dYAd7y+lAfOHhNby9yVzW4vVY2t9M9IpihL2w/J91eY6WDOTTNwewJxj6+taeGGlxfi9gQUeEVERGS/oMAr+4WtzX7ue3clg3KcXDWjtFvXHDo4l0MHt+82HK+D8bD8NF65agrnPvE5m9w+3lxcxU3HDKOs1sPgXCf/985KQhGDkQXp3HnSgZTmOQETB5Xm8OKXm3jk/TW8+k0FG+u9/OOqKTT7wyRbzditZkLhCC5nEklWMzaLGROweHMjt7y6mPdXbCG0reNyis1Cks2MLximNRhhY72PjfW+WI1vLKrk75cfTFIno7SPXziRW19fzafr6rjhpUWYzzdx7Mh+seOBUIRP1tXy6bp6Vm9pJsVu4fyDijCbTFQ1tirwyh5TmOlQmBUREZFeQ4FXelyjL8hFz3zJyupmAEpynPzgoIF79DWKs50886PJ/OSFbyir8/LMxxs45sB87ntvNQBHjsjjhqOGYjGbcHuCFGQm4wOunD4IfyjM85+V81WZmx//7RtuO34ENosZfyhCU2sQs8lEIBShuTXE20urWFa5vbnV5BIXYwrTMYDxRRkcO7KAmmY/62tbWF7ZxOotLby/sobFmxv59ZvL+b/TR8Wt35lk5S8XTuTmVxbx7rItXPPCN5iBLKeNQblpLNzUgH+nrZNCYYMrpw+if0Zy3HuKiIiIiCQ6BV7Z6+KNurbZUNvCdS8tZGV1M0nWaIj81evLyEqxM6HYRV5a/KnN0VvG2Y/WMDDi7OvrC4ZIsVv48w8ncOM/FrGqupmXv94MQG6qnXvOGI3ZDG5PEJfTRrItut8vwOHDc4lEDF74YiOb3D6u+fu3Xb5fu8XM8aP7ce6kIiaXuDocz09PwpViZUppdI3vJ2trueyvX/PPBZsZWZDO2ROLAAiGtr+/YMjAsES476wxpCQt57VvKogANS1Balrqgei2ReOLMhmSl8qzn5Tx/soaDh2SzbD89C7rFRERERFJVAq80mP8wTC3vLqEpRVNpCVZmFDsoqbZz4qqZv7ntSU8+6PJ5KfHH520dNqfybTtqz1fMEKK3Upaso1/XDWFq2Yt4JuNDQRDEVJtEXJSkzCZaLd22BcM4w2EKchM4bJppZRkO3j20400+IJkOGxYzSYc24Jx0rYpzmMGZHLe5CKyU5MwDKPT/YktZlNsH+GZw/O49bgR3PPOSn47ewXD8tKYPCir3T68dqsZs8WM1WLif08dSV5aEv9ZtoWRhelMLHYxPD+N4flpsXum2K08/P4a7n93FWMLM5k0KGuXfx8iIiIiIolGgVd6RDAc4dqXFvJ1mZtkq5mctGTmr45uD1SQkUxlYys3/3MRr//0MFKTOv9nWufxx0Zlu2p0teO2PQ6bhb9ffjC+YJgUe/Te8Qah2/a2BQOL2cRpE4o4/+CSOHffnmp9wRDeQBhfMESytX0q/7qsjpXVLYzol8rE4vYB9Ippg1hW2cgbi6q49qVv+dfVh5LeSQ8rk8nEdUcO5bojh8aea/IFafGHmL96K3NXbiVsRDioxMWXZW5ueXUxs6+bhkNbFomIiIhIH2Pe9Skie941L3zDf5dvwWY2UZLjZEOtB4s5Ghy3tvj5f/buO7yts3rg+PdqL0853o4d25nOHk2a7nRvuqFAF5QfHcwOaBllF1KgQKHMbigt3XunTWfa7OFM2/Hetjy01/39IVmxY8sjcWLHOR8ePQ+Rrq5eybrqPfc97znJZh3lLS5ue2rzoPtxRCs7O1yBQbcz63WkWgyx1j2KouAJhNjT7KTN5RvwORaDlk6Pn/pOL+1xttlfT5Ds9of6Pbaz0YnLH2BnY/8epoqi8OuL5zIrK5F2l58bH99Am3N4r+nyBbnj2a2ceM97/OiFUlbtamb17lZsRj1pNgMVrS5++er2Ye1LCDF8/ooKPKWlBOrrx3ooQgghhIhDAl5x2KmqypqKyLrTublJ7GmOBIAnTk1Dp1EIhFTy7ZGqwu/vbonOsg4sxarHoNOQYo3f0iee3m2QBhIJjhUCwTBtTv+AQez+LAYtWo2CpddsapvLR1mzk+wkI1aDnhmZA7cUMhu03Hn2dFIsekrru7js7x8P631Utbl5d1dLv/s313YwOzuyfvfjsrZh7UsIMTRdSgqK2Uz9bbdTecmllJ97ngS9QgghxDglAa847BRF4caTC9FqFNZXdzAnJxKUvburhWBYJTPJxObaTgB+fP6s2MzvQOxWI6gqm2o6KGvqHtE4UqPBcuogwXKqVU+CWY/dZugTxMZj1uuwW42Y9fvSsB2uAP5QmMl2G19als/iAjulDZ08v6mOrXUdfZ7vC6l8Y0UROclm2pz7AvF739rFpxVt+PerxAwwKzuRP1wxj8X5KWRG1zwrwKLJyby3uxWtovCTC0qGHLsQYnj02dkUvfIyBc88TfY9K1E9HoIOx1gPSwghhBADkDW8YkzccHIx2clmvvPkZjbVdLJwcjIbqjtIMOli6cOfX5LHFUuGbk9U5XDj9gWpcrgpzkgY9hjsVmNs3W+8QtJ2q5FUizFu8anhSLHqY+uMe+xtjYy5vMXFnJzk2P2TU8xoNPC3Ly3g0Q9289vo/Q9/XMWj65ow67UcMyWV44rtLC20k5dixqTXsqzQzknT0lFVlco2N+/ubOa+d8sAuPOcGZw4rW+/YiHEwdFnZ6PPzh7rYQghhBBiCBLwijFz4fwcGjq9/Ob1XWyo7uCskgzWVTno9gZJNuv4xqnFw9pPfoqFKoeb/BTLIR7xgekJrN3+EG0uP2a9lilpFsqaXRRNsvbZtjgjgfy0SMrzmbPSYwHv+XOzWVPjpM3lZ/XuFlbv3pfCnGo1MCnBSHaSiYxEE4kmPQ9+tDeyv0lWLl6Ye1jepxBCCCGEEOONBLxiTJ02K52GTi+PflLF66VNAExOtfDA1YvQKkrcmddweF/Ln6J0G0XpkSAxFA6j0Qycqa+iwgA9egHCqChxHoOBGh1F96mCogw8SFUN07eCc6SglScQYmZGAiVZSdHtBn5+WbMr9v9/cdFs0EUKUH24p5WPylvZWtuJJxCm3eWn3eVnV2PflO6sJBOXLMxGRe33GltrOyhrcTJ1ko3Zucnx33ecqe3BeisP9jwhhBBCCCEOJwl4xSE3WPCjqgqXL84jEArz389qSDTpeOiaxRROGriwU+99DrTbDrc/lj6cOkCbIm8w1Kc9UWx/Kn329+6ORrbWdTEnJ5FTZmYO8h7iB36RMe57Xu/WSBqNJu4+DbrI/QVpll73abBYDCycbCA9wciyIjtZiUYSTHrqO73Ud3ho7PRS1+GlodNDgknHD8+dhUmvHTA4LWtx4vIFqWh1DRrwCiGEEEIIcSSTgFeMqVSrnnZXgO+ePpUzZ2UyZZKVyakjT01eW9nGzgYnyRYtxemJOFyBAQPe3m2Dege8+9ta14XTF2BrXRenzMwc8XgGYtbvC7LjzZDuaeqmst1NQaqFE6ZlDLhNfacXrz9EQ5ePvFQryRYDs7ISRzSrWjzJRlmLk8I069Abi1FX1+HB4fIP+FhZc/+2VUIIIYQQ4sBIwCvGVO/CUSdNNx3wfnY2OHH6I9WQZ2bFb1PUe5Z1MHNyEmMzvCPVE3zPzLKxuMA+oudWtUeKWVW2u8lJGLgAV3aSifpOL9lJB/55zclNZk5u8pCpyfG0uXy0uwKkWvWxv58YnroOD6f9bjWeQPw2V2a9lhSr4TCOSgghhBBiYpKAV0wIM7Js7GxwMis7IbaedyC9Z1njqXO4SbQYuGRRLjkHUAirJ/je1egcccCbn2qJzfDGk5tiIXeMC3T17mEsAe/IOFx+PIEQf7hiPsVxvqspVgM5yebDPDIhhBBCiInnqA14V69ezUMPPcTDDz881kMRo2BJgZ0lBXbCBzhj2VtjlxdfIExjl/eAAt6e4Ht6Zt9gpqdQVPEkG7NzkgZ87tSMBKZlRmaVXS7XgNuMBz2p6IP1MBaDK06P/z0QQgghhBCj46gMeFevXs2ll17K448/PtZDESPkCQQHLDrVW7vLN2jhqqFkJppo7PKSmXhgKcM9wff+6cI9haLKWpzjKtD5cE8zpfXdlGQncPzU9GE9p3cquhBCCCGEEOPVURfw9g52Tz/99IPal8/nw+fzxf7d1dV1sMMTQxiq6NT7u5p4b3cbhXYLy6dOGlbAu7W2g8o2F1PSrJTkJJOTYjmgmd2h9BSKKh6iAvXhVlrfTbc3QGl997ADXiGEEEIIIY4EAzcsnaA+/vhjzj33XP76179y+umn097ezvXXX4/dbicjI4Pvfve7eL3eYe/v7rvvJikpKXbLy8s7hKOfmFR18Nv+LAYtWo0St+jUtvpuwqEwFW3ufoWrVFUd8Fbe6sQbCFHecmhTiOfkJnPRgtxosajhv+d94x/ZZ7XveQO/b1VVCYVV5uYmkmozMjc3kbCq9rkJIYQQQghxJDuqAt6ioiLy8/NZuXIlVVVVHH/88XR3d3P//ffzta99jfvvv5+LLrpo2JVr77jjDjo7O2O3mpqaQ/wOJh5FGdnNYtCRZjNiMehifW5736Zn2LCZ9RxXlEqazYRGUWI3bbT3bV2Hh3VVDuo6PCiKQmGaDa1Ww5Q0a5/te9/ij7//GHpuPb12R/rYgX5Wg42x3e2nrMVFu9u/3zgUlhVN4trjprCsaNIIxhL/fY+kPZIQQgghhBCH0lGV0pyRkcGqVatYsWIFs2bN4qqrruKvf/0rAFdccQXLli3jvPPO45VXXuG8884bcn9GoxGjUdYxjidnzM7mjNnZg27T0NPHttNLboqFubnJzM1NPqICtVXbG9lc18m8nCRWzBq6T/BQVZUfX1PJ+ioHi/JTuHJZwSEYsRBCCCGEEIffUTXDC/uC3oKCAr773e/2eezcc8+lsLCQrVu3jtHoxOGQbNbjC4VJNh+5FYY313Xi9AbZXNc5rO1TrXoMOk3cqsrrqxw4fQHWVzlGc5hCCCGEEEKMqQkd8K5btw63293v/oyMDN59912Kior63B8Khejq6qKkpORwDVGMgch61WRSbWM/O+8JBGlz+fAEgiN63rycJGwmHfOGWe3ZbjUyNd0Wt7LyovwUbEY9i/JTRjQOIYQQQgghxrMJm9JcWlrKySefzOLFi3n11VexWPpW3U1P71+N9s4776SoqIhzzz33cA1TjAGLQRtrbTTW+ladHv7huGJW5rBSmYfrymUFkso8iuo6PDhc/gEfK2t2HubRCCGEEEIcvSZswHvvvffypS99iSeffJJzzjlnwKAXIq2FnnvuOR588EF8Ph8vvPACWu3YB0JHm621HbGWPXNykw/pa5n18Xv4Hm7jKfgWo6Ouw8Npv1uNJxCKu41ZryXFajiMoxJCCCGEODpNyIC3s7OT1157jV27dvGVr3yFM844I27QazQacblc3HnnnZx00klHVOGiiaSsxYnLF6SsxXnIA97xxKzXjWhmV4x/DpcfTyDEH66YT3H6wD2XU6wGcpLNh3lk4lDyV1QAoEtJQZ89eOE8IYQQQhw+E/JMOyEhgQceeACbzcaSJUt48803Bw16v/KVr4zRSEWP4km22AzvwRispZSqqof1gsbQ7a0GHkvP81qd3j73hcPhIVsFifGjON3G7GGusRZHLl1KCorZTP1ttwOgmM0UvfKyBL1CCCHEODEhi1ZpNBrOOuus2L97gt7NmzdzzjnnxApZ/etf/6K+vn6shinY1891bl4KFy/MY25eyiHr53q4e8eu2t7IvW/tYtX2xjjjGbyfrsMVGJXxD/YcrUYTt/fwYP2HhRAR+uxsil55mYJnnib7npWoHg9Bh1Q7F0IIIcaLCTnDO5D9Z3pPP/10Hn30Uc4555yxHpqYoHq3DjqQAlMpcVoICSHGF3129ohmdAP19XGDYkmJFkIIIUbXURPwwr6g9+STT6ahoYF3332XbDmxEIfIvJwkNtd1Drt10P7itRASQhy5AvX1lJ97HqrHM+DjkhIthBBCjK6jKuAFWL16Nbm5uRLsTnBtLh/trgCpVv2YBY6j3Tpof4ezsrUQR6qe2dSeolJjLehwoHo8ZN+zEkNhYZ/H/BUV1N92O0GHQwJeIYQQYpQcVQHv9u3beeCBByTYPQq0uwL4g2HaXYEJO1N6tFa2FmK49p9NVcxmdCkpYzyqCENhIeaSkrEehhBCCDHhHVUB76xZs9i8eTMGg/S/nOhSrfrYDO9ENVqVrYWYqPafTZX1sUIIIcTR56gKeAEJdo8Sdqtxws7s9piTmzyqM7tfe3gNG6o7WTg5iX9cs2zU9ivEWJPZVCGEEOLoddQFvEKMlD8YHvRxvTZ+P90DaXfU0763dxtfVYWwChri9/Y9kNfq/d4auvyk2Qw0dPlj9xt0E7JzmRBCCCGEOEpIwCsmlEPRT/fHz22itMFJSZaNFbOy0WoUTpg6aVhjOZDx9Dyl91MVBTSKwqFqjauqKglGLU3+EPOzJEVaHJyyZmfcx1KsBnKSzYdxNEIIIYQ4mknAK8QQShucBIIhPix38MT6BgB+dN4svrwsf4xHNnr+trqcj/d2oNcq/OSC2WM9HHGESrEaMOu1fPvJTXG3Meu1vH3LSRL0CiGEEOKwkIBXiCGUZNlYV9VJY5cvdt/PX95OeoKRM0v6tx2qdbip7/SSnWQiL9U6KmN4b0cTexwBSrITOb0ka1T22WP17hbufXsPAIGQyk2Pb+DVb51AeoJpVF9HTHw5yWbevuUkHC7/gI+XNTv59pObcLj8EvAKIYQQ4rCQBXpCDOFnF83n6ycXEwxH1s/qNJG84u31XQNuX9/pxesPUd/pHbUxbG3oxOkNsqmmc9T22WNXY9/30eUN0tAxemMXR5ecZDOzc5IGvBWnS7q8EEIIIQ4vCXiFGIZz52Zz2swMAIJhlaVTUvn6SUUDbpudZMJk0JKdNHozpHOykrCZdMzPS+r3WJvLx55mJ63OAwtSrz62gIWTk2P/vu64KczLS467vRBCCCGEEEcKSWkWYhi0GoXfXjaXO57dik6j8LMLZ2M2aAfcNjfFQm6KZVRf/+SZGZxjsQ5YtKrdFcAfDNPuCpBmG3mQbdRruf+LC7n9ma1MsVu4/czpozBiIY5e/ooKgAPu+3uwzxdCCCHEPhLwCjFMFoOOP35+wVgPo59Uq552V4BUq/4g9mHkX1ctHsVRCXH00aWkoJjN1N92OwCK2UzRKy8PO2g92OcLIYQQoj8JeMURKRSO3xtXqzm6MvXtViN2q/GQtGQSQgyfPjuboldeJuhw4K+ooP622wk6HMMOWA/2+UIIIYToTwJeIYZg0B3eALoncO0dwCqKgkYz+gHt4X5vQkx0+uzsgwpQD/b5QgghhOhLAl4hhBihug7PoK13hBBCCCHE+CABrxBCjEBdh4fTfrcaTyAUdxuzXkuK1XAYRyXGu0B9fSxVWQghhBCHjwS8QogJZ7AZWIAUq4GcZPMB7dvh8uMJhPjDFfPj9pU9mP2LiSdQX0/5ueehejxApBiVLiVljEclhBBCHB0k4BVCTCjDnYF9+5aTDiooLU63MTunf19kcXAO5cWKseCvqMAPqB4P2fesxFBYKO2GhBBCiMNIAl5xxPvdGzvYUN3BwsnJ3HLmzLEejhhjQ83AljU7+faTm3C4/EdU4DSRxFvn3Oby8/XH1h/yixWHw0AthiyLFkmgK4QQQhxmEvCKI96G6g48/hAbqjvGeihiHJEZ2PEnxWrArNfy7Sc3xd3GrNfyyHXHYB9gDfSRdLGid4shQGZ1hRBCiDEiAa844i2cnByb4R2MqqqDPi59bIU4tHKSzbx9y0kTKmV5MKPRYqinyJUEzEIIIcSBkYBXHJG0mn39Y28/u2QMRyImImk7dOjkJJsnTEB7KA2UEl30ystgG7hQmhBCCCEGJgGvEEL0Im2HxHjQOyXaX1FB/W23R9KjJeAVQgghRkQCXiGE6EXaDh35JkrP29FIiRZCCCGOdhLwCiGOOKORchxvu577pejV+DXY3zipuw3XFy6VnrdCCCGEACTgFUIcYQ425Xi4lYIlZXn8Gc7frsRZz289HsJ3/AR1cgEkJbNLtUJd57BfQ2bvhRBCiIlDAl4hxLi0vb4TW3f/ytplzc6DSjk+2ioFTyT9/nZNjdDZEXu80xvg0f9uAuCbn3RQvqMVaAXKhv0aR0qfXyGEEEIMjwS8o6in7U1XV9cYj0QMZP+2ROur2tnT5GRqho1F+anjri2Ry+WK/f+uri5CofgzmkeCnuNiqPZQPY9f+qdVaIyWAbcx6TXMsOvIToj3NwvQ1RWI+xoJGkiI+9yhny/GTs/fLtDQwN5rLo+lLgMYgK8C3SYTv7xyMaSnj2jfFS0uvv/sVmoaW0nQjJ90do/TiTMUonXbNnxNTcDQx5EQQgghIhRV/qs5ampra8nLyxvrYQgxrtXU1JCbmxv3cTmOhBjaUMeREEIIISIk4B1F4XCY+vp6EhIS+swWdnV1kZeXR01NDYmJiWM4wgNzJI//SB47TKzxJyQk0N3dTXZ2NppefZT3F+84Gi+O5L+JjH1sjObYVVUd1nEkhBBCiAhJaR5FGo1m0CvuiYmJR9yJWm9H8viP5LHDxBl/UtLQaaJDHUfjxZH8N5Gxj43RGvtwjiMhhBBCRMjlYSGEEEIIIYQQE5IEvEIIIYQQQgghJiQJeA8Do9HIXXfdhdFoHOuhHJAjefxH8thBxj8eHcnvScY+No7ksQshhBBHOilaJYQQQgghhBBiQpIZXiGEEEIIIYQQE5IEvEIIIYQQQgghJiRpSzSKxnv/UCHG0nD7h8pxJER8chwJcfCkn7UQRxcJeEdRfX09eXl5Yz0MIca1mpqaQfvsynEkxNDkOBLi4A11HAkhJgYJeEdRQkICEPkBTUxMHOPRHF7DKX3mCQTx+EOYDVrM+n1fPZl8GJjL5SI7OxuInLxardYxHtHB6erqIi8vL3acxHM0H0ejoacOoScQ6nW8aftsIzN+R67xcBxNtN8mcfQZ7nEkhJgYJOAdRT0nkYmJiUfdifpwAt54n4icew9Mq90XpCQmJk6Yk8qhgq2j+TgaDT0B72CfnAS8R76xPI4m6m+TOPrIb6EQRwdZuCCEEEIIIYQQYkKSgFccMp5AkDaXD08gONZDEUIQSXNuc/lx++WYFEIIIcTRQQJecci4/SFCYRW3PzTWQxFCIMekEEIIIY4+EvCKQ8Zi0KLVKFgM2qE3FkIccnJMCiGEEOJoI0WrxCFj1uv6VGMWQowtsz5SsVkKtQghhBDiaCEzvEIIIYQQQgghJiQJeMVhI0WshBjf3P4grU6fFLUSQgghxIQh+aZilMRvxKuqKoqi9CmYY9ZrWbu3jd1NTqZl2pidk4zbH8JiiKRc9n5efAM/JtmaYjxTh2ha3ft77wmEhjwu1lW2sbPRyYxMG4sL7ISHOG5UFTRxjtfex6jFMPB/HobTc1uOQSGEEEKMFxLwilEz0Ml5bxaDNvY4wI7Gbrq9AdZXdZCVZMag08aC4R5b6zrYXNOJ3arnmEI7dqvxsL0fIQ63NpePdleAVKseu9UYC0DbnD7MBh2RC0sKZr02dhztbHTi9AfY2ehkcYEdgNK6DipaXRSmWSnJSR726+9/jAohhBBCHOkkpVmMmqFanpj1WuxWQyygnZmZgEGrpTDNAih9qse2uXxUtLjYXNNJq9PL3jYXDlfgcL0VIcZEuytAIBimPfpd76mqDAqhsEq7K0AorOIJ7DvGZmTasBn0zMi0xe7bUttFZauLLbVdI3r9nmM03uyuEEIIIcSRRs5qxKgZ6ezQkil2Zuf2T2UGcLgCBEJhbEYNLr+OjAQDKVb9oRq6EONCqlUfm+HtnTHRc2ylWvX0zPD2WFxgj83sxvZj0dHl1ZBqkZ94IYQQQhzd5GxIjJqelicjYdJpMOu1eAIh2lx+HE4fDk8Ak06DxaBjdk4yy80Gmrs87G114fGHyE2xHKJ3IMTYsluNpFoMKIpCm8sfy5jonRnRew1vrcNNY5eXzERTn+NiSaGd4ozEPheJ6hxuGrt8ZCYaD8kx5AkEewXo8p8WIYQQQowPktIsxoWedOgqhxtvIIQ3GKZwkpXsZAtajUKHJ4DXH6KxyzvWQxXisOhJZx4sY6KxyzvgcZFqNVKUbiO115r3xi4fvkCIxi7fIRnvUEsahBBCCCHGggS84pCodbhZW9lOrcM9rO17Tu7zUyyY9Fqykkyx++1WAwV2KyaDlsxE06EcthDjxv5r3geSmWga9nGRmWjEqNeSmRi/8Futw81nle3UtLtGPN7hBOhCCCGEEIeb5J2JUVHW1E2Vw01+ioXijAQaOr14AyEaOr3kJJuHfH5POrTdaojd17t9S26KRVKZhdjPYMdFeXM3NQ4PeSlmitITyEmxkJNiGbStUH2nF58/RH2nl7xU64jGYtbrMOvlPylCHCkC9fUEHQ4AdCkp6LOzx3hEQghxaMjZiRgVVQ43bl+QKoeb4owEspJMNHR6YzO1QojRp6oq5S0u8lLMGPebCa5xeHD7gtQ4PBSlJwxrf9lJJuo7vWTLcSvEhBaor6f83PNQPR4AFLOZoldelqBXCDEhScArRkV+iiU2wwt9Z57UwaaUDoInEKTW4cYTCJOTbMJulZN0ceTZv/fuQHpXbDbpIitRujwBvv/sVl7b1kiCUceZszO4YF42S6ekotdpyUsxx2Z4h6vnuO0piiWEmJiCDgeqx0P2PSsBqL/tdoIOhwS8QogJSQJeMSqK0m0UpUf6gPYOcNvdfjpcAZKtelIthn7PC4ZUNHFWkofCYXTagR8MhsI4fUHe2d5EeYuTeblJfGFpAQDaeDsEDuQ8fqiAXYID0dtQ35f9H213BfBHe++mWo2EVVD220fvglAaYGtdJ995cjO1HZHZmW5fkKfX1/H0+jrsVgPnzM3i/LlZHF+chqJEevj2CIfVQY8Rh9uHwz14AA6DfefVPmnTr22tY2N1FwsmJ3L2nJxhPP/AjlMhxMgZCgvHeghCCHHIScArRkW8oK/dFaDN6aWq3c28vCTsVmOfVipZSeY+z63vcPPXd/dQ0eLilBmTuP6kqbHHPP4gnkAYs16DXqvBpNPwSUUbFS0uAqFwLOA9VB76sIK1lQ6WFKRw7fFykiAOXO+jpXfvXYVIsOcNhPAEwjy3vprNtV3My03kokWTcfkC/H11OQ99VEkwrJKXaubey+cTCIV5aXMDr21roM3l57FPqnjskyrSE4ycWZLBZYvzSDbraO72k5FoJDs5/nr4Dve+ADxewDtYQLp/vL+xuotuX4CN1V29Al4hhBBCiMNDAl5xyHgCIUCl0xMg0aTDET2B7t1KJSspkm6pqiqvbG2g3eVnR0MX/pDKptqu/fYXjs1yba1t5/dv7WZbfWQbt7+Nbm+ABJN+/2GMmrWVDpz+AGsrHVx7/CF7GXGUsVuN/QLLnu/6uqoO/KEQm2u7+NwClVuf2sLaykiRmXPnZPHzz5XEvvOLC1L54Xkz+XBPK69ubeDN7U00d/t4bE01j62pJs1mYNHkFI4rTuOKJXlxsyeSLfrYDO9oWDA5MTbDK4QQQghxuEnAKw4Ztz9EktlAUVoCKJASPYHOTDTFZngj2wX42Us7eGp9LQDJZh12q4E52fsK7by0qZa1lQ4sBi0bazpjJ/0WgxaLQUur088vX9nBry+Ze8jez5KClNgMrxCHklmvwRMIszg/mc21Xdites6970Oau30YdRp+dN4sLl+c2y+zQq/VcNK0SZw6MwOPP8Q7O5t4eUsDH+xpodXp543tTbyxvYnfvL6Ts2ZncvqsTI4vTsPcq5VQqtVImi1+66KROntOjszsCiGEEGLMSMArRl1PgR1Q0WoUslPMfXpz9i5oFQqr/HlVOU+tr0VRwKTT0uEJ0uEJ8rf3K3mttIWcFDN7W5w0d0fWFgIYdBq+uHQyXz+xkIpWF1f+61Oe3lDHmSWZnDYr85C8r2uPL5SZXXFYRFr8wFdOKOb5TXXc8tRmVBWmptv4/eXzmJE19Gyp2aDlvLnZnDc3G48/xIdlrby1vZFVO5txuAM8s6GOZzbUYTFouWxxDj8+b/ZheGdCCCGEEIeXBLxi1PUU2NFqFDz+IJWtTjISTQP2C1VVlec21gFw13mzuGB+Dv/9rJpHPq6kudvH1rpOttZ1xrbXahQuXZTLjScVkhPdn91m5NrlBTz4USUr39jFKTMy0Gr2zXx5AsFYhVuLQb7y4sjhC4ZY+fouVBUuWpDDzy8sQR8nFXkwZoOW02dlcPqsDPyBEBtrOnhzexNvbW+irsPDk2tr+c5p0w/pkoChyHEqhBBCiENBzirEqLMYtLET14oWJ05PgE5PkFSrMTbTu66yjZ2NTmwGLc3dPkx6DZcvzsOo1/L1k4q47rgp7G7qprHLS53DQ1O3l8I0KydMnURmkolgKNznNW8+pZin19eyp9nJi5vruGhBbuyx3hVu5URaHG5r97axo7GbmZkJLJliH9FzX93aGEv//9VFszHqtAT2++6PlE6rYWmhnaWFdr531gzO/uP7VLa5+dvqcm47c8ZB7ftgyHEqhBBCiENh5FMFQgzBrNditxow67VkJZlAo5Bm1UeLWEXsbHTi8gd4e2czAEun2DHq96U9G3QaZmUncvqsDK45roDvnTWDyxbnkZk0cK/dRLOe60+MVE7+w9t7+gQFFoMWrUbpk1YtxOGyo7Ebly/IjsbuET/31a0NAFyxJA+jbvS/vwadhjvPmQnAgx9WUtPuHvXXGC45ToUQQghxKEjAKw6p3BQLS6fYyUm1Yu4V0M7ItGE16Klpj/QRPWFq2kG/1tXH5mO3Gqhud/PUuprY/Wa9DrvViFkvs0bi8JuZmYDVqGNmZsLQG/fi9AX5oKwVgDNLMg7F0ABYMSOd5UV2/KEwK1/fecheZyhynAohhBDiUJCAVxxyZr0Gu9XQZ+ZmcYGdSxbmsrMpMut1wtRJB/06FoOOG08uAuC+VWV4e80oCzFWlkyxc9WxBSNOZ169qwV/MEy+3cL0jJEFyyOhKAo/OGcmGgVe3dbI2r3tscc8gRBtLn+f7AwhhBBCiCOJXEoXo0JV1biPhcIqmgEurazZ24Y/GCYryUSB3Uwo3HdtYqvTh0bR4PIFeWJtNfNyk1lckApAIBQm2dy/wM4F87L514d7aej08tiaSr56fOGw38P+LV6EOBCDHQuRxwFN/O9aMBRGURReL42kM58xK4OQqkafCN2eADpd/GuVHW4/pkFmScOqSqrV0Oe+onQbly3O48m1Nfz8le28cNNxaDRKbF3ts+tr2NPsYsHkRC6Ynxcby4D7D6sMdSgdzLE21Od7sPsXYqTqOjw4XP4ht0uxGshJNh+GEQkhhOhNAl4xKgY7wdRoBn78gz2RdM0Tpqahi1N5Nqyq/OiFbXxc3gbAeXOz+OaKqZij6/32ZzZo+eapU7nj2a387s3dTLFbOHXmwbUpkpNnMRJDf19Uem+xNlrAbUamjSUFdjSKgj8YZtXOFiAS8Gp67VOn1Qz43R/IphoH7+5s4by5WUztNUusGWCM3z51Kq9saaC0vovnNtZxyaLcWD/gHQ3deIIhNlZ3ccH8wV9TUZRhBLzDGr4Q415dh4fTfrd6WFkQZr2Wt285SYJeIYQ4zCTgFYfc9vpOyltdFKVZKclJjt2/enfkhP7EQdbv/vP9Cj4ub0OnUQiFVV7e0sCaija+e/o0Pjc/Z8DnXLIgh7+vLqeyzc2/11THAt42V6SPb6pVj91qHL03KMRB2NnoxOkLsLPRyZKCSNrzx+VteAIhMhKMzMtNHtH+VFXl47JWHv6kki21kZZeL22p5/eXz2N+Xkrc59ltRm48uYiVb+zinjd2cdbsTKxGHWYDLJmSzMbqLhZMHrr/76HW5vLR7pLjWIwPjmjK/x+umE9xui3udmXNTr795CYcLr8EvEIIcZhJwCsOufJWF25fiPJWVyzgre/wUNHiQqPA8uKBA973drXw0MeVAPzg3JlkJ5v55Ss7qG53c+dz2/hgTys/OGdmv/RMnVbDd06bxree3MT66g463H6SLQY63AH8wTDtroCcKItxY0amLTbD6wmEcPqCvLatEYDTZ2WgGeZsbiis8tb2Jv62upzyFhcAeq1CdpKZqnY333piEysvmcuSKalx93HVsfn897Nqahwe/r66gmuOy8fhCnD81PRIKvM40O6S41iMP8XpNmbnJI31MIQQQgxAilaJQ64ozYrFqKUozRq778No9dlZWYkkDbAWt83p4xev7ADg80vyOHt2FvNyk3n0umP48rJ8NAq8tq2R8//8IRuqHf2ef+6cLGZkJuD0Bfnh89sIhsIkW/QYdBpSrf1fT4ixsqTAzpeX5bOkwI7bH8IfDLFqV6Rd1xnDrM7s8gX5wj/WcNvTWyhvcWExaPni0sk8e8NxPHLdMRxbaMcXDHPr05vZXt8Vdz8GnZbbz5wOwD8/qGBPk5NAKIzDFTio91jrcPNZZTu1joNve5RqleNYCCGEEMN31Aa8Tz75JHffffdYD+OoMCs7iQvm5fRJZ9ZFZ61KG7p4eUtDv+fUODy4/SHMei03n1Icu9+k13LTKcX8/UuLmJZhw+EOcP2j61lX1Tfo1WgUfnJ+CTqNwqvbGvnGExtJNOmZmm6TWSExblkMWrbWddHhDpBs1rOkIP5sbG9Pr69lR2M3CUYd1y7P57kbj+MbK6YyKcGISa9l5aVzyUg0EgipVLW5Bt3X6bMyKMlOxBcMU9bsRK/VkHKQwWV9pxevP0R9p/eg9gNgtxrlOBZCCCHEsB2VAW9LSwtXXXUVd955J7/85S/HejhHpYsW5PCFY/JQVbjlf5t5d2dzn8dnZCag0yh4AiEaBjhJnpGVyH+/uozji+14AiHufG4rHn/foiHHTEnlz1cuQK9VeKO0iW/8dyO+oLRXEeOXWa/l42j2w6kz09HHKebWWyAU5rE1VQDccsY0rl5e0C9rwuUL0tzlA4hVOo9HURTm5SUDkYI8Rek2Ug8yuMxOMmEyaMlOMh3UfoQQQgghRuqoDHjtdjtms5lf/epX/PCHPzzgoNfn89HV1dXnJvbpSWOs7/D0uf/FTTX87KXtLMlP4vx5WQTDKjc9vpE1FW2xbUx6LbOyIwVyNtd2DLh/s0HLvZfPJzPJRK3Dw/3vlfXb5oxZmfz1i4sw6DS8ub2JG/69AZ/0FB1X5DjaJxxWeXN7EwBnlgyvuvjr2xpp6vJhtxo4b27WgNt8urcdlcg6w0kJQwev0zIixXd2Nzn7PebxB6l1eKh1uIfdnzc3xcIxBankpliGtb0QQgghxGg5KgNejUbD5MmTueiii1i5cmUs6O3u7ub+++8f9n7uvvtukpKSYre8vPFR1GW86EljbOzqO0O7sbqLbl+AzbXd/OaSuSydkoIvGOZrj65nc01HbLt5uUnR7TuIx2rU8aNzZwLw8CdV7GzoHyytmJHOP7+8CJNew7u7Wvjqo+v7zQaLsTNRj6MDWbe6ubaTpi4fVoOW44rsQ26vqiqPfByZ3f3i0skY9doBt/sk2tZrWeHQ+wSYkRm52LS7qbvfY55AGKcviNMXwi3HkRBCCCHGuaMy4AWYMWMG27Zt47bbbosFvbNnz2bHjh3D3scdd9xBZ2dn7FZTU3MIR3zk6UljzEzsm8a4YHIiCUY9CyYnotdqeOiaJRxbaMflD3Hnc9ti282PplX2DoIHcsr0dM6YlUEorPKTl7YTCqv9tjlh6iQevHoJFoOWD8taufbhtbh8QSDS5mRPs5NW5+DrC1fvauLPq/awelfTMN69GK4j8Thy+4O0On24/cG428Rbt/pRWQt/W13Oh3ua+z3njdJIdeaTp6fHDV57hMIqD3xYya6mbsx6LZcvHvhCQVhV+XRvJOA9dpgB79Roe5Vahwenr+97NOs12Iw6bEYtFsPgYxRCHHkC9fV4SkvxlJYSqK8f6+EIIcRBO2rbEvUEvJdeeilf//rXuf/++6msrCQjY3hVUQGMRiNGY//0QHdpKYlLl4LSq52IMrzWIhNJboqF3BQLobCKqu4LQs+fl8v58yL/PxgKY9BpufeKeSy7exU7G7vp8gRIMOkpTrehALUdHmraXaTZ9n3W7S5/n/W41x1fwIdlrWyr7+LBj/bypWX5/cYzOyeRB69ZwlceWcune9u56sHPeOjaJbS7AgRC8ducqKqKoijsbOzGGwixs7GbE6els7Wug/IWF4VpVubmDtzf9ED+7D0fVa+PDFWN3Cbi1yjecTReqaqK2x8iFFZjhdX6Ph75O2Unmajv9PZbt7qz0YnXH2RHo5Pjiif12e/r0YD3xGlpdHkHrozc7vRT3e5m5Ru72NkYmYG9dFGkJ3WnO0CHO0BirzafOxu7cLgDWAxapqZb6XT7MeoHudapQpJZT3qCkeZuH7saupiZlYA3qGLSKRh12j59RNX9ri+FAU3/a069d4+GQTYgso54sOcDeLdtw1dRgbGwEFNJyaD7G8n+hTiaBerrKT/3PFRPZCmSYjZT9MrL6LOzx3hkQghx4I7qGd7S0lK6u7s588wzOe+881i5ciU/+tGPDrqQlW/vXgCC7e14y8sJtrePxpDHNUVR4t60GgWNsu/mC4ZZX9nOa9saqG5z0+Hx0+0Nxgrt7GzsRqtRKLBbmZ6ZAMDeVjd2mzF2sxi0aDVK7JaRaOJrJxYC8Md39tDU5e3zmproWBblp/DItceQaNKxobqDX7+2k2SLDr126DYns7ISSTDpmJUVSfcsb3Hh9gXZ09x/naOY2Hq+fwPNcCpK5HjIS7WydIqdvFRrn+NhRqYNk0HHzExbn/t3NTmpanNj0Go4YWpav++vRlHwBkL844MKvvroOnY2dmMz6vjBOTP45qlTMRu0mA1aUq2GPrdtdZE0/2WFdtITTSSa9eg0mri3nuO159gra3bhDaqEwioNnV6q2ty0u31xPxsl9hnEvx0MJXrzVVQQdrrwVVTE7uvZdai9HV95OaGj4LdXiNEUdDhQPR6y71lJ9j0rUT0ego7+rf+EEOJIclQHvBs2bODMM89k0aJF3HfffbH05ldffRW/33/A+zZOmQJE/8Ph88t/LPbj9oeodrjpdPup6fTgcAXwh8LYjJHgobRXn9CFkyMzpwP12t3f+fOyKMlOxO0P8YtXdvSZVe5tXl4y9125AID/flbDE2trQFWHbHNy/NRJfO3EIo6fGpmVK5pkxWLUxdI/xdHDrNditxr6ze4Ox/FT0/n6SUUcPzW9z/096czHFtmxGPon37y/u4VL/voJ/1tXS1iFM2Zl8MwNx3Lxwlw0g0SRPT2vjyseXjpzj2kZkYA3kjIdCYS9wTCB8PD78vYsF2hzxQ+QD5SxsJDmoIbNSnK/i07y2yvEwTEUFmIoLBzrYQghxKg4agPemTNnoqpqLNjtcdttt/Hee+9hMBgOeN+qz0fXRx/j3rqVQEMDqCresjLcW7fiWrcOf23taLyFI5bFoGVyioUki4G8JDMpVj0GrYaSaFXmrXWdsW0X5icDsL5q6BNXjaLw7dOmotMorNrZzFs74q+1XV6UxlXHRtKeH/igkt3N/YvzDGVOTjKfm58TN51ZiJF4vTTyfT11Rt9AuLnby61PbeabT2yiodNLZqKRP31+PndfPKdPmv9AOj0BtkWPp+OL00Y0np5KzR+Xt3LPG7v4aE8zWUkm9Jrh9+VtdwXwByPLBUabuaSE2iUn05lXRFW7G195Od3vvYfz409wbd2Ke9tWUFUCdXW4168nUFfXP/9aCCGEEBPehFzD+9RTTzF9+nTmzp0bdxuz2czatWux2/vPeuj1wzuZiyfs9eHZvQV92iTCRiMoCqHOLjw7d6DPjLQNMeTmHtRrHMnMei1LpkQ+9571sXarkS8cM5k3tzezqbojVnhqUXSGd0+zk6YuLxmJg/fxnJJm5SvHT+Hv71fwy1d2sLwwDZtp4K/5rWdM54M9rextdfH8xnrOnSNrlMTYqGx1sauxG51G4cRp+9b17mnq5tqH1+H0BdEqCl9aNpkrj5lM2jBaCwGsqWgjrEZSfW96fCPWaNpzssWA1aAjwaQjzWbkwgXZJJr6/u71zPBWtrqZnZXAxppOzp+XS7LZMOiMcm+pVj3trkBsuYAnEKnsbDFoD2h2vA9FIT/VQlW7m/xUC/7t2wm7Pfh27EBjMqOxmEFRCDQ2EuzoJNTZiS41FcVsHnrfQgghhJgwJtwM77p16/jCF77AihUr2LJly6DbDhTsjgoFtOkZqIqCbtIkdCkphH1etMnJhJ3dKEYjYffwW5UcLRZMTsFi0FLV7uavq8sBsNuMzI7O/P7xnT3D2s8NJxUxOdVCc7ePv67u35u3h9mgZeWlc1EUeHtHMxujadOeQIg2l3/YPUaFOFhr9kbWmi7KT4mtZQfYWNOB0xckJ9nMf65fyndOn4Z5BJWRE016NEqk0FNZs5PNtZ2sqWjn9W2NPLOhloc/ruS3b+7ipv9sIBAK93nu9IwEUix6/KEwu5tdLMhLwhMI0uEJ4AlEKjd7AkHa3X48gSD//aySW5/axJOfVcb2YbcamZpuiy0XcPtDOL2RPr6jcXxNzUjgtJkZTM1IwJCXh8ZiRj+lEI3VAhoNGpMJxWjEXVtHdwBcnSPP5BBCCCHEkW3CzfBqtVpmzZqFzWZjxYoVrFq1asCZ3oqKCp588knuuOOOUR+Defp0bIpCOBDAkJ2NYjSiTUzEU7qdkLMbb3k5jhdfxLJ4CSlnnL7viUd55dAks56fXVDCrU9v4d63djM7K5FFBal876wZfPnBz3hrexM/OGcmVuPgX1ujXsud58zk6/9ez6OfVLG9vitaGAh0GiX2/yfZjNxxzgwuXpDDMxvq+NnLO/jnVYvwBsIYdJoBK/DG4wkEe81cHfxh5QkED8m6R3HotLl8sdnMNNvgmQj762m9tXBy8oCPz8xKYEa0iNRIHFtk57VvnUBLtw+nL4jLF6TN6ScYVun2BXF6gzy/qY4ttZ3c+9Zubj9rRuy5Jr2Wuy+ew9f/vYEdjd0UTkrAEwgTCqt4AmHMevr8e31VB05fgA3VHXxh6cDjsRi0tLv8GLRKrIfvaM34GouLMRYXE/Z46K6sxtPRTdeecjTOLjztHaj19QTqa/CEAphmziLhuOVSrVmIXvwVFWM9BCGEOCQmXMA7ffp0amtrqays5KyzzooFvVOmTKGsrIwFCyLFil5++WXuvPNOkpKSuPHGG0d1DBqTCY1eDx4P/vp6NCYT/tpaXJ9+ihoK4a+qQpeYQNDRgcGeij4zE0NOzqiOYTzrndZo0vVNMrhkUS4flbfx3MY6vvfsVp76+rHMzU2iwG6hss3Nqp3NnD9v6NTjk6ZNYsWMdFbtbI7Nng1Eoyh85/RpvLq1kU01Hby1vYkTpqbFrcAbT982NQd/WPXsTxw5eq9XHWnAu6U2ss52bm7yqI8rI9HUZymAyxfE1it9eXFBCt96YhP/+bSaBZOTOW3mvtZsZ8zK5JzZmby6rZE7nt3Kf756DKBgjrY1Mus10eBXw6L8ZNZXdcQN2iPba8lNMceO/8HaOx0Mn9aAv7ER145SdM2NBFpb0SQlodZU48/MwFdVBX4fpmnTjurlJUIA6FJSUMxm6m+7HYi0ItKlpEjRNyHEhDHhAl6LxUJCQgJdXV28/vrrsaB3ypQpnHrqqbGA95vf/CaJiYlccskloz4GjcWCLiEBb3k5qs9PoLOTYFc3/s5O1Po61I5OAvX1BPaU0VxdTeLnLsT+hS+M+jjGq94nufsHvAA/v7CETTUd7G118aPnt3HfFxZwzpws7n+vnFe3NQwr4AVYeclcPipvJRAMowKBUBidVkNYVWl3+Vn5+i6eXFfDuXOz+OoJU7hvVRl/ebecc+Zk9UkrHY6ek/eRBMlD7c+lkdmnI8n+61WHy+MPsaspkmo7Ly/pUAxtUCdPT+ea5QU8/HEld71QytR0G3NykmOP33V+SazH9ZNra7nu+CmxNbxmvQ6zHtrdPo6ZksaZs7NIsQy+vtik7zubO5rHDYDq8WC2mvBrQNvWgreiArWyknA4HCliFQ6jy8/HUlKCNjFRAl5x1NNnZ1P0ysuxAFeXkoI+O1sCXiHEhDHh1vBCpOXQtm3bSExM5KmnnsLpdLJlyxa+sF9Qec0115CQMPI0weHSpaSgGA3oMzIId0WD3bZ2CIVQrFYIhfBu2ULzr+6m5jvfpeWxfxNsa5vwlUQH62EKYDXq+POVCzBoNby/p5XH1lRx9uxMANaUtw871ddq1HHGrEzOnZvNeXOzOX9eNhctyOGShblcf0IhVx4zGYAfPL+VLy6bTHqCkboOD/9bVzPi92TW67BbjYPO7rr9QVqdPtz+4LD3J44cPetVLQbdsP/OAKX1nYTCKukJRjKHKMp2IOK15+rt5hXFLJycjMsf4rant+Dttb52UoKRO86ZCcDv395NdXv/+gMOV4BAaPjtinocTHuneBSzGYvZiL2oAFNWFmp1NQQCEApBOLJOOVhVhePf/0EFwm73sD4jISYyfXY25pISzCUl6LOlgKMQYmKZcDO8EAl4S0tLOe6447jsssu47rrr2LRpE6eeemrcNb2jTlHQ2e3o7HbUUIjOp5+JBLsaDYpOh+pyoU1ORjGbCTY04HzrLfw1NdiWHoMuNfXQj+8w2v9k0qTTxGZ2w2EVzQCXXWZmJnDziiJ+/9Ye/vD2HubnJVOSnUhpfRdvljYxMysB7QDr7wKhMNqBdgjsaexGr9v3nOOL7by2rYGadg8/fbGUW86Yxvee2cp9q8q4aEEOqdZIa6pgKEyb08+2+i52NHQxNd1GeXM3m2o7mJ+bzM2nTh/kve/7/31ntrXDWLKt7vf/VSL1dsWhNFTs01P1OJ4+f+cBArlQKNxn7ejG6PrdOTlJhFVw+oKx74YvGA3QQipOXySAbnf58YfiD7Ky1YUxmnL87s5mnlhbw+KCFK48ZjJWo45AMEx2Sv9KxbecMY2bHt/I7iYnP3h+K3edXxJ77KySDJ5ZX8u6Kgd3PreVlZfMocXpJz3BSHZypLVYhytAslWPqoZRh7iWqg71NR7kb6Cq4A32XhbR9zNWTGY0ZjMms5nWvRXg86HPySH7d78j7PPSet+f8WzaRKC8nIZbbyP9+98j8Ywz+7ykLO0VQgghJo4JO8P7ySefcOaZZ7Jo0SLuv/9+Xn/9daZNm8YPf/jDwz6esMeD6vcDkHjBBaiByCxIqKODYENDZKNQCH9pKY0/+jHB9vhrTo9EkSJRA980mviP3XRyEWfNziQYVrnj2a2cMycyy/vm9iZSrQaSLf1vRr0WnUYZ8NbtCxIIqbGbTqvhi0sjvXhfL22icJKVmVkJdHuD3PVCKX98ew/XP7qOE1a+x/LfvMvXHlvPvW/v4eb/bmT1nlYcLv+g64P3N9TMNkTa0Kza2UxZk1STHa8UIgFR71t5czfv7m6mvLkbk05Bq9m3zrXf8/f7nm+Nrt+dl5eMoihYjTps0ZsxemFIp1Vi9yWa9bH/P9Ctp9ryG6WNPPhRJW5/iPd3t/L9Z7eyocpBIBwpyrb/LSvZzA/PnYkCPLexnpc21/c6TjX8+PxZGLQa1lS08/fV5TR2eKlsc6FRFCwGHclWAxaDDk20KNygtyH+N+jnryh9Lir0/+2I/I00ZnOsDVzY50OXmYHzrbfxrFsHwSBoNAQbG2m86y6cq1ejeqRyvhBCCDERTciAd8mSJTz33HMsWrSI++67D4DExERef/11/vOf/xz28aheL+bo2mH32rVYjjkGAOPsEmwnnbRvQ0XBu3kz1dd9hUBzM6qqHtWpdoqi8JuL55CbYqbG4WFPkxNFgfVVDho7vQe8X7c/xGOfVPHLV3eQmWRi6ZRUVBV+8Nw2bjtjGgCvbmvkT6vKWLWzheZuHxoFpqbbSE8wElbBotdiNuhYNEiBnv0NJ+25qt2N2x+k2uE54PcnRldPxeyeVjwDqe7w4PEFqe7wYDboSLUaMBuGl0CzuSfgzR299buvbGng32uqAThxWhqZSSY63AHufXsP//pgLx1u/4DPW5ifwpeOjVwE+uUrO9jT68JLvt3K/51UCMCr25rwBIMkR4tf9Q5AD4fhXDwCSLrkYhSzmVBrKy333kvnc88BoBiNsfTmcGcXdbffTuuq9yLTx0fxb64QQggxEU3IgHfx4sV8/PHHsWC3R2Ji4iFdsxuPt6qKUMCPYjETrKsDXfREOBBk0i3fJeOO76OxWGInWr5duyg7+RTq7/oJwba2CdWzt83lY0+zc9jrcBPNen5zyRwA3trexNIpkXTv9/e0HtDr72nq5ucvb+eDslaq2tw8+kklly7KxWbUsbvJyZa6Lq47roBZWQlcvDCHH583k/9ev5TNd53B698+ka+fVARAty/Eo19ZxrdOnzHEK45MfqoFi0HH5AFSTsXYGE4wNznZjNmoY3LyyP5uDrc/tiZ2zihVaH51awNPrI2sQ//cgmy+evwUfvm52Zw3NwtFgbWVDq59aB3v7mwe8ILaF47JY3mRHW8wzC1PbY6lUgNcvbyAqek2HO4A7+xooSij5/dUpcsTYNBc5P0M50JCPMO5eBQZlophTuT3o+vV1wi7XGisViY//BDWE07Yt1l3N23fu426W28l7B/4YoAQQgghjkwTMuAFWLo0TiPIMRCor0fR6UAfWRPq/ugj0Gjw7dqFZ/0GrMuXk3jB+X2fFA7T9b//0fiznxPqnjjprb1btwzXsYV2clPMuPwhCtNsAKze3TLi195S28Fv39xNm8uP3WrAoNVEgtzaTr60NFLA6i/vlnHl0sm89I3juefSuVy9vIBF+SlYorN158zJQqNE1l0OVLznYE3NSGDFjHSKMw7/hRkxsOHMJhZnJLBi+sj/bpuqOwAosFtGXBl8f9Vtbn78wjZe2hJZJnHZolwuWZiLoigYdBquWJLHT86fRU6yiU5PgJ+/vIP73yvvtx+NovCri+eQkWikqs3Nt57YSJszcoFKr9Vw1wUlKAq8tq2RT8rbos9SSDTrGWh1c63DzdqqdmodfY+XwzErbMjKwnbSiWjS0iKFq4Cwy0XH088w6bvfIf3WWyKzvQChMN2vvkrl5ZcTaG4+ZGMSQgghxOE1YQPe8cQ4ZQrGyZOxnHYahrlzIzO80XS61gceoOXv/6DjiScHfK7zzTepueFG/E1NhFyuI37GN9Wqx6DTjKh1i6IoLC+yA5BojgSeVW3u2FrF4fp0bzsqsHByMj8+bxYXzI9Uonx1awOL8pM5cVoagZDKgx/ujbuPSQlGjitOA+Chj+JvJyaOYc8mjlA4rPL7t3YBsLwo7YD3U9nm4q4XS/n8Pz/hze1NAFx5TF7s+91b4SQbd54zg6uiacvPbaijuav/8oAUi4F7Lp2HQathXaWD7zy5KXa8zclJ4rrlBQDc9vQWGjo9g14UaOjy4vOHaNjvdYablnwwdKmp2JYuJe2b30A7aVLs/s7nn8f50Ud4tu9An5+P1m6np3qef/duKs45F+eHH6KGR/YbI4QQQojxRwLew8AyezZpX/4y6ddeQ/5f7yft/74WeyxYW0v3yy8DoMvKwlhSgi4rq8/zfdu3U/G5i3B9thY1GCLsOXLXd/a0bundcqfW4WZtZd8ZIE8gRLs7gDvQd/YnwajDoI18bR1x1iHGU9cR+dzOmJWJ2aDl5GmTSDDqaHP5WVfl4P9OjKxPfG5jfTQ9c2DXnxDZ7sm1NbR2Dy81W0wcpfUdvLCpjm11HSN+rscfxOEO4AkEeXpDLdsburEZdXxjRfEBjeWdHU1c+c81vFHaSFiF44vT+P5Z0zl7Tlbc5+i0Gq45roD5eZGq0C9HZ4T3Ny8vmce+egwJRh2bazv5w9t7Yo/dduZ0SrITaXf5+eYTm9BrlLjthbISTRgNWrISTZQ1dfPOzib2NHUdsgsJ0LdClqWkBPtll5H923si7eAAQiEcjzxK96uv4t+9m1BbW+wiJEDY6aT2pptxr1s3ymMTQgghxOEmAe9horPbMRUXo01NxXbaaZEZhf2ofj+KyYTlmGNI++Y3+vTGUB0O6r75TdoefGBfCt4E0O72s7ayneYuDw29ClH1pDtur+vk32sq2d0YSevWaTWkJURSw9tdww9425w+ur2Rdi850fWxBp2GU2akA/Dq1kaOKUhhWoYNTyDEU+trgUibo72tLt7d2cxDH+3lJy+WUtXmYn5eMr5gmH98UHHA6xDFkam8xYXbH6S8xdXvsTaXj70tLtrjrFH3BMI0drhZtbOZe16PzO5+89Ri0g+g/259h4dfvbaDsArLCu08fO0SfnvZPPLt1mE9/8L5OUCkwFW8bIkZmYn8/HOzAfj3mirejs4gG/Va/vyFBdiMOtZVOlj5xq64r5ObYmFJfiq5KRaqHG7cviCVI1gOcDBrfXszFReTc889YIj8foRaWlBM/T93/eQ8AFSfj5obb8KzdetBva4QQgghxpYEvIebqqKGw1hOPqlf4BpqayNYX4/q86Ho9FiPP77vcwMBHI88St13v0vQ4TiMgz502l0Bks16/CGVrKR9J5896Y6tXV5qHW6c/sjJrk6jkJ5gij53+AFvWYsTgIwEE0adht1N3bywqY7ji+0YdRpqHB4+Km/j6miq5p9XlXHKb1dTctebnPmHD/jqo+v4xSs7eGxNFT96oZRTZ0YC5SfX1uBw+Q9bdVox9oomWbEYdBRN6h9YOlwB/OEwjjhr1M16DQ5PgOc21NHu9jM13cZVxxaMeAzBcJifvFSKyxdiTk4Sv71sLjMyEwfcNhQeuJDUccV27DYDDneA37y2i3d2NNHQ4elXyOqUGelcEz0ufvzCNqraIoF+QZo1VlDunx/s5eUt9UOOOz/FgsWooyDVMty3OmprfTVmM8ZpU0n5wudj96leL+aFCzH3rvmg0WKYGplxV51Oqq69Dl9V1VFdMV8IIYQ4kknAe5gpioIhMxPbihUYFi0Cfd+1rMGGBtRAAI3FgvXEEzHOmtXzxNg2rvc/oPFXdx/OYR8yqVY9ualWTpw2idyUfSfBZr2WVIue4owEclMsmHSRVEmNRiEjMXKhYCSFr8qaIwFvXqqZUFjlXx/u5ZWtkaI7x0fX5P599V4unJdNikVPlzdIdbubUFjFrNcyMyuBs0oyOaYgUiX6ta2NTE234vQFeWZD7SFZh/jEp5Wjvk9x8Eqyk7lwfg6zc5L7PZZi1WPQaEjptUb9v2squeXJjfx3TSVmQ6RPbk/RtZ9cUIJeO/Kf4Uc/rmJLbScWg5afXlCCTjPwPjbXdPDVR9bxyMeV/QJfnVbDhdF1vqt2NvPLV3byxX99xpX//JTvPLmJRz6uZGO1A18wxM0rilk4ORmXP8StT23GG11qcM6crNhSgNuf3sqOhq5Bx12ckcCpMzKYmjFwcD6Qg17rG01t1lgsGHJz8V/9NcLJKbGHPRs24N+7bz1+qKUFU0kJmmhFf9XpZO8ll+LbsePAXl8IIYQQY2q0F0+JYdClppK8YgWGzEza//Mfup99rs/jznfeQZuYiKmkhKTzzqUzHMa3c2ekqEp0nVn3q6/SceIJJJ9//kAvccRIMRtIMUdSDMO9Tsg9wRAef5jsFDNfTM/no7I2ttV3oVVgki0S8DZ0eujcb62tyx+MnYz3tjOaEt3Y6eXet3fT4Y4876UtDczMSkBR4JOKNj7c08pvL53LzsZuspPN5KaY8fiD2EyRMXZ6Amx7vJPtDV2cPjOdPc0unlxbw82nFDNwS5b+VWuHa31VR59/DzXBpBz4S4k+Bv+gw2oYJc7fNcViINGkQ6vRxGYEN9V04A0G2VTTweVLwvzh7TLCKpwxK4PpGQk49stUaHf5sBgjP82+YOR4D4bUWHugv60u46l1dQAszk/mpc11fZ5vM+mZnR3p6ftGaRPBsMrbO5pp6vJx/QlTcPtD1LZH1rMfX5yG1aBjd1M3e5qcVLW7cbgDrNrZzKqdkUrFBq2Gr54whWuWF1DWvI3dTU7ufG4bP7uwBIAbTi5iS20nn1S08dVH1/HodcdQmBY/rVoFNEN+V/dtYNbr+qzzVVV1GN/1+Bs0+SF0210k//z7qN7IMopQczPo9WhtNjTJyXQ9/0JkL0YDqs8fmem95lpyfv87bPtn3gghhBBiXJMZ3jFkmj6drB/8AP2M6f0e63zhBTybN+HdvQfFbI4UWwmH90U1oRBNK1cS9vsjlURVdeiIaIwoijLim8cfJqyqeAJhFEUhFH1rOq0mtt4xFFYpSLP2uRWnJ5CZZO53q2qLrBmscXjY3eSMjS0YVtla10WqJRLQ/ndtDVMm2Th7Thbz8pKx24x4gyphNXJLMOm4bFEuAJ9VOkhPiKSE9vQ97f/eB75pNEN/BvPykvp9hmLsDfV302o0aBQldpudk4QGDbNzknh+Uz1b6yIzs985beqA3w2TXovNqMNm1GHURX6idVoFWzQIfmlzAyqQmWjCpNNS3+Htc+v2BFiYn8LUDFtsxlWvVdha18nfVleQZNbHbnarkdNmZnDjycXce8V8/nv9Um48uYgrluSxaHIKSWY9/lCYf35QQZcnyA0nF6Eo8NzGOp7bEAm0tRqFlZfOISfZTH2Hlzuf20ZYHfxzGkuZiSa0ixZjvedeNDbbvgcCAVQgWL8vNVv1+WNLT8JdXdTcdBOOp5+W9GYhhBDiCCIB7+HW68xW0WrRWCwkXnAhhpKSvtuFw3S99jpdzz+Pd+NGVJcLxWLpE9SGW1opO+982p54Ave2bYf5jRxaZkMkaDDrI1/RULSoTmQNb+QEtNU5vDW8bn+Qytb+BYZ6mxTd5wd7WqhzDF4F+6zZmWQnRXqZZidHCmD984MKfMHRXcf75eWFo7o/ceisr2znkY8qeXdnU78Mg6uWT+HPX1zE5xbk8uvXdgJw/QlTSE80EQyHeWVLA6t3t9DQ6R0ykPr1azvxBMKY9BpmZSUMGjy+v7uFYFhlSpqVv35pEUlmPdsburjrxdJYxfL9GXVapqRZOXdOFt86bSp//Px85uUmEQip3PfuHqakWblkYeSCzy9f3cGuaOZEssXAHz8/H5NOw2d72/n9W7uH9bm1uXzsaXbSFqfI16HYR26KhcX5qeSedhIFzz+HfsqU2GNhhwPV13c/qs+HPjva4snnp+k3K2n64x9xrl17wGMWYqwE6uvxlJbir6gY66EIIcRhIwHvODDp2mvI+vnPsJ66ou8Dwb5VSY3FxWgS+659C1VX43js33grKo7oWYdgKMxf3ivj6fW1hMMqFr2OVIuejVUO/vl+BS3OyEmoVqPpFfAO7wS3rNk5RJIqdHsDLJ2SSliF/60feLa2Z7tAKMxV0SI+2+q6SLMZaOry8cz6urjPExPbriYnnZ5I4OUJDFzx+Pdv7abN5afAbuELx0wG4MEPK/nxi6V893+bOe++Dzn19+/z7Sc38fs3d/PKloY+laBf29YQayE0NydpyLW/b++IpCSfNjOdebnJPHD1YrKTzTR3+/jeM1vY2Tj4elsAjaLwfycVxb7j//pgL+fNzeT44jR8wTDf/d9mur2R5QHTMxP4aTTN+f73ynl+Ux1tLj+eAZYY9Gh3BfAHwyNaj7+/eoeH5i4v9UNcqBqIISeHwpdexHr66YNuF2xtjf32qt3ddDzyKK5PPxvXmTXiyFXX4WFbXeeQt3gXruIJ1NdTfu55VF5yKfW33Y5iNqNLSRn6iUIIcYSTNbxjqPfsjG/3biyLFuMu3Y7a2Nhv25Qvfwl9Xh6q203r/X9F9e+b3Qzu3Uvnk09iKSnBVHxg/TzH2vOb6vndm5FZocfWVPKri2YzKyuJ7Q3dOH0Bur2R4F+rgfRo0aoWpy+6nq//LFcorPK31eW8WdrIabMyhnz9xi4f3zx1Kp/ubef1bY1cfWwBqdZImnOHO8Bne9tZU9HG9oYuki0G/nD5POblJrG5tpNJCUZanX7+trqcK5bkoR16gaKYYKZn2AgEw0y2m2NZCT08gRDbG7p4bE0VALefOR29VkNLt4+HPqoEIN9uodYRWZO+obqDDdUdffbx9o5m1lS0AzAlzUJKNAU/HpcvyKd72wA4dWZG9DWsPHj1Ym78zwYqWl386PlSfnfZPCbbB6+YbDPquOmUYn7xyg7WVTn4YE8rd188m8v/vobqdje/eX0Xv4i2LjpnThbb67t45JMqfvDcNp742jKyk80D9ueFSNG6dleAVKt+wMeHw6TX4fKHMB1gP19FqyXvT3+k9ld343zssX33m83oc3Lwl5Wh+v1orNZIdo6qono8OB54gNTLLkWfnn7AYxdif3UdHk773epBLxT1MOu1vH3LSeREM42GEnQ4UD0esu9ZiaGwEF1Kyr7sBSGEmMAk4B0njEVF+Cgn++c/p/XPf8a3eXOfxx2P/Rvr8ceTdMH5pHzpi7Q//EisgBWAd8NGWh98kMxbbkFjNqOxDL/tx3jQu/hUfYeHhz+q5JJFuczKSmB7QzdZSSYq29xsb+hmxYx0DDoNHe4An+5tZ1lh357GrU4f339mC5/ujQQIu5u6h3z9cFhleoaNmVkJ7Gjo5tFPqshNMfPBnha21XX1mSFud/nZVt/F1csL+O7/NrO7yYnNqKXG4eGzve0cW9S/x7KY2BYVpLIoWsF7/0wLtz9Eu9NPWI2sd+3JUHD6gvijqfqzs5MwaDXsaXYSj9MXpMBuYcogBaF6+IJhAtGF73brvuDYbjOyOD+FilYX/lAYh9s/ZMALUJhmJTvJRI3DQ6vTT7LFwIlT03hyXS0N+80yLZicwiOfVOH2h3D7Q+yo72Bvm4cZmTaWFPQ9NuxWI3brwfUVz001k2ozHHSldNM3v4u/24X/+WcBUD2evtWbHQ6Sr7ySjscfjz1ec9PNTP7739AN0FddiAPhiGZF/OGK+RSn2+JuV9bs5NtPbsLh8g874O1hKCzEvP8yKiGEmMAkpXmcsMyZQ8rnPkfiCccz6VvfQjfATK3rww/pfO11dJlZmObOBUAx7DuZdX2yhu71Gwh7Rp7aN9a+uHQy1x1XwJeWTWZ5oR2TXmF9lQOjQcvZczJjvUpf3lyP1aDjomg7lQc+3NsnwNhS28Hlf/skFuwCJJn1Q1aFnZRgJNFs4AtLIqmmL26u5/73ytkaDXanptv48rLJzM9LBmBrXSeTUy0UTbISCqtMy0iIPW9tZRuPfVLF2sq20flwxLi2vrKdxz+tZn1l+4CPWwxaFuancNK0SYTCKivf3I2qqtG1spkAvLK1IRbsmvVa5uclc/ni3Ng+zp+bBRApgDWMok+pVkPsZLn3sfD4Z9X8b30tAFceM5l50e/zUNZWOqhxeDDpNJw+K4PGTi/PbYwUd/rK8fvWwDZ1efnJi6WR+48rYOmUVPa2eXD5AuxsjB/MHwyzXovdaog7ixyP2x+irVcPbYtBh+3GmzGvOHXfRqFQrOaCeckSLMcsif32Avi2baPln/8alfchRG/F6TZm5yTFvQ0WDAshhOhLAt5xyDp/Hpnf+97AQe+77+J6/320PevJAvtmRsMNDXS/+goa88iu9o4HJr2WH547i59dMJsrjsljSloCdqsOnz9MY5eXU6ZPIsGoo77Ty7oqB19cOhmDTkNpfRefVbajqir/+bSK7zy5iRanj6JJ1tg6SbcvxJLo7Fs82cmRys/Li+3MyExAAebkJHHzKUX8+uI53H3xHC6cn8NpMyPpi1vrOgFiPXxd0ZYxz26s472drTj9AXY2HJoTfABPIEiby4cnEBx6Y3FI7Wpy4vIH2NU08N+7JyD76QWzMESLOr0TbflzyxnTuXRRLtcuL+Dui+fw3I3Lee1bx/PwtUs4e3YkGE4w6QhGW3YNZ3a3x/JopsFHZa0A/G9dDfdGi0ldvjiXK5bkDWs/wXCYp6Lr2s+anUmSWc/fVpfjD4VZnJ8Sy2gIhVXufHYrHZ4Axek2bjsrUn1+RqYNq1HPjMzhnaB7AqHD8t32BEKEwmosddRi0JKel0n+X+4j6fLL922oqmiSkjDPnYsCJF96SSS9OarjscfoeOZZwm73IR2vEEIIIQ6MBLzjkMZiwTxrJtm/+AXmZcv6Pe768ENc778f+Ud0dlOTGJlh9Hz4Ed3vv3/EnXyV1nfwxNpq3iytZ3pmItcdX8gJ0zIwGjRkJpow6rWcOTuyFvGlzfXYbUYump8DwD/f38v3n93KX94tJ6zCuXOyePz6ZSwpiBTjaHH6OHdO1qCv31MASKMo/OGK+bxw83H88fPzuXhhbmwtL0BJdiIKUOvw0O7yc1yRHUWJBD3HFqbiD4Z5blMdeo2GGVmH7gq82x85We+ZnRKja2ttB89trGVrbceQ207PsGE16Jme0ffvvaepm7d3NrEnmlKfb7fyleMKgEgBK48/RJJZzx1nz+DmFcWcMSuDyamW2AxuT9Gpk6ZNoqo9cjwX7Jd+7A+G2dnYjdPbPzjsCXjXVLTxzPpa7nljFwAXzMviyujFoOF4f3crTV0+Ekw6zp6TRWOnl+ejs7vfPLU4tob+oY/28lmlA7New68vmY1RF5lxXVJg58vL8vulM8dzuL7bZr0WrUbpMzNc1tTNqp3NdN/wXRJ79TgPd3TQ/sADdDz9NIrJRNKllwCg6PUQCtH4q18RbJWMDiGEEGI8koB3nNKYzRjzcsn84Q+wnX8+aPr+qRSzGbSREzXFYCD77l9jmjePsNNJ/a23UfOjH+Hds+eIqSJa0eqmzemjst1Dhzsya52dZI60D0mJnOSfPzeSxvzqtkaCoTBfXBaZ5d3e0MUHe1rRaxW+c/o07r54DhaDjjTbvmrOPYV7hsOg08R6nu4vwaSPzbJtq+vEbjPGZo+PmWInK8lEQ6eXrXWdLM4ffFb5YFgMkZP1g123KAZW1uLE5QtS1jL0LP2iglSuXDo5toa3R5XDjccXpMqx7+LTF5flk5FopKnLx4Mf7d1/VzGqqrIqOgu8YsYkqqJttQr2m+Hd1dRNVbubNQOkU8/LTcZq0OJwB/j165F2SF9cOpkrFucNuxeuLxDiuY2R6uMXzs/GrNfy7IZaQqrKiVPTWDA5clFpc00Hf15VDsCtZ05n0eRU2lw+yg6g5dDh+m5bDJGZ996vU+3w4PYHqXJ4yPrlL7AsX97nOa4PP6L5nt8S9njQJCWhBgJok5NRXS6qv/EN3KWlkb7oQgghhBg3pGjVOKWxWNBYLOhSU0k660z81VX4d+6CaI9INbpOV9HrybrnHqzLj8UwtZjqq64m2NiIe9W7eE8+edxXbe5Zf1uYZsHlC5Jq0ZFs0aOqKuGwSu/z8mMLU7FbDbS5/KzZ28byoklcND+HJ9fVkJVk4ucXziYv1UwgWggoyRz5erd0+zDqhj7Bf/zT6gHvN+u1pNr2zfJmJpmoaHXx/p5WMhJNnD4znc/2tvPm9kZ+ddEcvvrIOl7c3MDc3OQ+6xv7vm8YKubovTZZVVVUNRwLVMx6ba+ZqZ7tRl4deqhrIcOMi8aVodpzxavs3fvx4kk2ylqcFE/qP0sfDqt9PurqdjeTbEbM0cBJVUHRqOSnWKhyuMlPscTGlGo1cNPJxfz4xVIeW1PFuXOzmJzad9a23eVnc20HDZ1eTHoNKRYDLn8IjQIGrYbGTm+sIFVLd+T3IBRWqWyLBNZGnZbdTZGWQ7OyE1lb6QDgrJIMzp6dwc6mbgyDtDTq9gWxRi/4vLatgU5PALvNwDEFqexs7OKTaLXom1cUo6oq3d4A33tmKyFV5aySDC5ZkIOqqnS4Ii28OlwBUntVlQ6rKhpN/9dvd/lwuAKkWPW9Cln1/1uqA9/d53HNIBtE/hYD//0np5ipdnjISzGjMRjI/vXdNPz857jeWw3R5SOhlhY6//fUvvcT/U0OVlfT8vd/kHPXj6WIlRBCCDGOSMB7BDBMnoxl6TI0ySl4P/0UvF4AdJmZZNx5B5alSyP/Tk0l9/6/UPPV6wm1t9P65z+jsdkwTZ0aqdw8Dqs3a6LVpGbnJJOVbKbdFUBRIvcritIn4NLrtJw7N4tHP6ni4/J2Ll6Yx08umMVps9JZlJ9CgkmPLxBCFz2Zn5wamQ3zBcOAwtdOKOQfH1QwyWbkqycUsKOhm3NmZ6HXaXj0k0qau70DjlGn1fSpZDsrK5GPy9uobHNhNWo5tsjOr1/bxY6GblKter5xajF/eHsPK1/fxbGFdmZlJw64XzE+KYrC3LwU5uYN3J+yd7C2sdrBZX//hHPnZHHxgixK67spyU7glBmZTM1IYGq0mFkPs17LF47J4+2dzby/u4U/vlPGg1cv7hOAO71BnlofCSqXFdrpiFYwz0oyk2jW4w2EY62vIs+LBHc9gXxIVWMp+qfOSGdDdQdnzsrg6uX5KIqCLxAm0RS/DZDVqGPB5GTaXX7eiaZVf+OUYpZMSeXWpyLV48+fm8WSglRUVeWHz2+jrsNDboqZ7509nVann6CqkmzV09DhQdVo8ATCsZnUeJcaHNEA2RHtydvTrmj/Ks6KMvilHW8gUh3aYtAOWMgq8pkNvIfijESKMxJjvzv69HQm33cf/qoqGu7+Ne733+9THR8iFx+Ns2bi274D97vv0r3iFJLOOGPc/daKialskMruPVKshhFXchZCiIlEAt4jgKm4mKzvfBs1HKb95Vdo+8c/CO3dS7CxkfaHH0GbNgnT1GLCfj+GggJy/vJnar96PYGqaupvvQ3d8uXk3nQTGpsVg9k8bqft2l0B/MEw7a5A3FYlJ0+fxKOfVPH29iY8/hBmg5aTpw/cB9Ns0GIz6nD6grQ4fczNS8Ko09Di9KFVNFwYXQO8P7c/hD8YIjlOr9MpaVa0ikKHO0Cr00+q1cCywlQ+Km/jtW2NfO3EQtZH+5Xe9PgGXrz5OBIGCTCG490dTVR1hZiZlcCKmZkHta/9eQLBXgHCxPtJWLWjkc21XczLTRz1z+4f71egqvDylgYK0yz4gmFK67s5ZUb811EUhS8ek8ua8jbe393CWzuaOGPWvu1VVeX93S0AnDg1jerozO1Q7YMCIRXDfpkMC/NTeOy6JbGLQCPx4Id7cftDTM9M4PSSDLbUdvJRWRtaReHbp00F4KXNDby4uQGtRuFnF5ZQ0erGqo/Mr0aqyCqxwlBDpSinWPWxGd52V4BAr9+CWoebhk4vWUkmclL6fw5tLl+vfr5KbA1w74DX0ycQHtn33JCfz6Rv3ExHQT6ut98hWFsLCih6A2nf+AbW5cdSe9PNBBsbab7713h37yHl4ovQZ2WhtQ6/0JgQw5USrUz+7Sc3DbltT7/eQ7fIRgghxjdZwzveRVtioCgoGg3WWTOZ9PWvY1y0EMVsxrNhAzVXXx3pyxsMouh0mEtKyPrtPQCoLhehzz4j0NExtu9jGFKtegw6TfSkdWA5yWYyEo24/CHe29U85D7ToqnIkbRmLQsmJwOwZu/ABWZanT7e29XC+3va2FDdgT/Yfz2eQachPxp89PT4PStaUff5jfW4fEF+fmEJmYmR3sF3PrdtyDTboWxr6MTpC7ClrqvP/f9bW8X3ntnM/9ZWHfC+J3oBrM21XTh9ATbXdg298Qg1dO7LCtAoYDPqKMlOGOQZEb6gyvKiyOnnL17egafXZ1/Z5qbG4UGvVTi2yE5NdA3w5JS+MzSqqsbS94FYT9/9HUiwW93m5vlNkcJU31hRjAL8bXVkje65czMpnGTDFwzxmzcia4OvWJzLniYnTR1uPMEwKdFjeKDCUAN5el01v31jFxur20mxGEi16tH3+i1o6PTiDYT6fN699b5YFm8N8MF+zy0lJWR///sUv/4a+qIiUEH1+wnU1aHPySH/0UcwlZQQ7uyk87HHaPj1b+h4480Dei0hhpKTbObtW07i5W8cP+jtD1fMxxMI4XD5x3rIQggxZiTgPcLoUlMxFuSTdNll6GfPBrMZNRCg9S9/oeyUFVRdeSWNP/kprX/6U+w5ajCId9s2NGYz3rIyut55J1LQapyxW41MTbfFnd0FsNsMnBKd0X1pS8OQ+0xL2Fe4CmDplMjaus/2tkfWYvbS5QmwpqI91gKm1uHhvd0t+IL9T5B7eiDujraiOXVmOllJJhq7vPzo+VJsJh2/uWQOOo3Cy1saePCjyj7PL2/u5u0d+yr4DqR3sZ/ZWUnYjHrm5vRNj15f3YHTG2R9dcdQH0VcE70A1rzcRGxGPfNyRze13OkLUlrfGft3hyfEjadM5aTpQ88iT0mzcO7cLNJsBuo6PDzw4b4CVh/sibQRWlyQisWgizvDG1LVPitVB7o4c6D++UEFobDK8iI7i/JT2FjdwcbqDvRaheui69Kf2VBHfYeX9AQj8/OS0aDS7QszJycpdgwPVBhqIBuqO3B6A2yIfo/3/y3ISjJh0mvJSjIN+PzeF8tMcfryjtb3XNHpKHzheSwnnABAxxNPUH3tdfj27EENRX4r1EAA/549dL/37kG9lhCDyUk2D9qrV/r1CiFExMTLXzwKaPQGQk3NhHVaNFOmoNFoCO7eDX4/3m2leLeV7tvYZMK0eDGh7i78DQ0416xBo4v82U1Tp47ROzhwdquRC+Zl88TammGtXUqIFt9x+YIkmPTMzk5Ep1Ho8gZpdflIT9h3Au0OhAhHi0nNyEhgR2M33kCYYKj/7GzPeqieoNRi0LHykrlc98ha3tvdwo9fKOXnF87m9rOm86tXd/KLV3aQbNZzyaJcIFoN1hekqt3db51njw7Xvh7Lp8zMwGKx9Cu2tGhyMuurO1gUnbk+EGa9bkKmMvdYMTMzbipzW69CSZurHbHU51NnxW9jtXpXI6X13RRPiv49orP3Bt3wrx/Oyk5mVnYyLl+In7y0nXVV+6os74peBDkm2larvCVaodneNzVW2W8dqmYUVyqsr4oUurp6eQEQCe4hUhyrosXFvNxkuqOtkFy+IO0uH1ajjhmZCX0KVA3XwsnJbKjuYGGc73FuiiVWrX2gXAm71RgLjuPlUvQt9HZwFI2GtBu+TkdqKs733sO3Ywe1N90MgCYpEY3RFKnirNHir61FnxNZPjHc6thCCCGEGD0yw3skUZRI4SmbFe/eCjShEITDaBJsmI89FmbOAJMJjEaUvFw0U6eiycok6PPh3raNtn//G9XrI+zzYZg8/D6cY80TCFHrcFPr8OAJhDBFT1qHM6MVis7W9qR16rQaMhIjQW7jfumR6QlGTHoNqgq7o8F0fqo5VrG2t1hw0evsek5uEisvnYtOo/Dq1kZ+8cp2rlteEKvU/MMXtlEZbS8zOcWMxagjPzX+uszkQVK7e1y+JJ/bz5rBwnw7rc6B0z2PVsNpi+NwBfBHCyUNN/W5tL4bpy9IWYubLy2NHEepFgPXn1A44jFmRGcsewJKgMauyN8xO9lMq9NHm8uPRqHfTM3+s5WJ5oNbJ97DHwzjiLYG6+n7e8LUNM6dm0VYhR+9sI2yZifXHVfAssJUXP4Qv359N/9bV0dzt3/E6fsef5AVMzP50XmzuHTxvt8lTyBEm8uPJzD+Uu0VjQZ9ejrmpUsxn3zyvrZxGg1KQiKqVkvI4SDY1krDvX+g9ZFHcK1fP6ZjFke3smYn5dH/rpU3O9lW19nvVtfhGeNRCiHEoTFxp3UmKI3FgsFiwTxjBoGa2khFY4MBY+EU7FdfRcjro/Pll/BVV6M2NEAoTFijRZeTQ7C5BV1aGilnXnhEze66/SGcvshJr9mgxRidSRtOwNuTnqzrNf2VlWSirsNDQ6eXubn7ttUoClPsVnY0dhMKqxi0GmZmDZwG2zNTs/+p/SnT0/nlRbO549mtPLexHptRz13nzWRHQxcfl7fx7Sc38ZtL5pKWYKA4zsxuj8FSu3vrCdraXQHSbAOnfB6Negez8T7L3oWS5uUmxmZ4B1OSnRCrxrysaBIZiSaOLbKTah35zGZPv2end1/A2xS9EJOZZGJXY2S2N99ujV3o6S0jwURTt5dUiwHNKM0eOtyRtX4Wg5YEU2R8iqJw+5nTqW13s7m2k68+so7nblzOP768iPtWlfHYmiq21nVy0+MbWTolle+ePg1/MEhpQzclWQkcP3XgwnIAnkA4WtgqjLnXR9h7ze1ozcz2fd2DK9ZmyM0lNTeXxJNOxDF7Nm3/+lck4IVIanMgAPUNePZWop+UBoBt8eJRfhdCDK53cauijlr+DHzryU2UJ7f227anuJVUdBZCTDRHbcAbCoXYvXs3aWlpTJo0aayHM2Ip555LuLsbz67dBDs6cG7eTCgYJOmUU1C0WjCaIBgCvQ6MRnS5OYTa2wkHgwSamo6ogNdi0GIzaoHIjFZP6mi8Ij299QS82l4Bb2bSwDO8APl2C7ubugmpkcAmXppqT2wx0GzWmSWZ+INhfvxCpNeqSa/h1xfP4ew/fsDm2k6eWl/D55dMHnZAO5TeQZvYZzifS+9U2MFSn3s7aXpmn3W6/3dS0aDb+4Nhun2BWBXh5F5RXSzgjc7wdnkCuKJFlTITTbxZ2gTAjMyBL44YdBryBqhafDA6orO7mYmmPim4Bp2Guy+Zw1ceXkdVu5sbH9/Ao9cdw53nzOSrx0/hr6srePzTaj7d284V/1hDgd3C4vxkgEEDXrM+0rbIrO97rFkM2lhAeij0DagP/D+FGpOJlHPOxre3As/mLeDxRH4gQkEUkwnF6cSzfQe6nIGrwgtxKPUUt3K4/LB7J7wHf7xiPkyb0We7smYn335yEw6XXwJeIcSEc1SmNL/77rsUFxcza9YssrOz+c1vfjPWQxoxnd1OwoknYjtmCfpJaegMRvzl5bjWrIm0wjAZCWdkoKRNwrpoIdm33opl3jz0CYn46+vHevgjYtZro2v4zJj1vQLekaQ09w54e1Kau/oHvAadhiVTUpmbm0huSvz/6PfsLRwne/P8edn84NyZAPzzg708vb429u/H1lTT4Rm9ipl2q5HidBtpNhNfe3gNi3/2Bl97eM2o7f9I1fO5jNaFhQNx0Z8/YPqPXuf0363G4fLT3mtdNuwLeF3RgLfWEUkpTLFEii9tqukAYF5e0mEbc3u0mmvmAAWiUiwG7rlsLlaDljUV7fzkxVJUVSU90cRd58/i3VtP4gvH5KHTKFS2uXl6Qz2PflzJy5vr4r6e2aAj1WrAbOgbdJrjFJ8aLaNVxEpjsaBLTSX1oouwLlqImppCWFEIJSQS7OxENRoh4EejnZhF4cT411Pcqii6LKIo3SbFrYQQR5WjboZ3/fr1XHbZZfzpT3/ixBNP5E9/+hN33HEHK1asYMmSJSPal8/nw+fbtz6wq2v0254MxjJnDpY5czAUTKHz9dcJtrYS6uzEsmAhaV/8Ev7qKtRwGJ3djsZsxrp4Mb7ycoxFg89IHU6DrfZT42yg1+yb4Q3tN8vr8YfQ6/Y9KRANisOqSpvTh6IoGKMn0HUdHpq7I38/tz8Ua/Gi0ygkmvSxdYyJZn2/dijt0ceCIZWGzoHXPS2cnMy3Tp3KH9/Zw59WlfGNU4pZNiWVNXvbueuF7Tx/4/JBW8b0njxW1cgtXtZqKBxGURSq270kmnVUt3sJhfd9NlrN+L22NdLjKBRW+e3rpbFiXbeeVdLncVVV+8zoj7ah1qh6gyG0Gg0bqx3saIqs2XZ4gtz+zBbuvXwu3qAJky7yHbRGgy2nL0g4HKY22oIoyaxnW20nOxsiKc1JJgN7ohXBfcHwoK20A6Ewe6NrxQfS5vQNerGo5/ucajXQ5Qn2e9ys13LX+SV875ktPP5ZDemJJi5blBd9VOGmk4v56vFTuP+9cp7fVE99p4973y7jjJJMFEUhHFbRD1LgKxxWUYb6ug6Rvj3UKmKF0S/WZpk9G11KCub16wk5XRAKEezuwvnZ2kiwqx95yrsQQgghDt5RF/DeeeedrFy5kiuvvBKAu+++m2eeeYYXX3xxxAHv3XffzU9/+tNDMcwRSVh+LLaFC2h/6SXCHZ2EA34UnRZjYSH++np81dWEPR5MRUWYS0pQxlHwM1RYMtB5rTGa+hgKqyiK0ie4Mem1aLX7/h2OBidGvZacFAt6rYacFDN/ebeMbm+QvFQzFoOOE6amxV2D2eEOMCu777pOffQ1tBoFo27gmRtvIMzli/PwBUP8bXUFf11dzspL5rKtvovtDV1853+b+L+TCpmTk7z/u46+933vQ1EUNBpN3PP8nm1n5ySwra6b2TlD94KNPG9Ymx1SB3Icra/uwOMPD9iOSVGUQavhHmyl3J7v29baDspanBRPsjEnNzn2uEZR2Nvq4sb/bMAXDLN0SioVrS6q2txc/+gG/nzlApYVRtpj6aLfo7AKgZBKfbRoTEaiidoODyFVJdVqIC/VHBv3pYtzSU+IP2vd4fLHCrMNxOULxioeD6SnR3W+3TrgMRFWVY4rTuOGk4u4/71y/vTOHjITTZwwdd/SkMl2K7++ZC5XLy/g8r9/QkWri1+9uoP0BBMl2QmcND0j7usrmqH/RoM+PoyaWYfqe2/IyUFjNseyaDTRyuqhrm4IBfddLFHVvu9hPByIQgghxAQ1fiKfw6C7u5tdu3Zx7bXXxu7TarUsXbqU+gNI873jjjvo7OyM3WpqakZzuMOmRKs32xYuxDS1GMVqo/vDD/HX1xPq7CTU2opvTxn++npU75Ffybf3utqh0poDA6Q024w6kqIVbesPsCqlJla0auiz6y8tzefEqWkEwyr3ry7nGyuKAXhze1Os5+poufuS+UxOs/FZVecBv7fD7UCOo0WTkzEbNAfVjulglbU4cfmClLX0bY/V6vTxtUfX0eEOMCcnifu/tJD//d+xzMhMoM3l5+qH1vLylsjvjaLsay7k9AVjKc1pNiM7owWrZmYmHNZ2Nj0pzfF63va48pjJnD07k7AKP3huG69s7d8Xe2ZWIt89fRoAT6+vo6HTzc6GwduJ1XV4WFfVHpvtHvcUpc9NZ7fHsm90yclobTYUjUKwtY32p5/G+fHHkQrO7e2EPUfGMSpGT1mcCsk9t+G02xNCCDEyR9UMb0JCAi+99FK/k8fU1FRcrvgpgPEYjUaMxrFbH7g/jdmMr7IK58cfocvMItjaii4ri7DXR6iziaDDgWFKAQnLlqGxjG6hm8PJ2DvgDYUxE39tXGiAolUQKU61pbaTHQ3dFKcPbza0t31Fq4azrcJtZ85gc+2nlDU7aez0cnxxGh+WtfLMhjquP6EQ/SCpzSPxyCeVvLk9Uujoe89s5ZFrl6A5hOm9o+FAjqOeNGZVVXlrexOTUy1Mj1PY6VApnmSLzfD2cPuD3PjvDdQ6POSlmPnrlxZiMeiwGHT8+6tLufWpzby3q4Vv/HcTlW1url2ej9Wow+kL0u0NxNqCTEow8mlFZKZ1RpxK4YdKmzP+Gt7eFEXh+2dFCt+8tq2RX726g053gC8ck9dnu6uOLeCN0ibWVzn4oKydn5yXHnd2HKCpy4vPH6axyzvoTPSRQGM2YygsJNDQSPena1BU0KfZsS5egi45CUN+PpbZs8d6mOIw6F0teShmvZaUA6j6LoQQYmBHVcALMGfOnH73abVawr3WO95xxx2kpKRw++23H86hHbRAczP+ykoCXh/Bigp8dXUYMjPR21NRDEbCPh/BtsiswpEc8Oo0CooSCTbd/mBstnYgPQFvMNQ3Ml2Un8KW2k4+KmvlvLlZIx5DTwjp8gepc3jIGaTAFUTWQ95y+jR+/GIpj35SxZ+vXMC2uk4qWlz85rWd3HBK0UEXV6rv8LDyjd1ApE/wx+VtPLOhlssW5w3xzCPX0+tr+f6zWwH44PZTyD6M1UXn5Cb3C9Z++Hwp2+q7SLbo+cfVi7Hb9v1NrUYdf75yIfe8vpNHPqnid2/upqbNjVmvwekDlz8Um+FNtuhiM8fxKjQfCqGwGmtLlDVIWnQPnVbDD86ZSbLFwH8/q+Yv75Wh0yrcHM1igMjFprsvnsP5931IVZubTm8Yh2ff7Pj+n2FGoonGLm+suNyRTDGZMGRm4mhrJdzWDi4XoUAAQ2srisFAqKsLdf/0ZjEh9amWPIQUq0EqJQshxCg6qlKa41EUJba26o477uCNN97g+uuvH+NRjUA0lU6fno4mKQlNKAQaDaG2Njzr1tG9cSNdH3yA64MPCLS0DFl0Zyy9s72B3725i3e290+P7KEoClPSrECkAvJgCtIigf0vXtlBp2dfhdxjC+3oNAo7G7t5fmP8CrLxpNmMGLQaXL4Q1z2yln9+UIEn2k4mnlNnZpCfaiGkqnS4A9x+1nQA3t/TimO/6r0HotMTiKV491T/beryDfaUI17v1lK9/75jpafgU5rNiH2AGRqtRuF7Z8/gpxfMAuCFzfW4A5HvjQIEoxfeFJQBK4wfaptqOgirkGzR9wnWB6MoCjefUszJ0yJreAdKyZySZuXYosi65ao2N8WTbFiNuj6z4wBvb2/guQ11dLn9R/zsLgCKQtDhINjYCF4vhELQ1kr3po10PP0U3e+sIuRwjPUoxWHSUy15qJsEu0IIMbok4GVfwNsT7L7zzjukpKSM9bBGTJ+Tg3XhApLOOANjbi66xETQ61EbGlErKvBt3IjjT3+i7NTTaFp5z7hcP7aptgunN8AnFe2Utzhpcw0csP0o2uLn0U+qWFvZHnd/Pzm/hJxkM9Xtbu5+dUcsKE1PNPHVE6YA8NiaKqrbRrZeMMVq4KcXzmJWViKBkMp/P6vhqoc+49NowZ+BhFWVpu5IgJZvt3DC1DQA9ra6sBoPvmXJzKxEroymk3Z5gxTYLVx3fMFB73c8u/7EQv7vxELuvXweMw9z6u9A7rl0LnabgbJmJ996YlPcNeZfXpbPtAwbvmAYly/yncxINJFvj1zIaXf5YynaG6OtiQ6Ht3ZE0uEvWZg74krXVe2RY2hJQf/fTl8wxNq9keP0uGI7c3KTuWhBbr/Z3c21XTh9AbbUHd6K94dSuLsb25Jj0BROQUmfhKrREt5Win/LVjoff5yKCy+k+4MPxnqYQgghxIQ1IQPe119/nT179gx7e61WywsvvHBEB7s9DHl5GAvySbvqy9ivvx7FZIKuLsIuF7pJk8BoBJ+P9gcfZO+ll0VmF3p63owD83MTsZn05Kda8AfD/fqWAngCIUpykrh4QQ4A3392a9zZ1UkJRv551SJSLHrKW1zc88ZOgtH2Q2eWZHLKjHTCKry0pWHEM4RZSWZuPqWIX3yuhKwkE21OPz97aUfc/TR2evEGwui1CjkpZnKSzaRY9ATDKs2jNBP7/bNnMCXNikGrcM9lc7EYJvaqBZNey+1nzeCC+TljPRQAclMs/O1LC7EYtHxS3saPnt82YEaFoihcuig39m+9VsFuNVBgj8xqNnZ5WZAX+R3aOEAl6kOhosVJRYsLnabv2IajsdPD3lYXGgWOmZLa7/E15e24/CFSrQZyU814AvuOV08gRJvLjycQYl5uIjajnrk5B3fxwhMI0uby4Qn0b6t0uBny8jCXzMJ+0cXoJ+dDSwuqMzoLrtUSam2j/tbbqPvRjwkHx368QgghxEQz4QLeTZs28bnPfY6TTz552EFvUVERxcXFR3ywqygKpuJiEk85BfPMmdhml6C2NBN2OkFRSL/zDtJvv43se1aiSUjAX15O+bnn0fH662M99JhTZ2VxyxnTOWdeNgadhlRr3/W5nkBknaPbF+TmFcWkJxipanNzzn0f8I/3K2LrD3srSLNy/xcXYtRp2FDdwV/eLYutm/v6SYVMSbPiCYR4/NPqWDA8XIqisLwojYeuWULRpMh+nt1QO+C2FdHeqJNTLeg0GhRFYW50hmtLbed+7zMYd3Z7MFajjuduPJZ3bjmJhZOP3O/ykawkO4l7r5iPVqPw4uZ6/vjOwL9Dn5ufE0tXnpRgRKNRKIim6jd2elkQrUC9o6FryHT50fD2jmYgErCmDTOduccn0QJbs3OSSDT1X1PfM3O8vMgeXXu/7/24/SFCYRW3P8Rps7K4eUUxK2ZmHujb6LfPsaQoCqapU0lcsQIlFCRUU4MazaxJ+eKVTH74IYzTphHu7KTrqaeo+vJVhDo7h9irEEIIIUZiwgW8wWCQoqIi0tLSBg16a2true+++wC44YYb+PDDD4/oYHcgGrMZ66mnoUlMBFWl8a6foLUlkHDGGUx++CF0GRmE2ttpuPU2qr5+A97du8d6yDF2q5GiSbZ+hZzc/hAGrYZASGVSgpHfXT6PJLOemnYPK9/Yxam/X83tT29hY7Wjz8zavLxkvnv6NDQKvLurhX+vqQLAqNPyvbNmYNRpqHF4eGVr4wGN16DT8OVl+QA8u7EOp6//TM3e1sisTmHavnWL83KTAHjgw700d+1bj9pzwj6QmnYXn+5to6Z94MriCSb9YS3eJCJqHW7WVrbT3OXlxGmT+MkFkUrSf19dwcMfV/ab6Z2UYOSUGekAsQJNU6IpzY1dXrKTzWQmmgiGVbbVHdogqNXpY311ZC3paTPj98iN55PySMC7PLpOt7dwWGVVNJg+b24WWo2CxbAvhd9i0Pa772Adin0eLGNhIcaZM9EkJwPQ8fQzBFtayHvoQdK+8Q3Q6fBu3Ej52efg+uyzsR2sEEIIMYFMuHzHGTNm0NTUxM6dOzn11FM5+eSTee+998jNzaW6uprp0yOFgp544gluu+02jEYjX/va17AcwVWL41HMZnLu+jHuc8+h/rbbCTY20vD97+MtLSXtphvJ/dtfabj9e/j27MG9ejV7V69GX1jIlCeeQJt4eFu8DFfPCaxRp8Gk07BsSiof3HYyL29t4L+fVbO1rouXtzTw8pYGpmXY+MKSyVy0MAe9VsP8vGRuOLmIv7xbzjMb6ki2GDh3ThbpCUZOm5XOK1sa+XRvO3argdk5SbHX7PQEqGobOLhsd/rZEy3So6oqdquBNpefX7+2k7NnZ3Js4b5DrKwpsl1uihm3L0ggFObzx+Tx7MY6qtrdfOmBz/j3V49hUoIJs16Dq9caShWVsBpGg0JDpxdfIERD5762Laq6r1XSSAxWwCw8RPXYwV5uPFedHU7RtsHGH47z/KYuH4FgmDaXn/REE5cszKGhw8P975Xzm9d2Ut7czQ/PnYVGgWD0b3vNsfm8t6uZZYV2gqEwudFq3y3dPjrcAUqyE2ns8vK398v5rLKdpVPsuP1BnIMEcjUOD3vjfF8BbEY9VmPfn/7XtjWiqjA13YbNqMPhjp9d0OH291nf6wuGWFcVCZbn5yXj9gcJ97pYs7mmgxanD6tRy3FFaeh1GgzafYUCTdFjGSJ/m3BYRTPIpVhVZfDHibR1Meu1ve7Z3+H/fppLSsj9+c8I//AHVF52Of49e6i/9TbsX7ue1K98BfOiRTT+8IcEamupvuZaEi44n/Sbb8aQE03XH8fHlBBCCDGejTjgfeGFF6ipqeHzn/88aWlph2JMB8Vms2EymQgEAqxatYoVK1Zw8sknM3XqVObNm8cf//hHAG699VZMJhOXXXbZGI/40Oh9wm5dsoSi116l6Te/oeOJJ3E8+ijBxkbMxyzB/vWvE6ivx7NhA8533yVQUUHbf/5N6uWXozGbD3n7okEDqgEe6ulp2jtoSTBrOKMkg8UFqexq7OKN0ibe2t7E7iYnP315O69ua+AvVy5k4eRkFuWnYNRp+f1bu3nwo70sL7Rz0vRJfP3EIpJMeh7/rIa3dzRz1pxMiqIVZEvrO+OmeG6t7ezTL3FubhLv7mphfZWDJQUppPdqrVIb7bE6Ly+ZtAQjiqIwKcHEI9cdw5X/WMOeZidfeXgd/75+KYkmPalx+jBmJZlo6PSS1atPqlajjOsgczSNtJjSaIv3OWckGmns8pGeYIwFW7eeMY0Ek46Vb+zi6fV17G11c9/n58fWVi8ttLPuB6dhMWhRFIXMRBNGnQZfMIzdauArx09he0MXDZ1ePi5v4+PyNuxWA6fPyuD0WekU7lflGODHu0sH7ets0GmZn7cvm8XlC7K+ajsAVy8voCjdRsIAack9tBpNnwrUH5W14guGyUg0sjS6flfX6/VX7YrM7p48LR2LUYfK4LFbh9ePwxUgxarvk+FR63DT0OUlK9EUt4LznqZuqtrd5KdamJoxvi7a9XxvtCYTU555mqprrsW7YQNtf/s7/vIKMu68k+xf/5qqL30JwmGcr7+Bcdo0ks44Y1/QK4QQQogRG3FKs1ar5Ze//CVZWVmcd955PPHEE3jGWbXfGTNmsG3bNux2O6+88gptbW188skn3HDDDX22u/nmmydcGnM8GrOZrJ/8hMxf/hI0GrrffJPmX/yS+ltuoeV3vwO9Dl1mZN2ca80a1GCIsMeDGg7vK2o1TgpbDaTdFWnJk2+38pMLSnjvtpP54bkzsRl1rK10cP6fP2RjNGXzhpMK+fySPFQVbn16M03RVOIvLJ3M4vwU/KEwv3xlB3UdI/9eF06ykmTW4wuG+xQbCoVV9kbX8Ban9w1SJqdaePi6Y0ix6NlW38VXH1mH2x+/eE1uioUlBakTo23LBJKTYmFRfgo5vf4ukXXiRTx09RISTDrWVzm46K8fs6W2I7aN1aiLBUMajRJrSVLjcJNvt/K//zuWv1y5gAvmZWMz6mhz+XlibQ1feWQ9X3l4Hf/9rBpf4MDXqr5R2ojbH2JyqoXF+SP/PfxgTysAxxenDXgx4O3tkYD3tFnDS5V2RI/l/Vt1NXR58flDNPRK/d9fVbsbty9IZfvIqq4fbopeT/6/H8N+4w2g1dL91luUnXJKJNiN0iYl4autI9DcPOActRBCCCGGZ8QB73nnnUdtbS2vvPIKdrudr33ta2RkZHDNNdfw9ttvEw6PrOjPoTBjxgxKS0vxeDxcd911XHLJJcyYMYNTTz11RNWbJ6KUSy4m75//IOnCCzFMmwaGyEyN8823Ir0igcDeSspvugnH628QqK/HW15OsD1+65/xINWqx6DTkJ1swm41kJlo4ivHT+HJ/1vGlDQrTV0+vvjAZ/z3s2og0tZoZlYCDneA7/xvM6GwikZRuPXMaWQmmmjq8vHtJzbF1iYOl0ZRmJ8XSYdeV+WIBa61Dje+YBijTjNgj8XidBsPXRsJitZVOvj6Yxv4rKL1ID8VMV6cNH0SL9x0HMXpNpq6fHz+n5/yXJz+zz1pzbWOyAWXyHcqhe+dNYMXbz6O28+czglT09BrFSpaXfzj/b387q0D+10LhdVYH+qLFuSMOEtAVVU+LIt8T3vabPW2t9VJWYsTnUaJ9ekdSkr0WE7Zr2BdVqIJo0FLVq+sif3lp1qwGHUUpI7vi0GKoqDRaEj/5jfJf+RhFKu194Ogi2SxuFa9Q90f/kj7M88SbBvZb5EQQgghIg6oaJVWq+WMM87gkUceoampiX/+85+0t7dz9tln84Mf/GC0xzhiM2bMYO3atVx44YVkZWXx2GOPsWrVKtLS0vj2t7891sMbc7bjjiP7N7+m6IXnmbl5E7l/+ytKdKZbMRhAp4PGRrpefZWWhx7CU1pK0OEY41EPzm41MjXdRqqlbxrwrKxEXrz5OM6anUkgpPKjF0r54ztlGPVa/vT5BVgNWj7b285T6yOVlRNMeu65bC4l2Ym4/SF+8coONlSP7L0Xp9tIMOlw+0M8tS6y37LoOt80mxE1znxNSXYSD1y9GItBy4dlrdz69JZBX2drbQfPbaxla6/ZQjEyh/MznJJm5dkbjmVWViL+YJjbnt4SK57WW0/AWzPALKVRp2VZoZ2fXVjCMzccyw0nFQLwzo4m2l39K5QP5ZOKNpq6fSSZ9Zw6M33Ez69qc1Pr8KDXKiyd0r9gVU/l5xmZCSSa46dJ95ZqMVCc3r9gXW6KhSX5qeQkxw9mp2YkcOrMjHGXzjwYy6JFTH13FfbbbkO3/Fg0FgsaY2TJA4EA6t69dH/4Ib69ewm7x/fMtRifAvX1eEpL8VdUjPVQhBBiTBx0lWaPx0NnZyednZ2EQiEMhoHXHR5OCxcu5PHHHycrK4uHHnoIjUaD3W5n1apVPProo2M9vHEn4aSTmP7B+6T//GcYFgfaZCwAAQAASURBVC1EmZQGSUlgNBBoacW1fj1hj2dcpzQPpKdqbofbz/1XLuC7p00F4J8fRNoXTUmzxirp/m9dDQ3RFOZUq4FfXTSb06IBwIaqjhG9rkZRWJCXDMA/3q+g3eWPnezXdXi46T8b4/bqXTA5hX9ctQizXkubc/C+wGUtTty+EGUtzhGNb38f7Gnmb++V88Ge5oPaz5GorMWJyxcc8Wf4cVkLD3xQwcdlLYNu1+bysafZyda6Du5bVcaFf/mY7Q1dsccbB0jPnRYN1j4ub4tbIAsiF2c6ot8jjaIQGGFLLQBPNAMhEArT5Rl5D9ietP8paVbMAxTSauz0Rsd6eOsj9u7tO9h944U2IYH0666l+F//Iu2OO7CcfRa6BQvAagOjEbWrk5DLdUT+DouxFaivp/zc86i85FLqb7sdxWxGd5Qs5RJCiB4HFPB6vV6eeuopPve5z5GVlcVdd93FggUL+Oyzz/jpT3862mMcseXLl/PGG2/Egt0edrsdu73/LMRRS1FiN0Wnw37ZZRQ+9BBTn3ySgj/cS8Lxx6MxGFCsVnzl5YS98dfOjUcNnV680WrGPT13Z2Ul4guGeXJtDQCfm5/NcUV2AiGVv7+/7+q3TquJtWdpc4585mxqho30BCPdviB/XlXGkoJUVl4yF5NewycVbXzxX5+ys7FrwOcuL0rj/dtP5uefK4ndt76qf0p58SQbFqOW4gEKF41EaV033b4ApXXdB7WfI1HxJBtWo27En+GOhm6c3gA7Ggb/zHY1Orn1qc1c8OeP+f1bu9nb6sKk13DBvCweuHox3z1tWr/nHFdkJ8Goo7HLy7rK+NkFuxq7Y9/jO8+ZQcYgqb7xrJiRwczMBNz+EH98Z/ewKlj3ZohWVw7GaaGVZotcAB2siNahMFAf3vHSm7ef3r/DGg32Sy8h7xe/IO9HPyTthq9jPXYZhmnT8e7cOe6XlojxJ+hwoHo8ZN+zkoJnnqbolZfRZ2eP9bCEEOKwGvFZyDPPPENGRgbXXXcdiYmJvPjii9TW1vKHP/yBxYsXH4oxHpAzzjijT7ArRsY0dSrp111H8ucuxJiegarTRWYXjiBZSSZMem2smrHy/+zdd3xb1dnA8d/VnrblvVecxM6eQAJZ7A1hl1UKpUALtIwOWlpKW0oLbSmlpVCgb6FQNoWwCiRAWAmZzrST2I73tmVbe973D9myHW/Hjtf5fj4GR7r36kg6V9ZzzznPI0lctzxUK/f5r8vxBYJIksQvzpuFSiGxtdTK1yVN3fYHsDq9fdbE7YtCksJTRF/fUcn+mjbOnJPI8zccT0qUnqoWF5c9sZn39tT0un+MSctFi1LD/37wvcIebZibGsXahanMTY0aUtuONDvFjFmrZnZK39NA//JRIVc/vZm/fFR4VI813gz3NcxLMmPSqclL6vs1c3r93P/2PnZXtiJJoUD2D5fMY9NPTuZPly1g1Yw4FL1knNaqlZw2O3Sx5Z3d1b0e2x8I8vAHBwjKsGZmXLie71ApFRJ3nj4TtTLU/z/aXzek/TXtgazX3/vocpw5NC3ZFxja+VNpdbK1rJlK6/Cm8PZWh3c81ubtjyo6mpgLLyT+ppvQpqagjIsn0Dq69ZiFyUuTnY1+9mwR7AqCMCUNeZ6Z2WzmiSee4IILLpiUtWuF7gx5eaiiolDodCj0obWFQaeToMs1omWL2tw+7nljDwvTovj2iuxB7ePyBXB6Axg0XWtuhqaRNjt8RBvVLM2MDm9r9/g5JS+BGOMBalvdfLivjnPmJTEtzsQFC5J5fUcVT35WwoL0KLQqJdEmDWqlhC8gh2qrmnsvTdSXVIues+cm8t6eWn7/fiH/+tZSZiSYefHG4/nR67vZXNLM91/KZ29VK0syo6lpcVHd6qam1UV1i5vKhs7RnIIaG69sq+Cq4zOG1IbBWDE9nhXT+w+YtpS14PT42DLE6d2TVW5SBAkR+h6JlToEgzJ3vbKLg3V2YowaXr7phHCZK/8gph6fNy+ZN3ZU8dmhBlpdPiKPWP/60tYKihscROhU3HZyzlE9l/RoA9cuy+SZLw7zxMZiZiaaSY4a3GixVtV/wNtRzqve1vfskN7O464ZmYeTjbx7Hd6+b5sINCkpRKxeja+2FnViIr4JduFREARBEMbakIdATz/9dL7xjW/0Gew6HI6jbpQwfqhiYjDMmYMuJycc3AZdrnDZopHyy3X7eHd3Db95t4AviwaXnbivKYrNDh8+f5DmLmVNnN4AeytbeCu/ipNzQ9li/7WpNHz/5UvSiDVpqGvz8Fp7oilFe11UgIZ+vrD35wenzkCnVrCzooX39oSyYEcZNDz2jYV8e0UWAE99fpib/r2dX769n398VsLbu2rYXmalpsXT7Vh//PBgn2t/R9txGVEYtGqOy4gCoLl9bWqTw9P/jpOU1eHDFwiVzml2eCiut9Pc5bX40/qD/G9fHRqlgieuXhQOdgdrRoKZGQkmfAGZD/bVdruv0urkuU2hZFe3npzTrQ70cF28KJWZCWYc3gBPflYy6KnN6gEC3o4R3hZnz37bsb65qM5Gg81Dk73z9RtMRuZJr8tUZ01qKsYlS9Ckpk64pSWCIAiCMNZGLJNISUkJ9957L6mpqTz00EMjdVhhHFLo9eER3qNl9/hZX1DHGzs6S7Tc+cou/vvd5UTo1Zi0fXdRg0YZHhnqIMuhEkUdI7wdX9wNGiW1rV5AJi/JjFopsbO8hV0VLcxLjUStUnD9SVk89L8DvLq9klUz40mM1JEQoaPC6mLrYSsuT8+1f00Obzhx0JHiTFq2lTazcnosH+6v55dv76O61UVSpA6zTsW3lmeSFqXnuc1lqJQKEsxaEiJ0GLVKYk0a9Ao/334kdKxIvQqr08cjHx3kvvNm9XisoCzT4vJhdfiwGNVEGzpHo2VZpqTBTnmLi/QoPTldMtgGgzJufwCXL4hereg2AibLcrhMze2n5XZ7vI5aqc0OX49suhPdQMFeMBjEYlSHX+uuwa/FoOEfn5Xwt0+KAbjhpEye3VTGnz46hEopoVRIIINJp0KlUKBSSmhVClbNiGN2cqicVZPDi1mn4ozZiRysK2LdrmrOnZeIJEkEgjK/fHs/voBMpF7FB3tr+XBf92nIDq+f/mbgm7RKNpf0LHFz6qx4ihrsbC+z8sq2Sk7uY5q0yxcIT2X2t09V9viD2Nz+8Otn0oZGpGPag3Gr04vPHww9f0BWSDQ7fLS5vNS2ukmJ0mPSqsLlvlMthj5HdmVZpqp9NkRypK73rM1Dq640oSh0U/gigCAIgiAMw5AC3oKCAu677z4OHDhARkYGv/71r5k1axY///nPeeSRR5g5cya33XbbaLVVGCcUBsOITWVusnv41dv7Abjq+HQ+P9RIebOT371fyG8vmtvnfpIkYdCoMGi6d2GFIlSiqGsQ1jHF2ef3U1hrY0lGFGfPTeKt/Gpe2lrBogwLs5IiyEuK4PODjWwqaeKlrRU8cfVi8pIj2FZmxe7xo+ol8Y5KqSCtjy/mVqcXhzfAooxoCmvtlDc7+ddXpVx3YiYBWYckSZw2O5HTZid226+mNTRy7nJ2zpZwegOgVPH81+V847h0ZiZ2Xzsq0X3UsWvAC1De4sLl8VPe4uoW8EoSuHxBAkEZly/Y7fVUSlKfdVktXS4qDLV261g72vYqJKlHH2tuD353VbTyp48OAnDRwhQ+PdhIQU3vycm6euHrcs6em8h3V+cQpVcTa9Zy+dI0/vFZCYcbHTTavcxJieSFzWXh8kOtLj/by1t6HCs5SkdmjLHH7R3cvt5HY+PNOlbPjGNDQT1Pf36YFdNjw1OSu9KoFOELUVGGUGDrCwTDt8myHF6bHGvSIkkQlKHV5SO2fcRXkkIXpqxOLylRBow6FTEmTfuAZt/9DkLneHWrG7c3QHVr71OeJ1qfHIqR+uwVBEEQhKli0FOaHQ4Ha9asYe/evSxYsICKigrOPPNMrrrqKv71r3/x5JNPkp+fz7Jly0azvcIkIssy97+9n2aHlxkJJu46fSYPrJ2DQoJ1u6pZXzC0BDp96Zji7AuCQaXgk8JG8hJDU0zX7aoOTxOWJImfnzcLtVLik8J69la1kh4d+nJ5NFOJlQqJS5akEmPUYHP7eWVrBZ4hlkbxBWRijBoCQZlfvbO/11FIi1GNWqnodV1pepQevVZFelTPUXm9WsGbOyq47629vLD58KDaE2vSMSPBTKxpaow2vbD5MHe8tLPX16ejBrTHF+Sm57fjC8gcn2WhxeWjoKYNk1bFd1dP47urp3HTymyuPj6d76zM5voTM7l2WUY4udl7e2q5/B+beG1HJb5AkEi9Opwp/K38aqqsLh7dcGjAtg5mjXBfTpwWS3KUDrvHzx8+HDhrc3ikNyjjD/Z8XJVSEa6N3WD34PL5aXZ4cPkCxBi1zE2JJC3GQKpFP6T1tcmROnQaJcmRU6P/CYIgCIIwfIMOeD/66CPi4+PZvXs3zz77LDt27CA3N5dNmzaxe/durrvuOpEVWRiS13dU8XFhPSqlxIMXzUOjUrAw3cK3Tgytbf3V2/tptB/9GtFooxq1SkFekok6mxedSsLpDZCbaMbjD/La9srwttPiTJzSHmS8v7dmRAJeCCXMueK4NIwaJXVtHl7eVtFrgNAXidBUV7VS4qviJv53xLpOgGiDlmlxJqINWh7bcIBv/nMzj204AEBOgpmTZ8Z3G93tYNCo2F9jw+3zs7W0ZbhPcVLbVtqC3etjWx+vj9Pr5zv/3k6DzUNmjIEZCWY+LqxHIcHPzsnjggUpXLAghYsWpXLhwhSuW57Jt1dkc/Oqafz6gjk8de1ichPNODwB/vZJMZc9sYkth5s5aXosAMX1Nu5/ez+uPkZnu6q3edlRbuVwo2PIGcaVColLFqWiUkhsKm4aMGuzVtUZpPr8vT9OxzreBpsHlzdAIEh43b1erSTGqBlyMqlUi4HjMqOHldBKEARBEISpZdARanl5OatXr0alCk1bkySJU089lfPOO4/4+OGVxBCmLqvDy/3r9gFw65qcblN0v7cmhxkJJpodXn79zv6jfqyOEbhl02K5ZHEK6bEm5qdGcvUJ6UBoOmnXkayz5oSmGL+3uyZc27TV5TuqkTMIJau6dGkaKoXEoTo7T302uNFUgDV5oURb0e1rIh94t5BgP4HMtjIrTq+fbWV913HtamlmFCatmqWZUYNu01SyJDMKk0bNkj5en1+9U8C+6jZijBquOj6d5zeXA/Dd1TksSrcMePzZyZE8/c0l/OSsXCJ0KooaHNzw7DZe2BxKTlXS6GRTSVM4K/JA3L4gNa1uCmpsbC1txuoYfC3phAgd1y3PBOAvHxfh9Pr73FbZpaySt4/zw9JlhFevUaJUMGFKAwmCIAiCMPENOuD1er1oNN2zgWq1WlGaSBgWu8ePo/2LdMcoage1UiK5feptXdvIZiQ9OS+RH5w6g5PzErlgQQpGjZLDjY5ugeGqGfFEGzVUt7r55+eH0auV+AIyHx9oGHT22r6kROnJbQ/uq1oGn+X6xGmhkb6ObLhVLa5+R52XZFgwaFQsyegZbBXV2fj4QD1FdbbwbVedkMWfr1jIVSdkDbpNU8lVJ2TxSD+vT037e5kSpcfrDyIDCgmmJww+O7NCkjh/fjL/vv44rliahgTsrQ6t/+1433WDGAk9srRvUAbPEC/WfOO4NNRKCZvbT4Ot71kWr2yrACBCp+o1iN1eZmVLaai8VoJZi16tItqonZDlgQRBEARBmJiGlLTqvffeo7a2cyplQUEBLper223nnHMO3/jGN0auhcKklBZt4OZV03j802J+/c5+FmVYwgly3sqv5tMDDaiUEvedN3vU2mDSqjhnXhKvbKvkte2VLM2KAUCvUfKny+Zzw7PbWLe7mjnJEeyraaOw1ka8WcuCtKhhP6bd46egNhRoXrwoZdD7HWwPTuenRvHpwQYAzLq+T9/bTpkZ/v3IIL28xcXB2ja+Lm7i/AVJzE6OGnQ7hN796oLZnP/XL9ld1UqaRc/yaTF8VdzEr9/Zz9+uXESUYfClgyL0an52Th6RejVPflYChN7D3EQzhbW2AfYOBbiSBBE6NZF6NVGG/rOd96bF5cMXkFFIkBTZezb2FqeXf3weat/tp0xHfURSN68/yE/f2EMgKHPevCROzIkdUhsEQRAEQRBGwqBHeDMyMkhOTqa2tjb8Y7FYetxmsw38hUwQIFSjdmaiGavTx/3r9iHLMtUtLh58vwCA29bkkJcUMaptuHxJGgD/21eLzd05YrpsWiw/OiMUNO6raWNOcqgdnxc1UmF1DvvxtpU2EwjKpFr0Qwqc97eP9M1OCbXDrFX1mjV6IO/srqao1obV4cOgUVLaOPznInTKiDHy58sXIEnw7t5aFmdEkWrR02j38uD7hUNaR9vhltXTsLRnQV6SGc2DF80NJ4nqT4xRzXGZ0cxOjiDVoh9ysAtQ1hTqF8lRejR9TKN++ovD2Nx+psebuHBBz4s3//flYYoa7EQbNdx/wZxJnTlZEARBEITxa9DfhC699FIuvfTS0WyLMMVoVAp+u3Yul/9jE58caOC/O6t4Z3cNDk+ABWlRXHfi6E+vXZgeRU68iaJ6O+/uruGK49LD931zeSb7qttYt6uag3U2smONlDQ6eG9PLRcsSCYxYmgZYj3+ANtLQ1OnT5oeO6QA4ECdHRQapseHpshGGnpmYh7IoTobt72YD8DjVy7AG5DJjBVLEkbKmtx4rliSxotbK3hy42F+dOZM/vDhAfIrWvjGU5sxaJTo1ErUSokInRqdWolOrcCsU7N2YQo58d2nPysVEm9+70T+9WUp58xLIifexB2nTef3/zvQbzu0amW3tbXDUdoUKomVGdN7/yhvdvLy1tB05jtPm9Hj8apbXDz2cREA95yVS6R+6P1VEARBEARhJAz90r8gjKCZiWZuW5PDI+sP8ct1+wjKocytv10756i/tA+GJElctiSV375XyGvbK7sFvJIk8esL57CpuJEGu5c2l4+ECC11bR7+u7OKc+cmDemx8stbcPuDRBs1Qx659vqDWCLVmDvqnx4RQLh9AVy+IHq1Ar2699P6qc87k2RtL2vh3nNnHfWaZKG731w4h6IGO1tLrTz1eQnfXZ3DYx8fotXl63fN9YaCOp7+5pIeWYejjRruPH1G+N9XHpc+YMA7EjpGeDP6qOf7lw2H8Adllk+L4YTsmB73//qd/bh8AZZmWli7cPBT9wVhPKlqcQ2Y8K2o3n6MWiMIgiAMlwh4hTF33YlZfHKggfyKFgDuOn0G6X180T4afQV3Fy5I5qH/HWBPVSuFNW3M6FK6R6NUcNacRF7dXkWjw8v0OCNqi4JKq4t1u2vIS4zoEXx28PqDtLUHOYGgzKaSJgAWpEXi9gZw9VGL191esqXj/x1mJZlpdYcSfUXo1XR9Ni5fkEBQxuULolN3f57eQIDGVi9v5leFb3t9ZxU/OHU6GpUCjar3BEKBoEx/g9DH4oLEWBjoGoDcvka2NwqFxI/OmMntL+VT0+rms4MNPPut47A6vbh9ATy+IM1OLzq1Eo8viMsX4IN9tRyqt/Oj13bz96sW4/EH+n3dB8PXT5Iqf1CmprXvhGkmrYrihtCX+FizlpYjAvW9FS1saC+5dNvJOT3Ka31xqJEP99ehVEjcf35oDX6Pc6+fJyiHduj7/n5ef0EYKVUtLk7948Y+P6e70quVWIyDX6cvCIIgHFsi4BXGVMf6wj9fvoArn9rMwgwLN5yUNez1fv3t11ed6DizjpNz4/lwfx1v5ldx7zmzut3/i/Nmc+bcJK55ZguHGhz86IwZ5Fe08uH+OvbXtLImN45VM+J6HLfB5iGtPQP1+oI6HJ4A0UYNt508HaVCIt7c+5TomPYvTg5H99Pz1FkJ4VHCKIOars/UoFHi9AYwaJQojngNlAoF/95cji8gszjDQnWLi5pWNx8V1HPe/OQ+X7OpGlR0fT1cPn/4de0YOZckqd/XZklmNE9/cwkX//0rtpVZ2VTSxJ2ndY7Sev3Bbo9x2dI0Lvn7V5Q2OXnog0LuO282WnXfa3Xf+/5JbC+18vO39qGUJJ64ZhFzUiLD99e0uvudQry9zNrnRRqAoCxT/WUoO/q8lEji2+voQihw/U/7VOZLF6exJDO6275uX4A/rT8IwLdOzOxzJkO/5/cAVxwkaYD9BWEEWB1eXL4Af758QY/lBkeyGDWkRPWe3E0QBEEYe0PPeiMIoyAz1shX95zC365cNCZfZi9dnArAmzur8Ph7XtE/ITuGe8/JA+ChDw5yfHY0Fy1MISjDYx8X8f7emj6P7Q8EeX17JQDnz0/ukc12sI7LjKbV2R7wHhGw6FRKYoyaXsu92D1+XtwSqgt744qs8HPtWIMp9M3pDRAIyji9A4/ydDUrKYLfrp0LhPrHc5tK8fQxUpQYoeNvVy1Cq1LwyYEGnmrPfNyfU2clcMbsBAKyzC/X7afB5iE4QtPTHR4/ze3TODOOWMO7obCefdVtGDRKbj8lp8e+T24soaLZRWKEju+fMn1E2iMIYykn3sSclMh+f0SwKwiCML6JEV5BAFZMjw2vz91QUM/ZXdbnyrKMwxPg5Nw4dpQn8fauGn79TgF3njads+Yk8v7eWp75ohSHJ8DFi0JB8OFGB1+XNFHe7GJ/TRsuX2iU8Kw5icNqX2KkllSLPjy91NhL5t0mh4dmh49oo5oYY+eo3CvbKrC5/WTHGVkzM55ZSRH89ZMivj7czOFGR7cp3L2ptDq54+VdnD47gRtXZA+r/RNV15HzoVq7MIX8ihae21TGfev28+iGIq5YmsZlS1JJjuoeSM5PjeK3a+dy16u7eOHrcnLiTZw5QF+56/QZ5Fe0UNXi4vy/folSkrAY1Zh1amJNGixGDRaDhhiThmXZMeHa1gOpba99HW/WdutnXn+Qv39aDMC3V2QRf0TSttJGR7hM0Q9OzRlWdmhBEARBEISRJr6RCAKgUiq4aFEqf/+0mBe+LuesOYlIksQv3trLq9sr8fo71ymqlRK+gMyfPjrEZUtSuXRxKq9ur+SlrRW8sq0CjUqB29d9XaNZp+Kmldm9BqqDsTg9GkmSsDpDI2/6XgKwZocPrz9Is8MXDnhLGx3884tSAL59UhYKhURSlJ5VM+L45EADr26r4GdHTOE+0uVPfkVtm5cd5VZOyI5hbpfps5OdXq3qMwnYYNx7Th7JUXqe+6qU6lY3j39azJOflXDHqdP5zspp3bY9b34yB+tsPPlZCQ++V0h2nLHfixFmnZpfXzCH+9/eT1WLi4As02j30mj3crjR0W3bJz4t5qTpsVyxNL2Po4UEgjLb2jOJH5mh+eWtFVS3uIk1abjhpJ4Z1B/64ABef5BF6VEsTLf0+zjD1fWiTqxpaFnSBUEQBEGYmkTAK0wpLl+gy5rM7kHjZUvSePrzEjaXNPHfnVVctCiV/TVt3YJdAF9A5rIlqbyyrZJXt1fywIVzMGpV/OurUoIyuH1BDBol0+JMHJ8VzbzUSDJjjT3W1g7E6fGHf1+WE4PN7eOTwnoAYgydCVI6npO+fd1ntDE03fnDfbXc/epubB4/qRY95y9IBkLrLMubne2/953cqIPV2dmORrtnSM9hqlMrFdy0MpsbTsxkQ2E9z35VxqaSJv7w4UFmJkb0WPt9x6kz2FPVylfFTTz0vwP849rF/fabuamRvHbLMvyBIFanj2aHl6J6Ox5/EKvDi9UZCn53lLfw2cFGPjvYyPR4E+fOSyI30dxt+UB5s5N/byqjrL1vLJsWG77vQK2Nf3wWGr29aWU2Bk3PPx0dia4uWJBCjGl0Evh0vagjAl5BEARBEAZDBLzClNJ1TeaRAW96tIEbV2Tz+KfF/Oqd/azJjeef31zKM18c5l9flWLvEoDesnoaHn+Qt/Kr+esnRTx8yTwyYgy4vAESI3WkROlpdnjDSauG4787O7Mqr5oRy0tbK2hz+0mK1DEzsXPkr+M56TUqUi0a6mwufvz6bl7ZFlo3vCg9ikevWIi2PRvzIx8dpLjBQZxJy60n91yHeaTV0y18WNhEukXH6l6ScwkDUykVnDE7kTNmJ3Lvm3t54etyfvjqLt783ondphorFBI/PH0mVz3zNfuq23hndw3nz08e1PHjzFrizFoi9OoeSatKGx28tLWCDQV1HKq388j6Q2TEGDhzdiKzkyN4Z3cN6wvqCMqhadzfP2U6584PTet3ev384q19+IMyK2fEcvbc3qda58SbKGlw4PYFiDFq+5xifzSijerwMQVBEARBEAZDBLzClNLbmsw9lS2UNzvJiDFw/vxk3ttTQ2mTkzd3VvGtE7P4wanTuf6kTJ76/DBflzTxy/Nnk2ox8MvzZ/PZwQaqW9y8uKWC65Znjlg7ZVnmnT2dibC8viDPbw4lnrp0cSopls4gqetz8vgC3PpCPtvKQtNSbzgxkztPnxEuPfT14Sb+76tSAB5YO4foQZTSePya43H7AmiUCpEddwT85MxcdlW2sreqle+/tJMXvn0CGlVnIrNYs5Zvr8jiLxuKePyTYlZNjyPScHQBXmaskZ+clcu3Tszkb58UseVwM2VNTp78rASVQsIfDCW8WpJh4dIlqazscmHjkY8OUd7sJM6s5adn5fXZB3ITzXy4r47CWhsQGo1tc4VGmQ0aVa8J1YYqxqglxqgV/VAQBEEQhEETWZqFKUWv7pnNuKjBjtPr53CjkxiThrkpoVIqb+zoHGGNMmj44RkzefXmZcxJiUSpkIjUq7l5VWgd5ru7a9hf3TZi7TxQZ6e00Rn+92s7Kml1+ciKNXL7KdNJtXSOHOvVSqINGlQKiVtf3Mm2MisGjZK/XrmQe8+dFc4K7QsE+cnre5BluGxJKmty4wfdHp1aiWKS1t09FrYebuK5TaVsPdyERqXg0csXEKFTsauylYc/KOyx/aWLU8mOM9Lq8vH3jcUj1o6ECB0XLEjhdxfN5dx5SRg0SvxBGYtBzfdWT+M7K7OJ1Ktx+wJ8eqCen/13L+/srkECfnnerH4D77zE0HlTUBM6D6KNagIyRGjVQ85yLQjC2Ciqt7O3KnRBrrg+tEyhZNte9n7yNXt3HGBvVStVLX3X8RYEQRiPxAivMGlUNDuobnWTHKkjLdo46P1y4kzhEd5oo5Y0iwGNUsH+mjb2V7eRl9Q5ffjIkaWF6RZOyY1nQ2E9f/2kiD9eNn9ERrLe29O9zNF/vq4AlNy6JgdlL4FnIChz96u7WF9Qj1al4JlvLuGE7Jhu22w80ECl1UW0UcM9Z+cddRuFwSuoteHw+CmotTE/zUJatIGHLpnHzc/v4NlNZSzJjOaM2Z1ThVVKBT88Yya3PL+DdfnVnDc/mdnJvde0HQ6zTs3585M5fVYCB+vszEgwoZAkdpZb2VZmZV91W7cg9dsrsliU0X8iqtz286S4wY4vECTGqMWgUQ07y7UgCMeOpf1C8A9ezg/fFue08g+lGt2DvwTArVTznVN+hD0ylvV3rRLlmARBmDBEwCtMSHIvNUerW934/EFq29ykWHqunQ0GZXobpJyTEkluYgQKhYQsyyzNimJ9oYGDdXZe217Bj86c2WuQCRAIBrlmWQa7K1upt3n45bp95CZGkBylR69RoFUpiDZqegTKTf0kf0qO1PG/vbXdbmt1+chIMHHu3ESCwe7PXZZlfvT6HtbtqkGlkHj4knnkJUXQ2l7CKBCUidCrebW9FvDahSnhkjGyLPf6Wnbc19fU0dB+fT6FMZ1y6gsMnIir/1rIg6ln2/fz6+31nJVoprDORm6COdQPFbBmZjzfPimLp784zP1v72NZdjRmnRqPP0BAVpCXFMEZsxP4YF8dD75XwJPXLEalVNDi9NHfU2ywefD3s4HbF0DWhd5/tUIiEAzy/OYydlW24umSoC0hQsuamfGcnBtPXlIE/kDoeXkDQdy91BSOMWowaVXYPX6K6u3kJkagUynRtU+n73hZgsgoBnqJB+g+Yq6BIIyslCg96+9ahbW9BnfYdUsJtLYglZeie/CX/Gp1KrdscWB1eEXAKwjChDFlA16fz0dhYSGxsbEkJSUNvIMw7iVH6qhtc5MY0Xv2VkmS6CsOUynB7Q/i9AY4PjuOn5yp4Ppnt7FuVzV3nT4DVR8B0sL0KDQqJX++YgHX/nMLxQ0Oihu6l4TRq5VkxRr53pppnN4+ildQ09ZnEH2ozk69zYNZ1/30vPXkHNTtwUNVi5O6Ng/xZg3PbirnvzurUEihdbkrj0gspVRIWB1eNh5sAOAbx6WHA76+gt2O/foKXLeUNFFQayMv0czSrJhetxkPmuwefv7WPuanRXLTEWWARktvr9mSrBiWtL9OXS8k/PCMmXx8oJ6SBgd/+6SYX5w3i6RIfbif3ntOHptLmilucPD2rhpuWpWNPxAMr8nujc8f7DdLckKEjlSLnro2N3e8vIuvDzeH70uK1HHG7ATOmZvMvNTIXp+Lyxfoo+9KzEgwsaO8hcJaG3lJvY9IS4O4niCJkFYQjrmUKH3PILa9DJ1rn4lSIM2iBxxH7ioIgjCuTck1vF9++SXZ2dksWLCA5ORkzjrrLEpKSsa6WcJRSrUYWJRu6XV0dzC6ZnBeMT2WeLMWq9PHJwfqB9x32bQY3v/+Cn5z4RyuPzGTNTPjyIwxoFRIuHwB9te0cccruzjQntCnP+t2VQNwal5C+La0aD0XdMnWW9fmwe0L8LdPinnmi8MA/OLcWZwxOxGb20dFs7PbMV/bUUkgKLM4w0JOvKnbfS5fgCaHF1cvo3Z9Kai1Yff4KBjE8xkrvkCQ21/K56P9dfzhg4O8srXimLfhUJ2N9YV1HKrr/XXSqBT88rxQHeTnNpdRWNt9HXiMScs9Z+cC8PinRZS0l/45Wp8eaODcv3zB14ebMWiUfOvETF67+QQ+++FqfnxWLvPTooY1Sp/bnj28Yx3vcLh8fpocHlw+/8AbC4IgCIIgDGDKBbx1dXVceOGF/OUvf8Hn87FhwwbKyspYtGgRn3/++ZCO5fF4aGtr6/YjTFwGjZIDNa18tL+Wgpo2LlqUAsAbO6oH3DcYlHF6/RyfFc3PzsnjmeuW8sEdK9nzy9P58I6VrJoRh9cf5M5X8vH0E1j6AkHea5/OfPqszqRSN67IDo8yf1XcwGcH6vj3plJebi89dPfpM7hwYQqljQ4u+NtXrH38K7a3Z2qWZZlX27e7fElat8c7VGfj/b01FNXahpRYKC/RjEmrJq9LeaThGq3z6Pf/K2TL4ebwNPZfvr2P0//4KT97PX9Ejj8YZVYnLo+fMquz1/u3ljZR2uRkaaaFQFDmvnX7e4y6nzcviRXTY/EFZH7+5j4CwcFMue6dLxDk8U+KuOHZbTQ7fcxKMrPu1hO595w8FqZbjjoxWUe5rMKjuBDi8gXDF54EQRAEQRCO1pQLeF9//XXmzJnD2rVrUSgUnHzyyWzZsoWlS5dy9tlnk5+fP+hjPfjgg0RGRoZ/0tLSBt5JGLf0aiWNDi/+QJCiBjuXLE4F4PNDDZQ39R6wAGw53Myqhz/lwr99xWmPfMbJf9jIlvZpomqlgqxYI7+7eC4xRg0H6+w89MGBfo/V6vIRb9ZSae3MhPnMFyVc9PhXXPX019z/dgEvbKnkg/2hkec7T53Blcel4/YFuOWFHTQ7vPiDMg+8W4Asy2wvs3K40YFRo+Sced2n75c1OwkGg1S1uYaUWGhpVgzXLssckenMo3EebSio49mvygB47BsLOW1WAr6ATEWLiz1Vx+7CVIbFgF6rIqOPWQeFtXbsHh8rpseiVyvZcrg5PMLfQZIk7jtvFgaNkp0VLTz9+eFhBb0dic06LpJcuyyD125eRlbs4BO8DWRGwuBHeF0+P81Ob7eR3Ganh9oWJ61On0h0JQiCIAjCiJhyAS9AaWlpt1EUk8nEunXrmD17Npdffjk+n29Qx7nnnntobW0N/1RUHPspk8LIyokzYdSqyIkzMS3OxPJpMQRl+N5/dmD3dJ9iKcsyz20q4+qnv6aqxYVJq0KjUlDW7OTKpzbz5MbicIKpWJOW3108F4DnNpWFA+KufP5gOEhbuzAFX6Czj5Y1udhd1cqmkiYO1dtpak8scv2JmXxvTWhtqscfpMnemXCkwe4hIMv888tSAM5fkIJR231dcEa0gSiDlrnJkSOSXXo4RuM8+vxQIxAa0T59diIPXTKPSL0afxBSonpf4z0apieYOTU3gekJvY+E5yaaMGnVnJAdza0nh97H375XSJW1e9mP5Cg9Pz5zJgCvbq/krlfy+0181puPC+vJr2hFr1by96sXcd95s9CO8HvesU7cpB04PUTHSK7L15koy+rwoVAoQAK9esqmmBAEQRAEYQRNuYD37LPPprKykr/97W/dbtfr9bz00kuUl5fz2muvDepYWq2WiIiIbj/C+PSXjwq59p+befj9/f1uNzc1irULU5mbGgXAHy6dR5wpNDJ796u7wiNrLm+AH762m9++V4g/KHPe/CQ2//Rktt17KhctTCEow58+OsR3/r2d5vbgdPXMeK5bngnAoxsO0WjrHrC8sbOKqhYXcWYtt6yexjfbtwX4v+uW8I9rFvPo5Qt4cO1c7j0njwfXzuWes/LCay0j9WruOn1GeJ+rjk9nf3UbXxU3oVRI3LKqZ9Km6QlmTs3rOyA7FkbjPGpzhy5aZceFRi9NWhUJEVoArl6efdTHHylLM2O45oQMlmbG8J0V2SxKj8Lu8fPj13f3GMW9dEkav794Ljq1gh3lLdzw7DZ2tE9bH4g/GORfX5UCcPUJ6Zw+K6H/HYahuMHO0+3ryX98Zu6A2+vVCpQKCb2688+QxahGrVQQbey73q8gCIIgCMJQTLmANzMzkzvvvJO77rqL9evX97jvrLPOYvv27WPUOmG0bClrweXxsamXkdX+JEXq+euVC9GoFHxS2MCf1x+kotnJFf/YzNu7alAqJO49J48/X74Ag0aFSaviiqWprF2YglalYOPBBi7425fh9bR3nzGD2ckR2Nx+Hv7gQDioqWtz83J7UqWfnpVLhK77F/7jsmI4OTee8+Ync/nSNK4/MYvLl6b1yJZ72ZJUvnFcGkszLVx1fDpPbgwlY7t4UQrpMcNL5jURtblCo/ER+s7XseP3jmB4vFEpFTxy+QKMWiU7ylt46rOeifTOm5/M41ctIivWSLPDy52v5PPsV6UDTnFev7+eSquLSL06vDZ9JMmyzP3r9uMPyKyeEccZswcOqPVqFdEGTbeR3GiDlmlxJmKMx24UXhAEQRCEyW3KBbwADzzwAKeffjrnnXdej9HctrY2pk07NuVLhGPnuIwo9Fo1y7KiqbQ62VLaTGUfiYSOND8tigcunAPAPz47zAV/+5LCWhsxRg3/d90Srj8pq1tG24P1DnKTTNx68jSyY43Utrq56umveXlrBVqVkj9fvgC9WsHe6rZwkPvkxhI8/iDzUiM5v0s25qGSJIkfn5nLU9cu4XCjgy+Lm1BKEreumT7sY05EtvagNqJLaaeOiwg21/Cy/w613zQ5PO3Tzwc/9Tg92sC954SyNv/t02J2VbT0us2T1yzm7LlJBGV45ovDXPGPTTzzeQnVLa4e2/sDQZ5tH929YmkaBs3ITxX+785qtpVZ0auV/OzcvF4zPHe8fr21URAEQRAEYbRMyYBXpVLx+uuvc9lll3HppZdy2WWX8fzzz/Od73yH+vp6vvnNb451E4URdvtpuTx3/Qn88KxZVLe6cXsDVLe6B73/+QuSuWllaCqswxNgVpKZN767nOOyontsOzPBhFGjZnl2LK9/dznnzU9qz8C7j80lTWTGGrl5deiiyotbyvm/L0v5+nBzaNrx6mnDKgfTm47R3fPmJ02p0V2ANncvI7ztwe9wR3iH2m+aHT58/iDNjqE93nnzkjh7biKBoMyPXt+Nw9MzQNeplfzkrFx+enYeZp2KujYPz24q44p/bObeN/fy/t4anN7Qfh/sq6O61U2UQc3ahSM/umt1ePnDh6FEbFcdn96zjme7jtevrq3vCwAvbinl7lfz+deXxSPeTkEQBEEQpqZJGfBu3LiRsrKyfrfRaDQ8++yzvPPOO7hcLv7whz+g1WrZuHEjBsPUCg6mmuRIHTqNkuTIoU2b/MGp07l0cSprFyZz77mzSIzUIctyj5/ESB15SSbizFqMGiV/uGQeFyxIJhCUuf3FnVRZnaycHsvJufEEZXhteyhr7toFyaRG6fEHg+GfDv5gkDaXD7un9x+XL0BQlsM/+RUt4dHd60/K7LWdsizjDwTxB2X8QZnSJgcNdk/43/5+psn2dbyOn7Hk84deKwhl3vb5g/j8wXDCrhZn/wGoLPf+kxypw6AduN90bB9tVKNWhdajdj1OMNj/a1fSaOfk3DjizBoqml3t2ba77k/4fT59dgKv3bKMn5+bx9JMCxKwv6aN371/gLWPf8UD7xbw7KZSAC5bkoZCIeH2B/AH5D5/fL4gAVnu8ycYDHZrz0MfHKDF6SPGqObaZenIwb5fP51GGV5L3ZvtZS04PD6+LmkG5H5+BEEQBEEQBmfSpcHcs2cPZ599NvHx8Xz66adkZGT0u/0555zDOeecc4xaJ4yUvkZBlf2NjrbflRZtJC26eymW/kZVdV3WGN5+Sg7VrW6SI3VoVUpkufOakdMXwOUNUNrkABnqZDdp0aGLJ7+7aC5F9Xb2Vbdx24s7+c+3j+eRy+Zz4eNfcbjRQXKUjl+cN6vbdFNFlzYpJAlZkvpsp1mnxtRl+u7/tWdmXrsohTkpUX2/Ju0vy77qVi55YhNJkXreu/0kdANk7z2yHZVWZ/h1Se2jBM+xolIqsLWP8FoMmnD94qj20d4js20fqa+ukGoxDOq5dbw2MUZdr2tRFQpFn48BUNLgwOMLcvnSNB7/pJg386tJiNBxxdI0MmONZMcZe7z+OXFmrlueRU2ri1e3VfBmfjVlTU4+3F8HQKxJw00rs9FrlASCco+1313p1EqUyr6vhap1apQKiTaXj5e2VvBWfjWSBA+snUNatBFZ7vkaHtk/+urHc1Mi+LqkmeOze86cEISRUtXiwurw9rtNUb39GLVGEARBGG2TLuB1uVykp6cjyzKrV6/uM+htaGjgf//7H9dcc80YtFKYqHoLlju4vAECQZkovRq3P0hiRGewo1MreeLqxZz/1y/YU9XGL97ax0OXzOPxqxbyxw8PcuOK7BFbW5lf3sLGgw0oFRKrZsRSZXX2G6gFgzK/fHs/voBMebOTv28s5o5TZ/S5fW+6Tvcd64DX6w/i8gWA7lOazR1Jq1zHPmmVy+fH6Q1g0CjRqUIXE/q6SJAdZ6S4wcGq6bE02738Z0sFT35WwpOflTA/NZLz5idzztwk4iN6BtNJkXq+vSKb76zMJr+ihf/urOLrkmbuOG0G+hGoayvLMjvLrby8rYJ3dtfgbi8pdPLMOGJMfY/cDrZ/fOukaVx3YmjpQJPDQ7PDR7RRTYyx72MLwlBUtbg49Y8bw58R/dGrlViMmmPQKkEQBGE0TbqANzc3l8bGRvbs2cPq1avDQW9SUhLV1dVkZmYC8NRTT/Gzn/2MQCDAddddN6ZtFiYHvUaJyxsgO96MQa3sMfEy1RLK+HzNM1v4b341c1OjuHZZBk9cvXjIj+UPBGmweUg6Yr1kMCjzcPt6ypXTY7EYNNS29R9kvLGzip3lLUhSaOrps1+Vce2yjCEFGcmRunDwNtZsXdbodq0H27mGd3hJq46Gs/1iiNMbCAe8fQWBc5IjmZsSRZPDy40rskiPNvB5USObS5rZVdnKrspWfvteAcumxXD+/GROn5XYLbCH0CjzwnQLC9MtI9J+m9vHW/nVvLilnMJaW/j2nHgT585L5Pis6G4XeI40nP7R7PDhbV8DLQJeYaRYHV5cvgB/vnwBOfGmfre1GDV9rkkXBEEQJo5JF/BGRESgVquRJIlPP/00HPTm5uaSlZXF448/DsA999xDMBjkzDPPHOMWC5OFQa3EMMBU4OXTYrnnrFweeK+Q375XwHFZFnITh1Z3VpZlrvu/reyubOWvVy5k5Yy48H3/91Upm4qb0KgU3LQyG6VS6jcQcfsCPPRBKEC++/QZvLu7lv01bTy5sYSfnp036DYNdrrvsdAR0OrUCrpOnO2Ypr2vqoUlv/qARemR/OO6E45JmwwaZXiEt8NAQaBerQQ0XLMsk5tWTaPB5uG9PTW8tauaneUtfFnUxJdFTfz8rX38+bIFnDEncVTa/s7uau55Yw9Ob2hETKtSsHJ6LDetmsai9KhBJVkbTv+INqrDI7yCMNJy4k3MSYkc62YIgiAIx8CkC3ghNMq7d+9eTjnlFD744ANmzpxJdXU1+/btC28jSRL33nvvGLZSmKpuOCmLTwob+KqkiS+KmoYc8ALsqmwF4PNDjd0C3o8L6wG467QZHJcdM+BxPP5geE1rYoSOGFNo+l7zAOvbxrMYowatSoHbF+SvnxRx+ynTsXv8PP5pKPOvPygTCATYUd56zNqkV6vC9WY7knoNFAQaNMpuAXKcWcs3l2dy9QkZ7KlqZd2uat7Kr6LZ4WN7uXXEAl6PL8DB6jZ2VbSwu6qVd3ZV4w/K5MSZOH9BEityYkmLMYz6qGuMUStGdgVBEARBOGqTNuDdt28fK1as4Pbbb+ess86ioKCA0047bVCJrARhqJocHqwOH5Z+1hu6fAGa7B5AIivOyFclTbQ4hx5YSpLEjSuyeHVbJVefkN7tvo5ANTfRPKhjRerVfG/1NB5Zf4ifv7UPpzeAWilx65qcIbdrvIjQq/nVBXP48eu7+esnRcxKjuDNnVUU1duJN2tZlhXJl0XNLEqfmKM7nxTWsqO8leMzo9hQUEezw9dreazBKmmws6O8hd2VreyubOFAnQ1foPuE/NUz43jqmiUoFBKy3DPpldMbwOULoFcr20emBUEQBEEQxodJG/Du2LGDSy+9FI1Gw4svvkhjYyOrV6/m5ptv5v333x/rJgqTjLV9vaG1n/WGTm8Auyc0LdSoDQUFA5XI6cvtp0zn9lOm97g9FFDTbwKhI924Ipt1u6opbnAA8K3lmWTG9p6Ya6JYuzCFvVWt/HtzGbe9uJNAUEatlPjrlQtZkGZBo5q4Fdn2VNmwe/x8UdREebMLpULi+GEGvP/88jC/fa+wx+0ROhWLMizMT41iYXoUJ06LRdFPZmeXL7RGuSPoFQRBEARBGC8mZcC7YMECvv/973PJJZfw4osvolKpSExM5NNPPx3rpgmTlMWoJr/cis3jB1kmJ6HnCKtBo8SkVQISce0BqXUYI7x9CQZlrO0BdPQQMotqVAoeuHAOVz79NXEmLbesnjZibRpLPzkrl8LaNraWWgH4xbmzWZA2MkmcxtLcFDM7yluxe0Lv9bzUSMy6oa9z3V3ZwkP/C63fnpFgIi8pgpj2JD2xJg3nzk/B5fPj8gbwBALoFd3/XOypaqG4wcG0OCPT4swi2BUEQRAEYVyalAHvypUreeONNzjvvPNQqTqfYmLi6CR1EaY2py8ASPh8QVQKBWVWZ68Br16tDK/ZjDd3BLx9j/AOdR1tq8tHIBiainpkwDtQiZclmdGsu/VEIvXqYQVP45FaqeDRKxby8zf3Mj8tisuXpg37WB8X1LK5pJn0aD1nzUsm1jR22ajX5CZycl4SP3hpJwAnTosd8jHsHj93vLILf1Bmzcw4vrtmGlE6NfU2D1a7h4y40Ai/yxsgKIf+r1d3/3NR3ODA6fFT3OBgbkpUeL2xfGR6ckEQBEEQhDE0KQNegLVr1451E4RJoqLZ0Wu9VICgLIfr78ZG6Gh1+8iwGMKJiegjg21kexkZq8NLIBjsdp9CocDjC3DTv7eHb5MBTyCASu59Kq4sy+EA2ahRolZKlNTZqGxzkxqhIyhJvZZ4kUM7AzCzPUiXJ1HEEmvS8vcjyj7Jstzvc5RluUfm4V2VbTTbvTi9fk6Y5jvqgLe/lzgoy/Q34bqs0c7BehsbDzYAsCw7ulsf8vkDyMq+R1qDQZlfvbOfsiYncWYtD18yj0i9GqvLh1mvRkow4/UH2FVhJUKnItKgQadWhl+zQCAIKJgeb6SsyUVGjD58oeWLQ/UcqrOTFWvg5LykPtswiMTOwzaY/juYzNKCIAiCIEwOkzbgFYSR0le9VAh9ce4oOTM7JbLHlM6+vljHmjunNKuU3cMbhSRx//uF3eqdqhQSWo26z+PJshxegxtn1qKQJMpaXLg8fsqCMvNTI8MjvF2PoVYOby3reA4YBlqfO1BAJElSj+c3J9lMq8tLZowBywBlco72pVHQ8/G7qmh1sbuqlTa3H71aycJ0C4ou2xs0KpT9vK/v7q7hjZ1VKCS4bnlGeL23SavC6Q1NS97f7MDtDRCQZabFHzlbQYEkSeQlRZGXFNXtnt2Vbbh8flor/Zwyq++Atz/juW8JgiAIgjDxiIBXEAYwmHqpQ127aDGEphy3OH09RhTf2V3N81+Xd9u+stlJRmL/o4pNju4Jq9Kj9JS3uEiP0hNr0o3pNNyJ7vQ5yZw+J3msmwFASoSe2tbQe700c2gJuKqsLn7+1l4AzpqTyGl5CeH7OvqxLMskRuiobXP3W8MZoLrFSV2bh4QILRajhswYPaVNDiL1GtYX1pFhMTC9l+n9giAIgiAIx8rETVUqCMdIqsXAcZnR/dZMHSqLITRK6A/K4Tq4AIcbHfzk9T0AfGdlVvj2De31dfvTMaW5Y/1uToKZk2fG97qeWJi4MmONWB2htd/Lcwa/fjcQlLn71V3Y3H4WpkXxyOUL+gxGUy0GlmQM3Ofr2jx4fAHq2jy4vEGOnxbHN47PIjnaEJpdYHUO/okJgiAIgiCMAhHwCsIx5vT6cXg7M9rWt4VG63ZVtPCd57bj8AaYlxLZrRbuYALeJnso4I0xaiiqs/HxgXqK6mwD7CVMNL5AkC2lzQCcOC1m0Pv9ef1BtpVZMWqV/PmKBcOezt7B5fOjU0kgQUKEFr1GgUKScPtCmcr9wSAZI3iRSBAEQRAEYTjElGZBOMac7UmucuJN7Klq5aEPDhBn1vLS1gpkOZTB+alrF+MNeML7VDQPPFLmDYQSF2lUCsrb1++Wt7gGPcJ7qM5GWbOTjGgxDfVYGc5rHgzK+Nrf68EGrU99XsITG0sAuP/82aRHDy4QdXoD4XJDHVmYO7i8QeIi9CRESuFZBXo1lDR4SY40oI5RMD3eNKjHGQ2iPwuCIAiCACLgFYRjzqBR4vDIXH9iBj98bQ/rCzpHb+elRPLrC2cTH6Fj9+GW8O0q5cCJfEza0Olsd/u7rd8drLJmJ06Pn7Jm55QNELqWbzoWa56H85pr1UpOyI7h80ONbCisI2eAoPL5zWXhert3nT6DCxak9Lt9cb2dcquLdIsei0lLICjj8gV6BLx6jQKXN4he0z3ojjKosTpDr+FYEv1ZmMp81dX4rVa8JSWjcvyievuA21ja63oLgiCMNRHwCsIxZtCo0KoURBk1nD03kXW7asiINrBqRizzUiKxuf00Oby0ujtr9KoUAwe8Zl3odLZ5/OQkmIe8djcj2hAeEZuqmh2+cPmmYxHwDvc1PzUvns8PNbK+oJ6bVk7rdRt/IMjfPy3mLx8XAXDL6mncvKr3bbsqt7pwev2UW10kWwzhEd6O0V6tSoFBo0KvVtFeXatb8qqkSD2xpp61no810Z+FqcpXXU3xOeciu1wASHo9KotlRI4doVejVyv5wcv5A26rVytZf9cqEfQKgjDmRMArCEfJ5Qvg9IZGwIaSrTk1Ss/aRSlcvCiF47NjaLB5qLN50CklAkGZSF3nCJlKMfDUVbM2tL3d7WdvVQvFDQ6mxRmZkxI1qPZMTzBP+ZGwaKM6PMJ7LAzlNd9T2UJRg50Z8SZOyU3gvnX7ya9oocHmIc7cPcAsb3Zy96u72FneAsBlS1K549Tpg3qcdIs+PMJr0HROZW5yeAkEZdy+IO1JxoHQWt5D9fZwceGkyPHx5Vb0Z2Gq8lutyC4XyQ8/hCY7G5XFgjp5ZLLMx5m1rL9rFdb2JIl9Kaq384OX87E6vCLgFQRhzImAVxCOUseaXGeXRFSDkR1nIjuuczpqcpSe5Cg9bl8Aly9IUpcvCYqhjPC6fRQ3OGhxethV6Sc7zoRROzoBXEWzI1yyKS3aOCqPcSzFGLXEGLUjVgvW5vbxzy9KWTkjloXpRzfCUtRgx+HxU9rs5MykSOalRLK7qpUNhfVcsTQNCNUYfjO/il+/W4DDE8CkVZETZ2RmvHHQz2lavKnX2QFWu4dyq4uMaH14zS6E1vLGGDQ0Ob0kRIz9yK4gCCGa7Gz0s2eP+HFTovQiiBUEYUIRAa8gDKB94KpPBo0yPMLbbVM5/J9ej9lX/NFRD9Uhd15BVysknN5An0GLLMsYtaFg2+b2kxNvZG9VgLQoAy5fEINm6O3oj9z+olS3unF7A1S3uruVsBmpgHEicvsCQCi51K3/2cnGgw08/mkRT1y9mGXtWZU1/SSb6us9yYkzUdRgJyvGQDAos3JGLLurWlm/v47LFqfS6vLxi3X7eH9vLQBLMixE6EJ9YmdFK9e2HycYlAnQ9/sTlGV6a16bx4dercDm8Yfffwit5U22GJiWYEKnUiLLA58z/XUPeaCdmdr9SxAEQRCEoREBryAMYKDv1qG1jD1PJbmPYBdAqZD6DV5Djyt1216rUvTZGFmWiWhfUGnz+JmdHEVWrAmXL4hO3XdwJUlHFzwkR+rCI7wTxbEKlv76SREbDzYA4PEHueWF7fzjmiUclxXdb5+SpN77xrw0C/PSLASDof6xYkYcf/2kmK9KmvjsUCM/f2svNa1ulAqJ76zM4u7Tc3n844NsKmlmYZopnNE5IPUfUEr0/vjxZh21bW5iTVqUXWYcGDUqugz4ho8yVkQwLAijpyMJ1khOkxYEQRhtIuAVhGGotDrDgV6qxYjL5++yjrf302q4a31hcOVnzLrONbwQSo5l0AxuxGy4Ui2GbiO7QsiGgjoe/7QYgF9dMJsNBfVsPNjAzc9v56lrl7B8CPVz+zI7yUyqRU+l1cW3n9sGQJpFz/0XzGZeahRKhcRtp87ktl727UgyFW/WYHMHyIozolV19skP9lSRX9nGgtQIzpgbyuqcYjEQbdTg8gVx+QI02T1dzoGefWAw54QgCBODymJB0uup/uGPgFAirGnvviOCXkEQJoTBFXEUBKGbrlN5ofs63r4MZpu+DKYskbm9LJE3EMTj63yMV7aW8f0Xd/D8psNDflxh6Eoa7Pzo9d0AXH1COpctSeMvVyzgxJwYnN4ANz63jZ3l1mEff0dZMy9uLaewxsYZsxPDt1+2JJXXb1nGvNSoHiWEjlTX5sHjC/DYx8Wc89gXnPLHjfzn6zI8/lC/ya9sw+72k1/Z1m0/ly8Y7sNHngNHOpr+3uH9PVX89t0C3t9TNexjCIJw9NTJyUx79x0yX3+N5IcfQna58FuH/zkmCIJwLImAVxCGITlSh06jDE/lNWiUKBVSt0BjW2kTz20qZevhpj636U9xvS38u9cfHHD7rtNMPz/UwAtfl7GttImd5a3YvT62loovJ6MtGJT5/kv5ODwBlmRY+PGZuUCodu7frlzECdnROL0BvvnPrdS1dQaKWw937yv9OVBvx+nxcaDezvUnZnLBgmSeuHoRv7toHrEmHdEGDQZN3yOqJfU2yhttFNa28t+dVSgkqGl18/O39nHqnzby4pYyIrRKTDoVC1Ijuu2rVyvCffjIc+BIQ+3vvdlZ3obN42NnedvAGwuCMKrUycnoZ89Gk5091k0RBEEYEhHwCsIwpFoMHJcZHZ7KqVeriDFqu03dLKi14fD4Kai1tW+jJMaoGfR05ooWV/j3/IpWnv687xFajy/ALS/sAMBiUFPV4sLh8XOgzs7C9EhMGjVLM0emDqPQt4AsU2/zALAk09JtKrpOrWTVjDggtM7a6uxMSnZkX+nPzHgTBq2amfEmkiL1PHLZAk6flTjgfh0qWlzoNGokSYE/KKPosua1zeXH5vITadTx47PywtOZO+g1KiyGUB8+8hw4Um/nxFAtTI/ArFWzMD1i4I0FQRAEQRB6IRZWCcIoyUs0U1BrIy9xeLVA044o+/DnDYcw69Vc3l6CpoPPH+TuV3fz+aFG9GolT16zGAmZA3V2ZiaYWJwRzeXHZQ73aQhDoFYq+Pm5edz96m6e+vwwJ+fGMy81CoBPD9Tz8AcHAPjRGTPJTewM4obSVxZlRLMoI3rYbUyL0lPR4mJFTizfXT2Nxz8tJjFCx6VLUpmVZMbu8ZMZMz5KTJ01N4Wzjgi6BWEy8lVXh6cIDzchVMcxOhJLCYIgCCEi4BWEUbIkM4alWcNPThTVJfXtTauyeGpTDb95dz9mnYqz5yYB4A8E+fHru/n0YANalYKnv7mYpZnR4ceH0U1aJfR07rxkPi6s5709tfzwtd3897vLKWtycucruwjKcMniVG5e1X1K4NKso+srQ5EdbyY7PhRYf+vETE7IimZ6ool4c+gCS6gskch0LAjHiq+6muJzzkV2hWb1SHo9qX/5C8poy6CD396OobKIWT2CIAggAl5BGHeK6myUt7hwORzh224/eTp2v4oXt1bw0zf2YNSqOCknlnvf3MuH++tQKSSevHoxy6fFAvDloQb219iYlWRmeU7sWD2VKeu+82azvcxKWZOTe9/cy/YyK05vgGXZMdidbub/8gOmxxl4/daVwzr+V8UNFNTYyEsys3xa3LDb2eL0EWvWUlDTRkGNjbQoPZlxpmEfTxCEofNbrcguF8kPP4QyykLl7bdTceONwOCzIXc9hiY7W5QNEgRB6EKs4RWEcaa8xYXL48fu8YVvkySJe87K5dx5SfiDMne+nM/tL+7knd01KBUSf7h0PqtmdgY++2ts2D0+9tcMvCZUGHmRejW/u2geAO/tqaWuzUNOnIlHr1hAZYsbfxAONTiHffyCmvY1v0f5/kYZ1KiVCuxuP25voNu6cUEQji1NdjamFScdVTZkTXY2+tmzRbArCILQhRjhFaaM/qb2yjJIfczi7O++0WhLevsaS8mn6ra9JMGvL5iD3e3n04MNbDzYgEKC3100l5Nz47sdc3ZS55rQ0L7HbopqX09tqDOrZVnu83U6ls9nqDrafEJ2NNcuy+C5TWXEGDX8/Jw89te0khqlpcrqIifOMKznJyMzK8lMYa2N3EQzMvIR94PUz2vdtT9EG7VEG7Ugy1S0uELrxmW53/cqdNfRvf79Hn8UzjdBmEjUycmTJmAtqrcPuI3FqCHliJwVgiAII0kEvIIwAEkaXoA13KAsJ8FMToKZt7d3TmmWJAmjTg1IPHH1Ym789zY2lzTx4Np5XLw4NRwQd5iZHEFcpB69WoHV5cegUfZbqmaoxnPAOdZ0XbJw33tOHvNSI1maGc3BOhtOj59Llmby92sThn18hSSxPCeO5TmdI/pNDg/NDh/RRnUogG3n8gVwegMYNMpwdnClQurx/k1PjGB6exKtwaz5Ppr3f6DDS5IkAl5BmOAs7RUJfvBy/oDb6tVK1t+1SgS9giCMGhHwCkIfDtXZKGt2khFtYEbisS+Lkh7d+x9/rVrJs986DrvHj1mn7nWbqhY3dpePgCwzMzGiPegZm9Pd5fPj9Abw+/xj8vhjSa1UcPGiVCBUOqqs2Ul6dO9lfGD4fa7Z4cPrD7YHvaGA1+ULUGl1oWkvjTTYclijraM/hIJw8SdIECajlCg96+9ahdXh7Xe7ono7P3g5H6vDKwJeQRBGjfi2IUxZWw83haf9dmQ07qqs2YnT46es2TkmAW9OfN8laiRJ6jPYBdCrFbg8EmaNCqVCwqAZu2DH6Q0QCMq4vIEh7be9tJk18yZP/dXpCWamJ5j7HeHs6HOlQ+xz0UZ1eIS3g9MbQKOU8AaCY/r+H6mjPzi9ARHwCiOqqsU1qABLODZSovQiiBUEYVyYst82HA4H7777LiqVigsvvBCFQuTvmmoKakOJnQpqbb0GvBnRhvBo20STajEQbdSGp7KO5RRkg0YZCm6GGHQdrLexZpTaNF4Nt8/FGLXEtI/sdsTTHUFu1+nM40FHfxhPQbgw8VW1uDj1jxtx+Qa+sKZXK7F0KfsmCIIgTG5TMuDdsWMHa9euxe/3U1dXx/nnn88bb7wx1s0SjrG8xM7ETr3pGJGbCNz+7ms1O37GA71ahV6toi3gGdJ+M/oZ4Z6sOvrcYC9Q9LZGt8N46gNddfQHQRhJVocXly/Any9fQE58/6W1RJIkQRCEqWXKfeuorKzknHPO4ZFHHuGKK65gw4YNnHrqqbhcLvT6of0B9Hg8eDydX+Lb2tpGurnCKFqaFcPSrNDI7lAzCI833aeJjr8gpz99nUeLM6PHqkkTxkR+3wVhNOTEm5iTEjnWzRAEQRDGkSk3j/ePf/wjp556KldccQUAJ5xwAunp6axbt44nnniCurq6QR/rwQcfJDIyMvyTlpY2Ws0WhG5cPj9NDg+u9kRQBo1yzNfqDpc4j4bvyPfd5QvQ3D7SJQiCIAiCIEzBgLekpISWlhYgVH7jJz/5Ca2trTz77LPcd999zJkzh4KCgkEd65577qG1tTX8U1FRMYotF4ROXUf2AHQqJTHtZSAmGnEeDZ9e3f19P7JfCIIgCIIgTHVTbkrzDTfcwNq1aznxxBNxu900NDSwd+9eUlNTaW5uZtmyZfz4xz9m3bp1Ax5Lq9Wi1WoH3E4Q+rK7soWiBjs5cSbmpUYNer/JlPhHnEcjZ7T6xZ4u/XTuEPqpIAiCIAjCWJtyAe/555/Ptm3b2LdvHx9++CHHHXccqamhOpnR0dF861vf4vnnnx/jVgpTRVGDHafHT1GDfUgBb8/EP3K/65BHOktzb48l6quOPb1aiU6t5Gje7d7e26IGOw6Pn301bSRbDANmfj66/tZ/Xz764wuCIAiCMJVMyW+lCxcuZOHChTzzzDNkZWV1u2/nzp2sWTPViqFMDf19ST7W35872pITZ+JQfWjkrOO28vJyGhsbcblc4e137doVTqoWGxtLenr6kUc8Ju3uT9fptAZNz4+Wob7GkiRNyMBmoDYf7VMa8PhHd/hejz+tvZ/GmTRHnSRroOc/0RPICYIgCIIwvkzJgLfDypUreeihh8jMzGTlypU89dRT7Ny5k82bN49104QpYn6ahflplvC/y8vLmTlzJm63u9t2J510Uvh3nU7HgQMHegl6x9ZkmmYtdDcv1cK8VEu3UXxBEARBEISJYNIGvHa7HYPBgELRd16un/3sZxw8eJDrr78egNNOO42NGzcSHS3KoQhjo7GxsUeweyS3201jY+OAAe+eqhaKGxxMizMyL9XS77YjQdRXnfz0ahU6lfKYzIgQ64YFQRAEQRgJk+7b6cGDB7nxxhv57LPPSEtL44033mDJkiW9bqvRaHjxxRd5+OGHCQaDIzZi5nA4UCrFCIgwdF2nMQ+0ncPhCP9b7mUe6L7yBpweP26Xk2kWzYi1MfR4/d/fW0DUtb2DIc6jsTHQeyvL8oAB79FMRe/oy3vL63F6/Ox1Ocm2qEfs+BPdUM8jQRAEQZjqJlXAe/jwYU466STuuOMOHnzwQe666y5++tOf8uGHH/bYtqWlhQ0bNnDxxReHk1aNlOTk5BE9niAcqesU58lKnEeCIAiCIAjC0ZpUdXjvuOMO7rvvPu655x6WL1/OD3/4Q8xmM6WlpTQ3N3fb9rHHHuPSSy/lP//5zxi1VhAEQRAEQRAEQRhNk2aE1+FwsHfvXv773/+Gb3vllVf49NNPycrKQqVS8bvf/Y677roLCK3fdTgcozJSVl1dTURExIgfV5j88vPzB9Unv/jiCxYsWBD+d9cpzcX1NiqsLtIseqKMGlocPqKMamJNuhFr53CmNLe1tQ1p1FacR2NjOFOat5c2caDOzswEE4szY0ZkSnN/pvKU5qGeR+NZVYsLq8M7IscqqrePyHGEsTGY989i1JASpT8GrREEYbKZNAGv0Wjk2WefDX8Ruvfee9mxYwfvvPMOCxcu5De/+Q133303K1euZOnSpSgUCn73u9+NWluMRuOoHFuY3DpKDw1mu659rGuQ0OhxIKu0NHoUGIwaVDoVHhQj2ieHE/AGAoEhPYY4j8bGcALe0rYG/AoNpW1BVhqNIuAdRUM9j8arqhYXp/5xIy7fyD0fvVqJxTiy+QomAl91Nd6SkrFuxrBYjBr0aiU/eDl/wG31aiXr71olgl5BEIZs0gS8ACeeeGL499TUVL744gvi4+MB+M1vfsMLL7zApk2bWLp06Vg1URBGXbpFT7nVRbpFj8WoxurwYTGqB95REIYpL9FMQa2NvETzWDdFmCCsDi8uX4A/X76AnHjTiBxzKo4A+qqrKT7nXGSXC0mvR2UZ/Yz8IyklSs/6u1YNONJfVG/nBy/nY3V4p9x7LAjC0ZtUAW9XN998c7d/BwIBXC4Xubm5Y9QiQRhYbGwsOp2u39JEOp2O2NjYPu/PSTCTk9AZeEQbNEiS1OfI2ciPlsm9jhJ+Wlg7wo8jjBdLs2JYmhUDDDxCK8u9zwDoEAyGRpA/Kaxld6WNealm1uQmdttmKo/wTjY58SbmpESOdTMmLL/ViuxykfzwQxgWL0Y9Aae7p0TpRRArCMKomrQB75F+9atfkZ2dzWmnnTbWTRGEPqWnp3PgwAEaGxtxuVzh9bxffPFFeLpzbGxsjxJaxzoA6O/h+op39lbZRqcxwogazZJDg318SZLIr2jD7vGRX9HGyXlJo/qYgjDRabKzJ2SwKwiCcCxM+oC3sLCQBx98kPz8fN5//30xMiCMe+np6aSnp3ert7lgwYJhr2fdXdlCUYOdnDgT81KjRqiVQzcnRUx3ncr2dOmHcwfRD+enRrCrso35qSJxmSAIgiAIwzepyhIdqaqqivvuu4/ly5ezdevWSZPZUhCGoqjBjtPjp6hhbLOYrpqZMKaPL4ytogY7Do+f4kH2w1NmJXHn6TM5ZZYY3RUEQRAEYfgm9QhvSkoKL7/88lg3QxDGVE6cKTyyJghjpaMfThP9UBCAUMIpv9U6YTMsC4IgTBSTOuAVBAHmpUaN6VRmQQCYmxrF3NSoAcseCcJU0DW7MtBvhuWOgFhlsYh1uoIgCMMgAl5BEARBEIRjqGt2ZU12dq/BrMpiQdLrqf7hj4BQUDzt3XdE0CsIgjBEIuAVBEEQBEEYA5rsbPSzZ/d6nzo5mWnvvhOe9lz9wx/ht1rHTcDrLSkRo86CIEwIIuAVBGHEFdXZKLe6SLfou9UEFoQOh+pslDU7yYg2MF30EUHolTo5edwFlF1HnsWosyAIE8GkztIsCMLYKLe6cHr9lFtdY90UYZwqa3bi9Pgpa3aOdVMEQRiCjpHn5IcfQna58FutY90kQRCEfomAVxAmAVmW+/051tItegwaFekW/TF/bGF0DdTXBtvfMqINGLQqMqINo9xiQRBGmjo5GU129lg3QxAEYVDElGZBmOQkSUKSpGP6eNMTI5ieGNHjdmHyG6i/ddw1IzGCGUf0kY79BUEQelNUP3Adb4tRQ0qUuNgqCEInEfAKgiAIgiBMIFOthq/FqEGvVvKDl/MH3FavVrL+rlUi6BUEIUwEvIIgCIIgjJiqFhdWh7ffbQYzUif0big1fCeLlCg96+9aNah+9YOX87E6vCLgFQQhTAS8giAIgiCMiKoWF6f+cSMuX2DAbfVqJRaj5hi0avLwlpTghQFr+E5GKVF6EcQKgjAsIuAVBEEQhClsMCOyMLi1kVaHF5cvwJ8vX0BOvOmojyeEdC0FBKFRXcPixVMi0BUEQThaIuAVBEEQhClqqCOyT1yzGL3kC9+2r7oVg8Ef/nfHVOWceBNzUiJHvsFTVEcpoI4SQONpVLdjHfF4apMgCEJXIuAVBEEQhClqsCOyTQ4vN/97O9/85xaCXnf49kv+vgmFRtdtWzFVeXSok5PHVUDZ26hz6l/+gjLaIoJfQRDGFRHwCoIgCMIktb+6FZOt79rIQxmR7Uga5HQ6OP6R0G2v3bIMg8HYbTsxVblvkym7ctdR50Czlcrbb6fixhuBUPA77d13RNArCMK4IALeESTLoS8VbW1tY9wSYTJwOBzh39va2ggE+p5y2NH3OmwvbeZgvY0Z8WYWZ0aPi9qmHefFkW09kjiPxreB+hqIWrqjaajn0SV/+RiF1tDvtjq1AlXATVtb/++bWQFms4RD0bldmknCaDxyPx9tbT6E7nw1NRy+9LJu2ZWdKhW+ifxZZzKFftLSiHvpRfwtLXhLS6m99+c0V1SgN/W/jnuk2W1tBD1OdpfUYLf1/bo67DZg4PNIEITJQZLF2T5iKisrSUtLG+tmCMK4VlFRQWpqap/3i/NIEAYmziNBOHoDnUeCIEwOIuAdQcFgkOrqasxmc7cRjra2NtLS0qioqCAiImIMWzg8E7n9E7ntMLnabzabsdlsJCcno1Ao+tynr/NovJjI74lo+9gYybbLsjxhzqOJ9J6Jto6O8drWwZ5HgiBMDmJK8whSKBT9XimMiIgYVx/4QzWR2z+R2w6Tp/2RkQNnbR3oPBovJvJ7Ito+Nkaq7RPtPJpI75lo6+gYj20dzHkkCMLkIC5rCYIgCIIgCIIgCJOSCHgFQRAEQRAEQRCESUkEvMeAVqvlvvvuQ6vVjnVThmUit38itx1E+8ejifycRNvHxkRu+9GYSM9btHV0TKS2CoIweYmkVYIgCIIgCIIgCMKkJEZ4BUEQBEEQBEEQhElJBLyCIAiCIAiCIAjCpCTKEo2g8VD3UBDGq4lUP1QQxitxHgnC0RPnkSAcvYlUz1oEvCOourqatLS0sW6GIIxrFRUV/dYHFeeRIAxMnEeCcPTEeSQIR2+g82g8EAHvCDKbzUDojR9vBdaFicfhcJCcnAyE/ugajcY+tz0y99z20mYO1tuYEW9mUYal3yvTx+qqdVtbG2lpaeHzpC/iPBrfevS1smYO1duZHm9icUY0siwP2KfESMnwDfU8WvfFLlbOyehzO1mGwb4dHe/bUD6bBGE8En+PhMliLD+PB3sejQci4B1BHV8GIiIixAejcNSUSmX494iIiCEFvGvmRbCmy33jIeAd7OOJ82h869HX5nb2tY77RcA7+gb7Gq+ck9HveTScgHcon02CMJ6Jv0fCRDcePo8nwt/08T3hWhAEQRAEQRAEQRCGSQS8giAIgiAIgiAIwqQkAl5BEARBEARBEARhUhIBryAIgiAIgiAIgjApiYBXEARBEARBEARBmJRElmZBmOT2VbVyqMFOTpyJualRY90cYYraU9lCUXs/nJdmGevmCIOwu7IlXHJqnvjsEARBECYoMcIrCJNccaMdh8dPUYN9rJsiTGFFDXacnoDohxPIoXo7To+fQ/XiPRMEQRAmLjHCK0wZR9YPPdJ4ryPWX/MDQbnPWprT40wcqA+NrB2LtoTqsPZ++9AeQ+5zn/H+Xo2m4ACvoyyDYoCXp7/Xb6C3KdhPXwvt33sd3pw4E0UNdqbHmSb8uTiZyOH/9DQ93hQe4Q3KoGjfsOv7d+R5Opg6zKHthl7/VxAEQRCGQwS8gjAJSJLU55fHvJQoZqeKKaTCyOivr3Xe33ODeWkW5qVZhnzhQzg6fb0fHRR9RbvAvNSo8FRm8a4JgiAIE5WY0iwIgiAIgiAIgiBMSiLgFaasrYebeG5TKVsPN411U4bM5fPT5PDg8vnHuimjZntZ81g3YcJxef1YnV5cvsBYN0WY4ly+AE2Ozr44kT9vBUEQhIltyga8b731Ft/73vf49a9/TUtLy1g3RxgDBbU2HB4/BbW2sW7KkDm9AQJBGae3/8DG5fXT7PDi9E68wFgkyhk6ly/YrV8cGXQIwrFy5GdUx+ft7so2mhxe3H7RJwVBEIRjY8oFvMFgkKuuuopbb72Vqqoq/vCHP7B69Wq8Xu9YN004xvISzRi1KvISzWPdlCEzaJQoFRIGjbLf7Y4MgCaS6fEjl2RrqtCrFd36xWAvjAjCSDvyM6rj8zYjVi/6pCAIgnBMTbmkVY8++iiHDx+msLAQo9FIfn4+S5Ys4fnnn+f6668f0rE8Hg8ejyf877a2tpFurjCKlmbFsDQrZqybMSx6tQq9euDTV69W4PIFBwyMx1Jf59HijOixatKEpdeo0Kk7szQbNEqc3sC4fv+FkTHe/h7p1Ur06s5+1/F56/IFRJ8UBEEQjqkpN8L7l7/8hWeffRaj0QjAggULWLlyJTt37hzysR588EEiIyPDP2lpaSPdXEEYFLcvNHXZ1T51udnhobjejssXINqowaAZv9e2xHk0fB1T1qusTorr7TQ7PN3u16uVxBg13QIPYXIar+fRkdPqnRN4mYUgCEJffNXV+Kqrx7oZQh+mVMBrs9lYtmwZ06dP73Z7dnY2DodjyMe75557aG1tDf9UVFSMVFMFoV+vbKvgBy/vpLbVDXROXXb5ggB8uKeG574q5cM9NcesTR2JtNz+AIfqbKwvqONQ3cDroyfzeeT1B3ljRyVlTUP/fOnPV0UNPPN5CZ8dqCcQlKlt8+ALBLE6fOFtRmP9rsvnp3mEkmIdqrOxvnBwfUQY2NGcR4fqbHw0yPO1Py5fgGanr1v/6Dqt3uULUNLgwOby0ezw8fnBev7+aRGfH6w/qscVBEEYS77qaorPOZfic84VQe84NekDXp/Px9NPPw2A2WzmhRde6LGNwWAgGAyG/11dXc3hw4cHPLZWqyUiIqLbjyCMJn8gyC/X7ePHr+/mrfxqvvWvLbS5feG1m3p16JQus7rw+AMcqLdRXG+n0e4e9bZ1/WJb1uzE6fFT1uwccL/JfB7dt24vd76yi3Mf+4LNJSOXnbagxobd7aOk0YlSIZEYoUWtVGAxqsPbjMb63b6O2eTwcKjeTtMRI8z9KbM6cXn8lFkH7iPCwI7mPCptP19LB3G+Njk8FPWRUK63/tF1La/TGyBCpyIgy0Qb1eytbsPm9rG3WiwHEgRh4vJbrcguF7LLhd9qHevmCL2Y1AGvz+fj8ssv58YbbwxPWZYkqcd2kiSFA97q6mrWrFnDBx98cEzbKowtWZb7/AkG+75PluU+j7nlcCPPbSply+HGXo4Z7PfxOn46BIMy9W0ubnh2K89uKgXArFNRWGvjpn9vw+sLYjGo0amVyLLMsqxoMmIM5CVG4AsGae4y8jdaun6xzYw2YNKpyIw2IMuEf8aD/t7Lgd7TwWhxemlxenn68xJe3BIaZbO5/Vz7zBbe2FFJi9NLUJa7/Ww93MTzm0MlW4IDPL4sy+QlmTHp1MxPi8RiUJMcpSc7zojFoAFC+/eV2Cwo062P9fwJth+j50/HMfVqZbf31erwhUeYu97e1w9AhsWAXqsiw2Lo3r5+29b9vBCOngxkRhswatvPV3p/95FlZKDZ4cMbCHbbv+Ona5/rOJd0KgXRBjU6lQKDRkmsWceclEjMWjULUiNJiNSxIDUSX0Bme1kzr2ytYHtZM76AHP4ZzDl7tOetIAjCSPCWlIhR3nFo/C7sO0odwa7X6yUzM5NHH32Uf/3rX71uq1AokGU5HOzeeOON3Hzzzce2wcKo6+1ix2gqrLVj9/gorLWzNPPokmNVt7i49dX9HKq3o1MrePCiuaRZDFz7zy1sLmnmF+v28egVC8LPcVVuAqtyE2hyeLA6fN1G/kZCby+lQaPCoFEhyzI5CWZyEswD7tP/Y0jH/D0bKbsqWvjd+4UAfGdlNgfrbHx6oIG7X93FT8/O4/qTsrptf6AuVLLlQJ2NxZn9J+tSSBIn5sRxYk5cn9tIkhR+P3rcdxSBgV6tRKdS9HhfLEZ1uJ8N9JaF3leYkRjBjMSeo5AT9C2fsCRgeoKZ6Qn9Z6uX27eNNqpxOjqvlUvtP9B3/6DL/R3ryb3+IEuyYlmSFRu+v6jegdPnp6jewbxUy/CflCAIwhip/uGPkPR6pr37Durk5LFujtBuUo7wdg12X3/9dW6//XZeeukl6ut7XyckSRI1NTXhYPfuu+8+xi0WJqPcRBMmrZrcxL7L6+ypbOG/OyvZU9nS77GueGoTh+rtxJo0/Otbx3H6rETykiJ45PIFKBUSb++u4Q8fHuyxX4xRS068iViT7mifjjBIDTYPd7+6C39Q5tS8eG5Zlc0fLp3HRYtSCMrwm3cL+MuGQ91GpGYmhEq2zBwg6BgrA/XTjn4WY9Qe24YJx1zHe92XvVWtg/pM601OvBGDWkVOvPEoWigIgjA2Uv/6GMkPPySmNo9Dky7gPTLY1Wq13HDDDWg0Gp544ole91EqlWzYsEEEu8KIWpoZwzUnZPQ7ulvUYMfh8VPU0PuauA5Ndh/T4028eOMJzE2JDN9+Uk4s958/G4DHPy3mha/LKKqz8XFhPe/vqeLN/Cr2VLWMyPM5Wp8W1o51E0ad1x/k7ld30WD3kh1n5P7zZyNJEiqFgp+fk8d3VoRGdv+8/hC/eGsfgfbpuYszo7ny+IwBR3fHSlGDHacn0Gc/7ehzRSOYgOq5r0r4/os7eO6rkhE7pjA86/fV8MhHB1i/r4ZD7e91h71VLbyxs5Ld7QFucWP/faUvLU4vL26poKbVzdyUqBFsvSAIwrGhSkpCk5091s0QejHpAl6FQsHy5cvDwS5AREQE119/PX//+9/xer099rn66qt57LHHRLArHHM5cSaMWhU5cT1HTN7f25lhecWMWJ6/4TiSo/Q9tlu7MIXvrZ4GwC/e2scnBxtwev3sLG/D6fFT3DCyGYKHa2/V5M/G++iGg+yqbMWkVfHIZfMxajunFEuSxHfX5PCTs3KRJHjh63IefK9gDFs7eDlxJgxaZa/9FKDc6sLp9VNudY3YY24va8Hh9bG9rGXEjikMT35lKza3n/zKVsqanbh8nSWFihsdOLtctJsW239f6Y0vEOSaZ7bw+o4qHt1wiCc2ioscgiAIwsiZdGt4lUplr4Hr7bffzmOPPcbLL7/MNddc0+2++fPnM3/+/GPVRGGKcvkCOL0BDJrQOjeAualRzE2N6rZdeXk5jY2NPPXm1vBtN+ZJHNq/B0mCmJhY0tLTu+1zy+pp7K1uY+PBBqqtLqbFmViYHoEnANPixsf0wDkp43O6bldNDg/NDh/RRvWwpoHXtJeJijZqiDX1Pr13TnIERo0Ku8dP5VEEiF37U8e6yK639VV7ubjBRoXVRZpFT5RBQ4vTR5RB3e905K79tLfkQOkWPeVWF+mWzgsyLp+/S/uG/qdmcUYU28taWJwRNeR9hZG1IDWS3VWtJCrs2CsbqK5vCd/nryuh3iWTZjFQXh5kTlpaj8+0IxXV2ahuc5EUoSfSoKbB5qW8S4bo3VUt1La6cPsC6NQKYs069Gplt/NTTJ8XBEEQBmvSBbx9yc7O5oILLuDRRx/tEfAKwrHQtWRHR8B7pPLycvLycnG7u5cRWrN6Vfh3rVZH/p593YJeSZJIjw5luzXr1ZycGz8Kz+DorM5NHOsmDKjZ4cPrD2W1Hk7Ae89ZeXx+qJHyZie/emc/v7tobrcEPlsPN/P9l/NxegPMT43kwYvmDrutXftTR8Db9ba+At4KqwuX10+F1QVI+AJBWpy+owogektS1r19Q/9Tc+3ybK5dPuwmCSPo1NlJTDd5yctb1OOz6bqLzw7/rtPpKCgoICMjo9/jlVmd+ANBqlpdIElIwJXHp/PMF6FygMdlWnB4A9jcPsyyGkN7H+96foqAVxAEQRisSTeluT933HEH27dv58svvxzrpggTwKE6G+sL6iiq7zkVt9LqZEtpM5VDqCHaV5mYrhobG3t8oTySx+Omqamxx+0aZSiw8nUpGSIMTbRRjUalIHqYWa3jzFoevmQeKoXEB/vq+E97WSKATw7U873/7MTpDaBWSGgkGYtR023/4jobnxyo52DtwHVJe+tPg+ljaRY9bq8fm8uL2+dHrVQQZRjZLN6DbYswcQzms8ntdtPY2POzqUNRnY0NhXUQDKLXKHlm4yFuenYrD767j2+dmMEF85P4xnFpzIg3UdHkwOcPYNQown3oaM9PQRAEYWqaMiO8ACtWrGDx4sU8+uijnHjiiaP2OA6HA6VSfMmbSHqbpllY2YjL68ftdJJs6n5tqLimGbc3gMvpILqPgYbejqkDgl4fDo/ca+kOl2twU1xdLhcOR/e1uUGfm6DXjcPh6HHfsSzv01c9zCPbNJDROo/6q9epA5KNEpIUGHJ7O0yPVvO9k1J5ZP0h/vjubrIilVS3unjgnUICsozFoCYYCFJc09zjMQ5UN+L2BnDYHaSYe3/uXdsf7k9dUhPoANnnx+Hz9Lp/okFBrUGB2xegpc3OgjQLJQ1WthxykRqlJyvWhELRd3+R5d77bndSl/b5cXi7t6W/3QdTZ7e/9k12I30eDbZ+7dF8NnUorAp9puo1KhalxFJW30oAKKt3YVQE+eXZ04FQIqxAIEDQp8Ss0oc+17yd5yf4cTj8PY4/UcuYCcfecD/fBUGYmCZ0wNva2orNZiM1NXXQ+9xxxx3U1NQMvOFRSBZ1t4RRdtrJq/u87zePwG+OXVNGzWQ5j875Y/d/V3T5PeKBY9oUYQo61ufRypUrh7Vf9M9HuCGCIAiC0G5CTmn2+/3cdtttxMTEkJaWxvTp03nttdcGte9VV10lsjELgiAIgiAIgiBMARNyhPdnP/sZ+/fv5/Dhw7hcLn77299y6aWXcscdd/DHP/6x27SmZ555hunTpw/7qvNwVFdXExERccweTxicgabuDWc6XMcxmxxeAkEZpUIiptu6zN6PGZoW2vP2/Px8VqxYMeDjrv9kI/PnL+h2vBe+LuNPHx1i7cJkLl6UzMF6BzPijRQ3Oml1ejFq1XzjuPQexxrpaYB9vcxtbW2kpAx+tGmszqPeptQ+8WkRDq8Po0bNzatzBpjy2/l7SYOd37y7nxXT47hueWb4tT6al/ylLeU4vT4MGjWXLU3rcb8vEETR/gAvbiplV2Ur81Mj+cayzPb2yaiVvV/r3F7WRGmTi+xYA/PSLD3utzq8BIMySqUCt9dPrc1NojmU3Kvj9+Qo/aCnPPemr3Oj295TeOpqW1vbkEZtR+o8ys/P56STThpwuy+++IIFCxYMuF2wlw+K/LJmXtlagT8YJNKo4aKFKeSXWdlcaiUnzsCKGQnEm7UkRxl67Pv058U02T0YdSpuO3lGn48r01/v624q97PJbqjnkSAIE9uEC3hlWeaJJ55g/fr1pKWFvuz961//YtmyZXz3u99FpVLx0EMPARAIBPjnP//Jrl27OHDgACkpKcekjUajEaNxfJSCETqNVsDr8gVQeBUokIkxacMZc9uP2ud+vT2cXt+zzm5v9Hp9tz4myzJGoxGFRgcqHeU2Gb9CQ7lNZm5GHPtr7OQmmnrtl8cq4A0EAkM6zlidR70FvIunJbC32sacZHPodR5kwDvXaOTl7yX02OZoXvJ5mfEcrLMzI6H397Mj4HV5A7y5v5nyZifltiA3nDILSZL6DXjL2hqRlRoq7TLLejm2Uq3D5Qtg1KrQq5XMoLMUUlpCNHq1ckhrfHsjAt7+jdV5NNzPpr70FvCW25qItkTQ4vBy+rwUZmfE8NKOBtxBFYesAb4/LbHPjN8LsxLYUdHC/JTIfh9fBLwCDP08EgRhYptwAa8kSXi9Xqqrq7vdftNNN+H3+7n11ltZs2YNZ511Fkqlkvfff5+33377mAW7wtTj9AbQqBQoFdIRwe6x1RHE+IJBZiaYOFBnZ2aCiUXp0SzOiBmzdk0Gq3MTx01ZpcWZ0SzOjO7z/mBQ5u091fzhw4PUttcF3l9r4/639/Pzc2fRX76n3EQThxudvdZudnsDuHyh8jBd+3lv5ZEEYThmJpjC/1/U3sfnp0ZgdXmZnWimusVNcpSu16B3xYx4VuX2vLgkCIIgCBMu4AU488wzuf/++znrrLPQaDqnj37ve9/j448/5le/+hVnnXUWABEREVx11VVj1dRxIehy0frWOtwHCtHPm49x+TLUCVP3i0Gl1Ul1q5vkSB2plp5T44bKoFG21z09+i/7sbGxaLVaPJ7eM+xCqA5vbExsj9vV7bV9fQGZJZkxLMkMBbmyLNPk8GB1+LAY1aJ+5TDtq26hpNFJdqyBuak9p/v2x+Xzh/vIcGrSDoYsy2woqOfRDYfYVx0qa5QcpePkmfG8sKWcF74up67NzSOXze9zhHdpZgyL0qN7HQJz+UKBbccIb4eR7P9HGulzdbzwFBXR+tZbICkwnnQihoULkdTju9RObGwsOp2u39JEOp2O2Nien02DtSgzmoRIHXVtHqqsTlIsBq5ansUlS9OpbnGjUUq4fEH0vbxUVpe3z884Z9eLNRrluO5XAbsD64v/wfHZ50Scfx5Ra9ciqSbkVzVBEIRxY0J+iv72t79l8eLF3HjjjfzrX//qNu3o+9//PmvWrBnktLrJL9DSQskFF+KvqwOg5cWXAIi97Vbivve9sWzamKludeP2BqhudY/Il50jR7yGoqCmjV+/U8ApefFctzyT9PR0Pt2yi6raOiS/j4vPORWA2Tc9wpPXLUeSJGJjYklL77kWV9sexLh9PadqWR0+vP4gVodPBLzDVNLoxOn1U9LoHHLA230UdGQ/doNBmQ/31/HXj4vYXxMKdI0aJTevnsZ1yzPRqZUclx3N3a/uZn1BPTc8u42nv7kEs25oAZZerQwHDUfePloju9WtbjwjeK6OB83PPkvdg78L/7vpH/9AlZhI9pv/RRkVNXYNG0B6ejoHDhygsbERl8sVXs/7xRdfhKc7x8bGkt7LZ9NQ1LV5cPsC1LV5SGl/z/VqFclRuvZgt/eLNS0OHw02N+XNTuanRXb7nOt6sUavUY7434CR4rdaOXz+BfgbGgBwbt1K65tvkfnC82PcMkEQhIltQga8eXl5PPvss1xxxRUEAgGeeeYZtNrQHze/309KSsqUDHZlWcZXVYWvrg51QgLqlBRce/bgr6tD0umIvGgtzq+34C0uxvbhR8TecguSYkIm6j4qyZG68NX9Y61jpE+vVqBXK7nnjT3sqmxlU0kT7+yu4fcXz2XuzGxysjMJejvrXp68/HgWLlzUb7/Wt4+wudoDXqfXH/6CaDGqw6MfwvBkxxrCI7xDNVqjoGVNDm55fgeFtTYgFOhedUI615+U1e0L/1lzkog2arjp39vZUmrlyqe+5v++tZRY0+Avfug0SnQaZZ9r4TvW8oZGsUfmeY7luToSwp/JtbWoExNR6PXYPv4EAP3ChaiSkrB/9BH+2lrcxcWhkd5x/Jmcnp5Oenp6txqmCxYsGNG19gkRWuraPCREhPqm2+vH7ZfRqxVEGzTdtnX5Oj/jooxqypqd6NUSNS0uILTEpKM/dr1YMx77lSzLeEvL8Dc0IOn1RK1di/U//8G1fTv2rVvRJCWh6bo0awp+xxEEQRiuCRnwAlx66aUolUquvfZaduzYwd13343BYOBnP/sZDzwwdYtbesrL8TU04iwqQhMfj/3zzwFQRUeT8JOf0PbRR9TcdTeeAwcouXAtURdfjOWSi1FMoSRbqRbDmF3V7xjpc/mCbDzYwK7KVvRqJSqFRH5FC+c+9gW3rsnhltXT8HY5PZdNG3gNbkcw1eLwAuDyBcOPFWPUipHdozQ7OYrZyVHD2levVo3KyO7dr+6msNaGSaviWydmct3yTEw6VThLc1fHZ8Xwwg3Hc/2z29hb3cZlT27mueuPI9XSMxnR+n017KmyMTfFzKmzkwbVntFYyzuW5+pI8ZSVEWhtw1VWRqCyEvfBAwDh6aole3bjq6ik/NpvEnX99STeeceUvGDbIcViYG+VlfX765ifZmZpVlz4c+zIqcxdP+Mseg0ROiWFtXaSI3TER+hx+UIXYDp+IJS0ajz2K8dXX1H/+1DCTVV0NDHfvgHrf/4Tum/z1zi1GkzLlqGbNg3FIBOICYIgCCHj91LyIFx00UXs3r2b5cuX88ADD/DHP/6R3//+91xzzTVj3bRjS5ZBlgk0N+MsLMS5bSv+llYCzVbs774HgH7RImSXC3VSMsq4OAC8Bw9S/+CDHDxpBRW33Y5j+3aCweCA2YyF4TNolCgVEioFPPS/0BffG1dk8cEdKzg5Nx5fQOaR9Yc488+fs6GgLrzfCdndA96qFhfv7qnp9l7p2oMMuyc0wqtXdyTSGtvT3OXz0+Tw4PL5x7Qdk82LW8vZXmbFqFHy/vdXcMdpM7AYNf3uMzslkhdvPJ7kKB2HGx1c9uQmiurtPbbbU2XD7vGxo7wVq8OL2ztwRtOOvm3QKCmqs7GhsI6iOluP7Vy+wNTpD7KMLCnwlJVhW78e6//9i6C1BYXJhC4vD39zM6ZVq0GlgkCAlqeeovjMs6h77DHcxcXIshz+mQycXj+Ndg9Ob//v/a6KUP/bVWFDp5LCn2NVLU6+Kmlkf3ULLp+fmhYnm0saqWlxAtDmDhBlUOMNBMY8ieBgyLKMv9lK1V13U3HDt/EcPAiAKjERd0UFmuxsAGz/+x+ehkbqn3kG60frmRy9QRAE4diZsCO8HbKzs3n66afHuhnjgreuDn9VFb6WVgI2G+6WVoKtraBUEnf77UgqNY6NGwm0rw/S5OQgezz4Kiqwf/QR9o8+QpWSgvmcszEsPY6IFQPXXBSGpmOU77lNZZQ2OYkxarhxZTYmrYqnr13Mul3V3P/2fkoaHdz2Yn54vzhz99HZn7y+m21lVvRqJSfnxgOdI7yBYLD93yoMmoHLMY228Kj2IIKm0TAZ1/PXtbn5/fuhCyZ3nT6TlF5GafuSFWvktZuXce0/t1JUb+eyJzfx7xuOY3ZyJHsqW9hZZqXV4QaFRFasmSaHF6NGSZKm+2McOYW546fJ4WHT4Wbac6iRk2Dutt9ormcedyQJVYQZT3U17o2fQfu5mfrYX1AnhUbOo666ElViIo4vv8S5fTu+sjKa//4EnuJiTGecib+qEl3eLCJOXD6Wz2REdH3vdf0Eo/PTzOyqsDE/zYxOo8KgCZ2/O8rqKGqwoVUqqbd7abJ7MGiUNNhDs1rizBo2HmhFIkhilJ5mh4+ECC2zkyOPyfMbjso778S1eXO321zbt1Nzx50kPfR7Kr99I77iYoJKJahUuHflw/nnjU1jBUEQJqhJ/m1j8uoaxMiyjAQodDrUqakE2tpQREYiy6AwGgk6HLS99x6Wb3wDRUTnl09NWhpJv30A967dtLz5JvaNG/FXVdH2yqv46xtCAe+RwdIkCxw6hJ5m74HhT17dyd5qG7OTzXz/tFyUComY9rWPsiyjak8WVVxno7zFRXqUnmntX/KlXo7ZaPfw5MZiAG5YkYUvIGN1+gBYOSOe/30/ml+/W8C6baXhfQLBIE6PD4WkwO72s6PcCsCWw00cnxUq39ExmuENdB8RCgZl+lsWONrBYMf6Vf0oZPHtTdfA+nCjnVv/s5N5qZHoVXCwzkFuookHLl4wii3o/wJD6L0ZqA5t3/e3OX38/M292D1+ZidHcMGCZNpcvvD9Vqe332AiEAgSY9Lx3LeO46bnt7OnqpXv/Hs7r9+8nAqrC08ggFmv4fhpMUyLM7ePxknhpxUqXxlkV3kzZVYXGRY9S7I6ZyC0OnzEGTU0OLxk9DJtdDDrmQe6RjOePoZ6u6DU8f7JsowmKQkpGEARG0uwvh5NZibKuDhkrxdZlql/4Lc4vvgCAN3Chaji47F/8AGO/32AZ38BpjPOwFN0CCZBwNv1ve/vQtzps5M5fXbod68/gKL9A0ySIEKrwukLolZImLVKjDo10QY1NW1uDtbaKG2yY3X4eP7rKmrb3EgSXLcsk1tPzkGnVhKUZQyanl99fvzKDvZWtTEnJYLfX7YIAMUgx1KH9Bna9bPZ48G9c2e3uzXZ2XhLSkCSwtmZJbUayWwiaLejyZkOwSBy+2ty5OfFZLu4JwiCMBJEwDtBtX66Edv6j5AlCXVCAhKgjI1DaTKhTk7GW1eP12pFio0Fh4Pmf/+b6G9dR8Rpp9Hw8B8AUMbH46usRGEykvDTe4i7/Taq7rwLT2Eh9vffw3nlN9CkpOC3WlFZLKii+679ORH0/0Wg7y82+6pteAMBdpS3cvqfP8MfkPnp2Xl847i00HGBJoeHr0u7j2r1dcRXt1VidfpIteiZkWDm0ie+4rrlWZwzLzTiE2vW8egVCzltehQXPtLZdo1SgUJSkF/ZQrD94Afr7GjaH9TQXirG5Q2wr7qV4gYH0+KMzE6OPKZfgo58qNBIs4q2YN+llkaD3e3n1v/spKTRQUmjg8QILfEmNYW1PafwdqXor1DtCDtUZ6PM6iTDYmD6ESOhR9pT2UJRg52GNg/rC+pRShK/OG9W+IJLV72t4e0QlCQkCaJNGv553RIu/vsmypud3PTvbZyaF4/bHyDGrCUlUk+MSYPeqwxPVw6ToKrVg9MToKrVw9IujxdhUDEtwcxSo5pYU8+kQB39YTKxbdpM24cfEnS7MC1ZSsSa1ahiYrBv2oz9i8+RPV4U06aBy4W3tBRX/i4izzkbT0lJONgFcO/cSeqTT6CbOYPGvzyGv7wc+86dxFx9Ne6iIlQWC4zz8kX96freD3bmiVKh4EBtG6VNTrRKCbNeQ1D20eL0Mj/NQlKUnsMNDiqbnby9q5rt5S2dyzo0SlzeAP/3VSkbDzbwwNo5zEnpfbR3T1UbnkCAPVVtI/Nk++FvbsZvteLatg3Z40HS65FdoSSF3pISADSZmXj27QNCr1WwzYZ2xgwkZKwfrUelUeNtbMJdWEDQ6UQVZUFp0KNfuEjMzhIEQTjChF7DO+XIMkGnE39TE66dO/HX1+OvrMJbVoa3vALHV18he7zIfj/mpUsx5ExDM3cuKJUEmppoe/c93AcPEvPdW9AvWoTjs88ovfQyyq6+hrJrrsWZn0/Kn/8ceiiPl6o778KxaxeB1jb8VuuYT40dK7OTzagUCurtXhyeAB5/kPvW7ePWF3fS4vTi9Po53OjArFPhD0J6VN/TS5vsHv75xWEAblk1jYf/d4DiBge/XLePL4sau227pn2q8pE2FTeFfy+sbQu/L50jvEEO1tlxev0UNzjYUdbMc5tK2Xq4qdfjTUbBoMxP/rubkkYHEbrQF+zaNg92T5DcRNMYt65TmdWJy+OnzOoccNuiBjtWu5e/fxb6QnzNsgxyEyOO6vHNOjWPXD4fjUrB7qo29lS3kRVr4oaTsslJMKNXK4kxanpdCzkzwUSsWcvMhM7X8/MDdby0pYLSBluvwe5QbS1t4t+by9haOs76bpfP4qDDgXvfXvxVlfhr6/BWlOMqKMCxbRuObVvx19UjKST0WZkoYkIj4U1PPYX7wAEkkwnjipNCQaxSif6441AlJmI+7TTU7eV9FD4fKksU/voGvDU1Aw9/T3BbDjfx7FelbOnyeVXa5MTpDeANQkKkjrQYAya9mqQoPYGgzFOfF3HLCzvYeKgJuydAhE7FrWty+OSuVfztyoXEmjSUNDq46umvue+tvXj8PZdXzEmJQKtUMifl6M6pwfBbrfjrG6j/U+iKpjYvN7xeV9Lp0C9ZgvnMM2l9510AFCYTmmnZKLUaZLsD9/59+G127F9+ia+qGl91Da6DB/BUVmH/bCNBp3PS9xNBEIShmFyX2aeAoMuF7A+g0OuRJQWSBEEZZIcNbVo6QacD3ew5SAE/utw8/BXl+FJT8ZeVUfPTn/Y4nqRWg0qFr6yM2p/+DEnTmfTGX1ND2/r1qOLjiTzllPE1j/AY+vVF8/nZf/dSUFdJrEnDVcdn8PinRXy4r449la38+oI5pFr0JEeF1kf2lw35r58U4/AGyEsKjQAfaE/qE5BlfvTabp7+5hJOyO5/JH1zSecXQavTR12bh8RIXbcpoqkWHTWtHqbFGdlT1YrD46eg1sbSrIGzPU8GT35WwoaCetRKiaeuXcK7e2p4blMZtTYPVxyfNdbNC8uwGMIjvAPJiTPx8M6DtDh9pETpuXn1tBFpw+zkSH56di6/XLefjwsbWDU9blD75SVHknfE2si91TYcHj87K1o5fU7yUbetsNaOw+OjsNbO0szx1Xc7PovBg9ISTRAJ2R0qh+O32VDJoDRHIMfFojCZwe9DM2M6weZmfGVllF15FSgU4O9M4OTasoXSCy4Mfda2Byz+1lacX36FdsZ09PGDe28msoIaG3avj4IaG8e1f15lxhgobXKSGWNAq1JS1eoi0azjnd3V/PXjIoobQmWSInQqrl2WydUnpIfrTK+eGc9b34viwfcLeWd3DW/srCa/spUHL5rLkozOz9qH2qcxjzpJQmWxYPvyS4JtodFk947Oac2y241r2zZc27Z17qNSoYmKQg4GCXo96PJmIfm8qFNS8JaXE3Q5kfx+AnYbuhkzCLpcIpOzIAhCFyLgnWAUej1BlwvzihUY5s7FffAAyvgE/FWVKCIi0OXkYFy8OJS1uaUF+65dKKOjaXvnXXzFxaAIrUqSVCo0ubkYl52A7PbQtmEDgdJSZK8XSadDGRuLMj4eORDE39iIu6QE3bRpSIbxVcrhWPhofx2vbq9EkuBPly1g2bQYVs+M4wcv51PW5OQ7/97GefOTuf7EzH6D3Uqrixe+LgPgtjU5/Pb9QgBuWplNfkULXx9u5vsv7WT9nSvDX9aOVNvqpqzZiVKSSIzUUdXioqC2jcRIHVqVIvw9OTHSwHFZsQB4fAH21djIS+x/yuxksbPcymOfHALgvvNmMy81irykCAprbGwpbeYHL+ez/s6V42Kt2/QE84BTmTvUtLr5on0WwL3n5g0qA+2H+2p56vPD3Lwqm1PyEvrc7vIlaWw9bOXdPTX8ecMhdBolF8xP7nW6dH/mJJvZW23rNup7NHITTRTW2sfVqHyHjs9iAOOC+cguJ8GWVmSFAkmjRaHXEXX6aaiTQ4G/p6QEV0EBSnME7vz80OdxMIgUEYE6JQVZlgnU1RFsaQFZRhEVhSo1BckSja/FiqqlBZXFgncMn/OxkJdkpqDGRl5S53mRmxRJblLnxZW0aAPX/HML20pDuQwidCquPiGDa5dl9PrZGWXQ8PuL53HarIRQUsAGB1c+9TWnz0rg3HlJrJ4Z3++69xElyyj0erSpqeiOOy60hleWUZjb8z5oNARsNvD5UFgsoNejmzsXSadDodViWLgQ0wknIHs8KGNi8U3PwV9RiaRQICMTsWqlCHYFQRCOIALeiUSSULQHnKqoSIJuF6r4eIJuF+qERFQxMUiShOx2h6/w6tLSwOPBcNKJsGQxSosFfd4sZL8PTUYGtq1b8RQUoEhJRmk2E3Q5ifrGlSiQ8bfZQoG00YSkVIWOaTAQdDrDx1dMgQD4kwOhrNZXH58Rroc7JyWSN793Ivev28eb+dW81f5z0cIUfnvRHLSqnl+ePthXiy8gszTTwqIMCzUtbgDOmpvEmXMSWfv4VzTavdjc/j4DXqTQmuGALNPmDiUq6livKUkSuYmhL4t3v7qLF759PGqlgkUZ0SweZ6Njoym/ogVZhpNyYrl4USoAaqWChy+dx6qHP6W4wU6L0zdgCZ+RMJQ1un0JBmX++kkRj6wPBfHnzUti+bTYfveRZZlnvyrj7+3J0R7/tJg1ufF9ru2VJIn7L5jNwTobh+rt3P3qbv76cRG3nTyd8+cnDTrwXTEzgRUzE1D1lyVtCJZmxoy7kd0OHUGFt7oaSaVGm5WN/asvUUZEoNRqMC5ZArKMu6gIb3k5mvR0Ik85BXViIi0qJdo5c1Baoog6/3zc5RV4du4k4PfhLCgApxOlyYx+4UICjQ2ozBEoI6MAwkH2ZHVcVkx4ZLcvkiRxoDY0O2ZJhoV/XLu4vdxb//3u1LwEFqZF8bv/HeC9PTW8v7eW9/fWYtQouWBBCmsXJmHzBEiz6MmOG/kLhB1/O4MuF8qYWPRLl6CIjm6fLeAn6HAQkCQ0koRh6RJMixbjd9hRmUx4a2sBCdeePcheL5rUVPC4UUdGEmxuBllGFRsrgl1BEIReiIB3PJLl8B9FhV6P1OUPmCRJBF0uJLUGhVaLPmc6AVsbvqYmPLU1aJOS8FZVoYyIJOhy4a2ogKCMKioKZZQFFArUyUloUlKQdDrs27ahTUtHVihRpCrQzZtP9PnnIbc/ftDlwm+1otDpQn9I29sm+wPhAHiyq7KGvmDOS+0+fdOkVfHQJfO4+oQMnv68hA/21/HGzirqbR6euHpROIlUh88OhgLn02cloFYqmJ5gorDWxoFaG432UEKnOckRJPezBjgxQsfZc5N4d08NNrefKL2aZV1q9D56+QIufmITW0utvLy1gqtPyBiR12AiMba/7h3JvDpougRtJt2x+ejrukZ3OAGv0+vnrld28b99oZrM1y7L4PaTc/rdxx8I8uD6Q7yVXw2AUiFRaXWxuaSp30DZpFXx0ndO4D9fl/PPLw9T2uTkrld3hYLty+YzPy1qyO2fTGRZDn8uKvR6JJ0u9LtaTdDnQ6HVoMnIIOAMrcf2NzWhtFjwVlQQdLlxFxWBJKGOi8e0bBn+xka0WVnocnPRZWTQVFWJ0uslmJmFJiYazZy5RCxZ3OMzOGjrWdt4qlEqJH5+7ix+9Npudla0UNLgYPogZxVYjBoe+8ZCbl6VzTu7a3h3dw1VLS7+s6Wcd3ZXc8bsRE6fnTA6AW94Gjxo01LRXnkl/uZmAq2t+G12vCXFuPbtRxkZgSoyEv2c2Z3POToa55497bV7m/G3tiKp1Mh+H9oZMwk0NaIwGgnYHeH+2VXXvjsV/m4LgiB0JQLecaJbmSGXKzxqAOCvqsJbUYEmPR2FTod9Zz5BWxuyJKEymghKoImyIHs8qCzRBN1uVNHRKPR6NGlpeCsq0M+ZgyRJKM3m0H3tf/B006bhPnAAyzlnY1q6NNwGyWAIb6NJTe1oJNA5lW+qXEmuagkFvH3VOl2cYWFxxmK+KGrkpn9v54uiRq56+mv+ed1SottHEd2+AF8fbgZg5YzQOry5qZEU1trYU9VCfnkLEBrtHcj31kzjg321+IMy585L6hbYTU8wc/fpM7hv3X7+9NFBok0aZsSZetRCncxM7QGvze3rdrvdE1orqVMrUA9xqu5wDWWN7pG8/iDffWEnGw/+P3vnHd5Wefbh+xxtybItee8Rx45jZ4cMkgABwt57FCibAqVsWlahUFpGCx+0jELZezTsFRIgELK3HTuJ996Sh7Z0zvfHkRQ7HnFCAoTovi5fiXWGjuQz3ud9nuf3a0erErnvlCLOmp4xwIJoZ/o8fv60sIQ1NTZEAW5ckE+DzcWbq+t5d23DLjPDJp2ayw/J5bdzsnlleS3PfldFdYeDi19czbtXzSY3QQkq3N4ALl8Ag0aF/ieym/qp2DmwDd/nJAnJ5cJVWYVz4wYCNhtqaxzavDy08XF4u7qULKxajdpixW+zoYqNRZuRgbuyEkGrg0AAGcL3WtFgUCx3TCYMEyfi3roV66mnDLgXA2jT0sL/F/U/Xgzs18Apk1N5dXkNmxp7uPHtjbx5xUxiDKOv2ihKjaEoNYZbjy5geVUnf/u0nC3NPbyztoHVNV0YNGrm5O3d6oLQs1NltobPK3Vcv/eYfxh9q1fj3roVfUHBgGWSy4UhdwzulmbUZjNIMqLRiGiOQvZ4kKPM+LvtyLU1uGpqkDxucLkQLVbUOi2iyYQuMwt/Vxdiv0kb+lUIRYgQIcKvlUjA+wvBVVqKp6oKXW4u2pQURI0GX1sbgd4ePJWVCCo13oYGfB2dONesQdBpkVxuBGQ0WVnoU1LQ5eYi6LRoghZC/q4uEARFxMLtBn8AVKoBgWrUQQcNGlwNS/CBKPYLhn/ttPW6aQoGvOkjZF5BKaF97bKZXPzCajY2dHP6Uz/wxuWzSI7R831FBx6/RHK0nrGJUdicPiakxfDOmga+KmujpduNShA4avzwfZYh0i1Grj9yLB9tbB4yg3vOQZn89/sa6rqcfLapGcP09AMq4DUHs7d9bv+A13uDv0fpfjpbl93p0e2PJMnc8u5Gvt3WjkGj4qVLDuKg7JHFzFp73Fzz2jq2t/Wh14jcf0ox88YmUNfl5M3V9fxQ0UmDzUly9K4Dpiidmt8dNobfzMrkN/9dxaaGbn77wmre+93BCIJMbaeLKJ2auCjtry7gdZWW4ty0CU16Bqai8eEMq9piwV1dTfennxDo7kZ2OJEzvIjmKHSJifiamvC3taHNUe7DaosFX3Mzge5utGlpqKOjkXw+tKmpQ94/R7wX9wtGDpR7764QBIExCSa2t/VR1+Xkwc+38sCpE3Z7P16/xLpaO+fPzMQvSTy6aDs1nU5+89+VHFmYyB+PHUesUUOXw4fVpBlRp2FXjObZOdJ5oIqJwRRnRW2xILlc+FpakDweRJ0OdWwMotGAY9MmvFu3glaLWqPF29GOceo0tOlpqGNj8bW2oolPwNveTqCrE11uLobi4j3+TBEiRIiwPxCxJfqF4KmuRnK68FRXKzP4KhWS34+3vQNvQwPepkaEmBgCti7QaUGjQQr4QRTxtrRg+/hj3DW16HJzUVksyJKkWAl5vPhaWpRssVo17GArwtBUtDnwSzJqUSBxFIHC5IxY3r5yFsnRemo6nZzx9HKq2vv4JtgHfEh+fHgmfWLQD7KlW+nlnZFrJS5qdIOpC2dn885Vs4csf9aqRW4+Kh+Ab7Z1YP6pxFh+IYQzvJ6hA17zT1TOvKfIssy9H2/hw43NaFQCT/1m6i6D3fKWHs5/biXb2/qwmrQ8/ZtpzAuqLWdajczOjUMG3lvXuFvHYtZr+O9F08m0Gqm3ubj0pdW02D2oBCWbPBrhrP0CWQ7/eOrqEABfXa2S0d26lb41a7AvXoyrpARVrEUREIqJQXY4kFVq+jasx7V5M96WVqSe7vB92NfWhuRyE+juVqxlIvffvcrMHCuzcq2IAnywoYkvSlt2a/uWbjfnPruCR7/axh3vl/BVWRtvXjGLC2ZlIQrwVVkbJzzxPY8vrqDX7aPLMXx1xb5GbbWiTohH1OvDTg2q2FhURhOi0Yhx2jREgwHH19/g3l6Br7cPr92OOjGJgNdDwOWmb/kK3DW1OLdvw9vYgOT24KmujlgYRYjwI/A1NYX9syP8cvllj/wOIHQ5ObjKywm43XR9/AlyXy+Sx4unsQHfljL87e10Pv4EBIL+gaKIoNEgpqSgycjA7/Hgb2nFs3UrnpLNiPHxyDLocrKJPfpoVOaoA6J3R5KGf3DLsowoDl22JcvykLZLLq8SJKXE6JEkedD+u10+NMGS4ia7ixiDBmuUjmcvnMY1r6+jrsvFmc+sCPePzsmLJyDJuH0BEs06ovVqeoKB2ILCRDy+AB6Nsq7HL4Xfx+OXCEgBtMNcsX1uP96Asn5AkilOi2Fcspnyll7+t6GJ6cOIwPgCMoKgfKb6LidRejUW446ywOFKf3flyfxzlsfpg99fr9s/wG/T5lT6pKN0+/a2t6vvJiCBKA6/zn2flPPy8loE4E/HjiMn3kRd1w6f3s4+D9GGHVnqVdVd3PdxGS5fgKw4I9cenodeo6K6wxFeZ+7YeJZXdfLBhkaOLUoasYe50eYa9He/96Qirn9rA5sbe7j341L+esoErFEadBrVoLGyLIMsjHQdjuxwJsvyz3L+hDQLZH8AZ+kWBLUKv82OY+lS/M3NCCqVUurc2zsgQOhbvBjdxIn4uu3IAQlvRQW2//wH/UEzMB95BDJgPugg1FbrAWvtNhSjjbFGup5Om5bBadMyeHTRNp76too/f1hKYUo0yTHDT07WdzrQqES2NPdw/ydl2Jw+onQqfAGZ77Z3cNYzy7n+iLE8fvZkXlxey5paGy8vr+WL0hauP2IsabH6Qeencbgb814klB0OCV+FcJZtoW/JErxl5fjb2sI2V4HaWsSkJFxOJwRVn9Fo0BQUYJw6BcnrJdDbizYjA19T047WpQgRIowaX1MTlcefgOxyIRgMqC0W/DZFPd5bVYXaYgkr9Uf4eYkEvD8xoYe3p6ICb2Mj2vR0vK1tuLdtRZOcgr+lmb6SEqSWVmSnE39NDfJQqpyShOzxKFZCXV2g0yHGxuBetgzZF5yFNplwl8QjBySSr/t9+P0jvTpDM9S3EgpG0yzGIZVqBUFAlmVeX1XHY4u2kxit4/nfHkRKrIFnL5zODW9tZEuz4rUoCjAnqPIcY9CgEkUmZ1hYur0dvUbkpElpmHRqQkKjqn7BuUoUMOo1aIdQfwYlcAUl6L729fXYnV6uO2Is939SxjtrGrj6sDwyrIMnO0LB7vo6G+c/t5IEs46Prp07IKDa3whlyfs8/qBVk/I9hiYQog379ra3q+tLFHdcg2trutjW2kd+UhTTsq28vLyGF3+oAeAPR4wdZCXU6/ZR2txDW4+H7W19bG/tpbrDgSTDtKxY7j+lmLZe7yCLlVm5cSRF62jt8bCi2jbsBAiAHPwBpdxza4tiEfOXk4u49d1NrKm1899l1fzttAnhz+Hy+nH5JAwaEb1m8PfbYHPS1O0mNUZPWuzIk26CIPwk96id78Xu6hqknm7Fiq22Bs/WbcjDCUSpVGjS0wnYbEg9PbhXrkQwGpV7dfD+61yyBL/NhiY3B+vJJ0fuvTsx2q9CoxJ3+b2dNT2dRWVtVLT18ecPSnjxkhkD7p/9kYFPN7fw1LeV+CWZrDgjdx0/Hl9A4uEvtlLV4eAvn5RxTFEyT/1mCku3dfDwF9to6XHzp4UlLNnaxsNnTBokivdTIRqNiAYD/q4uVOZoej/9DNeyZeHluvx8RQirowOpoWHgxj4fvpISuquqUCcmokpOxivLdH34EaJahX58EdqkxLCquD4vLzJJEyHCCPhtNmSXi9SHH8I4bVo4uBUMBppuuRXBYGDMJx9Hgt5fAJGA9yfC39kZ7gMD6Fu9GlGjBVnGVVGBr6qa3g8+INDRSaCzc8D0t8pqJWCzKa+JIkjKwF2MjkY0m/E3KmWKUnv7wDd1OJBNJvpWraLzjTfQ5+crHr0RRk2DbeT+Xb8k8Y8vt/POWmVg0drj4bo31vPcRdOxmrS8dtkMrnhlLatrbEzOiCXWOFBU5aBsJeA9sjAprC68p9R0OLj6tXW09SqZzGUVHczIsbKquot/LtrGo2dPHnI7lzfAre9uwheQabK7ue/jLTx85qQfdSw/J6GS5YAk4/IFwtmXcEnzT9jDOxKNNidflbUSik0b7W7u/WgLABcfnM2pU9Nw+wJ8sKGJksZutrf10Rwsf9+Z4yYkc8vRBWhUIm29g51aVaLAscUpvPhDDZ9ubuaa+WN2GUTUdTr566dlVHc4mJlj5Z6Tirj9uELu/aiUt9c0kBpr4LojxgLg8knB71tiKEetpm43Hm+Apm73LgPefUn/+7DKaiXQ1RW+FzuWfou/oRF/U9OOShpBQDAYkJ3OgTsKBJB6e4k+7jh8zU04vl2K3NMTXizo9chuN/7aGnxuN51vvok+P5+o6dN/wk974NDe5+V3h+Zy1welrKqx8d/vqrni0NxB63n9Ek9/W8lnJYrq+ZwxcdxwZD6GYB/6P8+axMvLa/nf+kY+L21ha2svD50xgY+vm8OzS6t5flk1i7a0ceu7m3jkzIm77VO91xCEsACWu6ws/HLS3XdhOfNMZJ+Pni++xP7uu7iDNkYDCE6m+2tqQBDwZpRimjcPNxDo7MDf1kagt1fRAgleLwPEtSJEiDAAbW5uOKjVpKYy5pOPca5dS9MttyoTn5GA92cn0sP7E+AsKaFr4UJcZWX47XZFvTM+ATn4sOx58y16Fy7EW76VQEeHEtiq1agsFgSdjkDQY0+MjQ0Hu6rERKSeHvyNjWjHjkU/dSoxp58+4H3F+HhU8fForFYkp0uxxYj06uwWDTZloDuUQnOfx88t72zinbUNCMClc3OIj9JS2e7gprc34vEHMOs1vPDbg7jnxPH8/bTBgioXzs7mvpOLuev48T/qOLe19nL5y2to6/WQYTEgCop/8LHFyQC8v6GR0qbuIbd9+Iut1HQ6sZq0iAK8vwe9cL8kDBpVOLvT20+46pfWw9vS4yE5WocvAA6Pn5vf2Ygsw6lT0rjo4Cx8AYm7PijlyW8qWbq9IxzsJkRpmTc2nkvmZPP30ybwv9/N5vbjCnepPH1EYSJalUh1h4MN9fZh15Nlmc82N3PN6+vCZdErq7t4fPF2ZuVauXa+Yov02FfbeXtNPQAGjYhKFDBohj6G1Bg9Oq2K1BFKTfcVzpISbB9+iLOkJKxr4LfZFJ0Dux0xJgb7O2/jXr4Cf309BAKoU1IQzWaQZSXYFQQ06WmYjzuW6BNPRIyJIdDVhf3NN/HV1aPJ7RdcqVTIbrdSPjq+CJ3ViuzxKr2SEfYJKTF6suJM/D5o2fV/i7ezuXHg/a6jz8PFL6zms5JWBOCCWVn86dhx4WAXlGzypXNzuP/kYqwmLdUdDs79z0peX1nHtYfn8dT5U9GoBL7c0sod75eM2EKzL5FlGcFgQGW1opk8CUGrTKS2PvA3+pYtQ5YkVLExuMvKFM/e7GxSH3mY2HPPQYyK2nln+Ovq6F24EMeKlUh6PYJGi2A247fZcJWV0bVwIc6Skp/hk0aIsH+iSU1Fmzt40i3Cz8cvY+T3K8dTVYUgg6+1FfPBByv9N34/7nXr6fj0UySnUwlwY2KIPfccfPX19HzwoZLVBRAE1ImJ+Ftbw/uMOfUUfFVV9H61GO/27QhaLdHHHIMmKwtfbS0IAnJUFJq4ODQpKYhGA7I/oGQaxo4dvTLzAYwkyWxr7QMgbacMryzLXPbSGtbV2dCpFUXc+eMSOaIwkctfXsO6Ojs3vLWRB06dQE68aVg/XFmWmZVr/VFBWGefh6tfW0ev28+4ZDP/Om8Kjy+u4MONTXxW0sJR45P4cksrj3yxlRcunjFg23W1Nl5ZUQvAP86axMqqTp7+toq73i9hTl78gH7e/QVBEIjSqel2+ejo85AUFBvrdAR7eH+igLfT4RlR2TU5WgfpFvJ9AS57eS1+SebESSn8/vA8BEHg319XsKq6C71G5KLZ2RQkm8lLjEKSZeJHKW7WH7New7z8eBaXtfH0t1U8ef7UQWWfLm+Ah7/YxrdBz+ipmbEcmp/AY19t57OSFsYkRHHy5FR8ksxT31Ryx8ISvP4A8wsSSQtaLw01p5ZuMZI+wvJ9iaeqCqnPgau8HMO4cQTsdjRJSfStWEnfsmX0ffIJgdZWUKnQFxXhra7G39wMgGg2E3XYYUTNnYsYZUJfpPiixp51Jq5162l/9FG81dVYLr0EW1WVch9PS0Pq6UFMSUGbmoq/uxt3eRmGyVN+2g9+ANBgc9Lc7SYlRk9xWjTFadFsbOjm85IWbnxrIy9forSXODx+znp6BY12F0atiluOKmBGzvBCcFMyY/nXOVN4/odqvipr45+LtvNVWRtXHZrLP8+axA1vbeSjjc3YnT6uP3Is07JGFpXbV7hKStAlJeONjwe7HZxOer/4AtOsWXgqKsKVCUl33I46IQFdXh7G2bPxlJTQu3gJ3srK8L4kpxP3Dz/gb2jAtOBIUKvx6vT4WlsRZOU6MkbUnCNEiLCfEsnw/gRo0tKQpACi2RwWm3AtX07vxx8jOZ0Ypk3DdPDBBDo76fzXv+n54MOBO5DlAcGu+fjjUFksaDIySbrzTtTJycheL20PPEDM6aejio9XRpVtbRAdjSoxEdEah7e2Bl9LK+6tW3/Kj79fIssyD3xaxoZ6O6IAE9NjBixv6/Wwrk7xOv3PhdOYPy4RgPwkM4+cMQmtSmR1jY3jHv+OBz4to8flQ5ZlGm0uPt7UxN8+K+fsZ5Zz0ANfseDRpVzy4mrszsGlqKOhx+3HEVQkFkUBt09CrVICGbvTy+8OGxPO+Fa29w3YtrVHyRpG69VMyVCCm9A+Xd4A+ysFyYoV0H0fl+H1S2xqsPPWaiUbWfATWTR1OXx4/dKwyq5pFiPTsiz8UNWJyxdgeraFh8+YhBgsNf5+uxJ03nbMOM6bmcm0LAsxP7K3+sSJqYgCLN3Wzp3vbybQL0PVaHNy/rMr+HZbOypR4LJ5OTxw2gSOnZDCoQXKeVHTqWR8bz4qnxMnpRCQZJ77rpo1NV10BScUfgydDqU3uaNv6NLt3UVyOlHHx4NOizY1FUFU4atvoPPFF2l78O90v/oqgdZWBJMJ0WzGvWmTIkoVRFCp8Le14dq8GU9dHa6NG3Ft3Ii/uRlPUJVTnZBA/GWXE3XEEeD3E6ivR5WbizYvD9FsRmU2oxszBuHnKn/9FbK9tZevyltZXd2J2xsIVz8IgsBfTi4iLVZPXZeTs55ZQVlzDx6/RFuvss4JE1NGDHZDRBs0/N85k7nv5CIMWhWbGrq5+rX1/OvrSi6crag4f7e9g9OfWs7lL6+hpHHoCpp9iaeqCm1aGsZp09BmZwPg7+xC8njQFRYqKwWdHLxVVXirqpB6ejBMm4YmZWjPd39dHd3Pv4D9jTdwrFiOmJCAv68XWafH39mpTNBHKsUiRIiwnxHJ8P4E6LOz8dTU4CopwV1Whq+iEveaNQCok5KQPR48TU1DbhsulfN68TU2gizjra6m99PPQJZRJyWCfkf20fHdd0jBXjLZ70cTZULQanCtWoUsCgT6ejHPm7tvP/CvgCeWVPDfZTUA/PWUYnITBpaB7fB0VVOUOjAYPijHyutXzOSxRdv4vqKT55fVsHB9Y7C/cuigYEV1F2f/ZwVPnT+VvKSoIdcZjpx4E4+ePZm73i9hS5NiT9PtUoKsGxfkk2E1cvi4JL4qa+WV5bXcc1JReNsjxyeRFWekttPJKytqWVHVCShltQnmPfeb/Lm57+Qiznx6ORvq7dyxcDOra2z4AjKHj0tkY20Hj3xextTMGP7z21n77BisJsW7UyVCj9uHWacesm/226Bl1TkHZYSFcAKSTJdDmQDZebLlx5Adb+Kmowr456JtvL++CQGB+04pZm1tF9e/uQGb00esQcNdJ4xnQvB9ZVmmLCi8FrJHEgSBO44r5MvSVuq6XDTaXNgcPqw/wqMUBk4SxEf9+PJnyeVCPyYPVXQ0jmXLsL/1ttKj2w+V1Yrs9yPZ7YhxceD1hoPegN2Oc+VKnCtXIuh06MePRz9xAqJOT88nnwAQdfTR2N58A2fwno4ootJq0aelos3OJtDdg+x2ocvJ+dGfJ4JCrc2Jy+PHE5DQa1Wk9CuVjzFoePWymVz64hqqOhxc/MJqPvz9HG49poC/flLO/9Y1MjMnjnHJu574EgSB06elM29sPC8vr+XN1fVsbella0svBUlRxBo1rKy2sWhLK4u2tHJkYSJ/OCI/fO3sK0L3EV1uLv7ubtRJSQiBAN4tW3AsXUr3+++DRoPKYiFgs9G3eDFRhxyCt6oKT2UVnm3bFK2Q/qjVYYVnZBlfZSX2ykoEkwlNdhaS14c+KRFtRgaiYWRP+ggRIkT4pREJeH8CRIMBWZbx1tbhLytTSpUFAW1ODt6qqgHZ2/7EnnkGjuUr8AWVFjVZmfhq6/Bs2SFS4W9tU/4TfFi5Vq/esQOvl5633kY7YQKm2bNAq0NlMqIdZmb350SWZb7c0kpOvIn8nygDNxwvL6/h0a+2A3D3CYWcNnWwXUOfRwkohxOayo5TgtBNDd088GkZle1KZkwtChSmRDMhLYYpmbFMzojF4Qnwu9fWUtfl5JxnV/DY2ZOZOzZ+t455Vm4cr1w6k1ve3Rguwz51ShrTgwHKBbOz+KqslffWNnDL0QXh49aoRH5/eB43v7OJf39dgdsnoVEJXDN/zKjet7/6bqhk9ZdAVpyJR86cxJWvruWjTUp5ak68iQdPn8DlL67G4w+wrm7fZmTiTDrW1tq5+rV1BCQZvUYkIUpHgllHYrSehCgdFpOGkiYlmAz55oKSmQ/IirJ3rHHvimzNyYsnJUbPLe9uYuH6RuptTtbX2QlIMuNTo7n16IIB/s41nU5aezxoVSJTMmPDrydG6zl3RiYv/lDD66vqOXlK2m4dh8sXwOkNYNSqwn6+oUkCq2nvfOaQsE/f98vofOwx5TWTCePBB0MggHPTJkU3ARCjohAEgUBvL6jVxJx6Kn1ff02gpwd8PmSPB9f69bg2bUJltYLfj+nQQ/FWV2N/9VXlDTUa1NOno8vOApUab109mvQ0LGec/qu3hNtXNNpd4bLl0D0my2Kk1uZkXHI0Y4PPi/4WZKmxBl689CCO+sdSbE4ff3x3M89eOI01NTa+KG3l75+X8/jZk0etRp8Yrefmowu4bF4Oz39fw6sra9kavM8Wp0VjMWhYVtnJV2VtfFXWxuHjErn5qALGp0bv5W9jIMYJE9CPGUP3t9/ilmVUaWkEGhtp+9vfB6znWPYDUl8frjVrw6+J0dHox49HNJnoW7wYJEkRaNvJFUJ2OPCWbsHX0oomJZn4/PyIcnOEXxWNdhc2x64r7Cwm7aD2tgj7D5GAdx8gOZ34fb4dvrc6Hcbx4+l5/wMCNhuatDSS//YAjdcqVkHanBy8/QVNBAFVcjIqaxyy3688XEQR7ZgxgKD06O5E9Ikn0rNw4dDH09eHNjcX2edDbYrC19Lyi/Pce3tNPbe9t5lYo4Yvrz+ExOifXtwG4J019fzlY2VC4fojxnLxnBy8/fxwQ/QFM7y7UlY+ND+Bg8fEsayiA5NOTXFqDAatCrcvgErcUeL4zpWzue7N9ayrs3P5y2u4/bhCTp2we6qYaRYDz//2IP61pIKWHjd/CCroAszLiyc7zkhNp5MPNjRx3szM8LITJqby9LdVVLQpA7hMi2HUwWtTtxt3UH33lxTwAhySn8AfjhjLY19tx6RT8e/zpmDWaxifEkWDzcnUzH2bhantdHDz2xvDZcNun0S9zUW9bbDNWHFa9ICMemfw4WsxalGLI5fCbm/tpdvlQ60SUYsCzd3uoO2VgFoU0GtUYaumEMdOSEEGxWaoRtEKOHFiCn85pZgm+8DjC2X9p2TGDrI7un7BWL4obaGp281/llZz78lFjBanN0BAknF6A+GAN86kI86k+1H2PbIsK365oR+3W7mPAurkZJLuupPOZ5/DvWHDgO2kvj7o6wOVCt3YsQRsNsXr3GBA1mgUu7eg2m2gvR11YgJJt91K98L3cQZtYTRZWViOPw5/RyeuTRvRJCdjHF8YCXZ/BM3Be0xzv3vM2CRzONAdjiSzniMK4li8rZNllZ28tLyWv55azKaGbpq73fxj0Tb+fOL4cAvBaIg1arnxqHwumJ3Fs99V8dbqekoaQ9UPFqJ0ar7d1s6S8jZ+qOzgmQumh9tE9hWiwYB5xgzweBD0p2B/5x3k7m7wKNVEIcXw/sEuoCiM19ch9fWhTk3F39Q0INgNbRfyyZM7O3F+/Q3eOXNQx8aGVZtDvsCiwaBkfiPBcIT9iEa7iyP/8S0u365buAwaFV/ddGgk6N1PiQS8+wDJ7UYWROUhYDQiCALehgZ8QYGI2LPOxFNSopQei+LAYBdAljHk56OKjkY7diyatDTFvDolBXV0DN07Bbzxv7+WuMsvJ/Hmm+j9/HNa77t/wPJATw+iWo12zBhkjwdNcvI+/fy7S32Xk78E7VjsTh9//N9m/nvR9L3qWfnE4q1sqLdTlBLNTccUDli2ucFOVUcfydEGHvxiKwFJ5sSJKVx3RN6w++v17Chp3hUalchhBYkjrhMXpeOF387gzx+W8P6GJu7/pIzSut23gdBrVNx8dMGg10VR4Dezsrj/kzJeXl7DWdPTw5YaKlHguiPyuO6NDWjVIi9eMnPU75caow9neH+JXHlILrnxJsYkRIXL0u8+eSIPnDG0n/Hewu0L8LvX1tHr8TMty8ILv52OzemjrcdNW6+H9l4PbcGfHreP3x6cPWD7jj5lsBofNbJo2JNfV/D6qvpdHs/sMXFcd3geBu2O8/W4CSmIgsC/lmznjGnpXHRw9pDXXCjgnZU7+HyM1mv4++kTuOj51byyopajipI4eMzoqhOMWlU4w7u3kVwuZH8AX0sLUm8fzu++UxaoVLQ99PDASUNBUALbqChEkwnRakFjsYAUQGW1oMsfi2fbdvzNzQg6HbLHgxgbQ/rTT6NJSSHmtFPpfPZZAHwVFcheL+7SUiSnC39nJ/qCwddjhNGTEqMPZ3h3l3+eO423Vtdz9wel/HPRNmbmWrntmAJufXcza2ptvLu2gbOmZ+z2fhPMOm4/rpCL52Tz9LdVLFzXyOrgxNG8sfE4PH5l8vKlNTx+7hSOKd6Hz1xBQB0Xh2n6dHxtbejHjsW9pRQ5GPDKbjcxp55C98L3ldW1WmSvF39bG5IzOEZRqRBNJiSHQznHvV5ktzt8vgt6HbLbg2vVKlr++gBRxx1H/BmKK0ToWgsFvREi7E/YHF5cvgCPnT2ZvMTh28kq2vq4/q0N2BzeSMC7nxIJePcBol6PoFYpHnkVFagtFnxNTUh2OwCB3l66nvsvAKqYmB1qzCFUKtBqcW/bhuxwgCDgt9kQTSaEfg8UdWIi/rY2nKvXYL3oItxl5XT99/nwckGjAbUawWpFZbEoiqFdXbi3blV628aO5eemy+Hh+jfX4/AGKEg2U93uYEl5Gy8sq+a0qemDfGtDDKeZIcnykBPM6+vtgMzaejvyThtXdTgQgMcWb6ezz0tKjJ47ji/EHczsev0BDJqBl0pvsEdWALa29DIUbT3uYUWGet0+EsyDB3BnTksnK87I44sreG9tY/j1PrcfP37UxqGzfQFJRhxmfsAXkHD7Apw4KZV/LtpGeUsv93xUyp3Hj8cvyWhVAkePT+La+WPQqkVcXv+g72g4+qvv/hIRBIGjin7aCZ7qTgePfLGVsuZeYg0abj2mgI5gxjbOrMMapeXwcYMnQDzBGea2Xjc1HYq6arRBQ8dOfd8rqjtxegOUNHbz7TalHNdq0iDLEJDl8LURkGQkWcbjk1he2UlFWx9nH5RBolkXFjlLNOv4y8mK8ur6Ojug9BuHggu700t5s3J+T8qIwen14/VL2J07hLgmpMVyxrR03l3bwM1vb+Rf500mJyEKq3Hofl5ZVpJABs2OUuadl8PI59+Ik2HB0syKzRV0v/0+sd9+oWRv1WpUUVF4tm7dkb0SBExzDlYmHUXlnu3bWE/oG1fFxyP1OcKqzfpJk3CtWoXp4DloUlLofP55Ovvdc8WUFFSxFsSYGPx2G4ZJk39x1TT7G6mxhvAAU5ZlKtr6qLU5ybIYBwxQJRnEIc6bM6el8d22dhaVtXHT25u498TxXDovm6e+qeKVFbXkJpgoThtc7bGpoZuVNZ2DXt+ZiWkxHF+cwqsra1lc1sZ32zsQ2BGoX/3aWm4+qoAjC5MYO0p9hj2Z7NWmp6MrGIerqhrBaEIQRKSuLgB6Pv0UXUEBnq1bUcXF4W9uxhc8p2W/H9njQXIobTeyz0fsOWfT/b+FyB4PqsREAm3B1qlAANfKlQS8HqImTVT8gPX6HdndCBH2U/ISo4a8D0T49RAJePcBotGI2mzGXVmpeD7a7Ti+XwayjK6oCNtrrwMQfdKJ9C76CgDzcceFB2Co1egylEGSr7YGVbxSEiX5fEi2LmUdWUabk60EvCtX0vH0M3S98AIEAqBWE3/173DXN+DZtg1tQQH6PCW49dbXIzldeOvqfhEB72sr6lhbZ8egUfGPMyeyuKyNR7/aziNfbmNmTtywAe9wiIIwZMBbnBLN2jo707NiBw0mcuNNfLCxiRVVXQgCPHj6hAG9XRqVGBYT2tRgRyUK4fIXo1Y1yNolRLfLj1E79CVmd/qxRg09qL90bi5jEqK49c0d/dgalYhOoxrWazUpWo/HF2BdnY0VVV3IyFw7Pw+9RimfBkXM5e+nTeC6Nzfw6oo6suJMXDArK7zPSZmxuDx+am3OXZYL7s3s+95m57Lbn5rPNrfw6eYWBOCOEwoHiX/JjPz9WYza8N8sJcYwqBx5e1sfXQ4v3wWD3eK06AHCab6AxJRMS/j3+i4nb6yqo73Xw3PfVXHCxFSy40zDvr9RqybTqixfX2dHBgpTzEzOUPbZ6x6sOn39kWNZXtlJo93FM0urue2YcUNaMYFy+xr5/PmRCrDBQXhru524JZ8huZzoCgsxLziSjsefAFEk+rjj6P7f/xC0WrQ5OYqac0IijmBpcoiA3a74oAPa/Hxcq1YBSv9jw++vwxUSqtLrUWVmEn3IIehzc/E1NmCcMAFNwu714h9IjPYesvMdr6bLidPjp6Zr4H1Kr1Ex1B5dvgC3HTuOTY3dVHc4+GBjE38+cTyNNjcfbmzi8cUVvHvVbOJ3uk6/3tqOeqd7u9PrRy3ueB4A6NUiqbEGbj16HGdPz+CVFbVh3+xEs462Xg8Pf7EVty/ADQvyR/WZ9xQh4Cdm7lykvl68Tc0IaWnIlZXITieeoDtDaPLGU16OvriY6GOPof2Jf+3YiSShycpCX1yMa+1aVFEmDOMPo++bb5TlgQDeDRvp+eJLog6eDRCZ1IkQIcIvnkjAuw9wrFuHPi8PtcWC32bDuXIlru+/ByDqyCPo/L9S5f9HHEGgswvHsmU4V67EOGMGgkZDwG7H164ot0peH3JHB4GuLgKdneFZWE1mJs4NGwFQp6XS9dxzAAgmE7oZM/D7fBhnzsB6+mlokpMR1CpEgwFtRgbeujq0mZk7H/ZPzrbWXp5YUgHA9Ufm8d7aRgqSzUzNjGVdnZ27PyjhnasORhwudbkb3HRMIbIsDznIijfreGetIgx2+bzcsNDTztR2Ojj53z+gVYtcNldRXDUME9CCko3ocnip73LS1O1CoxKJNWqIMWjw+WXS/Hp06qGDs8MKErntmHFc8uCOfe2MyxtgbZ2NlVWdrKjuYlODHV9gx3rfb+/g2QunD/D4PaoomZsW5POPRdv426dlZFqNHDU+CdghBJP1C87Y/tLZ0tTD/y1WBM9+OyebnHgTD3++leK0aI4pTh71IL+jT8kID1XS3OPy8UNFJzKQHWdkfMrIwjgZViO/O2wMb6yqo77LxbtrGxAFOH5Cyi6P56ONiqLx/F2U5Bu1au45aTyXv7yWr8raOHFSCnmJUQOEqZxePzaHD4tJs1cUmPvT//rwVFbibWggye9C8infY8p9f6H1gb8BEH3C8UQfewy9X36J1NdHz6efImh1BBxO1ElJePv6WXcFe38FoxHvtm0AaHNzcSxbhr++Xqm6SUtDm51F1CGHED1rFqLBQNTs2fjb2tBm7H65bISRybYaqelykm0d3X3K6Q1g1mu496QifvfaOt5b18icvHjuPL6QLc09VLT1cet7m3j2wunDTl4CVLb38enmFtSiwOwxcUxMixn0bMqKM3Hn8eNZXtXJ/Z9soa3XEw56H19SQZRezeXzcn/U5x8JbUYG3vp6kq68EikQwN/RQfcXX+LbUkqgqwvJZt+xsizj3rwZz/btymR7EMFkov3Bh8JlVN6qauIuvxwE6Pv6G9BowOej87nnCPh8xJ15huIe0diIr6UFTXJyJACOECHCL45IwLsPkNwefC0tmKZPRx0XF87oAgTa2sP/d/6wnLjfXYVj1SoCnZ04li5FV1iI7PWCKBLo6MBXXz/AF1JlsaCfOBHH0qUgywh6Hf6GRqUPLSEB07x5aJKTkN1uZJsdVXExaqs17P+rz8v7RWR2fQGJG9/egDcgMTcvnia7m5eW16IWBf5x1kTKW3pZW2fn+WXVXLYPBwiSJHPT25vocfkpTo0eUZ34haBNkdcvhYV9+vcftvW6KW/upbbTSU2ng6p2xy6FEIxaFfFROs6ans6kjNgByw7N35EdKm/pYdqYHVm5576v4uEvtg4IcEEpoxuXbGZ9nZ2NDd2c8uQynjp/KgXJO4Kiy+blUNPp4L11jdzw1gbevWo2hSnRoxKCiTA8PW4fV7+2Fq9fYkaOlXNmZHDzO5vY3NDNZyUtfLO1nZuPLiBuF325AJ3BHt6ds7udDg/LK7vwSzIJZh3Ts62jCqLNeg2XzMnhk03NrKm18faaBmo6nVw+NwfdMBnxirY+1tXZUQkCJ01K3eV7TMuycu6MTN5YVccDn5ZzaH4ifkkOC1PZgpZDtr1kOTQc3oYGJKeLZJ1Ae3w8/pYWZXAebB1RWyyo4+OJPvkk7G+8SaBTyeDS3Dys4I7sdCJoNGhycsKBL6KIdsoURKsFbWIS+Hzhe60+NxexuHiffcYDmd29T4V6xQ/JT+CqQ3J56tsq7vmwlPd+dzD/OHMS5z67gtU1Np78ppLfHz60bkNZcw9fbmlV2gYkmW+2tlPS2M2h+QmMHaLvb3ZuHPefXMzdH5bS1ushIUpHe5+HBz4tx+Hx84cjxu6TKhn92LHh57uzpAR1lBl9ehqi0YCnsgqhooKA3a4IUUlKy07/YBcUVeadUcfHE3f11TiW/YDs9YZbAtxlW8Lr+FpakFzuX6QoZoQIESKMLP8ZYY8Q9TpFGEqWQZaJv+Jy9JMnAWB/4w30kycr/3/rLWwvvUzC9deHBSNca9bg3rQJxzff4C4pGRDsAgRsNhzffhtuhJPdHgSdDrGgAPPxx2M59RRijz0WfUEB6swM1BbLAFGJXwr//b6aksYeYgwazp+ZycvLFREZvyTz5DdV3LhAeWg/9MVW6ruc++w4nl9WzfKqTgwakYfOmDhsybDD4+ftNQ3h37cEfUkNwYD3h8oOrn9zA09+U8knm5spberB5QsgCpAea2BGtpXpWRbGJkaRaNahUSmDHac3QF2Xk8eXVNC+U69m/+zxV2Vt4f97/AGe/a4aX0BGJQqcNCmFPx5TwFtXzOKrGw/h4TMn8ujZk8m0GmiyuznvuZVhH1VQSgn/fGIRM3OsOL0BLnt5zQBLjwh7xv0fb6Gm00miWcefjhvH26vr2dzQjV6jlECurrFxyYurR3U+h3p++/eAy7LMXz7agssXwKxXMycvbsSM1M6oVSInT0ljwfgkVKLAquou7ni/hI82Ng05MbNwvXK+H5IfP2rV9GsPzyPTaqS1x8MDn5aFS/6NWhV6jYjD60ev2bePHW16OqLRgD4vD+uVV4BKRe/nX6DNUyazbG++hf3DjyAQIPacc9BPnoygC04sDNW7LoqoLBZkny8c7IpWK0L+WJL+cB1xZ52FJi0V/dixv8h77YGOQaMizqTFoFFxw4J8ilOj6XH7uX3hZrLjjNxz4ngAnv++ekg9hpoOB1+UKsFuYYqZ+QUJ6DUiHX1e3lvXyA+VQ/f5Tsm08PfTJmDSqmjv85AQnOj6v8UV4SqQfYmo14MoopswkZj580n/893EXH65YqklDXYeAKWSYWfUyck0XH0NdRdepEzGA5pgy1WgrR1nWTnIMprkZESD/hcnihnh10ej3UVJY/cufxrtkftwhB1EMrz7AOOUKfjr63GsXYsuNxfD+PFkvfYa1b+5AO/69XjKyoi78go6n/kPvYsW0btoERgMiBYL+P2KQqLPt+OhJACCOPghJcsIJhOq3FwSrrwC47hxYfEIfd6Omer+tgG/FMuAth4luIs1avD4A8gonqOSrGS3MoJltV6/RLfLx74oDGzpdof9dm89ZhzZ8cP3NQZkxUc1FBiEvG4zLUb+/XUF321X+ilVgsCC8UlkxRmRZZiYHjNkEN1sd5FqMVDW3MOjX23H65eCQlY7MnrtvTtm3kPWNrIsc/cHpbT3eojSqVnxp8Nx+yU6+jzIMrh8EnEmHYcVJPDBNXO44pW1rK6xccfCEt6+clZYmVmrFjkkP4GV1V109nnx+KRhy6sjjI6Wnh3KyiatKlyWHK3XIAjQ2uPB4QnQ4xrcA7sziWYdFW19vLCsmqmZseFe8JZu5ZzIsBj3+O81OTOWmTlWnlhSQVuvh3fWNrCpsZvbji4Inx9A2I5rc2M3r66oJSlaT1K0DpNOhUGrGtIuyaBRcfeJ47nspTW8v6GJO48vJM6kDPQNWjVZcabdCtJHYkCZvywrpcz19aiTkjBMnKjcC8vLUaen46+tRWWNQzduHJ7ycmwvvKBsF/QvHw4xOhrJ4VAsigwGhJgYDNOno46zEjV3HsYJEwCImj4d2OleG+EXh0Yl8vfTJ3Lm08tZXWPjnbUNnH1QBu+ta2RldReXvbSGpy+YRlE//9xe947zIyXGQEqMnliDlhafci32F3DbmaLUGB46YyJ//N9m2vu84Xad/1tcwaT0WOYPIV63t9CmpqK2WHbYIwJqqxVT4Tg6n/svzvXrFZutwI7JLtkZnIwLWiESCOBvaVFe8wWFGo1GfE1KH7Dk9eLcsJ6oqVPwNjfj7+xEFRsbvjZ/yVoPEfZPdtdG6OkLpoWfQUMRsmOM8OvngA54h+vp3Bt4qquR+hx4qqowFBUh2WyYjzuOrvJyZJcLd0kp5mOOxrlqtSKKEvSMHEBoMCYD8sBgVzAYEFJTkN0eRIMef1sbjBu3wx6g30ytaDT+4nwgf394Hh9vaqK208mq6i4Oy0/gm21KufcNR+ZzT9Cm6NwZmXtVOa9/T+HfPyvH6Q0wNTOWM6amjbhdlFbNeTMzeeqbSiRZsaU4d0YGz31fhc3pQwBOmJjCmdMzwoIm5c29w2aMBUFAqxb5eJMycJiUHhO2zQnx98+3hv9/5SFKWfcrK2p5J9iD+e/zpmDUqhEEPyatChAGlFjHGrX8+7ypLHj0W7Y09/DS8louDfYeb23pDWcZ7jiukGiDhk6Hhy6HD6tJM6zgUITh+ctJRZz4xPdsae7l2e+quXhONiurumjpUQbGCWYdtxydT9EozufrDh9LWXMPle0O/v5ZOfecVIQoCFw2L5e/flrG1tZechNMu/SBHo78JDMPnT6RVTVdvL6yjq0tvbzwQ43Smx68J/7hyHw2NnRT0dbHPxdtG7C9ShCIN2tJNOtJtxi4+rA80ixKkDclI5bcBBNV7Q6+Kmvj1CnKtbW7NkS7ez6GBPk8VVWo4xOQXC40KSlIvUp1Q+/HHxN/7bV4CgvxbNuGp6JCsW5RqdAXFmKcORPZ68G5Zq0i8CNJinUcgEaDfs4cBFHAMGECuox0oufNHXQMv8R7bYSB51Km1cg188fwyJfb+OeibcwbG88/zpzEVa+upaSph8teWsOT508Jb1ucFk17n4dNDd0sKW9DQHkkq0WBGTlWDh4ztOZDiLGJZm4/tpDbF25mXZ2duXlxfF/RyY1vb+Tj6+buM4uToc5F0WBAnZhI8t130/i3v+ErL0cWBPB4kPu7RcgyBAIIRiNRhxyCNjsbye8Dnx/HsmV4KypArSYgivg7OvBUVeGtqwe/H299Pboxw7cGRYjwYxitjVCnw8tVr6zloudX7XKfBo0KywhBcYRfBwdswHv//fdTUVHB888/jzhEpuLH4CorIzonB+fGjQhaLQGbDU91NbLDgX76dFzffTdIDXRI/H7FVkitQjCaQKtB6nOgzcpCN2Uyutxcpd9GpcY4cWK4f2x/yC5YTFoePH0iF7+4mtdX1fOPMybS6/YxMT2W1TVdNHe7SYs1cMfxhbve2W7g9AYISDLLKjr4YGMTggD3nlQ0ojBWe6+HO94vYVmFUrp2dFESBo2KxxcrglvJMXp+d+gYCpJ3r//1o41NbG/rw6BRDfJhXV3TxffBrDEo2bHVNV3c90kZALcdM455YxX1boNGTWqsekhbIiXIKuDO90t5Ysl2jhqfRIJZx83vbMTrlzisIIHfzFIEzLocPnx+iS6HLxLw7gHZ8SYeDg6c31nTQFFqNPecPJ6/fVpOcVoMVx6aS5ROjTQKy6fkGD33nVzMDW9vYOn2Dt5YVcf5M7M4sjCRZ5ZW0tHnZX2dnblj91wF2KBVcWh+Ahajhn8s2sZ32ztIjTFw3MQUQPGYfvo304JiV07aej209Lhp73ETkGVaezy09njY3NiNX5J58PSJgDKZc8KEFB5fUsHHm5rDAe9wNkTD0RXs+R3ufHSVluKpqgpX0YQEe9RJSUg93UheL6LBgG5sPq6VK5EcDtoefHDwGwUCuEtKcJeUDHxdo0GVmIgqLg79pEno88YgI6CONkfEqPYz+p9LydEqLpiVxeKyNtbX2/nLR1t46jdTefbC6Vz7xnrW1tq48pW1HFWUTE68CUEQmF+QgEYlsrbWhgyMTYxiXn480XrNoEoHh8fPP7/aRlK0nsvm5iAKAtOyLJw/M5NXV9axttZOflIU21r7uPb19bx1xawBqs/7EtFoRJuaiuRykXztNThWriTQ00PAH6D388+R29vDmVxVXByB3l56P/980H4EoxExJQVNejr+7h66l3yNYcIE5IAf0Wym+4sv8NtsGIuLw1UQESLsTUZjI/TVTYdiC7YHjYTFpI146x4AHJABb1tbG/fdd1+47GZvB72eqiqM556LqNeHbYn8vb0EOjvR5uXhrq9Dbu9QSpTVapBlRJVK8YqUJFCJoNagslqRZYmEK66g9e8PEnfBlQRcLmR7NwQk1EYjlvPOG/De+1N2Yf64RE6fmsZ76xp5ZNE23rlqNmtrbPzhrQ0IwH0nFxG1hxmsoVhb28XGhh7SYnX8c5ESrJ57UAbFaYq/6FAsq+jgj+9tptPhRa8RuWBWFh9vaqa5240gwDFFyZwzI2O3y0sbbE4Wrld8di86OGuAJYYky/zjy4EZteZuF7e8u5mAJDNvbDzHFieN+r1Om5LGRxubWVndxZ8/LCU3wcT2tj7io7T89ZTicJWD1aQJZ0Ei7BnHFCdz1vR03l7TwMOfb+WpC6bywsUH7dG+itNi+MMRY3nky228sqKWY4tTsJq0TEyP4eut7TTaXTTZXaT+yAf1xPRYfjMzi1dW1PL2mnqSYvRMCA4krCYt18wfKORjd3rxBSRaezxsb+vlvo/LWFLWRmuPm6Rgr+/xE5WA97vt7fS4fANsvkbLrs5HT0WFUkVTUYGxuDgs2CNLEp6qKqTePjyNDRgOPxxPdzdSSwu43eDxDG/krdEgxsdjPGi6MlnZ3YM2IwN9/lgsp5yy258hwi+Dnc8lURS49+QiznhqOcsqO/lwYxMnT07jqfOncsNbG1hW2cnHm5o5fkIyuQlRCILA3Lw40mL16DSqYQfHsizz2OLt4RYXAcKqzOfPzKK608Gyik48folovZoN9Xbu/aiU+04u3ituBKMhlPlVx8WFg1HbBx8gSBK+5mYCnR14S7cQ6Az2JqtUoA1mv/x+5UPFxxNz3HH0rV+HZO/G6XAQe8zRaNPT8dtsOFauBK8Pj8EQCXgj/Gyk9fPwjhDhgAx4ExMTMZlM/Pvf/+aiiy4C9izo9Xg8eDw7hIZ6guVvuhylbDRkS4QsE3A4kNUqkCRM0w9CEBSFYLmvF38ggMocjcpkQl9QgK+6Cvx+xKgovI1NdL7yCtq8PGS/H3WsBSE9Hbmv71dRNnTjgnxWVHXRaHdx70dbWFerlFVZTVqmZllG3Nbl84dLJA2aXZ/K35S3U9vVx2ebvWxv6yMuSsvNRxUMua4vIPHE4gqe+74agIJkM5fPzeGP/9uMX5LJtBp56IyJIe2w3cLtC/De+kYkGWblWjl4TNyA5Z+VtLCluSdYpqxw67ubsDn95MabuPjgbNp6vaRbhu857o8gKIO7k/61jB8qO8MiK387bcIAFeA4k26PM7v1XQ6aut2kxujJsI7uuEIMdx3tr1w2L4ey5h42N/Zwz4db+Pd5U4ZVQt4Vx09I4ZNNzZS19PLy8hquPzKfaIOGgiQz5S29rKuzkRitG7KfdndYMD6JJruLxeVtPP1tJbNyrIwbxu5IJQrEGvUkmPUUp8XwyaZm1tXZeXdtQzg4zk8yh7NYX25p5Yxpu6/auqvzUTdmDJ7KysH3waBYoLumGtFkQna1Y5xQjDhzBoJWR9/GjdDTjTo9A1GjRjSZEI1GfG1t4HShnzAB86yZONZvQGhtQdTr9ot77a/tOtqbxJl0uLwBKtsdxJu06DQialHgkrnZPP1tFQ9+vpWDx8STYNbx+LlTuOXdTSwpb+PjTc0cXZRMQbIZQRAGtZ3szCebm/l2W3tYj+KdtQ2kxOg5YWIqKlHgsbMnc8IT31Pb6WRmjpWV1V28vqoehyfAg2dM+Nl0FHS5ufi7ukAlok5MAK0Wf0Mj2sJCNImJ+NvbUCclg9eDp6kZ0RylVFDo9UhRftTRMbhqanFt344mKRldTg7+rq794rqJECHCgcEBGfACjBkzhvHjx/Pmm29yzjnnAHDPPffw6KOP8thjj42qt/dvf/sb995776DXjUVFIAio4+JQx8XhrqhAY43Dq9cjGAyoMtLRZWfj7ehErdfTt2olAY8XuacHf3U1SDKiVot59mxUFgtqiwXHhg34mpoRo83ELlig9PD+CgQhTDo1951cxKUvrWHRllYAdGqRsfG60Lh1SPyShMPjJyDJODwy2mCvrCTDcNVhcWYNdTaRjQ3dANx1/HgMOjVuv0SP248mGDQ02V3c8X4JmxuV9eJMWuq7HNzy7iZkQCVAl8PD5S+v4fgJKcOW1bT3eYYsX/14UxNdDi8Wo4azpmfg8u3oz1YCbaW39oLZmdwRfH1bax9xsWYeOmMCIJAUrUNmx74Dkow8TNAjyTJZViPXHjaGfwZFui6YlcncvHhkSUKWhz6P/BKIwtB/AFmWBwgctfS48foCtPS4SbcYd+vcHO46Gg5/YGiF0f6oh+mdHg27qjhWev+HX64SRO46YTxXvrKWqnYHjy+u4Jajd0ys+PwSbu/wghvuncQ4Lp2bw83vbuKjTc2cMiUNjy9AdpyRmk4HDk+ATfXdA8rpHV5/2D93KNJiDeGJpf5IyFhNWrocXq55fR13HF+IxTi4r8njD5AcvWPW/PBxiayrs/POmnoWFCah14hE6aI5tjiZba0VLFzfyMmTd9gaSTLD9raHGOnskQERGUNxMYag9U9/AStZEEAQ0GVmIbtdyPHxyA4Hot4AajVREybgrapCnZiA1NOLSqtFHWshet4hqC2xiHo92tRUBEFAcrkRDfr9Iku1u9fRr5GRLt2mbjdub4AuwUeUVkVAkjlhQgpLt3WwpbmHO98v4bGzJqPXqHj49Imc8+wKtrf18XlJC16/xPjUoSeAtrb2sqS8HYfHH1bvT4s1IMmKwM7jSyr4vKSFiekx3HLMOP5ycjFXv7qOldVdHDchmS9KW/lgYxP1NicPnj6RaIOGRPPoJh731hjAWFyMsbgYx9q1eFvbMBYVoRszBl9bG33ff48uIxPRZCR6/uF0vPYaksOBe+tWjMVF+O12TAcfrGSEfX4CnR2RaogIESL84jhgA96CggJKSko4//zzw0HvW2+9xYMPPjjqh8if/vQnbrzxxvDvPT09ZAzR26W2KJnK6COOQPZ40CQlobZaUVVVEejpQTe+CF91FYLBiDopEXw+tJmZGIqKwsrKhoICBEEI9+mq9oM+3dEQa9RyeGESlx+Sy3+WVqEWBd773cEUp8XsMvDQa1T0un3c/b9N2Jw+ZmRbuH5BYfjv12Bz0tztJiVGT7rFyOR0C89/X4dfkjm6KIljinfYJ+jUIlqVClmWueHtDVS2OzDr1Pzl5CJueHvjgPcNyNDnUYKS6g4H8wuGVtoMSDJjEgZmO9fW2lhXZ0dAybBOzx4oePLy8hqaut0kR+u5fN6YcMCrFgWeOn8a07KGFkhxBQI0dzpw+yWSo/VY+2XGdGrFGuaqw8ZQ0tSDPyBxx3GF6DQqtrf2UmtzkmUxDvK2FPoFu1uaesiwGjDrhy4vTY7W09KjHPfuMtrrqD+PLSpjXV03UzNjuH7B3u3z7s/uVhEA5MSbyE0w8cS5U7jw+VV8VtLC5IxYLp+XgyAIOL1+hBFCugyrEX2/jHCG1ciHG5tYur2Dt9fU89uDc1CrBIrTYnjky23UdDr4w5F5pMQo94Tznls5ZD93iC1N3UQP83fUqhThM5vTx7+WKIH6zlknt08a0HM4b2wCL/5QQ0efl5XVnRw5PglBEDhuQgr/t7iCVdVd2F2+cPAsjKKHecR78C62F9hxzw3ZsnlSUlBFR+Pvc+BauwZ9YSFqqxXRakXu60UwmtAmK/flkPCfJjkZX0vLfmOzsifX0f7AqIO6XZwXqTH64L1Vh0GrwubwYTFpeOTMiZzy7x/4obKTK15dyzO/mUaCWce7V83m/k/KeGdtA4vL25iVa+X8mVmD9jv+z18Meq3eNlCAcltbX/hzTM6I5Xfzx/CvJRUs2tLKTQvy+dfXFayrs3P5y2t49OzJuwx4H/ikhFXVNiZnxHDvKZN29c2MmtC5rlgMGdAkJiED/pYWxLh4ZJRssK+lGW12NsYpU9BlZaFJS8NVUhLuqY8QIUKEXxoHbMA7btw4SoIiJdOnTycpKYmWlhbWrVuHJEmjKm/W6XTodLueiQ1lenfGGMxOmJxOpZwIxTYgJDoluVz4u7oQDQa06ekDRKl+Ddnd/ty4IB9BgIlpsaNWZTZoVNz+v818WdaBYQhvz+ZuN25fgOZuJev49bYOGuwuYo0a7jph/JD73N7WR2W7A51a5N2rDiY5ZuQAzjMKafwQdqc3XCJ97ITkQcGu3enl+e9rALjpqHzW1naFl91+3Dhm5AyvBurySticPgKSjF3tGxDwhtCoRJ65YNqA1+rsTlweP7U256CAN8QzSyt5+IttJEXreOY304b8+6RbjKRb9qx/fLTXUX/W1XXj9gVYV9e9T9XWQyJnTm9g1AFviDl58dywIJ9/fLmNv31WzrKKDv5++oQB3rqj5cYF+XxX0cGnm1uYlxdPfrKZWblWJmXEsLG+m1dX1A3IIvdnd74fURCYlB7D5sYeajqdPL+shisPyUUcYXuVqAS3Ly+v5aNNzRxRqEwA5SZEMS5ZKb2+9vX1XDs/j1m5g89hl8+PyxvAsBuTCiMiCKiscaisyj1XBWjSdpRUR02aGL6PikYjUr/7L4CgVilqtnFxaNN3vxT752JPrqMDidA9SpJlBASsRuW7shp1vHjxQVz92jo21Ns55cllPHfhdLKCHr1GrYqXltfy108VVf9QT+7uUtXRR1uvm0Sznt/MzGRjvZ3vtnfwwg813HXCeB5dtI2aTieXvrSGx86ezLQsy7CK5quqbbh8AZZXdQ25fLcJXt/a9PTwOR+y2IqaPHnAqpYTTwAYYHcEYJwwYb+ohIgQIcKByU8jDfgzUllZyUsvvYTNNrCEb9y4cZSWllJXV8f8+fO57bbbeOutt3j99de55JJLkIYxZt8XiEZj+EHT/wEiuVzhbENoPXVc3H4lTDVa9BoVfzq2kOODCrGj4ZNNzXwUtPVx+SSauj14/DsC0JQYPXqNipQYPeUtPTz9bSWg2PDERw09MPw2aI00MyeONIuBx5dsH/EYNjV2s6yiY6An6BDIssyz31XT6/aTaTVy+hA2SP/9vpo+j5/CFDPHT0jh/qAiM8DZB42cqTFoRSxGDT9UtnHHws1c89oafKMo/c2MNWLQqckaJlj937oGHv5CEdBq7fFwzrMr+KykeZf73ddMzYxBr1GRE2/k8H9+y9WvrRvV591djFolOz5aK52dueawMdx5fCE6tcjS7R0c/dh3fLChaZfny86MS4nmxIlKWfDrq+oBJfMVUvdeWdVFg805aLuAJOP2ybi8Em6fhNcv4fFLOL0BvH5JKYXf6ViMWjVXHzYGlSiwttbGhxuGL48OcXRRMhqVQEVbH+UtveHXrzp0DIIAq6q7uPD5VZz872V8sKEp7PEL4PIGkGTl37dW1XDbuxt4cVnliO/XYHOypnboz7wrBMPA+6hoNCqTiBolA622Wn+V99gICm+uquXmdzbwxqqa8GuzcuN49OxJJEXraO52c9Yzy/lmazuCIHDr0QX87lClF/XRr7bz5DcVe/S+voDMTW9vxOHxIwgCd58wnpx4E+29Hu7/ZAu/PzyPsYlRdDm8XPnKWr4obRl2XzNyLBg0KmYPMYG0txCNRtRWZf9y8Lkamrz/tY5DIvy8NNpdlDR2j/gT8c2NsKf8qgPep556innz5tHU1ERra+uAZePGjWP16tXMnz+fG264gWuvvZbTTjuNN998k0AgsNsD0r1KsAdNNBjC2YYIA+l1+/jzh6UAnDQpFZNWRaPdPSBITLcYOSjbSrrFyJ3vl+ILyBw+LpETRgiqv9mqBLyH5ifwdXkb/w1mZIdDkuHlFbW8u65hxPW+2dbOxoZuNCqBqw/LHdTD2N7r4Z01yj6unZ/Hwg2N1HXtKIvr3+c7FHq1itwEM5sbellVY+Pz0jae/HrXA7O8xCiOHJc0ZHa3tLGbPy1UqiAunJ3FofkJuH0Sv39jw4iDsZ+C6xcU8tIlM1lT20N9l4svt7TyryV7NhAdCYNGTZxJt8eZR0EQuHRuDu/9bjZFqdH0uv3cvrCEv39evtv7uu6IPEQBNjd2U9qk9ApmWo3MzLEiAx/sFJjKsozP36+3VYaApAy87S4fHQ4vrb0emns8g/qG85PMXDhbKd/8eHNzuJ99OGIMGg7NV2yyPt28Y0Lk+IkpfHn9IZw/MxO9RqSsuZdb39vE0Y8tpStoF2HQqhAF5d91dXb6PH5W7CJz1dKj9GOGPI5/LAPutb+y6pkIA1lXa6fP42NtrX3A64fmJ/L5Hw5hdm4cDm+A37+xnieWVOALyPz+8DxuWpAPwL++rgxfD7sz4aIWBba19vGXoMd8tEHDfy6YxtTMWByeAPd8tIVzDspgVq4Vly/Aze9s5N21Qz9Xbj++mPevnbdXy5mHJDIOifAT0Wh3ceQ/vuWEJ74f8ef6tzZEfHMj7BG/2pLmbdu28cc//pENGzaQE1RN7k9BQQE5OTmcd955XHvtteHXTzvtNE477bSf8lCHZSjj+AgKDk+AHrfiF3jBrEzaez0sr+qk0e4acv0N9XYAbj4qf9jyzo31djY22FEJAocVJPDAp2VDrtcftSjglxRP0uGQZJmPNipBwBnT0sNldf3pH3Dc9UEph+7kr+oaZel0UvSOh8CPDQba+zwEJOU4J6bHYNapwxnwNbU2bE4f45KjmJ49uFz/p8AvybT37fjeW/dS8LMvKEqN4f2rD+aO90t4e01DWDhttPgDEs9+V03wzxE+X3rdPuq7lEG3awghrNFO24X+zv0ZnxKNTi3i8Ut09g1/fu+8D69/4L6y403cc1IRfzhyLGc/s4LqDgeNdhcOjx+rSYtBow5PKEzNjGVdnZ2iYQSCQgzXM17R2kudzUWGxcDYpJH30Z/IvfbAYWpWLGtr7UzLih20LMag4Q+Hj8EvSayusfHUt5V8vbWNB04t5tK5OZS39PLJ5mbeXlPPhLQYOkfh8RkiJ95EVYeDb7a1s6HezuSMWKINGv7vnCnc/8kWviht5YHPyvnrKcVkxZl4a3U9t723Cb1G5ISJqbt+g31E5NqI8FNgc3hx+QI8dvZk8hJHVkOP+OZG2BN+tQHvwoULOeSQQ8LBbiAQ4NVXX2XVqlUUFxdz6aWXsnTpUtTqX+1X8KskJCIUa9Rw0qRU3t/QxDWvr6et14NWLfKnY8YNuZ1OLeL0BkZUhw2VPJ84KZX4KB2ra3bdH+WXZKL1as6bkTnsOqVNPbT1ejBqVRwxbmiBqwyrkecums79n5RR0dbHwg1N5MYbqQ8ur+t0kJFgweUL9BNRGlxme/PRhUQbdNR1OfnjsT9OzOnQ/AR+e3AWL/5Qy83vbAq/ftncHPKSTNicXspb+n62gFejEnn4jIlc+cpaUmL03DrM3/6XglolhtXEp2TEjno7p9fPjW9vDJZYwkWzs5mWZcHm8PKXj7fQ1O0mPkrLZfMGTuwJgoBGpWR0QRFzEgSlT1evEVGJAqIgoBaFQdeFLMu8+EMNHr9EVpyRuTtNwOzMttZevg5WR5wxbXC5vizLPPNtFdUdDgQBHjljIhnWwYPos2dkc/YMRuwZhuF7xutsLpxePzVdzt0KeCMcOJwzI4tzZ2QPu7yy08mRhQnkxkfxeWkL5S29nPWfFVx96BhOnZLKJ5ubWbSllbuOH8/EUepNgOJIcOLEFN7f0MSzS6v49/lTAdCqRe45qQhREPispIW7PijhuQunoxKU9oUb3tqITq1iwfjRe69HiLC/kpcYNWodlwgRdodfbUmz0+mkM2ic7vF4OOqoo/jrX/9KTU0N119/Pccee+zPfIQR9oSQiJDbF+Daw/NQiQJtvUr26fK5Obh8gUFlZp0ODxqVMoAeLlNa0tjNdxUdqASBKw7Jpbylhx63nyjdyBMiggC/O3QM1hHKa0K+t7Nz4wao7+5McVoMr146g5uOykerEqnq2PE57vu4DF9AGiCiNBRWk44/HlvIk+dPG7U4kssXoDM4uzrwswncfmwhx09QSsA1KoG/nzaBPx47jvzEKKK0GsYljzwTu685ND+Br28+lE+vmzfi3+CXgMsX4Icq5VyYtguP6RBlzT1c+Pwqvtnajk4t8vg5Uzi2OJmWbje3LyyhrsuFxajhjuMLiR3CQkitEtBrgj9aEZ1GRK8RidZrMGnVGDSqISeBvtnWTmlTDxqVwKVzckb0+ZVlmee+U0r/5xckDFke/8SSinB7wH0nFXHS5B1Bscvnp8vhweXzh3/v7Pf77pBpMWDUqskeIpiOEGFXuHx+UqJ16NRqjhofz8NnTGDOmDj8AZnHl1Twz0XbsJq09Lj9fFfRsdtieRfPyUEtCqyptbG+boeuiCgI3HlCIfMLEvAFZK58dS0nTUrllMmp+CWZa19fz3fb2/f2x40QIUKEA4ZfbcA7c+ZMli9fzqpVq/jnP/+J2WymtLSUTz75hMWLF7N06VLeeuutn/swI+wmIREhvUZFdpwpLP50ULaFQ/IT8PgVVeb+dDl84UH9zr2KIZ4NDthPmJhCptXIymoluzt9F4HJmHgTuQnDB31evxTOFM/J23UmVK0SuXZ+Hp9eN5epmbHh18tbenls8TYa7S66Xd49FlEKsbnBzsL1DZQ2do8YRIuiwENnTOAvJxXxzpWzOWOaouA5IS2W38zK+tmyu/1JiTFg+JHfx95ic4Od9zc0sKnBPmhZk91FVbsDgGmZw59XsizzQ2UHl7y4mlOf/IGSxh5ijRpevPggFoxPorbTwR3vl9DW6yE5WsdfTy0mc4QATxCE3RqYO71+3gn2Dp4+NZ3UXZSOLavsZEtzDzq1yEVBEa3+PPtdFU8E+6vvPL6QM6cPFGDrL1oV+n2kSZ2RyEsyc/i4RNKtxj0OmiMcuLh8EkVpsZwwKZVYox6jVs11R+Tx4OkTiDao2dLcG1bm79+rPlqSY/ScNEkpT/7P0qoBy9SiyH2nFDN7TBxun8RlL6/lgllZHFOUhDcg8btX17GttXeo3UaIECFChF3wqw14jznmGCZPnsxFF13EZ599xm233YZGo2S85s6dy8yZM6muHlmQKMIvj5CIUChTesdxhdx70niePH8qqbF6dGpFlbk/VpMmXP7rHkL8qaSxm++2dyAKcMUhiuXEqmDAO3MEKyCAuGHUnkNsqLfj9knER2l32ZfSnzGJUYMshP77XQ0NXU5AGLKceXeoaO/D4fFT2dG3SyVinVrFeTMzI2VGo6CivQ+XJ8D2IZQktwSFpvISTUMKbvgDEp9ububcZ1dyyYtr+KGyE5UocPyEFN6+cjZTMi2sq7Vxz0dbsDt9ZMcZuf/UYpL2wPt4OGRZprSpB69fojBZCR5HwheQeGGZch89bUraIPXzTzc389DnWwHFamuogLi/aFXo9x+jjA3sshIiQoShMARL/Q0aEatJg1YtkmjWceLEVD66Zi7RBjWO4Dn19dY2HJ7dn1D57ZxsNCqBdXV21tYOdI/QqET+ftoEZufG0efxc8lLa/jdYXlhIa0rXl6L3Tn6vuEIESJEiKDwq21gFUWRN998kzlz5lBeXk5paSmzZ88GwOFwUFlZyZw5c37mo4wwkhq2jNJ3OOx2goBRp+a8mVnYnR7cPonMOANxJt2A/VqNWmIMGuptLno9PryBgYPgfwXVjA8eE0e3y8u6Wnc4K2sx7roseEtzz7DH+E1Q5GlKpoVu947BkSgQtmaRZHlAz2JI4MrtGlia7Q1IPL6kgpcumY4sQ0mjncr2PtJiDeQkmDFoRfTqwUGCLA/+nvMSoqhs72NMfBQGzdD9wL9W+n8XmxvsVLT3kZcQxYT02H5rjZQRlRnutM1LiKK600FOvGnQOuuDwmnjU6IHCIq5fAE+29zCu2sbwq9rVSIHj4nj8HGJxEfpqGjt4/11jTz7fRW+gIxZpybOpOXZpYMn7RRBtBF8cwXwDWO75vAoPeJ6jciZ0zNweAYHjC5fAI9PwuML8Mx3VbT2eLAYNRw/MRWPT0IQZKSggNWbQQulsw9K55iiZLocHqL1A6+p/qJVAE5PgG6XD1nWoBvyfJZRjZCxlmWlEiTU6x4hwmhRzkXl//FmPYnBySQZGbNBzby8BD7Z3EyUTk2fx8+SrW27tf/FZYpbxNRMCyuru3jo83Iun5czoALDoFHxt9OL+f3rG9jc2M3FL67i4TMmUtPpoLbLyVWvrePRsyehFkUSzXtvsitChAgRfs38agNeUJSYv/32W0488URuuOEGPB4PeXl5PPDAAxx99NHMnz//5z7ECHuIWhxYpmlz+vH5JWxOP/FROwYBIZEnrVopZghI8oBB9JamHkUMCMXeSJKhot2Bxy9h1qtJtRh49KxJZMebCEgyD3+hZKtuWDAWnVrF0m3tw8rje30SZcFg+KyD0hnTr/RZkmWsJi1Lt7VzzevruPuE8eFSz5Da7c7KuQKwtaWXD9Y3c8UhueEsbVlLL1nxUbh9Eibt4Es6ODcwgAnpseEAb7hy11Df8y8N9QjCY7tLRXsfTk+Aiva+AQHvyBXAwy+ckB5LcVrMkN/p6mDVwJSM2PBnqO9y8vs31octemIMGqZlWTi8IBFTsH/c45dYW9vF66vqwirNvR4/a+vsQx7DjBwrF8zKGvYY67qcZMebhnz94aBd0s1HFQwrkuMLSHQ7fdz1YQmV7Q4E4I7jxjM5KMQlyzIalYjbF2BtsE/xqPHJ+AISbb0eREHEhOL3OxTdLh++gITd6cNqGpgx7nJ4sDt9WEyaAdf5zuwcRMMOwTujVjXse0fYfxlt2f5IkyX92Vm/weXzMiPHwiebm8Mq+1+UtvJ/Z08GQaC528WDn5Ujo1w/O4uybWqwh1tqZudaWVNro6bTSXlLLzk7XY9GrZrHzpnE1a+uZ2trL3/6Xwl/PG4ct/9vMyurunjy60quO2LsqD5HhAgRIkT4FZc0hygqKmLjxo3cdNNNPPfcc9x6662ccMIJPP/88z/3oUXYi1hNGjRqpQytP6HSxlDAu7N1y5PfKMrMM3Ot4V7FkMdpYUr0gMyrShT447Hj+OOx44bMPO3MluYefAGZ7DgjuUMEGABXvLIWX0Dmrg9KBy3r3wcaY9CELWb+uWgbVcGspEmnpjDZ/KNLQA9U8hKiMOpU5I3Qh7036HH7wpUAE/sF1m+sqqPL4SU5Ws+NC/J556rZzO8X7AJ8t72dV1cqwe5Ivb8/Bl9A4qUfapBkmJwRy1HDBLuyLPPxpibOeXYFle0O4qO0PHfhdI4pTh607ppaG16/Us4/IS0ajUpErxaR5JFLjWONGjQqkdghqivsTiUY7nL4dvszRsqcI/wYGm1Oelw+9BoxfA4t3dYeLmtOiTEwNaj5sKv+3miDJqzRsLisDV9gcMWFWa/hifMmkxNvoq3Xw6NfbuPmoxUv4FdX1PF5yc/rhR4hwoFIRVsfJY3d4Z/KYPtSZb/Xh7PHjPDzst9Oc7/55ps8+OCD2Gw25s+fzx133EFeXt6Q65rNZu655x7uueeen/YgI+wxnQ4PNoeSyYkzjdwnCxBn0hFn0oUVh0O2PTtKG3dky0KUt/SyaEsrgkBYSAR2BLxFKYqtSUCSae5209LtoqXHjdWkY3qWBZU4cqZgYzBgPaIwccjsQ3WHY9hty5p7eGNVXfj3kyel8Oq6tvBnuO1/m3nr8lnhrOTIRawRhqN/pntfsq7WhiRDltVIglk5n13eAEvKlb/p7ceNY/JOwazHF+D9jU2sCCo7z8uL55QpaeGs6d7kww1NtHS7idarOfugjCHP1z6Pn4c+Lw/bD83Ni+evpxQP28e+rKIDgNx4E0admvgoPU6fH5dXGnFyxmrSDcrshog1asIZ3t0lUuYc4cfQ1ushzqShIMnMxoZurCYtXQ4vGxvsHJyXAMCxxSmsr7OxpbmH6o4+cuKHn0ibmxfHpoZumrrdvL2mgbOnpw+qXok1avn3eVO49KU11NtcLNrSxoWzs3h5eS1//aSMKRkWJqRHtBUiRNjXWExaDBoV17+1YcDrY+wN/Av4w1sbqIxVnnkGjYqvbjqUkRVgIvzU7JcB74svvsi9997LAw88gMvl4h//+AeTJ0/mhRde4Mwzzxyw7urVq0lNTSUtbbA3ZIRfLjaHD69fyeSMJuAN0T+LE+pNNWhUqMXBtkRPBnt3jy1ODmd3azoclLcoSphFadG8s7aBjzY2sVN1MbkJJh48bcKwx9Hn9ocVeYcT/vnfuoYBv9d2OsiKM9Hl8PJiMNsWosHuZka2hVU1NgRgTY2Nt9c2cM5BAxVvQZks6HL4sI5ysiDCviek+j2jnwjad9vbcXoDpMTombiTL68/IPH4ku00dbsRgGOKklkwPmm3bVBGQ32XMxx4nz8ra0grLrcvwJWvrKWuy4lKFLj+iLFcNDsbcYRJn5Ad14wcqxLkasCoUWPUMOJ2IxEKhnc12TQUQ5U5R4gwGjodHpBldFoVp01NY2NDN16/8ixZWd3F7DHxCIJAglnHzJw4lld18vIPtUzNslCYEj2oZBmUDO65MzJ4bWUdle0OPi1pGTDxGiLerOPhMydy2UtrWFnVRUGSmTl5cSyr6OSKV9bwxQ2HDOqLjxAhwt4lLdbAVzcdis2xk2jctnL4BqW1IX8cFW19XP/WBmwObzjg9VZVobZY0KQOvr4j/HTsl0//u+66i+eff54FCxYAcP7553P55ZdzzjnnEAgEOOeccwAIBAJccMEFBAIBli1bRmLiyIqjEX5+Fm9pZmNDD7nxRorTLUNmckJ9uaEsbn+MWhUNNmdQjVkmzqTjww1NfLlFEQvJjlP6qjbW2/miVMnu/u6wMXT2KTexRUFRkVk5VpKj9awNZuY0KoHkaD1J0XrW1dmoanfQ3je8WmZHnwcZSI3VkzKMrctXZQMFT37/xnpeu2wmDq8f704lbr1uHylxyqBJEJS+3LaegfZLIbr2cLIgwujZnUkFf0Diww1NgJIVDeELKDMaNqeX2k7ngEGxJEN7n+IvfWh+AkcVDS4Z3lu0B32sE806itNiwoJT/fEFpHCZ1tWHjeHiOTm73G8oNpcRMGiVzFUow2vSDd1H+2VJE1tb+yhKNXNowcDP3GR30trjISlaR4Z16BaBCBH2BV0OHymxRrLUIplWA48vrqDT4UUlCtR0OvlueweH5CtZ3qOLktnc2E2X08tXZa18VdaKTi2SFK2nOC2aMQkmLEHP7AyLgXSLgeoOJ7YRyvTzk8zcdcJ47lhYwivLa/nnWZOo63RSb3Px9DeV3HrMuJ/ke4gQYU9otLsGB4o7UTGEs8EvjbRYA2k7jedc9ihqUJw1DDs5WagtFgSDgaZbbkUwGBjzyceRoPdnZL/r4ZUkiaamJnS6HYNMnU7HSy+9xIUXXsjFF19MaanSD6lSqVi4cCFHHnkkVmukuGB/YGNDD30eH1UdTsYkRg0ZTIzUi6dXi4CAShDocvj4ZFMTN7+7EYALZmVyaH4CLm+Aez7cAsCpk9PITzKH97uiSsnEHVWk9DCGVJqvOnQMj587hTuOLwxbIkkjKEyHlg3X69vc7aK6w4FKFHj/6oNJiNKxrbWP697YQEq0nuN26omclmXhiy0twX1DaoyeS+YOHXSE7DR27meOsPfoP6mwK5aUt9HU7cZq0nJ00Y7e2GOKk5mebcHtk7jnw9KwvyeAVi1y/ATlwfhDVSedweB3XzA2KQoBpWTTNozliVmv4bQpSpXMJ5uaBwmqDcVFs7MBeG1FLV3BySGXVxqxh3d9fTd9Hj+lTYP9Rlt7PHh8AVp79t13ESHCUPS/p+rUKm45ugAgXGnw/oZGGm2Kqr7FpOX24wr5zawspmVZMOnUePwSdV1OPt3cwhNLKvnX15V8XtLCV2VtVHcoVRPHFA/dNx9iwfgkTpqUigw89MXWsIXe88uqaekeevIzQoSfm0a7iyP/8S0nPPH9iD/Xv7UBg0Y1rAjo/ogmNZUxn3xM6sMPIbtc+G17vx0pwujZ7wJeURSZMWMGjz322IDXBUHgmWeeobCwkDvvvDP8emFhIU899RRq9X6ZzD7gmJQeTZROw6T06GHX2ZVvbEjAqqXbxa3vbcYXkDlhYgp/OlaZBf/T/zazpbkHi1HD9Qt2KF2urOrE45dIidEzNuiZG+ol7OgXcISErEayVArFA8OVXq6qVm58UzJiGZcSzTMXTMOoVbG8qpO31tRzxLhE5uTFhdf/srQlnBEE+OupxUOWnoLSzzx2mMmCCHuHkSYVllW088zSSr7frmTwX15RC8DZ09PR9atIUIkCdx0/HqtJS3WHg6eXVg3Yz7yx8eTGm/D6JV5aXhO2sdrbmPWacHZ5c0P3sOv99uBszHo1VR0OFq5v3OV+T5yYQqbViN3l49Xgd2DQiojC8NfulIwYonRqilLNg5YlRevQaVQkRUfO6wg/LTvfU8+cls7E9Bi8fokYg4aAJPPS8ho8wTJnk07NQdlWLpydzf2nFHPzUQVMy7KQaTUgCNDl8LKqxsby4ATr4eMSRuWnfcOCsaTG6mnudrOiupPpWcqE2aOLtu27Dx8hwo/A5vDi8gV47OzJfPz7uSP+fHXToYMyqPs7mtRUtLm5P/dhRGA/DHgB7rnnHhYuXMgTTzwx4HWtVsvdd9/N559//jMdWYQfyxHjU7jxqAKOGJ8y7DoGjQqrUTOsd2ycSUeUTsVN72zC6Q0wJy+OR86ciCgKPPlNJZ+VtKBRCTxx7pQBg4yQZ+5hBQnhXsm44GxjR7/y5VCp5khJrlAGbDgLjJBFTagMbnxqNP93zmRUosCaWhufl7Zy0qQdfedVHc6wKNXJk1M5rGDPy/O3t/byVVkr21sHZ9EijI6RJhW2NPfS5/ZR2txLeUsPyyo6EQQ4b2bmoHUtJi1/DE7EvLe2gVXVneFloiBw3oxMTFoV9TYXr66sHbLceG8QEr7Z3Dh8wBtt0PDbg7MBeGLJ9rA67XCoVSLXzleEBN9a00C3y4dRo/gHD2cLdFRxKlfPHzuonBkgNdbIlEwLqbHGIbaMEOGnQxQF/nJSEYKg2GiZtCpaezwsXDd4IkgUBDKsRiZnxPLbg7O55ah8zpqWxrTMWKwmLcWp0czKGV0Fmkmn5s8njkcAPtnUwuGFynPgnbX1kft5hF80eYlRFKfFjPjzawt2I/yy2C8D3qOPPprbbruNP/zhDzz55JMDlmVkZKDVakfMvkX4ddDp8LC9rU8RFOmH0+vn8pfX0t7roSApiqfOn4pOreLL0lYeX6wIVf35xPEc1G+QUd/lpLLdgUoQBvRZWsMB7+AMb//gw+tXbF1C4j+hkmbVEF62Xr/E+qCH6iFjd7zXvLEJ/OWkIkDpJQ6p84aQg8dz9wnjR/HtDE9tlxOnx09tl/NH7SfC0IxPMROl15BlNfC7V9cBcNT4JNItQwdqs3LjOH2qMrnxwKflAwLJuCgdl8zNQSUKbG7s5qNNTfvkmEMB77aW3gGl1Ttz6pQ00mINdDq8/Pf76l3u99QpaRQkm+l2+Xjym4q9drwRIvzcTMqI5eygd7peIyIAy6s6Wb8LFXW9RsW4lGiOn5jCtfPHcNrUtBHF6CRZ5vOSFjbW2wGYkmnhgtmKz/Zz31VzWEE8kgwPBj20I0SIECHCYPbbOt+//e1v+P1+rrnmGlasWMGdd96J0WjkT3/6E9dcc80+UTONsPcZaWJChh3p1EHbKUrO/oCEzeHDGhQB6XP7ueHtjZQ29WA1aXni3KmoRJG1tTZueXcTACdNSmFyeizbW3bMiH+4UQkk8pOjaOvz0BYMcN3BErWWbjctO4lEtfd5aLK7aLS7qGjro7LdQWW7g4QoLQ22HT5sfe6BmbD1dTY8folEs5Il7B84nzoljW+3tfPlllY+2DA4W3DX8YXhILw/kiQPOOf/vWQra2vtTMuK5er5BQNmtrKsRuq6nGRajUiSjDjCtNeBeB0p3+Xwy2VZHvF7mZplZUtjN3d/sIX2Pi8pMXpuP64w3Lfq8Uvo1AO/9KsOyWVdnY3qDifvrWvg/JmZ4feIj9Jx6uQ03l3XEK5C2BU7K4D3J8Gsp9s1sPdYlmXMejW9bj/fV3aQPYxvNMBZ09N59KvtvLi8hgVFiYNKMWVp4ETPjQvGcuUr63jxhxrOmp5Baqweo1Y9rPWYJEmjOO9+3Hm5q/nQA/C0j7AH3HJ0AR9sbKLT4aMgyczW1l7eXF1HusUw6D7t9QfodY9cFQFQ2+kMOwV4/RLfV3TQYHMhCoogVoJZR0CSiTNp6XR4cXklREERQVxW0cH07MHZYq1qdLmNyHkfIUKEXyu7HfC2t7djtVpRqX5eL0NBEHjkkUc4+OCDuf322ykoKECtVnPttddy3333/azHFmH07GpgO+xSASwmTXjAHNrP40sqWFzehkYl8H/nTCbNYqCjz8O1b6zH45eYnBHLJXNy6D/e9folShoV79354xJJNO8YwIeC0R6Xj4xghi5kcZRo1lPd4SBKp8bXT1VZqxbD5dZGjSpseRQi5K97WEEC+iFKO5/+zVRufW8Tby+vHPD6YfkJnDJl6GyA0hfmwe70EWvUsKbWjtPrY02tPbw8xNgkM2ODQl2hCYfXVlSzpsbO9OxYzp+1awXe/ZldnnNDLH5jVU14AuGcg7J2GRC/trqB9j4vBq2KJ8+bSkI/r9rUGD3GIfqv/3XeVE799w9sb+vD7vJxwsQdao5z8uIxG9S8sKwGAbjy0FyKUof23/zLx6VoRpjFcHj8HNyviiFEXkIU6+vtVLY5iDUOLxxySH4C325rZ12dnf9+V8O9JxcNWB6Q5HAVBMAhYxOYnatYtTy+eDsPnTERCFqPBSes+ge8KlGg0e6iudtNSox+2Mx4hAj7ktFM9sVF6bj16ALu/WgLTXYnxanRlDT18PGmZl69dMYAX92CZPOI12WI+z7Zgk4t4vD42djQHZ4ok2RFAG9GjgWdWsX0bAtLyttYWd3F9GwLa2psPPzFVt68YtYBOVEZIUKECCOx2yXNL730EmlpaVx//fWsWbNmXxzTbnHaaadRXl5OU1MTNpuNRx999GcPxiP8NMSZdOT166N8b10DzwXLLO87uZipmRa8fok/vLmBlm43qTF6bju6YJCQ1IqqTly+ABajhsLkgWJZoYG/wxvA6VVm50Meov2Vajv79fh2u3yEwl/1ECXNyyqUUuVDg/27OyMIAg+cOmGAaJVJq+L+U4tHHMjYnT58AYnWHjdFKWaMWg3Ts2KHXb8/a2rs9Hl9rKmxj2r9A421tXb6PD7WBicQRuL1VfXUdbkQBHj49IkUJA8WYAqxrbWXu94vYU1NF+NTornl6HwAnvqmkvqdSs7Pm5HJ0UXJyMALy2posO3dkvTcBCWrW9neN6IKsyAI/OHIsQjA56UtlDYN3/cbWv/m4Of6aFNzeH2LSYNWJQ5pPdbc7cbtDdC8h+qzLp+fTocHl2/XGbUIEX4MZ03PoDDZTK8nQLrFgFmvZkO9nb99Vr7HQnPtvR5W1dhwegPo1CJTM2MxaVV4/BKbG3qQZJlYo4brj1Suq9KmHnRqkQ31dhYFLfgiRIgQIcIOdjvgveSSS7jrrrtYtWoVBx10EIWFhdx///1UV++6n2tfkpKSQlRU1M96DBF+PlbXdHH7/0oAuGJeLidOSkWWZe79qJQN9Xai9Wr+dOw4ovRDeH8GBwizxsSFg9kQes0Or9+QcFVolVCfriTLYc9UgG6XP5wZ3jm4brA5qetSbCjmDJFlA8Vzt6bDwTnB/jCA648cu0tBh9ighdLN72ziheV1XHdEPtccXjDiNiGmZ8cSpdUwPTt2VOsfaEzLiiVKp2HaLiYQlm5r5++fKb10Nx6Zz+HjhhYXc/sCPPLlVk544nteXVnHZS+vpbnbxSVzcpiaGYvHL/G3z8oGVA4IgsD1R44lN96Exy/xzLdVw9oI7QmpsQa0ahGnN8DWlp4R1x2XHM1xExRhuce+2r5LzYSi1BhOnKis//AX25BledCEVX9SYvTotSpSYnatXDsUI1mXRYiwN1GJAnedqOgqfF7ayqVBu7jXVtZxwhPf80Vpy6g1RSRZprbTwcaGbgKSEtTOyLFiNWmZmB6DShSwu3xsb1U8Sy+ek8NB2RZc3kDY2/e+j7fs9cmwCBEiRNjf2e2A12q1cs011/DDDz9QWVnJ+eefz2uvvcaYMWOYO3cuX3311b44zggHOC5fQOlXGkJQp9Hu4qpX1+ENSBxZmMjvD1eUYT/Y2MT7G5oQBXjkzEmDSotB6c3d2NCNAMzOjRu0HCAmGEi29ypBbahc0+NTgpFup2+AZVCPy0cgOMBR71TC9kOlkt2dlB6DWT8ws/XZ5mZm/PUrJt67iKMe+47fv7khvOzcGYMVfnfGatKxvKqL8pZe3D6Ja19fH7bJ2JUy8/mzcnj0nCm/+nLmPeXcGdk8cuZkzp2Rzffb2nhs0TaWlLUMWKczWDovyXDK5FQumZM95L4abS6Of+J7/v11Jb6A0jvb7fJx1weliKLALUcXYNar2dbax3trB/bialQi587IJDlaj93l47/fV+81gT6VKJAdp5QPh/yoR+Kqw3LRqUU2NXTz3faOXa5//YJ8NCqBldVdLKvcIcjm9CrXdv/gNN1i5KBs6x6XM+/KuixChL3J1EwLp0xRWhA+L2nh3pOKSIjSUdfl5A9vbuDC51dRNwqRwGeWVlHXpeg/pFsMTM2MDff7m3RqilOVCqR6m6IdoRIF/n76REw6FS09bmIMatp6PZz37IoBiu8RIkSIcKDzo1Sac3NzufPOOyktLeWhhx5i9erVEUugCPuE4TI2Xr8S2HU5vBSlRvPAqcXhLG3I+uf8mVnDZlMXB1WVcxNMxEUN7e9pDvZbhoR+Mq3KIPzppZV4/RK6oEJnCK1KYFtQdCTNMjDI3tKkZM6mZ1kGvL68spMb3t5Ap0PJ2EXr1YxN3CEctHPmeTg6B2SafeGSuogy896jtKWXXo+PTQ0Ds6B9Hn9YYXk4j+RGu4uLX1pNVbuDpGgdT/9mKv+5YBpAWIU1PkrHCcFsaHWHY9A+DFpVuEza5vSyN/Xo46MG+04Ph0oQ0AYH43anbxdrQ7xJS4xBmeTp6N2xf5dPubaHmszaUwwaNXEmHQbNfqvLGGE/4+ajCogxaChv6eWjjU0svGY2Vx82Br1GZHWNjd+/vp6Xd+Gn3dytBLsqUSAtVj+gFx4gxqAJa0iEnoXpFiN3Ha9kmLtdfqxGDa09Hi58fhUPflY+oup6hAgRIhwo/KiAd8OGDdxyyy1kZmZy++23s2DBAk499dS9dWwRIoQZLmPzyJdbwyXLT50/dYC/pyG47nBZHkmWwzZCUzItQ64DSlkzgCs4wLj6sDHEB5WYv9vegU6t4uAxSnbYoBHRqkXa+7xoVSIXBu0jQtR0KgFMqF8SYGtLD1e9uhZfQOa44mQ233MUG/98FB9cO3fXX8xOXHFILtlxRlSiwKNnTw5nkbOsRow6NVnWiADQj6Uo2YxZp2Fi+sB+76w4Ew+drggyvbqyjieWDLThabS7+O0Lq2m0u8mOM/L+1XM4uig5XLYc10/VtaZDmZgIiYv1p6q9j6VBteazp2cMGhT/GEJBp2UE0aoQD32xlV63n4JkM8dNHOybuzNvr22go89LnElLZpyBDzY0srnRjkGjXNvD+WrvKyJ9vhH2JvFROp67cDpROjVram1c8co6ZuRY+eS6eRyan4Bfknl1ZR1XvbaWDcHJrZ258ch8TDoVAUlmba19wESSLMuUtfTil2SidGpy+ympnzo1nRuC/bxdTh9jE6OQZXh+WTWnPfUDJSP4a0eIECHCgcBuB7zNzc088MADFBUVMWXKFL7//nv+9Kc/0dTUxMcff8ycOXP2xXFGOIBx+QI4vQGMWtWAQXGj3cXLy2sB+OupxWTsFMyZgsGvwzv0gLa0sYe2Xg9GrYrClOGFhXQa5TIJiVbFRen484lFGDQq2no9fLe9g8IUM8dNSOaYomTW1SmDi9ljrOGMGSgDllDGLmT70tbj5pKX1tDn8TMtK5ZHzpw0bHZwNJj1Gj76/VyW3jKfBeOTwq+PTTJzZGHSkAFUhN1jbn4i1y/I5/DCwUHe6dPSueO4cQA8vbQq7FXbZHdx8YurabS7yLAY+Pf5U+jzKAFXSPAsVGEgyzJbmpXs8fiUgUF1n9vPe+sakYHZY+KYmB67Vz+bM5ihtgxhfdWfJeVtfLO1HZUocOfxhYNK93fG4wvw7NIqAH5/eB71XS4cHj9V7Q6MWhVxJu2giamR2hj2BpE+3wh7mwnpMTx30XTMejVbmnr47Quruev9Eq45bAy3HzcOq0mZKL31vU08/MXWQfZgsUYtE9NiiDVo8Esy6+ps4aqf1h4P7b0eBKAo1TxIH+Kqw8Zw2zHKvWd7Wx9TMy3EmTRUtPVx6pPLeGLJdvyBPRPRihAhQoT9nd0OeF977TVefvllzj77bCorK1m+fDnXXHMN8fFDl4xGiPBjGW5g+tDn5Xj8EjNyrBwfFNDpjykYODo9Qw9ovwpmd+flxaPpZx+xc0+kXq0KH0eInHgTfzx2HALK4GJ9vZ20WANVHQ5cvgDRejVTdxI46uzz4vAEEAXCFkdvra6npdvNmAQT/7lgOrq9kOUyatUk76HYT4Qfz3kzM7nhyLEA/GPRNv79dQUXv7iaBpuLtFgDL/72ILQqVdiSJzSgDWV4G+1uul0+NCqBvMSBQnyPL9lOt8tHfJSW06ek7fVjd3hDGd7Byskhet0+Hv5iKwAXzs4ifxSTKO+sbaCt10NKjJ5Tp6SRm2DCpFMPqHTYmX0dkEb6fCPsCyZnxPLpdfM4f2YmGpXAD5WdnPWfFXy9tZ0/nzCeEyemIACLylq59KXVfLO1bcD2apXIlMxY4kxaJBk21NtpsLnC3rw58aZB+g8hLpmbwz/OnIRGJbCuzkaG1cTh45Ts8j8XbeOMp5dT2d63r7+CCBEiRPjFsdsB71VXXUV5eTl33303ubm5++KYIhygdDo8bG/ro9MxsH9wqIHp2lobH25sRhDg7hMKh7TrMQXXHyrD6/IG+KFSEdo5onCHkq4kyzz5dSV//qAknO3akeEdOPCelmVherYleDx21tfZ2RwsHZuVGzco6xUqZ04LquEClAYzeSdPTg2/T4T9n8vn5XLFPOX++O9vKqkPBrsvX3LB5cQzAACiUUlEQVQQyTH6sCUPskxFmzIADWV4y4LnxNhEc/g8ASWruqS8DVGAC2dn75XJkZ0JnePWETK8z39fTZfDS068iUvm7FrkzOML8J9gdvfKQ3LRqkUmpMVy8uQ0JqTFDrvdvg5II32+EfYVCWYdd50wns/+MI+TJ6ciCDt0Gly+AHcdP57ceBM9bj8PfFbOO2vrB2yvEgUmpceQaNYhy1AeLGU269Vkx4/clnLCpFSevfAgonSKPVJdp4u7TxiPWa9mY4OdE574jpd+qAk7CUSIECHCgcBuP+kj1j8R9ib9H7k2hw9/MOtlNemUTKsgoNeown20MiBJMvd+tAWAUyankWk10etWgtNejx9DMCOrDf7b5/bj8Uk02BRvVICVVZ24fRIJZp2iNNvYjbexh9pOR7ic9I1V9eQlRoV7d7tdXuw72cBkWIw4PAG2NPewptYGKJYqiWYdHr80ILu8tUUJbDKsRlxeCdkoUx58rzEJZlzegJJNHqYncyQ1XlmWR/ToHX47pZfR5ZMwaMRBfZTD7XNXysB7cix7i10dmzTMd/V1WQubG3uYmhnDnLFD2wkB+AISKlnZvqXHTXufh4QoHcnRSlbd75dRq+G6I8bg9Pp5dWUdydF6/n7aBJJj9AQksBi0WAxaqjscdAUzvFajBkmSWRaciEmM1oU9a7scXh5dpGRVJ6TF0N7rCauG74w/ICPLw5cuBmSZ2s6hxct63UqJpUYl0O0abHm0rtbG11vbEYCbj8rHL0n4vQPfK2SnEiKU3U2O1nPKlDQC0o6/kcsn4fIFMGhUGIITPo12Fy3dbpJj9GErrp3/pj/m/FKulV2t9fOdvxF+PaRbjDx4+kQumJXFQ1+Us6raxldlSjvA0cVJFKaY+WRzC89+V02P28/FB2cRkOSwsFVBUhQC0BosZc5PjMIfkAGZth4P935UGn4vWZZpsrsxaFVYTVrm5cfzTXk7Fe19PP1tJY+cMZEXfqhhRVUX93xUyjdb23jkzIkDJs6GyxxHiDAUjXYXNsfI1nihCd0IEX5u9mhq2+l08swzz7Bhwwa6uroGDEZOO+00Lrnkkr12gBEOHCwmDTaHD4tpx0N3qGHnBxuaKGnqwaRVcd3heQMGrzq1GBarCg263T4Jg1ZFtEGDRqWsvCmYiT0sP4G4KB31XS4MGhWbGnaIe9R0OEiJ0Yf3LwhCuPc2xIWzs4g1avnzh6V8u60dlSDw4GkTyY434fEFwpZGoARHAPlJZmKMGpxeP/U2RZVzXIoZo1aNShTCg/n+g3pBEEYc5PffbncQRSXoUFRypQGiXwcamxt76PP42NjQw7z8pGHX6/9dt/Z68HgDtMoeUmKU4MyoU6MOnmd/ObmIw8YlEGfSkWE1YNSqkaQdAXeMUUNPsI8v3qxDEIRwMJqfaEavViHJMm+uqsflk8iJNw1S/t6ZY4qTmZ5lHXb5ujrbkBZdfkkK22uNS44ecO4CODx+ngv2JJ8/M5O5YxOG3L8sy+iCk00ef4DnvlO2ufqwMZj1mgGTM/0VmkOZ3JZuNy5fgOZu9x7bEkWI8FOhH0WlRXa8iSfOnUp5Sw9Pf1PF8qpOPtnUgl4jcsjYeJZu7+Ct1fWoBLj3pCJUqh33ckmWWVzWRlK0bkDP/v0fbwlPlrm8AUoau+l0eBGA4rQY0iwGZuRY2VBvp63Xw03vbOKfZ03i8HGJ/PPLbXyzrZ0b397IY2dPRqOOVBdF2D0a7S6O/Me3o9JYMGhUu9SFiBBhX7Pbo1u/38+8efOQZRmDwYDb7Wbs2LF89tlnREdHc8UVV+yL44xwAGA16bCahrYGCtFgc/LAp2UAXH5ILvHm4dcfqaQ55HVYnLZDFKimU+m/1apFvH6JLqcXlzcQ7u91DiN+JQoCdxxXSHyUlrGJ5kFBcYjqDmWmM7R8a7AnKz5KS4xB/bP1Ehq1qrAo2IHMhLRoNjf2UJw6emGvJLOO1l4PScOch4IgcMS44YNng1ZFb7B0Ps6ko8/jp9GunJuh/tbFZW1sbe1FqxK5+OBsVtXs2iN3TwhVI4gCmA2DHw1Pfl1Ja4+H1Bg91wS9rnfFu2saaOlRsrVnTE8ftHyocy85Rk9zt5uUvdyH7vL5hxS/ixBhXxM6zw/KtnLI5Qn8UNnBw19sY0O9naXbO5iRbWF1rY3XV9XT3O3mj8eNCz93REEYIEDYH1mWw/29gWCJsgxsbuzG7QuQm2Di2AlJbGvpY12dnatfW8dfTy3myfOncvVr61i6vYNb3t3Ew2dOHKBjESHCrrAFBQUfO3vyIK2JnbGYtOFqnQgRfi52+w63aNEiPB4Pq1ev5tRTT2X+/Pm8+eablJeXIwgCKSmDxYMiRNhbPP1tFZ0OL2mxBn4zM5OVVZ0c//j33PT2RhZtacXdb7YxJFoV8kYN4fQGwuqYoUG1LyCFS2/GJZvDPYxN3a7wQMAxjPgVKLP8fzgin+OGEM8KEVJoDtlJhERI0i0GbI5d+5juK9bWdPLumnrW1nT+bMfwS2B+YTLXHZnPIQXDB6ghVlZ18PLyGpZubeOyF9eE1Zh3F5dXCmdp4qK0bGqwI8uKaJTFqKXJ7uL9DY0AnDU9naTofSdGFpoYitKpB1kdrauz8b/1ynHcdHT+qCoBZFnmlRWKivrl83LDWd/+GDSKQnP/ADQt1sBB2da9nt2NqDJH+LkwatUDzvODx8Rz45FjOGp8IoIAq2psTMmIRaMS+HprO3ctLAm30gyH2xdgTY2N0qaecBvB3LHx5ASfL9vb+tjS3INGJfKfC6axYHwSfknmtvc2U9rUw2PnTEarEllc3saf/rc5ouAcYY/IS4yiOC1mxJ9IsBvhl8BuB7wVFRUcdthhqFQqdDodDocyiE9JSeH888/nyy+/3OsHGSECKBY+765tAODGBWPRqETu/WgLdV1OvtzSyo1vb+Tox5ayJpgBCwe8Ow0cWrqVDFqMQRMeuNd3OcOiIOmxhvANusnuUsSFgD7Pnvt1Ojx+WnuUnstQhjckTpSXEDWgjPunprSpl163j9Km3p/tGPY3trb2UdPRy8Nfbsfu8vHiD7WUt/Ts9n4MWhGbc4dK8/qgP2dugjJjvqK6E78kU5hiZm7evlXC73HtCHj7I8kyD36m9A+fPDmVqSN4VvdnXZ2d7W19GDQqTp+69xWld5eIKnOEXxJbmvsYmxjFSRNTgqrKdsanRKNXi6yqsfHH/20O9/LuTG2ng5XVimWRKCiTtDNzrETp1BQkmxkXtNmr73KxsqoLnUbFI2dM5DczMwFFPb6ksYdHz56EWhT4orSVv3++9Sf77BEiRIjwU7PbAa/H40GvV7IMaWlpbNmyJbysp6eHQCAyex5h9ylttPPRxkZKG+3h15rsLlbXdNFgU3oa19bZ8PglCpKiOLIwEbvLF+6BPXt6OhajBocnwOoaRTxKF+xL8vgHnpNNdqWXtn/JpD9YDub2Beh1+8NZYUEQMOvViAI02Fx7bOmwJGiBlG4xEGNQgtsV1UpgfkRhEnG7KOXelxSlmjHrNRTtRinvr53NDXYWrm9gc4N9yOUFSVF0u/zY+/lofrO1fbffRyUIuH3KoNZi1IbLio2aUA+ssiwhSrdPhcC8fonvtivHv7OlVUCSw1no/KTRixZ+H9xfUao53NP8cxJSZdYPkWkOsb21l6/KW9m2B5MXESLsDqH77hnT0nj2wunoNSIbG7qZmWPFpFOxuaGbR77YOqQIX3uvJ1zCPC3LQna8acD9ITvORIZVmbQN3aNEUeDWYwq4PmiZ9vS3lSRF63nkzEmA0n4QaqeIECFChF8bP6ppY8GCBWzatInzzjuPa665hueee44FCxbsrWOLcABR1eGgrcfDiqouuoK2RK09HtxeRbwGoDkYqI5JjEIQhPADXxTgzhPGMykjFlBKQ0ERFwJFibg/9cEAOjQgABiTEEWsUYMvILOyuitcfjw2MQqTTs1hBYpq76sr6vbo84Uy06dMVjJd9V1OqtodqEWBlBj9ICumn5K5YxO58tAxzB1BmfhAo6K9D6cnQMUwExwzc+OZmTMw4xpS9x4NnQ4Ple19YRVmUDKQ41OUnvLQOVoQ9LitCp6Pu0u3y8cbq+r4vKSF7W29+IYoW5RlmS9LW7A5fZj16gE2XQAalcilcxX7oWeXVoeVnHfFhqAAnEmrHrE80+UL0BnsBwvR6fBQMYRF2b6m1ubE5fFT0zW0inWECHuL/vfdQ/MTePSsyQB8u72Do4uSEQX4qqyN11cOfuZMy7IQHeyzHypIlWRFxRkUZecQgiBwyZxsjhiXiF+S+fOHpRyaH8/MHCsBWealH2r2/geNECFChF8Aux3wXnDBBVx//fUAmM1mlixZgiAI1NXV8eqrrzJ79uy9fYwRDgBCfa1JMbpwP2tStA69VhXOxDYFH+whhdlQwBsKbLv6gvYuwf7b0Ix3YCe/wYZgVrh/j6BKFJiRbSVar8YbkJCD7x/qmfzNLKUUbHFZ627Ngrf3evjXku1sqLejFgVO+X/2zjo8iqsL4++sW7ISdw8BEiC4FSiU4lCo0Bb6tVB3oUK/lrrLV1raUnelrrS4u1uQBOKebLJZl7nfH7s77CYbJWQ34f6eh4fszOzk7GbuzD33nPOebKfDuyXX2Xqmb3QwhAKeX2t4O0JzPZO7G7UGC/Iq9dwii5vUMAVkYj5Sw5qPaJpdmQMSVzsddw/mtlBntMHmYJFXZeDOIeDz0NclolasNcHBEm5clNSZvOrT28qanArsKdBidU4F3tt4Go/9egSrjlVgy6lqFNYYYXOwOFRcj5zyBjAMMKNftM/63CsGxSAxRIY6kw2fbi1o9fcSQnDIlZ6dEelbxM2Nr9parcEGq53t8nGRoJZBKhYgUUPVoSmdS2uLOJMzI/HgpekAgF/3l2C2qwzg4y352HTSO3uEYRjOkS2tMzdpl1ehM8NiZyES8JqIKDIMg8em9UaQRICjpTp8uaMA1w1PAAB8u6uwieYFhUKh9ATa7fBGREQgNvas2mZ2dja+/vpr/PHHH7jyyis71TjKhUPfGBVmD4xB70glcivq8fHm0yisMXiJ15S6Ir3RLgeYc3hdjm2N4WwtpOd2lvh2eOMatXcR8nkYlhQCpVQIiZDHRdsAZyuhoUkasAT41seKe2OOldbjxZU5mLx0Ez50tWWZNSAaYS41X3f6aEKIDOV1pnOq4XU7n9V6c4fP0V5qXQ5JbQA76m1xyrUGG6yOpo5VVqwKs7NjkeXRBqQx7r7KZ6P2pjZHP1UyIYR8HlSu9Ha5y8mMV8sgEfJgZwnK6k1QyUQIkYtAyFnRs7bCEoIjLic8LVyBYIkADpagqsGC7adr8P2eIixbm4s1ORUAgIvSQptteSTg83D/ROdk/LcDJThV0XK9d0GtEXUmGwQ8BlcMTmjxWHdtrclqx6lKPWqNVqjlQogEvC6vbU+LCMIlGRFIjwxu/WAKpR20ZRHnjnEpuKR3OFgC/HukAhP7OLMtXvz7OE42GnPBUiGnNZFT1uCV+pxf7cpiUku5BWFPwoLEeGhSLwDAO+vzEK+RIkEjQ4PZzmUjUSgUSk+C6tBTAgaNXIyUcAWK6yzQm22cirEbd2qzO8LrdmS5CK/LsQlROJ1K93PeM8BrsNg5x9iXcqBIwMOolBBcnB7eJNJ1nSvK+/fhck7l2RObg8WqY+W49as9uO3rfVh3ogp2lmBgvAqvXdkP/53WG4AzhXP3GWedcWaMEmCYdtXwFmuN2OVR2+wP51Pjckg0fhTbao22fC9quRAifsccqxOuCejwZA2XhfDHoTKU65wLKu56UF/OYYhcjJQwBdeGyy0UxeMxiHdFFwtdabXuKG9705oLaozQme2QCHi4aXQSnpjeB49OycCwJA16RwVBLubDQQhYAiSFyjE0sfnevQAwNEmDcb3CwBLg5X+O+6wtdHPQFd2N00ihkgm53ti+cCs1m2wsbC6HIEQuRmq4wq+17RRKZ9KWRRyGYXDjqCTEqqTQW+w4VqpDdrwKFjuLJ3492qQ0ID1CAT6PQb3JxmlT1BmtqDfZwDDg7iW+uGxANIYna2Cxs3jmzxxcOywOAPDp1jNg2ebHNoVCoXRH2t2Hl0LpTHxNmvtGB+F4uR6ZUUFe+90pzZHBEhitDugtZ8U4avQWmFziP0ESPix2B+ys87WDJbDYHThV2YD8GoPrGAHyPBwIs83RtA+h66HfYLJh/fFKEEIQ5eoRumztKYxKDYVYyMeQBCH0Zjvu/HY/56QI+QzGpYfh+pGJ6O2OFBPA7mCxPa8aVgeLiCAxUkLliFdLfX4PntsIIc5aZMYZ6TZbHSitNyNGLYNGLkStwdbpzifLEjSnk6SRiQPWGXF/bRq5EFqDDWq50LuO2/U9Oo9pvfdzY6wOAkJYLuKSFKZARlQQyurNyKvUIytaiRiVjKsHLdAakRp+VhDMxhLwGKdBOrNz8UUm5nPXa0SQBCcr9DhVqUefaCWiVFKgQIuTFQ0YlRqKCp2Fa6Hli6RQOYxWO/YXOhdV0iODYHWwgMMZTY3XyBARLAEhBPUmG2oMVsRrZJx4ltnmwL7COp/nHp8Rjq251didr8VvB0pwcYbvuu8DLod3QJwKapkz48J9PTfnJ7uvY+ffq/kJNyEEPF5ra7XNi2S1dG7u3edRIIxyYaKRiaBpNBZ8YXE4sGB0At5am4eSOjNC5GKu3/eX2wtw80XJAACzjQXDOIUQC2qMOF6ug0zMR57r3hCqEMPqYFFvtGF7nu+WczP7RWNfQR32FGiR6soCya8xYk1ORbO9fwE6PigUSveDOrwUv9K43ycAjEgJw4iUMBBCuAer1c6iSu+M4MaopRALeBDynVEjAY/h6v8kAh7UMhEYhoHUI0LrPJ6HBrOzPikyWOLVfmV6/yiMSA7lfpfF7kCQxOlA/nGwFGaXWu6AOBXK6suxO1+LgQlqgGEQp5Hhhz1FKKw1QiUVYuGoRMwdEgelVAh+Yyca4KK7E/tEYHzv1nu+esLAmdJdWm9GtFICBs5oYYi8Yyq+Lb3Hc9eytSewp0CLwQlq3D2hV6vv9Sdus9zfS9P9TMu2t/K5BHwGRbUmGK0OCPkMUsLk6BsVjPXHq3C4pB4LRyeCYRgkqGXIrzUiQS3z+n18hkG5zoSiGgNOVTgnp569b0elhmBzbjXqTTYMTlRDIeHj94OlKKkzYVCCCo/8dKhF+2QiPsZnhOPdDXkAgKuHxHGiawDQ38MJ9cX2vJpm3cVQhRiX9onAX4fL8e6GPEzoHQGJsGn01h3hHZSggdXBevXZBXxfO6EKCUIVkjY5pOeC8+9/Xn8FhdIE9zX/xqrj2JWvxdBENe6/NKPJcSlhCijEQiyd2x93frMfh0rqMSYtFBUNFvy4txg3jExEWkQQ5g9PAMM4M4ue/TMHVQ0WMACnqH7T6CTEh8hwptoAWzMRW6VchJkDovHj3mL8eqAEl2fH4utdhfh0a36LDi+FQqF0N2hKM6VbUKEzgxBnyrG7RvesSjPDpSlr5CJuYuFZuuQ+1t0LNzzYd1TvdJUeN3y6C1d/sAM7TjddFe8bEwwRn4cag5WL5gLAWlfboetHJuCOi1O5tOrGEEKw0SVA0lx0rDVi1TIM9aht7gr2FGhhtNqxp0DbZb8zkDnpclSTwxQQ8nnoHe2M4udW6nHaFWFJiwjGxN6RSItoWg9a0WBBg9nB1aV7Lr64o8F5lXo4WILUMAXEAudijbv+vC32ldWbIRbwMDw5pOMf1AeT+kYiMliCCp0Fn23Nb7LfameRU+aMfveOCvISozof+FJ5Pp/vo1DOhV35WhgsNuzK930vjVJKkR2vxpj0cCydOwAMA2w6VY30CAWnrOy5KCTk83DlIKeuyo7TtWAJkBquQHxI254PY9PDkBQqh9nG4kyNATwG2H66BsdKaWsuCqUzsZ4+DVtpqb/NuGChDi/F7xRrjdhdcLYm1ReeglWN1Zf5PIZb1dYozkauPKPH7gXuSp3zPG71ZU9259fivu8PoLLBAoudxZO/H8X6E5Vex4gFfE5Jd78r7dNsc3Cqy5e0ErHNrdSjpM4EkYCHEe1wRDYcr8Cba09izdGyNr+nMxmcoIZMJMDgBLVffv+5sC6nHEtXn8S6nPJOO6c7ndndl9YtcGa0OnDYo9VQc0QEiREk4aPalbWQ7KEGHauWQiLkwWxnUaw1QsDnId3VnuhoGyehG046r9sRySE+I7Bt5VipDm+sPokTHvX0IgEP90909vL8YnsBSho54ScrGmB1sFDLhIjXyCBroX63M2is8ux0ZC0w2VpWm/WlDk2hnG+GJqohFwsxNLH1e+mE3hF4ZLIzCnymygCxgIedZ2rx+0HvSXNmTLCXyOLFGWFttofHYzB/eDwEPAbb8mrQ3yXU98q/x1t8JlMolLYhUKvBSKUofehh5E2bTp1eP0EdXorfKdOZYbE6UKZrXmW4cUsiwNvhrXG1JNKb7dxDmsfzdHidx1Y2OB2MiCDvCOzJigY89sthGK0O9I9V4uJeYXCwBC/8ldOkXjI7XgUAOF7eAJPVgW15NTDbWMSopMiIDEJLrDzidLr6xSpbFPJpzOFSHfQWG/YXt731TWdy94Re+HzhcC6duTtxqFiHBosNh4o7L2LhdnjdbYuilBIESQSwswRB4tYrRaKUUgxNDuVaXGVGn52s8nkMd94jJU6b+7j2H22DMw0Am046F2DG9Wr7xLcxVQ0WvL0+F6cq9XhvY55XW6TxGeEYkqiG1cHi7fW5Xu875LpGB8SpEKoQN0ln7gjFWiN25/teFDNZ7ThcrEVOaR1qDJZWHdmSOue5tHoL+DzmvDvkFIon91+agW9vGeEzndkXN41Owtj0MNhYwvWYf/6vHFQ3nFWfZxgGVwyOgVjAQ6RSwjmtbSVSKcXUflEAwPUe355X267e4hQKxTfC6Gik/PUnol99BcRkgl1LM+X8AXV4KX4nKlgCsYiPKB9RVzfuSFiYh6N6sLgOABAeJOZUcvNrjJi8dDOe+O0I9uTXAnCqwLoFqewOVxp0o1YNG45XgSVOB+Gly/vh0am9MSUzEgTA+hOVsHhM9p1p02fFsHadcf6eMWmhLdaG7jxTg+WuusphiWqsyfGt4OvGs51OVnQwFGIhsmOVzR5P8U2/2GAEiYXoF9t5rWbcvZ5/2V8CvauVR4PZDoYBLkpve6p6g8kZhQxttAAz0BVJ/3DzaZTWmTAgTgUA2HKquk3ndWc8JIS03AO3JXQeSuQWOwuLq44dcE6w+0Y7r0XPibfF7sC3u5xtu0amdF4qdZlLqM2t1O6J2cbC5iDQWxzQGmxcm6PmHNmyejPMNge0JhtC5KJOccgplPMFwzB4ckYfyER8lNaZIRE6S2peX33Sqyd8lFKKp2f2xcOTevlsRdQak/pGIiMyCA1mO8KCRLA6WPyyv6QzPwqFcsEijI6GKDnZ32Zc0FCHl+J3YtUyDElouSbV3QZIKT2rRPzrfmdayLR+URidFoovFg7BsCQN7CzBij3FuO2rfQCcEVn3BCDS5Rh7TpzL6k0o0prA5zG4c1wqhHweeAyDu8enIUYlhdHqwGYPR+NMlQGEOJ0elUyEw65ep/1dTokvimqNuPub/bCzBNP6RWFwohpGix0Ftc2njNV5tNMZlxGBeyek45K+Uc0eT/HN+N6RuG9iOsb3juy0c941PhVRSgnya4y47au9ePqPYwCAeyekIa6FViCNcTu6nk4jAFwzNJ6bfD75+1H0iQ5GmEIMnbnlNF03ia76PbcqeUdIDpMjWuUcL70ig7zGnt6jX+c1w+K57d/sLERZvRlRSgnmDWu5/257iFJKIBHxuYUtT9RyIUIUYoQoRFDLha42R2JIhb4j7VFKCSTCpueiNb2UQCUhRI7PFgxFsEQAs42FWMBDvcmG/6066dWfW9lKC7CW4PMYPD2zLwBwGVP/HKngSncoFAqlO0MdXkpAcbi4Dr/uL8FhV/TWTWOH93h5A05UNEDIZzA10+kEZsersXzeQHx141Cv+ljPn90T+FKPlfHdLvGQ0amhUMvP1gCLBDzceXEqAGBXfi2qXE6JO8U5NVwBlhAccaWZvrMhF1lPrcLIl9bh0jc24ar3d+CGT3fhrm/2YcFnu6E12tAnKhgvzs5CjEoGmViAhBacI1UA97i90FHLRFh69QAI+Qx252thcxBM7BOBW8e0bwXXnVpf2cjhFQl4eGpmX6hkQpyuMuCzrfmYmtV2h91dE3y6quMOL8MwuH1sCsb1CsN8D6cWAH7cWwy9xY6kUDmXNl2tt+BTl4jVw5N6AQw6zYGMVcswpBmhthC5GEMSNRiSGNKmVlkxKt/nojW9lEBmUIIa3948HKEKESx2lutO8OaaU8jppNTjoUkaTO8XBZYAoa706f/+chh1RmunnJ9CoVD8BXV4KQFFXpUBBqsdeR4T9dyKBq59i9vh/dWVanVxr3AoZd6O4eBEDT5dMARf3jgUD03qhf+MOBtpilY6a4DdEV6zzYFDRU6HdUb/6Cb2DE3SIDlUDkKAf4+WgxDC1TilhMlRo7fCYHFAxOehqNYEk82BygYLTlcbcKikHltza/Dv0QoU1BgRIhfh5SuyIBXxkRSmwCW9I5AW0XzNb6D2uaU4GRCnwsOTnTXNKWFyvHR5VpNU+caYbHZoTWedwLBmHF73vocnOc+/6mg5xmeEo62ZismhzlTm01XN9+ttCyEKMa4aHOelOm61s/h6ZwEA4IaRiZw43PINeTBaHegbHYyZ/aO7nQPZWio0heJvMqKC8f2tIxCtlMDOEgh4DCx2Fu+uz+P6bp8ri6dkQCLkoVpvhVzMx/HyBlzz4U5uwZdCoTixlZbCevq0v82gtBHah5cSUKSEyZFXZUCMWoJaow3/HC7BqmOVKKp1OsDBEgGsdhZ/H3aKP80a0NRJdeOM+mi8tkW5IrxldWawhOBQcT2sDhahChH6N1MfOzY9DIW1RhTUGLH+eBUMFmf/1XiNDLkuxzwsSIySOhPGpYfhgYnpaLDYUG+0wWhzoMFsh85kw4iUECSeQ00lJfD4z4hEDE7QIDFUBpmo9dup0eoAyxKYbA5IhXyEu+rWi5pJbR+SpEGUUoKyejNyyhswLCkE2320y2pMcpjL4a1uPsLrYAl+2FOE3w+WYt6wBEzr17Z0+S251dAabYhWSjAp06lKfqK8Ab8fcJYY3D0+FTyX42i0OtrtQJpsDu59XVlfKxV27e+jUDpCYogcX940FNd+uBMVOgv4DGBnCT7cfAbzhzkwMjW0xfc7WIJla08hWi3FVYPjmuyPUUlx25gULF17CjKhAFKh0+md+8EOfH3TUEQppT7OSqFcWNhKS5E3bTqIyQRGKoVA3f06WFxoXLAOb1FREb7//nskJCTgyiuv9Lc5PRrPnoE1BgtqDTZo5EKEyMVgWdZL6CkzRonMGCVqjTY4WIKjpTrIRXwYXFGiIKkQa3MqUGeyQS0TQi0TefULtLNss7XA1XoLGAACHgOrg0VepZ5zHvrHqmD2EOXxRMBnMChBjZ1narnj49QyGK0O5LscCqnQmSyRFavkerLaHSwE/K5JorCzBIDze64zWqEz2xHvkS4t7CI7LgQ8LmcAQG9XOxD3doud5dpgHSvV4dGfD6F3VDDumZAGjVwEqx2QCnhwEMKpM686VoF/j5Xjkt4RzsiNx/kv7ROBz7cX4J8jZbhiYGybHF63wFRZvRl7C2q9WhOdqmxAjd6Kf46Uo6TOmenw0j/HcaBIi8wYJUxWh5cauid2lsW/RysAAP8ZkQA+GLAOFm+sPgkCp+hbWkQQCAEkAj4kAn6T74wA4IE0PbkLz8iw+/2eEAK0oA3XJjztMdvPOtju33eu56dQOgwhLYwOJwkaORZNTMe7G/KQX2MEj3G23vtyRyFMNhYTPHq815tsyPPoNFBYa8SpSj1OVeqRHavislKEfAbFWuf9aFq/KHy7uxAVOgvmDIzB1txqnKk2YM6727B0bjaGJnsvJDcHjw4kSg/FrtWCmEyIfvUVyAYNgjC6+eALJTC4IB3ebdu24bLLLsN9992H9PR0f5tzQVFrsMFqZ1FrsCFELgbDMD6VjaVCPkw2B/pGK3G4RAeRy2FTSYX4aMsZAMBFaaFN1ChtDtKsQmX/WBWCpUL8e7QCpyr1qGywoEZvhYjPw9VD470cRE8mZUZialYUbv9qH8pdrZOm9YvC5MxIrMlxTv6tLvXnXpFBELh+v4DHb1G1uTU838swTCvpss7f//CKffjzSCUIAb64cWiTCHd7aC09N1A5l++8LYh9OGGeuJ1drcGK6z7eCaPVgZMVeqw8Uo7/jEjATaOToBA70/BHpITi+hGJ+Hx7Ph758TB+vj0IyWFy8D0+w42jk/DFjgIcKKrHq1f0R7BEAJ3Zjun9ojilZE/MNgc0chGUUiHqTTZUNVi4ml6WEKw6WoEjJTrYWQIhn0FksARFWhP+PVoBq4NFv1gVpxLdmH+PlKPeZEN4kBjXj0iEWMjH6mMV2FOghUjAw5AENU5X6KCUCppPyW9lNu8ZGfb1t3TeM1o+R8t4v9nTwW5O6IpCOVfael/it3Fxcu+ZajgcDoTKhag22Din98e9xRgYr8I1Q51190arHZ7XvPO1E5mIz5UreFonEfJx57hUPPH7UfyyrwS3jk3Gn4fKUKw14a5v9+GlOVlgeAziVVKktFCWQ6H0dETJydTZ7SZckGGfm266Cf/73//w3//+F/379+/weSwWC3Q6ndc/Ssto5EKIBDxoWhFkkon4UMuEuHZ4Il68vB/csVcHIdhwogoAMLYd7V88SXLVN7ojVSNTQqBopXeqSMDDrWPPChINTlTDwRJOFMitspsWruiQTZ3F+pM1sDkI7CzBIz8dgsHSNlVff9ITx5HdweKBFQdgtDoQr5E5e9baWXy0+QymvLkZn2/Lh9UVhX10agaGJmqgt9hx21d7oW+kxBytkmJUijNN8bcDJejnSr0/UFTXog2xameUtljrFGirarDg1X9O4EBRPewsQZRSgssHxmBS3wj0ilQ4W3Adr/KKBnniYAlW7CkC4OwNKhbyYbWzeGFlDgBgYu9wSIV8VOmt0HoojLeX1lSWO5ueUrvbE8cRpXmOlOhgd7CQCXm4tE8EWHLWaX381yP4+3CZz/eZbWczmar1zYtRjesVhtnZMSAA3tt4Gpf2iUByqBzVeise/ukwTpbpUOgh/kihUCiBzAXn8Obn5yMnJwezZ8/mtn3//feYNWsW5s6dix07drT5XC+++CKUSiX3Ly6uaT0MxZsQuRhp4Yp2CTIRQrieoNvzauBgCVLDFdyEvr24HV43E/tEtOl9Q5M0uH1sMu67JA2hCjGKao2w2FlnRMrmAJ/H+LVG15nq7ewFGyIXoajWhJdWHvebPW2lJ46j/60+iR2nayET8fH2tdn4fOFQvDtvIJJD5dAabXjmz2OY/OYm/HOkDAIeg2XXZCNSKcHpagMe/ukQWNY7DHrloFgAwE/7StA3OhgM43Rk3f2pfRGjOuvwbjhRiSW/HkFOeQP4PAYjU0IwLSsSQRIhGIbB6NRQpIbJQQD8fbic62HtyeZTVSitNyNIIuCiR9/sKkRBjRGhChFuGZOEkCARolUSqD0WtHbn1+DLHQXYnd96KrY/kAoFXepgny964jiiNE9mTDBEAj4GxCnxzrXZuGJQDJc8wRLg/u8PYktu077dnqrpnr3eG8MwDO6/JA3XDHVeR59szcfI1BD0ilCg1mDFuxtPQ2/s+MIWhUKhdCUXnMPrric9c8aZFvvQQw/hkUceQXJyMs6cOYMxY8Zg/fr1bTrXo48+ivr6eu5fUVHRebP7QsZkc8DmShlefcwZlR2XHtbh83k6vIkhMk7gpy1M7x/NOcjHXK0g4lyOd2KIDCKBf4aU2ebA478eAQAsHJWEpVcPAAB8t7sIu840dV4CiZ42jjaeqOTa87w4JwtpEUFgGAYXZ4Tjt7tG4YnpfRAiF6Ggxog7v9mPWe9sxXN/HYPEde2szanEVzsLvc55Se9wKKVClNWbUWuwIdWVotxSlFfjarG16lgFPttWALOdRVq4AhN7h7uc5rNJjDyGwdheYUgKlcFBCJ79Mwcl2rPRG7uDxfe7nX+XywZEQy4WwMESvLcxDwBw34R0ZMWoMaNfDC5KC/da0DperofBYsPx8nNTjKa0TE8bR5SWefGKAdjw8Hi8ee1gCPg8vDynH64YFAPAWYNudbC49cu9KNZ6C+KZPFTTW1Nedrcmu3F0EgDgqx2FGJMehsGJajSY7Xj01yM4Xk4zCSgUSuBzwTm8iYmJSEhIwAsvvICjR49ixYoV2LdvH9544w1s374dY8eOxZNPPtmmc4nFYgQHB3v9o3Q+bmcXAMpd7YTSIjqeOuxZSpUcpuhQzWe9yYavXU6JOy1s8DnUy54rZpsDOlcqbLxGhiilBGKXAxXo7SR6yjgy2eyoMVhwpsaZ5h6vkTXJHhDweZg7JA7rHhyHOy9OhUTIw9FSHf44VIb8mrMT09pGkRexkI9LXecqrDWiX6wKgFMdmTRW0QKwt0CLn/eV+LTT6vAtzuYZVbY6WOjMZ6M3X+4oQH6NEQqxADNdyugNZhvXTqkltfSMSAXkYiEyIpuO2RqDBacq9S1Gmihto6eMI0rH4PEYPDQpA+MzwkGIU4TKaHXgk60FXIYUcHYhDACCJK33emcYBjeMTMRtrpKe73YXYfm8gRierIHR6sDb63I7/8NQKBRKJ9O9c7jawKpVq5CQkIBevZz9LBmGwVNPPYUFCxagvr4es2bNgkbjdFT4fD6uvPJKLF261I8WUxrj2SokRi2FrqwBVQ2WDqcPHyk5uyKd14E+pYQQLFuXizqjDUESAWoMVoQoRHjoUv8JoKlkIjwwMR0vrTyOl/85jsQQOSx2FsOTNZiSGek3uy4k3OJHEzLCsWxtLgprjdhxuhYjUkKaHKsQC/DAxHTMHxaPXw+Ugsc4W1uFKsQIVYi4WnCbg8XqYxUYlqzBgDgVfthbjPJ6M0anhkLIZ6C32FGuM3OtQggh2F+oxeZT1SAA+sUqsXBUEtafqMTKw+WcOmtBjRFDEtXchNdodWD1sQpUNljAY4AHJqZz6tP7C7X4YU8xAODeCWnce4IlQvB5DBwsQYPZDnkzdfBDEkMwJLHpdwA0FbGjUCgdRyHm4+mZfVFQY0BelQFSIR/1Jhs+3ZaPO8alQMjnITM6GFcPiYNYwENmjO9WfL64ekg8fj9YitI6M1Ydq8ATM/pg6ptb8M+RchTUGJBAW+5RKJQApkdHeP/991/MmzcP1dXedSw33HADbr/9dvz999/YsmUL7PazIjFr1qzB1KlTu9pUSgsI+QynvBztmthX6DoeETpaWs/9XFhr9FKtBID/rTqBy5dvw2v/nsDeglo4GtVTrsmpxPa8GvAZBg2uqOorl/fj1C79xQ0jEzEkUQ2j1YFjZTrIxXy8NKdft1Vb7m64xY/CgyWYM9CZWvjZtvwW3xMeLMEtY5Jx00XJmDUgBqNSQ5ESfjbr4P2Np3HPdwdw0+d7keWanJbrzODzGCSFOp3iUy6RKZYQrMmpwCaXs3txrzDcOyENKpkQs7Nj8PLlWRjt6tGZV2XAD3tKsPNMLSp0Zvx2oBSVDRaIBTzMGRiLCb2d0eQ6oxWvrXK2HJqSGYnRaWd7fPJ4DEJc0aKqFmqJW6KtInYUCqV1ZCIBYtVSLJ83kOt0IOQzKKw1YsWeIhBCwDAMhiRq0C9W1a62QXwegznZTi2Bz7flo1dEEMamh4ElwMeuzgkUCsVJbqUeR0rqvf65BSHzXPtKqOhbl9JjI7z//vsv5s+fj19++QWjRo1qsv+dd95BSEgIXnjhBUycOBHXXHMN1qxZg9zcXHz88cd+sJjSHAzDQCrkQ2+xI0IpAQCuPVB7cbAEOWUN3GtCnDemrBgVAGef0a93FsLOEuRW6vHljgIESwQYlqzB8OQQRCuleH/jaQCARMiDwerAtUPjMN6j76Fnr+FQhaSDn7r98HkMXr68H2Ys2wKD1YHHp/VBTAeFvSjtRyoUQCoUwGRz4LoRCfhmVyE2nXSqHqd0UL17xV5nHebhknqkRSggEvBgsbOoM9mQFq7AyYoG5FboMTwpBL8fLEGeSzV87uBYTM6M9ErXV8tFuOmiJEiEDI6VNqC03oxDxfU4VOxcAAqWCDA5M5LrwbuvQIv/rT6JWoMVcRopbhmTjMaEKsSobLC0KJ7VEiFyMY3sUiidRI3BAq3BBo1ChOcuy8SiHw7C5iDgMcC+wjpEBku4xayOMK1fFD7ecgYnK/TYlleDW8YkY+PJKvy4txj3Tkjz+6IvpXMoqTNBa2hewRtwzpsoTVHLRZAK+bjv+wNN9qXUFeNtAPd+fwB5qmpIhXysWTSWE5iknF96pMPr6eyOHj0aBoMBy5cvx4EDB5CYmIi77roLkZGRePbZZzFnzhy8//77+PXXXzF8+HB88sknUCj821rmQmXl4RLsL9QhOz4YU7JioDU625uo5UJIRU6HN1ThjCi5a3nby+lqPUw2B+QiPvrHqbAtrwYnyhs4h7eszsz1J52cGYmNJ6qgM9ux+lglVh+r5M4TLBVAZ7IjSinBbeNSYLI5uNRrzzTNrnR4ASBOI8OXNw5DkdZIU5n9SEKIHBMyIrAmpwKfb8/HM7MyO3Qeo+WswIyQz0PvyCAcLK5Heb0ZKWEKMIwzuvrFjnzU6K0Q8Bhc2icCU7Kimj2nSibC1KxIFGlN2Hm6FnUmGyKDxZjYJwISIR92B4v3NubhtwOlAJxqz0um9YFE2LRtT1iQGChDhx3etmCy2bm+vN1dSZlCOZ9oDTZYHSy0BhvmDIzBzjM1WLGnGEK+c6Hs7yPliAiWtCuV2ROFWIApmZH4eX8JPtuWjw+uG4TMmGAcKdHhyx0FuO+SdPx7uAT7i3TIjgvGlH6xnfwJKeebkjoTLnl9o5ead3NIhXyoPWrCKc7n5ZpFY30vGJw8DmwA3pw7ALmqWNz3/QFoDVbq8HYRPXL2sHLlSpjNZjAMg+LiYkyYMAHBwcFISUnBe++9hw8//BDr1q1D3759kZ2djffee8/fJvdomkrqeOzzENw5VNwAo82OQ8UNmJxJUG+wwcayqDPYOGdSLXM7vCYvUR03VjsLi923KE9upXNVGgAilRJuAr8nX4sBcWqkRyhwptq5ahmnluGpGX1hm8ri78Nl2F9Uhx2na1HVYIFIwIPOZAePAW4clYRSrQlmK4tUVxRPIxdyEd6O4P5OPL8bQghYlm1WYMudqgYAWbFKZMW2bULjQ+/Iiw7oeV0AtPylEcIChMH1IxKwJqcCvx0oxb3j0zixmDqDFYIW1LwJIdyxRo9JB0sI+sYocbC4HgU1BkSrJIgKlqC03owavRUSIQ9TMiMRKhdzfaF9YbWzsLNAlFKKmQOiUWuwQiMTgcdjUFZvxtbcamhd7UamZ0VhwahESIR8D1ErAjvrHGPua7xCZ+a2sSyBiGn+87EsAcNruZrGMwvfXRtttDpcDi9p9bo925GUQumZ+BKrU8uF3CIxIQRPzeiDjSerUKGzIFgigM5sx9c7C3HnxSlcJocbo8WOolpjk3M2ZnRqKH7eX4J1xytxsqIBC0YmYtEPh/Dp1nzMyY7B6RoTRAIGp2toumZ3RGuwwmRzYOncAdycpjnUchF11nwQo5L6/F5MdQrkA86MLxUNrHU1PdLhfeONN2CxWDB58mQkJyfj2muv5ZSXKyoqcPHFF+O6667Dvn37/GzphUFLU89ak41zDjNjFNhXqENmjLOGUel6eKvkQkiEzgmye4JdY7AiNVwBId974mxzsAgP8h1VtTlY7oGeECLn+viW1ZthsTsQLBVyqrPJYXIES52/67oRiVgwigEhBFtza3DH1/tgtbO48+JUDE1SQ2e2w2yzc85hV6RpGq12mGwspEIeZCIBhHxeh9SmKZ2LO6VQLRdiZEoI+sUqcai4Hj+4Uv4Ap1JzS38qhmEgcDmEVo/FGwGPh34xSnwDwEEIJvWNBCHA+5tOI1olwePT+iBKKUFlg6XFHtWp4YomAjN2lsVnW/Px1ZFy2FmC8CAxXpyThbE+2n/ZWZar/UsLDwIArDlWiRtGJsJsZyER8CEW+v6AuRUNKNWZEa2UICUsqIXv4OzPMhGfi/AyjHORxmRzeER9m0aeW4MOFUqg0tb7OM/HolHjZ49UJMALs7Nwz7f7oTPbEauSorjOhO93F+Gn20d6pSAfK63ntDJaQiFR4KK0UGw+VY1vdxXhoUm90GdLPo6V6fD83zm4YmA0jpXpkRoma9PnoAQmqeGKDmcCUCiBSI8UrWIYBu+++y7mz58PvV6PJUuWcPsiIiLwyiuvYP/+/SgtLfWjlRTAmf5rc6X/TusXiyXT+2CaKw1KIxMhNVyBELkYYoFzUivm8yAR8sCS9qc121mWEwmI18gQqhBDxOfB6mA5R7fA1RomMbSp4uSRUh2Wrj2FBosdWTHBuHt8KqLVUoQHSxDdxbWyJhsLB0tgsvmOZlP8Q63BBpsrpZBhGCwc5exfuWJPURPxs9ZoHMEhhHDCVaerDGAJwcQ+EXhyeh+8ekU/RCk7lj5fVGvETZ/vwTsb8mBnCSb0Dsdf94z26ew25vJBMRAJeDhUUo+1OZUwWhww25tPhSusM8FstaNI2/boj0wkQKhCDJno7PqsZ9SXQqE0T2ZMMO65xLnYVlxnQkSwGKX1Ztzw2e4O99CdPzweAPDzvhKYbQ48N7svBDwGq49VwmgjuHVsCi5K73itMIVCoXQ2PdLhBc46vW+++WaTldDQ0FAIBAIEBTUfYaCcP0w2B2pcaTMauRDCNqi0ysVOh9dsZ7noVHErk2aT1YFtedWco1Fe76zPlYn4CJGLwGMYRKmcToI78ss5vK7fYbWz+P1gKS5fvg0z396KvQVaSIQ8vHHVAAj5PGjkYqS5nPKuRCrkgc9jIBWe+xB294812eytH0xpEY1cCCGfB7Xrep7UJwLBEgHK6s3YfrqmXedy91V2Y7Q6kBaugIjPg9HqQFm9U615QLzKyxlsKxU6M95aewpzP9iBg8X1UIgFeHZWX7w8J4srHWiNELkYl7l68C7fkIfX/j2OtcfKmj0+XiWFRCRA3DkuELkVsWWi9kd3KZSejuczViUVYcHIJFw5yLmQbLI6ECwR4ER5A+a8uw1L15yEtYVFKl+MSglFUqgceosdvx4oQUZkMG66yLm49+yfOSioMcLchhpQCoVC6Sp6rMMLOJ3e6dOnN9n+7rvv4tprr6UOr58wWh3ILdfhnyNlqNVbfTqMJpsDtUYb1zKI70rzMlodSNA4U6UKW6g3qtZbcO1HO3DzF3uxYo9T6bao1ukgx2lkXNqYu81RsdYEQgjXlzcxRIb8agMmvrERi1YcxL7COgj5DGYNiMbPt4/ssOpuZyETCRAiF3XI0WkMjZZ1HiFyMZLD5NDInNezWMjH9H5Oh/CzbfkedbCtU9qoZUG9yQYBn4fEUOf1f7K8wdfb2sQv+0sw7a0t+HRbPkw2BwYlqPH9LcMxvV+0V0olIQQnKxpQojV5pVd7Mn9YAgAgt0oPvcWG42XN25UaEYTRKaEtpjO3BanQuWjVkXRmCqWn4+ue/sT0PugTFQyd2Y5olRRj0kJhZwne3ZCH2e9uQ1ULdf+N4fEYXO5qvfbtTufz9baxKUgJk6PWYMVjvxzGqYqO358oFAqls+mRNbzNkZ+fj8WLF+P06dNYvXq1v825YJGJ+CjVmeFgWRTWGZEW0XTy635gm2wsJAKCE66Hp1omQna8CquOVeD73UWY0T+qidO3t0CLRT8c5B7gz/2Vg5EpIRC5hIIKa4zQm+2QifkoqHG2cRHyGfy4txjFWhMkQh5SwuS47et9KK0zI0whxvzh8bhmaLxTlbaH4VkjSel8rh0aj+92F2Ld8Uo88vMhLJrYC4I21MoFS7yzHqSuv09WjBInK/T4eX8JLkoLhYDf/nXLPw+VwuFKkb5hZCLG9Qrz2ZPzoy1n8PI/J7jXGrkI4UFiRAZLEB4sRniQBHsKagE4FxhlIgEy2yiYRqFQzg++7uliIR9vXT0Ac5Zvw/HyBmgNVvx3agY+2HQapyr1eOzXI3h+diYig1svjdh9ppbrvWtzidWJBDw8d1km5n+8C/sK6/DzvhIMSw5t6TQUCoXSZXTbCO+JEydw9913Y/78+fj4449htbbcM2z37t1YuHAhRowYga1bt0KppJMyfyEV8pEZrYRKLka8yrewhTtlUcxnsGxdLqr1VgRJBLi4VxiuH5HIifN8tjWfew8hBN/uKsSCz3ajqsGChBAZlC7hqUU/HETfqCCEBYlhsjnw5+FS7CvQorTeDBGfh4HxKry48jgA4L5L0rFiTzEOFtUjSCLAT3eMwD0T0nqksws4+8eGyMW05ct5ok90MN64agB4DPDTvhI8/3dOm+p5Y9RSZEQ6F4MkQh6XZjxrQAyCJQIUa034+0h5h2zKd6XuL56SgfEZ4T6dXZPVgQ82OXtOux30WoMVx8sbsOFkFVbsKcbb63Ox43QtZCI+3p8/EG9dMxCXDWi5FYkzhd4KI02hp1DOC81lQMSHyLHi1hFIDpOjosGC5Rvy8NxlWYjXyFDZYMHinw6jpIVSIUII/jpUhhs/3wOt0YbeUUH49IYh3P7+cSo8eGk6AODHfSXY0c4yDgqFQjlfdEuHd/v27Rg1ahRYloVIJMLdd9+N7Oxs5OTkNDm2srISFosFQ4YMwbp163DvvfdCKOxYuxhK55EWEYRLMiKalb03Wu3IrWjAjZ/vwdK1pwAAN41OgljIh1TEx30uxdvv9xQjr0oPndmGR38+gg82nYGDJZiWFYUfbh2BX+4YCbVMiJyyBmw9XYuZ/aIh5DMo1pqw4WQVAGBMWig2n6pBg9mOfjFKDElUY9m6XADAveNTUamzoFjberuGzua7XfmtHvPDngI88tNB/LCn4PwbROkQ3+7Kx8aTlbhqcAz4PAZ/HS7DM38ea5PT+/78QRjXKwzL5w3itinEAswf7kwj/n53EeqMLS/2NabBbEOtq0dgYkjzSqo/7C2G1mhDmEKEw09dil3/nYA/7x6FD64biOcvy8S9E1IxOzsa0/tFYdk1A9AvTtWkd2ONwYKtudVYd6KCG0MmGwFLCEzWsynSP+0rxGO/HsZP+wrb9VlyKxqw9ngFcmn6JIUCwPncWLTiQLPPj+QwBb65aTgyY5TQGm1YtOIAFl2ajli1FDUGKxb/cpjLfPLE5mCxbF0u3t90GnaWYFpWJL66cViT9kb/GZGAaf2i4GAJ7vx6X5PSDAqFQvEH3dLhveOOO/Daa6/hnXfewSeffIIjR45AKBRi9OjROHjwIHecw+HAxIkTcdlll8FiaXt9CsX//Hu0And+sx+78rWQCvl4+fIs3D0+lds/KjUUY9JD4WAJnv8rBzd+tgdbcqsh5DN4YnofvHx5FuRiASKCJXjp8n4AgEPF9ahoMGNS30juPLFqKcRCHk5UNEDIZ/DsZX3x2C9HYGcJLukdjv5xKlhsDpS2UxG6M9hfUN/qMXsK6qA327GnoO78G0TpEHsL6qC32GC1E7w5dwD4DIOVh8vx1O9Hud61zRGnkeGT64c0UUye0DscqWFyGK0OfLmjfYsd7ghOZLCk2Rpwm4PlUhavHhLnEmgTISMyGGPSwzB3SBzuHp+GZ2Zl4okZfZAWEQyWJU0cXq3BhvJ6E2oarCjXOceQVMi4IsqEi/TuK6yH3mzDvsLWr3lPCrRGGC12FLSyIEWF2SgXCrvz69BgtmF3fl2zx2jkInyxcCiGJ4fAYHXgwR8OYk52NJJC5agzOhePcyv13PE1egse/fkIVudUgscAD16ajleu6MeVWXjCMAyemdkXGZFBqDFYcdtXezkBK6PVjmq9hdPmoFAolK6i2zm8LMvi4MGDyMrK4rYlJydj48aNSElJwbRp01BT40yj4fP5ePbZZ1FVVQW9Xt/cKSkBhNXO4oW/c/DYL0dQY7AiIzIIv981ClcNjmvSn/DeCWmQCvk4WaFHWb0ZUUoJll2TjblDvI8dnRqKWy5KBgD8e7Qc4UESDE8OQZhCjHHp4Vh33BnpvW1sCg6X6HC8vAFKqRDPzc5ERLAEYiEf0Y1avrzw1xHMWLYJL/515Lx9F9kJrafdD05QQSERYHCC6rzZQTk3BiWooBALMShBhalZUXh+dib4PAb/Hq3AU78da9Xp9QWPYXCT65ped7wKJ9ohYFXsiri4xa988ffhMpTUmaCRi3Dr2JRmj5MK+dDIRAAIirWmJsqsarkQkUopQoJEXG2gM4VeBIDhIr0D45VQSIQYGN++UpMEtQwysQAJ6pZ7flJhNsqFwpBEFYIkQgxJVLV4nEIswIfXDcLEPhGwOQjeXp+HS3uHIz1CgQaLHY//egTHy5zPwwdWHMSJigbIxXw8Mb0PFo5OarFfsFTEx7JrsqGSCXGouB6P/3oEhBA6DikUit/odkV7PB4PKSkp+O677zBo0Nk0P6VSiT///BP9+/fHE088gXfeeQcAMHPmTEyfPt1nk3ZK59G4Z6gnLEvQ3LPRzgL1JjPqjHboLVY8+VsODpU4ozxXD4nDQ5emQyzkw2DxXhE22xwID5LgjnEp+N/qkxidFopHp2RALOCB9WHL7eOS8dfhUpTUmfH7wRJcMzQeI1NCsPJIGUw2h1OYalg8pi/bCgC48+IU10QesNgcyK3Sw2x1ICnMmYJ9okIPmcvZbjExtYWdLcwXnJ9/aCJubvkQXDk4AVcOTmjlKEpXQggA5uwf/uqhCbh6qPNvREAwNj0UL87OwqO/HMaqYxVgCcHTs/pC4LpHmW0OGATNR0CMVjtEAj6SwxQYmx6GjSer8L/VJ/HC7CwES4WoM1nRkibWKVfkJl4jg8WH8jLLsvhwszO6e+3QeIgFPK/xbbax0JmtAAjUMhEkAh4sNhYyER8WG+t1bIhcjFGp3rXv7t1SEQ8mKwupiIc52fGYk+29vy2kRgQhtZHoHSFNxxYVZqNcKFw9NBFXD23bsWIhH2/OHYAnfjuKH/cV4/3NZzBvWDz4PAY5ZQ1Y8ttR2FkCO0sQr5Hi0SkZiAiSwOZofZBq5CK8cdUA3Pj5bvy4txjpEQrMHRIHB0sg5DNequ9uQUkKhUI5X3Q7hxcA7rvvPtx3332YPn06xo4dy20PDw/HU089hUcffZRzeAFQZ9fPMAyaXQ1mGII6ox2782vxwt/HobfYoZQK8eLsTIxOCwW/mb9dpNKZjnnnxan4z4gEBLkUbR0sAa+Z2f43Nw3D7OXbUa23olJnxoTeEfjf6pPgMcA787Lxza5CVOktSNDIcNXgOOjMdshEfJTUm2CyOFBUZ8Sfh8sg5POQHafC3sI6DIhTgUHrirvtwf1deX5nDMOcl+u4Naeb0pSWIhsA0JpockSwBFcOjoVKJsSd3+zDmpxKMAyDt64eAImQD73F3uI11SdKCamrL/WLl2fh6vd3oLDWiHc35OLTBUPB5zEQ8pt/v7t+NyMymBN182TTySocL2+ATMTH7IExTVSg7awdRtcClEzEQiEWQC0XQmuwQS0XQijgt/gd8V22yUVCyNvW7teL1r5/X0iFAirKRulRtHUciFtp3SUS8PDqlf2glAnx8ZYz+HpnIW4anQSVVMT1Dp/YOwKvXNkPCrEAVjvbJmV4G4/BqNRQPDSpF17+5wRe+ecEMqOVGJyoaZPdFAqF0pl0S0/w9ttvx4QJEzBr1izs2LHDa9+IESNgMBhajDhSAou9BbV48vej0FvsGBivwh93jcKlHnW2rREkaZsIWXiwBK9d2Q8M4xS7emCFs977+pGJiFRK8MEmZ1Tr4cm9YGcJl3oVo5RCKubjUFE93lybi9dWnURGlAqfLRiGuyf0arOdtI7wwsBkc6DGYMWpigbszq9tVvBsYp8ILJ83ECIBD6uPVWD+x7ugNbRPgEotE2H5/IEIkgiwr7AOS1ypgy1R7KrhTQ6V+9z/0RanMvNl2TGIUjZtUSIT8SEXCyAX87mIaYhcjFQf/bRbg44JCsX/MAyDRyb34hSWP9pyBrFqKW6+KAlLpvfG29dmQyHu2ILRwlFJmJoVBTtLcMfX+7hafgqFQulKuqXDy+Px8OOPP2LAgAGYMGECPvjgA7CuOrhPPvkEM2fO7FAUgNI1GK2utiRWO77eWYBHfzkCm4NgUt8IfLlwaBPVx85kVGoobnfVJOotdsSppbhnQireWpsLk82BgfEqTMmMBEBQb7IBIEgMUyBBI8cHrjRPAHj6z2NN0qxbg9YvXRi4/84FWiPMVgfKWhA8m9A7Al8sHIpgiQB7C7S48v3t7VY1TQ5T4I25A8DnMfj9YCl+2V/S7LFWO4sK14QzOaypQvrBojrsztdCwGNw57iUJm1NAEAi4CFWLUWsWuZzf3ugY4JCCQwYhkHfqCBc2iccDONUaS+pM+HqIfHNZk219bwvzM5Er4gg1JlsuOPrfdCbbZ1oOYVCobROt3R4AUChUODff//FbbfdhrvuugsxMTFISkrC9u3b8d577/nbPEoLmGwsHCzBL/tL8dTvx0AIcO3QOLx1dbbP9KvCWiNmvr0Fr606Abuj/QI/jblrfCpGp4VCyGfw3OxMlGhN+GlfMQDgsam9XYsljCvd0/mgf+aPYzBaHRicoEasWoryejNX59hW3L2FaR1hz8b9d05QyyAR8X1GST0ZlqTBD7eNQJRSgrwqA677eBfq29lqaFRqKB6b1hsA8PXOQmx0tdxqTG6lHiwBgiQChCqa5hN/tj0fADBrQPR5XXhyQ8cEhRI4HCtrQGqYAjOyoiDkM/j7cDnu++7AOWfMyUQCvDNvIFQyIY6U1OOOr/fRLDwKhdKldFuHFwDEYjFef/11FBQU4K233sJHH32E7du3IyQkxN+mUVpAKuSBz2NQ3XA28hWvkTUrtPPPkXLkVRnw6dZ83P71PlfktePweQzenz8QWxePx/DkEHyxvQCEAJP6RmBgghpA04n4GVdfwutHJmB6vygATke8PTjVacW0lrCHIxXyESIXIS0iCEMSNYhtRUEYANIjgvDNTcMAAJUNFtQa23+NXzM0Hte5+vMuXXMKO8/UeO0v1hrx0j/HAQBDEzU+s2AMZmfWQrnODNZHn2CTzYFaow25raRrtxU6JiiUwKFPVBDkYgFmZ0fj/esGOcstciqwJqfynM8dr5HhsxuGQCTgYceZWpygvbMpFEoX0q0dXjdRUVG48sorMWHCBCpQFUAUa43YlV+LkkYpmjKRsy3JnRen4tqh8QCAl/45gSd+O+ozgnuwuI77eVteDa7+YAfyqs6tzZSAz4NSKkSd0YrfD5YCAGb2j+L2u50Wd8rmqBTnIsr2vBrsztcCAIYmUfENSudRUON0HiOCxUgMad1J9sUjUzIwJs3Zn/rllSewv7AOgLP37uO/HEGd0YakUDmevSzT5/sfnZoBiZCHrbk1+Hx70/6+XunatpbTtSkUSvdidHo4bhmbgtHp4bgoLQw3jU4CALy4MgcW27mXHfSJVuKi1FAAwO8Hmi+9oFAolM6GeoeU80ZpvRlmq6OJSMVv+4vwxG9H8NehEjw1sw/+OzUDDAN8u7sIj//q3deWEIJDxc42RU9O74NolQSFtUbM+2gX1p8491Xnn/eVwGxjEa+RIUTRvODOhIwIAMC/RytwoKgOADA6lWYSUDoP9/U8OjW0wxoEfB6Du8anYURyCOwswfN/52Dd8Uo8/usR1BptSAiR4dlZfaGW+ZZHTgpV4MFLnUJsL/9zHKcaRWG80rWFradrUyiU7sutY5MRESxGUa0Jn23L75RzTuzjfJauPe677IJCoVDOB9ThpZw3opUSSER8RAZ7T4r3FtZDb7Fhb2E9GIbBiGQNpmc5VZn/OFQGi/3sSnKR1oRagxVCPoPLsmPw3c3DMShBDb3Fjpu+2IMPN5/ucC2QgyX4ZmchAGBqViRiWqhZHJ6sgUzER43BCgdLkBQq90pVpWqzlHPFXXd7UVroOZ2Hz2Pw4KR0DEpQw2pnsXTNKdQYrIjTSPHsrL4I9tGKyJNrh8ZjbHoYLHYW96846NUvUyrkQyMTIrUd6dr+gI5HCuXckYkEeGiScwHs3Q15nODduTA+IxwCHoMz1QbkVxvO+XwUSnfDevo0UFHubzMuOKjDSzlvxKplGJqoaeJIDopXQiEWYlC8EgBwpsaIzOggSIV8WOwsjpbquGMPudKZe0cFQyTgIUQhxkf/GYzLB8aAEODFlcfx0I+HOpRutflUFYq0JgRLBLhtbApCFGLUGKww+TiXWMj3ckRGp3o7JVRtlnIu5FcbkF9jhIDHYFjSuWcOCPk8LJ7SC1kxzjEWq5biucsyoWomsusJwzB45fIsqKRCHC3V4c21p7hWS77GRiBCxyOF0jnM7B+N7HgVjFYHXl918pzPp5KJMMxVDrTqGJ30Uy4cBGo1GKkUpQ89DN7CqxFm1PrbpAsKqhRC6RRaCrKyBODh7AEzB8Ri5oBYmGwO1BmtiFZKwADoH6fEjtO12FOgRXa8GnUGG3adqQUAZEQEefUovWl0IhJD5Fi65hR+3l+C09UGvHpFP8jEfKilvif1VofDSwX6qx3O6O7lg2IhFvJgsJydJEsEZ49jCcAwwPjeEfj3aAUAYHRaKNxxLz6cqZ5Gq6MdarOk2e+MttTyL60lDBBC0NKfyLm/+QNYFmAY71/iTmcelKCG0Wpv0bHUGqyQiQSw2B2w2lmwBAhViLjfqTPZoJIJIeDx8NjUDOwp0KJfrBJBEiEcLAEBwHp8SJYlXm1HrHYHlDIRnp7VF/d+dwDvbcxDv1gl+sWqYHew4MlEEPE7do2abHZunJxPoar2j0cK5cKmpUypx6b2xhXvbccv+0swa0A0+sepWj2f3mKHrJm2ZWPSw7A1rwZ/Hy7HNUPjoWlj/276aKR0Z4TR0Uj5608Y9+5F6UMPI9hKMxy6EurwUjqFlib4PMbd3Mcbk805WU8MVWBQggaldWbsOF2L/YV1EPAYiIU8HCtzRnv7x6kgEpxNSODzeLhqSBwSQ+VY/PMhHCiqw61f7sXrV/VDSDMPTzHDh9jlyJ6u0mPzqWowDDAiWYN/j5YjKliClPAgyER8r88j4DkftBMywiEVOmsYR6SEQOByEhjGmfolE7V9ONGODN2Xo6V1yKsyICVMjswYVZP9DMO0PB585NVsPFkNABjXKwxCPo97f0GNAU/+fhRaow1WO+v850PYbe7gONw+ztlfOilUjhj12ayKQYne4mosIRDyedCZbHj5n+P4eV8Jbh2bjHsnpIFhGE4tfVLfSFyWHY1f95fiwR8O4sGJabg4IwIFNUaEKIQIkbe/ftcz8tqe8dIeOjIeKZQLHSH/7I3pcHEdcqv0SA1TICtWhUEJalwxKAY/7i3B/1adxFc3DWu1N6+Qz4OA7zuJ8JI+EXhp5XEcK9OhWm9ts8NLoXR3hNHREGlpZNcf0JRmSqfT1vq5xq1/hiQ6WwLtya8FIQRmmwMnK5xqzJkxwT7PMTRJg/evG4RQhQinqw249ct9yK1sXcHZHd0dlx4Go80Bo8WBIq2Jiwx52m622VFrsEIq5OHH20fgx9tHQCGmk+kLlbwqA4xWO/KqOmd11mxzYMdpZwuhcenhXvu+2VWIvCoDag1W6C32Js6ue8q5Yk8Rjpfp0BYIIfjzUCku+d9GfLurCBY7i7fW5uLNtaeaHPv41D6I18igtzjwxY4imGwsbA4WtYaOtQajfXcplMAnp7QeVToLckqdgpEmmwM3jU6GXMTHoZJ6/HW47JzOH6oQY0C8CgDw1+HSJvtpDT6FQulsqMNL6XTaWj/nFMA52/qnb7QSYgEPWqMNp6sMOF7eAAdLEKoQNRG+8iQtPAgfXT8YCRoZKhssmPfRTuwtaH4F7XSVnhOrumFkIlJC5ZCJ+UgLV/i03WRj4WAJNp2oxNbcaq/+wZSeT+PJV0qYHDKRAClh8g6db1teFT7Zchrb8pwiVTvP1MJiZxGllCA9QsEdZ7DYse64M9X5qRl98PVNw/DjbSPwyfWD8fc9o7Hm/jFYt2gsLukdDgLg9dUnYWebRn89Kao14pYv9+Kebw+gWm9FmEKMMWnOmuG31ubinfW5XscrJAK8fmV/CHgMTlcbsPFkFYR8HjTyloWvmsPdd5dGXymUwCXKrcLuEqWr0VsAANeNcPb5fmP1SRgt5+aMujsfrD/etNsCrcGnUABbaSlspU0XhCgdgzq8lE6no1EckYCHAa7aoN0FWhxxiVdlRitbrWuNUkrx4X8GIzMmGPUmGxZ+tht5PiK9DpZgya9HYXWwGJkSgtFpoegbo8LM/jHoF6v2abtUyAOfx+B0tRF6sw3HyhqanJfSc2k8+eobrcKsATE+05nbwvGyBugtdhx3XUebTp1VZ/a8zjecqOJaZo3PCEe8RoaIYAlUMhFkIgEErtTnO8alQiEW4FSlHn8fbl4EZl+BFtPe2oJNJ6sh4vNw2YBo3DshBZP6RuGeCakAgNdXncSr/57wUmbOilXi/onpAIA31pyESMB0KJ2ZQqF0DwYnajCtfzQGc+UQzvvSlYPiEKeWorLBglf+PQGW7XhtzvjezmyWo6U61HrocwA0E4RCsZWWIm/adORNm06d3k6CLrNTOh2pUNBhQZpBCWrsPFOLPfm1iHXVIdaZ2pY+abY7wHM5DBY7i2qDBSlQeB3zzvpcbD9dA4mQhyXTezdxpH3ZLhEKIBU5RbWOlTWgT1RQhz4bpXvS2QJIGVFBOF7WgAzXdeQWdtmaW4N6j2vdnb5crbfgz0NlUEqFkIn5sNpZRCmlkIv4kIkEOFxSz4lcNZibHytb86phsjmQHqHAu/MGghCCIq0JcWopUsKDIOTz8Pqqk/hkaz52nKnFa1f0Q3KYc/yEyJ1CcGYbC53J3mXiUxQKpeuRCvlc5hUAhChEkIr4kIn4eGRyBu76dj9+3FuMBrMNL8zO8hKDbCs5rhIMHsOAz2v9OUxpnpI6k5eoZ3Oo5aIW2y9SAge7VgtiMnE/C6Oj/WxR94feUSgBxdAkDd7dkIddZ2pxw8iBeHtdHg4U1SG/2oDE0OZTSLfkVuPpP45CZ7JDIRbg2Vl9m7R32ZZXw9UpPndZJnpF+q4Lbo6RqWEYmRrGvT5aUoe8agOSQuToH6du17ma41RFA/JrjUjUyBATRFe3A4G2TL5yKxpQWGdCvEqK1IiWF0RGpoRhZMrZ6+jmMcn441AZCmuNePjHQ3h8WgYYhsGM/lFYm1OBg8X1eOXfE63aeUnvcMwdEtfsfrdQ2qAENefIpoSftfXOi1ORGq7A4p8O41ipDnOWb8PiKRnIjFbiyd+PAgAm941Ar8gg6C12LupNJ6YUSs9GKuSjpNaIA1ojUsPkeOnyLDz+6xH8e7QClQ0WLLsmu00tz9zozDa89PdxAEC/WCXWHCvF5YMSzpf5PZqSOhMueX1jm1rGSYV8rFk0ljq9lAsSOlOhBBSDEtTgMUCR1gRCCEalhWDTyWr8eqAU912S1uT4eqMNn2/Px9eumtxekUFYdk024jUyr+MqdGY89OMhEAIMT9agV4SiybnaS161AUaLA6cq9Z3m8BbUGmG02JFfa0RMEI0kdxcK60wwWeworDO16vA2JreiATP6R+HDTWewOqcCvSIVmDskHgIeD8/MysSnW8+grN4Mg9UOo8UBndkGs42F0WqHO6NwWlYUHpiY3iRS4om7FVFLx0zqG4k+kUF49Jcj2JZXg6d+PwaRgAernUWMSoJ4jXOiRNv+UCgXFgVa57OpvMGM6f2iER4kxr3fHcD+wjrM/2gn3r52YIuL0p4sXX0SVXoLJAIeVBIBduXXUYe3g2hd/dGXzh2A1PDm5zW5lXrc9/0BaA1W6vBSLkiow0s575htDo/0x5YnyAqxAJnRShwqqceBojrMHhCDTSer8dfhMtw+Lhl8hsHhknpsOFmFg0X1yCnTcR1+5w6Jw21jkxGl9L6Z2xwsHlhxELUGK2JUEkzJjERetQF9XTWYtQYLtAYb1PL2tVpJCZVzEd7OIkEj4yK8lO5DvErKRXjby8lKPVRSASZnRuD3g2VYvvE0MmOU6ButhEYuwqJLe3kdX9VggUYucimZs7CzLIIkrYtIuZ1jzzR+k9UOk42FVMiD1CUkFR4swUf/GYwvdhTg9VXOet6wIBEm9glHVowSAE05pFAuNBLUMhRojYgMcj4jhyaF4Msbh+G2r/Yiv8aI6cu2oE9UMMakh2FsrzAkhPh+hu3Or8VP+0oAAImhMpjtDgxNVAHouj7dPZHUcAUyXfdnSveh2a4ilXq4Z8tVDRbEd5lFPRd6R6GcdzxFf5pzeE0eTvGQRLXL4a3HQ5N6ISJYjAqdBbd/tQ9nagwwWLxTd1LDFbhlTDLGpof5VKlduuYU9hZooRAL8MT03jDZWaR4rERrDTau1Up7HN6+MSr0jVFxdcOdQVpEENJdqdYGA21K3l1IjQhqd2TXTXq4Aicr9bhuWDzsDoK/j5Tjqd+P4ePrByNY2rwjyzAMpCI+gLZFWd0CM54BXrcCucnGQuqRkcjjMbhhZCJGJIfgj4OlmDskDnF0EYZCuWBx3+MMHurMqeEKfHPTMNz//QEcKqnHsTIdjpXp8N7GPGjkIoxJC8OY9FCMSAmBTCSA2ebAM38cAwAkhciQFi5H/1gVpvaLAdB4rkCnp+eD1to2tqWt44WOrbQU1tOnz/k8EgEP931/wOe+lLpivO36+fav9uKDjN40Mn+O0DsKpZNoXq3RM/2x8VHuukLPB92QJA0+3pqPfYVaEADT+0Xj4y1nONVmpVSI7DgVLkoLxbBkDUIVYte5CMC6/nexNqcSH285AwB4/rK+uKRPpMfvdh6nlgu5CG8g4Dbf42OAEOe/TvStKR2GeP1tfNGSqnjj92bHa5Ad71RDjQ2RYX9RHcrqzXhhZQ6evyyzyblYQkBaGG8OlsDuaLrw414MYsjZa18iYGC2O/93byOuYwCgV0QQerkjzG0WZG39QEJavpBbus5Ja18+Wv7+KRRKxyGEwHN0hQeJ8fVNw1Cjt2BLbjU2nqzG1txq1Bqs+PVACX49UAIhn8HgBA3EAh4Ka41QiPm4ZmgcJmdFgWWdz3+JkA+pkMdlnPi8x7XxHkTHf1PUcmcLyOYcLE+kQj7U8rbXZF9IuNWTickERiqFQN3xcrbl8wdBF5fie+fJ48AG54/h2nJoTxciZmAv38dS2gR1eCnnnbyKBpyqMiA1TI6sWJX3TsY5ufV0ioe4WiHk1xjB5zG4a3wqbA4WIXIRLkoLQ9/oYDAMIOQ37arlcLCoNlhRWmdCYa0RS35ziu0sHJWIGQNi4OsxGCIXI0QubvYhyT8PD8/WHCJ371dK19P6n/vcrgdeCzW0Qj4fL83ph5u/2IOtuTVYdawCN45O9jomRiVt8Rw2B9tM1oFzm4DP4/bLxULIxd5HObMwzsUhbbqtWGtEab0Z0UoJYtU0UkyhBBptdRIVHuUTnplZ8SFyXBsix7XDEmC1s9h5pgbrjldh3fFKFNYasf10Dfe+zKggVDeYUVlvRmKYAlKh81kuFQm8Mk0onUeMSoo1i8ZSNedzxK2eHP3qK5ANGnRO6slhQWLEN5OGbqpTIN/188N7vwFZ+DNsf/9F1ZrPAerwUs4b7ofhyTIdTCxBbpWhqcML74emux1CWrgCpyr12FugxcQ+EVgyvY/Xe9xRnn0FWny7uxCFtSaU1plQoTPD5vCecWfHqfDI5Izz9jnPB+6IN+XCQibio090EB6e3AvP/ZWDV/45gYHxamTHe68iE0JQ2WDBmWoDyuvN6BMdjLRwRSsLKc7rqfEhRo86Xpno/DwSSuvNsFgdKK03U4eXQukhNO5R7vkcH5UaitGpYVgyrTdOVxmw/kQl/jpchjiNFLFqGaKVYpTUm5AdrwbjYwHPZLW7+vMy0MiFnMYApePEqKTUke0kRMnJXeJ8Op5+Ga//fhAP7/2Gtic6R+gdhHLecD8MI9VSVDRYkRrmW9zJV43v0CQNTlXqsTu/FhP7RDR5z/HyBry59hTW5lQ22cfnMYgIFiNaJUVauAL3X5IOkYDX9ozMAEAm4sPQQhSP0jORCgUQ8fm4YWQi9hfW4a/DZbjnu/24d0Ia8muMyK82IL/a4FTztnrXssdrZJjQOxwX9wrDkEQNBI0yIBzNqDR71vG2o7NIu4hWSrgIL4VC6Rl4ZmY1p9XBMAxSwhVICVdgSlYkKnQW6IwWNFgdSG5B1dlkY2Fw3eOkIj6N/FIuTMIjUBgU7m8regTU4aWcN9wPwewETYvqzL5anAxN1ODrnYXYnlfjrBnyCEstW3cKy9blghDn5P3ygTEYnRqKaJUUUUoxwoMkTSb7gFONudZgg0YuREjjPM4AQyoUBLyNlPMHwzB4fnYmjpTWo6DGiEd+OtzkGD6PQaxKitAgMQ6X1KOw1ohPt+bj06350MhFePvabK48AACsdmcNb+N0Z8+6ufNFrFrWaZHdmm40jimUnow7I8tNa63KYlQyxKhkqDVaWtXNkAp5kIuc5RXn895EoVAuDKjDSzlvuB+GrYnMNH5oAsCIlBCIBDwcL2/A34fLMa1fFADg94OleGttLgBgcmYkHrw0HYQlKNAaoZQIEKWUYv3xChwq0aFfTDDG9z4rUqU12GC1u9WY6USZEthU6cy4cXQivtlZBI1chMQQOZJC5UgIkSE5TI5YtQwigXMiaLDYsSW3GmtyKrAupxK1Bivu+mY/fr1jJKJUUpyu0uO3A6UAgKJaI9bllHNjQyYSNBvZDcQ2IbV0HFMoAYev53hzaA02/HGgGCcq9BiTFopJWdFerdEAZz1vDE1jplAonQRdNqMEJGFBYtw2xinW89xfx6Az2XC0pB7//dkZ6bpuRAKeuywTKWEKFGiNMFkcKNAaUWu0YsPJalTrTDhUovM6p1ouhEjAgyZA1JgpPYsagwWnKvWdJjZWVGdCqFyMhyf3wpc3DsOzl2Vi4egkjM8IR3KYgnN2AUAuFmBS30i8ekV/rH9wHHpHBaHWYMWd3+xHg9mG+78/CKPVgXi1FEmhsiZjozka1+gFAho6jimUgKCj9zy1XIgTFXrYHA4cKKrnSiooFArlfEEdXsp5p9ZoRW4HHoq3jElGcqgc1Xornvz9KG7/eh8sdhajU0Nwx9gULnUqQS2DVMxHglqGOoMNKaEy8Ph89IsJ9jqfRi5GWriCRoUo5wXPyGNnEKeSQiLiI85DZKTWYEF+tQG1LYwlqYiPd+cNhEoqxOGSekx9awuOlemglglx34RUBImFTcZGc8hEfPB5TItpil1NCB3HFEpA0NF7nkYmxpj0UKhkYgyIU4LPaz5tudZgQV6lvsV7HoXSk+isPr8Ub2i+COW8UKw1okxnBghBaZ0ZSokAgLxdk1SRgIdnZvXF/I934c9DZQCAyGAxXro8CyEKEdeWSMDn4eL0cPB4DGoMFoxKC8f0AbS+j9K1aORCrrb0XDHZ7FDJRYhSS71SieuMNthZFlqDDZpG13eJ1ohynRkRQc7WP0uvHoCFn+1Geb0ZAPDinCxM6B2BE+X1yK004EhJHTJjVF7nKHadIzJYgli1HFKhIGBSmSkUSmBxLve8a4Ym4pqhTvX4Y2X1yKsyICVMDpVMxN2DYtQylNeZ0GB2wGKzc8902uaM0lNp3OcXSpW/Teox0JkM5bxQpnO2ITlTY0SYXACtyY6sNj4U3W2KRHwGw5JDMDs7Br/sL4FczMegBBWMVhZmmwMF1QZ8sPk0fthbgnvGp+L+ienQyERNHN2uaLtCubBo3EoLONvPuVPOb3XAwTr/93Q4VTIh6o02qDzGksl1fRfWGFCiNeFEWQOGJmkwKjUUj07tjZdXHseCUYmY0DsC/xwuwbrjVVw6dGOHt1xnhsXKutoHNa+gej7wrBem45RCCXxauueZbQ7uudvaollelQFGqx3b82pgtNoh4POgM1lhthPEKCUYEK+BWHD2HKX1ZphpmzNKD6Rxn98TpGufwz0ZOqugdAqNhamigiUobzCjd5QCPB6DcIUEGpmoyXEsIeA1yqx31w2aWQKJkOCxqRk4WlKHILEAPDAQ8nkwWh04XFqPPw+VAwA+3noG/xmRgCCJACKBd/plV7RdoVw4EOJd2yppdL0RAK03wWqp5RSBVMR3Orsib9E3tUwEpUQAPp/HbXdf30qZCCcr9AhVCFGmMyMhVI7/jEjAnIExUIgFYAnBiQo9QhQilGjNSA2Xg200HiODJahssCA8SOz6JM19B83bz7JNe/02+fQ+9nt+p9ThpVC6N57PXWkLa90OQpAWLkdBjQkggJ0lsFjtqNXbIBMLUKO3IjlMDomQz92RopUSlOnMiAqW+LxL0YZ+lO4O1+e3pN7fpvQY6KyC0ikwjWawnm1IGrcV8oSHppNfzzZFQj4PIQoxbhubhN0F9ciIkMPmYCEX8fD7gTKYbE4xHYPFgW93F+G2sSlNfpfn+ZqzI5Bwm+hpKsO07kRQugaGYVq+plpRJXeeo/l9fB4DuUgAeTNOH4/hgWEYGK0O1/VPwOcxSApVQMznoaTejOhgCZfyr/ZY5UkNk+NYuR6TM8PRJ1rV5NwxahmiVTKcSwvotlyrvvb7ak/m+710IFAo/qKt489zPPNauKHwwUNGlAoZUSqcrmpATpkOIj4PQVIhCmtNyIwJhkYuds4jXO/pzDZnFArlwoA6vJROw1eaZ3PsPlODnPIG9IkKwuDEEK99vtoZzR4Yj9kDgRqDFQ6WYH+hFhtOVoFhgAUjE/HJ1nx8tjUfN4xM5Cb6bpxtV+ilTuk82lPbuju/BsfL9ciIVGBIo2u9vbjHmFTIh8nmjIjyeQxC5E6nNjUiCMnhimYnpTOz4zCdkCa9eM8n/xwuwf4iHbLjgjE5K6bZ4+g4pVB6Dh0Zz/UmG/QWByKDBZgxIBY8xik2ufpoKY6WNiAjUoGp/XzfQzznH/Q+QqFQGnNBqzQfOnQIx44d87cZPYLcigb8c6QcuRW6NrUwySlvgMFix4kKvdf2Yq0RuwtqUaw1+nyfO/rzxppTAICrBsfhwUm9EK2SoMZgxc/7Ss7xk1Aoncvxcj0MFhuOl+tbP7gV3Gm/JpvT6XWqm/peXMqraMD6E5XIq2gA4BS12ltQi7I60znb0R72F+mgt9iwv6htrZAoFMqFyckKPQxWO4pqTeAxTsX50joj1uRUokpvwr7CeuRWNGDt8Qrkuu5rbgKxhRqFQgkcLkiHt6SkBKNGjUL//v3Rt29fPPTQQ/42qdtToDWCZVmU1lva1MKkd2QQ5GIBekUovLa7xa7KdGaf75MK+dh8sgrHyxsQLBHg/olpEPJ5WDgqCQDw8ZYzTeqEKRR/khGpgFwsREakovWDW8HdJkgq5EMm4iNELmp2vBXWmWCy2FHocnDLdWZYbCwqG7q2vUd2XDAUYiGy49rWColCoVyYpEcoIBcJ0NeVxiwVClChsyBeIwUDBgPjlSjQGmG02FHQaFE8EFuoUSiUwOGCy/vQ6XSYMGECrr76avzzzz/45ptvcOedd+LZZ5+FRCLxt3ndlgRXPU2CWtZqOjMADEkKwZCkEJ9iV24xiubQGq0AAJYADSZnq4Kztbx2sATg0zI/SoAwJDGk01KZZSI+NDJhm+ro4lVSFNaZEO/q4xsZLEG5zuwSpOo6JmfFtJjKTKFQKACQHa9BdrzGa1tEsBgjU8OhkgqgkotRqjWiEmfnHG7cpVAUCoXiiwvO4X399deRnp6Op556CgCwYMECPPXUUzh+/Djq6uowcuRIiERtk/K1WCywWM5GS3S6CzdlLzUiCKkRQed8nsZiV774z4hErDxSjn2Fdbjj63147cr+eHtdLgBg8ZQM8M9FcYfS5dBx1DreqtBtS8xJiQhCiseYjFHLEKOWNVFmpvQM6Dii9ESiVU4hvVqDBQ6WIFotQ1asyt9mUSiUbsYFl9K8b98+KJVK7vU777wDrVaLOXPm4NJLL8XAgQNRUVHRpnO9+OKLUCqV3L+4uLjzZfYFT7HWiN35ztpekYCHt64ZgDCFCKcq9bjq/e2w2FmMTAnBrAHR/jaV0k7oOGqdzkjX81cNL6VroOOI0pOR+rgHtqb5QaFcaBwtrceRkpb/lVygc4ALLsI7bdo03HnnnRCJRDAajVi5ciU2btyIYcOG4fjx4xg7diweeughfPHFF62e69FHH8UDDzzAvdbpdHSS0YgagwVagw1qudBng3qznXWJTBAADLR6C7RmG6KCJYhxpWICQJmr0XyZq9H87jM1GJSoxtqcSljsLMQCHp6Z1Ze2LOmG0HHUcYq1RpTrzIgMlrTapsOzhjfGdeyPewqxr7AOA+NVuGJwPABAa7Sg1mCDppkx2xwmm92lIM2jKql+gI4jSk/h532F2F9Yj+x4JeYMjEetwYJ6ow0qudArbXnPmRoUa02IVUsRohBTlWbKBc8Vy7eDJ2q5PFMq5GPNorFec+wLgR5/V9DpdAgOPiuWctttt0GtVuPw4cPIycnB4sWLMWzYMABARkYG7rrrLnz77bdtOrdYLIZY3LX1cN0NrcEGm51FrcHmc/LsTtXUmWwIlgpRoDVCzOehTGf2GoxRSgnK6s2IUjoH8sFiHeQiPgYnqLE7X4uHJ/dCQoi8yz4XpfOg46h1mktpLteZYbayKNeZW3V4fdXw7iusg95sw77CurMOr8EGawtjtjUbTTYWsrZVhVA6ETqOKD2F/YX1TmX3wnrMGQjUGW2wO1hoG92TrHYCljj/97xHUoeX0hPJq9QDqvom241GA/fzK1f0Q2ZCeLPnyK3U477vD0BrsFKHtydx5swZjBs3Dvfccw8WLVrEbZ87dy7mzp2Liy++uEm9bmlpKQYOHNjVpvZY1HIhF+H1VZMrEfJgsrJQyYQAGCRqZKg32xEeJIadJXDP7WNUUm5wEkIwIDYY+4vqMTUzAk/M6IuQZs7fGbhrHj/fehq787UYkqjG9aOSuf1d2dOU0v1pfJ3WGLwjqg7Wme0AAN/sPIN9hXXIig7GzOxYSIQ82BwEPJ7zHJHBElQ1WBAWJIadZQEALMtCwG+a+hytkiJaJfWq4R0Yr+IivG7UciFnT3uQifhchJdCoVDagvN+582wJDWOlOiQGROMFbvysa+wDgkaKYamhqLGIITGtaI2MEGNQq0R8WoZd/+hKs2U7oKjVtvqMWq5iFvkvvf7A8hTVTc5hrWe7WoyKEGNtBhlk2MoPdzh/fXXX2G32/Hggw8CgJfTCwCZmZl44YUXMGLECAwbNgxffPEFfv75Z+zcudMf5nZrmkslDlVIWowSSQR8SIVnL0ON/OwCBCGk2fPOzI7DrOw4nKrUw2pnUWeyIyzo/Kps787XwmC1Y3e+FtePOq+/ihLAtLa+0d60+lofEVX3KfYU1MFoseFgiQ43jHbuc7CE2x+lkiKq0Sotj8drcRGGxzCcjVcNScBVQxK89ocqJAhVtH8syUQCGlmhUCjtwpfI5PT+sZje3/nzfd/tg8FqR53JjnG9o7zuk43FMqlKM6U7IFCrwUilKL7nHqT89SeE0c1rz8SopFg+fxAMa4A35w4A0jOaHGM0GjDsDefP0RdY1LY99OjZSUZGBoYOHYqBAwf6dHqfeeYZ7Nixg1NmjouLw6pVq5CYmOgniyntRdPBaFRHGJKo5iK8FEpn0dI1PChehb2FdRjkEYGlUCiUCwX3PbBvVBBEAl6XPOsplPOJMDoasW+9haKbb4Zdq23R4QWAsCAxDABSwhWQ+ojeGgw92pXrNHr0t5SRkYEjR47gl19+AQDO6a2rq0NUVBTuuOMO7NixA+vXrwfLsrj44oshFNKbqT8oqzfhive2Y3CCGmmhUuwtqsewRDXuGJ/e4vtC5OJ21RmeC9ePSqaRXUqn09I1PH9EEuaPaNt5lq87ib2F9RiepMat49I60UIKhULxDzqTDfUmZw1vWrjC3+Z0KSV1JmgN1haPya3Ud5E1lM6Er6GBk66mRzu8iYmJKCsrg8lkwpIlSwA4nd6YmBgcPHgQAMDn83HJJZf400wKgM+3FaC83ow/D5UhKyYYYj6wt7AOJpuDq8uh6UqUCwmTzQ6T1QGpyDvtvzn2FtbDZLXhQHFTUQsKhULpjuwpdJZ17C30fV/rqXOEkjoTLnl9I0w2R6vHSoV8qOVUKbCnYz19GgK1utWIMMU3PdrhZRgGKSkpyMnJwcCBA2G1WhEZGYmSkhJ89tlnTWp6Kf5Db7FxPydopNCZ7RgUr/JSXuxJDzMKpTVMVgcMFgfqjDZEqSStOr2D4pXYW1iPAbFUsIJCofQMBsersKewDoPifd/XPOcI7tc9oTWR1mCFyebA0rkDkNpKZFstF11wirsXEu6a39KHHgYjlbZa90vxTfe+I7QBz7TmP/74A0eOHMG7776LBx98ECKRCHfffbe/TaQAuGl0Mqr1VmREBuHeCWlgGAaEEFgdLFVepFyQSEV81BltEPIZZ6S3FYf3dlf6//lSK6dQKJSu5s4JvQAAzd3WPNWZe2JrotRwBTKp6m5AYCsthfX06S7/vcLoaKT89SeMe/ei9KGH21T3S2lKz7gjtEBGRgaWLFkCtVqNtWvXIiQkBEuWLIFUKsWIEW0sjqN0KsVaI0rrzYhWShCldK5KRijFeGF2FqQinpfKbW5FA3Kr9EgNUyArVuUniymUrkcqFCBKJeHSmtvD4ZI67MnXQiHiYURqWKs9eikUCiUQKa0zokJnQUSw2Od9TCp0pjIfKq7D0TIdYpUSDE4K8YOlbYfW5nY/bKWlyJs2HcRkAiOVQqDu2hpcYXQ0RNrW2xhRmqfHO7wLFizAvn378NlnnyEk5OxN0C1gRel6SuvNMFsdKK03cw6vVCiA1IdeWG6VHgaLHblVeurwUi44nOOi/bfp01UGlOvMEPAYJOnM1OGlUCjdkgqdBRabAxU6S4v3sdxKPewOFlV6a0BHd2ltbvfErtWCmEyIfvUVyAYNohHWbkjg3hVawWQy4eeff4ZWq8W4ceOQmZnp87jExET88ccfXWwdxRducQm1VAgtgGhl670+U8MUXISXQqG0jeQwOWoNVihEPEQGt72nrtFq7zE1cBQKpfsTESzmIrwtkRquQG6lHrFqKar1Fr/cw9oauaW1ud0XUXIydXa7Kd1yRlNcXIzx48cjKCgIJpMJd999N+bNm4d3330XwcHBXsc6HA7w+bT+83zTUtkgSwAGZ8UlNAoxUsOdzeIdLAHg+82EAJkxSkSrpag12FBjsEAj81jtZJo2rKdQAp2Wxkqt0QKtwQaVTAhNM62KCCEAaf7aJyxBZrQSmdFK7/d44fv9PbEGjkKhdF+iVTJEq2QghLR474xRSyEVCQAQD6HL5u9hnS110N7I7ZAkDXVmKV74q0b4QqFbzmhuvvlmXHXVVXjuuecAAL/++isWLlyIMWPGYM2aNQgNDQXgdHavvPJKZGZm4plnnvGnyRc0DJy+qae4hNtX5fOZZqbeTjeYAVBrsMFqZ1FrsHn1K2W6yOHlUcea0om0dDm5r3Wt0YawIN+RWR7Da/H8PIbX4bHhOUYpFArlfMPnte1e1ZqDWmuwwWZn4SAEGjnT5fcwqqpMORf8XSN8IdDtHF6WZbFmzRq89NJL3LbLLrsMvXr1wvjx4zFjxgxs2rQJQqEQfD4fffr0wbJly3DrrbciJibGj5ZT3OISbnbn1+B4uR69IhQY2oLIhEYuRK3BBo3cR5EvhdKD6Mi17h5HGZEKDEk8N7EWd80wXeOhUCjdCY1ciE0nKlFeb8WgRBWGJoX6xQ6qqtwzcdSeH8Eod29dWiN8/mk5VBCA8Hg8aDQabNiwwWt779698ffff+PAgQN4/fXXue3PPfccjhw5Qp3dAMBkc6DGtQoKAMfL9TBYbDhW1tDi+0LkYqSFK7yiuxRKT8R9rYcq2l536x5Hx8u9VT0bjzcKhULpqYTIxWiwOACGtDqnAACTzd4FVlF6ArbSUhTfc0+nRl49e+vmTZsOe1kZgK6rEc6t1ONISX2L/0rqTOfdjq6k20V4AeD666/H008/jVmzZiExMZHbnp2djYcffhjLly/H4sWLue3U2Q0MPOsDpUI+MiIVXIS3MTUGCxfpoo4uhdI87nGUEXl2HNUYLDhdZUCQRICwIIlXZgWFQqH0JNzzhWilGKX1QEZkUKvvMVnpQiClbbijr3Effthpzmjj3rqmo0c75bytoZaLIBXycd/3B1o9VirkY82isT0m/b5bOrxPPPEEfvvtN0yZMgXr169HZGQkt2/27Nl47rnnQAjpshpPSttoXB84JDEEgxNDfNbwNle3S6FQvBmSGNIklbnWYAOfYdBgtiMhhDq7FAql5+Ku300IDcIlfaLQnCifJ+3tbU6h8DWdW1crjI6GDAAjlaJm+XtdUrsbo5JizaKxbVITv+/7A9AarNTh9ScKhQIrV67E2LFjMWrUKPz444/Izs4GAOzcuRPDhw+nzm4A0riGtyVo3S6F0nHc40YjF9LoLoVC6dF0ZL7Q3v7mx0rroWhoXjkrt1Lf7D4KpTnckV67VguBWt0l6cwxKmmbndjWrmt9g64zTOoSuqXDCwDJycnYvn07rr76agwZMgRTp06FTCbDpk2bsGrVKn+bRzlHQuRiaOTiNqzTUiiUxoTIxTQzgkKhXBB0xf3uqvd3gCeWtXiMVMiHWi5q8RgKpTHC6OiAE6lqa+ozazF2jUGdQLd1eAEgNjYWmzdvxl9//YX169dDo9Fg6dKlXinOFAqFQqFQKBRKR3lpThb6JUe1eAxtN0TpKbQ19fnQ6TLMW9o1Np0r3drhBZy9WKdPn47p06f72xQQV6M4na77hPg7C1898kw2O0xWB6Si5lOZ3b1227sP6Lo+vP7CYDBwP+t0Ojgc3Vtkwz0uSCsNFXv6OGrt8wMtX9tt+f6ae7/J5vAYk83f/nv40OrWBMI46mn3Jkrg05b75lm8b2DecxHnfa+94yhCyiI+qLUbow06na0ddlICHVtVFYxHjkDvcECn18Pmx3mJSa/3acf5uh8H8YCgVq75CikLoL3j0z8wpDtY2U0oLi5GXFycv82gUAKaoqIixMbGNrufjiMKpXXoOKJQzh06jiiUc6e1cRQIUIe3E2FZFqWlpQgKCvKKsOh0OsTFxaGoqAjBwcF+tLBjdGf7u7PtQM+yPygoCA0NDYiOjgaP13wL8ObGUaDQnf8m1Hb/0Jm2E0K6zTjqTn8zauv5IVBtDYRxFKjfzbnSEz8X/Uy+aes4CgS6fUpzIMHj8Vpc4QgODu7WA6U729+dbQd6jv1KpbLVY1sbR4FCd/6bUNv9Q2fZ3t3GUXf6m1Fbzw+BaGugjKNA/G46g574uehnakpbxlEgENjuOIVCoVAoFAqFQqFQKB2EOrwUCoVCoVAoFAqFQumRUIe3CxCLxXjyySchFnfPvpjd2f7ubDtA7Q9EuvNnorb7h+5s+7nQnT43tfX80J1s7Wp66nfTEz8X/UzdHypaRaFQKBQKhUKhUCiUHgmN8FIoFAqFQqFQKBQKpUdCHV4KhUKhUCgUCoVCofRIqMNLoVAoFAqFQqFQKJQeCXV4KRQKhUKhUCgUCoXSI6EOL4VCOW/YbDZ/m9Dp1NTUwGKx+NsMSjeiJ44DCqU90DFA6WnQa7p7QR3eLmDt2rV47LHH8Oabb6KiosLf5rSLuro63HfffRg0aBA2b97sb3PaTW1tLT766CO8/fbbKC8v97c57ebnn3/GpZdeiqlTp+LgwYP+Nqdd1NTUYNiwYfjoo4/8bUqnUVVVhYsvvhjffvutv01pN2azGU899RRGjhyJu+66C3q93t8mtRmDweBvEzqMVqvFiBEj8N577/nbFL/wxx9/YPLkyZgyZQr27t3rb3NaZMeOHViyZAlee+01FBQU+NucFsnPz8eyZcvw8ccfB/z4+PTTTzFkyBBUV1f725SAZ+XKlZgyZQouvfRS7Ny509/mdAp79uzBzJkzMWXKlIC/VtvKyZMn0bt3b6xZs8bfpnQqq1evxquvvor169f725TOh1DOK4sXLyaxsbHk6quvJjExMUQmk5H33nvP32a1CYfDQUaOHEluvvlm0tDQ4G9z2s2GDRtIaGgo6dWrF1EqlSQkJIScOXPG32a1mSeeeIIkJiaSJ554ggwZMoTExMQQlmX9bVabeeedd4harSYMw5APP/zQ3+acM5WVlSQrK4ssXrzY36a0G51ORwYPHkwmTZpEHn/8caJSqcgDDzzgb7PaRF1dHenfvz956KGH/G1Kh3j//fe5cbB8+XJ/m9OlvPDCCyQ+Pp4sWbKEjBgxgoSFhRG73e5vs3zyxhtvkPDwcHL11VeT5ORkIhQKyQsvvOBvs3zy0UcfEblcTvr27UvEYjHJzMwkRqPR32Y1S1RUFFGr1SQrK4tUVVX525yA5fXXXycxMTHk8ccfJxdddBFRq9XEbDb726xz4tChQyQkJISsWLHC36Z0KnfffTdRq9VEKpWS1atX+9ucc0av15OpU6eSkJAQkpqaSgAE7P2vo1CH9zyydetWEhERQWpqagghhJhMJvLAAw8QAOSxxx7zs3Wt8+uvv5K0tDTicDi8tgfyg9VNeXk50Wg05I8//iCEEFJdXU3i4+PJ3Llz/WxZ2/j7779JXFwcNzmora0lPB6PmEwmP1vWdlavXk2mT59OFi1a1O2dXl/O7pkzZ8jPP/9M9u7d60fL2sb1119PbrjhBu718uXLyezZs/1oUdt5+eWXycCBAwnDMN3S6V2/fj2ZPHkyeeSRRy4op3fdunUkKiqKlJWVEUKciy5CoZDU1dX52bKmnDp1igQFBZGCggJCCCF2u50899xzhMfjkRtvvDGgFhq3bt1KwsLCyOHDhwkhhBw+fJiIxWLy4osv+tmy5hk7diz56quvSExMDHV6m2HLli0kPDycFBUVEUIIMRgMRCqVkoqKCj9bdm5cccUV5Mknn/TaxrJst5rL+GLZsmXk3nvvJZdddlmPcHpvvPFGcvnll3N/l0ceeYQIhUJSUlLiZ8s6D+rwnkdeeuklMmnSpCbb//e//xEAAR/pffLJJ8mMGTO411u3biVZWVkEAElKSgroAf7EE080mRy/9tprJDY21k8WtY8rr7yS3HPPPdzro0ePkpiYGDJx4kQyaNCggL92CCGkqKiIJCYmEkKIl9P7wQcfdAv7PZk7dy4JDw8ntbW1xOFwkPvvv5/weDwiEokIADJz5kyi1+v9baZPTCYTEYlEZNu2bdy2JUuWkFGjRpGBAweSiRMnkv379/vPwBZwOBwkJSWFnDp1irz//vvd0uktKyvj7jueTu8nn3xC3n77bT9bd/647rrryC233MK9zs3NJZGRkWTSpElk4MCBZNmyZX60zpvPPvuM9O/fv8n2r776ivB4PPLUU091vVHNMHPmTPL11197bZs/fz6ZPn26nyxqnVtvvZV89NFH5NSpU5zTe+LECXLllVcG5AKIP7j55pvJf/7zH+51YWEhCQ8PJ1OmTCHZ2dnkf//7nx+t6zgpKSnk+++/J4Q4Hd0XX3yRqFQqwjAMmTZtWrdd/Fi9ejWZPHkysVqtXk7v4sWLA3pu7IvS0lISGxtL6uvruW0NDQ1EIBCQX3/91Y+WdS7U4T2PfPnllyQ4OJjU1tY22ffAAw8QmUxGysvL/WBZ21i2bBkJCwsjJpOJHDx4kISEhJA333yTrF+/nkyZMoVIpVJy8uRJf5vpkzlz5pDTp097bfvrr79IUFCQnyxqHzfeeCNJTEwkx44dI3v27CG9evUiV111Ffnpp5/IbbfdRgCQjz76yN9mtkpwcDCXDu92esPDw0leXp6fLWsflZWVJDMzkwwcOJDcddddZNSoUeT06dPEbreTFStWEIVCQebPn+9vM31it9uJUqkk1113HamqqiIff/wxUSqV5IUXXiArVqwggwYNIiqViossBBIGg8Erqt5dnV6VSsVN7N1Ob2hoKDl16pSfLTt/3HnnnSQ2NpYcPnyYHDhwgGRmZpI5c+aQn376idx9990EQMA4/KtXryYCgYDk5+c32ffKK68QPp9PcnJy/GBZU4YOHUpsNpvXtueff55cdNFFfrKodd544w1y//33E0II5/Ty+XyvRd0LnUWLFpGIiAiyf/9+cvjwYTJgwAAyY8YM8uOPP5L777+fMAxDXnvtNX+b2W6GDRtGbrvtNkKIMxDRr18/8tdff5FvvvmGREVFkbFjx/rXwA5SVFRE4uLiCCGEc3r5fD7p27cv0Wq1/jWunaxdu5YsWrSoyfakpCTy+eef+8Gi8wN1eM8jDQ0NJDw83GcarclkItHR0WTp0qV+sKxtFBYWEqFQSJ544gly4403ek1OzGYziY+PJ88//7wfLWweX2nXGzZsIAqFwmubO4Ut0CguLiaDBw8mEomEpKWlkVmzZnntv+qqq8igQYP8Y1w7GDRoENm5cychhJAPPviAKBSKbpve7HZ6g4KCmqSZLVu2jPB4PK58IdD46aefSEhICImMjCShoaHkm2++4fbV1tYSlUpFXn31VT9a2HZ8Ob05OTnEarX60aqWGTZsGNm6dSshhJCPP/6YGwc9Ob25vLycDB8+nIjFYpKent4k2+k///kP6du3r5+s88Zut5P09HQyfvz4JjXGDoeDZGZmBkztvq9n29KlS8no0aO513a7PaBSEVeuXEkmTpxICHHebwYMGEAUCgVNb/agqqqKjB49mhsv48aN80qlv+WWW0hKSoofLewYr776KhGJRGT//v0kNDSUK3EghJBVq1YRAAE7D2uNoKAgLiq6aNEiolAouk16c0NDAzc3I8T3fSUzM5N89tln3OuioqKAKu9oL1SluROpqanB6tWrUVlZCQBQKBT48MMPsWLFCtx///1ex0okEowdO5Y7NhA4evQoNmzYALvdDgCIi4vDo48+iueeew4rV67EgAEDuGPFYjESExMhlUr9ZK03q1evxooVK7jXvuzi8XhgWZZ7/dxzz+HKK68EIaRLbGyJ559/3ksVNCYmBrt374bJZEK/fv0wc+ZMr+MHDBgAoVDY1Wb6pKqqClOnTkVNTU2Tfb169cKRI0fw4Ycf4vnnn8fBgwfxwAMP4JZbbul26s1hYWFYt24dbrzxRoSHh3vtGzNmDFiWhdFo9JN1LTNnzhxUV1cjLy8POp0Os2bN4vap1WrEx8cHzPWk1+tRV1fX7P5bbrkF7733Hl577TU8/PDD2LNnD8aNG4f9+/d3nZHNoNfrodVqm2x3j4NPP/0UTz/9NPbv34+HH34Yd9xxB95//30/WHr+iYiIwPbt22E2mzFkyJCAvofx+Xx8+umn2LJlC66//no4HA5uH4/Hw8SJEwPmWd3as83hcGDevHl46qmnutiy5nFf/1qtFhMmTMDkyZOxf/9+1NbWYvz48T6fHT2d119/HSdOnOBeh4aGYvPmzTCbzbjoooswY8YMMAzD7Q+k8dIcv//+O+69916vbXfddRfS0tIwc+ZMKBQKREZGcvsyMjLAMAwkEklXm9pmDAYDPv/8c3zxxRdN9qWlpeHIkSN46KGHsHHjRuTl5WHSpEmYOXMm1q5d6wdr24Zer8eUKVNw9dVXc/eN1u4rx48fx/Dhw7F9+/YutbVT8bfH3VN49dVXiVgsJgzDEJVK5SVks3z5csIwDLn66qu5VIe6ujqSkJBA1qxZ4yeLz6LVasmMGTMIj8cjAMjw4cOJxWIhhDhrLm644QYCgMyaNYuLoqxbt46EhIR4rdb5i7KyMiKVSgmfz+dqRXyxefNmIpFICCGEPPvss6RPnz4BIQjx4YcfEgAkMTHRZ0rdlClTyNSpU7mVtfr6epKenh4QqSatKRc/88wzpG/fviQhIcErjXnx4sXkzz//7Coz20VFRQW5+eabicFgaPN73nrrLZ81gF1NZWVli6rqWq2W8Hg8r1XbtWvXkpCQEL+XV9TV1ZFrr72W8Pl8AoBcc801TVI3PXFHeoOCgvx+LdXX15PrrruOs/2qq67yiji/8MILpG/fviQ+Pt4rjfmxxx4jv/32mz9M7lJmz55NJkyYwAkgNjQ0kMzMTPL+++/72TJvfvjhByIUCsmkSZO4Z5vJZCIDBgwgX331lZ+ta55ly5aR4cOHE7vdTubOnUumTZsWUOq+LMsSqVTaJFJ+6tQpMm/evHbda3sCP/74IwFAIiMjyfHjx5vsv+aaa8jo0aO5bAODwUCys7PJm2++2dWmtpnffvuNhIWFkR07djTZl5+fT5KTkwkArv6cZVly6623kjlz5nS1qW2muLiYJCcnk5tuuomsXr26iYDrtddeSzIzM8ngwYO5ub3VaiULFy4kBw4c8IPFrdPQ0EBGjx5NJk+eTACQX375pdlj+/fvTz766COSk5NDYmJiAvoe2Baow9sJvP322yQrK4vk5OSQuro6MmzYMHL33Xd7HfPbb7+R8PBwolQqybRp00h0dHRApEixLEvGjx9Pbr31VlJfX092795NJBIJ2bhxo9cxr7zyCpHJZCQ1NZVMnDiRhIaGkrVr1/rR8rOYTCaiVqvJTTfd1KLTu3XrViKRSALK2SXEKYwybdo00qtXL59O7z///EN4PB4ZPXo0Wbx4MUlOTib33nuvf4z1oC1tek6cOEEGDx7crWp2Z8yYQYRCIRk7dmybJmI///wzCQ0N9UoP8hezZ88mQqGQXHTRRc06vQsXLiR8Pp/ceOON5LbbbiMajcbvC282m40MHTqUXHfddWTPnj3k448/JgKBoMW2CLt37yYhISF+d3btdjsZOXIkmTdvHtm9ezf59NNPiVAoJM888wx3zKlTp8jgwYN7dM1uS6xbt47w+XwyfPhwsnjxYpKWlsbV9QUaGzZsIAkJCUQmk5EpU6aQpKQksmDBgoBO5XvnnXfIkCFDAtLZdbNo0aKAmPMEAn/++ScZP348yc7O9un0bt26lQiFQjJkyBCyePFikpGREdDXYEvOrpuKigoyY8YMwjAMGTduHMnMzCQjRowI6HrXK6+8kjz44IPN7l+/fj256KKLAvozeOJ2dt3X0pgxY8i4ceOaPT47O5s8+OCDPcLZJYQ6vOeMuxbXc0L/6KOPkkcffZR8/vnnZPfu3dz2hoYG8uWXX5KXXnqJq+fyN7///ju56KKLvG6kvXv3Jj/++CP58ssvSWlpKbe9srKSfPXVV+Tzzz8PuLqbxMREUlRU5OX0ms1m8vfff3PH7NixgwAIKGeXEEL27NlDhg8fTsrKyryc3pycHE4oZdWqVWTSpElk4sSJLUaxuwqtVkuysrI4VUmWZcnrr79OkpOTiVwuJ9OnT+9WTq6bM2fOkMTERLJt2zaiUqladHr37t1Lhg0bRkaNGhUQKseFhYUkPj6e7Nixg6jV6madXpvNRp5//nkycuRIMn/+/IAQ4/n000/JxRdf3KRmrbmoeV5eHomIiPC7s0uIU5xwzJgxXqv/d9xxR8DUp55PTp06Rd58802u/VtLrFu3jkyZMoVMmDDBL5OnZ599ts0iWWazmaxYsYK8+OKLflkMeuONN8hLL73U5uPfe+89AsAvzu4nn3xC/vvf/3bp7+zu5ObmkoyMDFJTU+Pl9Obn53PPkk2bNpFp06aR8ePHe2XkBBorV64kIpGIrF+/nhDiVPy99tpriVKpJFFRUWTJkiVemToHDhwg77//Pvn777+bREwDDalU6jWH37FjB1mwYAG59tpru0WdrieNnV1CCPnll18IgGaj0YMHDyZ8Pr9HOLuEUIf3nNmxYwe5/vrrudelpaUkKiqKREZGkvT0dAKAPPvss/4zsBUefPBBr8nKDz/8QBiGIRkZGSQsLIwolcoWV+0ChcmTJ5N//vmHsCzLOb2DBw8m11xzDXeMVqsll19+eUA5u4Q4G34HBwcTQgjn9CYkJJCYmJiAcG594XA4yIIFC4hSqSQ7d+4kN998M8nKyiKffPIJWb58OUlISCAREREBqfzbEu+99x7Xz3LXrl2tOr3V1dVdaV6LfPTRR9y9Zu/evS06vYHGpZdeSjZv3uy17ZtvviEhISE+j2dZNmBSxqZOncpN9tysWLGCKJVKv9jTVbz++utEJpORPn36EADk6aef9rdJzXLgwAEiFAoDShm6OU6dOsXZ2lan99ixY2T+/Pld7uxWVlYSoVBIGIYhjz76aJf+7u6Mw+EgCoWCmM1mL6c3MTGx2wk6njlzhiQkJJDx48eTvLw8Eh8fTxYuXEi+//57smjRIiIQCMi8efP8bWa7YVmWiEQi8vPPPxNCnJl4SqWSLFiwgIwaNYowDEO+/PJLP1vZdk6dOkUWLVrktajscDhIcnIyWbBggc/3vPjiiz3G2SWEOrydgrvWx2g0kr59+5L77ruPW9F65plnCJ/PJ4WFhf40sVnq6uo4dbZt27aR0NBQLmrS0NBARo4cSUaMGOFPE9vEfffdR15//XVCiDPqHhMTQwB0m8EaGxvLKRWuW7eOq+/xVdMbKLidXplMRtLS0rxU/ioqKkhERAS59dZb/Whh+3E4HF79dJtzegMxtczhcHg5t805vYFo+5YtW5rYtXr1aqJSqfxkUdvZunVrk0jF+vXrmyjC9yR++eUXkp6eTs6cOUMIcUZPm1ONX7duHZk0aZJfF16qq6tJbGwseeyxx1p0erdt20bGjx/v1xRFo9FIQkJCyAsvvNCi03vgwAEyZswYvy7gsixLlEolWbp0aYtOb05ODrnoootIcXFxF1sYuGRmZpKDBw8SQpz3agBErVb7rOkNdNxOr0wmI48//rjXvs8++4wA8OoD3124+OKLyciRI4lOpyPh4eHk0KFD3L4FCxaQqKgoP1rXOSxdupSIxWJSWVnpb1POO9Th7WQaRyksFgthGIZs2rTJTxa1HYPBQPbt2+e17dtvvyXh4eF+sqjtvPfee2TBggXEbDaTadOmkblz57Za0xtIXHLJJeSvv/7ixAGWLl3abE1vIOF2ev/3v/812XfPPfeQkSNH+sGqzqWx07tt2zZy0UUXBXw6FiFNnd4dO3aQUaNGNWm9EoisW7eOy3wgxFmzO3v2bD9a1HY2bdpEZDIZ93rfvn1k1qxZAbnY0BH69+9Pdu3axb3+7rvvyOTJk8njjz9OXn31VS/ndu3atUQqlZJXXnnFH6ZyhIaGkrq6Oi+nt6CgwMv53bp1KwkKCiJPPPGEHy0lJCEhgZSUlHg5vWVlZeSNN97gjtm/fz8JCQnxey/b/v37k6NHj5J33nmHc3pramrIyy+/zB1z7NgxEhkZSRYuXOhHSwOLK664gnz99dckPz+fJCUlkZdffrnZmt7ugNvpra2t9drOsixRqVTkgw8+8JNlHWf9+vUEALn88svJJZdc4rVv48aNhM/nd4tnaUvodDqiVCoDOhO1s6AO73lm7969RC6Xc726uhuPPvpot5hkbtiwgQwYMIBzdu12O5fe3K9fvxbVXgOBu+66iyxcuNBLHMCd3vzkk0/617hWcDgcPqMM8+fPJzfffLMfLOp83E7v0KFDSXh4OFm1apW/TWozbqd3yJAhJCIigvzzzz/+NqlNrF+/ngQFBRFCnM5uoNTstoXNmzcTqVRKCHE6u5GRkT1GjdlsNnv11K2pqSHp6emkT58+ZP78+USpVJJ+/fp5ZXwcOXLE787+qFGjOO0Mt9MbHh5Oli1b5nXc0aNH/T6JnTRpEvn3338JIYRzesPDw7lyCzcnTpzwu0DV3LlzuUVlt9MbHh5OHnnkEa/jcnNzLzg15pZ4/PHHybx580hSUhJ59913CSGES28OBFHKjuCra4fBYCAikShgdGvay/PPP08AkKioKKLT6bjtzz33HNdburvzwAMPkKioqIDuZd8ZUIf3PFJVVUWysrJ8Rr+6A+vXrycajSYgRG1ao6amhqhUKs7ZdcOybLdQ0Pv+++99igPU1tb6faLYEdauXUtUKhXJzc31tymdxvLly4lUKu1Wzq6bDz/8kIjF4m7j7BLiXMRSKBTdztklxJmiLZFIepyz68bTyZoyZQq58847uftuTk4OkUgk5NNPP/WTdb658cYbuTZIBQUFRKVSBWxN77333suV6JSVlZHw8PB21fR2JU8++SRZsmQJIcT5vEpISCAAaE1vK7i7L7idXTd1dXV+X3DpLBwOB7npppvIlClT/G3KObF06VIiFArJ4MGDyRdffEEefvhhEhUVRU6ePOlv0zqF/Pz8HiVO1RzU4T0PnDlzhrz55pskLi6uW6YJbNq0idx7770kISGBbNiwwd/mtJkTJ0506wfF0aNH/W3CObNhwwZy9913k/j4+G517bTGtm3bul1k182OHTu6VWTXzaZNm4hAIOh2zi4hzuuFz+f3SGe3Mb6iOoMHDw64hd5XX32V3HPPPaSgoIAkJyeTZcuWtVrT6y+WL19OFi5cSMrKykhGRgZ58cUXW63p9RfffvstmTNnDqmtrSXZ2dlk8eLFXunNlObpCc98XzQ0NJAvvviCjBo1ikybNq1bBB1a49ixY+TWW28lo0ePJrfccgunudJTmDt3Lvnhhx/8bcZ5hTq854GSkhKydOlSTtCju/Hvv/+S7777rluou1ICi3Xr1pHvvvvOK/WnJ/DVV191S2eXEOeEtLs5u4QQcvz4caJQKLqds0uIUxFToVD0eGfXF2VlZUQmkwXcZP6PP/4g/fv355xdN4899hi5+uqr/WhZU9avX08yMjI4Z9fNCy+8QGbOnBlQ+gH79+8nCQkJnLPr5p133iETJkwgFovFj9Z1PR999BEn4DR58uRu0eWiNTZt2kQGDRpExGIxGThwIPnmm29aPN7hcJB33303oNOYCwsLydSpU4lUKiVJSUnkueee6/bXqsFgILfddhtRKpUkLCyM3H777W0WtfN3aURXQB1eCoVCoQQkgdbvuz10Z9s7SkVFBRkxYoSX4xMoVFZWkujo6CY1u4SQgHIgCXGmtSYkJDSp2SUk8Gw1mUykV69ePv/mgWbr+ebjjz8mKSkpZOXKleTPP/8k48aNI3w+32e2w+bNm5uIhAYiBw4cIKGhoeSzzz4jGzduJAsXLiQMw5B58+Y1cRCLioq4Nj6BjE6nI8nJyeTxxx8n27dvJ08//TSRy+Vk4MCBTTJWzGZztxHcmjFjBrniiivItm3byAcffEBiYmJIRESEl8Cgm08++eSCC2pRh5dCoVAoFEqHKS8vJw8++CCJi4sjL7zwQsDqDnQn8Uhqa/cjOTnZKyOFZVny3//+lwAgr732mtexI0aMIBqNJuB1Lv7zn/80Wcz46aefiEQiIXPmzPFa1LjvvvsIn88nf/zxR1eb2S4++OADMnr0aK9tR44cIXFxcaRXr16kurqa2/7jjz8SAOS///1vV5vZLg4dOsT1dnZTXV1NRo8eTYKCgrwWV06fPk2kUikZN25cwN6rzwc8UCgUCoVCoXSQiIgITJo0CUePHsWjjz4KhmH8bZJPgoOD/W1Cm6G2dj+qqqpgt9u51wzD4Pnnn8eSJUvw8MMPY9OmTdy+3377DbfffjsSExP9YGnbafyZAGDOnDn46aef8Ntvv+HVV1/ltr/66qt4+OGHMWrUqK42s134+kx9+/bFhg0bUFdXhxtuuIHbfvnll+Ott97CVVdd1cVWto+qqio4HA4QQrhtISEh+Pfff9G3b1/MmTMHer0eAJCUlIQ///wTt9xyS8Deq88HDPH8digUCoVCoVAoFEq7mDFjBrRaLTZt2gQe72w8iRCCyZMnw2g0YvPmzX60sP28/PLLePXVV5GTk4OwsDCvfc888wxee+01lJSUICgoyE8Wtp+tW7di9OjR2LBhA8aOHeu1b+3atbjkkkuwZcuWgHfcPdHpdIiKisKTTz6Jhx9+2GtfaWkp+vTpgyVLlmDRokV+stD/0AgvhUKhUCgUCoVyDjz11FPYuXMnHnnkEa/tDMPg8ccfx9atW2EymfxkXce4/fbbIZVKcdVVV8FisXjtW7RoEex2O3bt2uUn6zrGqFGjMGXKFMybNw+FhYVe+yZMmICRI0dizZo1frKuYwQHB+ORRx7BkiVLsH79eq990dHRuPHGG7vdZ+psqMNLoVAoFAqFQqGcA4MGDcLSpUvx2muvYfHixV7ppRqNBlKpFEKh0I8Wtp/g4GCsWLECO3fuxMyZM9HQ0MDtk0qlkMlk3TKl/bPPPoNEIsHFF1+M3Nxcr30ajaZbfqb//ve/GD9+PGbMmIFVq1Z57euun6kzoQ4vhUKhUCgUCoVyjtx5551466238Nprr2HixInYvHkzDh8+jDvvvBP33XcfBAKBv01sNyNGjMDKlSuxa9cuDBo0CD/99BNyc3Nx9913IzMzE0OGDPG3ie0mPDwc69evh1QqxeDBg/HWW28hLy8P77//Pvbt24f/t3dnsTH9fRzHP502U0pqCBJ7LNHE1jQkJNTMtJaoJZZaWlN7iMRFE0SCRAZx4waRSBMV2zBBmCZqT6ZaUhSxlARFcNGqpUJLtGr+F+I8zzytev4izszxft3N9/c7yfdivjP5nGUmJyfH7Bb/tbi4OB0/flzp6enKyMjQqlWrdP/+fZ05c0Y7d+5Ubm6u2S2aimd4AQAAgN+ktLRUa9euVXFxsRISEpSbmyuv1xv2bG+0efHihdasWaNAIKCGhgZNnz5deXl5cjgcZrf2yz5+/KiNGzdq9+7devPmjYYPH649e/ZowIABZrf2y0KhkPLy8rR161Y9efJEvXv31o4dOzRp0iSzWzMVgRcAAAD4zerr6xUbG6vY2FizW/ltGhsb1djYKLvdbnYrv00oFNLnz5/VqlUrs1v5rT59+qTWrVub3UZEIPACAAAAACwpeu+tAAAAAACgBQReAAAAAIAlEXgBAAAAAJZE4AUAAAAAWBKBFwAAAABgSQReAAAAAIAlEXgBAAAAAJZE4AUAAAAAWBKBFwAAAABgSQReAAAAAIAlEXgBAAAAAJZE4AUAAAAAWBKBFwAAAABgSQReAAAAAIAlEXgBAAAAAJZE4AUAAAAAWBKBFwAAAABgSQReAAAAAIAlEXgBAAAAAJZE4AUAAAAAWBKBFwAAAABgSXFmNwD8quPHj6t///4aNGhQk7Xa2lqdPHlSTqdTXbp0MaE7ILJdv35dNTU1Gjt2rFELhUI6cuSIevXqpREjRhj19+/f69SpUxozZow6duxoRrtAxCsqKlJVVZUkKT4+Xj179lRKSopsNq4tAICZCLyIWocOHVJ1dbWKi4ubrPl8PuXm5qqystKEzoDIV1paqnXr1unt27eKi/v2VXDnzh3NmTNHKSkpunnzprH39OnTmjt3rl69emVWu0DE27x5syoqKjRixAh9/vxZV69eVfv27XX27Fl1797d7PYA4K/FaUdErcWLF6ukpESPHj1qspafn68ZM2bI4XDo6tWrKikpabLnypUrzdaBv4Hb7daHDx9048YNoxYMBuVyuVReXq6ampqwenJysjp06MA8AS0YNWqU/H6/Tpw4oYcPH+rjx49at26dsc78AC2rqamR3+835qi8vDxsva6uTn6/P+w7Svp2Z19zdUAi8CKKjR8/Xj169NCePXvC6nfv3lVZWZmWLFkiSSovL9f06dP15csXY08oFFJWVpZKS0v/aM9ApBg4cKA6d+6soqIio1ZUVKQpU6YoOTk57M6JoqIiud1uScwT8P9q27atUlNTdfv2baPG/AAtq6mpUSAQUCAQ0N69e5Wenq6JEyeqoaFBkpSQkKA1a9Zo3759YccdPXpUy5cvV0JCghltI8IReBG1bDabFi5cqP3796uxsdGo5+fnq1+/fnI6nZKkzMxMffjwQWfPnjX2XL58Wc+fP1d2dvYf7xuIBDExMXK5XAoGg5Kkr1+/qri4WC6XS06n06hXVlbqwYMHSktLk8Q8Af/G48eP1blzZ+M18wO0rE+fPsYV3oKCAlVUVKiiosK4uBETE6OsrCz5fL6w43w+n2bOnKn4+Hgz2kaEI/Aiqi1atEhVVVU6ffq0JKm+vl4HDx7UokWLFBMTI0lq166dJk2aFPbh6PP55HQ6ea4KfzW3261Lly6poaFBt27dUigUUnJycljgDQaDio2NVWpqqiTmCWjJs2fP5Pf7tX//fs2bN0+3bt3S+vXrjXXmB/i5r1+/qqysTCdOnFBhYaF69uypa9euGesej0fXr1/Xw4cPJX07MRsMBuXxeMxqGRGOwIuo1qtXL6Wnpxtn/goKCvTu3TstWLAgbN/cuXNVUFCg2tpaNTQ06OjRo3ww4q/ndrtVV1ensrIyBYNBjR49WjabTampqbp3755ev36tYDCoYcOGKTEx0TiOeQKa9+LFCwUCAR07dkyBQEBZWVnGyaLvmB/gx54+faqkpCTNmjVLu3fvViAQ0PPnz1VdXW3sGTRokIYMGWKcODp8+LB69OjRZNaA7/iVZkS9xYsXKycnR9XV1crPz1dGRkaTvyKaOHGi4uPjFQgE1K5dO9XV1SkzM9OkjoHIkJSUpK5duyoYDKq0tFTp6emSJIfDocGDB+vixYsKBoOaNWtW2HHME9C8UaNG6eDBg5K+3c48dOhQJSUlafXq1cYe5gf4sU2bNql///4qLCw0ah6PR+/evQvb5/F4lJeXJ6/XK5/Pp+zsbOPOPuB/cYUXUW/atGlKTEzUli1bdP78eePHqv6b3W5XZmamfD6ffD6fJk+eHHbFCvhbuVwuXbhwQSUlJXK5XEbd6XTqwIEDevz4sfGDVd8xT8DP9e3bV16vV16v1/h/Xon5AVpSVVWlpKQk4/X79+91/vz5Jvuys7P19OlT7du3Tzdv3uQuCbSIK7yIena7XTk5Odq2bZu6dOmijIyMZvd5PB6lpaXJbrfL7/f/4S6ByJSWlqYlS5bI4XAoOTnZqDudTm3fvl12u10jR45schzzBPzc8uXLtX37dnm9Xu3atcuoMz9A86ZOnaqVK1eqU6dOSkxMVF5enurr65vs69atm5xOp1asWKGUlBQNGDDAhG4RLQi8sIRly5apsrJSY8aMUVxc82/r1NRUzZ8/X/X19ZowYcIf7hCITOPGjdPs2bM1ePBg2Wz/ueln9OjRmj17tvr06dPs3zwwT0A4t9sth8MRVrPb7dq5c6cOHz6suro6tWnTRhLzA/zI0qVL1b59e507d042m00bNmxQbW2tXr582WTvypUrdeDAAc2YMcOEThFNYkKhUMjsJgAAAAAA+N14hhcAAAAAYEkEXgAAAACAJRF4AQAAAACWROAFAAAAAFgSgRcAAAAAYEkEXgAAAACAJRF4AQAAAACWROAFAAAAAFgSgRcAAAAAYEkEXgAAAACAJRF4AQAAAACWROAFAAAAAFjSPwi8fk2w+jgrAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for name, w in [\n", - " (\"no discrepancy\", walker_vol_plain),\n", - " (\"GP discrepancy\", walker_vol_gp),\n", - "]:\n", - " ch = w.model_sampler.chain\n", - " print(\n", - " f\"{name:16s} \"\n", - " + \" \".join(\n", - " f\"{p.name}={ch[:, i].mean():.2f}±{ch[:, i].std():.2f}\"\n", - " for i, p in enumerate(omp_vol.params)\n", - " )\n", - " )\n", - "print(\n", - " \"truth \"\n", - " + \" \".join(f\"{p.name}={v:.2f}\" for p, v in zip(omp_vol.params, volume_truth))\n", - ")\n", - "\n", - "fig = corner.corner(\n", - " walker_vol_gp.model_sampler.chain,\n", - " labels=[p.name for p in omp_vol.params],\n", - " truths=volume_truth,\n", - " truth_color=\"k\",\n", - " color=\"tab:blue\",\n", - ")\n", - "corner.corner(walker_vol_plain.model_sampler.chain, fig=fig, color=\"tab:red\")\n", - "plt.plot([], [], color=\"tab:blue\", label=\"with GP discrepancy\")\n", - "plt.plot([], [], color=\"tab:red\", label=\"no discrepancy\")\n", - "fig.legend(loc=\"upper right\");" - ] - }, - { - "cell_type": "markdown", - "id": "65f86f5a", - "metadata": {}, - "source": [ - "## The learned discrepancy vs the missing physics\n", - "\n", - "Conditioning the GP on the posterior-mean residuals\n", - "(`rxmc.predictive.gp_posterior_predictive`) recovers the smooth angular\n", - "structure the deficient model cannot produce — compare it with the *true*\n", - "defect, the difference between the full and volume-only potentials.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "d4fb9bb3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:59.633011Z", - "iopub.status.busy": "2026-08-11T03:08:59.632823Z", - "iopub.status.idle": "2026-08-11T03:08:59.846393Z", - "shell.execute_reply": "2026-08-11T03:08:59.845548Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mp_vol = walker_vol_gp.model_sampler.chain.mean(axis=0)\n", - "theta_xs = walker_vol_gp.likelihood_samplers[0].chain.mean(axis=0)\n", - "residuals_xs = obs_xs.y - omp_vol.evaluate(obs_xs, *mp_vol)\n", - "\n", - "angles_vis_rad = obs_xs.visualization_workspace.angles\n", - "disc_mean, disc_cov = rxmc.predictive.gp_posterior_predictive(\n", - " kernel_xs,\n", - " theta_xs,\n", - " obs_xs.x,\n", - " residuals_xs,\n", - " angles_vis_rad,\n", - " train_noise_var=obs_xs.y_stat_err**2,\n", - ")\n", - "\n", - "true_defect = omp_full.visualizable_model_prediction(\n", - " obs_xs, *full_truth\n", - ") - omp_vol.visualizable_model_prediction(obs_xs, *volume_truth)\n", - "\n", - "angles_vis_deg = np.rad2deg(angles_vis_rad)\n", - "disc_std = np.sqrt(np.diag(disc_cov))\n", - "plt.plot(angles_vis_deg, true_defect, \"k:\", label=\"true defect (full - volume)\")\n", - "plt.plot(angles_vis_deg, disc_mean, color=\"tab:blue\", label=\"GP posterior mean\")\n", - "plt.fill_between(\n", - " angles_vis_deg,\n", - " disc_mean - disc_std,\n", - " disc_mean + disc_std,\n", - " alpha=0.3,\n", - " color=\"tab:blue\",\n", - ")\n", - "plt.plot(np.rad2deg(obs_xs.x), residuals_xs, \".\", color=\"gray\", label=\"residuals\")\n", - "plt.xlabel(r\"$\\theta$ [deg]\")\n", - "plt.ylabel(r\"$\\delta(d\\sigma/d\\Omega)$ [b/sr]\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "id": "35d64f79", - "metadata": {}, - "source": [ - "## Takeaways, continued\n", - "\n", - "- **Same API, real physics**: `kernel_term(kernel)` on a\n", - " differential cross section works exactly as in the toy — the kernel just\n", - " acts on the angle grid (radians), so its length scale is angular.\n", - "- Without the discrepancy term the deficient potential's parameters must\n", - " contort to mimic the missing surface absorption (note $V_v$ lands many\n", - " $\\sigma$ from the truth, and $W_v$ inflates to play the role of $W_d$);\n", - " with it, the GP absorbs the angle-correlated defect and the geometry\n", - " parameters relax to the truth. $W_v$ remains partially biased — volume and\n", - " surface absorption are genuinely degenerate at one energy, and no\n", - " discrepancy model can restore information the data do not contain.\n", - "- `rxmc.predictive.gp_posterior_predictive` reconstructs the learned\n", - " discrepancy on any angle grid — useful for comparing against candidate\n", - " missing-physics terms.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/linear_calibration_demo.ipynb b/examples/linear_calibration_demo.ipynb deleted file mode 100644 index 500f59f..0000000 --- a/examples/linear_calibration_demo.ipynb +++ /dev/null @@ -1,1851 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "5add9362-edc2-4c55-a0ff-1fc5cf1d4ed7", - "metadata": {}, - "source": [ - "# Calibration of a line\n", - "\n", - "Simple demo demonstrating the workflow of `rxmc`." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "69d96b52-427c-4345-8622-d2726544a77c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:07.173521Z", - "iopub.status.busy": "2026-09-09T18:23:07.173398Z", - "iopub.status.idle": "2026-09-09T18:23:09.655789Z", - "shell.execute_reply": "2026-09-09T18:23:09.655058Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using database version X4-2024-12-31 located in: /home/kyle/db/exfor/unpack_exfor-2024/X4-2024-12-31\n" - ] - } - ], - "source": [ - "from collections import OrderedDict\n", - "\n", - "import corner\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "from scipy import stats\n", - "\n", - "import rxmc" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "315006bb-c255-4555-b458-e00bfef26ef9", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:09.657738Z", - "iopub.status.busy": "2026-09-09T18:23:09.657494Z", - "iopub.status.idle": "2026-09-09T18:23:09.660733Z", - "shell.execute_reply": "2026-09-09T18:23:09.659881Z" - } - }, - "outputs": [], - "source": [ - "rng = np.random.default_rng(49)" - ] - }, - { - "cell_type": "markdown", - "id": "4443b51f-20b9-42f4-8292-1bd5098c8f17", - "metadata": {}, - "source": [ - "## define parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ab500ce6-552f-4c9e-9b0f-648d456e4a53", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:09.662224Z", - "iopub.status.busy": "2026-09-09T18:23:09.661954Z", - "iopub.status.idle": "2026-09-09T18:23:09.665195Z", - "shell.execute_reply": "2026-09-09T18:23:09.664616Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on class Parameter in module rxmc.params:\n", - "\n", - "class Parameter(builtins.object)\n", - " | Parameter(name, dtype=, unit='', latex_name=None, bounds=(-inf, inf))\n", - " |\n", - " | A single scalar model parameter.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | name : str\n", - " | Human-readable name of the parameter.\n", - " | dtype : type, optional\n", - " | Data type of the parameter value. Defaults to ``float``.\n", - " | unit : str, optional\n", - " | Physical unit string (e.g. ``\"MeV\"``). Defaults to ``\"\"``.\n", - " | latex_name : str, optional\n", - " | LaTeX representation used in plots and documentation. Defaults to\n", - " | ``name`` when not supplied.\n", - " | bounds : tuple of float, optional\n", - " | ``(lower, upper)`` bounds for the parameter. Defaults to\n", - " | ``(-np.inf, np.inf)``.\n", - " |\n", - " | Methods defined here:\n", - " |\n", - " | __eq__(self, other)\n", - " | Return self==value.\n", - " |\n", - " | __init__(self, name, dtype=, unit='', latex_name=None, bounds=(-inf, inf))\n", - " | Initialize self. See help(type(self)) for accurate signature.\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Data descriptors defined here:\n", - " |\n", - " | __dict__\n", - " | dictionary for instance variables\n", - " |\n", - " | __weakref__\n", - " | list of weak references to the object\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Data and other attributes defined here:\n", - " |\n", - " | __hash__ = None\n", - "\n" - ] - } - ], - "source": [ - "help(rxmc.params.Parameter)" - ] - }, - { - "cell_type": "markdown", - "id": "fcf6b674-c76d-47a4-852c-11e57538e625", - "metadata": {}, - "source": [ - "## make the model" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "0669a9b0-3941-429b-887f-edcfeb909453", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:09.666500Z", - "iopub.status.busy": "2026-09-09T18:23:09.666383Z", - "iopub.status.idle": "2026-09-09T18:23:09.669683Z", - "shell.execute_reply": "2026-09-09T18:23:09.668842Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on class PhysicalModel in module rxmc.physical_model:\n", - "\n", - "class PhysicalModel(builtins.object)\n", - " | PhysicalModel(params: list[rxmc.params.Parameter], transform=None)\n", - " |\n", - " | Abstract base class for parametric physical models.\n", - " |\n", - " | Represents an arbitrary parametric model\n", - " | $y_{\\mathrm{model}}(x;\\,\\alpha)$ for comparison to an experimental\n", - " | measurement $\\{x_i,\\, y(x_i)\\}$ encapsulated in an\n", - " | :class:`~rxmc.observation.Observation`.\n", - " |\n", - " | Subclasses implement :meth:`evaluate` in physical space. An optional\n", - " | *parametric* ``transform`` (see :mod:`rxmc.transforms`) is applied on top by\n", - " | :meth:`__call__`; its parameters are appended to :attr:`params` so they flow\n", - " | through the ordinary model-parameter machinery (priors, ``split_parameters``).\n", - " | Typical uses are a latent normalisation :func:`rxmc.transforms.scale` or one\n", - " | per dataset via :func:`rxmc.transforms.per_observation_scaling`. Comparison-\n", - " | space transforms (e.g. comparing in log space) are *not* the model's\n", - " | business: declare them on the :class:`~rxmc.observation.Observation`.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | params : list of Parameter\n", - " | Physical parameters of the model. Each entry should carry a name\n", - " | and a data type.\n", - " | transform : Transform or callable, optional\n", - " | Model-side transform ``y -> transform(y, *values)`` applied after\n", - " | :meth:`evaluate`. Its parameters (if any) are appended to ``params``.\n", - " |\n", - " | Methods defined here:\n", - " |\n", - " | __call__(self, observation: rxmc.observation.Observation, *params) -> numpy.ndarray\n", - " | Physical-space :meth:`evaluate` followed by the model transform.\n", - " |\n", - " | __init__(self, params: list[rxmc.params.Parameter], transform=None)\n", - " | Initialize self. See help(type(self)) for accurate signature.\n", - " |\n", - " | apply_transform(self, observation, y, transform_values=())\n", - " | Apply the model-side transform to a physical-space prediction.\n", - " |\n", - " | evaluate(self, observation: rxmc.observation.Observation, *params) -> numpy.ndarray\n", - " | Evaluate the model at the given parameter values.\n", - " |\n", - " | Must be overridden by subclasses.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | observation : Observation\n", - " | Observation containing the independent-variable grid.\n", - " | *params : float\n", - " | Model parameter values.\n", - " |\n", - " | Returns\n", - " | -------\n", - " | np.ndarray\n", - " | Predicted observable values on the observation grid.\n", - " |\n", - " | Raises\n", - " | ------\n", - " | NotImplementedError\n", - " | Always — subclasses must implement this method.\n", - " |\n", - " | split_params(self, params)\n", - " | Split a full parameter tuple into ``(base_params, transform_values)``.\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Data descriptors defined here:\n", - " |\n", - " | __dict__\n", - " | dictionary for instance variables\n", - " |\n", - " | __weakref__\n", - " | list of weak references to the object\n", - "\n" - ] - } - ], - "source": [ - "help(rxmc.physical_model.PhysicalModel)" - ] - }, - { - "cell_type": "markdown", - "id": "b497f926-7772-4b57-a8e4-707e69efdee1", - "metadata": {}, - "source": [ - "# Clearly to make a model, we need to understand these things called `Observation`s. \n", - "\n", - "This is an important detail, the whole point of `rxmc` is to compare predictions of a `PhysicalModel` to data contained in an `Observation`, to calibrate the parameters of the `PhysicalModel`." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "3c64a7e3-c4a4-41e4-a7de-34eb7ab4728a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:09.670969Z", - "iopub.status.busy": "2026-09-09T18:23:09.670850Z", - "iopub.status.idle": "2026-09-09T18:23:09.674074Z", - "shell.execute_reply": "2026-09-09T18:23:09.673577Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on class Observation in module rxmc.observation:\n", - "\n", - "class Observation(builtins.object)\n", - " | Observation(x: numpy.ndarray, y: numpy.ndarray, y_stat_err=None, y_sys_err_normalization=None, y_sys_err_offset=None, label=None, transform=None, mask=None)\n", - " |\n", - " | Experimental data: ``x``, ``y``, and the statistical error on ``y``.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | x : np.ndarray\n", - " | Independent-variable data.\n", - " | y : np.ndarray\n", - " | Dependent-variable data, same shape as ``x``.\n", - " | y_stat_err : np.ndarray, optional\n", - " | Statistical (uncorrelated) error on ``y``. Defaults to zeros.\n", - " | y_sys_err_normalization : float or np.ndarray, optional\n", - " | Reported *fractional* (dimensionless) normalisation uncertainty —\n", - " | inert metadata; see :meth:`systematic_terms`.\n", - " | y_sys_err_offset : float or np.ndarray, optional\n", - " | Reported *absolute* offset uncertainty, in the same units as ``y`` —\n", - " | inert metadata; see :meth:`systematic_terms`.\n", - " | label : str, optional\n", - " | Human-readable dataset identifier used in error messages.\n", - " | transform : Transform or callable, optional\n", - " | Parameter-free *comparison-space* transform (see :mod:`rxmc.transforms`).\n", - " | Pass **raw** ``y``: the observation stores ``y = transform(y_raw)`` and\n", - " | propagates ``y_stat_err`` by the delta method, and the\n", - " | :class:`~rxmc.constraint.Constraint` applies the same transform to the\n", - " | model prediction — so ``transform=rxmc.transforms.log`` compares in log\n", - " | space with the model written once, in physical space.\n", - " | mask : array_like of bool, optional\n", - " | Which points are *active* in a likelihood (default all). Inactive\n", - " | points stay in the block (supports/terms are authored over all points)\n", - " | but are excluded from the residual; use :meth:`masked` /\n", - " | :meth:`masked_where` to derive fit/held-out views.\n", - " |\n", - " | Attributes\n", - " | ----------\n", - " | x, y : np.ndarray\n", - " | The data, ``y`` in comparison space.\n", - " | y_raw, y_stat_err_raw : np.ndarray\n", - " | ``y`` and its statistical error as given (physical space).\n", - " | y_stat_err : np.ndarray\n", - " | Statistical error on ``y`` in comparison space (raw, not squared).\n", - " | transform : Transform\n", - " | The comparison-space transform (identity by default).\n", - " | mask : np.ndarray of bool\n", - " | Active points.\n", - " | identity : Observation\n", - " | The root observation this one is a view of. Views made by\n", - " | :meth:`masked` share it, so anything routing by observation (e.g.\n", - " | :func:`rxmc.transforms.per_observation_scaling`) treats a masked view\n", - " | and its root as the same dataset.\n", - " | y_sys_err_normalization : float or np.ndarray or None\n", - " | Fractional normalisation uncertainty (dimensionless).\n", - " | y_sys_err_offset : float or np.ndarray or None\n", - " | Absolute offset uncertainty (units of ``y``).\n", - " | label : str or None\n", - " | Human-readable dataset identifier.\n", - " | n_data_pts : int\n", - " | Number of data points.\n", - " |\n", - " | Methods defined here:\n", - " |\n", - " | __init__(self, x: numpy.ndarray, y: numpy.ndarray, y_stat_err=None, y_sys_err_normalization=None, y_sys_err_offset=None, label=None, transform=None, mask=None)\n", - " | Initialize self. See help(type(self)) for accurate signature.\n", - " |\n", - " | masked(self, mask, label=None)\n", - " | A shallow copy of this observation with a new point mask.\n", - " |\n", - " | No data or pre-computed workspaces are rebuilt: the copy shares them and\n", - " | only changes which points enter a likelihood.\n", - " |\n", - " | masked_where(self, predicate, label=None)\n", - " | :meth:`masked` with ``mask = predicate(x)`` (points where it is True).\n", - " |\n", - " | num_pts_within_interval(self, ylow: numpy.ndarray, yhigh: numpy.ndarray, xlim=None)\n", - " | Number of active points of ``y`` that fall within ``[ylow, yhigh)``.\n", - " |\n", - " | Useful for empirical-coverage diagnostics. ``ylow``/``yhigh`` are in\n", - " | comparison space and indexed over *all* points of the block.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | ylow, yhigh : np.ndarray\n", - " | Interval bounds, same shape as ``y``.\n", - " | xlim : tuple, optional\n", - " | ``(x_min, x_max)`` range to restrict the count.\n", - " |\n", - " | statistical_term(self, support=None)\n", - " | The always-on, genuinely uncorrelated statistical diagonal.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | support : np.ndarray, optional\n", - " | Indices of this observation's block in the stacked vector\n", - " | (``None`` for a single-observation constraint).\n", - " |\n", - " | Returns\n", - " | -------\n", - " | Term\n", - " | ``diag(y_stat_err**2)`` on ``support`` (comparison space).\n", - " |\n", - " | systematic_terms(self, support=None) -> list\n", - " | This dataset's reported correlated systematics as fixed rank-one terms.\n", - " |\n", - " | Opt-in — **not** added to any covariance automatically. Pass the result\n", - " | via ``Constraint(extra_terms=[*obs.systematic_terms(), ...])``.\n", - " | Zero magnitudes are skipped, so an observation without reported\n", - " | systematics yields an empty list. Magnitudes are reported in physical\n", - " | space and propagated to the comparison space by the delta method\n", - " | (``|t'| * omega`` for an offset, ``|t'(ym_raw)| * eta * ym_raw`` for a\n", - " | normalisation).\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | support : np.ndarray, optional\n", - " | Indices of this observation's block in the stacked vector\n", - " | (``None`` for a single-observation constraint).\n", - " |\n", - " | Returns\n", - " | -------\n", - " | list of Term\n", - " | The absolute offset mode (``outer(omega, omega)``) first, then the\n", - " | fractional, prediction-scaled normalisation mode\n", - " | (``eta**2 * outer(ym, ym)``).\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Readonly properties defined here:\n", - " |\n", - " | log_jacobian\n", - " | ``sum(log |t'(y_raw)|)`` over the active points.\n", - " |\n", - " | The log-Jacobian of the comparison-space transform: a constant in the\n", - " | parameters, needed only to compare marginal likelihoods (log Z) across\n", - " | different comparison spaces (``log Z_raw = log Z_transformed +\n", - " | log_jacobian``). Zero for the identity.\n", - " |\n", - " | n_active\n", - " | Number of active (unmasked) points.\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Data descriptors defined here:\n", - " |\n", - " | __dict__\n", - " | dictionary for instance variables\n", - " |\n", - " | __weakref__\n", - " | list of weak references to the object\n", - "\n" - ] - } - ], - "source": [ - "help(rxmc.observation.Observation)" - ] - }, - { - "cell_type": "markdown", - "id": "09fdde46-187c-4849-947e-15d98bcef3ae", - "metadata": {}, - "source": [ - "Ok, so basically it's just some `x` and `y`, ans some information about the errors of `y`. We can think of `Observation`s like experimental measurements of an observable, and we want to build a model to make uncertainty-quantified predictions of that observable." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "9f859e53-c6f9-4d88-b7bf-bbc5bc9ab686", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:09.675382Z", - "iopub.status.busy": "2026-09-09T18:23:09.675238Z", - "iopub.status.idle": "2026-09-09T18:23:09.679017Z", - "shell.execute_reply": "2026-09-09T18:23:09.678407Z" - } - }, - "outputs": [], - "source": [ - "class LinearModel(rxmc.physical_model.PhysicalModel):\n", - " def __init__(self):\n", - " params = [\n", - " rxmc.params.Parameter(\"m\", float, \"no-units\"),\n", - " rxmc.params.Parameter(\"b\", float, \"y-units\"),\n", - " ]\n", - " super().__init__(params)\n", - "\n", - " def evaluate(self, observation, m, b):\n", - " return self.y(observation.x, m, b)\n", - "\n", - " def y(self, x, m, b):\n", - " # useful to have a function hat takes in an array-like x\n", - " # rather than an Observation, e.g. for plotting\n", - " return m * x + b" - ] - }, - { - "cell_type": "markdown", - "id": "74fd0a56-9c1e-4a99-a20a-286d373ced62", - "metadata": {}, - "source": [ - "Well that's not too complicated." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b83c6308-935b-4c8a-b1ef-0d65677ec809", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:09.680343Z", - "iopub.status.busy": "2026-09-09T18:23:09.680183Z", - "iopub.status.idle": "2026-09-09T18:23:09.682461Z", - "shell.execute_reply": "2026-09-09T18:23:09.681975Z" - } - }, - "outputs": [], - "source": [ - "my_model = LinearModel()" - ] - }, - { - "cell_type": "markdown", - "id": "a841a40b-cddf-4921-a660-2ee3bfa3de71", - "metadata": {}, - "source": [ - "Let's test it out:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "27f09315-06dc-44ee-8190-f5d393974b68", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:09.683764Z", - "iopub.status.busy": "2026-09-09T18:23:09.683648Z", - "iopub.status.idle": "2026-09-09T18:23:09.688346Z", - "shell.execute_reply": "2026-09-09T18:23:09.687910Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1., 2., 3.])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# true params are obviously m = 1, b = 0\n", - "observation = rxmc.observation.Observation(x=np.array([1, 2, 3]), y=np.array([1, 2, 3]))\n", - "my_model(observation, 1, 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "f5de0b99-d85b-4acf-bfb4-eabdc4580688", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:09.689720Z", - "iopub.status.busy": "2026-09-09T18:23:09.689590Z", - "iopub.status.idle": "2026-09-09T18:23:09.692850Z", - "shell.execute_reply": "2026-09-09T18:23:09.692199Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([3, 5, 7])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "my_model.y(observation.x, 2, 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "bf846a07-d5d2-4f34-838c-06b1deca0739", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:09.694092Z", - "iopub.status.busy": "2026-09-09T18:23:09.693952Z", - "iopub.status.idle": "2026-09-09T18:23:09.696803Z", - "shell.execute_reply": "2026-09-09T18:23:09.696336Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([3., 5., 7.])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# now with parameters that are obviously wrong\n", - "my_model(observation, 2, 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "fb8188e6-7e47-479f-8b72-a8825ae5dacd", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:09.698094Z", - "iopub.status.busy": "2026-09-09T18:23:09.697955Z", - "iopub.status.idle": "2026-09-09T18:23:09.700984Z", - "shell.execute_reply": "2026-09-09T18:23:09.700393Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[3. 5. 7.]\n", - "[3. 5. 7.]\n" - ] - } - ], - "source": [ - "# just to show some ways that may be convenient to pass around params\n", - "# note they must be in the same order as in my_model.evaluate,\n", - "# which should be in the same order as my_model.params\n", - "\n", - "p = [2, 1]\n", - "print(my_model(observation, *p))\n", - "p = np.array([2, 1])\n", - "print(my_model(observation, *p))" - ] - }, - { - "cell_type": "markdown", - "id": "0f73f0a7-667b-485f-9753-591d17464409", - "metadata": {}, - "source": [ - "## define a prior\n", - "\n", - "Let's imagine this line corresponds to some physics, and we have some backround knowledge to inform us what we expect $m$ and $b$ to be. We can encode that into a prior distribution:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "d251f717-09be-4280-b59d-de137fd7cfc2", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:09.702304Z", - "iopub.status.busy": "2026-09-09T18:23:09.702188Z", - "iopub.status.idle": "2026-09-09T18:23:09.704818Z", - "shell.execute_reply": "2026-09-09T18:23:09.704037Z" - } - }, - "outputs": [], - "source": [ - "prior_mean = OrderedDict(\n", - " [\n", - " (\"m\", 1),\n", - " (\"b\", 1),\n", - " ]\n", - ")\n", - "prior_std_dev = OrderedDict(\n", - " [\n", - " (\"m\", 1),\n", - " (\"b\", 1),\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "016fc2de-c206-44c1-916a-a3b663a8fd25", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:09.706006Z", - "iopub.status.busy": "2026-09-09T18:23:09.705881Z", - "iopub.status.idle": "2026-09-09T18:23:09.708580Z", - "shell.execute_reply": "2026-09-09T18:23:09.707834Z" - } - }, - "outputs": [], - "source": [ - "covariance = np.diag(list(prior_std_dev.values())) ** 2\n", - "mean = np.array(list(prior_mean.values()))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "d28fcb22-9ca7-4846-9cba-24b7b4808e2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:09.709810Z", - "iopub.status.busy": "2026-09-09T18:23:09.709694Z", - "iopub.status.idle": "2026-09-09T18:23:09.712937Z", - "shell.execute_reply": "2026-09-09T18:23:09.712205Z" - } - }, - "outputs": [], - "source": [ - "n_prior_samples = 1000\n", - "prior_distribution = stats.multivariate_normal(mean, covariance)\n", - "prior_samples = prior_distribution.rvs(size=n_prior_samples, random_state=rng)" - ] - }, - { - "cell_type": "markdown", - "id": "a5f9acb2-90e3-4e7e-bcd1-6c12ef2baa44", - "metadata": {}, - "source": [ - "Let's plot some samples of lines from this prior" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "70a97727-68ec-4eea-880f-203a3f0817cc", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:09.714160Z", - "iopub.status.busy": "2026-09-09T18:23:09.714001Z", - "iopub.status.idle": "2026-09-09T18:23:09.719998Z", - "shell.execute_reply": "2026-09-09T18:23:09.719429Z" - } - }, - "outputs": [], - "source": [ - "x = np.linspace(0, 1, 10)\n", - "\n", - "# array to hold the lines\n", - "y = np.zeros((n_prior_samples, len(x)))\n", - "\n", - "# propagate the prior through to the observation\n", - "for i in range(n_prior_samples):\n", - " sample = prior_samples[i, :]\n", - " y[i, :] = my_model.y(x, *sample)\n", - "\n", - "# grab confidence intervals for plotting\n", - "upper, lower = np.percentile(y, [5, 95], axis=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "e8476136-5ab0-42c7-a3fe-88539ee7a019", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:09.721383Z", - "iopub.status.busy": "2026-09-09T18:23:09.721216Z", - "iopub.status.idle": "2026-09-09T18:23:11.427947Z", - "shell.execute_reply": "2026-09-09T18:23:11.427321Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'prior')" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for i in np.random.choice(n_prior_samples, 1000):\n", - " plt.plot(x, y[i, :], zorder=1, alpha=0.2)\n", - " # pass\n", - "\n", - "plt.fill_between(\n", - " x, lower, upper, alpha=0.5, zorder=2, label=r\"inner 90$^\\text{th}$ pctl\"\n", - ")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.legend()\n", - "plt.title(\"prior\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "30ed8fc8-d5e9-4e58-85e8-aba6b8683d00", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:11.429402Z", - "iopub.status.busy": "2026-09-09T18:23:11.429243Z", - "iopub.status.idle": "2026-09-09T18:23:11.567585Z", - "shell.execute_reply": "2026-09-09T18:23:11.566891Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'prior')" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = corner.corner(prior_samples, labels=[p.name for p in my_model.params])\n", - "fig.suptitle(\"prior\")" - ] - }, - { - "cell_type": "markdown", - "id": "278a52b1-4326-43a4-8157-0b9e7e24a352", - "metadata": {}, - "source": [ - "## Now let's update our prior by comparing to some data\n", - "\n", - "This will require learning how `rxmc` encodes our assumptions about the error on an experimental `Observation`: as a covariance built from explicit terms, evaluated under a likelihood functional. These are a necessary ingredient for comparing to the predictions of a `PhysicalModel`.\n", - "\n", - "In our case we will mock experimental data by synthetically generate some data with noise about a \"true\" $m$ and $b$. Our calibration posterior should converge to be centered about this true point.\n", - "\n", - "Let us assume that the experimentalists made a perfect estimate of the experimental noise in their setup. That is, the error bars they report will correspond exactly to the true distribution from which we sample.\n", - "\n", - "This noise will correspond to statistical noise. Later on we will look at systematic experimental error." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "d07c4b8c-fb40-4af3-9a76-7c798943a213", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:11.568951Z", - "iopub.status.busy": "2026-09-09T18:23:11.568808Z", - "iopub.status.idle": "2026-09-09T18:23:11.572069Z", - "shell.execute_reply": "2026-09-09T18:23:11.571523Z" - } - }, - "outputs": [], - "source": [ - "true_params = OrderedDict(\n", - " [\n", - " (\"m\", 0.6),\n", - " (\"b\", 2),\n", - " ]\n", - ")\n", - "\n", - "x = np.linspace(0, 1, 10)\n", - "noise = 0.1\n", - "y_exp = my_model.y(x, *list(true_params.values())) + rng.normal(\n", - " scale=noise, size=len(x)\n", - ")\n", - "y_stat_err = noise * np.ones_like(y_exp) # noise is just a constant fraction of y\n", - "obs1 = rxmc.observation.Observation(x=x, y=y_exp, y_stat_err=y_stat_err)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "664d9b5e-d7d2-4d08-91f6-763e75b40ab4", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:11.573283Z", - "iopub.status.busy": "2026-09-09T18:23:11.573166Z", - "iopub.status.idle": "2026-09-09T18:23:11.702710Z", - "shell.execute_reply": "2026-09-09T18:23:11.701937Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'experimental constraint')" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.errorbar(\n", - " x,\n", - " y_exp,\n", - " y_stat_err,\n", - " color=\"k\",\n", - " marker=\".\",\n", - " linestyle=\"none\",\n", - " label=\"obs1\",\n", - ")\n", - "plt.plot(x, my_model.y(x, *list(true_params.values())), \"k--\", label=\"truth\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "\n", - "plt.fill_between(\n", - " x,\n", - " lower,\n", - " upper,\n", - " alpha=0.5,\n", - " zorder=2,\n", - " label=r\"prior inner 90$^\\text{th}$ pctl\",\n", - ")\n", - "\n", - "plt.legend()\n", - "plt.title(\"experimental constraint\")" - ] - }, - { - "cell_type": "markdown", - "id": "766bb583-3be2-469a-85bb-4f364610f175", - "metadata": {}, - "source": [ - "Clearly, our prior is at odds with our observation. We will now determine a posterior distribution of $m$ and $b$ that takes `obs1` into account. To do this, we will need to think about a likelihood for `obs1` — in `rxmc` that means a covariance model (here, just the reported statistical errors) and a likelihood functional (the default `GaussianLikelihood`)." - ] - }, - { - "cell_type": "markdown", - "id": "4818c696-b984-4145-b911-5307782e0dd5", - "metadata": {}, - "source": [ - "## set up the likelihood and `Constraint`\n", - "\n", - "We will use the simplest assumption about the error on y: that the experimentalists exactly reported the statistical error, and there is no systematic error at all. This implies that each data point in `obs1.y`, say `obs1.y[i]` can be modeled as being an random variate, each sampled independently from normal distributions with mean `obs1.y[i]` and with standard deviation `obs1.y_stat_err[i]`.\n", - "\n", - "This is exactly the default behavior: a `Constraint` automatically includes each observation's statistical diagonal in its covariance, and evaluates it under the default `GaussianLikelihood`:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "1ca25e05-9b9d-4c22-94bf-0caf31854835", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:11.704213Z", - "iopub.status.busy": "2026-09-09T18:23:11.704025Z", - "iopub.status.idle": "2026-09-09T18:23:11.708161Z", - "shell.execute_reply": "2026-09-09T18:23:11.707585Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on class GaussianLikelihood in module rxmc.likelihood_model:\n", - "\n", - "class GaussianLikelihood(Likelihood)\n", - " | Multivariate-normal likelihood over the stacked residual.\n", - " |\n", - " | Parameter-free — all uncertainty lives on the covariance terms.\n", - " |\n", - " | Method resolution order:\n", - " | GaussianLikelihood\n", - " | Likelihood\n", - " | builtins.object\n", - " |\n", - " | Methods defined here:\n", - " |\n", - " | log_likelihood(self, d2, logdet, n, *like_params)\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Data and other attributes defined here:\n", - " |\n", - " | __annotations__ = {}\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Methods inherited from Likelihood:\n", - " |\n", - " | chi2(self, d2, logdet, n, *like_params)\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Data descriptors inherited from Likelihood:\n", - " |\n", - " | __dict__\n", - " | dictionary for instance variables\n", - " |\n", - " | __weakref__\n", - " | list of weak references to the object\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Data and other attributes inherited from Likelihood:\n", - " |\n", - " | n_params = 0\n", - " |\n", - " | params = ()\n", - "\n" - ] - } - ], - "source": [ - "help(rxmc.likelihood_model.GaussianLikelihood)" - ] - }, - { - "cell_type": "markdown", - "id": "685dec98-6cf8-49e4-b1aa-3bad227ffe8b", - "metadata": {}, - "source": [ - "With a purely statistical (diagonal) covariance, the likelihood is proportional to the familiar form:\n", - "\n", - "\\begin{equation}\n", - " \\mathcal{L}(\\alpha|y) \\propto e^{ - \\chi^2(\\alpha,y) }\n", - "\\end{equation}\n", - "\n", - "where the Chi-squared is simply\n", - "\n", - "\\begin{equation}\n", - "\\chi^2(\\alpha,y) = \\sum_i \\frac{(y(x_i) - y_m(x_i;\\alpha) )^2}{\\sigma^2_i}\n", - "\\end{equation}\n", - "\n", - "In a later tutorial we will use more complicated `Observation`s that include systematic error, and look at other `LikelihoodModel`s." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "8b698c69-7a23-4955-ab7a-15107774bec9", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:11.709520Z", - "iopub.status.busy": "2026-09-09T18:23:11.709400Z", - "iopub.status.idle": "2026-09-09T18:23:11.711656Z", - "shell.execute_reply": "2026-09-09T18:23:11.711076Z" - } - }, - "outputs": [], - "source": [ - "likelihood_model = rxmc.likelihood_model.GaussianLikelihood()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "211990b6-85ba-47b1-89c4-796c5b396168", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:11.713223Z", - "iopub.status.busy": "2026-09-09T18:23:11.713084Z", - "iopub.status.idle": "2026-09-09T18:23:11.715482Z", - "shell.execute_reply": "2026-09-09T18:23:11.714951Z" - } - }, - "outputs": [], - "source": [ - "constraint = rxmc.constraint.Constraint(\n", - " [obs1],\n", - " my_model,\n", - " likelihood_model,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "6e59d72f-7175-4ac8-9b08-bdeb1119379f", - "metadata": {}, - "source": [ - "Let's test this `constraint` thing out. What is the reduced $\\chi^2$ for the prior mean?" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "51b72666-3986-42f1-8262-8de23eb87ee3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:11.716786Z", - "iopub.status.busy": "2026-09-09T18:23:11.716668Z", - "iopub.status.idle": "2026-09-09T18:23:11.720105Z", - "shell.execute_reply": "2026-09-09T18:23:11.719481Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "64.18631209285807" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "constraint.chi2(prior_distribution.mean) / constraint.n_data_pts" - ] - }, - { - "cell_type": "markdown", - "id": "5cc86910-5ded-4339-ad1b-0ff82fabd5f6", - "metadata": {}, - "source": [ - "Here is proof that, in this case, we reduce to the form described above:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "a845e4ff-1d51-455b-97c8-e7ad561a89ce", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:11.721495Z", - "iopub.status.busy": "2026-09-09T18:23:11.721360Z", - "iopub.status.idle": "2026-09-09T18:23:11.724730Z", - "shell.execute_reply": "2026-09-09T18:23:11.724208Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "np.float64(64.18631209285807)" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y = my_model(obs1, *prior_distribution.mean)\n", - "np.sum((y - obs1.y) ** 2 / y_stat_err**2) / constraint.n_data_pts" - ] - }, - { - "cell_type": "markdown", - "id": "fbb7e0d9-8a86-4228-9372-ee63dfad5ee3", - "metadata": {}, - "source": [ - "## running the calibration" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "dd9fa5b9-10ab-4649-96c0-58f9cb6896f6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:11.725920Z", - "iopub.status.busy": "2026-09-09T18:23:11.725804Z", - "iopub.status.idle": "2026-09-09T18:23:11.728856Z", - "shell.execute_reply": "2026-09-09T18:23:11.728456Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on class Walker in module rxmc.walker:\n", - "\n", - "class Walker(builtins.object)\n", - " | Walker(model_sampler: rxmc.param_sampling.Sampler, evidence: rxmc.evidence.Evidence, likelihood_samplers: list[rxmc.param_sampling.Sampler] | None = None, rng: numpy.random._generator.Generator | None = None)\n", - " |\n", - " | Gibbs-style MCMC coordinator for a Bayesian calibration problem.\n", - " |\n", - " | Manages one sampler for the physical-model parameters and, optionally,\n", - " | per-constraint samplers for parametric likelihood parameters. The samplers\n", - " | alternate in a Gibbs framework: model parameters are updated with the\n", - " | likelihood parameters held fixed, then each set of likelihood parameters is\n", - " | updated with the model parameters held fixed.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | model_sampler : Sampler\n", - " | Sampler for the physical-model parameters.\n", - " | evidence : Evidence\n", - " | Evidence object containing the observations and likelihood models.\n", - " | likelihood_samplers : list of Sampler, optional\n", - " | One sampler per entry in ``evidence.parametric_constraints``.\n", - " | rng : np.random.Generator, optional\n", - " | Random number generator. Defaults to ``default_rng(42)``.\n", - " |\n", - " | Raises\n", - " | ------\n", - " | ValueError\n", - " | If the physical-model parameters in *evidence* and *model_sampler* do\n", - " | not match.\n", - " | ValueError\n", - " | If the number of *likelihood_samplers* does not equal the number of\n", - " | parametric constraints in *evidence*.\n", - " | ValueError\n", - " | If any likelihood sampler's parameters do not match those of the\n", - " | corresponding parametric constraint.\n", - " |\n", - " | Methods defined here:\n", - " |\n", - " | __init__(self, model_sampler: rxmc.param_sampling.Sampler, evidence: rxmc.evidence.Evidence, likelihood_samplers: list[rxmc.param_sampling.Sampler] | None = None, rng: numpy.random._generator.Generator | None = None)\n", - " | Initialize self. See help(type(self)) for accurate signature.\n", - " |\n", - " | log_likelihood(self, model_params, likelihood_params)\n", - " |\n", - " | log_posterior(self, model_params, likelihood_params)\n", - " |\n", - " | log_prior(self, model_params, likelihood_params)\n", - " | Log prior probability of model and likelihood parameters.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | model_params : tuple\n", - " | Physical-model parameter values.\n", - " | likelihood_params : list of tuple\n", - " | One tuple of likelihood parameter values per parametric constraint.\n", - " |\n", - " | Returns\n", - " | -------\n", - " | float\n", - " | Sum of log prior densities for model and likelihood parameters.\n", - " |\n", - " | run_likelihood_batches(self, n_steps, starting_locations, model_params, burn=False)\n", - " | Sample each set of likelihood parameters for fixed model parameters.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | n_steps : int\n", - " | Number of MCMC steps per likelihood sampler.\n", - " | starting_locations : list of np.ndarray\n", - " | Starting locations for each likelihood sampler.\n", - " | model_params : tuple\n", - " | Fixed physical-model parameter values.\n", - " | burn : bool, optional\n", - " | If ``True``, treat as burn-in (samples are not recorded).\n", - " |\n", - " | run_model_batch(self, n_steps, x0, likelihood_params=None, burn=False)\n", - " | Sample model parameters for fixed likelihood parameters.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | n_steps : int\n", - " | Number of MCMC steps.\n", - " | x0 : np.ndarray\n", - " | Starting location for the model parameters.\n", - " | likelihood_params : list of tuple, optional\n", - " | Fixed values of the likelihood parameters for each parametric\n", - " | constraint. Defaults to ``[]``.\n", - " | burn : bool, optional\n", - " | If ``True``, treat as burn-in (samples are not recorded).\n", - " |\n", - " | walk(self, n_steps: int, burnin: int = 0, batch_size: int = None, verbose: bool = True)\n", - " | Run the full MCMC chain.\n", - " |\n", - " | Updates the internal state of ``model_sampler`` and each entry of\n", - " | ``likelihood_samplers`` with the accumulated chain, log posteriors,\n", - " | and acceptance statistics.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | n_steps : int\n", - " | Total number of active (post-burn-in) steps.\n", - " | burnin : int, optional\n", - " | Number of burn-in steps discarded before recording.\n", - " | Defaults to ``0``.\n", - " | batch_size : int, optional\n", - " | Steps per batch. If ``None`` the entire chain is one batch.\n", - " | verbose : bool, optional\n", - " | Print batch completion messages. Defaults to ``True``.\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Data descriptors defined here:\n", - " |\n", - " | __dict__\n", - " | dictionary for instance variables\n", - " |\n", - " | __weakref__\n", - " | list of weak references to the object\n", - "\n" - ] - } - ], - "source": [ - "help(rxmc.walker.Walker)" - ] - }, - { - "cell_type": "markdown", - "id": "cc7f9eff-e473-4d7d-b081-4ba1fa6a94b5", - "metadata": {}, - "source": [ - "First we need to put together our `Evidence`. With one constraint this seems trivial, but this will be useful down the road when we may want to combine multiple constraints together." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "1ff7c728-b0ac-4c48-8779-19da58277cc9", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:11.730328Z", - "iopub.status.busy": "2026-09-09T18:23:11.730196Z", - "iopub.status.idle": "2026-09-09T18:23:11.732694Z", - "shell.execute_reply": "2026-09-09T18:23:11.731967Z" - } - }, - "outputs": [], - "source": [ - "evidence = rxmc.evidence.Evidence([constraint])" - ] - }, - { - "cell_type": "markdown", - "id": "bf1e4ad1-a94a-42c2-b084-c29b9945c29e", - "metadata": {}, - "source": [ - "Another chore we have to do is configure how we will sample our parameter space." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "5470a662-4880-418d-85aa-deadeca0cfe7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:11.734066Z", - "iopub.status.busy": "2026-09-09T18:23:11.733940Z", - "iopub.status.idle": "2026-09-09T18:23:11.736842Z", - "shell.execute_reply": "2026-09-09T18:23:11.736379Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on class MetropolisHastingsSampler in module rxmc.param_sampling:\n", - "\n", - "class MetropolisHastingsSampler(Sampler)\n", - " | MetropolisHastingsSampler(params: list[rxmc.params.Parameter], prior, starting_location: numpy.ndarray, proposal: rxmc.proposal.ProposalDistribution)\n", - " |\n", - " | Metropolis-Hastings sampler with a fixed proposal distribution.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | params : list of Parameter\n", - " | Parameters to sample.\n", - " | prior : object\n", - " | Prior distribution with a callable ``logpdf(x)`` method.\n", - " | starting_location : np.ndarray, shape (ndim,)\n", - " | Initial parameter vector.\n", - " | proposal : ProposalDistribution\n", - " | Callable proposal distribution. Must accept ``(x, rng)`` and return\n", - " | a proposed parameter vector.\n", - " |\n", - " | Method resolution order:\n", - " | MetropolisHastingsSampler\n", - " | Sampler\n", - " | builtins.object\n", - " |\n", - " | Methods defined here:\n", - " |\n", - " | __init__(self, params: list[rxmc.params.Parameter], prior, starting_location: numpy.ndarray, proposal: rxmc.proposal.ProposalDistribution)\n", - " | Initialize self. See help(type(self)) for accurate signature.\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Methods inherited from Sampler:\n", - " |\n", - " | batch_acceptance_fractions(self) -> numpy.ndarray\n", - " | Acceptance fraction for each completed batch.\n", - " |\n", - " | Returns\n", - " | -------\n", - " | np.ndarray\n", - " | Per-batch acceptance fractions, or ``[0.0]`` if no batches run.\n", - " |\n", - " | most_recent_batch_acceptance_fraction(self) -> float\n", - " | Acceptance fraction of the most recent batch.\n", - " |\n", - " | Returns\n", - " | -------\n", - " | float\n", - " | Fraction of proposals accepted in the last batch, or ``0.0`` if\n", - " | no batches have been run.\n", - " |\n", - " | overall_acceptance_fraction(self) -> float\n", - " | Overall acceptance fraction across all completed batches.\n", - " |\n", - " | Returns\n", - " | -------\n", - " | float\n", - " | Total accepted / total proposed, or ``0.0`` if no batches run.\n", - " |\n", - " | record_batch(self, n_steps: int, n_accepted: int, chain: numpy.ndarray, logp_chain: numpy.ndarray)\n", - " | Append a completed batch to the running chain.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | n_steps : int\n", - " | Number of steps in the batch.\n", - " | n_accepted : int\n", - " | Number of accepted proposals in the batch.\n", - " | chain : np.ndarray, shape (n_steps, ndim)\n", - " | Sampled parameter vectors.\n", - " | logp_chain : np.ndarray, shape (n_steps,)\n", - " | Log posterior values for the batch.\n", - " |\n", - " | sample(self, n_steps: int, starting_location: numpy.ndarray, rng: numpy.random._generator.Generator, log_posterior: Callable[[numpy.ndarray], float], burn: bool = False)\n", - " | Run the sampling algorithm for one batch.\n", - " |\n", - " | Updates ``self.state`` to the last sample; records the batch unless\n", - " | *burn* is ``True``.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | n_steps : int\n", - " | Number of steps to run.\n", - " | starting_location : np.ndarray, shape (ndim,)\n", - " | Starting parameter vector for this batch.\n", - " | rng : np.random.Generator\n", - " | Random number generator.\n", - " | log_posterior : callable\n", - " | Function ``f(x) -> float`` returning the log posterior at ``x``.\n", - " | burn : bool, optional\n", - " | If ``True``, discard samples (burn-in); only ``self.state`` is\n", - " | updated. Defaults to ``False``.\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Data descriptors inherited from Sampler:\n", - " |\n", - " | __dict__\n", - " | dictionary for instance variables\n", - " |\n", - " | __weakref__\n", - " | list of weak references to the object\n", - "\n" - ] - } - ], - "source": [ - "help(rxmc.param_sampling.MetropolisHastingsSampler)" - ] - }, - { - "cell_type": "markdown", - "id": "628ad075-8062-4420-8e1f-76a5ed69f3b4", - "metadata": {}, - "source": [ - "This means we have to decide on a proposal distribution. We will use a simple form: just sampling from a scaled down version of the prior about the previos point:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "9c0ac067-047a-4357-a462-415e32d0481c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:11.738159Z", - "iopub.status.busy": "2026-09-09T18:23:11.737999Z", - "iopub.status.idle": "2026-09-09T18:23:11.740430Z", - "shell.execute_reply": "2026-09-09T18:23:11.739854Z" - } - }, - "outputs": [], - "source": [ - "def proposal_distribution(x, rng):\n", - " return stats.multivariate_normal.rvs(\n", - " mean=x, cov=prior_distribution.cov / 100, random_state=rng\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "5362bf0b-76d4-419b-af34-2ee94e3b2dda", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:11.741585Z", - "iopub.status.busy": "2026-09-09T18:23:11.741426Z", - "iopub.status.idle": "2026-09-09T18:23:11.743814Z", - "shell.execute_reply": "2026-09-09T18:23:11.743184Z" - } - }, - "outputs": [], - "source": [ - "sampling_config = rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=my_model.params,\n", - " starting_location=prior_distribution.mean,\n", - " proposal=proposal_distribution,\n", - " prior=prior_distribution,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "784ebdb9-b5f2-4a2f-9882-a65d2f107c05", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:11.744999Z", - "iopub.status.busy": "2026-09-09T18:23:11.744868Z", - "iopub.status.idle": "2026-09-09T18:23:11.747235Z", - "shell.execute_reply": "2026-09-09T18:23:11.746588Z" - } - }, - "outputs": [], - "source": [ - "walker = rxmc.walker.Walker(\n", - " sampling_config,\n", - " evidence,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "194af035-fafd-4c64-8b58-0f7af34bb685", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:11.748479Z", - "iopub.status.busy": "2026-09-09T18:23:11.748344Z", - "iopub.status.idle": "2026-09-09T18:23:17.635768Z", - "shell.execute_reply": "2026-09-09T18:23:17.635315Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/1 completed, 1000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 1/1 completed, 20000 steps. \n", - " Model parameter acceptance fraction: 0.273\n", - "CPU times: user 5.89 s, sys: 40.5 ms, total: 5.93 s\n", - "Wall time: 5.88 s\n" - ] - } - ], - "source": [ - "%%time\n", - "walker.walk(\n", - " n_steps=20000,\n", - " burnin=1000,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "b57ce430-2014-4999-9e4f-3f16f0c3c8fc", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:17.637295Z", - "iopub.status.busy": "2026-09-09T18:23:17.637172Z", - "iopub.status.idle": "2026-09-09T18:23:17.639377Z", - "shell.execute_reply": "2026-09-09T18:23:17.638835Z" - } - }, - "outputs": [], - "source": [ - "chain = walker.model_sampler.chain\n", - "logp = walker.model_sampler.logp_chain" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "8bd838cf-8ed2-43b9-a545-416b9f5b802c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:17.640592Z", - "iopub.status.busy": "2026-09-09T18:23:17.640474Z", - "iopub.status.idle": "2026-09-09T18:23:17.643253Z", - "shell.execute_reply": "2026-09-09T18:23:17.642785Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(20000, 2)" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "chain.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "122e8c8e-4975-42b0-96ef-611cb9187c85", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:17.644574Z", - "iopub.status.busy": "2026-09-09T18:23:17.644455Z", - "iopub.status.idle": "2026-09-09T18:23:17.944915Z", - "shell.execute_reply": "2026-09-09T18:23:17.944215Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0, '$i$')" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(chain.shape[1] + 1, 1, figsize=(8, 8), sharex=True)\n", - "for i in range(chain.shape[1]):\n", - " axes[i].plot(chain[:, i])\n", - " axes[i].set_ylabel(f\"${my_model.params[i].latex_name}$ [{my_model.params[i].unit}]\")\n", - " true_value = true_params[my_model.params[i].name]\n", - " axes[i].hlines(true_value, 0, len(chain), \"r\", linestyle=\"--\")\n", - "\n", - "\n", - "axes[-1].plot(logp)\n", - "axes[-1].set_ylabel(r\"$\\log{\\mathcal{L}(\\alpha_i | \\mathcal{O})}$\")\n", - "\n", - "axes[-1].set_xlabel(r\"$i$\")\n", - "# plt.legend(title=\"chains\", ncol=3,)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "6b20814a-1595-4881-9190-6f03ee4811e3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:17.946425Z", - "iopub.status.busy": "2026-09-09T18:23:17.946297Z", - "iopub.status.idle": "2026-09-09T18:23:17.949919Z", - "shell.execute_reply": "2026-09-09T18:23:17.949142Z" - } - }, - "outputs": [], - "source": [ - "posterior_range = np.vstack([np.min(chain, axis=0), np.max(chain, axis=0)]).T" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "21ca84ce-5d9a-44db-8651-f9c80e9b1f68", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:17.951275Z", - "iopub.status.busy": "2026-09-09T18:23:17.951157Z", - "iopub.status.idle": "2026-09-09T18:23:18.106585Z", - "shell.execute_reply": "2026-09-09T18:23:18.105865Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'posterior')" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = corner.corner(\n", - " chain,\n", - " labels=[p.name for p in my_model.params],\n", - " label=\"posterior\",\n", - " truths=[true_params[\"m\"], true_params[\"b\"]],\n", - ")\n", - "fig.suptitle(\"posterior\")" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "4aaec946-4f48-47e0-ad46-f6d71e866341", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:18.107941Z", - "iopub.status.busy": "2026-09-09T18:23:18.107811Z", - "iopub.status.idle": "2026-09-09T18:23:18.110248Z", - "shell.execute_reply": "2026-09-09T18:23:18.109767Z" - } - }, - "outputs": [], - "source": [ - "x_full = np.linspace(-1, 2, 10)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "2aab97fb-db36-4a05-aef0-8774173188df", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:18.111547Z", - "iopub.status.busy": "2026-09-09T18:23:18.111429Z", - "iopub.status.idle": "2026-09-09T18:23:18.178487Z", - "shell.execute_reply": "2026-09-09T18:23:18.177824Z" - } - }, - "outputs": [], - "source": [ - "n_posterior_samples = chain.shape[0]\n", - "y = np.zeros((n_posterior_samples, len(x_full)))\n", - "for i in range(n_posterior_samples):\n", - " sample = chain[i, :]\n", - " y[i, :] = my_model.y(x_full, *sample)\n", - "\n", - "upper, median, lower = np.percentile(y, [5, 50, 95], axis=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "92fc68f8-9be2-4e5f-9bf0-65eb796f5e0a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-09T18:23:18.180170Z", - "iopub.status.busy": "2026-09-09T18:23:18.180004Z", - "iopub.status.idle": "2026-09-09T18:23:18.301745Z", - "shell.execute_reply": "2026-09-09T18:23:18.300991Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(x_full, my_model.y(x_full, *list(true_params.values())), \"k--\", label=\"truth\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.errorbar(\n", - " x,\n", - " obs1.y,\n", - " y_stat_err,\n", - " color=\"k\",\n", - " marker=\"o\",\n", - " linestyle=\"none\",\n", - " label=\"experiment\",\n", - ")\n", - "plt.fill_between(\n", - " x_full,\n", - " lower,\n", - " upper,\n", - " alpha=0.5,\n", - " zorder=2,\n", - " label=r\"posterior inner 90$^\\text{th}$ pctl\",\n", - ")\n", - "plt.plot(x_full, median, \"m:\", label=\"posterior median\")\n", - "\n", - "plt.legend()\n", - "# plt.title(\"predictive posterior\")" - ] - }, - { - "cell_type": "markdown", - "id": "cf93c216-e2e7-44f0-91d5-09e0725a9fdf", - "metadata": {}, - "source": [ - "# Nice!\n", - "\n", - "Hopefully this simple example served to illustrate the basic function of the working pieces of `rxmc`. The true power is the ability to compose different `Constraint`s, and easily manage and test different model forms for both the `PhysicalModel` and the covariance terms describing the uncertainty. \n", - "\n", - "\n", - "Check out the other demos to see how `rxmc` helps us handle more realistic problems." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/measurement_to_calibration.ipynb b/examples/measurement_to_calibration.ipynb deleted file mode 100644 index 19f4055..0000000 --- a/examples/measurement_to_calibration.ipynb +++ /dev/null @@ -1,618 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "78ffc016", - "metadata": {}, - "source": [ - "# From a measurement to a calibrated potential\n", - "\n", - "This notebook walks the **production path**: an EXFOR-shaped measurement —\n", - "reported in its own units, with statistical *and* systematic errors — is turned\n", - "into an `ElasticDifferentialXSObservation` via `from_measurement`, its reported\n", - "systematics are composed as explicit covariance terms, and a small optical\n", - "potential is calibrated against it.\n", - "\n", - "The unit contract, up front:\n", - "\n", - "- **dimensionful** errors (statistical, absolute offset) are divided by the unit\n", - " normalization `norm` when the observation is built;\n", - "- the **fractional** normalization error is dimensionless and passes through\n", - " untouched;\n", - "- `norm` itself is retained as `obs.norm` for provenance.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "9585b106", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:06.821526Z", - "iopub.status.busy": "2026-08-11T03:07:06.821389Z", - "iopub.status.idle": "2026-08-11T03:07:09.182945Z", - "shell.execute_reply": "2026-08-11T03:07:09.182237Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using database version X4-2024-12-31 located in: /home/kyle/db/exfor/unpack_exfor-2024/X4-2024-12-31\n" - ] - } - ], - "source": [ - "from types import SimpleNamespace\n", - "\n", - "import corner\n", - "import jitr\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from jitr.optical_potentials.potential_forms import (\n", - " thomas_safe,\n", - " woods_saxon_prime_safe,\n", - " woods_saxon_safe,\n", - ")\n", - "from scipy import stats\n", - "\n", - "import rxmc\n", - "from rxmc.params import Parameter\n", - "\n", - "rng = np.random.default_rng(11)" - ] - }, - { - "cell_type": "markdown", - "id": "213805bd", - "metadata": {}, - "source": [ - "## The reaction and optical model" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "63f8c4a7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.184651Z", - "iopub.status.busy": "2026-08-11T03:07:09.184357Z", - "iopub.status.idle": "2026-08-11T03:07:09.213438Z", - "shell.execute_reply": "2026-08-11T03:07:09.212848Z" - } - }, - "outputs": [], - "source": [ - "Ca40 = (40, 20)\n", - "neutron = (1, 0)\n", - "E_lab = 14.1\n", - "\n", - "rxn = jitr.reactions.ElasticReaction(target=Ca40, projectile=neutron)\n", - "\n", - "mso = 1.0 / jitr.utils.constants.WAVENUMBER_PION\n", - "\n", - "\n", - "def central_potential(r, Vv, Wv, Rv, av, Wd, Rd, ad):\n", - " return -(Vv + 1j * Wv) * woods_saxon_safe(r, Rv, av) + (\n", - " 4j * ad * Wd\n", - " ) * woods_saxon_prime_safe(r, Rd, ad)\n", - "\n", - "\n", - "def spin_orbit_potential(r, Vso, Wso, Rso, aso):\n", - " return (Vso + 1j * Wso) * mso**2 * thomas_safe(r, Rso, aso)\n", - "\n", - "\n", - "R = 1.2 * 40 ** (1 / 3)\n", - "fixed_spin_orbit = (6.0, -3, R, 0.45)\n", - "\n", - "\n", - "def extract_params(ws, *x):\n", - " Vv, Wv, Rv, av, Wd, Rd, ad = x\n", - " central_params = (Vv, Wv, Rv, av, Wd, Rd, ad)\n", - " return central_params, fixed_spin_orbit\n", - "\n", - "\n", - "params = [\n", - " Parameter(\"Vv\", unit=\"MeV\"),\n", - " Parameter(\"Wv\", unit=\"MeV\"),\n", - " Parameter(\"Rv\", unit=\"fm\"),\n", - " Parameter(\"av\", unit=\"fm\"),\n", - " Parameter(\"Wd\", unit=\"MeV\"),\n", - " Parameter(\"Rd\", unit=\"fm\"),\n", - " Parameter(\"ad\", unit=\"fm\"),\n", - "]\n", - "\n", - "omp = rxmc.elastic_diffxs_model.ElasticDifferentialXSModel(\n", - " \"dXS/dA\",\n", - " interaction_central=central_potential,\n", - " interaction_spin_orbit=spin_orbit_potential,\n", - " calculate_interaction_from_params=extract_params,\n", - " params=params,\n", - " model_name=\"measurement_demo\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "895e8324", - "metadata": {}, - "source": [ - "## A measurement, as EXFOR reports it\n", - "\n", - "EXFOR entries report cross sections in their own units — here **mb/sr** — with a\n", - "statistical error column and, often, scalar systematic errors: a *fractional*\n", - "normalization uncertainty (e.g. from the flux calibration) and an *absolute*\n", - "offset uncertainty (e.g. from background subtraction), in the same units as the\n", - "data. We mock up such a measurement (`exfor_tools.Distribution` carries exactly\n", - "these fields) from a known truth so we can check the calibration at the end.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c7dff4fd", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.215225Z", - "iopub.status.busy": "2026-08-11T03:07:09.215072Z", - "iopub.status.idle": "2026-08-11T03:07:20.015257Z", - "shell.execute_reply": "2026-08-11T03:07:20.014738Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "angles_deg = np.linspace(5.0, 160.0, 20)\n", - "true_params = np.array(\n", - " [48.0, 3.5, 1.1 * 40 ** (1 / 3), 0.7, 21, 1.2 * 40 ** (1 / 3), 0.5]\n", - ")\n", - "\n", - "template_obs = rxmc.elastic_diffxs_observation.ElasticDifferentialXSObservation(\n", - " x=angles_deg,\n", - " y=np.ones_like(angles_deg, dtype=float),\n", - " Elab=E_lab,\n", - " reaction=rxn,\n", - " quantity=\"dXS/dA\",\n", - " measurement_quantity=\"dXS/dA\",\n", - " y_units=\"barn / steradian\",\n", - " dataset_label=\"template\",\n", - ")\n", - "\n", - "y_true_b = omp.evaluate(template_obs, *true_params) # b/sr\n", - "y_true_mb = y_true_b * 1000.0 # the measurement is reported in mb/sr\n", - "\n", - "stat_err_mb = 0.08 * np.maximum(y_true_mb, 1e-1)\n", - "y_mb = np.clip(y_true_mb + rng.normal(scale=stat_err_mb), 1e-3, None)\n", - "\n", - "measurement = SimpleNamespace(\n", - " x=angles_deg, # degrees\n", - " y=y_mb, # mb/sr\n", - " Einc=E_lab,\n", - " quantity=\"dXS/dA\",\n", - " y_units=\"mb/sr\",\n", - " statistical_err=stat_err_mb, # mb/sr\n", - " systematic_norm_err=0.04, # fractional (dimensionless)\n", - " systematic_offset_err=2.0, # absolute, mb/sr\n", - " subentry=\"toy-subentry\",\n", - ")\n", - "\n", - "plt.errorbar(\n", - " measurement.x, measurement.y, measurement.statistical_err, ls=\"none\", marker=\".\"\n", - ")\n", - "plt.xlabel(r\"$\\theta$ [deg]\")\n", - "plt.ylabel(r\"$d\\sigma/d\\Omega$ [mb/sr]\")\n", - "plt.yscale(\"log\")\n", - "plt.title(\"A mock EXFOR-style measurement of n + $^{40}$Ca\");" - ] - }, - { - "cell_type": "markdown", - "id": "ac41e21a", - "metadata": {}, - "source": [ - "## `from_measurement` keeps the systematics as inert metadata\n", - "\n", - "The observation stores its data in internal units (b/sr). Everything\n", - "**dimensionful** — `y`, the statistical error, the absolute offset error — is\n", - "divided by `obs.norm` (here $10^3$, mb $\\to$ b); the **fractional** normalization\n", - "error is dimensionless and untouched. The systematics are *metadata*: they do\n", - "not enter any covariance until you ask.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "480644cd", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:20.017049Z", - "iopub.status.busy": "2026-08-11T03:07:20.016896Z", - "iopub.status.idle": "2026-08-11T03:07:21.846957Z", - "shell.execute_reply": "2026-08-11T03:07:21.846224Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "unit normalization obs.norm = 1000.0\n", - "fractional norm error 0.04 (passed through)\n", - "absolute offset error 0.002 b/sr (= 2.0 mb/sr / norm)\n", - "y[0]: measurement 1834.19 mb/sr -> stored 1.83419 b/sr\n" - ] - } - ], - "source": [ - "obs = rxmc.elastic_diffxs_observation.ElasticDifferentialXSObservation.from_measurement(\n", - " measurement=measurement,\n", - " reaction=rxn,\n", - " quantity=\"dXS/dA\",\n", - ")\n", - "\n", - "print(f\"unit normalization obs.norm = {obs.norm}\")\n", - "print(f\"fractional norm error {obs.y_sys_err_normalization} (passed through)\")\n", - "print(f\"absolute offset error {obs.y_sys_err_offset} b/sr (= 2.0 mb/sr / norm)\")\n", - "print(f\"y[0]: measurement {measurement.y[0]:.2f} mb/sr -> stored {obs.y[0]:.5f} b/sr\")" - ] - }, - { - "cell_type": "markdown", - "id": "5b9b64d7", - "metadata": {}, - "source": [ - "## Nothing is folded in silently — systematics are explicit terms\n", - "\n", - "By design there is **no compatibility path that re-folds systematics into the\n", - "covariance automatically**: the default constraint covariance is the statistical\n", - "diagonal only. `obs.systematic_terms()` turns the retained metadata into\n", - "fixed rank-one terms — the absolute offset mode\n", - "$\\Sigma \\mathrel{+}= \\omega\\omega^T$ and the prediction-scaled normalization mode\n", - "$\\Sigma \\mathrel{+}= \\eta^2\\, y_m y_m^T$ — which you pass in as `extra_terms`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "551698f3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:21.848441Z", - "iopub.status.busy": "2026-08-11T03:07:21.848289Z", - "iopub.status.idle": "2026-08-11T03:07:22.186513Z", - "shell.execute_reply": "2026-08-11T03:07:22.185864Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Term', 'Term']\n" - ] - }, - { - "data": { - "image/png": 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3NzecOHECLi4uBcYfOHAAW7ZswcaNGzFw4EBV+ZkzZ1Rzv4ujX+rkLnjOzMzMt46+P0/uObOzs/O8l5mZmWcR9pt3wHKZmZmpHYC9zd/fH7Vr18bSpUvRv39/LF26FGZmZvjggw80xlasWBHx8fH59v/tdRC5ZW/encxNDO7u7nnuCtauXRuDBg0S1X+btt+9hYUFFAqF2u+0oEGkFKmpqUhPT4ezs3Oh2yKikq1p06Zo2rSpqCw4OBje3t467Vzo4OCQp0yhUMDS0lLtP7xr166dZ0OV/H5zgf//Xc69OCcIQp5/mGdmZub5LVTXr4Jo22dzc3O1/RUEQavcVaNGDVy7dg1Hjx7F8ePHcfz4cfz000/w8/PDoUOHVHnxww8/xIwZM7B06VIsWrQIS5cuRYUKFdC9e3fJbRX2M+d6u73cC6r5lb/5ffz222+4cOECrly5ggYNGqjKly9fjt9//73AfhYk9+9CqVTme4G3sN8TkToc5JVACoUCrVu3RuvWrQEAp06dQuvWrbF69WrVNBBzc3PRIutcuTshtmzZUlR+6tSpPHXza6Mo+qWOt7c3AOD8+fP5TkmQ8nm0kfvDnbvhSy6lUonLly+LftgLK/dK56RJk3Dy5Els2rQJHTt2zHOVVx1vb2+cPXs23/5fvnxZNCUlJSUFd+7cEU3zyP1+33nnnXx36SyItt+9hYUFKlasiEePHonKX716hTt37sDLy0vjuTT93Vy5cgUA8vzDj4hIF97e3ti8eTMCAwMLvMOSK3d34jdFRESINuJo0KAB/vrrL1y/fl00WyQ+Ph4PHjzIs4OzOrkDRnW/h9r2uUGDBti8eTOio6NF+ebq1atqL8apY21tjc6dO6Nz584AgGXLlmHMmDE4ePCgaslGuXLl8P7772PdunXo06cPLl68iEmTJuWZ3aOpLX18Zn24c+cO7Ozs8vw74O2cV1B/1fH29samTZtw6dIlVV5WR5vvnEgK7q5ZwoSGhuLBgweistxpK29eyXFxccnzj2rg/6chvrkL4e3bt7Fp06Y8dfNroyj6pU6nTp3QqFEjzJw5E/fv31eVK5VK1S6cUj6PNrp06YJq1arhu+++E029WLx4Me7evYtPP/1Up3bz8+GHH8LW1hZ9+/ZFenq6aBpLQdq3b4/Y2Ng8u3N26dIFVatWxdy5c/Hq1StV+dy5c/NcJe7cuTMaN26MadOm5flvkpCQgN27dxfYBynffbt27bBnzx7Rd6quT/nR9Hdz5swZKBQKtG/fXqv2iIgKMmrUKDg7O+OTTz5BcnKy6L3Y2FgcP35cVHbx4kXRw9gjIiIQEhKCDz74QJUDhw8fDltbW9Vu0cDrgcCXX36JnJwc1WMbCmJnZ4cyZcqo/T3Uts/Dhg2DjY0NvvrqKyiVSgCv7zrOmzcvz7RVdd7+LQfU53sA+PTTT5GUlKR6lMKIESMkt6WPz6wP9erVQ3Jysugh5//88w9OnDghqldQf9UZMWIEKlSogAkTJojWXKanp6v6L+U7J9IW7+SVMMnJyejYsSOcnJxQu3ZtpKWl4dChQwgMDBQliI8++gijR49Gr169ULVqVdVzxdq3b48hQ4bgo48+wq5du6BQKHD9+nXMmzcvzzPM8mujKPqljrm5Ofbs2YP+/fujYcOGaNu2LRwcHPDvv/+qnjEj5fNow9raGrt27ULv3r1V54yNjcWpU6fw5Zdf6v1h22XLlsWgQYOwYsUKODo6an01buDAgZg8eTI2b94sesacjY0NtmzZgh49eqBBgwbw8/NDVFQUunfvDk9PT9H6BHNzc+zduxdDhw6Fh4cH2rVrh4oVK+L+/fu4ffs2Jk+eXGAfpHz3c+bMwZEjR9C4cWO0bdsW169fx4cffijaVKAgmv5uNm/ejE6dOuk0LZiI6G2Ojo74+++/MXjwYLi7u8PPzw92dna4ffs24uLiMH/+fFH9Tz75BLNnz4aNjQ0sLS1x4MAB+Pr64qefflLVqVatGv766y8MGzYMDRs2RPPmzXH58mVERUVhxYoVWs9EGDVqFH777TfEx8ejXLlyqmewadtnNzc3rF27Fh9++CG8vLzQuHFjRERE4Ouvv8bevXs1nj86OhqffvopatWqhRo1auD58+c4fPgwRo8eLdo4Bnh9l8rX1xdnz56Fr69vno1JtG2rsJ9ZH0aNGoWNGzeiXbt26NGjB16+fIn4+HjMnDkzzzKL/PqrToUKFXDgwAEMGDAAderUwbvvvguFQoGwsDBMnToVbdu2lfSdE2lLIUiZr0cGkZOTg/Pnz+PmzZsoVaoUGjZsqHajkMuXL+PixYtITU2Fk5MT+vfvr3rv/PnziIyMhKOjIwICApCdnY21a9eiXbt2oh/h/Nr4448/4OzsLPpxKWy/1LWZ699//8WVK1dQqlQp+Pj45NlERJvPU1D7b8vKykJoaCju3r2LMmXKwM/PTzT9EQBWrlyJmjVr5nnI7IEDB/D8+XPRD3pB5969ezd69OiBCRMmaP2cIgAYM2YMTp06hcuXL+dZ35GcnIxDhw4hMTERzZs3R/369bF9+3bk5OSgb9++edq6du0awsLCkJmZiZo1a8LHx0d0RffIkSO4f/++2juN2v4tJSYm4uDBg3j16hX8/PxQq1Yt1YN9cx9GW9B3ld/fza1bt1CnTh3s27dPNY2FiEzLH3/8AXd3d9UzM7W1c+dOZGRkiPLjm3J3WLx27RrMzMzg4eEBHx8f1ZS8uLg4VK5cGQsWLMD48eNVv5UeHh5o06aN2k0xUlJScOzYMTx69AgVKlRAu3btROuiU1NTsWrVKvj5+aFRo0Zq+3T69Glcu3YNGRkZqFu3rmhHZk19zvXkyRMcPnwYOTk5aNeuHVxdXfPNa29LS0vD2bNncffuXZQrVw7NmjXLkyNzBQUF4csvv8Ty5cvx0Ucf6dRWYT9zft9peno6VqxYgdatW4uWDmRmZuL3339Hy5YtRVMolUoljh07hvv376NatWpo3749YmJisGfPHrz33nuq5+Hl19+cnBwsXboUvr6+eQb12dnZOHnyJG7fvg1HR0e0bNlStDOslO+cSBsc5BEVsf/85z9YvHgxIiIiCtxJ9G2PHz+Gh4eHar2DqRo6dCgePnyIo0ePFndXiMjEvDnImzhxYnF3p0Rq164dLly4gMePH+tloy0i0g9O1yQqQqmpqdi0aRPatGkjaYAHvH4m37Zt27ReKG+MlEolWrZsCX9//+LuChERveXu3bs4ceIExo4dywEeUQnDQR5REYiPj8fXX3+NM2fOIDU1VdI0zTeZ+lx8MzMzjB07tri7QUREb7h69SoWL16MgwcPwsXFBTNnzizuLhHRWzjIIyoClpaWqFu3Lnx8fNCxY0dUqVKluLtEREQS2dvbY9GiRfDz8yvurpQopUuXRoMGDfDuu++iS5cukp/5R0RFj2vyiIhkKj09HZmZmTrFWllZcWtuIiKSHeY+7fBOHhGRDKWnp6NG9TKIe5qjU3ylSpVw7949k0l2REQkf8x92uMgj4hIhjIzMxH3NAf3LlaHvZ2ZpNikZCVqNIlGZmamSSQ6IiIyDsx92jOaQZ5SqcSjR49gZ2en9rk1REQlhSAISE5OhouLC8zMpCWpt5Uu8/qQIoeT9I0Gcx8RyQVzn2EZzSDv0aNHfGgkEclKTEwMqlatWtzdIBlj7iMiuWHuMwyjGeTlPp8lOswN9mWkXx3oXbuhvrtERKRWNrJwEvv08lwpJQQoIe3ypNT6VHIVNvf16ddf53ObP0/QOVb5shCxaek6x4J7zREVG+Y+wyqSQV5WVhb279+P6OhoeHh4ICAgQONtWV1i3pQ7TcW+jJnkOboAYKGwlBxDRKST/+UZfUyvU0IJpQ4xpH+yzH3m1pJjcpmb6R6rVFgVIla3DRdeM61/5BGVKMx9BqX3QV5ycjLat2+PxMRE+Pn54aeffoKnpyf27NkDKyv1P+q6xBAREZAjCMiReHdCan3SjLmPiMhwmPs00/sgLygoCE+ePMHly5dRtmxZPHz4EPXq1cPvv/+OcePG6S2GiIg4ZaWkYO4jIjIc5j7NCre1jRqbN2/GgAEDULZsWQBAlSpV0K1bN/z11196jSEiotdJK0fiYWqJzhCY+4iIDIe5TzO9DvIyMzNx584d1KlTR1Tu6emJ69ev6y0GADIyMpCUlCQ6iIiIDI25j4iIShq9DvJevXoFQRBUVyVzlS1bFsnJyXqLAV5Pc3FwcFAd3EKaiExR7pQVqQfpD3MfEZFhMfdpptdBnq2tLQDkubKYmJiI0qVL6y0GAKZPn47ExETVERMTU5iuExHJUu7ic6kH6Q9zHxGRYTH3aabXjVesra1RvXp13LlzR1R+584deHh46C0mN87aWvftm4mIjIHyf4fUGNIf5j4iIsNi7tNM7xuv9OzZE5s3b0Z6+uuHlcbHx2P37t3o3bu3qs7p06exePFiSTFERJSX1IXnuQfpF3MfEZHhMPdppvdB3owZM6BUKtGmTRt89dVXaNOmDapXry7aDvrQoUOYMWOGpBgiIsorR9DtIP1i7iMiMhzmPs30/pw8JycnhIeHIyQkBA8ePMDkyZMxcOBA2NjYqOq0bNkSOTk5kmKIiKj45OTk4MiRI4iOjoaHhwfatGkDhUJR6JhHjx7h9OnTyMrKQpMmTVC7dm2d2tGlf/rE3EdEZHwEQcCZM2dUuaVp06Z6iYmPj8f58+eRlZUFLy+vPJtoXb58GWFhYaIyKysrDBo0SOu+KwTBOFYhJiUlwcHBAS9v1YS9nfQblJ1cvPTfKSIiNbKFLBzHTiQmJsLe3l6nNnJ/8y5dc4adxN+85GQlvOo91fr8r169QkBAAB49eoSWLVvi2LFj8Pb2xvbt22FpaalzzLhx47B37140adIE5ubm2LNnD8aOHYt58+ZJakeX/hmLwua+wK6DdT63+bMEnWOV8S91j01L0zkWxvFPHiJZklvuS09PR/fu3REZGQkfHx+cPHkSHTt2REhICMzNzXWOmTZtGkJCQlC/fn2Ym5vj2LFjmDp1KmbNmqVqZ+7cuVi4cCG6du2qKrO1tcWvv/6q9efV+508IiIyHCUUyIG0O1ZKifX/+9//Ijo6GpcvX0b58uURHR2NBg0aYMWKFRg7dqzOMe3bt0dwcDAsLF6nohMnTqBNmzbo0aMH3n33Xa3b0aV/REQkX4bIffPmzUNkZCQuXbqEihUrIioqCl5eXlizZg1Gjhypc0yDBg0QFRWlmrVx8OBBBAYGIiAgAC1btlS15e7ujjVr1kjq85uMbpDXu3ZDWCikX7m99ZuPzuesPea8zrFERIWhFF4fUmOk2LRpEwYMGIDy5csDAKpXr45u3bph06ZN+Q6itInp06ePKObdd9+FlZUVbty4oRrkadOOLv0zNn369YeFufRdN29+nP/jGjSpOyNO51jhjWmr0oN5N47I1Bki94WEhGDAgAGoWLEiAMDDwwNdunRBSEhIvoM8bWKGDh0qiunUqROsrKwQGRkpGuSlpKRg69atsLGxQePGjeHi4iKp/3rfeIWIiAwn539XM6UewOtpL28eGRkZedrPyspCVFQU6tatKyqvW7curl27prZPusQAwIEDB5CZmYkmTZpo3Y6u5yIiIvkyRO67efMm6tevLypv0KABIiMj1fZJlxgAOHr0KDIzM9GoUSNR+ePHj7Fu3Tr8+OOPqFGjBubMmaPVd5OLgzwiIhkrTKJzdXWFg4OD6ggKCsrT/qtXryAIAsqWLSsqL1euXJ4HeRcm5uHDhxg5ciSGDBmiGuRp044u5yIiInkr6tyXkpICpVKZJ7eUL18eiYmJavukS8zTp08xcuRIvPfee2jevLmqPCAgANHR0di5cydCQ0OxefNmzJw5E/v379f6OzK66ZpERKSdmJgY0eJzdQ/Zzl0zkJycLCpPSkqCra2t2nalxjx58gQdO3ZE/fr1sWLFCknt6NI/IiIyXdrkvlKlSgF4fSHxTcnJyar3ChsTHx+PgIAAuLq6Yu3ataL3fHzEy8h69OgBLy8v7N27F507d87vo4lwkEdEJGNKQQGlIHHx+f/q29vba9xhzMbGBq6urrh3756o/O7du/Dw8Ch0zNOnT9G+fXtUrVoVO3fuFCVbbdrRpX9ERCRvhsh9Li4uuH//vqj8/v37cHd3L3TMy5cv0bFjR5QpUwb79u1D6dKa10fb2trixYsXGuvl4nRNIiIZK8yUFW316NEDmzdvVq1bSExMxO7du9GjRw9VnfPnz2P58uWSYp49e4b27dvDxcUFu3btUnulU5t2tKlDRETGwxC5r1u3bti6dSuys7MBvL5Dt3v3bnTr1k1V59KlSwgJCZEUk5CQgI4dO8LW1hb79+9HmTJl8pz7wYMHotd37txBWFgYfH19te6/0T0nry16cndNIirR9PmsoKORrigj8VlBKclKtG8Qo/X5nzx5gubNm8PV1RUBAQHYvn07srKycPr0adjZ2QEAZs2aheDgYCQkJGgd4+3tjaioKHzzzTeiAZ6Pj49qqoo27WhTx1jl/h20a/RFMeyueUfnWGVyis6xgppNEoio5JNb7ouNjYWPjw8aNmyIwMBAbN68Gc+fP8f58+dV6+7ezn3axLRo0QKRkZEICgoSDfC8vLzg5eUFAGjdujVq166Nxo0b4/nz5/jtt99Qt25d7N+/P9/pom/jnTwiIhkT/jdlRcohSJziUrFiRYSHh6Nv375ISEjA6NGjce7cOdEAysfHB6NHj5YU06ZNGwwfPhzR0dG4ceOG6nj+/LmkdrSpQ0RExsMQua9q1aoIDw/Hu+++ixs3bqBPnz64cOGCaGMVLy8vDBo0SFJMgwYN8N577+Hff//F8ePHVceb0zxDQ0Ph7++PO3fuICsrC8uWLcOxY8e0HuABvJOnwjt5RGQo+ryaeehKdZSWeDXzVbISAQ2jC3V+Khl4J4+I5IK5z7B4J4+IiIiIiMiIcHdNIiIZyxHMkCNIu16XYxTzN4iIyFQx92nGQR4RkYwpoYBS4qQMJUws0xERkVFh7tOMgzwiIhnTZVtoqfWJiIhKEuY+zTjIIyKSMd2mrJjW1UwiIjIuzH2acZBHRCRjr6esSLs6KbU+ERFRScLcpxkHef9TmMcgKJo11DlWuHBF51giIiIAMH+eAHMz6Y9QqDsjTudzKmu46BwrXLymcywREWnGQR4RkYwpYYYcLj4nIiITwtynGQd5REQyxnUJRERkapj7NOMgj4hIxpQw4zbSRERkUpj7NOMgj4hIxnIEBXIEidtIS6xPRERUkjD3acZBHhGRjOXosC4hx8SuZhIRkXFh7tNM2rdDREREREREJRrv5BERyZhSMINS4uJzpYktPiciIuPC3KcZB3lERDLGKStERGRqmPs04yCPiEjGlJC+mFxZNF0hIiIyCOY+zfS+Ju/Ro0eYNm0afH190aRJE4wZMwYxMTEFxixevBhubm6i45133tF314iIjE7uNtJSD9Iv5j4iIsNh7tNM73fyevfujb59+yI4OBjm5uaYNWsWWrVqhfDwcDg6OqqNSUhIQLly5bB9+3ZVmZmZaf2HICLShW4PhOXvq74x9xERGQ5zn2Z6H+SdOnUKFhb/3+yff/6JcuXK4cCBAxg8eHC+cdbW1nBzc9N3d4iIiIoccx8REZUkeh/SvpnkACAnJweCIMDc3LzAuBs3bqBhw4Zo1qwZPv30U8TFxem7a0RERkcJhU4H6RdzHxGR4TD3aVbkG6988803KFu2LAICAvKtU7p0aUybNg2BgYFISEjAnDlz4O3tjcjISJQvX15tTEZGBjIyMlSvk5KS9N53IqKSjlNWSibmPiKiosPcp1mRDvJ+++03/Pbbb9i5c2e+CQsAJkyYIFqH0LRpU9SoUQNLly7FV199pTYmKCgIs2fP1nufdSFcuKJzrMLaWvfzvpHoicg06baNtGklOkMrjtynfJkApcJKcl+FnBzJMarYi9d0jlVY6v7PDyFD9z4TkXFg7tOsyD7tqlWrMGHCBISEhKBz584Fd+KtheZ2dnZo2LAhrl3LP4FMnz4diYmJqkPTLmZERMZIKSh0OqhoMPcRERU95j7NiuRO3urVqzF27Fj88ccf6Nu3r+R4QRAQHR2NBg0a5FvH2toa1oW4C0ZEZAyUOlzNNLVtpA2FuY+IyDCY+zTT+yBv3bp1GDNmDP744w/069dPbZ3g4GCsWrUKly9fBgBMnjwZEyZMQLVq1ZCZmYkZM2bgwYMHGDZsmL67R0RkVJSCGZQS1xlIrQ8Ad+/exeLFixEdHQ0PDw9MnDgRlSpVKnRMTEwMli9fjrCwMEyZMgVt27YVvT927Fi1d6uaNm2KWbNmAQA2bdqE9evXi94vU6YM/vzzT8mfU1fMfUREhmOo3Cdnev+0Y8aMgUKhwOeffy56wGtwcLCqTkJCAh48eKB63axZM/j7+8PR0RH29vY4dOgQ9u/fj2bNmum7e0REJNGdO3fQtGlTPHr0CL169UJERASaNm2KZ8+eFSpm7dq18PPzg5mZGfbu3YvY2Ng87QwYMABjxoxRHQMHDsTevXthY2OjqhMVFYXbt2+L6n344Yf6/RI0YO4jIqKSRCEIgqDPBqOjo6GuybJly6Js2bIAXie6pKQkVKtWTVTn5cuXsLW11WkqSlJSEhwcHNAWPWGhsNSp78WBG68QmZ5sIQvHsROJiYmwt7fXqY3c37xvz7eHTRlpkzLSU7Lxtc9Rrc8/fPhwXL16FRcuXIBCoUBmZiZq1aqFQYMG4YcfftA5Ji4uDk5OTjA3N4dCocD69esxZMiQAvuyZMkSTJw4ETExMaq7gnPnzsWePXtw9uxZSd+DPhV37mtvOxAWht54JStb59jCbbzC3EckR3LLfXKn9+ma1atX11jnzaT3pnLlyum7O0RERs0QU1YOHDiA8ePHQ6F4vWjdysoK3bt3x4EDB/Id5GkTo2m6pzorV65E9+7d88Q+ePAA77//PmxsbODj44OPPvoIlpaGu+DH3EdEZDicrqmZaX1aIiIjkwMgBwqJx2tJSUmiI0PNHZLU1FQ8ffoUVatWFZW7urri3r17avukS4w2wsPDER4ejlGjRonKzczM0K5dO3Tp0gXe3t6YN28eWrRoofbzEBGR/BUm95mKIn8YOhERFZ3CXM10dXUVlc+cOVO1mUmuzMxMAICtra2o3NbWVvXe23SJ0caKFStQrVo1dOrUSVQ+btw40dSbPn36oE6dOli2bBnGjx+v8/mIiKhk4p08zTjIIyKSsRzBDDkSE1du/ZiYGNHgSN2asDJlysDc3Bzx8fGi8hcvXqideqhrjCbp6ekICQnBxIkT8zxf7u21FVWqVIG3tzcuXryo07mIiKhkK0zuMxUc5BERmSh7e3uNi88tLCxQv359REREiMovXbqERo0a6S1Gk61btyIpKQkjRozQqv7Lly9Ru3Ztnc5FREQkd6Y1pCUiMjICFFBKPAQoJJ1j2LBh+Ouvv3D//n0AQEREBA4ePCh6nltISAgGDhwoKUaKlStXIjAwMM8UUwBYvnw5srKyVK/XrFmDyMhI9O7dW6dzERFRyWaI3Cd3vJNHRCRjhpiyMn78eJw/fx7vvPMOGjZsiEuXLmHEiBF4//33VXVu3bqFAwcOSIqJiIjAV199pXo9f/58/Pnnn+jUqRP+85//qMrv3r2L48ePY9u2bWr7Fxsbixo1asDd3R3Pnz9HbGwsFi5ciK5du0r6nEREJA+crqkZB3lERDKmFBRQCtKuTkqtb2lpiU2bNuHmzZt48OABatWqhRo1aojqDBo0CC1atJAUU6VKFYwZMwYAVP8XQJ7nyFlZWWHXrl0IDAxU27/Zs2dj8uTJuHz5MmxtbVGnTh2ULl1a0mckIiL5METukzsO8oiIZCwHZsiROPNeav1cderUQZ06ddS+V7t2bbVr4AqKqVChArp166bxvFWrVs3zOIa32dvbo3Xr1hrbIiIi+TNk7pMrDvKKmcDnOBFRIfBqJgGAMi0dSoUOT4ESBP13RpvTZpjaE6uISJ+Y+zQzrSEtERERERGRkeOdPCIiGVPCDEqJ1+uk1iciIipJmPs04yCPiEjGcgQFciROQZFan4iIqCRh7tOMgzwiIhnjugQiIjI1zH2acZBHRCRjgmAGpcRn/wgm9qwgIiIyLsx9mnGQR0QkYzlQIAcSp6xIrE9ERFSSMPdpZlpDWiIiIiIiIiPHO3lERDKmFKSvM1AWz6PRiIiI9IK5TzMO8oiIZEypw7oEqfWJiIhKEkPmvri4OMTExKBmzZpwdHTUS4wgCLhz5w6ysrJQs2ZNWFtb6+3cuZjpiYhkTAmFTgcREZFcGSL3KZVKfPzxx3Bzc8MHH3yAKlWq4Kuvvip0zNKlS+Hm5obAwED06tULLi4uWLt2baHP/TbeySMikjE+K4iIiEyNIXLf0qVLsWnTJly6dAmenp44c+YM2rRpgyZNmqBPnz46xzx79gynT59GlSpVAAArVqzAyJEj4e3tjYYNG+p87rfxTh4RkYzlTlmRehAREcmVIXLfqlWr0K9fP3h6egIAWrRogQ4dOmDVqlWFivnmm29UAzwAGDlyJMzMzHDmzJlCnfttzPRERERERGQSkpKSREdGRkaeOjk5Obhy5QqaNGkiKvfx8UF4eLjadnWJAYCIiAhkZWWhVq1ahWrnbRzkERHJmBIKKAWJB9fkERGRjBUm97m6usLBwUF1BAUF5Wk/OTkZWVlZeTY7cXR0RHx8vNo+6RKTmpqKESNGoHXr1mjXrp3O7ajDNXlERDIm6LCYXOAgz/gIAgAT2x+ciExWYXJfTEwM7O3tVeXqdra0tLQEAKSnp4vK09LSYGVlpbZ9qTEZGRno06cPXr16hf3790OhUOh8bnU4yCMikrHcK5RSY4iIiOSqMLnP3t5eNMhTp3Tp0nB0dMTDhw9F5Q8fPkS1atUKHZOZmYk+ffrg7t27OH78OCpWrFioc6uj90He7du3cePGDVGZpaUlOnXqpDH21q1biI6OhoeHB9zc3PTdNSIio8Pn5JUMzH1ERIZjiNzn7++P3bt344svvgDweq3c3r170bNnT1WdBw8e4NGjR/D19dU6JneAFxUVhWPHjsHFxUWnc2ui90Hen3/+ifnz56NVq1aqsjJlyhSY6LKzszF48GDs378fDRs2xKVLl/DBBx9g8eLFqluXRESUF+/klQzMfUREhmOI3Pf111/Dx8cHH3/8Mbp164b169cjOTkZkydPVtVZtWoVgoODkZCQoHXMwIEDERoaipUrV+LevXu4d+8eAKBatWqqO3XatKNJkUzXrFOnDvbs2aN1/YULF+LIkSO4fPky3NzccOnSJfj6+qJVq1YYNGhQUXSRiIhIr5j7iIiMR7169XDmzBn8/PPPCA4OhoeHB86ePSt6/EG1atXQokULSTGJiYlo1KgRfvnlF9H5RowYgREjRmjdjiZFMshLT09HaGgobGxsUK9ePdjZ2RVYf926dejfv79qmoqXlxc6deqEdevWMdERERVAqcPic+6uWTSY+4iIDMNQua9hw4ZYvXp1vu+/OTDTNubIkSN6ObcmRbIw4+bNm/jyyy8xYsQIuLi4YMmSJfnWzc7OxtWrV9GoUSNRuZeXFyIiIvKNy8jIyPOcCyIiUyN5C2kdpriQdpj7iIgMg7lPM73fyfPz88ODBw/g5OQEAFizZg1GjBiBBg0aoE2bNnnqp6SkICcnB+XLlxeVOzo6qua3qhMUFITZs2frte9ERHLDNXklA3MfEZHhMPdppvc7eW3atFElOQD44IMPUL9+fWzfvl1t/dznPaSmporKU1NTC3wWxPTp05GYmKg6YmJi9NB7IiJ54dXMkoG5j4jIcJj7NDPIPtrlypVDXFyc2vdsbW3h7OyM2NhYUXlMTAxq1KiRb5vW1taq51xo87wLIiJjZKhEFxISgnr16qF06dLw8vLCvn37Ch2TlpaG1atXw8fHBxYWFggJCcnTxvfffw8LCwvRUaFCBb30r6gx9xERFQ0O8jTT+yDvxYsXotePHj1CWFgYvL29VWW3bt3CwYMHVa8DAwOxY8cOCIIA4PXzI3bv3o3AwEB9d4+IiCQ6dOgQhg8fjilTpuD+/fsYNGgQevXqhfDw8ELFLF++HKGhofjll1+Qk5MDpVKZpx2lUommTZsiPT1ddTx58qTQ/dM35j4iIipJFEJudtGT5s2bo02bNmjcuDGeP3+O4OBg2NnZ4cSJE6orjrNmzRI9U+LOnTto2rQpOnXqhK5duyIkJARXrlxBeHi4aPpLQZKSkuDg4IC26AkLhaU+PxIRkV5lC1k4jp1ITEzU+U5M7m9ex30fw6K0tbTzv8rA4S7LtD5/QEAAbG1tsWPHDlVZs2bNULduXaxbt04vMQqFAuvXr8eQIUNE5XPnzsWePXtw9uxZvfZP35j7iIgKJrfcJ3d6v5N37NgxuLq6Yu/evbhy5QqmT5+OCxcuiL7M2rVri65Uuru74+LFi6hcuTJ27NiBRo0a4cKFC1onOSIiU1XUU1YEQcDp06fRrl07UXmHDh1w+vRpvcUU5PLlyyhXrhwqV66Mnj174tq1a0V2Ll0x9xERGQ6na2qm9901bW1t8Z///Af/+c9/8q0zaNCgPM8AqlmzJhYsWKDv7hARGbXC7DD29vb71tbWsLYWXxlNTk7Gq1ev4OzsLCp3cnLKd72ZLjH5cXZ2xrJlyxAYGIiEhATMmDEDrVq1wpUrV1C1alW9nqswmPuIiAyHu2tqZpCNV4iIqGgU5mqmq6srHBwcVEdQUJDW5zUzM4PU2f66xIwePRpDhw6Fk5MTPDw8sH79epQqVQq///673s9FRETywDt5mun9Th4RERlOYa5mxsTEiKYTvn0XDwDs7Oxga2uLZ8+eicqfPn2KihUrqm1flxhtWVlZwdPTE1FRUUV+LiIiKpl4J08z3skjIjJRb2/Fr26Qp1Ao4Ovri9DQUFH50aNH0bJlS7Xt6hKjraysLNy6dQuVK1cu8nMRERHJFQd5REQyJggKnQ4pJk2ahF27diEkJATJyclYtGgRwsLCMGHCBFWdOXPmiJ5fp02MNt5//32cPn0aaWlpiImJwYgRIxAfH49Ro0bp/VxERCQPhsh9csdBHhGRjCmh0OmQomvXrli2bBm++uorlC1bFkuWLMFff/2FZs2a/X8/lEpkZ2dLijl69KjqAecAMHz4cFhYWODjjz9W1RkzZgy+/vprODk5oUmTJnj58iXOnDmDunXrSjoXEREZD0PkPrnT+3PyigufFUREcqHPZwU13zFep2cFnev1i16fFaRUKiEIAszNzbWOEQQBOTk5ecrNzMxgZsZrkNpg7iMiuTDG3FeSceMVIiIZ02UKSlFMWdFlUKZQKFR38YiIiLRVUnJfScbsSkQkY9xhjIiITA1zn2Yc5BERyRivZhIRkalh7tOMix6IiIiIiIiMCO/kERHJmKDDlBVTu5pJRETGhblPMw7yiIhkTAAgdY9ko9hSmYiITBZzn2Yc5BERyZgSCigkPvvH1J4VRERExoW5TzMO8oiIZIyLz4mIyNQw92nGQR4RkYwpBQUU3EaaiIhMCHOfZtxdk4iIiIiIyIjwTh4RkYwJgg6Lz01t9TkRERkV5j7NOMgjIpIxrksgIiJTw9ynGQd5REQyxkRHRESmhrlPMw7yiIhkjIvPiYjI1DD3acZBHhGRjHFdAhERmRrmPs24uyYREREREZER4Z08IiIZe301U+q6hCLqDBERkQEw92nGQR4RkYxx8TkREZka5j7NOMgjIpIx4X+H1BgiIiK5Yu7TjIM8IiIZ49VMIiIyNcx9mul9kDdq1CjExMTkKW/WrBm+/fZbtTEbN27E2rVrRWVlypTBli1b9N09IiLjwsuZJQJzHxGRATH3aaT3Qd7w4cORkpKiev3y5UsMGjQI7du3zzfmzp07iI6OxoIFC1RllpaW+u4aERFRkWDuIyKikkTvg7zWrVuLXi9atAiWlpYYPnx4gXEODg4IDAzUd3eIiIybDlNWYGJTVgyBuY+IyICY+zQq8jV5K1euRI8ePVCxYsUC60VHR6Nfv36wsbGBj48PRo8eDWtr66LuHhGRrPGBsCUTcx8RUdFh7tOsSAd5Fy9eREREBP773/8WWM/MzAwBAQEIDAxEQkICfv75Z6xatQpnz57NN9llZGQgIyND9TopKUmvfScikgNDLj6Pjo7GgwcPUKtWLVSuXFlvMXFxcYiMjESDBg1QqVKlPO9nZ2fjxo0byMrKQu3atVG6dGnR+/fv38ft27dFZZaWlmjTpo2Wn0y/mPuIiIoWN17RrEgHeStXrkT16tXh7+9fYL3x48ejTJkyqte9evVC7dq18dtvv2HChAlqY4KCgjB79my99peISHYEhfQpKBLr5+TkYMSIEdiyZQs8PT1x7do1fPLJJ5g/f36hYq5evYo5c+bg1KlTePjwIdavX48hQ4aI2gkODsa8efNQtmxZmJubIzo6Gj/++CNGjx6tqvPHH3/gp59+QrNmzVRlZcqUKbZBHnMfEVERM0Duk7siG+SlpaUhJCQEkydPhpmZWYF130xyAFC5cmU0btwYYWFh+cZMnz4dkyZNUr1OSkqCq6tr4TpNRCQzhpiysnjxYuzatQsRERGoVasW/v33X7Rq1QrNmzdH//79dY65d+8eevfujfXr1xd45yosLAzOzs4AgPXr1+ODDz6Aj48PvLy8VPXq1q2Lw4cPS/tgRYC5j4io6HG6pmYFZ6BC2Lp1K1JSUvDhhx/qFB8fHw8rK6t837e2toa9vb3oICIi/VuzZg369++PWrVqAQCaNm2KgIAArFmzplAx3bp1w8CBAwv8rZ82bZpqgAcAQ4YMgYWFBc6fPy+ql5mZiXPnziEiIgLp6ek6fEr9YO4jIqKSoMgGeStWrEDnzp1RtWrVPO/98ccf6Nu3r+r10qVLkZmZKYq9evUq+vTpU1TdIyIyDoKOB17fBXrzeHOtV67s7GxERkbC29tbVO7t7Y1Lly6p7ZIuMdoKCwtDZmYmateuLSqPjIzEJ598gj59+qBSpUpYsWJFoc6jK+Y+IiIDKETuMxVFMl3z9u3bOHHiBHbs2JHv+29Oq3n+/Dlq1KgBNzc3PH/+HE+fPsWSJUvQuXPnougeEZHRKMzi87en+c2cOROzZs0SlaWkpCA7Oxvly5cXlTs6OuLly5dq29clRhu5d8jatWsnWm/XokUL3L9/Hy4uLgBeD55Gjx4NT0/PPI82KErMfUREhsGNVzQrkkFeqVKlsG/fvnwXnQ8ZMgR+fn6q119//TUmTZqEK1euwNbWFh4eHihVqlRRdI2IyPjoeHUyJiZGNN1P3bq43KmDaWlpovLU1NR8pxXqEqNJWloaevbsCaVSib/++gsKxf8n6w4dOojqjh07FosXL8bWrVsNOshj7iMiMiATuzMnVZEM8qpUqYIqVark+36tWrVU6zRylS5dGr6+vkXRHSIio1WYq5narOmytbWFk5MTYmNjReWxsbFwc3PTW0xB0tPT0aNHD8TFxeHYsWOoUKGCxpgKFSrg8ePHks9VGMx9RESGYcg7eYIgICUlBXZ2dnqNUSqViI+Ph52dXZ6LrGlpaXj16pWozMzMLM8MmYIU2Zo8IiIyAAOsSwgICMDOnTtVr7OysrBnzx4EBASoyu7evYvQ0FBJMdrIHeA9evQIR48eFW3CkisxMVH0Oi4uDuHh4WjUqJGkcxERkUwYaE3eggULUKFCBTg6OqJy5cpYt25doWOeP3+OoKAg1KpVC05OTti8eXOeNubPn4/KlSvD09NTdbz5mCBtFOlz8oiISP6+/vpr+Pj4YPjw4ejWrRs2bNiAjIwMTJ48WVVn3bp1CA4ORkJCgtYx8fHxoscFXL16FYcPH0aVKlVQt25dAEDfvn1x5swZLFu2DFeuXMGVK1cAADVr1kTNmjUBAP7+/ggMDETjxo3x/PlzzJ8/H1WrVsXYsWOL+qshIiIjtWXLFnzxxRfYvn07OnXqhA0bNmDEiBGoUaOGaOq91JidO3ciMTERR44cUeUxdZo0aYKzZ8/q3H/eySMikjWFjof26tSpg3PnzsHW1hZr1qyBm5sbzp07h0qVKqnq1KxZE23btpUUEx0djR9++AE//PADOnTogAsXLuCHH37A/v37VXWUSiWaN2+OVatWqer+8MMPOHnypKrOkSNHYGdnhw0bNuCff/7BuHHjEB4ejrJly0r6nEREJBdFn/sWLVqE3r17o0uXLjA3N8ewYcPg6+uLxYsXFypm5MiR+OGHH1CjRg2NfUhLS0N2drakfufinTwiIjnTZQqKDlNWPD09sXTp0nzfHzZsGIYNGyYppnHjxhofYL5v3z6NfbOzs8Pnn3+usR4RERmJIs59SqUS58+fx08//SQqb9OmDTZs2KC3mIJcvHgR5cuXh1KphJeXF+bPny9pMzHeySMikjM+K4iIiExNET8jNiUlBenp6XBychKVOzk54dmzZ2q7pEtMfmrUqIF9+/YhOTkZz549Q9OmTREQEIBbt25p3QYHeUREciYodDuIiIjkqhC5z9XVFQ4ODqojKCgoT/O5j+nJyckRlWdnZ8PMTP3wSZeY/AwePBj+/v6wsLCAvb09fvnlFzg5OWHNmjVat8HpmkREMiYIrw+pMURERHJVmNynzTNi7ezsYGdnhydPnojKnzx5gsqVK6ttX5cYbZmbm6NmzZq4e/eu1jG8k0dERERERCYh9xmxuYe6QR4A+Pn55Vk3fujQIdHOmqmpqYiPj5cUow3hrRFsamoqrl27JulZsxzkERHJGdfkERGRqTFA7vviiy9w6NAh/Pzzz4iKisJXX32FW7duiR4F9OOPP4oeg6BNTFZWFp4/f47nz58DeL2W7/nz50hJSVHVCQgIwLZt23Dv3j1cuHABffv2RWZmpqRHA3GQR0QkZ1yTR0REpsYAuc/Pzw/bt2/H5s2b0bZtW5w6dQoHDx5EvXr1VHVsbW3h6OgoKebMmTOqB5w7OjpixowZ8PT0xNdff62qs3DhQmzduhX+/v4YOXIkXFxcEBERgerVq2vdf67JIyKSMYXw+pAaQ0REJFeGyn3dunVDt27d8n1/6tSpmDp1qqSYd999V3UXLz/16tXT6bELb+Igj4hIzgz0nDwiIqISg7lPIw7yiIjkTJfpl5yuSUREcsbcpxHX5BERERERERkR3skjIpIzTlkhIiJTw9ynEQd5RERyxkRHRESmhrlPIw7yiIjkjImOiIhMDXOfRhzkERHJGRefExGRqWHu04iDPCIiGeNz8oiIyNQw92nGQR4RkZxxygoREZka5j6N+AgFIiIiIiIiI8JBHhERERERkRHhdE0iIhlTQId1CUXSEyIiIsNg7tOMgzwiIjnjDmNERGRqmPs04iCPiEjOuPiciIhMDXOfRjqtycvIyMDt27eRkpKSb5309HTExMQgMzNT63Z1iSEiMmmCjgdJxtxHRFRCMPdpJGmQFxsbi2nTpqFmzZrw8PDAjh071NabOXMmypcvDy8vL1SoUAGLFi3S2LYuMUREREWNuY+IiORG0iAvNDQU5cqVQ3h4eL511q9fj59++gmHDx/Gixcv8Mcff+Czzz7D33//rdcYIiL6/wfCSj1Ie8x9REQlC3OfZpLW5A0ePFhjnaVLl+K9995Dy5YtAQA9evSAn58fli5dCn9/f73FEBERDLYuITk5GRs3bkR0dDQ8PDwwcOBA2NjYFDomMzMTW7ZsQVhYGAYNGgRvb2+d2tGlf9pi7iMiKmG4Jk8jvT4nT6lUIiwsDC1atBCVt27dGv/++6/eYoiI6H8MsC7h+fPn8Pb2xsqVKwEA8+fPR6tWrfDq1atCxezevRs1a9bErl27MH/+fFy7dk2ndnTpnz4x9xERGRjX5Gmk10FecnIyMjIy4OjoKCqvUKECnj9/rrcY4PUC+KSkJNFBRGRqDDFlZe7cuVAoFDh+/Di+++47hIaGIjo6GosXLy5UTM2aNREWFoY///yzUO3o0j99Yu4jIjIsTtfUTK+DPDOz181lZWWJyjMzM2Fubq63GAAICgqCg4OD6nB1dS1M14mI5Cn3WUFSDwl27NiBfv36oVSpUgCA8uXLo3v37vluQKJtTP369eHs7Fzoc+vSP31i7iMiMjAD5D650+sgz87ODg4ODoiLixOVx8XFoWrVqnqLAYDp06cjMTFRdcTExBT+AxARmZC37whlZGTkqZORkYHo6Gi4u7uLyt3d3XHr1i217eoSo2s7+jpXYTD3ERFRSaPXQR4AtG3bFgcPHhSV7d+/H23btlW9jo+Px7179yTFvM3a2hr29vaig4jI5BRiXYKrq6vorlBQUFCe5lNTUwEgz2+sg4NDvmvedInRtR19nauwmPuIiAyIa/I0krS7ZlpaGh4+fKh6/eTJE9y+fRsODg5wcnICAHz11Vdo3bo1vvnmG3Tv3h1r1qxBTEwMJk+erIr75ZdfEBwcjISEBK1jiIgoL13WGeTWj4mJEQ0SrK2t89QtXbo0FAqF6vc6V0JCAuzs7NS2r0uMru3o61wFYe4jIipZCpP7TIWkO3nh4eEIDAxEYGAg3N3dsXTpUgQGBmLBggWqOs2aNcOhQ4dw4cIFfPDBB3j48CFCQ0NRq1YtVZ3y5cujZs2akmKIiEiNQlzNfPuOkLpBnpWVFdzd3XHz5k1R+Y0bN1C3bl21XdIlRtd29HWugjD3ERGVMLyTp5GkO3ktW7bE7du3NdZr06YN2rRpk+/748ePx/jx4yXFEBGRGrrsGCaxfr9+/bBhwwbMmDEDDg4OePjwIfbs2YO5c+eq6hw6dAj//PMPvv32W61j9HVufZ0rP8x9REQljAFyn9zpfU0eEREZkAGuZn7xxRdwcnJCs2bNMHLkSLRo0QI+Pj74+OOPVXVOnz6NRYsWSYqJiorClClTMGXKFADAxo0bMWXKFGzevFlSO9rUISIiI8I7eRpJupNHRESmx97eHmfOnMG+ffvw4MED9OvXDwEBAarHAABAQEAAKlSoICnG2toalSpVAgD89NNPqnIHBwdJ7WhTh4iIyJRwkEdEJGe6XJ3U4WqmpaUlevbsme/7LVu2RMuWLSXFVKtWTXUXrzDn1rYOEREZCQPlPjnjII+ISMa4wxgREZka5j7NOJeFiIiIiIjIiPBOHhGRnHHKChERmRrmPo04yCMikjFOWSEiIlPD3KcZp2sSEREREREZEd7JIyKSOxO7OklERMTcVzAO8oiI5IzrEoiIyNQw92nEQR4RkYxxXQIREZka5j7NOMgjIpIzXs0kIiJTw9ynEQd5REQyxquZRERkapj7NOPumkREREREREaEd/KIiOSMU1aIiMjUMPdpxEEeEZGcMdEREZGpYe7TiIM8IiIZ47oEIiIyNcx9mnGQR0QkZ7yaSUREpoa5TyMO8oiI5IyJjoiITA1zn0bcXZOIiIiIiMiIcJBHRCRjuesSpB5ERERyZajct2fPHrRs2RJVqlRB27Zt8c8//xQ6Jjs7G9u3b0dAQAAqVKiALVu26O3cb+Igj4hIzgQdDyIiIrkyQO77559/0Lt3b/Tt2xfHjx9Hy5YtERAQgGvXrhUq5pdffsHatWsxadIkvHjxAunp6Xo599sUgiAYRbpPSkqCg4MD2qInLBSWxd0dIqJ8ZQtZOI6dSExMhL29vU5t5P7m1R33PcytbSTF5mSk4/riLwt1fioZmPuISC7klvu6du0KANi7d6+qrFGjRmjatClWrlypc4wgCFAoFAAAhUKB9evXY8iQIYU+99t4J4+ISM54J4+IiEyNge7kdezYUVQWEBBQ4LRJbWJyB3j6PvfbuLsmEZGccYcxIiIyNYXIfUlJSaJia2trWFtbi8qSk5ORnJyMihUrisorVqyIx48fq21el5iibId38oiIiIiIyCS4urrCwcFBdQQFBeWpk7uazdzcXFRuYWEBpVKptl1dYoqyHcl38tLT0/HXX38hLCwMQ4cORZMmTfLUOXPmDEJDQ5GVlYVmzZohMDCwwDb//vtv0ZxTAChVqpTaL52IiP6f4n+H1BiShrmPiKjkKEzui4mJEa3Je/suHgCUKVMGNjY2ePbsmaj82bNncHJyUtu+LjFF2Y6kO3m7du2Cu7s7Dh06hIULF+L69et56nTt2hVTpkxBQkIC0tPT8cEHH6BXr14FjjzPnTuHHTt2wM3NTXVUq1ZNSteIiEyTgdbknTlzBn369EGTJk0wcOBAREZG6iVGU50ePXqgQYMGeY5PP/1UVef333/P837Lli2lf8h8MPcREZUwhch99vb2okPdIM/MzAzNmjXDyZMnReWhoaFo3ry52i7pElOU7Ui6k+fh4YHLly/D0dERGzZsUFtn7ty5aNy4ser14MGDUb9+fRw8eBCdO3fOt+1KlSph4sSJUrpDRGTydHn2j9T64eHhaNeuHcaPH4/PPvsMa9euRevWrXHp0iW4ubnpHKNNnfnz5yMjI0PV7uPHjxEQEICxY8eqyp4+fQpzc3NRXnp7mkthMPcREZUshsh948ePx+DBg7Fv3z506tQJISEhOHv2LI4dO6aq8+OPP2LZsmW4c+eO1jH6OrcmkgZ5devW1VjnzSQHAHXq1IGVlRUePnxYYNyTJ0/w9ddfw8bGBj4+PvD395fSNSIi02SAjVfmzp2LFi1a4McffwQAtG7dGv/88w/mzZuHxYsX6xyjTR0PDw9Ru3v27EGpUqUwePBgUXmpUqXQoEEDaR9MS8x9REQljAFyX9++fRETE4OhQ4ciOTkZ5cuXx8qVK+Hn56eqk5qaihcvXkiKOXHiBPr06aN6PXbsWEycOBFDhw7FggULtG5HkyLfXTMkJATZ2dlo3bp1gfUqVqwIMzMzPH36FH379kWHDh2wdevWfLcZzcjIEF3dfXunHCIik1HEu2UeO3YM06dPV71WKBTo0qULDh06VKgYXdpdtWoV+vXrh7Jly4rKo6Ki0KJFC9Vgadq0aShfvryUj6lXzH1EREXMADtFf/bZZ5g4cSJSUlJgZ2eX5/2pU6di/PjxkmJatGiBGzdu5Cm3sRE/909TO5oU6SDvypUr+PTTTzF16lR4enrmW2/o0KGYMWOG6vXo0aPh7e2NdevWYfjw4WpjgoKCMHv2bL33mYhITgozZUWbbaRfvXqFly9fonLlyqLyypUrIyYmRm372sTo0u6JEycQFRWFVatW5en3mDFjEBgYiISEBHz33XfYtGkTLl++XCwPe2fuIyIqWoaYrqmKUyjyHWTZ2trC1tZWUoylpSUqVKhQ6HNrUmSPULh+/Tr8/f3Rr18/fP/99wXWrV69uuh1/fr10aRJkwIf+Dd9+nQkJiaqjvz+UUBEROpps410dnY2AMDKykpUbm1tjaysLLXtahOjS7srV66Ep6dnnrtjn332Gb777jv4+fmhe/fuOHDgAJKSkvDrr7+qbacoMfcREVFJUCR38m7evIn27dujc+fOWL58uVZPdn9bZmZmvokeUH/FmYjI5BRiXYI220jb2dnByspKtOYAAF68eJHvlUhtYqS2m5SUhC1btuDbb7/N856FhTiVlS1bFu+8845WO4DqE3MfEZGBGGBNntzp/U7erVu30K5dO3Tu3BkrV66EmVneUxw4cEC0DuPw4cOi948fP47w8HAEBATou3tEREYld8qK1APQfhtpLy8vnDt3TlR++vRptc+K0zZGarsbN25EdnY2hg0bptX38vDhQ4NO1WTuIyIynMLkPlMhaZB369YtTJw4UbXd8x9//IGJEydi06ZNqjoBAQFITk5GmTJlMGnSJFX9AwcOqOqcPXsWS5cuVb1et24dmjRpgpEjR6Jnz54IDAzEp59+ikGDBhXy4xERGTkDPCfv448/xpYtW3Dx4kUAwKFDh3D8+HGMHj1aVefXX38VPZtOmxht6uRauXIlevfurfYu3zfffIPnz58DAHJycjB79mzcvXs3zw6cumLuIyIqYQz0jFg5kzRds1SpUqpnF+Vu8QkAjo6Oqv89ZcoU1VqLN725E1pgYCAqVqyoer1u3Tpcv34d586dg62tLX7++We4u7tL6RoRkUkyxOLzESNG4ObNm2jVqhUcHR3x8uVLBAUFoUuXLqo6T58+xbVr1yTFaFMHeL2RyYULF9SuGQSAatWqoXHjxjAzM8PLly/h7OyMrVu3olWrVtI+aD6Y+4iIShZDbrwiVwpBEIziIyclJcHBwQFt0RMWCsvi7g4RUb6yhSwcx04kJibqPKUw9zfvnQ+/h7mVjeaAN+RkpuPy6i8lnz85ORlxcXGoUqVKnt3Enj59ihcvXuR5plxBMdrWefnyJR4+fIj69esXuM4tNjYWtra2xfroBENj7iMiuZBr7pOrIn9OHhERGQc7O7t8t3J2dnaGs7OzpBht65QrVw7lypXT2L+qVatqrENERGQKOMgjIpIz7jBGRESmhrlPIw7yiIhkjOsSiIjI1DD3acZBHhGRnPFqJhERmRrmPo04yCMikjGFIEAhcf8sqfWJiIhKEuY+zTjIIyKSM17NJCIiU8Pcp5Gkh6ETERERERFRycY7eUREMsbF50REZGqY+zTjII+ISM44ZYWIiEwNc59GHOQREckYr2YSEZGpYe7TjIM8IiI549VMIiIyNcx9GnGQR0QkY7yaSUREpoa5TzPurklERERERGREeCePiEjOOGWFiIhMDXOfRhzkERHJnKlNQSEiImLuKxgHeUREciYIrw+pMURERHLF3KcRB3lERDLGxedERGRqmPs04yCPiEjOuC6BiIhMDXOfRtxdk4iIiIiIyIjwTh4RkYwplK8PqTFERERyxdynGQd5RERyxikrRERkapj7NOIgj4hIxrj4nIiITA1zn2Yc5BERyRm3kSYiIlPD3KcRB3lERDLGq5lERGRqmPs04yCPiIi0kp2djZcvX8LR0RFmZtptzqxNTEF1kpOTkZiYKCozMzODi4uLXvpHRERkjJgFiYjkTNDxkCgoKAjly5dHjRo14OTkhOXLl+slRlOdhQsXwt3dHb6+vqrD399fL/0jIiKZMlDukzNJg7y0tDSsXr0aPj4+sLCwQEhISJ4633//PSwsLERHhQoVNLYdEhKCevXqoXTp0vDy8sK+ffukdI2IyCTlTlmRekixceNGzJkzBzt27EBKSgqWLl2KMWPG4NixY4WK0bbdxo0bIzY2VnVcvXq10P2TgrmPiKhkMUTukztJg7zly5cjNDQUv/zyC3JycqBU5n3ghFKpRNOmTZGenq46njx5UmC7hw4dwvDhwzFlyhTcv38fgwYNQq9evRAeHi7t0xARmZrcxedSDwmWLFmC3r17o3379gCA/v37o3Xr1vj1118LFSOl3YSEBKSlpemtf1Iw9xERlTAGyH1yJ2mQN378eKxZswa+vr4a6755NdPc3LzAuvPmzUPXrl0xYsQIODk5YerUqWjUqBEWLFggpXtERCanqK9mKpVK/Pvvv2jVqpWo3M/PD+fPn9c5Rkq7Fy5cgKurKxwcHNCoUSMcPXq0UP2TirmPiKhk4Z08zYpkTd7ly5dRrlw5VK5cGT179sS1a9fyrSsIAk6fPo127dqJyjt06IDTp08XRfeIiIxHIdYlJCUliY6MjIw8zScnJyMjIyPP1MMKFSrg2bNnarukTYy27Xp4eOD48eNISkpCYmIiOnTogK5du+L69es696+oMPcRERkI1+RppPdBnrOzM5YtW4Zbt27hxIkTsLGxQatWrRAbG6u2fnJyMl69egVnZ2dRuZOTE+Li4vI9T0ZGRp5/oBARkfZy747lHkFBQXnq5O5SmZ2dLSrPysrK906VNjHatjtgwAD4+flBoVCgVKlSmDdvHipVqoS1a9fq3L+iwNxHREQlid4foTB69GjV/3ZycsL69evh5uaG33//HXPmzNG6HTMzMwgFzJ0NCgrC7NmzC9VXIiK5K8yzgmJiYmBvb68qt7a2zlPXzs4O9vb2eQYeT548QZUqVdS2r02MLu0Cr3ODm5sb7t+/X6h29I25j4jIcPicPM2K/BEKVlZW8PT0RFRUlNr37ezsYGtrm2dazdOnT1GxYsV8250+fToSExNVR0xMjF77TUQkC0pBtwOAvb296FA3yAOAd999F3///beo7MCBA3j33XdVr5OSkvDo0SNJMdrUeXuTk5SUFERGRqJmzZqS2jE05j4ioiJUiNxnKop8kJeVlYVbt26hcuXKat9XKBTw9fVFaGioqPzo0aNo2bJlvu1aW1vn+QcKEZHJMcC6hC+//BJHjhzB999/jytXrmDy5Mm4d+8eJk+erKrz888/o169epJitKnToUMHhISE4Pr16/jnn3/Qq1cvCIKAsWPHSmrH0Jj7iIiKENfkaaT3Qd7777+P06dPIy0tDTExMRgxYgTi4+MxatQoVZ05c+aIFslPmjQJu3btQkhICJKTk7Fo0SKEhYVhwoQJ+u4eEZFRUUCHHcYknqNFixbYu3cv/v77b/Tq1QtXr17FkSNHUKdOHVUde3t70fRIbWK0qbN8+XIcOXIE/fr1w+TJk+Hp6YkrV67A1dVVUjtFjbmPiMhwDJH75E4hFDT5/y1Hjx5FQEAAACAnJwdmZmZQKBQYOXIkli1bBgAIDQ3FnDlzcO7cOdja2sLHxwffffcdGjVqpGpn1qxZCA4ORkJCgqps1apV+Pbbb/HgwQN4eHggKCgIvXv31vqDJCUlwcHBAW3RExYKS63jiIgMLVvIwnHsRGJios53YnJ/81p1mAULCxtp589Ox6kjswp1flPC3EdEVHjMfYYlaZAnCAJycnLylJuZmal2ONOGUqmEIAh63fmMiY6I5IKJTl6Y+4iICo+5z7Ak7a6pUChgYVH4DTmlJEUiIsofdxgresx9REQlC3OfZnp/hAIRERmQLovJTSzRERGRkWHu04iXFYmIZEwhCDodREREcmWo3Hfp0iUMGzYMbdq0wUcffYTbt2/rJUZTnTVr1sDX11d0+Pv7S+o7B3lERHKm1PEgIiKSKwPkvsjISLRu3RplypTBF198gVevXsHX1xexsbGFitGmTmxsLFJSUhAcHKw6goKCJPWf0zWJiGRMl6uTvJNHRERyZojc9+2338LLywu//vorACAgIAB16tTB/PnzsWDBAp1jtG23TJky8PX1ldTnN/FOHhGRnPGBsEREZGoMkPuOHDmC7t27q16bm5uja9euOHz4cKFitG33zp078Pf3R/fu3fHtt98iJSVFUv95J4+IiIiIiExCUlKS6LW1tTWsra1FZa9evcKLFy/g4uIiKndxcUF0dLTadrWJ0bZdS0tLDBo0CIGBgUhISMB///tf/PHHHwgLC0Pp0qW1+pwc5BERyZkgvD6kxhAREclVIXKfq6urqHjmzJmYNWuWqCwrKwsA8gz+SpUqpXrvbdrEaNvuxIkTRXX8/f1Rq1YtLF26FFOmTMn/M76BgzwiIhnjs4KIiMjUFCb3xcTEiB6G/vaACwDs7OxgaWmJ+Ph4UfmLFy/g6Oiotn1tYrRt9+0+VahQAV5eXoiIiNDwKf8f1+QREclZ7tVMqQcREZFcFSL32dvbiw51gzxzc3O88847uHDhgqj83LlzaNy4sdouaROjS7u54uLitJ6qCXCQR0QkawqlbgcREZFcGSL3jRw5Elu2bMG1a9cAACdPnsSRI0cwcuRIVZ0VK1aInl+nTYw2df773/+qNloRBAHz5s3DzZs3MWDAAK37z+maRERyxjV5RERkagyQ+8aMGYPIyEg0btwYbm5uiI6OxvTp09GrVy9VndjYWNFdOW1itKljY2MDDw8PlC9fHvHx8VAoFNiwYQPatWundf8VgmAc2T4pKQkODg5oi56wUFgWd3eIiPKVLWThOHYiMTFRtC5ACtVvns9XsLCwkXb+7HQcP/9doc5PJQNzHxHJhVxz37NnzxAbGws3NzeUK1dO9F5sbCzi4uLQtGlTrWO0rZOTk4OoqCjY2tqiatWqMDOTNgGTd/KIiORMl+feGcWlPSIiMlkGzH1OTk5wcnJS+17VqlVRtWpVSTHa1jE3N4enp6e0zr6BgzwiIhlTCAIUEidkSK1PRERUkjD3acZBHhGRnHFNHhERmRrmPo04yCMikjMBgNTdMk0rzxERkbFh7tOIgzwiIhnjlBUiIjI1zH2a8Tl5RERERERERoR38oiI5EyADusSiqQnREREhsHcpxEHeUREcsbF50REZGqY+zTiII+ISM6UABQ6xBAREckVc59GHOQREckYF58TEZGpYe7TjIM8IiI5M9CUFaVSiZMnTyI6OhoeHh7w9fXVS4w2dZ49e4Zz584hKysLjRs3hpubm+j9S5cu4d9//xWVWVtbY+jQodI+JBERyQOna2rEQR4RERUoLS0NXbt2xa1bt9C8eXOcOHECbdq0waZNm2Bubq5zjDZ1Jk+ejM2bN+Odd96Bubk5Bg8ejEmTJmHu3Lmqc+3Zswe//PILevTooSqztbXlII+IiEwWB3lERHJmgKuZP/30E27cuIGIiAg4OTnhzp07aNSoEVatWoVRo0bpHKNNnSZNmuD777+HtbU1AODw4cPw9/dH586d0apVK9X5atasiRUrVkj7HoiISJ54J08jnQZ5YWFhCAsLQ7t27eDu7i56b8uWLUhISMgT4+rqik6dOqltj1NtiIh0ZIBEt3HjRgwYMABOTk4AAHd3d3Tp0gUbN27Md5CnTYw2dQYNGiRqt2PHjrCyssLVq1dFg7zk5GRs2rQJNjY28Pb2hqurq6TPqA3mPiKiEoKDPI0kPQz99OnTaN68OUaMGIFRo0bhzJkzeepERETg7NmzquPUqVMYNWoUdu7cmW+7e/bswZdffimKu3DhgvRPQ0RkapQ6HgCSkpJER0ZGRp7ms7KycOvWLdSrV09UXr9+fURGRqrtkjYxurQLvL6Tl5mZCS8vL1H5s2fP8Ndff+GXX35BrVq18M033+TbhlTMfUREJUwhcp+pkHQnLycnBwsXLoSvry8UCvX7ln777bei13v27MGaNWvw0UcfFdg2p9oQEUlXmB3G3r7bNXPmTMyaNUtUlpKSAqVSiXLlyonKy5cvj6SkJLXtaxOjS7tPnjzByJEj0b9/f/j4+KjKAwMDMWnSJNja2gIA9u3bh27duqF58+bo2rWr2rakYO4jIipZuLumZpIGeX5+fpJPsHLlSjRu3Bje3t4F1jPEVBsiIqNTiCkrMTExsLe3VxXnrnt7U6lSpQC8/o1+U3Jysuo9XWKktvvixQv4+/ujRo0aWLNmjei9pk2bil536dIFXl5e2L9/v14Gecx9REQlDKdraiRpuqZUT548wZ49ezB69GiNdaVOtcnIyMgz1YiIiLRnb28vOtQN8mxsbFClShXcv39fVH7v3j3UqlVLbbvaxEhp98WLF+jQoQPKlSuHvXv35ju4fFOpUqUQHx+vsV5RYO4jIqLiVqSDvLVr18LKyirPwvm3BQYG4v79+9i6dSuOHDmC7du3Y+7cudi7d2++MUFBQXBwcFAdvPpJRCZJKeh2SNC9e3ds3boVWVlZAF5Ptdy9eze6d++uqhMWFob169dLitGmTnx8PDp27AgHBwfs27cPpUuXztO/tweKt2/fRlhYGFq0aCHpc+oLcx8RUREzQO6TO4Ug6HbvUqFQYP369RgyZEi+dTw9PdGyZUusWrVKcvve3t5o2bIlFi9erPb9jIwM0SYBSUlJcHV1RVv0hIXCUvL5iIgMJVvIwnHsRGJiomi6pBRJSUlwcHBAx5oTYGGe9w5cgefPycDhuwu1Pv+jR4/g4+MDT09PdOrUCVu2bEFSUhLOnj0LBwcHAMCsWbMQHBys2mFSmxht6jRv3hzXrl3D3LlzRQM8b29v1VTId999F25ubmjcuDGeP3+O33//He+88w727t0LGxsbSd+NJsx9RES6kVvuk7sie07eyZMncfPmTaxevVqneE1TbaytrdVOLSIiMi06rEuAtPouLi4IDw/HqlWrcP/+fbz//vsYMWKEKEl6e3tj2LBhkmK0bbdhw4a4cuWKqE/Ozs6qQd6xY8ewbds2nDt3Dra2tli9ejW6desm6TPqC3MfEZEhFH3uk7siG+StXLkS9evXVztdJiwsDFevXlU9C+j+/ftwc3NTvZ871WbgwIFF1T0iIuNgoMXnTk5OmDZtWr7v9+jRAz169JAUo02dpUuXauybubk5+vXrh379+mmsW9SY+4iIDIAbr2gkaZD36NEj7Nu3T/X6+PHjSE9Ph6enJ1q3bq0qT05OxubNm/Hdd9+pbWfXrl0IDg5WJbphw4blmWrTsmXLfB+yS0RE/6MUIPnqpImtSygs5j4iohKGuU8jSRuvJCYmqh7YOnLkSADA2bNncefOHVG927dvY+DAgapE9ra3p/UcO3YM3bt3x8OHD2Fubo7Vq1fjyJEjel9LQUREJBVzHxERyY3OG6+UNLkLMbn4nIhKOr0uPq/2CSzMJC4+V2bg8INfTWbxuTFj7iMiuWDuM6wiW5NHREQGwHUJRERkapj7NOIgj4hIzrgugYiITA1zn0Yc5BERyRmvZhIRkalh7tOIgzwiIjkToEOiK5KeEBERGQZzn0aSdtckIiIiIiKiko138oiI5IxTVoiIyNQw92nEQR4RkZwplQCUOsQQERHJFHOfRhzkERHJGa9mEhGRqWHu04iDPCIiOWOiIyIiU8PcpxEHeUREcsZnBRERkalh7tOIu2sSEREREREZEd7JIyKSMUFQQhCkLSaXWp+IiKgkYe7TjIM8IiI5EwTpU1BMbF0CEREZGeY+jTjIIyKSM0GHdQkmluiIiMjIMPdpxEEeEZGcKZWAQuIUFBObskJEREaGuU8jDvKIiOSMVzOJiMjUMPdpxEEeEZGMCUolBIlXM01t8TkRERkX5j7N+AgFIiIiIiIiI8I7eUREcsYpK0REZGqY+zTiII+ISM6UAqBgoiMiIhPC3KcRB3lERHImCACk7jBmWomOiIiMDHOfRhzkERHJmKAUIEi8minokOhiYmKwdOlSREdHw8PDA+PGjUOFChUKHWPIOkREZBwMlfvkzGgGebn/4bKRJXmKLhGRIWUjC4CeEo6ghPSrmdLq379/H82aNUPr1q3RpUsX/Pnnn1izZg0uXrwIR0dHnWMMWcdYMfcRkVzILffJnUIwkmFtbGwsXF1di7sbRERai4mJQdWqVXWKTUpKgoODA9qZ94GFwlJSbLaQhWM525CYmAh7e3uN9UeOHImwsDBcvHgRZmZmSE9PR61atfDBBx9g7ty5OscYso6xYu4jIrmRS+6TO6O5k+fi4oKYmBjY2dlBoVCI3ktKSoKrqytiYmJM4j9qYfC7kobfl/b4Xf0/QRCQnJwMFxeXwrdlgCkr+/btwyeffAIzs9dP3bGxsUH37t2xb9++fAdR2sQYso6xYu7TD35X0vD70h6/q/8nt9wnd0YzyDMzM9N4VcDe3t7k/x9MW/yupOH3pT1+V685ODjopZ1sIUPyFJTcKTNJSUmicmtra1hbW4vK0tLSEBcXh2rVqonKq1Wrho0bN6ptX5sYQ9YxZsx9+sXvShp+X9rjd/VaSch9psJoBnlERKbEysoKlSpVwsm4fTrFlylTJs80v5kzZ2LWrFmisoyMDACAra1tnvj09HS1bWsTY8g6RERkHAqb+ypVqgQrKys996pk4iCPiEiGbGxscO/ePWRmZuoULwhCnul9b9/FA14PlszNzfHy5UtR+YsXL1C2bFm1bWsTY8g6RERkHAqb+6ysrGBjY6PnXpVMJjHIs7a2xsyZM9X+A4bE+F1Jw+9Le/yu9M/GxqbIk5WFhQXq1q2Ly5cvi8ovX76Md955R+cYQ9YxVfz/Oe3xu5KG35f2+F3pnyFyn1EQiIiICvDDDz8ITk5OQkxMjCAIghAZGSnY2NgIa9euVdXZtGmTMGzYMEkxhqxDRERkSozmEQpERFQ0MjMz0a9fP5w8eRJeXl64cOEC+vbti5UrV6qmfM6aNQvBwcFISEjQOsaQdYiIiEwJB3lERKSViIgIPHjwALVq1ULdunVF7127dg23b99Gjx49tI4pjjpERESmgIM8IiIiIiIiI2L0G69kZmbi1KlTSE5ORrNmzVC5cuXi7lKJ9O+//+L27duisrJlyyIwMLCYelSyZGVl4fDhw8jMzETPnj3V1klKSsKpU6cAAK1atTLZ5+EIgoDQ0FDExcXhvffeg6Wlpej9w4cP4/nz56IyNzc3+Pr6GrKbREaNuU87zH0FY+7THnMflTRGfSfv7t278Pf3h7m5OapUqYLz588jODgYo0aNKu6ulThjxozB3r170apVK1WZq6srfvrpp2LsVcnw7bffYvny5TA3N1c9ePltx48fR58+feDu7g6FQoHbt29j27ZtaNu2reE7XIxWrVqFH374AYIg4Pbt23j58mWebex9fX2RmpqKevXqqcr8/Pzw6aefGri3RMaJuU97zH35Y+7THnMflURGfSdv9OjRqFGjBg4cOAALCwusWLECn3zyCTp27IgaNWoUd/dKnObNm+PPP/8s7m6UOPb29jh37hw2bNiAefPm5Xk/IyMDgwcPxrBhwxAcHAwAGDduHIYMGYK7d++azEM3c+3duxc3b95E9+7d863Tv39/zJgxw4C9IjIdzH3SMPepx9wnDXMflTRmxd2BovL48WMcOXIE48ePh4XF67HsBx98ADs7O/z111/F3LuS6eXLl9i1axdCQ0NVO+QRMGHChAKnOh07dgyPHj3CZ599piqbPHkyHj58iGPHjhmiiyXGiBEj4OHhobFeTEwMtm/fjnPnziE9Pd0APSMyDcx90jH3qcfcpz3mPiqJjHaQFxkZCQBo0KCBqiz3oblXrlwprm6VaBEREfjtt98wfvx4VK9eHWvWrCnuLsnClStXYGdnh+rVq6vKatSogdKlS/NvLR8HDhzAypUr8f7776N27doIDQ0t7i4RGQXmPumY+3TD3Ccdcx8ZktEO8hITEwEA5cuXF5U7OjrySp0aAwcORExMDPbt24eIiAjMnj0bo0aN4g+1FhITE/P8nQH8W8vP7Nmzce/ePezZswdRUVHw9/dH//79Vf8/S0S6Y+6ThrlPd8x90jD3kaEZ7SDP2toaAJCSkiIqT0lJgY2NTXF0qURr27YtSpUqpXo9ceJEODg4YP/+/cXYK3mwtrbO83cG8G8tP506dYKZ2eufHnNzc8yaNQtPnz7FuXPnirlnRPLH3CcNc5/umPukYe4jQzPaQZ67uzsA4MGDB6Ly6Oho1KxZszi6JDt2dnZ49uxZcXejxHN3d8fLly+RnJysKktKSkJCQgL/1rSQu902/9aICo+5r/CY+7TD3Fc4zH1U1Ix2kFe3bl24ublh8+bNqrKLFy/izp076Nq1azH2rORRKpV4+vSpqOzixYuIjo5Gs2bNiqlX8tGxY0dYWFhg27ZtqrLNmzfDwsICHTt2LMaelTyJiYlIS0sTlW3btg0KhQJNmzYtpl4RGQ/mPu0x9xUOc5/2mPuoOBjtIxQUCgWCg4PRt29fmJmZoVq1aliwYAHee+89tGnTpri7V6Lk5OSgTZs26Ny5M+rXr48HDx5g0aJF6Nq1K957773i7l6xCw0NxePHj3Hp0iWkp6erttru2rUr7Ozs4OzsjBkzZmDcuHF4+PAhACAoKAhff/01nJ2di7PrBhceHo6bN28iPDwcwOskZmtrCz8/P1SpUgWPHz/Ge++9hz59+qBGjRqIiIjAsmXLMG3aNNSpU6eYe08kf8x92mPuKxhzn/aY+6gkMuqHoQPA+fPn8ccffyA5ORmtWrXChx9+CHNz8+LuVonz6tUrrF27FuHh4ShXrhz8/PwKfNaLKfnhhx9w6dKlPOU///wzXFxcVK937dqFPXv2AAC6deuGHj16GKqLJca6deuwb9++POVTpkxRXa2Mi4vDmjVrcOvWLbi4uKB79+5o3ry5obtKZNSY+7TD3Jc/5j7tMfdRSWT0gzwiIiIiIiJTYrRr8oiIiIiIiEwRB3lERERERERGhIM8IiIiIiIiI8JBHhERERERkRHhII+IiIiIiMiIcJBHRERERERkRDjIIyIiIiIiMiIc5BERERERERkRDvKIiIiIiIiMCAd5RERERERERoSDPCIiIiIiIiPCQR4REREREZER+T9hBWX01/+tgAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "terms = obs.systematic_terms()\n", - "print([type(t).__name__ for t in terms])\n", - "\n", - "constraint_stat = rxmc.constraint.Constraint([obs], omp)\n", - "constraint = rxmc.constraint.Constraint([obs], omp, extra_terms=terms)\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(9, 4))\n", - "for a, (c, title) in zip(\n", - " axes,\n", - " [\n", - " (constraint_stat, \"statistical only (default)\"),\n", - " (constraint, \"+ reported systematics\"),\n", - " ],\n", - "):\n", - " im = a.imshow(c.covariance_matrix(true_params), cmap=\"viridis\")\n", - " a.set_title(title)\n", - " fig.colorbar(im, ax=a, fraction=0.046)\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "293e0e6e", - "metadata": {}, - "source": [ - "## Guardrail: a measurement with no statistical error\n", - "\n", - "EXFOR subentries that report only a systematic error come back with\n", - "`statistical_err = 0`. The old pipeline would let that propagate and crash deep\n", - "inside the sampler; now the `Constraint` fails fast, names the dataset, and\n", - "suggests the remedies.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "5321385f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:22.187926Z", - "iopub.status.busy": "2026-08-11T03:07:22.187773Z", - "iopub.status.idle": "2026-08-11T03:07:24.064179Z", - "shell.execute_reply": "2026-08-11T03:07:24.063596Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Constraint covariance over [toy-subentry-nostat] is singular (Cholesky factorization failed). The covariance diagonal is zero on rows belonging to ['toy-subentry-nostat']: these datasets report zero statistical error and no other covariance term covers their points. Remedies: pass the dataset's reported systematics as terms (extra_terms=[*obs.systematic_terms()]; for a multi-observation constraint place them with support= from rxmc.covariance.stacked_supports(observations)), add a noise_term or a fixed Term covering those points, or compose the full covariance explicitly with include_statistical_term=False.\n" - ] - } - ], - "source": [ - "measurement_nostat = SimpleNamespace(**{**vars(measurement)})\n", - "measurement_nostat.statistical_err = np.zeros_like(y_mb)\n", - "measurement_nostat.subentry = \"toy-subentry-nostat\"\n", - "\n", - "obs_nostat = (\n", - " rxmc.elastic_diffxs_observation.ElasticDifferentialXSObservation.from_measurement(\n", - " measurement=measurement_nostat,\n", - " reaction=rxn,\n", - " quantity=\"dXS/dA\",\n", - " )\n", - ")\n", - "\n", - "try:\n", - " rxmc.constraint.Constraint([obs_nostat], omp)\n", - "except ValueError as err:\n", - " print(err)" - ] - }, - { - "cell_type": "markdown", - "id": "d98cea7e", - "metadata": {}, - "source": [ - "As the message says: supply the dataset's reported systematics as terms, add a\n", - "`noise_term` (a free noise nuisance), or compose the covariance explicitly. Note\n", - "that the offset and normalization modes alone are rank-two, so a dataset with\n", - "*only* systematic errors still needs a diagonal contribution (a noise term or an\n", - "error floor) to make $\\Sigma$ positive definite. Our measurement has statistical\n", - "errors, so we proceed.\n" - ] - }, - { - "cell_type": "markdown", - "id": "9489bae8", - "metadata": {}, - "source": [ - "## Calibrate" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "17319787", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:24.065831Z", - "iopub.status.busy": "2026-08-11T03:07:24.065682Z", - "iopub.status.idle": "2026-08-11T03:07:24.069721Z", - "shell.execute_reply": "2026-08-11T03:07:24.069224Z" - } - }, - "outputs": [], - "source": [ - "evidence = rxmc.evidence.Evidence(constraints=[constraint])\n", - "\n", - "prior_mean = np.array(\n", - " [50.0, 3, 1.2 * 40 ** (1 / 3), 0.65, 18, 1.2 * 40 ** (1 / 3), 0.65]\n", - ")\n", - "prior_cov = np.diag([7, 7, 0.2, 0.2, 10, 0.2, 0.2]) ** 2\n", - "prior = stats.multivariate_normal(mean=prior_mean, cov=prior_cov)\n", - "\n", - "walker = rxmc.walker.Walker(\n", - " model_sampler=rxmc.param_sampling.BatchedAdaptiveMetropolisSampler(\n", - " params=omp.params,\n", - " prior=prior,\n", - " starting_location=prior_mean,\n", - " initial_proposal_cov=prior_cov / 100,\n", - " ),\n", - " evidence=evidence,\n", - " rng=np.random.default_rng(7),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "84438127", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:24.071394Z", - "iopub.status.busy": "2026-08-11T03:07:24.071248Z", - "iopub.status.idle": "2026-08-11T03:07:40.796744Z", - "shell.execute_reply": "2026-08-11T03:07:40.795972Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 16.7 s, sys: 1.4 ms, total: 16.7 s\n", - "Wall time: 16.7 s\n" - ] - }, - { - "data": { - "text/plain": [ - "0.206125" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%time\n", - "walker.walk(n_steps=8000, burnin=1000, batch_size=1000, verbose=False)\n", - "samples = walker.model_sampler.chain\n", - "walker.model_sampler.overall_acceptance_fraction()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "f7b9a4c4", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:40.798239Z", - "iopub.status.busy": "2026-08-11T03:07:40.798069Z", - "iopub.status.idle": "2026-08-11T03:07:42.595527Z", - "shell.execute_reply": "2026-08-11T03:07:42.594833Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = corner.corner(\n", - " samples,\n", - " labels=[p.name for p in omp.params],\n", - " truths=true_params,\n", - " truth_color=\"k\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "dccca3bf", - "metadata": {}, - "source": [ - "## Predictive check" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "1ec6c533", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:42.600185Z", - "iopub.status.busy": "2026-08-11T03:07:42.600011Z", - "iopub.status.idle": "2026-08-11T03:07:42.979739Z", - "shell.execute_reply": "2026-08-11T03:07:42.978808Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "draw_indices = np.linspace(0, samples.shape[0] - 1, 60, dtype=int)\n", - "y_draws = np.array(\n", - " [omp.visualizable_model_prediction(obs, *d) for d in samples[draw_indices]]\n", - ")\n", - "y_low, y_high = np.percentile(y_draws, [5, 95], axis=0)\n", - "\n", - "angles_plot = np.rad2deg(obs.visualization_workspace.angles)\n", - "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n", - "ax.errorbar(\n", - " np.rad2deg(obs.x), obs.y, yerr=obs.y_stat_err, ls=\"none\", marker=\"o\", label=\"data\"\n", - ")\n", - "ax.plot(\n", - " angles_plot,\n", - " omp.visualizable_model_prediction(obs, *true_params),\n", - " \"k:\",\n", - " label=\"truth\",\n", - ")\n", - "ax.fill_between(angles_plot, y_low, y_high, alpha=0.3, label=\"90% predictive band\")\n", - "ax.set_xlabel(r\"$\\theta$ [deg]\")\n", - "ax.set_ylabel(r\"$d\\sigma/d\\Omega$ [b/sr]\")\n", - "ax.set_yscale(\"log\")\n", - "ax.legend()\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "e95379dc", - "metadata": {}, - "source": [ - "## Takeaways\n", - "\n", - "- **The unit contract**: `from_measurement` divides everything dimensionful\n", - " (data, statistical error, absolute offset error) by `obs.norm` and passes the\n", - " fractional normalization error through untouched. `obs.norm` is retained, so\n", - " you can always convert back.\n", - "- **Systematics are opt-in**: they ride along as metadata and become covariance\n", - " terms only via `obs.systematic_terms()` passed to\n", - " `Constraint(extra_terms=...)` — nothing correlated is hidden in a default.\n", - "- **The guardrail**: a dataset contributing zero variance fails at construction\n", - " with a message naming the subentry, not with an opaque `LinAlgError` mid-chain.\n", - "- To adapt to production: replace the `SimpleNamespace` with an\n", - " `exfor_tools.Distribution` — the fields are the same.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/normalization_inference.ipynb b/examples/normalization_inference.ipynb deleted file mode 100644 index 6b89d43..0000000 --- a/examples/normalization_inference.ipynb +++ /dev/null @@ -1,2196 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "196a680f-91b8-4c45-8894-f56175a73082", - "metadata": {}, - "source": [ - "# Bayesian calibration of polynomials, including inference of overall normalization" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c549ae62-7f81-4a8e-a54e-a7331a298c90", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:06.821557Z", - "iopub.status.busy": "2026-08-11T03:07:06.821382Z", - "iopub.status.idle": "2026-08-11T03:07:07.527172Z", - "shell.execute_reply": "2026-08-11T03:07:07.526332Z" - } - }, - "outputs": [], - "source": [ - "import corner" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a55118c3-ccbf-45bf-81a9-bdf6e9daf46c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:07.528701Z", - "iopub.status.busy": "2026-08-11T03:07:07.528499Z", - "iopub.status.idle": "2026-08-11T03:07:07.958886Z", - "shell.execute_reply": "2026-08-11T03:07:07.958243Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "from scipy import stats" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "22715d9f-6d09-4444-b9a5-243a1274c05c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:07.960611Z", - "iopub.status.busy": "2026-08-11T03:07:07.960418Z", - "iopub.status.idle": "2026-08-11T03:07:09.174374Z", - "shell.execute_reply": "2026-08-11T03:07:09.173736Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using database version X4-2024-12-31 located in: /home/kyle/db/exfor/unpack_exfor-2024/X4-2024-12-31\n" - ] - } - ], - "source": [ - "import rxmc" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1f8e822c-4807-452b-b4b9-a6e25af16006", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.175847Z", - "iopub.status.busy": "2026-08-11T03:07:09.175620Z", - "iopub.status.idle": "2026-08-11T03:07:09.178149Z", - "shell.execute_reply": "2026-08-11T03:07:09.177591Z" - } - }, - "outputs": [], - "source": [ - "rng = np.random.default_rng(42)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "31abc19d-08d7-49c4-98f8-0c58d235fcd8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.179441Z", - "iopub.status.busy": "2026-08-11T03:07:09.179298Z", - "iopub.status.idle": "2026-08-11T03:07:09.181657Z", - "shell.execute_reply": "2026-08-11T03:07:09.180998Z" - } - }, - "outputs": [], - "source": [ - "poly4 = rxmc.physical_model.Polynomial(4)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "715c0ed6-62e7-41da-b5c0-4d0e12c0d414", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.182953Z", - "iopub.status.busy": "2026-08-11T03:07:09.182815Z", - "iopub.status.idle": "2026-08-11T03:07:09.185509Z", - "shell.execute_reply": "2026-08-11T03:07:09.184903Z" - } - }, - "outputs": [], - "source": [ - "true_params = [1, 0.5, -0.1, -0.4, 0.1]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "67038d92-ffcb-491c-8d62-eb9a4843e62d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.186991Z", - "iopub.status.busy": "2026-08-11T03:07:09.186844Z", - "iopub.status.idle": "2026-08-11T03:07:09.190165Z", - "shell.execute_reply": "2026-08-11T03:07:09.189599Z" - } - }, - "outputs": [], - "source": [ - "settings = [\n", - " {\n", - " \"domain\": [-0.3, 0.4],\n", - " \"N\": 50,\n", - " \"noise\": 0.1,\n", - " \"systematic_err\": 0.1,\n", - " },\n", - " {\n", - " \"domain\": [0.3, 0.5],\n", - " \"N\": 30,\n", - " \"noise\": 0.1,\n", - " \"systematic_err\": 0.5,\n", - " },\n", - " {\n", - " \"domain\": [0.1, 0.6],\n", - " \"N\": 25,\n", - " \"noise\": 0.1,\n", - " \"systematic_err\": 0.2,\n", - " },\n", - " {\n", - " \"domain\": [-0.5, 0.1],\n", - " \"N\": 15,\n", - " \"noise\": 0.2,\n", - " \"systematic_err\": 0.6,\n", - " },\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "abf487e6-041c-457c-9216-5a15e3c08f6c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.191643Z", - "iopub.status.busy": "2026-08-11T03:07:09.191485Z", - "iopub.status.idle": "2026-08-11T03:07:09.195421Z", - "shell.execute_reply": "2026-08-11T03:07:09.194734Z" - } - }, - "outputs": [], - "source": [ - "def generate_observations(settings, true_model, rng, true_params, scale_err=True):\n", - " obs = []\n", - " for setting in settings:\n", - " x0, x1 = setting[\"domain\"]\n", - " synthetic_obs = rxmc.observation.Observation(\n", - " x=rng.random(setting[\"N\"]) * (x1 - x0) + x0,\n", - " y=np.zeros(setting[\"N\"]),\n", - " y_stat_err=np.ones(setting[\"N\"]) * setting[\"noise\"],\n", - " )\n", - " renormalization = rng.normal(1, setting[\"systematic_err\"])\n", - " y_true = true_model(synthetic_obs, *true_params)\n", - " if scale_err:\n", - " synthetic_obs.y = rng.normal(y_true, setting[\"noise\"]) * renormalization\n", - " else:\n", - " synthetic_obs.y = rng.normal(y_true * renormalization, setting[\"noise\"])\n", - "\n", - " synthetic_obs.renormalization = renormalization\n", - " synthetic_obs.sys_norm_err = setting[\"systematic_err\"]\n", - "\n", - " obs.append(synthetic_obs)\n", - " return obs" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "b1fa98b8-d52d-44f1-849b-ad89b318c37b", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.196840Z", - "iopub.status.busy": "2026-08-11T03:07:09.196635Z", - "iopub.status.idle": "2026-08-11T03:07:09.199797Z", - "shell.execute_reply": "2026-08-11T03:07:09.199178Z" - } - }, - "outputs": [], - "source": [ - "observations = generate_observations(settings, poly4, rng, true_params, scale_err=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "30849732-090e-4c2d-be1e-f212fb83845e", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.201576Z", - "iopub.status.busy": "2026-08-11T03:07:09.201373Z", - "iopub.status.idle": "2026-08-11T03:07:09.204499Z", - "shell.execute_reply": "2026-08-11T03:07:09.203733Z" - } - }, - "outputs": [], - "source": [ - "observations_unreported_sys_err = [\n", - " rxmc.observation.Observation(\n", - " x=obs.x,\n", - " y=obs.y,\n", - " y_stat_err=obs.y_stat_err,\n", - " )\n", - " for obs in observations\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "b56c8f2a-f6e3-46a9-bd46-6799a7edf0d6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.206187Z", - "iopub.status.busy": "2026-08-11T03:07:09.205934Z", - "iopub.status.idle": "2026-08-11T03:07:09.209932Z", - "shell.execute_reply": "2026-08-11T03:07:09.208921Z" - } - }, - "outputs": [], - "source": [ - "N_fine = 100\n", - "domain_fine = (-1, 1)\n", - "truth = rxmc.observation.Observation(\n", - " x=np.linspace(*domain_fine, N_fine), y=np.zeros(N_fine)\n", - ")\n", - "truth.y = poly4(truth, *true_params)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "d7349134-bbd3-4fef-b7d0-99d501afad7d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.211828Z", - "iopub.status.busy": "2026-08-11T03:07:09.211561Z", - "iopub.status.idle": "2026-08-11T03:07:09.472241Z", - "shell.execute_reply": "2026-08-11T03:07:09.471426Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(truth.x, truth.y, \"k:\", label=\"truth\")\n", - "for synthetic_observation in observations:\n", - " plt.errorbar(\n", - " synthetic_observation.x,\n", - " synthetic_observation.y,\n", - " synthetic_observation.y_stat_err,\n", - " linestyle=\"none\",\n", - " marker=\".\",\n", - " label=f\"renormalization = {synthetic_observation.renormalization:1.3f}\",\n", - " )\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "65d77c5b-4498-4eb7-9e10-23ae52198504", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.473577Z", - "iopub.status.busy": "2026-08-11T03:07:09.473406Z", - "iopub.status.idle": "2026-08-11T03:07:09.476110Z", - "shell.execute_reply": "2026-08-11T03:07:09.475205Z" - } - }, - "outputs": [], - "source": [ - "correct_model = poly4" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "ed7e3fce-125d-49a9-8742-718e06d3d764", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.477481Z", - "iopub.status.busy": "2026-08-11T03:07:09.477328Z", - "iopub.status.idle": "2026-08-11T03:07:09.483737Z", - "shell.execute_reply": "2026-08-11T03:07:09.482771Z" - } - }, - "outputs": [], - "source": [ - "# block supports come from the library helper\n", - "_block_supports = rxmc.covariance.stacked_supports\n", - "\n", - "\n", - "evidence_models = {}\n", - "\n", - "# Unknown per-dataset normalization: a latent scale rho_i for each dataset, routed\n", - "# to it by identity and sampled as ordinary *model* parameters. This is the v2\n", - "# form of the old per-dataset UnknownNormalizationModel -- rho moves onto the\n", - "# PhysicalModel (via rxmc.transforms.per_observation_scaling) and is sampled in\n", - "# the model block (jointly with the physics) rather than as a per-constraint\n", - "# covariance nuisance. All constraints share the one model instance, so they\n", - "# still share a single model-parameter vector.\n", - "norm_model = rxmc.physical_model.Polynomial(\n", - " order=correct_model.order,\n", - " transform=rxmc.transforms.per_observation_scaling(observations),\n", - ")\n", - "evidence_models[\"unknown_norm\"] = rxmc.evidence.Evidence(\n", - " [rxmc.constraint.Constraint([obs], norm_model) for obs in observations]\n", - ")\n", - "\n", - "# reported systematic errors folded in as fixed per-dataset normalization modes\n", - "evidence_models[\"marginalized_sys_err\"] = rxmc.evidence.Evidence(\n", - " [\n", - " rxmc.constraint.Constraint(\n", - " observations,\n", - " correct_model,\n", - " extra_terms=[\n", - " rxmc.covariance.normalization_term(\n", - " magnitude=o.sys_norm_err, support=sup\n", - " )\n", - " for o, sup in zip(observations, _block_supports(observations))\n", - " ],\n", - " )\n", - " ]\n", - ")\n", - "\n", - "# systematic errors omitted entirely (statistical only)\n", - "evidence_models[\"unreported_sys_err\"] = rxmc.evidence.Evidence(\n", - " [rxmc.constraint.Constraint(observations_unreported_sys_err, correct_model)]\n", - ")\n", - "\n", - "# omitted systematics absorbed by a single unknown (averaging) model-error term\n", - "gamma = rxmc.params.Parameter(\n", - " \"log fractional err\", float, latex_name=r\"\\gamma\", unit=\"dimensionless\"\n", - ")\n", - "_N_unreported = sum(o.n_data_pts for o in observations_unreported_sys_err)\n", - "evidence_models[\"unreported_sys_err_with_unknown_model_err\"] = rxmc.evidence.Evidence(\n", - " [\n", - " rxmc.constraint.Constraint(\n", - " observations_unreported_sys_err,\n", - " correct_model,\n", - " extra_terms=[\n", - " rxmc.covariance.model_error_term(\n", - " gamma,\n", - " averaging=True,\n", - " support=np.arange(_N_unreported),\n", - " )\n", - " ],\n", - " )\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "1cf0e309-c459-4c42-90aa-7cec410bc017", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.485199Z", - "iopub.status.busy": "2026-08-11T03:07:09.485039Z", - "iopub.status.idle": "2026-08-11T03:07:09.488431Z", - "shell.execute_reply": "2026-08-11T03:07:09.487748Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['unknown_norm', 'marginalized_sys_err', 'unreported_sys_err', 'unreported_sys_err_with_unknown_model_err'])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evidence_models.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "94719fd0-383c-4a7d-aec5-f904bec9a357", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.489984Z", - "iopub.status.busy": "2026-08-11T03:07:09.489815Z", - "iopub.status.idle": "2026-08-11T03:07:09.493318Z", - "shell.execute_reply": "2026-08-11T03:07:09.492611Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "hyperparameters for each model:\n", - "unknown_norm: []\n", - "marginalized_sys_err: []\n", - "unreported_sys_err: []\n", - "unreported_sys_err_with_unknown_model_err: ['log fractional err']\n" - ] - } - ], - "source": [ - "print(\"hyperparameters for each model:\")\n", - "hyperparams = {}\n", - "for key, evidence_model in evidence_models.items():\n", - " hyperparams[key] = []\n", - " for constraint in evidence_model.parametric_constraints:\n", - " for p in constraint.params:\n", - " hyperparams[key].append(p)\n", - " print(f\"{key}: {[p.name for p in hyperparams[key]]}\")" - ] - }, - { - "cell_type": "markdown", - "id": "7255a93d-771b-448a-962b-4f285d7fa312", - "metadata": {}, - "source": [ - "## Priors\n", - "We will put a tight prior on $a_0$, to avoid identifiability issues, as $a_0$ corresponds to an additive offset of the entire model, which is pretty confounding with a multiplicative bias." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "5ccbf1c3-e622-4476-9216-6bbe692570bf", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.494912Z", - "iopub.status.busy": "2026-08-11T03:07:09.494703Z", - "iopub.status.idle": "2026-08-11T03:07:09.498523Z", - "shell.execute_reply": "2026-08-11T03:07:09.497852Z" - } - }, - "outputs": [], - "source": [ - "cov = np.diag(np.ones(len(correct_model.params)))\n", - "cov[0, 0] = 0.001\n", - "model_prior = stats.multivariate_normal(mean=np.array([1, 0, 0, 0, 0]), cov=cov)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "e75ea172-6a1b-4411-a4e4-b38311461ce2", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.500201Z", - "iopub.status.busy": "2026-08-11T03:07:09.499992Z", - "iopub.status.idle": "2026-08-11T03:07:09.504289Z", - "shell.execute_reply": "2026-08-11T03:07:09.503599Z" - } - }, - "outputs": [], - "source": [ - "unknown_log_norm_priors = [\n", - " stats.multivariate_normal(\n", - " mean=[0], cov=[[np.log(1 + (setting[\"systematic_err\"]) ** 2)]]\n", - " )\n", - " for setting in settings\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "6c8372b5-8418-4142-9284-642561a76405", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.505849Z", - "iopub.status.busy": "2026-08-11T03:07:09.505662Z", - "iopub.status.idle": "2026-08-11T03:07:09.509179Z", - "shell.execute_reply": "2026-08-11T03:07:09.508156Z" - } - }, - "outputs": [], - "source": [ - "unknown_model_err_prior = stats.multivariate_normal(mean=[np.log(0.1)], cov=[[0.01]])" - ] - }, - { - "cell_type": "markdown", - "id": "e0529a9e-b937-4a3e-ab0c-8660c5fe6d23", - "metadata": {}, - "source": [ - "## Walkers" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "0d765083-b158-4170-beac-430689778a0a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.511081Z", - "iopub.status.busy": "2026-08-11T03:07:09.510871Z", - "iopub.status.idle": "2026-08-11T03:07:09.513731Z", - "shell.execute_reply": "2026-08-11T03:07:09.512908Z" - } - }, - "outputs": [], - "source": [ - "walkers = {}" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "8986c8a1-c24a-4693-a94c-fda660e36d95", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.515337Z", - "iopub.status.busy": "2026-08-11T03:07:09.515114Z", - "iopub.status.idle": "2026-08-11T03:07:09.520762Z", - "shell.execute_reply": "2026-08-11T03:07:09.519950Z" - } - }, - "outputs": [], - "source": [ - "# combined prior over [physics params, per-dataset log_rho] (block-diagonal):\n", - "# the per-dataset scales are now model parameters, so they are sampled in the\n", - "# model block -- this walker has no separate likelihood samplers.\n", - "n_physics = len(correct_model.params)\n", - "rho_prior_vars = np.array(\n", - " [np.log(1 + setting[\"systematic_err\"] ** 2) for setting in settings]\n", - ")\n", - "norm_prior_mean = np.concatenate([model_prior.mean, np.zeros(len(observations))])\n", - "norm_prior_cov = np.zeros((n_physics + len(observations),) * 2)\n", - "norm_prior_cov[:n_physics, :n_physics] = model_prior.cov\n", - "norm_prior_cov[n_physics:, n_physics:] = np.diag(rho_prior_vars)\n", - "norm_model_prior = stats.multivariate_normal(mean=norm_prior_mean, cov=norm_prior_cov)\n", - "\n", - "walkers[\"unknown_norm\"] = rxmc.walker.Walker(\n", - " model_sampler=rxmc.param_sampling.BatchedAdaptiveMetropolisSampler(\n", - " norm_model.params,\n", - " starting_location=norm_model_prior.mean,\n", - " prior=norm_model_prior,\n", - " initial_proposal_cov=norm_model_prior.cov,\n", - " ),\n", - " evidence=evidence_models[\"unknown_norm\"],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "a55021b0-5fbf-4c62-a6e1-4754bccf4236", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.522378Z", - "iopub.status.busy": "2026-08-11T03:07:09.522190Z", - "iopub.status.idle": "2026-08-11T03:07:09.526059Z", - "shell.execute_reply": "2026-08-11T03:07:09.525352Z" - } - }, - "outputs": [], - "source": [ - "walkers[\"unreported_sys_err_with_unknown_model_err\"] = rxmc.walker.Walker(\n", - " model_sampler=rxmc.param_sampling.BatchedAdaptiveMetropolisSampler(\n", - " correct_model.params,\n", - " starting_location=model_prior.mean,\n", - " prior=model_prior,\n", - " initial_proposal_cov=model_prior.cov,\n", - " ),\n", - " evidence=evidence_models[\"unreported_sys_err_with_unknown_model_err\"],\n", - " likelihood_samplers=[\n", - " rxmc.param_sampling.BatchedAdaptiveMetropolisSampler(\n", - " params=hyperparams[\"unreported_sys_err_with_unknown_model_err\"],\n", - " starting_location=unknown_model_err_prior.mean,\n", - " prior=unknown_model_err_prior,\n", - " initial_proposal_cov=unknown_model_err_prior.cov,\n", - " )\n", - " ],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "c9b000c6-4219-4e7f-9c47-abe793d631ff", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.527512Z", - "iopub.status.busy": "2026-08-11T03:07:09.527343Z", - "iopub.status.idle": "2026-08-11T03:07:09.530468Z", - "shell.execute_reply": "2026-08-11T03:07:09.529824Z" - } - }, - "outputs": [], - "source": [ - "walkers[\"marginalized_sys_err\"] = rxmc.walker.Walker(\n", - " model_sampler=rxmc.param_sampling.BatchedAdaptiveMetropolisSampler(\n", - " correct_model.params,\n", - " starting_location=model_prior.mean,\n", - " prior=model_prior,\n", - " initial_proposal_cov=model_prior.cov,\n", - " ),\n", - " evidence=evidence_models[\"marginalized_sys_err\"],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "53080b07-1708-48e3-ace7-89e34a9a5494", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.531884Z", - "iopub.status.busy": "2026-08-11T03:07:09.531684Z", - "iopub.status.idle": "2026-08-11T03:07:09.534956Z", - "shell.execute_reply": "2026-08-11T03:07:09.534233Z" - } - }, - "outputs": [], - "source": [ - "walkers[\"unreported_sys_err\"] = rxmc.walker.Walker(\n", - " model_sampler=rxmc.param_sampling.BatchedAdaptiveMetropolisSampler(\n", - " correct_model.params,\n", - " starting_location=model_prior.mean,\n", - " prior=model_prior,\n", - " initial_proposal_cov=model_prior.cov,\n", - " ),\n", - " evidence=evidence_models[\"unreported_sys_err\"],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "5edcfc20-5f56-4851-ac52-4883236950a8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:07:09.536899Z", - "iopub.status.busy": "2026-08-11T03:07:09.536665Z", - "iopub.status.idle": "2026-08-11T03:08:43.365354Z", - "shell.execute_reply": "2026-08-11T03:08:43.364712Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Running unknown_norm\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/4 completed, 2000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 2/4 completed, 2000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 3/4 completed, 2000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 4/4 completed, 2000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 1/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.247\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 2/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.306\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 3/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.266\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 4/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.277\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 5/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.294\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 6/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.286\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 7/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.276\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 8/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.248\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 9/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.292\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 10/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.288\n", - "\n", - "Running unreported_sys_err_with_unknown_model_err\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/4 completed, 2000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 2/4 completed, 2000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 3/4 completed, 2000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 4/4 completed, 2000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 1/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.283\n", - " Likelihood parameter acceptance fractions: [0.423]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 2/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.281\n", - " Likelihood parameter acceptance fractions: [0.451]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 3/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.291\n", - " Likelihood parameter acceptance fractions: [0.4215]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 4/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.302\n", - " Likelihood parameter acceptance fractions: [0.455]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 5/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.311\n", - " Likelihood parameter acceptance fractions: [0.436]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 6/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.290\n", - " Likelihood parameter acceptance fractions: [0.4425]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 7/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.278\n", - " Likelihood parameter acceptance fractions: [0.4445]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 8/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.267\n", - " Likelihood parameter acceptance fractions: [0.474]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 9/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.305\n", - " Likelihood parameter acceptance fractions: [0.4405]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 10/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.292\n", - " Likelihood parameter acceptance fractions: [0.4275]\n", - "\n", - "Running marginalized_sys_err\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/4 completed, 2000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 2/4 completed, 2000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 3/4 completed, 2000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 4/4 completed, 2000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 1/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.294\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 2/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.288\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 3/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.288\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 4/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.264\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 5/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.290\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 6/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.311\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 7/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.287\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 8/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.292\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 9/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.310\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 10/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.301\n", - "\n", - "Running unreported_sys_err\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/4 completed, 2000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 2/4 completed, 2000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 3/4 completed, 2000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 4/4 completed, 2000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 1/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.301\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 2/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.284\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 3/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.289\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 4/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.295\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 5/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.282\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 6/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.296\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 7/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.310\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 8/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.315\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 9/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.290\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 10/10 completed, 2000 steps. \n", - " Model parameter acceptance fraction: 0.291\n" - ] - } - ], - "source": [ - "for key, walker in walkers.items():\n", - " print(f\"\\nRunning {key}\")\n", - " walker.walk(n_steps=20000, burnin=8000, batch_size=2000)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "6ebeb608-b331-4114-827a-31b74773ae0e", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:43.367063Z", - "iopub.status.busy": "2026-08-11T03:08:43.366905Z", - "iopub.status.idle": "2026-08-11T03:08:43.370243Z", - "shell.execute_reply": "2026-08-11T03:08:43.369708Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'unknown_norm': 'tab:blue',\n", - " 'unreported_sys_err_with_unknown_model_err': 'tab:orange',\n", - " 'marginalized_sys_err': 'tab:purple',\n", - " 'unreported_sys_err': 'tab:green'}" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "colors = dict(\n", - " zip(walkers.keys(), [\"tab:blue\", \"tab:orange\", \"tab:purple\", \"tab:green\"])\n", - ")\n", - "colors" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "c37c4279-fe7f-4e7a-b261-b368abcc34ae", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:43.371935Z", - "iopub.status.busy": "2026-08-11T03:08:43.371791Z", - "iopub.status.idle": "2026-08-11T03:08:44.883123Z", - "shell.execute_reply": "2026-08-11T03:08:44.882505Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model = correct_model\n", - "fig, axes = plt.subplots(\n", - " walkers[\"marginalized_sys_err\"].model_sampler.chain.shape[1] + 1,\n", - " 1,\n", - " figsize=(8, 8),\n", - " sharex=True,\n", - ")\n", - "for i in range(walker.model_sampler.chain.shape[1]):\n", - " # plot walkers\n", - " for key, walker in walkers.items():\n", - " axes[i].plot(walker.model_sampler.chain[:, i], alpha=0.4, color=colors[key])\n", - "\n", - " axes[i].set_ylabel(f\"${model.params[i].latex_name}$\")\n", - " true_value = true_params[i]\n", - " axes[i].hlines(true_value, 0, len(walker.model_sampler.chain), \"r\", linestyle=\"--\")\n", - "\n", - "# plot likelihoods\n", - "for key, walker in walkers.items():\n", - " axes[-1].plot(walker.model_sampler.logp_chain, alpha=0.4, color=colors[key])" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "a9dd5361-1428-4b53-a50f-a8d2cc71917f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:44.886306Z", - "iopub.status.busy": "2026-08-11T03:08:44.886135Z", - "iopub.status.idle": "2026-08-11T03:08:44.892077Z", - "shell.execute_reply": "2026-08-11T03:08:44.891619Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(20000, 5)" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prior_samples = model_prior.rvs(walker.model_sampler.chain.shape[0])\n", - "prior_samples.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "f716e401-7712-49f0-9771-5e90cb016cb4", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:44.894051Z", - "iopub.status.busy": "2026-08-11T03:08:44.893889Z", - "iopub.status.idle": "2026-08-11T03:08:44.902271Z", - "shell.execute_reply": "2026-08-11T03:08:44.901462Z" - } - }, - "outputs": [], - "source": [ - "domain = np.zeros((len(walkers), correct_model.n_params, 2))\n", - "for i, (key, walker) in enumerate(walkers.items()):\n", - " chain = walker.model_sampler.chain[:, : correct_model.n_params]\n", - " domain[i, ...] = np.array(\n", - " [\n", - " np.min(chain, axis=0),\n", - " np.max(chain, axis=0),\n", - " ]\n", - " ).T\n", - "domain = np.array([np.min(domain[:, :, 0], axis=0), np.max(domain[..., 1], axis=0)])" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "a45db89b-5c4d-43ea-893a-336a88d5432a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:44.904009Z", - "iopub.status.busy": "2026-08-11T03:08:44.903854Z", - "iopub.status.idle": "2026-08-11T03:08:44.906797Z", - "shell.execute_reply": "2026-08-11T03:08:44.906226Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(2, 5)" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "domain.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "2986eac1-ea7a-4f66-926e-8e6cd1121548", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:44.908463Z", - "iopub.status.busy": "2026-08-11T03:08:44.908324Z", - "iopub.status.idle": "2026-08-11T03:08:46.895046Z", - "shell.execute_reply": "2026-08-11T03:08:46.894436Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "for key, walker in walkers.items():\n", - " corner.corner(\n", - " walker.model_sampler.chain[:, : correct_model.n_params],\n", - " fig=fig,\n", - " color=colors[key],\n", - " range=domain.T,\n", - " labels=[f\"${p.latex_name}$\" for p in correct_model.params],\n", - " truths=true_params,\n", - " labelpad=0.1,\n", - " max_n_ticks=2,\n", - " truth_color=\"k\",\n", - " )\n", - "\n", - " plt.plot([], [], color=colors[key], label=key)\n", - "fig.legend()\n", - "# plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "6c8a7777-4e29-4137-a7eb-9e69189ac9a1", - "metadata": {}, - "source": [ - "As expected, `unknown_norm` and `marginalized_sys_err` are the same. This is because they originate from the same exact statistical model, in which the normalization of each data set is a random variable. The latter explicitly marginalizes over the renormalization according to $\\mathcal{N}(1,\\sigma_{sys})$, whereas the former takes that distribution as a prior but attempts to learn the real distribution by conditioning the data. \n", - "\n", - "In this case the underlying process for generating $N$ is exactly what the experimentalists report (that is, it is $\\mathcal{N}(1,\\sigma_{sys})$). If the experimentally reported $\\sigma_{sys}$ was incorrect, then `marginalized_sys_err` would not be marginalizing over the correct distribution, and would converge to something wrong (try it!). On the other hand, with `unknown_norm`, incorrect experimentally reported $\\sigma_{sys}$ just means a bad prior: $\\sigma_{sys}$ doesn't enter into the likelihood at all. Eventually, sampling should converge to the appropriate distribution.\n", - "\n", - "Of course, the other two methods, which do not account for systematic error at all, fail to converge to the region of the truth." - ] - }, - { - "cell_type": "markdown", - "id": "dff9485a-74ec-4896-8122-374d4e407a39", - "metadata": {}, - "source": [ - "## Let's explore the performance of the inference of the normalizations from the \"unknown norm\" model\n", - "\n", - "We will look at the posteriors of $\\rho_i$, the renormalization of the model predictions with respect to each data set. We will look at the *maxima a posteriori* (MAP) and compare them to the values we actually renormalized by when generating the synthetic data. Then we will plot the experimental values renormalized by the MAP $\\rho$s, and we should see that they re-align themselves with the truth." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "9a63cc20-d38d-4e1b-893d-831306dbb73a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:46.898047Z", - "iopub.status.busy": "2026-08-11T03:08:46.897873Z", - "iopub.status.idle": "2026-08-11T03:08:46.902313Z", - "shell.execute_reply": "2026-08-11T03:08:46.901650Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(20000, 4)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# per-dataset rho posteriors live in the model-block chain, after the physics\n", - "n_physics = len(correct_model.params)\n", - "norm_chain = np.exp(walkers[\"unknown_norm\"].model_sampler.chain[:, n_physics:])\n", - "norm_chain.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "00efd03d-7fdf-4f99-bc68-413a75ce2b72", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:46.904501Z", - "iopub.status.busy": "2026-08-11T03:08:46.904332Z", - "iopub.status.idle": "2026-08-11T03:08:46.907957Z", - "shell.execute_reply": "2026-08-11T03:08:46.907231Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(20000,)" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "norm_log_posterior_vals = walkers[\"unknown_norm\"].model_sampler.logp_chain\n", - "norm_log_posterior_vals.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "328e1d8e-d6b0-4429-a852-394eb3613895", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:46.910019Z", - "iopub.status.busy": "2026-08-11T03:08:46.909844Z", - "iopub.status.idle": "2026-08-11T03:08:46.913968Z", - "shell.execute_reply": "2026-08-11T03:08:46.913283Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1.0519558 , 0.53718276, 1.2928494 , 1.00432021])" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "map_idx = np.argmax(walkers[\"unknown_norm\"].model_sampler.logp_chain)\n", - "N_data_sets = len(settings)\n", - "maps = norm_chain[map_idx, :]\n", - "maps" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "868b71f7-0de1-43b9-a593-295f7ff36099", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:46.915908Z", - "iopub.status.busy": "2026-08-11T03:08:46.915735Z", - "iopub.status.idle": "2026-08-11T03:08:46.919544Z", - "shell.execute_reply": "2026-08-11T03:08:46.918903Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1.06789136, 0.53671203, 1.3071512 , 1.05429387])" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.array([obs.renormalization for obs in observations])" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "7f72afa9-bf06-4e3b-83d2-8344ae36fc06", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:46.921824Z", - "iopub.status.busy": "2026-08-11T03:08:46.921656Z", - "iopub.status.idle": "2026-08-11T03:08:46.925448Z", - "shell.execute_reply": "2026-08-11T03:08:46.924832Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1.04462468, 0.5287628 , 1.28785673, 0.99731649])" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.mean(norm_chain, axis=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "b7acf84b-e98c-4743-9e62-2c3eb16bf677", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:46.927658Z", - "iopub.status.busy": "2026-08-11T03:08:46.927485Z", - "iopub.status.idle": "2026-08-11T03:08:47.583693Z", - "shell.execute_reply": "2026-08-11T03:08:47.582931Z" - } - }, - "outputs": [ - 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = corner.corner(\n", - " norm_chain,\n", - " labels=range(len(settings)),\n", - " truths=[obs.renormalization for obs in observations],\n", - " labelpad=0.1,\n", - " max_n_ticks=2,\n", - " color=\"tab:blue\",\n", - " truth_color=\"red\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "9861dc77-5683-47b3-84d6-22409195355b", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:47.586579Z", - "iopub.status.busy": "2026-08-11T03:08:47.586404Z", - "iopub.status.idle": "2026-08-11T03:08:47.907564Z", - "shell.execute_reply": "2026-08-11T03:08:47.906802Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Renormalizing experimental data sets based on MAP of $p(\\\\rho)$')" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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5r4OEhAS2a9euAuunCHI8PT3ZzZs3ufK1a9cyAGz79u0Fnn/o0CGVv+M6deqwfv36cX8rjDH24sULdvLkSW5b29fjTz/9xACw/fv3c2Xnzp1j9evX1yrI+eqrr5hQKOQFKmfPnmUikYh9+umnXJni765fv37c6zQ7O5t17dqVOTo6sqysrALvozg/MDCQTZs2jbm7u3P7/vjjD2ZoaMiSkpI0Bjlt2rRhffr04ba//PJLZm9vr/L5pOlzq3PnzkwgEBT4d3/gwAEGgC1ZsoQre/bsGatRowZr2bIl7/dlbW3Ne78piOKLZvPmzblgSS6Xs1GjRjGhUMiePXvGGMv9Yg+A3bt3T+11pkyZwiwsLLj3LcYYe/nyJRMIBOyjjz7iHbtgwQKt3ieUfbBBzsGDB9nhw4fZ9u3b2aBBg1Siz/nz56t908zOzma2tra8F6SxsTHz8PDg/bEwlvuiHzBgALfdpUsXZm9vr/LCUXzwHT9+nDGWF0zk/wUzlvch98MPP/DKf/75ZwaAff/997zyX3/9lQFQiciTk5PZlStX2NGjR9nhw4fZnDlzGABegFHUIOf06dNMX1+ftW/fnmVmZnLlmoKcadOm8c4PDw9nANjvv//OGMsNHsRiMZs8ebLKvdq0aVNokBMVFcX09PTYzJkzVfYtXryYSSQSXgDz5MkTZmJiwvr168f69u3LTExM2JMnT3jnjRw5kgFg9+/f55Xfvn2bAWC//fYbY4xx3ziVgwaF5s2bs169enHbTZo0YUZGRiwxMVHlWE1BTnF//xs2bGAA2MWLF1Xu5enpyfr27cttN2jQgFlaWrL09HTecT169GCenp7cdmFBjkwmY/fv32cnT55khw8fZocPH2YSiYQtXryYO0abIEcmkzFLS0s2cOBAlX2+vr5qX69paWns+vXr7NixY+zw4cPMz8+PAeAFWIUFOTExMezChQvsyJEj7PDhw2zkyJFMIpFwX0rUuXr1KgPA/vrrL43H7Nmzh/e6V9a2bVvWuXPnQuvYuXNn1rRpU5X3nsIoghzl9zEFT09P1qRJE16ZXC5njx494v0OzczM2JdffskdY2RkpPa1qlCU16OTkxPv+SsoXn8FBTmZmZnMyMhI7d/J2LFjmb6+PvfFVfF3lz8oVAQGd+7c0Xgf5fMDAwPZnTt3GAD233//McZyX+cjR45kjDG1QU5QUBDXKqvw4sULJhAIVFpm1H1u9e/fnwFQ2/KjrGXLlszV1VXl7/W3335TeS8oSpAzYMAAZmBgwAvQGctt0QPAdu/ezRhjbOnSpQwA78uewpMnT5hQKGR//PGHyr46deqw5s2b88oU73MPHjzQqo6MMfbBjsnZvn07BAIBYmJicPPmTfTq1QszZ87k9l+7dg2WlpZ49OgRHj16BJYbEIIxBmtrazx48IB3vWbNmqmMGnd2dkZERAS3HRAQgDZt2qiM3WjTpg23X7mfvaCpcooxGgo1a9ZUW67oG42MjIS9vT0YY5g/fz5++eUXODs7w83NDRKJhMuX8fr1a636g/N7+PAhhg4dCjc3N/j7+6udBpmfl5eXynPQ09Pj/s8eP36M7Oxstf2vzZs3x7Vr1wq8/q1btyCTySAWi3H8+HHu9wfk9oVnZWXh2bNn3FTUevXqYdOmTdz0+P3796NevXoq1zUwMFAZH9CsWTOIRCIup42ibgkJCSr3NjU1Vfn7qVOnTpHGqRT393/t2jUIBALExcWp1MvMzEylXurGdzg7O+Pu3bta1fPcuXOYMGECUlNT0bhxY5iZmUEgEIAxVuR8S6GhoUhISFD7uvD29saJEyd4ZT/++COWLl2KatWqoXbt2jA0NOTyq2hz78zMTPzvf//D/v374eHhAQcHB4hEIrx8+RJZWVmIjY3VOGW+WbNm8PDwwOjRo3H06FF069YNXbp04Y1VUPyNJCcnq/wuFOO/CjNy5EhMmjQJnp6eGDp0KDp27IhWrVppHPuQn7r/y5YtW2Ljxo2QSqXQ19fHlStXMHbsWMTHx3PjqQQCAXJycnj/jyNGjMCff/6JkJAQ9OnTBx07dkTjxo2590VtX4+Ojo4IDw9XOwDX29sbu3fvLvA5PX36FOnp6dwYQGVt2rTB9u3b8fDhQ7Rv354rz/9epBhXExERobJPEy8vLzRq1Ajbtm2DpaUl7ty5gxUrVmg8fuPGjTAxMUFmZiZvQkft2rWxceNGDBgwQOUcxeeWWCyGu7s7Ll68iI4dOxZYr8DAQAwcOFBlbJTy505h19B03f79+/PGIAHgxhwqxucpcl+pG7N48OBBCAQCtSlJMjMzVWbyKj5XFGNKtfHBBjnKo9Rv3LiBLl26YMyYMfjrr78A5L7oZDKZ2tktdevWVQkE1A3uFIvFyMzM5LazsrLUDuJSlCkfCwDVqlXTWP/891P8AWkqV1z7r7/+wooVK7B9+3aMGTOGO27v3r24fv0696ZTFFFRUfD19YVEIsHJkydhZWWl1Xn56yoUCqGvr8/VVfGHbGRkpHKuNjMJFInhrl69yg2cVNa/f3+VYEzxghQKhRr//9UN6hQKhTAwMODqnJ6eDj09PezatUvlWDMzM3Tq1IlXVtDvWp3i/v4V9dqxY4fKNS0tLVXesLT5u9YkLS0NQ4YMQdu2bXHo0CGuLnK5HAYGBkX+WyvK38N///2HOXPm4IcffsCcOXO48gsXLuDixYta3XvlypXYt28fLl68yEstsXjxYu6LjyYGBga4desWNm/ejH/++Qeff/45kpKS0KVLF+zatQvVq1dHeno6BAIB9u/fr3K+oaEhunTpUmgdJ06ciHr16mH37t3Yu3cvFi1aBAsLC6xcuRKTJk0q9HxN/5eMMeTk5IAxhiFDhqB+/foICgrivV4sLCx4/wcbNmxAjx49cPjwYaxatQozZsyAu7s7tmzZgg4dOmj9eizp615xflHeawt73Whr7NixWLp0KUQiEZycnDT+DrOzs7Fz5040adIE27dv5+1zd3fHP//8g8jISJUPeeXPLW3IZDJIpdIi/V9oIyEhAa9evcLs2bNV9im+KDVs2BAAuLxXCQkJKu9z586dg5ubm8pnRlxcHF6/fq0yWy0uLg4AYGtrq3VdP9ggR1mrVq0wd+5cLF68GEeOHEH//v3h5uaGJ0+e4NChQ6WWJNDFxQUvXrxQKVeUubq68spLM8mTwuXLl2FgYIBRo0bxyoub2TI1NRV9+vRBTEwMzp8/D3d399KoJoC8b1OhoaEq+0JCQgo9383NDUDuG8///ve/Qo9/9OgRpkyZgsGDB+P169dc0rH8MzISEhKQmJjIe2OMiYlBSkoKl4jOzc0NMpkM69atU5mtok5Z/K7VcXNzg1Qq5aatlgZNdX/06BESExMxZswY3re4Z8+eIScnp8j3cXR0hFAoVPu7V8zgUlDM+Mr/Qa/u71xT/S9fvgwPDw+V3FnavlZMTEwwY8YMzJgxAzKZDMePH8fQoUOxYMECbNmyBW5ubmCM4ZdfflF57WtbRwBo27YtV8eoqChMmjQJkydPRs+ePQvNaaXp/7JatWowNDTE48eP8fbtWyxfvpwX4ERGRqpkHRYIBBg6dCiGDh0KILd1d9CgQRg1ahTCw8O1fj1KpVIYGhpq9XtWR/EaLMp7bWkZNWoU5s6diw0bNmDhwoUaf2+HDx9GfHw8jhw5Amtra5X9Dg4O2LJlCxYtWlSi+ujp6cHBwaHU/y8CAwMBQGVGJgCsW7cO9erV41qVFbMRQ0JCVGYSBwYGqr3/X3/9BZlMxv0tKYSEhMDMzKxIudooxe97X375Jezs7DB//nzI5XJMnDgRKSkp+OOPP1SOlclkiI6OLvI9hg4disDAQFy9epVXvmbNGhgYGKBv377Frr+27OzskJWVxUvnHhcXp/abfWFkMhmGDRuGe/fuYefOnWqbh0vC2dkZzZs3x7Zt23jNk2/evMGZM2cKPb9FixZo3Lgxfv31V7XfVpSb2lNSUjB48GA4ODhg69atOHDgAKRSKYYNG6aS30IsFmP9+vW8st9//x0CgQBDhgwBAIwZMwYikUhjc3VUVFSh9S8LY8eOhb6+fqnWS/EmrZiuraDoylHusgVyu5G07U5RZmpqCh8fH+zZs4d3r/j4eBw6dKjQe6elpWHjxo1q65+/7oprvHv3jrfURFBQEE6fPl1oXWNjY3nn6enpoX///qhevTrevn0LIPcDUSKRaPW70FTH/N1uNWrUwMCBAyGXyxETE1NoPXfv3s2rZ0hICE6dOsV9uGj6Ha5YsYIXuObk5Kjcr1GjRujQoQPevXsHxpjWr0d9fX0MGDAABw8e5L1PpaWlYd++fYU+p2rVqqFDhw7YtWsXEhMTeedv3rwZTZs2LVaXvDbs7OwwZ84c9OvXD+PHj9d43MaNG9G8eXO1AQ6QmwR08+bNkMvlJa7T0KFDceXKFdy7d48rY4zht99+g6mpabEyXiu65W/fvs0r379/P27evIlffvmFK2vbti309fVx69Yt3rEhISFITEzE48ePER8fz5VHRETgm2++weDBg1XSsNy4cQPt27fXKm2IArXkvGdsbIz58+djxowZ2LlzJ8aOHYsVK1Zg1qxZuHbtGrp06QKJRILg4GD8/fffWLRoEUaOHFmke8ydOxfnzp1D79698dVXX8HBwQGHDx/GyZMnsWXLlmKnxC+KcePG4eeff4avry+mTZuGpKQkbN++HVOmTMGCBQuKdK0dO3bgxIkT8PHxgb6+vkqiQEWenJLYsGEDOnXqhI4dO2LixInIyMjAwYMHMXr0aKxZs6bAb7hCoRB//fUXevbsiYYNG+J///sfnJycEBUVhStXruDdu3dcwDlx4kRERkbi5s2bMDU1hampKXbt2oXevXtj4cKF8PPz465ra2uLt2/fYvr06WjZsiVu3LiBdevWYe7cuWjatCmA3H717du3Y8KECQgODka/fv1gbm7OrdE0aNAgLFy4sET/N8VRr149bN26FZMmTcKTJ0/Qp08fmJub48WLFzh8+DCGDRuGuXPnFuma7u7ucHV1xerVqwHkdjMo8uQMGjQIS5cuRVZWFmrUqIEjR45wY3OKY82aNWjbti3atGmDzz77DIwx7N27F2PGjMHKlSu544YMGYJly5bho48+wuzZs5GdnY3t27dj9OjR+Oqrr3jX7NatGw4dOgQ/Pz/Uq1cPYrEYvr6+mD59OpdldtSoUYiIiMD+/fvx2Wef8e6lzv379zFp0iQMHjwYDRo0gEgkwrFjxxAdHY0NGzYAyA3id+/ejTFjxuDly5cYMGAALC0tERISgqNHj8LHxwffffddgXUcPXo0jI2N0bFjR24sy08//YRu3bpptezBgAED0KtXLwwbNgypqan46aef4ObmhmXLlgHI7WoYPXo0Vq5cCYFAABcXF5w8eRI1a9bktVBmZGSgRYsW6Ny5M7eMy71797Br1y589dVXEAgEEAgEWr8ef/zxR1y5cgVt2rTBjBkzIBKJsGfPHowYMYL7PynIhg0b0LlzZ7Ru3RpTp06Fnp4e1q9fj4yMjDJfIuX7778vcH9ISAjOnz+Pb775RuMxvXr1wubNm3HmzJkSL7uxZMkSXLp0CV27dsVXX30FOzs7HDhwABcuXMCePXs05tEqSGBgIJydnfHXX3/B2NgYzZo1w/Xr1/Hnn3/i119/5dXZxMQEvr6+OHbsGBYvXsy7BpA77q979+6YMGECYmNj8fvvv8PKyop7nSi8ePECT58+LfL75gcX5LRo0QKZmZlqP3wnT56M69ev4969exg7diy+/vpr9O3bF/v378f58+dhaGiIOnXq4PTp07z1cfr27as28V/r1q153TeGhoa4ePEi9u3bh/Pnz+PBgwfw8PDA8uXL0aBBA+44xfIJ6hJk1axZE/3791dJVFW9enX0799fpW/T3t4e/fv3574xODg44MGDB1i7di3OnDkDBwcHHDhwAKmpqbh16xZv3aDOnTvztvMv62BnZ8cla1L3xuHr6wuhUKiyrIOVlRX3rTa/fv36oVGjRtx206ZNcf/+faxfvx5nz55F7dq1sX//fvz0008QCASFJj2rU6cOHj9+jH379uHatWsIDAyEk5MTJk6ciN69ewMAbt68yX0IKvqRgdy1aFavXo0LFy6oJPn6+eefsX37dpw5cwZisRhHjx5VWdJg+PDhaNeuHXbv3s0N+FUEP8rPsXPnzhoTj+Vf1qGkv38gtwWhQ4cOXL2EQiFq1aqF3bt38/4Ou3btqnasUJMmTXitjiKRCKdPn8aGDRtw+PBh5OTkYPz48ahVqxb279+PzZs349q1awgJCcG4cePQv39/hIaGcgEhkBsoqRsjlV+tWrVw//59rF27FpcuXYKTkxN27tyJZ8+eITg4mBvHYWFhgYCAAPzxxx+4cOECqlWrho0bN8LMzAxXrlzhNXd/8sknEIlE+O+//3Dr1i0YGRnB19cX3t7eCAwMxJYtW3DixAnUq1cPp0+fxq1btxAcHAwDAwON9ezatStu3ryJ3bt34/Lly5DJZGjYsCFWrlzJ+zsaPHgwWrdujV27duHmzZvc/8XGjRt5QYqmOp47dw6nT5/GuXPnuOVp1q9fj759+xb4BcDGxgb9+/dHv379MHDgQGzcuBExMTGYPn06PvvsM17Cyq1bt6JTp07477//EBYWhsGDB2Po0KFISUnhuh/MzMwQHByMAwcO4ObNm4iPj0fNmjVx4cIFboAroN3rEcj9Ow8MDMTatWtx5coV1KhRg6vjw4cPCx2kX7duXTx69Ahbt27lxhqOGDECEyZM4HXTKv7u8v8uzc3NtVrCRnF+YfVp27Yt95nx4MED9OvXD4MGDdJ4fPfu3TFgwACue66gz63CmJqa4tq1a9i9ezf+++8/BAYGomnTpvj1119Rp04d3rG9e/dWmbygTmBgINq3b49vvvkG69evx6FDh+Do6Ihbt26pTdo4bdo0dO/eHY8fP+beYwIDA6Gvr4/Tp09j/vz5+PHHHyEWizF69Gh88803Ku9xO3fuhK2tLddarq0PboFOUjUMHDgQ9+/f12psTmkaNWoULl68qHYNGkIIqerS09NhZmYGPz8/lVbRgnTu3BkODg7YuXMngNyAKjIyUmVGpzqpqalwdXXF4sWLMW3atCLVl8bkkAotIyNDpf/+xYsXOHHihNoploQQQsrOgwcPIJPJirzMxi+//IL09HRuwHpgYKBWrUZAbrqFXr16YcqUKUWu7wfXXUUql4SEBPTo0QNDhw5FrVq1EBISgjVr1sDd3V0nY1oIIeRDphh0rNzlrg1PT08cPHgQQO6i2NHR0bwu64L06NEDPXr0KFpF36Mgh1RoNWrUwPHjx7Fr1y6cOnUKenp6WLZsGcaMGaOT1eKbN29e4FgMQgipykQiEYYNG4YaNWoU+xomJia4fv26ypTyskBjcgghhBBSJdGYHEIIIYRUSRTkEEIIIaRK+uDG5MjlckRFRcHU1LTcUukTQgghpGQYY0hJSUGNGjW0zhn0wQU5UVFRapPsEUIIIaTii4iI0Hr9qg8uyFFk8oyIiCh2anlCCCGElK/k5GQ4OjryMnIX5oMLchRdVGZmZhTkEEIIIZVMUYaa0MBjQgghhFRJFOQQQgghpEqiIIcQQgghVRIFOYQQQgipkijIIYQQQkiVREEOIYQQQqokCnIIIYQQUiVRkEMIIYSQKomCHEIIIYRUSTrPeHzv3j3s378ftra2mDVrVqHHZ2dn4+DBgwgKCoJYLIa3tzd69OhRDjUlhBBCSGWisyBHKpWiffv2SE9Ph1gsBmOs0CAnLS0N7dq1Q2ZmJkaMGIHU1FQMHz4cvr6+2LFjRznVnBBCCCGVgc6CHIFAgJ9//hmtW7fGzJkzceXKlULPuXDhAu7du4fw8HBuJXEvLy98/PHH+Pnnn2FjY1PW1SaEEEJIJaGzMTl6enpo3bp1kc6pVq0aACA1NZUrS01NhampKYyMjEq1foQQQgip3HQ+JqcoWrZsid9//x0fffQRvL29kZaWhuDgYBw+fFhjkJOVlYWsrCxuOzk5ubyqSwghhBAdqlSzq7Kzs3Hjxg1IpVI4OjrC0dER7969w507dzSe4+fnB3Nzc+5H0c1FCCGkCLLTgCXmuT/ZabquDSFaqVQtOZs3b4a/vz9CQ0O58Te+vr7o1KkTfHx84OnpqXLOvHnzeAOak5OTKdAhhBBCPgCVKsh5+vQpXF1deQOMmzdvzu1TF+RIJBJIJJLyqiIhhBBCKogK3V2VkZGBmTNn4tatWwCAhg0b4vnz5wgLC+OOOXPmDACgQYMGuqgiIYQQQioonbbk/PDDD3jz5g0uXbqEqKgozJw5EwCwcuVKiMViZGVl4ddff4WnpydatmyJcePG4e+//0bz5s3Rt29fpKWl4ejRo/j666/RqFEjXT4VQgghhFQwOg1yatasCYlEAhcXF165QCAAABgaGuKXX35By5YtAQAikQinT5/G9evX8eTJE4jFYixbtgz16tUr76oTQgghpIITMMaYritRnpKTk2Fubo6kpCSYmZnpujqEEFI5ZKcBy2vkPp4fBYiNdVsf8sEpzud3hR6TQwghhBBSXBTkEEIIIaRKoiCHEEIIIVUSBTmEEEIIqZIoyCGEEEJIlURBDiGEEEKqJApyCCGEEFIlUZBDCCGEkCqJghxCCCGEVEkU5BBCCCGkSqIghxBCCCFVEgU5hBBCiiY5Stc1IEQrFOQQQggp3L09eY//aAkE7NBdXQjREgU5hBBCCpb0Gjg1J2+byYFjM3PLCanAKMghhBBSsPiXuYGNMiYD4kN0Ux9CtERBDiGEkIJZuQOCfB8XAj3Ayk039SFESxTkEEIIKZh5TaDXyrxtgR7Qd3VuOSEVGAU5hBBCCuc5Iu/x1JtAszG6qwshWqIghxBCSNGY1dB1DQjRCgU5hBBCCKmSKMghhBBCSJVEQQ4hhBBCqiQKcgghhBBSJVGQQwghhJAqiYIcQgghhFRJFOQQQgjRjew0YIl57k92mq5rQ6ogCnIIIYQQUiVRkEMIIYSQKomCHEIIIYRUSRTkEEIIIaRK0tflzRljOH36NPbv348aNWrg+++/1+q8iIgIbNu2DaGhoWjatCkmT54MsVhcxrUlhBBCSGWis5acnJwc1K5dGz///DNCQ0Nx+vRprc67ePEi6tevj6dPn6JDhw54/fo1hg8fXsa1JYQQQkhlo7OWHD09PZw5cwZubm6YOXMmrly5Uug5mZmZGDFiBMaNG4fffvuNK4+JiSnLqhJCCCGkEtJZS45QKISbm1uRzjl+/DjevHmDr776ildua2tbmlUjhBBCSBWg0zE5RXXv3j3UqFEDGRkZ+OKLL5Ceno6mTZtiwoQJGsfkZGVlISsri9tOTk4ur+oSQgghRIcq1eyq1NRUZGRkYMCAAXB0dESTJk3w66+/on379sjJyVF7jp+fH8zNzbkfR0fHcq41IYRUAWJjYElS7o/YWNe1IUQrlaolx9zcHAkJCTh8+DA6duwIAPD19YWLiwtOnDiBAQMGqJwzb948zJo1i9tOTk6mQIcQQgj5AFSqIKdJkyYAgHr16nFlzs7OMDQ0RFRUlNpzJBIJJBJJudSPEEIIIRVHhe6uSk9Px7Bhw3Dp0iUAQM+ePVGtWjUcPHiQO+bEiRPIzMyEt7e3rqpJCCGEkApIpy05X331FSIiIhAYGIiYmBgMGzYMALB9+3ZIJBJkZ2dj//796NmzJzp27AgjIyPs3bsXQ4cOxb59+2BgYICrV6/Cz88PXl5eunwqhBBSdWSnActr5D6eH0VjcEilpdMgp2vXrkhMTFQZS6OnpwcAMDY2xt69e3mtNF26dEFYWBiuXLkCoVCIHTt2wN7evjyrTQghhJBKQKdBTs+ePQvcLxKJuNYdZaampujVq1dZVYsQQgghVUCFHpNDCCGEEFJcFOQQQgjRvWT1M2QJKQkKcgghhOjGvT15j/9oCQTs0F1dSJVEQQ4hhJDyl/QaODUnb5vJgWMzc8sJKSUU5BBSiuTp6XhSzwNP6nlAnp6u6+oQUnHFv8wNbJQxGRAfopv6kCqJghxCCCHlz8odEOT7CBLoAVZuuqkPqZIoyCGEEFL+zGsCvVbmbQv0gL6rc8sJKSUU5BBCCNENzxF5j6feBJqN0V1dSJVEQQ4hhLxHY6p0yKyGrmtAqiAKcgghhBBSJVGQQwghhJAqiYIcQgghhFRJFOQQQgghpEqiIIcQQgghVRIFOYQQQgipkijIIYQQohmtDk4qMQpyCCGE8NHq4KSKoCCHEEJIHlodnFQhFOQQQgjJQ6uDkyqEghxCCCF5aHVwUoVQkEMIISQPrQ5OqhAKcgghhPAVZ3Xw7DRgiXnuT3Za2dWNkCKgIIcQQohmtDo4qcQoyCGEEEJIlURBDiGEEEKqJH1dV4AQQsgHSmwMLEnSdS1IFUYtOYQQQgipkijIIYQQQkiVREEOIYQQQqokCnIIIYRUGOk56Wi0vREabW+E9Jx0XVeHVHI6DXIyMjKwbds29OrVC5MnTy7SuTk5ORg+fDg6deqEmJiYMqohIYQQXfHe403BDikRnc2uys7ORq1atdC1a1cIBALcvXu3SOfPnz8f9+7dw9OnT5GVlVVGtSREe/L0dAQ389J1NQhRLzsNWP4+sd/8qNyZTYRUcTpryRGJRHj06BF27NiBOnXqFOnc06dP48iRI/Dz8yuj2hFCCCGkstNZkCMQCGBpaVnk86KjozFhwgTs3LkTJiYmZVAzQgghFRmN2yHaqlQDj+VyOUaPHo1PP/0U3t7eWp2TlZWF5ORk3g8hhBBCqr5KFeSsWLECGRkZmDdvntbn+Pn5wdzcnPtxdHQswxoSQgghpKKoVMs6bNy4EUZGRujatSsAICEhAQAwdOhQjBw5EtOmTVM5Z968eZg1axa3nZycTIEOIYRUNIqB0QIB4ELv0aR0VKogZ+/evcjMzOS27969i9mzZ2PWrFlo2rSp2nMkEgkkEkl5VZEQQiqm7HT+Y5pdRT4AFTrISU1NRZ8+fTB37lz07NkTrVq14u2XSqUAgNatW8PBwUEXVSSEEEJIBaXTIGf8+PEIDQ3FixcvkJSUhE6dOgEATp06BUNDQ0ilUly6dAnjxo3TZTUJIeTDUtLVwZOjAJvapVcfQopJp0HOZ599hrS0NJVysVgMADAxMcGFCxdQr149ted7eXnhwoULsLW1LdN6EkIIKcS9PXmP/2gJ9P0VaDZGd/UhBDoOclq0aFHgfn19fa51Rx1LS8sC9xNCCCkHSa+BU3PytpkcODYTcO8KmNfUWbUIqVRTyAkhhFRA8S9zAxtlTAbEh+imPoS8R0EOIWUk5+1bXVeBkPJh5Q4I8n2cCPQAKzfd1IeQ9yjIIaSMhPj2QeLff+u6GoSUPfOaQK+VedsCPaDvauqqIjpHQQ4hpUSl5UYux5tFi5ETHa2bChFSnjxH5D2eerNMBx1nSDNUHtN6VkQdCnIIKSU54eGqhXI5sl+pKSekKjOroesaEAKAghxCSo3IyUm1UCiE2FlNOanwaEwVIZUfBTmElBKRnR2/QChE9WVLIbK3102FSJEl+vtzjyvamKoPpTvmqInqchNv0yngJMVDQQ4hZcTtxHFYDBmi62oQLeVER+Ptd9/nFVTlMVUpb3RdA7Wi9fTgZ22pUt7fvz8OPT+kgxqRyo6CHELKiErLDqnQssNeAfJ8uV6q0piqhwfyHm/oCATs0F1dNAgX6UMuEKiUMzAsvb4U0WnFCzg/lFYwooqCHEIIASB2cQaE+d4Sq8qYqqTXwJmFeduKjMRJr3VWJXWccqQQMqZ2n5zJEZESoVL+Lv1dWVeLVGIU5BBCCACRvT3sFi7IK6hKY6o0ZSR+FwQsMc/9yVZdR7C82ctkmBeXoHafUCCEo6kjAOBEyAmufNjxYdSVRTSiIIcQQt6zGDCAe1ylxlRpykhs6VLgaenZUrjMPQGXuSeQni0tu/opGZCSChsp/15CgRCLWy+GvbE9otOi8dOdn7h9csix9PpSGpxM1KIghxBC1KjsY6p4AYqhHdDju7ydAmFuRuKKlM/m/SrmBgDORkRhYEoqt8u/vz8G1R4EAAhPDocc/FYpOZMjMiWy3KpKKg8KcgghpAootNWl0Ud5j0f8pZqRODtdd11X+VYx1wOwODYedu9bdOyM8gJOJzMnCPN9dAkFQjiYOpRLVUnlQkEOIYR8CJRnV+0ZWrFmV6kZM6QHwDFHNVizN7bHl82/5LYVXVnKgRAhChTkEEJIFSdI1jC7Kjmq5BfPTstt/VmufdeXypRuNWOGZAAiRPpqz/d18+Ue7/Pdx3VlEZIfBTmEEFLFCeJD1M+u+r25biqUX75VzGUAltpY4a2++iBHWTWjamVYMVLZUZBDCCFVHLNyUz+7qiJ5v4p5JgAfxxo4bGqi2/qQKoGCHEIIqeKYWU3V2VW9ftBdhQogFwi0asEhRBsU5BBCSAWXni1F/UWnS3YR5dlVn1ziWk4IqcooyCGEkA+NaXVd16BMee/xpnWqCACA2gQJIRWOPD0dwc28AAB1A+5CaGSk4xoVTrm1JWiZD4zEH+Db6/woQGxcrrd8l/4O1obW5XpPUnl8gK9CQggpfxUqCFpVS3f3LgX5165a3GYxsmXZOqwRqagoyCGElJvK2EJT0ZXKeB3FtQQCeLs4Antb4eaImwDEpXLd0qRp7SqmZvXyt+lv4WruWp7VIxUMjckhhJDSIsiGqcdceO9tWqbjQXS6GKUi+Z+OVi7XtHYVg2qQQ+tZEQpyCCGkEhCZ3+UeD/AfgEPPD+mwNrqjae0qAQQqx9J6VoSCHEIIKUOKhTNL0qX0Nv0tJPZHuW1FF010WnRpVLFS0bR21Xzv+SrHqlvPSqetYKTcUZBDCCEVXERKOAQCfneMnMkRkRKh9TXSpRlo5OqERq5OSBeotnqUinJayVzd2lX93PtpPP7oy7wA8UNuBfsQUZBDCCFqKFpgXOaeQHq26mrY5clWUgOM8QMToUAIR1NHtccXq74pb4pTNe2U4Tiewtauik6Lht9NP277Q24F+xBRkENIKUn09y9wm5DisjWyQ1Z0XkuFoovG3ti+9G6yoSP01MxQAgAxspH0fW1giTnSU5NL755qGDGGh8NvvJ/dVXKaBioXpRWMVF46DXLi4uKwatUqtGjRAsOGDdPqnOPHj2P48OHw9vbGkCFDcPp06UydJKQkcqKj8fa773llb7/7HjnR9G2RlI6cJC/usX9/fwyqPUjrc6OTMgs/iMkhBmAnzWsFOng3d3ZSNsRom7UG+6WdtL5nSRmJjPBw7EM8HPsQRqLipxrQNFBZUysYqVp0FuRkZ2ejcePGiIqKgpOTE168eFHoOb/++iv+/PNP9O/fH2vWrEGrVq3Qp08f7Ny5sxxqTIhm2WGvADn/2yLkcmS/CtdNhUiVpm5ArbK3yZlcgAIA3X6+hIOPUwu9rgBA99R02EmliE7KxOKjj7l9cggxXzoR0SmVK+mevbE95nnP47bLpBWMVFg6SwYoEonw8uVLGBgYYObMmXj16lWh50yZMgUzZszgtr29vREUFIQ//vgDo0ePLsvqElIgsYszIBTyAx2hEGJnJ91VilRZ3nu8AUBNwj4GQADfNZchUPoOK2fA4uOhMHQ3g1CkubuJAfg6IRGzExLx/NYuyJk7b78MeghPyIKbhvPTpRnwds39m78pzYBROS/xoEk/9374/mZuS6t/f39KEPgB0VlLjkAggIGBQZHOkUgkKmX6+vqQ5/8GTUg5E9nbw27hAl6Z3cIFENnTt0VSnhSDk4UqqfHkDJBn22h1th6AureXoIYgjrdfDzI4Waq+D3PnpxS9e7Y4U7oN9Q3VPtZGYa1gpGqp1AOPnz9/jt27d2PIkCEaj8nKykJycjLvh5CyYDFgQIHbhBSF8uyujGxZia8nFABCcazWxwuYDEva5QUQepBhuf5m2JvmW+rh3h7uocEWHwxMKbxbjKZ0k/JSaYOcmJgY9OvXD23atMGXX36p8Tg/Pz+Ym5tzP46ONNiMEFL1KafCEQqApX1cC+yqQr6MwUygh/beLQHkzq66IpmBj/Uv8k9JjgJOzcm7ApNjcWw8b/ByftHp71SmdC++thiN9rZC+oI3wJKkUl3JPDU1FdH5JgDEx8fj0qVLuH37Nq/89u3buHDhAmJiYriyzMxMhIeHIyEhodTqRMpPpQxy4uLi0K1bN9jZ2eHIkSPQ09PTeOy8efOQlJTE/URE0LRBQj4kFSnfjULZ1UnRSSXHwj61udKzszpicEsXblugYaq4olQGILv3SjCzmgAAPchRXRCvekJ8KMD4wwX0ADjmSIEfa+XmxUmNAZbX4PaHp0SoTOkuquvXr6uUXbp0Se2xrVq1Qq1a/FXXr1+/jk6dOuHTTz/llX/xxRfo0qUL/vvvP64sICAAzs7OaNGiBe/YqVOnwtvbGydPnuTKEhISsGPHDpw/f77Iz4mUjUoX5MTHx6Nr166wsLDAiRMnYFTIKsYSiQRmZma8H0IIqag0LgPBAGmaG+Q5Bb2H5cDQaQOMa63AgKbVuVJ7c/74R/X5jhmyAYy3rwYfxxqQeY4ovLJWroCA/zEiAxAh0jynxcnUUWVKt0J6ejpycnK47UuXLsHX1xczZ87kHTd16lS156pjYmICoZB/P3Nzc9SrVw/u7vyB1bVq1YKHhwcsLCy4sqysLEgkEhgb81uXHj16hFu3biE1Na97Ljg4GGPHjsWECRN4x86bNw/9+vXjBT8ymQwyWcm7IUnBKnSQk5KSgoYNG+LIkSMAcqPkbt26wdzcHCdPnlT5oyOEVE0VrTUmPScdjbY3QqPtjcp0tXGF4w/eABAhI/wTpL2Yi+zE5uoPFAD6xiGFdEtBzXrdAARCyAHcMTTAW30tJ96a1QB6rcy7rkCIpTZWBZ5vb1QN0+pP47aFSkFStWrVeK00aWlpOHnypEorTfPmqs+/adOmKmVv09/i0qVLSEpK4pW3a9cOT548wf79+3nl27ZtQ1BQELp27cqVde7cGZmZmQgICOAd+8svv+DIkSNo164dVyYSidC9e3e0bduWd+ylS5dw7NgxJCYmcmUBAQEwNjZGt27deMcmJSWBaWhpI0Wn0yBn4MCBaNiwIXbt2oXHjx+jYcOGaNiwITIyMgDkRrqPHz/m+kJ/+uknBAYGIjIyEt7e3tzxbdq00eXTIISQXFq1thTdj6eDoTxzKuvNoFK/B3p8B1acNa2UWnwyJ5zGYVMT3u6nz4J52ydPnsTkdpO57X2++3j7ldOJeHl54c8//8TPP//MO2bDhg0q1bBXM5NxgP8AnAg/AUEprNWVf1hEs2bN0K9fP9SokdcV5+XlhTNnzmD37t28Y5cvX461a9eiZcuWXNmLFy+QlZUFab7xS3379oW1tTUv0a1MJqPAp5h0licHAH788UdkZqpm4lRMFTczM8PDhw/h4OAAAJg+fbrazMgFjckhhHx40rOlXHfPnYVdCzm6dOQm38ttbQHkOHg3CqNb1VJ77NtkLTIQK5GrfL4JC50OrsnAlFSoTd7R6CNk/LuQ28yQZsBQT6zuSI3eqFmWqn379oj5Ki8gq1O3Lm+/8tpT4eHhcLBz4Lbt7OzwySefFKkOyhTrVDWtptrKU546deqETp068co++ugjtGzZktfNxhhDUFAQEhISeMHT0aNHMWXKFIwYMQK//PJLeVW7StBpkJN/MFh+QqEQDRs25Lbt7OxgZ0c5DgghFYf3Hm8c8b30PjtwXmvL4iPB6OZRE+aGIpVz+v52tUj3EAryBzpy9dPBC/mybyeVYnFsvIYxOXzv0t+hmqEYph5zAQDpYQIYqWtNyM77kB7Qxwf4xoG329iIP6zAzdUVb9++RZcjXVQuZWVlpUXNikbO5IhMiSz8wHKmp6enMiZIIBDg9evXCAoKgoeHB1d+8+ZNvHv3Dmlp/Cjy448/Ru3atTFz5kzY2BQv6K3qKvSYHEIIqQxexWVwQYhAPwl6Ri/B9JIQFqt+vE7usdp3P3zlUxfKM6ck1Q9x426Ul28ARJrH6wBwypGioHbvEyZ5EzmGHR+GE6HHVI45ePBg3kZyFG9fZkaWyvEhL1/ytoVCYbmOpxQKhHAwdSj8wApCIpGgadOm0Fca17RkyRJcu3YNn3/+OVcWERGBAwcOwM/PD2JxXovbvXv3EBAQQEly39NpSw4hhJQ25a6q8uJsbQihANAzuw1J9UMQCBgYE+BJqhBNoClZqfbjRH74Jxi5M6e2QSiO5QKcsPhE3vpSgABZbwZB3/iZ2uuEi/QhA9QGOtHpMfjJypLblkOOVXdWqBznEK/UCvVHS6DHd9zmndu30Pbf4bzj889sKk+Kdaoqe5ZjAwMDtG7dmldmamqKTZs24dWrV7xZw9999x0OHjyI77//HvPnzy/vqlY41JJDCCElZG9ugNm97LgABwAEAoZf7/sVsGxBEQeSqpk5FR6XWaTxOm/19bHUxkrtnUOTXkGeb4AuU3Nkp4x/lA6QA2fyxvHo66t2zWFVwcMSylJRV2uvTCwsLDBx4kQsW7aMV25sbAwTExPerK27d++iW7du2Lx5c3lXU+coyCGEcCraVO3KpKFrDhfgKMiZHHvuKk89zutyys0oUzJO1gYQqjQIaRiv895hUxO8UjNZY3SfkRDmG3MjYKqtTYL8ifzyJQO0k0rRIiNTY8LB0mAkMsLDsQ/xcOxDGImMeGW5i5a+r0slb8Epju3btyMuLo43zf7o0aM4d+4cL3EhAMTGar/MR2VFQQ4hZaBuwF0IC0lUScpGUYIzeXo6ntTzwJN6HpBrSCYHANFJhc+GcjR1AssXFAghxJaLKUolAgByGLr8AQgKGTMhyIapx9zcgb+CbLXT0+3MJFjar4HSSQwiq8tqL6e8XlR/xxo4ZMIfF5P6JhlfxuctXSAUCDG7xVyV67B8yf+UkwHqBR3B6YgobIl+p34GF6AyjoeUPrFYzOsmHDt2LH788UdMnpw3dT8xMREODg5o3bo1L39PVUNBDiGEqHHwft56R91+voT9t8MLPN7OyA5Z0f24bcYE+Nj1C8hyzPMdKQTkmlfyVosJoSkZ4GAv/qDanPiOSHsxF/6BbwAAhshE8g/V4Hcjb+yMXCDAUhsrRCu16Jw7dw6+qXmB3j7fffB17cttxzBzPJI5I95rRt7NBHq8MTniS37ceB+NI47+aAk9pUU9i6O8kzFWdm5ubpg9ezZ69OjBlV27dg3Z2dlITk6GuXne3+j9+/d5WZwrOwpyCCEknxgDcyz95zm3LWfA/EOP8CYpo8DzcpK8uMdpL2dhmMeQIncnqRydY4bcocLaJAPMO+b743n1Dxfpq4y3kQsEvOUXGnjU5+1Xzl/T760NHFgSGuq9gsXt1QiUueXumHoTaPRR3t2ZFjN6mBzik3MKXMSTlL3evXvj9evX2LZtG5csUS6XY8CAAbC3t1e7PlhlREEOIYTkE2ViozKgV8aYxinh6jCpOezNDVS6k5Snf2sjdxBx/kip8GSAyvV3ypGqjLcRMpa7kGYhbDMMsSwtEHrvxxvpCRgaCcPwllnkLuugRKUrSwMBk2l1b1K2qlevzlt4NCoqCiKRCHp6emjSpAlX/uTJE7x580YXVSwxCnIIIUWm7ViWykZoZASPp0/Q8cRfKi0wegIBXGyKPs6K352UA7HFnaLVSRyL/DOxBPoJEEqi8S79ncrxijw9eqK89ZrsZTLMi1Mab8MYFsfGw16LBSIdMw24AEdBXyBHrFy1JSm74zxE6enhloEEbwrIRM8EegUu4lna1A1UJqocHBwQHByMe/fu8Ra//vzzz+Ho6KiyXEVlQEEOIYTkU93ckNcCIxQAywc1RHVzw5JduBhLKOW2+uQFIyLzWzCu9QOE+ukYdnwYjr48nG/fChg5b4RxrRUQmd/m9vVLzcuW6x/5BoNS1azBoEaEQSZk+QZUS5kQNkLV1qjDpibwcayBidXt0FPN4GYAgECIbCbTfhFQUq4EAgFcXV257aysLGRmZkIul/PWiXz37l2lGLBMQQ4hhKih3AJzdlZHfNzCqdzrIM8xgzTNDXg/bVugnwRJ9cNQDK+RQw6/299BoJ+ktC+31YW97xqDfpLKde3yt+DMjwLE6ls4YgwzsMi4KaQs9+NCyoQIkNeGnSCRd1y0nh78Hq2HonLqBjcDAD65BFkpLJhJyodEIsHly5cREhLCC36WLVsGBwcHbNy4UYe1KxwFOYQQUgh7c40TossOEyLtxdz3C37mJtkTimPV5uIRimPV7hMIGATi+GJXwVCUG6ActYvFa4EZHsuc8Prjf9BSL1jl2HCRPuT5utXyD24GAJhW13g/zYkTia65uLhwjxljCAgIQFpaGm/9LalUWuFWS6cghxBSKipDIsH8+W7kGRlqH5eL958FmnPw6CHvLfp960i2jdpcPPJsG/X7BEKc/3xwqVTXVpCEBnrhsLVX36LllCOFMF9/nDaDm0+EnOAeD/AfgEPPD5W8sqRMCQQCXL16FVeuXEHnzp258vXr16NZs2Y4fvy4DmvHR0EOIaRK8w/MSz6nTb6b8pCb5ya3dUZRpwsXLuDjjz/GjRuKjL2qXTpMao6sN4O4YIYxAYa5fQEmNVfZp1i3yV5pKrgm6dlSuCy6CK/M9cV+TvYyGeY1nMJtaxzc/PAAb/OnOz9xj+WQY+n1pYhOiwap2AQCAdq2bctNP2eMYcOGDbh37x7Cw3X/GlOgkV+EkEolOikTbrYmWh+//OQT7rEi302HOrYlH0RcTPIcM2S9GQSudeZ9nVq8PYYDBw4gybYxYOKp8fycpBaQptXJnXWVY4MuHXzwJ27k2xeDbWM7ob1zEyBbuwHGpaGfY1f8ee83+MXEwSVHqn721pmFMGIMD0PDcWviMUw8P5W3W87kiEiJgL2xfTnVmpQGgUCACxcuYNOmTRg3bpyuq8OhlhxCSIV38G4k97iorTElzXejjvISCeq2C6xPtg3yv/XKGEP3gcMxcfqXCFYb4DDeYyY1hyzdFfN7NlXpGszdVwvp6aZa16lElAcsiw3hmiNFq8wszdPTlRIGOpk6Qpjv/0IoEMLR1LEsakrKmLW1Nb7++mve9HNdoyCHEFKhvUnOxOKjj7ltbbMPK+SfyCMUoFj5bhSi06Lhd9OPV+Z3q6DVxvPdXxwL5FvkUk8gQO8OLTBhxlwNa5MrBTKCdBg6bYBxrRUY0KwanK3VTNOGHI5WWgyWnh8FLEkCxKrXyJ+D520BeW+UhYv0C156VClhoL1RNczznsdtc11s1IpDSgkFOYSQCi0sLqNkrTH5zpUzwNxQVOz6hCeHQ54vSJEzOSJTIlSOlUqlOHzYn1cmFCXnTu1+XzHlHDyuNsZql4HgPQkBg75xCJc1WXXmlxyS6odgZ6Z+fSx5jhluyurjDbNS2ScyD+QeDzs+DMuuL+O2BzhUV5/3RokgJRpv9fWx1MZKQ7AG3lpXANDPPW+9L//+/hhUe1CB9yCkKCjIIYRUaE6ylCJlH36bzJ+tVNoTWp3MnFS6WAAgKP6xShljDLNnf6lSLra4A4F+7vpVyjl48ichVAQsWicRFGTAuNYKjVmVsxObI+3FXIzPXoC2WWuw/25eqn6BfhIkdieV7izHidC8mU+KvDdvM2L4F1VabNNgiw8GpqTisKkJNK7brrTWVX52RnaanxshxUBBDiGkwkn09+cepw4bjD8s8sbkFJZ9OCK+bJeZsDe2x1TPqSrla++vgSBf4j2RSISZM2fmbZvf5R4b1/oZIvPbKi0xeUkIc2BcawX0jZ8BTH2U09zvEOovOp1XIJBpXBcrmlm9H/Cc+7YvhxDzjwZz3X7q8uzkJxcIsOdh3kBuJEcBp+bk3Z7JsTg2HnZSKRgl/CMVAAU5hBCdyHmrfgxLTnQ03n73fV6BXA6X7Wtgk5EIoPDsw0FvUkqzmmrVs66nUpablC838Z48xwxgAkQnZWL69OkA3reU2OcNUBYIcjMSaxzLI2CQptVB2ou5UEw3B/hdSsZuv/GWbijIK7k9VAc8g+v2U5dnJz/GBNh6JTuvqys+lDeQGMjN7qPLxTdpnSqijIIcQohampPUFZ9yC02Ibx8k/v23yjHZYa8AOf+DE3I5qqfmdu8UlH34TVIGfj//olTqWhB1s3+EAiHaxARyXUKACN1+vsTNDNOUkfhlQpj6mzDwWl4A1S4lRaCUvwVJHWdhNNQNeFZ0+zGpObLe9s57PoyhpVVnXk6erDeDIJVaIUz+vlvJypU3kBjIXWUrQqQPvQqW+ZZ8mCjIIYRwSjJVuzDqWmjeLFqMnGh+4jexizMgzPfWJBTijYkNABSYTTk0Nk1lkHJZiA2N5W0rZgUtXfQrv0uIgZsZpq6lhDEBaho7QD0BBPop0DN6yQUxmgKl3BlbBbMXxL8f8Cx/X2fVbr+cRG/usX/kGyxo8TXSXsxB+qv/Ie3FXOQktYAeZHARvm99MqsB9FqZ93wEQiy1yW3lERdaI0LKHgU5hFRh8vR0PKnngSf1PCBPL3isypukjBJN1S6Mphaa7Ff8QEpkbw+7hQvyCoRCmMxfiFhDi0Lv4WpjrDJGtzSGhiQnpyAgIIDbrl+/Pm+/YlbQq7gM5H9bVQRdTGqOrOi8mUSKlhFbDYNtReZ3VFYU1xQoHZ/Sv9DnIGQMjiY3YFxrBYCcQrv97GQy2JtJILY9B1m6K5jUHHqQYbn+ZlQXKK2H5TmCe5g54TQOm5rAKUdanAXXi4XWuyIFoSCHEAJAfSuIjDEER5fOGBdNLTRiZ9UPWosBA7jHbieOw3DAwOLfuBRadurUqYOBAwdCKs1tRRLki5wUs4Lq2VupzART3s5J8uIep72chZykFmrvl7ui+FGu1UbRLQWA16VUWKAEgJv9ZADgdEQUBmdGAQKm9aKjYovcYGub+DtckczAx/oXNebXYaa5+W3CRfqlPqtNmXLyRVrvihSEghxCCABoyNECOFmpDt5U7jLSdjFOdS001Zcthci+4MRvIjvtpxWHxqapfLgW9cOWMYbkZP4MJYlEDCMjI0REqObCUZZ/CrhQgHxTwpXuIzXXeJ2CuqVykppyZWkh0zUGSu/S3wFJr3mzn/SA3PWkULSVyYWiZLTUe8JvwSnAW319ZBfpDtrLn4yR1rsiBaEghxACIPcDen5vD5Vybb/xaxP45G+hsRgypGiVLISmQE1bd+/eRcuWLTFoED8h3YULF/H48WO4uroWeo28KeC5M8FGt3ZB2ApfBC3z0boemrqlcpeEUCqTmvG28yfzO/R4p9rZT3UEBQdrpUFWRlPINSVjjFCTjJEQCnIIIZwBTWuU272K0kKjLU2BmrZsbGwQGBiIq1ev4s2bvER5bm6uEObvatOCtgFifprG7xTU+qMumd/SF3sRra+a3TlcXrNY9SqUuIhTtpOjCj8mH3XJGGm9K6IJBTmEkALVX3QaLnNPaN0tVVTp2VK4zD1RavfQNlCLjIzE4kWLeGXOzs7Yv38/wsPD4e7siLAVvghb4QsjsX6J61VU+cfv3P9yYYGtQeq6uORMjoi2qokL30J1SYfCeGWuK/I5KuZHAUH+edt/tISeUsZkbdgb29N6V0RrFOQQQj5Iz549w7r161XKBw8eDFtbWx3USLOCWnAU1HVxCQVCODYdV0a1KoZ8GZLB5BCfnAM7adGCW1rvimhLp0HO8+fPMXv2bDg6OqJbt25anRMbG4sJEybA2dkZdevWxaJFi5CTk1PGNSXkwyGRZuGU/2yc8p8NiTSrWNfIv35URdS5c2eMGz9e19UoNSrJ/BQtHEbVVI41RBaMllsDS8yB7LTyq6SaDMkCJitRhmRa74oUpMhBjp+fH2JiYgo/sBBZWVnw9fVFtWrV0LlzZyQmJhZ6DmMMffr0wbNnz3DkyBGsX78eGzduxOzZs0tcH0JKSmhkBI+nT+Dx9AmERh9eOnn/wLzxFX1/u1qqiQTLgkAgwMoffijfmzJAmuaWu+xDGVCeebXPd1/RWziYoMzqBkBthmQm0EOEqPy7A8mHochBzoMHD1CrVi2sXLkSWVnF+5YHABKJBMHBwZgzZw6srLTrHz579ixu3ryJLVu2wNPTE507d8by5cuxbt06xMcXbUokIaT0vEnKwPKTeQs3FjeRYFmP/9Gl3GzSImSEf5K77AMrZkM6E6t/nG/b2cy5iHUDABHSXsxFdmLzYlXNSN8wd92o4TdgpG5Zh3wZkiHQQ3bvlXirT0EOKRtFfpXt3bsXu3fvxubNm+Hh4YG//vqr2DfPn1CrMP/99x+cnJxQp04drqxHjx7IycnBjRs3il0PQkjJaEokqFj8sTz9+++5cr9nYfKySSve84TIncyte2+SMnmZrgEhst4MQjQr+uBkrShlSMbUm5ApbxNSyor1VaJPnz549OgRpk2bhv/9739o3749bt/WbiXckoiKioJdvmmnim3l6Z7KsrKykJyczPshhJQudflplBd/LGtMqdWgrtKXIE22XAkty+qoUL+mVtHzyBiJ9YuUb0cboXEZauomRLi8HMa6mJVfygLyYSr2wGORSIRZs2bhxYsXaNCgAdq0aYPRo0cjMjKy8JOLiTEGPT3+tx+hUAihUAh5/jVx3vPz84O5uTn34+hIuRQIKW3589OoW/yxLDx9+hQjR47EV199xZU5ORf+Gl915hn+/O9lWVaNR32SwtJb+MBIrF/khIMKrtaGauomh5OwBGtCiY1zp4trwUhklNvFNfYhjEQf3lg2UraKFeRIpVLcu3cPGzduxPz583Hz5k0wxrB3717UqVMHP/zwg8agoyRsbW1VBj3HxcVBLpdrnPI5b948JCUlcT+FpWUnhBSPcn6aY9PbFrj4Y2kJDw/Hnj17sH79+iK30v5w6inepBR/XGFR5C33oAhs5ABkAKCTHDzKqpsb5Ft6Qg5J9UOw13IJB42ylboqZ79Qu9YVIWWtyEHOoEGDYGpqiqZNm2Lx4sWIjY3F8OHDceHCBaSkpODIkSPYvn07xo4dW+qV9fb2RkhICKKi8r4hXLp0CQKBAC1aqF+/RSKRwMzMjPdDCClbdmbFy/SrjdP/nOEed+/eHV9//TUuX75c4GtbXceQnAGvEspvqnvucg85MHTakLsSuED9F8Eitcq8j5mik0r2PAZ7OcAAmdgm/g7GtVZAbHGnRNcjpKIocpDTsmVLbNu2DWFhYYiKisKhQ4cwZ84ctG/fHoaGhujevTuuXr0Kf39/xMXFlahyycnJsLGxwb59+wAAvr6+cHV1xaxZs5CamoqoqCgsXboUgwYNQs2aZZSmnBCic8qtHd/M/5q3GviKFSvQtGlTTacCAKZ3qaVSpicQwNmyeMGYkcgIN0fcLPqJAkDfOARCUcnHBubOgMpdsqHbz5dKPGVfAKCl3hPt6laM5RgI0YUiBzlz587Fxx9/DGdnzdMTLS0t0bx5cyQlJRV4rfbt28PGxgZ//vkn7t+/DxsbG9jY2CAtLTc5lVwuR1xcHDIzc7+lSCQSnDhxAq9evYKVlRVcXFxQr149bN68uahPgxBSSQ0aNLDI6SvGtOG/X3FjhkwlpVm1oilBK4w8xwxZbwZB0UalmLJf0hYdBTupFELGIEjJm9AxWHg174A/WgIBO0rlXoSUpTLrDL5w4UKhxxw7doz7RqbM2Di339bc3BwxMTEwNTXl9tWrVw/Xr19Heno69PX1IRaLVc4nhFQM8vR0BDfLXYOpbsBdrc87f/48amvY99mX87n3iOI6O6sj3GxNIC1ha3Nx5W+F8RvUqEhjmHJXI+d/R5UxhvD4ouUlys+IMTwMVWoRWt8Bd3r9go6HhFgq+TyvnMmBYzMB966AoUWJ7klIWdLpiDcLC4sC9wsEAtjY2KjdZ/QBZpQlpDwwAPdt3FEjNVZndXBzc+ceJyUn43hoXgtFcYKC/Iq7OrgyxaygotLUCtOhjq3Ws9GE4ljkDl7OC3T0BAI4WWl3fsDC7sAqLQ5kcohPzYKX8DPo5Vv8E0wGxIcAru1zBxUTUgFRmklCPkDp2VLUX3QaABC0zIcb8+IfGIVsPTHmtvsUYHIIy2CWZH7h4aozHl1cnKEo9V51DVJRXrdScYKCCoOJkfZivkqxInGi1kGOKBmS6oeQ9WYwAAHX/VYawVt+AibDHyO8gINC/rpTAj3Ayq3U70dIaaJVyAkhAJSWZlBkIhcIIdfT/D0oxsAc923cEZ1csmnYT58+KXA/EwjUZlNu7Xde7fIPxZ2SnfPuXbHOK46iJk5UDkQVLUiBn26EojXo7KyOZTdlX6AHOLZUWY4BfVcD5jThg1RsFOQQQgBoysqbR3lQ68H70RjrswBz232KHutuqczsKcoq5N27dy9wv4AxNcnqikZTfZKOHecehw0egsS//y7ZjZA3BTxsha/GgKu0EycWuQUnRX2GeBXKwUy+5RjQbEzB54qN1D8mpBxRkEPIB8Jj0T8FLn6pPitvnvD43ORub5IysPSf52DvV5NWdB/tuP6KO1bTKuQHDhzgHstkucnwClvDTgDVoKCo+v52VaUsJzoa71YqtU7I5XizaDFyoqOLfoMiUk6cWKatMJps6KjdcZqCGVqOgVQSFOQQQgAoZeVVt3o0AENR7pIqmhbj/O3cC25bEfhExqfxjuvt68s9TkvXfvHO/NmUC2Ik1sf1eV14wZC6FqrssFdA/jFHcjmyX5Us30xRaWqFKdPlDpiWY60omCGVHAU5hBDOYC8HiGXZGPz8AoT5PgiHb7yB/bfDuRYfm4xENI55AZuMRAihuhKTjDF0HzKKV2aiNPXb7H1qiIN3i7benTbZlAvregMAsYszIMz3FigUQuxczq0qZU1sDIw9VrJrZKcByytOwEPrXRFtUZBDSAUnT0/Hk3oeeFLPA3ItWz/Ss6VwmXsCHov+KfL9BAAmPT6Bny+u4bXqKFpnAOA3kzBsP/09fri6HttPf49fTMNVupGEAiDqSaDG+3gs+gchMalYfPSxyr6SDmYurOsNAET29qg2Z05egVCI6suWQmRvX6J7V0hW7oBAw9s9ZS8mVRgFOYSUUHGCkMogUyTJm2n1nowxvHoaBrfdayF833YjBEOd3b+jXfp97jihAPAb1Aj7t64v8B6v4tS3uIQnlCypXd6CmHn1Uce8bx/uscvBv2ExZEiJ7qtrGls4zGvyZ0cpr+b1R0vo3dtVbnUkpDxRkEMIUatGaqzK+Bw9gQA1UmPUjmV5tuMXblOxCnnPnj0LvIeztfoWF8X4n5LIXRAzrz6FEVWrVuJ7VmjKs6OUg9f3Cf/sUMJVxwmpgCjIIYSoZZuZBH153kwsbqpzgzpg+WdECYXw7JU3qFjbVcjtzQ14LS4KI3few2nnlsWruBra1Kc88+ToXL7xVgImg5OwnJ6/2Dg3Q/KSpNzHhJQhCnIIIRrpKX0YKqY67zl7AX80GpB30PuxLN9tKLhrShPlFhcFOQPWNBmCGAPzYl1TW2WRJ6dSyDc+hwn0EC6v4i1Z5INEQQ4hRCuKqc49e/XCWafmXHl1f/8yGcsiFwrxxkT92nWlQZd5cnSux3d5jwV6yO71M97CSnf1IaSMUJBTypiGHCOEVBWmpia8bf1qdgD4yykUd2kFZUK5HNXLcJHQipInRycafZT3eOpNyDxHaT62NNAMLqIjFOSUouDgYHh6euLevXu6rgohpS45OaXc7iUUAJ/f/xu2mWW3unV558nRZrkHnTCrASOxPp4sK3iQeJHd25P3+I+WQMCO0r0+IVqgIKcUzZ49Gw8ePMDXX3+t66oQUmSPHz/GhAkTNO43MzMtt7qc+bQlfF7dKtN7fFB5crShHJSUVNJr4JTS/y2TA8dm5pYTUo4oyClFO3bswPjx47FjB31jIZVPSEgIb20pXbI3k5Tq9YzE+gha5qNSXtXy5BRb/qBEobjdTPEvVZeOYDIgPqR41yOkmCpQm2nlZ2lpiS1btvDKjh49ijZt2sDGpuwGUBJSVKmpaSplvr6+mD59OoqeI7lyUHQXaVLl8+QURF1QAgAJYYBN7aJfT5FhWfmaAj3Ayq3YVSSkOKglpwxdunQJgwcPhqenJ6I/hBkbpNLYvm0b91gxWF4oFMLPz09HNSI6pWnZB0uX4l0vf4ZlgR7Qd3VuOSHliIKcMmRlZQV3d3d06NABdnZ2uq4OIZxRo0dzj18nqLbqVBQxBuaQCwR4m5yp66pUbSrLPrxXklXIlTMsT70JNBtT/GsRUkzUXVWGGjVqhDt37oAxBsH7DLHZ2dlISEigoIfo1NkXydzjnr9ehd+gRvi4RcVaffu0c0v86jkETCBE39+uqj0mOikTbrYmavdVdYV1vxWZ5wjg5OzSu56ykgRLhJQAteSUMRMTE5ia5s1KmTt3Lho2bIgTJ07osFakKgsPD0dMTIxKuXVG7nTs6KRM3srfitXF3ySVbFHM0hSdnMUFOAB4i3j6B+YNhu328yXsv/0B5LWpSOZH0XIMpNKgIKccpaen4/z584iNjeVadggpTcuWLYOrqyt+/+13AEC38Dvcvo3nfkSPsJsIDE9QWflbxhjCYivOCuoR6XIuwMlv+ckn3OOKGKBpo8LmzFGYH6X92lLZacAS89yf7Irb9Uk+TBTklCMjIyPcvHkThw4dQu/evbny5OTkAs4iRLP4+HhkZOR9wDdu3BhyuRyhYWGwyUjEZw/8uX1CMHx+/2+s2Pkf8ofYegIBXGyMyry+QiMjOD94iF4DViFLX/M0cXWrk+sJBPh9RNMKH6ARQioOCnLKmUQiwcCBA7ntlJQUeHp6Yvz48UhKKrvsrqTqWbhwIWrWrIm9e/dyZX369EFQUBB27NiOGqmxEIIfEegxBvuUWChHOdzq4uaGZZP5thjyr06uqKOXs6Xa4Kc8AjTyHi3RQCoRCnJ07OTJkwgNDcWlS5cgEol0XR1SwSmvjWZmZobMzExcvHiRK9PX14eHhwcAIMrEBvJ8bTYygQBvTGygvMSaYnVxde4u7FZ6lVcjOknzrCnl1ckVdaxubqg2+Klublim9fzg0RINpJKiIEfHPv74Y1y7dg3btm2DkVHet9HXryn9OVF19do17vHEiRNx/fp1bN++XeW4g3cjEWtogV89h0D2fvyXTCDAmiZDEGtowWsNUawuXlxFHVNy8G4k97jbz5d425oo11Fd8EPKEC3RQCqxCjji7cPTunVr3vbx48cxcOBAfPnll1ixYoWOakV0KT4+HlZWVirlp//5Bx26dwcAWFtbw9raWuWYN0kZ3OypMy7eCLCrixqpMYgytkGsUW53z9J+DfDNkccq55aENlOalesG5A4cVt4uqoICtFKfYl3ZiY1zBxMDRRsgXNASDZTcj1Rw1JJTAZ04cQJSqVTX1SA6kJOTgz59+sDe3h4REREq+5ctW1boNUJj03iDc2MNLfDAtjYSJbkzZc7O6shrDSmI9N1b7Squpfx1A6CyTSoYddmQaYkGUklQkFMBrVu3DmfOnMH8+fO5slevXuHQoUO8MRmk8mOMISQkb9FCkUiE1NRU5OTk4OzZsyrHa5N6wNVGdWaSUC6H8P3fTmHdU4n+/tzjNwMG4F7zDJWpzgzAfRt3xBiYF1qfwuqmJxDg+rwuahfQJBUALdFAKjEKciqo7t27w8zMjNueO3cuBg8ejFmzZumwVqQ0vXr1Cu7u7mjevDmysrK48lWrVuHZs2cYP358sa6rMjhXLsfn9/9WmTauTk50NN5+931egVyON4sWI0dp7bWDdyORrSfG3HafYqzPAuwP1H62DQ0crqRoiQZSSek0yHn16hV69+4NAwMDmJub45NPPkF6esH5Lo4ePQovLy8YGRnB3NwcPXr0wP3798upxrohl8tRq1YtmJqaYsyYvDeXnJwcatmpRMLCwnDz5k1u28HBAdnZ2cjJycGjR4+48ubNm6N27WKs/KxE0R0lkmVj25nv4fPqllbnZYe9AuT5xl/I5ch+lZtVmBtT875FiQmEWHg8uEjJ+GjgcAWgGJ+jbcI/ZbREA6lEdBbkSKVS9O7dG3p6eggLC8ONGzdw/vx5fPrppxrPCQ4OxuDBgzFo0CDExcXh5cuXsLCwQM+ePSGTycqx9uVLKBTi22+/xevXr9G0aVOufOXKlWjWrBlOnz6tw9oRbRw6dAiurq6YMmUKV6anp4cTJ07g7du38PLyKpP7ChmDbab2+ZfELs6AMN/bglAIsXNuIKJuTI2ModjJ+JS7zip8FmBCSKWjsyDn1KlTCAoKwh9//AF7e3t4eHjg22+/xa5du/D2rfrBjo8ePYJUKsWMGTNgaGgIGxsb/O9//0N0dLTGc6oS5TWw5HI5Nm/ejHv37iEuLk6HtSL5PXv2DMuXL+eNqenYsSMkEgksLS2RlpY3s6VJkya81AG6JrK3h93CBXkFQiGqL1sKkb09AE1jakDJ+AghFZLOgpxr167B1dUVTk55TdVdunSBXC7nNekr69SpE2rWrImffvoJiYmJeP36NdavX49u3bqhRo0PqwlVKBTi9u3bWLVqFYYOHcqV//333/j000953R+kbOVvRdyyZQsWLFiATZs2cWXW1tZ4+/Ytzp8/D2Pjir24ocWAAdxjtxPHYTFkCLfNjal5300qlMvxXZ+6NKaGEFIh6SzIefv2LWxtbXllNjY2EAgEGltlrK2tcfDgQaxbtw6WlpZwcHBAWFgYdu3apfE+WVlZSE5O5v1UFdbW1vjyyy95mZJXr16N9evX4/Dhwzqs2Ydj8uTJcHFx4ZUNHjwYvXv3Rp8+fXjl5uZFm4lUEYjs7FTKBns5QCzLxg9X1mHbme/xcdMP6wsGIaTyqJCzqzRNk33y5Am6d++OGTNmIDU1FW/fvkWdOnXQsWNH3iKFyvz8/GBubs79ODo6lmXVde7bb7/FRx99hIkTJ3JlV69exfDhw/Hvv//qsGaVm1QqxdWrV7FlyxZeeXR0NBISEnhlLVq0wIkTJzBq1KjyrGK5EgBoHPuySON9yAemJIObCSklOgtyqlevjnfv3vHKYmJiwBiD/fv+//y2bdsGGxsbzJs3D8bGxqhWrRp+/fVXBAcHaxx8O2/ePCQlJXE/6hKsVSWdO3fG/v37ed1327dvx759+7Bv3z7esdnZ2eVdvUpDJpPxWv3CwsLQrl07TJkyBampqVz5/PnzcfrMmXKrV3AzLzyp5wF5IbMQCSGE6DDIadu2LcLCwhAWFsaVnTt3Dnp6evD29ubKpFIpN01aJBKpTJmWv5/uqmlxS4lEAjMzM97Ph2bKlCmYMWMGJkyYwJWFh4fD2toaH3/8Mfd/SHJt3LgRNjY2vGSMinw2gwYNQmJiIlfu7e2Ntm3a6KCWlU/OBzA5oFKjlhdSBeksyPHx8UHjxo0xefJkvHr1CoGBgVi4cCHGjRvHjdVJTEyESCTiFiAcMGAAIiMjsXjxYsTFxSE8PBxTp06Fvb092rZtq6unUuE1a9YMq1ev5v0f/fPPP0hNTcWbN28gVJoyvHHjRhw9epQ3A6gqW7BgAby9vREcHMyV2djYIDExEbdv3+bKBAIBbt++jX379sHBQbslEQg/e3KIbx8k/v237ipDCPng6CzIUeQIMTIyQuPGjeHj44M+ffrg999/544RCATQ09PjPoSbN2+OI0eO4OzZs6hduzZatmyJnJwc/Pvvv7CwsNDRM6mc/ve//+H27dvw8/PjyqRSKWbPno3+/fvjxYsXXHlYWBgCAgKQmZmpi6qWiufPn2Pq1KmYOXMmr/zy5cu4desWrimt7t21a1fcunULV69eLedaVi2asidLlbInkyomWfvs14SUB51m3HJwcChwFpC5ubnKQpW9e/dG7969y7pqVZ5AIEDz5s15ZampqRg5ciRu3LiBBg3yUu9v2rQJ33//PSZPnoz169cDyF1z6fDhw3BxcUGTJk2gp6dXrvVXYIwhJSWF1w25atUqHD9+HLNmzUK/fv0AABkZGVi7di3Mzc3x888/c4HzrFmzMHnyZHTp0oU738zMDC1atCjfJ1IFacqeLK3i4+I+OPf25D3+oyXQ91da9oFUGJRWlHAsLCywdu1alXKpVApLS0s0atSIK4uOjsbgwYMhFAqRmZnJBTkbN27EzZs38dFHH6FHjx4Acqfx3759G2ZmZmjcuDF3jbS0NMhkMhgYGEAsFnP3ioyMRGZmJurVq8cde/bsWdy+fRsdO3ZEm/djYJ4/fw5PT09IJBLEx8dzxz558gSXLl1C586duSCnbt26mDNnDho1agSZTMYFOQOUcsKQ0sVlT1YOdIRC6Ds6AojXeB6pRJJeA6fm5G0zOXBsJuDelRbwJBUCBTmkUCtWrICfnx8v6V18fDxat24NqVTKG/R99uxZHDhwAA0bNuSCnDdv3qB9+/YwNDTkrU02ffp0bN26FT/++CNmz57NHevq6qpy7P79+7Fp0yYsW7aMC3Ls7OyQnp6O9PR0pKWlcUn2xo0bh86dO/MGsEskEvzwww9l8L9TOSmWUChLiuzJb5d9m1vwPnuyvr09gMLXm9NUx/KoO9FS/MvcwEYZkwHxIRTkkAqBghyiFYFAAH39vD+XBg0a8MaxKIwZMwaNGjVCu3btuLKcnBzUqVMHEomEd6yiK1K5S1IsFsPQ0BDGxsaQSqXcPTt06ACZTMZrCTIzM8Pz589Rs2ZNGBrmZdxt37492rdvX8JnTEqDxYABXJDjduI4JK6uSM+WFnIWqTSs3AGBkB/oCPQAKzfd1YkQJRTkkFLl6+sLX1/+t+zatWvzZi8pbN26FRs2bOCN51G0zuQ3evRojB49WqW8Vq1apVBrUh7UZU8mlZx5TaDXSuBkbkssBHpA39XUikMqDApyiM7o6enpbMDyh+jJsp4QVqDFQEkV4TkiL8iZehOwqa3b+hCihIIcQkpRztu3kLi6luo1lXPNkNIlVOrmVH5M8lEkCiyMGa1jRiqWCrl2FSGVSVkmvFPJNaMor8TZgxUDh8NW+MJITN+zCCFlh4IcQkpAU8K7nFJKeKc21wyAnEJyzSgCiSfLepZKPQghpDKiIIeQEtCU8C77VXipXJ/LNZOPyNGxVK5PCCFVGQU5hJSA2iBEKITY2alUrq/INaNSruOZSln6EvQasArODx7SYGZCSIVFQQ4hJaAShLxPeCeyty+1e1hQVmZCCCkWGvVHSAmpS3hXlVHGYUJIZUEtOYSUIl13IxFCCMlDLTmEkA+W0MgIHk+f6LoahJAyQi05hBBCCKmSKMghhBBCSJVEQQ4hhBBCqiQKcgghhBBSJdHAY0LIB0cmkyEnJ0fX1agasrMAk/cZuDOzALmebutDKi2RSAQ9vdL9+6EghxDywWCMITo6GomJibquStXB5EDbn3IfR74BBNRBQIrPwsIC9vb2EAgEpXI9CnIIIR8MRYBTrVo1GBkZldob6QdNLgNis3If27gAQmrJIUXHGEN6ejrevXsHAKhevXqpXJeCHELIB0Emk3EBjrW1ta6rU3XIZYD++2DRwICCHFJshoaGAIB3796hWrVqpdJ1RUEOIeSDoBiDY0QLipYuoR5Qo6mua0GqCMXrMycnp1SCHOo8JYR8UKiLipCKq7Rfn9SSQ0gVRssWEG1JpVLo69NHAqlaqCWHEEIqOKlUWqbXS0xMhEgkwp07d0r1PoToGgU5hBBSgWVmZkIkEuHKlSsV8nqEVGQU5BBCSAUmk8m4f6VSKbet3BrDGANjTKVc+RqK/ZquR0hVREEOIYRUYDVr1gQAdO3aFQYGBvDy8uK6l37++Wc0btwYEokE69atw4sXLyASiRAWFsa7hkQiwenTpzVeT+HKlSvw9vaGkZERXF1dsX///vJ5koSUEQpyCCEfvLS0NKSlpXGtHQCQnZ2NtLQ0ZGVlqT1WLpdzZTk5OUhLS0NmZqZWxxZFdHQ0AODixYuQSqW4d+8et2/NmjXYvHkzMjMz8dlnn5X4emvXrsW6desQHx+Pzz//HOPGjeOOJ6QyqhBBTkJCAtLS0op0jlwuR3x8fBnViBDyITExMYGJiQliY2O5sh9//BEmJiaYNm0a79hq1arBxMQE4eHhXNkff/wBExMTTJw4kXesi4sLTExM8ORJ3gy3bdu2lVq9v/nmG7Ro0QJCYem8lS9btgzNmjWDgYEBZs6cCQC4e/duqVybEF3QaZDz6NEjNGvWDDVq1IClpSX69etXaOAik8mwYMEC2NjYoE6dOnB3d8fx48fLqcaEVH2KaeceT59ASInzKrT69euX6vXc3d25xwKBAObm5khISCjVexBSnnQW5GRmZqJPnz5o2LAhEhISEBUVhfDwcEyYMKHA86ZOnYodO3bg3LlziI2NxbVr13jNrYQQUlSpqalITU2FjY0NV/bVV18hNTUVv//+O+/Yd+/eITU1FU5OTlzZ1KlTkZqais2bN/OODQsLQ2pqKjw8PLiycePGlVq9RSJRoccoD0ouDCVKJFWNzjI/HT9+HBEREVi5ciUMDAxgYGCAxYsXY/DgwYiMjISDg4PKOcHBwfjzzz/x999/o2nT3DTidnZ2WLhwYXlXnxBShRgbG6uUicViiMVirY4ViURqAw5NxxaFvr4+BAKBVrOgLC0tAQDx8fFwcXEBALx8+ZI3Jqgo1yOkstNZS86tW7fg5uYGe3t7rqx9+/ZgjOH27dtqzzl16hQkEgn69u2LjIwMJCcnl1d1CalQct6+1XUVSDnR19dHzZo1cfXqVWRlZRUYnFhbW6NBgwZYuXIl4uLi8OzZM0yePLnY1yOkstNZkBMTE8NrGgYAKysrCIVCxMTEqD0nIiICDg4OmDdvHuzs7FCjRg3UqlWrwDE5WVlZSE5O5v0QUtmF+PZB4t9/67oapJz8+uuv2L59O8zNzeHl5QWBQAA9PT213Ut79uxBREQE3Nzc8NFHH2HSpEmQSCS8wcnaXk9fX7/UBjUTogs6664SCoUqSatkMhnkcrnGlUcFAgFevnyJ5ORkxMbGQl9fH99++y2GDh2KR48e8QbNKfj5+WHp0qVl8hxI1SVPT0dws9z8IXUD7la8AbhyOd4sWgzjdu0gUmoNJVXToEGDMGjQIF6ZpqUeGjdujKtXr/LKhg8fXqzrRUZGFqe6hFQYOgvRHRwcVPIvvH3fBK9IVqXuHABYtGgRxGIxhEIhFi5cCLlcjvPnz6s9Z968eUhKSuJ+IiIiSvFZEKJDcjmyX4UXfhwhhHygdBbkdOjQAZGRkXj69ClX9s8//0AsFqNVq1YAcmcFREZGIj09HQDQuXNnAEBKSgp3Tnp6OqRSKUxMTNTeRyKRwMzMjPdDSJUgFELs7FT4cYQQ8oHSWZDTpUsXtGvXDmPHjsXNmzdx6tQpzJ8/H9OnT4eFhQUAICkpCY6Ojjhw4AAAoEmTJhg0aBCmTJmCW7du4cGDBxg7dixq1qyJnj176uqpEFL+hEJUX7aUuqoIIaQAOhuTIxAIcOTIESxYsABjxoyBRCLBjBkzMHfuXO4YoVCImjVr8qZh7ty5E8uWLcP48eMhFArRokULXL58mZs6SciHwO3EcUhcXXVdDUIIqdB0FuQAubOp1q1bp3G/mZmZysA3IyMjrFixAitWrCjr6hFSYYns7HRdBa0osicTQogu0NxAQgghhFRJFOQQQgghpEqiIIcQQgghVRIFOYQQUkTp2VK4zD0Bl7knkJ6tPikfIUT3dDrwmBBCyIft1atXOHXqFNzd3dG9e3etznn9+jXOnTsHgUCAbt26oXr16irH5OTk4OLFi4iIiEDLli3RsGFD3n6pVIqLFy/i1atXsLS0RIcOHVSWGirM4cOH8fr1a3Tp0gX169fn7bt69SoCAwPRsGFDdOrUibcvOjoaf//9N5o3b87lhVOIjIyEv78/gNxZyLa2tvDy8lKb0b+0PX/+HP/++y8aNmyIDh06aHVOWFgYLl68CLFYjO7du8PW1pbbFxsbi3379qmcM3jwYLW/s7JALTmEEELK3bt379C3b1906tQJ3377LbZv367VeYcPH0bt2rWxf/9+7N69G7Vr18aJEyd4xzx79gwNGzbEzJkzcf36dUyaNAnffvstt//Nmzdo0KABpk6dimvXruH333+Hi4sLjh07VqTn8Ouvv2LGjBlYsmSJyr7JkydjxowZaj/k169fjxkzZmD69Okq+54+fYrp06fj7t27ePLkCXbu3AkPDw9e/UtbeHg4evTogd69e2PJkiVcbrrC7NixA/Xr18fhw4exadMm1KpVC5cuXeL2R0ZGYvr06bh37x6ePn3K/WRkZJTVU1FBLTmEEFKJBAcH4+3bt3BycoKLi4uuq1NsMpkMn3zyCXx9fdG7d2+tzklNTcWkSZPw9ddfY/HixQCAOXPmYOLEiQgLC4OBgQFycnLQr18/NGzYEAcOHODWQrxy5Qp3nXXr1iElJQUvX76EoaEhAGDs2LFYuHAh+vbtW6Tn0bVrVxw9ehRxcXGwtrYGANy4cQNv3rxBs2bNVI6Xy+XYunUrlixZgqVLl+L+/fto0qSJynHffvstt5TRmjVrMHPmTIwePbpMfucymQxffvklevTogdatW2t1TkxMDD799FP4+flhxowZAIBPPvkE48ePx4sXL3gLu65YsaLIrWSlhYIcQkqorHPBKK6vvGgoqTiikzLhZqt+WZnSFBsbi7Fjx+Lq1auwsLBAeHg4Vq1ahVmzZpX5vdUJDw/H0aNHCzymVatWaN68udp91atXL3JA8e+//yIhIQFTpkzhyqZOnYoff/wRFy9eRM+ePXHs2DEEBwfj5MmTvMWe27Vrxz3W19eHoaEhDAwMuDJLS0vo6xf9I9HNzQ1SqRS7du3iPuw3bdqEUaNG4e7du2qfQ2JiIr788kvcunULmzdvxpo1awq8R7du3cAYw9OnT9UGOc+ePcOZM2cKvEbnzp3RoEEDtftcXV3hWsTkosePH4dMJsPEiRO5sqlTp2Ljxo24desWrxvuyJEj0NPTQ926dbUOokoLdVcRQkgRHbybl6S028+XsP922S6UKpPJ4OPjA3t7e8TExCAsLAxff/01vv32W+Tk5JTaff755x9kZ2drdWxaWhqvC0LdT1xcXKnVDQCCgoJgZWUFO6VkmM7OzjA0NERQUBCA3LEwLi4usLW15bq0lNdIBIDPP/8c9erVQ58+fbBq1SpMmzYNZ8+eLTA5bUEmTpyIzZs3A8htbdq/fz8mTJig9thNmzZh5MiRMDIywieffIJdu3YhMzOzwOs/ePAAAODkpH6tuqSkpEJ/FwkJCcV6bpoEBQXBwcGBt26kh4cHt0/B0NAQ/v7+OH36NPr27YsOHTqUel0KQi05hBBSBG+SMrD46GNuW86A+YceoUMdW1Q3NyyTe+7YsQNxcXFYu3YtRCIRAGDo0KFYsWIFIiMjERcXh+rVq6NmzZpFuu7Nmzfh7OwM+/droM2ePRsXL17UqmvBw8MDv//+e9GfTAmkpKRwaxsqs7S0RHJyMgAgLi4OQqEQrVq14louJk2ahFmzZuH7778HkLtkkJWVFe7cuQM7Ozuu20osFherXoMHD8a0adNw584d3Lt3D/Xq1VPbBRUbG4ujR4/i5s2bAIDevXvD2NgYhw4dwogRI3jHbtu2DRYWFoiIiMCff/6J4cOHqwxuVmjRogVatGhRrLoXl7rfhVgshpGREfe7sLe3x5MnT+Ds7Awg9/l7eXlhzpw52LhxY7nUk4IcQggpgtDYNMgZv0zGGMJi08ssyDl06BAGDx4MiUTClaWnpwMALCws8MMPP6Bnz55FDnLWrVuHYcOGFWuB45J2VxWHoaEhUlJSVMqTk5NhZGQEIHfpn5CQEBw7dgx9+vQBkNu10rdvX3z00Udo0qQJ5s6dywUkiv/TL774Av3790dISAivm0sbBgYGGD58ODZv3ox79+5pbMXZsWMHjI2NceXKFW6MkKurKzZv3qwS5Lx8+RImJiawtrbGnj17Chy3VNLuquJQ97uQSqXIyMjgfhf2+RYQtrGxwbhx47B169ZSq0dhKMghhJAicLUxhlAAXqCjJxDAxcaozO557do1lQ+5S5cuwdPTEzk5OXj58iWuXLmCzMxM9OrVCzdv3oS3tzcePHiAGjVq4OnTp/Dx8YG+vj6SkpIQGBiIevXqITQ0FJcuXUJiYiIXEGRnZ+PixYswMTEpMEBRdFcVpHbt2iV/8vmuFxcXh+TkZJiZmQHIHQCbmprK3atOnToAgB49enDnKaamP3r0CE2aNMHNmzfRuXNnXtDYs2dPrF69GlFRUXB0dCxy3SZOnIj27dsDgErAorB582a0bduW9//WuHFj/PnnnwgJCYGbmxtXrjzwuDCK7qqCeHp6anUtbdWuXRuvX79GdnY21wIWGhoKxliBv3eJRILExMRSrUtBKMghhJAiqG5uiKX9GuCbI7ldVkIBsHxQwzJrxQkNDUV8fDzevHnDlSUmJuKPP/7AokWLEBsbi9DQUGRmZiIyMhLt2rXD/PnzYW5uDlNTU0ybNg0zZszAvXv3YGJigoiICHz33Xf46aefEB4eDoFAgNDQUHTu3BkAMH78eBgYGODu3bv45ptvMHnyZLX1Ko/uqqdPn+Ls2bP45JNPIBaL0aNHD4hEIuzbtw+ffPIJAGDnzp0wNjZG165dAQB9+/bF7NmzERAQwA1+DQwMBAAu14yrqysCAgLAGINAIAAA3L17FwYGBlz+ltDQUJw4cQJjx46FqalpoXX18vLCrFmzUKNGDZibm6vsv379Op4+fYqLFy/ycskAwP3797FlyxZ89913xflvKpfuqnv37uHatWv47LPPAOR2tX3++efw9/fHRx99BCD3d2FtbY22bdsCyM27oxzw5OTk4MCBA7xB4GWNghxCCCmiwV4OXJBzdlbHMp1dFRAQAHNzc2zfvh329vYwNDTEqlWr4OXlhcmTJ3MJ8Xr27IkBAwZw582ZMwc+Pj4ar9ukSRN07NhRpbtqyZIlaN26NW7evIlly5ZpDHJKw9q1ayGXyxEeHo74+Hj8/vvvMDExwbhx4wDkTsWePn06Ro0aBbFYDBsbG/j5+WHmzJkIDg6GTCbD+vXrsXr1aq5lx93dHYsWLcKAAQMwefJkMMawYcMGjB49mgt6Fi1ahHbt2qFHjx7o3r07QkJCsHXrVqxYsYKbYXXnzh18/vnnGDp0qFZBDoACg5RNmzahbdu2KgEOAAwYMAC//PILli5dWpT/vlKTk5ODP//8EwDw9u1bPHz4EL///jusra0xfPhwAMDZs2cxd+5cLshxcXHBwoULMWnSJNy9exepqanYuHEjtm3bxrXsHDx4EEePHkWXLl0gFovx119/ISUlRW3uoLJCQQ4hhJSAvblB4QeVwN27d+Hl5YVvv/0WK1euREpKCiZMmIDPP/+ca4VQ/KtMefyFvr4+pNLc5SdSU1O5cnXnKb55W1tbIy0trVSfS36KQKVLly4AcltuLC0tuf0eHh6YOnUqr1tp5syZaN68OU6cOAGBQICLFy+qZA1etGgROnbsiH/++QdisRjbtm3jBXKNGzfGy5cvsX//frx69QouLi64fv06L6/NlStXMGbMGN5MrvwGDhyoMu5E2eDBg1GjRg0AQLVq1TBo0CC1x3300UcIDQ1FZGQkHB0dMXXqVN6spbKmmJ4OAL6+vgByfxeKugNA06ZNMXXqVN55S5YsQfv27XHmzBlYWVnhxo0bvP/DuXPnomfPnjh16hTS0tLw9ddfY8iQIbyp+2VNwBhjhR9WdSQnJ8Pc3BxJSUlc5E9Ifso5aeoG3IXQqOzGW2irItapMsnMzERoaChcXV1L/Cabni1F/UWnAQBBy3xgJC6774s9e/ZEo0aN8OOPP2o8ZtGiRQgJCUGfPn3Qq1cvdO3aFf7+/tyYjo8++gimpqZo06YN9uzZA4FAgLNnz2LOnDmIiYmBj48P+vTpg1atWnGzq168eIFJkybh4sWLZfbcKrIff/wRI0eO5H3Qk7JX0Ou0OJ/flCeHEEIqsICAALWZc5VNmzYNdnZ2OHLkCFJSUuDj48PNcAGA3377DZaWlnjy5Am+++47bvzNF198AQsLCxw5cgRpaWno1asX12piamrKtbB8iL766isKcKoAaskhRI2K2GpSEetUmZRmS055ycrKwo8//oiJEyeW24KGhOhSabfk0JgcQgipoCQSCRYuXKjrahBSaVF3FSGEEEKqJApyCCGEEFIlUZBDCCGEkCqJghxCCCGEVEkU5BBCCCGkSqIghxBCCCFVEk0hJ4SQospOA5a/TxQ3PwoQG+u2PpWYXC7HmzdvYGZmptUaUSEhIZDL5bwyGxsbWFhY8MoYY4iOjoaVlRVvWQggd32mlJQUlWvr6enB1dVV67q/fv0aGRkZsLe3V1mGISEhAXFxcTA3N1dZryo7Oxvh4eFq652RkYHXr18DyF12w9bWttxyuslkMrx58waWlpYwNi78b/rFixcqZdWqVdNY34iICOTk5PBWWy9r1JJDCCFEJ06ePAlHR0c0bNgQ1tbWGD16NLKysgo8p3Hjxmjfvj169uzJ/ezfv5/bn5KSggULFsDW1hbNmzeHmZkZevfuzQUOQO6SDcrnK5bO6NGjR5HqP3LkSNSuXRuLFi1S2Td8+HDUrl0b33zzjcq+HTt2oHbt2hg/frzKvqtXr6J27dro3r07fHx8UL16ddSrVw/nzp0rUt2K6q+//kL16tXRuHFjWFlZYcqUKdx6Z+pIpVLUrl0bHTt25P0/njhxQu3xly5dgqurK+rXr19WT0EtCnIIIYSUu/DwcAwePBiff/454uPj8fz5c1y8eBHz588v9Nx169bhxYsX3I/ySunPnz+HnZ0dQkND8fr1a0RFRSEuLg6TJk3ijlm1ahXv/Hv37kFfXx8jR44s8vNo0KABdu7ciezsbN5zu3jxIrfYaX6bNm1C3759cfz4cbx9+1btMZcvX8aLFy+QkJCAli1bYvDgwUhISChy/bQRFBSEESNGYNmyZYiPj8f9+/dx8OBB+Pn5FXru7t27ef+XilXLlcXFxWHcuHEYM2ZMWVS/QBTkEEJIJcAYw2+//QZXV1cYGRnBw8MDV65c0Vl90tPTeR9u6n4K+lDetm0bzM3N8dVXX0EgEMDZ2RnTpk3D5s2bkZOTU+C9s7KyEBkZCZlMprKvWbNm+Pzzz7muL2tra/j4+CA4OFjj9fbv34/09HRMmDBBy2efp127drCyssLRo0e5si1btsDX1xfVqlVTOf7x48e4c+cO1q5di4YNG2L79u0FXl8sFuPLL79EUlISAgIC1B6Tmppa6O8iOTlZ4z02bdoEFxcXTJkyBQBQr149TJo0CevXry/0+Su61/J3ISobP348Jk6ciJYtWxZ6vdJGQQ4hhJREclS53ObTTz/F5s2bsWPHDgQEBMDZ2Rljx44t1XukpKRA2+UM7969q9Llk/9n7969Gs+/ffs2WrZsCaEw72OoXbt2SEpKwvPnzwu896hRo+Dl5QVjY2OMHz9ebTAVGRmJ4OBgHD58GFu2bMEXX3yh8XqbN2+Gj48PnJyctHjmqiZMmIAtW7YAyB1jtHXrVkycOFHtsZs2bULv3r3h4OCA//3vf9i8eXOh109KSgIAlXE/CufOnSv0d6GpGwnI/V20bt2aV9auXTtERUUhKqrgv+/+/fujadOmMDY2xtSpU5Gamsrbv3r1asTHx2PevHmFPs8ywXRMKpWyoKAgFhISUuRznz9/zm7fvs2ys7O1PicpKYkBYElJSUW+H/lwyNLSWFDdeiyobj0mS0vTdXUYYxWzTpVJRkYGCwoKYhkZGSW/2M0NjC02y/1ZYsHY3e0lv2YBLly4wExNTVlkZCRXdvnyZQaAvXr1iqWnpxfpfVAhLS2N5eTkcNsNGjRgMTExpVLnwrRq1YqNGzeOV/b06VMGgF26dEnjed999x1LTk5mjDEWFBTEatWqxQYOHKhy3IABA5ijoyPT09NjAwcOZImJiWqvFxQUxACwQ4cOFfk5dOzYkU2ePJm9efOGSSQSFhERwf755x9Ws2ZNJpVKWdu2bdnkyZO547OyspiNjQ07evQoYyz388jY2Jj3fP/9918GgF2+fJk9f/6cnT9/nnl6erL69euzzMzMItdRG/Xq1WMzZ87kld24cYMBYPfv31d7jlQqZd9//z1Le/9eFBgYyBwcHNj48eO5YwICApitrS0LDQ1ljDG2bt06JpFICqxLQa/T4nx+63R21dWrVzFs2DDIZDKkpaWhbt26OHz4MGrWrFnouY8ePYK3tzfS09MREREBBweHcqgxIeSDl/QaODUnb5vJgWMzAfeugHnh713FsWPHDgwYMID33igWiwHkLuL5xRdfoGfPnhgwYECRrvvZZ59h2LBh6NmzZ5HrlJ6eXui3fGtra1haWqrdJxQKVbqlFONa9PT0NF5zwYIF3GMPDw989913GDZsGBISEnj3Onz4MIDcmVQfffQRfH191Xbvbd68Gfb29ujbt2+Bz6Ug9vb28PHxwfbt23Hv3j2MGzdO7XPw9/eHTCZDnTp1uJlJ3bp1w+bNm9GhQwfesaNHj4a+vj6sra3Rvn17zJ07V2WWmEJqaiqio6MLrGNBs56K87vQ09PjjZ/y9PTEwoULMW3aNKxfvx4ikQgff/wxpk2bBqlUihcvXiAmJgaMMbx48QK2trYwNzcvsM6lQWdBTmpqKgYNGoQRI0bgl19+QVZWFrp164bRo0fj/PnzBZ6bnp6OYcOG4ZNPPsHq1avLp8KEEAIA8S9zAxtlTAbEh5RZkHPhwgWV7pb79++jRo0asLS0RHZ2NtLS0pCYmAgzMzOkp6fD2NgYcrkcMpkMWVlZ3BgVuVyOjIwMSCQSlfMUpFIp9PUL/ni4e/eu2tlBymbNmoXPPvtM7T5HR0eVD2bFdlG+tLq4uAAAXr16pTagsrOzw9y5c9G7d29ERUWhRo0a3L6cnBzs3LkTEydOLPT5FmbixImYOnUqYmJisGLFCrXHbNq0CWKxGL6+vrzyN2/eYM2aNbwP/cuXL2v9/3Du3Dl8+eWXBR7z7bffqh0UDBT8u9Cm0UHBxcUFUqkUUVFRcHR0hFwux44dO7Bjxw4AQHJyMnJyctCzZ08sWbIEo0aN0vraxaZ1m08p27VrF9PX12dxcXFc2cmTJxkA9vLlywLPnThxIvvss8+4Zr2IiAit70vdVUQbyl1DmcXoSi0L1F1VMqXWXZUYmdtFpeiuWmzG2BLL3PIyEBcXxwCwn376iSuTyWTM29ubzZo1i12+fJmJxWJmZGTEzM3NWVBQEPPy8mIzZ85ktra2bMuWLczd3Z2lpKQwxhh7+PAh69q1Kzt79iwTiUTM2NiYmZubs5CQENagQQP25ZdfMisrK2ZjY8OuXLlSJs+JsdyuC0NDQ67riTHGZs6cyVxcXLjt5ORk9vz5cyaTyRhjjNe1prBq1Sqmr6/PEhISNB6zZcsWJhQKuWMUDh48yAQCAXvx4oXKOampqez58+dqr6eg6K5S3NfLy4sNHTqU26/cXRUWFsaEQiF78uSJynXq1q3L1q1bxxhjxfpcKyk/Pz9mZWXFsrKyuLJx48YxT09PbjshIYH3/6Tu/2XhwoXM2NhYY7eaLrqrdDbwOCAgAG5ubrCysuLKvL29uX2aHDhwANeuXcOqVavKvI7kw5Xo7889DvHtg8S//9ZdZUjFYl4T6LUyb1ugB/RdXWatOHfv3oVIJMLWrVsREhKCN2/e4JNPPsHbt2+xePFitGvXDuPHj8fu3buRmJgIDw8PALktGG/fvtXY2tK1a1eMGDECf//9NxITE7kkeG5uboiLi8OaNWvKtKV8zJgxqF69OoYPH44bN25g48aNWLt2LZYsWcIdc/DgQdSuXZubGbR3715MnDgRp0+fxr1797B69WosXrwYX3zxBZdUb9myZViwYAEuXbqE+/fvY/PmzZgzZw4mTJigknhv8+bN6NKlC9zd3VXq9++//6J27dq8/DoF0dfXx507d3DgwAG1+7ds2YLatWujXr16Kvv69++PTZs2aXWfsjB58mRIJBKMHj0aN2/exJo1a7Bz504sXryYO2bTpk2oW7cut/3nn39i6tSpOHv2LAIDA7FixQr8+OOPmD9/vsZuNV3QWXdVXFwcrK2teWWWlpYQCoWIi4tTe05oaCimTZuG06dPw9DQUKv7ZGVl8ZJLFTSNjhAAyImOxtvvvs8rkMvxZtFiGLdrB5G9ve4qRioOzxHAydm5j6feBGzU50MpDQEBAWjSpAk+/vhjNGrUCBkZGejatSvOnTtXYCbc4cOHQyAQAAD3LwC1066VDR06FADQokULbNiwoRSegXpGRka4ePEivvnmG0yaNAlWVlbYvHkzrwvDzMwM7u7u3LiQUaNGQSQSYc2aNYiMjISLiwu2b9+OwYMHc+csXLgQf/75J7799lvExsbCyckJq1evxrBhw3j3j4+Px8uXLzXmglGM+yxoxlXNmjXVThNXcHBw4PZfuXJFY56YIUOG4NChQ3j58iWMjIzg7u5e4u6zorC0tMTly5fxzTffYMKECahWrRoOHDjAG+NlaWmJWrVqcduffvoptm/fjlWrViE6Ohpubm44dOgQevfurfE+5ubmagPKsqSzIEckEqlktszJyYFcLodIJFJ7zoQJE9C7d2/IZDLcuXMHz549AwA8ePAAjDE4OjqqnOPn54elS5eW/hMgVVZ22Csgf84HuRzZr8IpyCGqzGoUfkwJ3L17F82aNcPs2bMxa9Ys5OTkqHxTNjY2RkhICG9sjfKA0Zo1a8Lf3x+dO3fG2rVreee9fPmSd55yQMS0nE5eXI6Ojti2bZvG/YMGDcKgQYO4bYFAgGHDhqkELMrEYjGmT5+O6dOnF3hvKysrPH36VOP+J0+eYPXq1bz/j/x2795d4D327dvHPS4oY3GLFi24afPu7u5ql0soa+7u7tizZ4/G/RMnTuRNixcKhRg/fnyh47KUDR8+XOO4oLKis+4qJycnlZH5imZBTZGzqakpHj16hClTpmDKlCn49ddfAQDz5s3DX3/9pfacefPmISkpifuJiIgoxWdBqiKxizMgzPfSEAohdi5eDo3SIjQygsfTJ/B4+gRCIyOd1oWUn4CAADRr1gxA7geLuq6AYcOGYfPmzXB1dUVwcDBMTU15+We+++47rFixAj4+PqhXrx6Xb2XkyJH4/fff4erqilevXsHMzIw7T09PT2Nelg/B7t270apVK11Xg5SQgJV1qK7BtWvX0LZtW+5bCgCsWbMG8+bNw9u3b2FiYgKZTIbAwEC4urqqdG0BwNmzZ9G9e/ciTSFPTk6Gubk5kpKSym3RM1L5xO/Zg7fLvs3dEApRfdlSWAwZottKkRLJzMxEaGgoXF1dYWBgULKLldMCnVlZWejevTv++OMPNGrUqEzuQUhFUtDrtDif3zpryWnTpg18fX0xcuRIHDlyBFu3bsXChQsxf/587ttDSkoKWrRogWPHjumqmuQDZaHUF+124jgFOIRPbAwsScr9KcMVyCUSCf777z8KcAgpJp0mA/zrr7+wcuVK/Pzzz5BIJPjll194fX76+vrw8vKCjY2N2vPNzMzg5eXFJcUipCyI7Ox0XQVCCCHFoLPuKl2h7iqiDXl6OoKbeQEA6gbcpTEwVUCpdlcRQspElemuIoQQQggpSxTkEEIIIaRKoiCHEEIIIVUSBTmEEEIIqZIoyCGEkCJKz0lHo+2N0Gh7I6TnpOu6OoQQDSjIIYQQohPPnz/H7NmzUa9ePcyePVurcx4/foxJkyahWbNmaN++PZYsWaKyJmFcXBymTp2Kxo0bo02bNrylLABg9uzZqFWrlspPmzZtilT/kSNHolatWli/fr3KPsU9Fi1apLLv3LlzqFWrFhYuXKiy79q1a1x9ateujTZt2mDmzJmIjo4uUt2K6vHjx5g+fTrq1Kmj1VJIjDH4+/tj8ODBaNKkCXx9fXHw4EGV486dO4f+/fujUaNG6NGjB06cOFEW1deIghxCCCHl7vHjx/D19YWdnR2srKy0+hCPiYnBpEmT0KFDB2zbto1b0meIUrJOmUwGHx8f3L9/H2vXrsXnn3+Or7/+GitX5q0c/9VXX+Gff/7hfk6ePImkpCTUr1+/SM/h9evXSEpKwm+//cYrT01NxZ9//onk5GS8e/dO5bx169YBANauXYvMzEzevvT0dLx8+RLbtm3DqVOn8O233+LChQvo3r07srOzi1Q/bV27dg0fffQRatWqBQMDA8TExBR6zvr167F9+3aMGjUKO3bsQN++fTFy5EheQHnixAn06NED7dq1w65duzBixAgMHz4cx48fL5PnoRb7wCQlJTEALCkpSddVIRWYLC2NBdWtx4Lq1mOytDRdV4eUgoyMDBYUFMQyMjJKfK207DTWcFtD1nBbQ5aWXX5/H48ePWJTpkxhAwcOZDNnzmTv3r0rt3uXtpycHCaXyxljjPn4+LCRI0cWeo5MJuPOUdi1axcTCAQsMzOTMcbY4cOHmUAgYOHh4dwxK1b8v737jorq6v4G/qUPvShdRAQRRI1iARQr9hJRYxejscUWfcToY6LRRGNN1GisARXs0UfUgC0iGmMiEgs2VBALRlHpCEib/f7By/15nWEcYAYU92ct13LOPefMOTN3Zjan3LuMTE1NKT8/X269x48fJwB04cKFcvWhQ4cONHr0aDI3N6e///5bSP/ll1+odevW1LZtW5o4caKozPPnz0lHR4f++usvsrGxoV27domO//777wSAkpKShLQzZ85UqH3KKigoEP7v5eVFU6ZMKVeZUlOnTqUmTZoIj3v16kX9+vUT5Zk9ezZ99NFHZdar6HNakd9vHslhjLFKeJb7rEqeZ/fu3fD19YWFhQXat2+P//3vfwrvxq1u586dkzvl8/q/N6eJXqetra3wDt/yaGpqisq8evUKERERaNmypXDj0jNnzqBRo0ZwcHAQ8vXq1QuZmZm4cuWK3HqDg4PRpEkTeHl5las9QMmtN0aMGIGtW7cKaUFBQaKr978uNDQUHh4e8PHxwZgxYxAUFPTW5zAzMwNQMkIkz+HDh9/6XuzZs6fM+nV0dN7aBmXKEJHo/cnOzoa5ubkoj4WFBWJjY5GWllbu56yIar2tA2OMvY+O3Dsi/N//kD8WtFmAAQ0GqO357t+/j/Hjx+Po0aPo0KEDAKB+/frw9/fHixcvYGlpqZLnKZ1uMDU1fWveFi1a4Pjx4wrzyLuxsirMmjULYWFhSE5ORtOmTXH06FHhWFJSEmxsbET5Sx8/fvxYJpBJTU3F4cOH8cMPP1S4PWPHjkX79u2xevVqPHjwANevX8fQoUMRGhoqkzc4OBhTp04FAIwfPx4rVqxAYmIi6tevL7fuwsJCrF69GiYmJmjevLncPH5+fm99L6ysrMrZq/IpnWL76quvhLSuXbti1apVCAwMROPGjfHkyRMhGExKSoKFhYVa2wRwkMMYY+WSnJOMpdFLhcdSSPHt39+ijV0b2BjaKChZcZs2bUK7du2EAAcAHB0dQUQoKCjAihUr4O3tjfbt25er3kWLFqFr167w9vYGAMTGxqKwsFCpsgYGBnBxcSnX86nKl19+ifHjx+Pu3buYO3cuPv30U2FBq1QqlRllKL2/YXFxsUxdO3bsgKamJkaOHFnh9jRr1gwNGjTA/v37ERsbi0GDBsm97cBff/2Fhw8fYsSIEQAAJycndO7cGcHBwfj+++9Fedu1awctLS0kJyfDzs4O+/fvLzMoMDIyqrb3AgBSUlLQp08f+Pj4YPbs2UL6f//7Xzx58gQtW7aEhYUFCgsLMWHCBCxZsgRURXeU4iCHMcbK4VHWI0ghFaVJSYqk7CS1BTknT57EsGHDRGmJiYkwMDBA7dq1kZiYCFdX13LXe+/ePbRq1apCbTp37hzGjBmjMM/MmTMxefLkCtWviLW1NaytrdGwYUPY2dmhZcuWuHjxIlq3bo3atWsjNjZWlD8lJQUA5N7sOTg4GJ988onMtEp5jR07Fps3b0ZCQgIOHjwoN09QUBCKi4vRokULIS09PR03b97Ed999By0tLSF9x44dsLW1Re3atd86snb48GEEBgYqzLNo0SKZc0gVUlNT4efnB2traxw+fBja2v8XVujo6GDjxo1Yt24dkpOTYWVlJezAqlOnjsrbIg8HOYwxVg51TepCE5qiQEdTQxMOxg4KSlXcq1evcOPGDRgbG4vS9+/fj969e+PmzZsICwtDVFQUli1bht27d2PRokXw9/dHaGgoRo4cid27dyM0NBT6+vpITEzEmjVrMHz4cERERCA6OhoLFy7EgQMHAAB//vkntm/fDiMjI6xcuRK2trZy21Wd01WvKw0AsrOzAQCtWrXCzp07kZOTA0NDQwAlfdLW1kazZs1EZS9evIgbN25g/fr1lW7H8OHDERgYCAcHB7Rr107m+MuXL/Hrr79ix44dMtNO3t7eOHbsGPr06SOk1atXT+lAoLqmq9LS0tClSxeYmZkhIiICBmXcyFhbW1voy8GDB9GyZUu5Aac68MJjxhgrBxtDG8z1mis81tTQxAKfBWobxYmNjUVRUREiIiKEIf59+/bh8OHDWLhwIVxcXNC2bVuMHTsWa9asga2tLa5fv46dO3di2rRpaNu2La5cuSJM1eTm5uLWrVtwd3dH69atMXHiRKxZs0ZY1xMWFobp06fDzMxM4fVSSqerFP2r7OjIwYMH4eLiIgQw4eHhCAsLQ1FREYCSH9n//ve/sLGxgY+PDwBg0KBBMDAwwDfffIPi4mK8ePECy5cvx+DBg2Wme4KDg+Hq6ip3mu/UqVNwcXHBv//+q1RbzczMcPfuXfz5559yj+/Zswf6+voYMGCAzOvUp08fpRYgl6V0ukrRP2Xv2l2W4OBguLm5CY/T09PRpUsXmJiY4OjRo0JA+bp79+5h27ZtKC4uBhEhKCgIhw4dwvLlyyvVlvLgkRzGGCunj50/xvfRJWsoDvU7BCdTJ7U91+XLl+Hq6goigru7O/T09PDgwQPs2LFDuK6LlZUVXF1dhbU1APD9998rnMIyNTWFpaUl3NzcROWWLFkCe3t72NnZqWWq6XVubm4oKirC06dPoampCRcXF9ja2uLcuXMAgKysLNy7d08I0Dw9PTF37lyMHj0axsbGSElJgZ+fH06dOiWMIpibm+PIkSMYNWoUgoKCkJeXhx49esjs9MrNzcXevXsxf/58uW1LSEhAbm5uuRbHvr6j603BwcHo06ePaEqqlL+/PwYNGqT2C/6VJTc3F02bNgVQsjj79u3bOH78OFxdXYVF3enp6UhISBDK/Pzzz7hy5Qrs7e3x0UcfCel6enq4efMmAMDe3h7R0dGYNWsWiAi1atVCWFgYOnfuXGV94yCHMcYqwdrAWq31X7p0CZ6enti5cyfOnTuH7Oxs+Pr6ikZJdHR0ZC4U9/rUgampKZKSkuDu7o7o6GiF5Uq3YmtpacldqKtKr49OlXp9TcfAgQPh6+srjELY2dkhJCQERUVFeP78OaysrET5S7Vt2xYJCQl48uQJDA0NhS3Yr9PS0sKlS5fKnBI6duwYlixZAn19/TLbv2vXLmFRszz79u0Tju/atavMKZo+ffogLi4OJiYmaNu2LeLj48ucJlQHfX19udNdpecCAIwbNw79+/cXHk+ZMkXuGh9Nzf+bIJJIJNi0aRN+/PFHZGdny+x6qwoc5DDG2Dvs8uXLGDZsGLS0tNCxY0e5eXx8fDB16lSsWrUKu3fvljk+ZswYtG3bFvb29mjQoIGo3Pjx47FkyRJhTU5VcnZ2Vnjc2NhYZi0SUBII2dnZKSyroaEBe3v7Mo/r6ekp3JG0bt06hSMzABTW/+ZxRX3V1tYWtaWqd0ppaGi89TnNzMxEwaKFhYXSo1yGhoZyp7OqAgc5jDH2jiooKMCNGzfg6empMN/w4cPh6+uLp0+fwtbWFtu3bxctNJ06dSo+/vhjaGlpwcLCAg8ePAAAfPbZZ/Dz80NycjIsLS2xe/du4YfMwcFB4cX8arq6detWdxOYCmhQVW1Wf0dkZWXB1NQUmZmZlV6IxWouaW4u7niWbPNsePkSNMvYNcDeH69evcL9+/fh5OQEiURS3c1RChEJu4TKe3Vgxt5Hij6nFfn95pEcxhh7R2loaMDIyKi6m8HYe4u3kDPGGGOsRuIghzHGGGM1Egc5jDHGGKuROMhhjDHGWI3EQQ5jjDHGaiQOchhjrJykubmIc3NHnJs7pLm51d0cxlgZeAs5Y4yxakFEiIyMRHh4ODw9PTFq1Cilyp0/fx4RERHQ0NBA3759RffeysjIwLx582TKTJgwQbg/0+uys7Mxf/58mJqaKrwhqTw//fQT4uPjMXjwYJmbfP7666/4448/0K5dOwwZMkR0LD4+Hj/99BP8/PxEt0oAgDt37mDdunUASi4hYGlpidatW6NHjx7lalt5SaVSHDt2DCdOnEC7du0waNAgpcpFRkbi999/h66uLvr37y9zh3WpVIoDBw4gJiYG5ubmGDZsGJyc1HevtzfxSA5jjLEql5SUBDc3NyxduhRHjx7FyZMnlSq3Zs0adO3aFfn5+cjJyUHHjh2xadMm4fjLly+xfv16mJubw83NTfgn7/YQADBx4kTs2bMHISEh5e5DWFgYfvnlFyxevFiULpVK8eWXXyIoKAhRUVEy5davX4+tW7dizpw5MseSkpKwfv162NjYoGHDhsjOzsbgwYMREBBQ7vYp6/bt22jQoAF+/vlnHDp0CGfPnlWq3Lfffov+/fuDiJCSkgIvLy/s2bNHlGfIkCEIDAyEgYEBrly5giZNmuDixYvq6IZ89IHJzMwkAJSZmVndTWHvsOKcHLrV0I1uNXSj4pyc6m4OU4G8vDy6desW5eXlVbqu6jo/8vPz6dChQ7R582Y6fvx4lT2vOqSmptLdu3eJiKh79+40YsSIt5Z58eIFSSQS2rx5s5D2448/kpGRkfCdnpSURAAoLi7urfUFBweTj48PzZs3jxwdHcvdhw4dOlD//v1JT0+PHj58KKQfO3aMHBwcyMfHhyZOnCgqk5+fT7Vq1aLg4GCSSCR09uxZ0fHff/+dAFBSUpKQtmvXLgJAN2/eLHcblZGcnEz3798nIiIvLy+aMmXKW8s8ePCAtLS0aN++fULa/PnzqXbt2pSfn09ERMePHycAdOPGDSFP//79ycfHp8x6FX1OK/L7zdNVjDFWCYXPnkGvCobfb9++jf79+8PAwACWlpY4ffo0Pv/8c6xdu1btzy1PXFwc1q9frzBP37590b17d7nHynODx1InT55EYWEhhg4dKqQFBAQgMDAQkZGRoqmfjRs3Qk9PDw0aNMDgwYNhamoqquv27dv4+uuvcf78eYSGhparHa+zsrJCz549sW3bNixYsAAAEBQUhNGjR+P06dMy+cPCwqCjo4NRo0YhKioKQUFBMlNdb2rWrBkA4NGjR2jUqJHM8ZiYmLeORA0dOhS+vr5yj1lbWyssK09ERAQkEgn8/f2FtICAACxatAjnz59Hp06dcPjwYTRv3hweHh6iPAMGDBDuIq9u1T5dlZ2djaioKPz9998oKipSqszDhw8RFRWFhIQENbeOMcZkZRw6JPw/sXcfZKj5Dt45OTno3r07AgICcOnSJRw/fhwrV65EUFAQcnJyVPY8GzZsQK6SC6kNDQ1F00Hy/tWqVUtlbQNK1rLUqlVLdN8iS0tLGBkZIT4+XkiztraGpqYmTExM8Msvv8DNzQ03btwQjr969QpDhgzBsmXLUL9+/Uq3a+zYsdi+fTuICC9evEB4eDjGjBkjN29wcDDGjBkDbW1tTJgwAQcOHEBmZqbC+k+fPg0NDQ24ubnJPW5qavrW98Lc3LzS/XxdfHw87O3toaurK6Q5OTlBQ0NDeC/i4+Nl1t+Uvt5V9ftdrSM54eHhGDlyJOrVq4esrCxoaGjg6NGjaNiwodz8V65cwbRp0/D48WM4OTnh2rVraNq0KQ4cOKDyDxNjjMlTmJyMZ4u//78EqRRPv1kAQ19f6NjYqOU5t2zZAhMTE8ydO1dI69ixI/Ly8pCSkoKoqCi4uLiU+SNYlkOHDqFx48ZwcXEBUBLkDB48GAZK3JC2bt26mDp1avk6Ukl5eXly19aYmJgIwZmFhQVu3boljBJ99dVX6Ny5M6ZMmSKsNQkMDISrqys+/fRTlbSrZ8+eKCgoQGRkJGJjY+Hr6yt3ce3Dhw9x+vRpYQ1Ru3btUK9ePezevRuTJk0S5Z0/fz4MDQ2RlJSEY8eO4ZtvvkG9evXkPr+rqytcXV1V0hdlyXsvtLW1oa+vL7wXeXl5sLe3F+UpDVCVDaYrq9qCnLS0NIwcORKBgYGYP38+pFIp+vbti4CAgDIXJb148QIrVqxAmzZtAACZmZnw8fHBzJkzK7RojDHGyqvgwUNAKhUnSqUoePhIbUFOeHg4+vXrJ7oTeUpKCjQ1NWFhYYHw8HD06NGjQkGORCIRgpzyqOx0VUUYGRkhIyNDJj09PV348TQwMBAFaZqamhg+fDi++OILFBUVITU1FRs2bMDw4cOFIO3ixYtIS0vD1KlTMW7cOGF6SFlaWlr49NNPsXXrVly9ehXz58+Xmy84OBjGxsZYtWqVkKanp4egoCCZIMfZ2Rnm5ubw9fXFjz/+qPA9qux0VUXIey8KCgqQm5srvBfy8qSnpwOA0ncRr6xqC3IOHz6MvLw8zJgxA0DJiThr1ix07twZt2/flvth7datm+ixqakpevfujYiIiKpoMmOMQbeeI6CpKQ50NDWh61hXLc8nlUpx8eJFDB48WJR+8uRJtG3bFqmpqfjnn3+QkZGBq1evYvLkyTh06BC6deuG33//Hc2bN8dff/2Fzz//HLq6unj+/DmOHz+ONm3a4OrVqygqKsKFCxfwxRdfAABSU1Nx4MABGBkZYdiwYdDS0pLbrtLpKkVUPcLu4eGBtLQ00XqOhw8fIi8vT+5alVL5+fnCcggjIyNhi3apxMRE6OjoKNyF9TafffYZ3NzcYGJiIrMtHCh5H7dv346RI0eKZivc3Nwwa9YsXL16VRRcjR49GnXq1FHquUunqxRR9XSVh4cHfv75Z7x8+RJGRkYASgJfAMJ74eHhgfDwcFG5uLg4aGlpVdnIU7UFObGxsahfv77ohCp9g2NjY5X+i+T8+fMK8+bn5yM/P194nJWVVbEGM8YYAB0bG1jP+xrPvltUkqCpCdvvvlXbKM6dO3fw8uVL3Lp1S0hLTEzExo0by/zrfcuWLdi9eze8vb3RvHlzrF27Fp999pkQ5ISGhgoj4m+aNm0amjdvjtOnTyMxMRHffPON3HxVMV0VHR2NHTt2YOXKldDX10fXrl1hZmaGjRs3Cot8169fDysrK3Ts2BEA8Ndff6FJkybCb0tWVhY2b96Mzp07Q1tbG9ra2jLtTklJwa1bt0Tp165dw5YtW/Ddd98ptUDaxcUF27ZtQ61atSCRSGSOnzhxAs+fP8fSpUuFoKDU0aNHERQUhJ9//rlcr0+pqpiuioyMxJEjR/DTTz8BAPr06YOpU6ciODgY06dPB1DyXjg5OaF169YAgMGDB2PVqlWIjIyEn58fioqKsHnzZnTv3h1mZmZqbW+pagtyMjIyZE4cMzMzaGlpyR2OlGflypW4dOkSLly4UGaepUuXlvsCT4wxpoiZv78Q5NSPCFfr7qrLly/D0tISERERICLo6+sLi1dLRwxatmyJHj16iHa6LFu2THSRvDe5uLigWbNmGDp0qOhCc2vWrEGjRo1w7do1zJ49W239AoDp06ejuLgYcXFxkEgkmDp1KszNzbFoUclrWzoltnjxYujr68PIyAhBQUEYOXIkLl68iOLiYpw7dw6//vqrEFg8f/4cLVu2hLu7O4yMjBAZGYk6depgy5Yt5WrbjRs3EBQUhO+++07pMoquZRMcHAw/Pz+ZAAcA/P39MWfOHPzwww/laqOqFBQUYObMmQCABw8eIDc3F1OnToWdnR2++uorACVrYtevXy8EOZaWltiwYQMmTZqE06dP4+XLl4iJicFvv/0GTc2SPU1eXl6YO3cu+vfvj549eyI+Pl5YQ1ZVqi3I0dXVlVl4lJ+fj+LiYtFq7bJs374dX3/9NXbt2iVzhcXXzZ07V3jzgJKo3sHBoeINZx8ETQMDuN+Oq+5msPeATgW235bHpUuX0KpVK6xfvx6bNm1CdnY2QkJC0Lt3byGPpqYmiEhU7vWpDj09PeTl5cHIyAjPnz9XWK50GsjAwACvXr1SR5cEDRs2hFQqFY3Gvx4EeHt7Y926daI1NgMGDMDdu3cRGRkJDQ0NbNu2Dba2tsJxf39/dOrUCX/88QfS09MxZcoUeHt7i9YzvalXr14yu6xOnz6N6dOnKxzFmT59usJpoJkzZwrlu3XrBk9PT7n5PvnkExQUFCAtLQ1ubm5Yt26dyqeXFHl959brV4t+fbqxS5cuMpcr+PTTT9G+fXucOXMGurq62Lt3LywtLUV5lixZgiFDhiAmJgaDBw9G9+7d5QZ6aqP0FXVUbMmSJWRpaUlSqVRIu3v3LgGgqKgohWVDQkJIR0eH9uzZU+7n5YsBMvZhel8vBtihQweaN2+ewjzLli2jTp060YIFC+jZs2fUokUL0cXkxo8fT506daJZs2aRr68v+fn5ERHRwoULqWvXrrRgwQJKTU0lDw8PevHiBRERxcfHU4cOHdTWr3fd3r17+XeiGqj6YoDVdp2cHj164MWLF/jzzz+FtAMHDsDU1FQYYi0qKkJ4eDgeP34s5Nm5cyfGjx+PkJAQ0QWhGGOspiEiXL16tcwRgFLTpk3DsGHDhNGKCRMmiHavrF27FsOHD0fDhg0REhIi3CMqMDAQAwcOFMpNnjxZGDWxsLDA6NGj1dCr98OQIUOqbAcQUx8NojfGKqtQ6RUf582bh7S0NHz77bdYtWoVJk+eDKBk3Y65uTm2bduG0aNH47fffkP//v0xYsQI0c3DdHR0lN6mmJWVBVNTU2RmZvIJzNgH5NWrV7h//z6cnJzkLgx9FxUUFODIkSPo2rWrzBV7GauJFH1OK/L7Xa0XA9y2bRuCgoJw+vRp6Onp4X//+59onllHRwe9e/cW1tCkpaWhR48eSE1NFd2QzcjISKXXYmCMsXeBrq4uPvnkk+puBmPvrWodyakOPJLD2IfpfRzJYexDo+qRnGq/dxVjjDHGmDpwkMMY+6B8YIPXjL1XVP355CCHMfZB0NHRAVB1NwZkjJVf6eez9PNaWdW68JgxxqqKlpYWzMzMhIvhGRgYKLxAHGOs6hARcnNz8fz5c+HuB6rAQQ5j7INh8//vL/X6VX8ZY+8OMzMz4XOqChzkMMY+GBoaGrC1tYWVlRUKCwuruzmMsdfo6OiobASnFAc5jLEPjpaWlsq/TBlj7x5eeMwYY4yxGomDHMYYY4zVSBzkMMYYY6xG+uDW5JReaCgrK6uaW8IYY4wxZZX+bpfngoEfXJCTnZ0NAMJNPxljjDH2/sjOzoapqalSeT+4G3RKpVI8efIExsbGKr8QWFZWFhwcHJCUlFQjb/7J/Xv/1fQ+1vT+ATW/j9y/95+6+khEyM7Ohp2dHTQ1lVtt88GN5GhqaqJOnTpqfQ4TE5Mae/IC3L+aoKb3sab3D6j5feT+vf/U0UdlR3BK8cJjxhhjjNVIHOQwxhhjrEbiIEeF9PT0sGDBAujp6VV3U9SC+/f+q+l9rOn9A2p+H7l/7793qY8f3MJjxhhjjH0YeCSHMcYYYzUSBzmMMcYYq5E4yGGMMcZYjfTBXSensp48eYJffvkFJ06cwMCBAxEYGPjWMsnJyViyZAmuXbsGKysrTJ48GR07dix3nqoglUqxefNmHD58GMXFxejVqxemTZsGbW35p0pMTAymTZsm99jq1avh4+MDAOjduzdSU1NFx8eMGYOJEyeqtgNKuHDhAtauXYsnT57A3d0dc+fORd26dcvMv27dOuzatUuUZmtri7CwsErVqy7p6elYunQpYmJiYG5ujnHjxqFXr15l5iciHDp0CGFhYXjy5AlcXFwwbdo0eHh4CHni4+MREBAgU3bLli1o2rSpWvoBADt37sTevXuRl5eHTp06ITAwEPr6+pUuU5F61eHGjRv44YcfcP/+fTg7O2P27Nlwc3MrM39OTg6Cg4MRFRWF/Px8tGjRAjNmzECtWrWEPLt378batWtF5fT09HD27Fm19aMsOTk5+OGHH3D27FkYGhpixIgRGDp0aJn509LS5J6rS5cuRadOnSpcrzqFhYUhJCQEmZmZaNOmDebMmVPmtWHK6h8ATJs2DSNGjAAAfPHFF7h48aLoeIcOHbB8+XLVNl4JiYmJ2Lx5M/744w9MnDgRo0ePfmuZ+/fvY9myZbh9+zYcHBwwY8YMtGzZstx5VIKY0i5evEh169alefPmkbu7O02ZMuWtZbKzs8nZ2Zm6detG4eHhNH/+fNLW1qZTp06VK09VmTlzJtWuXZtCQ0Npz549ZGdnR2PHji0zf2ZmJv3999+if6NGjSIDAwPKyMgQ8llbW9PChQtF+ZKSkqqiSyIXLlwgXV1dmjVrFkVERNDHH39M9vb2lJKSUmaZwMBA8vb2FrX98uXLla5XHQoKCqhZs2bUpk0bOnLkCC1btoy0tbXp119/LbPMzJkzacCAARQaGkqnTp2iSZMmka6uLl24cEHIc+XKFQJAx48fF70OWVlZauvLihUryNDQkDZt2kQHDhyghg0bUu/evStdpiL1qsOdO3fI2NiYxo0bR0ePHqWAgAAyMzOjxMTEMst4eXnR9OnT6eDBgxQeHk7t2rUjZ2dnSk9PF/KsXLmSGjRoIHqfoqOjq6BHsvz8/KhJkyZ08OBBWr9+PUkkElq3bl2Z+Z8+fUoAaNeuXaL2p6amVqpeddm6dSvp6urS6tWr6dChQ+Tp6Uk+Pj5UXFwsN39BQYHM9+XChQsJAF25ckXI5+fnRwEBAaJ8d+7cqaJe/Z+IiAhydnamJUuWkI2NDS1atOitZZ4+fUrW1tY0aNAgOnr0KE2bNo0kEonoO1OZPKrCQU455OTkUGFhIRGVfNkoE+SsWrWKjI2NKScnR0gbMmQIeXt7lytPVXj69ClpaWnRnj17hLTffvuNNDQ0KD4+Xqk6pFIp1a9fn0aPHi1Kt7a2ph07dqi0vRXRrVs30Q9afn4+WVlZ0cKFC8ssExgYSN27d1d5veoQGhpK2tra9Pz5cyFtypQp5OLiUmaZ7OxsmTQfHx8KCAgQHpcGOS9evFBtg8uQm5tLRkZGtHr1aiEtJiaGANCff/5Z4TIVqVddRo0aRS1bthQeS6VScnd3p88//7zMMm++VxkZGaStrU0hISFC2sqVK+mjjz5SeXvL6+TJkwSA4uLihLRFixaRhYUFFRQUyC1TGuRcv35dpfWqQ3FxMdna2tLXX38tpN2/f580NDTo0KFDStfTp08f0XlAVBLkzJkzR2VtraisrCwhYHN0dFQqyJk9ezY5OjpSUVGRkNahQwfy9/cvVx5V4TU55WBgYFDmtE1ZIiMj4efnBwMDAyGtX79+iI6OFm4WqkyeqnDmzBkUFxejT58+Qlr37t2hq6uLyMhIpeqIiopCYmIixo8fL3Ns7dq16NixI0aNGoWjR4+qrN3KKioqwtmzZ9G3b18hTVdXFz169MCpU6cUlr1+/Tr8/Pzg7++P5cuXIy8vTyX1qlpkZCS8vb1haWkppPXr1w8JCQl4+PCh3DJGRkZy0woKCmTSR44ciS5dumDKlCm4e/eu6hr+hujoaLx8+VL0mrZs2RJ2dnZlvqbKlKlIveoSGRkp+qxpaGigb9++Ctvx5nslkUigra0t8149evQI3bp1Q58+fbBw4ULh7s1VKTIyEi4uLqLpt379+iEtLQ2XL19WWPaLL75A586dMWHCBFy9elVl9arSrVu38PTpU9G5VK9ePTRp0kTpc+nJkyc4duyY3O/LI0eOoGPHjhgyZAi2bdtWrjtvq4qxsbHS94gqFRkZiZ49e0JLS0tI69evH06dOiX0QZk8qsJBjpo9fPgQdnZ2ojQ7OzsQEZKSkpTOU1VtNTExEX2R6ujowNLSsswfyDcFBwejUaNGaNOmjSjdxcUFY8aMwbx58+Ds7IyBAwdi2bJlKm3/2zx79gz5+flyX2tF/ZNIJBgxYgRmz56NAQMGYNu2bWjbtq3ww1LRetWhrHOp9JgyLly4gMjISPj7+4vSu3XrhjFjxuA///kPMjIy0LRpU0RHR6uk3W8qbWt5XlNlylSkXnUoLi7Gv//+W+l2rFq1CkDJHyOltLS0MGzYMMyYMQMBAQEIDw9Hs2bNqjzQqei56OPjg88++wyzZ8+GlpYWWrVqhfDw8ErXq2qqOJdCQkIgkUgwbNgwUXrt2rUxatQozJs3D76+vpgzZ47cNXHvorLen5cvXyI9PV3pPKryQS88Pn/+/FsXDk+ZMqVSJ1dhYaHMVR9LFzgWFhYqnaeiunbtqnA0yNnZWVhUK68dpW1Rph0ZGRk4ePAgli5dKnMsMjJSqLtLly4wNTXF7NmzMXny5ErdwO3HH3/E/v37FebZsWMHGjRoIPRB3mutqH/z588XlencuTNcXFwQGhqKcePGVbheZdy8eRNjx45VmGfEiBHC4u/KnksPHjzAwIEDMXToUNFCTnd3d5w4cUJ4XLqQfM6cOThz5oyy3VFaaVt1dXVF6YpeU2XKVKRedVB0zhQVFYGIoKGhobCO3377Dd988w22bNkCBwcHIX3y5Mmiert37w4XFxesXbsW8+bNU2EvFKvIuVi7dm2cO3dO+Au/R48eyMvLw6xZs4RRL3V+X5aHovfw9ZFeRbZu3YqhQ4fC2NhYlB4SEiL6vnR3d0fXrl3VtzhXhar7N+9NH3SQ4+HhgTVr1ijMU9ndMRYWFkhLSxOlle4yKt0RoUyeilq6dCmKiorKPP76FJmFhQXS09NlvmBTU1OVaseuXbtARHKDwjdP6C5duqCwsBBxcXHw8vJSpityDRw4EG3btlWYx97eHkBJ/wDIfa0V9e/NttepUwdubm6IjY2tVL3KcHR0fOs5+vpfRJU5lx49eoTOnTvDy8sLISEhomPygt8uXbrg+++/V1hnRZW+punp6ahdu7aQnpqaCk9PzwqXqUi96iCRSKCvry/3vbKwsHhrgHP8+HEMGjQIK1euxJgxY0TH3nyvzMzM0LJlS+F8rSoWFhZ48OCBKO1t56K85QBdunRBaGgo8vLyoK+vX6F61eH1z72VlZWoLa8HnWU5e/YsEhISsHPnTpljb76HHTt2hJaWFmJjY9/5IKes7yBNTU2YmZkpnUdVPuggx8zMDN7e3mp9Dk9PT0RFRYnSoqOjYWlpKfz4KpOnosrzgfD09ERRURGuXr2K5s2bAwDu3buHtLQ04bEiwcHBGDBggFJfNMnJyQAAQ0NDpdsnT7169VCvXj2l8pqYmMDZ2RkxMTGiUYro6Gil+leKiPDs2TOh7aqqVx4jI6NynaOenp7YvHmzKFCNjo6Gvr4+GjZsWGa5pKQkdOrUCU2bNsW+ffuUWnuWnJxc6fevLKUBR0xMDHr27AmgJDCJj4/H7NmzK1ymIvWqi6enJ2JiYkRpypwzJ06cQP/+/bF06VLMmDFDqedKTk6GjY1NRZtaIZ6enti5cydyc3OFP6aio6OhoaGBjz76SOl6kpOToaurCx0dHZXWW1lNmzaFtrY2YmJihPVBBQUFiI2Nxccff/zW8kFBQWjSpIlSf+SlpKSguLhYbZ83VSrrvG7UqJEQvCmTR2VUvpT5A1HW7qq///6bvLy86MGDB0REdOnSJdLQ0KD9+/cTEdG///5L9vb2opXzyuSpClKplBo3bkwDBgwQVtQHBASQk5OTaNdC9+7dadOmTaKyly5dIgB0+vRpmXr//PNPOn78uPA4OTmZWrVqRU2aNCGpVKqm3si3ZMkSsrS0FLbpHjt2jDQ0NCgqKkrIs3r1atEq/8WLF1NeXh4RleyomD9/PmlqatI///xTrnqrQkJCAuno6NDGjRuJiCgtLY1cXV1FlwGIi4sjLy8vio2NJSKix48fk7OzM/n7+5e5O2X79u2iHXYXLlwgY2NjCgwMVFtfOnfuTO3bt6dXr14REdGsWbPIwsJCdGmC4cOH05IlS8pVRpk8VWHr1q1kaGhIV69eJSKi6Oho0tPTE+1uDA0Npfbt2wuPT548SRKJhFatWlVmvStWrKDMzEzh8bp16wgAHTt2TA29KNuLFy/IxMSEFixYQEQlO9tat24t2oX4/Plz8vLyEr439u/fL7weREQ3b94kGxsbGjFiRLnqrSqDBw+mZs2aCbveli1bRgYGBvTvv/8KeSZNmkSzZ88WlcvIyCB9fX1au3atTJ3379+nrVu3Ct/BOTk5NGjQIDI3N6e0tDQ19kaxsnZXhYeHk5eXl/AaHD16lLS0tITvvtu3b5OpqaloR6MyeVSFg5xyyM3NJS8vL/Ly8iIjIyOytrYmLy8vGj58uJDn2LFjMtsbN23aRAYGBuTi4kISiYQGDBgg/GiWJ09ViIuLo4YNG5KlpSXZ2NhQvXr1ZK5dUKtWLdG2SSKiyZMnk4uLi9yg5fHjx9S/f3+qVasWNW7cmCQSCfXq1UsIBKtSYWEhjRo1ivT09MjV1ZUkEgmtXLlSlGf69Onk6OgoPF68eDFZWlqSh4cHWVpakqOjI4WFhZW73qqyd+9eMjExofr165OBgQF17dpV9KNXumX63LlzREQ0bNgwAkCenp7C+e3l5SW6DMDZs2epadOm5OjoSA0aNCCJREIzZ86k/Px8tfUjKSmJPD09yczMjBwcHMja2lomiPbw8BAFcMqUUSZPVZBKpTRjxgzS1dUlV1dX0tXVlfnDZunSpaSnpyc8trS0JH19fdH75OXlRevXrxfybNiwgWxtbcnd3Z1sbW3JysqKtm3bVlXdEjlx4gRZWlpS3bp1ydTUlLy8vOjp06fC8aSkJAIg/IF3+fJl8vb2Jnt7e3JzcyNdXV0aN26czPWY3lZvVUlJSaF27dqRsbExOTk5kbm5OR0+fFiUp0OHDtSvXz9R2oYNG0gikcgNWl6+fEmTJk0ic3NzatKkCRkbG1OLFi0oJiZGnV2RKykpSTjHdHV1qU6dOuTl5UXTpk0T8mzbto0AiK7VtHjxYuG7UFdXl8aOHSvaLq5sHlXgu5CXg1QqlbkKJVCyrqX0qq+ZmZmIi4tDs2bNIJFIhDwvX75EfHw8rKysypyCUiZPVSAi3L59G1KpFO7u7jJbCC9dugRra2vUqVNHSLt+/ToMDQ1Rv379MuvNyMhAUlIS6tatC1NTU7W1XxnJycl4+vQp6tevL9OWhw8fykzRFRUV4e7duzAxMYG9vX2ZayYU1VuV8vLycOfOHZibm8PR0VF0LCcnB9evX4eHhweMjY0RHx8vczVqoGSqrHHjxqK0pKQk5OTkwMnJSfXDymWIj49HXl4e3N3dhSmLUteuXYORkZHMeaeoTHnyVIWUlBQ8evQIjo6OMlO9T548wePHj9G6dWsAwD///CN3jZ29vb1oHUhxcTHi4+Ohr68PBweHcm8DVqWCggLcvn0bBgYGcHFxkTl2+fJluLq6CmtcgJLPUWpqKurXr1/mlagV1VvV7t+/j6ysLLi5ucl8Lm7dugVtbW24uroKaQkJCXj16pXM5+t1eXl5SEhIgI2NjeiSEFUpPz8fV65ckUk3NTWFu7s7AODFixe4d+8eWrVqJdoSnpGRgfv378POzg7W1tZy61cmT2VxkMMYY4yxGomvk8MYY4yxGomDHMYYY4zVSBzkMMYYY6xG4iCHMcYYYzUSBzmMMcYYq5E4yGGMMcZYjcRBDmOMMcZqJA5yGGOMMVYjcZDDGGOMsRqJgxzG2Hvvyy+/xNy5c4XHUqkUU6dORWBgYDW2ijFW3fi2Doyx915kZCS6deuGmzdvws3NDf/5z39w5MgR/PXXX2q7Jw5j7N3HQQ5jrEbo0qULateujVatWmH58uU4f/48GjRoUN3NYoxVIw5yGGM1QkxMDLy9vWFoaIjIyEi0atWqupvEGKtmvCaHMVYj3Lt3D0SE5s2bc4DDGAPAQQ5jrAaIiorC2LFjsWrVKpw/fx6RkZHV3STG2DuAp6sYY++1a9euoX379li5ciXGjx+P0aNHIy4uDtHR0dXdNMZYNeMghzH23nr06BF8fHwwbtw4fPvttwCAxMRENGzYEPv27cOAAQOquYWMserEQQ5j7L2VkJCA5ORk+Pr6itIvXboEAwMDuLu7V1PLGGPvAg5yGGOMMVYj8cJjxhhjjNVIHOQwxhhjrEbiIIcxxhhjNRIHOYwxxhirkTjIYYwxxliNxEEOY4wxxmokDnIYY4wxViNxkMMYY4yxGomDHMYYY4zVSBzkMMYYY6xG4iCHMcYYYzUSBzmMMcYYq5H+H6l6uvAH68ZuAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(truth.x, truth.y, \"k:\", label=\"truth\")\n", - "for i, synthetic_observation in enumerate(observations):\n", - " rho_hat = maps[i]\n", - " plt.errorbar(\n", - " synthetic_observation.x,\n", - " synthetic_observation.y / rho_hat,\n", - " synthetic_observation.y_stat_err,\n", - " linestyle=\"none\",\n", - " marker=\".\",\n", - " label=r\"$\\rho_{\\text{truth}}$ = \"\n", - " + f\"{synthetic_observation.renormalization:1.3f},\"\n", - " + r\" MAP = \"\n", - " + f\"{rho_hat:1.2f}\",\n", - " )\n", - "plt.xlabel(\"$x$\")\n", - "plt.ylabel(\"$y$\")\n", - "plt.legend()\n", - "plt.title(r\"Renormalizing experimental data sets based on MAP of $p(\\rho)$\")" - ] - }, - { - "cell_type": "markdown", - "id": "11acfd96-4b06-48bf-8664-a0e42ddb9db2", - "metadata": {}, - "source": [ - "## predictive posteriors" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "e1708552-11c0-436f-bbc3-58d778e99774", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:47.909398Z", - "iopub.status.busy": "2026-08-11T03:08:47.909244Z", - "iopub.status.idle": "2026-08-11T03:08:47.912488Z", - "shell.execute_reply": "2026-08-11T03:08:47.911972Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(20000, 5)" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "walker.model_sampler.chain.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "17ae70aa-589c-42ac-b132-f918a3160ce4", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:47.914250Z", - "iopub.status.busy": "2026-08-11T03:08:47.914039Z", - "iopub.status.idle": "2026-08-11T03:08:47.917123Z", - "shell.execute_reply": "2026-08-11T03:08:47.916447Z" - } - }, - "outputs": [], - "source": [ - "def predictive_post(chain, model, obs, n_samples, intervals):\n", - " draw_idxs = rng.choice(np.arange(chain.shape[0]), n_samples)\n", - " draws = chain[draw_idxs, :]\n", - " ym = np.array([model(obs, *p) for p in draws])\n", - " return np.percentile(ym, intervals, axis=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "9c48e1df-130d-445e-8be3-6c35e33642e6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:47.918696Z", - "iopub.status.busy": "2026-08-11T03:08:47.918538Z", - "iopub.status.idle": "2026-08-11T03:08:48.194795Z", - "shell.execute_reply": "2026-08-11T03:08:48.194067Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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mNbSdYbt0bvgAQ9uTXmsu0ZagWGN3e4G5C5qZNiM2rP40SjqdZubMmXXVHxGiWyAQCHYh4vG4yOkWCLYTnV4noXCIRLxaoGVzWabHpjOpZdLYdSi/DjQZ4snhbS+ZYIUgGh226MbsGwRo4HvJ9X1SZo6oHiXHdJKtcfRIIGn1kE8sHiOZHOYxDZP+tWtqISZS7uJYlkWhUNje3dghyefzQ3pj61lnLBgv/diRMQwD27Yb2kacd4FAUE5noRNd1ms+Z7kWkyJjKLg9DzrfBXXH++XLsF1yqS4KTis2MbSwUnrO8bxBtty+DE902wUwM2PzZwtB2B/TNDFNc0TauuKKKzjttNNGpK1djf32249f/OIXpce1BNa8efP43e9+N9Zdq2K89GNH5vDDD+eWW25paJv+7xGBQLDrYrkWqUKKiBapes7xHBRZIaEPbVEYMQq9kNoAofjY7XMEcD2fdN7CzRm0G62EEuHSKHPBdjEddzv3cGAat5fYBVj9DJjp+tY3+taLDHOYX0/AvKNB2/HuxEaLz33ucziOw//93/9t767s0sRiMTRNKz1etGgR11xzDeeff/527JVAIBAIxiNpK03eyTM5OrnqubyTJ6pGSepjaInId4HRAy1zxm6fI0DecnAKJp6ZwFISKPjEfZ+C42HaHiFVGbqR7UTjotuzIdsO+KDU/omkArM3+Dc0DEO7awUi37OB+kS3bdvYtl3hWXRdF8MwiMeDuznHcTBNk1gsVnpeUSpfpP7rGIZBOByu8OzYtl0huooUR6FDodCA7ZdjWRa6Xn0uy9vxfZ9CoYCiKDiOg+M4ZLNZAKLRaEVMzUD9KuJ5HpIk1eU/qkX/46l1zqH6HNbathHqOd/F8xSJRMjn8+i6jqqqOI6D53k1z3MRy7LwPK80yczzPPL5PJFIpNTn8nWef/75UnuGYeD7PqZpks1mkSSp4riL7Q0VJ9SfXC5HOByuOGeGYaCqaulclB9nI/vI5XIoikI4HG6oDd/3cV0XVa38+mj0/A2330XKtx/oM9y/j/UcRyNtNLpevQzUXq33tKZp5HI5YrEYkiRhmiaqqg77cyYQCEaHrJXF9V3UGhMW83ae6fHp6PXoqpHA96FnLUgSGL3Db6e4rdEbtDUcioO4Rs+Qq9quTz5n4nelMOwZNM2YiOFBygh+ZQ5pMlaD15KxZHg9s7KQ64B859B/aij4q2fd/n+5jmBfDfDTn/6Ugw46qGLZ008/XTGr9Pe//z2zZs3iuuuuY/bs2UQiEQ444ABef/31qnX+67/+i9bWViZNmsTq1asBuOmmm5g5cybRaJTp06fzne98B9fd+nPG2WefzWmnncYpp5xCS0sLkUiEs846C8MwSus4jsPll19een7evHn89re/rej32Wefzemnn85nP/tZYrEYRx99NNdeey133XUX9957L1OnTmXq1KksX768rn5ZlsUFF1xANBqlubmZT37yk7S1tdV9bp944gn23XdfotEoU6ZM4ZJLLsEwDJ544glaW1vp6an8wPzHf/wHH//4xwfdth7qOd/9zxMEloqrr76aQw45hHg8zlVXXTXofm6++WYOP/zw0uP77ruPRCLBzTffXFr2qU99imuuuQaotA6ccMIJrF+/nksvvZSpU6cyY8aM0jZvvvkmRx55JIlEgpaWFr7zne/UddwATU1NPP744xXLDj744Aqbw7x587jmmmsa2sd3v/td5syZw5tvvll3G21tbZx88smEw2HC4TCHH344r776aun5Rs/fcPpdzrx58/jqV7/KoYceSjKZpKmpif/+7//mhRde4MADDyQSiTB16lR+//vfN3QcEEQ/HXPMMYRCIaZOncpll11W5efO5XJ8/vOfp6mpiVgsxsKFC7nvvvvq7n9/6mmv1nt65cqVJBIJfvSjHzFr1ixaWlp48MEHh90PgUAwOvQUemoKbgDbs2kNt45dZ8wMdL0PWgysDKTWD+/PygTtpTcMv40idaxrdq3F7dpArj0PycnoIQ1V3ir2w+r4FdwwXNEdbYXJC2HKPkP/6Yngr551+/9NXgjRCSN8yAHd3d288sorvPbaa6RSKfbcc88qW0B3dzfLly9n5cqVZLNZ5s2bx/XXX8///u//8sADD2BZFk888QS33347N910U8W2f/jDH9h///3ZvHkzr732Gn/7299KYgPg+9//PnfeeSd//vOfMQyDq6++mjPPPJOXXnqpop3777+fo446ip6eHl588UW+973vccYZZ/DZz36WbDZLNpvl8MMPr6tf3/3ud3nsscdYvnw57e3tHHnkkXX7fF3X5ZRTTuHf//3fyeVyrFixgjlz5vDyyy9zwgknMGnSJH7zm9+U1s/n89x1112cd955g247FPWe7/7nqcjPfvYz/uu//ot8Ps/1118/6L6WLFnCG2+8Ubp5WLZsGVOmTGHZsmVAMHL77LPPsmTJkqptn3nmGWbNmsXNN99MNpult7e39Nwvf/lLvv/975PJZLjrrrv41re+xd/+9rchj70R6t2H7/tcfPHF/OxnP+Ovf/0rixcvrruNM844g66uLlavXk1PTw/77rsvJ554YukXl+Gcv209N3fffTc/+tGPyGQy/Pd//zdXXnklZ511Fj//+c8xTZMrrriCc889l3R6qx1uqOMAOPPMM1EUhS1btrBixQrWrl3L22+/XbHv0047jU2bNvH2229jGAbXXXcdZ555ZsX7rxHqbW+g9/Svf/1rnnrqKfL5PCeddNKw+iAQCEYH27PpKnQRVasTOmzPRpXUsfVz57uCCZRqCJK7DU+jTdkn2BZg8t7Db6NOnWhNXEQquSc5ZS55bTb6hFYKtovj+qiKBBJkTQeGnRg++gwzVFEKomHq+pMaXL/fH8P8uWLIQ5D4yU9+Uhpp/tKXvsSrr76KZVkV691yyy1MnDgRCATLD37wA775zW+yaNEiCoUCc+bM4aKLLuL222+v2G6vvfbi6quvJhwOs2jRIq699lr+93//tzRadsMNN3DVVVdxyCGHoOs65513Hscffzw33HBDRTuHHHIIX/ziF0tWlVrU26//+Z//4eqrr2b//fcnFApx2WWXsf/++9d1vnK5HL29vRx55JGoqkpLSwtf/vKXOfLII5FlmXPOOYdbb721tP7vf/97JEniU5/61KDbDkYj53ug83ThhReydOnSuqwLBxxwAMlkkr/85S9AIBqvuOIK/vKXv+B5Hq+++iqZTGbIfvfnoosu4ogjjkCWZY4//nj2228/nn/++YbaGIl9OI7DWWedxZ///Gf+9re/sWDBgrrbeP3113nyySe55ZZbmD59emlk1bIs7rzzTmB4529bz80Xv/hFDjvsMGRZ5oILLkBVVS644AIWL16MLMt8/vOfJ5PJlARzPcfx2muv8cwzz3DLLbcwadIkmpub+d///d8Ka9M//vEPHn74YW6++WZaW1sxTZOlS5fyz//8z6V2GqGR9gZ6T//nf/4ne+yxR8P7FggEo0/OymE4Rk3RnbfzRLUx9nNntoCibbtGK1pKtrWNIbb3JYms5VEoyOS7LdTmZgpyKPBwazKxkEo8pOD6ULB3tvSSnYCWlhaamppKj5uamvA8r2JELJlMMm3atNLj9evX09nZyYUXXsjEiROZNGkSkydP5mtf+xodHR0V7R944IEVnukPfehDFAoF1q5dS1tbG729vRWjjBDYBlasWFGxrL8wqkU9/dqyZQu9vb1V1psPfehDQ7ZfPBef+9znOO644zjnnHO4/fbb6e7uLj1/3nnn8dprr5UsOrfeeiunn346kUhkyG235biKDHSe6jl/RWRZ5sgjj2TZsmV0dnby7rvvcsEFF6DrOm+88QbLli3jwAMPrHjf1MPcuXMrHjc1NVWMhI8E9ezjyiuvZNmyZfz1r39l5syZDbWxYsUKQqEQ++yzT+n5SCTCPvvsU3rPDuf8beu5Kd9elmVisRhz5swpLYtGoyiK0tBxrFixglgsxl577VVaZ9KkSRXn7O9//zsQTJ4tf2/+6U9/YsuWLXX3fzjtjcR7XSAQjC0ZK4Pt2WhK9bykvJOnNdxa87lRwcpDtg1CY5tjvS2YjkfOdDDTYNsSbnJCSXCHtWD+iiLLxEMKnr+zjXSPY2pNDvRqZDbWM4lwoMmIDz30UMnaUfzbtGlTxTrlnuPyx6qqlkaoaq3Tf/LTYBMiG+lXsd2B+lUPP/nJT/jb3/7GPvvsw2233cbcuXN55plnAJg1axbHHXcct956K6tWreKZZ57h3HPPrWvbbTmuIgOdp0bOHwQWiaeffpply5axePFiEokERx11VGlZLWvJUAx3wupA2w73/XzyySfT29tb9StBPW3Isoznefj9vsz6v2cbPX/bcm4G2n5bj0NRlJrnuP9nRVVVuru7q96bd91113AOpe72Ruq9LhAIxo5es7eqAmURx3VojYyhn9voDjzd+thUbNxWfHxyBYdCFoxuAyUWwlCSFYK7iCLL6OPY1z1+ezZMWlpaqkZR33nnnRFpe+bMmUyZMoWHH354yHVfeumligv3888/TyKRYObMmaVRrP4/oz/33HMVI3ADoet6hQCop1/Ffb7wwgulZb7vlyZh1ss+++zDpZdeylNPPcVRRx3FL3/5y9Jz559/PnfeeSc/+clP2HvvvTn44IPr3rYWjZzvkWLJkiX84x//4N577y0JxCVLlvDkk08O6Ocu0v91GQlaWlro6uoqPTYMg7Vr1w6rrSOOOIJHHnmEb37zm9x4440NbbvPPvtg2zavvPJKaVkmk+HNN9+seM9uy/kbC+o5jr333hvDMComVm/YsIENGzaUHh988MHYts1jjz02Iv0a6fYEAsH4wfVcOo3O2n5u10aTtbH1c2faAzuHvGMkHBVsj1zBpZBRcE0DKz6JkK5XCW4A1/OwHGEvGTM+8pGPkEqluPHGG2lvb+fRRx/l+9///oi0LUkS3/3ud/mf//kfbrrpJtavX8/KlSu5+eabufjiiyvWXbt2LRdffDHr16/n6aef5pvf/CZf/epXS6NpV199NT/4wQ946KGH2LhxI9///vd59tlnueKKK4bsx9y5c3n99dfZsGED2WwW3/fr6tell17K9773PR577DE2bNjAFVdcUWVnGYiVK1dy0kknsWzZMjo6Onj55Zf5xz/+wcKFC0vrfPKTn0RRFG644QbOO++8hratRSPne6Q44IADaGpq4u6772bp0qVAIBr/9Kc/Dennnjt3Ls8++yzd3d3kcrkR6c/SpUv50Y9+xPvvv88HH3zAueeeSz6fH3Z7Rx55JA8//DBf//rXqyajDsbChQs5+eSTufDCC/n73//OqlWrOPfcc5kwYQKnn356ab1tOX9jQT3HsWjRIj72sY9x4YUX8uabb/Luu+9yzjnnVNxQLVy4kDPPPJPPfe5z/OEPf6CtrY1XX32Vyy+/nNtuu21Y/RrJ9gQCwfghY2XI2TmiWg0/t5MnokXGTnS7NqQ3BhMXdwBc3ydl2OSyEpkeEyUCanzCgII7a7rI2/gL6mgy/GBZp86KiE5fRUl7GEKh3n2UMXv2bO68806+/e1v88Mf/pCDDjqIb33rW1x22WWldTRNK2V2F1EUpZRzO9A6EIzmTpgwgRtuuIHvfve7TJ48meOPP56vfe1rFeudfvrpeJ7H0qVLyefz/Ou//itf//rXS89fdNFF2LbNZZddRnt7OwsWLOCRRx5h0aJFpXWKkWb9ufDCC3nhhRc4+OCDyWQyPP7443X167LLLiOVSnHuueei6zrHHnssX/jCF6qi/moxf/58zjnnHL797W/z5ptv0traytlnn83ll19ecV7PPPNMbr75Zs4888yGth2Ieo5roPPUv3hNPciyzHHHHcfjjz/ORz7yESAQYbNnz2b69OkVfuT+7X/ve9/joosuYs8998R1XXp7e2v2IRKJDDoxtpwf/ehHfPGLX+TII49kypQpnHfeebz//vsVeeP17KN8naOOOoqHH36Yz3zmM8RiMS644IK62rjtttu48sor+fjHP45lWXzkIx/hiSeeqFhnW85fo+em1vbxeLwq37r/snqO4/bbb+dLX/oSS5cuZerUqZx11llks9mK8/6rX/2K6667jq9//ets2rSJefPm8a//+q8VNyGNvAeH217Ry95ozrlAIBgbMlYGy7MIKdXfbTk7x5ymOQNGCY44+e4gFzsxbeh1xwGG5dLRY5Hr0pC9DGqiFTnaXLVeUXArEoS18ftdKPn9zY195PN53nnnHRYuXFhZ9ERUpByS0047jXg8vkuWfz7llFMAuOeee7ZzTwQCQTkDfqcLBIJR5fX211mbXsu0eLXQXZ9Zz8FTDmZmsnpy+6jQ9hZsfDWoQul70PYmNM2ESMvw2jN6ggztKfv0Jc4Ng541wb/9KmM6nk9HymTNOgc7I9GsdaDO2A83PrVivXLBHQ+pdGzJs9e+E9lt5thMFE2n0zQ1NZFKpUgmB99n47dWWhjmHtVXJXIMkLUdSnDvyJTnFPenf9XL/hiGwYsvvsgDDzxQymUe7X02SrEyaS1qVZAcbcZbf8YTO9u52dmORyAQ1Ift2XQYHTWtJaZrosv6GFpLHOhdt8NMoMyZDm2dDnZWIaJncZQInt5EubGkv+AedlXMMWJ4v2doYeoty74rMpDdYTyTSqUqqij25+mnn66aGFnORz/6UVatWsU3v/lNPvzhD4/JPhvlT3/6E6eddlrN58LhMJ2dnSO2rx2xP+OJne3c7GzHIxAI6iNtpsnbeSZGJ1Y9l7fzxLTY2Iluozso1x6fMjb72wYs16OjxybTJRGOSCQpkI3OIOuqxD0PRZZ3OMEN2+LpFgzIjjjxqampadBR56F47rnnxnyfjfIv//IvY7q/oRhv/RlP7GznZmc7HoFAUB8ZK4PjO2hy9dyOnJ1jr5a9UMYqRSTTBp4TFMUZx/j4pLI2Wza7QaXOqAsFmVByMpYEWdMlooLh7FiCG3bC9BKBQCAQCASC8UCn0Yku61XLPT+ItZsQnjA2HXEdSG8AvTogYrxRsD3Wb7LBUkk2KchWFj/UjB9uDgS2D4btgj+Q4BbFcQQCgUAgEAh2GUzXpKfQQ0SLVD1nOAYRNUJTqLEKx8Mm3xVMegyP0f6Gief7bOm0yfVKJOIKsuSBY+LFp4As4/YvbNY/C8T3RRl4gUAgEAgEgl2JjJUh5+RqFsXJ2TmaQ801J1iOCtl28Nxxby3JGA5btrhEdBU9BJKdBy2GG5mw1cMtQyKsosiB1cQtFiL0fbKmI8rACwQCgUAgEOxKpM00nu/VzOA2XZPJsclj0xHXCWL9xrm1xHI8Nmy08U2ZSCywjEhWDjc+GVfWKyZNyrJEPKSi9Hm8Hdcjazq4/vjO6RYTKQUCgUAgEAhGmPZ8e82COLZno0gKTfoYWku6V4MeBatsQndxRDi1Hgqp4bVdrNnSs3b4kxnNND7Q1bWazMYQySiohg+uBb6PHW6tnVIiBcI7azrkzKBicDysYA03L3wMGL89EwgEAoFAINgBydt5UmaqprWkGBWYDI1N8RYybWDlYKyqXg6DfMFnU5uCpkhoWnAzINs57Ng0Mn50h0spGYjx+wrsZNxyyy2sXr2a6667blTbHa39lHPrrbfy97//nf/5n/8ZtX0IBAKBQLCjkrEy5J08zeHmqueydpZ5TfNqxgiOOK4N6fUwcU+I9csKH8mKlC2zh12R0u5czYYenx53IhNbVTxNBt/F8yAbnoLSZyWpEtx9Hm7Xh1hIoWC7ZE23lAwzHhmW6HZsF88dG6O6rEio2hhlWI4iq1ev5s033xz1dkdrP+WsW7eOf/zjH6O6D4FAIBAIdlRSZgofH7mfEPV9H8/3xi4qMN8FRgoSU4dedzvRlfLpTek0Nys4nofkgGZnMJQYfnjCkII7HlJQZJm4LJE1nXGdXtKw6HZsl40rezDzzmj0p4pQVGXG/JYdXnh/6UtfIpfL7TT7EQgEAoFAUI3v+7Qb7UTU6qjAglsgrIbHLiow0wZ44za1JGM4bGlXUGWJaFTBcjxsx8U3cvitexGPhOoS3EDJ490zji0oDYtuz/Ux8w6KKqOM8gxR1/Yw804wql7n++XJJ5/kxhtv5PLLL+eOO+5g7dq1LF68mGuuuYaXXnqJn/70p/T09HDcccfx1a9+FbnvxVq5ciXnnXceAKFQiD333JMvf/nLLFq0qKrtb37zm9x4441s3LiR3/zmN8yYMYM//vGP/PKXv0SWZY466iji8ThvvPFGyYLx8MMPV9g+rrvuOlKpFPvvvz/3338/qVSKE088kS984QtIfW+Y3/3ud9x8880AJJNJDjjgAC6//HJaWgb+Gah8P93d3XziE5+oWmfy5Mncd999AKxYsYIf/ehHvPfee8yYMYN///d/Z+nSpRXrP/LII/zsZz9DkiSOOuooXNet78UAOjs7ufHGG3n11VeZMGECp556Kp/85Cd54YUXuOqqq3j44YeJRrd63u644w4eeugh7rrrrgG3rYehjqt4/vfee29++9vfoqoq9957L2eccQYf//jHWbVqFX/5y1844ogj+PrXv1738QoEAoFg5HA8h/vfvx+Ak/c4uWYSyHgj7+TJWJkBowKTepK4NgZJIq49rgviuJ7Pxs0OhqHS3BSMTquKhFMw8NQwfmxi/YK7iCShKDuR6C6iaDKaPvqjz67T2M8EHR0dPProo2zYsIGvfe1rAFx88cU88cQTeJ7HVVddhWVZfOUrX0FRFC6++GIAZsyYwQ9+8AMADMPg8ccfZ/Hixbz55pvMmzev1Paf//xnVq1axdVXX83s2bNpbW3lwQcf5NOf/jRf//rX+dCHPsSvf/1rHnroIQ455JBSv/rbPt5//33uuOMOjjnmGD73uc/R1tbGJZdcgiRJfOELXwDgyCOPZLfddgOgt7eXn/70pxxxxBG8/vrrqGrtl658P4lEonRMAK7rcu6555ZuNF588UX+6Z/+iYsuuoiTTz6Zd999l0996lP8/Oc/5zOf+QwAf/7znznppJO4+uqrWbx4MbfddhsPP/xwxbENxsknn0wsFuOiiy7CMAxuv/12ZFnmhBNO4K233uKee+7h7LPPLq1/ww038E//9E+Dbvvxj3980H3Wc1zvv/8+t99+O0ceeSRf+tKXmDo1+OntlVde4Q9/+APnnXcel112GbNnz67rOAUCgUAwuuwoAjxtpik4BVrDrVXPGbbBHs17lAbXRpVcJxi9kJw++vsaBu3dDt2dPsmYhyyD4/sYlovuZHGbZmISBtslXHQ6DCW4gYLtYjn1DwyONePzHbuNuK7L3XffzV577QXAe++9xzXXXMOaNWtKIuof//gHDzzwQEl0x+NxjjjiiFIbxx13HO+++y4/+9nPKoSr4zjccccdfOhDHyotu/baa/nCF77AN77xDQBOPPFEFi5cOGQ/W1tbue+++9C0YBj/zTff5L777iuJ7hkzZjBjxozS+ieccALTp0/niSeeKAnTwdA0reKYLr74YnK5HHfeeScAl1xyCV/+8pf51re+BcDxxx+P53l8+9vfLonTb3zjG3z+85/nm9/8JgAf+9jHWLBgwZD7huB1eO6553j22Wc5/PDDAfjMZz5DPp9H13X+7d/+jVtvvbUkul999VVee+017r777kG3HYp6jguCm5I//OEPRCKVPwEec8wx/L//9//qOkaBQCAQCMrpKfQAVAlr13ORJZnmUPPYdCTbDvjjMrUkV3DYtNlBlWU0BTwvENyK7xDSNZymqTiKjNnnzw6rcl2C27Q9Qur4tSOPv1diBEgmkyXBDTB9+nQmT55cMWo5ffp0tmzZUrHdI488wl133cWGDRswTZPVq1eXBHGRcDhcIbgdx+H1118vCTwIPmgf/ehHeeuttwbt54EHHljR/qxZs3jiiSdKj03T5Fe/+hVPPfUUbW1tuK6LYRh88MEH9Z2IMm677TZ+/OMf89RTTzFz5kwKhQLPP/88qVSKZcuW4fs+vu/T3d3Nu+++G0z28DxeffXVCnuFLMsce+yxvP3220PuU1EUjjjiCL785S9z8cUXs2TJEmbMmFGyk5x//vnceOONvP/+++yxxx7ceuutHHHEEey5554Ag247EPUcV/GL8KCDDqoS3MX9CgQCgUDQKJ7v0WF01LaWODliWmxs/NyO1VcQJzH6+2oQ2/XYsNmhkIXmFgkv52M6HnJIIurm8CMteKEmwn2TUE3bC8S3VIfg1mSsGs+PF3ZK0d1fKEuSVHOZX1Yq9I477uCLX/wi3/jGNzjttNNIJBLccMMNGIZRsV0sFqt4bBgGjuMQj1d6pvo/rref5X36j//4D5577jkuu+wy5syZQygU4t/+7d+q+jQUy5cv5/Of/zy33HILH/nIRwDI5/N4nsfZZ5/NYYcdVrWNJEnk8/max5ZI1P8h/tOf/sTPf/5z/u///o/Pf/7z7L///vz6179m9913Z+HChXz4wx/mV7/6Fd/4xjf4zW9+ww033FDXtgNRz3EV6f9aDrVcIBAIBOOb7W1BydpZsnaWpF6dwZ2zc8yMz0RX9NHvSL4zKHgzzqwlPj5tXQ6d7T6JuAxSn+CWIKQBloMXm1qKHwyrW0e7VVkaUnCHNYXMWB5Qg+yUons43HPPPVxwwQVcdtllpWXf/va30fXBPxyJRILW1lbeffddlixZUlr+3nvvjUif7rzzzpKH2bIsOjs7G2pj06ZNnHzyyVx44YWcf/75peUtLS1MmDABwzAGHNlNJBJMnDiRlStXVhzbypUr695/NBrlK1/5Cl/5ylfI5XKceOKJfOMb3yhZXM4//3yuueYaFi1ahOM4nHLKKXVvW4t6jksgEAgEgtEgbaYxXbNmJUrHc5gYmVhjq9HoyBbGo7WkN+uwaaOLrsooOoGHW4KQKuNbOfxQHDfSF6fY5+FGCgS34/oUyj3eVAvu8c74HYMfY5LJJG+//TaeF9xRPfjggxVWj8H4t3/7N370ox/R3d0NBBP5HnrooRHpUzEP2/d9rr76arLZ7BBbbcU0TU4++WQWLlxYMYIMlCZs3nDDDbz88sul5WvXruUnP/lJ6fHZZ5/NTTfdVBL7zz33HA8//HBd++/p6eGmm27CsiwgENGRSARF2frBOPXUU8lms1x66aWceuqppVHmeratRb3HJRAIBALBSNNV6EKW5Co/t+mYhOTQ2FShtAtBQZyxqnhZJ6bjsnqtg2tCJBYIblmSCKmBFJXsXDDKrehVkyZjIZWQFox6F+xgouSOJrhhG0a63TEIHx+LfRS55pprOP7445kzZw6xWIxcLseRRx5Z17bf+MY3ePHFF5k7dy6zZ88mlUqxdOnShqL1anHjjTfy7//+7/zmN7+ht7eX2bNnM2fOnLq3f+GFF3jxxRfZZ599Kkaqi5GB3/rWt8hmsxx55JHMmTMH27YBuP7660vrfv3rX+fFF19k3rx5zJ49m3Q6zXHHHVfXhMZYLMa6deuYOnUqc+bMoa2tjUmTJlWI32g0yumnn85PfvITzj333Ia2HYh6jksgEAgEgpHEdm068h014wBzdo64HicxFh7rXDsU0kGlyXGC6/ls3GLT0+MSj8kU7EBwRzQZbAnJMfBDTXixiQOmlBSFtWl7mI4HPjuU4AaQ/HITcRn5fJ533nmHhQsXVkxeG+/FcTo7O/nggw849NBDS8va29tZu3YtBx98cGnZ5s2b2bx5MwcddFBpmWmarFixAlmWWbhwIevXr8cwjFJWd622y3n77beRZZk999yTs88+G0mSuOOOOwBYs2YNuVyOvffeG4APPvgA27YrkkA2bdpEW1sbBx54YGlZKpXivffeo6Wlhd13353XXnuNSZMmlVJN+rdb/jiVStWsHBkKhSrORSaTYeXKlbS2tjJnzpyaUUbvvPMOkiSx5557snHjRlKpFPvuu+9AL0MFhmGwYsUKmpubmTt3btXz1157Lb/73e945513Gt52MAY7rlrnH4IElWnTpjFt2rSG9iUQjHcG+k4XCMYz5R7tj8/7OA+uehAY2K+9PT3dnUYnz258linRKVX73ZDZwMLWhSyYUF/61zax9nnoWT206B7JMvBT9hmwDLyPT3u3zapVLqokIenBgGpUV5AkCTnXhmx0YU/eH7d1ryFTSlKGDT4gQVOkuohL26YMC/abzG4zx2akP51O09TURCqVIpkcfJ8NvxtVTWHG/JZxWwZ+4sSJTJxY6ZmaPHkykydPrlhWS1iFQiH233//0uP+Iq9W2wAbN27knXfe4dhjjwUCe8m9997LbbfdVlqn/wh1rcmA06dPZ/r0ykkPTU1NLF68uPT4gAMOqHi+f7vlj5uamuryNScSiYp91KI8AnHWrFlDtllOJBKpuJEoJ5/P8/Of/5yrrrqq4W2HYrDjGmgyZvlNmEAgEAgE9dJT6MHzvCrB7XouSIxNVKCZgcxmCI/Bvuokazhs3OiCA1Jsq4OhYHvBSLdn46tRvNjkumIBi4IbnyqPt+t5O1cZeAiEd70VIncFmpubue666zj//POJRqOsWrWKSy+9lM9+9rPbu2ujzmCi/vrrrx/wVwGAiy66iD/84Q9MnTq1VA10tPcpEAgEAsFI4/s+bfk2Ilp1DG3OzhFX47SEhzma3Ai5jkB4R8dowuYQWK7Hxk0O+SzoUb9kKfEIPN2G7ZEwszjNc8lIsfpyuPssJcXHEFhPXM8jawa2lfHK+JrWuoMSi8V49NFH2bx5M+3t7eyxxx67TOxceeGg/syfP3/QbS+88ELOOecc9ttvvwErbI70PgUCgUAgGGkydoaUmarp2c7YGeY2za2ZaDKi+D70rgsmIuIHj4dav/ivP8zR4fI2qGzD9X22dFh0dXgoOqgywcg2PjLB/w3TouCCEZ6G60t1C26o9Hi7no/j+SgShLXxmxEiRPcIsit6gbcllq9eT/hI7lMgEAgEOxaWa2G6JobTWI2KsSRVSGE6JpMikyqWe76H7/tVy0eFQi+0vQ2OAUZ3/dulNwR/20J7ZTFAH8jmXLo2hlFMhVjUJeRIYGwdhVYA1cySD0/F0puJ6fUL7iLBCLeP02d5jodVDKxtO5ZRRIhugUAgEAgE45LVqdW8uOVFCk4By7GwPIu4HsewDRKh8VNtsdPoRJXVqiCCvJ0nqkbHxs+d7Qj+bd2zvvV9PxDbegIizcPbp9ELVgaSu0HZsectl/VdDmlPITEBNF3G62/78D1s28NMzgZZo2C7xGWpop2hYgFdz8Pxto7oF5zx6+cGIboFAoFAIBCMQ/J2nqfXPU3ezpPUk6zLrOOtrreYFJmE4zocO+dY5jXN297dxHRNOowOYnq1rTRjZ5gRn0FUG+XEIM+D3jUQn1x/EonvBaI70jz89BIIRHekuZReYrkeW3oLdKV9wgmZSFzGrxLcPmY+gxduIdw8BTSFrOmSNR3iIRUkqS7BnTVdFCkY4S44Qbl4xxu/wnv8Gl8EAoFAIBDssmTtLBk7Q1JPIksy3YVuDMcgpIToNXtLgrweHM/hnnfv4Z5378HxRjbyOGWmyNt5Ymql6PZ9H8d1mByZPMCWI4jRDbluCDeN/r4GwfV8OtMWmzZ7KJJMMi5XjFwD4PsYtgd2Bq1pGkoohiLLxEMKrg9Z08FoRHD3ifSwpgQFdJxtq5EymgjRLRAIBAKBYNwR1+IktARpK03eydNT6CGuxYloEaZEp5C20mTtyirNjuewfPNy/rbxb2SszJj0s6fQg4eHIleKQ8MxCGvhsUktybaBZ4MaHv19DYCPT2/eYv0mCy8v09I0sOD2nQK6HkKKb70hKQlvDyzbQ29AcBcJawq6On6L5QjRLRAIBAKBYNwR1aIsnbWUqBbFdE2SepKoFi3F8yX1ZFX1xzWpNaxJrWF1ajV3rbiLValVo9pHz/fYkttSNcoNwUj9hNAEYtoop5m5NvSsBb26EuZYkjNdNnaaWFkFPUy1wuwT3J7vE/FySNGJ+P1K1dtlNWBc16tKYBlMcBf34Y5RHZnhIET3Ls7jjz/OPffcs727IRAIBLsco2l52FmY2zSXQ6Yewoenf5gvHvBFWiOtZO0sLeGWkiAvkrfzLFu/DNuzCSvhhi0owyFjZchYmZrC2nRNpsam1qzyPKLkOoMJjdvRWmI6gY+7kJYJySqhsIRhuXhlkYIlwa34KLKEF5864KTJeHir1aQovOsR3FnT2brPcciwJlKarjlmXxCqrI5+tuUOxoMPPojneXzyk5/c5rYefvhh1qxZwymnnDICPRMIBAKBYGTRFR1d0VnQuoBDph6C6ZqcsucpVeklRQ94WAmjyAqTo5NJWalgxFmZMCp96zV7MV2TkFqpUwpOgZAcGhtrSWZLMClS2T5VC10PunIm6V6QTI1oXEKWZQzbw7CCipGW0ye4dQXV6MKPTMALb31Nak2ajIcoTa4Mawo5a2jB7fo7WU636Zos37y8ykc1WsS1OIdOO1QI7zLuvfdeHMcZEdEtEAgEAsGOQlGA16r8WPSAF9wCYcK059uZFJ1UZUGpheM53P/+/QCcvMfJVaXcB6I9315Tn2TtLE3hpprFckYUuwCp9RBODr3uKODjkzNtOgo25CNoukRgbQ8qTxq2F5RuByK6gowLvosbnwp9mdwDpZQEHm/IFlxyposiDy244yEFS9qJRLfjOWTtLLocvPFHE8u1yNpZHM+pW3S//PLLvPzyy3z+858vLVuzZg3/93//xzXXXAPA22+/zaOPPsrnPvc5/vznP7Nu3ToOOOAAlixZUtqmuM6FF17Ifffdx8aNG/n85z9PS0sLpmny0EMPsXr1ambNmsWJJ55IPL71Q33//fej6zrz58/nL3/5C/l8nk9+8pPMmjWroq89PT088MADdHR0MH/+fP75n/8ZRVGq2lmwYAGPPvoomqax++6788Ybb+B5Ht/97ncBOPvss5k5c+aQ/QLYtGlTqd2jjjqqvheijCeffJJ//OMfTJ48mRNOOIHW1lZeffVVli1bxiWXXFKx7rPPPssbb7zBf/zHfwy4bT3Ue77Lz9OFF17ITTfdxLHHHks6neb5559nn3324YQTTmj4mAUCgUAw/olqUZbMXMIT656g4BYqLCij8eu84Rh0G901rSWGY7BX817Ioy0As1uCojhNs4ZcdTTIWx5dGQ9fCuH7EqHqe6EKZDODH2rGiwTX/6FiAYekn+CuVVxnPDHs3umKTkSNjOrfcET9Cy+8wE033VSx7P333+frX/966fEbb7zBNddcw+GHH84DDzzAypUr+Zd/+Re+//3vV61z6KGH8vjjj5PL5fB9n/Xr17Pffvvxox/9iE2bNvGLX/yCvffem9WrV5e2veuuu/jqV7/KscceyyuvvMKDDz7IokWLePHFF0vrvPPOO8yfP59f/OIXrFu3jq985SscddRRWJZV1c7HPvYxVqxYgWma2LaN67q4rkuhUKBQKNTdrzfeeINFixZx77338vrrr/NP//RPPProo3Wf2zPOOIPzzjuPNWvW8Nhjj3HUUUfxzjvv0NrayuWXX87LL79csf6VV17JO++8M+i2Q9HI+S4/TxCUiz/jjDO46KKL2LJlC7Zt132sAoFAINjxmNM0hzlNc5jbNJdT5586qjneKTNF3slXZXDbro0mabRsS/Z1Pfg+dL4Psg7y2Cd2WK5HR9qikI1jGxKR8nuPMg93WFOQJQnDcvDsAl5yBshq/TncMsRC1R7vHU1wwy5cHMcwDP7zP/+Tf/mXfwHgoIMO4sorr+Sqq66qWOeaa67h9NNPLy0766yzWLp0KT/5yU9Ky8455xyuuOKKigmJa9as4e2332aPPfYobXfxxRfz3HPPAfCVr3yFQw89lD/+8Y9IksQ3v/lNFixYwC233MJXv/rVUjttbW28++67TJkypbTsd7/7HY7jlEa6Af7lX/5lyH5deumlHH/88dx9990AXHHFFcyfP58FCxYMeb4ymQy//e1veeWVVzjwwAMB6OzsJJ/PM2vWLI499lhuvfVWFi9eDMDKlSt57rnnuPnmmwfddii+8IUv1HW+a50ngHg8zjPPPFPxC4JAIBAIdl5UWUWVVSLqEMOu20h3X6n1/qPZGTtDMpSkSR/liY1GD/SsgubZo7ufGrieT1fGItOjYhlhotPKXB/lkyb1QHBHNBkzmyIvx1BCLdjDyOEu93hHdZW8tWMJbtiFRXc4HOaf//mfS48PPPBAenp6SKVSNDUFHxRFUSomGGazWR599FHOPvtsfvCDH+D7Pr7vk81meeGFFyraP/roo0uCG+CCCy7gqKOOore3l2g0ypNPPskf/vCH0qzmiRMncuqpp/LII49UiO6lS5dWCcn+1NMv0zR56qmneOSRR0rbzZo1i+OPP76u8xWJRGhpaeF3v/sds2fPZsKECUycOLH0/Pnnn8+FF17IDTfcQDgc5tZbb+XAAw/kwAMPxHGcQbfdluMa6jydcsopQnALBAKBYERxPIe2fFtNa0nezjMnOacqt3vE6d0IRgomjXK1y374+KQLFt1dHlZOIxKxkYqHWkNwF7eKUCCdmI9hy0BjghsqPd6ZQmAXiod3HMENu3BkYCwWq4jxUdXg/qPcgtDc3FxaDsFoquu6SJJENpsll8uRz+eZP38+F154YUX706ZNq3g8ffp0ADZu3MjmzZvxPK+0rMiMGTPYuHFjxbJ6xGk9/dqyZQue5w3Yr6FQVZWHHnqIV155hRkzZnDQQQdx3XXXlc7XJz/5STRN47777sN1Xe644w7OPffcurbdluMa6jzVc/4EAoFAIGiEtJUma2WrRLfjOUiSxITw6KSlbN2RCR3vgDb2xXDylsvmDhujV0HVQFVrxAJWCG6QrCyEkhCbVFqmKdVRikPFAipDPAbGdRn4nW6kW1VVHKdywkQ2OzJJK62trUiSxHHHHcdnP/vZQdfdsmVLzcfTp08nGo0iy3LVOps3b65LBPfP/KynX1OmTEGSJLZs2cJ+++03YD8H4/DDD+eJJ57AMAwef/xxPve5z2GaJldffTW6rnPWWWdx6623kkgk6O7u5owzzqhr24Fo5HwLBAKBYMdjuKkh25tuoxvbt6vmnmXtLAktQXO4eXQ7kG2DzCYY7f30I285bOo06doCmuwTj/jQ5xQdSHDj+0h2jnxyD0xfQ9dkXDcQ1/EQpZHqenO4kSCiKhhOYDUpX7dgu6IM/FgyZ84cNmzYQHd3d2nZAw88MCJtNzc3c8wxx3DDDTeUJusBuK7Lq6++WrHuX/7yF9asWVN6XPQ7t7S0EAqFWLJkCb/61a9Kz6dSKe6555660jWSyWTFjUQ9/QqHwxx11FH8+te/Lj2/ceNG/vznP9d17D09Pbz33ntAYDX5xCc+weGHH877779fWuf888/n6aef5nvf+x4nnXQSLS0tdW9bi0bOt0AgEAgEY4Hne2zKbarpGc9aWabGpqLJo5iZ7fvQuw48Z0wnUOYth46URa5LQfEVlJCH7QRVI4uFcKoENyA5eSw5TF5vJaTJRDSFeEhFkQKPtut5dQvuoodb1/pKxpdNrixNzBzHZeB3jFvKBvjoRz/KXnvtxTHHHMOJJ57Iq6++yvr160es/Z///Occf/zx7L///vzzP/8z+XyeZ555hnPOOYeDDjqotN6MGTM45phjOPnkk3n//fd57LHHeOyxx0rP33TTTRx99NEcd9xxHHDAAfzxj39k1qxZXHTRRUP2YenSpZxxxhlcdtllNDc3c/bZZ9fVr+uuu44lS5Zw4oknsmjRIu6///667SW2bXPSSSex5557svfee7N27Vr+/Oc/VxzTggUL+PCHP8yzzz7Ld77znYa23dbzLRAIBAIBBKPnjudgOAZhdav9wnItTNfEsI2qwjqNkDJTpMxUVeEbx3OQkJgUnTTAliOE0QNdqwNhaqaDx8OhmAJi9A69S9ujO2OS6dCw8yrNcT8Qy4UMBccDK0tUV5HtGpaRTBtG0wL0SGyrh1uSiIdUsqZDthCMTNebw10cGS95vE2XlBE4HEKajDWOPd7DFt2Waw290jYynH1omsbzzz/PXXfdRWdnJ5dddhlz586tGFXee++9ufTSSyu2mzp1KldffTXRaHTAdQDmzp3Lm2++yYMPPsiKFSuYP38+l156acWkSYBjjjmGSy65hKeeeoopU6Zw/fXXV6yz7777snLlSu69917a29v5zne+w8knn4ymbb07/tSnPoWuV8cmnnTSSTz00EO8+OKLZDIZfN+vq1+LFy/mzTff5Pe//z26rnP//fezYcMGent7hzyvkydP5vXXX+eRRx7hzTff5Mgjj+SHP/xhlUd86dKlrFu3jo9+9KMNb1uLeo5roPP01a9+lX333XfIfQgEAoFg52BNag1rUmuwPZu7VtzF8XOPZ17TPFanVvPilhcpOAUc1+HYOccOO06wp9CD7dpV9UMyVpBaMupVKNObofsDiPeJ+9Q2DixameBvAEzHI23Y5LqjFLJREjELuQCq7weCG9DNblS3Wuw6poEthVCapqL3nzQpSUGlSTMQ3WFNqVtwF1FkGVX2cNzgBiKsygx8JNsfyfdrF6nP5/O88847LFy4sCREQVSkrIfTTjuNeDzOL37xi+3dlTHF8zwWLVrEmWeeWSpEJBAIxgcDfacLth87qp95JBnqHJQ///F5H+fBVQ8OuG7eznP7W7fzpzV/IqyEOXDygUyKTuKkPU7i3nfv5aW2l0jqSaZEp9AaaeX0BaeXMrbrfS083+O5Tc+RsTJMjFRO1N+Q2cDC1oUsmDB0DO+wcSx47zFwLTD7dFjLMCMDfR/a34LkbhBprrmKYbt05yxSXRK5bplIDJS+U2NYLlhZdLObQmwmmiajK1tFseV6+Okt0DIHZUr1OSm3lADVwrqOHO6ipURVJBzPR5HA6DHZa99J7DZzbKp0ptNpmpqaSKVSJJOD77PhT3hICXHotENHpbpTLVRZ3aEE946KaZr88Ic/HPD5c845hxkzZgz4/C9+8Qv+9Kc/0dvbW6pAOdr7FAgEgh2dkbI87KyossopewXRvUPpjqydJWNnCCthFFlhcnQyKStFW74tyM7Wk2iKxsTIRJZvWY4kSZy16KyGbnbSZpreQi/NoeaK5bZrI0tylRAfcbJtgR2kaTewcsGyYVe97Ev5kKSabZiOR6/hkOmVyfUqhKKgaGxNKUEiqquoroyryfRVe0dXZSzHwzENdD2C1Dyd/qO7VR5uAm92aXKlJNUtuIvRg8U2C/ZOll4SUkJCCA/CQHaH8Yzv+xQKhUGfHwzTNDn44IP54Q9/yIQJ9UUlbes+BQKBYEdmJC0PuwLlArwWcS1OQktQcAuECdOeb2dSdBJTolNIaAnSVpqknqQ9305YDVfpmHpugHrMHizPIqRWW0uaQ820hEbRWlKcQCnLoz6B0nZ9eg2LTNon16Oi66DpVMcC9nm4iyPctuthu4HoDTsZ5JY5uKHK0d+BJk3293gj1S+4YavHu3cca4dd77esMeDUU0/d3l1omHA4XFHhslG++MUvjvk+BQKBYEclb+d5et3T5O08ST1Jr9nL0+ueZuqCqVVlxQX1EdWiLJm5hCfWPUHBLdASbmHprKW0RlpZOmspb3W/RdbOslfLXjSFmyri/uq5AfJ9n03ZTYSV6mzsnJNjXvO80S2IU+gNYgIjo5sB7vk+mYJFOuWR61KQZdDDDJrDDcEId1FwS46BFopgJ6ZWrDNoSokUjJwXC99E1PoFdxFFltHV8TuRcvz2TCAQCASCnZSiFaJoeZgSnRIUXBmj+VIQ2DXuefce7nn3njGzjI42c5rmMKdpDnOb5nLq/FNLwnlu01wOmXoIh047lFPnn1phAym/AYpr8dINUN7OV7SdtgJrSUKvHAU3XRNN1miNtI7uwWW2gG2AXl0Fc6Tw8ckUbDa0W2Q6FTxPIhxlSMENYDlbbR2KmcYITcQvG+WuJxYwb219HxpOECdYzmCCu7iP8n6MN8RIt0AgEAgEfYzV5MaiFaJoeWjLt9EaaSWuxUdlf7sKqqxy6LRDAarEsa7o6Ipela9d6wao2+wma2crfnXoKfRQcAtMVidXbJ+xMrSEW6p83iOKY0L3KtBH1/eft1zaum0ynRK4HhNaZPAHKXzTh+V62C5oikwIC1sLkQlNQbPdCr91XTncYaXk6S4voFOP4M6abs3+jReGHOn2xnE5TYFAIBDUR/G7vH9FW8H2IapFWTprKVEtStbOlqwQwloy9pTfANmuTVu+jaSerLgB8n2fzbnNFbnfRfJOnunx6cjDntBYB+mNkO+CyOh5xi3Xo6PbJtOhoEkaejQY3R5KcDuuh+34aEpg7ZAKKZSm6WixJkzbI9cnnustfKPIcinHu1hAJ2c6dQluRYKwNn5NHAPewofDYWRZZvXq1cyYMYNQKCS+rAUCgWAHw/d9TNNk48aNyLJMKCQmwY8XipYH0zU5Zc9TRHrJdqJ4A1T0fC8IL6i6AcrYGXoKPVWj54ZjEFbCtIZH0VriOtD1PqiRUZtA6Xg+7T0WHZtB8mTiCfB8hYK9NUO7luC2HQ/f9dFUKRDcTgEUDS8xtW+E2y9laMfD9Re+AUrCO2U4OK6PqkhDCu54SMVg9OvIDJcBRbcsyyxatIg1a9awevXqseyTQCAQCEaYeDzOXnvthTyOq7XtipQsD1p1SfHhILK/h8dQN0A9hR4Mx6iqNpmxMrSGW0nqo5gJndkM2XZI1FdBulFc36ezx2bTeh/ZkYkmQcKv8EZbjkdEkytEs+V4OK5HRJGQ+tJLJDOFm9gNP5TE9Twcb2uSSMHxKkVzPTncZX1wvKAKZvl6Q9pWxhmDfhpDoRB77bUXtm3jODvHJIsdhWw2y1FHHcUzzzxDPD76Hr/uzjxvvdnJhIkRdEXCsF1cH6K6jCzJeL5HbyHPB8ZbJPUoulwdiVhwPHwfQn0zh03Hw/d9dEUmFlJRZAnf8ynkHVqmRonExzZWsS3fxsIJC5kWH7oSpkCwM6GqKpqmiV8rBeOS8ZJVPtANkO/7bM5urooY9H2fglNgWnza6H22PC/wcktKX0j2CDfvQ0evy/p2FxyZaFJConLSJASFcAx7q/C2nCAaMKTIqIqMC8Eot6zhJaZViuGwSsHxMPvys8Oa0lDhm5AmE1blKo/3jia4oY6JlJIkoev6Dpc7vaPjOA4rV64kHA6PSfW4rO4heSphLfCrxfTgA5Ezg9gew3HRpRiaqpByMySl5q0b+0HVKs+HiCZjF2PwZZ+C7ZG2LDxZJx7SQAYHl3TaINSsIctj9yHxbI+0n2b36O5jtk+BQCAQDMyOkFWetbN0m901rSURLcKE8ChG+OU6IL0JYiNfdMfzfTp7YX0afFkmXkNwFy0lEV0pCW9ZlnBcD02R0SSZopsjGOWegaPGq8RwcYTbtD18wHW9hgrfQFmOt+kSUYN0kx1JcIOIDBSUoSnVXqqioMaHZFgnqbVgeebW9foJ7nIRLcsSEU1GkSXSBQenbyJXKKJSyNoUsvZYHRoAUTVKV6EL2x3b/Qrqx3cceu66m5677sYXv64JBDs19Ub1bW96Cj0U7EJV6knaSjMpMmn0Emd8H3rWgO9CjQmc29Q0PpvbHdZtDiN56qCCG0CWJCK6guf7OK6HqlTmYUuOAbKGHZtK1qothsOagq7JWLaH6zUmuIOdVOuS2oJ7/BbHEaJbMCBuv6pOru8TV5K4xdKxgwjuIo7no0gytuNhmMGEDFmRkWSJTLeB543dhyOqRcnZOdJWesz2KRAIBILajIes8qEoppboil5hIfF9H9uzmRqbOnrWEqMHetdCdGRHuT3fY/1Gh7VrHFRFJp7wBxXcRYoTIoHg2l3UCL6PVEhjxaaRITZoSonrbvVo22719X/IWMAauqT/PsZzGXghugU1KXmlZEiEVRQ5iO3RpTAKMq7nDim4LacvRkiVSIRV0qaN5W6/0W5N1nB9l4yVGbN9CgQCQb0MVaxmvBezabR/9UT1jRaWa5GxMhi2Meh6GTtDl9FVZS3J2Tliamx0U0t614FdGNFiOK7nsXa9w/urTHRdIhpx6y58Y/dZSooj3obtBYLbNnDVKBl9St053CFNxrS9UjoK1Ff4ppYuKRXQ6duHN47LwO/yorurq4vrr7+eI488kiuvvHLI9deuXcthhx1W9ffSSy9VrJdKpbjqqqtYunQpJ510Eg888MBoHcKI039ygixvzct07RAKIVJWvm7BrasymiqjKDJ50wH8raPdXQaeO3Z3pZqs0Wl0jtn+BALB6CHsSDs22yurfE1qDS9ueZHlm5fz2xW/ZVVq1YDrduY7yTv5qj71mr1MjU0dvb6aGeheDdGR84vbjsfatS4bN7hEIhJoPp7nU3DqF9y6KldYTQq2g+c6ZMLTkPRI3TncYa1SeNctuGvokqzp4rheaR87ZE73rsD69es57LDD+OxnP4vv+6xcuXLIbQzDYPny5fzxj39k0qSt0UHz588v/d91XY4//ngArr76at577z0+85nP8Ktf/Yozzzxz5A9kBBlwNnDRS2WC7IXJORmao/G6BHeRiKpgOi6W66MrEuGoSj5jk89YxJtH1q82EFEtSk+hB8u10BUxOVggEAi2J2OdVW65FsvWLyNv50nqyZKP/NS9Tq1a1/VcNmQ2ENMqR5odz0FCYmps6uh1tGcddL0H8amBzaQWZp9VsmfNkM0ZBVi3SaGrVyYZ9wjJPqYRlEyXnQ5iEQPZqZXD7eK7EFZAUxXom9IlAzHPx01tJpeYiRefWn/hmz7KJ1cCdQvu/rokCH0IRszjYQVrNIsUbSO7tOieMmUKH3zwAeFwmM985jMNxSIeeOCB7LbbbjWfu++++3j55ZdZt24dM2bMAGDz5s187Wtf44wzzhi3sV2u59cVv5OQm+mlq+ZzAwlugOJnLVtwaIlpSLKMospkOk0icR1FHf0PSlSN0pZvI22lmRgZ+dngAoFAsCMxHuL6ilF9mqJxz7v3ACOfMa7KKqfsdQrt+XbuePuOqpLvGbvadthd6KbH7KnK5u41e2kJt9AaGSVriW1A25ugRUYklSOVlVi/SSGTk2hKuqiqVNdcQ9txcVxQi4K7P56DL6uYLXvUjjOsIxZwKHbEWMDB2KVF97bEIJ577rm4rsv8+fO56KKLWLhwYem5J598koMOOqgkuAE++clPcv311/Puu+9WjIqPJyzHI1aHH6s1kmSDTSmzszjaPZjghmDihev5WI5NNKQQVhXCUZVs2iKXMkm2jkxxiMFQZRXP94ToFggEAzIehOhYsCPE9TVKUVwPRLmPPKknacu30RppJaFVv85t+TZcz0WTtwpK3/fJ23nmT5g/eoWHUhvBKcCUfQevQFkc4W6ZU/Np3/fp7PBY1+5QwKNpenC9LvqxPc0j7q2nEGolp8Yq7CWW42GrgaVEUWX6m0A938dOteFN2JNw01RyTqARSvqhwRxuoDLHmzoEd9k+YqGgembWdPF8MZFyp+KYY47hnHPO4bLLLsMwDPbff3+eeeaZ0vNr165l+vTKylHFx2vXrq3ZpmmapNPpir+xRq5nAkRIIaHFiWs6LnbwwfX8ugS3YXsocuAFM2wXDx8kCV2XyXQVcCy3arvRIKSE6Mh1jMm+BALBjkVRiNbj992R2VHi+kaagXzk/QviFJwCm7ObaQo1VSwv+rsnRSpHv0cMx4TOd0GPb1PJd9fx2bjRZdUaB9v3aGkO4nsrJk1qCrIsEe7zaRuWi+f7VR7u/ni+T8HIB3VcJs5D1VTiIQXXJ5jI6DVY+EZTqjzejQjueEgJUlj6PN7jOb1klx7pHg7z5s3jiSeeKFlEPvaxj9HT08MVV1zBCy+8AIBt2yQSlXfNkUik9Fwtvv/973PttdeOYs+HRleV+iZASFFCcgQfG9nTMKzgDT6U4JYliPTdwRp9H7iIpqBHVHIpi1yvSdPk0S8EFFWj9Jq9FJwC4RHOPhUIBOObwcqklwvRcr/v1AWjM2Gu/6jsWCaS1Irr6za7ydrZUZ/IuL2p5SPvf+47jU6ydpbp8coBtF6zl1mJWVVpJiNGagPkO6Bp9rCbsMxAcG/pcNBCPrGIXBp9rkgpKXpMpKCmhmF7GH2DX4MJbsNy0a0U6oRZuJEWIKgQGQ9BtuCSKQTnMh5uLIe73ONt2qDI9Qnu0j76PN4949iCIka6G0TX9SpP9rHHHsvrr79eejxhwgS6u7sr1unqCjzQra21PWBXXXUVqVSq9Ld+/foR7vnQVOfL135jq5JKQm3C8gsVEynVGpMqqwS3BEigKjKG5eL6HiChhxUyPQWswuhfdCJqBMMxRF63QCCoYEfIjR4p4lqcmBrj/d73Wd27ms25zWMW1zce0BWdhJ6oGuGGwJaxMbsRVVaRyyblOZ4DPlVCfMQojnJrwx/lzud81qxx2NLuEIoOIrj7X/AlqfJ6rtS4nvcJbtXJo4cjeMndKoSD0q/N/o9h6FhArWy/iiI3NDGzeBxKjb6PF4ToHgG2bNlCLLZ1dvNBBx3Ea6+9VjExc/ny5Wiaxt57712zjVAoRDKZrPgba+zy6L4h3thJtRnDtnAcH1WVkGVKVpMiNQV3H5oi4XhbMzq1kIJteWR7CqN6jACKrAS+blOIboFgV8TzPV5pe4V73723YoRze+ZGjyT1ZFBHtShLZi5BkzUKbqFks9AVfVxngY8WxV8dTtnrFPJOnk6js8pakjbTNIeaR28CZWpDUPY92nj7vu+T6vVZvdqhrdtGibhEQkp9gpvAw12sNFluNSlSFNwyPhEvh5ecia/HyztA1nSg7HqfNZ2tBXRoLIe7WLmyPMe7Xp+45YyNVXU4CNE9BKtWrarI4b7zzjsrogVfffVVbrnlFk477bTSsjPOOINcLsfNN98MQDqd5qabbuLUU0+tsp2MJ2zXD97gdbyxZS+E60ooik9IlQNvmLRVeA8muAHwg7SUtFEsDy8Rjqhkey3M/OgXzAmrYdqN9lHfj0Ag2HGoNzd6PBepacSTPqdpDnOa5jC3aS6nzj91h59EOVJ05jspONVl37N2lt0Su1VMrBwxHKtvlDva8Ci35/l0dfisXuOQNR0icZBlCcvx6hLcdpmHO7ieVwrvkuCWJGJuCj/SgpsoG+3vpxl0Ta7weAdVIhvL4Y7083g3NDGzVtLKOGGX93Qfd9xxZDIZ3nvvPXzf57DDDiORSPD4448DkM/nWb58OalUCgg83f/6r/9KZ2cn4XCYtWvX8rnPfY7/+q//KrU5c+ZM7rzzTs477zz+3//7f3R0dLB48WL+53/+Z7scY71oitTnpfJAGvyNrXgRoloYX7YAtXR3a9huyeMtywMLbsN2kSQJCTAsl0RYQtUVrIJDpttEj6ijGq0Y1aJkzAx5u7rogUAg2HUZ69zochzP4ZW2VwD4+LyPN5yOMRxPuiqrqLJaJTB3VRzPYUO2Ops7b+eJqJGq+MARI70Bcp3QNLOhzRwH2jd5bGnzcGWHaFRCVZTSZMjiQPHApd09LNdD08s83DU83rIkEZFtwMdtngPFOhcDiOGSx9t0SRnBjWmjOdyVHu+hdUlR1FvDiCYcK3Z50f3973+/Kp9bVbeelt13353nn3+eRYsWAXD44YfzyiuvsHHjRtLpNHPnziUcrp6M96lPfYoTTzyRFStW0NTUxNy5c0f3QEYAteyNqsrSoG/suB6hyUuQczNElL4vcwl0VabQJ7p1VR5QcBerWQIUHJeQq6ArEqGoRj5tEmvSiSRGr3hNRI3QU+ghbaWF6BYIBBUUc6Nr+X3HM7vy5MiRoqfQQ6/ZWzObe7fEbiT1UbB+ujZ0vgdquKFR7kJBZku7SqflgeYQDkul67iuSCXBrchSbUuJ6yG5Proio/afNCkFwQhFe4euSMhGCje5G17RXjPE6LMiy6iyh+MGFpNwjYmZQ6WUhFW5FCU4lC4pivrqxPXxwy4vuhcvXjzo85FIhMMOO6xq+YwZMypyuGsRDoc54IADtqV7Y4rluOhS8MZ2+qwm5Xel/d/Yze4EeuytRXI8z6dge6UiOIV+Od79BXdxueRJ5C0HNaIFBXJ8SHcZ6FE1mEgxCsiSjI9P2kyPblUxgUAgaADHc3A8B8MxaqYrDZYhPlAG9Vh60nf0jPMtuS14vldhIXG9IPt5emz66PwCm9oA2XZoql1wrz++75NJw5aNGhnDR2pyCIfkrcK6z1ICgeB2+2J9y9NIijG/UUVCVuWqWjlenyWklNud70XWInhNMxvK4XZcH1WRcLx+Od7Un8NNA7pkvDN+x+AFY47X9+GJhdRKLxW139hRJQ6Sj+9Xe7j7e7wHEtwAuiJjOd7WYPyYipGxMdLWqB5vRI3QbrTj+/2/bgQCgWBkqccHvia1hjWpNaxOreauFXdV+bGH8mvX60kfLXb0jHPDMUopLuWkrTRNoabRKahWMco99Dho0b+9bq1LtuCh6haRGoK76OEOawqaImO7Qcl3YGsOtyqh1hjYKvdwRzSZiOKhugUy0d1wlHDDhW9iIbXK491oDne9umS8s8uPdAu2oqty6cNT4aVyPPCr/VhRJYYm6ZiuheuqVZMm+3u8A993peCG4LPm+ZAu2OhK8PORqsmkOwuE4xrqKH2YomqUjJUh7+Sr/HsCgUAwluTtPMvWL8P2bMJKuMqPXa9fe3t50oebcV6eVT4Sk1IHy2EfimI294x45a/YWTvLvhP3RatV6nxbSW+EbFtdo9y27dOxxaej08WVXTTdRldlvAEEd1GIF0e4A493X10NRUavVdm9n+BGkpDNHtSmqXixyWQLfZ6VOv3VRc1Qy+PdaA53PbpkvCNGugUllH5iOFwU0D4gUfXGjsgRNClEysrXTimRqJhFrCvVghvom2ENjudi9N3BhqIqZsEm12OO1OFVEVEjFJyCiA4UCATbnaIfO6yEUWSFydHJFRnhjWSID5ZBPdr939aM83riDkcDz/fYkNmALusV2dx5O09YCTM5Onnkd+o6wSi3EhpylDuX9dm4zmNLu4ujOMga6FpZQbshUkr6F7oZrPBNueCW7BzIGl7TbKKhUGndiNpY4RsIhHekTBNE9QYK3/QxlC4JjmP8VqQUolswIEFMD6U3eEVeJuD7EpqXxPYLA6aUmGV5mZZbmeMNVJSPj+saBdvtuxOXCIXVoGCOMTqRXEVvXspKjUr7AoFAUC9FP3bBLeB6Lu359oqM8PFezGYkMs63pz2lu9BNR76D5nBz1fLpselVmd0jQnoDZLZAbGDbSmAn8Vi/1qMn5WLKFi4eofICMHXmcA/2uJbgxveQzDRuciZ+KEne2notNpygVHs59cQCGmWaIG9V5njXa1sZTJe4njeuy8AL0S2oSfmHpymiVXmpin6spJpEU/3BU0p0mVion8ebSsGtq8EouE8QIejjo4VUHMsj010YNd91VI3Slmsb13fGAoFg52egYjVFa8ZQz29vttVPXm5PiWvxkj0lb+dHuecBG7MbcX2XkLJ1NNd0TRRJYbdkfRMcG8K1YeOrQfTeALYVs+Czab3Pxg0eOcvG05y+a6kUTJT0/boFdzGHOxZSt3q8+6wmNQU3IBvd+OEJuPFpW8VwWKEpoqJIgVWkKLwbyeFuiqjEw5Ue70Z94oPpklrnYLwgPN2CKmp9eMq9VK7n43g+igQTI0k2Ogqu76JIfR+0ASZNljzetocig+tSEtxFdEWh4LhoTvAzVDimkes1iTbpROIjHyEY0SJkrAw5O0dC3/Fm2u9M+I5D7z33kH/170QOPHB7d0ewE2O5FgWngOWO7mTtRikWq3E8h1Pnn0pLuKWh57c32+In355xhxkrw6bsJppDzRXLewo9TI5OZkJ4wsjvNLUeOt6B3Q6FfoM+vu+TSUHbFo901sVVHXRNIlTmjzZsN/A1A57mBeEF+JUjxwS/MJcGtxTA90pebtv2UBwPy3KQdb8vxjdoQ3KC6tBO02yyjlwlhuMhlazpkDXdUixgIznciiSVPN5ZMxhBr1dwD6VLwtr4HU8WoltQwWB3q2FNCd7YfZmb8bCK5ccJyWFMr0BUiQ2aUlIsoJMzXVwXFKXaVybLIEuQytloCSnIDpUg3WkQiqjIIxwhGFbCdLqdpK20EN0CwS7A6tRqXtryEmvTa/F8j8OmH8aeLXtu726VGKpYzXgvZjPcjPOxjDssn7wJQUxg3s7TGt5afr0Y3TgrOavC4z0iOBZseAU8DzpXVjxlWTJdKZ3uHg3TdUC2SOgyir21Dwqg9MUAAsS99TXnSzl9OdxRpTqlJAIoTvDLs2p0EXZ6wCj6wz1kM401/WAySlNtMSxJxEMqKcMpxQI2UvgGgpuHmA45MxipjjUguIvU0iUG4+tmuhwhugUlbNcb8uchp8yTXXA8wppOTEmScrqJyrGBBXcfxZ+zgvYCv1r5ep7n43hguy450yUZkQhFggjBfMYi3lydW7stSJKELMl0Gp1VM9YFAsHORdHCYNgGYSWMYRssW7+MGfEZ48amMdbUygXfHlnbRXvKW91vkbWzLAgvGBP7jOVarEuvI6EnKjK4e81eWsOto1OBMrUeetdBuAmSu4Ek4XmQTkt09Mpk8z52yATJJ95XnbliLNz3KTgestMBQCHUGhSeKeu/7QT2EV2RkVWZ/gZKz/exLAfV6MJTI5ihBFrfIJhU6MFu2YN0ZObgo89lvnDHC2IAy9erJxaw3JNdsF3islSxXj22lf66ZDwjRLeghF3vz0NhlUJZrnaT2kyHtWVowV3u4VbkktWkuH551ncyomE6LgVLIqKrWyMEYyMfIRjX4nTkO7BcC10ZvSqYAoFg+1K0MCT0BBk7gyZrdBW6dtmKjcVccNuzuWvFXRw/93hcz+XFLS9ScAo4rsOxc45lXtO8MenP9og7bMu3kbbSTI9PLy3zfA/DNljUuqiiSM6I4JjQ+S5EWsAxINKMYUh0dvr09njYuBghG02JkegTqhWGkaKHW/WJRYKEl5waI1/mx7YcD1sJSrurAxS+MSwXWfcJOz2YoQSmEsWTZXQsJCVMJj4HR9brs3uocslqEg8FI9gN5XCHg2t60WpSXL8Rn3i5LnG88Su8hegWlNAa+Hmo3EslE8F0XFzFI6qrQwvuvrvpco+3rshYrlcRPaipMjnLQVVkQlGVXMoi023SMmVkL44xLUZbro1es3d0YqEEAsG4oGhhaM+3055vp9voxvVdOvOdDX/2R2s0eKiKlCNF3s7zzIZnaAm3kNSTpK00j615DNd1G87aHkmGa08ZDp7vsT6zHk3WKiwkKTNFMpRkSnTKyO+0d31QfTIyAat7E+l2j65Oibzp4akOBdchrCpEdaWmUK2YNOn0zZfSFQwruJbKsoTTN2myrlhAQ0JTZTxZxnZc5EI3Vnw3rFBrQ/7qco93RA3STeotfLPVJ75VeCuKjFWv4O6nS3oct2r98cL4dZsLxhythl96sLvVsKagazKKF0YlhKI6dQtuoOTxxt+a1V0ePaj2tZW3HDwgFFHJdhcw8/aIHrcqq3h49BR6RrRdgUAwvohqUQ6ffjgZO4PlWsS0GBMjE3l+8/OllIyi3/eUvU4ZsLDKaEXbDVWRciSpNXGxy+ii2+ze5qztHYUuo4uOfEfFhFTf98lYGWYnZ4/8TY9dgM6VFPwkHV0q6zbE2bjRx3AdDMnE8jxC9QrusudlSSKiK3i+j+N6qPUK7jJ0VSbkpLDUOIX4bsTDamP+6j6Pd3FeF35jghuKBXQUXA8s20NvQHAXCWsKujqyv4aPJEJ0C0r0T+Wr5+ch1/UISRFCUoS8U13MYEDB3YfXb6f9H2uKTNqwMSwHVVfwPY90p1GV972txNQYW3JbcL3xe4csEAi2ndZIK5Mik5gcncy85nns1bJXQ8JytKLtBqpIOVqRebVytVsjrUwITdimrO0diU3ZTXi+V2ErzNk5YlqMqbGpI7ov3/cxNq5iy5oU67Y0saVNxvJc/JCNK7moioTWl71dZY6oIxawOJEQgrlR/S/oA8UCbm2ggO/YWMnZ+GoE262+xg5p9+i3z/6P64kFLN+v63pVx1GfLhmdiOGRQIhuQQnLcUtv8Eb8WMmIxsRwK1nHqAjcH1JwFz3ccvDzmCxX5ngHxXWCn8typovleoSiGrmURT49srOTY3qMtJUmY2VGtF2BQDC+SGgJoloU13fxfb9hYbktlRcHq7Y4VEXKkaZWrvbxc47no7OOpTsj8V5HJ02h5nGVBT6SpK00m3KbqmIXe8weZsRnjFiale/75NMWm1e0se7va+hKJ/AUkMMmLjbg4wOqLBPtE9SG5W4dgGowh7s44l3K8aYOwe172NluzNg0Ii1Tq/KvoYEcbhkSYRVFrszxbjSHuyrHm/p1Sf/Bu/GE8HQLSnh9b/CwppCzGvNjTQg1saEAtuNTvE+vS3CXebjLPd5hTcZyPLy+n6hs1ydnOSRDKooqk+40CMfUEZtUGVJC2J5Nj9lTVZFMMLb4joObyeDl8yjJ5PbujmAnI6JFmD9hPpuym4ZVxGW40XZFS8pAExTLK1KGCdOeb2dSdFKpXcf1uPulDazpzDG7NbZ12csbADh18W6oilwVhzcYtSYuFmyLWdH9cHyTU/f6LC2RUajEOA7Ykg1iAidGtlaDNBwDXdaZkdj2JCvf9zEyNr1teTJdBn5qE6qXxku2UnAcZBd0BUyPCjEc0WQM28OwXMKa0ncdHFhw246LrVZ6uMs93roqU7AHE9w+VrYbOzwBbeJcFEVB6busFsMSiv9vxF9d7vGO6YGgbjSHu9zj3YguGc853eO3Z4IxR1dlXC/IzGx0AkREiRHWdCTFwXb8hgU3UBLesgQFy8PztiahhFQZx/HImS5aVME0bLLd5ogef0gJ0ZZrG7Xql4KhcTo7sVavJr98OT2/+Q3mqrErAy3YdZgYmcicpjkcMvUQTpt/WkPpHMOpvFiPJWV7VZzUFZ2EnqiYuKjKGmElPm6zwIdisF8UIKg0uS6zrmo0u7vQzdTYVFpCwy86VBrZfj/F+re7SHXkUTUb1d6AIWsUbB9NlVEVCcvxkaFSDPeJY7kvvWMowe24VE2aLPd4Dyq4Aatg4Ekq2qQ9UEKx0vKwppRGvBsV3MXjiIeCypU508X1GhPcUOnxbkSXjHiu+ggiRroFAUYvWmYDNM2r+cEEBv15KKrECElhTNdEH+RtNaDgHgoJQpoSfIFYoEdUMt0FIkmNUGRkIp3iWpwes4e8kyemxYbeQDCiePk8hRUr8C0LOR7H7e0l88STaGdORY7ufD9vC7YvqqxWic16aTTart5qi/VUnPR8l4JTwHAMEpqIOO3PUL8oQFAMJ2Wl2C2+tby76ZhISMxKzqrI664X3wvEdm9HnmxXAc/z0aIqvgSZtlVI2RRyYhphVe6zf7jofeJ4wGvuIFiOh++CqoBSY3Br6A77GAUL3bNQJu0J8caTu4a0e9TBULaVIanDtjKeGN+9E4wdromf70Z308RC1V6qod7YqqQSIk7ONtBUCU2VsB2/wuM9pOAuq2YZ1uUqj7ckBZv05CxcScJzPdIdhRGbVBlRIxiOIVJMthNuNotXKCBFIkiahjplCl46jZvZOZMTBCODZ1m46TRefnQmHA5ErRHigag1aXEgH7kqq4TVcM1R5i5rA13WBtakg3ST1anVI3IsOwv1/KJguRare1cTVaMVI6KdhaBAWrndpB5cN5jcv35FN+vf6SbdYSCHFfyoQodhsbGtHVIbCcWbUYuC23JRJImQWkOklnm4w1oNjzdbPdyqAlqNpI5yD3dYq/Z4U3ze6IApe0PL7Ko2ysVwLY93I/O+gkqT/TzeNOYTH44uGY+M/x4KxgzFzRO3e1CVvp90+t7gnlffBIgwTUiyg64GP3WVC+9GBHdEk1FkqWQ1KQpvy/FwvWDEO2faSGGFfNocsUmVkiShSApdRteItCdoDCUeRw6H8Q0D37Zx2tqQk0mUxM6ZnCDYdsxVq8gvX05unNuR6rWkqLLKh6Z8iA9N+VBVXKHh5HkvsxzHtwnJQbrJsvVPY3mFsTyUcU09k1w35zbTZXZVWEgKTgEZmdnJ2XVbE2zLpbctz7q3utmwsgcjbaHGVNywQqdhkTJsdEWm1e0Ex8SUIpViWK3try6fNBlcB+UK4V0+aXIowV26lpZNrvT7/pXMXvRYC0yaD/3ea/3FcLnVpGC7jRW+CSl9miKwmhSFd0OFb0LqsHTJeETYSwQlNElCybfjJXdD0SLBJIaCS6bgAEHVqMH8WEk9ju4peL6HLBU9ZkGCiY0fpJTUIbhLWd/lkyut4O646BO3HC/4eU6WSHcYhKIqmr7tkyrjWpz2fDumaxJSQtvcnqB+5GiU8IIF2Bs34WWzKC0tJI79qLCWCGri5fNkn3qqNOF2vNuRallS+k+EHMxul7GyWH6eyaHZzG1uZlpsGp1GF3E3hy6PXhGdRmhkEudoMNQk1/JRbkXeer3oMrqYmZw55Ci37/kYWZtsT4F0ZwGrYKNqMqGkTt526EkX8H2fRERDU2TkQg9qoR0z1oLlethu2aRJu84c7n6TK6HMw91vWtNAKSVFj7dhueQtF8kxiCg+3oTd8ftZKQcSw+UF8UwbFLnBHO7yyZWF4Dga9YkHHu/6dMl4ZcfpqWD0kSSw8sj5TgCUfh+m/o+h8gM6IZwkJEUwy0Ze1LJiObIs1S+4S32iYlmxPV2VcX0fR/EpGHYwO3wEJkDGtBg5O0dvoXeb2xI0jjpxIvrcuUQPPZSW008nNG9syk8LdjzcTBY3k0FJJncYO1IjlpT+JPQ4ITlOwc1VWFRCiph/UmSoXxS25LbQbXZXjHIbjoEiKcxOzh7Qy+1YLqmOfGAhebuLzg1ZJNlHb9IxVYktmQJb0gU0RWJCPBQUmvM95NQG8F0UfevrHVwH6y98A4AkVV4Hlep+DhULWBJ7notipvGbZ+NFK28yhhp91sr2qyi1R+oHHX2WpGC7Gu0VGWoUvR5dMp7LwAvRLdiKBOhR5NxmcO3AO1VmB6nwUlH9AQ3JYWJqnIIXzBgvz+FWVQmnn8d7SMFN4F1zHB9Vlao83mFVoWB5uCpke0yMzLZXqlRkBR+fHlP4urcXkqqiJBLjcrRSMH5QEnGURAI3nR4RO5Ljevxm+Tp+s3wdjjv+LtoRNcpe8cPQ5UhJUC6ZuXTcjHKPF4q/KBw67dCKZBrLtVidWk1EjVSNcs9IzKA13FrRTnFiZPvaNGve7GLjuz0YGRsloqIkNNKeR1fWojtv4bgezVGdeHjrpH4534Wc78AJNZfEsKrIOK5XeR2EunK4i5Uma3m8h87h7vN04xOxe3Bik0mFplWs0oi/WteCEu3lHu96c7iLlSZrebzrta0MqUvGcRl4YS8RVODpCeRcB4VUO25kcunDoyhb8zLjIZWCUztGqFmdQKfdXtPDLfVZTcBDV+S6BHdF9GCfSDdsr1TC1sXHcEGRPFIdBqGIgrKN2d1RLcqW7Bb2bN6z4stZIBCMH+RolPgxx2C8+RZuNku4ZcGY2JEc1+N3L63n9VQ3i2cPP1qukf3d/fIGXM+nRZvOrOh+7D01wmfnn0pEjfESG0a9D2PFSNlTdEVHV/SKXxS25LbQVehiemx6aZnhGKiyWjHKbVsu+ZRJqsPASFu4ro8aklETGnnbJZ0pYLseyUggnxRJJqT3E6qeg5LegCvJGK5cIYYlwHY9ZNcj1CeWPaS6Ct8E10G/ZDWJ9Q1A1SO4Pd8n6qaRIwnU1j0o+OqQ1/MitcSwxNYc77AqN1T4Jqwp0LdN1nSJ9zk5G/GJD6pLxnEZeCG6BVtxLSQrh2UayJlNxJumlj48JS+V6ZIyAi9VrQ9oTE3g+5C3HBRZrvBwV3i8HbfvbrVOwQ21Pd6yjCSBK8sUchbprgLNU6LDinwqEtfidBvdpKwUE8ITht2OQCAYXULz5hE99FD8QoGWz566SxRTUmVtq0VFlBSoC9u1BxzlnpucS0JuItNdIJ8yyXSbWAUbSZaQwyqu75F3POxccE2L6QqGI2HawcmvdR1Uch2Q3oihJlH9XDBpss/DHQJk18MtZCg4HlhZorqK3N/jDdh9I9whRUaTZOjLDIj6PgXHwzYCK5UqRyr2UaJvPd/3ifomCh7O5L1RwgnifUJ6sOs5DDz6XOnx9kBqMIe7hse7UZ/4QLrEGscebyG6BSVkK4edNvFdm3h+PY69B56ydSRHkWVU2cNxi/aO6jd2iAi+o2ErZjB5pd9nR1fkQHATfMDqFtxFpEC8F/pEt65uLSKgagqZ7gLhuEYkPvz8Wl3RsT2bXrNXiG6BYJwj6zrourAjCQZkc25z1Sh3Op/ByYFuN7FuQzdWwQnmBekKblgmZ7m4hoWuSGiKTEjdKgZtzx/4OuiY0PEOdj6FrrtBLGA/MRzqE8MAutmN6lZfSx3Xw3d9IkpgS6FfSFfY80uLwmZXtWj3fUzHR/Z9IoqH7FrYux1e8nHXcz0fyu4RVuXSaLcqSw0VvgGgL9IwZwaaIKzVyCwfwrZS6zgyVb0YPwjRLShR0JqQk63B6HS+HSnbDmXFGQq2i+P6qIqE0xfXU/5BdD0P29aIK3FcOT/gpEmkQHC7LliSVyGsBxXcBD7xQp9PPOhTYDVRFRnL99A9n3SHgR5Wh1cwoI+QGmJLbgtzk3O3adRcIBCML1RZ5dN7frrhqnVFm4fnebg7edVax5WwHYm85RIemdpj243yUW7PBsMoYKRt1qXWM1Wajh/RcUI+fkSh4HikCyaOB01RlbhaLQKHug6S3ohlmXgTFxLRVWq5iw3LBSuLbnZTiM3E1WT0sgmGlrv1OigpclUbXt+1VDc6AMhGJpUidsv34QERBTA6sJtm4iZn1n0cjfirVVnCcYPql+XCuh6feM4KfOIAOSuwmpSEdZ0+8f7HMZ5/Ahq/Y/CC7YBERFeRZRk/nETJtyNZOaDywxMLqRV5mfh+6QOqyhLTopOw+t+W95s0GdaUqgI69Qjucp94eY63IoHj+XiqTD5jke3ZtuzaYmGF8nxXgUAwvhnvkyHLcVyP3764nhdXd+PVkbbgej4vrelmTWcOt6wgmOV4pA2bvOU03Ieih/qUvU4p5YKv6sjx3oYY722I838vrOP99h33O9D3fdZ1rWdDx2acdpUtq1N0rs/Sne5FVTSmNc8mp0r02DYZ0yZr2WiKzKSETkSrFppDXQe9XCdu1xoINxPRNZDkyj8kDNvHQyKsq0E9C03GdsFyAUnGcsF2g6I3uqpWteEh9V0HZUJq8CdLQaSgh1Sxj4imoFo9+NFJuM3zKI5W1Xs9r9dfHQupVQV0Gs3h7p/jXa/grnUcBXv8fvbFSDdgmibPPvssEyZM4MADD6xrm9WrV7NhwwZ23313pk+fXvFcLpfjr3/9a9U2Bx98MK2trVXLxwuKLJUmcvhqFKmQQs53kpPCVR+eWl6qoh/LtJOAj+97SJI8YEpJhcfbdcGnbsFdHEUv93i7+FiOTUtIJ9UZjHZHEsOzmUTUCF1GFz2FHhL64CWeBQLBjsX2zpMeLo7nYzkKVl9Q0wcdWZ77oJOC7eF4Hv+0zzT2mDz8YlJ5y+GpFZ00y3uQTCosX9XDW5sy/PAz+5GM7Bgl513bw81KeAWZ1YU2Xu54PRixDzlImoKkyHQUepkmz8Gzo1iuA0goktR3Xavfl1xxHczmiHR9gIqPHokPHQvYZwcpjnDbbjBhEspyuPtRlVLS10b/HG+AiK6gmr2ghHEm7A5qqL7j6Hc9r9fuUeHxdjzwG/eJ9/d4N+oTLx5H7zj+JWqXFt25XI5rr72WO++8E8MwOOqoo/jDH/4w6DbPPPMMl1xyCV1dXey222689tprnHjiifz6178mHA6im9avX8/HPvYxlixZQii0tcDKD37wg3Euuisf+1oMN7URU5lIKByuniwiy0TUPssIENWDD09UiaFLYUzfJExk0JQSXZVLgrvo1+7PoNUsJQipQei/ggSygiX5hFyfni15FFVGjzT+NpckCV3R2ZzdzMzETGExEQgEJWRJ4cDmj/HpPXdDHaNJWx0Zk9WdeXrdFp5boXHghBR/fa+bvOWSDGv05Cwef3sL05vnBN/FwyBTcEgbNsmwhqZAMqKRM4NCJONVdLuOh2U4mIaDkbZIpwysdTq+57O6dR29fi8T41MwHHBth4yTJiTFmB2dRVzT8Dy/VGglojYwEbCP4Dro4/asQyn0oDVPaziHO7gOehWP+zNoLKAkEdZk8n2iO6TIKK4BnoszaS/8ULLO46i+nvc/jsFGn8OaUhLcSDQkuIvHEdXVbX49ap2/8cIuLbozmQwTJ07k9ddf5/Of/zyOM/TPc21tbfz85z8vjYivX7+exYsX873vfY/vfOc7Fevecccd7LbbbqPS99Gg/6+xphLBz7URdbrRtJk11vcwyvIw81bgCQvLEWJKjJyTxUcfMhaw+AHFDx6Xf2DqKR9fmcnpY1oeeljBNh26t+SYOCOOOoxqlQk9QZfZRdbOitFuwS6P7zj03nsfAM2f/hSSuktfPkaFgUbgLcdjxZY0luOhaQ55Ex59s42C7ZEMa+iqzNSmMF05i0zBGbboToRVkhGN9OYMybBC2nCIh1US4fHzWjuWi2k4WIaDkbHIZ2wc08VzfZDBV8EOuRhugay9FtsJ0e24xEIKqgyebLBHdG/iWgJ8v8KWYzguikKF0KvHJmGntxDObsQNtVBwIKL5W8XkUIVvoCq3u+o6WEcOd7mlwrItVKcXb8KeeNHJdR9Hret5+XHUY/cov57393jXY1sZidej//kcT4yfT9J2YOrUqVxxxRUNbXPKKZVfiDNnzuSYY45h+fLlVeu++eabrF69mr322ospU6ZsU1/HAtfz8XwfWZL6skElQqEYkdxG7PiEinKxFR+esIrr+xV5mc3qBLbkO9C0prpjAYuPIfjCqUdwl0bR9b6iAbaL7XrkbWiK6hSyFj1teSZMj1VUwqqHiBqh0+iku9AtRLdgp0II6B2Lgu1ScFwimoIr+ySjQfpFSFVIF4KR6S2pAhMToW0SyFFd5bhFU/jHxlRJvC+clhy2iN9WPNfDKrhYhUBk59MWluFiWy6+F4hsWZPxI8FIcd5ysSyXnOnT7nQSknRmhHcjpqsgQbfdSbPWyuTQtEoRGVZQJKkiN1qR5boEXj6bIZpeSyQUxglHMazKWhJDCu6+8vBFS0kxlxuCEe96C9+U9oGH09tFLjoNNbEbSl+6V93+6hrXc6DhHO7iYwhGvBvyiW/D65E13Zrnebwgvmm3Edu2eemll/jYxz5W9dxXvvIVmpubef311/nMZz7Dz372M6IDxFqZpolpmqXH6XR61Po8IFIw41mRJVzPR1NkVL0Zcu3IvWtxWxeALNf88CiSVPKEZU0H3AguPmFVqjsWsNzj7fkurkd9grtM1Ef6Poh5y0WRHJJxnWyviaJJtEyOIdXoy4CnQ5IIKSE2ZzczKzFLWEwEAsE24Xk+L6/twexez2cO2vrroev7/PbF9SiyxKmLdwsi4soIawphNZi/4mkS6bzE3q06R+wxidVdObKmw4RYguMWTd1mgbz7pDiHz5tI3rQJaSqRYfxKOFwqrCIZCyNjY1sObl8mtqRKSKqMFFGwXY+C4+I7Lp4dzEmCvlFhP0+v18Uife+S4LY9C9d3mRmegyZpNUVkyVNsuqUYukEFnmERzawl4ufxwlMCYawrJeFd7M9AgtsppZQo/a6Dgcfb831czx9UcJuOh6dt3Yec70FNtJJrmkvBBtV1hj6Ooa7n0FjhGyo93q7n43h+Q4VvhvV69B1HWBP2kp2WK6+8ks7OTi699NLSsmQyybJlyzj66KMBePfddzniiCP42te+xk033VSzne9///tce+21Y9HlAdGUYlSQjyJvFcNeZAJKdjOEW7BiUwa8Ww0KB0DOdAlJMRJaBEeyUKgsUzxYSklwZ+/i9v3KFdHrF9xAqYCO6/mkDZuQJhGJaqQ7CiiqQtPECI2QDCXpLnSTsTMk9Z2/8IZAsL0oRvIBNYXneKXefgdRhZ/BTVVWkHRcj3TBJqppAwrc4kBI4NlViIXg2IWT2X1SksPnTaRgu/zroTNHzHetqzKqrOFXffmOHL7nY1sudt9IdqVVxMOXAE1GUmU81cf2fVzXp2DYGLZDSAtSMzRFKhsQUXANmy63HQ+PllCsdP3osbuZEprOBHXiwKO2fQVbUoZTiqEbTOCFjC3EzA68aGvpWij3ZU8XUzzCWm3BbTtBDvfA10G/lFIzqOD2KQluyUyDrOJN2J1YKF73cQx1PQeINVL4po9w37W4mKEdDzfoE2/w9Sgeh9E/PW0cIUT3NvBf//Vf/OQnP+Ghhx5izpw5peXTp0+vSDTZa6+9+I//+A9++ctfDii6r7rqKi655JLS43Q6zcyZ1T7q0cT1QCv9f6vVBEXDV8PQs5q8H0bR4wPerZa+aKQoqh+h4BqE5K2iu55YwHJvueVWetsGFdxl20hIaKpEb95mUkJBDyuk2vOomkysKVS1zUBE1Agd+Q66jW4hugWCnRzLCSLP8tbITRwsF+WfOrAy6aojY7KqM0uvYRHRFPae0TRgO/GwysS4hpzo5fAFYXafHAc/EGi6Km83C0i9FL3YdsGlkLMxMha26eI6Hq7r48sSkirhqWAqgcVRdl1wg19fFSmwkPj4NMf0AcVXp93FxFgze6i743gSugx5N4cm68wIzSJveYOP2pb5gR0viM8rX68o8HQ7TSKzGl+LgFwWZu77FZ5iy/GqRLPVV2kyogQ53P0pF9wAluujq2XXuj5LiexDSO0rL+8UkJwC7sSFeOGW0rV4qOMYbPS5vI2C7RKXpYr16rF7OGXHUXC8yvXq8YnX+XoMeBzjjPH9KR3HXHfddXzrW9/ij3/8I8ccc8yQ60+YMIEtW7YM+HwoFKpIOtkeeH2WEl2RSvFDxTtoV0tgpzYTVtajTdt7SD8WQNxqYZO1iia1BaQGc7h1pVQgoOjxrktwl+0jpmikDIu0YTMhpuG5Pt2bg9zxRoR3WA2zKbeJ2cnZwmIyyniWhVco4Fvjd6RCsJUddXS6Fo3E7xV9t3lr+JMW85bDO5tTWI5PPBQkNryzOT2o4JdlCV1z0Md5wRrf90sj2GbeIZ82MfMOlhlMtPPxQZVBkXBDErYrkbMcLMMjElL66i546IpcElIFO7AchnVlQIHXYxi0WWtJhsJEZB3b8fF9h7SXYm54D2Q3Vr9NQpWrPMWlehSeRTK1EsnO4cUmlR94hb8aqPR4l+ZLBaXd1ZqFbyo93JbrV3i8K/ahBtdBz3eQjB7clrm48al1H0e9/mrYajUpfz0a8YkXHK/C491oDvewjmMcIkT3EGSzWZ599tmKjO0bbriBb3zjGzzwwAMcd9xxVdv09PTQ0tJSseyRRx5h//33H5M+Dxe5zFJSnvsZUmRM10OJtBIzO3BzbbiJshGbAT48kyPNbDCD2ciKIuM0UPhma3xgIKJ9PDzPr1twF/eRDGv0GjYhVSYRVTHzDl2bcvg+xJvrE94JPUFPoYe0laYpNPBIlGDbMFetIv/ii1hr14HrEvvw4YT32mt7d0swCow3sZ63HB5/u62u+L2OjMnbm9OYjo/jeRy3aHiT5DMFh4LtEdZkNEUeNJ6vOALveTIzlKV8ao/9UGV13BQAKk14NBwKOZtcysIuODi2F5RWVyQkTcYPK1i+h+OC53n4TvBrajCiKjExoZflPnsloacoMlYdAq/L3kRBSjFZm9ZXcdSj2+xFV+MkpWkN+5LLPcURtS9NA4+ksQ615wOclt23NjBASkm5x1uWJRzXQ1NkNKm6tHutSZPFEe5glJ++62DfPhwJfB8514UXn4LbPLskboc8job91TT8epTvo9zj7QOuO8QvDvW+HjuQ4AYhunnyySexbZu2tjZc1+XRRx9F0zQ++tGPArBmzRo+9rGP8fjjj3Psscfyy1/+kksvvZRLLrkEz/N49NFHAUgkEnzkIx8B4Prrr+eDDz7g+OOPJxwOc/fdd/PXv/6Vhx56aLsdZz1UaOG+D33OcjH7vtjDoRC+FEJp/wdeKImvxwe9W01oCeJ6GNOx0P0QaoOFbyAQ3j4ejtPnbdPrF9wAkizRFNH6/N0KoaiGZdh0b8qC5xNrCQ05eh1RI3QYHXQXuoXoHiW8fJ7sU0/h5fNIoRBuPk/26afRd9sNeYDJxwLBSFGeTz1Y/J7leLy9OU3ecmmK6PTkLJ54u41YSGs4GzgRVglrMgU7GPnMFGrH833QkeWFVZ2s7syRMx0mJ+obLBitG5v+XuxC1iafsXBMF9cNPMaSKoMm4cgShuNhOi46MpIkocoSmiKh9J2vrOkg1yhKUyrYUggqFOpDCDzTy9LjbySuJPoEN8iKhy0XmKnsjkq4IYEHVHiKi/nVSbsdJbMJT4v1VZlk0FjA8smVnuujFgvf1CG4iwTXQUo3WeX7kM0UbtNM3Am7U3Dluo+jUX91o69HLTEc1hR8wOob8Y6Ht/31qHkc47gM/C4vun/84x+TzWaJxYI4vJtuuol4PF4S3YlEghNOOIGJEycC0N3dzQknnMBbb73FW2+9VWpnzpw5JdH93e9+lz/+8Y88+OCDpNNp9t13X26++WZmzJgxxke3bfQfQ/EAWU+idq/C61mFM2kRWcsfOChfjqIRIe8b6ITwPH9rhmexzTpiAb0yT5jj+ej9RPdQthUI0gFShsWEqI4e0bAMh+7NOTzfJzEhPKTwDithNuc2Myc5R1hMRgE3k8XNZFCSSbxcHiWZxE2ncTNZIboFo05lPvXA8XtpwyZl2DRFt4rzjozZUEEOz/N4eW0vtuOy15QkG3sNsmbteL7yEfiQqpA2bDanDPKWQ3gUPdye5/HSmm58JBbPaqGQDSY4Bl5sG7vg4DoenuvjyRKyKuGrEo6m4LhekF1d8DHsQHwpikxE73eNqMNeYLtbv/td1wtGhsq+f4sCT8Kj199I3s8zRZ1Wer7L6qBVnkyrNrXUXv97jyFtEmXVDZVCN1J6Nb4aQVLypeMYKhbQKTuO4DpYKQrriQWsuA72ebwlO4unx3Bb9sCgunL0QMdRfKyU72cEX4/BRtHdsl9ntvX1GOg4RBn4cczvf//7QZ+fPXt2aTQb4PLLL+fyyy8fst1PfOITfOITn9jm/m0vyr8EiqMxhuUS0WS8UAI5tZ6C2oIbnTrgB9RyfKK0kJXWEtH77Cq2WxLXjeZwO55f6fGmfp94WFNw/WAmdiIioUdUrIJD7+Y8vg/JCeFB4wSTepIeQ1hMRgslEUdJJCik0/iu+/+z999RlmZ3fS/82fvJJ5/KoXP3TE/QzGjyKAAaIZlhQAIEGkDC+CW8yzhcLublOsh+fS/LATDI5lq+XL8Gy3g5gO1LNNiAJESyUU6TQ+dQuerkJ+693z+ec05VdVd3V/X0TPdI57tWrapz6pwnh+/z29/f94tqtbAqFazy9Udaj3BrIlOaX/3MWT53psGDB+vX/sIecT3NkFv9qTtxRrVQIs40v/nFi8Mq8YmVDl8+32CpHbPYijg0XmKxGTF2haa+3aAaOMzWAu6eLVMNvMvcS4YV+MChFWVYQhAmiuVWzFjJv8JUXx20NuhY4YQKkRqy8z3OijWMNhjRr2JbgsSB1ALIm9ukye1hbSmwpCDVipJnUXBtesl2Le5e9byOJS7TFG8leF2xxkJ4njF7fFgUaadNhHKZKxylGrikymzXFLO7RsBOrLAkFGWKap0iTlLcSg0Zru06+CZVGtvKt0uYKHqJIcAgjCFTmjAz2HJ3PtxZX+MtVIilDWr2Xnp2ddfr8Xrsj93oxF/t/rjaeuhRDPwIbyTs9NQ91HinCguLREtk6xyl8gSWvLyrZ3DyjHlV1o1ACLZNw7VzIr8XH+68wr3ZXAnsvjHTsdAGokwhonxIyvVtUqloLObNlZXxK1e8fdtnOVxmLVobke7XALJQoPTOdxI+/TTZyirW9DSlxx//qq9y32ra5jc69tIMeSkG/tRRqnjqoXl+79nl4f8GFec40xyoB5xd73F2vcv9+6u8665pPn+msedlXWnHPL/Y5ux6D4HhrtnaZaR7WIG/mDdYtuKMVEl+5+kFbNvi0PirPz+00iShIg5Twk5KuxGRnu/hb6SkGlpuQhcFbl7FztKMsJcnDRY9G0tuTwzciXxt1eIW3fz+sHc976am2Hcsukk+D2nHnOmcxJUersylN6lKacRtDgd3M12oYUk5rKjGW6qgu24EdAX26ik81aYbTOT3QZ0HFClb41kWWhsyYzAYTD9oOdOaQUEYk9s+2lKSaU2YZrhZfo8SQiAQ/b+3F8LjLB858B256YqiMtLeOmn9OMqd3LO++rXcH7vVib+q/XGN9Rj5dI/whoBIutBeJM00ruhbEaWbJ0+x7wuan/gRpayF6kyjxo5um87WE9QSFZzEITUJrvRy4p1ookQj5d6Cb2B7cyWwp8ZM2V+nMFVICUXXxnHzLurd2AkGVsBCJ5eYDDSDI9w4eEeOUHjkEZAWhQcfxDty5GYv0givATKl+czpDc6u9bh//417gN1LM+SVcCX7va2ab8eCwLVphinvefMsh8ZLeybdmcpj3XPZiKQXK55faDFW3F6ZH1Tgv3CmwUY3FwHXiy7dJONjzy3ywUcP7Gm+AFmqSMK86THsJPRauR47SzUKMBZE0rBiFBthysqyoms0d85VGCu6WEJQv4pl347Vzi0EaeD7vFc971ZNcXdY7RS82DtJV7eYcnJZidGGhXCJcWeOw6V9oDUMmzMt1tfXeGVplUqlyv75mXwUVCn+5I//mCTNeOc7H0daFp1Y8cpLL3Hyxee4d1+Re+s9jFPCj1f41Oe+hGMSbrv9doJiDYCVlTUWFpcYr49xYP9+DOAZOH36LEbD7Nw8nuvmmySOSXtNpOdSsjoIkd/PDNt/S2koyvy3UCA0GGXQ4Tq9yhE65YMkiRo6eXRjgxT59pZsFtAsKSi8DvtjL42Zg/0Bm8R7L4T7auuR3ML35xHpHmEIQ06qB+T06t3AAuMUsTZeRhcmMH5+87z0BNWmgC8DIh0NqxDXWIhr2gJeC1eTrQwcWgY3sKJr43g2WqU0FrtYtsQv7uzHVXEruYtJ3KLm1/a8XCNcG9J1kb6PcG+MR/IIXzvYbTPk9WC75tuiG2fUCg5T5euTeKT9KmklcAgTPXQu2eqLPMDRyRL37qtyYrWDmyqKns10xafRS2lH2VXnY4yBzNDZiFCx7lv35THqKlNoBDgCLSGy+9d/nbtCbPRSlDY5ick0Ly22eeTwGPaV+mZugH3bteQFSZLw9DPPEYURjz36MEvJAkvJRc4+fZ4/fPGPuOvuNzFz2zyeLDIvZ/n+7/t+sjTlV//Tf8Ky8un93u/9Pr/3sU/wxDd9Ex/4nqfy7aQ1v/ALvwDAo489hrY80IaXn3uBL//xxzj06GE27rkfbblYCF589gweKbcfehNWwUcKwcriKp/71Ge44/gxbj9yCCENtmX45Cd+gyjs8Ff/2l9mYnwCY+Bzn/8Kf/qJ3+fI0WN8y7e9H20EGPj3//FXiKOYb33PexmrT6A1LC2vcuLEacbqExw5chjiFhQP0dUzeFFG2e8HvhmJlGCEwAgwIq+Su3ZeVY4yjdG5z7kU+ejzQKN9pdj0a+2Pa2IXspVr4Y1oC3g1jEj3CEMksohbnMBzJPpKujJHU9LniLxxOnaRQrqG1TpH5paJ+hqtrSeoFBY1Z5zz0Wm0rvRtkxjKS7ZqvPfqww1cpvHejU48yfLhvE6kkEIS9F1Nwk7CxkKP8f0lXO/yC4xne8S9mPV4fUS6RxjhFsNumyGvB1s13wMSf2nT417gSIFvS1phSqY1rTCl6DlXJDZl38F3JL04Q2nDUitiquxftm5brfu67YT0fA+TaM6Z9b51n8RYkAiB8HKZgzGGKFIgoOK7/WF/gTHgOjlRKvkWjTBFk1cjL9Xi7kXPW/TytMaBFjdNEj7/+c/TbHd529c/Prx//Mqv/Aqf/OQnee973su3vudbUVqz0ujwz3/+n4FKueP+j/BK9BKBE7C+ss6LL75IZWqC2tFx7qvex6Q9QZamQE7WgyDIA16qdabG65SCAu1uQtcIbGN47JF34tgOaysxhSCvDB+fn2DukYOMzU8ye3w/riNxbMH+Z+pMiTWO3FWjODGNlNDQY5xaLzN/1xiH758eShWdSZuk6zB1+z4m+rbD6qUvcK5xmnv8EvN3zQ5dUD71/J/QbLb4gb/xQQ7sn0YrePZ3v8i//+1/xSOPvIWvf+dtaFXHzL+ZH/iOD3Dx/AK/9Esf5U1vuockVLz4/At84hN/xNEjR/n6r/sGHCNAQBwnFEoB0pMg8yC8XqKwZJ45GqaKsKOo+nafpMs96asH8pKtGu+96sSBG3ZcaTNqpBzhjQBx5bjZYSOHYyH7N4yeEIRWlaC9ROqOE7sTO56gFbuK0ln/JBdDMrxV4+3ZFnG2e8K9KSnZ1HjbUuy6MbPk2WigE6X5stgWQcmh20zYWOgwMV/C2uFCU3AKXGhf4GD5IJa8vuapEUYY4cbj0mbIsWKZd981c8OSGgea716c4jn2FSPbr4Uk06TacHSyxImVLqudhIJncWyqdMUGUNeWzFYCmr2UKFXUCy7vvmsGB4HuZZhUs3iiSdzNXUaSvg1hHGVkwtC28ghtbQaFiQzXkpQDmzBROLa1jRh9/fEJTq12WW7HJJlirZNQK7jUAwf3EknAoKluKzFSSrG8tMRGo8Fdd945JEYf/2//lT/70z/liSe+mbe943E6sSLt9fg/P/J/IRyfd7zj8eH9I0kSNtbXaTQ2huSrWi5xcH6GcqXIYnKCUEeUzQx33nUXlfFJ3EmLQ8X9zPhzSCH5v//lv8T3AxzbpdGM6PRS3v7Q1/P4o+/I7zGpxrIlhcDh73zor+EHDq5v43kWrtrg0SkX+x1fh6jMbNsff/2vfx8sPQPVAIL8RvPWtz7MW9/68GX7+6f+8T++7L33vuc9vPddb4fmuW3vf/jD/5Rut8vMzAxCCCwbjhzZx3ve+25uPzBDpRDCgUdhbD/nV09ycf0ih++eYf62vCH59//ns3zkl/8x3/Lkt/J9f/k7yBJNEmV871PfR7vR5X//+/8Hhw/lctDe+govnniRA4cPcOjIUeL+sRemiiRNSTJD4Fk41uX34p311Zsa70GT41514tc6rrZhB1JfkoJOnI3cS0Z4Y8CxxDU7p+XA/3LQXIlDnEjMxhn82RqeE1w23UAU0colsyJKTmlIhmW/WztMcmcU2LsP91aNd4rZk05cAsaSdKIU6YNnWxTKLr12wsZyyNhMAXlJY1vVrbISrrARbzARTOxl844wwgivMbY2Q37g0f03LMp9ANeW2NLBXHKB2a1jysmVDn9+co3Taz2UUhyZLGNZkkPjAa8st4eBOzs1gPq2YNK12Vf0+JZ9YzgrMWdPt4nP5dany56HcCTKAuMJ4tSwkWVICbYUuPYm+Sl4Fp1I0Yl21vO6tsW+sQKrnZherNhXK/DgobEh4d6qxf3il5/jpRde4N67buf+B+4HoNls8aM/+qMIIfnFX/530PfhDntdzp8/x+LSwlCLq7wCx+++h1q5SJ7PmMv7nnjiCb7u7W+nNja+Sb4KHj/7sz/LufA0L/eeYzaYIk1hfGoeeyyg7PnM2YfodTKyREEWELYN3U6E4wgmxwKqNZ9iycUPcnLtuRaWI7c30YcNOPsVMD2o7t/7gXKdOHb06GXv3XPPPdxz+2EI12HufqgfBuDChQuEYYi7RYr38MMP8Xf+7t/i2LFjlOq59MkYw6ee+QRxlHD0zdPsmxsj6qb80f/4OB/9V7/Mww8+wo/+rz9Kwbcpl3z+xS/8Apbj8OS3vpdCeYwk0yitsPpp1cCOZHirxnsge9qrTnzrcRWn7F0n3td4b9zCEpQR6R5hiMsO053skLa2VYvcIirzati9ZaxwEfzD2yahtCZLPcpWmUx2QWy/kVyqJbui5dJVXEpsKUiHjSdiT42ZjiXASBq9lFohJ95+yaGzHmLbkupUsO1i7FgO2mgWu4sj0j3CCDcY12P3dymu1Az5WmG3jilJpvn488v0kixvnkwUJ1c62JbgxEqHMNHDwJ0/eHaBCW8/toawl7J6skV0ustYpikpxcsvbTBZ9UmF4E231UEIYpM3i9tCktv4mTwQzJZDmcgA1iXX2UtfQ+4AUS86HBwr8Y13TaLThH/4D/8hqyur/NN/9k9xLEGcwnPPPcvv/7ffwaTvHpLueq1KuVKhXJ+k0+0xM1HFkpLHH3+c++67j7m5ORACy5JYGn7i//MTlxG06elp1OTkZQSvkaxzpnuCwBTREYRhQjfrEOou84UjaGVju1Cp+5TKDkEh/ykUHFzPunbOQtKF85+D3trrSrivvDw96K3C7H0weXwbAQ2C7UWuBx98kAcffHDbe0II1tbWuHDhAgcPHsS2bUp1n8nDBcYP++y7Y4z6bJFuIyZsJ3zmTz5LnCa895u+hbpnY6Tg45/4Q37tt3+HRx57O9/w7m/CEhAEDkrroVYedndcXUu2MjiuIPd330tjZn+FsXaozt8qGJHuEYbIto7I7NJ/NFMa27bAK5Otn0UE41h+BdgcgrKlYK4wzSvRC9sn0CfDCHAtSaIu0Xizex9uKXPCnWUGsUXjvRuduMGQZIpGD+pFgWtJvMChudJDSJFbCW75XsWtcLFzkaO1owT25ZX9EUa4GbhZ9oM3gijDq7P7u1nYrWNKkmlW2lHuAR44dOO8ebIdZ9haYjLFmG3jJ4bJBNqnOrwsVyhYFt044fyFJlGmsRxYMYo/X2jy9pKNa1uQQTlwcPr7O7/uaiwp8WyJuLQK0SctA1ndeqvNmdOLvOmO25B9AvNrv/Hb/O4XTrL/jvu4Z/5eXNtC2AHPPvssWZqytLxCoTqGJeHuO+8gzTJuv/PO4SyEEPzzX/j/XUaM5vftY37fPiAnX0maJxsqpbf7LffXo9VLESq32et0YzpZh5eiZ+kSMuZMkUiDVwbjKB4df4g7Jo5TKDoEvo19PY1/WQwXvwitC1A7uJk4ebOQRdBZhOl7YPru624i9H2fo5dU0d///u/i/e//ruFrpTTdVsj3/PC3ce70RaZnp0lDRZZoLp64yMIrJ0juuI2DY4WcOGeK//df+etUazV+4id+gsmxOrHSpFlGOXCJMr1d483efLgHUfOCzQr4bnXiSXZ5Q/KtghHpHmEIrQ1JpnEtcU3CnWaa1NI4g0hbq0zaXCBeOw0zbwKxfQgqy6pYkUSZDEvYO5JhS24P0EnULgn3Fg232KLxdi2568bMgmcjgFaYUAlc3L5es7HUBWOoTARD4l1yS1zoXGC1t8r+yi1QCRlhhJuEG0WUb4Td3+sFpQ2fP7uBFIK3HR27pmPKYBt1kwxhoBunmNQQNRPqQuBpjUg0aRxhORZrPUUhcMgsCB3DeqLZMAbjCLTIK9AaQ+A6VINcotFLcsIKm9fdwLW3Ee6w1+PMmTMkWCypKp4jeexIjR/7638Fbbn83D/5GQ7MThFlGjcoorKEOAyH35dS8qP/y49SKJVwgtLw2v7Yww/w5jffR5zmD1++LffUQOdKiVKGdi9hrZXgWxKtDd04Q1jg+w6xNFh1i4ZawNYp99SO4nsulmVY6i3wQP0e7p28F1u+imNFpXDxS7B2Iq9w3+yeHZVA6yJM3gGz977my2NZkkq9yF/9sR8GcuvFNFZEvZTv+J4nOX7XbdQrY6h2RqlgE/bWaS6dp7uxzMxYjUjldsK/+zu/wx/+wX/n27/923jnNz05JN69OCPVe7MFFGxprtzLcWXfuv1Wt9bVbISbCikFqdIMXKuuHGmrSZTGcbdEHwuBUxqD7grdxjIqGN+mxypZZQJZJFQhJau8IxkearxTPfTc3Avhhks03pkaNmzuVieeZvoy4r2x1E+tnAxyLbiQuNLlQucC+8r7RrHwNxLSovDQQ9S+830Ie3R5upXxaomy0oZWlPabt/Q1yavJMhq/9usAt8zxcS3HlF6S8bFnFkm6GROORYDh3FqKpWGyIJksB6ynCcWSy1kd0lQZxcDj6IEqtps3rVcDl8CRNEKNIwVJZqj6zrDpfRgQ0tdnWxJWFi5w+swZzMRtQ83vH/7hJ/m3//FXuf/Bh3jbt34AKQSWZTEzNUk3jNhotqjWxgB462OP4E4fZTHavr6PPPbojnre7VpcDeJyYmSMQStDL0yJQoUFZFKQCQE22I5F5kDqSzzfYtZ3qBRdSkUHP7B5pfECNJvcUz6MIx2MMVzoXGCuPMud43e+SsKd5RXulRegMgfWzraxrxt0llfbx4/mOm7r9T/WhcyTm93A5r633M5dDxwlbCd0NmJ6rZiiXeXf/tIvs9ZYZrJaQBlDpgyLZ18hDCOk4+H0OcWFlQZ/62//LQ7Mz/IP/vf/72XzupJLyW6OqwG2Psgl12FN+Hrh5l+1RrhlsHU02pJiZ0mJ0ghlcC15uV+r7WNbHZz2OZRXwff84cljS4eaM8ZCdA5LF65Yfc4r3qD6xN/dYYj8WraAriVzwt1fp700Zjp2nvrVDBOqWyveyz2MMVSnCkgpqHk1VsIVGnGDul/feYOOMMJXMV6NL3Y7SlntxHz61DpCCL7h+BQlz+alpXZfS2zvaIl3q2Enx5R33jaFCRUb6zEXFjv0TreZ6WlsYLLq0nRstISp6QLCsuglMRXX4vBEGTC8664pAneT9EkBM9WA5XZMpnMHqXv2VXEdi163yyuvnCBOM26/6x4gJyo/97P/hOWVVd73V/8u45PTxKlicm4/Y+MTVIv+tmv7P/nZn8N1HbpxRtb3bR6vlgkKEWmnQ6bzSuO17Nt8O7cSNMYgNag4lxqoLI9n1NoQK01sDH7BplDxcHwLz7fxfItCYBO4DpaVR8k7lsTqX7vPtM5wpn2CqcIETj8BeS1ao+SUuHv87lcn89MKFr6cE+7yLNjX571+w6Cz3NWkdgDmHwT71sgscDwLxwuoTAQkUUZnIyJYdKkW64SdBL/o4NmSf/j3P8SJk6cpVmrYlqSXZLxy6jRJktJpbmBteVj+pV/8Jc5fvMgT7/l23nTXnVc9riDv39pNY2b7td0Urwq39hVthNcVqTI45IRbDaQmWwjpUIphCaQ99DEZQhtDaJdxwlV0tErXmt2m0avaNU5kJ/EtQ6FfxbkUSaZRCiwr9xK9VOO9Gx/ugU58QN4TsfN6XK0xsxlmaJ1QL+bEWwhBcyXEALXJAM/2SHspy73lEeke4XXDrVTtvV5f7F6ScXEjJM7yKPGNbsKfvbzC246N89+fWWS5FXNuvcf3PHzgmuRd2Db1735q8w31+lqFGWM4UAl4dKZG3Mt453gVazHidNgmVYZIKdCwEmf4voVKU4wrsKVEI/Ck4PBkiTfvq/Ll802UNuhLViFK8mtg3Rd0eiHz5QLT1ZwYPv/c8/z0z/4s+w4f4x/8ZE66u4ni+J13UZ3tcb6jWVEtwlRx1/xh/q//88MA/Pkr68Ppu65DlCoyldsKZtpwarXDC4stVjsJRhtumypRKbjbCLfRBpVqVKbRqaITZmiT67kzY0g8iR84WAUPx5cIR1JxJMWiS6Xo4NgSR+bE2rHEFUcMV8NVnlt7joJToODksfetpEWmM+6bvu/VZSZoBQtfgeVnoTQNO7hvva7YSrj3PXzzl+cKcH2bsdkS5fGA1mpIcymktRrhBhZ+0eGO228DQBlDxbcp338P4//HT9LrdjBaI/qc4MtPP83SepNvUmp4XJ09c4bf+u3f5r777uPrv+7rhv0HthRkyuQSpi3ylFcd4PM6Y0S6R9iE2ZSUJFnu8wq5ZGPw2rEFtiW59NY2iJuV0sYrlPDCCzSCOp3Yz4m3EJAVsPGw7BS5w1DgpWR4QLAHxFub3RHurVX0ROhtATq7bcz0HQspoBNlVAIb25H4BYfWSgjGUJsuUvbKnGuf43D1MK51a1QjRhjh9cL1+mI/c6HFSichyTTnN3ocnSzSDFMmSh6HJ4pIIXj08DhHJ29uE+VODaLGGEyisCOFnRkuPL9BlijMQoilDZ1aTCZBOJLMgk5kODBX4nQnJFGaWsFFCsHJ1S6x0lgCZusBRc/Csy1SpYhTRaOxTnNtlSO330GcaWxLcPLEKzQbDSbkYXjgEAD7Dh5iZt9B9s3PU+wXBzpxxvf+pR/ms6fWeXmlg2NZdKKUV5Y7HBwrXObscClp6cUpz1xoDXMVwkzz+VPrvO3wOGXXphvGYAADwhEYCZEjwHeplV2CwEY6Et+3qRQcHNvqVyjzhw1rh2LLldBJOjyz8gypTpko5m5RcRbTilu8aeJNzJXmrn8Haw2Lz+R+28Vp6BP6m4ZLCbdbvLnLsws4rsX4XInKxBbyvRZiOzn5tvouQr5jUbnjEI1uwmonoRrkIxp/5X/5MU6fPMk9d942rHA/99xz/Mkf/zHNZosHHnnLUMP9P//sfzA2Oc3svgNAPqLzRiPcMCLdI2zBVknJUOOsNsm3Y0l2yoMYEu6+d7ehguwuUYmWaBYPDrWGrigw7lcIdZeA7U/wO5HhnTTee/Hh3lyPvsZbKTB704lHqaIdZ1Q8G8vJI+JbqxFCCMqTZRZ7C6yGq6/u4j/C1zRulPvHzcBefbF7ScZnTufpiMoYeoni82cafN1tE5R9G9uSm83ZrzNsS/KBR/Mb+ouLrbxBNFFkUcY3HJlgwrFJz3dRkeJO6WBcaLQilJB0LY22BF3LkCqDlRosISj7NlNlj+V2QpppHjpY4z9/7jxpZrALgjDRXFzvst5sAeBYFivLi/zDX/wvOLbDP/mbRwhcG8eSlMtlsixFWptpfX65yk/943+0bVi+5Nk0w4xmmPYlGjBZ9uglijDVlLZYvEWJItvS3JanU2pUpKgg6SaasoS0k5KmGuULrKqH7Vs4nsT2JNK28D2Liu9gW/Iyacj1IlYxz649y0a8Mby+piplqbfEsfoxjlSPXP/EtYbFZ2HxK1CcAPcWINyNs1A/+IYh3FsxIN/ViYBOI6a5HNJtxnlPVdHF8SyKffIdJor1bkynmzE3N89dxw5tk5TcceedfOd3fhcTs/uGhFsrxS/8379Alqb8zM/9M6ypGeJME/ZCykX/DUO4YUS6R9iCS+XTri2HhHvwmkviVS8l3IOTx/hVrM5FSoVxmuQVq4JjMyGmeCV8fts0rlZ9ljK38Ev6foaevXvCvX09csKNYE+Nmb7dj5Ylo+TbWHa/4r0a5olhBYsLnQvMFmdHDZUj7BlvRJu8S+FYAiE1memxGubD/oMfZRSpTkl1itGG1W7ECxunMf46OlE0lI2vC8xNWSyEhp5ZIBUxzdRmuRfgOy6OdLClja1f+/MrSxWtVszHPnMBNhKmhaRzqs0fn+/x4KE6UZgRY0hRIC1CKfj8mXXOrIUcmyxQCRyU6kvcyEcDDAZHShxXEqcpidJYlsBC0Fi+yMLyKoVFzfFHvhGAQ/vncV2HUqlMp9NhYm6St902DryJE6tdjk4UUMoQZnpnfbUQjBUdAtfiYjOi5Nm0woxy0G++JHemyNKMTjvBkxIVCroiN0cWxhA4kiaaDcfQcjVj8wX23T1GteQRuBaOnZNrW+a/d5IKvhpEWcRXVr7CufY55kvzSCHJdMbF7kUOVw9z19hd158IrDJYfBpO/QlM3A7uTT7fdAbN829Ywr0VtmtRmypQGffptRKaqyGdjYheO6FQzsl3ybPxHUk3zujGGZ04w3esocXpoYMHmZiZ3+ZS0m42uf/++7l44SJHDu6jGWVg4Hd+57/yxx//Pb73e7+XJ598crgcoxj4Ed6QGBDdra+3VrqvRLgBjB0g4hbp2hmo3wHCIswUBVlCYqGMwhLWruQeyRbiH2fbNd678eFO+o08iPzzl2rVr6oTF+DZkkaY35DKgYPlSFxj01zp4U94LMtlWkmLqlfd9bYdYYQ3kk0eQKYzOiYiJGGjdZpQx2yELX73xHMYMipjMyA3z1WDQZBrdSUytxGNMi52LqKsHq4Ltiuw3A6R9Hhm9RwNfZ7QSjnbW+XPL17AtixsYWNLG6kUvef/O0Vtc+AddxMUKniWh2/7BHYwbLDbLZTSpKEiiTLiXkbYSUhCxVozpHuuQz0GbINTtlhJMhpC00GTKYNrWQSuTcG1cCyZ24kKMSTDA/SSrC+NU/zmb/0WF7/8Zzz6/X87l7wJkF6AUSmd5mbrV6IMT73/KaSwqFQqRKnaFsWttaGT5DHuV0rrU9pw52yF8xtdoiijWPS4s1YkbaVExpB1UxKjsS1JUHaRfl6xthyLuid5cNbjK//zNJ1mzPhYwHe89SB3zFVfdfV6NwizkK+sfIXz7fPMl+axpY3Siovdi+wr7ePu8btxrtddJIvzpsnTf5qTbe8WINyNszB2+A1PuLdCWpJS3adY84i7GY2VHs2lHlEvpVhxsS2Zu/K4NmGi6CYZcZrhOZIoVZfZAtbqdf7m3/ybYAzRlvv5mbNniVNFpVIZzntldY3/5zf+O//b7X8RqOy8gDcRt96VfYRbAkMNd3+od6vGO+DqhBsAY+hZVWRvmUp1FlGaohNnpFkBT/hEuodjirv34XY3Nd3D5kp2R7i3zmPweqDx3k1jZpTlN6cw0Vgyo+jZ2K6FAeJVRS9tslxfHpHuEfaEdpTR6KU0egmdKOPe/RUavXRX7h+vNYwx9LIe7aRNO2nTiBs0ww3W1cukKPzlKpbtgLYwKCQONa9OwfOQVwkUWSdhphiz1GgSa0XZcpkrl5gK5im5FoFIcExE1ZlgrjgF0qCMQmlFonp0rJSGE9NaexbRchAIHOng2R5lp0zJKbORtHGlT5iNU5IFhBAYk3sOJ32SHbYTom5KFivSTKNMHueohCFzBVlBsthOcZXEihJqRY9K4PDYkQlsCV8827xs3QZk+Nzp0/z+7/429bExnvrg99OJM4SQZFmGRuJmPVw7IMk00+NjPHBklifv28dnTm2QKkWUalzbwrHyNMk41UM3J9N3ALH6VoHDkUVj0JlBpYp2LyVLNVOWzd3VMp1Mcdd8hfFxH7fgEGpFt23jehbzd9fxPZvAtvPGRivXXh82Zc6uh3z69AaPHBrjztnK60K4e2mPr6x8hYudi8yV5rCljTaai92LzBRmuHfyXvzrdRdJunDhC7B+Erwq2N6NXfjrWZ6oCbP3w74Hv2oI91YIIfBLDtPFCuW6z9rFDu2NGNfPNd+uJXEDSeBa9OKM9V5ClCrGCu7OLiWZ3qbh/tDf/AleOXWG+dlpIJdcfeErz3LuzBk87ybv3ytgRLpH2IQBjN4eSmPl7w0q3GmqsTJNkmRI1/SHK832eHggTBRaWBRcF9k6S1qoU/IciME1Ndbii9RkYU/66gHBHxBvYE+EG7ZrvLVRKM2uGjOL/Q3QSxRCiHyI1bXyAIF1yQveCQ7VDu252jbC1y7Kvk3Ft4lSjefAn7y4sk0C8HpiK8luxk1WwhXaSZsoizAYPMvDxaGMj4NNpbwfYVkkqcIVHQBcy70q4QYouBZTFQ9/WSJMvg2mKz6FHZpFhBBY0sLGBgt8Y5MqDxSMl+aRros2mlSlxCpmNVrlXPsCZ3vrYCR/eOIsvggoqip27GIlLq7ykcbFSMASKAnaFXRiTZIqip6F7zk8cKDOxUZEmGoC1+aOmQrGCDxHbqs6P/30V/j0s+fxpw4RVz0sIZAq4bOf+TQTk5P84A/+AM0wJc40b337N/DAB7+dCz2Lkytd5mo+nmPh9jXWqVKkyuA7Eqf/nu9apMrQizPiLJcgSQ2egaiTDu34AIyAUOXa8vJYgOVZqCik4AgO3ztBwXP71UPBS90YKQQHxkt4O+hhM5UXJQLHet309b20x5dXvszF7ibhNsaw0Flg3B/n3sl7h+4le0bYyKPdW+ehsi8PnTHmMrnkrjG4313vNLqrEHfg+LfAzD23jC3gawUhBMWah1/OzQjWLnZprYa5baRn5eS74FLwbDpRSi9VZHFe+R6Q752aJgPP4bajh4lTnVteasOhgwf45m9613Wnd77WGJHuEYaQ0QZmvY1QuS3gpTHSAWBlGq0NdriGn21AeHmFO84MtjF4jkRgkO0L6OIUqnaQkmdTiessxueQtsljjC/B1arPUgp8RxIl/ZQqd/eEewDXljnh3hICtFuduGfLoZ9t0bNwfZuqrnJ+YZFT1fPctv/Qtsj4EUa4EgquzbvunOL3nl0kTjWFqsWds5VrVrm11nzuTIO1z5zjqUcPDs/TTGk+cyq3gnvf/XPXjIHPjKJNxEbzFEvxKs24SZjlCYS+7VOwC4z5Y0MibZSipQQkITqKsIq7r8wZY3JtswVv3lfmU6eW6ZHSSQxxliJQaJNXpPcCKSSe7eEIB18XKBjFO6fq9Dox7YUey0mLSJ/FSLCkhWX7FGWZshyjYIoUrRJa2UgJk1uqa7O1gOMzFZJMcd/+Kr5ts7G2wtrSAm9+4P7h/P/gDz7Gs2uK2x6sIecnKXk2t91+G3/xL/5Fjh8/jiUERddGa8PY2Dj7psdZPLOBtCRFz+Ftt41jSUk3SnPLVksM0/SMMahMIxXoXgbdlMO+xyOzFbLMIF2JVfSwPAvLESgLqr5NueAQODZSwNPNHpYQHJuuDMl1pjYT+65WvZZS8sjhMZ56aN81j6VXi07S4csrX2axu8h8aR5L5k2di71Fym6Z+ybvo+yWr3PiK3Dyk7D0HBTGIHlx839Lz7y6BW+dz392C6Oht54H8Nz2jVA/fMuSw9cCliWpzxQp1jw2Fro0lnuEnYRi1cOyJa4lqRddilmeSNpLMzC59/vWht+t8B0Lpc3QY/74kYOMFSZvxurtCiPSPcIQqVUgdMu4lkTaO9sCJkmGHa6h7YDYK+NsJbR9vZVy8gAHI0RePHdLWI2T6GCMUBYoyjKOdIlUgqe3+3XvRu6xVWueZPqyz+1GJ77VzjdR2zXeV9OJ56Tfoh2lRJmiFjgEBRcrs3j65ZcoqzqT+8rYO9m8jDDCJTgyWeLIRIlEaR4+VKfovbqRkkxpUm3oJRn+DuQ9VjHr4TqrnWVOxi/QSdt4FzyCUpWSU6LiVPnjl9YAxePH/W2V6/TiReLnn0MnCWhF8NBDMDmNMRpNSitpQZZLQTKTXUaghRC5HMQ3jFcStEwYLxlst8tKuEKmFZFZ7TdSZpzvpEMSbAkLoTQdK8E2gkISIjODig1xLyMOU7Ik94xWAFIgpY/jeqANSuWSMmlSWmqdDbWElUqM9ijJGtOFcaSuUBAlbGFjtAGd4Vp5JXp1bZUPfehDSJXwb/7NR4fr9ODDj5CeblIslvBsAQJ83+e93/Zt+QdM7is8QK5H3b5dolQRJQrHGOxMEDYTdCcFA91WApYksiEuWwjHoni4SrVvzef1Q8p2suTbLbm+2ViP1nlm5RnWorUh4dZGs9BdoGgXuW/qOr24tYaNU3DusxA3YO7NMDiem+fy39X917fQxuRk2y1DsMtlyxLoLMK+h2DfI1C6dYnhaw3Xt5k6VKE8HrC+0KG9FiGloFDxkJbAswWu7VDM8sr3ei9BCHB3SOZUWpPpzXMqym7dJkoYke4RtkBJFzuoYF8p+CZRSNfgZxvEXpnYKqBl396rr7fWtiHo+8UOpmHcErK7hFo7TVy9napXYkJXacRtwtQbEtvdBt9oA4GbXzy3abzF7gj3Vp34QEoz0HjvpjEzVRpLSIyGVpgSuBbjxTFa8Tpnzi2Q9BSTB8oUKl/dQ4Yj3BjYVn6cDY7XTGn+8+fy6tleqownVzqcXO2QKsO//9RZnrx3jmNTJVKVsh6ts9xbZrG7SCftkC4uoV5+gUKkKOsiwUMP4c4VUFcIl9FRRO+LXyBMQrKSR7O3hP7CJ3EffoTIrCFFLgWouhUCOyCwAzzLw5EOjuXkpFkIpJAkqebj9vNEOmTS8jlaGuPt87OkSvFx61ki1eVAYZIHp2cwQpGqlCiNaDWaNFVAB0n71AKx0qSZwgiDZVlImc9P4CCMRZrkYS8l1ybTJr8uWC5VuwwGOklCaEI6rNDqLWIJm0AGvPSlE3zsNz/OkQe+hUfu/3o8W3J03wwTY2PU6nXW1xukWhKninsfeASmW5xvhDv2tXTiDGXMsDqntSFONCLVECnWlnqkmUYIMKkhsTQULdKag3QFU7dVKRQdfM9m4Su5pevxA9U8R+BVEOlbwaZyEOP+7OqzhCpkrjSHFHLYNDnmjXHv1L2M+WN7n3jSy6vYqy+BU4Tq3dv/H/U1+cF1BpsZnZPuoLa7aYQbkIUw/wDM3vdVqd/eK4QQFCouQalOZzJmfaFLeyPCcS2CsoMQffJdcin5Dt04pROrXIrSl+BtS0n17aHmO7s0YeoWwoh0jzDE1hv/VlzWNBkKHFuiZW4paMhvJtqYYbjOpYjtKrq9QKEwjlOcY1xN0sjWkSInzq4lSdTegm+AbRpvKQXZbgl3fx5bNd4G3V+P3evEtTZ0Y4WTWaQypVdYJ2zXOP/COhP7S9SmizfcTmuEES5FL8n4+PPLJFmuCV7vRfzW0y/wzrt91uMl2kkbg6HiVpixx+g+/wXCrsYqFlGdDuHnv4A9NgbOJgHLdEZXRYRpSNhYoxsvY5UdAukwUz1AoRFTL9zBH1ttLFy+ft/9lPxrN7lFMsMRJSwEjggIrDJ1v55XZkUN27hU5CQTTKMiTdhOsNsJTquG2ribVGgC634iH1KZEmUxPdUhMh0yEjLdppvFSAEF4WK0hyc9LMsmzRie5wKLV77yAqdOnOCbn/xmyrUykQqJC1160x0uWs+yKCssZEeoyTo/+3M/idE+Fxohzy8sE6c5cbWkvNwutE+400zjGSBSoAxZKyXtZaSJoms0dt1loubh+w6nTuSWa3feM8kJ8uvt0X1VHNsiU3pI3F1752vTbnEr2FRmOuNE4wQvbryIJz3mS/MApDplobPATHGGeybvoeJeh/tEawEWvwztpTzW/WamOuos14/bfu5OMn4MdqjWfi1DSEF5zKdQdWmvRqwvdGmuRri+RVB0EDIn347l4jqKTpjSjVIcO7/3b7XNHJwjG5m6xlxvHkZ7f4QhduqXuYxwb6mBu/2KeDZwNbkC4U4yTYqN53g4vfOkpTHKdgUpBJ4NYbIpGdmLvhq2BOgkfa35Hgj39vXQZJnpL8PudeJS5k2VUapI44CzXGB+4gBW7LF4skXUyZg8UMbxRnKTEV47tKOMVpThOhnGapO4S7zYXGVieYzZSo3p4jR2PwU2W99A9XpYxQLYNla9jm61SDodoqJDK11FkbLYTfjKuZiCVebbjz+AdpfhwjMUi3V818Yam6YweQxPnAQYTv96oDJN2EmQnZRiKyM60+G0WEUp0AKMJciEpiUNCRY2BWqej22JPO22f92Jsoj1qIcyEdJO6eo27axFL+uxsHyB9XaLg/uP4gmfqhfw3DPPcO7cWe686y7uv//NlOwydx28n7Fv2U9TuBiRcC4+xbnkFL7w8USJZy4aWpmk7JRJlGK9GeNIINPE3RSdatq9vKnL9yyMI0ltkCWbqWNVilaKWhWMHary0P0z+G4+SvD55dw2sOjlYTjADff+vxVsKsMs5Pm15znVOsWYN0ap75MdZzFLvSX2l/fzpok37b1pUqV5ZXvp2VxaUjsI1+vlfSMQt6G7ArX9MHNvHsIzwhVhWZLadIFS3aO9HrGx1KO1HuK4Nn7JQcp81Mq1JJ0oZaUdY0lBtehuG2XyHWvHXrFbBSPSPcIQV5SUbLUF3KpHNAa9RUuVKYNrb79JbLUetN06oruE1TpPsXYAXwZ0VQ/J5sVVG7OduO9C7rFVz6W12fTk3vLetWQr29ZDG1y5w3pcoYou+l7eUeJxvr3ISfsCd4/fju1aNJa6pHHG1KEKQWkkN/lqhMkyGr/26wDUvvN9CPv1vawaY9CiQyLP0eQFbBmy2KkxVRzjSP3AZY1HshBgFQok3R6m6NNpLhCVPMqyTWCqVJ1Jinadt8wepbfRwZYOx2b2kT3usfL0WXSni1WvU37XN2IK1+kmoQ1uZnBCRbYUcvrpVc4stume65LEKV8+t8FEzWf/RBFj8ofbfDQKPDSeLfHs7ValSmvi1KJilym6NdJMMR84aKN5/uVn+fmf/hcUJqrc/7ffSk+3aGRNbn/sKAfumWdsf41YR6AcLMvlwL45Xl7qAD6TTgUhDJGOWO6tcTFbIfLB6AI1PU0SWUhRJol9okxjFWzGJwNKJYdSwcULLE49k6fp3nXbGHffPj6UD3mO1ddbi2EaZnYFic+NQDvKaIUpFd/BtSUzVZ+1bvK62VSuhqs8v/Y8K70VZoozuFZ+TeylPdaiNY7Vj3Hn2J3D93eNzjIsP5d7Xgdj4N9E+1atcu02Aubuh8njN9+e8A0E27WozxQpj/t01mM2Fnu01yNsR1KouMNmS9+xaIV5uE7gbnp6YwY9HLcmRqR7hCFS1bf+E2JXPtxhqoeSkkyZoY/3gJRe6vUNYPwaVuM0fmGcilXnTPc8k24Bz7aIszymeEiudxl8MyDDthSXabz3qhMfaD+HGm92rxP3XZtxU+WF5ilq9iwz5RKV8YD2RsSFFxtMHSpTGb+JQ50jfFVBG8VSd4nF8CKLnUWmJjawT4NO60wFkzx2aGLHeGTt2kRvOsryhecQaZvxwhx3PvIXmD18HwWrxPrKMgDjwTi2jIff844cofDoo5goov7dT2FVKkRJtqtlVakmDjPiXkpjI6K4mjDWUrhZSk9GXCj2+PRCk64LXSWIegm/9/wS3/HmOQ5MlIgG5ygaizz8pRNnw2HlrdrOP/nEx/i1X/81nvimJ3j/U+9HCskdR+5kvLSffZMHOGYfwS0ENOMOx+65m0x02UjXaSYdelmEY0kK+KQmxdIeaaIwypDGEjsqUs9gOe6RuilnrdN0xzRHJid40z0B03XNeKFIza8Mbf+yLY3a11u53hpR/2pQ9m0qgUNroU3Fd1hsRkyUPcr+a0sFemmPU81TnG6eJjUp8+U8ZdIYw1q0RqpS7qjfwe1jt+9txCTpwurLeYVbZ7kd4PUG59wIJJ3cLaU8lVe3K3M3b1ne4LAdi9p0gfKYT3sjYmOhS2s1olTPnU4Kbj4i1I0zOkmG6D+M9xKF3qML0uuJEekeYROmr6+281SoqxHuKNtsmpRCDCvcW2PjLyXc0E+qzJYQKy/hlqdAaHxbImROisM0J96+kwfZ7NWHe6vGO1+PvenE8wr3ZnMlsPvGTMfCo8KyWeBs+yLo/UyWfcpjeSTuwssN0khRnx3pvEe4fsQmY9Vqcqr3JQqLeUPdmD/G/bPjfKosyIzh3XdNb2uQ00bTSTq0khYCQXlmlruPPEo9lhz89qfwyxUwmixq4cerCKOhZSiECwhjoJGfC5boQABSNaHVxdaab5jNMAjs3jLEEoREKYgjQxwZem1Fr5MRxfl5Fevcx7+FxrGgJQyNTBEqNRy1KroWUaL40vkmtaKLMQLftjD9c7LoWfS04eN/9Ge8+PyzvO+p76FcKlLybFzXodVs8sILz+crbwxhZviZf/KzlHx7iyNKlTgt4zmSafswDTrEok0va7PWXSXsbZDKdS6GRcpumWKtyKFSndqBMv/9+UVSBUdmSxgMgZfRZYXnNhbx2h5lt8xMYYaaX6Pm1bYR5t1Usl+rRseCa/Puu6Z5+kKTTpwxVizz7rtmXrMqd6bzBNKXNl6iGTcZD8YpOnkTYaISlrpLVLwK907ey1xxbvcPJVrlVe3l53LP6+LUzU2X1Arai/m9cuaevLrtXuco0AjbYDmS2lSBQsVl5Wyb5kqPoOTi+jnprhVcfNeiHWWsdfMigfc6ectfD0ake4QhLJlXuK9KuIE4M7kt4CUa7gEpHRDvSwn3AMqroVZfZsyrUnELxCbCJwCxSbwHPtx70VfDdo13lGik3Ftj5uZ6DIg3ewvwQeJZPrFcoZtME69rxksu5ZKDjhVLp1skccbk/jL2DlXIEUa4EnppjwvNczyrT3DCWSNW00wGx/AdD7QiiSMCIiyR4cfrmDgjTLs04w0ylVAWDrc5JaasAjWhSOI1QONd/BRIQGuEyphdXkcYhZV2mV+1yZwATo+B0Tjd53L51okALAuUZmZlHa0F0dNjZKlNL7Lo9CziMEOnEZlbRNoCYRmkBNfAEbVOTStKsctUWOJgVmc5a9FOOowjkZlDtRRg9RI6G4KJaoCKE3rdNoViCVtKSq7Fb//u77K4uMg9b36Ar3/LwyAEDz/yCPP79nHbsWNbHETYRri10pBqsm5KM0zR2uB7DmP+FHO1ee7eZ2GcBVIn5C13+rT1OmHWJTNNxrG5fd4H7fD4HdP8z1fWSZRNzZuk6DlEWUQ37fLs2rMIBEWnyEQwwUQwQcWrEFhXd654rRsdj06WeMuRCaJU8YFH978m7iWDCvbLGy+z2F2kYBfYV943tKBsxA06SYeD1YMcrx8f6rp3MeFcJ73wlbxh0i1A/dCmFeDNQNSC3ipUZmH6TXnz5teQ9/brBde3mTlaxfVt1i50yBI9dDnxbQu3mCdbtnoJbbm7Ebibga950r22tsYv//Iv85u/+Zu89a1v5Wd+5meu+Z1ms8lP//RP86lPfYpqtcoP/MAP8G0DX9Y9fOYNh77kxDa5D/f1DJVqY4gU+EIyGa5x0fbY0F18a/eyi2vJPa6JXchWroWryVYqdpW1dJUxp0FJTLDQjIhTnYdWlB3WL3ZJI8XYbJFi1RuF6YxwVURZxIX2BU61TtHorYGOmVQOkdI4zQuQtiHpIdKEQ9FFjFE0TjxNpFMKxXHm7TIzfo1xp5JHaEsLoyEWFgg7rxDaDggLjKAb+IABf5EwKJI4VURtEoxGO2cB0JUDJKlFGBoWuyXCHviygjQSJRSaDGM3cVUbu1RHSgtLGKQwZFmGJWHCF0wUwVId/ATeVE1oLq0Rq4yCkdSVy3gYMrdWYfVMzK//P7/O28pVJo7djr30ZZQb8O1vuZNW7xAHahLRW0VYNrXAoXrbYZCCTpyRZRoHQbeZ5FIRnXM027XwizYTs0WqNZ+g4BAENr6fNzY+3a+aPTS/D4QepnVe7Cwi5CsktHlhNeKLF5so5aG04ZHD48zXg2FUudKKbtblXPscp5qn8GyPol1iOYLAKhNm45S3kPDXq9HRtfOCyI2ucGujWQ1XOdc+x8XORbTRTBenh0m9mc5Y6i7h2z73Td7HwcpBrN02O3bXYO0VWHkB1k/BobflriA3CzqD9sVcQz53P0zcDs5NXJ6vAViWZGJ/Ca9gs3ymTXs9olT3kTK30yy5uXe96SqcHWLkbwW8IUn3z/zMz9BoNPjBH/xBbrvttuuezrlz53jsscf47u/+bowxvPjii9f8jlKKv/AX/gIAf/fv/l1efvllvuu7vot/82/+Dd/3fd+368/cilDa9KUiubxkoK8eNFCGqUaTp0INgm+2YquGG7hM4z3QidtC4PhFjAqZ6qasuCE4E9vIsO/m8pJtGm/24MMtGcpLtmq896oTBy7TeF9LJ25hk6awZC4yW50mySStOCVSmrJnU6x49JoJvUZCedynPlscPrGP8LUHlabs//L/oHLSI3z8ic33Vcjplac503iZRm+FsoF9WUYcL6J0h7BtwVoFLBdsl1RYrApIpcTxyhwPxpieehNle4cHWqVg8KDrFPKqNYA2lzdMkx/zSWRoRx5h4pC9bEjSjOVmzEtnI1Kj6GUpR6ZK1AoOSglc6VAUHsoPhpVIAxih6IkORoDyfDIEXbtEdcxibNrl+QtrLDVaRAbuPDaDW7SpVyp0s3zxtMowSZssbPL4PXPYliDJ1tEX1nEti0TbxJlFLxZkws0Dckpl/ILL1ESRoFogKBcISkW8wMXawQc9u6QRy5Y2db9O3a+zr3SAl86O0YqbLK6skmYtXKfLQq/HH51Y54m7DlEPSkghsaRFxa0Mre+iLKIVt1mIFgD40/PLjAd1JouTVNwKYeTesEbHG6UD3w0ynbHSW+FM6wxLvSUMhjF/jKB/7Blj2Ig36KZdZouz3DF2B3V/lx7ZvXVYP5ETbZXkRLs0mR/3NwNa5dX2pAeTd+a+2+Xpm7MsX4MQQlCZCHB9m+WzrZx413KdN+Qj7CXPxnNGpPuG4Z577uFDH/oQP/3TP83b3/52fuiHfoj3v//9FPcQSwwwPT3NiRMn8H2f7/qu7yLLrj0k8eu//ut87nOf4+zZs8zP596iCwsLfOhDH+KDH/wgQohdfeaWhGBIsgPXIkw29dXRoGnS2R58M8BOTZOwSbxtSwwbM31bQirRxUkqvfP4yQqJVUPJ6jYyvFXjHTibTY57kXts1XgPmjX3qhPfqvEeNGteSydelFVC1umZFjUvv7lk2tBJMrpJRsVzKNiS1lpIZyOiMhlQny7il25iE9BNhrBt6t/91M1ejNcVJ1c6nFjpsiGKrMUZ+y68Qnd6Bbn4Wdbii3wx1FSkxT7pI6WFETYGi4QCkTcBpTEik7KWtlFoXFFigjpvLZUpuR7sRLh3AWMgSSRJaNHrSE68rIgjTbtRJTWGoJOAA6c7LSKR4DiCOFOcXe8SuGU82yKQFqQ7N2BjwJKQpSnra6vcPnmMMMu13I6KWF06S61tGL//6wDwgiI/8tf+Bnzm0ygEPVlGurmNWC+B2BjCWGFLCFyD62Xsq2sKXo+g/SW8Sh2/XMlHlXoORDasu+AGeaqgV8oDS5xC7ussr16xdKWPi4Wni+xzJxB2yKEaXOwu0ox6hKoFQNEpUnAKeFbuXuHbPr7t8133TaKNppf2WI1WudC9gCUsLDzaImQ5gqpX4XxDMV2uvOaNjpdiN4TdGEMn7bARbXCmdYbVaBVb2EwEE9vcR7ppl/VwnYpX4YGpB5gvzw8r31eE1tBbg+b5nHAnvZxou6U8aOZmwBiIGhA2oDAO9z4FtQM3t3Hzaxh+yWHuWI2lUy2aK+GwwfJWxxuSdD/55JM8+eSTfOELX+CjH/0oP/7jP86P/uiP8tRTT/GDP/iDvPWtb93VdFx370/Kn/jEJ3jggQeGZBrg277t2/jwhz/MSy+9xPHjx3f1mVsRjrXpdyu3EO9ekhvNB67F5VmVVybcWzXeqWJTJz68EQuCYB+l9kWaay/jV44TFKubZHiLxjvsa7z3Qrhhu8Y7HK7H3nTiWzXeKWZXOvGK57OaNVlOFqg5Y5Q8O9eWaki1Zk1FxK5Dqexga3JbpLWI+kyRsbniG+LiMcLucFnCpI4hbhN2Gnz5M89yX/IKk9Z5Qi1ZOfki/7ETEut1ZPkQ86UDOI6/qVnVGmO5pELTTmLO9FYp2Db7/AlmnDHO6lW0Ebm59R6hMkOnq+k1BGkoecUqsNZwSSRsuBmIjFhGbLQjsmaHI1MllDYU+x70vmfRTTIyZagF8gqEW4GA/WMFwPCxj32MOI45PlVkam6eVBnK5QrlI4d56/Htzg+BX2DVuPSMi98EzwXbMbiuYGJCUiraBIEg8CW+J/LihtGwtJLHfQf1nDjpNPd0VgmETeis5u9BX3fiYVseH5isgF+DRrpJxp1CLskh9wOu+DYvxJqKKBGHHserc7zr0DSpyYnmcm+ZjWiDRCW4lkvRKVJ0ikghkUJScktDPXOmM3pZj8MzbZ5fOcdiAhVR5+6p/ZxpZ5TcUk7i7cJQvvK6QGXwxX+X/3nfB2ipHhvxBovdRRpxgyiN8GyPmcLMNueRVKUs95ZxpMPxseMcrh6+tvd21Mzt/zZO5w2SOsvlG6WbXEmOO9BbAa+Sx7iPHb65oTsjALlEbPpIBQQ0lnuU6/4tf+98Q5LuAR544AEeeOABPvzhD/Mbv/Eb/KN/9I/41//6X3PHHXfwIz/yI/zwD//wnqvf18KZM2eYm9t+Mxi8PnPmDMePH9/VZy5FHMfE8aY9V6vVuqHLvRtcepu+9NDd6VC+EuEewLYEaT8cSkpxWYOJFJKivZ/l8DnGexcwjsT4W1LIBMOIeAB7B7J8LbnHpYE9VwzwuUoV3ZaCtP/Aka/HJR/YQbZStiqsJkvMefsp2RUsS6K0zpvAfAulYb0T47sW5aqLzDTLZ9tEvZSpA2W8wqiC8lWBpEMhXMRNG3DixdxWLAtJuhGl5hoHy4azkc2yE9H2ITYutbhCpTaBtIPLzpmFVPMFFPHGOraY4V2HDvGm6hRn1rosNmMybfjzkxH37q8yfYURfGMMqZLEmU1vWdPpadodTaenaKwAwpCVU4Rr4TgOnm9QmaBoQU8YtATXyb2yozS36upGuXXX6bUuYyWXrUrdleUVfu/jHyfNNN//ge9FCoE2MDE+SScMiZMUq/9g4Xoe81OHqdUrdForZJkkCRVCa5SxqIkecwdtShUH3xcEvsDeQSKyI4TIZQmWC+xwb9AKVAxZnJO/5rmccEXNvGHP8cEKGN+ISe0Sb5sf59y5Jq3QYWJmmnffNcN4oQSUmSnOcFwfz7XgSZOV3grr0ToLnQU0msAOKNgFAjtACIEtbSpuhXunSyyt+cRZyltvqyCslBfWXwDy66Vv+ZTcEmP+GAWngG/l1fPADvbub30VGGOIVEQvbtNLGrRVzMrF/0Er65KoBN/2KbtlJvyJbaO3URaxEW+gjWa2NMux2jHGg/Erzyjp5gS7eR7aC5D28hGa4uTN9bfWKt/vcSvf71N3w8Sxm+sBPsJlsB2LmcNVhBBsLHUp129tXf0bmnQD9Ho9fu3Xfo2PfvSjPPfcc7zjHe/g0Ucf5cMf/jAf+chH+NSnPsXExI1LgkrTlHK5vO29IAiG/9vtZy7FT/3UT/GTP/mTN2w5XzUGw8CAZ0liNdBXb15cr0W4t3p9SynIlEYAWy+jYaopyiJOoYrOEuzWWRT7Mf0LW5LlSZG2LYbkeqs0ZLc+3AiGUfPbNN7sTSc+iJoXWzTeV4yotwq0VIOl5CK2KZKkGteRKJV7Cpc8i6JnE2WalXZE4FiUKg7ttYg0VEwcKFEe829dOdII26C15lc+cw4hNO+/u4QdN6F1AdFdZ3r1PAgQ4Tx4BQhqeEVBexGeaZ9hPWigjaFoJpn3XdqdHp04I1E6l2kAsU652N3g2TjFj4vMVg4xltY5eSHjQCnl82caw4Cqbprx7IUW1TmVW+0ZQ5JAFBl6PU2roWis1Ym0BSditADPFQhXo90MicF1DE4iCM1m8JWFQWCQWuGQcWzMZr0Zk8SaqudSC0BEGzz/pRNMV1xmyhYibmKZhJULp9BZguptcO9MgbVOwvkDh6kpwaoJsFYiVCwgtoi7gjiCoi0Yq2iCGQdPCnpPn8IVivF5G/la2NxJC2Qhr2gPEG70K651UAkiblHuLiJMxsFqnW8rbhAam7dNrFNonIeoCn4FbB/L9qk5PjV/koOl/UQ6oZW0aEQNlsNlWkmL9WgdgIJToGAXsHGwLYFtuYwF5W2a80xnuS48abHcy/3UjTG4lotrufi2T8Wt4FkenuVhSxtb2jjSQYo8rl4pQ6jy9Mt20gJhSHVKpjMynZHqlDALWY/WCdOQKO2iOufykcosou7Xh3KZAYwxtJM2raSFIx2mC9McKB9gqjC1c6Nk3MkdP1oLudVe0gZp59u4OHVz3T+yONeRqwj8Osy+GarzUBi7ecs0wlVhOZLpQ3mxrrHUw4x8um88Pv3pT/PRj36UX/3VX6VQKPCX/tJf4hd/8Rc5duwYAP/gH/wD3v3ud/Nbv/Vb/NAP/dANm+/Y2Bjr6+vb3ltbWwNgfHx815+5FH/n7/wdfvzHf3z4utVqsX///hu23HvCJcE3UghkX5MdJpqSMaSZJrV2R7gHOnFBLjWRSuP1/68RTHplLiQFuk5GNTNYzbMoDhLbpe1kuE9uhw2esPvgmz4ZtuSmxjtwLBK1S8K9ZR5ii8bbteRVGzMrVo1zvfP43iTjfi0PK7ElnTgbEm/XErQSQ5gkxEoRBDa9MOXiSw3G50uMzRd3bPYa4RaBVgjVRaYdxhtfptI7jbBKeZXO8sAp0Q3mculCaRKkIFQJZ6MldG2FZK2J3fawjE3Zc9noJSSJot2I+NzpdW6bKSLchMwoJqwac/EsIgUty4yXPFpRyno3pZeonBgLScVz6PQMC8sZjja0O4YwNMSJRmuDkBqFAislKBmKvoM0GWvNHhvra6BTLnTW0bKE7xbx4xDPEsSqiUeCVi1kIqk6MF2wUNjcM1fh1HrE8y+e4pUvfYp77riNb373O4i0jV+q8q7HH2dudgbHsem0Ik5eaGP3MgIBViclTFuM+RpRCDkw7vCmfXWK6RLStqBYQCuNEiEGmZNg47x+5EyIfH86AbgVekF/vrVxomIBS6d4jpuPYvTWt8hVBEgHsgicAL84ie+WmPJK3BbM0w3maJmMhopYiTdoJS3CNKKRbmALl04SUPQKQ9mGLe1cksKmxZ4xOWlOVEKYhjTjJsootNEIBAaDQAzt+oyGVzobCAR/euEkYFBGYbZIBy1h4Vs+nu1RsYvYXi3/hz82bLodVMK7aZde2qPklLi9fjuzpVnqXn17sUCledU4aub+2u3FfBtJG7xqHtl+M23/tMor2lErjw4tTsH4kdz+byQjeUPAciTThysImcs1b1W8IUn33/t7f4+f+qmf4oknnuCXf/mXec973oN9SfSy4zh88zd/M553Y4enHnjgAX7u536OLMuG8/z0pz+N4zjcfffdu/7MpfA874Yv656hEkTcJso0xhgKjoXs6zItoIAhSrJ+U2UbzwdHSEi2T2bg9W0PmybzaXiAVBoV5fMg6VBwbWQmmFAep9U6VasOSQezfpKsfBgnKG+S4R003nv14R5qvFNNN851L3vViW/TeGdq2LC5k07cMj69dJ0NZ4F5p//AJcRQ492J+stgCeoFD9V/GMECRxmiU02iXsrEvtIoRv5WQtLNyVV3DdZPYbe+AFnImNPA1SGY0uYwtE5xs7yymPUEF9Imr4RLbGRdpt2AB6tVovXTrBtJqB10FOHYkqoV0Qo7fOG85usOzHNncYYKAS1zjgs6xUmb9JohY75D1XSppBF+N8HBYeWcxMLh3EstbCmxHIPjQMlR2CTILKGtltDCUA4DZOqQYXNxNUQbjbaLvGBNsJo4HKjVuG1mklRIwvOa9W5Eq3wP81Pj/Mt//W95biXhW7/lm7lr/20sqgad+QnOfvk8c2MPsDLxltyyz8DMQ/eQZopVYUgczbnmGht+jOPB7dMBy0lINbC4uNZiYjYgOFBHpgdyImvy64UQCRIFjdMg+wm6BsD0q9ROTuQsp/93v8oaNq5/Xw++GzZyEq0Nbtrsv2cN9y2mmM97awXe6FwT3V3JHxTidv7aaISAkrQp2QFzto92i3TcEk23wprRtExEmjRZThpomVepAyu3I/Qsb1hBFkIMK91XgjEGg8EYQ6YURcsAhppbw7FtLHEVC1ilhn9mOqObdeimXZRR+JZPxatwx9gdTBemc8221rlEJA37Gu2lvKIdNyGNoLsMxenc11pIMH0Zx/Xuj+tB3MqPncY5SFrkF/ExmLozr2oXJzePnRHeMLBsydTBCtKSt2wOxhuSdD/55JP8yI/8CPv27bvq5/7W3/pbr3peJ0+e5AMf+AAf+chHePjhh/ngBz/IT/7kT/Iv/sW/4Md+7MdotVr8/M//PE899dRQUrKbz9yKkEmXtBUjjSFwJCK7RAsNuFluGyizEC+OIdn+GWMMWapxhcCzxWXNVF4/zRLAjdexVU526ybmjGrneXMaRNqgAAj/DgxbNFqibwWYbLEj3CXhHq6HFFhy817i7lBFvpZsxbVkTrjJXRiu1pg55tVp6iVa2T4qdq2/HgLfsYbE33csEAJLCAperv2OtSJE0TvfZmM9ZGK6yPhcCS94Q562b2xo3Xcu2MibvNZPk3WWeOHcChqHI1kbhI0EMruYD1E3z/W/C4XeBg1CPruoWM46FKTNPivAmA7FtM2Y3GDGQCPwSZWhIQ0pF5kRRepRkfsSm3GrjVFN7ndXUECn5VHRFWaosvhim1rbMBEalInQtmS6mjEhz+GYlPTUeRA29qGjGLdEKAN69iTLbeikx3jgwCztTPBSY4OVksEIwXTF49xSB58iDVHhxAvPoV46hztxgEQW6BiP9VaPTi/l/MVleOA42hjmpub4+3/779PqpnTWI3zXwi24VKZ9ynWfoOggXcmLn5U8d2qDiu9wTniMjU2QOjYr7SbNUg3mDuQn1wC9LpH6DAIFx78FbJk3Q6p0k+CFjVyqkPQg7ebv6zSP5hbkRNxyNkn5Xkhb6/xwfxb7xE+0uhTDfv9NM9y58QU2q6VZtPme6b+O26BTpEqpqJSCgbeshGjhcHtxip7r0JMWLctiHUMoJRtojBAgbVzLx7MDXMfHsQvYlptXI7ZACIGg34tiBFa/GdS13GFg0FYMquexiomTHnHSxACyt0TRDjgUTDHuVqjaASXhILIEVl7OCXZvPd8Hcaf/wKRymz/bz7d7cTLfiK0Lu9/2O2GwP/aKLM6XTZCPRE3emTdGFqdGPttfBbDsTanJrYg35N17t+4ku8G73/1u2u02L7/8MsYYHnvsMcrlMh/72MeAXDP+6U9/mmYzfxLfv38//+E//Ad+6Id+iH/+z/85KysrPPTQQ3zkIx8ZTnM3n7kVETlVZGWcwLF2ND9IlCZNNX73HIk3RsctEbibT5O6T3YludOJunwSeRU36eDG60TF/ShH4lqSgknxo4ymBt8EWCVwkya0zqOqBzB2XsXR2uTR7v37RHSJxnu3PtxKDQP1LtN470UnPiDvidDbquVbdeJFu8hy0mYxOk+5mDd8KK3pJmrIKbqJouSxJaJa5A83lsS4gmasaLy8zsWLHabmSkzMFimMGi1fO2idE4eoCb2NvMErbOQ6T8uD8jSifpjFcA204mCaN7o1yseodU9CZR6KeRdjO+nxpZXnWc4i7iqOMesewu5X0ZSBTnsdJToIAVF5nka8QhR3kPadeHKaYq2IOz1DMxF0O4qNVGESja7uJ6iUCB1BYmtKfoRoxdgq4fi0jRMUidw6qlAm3qiipUtw4FG6wiPTErHwecJul9CbQAc1fGXw/Ih2o41nCxabPTphxisrXXqdFr/90V/iLZbirU/uI1YKSwie+o738sKKplaps74ckrUShCNxXIs75suUKh7FokOh6GJd4pv77rtnePpiaxhH/q67pvnMqavYwUkLk4HODFoUkZUrdIlq3W+GjHKClYZ9ctvJH5ySbv6+SsiZb7+50vY2JUFbq5xhIyd4U3cPK90bjRUAzNQ4G81cOsh0Lh3aERtn8t/1g1devz6ENiynK2A090xWKei+08ryc5hgnNBx6KmUnk7o6pSWSenolC6GBI1GbKn6W1i2hyVdpJU/ZCgDWbeFEZLVtXUQubxEaYUU9PWwBhuJb6BioBb2KBlDsblOVdg4XieXZGiVV/Oh36Tq5NvQL+fb8VI7PWNg+Vmo7IOgds1tsSMu3R+7gUr7D2QdKEzk8x4/BqUp8G7dQtgIX314Q5LuG4mf+qmfusyfe6tU5ejRo/z5n/85d9111/C9973vfTz55JO88MILVKtVDh8+fNl0d/OZWw+CwLWv7O6hwHFyDbd0bXoIwjSvimv6hFvInePjh+E6goJrYyuJcuTQ2cS1Pcqiwnm9TNEu4lgCE9QRYQPZvoCq7kdjbSfDbNd4SyH2FHzj2nJIsAfEW5vdEe6t80iE3hags1NjZtWusZwuMq3mKckqnVhhCSh5+bG2qfHOCfcguroS9KOrPUNStIm6Ka+8uM7pM02m50tMz5UoF12sUarlq0fSy5u7umvQXcorkGmUH8tOIffm3VoJ0yYfHhdmeLybwQEjBKnRnAtXeLFzkUW1SsUqM+ONbd9XJv+uFtCWCR3R48HJWZbWbZY60xSdCtOiwssvacJIE8cZKnIRokshiJgoaGwUCEFsHEKrRM+pII4cgaBMrB16yoDdwRLQoYASkqIH0aWjN47F8Zkyn3r+NItLy9TqVerj0/mDtesxe89bOKDW6CSgu4poI2aivp962sLyLPYfrXDONdie5KG3zONcY3j30jjygmtflXTHp06RnDqFyTI2fuVXqDzxBN6RI5d/UEqQwZW1uFmyKX9IezkJjxq5fWDShWytHw7UJ+MD8o7JUzuF2dQfC3nJ31c4Dwfv70a3PJi+kPlxJ0VObAtjiOo+CkGdYZunMWAUSiXEWUSYRSQqJtMpqU5Js5Q4iYlVF5W0IWygyjPMd/NKb22tSWDbeMLBkxa2kNjCxhGSQEgC6eBaDmgrX540zMm0P59LaYTcowxjC0G/Xg232DzHrjoNneUa7aSdfy6ow8zduf1gUB9FtY9wU/A1T7ofeuihq/4/CAIee+yxy973fZ83v/nNV/3ubj5zK8HqR6leim0uJf3ra94gmft4d/v+11ubJrfh0sbMvuTE3ZJcmSpNyZQRcpnNgrHABBVEuA5IwmAWKa1tZPhSjffV9NU7keGdNN571Ylv03grBeZynbgnfZpZg/O9s8zbd2LLXNc92FaXarwRUPKszaFfIXBtC7dq0fNTWs2EMy9usHShQ30yYGq2SKXi4zty5HayFwzsytqLeTV7/VQuRxg/lnvyFqf3fHM2xrCUNDkVL7AUNyjIgHFZv+J+6WYhDWKKaZG53mEOyXF0UkLIGpOySLcLqZviuhlFu8dKdB6RZLiMIfxJVHGc1C7SVA4y7DHuWIjKOEhJyRg6vYRWf+iqaEx+XPW7+1vNJp//7Ge5a+rtFEplqoHDbAAnl8/gqynGDhxAakMBwcP3fz2HwmV63S6y4nD4eJ1CyeXCyxLLlRw6WucLrRAgD6HZBXYbR657PTqf/CQmSRBBgNpo0P74J3C+bwZZuIb386Ww8wTPyyqtWvVJeG+TjIcb+fGRdKDZl0MYgx/3UNKFrDR8cLopEAKEjSXt3P3kap8NN6B5DjV5N2Evr86/5cDktR/YtcrdWCA/J6R16zYXbm2IFOQ+69P3QHkmf2i2vuYpzwg3GaMjcIQhdjLIuMwWcDCUSE6yB3aCAP5uCPcl/3dtOUytLMkiJeETmZhADCqKFsqtoDtLuFrg1Oe3k2EBnm0Ng29ca2/BN5ATb9fKK9SQT2+vOvF8PdRwtHqnxsyKrHE+XKBWnGE+uMQWS+QjAO0oH3UJbGtHrWWUKlIFtbqPZ0uSXkrjYpeNpR7BmE9twmdyokDRs3FGjic7Y0C0Wxf7dmWdnEh4ldwWTMi+7nTviEzMCb3G+XaG5ZWY9ccQRiLEwCrUkKaGOIFunHK+3aCxbphsH6MW19DuGC0BSE1QgtlxQxK2ESoGY+P6RTq6xlpiMMEdHJ4+AgI6sUIKg+fEuXZ3gH7I1cD1P4tCrJI/bGh45cQrfKmnuGe+wsOPvgXPlhzZN0/J9SkXalxsxliORezC2KzPfZ2YMytt2ocqHDySyzvss699QqBqd9DtNiIIEJaFPT2NbjRQ7c7eSfeVIK1canCp3ECr/JjpE3ErbvP22jrEbVS8QTFcyj/XjPKES9vvy1RuzcZnSwredefUzV6MGwet8lGpuJlff/0KTN0FlZlcSmLfmvthhK9NjEj3CEMovf31bny44y1fGuirh2TyGoR7MI8BfOFR1EWaNAn6zZMaSLRAulWCeBnT1KjK3OYNzUCcbarHE6Wx5HZSvBsf7mTLesTZdo33bnXiA8KNyV9vnZfWBqUcQNE0i8wyidxKjoyhl2zKnMJMYVlsI95RqohTjefIvPES8IoubsEhixTdlZBXFtqcKbnUJwLmZ0tUS96o+g2b0pHWQk62h0S7mkc5D4aprzNiOkOzQJcXk1NImtxvz1Jwa6Rp7kYT9XLTilNnMtLE0FQdeiamqurMxEWm0mVcKyEuG6pBRiTbyMxBRXWUXUKVD6DcCp1mF3XxC/i9CPnsM8QTRdLxCSwBgWtvJ9yQn4OJot1q8YlPfIIv/WrEL3zk54cX/smpKY45ZYJgnN56iBQC29jsn5/l+MEqv/bcIhvA0WNVnrh7mvofvcjC+mvjgWtbku99ZP+OlVerXEKWy5gwhCAgW1rCnpzEKpd2mNINhrRyIudf0pyVJZiow1LjFWzVw4wXIG7k5DxqbdoGSis/riwn12bvtYFzhMth9KZ0xOj8QWnyeG7xd7NDdUYY4SoYke4RhlDaoI1BCrGn4Juhpju53EP7qoS7Hw8/mEeSaUqqzJJaBatPuFOVW2I5LsbUEOEats5QlX0Y298kw+6mpntrgM6ug29E3vw50HQPmyvZHeHeOo/B64HGe+s8pv0JVtMlmtk8dacf2mTMUMNd8q2hpnuo8ZZyR8I9gBAC6UkEDuVU4ihon+vwxbMtqpMFZmdKjI/5BF9r1e8sye3JmhehfTGvhg0q2rUDGG3o/MmfAlD6+q9DWNdnMdXRXb5oVjmnI+K4wERYZ2PZYVFlxJlGZdBu5I40DSJir0sp8DgqD1Onzpk4JBAXcYix4xWELrFeOEzTnaI6No9fLFNwHcJuyMpzXyTNFMr2IA5Zf+Y5qm99jFK5gOpLRjaaG/zGr/8xs7Oz3PfgwyhjmKwUSJtrdFLFK6cvMFMbox1KalNHefTwUe69c4rJqSJB0eHCCYHlWrz9oX2ctxhqrsuOZP0a2+LVwrYkH3j0wGXvy0KB0uOP0/6Dj2HiGKtep/yub7xxVe7rQV+i0gtm8tf79uX66yzelKcMXFWcUl6J7fYj501fHz5o3hw0cI7I+M7QalOD3+n3W+gMxo7l1oPFiVtX8jLCCFswIt0jbELkxNmSAqXNFQm3MSZvmnQ3NdwDx5IB8QauSrizYSiNNZyHa0vGdIXTmU07i3CMgxACb+jTbeXNlVEDa/1luqUDaLu0PQVyoPFO9dBZZC8+3IOHiAHxztdjbzrxrRpvbRRKs2UeFii4GJ+jateRyE3CvUXDPdR4xwpbajJldiTcAErrzcbMcn7jDrQh6iU0Fnq8uNjFLjhUx30mxnxq9YDAs/Hsm18BN1lG49d+HYDad74PYb/KS5LW0FvLZSPNs/3Ktcg9s7dWtAF29Ne5yrIakzvVpIY4NrSihJdWFllMVyh3BKVsCh1XaGcaIxWWpyh4Es8DN9F0TZeeL5i1J9lvzREID1/1KKUruISkwme9eJS5uYNczGJSZXCCMr6bO0C4aYIb94i9AmFqkIUKIgqJ1pbx/H1Ydn5sXDh3nk/8p//E3ffdz933P4QnBG1l884nvpP3PfgwbjFAWIKJoqIXdegerfDQY3O4rk2mNM7FxnCdt2mu+9Ky2AhaUUovya6pxb6R8A4fxtm/Hx1FVL/923Dn5l63ee8aQuTNto6/PcHQmE0yPvSwbkG4nr8ON/rNmvlHg6iDkh4kRXC9mxscA7n7h0r6lfrXgNxqBS//Qf73sXeDTvokO8zJ9aCZ2a/kRLtQyyva7k186BphhOvAiHSPMIRj5QRMaYMlr0BUDaSpxnIF3iUabtn3no7STe/pnQh3mmmM2rn6XHMK1NIyG6pFXVY3CfcQAuPX0BtnceIId+IYwqtv/TdB3/96YAu4l+AbyDXevrPpBe67e9eJu7bMCXef1wXu5jxqdp2VZIkx+yJlZi4j3Pl65I2WzTAjUwbbEtcm3FsaM4UUBCUPJ3Bod1PSMKO30OPsQsg5t0FQdinVPGoVj0LRxfMsLCmwpbjpRHzPGHho99Zh+bm8wqjS/AZd6bss7HmShijOf+IoT3MMQ+hFijhRrKs2F81FTnaXCUzAuK5gWZqGp7BFQrEIfpDvr8RkrOt1HO1xp3OESVPFjrt4sg1OkUbhAJawyHDp+DOEskCqIhxL4G+x5JSBTxAEOElIFxe71+WzX/ky/+U//Fv+tw99iHvuuw+Aubk5Hn3L4xy//U3EGwnaEQgBt+2vMPvwPOVqQMGXtBY/zUY7JSk4yF2OgJyKJJ+MS6ydXCdD8O67prf9P8k0UaroJRmV4MZqaeNTp0jPncNkGc3f/M0ru5fcithKxrkkTnzoppKTcTvu8LaxZk7Ksy7E6/nx3FkBYeck1PI2deOv9fnaPA+LX8kJd3cV5u6H+qEbM+0BmU/D3NLPqPxh2S2AU4TyXP7w4pXBK+XE+412fRphhC0Yke4RhlAanOHfm1KTAXS/wu31kybNDk2TWzXaSaYvczNJMk2mNIElEDvc6A0wJuoss54HNCgzfBgYIM4M0inlyX+ts2iToQuTQ1K7VZ+tdE6gtpLm3fhwX74e2z+3G9nKVo18ojY13rZ08IXPS+2XOR6UmSxUdm6a3LIMmTYorbd97kqEe3M98mRQaUu8sosloeBYZIkmbiWsrEWcVxolBEHRplx28QoOhcCmWHLxAhvHln0yLm8tW8Is3nSWaJ3P7d56a/nP/kf3NNScKkG7q4lTQ7Iq6PYEHRmTpZBlBq1zVYpGE5GwJBdZlit4lmQirSGNwMvaaNN3ERTg9Y/tru6xGLZIWgUqUZVmt02xnlGsTtAtz+CUJ2iHLYpiHUF+vEepxrEEzha5S6fd4Q/+4A9onjnLvU4RO4wxXkBjeo70lVc4c2aBfXO3EzdjAqvED/3A/4uJyQLVsYBiIEnXE2wJtfkywrYxl9ik7ga9JOOPmhZtrSh5NhvdhI8/t0TRc3BtycmVDv/zxCpRqsm05ok3zXJs6sZorm+oe8mthqu6qYSbsor9j25Gqac96HU2pSrQT+J08x9p9726+7+vt1KeRXD2U/lvt5g/0C5+GebuyxtGrwWttlfJVZLLQ5D5egyWWdpQqINbgiPvyLeFWxqlQo7wVYcR6R5hCD2QlFgil1ckaigPGWq4Ac8W6Gu4lMAlGu8tOnHPktiWvGxwfzCPqlWmSkBqUoQWgBwS77gfUe9YAuwiWA6ydQ50hi5Ok2g2ybAlL9N47zb4ZqATB7ZrvMXuCPdWnXiitmu8MWDrEutqkTV9hilxz2XT2KbhtuVlGu/dEO6tOnHIHS56qaLk27iBnY9IRBk2AhuINmK6KxErAxtiR2I8SaHsUK14OJ5F4DsEroVtSWwpXt/quDE5yW6cy4l23G+icgp5NcwJyDfu9iYqQ06c00yTppAkhjg1xN2M1bUqsbLQzyS5hCeRODbIMni+oGgLpBREacZSusFFs0BIj4op4hiXtskt8rSBpE+6tTGkWtHSGwgFwUaBI12BsATLpsaqmuWRmSOkRhJnZsiZEiNIlc5HWcIeXZWxtSr6q//5PyPdgCPf9O2kYYY6fDcP3PtmHv/W9zMxWaFYcimpFDuweeSt+/D9/PJusozGDVAntKOMjhLUpSK1JDNVn5V2PHwQ/Pjzy/QSRcV32OgmfOy5ReZqh26IBOV1cS+5DlxJg35DIK28uuvt8OAylKpEfR/xLeE/g/dMluvrTJafO0k3Hw3aOJNfLwZkXAx+y03v6wFJjxoQbfTPKZET76iZ22v6tZxUY/pBOdmmFMSQvz8g/oPwoeJ4bsfpV/Lz1Q7y6r+wc6kN5KNTI2u/Eb5KMTqyRxhifX2N2ErZt2/fpq45UUNbwKEPd7g7W8CtGm8pRa4XtSSOkJBsn8TWxswxp8BYWmMpWyUQPkprQKKNwRiD61gIlc/D2AFCWMj2AlmakgZTOI47JMNbNd6uJUnU3oJv8mlsarylFGS7Jdz9eWzVeBs0WhsMgll/kpVsgYlkkmlvU5+6U9PkVo13YPfdTXZDuLfpxHPi3YkzLEuSpJrAty+TrRhjSBNFq5NimglpJ2NtKUY4gkRAJqFYdBCWxPUsSgUH15d4ro0tJbaVk3Ap2EbGB39JkcsdLCmQehdOGFkC7QXM+klMexGjUoxXRRdn0MJCKU2WgmpnmBZEIkYZSZZBlmqixJDEAp3lDiJKQao1KlOIxEZKTeBpxksudq+/H4ub+7aRdDmdXGSVNXzpMCXHEUIQpYpMgRSGxEBXwXovIVIhi+sXuK00zjE5wanE5bRbpePUmZ0cp5coUi0p+RbNMCXONK6BjLyp+A//4Pf5L3/6ZY4dPcY3P3gUAMf3+aYn30e9Mo4INcrJ8EsBjz6yn/qYT7niIR3J05/LHTNs5waw7EtQ9m1KluG8tnCVZrEZMVH2+P63HqQdZfzrPz1Jxc+r3jNVn7VuQju6Mbrvm+peciti0Hy504DOoLqs0/7vLK8w62yTHKs0P69U3P9ctkmahymTpp9q6eUPuE4hJ/ZBNa9yCwm2vZlCafcDiSw3J83S6dsnuv0I+KtIYdTeR15GGOGNiBHpHgEAozXPfOVLPH3yRd73ne/j3nvvI3Ak3UQNbQFzV5JLSNJVbAFl3yM4TBRaGexBY+ZVCPegKj4mqywwCMqRfeKdp+ZdSieM5ZLZQGeRQGfI+j6gryfdovEeSEa26qs3J3Jll5JhgE6SE2Z7D4R7ANeWGDRZZvrLIJHSw8s8zoQnKdtVClbxyi4lWzTegwbPkr97wg15hbzkQSdSKK1xr9CYqY0h1Aa/aFPy/DwxUWmyRGNnml4vo91IMeT7PLQlWCAsieVIpGeRoMl0LrNwpUAbMMqgVf7gFCuFLS1sodFrNlIYai+tI2wLIQVCCkzcQvZWkM1zEDfRxkG7eZx5ogxhmJCkILTAEhJigQwD0jVAaozJR0YsCUVf4toC2wNpC4SwiBPorOQ3+1JweRKrMorzyQqnkgskImbSruGKzVjrXLevSZUh1rCWCiK3QdFqMRkdQJrD+MfuYCFUnI5iytKmFWaUA4fAkVhS8snf/2985vNf5vseepDZiXEmjk/yHMeQf/YVTGZobkSksUZpzXe+9z1UizYv/t7vs9LqUDpc5tBtdey+lCW71PNzF9jaFHktDXbBtXlHVXGhoVmLM8aKLu++a2ZIqiuBQ2uhTcV3hoS87N+YW8wt6V5yq0JafVnGLuQfO0HrXFvdj4Nn4nb445/JZS4H3gJ3fitM3tFPo/wackMaYYQbgBHpHgGAKIqo18coFAscv/04kFv2RVGE63m5xAS2E95d+HBnapOka2029YeD93Yg3AA1q4wvfHomwjabcgGlDPISjXeqDAoLK6jjJhvQ0LmlYD+yW+8wz23Lugsf7kxfuh5sI9W7ka3oLdPItMGVgopVYyld4Hx4in3ucdKMK7uUXLIeyhisbetxZcK9dVsNv6802Ns191eSrUhL4gYSjCFzBFaf37mWyEl1lhNqHSq6jYQk1bi2wNgW8WDiMq98a62JIoUxBq0UdPpypFOt/DNxi6y9ikyaeKTgBAhnDtOvkGMEmdZocnmLtPLGV0tKLGEwdYERIpdHCXnFlFS9haNmWmOxuc1busOJ6AJL6SoFK2DOnrhMQtMMU1Y7MUpDkmSE9gZFUcMWjxLU7uF8FjBrTXDbXMrZ5hIbrQ5Zs8vbv+EB3P7+feWVk5w5c5qF+VkmxsbpdhLGa/N88H0fxPU9SgWH8UNFShWXSsVDCM3CJ1Mqkx7vfHT/kHDD3qUOlzZFPvGmWQ6NX53EHvY1j3sdLh4Z4wNvOTgk6gXX5t13TfP0hSadOGOsWN5GyG8EvMOHcQ8fxmQZ9e/9Hux6/dpfGmF3UBl88d/lf9//F/Pq9QAz9+Q66yyCB38gr3SPMMII14UR6R4BgKBQ4MEHH+DrnvhGHNcZkuHf/73fY3V5gSeefA/zfdmJBbsOvhl4fduWGEpNCn3yqPtkd6f4eFe4TMgap+IFxqWL61goZYZSk0FNLlWbDYaOJTB231KweQZVmUfZxb68JU+ajLPtGu/dBt8MNNy2FJdpvPeqE8+02abxHrPHOdM7h6WqzBfmru5SInOC00u2a7x3Q7i3VtEdSwylJgNyvVedeKoMcaoRUuAH9nAeliOpXcPesFyw8/UIE1qepkRCLdhAdi+Sxi20K3BrVYRTv2w58uPK4FjW8LhSQuMJg7QMKez4ILd1PQbHri8MyuR9AEJqLKNYUGuc6MWEKmHCrhPYDpciVZpzGyFSJwSyQ9duEcazCH0/STCHTmwcYXAtweHxIvtLkv/8+39AevYr/MC7/9VwW33d4+/izfc+xMHU0AklXqqZni1TlwYnsLn34VmcLdsxjZPLluV6sFNT5MeeW+SDuyDtnjBUfOcyQn10ssRbjkwMfb2vVTm/Hj20sG2EbSODkSfz64qBnMUt3uwlGWGENzRGpHuEbdhKuNMk5uypV4jCkHq5OAyfkUoTZxpt745wD/XVfalJlGl83ff6vkolsmDKKC5i27kPeF7hzqUmqj+UrqxNwp0jtxQUcQuxfookmEEGY0MyvFXj7Tt5kM1efbi3arxdWxLtlnD35+FKwUDjnY8nOAhts5SdZl5OcKlQcycyvFXjXXRzArdbwr2pE9/UePuORTfZm058UGSN0y1pnlcI8LlsPVwLoXqU03VMchahuthnzhL7kySFKQLP3bE580YcV0PC7VgkIvdQl7ZgNW2wmJxkzbTxxAwzzsRlzjn091gvDHGTDYwTsWEVmUwOoNKD9NxxVpdW+cyLX+KuuTLveeB7AKhXq0zUKsyO3Uur2SbsQbudcHjuCOVjNs4Ln6fqp+x7bI6g7HMPM5fN90Zip6bItW5CmOohEb4euco2X+8RRhhhhBG2YXRlHGEIkXShvUiaaVwBZVvyE3/5g1w4d57pooZkhTjTfO5LX6bTbnPPmx+kPDl52XTSTGEU+BY4tsVAXyCBojakYYcEcMOV3Ic7vZwYxZmmphXjIiOKFimLnIi65LIInUb91w0sS0J66SQMWdzGj5pIcQRtz/S78jeJ98CHO9ijD/dWjXeU5FX0vTRmwvbmSoBxr86GXuZseIKjxTuxRE5ar1h93kK8u/FA4717wg3bNd7dfhV9L42ZwHB6A+J9VcIdZTiqR8n0kBuLyN4KImpRSS8QGZdIzCOymGK6gVSX748bdVxJA4EtkWh01iRFsdR7llNqFZWGjFPEETGWSS8/rlSGiTu4wmE9kJxcqROt1KkGkxyQCWbM46ySPH/i81xo1bDkB1Bao5XmvU+8h7unKrmFpS247Xid+niBSskiXOl72wfXviTHRhAaQS9RVK8z7fpKTZE3SoM9wggjjDDC5RhdYUcYwtAnJYKctAiB67ocProZQKGU4uSJV0iSlMPH7uRSzp1muaODPSBGe16IfBm0gaLjMKmrnNZLQ9K9u0nkfsfCDrBJERsvg4rRlfm8g34XuJYt4LUX4tqyla0QQjLuTHAhPodnBRzwj6CNubrcYxe4Wnz87tbj2rKVa31fxW3i9gbF3hKFdA2RdBBCou0gdyGxSrm++iqrdyOPK8/u7w8NGzLkrN2kpbsUKeCLK1wSjaK7sUI7TPD3H6Nb9Dms5/jiFy8SrHXZJ57hUNFlJW5j7z/MA3/jf+WuY0dpNSLCbkraTZG+xfzhCuOTRSpVF8/dtPQLd7kaJ1a6fDIu0TOSlU+f5Yl756/LC/taTZE7Qdg21aeeYu1z5/c8vxFGGGGEEUake4QtSGQRtziB58gr+3A7mr/wje/iCycWGTt2P5mXuz588Ytf5JWTp7n/oYc5MD+HZUsuHZweyFZsGeDHa3SCSbKtMoDhPPpe30JQVwXOxBGprGCLXEesLI1LI19mt7ZNXqKBJFUIJ4+PN4AxChmuI7UiK88R4qFNnjSZZHq7xps9+HBLhvKSrRrvverEYeAtblOxq5wJT+AKn4KZ2pXco+jlKaDbNN5cm3Bv1YkP5CVbNd571YlDv+JtDAERMm5h2ktk3QaBSfBcPw8xKu8bro9Rio5cRwsoOxaxP0HXLm6TLSWZJrVzScmNOq4ik3IhWeCk38EgGXMPYpBIkR9XkVNDGoXvOYikS2O9wX/9oy+yalk8dfcjHCweZd49xMmphOnGMoesDKtYI4tiqmfPM/XYw7QbKdWqx8FjNc75YBdsjh0f39b8uBf0koyPv7BMW0vqUrHRS6/bC1vYNm/+/u/imU+dvaIG+zX1oB5hhBFG+BrEiHSPsAnBNXWwgWNRGa/zjftuoyckYaLwHcmnPvs5llfWmJmd4djBfZdNeqtLiW9LZCr6Mg891FdHOzRmlmWRsizSNT0KujRsmrT6xGUQFAMSyxI54RZie3y8sDBB3mCpV0NEcZqgNN6XiWxqvANns8lxLz7cWzXeg2bNverEB1ITx/axiXm++QJ3FF3mihO7knuUpNgWoDNoctyVvnqoE9/UeA+aNXetE7clIuthxevo1RVM2kKQEmsb3CKOP7Hjg1yUajTgC7AE+LakN3AecS0yZS7TcL+a40oAa6rB2fQCG6qJj42jXLQW2HZ+XL3yyit8+ulXuPv4Ue45fhS8MmL6Li64X6RWrrKPwxwObkcbw/EDByisLxEZ0NJDOgF+2ub2/T5Tt81SqXoYIfhss3fZsu8V7SijFaXUpcIVhumKx3qYviov7JEGe4QRRhjh9cPoSjvCEI4lrk64XQs58OnuO0OE///23jxcsqo+9/+sPdZ06sxDT/Q8IXTTDdgochlEEFCJYpQITugv3osagyZRuI9RriZqggnXIXqjxKgx16sBIwTijeKEQ/qCCorM9GDP4xlr2LX3Xuv3x67ap6ZzTp3uc7pP91mf56nndO3ae629qnZXvbXqXe/XlxR9yRVXXsXjv36U887ZGB/6/PPP88Mf/pDzzj+fVWvPHE+TKHttKxUbC6WQfCnytNYvzDSFSa/ZxVPFHbgiNT6r7Vedc2VxpaRRcMcIimYWggLp3C6UKqAy/SgrMS68yx7v6QhuqPV4F+JxTM8nXu3xtskyxgH2h8/SKdMkzaoYt4lmn6sXVxajc5iO4IZaj/dosZxfPYVPPCFKpLxRxJH9GLkD2MKkpExKZgrP7GgxQaQqirLquqo8l60I7lauK0957Ar2sy84iEDQIzp5dueTDI/lWN/XR9JNQ0mRNKHN8BgcHKSQXoLnphiWQ7zzrTezKrOOdrMDz5fkxkoUSyamlaVvZBddScURBVY2xfIX9OO0Ra/bsSxIbEZbwiKbsNkmTTqNkAMjHj3ZhPZha04PAi+KJSzldCyh5rRFv1trYhokYrNYwOqsaBGVyJahYmBggOVnXFMjmB/91aPs2r2b3gWLWbPuBePl4EuluGxDvZRqJpfTKoNQJqERkjAbRaRpCiq6plmSCpTLxwNOMgMyxMgfhsIRZMcyZKIzLhEPYDURy1PFAtb3O2GiyySz6JYh8MtfanqcPobDg2wvPMOa1AuwDHtqu4cQmOZ4IaFmyRtTxQKaU9wHKHolgtwgGf8oydIg+AUMbxBppZDZhRiAKotdw5j8i1wlQaSm5lLVdQVgNUsQmSDfvUL1MxOqkG0ju9j6u19SEB6bV23EFQ5+EHJ0eBRvbJTBw4doT9oYpTF6Fy7hgoUvILtwJcOBz0g4xOLEYpYn16ByJoeLeWwhSKdsMgvaSLctY+3PfolRyDMysJrhcy+YdtEWWSqhikVkPo+ZzTbdJ+VYXL6uj+d/IhlRJktT9oxnYWs0J4XDz8D2hyAoRJnhZ74Ketec7LPSaGYc/W6tiQmqJ+RazOEOQollGpEoLVsCKvu+9GUvo3tgIcuXLYuF0ZEjR/jy33+ac9cu5r/83lspltM7KqXmKzaPiogqBRJHJugw2/BEgTS1vtPYwy0ERjlrGowawekFMi4fHxW4jOwm5tAOOPosQbKfwO3BclOxuK62hrSaw40gLjVf4/Fmej7xqNQ8tJk97Pf24hoJlidWky/JKe0eJT+qNBmGssHj3WoONwKSlkkhKHu8HRNkCRF4+GODGKP7aQtz2CYoJ4NK98bjlIxnZBuGIAglAsbHXH9d1RX8aeW6mkpwj46NsX3XXrp7ekh02Gz39/H04LM88stHyFptvGj1+fihIggViwf6sVQPPR0uhB5B10rMVC8dhsOhwlGKssgCsZyOsUX4JWjLmCxalqGzO0UiY/HMryUqXEpbeB7S8zi8+iXI1PSyjL1t28hv3YosFiEMaLviCtwVK5ruu7I3zaXuGAUlePmWM2hv03nVmlOcUg6eegD8HCTaoXAUnr4f2hfpXHDNaYcW3ZoYKVUkck0xpeD2A4lvVnlty2KqIpAA7ESKLS98YY0w2rZtG0pKgiAs+3mjUvHPPPUU3T29pLIdsegtlf28rmWxyOrmSe95lFJxfrNSqomH26gR3g2CuwplJQmEC2MHSXujGOluZLKDAu64+IfWC9+UhbppjHu8k7ZJKWxRcFf1IZD4gUna6GBXYTtByaTfWUpbwmotFtAyajzeQGuFb8KANlHE9jwcv0gxN0IY5EkYAYHvIXwfx01jtnUj65I+KgK5WgwLokIyQPPrqk50t3JdVffhlTwOHzrMosWL43Hc98B3eHrHc2y4bAP9mQF8I2RZ+xIO9Cylr7ubUtlzbxmwoCuFQGJm+wnb+lF2hmLgs3toL1ZgsMpdy9K2M+jqS9HZnSDTkcAun0e1bUQ4DqbjIN3pld6W+Txj3/9+PMMdDg0x+r0HsW8cmHC23BUKVyhSzjGkuGg0s0VDVcsW5UVxBLzhSHCbDmQXQu5QtF2Lbs1phhbdmhjDEPihxI+cAZOUdpeUQontVHltm3hxm81Enn/++axY1IscO4Qs9yHDgLvvuRu/5POO/3Yz2c5ucnV+3k7ZTspIkmjbnCgAAHfoSURBVFdF0iI5Hgto13q46z3eQFPBDdHMr2+amKluLEowth+jcIR0op2C1U5BpQEx7Rzu2OPtyzhDe7o+8XGPt4sRptnuP0vSsWgXyxraaJpS0sTj3TSHW0rwxyiODWN6I7SHo5hhEVSISeSpL4YmOWWjrCR20saaYBxeIDHc2te8MuZWrit/iutqeKyAHwS0pdMkbYOxsRx3fPIOhBD899tuw1aKvB+SWtqOcEyG0iMsFUvpNBLIFFx88SXxkG3lYYUFPOHgW+0YbWcQSEF+KMegf5DeZDfnLN7AyoWLaOtM4MySbzocHSMcHcXMZhG2jdXfT3jkKOHo2LQtKhrNKUkiC247FMvCe2QvZHqj7RrNaYYW3ZqY6iQz0xDNLSWhRIQKxzQaxZcQ5Qi9SF05VvMFdJn2TgwrwLQj8TWay7NkyRIGB4dY0N9HMZCEUvGTn/yEA3t28uIXvZhVq1fTa3bxu2AfSZKEoUJA00WTdrXH2xBNBbcfSoRU2OUkFEUCkglEWMLIHybJUYpmljDRjpNqn1bhm0q/pgFhWWg6TWLiprKtOKaBH4QkzTQCyY7iczimw4C7KN5n0lhAIaIowLLwT9hRXB5BEaM0hvCGEfmj+GNHSOYPYWYXItwUMtEORlT6XADCD2O/uzOBv9qv5Ls3sXs4pogF90TXla/ArLquCvk8iWQCIaL2Hvl//8kPfvwTNmzYwCuvfnn0pSKTJp1OY1kWg0NDyITHHrmbzFltXLHxZWRFJv5VxCi/JtIvYfpjWG4SP7OQUcsllCbOWIAyAmRqlHP713LeknPobu9oWhVzJjHbMphtbRRHRjCzWYIDBzC7ujHbpp+9rdGckjhpWHc17PsVeKOQ6oa11+hZbs1piRbdmhg/VNhEwiisWE2qRG3Fl5wyBYZlUO/GlUpR9MNYVBX9Wi9uxSqglCJpG8jy5vb2dt70pjcjw5BSqAilwjQEzz//HIcOHmTTOVH1yT6rm+dGf8evdv6aZZ0Zejo7Gs4BIg83EC+O9FE1Hm8/VIQhuIbAMEVNG8p0kKZDyfOwvEFs7yhBLomZ7kIksig7BYiWcrjDEEwTQkmDx3s6PnHTgETYRlGO8Hz+qSjRxRloKYc7VwoxVYAR5CjlxnDCESx/NEoJUFBQDtJ0SafakZlelKj9clAqfwGqXBP1nvuKv9oR0RegifLdofl1JaUk75XAcsiYAtMUfPozn+bwocPc/M530tfXh1SKVLYdJUNGR4Zrrqs/+qM/IrQk24v72FvahWPl6LUGsOqsL37gQ3EUUxgU3G5GnB4s5aCCIdI2JHolKq144cBm1vesx2mxiFIrTJZ3baRSZC67jMLjvyUcGyPRuY62y186Z2e5dXb3KcCx2jwmSg8xLTjvrTN/ntX0rIFlF0X9n/tWnV6iOW3Rolszjhr/6b8UyHEvrmXE921LRAvc6g5tWNwGNV5co3xfKkXKNhFBE9uKEjWZzNf93qt45rntLFq6HJQiTYqxfTl+9cyj5FNZ/svFF8fHHj58iEymDWE5NR5uH1Xj8fbD6L5pRgsMG744UF6YadrYbhcoiSrmkcN7sHMHwM1QsNpRZpJkMt1SLGBFYFeEt1StCe5qUV8SEoIshXCIZ3NP4geKND3NBbf0kcUxvNwIqdIwKZlDhEW8kk9JWIhkBpFqo+Cr6PWggCiMNR9H1etReY0rwrt60aQ7wa8aldfcNQUjw8M4yRR++W1n27NPc/c93+L8BYu44Lxzsc1ItCeT0eLAo0eO0NPbS6EUsnrlSt73nj8ik0nH15VtKw6LQZ7P7yOn8vRaadLSQIrq50MRFkYI/RKh3UGY6AGRJpuwSKZMjPaAUXOYTN8q1vecw7L2ZRii2W8js4e7YgWpLVtQxSKdr3/dhOklcx0tyE9h5kJ6iOVGNz3DrTmN0aJbE1P903+tF7cSQWfQbO3WRGkS9R5vKIt6fwKhWlcEpb+vj96y6Kp4vBc6vfT1ddOb6ao5/j+++138UHHFy19Of083BlD0ioDAtJwaj3eU9d1kHNC4MFMY2MkMXpCiGPoYuVEsNUjCSSC8JCrRibJTKCsJonlKSTOP93R94hWPN0EHQ6UjPFV6gjPbzqbd7gPpI0p5DD8X+SILQ4TFHEkkruOgrAQy0YmdNAl8SV4pKGeST+f1MISI869z9b79chtBEHDo4EEKxSILFi+NF03+w11fZPeu3Vx//fWsWL02ajvVRohBMTeGXXUKr3nNdaRSKWzHia+rdCoRX1cJS7C3NMSu/D4Gw2GSwmWR04sVFqGqoLoKSpRyw3hkUImFuKkOMhmHbIdLIuNgWCHbdh6lQ7ms3rqPTquAuO6MaIXlMWAYBn/wwiUIa/pvq4bjgOPM2gx3K5GEmnnKXEwPOdbZeo1mjqOvZE1Mve3YsYxYcFfuo2rnuCeNbxOChG3EBUpc05g4v3qCqoOGEHGcIMD5qzbinOFSKhyK9ykWiyTTGWSuQE9nR+zhfvKJJ3nsscc488wz2bDp3Hj/keGjZNvacKvHQRPBXfdceMpCum3YhoGBD4WjWEM7CFO94KbxjAwhNo7jYhsJUEZNAR3HjH4xAHCt1gV39Tn4QUCHaGPY28eu4Z9ijfSwIOchkGCYSEzyykIkOnFdp8HuMZOvx46dOzm8fw9rVq9iUTb6FrNv3z7u+uIXyXb18I7/+t/iX066urrYv38/uXw+vq56e3t4+9vfTueh/TU53Z2dnU2vK6UUI3KMfeEh9stD+FLRbXSQMO2qPHFFGEqC/AhBYIC7gHR3P21dGdJtDm7awjQNCkGBfWP7GRDtrBP9pJle6sipxHQiCTWnATKAnT+L/r3xD6YWrDo9RKM5YWjRDQwNDfHYY4/R3t7Oxo0bJ1089eyzz/L88883fexlL3sZpmmSy+V46KGHGh4///zz6e7unrHznm1KgWy4Xz3TPVVecqXMdwUvlBimoHqSeTKBV+nDqxL+QSBYYPbwNHtQKpoVFJbDVVddg4HEriqeUyhGXvBUpm18m+dx3789gFAhb371yxBEgnvPvv2UvBIDvd24VuMHTfVz4UuJsB1MJ4OUIcpKEBbGEMEgKUNgWla0ENGwkU4arAShsPClGW0XFl5Q6/GOBHeAlJKkBaYMo5gNpUCFIH3CYh6nOIaQHv2BR07kedrcScGzWJR9AVai57hfj4LnMzQySirhkm6Lnofh4WHuv/9+wjDkhhtvjF+Px3/zG5568gls02LROSsB6O7uIt3eRXtHJ645/svJNddcw6tf/WqEGP/iYZoW2bYs/oH9NTPdza6rETnGvuAgB4OjBAQkZYY2w4pfDzCQgULmAsLiCHYyS/viAdoX9OJmnDjmD2C0NMqwN8yajjX0GwGOEvXJhS1RsVOoIGBo18PTb+AEMFkkoZVKaTuIRqeHaDQnkHkvur/yla9w8803s2bNGvbs2cPixYt54IEH6O/vb7r/z3/+c/75n/+5Ztujjz5KqVTi4MGDAOzatYurrrqKSy65BNcdn0/9+Mc/fsqI7noxXO3xTtKa4K4tHz/u8U6hMIiSUPyw9TLfFQ9xSmVJ4ZBXRcyaHO5az8iLX/QiNm8+l1Io4/LxI8NFEqk0tgDDMAlVNMP93LPPsnPb85x3/vmc9YIXAFAoFPjpz39OOtPGpk2bY5/44MgY+QK0O2ADPiahlcF0jGhhpgoRMgDpYeTzKBmiQkXCMHFsh9Bw8bDwkSSQoPxoEatUOGZUxjz6RUGVc6wVYRCgFNiWg+W4lJwUSdlJIErsCHYyFP6OgaJFh5lteD3CICRfyBNiYDtuVBK9UOChn2+l5Adcc+kF8etx3wP/zhO/eYwrLn8pL37xiwGwTJNnnn4aTIOxQgnbskjaBiuXnYFpmbR398R528JOcPM739kQC+iW86vrryuv5DPGeF53/Ws+pvLs8w9xIDiMT0DWyCBCG4XCsQyUhHxJ4pUC3FKeRCJB2xlLSC1YjJtJIep+LThaOIoXepzVcxYrMksZEc9EX2pOU3QkoWZKdHqIRnPCmNeie9u2bbz97W/n7/7u73j7299OoVDgJS95Ce9617v45je/2fSYN73pTbzpTW+K7wdBwOLFi3njG9+IVefl/OpXv8riStGOUwEFKFlbzMWMtlUmCn1fYgaSUinAcFR50aSifqqwUAqjHG7bpLJcMWkLCiVJsRTgBBLfl9i2EfdRjSxbLQyI+4j/7dt0yCy71CG6VCS+GpdElouyCJOka5Y93Iq+nk6ue/W1eCWfoHQUPwgRFrRnUnT3dNPR3k7F6zA2NsqevXtJpTJsOf+8uI9Hf/H/+N2evWzZvJE1i7oJQ0XJK/Kf//lTkskUF73kJSjTAdNh1+49DI3m6O/toa8zFQm80ghjhw8hMXEyNkoIpNtOqVAk5/uk0mkSySglJZSSfQcOESjBooUD5XhDOLR3H4ePDtLT00NfIsPB0ggH1G/Z+dCTpMdcbrzhxvh5uO/++/j1b37LRf/lEi5+yYswUEgZ8vMf/wDhJLj0gk0kyq9HNp3EJMQrFuLXJJ1OcfU1ryDZ1o6BImlHddvP23wOmzedQ8EP8cp2H2nL8de87ppodl25psBD4asodcYvX1ehWeC50mH2h0coqRJZI0OnkcXzJVJJTCEolSQyAEeGpJ0c7Ys6SC5ehZlt/GKrlOJg/iCmYbKpfxOLM4vH8xxPY3QkoaYldHqIRnNCmNei+5//+Z/p7OzkpptuAiCZTPKe97yHt73tbQwNDdHR0TFlG/fddx8HDhzg7W9/e8Njjz/+ONu3b2fNmjUTzpzPJYziIOroKCKMYgGtOpN3EjADiZQKq3CERDAIhcYZbi9QWErh2kaDVSdTtjhIIJHb1XSGWymF70tcIXAtUdOHCZhS0Rs47FMBRmEvVjlTuppQRjncbrlCZDUuYIbRLLzpDeOEo5y3ZhHnrSnnX48dABQZily6eS2GUFi5A/HxXVaBIBmQtQKkUjiFg/hjIzC8F0pJjLFoX4Xi8I7fcvjQQbpYi5GIfsr3cmP8autPsV2Hiy66BIBEkOPZJ3/N3n17Wb9uHUuXLQcgn8vx5CM/xxCCFZ0vjc9h6He/Zf/u3aRXr4WlS+nzYUwOcZjnyWMwMvhbOkQaASxO+ox1QJe/H3tkJwBtSnLNi9bjJhIYhSNI0ySR28XLz13O1eevjF634Z3x67FpWWfkd/f2Qqnx9ahYRjJyV9NEl6Cc795wXUlJKjyIL0HKxRTyuxn0RjjACCUC2kjRLRxgKPoSJRWWMgilhSUEKStPNitILF2H2bMSrMaoP6kke8f2knWybOjdQG+qt/z6nP6capGEmpOITg/RaGadeS26H330UTZs2IBRJco2bdpEEAQ88cQT8c/rk3HXXXdxwQUXcPbZZzc89p73vIeOjg4ee+wxXvva1/L3f//3pCb4sPM8D8/z4vsjIyPHMKLjwzdTFJw2HNPAsJrHApZKAVbhCNJK4rlt2NWiWamosI2tSNomSogGYeMHEqlGMYICJbcLw7FqbAhRnF6I6QgSE+Q+FwNJmx/SSZojlsECu7Z4TRAqfCmjBY9m4zlIBX4QYnrDKNPFt1NY1UVfVLkIUKaNJR0DGKJWoJ3zosvwA0lYyoMs4TtZrLYEa87ZgiEMVKIdqcALQuy2PlKhgZXtQyWi2aPANwjddoyqkuElO0vJzuCZKXwni0q0R+NwbEIrgzCM+HiARNdC2nwLs60sIE2XtNvO+pXn4Tkhj5pDDJgGPXSz9oUvZfNFV2LWRT2ef9kro3EUR6H8eiQcq+Z1a/X1MIJoprvodpOoiw/0g+jXk6bXlZQUOcRRmWck3MMhJyCwLLqsHjpEZM0KFeS96JcT0zAxTZOOVECbNUSisxejfw2k+xotToAvffbl9tGX7GND7wba3doZPFkqoQp5hOMiylGFcxnLNHjh8q74361wukQSajQazanOvBbdQ0NDDR7ryv3BwcEpj9+7dy/f+c53+Pu///ua7dlslh/+8IdcXM6RfuaZZ3jJS17Cbbfdxp133tm0rY997GPcfvvtxzCKmSM0HKxkFmuCwjeFUojhKBLBIJ7bhmemkEbZj13xcFuRh1s0EdylQOKbEjcBrucx5mTIIyI7ghBVfQhc20BN5BO3FOlUG4vlQvZzhBwWKSua7fZDRWhITMvAbCa4KaeUWOCEo/h2Ct9wkcKIC+h4gUSZkU88MlLU4oeK0JTYrsD2AwpWEmElOWNVFwYQVvqwBWdt3DR++uW/bZ1JLrvq91BKYQXDABTNBC/Y/CI2vzASUqXyOFLZJC9/xe/VHA+wbM0LWLh8HQJwvIPxOFas3IBtCorKY1tpmN2iwLJUP7bVR8KozmuZ4dcjGUX15aw0+Sqff6UP2zEarqu8LHLo8O/YNvY8YyJP8qgg07MKOzmAaRhIM8ptzxcUBoJMyqQta5C28yREHtG+DLpWTDgz54UeB3IHWNK2hLN6ziJl137hjVM9CnkMN0HizDObtnM6MNuRhCcCYVmkzjsv/rdmmpyIGL6JCuxoNBqAphWy5w2O41AoFGq25fP5+LGp+Md//EdSqRSvf/3ra7YvXLgwFtwAa9as4eabb+buu++esK1bb72V4eHh+LZr167pDGVGMAwx9YJGJyolblsGthlFv3mBrF00OUUMnV2eCa3sWyiFBIGc3sJMQ9BlttHjtDEqc5EfuFL4xjBqKlDG46AqFtA2AIFlRvaTUEbHezULMxup7iOaHRexjabkh5G/e5LoQaCmDyEEQggc20QpNWPjEKFNr9FFynLYEezmUe9Jdvn78VRp1l4Pw4jaqBT/8Zok0xSlx8HgCE+UnueRsV/z6MFfkCdPJw69RQv30CBChuRLIcOjIV4eOlIWSxbYLFps0JM8StIpIfrWQ++ZEwruvJ/nQP4AK9tXck7fOQ2CuybVI5MhzOcpPvUUsvz/X6M57QkDeORL0U0Gx99epcDO9h/D//sCHHrm+NvUaE4z5vV0wbJly/j5z39es2337t3xY5OhlOIf/uEfeMMb3kA6PbUHrpJTPBGu69YknZwMmmnExkqT43OVTnnmMqikmrQguB3LgNL4Y0nbIFcK4xi6lgRe+XFLmCyyBxiSzxLKECWM1oSqVTuOaH+jXLmSlgS3bQqo+pxyLYOiH5bj62hJcFfvYZT7LfkhoVIzNo4ESTIqwZjK82xpO3uDg/SJHjroJG25M/p6QG0BHRkqlCHJizwHgzyD4QhjMkdReYCBVYIeL4GNjH4ZcQVFT8GYxEo4ZNsE7e0GqbTAJID8kSjSrGc1pGqLI1VTiQRc17mOtV1rsYzGt7maVA/LxMxmkfk84diYtl9oTj/CAH75Zfjdf8KSLTPf9sNfhO0/Ks9wd86NAjsazRxkXovul7/85Xz+859n+/btLF8eLVy7++67WbFiBatXrwZgbGyMn/zkJw0Z2z/60Y94/vnn+T//5/80tDs4OEhnZ2fNtgceeICNGzfO4miOnwktJdWzndWJFEoh5fj9IFQ4Vq1AmzKHu8n9mr0mEXgA3WYHKdKMqTxZkUEqRX3VmakK31TGWiEMFUad4G1l9nnScTCx4K7ut/Z8ZmYcQgjaRJqMSDEYjPGk3E6HeYhl1gA9qquhSNCk45ji9QhUwKCfYyTMMyJHGVU5pFlCorCxSAqXlJEiCBQ4IVg2JT/At5L4eYHhJOjqdujoMkkko9lzSjnwRqKiHV2rwJnYez1YHKQYFjmr5yxWdqycsKR7TapHW4ZwZAQzk8HMzO1UD2FZdL7+dSf7NDSaWoIi+MVjL7BjWnDeW8fvhzMw867RzEHmteh+5StfycUXX8yrXvUq/vRP/5TnnnuOz33uc3zjG9+I99mxYwdXXXUV3/3ud7n88svj7XfddRebNm3i3HPPbWj3k5/8JM8//zxXXHEFiUSCb3zjGzz00EP827/92wkZ17Hih+WYt2o/b4v2giBUcY53RVxPXfimHAtYrlxZLOd4x2JuCoEHQGjSJ3p5XuzAtgz8ILI1VETplEJV1YrhsCyuYVxcTym4FZSCqI9KpnnJD2vE9XRsK6YZWVVmehyBhBQp0maKopHnydI2MuIAGQltYRHTT6FCC1c4ZBybkg8jnk/KsTCEgVCKvC8JVIhrC4rKRypJoCRFeYQhmedwfi/50AMhcUwbI7BIyzZStl0zDgDTsij1DOANlnCCIt0dAe1rFpBe7ETpKUpB7kg0uJ410H4GmGbj2Mscyh9CoTin9xyWtC2ZtMhVfaqHmUqRWLfulPY8azQxMoDQj0TvY/87iv+UsxiRaSXATkQFdpKGLrCj0UzAvBbdQggeeOABPvWpT3HPPffQ3t7O9773PS655JJ4n7a2Nq688kp6enribWEYksvl+MAHPtC03Y9+9KPce++93HfffYyMjHD22Wfzmc98hkWLFs32kI4PFRWwccpWiUkrGwbjiyYNIeIZ7uqy8ZMJblVOxTCEEfeRtI24gE7CNikFkwtuPwjxLcmA3cURcZi8ypO207FgtU0jFsPNZ4YVpXB80aQB5RluIxaswKSCWxHF5VX34VoGXkV4WyZ+ONUMtyQ0a/uoWE1maxwubbSpNHlVYFCOcUgNUij6CAQpy8byLISKFnQqX+Ga0RcriYq87CWFVBKJQiFRwTBCgiF66TbbSZQXukmTmnEU/BClwELgewaJtnbaFiTIGAHZlV2Yne3R9SZlNFPmZqB7VfQBPgGVDG7bsNnQu4EFmQUT7ltNnOpRTi8xToH0Eo1mSg4/G92kD7/4h2hbeuL/Py0z2UJMy4WBs2H/b3SBHY1mEua16IYom/v973//hI8vXbqU73znOzXbTNPknnvumbTdV73qVbzqVa+akXM8UZhGNMM9qeAGvEBFsYB1YrgirivCe7JKk4Evx4vdVNqoEt7F8mzoZII7iKtZJlhIH8/428moVCxYJxeqlRSSRjFc742ebIY7DCTCbfRwx8I7iMYxqeCWjX1Ue7xnaxyGMMiINEoYhGFA1ujGsQwCQkKi87YthRdKglCiFCRsCxOBIQwMIRAYGOWKkqECrHTTcRRKIV7gYyoTS5m4tqCtV5BJK8LREmBjVBIpZABjhyDdA71rwG1rOm6IBPe+3D7SdpqNvRvjDO5WMRwHLPOYysBrTizaWtMCpRw88++R1cNyYOxgVHdg6YXQZG3DjJLphyVtsHsr2MkoWeh40EkomtOQeS+6NdOgbDmxVJTDPdnP9xNREfVOVRLKdCkFEhWCZYJZFqK9Zid7gv3kVJ6UmHp2xfMlplK4lsn0z6BskwhC7DhBZPr4oQIJpkGDh7xVZn4cAhsLGyu2lBsyms9GgGM0fnmYbByq3L4KwZY2CcegLSvIZARuQqCkpCY/KCiBNwbtC6MZbnvi2WepJPvG9pF1s2zs3Uh3srESpUYzryiOwOAOKI1BSZX/nYvEq3MM6xWqZ7c3/sHU+1tO9H/WahIKMJ3IwkoSSlCIjjvzVdEXcI3mFGdeRwZqagmlKvurx2Pf4inAir8acG1j6ljAcpxgpVIhjC/MNCuVJuup8nAn7PH4uurFgZU+LBNsa9zfmzBcFlp9jMgCXiko+6vHY/iq8QKJAmzLoEnxxFp/dVUMXzwOIsuEKUTTGfC4D6VwLDOOE6w+i/E+wGxS5KTaw32qjsMPFcVSiCwapEybni6TgYUGXd2R4G484SKURqFrGfSum1Jw7xndQ2eik819m7Xg1mggms0e2QthAQyjvJhxiFPqo76Ug6ceAD8X/cpVSUIp5U72mWk0x42e6daMI8btHpXYt4Iv40WOsmqGu2nhmyYe7orVxDJFvDAzYRngTx1DV+3xrl6sGWVLNy6o6zY6seU+ChTpsqJZnWpvdGWRYyQiDYTXpGJm00WT497oyiJHIQSOaUDQzH5T6+Gu8XhXLXKMFk1OLrgrlpJTaRxhqKL+fYFrWWS7DDJZQSLBhL+OiLAARgL6z4LOpWBMvGAylCH7cvvoTfWysW8jWUcv1tJogMialV0IB5+MFk6m+6JZ74ZcojqMuvSQk0lxBLzhY09C0WjmMKfQ11/NbGObIrZ7VPKWpVLky7PNkRhuPG4iwe1UFdCZVqGVOo93ZcZ78iQUBYHNgNlLySiiyrPjFU+xUgrPD8dF5BQzw9Uzv3ZV4ZkaMdykjYlSSlyrqoBOq4VvqsZ5KoxDSkXRl3geGKFJV7vFwoUmPX2CZFI0F9xKIcKyKFj6YuhaPqng9kOfvWN76Uv1salvkxbcGk01iSx0LosWIKe6oeOMaG2ElTjx5yKriu/4xSgnfOfPpk5RSWTBbY+SUMJSNHOf7NBJKJrTAi26NTH1kqj+4mh2sUwVC2hVL6ozxLQKrUQnVc5qbtJehep4wzPcXpJGgrwqTmscU8UCmjULEKewYkywaLL6OHMagnuujyOKTJQEPgjfJJuyWLDQpK9fkExNILYhTihRwiJMLI5mtCbx+BeCAvtz+zkjewab+jaRORaPqkZzOuOkYc1VYCWjmMC2gShVpJnHeq7ipGHd1dFfnYSiOc3Q9hJNcyqebsA1DbwwKvWetMdF0dQ53ONi2DAEQSgRUFOMpZXy8UEosUwDKdV4jneTPqJZ9CQLzB62BbtJE3mCK15o2zDwZSXHe7yv6ZRdN4SILRpO1T7TyeGWSsUWjcqcbiuFb+baOEIpGSv4OKaJ8G0StqC93yDTJpp+qag9mQByhyHZjnQXo8zJ4/oqVSZXd65mXdc6bNOevH2NZr7Sszq6hT6c+9bID63K9hIZRDPIpdzMiVgZRDPYKFiweWba7FkDyy6KFoCe+1adXqI5bdCiW9NIk9lno+zJLpQkGaXwA4lvtia4K5YSQeTxNkKJW35cIlovH18+r0IpJF2uhNnMttJndbMvPExOFrCkWyMihVn2RvuKJIogVITGdO0ekeAMpMJG4fnRgsaWy8cjYm90omyDaUVwz51xCECBEjjSxjENsh2KtkyA09HCj2eBF5V0b1sAXStRzz086e5HC0fxQo+ze85mRceKCatMajSaMoYV3ZyqYk9jB6P87sJQtEB+3dWzH8snQ9i1NfoCMF0sN7rpGW7NaYQW3ZpxwhLCG6VYFngp28QoL3g0gRSKYikoL6ocxU2ALQwo1TZTiQW06hZNuoARSsJi1AelsajaYf2iSsAvz3C7plHTR6pcmMcvjAFgGcmGhZkZYJFM81RpN910kLAtjHJVYbN8HqUgLC9GzGM7YAkBdZWHK5UmzXixYbTdAQKpkKU8pVCCWcC1zLiPaoKyULUNo6aPhIJSKAm8KDDPFG5NHzHl/VQ5FvBkjyNCIEMDC5O2DLRlFYlgOLIneVNUdPQLUSpB92roWVUeSXOUUhzIH8AxHDb3b2ZRZtExxVTONjo/WnPSqY7jO/v3Gx8PPNj3aCSuO5ZEiSC//Aoc3R4V0TneWD4ZRP7roDT1vhrNPEaLbk2MUcrhj3gYSpG0DURdooUBOEEUG2gEBVzPg1JdLnO58I1TiQWsE9RuWTQDON5RrLBx1jIIJSpUJE2BZTaK+oRU8aaEd6RRtCtFr6/YJwNK1kHSsnamxATsMBqHCD1s3284T4UiDCR2JU6v7rmwy1UgAezSCGaTcYRSoqTCNaIFjNRN9jhKxZscf6Th+YYoBUQoRdIyEGHt4ydyHEGoohl2TJA2bQlJts0n6QYYIeMLAnIHG46P8QvRjotfGMUCGiaEzRdVSSXZO7aXdredDb0b6En2NN1Po9G0wPBuOPwc+GMw+DvoWAb7Hosey/SNx/K1L5r+zHJcAbNEtLBj7n0x1mjmClp0a2KKdjtGtpukbSKbpZSEEt+XJHK7KLldjDmZqMBNGamISrsTVZJsJqcKpRBKYzjeUYrpJYS2Ec2+VvcRKGxLIEyjoY1KH07hEABjyV6SdSkehVKIAs4QnTzlP0/J7MAS4+fph5IwVDiFg/hOloKVrClwU1kYKNwok7xZ2JbnRzPDdmmEUrKP0BTYVeOI+gDTBGE2tlHpw/KGonN2O3Dq8rZju4dloARNYgFnfxyeL/GCENs2MZRJMiHItgvSGYFZPUk9eiD629bfpBeigjdBIfJqdpwx6QdzIIMoEjDZy4beDbS72s+p0RwzgQdHniN6B1EQ5CPLh2FBpvf4YvkqFTBDL8rV9/NRLnjgtXb8dArmaDSnAfoK11QhSDrWJP5qsO3IX204FnkEBT+aFZeUBbcwJo8FRJByLKzQILQNytXe4+xpP4yK3kzoEy/3UfElByLK8q4srqz0kXRMEnRzRA1yRA7TKzqBii85St2wTANhmXhEpe1dy4grNE7prwZcy8QMDUJTUJ4sxjZFVR+T+KvLfVQeD4WgFMjYT13pY3J/9eyNo5KrHobgSIdkwiTbDumMwLabCOY45rFJX8WR6EO5dy20L5lUcHuhx4HcARa3LebsnrNJ2VPYVTQazeR4I+Uqr2fA0ecBIxK33WthZNd4QZ1M7/Rj+Yoj0cJMOxm1k8hC/lDkE9doNA1o0a2JMQ0x9YLG8uxmtHgxKqCTK4VV21rI4S5bICoz3H4o4yI6LS/MLLdRXUCnwvjCTJMl1gIGvVGKysOUTtWCxso4wLGiRYnF8jeAlhc0lr3PlZnhUMpYtLa8oLFs96guPFOhtYWZMz0ORTEIUZ7AkgYZ1yTbLsi0CRznGH42Lg5Hi6h6100ZCZgP8hwtDbGyfSXre9bjmqdQzJlGMxc58hzs+QWM7YsiOp1UFMG34hJYfQX85G+mF8tXv/AykY2K2PiFSHi3L47+j+svyxpNU7To1sQ0KY7YJEFk3KRgCBHHCQIkplP4poxjGbHgrtyvp1kSSowQJGyDfFl0u2Ztifp2o40FVg/bvb100YllmmUxPG7WMBiP4ZvoHGDyOD27apa4cr9hHEyeUuJYBl5ZMNuG0WISykyNQxGEkjAUGL5BwjHJZg3a2iYo2d4KxaEovaCvLLgnYUwVCYtHWdu9nrVda7GM+fPWpBdiamYFGcAz34mEcseyaNFkYSgqQHXmta3H8lWE9r5fw/aHIptY9cLLNVfBE/dG+6W6o8I8FWHe7JwqkYU6BlAzD5k/n2yaKQnrTL+t5HB7VQcVfVkriqcqfFPuo/5+dV+TCu5yH0V/vA0vlBjm+Iy9EIJe+tjDEYqiSKfZWFBFQixUK+dQL4pbya+uv18tvFvJ4a5+LnwpEWZtX63kcB/LOMJQIQOFXxJYmHR0GGQ7Ji/bPiWFQUBB3/qoQMckDKocJQLO6z6bld2rdSSgRjMThH4kcBPtYNpRVcrDz0RZ2j3llJKpYvkOPwPbfxTNmP/qa5FQzi6qXXhZnQu+7L/Azz9TFual2qjA+sjCM18FXStm/WnQaOYS+tNNExNKFZVTZ3qFb9KOGZeML/gyekNtRXCH432kXSsuGV8Rn60I7uo+0uV+CuWy9ZVx2MrhDGsBnlFEqlqRL9W4GE7Y5nip9SoBPLXglrEYTthmXDimIsSnFNzl/ip9JGwzLrUu4z6mENzHMI6w/LjvC1Tg0JY0GVho0ts/Sdn2VigMRn+nENxSSfbn9oGADcYZrGw/eRncqlQiHBlB5vMnpX+NZsYx7UhwV8qpl8YiC0i9b1uGUYn2R74UzWBXKOXgqQeixZGGFf31ctG6jezCSDwXR6J9DSvqb8ePokhQtw3yR+HA49HsdlCK/h160Ux4RbQ3mw3XaE5j9Ey3ZhwRJX+YhiCUakLBrSoLGp1xMVxJLCmUwriS5WSCO4hTSsYXTVb++mEkpEOpJhXcXiCRdm0f1R7v6nEscfoY9gYZkiN0mR1REyi8IETY42LYIPJSl/wwFqyTCe5QVlJKxsVw9DcS3gqBlGpSwe2HCmXV9lHt8TaMqI2JBPd0x1EpUhTKqGx7wjZpTwZkBgzMZoskp0NxCFCRhzszQZIJUULJ/tx+Op12lhhL6RKZk5bBHRw+TPHJp5ClEoQBbVdcgbtCz8BpTlEMC5a+OPr3ovNg/2+gOApOJvJat1oSvjgCXnmmvDAEbhpKo1DKN194WTOz7kTFrw78NooRLJYXcxrWuGjPHYoWeWo08wgtujUxcZKGVJiGmGCGG3xfYjoiiqerEkpGeZa1spAvYTcX3H4Q5XDbVmMfjmXEghuYXHArGkV9WaTnSmHdOAwW2wv4rfcsJeVjYSEDiWkJ7LpzMCgvSgzC8jk1F9xBqFBSYZqNHm7bFLHgBia0lPihmjClxLUMin6IlArDEBPOcE9nHEoqAqUQ0sAKDbKdgvakxPFLx/9u4I1Gs11TzHAXgyIH8wdZ3LaYMzvWEojDx9nxsSNLJYpPPUWYz2N1dBAODTH6vQexbxzASOnFYJpTDCWjoje7H4YlWyLbx7KLyrPVDux/rPW2EllwyzPlKLDboiUkQaF24aVfTiqpzKwffLL89wkYOxAd6+cioe4NR7+E2qnID+5OMy1FoznF0aJbExNKsON/R1aTakFbiexzy5UmVRMxXO1LLgWyQTSXypUmk2aUw11PteAGKIUKx6rqp2wpMVRZyDa1rYwfXz2ObqODfquHvf5BOlRnuWCM0ZB/DdQs7vTDRm90xe7hGhOMA2LBXdm/XjR7gYwWPzYR05Vj4vakQpq1frAoFlC2NA6pIF8KSVoGpjQxMWjvgWy7wPBpKNwzXVRhhOKO3xEmDdLLeploznq0NMqwN8yqzlWs71qPrQyGjq/r40IVi8hiETObRdg2Vn8/4ZGjhKNjWnRrTk2EEc10b3pjdN+wIgEs5fQK1zjpqFT83l/C6H7oXAh9F0Gio3HhpQyime7lF0cz64XB6DzcbCTSR/ZG+5hOZHMZ3Q9b/uvEXnLTgvPeesxPgUYzV9GiWxMjK5YSU8QWjcpMcuyvBlxLIKfwVwOx1aQivCs+cdc0sJoWvqn1cJdCFYvGKDmlqg/LiGwXdW3UxhvWj8NgkdnPPm+QvCrQYwpUk8+gau8zEFs03Nj+Ml7a3TSaFL6h1sPtxyXUx+0hlT5sM/JONxa+qfVwV6wmlRnxuA/Ks+qTjMM0BEhFwjbBN7AtQUe3IJ0+joWS1RSHwXIIk8tRVseEux0pHKEUljir5yxWtK/ANExU0KTm/AlEJBIYiQThyAiio4PgwAHMrm7MtsYFtxrNvKNnDSx7SSSgF28BNxX9u1osxxUp/WjRZc/aqEKtNwaj+2Bwe9lWYkYz4NlF0L0yqoSp0cwz9EJKTYxRsWKURW9lUWIQyHEx7JgtxQJW9q0srvSqxHC9DQKaL5p0LCNeXOkFsrYPo8kMd/3izybjMIMES8wBfLOAbDI1XL/YsOKNrixKrBbDVouxgLYpahZXVvfRTPQ2WzTpWka8uDIM1XgfdlScvdk4Kr8YGAiStgW+SSIp6OkXZDLHsVCymuJQNIO1cPOEglsqyb7cPgSCzf2bWdWxCtMwm+57ojEch8S6dZipFOHYGGZnJ22Xv1TPcms0FSw3WoBpOY2PVVektNxo8eThp8HtgHRP5AUfOwT5I5EoF0a0b/oYCvFoNKcBeqZbE1Ojhau80ZVYwKQdFU+pYZKUkorwLpRCZKiwKmK4VNvEZCkljhXZJoLKOUwSPdg0baXJOJYm+hjxBhliN510xLtOlFJSvSgxVFULGusmaSdLKaleXAmtFr6pHWfF412JBYz6aPzm4AWSQCpMAa5tYmHglwSZDHR0TVBR8lioTilJ9QLPNuwSypC9ub10up2c3Xs2Pcmemel7BrF6eki98IUo36fz9a/DzJ5eYkDngM8TAi/ybptuVARnKmQAO38W+cCXbGleqGEq6itSti0ol5yXUX739h9HdhLDit7XS2PQsQTWXhXNlocn95cujeZEo0W3ZkKa2SZq3pZbiAUM6nzJqFqR2EosYLU3Oqj3eNNCvGHdfUvYnGEv4Al+R0n52CSnjAUMq8ehFPWzy63kcMuqsYehwqgT1a3kcNffr+/JC0K8UJGwDJK2CVIQ+oL2DmjvFJHVZCbIHY58l73rop+Jw3qzEJTCEvtz+1mYWchZPWfR5rTNTN+zgHAcjGRSz3BrTk0OPxMVrvHzYCdgwcYT0299RcrRfeVZ8USUUGImon9nBsopKEdg0QvHc8KhscqlRnMao+0lmqZUi+FUTf51eYcWC99UxHBDjjfR4r7p5HDX53jX9zFVnnj1ODpFBwMiy2A42lLhm4oYrsm/VpU+phbc1X3U53jX9zFlNcvqHO/49YieSy9QZByTtGOhfAMZCjq7obNrhgS3UmXB7UDfmRP6MvN+ngP5AyxvX86mvk1zWnBrNKc0cZ52DhJt0f39vzkxGdhOOprRNt3xipQDZ0dWE280spRYiSjZJNlRPqhq+qDyZWH7j+H/fQEOPTP756zRnES06NY0UD/7LKq90eUIu2LQuuB2LKPG410MJFKWs75bFNxGncfbD8Lo1qLgrh+HFygW0Y2hLMbCfMt2j2qPdymMhHQpkC0L7kpaSUV4h2H51qrgtgwEVR7vQCKlJFcKUUBH0iZpm/hFgWFCT68g227MjH9bKcgfimbS+s+MPJtNOFo8ypA3xLrOdWzo3UDCShx/3xqNpjnVedqmMz7zXJ+BLQSc8SI49y3Rr1QzRaUiZffqKNWkks/vtkWz3rJULv8+Aka5MibUfllw23TBHM28QNtLNDGilIPR/fiBxBFlz7A/LtbS5RneUiAxgkOkkwWMoFkOd4gKIWGCbZngRdsNIC0VfmGMEuAUDjX0AcQ53IYiSimp6iMBmEGI9PLl+wdr+qggpZpyHIQOS3yLHc5BhPQQddUQw1CCBMeIit9UYvVMIKEUgVfAByxvqOzxbhyHH6pYaIuwyqtebl+WM24dhmr6GG9CEYYKq9xGdR8JFS2oLPqSJCO4doDhCQpFk0RC0pkKcKWC0YaXqBa//CE3emDiSDElo0VRbQug7wVNfwaWSnJIjNKpYHPfZha3LT5pBW80mnlDdZ52Ihv9dduOPwM7DKJKlb/7eYMtsAHDim7VXnInHb1X7HssmgV30tGtUpyn/stCpWBOcWTiKEGN5hRHz3RrYqLKhhJDTJyBPRV+EBKEYFUE97RPYrzwjVuOBZwuUrY+jn7RQbtIM6jGaraH5YqNZkVwT5dKpUmIYwGn30TURiUWsGYcSkXFeYCMa5G0LQSCfNEknQ7p7gpwnSk+KKcxFgpD0L4QFm1qKrj90Gcfw7ST5LyB81iSXaIFt0ZzIqjkaTvpcuXJdGTxmEq4VipXnvEimK00oUxfdOteBWe8uDaxpPrLQliKsryTHTrVRHNao2e6NTElI42T7sG1jYlzuG1JRu6i6HaTs9I19pJSIPGtyO5hWk3yq8t2D8tIkvCOMJbsJRDGuL0k7kPF0YRNc7gtSYKDABQTfYRV9pLYUuKKlsYh3QX0G708r7aREwlSRiKylJiR3cMwGzO0K3YPU7g4/ggFt4Owzl7iBTIu7Q6NGSOVPpxyaZiS01FjL4ktJeXS7tXHS6KqnqYjyDgGVv4AgdWBFybJ9kNHp8CcoOBOU7xRyB2Etv4o0qvu+SJ/CLqWRR7uJh+IOT/HkfwhFoku1hoL6E50tdz1yUzWqPStgoChu+85Keeg0cwIPWvGK082TS8RsPRFsPnNza0lgRcJ34kWM8qgPIOeiSwj1VSXnTeatG2YzSMHK18W9v0qeg+qrnKp0ZymzHvRPTw8zMc//nH+8z//k/b2dt761rdy7bXXTrj/zp07ef3rX9+w/dOf/jTnn3/+Mbc7JxATl12P/dV2lJGdsAzy5UWJScckKBeyacVfnbAMDF+QtM2oeI0vSdgGxWkszKzMoodljzeAZYrWF2ZWjaNbdDDq97FH7UFIC6FES/5qxzQQQeQ1LwVRlnjl360uzKzMolc83mBgmmLChZnR7HmUTuLaJoaAki8IpKCjt1xhcqYSSmIPdxp61zcIbqUUhwuHCVTA+q719BoBlpgb+dsazbzDcqMFi1NZQeoZOwAHHi8L7wDOfFVUor1CcQSG90S/djmpmU1GqXxZCIqNVS41mtOQeS26wzDkiiuuAOC///f/zrPPPstrX/tavvSlL3HjjTc2PaZQKLB161buvfdeent74+1r1649rnbnAg0WBmhc0FiZcy0L20rFx+j41hY0VvzVhiDO8c6X22g5h7vs4a7054cSP6T1hZl141isFpDzCxwOj9Jvdbe0oLGS022I8Rxvz4/G0XIOt1/13JdzvENJg+CuzG5bpiBhWeOVLQsKGRp09kjaOmao4E35+SJ/GOxUNMNd92FYCkscyB2g3W3nnO5zGHB7GRZPzEzfGo3m2Am8KDWkMmt97lsmto8EXiS4/XxU3r2ymLF90XgiycieqKCNm4lytivJKDMlkC03uukZbs08YF6L7nvuuYdHHnmE3/3udyxatAiAffv2cdttt3HDDTdMKmA2bdrE4sWLZ7zdk0nDWTWLBayeRRHRrKosR981rdA4RQ53vTBtJlSnigW0TEFZ60azvC0kodSPwzEtlspF5FSeMTFGF7WzulPlcLcyjqliAU1TUJ60r/niEUUL1s5uAxTykeDv7vRIZ9PH5MGfkPyRKGWgb33Dh+uQN8RYaYyl7UtZ27mWjJM56eXcNRoN0az1/t/Uzlp3rZh4/6AIfrH5YsZ0b/S49Mezt894UfS4n9ez0hrNMTCvF1I++OCDbN68ORbGANdeey27du3imWcmzwu96aabeOlLX8rNN9/Mk08+OWPtnkyCagN1izncQSixTKMqx7u2iMykdg+IcrsBt2yzqM7xrvTRqm3FMg2Cuhzv6Ywja6dZbi0mH5QoqPGyma0UvvHKfdqGUXO/wnRyuMdzvGW8IDTlWOXzBxTkc2BZ0N0nSCdnWPDGOdzrIdkZbw5kwJ7RPUgpOafvHM7pPYeMk5nZvjUazbEReOOz0PURfIEXebLr4/isRBQBOtFiRisRxfz5heaPhwH84h/HK1saFpz31ujWzN+t0cxz5rXo3rlzJwsXLqzZVrm/c+fOCY+77LLLeOtb38qf/MmfUCgU2LhxIz/+8Y+Pq13P8xgZGam5nWikjDKnWxGqfpUYdi1jPMe7LLxbqTRZ2TfpmFhWYwGdKQW3rO3DrcrxPtZxLHF7WGz2c7A0RKBkC4Jb4flVhW9MUVtAh6kFtypH/1X6iK0jgcS1DNLuuJ1ESRjLgZuAnj5BMtnQ3PFRGIwWWvWth9T4gsix0hj7xvbRn+7nhQteyPL25ZizlXig0WimT7NZ68IQ7Ht04gI0lgv9Z0XrNpotZrSTsO6aKIe7lDu+xY6GGS24nOmccI3mFGJeX/m+79PWVlspL1lWMb7vNzuEFStW8L3vfS+2iFx11VUMDg7yZ3/2Z/znf/7nMbf7sY99jNtvv/3YBzMDGIaIvdEwsb86CCWlUGI7VWK4icd7MsHtBQpZ10elgE6hFJKbyideiQV0a/uo93hPdxzCMFidXEyuUGB/6Sg9RufEglsRxwJWe7grBXSiDO3oJCYT3H6oalJK/EBiGwaZhIFTFVcYhpGlJN0WVZi0bTH9RVOTURyOBtW7HtLdQDl7O38IheLMnjNZ2b4S27Rnrk+NRjMzVM9aJ43yrHQ77PhpuVple3PPtpWAxS+M/IXNFjNm+mFpe2QzOZbFjjKMZsqDUrTQU6OZx8zrme6uri6OHj1as+3IkSMAdHd3Nz3GcZwGT/bll1/OY489dlzt3nrrrQwPD8e3Xbt2TW8wM0B1HLVpiOZWjFAShAqnmRguV42s4DTLyC5XpFSVBJF6j7eoLVfuTOATr9gumon66mOOZRyO4bDaPQNXOByVQ1gTpIFUKlI6ltHU012dIjKRpaSSw+1aBmF5dt+2DFKuVSu4AygWINsO3T1lwT2T+Pnow7FnbVza3Qs8do/tJmNnOH/gfNZ2rtWCW6OZq1jueD53ZdZ66UsgKDTOfhdHxkuw7/gJ7N4azZRPNINtuVEb053hHjsY3Y48Bzt/EnnONZp5zLwW3Zs3b+bRRx8lqFoEtnXrVmzb5gUveEHL7ezfv590evzN6FjadV2XbDZbczvR+OUFkaYhCCtWkypKgcQPFJYpsCfwVxfLpd0NISj6tR7vit0jVArXNmimZUuBJJQqFt71Hu9x28oEhW8q9pTjHIcTJllrryBlJNlbOoyvwpp9vCAah201H4cfKqRUsfCu93jHthUiQe6HEikVKcck5Zg1X4D8EngetHdAZ/c0M7hboZQHw4HetZBdAMBgcZBDhUOszK7khQteyEB6YM4uANZoNGUy/ZHQXv5f4Py3w8JzmhegMayqEuyZ6Et3xQ8+U4Qe2G7UvpOEzmWReA+9KQ/VaE5X5rXovuGGG8jlcnzmM58BYGRkhDvvvJPXve51sT1k27ZtXHDBBTz88MMAfO1rX+Ppp5+O2/jlL3/JZz/7Wa6//vpptTsnUZEVI2Gbtd5oqhY0WtGCxXrqPdz1Hu/6jOxmAq7aw52wzQaPd3Ufkwnuiof7eMexwO1gU3I1vVYne0tHKJYXV3rlmXrXMhGNmS81Hm7HNBo83jWLJk2BHypMU5B2bVzLqGmx5EHgQ0cXdHTNYAZ3fLL56CfmxS+E7EIKQYFdo7tQSrG5fzMb+jaQsusLbWg0mjlL9ax0dbXKas+2DGpLsCfaIwuIN4NriYojkdi3k5HIb1swPsuu0cxT5rWne8mSJXzta1/jbW97G5/61Kc4dOgQ5513Hp/+9KfjffL5PFu3bmV4eBiIPN1veMMbOHz4MIlEgp07d/KOd7yDT3ziE9Nqdy5SbcWo9UaXkzlMA6fJ2rmJFk3We7yh7K/2W8jhprnHuz7rO2aCRZPHO46UmeLs5Eqcgssebz9pI0lKJCMPd5PQkGaLJpt5vIUQGAJkqKLFk7bVMGPuFUEq6OoVpDPM/EyzX4w+APvOxM8u4FBuHwYGK7IrWN6xnHZXR4JpNKc8zQrQlHLjM+BuWyS2nQy4db+wBt4kVS7LVBZIbnpj7QLJRHZczNtJGN0XVb3VZd4185h5LboBXvOa13D11Vfz1FNP0d7ezvLly2seX7lyJT//+c8588wzAXjRi17EL37xC/bs2cPIyAjLly8nkUhMu925SP3Er2ONV3us3EfV2SQmSykRgoRtxIVv3HK0YD2TpZQYQuCaBl75PBKtFL6p93gf5zgcw+HM5FKcgsPvwj1IEdJDoyCdLKXEIIoT9KVEKjCEwhBRYotlioaQ9GIh+tvdK0inZ8HWEZSgeJSwcwVHEilKxYMsSC1gZcdKepI92kqi0ZxO1BegqSnBPhall1T84BUqnm8/Hy3QnG4lSicN614B+x6LhHe6V5d518x75r3oBkgkEpxzzjlNH0smk1xwwQUN2xctWlSTwz3ddk8Fmnmhq2eIW4kFLPrjbXihxDAF1ZPMreRwe1WCuejLqK+qPlrJ4T7ecZQCWGwNkDRctgW/41A4SK9yY63cSg63LyWBBIUiaVkkLBNTNckuz4NpQle3IDkbglv6yNwhBjNd5B2HnkQ7KztWMpAa0DGAGs2piFnOxw4D+NVXWzumMgNeykUz0XZV/mgpN+75TmQjW8ixVKLUZd41mhq06NY0pV4MV+4DJGlNqNaWXSe2mqRQGETpH37Yevl4CRRKIQVfki4vrmxFcM/kOM6gB8ezedbfwQE5RL+KFkGGcuJYQAl4fkgoIx940qmNAqwmnwPbhs4eQTI584Jbhj5DwzvJpTro7F7Duq41LMos0qkkGs18xHIjv3X9+15xpMrzbUd/S7nIhjJd4azLvGs0MVp0a8ZRgIqyq/1AYVsimhFW4zPDvi8xA0mpFGA4qjzrrBryogulMMrhtk2i9GlI2oJCSVIsBTiBxPcltm3EfVQjFRT8EAPiPir/Lvjh+KJEW473UXcOszWOATeLqZbzfPFp9sn9ZIMuHMvCNitPYu04in6AlJByLZK2gWlUz9SP75jPg+NCV4/AdRvPpSmVXZRqeA5rz0MyVBolN7aPjvYz2LzyShZ0LMc13an70Gg084tEdtzznciOe7/rPd/HS2WGXqOZJ2jRrYkxioOoo6OIUJEyG9M9koAZRNF2VuEIiWAQCs0L31jlWMB6b3CmbDmRQCK3q+kMt1IK35e4QuBaoqYPEzCrYgAzclfTRI8glLM6jgGlcEsZnlMZjhS30W9kMUStNUOh8P0QWwiSlomFgVGiASUhP3qYhBvSlfFw8hLyjftNSv5QdKt/HpRkKCxSkAEdvsemheez8MzX4KZ6ptmBRqOZN1R7vouj0f16z7dGo5k2WnRrYnwzRcFpwzENDCuyc1QjlaJUCrAKR5BWEs9tq825Lhe+Ce0oFlAJQf1crR9IpBrFCAqU3C4Mx6qxhUQRgSGmI0hYBnKC4jpGEAnMottNoi4+0A+i2frZHoerRlnjC56xPQ6Zg3QaSRLCASCUinxJ4iYFKcdqajsBkPkh8kWLVEeGrm6JbU0zUlKpSGxbqSgPt0xR+gz6eUIk3aluzgwl/R0rSKy6HJKd0+tDo9HMPyp+7KnSSwIv8mxP1++t0cxDtOjWxISGg5XMYllGg8iMvc+OIhEM4rlteGYKaZT92BXvsxV5n0UToVoKJL4pcRPgeh5jToY8Iq5MOd6HwLUN1ET+akuRTkbxHjkrTb7Kj13pw3aMEzKOlPBYnVzGtmAvQ8Ygg+RIkkAFNulkkoxrNaTCxM93CAW/RCYb0rkwhWUdg4dbyUh0uxl8O0k+9BgJC9iGyYLsYpYkeujxPSzTgaUXasGt0Whax3IjT/dEVrdKwklQiBZxnvkq6F1zYs9RozmF0KJbE2MYoqUFjRSiSo7SiKL4FCClanlBoy0MKJUzuP2o7UosYMsLM4NyFng5x7vgSwxDEEyRhDIb42hzXVaLpYzKHkYZYad3CGWPIkwbhzQJ5TTYU8IwigVsy4R0dgSYxyC4fRmQD4rk/TF8z8JRWZKmy5r0Qha63XTaGYQ3En1gLtwEmd5p96HRaE5zDLMxY7saYcDmJo/XJJy0Q+EoPH0/tC/SNhSNZgK06NbENNGpjUK1at7XKc8kB5U0kBYTRKjyNSdtg1wpjGMBWxLcVY9XF9CRocJqVXDP8DhSjknopbACl0WZfqRT4FB4hCPhMMNyLOpHWLjCwQodAs8k2wHtdkArVd19GVCUPp4sUQx9EGBikjJtltjtdLUtI5tZQMZMYldi//w8FAZh8XnQuXTqTo4TWSqhikVkPo+Z1QUwNJrTmpqEEweyCyF3KNquRbdG0xQtujUxE1oxqmefq39mVAopx+8HocKpm7GdMoe7yf2avVrI4Q7C8XOQspz6Ue8Tn+VxBFJR8iWdaYe2hI1ppBhQXeRkgZwqkg8LDMpRhr08h71h0u0SMynIF4ZQgChaVPJRpJLlYjkCgUIBFiauadNuZVieypA2k6RNlzbTxQ6fgkQP2OOebkIfRvdD/1nQs7bhfGcab9s28lu3IotFCAParrgCd8WKWe9Xo9FUcSLTQGoSTtphZG/0a5quOKnRTIgW3ZoYPxwXrNPNrw5CFedfV0Tp1IVvyrGA5cqVxXKOdyyuWyx8U+nDMkVsNamc84kYhxeE5H1Fd8YhnbAwy30IIciYKTKkwIKFRcWoCkgtKpLoLKFQhEaCUCnCzAC+iurK28LCNkxMYWJiYAmDhOmQMl0coy5Pu1lMoAxhZA90rYgSB4wmP2HMIDKfZ+z7349nuMOhIUa/9yD2jQMYqQkWX2k0mlObmqqWo5Dq1hUnNZop0KJbM46Kis04lkHRn6LSZDC+2NAQIp4Zri63PplQVeWUEkMYcR9RBnckvBO2SSmYXHD7QYhv1fZR7fGe9XEoheeHBCH0ZVxcu3mZe4BiQeH7gsWLbLq6qzzexXI2YGZx0+OmjVKR4M70Rz5uy5mZdichHB0jHB3FzGYRto3V30945Cjh6NgpIbqFZdH5+ted7NPQaGaH2Zz91hUnNZppoUW3JsY0opnhSYUq4AUqitOrE8MVUVoRrJMtaAx8OV74ptJGlfAu+iEwsb/aDyKxW99Htcd7NschlaJYCkkagp42B9ucuHx6Ia8IAxhYKGjvpGFR5YwydiCKDlx0bk2E4GxitmUw29oojoxgZrMEBw5gdnVjtp2Y/jUazUlEV5zUaFpGi25N65StGpaK8quPRTxWRL1TFsfNxPBUlAKJCsEywWy2+nMqjnMcfiAJpCLjWCSFXVthso58TiEVDCwyaO+cRbENkbdSSVi4GdLds9tXFUYqReayyyg8/lvCsTESnetou/ylp8Qst0ajmUV0xUmNpgYtujUxoVRli0Vky6j2RsfeZ4gztJvmV5etGECDN7rir7bKlSbD+hOo8ldX7CU1Hu+qPhIm2JbZtPBNxcM94+MwBV4QxRq2Jy1caSIaBjFObkwhBCxYZJBtn2XBHXhRVu6i86Bjyez21QR3xQpSW7agikU6X/86nV6i0cx1tCDWaE44s7vCSnNqISK7h2lEs9CyLFBV9WLDCWaG6xcbOpaBbUb51xVvdkUM11eQBBoWNJpGZAsxRLQ4UipVm5FtNdo56hdNzuQ4vCBk1AuwTEE2aZOwTQQTC+mxUYUhohnuWRfcMoSxg1FKSe/sJ5VMhOE4mNmsnuHWaDQajaYJWnRrYmxTxGK44o2WSpEvi95oxrnxuInSPaqF93QSRGJ/tagV3pMnoTRPKZmJcVimAAUZ1yKbsCcs6R4NRTE2ojBNGFhs0JadZcGtFOSPQvdKWLAhKnSh0Wg0Go1mzqHtJZqYenlYL22bfUObKk7PMgXlNZEYhphW4ZvopASGIZDlLG6rieCdKhbweMYRSEkQKNpTDiln4nSSaCiKsVGw7MhSks7MsuAGyB2EruWw5IVgJ2e/P41Go9FoNMeEFt2a5pTFMBCXaI+80eNCcuoc7nExXCnRLgC3ap9WcriDUGKZBlKqcY93kz4mm0U/lnH4gUSiaEtYJBxjUjtJNMMNjisYWCRIpU+A4C6ORNaSlZdCqmv2+9NoNPMH7fnWaGYcLbo1jTSZfTYqhWdKkoxS+IHEN6dn9xBEixKNUOKWH5eI1suul8+rUApJlytITte20uo4SkEk1LMJG3eKhBSlFKOjAvdECu6gCIUjUVJJ+4lfOKnRaDQajWZ6aNGtGScsIbxRikG06DBlmxh+JCBNIIWiWAoo+hKpRnETYAsDSrXNVGIBrcqiyXIbLmCEkrAY9UFpjJRjxX1U45dnuF3TqOkjVS5o4xfGALCMZE0fMeX9jmUcfhBliKddCzc0aIxZKeONoRSMHc7jJhULeiRJAyhM4zn3RqK/hcHWj5EBjO6DBZugd90xxS5qNBqNRqM5sWjRrYkxSjn8EQ9DKZK2gQgavdFOEMXtGUEB1/OgVLuPKhe+ccqxgPVi2C2LYQDHO4oVNs4iB6FEhYqkKbDMRlGfkCrelPCONIp2pfACdUzjCMNIcKccE8czwJv4+VIKRscsEu5hFmTyJEqy4VxbZnhXa/spCbnDsGQLLNoU/QSs0Wg0Go1mzqM/sTUxRbsdI9tN0jaRzdI9QonvSxK5XZTcLsacTFTgpoxURKXdiSpJNpsgLpRCKI3heEcpppcQ2gaOadT2EShsSyBMo6GNSh9O4RAAY8leknZtGklkW2Ha4whCRagkba6NY0+eAqKkYvRwnkTiEAtesJRE8hiDgAZ3Rn87l7a2/8hu6FoFy16sF05qNBqNRnMKoUW3pgpB0rEm8VeDbUfeZ8OxyCMo+NFssqQsuIUxub8aQcqxsEKD0DbiZBPHMsb7sMyJfeLlPio+60BEZeMriysrfUzuE28cx6gXYpsGbQmHxBSCW0rF6IggnVEMpAu4SQPEMYruOB6xhePHDoKVgsXnQqL92PrTaDQajUZzUtCiWxNjGmLqBY1lPRotXjQplEJypbBqWwsLGst2EKeq4mOl6mPLCzPLbSRtI15cWaGlhZlV47AMgVdStKUtki0Jbsi0CQa6JE6xvibmLFEchtCHpS+CTN+J6VOj0WgmQyecaDTTQhfH0cSYTa6GyWIBDSFwqw5KTKfwTZn6NqdT+AYAIaJ+y7hm8yzticYhlUJJ6M44rQvuTJRS4riT7j5z+HkoDMHCja3bUDQajUaj0cwptOjWxIR1k7at5HB7VQcVfRmtLqwwVeEbxqP5JrrfSg530R8/xguj/loZhyqXlk+5UQ73ZCipGB2uFtwnKDFEBjB6APrWR2XeNRqNRqPRnJJo0a2JCaWKBet0Ct+kHTMutV6oCO9WBHc43kfateKS8RXh3Wrhm0of6XI/hXK598nGoYBSoEhYBqmpCt9IxcgIpNtOsOAGGN0P2YUwcBYY+r+rRqPRaDSnKtrTDQwNDfHYY4/R3t7Oxo0bES3kHm/fvp3du3ezcuVKFi5cWPNYLpfjoYceajjm/PPPp7u7e8bOe8YRUfKHaQhCqSYU3KqyoNEZF8OVxJJCKYwrQE4muIM4pWR80WTlr1+erQ6lmlRwe4FE2rV9VHu8JxyHUnilkJQ18cLR6rGOjkA6fRIEtzcKhhkJbutEeVk0Go1Go9HMBvNedH/lK1/h5ptvZs2aNezZs4fFixfzwAMP0N/f33T/H//4x7z3ve/lyJEjLF68mEcffZSrr76aL3/5yyQSCQB27drFVVddxSWXXILrjoulj3/843NadNtmOataKkxDTDDDDb4vMR2BWyeGDSFI2CbFciRJwm4uuP0gyuG2rcY+HMuIBTcwueBWTRZNlkV6rhROOA6vXFY+aVmYxtSCO5GKBLd7IgW3DCF/GAY2QtvAietXo9FoNBrNrDCvf6/etm0bb3/727nzzjv55S9/yY4dOwB417veNeExBw4c4Atf+ALbt2/noYce4oknnuCHP/whf/EXf9Gw71e/+lW+853vxLdzzjlnlkYyM1R7uqutJhUqkX2iUmmyiRiu9mSXgjqPd3lbKZRYpqjJ567uoyK4AUph7fHjlhJwrQkWTVYdUz8Ov3x+bUkLc5J1k0opxkYgkRQsWCRwEye46uPYfsgMQK/2cWs0Go1Gczowr0X3P//zP9PZ2clNN90EQDKZ5D3veQ//+q//ytDQUNNjfv/3f59NmzbF95csWcJll13G1q1bG/Z9/PHHeeihhzhw4MCsnP9MI8tWjGbe6NhfDVGlySn81Q0eb8b91Y5pRJUm6/uv84nXe7yr+3AtA6PJLHW1h7t+HIGUBFKRTdjYU/ijc2PguJHgTiRPsOAujUWm8/4XgJ04sX1rNBqNRqOZFea16H700UfZsGEDRpUA27RpE0EQ8MQTT7TUhu/7PPzww6xd2zgj+Z73vIc/+ZM/YenSpdx4443k8/kJ2/E8j5GRkZrbicaoWDHKFo2KYA0COb6g0TFbigWs7FsR3l6VGLZbjAV0LCMW3l4ga/uYQnDXjyPnBRT9kGzCigvrTESxoEBA38BJENxKRkVwetZECyg1Go1Go9GcFsxr0T00NERXV1fNtornenBwsKU23v/+93P48GHe9773xduy2Sw//OEPefrpp9m6dSu//vWv+Y//+A9uu+22Cdv52Mc+Rnt7e3xbsmTJMYzo+KjRohXxC3EsYNJucrlMklJSLbyDso96ujncjhXNigfh1NGDTdNWyjnefiDJOFNHA/q+wvMEvX2CTPYEC26IBHemD/rWNX650Wg0Go1Gc8oyr0W34zgUCoWabZXZaMdxpjz+E5/4BJ///Oe55557WLZsWbx94cKFXHzxxfH9NWvWcPPNN3P33XdP2Natt97K8PBwfNu1a9c0RzPz1NdabKi92EIsYFDlr5ZSNXi8W4kFlFUe76De483U8YYFP6QtYZN2rUmjAWWoyI1Bdy90dp8Ewevno1zu/rPASZ34/jUajUaj0cwa8zq9ZNmyZfz85z+v2bZ79+74scm44447+PCHP8y9997LZZddNmVfXV1d7N+/f8LHXdetSTo52VSL4YRtUCzH8CVtAxNaLnxTEcOWKeI4wVTsE48Ecas53EGo4nLxiSZ9NBPclSSVbNJuKakk2y7o6RUtxUbOKDKMMrn71kP74hPbt0aj0Wg0mllnXs90v/zlL+exxx5j+/bt8ba7776bFStWsHr1agDGxsb4zne+w5EjR+J9/uZv/oY///M/59vf/jYve9nLGtptZk154IEH2Lhx4yyMYuapn30W1R5vP0RKRTGYnt2j2mpSDCRSqmkJbqPO4+0HYXSbRHD7gaTkSzqSdhyHOBG50SgasG+BwLROwiz36F5oWxDNcmtbiUaj0Wg0px3zeqb7la98JRdffDGvetWr+NM//VOee+45Pve5z/GNb3wj3mfHjh1cddVVfPe73+Xyyy/nrrvu4n3vex/vfe97kVLyne98B4C2tjYuvPBCAD75yU/y/PPPc8UVV5BIJPjGN77BQw89xL/927+dlHG2iijlYHQ/fiBxRBTJhz8uANPlfOxSIDGCQ6STBYygWQ53iAohYYJtmeBF2w0gLRV+YYwS4BQONfQBxDnchoKkZdT0kQDMIER6+fL9gzV9VAhDSdEL6Eo5JIomFOtOsuJSyR2kMJJHSEF/p4eblzDxetdavPJi18Gdxy6UvZGoCE6mDxaeo20lGo1Go9Gcpsxr0S2E4IEHHuBTn/oU99xzD+3t7Xzve9/jkksuifdpa2vjyiuvpKenB4CjR49y5ZVX8tvf/pbf/va38X7Lli2LRfdHP/pR7r33Xu677z5GRkY4++yz+cxnPsOiRYtO6Pimi6IsdiuC+xiEpB+EBCFYFcE97ZMYL3wzUSzgVIRSMlYMaEvaJOzJz8H3BX4oWNDvk041uNZnn6AU+dyXviQS3hqNRqPRaE5LhFKqcWWa5qQzMjJCe3s7w8PDZLPZWe/v4HPP89T3t5JZ0DOF3UOSye2i6HYTWOkae8lU/uqKbcUKciS8I4yll2AIY7y/afjEE8WDABQTfTX9SaUYyvukXYvOpI01ka1EKcLD2xmV/fQuTtM7cAw+7sIgDO8qW0KOwaklQ9i1FZZdBGdsiUq+n6KoIGDo7nsA6LjuNQhrXn+f12g0Gs08YTp6bV57ujV1iInLrsdi2I4yshOWUVN4plXBbZSrWRqGIGmP53iraS7MtC0zulUV0JFKMVTwSbsmHZMJbkBJxVjepqND0t13EhZOAozug971ka3kFBbcGo1Go9FopkZPR2libHPqSpNGxQxdXgBZKKeaRMe3mMNd9nAbApKOSaEUki+30XIOd9nDXenPDyW5kiRpmXQknUkXTiqlGB2FdDKgr19iTrHIclYoDIJpRzPcp4GPW1gWna9/3ck+DY1Go9Fo5ix6plsT0yA9p5p9FqLGc91sZnmqHO76C7DZBTnVLLplCkrl8vKd6amTSvI5sG3o6yli25PuOjsExWgB5cDZ0NZ/Ek5Ao9FoNBrNiUaLbk1MUL2OsEW7R6XSZLXVpMKUhW+Agh916prG+P2qNlqxrYwUAhxT0JV2cMzJL2mvqFAS+hYIkolw0n1nBSUjW0nXKuhedeL712g0Go1Gc1LQolsTI2XkzW5FcPtVYti1jPEc77LwbqXSZGXfpGNiWUac410R3lMKbhkJbsuAzpQTJa5MQuArikVBT78g236SsrBHD0CqBwbO0j5ujUaj0WjmEdrTrYkxDBEVnilPAE9c2l1SCiW2UyWGm3i8JxPcXqCQdX1UCugUSiG5qXziUpEvBZgp6Ew7U0YDylAxNgbdPZUS7ychtMcbi2a6F2wAN3Pi+9doNBqNRnPS0DPdmphqZ4ZpiOaWklAShAqnmRguV42s4DTL+i5XpFSVJJR6j7cQNeXanQl84vlSgCCylCSnENzVJd57+8UxZX8fNzKA3KGozHt2bue1azQajUajmXm06NbE+GE0+2sagrBiNamiFEj8QGGZAnsCf3WxXNrdEIKiX+vxrthWQqVwbYNm2rcUSEKpYuFd7/GWSpErBgggm7RIOlNbNMZGIZkW9J+sEu8AI3uhfRH0rdNl3jUajUajmYdo0a0ZR0V2j4Rdm38NVQsaLYHVZLFivYe73uNdn/XdLBe72sOdsM0Gj7dUipwXIBVkE1NXmwQo5BWmCX0DAsc9SWI3fxSsBCzYCJZ7cs5Bo9FoNBrNSUV7ujUx1ZaS6vxrP4yEt20aNJtYnmjRZL3HG8oebr+FHG4aPd5SKqSCrrRDomQ2RhzWt1lSBL5gwWJBOnOSBHfgRfGAS7ZAuufknINGo9FoNJqTjp7p1sTUT2DXe7ZbKnxTPYMtBAl7/Bi3HC1Yz2QpJYYQuKaBlIpAKjrTDinXmFJwh4GikBN090G2Y4qdZwulIltJ98roptFoNBqNZt6iRbdmQpp5uqtpJRaw6I8f44Wy1uNNazncxTCkFEg6UzYpx0BMIbmVjBZOdnRBd+9JKvEOMLof0t3Qr+MBNRqNRqOZ72h7iaYp9WK4ch8gSWuCu7Z8PLHVJIXCIEpC8cOpy8cHIXRlHNIJq+lMeT25HKTbBL0DJympBMAbLccDboRE9uScg0aj0Wg0mjmDFt2acRSgogxuP1DYlog83ErGXm7fl5iBpFQKMBwVCW5UTcIIEC2ghCgWsJyJnbQFhZKkWApwAonvS2zbiPuoRioo+CFhKMk4FinHwITxfird1fXr+1HFye4esK3Gx8fHqsb/1vXdMtVtUNVG6MPYQVh0ro4H1Gg0Go1GA2jRranCKA6ijo4iQkXKbEwpSQJmIJFSYRWOkAgGodC88I1VjgWst3ZkypYTCSRyu5rOcCulIkGOImkapEITuzSBE2pwe3XX5MdsOjs8MvkiFFoY9Mju6HY8HPxt9clHedxLL9TxgBqNRqPRaGK06NbE+GaKgtOGYxoYlkH9/K9UilIpwCocQVpJPLetNq+7XPgmtKNYQCVEQ91HP5BINYoRFCi5XRhOrWUkiggMEXZ5EaVr4TSLBswdjP6m++JNhbzA7VB0nxEinCkGq1Qktp02SHZM9dQ0pzAEpVHILh4X17lDkaVkyQvBtI+tXY1Go9FoNKcdWnRrYkLDwUpmsSyjQSzHHm5HkQgG8dw2PDOFNMp+7IqH24o83KKJ4C4FEt+UuAlwPY8xJ0MeEVemHO8j2t91LFzXbD5bXBor7xSVUw9DRSCieECnvYXZZSUj0Z3sgGTndJ6muvMYjdoQRlTm3U7B4vOOXchrNBqNRqM5LdGiWxNjGKK1WMBCVJFSGlEBHQXlDO3yoskpYgFtYUCpnMHtR227poEXSgwRZZPYtiA9QVvNyI1CWztk24/zSThWQj+afV9wDrQvOUknodFoNBqNZq6iIwM1MU30dqPgrsKxDCzTIAhly4K7XtRX2vTKySiWITANQcax41LwU+EVFaYVxQOetLSS0b3QcQb0rdc+bo1Go9FoNA1o0a2JmdBSMkksoJTjRwVhY1LIlDnc1f+WCj+UpFwT22xNuEqpKBQiwZ1MnSSxWxgEK1ku8z6VmVyj0Wg0Gs18RItuTYwfjkf/TTeH2zYjq0l1AZ2pC98w3odjEipIOWZNFcupyI9Bpk3Q0XmSBLcMoDgM/WdCquvknINGo9FoNJo5jxbdmnFUVMAmlC1UmgxqLSWOZdQI76kEtyqnlFT68ANJNmmRdq0pK05W8H2BUtEst2mdJNGdH4xsJd2rTk7/Go1Go9FoTgn0QkpNjGlECSJFfxLBDXiBimIB6zzcFXFdqVw5WaXJwJcYRJ7uQCoMIUg7Vss+bqUgXzDpPEOQzhzDYGcCbxQyfVGZdx0PqNFoNBqNZhL0TLemdcqWE6VUHPM3XeIc7rKlRBJ5wafj4wYoegaOI+nuEQ0FeE4IMoxsJcsugkzvie9fo9FoNBrNKYUW3ZqYsDzjnLDNsjiWNaXOC+VKkq5tTB0L2MTjXfGJm0Lglu0gni+n7eOWUlEqGXR1BjjuyUor2QddK6B3zcnpX6PRaDQazSmFtpdoxhHElpKkY1IohRR8ScI2otLt5RnuCQvfNPFwV6wmlilin3jCMsAXeH6Ia1tRMZ0WfdxQXjyZDmnPBjMx6unjjUbFcBaeA5Z7cs5Bo9FoNBrNKYWe6dbE2KaIPdyVRBGpFPlSWLVosvG4iQR39eLK+oWZoZQIY3o+bgDfVygFXV0+ZpPq8LOODCF/GHrXQdvASTgBjUaj0Wg0pyLzXnQPDw9z6623cumll/J7v/d7fPvb356RY46l3ZNNvfStvziaXSxTpZRYVT5tw4hEvVKKIISUPT0ft1KKfA7auwTplJz6gNlgbD9k+rWtRKPRaDQazbSY16I7DEOuuOIKvv/973PLLbdw0UUX8drXvpZ/+qd/Oq5jjqXdOUfF0w24ZnSZVO5XmDqHezx6sFK5shIn6FiRd3w6eEVwHOjqESen6GNpLKogNHA22MmTcAIajUaj0WhOVea1p/uee+7hkUce4Xe/+x2LFi0CYN++fdx2223ccMMNTVMxWjnmWNqdU9QVvjGEwCh7sgslSUYp/EDim60J7oqlRBAVw0kACWt66SdSKrwi9C8SuK6A/MwNt7UTCGHsYFR1sm3BCe5co9FoNBrNqc68Ft0PPvggmzdvjoUxwLXXXssnP/lJnnnmGdauXXtMxxxLu3OCsITwRikGEqUUKdvE8CNhbAIpFMVSUF5UOYqbAFsYUKptppL1bVUtmgRwlMIPAjKmF9lOvLGmOeDNyI8K0klJR0JCAfBGogcKg8c21koqS2Gotf1H90H7Yuhd2/I5azQajUaj0VSY16J7586dLFy4sGZb5f7OnTubiuNWjjmWdj3Pw/O8+P7w8DAAIyMj0x3WMZHP5QgLI+SPjKIAxzRQAsK6/UQo8SXg+9jBWMPjUkEplAjAtoyax/1Q4poGhm3iCQGlvS2dWxAIPM+iOzFG6bBf+2DxuWmNs4GiByOHJ9/HL4BpEyy+BFXwoeBPvr9Go9FoNJp5QUWnKVWf69bIvBbdvu/T1tZWsy2ZTMaPHesxx9Luxz72MW6//faG7UuWLJlqGJoTxvtP9gloNBqNRqOZg4yOjtLe3j7pPvNadHd1dXH06NGabUeOHAGgu7v7mI85lnZvvfVW3vve98b3pZQcPXqU7u7uE+IBHxkZYcmSJezatYtsNjvr/Z1K6OemOfp5mRj93DRHPy8To5+b5ujnpTn6eZmYE/3cKKUYHR1tcDg0Y16L7s2bN3PHHXcQBAGWFT0VW7duxbZtXvCCFxzzMcfSruu6uG5toZWOjo6ZGOa0yGaz+j/wBOjnpjn6eZkY/dw0Rz8vE6Ofm+bo56U5+nmZmBP53Ew1w11hXkcG3nDDDeRyOT7zmc8A0bejO++8k9e97nWxPWTbtm1ccMEFPPzwwy0f08o+Go1Go9FoNJr5w7wW3UuWLOFrX/sat99+OytWrGDRokX09vby6U9/Ot4nn8+zdevWeGFjK8e0so9Go9FoNBqNZv4wr+0lAK95zWu4+uqreeqpp2hvb2f58uU1j69cuZKf//znnHnmmS0f0+o+cwnXdfnQhz7UYHHR6OdmIvTzMjH6uWmOfl4mRj83zdHPS3P08zIxc/m5EaqVjBONRqPRaDQajUZzzMxre4lGo9FoNBqNRnMi0KJbo9FoNBqNRqOZZbTo1mg0Go1Go9FoZpl5v5ByPiGl5P/+3//LF7/4Rfbs2cMDDzxAV1fXlMfdfffdfOUrX2F0dJQLL7yQP/uzP2uIPmxln7nMnj17+NjHPsbjjz/OwMAA73rXu3jJS14y4f4f/OAH+e53v9uwvbe3l/vuuw+An/70p7zvfe9r2Odb3/oWCxYsmLmTn2X+8R//kW9+85t4nsfll1/OLbfcMukCleuvv54dO3bUbPv93//9hudiuu3ONZ577jk+/vGP8+yzz7J06VLe+973cs4550y4v+d5/OM//iPf/e53GRsb45xzzuGP//iPGRgYiPe59957+cu//MuGY3/84x/jOM5sDOOYkVLyuc99jnvvvRelFNdccw3vfOc749oEx3rMsbQ71/j1r3/NJz/5SXbs2MGqVat4//vfz5o1aybcP5fL8cUvfpEf/vCHeJ7H+eefz3ve856a9+evfvWrfPazn605LpVK8f3vf3/WxjHTlEol7rzzTv7jP/4Dx3G47rrruOmmmyYsABeGIRdeeGHD9ve///28+tWvPuZ25yI/+9nP+PSnP82+ffs488wzue2221i8ePGE+7/85S9naGioYfs111zDBz/4QQDuvPNOvv71r9c8vmTJEr75zW/O6LnPJkNDQ3zlK1/h7rvvZuPGjXzqU5+a8phcLsdf/dVf8dBDD5HJZLjxxht53eteN+19ZoNT511Mc9y8/vWvZ3R0lI0bN3LPPfdQKpWmPOYLX/gC7373u/mrv/orli5dyoc//GF+9KMf8aMf/Sh+Q2tln7nM8PAwL37xizn77LP5sz/7Mx566CEuu+wyHnzwQS666KKmx7z5zW/mmmuuie8rpXjFK17BunXr4m2Dg4P86le/4kc/+lHNsa180ZkrfOQjH+GOO+7gb/7mb2hvb+cDH/gAjzzyCP/yL/8y4TGPPvoor3jFK3jta18bb6sWlsfa7lxi9+7dXHDBBVx55ZV84AMf4N577+UlL3kJW7dunbAA1ite8QpWr17N9ddfTyaT4c477+Tcc8/ll7/8Jf39/QAcPHiQnTt38q1vfavmWNu2Z31M0+WWW27h61//Op/85CcxTZP3vve9PPnkk3z+858/rmOOpd25xJNPPsmFF17IDTfcwAc+8AG+9rWv8aIXvYhf/epXnHHGGU2PueSSS3jJS17Cm9/8ZizL4uMf/zhf//rXefjhh+PiHnv27GF4eJgvfelL8XGmaZ6QMc0Ub3zjG3nkkUf4+Mc/zujoKLfccgu7du3iwx/+cNP9lVJs3bqVz372s2zevDnevmLFiuNqd67xs5/9jEsuuYRbbrmFN77xjXzuc5/jxS9+MY899hidnZ1Nj/mLv/gLfN+P7z/33HO88Y1v5F3vele8bceOHViWxR133BFvSyQSszeQGebIkSNs2LCB17zmNdi2zRNPPDHlMZXP4sHBQT784Q+ze/du3vSmN3H06FH+63/9ry3vM2sozbxhaGhIKaXUd7/7XQWoffv2Tbp/EASqr69PfehDH4q3PfvsswpQ//Zv/9byPnOdv/zLv1Td3d2qWCzG26699lp1ySWXtNzG9773PQWon/zkJ/G2++67T7muO6PneiIZGRlRyWRSfe5zn4u3PfTQQwpQjzzyyITHrV27Vn3605+e8XbnEn/0R3+k1qxZo8IwjLddcMEF6vrrr5/wmNHR0Zr7hUJBtbW1qf/5P/9nvO0LX/iCWrp06Yyf70yze/duZRiG+uY3vxlv+9a3vqWEEGrbtm3HfMyxtDvXeMMb3qC2bNkS3w/DUK1Zs0a9613vmvCY+mvj6NGjyjAM9bWvfS3e9rGPfUyde+65M3/CJ4hf/vKXClAPPfRQvO1zn/uccl03/myqx/d9Bagf/OAHM9ruXOOyyy5T1157bXy/WCyq7u5u9dGPfrTlNj7wgQ+ozs5OVSgU4m3vec971DXXXDOTp3pC8X1f5XI5pZRSb37zm9VLX/rSKY954IEHFKCeffbZeNuHPvQh1dPTo3zfb3mf2UJ7uucRrZYprfCb3/yGgwcP8spXvjLetmrVKs4880y+973vtbzPXOfBBx/kZS97WY214dprr+Whhx7C87yW2rjrrrtYv359w0+hQRBwzTXXcOWVV/K+972PPXv2zOi5zyY//elPKRQKNa/thRdeSHd395Sv7V133cXFF1/MDTfcwN133z1j7c4VHnzwQa6++moMY/wt9FWvelVTy1GFTCZTc99xHBzHafjF6ciRI7z85S/n6quv5rbbbuPIkSMze/IzwA9+8IPY+lHhqquuwrKsCe0OrRxzLO3ONR588MGaa9swDF7xildMem3XXxuJRALLshqujR07dvCyl72MV7ziFdx+++2Mjo7O7MnPIg8++CCdnZ0175HXXnstnufxk5/8ZNJjP/jBD3LppZdy00038bOf/WzG2p0L+L7PQw89VHPNuK7LlVde2fL7YRiGfPnLX+bGG29smMl+9NFHueyyy/i93/s9/vqv/5pisTij5z+bWJZFKpWa1jEPPvgg69atY9WqVfG2a6+9lsOHD/PYY4+1vM9soUW3ZkJ27twJwMKFC2u2L1y4MH6slX3mOjt37mx6/mEYtiSSBwcH+da3vsX/9//9fzXbhRBcd911vP3tb+fmm2/mySef5Mwzz+T555+f0fOfLXbu3IkQosYaIoRgwYIFk762Z5xxBm9961v54Ac/yNlnn81b3vIWPvCBDxx3u3OJia6ZI0eOkM/nW2rjf/2v/8Xw8HCNwDQMg9e//vW8613v4m1vexs//vGPOeusszh06NCMnv/xsnPnTjo7O0kmk/E213Xp7u6e8DVs5ZhjaXcu4XkeBw4cOO73wzvuuAPLsnjZy14Wb7Msiz/4gz/gve99LzfeeCPf/va32bx5M2NjYzN2/rPJzp07WbBgQY3lcGBgANM0J31uzjnnHG666SZuvfVWurq6uPjii/nKV75y3O3OFfbu3Yvv+8d1zTzwwAPs27ev4TMokUhw44038v73v5/XvOY1fPGLX+Siiy6qsaWcbkz03lx5rNV9Zgvt6T6Fefvb387jjz8+4eOWZR3XN/3Kf8z6xW3JZDJ+rJV9TjQ/+MEPuPXWWyfd55ZbbuH1r389EI2h2flXHpuKr33tayileNOb3lSz/fLLL68RVK94xSvYvHkzH/rQh/inf/qnlsYy07z61a9m3759Ez7e19fHvffeC0RjtyyrwTc61Wt73333xc/n5ZdfzsDAAG9729t497vfzaJFi4653dnkX/7lX2p8j8346Ec/yuWXXw4c/zXzgx/8gFtuuYW/+qu/Yv369fH2G264gZtuuim+f9VVV7Fu3To+8YlPTHl+J5Jm44fJX8NWjjmWducSM/F++K//+q/cfvvt/MM//AOLFi2Kt7/73e+uafeKK65g1apVfOYzn6n5UjtXafbaCiFwHGfC58ayLLZu3RovIr7iiisAeO973xu/3x5Lu3OJmbhm7rrrLrZs2cLZZ59ds/3222+vaffiiy9mzZo1fO1rX+Mtb3nL8Z34HKWV9+bjff8+HrToPoW55ZZbJv158XgXMVYW/B09erRm8d+RI0dYvXp1y/ucaDZu3Midd9456T7Lli2L/93V1cXRo0drHq/8pN/d3T1lf3fddRevfvWrG/at/09tmiaXXnopDz744JRtzhZ//ud/Pqllpvqcu7q68H2fsbGxmp+/jxw5MunzUj/uyy+/HCkljz/+OIsWLTrmdmeTiy66aNKkAKDmep7omrFte8rUnspPybfeeiu33HJLzWP1z10qlYoXVM0lmo0fJn8NWznmWNqdS6TTaVzXbXpttHL+999/P9dffz133nlnw5f4+mujq6uLzZs3z7lrYyKavbaFQoFCoTDpc1Of2nP55ZfzyU9+kj179sTvJ8fS7lyh+jO0mlavmQMHDnD//fc3XWhcf80sXbqU1atXnzLXzLHQ1dXVsOCy/vO8lX1mCy26T2EmSkmYKTZu3Ihpmjz88MOx96lYLPLrX/86jtZpZZ8TTVdXFxdccEHL+2/evJmHH364ZtvWrVtZvHgxPT09kx77i1/8gkcffZRPfvKTLfW1f/9+0ul0y+c202zatKnlfStpAQ8//DCXXnopEKVr7NixY1rt7N+/HyAe90y1O5P09/fHCSKtMNE1s3Hjxhqfdz0//elPufrqq3nf+97Hhz70oZb62r9/Px0dHS2f24lg8+bNeJ7Hb37zm3h27emnn2ZkZGTC17CVY46l3bmEEIJzzjmn6bUx1fk/8MADXHfddfz1X/91TQLFZOzfv79mAmEus3nzZj7xiU9w4MCB+P/a1q1bgem9L+3fvx8hROz1nal2TxZdXV0sXbqUhx9+uCbxqZVrBqLo1WQyyfXXXz/lvlJKDh48eFI/g2abzZs3841vfINisRj727du3YphGGzYsKHlfWaNWV2mqZmTTJZe8od/+Ifq1ltvje+/5jWvUZs3b1ZjY2NKKaU++tGPqnQ6XXNsK/vMZX72s58pIYS69957lVJK7dy5U/X399cksvzoRz9SW7ZsUXv37q059r/9t/+mVq5cqaSUDe3+3d/9ndqzZ098//7771eWZam//du/nZVxzAYvfvGL1eWXX65KpZJSSql3vetdqr+/P36tlVLqta99rbrjjjuUUkr94he/UPfcc0/82JEjR9Qll1yili9fHrfRartzmXvuuUc5jqN++tOfKqWU+s1vfqMymUxNIss999yjtmzZojzPU0pF11lbW5v68z//8wnb/Zu/+Rt15MiR+P5XvvIVBaj//b//9yyN5NgIw1CtX79e/f7v/74Kw1BJKdUb3vAGtXLlyprV/5dffrn6whe+0PIxrbY7l/n7v/97lclk1K9//WulVPS6O45Tk8jypS99SV166aXx/X//939XiURCfepTn5qw3U984hNqZGQkvv+3f/u3ClDf/e53Z2EUM08ul1MLFixQ73znO5VSUTLFy172spqkF8/z1JYtW+L3kPvvv1/97Gc/ix/ftm2bWrVqlbryyiun1e5c53/8j/+h+vr61I4dO5RSUfKVEKImkeWOO+5Qr33taxuOXbNmjfrDP/zDpu1+5CMfidNMgiBQt956qzJNUz366KOzMIrZZaL0kr1796otW7aoH/3oR0oppQ4cOKDa2trURz7yEaVUdH2ce+65NekwrewzW2jRPY/47Gc/q7Zs2aLWr1+vALV582a1ZcuWmjftCy+8UF133XXx/YMHD6oLL7xQtbW1qWXLlqmuri5133331bTbyj5znU996lMqmUyq1atXq0Qioa6//vpYLCkVxZYBavv27fG2fD6vOjo61Mc+9rGmbX77299Wq1atUqtWrVLLli1TmUxGfeQjH2kq0Ocq27dvVxs2bFBdXV1q0aJFauHChTWxiEoptXLlyvgD7+DBg+oNb3iD6urqUmeddZZKpVLq0ksvVU8//fS0253rfPCDH1Su66o1a9Yox3HUzTffXPPafu5zn1NA/KG3evVq5TiO2rJlS83tE5/4RHzMl7/8ZbVkyRK1du1atXjxYtXV1aU+85nPnPCxtcLjjz+uVq9erfr6+lR/f79asWJFw4d5e3t7zZfXVo5pZZ+5jJRSvfvd71aO46g1a9Yo13XVbbfdVrPPRz7yEZVOp+P7nZ2dKpVKNVwbn//85+N9Pv3pT6uBgQG1fv16NTAwoAYGBtRXvvKVEzaumeBnP/uZWrx4sVq0aFH8HvH888/HjxcKBQXEX16feuopddlll6mBgQF15plnKsdx1B/8wR+oQ4cOTavduU6pVFI33HCDcl03/gyqn5x55zvfqVauXFmz7cc//rEC1MMPP9y03dtvv1319PSoF7zgBaqnp0ctW7ZMffvb356tYcwKr3zlK9WWLVtUT0+PymazasuWLerCCy+MH9++fbsC1Le+9a142wMPPKB6enrU0qVLVTabVS960YvUgQMHatptZZ/ZQCil1OzOpWvmCrt27WqaxrF69erYx/TEE09g23aDH3vbtm2Mjo6ybt26CasGtrLPXGZ0dJTnnnuO/v7+hpXNg4ODPP3002zatCkeWy6X4ze/+Q1nnXVWQ+RXNdu2bSMMQ5YtWzYni5y0wjPPPIPneaxfv76hMuCjjz5KR0dHzc/co6Oj7Nixg8WLF09Y3GGqdk8FBgcH43H29vbWPHbw4EG2bdvGli1bEELwq1/9qqmfvr+/n+XLl8f3pZQ899xzWJbF0qVL53QBFKUUTz75JEop1q9f32CteeSRRxgYGKjxy091TKv7zHUOHTrE7t27Wbp0aUNBrD179rB3717OP/98ILJahWHY0MbixYtrnrswDHn22WdJJpMsWbLklHxegiDgySefxHEc1q5dW/OYKhfDWbFiBX19ffH2w4cPs3//fpYvXz6hNWKydk8V9u3bx/79+1m5cmVcFKnCjh07GBoaqql6u3fvXnbv3s0LX/jCCdv0fZ9nn32W9vZ2Fi5ceEoUrKum2fumEIItW7YAUWLQr371K9auXVvzWVMqlXjyySfJZDKsXLmyadut7DPTaNGt0Wg0Go1Go9HMMqfe12SNRqPRaDQajeYUQ4tujUaj0Wg0Go1mltGiW6PRaDQajUajmWW06NZoNBqNRqPRaGYZLbo1Go1Go9FoNJpZRotujUaj0Wg0Go1mltGiW6PRaDQajUajmWW06NZoNBqNRqPRaGYZLbo1Go1Go9FoNJpZRotujUaj0cwaIyMjfP3rX2f37t012++//362bt16ks5Ko9FoTjxadGs0Go1m1shms3zzm9/kuuuuIwgCAL7xjW9w3XXXkUwmT/LZaTQazYlDKKXUyT4JjUaj0Zy+HD16lA0bNvCWt7yFd7zjHWzYsIEPfehD/PEf//HJPjWNRqM5YWjRrdFoNJpZ58EHH+TlL385a9euZdGiRXznO99BCHGyT0uj0WhOGFp0azQajeaE8NKXvpTvf//7PPHEE6xfv/5kn45Go9GcULSnW6PRaDSzzv33389DDz3E6tWruf3220/26Wg0Gs0JR4tujUaj0cwqBw4c4KabbuL222/nX//1X7n33nv56le/erJPS6PRaE4o2l6i0Wg0mlnlmmuuYXR0lB/+8IcYhsFnP/tZbrvtNh577DGWLVt2sk9Po9FoTgh6pluj0Wg0s8Yvf/lLstksX/3qVzGM6CPnne98J+94xzu49957T/LZaTQazYlDz3RrNBqNRqPRaDSzjJ7p1mg0Go1Go9FoZhktujUajUaj0Wg0mllGi26NRqPRaDQajWaW0aJbo9FoNBqNRqOZZbTo1mg0Go1Go9FoZhktujUajUaj0Wg0mllGi26NRqPRaDQajWaW0aJbo9FoNBqNRqOZZbTo1mg0Go1Go9FoZhktujUajUaj0Wg0mllGi26NRqPRaDQajWaW0aJbo9FoNBqNRqOZZf5/+FHDaeium6AAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(8, 4))\n", - "plt.plot(truth.x, truth.y, \"k:\", label=\"truth\")\n", - "\n", - "for synthetic_observation in observations:\n", - " plt.errorbar(\n", - " synthetic_observation.x,\n", - " synthetic_observation.y,\n", - " synthetic_observation.y_stat_err,\n", - " linestyle=\"none\",\n", - " marker=\".\",\n", - " alpha=0.4,\n", - " # label=f\"renormalization = {synthetic_observation.renormalization:1.3f}\",\n", - " )\n", - "\n", - "hatches = [\"//\\\\//\\\\\", \"|-\", \"\", \"\"]\n", - "alphas = [0.1, 0.25, 0.25, 0.25]\n", - "for i, (key, walker) in enumerate(walkers.items()):\n", - " intervals = predictive_post(\n", - " walker.model_sampler.chain[:, : correct_model.n_params],\n", - " correct_model,\n", - " truth,\n", - " 1000,\n", - " [16, 84],\n", - " )\n", - " plt.fill_between(\n", - " truth.x,\n", - " intervals[0],\n", - " intervals[1],\n", - " alpha=alphas[i],\n", - " color=colors[key],\n", - " label=key,\n", - " hatch=hatches[i],\n", - " zorder=99,\n", - " )\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.ylim([0, 2])\n", - "fig.legend(loc=\"upper left\", framealpha=1)" - ] - }, - { - "cell_type": "markdown", - "id": "gal00", - "metadata": {}, - "source": [ - "# Covariance-structure gallery\n", - "\n", - "The sections above inferred unknown **normalisations** — a single rank-one\n", - "systematic per dataset. But a `Constraint`'s covariance is assembled from\n", - "arbitrary `Term`s, so the *same* machinery expresses far richer uncertainty\n", - "structure. This gallery surveys four cases the covariance API handles, each just a\n", - "different `Term` (or `support`) on the stacked residual:\n", - "\n", - "1. a **systematic correlated across $x$** (smoothly, not a flat normalisation);\n", - "2. **correlated statistical errors** — and why ignoring them is overconfident;\n", - "3. **unknown / misreported magnitudes** (the free-$\\gamma$ model error above);\n", - "4. a systematic **shared across datasets** (cross-block coupling).\n", - "\n", - "A small helper normalises a covariance to a correlation matrix for plotting." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "gal01", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:48.196610Z", - "iopub.status.busy": "2026-08-11T03:08:48.196446Z", - "iopub.status.idle": "2026-08-11T03:08:48.297327Z", - "shell.execute_reply": "2026-08-11T03:08:48.296552Z" - } - }, - "outputs": [], - "source": [ - "from sklearn.gaussian_process.kernels import RBF, ConstantKernel\n", - "\n", - "\n", - "def correlation(Sigma):\n", - " d = np.sqrt(np.diag(Sigma))\n", - " return Sigma / np.outer(d, d)" - ] - }, - { - "cell_type": "markdown", - "id": "gal02", - "metadata": {}, - "source": [ - "## 1. A systematic correlated across $x$\n", - "\n", - "A flat `normalization_term` is a *rank-one* mode: every point is **100 %**\n", - "correlated (correlation matrix all ones off-diagonal). A systematic that varies\n", - "smoothly with $x$ — e.g. an energy-dependent efficiency — instead has correlation\n", - "that **decays** with separation. That is a `kernel_term` (a GP prior on the\n", - "systematic). Same API, richer structure." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "gal03", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:48.299491Z", - "iopub.status.busy": "2026-08-11T03:08:48.299204Z", - "iopub.status.idle": "2026-08-11T03:08:48.645254Z", - "shell.execute_reply": "2026-08-11T03:08:48.644419Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "o0 = observations[0]\n", - "xspan = o0.x.max() - o0.x.min()\n", - "\n", - "# (a) flat normalization: rank-one, uniform correlation\n", - "c_flat = rxmc.constraint.Constraint(\n", - " [o0],\n", - " correct_model,\n", - " extra_terms=[rxmc.covariance.normalization_term(magnitude=0.05)],\n", - ")\n", - "# (b) smooth correlated systematic: a GP (kernel_term) over x\n", - "sys_kernel = ConstantKernel(0.05**2) * RBF(length_scale=xspan / 4)\n", - "c_smooth = rxmc.constraint.Constraint(\n", - " [o0],\n", - " correct_model,\n", - " extra_terms=[rxmc.covariance.kernel_term(sys_kernel)],\n", - ")\n", - "\n", - "S_flat = c_flat.covariance_matrix(true_params)\n", - "S_smooth = c_smooth.covariance_matrix(true_params, tuple(sys_kernel.theta))\n", - "\n", - "fig, ax = plt.subplots(1, 2, figsize=(9, 4))\n", - "for a, S, t in [\n", - " (ax[0], S_flat, \"flat normalization (rank-one)\"),\n", - " (ax[1], S_smooth, \"smooth systematic (kernel_term)\"),\n", - "]:\n", - " im = a.imshow(correlation(S), vmin=-1, vmax=1, cmap=\"RdBu_r\")\n", - " a.set_title(t)\n", - " fig.colorbar(im, ax=a, fraction=0.046)\n", - "fig.suptitle(\"correlation of one dataset under two systematic structures\");" - ] - }, - { - "cell_type": "markdown", - "id": "gal04", - "metadata": {}, - "source": [ - "## 2. Correlated statistical errors\n", - "\n", - "Statistical errors are usually taken as independent (a diagonal covariance). When\n", - "they are **not** — shared backgrounds, unfolding, detector resolution — ignoring\n", - "the correlation makes the posterior **overconfident**. We draw line data with\n", - "exponentially-correlated noise and fit it two ways: a naive diagonal, and the\n", - "correct correlated covariance supplied as a fixed `Term`." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "gal05", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:48.647141Z", - "iopub.status.busy": "2026-08-11T03:08:48.646980Z", - "iopub.status.idle": "2026-08-11T03:08:54.150726Z", - "shell.execute_reply": "2026-08-11T03:08:54.150004Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "naive diagonal a0 = 1.101 ± 0.062 a1 = 0.758 ± 0.027\n", - "correlated a0 = 1.048 ± 0.129 a1 = 0.779 ± 0.048\n" - ] - } - ], - "source": [ - "line = rxmc.physical_model.Polynomial(1) # a0 + a1 x\n", - "a_true = [1.0, 0.8]\n", - "xs = np.linspace(0.0, 4.0, 20)\n", - "ell, sig = 1.2, 0.15\n", - "R = np.exp(-np.abs(xs[:, None] - xs[None, :]) / ell) # exponential correlation\n", - "C = sig**2 * R\n", - "y_corr = line(rxmc.observation.Observation(x=xs, y=np.zeros_like(xs)), *a_true)\n", - "y_corr = y_corr + rng.multivariate_normal(np.zeros(xs.size), C)\n", - "obs_corr = rxmc.observation.Observation(x=xs, y=y_corr)\n", - "\n", - "c_naive = rxmc.constraint.Constraint(\n", - " [obs_corr],\n", - " line,\n", - " extra_terms=[rxmc.covariance.Term(sig * np.ones(xs.size), kind=\"diag\")],\n", - ")\n", - "c_correlated = rxmc.constraint.Constraint(\n", - " [obs_corr],\n", - " line,\n", - " extra_terms=[rxmc.covariance.Term(C)],\n", - ")\n", - "\n", - "\n", - "def fit_line(constraint, seed=7):\n", - " evidence = rxmc.evidence.Evidence([constraint])\n", - " prior = stats.multivariate_normal(mean=a_true, cov=np.diag([0.5, 0.5]) ** 2)\n", - " sampler = rxmc.param_sampling.BatchedAdaptiveMetropolisSampler(\n", - " params=line.params,\n", - " starting_location=prior.mean,\n", - " prior=prior,\n", - " initial_proposal_cov=prior.cov / 100,\n", - " )\n", - " walker = rxmc.walker.Walker(sampler, evidence, rng=np.random.default_rng(seed))\n", - " walker.walk(n_steps=6000, burnin=2000, batch_size=1000, verbose=False)\n", - " return walker.model_sampler.chain\n", - "\n", - "\n", - "chain_naive = fit_line(c_naive)\n", - "chain_correlated = fit_line(c_correlated)\n", - "for name, ch in [(\"naive diagonal\", chain_naive), (\"correlated\", chain_correlated)]:\n", - " print(\n", - " f\"{name:14s} a0 = {ch[:,0].mean():.3f} ± {ch[:,0].std():.3f} \"\n", - " f\"a1 = {ch[:,1].mean():.3f} ± {ch[:,1].std():.3f}\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "gal06", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:54.152422Z", - "iopub.status.busy": "2026-08-11T03:08:54.152265Z", - "iopub.status.idle": "2026-08-11T03:08:54.370278Z", - "shell.execute_reply": "2026-08-11T03:08:54.369516Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = corner.corner(\n", - " chain_naive, labels=[\"a0\", \"a1\"], color=\"tab:red\", truths=a_true, truth_color=\"k\"\n", - ")\n", - "corner.corner(chain_correlated, fig=fig, color=\"tab:blue\")\n", - "plt.plot([], [], color=\"tab:red\", label=\"naive diagonal (overconfident)\")\n", - "plt.plot([], [], color=\"tab:blue\", label=\"correlated fixed Term\")\n", - "fig.legend(loc=\"upper right\");" - ] - }, - { - "cell_type": "markdown", - "id": "gal07", - "metadata": {}, - "source": [ - "The naive-diagonal posterior is visibly **tighter** than the correct one:\n", - "treating correlated noise as independent over-counts the information. The\n", - "a fixed `Term` with the true correlation restores honest uncertainty." - ] - }, - { - "cell_type": "markdown", - "id": "gal08", - "metadata": {}, - "source": [ - "## 3. Unknown / misreported magnitudes\n", - "\n", - "When the *size* of an uncertainty is itself unknown, it becomes a sampled\n", - "`Parameter`. The `unreported_sys_err_with_unknown_model_err` scenario above does\n", - "exactly this with a free $\\gamma$ (`model_error_term`, a diagonal inflation). Here\n", - "we just visualise how the covariance diagonal grows as $\\gamma$ increases —\n", - "inference slides along this family to whatever the data support." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "gal09", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:54.372438Z", - "iopub.status.busy": "2026-08-11T03:08:54.372273Z", - "iopub.status.idle": "2026-08-11T03:08:54.586555Z", - "shell.execute_reply": "2026-08-11T03:08:54.585914Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<>:12: SyntaxWarning: invalid escape sequence '\\g'\n", - "<>:17: SyntaxWarning: invalid escape sequence '\\g'\n", - "<>:12: SyntaxWarning: invalid escape sequence '\\g'\n", - "<>:17: SyntaxWarning: invalid escape sequence '\\g'\n", - "/tmp/ipykernel_402344/1563835932.py:12: SyntaxWarning: invalid escape sequence '\\g'\n", - " plt.plot(o.x, np.sqrt(np.diag(S)), marker=\".\", label=f\"$\\gamma$ = {g:.2f}\")\n", - "/tmp/ipykernel_402344/1563835932.py:17: SyntaxWarning: invalid escape sequence '\\g'\n", - " plt.title(\"unknown model error inflates the diagonal by a sampled $\\gamma$\");\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "o = observations[0]\n", - "gamma = rxmc.params.Parameter(\"log gamma\", float)\n", - "c_me = rxmc.constraint.Constraint(\n", - " [o],\n", - " correct_model,\n", - " extra_terms=[rxmc.covariance.model_error_term(gamma, averaging=True)],\n", - ")\n", - "plt.figure(figsize=(7, 4))\n", - "for g in [0.02, 0.05, 0.10]:\n", - " S = c_me.covariance_matrix(true_params, (np.log(g),))\n", - " plt.plot(o.x, np.sqrt(np.diag(S)), marker=\".\", label=f\"$\\gamma$ = {g:.2f}\")\n", - "plt.plot(o.x, o.y_stat_err, \"k:\", label=\"reported stat. err\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"total std. dev.\")\n", - "plt.legend()\n", - "plt.title(\"unknown model error inflates the diagonal by a sampled $\\gamma$\");" - ] - }, - { - "cell_type": "markdown", - "id": "gal10", - "metadata": {}, - "source": [ - "## 4. A systematic shared across datasets\n", - "\n", - "If two datasets share a systematic (a common calibration), the coupling is a\n", - "single `Term` whose `support` spans **both** blocks — off-diagonal correlation\n", - "between datasets. Treating them independently throws that coupling away. (The\n", - "dedicated `correlated_observations.ipynb` notebook explores the inference\n", - "consequences; here we just show the structure.)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "gal11", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:54.588808Z", - "iopub.status.busy": "2026-08-11T03:08:54.588638Z", - "iopub.status.idle": "2026-08-11T03:08:54.933783Z", - "shell.execute_reply": "2026-08-11T03:08:54.933233Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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cXb+qJSUloWPHjti5c6ey0/qVK1ewa9cuPPPMM5qX17vvvovjx49j2bJlaNasGecuoLescuXKAUgPxetsm11B8+bN8fvvv2Pfvn0A0v3/e/Xqhdq1a3PpZPbaYsKECfDx8UGHDh3w999/gzGGBw8e4Ouvv5YOBPXkGzNmDOLj49GzZ0/cvn0bqampWLFiBT7//HN07doVlStXBpC+RigsLAyjR49GVFQUkpKSMHv2bNy7d89u/cePH8dvv/2GtLQ03LhxAz179oS3tzeGDh0qba+ea2b0PlNjtP8zY6ezNjRr1gypqalYs2YNt26jWbNmWLNmDVJSUrjjgP5raQsjz7wr7nM9Zei9xnqvgz17MnMvyXD2/td7HY3qQkREBHbu3ImiRYuiQYMGOHDggFvbTxBPLFm5ip14spk1axbLnz8/8/HxYXnz5mWjR49mW7du1UTsETl79ix76623WIECBVhAQADz8/NjtWrV4qLAZESamjBhgiZ/tWrVWJMmTQzbIiuTMcYuX77MevTowUJCQpivry8LCAhglStXZjNmzGAJCQlKug0bNrBy5coxk8nEgoKC2KBBg9iOHTt0RbJZuXIla9y4MQsMDGSBgYEsODiY9ezZk12+fFmTNjExkYWFhTEAbMmSJU6XNXToUBYQEMBy587NQkND2bp16wy1WQwtmcH333/PALDz589zx21Fwnn48CHr1q0b8/PzY35+fqxChQps69at7I033mChoaG67LUVzYax9FCXHTt2ZP7+/szPz4+Fh4ezIUOGsIcPH9q7DLrz7d27l9WrV49ZLBbm6+vLwsPD2UcffaSEE81gy5YtrFSpUsxsNrM8efKwMWPGsG3bttmNXnXy5EnWs2dPFhAQwACw6tWrs4MHD3Jl2muvnmtm5D4Tsdf/9u6D06dPMwDsxx9/NGynURsYSw8pGxgYyACwM2fOaOzInTu3JpIYY/qvpS30PvOuuM/1lqH3Guu9Dvbsycy9JOKK+1/vdXT0fNu6n2NiYljTpk1ZQEAA27x5s8vbTxBPOibGVI62BOEG4uLiEBAQACB9ej4qKgrBwcGK24OjvLly5bIZCeTu3bsICAjQ/JoUFRUFk8lk09XAkS32yhSJiYlBQECAXf91AIiNjUWuXLlgNpuRmpqKhw8f6m43Ywzx8fGKrfaoUKECbt++jevXr8PHx8fpsqxWK6KiopCWloagoCCbZdlrc2JiImJjY5E3b17uXFJSEmJiYuwez5Mnj2a2yWq1IikpSen/2NhYJCcnayID2bI3OTkZ0dHRNsvNyBMXF8e5XehBT76UlBQkJiY6LFvdh7buv++++w7vvPMOLl26hGLFiiE1NRXJycnIlSuXpixH7RXrs4Xe+0zEVv/buw/S0tLw4MED5M6d2+Z6KT126rUhg4cPHyI1NVWzQ/3du3cduhLpvZa20PvMZ+Y+N1qGkWvs6DrI7HH2XlIj3kOZuf/1Xkd7z7ej+1m8j1zRfoJ40qFBB0HkUE6cOIGKFStiyJAh+OKLL7LaHMIFiIMOgiAIgnhSoDUdBJEDSUtLw6effgpfX18MGjQoq80hCIIgCIKQQoMOgshhDBkyBEFBQdi4cSPmzp2LokWLZrVJBEEQBEEQUsi9iiByGLGxsUhJSXEYs57IedjzIycIgiCInA4NOgiCIAiCIAiCcCv0UxpBEARBEARBEG6FBh0EQRAEQRAEQbgVGnQQBEEQBEEQBOFWaNBBEARBEARBEIRboUEHQRAEQRAEQRBuhQYdBEEQBEEQBEG4FRp0EARBEARBEAThVmjQQRAEQRAEQRCEW6FBB0EQBEEQBEEQboUGHQRBEARBEARBuBWPGHSsXr0aw4cPz2ozdDNr1ixMnz49q83I9kRFRWHo0KHYu3dvVpuSrUlISMCwYcNw+vTprDblsXD8+HEMHz4cycnJWW0K8YQwfvx4LF261OXlfv311/jf//7n8nKzkiexTe6G+ozwFDxi0LFt2zZ8+eWXWW2GbhYvXoxffvklq83I9sTExGDatGk4cuSI4bwXL17E0KFDcfbsWTdYZhx32vP5559j8eLFKFWqlMvLftzExcXhgw8+wIcffoikpCSbaUqXLo2ffvoJ33zzzWO2jnhSmTlzJjZu3OjycufPn48lS5a4vNys5HG1afTo0Vi+fLnb63kcZKbPsls/ZDd7iOyFRww6CEIkMjIS06ZNw6VLl7LaFADus+fBgwf47LPP8OGHH8Lb29ulZWcFixcvxueff46pU6dixYoVNtP4+/vjvffew8SJExETE/OYLSSeRMaOHYuXXnopq80gVEyfPh2bN2/OajOynOzWD9nNHiJ7QYMOgniCmTdvHlJTU/HKK69ktSkuYc6cOWjQoAFq1KiBOXPm2E3Xo0cPxMbG0owh4RLeeecdNG/ePKvNIAiCyNF4ZbUBruDo0aPYsWMH7t27hxIlSqBVq1YoUKCAzbSnTp3CypUrYbVa0apVK1SvXp07f+DAASxbtgwAYDKZEBgYiMqVK6NVq1bw8fFR0q1evRr79+/HpEmTcPLkSaxevRqMMYwYMQIAkJycjDVr1uD48eMAgJo1a6JVq1YwmUxcfVeuXMGyZcsQFRWF2rVro2XLlrrbPWPGDOWXcYvFgnz58qFx48aoUqWKJm1ycjLWrVuHY8eOwdvbG3Xq1EHDhg2V82PHjsUzzzyDjh07Ys2aNfjrr79Qt25dxZ7jx49j48aNePDgAYoUKYIXX3xR08e3bt3CqlWrcO3aNYSHh6N+/fp49tlnDaexx/bt27Fjxw4EBASgc+fO3PXI4MGDB/jkk0+Uz76+vihRogTat2+PsLAwAMCff/6JWbNmAUj/ErtlyxYAQPv27fH888/rKsNoe65evYo1a9YgMjISefPmxQsvvIBy5crpsicz/TZ//nw0bNgQefLksXn+2rVrWLt2La5cuYKCBQuidevWKFasmO6+dGU/OOLkyZPYv38/Fi9ejISEBLz22mu4ePEiSpYsqUlboEAB1KpVCz///DPefvttXeUThD3Gjx+P8uXLo0uXLsqx4cOHo3bt2mjTpg1WrlyJo0ePomDBgujWrRtCQkI0Zfzzzz9YsWIFYmNjUa9ePTRt2tRufQkJCVi9ejVOnjwJi8WC2rVrawY9GfW3atUKS5cuxalTp1C4cGG8/PLLCA4OzlSZWd0mWf2JiYkYNWoUEhMTceDAAQwdOhQAULJkSbzzzjt263dnf7Vu3RqrVq3C0aNH0aRJEzRq1MiuHXr77Msvv8S1a9cAAF5eXsifPz+aNm2KihUr6u4HR2VkkJycjNWrV+PMmTOwWCx49tln0bJlS5jN/O/Ssj5x9roQnkWOn+kYPXo0ateujb///ht+fn7Yt28fGjRoYNP/dtmyZRg4cCCSkpKwf/9+1KhRAwsXLuTS+Pv7o0CBAihQoADCw8Nx//59vP3226hRowbi4+OVdBnrRObPn4/+/fsjNjYWu3btApA+sClfvjw++OADxMbGIj4+Hm+++SYaN27MlbF+/XqUK1cOS5YsgdVqxY8//oj+/fvrbntoaKhia+7cuXHw4EHUrFkT48aN49L9/fffKFeuHAYMGIA7d+4gPj4eEyZMQPfu3ZU0M2bMwPr169GlSxesXr0acXFx+PPPPwEAQ4cOReXKlXHo0CF4eXnh559/RsmSJTn3lm3btqFo0aJYtWoVvLy8cOnSJbzxxhsYNWqUoTT26NOnD5o2bYp//vkH0dHR6N69O7Zt26ZJZ7FYlD7JGBTNnDkTpUqVwuHDhwGkX+O8efMCAPLkyaOkDQgI0F2GkfbMnDkTJUuWxMqVK+Ht7Y2jR4+iYsWKyjojR/Y4228PHjzAsWPHUKtWLZvnp0+fjhIlSiizAadOnULr1q2xcuXKLOkHR3z//fcoUKAAOnTogK5duyJv3rzS2Y7atWvj0KFD5GJFZBpbazq+/PJLbN68GV27dsWmTZtgtVoxefJkVK5cGVFRUVzaFStWoHz58li1ahWsVitmzpyJYcOG2azryJEjKFOmDEaPHo2EhARERUWhR48eaNWqFbeO6csvv8TGjRvRrl077Ny5E6mpqZg6dSoqVKiAc+fOOV1mVrfJUf0mkwkFChSAyWTi3tcZGmoPd/XXpk2b0L59e2zcuBHR0dGcNooY6TP1+z0gIAB79uxB1apVMW3aNN394KgMALh//z4qVaqEkSNHIi4uDgkJCZg9ezaqV68Oq9Wqu0+cvS6Eh8FyMPHx8czLy4t9+umn3PG4uDh2/vx55fOgQYOYxWJh/fv3Z1arVTnerFkzVrRoUe6YLW7evMmCg4PZ+PHjNWW++eabSv6HDx+ypKQkVqJECValShUWGxurpL927RoLDg5m77//PmOMsejoaJY3b17WokULlpKSoqSbOnUqCwkJYTVr1nSiRxj7/vvvmdlsVtqfmJjIihYtyipVqsQePHjApT1+/Ljyf2hoKAsJCWGrV69Wjj18+JAtWbKEAWBffvmlcjw1NZW1b9+e+fv7s6tXrzLGGGvSpAmrX7++xp5jx44p/+tJY4tFixYxAOzHH39UjiUmJrIWLVowAGz69OnS/Glpaaxx48asTp06yrHt27czAGz9+vXSvLIy9LRn+/btzGQysXHjxnFpZs+ezcxmM/vrr78c2uNsv+3atYsBYL/++qvm3ObNmxkANmLECO54YmIiO3v2rN0y3d0P9khMTGShoaFs9OjRyrGhQ4eyAgUKcM+Pmjlz5jAAbP/+/dKyCcIR+fPnZ2+88QZ3zNfXl+XNm5ft3r1bOXbu3DlmNpvZJ598ohy7c+cOCwwMZB06dGBpaWnK8Y8//pgFBwezJk2aKMfi4uLYU089xerUqcMSEhKU45cuXWK5cuViY8eO5eoPCQlh27ZtU47dv3+fFStWjHt/GC0zq9ukp37GGAsODmZvvfUW04u7+iskJIRt3bpVOfbw4UOb9RvpM3t8+eWXzNvbm127dk05ZrQfxDKmT5/OzGYzu3v3Lpfu+PHjyncbI31i1B7Cs8jRg46YmBhmMpnYq6++aveLB2PpAwQA7PTp09zxGTNmMADcA8xY+iBj7ty5bNSoUWzo0KHs/fffZxEREaxt27aaMg8fPszlXblyJQPAli9frrHj7bffZuHh4Ywxxn755RcGgBMrxtK/XAUFBekedBw8eJBNmzaNDRs2jL3//vvsjTfeYADY4sWLGWOMLV26lAFgy5Ytk5YTGhrKKlSooDneunVrFhoaypKTk7njf/31FwPApk6dyhhj7Pnnn2dly5bVCJcaPWls0apVK/bUU09xQs3Yo7aJg46EhAS2cuVKNn78eOX61a5dm/n5+SllOBp06ClDT3s6derEcufOrbk/09LSWHBwMBsyZIhDe5ztt2XLljEAbMOGDZpz7du3ZyEhISwxMVFaxuPuB3v8+uuvzGKxsMjISOXYxYsXmclkYitXrrSZJ+NZ/O2336RlE4Qj7A06GjRooElbsWJF7l0xe/ZsBoD98ccfXLrY2Fjm6+vLfdmcP38+A8A2btyoKbdHjx6sePHiXP21a9fWpJs6dSoDwE6dOuVUmVndJj31M+bcoMMd/VWjRg1d9Rvpswz27dvHpk6dqrzfe/bsyQCwtWvXKmkc9YOjMqZNm8YAcAM9ESN9QoMOQkaOXtMRGBiIUaNGYeLEiVi/fj2aNWuGhg0b4oUXXkD+/Pm5tGazWRMyNCPN9evXUbBgQQDAypUr0b17d1SuXBkNGjRAeHg4zGYzfHx8cP/+fY0N5cuX5z5nrOHYsGEDDh48CJY+sAOQ7pN+584dxMTEKNO5Yn5fX1+bPuq26NmzJ5YuXYoOHTqgXLlyCAgIUNY5ZNh66tQpAEDlypUdlifaAgDnzp1DmTJlNJGPKlSoAJPJpLRj1KhR6NKlC4oUKYKGDRuiQYMGaN26NZ555hklj540tjh37hzKli2r8S+tUKGCJu2lS5fQpEkTWK1WtG3bFgULFoS3tzeCgoKQmJiI+Ph4BAYGSuvTW4ae9hw/fhwBAQGKq1HG/cAYg5eXFy5evCi1JTP9ZrFYAABpaWmac6dOnUK5cuXg6+ubI/rh+++/R1hYGL766ivueN68efH999/jxRdf1OTJaHdGPxCEqylbtqzmWP78+XH9+nXlsz2tDwgIQJEiRbhjGe+P3377Ddu2bePeH+fPn8e///6LtLQ05Z62pdkZunju3DmUL1/ecJlZ3SY99TuLO/rLVpm2MNJnVqsVXbp0wYYNG9CxY0eUKVMG/v7+ih22vouI6C2jd+/e+OGHH1C/fn1Ur14dDRs2RNOmTdGkSROljUb7hCDskaMHHUD6Ar8ePXpg7dq12LNnD95//30MHDgQP/30Ezp37qyk8/b2hpcX39yMByQ1NRVA+hehd955B/Xr18eGDRu4tN99952mbl9fX/j5+dm0K1++fJqFd+3atUO7du3g5eWlLCi39YXQ1jGRTZs2YcGCBfj555/Rs2dP5fiBAwcwadIk5bOsHhFbCwVNJhPn15mB1WoFY0wpv3nz5vjnn3+wdu1a7Nq1C7NmzcKwYcPQv39/ZaNDPWlsYTKZdPfT2LFjERUVhXPnziE0NFQ5/u+//+qOs6+3DL3t8ff31yy8BoCPPvoIxYsXd2iPs/2WL18+ALZfUPb6VE126YeLFy9ix44dGD58uGZBfJ8+fTB16lRcvXoVhQoV4s5ltDujHwjC1fj7+2uOWSwW5Z0CyDXY3jOYP39+5MqVizvWuXNn7p3mqEwxaIneMrO6TXrqdxZ39Jet96YtjPTZqlWrsGLFCqxYsQIdOnRQjm/ZsgVffPGFrvr0lhEaGoojR45g69at2LZtG3bt2oXPP/8clSpVwvbt2znN1dsnBGGPHD/oAIAyZcqgTJkyGDJkCGJjY1G9enWMGTPG8IMQFRWFW7duKRGDMrhx4wYuX75sNyKWmowZhRo1aqBdu3Z20z399NMA0hd5q78sxcfH4+LFi7p+/QeA+vXrc8cPHDhg055Dhw6hTJkyDu0XqVChAnbu3InExERugHXs2DGuHUC6ePXq1Qu9evUCYwx9+vTBt99+iw8++ED5JUdPGpGnn34aBw4cQGpqKjdwzPj1Rc25c+dQoUIF7ksyoO2XjAFnxq81zpShpz2VK1fG1q1bMXjwYM2gV689euqxRaVKlWCxWJTZLjWVK1fG+vXrERMTg9y5c9vMnxX9YIs5c+Ygf/78mDhxouaLAWMMc+fOxQ8//IAxY8Zw506cOAEfHx+HzxJBuBO11qvfLQ8fPkRkZCQ34M7Q63r16qFx48YOy/777781xzJ0MeO+N1qmHtzZJr1YLBa7emmPrOovwFif6X2/A/b7wUgZ3t7eaNmypRKtcs2aNWjXrh3mz5+PgQMHGuoTZ64L4Tnk6OhVN2/exJ49e7hjAQEBCA4OdmojtJCQEOTPnx979+5VjqWlpWHYsGF2Q46KtG7dGhUrVsTw4cNx+/Zt7lxsbKzyC3GbNm0QERGBTz/9FAkJCUqayZMna35JsEXGFLTa1suXL2PBggVculatWqFChQoYO3Ysrl69qhxnjGH79u0O63nrrbfw8OFDfPbZZ8qxpKQkjBkzBsHBwcr+D6tXr0ZKSoqSRh3JIuNLpp40tnjzzTdx69YtfPvtt8qx6Oho/Pzzz5q0ZcuWxalTp/DgwQPl2Lx58zRT8xEREQDS7yFny9DTnvfffx/R0dEYNmyYZsbo7NmzOHHihEN7nO23wMBAPPfcczZfMkOGDEF8fDw++OAD7le2hw8fKju8Z0U/iKSmpmLevHlo0aKFZsCRUVeLFi3www8/aMrdv38/6tSpY/OXU4J4XHTo0AF58+bFhAkTkJycrByfOHEigoKCuLSdOnVCqVKl8MEHH2hmKKOiopRw2hlcu3ZNiTYHpLsKz5gxA40bN0aJEiWcKjOr26SXiIgIm3opI6v6CzDWZ7be7+fPn7e5a7m9ftBbxq5du3Dnzh1NmQCU71FG+sSZ60J4Djl6psNkMmHEiBF4+PAhnn32WQQFBeHAgQO4evWqzYdTD19//TV69eqFunXromLFiti/fz969eqFf/75R1d+Ly8vrF27Fq+88gpKly6NZs2aIX/+/Pj3339x4sQJDBgwAC1atIC/vz+WLFmC9u3b49lnn0XDhg1x7tw5NGjQABUqVEBiYqK0nmbNmqFz58547bXXsHbtWphMJhw5cgSTJ0/mZlgy7OnUqRMqVqyIpk2bIjg4GH/88QcaN24sjScOpA9aJk2ahNGjR2Pbtm0oW7Ysdu3ahXv37mH58uUIDw8HkB7+d9CgQahSpQqeeuopXLp0Cdu2bcOnn36qrJfRk8YWL7zwAoYPH4733nsP69evR+HChfHnn39i7Nix2LRpE5d27Nix2Lx5M6pUqYKWLVvi0qVL8Pb2Rt++fblQwiVLlkS9evUwcuRI/Pnnn/D391f2xdBbhp721KxZE0uWLEHfvn2xdu1a1K5dG2azGWfOnEFUVBR++uknh/Y4228A8Oqrr6J///64desWt86pVq1amD9/Pt5++23s2rULderUQVRUFI4ePYoZM2YY6ktX9oPI77//jps3b0r3r2nVqhUWLFiAzZs3o0WLFgDSB+B//vknfvzxR2n/EIS7CQ4OxuLFi9GpUydUrlwZzz//PE6ePIn27dsre+Jk4Ovri40bN+Lll19GqVKl0LRpU4SHh+Off/7BqVOnMGzYMG5fh5deegm//vorfvrpJ+TJkwfr169HSEgI5s2b53SZWd0mvbz66qsYPnw4unfvjoiICF37QWRVfwHG+qx9+/Zo3bo1unbtii5duiA1NRV///03xo0bh06dOunqB71lXL9+Hb1790bp0qVRqlQpxMbG4vfff0eLFi3w6quvGu4TZ64L4TmY2BMwD3bq1CkcPnwYsbGxKFKkCJo0acItkN2+fTuOHTuGwYMHc/kuXLiAVatWKQ9HBpcuXcLOnTuRkpKC+vXro2zZsli0aBEA4OWXX5aWqebw4cM4fPgwkpOTUaJECdSpU0fzi8aDBw+wbt06REVFoWbNmqhWrRoWL16MtLQ0dOvWzWHbd+3ahRMnTiA0NBStW7cGYwz/+9//0KxZM25zNsYY9u7di7///hv+/v6oWbMmt6AtY/8Fe7vuXr16Fdu2bcPDhw9RqFAhNG/eXLMg+9atW9i3bx+uXbuG/Pnzo0GDBhp/ej1p7HHy5Ens2rUL/v7+aNOmDfz9/fHdd9+hadOm3EL5mJgYbNiwAbdv38bTTz+NBg0a4I8//sCePXswaNAg5debxMREbNiwAZcvX0ZKSgoaN26MqlWrGipDb3sSEhKwY8cOXLx4EYGBgShfvjxq1KjB/Xovs8fZfouLi0PRokXx0UcfKZs1qYmKisLWrVtx7do1FCpUSLORYFb0g5rNmzfj2LFjePPNN+36Tj98+BBz5sxBtWrVlEH0+PHjMWPGDFy+fNnuuiuC0MusWbNQsmRJTh+/+uorPPvss5ofbpYvX464uDj06tWLO3737l2sW7cOcXFxqFu3LipVqoQFCxbA19eX23Qwg0OHDuHIkSNIS0tDiRIlUK9ePWXvHgDw8/PD22+/jS+//BJbt27FmTNnUKhQIbRq1cpugAhHZWZ1m4zWv2/fPhw9ehQJCQkoVKgQunbtarPdj7u/ZBjps23btuHMmTMIDw9H69atkZiYiB9//BFt2rTh3t+yftBTRlJSEvbs2YNz584hICAAlStXRqVKlZzqE0f2EJ7NEzHoIAjCPt988w0mTZqEixcv6nLdy+lER0ejePHimDRpEvr27ZvV5riNa9euYcmSJfD399e96/rJkyexZcsWeHl5oXXr1jYX8OtJQ2Q9GV+ixYhuhG2ov4icTFpaGtasWYMTJ04oM06OiIuLw2+//Ybr16+jfPnyaN26teYHPj1pXEmOXtNBEIRj+vXrhw8//BDXrl3LalMeC9euXcOYMWPwxhtvZLUpbqNz586oXbs2fvrpJ91fojJ2Gf7rr7+wbds2VKhQAb///rvhNARBEMTjY9WqVShZsiRmzZqF0aNH48yZMw7z3L59G1WqVMHnn3+Of/75B2+//TZeeOEFbg2nnjQux31bgBAEQRDuYNWqVSwlJYWNHDmSlS1b1mH6W7duMX9/fzZr1izlWMampxkbf+pJQ2QffH192aBBg7LajBwD9ReRUzlw4ACLjIxkDx48YADYmjVrHObp27cve/rpp5Ud5C9dusR8fX3Zjz/+aCiNq6GZDoIgiBxG+/btDYUeXrt2LdLS0tCjRw/lWJ8+fXDjxg0luo2eNET2YfLkyWjfvn1Wm5FjoP4icio1a9bU7EPliOXLl6N79+7KmsZixYqhWbNmWLZsmaE0rsZt0avi4+Nx69YtFCxYULrrMUEQxJNCYmIiFw7TCEy12WYGvr6+LtHPM2fOoGDBglzwh9KlSysRxBo2bKgrjYyrV6/Cx8eHNmN8TMiCmBBaqL8Id+Cs5rtT7+/cuYN79+5p9mYrU6YMfvvtN91p3IFbBh0jR47EF198gaCgIMTFxWHChAkYMmSIO6oiCILIFiQmJiLUPxDxcM4fNjAwELGxsdyxsWPH4uOPP860bbGxsQgODuaOmc1mrk49aWxx5MgRvPLKK7h58yaSkpJQs2ZNLFq0SNdmqgRBEDmVzGi+u/UegEbPQ0JCOL13lMYduHzQMW/ePHz11VfYsWMHatasiXXr1qFdu3aoUKGCEkOfIAjiSSM5ORnxSEMvPAUfgzE6kmHFz7HXEBkZyYXVdtUscUBAAKKjo7ljVqsVsbGxSrhLPWlEEhIS0L59ezRv3hyzZ89GQkICmjdvjp49e2Lz5s0usZ0gCCI74qzmPw69B6DR86ioKE7vHaVxBy4fdMyePRudOnVCzZo1AaTv0N2gQQN89913ugcdVqsV169fR+7cud0auosgCCIDxhhiYmJQsGBBmM3OL3fzgRk+JoP5/wtcHhQUpNnLxxWULVsW169fR3x8vBI2+eLFi7BarcrOxXrSiKxbtw7Xrl3DxIkTYbFYEBgYiNGjR6N169a4ePEiSpYs6dA20nuCILKCLNN8N+t9vnz5kCdPHly4cIE7fv78eUXL9aRxBy4ddFitVhw+fBg9e/bkjtepU8fujsO2uH79OgoXLuxK0wiCIHQRGRlpeNGeGovJBIvBL88WmJQXkStgjOGTTz5By5YtUb16dbRp0wbvvvsuFi5cqIQS/uGHH5AvXz7Uq1cPAHSlETl06BCKFi3KuVLVqVMHAPDXX3/pGnSQ3hMEkZU8bs13td4D6Rvpnj17Fv379wcAdOjQAb/88gsGDx4MHx8fXL16FZs3b8Y333yj5NGTxtW4dNARExOD5ORkhIWFccfDwsJw9+5du/mSkpKQlJSkfGb/7VfY/b8pqykbxnPpWfKjtBBGp+ZcubnPadH3lf9NZotdG8yBvF8bS04UEjyqxxrP+7uZvLwffbBa+XM+qukydTobdZhzPRrxWlV2i/Vr6vDz5z6L9nFpVX1gysXvKI7UFFWZwvSalfdZtMbH2LWHqdKaxTpE1HmF/lHbqrkeqrQsMZ7Pp+ln+/eL+vqwxASpqdy1FMpR1yHeg7V7Dcaeb8dIy84s4r3NrG6Ms+1GZO1wdxtj4hNQqttQ5M6d23FiCWYTYDH4g70ZMPQSmjdvHq5evYo9e/bg3r17mDhxIgBgyJAhCAgIQFpaGkaPHo2QkBBUr14dBQoUwJQpUzBgwAD88ccfiI+Px9KlS7Fw4UL4+PgAgK40Ivfu3dPofXBwMLy9ve1qviO9B8BpPvf8AlLNTxN0U6/my/Qe4DVV1Be1hnEaAfA6JdF7QNB88VdXdR1O6j0gaL5K79PLVWm+TO8Fe8TnUKr5wrtC3T+a51vdX6KmqzRfqvcA15fi9ZFpvuZaqsoR61Dfg7XfGoM9u3fbLZfIPsTExKBU6dKPXfON6v2ZM2ewbNkyJCamPxOLFy/G0aNHUbt2bTRp0gRAevTBVatWKYOOiRMnok6dOqhfvz7q1q2LlStXok6dOnj11VeVcvWkcTUuHXRYLOmiIa7kT0pKkoZ3nDRpEsaNG6c57gMzfGBGUAC/izLzVgmy+AIS0qalPRIV+QtIqCNJEH31oMPEi6x80OH36IMojt72bbemCWIoHXTwtov2cWm5QYewO7V60OEvnBNfQqZUu/bwgw4HO2DLBh0W1aBDvB7ql7mFf3pNXvwXJNn9or4+zP7toUmrGXSo6hDvQYvZjKAA/ouCq6FBhwttyKSLj9MzHQZISkpCYmIi6tWrh3r16ikvo4wv8GazGSNHjsRzzz2n5Bk4cCDq1q2LzZs3w8vLC6NHj9ZMo+tJw9ltsWj0Pi0tDampqXY135HeA+A0X9RJmeanCbqpV/Nleg/wmiofdPjx59Q6JdF7QNB86aDDOb0HBM0XBx1qzZfpvWCPdtAh0XzZoMMiPN/qayK+O1WaL9V7QBh08NdHpvmaa6kedEiupcVicYvLDOE+HrfmG9X7tLQ0ReNHjhwJIH0Re0rKo2e4efPm3IxxREQEjh07hqVLl+L69euYMmUKOnTooHxP15vG1bh00BEYGIiQkBDcuHGDO37z5k3p1NXw4cPx3nvvKZ+jo6NRuHBhTNkwHkEBuTDw+Q+59F/vnKT8rxFVQSjUn2UvIFHUIOzIqBZETR2SQQcnqt7Cr4VW+4MXmeBpBh1CuZq86nPqQYevIMB2bAEAZrX/ZV3TZvUXRbHNAkzVz6I9HOL1UJcrfvkU7wlJ/SZf1WBAbIcmrXrQYeBeMpntXxPZF2dZHQ4wqa9XJsrhcNDP7sAEb6fOOVVXivz668XixEyH0Z586623pOfNZrMy+6GmWrVqqFatmjSvnjQZFC5cGKtWreKO3bp1C4wxu5rvSO8BcJqv1ntArvkabdb7nEr0XlOHZNCh+eFEolNiObIfNbhBh5N6D/AaJv7QqrZHqveCPZp2STSfif2sU/Ol705n9R6Qar7GNiOaT3gURjXfqN4//fTTNvVcTevWrdG6dWvuWFBQkOIqaw89aVyJyzcHbNiwITZs2KB8Zoxh3bp10hjvvr6+yoIady2sIQiCeBxk/Opl9C8n0rBhQ9y6dQtHjhxRjq1duxY+Pj7K2g4R0nuCIJ4kPEXvXYHLo1eNGjUKderUwYgRI9C2bVv89NNPuHHjBoYOHerqqgiCILIdj2OmI7tQt25dtGjRAr169cKXX36JqKgofPTRRxgyZAhCQkKy2jyCIAi34+6ZjicJlw86qlWrhq1bt+Kzzz7D2rVrUbp0aezatQslSpRwdVUEQRDZjsexpiM7sWzZMkyYMAHDhg2Dr68vRo0ahUGDBmW1WQRBEI8Fd6/peJJwy47kGYsbMwtLTgLzNmt8egc1GK78//VWMbJVov3PEp9MzfoGTTQTSSQllX8pExfnSfxtNbaq/FatiXF8ObJ1Iw7K5VD3gSSaFhMX9Qm+uGLEKO6cuj9k/s/QLkK0W6akTRpbJO3S+HirAwQIfS7C+SKKC8nVNoh1MCtYRtniOWcXQztaT+HsWhGJr7TTthrByDoRma1686nyaqLeOIkJxv1Wc/IrKDAwEJ999lmmy8nQe4Bfx6HWe0Cu+TLdFuHWNzjIx50X7h+15jur9wCvP9J1Iw7K5RDbL4mmpdZ8Z/UeEDTfSb23ZZ9dexxEheQ0XwwQINF8zTOsXkgu9sdjWN9GZF+Man5O1vvM4vI1HQRBEARBEARBEGrcMtNBEAThqXiaexVBEIQnQ+5V+qFBB0EQhAvxpIXkBEEQng4tJNdP9h50mM2A2azxw1f79A5qwu/0PP3PGdxnmT+nDJnvv/acau8Ls7CHhszXU1qO/XPacoSY6jrTirHouTocxD5nkr6UPnuirer/ZRvSZKb9sntA2ueSDcNk94etdvyXXrPBnTalXaTXVYDbc8VF+Zy11YgftxFbnfWj1uxPYOfaOEv6C8joTAeRofcAfy3ENRwyzXf4DNvBUT6TTBtVmu+s3juqQ7dtjtJaJHWotVAoxh16r7FHk1fne8zRu1Ki29LvB5J3hbRfCY/DqOZ78t2SvQcdBEEQOQya6SAIgvAcaKZDPzToIAiCcCG0poMgCMJzoDUd+snWgw5zrtwwB+TShCFUh8QT3akGVH+X+/zN/i+V/2VToKZcufkDQhg+zl1ECHXITbsKriSc7aK7ihCm1+wXYLcc3XUAsKqnyZ1sM2cLtC4yZok96nCL5gBht2ExreqzLNyk5np4PboG4vUQw03Kwiar6xTDBItIr6XKBpNfLj6j2QJzYEj6OQehiPXiaDpfVq5JEhpT5u7mtK1G0krqNGKPrH80bf4vrdnkYyu5YcxOzHRQGMFHeg8Iz6UQAlWm+Wq9Bwzon0TvAUFjJKGkNRqmfm/I9F4ox0gdVlHTnGyz2h6p3gv2iM8Tp/kSvQf0a75a7wFBbx2EzOXcxsTvErK+kl1L8Z0jaj7hURjVfE/W+2w96CAIgshp0EwHQRCE50AzHfrx5AEXQRAEQRAEQRCPAZrpIAiCcCG0kJwgCMJzoIXk+snWg4606PtIS0uQrukQfW9Fn96BtYco/3+1eazdusyCX6roF6quxxofzZ9Try+Q+Kw69D1NTVH+TYu6Z7d+jb+t4Bss2scnflSOOSWZt0dVv9oWQOu3y+Jj7NrD+fEK5YhwazqE/uF8aMXroUrLEuP5fJJ+Fu8X9fWxJsZJbeWupejzra5D6FdY02CN5q/no3NW28cBQyGepeVmphx7Zbqy3GxCWlyCS8qhQYdzZOg9IF/TIdN8td4D+jXfUR1qTdWsL5Cst+B0SqL3AK/5Gn1R1eGs3gO85jNRm1WfZXov2qNZfyjRfM37Ua3VMk0VNV2t+Q7eqybJmg6Z5muupex9JGo+4VHQoEM/2XrQQRAEkdOgNR0EQRCeA63p0A8NOgiCIFyIBU7MdBjZeZEgCILINhjVfE/W+2w96DCZLcofh4GdQdXT64ObjePOqXe6le7OLdYj242aPyPfZdbQjuT26zC0y7akfm43dQM7kmvOyWwT4PLKQj8auQcM7Ugu2dlWtFVvOZIdyXUfN4jMHcNluMhWGbJ2GGmj7Frazeei9pmdmOkwG0z/JKLWerlu2td80Z1KrfnizuZyfTGwG7X6f6n2GNmRXLKTt8w2WzbYScvpvXhOyCbuSK4+r/n+ZGT3cll4d533gLEdyfXv5i7dpZ52JCdUGNV8T9b7bD3oIAiCyGk4tabDc99BBEEQORrDazo8WO9p0EEQBOFCnFrT4cG/fBEEQeRkDK/p8GC9f7LCzxAEQRAEQRAEke3wqJkO0ad3UJMxyv/TD34jzywLbZqN0YQofAx++VKc7EdNO+BtJ2UOQ1xf4OT1kYYtzuprbgBXrUVxy5oWnZB7VfZBrflqvQccaH4O1XtAvg7qsZOJfuTa8aToPfFEQu5V+vGoQQdBEIS7IfcqgiAIz4Hcq/RDgw6CIAgXYjaZDEcn8eRoJgRBEDkZo5rvyXqfrQcd5sBgmANzaXYcNfn62ckBmHLl5stQ7awqhrVTT68PqDGQP3d4Fl+ueppatMdbtUOt6NahSqvZydab/2zyy/XIbpnbjWTXc01aAXUfmISdbVnqo11VxV1vNbugS+xR72Zr9ufLEeF3JOf7Qx2W0Cz2uXoXeG/7+QAH94t652OxHEla6e65quuYkdYcGCItmzCOq2XbbJJff72YLCaYzMasM7n5JcQYQ1qabZczL69Hr4HU1FTNeYvF4nb7AJXeA7xuSp5fgNd8s9BGtd6J7lRqzZfqvWiPqBNq/TOgU6JOmGUukZJdz/XqfXqdj/RYrfcAr/lSvRfsEXcvl2m+dkdyVZ8Ium3W++50Uu815ThIq+5nzY7kouYTHoVRzX8cepqWlgbGtBuCmM1mmP97ZqxWK6yCC6TJZILFjSGgaSE5QRCECzFbTE79GeH69et48cUX4e/vj9y5c6N3796Ijo62m37hwoXw8/Pj/nx9feHt7Y1Tp04p6by9veHr68ulmzVrlt1yCYIgPB136z0ATJ8+HUWKFIGXlxcqVqyITZs2SdMXLVpUo/ne3t548803lTQffvghfHx8uDRly5Y1bJsRaNBBEAThSixmmAz+waJfiq1WK9q1a4eYmBhcvHgRhw8fxp9//onXXnvNbp5u3bohNTWV++vYsSOefvppVKhQgUu7ceNGLl2/fv2c7gqCIIgnHjfqPQD8+uuv+OCDDzB9+nTcv38fL7/8Mtq1a4fTp0/bzXP16lVOx/fu3QsAeOmll7h0zZs359JduHDBePsNQIMOgiAIF2Iym9Kn2438GZia37lzJ/766y98++23KFiwIEqXLo3JkydjxYoVuHTpkq4y7t69i9WrV6NPnz7ONpMgCIKAE5pv0P122rRp6N69O9q3b4+goCCMHDkSxYoVw4wZM3SXMXfuXBQpUgTNmzc32jyX4tSajgsXLuDo0aOoUaMGihQpYjPN8ePHcfnyZZQuXRrlypVzyjiWnAiWZAYEn1HOf17ieyumlfm+ij69A6q+w59X+QOL/pycT68YIjDN/jmWlMB9NqlstybE2T3nKAyhxj41krUpLOlRPiauWRDXbQj22UvLHPgGiv7AdtMliX3+qA80tkjuAVkISU2fy8JNiv2jziv4GINZwRLjbObTrAGSYST8pTtC5uYkW51A+txkI/bt24fw8HCUL19eOda4cWMAwP79+1G8eHGHZcyfPx8mkwk9e/bUnOvcuTPi4+NRrFgxvP766+jfvz927doFHx8fNG3a1GZ5Dx48wB9//AFfX1/Url0bfn7ydRi2UPQe4HRT4z8v0XzNNdSp+TK915SrCU+t0mNRz9Q6JdF7gNcf8ZxM83XrPcD3laCpas13Vu8Buebr1XuNfeK7U22P7J0P/ZrvMLywun/E/hA1nyBcREJCAo4ePYpBgwZxxxs1aoR9+/bpKiMuLg6LFi3C0KFDlfUcGezatQt+fn4IDAxE7dq1MXXqVKe/s+vB0JNy+PBhtGjRAi1btkSXLl2wa9cuTZrk5GS8+OKLaNSoEb766ivUqFEDb7zxhs0FLQRBEE8amVnTER0dzf0lJSVpyr916xbCw8O5YwEBAciVKxdu3bqly8a5c+eiU6dOyJs3L3f89ddfx8GDB/Hw4UNMnToV48aNQ0REBPr27YuhQ4faLOu3335D0aJF8fHHH+Pdd99FqVKlcPz4cV12EARB5HTcqfd37tyB1WrVaH6+fPl06/3SpUsRFxeH119/nTterFgxLFq0CPfu3cPBgwfh7e2N+vXr4/bt2072hGMMDTru3buHIUOG4Pz583bTfPXVV9i7dy+OHTuGrVu3Yv/+/fj111+xYMGCTBtLEASR3TGZzU79AUDhwoURHBys/E2aNEl3vYwxXVFRDhw4gJMnT9p0rZo7dy7KlCmDXLlyoW3btmjWrBmSkpLQpUsXm2U9ePAAvXr1wrBhw5Rya9asaXMGhSAI4kkkK/TearXqjoI1Z84ctGrVCoUKFeKO9+/fH+3atUNAQABKlCiBBQsWIDk5GfPnz9ffeIMYcq9q1qyZwzTz589H165dlcY9/fTTaNWqFebPn08vIoIgnniciU5i/i8AcGRkJIKCgpTjvr6+mrQRERGaX6JiY2ORkJCAAgUKOKxr7ty5KF26NBo0aCC3yWzG4MGDsWbNGsTExNhMs3r1aiQmJmLAgAEA0sMtvv/++6hbty6OHz+OSpUqObSHIAgiJ2NU843ofb58+WA2mzWaf+fOHV16f/bsWezduxerVq1ymDZXrlwoUaKEWxeTu3SfjtTUVJw+fVp5AWVQqVIlfPfdd3bzJSUlcdNKSuhHsxkwmzWxxjk/VTFGt+CXaZKkVfuJivlkMd2/2T3FZjtswdku1i+Lry6eM7C+QL4Wwf7kFm+rUKaQlsnKkdgmTSueU+fV3ANm2/9DG5tedr+ozzn06ZWhKldbjkmpRzznrNOhI1tl5crycvumZMJWk8Q/XIOT10Bmq9586ryZuv7q8v5bLGgoz39PQlBQEPcSskXdunUxYsQInDhxAs888wwAYOvWrQCA2rVrK+lSU1M1e2zExcVh8eLFGDVqlK5fyY4dOwYvLy/kymV7H4Ljx4+jaNGinM0ZAw17gw5Heg/ItUim+VrdVKUV49Gr0sr0HtCv+VrtUeuUAy00S9rMlekGvRfKdVrvbdgnTas+J+aTvjtVeivTezGvkb6T4eB7B+FZGNV8I3rv5+eHatWqYfv27ejdu7dyfOvWrWjVqpXy2Wq1gjGm2WNj7ty5iIiIQJs2bRzaFRsbi4sXL6Jjx46622IUl65+io2NRVpaGvLkycMdDw0NxcOHD+3mmzRpEjfFVLhwYVeaRRAE8dhIfwEZDZur/4X1/PPPo1atWujXrx/++ecfnDhxAh9++CG6du2KokWLAkgfcHh7e2uimyxZsgSJiYncyyuDH374AZ999hnOnz+PBw8eYMmSJRg/fjz69OkDHx/bm6hFRUVp9D4wMBA+Pj52NZ/0niCIJwnjmm/sR6kPPvgACxcuxOLFi3Hnzh2MHj0a165d437gf++991CyZEkuX2pqKn7++We89tpr3CawGbRp0wa7d+/Gw4cPcfr0abz88suwWCzS8OuZxaWDjoypofj4eO54bGysNJrJ8OHDERUVpfxFRka60iyCIIjHhrs3BzSZTFi1ahUiIiJQtWpVNGzYEA0bNsScOXO4NBaLRROp5KeffsKLL76I/Pnza8rt2rUrEhMT0bZtWxQvXhyTJk3C+PHj8c0332jSZuDr66vR+5SUFCQnJ9vVfNJ7giCeJNy9OWCXLl0wffp0jBo1CsWKFcP69euxdu1alC5dWkljsVg0A4t169bh/v37eOONN2yW+8EHH2DChAkoUaIEWrVqhaCgIBw6dAhPPfWU8U7QiUvdq/z9/VGgQAFcuXKFO37lyhWUKFHCbj5fX1+bvmzW+FhYTWkw+fAvL1kIVObN/yJnjVft0iubAnUQdk89vT7w+Q+5c19vHf8on+i6oXYBcVCHOtSgNdF++FaxDrPgNqC3zWax71T2iKNRMdQhS3z0RUPjPqP67GhUy7nIiNfZjm0A35dqWwCApabwn2Uhc1V9J/a5CNcW4cuc2garWIcqZK6rYrhpyjEQilevDZmx1dm8YqhmaThddWhmsSDZsy6G+Mw4HJ+gTesEJpPxOOwmq7H0+fPnx+LFi+2et1gsSE1N1RzfsWOH3TwBAQEYO3Ysxo4dq9uOEiVKYOHChbBarcoAJ0P/7Wm+I70HeC1wFAJVrfmc9gG6NV+m9wCv+Wq9BwQNE/VfUocYWlatP5o2qjVVpveAbs0X7VErmkzvRXvE50mm+Zr3o/o6i2nVui2+O9XvH4neA0Jfin0n0XxNO1SaL/aHRvMJj8Ko5hvVewDo27cv+vbta/f8tGnTMG3aNO5Yu3btkJycbDdPxg9WjxOXB5du1aoVVqxYAet/D3diYiLWrFnD+Z4RBEEQOZ9WrVrhwYMHypoSIN2FKzg4GHXq1MlCywiCIIjshqGZjtu3b3N7cxw6dAh+fn4oXrw4qlWrBgAYM2YMnnvuOXTq1AmtW7fGokWL4OXlhffee8+1lhMEQWRDzBYzzBZjv+eYWfbcXGzLli14+PAhzp07h6ioKCxbtgwA0L59e3h7e6NChQp466230LNnT3z00UeIiorCp59+iq+//tqpDQIJgiByGkY1P7vq/ePA0KDj1q1bWLRoEQCgU6dOuHbtGhYtWoSmTZsqg45ixYrh8OHDmDlzJnbs2IHnn38eixYtQmhoqOutJwiCyGY4Fb2KGZ9ufxxs3LgRly5dgo+PD6pVq6bof+vWreHtne7yMmvWLNStWxdbtmyBr68vfv/9d13h1QmCIJ4EDEevyqZ6/zgwNOioWLGi8kuXjMKFCxva5MQeJi9v5Y9D5ucthsTzUq3xkIX9E9aCyHziRZ/eQU3GPDq3faJQ/yPbtXXw/qVcyEKxzepzVqEdQlquzSKyUIPqcsQyRR9jdbtEe6yqtLJrByib5Ni0R12n4Ler7kvRp9fQ/aJuh5hPRBb+UnadYbJ7TeyFb800T4qPsTTktbPhLm3nM3k5CO2rkydp0DF16lSHaUwmE3r27JnpfZjUWs89i6IWSzRf85zp1XwHYZ3Vmq/We4DXfFFD+Drs670mr3hOrbFO6j0gaKyod2oNk+i9xh7xOZRcO5PMHvG6qnRd1FS15kv1XixX03cSzZeFuJddZ8LjoEGHfly6kJwgCMLTeZLcqwiCIAg55F6lHxp0EARBuBInZjrgwb98EQRB5GiMar4H6332HnRYrY/+VHBTq2ZhyloMh6kOZyitSwz7arWdDlqXGPX0+qBGo7hz6tCLmjCEkvC6svo158SQgRJXAXUfaOxR96vY5ylC2DWJrbIQkrK0sl3QNW1Ksx+yUZzul94v6pCWopuWJLyuBkkISYCBpf7Xfw5C20p37zbiMiVzKXMWRzuLq+HCVDpwj9Frq5H6ncDhzuk6MZtMMBsMmWvWsTv4E49a61XPmva5tK/5Gk2V1qdTb4VyRRdateaLoXbVGivVe0c2qM85qfcae8R+Vfe5TO8Fe4y8xzS2ynZBV6dNE+tX12Ff7wHhfhFD73LvBif13tZnwqMwqvmerPfZe9BBEASRw8jYddZQHnFdFEEQBJEjMKr5nqz3nttygiAIgiAIgiAeCzTTQRAE4ULMFhPMBtd0mJ3YoZYgCILIeoxqvifrfbYedJh8fGHy8dOGp/N5tOmU6IepPucoLYcYOk/wIVWH9hN9cdVh90Sf3oHPf6j8P/3PGXwdYjlqW4V2cOH7BF9Tk6/QZpmPr9qH1tf+5l3akL3CpJjgN8uhXtPhKJSgqp+l9ojXVRbuUrjOZr8A++X6+j9K58Cvm7NP5v8r9p3JDJM9G5xdJ+GI7LSmI4dgSpFff93lOBMy14NfQhkoeg/w4VvFZ1+i+Y7ScqifU4neA7ymitqo1ny13gOC5kv0XvNZEx760b3prN7bysud40L2Oqf3gAPNF/tZp+ZLQ9o7qfeAXPM1tnHr1OyvDSQ8D8Mhcz1Y77P1oIMgCCKnQWs6CIIgPAda06EfGnQQBEG4ELMFTrhXuckYgiAIwq0Y1XxP1nsadBAEQbgQk9kEk8GQuUbTEwRBENkDo5rvyXqfvQcdXt6Al7fjdQFqRB9Wzk/Vvn+vyUv0GRWGoiofV9GnV22fuPeF2qd3QPV3+XMHv+HrUJcr1qHyMRbr0KSV+Zeq13SI/aHuZ7FMIRY6VP6uoj3c1KEDX1dujYl4DdSI61jU5TpY08FkBpj128r5mYt9p+oPTTtMkmsi9qvoS+007vAxfrL9lqXPjQHMZid2JE/z3Ol2hf/0HtCxFkwNpyHiWjSdmi/Re7Fc0Ta1/onr9tSaL9V74bO4poTTWCf1HhB0S+xjdf0SvRft0biKSOzRrDHRqfmaNkrWdOjWe0Cu+WI/q+sR19XI2kE88RjVfE/W++w96CAIgshhOLWQ3OgO5gRBEES2wPBCcg/Wexp0EARBuBCnFpIbTE8QBEFkDwwvJPdgvc/Wgw6WnAjmbda4z7DkxEcfxKlVcRpYkpZLJ06ZJyXwCVR5uTIBbhqYia4+qs/i9PqAGgO5z9MPz7Jfh7p+B6FLNXntlSNrsyRkIwBYE+Ls1yEJmatxDdMZhlVzPVT5WJLYV7ztTBLukXNbk/WbiOD+oO4PsyacLgNLjLdpm8atQ4YR1yt1ua5y2XKXrdkElpyU1SZ4NIreA/zzLdFCgNd8R2m5fGq3WInea8oVngNOjwU9U2u+TO81dYj1S3RSr94DjtpsPyy7Xr0H5O7GRsJuc/aJ3wHUmu+k3gPOa77YHxrNJwjCJtl60EEQBJHTMJnNMBkcdBlNTxAEQWQPjGq+J+s9DToIgiBciNnixEJyD55uJwiCyMkY1XxP1nsadBAEQbgSJ9Z0wINfQgRBEDkao5rvwXqfrQcd5lxBMAfk0oTL49YiCL6UZr8AvhC1f6csfKJfLv6zJJQes4hhTlUhG0X/Vh/VGhOhTNGnd0DVdx6dE8MrqusQ7OHqsGWfvXIkbdaUKbRL+sio13QI10PsHw5ZqEVJeElpmGTIfaXV7XQkA1yfiPedpF9hNsOUK3f6OVkoTEf1y66rABfS0kX5nLXVXW10Fk2I5//qNDPXyKHJ7MRCcg+ebs8gQ+8B/hkW155JNV/059ep+Y7CJXOaKmqIWu8E3VTrlEzvAUHzxTrU/zup94C8zepyndX79DoeXQ+p3gP6NV8SClmq92Jaoe9k7dJcS1U54hoOjeYTHoVRzfdkvc/Wgw6CIIicBq3pIAiC8BxoTYd+aNBBEAThQtLDJxqbsTFZ9M8GEQRBENkHo5rvyXqfrQcd1uj7sKYlaKY5rYmPwtVpdoYWpnPTou7ZT6vCLOQTQ+Kp86rrB8TdsYWdsyXuVeI0sCy84tfbJ+qrw4Z9XFr1VHNKsl17zP68W5ToksLiY+zWoZ6aNwt1yMJNiu0wSUIUcq4AmuvBu2PI7hf1tLhVaJMmLedeJYRpzAiJC+29BGsarNH/3YcO7lcpRsIySnbsdRpnbXWwY7xbbJVhx560uAQbiY1D+3Q4R4beA/yzptFbyf2j1nubaVWon1OZ3os2aHfHVu2cLXGvkuk9wGs+p/cO6tCr9wCv+aI9as13Vu8BQfNl4YXBt0X80qa+JhqXMu562Nd7QHjnCW5Qas2X6j3Aab5a7wEbmk94FLRPh36y9aCDIAiCsM3u3buxadMmeHl5oX379qhcubI0/eDBgzXHOnfujHr16mWqXIIgCMK93Lt3D7/88guuX7+O8uXL45VXXoGPj4/d9KtXr8a2bdu4Y3nz5sWYMWMyVW5m8dzhFkEQhBswm81O/Rlh8uTJaNWqFeLi4nD9+nXUqFEDv/76qzTP119/jeTkZBQrVkz5y507d6bLJQiC8GTcrfdXrlxBpUqVsHLlSvj4+GDy5Mlo1KgRkpOT7ebZtWsXNm7cyOl9oUKFMl1uZqGZDoIgCBfibveqa9euYcyYMfjhhx/Qo0cPAEBERAQGDBiAjh07ws/Pz27ejh07omnTpi4vlyAIwlNxt3vVqFGjEBERgc2bN8PLywvvvvsuSpYsiblz5+Kdd96xm69o0aI2Z7gzW25mMDzoOHXqFHbt2oWUlBQ899xzqFWrliZNQkICVq5cicuXL6N06dJ48cUX4eXlxPjGbH70p8IkCZeqCRGo/iwbXToIu8qF3XNQJ3/uUZ2ahUaSckSf3kGNRtk9J64V0dtmjT2SEIVimxm3jkXw01V/cHR9rPr6RxPeUR2SVVOHc/eLo7CZXLliObI6YNL4HdtP6yTidba6YW2Es7Y6yie7X9U+4bJzDm1Q5bXTNzL/fyO4e9Cxbt06WCwWdO7cWTnWq1cvjBs3Drt370azZs3s5l28eDG2bt2K4sWLo0OHDggPD5eW27lzZ4wbNw4DBgxA48aN0bZtWwQGBmrKPXz4MLZt2wZfX1+88MILKF68uO72KKi1Xq2bEi0WP2u1WZ/mO6qD11R9eg8ImuZAC9W6rtZ78Zwjbdat+ZIw5FK9B/iw6GIlkr5S673GHsk7WBO+W6IZ0mvp6D3P5RP60Szpu8exFo3Itrhz0MEYw+rVqzFq1Cjle3T+/PnRvHlzrFq1Sjo4uHLlCkaOHIncuXOjdu3aaNCggUvKzQyG3ozdunVD165dcfToUZw5cwYtW7bEq6++yqV58OABatSogcmTJ+PmzZsYPnw4GjZsiMTERNuFEgRBPEGYTGYlhKLuP1O6FEdHR3N/SUlJmvLPnTuHggULcjMPxYsXh8Viwfnz5+3alTt3bphMJuTKlQsLFixA2bJlsXv3brvlbtq0CS+88AJMJhMuXbqEb7/9FiVLlsTJkye5cr/44gs8//zzOHPmDHbu3IkKFSpg/fr1mepDgiCInIJhzTeg97dv30ZUVBRKlizJHS9ZsqRU7wEgLCwMPj4+uHLlClq3bo3XXnvNJeVmBkPTD3379kXDhg2Vz2+88QaqVauGHj16KFP2n376KeLi4nD8+HEEBgZi5MiRKFu2LGbNmoUhQ4a41HiCIIjsRmZmOgoXLswdHzt2LD7++GPuWHx8vGYthslkQkBAAOLj+ag6av7++28ULVoUADB69Gi8/PLLeP3115UXjFhuREQE/vzzT5QsWRItW7bE0KFD0aRJE7z//vvYsGEDACAyMhIfffQR5syZg169egEABg4ciLfeeguXLl2C5TFs9kgQBJGVODvToVfvAWg0PygoSKr3AwYMUPQeAHr37o06dergxRdfRPv27Z0uN7MYGnSoBxwAUKVKFfj4+ODSpUvKseXLl+Oll15SpuDz5cuHtm3bYtmyZcYHHVbroz/xuN08Qqg/2TSwJJ+sDk3YP/WUscRWTRhCmVuSUI5s6v2bvZ/bLVfWZs1O0eo6Ne3QhoG1l5ar31EoQUn/yPqDS6uxTQypqfN+ceSuo7scsc1Mc61djXid3V2fu5C1w0gbtWFVVfeZ+Ny52DUiM4OOyMhIBAUFKcd9fX01aXPnzo2HDx9yx9LS0hATE6N5gahRv4CAdJesxYsX4+bNmyhQoICm3IoVK2rKrVGjBlasWKGkWbNmDXx8fNC1a1fl2Jtvvonp06fj4MGDqF27tuPGZ6DWepkWafI9up5STZXkc1QHr2kSNz+JTjm0TZVX5l4r03ub5dqxR/pelem9kFb7PpRoj6R/ZP2hfVep2+Gk3jtMa6ScnKm3hGtwdtChV+8BaDT/wYMHhvS+Zs2ayux2+/btnS43s2RqIfmKFSuQnJysrOtITk7GpUuXULp0aS5d6dKlsW7dOrvlJCUlcdNK0dHRmTGLIAgiyzBbzDAbHHRkpA8KCuJeQraoUKECrl+/jujoaCXt2bNnwRhDhQoVdNeZEaEkJSVFV7nJyclYsWIFN5A4e/YsChcuzL0sy5Qpo5yzNeggvScI4knCqOYb0fuwsDCEh4fjzJkz3PEzZ84Y0nsgXfMz9N6V5RrB6ZC558+fx1tvvYV3330XFStWBADExaVvyhMcHMylDQkJQWxsrN2yJk2ahODgYOVPnHIiCIIg0mnTpg18fHzwww8/KMdmzpyJQoUKKV/yrVYrBg8ejD179gAAjh49inv3Hm2cl5ycjG+//RblypVT9NZRue+88w7u37+PiRMf/QofGxur0Xs/Pz/4+PjY1XzSe4IgCP289NJLWLBggfId+9y5c9i6dSteeuklJc3KlSsxfvx45fOWLVu4MlavXo2LFy+iefPmhsp1NU7NdPz7779o2rQpGjVqhK+//lo5HhCQvqNpVFQUl/7hw4c2I55kMHz4cLz33nvK5+joaHoREQSRIzGZTTAZjMNuMksdgThCQ0Mxc+ZMvPXWW9i1axfi4+OxZ88erFy5UolCYrVa8fXXX6NUqVKoV68eoqOjUa9ePZQpUwZhYWHYtWsXTCYTFi1apKvcYcOGYcWKFdiyZQsX6z0wMFCj94mJiUhOTrar+aT3BEE8SRjVfCN6DwDjxo3Drl27UL16ddSoUQMbNmxAu3bt8Morryhpdu7ciVWrVimb/33zzTcYOXIkKlasiBs3bmDr1q0YMWIE2rRpY6hcV2N40HH58mU0bNgQzz33HBYuXMgtFPTx8UHx4sU1K9/Pnz+PsmXL2i3T19fXpi+byc8fJr9cMHlLdkcUw+X58LHkzX4BdtPK8mnredROs+jrqQ6fl5rCl+vrZzudAxs09qjyij69A+sO5T5/s3uK/UrUoSh9hTpU11JTv+i3m2J/8xiTqn9Mvv72bRHLdRQKUo0sZK+R+0Vtn3hdZWmN3Esms3JME4rSgC+wobUHah90F61ZcNZWR2somMRWmTQbk20VduwxpRgIwSvB3SFzgfT1GPXq1cO2bdvg5eWFH3/8EREREcp5i8WCL7/8Es8//zwAoH79+vjzzz+xfft23Lx5E926dUP9+vXh7e3tsNwvvvgCc+fOxebNm1GtWjUufdmyZTF37lwkJSUpGn7u3DnlnC0c6T1g8BlWPW+c3ttIay+ftg7+PuQ0X9RxleZrNFWi+WL93Gchn1rzndV7jX3CIn+ufif1Pr0OieaLGiIJ02svnViO9F4BhHeeYJskJLcmbWa+PxBPNO7epyM0NBSHDh3Chg0bcP36dbz22muaNdYdOnRApUqVlM+rV6/G8ePH8eeffyIoKAgzZ87UrPPQU66rMTTouHLlCho2bIjq1atj0aJFNvfe6NSpE5YsWYJRo0YhMDAQt2/fxpo1azB69GiXGU0QBJFdeRyDDgAoUaIESpQoYbs8k0mzKVRAQABeeOEFQ+UOGzYMc+bMwebNm/Hcc89p0rZt2xaDBw/G4sWLlehVc+bMQaFChVCjRg2DLSIIgsh5uHvQAaT/WNO+fXu75xs0aMDtwwEAlSpV4gYizpTragwNOpo1a4a7d++iXLlynF9vw4YNldHRiBEjsH79etSpUweNGjXCunXr8PTTT6Nfv34uNZwgCCI7khGz3Wie7Mb333+PKVOmoG3btli7di3Wrl0LIH1Ge8SIEQDSQz5OnjwZ77zzDnbt2oWoqCj8/vvvWLFiBYXLJQjCIzCq+dlR7x8XhgYdPXv2RGpqqjRNnjx5cOjQIaxYsQJXrlzBpEmTnN+RnCAIIodhslhgNviFW9xxOTtQsmRJjB071mG69957Dw0bNsTWrVvh6+uLKVOmOLcjOUEQRA7EqOZnR71/XBgaCYwaNcpxIgD+/v7o3r27UwapscbHwmpK0/hLsuRHu5uLfqBW8XO8KhyjzGdU9DNP5ndQV9fDlQnA5PXIp1QWs9wk+KWKdTDVjWhNjLNbv1iH6NM78PkPlf+/2ihcM/XaFP4MmMo3WXNO3GMk8dHmMRpff9VnR+N5JusfdZvF66FKq7YF4Nsh5pXt3yD2uYhJ7bstuV/EexDMCvZf2Uws1ME6Eq4YqXX2y2UGf3XXUyYAp2119py7yKjTGp/gkvIel3uVu2ncuDEaN26sK23VqlVRtWrVTNWXofcA7yMv02KAf95Ebdar+Q7rUJWr1ntAvi8Rp1MSvQd4/ZGte5LpPSBovrg2RV2moJPcOYnei/aI6zRkd7Lm/ajuH4mmat4N6vePRO8BoS8FDZNpvklcnym5XzSaT3gUj8O96kmBph8IgiBcyJMy6CAIgiAcQ4MO/XhuywmCIAiCIAiCeCw8cTMdMl+5zIQr5ciKqVR12D8HSdXT64NbTOTPbVb5aGvakQJnyNH9agCunc66LLmqjZkph3M3kFyrLLnPRfe3NH3nHJUjy5eR1kXtNZmdWEjuKhc4D0Sv5jutS8DjfxbE8K2SpKILrVrzOb0HhHY4p/dADu5XA2hccekZJexgVPM9We+fuEEHQRBEVkLuVQRBEJ4DuVfphwYdBEEQLsRkNhkfdBjcoZYgCILIHhjVfE/Wexp0EARBuBByryIIgvAcyL1KP9l60GEyW5Q/DiO+9RKfUXW5Gr9gI/6ckvUW/DoAR3Wo7DHi6yrapsor+vQObjZO+f+b/V/qtlVsFxeGVRNKVf/1kY33uWti4B4wdL+or12m1kmoyrHlY6637Me95sPQfZa1azw0a4dcUKY7sKlZOvJ4Oup+k+umTIv1r+mT64s+vQd4DZNrj1z/dd8DEr0HeM1X6z3Aa77MVqneA7zmG+g73XovliupQ9rnoj2O3hV6Ecvx4H0XCOOa78l6n60HHQRBEDkOs8X4wMaDX0IEQRA5GqOa78F6T4MOgiAIV2I2G49048HT7QRBEDkao5rvwXqfrQcdplyBMOXKBZMvvyM5VLuTitOaply5uc/mlGTVB8nUu1+A3TpEzJJpenEnV7XtGvcQb35nW5Nfrkf/q+02UAcgbL4i1KmeXh9Yewh3bvqfM2zaAkDjQiXbvVa9K685IIg7p929/FG54s7zfIVCn6t3shWntsVrJ7tfVHU62lVW77XU3EtmM8zCfelynA0nayTsbGZCY+rFQMhccfdnaTn2ylSlNZt8bCQmHhcZeg8ImiY8zzLNNwu6qVvzJXoP8Jov1q/WP1GLudCyEr0HeM03Uofm64uqTtGFVq35ar3X2CPRe4094o7kKs2X6T2gX/M1O5Kr+0ei9wDfl2J9Ms03di0FzScIwibZetBBEASR0zBZLIZ9vMknnCAIImdiVPM9We9p0EEQBOFKaE0HQRCE50BrOnRDgw6CIAhXYjY7MejwXB9fgiCIHI1Rzfdgvc/eg47UFCA1RRMakyUnPvogXmjR9zM1RfnfZBZCu3LpeF9glpTIfebWVCTz59R1quvTIPiBsqQEvg51OZI6NCFqRf9fzgbeHrVfqujTO6D6u8r/3x79H1+m4Jsr9o+9tJp2iGnTZH75jx5MJvpqy2wRypTeL/bS2YC7B4R7ibNB9DFm7FHZmVknIRU14b6TrIXQpNWbz1lbs3MbVXkdXX+90D4dTvKf3gN8OGTNdZFovqi/ejVfpvcaGyTvGA0qzZfpvaM6OM2X6j2gvvfFtWdqzVfrPcBrvrN6D8ifI716DxjQfJneA05rvuYeUN1Lmv5wsCaIeLKhfTr0k70HHQRBEDkNkxPuVSbPnW4nCILI0RjVfA/Wexp0EARBuBJa00EQBOE50JoO3dCggyAIwoWQexVBEITnQO5V+snWgw6TXwBM/rnkMbqFEaNZjJet9neVjC7FfExMq/qsuV3U+0CIccjVtot+n8KNp44hbvaX7BviINa59HZWxz4X4sSrfXr7V+7LnzvxI1+OuK5EbY/Kx9dR/HIuxrsYX10dF93B2h0un4H7hetzsSDJ3iDS+P9i7HmzWbNfiWKbuMeJi0LpcXH03VCmK8vNLpjTTFltgkeTofeAg314ZJovrm/QqfkyvQcEbRB1Sr3XkGzPCIneA4LmS9Z0OKv3AK/54ro9teY7q/fpddjXfM3eOup3p7j3kfqzs3oP8O88Wd/J9N7GeS6tbL8RgiAUsvWggyAIIsdB7lUEQRCeA7lX6YYGHQRBEK6EQuYSBEF4DhQyVzfZe9BhTQOsaWBWIZSe2nVEyKIJ3ydJK80nThnbKRMQ3IDEMH+qm8tkFUOQCmFX1aFmJXVoQn5K2izC9YGkfnF6vf8zr3Gfpx/8xm4dXJ87CJeqrlPsH2YnnSat5trpv18g63ONrWa756RhYRl7dF84CEMrtpPDiKip26U/l+4yAffYmpW/AEnbYwDakdxJ/tN7gH/WHD+X+p9hu/kkei+Wq3EDUmu+6ELF6ZR9vXVUB/eMOKv3gg1i/WrNd1bvAbnmy3RcExpf8m7g9E2i94D4ztP//UAsV1YO4dnQjuT6yd6DDoIgiJyG2Wz8lywnf/mKjY2FxWKBv7+/7jwxMTHInTu3zXM3b97UHAsKCkKuXLlspCYIgiAMa76Tep+WloaYmBgEBwfDZNK3BjEpKQkmkwk+Pj6aczExMYiLi+OOWSwWhIeHO2WfHjx3jocgCMIdZPj3Gv0zwIULF1CvXj3kzZsXQUFBaNOmDe7cuWM3/Z07d/Dhhx8if/78eOqppxAUFIRBgwYhMZHf5CwiIgIVKlRA5cqVlb/Fixc71Q0EQRAegZv1HgDGjh2LPHnyICIiAk899RSWLFkiTb9y5UrUrl0boaGhCA4ORs2aNbF//34uzbhx41C0aFFO71u3bm3YNiPQoIMgCMKFmMwWp/70kpqainbt2iE8PBwPHjzAzZs3cefOHXTv3t1unu3btyM8PBx///03oqOjceDAASxfvhxDhw7VpF2yZAlu3ryp/L322ms2SiQIgiAA5zTfCP/73//wxRdfYP369YiLi8PYsWPRrVs3HDlyxG6epUuX4quvvsLDhw/x8OFD1KhRA61atdLMZjdq1IjT+0OHDjnVB3ox7F61a9curF69Gnfv3kWZMmXw2muvISIigksTGRmJWbNm4fLlyyhdujT69++PsLAww8ZZ42NgNaVqwtGxxPhH/wvTVGbR3zY+xm5aab4EfspJnVddPwAwLpyt4OupDuHoy7fDKtShtk5tt6M6TMI6EtE+7pyqHWJvsCTVr56C/7Ho0zugxkDl/6+3T+QLUuXVBFrUrEdRpfC1H3aQs01IK/ajJoSi5H4xqXx6NX0uCXEphthkiY9s0NxlVius/5Wt8Qc34BtsRKg4f2gXrZNw1laNH7ekD1xlqzOw+ATXFGRywr3KpD/91q1bcfr0aaxZswYBAQEICAjAhAkT0LJlS5w7dw5lypTR5HnppZe4zxUqVMBrr72GBQsW4Ntvv+XOpaWlISoqCsHBwbh//z7mzZuH48ePIyQkBK1atUKLFi249Iwx/PLLL9iyZQt8fX3RuXNnNGvWzEDj08nQe0B41kS9lWi+o2fYbj6J3os2ME04W9X9LYbsleiURn/V7ypJHc7qvVinRlNVWizTe0DQfOFdwX2S6T2gX/Ml706Z3gN8H5iE9R6y7wea7x3q+yVRfi0JD8Oo5hvQewCYPn06evTogbp16wIA3nrrLXz77beYNWsW/ve//9nM8+uvvyr/e3l5YeLEifj222+xd+9edOrUiUsbHR0Nf39/eHvbD03tKgy1fNSoUZg0aRIKFy6MBg0aYM+ePXjmmWdw4cIFJc2///6LqlWr4vTp02jYsCF2796N6tWr4969ey43niAIIrvh7pmOgwcPokCBAihZsqRyrH79+so5vVy6dAn58+fXHH/hhRdQsGBBhISEoHTp0rh69SqaNGmCkJAQdOnSBSNGjODS9+vXD0OHDkXlypURERGBF154AbNnz9ZtB0EQRE7GnXofFxeHkydPol69etzx+vXrG9Z7ABrN37JlCyIiIhAYGIjatWsbKtMZDM10DBw4EPny5VM+9+7dGwULFsSSJUuUF9GECRNQqFAhLF++HGazGT179kSpUqXw5ZdfYuLEifaKJgiC8Hiio6O5z76+vvD19eWO3blzRzNz7O/vj4CAANy+fVtXPdu2bcPChQuxYMEC7viHH36IDz/8EKGhodiyZQt69uyJ2NhY9OzZU6nns88+w6effgoAOHXqFL777jts2rRJmd3w9fXFsGHD0Lt3b/j50aZpBEEQttCj9/fu3QNjTKP54eHhuvU+OTkZ/fr1w3PPPYc6deoox59++mns2rULtWrVQkxMDIYMGYJmzZrhxIkTKFy4sJOtkmNo0KEecADArVu3EBMTg+LFiyvH1q1bh379+sH831STn58f2rZti3Xr1hkfdFitj/5057Ef1lO61t+A64g27J9qwkhiqyy0oUO4kIny/pC6q6hDJmbCHvX0+qBGo7hzX20e+6h+N4UW5GzXTOE7Ga5Vc87AfaeTzIRlleWV/XIic29ytkxHyMo10geye9kd/eESMrFPhyj0Y8eOxccff8wdM5lMSE1N1RSRlpYGi45QjEeOHEGnTp0waNAgvPLKK9y5zz77TPm/adOm+Pjjj/Huu+9i+vTp8PX1xYULFzi9X79+PUJCQtCkSRPl2EsvvYQRI0Zg3759aNy4sUN7FNRa76TmOwp9ay+fI/jwrYKDgDs035FbkroO2f0shul10h7RhVat+Wq9B9yj+Rq7ufdh9tV7wkNwcp8OvXoPQKP5qampuvQ+LS0NPXv2RGRkJPbs2aN8NwfArdcLDg7Gd999h99++w2//vorhg0bpr89BjC8piMyMhJDhgxBXFwcjhw5ggkTJuDll18GACQkJODmzZsoUqQIl6dIkSJYuHCh3TKTkpKQlJSkfBZHfwRBEDmGTITMjYyMRFBQkHJY/NULAJ566inNL1xRUVFITExEwYIFpdUcO3YMzZo1Q48ePfDFF184NKtUqVJIS0tD27ZtcePGDYSHh2P58uXK+X/++QeFChXiXmQZ+v/PP//YHHSQ3hME8UThZMhcPXqfP39+eHl54datW9zxW7duOdT7tLQ09OrVC3v37sWOHTtQtGhRaXofHx8UKVJEccVyB4bXPwUHB+Pll19Gp06dULlyZcyYMUMxMONFIsZ0DwwM1IRmVDNp0iQEBwcrf+6a1iEIgnA3GRtFGf0D0vfEUP/ZegnVr18f9+/fx19//aUc27hxI8xms7LQEEjfcyM+/tGi2uPHj6NJkyZ4+eWXMX36dE25jGm3kfzjjz/g4+OD7t27o3379jh69CgXQjcpKUmj997e3vDx8bGr+aT3BEE8SbhT7318fFCzZk1s3rxZOcYYw6ZNm5S1fED6nhvqsOlWqxW9e/fGzp07sWPHDpQqVUpTtqj5Dx48wPnz57nZbFdjeNARFBSEzp07480338S6desQEhKCcePGAUgfXFgsFjx48IDLc+/ePYSEhNgtc/jw4YiKilL+IiMjjZpFEASRPXDzPh21atVC06ZN0adPHxw8eBA7duzA+++/j9dff1355Ss1NRURERH44YcfAKSvvWjSpAmaNGmCUaNGKeER1b+e/e9//8MHH3yA/fv34/z585g5cyY++eQTDBkyBL1798bEiRMxffp0jBw5Ugm7GBwcrNH72NhYJCcn29V80nuCIJ4o3LxPx8iRI7Fs2TLMmDEDp0+fRv/+/fHw4UMMGDBASTN69Gg899xzyufXX38d69atw6JFixAYGKhovvqHqHr16mHlypW4cOECdu/ejfbt2yMoKAivv/565vvEDpnakdxsNqNMmTK4cuVKemFeXihfvjyOHz/OpTt+/DgqVapktxxbi2eAdF9VZk0ztE5D5vup/R3Pfj6pb7BkDYHGz1zqCyyxVVK/dk2J4IsqKUd9s4u+t7zfsnBO06+P6hR9egc3G6f8/83uKZDB2SeEglTbamQdjZhWuq5HvcbFkS+yKhymySz2uYO1MvbKFq+dkzuVau87Vbli+E+dPtfSMm2U6wrcsRZEb1qWmuwgpU6c2fzJYPply5ZhxIgR6NatG7y8vNCrVy+MGTNGOW8ymZA/f34EBAQAADZt2gSLxYKdO3eicuXKSjovLy9cvXoVQPpLKmPgcfPmTRQvXhxz5szh1n2UK1cOaWlpuHbtGgoUKIBKlSrh22+/RXR0tOImkKH/9jTfod4DhtZpyNZ36dV8h2tBJGsIuLxSnZKsS4CD9xqnzfb1XmOPcF+ZZP2qPifRe4DXfLXeA3LN1zyXas2XhdKWrKOR6T0gvrvl7woOIfwxp/kuWitDPCEY1XyDet+qVSssWrQIn3/+OaZMmYLy5ctj+/bt3FKGoKAgZSfxpKQkbNiwAT4+PujcuTNX1oQJE9CnTx8AwKxZszB58mQMGzYMQUFBqFOnDpYuXerWHckNDTrmz5+P7t27K/67Fy9exObNm/Huu+8qaXr06IFp06bho48+QqFChXDy5EmsX7+eQigSBOERmMxmmAwOyIymDw4OxowZM+yet1gs3CZQgwcPxuDBg6Vlent7491331X0fPv27VxY3rS0NPzvf/9DaGgonn76aQBA27Zt4efnp8yAMMYwbdo0VKpUSfpDE0EQxJOCUc03qvcA0KlTJ83+GmrGjx+P8ePHA0j/YUfcBNAWlSpV4vbzeBwYGnQcP34cY8aMQZkyZZCQkIA///wTL730Ej766CMlzZAhQ7Bv3z48++yzqFy5Mg4dOoRXXnlFCblIEATxRGNyYqbDlHWbItrD398f7dq1g7e3N8LDw3Hy5En4+flh2bJlSijcvHnz4ueff0bPnj2xZs0aREdHIyoqCuvXr89i6wmCIB4TRjU/G+r948LQoGPq1Kn46KOPcOTIEXh7e6Ns2bIoUKAAl8bHxwe//fYbjh07hitXrqBUqVIoX768U8aZcwXCnCsXTN4+3HHNbq3qPAFB/AH1FKlsd1r/AL4OMRSZ6obSlKKyR3RL4mwX7BbbZfJ7ZINZ4mqkqcPXn08q2sedVO1ILvQVS3608FNti606rZJz6un1gc9/yJ2b/if/66w6r7gDLGebeD3UfSm6EAhpZfeLSXXdzQ5+feD6WUyrqkO8l2Ayw5xLuC8zbHN3+FZCN2aWKW/TJ45atWrhr7/+wvHjx3Hjxg0UKlQIzzzzjOY5ad++PS5fvowDBw7A19cXderUcWp/jgy9B3htlD2/gKBj4o7gOjVfpveAoKmijqs1TNB07t0g0XtA0HyJW5Szeg/wfaXWe9Eemd6L50V3KrXmy/QeMKD54j2gfh86qfeAXPPFfub6UqhDo/kEQdjE8Fs2NDQUTZs2dZju2WefxbPPPuuUUQRBEDkWkwkwGZw+N0l3lMgyLBYLqlSpgipVqkjT5cmTB61atXpMVhEEQWQjjGp+NtX7xwH9tEcQBOFKTGYnBh2uX5RPEARBPAaMar4H6z0NOgiCIFwIM5nBDL5UjKYnCIIgsgdGNd+T9T5nDjok4UDthiZ1VE6m7FGHTNRfphhmzyQrxw2+/kbCimrQmVf06R1Q/V3+/MFv7Nqje32DaIvony29X+yHQXRYj95zeNQ2R23Se00yU44sLxemUhLC0hEmSbhjvfkcIbNVbz6jeXVBMx2uxVGoZr33lzv03kC5Ur0Xy3HT2i6nNd9APrXmy/RetMfQc6i2R6b3AH+/aMINS65dJvSe8DBopkM3OXPQQRAEkV0xmYz77Hqwjy9BEESOxqjme7De06CDIAjClZjNxjdOdMNGiwRBEMRjwKjme7De06CDIAjChdCaDoIgCM+B1nToJ3sPOqxWwGrV+MJyfqBCFtFnVZZWmk/0v5WkVe8uqfGZTZP4rEr8QjV1WGV+qfbbLML1gVCOus2iv7GmTIkvLNfnQj7Rp3dAjYGPzgnrP5gd2wChLyXt0NgjGqs658jfWTohqrJBWw5T6mHCGUO+wUbWSajPG1mboe4PWZkO4PI6m88RMlt15lPnzdQaJyLz/Kf3AP8Ma7RQyMYkz7BezZfpvZhW3E2Yq1OqU/rWfQGC3gPC2jMn9V4oR7bGxFm9F8uR6T3Aa774DDPpu9N+O6T3QGb6zk79jsohCOIR2XvQQRAEkdOgheQEQRCeAy0k1w0NOgiCIFwJDToIgiA8Bxp06CZ7Dzq8vAEvb5h8/ewmMQnh8kw+fFqTl/ejD5KQfCYvH91maepQ2yDUobbdYR0qW2V1iNPJ6nzp9ehss4/9ftWUaRXKkVwTpCTbrUOchpaFV/z2+NxHH4TpbK5ccWpbsJ3LJ7lfTGK/iqivj9ivsutsMj2qx4CLnRRH4SVl5UpdPiT3jtMuBPavhwZZnUbskfWPmO+/tKYUF4VUpUGHc/yn9wCc1nyT+Oy7QfPF+tV1iHZL6xA1VlIHp/kyvRfsEdGr+c7qvViHTO8BXvM5vQc4zdfYbbXfHyLqvtS8V2WaL/azul+NXGfiyYcGHbrJ3oMOgiCIHAYzmZxYSO65IRQJgiByMkY135P1ngYdBEEQroRmOgiCIDwHmunQDQ06CIIgXAltDkgQBOE50OaAusnWgw6T2aL1nzWK2t9WVpaDzVrU/pya8JzqOozYJqtP5jcsphVDosp8enX2gaZM+7lslCOpX1Ku6NPbv9Ibyv/TD89yqj7AQZsNpONsl9WpuZdM9tM7WpvhLHrLNVK/u2x1tk5n7XF3O2imwylcrfeA85ov1T+xDr22OUC6NlD9vxv0Xiz3ceg9wGu+Wu8B5zXfyD0kS6tZt2dI8wmPgmY6dOO5LScIgiAIgiAI4rGQrWc6CIIgchq0IzlBEITnQDuS6ydbDzpYciJYklmzyytLTnz0QZzyFMLcydKqw/mZxXxJidxnqEPWJgvnUlNslgmIoV2FXUyTEvi0KhusCXF2z8l2h7VpnxqJixBThT4Up+w1O7aL/WMnLXMw1c2FghTapZ5eH1D1Hf6ceidboa9k94BmylyF2OciMpng+kMM4cisYIn/lZ3dQubK8rksZK4BjLg+yWzVm0+VV/rcGMFkNu5u4cEvoQwUvQc4zddcF4nmy9KKGqbWfJnea8pV6b1Yrja0q2rnbIneA7z+aMLgynYE16v3wmcmhLplkr7Sq/eAXPM14d5V7RLdqdSaL4ba5TRf9s6H85qvW+9t2EB4GEY134P1PlsPOgiCIHIctKaDIAjCc6A1HbqhQQdBEIQroUEHQRCE50CDDt3QoIMgCMKV0KCDIAjCc6BBh26y96DDyxvw8obJ20d3FpOXj/BZ5WupCUPorUon1CH60KrDyQr+m5x9gs+q+pzWT1fwb1WdF32D1aH9NH6xoj0y/1J16F1ZOrFMq+AX6/vIPtEek1X1QDnwdVX722r8oVWIPr0Dqr/76NzBb/jERvz7JX0uTSsL2SjeSyaT/bLFfnVZ6EVnfYxl+bKb37Jr2yh9HgxAO5I7yX96D8BpzddcQy7UuKiTqjokei+Wq7FNpX/iOX4tngPdVmmEGMqV01gn9d5hWnUbJXov2sPpvQ37uPpFe3RqvlrvAUHzndR7R/Vr0hrRfMKjoB3J9ZO9Bx0EQRA5DZrpIAiC8BxopkM3NOggCILIgaxatQobN26El5cXOnbsiEaNGrkkjzPlEgRBEO7jypUrmDNnDq5fv47y5cvjrbfeQmBgYKbzOFNuZvDc4RZBEIQ7MJmc+zPAsGHD8Prrr6Nw4cIICgpCy5YtMWuWZAdnnXmcKZcgCMKjcbPenzt3DpUrV8bJkydRqVIlLFq0CHXq1EF8fHym8jhTbmZxeqZj1apV+Oijj9CiRQt8/fXX3Lk9e/ZgypQpuHz5MkqXLo3Ro0fj2WefNVwHS4wHszCNLyxLVHWI4HvLBJ9aWVppPjF+tyovVyYApo7bLvrtqj87in2ujpOeyNfP7Zshq8OGfXwdqnaIfsOSWOxiHVx8c409Kv9oR3s9qNPa2T8B0F4PtU/vgBoDuXPf7P2c+6x3/wWxzzWo9x8R9zFR1aG5y6zM7jURY9zLkPkUy8o1kk9vmY5wVZ2PE5aY4DiRHtzsXnXp0iV8/vnnWLZsGTp06AAACAkJwbBhw9CrVy8EBAQ4lUeWpkuXLujatSuuXbuGrVu34qmnnlLKjo2NxcSJE7Flyxb4+vqiS5cuGDhwIMwG1ycpeg/wz5r47Eg031Fau/kkei+Wy4R9OmQaz7VDovfpdTyyQdQX6XtEp94DvOY7rfcae8T1MLJ3lZBWsteO+pqI6/bUmu+s3gMONF/sZ/X7SKiDfr31cNzsXjVq1ChUqFABy5Ytg8lkQq9evVC0aFHMnj0bQ4YMcTqPM+VmFqeelStXrmDAgAGwWCy4du0ad+6vv/5C06ZN8cwzz+C7775DaGgo6tevj3/++cclBhMEQWRnMnanNfqnl/Xr18PPzw9t2rRRjr388suIiYnBrl27nM4jS9OzZ0/ExMTg7NmzSEnhv3R37twZa9euxeTJkzFkyBBMmDABI0eO1N0egiCInIw79Z4xhnXr1qFLly4w/TdDEhISgpYtW+L33393Oo8z5boCwzMdaWlp6NatG0aPHo3Vq1drzn/yySeoW7cuPv30UwBArVq1sHPnTkybNg0zZszQpCcIgniiyMRMR3R0NHfY19cXvr6+3LF//vkHERER8PF59Et9oUKFYLFY7P64oyePvTRmsxnHjx/HDz/8gJYtW3Ll7tu3Dxs3bsThw4dRpUoVAMCDBw/Qv39/DBs2DCEhIcb6gSAIIqfh5EyHHr2/desW4uLiULRoUe540aJFcfDgQZvF68njTLmuwPCgY+zYsQgLC0Pfvn1tDjq2b9+OESNGKJ9NJhNat26NDRs2GDbO5OWdHopOnGqWhC7VhATUGeZUMw0vhstTT0sL0+t8WEQxBKrF9v826uTCMooh+NRphTrEMI2a6X91WrUNYl+pwyAK55jVvq2iPZxrj+DCBQOhIDkkoTDF6fWBdYdyn7/ZPcVusVzfSfpNk1bm/iDaajLZbZsm3KSL3JK4ct1RpgvLzTZ4ya+/XtLDJxrz2c1IX7hwYe742LFj8fHHH3PHEhISNAv9TCYTAgICkJBg20VMTx5baS5fvgzGGF5++WXNyxBI1/v8+fMrAw4AaNOmDfr27Yt9+/ahdevWklbzKHoP8PeWg9ClnBYZCHPKaapE7wFeU7Whz2Uarz4n0VAImi++j1R1OK33AN9OMdS56pxU7wV7RLdLru9kei/aIyIJd6/WfKf1HpBqviatzG3MReG2iZyJUc03qvcANNqcO3duqd47yuNMua7A0KBj27ZtmDdvHo4ePWrzfGxsLB4+fIiIiAjueIECBXD16lW75SYlJSEpKUn5LI7+CIIgcgqMpf8ZzQMAkZGRCAoKUo7b+qIfHByMBw8ecMdSU1MRExOD4OBgm+XrySOmSU1NxSuvvAIAKFeunM1yIyMjUaBAAe5Y/vz5YTKZ7Go+6T1BEE8SRjXfqN4D0Oj3/fv3pXrvKI8z5boC3fNBd+/eRc+ePTFnzhyEhYXZTJP2368Y6ul5IL0jU1NT7ZY9adIkBAcHK3/i6I8gCCKnYGXMqT8ACAoK4v5svYQqVqyI69ev4/79+8qxEydOgDGGihUr2rRJTx4xzejRowFAWm5aWppG7y0WC7y8vOxqPuk9QRBPEu7U+7x586JgwYI4ceIEd/zvv/+2q8t68jhTrivQPejYs2cP7ty5g8GDB6NcuXIoV64cduzYgU2bNqFcuXK4du0acufODR8fH9y9e5fLe/fuXYSGhtote/jw4YiKilL+IiMjnW8RQRDEE0zr1q0RGBjIrZH76quvUKpUKdSoUQMAYLVa0aNHD2zevFl3HjHN0qVLcfz4cXh7e6N3797o1asXAKBJkyaYOHEiACA0NFSj91FRUUhJSbGr+aT3BEEQ+unWrRt+/vlnZVbiyJEj2LlzJ7p166akWbBgARdxSk8ePWlcjW73qqZNm+L48ePcsXfeeQc+Pj74+uuvkS9fPpjNZlStWhUHDhxAv379lHT79u1D9erV7ZZta/EMALDkJDBvM0RPOS5cnSR0qaO0aky+ftJyIAmXx4VFFHxEzX6PwleKs29iWnW5ViGUH7+GQgg7KJYrCxko8ZVW59OE003TH6ZR7ePLNL7AVrtpRbh1NJI2iedEn96Bz3+o/P/11vF8ZpXvtKbPDawBUveHxheYWR+FZnQUQliGkTUUklCUTudz1lZ3tdFZ7IRmZslJNhIbh0H7rOvJo5fg4GDMmzdPGVTEx8fj8uXLWLNmjRKm1mq14pdffkGtWrXQrFkzXXnENP7+/vD19cXcuXNRuXJlHDx4EL1798b//vc/PPPMMwCA5557DlOnTsXNmzcVN6t9+/YBgF3Nd6T3ADjNl2mxeN5RWjVqzTdShyaUqkrH1XoP8NdVpvcArz/aNRT2NV+33gOc5mvelWq9dVLvAUHzndR7W/bZOyfTe0DQfEG3uT53pD2SsPkazSc8CqOab/T9MGbMGBw4cADPPPMMKleujD179uCNN95Ax44dlTR//vknVq1ahS+//FJ3Hj1pXI3uQUdgYKDGrzcgIAB+fn7c8bfeegvvvvsu+vfvjxo1amD9+vXYuXMn1q5d6zqrCYIgsilWlv5nNI8R2rdvj0uXLmHPnj3w8vJCgwYNkDt3buW8xWLB/PnzlVkMPXkcpbl58yYAoGTJksifPz+A9NmRggULYtSoUZg9ezYSEhIwYcIENG3aFCVLljTWKIIgiByIUc03qve5c+fGzp07sX//fly/fh2fffaZ8sNPBj179kTDhg0N5dGTxtU4vTmgPV599VWcP38eDRo0QFBQEOLi4jBlyhS0aNHC1VURBEFkOxhjYAZXkhtNDwBhYWF48cUXbZ4zmUzo0aOHoTxG0mTg7++P3377Dd26dUNoaCiSkpJQo0YN/Pzzz7ryEwRB5HSMar4zem82m1G3bl2756tVq4Zq1aoZyqM3jSvJ1KDju+++s7nr7CeffIIRI0bg9u3biIiIgJ+fn43cOjCbbe4oa5KEIRSnk6VpJWimWlV5tedU4QzNctcnKVw5ktCLmnxCeEOdaTXhbCXhfTUubpK+lAaOE21V/y/aoz6XCbcb9fT6oCZjuHPf7P/Sfh2ae0sShtbRvfVferEOI9JjaEdyN+Rz1la3tdHJXddFe0x2ro2zPI6ZjqygRo0aOH36NLcbOQBUrVoVZ86cQWRkJHx9fZEvXz7nKtCj9xnpuM8y3dSn+Y7ymWTa6KzmS99Vrtd7QNBY2XtMKMYdeq+xRzzn5PMoutCqNV+t95o6ZHoPSMMfE56Nu2c6niQyNegoVKiQ3XMBAQEoXrx4ZoonCILIkTyJ75RcuXLZDZ0LaGPOEwRBeApPoua7A5e7VxEEQXgyT+pMB0EQBKGFZjr0Q4MOgiAIF/K41nQQBEEQWc/jWNPxpJCtBx0mH1+YfPxg8vXnT0h8600+fvY/y/xSfRysO+FCgAo+vOoQgUKoQ852mX+tYIPJL5ekfj7soKZ/ZCF11Ws6JG3WnBPrVIdUFMMgquo3+QfYPSfmdXgN1IghJdW2ieELVW0WfXoH1n4U1/qbvZ9Lq5RdSy6d2A6TWRNKUzklrdEA4r3lIKyyPWT2OGuroXxiv6rbkZk2qu97O/lMKZlYj6XC+t+f0TyeTobeAw6eNYnma549ZzVfEy5adYVE7VF9lr2rZHoPCJovCVftrN7bqtPuOZnei+eF+jnNl+i9I3s4nNR7gNd8td4Dcs2Xfu8Q0xp5dxFPHEY135P1nlZDEQRBEARBEAThVrL1TAdBEEROg7H0P6N5CIIgiJyHUc33ZL3P1oMOlpgAZoFmila2i6i4k7beHUeZMEVrTbC/O7Vst3DNjuSSaXlxx1X1tJM1PsZuHdopa/v9I8K1Q7LrrjgFptmhVrCPO6eaQteEVJbsUCtO4avdETTXQzWdzcT2CtdAdg+op9cH1h3KndPsXi5x9VHvUCveg2BWWOOj/8tHO5Lrzucu7OxIbk1McE3xtJDcKRS9B7hnTaO3Es13lJbLp9J8md6L5Yq7hXM7kktcb2V6D/CaL92R3Em9B3jNl9njrN4DguY72JFcrfmi+5n6mojuS5zmS/Qe4PtAdKdSa75U7wHpjuQazSc8ClpIrp9sPeggCILIadBCcoIgCM+BFpLrhwYdBEEQLoQWkhMEQXgOtJBcPzToIAiCcCEMTqzpcIslBEEQhLsxqvmerPc5f9AhCWOXGWS+wO7Il6ly3NEHYlhK4TST+O1mN7i+lPSV6NM7qMkY7vM3u6fYr8TRNXDmvngc6xtcVacsX1a0Q4ab7bEyBqvBUYfR9B6NG/QuM7r92DXfTe889XMh1XsgW2u+ph91ar5L9Z7wKIxqvifrfc4fdBAEQWQjGIz/kuW5ryCCIIicjVHN92S9p+E6QRAEQRAEQRBuhWY6CIIgXAiFzCUIgvAcKGSufrL1oMPk4wuTjx9MvnyMbm56RvCtFON5qz/LfGbFfDI000OS9Q2c7WLsdYkNGnukdfjzn1Vxy6VtFvpVHSddE4veKu9nDnX9gm3iHgmc77BQp9p2TZ9z+5YIZQp7rnCI94vaPqFfRZ/egc9/qPw//c8ZdqvQ9I3JrBzT7DFgZA8Lrki5/7esXHVevekcpXUVsjqN2CPdn0G8X/5La04TPdmdxInNAT16vv0/MvQe4LVJqreQ66ZbNF+yvkHUVJnmy95V8jrs6z2gX/PFfTG4/aac1HuNfTK9B7j+Ee0220knlivVe4DrS+376FG/yvQeMKj5hGdhVPM9WO+z9aCDIAgip2EFg9XgW8VoeoIgCCJ7YFTzPVnvadBBEAThQpgTMx0eHMyEIAgiR2NU8z1Z77P3oMNs/u9PEgJPc06YMtYZLtVROXbrd5hW5bIkprNI6jRUhzAVrrPNGncVs8q9yGHIXMkUPleO/lgF2lCHsjpULkKO8knvF7Pt/22gnl4fUP1d7tw3+7+U5lXsFa+VNJcEB7ZKy1W7Gxiow0XOR3JkdRqxR3bf20vrojCYtKbDSTL0HjCmhTKN1av5jsLVyjREYhtnj0zvDdUh0Xsb5+2l5fReqNNpvXdQv8wevXqvscdR+6X3kn1bRXcqteY70nvCs6A1HfrJ3oMOgiCIHAbNdBAEQXgONNOhHxp0EARBuBBa00EQBOE50JoO/dA+HQRBEARBEARBuJVsPdPBkpPAvLXjIpYY/+iD4JPJhPB5LDnRblq76QCwhDg+gSovVz8gDd/KhWEVwilahTrMan9bI3UIiG0RKnmUTuyrJEk+oU6WGGcnIfg2S0IdatKK4SZ12qZpr8ZW+/eL3XQO0oo+vQNrD1H+n37wG6FgK6z/9VdmwtA6CpNrr1wj+fSW6QhZWF4jYXEfJywxwTXlkHuVU+jSe0Cq+Rot0Kn5Mr3X2CDTNCHstlrTZHpvqA4BvXoPCH3lDr0H9L8PAddovkzvAf2a72Atilrz1XoP2NB8wqMg9yr9ZOtBB0EQRE7DyhisBt8qRtMTBEEQ2QOjmu/Jek+DDoIgCBeSZk3/M5rncXD58mV4eXnhqaee0pU+NTUVkZGRiIiIgJ+f9lfpEydOaI4VLFgQefPmzbStBEEQOQGjmv+49D46Ohq3b99GoUKFbOq3LW7dugUAyJ8/v+bczZs3cffuXe6Yj48PypQpo9smQ4MOxhjS0gRXCZMJFjEUoIsw58oNc0Au7VSzOrSfuKuqXy7+c0qy3bSyfLKwhFYxLKLaTUmcTlbvuOrFuzOJ0+tqG8xiOeq0oruKsBuqaB+XVr3ruF+AfVsd7LAqm4hmqnvE7B8gSSm41gj9w10DcbdyVVrpbuWQ71irbidzcB/L+kQ9vT6gxkD+ZDELzIEhtsuUXedMwJXrjjINlOso1O5jCcWrAxNzTT9lx5mOY8eOoWvXrrh16xZSUlLw9NNPY+nSpShSpIjN9JGRkRg7diyWLl2K8PBwXL9+HR06dMDMmTMREhICkyn9qlWsWBHFihVDQMCj53zkyJF45ZVXDNuo6D3AP8OiTko0X633ttLay+coDK1aUzV6on4uZDol0XtA0Hzx2VLrpJN6n16nSo9FWyX6plfvAbnma1wt1ZovujfJ3p120gH69R6Qa75evQcEzS8n7HpOPPFkt5kOq9WKAQMGYM6cOQgNDUVMTAymTp2Kt99+226eOXPm4LPPPkNMTAzS0tIQFBSEGTNmoGXLlkqazz//HLNnz0bRokWVY0899RQ2btyo2zZDC8k/+eQTeHt7w8/PT/mzNRr68ccfUaJECXh5eaFcuXJYtWqVkWoIgiByLFbGkGbwz50voeTkZLz44ouoWbMm7t27h7t37yIgIEA6MDhy5Ahq1aqF2NhYXLlyBSkpKVi0aBFCQ0Px448/cmk/+eQTFCtWDBcuXMCVK1ewdetWxMVJ1gAQBEE8QRjVfHcPOr766issXLgQR48exfXr1zFv3jz069cP+/bts5vn+PHj2LBhA27evInbt2+je/fu6NSpEyIjI7l0devWxYkTJ5Q/IwMOwInoVTVr1kRqaqryJ061rFu3Dn379sXEiRPx4MED9OvXD126dMGhQ4eMVkUQBJHjSN8oihn8c589GzduxL///ouJEyfCbDbDz88PY8aMwb59+2y6RwFAu3bt0KtXLwDAjh07kJaWhjFjxqBIkSJ4/fXXubTDhw9HTEwMzp49i4MHD2L37t3o27ev+xpEEASRjTCu+e61Z/bs2ejVqxfKly8PAOjUqROqVq2K//3vf3bzfPPNNyhZsiSAdA+mDz74APHx8fjjjz+4dFarFZcuXdJ899eLy0PmTps2De3atUO3bt2QO3duDBw4EFWrVsXXX3/t6qoIgiCyHRn+vUb/3MVff/2FggULonDhwsqx2rVrK+f0cvLkSa6MDK5cuYILFy6gdOnSmDx5MkaMGIGFCxfi+vXrmTeeIAgim5Od9D4mJgbnzp1DzZo1ueN16tQxrPcANJq/efNm1K9fH8WKFUPp0qWxefNmQ/YZXkh+9OhR+Pv7w8/PDzVq1MBnn32GypUrA0hf83HgwAFMmjSJy9O4cWMsXrzYaFWo3WswLGYzYBLGRkxyxSS+sFLEfJo6TPrOiZu+qG0XndfF4a7ap1Vjt846bNpnB9GHVj3lJzsHaEMf2kO0TYOqXJPYQZI+V6cV+1EsR9Yfavsc9ZusLeq8xfh76dy/V5G/dlcAQIEiYUI+eZV8/QbSqst1VT5nbc3ObVTlTdN7T7uR6Oho7rOvry98fX25Y4wx5YVgj8DAQBQrVgwAcO/ePYSGhmrKDQwM1PVr1QsvvIDY2FikpaXh9ddfR2pqKry80l8dTZo0wdmzZxEZGYkzZ86gdevWiI6OVt4FHTt2dFh+BrXfGuO29YEE4VZU6zjOnTmDiFzBymdO8x+HFhrJK8uXGVvd0U5n2yjmVeXLas3Xo/cAcOPGDdy7d09aVqlSpeDn54f79+8DgEbzQ0NDdc9OxMXF4e2330bDhg25wUuNGjVw9uxZlClTBikpKfjoo4/Qvn17HDt2DKVLl9ZVtqFBR8GCBTF//ny0bNkSDx8+xIgRI1C/fn38/fffKFq0KGJiYhAfH4/w8HAuX758+ZQV8bZISkpCUlKS8jnjQuz5dgyCAvy1i7/UMcOFwYK4YNcafc9uWlk+TVxybg8N/px6kRtLFRYyqhbumYQFb2I8cVOu3LbtFutwsJBcGlNd1Q6zqj6Aj31uDgjizwkLNK3xMfbrUNlnziWUo4nbbn+BpLTP1QvAZXuaiHnF+0V1fazxvABoFrCq6tQsNFXVId5L+Wt3RVcUBAB89f0ovg4joucgjrzdcl2Vz1lbs3MbVXmj4xKQv+NAGxmMkZmF5OKvSmPHjsXHH3/MHUtJScHLL78sLa927dr4/vvvAQBeXl5ITk7WpElOToa3t7fmeAYmkwlvvfUW6tevjz59+qB9+/ZYvnw5ChQogE8++QQAUKJECTx48AAAUK5cOYwZMwZvvPEGzGazXc23q/e7dyMoKMhmHoLIKUTkClb0HhA0/3FooZG8snyZsdUd7XS2jWJeVb6s0nwjeg+kr5P+9ddfpWUuX74cZcuWVX4QEjU/KSlJqvfqdJ06dUJiYiIWLVrEnXvppZeU/729vTFlyhQsWLAAS5YswciRIx2WDRgcdKh9eXPnzo0ffvgBxYsXx5w5czBhwgS7+axWqxLtxBaTJk3CuHHjjJhCEASRLclYLGg0D5AeNUr9xdvWr14+Pj5212LYonDhwrh58yYYY4oO37t3D8nJyTbdpdR19+jRA61atcIHH3yAjz/+GFOmTMG4ceMwYcIEmG289AsVKgSr1Qqz2WxX80nvCYJ4kjCq+Ub0HgBGjBiBESNG6Co7X7588Pb2xo0bN7jjN27ckOo9kD5Q6dixI/755x/s2LHDZqAoNRaLBREREZrF5jIytU+Ht7c3ypQpgwsXLgBIH4gEBATg9u3bXLo7d+6gQIECdssZPnw43nvvPeVzdHS0vHPU4QsdhFN0GrFc9a/uBvLJ6xDCNKptN1COZodnp+2RpJX1RyZ21dZta2bCvkruFyN1cHkdhNhUU6BImPJr1+AWE7lzX20cZSuLcVy1k7fs1yMjvyw5m0/mHumie8DdWKH1+NOTBwCCgoJc/mt/o0aN8P7772Pfvn2oW7cuAGDt2rXw8vJCvXr1lHQnTpzg9tjYu3cvWrVqhffee0/59e2ZZ55BfHw8bty4gfDwcERERHB6v3PnTuTKlQvx8fF2Nd+w3hNEDkKt9wCv+R6h90bzSrYDcLqdj1HvAeOa70699/b2xvPPP49169ahT58+ANL3W9q4cSM3cXDz5k1ER0cre2xkDDjOnz+P7du3o2DBgpqyk5OT4ePjw5Vx7tw59O7dW7d9mVpInpycjDNnzigbTZlMJtSpUwfbt2/n0m3duhV16tSxW46vr6/S+e64CARBEI+LNCtz6s9dVKlSBR06dMDrr7+OdevWYdmyZXj//fcxcOBAxRU2NTUVFStWVKbw//zzT7Rq1QqtW7dGly5dlPCImzZtgq+vrxI6d//+/bh69SqWLl2K8ePHY8qUKWjfvj3MZjNq1apl0x7Se4IgniSyk94D6W5av//+O8aNG4fdu3ejZ8+eSE1NxYABA5Q0kydPRvPmzQGkrxPs0qUL9u3bh2+//RYPHjxQNF+9lqRGjRr4/vvvceDAAaxcuRItW7ZEwYIFNRENZRia6ejYsSP69++PKlWq4O7duxg1ahSio6O58IhDhw5FmzZt8MMPP6Bt27aYN28ejh07Jg3VRRAE8aTAnFjTwdwct/2XX37B5MmTMX78eHh5eWHEiBEYOPCRL7PJZMLTTz+tLD48fPgwAgMDsWvXLnTs2BEWiwWxsbG4fv06Bg0aBD8/P/Tt2xdBQUE4ePAg3nzzTTRo0ADffPMNpkyZgp49e0pntwmCIJ4UjGq+u/W+fv362LhxI7788kusXr0a5cuXx969ezl3qYiICJQtWxZA+jrBixcvomDBgtwsNAB89NFH6NGjBwDgt99+w7Rp0/Djjz8iKCgIHTt2xJAhQ5A7N78+WIahQcewYcMwfvx4/PHHH8iVKxdq1KiBgwcPclugN2/eHD/99BMmTJiAAQMGoHTp0li1ahWqVKlipCqCIIgcSRpL/zOax534+/tj3LhxdtdSWCwWbp1I37598corr2DKlClYsmQJbty4gRIlSmDkyJHKlL3JZMIrr7yCBg0aYMCAAdiyZQsOHDiALl264PPPP3dvgwiCILIJRjXf3XoPpEeNbdy4sd3zw4YNw7BhwwDoXydYtGhRfPPNN5myy9Cgo2bNmli7dq3DdN26dUO3bt2cNkqDJOKReO1YmphWHbXAQDhd4ZzsHuHWNEhshVWsn4/GwNluwLdRGxHKyTBw6n7V9KPQH9K+s+pLJ5Yra7OmjfbXlJisYiQN+/eLyU46W6jr0UQIVJ8Ty2FQ+kT06dWs8Vivb7GYNlqIgXtAb4SSzEQkcRWyOg3Zrr7PMhEFRQeZiV6VncidOzcmTJggDRICpEc1XL58+WOyiiCyOSq9B3jNd1rvAXlIfVfpuLPRq1yFO/RezOtivQecj17libi+9wmCIAiCIAiCIFRkKnoVQRAEwePMQkF3LywkCIIg3INRzfdkvadBB0EQhAt5UtyrCIIgCMeQe5V+PHvQ4aJYzrr3rHCDL6HhenS2WdzvxMheHOr6NftyGClHZx3ac483Rndm6hd9ege3+tTuObeQHdZtuILH9WzpIDsuJCcIIuuR6b2t824hq9dtuIocrPmerPeePeggCIJwMTTTQRAE4TnQTId+aNBBEAThQqxWBqtBn12j6QmCIIjsgVHN92S9zxmDjsy4y2SjKTgih5EFblrq6XXN1LsQbjdb46op/Bz4/FqdcK/y4HcQQXgsUvfanKr3gPOanwP1HjCu+Z6s9zlj0EEQBJFDIPcqgiAIz4Hcq/RDgw6CIAgXksYY0gy+VIymJwiCILIHRjXfk/U+Z85lEQRBEARBEASRY8jxMx1iSFaX4Wy5Yj5ZOYL/Ihem1kD9mrC0unOK9shs5c/pDhOcmTp14vAeUJ2Xps2ELdJyTXDKV1X06R3cYqLdc1KM1O2sT600hLGbfttwh60ugBaSE4QH46TeA7yuq/VePOcQvfVnRgsft+a7y1YXQAvJ9ZPjBx0EQRDZiTQ4sU+HWywhCIIg3I1RzfdkvadBB0EQhAuhheQEQRCeAy0k10/OHHRIXFnEnbRdBedO5JYa3ERW784tIN2h3E2uT66Cs91V07UGypFOvW8e6xp7ZDyOcIbidZbdH0bc+tR5M+MOqANaSE4QhE2c1HtAcK99UvQekGuzs7qdmXeFE9BCcv3kzEEHQRBENsVqZUijNR0EQRAegVHN92S9p0EHQRCEC0lzYtBhND1BEASRPTCq+Z6s9zToIAiCcCE06CAIgvAcaNChn2w96DCZLS4NiStdT2CErFhfoPKvNLmsTLEdKU4V47J+zQoel9+qUp9rfE1Fn97Bzcbx52XhFWX3r6vWUGQ1Rp7RjLTZYN0QQRBPGC5aT6bWfLfovZj2Sdd7o/kIl5CtBx0EQRA5jTSr8V+y0qxuMoYgCIJwK0Y135P1ngYdBEEQLoTcqwiCIDwHcq/SDw06CIIgXAgNOgiCIDwHGnToJ1sPOpg1DcyaBpNV8LtX+RqKl46lCX6I1kfzWJq1BjKfReEck5yT+kFyn73t2gYItmvKUe0T4qgdVsncnU5btf0o9Ie07+zXL7XdkH/po74UyzRyv5jUtjrwYVXXI66r4c6J5TDY7xPZtTKANKb7+hH6C5LZ4yJbDeEOe+xeC9f4MFPIXILwYLJY7wEDmu8Jei/Lm0Wa78l6n60HHQRBEDmNNObETIcHbxZFEASRkzGq+Z6s9zToIAiCcCHkXkUQBOE5kHuVfpwedPz7778wmUwoWrSozfP379/HtWvXULRoUQQFBTltIABpWDOHIXVlIVFleZ0NpSbmk9bB22ayOBfKTROy1tkwsKpyOFtgwy3JLHH3MlK/C0LWae4ByWfp/ZIJW0yyMHwmuCY0r4Ey1NPrg1t9yp+Thld0UQjhx1GO0/e5e8MkZ8dBh9VqxQ8//ICNGzfCy8sLHTt2RJcuXaR5WrZsqfyfmpqKxMREvPfee+jYsaOm3A0bNiAhIQGtW7fGu+++67Z2EES2J4v1HuA13+P1PrN5dZAdBx0nTpzAzJkzcf36dZQvXx7vvfcewsPD7aafM2cOli1bxh3Lnz8/fvrpp0yVK2L4Suzbtw8VKlRAjRo10LJlS9SrVw+RkZHKecYYBg0ahIiICLz44ovIly8fJkyYYLQagiAIwkW88847GD16NJo1a4YaNWrgtddew+TJk6V5Nm7ciHr16sFkMmH37t2IjIxEv379MHfuXK7cjz76CNu3b8fu3bvRv39/FCpUiHsnEARBEI+PY8eOoWbNmkhJScFLL72EgwcPolatWoiKirKb58yZM7h79y4GDx6s/L3++uuZLlfE0KDj/PnzaN68OTp27IibN2/i9OnT+OKLL/Dvv/8qab7//nv8+OOP+PPPP3Hx4kVs3LgR48ePx+rVq41URRAEkSNJtTKn/tzF2bNn8f3332PevHno27cvhgwZgs8++wzjx49HdHS0NO+2bdtw4cIFnDt3DpcvX8Y///yDpKQkrlyr1Ypu3bohOjoaX331FW7cuIGuXbu6rT0EQRDZieyk9wAwevRo1KlTB99//z26deuGNWvW4OHDh5g5c6Y0X1hYGFq2bKn8NWjQwCXlqjE06Pj0009RuHBhTJgwAeb/pqtq1KiB559/Xknz/fffo3PnzqhYsSIAoEGDBmjcuDHmzJljpCqCIIgcScZUu9E/d7Fx40YEBASgadOmyrFOnTohISEBO3fulObdvn07wsLC8PXXX+PYsWPIlSsX+vXrp5Tr6+uLqKgojBkzBgDQtWtXWK1W7N+/H+fOnXNbmwiCILIL2Unv09LSsGXLFrz44ovKsVy5cqFVq1bYsGGDNO/p06fRqVMnvPrqq5g9ezZSU1NdUq4aQ2s6Nm/ejG7duiE1NRXnzp1Dvnz5OF+utLQ0HDt2TDMlU7NmTfzwww9GqpIjW+Nhcc+29twaBrfU4CYMrWFIcaoKzZoSSRg6aVo3rbdwFfy6jezmC2u/f6ThdKX+vlnQ52KdsvtDFu5QZru90Mwuam9mQuaKMw++vr7w9fXNlD3//vsvIiIiYFFpY4ECBeDt7c3NUouEhYXhwYMHGDhwIH7//Xc899xzmDdvHrp166aU6+/vj4IFCyrvgYxyU1JScPjwYZQpUyZTthPEE8Vj0HuA13VNON3srPmGwubbSWcLe++RLNJ8d+r9rVu3kJCQgMKFC3PHCxUqhN27d9vN5+XlhRYtWqBJkya4desWPv30UyxYsAA7duyAxWJxulxNPXoTMsZw48YN3L17F2XKlIGfnx+uXbuGKlWqYMGCBShcuDBiYmKQkpKC0NBQLm9YWBju3btnt+ykpCRlyh7QXgiCIIicQhpjhkMiZqQXBX3s2LH4+OOPuWMpKSlo27attLwqVapg0qRJAIDk5GT4+/tr0vj5+SE5OdluGd27d8fKlSuxfv167NixA/7+/ujRowcSExPx+uuvIzk5GSaTSaP3fn5+SEtLs6v5pPcEQTxJGNV8I3oPADNnznS4RGHmzJkoUaKEoumi5ufKlUuq96NGjUJgYKDyuU2bNqhQoQIWLFiA3r17O12uiO5Bh8lkgsViwYoVK3DgwAGUK1cOUVFRaN68Ofr27Yv169fD2zt9wzb1CwUAEhISlHO2mDRpEsaNG6fbaIIgiOxKZqJXRUZGctH+bP3qZbFYMHjwYGl5YWFhyv8hISG4f/8+dz4lJQUxMTHIkyeP3TL8/f1x5coV5M+fH1evXsXmzZvRokUL9OnTB9WrV0dISIhmAJFRLgC7mk96TxDEk4Sz0av06D0ANGrUCCVKlJCWmTHbHBISAgAazb93755U79UDDgAoWbIkypYti8OHD6N3795OlytiyL2qSJEiqFatGsqVKwcACA4ORu/evTF06FAwxhAQEIC8efPi+vXrXL7r16+jSJEidssdPnw43nvvPeVzdHS0ZgSoF81O2p6Os9OVBpDuTm4Ema3ZANmO5E4j7pTq5tB+gIGpd1dd16zAyL2UkdZF7c3MoCMoKMhhiHGz2cyFs3VE5cqVMWnSJNy+fRv58uUDABw5cgQA8Oyzz9rNlxEOfeDAgTCZTMrLxtfXF7t27ULlypURHx+Pa9euKXkyygVgV/NdqfcEkaPIYr0HHLjX5lTNd0bvbeVzEmcHHXr0HgDKly+P8uXL6yo7JCQERYsWxZEjR7iAHkeOHJHqvS3u378PHx8fl5Zr6I5v3rw5bty4wR27efMm8uTJA5Mp/WtYkyZNsHbtWuW81WrFunXr0KRJE7vl+vr6Kp2v9yIQBEFkR7LbQvJWrVohLCwMU6ZMAZDuKvvZZ5/hmWeeQdWqVQGk63TLli2xZs0aAOkLyAsXLgyTyYQbN27g9u3bmDRpEmrWrImkpCSEhoaiVatWCAkJwb1793Dw4EGl3IIFC8LX1xd16tSxaQ/pPUEQTxLZSe8BoHfv3vj555+V7+u7d+/G3r170bt3byXN7Nmzuc8zZsxAmupH+88//xzXr19Hhw4dDJXrCEMzHcOGDUPVqlXx4Ycf4oUXXsDJkyfx5ZdfclPlo0aNQq1atTBw4EC0bdsWP//8M+7fv4/333/fSFUEQRCECwgICMCiRYvQpUsXrFu3DomJibBarfj999+VH4usVis2btyIF154AUC6e1afPn0QFBSERo0awWq14umnn4bFYkHJkiXRtm1bBAQEYPny5WjZsiWef/555MmTR9lEcPjw4TSYIAiCyAJGjBiBo0ePoly5cihXrhyOHz+O4cOHczPkp0+f5qIXXr58GYULF0aJEiVw48YNxMTEYN68edyPR3rKdYShQUfRokVx4MABTJ48GSNGjEBERAR++uknbofaSpUqYffu3fj888/x8ccfo3Tp0ti3b5/UvYogCOJJIY1ZkSa6UejI404aNWqEyMhI/PXXX/Dy8kK1atW4NRcWiwXr169HhQoVAAAVK1bEgQMHcPbsWcycORN//fUX/Pz8UL16dXzwwQeK/2+jRo1w8+ZNfPDBB/jrr78QGhqKrl274s0333RrewiCILILRjXf3Xrv6+uL3377DWfPnsX169dRtmxZFCxYkEvz9ttvc7MYU6ZMwahRo3DixAkEBQWhdOnSmjUmesp1hKFBBwCULl2a25HWFtWqVcPChQuNFq0flR+eOEnlMl97AZetW5DVkdXrUR5HG8U6uM/2gw1obZOkleSV3i/uWlPC8MiX15EPr0y4ZHk1/aoqR5LPmL+vAaFU12nwC7hdMnN/cn687vWrzkzIXHfi7++PevXq2TxnMpls/lpVtmxZfP3119Jy8+TJQ/swEUQGar0HHOi2G/TeQV79a/qc1HujeWU4q/lOvg+dxdmQue6mbNmyKFu2rM1zGbMVaoKCguy6xeot1xGGBx0EQRCEfdKsDGYnF5ITBEEQOQujmu/Jek+DDoIgCBeSagVMBl8qqe6dbScIgiDchFHN92S9z5mDDpXbi7jDdU7ekdwtthvZ5dtJd6JM7Uiut/7MuDpJ7hcjdTi9I7lJkv4x7VBrN61wrZzeyVZan4E2Po4dye2ldZE7Hc10EIQHk531HuB005B7rRGc1Xx37UhuL20Wab4n633OHHQQBEFkU2jQQRAE4TnQoEM/NOggCIJwITToIAiC8Bxo0KEf92+HSRAEQRAEQRCER5MzZjokYVbF8aIm7CwXKs2A/55Qp+5xqZGQsEJYOc52A+FbtWFoZWH4JD6S6n7V9KPQHzL/Skn90pC5hvw5H/WlWKbJKobvczJkroC6HjE0M3dOLEcWMtfZ0IKacgzcAzJU+WT+vgDw1foRztXhpD3Sc476Q0+ZLgoZnV1D5hIE8RiQhczNTChZrhzX6z3gYE1fdtJ7QN4fesvNIs33ZL3PGYOO/7d35+FRVOkawN8OWZqsSMCEJQhEZMKEyK6GgEJIEAV0lB0FFHEZUeLgOPDADDBch6g4MIOyqPEGBxkElUhQQAENgmGVJUEWRUKCCaAQkpCFpLvP/YOboqs7Xenq9JbU+3seHu2qU3VOny6+5nSd8xURUSNhNAnV2au0fLudiKgxUxvztRzvOeggInIiIQSEyi8VIbT7JURE1JipjflajvccdBAROZHJJFTfPtfy7XYiosZMbczXcrxvHIOOBj2jQYNr5Z3wni2fGaK4hsMF9TeIk3JvN9r6XcRyTm/KsH/Y3Oc0nr6WHCCEUP1LlpZ/+SIi76MU7+va7xSNMN4D6mO+luN94xh0EBE1EsLkwPQqDf/yRUTUmKmN+VqO9xx0EBE5EadXERFpB6dX2Y+DDiJbvG2alJfdeja/vW5169083W5D0kQSEWmRF8d7wGJ6rUV6dcZ8soWDDiIiJxKmG3/UHkNERI2P2piv5XjPQQcRkRNxITkRkXZwIbn9OOggInIirukgItIOrumwX6MfdOhcNe/e4rzm9SheLpbtUWqfxZxNWZpaFe/Lsg8cvpwV26rQHyrS6TqtrQrntGK2X7FsA64lxfPq4Nj8XGfN6VVzHgfrtJzTmzL0f2zucxpH+8fFc6WZvYpIwxyN92jAcY6epwH1mcd183hvuc9pGtI3XhbztRzvG/2gg4jIqzgw6ICGv4SIiBo1tTFfw/Gegw4iIicyCQGdyjm7Jg3P8SUiaszUxnwtx/tGP+iwnNqjc1M9Nql5crdlHUazYy3PozB9R9XTwpWoOI+jdVodJ3vt59A562VWh+VfdZ2NcgBUTbcyf19W16DAzRSCam7zWqYddEcKRfM6G9BWxVvvSk+ydfd7dEGdQjgwvUrDX0JETYp5vAccj6PeHgvNjlWcXlvfk8u9+XvNTmpjvpbjfaMfdBAReROu6SAi0g6u6bCfdz19hoiIiIiImhze6SAiciKTCdCpTpnrosYQEZFLqY35Wo73qgYdGzduRElJidX2du3aISkpSXothEB2djbOnTuHLl26oE+fPg1vaWPhrBS+nkiZq4KjKXObKpelbm4CLOf0pgz7h+L+xs5bHw54/fp1HDlyBL6+vujRoweaNbN9zV64cAFbt27F8ePHrfZ17twZ48ePR4sWLbB9+3YAwJUrV5Cbmws/Pz+MGjUKMTExLnsfROTdzGN6U4/3gPc+HPDnn39GYWEh7rjjDtx6662KZb/99ltcv37dantkZCRiY2MBAD/++CPOnTsn29+8eXP079/f7japGnTs378fRUVF0muj0Yg1a9bg2WeflQYdVVVVGDFiBHJzc9GvXz/s3r0bQ4YMwdq1axW/5IiImgJhuvFH7TGutGvXLowePRphYWGoqqqCn58fMjMz0a1btzrLHz9+HG+99RYKCwulbWVlZbh27Rr8/PwwduxYAEBSUhI6duyI/Px8hIWFwWAwYPHixdi+fTv69u3r2jdFROQF1MZ8V8f76upqjB8/Hl9++SW6dOmCEydOYNasWZg3b57NY5YtW4YrV65Irw0GA7KyspCSkoIlS5YAAFatWoX09HT06NFDKhcZGem6QceiRYtkrz///HOsWbMGU6dOlbYtXrwYubm5OHLkCCIiIvDjjz+iR48eSE9Pl5UjImqKTCbhwPQq1/3yVVFRgTFjxmDChAlYsmQJTCYTHnnkEYwfPx5Hjx6t85jExEQcPHhQti0hIQFHjhzB8OHD0bJlS2n7hQsX8Prrr2PmzJkAgAkTJmDy5Mn44YcfXPaeiIi8hdqY7+onki9atAjfffcdTp48iXbt2uGbb77B4MGDcc899yA5ObnOY9avXy97nZGRgaysLEyZMkW2vU+fPti6davDbWvQQvK0tDT06NFDNn1q7dq1GDt2LCIiIgAAXbp0wQMPPIC1a9c2pCoiokahNpOJ2j+usmXLFvz666+YNWsWAMDHxwezZ8/GsWPH8P3339t1jlOnTmHPnj0oLy/HtGnTZPsMBgPi4uKkuyIzZszAiRMncPjwYee+ESIiL+RN8R4A0tPTMWnSJLRr1w4AcN9996F///5IT0+3+xxpaWno27cv7rzzTtn2qqoqZGdnIzc3t87pWPVxeCH5pUuXsHnzZixdulTaVlNTg1OnTuGll16SlY2NjcXy5cttnuv69euyxpeWljraLOdx9JkNrnpmhjue06HEog6u45BTfE4HySit8bDM994YNSRlrmXsCwgIQEBAQIPac+TIEbRt21b6IQgAevXqJe2r/X8laWlp0Ov1aNOmDQYPHixvuxCYO3cucnNzMXDgQLz99tsAgNzcXPTs2dPqXF4Z74nIJepd06fBmO/KeF9SUoK8vDyruN6rVy989dVXdp2jsLAQW7ZswYoVK6z2ZWdn48UXX8SlS5dQUVGBZcuWYdy4cXa3z+FBxwcffABfX19MnDhR2nbt2jWYTCa0aNFCVrZly5Z1LkCvtWjRIixYsMDRphAReY2GPJE8KipKtn3evHmYP3++vKzJhJ07dyqeLzw8XPoHf3FxsWw6FAD4+fkhJCQExcXF9bbNYDBg9erVMBqNmDZtGnS6m8PqgQMHoqqqCvv27UNRURGSkpIwffp0+Pv724z5jPdE1JQ4+kRye+I9cONOc0FBgeI57777bgQHB0sx3TLmh4eH2xXvAWD16tVo3ry51WAiMTERf/nLX9C6dWsAQGpqKiZNmoSYmBirOyK2ODzoeP/99zFmzBiEhYVJ25o3bw4AKC8vl5UtKyuT9tVl9uzZ+NOf/iS9Li0ttfowiIiauoKCAoSGhkqv6/rVy2g0IjU1VfE8vXr1kgYd/v7+qKystCpTVVUFf3//etu0efNmXLp0Cc2aNbOa39u9e3d88803AIA2bdpgzpw5mDhxIoQQNmM+4z0RkX3xHgC++uorZGRkKJ5r1apVCA4OlmK6ZcyvqKiwK94DN/59P27cOISEhMi2Dxs2TPZ61qxZeOutt5CRkeHaQcd3332HEydO4N1335Vt1+v1aNu2LfLy8mTb8/LyEB0dbfN8tm4p6Xya1Z2K1Gyb5X6dQoYsq9SySlOELM9rniJWaapVA1KnytpueR6fm8tvLKfvKKbMVdNWFWWVUsQq9avT2qqGwvVi3q/11SE71sfH9j6rA83KeyClssPHOq2tykvHzG+vpwz9H5v7rNqj4u+votqyTnq/DZleFRoaKvsSqoufn5+UqtYeHTt2xIULF2A0GqUMghcuXEBNTQ06duxY7/FpaWkICQlBYmIi2rRpI9sXHR2NNWvWSOdu1aqVlA7SVsx3xhQCIq9lHu8Bz8d8R2JhQynEfMvpVOYx32qqlXl7nB3v1R6nwNHpVfbEewCYPn06pk+fbte5IyIioNfrcf78edn28+fP2xXvs7Ky8NNPP+HDDz+0q77w8HBZVtv6OLSQPC0tDTExMXWmyRo+fDg++eQTGAwGADfuemRmZmL48OGOVEVE1KgI4cBCchfmbU9KSkJZWRl27Nghbdu4cSP0ej0GDhwotXn79u1WX1S1c3vLysqsFpCXlJTggQceQGlpqZTN5IsvvkBAQABuueUWxMfHu+w9ERF5C9Ux34XxvlmzZkhMTMRnn30mbausrMTWrVtlmatOnz6NPXv2WB3/3nvvIS4uDv369bPaZzll9uzZszh58qTddzkAB+50XLt2DevXr8ff//73Ovf/9a9/Rb9+/fDggw/i/vvvx4YNGxASEoKUlBS1VRERNTrCJFSnRHRlNpOYmBhMnToVTzzxBBYuXIiKigrMnj0bc+bMkabHGo1GJCUlYdmyZbJf1NLT0+Hr64vIyEjcf//9svNmZGRg3bp1SExMxLhx49CtWzccPHgQOp0OS5YssftWPhFRY6Y25rs6e9XChQvRv39/PPfccxg8eDDeffddhISE4Pnnn5fKLF++HBkZGbKZSSUlJfjkk0/w+uuv13ne+Ph4jBs3Dt27d0dRURFef/11dO/eHZMnT7a7barvdJw+fRqPPvooJk2aVOf+9u3b4/Dhwxg4cCBOnjyJRx55BAcOHLBaXE5E1BTVPp1W7R9XWrVqFebOnYtNmzbhm2++wTvvvIO5c29OZfDx8UFiYqLV2or8/HzcddddSE1NhY/FlInJkydj1qxZiI6ORrdu3VBVVYUJEyZg165dqr6EiIgaM2+L9z179kR2djYMBgPS09MRFxeH7Oxs2b/Du3btioSEBNlxBw8eREJCAh577LE6z7t79240a9YMH3zwAfbt24fZs2dj7969CAwMtLttOuGu57GrUFpairCwMFzMeBuhQc2h89fL9ovqKun/rdZ0BMoXvphKL998oTB/zye4hc06LOsxVcjTnOl8b/6iZ7mewbztOl8/xTp8gm7O7TOWXJbtk60psajDRx8ke23ZPnnhm+fxsegrYai5ua+5/JzCaJEyt6LMZnvM5176BCrPV5SlmrXoH/O2Wn0eZmVFVYX8OIV+trpezD4fU5U8AYIl2WdpuTbFvA6Lfr1z7PM4/G7ddwZhUng0aT1rIRSZn7ch57F1TmeeV4HifF8nKy2vRMQjL6KkpMSuebZWx/9/3Or4xAfw8bc/CAOAqboCef87yeG6GzMp3l+4oLn3Tk1Pz99FOxbvAcdjqitis4fjPdB0Y76W473D2auIiMiaySQAL3oiORERuY7amK/leM9BBxGREwmTUfXDM/mwTSKixkltzNdyvPfqQYfNlLlKx6hImeswpbSrrrqYFFLmKh+nkOrWqj9q4AhVqYiVOOvzUVWn/beMlVLm2l+fl6fMNf/sPPJ5yOt0OJ2uEsvr08kpc4mIJJ6O+fbG+4bU0RBmdTqcTrc+tr7XGPPdzqsHHUREjQ3vdBARaQfvdNiPgw4iIicSJpMDg456FpgSEZFXUhvztRzvOeggInIiYTRaZXuz5xgiImp81MZ8Lcd7rx501N6y0sEilarCvHOlD1NpJFrfOgnZsVYpYt0wL9BsZGyVllfxOItUt+bHKaS6VaNBtxWV+tX8s7Wqw+KacLB+nYpfHGTpfVVVCtupEtX84qFm7ZBSylx7Py+rz8rBtqqh0DZV830dqdNJt7yFcGB6ldDulxBRk+KKeA8oxyel9LZqYpHs+9gN8d6yTgt2r+lztD4PxXwtx3uvHnQQETU2XNNBRKQdXNNhPw46iIiciIMOIiLt4KDDfo1z0OGBNGeyJ4K7vXY3cUO/WqXXtfdAJ7XNaWmTVVUK+28/u+Gprw7zdNscTafrZhx0EGkY470T2+BgOl0346DDfl5wVRERERERUVPWOO90EBF5KabMJSLSDqbMtR8HHURETmQyGVVnRTFp+HY7EVFjpjbmazneN7lBh66Z7Tn7VusJHP3gPbEuwIyqdQlKZa321TilPY21X+sje5/Omu/qtPM4eE0ofVaW53RHoHSwTqX5vgCw9Kt5ts9ZW6eTrj+u6SCiOnk63gP2x3x3xSQH61Rc02ce7y3PK/se90zM13K8b3KDDiIiT+Kgg4hIOzjosB8HHUREzmQ0Qvio/FLR8BNqiYgaNbUxX8PxnoMOIiInEkL9mg4tP6GWiKgxUxvztRzvOehoLMzngmo484HTuTsXubPWSThr/Ysn1m24geWc3pSkBTf3uTinuzCZ1A86+HeaqGly1joJV8R8DcR7wPtivpbjPZ/TQURERERELsU7HURETiQcSJmr5YWFRESNmdqYr+V43zgHHWYfmLDYJRQW6DTkg5YdayvlZgPJ2m5Zh+lmHVbvQ+l9KbVVzXkUNChrg739alWHn911Kl0vOvPbnPV8ruZt19lf+41Kbd1OVXObVTbFrp4+Nz+v5RQyxetFoT0Ot9VJt5LVXJ8WZRXTK9buc9IXwY1b7eres5ZvtxM1Ka6I94C6uG3vd4Ur4r3aY5XYG5MV4j0gj/myfR6K+VqO941z0EFE5KV4p4OISDt4p8N+HHQQETkRBx1ERNrBQYf9OOggInIik8kIHQcdRESaoDbmazneN85Bh9lce53FvHtdMyellbNgXo/lugCn1WHedoV1Ilbv2XLtgfkLpfUmlvscXJtiVb/CXyintVUNhetFTR2yY9Wk2tUplHdWyl41/aO0Vsa8PU5L0ajiPSql8FWT3leh7bbm+1bDOfNshdEE6FQOOozaneNL1KR4c7wHLGKqj3JZRzka81Wt67RRrg621vR5KuZrOd4zZS4RkRMJYYQwqfzj4odFXbt2DatWrcK9996LUaNG2XXM9evXkZqaikGDBiEpKQkrVqyAyWIBpD1liIiaMtUx3w0PB9y3bx+mTJmCPn36YPfu3XYds2fPHowZMwYJCQmYNm0a8vLyHCqjxCvvdAhx4/fvsopKAICuRv4lJqqvS/9v+cu1j85f9tpYXmm20/Zo2PI4UV0le21ej6miUr7P1yw7ksVo3LztOl95xiXz9wEAPuLmxyFrN5R/obfsH1n7HHzPPkZ5fibL9yUs+kC2z1B98zxC+RKTZYSqsf2PFavPw6wvRZVFW3xrLI61fb2Y12myPI8FWZ9Y/JJj3gadkNdhNJlQWq587gZz9C6Aml+P3HFL2A13OiyPq/21q/a/tfGnKYmJicGwYcMQGRmJo0eP2nXM5MmTsX//fixevBjl5eWYMWMG8vPzsWjRIlVl6iPF+7IydW+KyAt5dbxXU9ZdU4DccKfD/FjzuxtNNea/+uqr2LRpEyZOnIjVq1fj6tWr9R6TnZ2NQYMGYebMmXjqqaewcuVKxMfH49ixY2jVqpXdZeqjE17Y2+fPn0dUVJSnm0FEGlRQUID27durPq60tBRhYWHw7/kEdM386z/AjDBWo/rw/6KkpAShoaGq665PWVkZQkJCMHfuXHz88cc4efKkYvljx47hzjvvRFZWFgYOHAgAeP/99/Hss8+iqKgI4eHhdpWxB+M9EXmSu2O+u+L91atXccsttyAzMxPDhw9XPCY5ORl6vR6bNm0CANTU1CAqKgrPPPMMFixYYHeZ+njlnY62bduioKAAQgh06NABBQUFLvlgGrPS0lJERUWxb2xg/9jGvqmbEAJlZWVo27Ztw85jMqpf0+HiXxVDQkJUld+xYwdCQ0ORkJAgbRsxYgSmTp2Kb7/9Fg8//LBdZezBeG8f/r21jX2jjP1TN0/FfG+L9waDAVlZWfj3v/8tbfPz88PQoUOxY8cOLFiwwK4y9vDKQYePjw/at2+P0tJSAEBoaCj/otjAvlHG/rGNfWMtLCyswecQNVXqv1SMN6YE1sa8WgEBAQgICGhwm9TKz89HZGQkfMymEbZu3Rp+fn7Iz8+3u4w9GO/VYf/Yxr5Rxv6x5pGY72Xx/uLFi6iurrYafLVt2xZff/213WXs4ZWDDiKixsbf3x+RkZG48MN6h44PDg62mmY0b948zJ8/X7aturoa8fHxiue666678PbbbzvUDuDGbfO6vvz0ej1qamrsLkNE1FQ1JObbG+8B4I033sBHH32keL61a9fijjvuUN0OAFK8toznzZs3l8X7+srYg4MOIiIn0Ov1OHv2LKqrq+svXAchBHQ6eQKHuv5R7+fnh5UrVyqeq6G/ZoaHh+Py5cuybdXV1SgrK5PWathThoioqWpIzLc33gPA2LFjMWjQIMXzNWRdXMuWLQHAKp7/9ttvUiy3p4w9vHrQERAQgHnz5nnkdpO3Y98oY//Yxr5xHb1eD71e79I6dDod+vTp49I6evXqhaKiIvzyyy9o164dgBspGGv32VtGDV6Xytg/trFvlLF/XMcdMb9Dhw7o0KGDy84fGhqK22+/HQcOHMD48eOl7fv27ZNiuT1l7CKIiEgT5syZI7p27Wq13WAwiN69e4uPPvpICCFEZWWliIqKEk899ZQwmUyiurpaJCcni759+0rH2FOGiIg8o7i4WAAQmZmZVvvefPNNMWLECOl1amqqCA8PFz/99JMQQojMzEyh0+lEVlaWqjL18eo7HURE1HBPPPEEcnJyUFhYiOLiYulOybZt2xAeHg4hBA4dOoRLly4BuPHr3aefforRo0cjMjIS1dXV6NChAzZu3Cid054yRETkXjt37sQrr7wCo/HG4vaXXnoJ8+fPx+OPP44ZM2YAuJEI5NixY9IxL7/8Mk6fPo1u3bqhffv2KCoqwj//+U8pHbq9Zerjlc/pICIi5zl58iSuXbtmtb1Hjx7w9b3x29PBgwdx2223oXXr1tJ+k8mE06dPw9fXF7fffnud57anDBERuUdxcTHOnDljtT0yMlJ6HklBQQGuXr2K7t27y8r8+uuvuHDhAjp16oTg4OA6z29PGVs46CAiIiIiIpfy2ulVhw8fRl5eHm6//XarkZiWXL9+HYcPH8aVK1fQrVs3dOzYsc5yp0+fxg8//IA2bdqgX79+VlkRmrqzZ89i3759iIuLQ7du3WT7DAYDsrOzceXKFfTs2dOlC7K80fHjx3HmzBnExsaic+fOVvuvXLmC7777Dv7+/khISEBgYKAHWklaxmvwpry8POTm5qJVq1bo1asX/P2tn3RcUVGB3bt3S+mTazPLaElGRgZMJhMeeeQRq315eXk4cuQIWrVqhXvuuQfNmjXzQAs9o6SkBHv37kVAQADi4+Otrh8hBPbv34+ioiJ069bN4TSrRA5RvzTFta5fvy5GjhwpwsPDRXJysggLCxMTJ04URqPR001zu7S0NHHbbbeJu+66SwwbNkwEBQWJJ5980qovUlJSRHBwsEhKShKRkZFiwIABorS01EOtdr+KigoRGxsr/Pz8xMKFC2X7ioqKRGxsrOjYsaMYPHiwaN68uXjttdc81FL3unz5shgyZIho1aqVeOihh0T37t3FnDlzZGUyMjJESEiIiI+PF3FxcSIiIkIcPHjQQy0mLeI1eEN+fr4YOnSo6NSpk3jwwQdF165dRadOnaz64uDBgyIiIkLExcWJ+Ph4ERISIjIyMjzUas946623hL+/vwgLC7Pa9+qrr4rAwECRmJgoOnToIOLi4sTFixfd30gPeOedd0RISIhISEgQw4YNEz179hR5eXnS/tLSUjFgwADRpk0bkZSUJIKCgkRKSooHW0xa43WDjjfeeEO0atVKFBQUCCGEOHXqlAgMDBTvvfeeh1vmfmvXrpUFyx9++EHo9XqRlpYmbcvMzBTNmjUTBw4cEEII8dtvv4kOHTqIl19+2e3t9ZRp06aJGTNmiHbt2lkNOsaOHSt69+4tKisrhRBCbNy4Ueh0OvH99997oqlulZycLHr37i1KSkqEEEKYTCaxadMmaX9xcbEICwuT9dm4ceNETEyMMJlMbm8vaQ+vwZuOHz8utm3bJr02Go1i9OjRIiYmRtpmMpnE7373OzFx4kRp27x580SLFi3E1atX3dpeTzl69Kho3769eOWVV6wGHfv37xcAxJYtW4QQQpSXl4u4uDjx2GOPeaCl7rVt2zah0+lkA9BTp06JEydOSK9nzpwpOnXqJC5fviyEEGLfvn3Cx8dHbN682e3tJW3yukHHnXfeKZ577jnZtjFjxoj77rvPQy3yLj179hQvvPCC9Hrs2LFi0KBBsjJ/+9vfRGRkpLub5hHr168XsbGxorKy0mrQUV5eLvz9/WWDNCGE6Ny5c5MflB04cEAAEDt37rRZ5j//+Y/w8/MTxcXF0rbaL20t/tJM7sdrUNmHH34oAIiqqiohxM2/14cOHZLKXL58Wfj6+oo1a9Z4qpluU15eLmJiYsTGjRvFkiVLrAYdM2bMkA3ShBBi+fLlQq/XSz88NVX33nuveOCBBxTLREREiAULFsi2DRw4UIwbN86VTSOS+Hhyapclk8mE48ePIzY2Vra9e/fuyMnJ8VCrvEdhYSFOnDgh65+cnJw6++vChQv47bff3N1Etzp79iymT5+ODz/8sM6H85w6dQrV1dWavJ527doFvV6P+Ph47Nq1C1988QUKCgpkZXJyctC+fXu0aNFC2hYXFyftI3I1XoPKtm/fji5dukgPdavtE/OY1rJlS7Rr104T/TV9+nQkJCTg4YcfrnO/re/Dqqoq/PTTT25ooWfUrltMTk5Gfn4+PvvsM+zfvx8Gg0Eq8+uvv+LixYua/D4k7+FVC8krKipgMBisFsWFh4fj6tWrnmmUl6ipqcFjjz2G6OhoTJo0SdpeUlJSZ38BwNWrV9GqVSu3ttNdDAYDxo8fj1mzZkn/SLFUUlICAHX2z/Hjx13eRk+6dOkSwsLCMHToUBgMBgQFBWHXrl1ISUnBokWLANR97QQEBCAwMFDzf9/IPXgN2rZx40asXr0aGzZskLaVlJQgKCjIanGwFr4j161bhz179uD777+3WaakpARdunSRbTP/Pmyqrl69iurqamRlZWHJkiWIi4tDTk4Omjdvjs2bN6Nz586K34dNuW/Iu3jVnY7aX3Ms88lfu3bN5Y+Z92ZGoxETJ07EmTNnsHnzZllfBAQE1NlfAJp0n61cuRL5+fmIjIzEunXrsG7dOlRWViInJweffvopAG1fT3q9HhcvXsTIkSOxe/dubNu2DZ9//jlSU1ORlZUFoO5rx2Qyoaqqqsn3D3kHXoN127FjByZMmIDU1FRZdqaAgABUVlbCZDLJyjf1mFZdXY1nnnkGI0eORGZmJtatW4fDhw+jpqYG69atw88//wxAu9+Hte8tNzcXubm52LRpE06dOoXQ0FDpYXBa/j4k7+FVgw4/Pz9ERUUhPz9ftv3cuXN1pvrUgtoBx969e/H1119bpcyNjo6us7/0ej3atGnjxpa6V2RkJAYOHIjPPvsMGRkZyMjIQGVlJU6cOIHPP/8cAKRrRovXU3R0NADg0UcflbYNHjwYLVq0wKFDh6QyhYWFslvw+fn5MJlMTb5/yDvwGrS2c+dOjBw5EvPmzcOf//xn2b7o6GiYTCacP39e2mYwGFBYWNik+0sIgWHDhqGgoECK9zk5OaipqUFGRgbOnTsHwPb3IYAm3T/BwcGIiIjA0KFDpYe1+fv7Y8SIEVK8b9u2LfR6vSa/D8mLeHpRiaVnnnlGxMbGipqaGiGEEJWVlSIqKkrMmjXLwy1zP4PBIMaNGyeioqLEmTNn6ixTu5iuNnOJyWQSAwYMEA8//LA7m+oV6spe1bt3b1nmkp9//ln4+PiIjz/+2N3Nc6uLFy8Kf39/sXXrVmnbL7/8Inx8fMT69euFEDcym+h0OlnmktTUVBEaGirKy8vd3mbSHl6Dcl9//bUIDAwU//jHP+rcX15eLkJCQsTixYulbRkZGUKn04nTp0+7q5leoa6F5P/973+Fr6+vOH/+vLRt1KhR4u6773Zz69xv6tSpYsiQIbJtEydOFP369ZNeP/TQQ7KkPMXFxSIkJEQsXbrUbe0kbfOqNR0AMHfuXPTp0wcPPfQQhg8fjg0bNsDHxwczZ870dNPc7oUXXsD69euxcOFC7N+/H/v37wcAREVFoX///gCAp59+Gu+//z6Sk5MxZcoUZGVl4ciRI9i7d68nm+413nzzTSQnJyMoKAixsbF4++23ce+99+IPf/iDp5vmUrfeeisWLFiAKVOmYObMmQgKCsLy5cvRr18/aRHmHXfcgRdeeAFTpkzBK6+8gmvXriE1NRVLly7V9MPZyH14Dd6Uk5OD4cOHo1evXujUqRPWrVsn7RsxYgSCgoIQGBiI1157DS+99BJKS0sRHByM1157DS+++KLVWgYtGj16NFasWIGhQ4fi2WefxdGjR7Fp0ybs3LnT001zufnz56Nv376YNGkS7rvvPhw6dAgbNmxAZmamVObVV1/FPffcg/Hjx2PAgAFIT0/HbbfdhqefftqDLSct0QkhhKcbYamwsBArV67EuXPnEB0djT/+8Y9NdkG0kjlz5uDMmTNW2++++26kpKRIr8vKyrBixQocP34cbdq0wdNPP63J26XPP/88hgwZYjWgOHbsGNLT03HlyhX06dMH06ZNk+a3NnXbtm1DRkYGhBDo3bs3pkyZAj8/P1mZjz76CF9++SX8/f0xatQoJCYmeqi1pFW8BoHs7Gz861//qnPfsmXL0Lp1a+n19u3b8cknn6C6uhrJyckYO3asu5rpNbZs2YKPP/4YaWlpsu1VVVV45513cOjQIYSHh+PJJ5+0ytjUVF28eBErV67E2bNn0b59ezz++OPo2rWrrMyZM2fw7rvvoqioCL///e/x3HPPISQkxEMtJq3xykEHERERERE1HV61kJyIiIiIiJoeDjqIiIiIiMilOOggIiIiIiKX4qCDiIiIiIhcioMOIiIiIiJyKQ46iIiIiIjIpTjoICIiIiIil+Kgg4iIiIiIXIqDDiIiIiIicikOOoiIiIiIyKU46CAiIiIiIpfioIOIiIiIiFzq/wCa1s0WrduaDQAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pair = observations[:2]\n", - "supports = _block_supports(pair)\n", - "full = np.concatenate(supports)\n", - "\n", - "c_shared = rxmc.constraint.Constraint(\n", - " pair,\n", - " correct_model,\n", - " extra_terms=[rxmc.covariance.normalization_term(magnitude=0.06, support=full)],\n", - ")\n", - "c_separate = rxmc.constraint.Constraint(\n", - " pair,\n", - " correct_model,\n", - " extra_terms=[\n", - " rxmc.covariance.normalization_term(magnitude=0.06, support=s) for s in supports\n", - " ],\n", - ")\n", - "n1 = pair[0].n_data_pts\n", - "fig, ax = plt.subplots(1, 2, figsize=(9, 4))\n", - "for a, c, t in [\n", - " (ax[0], c_shared, \"shared across datasets (case A)\"),\n", - " (ax[1], c_separate, \"independent per dataset\"),\n", - "]:\n", - " im = a.imshow(\n", - " correlation(c.covariance_matrix(true_params)), vmin=-1, vmax=1, cmap=\"RdBu_r\"\n", - " )\n", - " a.axhline(n1 - 0.5, color=\"k\", lw=0.6)\n", - " a.axvline(n1 - 0.5, color=\"k\", lw=0.6)\n", - " a.set_title(t)\n", - " fig.colorbar(im, ax=a, fraction=0.046)\n", - "fig.suptitle(\"a shared systematic couples the two datasets' blocks\");" - ] - }, - { - "cell_type": "markdown", - "id": "gal12", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "Every case above is the *same* `Constraint` machinery with a different `Term`:\n", - "\n", - "| case | `Term` | structure |\n", - "|------|--------|-----------|\n", - "| flat normalisation | `normalization_term` | rank-one, uniform |\n", - "| correlated-across-$x$ systematic | `kernel_term` | smooth/banded |\n", - "| correlated statistical errors | fixed `Term` | arbitrary off-diagonal |\n", - "| unknown magnitude | `model_error_term` (free $\\gamma$) | sampled diagonal |\n", - "| shared across datasets | cross-block `normalization_term` | off-diagonal blocks |\n", - "\n", - "Declaring the uncertainty *is* the modelling choice — the inference machinery is\n", - "unchanged." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/overconfidence.ipynb b/examples/overconfidence.ipynb deleted file mode 100644 index e9bc6de..0000000 --- a/examples/overconfidence.ipynb +++ /dev/null @@ -1,1489 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "196a680f-91b8-4c45-8894-f56175a73082", - "metadata": {}, - "source": [ - "# Large N implies overconfidence? Do we need to re-scale the Likelihood?" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "7bb6c48f-b3e8-424e-962a-747343e60742", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:29.033340Z", - "iopub.status.busy": "2026-08-11T03:10:29.033193Z", - "iopub.status.idle": "2026-08-11T03:10:29.036142Z", - "shell.execute_reply": "2026-08-11T03:10:29.035405Z" - } - }, - "outputs": [], - "source": [ - "from collections import OrderedDict" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "b11241d5-93d5-4e8f-8ca6-380154ca9a19", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:29.037856Z", - "iopub.status.busy": "2026-08-11T03:10:29.037707Z", - "iopub.status.idle": "2026-08-11T03:10:29.696403Z", - "shell.execute_reply": "2026-08-11T03:10:29.695590Z" - } - }, - "outputs": [], - "source": [ - "import corner" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c549ae62-7f81-4a8e-a54e-a7331a298c90", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:29.697955Z", - "iopub.status.busy": "2026-08-11T03:10:29.697725Z", - "iopub.status.idle": "2026-08-11T03:10:29.700409Z", - "shell.execute_reply": "2026-08-11T03:10:29.699835Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a55118c3-ccbf-45bf-81a9-bdf6e9daf46c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:29.701673Z", - "iopub.status.busy": "2026-08-11T03:10:29.701542Z", - "iopub.status.idle": "2026-08-11T03:10:30.169160Z", - "shell.execute_reply": "2026-08-11T03:10:30.168460Z" - } - }, - "outputs": [], - "source": [ - "from matplotlib import pyplot as plt\n", - "from scipy import stats\n", - "from scipy.stats import norm" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "22715d9f-6d09-4444-b9a5-243a1274c05c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:30.170611Z", - "iopub.status.busy": "2026-08-11T03:10:30.170462Z", - "iopub.status.idle": "2026-08-11T03:10:31.456765Z", - "shell.execute_reply": "2026-08-11T03:10:31.455781Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using database version X4-2024-12-31 located in: /home/kyle/db/exfor/unpack_exfor-2024/X4-2024-12-31\n" - ] - } - ], - "source": [ - "import rxmc" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "abee925c-9d84-443e-9e6c-ca3dd5a29afe", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:31.458299Z", - "iopub.status.busy": "2026-08-11T03:10:31.458063Z", - "iopub.status.idle": "2026-08-11T03:10:31.460632Z", - "shell.execute_reply": "2026-08-11T03:10:31.459993Z" - } - }, - "outputs": [], - "source": [ - "true_params = OrderedDict(\n", - " [\n", - " (\"m\", 2),\n", - " (\"b\", 4),\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "1f8e822c-4807-452b-b4b9-a6e25af16006", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:31.462100Z", - "iopub.status.busy": "2026-08-11T03:10:31.461938Z", - "iopub.status.idle": "2026-08-11T03:10:31.465013Z", - "shell.execute_reply": "2026-08-11T03:10:31.464140Z" - } - }, - "outputs": [], - "source": [ - "rng = np.random.default_rng(42)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6c697651-05eb-4082-962e-144f86fd7dbf", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:31.466337Z", - "iopub.status.busy": "2026-08-11T03:10:31.466177Z", - "iopub.status.idle": "2026-08-11T03:10:31.469348Z", - "shell.execute_reply": "2026-08-11T03:10:31.468687Z" - } - }, - "outputs": [], - "source": [ - "noise = 0.05\n", - "N = 200\n", - "# x_data = np.sort(np.random.rand(N))\n", - "x_data = np.linspace(0, 1, N)\n", - "y_true = true_params[\"m\"] * x_data + true_params[\"b\"]\n", - "y_err = np.array([rng.normal(0, noise * y) for y in y_true])\n", - "y_data = y_true + y_err\n", - "\n", - "reported_stat_err = noise * y_data" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "31abc19d-08d7-49c4-98f8-0c58d235fcd8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:31.470724Z", - "iopub.status.busy": "2026-08-11T03:10:31.470580Z", - "iopub.status.idle": "2026-08-11T03:10:31.473989Z", - "shell.execute_reply": "2026-08-11T03:10:31.473286Z" - } - }, - "outputs": [], - "source": [ - "class LinearModel(rxmc.physical_model.PhysicalModel):\n", - " def __init__(self):\n", - " params = [\n", - " rxmc.params.Parameter(\"m\", float, \"no-units\"),\n", - " rxmc.params.Parameter(\"b\", float, \"y-units\"),\n", - " ]\n", - " super().__init__(params)\n", - "\n", - " def evaluate(self, observation, m, b):\n", - " return self.y(observation.x, m, b)\n", - "\n", - " def y(self, x, m, b):\n", - " # useful to have a function hat takes in an array-like x\n", - " # rather than an Observation, e.g. for plotting\n", - " return m * x + b" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "5ee482a3-6a9d-4fd2-bcb1-6fbf4d400502", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:31.475264Z", - "iopub.status.busy": "2026-08-11T03:10:31.475120Z", - "iopub.status.idle": "2026-08-11T03:10:31.477479Z", - "shell.execute_reply": "2026-08-11T03:10:31.476846Z" - } - }, - "outputs": [], - "source": [ - "prior_mean = OrderedDict(\n", - " [\n", - " (\"m\", 1),\n", - " (\"b\", 4),\n", - " ]\n", - ")\n", - "prior_std_dev = OrderedDict(\n", - " [\n", - " (\"m\", 0.1),\n", - " (\"b\", 0.5),\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "591b9219-baf9-4a06-80bb-89b8a76b4248", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:31.478771Z", - "iopub.status.busy": "2026-08-11T03:10:31.478651Z", - "iopub.status.idle": "2026-08-11T03:10:31.483966Z", - "shell.execute_reply": "2026-08-11T03:10:31.483336Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 4])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "covariance = np.diag(list(prior_std_dev.values())) ** 2\n", - "mean = np.array(list(prior_mean.values()))\n", - "prior_distribution = stats.multivariate_normal(mean, covariance)\n", - "mean" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "66395e1c-6724-4dbf-89e2-b83b94f8ba89", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:31.485277Z", - "iopub.status.busy": "2026-08-11T03:10:31.485124Z", - "iopub.status.idle": "2026-08-11T03:10:31.487577Z", - "shell.execute_reply": "2026-08-11T03:10:31.486860Z" - } - }, - "outputs": [], - "source": [ - "my_model = LinearModel()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "22e4b752-4be1-457e-b934-8316031e2cfc", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:31.488915Z", - "iopub.status.busy": "2026-08-11T03:10:31.488798Z", - "iopub.status.idle": "2026-08-11T03:10:31.491463Z", - "shell.execute_reply": "2026-08-11T03:10:31.490701Z" - } - }, - "outputs": [], - "source": [ - "observation = rxmc.observation.Observation(\n", - " x=x_data,\n", - " y=y_data,\n", - " y_stat_err=y_true * noise,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "0fc8e25a-7544-4b80-ad8b-4b48447dc998", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:31.492723Z", - "iopub.status.busy": "2026-08-11T03:10:31.492604Z", - "iopub.status.idle": "2026-08-11T03:10:31.633177Z", - "shell.execute_reply": "2026-08-11T03:10:31.632503Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.errorbar(\n", - " x_data,\n", - " y_data,\n", - " noise * y_data,\n", - " color=\"k\",\n", - " marker=\"o\",\n", - " linestyle=\"none\",\n", - " label=\"experiment\",\n", - ")\n", - "plt.plot(x_data, y_true, \"k--\", label=\"truth\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "54636463-a169-43d1-9498-dece057a7550", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:31.634679Z", - "iopub.status.busy": "2026-08-11T03:10:31.634544Z", - "iopub.status.idle": "2026-08-11T03:10:31.637091Z", - "shell.execute_reply": "2026-08-11T03:10:31.636348Z" - } - }, - "outputs": [], - "source": [ - "likelihood = rxmc.likelihood_model.GaussianLikelihood()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "2348c4ad-f2ce-4b38-abc3-2048aa2ed11a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:31.638424Z", - "iopub.status.busy": "2026-08-11T03:10:31.638294Z", - "iopub.status.idle": "2026-08-11T03:10:31.647486Z", - "shell.execute_reply": "2026-08-11T03:10:31.646900Z" - } - }, - "outputs": [], - "source": [ - "evidence = rxmc.evidence.Evidence(\n", - " [\n", - " rxmc.constraint.Constraint(\n", - " [observation],\n", - " my_model,\n", - " likelihood,\n", - " )\n", - " ],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "ed7e3fce-125d-49a9-8742-718e06d3d764", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:31.648771Z", - "iopub.status.busy": "2026-08-11T03:10:31.648639Z", - "iopub.status.idle": "2026-08-11T03:10:31.651407Z", - "shell.execute_reply": "2026-08-11T03:10:31.650875Z" - } - }, - "outputs": [], - "source": [ - "evidence_scaled = rxmc.evidence.Evidence(\n", - " [\n", - " rxmc.constraint.Constraint(\n", - " [observation],\n", - " my_model,\n", - " likelihood,\n", - " )\n", - " ],\n", - " weights=np.array([2 / N]),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "687d0733", - "metadata": {}, - "source": [ - "## The same tempering as a config-level knob\n", - "\n", - "`Evidence(weights=...)` tempers the likelihood **per constraint**;\n", - "`CalibrationConfig(likelihood_scaling=...)` tempers the **whole** likelihood.\n", - "For a single constraint they are two spellings of the same thing — and both\n", - "propagate consistently into the Gibbs conditionals\n", - "(`CalibrationConfig.conditional_posterior` applies\n", - "`likelihood_scaling * weight` to the marginal likelihood, with the prior\n", - "untouched), so the tempered joint is what every sampling block targets.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "c4713daf", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:31.652760Z", - "iopub.status.busy": "2026-08-11T03:10:31.652627Z", - "iopub.status.idle": "2026-08-11T03:10:31.656788Z", - "shell.execute_reply": "2026-08-11T03:10:31.656280Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "likelihood_scaling = 2/N : 0.173502\n", - "Evidence weights = [2/N] : 0.173502\n" - ] - } - ], - "source": [ - "model_config = rxmc.config.ParameterConfig(\n", - " params=my_model.params,\n", - " prior=prior_distribution,\n", - " initial_proposal_distribution=prior_distribution,\n", - ")\n", - "\n", - "config_scaling = rxmc.config.CalibrationConfig(\n", - " evidence=rxmc.evidence.Evidence(\n", - " [rxmc.constraint.Constraint([observation], my_model, likelihood)]\n", - " ),\n", - " model_config=model_config,\n", - " likelihood_scaling=2 / N,\n", - ")\n", - "config_weights = rxmc.config.CalibrationConfig(\n", - " evidence=evidence_scaled,\n", - " model_config=model_config,\n", - ")\n", - "\n", - "x_test = np.array([2.0, 4.0])\n", - "print(f\"likelihood_scaling = 2/N : {config_scaling.log_likelihood(x_test):.6f}\")\n", - "print(f\"Evidence weights = [2/N] : {config_weights.log_likelihood(x_test):.6f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "7739ec4d-bded-476c-8a7f-bb8feac5ff3f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:31.658182Z", - "iopub.status.busy": "2026-08-11T03:10:31.658046Z", - "iopub.status.idle": "2026-08-11T03:10:31.660370Z", - "shell.execute_reply": "2026-08-11T03:10:31.659836Z" - } - }, - "outputs": [], - "source": [ - "def proposal_distribution(x, rng):\n", - " return stats.multivariate_normal.rvs(\n", - " mean=x, cov=prior_distribution.cov / 100, random_state=rng\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "79791e26-f2d9-42da-b896-51c326a09540", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:31.661680Z", - "iopub.status.busy": "2026-08-11T03:10:31.661546Z", - "iopub.status.idle": "2026-08-11T03:10:31.663993Z", - "shell.execute_reply": "2026-08-11T03:10:31.663525Z" - } - }, - "outputs": [], - "source": [ - "walker_scaled = rxmc.walker.Walker(\n", - " rxmc.param_sampling.BatchedAdaptiveMetropolisSampler(\n", - " params=my_model.params,\n", - " starting_location=prior_distribution.mean,\n", - " prior=prior_distribution,\n", - " initial_proposal_cov=prior_distribution.cov / 100,\n", - " ),\n", - " evidence_scaled,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "38e27c49-fbb4-473f-9b1c-68ca55ecb076", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:31.665565Z", - "iopub.status.busy": "2026-08-11T03:10:31.665432Z", - "iopub.status.idle": "2026-08-11T03:10:31.667902Z", - "shell.execute_reply": "2026-08-11T03:10:31.667392Z" - } - }, - "outputs": [], - "source": [ - "walker = rxmc.walker.Walker(\n", - " rxmc.param_sampling.BatchedAdaptiveMetropolisSampler(\n", - " params=my_model.params,\n", - " starting_location=prior_distribution.mean,\n", - " prior=prior_distribution,\n", - " initial_proposal_cov=prior_distribution.cov / 100,\n", - " ),\n", - " evidence,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "214aaf01-78ec-4c0b-a74e-a67d59b0e4a0", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:31.669280Z", - "iopub.status.busy": "2026-08-11T03:10:31.669138Z", - "iopub.status.idle": "2026-08-11T03:10:35.105323Z", - "shell.execute_reply": "2026-08-11T03:10:35.104725Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/2 completed, 500 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 2/2 completed, 500 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 1/20 completed, 500 steps. \n", - " Model parameter acceptance fraction: 0.390\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 2/20 completed, 500 steps. \n", - " Model parameter acceptance fraction: 0.364\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 3/20 completed, 500 steps. \n", - " Model parameter acceptance fraction: 0.336\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 4/20 completed, 500 steps. \n", - " Model parameter acceptance fraction: 0.360\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 5/20 completed, 500 steps. \n", - " Model parameter acceptance fraction: 0.394\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 6/20 completed, 500 steps. \n", - " Model parameter acceptance fraction: 0.386\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 7/20 completed, 500 steps. \n", - " Model parameter acceptance fraction: 0.390\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 8/20 completed, 500 steps. \n", - " Model parameter acceptance fraction: 0.358\n" - ] - }, - { - "name": "stdout", - "output_type": 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500 steps. \n", - " Model parameter acceptance fraction: 0.400\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 3/20 completed, 500 steps. \n", - " Model parameter acceptance fraction: 0.394\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 4/20 completed, 500 steps. \n", - " Model parameter acceptance fraction: 0.352\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 5/20 completed, 500 steps. \n", - " Model parameter acceptance fraction: 0.358\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 6/20 completed, 500 steps. \n", - " Model parameter acceptance fraction: 0.362\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 7/20 completed, 500 steps. \n", - " Model parameter acceptance fraction: 0.350\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 8/20 completed, 500 steps. \n", 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acceptance fraction: 0.372\n", - "CPU times: user 3.44 s, sys: 13.1 ms, total: 3.45 s\n", - "Wall time: 3.44 s\n" - ] - } - ], - "source": [ - "%%time\n", - "walker.walk(n_steps=10000, burnin=1000, batch_size=500)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "5a1c1318-3a89-49fb-bfe9-815bd2689807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:38.554263Z", - "iopub.status.busy": "2026-08-11T03:10:38.554119Z", - "iopub.status.idle": "2026-08-11T03:10:39.693393Z", - "shell.execute_reply": "2026-08-11T03:10:39.692867Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = corner.corner(\n", - " walker_scaled.model_sampler.chain,\n", - " color=\"tab:green\",\n", - " truths=[true_params[\"m\"], true_params[\"b\"]],\n", - " labels=[\"m\", \"b\"],\n", - " show_titles=True,\n", - ")\n", - "\n", - "_ = corner.corner(\n", - " walker.model_sampler.chain,\n", - " color=\"tab:orange\",\n", - " fig=fig,\n", - ")\n", - "_ = corner.corner(\n", - " prior_distribution.rvs(10000),\n", - " fig=fig,\n", - ")\n", - "plt.plot([], [], color=\"tab:blue\", label=\"truth: $m=2, b=4$\")\n", - "plt.plot([], [], color=\"k\", label=\"prior\")\n", - "\n", - "plt.plot([], [], color=\"tab:orange\", label=\"posterior\")\n", - "plt.plot([], [], color=\"tab:green\", label=\"posterior, $k/N$ scaling\")\n", - "fig.text(0.6, 0.75, \"$y=mx+b$\", fontsize=20)\n", - "fig.legend(loc=\"upper right\")\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "2a678f70-58e3-4c4f-a290-7125f334b823", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:39.694861Z", - "iopub.status.busy": "2026-08-11T03:10:39.694706Z", - "iopub.status.idle": "2026-08-11T03:10:39.698023Z", - "shell.execute_reply": "2026-08-11T03:10:39.697468Z" - } - }, - "outputs": [], - "source": [ - "def predictive_posterior(\n", - " walker, model, x, x_data, y_exp, y_err, percentile_bounds, added_noise=0.0\n", - "):\n", - " n_posterior_samples = walker.model_sampler.chain.shape[0]\n", - " y = np.zeros((n_posterior_samples, len(x)))\n", - " for i in range(n_posterior_samples):\n", - " sample = walker.model_sampler.chain[i, :]\n", - " y[i, :] = np.random.normal(loc=model.y(x, *sample), scale=added_noise)\n", - "\n", - " percentiles = np.percentile(y, percentile_bounds, axis=0)\n", - " return percentiles" - ] - }, - { - "cell_type": "markdown", - "id": "553d614e-1779-44a9-ab0e-a35260fd4d0b", - "metadata": {}, - "source": [ - "## Empirical coverage" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "c80f57d4-3ca9-46ba-8e4f-afe91f169274", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:39.699471Z", - "iopub.status.busy": "2026-08-11T03:10:39.699335Z", - "iopub.status.idle": "2026-08-11T03:10:39.702290Z", - "shell.execute_reply": "2026-08-11T03:10:39.701847Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([10, 20, 30, 40, 50, 60, 70, 80, 90])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "spacing = 10\n", - "inner_pctls = np.arange(spacing, 100, spacing)\n", - "inner_pctls" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "a239e4c5-a93e-4cbf-a7a9-95fe571c82de", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:39.703609Z", - "iopub.status.busy": "2026-08-11T03:10:39.703490Z", - "iopub.status.idle": "2026-08-11T03:10:39.706322Z", - "shell.execute_reply": "2026-08-11T03:10:39.705900Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 5., 10., 15., 20., 25., 30., 35., 40., 45., 55., 60., 65., 70.,\n", - " 75., 80., 85., 90., 95.])" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pb = np.hstack([50 - np.flip(inner_pctls) / 2, 50 + inner_pctls / 2])\n", - "pb" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "5fafba0a-fc4a-4a49-86cf-1ec706eff1ae", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:39.707604Z", - "iopub.status.busy": "2026-08-11T03:10:39.707486Z", - "iopub.status.idle": "2026-08-11T03:10:39.709889Z", - "shell.execute_reply": "2026-08-11T03:10:39.709286Z" - } - }, - "outputs": [], - "source": [ - "lower_bounds = np.flip(pb[: inner_pctls.shape[0]])\n", - "upper_bounds = pb[inner_pctls.shape[0] :]" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "b3b2f360-6926-4a3a-ab12-b54150b4392f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:39.711108Z", - "iopub.status.busy": "2026-08-11T03:10:39.710993Z", - "iopub.status.idle": "2026-08-11T03:10:39.713326Z", - "shell.execute_reply": "2026-08-11T03:10:39.712884Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "45.0 55.0\n", - "40.0 60.0\n", - "35.0 65.0\n", - "30.0 70.0\n", - "25.0 75.0\n", - "20.0 80.0\n", - "15.0 85.0\n", - "10.0 90.0\n", - "5.0 95.0\n" - ] - } - ], - "source": [ - "for l, u in zip(lower_bounds, upper_bounds):\n", - " print(l, u)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "707ad8cb-8b56-4a88-a90f-64efc51cc4de", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:39.714736Z", - "iopub.status.busy": "2026-08-11T03:10:39.714618Z", - "iopub.status.idle": "2026-08-11T03:10:40.022224Z", - "shell.execute_reply": "2026-08-11T03:10:40.021499Z" - } - }, - "outputs": [], - "source": [ - "pctls_scaled = predictive_posterior(\n", - " walker_scaled,\n", - " my_model,\n", - " x_data,\n", - " x_data,\n", - " y_data,\n", - " noise,\n", - " percentile_bounds=pb,\n", - " added_noise=reported_stat_err,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "ae2f5205-f199-4168-a91a-367d4e3eb91b", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:40.023673Z", - "iopub.status.busy": "2026-08-11T03:10:40.023539Z", - "iopub.status.idle": "2026-08-11T03:10:40.328092Z", - "shell.execute_reply": "2026-08-11T03:10:40.327564Z" - } - }, - "outputs": [], - "source": [ - "pctls = predictive_posterior(\n", - " walker,\n", - " my_model,\n", - " x_data,\n", - " x_data,\n", - " y_data,\n", - " noise,\n", - " percentile_bounds=pb,\n", - " added_noise=reported_stat_err,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "37f7ea46-cddf-4a4d-ad5c-a561ed6792d6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:40.329758Z", - "iopub.status.busy": "2026-08-11T03:10:40.329622Z", - "iopub.status.idle": "2026-08-11T03:10:40.616031Z", - "shell.execute_reply": "2026-08-11T03:10:40.615425Z" - } - }, - "outputs": [], - "source": [ - "pctls_unbroadened = predictive_posterior(\n", - " walker,\n", - " my_model,\n", - " x_data,\n", - " x_data,\n", - " y_data,\n", - " noise,\n", - " percentile_bounds=pb,\n", - " added_noise=0,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "75a16414-2acf-4fa1-8c9c-e17e39524709", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:40.617569Z", - "iopub.status.busy": "2026-08-11T03:10:40.617435Z", - "iopub.status.idle": "2026-08-11T03:10:40.619973Z", - "shell.execute_reply": "2026-08-11T03:10:40.619414Z" - } - }, - "outputs": [], - "source": [ - "lower = np.flip(pctls[: inner_pctls.shape[0], :], axis=0)\n", - "upper = pctls[inner_pctls.shape[0] :, :]" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "72e89091-7a24-4454-becb-f8b71b057e5b", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:40.621138Z", - "iopub.status.busy": "2026-08-11T03:10:40.621021Z", - "iopub.status.idle": "2026-08-11T03:10:40.623206Z", - "shell.execute_reply": "2026-08-11T03:10:40.622725Z" - } - }, - "outputs": [], - "source": [ - "lower_l = np.flip(pctls_unbroadened[: inner_pctls.shape[0], :], axis=0)\n", - "upper_l = pctls_unbroadened[inner_pctls.shape[0] :, :]" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "910d78cf-18ce-4b51-82d1-9b44d9728f74", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:40.624449Z", - "iopub.status.busy": "2026-08-11T03:10:40.624336Z", - "iopub.status.idle": "2026-08-11T03:10:40.626802Z", - "shell.execute_reply": "2026-08-11T03:10:40.626149Z" - } - }, - "outputs": [], - "source": [ - "lower_s = np.flip(pctls_scaled[: inner_pctls.shape[0], :], axis=0)\n", - "upper_s = pctls_scaled[inner_pctls.shape[0] :, :]" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "1b2a4f2a-d4fc-4db8-9b71-46be0b828e06", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:40.628270Z", - "iopub.status.busy": "2026-08-11T03:10:40.628119Z", - "iopub.status.idle": "2026-08-11T03:10:40.794660Z", - "shell.execute_reply": "2026-08-11T03:10:40.793817Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plt.fill_between(x_data, lower_bounds[-2,:], upper_bounds[-2,:], alpha=0.5)\n", - "plt.errorbar(\n", - " x_data, y_data, reported_stat_err, linestyle=\"none\", marker=\"o\", color=\"k\", zorder=0\n", - ")\n", - "plt.fill_between(\n", - " x_data,\n", - " lower_s[-1, :],\n", - " upper_s[-1, :],\n", - " alpha=0.3,\n", - " color=\"tab:green\",\n", - " label=\"posterior, $k/N$ scaling\",\n", - " hatch=\"//\",\n", - ")\n", - "\n", - "plt.fill_between(\n", - " x_data,\n", - " lower[-1, :],\n", - " upper[-1, :],\n", - " alpha=0.3,\n", - " color=\"tab:blue\",\n", - " label=\"posterior+ exp. err.\",\n", - " hatch=\"\\\\\",\n", - ")\n", - "plt.fill_between(\n", - " x_data,\n", - " lower_l[-1, :],\n", - " upper_l[-1, :],\n", - " alpha=0.7,\n", - " color=\"tab:orange\",\n", - " label=\"posterior\",\n", - ")\n", - "\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.legend()\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "023b3cd0-3daf-46d1-b748-6857a69a4d54", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:40.796167Z", - "iopub.status.busy": "2026-08-11T03:10:40.796024Z", - "iopub.status.idle": "2026-08-11T03:10:40.934389Z", - "shell.execute_reply": "2026-08-11T03:10:40.933670Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plt.fill_between(x_data, lower_bounds[-2,:], upper_bounds[-2,:], alpha=0.5)\n", - "plt.errorbar(\n", - " x_data, y_data, reported_stat_err, linestyle=\"none\", marker=\"o\", color=\"k\", zorder=0\n", - ")\n", - "plt.fill_between(\n", - " x_data, lower[-1, :], upper[-1, :], alpha=0.5, label=\"posterior + exp. err.\"\n", - ")\n", - "plt.fill_between(x_data, lower_l[-1, :], upper_l[-1, :], alpha=0.5, label=\"posterior\")\n", - "\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.legend()\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "22ca7234-5e2a-4249-a15e-9e7c773bd166", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:40.935793Z", - "iopub.status.busy": "2026-08-11T03:10:40.935651Z", - "iopub.status.idle": "2026-08-11T03:10:40.938704Z", - "shell.execute_reply": "2026-08-11T03:10:40.937930Z" - } - }, - "outputs": [], - "source": [ - "def coverage_counted(y, err, lower, upper):\n", - " coverage_integrated = np.zeros(len(lower))\n", - " for i, (l, u) in enumerate(zip(lower, upper)):\n", - " coverage_integrated[i] = np.mean(np.logical_and(y >= l, y < u))\n", - " return coverage_integrated" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "fafbe724-cab5-481d-8f43-ee8a07de0c69", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:40.940118Z", - "iopub.status.busy": "2026-08-11T03:10:40.939997Z", - "iopub.status.idle": "2026-08-11T03:10:40.943106Z", - "shell.execute_reply": "2026-08-11T03:10:40.942190Z" - } - }, - "outputs": [], - "source": [ - "def coverage(y, err, lower, upper):\n", - " coverage_integrated = np.zeros(len(lower))\n", - " for i, (l, u) in enumerate(zip(lower, upper)):\n", - " coverage_integrated[i] = np.mean(\n", - " norm.cdf((u - y) / err) - norm.cdf((l - y) / err)\n", - " )\n", - " return coverage_integrated" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "653ddf7d-568e-40c6-bd59-50d8bc93bf57", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:40.944421Z", - "iopub.status.busy": "2026-08-11T03:10:40.944303Z", - "iopub.status.idle": "2026-08-11T03:10:41.175228Z", - "shell.execute_reply": "2026-08-11T03:10:41.174577Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plt.plot(inner_pctls, coverage_sum * 100, label=\"sum\")\n", - "\n", - "\n", - "# cov = coverage_counted(y_data, reported_stat_err, lower_bounds, upper_bounds)\n", - "cov = coverage_counted(y_data, reported_stat_err, lower, upper)\n", - "\n", - "plt.plot(\n", - " inner_pctls,\n", - " cov * 100,\n", - " label=\"posterior + exp. err.\",\n", - ")\n", - "\n", - "cov = coverage_counted(y_data, reported_stat_err, lower_l, upper_l)\n", - "# cov = coverage_counted(y_data, reported_stat_err, lower_bounds, upper_bounds)\n", - "\n", - "plt.plot(\n", - " inner_pctls,\n", - " cov * 100,\n", - " label=\"posterior\",\n", - ")\n", - "\n", - "plt.plot(inner_pctls, inner_pctls, \"k--\", label=\"expected\")\n", - "plt.legend()\n", - "plt.xlabel(\"inner %\")\n", - "plt.ylabel(\"coverage %\")\n", - "plt.title(f\"$N = {N}$\")\n", - "plt.tight_layout()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/robust_likelihoods.ipynb b/examples/robust_likelihoods.ipynb deleted file mode 100644 index 78a8863..0000000 --- a/examples/robust_likelihoods.ipynb +++ /dev/null @@ -1,481 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d8f105f7", - "metadata": {}, - "source": [ - "# Robust likelihoods: Student-t vs the multivariate normal\n", - "\n", - "The multivariate-normal likelihood penalizes a residual quadratically, so a few\n", - "outliers — mislabeled points, an unreported background, a transcription error —\n", - "leave it **confidently wrong**: the posterior stays narrow around a biased value.\n", - "The **Student-t** likelihood keeps the same covariance $\\Sigma$ but replaces the\n", - "Gaussian functional with a heavy-tailed one carrying a degrees-of-freedom\n", - "parameter $\\nu$: small $\\nu$ means heavy tails, $\\nu \\to \\infty$ recovers the\n", - "Gaussian. Sampling $\\nu$ lets the *data* decide how heavy the tails need to be\n", - "— and, as we will see, the multivariate-t's honesty shows up as **wider,\n", - "truth-covering uncertainty**, not as outlier rejection.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "2bdf89cc", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-03T17:43:52.311562Z", - "iopub.status.busy": "2026-08-03T17:43:52.311358Z", - "iopub.status.idle": "2026-08-03T17:43:54.650591Z", - "shell.execute_reply": "2026-08-03T17:43:54.649500Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using database version X4-2024-12-31 located in: /home/kyle/db/exfor/unpack_exfor-2024/X4-2024-12-31\n" - ] - } - ], - "source": [ - "import corner\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "from scipy import stats\n", - "\n", - "import rxmc\n", - "\n", - "rng = np.random.default_rng(21)" - ] - }, - { - "cell_type": "markdown", - "id": "8e3985d0", - "metadata": {}, - "source": [ - "## A clean linear signal with a few gross outliers" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7594ebbb", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-03T17:43:54.653141Z", - "iopub.status.busy": "2026-08-03T17:43:54.652804Z", - "iopub.status.idle": "2026-08-03T17:43:54.806038Z", - "shell.execute_reply": "2026-08-03T17:43:54.805138Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "class LinearModel(rxmc.physical_model.PhysicalModel):\n", - " def __init__(self):\n", - " super().__init__(\n", - " [rxmc.params.Parameter(\"m\", float), rxmc.params.Parameter(\"b\", float)]\n", - " )\n", - "\n", - " def evaluate(self, observation, m, b):\n", - " return self.y(observation.x, m, b)\n", - "\n", - " def y(self, x, m, b):\n", - " return m * x + b\n", - "\n", - "\n", - "model = LinearModel()\n", - "m_true, b_true = 1.0, 0.5\n", - "noise = 0.05\n", - "\n", - "x = np.linspace(0.0, 4.0, 25)\n", - "y = model.y(x, m_true, b_true) + rng.normal(0.0, noise, x.size)\n", - "\n", - "outliers = np.array([5, 12, 19])\n", - "y[outliers] += np.array([10.0, 12.0, 9.0]) * noise # one-sided: a fake background\n", - "\n", - "obs = rxmc.observation.Observation(x=x, y=y, y_stat_err=noise * np.ones_like(y))\n", - "\n", - "xg = np.linspace(-0.2, 4.2, 100)\n", - "plt.plot(xg, model.y(xg, m_true, b_true), \"k:\", label=\"true signal\")\n", - "plt.errorbar(x, y, noise, ls=\"none\", marker=\".\", label=\"data\")\n", - "plt.plot(x[outliers], y[outliers], \"o\", mfc=\"none\", color=\"tab:red\", label=\"outliers\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "id": "34ea6351", - "metadata": {}, - "source": [ - "## The same constraint, two likelihood functionals\n", - "\n", - "The likelihood functional is orthogonal to the covariance: both constraints below\n", - "share the identical statistical $\\Sigma$; only the function of\n", - "$(d^2, \\log\\det\\Sigma, n)$ differs. The Student-t brings one likelihood-side\n", - "parameter, and the **full-tuple convention** applies everywhere: every\n", - "`Constraint` method takes covariance parameters followed by likelihood\n", - "parameters, in `constraint.params` order — including `chi2`, even though the\n", - "chi-squared statistic ignores $\\nu$.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b0ee0605", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-03T17:43:54.807838Z", - "iopub.status.busy": "2026-08-03T17:43:54.807661Z", - "iopub.status.idle": "2026-08-03T17:43:54.812937Z", - "shell.execute_reply": "2026-08-03T17:43:54.811975Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gaussian constraint params: []\n", - "Student-t constraint params: ['degrees_of_freedom']\n" - ] - } - ], - "source": [ - "nu_param = rxmc.params.Parameter(\n", - " \"degrees_of_freedom\", float, latex_name=r\"\\nu\", bounds=(1.0, 100.0)\n", - ")\n", - "\n", - "c_gauss = rxmc.constraint.Constraint([obs], model)\n", - "c_t = rxmc.constraint.Constraint(\n", - " [obs], model, likelihood=rxmc.likelihood_model.StudentT(nu_parameter=nu_param)\n", - ")\n", - "\n", - "print(\"Gaussian constraint params:\", [p.name for p in c_gauss.params])\n", - "print(\"Student-t constraint params:\", [p.name for p in c_t.params])" - ] - }, - { - "cell_type": "markdown", - "id": "89342ff5", - "metadata": {}, - "source": [ - "## Sampling $\\nu$ alongside the model\n", - "\n", - "$\\nu$ is a likelihood parameter, so it goes to a `likelihood_samplers` entry in\n", - "the `Walker` — the Gibbs framework alternates between the physics block and the\n", - "nuisance block.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "08a8ecf5", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-03T17:43:54.814637Z", - "iopub.status.busy": "2026-08-03T17:43:54.814441Z", - "iopub.status.idle": "2026-08-03T17:44:01.618243Z", - "shell.execute_reply": "2026-08-03T17:44:01.617393Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gaussian m = 1.002 ± 0.008 b = 0.566 ± 0.019 (truth b = 0.5 is 3.4 sigma away)\n", - "Student-t m = 1.002 ± 0.028 b = 0.562 ± 0.067 (truth b = 0.5 is 0.9 sigma away)\n" - ] - } - ], - "source": [ - "model_prior = stats.multivariate_normal(mean=[1.0, 0.5], cov=np.diag([0.3, 0.3]) ** 2)\n", - "\n", - "\n", - "def make_model_sampler():\n", - " return rxmc.param_sampling.BatchedAdaptiveMetropolisSampler(\n", - " params=model.params,\n", - " starting_location=model_prior.mean,\n", - " prior=model_prior,\n", - " initial_proposal_cov=model_prior.cov / 100,\n", - " )\n", - "\n", - "\n", - "nu_prior = stats.multivariate_normal(mean=[10.0], cov=[[49.0]])\n", - "nu_sampler = rxmc.param_sampling.BatchedAdaptiveMetropolisSampler(\n", - " params=list(c_t.params),\n", - " starting_location=np.array([10.0]),\n", - " prior=nu_prior,\n", - " initial_proposal_cov=np.array([[4.0]]),\n", - ")\n", - "\n", - "walker_gauss = rxmc.walker.Walker(\n", - " make_model_sampler(),\n", - " rxmc.evidence.Evidence([c_gauss]),\n", - " rng=np.random.default_rng(1),\n", - ")\n", - "walker_t = rxmc.walker.Walker(\n", - " make_model_sampler(),\n", - " rxmc.evidence.Evidence([c_t]),\n", - " likelihood_samplers=[nu_sampler],\n", - " rng=np.random.default_rng(2),\n", - ")\n", - "\n", - "for walker in (walker_gauss, walker_t):\n", - " walker.walk(n_steps=6000, burnin=2000, batch_size=1000, verbose=False)\n", - "\n", - "chain_gauss = walker_gauss.model_sampler.chain\n", - "chain_t = walker_t.model_sampler.chain\n", - "nu_chain = walker_t.likelihood_samplers[0].chain\n", - "\n", - "for name, ch in [(\"Gaussian\", chain_gauss), (\"Student-t\", chain_t)]:\n", - " z = abs(ch[:, 1].mean() - b_true) / ch[:, 1].std()\n", - " print(\n", - " f\"{name:10s} m = {ch[:, 0].mean():.3f} ± {ch[:, 0].std():.3f} \"\n", - " f\"b = {ch[:, 1].mean():.3f} ± {ch[:, 1].std():.3f} \"\n", - " f\"(truth b = {b_true} is {z:.1f} sigma away)\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9e6a0c15", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-03T17:44:01.620568Z", - "iopub.status.busy": "2026-08-03T17:44:01.620348Z", - "iopub.status.idle": "2026-08-03T17:44:01.822830Z", - "shell.execute_reply": "2026-08-03T17:44:01.821741Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = corner.corner(\n", - " chain_t,\n", - " labels=[\"m\", \"b\"],\n", - " truths=[m_true, b_true],\n", - " truth_color=\"k\",\n", - " color=\"tab:blue\",\n", - ")\n", - "corner.corner(chain_gauss, fig=fig, color=\"tab:red\")\n", - "plt.plot([], [], color=\"tab:blue\", label=\"Student-t\")\n", - "plt.plot([], [], color=\"tab:red\", label=\"Gaussian\")\n", - "fig.legend(loc=\"upper right\");" - ] - }, - { - "cell_type": "markdown", - "id": "883c01ed", - "metadata": {}, - "source": [ - "Both posteriors are shifted by the one-sided outliers — but the Gaussian is\n", - "**confidently wrong** (truth excluded at $\\sim 3.5\\sigma$) while the Student-t\n", - "inflates its uncertainty until the truth is covered ($\\lesssim 1\\sigma$).\n", - "\n", - "This is the multivariate-t's character: it carries **one** radial tail factor\n", - "for the whole stacked residual, so it cannot single out and reject individual\n", - "points — it broadens the posterior instead of relocating it. Per-point outlier\n", - "rejection would need per-point machinery (e.g. a free noise nuisance via\n", - "`noise_term`, or explicitly masking suspect points).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "a1f32cc9", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-03T17:44:01.825295Z", - "iopub.status.busy": "2026-08-03T17:44:01.825036Z", - "iopub.status.idle": "2026-08-03T17:44:02.702670Z", - "shell.execute_reply": "2026-08-03T17:44:02.701239Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(nu_chain[:, 0], bins=40, color=\"tab:blue\", alpha=0.7)\n", - "plt.xlabel(r\"$\\nu$\")\n", - "plt.ylabel(\"posterior samples\")\n", - "plt.title(f\"heavy tails demanded: median $\\\\nu$ = {np.median(nu_chain):.1f}\");" - ] - }, - { - "cell_type": "markdown", - "id": "fd330f6e", - "metadata": {}, - "source": [ - "## `chi2`: the fit statistic without the normalization\n", - "\n", - "`Constraint.chi2` returns the generalized chi-squared (the squared Mahalanobis\n", - "distance) under the same $\\Sigma$. Note the full-tuple convention: the\n", - "Student-t constraint requires $\\nu$ in the tuple even though the statistic\n", - "ignores it.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b945e231", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-03T17:44:02.704495Z", - "iopub.status.busy": "2026-08-03T17:44:02.704334Z", - "iopub.status.idle": "2026-08-03T17:44:02.709510Z", - "shell.execute_reply": "2026-08-03T17:44:02.708453Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gaussian chi2/n = 10.98\n", - "Student-t chi2/n = 10.98\n" - ] - } - ], - "source": [ - "map_gauss = chain_gauss[np.argmax(walker_gauss.model_sampler.logp_chain)]\n", - "map_t = chain_t[np.argmax(walker_t.model_sampler.logp_chain)]\n", - "nu_map = float(np.median(nu_chain))\n", - "\n", - "chi2_gauss = c_gauss.chi2(tuple(map_gauss))\n", - "chi2_t = c_t.chi2(tuple(map_t), (nu_map,))\n", - "print(f\"Gaussian chi2/n = {chi2_gauss / obs.n_data_pts:.2f}\")\n", - "print(f\"Student-t chi2/n = {chi2_t / obs.n_data_pts:.2f}\")" - ] - }, - { - "cell_type": "markdown", - "id": "1ab2a3ba", - "metadata": {}, - "source": [ - "## Predictive comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "57661fd9", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-03T17:44:02.711407Z", - "iopub.status.busy": "2026-08-03T17:44:02.711236Z", - "iopub.status.idle": "2026-08-03T17:44:02.833905Z", - "shell.execute_reply": "2026-08-03T17:44:02.832486Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def band(chain, levels=(5, 95)):\n", - " draws = chain[:: max(1, len(chain) // 300)]\n", - " ys = np.array([model.y(xg, *p) for p in draws])\n", - " return np.percentile(ys, levels, axis=0)\n", - "\n", - "\n", - "lo_g, hi_g = band(chain_gauss)\n", - "lo_t, hi_t = band(chain_t)\n", - "\n", - "plt.plot(xg, model.y(xg, m_true, b_true), \"k:\", label=\"true signal\")\n", - "plt.errorbar(x, y, noise, ls=\"none\", marker=\".\", color=\"gray\", label=\"data\")\n", - "plt.fill_between(xg, lo_g, hi_g, alpha=0.4, color=\"tab:red\", label=\"Gaussian\")\n", - "plt.fill_between(xg, lo_t, hi_t, alpha=0.4, color=\"tab:blue\", label=\"Student-t\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "id": "9e4a0f63", - "metadata": {}, - "source": [ - "## Takeaways\n", - "\n", - "- **The likelihood functional is a drop-in choice**: `Constraint(...,\n", - " likelihood=StudentT())` keeps the covariance model identical and changes only\n", - " the function of $(d^2, \\log\\det\\Sigma, n)$.\n", - "- **$\\nu$ is an ordinary nuisance parameter** — give it bounds on its\n", - " `Parameter`, a prior, and a `likelihood_samplers` entry, and the data choose\n", - " the tail weight.\n", - "- **The multivariate-t buys honesty, not outlier rejection**: one shared tail\n", - " factor widens the posterior to cover the truth where the Gaussian is\n", - " confidently biased; rejecting individual points needs per-point terms.\n", - "- **The full-tuple convention is uniform**: every method — `log_likelihood`,\n", - " `chi2`, `covariance_matrix` — takes covariance parameters then likelihood\n", - " parameters in `constraint.params` order, and raises on a wrong count instead\n", - " of guessing.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/sampling_algos.ipynb b/examples/sampling_algos.ipynb deleted file mode 100644 index 1ba8e58..0000000 --- a/examples/sampling_algos.ipynb +++ /dev/null @@ -1,891 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "196a680f-91b8-4c45-8894-f56175a73082", - "metadata": {}, - "source": [ - "# Comparison of sampling algorithms for calibration of a line with unknown model error" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "7bb6c48f-b3e8-424e-962a-747343e60742", - "metadata": {}, - "outputs": [], - "source": [ - "from collections import OrderedDict" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "b11241d5-93d5-4e8f-8ca6-380154ca9a19", - "metadata": {}, - "outputs": [], - "source": [ - "import corner" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c549ae62-7f81-4a8e-a54e-a7331a298c90", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a55118c3-ccbf-45bf-81a9-bdf6e9daf46c", - "metadata": {}, - "outputs": [], - "source": [ - "from matplotlib import pyplot as plt\n", - "from scipy import stats" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "22715d9f-6d09-4444-b9a5-243a1274c05c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using database version X4-2025-12-31 located in: /mnt/home/beyerkyl/x4db/unpack_exfor-2025/X4-2025-12-31\n" - ] - } - ], - "source": [ - "import rxmc" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "abee925c-9d84-443e-9e6c-ca3dd5a29afe", - "metadata": {}, - "outputs": [], - "source": [ - "true_params = OrderedDict(\n", - " [\n", - " (\"m\", 2),\n", - " (\"b\", 4),\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "1f8e822c-4807-452b-b4b9-a6e25af16006", - "metadata": {}, - "outputs": [], - "source": [ - "rng = np.random.default_rng(43)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6c697651-05eb-4082-962e-144f86fd7dbf", - "metadata": {}, - "outputs": [], - "source": [ - "noise = 0.05\n", - "N = 12\n", - "# x_data = rng.random(N)\n", - "x_data = np.linspace(0, 1, N)\n", - "y_true = true_params[\"m\"] * x_data + true_params[\"b\"]\n", - "y_err = np.array([rng.normal(0, noise * y) for y in y_true])\n", - "y_data = y_true + y_err\n", - "\n", - "# Define priors: (mean, variance) for normal, (alpha, beta) for inverse gamma\n", - "reported_stat_err = noise * np.mean(y_data)\n", - "m_prior = (2, 10)\n", - "b_prior = (1, 10)\n", - "sigma2_prior = (3, reported_stat_err / (3 - 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "31abc19d-08d7-49c4-98f8-0c58d235fcd8", - "metadata": {}, - "outputs": [], - "source": [ - "class LinearModel(rxmc.physical_model.PhysicalModel):\n", - " def __init__(self):\n", - " params = [\n", - " rxmc.params.Parameter(\"m\", float, \"no-units\"),\n", - " rxmc.params.Parameter(\"b\", float, \"y-units\"),\n", - " ]\n", - " super().__init__(params)\n", - "\n", - " def evaluate(self, observation, m, b):\n", - " return self.y(observation.x, m, b)\n", - "\n", - " def y(self, x, m, b):\n", - " # useful to have a function hat takes in an array-like x\n", - " # rather than an Observation, e.g. for plotting\n", - " return m * x + b" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "5ee482a3-6a9d-4fd2-bcb1-6fbf4d400502", - "metadata": {}, - "outputs": [], - "source": [ - "prior_mean = OrderedDict(\n", - " [\n", - " (\"m\", 1),\n", - " (\"b\", 5),\n", - " ]\n", - ")\n", - "prior_std_dev = OrderedDict(\n", - " [\n", - " (\"m\", 0.5),\n", - " (\"b\", 0.5),\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "591b9219-baf9-4a06-80bb-89b8a76b4248", - "metadata": {}, - "outputs": [], - "source": [ - "covariance = np.diag(list(prior_std_dev.values())) ** 2\n", - "mean = np.array(list(prior_mean.values()))\n", - "prior_distribution = stats.multivariate_normal(mean, covariance)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "66395e1c-6724-4dbf-89e2-b83b94f8ba89", - "metadata": {}, - "outputs": [], - "source": [ - "my_model = LinearModel()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "22e4b752-4be1-457e-b934-8316031e2cfc", - "metadata": {}, - "outputs": [], - "source": [ - "observation = rxmc.observation.Observation(\n", - " x=x_data,\n", - " y=y_data,\n", - " y_stat_err=y_true * noise,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "0fc8e25a-7544-4b80-ad8b-4b48447dc998", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.errorbar(\n", - " x_data,\n", - " y_data,\n", - " noise * y_data,\n", - " color=\"k\",\n", - " marker=\"o\",\n", - " linestyle=\"none\",\n", - " label=\"experiment with bias\",\n", - ")\n", - "plt.plot(x_data, y_true, \"k\", label=\"truth\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "54636463-a169-43d1-9498-dece057a7550", - "metadata": {}, - "outputs": [], - "source": [ - "log_noise = rxmc.params.Parameter(\n", - " \"log noise fraction\", float, latex_name=r\"\\log{\\epsilon}\", unit=\"dimensionless\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "ed7e3fce-125d-49a9-8742-718e06d3d764", - "metadata": {}, - "outputs": [], - "source": [ - "constraint = rxmc.constraint.Constraint(\n", - " [observation],\n", - " my_model,\n", - " extra_terms=[\n", - " rxmc.covariance.noise_fraction_term(\n", - " log_noise,\n", - " support=np.arange(observation.n_data_pts),\n", - " )\n", - " ],\n", - ")\n", - "evidence = rxmc.evidence.Evidence([constraint])" - ] - }, - { - "cell_type": "markdown", - "id": "de01662a-532d-40ae-b284-232a64e29f82", - "metadata": {}, - "source": "## Sampling configurations for the model parameters" - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "d16ee5a9-5db9-4830-af3a-75faffe0f018", - "metadata": {}, - "outputs": [], - "source": [ - "def proposal_distribution(x, rng):\n", - " return stats.multivariate_normal.rvs(\n", - " mean=x, cov=prior_distribution.cov / 100, random_state=rng\n", - " )\n", - "\n", - "\n", - "metropolis_model = rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=my_model.params,\n", - " starting_location=prior_distribution.mean,\n", - " proposal=proposal_distribution,\n", - " prior=prior_distribution,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "16efc114-712a-429a-8a21-38f8785a099f", - "metadata": {}, - "outputs": [], - "source": [ - "adaptive_model = rxmc.param_sampling.BatchedAdaptiveMetropolisSampler(\n", - " params=my_model.params,\n", - " starting_location=prior_distribution.mean,\n", - " prior=prior_distribution,\n", - " initial_proposal_cov=prior_distribution.cov / 1000,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "f9852933-dad1-484e-89e8-b13e19e8d166", - "metadata": {}, - "source": "## Sampling configurations for the likelihood parameters" - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "e65ff4ba-f8fb-4f2e-b113-206a8519d389", - "metadata": {}, - "outputs": [], - "source": [ - "noise_prior = stats.norm(loc=np.log(0.01), scale=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "a4b60043-c490-4b10-b65b-6c6b11b36f17", - "metadata": {}, - "outputs": [], - "source": [ - "def proposal_distribution_log_noise(x, rng):\n", - " return np.atleast_1d(stats.norm.rvs(loc=x, scale=0.1, random_state=rng))\n", - "\n", - "\n", - "metropolis_likelihood = rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=list(constraint.params),\n", - " starting_location=np.array(noise_prior.mean()),\n", - " proposal=proposal_distribution_log_noise,\n", - " prior=noise_prior,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "cdab8ed0-1b9e-470c-b4c3-dca64d89d736", - "metadata": {}, - "outputs": [], - "source": [ - "adaptive_likelihood = rxmc.param_sampling.BatchedAdaptiveMetropolisSampler(\n", - " params=list(constraint.params),\n", - " starting_location=np.array(noise_prior.mean()),\n", - " prior=noise_prior,\n", - " initial_proposal_cov=np.array([[1]]),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "e659a30a-50c7-4938-8292-cbc89d8ede1b", - "metadata": {}, - "source": [ - "## Walkers" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "38e27c49-fbb4-473f-9b1c-68ca55ecb076", - "metadata": {}, - "outputs": [], - "source": [ - "walker_metropolis = rxmc.walker.Walker(\n", - " metropolis_model,\n", - " evidence,\n", - " likelihood_samplers=[metropolis_likelihood],\n", - ")\n", - "walker_adaptive = rxmc.walker.Walker(\n", - " adaptive_model,\n", - " evidence,\n", - " likelihood_samplers=[adaptive_likelihood],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "69de493c-ca79-4369-89d7-29117f10e45b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/10 completed, 100 steps.\n", - "Burn-in batch 2/10 completed, 100 steps.\n", - "Burn-in batch 3/10 completed, 100 steps.\n", - "Burn-in batch 4/10 completed, 100 steps.\n", - "Burn-in batch 5/10 completed, 100 steps.\n", - "Burn-in batch 6/10 completed, 100 steps.\n", - "Burn-in batch 7/10 completed, 100 steps.\n", - "Burn-in batch 8/10 completed, 100 steps.\n", - "Burn-in batch 9/10 completed, 100 steps.\n", - "Burn-in batch 10/10 completed, 100 steps.\n", - "Batch: 1/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.310\n", - " Likelihood parameter acceptance fractions: [0.46]\n", - "Batch: 2/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.310\n", - " Likelihood parameter acceptance fractions: [0.53]\n", - "Batch: 3/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.650\n", - " Likelihood parameter acceptance fractions: [0.49]\n", - "Batch: 4/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.390\n", - " Likelihood parameter acceptance fractions: [0.39]\n", - "Batch: 5/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.320\n", - " Likelihood parameter acceptance fractions: [0.44]\n", - "Batch: 6/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.330\n", - " Likelihood parameter acceptance fractions: [0.43]\n", - "Batch: 7/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.290\n", - " Likelihood parameter acceptance fractions: [0.44]\n", - "Batch: 8/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.600\n", - " Likelihood parameter acceptance fractions: [0.42]\n", - "Batch: 9/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.380\n", - " Likelihood parameter acceptance fractions: [0.51]\n", - "Batch: 10/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.290\n", - " Likelihood parameter acceptance fractions: [0.52]\n", - "Batch: 11/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.440\n", - " Likelihood parameter acceptance fractions: [0.39]\n", - "Batch: 12/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.500\n", - " Likelihood parameter acceptance fractions: [0.26]\n", - "Batch: 13/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.330\n", - " Likelihood parameter acceptance fractions: [0.47]\n", - "Batch: 14/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.410\n", - " Likelihood parameter acceptance fractions: [0.48]\n", - "Batch: 15/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.440\n", - " Likelihood parameter acceptance fractions: [0.41]\n", - "Batch: 16/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.380\n", - " Likelihood parameter acceptance fractions: [0.44]\n", - "Batch: 17/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.190\n", - " Likelihood parameter acceptance fractions: [0.46]\n", - "Batch: 18/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.450\n", - " Likelihood parameter acceptance fractions: [0.34]\n", - "Batch: 19/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.350\n", - " Likelihood parameter acceptance fractions: [0.49]\n", - "Batch: 20/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.370\n", - " Likelihood parameter acceptance fractions: [0.5]\n", 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0.310\n", - " Likelihood parameter acceptance fractions: [0.43]\n", - "Batch: 28/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.540\n", - " Likelihood parameter acceptance fractions: [0.45]\n", - "Batch: 29/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.290\n", - " Likelihood parameter acceptance fractions: [0.45]\n", - "Batch: 30/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.350\n", - " Likelihood parameter acceptance fractions: [0.42]\n", - "Batch: 31/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.390\n", - " Likelihood parameter acceptance fractions: [0.37]\n", - "Batch: 32/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.410\n", - " Likelihood parameter acceptance fractions: [0.52]\n", - "Batch: 33/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.450\n", - " Likelihood parameter acceptance fractions: [0.46]\n", - "Batch: 34/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.440\n", - " Likelihood parameter acceptance fractions: [0.49]\n", - "Batch: 35/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.310\n", - " Likelihood parameter acceptance fractions: [0.42]\n", - "Batch: 36/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.430\n", - " Likelihood parameter acceptance fractions: [0.51]\n", - "Batch: 37/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.310\n", - " Likelihood parameter acceptance fractions: [0.58]\n", - "Batch: 38/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.420\n", - " Likelihood parameter acceptance fractions: [0.46]\n", - "Batch: 39/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.260\n", - " Likelihood parameter acceptance fractions: [0.59]\n", - "Batch: 40/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.360\n", - " Likelihood parameter acceptance fractions: [0.38]\n", - "Batch: 41/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.550\n", - " Likelihood parameter acceptance fractions: [0.38]\n", - "Batch: 42/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.200\n", - " Likelihood parameter acceptance fractions: [0.44]\n", - "Batch: 43/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.610\n", - " Likelihood parameter acceptance fractions: [0.43]\n", - "Batch: 44/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.430\n", - " Likelihood parameter acceptance fractions: [0.38]\n", - "Batch: 45/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.160\n", - " Likelihood parameter acceptance fractions: [0.32]\n", - "Batch: 46/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.330\n", - " Likelihood parameter acceptance fractions: [0.56]\n", - "Batch: 47/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.410\n", - " Likelihood parameter acceptance fractions: [0.37]\n", - "Batch: 48/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.320\n", - " Likelihood parameter acceptance fractions: [0.36]\n", - "Batch: 49/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.230\n", - " Likelihood parameter acceptance fractions: [0.4]\n", - "Batch: 50/50 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.390\n", - " Likelihood parameter acceptance fractions: [0.43]\n", - "CPU times: user 4.8 s, sys: 224 ms, total: 5.02 s\n", - "Wall time: 4.78 s\n" - ] - } - ], - "source": [ - "%%time\n", - "walker_adaptive.walk(n_steps=5000, burnin=1000, batch_size=100)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "eeee43dd-2c5d-43df-9ed2-7610abe4899f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/1 completed, 1000 steps.\n", - "Batch: 1/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.670\n", - " Likelihood parameter acceptance fractions: [0.825]\n", - "Batch: 2/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.750\n", - " Likelihood parameter acceptance fractions: [0.825]\n", - "Batch: 3/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.799\n", - " Likelihood parameter acceptance fractions: [0.839]\n", - "Batch: 4/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.870\n", - " Likelihood parameter acceptance fractions: [0.83]\n", - "Batch: 5/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.725\n", - " Likelihood parameter acceptance fractions: [0.827]\n", - "Batch: 6/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.797\n", - " Likelihood parameter acceptance fractions: [0.839]\n", - "Batch: 7/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.690\n", - " Likelihood parameter acceptance fractions: [0.839]\n", - "Batch: 8/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.614\n", - " Likelihood parameter acceptance fractions: [0.847]\n", - "Batch: 9/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.651\n", - " Likelihood parameter acceptance fractions: [0.835]\n", - "Batch: 10/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.784\n", - " Likelihood parameter acceptance fractions: [0.834]\n", - "Batch: 11/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.722\n", - " Likelihood parameter acceptance fractions: [0.82]\n", - "Batch: 12/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.821\n", - " Likelihood parameter acceptance fractions: [0.816]\n", - "Batch: 13/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.766\n", - " Likelihood parameter acceptance fractions: [0.819]\n", - "Batch: 14/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.637\n", - " Likelihood parameter acceptance fractions: [0.815]\n", - "Batch: 15/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.768\n", - " Likelihood parameter acceptance fractions: [0.839]\n", - "Batch: 16/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.642\n", - " Likelihood parameter acceptance fractions: [0.851]\n", - "Batch: 17/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.621\n", - " Likelihood parameter acceptance fractions: [0.833]\n", - "Batch: 18/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.646\n", - " Likelihood parameter acceptance fractions: [0.838]\n", - "Batch: 19/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.753\n", - " Likelihood parameter acceptance fractions: [0.849]\n", - "Batch: 20/20 completed, 1000 steps. \n", - " Model parameter acceptance fraction: 0.718\n", - " Likelihood parameter acceptance fractions: [0.834]\n", - "CPU times: user 13.2 s, sys: 130 ms, total: 13.4 s\n", - "Wall time: 13.4 s\n" - ] - } - ], - "source": [ - "%%time\n", - "walker_metropolis.walk(n_steps=20000, burnin=1000, batch_size=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "d7dbdf6f-a1ab-484f-ae5b-a635aeafe4e5", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_chains(walker, model, true_params):\n", - " fig, axes = plt.subplots(\n", - " walker.model_sampler.chain.shape[1] + 3, 1, figsize=(8, 8), sharex=True\n", - " )\n", - " for i in range(walker.model_sampler.chain.shape[1]):\n", - " axes[i].plot(walker.model_sampler.chain[:, i])\n", - " axes[i].set_ylabel(f\"${model.params[i].latex_name}$ [{model.params[i].unit}]\")\n", - " true_value = true_params[model.params[i].name]\n", - " axes[i].hlines(\n", - " true_value, 0, len(walker.model_sampler.chain), \"r\", linestyle=\"--\"\n", - " )\n", - "\n", - " axes[-3].plot(walker.model_sampler.logp_chain)\n", - " axes[-3].set_ylabel(r\"$\\log{\\mathcal{L}(\\alpha_i | \\mathcal{O})}$\")\n", - "\n", - " lmp = walker.likelihood_samplers[0].params[0]\n", - " axes[-2].plot(walker.likelihood_samplers[0].chain)\n", - " axes[-2].set_ylabel(f\"${lmp.latex_name}$ [{lmp.unit}]\")\n", - " axes[-2].hlines(\n", - " np.log(noise), 0, len(walker.likelihood_samplers[0].chain), \"r\", linestyle=\"--\"\n", - " )\n", - "\n", - " axes[-1].plot(walker.likelihood_samplers[0].logp_chain)\n", - " axes[-1].set_ylabel(r\"$\\log{\\mathcal{L}(\\alpha_i | \\mathcal{O})}$\")\n", - "\n", - " axes[-1].set_xlabel(r\"$i$\")\n", - " # plt.legend(title=\"chains\", ncol=3,)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "5177b6d1-ab00-475a-aec5-905af482662c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_chains(walker_adaptive, my_model, true_params)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "a0c29d13-2373-452e-9e5e-a31dbcb4f91a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'posterior')" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = corner.corner(\n", - " np.hstack(\n", - " [\n", - " walker_metropolis.model_sampler.chain,\n", - " walker_metropolis.likelihood_samplers[0].chain,\n", - " ]\n", - " ),\n", - " labels=[p.name for p in my_model.params]\n", - " + [walker_metropolis.likelihood_samplers[0].params[0].name],\n", - " label=\"posterior\",\n", - " truths=[true_params[\"m\"], true_params[\"b\"], np.log(noise)],\n", - " color=\"tab:blue\",\n", - ")\n", - "corner.corner(\n", - " np.hstack(\n", - " [\n", - " walker_adaptive.model_sampler.chain,\n", - " walker_adaptive.likelihood_samplers[0].chain,\n", - " ]\n", - " ),\n", - " fig=fig,\n", - " color=\"tab:orange\",\n", - ")\n", - "\n", - "fig.suptitle(\"posterior\")" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "4b6154cb-284f-43f9-b03f-cef4329b0efd", - "metadata": {}, - "outputs": [], - "source": [ - "x = np.linspace(-0.5, 1.5, 10)\n", - "n_posterior_samples = walker_metropolis.model_sampler.chain.shape[0]\n", - "y1 = np.zeros((n_posterior_samples, len(x)))\n", - "for i in range(n_posterior_samples):\n", - " sample = walker_metropolis.model_sampler.chain[i, :]\n", - " y1[i, :] = my_model.y(x, *sample)\n", - "\n", - "n_posterior_samples = walker_adaptive.model_sampler.chain.shape[0]\n", - "y2 = np.zeros((n_posterior_samples, len(x)))\n", - "for i in range(n_posterior_samples):\n", - " sample = walker_adaptive.model_sampler.chain[i, :]\n", - " y2[i, :] = my_model.y(x, *sample)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "2a678f70-58e3-4c4f-a290-7125f334b823", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Metropolis Hastings\n", - "upper, med, lower = np.percentile(y1, [5, 50, 95], axis=0)\n", - "p = plt.fill_between(\n", - " x, lower, upper, alpha=0.3, zorder=2, color=\"tab:blue\", label=\"Metropolis-Hastings\"\n", - ")\n", - "plt.plot(x, med, \":\", color=p.get_facecolor())\n", - "\n", - "# adaptive\n", - "upper, med, lower = np.percentile(y2, [5, 50, 95], axis=0)\n", - "p = plt.fill_between(\n", - " x,\n", - " lower,\n", - " upper,\n", - " alpha=0.3,\n", - " zorder=2,\n", - " color=\"tab:orange\",\n", - " label=\"Adaptive Metropolis\",\n", - ")\n", - "plt.plot(x, med, \":\", color=p.get_facecolor())\n", - "\n", - "# truth\n", - "plt.plot(x, my_model.y(x, *true_params.values()), \"k--\", label=\"truth\")\n", - "plt.errorbar(\n", - " x_data,\n", - " y_data,\n", - " noise * y_data,\n", - " color=\"k\",\n", - " marker=\"o\",\n", - " linestyle=\"none\",\n", - " label=\"experiment with bias\",\n", - ")\n", - "\n", - "plt.xlabel(r\"$x$\")\n", - "plt.ylabel(r\"$y$\")\n", - "plt.legend()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/systematic_err_demo.ipynb b/examples/systematic_err_demo.ipynb deleted file mode 100644 index 7c09426..0000000 --- a/examples/systematic_err_demo.ipynb +++ /dev/null @@ -1,6398 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "5add9362-edc2-4c55-a0ff-1fc5cf1d4ed7", - "metadata": {}, - "source": [ - "# Introduction to the likelihood" - ] - }, - { - "cell_type": "markdown", - "id": "5e1bd043-2bcc-4323-8b6b-0ae67c834ec1", - "metadata": {}, - "source": [ - "When we are fitting the parameters $\\alpha$ in some model $\\mathcal{M}(x;\\alpha)$ to some data $\\mathcal{D} = \\{x_i , y_i\\}$, how do we judge how well a given model prediction $\\{ x_i , y_m(x_i;\\alpha) \\equiv \\mathcal{M}(x_i;\\alpha) \\} $ describes our data $\\mathcal{D}$?" - ] - }, - { - "cell_type": "markdown", - "id": "6dba0045-1a53-46f1-9ee4-7da617d9b630", - "metadata": {}, - "source": [ - "We have to come up with a model for the *likelihood*. This is the conditional probability that our data $\\mathcal{D}$ (also called evidence) is described by a given model prediction $y_m(x_i;\\alpha)$. We can write it as $p(\\mathcal{D} | y_m(x_i;\\alpha))$ or $p(\\mathcal{D} | \\mathcal{M}, \\alpha)$. Sometimes we use $\\mathcal{L}$ instead of $p$ to write the likelihood, but it is just a conditional probability. It tells you the probability, given model $\\mathcal{M}$, with a fixed set of parameters $\\alpha$, that some data $\\mathcal{D} = \\{ x_i,y_i \\}$ is the result.\n", - "\n", - "Every method for fitting or calibrating a model (whether it is explicitly stated or not) makes some assumption about the likelihood.\n", - "\n", - "How do we come up with a *defensible* form for the likelihood for a given set of evidence and a given model? " - ] - }, - { - "cell_type": "markdown", - "id": "a3a790c4-f5cf-42b1-8c0d-c115116ea8b6", - "metadata": {}, - "source": [ - "## A very simple likelihood model \n", - "\n", - "What if there are no errors in our model or in $\\{ x_i, y_i\\}$ at all? Then our model must exactly reproduce $y_i$, and our lileihood is:\n", - "\n", - "\\begin{equation}\n", - "p(\\{ x_i,y_i \\} | y_m(x_i;\\alpha) ) = \\begin{cases} 1 & y_m(x_i;\\alpha) = y_i \\\\ 0 & \\rm{otherwise} \\end{cases}\n", - "\\end{equation}\n", - "\n", - "Can you guess what issues this might have?" - ] - }, - { - "cell_type": "markdown", - "id": "8f4629f7-fe33-4550-ade1-bf425eb4eebf", - "metadata": {}, - "source": [ - "## The general case\n", - "\n", - "In real cases, our evidence $\\mathcal{D}$ is more than just a set of numbers $\\{x_i , y_i\\}$. It is, in fact, a probability distribution itself: $\\mathcal{D} \\equiv p( \\vec{x}, \\vec{y} )$; a given $\\{x,y\\}$ pair is a measurement of a random variable. Based on the uncertainty information provided by the experimentalists, and the limitations in our model, we must come up with a reasonable model for what $p( \\vec{x}, \\vec{y} )$ is, and use it to construct a likelihood. \n", - "\n", - "Then, when we do a frequentist model fit, we are searching through $\\alpha$-space trying to find the $\\alpha$ that corresponds to $\\text{max} \\left[ p(\\mathcal{D}| \\mathcal{M},\\alpha) \\right]$. This is called [Maximum Likelihood Estimation (MLE)](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation). \n", - "\n", - "As we shall see, minimizing the $\\chi^2$ is just a special case of MLE, subject to certain assumptions (which may not always be appropriate!).\n", - "\n", - "When we do a Bayesian calibration, we sample $\\alpha$-space in such a way that the samples converge on a distribution that is related to the likelihood, called the posterior $p(\\alpha | \\mathcal{D}, \\mathcal{M})$, which just modifies the likelihood to include prior belief about $\\alpha$: \n", - "\n", - "\\begin{equation}\n", - "p(\\alpha | \\mathcal{D}, \\mathcal{M}) \\propto p(\\mathcal{D}| \\mathcal{M},\\alpha) p(\\alpha).\n", - "\\end{equation}\n", - "\n", - "This is just the result of Bayes theorem. In Bayesian calibration as well, one often sees the likelihood $p(\\mathcal{D}| \\mathcal{M},\\alpha)$ modeled as $\\exp(-\\chi^2)$. Again, this may not always be appropriate! " - ] - }, - { - "cell_type": "markdown", - "id": "d3cc697b-c225-4b07-8cef-ba27d4149b03", - "metadata": {}, - "source": [ - "## Under what assumptions is a $\\chi^2$-distribution appropriate for the likelihood?\n", - "\n", - "Let us *assume* there is some ground truth $\\{x_i, y_i^\\rm{true}\\}$, and futhermore assume that our model, for some unknown true parameters, is able to exactly replicate the truth:\n", - "\n", - "\\begin{equation}\n", - "M(x_i;\\alpha^\\rm{true}) = y_i^\\rm{true}\n", - "\\end{equation}\n", - "\n", - "This may be a big assumption, and, in realistic scenarios, one may want to to instead use \n", - "\n", - "\\begin{equation}\n", - "M(x_i;\\alpha^\\rm{true}) = y_i^\\rm{true} + \\epsilon_i\n", - "\\end{equation}\n", - "\n", - "Where $\\epsilon_i$ is another random variable describing the model error. We will ignore this for now." - ] - }, - { - "cell_type": "markdown", - "id": "bb2fdf08-fe00-4463-a35f-2767f35b1a7e", - "metadata": {}, - "source": [ - "Now we need more assumptions: the measured values $\\vec{x}, \\vec{y}$ are random vectors from some multivariate probability distribution $p(\\vec{x},\\vec{y})$.\n", - "\n", - "Let's assume:\n", - "- there is no error in $x$; $x_i = x_i^\\rm{true}$\n", - "- each $y_i$ is really a number of counts in the bin $x_i = \\left[ x_{i0} , x_{if} \\right]$\n", - "- each count is independent of all the others (this is akin to saying there is no systematic error; later in the demo we will see a case for which this assumption is disastrous!)\n", - "\n", - "\n", - "If there are $N$ total counts, then $p_i = y_i^\\rm{true}/N$ is the probability of a count being in bin $i$, and $ 1 - p_i$ is the probability of not being in bin $i$. Our model prediction is for the probability of a single count being in bin $i$ is just $y_m(x_i;\\alpha) / N$. The probability of getting exactly $y$ counts in bin $i$ in $N$ trials is a binomial distribution:\n", - "\n", - "\\begin{equation}\n", - "p(y_i | N, p_i ) = B(N,p_i) \\equiv \\binom{N}{y_i} p_i^{y_i} (1-p_i)^{N-y_i} \n", - "\\end{equation}\n", - "\n", - "Plugging in our model prediction for $p_i$, we have the likelihood \n", - "\n", - "\\begin{equation}\n", - "p(y_i | N, p_i = \\mathcal{M}(x_i;\\alpha)/N ) = B(N,\\mathcal{M}(x_i;\\alpha)/N) \\equiv \\binom{N}{y_i} \\left( \\frac{y_m(x_i;\\alpha)}{N} \\right)^{y_i} \\left( 1- \\frac{y_m(x_i;\\alpha)}{ N} \\right)^{N-y_i} \n", - "\\end{equation}\n", - "\n", - "The total liklelihood is the product of this probability for every bin:\n", - "\n", - "\\begin{equation}\n", - "p( \\mathcal{D} | N, \\mathcal{M} , \\alpha)) = \\prod_i B(N,\\mathcal{M}(x_i;\\alpha)/N) \\equiv \\prod_i \\binom{N}{y_i} \\left( \\frac{y_m(x_i;\\alpha)}{N} \\right)^{y_i} \\left( 1- \\frac{y_m(x_i;\\alpha)}{ N} \\right)^{N-y_i} \n", - "\\end{equation}\n", - "\n", - "As long as $N$ is known exactly (it's own can of worms), then this is exactly what we are looking for! In principle, one could exactly use this formula in this situation.\n" - ] - }, - { - "cell_type": "markdown", - "id": "304c3c77-b950-4c16-ba88-bf8c3a4cebf9", - "metadata": {}, - "source": [ - "How do we get the $\\chi^2$ form? Well, when $N \\rightarrow \\infty$ and $p_i^\\rm{true} \\rightarrow 0$ such that $N p_i^\\rm{true} = y_i^\\rm{true}$ stays constant, the binomial distribution limits to a Poission distribution, with mean $y_i^{\\rm{true}}$ and standard deviation $\\sigma_i = \\sqrt{y_i^{\\rm{true}}}$:\n", - "\n", - "\\begin{equation}\n", - "p( \\mathcal{D} | N, \\mathcal{M} , \\alpha) \\rightarrow \\prod_i \\frac{ (y_i^{\\rm{true}})^y }{ y\\! } e^{-y_i^{\\rm{true}}}\n", - "\\end{equation}\n", - "\n", - "Then using the Central Limit Theorem, we find in the limit as $N \\rightarrow \\infty$ and $0 < p_i < 1$ fixed, that our Poisson distribution becomes a normal distribution:\n", - "\n", - "\\begin{align}\n", - "p( \\mathcal{D} | N, \\mathcal{M} , \\alpha) &\\rightarrow \\prod_i \\frac{1}{\\sqrt{2 \\pi y_i^{\\rm{true}}}} \\exp{ - \\frac{\\left( y_i - y_i^{\\rm{true}}\\right)^2}{ 2 y_i^{\\rm{true}}} }\n", - "\\end{align}\n", - "\n", - "Plugging in $y_m(x_i;\\alpha)$ for $y_i^\\rm{true}$ and $\\sigma_i$ for the standard deviation, we have:\n", - "\n", - "\\begin{equation}\n", - "p( \\mathcal{D} | N, \\mathcal{M} , \\alpha) = \\prod_i \\frac{1}{\\sqrt{2 \\pi \\sigma_i^2}} \\exp{ \\left( \\frac{(y_i - y_m(x_i;\\alpha))^2}{2 \\sigma_i^2} \\right)}\n", - "\\end{equation}\n", - "\n", - "Take the log of this function, one of the terms will be of the $\\chi^2$ form:\n", - "\n", - "\n", - "\\begin{equation}\n", - "\\log p( \\mathcal{D} | N, \\mathcal{M} , \\alpha) \\propto -\\frac{1}{2} \\sum_i \\frac{(y_i - y_m(x_i;\\alpha))^2}{\\sigma_i^2} + \\dots \\equiv \\chi^2 + \\dots \n", - "\\end{equation}\n", - "\n", - "\n", - "It is a useful excercise to take this log and see what the other terms are exactly. In some likelihood models, where the covariance is not known a priori but has parameters that are fit along side the physical model, these other terms will also play a role.\n", - "\n", - "If you've taken a stats course, you've probably done these two limits (Binomial to Poission and Poission to Normal) at some point or another. A nice pedagogical source for the derivations is [The Knolly Bible, Ch. 3](https://indico-tdli.sjtu.edu.cn/event/171/contributions/2123/attachments/982/1592/Knoll4thEdition.pdf). Also, I would like to mention that chatgpt was also useful in preparing this discussion.\n" - ] - }, - { - "cell_type": "markdown", - "id": "3b8ef1af-2756-4689-921d-6073c945918e", - "metadata": {}, - "source": [ - "## too long; didn't read:\n", - "\n", - "The main point I want to get across is is that we had to make many assumptions to get our likeliood to look like a $\\chi^2$! These assumptions are not valid in every case. Whether you are doing Bayesian calibration or frequentist model fitting, whether or not you're even doing uncertainty quantification (which you should be doing), or you just care about finding the \"best\" parameters, it is your job to come up with a *likelihood model*. That is, a model for the function $p(\\{x_i,y_i\\}| \\mathcal{M},\\alpha)$. You must carefully consider the data and its errors, as well as your physics model and its errors, to come up with such a model. You must also think carefully about the implications of any simplifying assumptions you make (e.g. ignoring systematic error), and transparently disclose them." - ] - }, - { - "cell_type": "markdown", - "id": "d6095157-c38c-42d1-be67-ae5a10be46e0", - "metadata": {}, - "source": [ - "## In this demo: the multivariate normal liklelihood model \n", - "\n", - "In this demo, we will introduce some systematic error. That is, instead of measuring the number of counts in a bin, $y_i$ will instead be some derived quantity like a cross section, which includes some normalization. In particular, we will consider a scenario where this normalization is not known exactly.\n", - "\n", - "This will break the assumption from above that each $y_i$ is indepent from each other. If the limiting conditions leading to a normal distribution are still valid, but there is some covariance between $y_i$'s for different points $x_i$, one can write the likelihood more generally as:\n", - "\n", - "\\begin{equation}\n", - "p( \\mathcal{D} | N, \\mathcal{M} , \\alpha) = \\frac{1}{\\sqrt{(2 \\pi)^k |\\mathbf{\\Sigma}|}} \\exp{\\left( -\\frac{1}{2} \\vec{\\Delta}^T \\cdot \\mathbf{\\Sigma}^{-1} \\cdot \\vec{\\Delta} \\right)},\n", - "\\end{equation}\n", - "\n", - "where $\\Delta \\equiv (y_i - y_m(x_i;\\alpha))$ and $k$ is the number of elements in the vector $\\vec{\\Delta}$. Then, one must find a model for the covariance matrix $\\mathbf{\\Sigma}$. If $\\mathbf{\\Sigma}$ is diagonal, this will resemble the $\\exp{\\left( -\\frac{1}{2}\\chi^2 \\right)}$ form. The demo will begin with this assmumption, and test out different models for the covariance matrix $\\mathbf{\\Sigma}$.\n", - "\n", - "In fact, one can show that this is a very good assumption for many cases due to the Central Limit Theorem, as any bounded, finite-variance distribution looks like a normal distribution near the mean. One can think of the generalized $\\chi^2 \\equiv \\vec{\\Delta}^T \\cdot \\mathbf{\\Sigma}^{-1} \\cdot \\vec{\\Delta}$ (which is also the squared [Mahalanobis distance](https://en.wikipedia.org/wiki/Mahalanobis_distance)) as the truncation of the Taylor expansion of the log probability of an arbitrary distribution to 2nd order. See [Bayesian Data Analysis by Gelman, Ch. 4](https://sites.stat.columbia.edu/gelman/book/BDA3.pdf). In other words, for cases in which there are many samples and the CLT applies, the above model for the likelihood function as a generalized multivariate normal is often adequate. The job of the modeler is then to come up with a model for $\\mathbf{\\Sigma}$, given their knowledge of the experimental evidence and the model. This is what we will be doing in the demo.\n", - "\n", - "Some good reading is [D'Agostini, 1994](https://s3.cern.ch/inspire-prod-files-a/af06df9041f5b73dcdf6d1ae8172caa1)." - ] - }, - { - "cell_type": "markdown", - "id": "3bb1e807-4a14-4dc2-b3d2-539edd155b77", - "metadata": {}, - "source": [ - "# Comparing likelihood models: there is a right way, and many wrong ways!" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "69d96b52-427c-4345-8622-d2726544a77c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:08:57.685052Z", - "iopub.status.busy": "2026-08-11T03:08:57.684884Z", - "iopub.status.idle": "2026-08-11T03:09:00.461343Z", - "shell.execute_reply": "2026-08-11T03:09:00.460616Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using database version X4-2024-12-31 located in: /home/kyle/db/exfor/unpack_exfor-2024/X4-2024-12-31\n" - ] - } - ], - "source": [ - "from collections import OrderedDict\n", - "\n", - "import corner\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "from scipy import stats\n", - "\n", - "import rxmc" - ] - }, - { - "cell_type": "markdown", - "id": "baaea2d0-41de-4876-9930-a9e4e7b662e8", - "metadata": {}, - "source": [ - "## Plotting functions" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "81124826-23ca-4713-9b4b-9ed6626dc567", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:00.463594Z", - "iopub.status.busy": "2026-08-11T03:09:00.463332Z", - "iopub.status.idle": "2026-08-11T03:09:00.467741Z", - "shell.execute_reply": "2026-08-11T03:09:00.467131Z" - } - }, - "outputs": [], - "source": [ - "def plot_chains(walker, model, true_params):\n", - " fig, axes = plt.subplots(\n", - " walker.model_sampler.chain.shape[1] + 1, 1, figsize=(8, 8), sharex=True\n", - " )\n", - " for i in range(walker.model_sampler.chain.shape[1]):\n", - " axes[i].plot(walker.model_sampler.chain[:, i])\n", - " axes[i].set_ylabel(f\"${model.params[i].latex_name}$ [{model.params[i].unit}]\")\n", - " true_value = true_params[model.params[i].name]\n", - " axes[i].hlines(\n", - " true_value, 0, len(walker.model_sampler.chain), \"r\", linestyle=\"--\"\n", - " )\n", - "\n", - " axes[-1].plot(walker.model_sampler.logp_chain)\n", - " axes[-1].set_ylabel(r\"$\\log{\\mathcal{L}(\\alpha_i | \\mathcal{O})}$\")\n", - " axes[-1].set_xlabel(r\"$i$\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "dbdd23dd-ecef-43e4-9954-572e7fe1da0f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:00.469457Z", - "iopub.status.busy": "2026-08-11T03:09:00.469307Z", - "iopub.status.idle": "2026-08-11T03:09:00.472158Z", - "shell.execute_reply": "2026-08-11T03:09:00.471582Z" - } - }, - "outputs": [], - "source": [ - "def plot_posterior_corner(walker, true_params):\n", - " fig = corner.corner(\n", - " walker.model_sampler.chain,\n", - " labels=[p.name for p in my_model.params],\n", - " label=\"posterior\",\n", - " truths=[true_params[\"m\"], true_params[\"b\"]],\n", - " )\n", - " fig.suptitle(\"posterior\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "72a48951-4660-4d4e-bb8b-4c8980282eb0", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:00.473618Z", - "iopub.status.busy": "2026-08-11T03:09:00.473479Z", - "iopub.status.idle": "2026-08-11T03:09:00.477590Z", - "shell.execute_reply": "2026-08-11T03:09:00.477012Z" - } - }, - "outputs": [], - "source": [ - "def plot_predictive_post(walker, model, x, y_exp, y_err, y_true, x_true=None):\n", - " if x_true is None:\n", - " x_true = x\n", - " n_posterior_samples = walker.model_sampler.chain.shape[0]\n", - " y = np.zeros((n_posterior_samples, len(x)))\n", - " for i in range(n_posterior_samples):\n", - " sample = walker.model_sampler.chain[i, :]\n", - " y[i, :] = model.y(x, *sample)\n", - "\n", - " upper, median, lower = np.percentile(y, [5, 50, 95], axis=0)\n", - " plt.errorbar(\n", - " x,\n", - " y_exp,\n", - " y_err,\n", - " color=\"k\",\n", - " marker=\"o\",\n", - " linestyle=\"none\",\n", - " label=\"experiment\",\n", - " )\n", - "\n", - " plt.plot(x, y_true, \"k--\", label=\"truth\")\n", - " plt.plot(x, median, \"m:\", label=\"posterior median\")\n", - " plt.fill_between(\n", - " x,\n", - " lower,\n", - " upper,\n", - " alpha=0.5,\n", - " zorder=2,\n", - " label=r\"posterior inner 90$^\\text{th}$ pctl\",\n", - " )\n", - " plt.legend()\n", - " plt.xlabel(\"x\")\n", - " plt.ylabel(\"y\")" - ] - }, - { - "cell_type": "markdown", - "id": "fcf6b674-c76d-47a4-852c-11e57538e625", - "metadata": {}, - "source": [ - "## make the model" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9f859e53-c6f9-4d88-b7bf-bbc5bc9ab686", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:00.479244Z", - "iopub.status.busy": "2026-08-11T03:09:00.479091Z", - "iopub.status.idle": "2026-08-11T03:09:00.482357Z", - "shell.execute_reply": "2026-08-11T03:09:00.481750Z" - } - }, - "outputs": [], - "source": [ - "class LinearModel(rxmc.physical_model.PhysicalModel):\n", - " def __init__(self):\n", - " params = [\n", - " rxmc.params.Parameter(\"m\", float, \"no-units\"),\n", - " rxmc.params.Parameter(\"b\", float, \"y-units\"),\n", - " ]\n", - " super().__init__(params)\n", - "\n", - " def y(self, x, m, b):\n", - " return m * x + b\n", - "\n", - " def evaluate(self, observation, m, b):\n", - " return self.y(observation.x, m, b)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "931eb9c2-6dfe-4538-98c1-c940333f5406", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:00.483849Z", - "iopub.status.busy": "2026-08-11T03:09:00.483706Z", - "iopub.status.idle": "2026-08-11T03:09:00.486203Z", - "shell.execute_reply": "2026-08-11T03:09:00.485466Z" - } - }, - "outputs": [], - "source": [ - "my_model = LinearModel()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "6e6b4a56-38df-44ef-8cd0-0454ab01ad2a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:00.487761Z", - "iopub.status.busy": "2026-08-11T03:09:00.487619Z", - "iopub.status.idle": "2026-08-11T03:09:00.490169Z", - "shell.execute_reply": "2026-08-11T03:09:00.489481Z" - } - }, - "outputs": [], - "source": [ - "rng = np.random.default_rng(16)" - ] - }, - { - "cell_type": "markdown", - "id": "cf188ab7-8ee4-47b7-9d77-2e2e358a1bbf", - "metadata": {}, - "source": [ - "## set up prior" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "138cb996-aab0-4a80-b0ae-eb07677b33cd", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:00.491723Z", - "iopub.status.busy": "2026-08-11T03:09:00.491579Z", - "iopub.status.idle": "2026-08-11T03:09:00.494136Z", - "shell.execute_reply": "2026-08-11T03:09:00.493380Z" - } - }, - "outputs": [], - "source": [ - "true_params = OrderedDict(\n", - " [\n", - " (\"m\", 0.6),\n", - " (\"b\", 2),\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ee04e0a-9d10-479c-b0d5-cd4e2b2bd55b", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:00.495610Z", - "iopub.status.busy": "2026-08-11T03:09:00.495466Z", - "iopub.status.idle": "2026-08-11T03:09:00.498045Z", - "shell.execute_reply": "2026-08-11T03:09:00.497448Z" - } - }, - "outputs": [], - "source": [ - "prior_mean = OrderedDict(\n", - " [\n", - " (\"m\", 2),\n", - " (\"b\", 5),\n", - " ]\n", - ")\n", - "prior_std_dev = OrderedDict(\n", - " [\n", - " (\"m\", 2),\n", - " (\"b\", 2),\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "5107c785-2e6e-4c10-a5b0-c8eaf906549f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:00.499407Z", - "iopub.status.busy": "2026-08-11T03:09:00.499248Z", - "iopub.status.idle": "2026-08-11T03:09:00.502299Z", - "shell.execute_reply": "2026-08-11T03:09:00.501656Z" - } - }, - "outputs": [], - "source": [ - "covariance = np.diag(list(prior_std_dev.values())) ** 2\n", - "mean = np.array(list(prior_mean.values()))\n", - "prior_distribution = stats.multivariate_normal(mean, covariance)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "37bd2c80-2873-4f73-b994-2101b42bec7f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:00.503959Z", - "iopub.status.busy": "2026-08-11T03:09:00.503806Z", - "iopub.status.idle": "2026-08-11T03:09:00.507359Z", - "shell.execute_reply": "2026-08-11T03:09:00.506602Z" - } - }, - "outputs": [], - "source": [ - "systematic_fractional_err = 0.1\n", - "# choose a normalization 1 std deviation below the mean\n", - "N = 1 - systematic_fractional_err\n", - "noise_fraction = 0.05\n", - "x = np.linspace(0.01, 1.0, 15, dtype=float)\n", - "y_true = my_model.y(x, *list(true_params.values()))\n", - "y_exp = (y_true + rng.normal(scale=noise_fraction * y_true, size=len(x))) * N\n", - "y_stat_err = noise_fraction * y_exp * N" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "7247bee4-67b8-4ac6-8f21-74373b733177", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:00.509189Z", - "iopub.status.busy": "2026-08-11T03:09:00.509040Z", - "iopub.status.idle": "2026-08-11T03:09:00.748450Z", - "shell.execute_reply": "2026-08-11T03:09:00.747747Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'experimental constraint with bias')" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.errorbar(\n", - " x,\n", - " y_exp,\n", - " y_stat_err,\n", - " color=\"k\",\n", - " marker=\"o\",\n", - " linestyle=\"none\",\n", - " label=\"experiment with bias\",\n", - ")\n", - "plt.plot(x, y_true, \"k--\", label=\"truth\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.legend()\n", - "plt.title(\"experimental constraint with bias\")" - ] - }, - { - "cell_type": "markdown", - "id": "b5284f41-4a29-4c2a-b1ba-3497910bc120", - "metadata": {}, - "source": [ - "## Compare Likelihood Models\n", - "We will look at a few different cases:\n", - "1. Covariance is fixed to just statistical error (disregarding systematic error)\n", - "\n", - " \\begin{equation}\n", - " \\Sigma_{ij} = \\delta_{ij} \\sigma_{stat,i}^2\n", - " \\end{equation}\n", - "\n", - "3. Covariance is just statistical error, but we fit the magnitude of the statistical noise $\\eta$ (disregarding systematic error). This means the covariance is not fixed, but will be updated during calibration.\n", - "\n", - " \\begin{equation}\n", - " \\Sigma_{ij} = \\delta_{ij} \\eta^2 y_m(x_j; \\alpha)^2\n", - " \\end{equation}\n", - "\n", - "\n", - "5. Systematic error is included properly in covariance, making the covariance a function of the model prediction. Again, this means the covariance is not fixed, but will be updated during calibration.\n", - "\n", - " \\begin{equation}\n", - " \\Sigma_{ij}(\\alpha) = \\delta_{ij} \\sigma_{stat,i}^2 + \\sigma_N^2 y_m(x_i; \\alpha) y_m(x_j; \\alpha)\n", - " \\end{equation}\n", - "\n", - "7. Systematic error is included improperly in covariance, using the experimental $y(x_i)$ instead of the model prediction $y_m(x_i;\\alpha)$. In this case the covariance is again fixed.\n", - "\n", - " \\begin{equation}\n", - " \\Sigma_{ij} = \\delta_{ij} \\sigma_{stat,i}^2 + \\sigma_N^2 y(x_i) y(x_j)\n", - " \\end{equation}\n", - "\n", - "9. Unknown *constant* statistical noise, inferred alongside the model (option 2b):\n", - "\n", - " \\begin{equation}\n", - " \\Sigma_{ij} = \\delta_{ij} \\epsilon_0^2\n", - " \\end{equation}\n", - "\n", - "11. An additive **offset** systematic, included as a rank-one mode with a constant basis (option 5):\n", - "\n", - " \\begin{equation}\n", - " \\Sigma_{ij} = \\delta_{ij} \\sigma_{stat,i}^2 + \\omega^2\n", - " \\end{equation}\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b5f05c68-32ab-467d-8635-94f6ffd9c4de", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:00.750539Z", - "iopub.status.busy": "2026-08-11T03:09:00.750376Z", - "iopub.status.idle": "2026-08-11T03:09:00.754153Z", - "shell.execute_reply": "2026-08-11T03:09:00.753542Z" - } - }, - "outputs": [], - "source": [ - "# 1 and 2 (reported statistical errors)\n", - "obs_stat_only = rxmc.observation.Observation(\n", - " x=x,\n", - " y=y_exp,\n", - " y_stat_err=y_stat_err,\n", - ")\n", - "\n", - "# 2 (unknown statistical error: reported errors ignored, inferred instead)\n", - "obs_unknown_stat = rxmc.observation.Observation(x=x, y=y_exp)\n", - "\n", - "# 3 (normalization systematic supplied as a covariance Term, scales with ym)\n", - "obs_sys_norm_correct = rxmc.observation.Observation(\n", - " x=x,\n", - " y=y_exp,\n", - " y_stat_err=y_stat_err,\n", - ")\n", - "\n", - "# 4 (the \"wrong\" fixed covariance, built from the DATA -> Peelle's Pertinent Puzzle)\n", - "obs_sys_norm_wrong = rxmc.observation.Observation(x=x, y=y_exp)\n", - "wrong_cov = np.diag(y_stat_err**2) + systematic_fractional_err**2 * np.outer(\n", - " y_exp, y_exp\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "4818c696-b984-4145-b911-5307782e0dd5", - "metadata": {}, - "source": [ - "## set up likelihood models and constraints" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "34d5d663-8735-4c87-b05d-12a886204959", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:00.756076Z", - "iopub.status.busy": "2026-08-11T03:09:00.755921Z", - "iopub.status.idle": "2026-08-11T03:09:00.758754Z", - "shell.execute_reply": "2026-08-11T03:09:00.758096Z" - } - }, - "outputs": [], - "source": [ - "# 1 and 3 - the default Gaussian likelihood\n", - "likelihood = rxmc.likelihood_model.GaussianLikelihood()\n", - "\n", - "# 2 - free noise-fraction parameter, sampled as a covariance nuisance\n", - "log_noise_fraction = rxmc.params.Parameter(\n", - " \"log noise fraction\", float, latex_name=r\"\\log{\\epsilon}\", unit=\"dimensionless\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "ea87d212-bb42-4174-b274-f785b8e29e8f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:00.760562Z", - "iopub.status.busy": "2026-08-11T03:09:00.760403Z", - "iopub.status.idle": "2026-08-11T03:09:00.765403Z", - "shell.execute_reply": "2026-08-11T03:09:00.764617Z" - } - }, - "outputs": [], - "source": [ - "# 1\n", - "evidence_stat_only = rxmc.evidence.Evidence(\n", - " [rxmc.constraint.Constraint([obs_stat_only], my_model, likelihood)]\n", - ")\n", - "\n", - "# 2\n", - "evidence_unknown_stat = rxmc.evidence.Evidence(\n", - " [\n", - " rxmc.constraint.Constraint(\n", - " [obs_unknown_stat],\n", - " my_model,\n", - " extra_terms=[rxmc.covariance.noise_fraction_term(log_noise_fraction)],\n", - " )\n", - " ]\n", - ")\n", - "\n", - "# 3\n", - "evidence_sys_correct = rxmc.evidence.Evidence(\n", - " [\n", - " rxmc.constraint.Constraint(\n", - " [obs_sys_norm_correct],\n", - " my_model,\n", - " extra_terms=[\n", - " rxmc.covariance.normalization_term(\n", - " magnitude=systematic_fractional_err,\n", - " )\n", - " ],\n", - " )\n", - " ]\n", - ")\n", - "\n", - "# 4\n", - "evidence_sys_wrong = rxmc.evidence.Evidence(\n", - " [\n", - " rxmc.constraint.Constraint(\n", - " [obs_sys_norm_wrong],\n", - " my_model,\n", - " extra_terms=[rxmc.covariance.Term(wrong_cov)],\n", - " )\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "f237192a-4f2f-4b2c-8d0f-30d3e71ca123", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:00.767237Z", - "iopub.status.busy": "2026-08-11T03:09:00.767062Z", - "iopub.status.idle": "2026-08-11T03:09:00.769908Z", - "shell.execute_reply": "2026-08-11T03:09:00.769230Z" - } - }, - "outputs": [], - "source": [ - "def proposal_distribution_model(x, rng):\n", - " return stats.multivariate_normal.rvs(\n", - " mean=x, cov=prior_distribution.cov / 1000, random_state=rng\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "fbb7e0d9-8a86-4228-9372-ee63dfad5ee3", - "metadata": {}, - "source": [ - "## Run option 1: fixed covariance, statistical error only" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "194af035-fafd-4c64-8b58-0f7af34bb685", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:00.771842Z", - "iopub.status.busy": "2026-08-11T03:09:00.771676Z", - "iopub.status.idle": "2026-08-11T03:09:00.774653Z", - "shell.execute_reply": "2026-08-11T03:09:00.773960Z" - } - }, - "outputs": [], - "source": [ - "walker1 = rxmc.walker.Walker(\n", - " rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=my_model.params,\n", - " starting_location=prior_distribution.mean,\n", - " proposal=proposal_distribution_model,\n", - " prior=prior_distribution,\n", - " ),\n", - " evidence_stat_only,\n", - " rng=rng,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "25f3b9a2-5bf0-47a3-94f4-17bc1f864d20", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:00.776494Z", - "iopub.status.busy": "2026-08-11T03:09:00.776325Z", - "iopub.status.idle": "2026-08-11T03:09:04.497031Z", - "shell.execute_reply": "2026-08-11T03:09:04.496394Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/1 completed, 1000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 1/1 completed, 10000 steps. \n", - " Model parameter acceptance fraction: 0.336\n", - "CPU times: user 3.72 s, sys: 25.3 ms, total: 3.75 s\n", - "Wall time: 3.72 s\n" - ] - } - ], - "source": [ - "%%time\n", - "walker1.walk(n_steps=10000, burnin=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "98de6f5c-2c40-44bf-9120-bd2f1c809273", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:04.498537Z", - "iopub.status.busy": "2026-08-11T03:09:04.498385Z", - "iopub.status.idle": "2026-08-11T03:09:05.597104Z", - "shell.execute_reply": "2026-08-11T03:09:05.596500Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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TgLwMPHgUE+duU7snQaqgYsKsn7DbeZGA8dRv6rY2ZsTjs5tQhxS5ajyp+mh2CV759yDeXpaK//ZleFATHecKHMkfzBD2cooqqOrZl16Al/9JQXqey5Zr+o97cCSrGE/+muxxOwTIIX1Ex0Ye1iX/glDqR68vOoQ1R4wtd6px42fb8Oeec7j58+0AgEYKWYNo4s9KMw554/nwNYJGVk3zIgcZl5YUBpSiFvhDUJROMKSrpul5Yqe/IL2FhoggNPFxkWEqJbWRTlRIoeirTacw+r31orFLb30kpODk6QTSqZlVefQtnOM4209eMOQkKoenvnf9Zq2S3a73GVbDzgNxkaHaBRG85lZUwizP87BardR/dg8Cdl8s3PrVdnxM5HI+pit8iFGbWdGijqE63FoiMwB5MiaRM+fiCu9rZh/6aQ8e/SUZf+4561E97608igFvrkb7F5Zqlp3+4x58t/U0Zv7nbvtWLKM5MAp5G2Ii6AYy/cdQvtlvLvF82U5KTrFDiyFoU5TswGg0s60b1BF9J1cuglWrJzj6eTI5S6oX7fwshPWR4unVWXEwCwPeWIWtJy5Q7+P+klW/yYGmXf9r71lM+GCjLkGQFjM1nDwvtlEmJ4avLTqE4zkl+N/yVLldZSGfJeG5dNZv4gTSNRYp9wuO47DjVB5u+GwbbqqZEPuTUpXx3tPrIdLM8rwOm9nAem5ooRJm3333XYSFhVH/PfPMM9QNOHjwIB599FFMnToVb7zxBoqKtI32bTYbvvvuO9xxxx2YPn06du3yXegNs9h8nH4QB8yxLfKKzawPl6TkKKqoNpwRDQD2nikAAKw06DQhMG/1Meqy5wocGoFdp/PcfzThev655yzWHz3v8aDkaSzRC5LlM2+gtKRIM2yHh4iHP7uJAoG/aBIfCQCo9kC9YqF46XlqE33v97uRU1yJGz/fRr+T5JBazTRrQmK389h7Jl82ioAeHl+4D4cyi/DSPym699U6E3JSacZ5ayXd0BMfmSfmVRzniAs7aPZqnM0vE4fm8tgBzPFfK/vd9lOOcfeUFxLJ6D2FKquK7aqHbSHHRh5ikyG1dgbr2Eetrrn++uvxwAMPaJZbuHAh9cF37dqFYcOG4aabbsLll1+Ozz77DAsXLsSOHTsQGRkpu09FRQXGjx+PjIwMPPbYY4iOjsbjjz+OefPmoXfv3tTHDjbMmC1542Utp5nzpK16X5ID3liFimo7djw/Co3i5PtMoOINJ6lTuaXOuMVvT+nh3G7kngiDPgDER8kvXapV62nMxS0ncrH1xAVMH9EOUeHyTi3KmlltgSxMEuHBG3GYfY1gZiBM0oxAY9Lkj+sjFXa0Qg2Z1cYft5/GS/8cRL+kuvj9gUs8rk9NG6eXimobPll3QjSh9/RdYed50btC7jLH1pgyJKcXoLiiGkPbN1Stj0TQ6r634igm90l0btdqdWZhOaqtPFrWd60ckA6KwnHUBESO8yw7nlKdRvHmYyTSzNrFkz9VYTZIF9aphNnY2Fh0794dI0aM0CybmpqK8+fpwlPMmDEDo0aNwhdffAEAmDx5MhITE/HVV19h+vTpsvvMmTMHBw4cwOHDh9GwoeMBuuWWW1BaGtyeq3rGHzOiGZimtZCpxpOa9dotCqGY1qbm4Ib+LQ0f1x8vZ5pD8jwPq513OvZocb6Y0IYanLxUVNswb/UxsZmCgr2VWr204aHkePXfg/imJlZqfFQYpg1tI1suWkHIpXm/hBCxd/NKq0QmHsHqBHGhxBFeqHmCcScgkR0jRRlfIT2ilhAh5+xjhJ93OByDdp3ON6U+Glkqt6QSX246pVnu991n8YFkVciMqAAi0wCZMlyN7amQ2njrjMvQNF6+z6k1h1ajzPM8Bs9eAwBImTnWaTY17TuX059wXdWEVQ7SeOv+fc7Vjk/+tistD0UV1RjZsZGh1MU70vLEmlmVuxKsYx+VMHvfffdRV0hbtqKiAmvXrnUKsgBQt25djBo1CkuXLlUUZr/66ivceuutTkEWAEJCQhAXF0fdRr8gEbajqlwzabvFgpLKUMWyAMDb7IiqqoCd48SDlZoQb7EAUY4Bhud5RFRVgKvZN6yiXLwvxwHRrhkvysqUR0XRcgWPiOpKWIiyUVyouO46hG1iebl8CIQayAHNUlmhfn5EvQeOZ+OGLvWVy0ZHu9pdWQlYHS864T6EVTquR3ZRBWLqxqGO4ERBlJUlKspxnQGE2aoRarMpt1lSNrLS6iwrtCOiyuLYFhkJhITg7m93IflENv6cNgCtJDaeTmrKAoDFWu2sy1Je6vwcWl7mqDciAgit6WvV1UCVe2zFr9cex9drjqMqNAywOOrlq62i88oqLMeHa47j6l7NEFVVgeqQUFhDHPWG2G0It1ajY0y8/LUIDwfCaq6vzQZUuJuJLFx3BFEArCEhOCBks7LZRM8NSksRUVnu2kacCwe7et8JC3NqKjjejiEv/YdmCZHOuviSUkAIcRQa6rhugOOZKFOxeaQsG1VVAbvFgsrQcNH5KBIS4rjPMmXJa9Ix1oKI6kqxoKdSb0R1JSrDCEe4sjJYSl39pkuc6x5GVlegIizSeWqaYwQ5nkiee+l9VBojLGWutthLSgBEOMvyPBBhrYJFYTwpy5NkQauocPQ3JRTGiMgqoo8J11KhrCyS5z68okxzjHhneSr+2HYSUTXtFZ5f8rpVhoY5o3o4xx4AXKlM/cQYofTcO+u2RjrH4VCb1TV2EGWiqgF7SSlC7DbYLCHILqpE0zphjmshwV5S6RwjBELsNkRUVoAjzimMOI50jLCXue5Bevp5hFk4tGsci9ysfISFhKA6JAz70gtQUVGF0PIycf8isFRXOT35Od4OvsR1/F4tJONVWJijHYCjP5bLO4xFVpYj3FqNqtAwh6CsY4zgeV6xrSgtBWJDkVMNTFmwFQDwx2090Depnnx5yRhhKXNdhw17TyGqqhxRNfedLysHwgllA3HefGmVWD4JlvRyvAlYrVY+Pz9f1z5Hjx7lAfCrVq0SbZ8+fTrftWtX2X3y8vJ4APy3337L/+9//+OnTp3KP/HEE/zevXtVj1VRUcEXFhY6/9LT03kAfGFhoa42e4Sji8v+rW7Tj+/wwhJX2ehoxbJbW3Tjt5+84CrboIFy3f36OYtZbXY+Pa6RctkuXcTt7dJFuWxSEp/07CI+6dlF/Mj/reWTm7RXLtugAX8su5ifveQwf764gueHD1cuGx3Nbzia46w7bdAI1evG87yzbPKg0eplS0pc53b77aplRz33G19RbXWUnT5dvd5Tp5ztWDBgsnrZlBRn2feH3KhedscOZ9k3RtypXnbtWueppc18S73sokWu6/D116plH7j6Oef1feDq51TLPjnhMWfZO6a8ot6G+fNdbVi7VrXsGyPu5B/7Za+j7I4dqmU/HXmrsw2THlig3oannuLv/W4nn/TsIn7I/V+ql50+3dXenBz1srff7ipbUqJadlHHIXzSs8T9UKt3wgTx86kxRtz9zU6qMSK5SXs+6dlF/At/7XeUTUpSLJvWuJXz+p48X6I5Rojo10+5bIMG4rIaY4TAPd/u5Fe3UakXENc7ZYp6WR1jBJ+T4yrrhTGi92srNMeIK297j5+16KDuMYKfP1+17HN3vckXlVfxSc8u4p+c8JhqWWGM2HM6j+d//VW17JMTHuOHzFntlTFC6Je7flumWvbco8/wby4+xCc9u4gffddH6m146ilXG06dUi37be8rnG3QM0bkZF5QLztlCp+aVeSqW62sZIywRimPEdahw8TPhsYY4U8KCwt5GnnNUGiu1157Ddu3OzwBT5w4gZYtW6Ju3bq48cYbwfM8VR2VNTO4aHL2DiAmJgYVMpoaAE5TghkzZuDkyZO48sorYbVa0a9fPyxatEjxWLNnz0Z8fLzzr0WLFlRt9CVWHXY8tNeYxGw7IYGTFEb0T/yajAXrT+D9lUc1y3qrnXrIK61CgZ/TeO5Kk3EOu0ih1QuQcYmNLMXVLhzPUUW1zbRFw0C7pAEwVPidEA/TVMvB8/qvLW18bAOvLl3YNOIrc5z3I5WYZd4CAGfzyw1fsyqFFOQAgtSQQB2O1ykZ7d+/H3feeSd2794NALjnnntQUFCARx55BLfffjs++eQTjB07VrOe9PR0tGzZEosXL8aECROc26dNm4Z9+/Zh586dbvuUlJQgNjYWV199Nf7++2/n9qlTp+LMmTPYsmWL7LEqKyudwjMAFBUVoUWLFigsLPSdeQKhxt9/tgA3fOry4hWWG9PmXOFWVqDKakfPmStg5zh8++BwDGpTX7GsE8LMoKLahl7P/e00MxjSrj6+uL2/q6xOM4NWr611fpWaGdQJD8Guly53fm/1+joADju+zY8OVjUzWJte6gx+/sJlrXHPkCTl86tTB62eWwwAmNq9IeZM6qpcVmFZsPNLywAAwzs2xMBW9fD28lSUh0Vg2/OjHZ7hlEuIrZ5b7FzqO/z6OKqydTgeya+MEbUjPMSCKpsdlaFhODbnSrR9fgnCbNWYMbot7rpU3m6UXELcfTwbt3zsyBL06lVd8eq/BwEAz43vhNsGJ2HO2lPo0qIeru7VXHG58alf92HxgUzHslmNmUGI3YbWsSFY9cQIAEDf11eijPDuljMz6N+qLr67e6B7ezXMDI5lF+Oq+Q5bPGtICK7s3wrvXd8LsNnQ+em/neUOvz4Okz7a7HQCsdYsNwJA3cgQ7H16qPz1AoCwMNy3cD+WH8wGx9sRWS2+DrteHI06QjgzL5gZdH5pGdVz70TBzOBQZiGu/dixBPnQyHZoHB+BF/45hEu7t8ArV3bF2LkbcHX7eMy5todcrej48nJUhkXglkEtMWtSd6CsDJ1fdIWXe2FCJ9wyuBUA4LpPt2JnjmMcXfPkcLSpYxGNEUIfTogOw9bnR6uaGQhlAcd9VDIz2HI8F3d/64hWs+7pEWgcF+ksO+3bndh44KyimcHDl7XD9Ik9XRsIM4Pvt6bhXEE5nh3XyTXxURgjpnyyxZke2flsGzAzEJ77Pk1jsPC+wc6frTY7DpwrQOem8YiMiwEsFvSbtRKFhaVO04Enx3SAzc5j7iqXfWxlaBgeGNUBH609ITIz+N91PTCxRzNxGyjMDIR70qRhPL6//xJc+tZahNqsWHB9d4zu0lhUJql+FP59aCg6v7EWNksIOjWJxbKHLpE1M8gqLMfId9ajOiQUjerFIKOwAiF2G27u1QijOjfG/d/vcZbdNmMU4qPD3MaIypJS9Jq5UlTvuK5NsOxglui5/+Lm3vhwyQEczZYXrn+afin+O3wBX20+BY6348jzw531dm0WJ3bwozQzmPzxZqRklznMsgBM7ZeIOePbyZYFIBojcorKMfzVJYpF7RYL/n5yFMZ/sBEA8O4V7XAt4TQnQjJGdH7iD+fnKX2bY8uJC85sbttfuhxxdQnZhxh7zhdXYtjbrve7neOQ+u5k5fPxMkVFRYiPj9eU13QHn9y2bRsGDBjg/L58+XL8+++/6NWrF26++Wbs3LmTSphNTExEvXr1sG/fPpEwm5ycjJ49e8ruExMTg7Zt26JdO3FHadeunazwKxAREYGIiAjF330CMVhf9fU6IFzF876Ou20kb7WhXG4fmbJy8Dyc9m4AUBEepb6vRGOuhsjmDgAXHiJbd0SoS7hWwmZ3DULW8Ajq89NTFhERzsFEuKZVEVGoioxyfncawRNl1YiNCEVxJRyDak07jueUICE6DA1i3PevDglDWajFWVY4bjkA1LxzBC11dUgYrFHRdOcXFuY6J+J8qiOjsPpMKT7dfAbAGYcwGxbmemGQbYuKcutrNksIikMiRO0th/zM32YJQXl4CDZklMMeFa0e6inEva/wdeyi4zu91kNCRNsnfZOMivBIlIfLaNEtFs3rJcTP5DmL2/nao6MBueDzHEffz1TKevIsk2XtUVZnXbboaNijIlEZFoHiCit+3nkG5dU2/HIoDy/eECEba1j67CI6WtS2aqLfVYVHAnAIK3xNWblziggPcx8/JM+96Pwl511qCcPaYzkY2Lo+Hvg71VnWHh0N1HHVY+chtjmWUB0paQPxon9pVRoA4IpB7dE9Md59Z+K5r4yIQnl4lWxbpWW1qA4JQ0V4pKieBWuO4Z0VR3FDvxZ4qyYCSW5JFRAS5hTSrFHRKKtyfwd8tPaEs15n2UiNsYJ47gvKqjB7yRFc0q6+a6ywWLA21eHEbQ0JhS062m2cuoBw/HAg1znZLam0OoS0UPc+Zqu2EONqzTZLCKxRdWCPqiM6p5KwCMTXkbwjQkLAR9dxO/dchLo/RyEh6NK+Gfbly2e0Q3i4MzMiz1lE9ZZL7osIlfGkLCwSVaGuMWjLyTwdzzInPxYQkDqlJxcfx7VDO1LVTNZbFRGN1kmhOFHmCEHJR0quMdFe3hai2aZARPcaRVRUFDIzMwEAKSkpKCsrcwqfFRUViiG1pHAch1tuuQVffvkl8vPzAQBr1qzB7t27ceuttzrLffTRR3j00Ued3++8804sXrwYJSUOoaesrAz//vsvhgwZovdUggqxB6aB/SU7BYLH4pGsItz65XbsPZPv3CaKhauzjRXVNvyx+6xioHc1eF58NL3LbKQnfHZRBc4VlGP0e+vRb9YqxVA8VVY7Br25WjHTmbHlMJfgaJMEzhfi22qhdFhyO23TLpS6a4D0oiQKJ6cXqKSz1V4TVyuidnrfbD6FV/5JMWTuYzZkH+naLM55sXak5eETIinLQz/tke6qyBXdmzo/i+45UUbt1KW/7T2TLwqhpMX/lqfioZ/24ur5m0Th3aT1al1/mvFDbUk4Ob0AhzLEcc/3pRdo1qmFtFXCfRLSqeYojV+U/a1cR3zon3ekY+GudDz6S7Jz29n8clGMaLnDni+uxGuLXElfVJ8lXv7dZeHc75HSPZXbfDhTvk+1rKeuiCFNuMh6jZq4KY1BNNAc0Yx3NccBdaOJybna8+vx0fyDbmH28ssvx8qVK3HrrbfixhtvxNSpU50vjjVr1mD06NHUdc2aNQvNmzdHx44dMWzYMEycOBEvv/yyKATY3r17sXr1auf3p59+Gl26dEG7du0wZswYtG3bFrGxsXj33Xf1nkpAMbFHU9Xff9vlylBlpHNLBSNP38OhKto2pbqlGZdu/XIHNh7LxTUfu8xDPEkp+s2WNDz52z4887t6amAlxMIaj5yiClSq2B2RNIt3TeLeX3kUB8+5PKmV8t4DjkxWSkHUjQiz5EtFaj52OFM7IQlt3bR98K1lnmcB04onKgfNHuovYOXfXv3vEL7detq0UE2eQNraX9apkeJ5r0s9r5oYxEp0lrgol3ZN2LovvQD7z5LRAZQvENlv0/PKcM3HWzB27gb8WiOsaSFM7qS56aXPg5bscTynBBM+2IiFO5WfP6V+XFBWhUkfbcaEeRtFQs7VNaGoPEHat6Tnocd/Qo4Zfx6gnmiJQvkR/CHKiKhdlzSVMAnZFPIehkgj80D5uZO7Tx0ax2i2y70eOExVZNpj1JZWmnxFD+SEQAkz5szSVRn1MGieH88f6L4LTZo0wapVqxAZGYmrr74ac+bMAQDs27cPl19+OXr16kVdV2xsLNatW4dly5ZhxowZSE1NxcyZM0VlHnroIcybN8/5PTw8HH/88QfWrl2LJ554AqtXr8bmzZtFobqCEQvH4cEf9yDlXKHs72kXCHs6A51NGghZqcNmF1Xgg1XHNPNWS/Pai+pWaODqI+KsMXIDqdpDdiijCP/ty5AdqHke+GO3YwBef5QuzrFof8mx1x89jwFvrsZ1NSFR9LD5RK4zeD0AnDiv7iSn5Gzm6UvNLtHm+8Mp6vfdnqUJBow5HtHss+OUipMdxaUv8UG6ZTWqrHZMrpkIxkeFgeM41Xt8z3fKmRJ/2ZmObScdWQnl4nBKhTjVF17NbxkF5Zi/5rhzs9okk8yuVaWQileahENL+Fi0PxOHMovw7B8HNNsqhVxR8CQ1sPwh1dtthvMprZZRqS2V1a5zph2GBG3uhZJKvPDXAeyumewVVbjOh9Qa160TTi04ybVhi0wqZB7a11cpjrLR4VaPEFxeZcN7K1Kd7/nF+zM19zFDuOzcNE6ijFAmWFN56xZmMzMz0bx5c3z++eeYNWsWYmIcs6OePXvi0UcfxZkzyrNgOTiOQ58+fTB+/HjZKAO9evXCZZdd5ra9c+fOGDduHLp06aL3FAKSf/dlYPGBTEz8cJPs7xy1T7c8tGYGM/87iPdXHcXrGjNGNW2ZJ8+COOWuuKKbvtiGh3/eqyiEmCmrJddkUNp/thBvLTuCNI2oDWRLW9WvI5qtSzNN0aLlmau5v8Rkw6aR5lELX41x0qtlTAjX3qd5XeXlSH+Z4VRU2/DvvgwUlGmbZ5ATTkHQ8+QRmPqZwymVRnZLu6DsBCdcucd+SXYunavx5aZT6PzyMvxbo5G1KvT7l/85KD6OCbdIqQpyfDP75e6mjZS04r2VqfL76TjGR2tPYA9hvqUXcqme9vxLKx2C6odrjuPH7WfwwA8OJ/GD51wrQqSQ3SAmwu2clE2c6M9ejwmMSDNrUJrVo3T4aO1xzFtzXPE9L4cZY5F0cqN2PYNTlDUgzP74448iTSntbwzPyCpyvbiMdDZaM4MlB7JE/5UwGhFGa1BS0ygIGouDGe7L5Tx4N4H/wNlCajtRQDyYhRLC6CfrTuCp3/Yp7nPPd7uQSSyJZhVWuDzhAbRQEZrUMKKZLat0aT7WSDThtPUF2mDmLc2sWhGaS/X4r8micGBG6NxU7J07e8lhPPLzXtz21Q7Nfb01uRBPKOXLzPzvoPwPcD3jOyjDywkT50d/2QtA2eZzt8Ssw4yXvNL5kVZUZocA06ovS8Zm9uT5El33+/1VRzH54y0i21c5lJQkpDBPe1zhfixNcWgbc2pW3kSmTxpCldI9pb0H2kIvr2j7TxNm0lOMmHqZ8Zw//9cBRft39+MF2huADlOD1BUWFiI21ngKy4sBoy8/Urik6WsXSipFD46bMGuoFS5UNbMq+2m1ncbJSElTQDbpeE4Jrpy/CVdRzoB5Xtpu8Tcl+8idaXlutojFFVbR9VFKBauFkoZGDfJlQGqweV5Z4yVFySFNtEylUFX7RjH49yFznTHVgiEoQbOLpwN6QVk1Fqw/Sd0mOaTmOn/tPQcAEvtUeeRaqEfwn7tKPu6zyMxA4SqpTTrVrlyeikOg3ndoahZdbFP342gfyIhm1m7nqeq2qqi+Mwvl44r+uuusIeE9R8EmVkBZeORlP6th54FVh7KRXaR8TLIqjlPuK0UV1cgpJoR6aoGawjFQ9I4Rl6X1kSDp2SJB9z56kE7uKnQ4+AlIFRl6tNfBAvVbdtmyZfjll19w8OBBlJeXIzc3V/R7aWkpli9fjl9//dX0RtYmbvxsm3YhDWiWrfvOWgUAWPH4MHRoHOse2tXDDqvqlFNTd0W1zc3+S+uw4mgGCtXL/JBfWuVM7Qi4gnhfKK1Ctc2OMAojfbLekkq6AaNaRkBsGBuh+DJQcwaTIuSE14OSpoUHcDbftTTM87zu5XuaQS7EwqFHYgKGdWiIDUfPY2zXxrqOIYcRExuqU1M5oVO5pbj2ky24vn8LTB+hHDMyu1B/5AxxE5SnUFr9Vu6lrU+YPSa73dPIKWr7PPfHfnx2Wz/9lcpgdPn/X2KyRiMg0hzHarNj3Acb0SQuEj9Mk4mtTJBUX3ml5pI5a9CpiXmxz9UcddUQmSjVfFQTwh3leEyT2GXf9c1OjOvWxPlda4VQ+N7j1RUAgH0vj0F8dJjOCYVaG8X3XDonq7LaEREaQnUsAWmEC7U+RZ6HUpQbKackGuM5S4/g1atUYqorwIs+q0xGg1SYpdbM2u12WK1W2O1252fyr2HDhvj8888xbpxCwHgGAGAfhcZFi5SMIuqlgJ01S33umlnPeqzaECnUPfq99Rg0e7Xot5PnSzD9x934J/mc7L6rD7uWxpVOUWr/CwBrU8+LBEvSe1PqOKIEeY2UtJO0KC3VzvhTxRlFBTMGmD01dsAAfZguJSqt6i+2UZ0aARCba6jx3B/7MWTOGuTKLItaOGDB+hPOBBA0FJRVY11qjiEtBgB8uuEk0i6U4e1l6tpx050liOp2aizTy2pmPbStzy6qkGhm9VNebVPM9pdsQmgrAbm4uTSQKymhFPZSaqbmKeccNvXP/XkAx3NKsOl4rnLhGqLCxe2WrkYpje1kkhJaQghhtrTSSm0XapfRzstN3EX78BA5vgIOUydyRcjd1El9xTA1u1h2u1obVFdcpN/dNLP6/Qqk5yzl682nnKtkBcS7SCqkKiF9Z2QYHLtFp6om8AecoRkd1KPBhAkTMGHCBKxduxbFxcW46qqrvNkuhgqvLzqE6PAQ3DigpWZZ4eVmJDRXZmE5msa7givnlVbhjcWHcXmXxlTd/Wy++0N3+fsbAMjb5PI8j8UHXN6dNEtgSpAaqs83nMSMCZ019zHyCCspPkQhxgzUaxQlzZyb44MHvmA0GgXhusi9mM8VlCMuMhSxRFKCX3Y6tNBLU7IwqHU9UXkbz2POUn0hviqtdtzx9U7cNaQ1Xr5Sv5OoWhgrErPvLenJr9TNj2YXo3FcpOzvWppZLY38wDfFk0+jsvoHq+W1vlpoeeEfzChE12bxAOg1W1IWER7k32w5hcFt67uVUVuKJtHjyKNUn1SAUjrchRL9MZsjwhxaxoMZhbjm4y0Y2LoevpfLyieBvA3CWFGtMWjYeV52PNSjBZReG6E/0Cpv7Ly6ZlZ6TGl3O5VbKpvkRr1C8VdyQvnuilR8WBPRI23OFaL+rWeVjoR8/1VZ7ZrCNOCIHa2mkRbXL/7eIMaVmGRXWh7+3ZeBp8d2FI3fgYBum9mRI0cyQTYA+Gm740GotNpwOFNZUyu8t6TjEM3QcOfX4qxqX2w8iT/2nMX9P+xWnVUafQFSOxrwjpSnahEXyAf+0w0nxfZXivUaaLjC4C0OFO7/ma5bcHLwmsuGSmRSLK0LApO03x3PKcGQOWsUhQC5l2GTOOPZaFYfURZKzbgrRj2glSAFG7lus+V4Lsa8vwGTPtoMI2egNzC8kiBi+BnX+H3JAfVQRaRm1wyt+PKD8v2D9sVvBD2mViRHsvQ7D1XV9KelB7JQZbVj4zFtzbG0DcJnLZt7m51HiMxE6YW/5ONoA3JRdsQIYyftPbDzvPoyv9R2VFK20EBYNLV++CERmg4QX8MftxsTZgXt9v6zBej12go8VuM4qYbFwomcg9UnGMqKrykLtuK7rafxznL9/hzehkqY/emnnzBo0CB8+OGHzs9Kfx9++KG328wAUFrjSHbXNzsx/oON+E0jnqf0pU7zIpBmNjmpES9VwOjY7z6QOQafFQezkEUIUBdKqjB27gZ8uemUYl23fin2Bn+RGFB3n85zi3HLw1xbIU+SP3h2XLqDTf54C/rOWoX0POUQS0rocaCRvqyEcEGnidBO5AskPMTiZpqQa0Aj5Wqr4395lQ2rDmXjzIUyt988QU+2JTP4arOjz5/KLVXQzKqrZq12HvvPFlA7uvh6HqaVvY/UepnRtMtqzGGkkOdtxClI4Fh2MZ5YmCzKJKYVdk3pGT6ard/hbeQ765CeV6Zb8CfHr/9qNNla8XZtdl53ghP36Aby7aBd+v5p+xlFW3AAuOGzbdgucYwVHR+O2Nh9Xl+JT9efAA1K11bOlM7qYXhEwHXNft2VjrIqG/5OpjOJ202EalPrDtKJg1xRX0R+0AuVmUGbNm0wceJEdOnSBXXq1MHEiRMVy9aWuK+BjvBS3nzcETj6+62ncX0/9zi9wtCyKy1ftF2uM38lIxzuTMvDP8nn8OTlHakfRBulV68UOWeYP/eew1O/7RM5vaXnl+nWlqyoWTbeeuICbvx8GzgOODX7CtGxtQb8CyWVqC9ZgpKzUeTAqcbLNYvjOSVYvD8Ttw1OQt064TXHki8r3S4EhV9yIBP3DW+r67g0116QR93Onfi65kg2hrVviNN5roExLirM7Yp+syVNV/vk+HDNMXxckzb0yOvjcDa/3HD6SpKlKVlYm5qDkR3lhSItVJ1VZF4jWjaeWqLE3FXHsGD9Cdw2OImidb5Hj9ClRyt+PKcE7Rq5Z4zqkRgvW56suVTiDLr7dD76JtVVPJbNzuP9lUfRN6kuPl53HDvT8p22n4BrzFbC7OHi681pVEvRJGQknA01SWhohFk9sizPy60USMwMeMe7JC2XbtK9XS0Rigzu5ncOk6a80irMXnqEamyUu125JZWiFMECnibCAVzXjNbmFnCcV8t60U4ljnor3K+Je33Uh/YZVMKsoHUlvzP8S4OYCOw/W+D8rjZz3Xw8F2drjMb7t6qLnWn5bqWrrHbZ1HpCBqzo8FBU6Qjir5ZtSAm32nmeGEhdv9LaM8qx4dh5oWrt40t44Mc9ePvaHmhFpOU9mOHu0MdDLBib+dwv3p+JTzecwPwb+2D0e+sBOAbO1yd1A2Bs6fV4TgkaxkYgPorOBorOZlmw1RZvJ/vpXd/swotXdEb/Vi4bWYe3sXkIxyPDBX26/iTeVwhLZYQ7v96JtDlXaBeUYR/xDEuRu8ykQ4/cddISJhbUaJu+23qaonWOF5nsy8zgXdLqOlrvesGEh+M4XS34Y89ZPDuuk/vxJAcUHAZF5yy5pl9sPIm+SX0Vj/XzjjOYv1a8vEzGxtZKf2q2nMBx5kyotRzAbBT2qiQ8gOUHxb4Tcvt/uOY43lNwKPQUuePJOaEq7+9+zjx4FCtkB1SbQIeHWpxmIWoIdWhNisRtgqhu1aQJkp/yZUwvAtFJzKM4s3l5ecjKyhL9FRcXa+94EVJRbTMUMFlgPBHeBHAE1r7+U1eqVaW+uet0Pm7+YrszfEi04Ekr2UFLQNl/tgBbKDx1BVYdztEuJEFOiWf2I+NJTuodp/Lw4t8pokFh1uLDmnWZYXN3psYc4MGf9mD/2UI896crNej321yCid5DpWYVY/R76zHwzVWaZQWtAo0wa3EKs+rLiLMWHxbZ4CoJT55CDr5mCrKeohamSQ6RMCtnZuBhNAMpDu96+e3eQKtvlVZaMerd9XjlnxRT2kA+m0sOZKLTS8vQ6aVlYodDyXG0hLpZi9WzJyql6xUwY8VAihn2xfla5hF2XjXNuRs8j4SocPEmmWKfb/AslrMansZfV14Jk/9Bze748s50YQxFqe1p4cXmMnrMDAD3+NCBqJk1JMy++eabiI+PR/369dG0aVPR38yZM81uY63gli+2Y/wHGw3vb5HxjKmo1vZ83iMJ9i+8DKXFtQY7C8ehed0o1TKekJ5X5mbG4HiRmvvUVKiEt6E51qbjuej2ynI3jQKJN8wMpIkXyHzngMM5pKTSqngspe0bayYoZF9SQhjQVJfGa34Tuqs09bDcQHl/TdpL4Xcz73h6Xk1EjQAafMl7Iecw4ywns+1CaSXxu3sJM1M6C20wogFWr1GZAxqhC//ccw4nc0vx7dbTpghopE33Qz/tcX5eobr6o35cmmdJzUTCbGGWg/KEWs8l1EpxbrPzuswZHH1L3AA5v4xig1ErqNogVaDovPRyfVBtQqlmqhdKmfZcLSmFEosPZFJpfQH5cUVuBTLQ0C3Mbt68GW+88Qbee+897NmzBwcOHBD9PfXUU95oZ1DD87xiBin6Soz9LH3puEImSfbXqD/Ewnl1Njb07bVu0RMA82UQNcN12mNV2ez4ZJ2yc4DDzEBnwzSQBj+X3otxczfi2o+3KIbc4nnIhpwxElOdRogQwiZJ7RS1lqf0LlVqIZhO0FY5qVczt21k1j5fRqaQOxa5tCivmTW7Db4956Up6mm0aUKXqSENh1RZbcehmrjdigKfpPeY8Wwrpcd21G+yMMuZU+deIk61HNnFlbruycv/HHSzIRUcHAUqvexg6d5euhOotNow48/9ogQcNKhpZmliHgNAIz3abwLS6ZlGIUFSWU0XPs6f6I46vX//fkydOhV33323N9pTK7n5i+0e16ElBCiH5hK/3oTlX/dQTepYOM7rdjJS430eJqvpoB5oXY9DiZozhN1uLB2kGh+tPYGnx7rb/JGkZhcrXq7yaptCQgL94o/aZbq+v8MJsVmCQ4uvt3ZHPzbvpguOg7QCWUJ0uNu2Li8vR88WCbhpQAtc0zvR4zbRdgeed2RdUko8QdYzrENDj9slewzwyChwjzDgr5cZ+Yzqea6EotIA9F9tPoWvNp/CqyqxiKX9fc0R/SZUgEMhIGhd/9x7Dk+P6yiK4y1gumaW49xCBQrvBTM1+WEW/e+IEzniCA3SSbsXLC5E0CbUkfLP3gz8vCNdMUujUlSRCyrpnGkyewLGR0cr5bMj95tUzq4VNrMtW7ZESYmxnNgXK1tO0BtqK2Ek+wsAt6UFIU6ju2ZWy8zA0OE9wuEMZO5Do+49Tk+IygU5lFmEe751OcB548WvVKfSIPWxgiaZJmYs7TEAYHiHBgBcL8l9Zwtx5YebkFnoWO7XdgAyVzNrRFiXY196AZ794wDWpRoTZIxw5zc7MeDN1chRCFdFTqg+ubkPAC+YGfDAqPfWmVqfJ8Ia+UI2s598KdEI0qBlEiFFet5SO0QBbwhwShkJjxkI96UED/33RDDz6NbckcJX73vJU3JLJSEaKQ+nlUFRqd19WiYo7kNrZmD0mohiWKvW775t+o97xFnhAk+W1S/Mjh49GsePH8cff/wBm823MRYvVs7ml2Fd6nnVMmTnIg29z0jiiOYIoTmkg4ZGG9SEN2+x5cQF2UxhnqAWBF6XJy6vHlVB9NKlr5YaJdsrXwwyNIMpaTd24Fwhvt6cBsARIksNu13f9YoMUx/ClGzEldASBvVocv7ccxZ/7XWP/6zn/PJKq5CSUYjfdqXjWLbYuVYQBBrHRaCOc8XBZAcwaDs86eFCaRW6vbLc8P6kQKjHPlPrvqpNepT6+5Xz9Wf/Eter73hGcdjMumvl8kurqNLv0uJIWKBzn5phLIRYMeRFbTWpcQrkSx2bKPdTFQZVfk2sq+zwSWtmINc9Siut+Gjtcbyx+JBmCDVHHcptXCHjD1JRbcfe9Hyq9vkL3WYGn3/+OQ4dOoQpU6YgLCwM0dHim/Pwww/j9ddfN62BDEcQZy3IB0iaklK+vENIXpt6Hlf1bEbxFHPILzW2JGMUM/O4C6h7cdKPnAfOFVKHHzPbBg5QDqDuyYtQbUmbRO0F4zy8RDbIL63CmQtlms4LejWzYSEWVYcbp1kNZZ1mRQMoqbTiiV8ddpGjOzd2pn6023lM/mSLsxzZLCUzl193nsUymReMcK9FiQTM7mtKDoUeVOlJoglSmC0x0TFIVZg17Sh0eON4ovS0NZ+1tItSYiNDFUNOAY57czxHn6ZXGBs54jklu5y3l7OFNNp6WX3YWHhIaRIZEk/MDBbuTMf/arJy8Tzw4kT1eP9qV3WBQvSIqDCXuOhNZ3Cj6BZmx4wZgyZNmij+3rFjR48axHCHZlmOfEYKKFLy8TyPR37eiz1nCnA0qxhPjumgWn6VwYc30PD2TF8OXy7JqGVF02JpShY2HlNfAQDUbYsVZFn8tvusZpY6wHzTEr2aWS1o76WN0Gaeyi2F1c6jT8u62HQ81xkmT8pqBVtMOUEWkO/Lvkq9Kr0O0ox6ZjK2a2Nn2llvhK0C1DW33np+9ZoKGT4OxCsKQv2LNVIHC9QJDwEATOrVXBQGUMri/XT1yeF0TIZvJw+0IacKyqrw0drjKKuyYViHhqK4wVLUJsRqYyeNIgGQ7x/kefyw/bS2MKtykZWiHhzKLMKAVvWwIy0PoynDiPkS3cJshw4d0KGDuuDD8D1GBoA9Nd6pyw5m4YnLL757WkpodqSJDoIVT6JmPPzzXqpyNJpZrbSqSth4HkXl5mnc9JrHaDWbWtAm6rlq/mYAwB8PXKIakD2vVJ9AKAh2ZJvN6sOXtmuATcdzqQW5V/5N0S5kkEaxkbiubyJ+231WVbOlhtZuem2rtdLu0iD0pcFt6mPrSZdfhc1Esw4A+EyiaROuhVpEFhK785lWL6cezkypbkflzkknz4visT++MFl3nUaOL+DmGM3zmLPsCPaeKXCGGdQyzTiTV6Z4bdUmY2GUY5VcXybbXVFtdwuJKLeHXr7efAp1wnWLjD5Dd8uys7ORnq6smm/SpAkSE/V5/O7duxfZ2dno2rUrWrRwT8lKsm/fPpw6JdY+xcXF4bLLLtN1zNoGubzYIzEe+zWcE0RLOQZsnYIX15l2ldjueUuWXXU4Gw+ObOedyv1AVpHy8qTQk4wu1tt5Hh+vO65dUIEpfRNFZjmuUHR0N1er3bSKQbnjrU/NQZuG7ilVncfWKVAJWh5yL7OE2XaNYhzCrMLIIG2q2bbtJBbOJWx6SzNboWL6EB5icUt0IA1VZAThVoVJbH+9GVcVcPURC0fXn4U+4I3x0TUhc5kZnCcmfGbaa8sh1ZRKz3Ftag4+XS+eDEjTG8shtwq15ki2av+l1czKjS3SasmESvJ1UB3K7Rieju/eRLcw+/333+Ppp59W/P3JJ5/EO++8Q1VXcXExJk6ciMOHD6Njx47YvXs3nnnmGbz66quK+3zyySf477//0L9/f+e2pKSkgBVmjdqwrTqUjYMZRXhkFJ0QdOJ8qTPkCs0hyZeUnfeOXWegoRpL0ovXQE8O7WAgR8Xu1aWZNVa3ndcZLkdyy6SH1WNm0LZhHc120/YRuWIllTbV+vVqB3/c7oiZSgrBZnVhzjkJkP/dl8MFx3HO0EBqwkDzhCjMmtQNd3+70+0517q0WSpRPbo0i3Oz39fK4kWD0Jd8GceXPK6F46j6c0W1HXmlVV6xXxUO73IA8+31SLtQpvq7M+kKgZ50tyR3fbMLIzoqh9C7QFmvrGZW5yUzcoXJ1L1mR00xA93C7MMPP4xp06aJtpWWluK///7DvHnz8OKLL1LX9eKLL+LcuXNITU1F3bp1sWbNGowaNQojRozAiBEjFPcbPHgwfv/9d71N9wtGnZim1TgX9WgRT71Pflk16tUJpxp0yJBMNru54ZAClWH/W4tGsZGyv1Va7V7TTkcQmhcy1mSworZ07xzsDM7deZ53izWphlSLJRUInf2a1jpAY5RWek5SzhUi7UIpJvZwJF2QExJsdruqwKo3YIiczaNZEzLh/qnV9vfec/huaxo+mNrblGMqtoVz3RdpkH2Sq3o1w8hOjRBqcdekauG4L/J1y22l8RjXwtk1fTwcCBEE9Bx2yJw1mNynueltEdogTFZ4nldM/OILpNfklX8Pmlq/2tgvTTCjhFwNeicAB84W4o3Fh3HTwJYY21XZB4pEHK0i8KRZ3aG5IiIikJCQIPpr3rw57r//fgwePBiLFy+mqofnefzwww+4++67UbduXQDAZZddhr59++L7779X3beoqAgrVqzA9u3bAz7mLU1qQzWUNAZy2Zx450xfu17SK9XRSYNbwKIhPa8cuxVsSnsmJnjtpUK+97o2i/POQQIE5zKUUc2snacOUSOHe3BveiLDQjSHaKWXxsQPN+Ghn/Ziz5l8xePyUNe+Gr1m5H5mTZSEOqtVUmA+tjAZe84UYO4q9XBrHrcFnFPQl3OgGdahIS5pWx+3D24l7KD/GKoOYO7HzC9TDn5Pi1Ctr1fFhOPpOW55tc1LURZcWmLA9w5gUrytFU45p2L+RzkAyLVRb6tnLT6E9UfP477vd2sXriHQdTDG3xoyJCYm4tgxuoHt7NmzyMvLQ8+ePUXbe/Xqhf3796vuu337dsyZMwe33XYbWrZsiZ9++km1fGVlJYqKikR/vqJLU8+EFz0DjtDZ9D6PdocxzEVNWAjntYGstplw0JyPcZtZoGOTWIN7ux/ZOcFT6eDDOzQExwFPjdGOxKI1oAuhieSuEc8DUrM4spjRBA/kbnVUMtzpqrPmf5mCLWnPRNeK0R97tKNUeAJpMyunmb19cBJ+umcQmsQ7Vl3krqInDmByEwTaPPfq6BcqzcAR/k7/apw3mikfmst/46W370W+SqQh2qdfNoqJTklTrR3Kx3XFAA5EMwPdwmx1dTVKSkpEf4WFhdi0aRO++eYbtGtHZ+NZWOiYoQhaWYH69eujoKBAcb9rr70W586dw5o1a5CamopnnnkGd955Jw4dOqS4z+zZsxEfH+/803IyM5P46DC0aVjH8P5Kz1ZxhXtndNpg6TwGk2Ud185ozEEtyJdhbZBrvRnN4Gh2MQa0rmdoX8dxJe2RtEuOp8d2xJ4XL8fITo003yjUL1o5uzbwUDuA0WtGrt7Uq+OejtcILptZ8Yl0aOxYCvVW+lyltsil1CV/V/tOewwl5IRZMx5j4dLqHRP2vTzGo+PaeaiGllLG/MFLMCnwV2guKf4cn2n7rdzE3BfN5gk5IQBlWf3C7AcffIDY2FjRX0JCAoYOHYqBAwfixhtvpKonPNwx6JaViQ2wS0pKEBHhvoQucPnllyMmxmVb8uyzzyImJkbVvGHGjBkoLCx0/qlFY/AGnqTUdBjEu2+vlNEMGHUoKK+21TrtoV7MDMAuRe+sOdCh6SpGu3zD2AiPXihFEuex9JoMeGp1WjgOdWuEQC1bX9pbqVRug0ocX6OjBOnxnVRfOcOQHgTBWirItWngGHt9qT2zcBwGtK6r+Lv0nsmZdnGcfGYjAbV04fION56fvxGb2brRYYiPDvPsuDwvyhJJi3a4J/04Q3MRHoepWcUqe3gXb0bl0ILWz4DnHZN+Er2pldXol+R41hIk/czO89hbE87T6MTbm+hek7r55ptx6aWXiisJDUXLli3RqFEj6npatmyJkJAQN8EyPT0drVu3pq6H4zjEx8cjJ0c5Z3pERISqgOxt1Bw79rx0Ofq8vlK5gAEzAyNc5LKs8yH1BkZjYwYqahMflwOYcTyx346LEg/A1TZeczJB2tmaFs1ATnvCAz/VRCCQI4wyNI8aTeOj8Ot9g7Hp2HnMW2M8xJlwGaSXTrhWPp2fcRoKAYrOtvXEBeq4qlLknl8znJT8ZTNrdDw6cd78qCzSlRwewHsrj5p+HFr8mRyIVj6stNox5v0Nom070twnGq3qR2tGa5CjVYM62HU6H/cNa4u3lh1xbrfzPBrGRng1QYon6B49mzZtikGDBon++vXrp0uQBYDIyEiMHDkSf/zxh3Nbfn4+Vq9ejfHjxzu3JScnY82aNQAAu92OvDzxTdu3bx/S0tLQp08fvafiM9RmXFrLgnYe1LNo4aWtNlb9fM8g2e21S9zSzxEvagNEZga1/Eq7HMA8WI3w4BLFRbprrY7llChm0QLEz6dWq99dof6iLamwYtzcDXhPppzWacVHeaZxExjQuh46eWirL1wIqaZbuFbv+1Dg4MB5rAnyJDW23GQoT+IA9u1dA9Cynj6tuKDd1SPMGrF1lFJQVh0wygtX0gTx94sRs3WdRiecSrfAbne1sVmCfFQgf+Kxt8C8efOQnZ2NN954Q/e+s2fPxtChQ3HPPfdg8ODB+PTTT9G2bVvcddddzjLz58/Htm3bkJKSApvNhksuuQSTJ09G165dcebMGcydOxejR4/Gdddd5+mpeA1PxmE9oUGcNlgqr822jeTtd/1pdC8lkGd/RhBMQnJLKpFyznfOh96gqKIa81U0fjE1DkhG+7w3uuHL/6hnpyLbqtXucpXg+oAjleTJ86WykyPZ5WriWdUVX1cDT1+MgiZUmtVJuD7eDuwvbov66pYZQkDPxHjsU1iqlROwnvld7KTM87zu0GpCrWomDt4gkFaIBYHLGc0gcF5DPkfrvsgl71DjTJ5+rSygHJGGTK5kNPSiN/F4XauqqgqVlcYEj379+mHXrl2IiorC8uXLceWVV2LTpk2IjHRJ/b1798aoUaMAAGFhYdixYwcaNGiAZcuWITMzE/Pnz8fy5csRGhq4adZ8ZV/isplVLqO0XGdkEGnbsI6umKC0hFo4Zz7w2oLVZscSylzoSgTChOOlv1MUBbqnx3Z0Zrgy2iu8ESRuu4atHyf67Fl/Vo8/qnxmqw9n4/4f6MPkaOHpkKO0uyf2/0bhNMwMjIyvT0rSd9dVWSGjWZY30meF8dqbq0JynM0rD5j1IWFMY8KseOwR7FZJXprY2TcNUTAVu2lgy9qVNMFsunbtinnz5in+/uCDD4q+x8XF4YknnvB2s4ISmmgGIRyHsV0bY/lBscbFyCDy47RB2H+2APfqiFVHg4XjUOpjbYW3Ka+2iWL7GiEQBvptRA55KWTKXk8GO0+EdiOisK8GZrnTEl5gO9PyfdMIBW4Z1BI/bHPZ8ypdE3+8xCwcZ7pmVurcohafl8o+ljcgVNccMql+NE4bsG00yrTvduHX+wb77HhqOOPM6sjUV2shus9bU3pg8sdbnKs1PRPjMbZbE7z0j7lJHORwal8l3blRXKTz10AUZj3WzCYlJaFDhw7aBS9ifKVRE1KBCrEu5XC8GNx7ol4h4NZBSWgSH4k+MjNIT9HzoHx9R3/tQgEAD+BwpmcmBsFlT2ZstPP0FHca8rgmbGY91mgqV6BmZmD2Aofe6zhrUne0IiIhBNIyIgeo3hgj9ywiVLzys/FYrmJZmueOB6/7igm1Wm3B9Fybi8vMwPE/EFaf/IVa/9l3ttDUVRGaJCHSMeCZ3/cjt6RK9rdAwGPNbCDbqgYK5wrc8zt7A57nsUllUAYAzuKaBZPo1RoKVTSIicBjo9ujoKwa32xJ01WHEmrpUqXERoZix/OjcP8Pu7HHixEJPIXnPQ9tE8gRvp6f0En03bDNLDwTaI30AdLxyl9DtNmaDj2X8OWJXdz2UdTMGm6RcTiOQ0Gp5xm3SDrrcJCjyapmpM8K+5iRGjdYkWYAu5ghNfsc3G3ozbxCav1VSTMraksA3i5Dmtnq6mp8+OGHGDNmDLp06YJRo0Zh9uzZKC/3jdAWbDwwoq1pde17ZQz6KmhD7Txg1VgTs3CcK6YfQYFOL1nywXtsdAc8Nrq9rv3V0NMWjnMsf5gVLN5r8O5Lm3oJZM3s1AEtRd/9FZrLCCK7bw9HabXdd59RNiUwW9Ohp6vcdal7KESl1hi1/6fNOy8QToQp4zjgsw0nFctKr11djecsLITTNWGmmUQaEmZr+rlcVjNvEygaUKkNpq/thwMJke0+x7mNJZFhxvxI9EzcAMo44oZa4l10C7M8z2Ps2LF45ZVX0KZNG9x8883o0qUL5s2bh8GDBxt2BqvNhJsQP1IgPipMcSC2U6QotHDymk+9AoR0Jh1q4jnq8+rmJP8DEx7600dKkQbK9gdKQpd0gmRU6PH0GkUbcByUW6nwBnLmP+VVNizan4H5a43HhJXD4wmBwv0zeqX07kd6bVs4TtUJS9rUV6/qqt4WjhPFFtYir1T7nWZkovn4wmT8b/kRv2hmzYycoQdpdj+mmXUh7ZMJklB9RlNVaw1vOcXi7Hr/7ssAoD6GB+Lt0i2BrFy5EseOHcPhw4exYMECvPDCC/jwww+RmpqK6upqLFy40BvtDGrCQ/Vd5nyNJbWwEBVhVuMlZpGZ8QF0S2niesTfzRTY9SCci4/kEcPwvOea1avmbzapNeYjnSB5cj+MXqYeifGyqw5aRBEaD0+7UbVMZj41TpwvxUM/7fXwqO54suwNmD811HtbyHS5HDSiGUi+D2xdX70t0KcJp9LMyjVEg9ySKny09oTHjqFG8GRiHKHzfaaGWwawixhpvGs9qweq9WpUM/XTbc7PBUT85JIKq0rs68C7X7p75ZEjRzBu3Dg0btxYtD0uLg6TJ09GamqqaY2rLfRpWVdXqKl3Vqhfw7Rcec9Xq43HykPqGUw4Tn7g0Pvyk2qz9ArsZuHUywbesyWCR/B66vZv5TBrIQUMKdIQbUaXzT3RYHMAIsLEy9M0kH3X03uUUSjWcpj1QtKLp+eheO0Mno5ezVtrwhnNYuHUE0robJMj1Je+fbSg7bNX9Ghq7oEN4okyuGk8XcD8a3o31yzjdAALdG2El4gOD8E1vZvjs1v7uj1zIzvqS0SlhNazdzLXldmNjHmcXVwRVCEydUsgjRo1wv79+2GXsc3cs2cPGjZUfuFdrHRrHo+9L4+hLr/5uLoTl5JD2XWfbtXMLW3h5O3F9GtmA2PwEdrhLe/KV6/sYko9PM87JYy3ru3uN+HfCDf0d9jDKl3h967v6WZmYrR77D1doDkhU4Tj0L5RrOsrxS5vXdvd2LEo8ZfGyYhNJLmqo/Q8GX3O9JqdSMvXiaB/qWodypFRTFdzNOF57WgGIzs2RAOTbPtjDS45CyiZbVzS1qXVbtNQPsEO7dhPo/0V+ukhDyK9vH1tj6ASukha1I3G+zf0wpiuTUTbOQ54amxHU46hp6uTq4edm8QqlguQ178I3W/UK664Aunp6bjyyivx77//Yvfu3ViyZAmuv/56bNq0Cddff7032hn06BFejAqKVRRLnA4zA/f69S6B+8mqwA2nmYGX2nPHEHfnGCPwcF3jtg1j0CsxwZR6uzWPw+HXxplSlxJCbyHPgWRyn0TTjrUjLU819awaFk48yNI8R8M7mKP9UMJb/dIsJipoCs2OM6t3t7Iq19J7o9gIhKkMOFIBW+tYHGd+Ipt+repp1nlZ58aqZfQ4iN491LNxSS5FLwC8NNE1eVeaiNFeuoMZ2gKqMJxEhRl/UK7v38JUJ2tfIppASi6sUYcvKXr6umh412HaEwjo7kGxsbFYt24deJ7H5MmT0a9fP1x55ZXIzs7GunXr0KxZM2+086JCqQ/dVSNY6fVOJHE4gLlv1yPM9mmZgCu6B8Z9Fl5kgRj3joRQzILjzItMsOjhoYjyslaC7I+llGlM/TFzlx5STpgd27WxpIz499wScx1Y/aeZpSun9KLzt80suXp0da/mqsKsmw+BxrEsnLmjxe4XR6NhbIRmuZskET+k1AkXa1sHtamHN67phiTC5EJA6/5qLfErRVAg26A0GZRu90RLLGiIR3i4pH77Ja1kt8cotK1xnPb98gVKdupmrh4YteDgoDI+BKBq1tB0qEOHDliyZAnKy8uRlpaGsrIyrF+/Hr179za7fRclSh1FGNTuHNJKs46RHRvKhqviFEJz6bEy+HP6EHRpZlygNhPhVALw2RIhtgX1bWM9DQkmQObmFri0XQPZsv6YXHAcJ345yDShfyuxN7X0Wft5xxmYif9sZg2YGWhcOwCGl3P1Pp+kwBRi4WTTewp0UFkOlW0LzDWTqh/jEIxKNBy5QizqAoo0qkGj2EjcPDBJZKPaol4UAGWb6Mu7NMb3dw/A65O6ob6KSYPUxj3EwuG+YW1E7VNq6zEiKsetg5Kw/PFh+PauAYrHUkPoczSTvigVTWVsZBh6t0xw2/6/KT1kyz86KvASPUmvvVl9VI/gSatkCcTXrUeLYGFhYUhKSkJERGDMcmoLSu8/p+BGUQenon04mu0eIkivzawv2fPS5ZplfDVTNGpDS0YzsHDuLyNv2tB6qgQmL610sGurYFcXCJMLuTZI+4n0WTM7/KbfhFlazazCPkrP0z3D2hhvlAwLbumD4TKOheQEjAPw0GXt3MoI6I6kwin3TyU7URpotLNKo7KFc09cIydYKN3Xz2/rh6HtG+CNSd0wtH1DxESEipx5pIRKtNmpr4/DjAmdJW3S7rs3DWyJZglRCPNyP28cF4HYSHUNsFwLuifGy5YNFDM5Er3mMrTQ3Bqbncf1n27FjZ9t0y6MwBjfpRi6pT/++CN69uyJuLg4REZGiv5mzJhhdhsvOpTSGwr9h0ZwcywRyP+29eQFt21mLHtf0V3bUzfSgG2UWhB0PQK+GegVToT28TwZIJxz08KEKnhsd1XRgAtRBryNMMg6BHLJbx7a1ZmJ9NbIvYylW8yMjyxHwAuzOpuXWDfa1HNqGBuB2y9JctseE0EIs5x6jE29wqyaZvaB4cZtL6WaQbmYx0rX28Jxbokl9AzJDo3sQDSKc2lx1RLokDazc2/oJfsc0Nxn5/loFG0SF4kvbuunWR/VsRR/dy9AayoRCEibJNfEBjH6HQhpVsnO5pdhx6k8t2gsntTpa3SP5MnJybjrrrtw1VVX4dtvv8Xvv/8u+rvtttu80c5aSZsG8loAMlSGiJreLR1jHhnlnn3LUVRc8PbB7i8NAakwO6ZLY4WSysy/SdvMpGl8lO561YR3i8I1MYPbZK6XXg2wsHwm9hQH9p8tdC8rOYlGsRGqHq20A7Kn2X5Eh3ETZhX28YeZgeSYssIssemyTo3UQz6Z0SY/vTQLKIPiK7VOrdlmPmuJdaNl+4rUMUa6LC5qj+S32Aj1eyqXXUnAE6cbafxVPY+dheNQV2IWIGcq4mw3ReVqmcWEn9o0qINJhH0teV2ohFkVn4Ure7r8KtY9PQKjDbxTpMdRL+OOGcKsmkLBU0T+VpLf5Nr494NDMLS9vGmXJ+hdmA3AuQB0W25v374dU6ZMweuvv+6N9lxU/Dn9El3lXZpZ8XZ5eyP3AbufxF6QRDqJb5ZgrtDpapW5uGxmzX+6Xru6m9s2vTN6R3leZGYgVwXPuwsJz43vpBprkLYpZq2c8+Dpbar8MdhRaDbI+/f8hE6GDzWsQ0NsOHpes5y/wmd6PIFReVIdv+mrv1FsJACHd3ur+tH4YGpvNI2PRKO4SBymCMuk5/mOCg9BUv1onL4gH4/bEfVCvr5mCXQxVOXom1QPn2885fwuK4wq7Mtx7vdMLTM5zdVX6wKC45V0IkBeFz19V1p2RMeGeHliF/y3LwMNYyNc2nOFNmlrXQHw6oXkzYpcn1+Y0BlvLDkMAJomC3ra5gnkPaexV06sG43pI9ph4zH18J2iY1D0ltMX3BVogSiwqqFbM9u4cWOEhnoW444BzJrUDQnR+pYMXEvqUg2UfFk9fVEad1DPw64Hsx+Q6DBHOzMUYu8aoW50GAYoCP66hRPBzACul4uSQEy+SBbeO0gz5BXZD7pIIlzcOKCF64tJ0qxcFjPleKS+4QXCzq9m2qDaBvGlV29ly3ruXuQCtMJiqJ9ic3lqA6/2nFYZiLj/xOUuhxseQM8WCc4lcU+WfK/qKR9VJam+su0rx3GoFx0u61DkyRxgbNfGeP+Gnvj6zv4AgIpq13Ua361JzbGV2uS+zayIJ3IIZgbSw5LfqZQTMsqE2wYn4Zs7B6BhbAS2zRiFlY8P00yKoHWuRnoIx4mv6yXt6uPNa7rj7Sk90ENHaETfrTJJJxbypfS+g2hWG7adzHPfL1R5PzWtv7/QPdJedtll2LVrF3bu3OmN9tRqBrdxBKRe8fgw3DJIeclfCeeSjqQzyw0UnEw5NaQvZ289vmbbKrWsifBwuQdLWFJ2vDAaC+8bJPub3ua7VgXVUw3z4EWDVOM4bQ0R2ZbZk7tLfnP9OFHhha9F36S6+HP6Jc66yPBicm2g2W42pEMS7XOhVF6tbil65IxBbZRXRLwF7cvGV6G5OhERB6TXzpMxYVAb+dS1ajVycGhvNzwz0k0Y9uQVzXEcrumdiA6N3aMrXN2rubOMHBZJJA5AfumXtF/3hMqamOTSkGci7SBFPXKrheR+TeIjRUobpTGQpg80r6u+WijnQEXWGxZiwU0DW+L6fi10jU++GsvEmllO2URCpzRLk7FNzuz8SpX3hqcrP95AtzD7999/48KFCxg4cCDatm2LXr16if7ef/99b7SzVvDjtIHY98oY2cGOBjXnAbltbg+3yjPgSXpDb0CbMlFAzaZOL2EhFlPi6z0woq3IAUx4OSmbGbh+oDkMWb5niwTRb6TpyTMGM8n88cAl6NOyrqLHO6C2bOr7NSoOHBLrEmlQZdpAaim0WqiWg16P5tPo8+4JtO0T31v5JU8zIB2MpKsonjy6Svuq1SmcW8PYCCRqCEhGUFsRUGqWXF+VExgEYdBI6DWSs/kOEwy3GL0ESn2ADM8m2PmS19vIs9+1mXzUAbLOuTf0EmUocy/kvo+SQKhHFvPmSEY2o32jGIRaODSICUf9OuEqfYW+/uYJUVT2sNL3f4OY8KDKUgkYsJnt0qULnnrqKcXf+/Tp41GDajOaOcY1EPow+YA2jY+UnVVxnL5OL13mqVKIqOAp1HaeOg/vq9ze0qPERoSiWCaRQPOEKDw7rhO+2Zzm3OaMZiDr8CIRZmkcHlSKkJejbp1w1I0OQ34ZnVOQEmXV7qF+lB3AfA/HOcwObHYe1/VNxCO/7HUrQ3qMa710yfvRvlGMKL6mUjpQOWi07GYjFWZvGdQSP2yTiaGreP/Mu4OT+4gD+EuTAMjdB7njz5ncHc/9eUC0zZiDj/Kk0VsKJ+Ewwzs2xKcbTrr/zgFD2zfAJiKVudq74tJ2DfHR2hOG2xNRM6k7nCVOOcuJro38Nfz01n7gwSMhKhwNYiKcewoY0bRbOGD6iLb4eJ3yObWoF42f7nGsmN3yxXbRtQKAIonT41NjOir2Yl0RObxqNOv62KZhDHa+MBoRYRZEhoUoTkj1TBaKyqsVs72RuJmPBZvBLAwIs/369UO/fp6F2GAYw2Wf5No2tX9LRa9taYcUBqqE6DAUSASbVMmg5q1lBNqXpF7NA3mul7Zr4DbQmYX0miq1UnqvHJdTcLpQ30f6mbYtJBESOylPbJzO5Dm0OPvSC9x+k+YUp2mbt+A4h+D+/g29hC1uZchlVa0Wku+7UZ0bi4RZmhcE4HiObh7YEv9bnkpV3iykz++sSd3lhVlyH+KzWbfv9sFJePWqrqJt4RJbPNmVCpkna+qAlm7CrLKZi+uH/q3qYmdavvO7SIso6QVm2KmqOR4OblMf3941ALd/tcPt97subY3ZS484tz0n46AotHewmoaSAqH/DpaYadCYGVwq402vd+xy35/zeAUju0gcVur6fokSBYELmpjAcvt5GzKihfKqA32LiiutVBNvqeAcfKKsh0kTzOC7775D3759kZiYiPHjx2P//v3U+86ZMwcNGjTAiy++6MUWBh6cRLMgH09TuTvKxVKUzlQDzyJGHbL1r0/qhqn9WyiWNes4gPLL72x+uai8IxKAUIfs21u3mYFakaT60bi+XyLur7nXnjgErVfw2l/66FC3jFo0bfMW0udA7mVABoun8qBWKKvHQTIhOtwtfaZaRiszUErvKcVMDeytMn4AdeuE69KA60VZM6u8j9p9NWMOL3dNSUcpORtqC+duv+qIAGHuPRJQiqxCftWT2pm8D0YWyWx23i2Rg6hdbiYE2nVyHAeOUsK5oZ/y+8JfSkpl+2p99dCM/W7CbBBKs1S3eu7cudTJEPSU/fXXXzFt2jQ8/PDDWL16NRITEzFy5EhkZ2dr7rtlyxZ8/vnniI+PR0mJe0arYELNNo9EzgGMg8KSCac8APSReZEK2jcBr2lmKR+ScpXsNVr1NoyNwJxre3glhqi0/ZpeuDU7LNqfKXqBSDOJSR3AaDSbanbFNjuPt6f0xHPjHdqdqf3V88KrodQXOjdVjr8YYSA5htnkFLu/jPVEF5BqdV4h7llYiAUbnxkpm0KTRLhy0qD/T4zxbjrN+jERbkH49WBEs95XZlyhEcQ8spmlWOVw+w3i+0riqS2q0rHFY7Z7AV8H8XdNrCUQG06cdw/X5DIrUNzNUN+x2Xk3YV5UvxFtL+ivq1zfJevxFkZ6m96+QrPaIB3jhWNUWvW9h/0J1chutVpx8OBBtwQJcn979+5FdTWdbd4bb7yBO++8E3fccQc6duyIBQsWIDQ0FB9//LHqfvn5+bjlllvw1VdfITbW984VZhMbSSl0caJ/js+cQmguAIUKgdPlHoU1R3JE333hrNhJJad6kUaecynkC0K4Ht4wo9WryRGK/295qtO0w8IBNw1MwujOrhiy1TYeeaVVzu+RFBOcB0cqp/isltg8Pzu+I76+oz9WPD5Ms14pRrS6glbJnzwt4/hGOgpqCVrkS/lodjHuHNLa+d3O82hRLxot6iqH7wKAzJqMOtIj+SLcD03/J/szmU7VrNbRtcF8zaxYYBWXKSh3PWeKCWpMRrqa5vY75TUgiwmppOXSAWvBOyfW4uNq9cs/H5CPjU5rZqAUfQJQd0YzAsfR92Opz8WO50fhwZFtseSRoYp9ZK7TnMm36H1c1OIVC0hNEYRD5JZUuRcOUKjWyiIjI7FhwwZs2LCBqtKHH35Ys0xhYSH2798vMhEICQnBZZddho0bN6ruO23aNEydOhXDhw+nak+gQyt0CcXIAaigrNotc4xQpl9SXaxNdS0Ru/bXPpYvzAym9m+BV/87ZEpd5LKv8IIz8pIcpvFikA72mkK/bBM4hIda8MXt/dHqucXOrSWEI1l9BQ0ISQuVOKg2yQgWERqCkZ0cwvPBmWMx6aPNIhtQNa7o0Qx7zhRQlRXwhT+ekOY4KiwE5dU2dJFk6pFLJ6rHzICMNSzVUgnyPe15SoUuXyjirumdiLeWHVHVoJPN0HNtZOvS0EgKTohS4Ut6DbWeQfG+NNKy+CsZ/3XR/kzxj6aYGchs49R/N/K8/DhtEP7cexY3Glh1WXXYobzQYyN8WadGzlCIUmidVx8c2Q4NYyPw8j8H3X5TWzWhmfy5hQ2EcjQDkqt6NnNzoo6JDMXTYx2rWlL/EoFJvZvjsYXJmvVHhFqcodCkGFkB1auZNWIzS5vWNpCg0sw+9NBDKCgooP6jyQ6WkZEBwJGEgaRx48bIzMyU2wUA8PHHH+P06dOYOXMmTdMBAJWVlSgqKhL9BSOcU0hzbeueGC+bAYyDK7ahTE2ax/KaVy/RVjNzvA9o7RI8nMKsgXo+udk9GgfpjS0d/Hnw+OfBIeibVBczJY4uZFtISCGL9Ow285KrOXzViQjF1b3oY88OrLm20eEhiKQ0H/CmA9jozo3w6a19sbxGy/zF7f1wQ78WbrbgctdebSlTCpkHvbomds1lNROCKX0TFY9BIqSedLfn9b40e8/Q1vj2rgH45V6HB3j7GrODRgrOL82IVNNmtY7sB0seHYo5k7vjsdHi9NtSe8uv7+ivWF9HiZMQzXXUcy6mxILX0LzKPRv0qaldn5vER2L6iHayygxa1qWK7eGlzZjYo6nrN8o61Yb1yLAQ3Da4lZuJFA8vmBlYxGYoSu+b0BAOIRJB2syVk1mT3DNJeoLesYNGYKbR3gY6fjNsEy5wSIhYexIaGgq7wpVNSUnBSy+9hB9//BFhYfT2kLNnz0Z8fLzzr0UL7zgHGYV2/OQk/wHHA6oczUChHopnocrmHVsZb73CG8dF4umxHfHSxC7O+HhqAlV0eAgW3NJH5NWaEB3mZtsIiB3myiXhqey8I8brHw9cgoEyjh1yTSBTBZP20mZOIGwaodX0CJtC0ZiIUKftbbgOodAoC27pK7s9xMJhbNcmTlOGIe0a4K0pPdwy6sk6gOmYQJHXSNBcvH9DLyy8dxDGCVEcaFdVOPXv3iA0xILhHRo6bce/uqM/bhnUEgvvGyzbDrFmVn8DOY7DGEnyErKapvFRmDqgpVtGInL8GtKuAUIs7gkEBH6YNlCyr3w5USY4H1xrElkHMI19/JX2WIr0uj9PZthTaSO5MhZD4RwppxFWjXkr/U45iYkIDcHtg5Nwda9maN1APiscB849aZBJ92Nij6ZukSfiiOtjzGZWfvuIjvIrGlQOYAGYBEEvfhNmGzZ0XPjz58Uzw/Pnzzt/k7Ju3ToUFxdjyJAhaNCgARo0aICUlBR8+umnaNCgAWwKAtiMGTNQWFjo/EtPTzf3ZHwE6RErEGrhVDKAibd3ax7v/E0Lo2k4Bc0VFSa/ZR4c2Q53X9ra+V0az1IgLjIU+14Zg3HdmoquhdJyUvvGsXjjmm74/Db3kHRa2mXpr5N7i7Xl5/LNS8NLUm1iukHh5ZxTXIkjWY5Vjev6qafaNYNx3Zo409XGRLibkWgiUy7UoBAuaLrjo8IwsE195zNH2xZ320RlvJVKukW9aMya1F3xpa43moYUDsCnt/bF21N6yNaphJ6hRhpSyewVADUHsIdkbNTVvOBJtMwMjNjM+gLaCU5S/Tp4e0oPPHl5Byqzh+wiGedMDyfIUnlMGDNmXt0NH0ztrdr+VirpjwWMPJedm8a5HTfOQ8dkJfMyuRVaAKAJGU8bajCQ8asw27p1a2zatEm0fcOGDRg4cKDsPtOmTUNGRgaOHDni/OvcuTNuv/12HDlyxE3LKxAREYG4uDjRXyBBOylyCrPENgvHySZNOJJVLCoXHxXmfAi0Bs5bBrU0nBnng6m9VH83Ohh/equ8lk4NpSgRHMfJLmm1aag8oN08MEk2Ze6jo1xLpvIhecTbzkvC3XRPdGW+aaUgfMshlwnnXiL9atdm6n1cz30gy8rl8PYm9wxrg5NvTsD+V8bItkcNLc2sXD3PjHM5jZE/Ky3V0dvMir+rPYP1PFg21gvZZ8mXtZHH1LEaxCGK0LzSXB9R7F8ZUyo1qBzMdJyNWoSQKX0TsfpJsZ/Gm5I00oB827UmCtIYqSRmRFigRXos8tqlSyLeSLm+Xws8PKq9IbMHnlfXzEq1+TTomehIlRJG3lPPjpOJC8y5P0vkhCzTgG2q0rVQOl8aMwO9mtlAFH39Gj/nkUcewRdffIHt27fDarXi7bffRkZGBu677z5nmccffxxDhw4F4HBEEzSywl9ISIhze/BCm3qyRhsksQOS035kF1WItpORA9Se02/u7I9Zk7obtunTiszAcUCbGs1Q/1Z1qesd27UJXryis3ZBybH0lFGa2ZKQE9ixXRuLTBBkHWAk3zcey5X87iqhx4ZYLsD48xM6Y/WTw7Hglj64vLO74K10XC20HHu8jUWy+kDbdiN9eFQn13XjOODGAQ7N2/0ysZn1HEOPmYEvlW9kOx6/vAPCQy14+LJ2hm6wfOhA7XrICYbe5XazbY/bNVKOsMJxQNuGMRhJLOfKPbNaLZITOtRs3D1dAY4KC8GGp0fSFZamqyaaekSSWMds1GxmpatictfYk3CS0ltC26+u7OnwPbj70tZ4YERbNzMmDpyobulqYRSlkE76hCih9OwYiTOrhVYUF3/gV2H20UcfxfTp0zF69GhER0fjk08+wZ9//okOHVwxGIuLi5Gfn69SS/DTpgFdPEiXZlYsAMk9eHbemM2sMDh7U1j5c/olWPXEMHRqoqw9FDSeck5VnqL0sqURJsnlmLFdm0iELPVjabVFjzCrtFTctmEMxnVrqpnelzyuWoxFwHNniM5N47D+6RGqZRKidSy9maS1kxMqSOc8juPw0sQu+HHaQMUkBFr3t32NcOQWzUBlH70Cmif2y+Sh+rSsi/2vjMGTKmlAaeoi208TeF9PVjYpNCYKHVVCAJKmA5/VrP4oLSfTPgdy/cqTpCWkjGb0vrSsHy0y1aHhqp7NfBr/Vs2eXS1yi8DQ9vpDlAm4aWYp93vnuh5YeO8gzKiJ503aowMyk1iI76fVRud5JbVFl4MM7UhC0/eUolr0bJHgtm3VE8OdfimBhCHjrKVLl2LZsmWIiopC9+7d0atXL3Tq1ElxmV8JjuPw5ptvYtasWSgtLZWNGTt37lxYrcoxR9etW6fLGSwQmTu1Fy6Zs4Z+B4kAJC/MSoPwuz6rDVCeRAKggQOHhOhwN2ed2IhQDG5bHysOZaN3ywQ8fnkH3DIoSbQko3firfTyIW1jRYItlWZWr2MJvVBZVE4fW/d6Sls9xeMqfJYt62FniIsMRRPJ8q00D/udl7TG+6uOUtVHrQ2VnFmLelGaayAt6kXjwZFtEVezwhAdHooh7ZRXfbT6jJAcQc8l1Hu9L2nnWWpTEmEJ06jNLPkfUDfdESDtMvUKT8pLq67PjeKUw9z1IMx8BO2XUgvkfBZky8ls8yRNLrn0b0SIMDqmX9+vhc9WCXjoizQixytXdcHiA8qRkNSQrsrJ3WO5axERGoKBROzcvkl1seCWPrj/hz3OfcQx0DnRGCSNB64ETffZdVpe6UfT95SiGcifc+AJsoABzey2bdtwxRVXIDk5GTt37sQTTzyBbt26ITY2FgMGDMBPP/2kvxEWi2Lyg5iYGCQkJCjum5CQgDp1tAfMQKZZQhSVBlJ4wBIIA/K60eGyNrOOJ0b/UCQMfFqaPaOovQfeub4n3rimG7683RGaR0/+bL3HEiCfc5qxlJzlSoUlI8vxZB2XdaZznmsaH4komRiqehAL8RplZbdpX9xnx3VCz8R4PDa6g9vL4hmJfZkuG17acpKCv9wr0Zoo7Pf02E64T8GsQIraY9IgJtypDZO+HJur2KTrzdb2ypWerF7In4Dc/W1RL0r0320fGWGPxpFUbDPr+E8r+9GF5tKevJPlLu/iiFShZj+rF63zUXOcJRVraolSlODcPtDRvXm8T82JPAkxBjgStTxxubHMetL3nfwqG6dbu+2wIxd/f+Qy1z2s9kFMLJpFASWb2bYNjWcR9DW6NbO7d+/Gddddh4ULFzq3ZWVlITk5Gfv27Qt6Lam/oLH3EZ6JXi0S8MHUXgixcOjaLA4ZBe4e8XaeV1xOpzMzkC+08ZmRmu00SlxkGG4e6J7f3ZuQs1aaF6P6kg3dbF70O1GAdqA04/2iliHJrazMz0rZ5UgeGNEWD4wQ2xQrdXM952Q0UUGYxT0Ej6fQahLJNjeICVfMkPbvQ0OQVK8O3lhymKremVd1VTQ58QTpac2/qTe610RDqROu1E+FscO1hcZ0JowQeNVsR+VQqp68Jmq3SCRr15R77equ6NUyAWO7NMZriw45Eys4Q/5ptEnueFraMbkEHwKtG9TB7hqtm3LscGUEQe22wUn4aO0J1bKiVnKemxjpISYiFJ2axFLZ5potZLtrZuXL/XLvIMxafEjW2YvYm/gkvoIcx2FU58aIDLOgotqOvi3rGm+0DBbOXXj1xGb2pYmdERFmwU/bz5jRPK+iWzPbtWtXN0GnSZMmGDduHJ599llcd911pjXuYuKa3uJQR3Le6KTm4+pezTGxRzNwnLyZAQ+6VI9SBCWJXIn2jWKobJfUUDpyCEUqw+gIfdpImpiiZBnll7SL0ip98Xf1DLpKwezd6/R8JBdVoVmdewEjS019TBq4DYcx4jR+N9IW2nLEwXomJiiW65GYgAhJYoo5Mh7zAp4sXzvapbBd8n1ij2ZIqglfpCSgytnM0qzwkGYGNC/en+8Z5Kpf4QQeGdUetw5Kws/3DFK9R3J9qU5EKG4dlIRGcZF4/epuaBATgcl9mqNxXGTNPurtkxtftc5LrU+/eEVn3DigJf54YLBiGTWE+/X4aG2tpcg+lzNfaNTiiu5NtQupYPRxcI9mIKOY4BzhLX+5dzB6q4xlbqtexHfhMD9OG4hhHRritavpEirQRrSQezapzAwUyiREh+PNa5THn0BC9xtp+PDhKC8vx7p167zQnIuX+OgwLH10qPO7bCIEhWFZqQOLZ4Tyn6UIxxW0MGQWJFOQHPyumnz3j41qL1daxJS+iSJPYi3uHNJaVnNzB+HMQ4Y5oXFCEmXEoRCOtLWert9vGZSEK7o3xTvX9dRsh5mknCtU/V3uvIQXux7UhQod9RjUzErt1cxAXbDmZD5pt18aemfqAGWzA08ciwBt+1A5tCaJ5K80EUJIYfYCRS54MnSZUvV1IkLx+qRubgHrpXRqEouIUAta1IuSXRmpWyccu14cjfeu7+Xc1rWZY2xUHC9kNbOqzVBdbUiIDsfsyd3RN0nbo12+bkflNHFc46Jc1yAi1ELtbe8pworJPUR4QTWULpfRMGZmmtWp1STci75J9fDdXQPcUnArQR++U12YbddI3mzA03EkENBtZsBxHK644gqMHTsWV111FcaOHYvevXujW7duiIjwzMbxYkcrRI3SwC1nW2rnlT191d4vgqNOzxYJ+OmegWgWH4UR76wD4J3Ycs9P6ITr+ye6pamUIyI0BF/fOQDjP9iIw5naKYmHdWiIXS9ejj6vrwTgmPU/fnl76ugRcsiFxBIgL2udcDpHGvLnyLAQfCSTTtdtHzM0ikQlZRraZrnDGXIQUtlHV0Yyaq9y8XdveGar1Un+FE/YufeS8RCW0qp+NNIuqMf1BLyYdlrNzlRJM+v8nShLcclJMwOtyBqA+LpS2cyqFGkaH4UdL4xGRKiFOprItKGtERFmwZC28o6BctVorWR4QwF6RfemWHwgE/cPpxMQAYfD478PDYGF4xAR6hjD5kzujuf+PIC5N/TyQisdCN3YSDxZUT0GnwcaEy8jqzCO/Xyn3pabPJIBE6S/xkaEorjSqqm9jQi1oNIa2DlvdWtm16xZg/vvvx+jRo1CdXU13nzzTfTv3x8xMTHo0aMHvvvuO2+086JAvPQrp5mVRzZAPq9sJ6v2cDUl8rNf0rYBWhG2Z3rtDZVmgSShIRZ0auKeJUWNB0a0Bcc5kjtoQWpx4qPD0K5RrOLLWC4MiVG+vnMAAO0B0IhCwJfL447j0fdF9WOK9zLqFVtUoW2vC8iHw4qNCMXgNvXRL6kumhjQLrsfg67cszWhe8JCODx0mfYqBO2TZjQNpWBTOqiNguZS5bzOF8uH23IlPNBnZkCWEVI9q058yH0pHgYtpVN8VJguISo2MgzTR7RTHC/knN7UImIA5pgOSZk7tReWPDJUlBWRhh6JCc5skYBjZSBtzhWY1Fu/va4ZjO/WxG2bYhQLg8eIMzHrnvg1zumefMkhPa/XJznME6TRLeQmZMUV8tF7AOCqXo44uZXV6oLqpmcvc342sirnC3TfwUOHDmHq1KmiqAVFRUVITk5GcnJywGXXCi7UNbOKeyktLSjUofd5en1SN7y97IhoqY0GuRR5ZgzZV/VshqHtGlDHJp0xvhN+3ZWOe4eqayhuG6zP+cxtBk58F0ISuZdRr8NX6Dms/CqB/nbTmGXQkERpty2nmeU4Dj/dM7Dmd8+vPa3Q1aFxLE6+OcH0KCFGbWY/v60f9qUXYIKCjaKxyYr7vnpf3sL5qJ2WSFimqH7loWxdbfAUOYFCK6SWN4aBsBAL9TJ2IDKuaxN0bhqHG/p7FoaQBjPHYbHiSJ+JkRINY8Srr7cOSsKkXs3wwapj+GLTKdX6SWddaSKldannASiH9XIePzYCR14fB543Fh7OF+gWZnv06OFmLxsXF4dhw4Zh2LBhZrXrokQ6g4sODxEtAet5EKQOYGLvdX3cOigJNw9oqftFLJei0awxQ08Yl/uGt6UKtRRN4QBGUi9auQ3OF7vkfM1YFvblspXS8cwwM6gXHY6MmnSOeuqjzW3uppkVnBtNfHHpEdb0PD/UNnIG+0K7RjGqKye01+jVK7vg1f8O1ezj2EZek83Hc2XTQCtBFdWFFBYo2qllRmM2NI6nUnz9TAcareu7R+SICg/Bo6O1VzFEeMvuxiCeTuDn3tALO9PycEUP90mnXLZNuYlURKgFcvEhIsJCdGV189QExNvoFrF79uwJnuexZMkSb7Tnokas0XCo9j+6qY9CCXXUHMCMxCg1olGiDQgdbLx/Q0/cN7wNhkiC1fMyYb702MzSYoYsVoewEWsQo27rbsSxTY7wUHG/e+3qbggL4fD61fripNLaNkqLeUNcUBOmvKV0J81rvHUMtWqVhEnho5KtMA00fijScVJPeV9gZKz0UljvgEVIrz6oTT38OG2gyJxNwEjkHG++cYxEUeEk++kdNyf1bo43rumuKEhKmyRnukW+h8mJZqAmPzCK7rP5/PPP8ffff2PixIkYP3485s6di/Xr16OwsNAb7buoEL8YONSrE47eLROIbfR18byy1iixbjRmXtUVj41u7xxUvEGVTKq+2jBmX9M7ETPGd6YSZIzEcJUytL3Y3s6Ma0gGw15wi7bTmRQjQtRDI9vBwrmiSYzu0hgHXh2LWwe30jXI02tDxeVoQq/phRRChnVoiD0vXW5KvWpe2SM7NkJiTdIFGocpI9BeYjlbfPL+XKPTzlIwM1CLnSs2M9BuqJ8seXQRDG00k/k39cbTYztiwS19Fe2JVSdUCttpnCvd6jL52ot9VaRxZk0+FlHho6Pa47Ga8Gu3DkrCrEndsPLxYaKUueTxI0Ittarf6R7d77nnHvTo0QP79u1DcnIyvvjiC6SmpsJqtaJ169aYMWMG7rnnHm+0tdYjp3EgtVB6+51aRxVyzd8+uBV613j7m03vlgnYe6ZA0qZa9PSoIAxh2qerfT3uGdoGG4/luvYw4RqGEeGQ6stoZrs1d9nayWtm9TOgdT3sfvFyka2zkfSptFkvpdoub2S1I9sdwokdDs2MdjNjfCfMXnrEecwf7h6IU7ml6N/KWLgmLaiFWZkvPRLj0b15POrHhKOlTu2asLgxpktjPDe+kyjdrIDe2+iNKBZmEwxtNJN2jWLRrpG6IsXIJbmsUyN8fHMfU5U047o2wbKDWbhlIGVmPtHKhXcnKmTVCdFhmNq/Bfq3qovWDWKcsgOZiIRsS1xUGBbeOxjXf7rVew30IbqF2fj4eIwZMwZjxoxxbqusrERKSgqSk5PRtKlnQY8vZuS8HvXah4nqoyjjaQpBNXq1SMDTYzqiZf1oXPrWWq8dxxMeGdUejy1M1q1BkoOUXTiV5BMkVMukXlguJz2uyXAuDWLC8cu9g5BY1yWEKAUQN4IZ/Y0+65b3BQRRggDJ8ZS8/o1ALgly4NCqQR3ZpVmzUNOUc9K3tXO7g9jIMPz38KWGjtugJsxgaIgF9yvYuTch0szSZKILBjkxGNoYDHAcp+jUaJQ3J3fHxJ5NMaoTne03J/ks1dSaioxJg3SSEBsZisqa+M1kW+4a0iqo0tVqYcq6W0REBPr27Yu+ffuaUd1FC9nRhBcjTdBxJcgg2Xml2sHIzcZu53FJO/OXyM1kUu/m6N+6HpqaEG5ElD1H+K9x/2hur5tgYcJFJDWzZCQhnofbYCh3OLMHZT210XrTKsVZNhOpaRCJJ9ohqR+LSKvsg4fIiGbWkz7x9rU9sP7oeUzpk6hZNoKwvS6v1nbu8seY89M9A3HT59t17BFoI6P/UZ1QmXi5hGetXaMYHM8pcfu9Xp1wTOzRjLo+TqyBEv9mqIXKVBGxX5VWnr66oz+e+X0/ZkzojHdXpDq3902qh2oZU8BgpXZZAAc5cs8AafhNE9hZ4KqezdCzhWuJzkwtES1yy6yB6BLWPCHK9CVoZ8xNmWPpr0v8vYhCG6UFGRGAtCWVC/XkC60RzTEm926OBjER1C8WX3iIi53+xL95ohkWbGIFAknUITXCUmcXo1zfvwU+urmPbufUNjTaaT+oPS9RSKighNB37q3JgHWfjkQHelj95HCv1OsNfH3b3pjUDfXrhON/U3p4VI9bs4kNGQXlHtUt5ecdZ5SPW0OPxAQse2wYhndoiLFdHTF7hRjbYSEW3aZAgYr5HhEMUxBehHUiQvHprX2RWVCOAa3p7eNenNhZpMHwhxApF9Bdb+KF4IKwTXL74ODNyeI81zQCl7RELkXKTy0ax0Xig6m9EGLhREv/cmGvzArNpYc6MkLNezf0gs3OU0cz8MXL8I/dZ52fpe0i07Tq5d3re2HmvwedAe85meV8b6KmZf3flB54Y/Fh3H5JElKzSoh9fNCwGlY/ORxZhRVoT5E5MBgiBQjX7vHRHTCoTT0MbqNPGKal9iwrm3dThWs/sE197HpxtMerTtIJHtn/ru2rvfKghwoy2QFFu+8d1gZJ9aMxsLUrEk+LelE4k6edbTDQYcJsACG2mXV9FmZTtESHh6BRrHjZnEaGlEuL6wlygmstSAGtCHm6FhnN7MZnRrqFm6EaN730Mr66l8tO+IUJnTF76WG8eEUX98PLHN9srae0PqVuQivIAr4JOSnEyQWArScviH7zRDPbPCEKn93Wz/ndE9t5I6gdoXFcJObd2BsARMKsL2nbMIZaMPOXLDukXX1sPn4BAymUEJkFjn4UFR6CyyhtM2s7aqtYwzs0wKrD2aYH8DfdfIpz2JBP6ZuIlHOFuKonvbmC7mNRlAkLsbitbOUU+X7V1hswYTaAIB+k4gqr4Xrkcyhrv9nbmTxrt8lIrrVbM+vCFXOTc9smV061Lh+8ju8Z1gY3DWwpij8rEBnqriX1tjxlxtIXaU/ZtqF3nKVa1ot2ajUKysTmH2ZqBElHJzNTbypB3l9h6VurXKAG/vdXBJWPbuqDf/dl4EoZs5im8ZHIJCZCq4/k+LJpAc2CW/pg28k8XKki+N00MAn1YyJEoSsDBXGCIsfnd67r6f3jGuzmNE6UwQCzmQ0gyL6olV5ODTmnMRqNqCfLorTUas2szDYtB5lAClUmJ8gCQLxM2mCzl26ll+HTWz13JiXtf8noDGby1NiOir/p0SJrQU5ujcTS1Av5Qr6sUyPFcmRqYamdb6Dgr+DwCdHhuG1wK9kIHn9OvwSfmdDHPWV4h4b+boIb47o1xatXdVXVuoZYHFELmsZ73ufUYhobQcX/y6sYnUyaOU75E6aZDSDM6vhyXtw0GtFw2gCeGjw1pgN+2HbGGcCZxGgu+WBDLhuS3O2liVYhl3zC33hTCzeiY0MkyaS31AvZ1bw1YKs5IGmlR/7t/sG497tdyK/R6A5TESwqq8nA5z4wM6B07LqkXQOseHwYIkItptwzb9DEhEglZtM0PkrUP+4a0tov7Zg1qZtfjhsI/Hb/YKw5koNpQ8299pzCZ29jdFioLTGO/S7Mnj9/Hr/88guys7PRvXt3TJkyBSEh6h6tKSkpWLlyJUpKStC1a1dcddVVCA31+6l4jFkCglznpBEhzbI/euiy9nhwZDvZl26t1szK2syqmxk8fnkHbD15AdMuVR5Qz1woFX2v58XYwLR4c/wza3D1xcRJrq2PjmqPVYezcZ/K8jwA9G9VD5/c0hdTP9sGAHjveuWlyAGt6+Krzae8Zi4hRWtFgaQDhROWP6kf4//nRQ7yso7vrs8vwgxiI0MNpYytLfRvVc87SUf8ppk1hi9CGPoCv57GiRMn0L17d/z999+w2WyYMWMGJkyYAJtNOXbgs88+izvvvBNZWVmoqKjAs88+iwEDBqC4uNiHLfcOZMe/R+ds8aWJLscdOS0UzXs91CTNLKD8AqzNNrO8TDQDLZvC1g3qYMfzo3CfQoB4wN32uHbMo8V421vfW/2OfNbGdnU47jx+eQcsfmSoW4xl+Xa5PjeQycQmMKZLE/xw90D8OG2Q8cbqgDRrCHbFTUiAvq39pcHz5zEvBuRsZr3FW9e6ouMYfU4D1dZdL35VZz777LNo164dVq5cCYvFgvvuuw8dOnTAL7/8gptvvll2n1tvvRVvvfWW8/tjjz2GJk2aYNGiRbjxxht91XSvQHYpPUGaAaAZkRUnVEaYpdFShXnRZlYISK03/mKw4owzq+EAJi0jh1SbHQh2tma3gYydu00SFcAovtDMksLs0yr2s0rwlEHzLBYOl7b33bNzItcVpaBz0ziVkoGP3HgYCATCc8wwH86Hs5RmRMSH2iKUGsVvU1ar1YrFixfj5ptvhqVm5tyqVSsMHz4cf//9t+J+3bqJbXwqKyvB8zwSEhK82FofQfRFvc5YZNB/WftAinemWTazcnx4Y2/cP7wtHr6sndeOEUhwkv/Sz3qQCmVevE3UmD1skud46+BW5tTpA1Nj8lkLNaABNJJEwxdUVLlWx/QkawlEpAlRGseZG4LQKGSzau961cWHL0XKEApliWYdATrZ04vfRqnTp0+joqICbduKl1fbtm2LrVu3qu6bmpqKzz//HEVFRdi4cSNmzpyJ8ePHK5avrKxEZaUrllpRUZFnjfcSZJIDvS8Q0mPXqM2sN6MZdG4aF/QaHi1E6WzlHMAMjjZSYTYQZuDeVCpNH6lscqEHX6wuR4Xpy1glJal+HXx2a9+As+tsU2uC67uHMvv9/kv81BIx/n6OmWbY+3j7Cqul06ZlQvcm+GjtCbOa5Df8puMpK3PEZoyLEws48fHxKC0tldvFSUREBJo0aYJ69eqhqqoKu3fvVt1n9uzZiI+Pd/61aNHC8xPwAvFRYXjjmm54fkIn3Z7BZNpbeZtZGjODAFD5BTFiYVYwMyC3Gav3+n6B11/NHqTJaxcX6R4KzAijOns/+Dw5iTR6f8d0bYK+SV5wRPGAGwe0xNNjO+KfB4f4uykec/PAJOfnp8Z0CBinJ7NSARvFm2ZlFzNmCJi0WEzoQ09crt88KhDxm/QSE+OY+RcUFIi2FxQUIDZW3Tu2VatWeOqppzBnzhzs3r0bW7ZswQcffKBYfsaMGSgsLHT+paene9x+b3HzwCTcO0y/ZooUACJltEVU0QyYMOsRDWLVtWtGB5t4SYrZQFComD1I09qO6oF8Jry1jBsI98IbhIda8ODIdujpg5i23iZKJjXyxcyIjo4QcGanVmU48OUkhYxAZHRllZkZeEjLli1Rp04dpKamYty4cc7tR44cQefOnanrSUhIQOfOnXHo0CHFMhEREYiICAw7KW/RualrAlBXJsg9jTOML5Im1GYaxUbi6zv7K5qIGBUApfsFwl0KNiHOW75gvvRcZtQu/PUMvXlNd6w8lI3JfZprF2boJpYwa4nxcra+7s3jceOAFiittGFEB+XkJrTMvKor8suqML5bUxNa51v8JsyGhIRg8uTJ+Pbbb3H//fcjIiICKSkp2LRpE37//Xdnud9++w1nzpzBk08+iaqqKuzZsweDBrnC05w+fRp79+7FVVdd5Y/TCBi0BCW1l3lsRCiKK63o2ize5FZdfIzsqDygmPXuCgRbt56JCabWF7QR20ROPMF6Egx/oBWD2ls0S4jC7Ze08t0BLzI6No7F7MndUVppdWrBvUVoiAWzJ/cwrb46EaFB2zf86qb61ltvYejQoRg4cCD69OnjDK91zTXXOMssX74c27Ztw5NPPgmO4/D888/DbrejS5cuKCgowOLFizF69Gg8+OCDfjyTwKJLM3dHK7XX7A/TBuLAuUKM7er7wN21nTMXypyfzXphDWlX35yKPKA2LD+bQQDMKxg6CISJoEAANYVhIhzH4cYBLf3dDEMEs8WBX4XZpk2bYv/+/Vi0aBGys7Nx2223YcSIEaIy119/PS699FIAQFhYGNasWYMdO3Zg7969qFOnDl544QV07drVD60PPBY9fCkWH8jE9BEyNrcq0mzPFglMOPESRUTwea30prQ8M66TKfUEEsGq0xSHXgviNwHD55BRZ4J2ZYJRKxjbtTF2ny7AqE7ed5r1Fn4PIBgdHY3rr79e8fcxY8a4bRswYAAGDBjgzWYFJd2ax6Nbc3lTgSqbD4JuMlQxK12wWqaoYCVYX+aBpOljBBes5zAChQW39IXVzgd1RCO/C7MM3xBhkiDFYHiD4opq7UIe4LVoBuRnJp0wdGBG2D4Gwww4jgv6UG1MwrlI6NREPdwZg+FP2jcKziD9TAhhGIVp9RkM82DC7EUCGzgZnuDtFKDejnVIkzSEwWAwGMEJE2YvEoLZS5Hhf9Y9NdKr9Xt7ssUmc4zAhvVPBsMTmDB7kVBbsnww/IO3syh5W9YMY/2fwWAwai1MmL1IYMIsw1O8me7Y22GtvNX/yfBKzMmSYRxmBsNgeAIbfWs5V/VsBgC4c0hrP7fk4sSscFyBwE/3DESbhnXw/d3BFxbPW6maI8NC8PTYjnjksnZoFBfplWMwzCPayysMDAbDP7DQXLWct6f0wAMj2rJoBn6iNunD+7WqhzVPjvBK3d42MwixeG9S8eDIdl6rm2EOL03sgrVHcgI2M1N8VJi/m8BgBDVMmK3lRIaFoHNT9/S2DN9gYY5HVHjbCqZ1/WjvHoAR0Nx9aWvcfWngrU69c11PnC+uRLtGTNnAYHgCE2YZDC9ilizbMDYC54srzaksAPGWzeyCW/pi+cEs3BWAggyDMaVvor+bwGDUCmqPQR+DEYCYpZkd2r6BKfVcbIzr1gTv39ALCdHh/m4Kg8FgMLwEE2YZDC9ilr7R297+fqeWnx6DwWAwvAcTZhkML2KWmUFtj6wWwmyLGQwGg2EQJswyGF7EYpIUWttlvQndm6Jtwzq4cUALfzeFwWAwGEEGcwBjMLyIWTJobY+KEBUeglVPDGdpZxkMBoOhG6aZZTC8iFnC2cUg4zFBlsFgMBhGYMIsg+FFzLJ1ZYIeg8FgMBjyMGGWwfAiZgmhrevXMaUeBoPBYDBqGwFhM1tQUIDc3Fy0bNkS4eF08SAzMzMRGhqKhg0berl1DIZxzNKn3jiwJXKKK3BJOxZvlsFgMBgMEr9qZq1WK+6++240btwYw4YNQ6NGjfDdd9+p7vPRRx+hdevW6Nu3Lzp16oROnTph3bp1vmkwg6ETs6wDYiJC8cIVXTCyYyNzKmQwGAwGo5bgV2H2zTffxKJFi3Do0CFkZGRg3rx5uPPOO5GcnCxb3maz4fDhw1i3bh0yMjKQk5ODK664AldffTXOnz/v28YzGBRc0b0ZAGBA63p+bgmDwWAwGLUTjud53l8HT0xMxB133IFZs2Y5t3Xu3BmjRo3C/PnzqerIzMxEs2bNsHTpUowbN45qn6KiIsTHx6OwsBBxcXGG2s5g0FBUUY1/kzMwslMjNE+I8ndzGAwGg8EIGmjlNb/ZzGZlZeHcuXMYOHCgaPvgwYOxe/du6noOHjwIAGjRggVbZwQecZFhuGVQkr+bwWAwGAxGrcVvwuyFCxcAAPXr1xdtr1+/PnJzc6nqKCwsxEMPPYQrrrgCXbt2VSxXWVmJyspK5/eioiIDLWYwGAwGg8FgBBp+s5kNDXXI0VVVVaLtlZWVCAsL09y/rKwMV111FSIiIjSdxmbPno34+HjnH9PiMhgMBoPBYNQO/CbMNm/eHBzHITMzU7Q9MzNTU9gsLy/HxIkTkZeXh1WrVqFePXXnmhkzZqCwsND5l56e7nH7GQwGg8FgMBj+x2/CbExMDPr3748lS5Y4t1VWVmLVqlUYOXKkc1tGRgaOHz/u/C4IsufPn8eaNWuo4sxGREQgLi5O9MdgMBgMBoPBCH78mjRh5syZmDhxIrp164bBgwfj/fffR506dXD//fc7y7z88svYtm0bUlJSYLVacfXVVyMlJQW//PILsrOzkZ2dDcCh6a1bt66/ToXBYDAYDAaD4Qf8KsyOGzcOixYtwrx58/Drr7+ie/fu2LRpExISEpxlmjdvjvbt2wMASkpKkJGRgYYNG+Lhhx8W1fXaa69h8uTJVMcVopExRzAGg8FgMBiMwESQ07SiyPo1zqy/OHv2LHMCYzAYDAaDwQgC0tPTkZiYqPj7RSnM2u12ZGRkIDY2FpxZ+UZVKCoqQosWLZCens7sdYMUdg+DG3b/gh92D4Mfdg+DG3/cP57nUVxcjGbNmsFiUXbz8quZgb+wWCyqEr63YM5nwQ+7h8ENu3/BD7uHwQ+7h8GNr+9ffHy8Zhm/RTNgMBgMBoPBYDA8hQmzDAaDwWAwGIyghQmzPiAiIgKvvPIKIiIi/N0UhkHYPQxu2P0Lftg9DH7YPQxuAvn+XZQOYAwGg8FgMBiM2gHTzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwgpZQfzfAH9jtdmRkZCA2NhYcx/m7OQwGg8FgMBgMCTzPo7i4GM2aNYPFoqx/vSiF2YyMDLRo0cLfzWAwGAwGg8FgaJCeno7ExETF3y9KYTY2NhaA4+LExcX5uTUMBoPBYDAYDClFRUVo0aKFU25T4qIUZgXTgri4OCbMMhgMBoPBYAQwWiahfhVmeZ7HihUrsGXLFoSGhuLSSy/FyJEjNffLzMzEDz/8gOzsbHTv3h033XQTwsLCfNBiBoPBYDAYDEYg4bdoBna7Hb1798bcuXMREhKC0tJSTJ48Gffee6/qfkePHkX37t2xdu1axMbG4o033sCYMWNgs9l81HIGg8FgMBgMRqDA8TzP++PAPM8jJSUF3bt3d25btWoVLr/8ciQnJ6Nnz56y+11zzTXIz8/H2rVrwXEczp49i7Zt2+Lzzz/HbbfdRnXsoqIixMfHo7CwkJkZMBgMBoPBYAQgtPKa38wMOI4TCbIA0LVrVwBAVlaWrDBbXV2NpUuXYt68eU77icTERIwYMQL//PMPtTDrF0pLlX8LCQEiI+nKWixAVJSxsmVlgNLcheOA6GhjZcvLAbtduR116hgrW1EBqGnc9ZSNjna0GwAqKwGr1ZyyUVGO6wwAVVVAdbU5ZSMjHf1Cb9nqakd5JSIigNBQ/WWtVse1UCI8HBBMffSUtdkc906JsDBHeb1l7XZHXzOjbGio41oAjmeirMycsnqeezZGyJdlY4T+smyMcHxmY4SxsoEKH0DMmjWLj46O5nNzc2V/P378OA+AX7FihWj7/fffz3fv3l2x3oqKCr6wsND5l56ezgPgCwsLTW2/Ko5uK/83YYK4bHS0ctnhw8VlGzRQLtuvn7hsUpJy2S5dxGW7dFEum5QkLtuvn3LZBg3EZYcPVy4bHS0uO2GC+nUjmTJFvWxJiavs7berl83JcZWdPl297KlTrrJPPaVeNiXFVfaVV9TL7tjhKvv22+pl1651lZ0/X73sokWusl9/rV72119dZX/9Vb3s11+7yi5apF52/nxX2bVr1cu+/bar7I4d6mVfecVVNiVFvexTT7nKnjqlXnb6dFfZnBz1srff7ipbUqJedsoUXoRaWTZGOP7YGOH6Y2OE44+NEY4/X40RPqawsJCnkdcCJgPYqlWr8Oqrr+Ldd99F/fr1ZcuU18yMpCEa4uLiUKYyu5k9ezbi4+OdfyzGLIPBYDAYDEbtwG82syQbN27E+PHj8cwzz+Dll19WLJeWlobWrVtj6dKlGDdunHP7vffei127dmHPnj2y+1VWVqKSWNIQ4pb51GY2EJYH2BKi4zNbQtRfli0hOj7zfO1eQmRjhOMzGyP0l2VjhONzbR8jfAytzazfhdnNmzdj3LhxeOKJJzBz5kzVsna7HfHx8Xjttdfw+OOPO7cPHToUSUlJ+OGHH6iOyRzAGAwGg8FgMAIbWnnNr2YGW7Zs0RRkf/rpJ7z55psAAIvFguuuuw7ffPON0+QgOTkZW7ZswQ033OCzdjMYDAaDwWAwAgO/aWZLSkrQvHlzREZG4rrrrhP9dvPNN2Pw4MEAgGnTpmHbtm1ISUkBAOTk5GDEiBEAgF69emHZsmW45ppr8OWXX1Ifm2lmGQwGg8FgMAKbgA/NFRoaijfeeEP2t/j4eOfnm2++GaNGjXJ+b9SoEfbu3Yvly5cjOzsbjzzyCAYNGuT19jIYDAaDwWAwAg+/28z6A6aZZTAYDAaDwQhsgsJmlsFgMBgMBoPB8AQmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAaDwWAwghYmzDIYDAajVrP5eC4+XH0MdvtF5+/MYFwUMGGWwfAihzKKMPb9Dfh991l/N4XhBWx2Hrd+uR3P/L7P301hqHDzF9vx7sqjWHwg099NYTAYXoAJswyGF5m1+BBSs4vx1G9M2KmNHM4swsZjufh111nYmNYv4DmTV+bvJjAYDC/AhFkGw4tsOXHB300wjTMXynDFvI34bMMJr9RfXFENq83ulbq9hZ0I082EWQaDwfAPTJhlMBhUfL3lFA5mFOHNJUdMr/t8cSW6v7oC4z7YaGq9W09cwOrD2abWScKBc362X3z5ZxgMhp85lVuK6iBTAngDJswyGAwqSiqsXqt7w9HzAIDjOSWm1VlRbcONn2/D3d/uwndb00yrl4RzybJMmGUwGD5lzZFsjHxnHV78K8XfTfE7TJhlMHzEqdxSfzfBI0JDvDdckEKhWRQTwvfL/xw0/wAQt5uZGQQ+F2H2dkYt5p3lRwEAC3el+7kl/ocJswyGj9iXXuDvJnhEqMUluZVVmaultXhBmj1XUG56nVLIdtvZSh+DwfAhFVabv5sQMDBhlsHwEd7QPvqS2MhQ5+elB7JMrdsb16awvNr8SiWIhFmm9WMwGAy/wIRZBiNIeG/lUXy16ZTfjl+vTrjzc1GF9wVFT/HFkrLIzIAJswwGw4cEuX7EVEK1izAYDH+zNjUH81YfAwDcdWlrv7TBm2GoOC+oZn0hWpLyK8suxWAwfIk3zLOCFaaZreXkFFXgg1XHgt75qDbgicD2X3KGiS0xBim4mS3MWrwxJvtAtuSJgzDNLIPB8CVMlnXBhNlaQnFFNa6evwnTvt0lWl59bdEhvL/qKMtAFeTsOp3v7yaAlF+tpguz3tDMel+4FGlmmSzLYDB8CMcMDZwwYbaWsC+9EPvOFmLV4WzkFFc6ty9LcTjq7A4AYehix5Nhh0zD6a/wQl41MzC1Nge+uEx6zAx+25WO3yQhdOx2HgvWn8C2k7UnU1wgE2jK83+Sz+HBn/aYGl+ZcfHANLMumM1sLYEUNMTaogAbvRmGCA+xoKomywvP+34Q+2zDCfxvearzu9lpZ71xPj4RZkEn4BdXVOPp3/cDACZ0b4o6EY6hd0lKJuYsdWRUS5tzhRdbygB8Y0eth0d/SQYAhFk4zJ3a27+NYTCCGKaZrSXICa1Wm50tfQYQnghspNDkj1sqTWG772yhqfV7wwHMFxM52oljRbVL+K+yuj6nMVt2nxKok/vzJZXahRgMhiJMmK0lXCipcn4WBJ99Zws098soKMer/x409aVaabXhAhuc3fDEvomclARCFqMW9aJMrY+8Mmadn6+vUqAKSgwX2UUV/m6CLKzrMIzgDSVAsMKE2QBj4c4zuObjzTicWaRrvycJBy9hYKysVl8K5nke936/C99sScOUBVt1t1WJUe+uR99Zq3A2v0y78EWER5pZ3rua2f1nC/Dkr/tk75mccGn2yzc0xHVxiirMyS7ma5tZky0vGF7g5x3pyGUTbUYtgYmyLpgwG2A8+8cB7D1TgPdXHvW4LjUTg0X7M9Bz5gqknHMIzWYO8GfzHWlE16WeN63O2oAnAw95K1cfzva0KW48+ksy/thzFm8sPuz224XSKrdtZpuvxEaGOT8XmZa5ywdmBpQ2s+J9TDo2U+cZ4mCGPkVBbSKrsAJrj+SwvlNLkCpIVh/Oxri5G3DoIuzjTJgNULI8WA4Thim1Zc9Hf0k2TQMWzOSXVuH1RYd0a8J9DXkrZy89olzQIEIc4q0yXvXyQprnL8NTuaW4+Ytt2HI8V1yzSe9Zn0cz8PIBv958CnOWHoHVZseHq49h4JurkVFQ7pVj2ew8ftlxhnnZ1zIGzV6NO7/ZiaUp5qajZvgHqTB797e7cCSrGPd+v8s/DfIjTJgNUDx5LwqzbrUqzA6tpMXxnGLMW30MhaZp3czhpX9S8OWmUxj/wUavH8ss86bTF7xnviHXRLm+aDdhSf2hn/Zg8/ELuOmL7V4RCn3RwxcSobaMtJvW5i2/tAoz/zuEBetPYPfpfLy78ihyiivx7grPV3Dk+HVXOp778wBGv7feK/Uz/MvGY7nahRhBBaltL6m8+BRVTJgNUMwI+B5IDimPLUzGeyuP4uO1x/3dFBGBthyTnleGimqbv5shQq4vmtE/SWccb9gE+6L//7T9jPOzNyeIlUQEhAris7cSQ+w9U3vjUgf6KoxvCJx3QyCRX1oVVIJgqMUlwq0/6jLr0zP0LdqfgesWbEFWYWA6R9LChNkAxTPNrPA/cAYswTZ3zZEcP7fEfxRrmHUczCjE0LfXumnD/kk+581maSKrmTWha4kiNIiOZ5Jm1sfd30zh+XhOMSZ8sBHLD2a51S06jpfOsTZnFprjBTOdYCOAXg0BQ2pWMXq/vhLdXlnucV02O49qH3iEdm8e7/xsNCLRQz/txc60fExZsMWsZvkFJswGKKYIC372rqa1tDxwthAP/LC71sfcFILmKyFkaxMc6ASEwOqBhBkvQ+VEH57XDfgjNJfjf3FFNfalF2hmBFPjkZ+TcSizCPd9v7umbu8LsAz/4Wvhkgmz7jzw425T6iksq8aQOWvQ9/WVOJ5TbEqdSoRYXJNOm4f3tDSINNJyMGE2QDEjTmsgmRkIyGndrpy/CUtTsjDtu4vPaJ0kAG8XAIVJiQmNFctnIt2sx3U76vftBRXMDCZ9tBlXf7QZ765M1dhDmaIKsW05OTENxOea4RneMhdRgvUhd06eN0eZcjSnGFlFFSiqsGJfurnJZaSQY5wnk2cAKA8w8za9MGE2QPEkVJbQv/2e/UvngFnbNbNa+PqFJoecQ5JsnFkTjqWkbVy4M929cBAgvExO1LwUt5xwjwwBGBOylbTY3sIbsdirrHbsTMvzePk1LbcUX206FXC25cEEi7WvjpFntNpmR7XNDl8uopD1Wz184VdoxKUPdJgwG6DUj4kwvO/c1UeRWVgeUDazNNh5Hkezi30eaUHA39crUG+XXLtMuVYKVXy+8ZTndcMfNrMQhRnbe6bAcF3StouEWcO1+pfn/tyP6xZslY1l/POOM3hzyWEqQXfEO+vw2qJD+GD1McUyVpsdf+w+i/S8sot+kizH2fxyVFrpJwNHs4sx5ZMtbmH0vMlfe89i0keb/eKYpHfssNl5DJ69BoNnr4GV6MPefqeIk7YEtzDqKUyYDVA80V78ueccbv5iu0eaWZ7nsfRAJg5mmLtMotYkOw+MeX8DXv33oGoduSWV+GHbabelWCOQnquexPalxd8Csxa0Chsz5hvmGxaI8fVS6trUHNz0xXbn93p1wlXLH88pwTsrtE0RZv53UHS9A22J2Eo5Vv25x+HI+M2WNNH2apsdM/48gM82nMQmHSGjPll3QvG377edxpO/7cPQt9fiZuKeMBxsOXEBd32zk7r8Pd/twq7T+aL+7W0eX7gPyekFeH3xIZ8cr2W9aOdnvU9YflkVcksqkVtSifwy13vJ248quZon0gjrOHBMRKjzczCnoWfCbIBSbfVslnXyfKlHL73dp/PxwI97cO0nxj0cZY9O0aTvt51W/f3Or3fixb9T8KyGQ5VAQVkVVU52X8gIakJgIIgo8vaxdOX04u2lc1/LfN9tTRN9D7XITw2EZk3+eLNiG8ll4K83p+GtZS4P/EAyM9h64gI6vLgUX2+W16Z/tzUNP+84I/ubALkSU6zTCUXJ1GArYeJxzkuJJWgoLKvG9pMXAnISu/m4vBmMHOeL/SfkmJcRUJ3eLROcn/XeLwtHOmKRqyi+08waPVIYkUr8li93eNYgP8KE2QBCZMyt0DNLKq34cftpnL6gvXTmiTArGMMHoh3NgXMObTFtFpter63EwDdXyyZsIF/avnjdmKVV25de4Dbg/rcvA0//ts8ncRLNOA+7lwd9X4sPEaEhou9KAuG4uRthtdndMvDxKsL9ykPZ8uUMtlUPUvt9q82OLcdzUVZlxRO/JsPOAzP/c9ee5ZVW4eV/DmLGnwdU7Vs96UqVCuNToNiEjvtgA274bBv+258p2v79ttN4+Oe9zu8BKOsGLcUV1ViXmqN7dfOf5AznZ723g5y3ksv93tfMujA6JoeFuMTAYI7BzITZACKvtMr5uUl8pGyZD9ccwwt/peD+H/Zo1ldQpm9GSy4XhiholfRQVF6NqZ9tFQWW9+eYveZINr7dkiYKQULG0/SF9kRtwNFz+Ks/2ozfd58VbXv45734bfdZjxyoZDOAyd01U0JzEdV5RTPrqrR5QpT5BwBwWadGzs9VkpenUqzW3JJK7D/nbr7zAMUzDYgv/cnz9OlmL5RUYsH6E8ihMqdxtX3s+xtEv3y39TRu+mI7Xl90SKSRklJW5XrOaF+0ep/BQHCaVCOzxt5zWYpYmH3p7xT8ty9DbhdZjmQVYd7qY7ocg49lF2Pyx5tFEyEpX22is0/3p7C98VguUmSeFzmqbXZ0f3UF7vh6Jz5YpWxTrYXe8yUdZ61EjCxvu3/wJoyhnvjnBBJMmA0gyOWJuKgw2TLrUx1ZPmhmUKsOKw9icvxLDK6hxNKD0ZAfC9afxLaTeXj+rwPObWLtEy8SdL3N4wv34ZV/D+LTDSdlf/eJmYGKskDvi3nJgUzZ7VmF5i6ryidNMNdo1huXnqyzfoy6/apRWjeo4/ysZjsqlfnkRMBlB+lWGshrX60juOSDP+3BnKVHcKcOW0kAuEBMsgHg4xpb1Z93pFNrQNW6iycCKU03JO9RsDL9hz14b+VRvLOcPtzbrMWHsedMAe77Xjnk4Swf2aN6ysQPN1GV+5VIL/3bbuOTer19knwOvL3iJEb8PiXJLqqgsmevEx6iWSYYCAhhtqqqCllZWaiqqtIuXIPNZkNRUfCqxGURecR4/hDEKwjESiSnFzg/k2nyqg16SWotd69NzREJumZis/M4m18m+9umY+dltxslt6QS325JQyGFJlwqBPI8j5RzhSiv0h9myNMesvt0vuieA/LLs7R2tHqx88oDsSl4WfMrrVc65/PWUrfRaB/bTuYBAA6amMKZ1MxmUk6ipNfFo2yHCtuLysnVF//jaUa1kzURGbadpLdzPZbtCNiv1l1ou1Kga8AFyMgHWtc8u6gCa45kg+d5t/FHb58ky5Mhsnyp0SYPVVRhxcA3V+Omz7Ud9gLFJMdT/CrMnj59Gs899xxatmyJpk2bYssWbWejkydPYvz48YiJiUHTpk3RpUsXbNpEN2sLdMjOeDyHfvlQCb3aM9KmlDQKLyirRkFZFZalZGLLCXpvYy1LhWPZnp+jEo8vTMalb61Fq+cWU+9jdOC565udeOXfg3j812S338jrCLjfkyUHsjDxw02OVIIeDKB6ySutwrWfbMGkjzZjV1qexnHcD+SpZvZcQblqXERTkjKQWgs/vIzVur9cPF9ajAizemOyqjWPfK7JcvPXHKerW/Ldkzuj1E8ulLqW48sMTBTleO2/Qxj7/gaR+QQ1JgkMwSFSBgdD5qzBXd/swr/7MjyO0UpCmhl4Oo4VVVQjp1jZLEg8mXY/1g6NsR2oPamr/SrM/vrrr4iPj8eSJUuoytvtdlx55ZXgOA7Z2dkoKSnBgw8+iAkTJiAzU37JNRBRWrYnO2Nplc3j0FN6X3qkAXx4qKtrPPFrMnq9thL3/7AHN32+nfrFKGdPxyt8Npt/VezRSEHCiMBjs4tn8vvPOuy51hzJcSsrDc8kVXL/XrMUZqa2jEZQyi9zrYLc+Pk21bKymlm9jZJwk+SY0vr+2usI47T7dB5+UfGGL1Z5RnwRrlitz3gisKoh0vxQ7iO15/UE0p4+Ksy1RBkZJnWAkz9/NTvbR39J1gzNR6J0/uQx2jQ0x8zgq82nkJpdLBonA4XjOSV49d+DIs2kmd0/GB3UtB4/4TnaeCzX7Z2m+3yJ8jYDz6cSPV5dgQFvrEZBmfyqtSlpwGuHLOtfYfbpp5/GjBkz0KhRI+3CAI4ePYpDhw7hpZdeQlxcHDiOw4MPPoiEhAR89dVXXm6tOWw/eQE9Z67AHxLnHcD9AcorcXTg77emYfaSw6jSGa7LyPtLcCghmyIN4UL7YpSb7ZphsO4pPM/j4Z/34qbPt4mWI2naU1Ftw7C312Lat/J2aOl5ZTh5vsQp7Err3H+uQLld2oc3jXzCDlKP3aWApxqH0xfEJiCzFolt94SYpNd+shXP/XlAFGqJ3Kf7qyuwNtV9EgEA7688SrTXo+YGFJ6mrfQU8t13ikhI0KdlXar9pcKstC99syWNun8pFSO3S4VsT/FmUpdj2cV44tdkHM8p1rXfpI8245staXjwJzonQjXsdh6bjuWKzKaC5fExIpdxcHeW1ruSQ5Z/jRjLzHpWU7Pk+4NSnFlaiiuqseOUtvY2GAjVLhI4REQ4vO5KS10DqN1uR0VFBbZu3eqvZuli2re7UFxpxZO/7cO1fRNFv0n74rdb07AvvQB7ajIJXdq+ga4Oa2Qp2KmxU3PYoKy2UWwEcmTiE9rsvFdeCKdyS9EgJhyxkeq2wucKyp3XlCSrqAKtNJxFtp64gHMF5YqxK4e+vRYAcEO/FnhrSg+3y5hpYjYbT66gnpeefAYwDw4uwwlJXnTpyyTtQikGt60v2vZFjSf2nCVHMLKj+4SY7HsHM4pQZbWLVhzMwB9Css0HB6U1kai0khFQKOuW2szKlKm02mWFUKlwoCR0WIklEE/T5/oC4Sye/WM/9pwpQHpeGX67/xLq/QX/hANnXV7/RrvJjzvO4KW/U9C6QR2sfWqEsUr8wCfrTmAeYepCK9hynLuCxhObWdH2mv97zuTjSGYxrundHFEGHK6UmmM0UYLAxyqJR4INKmH2hx9+wNy5c6krvfXWW/Hoo48abZMirVq1wtChQ/HUU0/h448/RoMGDTB37lwUFBQgJ0deOwMAlZWVqKx0vdgC1XFM2hm/3pwm+q4UU1EJIwJjlbVGo6gqzdLV1a5RjKwwO+GDjcgursAdl7TS3T4ldqXlYcqCrWjTsA7WPDlCtazSu+2ub3bi0GvjVPclB7280iqEKKxlLdyVjrem9HDbXqkab9N3klF2kZ4g6MZsZlPOFWJpSiZuHNASiXWjNcsbhXY1/90VqZgxobPX2iHFW44VdpGDifZ9SMstFQl3NKjazCoIrbTdl8YBrLzKJivMusWKptDMXiihdyyW4+cdZ3SvihlFmGTvTMuX/d0XQ4QQMozUugeDapZMLKIX6buV5nQLy6sxe8lhDG3fEJdIJtrOenjHMzr5Y4c/UFmVFdOGttHdPqX7/huxwmvkFtGGPAsGqITZrKwsREdHY8KECZplN27ciPR04yEx1OA4Dn/99RdmzpyJ++67D+Xl5bj++usxZcoUnD6tnDVq9uzZmDlzplfapBuVl4TWQCX9ecuJXPRMTEC6gte+Ec2soMUw21tdgAeP1Bov26PZ2ktp321Nw+7T+Xj3up4IVVH9bK9ZKjl53ngedhpHEVLL0+f1lZoCi/QSSB37SC3XhqO+y3uuRG5JFbYcz8Ul7Ro4txnNAHbf97txrqAcqVkl+OL2frraIT2mGXLhpxtO+lSYPZtfjreXHcEz4zphs4k57clJqpZdbl5pFUa8s073MVqoTD70TqqluNnMynQmJe2zdKtSPyT3P+RBIPiKahtm/CkfcaWi2oZNx3IxuG191IlQf5Vq9l/K8VZOySCa0Jg8gdp9Oh9P/bbPVJtrM6i02vDlplMY2bEROjeNky1Da7POgcNxSbxmmknirzvT8UvN356XLpctU1JpFa1eZBQYW5mTu+/SCZa/zY/8DbWZwYABA/Dcc89pVxgaiqwsuniJRqhfvz7mzZsn2tarVy/07t1bcZ8ZM2bgiSeecH4vKipCixYtvNZGvfA8T2W3yPO8aLZ80+fb0b9VXdw2uJVseSOaWcHMQE9cSLudx6HMInRqEisSOLWWp5cc0O4nL//jcAYZ3bkxruzZTLlNPtJqllZKszZp7SEuoCaQp1II9+Jjyx/c0/fZ64sPY+mjQ13HkSlD07UEU4w9ZxxapsLyavA8j4Ro7ZivW05cCIo84Vr97uN1J/DMuE74kjI4PU29pC261gtMT1IFEjVTnXKF1QUejvB+G4+ex/0j2rr9JhDCcZomH8q2sDxVOasBO3CBoopqfLb+JK7s2QyJdZWTbXy09jg+XHMctw9Owsyru4l+O1dQjvu/3224DXpQcnbNVvGCp8WTdObe5LP1J/HuyqN4e1kq0uZc4VFdHOceeee+73fjp3sGqe53nhiflJ7X91YeRf9W9ZzfhePkFFfgwNlCjOjYiC5BkaT6dak5bimGL25RllKYvf/++2Gz0Xmw6ylLQ1FREaqrq1G/vkONz/O8aMaVnJyMffv2Yfbs2Yp1REREOO1tA5HbvtqBXWn5+O3+wZplpTPknWn5uFVBmN2uYNi9/qhynFUhD7bagyF9buetOYa5NdlW7hvmWkKRq8OozEnGrI2NCHXL4a5Pbjf+2OvNqiZFGLcOZhTi0/UnFQ37/Yk0y5D8pISntkHleR52O4+eM1cAAI68rm7KIXCfj4QBXyAVrrRWTTJUbKvTiFTWeidAtKi1b3Cb+lihkFVq0kebAQBfbj6Fmwe2lC1TXGlFhxeX4umxHfHgyHby2kbKZ1SpHO2qVGFZNWYuOogpfRKdqxFvLj6MX3amY/7a4zj02li3fdLzy/DCXwfwY03Cl2+3nhYJsxdKKjFkzhrRPt6KbAEAyxTSehsda/dJYk/7kmqbHZe+tUZzPD9g4vI4x7lfqy0nLsBu52FRETTJsIuHM5Wfw3mrXZnIhFWCh3/ai+2n8jDvxt64SkVJI0A2Ly23FHd87Z74xKxU6TnFFWhQJ0L13AMRKpP9mJgYxMfHU1Wop2x5eTmysrJw/rxDuMrLy0NWVhZKSlzahCeeeALDhw93fn/ttdfw5Zdf4uTJk1i6dCmuvfZa3HjjjRg/fjzVMf2NXPfYeCwX5dU2fKaQmUpA2Qhc/pf6deQ1YGoZYYR88Xo0nWR8STK7VnGFgXiMFISGuF9FPc+xJ898GK2Xi8KxhLnI07/tx7/7MhQdyQIJOYFh47FcdHhxqVPrqr6/eBKWQ2mvu+u0q+6KahumfLIFz/6+X3O/RfszsJMivqIZ0HYluWQZctBk7ImTaE294UypViONXFZQVo2P1qo7l/yvJptVvtwEUcOhxvldodzwDg01WuhgzrIj+HPPOdz0hSu4/F7COXTxfveQj5+uP+kUZOV4exl9li4zMPv+V/rIPliObScvILuo0k3rKEUtvJt+OEPhB8l3wS1fKicnkPpZAC5F0xKZ/iWHyAa8VDtMFw1bT1zAxmPu5k8D3ljtkf2xvzDk2vvPP/8gOTkZAFBQUIBJkyahU6dOupzEAODff/9Fr169MH78eDRu3BjTp09Hr169sGDBAmeZ+Ph4NGjgst979NFHsXv3bowdOxYvv/wyHnzwQXz33XdGTiPgUIuNCtCFoSFRErwqVGzehBeMqmZW5TcSuZS7Zs0efVWvFL1HkZYX2umJHZ8cNEKlN3hj8WHNMmbcmm0n87DrdD4W7kp3804ntV5Hs4vx0E97cd0C70Q3Ka20imI+Gj03Jfmjx8wVSMtVt/uW7quWFcrwO9/AiRm5Fgt3nsFIAza9zmMqbKcNE5ae5+5vQGq7n6aYPEnJl4kJSt4GWkXBWpm41VJ4nhdpyYNFl8bzPKw2O77YeBIz/jzgvCa0mRCVnBBJaPu+QzOrbov8xcaT+GDVMdHvCZQZNncTk3KjYeIEhcLKQ9mKph9K/cpu5zFn6REsSxELzmrxxZVSvlttdlFYx0BCd2iu9PR0zJgxA3v2OEL7vP322zhz5gzuvvtuvPrqqxg+fLiq/SrJDTfcgBtuuEG1zLvvviv6npCQgI8//lhvs2sJSk4RXtDMqNnMSn7U88L0XlpRHwmzHh7HTC0K2ZRHf9nrlXrlvuuvT6KVNNBfycmK2sRFKYWxWXR9ZTkAIGXmWMRoOP0InMotxRGJOYmSrWtZlU20LCmH9Hqqr4DoE2+EpVVVzayJIpOSc5Xc8UsrrU7NloCnTi/k2LUzLQ8dm8R6VB8g33bSzljeodJ943db0zCykzjknHRfNXMUklf+ScFNA5OoyvqCxxYmY+WhbKfT7dT+LdC2UQyeU+gPUvT0wTMXyhAWyqFpvLL9sxzCpbba7JhVM2m/vn+is57wUP2CqTSqCLU5TU2xe75TXlVVehSWH8zCgvWOVZK0OVfAbucVzYS0uPObndh28gKWPjoU7Rp5/qyYiW5hdu3atRg8eDAiIyMBOLSrH374IUaOHInMzEysWrWKWphl6EOvZtao4JVRUA5vmZOboUGVsz/T807zpaG89B74Ikbo+qPnTfXa91iYNaE+8parRTrwZmrGf5LPOT+fOl+K7onxVCGvxs3d4LZNrb9Wa3Rm6YTIrBXXlHOFuPHzbXhgRFtEG9AgmTmpdp9Q8Rj3wQak59GZ5Rhpy3ULtmJ058a696OB9Dz35ipSpdWO91cedct69u3W0yKzHSUMpeo1gDSLWmgIhw9WHXWbrChB0+c5zpEUYNj/1gIAOjWJxY/TBqJ+TIRoXFaqSihCmgkUlFXjt11nMaB1PUN9TGo7T9sVaIopteeBH8UxxbefysP9PxjzRxDMEpYcyMIjowJLmNVtZlBVVYXqaoeNU2ZmJk6dOoUhQ4YAcNjLkvFcGe544gig1KHNNpm75cvtGtEMjGNGU+WuoDgTivpR1AZMYZnLrDAn0lp+UrG1M1S/TKaxI1nFGPP+etklu0qrDb/uVA+dl1tSKYraoJqylaqRngvEpJAqFQY4sTTrNR79Jdn5mQePwrJq/LxDOwyhnA2iJ4KftGuadcoP/7wXxRVWvL0sVWYCot1eteVHvRNr6fWx2XlZQVbvBF+LVYeNaay0IPssbdOEd8WZC/pWGz5YfUzUVwVoUmbnFutfQrbZeWw7ecEt0oseIkIt+HzjKerytO/R7CKX5vpIVjE+rPHveOo3l/mInAMY4OqDZCi6hTvT8d7Ko7j5i+2G3rtSEynaJDo0zw9te7TSl5OcKyjH6QvuZk+B6BumW5i95JJL8Ndff+Gjjz7Cgw8+iLFjxyI83OFotH37dgwapB7OgmEcvRpYcmujWPpoDifPl+qKZqBHG+YtcwAyfp8nh3j6930oKKvCJXPWeC20jllOX1U2O8Z/sBGP/OxuYnA0uwSLD7g7F3yx8RSe+UPbDvDjdS6nPjM0s6Rw4qmDyaJ9dE4TnnI02zEpWCJzHe088O++czJ70aF2TUM13hTuwrz+N4uc4EmG/ZO2j0z7rHS4l2rC6MmhFCqLtu1Kl8tTbbA3ogxohSTU+zy98Dfd0rsZVFq1bVazCisw+ePNuPe7XbDZeXy+8SSmfrYNd3y9Q1Quv7QKd3+zE3/tdU/dLkWvYEgjTDneS+KC32xJwwt/HcAfe8Rtko/f6/i/4Zgr+s+JmlB3NruxGfqJ86UiRYlS97Pa7OKkFRSY/W7leR5D5qzB8P+tQ3GF2EnTm9E5jKLbzKBLly54++238fHHH6Nx48b44osvADhCZFVXV2P06NGmN5KhjuJAT/zQNEGfvZD3HLXoy5IaVK3mkOGKPGn5ov2ZGNW5EbKKKrDsoCvkDc/zeGdFqqaHthS5dtM6OWgh5NQ+klWM5jL3V067TBu8/5cd6Xh6bCfNcjRjWkmlFc8QjjTfbEmjagMJ6fAhdZ4j20A2JzzEYijY+887ziA6PASfrDuBo9klmP7jHvx0z0Dd9aih9nxpPXtuwqxMGZ7nsfpwDkoVlo3Lqm2QukiFWjhnDFtpC/7bn4FbBhm3ubTqlFYqqu34eccZDO/QEM0SovSbWFEexxuvZLn7R5qG0ArgQtvcsp55kaIK7WNtPp7rzFR2KrcEv+xwrDaRWcuyCiswaPZqAMDqIzm4pneiWz0ktK+bj9Yex+7T+QinjCojJ/RKI1Fw4FSPTzoJkt7/Rt8zm0+46mgUGylb5uV/D4pW8ajMDEx+ZZOPbHZRhSj2tLnRJMxBtzDL8zzuu+8+PPDAA6LtvXr1wurVq2G322GhcTW8SPGkDygOgoqbjasD9CRN0PNGoBWS5685hndWHJX9Te4akp6lA99cRd8gGeTMIFcfztEtyAJKs2VfWu2KUQsCT2L2ZGYREYJm5SH9SVVITUCRF1/u2UUVTqekFvVc1+qmz8Whd3iex7EcYwkJHPsr/1ZXI6mEqplFDetSz2OairNI8pkC/LYrHQ+MaIsIGUcWab/deuKCR8KsEkpDx9xVR/FPcgbio8Kw75UximOfp73UG+9kOcGdtJXX+2jZFfZdcTALs5eaG0KJJgsieS52Xl5L94hOh1Ta8UaItqO1egEIyRC0y6VdKEXvlglu24UmKSUJoblWcmQSq4gRCnG6peZoNookIDTXcF2qdoQMAV5yn0lqhZnBu+++i2eeeUb3bwx39OZFVuqrexWCXHsij3hL3KJtk1SQ1RqTyAE118Nc7FKqrHZVwUANWVHWf7Isoigde8iMdGrtNeJw5amZgVqiALIfGFmCJl9Qalmkyqtt+G6rcgptLTyZLEiVzXLPxq7T6nF2H/xpD+auOoY/dsubSkibt/lErtcjRZAIiV0EraReEyu9mmAzkVsRIbcpnYv7Uq6wr3z5e7/frXspWotj2RQTNImORG4EOCh5t3209rgopJ0Uvc8Dzf2trLZTTVY2HstVtJm123msOSKfZGiOCRMJ2snU28u1j0VzCe/9jt50bvMJV8g/6f2hylrmY0xVoVZWVjqjHDC0mfjhJl3llfrqzzu0nYr0Du2q9je8q8zDP+91yxGthhlLZnLhrbz5aP22W9vJRw/eeM2aHTi9hNIBzAhGkmmQ9zci1IJvCVMFUqDW6gdajn2kxkfqrEHiqXZY7fEq0XCkOZ4jFublNGO0k4zn/zrgNOchmyS95wVl1bj1yx01x6Oq2iOkh9BrTlCpoE3TOo4ZyEW4EDuAube6uMKKT9bJr/z4KoY2ALy26JBmGZGzrcIdkAqb/1ueivdWyq+0Afon+DTCVFYRfTpfucP/vTcDPWeuwBkZByhPOEGkmKZdrj9KMcmgeQXoMbv6hPCbOHm+FIv2q8fB9zfUZgbJycnYtGkTNm/ejOLiYsyfP1/0e2lpKb788kvMmjXL9EYyHOg18M4pJnNHu7Y3T4jyyAlJqKqo3Ir/NBI9mMU7y1NRUmHF/1ak6hKejSDVapHOL7rRcAZR4lh2Mdo3pg99QjtwGzHcV22vjybo5KC/50yB02YPUBau5Nqdnl+GxLrR+G9fBqLCQzC2axPR72SikWrVpb3/s3fe8VGUWx//zW6STe+NNEIISYAACSV0Qi+CgoIUFbCLIoqoCNZrxYKKvZdre23XCgIKWEBREOlI74QOKYSQOu8fyW5mdqfP7O5scr73wzU7++zMmZmnnOc85zlH340XV4hbqb5cJ71hhuubqF8S4M3f9mLOCL5/tNCz02MFnPimuiQWzvVU1M1A5BUpHbSN3shSV8cKKhXc+iok8/ZjZWgdH+osXf05vbmUI4CLOJxH+PqvezCpW5qgzN9tLMIjnLS/XJTcI3fCrnTyrnSOLzS23vu1ezbecRMSfLexCC9OUhbKVM6f2WijA3dl6vFF//J0Bp/2mV2/fj1efvllnDlzBrW1tTh8mN/hhoeHY+zYsRg/frzhQhL64Vb0S/KSRK0AjvIK2oUnO9nT5VV4/AfxbFNGti3ncEt6VlSEPWbln9vs/23C17f01n5hA9kvYZlwZ5cWH2bjTcjE2HeyHP/9Yz8mdEuVrQc1dSw6Pfyjw/q57ZFhCA5o7Aa5v3fnxps7Ptto2Ln0KmT2dszwjuk6pQt7TgrXIbHlYpdEG6LyCH+xUcT1yhmlbjdyHCmuwPcbi/DKz7tlVx4OiITaEkqdC/Ctcp5KDiPFSs4mUmdxnly8HcdKLghOBKVEV1LfzmlY0THbREAP1TJGHHfeqrPxy4xuBoqV2WuuuQbXXHMNPv30U5w+fRrTp093p1xNkgXLdqJYKA+5QjzZLqUULrscTaebkMbohqvkPa4/WIyKqloEBWgfbPXO1M+WVyEqJEAwZqWnkbqTssoaPPTdVtTUsciIDZEoWb9czl3Gr6iq5SmzSn0tjTZMpEQF4fBZbaslekURuhfp2MLuH8i4E4nT5yphE1E6xdpSQrgyd7d2SeGCIezU0v+Zn2Us+Y08/L14CDMhEsJtOF7qvvjt9qxvSjnltOLn/EuxaCVSirgSpXPj4WIF0jmdV2F71rqZy5PI3cnX67WHClSLGScJqn1mJ06cSIqsSmrrWGw8VIwFy6TTVLoT7pKBkooo1TGzYFFbx5rCStCI+wZYsYFUCcI5v5X91h3xJdUoYcUKLJPuXG3iPiYl7iy/7DiBN35rXHEQeswHz/AthJ/9zbfCvyWSk9wZo287JEB1YBkHQu9AzXvh7q624+2mzdVBujy2zBH+yRkxRVSpYmlU/VVyvVmfb8Brv+xRrDjZZXP35KGoRN0kihcz1yCTBsvKvwstCpTSFQZ7lASiEalH525XPy0o6kGXL1+Or7/+GoMHD0ZYWBi+/vpr0bKDBw/GmDFjjJKvSfD00u1441dlg6QUejqO7cfKsONYWX3ucQWn4cYFdWbSm3+ivKoWn1xvbOxNLQyY/wu+me6+5fiz5VUIs2lTNO7+YiPKBQYupe/xq3+O4LnxeZquLYbagVFMiXCmprYOp85VITHCuA2gWhSqP/c2+jsLDX5FTorb00t24IqCNEQ2hMP6dK2y+zVS13v9qi6O3Ola0Kvs2P32PJFqWSuPLRJ2MVqwbBdmDs5yOV4rkWaYGw7Jk7f81T/1lrM2Lr6x3uX7jUdxc//Wiss7hwozwu+YZVnZd6HlVb388275QpDfdGkGzNQ8U6KCvS2CC4oss+fOncPhw4dRXFzs+FvsX3FxsZtF9j2MUGSB+socFshXrFqoUB7smZ/0LhHsP30eJ8sqFQfgdyf7TpXjoz8PuM1CmP/oT3hPQ5B/APhCZiOPN1DznFiWxZyvpK3DdkVq8jtr0GPecqxuCOdiBqu9kAhCFhgxv1yxGJD15zbm/nKTwzE8N1GX76agZVblOUoqqnnPSyp8mpolaW/x+d/ibS+ZE2tZyXv0txp7v0o3L/207ThGv/K7ql35WnhqiboQU1rHD6lfuSuSmqc2KBvFtqJSvLhceAXX6A1eTQ1FJqfRo0dj9OjRvM+E5xHyT1Kzq3BbUX3sP6M6DjFriadxt+KkdDOJUtSIW6Mhg5Wde/63GWfPV2NaYWucr6rB52sP4YgKv0wlYtrDzKzeW6/EfvTXAfRsHYOT59zn4ycGy9b7NzsrCzMHt5F08REbnPu2icWyf4WDjBvVhuyTgeM6FBbBHkDl7K7rYz/xPkv5GvqbXJl1jtfqAufWlLzHesujcX2MmjM59z323xrd550ou4BP1ygLQejsUbFbafIQ+14LmQxpYlwQcc9gWRZlPmBZlePQmfO46MWV4gVMpMueKfd8/y4HperyIVgIhKzR0KkZ1Q96MzC5M+YeXvmoiS+s9xHbA3vP+2E7/vP9Nl6KXjmU1JMTZZW8dI92jPHzU3fzLFjBq8pN+Lgr0tx7llo+NaoN2S/RKzNG8zmqDWiHzj6fUi4H5mn1wqhJZqHkPRrdtxihiBo9fy94fLlkHFj+tfluBmq5TWBD6Yky+cnczR//I3i8jgUOn9EeatIszP9R2m/XTO3ue5HIG95EkzK7dOlSDBs2DFlZWUhPT+f9e+KJJ4yWsUmjpmNjWdbF4KJmHLNfyow7EfWgZPOAr2LE0tKJ0gv4RsNO18HP/epybGTHFi7H3LUMqraasqyw4ipXNfhB4BvZfFg8Q99SFZMCKeyy+elIAX7n5xsMkYWLlKXMDC4kUtizh4nBlV5JX2h032KEDcCbb0AuAYQY9pJCS//c+M5a5DE6aYw32CTR35gNMw63qne2bN++HZdccgmmTJmCMWPGwN/fn/d9x44dDROuKXBBJhONmnFBqKg9e48S7J1QU2j4XJrW3fAxQm/o9eQKw6zoD1/S3iUepmAqSAMEP62ibtvlEFI85Hw8xUSVUtK/M8oXr0FgJbnmxTA6fTMgncrX7N3HYYGVAi58y6L8zRgdIF6PMcERFtGLE4oahamu1aBnTGJZ4OO/tKeW9jb20Gh928RKJiYx+RzS66hWZleuXIlRo0bhrbfecoc8TY71nMwvQqitn87dqpr0dACw5UgJPvzTdxu+EE3N0mw0RrqDCA3sQs/fG2+EYbRZ0cxQfdJl4uOqRa/6JdWmKmvMHZNTrr5zv12vwB/eaGVWKk2yUrw5odhxvDGdcr3rm7I2dK6yBu+u2if4nZ4+vI5l8elaY1OOe5ITZZVIjAiU3QTq6Q1gUhMmM66EqrbtR0VFITIy0g2iNE9UzbB11uU6FpjzlXjILV9lwbJdWLr1uLfFcAvc6tEnM9Z7gjQg1IcdEMgQ5o0Jxt/7zwq7Gch0vLxBwsNi20Wb2C0VE7ulevbiEtRIhLYye1uTU/S4VXPlLvmILEaP23oSINjr6rM/eS8uKvf5vff7PlUuAo8s3CZ4vKmtFqrB3lfKPQEzTLrteCJxilpUK7MDBgzA77//jj17tMdFbE50S4+S/F5N/WTBGp5L3I6aOIOE5+AqWnFhNi9KUo9Q9bvnf67hu7zR8VbV1gleV3YDmBcHCbtoflYLRnVM8p4gTogZDz9fewixoQGeFUYlcgYC1RYuE43bx0srUVFVa1i4R718u6HIkAD6epRZX1+ZcyizsvXWPJjRMqvazeCXX35BZWUlcnNz0blzZ4SFhfG+HzduHK6//nrDBPR15JRPqYb48hX5uPWT9ZyyBqSuFDlDUmSQ4HHCuzzwjbrUl+5G6WTKWwNMhYCPupzEK7afQPukcPhbLR5fyuPKZmTEK72Dzf+JJMuwx6o2M0ZbuIx2M9BL36d/9rYIhqNPmTVQEC9gXwTxcZ3c66hWZmNjYzF27FjR7xMTE3UJ1NSQ3UktUYHbJ0XwPs+VCWCvBLkNaYS5+N8/jcHfzTCkKh3XzdQxy8n84vJdiA+z4aoeLT0uN29yYIYX3ASQtXCpfMdG+LgaySkvxHB2FwnhNhwvrdSVfa7ahKlV1aDczcA9nVNIgBUZcaHYfIQfTUHqaiab3wHQoMwWFhaisLDQHbI0SfS8dHfUl11KA1wT5sMEHYieTFWeID7M5pLRS4l/18d/HcRVPVq6SyxR+JZZE7zgJoDRSkFTfC8dkiNclBdvYH+2Ukk65Ljl43/QIyOal8ral6h1uBlIl3PXRPv7GX2QHBWE+77egi8VZq1sEj6zhDr0uBl4ksggf/lChFfRE77JKPytFtw2qI1sOW/Va6E4uEp0EZZlcdv/rfd4IhCubHqUprdXmsOH0gzIKUZq33C8CXzVjWbeZR0MP2dCuPrnZK/zetwMVu89DYn9iqbH23Gbo0MCYPOz4oruabzj3h9t1KFamZ0/fz4YhhH9d9ddd7lDziaLVD32pEEgnJRZ09MyxtjwTVpJiggU/9IRB9MzsjhjFWg0EQrq9vHSC8bFjlUB18KhZ67inFrajJYTTyFX9dROtOTiFPsimfGhhp9z1pAs1b+x5wqRCGusiDX7vWuVldvoLcWGQyU4V1kj66/vrj7VbnDrnBaFu4dlN15P8jfukUUPqt0Mxo8fj7y8PN6x8vJyfP/99/jtt99w0003GSVbs0Br/Xzysg6YY4APrZ0m2F83Oa7q3hLPLPVeSB47USHyu9m5CkNyZBCOFHsm3eTqvaddjvlZ5Su310IDcV1mDRwhjDrVTf0y8MZv5rb6/rHnFNrEh+GVn3fXW5cMXq4VmiAppWVMMA6clk7i4A3coYxomUBZHZZZHzatQt8mtLu+2IjP1x5Cu6RwyXLu2pzKHfs7pzUq5VIx8t0VVUkPqpXZtLQ0pKWluRwfPXo0LrnkEuzYsQNt2sgvQxL1vPrzbtHvpDoHo+tSc7bk+Aqhgaqbq1tIiw6WLWPvdsMD/fD7nIFIn7PIpUzPjBhB5VMPW4tKeZ8D/S0+4/NoygmlGWVyYv7SHQgP8scvO07i/T/2IyRALvh8PUUKJ1h6qo/ZfcwNRcNzah0Xiv2nz3vcvccoWsWGYN+pct2uAmv2n5FVZguf+UXXNcTg9o9K+6D/XNzOLbLowVCf2S5dumD9+vXyBQkHr/4iHq9XqhMVmhm1bSHdGKTwkfG+WWOWV9RKKltVg5D2zl1qiTbGA/FKGTCKllS9NZRyn45R1o59p8rx0zZjEhv4wiT3XGUNthxpnMQoSZpwvqoG/RUqB1Yds4xAkyqzWt7r5V1SZM6pHvvz0bMBzEhyEsPkC3GwN9l/ZDJ9KsFbvrPc+q2kDxrcNh4Zcca7qejFMGW2uLgYixYtQkxMjFGnJCQQsjYtmJCHKT217chW4ldIeBezTDiUDND2fllKZEml2ECUWGa99Wj5G8CMOeeA+b8YtlPdlNZiGSKDpfsylmVx+lyV4lTgepTZoyXy1t9erT0/ZmrpS7ISpBU9LZMx+0/MEv0sKyFMVZ03ctXHaxNqRvhviV+4SxRdqFZm33vvPeTk5PD+tWnTBi1atEBZWRmuuuoqd8hJOCEU+zA2NACPjM5FVoK6WZPNz6LID5LQjs3PIjvIymFGPyUx7IYWby/xM4zCaAbuF0UQ/gYw/c/KaAuXr1Q5rpz9s+Mky7JQ5yOtp90pSV3rjWesxQgoJ6eW27DX+eeX7dTwa+NhGKhKz+uLkz1n1LoZtI43x0ZkZ1Q74eXn5+PWW2/ln8TPD2lpaRg8eDACAkgp8gT7T5W7HPP3q2+EWgZFM4R9asr8MWcgJr75J4rPV3tbFI9g36xgr4qhNj+cq6zhlfFEjWOgbEm17EKNbBl3oMQqEmbzQ1mlMvn0BJ8XQsmz65QSgcKsOLy4Qtz/350wChfNbxuYiRdX7MaZ8ipVPpoK9g/6HHKbiRiGr/BGKzB2aFHKzTbsMACmFbbGC8t3uXyXERuCvU7jrqGWWS/NqPn3IH8/tysIzegNVCuzeXl5LtEMCGO5Z3gO8tMiJTuHQW0TeLuMI4P9ER5Yb/lT28AYRt9SGiFPfeg6b0vhOeybk+1WLcH65YEHYrbn3iczFqt2n3J85g5gYu3WqkKbMjq+r5JnN31ApuLNVO5gx/Ey3mehjfG39G+NMfnJDoX75RWuyooY7u4bveGX7GeRtj4y4K9WfHpjD/y286Tkb7QodkI+9f+7uSfGvrZa9bmM4o4hWTh09jy++ucI73j3jBgXZdZIPJ1K2w7fb1+67AfXFiA4wBwbkZ3R7TP72Wef4bXXXjNCFqKBm/u3Ro+MGMnlLX+nAe6aXq0cfzv3U0r8aN1hmY31wAYfX+LgGf0helKiggyQxP04LLMNnycWpLqU8ZxlVh/JkcY98w+uLcCKOxszKHKXu8Wau5rQUEZbdxiGwf0j20pfE+aaDAspBZnxoUjnxGn+ZoPymMLudpXx9GRrwYQ82fflPPZYGuLIS/9GvSxCzzbU5t79G1JJMM5X1ad7Twx3jaUt9MiahGWWuwFMpqyZDAPO6FZmDx06hD17xHfkE+7BuTPiuvnsOcGfPY7v6qpIyJ3PCP6+fwiWzaLUx0B9J3Fld/3pUp+9vJN+YTyAYwNYQ7UK9PPSrm6FPrNSvD21q2yZgvRo2TI5iWGwWBjEcQdT3gYwYUHV+GwaHS+XATBOZhc7YK7EAkJKAcNol9Hbft9Gk6ogtJ6n8MaCDXfjKTdJAAAkNUxchVqRkFxGymqGeA5ydd3Iib3RUDpbEyNVrZwrHbejrqiu5X0nF5qIASO77NQU6JnhvUgbDAOE2PQvz3RKjdQvjDtxygBmr6eX5ie7FPWEjsBw/l8LsaEBiAqWX2G4Z0QOvr+1jzKZRG5cTNdSo4O5w81AiTKtJ7GA0Rgd5cmdVue+bWLddm4xlNyOc5FDZ88rsNqpf06VNZ4PY8AVc3huIu87+1gp1IyEFD3nuNa+gr3eOW8Wl3uFZgzJZUe3BhMYGIigIPNq600V56xG3MGEG2ZrbOcURZYFm58FLWOMn7GbaIzDB9cVOP5u1yIcGXGe25VZP2HQ/zDc+Tw/ub47/rp3kCHncnYzSI8NwSfXd5f9XdeWUYZcn4ueeq3UKhce6CeYaSw/LdLxt32wFzujmDKgRpkyWpFjoMzn2FSWWQEbl9wGv2CJRAt67q2dROzvNvGheGR0rimjlDiL5K/A2KHlLhKlUmO7CakIImKv4t2ru7rdQu9JN4N7L2qLuSNy8NpVXXjHfSGutBi6ldlbb70Vjz76qBGyEE5ItR1nxYg74I3gzDav6Z0uH1KlYQlu6cx+muT0FfytFscSU/eMaMSGiPtOuQMlaVVlz+FGC3qHlAgkCPiKaaHO4WbQeM/OlmmhjvOSvCRDru+4BsPA32rBqI4tNP3eonADmfPubzvvX13gelDiWmqOC2F0aC4LI399ljWXZVZovfb7jdI+srcOzBT9To+e/sF1BRjaLkHwu2fHd/JYrGW1OLdNq0W+HWipApletvQ5v1sxZW5gToIHDDOe02ZjQgNwU2FrtI5TZ5k1M6pHxpdeegnjx4/HkiVLUGdAPuWzZ89iwYIFmDZtGnbuVBZrbu3atXjkkUdwxx134Pnnn8fx48ZkuzEbUrMkFzcDzmeG97cCp+6G/7ojW43Z2sbbU7vikdHtMXNwlmcbLmPMJjurhcGX03oqsnJ6E3s2G35SAHkriNHWD/vp1MSO5GK1KLNVhAf6Iz7cdXJk83e9rtgthoukK1Yzf3GLm4GCcmbaACb0DPRYP3ceK5MvJEJsqA1vThH2uWZ0O8FoQ1ENEfIPlf2J+jsRei/cI/ddJL350BklGby47VSsTxKy7vuCZfb6Pq0UlRN7V81KmS0sLERNTQ0uueQSpKen44EHHsDevXvlfyjAa6+9htzcXPzzzz944403UFQkv8P0pZdeQp8+fXD69GmkpaXhp59+QlZWFrZu3apJBk8ytrP8RgqlOA/O3LGEF7sSjGwjtMenbQ60jgvFlJ7piAjy92jDrQ9/Zsxz7poejV6ZxvvaGbLc2XAKoaQJzqcXuprR74Rx+q/q3zP8Hz98SXuXMh9eV4D48EDEhtrwfzf0wGNjcmVkEpYmJlR4peDQGfmwVxan524USkObmcnN4EK1q5FFTjypDYrlVbWi3+nB/lz3njrnlvPrwflxOUfPEfyNhiog9JOWnKgTSuLbchmd5+qbz+W+i9oiI7bRGuk8CXN8EvSZVSWKaoxQZu8f1U5RObF31azcDDp27IivvvoKR44cwcyZM/HNN98gMzMTAwYMwEcffYSKCuXxBvv164fdu3fjiSeeUPybl19+GdOnT8cLL7yAO+64AwsXLkRsbCw++OADtbficfplqVNApOLO2WPK2uE2SmfFVjTkT0NBdwZBNqM/mB09DVdLx9a3TWwzClfmaplVUhW4A42haHzV9ZZZTttyevH3DM9B3zaNGad6to5Bh+SIxstyigspBHpbx+gGt4ycxHrfTL2WWaEsdTbZSBSsqdwMlmw95nJMrq2P68o3NDw62nXS4i6UTFb0IKqwqaBLyyiP9eUBHOOK2kvKle/cMpI3rro8G4nfu9syW23ASjcAXNk9TbaM2J348j5wzaLHxcVh1qxZ2Lx5M/766y/U1dVh8uTJaNGiBWbMmIEDBw7InqN9+/aqN48lJSXhzJkzjs8XLlzA+fPnkZJinNXTLEiNS86B1C08Zda5gQpX3d/vGYgVdxbi6l7pmmU0Au6GNU+ip2/a9fhFkt/nOUUdYFCf93vtfYO1X9SHcPjMco45KxRCz9/oOLr2uq9117Szz6zzJEYohjO32VoYBk+P7YikiEDMbwirxj1fSYV8RrhBOfGCxx8bk+tY7bFfU68y6/xKLAyjyIVAoxeHC9f2ll8mDZHYrCWG3CDtbJntkBLp+Ntb/ZNR/HXvIMwe3hiCqn1S/WTrm+m9RX/jsoqioLPUouwZ7YerpDi3iYjdp3BoLvcqs85JGrTy2JhcrJw9QLKM2L00K8ssl7Nnz+KVV17BzTffjN9//x0XX3wxHn30UWzcuBEdOnTApk2bjJLTwXvvvYdjx45hwIABuPrqq9GtWzdcffXVmDZtmuhvKisrUVpayvvnC0gNS87+l104u8B5CkTDh7YCu2r9rAwy4kIlG+mMgZnolxWH16/qrERkFzzdNKYVtlZc1rnz7ZQSIVKSj83PAquF4VkQ5HDsZHe65mWdpZfFlJAUEYiOCmV3N3ZfWefQXICyWb/Nz4InL+sgW65bepRsGaCx/jlvdFCKxcln1LnOyIVbYwCM75aKP+YOQlaCqz+fVFzYyzonY+GMPqJRN1Kjgxt9/BoeuJybgVTAeCF5lI7fQoqMlhUfIR9jLpnxofh8Wk/0yJCP68vFLt8MgY1eV3ZPc7lPMbctI/GEofPVKzsjNtTGGy/s/ZZaH345eYNUTjKUhEo03hrKONoK4LpxUepqean1fWycTBvyNgzDyMYSFrtPEy2wqEa1MltXV4effvoJkyZNQlJSEp599llceumlOHjwIL777jvMmDEDv/32GyZMmIAvv/zScIE3bNiAjRs3ol27dujYsSPS0tKwdOlSHDvmurRkZ968eYiIiHD8S02VTyJgBlgJKwu3I5pUkOpYZgScNoA1VNs3nEJwAMo6ijuHZuODawswPFfbbnBPM7KDcjmdbz8/TZmC5ECFESxYZHPdc+Pz1F1TAIZhUJgVJ19Q7jy6zwD8sPkYTpRdaLQQOvlvcxFSvPytFkX1MibEhoculvcPc0zmFGwMEcI58xF3/Oe6E3Dhtls5aw7XP9CZ3q1jkZscIaqgZiWE8p7pybJKnCqrlLzeq1dKT0qdr6XU0iVkvU3WYGWXu96yWYVonxTBy+alBmfLb2p0EO4f2U7QIm1HLqyXVjyRFvQiif4wRsLliVuv7OHy5N6NVHgzISYWpBrvI6/yfGKrmEJj79B2iVgwIQ8f69h8OyBbfz9tBGLPyebD+2dUS/7CCy/g4osvBsuy+P7777Fnzx7cd999SErih9QZOnSo4Uv/lZWVuPrqqzFz5ky88sormDVrFhYvXgybzYZ77rlH9Hdz585FSUmJ49+hQ4cMlctdSLoZcAYP53BNQhVV8JhWwQxGSmlXi5rOzDl6g7t8oi7rnGyqDTJiGPUWPl1zSNAyK/d4LQwQbLMqqpgsWFzTuxX6yw4OwhZxpbDgi8Md5G/pL7wKwH2OQldVKor9PC1EYnFywyWVVlSj8JmfMfqV3yXP2VUmU9m5Sr7iJiarc4pbofpdqsCFwhmlz6a6Vl1ttddD5/MfOVshaFHkljtTXqXqWkoRy6b07tVdBX2XlezUV0OLiCDkJgvHweXe/8QCeR9MAAjSEA3HeYLrbC1W226VLJM7uwHxf99QRmgDmIXBmPxkwRUWIW4W6B9mDckWKKmOqGB/7HhsOC7qwE/4oGYyIfaczG51lkK1Mjty5EgcOXIEn376KQYPHixa2S6//HLceOONugXkcvLkSZSUlCA/P593PC8vTzKsl81mQ3h4OO+fLyC1BMl97s4bxZQqEJ5I0+iuS4jFDVVzT/c6hX1R+lOp8C1C51IjU6gBWcK8TWVNrUvSBOe/Af6A8c303vjxjn6w+VkVPa+i4gsAGtNPyqE1dNTuE+dcNrFlxIUgwGpBFwWuDsKTSGWy2Cd5k3u2xCQBhcLKNJ6pqOSCI6+8kYi9C2efcKENYGoVzkfH5Cp+NrUqN8vYX7/zeCUUD7m+vLEdl5CblpCb0mc39qiPaSpwjnAdvrt2N7MoJyV5WLtEoeI8lEbK4E4KtCYq2fDQUN5nw50MGL5fubPrk/21d1DotiWU6jk3ORxr7xuMe4bnyEY20cLH1/eAzc/Kaytr7xuMv+9XsR9D5MEGB/jhQZGICPMUuH95E9XKbFZWFmJiPJcW9KOPPnIkZUhOTkZ0dDQWLVrk+L6iogIrVqxAx44dPSaTp6jlNDo1s17FHYDKnmJC13r3jJv6Zaj7oQxarJZPje0oaNlQsxvT+fdSYmjZdKIFT9tvuUvlRi0xvfHrXpworV/u5iuCzopEY/3OS41EZny9xUPNM5Az6tsvqccwHhHkj96ZMWgREYjurWLw7fTe+G32AMSHCVtM+RtMtF/YfhqbnxXzLuvgYoX2s1hUPSwl7VbImiQE97ZYCE8WqmrqkCGSFIBb/vc5A/H5TT0xuUdLxRPKk+ek3Smcsb8HsXog5WZgBM5uWmI7zrtL+JGKbQRUQp/MWLx7dVd8eXMv3nGxvldok9SiTUclr5Em46cphPNjdnbHMjqaAcC/N7FoBhd3TBK1WnNJFEgyw7KNFs4Qm777cSY6JADtklzliguzqXJbkZJDbLUrRmWYNE+jaPR677338PTTTys6oZqyq1evxrRp0zB37lwAwHPPPYdp06Zh4cKFjjK//PILPvvsMwD1HdI777yD9957D3369MGUKVPQrl07WCwWPPbYY4qu6U06q/TJ5Hao1/ZJV/47TgOVajtqB/j7R7XF61d1we2DlW/sUGJpERs45ozIkfyN0PKgmkHIOVSSlOKhRilR6zvGv47mn6rCvpw3qSANP97RD8vvLNScWMCZmjoWjyzcBkB6lUCPW4PUqgUX+yX1KCcMw+Dj63tg9dxBSIsJRligv0waTmnZuKIcKVYelunR0Xwrj9XKKLZkzhzcBnMVBKC/e2g2TykRr/f840LKbOv4EEFrzrZHhqE9Z0BOjgxCQatogbOKo/Z9illm5coL8fIV+bo2bk4qSMPjl0pbuYzeOc8wDAbmJCjeCFlR3Wjltz/rNfvPiBVHgJ+F138o8RwTKuMSXcDg6T0DOTeDhkmPhcGEbgpCXMmIZ3SKWqOehtR5PJhV11AUqfKnT5/G5s2b8ffff8uW3bhxI/z8lM0QoqOjkZeXBwDo3bsxTEhiYuPSx+TJkzF0aOPSw5gxY7Bv3z6sXr0ap0+fxrXXXou+ffvCavWM5UwL6x8YgrPnq2R3GDqTHhOMkR1bIDo4gNfonHcJOzcY3tJuwweh0ERqO8ywQH8Mz5VfllLC9AGt8crPewAot5b8cFtfXPTiyvrvGKBX6xjsPsEPOK5GQeelWg2wSnZMDOoHof9bcxB3yvg9vTG5C37cehzrDxYrF0ZAJjWo7TS/vqU3zlfVoFt6tFv8eYX8DF0UEBGhlVjXFSuzdsusiXyWuZL8e1QisorTLaZGB+PTG3tg4pt/AqifkOiJNiBYzsIgPTYEB8+cd5FVVExWeDPcRbktsO7gWZfjAVKTJoVyqg1BJmuZdVaiJMQY1TEJozomaQ6lpKQqCu0jaKHQpUadLPLCKOmTnH1duS4UXVtG4e8DrvWgjnWNT+x8LXdM7qWqDvd6AUoSRei4lhZ4z0PHs5F6p2J7WMwcMx5QqMwC9cv9H330kaKyd955p6Jy2dnZyM6WVgwKCwtdjsXExGDUqFGKrmEGokICEKXBRM8wDF65ot7X6rkfdziO95PZuS5c6QRSPKqWSDnX9E5vkMX1u4RwG+4eluNQZkVj3skMMMKDokZlEPJWgMfH5OKGvq0c+dTFOqoeGTHIT4vCh38eUC2HkToXw4jLGB7kp9gvTA88y6zTd2IZr5zfwzW90/He7/t5x2ocPpMyVlBIKzHuwKgBTEhh4y5rWi3y2f3sKLn/NvH1VjtuUaXKn3OIst2Pj4BFRD6GYUSfkdLXpPYZy1notQzUNj+LpvjFWlcJ1NZhJRvGlIhiL5KVEIqdx4WzldnLzLusA95euRfPj8/DxS+vAgBc2jlZUJmNCg5AWaV0pAjJiY+QHDI3xDAMb6+D1GrU6LxkfLLmEHq31u5WOSw3EXd+sdHxmWvx1kbj/Y3s0AKLNh1FgcymTumzuKK3bXoLRcrsjBkzcP311ys+aWCg1BIcoQmR8EAyRSFVBd25Aeyhi5Vn0BHLHuSs1Dg3MufEEYD2oPF1LCv5XMsqa2Cx1MfltfPEpR0w+3+bcNugNnhx+S7HcX+rRfOSvZpBdXDbBCz79zgmdEtFZY26TjJBwNfLHUhthBvXJQWrdp/CgOx40d8A9YNyTEj9wBcZ5I8TZZWOrFtKfWY9maFKrgbqsXCkx4bgtoGZiAwOgL/Votwyq0ATev/aggb5Go9pkTU80A9+DfXf2WfQTtkF4UgHSvskJc3c38o4NqHZT2uUKw0APDm2A+74bKPL8RG5idh14hwGtRX2cdW6GVFNHb6mdzqu7yvvI63kjPZ3cvewHNzwgfDqrL2eTCpIc2xW/PC6AgRYLThw+rxA+fqMiIu3HOMdc0aty5aiJyTpM9v4OdDfim8lEks0/MDlEDeRSqjND4+OycUD32xB91bRKDlfzfvOOXqIHNzLXdShBX67ewASItRHIJCqSk3azcBms8Fm892QDb7Ay1fk4/3f9+PuYcKWam7dk+sMhb5W4p/kKZyVVKXWH66i6mdh0KNVDN74dS+vTG0di9ToINUpIllWeiAVsnKM75aKwe0SEB0SwFNmhc7NZc29g/Dkku2YPsA1gLuace6psR2wem8SBrdNwEsrGq+fHBmEWwa0xn1fbxH9rXNYMnfBdQMRsua9NaWr7DkYhsHPd/dHZXUdzp6vwsJNR3GNyqx13EFqxZ2FSIwIRFVNHVbvOY0WkUEYIxPSSg2yCrbS84gcnzW0sY9Qei4lSqK97hVx/HjFfsY9LBXVIzshDPePbIv9p8vx0Z8HHcfFvES6tYrifc5LjcSGQ8Uu5ZRMWhmOh6S9z5TqO3+fMxBPLt6O4e3FXalu5bTZri2jYWFc7yU80B/LZrmuKDrkEhEhSdIPW93E4sFR7RSVV1MvpCKtJIS76gf2CeeBM67K7NKZ/RwTHjtcSS7LT8aBM+cdvtRqiAkJwGmRcGqNNaKRkAAryhsigRgxJI7rwo9jP7FbKqKC/dG1ZTQvtu+jY9oLToakcJYvTWPUCKlVSFHLrMlNs74fB6iJUNAqGqM6Jol+z53BOXdAznVPaNONcHo+aZmMjmsodl2LhUGb+FDsOiG8hGWHO4BZLQz6tol1KVNbx+KVKzrj+Z924ucdJxXLxLLSz0PM0hytwX0kPjxQIlmC8h4jJtTmqDPcDuj3OQMBQFKZ1YJQeCg5uMuwSjtDoUE4PNAfCKzftTtrSGO9VKo4cnWY8CB/BAf4ITgAGKEiyYZSOiRHwGphdKfmVWJ9VO4zq/y63KVksZ8pVawYhsH1fTPAsiwC/axIjgqC1cKI+jz3ah2LSQWp+L819bHAxSxzap+NEnmTI4Pw0qR8AMDuE2WCZe7iGBtSo4Px572DUPD4cl4ZOSu4mAIpl1FQjUVX+ftRXkbq8m9MFp+USl1C7PrPTciTlUsIJcv4zj6hamJhO+NcPDU6yOU9+VstvLF9/5MjcfpcJWJCbYqU2afHdsTs/23SJJ8Y0pZZ18aVHBmE3pmu462Z8N10D82M7pwZqnNnGOvke8gI/C1omZVRnN69upti+ZSmguXKZFdGL8tPxvMT8jCxWypulAgfxB3/GEbYH6+2jkXHlEjZ3cLOBPpbRAeANyZ3QU8dflNqMNPsl7vj/KbCDDyuM2ZicmQQMhv8Mr+/tY9oOedB84JKP7Pr+rRy/F3VsMxs41ii/dXEb9NAUIAVWx8ehuUi1jml71jK4sk5m6JzaXUpMmrTB8MwuH9UO1zTkIFLyrJ670VtkRkfipSoINEJlFp3IrH7F0tcoJT4sECHD70duUcmphRKZYOT+p0elLzfmoaOV0pJt7drIZS6dsjJcmm+fAQJ7gqV8DVcx0J+9B99D1mpK4jYfgEhclo0Tt71yHe5QExcIYSa1m+zB3hsNU8rpMyaBLlKKpTj/oWJeRjfNQWTe7TklU1piJpgYRobjdDAKNc5Kg1IDwBnzwv7wAm1bXun9dTYjnhsTC5uKmyN3OQIPDm2I9q1EE7LC7gG/BY6t73jVTrUvXt1V2TEhuC/1xbw3gB3kFMybuodFO1o7arc4ecUwQnSnhkXqjsiAMMwWDarEPufHCm5+cy5LSzdKp6qWqheP8AJ+n2qIR5ph+QITO7RErOGZCFCILuS0QT6W12WUe0oVRCNtMxqRYmbgetv5IWSUkbDGpbpV90zUNTnVkl951p/xapuWKDw4qSaBAXOO/nlmsnRkgu8zx9f3x1D2iXgqbGNsdKdNwwPyI4TbH+j88RX85SgpPr8vvsUAP4YJBY/WAipkJTcti4nC8uygnFduVyolt+Q57qSyZFHZXtyztRm9I7/727trdlyHOuUrjiVF3JP/HdC32n18/YkpMx6mIcvEV6ulquk3Mpkr9yj85Lx9LhOLinoLu+Sgq9v6YXfZg/gKSSu1xS/6JB2CdICOeG8m1kJSZFBuKpHS95vuSKFOZ2zZUwInri0A16/qktDWVf51ViIAWBgTgJW3NUf+WlRDgtLqM0PvVRaYt1s7JPFKGWai9d8qp2ua5V4uHIKnz3zkdXC4NExubhtkPIYyd7GOLusuni2/PPLX0HLnstahUEAxK6vJAU21z9RbSzr+LBAvDAxT5FPt/OkRexaQ9slINTm51IHe2fG4q0pXXmxi/9zcXteEpMRHVoIWv3ElHGlKLFw1zSsbnD1mbzUSHwxraeia/j78eVuEdFozWZUKJJ1LPBigxuIHlw2EvPGVmXneHpsR4zITXRZOTC6y4wK5iukp88pT6286La+vM8Xd1I28cmKd497obvR1BL27NmDl19+Gfv27UN8fDyGDRuGMWPGmDrWq1mY2isdfdrEYtCzv/KOyzUCqV3hrmUZ5DvNhgVzTUucxqi0qkIKp5T4XGvK0PYJmP0//vdXiGTOAep38NoHFufBzjmXvBCj85LQtkU44sJsmM8JhabX7im3XHxJpyR8t7EIgHA8YCWM65KCI8UVGCiSJeiiDon4YbO4hVMtN/XLwBu/7ZUtp6UeOVcPPb6n5rcnSKBkk5OBVl6bn2v/LWqZ1flglboJiF1HSZhhOatfQato9M8Wz6o1Ok9ZYoQnLs3Fpa/+4fgsZkB4c0pXsCyr6J31y4rDloeHoc19ix3HBMOc6azhSmKf+zco1bzrq7isH2cy2jMjBh9eVyC4aiF3L3Usq2lDmPM1pNK/K72x8d1SMb5bqnxBnTAMvw2M76bMVQCoj1gT6G9xWKuVKupmismtBtX2pKKiInTr1g3Lli1DfHw8jhw5gmuvvRZ5eXnYuXOnO2RscrTUkPZPy+yRSwuBnbJGLomwLItXr6yPifv0OOnUwlKXreHkcxcaXMVonxSOe4YLW1n+eWCIsjA1DIPsxDBEhwTwBn+jA18788LEPMffSZFBCPS3ICYkAD0ylHfcgf5W3DM8B91EYg72z47Hr3f31ylpI7OH5+CrW3rh61t6SZabf3kn1ed2rpdqIxdw8bbFXA9Kqp1YX5CbHO5YwQDgsnojhNBmxlW7TgmWlVI8lHQrQkk1hM8lYplV9Gvp8yhNUX3X0CwA4jFP89OisGRmoxXsaon6qqbPdfY1FarLzv66gHxUBC6RCtwpAhv6YZ6rGyMeK9gZrhsGw/At2Uqehn0ToPPEQ2t6XxefWR0bwFwwWA+0OO0NaaPSasptp+GBje/ak6EKPYVqs8mSJUuQnZ2N33//HZaG1lVaWop58+Zh1KhR+OeffxAaqixlXnNF2FqpwmdWQ0WMDHZ/XuWLOrTAzsdGyO7KlaKKs/6o5jx3Ds3iOahzO6xAf/XycC27ypZ7tXcO3HcfHuiHFXf2R4CfBbtPnHNke1I68IrC1rtprLizUHEEhvgw8UHRamHQOS0K+06VS56jS0txfzkxuMur2x8drmvjgTtjKbsbRT6zIvUuKz6M5296eVflFh0uckHtAffGpRR7e0rcDLjoMTbdOrANJhWkSbYbrnIQHGB8kCAGwnV5YkEqjpZUYP/p8/hp23EAwDMqJpBKrHDT+tcbAvirg0C8ggkSwI8H7hzFgnvOKhHfk1/u6o/NR0pc4lFrGdPk/EHN1ls4W2bVjmXc1xsVEoC3p3RFgJ9F1J/fl1F9RzabDXl5eQ5FFgDCw8Mxb948tG/fXnGWsOaMUP8h14i4HZlWZ+wYDWGk1OKsgApJKqX4cTdeGOV0rkXRVGuZldSZVIy7DFNvnY0NtfGWYlfdM1D5SRoYwUk9bF9ay4gLlR0E3pnaFYPbJuA+Ba4Zck9W0zvkdd7SiqxsggL1VzcNcpn+AIl653Q8zKZt09u1DZn8FF/XYMSuE6hg1YbbfoQUQTXqcEyoTdLgwNXR3LUaINSWggP8cN/Iduif3VhX1Ez+lLxG+6SW+wwrquuQHhuC58Z3wn8bkm2IwbXMaklqEx8eiEFtE1wUb60JcpwnQmpDuElhdLNgGv5nR81qJeB6P4PbJSjqV3wRRc1u37592LVrF1iWxahRo/DXX3/h2DFX/7u8vDwcOaItX3VzgmEYFx9ONeFctLY3XzBSDW2XgBv6tsLzE9QvTxtJHc8y652cKNw+V0s65EdGawulNahtAt6e2tUl5JsW3L2cVSwSRcOBL1R6EYSWkJXiPIHjPgZ7xJC81EjZ89gVGSVlG69tHGIT0Xlj5UPvcVutkIJppPsQLwa2m+qc1LyQ+5zUzB/VWDe5yvT3DT7+l3VOQaGMcsRVwEornC392p+VWKxiOZx/daKs0vG3c2QKdzOuS4rMJm3+c1drmfXh7k81ip7Md999h6ysLISHh2PkyJGw2Wzo06cPPv30U1RU1O+SLSoqwhdffIEhQ4a4VeCmwvV9MzCXs5NWznooFM1APcbVbOdGryZriJT4gf5W3DeyHS7N17YsahQ5nBBhXF8jTah47Nx6oNXyYIfrJ6l34NZqsdBipVJ3Jekb0zo2fTGtJ1Kjg/CeiljLRqJ0CVfpBi1un/H+Nd1w55AsvDmli8PPXQx7v/PmlC6iZdQu+atB7P5ax4VKZtlyPY/ricpVphKVQs4KbARS51WzQZhLZnwo7h/ZVpHxQGtb4o5dRhoGtCqzzv1qFWfTbTe9G8xUvvv5l3fCPw+I60wMUx8GbWSHFujVOgY9M9QlLvBlNyu1KBpqbr/9dhw5cgSffvopRowYgeTk+l2eV1xxBUJCQhAbG4vU1FTEx8cjJsYzweWbAsEqdnpzG4nW5Xc1P5NrBG9P7YqLOyUhO6HeIX2qyKYHwR3SysVAJ4UWIefJQDwnvaJNgw/vld3TkJscjvy0SEWZTyTvSWP/rSbOrxwa+31ZpKpJTmKYYVExxJAb0JR25t2dBrFu6dFYOXsgBmjcZOIpxCbBzke5jyE+PBAzBrVBfFgghrdPxMzBbUSXiu2udfFhgY627nw+dyJ1mcz4UMnUsyxPwXT9fpzCIPJKYHluBu55OEr7fbXv5vq+GaLGAyOX4OXOrxahtn/7oDayMVSl5l56+ystll2p98qAgcXC4JUrO+OTG3qojpHto4EJNKH4zSUlJSEpKQkjR450HCsrK8OGDRsc/9avX4//+7//w2OPPeYWYZs0MpWO56TuATcDuUbZPzse/bPjUVlTiz0nytG2hfAuy+iQADwyuj0YhsED32xpkEO5INf0SsfMzzagW7q6jUQ2Pys2/Wdo/W5QDS060N+KhTP6yhc0GO6jaR0Xitev6qxoJ7oZ+eDaAk0DYK/WsWgdF4KsBPmduzUyyqzSq396Yw98ue4w7v5yk8JfmAOxuiFlmeUdtzCYOTiLd+yqHmn46M+DAIB0TlYqsVeZn8pvm+5QegDgbk4q2cZriZfnKZicggtn9MHa/WcwvqtxoZX0rqLIIZbx0PE9528jrXGPclyVuN2oVKZGNeiRtFbgmQ9um4CP/zog+Tuj39Qdg7Pw/LL6SE7PjtfvHtctPQpr958FoH/SqLUtvnJFZ0z/5B99F/cwuqYhYWFh6Nu3L/r29fyg3yTgNEY1PrNaOyvu72YOlg4ez92BKoXNz4p2nLSnQkzpmQ4AjcqsojPXMzovCdmJYZr8B3W7BxiEmmxCzgzPbWGIDN5YbQrVGNA9wM+Cn+4oVDQJkVMilLYVhmHgp7DOewKlA25cmA2DcuKxfPsJ3vFzTkvoau6sXYvGxCOiWczAYN39g3H2fBXSYvihBuWyNKmCI/iUni1dvpZ6/RYR16zc5AjkJqtLriJHRpx7I/gwkE4Za3T7vrl/a1zXpxXPb577DJVMNN2Ns2X2vovaIjdZeiyqqWVx6Mx5Q+Xg+rG2T9JXrywM8PS4Thgw/xcA+h0DtVpmR3Zsgemf6Ly4h2l68Rl8FLk6x11O1LrBgPsrOUWvXxt1vjmaBZEryjBo2yJcdoeutxURoRnw3BE5yEkMw3V9Wqk4j5FSAS1jgmFhgN6t3fM+xZSJp8d21BWiSKk1nRuXWIjm4DLWRkCxWLLlGD9pgAHPwTkeZ0yoDZmcuJef3dgDhVlxsn64auDfg+tNFFcIx6vtmRGDfm0aNybZNITnU0OozQ//PDAEWx4e5rZrSPWB/A1g+l82y8JlAyg/PKS287puTNQuawLHlSwzPhQ39MsQPB830kNVbR3OV9Vqvqa7cX53et+l1OaypgYps97EAAurusvJd0Z/zh2E967uhmESvmh6MVK/uLpXOnpmxKBnhnd9tYXu6abC1lgys5+iLDvu4stpvbB0Zj8X65lRlFcJb6LJTPBMrGkxn1n78rsaC5y7k2OoQW8bcX4sRiz9y52ie0YM/nttAdJ1RGFwxl5vgwOsCBTwfRfzMvm/G3sggJNG9eKOylJ56iE6JMDtPuJKMGLiIrRRi7uZM0Gj9d35vFxRndOXi/H8hE7olxWHW/pnOo6JGXjGdUnBggl5SI4Mgs3PgozYEM0bx8RQsqdCKRYLw/P11vsuX72yC9onheOdqfJpmX0d77e8ZsxRTr50NfHjtMYxVOLMnxgRyMsRbnb+c0l7b4tgKHrTUzoTF2Zzq8+ttxVAMZ/Z727tjW1FpaoGGndtktOCGlGMtj6L7Tg3NFOSQpIjg7BsVj8EB/gJujxUSaR/ruN8ZbRbgaeRfd4GuKHxEKgCLSLqN6QG+VvRtoX0cr4Wbh7QWlG5S/NTXDasid3yzf1bIzI4AEvv6IcL1bWICbUZHn0jNzkCi27r43g+erAw/EevdzzITgzDotuahxsoKbNeZP/pxuxJctmutIZeETuHN5df3bVBxKs0wVvyBcR8ZltEBKkeXNy9icfX8VazzZRI4TkwJx4bDhULfuet+NDegGtt1PqeCrPi8OvOkwCAYbmuK3NWC4Odj41ARVWt6l31dpybmN5xLT8tEusPFmOozEpiqM3PYTUX2jimF72+snZclFcaVxRDyqwXkbIqONMqNgSxoQGICPJHkMb0njzLiqYzGAO1T3Gaip7vqduQ85lVgzvjpboTo5+1eMxobv9hjop6fd9W2HvyHL7ZUOTynY++TkHk+oUizipfS40uRW9P7YqtRaUI9LcgJ1HY8hrgZ9GVrjw5SnyCqWUvyJuTu2L9wbNOWa2kxzmRrLmmwNn9rzmF1tIL+cx6EbmwQlxCbH5Ydc9ALL69n+Y4htxfedM62lQUtuaEWV+Zkf5vZh7kzAC///CaGDyCA/xwuUiILTO5jbgb+yanbulRqlOe2vG3WpCXGimqyBrBpfnJvM/ceqRlZSQuzIah7RN5m+Pac6LrCI1zo/Pq/aezPOTXr4Z6n9nGz01yFdNNkGXWi6gdiNXk3BbCGz5vQpjFqmMkRt1RkgF+V82Jmjq+BqpngDKTm0Gaik2DxvvMeuY6RiH22pqim0GYzQ9lApnLurSMxsrZA0wfk9rfye+ZOxaoSZksxb0XtXW4SwhxfZ9WyE4MQ+eG+MjhgX4ovVCDDAM3LmrF2dXCpE3OlJBl1ou08PRGK67PLDUTQ9G76eKDawtwSackzOGkOPZlqg1c/peCOx9cNqsQn9/UU/O5zOBm8NKkfOSlRuK+kW0V/0Y0C5jBTdwsbkpKMcHrNJw3pzTuSu+YwvfTTI0O1m3wcBeTe7REbnI4BrXlZ9TjTjiMioLBdbMQqqd+VgsGZMc7/H4XTMxDdkIYHr+0gyHX14PzoqtZJ5BmhCyzXmTuiLa4UF1naCYaKcyyTNgUG+gNfTMw+3+bcJnTMppS+mXFOfl9+TaesnL2z47DvlPlSAwPlAwqrwQzLEtf3CkJF3dSF0bq1LlKweNaEo1I4WvN1kyWdr3YJyw9WzeGIIwKDvCWOKp5dEyu4PGKqsaVFaOS3HCzZSpJWDMwJwEDcxIMubZe6ieMnM18PtfqvAcps14kKiQAL07K99j1jAh6TQgzrksKMuJCZLOhNTXE9IU6D2mGs4flICshDAOy4+ULyzCkXQIe+m6rR3zpPryuAPd+vRlPXdZR97k+XXvI5VhOYhiSIoPw1S291AdOF3mpZnFTckbMnaAJ6bKCJEX6TghFMc6eb0x6EWhQYgt/qwXvXdMNldV1iA7xHYUfqPeZTY8JQVZCKMIC/Q17JlrY8OAQPPvjTlzfV3nSH29CymwzgjGJm0FTdGq3WBh0TY/2thimwVNWzqAAKyYVpBlyrqTIIGx8cCiCbe5fqu3bJg4rZw90y7lnD8/G2M71cTg7p0UZd2JG9INXyRUJi9SkLLOcx/3BtQX4ct1h3DPc912SuKsKRo4LRkxuvYGFqXeDWHJ7PzCMd8fKyOAAUYu6GSGf2WaEha/Neg3zDIOEu3BHLEdPEBHs77JJxde4pX+m5gxNgPgGMCNXc+xdUWq0/g2PUSEBWHPfIFzi5J7hmzVQnn5ZcXhxUj4ifcjNgFCGfYy2WJgmafRxJ77daxOaMSRLDEGI4Ck3A8J4WscJu1kYEZjfzve39sGw9gl4/5oCfSdqID4sEG2cfKbNsKGPkIa6CT40LmuH3AyaEWbZjaw1HS9hPsT8FY3Of06Ikxkfit0nzhl2vl6tY/DU2A7ISuBn3hrfNRVr95815Bq5yRF4Y7Kx+eIn92yJA2fOO2Ku1lHcYPNDEw4epMtqh9SKZoSf1bsbOB4dk4vokAA8PbaT5y9O6EJtffFVNwNf5J2pxiqFDMNgQrc05Dv52+anRTaWMfSKxhAZHID5l3fCqI717gZNKc5sfJjvb/YSgua8fMgyqx2yzDYjuCFLvNFmJvdoiau6p5EvUDOAlng9R8sYzwR756Wz9YE23BSq4JuTu2DXiXPokdE0N5c2pU16RkBRhrRDltlmhB+npXhrBugLgyChHLGxiFLDNj18zWrUFKx+Q9snYvqAzCbbb4bYyJ4GALOGZAEQj8dLyEM1qRnBtcx6KkMT0TwhN4Omh9UkPvdKodUB83N1r3T8secUhrdP9LYoXuW2QW1wY78M02Zw8wVImW1G+HF2XgUHUKMh5BmYE4+1+85gcDv5DDntWoRj29FSABTNoCnia8ZBWsI2PyE2P3x8fQ9vi2EKSJHVBymzzQiuZdaXUiES3uOdqV1RU8eKxl4VUxcomkHTw+Jln3u1JEXqj2FLEIRvQMpsM4Kbp7qanBoJBTAMA3+res3FT8NvCHPDdzMw//udPiATO4+XGZYhjiAI82IKZbayshInT55EXFwcbDabZNmioiLUCQQQDAkJQVSUgakbmyB3DsnC9xuLAADnq2q9LA3RlBmR28LbIhAG42s7rZMig/DFtF7eFoMgCA/g1WgG+/btw1133YW0tDSkpqZi9erVsr8ZMGAAevTo4fhXUFCA1NRUPPbYYx6Q2LdJj20M4UPLwIQ7CfCjQClNDX5oLi8KQhAE4YRXR5yvv/4aCQkJWLp0qeLf7NixA4cPH3b8e/PNNwEAV1xxhbvEbFKMyUtCSlQQBuTEeVsUognA3TFOCk7TxttxqgmCIMTwqpvBrFmzAACHDx/WfI63334b+fn56NKli1FiNWkWTMxHXR3L28xBEAQhB7fLoEABBEGYCZ9eCzx+/DgWLVqEG264wdui+BSkyBJGkZMYDgCIDZX2dSd8n6YauJ8gCN/HFBvAtPLf//4XAQEBsi4GlZWVqKysdHwuLS11t2gE0SwICrDi30eGw2phcOmrv3tbHMKNcN0MyDJLEISZ8GnL7Lvvvovx48cjIiJCsty8efMQERHh+JeamuohCQmi6RMUYKUNX80AWtAhCMKs+OwItGrVKuzYsUORi8HcuXNRUlLi+Hfo0CEPSEgQBNF0sHDcDFjRdBkEQRCex/RuBmfPnkVVVRUSEvjpNN955x20a9cOvXrJxxG02Wyy8WsJgiAIcRjaAEYQhEnxqmX2/PnzOHz4MI4dOwYAOHnyJA4fPszzab377rsxaNAg3u/KysrwxRdf0MYvgiAID8HN+kW6LEEQZsKryuyiRYvQo0cPjBkzBsnJybjjjjvQo0cPvP32244y0dHRSExM5P1uyZIliI2NxZQpUzwtMkEQRLPE38ogIy4E8WE2pEQFeVscgiAIBwzLNr8Fo9LSUkRERKCkpATh4eHeFocgmgSjXlqJLUfqV1X2PznSy9I0L9LnLHL87c5nX1vHoo5l4W/12e0WBEH4EEr1NeqRCIIwhM5pUd4WgXAzVgtDiixBEKbD9BvACILwDe4ZnoP4MBuG57bwtigEQRBEM4KUWYIgDCHE5odbB7bxthgEQRBEM4PWiwiCIAiCIAifhZRZgiAIgiAIwmchZZYgCIIgCILwWUiZJQiCIAiCIHwWUmYJgiAIgiAIn4WUWYIgCIIgCMJnIWWWIAiCIAiC8FlImSUIgiAIgiB8FlJmCYIgCIIgCJ+FlFmCIAgfJzY0AADQJj7Uy5IQBEF4HlJmCYIgfJwXJuYjNzkcT47t6G1RCIIgPA7DsizrbSE8TWlpKSIiIlBSUoLw8HBvi0MQBEEQBEE4oVRfI8ssQRAEQRAE4bOQMksQBEEQBKsk2WkAAG8USURBVEH4LKTMEgRBEARBED4LKbMEQRAEQRCEz0LKLEEQBEEQBOGz+HlbAG9gD+BQWlrqZUkIgiAIgiAIIex6mlzgrWapzJaVlQEAUlNTvSwJQRAEQRAEIUVZWRkiIiJEv2+WcWbr6upQVFSEsLAwMAzj9uuVlpYiNTUVhw4dori2Pgq9Q9+G3p/vQ+/Q96F36Nt44/2xLIuysjIkJSXBYhH3jG2WllmLxYKUlBSPXzc8PJwasI9D79C3offn+9A79H3oHfo2nn5/UhZZO7QBjCAIgiAIgvBZSJklCIIgCIIgfBZSZj2AzWbDQw89BJvN5m1RCI3QO/Rt6P35PvQOfR96h76Nmd9fs9wARhAEQRAEQTQNyDJLEARBEARB+CykzBIEQRAEQRA+CymzBEEQBEEQhM9CyixBEARBEAThs5AySxAEQRAEQfgspMwSBEEQBEEQPgspswRBEARBEITPQsosQRAEQRAE4bOQMksQBEEQBEH4LKTMEgRBEARBED4LKbMEQRAEQRCEz0LKLEEQBEEQBOGzkDJLEARBEARB+CykzBIEQRAEQRA+CymzBEEQBEEQhM9CyixBEARBEAThs5AySxAEQRAEQfgspMwSBEEQBEEQPgspswRBEARBEITPQsosQRAEQRAE4bOQMksQBEEQBEH4LKTMEgRBEARBED4LKbMEQRAEQRCEz0LKLEEQBEEQBOGzkDJLEARBEARB+CykzBIEQRAEQRA+CymzBEEQBEEQhM9CyixBEARBEAThs/h5WwBvUFdXh6KiIoSFhYFhGG+LQxAEQRAEQTjBsizKysqQlJQEi0Xc/tosldmioiKkpqZ6WwyCIAiCIAhChkOHDiElJUX0+2apzIaFhQGofzjh4eFeloYgCIIgCIJwprS0FKmpqQ69TYxmqczaXQvCw8NJmSUIgiAIgjAxci6htAGMIAiCIAiC8FlImSUIgiAIgiB8FlJmCYIgCIIgCJ+FlFmCIAiCIAjCZyFl1gf5Y88p/Lj1mEevWXy+CidKLxhyrpLz1fhh81FUVNUCAKpq6lBVU+f4vraOxeLNR7HpcLHkefaePIfq2jrJMlKwLIsaFb+vqqnDtqJSsCyr+Zpc/j1aiuX/Htf02zX7zuDXnSdV/WbLkRKs3nNaUVnuPdbVsdh0uBiVNbWqrqeEYyUXcOjMecky5yprsHb/GZReqFZ0znUHzuC4QXUVAEoqqvH3/jM4U16F3SfOOY4fPH0e5ZU1hl3Hcb3z1Vix/bhs3TxTXoV/Dp41/PpaOFZyAat2nZJtG2fKq1ByXtl79EUqa2pRWVOLMk5dPVpSgQXLduKP3adUnau6tk6yzz1RekFV/yXE7hNl+L81B1FZU4ujJRW8flgt5zhtoa6O1d1fFBVXYMexMl3n4HKhupZXP3/efgI7jpVJ1tnzVTWoq1Pe3x88fR4XqsXve8uREvxv3WHFYwjLslh34AyOFFfIlt10uBgrth8XHBOLiiuwtahE0TV9lWYZzcBsfLvhCP7YfRqPXZoLf6v4/OLfo6UY8cJKx+eFM/pgz8lzOHj6PLpnxKCgVbTjO5Zl8cLyXYgOCcCF6lpkxodiYE6CJvnW7DuD8W+sBgCsf2AIokICeN/X1Nbh8NkKjH9jNfq0icVz4/MA1A9wVguDuDCbo+wbv+7BvMXbAQCX5Sfj5LlKrNxV38nveeIiWC0MftlxAjd//A8AYMdjw2Hzs/KuN3/pDrz8824AwEUdEvHqlV1k74FlWVTW1CHQ3+r4POmtP7HnZDmWzuyH6IZ7qqtjseN4GVrFhjjK2rn5o3VYvv1E/X1M7oJh7RMFr1Vbx+L1X/fAamFwbe9WCPDjv9N/j5YiMTwQ419fjbLKGnxyfXf0yozllamsqXW5bzsl56sd7wMAphW2xpwROZL3X1Nbh1EvrQIA/DFnIJIigwAA5ZU1CPK3wmJp3Cl6rrIGI19cibzUSLwwMR/v/bEfjy7chv7ZcXj/mgJHuZNllej2+DIE+Vvx132DEB7oL3r933efwkd/HsCN/TKQnxYFoP4d9Ji3HADwztSuWL3nNO4cmo2gAP59T3hjNbYWlTo+D8iOw7tXd3Psbj1aUoHr3v8bmfGhuLZPK4x9bTUSwm1oEx+GqJAAvDQp3/HbC9W1+GPPKXRKiURMqA1KGPniShw+2ziYfHVLL5wtr8J1//0bAPD0uI4Y39U1bvW+U+WICPJ31C2gfiIaZvNHh5QI0euNf2M1dhwvw93DsjF9QCZ+2HwUJRXVmFSQhnUHzuCpJTvw0MXtcOkrf6Cqtg439G2F+0a2c/z+3VX78NO243jn6q4IDhDu4s9V1uC7DUXIS41EuyR9EV22HyvF8AX1/dKH1xVg/cFiJEUGYVwXfkzIkopq9HlqBc5X1WL7o8Nd2heXC9W1+G5jEbYfLcO1fdKREhWMYyUX8MC3WxAbGoAnLu2gO+FNRVUtHlu0DcEBVqTHhuB/6w7jgVHt8PvuU/jp3xO4rk8rjOzQAlYL/zpnyqvAAC794Ku/7MbTS3YAABgG2PKfYfC3WtBz3gpHmWHtE9ApNRITu6Xx6kVJRTV+330KO4+XoU9mLLqmR+Py11djw6FipMcEY/bwHDzwzRbcOjAT1/RuhbdX7sVji/5FflokzlfWK9DjuqTgiu4teeeV4vDZ8xj83G8AgOX/Hseyf08gLzUS30zvjSPF9YptWnQw3vt9H7qlR6NTaiTv90eKK7BoUxEmFaRh9pebsHjLMfTLisO0fhlYsHwX/i0qxfvXdkOXlvXjUl0di72nyhEfbkOwvxV+EmMdAPR6sv65/XXvICSEB+Kfg2fRMjpYcbvlcujMeRQ+8zO6pUejqrYOxeerse9UueP7hTP6IDeZ3yavfm8NftlRbzC4rk8r3HdRW14/aVda7fV44aYi3PrJegDAb3cPQFpMMABgw6Fi/Lj1GG7om4EJb6xGeVUt/tx7GpO6p6FjcoTkc/huYxFu/3QDAGD28Gzc0j9TtOwlL//u+PuqHml45JJc/LbrJNokhKF3w7P88LoCZCeGITzQH6t2nUJiRKDLfe87VY6nFm/Hzf1bu7xzM8OwRpmZDKS2thZlZWUIDQ2Fn5+4vn3hwgUEBgaqPn9paSkiIiJQUlLi0dBcRcUVWLnrJIbntkBEUOPAnz5nEQDgqbEdMKFbmujvuzz6E06XVzk+D22XgB+3NVr2nh7XEeM6p+DHbcfw9NId2HuynPf7/U+OlJXx3q83o+xCDXKTwjGxWxoigv3R/YllOF5aCaB+IE+JCsLUd9diaLsEXNY5GZe8/DtKKhotET/d0Q+B/lb0ffpnBPhZsObeQYgMDsDizUcdSqoQj43JxVU9WuKTvw7i3q83AwByEsOwZGY/Xjn787LTOi4EreNC8eaUrqLnvvmjdVi69Rj+nDsI8eGBOFZywaFIvXd1NwzIicdLy3fh2Z92On7z6pWdsWL7CVyan4zembEu13336q4YmJOAxxZuw9ur9qFfVhw+uLaA16ld16cVHhjVqGj8sfsUrnj7L6RFB+MgxyK5cEYfxIQGoEVEEHYcK8OwBb/h+j6tcP+odpjzv00IDvDDgxe3w0d/HsD932xxub8XJuahMCsOkcEBKK+swWu/7MHQ9glo2yIcN3zwN2rrWMekYe6IHLz+6x5c2b0lXv55NwpaRePzm3o6zrV06zHc9OE6APV1ps9TKxzKHLcO3fzROizeUr9CIFd3O/5nKUov1CA3ORxfTuuFsa/9AQA8JRUQVsydnzsAtIoNwde39EJkcADv+5v6ZeCN3/byytonRDuPl2Ho8785jvfIiMbH1/fgKSsnyyrx9sq9mFiQhiB/K06WVeLil1fxzndZ52R89c8R3jHuc6mprcOJskrHYAwAP9/VH+WVNY4Jxa0DMnHXsGyBJ9V4v9kJYfjqll5o/9BSAMCKOwsx8NlfAQDRIQE4w+kLuNe3/37uiByMyU/G3K82Y3ReEgKsFuw5eQ7TB2TiuZ924qUV9RPCyGB/zB6Wgyu6i78/MeYt/hdv/Nr4vPNSI7HhUDEAYO8TF/EG/ycXb8frv+5xfJ45uA2OFl/A9mOlaJcUgScuzQVQH4Ln4e+34r3f9zvO+c303njl5914Zmm9svjzXf2RHhMsqtCeOleJ4vPV+HD1flzeNRXtk8Jdyi7bdhzXf/A371jfNrGOdmJnycy+yEmsHyfKK2vQ9bFlsFoYrL1vMIICrKiqqUOAn8Wlnn4xrSeeWboDa/adcZFvQHYc3uNMDO/4bAO+Xt9YpzqmRGDTYWFL2j8PDEHnR38S/C4lKgit40Lx9/4zeHZ8Hga3jYfVwoBhGHyz/ghmfrbBMfkRalcAMLFbKj5dewgAMK5LCr5cdxhAYx2rrWNxrPSCQ0Gy+VlQKWHRtfcNT/zwL95saJs9MqLx6Y09RX8DNNbjpIhALJiYj/FvrEbntEh8dUtvAMDqPafx0Z8HcF3fVuiUEuky6eDyn++24v0/9kteb/+TIxusoBdwSackl+fTMSUCr1/VBUmRQThRegGFz/wCq4XBmvsGITjAD7M+38DrF3Y+NgIBfhZ0fWwZTp2rxA19W+Gtlft454wNDcBvswe4TDrfXrkXK3edQm0di1UNFv2IIH9sfGio7POyc//Itnhs0b9oHReCPU66gPN9cxm+4Ddsb7CI739yJE6UXsCf+84gIsgfhVlxoudxF0r1NVNZZouKivDmm2/i7bffxpEjR/Dzzz+jf//+LuUeeughvPDCCzh37hxatWqFl156CcOHD/e8wAr5YfNRPPfTTscS5cbDJXhgZDss+/c4enMscvf8bzNSo4LRKzMWe06ew8xPN2Byj5YY363e6sNVZAG4LBnP/nIT/jlw1tEROfP2yr24vm+G4Hd1dSweXbQNn/x1EADw/cYizFu8HU9c2sGhyALALztO4mx5Ff49Wop/j5YiOTKIp8gCwIQ3/8Sjo+sHpqqaOhwrvQCbn1VSkQWA+7/Zgqt6tEQFZ5lm+7EyfLvhCEbnJaOmtk6ww9pzshx7TpbjQnWtqLXHrnR9+c9hTOvX2jEoAkCAnwUnyi7wFFkAuKVB3i/XHcba+wa7nPPa9//GjseG4+1V9R3UbztPorq2Dps5g9Dnfx/iKbPrDtQvCx90Wlq3KzlXdE/D6XP1z/vtVftwY2GG433eOTRLUJEF4Ji9r7t/MP5vzUG8/PNuvPzzbnx+U0+HdcGO3TJut26v2XcGo15aiYUz+gIAgjjPsOR8NZz1hZLz1Si9UI0Dpxvv4Y89pyWV2dIL9UuQ/x4twy87TrgosXa4S2El56sx+d2/BMvtO1WOd3/fj1lDsnjHqwSW2OzTda4iCwB/7j2Dfw6eRbf0xhWNOz7bgFW7T+G93/fD38qgvMp1ydBZkeWy/VgpLnnpd6THBvOOX/fftZjWr7Xj88s/73ZRZs9V1mAZZ3Jax7K8tnDqXGP7P+PUFwhxrrIGizYdxYrtJ7CiYUUBAOb/uJNncSk+X417v96Mxxdtg83fiqUz+zlWU8orazDxzT+x+UgJvru1N7ISwnhtjKvIAnAosgDw4opdmDk4CzW1dfCzWniKLAAsWLbL8ffGwyUYnpuIqe+ugYUBuCu7mw4XY9PhYl6bfWrxdvx94AzuH9kOY/KTeee1K212/rv6AGYNycJtg9oAqF/1CLBacEFgGZxbp+08++NOvNUwUT5ZVul4J6fOVeKLvw/hxRW78eU0V8Xsx63HBBVZAPh5x0ks3FSEWZ9txMtX5PMU2fp7Fl8Sllq+P3y2wjHxnPbROsfxyT1a4sM/DwAA3lq5j2fJd4Y7ftgVWQB4Z9U+TOnZEjd9uI5Xn6QUWaB+XBvZMcmhyAL1be/g6fOwWhn8sfsUxuQnw99qwboDZ/HrzpO4oW8rR9mikgu4+8uNAIB/DhY7jt/y8TqcPV+NRZuPAgDG5CVhZMckDGnnugJpUWjFH/ta/YpXRmyIy3ebDpeg15Mr8NUtvXDZq384jrd7cGl9H+9kFrzy7T/xfzf0wKmG/txZkQXq2/TD323DnUOzEB/eaJh7bNG/LmWd3RdOn6vE/tPl6NIyWtA9ZPm/9e9ISpEVYjvHtePXnScx9d01js/OxhkzYSpl9r///S9YlsWXX36Jnj2FZ20vvfQSFixYgEWLFqGgoADz58/HmDFjsGXLFmRmipvgvcktTkrcJ38ddCiNHZ2WG694+y/sf3IkXvl5NzYfKcHs/21CYkQgz4XATpmAv56YIgvUN5CCVtFYf7AYzy/bicTwQBRmxWHuRW3xw5ajDksIF7uF1M6Ly3fxPtv8XZdIzpRXYZmTL+i2o8r8dT7+6wAeXbiNd+zh77dhWPtEDJz/C4pKxH3IqmrrcOpcJZ77aSem9ExHp5QIF2vMheo63Pv1Zvzvn8ZOesOhYgQHiC95AkC3x5cJHs++f4nLsRrOSOysfMv1qZ/8dRAFHOXKj5OL2nnSIMShsxW8Dp/rjiDFliONymWIrfFZrNx9Egwaha6prcOg537BqXNV4N7atxuK8MLExuV8O+sOnMHnaxufdW0di2kfiU9qGIbBtqJSnCi7gDX7zkgO6i8u3+Uy6Ah16lW1dY6O3Rln/zL7ZKOqtg4Ceqwsjy/6F1W1ddh5/Bzv+N6T5fD347/8V37ejekD6vssuzWey9nz1Xjw28bJi1r/6to6FgdOCw9kGzlKp53yqlqUV9Xi6SXb8czlnQAAX/x9CJuP1L+DS17+HaE2P3x5c09U1dThgW+3Sl5/wbJdGNY+EZe/vhrX9k6Xldc+aDq7KF6an4IXlvH7nSUNewZmfrYBVguDouIK3FRYP1mYt9hVEXjup50ozIrDrhPncNcXG9G3TazL8irgOskE6pXWBct24r9/7MdZjr9vHcvixQbr9rjXXduZkPJix+Zncaze3PjhOtFyQqhw4XRgV2T18OjCbSi7UM1TZJWycGORy7HrP1iLQ2cqUFFdi9PlVZhW2BrXvLcGpRdqYHXqKIUmGWedfK+/2VCEbzYUYf+TI/HN+iP4ct1hPD2uI5Iig2T7XWd+aFCQhXhIoN47j1kAsHb/Wfzne+k2AgCf/X0I246W4vsZfQBA1J+21unF93/mF5RV1uDD6woEn8/qvcr2R0jBVWSB+gkNKbMKmDt3LgDg8OHDomUWLFiA66+/Hn361L/4e++9F2+++SZef/11zJ8/3yNyGonQYF1bx/IsL1PeXaNpCVCIoyUX8NB39Q2s+Hw1th8rQ3iQP2x+2vYCijmpcC0NLAucLVe26eO+r10tj2fKq7B4y1FJRRYAth8twxM//IsNh4rx1T9H0DElAl/f0punUDor4wB4Fh+9jH9jNdZzlEnnTlmJn9+a/Y3WHG7HVlMrP4rtP6VuFi4EV7/bc4J/vvKqWoeFUG5QLb1Q7bB0KL92HS56sd7/sm+bWJnS4FnggPr67cwHf+zH/B93uhwHgCveqrf8Dm+fiFev7KxKVmfq6licPS9uMa12en/PLN3hUGa/3eBq7T11rhI/bG7c6OnsPiFHVU2ds7FIEWUXGifJzhtPzlXWOPxjlWD38bcrfVpgWRbVEpVtxv/VK4W9M+sVVO5KEpfRrzT6FK7cdcrFnUCMg6fP86zIdgqf+UXR74VIjgpycQNTymdrDmq+rl6EnoMShF7frhPnHOPHql2nMK2wtWMFZ8UOcYX5p23H8b2AcszF3i/M/HQD3pgsv6fCGanVF/vkTgkf/ansXdnPWVFViwlvCveZzkOH3Zg1+Z01yE+LVCyTMw9+uwX3DM9BiE2ZOii1AupNfCqawalTp7B371707duXd7xfv3746y/h5Uhf5PuNRYgP4zu5b1XRgKT4r4Df0DNLd0j6G0lRp9DlWi4ygRx3fLZRtszcrzbxljk3HS6R3SlvNFxFFnBVXpUud9lRa4WZ+9VmnmVYLReqa3nWXOf3q2Zn7x+71VsGfuf8RqmywUXIaqTEkrRk6zH8JbIkrITfd59Cp4d/5Fm4nfnib9dVE/tudKnBUw3cyc+f+06LTjalWLL1GD5vWOGxWrw/RNSyLGrr5HfZK3G90IKQ64petCqygL6JgbeQUz5r6up40R6EVg7svPXbXnwncT6uVXXN/jPIf/QnfLB6v2JZAeCYgdFQ1HDTR+skV6PEkFtZlOKD1QewZIvy6EgjXlhpWEQfI/F+T6WCEyfqB6XYWL7FJj4+3vGdEJWVlSgtLeX9MzPHSy8g3Wn5VId+wmODSCehVZmd9bm8klm/u1+dVUkLQr5BlTV1ipbn3QV3oyrLsqpl4foZsgrsbBXVtYoGfiFW7jqJV352HSi5+netik7MHaG8tKC07dj8LaqXI+1c+fZfgm4/XNbudw2jdfV7awEYN3hy73XLkVLNy8uz/7cJAL/+eos6Fjh9zj2KqhK4luqmgqfbptCSN7crOVJcgSveVmaQ4q5cCeHs1ge4roqYkaqaOvymINxiUXGFi/+snskRAJyvrsVX/xzG+oNnXVwfndl3qtwtEzy9mMrNQCl1ToN1TU2N5PLtvHnz8PDDD7tbLMOoqK6Fn5NyqWZpQ4rzIo6AWpVZJSz/94TXKn95VQ3OV3lvMOK6Gdz95SbehgolvLNK3O/OaCa/s8blWGVNHbg1Q41l1iyTd6VWBK2uNnpYtfsUHlbgVyfHsZILSIwIdPGr04uzm4w3qKtjBX1ZASDAanH0LSapbj6B2n7I3Rw6Ix9Htakj5+NaXcuKRqAQcq9Sw/qDZx2rQ70zY2TLm6Vv52KCebdykpKSAADHj/M3Qpw4ccLxnRBz585FSUmJ49+hQ+KbpMzA6j2nDbPEKkUqvq1e9otsQvEEl736By+eoKfhhibSO4Ao7UB+17C8L8brv+7Bfs7mAjWWWaUuKO5GqRQsC3hDdRPaeKmWmZ/V+40a/czN4GZQx7Ki8XK5mHHp06y868FJMqGMCi07Tg3iIKePV7KwZ8am5v2eSgWRkZHIzc3F8uXLHcfq6uqwYsUKx4YwIWw2G8LDw3n/zIyFYTyuCPy6Q355Qyu7ThiXxUULQn7CnsJIi7cZ+g81lj892dm8gVmUby3saoieYPQtmMHNoLaOxely4U1dXB7+fpuo5YrgU15pDhcggov3+h/uAoySflCJy5unMUFX1UhVVRWKi4sdPq3nzp1DcXExLlxoNKHfe++9eO+99/Dxxx9j7969mD59OiorK3HzzTd7S2zDqWVZlFZ4dml8kUQoEr0osaq4k/2nPLsJjIt9w5evKXZiKHXHraypxT3/2yxf0AMo3VDh6dUQd2C0Qq50h7M7qWPF3R24g6o3V2B8DTMqI80db/Y/3G5DiRhm7CtNpcx++eWXSE9PR69evRAREYGrrroK6enpWLBggaPMpEmT8Nprr+Gpp55Cz549sWvXLqxYsQItWrTwnuAScB26p/Zsqeg3J0ov4N3fm84ykLeX/7zp9mc3zH71j34fNW8/R0C5suSLPnBG+5t6ErvkRiuz3veYrb8ni8gKhy9s7DEjJuhKCCe8uTLEin4QKW/CCuT9aTeHK664AldccYVsuWuvvRbXXnutByTSjz1PdmxoAAbkxOO/q+V3F+8XCIDsy3hbSaio9t6S2p6T5Sg5X21Y6CVvI+czy7Ks18La6OWuLzYKZvzyBeyDi5FN7fuNRaZ4HhsOFQsmwyC0c6JM3m2D8Czetcw2XlyJ1d58qqzJlNmmSE5iGD6+vjvyUiPdtmRntTBeVxil8LZsQtlRPMmV7/yJgTkJuuKYAuboQHYek/Z/fv6nnXhxxW5M7qFsFcJM+PIytcMya2Bbm/F/612iqngDd8WPJQgzYWTbVQv3ykIhBF3Km3BuaSo3g6aIn9WC3pmxbvU9M0P4HCnkcnc3dbYcKUWIjqDWdsywsiNnmbUHdDcifSahHqOXKvUk4CAIQjnO2Qw9idpuw4w+16TMNgHcGSPWCLy5zG8W1IS0MjNN5DaaHMUNeepJ9yQIQi1quw0z9jOkzDYBzK7MHvdRH0oj2awhRaEr/B4kIsjfgHPqkYAwG2bcmEEQRNPCjP0MKbNNALMrsxeqPetmYMSSvtEYsfxrwv6DMBF/7fV8shWCIJSTHhPsbRGEUTm4mLGfIWW2CWCGTRruYGBOvKbfmWEHtjPuCDPrjdmxGWfkRD1/7Dnt04kfCKKpwzAMEsJt3hbDBbW9BvnMEm7Bz9o0lVmLyTe2qcEIJdD5DEwTej6EfliW9XrkEIJwZnDbBG+LYAgxDWE29cCg0b/dTKjeAGbCboaU2SZAkL/5ltUJPt5wM2if5Nm0zUu3HvPo9Qg+tSxrykFGC/2z47wtAiHByI4tMO+yDorKPnRxOzdLYwzjX18t+b3FwqBtC519KmPOtNlqLa0mvAVSZpsCEwvSvC2CWzhR1nQ2jnmj7bvDsi3Vid304TrJ33ZKiTBYGoJLbZ05B0otJIYHelsEQgIrw6BzWpTjM8MAfdvECpYVy95mNtbsl48D/u7VXXH3sGzN12AA+FnMp3ap7TbM2M+Y76kSqslLjfS2CG4hN7npKD9GrP5KzZ6v6O46oXHHGLJdJmmCFEEm3JjX1DDjIKMF8qAxNwzDf0dSE2cf0WUV0SIiCNMHZGr+vYVhTLnHRX2cWfNBymwTwIyNwwi8dV9PjVW2fKYGI9yapTocoQgO7vCpff3XPYaf05cwc1NjGHPuMtYC+YObG2fl1cKI908MzPMuM+NDEWD1ntrDMICJHocD1RvATDhpJmXWx5kxMBPpsSHeFsMteCvk2IRufCvnggl5guVu6NtK8Tl/3nFSj0iyCA3+ZtIHvDmAGE0rE7e3C00kQYmJqq4h3DM8x9siGArD8N8RwzDo2TrGpVyAn8VUE8DLOicjxOa9FSLGVKq9dkyoy5Iy68tsfXgY7hyajdhQW5PZMcrFG5bZaYWtXY7Z/ISbyciOSe4Wh4dzB8JVVoWelJmiQXx2Uw9UNZG0xpMKUr0tgig7j2t3AzETJqq6htBU3D/sOKtkDICre6W7lPvhtj6msrKfLKvULI8Rr7DePcM8z8OOWkurGaszKbM+TIjNz/F3eKCfREnfxGpCR3kuZrI4CGmzVpN0mk+P64j8tCjsP33e26I0ecyeQEUp3pyIueMRmnFZVg8WAZ/ZEJsfIoP5WQkz48MMm5iMzkvC+geG6DpHZnxok7CMGs2hM+r65ip3BE7Xibm1hSZIl5ZR8oW00ARbqFl8gcWGIU8PuFIbwARlMcfjc2AycZocDJqOBdCbyuzjl3YAwwB3D8vGyI4tDDlnU/FltuP8fuwfuUdH5yUJltVDlM5Yr95erbIwDE+5n395J+8Jw0FtoqG3ftvrJkm0Q8qsh2kqg40nMEsyCNGNDR4WT6rqCLsZuE0UVdjFMImhWDdmbsJmls1XyE+LxM7HRmD6gEw8OEp9jNSuAgYLljWP4mIExRVV4PY6Qk37hYn5AIzrhwxZ5od3+yGG4YfmGuKj7oErd7l3D4gWSJn1MIfOVLjlvE3DrZyPeSyzwr2ot2f5XIRE0bvk/OqVnXX93hXzPC9APC6mL9NUlFlvNy3/hg2LCeGBSIkKUvXbAAEfexYsxnVJwW2D2hgin7dZuvU477O9LxTeiGrMyzSialu8HE6AZYGOnHjbjEINbErPlm6SSBu1JuxoSJn1OMKVoCn6vOolKVLdIOJpTKXMCnTQeic4Rt+diR5Xk6WprPyYqW2pfaRCojc1NwPAaQOqgJuBUDkAuKkww20yycJ4tx9iwa/bSkUxmy+8GdNmkzJrEozOktIjIxrdW0Vr/r1Q3FJPUpAejYs6GOOvpoVL85Mdf0cHC/tpeTralKSbgZDLrM4qZXSnb4bu+NHR7b0tgtuICg5wWzDzNvGhbjqzMN6sK1omge9e3VXy901tA5gzjMMy6/odV3mLCQnA3BFtPSWWoCzerFssyypONsHFLJt57dSYUJklc6CHEQ8srQ/nup4eE4Kz56s0n8/fzwKodAo3ks+n9URljeevb3+Oz0/IQ4+MaOw6fg49MlzjJ9aXNU8HIyQLt6Mc1yUFFgb4/O/Das5qgGScs5ngcbXWqZQFB/iZMvsNALSIDER5ZY1bzj2kXQJ2nTjnlnMLYYa6YkeJIhoR1LiLX9gya9Zao41ZQ7J4vUOjLUaoH2r829tPQZ/PrPHSK1VmY8NsGNWxBTYfKcEBE0SFIcssIdocjFaMGEbfUp3Ub6/rozxZgB68MYdOjwl2/D2hWxruH9VO1Gru7WgGjMjfdrhi5ySG4elx6jagGHV7DquNCWyzYjJ8fH13Rb+/f6R6q9KnN/bAuC4pqn+nFpZ1n8+sp5VLT04UZw5ug4s7NcaMdg4v5fxIuYqrHTl5z5RXa5bPCIyKymAnPszGU9AtEpZZbpvztoXaYjFHP2RHTTV/+YrO+PXuAXji0g4oSNe+6moEdaTMEmKNWbdlVuCInvFA6qfXekqZ9XCfc1GHRFyar1zp8HSXuPdkueh3cst71bXqOx8t9xdmct9vsTqVnxapaOf6xII02TLOtG0RjrkjPJMByl1DjBIFwEi/Pk+0rdZxIXhzchfcPqgNXpqUj7emdMWCCXlICA/klXO2Qgm5P8nJ6+3NrFd2V19vpfCzWlBe2bhy1iKy/pkJTqo5WsaFau3xSY1QhBkwOF52Qfd5tMKyzpnTlP0uNarRyHJF9zTcMyLbWMFUcmUPc21IA0iZ9ThiExp3WGb1zECl5PFUt+zp7v++ke0EdyID9T7Iznha2Z752QbR74Te9aGzjctRi7ccVX09LXXSPmgL+VgeK/XeIGJH7I4sDCOriLfkWO3V4gk7BovG5eyOKRF4emxHw86tpCq8NCnfwOu5v3ElRgRiaPtEx7WGtEvAGI6vvB1nFwEhqxRXXiHZpWJEewIGjKEKdWVNLe+OrBKWWZtf4/4LbwfbZxjvRvxwrgdKVvfuGpqFYe3NFcLLTG5AdkiZ9TCillmDN+vo8w0yR2VV0tBnDcky7Hpqb5kBg9ZxIYZd32h2Hnf1cRRKOSmGlrFPavXJbQlDVCClJEmNcRd3SsLzE/Lqy+nc3X7vRe6x0rIs65CtRUSgx6OBBBi4I9IThkyl73Fqz3TeZ6GwRP4yMbH1KlCXCijZajGyT0+K4Nctpa5E3h5WvO277Hx5uTEuJMCKWwe2gZ9T2/K6C7a3ry8AKbMexl0bwFzOx+izbgT6N1aN727tjWfGNVp5PKXoKrmOp+I2inXSYpvD9KLWN1PuWYU2pD6+f2RbLL+zUNE5O6VGqpIBaLRa8cL2qD6L++DKxW2LcoPKS5Py0TlNmzLufObLOqv3n1X6Ll77ZQ8AoELHcq5WjMw+7Ql/dKWKzS0DMvHltJ6NvxOYsXGtj0KS63UxLMyKw8IZfTT/Xu9KnTMWC8MzzNgnH8NzEwEAeRr6Dk9Qct67vsss+H2Q1cIg2eQhKIUwoS5LyqyneXa88CYcoY41XceyJgNGl3UjPabR4hjgZ+FZeTzlQC+njHvKDxEQD311+2D3KNPX983Ah9cVKC4v90bs1cvPakHrOGU7+mNDbYqvb8dej820yYKLmFQMA009tKc2YuQkhCkqd6S4PinLbzuNzdCj5G0a+c7NsDJkx2ph0JXznoUss9z+W9h3WP/wH6wjXGJadLChs0oLw78j++TjjsFZuGd4jmMVwxlvv9cTZZVICJfv176Y1hO39G/NO2aENfTcBddoI4+NyRUtb6aIOVy8vZFPCFJmPczQ9onY/uhwl+PnBcJgtYpVvoTtPJAwDFBV02idyVQQkogbl9ZuyQO8H5tPiI0PDcVNhfWdzStXdFb1rMTQ0m/EhwXiThWuDmo2SKl56sIbwBr/9tTympQFymx1iItWS+Ca/WdkyzgPSFquFGyTV2SMeMWfXN8dlyuIvHDfRa4rByYdd0XR+ryEwhJx+1ohlwMj3o0ea3VSZBDuNXDyb2UY3j3ZRYsI9sfN/Vsb0h+7gzqFET86JEcgWWXmNyn6ZMYiwGrBs+M7CY7VvoYJdVlSZr1BoL/rwCRUOfR0XgyAYe0TeZ/l4F7P5mfBw5e0x/QBres383CXjRWK5c4UfNzwOCM7tsDPd/WXLN8/O85wGbS8HjU/0btpZN5lHTjn8gxmTHPIhSudsyuEuzbpOL9ztVe5c0iWaOIO/nn1y98rM1bQpcHfyWevu8CGSCNdA/RapGJClDwvbQhNDLMSwpAQbkP7pHCRpAkaL8ZB7/PVEolDDG9YDI1onXUsq8jlw+jb+/C6Amx5eBh6Z7qm0O6YEikuh7FiGIYZe3mfVWYrKytx/Phx1NV5d3ekUQgpAWo6DOeiR4ovqM404rw8NrVXOu4elgOGYUSthFK+UUEal8VaRATKF1LJ+9fIL9nHhKhfVleL20InCbzfsVzfTA/1PvblJ8FUlybrmZ0tS2aN0TpjUBvRKBtc3CX/3cOyFd2DVmXrRYEoCHqryhSnTVuCGGiZDfCzYNU9A/H9rcJ+rUasjLir/YzrkoLbBmaq+k39WNF4T55Yu7u5sLV8IRnq3538uxBMD67xBbw9pSsYhnG04XtG5CA80A8zG1zUoqUmXibrM+2Y0Wbhc8rs/v37MXjwYERERKBjx44ICwvD7bffjtpa72WrMgKjgxDnJofzPisajBQ62TJo9OftnWnsBqgb+rbCpzf2MPScSpFSGIR9Zr3b08hdn/u9GdwMjOSmftryu4v7zDKKdRu9FlAttUaJMqv32b9yRWcArkN9lkJ/Xa0xhi/plIQ3J3fhHdNrhfSTiS4AaH+PYtmP/K0W0T7UiGahN+W50CMNtflh/uWdMGuourilFufJnwe6wtzkCJdj6+4frOoctXWsYkXMuZyzD60SHhuTi8Ht+GG1WsWGYMODQzFzsHGReDyNt0PNCeFzyuwNN9yAiooKHD9+HMePH8fKlSvx9ttv44033vC2aLoQUjbU9OfOZS0MIxv70BmpvpL3cwZ49coumDUky+G3agQMUx/rtWWMe/ytrm9I9pCTqGxw5pISqX0znruoUJFu2FNdjyeU5qt7peOe4Z7b/KcXI6xW3N3yYlY0vRNisSxRDFwHdu49FbSKxrzLOqBjiquyoZShHJcoQL8VUijOsTNaH5eWVJ7GuBnoP4czj45pr+l3Fgt/8qdUNKMtuDEqN6k6909iiT6c69/Xt/RSFdZQ7Dx29E5MvA1ZZg1gz549GDJkCCIi6jvOzp07IzMzE3v27PGyZPronx3vckxPNiULw+/8lDQdq8QI4vxNu6Rw3DaoDcID/XlhvHh4uMLb0+yKzaDvGZGD/7uhB54bn6f63LcNboOuLaPw7OWN0ShiQ8WXh5QEub62dyvVcnDJT4t0/C0Uw5X7zjy1+9R+GaHJk96B7P6RbTG+awruG9nWLYOBux6REquqmnMIWagA9/kry4XcmtyjJSYVpBm6UqH3TClR8pNPrW1CLpmekNXKCEuW0eHKwmx+shkPe7UWXnnTKou3XY1aRATx3sTf9wlbdp3FzE+L0tTn6O3zxH5tQl3S65g796QAd9xxB5599ln07NkTLVu2xI8//oiioiJce+213hZNF71ax2DF9hO8Y0I5wJXi7OeqJAakZNYvrpXXpI48913UFuO7pqJNfChe/cV1cuNvtaBn6xjsPemaTECO5MggfHlzLwDA0PYJqGMbrWVaO5YHRrXF+kNnsf5gsabfc+tH91bReGdqV5RUVGPW5xsB8AcOb6bSVjKAZcSFSKbrBeqXo+PD9flTe+MxKE3zamHE3xNXmRVrp1oshkrwhjuNs+LgDp9mzRvAtDxnA2TX+xac+20p5ezLaT3x265TmNKzJbo+tszleyvDoEogmoGsDF4aOj64tgDL/j2Oa3qn443fGscGMaXc2y5kZodCcxnAtddeix49emDUqFHo168fZs+ejUceeQTt24svl1RWVqK0tJT3zxdQV1+cOiqGcdqtrWQDmMIr6WznadHBWHFnIf528nfS231YLAyyE8NgsTB4krOT3xm9HVVYoL/sREPJu2MYBq10uFRYnNxIBrVNQLpISBy1XU9njtXXE1yUK7zEzcPN44s7/MBW3TNAcdn4MHFFnRH5m4ucMpubHI5shf6vXCwC/sS85BOqz6gNMSuhVtJVtr22Ler3IQilvOUiGM3A8Yfw0xrSLgEFraJFw6LpTYKjhuiQAHRNj8asIVmisaatFgbtkhr3ZSg1cMhZdLulu64wGUG/rDg8MjoXgf5WZX2zQdfVn9nTGEkmFaQhUachwOz4nDJ7ySWX4Pjx4zhx4gROnDiB1atXY+7cuXj11VdFfzNv3jxEREQ4/qWmpnpQYu9gYfgNUolxSMqCZHQ/mhEXqikov1ImFqSJZtEyeoVa6ekC/CwuI79kelWZTlfunfDOrWJm9PiluYI7zNUgKJpSn2zRcxr74jyhG3DjNcshJQ83bqdYOTll9rvpfbD49r7ygjjVFW+49wkpPU+N7ShQUhi5d9u3TSzuULkB58tpPfHVLb0wtrO0MivoZtDwTMVeUXRwAD6/qSfGiiizLAtxdy6NiFnX5BTO4AArUqODeHVbaVuSO3datPvj0zrf993D6je/teQkKWIYcy/lq3XzGJ6biPAg4xbizfhsfEqZPXr0KFasWIE777wTUVH1M7j8/HyMGzcOH3zwgejv5s6di5KSEse/Q4cOeUpkj+Fct60WJ8usgsqvdBbo6wswYYHa3TeUkhqtbMOYHkVBqEMT01mdB9EOIn6XQH1ILyU+h2qRulUlj8Fopcr5Wblj5cwoBZwb55X72hPDA9G3TX3syhqZMIUWC+MWvz+jXsvzExr90Z2rNoP6NpVhUDD+D6/rjjSVGRZDbH7onBalyVpmb39iGySVnFJvv2XU5O33ewZqlsUMYwcvZCRTv8fir3sH4SXOBF7pOx6QHSfZl+p2DRE5QceUCHRSueHSyP7NhF4G+pTZ5cuX48Ybb0S7du0QERGBqKgodOrUCbfddhv++usvo2R0EBYWBoZhUFxczDt+9uxZhIeHC/8IgM1mQ3h4OO+fL6DHL+Xfo2XgNiW9cSKN7IS87Y4UHRKA96/phgUT8hAVrF+xFXpLg3LiMUcg487EgvpVgR4NQeelnoXccxLWS4TrTJsE/u7ut6d2xW2D2ujehCYGV/bh7Vu4HJP8gWgR91YcxaG53BWPVuI77qoJV7ns2TrGoeg+8cN2t8jlEobJTXCjmIg9i6fGKbfOmgnW6b9iKPWv9iZRAnFRFTdNmXKeCPn0zLhOvM8MwyAhPNBl/EtTYJBIjAjEoLaum7cbz61NRjn8rRZ8e2sfTOiqbJXZaDGaTGiuFStWoFOnThg7dixOnDiBK6+8EvPnz8eTTz6Jyy67DHv27MGAAQPQu3dvrF271jBhQ0NDMXbsWNx///1YuHAhtm3bhgULFuCbb77B1KlTDbuOWSjl5HG+sru67C3HSis0RDMQ/46fOYlfUGygKz5freCq3qF/djzG5Ce7TUGyWBhMK2zNsyRd3DEJdw/LwXvXdMPbU7vVl9PhZqBkqen7W/vg6l7peHBUO97xhPBAzBqShcQIV1cPox+JkuQZSi6pViwlESV4uMc0awh+Eo3Z3eqPXBtxx7Amdslu6a6Zx3wBu2FCLCGM/X47p0UhRKC9eNsAYBThchZdD+hIGXHKrPv92sTi/pFt8cn13SXLXd9XW8xrI9BbLz68rgDhGqImmdEyq/ouysvLMW3aNNx///24/PLLERQknL+4tLQUn3zyCaZOnYpt27bpFtTO+++/j+eeew5PPvkkTp8+jZYtW+Lzzz/H2LFjDbuGNxAaMI4UVzj+vqFvBj7+66D4750+19SyvHMqUXzUJE0QIykiEEUlFwAAn/3d9Nw59HBJXhIC/CwYwAnDdkleEj5dq+05KVHEO6REoIPEcpRZOiWjM0xZLQwu6tACS7ce1yGVMGoUKrvIzs85JzEM24+VOZUVvz++ZRa8v92t6HjKWMj38edfVO2k0x1ZBPVgf/2Xd0nFhoPF+Gr9EcFyVguDz27qiVEvreL/Xkc7vahDfQxfd75Gpe40qdHC+oIdI7qjaYWt8fqvykJ1cqtVZnwoLExj7FqGYRQpqlJ+8e4KzeX4XuHpxcr1bROnaTXAJMMGD9WW2eDgYGzbtg1TpkwRVWQBIDw8HNOmTcOGDRv0yOdCSEgIHnjgAaxatQr//vsvlixZ4vOKrBg2FfEp953ihzWqrWMlBwchjHAz+GPuIIUlBa7RVMwP4DwvGet4r9ax+PGOfpquoTT6hFqM8PMUzJgmcV5F13Rz9VDaQRe0isbH13dXFanAmfevKcDtg9o4Np/Iwc1oZWQ7eWtKV9ky9QHyvTt8qb3jyOAAjM5LcosscggpnnbLbFCAFc9NyBP4lfsqtydCKSqtkpd3kV4Wt7tf6UFrXOdAfyu2PTIcf8wZqPq3ou5qOh+9fFtXaoASz3BYIxc4GcDTTpsvzWIE4aL6rTMMAz+/xpnI2rVrsWDBArz11lvYvXu3S/mAAIm8wwQA4QYcGeyPqhrpDR1c/thzmve5po51ydolh1TSBC4umzMM6ivNGLtOCUJiCyYNEHlOzulC37umm6Lrprphk5ZRqB1AFUUzUFnPjFL6hCwvvTNjFW2Ss0vgLEpiRCDuGJKFOE5ED6mwRJFBAfBvUGiTI52tjtrvc0i7BLwztSt+uE08ykGQv/Syt1GqErcZ+TvN1LS8SnsoLW9S0GDFn1Sgzk3ME3ijt5WLdz6uSypemJin7xoM8M8DQ0S/lxpmAv2tLnVPCQsl2o87MaKLK6uskS0TGGDF+K7cSBvmG6t12XaeeOIJFBQU4I477sCNN96INm3aYNSoUdi3b59R8jV5RnZsgQ+vc/XJ6ZAcgRrOFnS1oTh6Z8aoHnCUJFYQQqxzuLiTdywjajAi/aq/n3GWj16tY3huCGK8f003wd3YRswHvGEgV1Q/3b0BzOnZhdr8cO9FOcrCWYkgPKlpPMa1eM6WSNEbFGDFz3f1x9KZ/ZAZz5/86H0sg9om8GKGOtNe4jvAPcPaqI4tEB/mvtB9nuLjG7pj5ewB6NsmTrKcfJg9HUIw9nO4r/20VBgZQm6Sa7UwGJ0nHfpMCdECm9SE5dGHvc9IjgzC8jsLDT+/UW+sPumIcEtVuonsac7GOTPanTQrsyzL4sknn8TTTz+NU6dO4dixY/jxxx8RGRmJgoICbNy40Ug5mywZsSGCM8GaWhb5qZEA6v2/UqKCUJgl3SHaSY4MwoyBbfhZuwz0SVRqdVMbOkSus73vIuG4sXowImvSlQUtXY6pXWZXg83PIpj+2EwI3r/I7XdOi1QYZ1aay5zif6p92s6dfajNDzf2a604zJr0uYWvU1LRuEkyKVLanzAlKhjZia5JD4zfqex0fobxyFI1l8jgAKzW4bIEeC4M1DW900W/87daFNUfd8paxNl7oYWEcPFJxetXdcaQdgm4dUAbRefy5ETZHsc82alduUsG5+vUX0unz6zcJEfX2eu5c6h8vGX7dey+6MNzEw24srHossy2bdsWd999N2JiYpCQkIAhQ4bgo48+wscff4xJkybh3Dn1aUObG2KVsbaOxfQBmZgxMBNvTu4Ki4XBf68tULQZ48oeaQj0tzptFJH/oWJHcMVO58IFtTZAd4StMWKGGWyT362vFCV9n/05GCG7UPgZd403E7oJWwCeubyTok5fbrL13Pg8x99KNkY5+4Fe1LGF5PdakPd40+tUJ3z46l7p+s7rxJSe/Ambu5VbBuLhyD6/qadbr60EbpSS2cPELepGobStC/mLak2Xbef1q7o4+olb+rfmfTc8twXemtIViSbbcAcAn93UA+O6pOAjp2gERloVvb3NQ/EGMInvlFqxAeDHO/ph4Yw+ig1rnkSzMsswDPLy8lBdXY2pU6fi4Ycfdnw3dOhQzJ07Fy+++KIhQjZpRGpjRLA/okICcOfQbMnd6IKnbKi6RlpmtTRaHwiZ6L589k7/BdQ/QzHJjFxuH56biNnDlW1C0svFHV3dTrISQtE6LlSgtCtG3HZLjvLunGMgPiwQ2x4ZpvncYhnnACcfUwPfX3pMCH7cxo/Y8PyETi6h2PQSFRLg8P/0BFKPqKCV98NzvXt1N4zs2AKLbuvjEnpOi3x6qoTcpqm8hhU+rZfIT4vCb7MH4J8Hhki6wihBqu47bzLSS+u4UMy/vBMve54aeZQgm6VR19nlMWazrvJzhAX6Izc5wpSbtTUrs3///TdmzZqFyZMnY8+ePXj//fd531911VWGxphtTmTGh2LGwEzd51GrSEkrSeoUY/nzmYN+WfXZk+RCxngKJZ2TvYRg2ky112MY3NI/E/+5uB3vmF6EzmCxMOjUMLC6yqH7kgIyuJ40KMCKjQ8OxdaHhwk+v+AATopOlQOFUH1Xax1Wymc39sANfVvhxn6uoYPaxIdpyvQlxOOX5jr+1upTrwVD6qAbu5/02BC8ckVntE9yNTRM7ZWOp8Z2wK9399d0buENpeLlefWUBQa3TeCFJhP1eVZZ9dRY8MSQqpYjOphv6VoNAW4JL6OuEseGirwjRp9vuw8M5dqVWT8/PyxZsgTPPfccpkyZgkWLFvG+ZxgG5eXlIr8m7AjVkWWzCtExJVL7ORn+f5Ui1dG0TwpHh+QIDG4bjzCF+ea9bZlVMiA+PbYT5o7I0bV0KRQFQthn1hjc0bFwsy8ZgoiQL07MQ6/WMS7HlSz9GTU5igj2R4jNz5DlRnsqWUDaT9roDRPdM2Jw38h2CBSINKDXHYcbR/fK7o3uBdxVDKWvQsoq5ozkM/KBwdSOv9WCCd3SVLUpPRY253fx9tSuisJLiU143Km45DT4fN/UMAnjTm61TmDkNih6CouFwdKZ/XAXxwfVX2OYMKXsOM6PVf3N9N6YPTwbdwzO4j0X6bCITQPNTzovLw+HDh3Cgw8+iIEDB6JdO/6y1rp163D69GmRXxOegFuB9frMBvpb8f2MPnh7ajfFnY6oz6yKTsseMijSgLSzQkQE++OmwtZoEaHdMmuxMFj/wBAsuq2PbnkUWdAN9JltvLDgn4bTMiYEn9zQw/HZfg+lF+SzxUk9m5sK+RZKhhHbhNZ40IjHd+fQRhcNs6xE6JWjbYtwfHdrb6y5l78B6wqVWQgBKI6jK0diuPl8Mt2F+hB0jX9f3hA+SUloQLHYqO7Yqf7zXf3x2Y09HJE45ozIwcrZA3AjJymB1jnYZzf11GyM0NpSbuyXgZAAK6YP4K+gZieGYVpha1zRPQ2j85JQKBPFQlY+GQHLOWG1lt9ZiJSoYNzSPxO3D26DNyZ30XVtnhw+oPKqz2PG4ZlnnsE999yD7OxsdO/eHW3btkV8fDwOHjyI//3vf3jyySeNkrPJYrfsGFlV7OfiZcBUoSTpJT0mGPtPn8eAHHU77oWu3i4pHJv/MxQhAX54/4/9hsjnDqJCAlDNc8J0n2lWTwpcT6H2VtW4VzgTExKAOQK+fHJnNDqusVDzkXUzcMP78pPKS60QoZUhLXFb40TCaxWkR2PN/jMYxdl0J/SsPruxBxYs24VHRrdXfW0xjFguNxruvUvVidjQAJw6V8X/LaemPyDgKy1WB4XKuotWsSE8Kz3DMEiNDsbGw8WOY1onYaE2P4/7Ud97UVvcMzxH0ADkZ7XgiUs7eFQeAJJ7D/TOs00yT5dElzLLMAyefvppTJo0Ca+++ip+/vlnHDt2DElJSXjkkUdw2223GSVnk+Pv+wfj8NkKh3O+WzDQzUANP95RiHOVNaoHjeQoYetomFw+bxPS6Oqh7qGKDf68czf8112Kq1k7LrHBLiokQPlqAedvQ2Lycv82eAOlVqTSa+qBt9Ijcz9hgX6YVtgaXVtGCX7/1pSuWPbvcQzjhPgReh/dM2Lwfze6uqWolZd/3Hwolen1q7pg3OurRb8XcjsRolfrGAxqm6Dwqu6Du//WK/GtdVzTHdF1gPoYyws3HQWgIBqKlC+185ci/Z1Z+3u1GNLr5efn46233jLiVM2G2FCbIw6e0dgrJ7djO156QfZ3Ri2TBvhZEO2n3vrxzlTpzFcmMTqKwvMpbPhvZU0t55j4831nalf835pDuFdBLF0ppSkvNRLpMcGGxEbVg56lUrVlxH4qF+v20Nnz8hdVgRaLuRF1ul9WHH7bedKAM0mj5p0Ob5/osgTLJSLYH2O7pIh+TwjTNT0aD45qh0cWbnMcUxyeiVMwRGLC40nlhrs64gtL2Z7gjiFZjcqsQY+kOTxZD+5PJTxNJ85SYUlFNfLTIkXLAkD3VtosIEYMyJd3SUFmvLIQTWalsrrRzcC+JHvojLKA5YPaJuDtqV15E5xAkc0DdoOA0HMP8LNgxZ398cG1BcqEFsCMYVcALXJJlz9eWqldGPsVZFx5PPEoX5qUz7+m+y+pCrl+x9OYsXqrqdsXdWghX8gH8faGYcK3UW2ZraiowIgRI1RfKCgoCIsXL1b9O0I99hmuc9BxOctr70xtyqwewgP9UHqhBoPbeX/JSy8tY4IxrksKYkIDFC/3SVHQKhpj8pLwzYYi3nF7Figxn0+jfJ+VEGrzwzkFub3FkKqSc0bk4M3f9uJMeZV4IZlzuJR1o6ondGZPWJsigvxhYfhLtoDxSpvW031+U0+cKa9C9yeWGyqPHGZUWpUgF64tMSIQV/dKd+whkFOE1dZBT/rdc9OJe2MDpa9bg5VGKZB2gdK+X8FMqFZm/f39MXHiRNUX8vf3Pb/Hpoa8/437quzgtglY9u9xl+O/3j0Au06cQ7d0Yd86X4JhGMy/vJPE9+rPt2Bivosya9+44U23i2HtE1CYFY+UqCBMeXeNy/dKd59bG4KXCj2aaYWt8eTi7Zpl9MTYyB1MpCYRYrIYpTgwDNN4Mrvl3o0VhHvfchvp/K0WRASZqf/3haFZmhBO1kEtd2OWJ8B62WfWjHAfw4Ru6iOICJ6TafpxZlUrs35+fpg2bZo7ZCEMQtS30On45v8MRYf//Kj/egrKhAVyg9E3EhUSYIqMPmYmLzUSGw4VOz5n2netelGbjQjyxxXd07CJsxuZy9jOKYgNtcla++XijN82qA1eXL5LdWgm0c0/aqy4qjdQuv6AkVEsjUiZC6hTTh4dk4sHvtmCeZep23Ft1ICWnRAmeFxr4pJu6VFYu/+sHpG8To1zOjoVyL0Xpe9tUE48lm8/gWt6p2uWRS38DWA+oDF5mEkFwinA7dAja8Qwn9kvvvjC8FA3zQktTvzqr8H/bRBnKTzQX3tVUPLWqc1pfwavX2VcvECjcKRMFrkrFvVK0/Bcaf8+e8IJsWo9c1AbLJnZF78LBIFv1+CXPCY/2XFsSIO7yjW90z1e57xZx3m+u3B9ptyYk5N7tMTWh4dhUoFaq4/YBEFciRfi2fHCqxfxYYH47tbeWH5noSqpZgxso6q8GRQAbmB9ALhIpp14gleu7IyvbumFmwtbe+ya3tYZzFAXpPCTSbsnJT6/T2j6GBbDZc2aNRgzZgz8/f2RnZ2NjIwMFBQUOP7FxekLHkzUo6fxO1do7iDkdn8lL7SmADdnX/EUiRF8q6TD2mfi+A51MvU0OiQAZ8qrHLGIxZRii4VBTqJwfNNPb+qB9QeL0ZuTUeylSfnYcKgYXVpG4Zcd+nb4K6my3GYjNO54qtrXPz/+M+e+gmHt+alCpXazK7qejhvjrtI4oyXzodkVEiGmD8hEfHggZn+5CQAQGKDOx56fEEc9Qs8s0N+Kzmmedfcybw/mPchCrQ3DRvtnnnnG4Rf777//4plnnkFaWhq+++47DB06FJWV+ncOE8qocd4JAohmRLIjlJLVSIxwtFeryI/rnIJOqZG4bZA6y427MKqTsp8nIsh7wd/lkwFIv6sfbuuL58Z3wi39xcM3yREe6I/CrDj4cXwVAv2t6JERA38R/wWu3HYXiMIs4Ym22lSUwm4G0g/KMMOUTFQFQy4h6vdrLpUkRSRetR0zqAoMw/CTCOg8l8/ihapjsuoqiWGhuRi97dT8dUy3ZXbx4sXo168fQkIaG6bFYkFubi5yc3Nx3XXX6b0EoZLT51wnDiwrbX11u2GWt7XSvdeyExRgxbfTe3vmYl5gaLsETCpIdW/iDRHkrMMC8ykeiRGBuKyze+OMylWzV6/ogsVbjmKESKgjMYXYjAhGUjD/+KOZGQMz8dKK3QAarbnBAVbMu6wDOiRHABBX8syyYsPP0MjdUKfuPLIbe9WdzqPIreCE2vxQmBWHQW3VZZMkjI3U4At9iW5l9s8//8R1112He++9FzfeeCMCAuqtRcXFxXjrrbdw99136xaSaIS3a1mEWpG9BM4VkvvROZuJ0R2+EW3Bpy0Q4OfRNgKLhcG8yzoaek6jUD0gc17t7YPaoGdr94SJ43bwEcH+mCjhN6pWmRW0zKo6g3aE/OOMtkCJzUe90S65zzoiyB8bHxoKm59FUUi8u4Zmu1M0FehzFXD81oe7RbkqGuhvwStXdjb0mr70vGRFVXwzPnTTGtGtsTz00EO44447cNdddyEnJwfPP/88PvjgA8yePRvPPfecETISHJQsFQjNdhlGeLC150a/qcHp/9rerRBgtUhm7/EWZlvOVEutnLnSp5DuHOUsLlLcMSQLPTL0K7N6kxh0SBb21RU7n1Q0A94xifM9Mro94sNsaBFhTPQGI1GjtDrLw302wQH6t2o4166IIH9Fiuy4Lim8DYPexCJTd6RQ4y8vuEHPJMqNWDcxrWE8evDi9h67ZlNDLqGLqnPp+7lH0N2r3HHHHXjrrbfQp08fBAYG4qOPPsI///yD5ORkvPbaa0bISKhEqSLBMMDzE/Jwc//Wjp3hD17cDnNG5KizzCq4nC/Nht2FL/ehyZFBOFLcmM1M7n3qUWaNQmudWzijD77dcAQzVPpaK40IIvVkpvRMx5Se6Zj95UZ8/vdhVde3Y1dePNXmlEwyA/wsePbyTqisqUNcmHvSeCshzctpnrlwlUx978p3O1exfmLOiBxMK8xAZLD39gV4C3cZbbzfI7sX3crsTz/9hE8++QRjxoxxHFu4cCGmT5+OAwcO6D094YQyNwPh74Vm6P5WC9onRfCOucOnzCyWAEIbj43JxTXvr1Vc3mi/P0+SmxyB3OQI+YLg1+vM+FBM6JqKnSfKsP5gcf33shvADIozK3AZd7oZaGFsFwN9pBXcnJnqlBBGJerzZUOB1FtsjoqsM/LZ3RSeB0B5Za1suUvzk3GhuhaLtxzjHTeDcUIO3VpLfHy8S6SCUaNG4ZdffsE999yDc+fO6b1Es8DI/kio4p0oq3TpPD3h6xYVXB/hYggnXa0P97268EU3iefGd8I1vdPRPzsOWQmhjuNy79AMnZ+nJ1AMGDw1riNmD8uRKSdPSUW1Djncj7cVKK21y0x9j8Ugy6yWnx4rvaD9gkbihn5CLJSfr2BY1Bunz0EB8upeXJgNrwr4KCeoTFrjDXQrs7fccgtuv/12LFmyhHc8IiICtbW1pMwqxL6jWm5nuhKFSMgyW5gV576OXOLEP9/VH/+7uSftRvVRLuucgocubg+GYdC3TWMIK3k3AzcLpgQPaC56/dKGtKuP/+qc4UytD6Unohl4W4HlolUHMkO1FELtxIsXZ1bDe2kZYw53i0FtExAcYMWI3ET5wg08Orrej/blK/J5xxfd1gdPje2AYe0ThH7mM3DHeNlIFQrfPcMwaBEhn2FP7HS+ENlFt5vB+PHjsWXLFowcORKZmZno1asXQkNDsXTpUqSnpyMxUXklbc4khAdiy8PDEKxgE4MQQf5WVFTXLyNUVLuGM7AwDLQnTJRBYoSIDA5Al5aUrhYw70CqFDXLoqotsx7QlK7ule7WTlnLLWQnhmHVPQMQG8r3I9VjsJJLoav5vCoULjMovmaQQQoxy6wn+olQnUkzjCIpMgh/3z+Yl41Sjsk903F511SXDX/tkyJcXOaEMHOyGbWoqeI2Je6DjMiGQZO3JcCgpAmPPPIItm7digkTJuD06dNYv349+vfvj4ULFxpx+mZDqM0PFhmNQWwJ4rObejj+Pnz2vMv3Fgbo3oqUSm9igpV3XahZ/jKDSwVX2p4ZMfjPJcbvjOZdT2LjVQBHibZa+QVSooJdBma1A67nw2NJX699knuXerUqJGYak43aba7FncbPKIddAwgO8FNdf5VErmgK6GrWTuH6CtLlx39f3tti2PQsJycHjzzyiFGnI1TCTQMZIGB9sloY3NgvA/MWb/egVML4erzY5gr3rZ27UB8zV0xnVetm4Ks1Qmn+84hgf9w+qA0sDIPwQH/Z8+px07APSN5wM1h8e1+s3nMakyTi9xL18CyzKlsAV5nX8p7ljCZE04JhgBsLMxAUYMVji/6VLCd43Ad6aNWW2erqavzzzz+Ky//5559qL6EIlmWxe/duFBUVueX8TQ2LhSElktBFZU2jowp38mSHW71MsQGMI9DGw8UeuJ7093cMycLtg5WF+zIiGoSnXgF3+bJti3Bc26cVL8WwO1AyITB7b2eYZVbDb92dvpzQjruarc3Piuv7ZiA5Utx31pfnOKp7nLq6OowZMwZjx47F0qVLUV3tuuv2/Pnz+PLLLzFkyBDceOONhgjKZdGiRUhPT0dhYSEKCwsxatQonDlzxvDr+CpCjUHthhKCcOYUJ03ygJz6DX1ilkkT6LI8ec5XyYel0XYNV+uaMS1Nl2nW7TBMfaa2wqw4DG7r+Q03E7ulIS81ErOHi2fzMkEVlMQi2nZUupjIvHChrt/Z1aW5YvZhUf7din8v1DfpvZ6ZUe1mYLPZ8O+//+KZZ57BVVddhfLycuTm5iI+Ph51dXU4evQotmzZgsTERMycORMzZswwVOA//vgDY8aMwQsvvIBbbrkFALB06VIcOXIE0dFN3ydUaxYp6rvMgNmHV2m40reKDXH53sIwDousc3pkOdwxqHh6oDLyeqqbucC13e1mcMeQLGMvoIKIYH98M723165vDNwNYNpf1uSeLbFo81H0VJE1jyyz5sXdb0bq1Yu6GfhAddHkMxsSEoL//Oc/mDt3LpYvX47Vq1fjyJEjsFgs6N69O5599ln069cPfn7G75h86KGHUFhY6FBkAWDYsGGGX6epQZZZQjcCCpaYEckMy1VSy2lGYWTKSC7qrXNC5zBGFsI9WETqjhLFlmtB65ERgz/nDhLNrCZkbTPTBjBPw43kYMZxkddsZcRTKr3iEF4Kz2dGdGmbNpsNF110ES666CKj5JGkqqoKv/32G5555hlUVFRg7969SEpKQlRUlEeu78uYwYexuePrr0BuBzm3wxzkhaVnZzztI27kEp1qw6xhqVEVXsM9l2hWiD1PLZFAEiNcg9rbwzUOyIlz+a45bwCLCbVhwYQ82PwsPhE/VQotVlYtJzShzu+CW4LNVVVVYevWrcjPzzf0vKdOnUJVVRV27tyJ1q1bIzIyEvv378fgwYPxwQcfIDIyUvB3lZWVvCxlpaWlhsplOgT6wvFdUz0vB9GkUDPGOsdNlcMeI9lIPD1eG2uZ1X8Ow90MjD2dVzDToCwWW1WJgqUkNNnvcwZi36lydGnpauxp7m4GY/KTvS2CIM7uWR53lfLs5QxF87Rk165duPXWW/H666+7fBcQEIDVq1fj5MmTuoRzxmqtb/zfffcd/v77b2zbtg379u3D1q1bcffdd4v+bt68eYiIiHD8S01tPordz3f1x9tTuqJXZqy3RXGgtcH4vmXTxCgQTu7567FMKtmdrhZPLCFyn4mRV1NvmXWPHIT74CqzXB/p9knhuKRTEm4qzHBkhOyfpT6DYnRIgKAiC9AGMLNxx+B6//P7LmrrlvNz+4fLGhR5oVjQvhyaS7Nl9u677wbLsnjrrbdw3XXXwd+fPxhNmjQJjz76KJ577jndQtqJi4tDcHAwLr/8ciQlJQEAEhISMGHCBHz++eeiv5s7dy5mzZrl+FxaWtqkFVrurL1VbIjgZh13XY9ousi+Zx39XXCA8UHQPb8BrP6CQku+ajFD0glnfM2YZ8JHyIOrUHLdwBiGwYuT6lc16+pYVNXWGZ4koDn7zJqRGQMzcVGHRGTGh2LvqXLFv5NSMsW+mTGoDTqlRqKrQBIFX1BaxdCszCYlJWHBggUoKSlxUWTtLFy40FBl1mKxYNCgQTh69Cjv+LFjxxATI76T02azwWZTt+xJmA9fG0ydMfvgKoec/FU12hMmu+PVesQyyw1e3/DfljEhePmKfESHBGg+r1ofd56F2AP3TTGr9cNVKMWi1FgsDAItxk/0zLjxqTljsTBokxDmctwdb8nfahHd0+DL1UKzm8ENN9yAWbNmISRE2Or32muvoaSkRLNgYjz88MP4/vvvMW/ePMdmsA8//BB33nmn4dciCLNwUYdEb4vgVvu7O5QjTw/Y3OuN6piEXq21u/aonfiUVLjG+zYaX7bamBGrAmXWE9cmzIU3jR6+XCs0W2bz8/ORkpKCFi1aoF+/fujVqxcKCwtRUFAAPz8/bNy4Eenp6QaK2njdX3/9Fc8++ywWLVqEtLQ0LF26FAMHDjT8WoQy6gc55S1Qq47h85ZNHepgcIBb9mqqQu75x4baeIkV1OCrcWZ5z8QkG8Dcddu+bLUxI9xNWJ6ONkNuBr6Bnkk+P1qGwqQJzS3OrJ05c+agoKAATz/9NP7zn/+gqqoKYWFhuPzyy2G1WpGV5Z6g2p07d8bHH3/slnM3BTyt9KlV0nxdKW2+KA/NpRZ39JWeVmYNjWZgcj90HxjbTA83PJZaw6xeK3lzDs3VpDA8MYpIaC5jL+MWdAdZGzhwIJYsWYLi4mIsX74cs2bNwr59+/D111+bchMDQXgLM1rb1FwgPMjVN95NhklD8JbPrBHoWXV2W5xZ95yWgPpQWXonO809NJevIPeWjH6L9mrx/a198NF13Q0+u3sxbO0yKCgIAwcOdCz3nzt3Ds8884xRpyeaEFr7UZu/bwe41oMZxp6b+rXGtqJSXCoSo1GXjE3AZ9ZIv98ZAzMx+Z01os+aaBpc0zsdh89WIDfZNUySOyGf2aYP9w0rzwBWX7BDSoTH/bj14jZHvNDQUDz00EPuOj0hgZIqKBRjzuxc3iUV328sQmGWa0Ybwv1kJ4Zhycx+ot/rWfp0TzQDN5zUCa613cjr9W0Th38eGIKoYPXxd922UUsk/apZEeoHzRaF4aGL23vlupHB2iNtEObB8MQoPhyv2q27SiyW5mtJ8yZKYnZG+WBnFhRgxRfTenlbDM2YeZ7bvVU0ggOsyBIID6MUPR2rO6yoXMUlwM/9fZHRSqSe0F7ugKIZ+D6PjG6PrUdK0T+bDAK+gFkygJlsDiiI97dIE4bx3PhOePO3vXh8TAfRMm9O7oK3Vu7FvMvEyxDGEWrzw7nKGm+LIUtwgB82PDhU1y5nk3kZ8M65YEKe8ReA+zaA6cEschDmY0rPdG+LQMhijNlDSz/gy32HbmW2uLhY9DubzYagoCC9lyAUclnnFFzWOUWyzND2iRja3rsxS5uThYd7p3o2RHrimem1XpptCTeAk+M+J1G7xVkKs0cdMBL+EqS53jVBNEXk2pnekFvKz2f+9q5LmT137hyiooRzP9tJTk7GDTfcgAceeIDcDghCIybTEw3HHbcX6G/F45fmoqSiGhlxoW64Ap+m/o4IgmjacPswX5um61Jmg4OD8cQTT+CVV17BPffcg7y8PNTW1uKvv/7C008/jYceeghWqxWPPPIIIiIiMHPmTIPEJggfoRkpOLrizLrpOV3ZvaV7TtwAz82gib9s3iqDDwx1tXXa0ysThBmQ6xdDbOIqnJH9kS8Ev9ClzFosFnzwwQdYuHAh8vLyHMf79++PDh06YP78+VixYgVSUlLwyCOPkDJLEE0YfUkTfKC3lMEsHb7b4sz6mOn5h83HvC0CQbiVmYPb4M+9p3FD3wxDzsdt41YLgzF5SSipqEar2BBDzu9OdCmzNTU12LNnD9q1a+fyXadOnbBt2zYAQPfu3XHkyBE9lyJMDOXGEIfvM6vjPCbVI7h+wLoUUpPenxy8pBFmfUnNlNKKam+LQBCqaRERhCB/KwL9LfC3Srtm5iZHYMODQ+AnU04pzj3Ygon5hpzXE+h6An5+foiJicF7773n8t2bb76JpKQkAMCOHTvQoQPtnifqaa5jfoRABq2mhNnS2XoCbhg8s9yDu6zcZrk/pQilbE0ID/SCJAShnBCbH369uz+W39lfUXILUUVWw4ZNXx6bdUczmD9/PqZMmYIPPvgA+fn5Dp/ZrVu34vvvvwcAfPLJJ5RAgWiWcK11+mI7mrOX4d6fHgmFUuX6Am3iQ3FTvwzEhdko373J4IaZe2FiHjYeKqGMaoRPEO+lSZcv92C6ldkrr7wSnTt3xmuvvYadO3fCYrFg0KBB+Oqrr5Ceng4AeOONN/RehiB8nqa+DK3n/ga3TcC4LinolBJhoETuh2EYzL2orbfF4GF/DV3To5AcGYSMOGP83Xyt+nKtWkPbJWJ0HimyBCGFL0/IDUma0LZtW7z44otGnIogmhRGKQBmVST4PrPasVoYzL+8k36BCAc2Pyt+mz3AsI1pvrZJLzUqGFuLSgEANg9kgCMIM8GLC604zqzvYogye+jQIbz66qvYvn07rFYrcnNzMX36dMTFUco8onnjy52DaprVzZoX7mtQ4nPXVPGzNt67L1ucCMJjmNVqogDd09WffvoJWVlZ+PbbbxEWFgabzYYPP/wQbdq0wbp164yQkSB8lqbuWsCl+dxpM8XHXjBFWSEIdfhYE+ehW5mdNWsW5s6di61bt+KDDz7Axx9/jJ07d+Kqq67CnDlzjJCRMDljG1LoFqRHe1kSwps0J8XdzNB7qKeOtFmiGaOlF/DlrkOXm0F1dTW2bduG2bNn84PtWq2YPXs2OnfurFtAgk9yZBCOFFd4WwweD49uj35ZcejTJtbbopgOo/oGX+hjfEFGQju+NtCRLksQ6vA1v3guuuPMBgYG4tChQy7f7d+/H+Hh4XpOT/gIgf5WjOzYosnHUfUmZlUkshPDAACRwf6mlbG54a7X4GuvlyyzBKEOX+7DdSmzDMNgwoQJuPTSS7Fw4UIcPXoUhw8fxueff47Jkydj4sSJRslJED6JUZ1DVkK90hjob65d2cEBftj68DD8de8gn57VE02POtJliWaMlhjgvtyD645m8OKLL+L222/H6NGjUVdXBwAICAjA9OnT8fDDD+sWkODjyzMnQjvju6biXGWNKf2SQ2z13QjVTXPgrvfAHRx9w+jpE0IShGmw+HAnrluZDQ0NxTvvvINnnnkGu3btgsViQVZWFiIifCv4OeE5mtcGFWPuNdDfilv6ZxpyLoLQgq+12qZsmR3WPhGv/LwHiZSelzASX2vkHAyJMwsA0dHR6N69u1GnIwjCx2hekxTC7CQ0YUWvY0okfrmrP+LDbd4WhTAp3N64OXTNqpXZCxcu4KqrrlJUNigoCB9++KFqoQiiqdAcOhHCXLhrUuFrdfnWgZnYcawUV3Rv6W1R3EJ6rDFpignCTkxIgLdF0IxqZZZhGMTGKgvBFBjYdGfG3sLXBpTmTnN6Xc3pXgnzkxwZhK9u6e1tMQjC9Dw9tiM2HSlG/+x4b4uiGdXKrM1mw+uvv+4OWYhmQnNSeprT5CM5KgjbjpZ6WwzCTVC0CoLwHdSMPeO7pWJ8t1T3CeMBDPOZJTwDDSiEWXl8TC6sDIPJPZvmsm5zpzlNzAiiadH0Gy8pswThRprT5CM+PBCvT+7ibTEIgiCIZoa5IrCr5MyZM1i2bBl27tzpbVEIgiC8jp+l+UyeCIIQpzkZUgAfVmZZlsWkSZMwfPhwvPrqq94Wx2PQUp9vMbR9AgAgLzXSu4IQBEEQRBPFZ90MnnrqKdhsNuTm5npbFIIQ5c4h2WgZE4IhbRO8LQpB6II7kW7C+QgIgvBBfNIy+9dff+GVV17BO++8421RCEKSiGB/XNenFdJigr0tCtEMcKeS2dyWLQnCl+FOPpvDiq7PWWZLSkowadIkvPnmm4iLi1P0m8rKSlRWVjo+l5ZS+CBv0hwaFkEQBEEQnsHnLLM33HADRo4ciREjRij+zbx58xAREeH4l5rq2/HUCIIghGBZcgAgCKL54VPK7Ndff40ff/wRQ4cOxbJly7Bs2TKcO3cOhw4dwrJly0Q78rlz56KkpMTx79ChQx6WnCAIgiAIwvM0h8VQn3IzsFgs6Nq1K1544QXHsePHj2Pt2rUoKSnBwIEDBfOS22w22Gw2T4rqNppDpSQIwnyQexBBEGbFp5TZ0aNHY/To0bxjeXl56N+/PxYsWOAdoQjV0KBIEARBEIRR+JSbAdE08LNQtSMId+BOj1krZxYaGeTvxisRBKGX5mY08inLrBA9evRAdna2t8UgFPD02I54YfkuPD2uo7dFIQhCJRYLgy+n9URlTR2iQgK8LQ5BEIQDn1dmX3/9dW+L4FGEfIJ9hfHdUjG+G0WSIAh34e5gBl3To917AYIgDKG5xYWm9V6CIAiCIIgmii8bwZRCyixBEARBEAThs5AySxAEQRAEQfgspMz6GE1/sYAgCIIgCKNoDnoDKbO+RnOolQRBEARBEAohZZYgCIIgCILwWUiZJQiCIAiCaEKwbk2hYj5ImSUIgiAIgmhCcOPMWi1N3z/R55MmNDeafpUkCIIgCEIPAX4WXNUjDecra5ESFeRtcdwOKbMEQRAEQRBNjMfGdPC2CB6D3AwIgiAIgiAIn4WUWYIgCIIgCMJnIWXWx2gOOZYJgiAIgiCUQsosQRAEQRAE4bOQMksQBEEQBEH4LKTMEgRBEARBED4LKbMEQRAEQRCEz0LKLEEQBEEQBOGzkDLrY1AsA4IgCIIgiEZImSUIgiAIgiB8FlJmfQwKM0sQBEEQBNEIKbMEQRAEQRCEz0LKLEEQBEEQBOGzkDLrYzC0BYwgCIIgCMIBKbMEQRAEQRCEz0LKLEEQBEEQBOGzkDJLEARBEARB+CykzPoYFJqLIAiCIAiiET9vC6CFM2fOYN26dfDz80NeXh6ioqK8LRJBEITXYBiAZYHY0ABvi0IQBOFxfMoyy7Isbr75ZnTo0AHPPPMMHnjgAbRs2RLvvvuut0UjCILwGi9MzEeYzQ9PXtbR26IQBEF4HJ+yzLIsi06dOuGFF15AQEC9BeK1117DTTfdhMGDByMtLc3LEhIEQXieSzolYVSHFrBYyA+JIIjmh09ZZi0WC6ZNm+ZQZAFg7NixqKmpwZYtW7woGUEQhHchRZYgiOaKTymzQixbtgwMw6Bdu3aiZSorK1FaWsr756uM65ICAOifHedlSQiCIAiCILyPT7kZOLN//37MnDkTN998M9LT00XLzZs3Dw8//LDnBHMj1/RuhU6pkWjbItzbohAEQRAEQXgdhmVZ1ttCaKGoqAiFhYXIycnBV199BX9/f9GylZWVqKysdHwuLS1FamoqSkpKEB5OSiFBEARBEITZKC0tRUREhKy+5pOW2aKiIgwYMABZWVn48ssvJRVZALDZbLDZbB6SjiAIgiAIgvAUPucze/ToUQwYMACZmZn46quvSEklCIIgCIJoxviUZbayshKDBg1CcXExxo8fj6+//trxXUFBATIyMrwoHUEQBEEQBOFpfEqZraqqQseO9UHBFy9ezPsuISGBlFmCIAiCIIhmhk8ps2FhYfj00091n8e+582XQ3QRBEEQBEE0Zex6mlysAp9SZo2irKwMAJCamuplSQiCIAiCIAgpysrKEBERIfq9z4bm0kNdXR2KiooQFhYGhnF/1hx7KLBDhw5RKDAfhd6hb0Pvz/ehd+j70Dv0bbzx/liWRVlZGZKSkmCxiMcsaJaWWYvFgpSUFI9fNzw8nBqwj0Pv0Leh9+f70Dv0fegd+jaefn9SFlk7PheaiyAIgiAIgiDskDJLEARBEARB+CykzHoAm82Ghx56iBI8+DD0Dn0ben++D71D34feoW9j5vfXLDeAEQRBEARBEE0DsswSBEEQBEEQPgspswRBEARBEITPQsosQRAEQRAE4bOQMutmysvLsW7dOuzdu9fbojR7ysrKsH79ehw7dky0TG1tLTZt2oTNmzejrq7OrWUI7axduxZ//fWX4HdlZWX4+++/sX//ftHfG1WGUA/Lsvj333+xY8cO0TLHjh3D2rVrcfr0abeXIdRRXV2N7du3Y8OGDSgpKREtt2PHDvzzzz+orKx0exlCmhMnTmDVqlU4e/asaJkjR47g77//Nk0Z1bCE2/j444/ZsLAwNisriw0LC2MHDBjAFhcXe1usZsf+/fvZCRMmsJGRkWxeXh4bHh7ODho0iD169Civ3D///MO2bNmSTU5OZhMTE9nWrVuzmzdvdksZQjuffPIJa7FY2JiYGJfv3nrrLTY4OJjNzs5mQ0JC2BEjRrDnzp1zSxlCPcuXL2fT09PZ1NRUNi8vj+3Rowd74MABx/c1NTXsddddxwYGBrLt2rVjbTYb++CDD/LOYVQZQj3ffvstm5iYyLZq1Yrt1KkTGxQUxN511128MkVFRWyXLl3YqKgoNiMjg42JiWF/+OEHt5QhpNm4cSM7ceJENiEhgQXAfv/99y5lqqqq2CuuuIINDAxk27ZtywYGBrJPPvmk18pohZRZN7Fr1y7W39+fffPNN1mWZdmzZ8+y2dnZ7DXXXONlyZofP//8M/vZZ5+xtbW1LMvWv4suXbqwF198saNMVVUVm5GRwU6dOpVlWZatq6tjL7/8cjYnJ8fxO6PKENrZvXs3m5yczN58880uyuymTZtYi8XCfvLJJyzLsuyJEyfY9PR0dsaMGYaXIdSzZcsW1mazsU888YTj2Lp169jVq1c7Pi9YsICNjIxkd+zYwbIsy65cuZL18/Njv/32W8PLEOqoqalhw8PD2TvvvNNx7JdffmEBsD/99JPj2PDhw9levXqxFRUVLMuy7EMPPcSGh4ezJ0+eNLwMIc0nn3zCfvLJJ+zx48dFldnHHnuMjY+PZ/fv38+yLMv++OOPLMMw7PLly71SRiukzLqJBx98kG3RogVbV1fnOPbyyy+zgYGB7Pnz570oGcGyLDt//nw2KirK8fnHH39kAbC7d+92HNuwYQMLgF25cqWhZQhtVFZWsl27dmXff/999plnnnFRZmfNmsVmZmbyjj355JNsREQEW1NTY2gZQj2TJk1iO3XqJFmmY8eO7LRp03jHBg8ezI4ePdrwMoQ6ysrKWIZh2C+//NJx7MKFC7yJ35EjR1iGYdhvvvnGUebcuXNsUFAQ+9prrxlahlDO2bNnRZXZjIwMF+t6jx492CuvvNIrZbRCPrNuYv369ejcuTMYhnEcKygowIULF7B9+3YvSkYA9T6XmZmZjs/r169HREQEWrdu7TjWqVMnBAQEYP369YaWIbQxd+5ctGrVClOnThX8fv369ejSpQvvWEFBAUpKShw+60aVIdSzfPlyjBo1ChUVFVi3bh0OHTrE+766uhpbt24VfPb2tmNUGUI9oaGhuP/++/Hggw/iiy++wNKlSzF58mT07NkTY8aMAQBs2LABLMvynn1ISAjatm3rePZGlSH0U1pair1790q2FU+W0YOf7jMQgpw5c4an0ABATEyM4zvCe3z99df4/PPP8e233zqOnTlzxvF+uMTExDjel1FlCPUsXrwYX3zxBTZu3Cha5syZM2jbti3vmHObM6oMoY7a2lqcOHEChw4dQnZ2NmJiYnDgwAHk5OTg008/RVpaGkpKSlBbW+vSfrhtx6gyhDbGjx+PH3/8EXfddRciIiJw/PhxvPjiiwgKCgLQ2D6knr1RZQj9ePJ9ufudkmXWTfj7++PChQu8YxUVFQCAgIAAb4hEAPj5559x5ZVX4oknnsDFF1/sOC70voD6d2Z/X0aVIdRRWVmJqVOn4oYbbsDWrVuxatUq7N+/HzU1NVi1ahVOnDgBQFmbM6oMoQ6r1QqLxYJFixbht99+w/r163Hw4EHU1dXhpptuAlD/3AEIPnvuuzGiDKGes2fPorCwEIMHD8aBAwewadMmfP3115g8eTK+//57APQOfQ1Pvi93v1NSZt1Ey5YtceTIEd4x++e0tDRviNTs+fXXX3HxxRfj3nvvxZw5c3jftWzZEqdOnUJVVZXjWHl5OUpKShzvy6gyhDqqq6uRlZWFpUuXYs6cOZgzZw4WLVqE8vJyzJkzB+vWrQOgrM0ZVYZQT8uWLTFs2DCkp6cDqF+2vvLKK7Fy5UoAQEREBCIjIwWfvf25G1WGUM8ff/yBM2fOYPr06Y5jvXr1Qn5+vkOZbdmyJQBIPnujyhD6SUxMhM1mk3zOniyjB1Jm3cSQIUPw119/OaxGAPDtt9+iTZs2joZKeI6VK1di5MiRuOeee3D//fe7fD9o0CBUV1djyZIljmPfffcdLBYLBg4caGgZQh2hoaFYtWoV79/06dMRERGBVatWYcSIEQDq29xvv/3Gi3357bffIj8/37G0ZVQZQj3Dhg1zGcgOHz6MuLg4x+fBgwc7FCMAqKmpwaJFizBkyBDDyxDqsL+nw4cPO47V1tbi6NGjju+6dOmC6OhofPfdd44ymzdvxr59+xzP3qgyhH6sVisGDBjAe86VlZVYsmSJ4zl7sowudG8hIwSprq5mO3fuzPbo0YP96quv2Mcff5y1Wq28naCEZ/j777/Z0NBQ9uKLL2ZXrlzJ+8eNNnHzzTeziYmJ7AcffMC+9957bExMDDtr1izeuYwqQ+hDKJpBRUUF265dO7Zfv37sN998wz744IOs1WplFy9ebHgZQj0HDhxgY2Nj2ZkzZ7JLly5l58+f77I7fdOmTWxwcDA7bdo09rvvvmPHjh3LxsfH82JCG1WGUEdNTQ3bq1cvNjs7m/3kk0/YH374gR03bhwbGhrK7tq1y1HulVdeYQMDA9kXXniB/fzzz9m2bduyQ4cO5Z3LqDKENKdOnWJXrlzJLl68mAXAPvXUU+zKlSvZffv2OcqsWbOGtdls7MyZM9nvvvuOHTlyJJuSksKePn3aK2W0wrAsy+pXiQkhiouL8dRTT2Ht2rWIiorC9ddfj2HDhnlbrGbHF198gRdeeEHwuxUrVjj8dWpra/Haa69h0aJFYBgGl1xyCW688UZYLI0LGEaVIfTx6aef4qOPPsLChQt5x0+dOoWnnnoK69evR0xMDKZNm4YBAwa4pQyhnr1792L+/PnYsWMHkpKScNVVV7n0iRs3bsTzzz+PgwcPIjs7G7Nnz0arVq3cUoZQR3l5OV555RX8+eefqKioQHZ2NmbMmOGy2fmLL77Axx9/jPPnz6Nfv36YNWsWgoOD3VKGEOeXX34RXImcNGkSz11k7dq1ePHFF1FUVIS2bdtizpw5SElJ4f3Gk2W0QMosQRAEQRAE4bOQqYggCIIgCILwWUiZJQiCIAiCIHwWUmYJgiAIgiAIn4WUWYIgCIIgCMJnIWWWIAiCIAiC8FlImSUIgiAIgiB8FlJmCYIgCIIgCJ+FlFmCIIgmwsiRI10SSRAEQTR1SJklCIJoAlRUVGDx4sWIjIz0tigEQRAexc/bAhAEQRD6sVqtWLNmDTp06OBtUQiCIDwKWWYJgiCaAB999BFeeeUV2Gw2b4tCEAThUUiZJQiCaAIsW7YMFgt16QRBND+o5yMIgmgCbNy4EXl5ed4WgyAIwuOQMksQBOHjXLhwATt27CBlliCIZgkpswRBED7Oli1bUFdXh06dOnlbFIIgCI9DyixBEISPs2HDBrRq1Qrh4eHeFoUgCMLjkDJLEATh45C/LEEQzRlSZgmC+P927ZgGYBgIgqCDwZ35A7L0gN4cUsQ6ZQbBlVsc4fbeYhb4rae7+/YIAN6rqrHWGnPO21MAPidmAQCI5WYAAEAsMQsAQCwxCwBALDELAEAsMQsAQCwxCwBALDELAEAsMQsAQCwxCwBALDELAEAsMQsAQCwxCwBArAPoqSc6HTlXHQAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_chains(walker=walker1, model=my_model, true_params=true_params)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "2106dc08-96ce-49df-8ae8-5a2cf7f45783", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:05.598567Z", - "iopub.status.busy": "2026-08-11T03:09:05.598415Z", - "iopub.status.idle": "2026-08-11T03:09:05.770222Z", - "shell.execute_reply": "2026-08-11T03:09:05.769551Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_posterior_corner(walker=walker1, true_params=true_params)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "0c8ab69c-5fad-4b2e-a505-b7df7b266a86", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:05.772006Z", - "iopub.status.busy": "2026-08-11T03:09:05.771852Z", - "iopub.status.idle": "2026-08-11T03:09:05.988574Z", - "shell.execute_reply": "2026-08-11T03:09:05.987835Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'option 1: fixed statistical error, systematic ignored')" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_predictive_post(\n", - " walker=walker1, model=my_model, x=x, y_exp=y_exp, y_err=y_stat_err, y_true=y_true\n", - ")\n", - "plt.title(\"option 1: fixed statistical error, systematic ignored\")" - ] - }, - { - "cell_type": "markdown", - "id": "2799a418-343d-428e-a2d2-93bae9444858", - "metadata": {}, - "source": [ - "## Run option 2: unknown statistical error" - ] - }, - { - "cell_type": "markdown", - "id": "57185a88-4e94-445a-af8c-c0da5cc72845", - "metadata": {}, - "source": [ - "We need to come up with a prior for the noise for option 2. We will keep it fairly wide and centered about the reported value." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "3750e61b-6028-4493-8bea-faa5d8ed6bbb", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:05.990533Z", - "iopub.status.busy": "2026-08-11T03:09:05.990369Z", - "iopub.status.idle": "2026-08-11T03:09:05.993357Z", - "shell.execute_reply": "2026-08-11T03:09:05.992820Z" - } - }, - "outputs": [], - "source": [ - "noise_prior = stats.norm(loc=np.log(noise_fraction), scale=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "1424209b-d14f-449e-a433-5265b1162bc0", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:05.994650Z", - "iopub.status.busy": "2026-08-11T03:09:05.994512Z", - "iopub.status.idle": "2026-08-11T03:09:05.996913Z", - "shell.execute_reply": "2026-08-11T03:09:05.996403Z" - } - }, - "outputs": [], - "source": [ - "def proposal_distribution_noise(x, rng):\n", - " return np.atleast_1d(stats.norm.rvs(loc=x, scale=0.1, random_state=rng))" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "396e21a8-3866-4f04-9ad3-8e401ddb8ccd", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:05.998435Z", - "iopub.status.busy": "2026-08-11T03:09:05.998305Z", - "iopub.status.idle": "2026-08-11T03:09:06.001312Z", - "shell.execute_reply": "2026-08-11T03:09:06.000753Z" - } - }, - "outputs": [], - "source": [ - "walker2 = rxmc.walker.Walker(\n", - " model_sampler=rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=my_model.params,\n", - " starting_location=prior_distribution.mean,\n", - " proposal=proposal_distribution_model,\n", - " prior=prior_distribution,\n", - " ),\n", - " evidence=evidence_unknown_stat,\n", - " likelihood_samplers=[\n", - " rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=[log_noise_fraction],\n", - " starting_location=noise_prior.mean(),\n", - " proposal=proposal_distribution_noise,\n", - " prior=noise_prior,\n", - " )\n", - " ],\n", - " rng=rng,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "92db7ab4-b071-4fc0-900c-1dde26ec5438", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:06.002626Z", - "iopub.status.busy": "2026-08-11T03:09:06.002490Z", - "iopub.status.idle": "2026-08-11T03:09:14.493812Z", - "shell.execute_reply": "2026-08-11T03:09:14.493183Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/10 completed, 100 steps.\n", - "Burn-in batch 2/10 completed, 100 steps.\n", - "Burn-in batch 3/10 completed, 100 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 4/10 completed, 100 steps.\n", - "Burn-in batch 5/10 completed, 100 steps.\n", - "Burn-in batch 6/10 completed, 100 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 7/10 completed, 100 steps.\n", - "Burn-in batch 8/10 completed, 100 steps.\n", - "Burn-in batch 9/10 completed, 100 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 10/10 completed, 100 steps.\n", - "Batch: 1/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.350\n", - " Likelihood parameter acceptance fractions: [0.8]\n", - "Batch: 2/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.320\n", - " Likelihood parameter acceptance fractions: [0.78]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 3/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.490\n", - " Likelihood parameter acceptance fractions: [0.86]\n", - "Batch: 4/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.260\n", - " Likelihood parameter acceptance fractions: [0.83]\n", - "Batch: 5/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.390\n", - " Likelihood parameter acceptance fractions: [0.82]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 6/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.280\n", - " Likelihood parameter acceptance fractions: [0.82]\n", - "Batch: 7/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.320\n", - " Likelihood parameter acceptance fractions: [0.81]\n", - "Batch: 8/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.460\n", - " Likelihood parameter acceptance fractions: [0.79]\n", - "Batch: 9/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.360\n", - " Likelihood parameter acceptance fractions: [0.81]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 10/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.490\n", - " Likelihood parameter acceptance fractions: [0.73]\n", - "Batch: 11/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.330\n", - " Likelihood parameter acceptance fractions: [0.85]\n", - "Batch: 12/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.450\n", - " Likelihood parameter acceptance fractions: [0.88]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 13/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.280\n", - " Likelihood parameter acceptance fractions: [0.91]\n", - "Batch: 14/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.300\n", - " Likelihood parameter acceptance fractions: [0.82]\n", - "Batch: 15/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.510\n", - " Likelihood parameter acceptance fractions: [0.79]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 16/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.390\n", - " Likelihood parameter acceptance fractions: [0.85]\n", - "Batch: 17/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.340\n", - " Likelihood parameter acceptance fractions: [0.86]\n", - "Batch: 18/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.310\n", - " Likelihood parameter acceptance fractions: [0.84]\n", - "Batch: 19/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.540\n", - " Likelihood parameter acceptance fractions: [0.89]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 20/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.400\n", - " Likelihood parameter acceptance fractions: [0.79]\n", - "Batch: 21/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.390\n", - " Likelihood parameter acceptance fractions: [0.79]\n", - "Batch: 22/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.420\n", - " Likelihood parameter acceptance fractions: [0.9]\n", - "Batch: 23/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.380\n", - " Likelihood parameter acceptance fractions: [0.82]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 24/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.350\n", - " Likelihood parameter acceptance fractions: [0.8]\n", - "Batch: 25/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.420\n", - " Likelihood parameter acceptance fractions: [0.78]\n", - "Batch: 26/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.380\n", - " Likelihood parameter acceptance fractions: [0.89]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 27/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.370\n", - " Likelihood parameter acceptance fractions: [0.82]\n", - "Batch: 28/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.330\n", - " Likelihood parameter acceptance fractions: [0.83]\n", - "Batch: 29/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.200\n", - " Likelihood parameter acceptance fractions: [0.86]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 30/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.320\n", - " Likelihood parameter acceptance fractions: [0.86]\n", - "Batch: 31/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.420\n", - " Likelihood parameter acceptance fractions: [0.85]\n", - "Batch: 32/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.220\n", - " Likelihood parameter acceptance fractions: [0.85]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 33/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.370\n", - " Likelihood parameter acceptance fractions: [0.85]\n", - "Batch: 34/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.280\n", - " Likelihood parameter acceptance fractions: [0.8]\n", - "Batch: 35/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.340\n", - " Likelihood parameter acceptance fractions: [0.8]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 36/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.310\n", - " Likelihood parameter acceptance fractions: [0.82]\n", - "Batch: 37/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.530\n", - " Likelihood parameter acceptance fractions: [0.83]\n", - "Batch: 38/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.220\n", - " Likelihood parameter acceptance fractions: [0.9]\n", - "Batch: 39/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.440\n", - " Likelihood parameter acceptance fractions: [0.76]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 40/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.410\n", - " Likelihood parameter acceptance fractions: [0.86]\n", - "Batch: 41/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.280\n", - " Likelihood parameter acceptance fractions: [0.88]\n", - "Batch: 42/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.360\n", - " Likelihood parameter acceptance fractions: [0.83]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 43/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.340\n", - " Likelihood parameter acceptance fractions: [0.81]\n", - "Batch: 44/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.330\n", - " Likelihood parameter acceptance fractions: [0.82]\n", - "Batch: 45/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.330\n", - " Likelihood parameter acceptance fractions: [0.83]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 46/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.420\n", - " Likelihood parameter acceptance fractions: [0.84]\n", - "Batch: 47/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.360\n", - " Likelihood parameter acceptance fractions: [0.84]\n", - "Batch: 48/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.290\n", - " Likelihood parameter acceptance fractions: [0.86]\n", - "Batch: 49/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.460\n", - " Likelihood parameter acceptance fractions: [0.86]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 50/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.310\n", - " Likelihood parameter acceptance fractions: [0.84]\n", - 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Likelihood parameter acceptance fractions: [0.84]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 63/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.490\n", - " Likelihood parameter acceptance fractions: [0.84]\n", - "Batch: 64/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.470\n", - " Likelihood parameter acceptance fractions: [0.82]\n", - "Batch: 65/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.310\n", - " Likelihood parameter acceptance fractions: [0.78]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 66/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.300\n", - " Likelihood parameter acceptance fractions: [0.72]\n", - "Batch: 67/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.320\n", - " Likelihood parameter acceptance fractions: [0.81]\n", - "Batch: 68/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.250\n", - " Likelihood parameter acceptance fractions: [0.85]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 69/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.300\n", - " Likelihood parameter acceptance fractions: [0.8]\n", - "Batch: 70/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.270\n", - " Likelihood parameter acceptance fractions: [0.78]\n", - "Batch: 71/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.230\n", - " Likelihood parameter acceptance fractions: [0.75]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 72/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.450\n", - " Likelihood parameter acceptance fractions: [0.79]\n", - "Batch: 73/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.370\n", - " Likelihood parameter acceptance 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acceptance fraction: 0.290\n", - " Likelihood parameter acceptance fractions: [0.82]\n", - "Batch: 80/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.350\n", - " Likelihood parameter acceptance fractions: [0.77]\n", - "Batch: 81/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.370\n", - " Likelihood parameter acceptance fractions: [0.84]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 82/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.390\n", - " Likelihood parameter acceptance fractions: [0.82]\n", - "Batch: 83/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.310\n", - " Likelihood parameter acceptance fractions: [0.86]\n", - "Batch: 84/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.340\n", - " Likelihood parameter acceptance fractions: [0.83]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - 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"name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 91/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.320\n", - " Likelihood parameter acceptance fractions: [0.82]\n", - "Batch: 92/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.360\n", - " Likelihood parameter acceptance fractions: [0.81]\n", - "Batch: 93/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.500\n", - " Likelihood parameter acceptance fractions: [0.81]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 94/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.280\n", - " Likelihood parameter acceptance fractions: [0.89]\n", - "Batch: 95/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.380\n", - " Likelihood parameter acceptance fractions: [0.83]\n", - "Batch: 96/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.310\n", - " Likelihood parameter acceptance fractions: [0.8]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 97/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.290\n", - " Likelihood parameter acceptance fractions: [0.77]\n", - "Batch: 98/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.330\n", - " Likelihood parameter acceptance fractions: [0.88]\n", - "Batch: 99/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.260\n", - " Likelihood parameter acceptance fractions: [0.81]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 100/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.390\n", - " Likelihood parameter acceptance fractions: [0.89]\n", - "CPU times: user 8.51 s, sys: 70.6 ms, total: 8.58 s\n", - "Wall time: 8.49 s\n" - ] - } - ], - "source": [ - "%%time\n", - "walker2.walk(\n", - " n_steps=10000,\n", - " burnin=1000,\n", - " batch_size=100,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "1a119f59-97fb-4791-9a18-3936d8083509", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:14.495247Z", - "iopub.status.busy": "2026-08-11T03:09:14.495087Z", - "iopub.status.idle": "2026-08-11T03:09:14.500168Z", - "shell.execute_reply": "2026-08-11T03:09:14.499467Z" - } - }, - "outputs": [], - "source": [ - "def plot_chains_with_err(walker, model, true_params):\n", - " fig, axes = plt.subplots(\n", - " walker.model_sampler.chain.shape[1] + 3, 1, figsize=(8, 8), sharex=True\n", - " )\n", - " for i in range(walker.model_sampler.chain.shape[1]):\n", - " axes[i].plot(walker.model_sampler.chain[:, i])\n", - " axes[i].set_ylabel(f\"${model.params[i].latex_name}$ [{model.params[i].unit}]\")\n", - " true_value = true_params[model.params[i].name]\n", - " axes[i].hlines(\n", - " true_value, 0, len(walker.model_sampler.chain), \"r\", linestyle=\"--\"\n", - " )\n", - "\n", - " axes[-3].plot(walker.model_sampler.logp_chain)\n", - " axes[-3].set_ylabel(r\"$\\log{\\mathcal{L}(\\alpha_i | \\mathcal{O})}$\")\n", - "\n", - " lmp = walker.likelihood_samplers[0].params[0]\n", - " axes[-2].plot(walker.likelihood_samplers[0].chain)\n", - " axes[-2].set_ylabel(f\"${lmp.latex_name}$ [{lmp.unit}]\")\n", - " axes[-2].hlines(\n", - " np.log(noise_fraction),\n", - " 0,\n", - " len(walker.likelihood_samplers[0].chain),\n", - " \"r\",\n", - " linestyle=\"--\",\n", - " )\n", - "\n", - " axes[-1].plot(walker.likelihood_samplers[0].logp_chain)\n", - " axes[-1].set_ylabel(r\"$\\log{\\mathcal{L}(\\alpha_i | \\mathcal{O})}$\")\n", - "\n", - " axes[-1].set_xlabel(r\"$i$\")\n", - " # plt.legend(title=\"chains\", ncol=3,)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "9f73ec88-faa9-4c74-b677-61706a3f1223", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:14.501674Z", - "iopub.status.busy": "2026-08-11T03:09:14.501524Z", - "iopub.status.idle": "2026-08-11T03:09:14.971962Z", - "shell.execute_reply": "2026-08-11T03:09:14.971410Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_chains_with_err(walker2, my_model, true_params)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "5d0920bf-a6cf-4fbd-a9d4-dcc213b655e3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:14.973831Z", - "iopub.status.busy": "2026-08-11T03:09:14.973674Z", - "iopub.status.idle": "2026-08-11T03:09:15.362339Z", - "shell.execute_reply": "2026-08-11T03:09:15.361416Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'posterior')" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = corner.corner(\n", - " np.hstack([walker2.model_sampler.chain, walker2.likelihood_samplers[0].chain]),\n", - " labels=[p.name for p in my_model.params]\n", - " + [walker2.likelihood_samplers[0].params[0].name],\n", - " label=\"posterior\",\n", - " truths=[true_params[\"m\"], true_params[\"b\"], np.log(noise_fraction)],\n", - ")\n", - "fig.suptitle(\"posterior\")" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "ccee96fa-f877-4cfe-a0bc-d250ca9066b0", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:15.364059Z", - "iopub.status.busy": "2026-08-11T03:09:15.363888Z", - "iopub.status.idle": "2026-08-11T03:09:15.586546Z", - "shell.execute_reply": "2026-08-11T03:09:15.585806Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'option 2: unknown statistical error, systematic ignored')" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_predictive_post(\n", - " walker=walker2, model=my_model, x=x, y_exp=y_exp, y_err=y_stat_err, y_true=y_true\n", - ")\n", - "plt.title(\"option 2: unknown statistical error, systematic ignored\")" - ] - }, - { - "cell_type": "markdown", - "id": "c9732c59", - "metadata": {}, - "source": [ - "## Run option 2b: unknown *constant* noise\n", - "\n", - "Option 2 inferred a **fractional** noise\n", - "($\\Sigma_{ij} = \\delta_{ij}\\,\\epsilon^2 y_m(x_j;\\alpha)^2$, via\n", - "`noise_fraction_term`). Its sibling `noise_term` infers a **constant** noise\n", - "floor,\n", - "\\begin{equation}\n", - "\\Sigma_{ij} = \\delta_{ij}\\, \\epsilon_0^2 .\n", - "\\end{equation}\n", - "Both are *additive* on top of any reported statistical diagonal — so to let the\n", - "inferred noise **replace** the reported statistics, build the `Observation`\n", - "without `y_stat_err` (as `obs_unknown_stat` is built), or pass\n", - "`include_statistical_term=False` to the `Constraint`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "061e55aa", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:15.588203Z", - "iopub.status.busy": "2026-08-11T03:09:15.588033Z", - "iopub.status.idle": "2026-08-11T03:09:15.591783Z", - "shell.execute_reply": "2026-08-11T03:09:15.591325Z" - } - }, - "outputs": [], - "source": [ - "log_noise = rxmc.params.Parameter(\n", - " \"log noise\", float, latex_name=r\"\\log{\\epsilon_0}\", unit=\"dimensionless\"\n", - ")\n", - "evidence_unknown_const = rxmc.evidence.Evidence(\n", - " [\n", - " rxmc.constraint.Constraint(\n", - " [obs_unknown_stat],\n", - " my_model,\n", - " extra_terms=[rxmc.covariance.noise_term(log_noise)],\n", - " )\n", - " ]\n", - ")\n", - "# the true absolute noise scale is noise_fraction * y; center the prior there\n", - "const_noise_prior = stats.norm(loc=np.log(noise_fraction * np.mean(y_exp)), scale=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "dc755e2d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:15.593503Z", - "iopub.status.busy": "2026-08-11T03:09:15.593357Z", - "iopub.status.idle": "2026-08-11T03:09:15.596519Z", - "shell.execute_reply": "2026-08-11T03:09:15.595886Z" - } - }, - "outputs": [], - "source": [ - "walker2b = rxmc.walker.Walker(\n", - " model_sampler=rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=my_model.params,\n", - " starting_location=prior_distribution.mean,\n", - " proposal=proposal_distribution_model,\n", - " prior=prior_distribution,\n", - " ),\n", - " evidence=evidence_unknown_const,\n", - " likelihood_samplers=[\n", - " rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=[log_noise],\n", - " starting_location=const_noise_prior.mean(),\n", - " proposal=proposal_distribution_noise,\n", - " prior=const_noise_prior,\n", - " )\n", - " ],\n", - " rng=rng,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "8c5b3773", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:15.597976Z", - "iopub.status.busy": "2026-08-11T03:09:15.597818Z", - "iopub.status.idle": "2026-08-11T03:09:23.667111Z", - "shell.execute_reply": "2026-08-11T03:09:23.666489Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/10 completed, 100 steps.\n", - "Burn-in batch 2/10 completed, 100 steps.\n", - "Burn-in batch 3/10 completed, 100 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 4/10 completed, 100 steps.\n", - "Burn-in batch 5/10 completed, 100 steps.\n", - "Burn-in batch 6/10 completed, 100 steps.\n", - "Burn-in batch 7/10 completed, 100 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 8/10 completed, 100 steps.\n", - "Burn-in batch 9/10 completed, 100 steps.\n", - "Burn-in batch 10/10 completed, 100 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 1/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.300\n", - " Likelihood parameter acceptance fractions: [0.72]\n", - "Batch: 2/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.490\n", - " Likelihood parameter acceptance fractions: [0.86]\n", - "Batch: 3/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.240\n", - " Likelihood parameter acceptance fractions: [0.83]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 4/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.240\n", - " Likelihood parameter acceptance fractions: [0.87]\n", - "Batch: 5/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.400\n", - " Likelihood parameter acceptance fractions: [0.87]\n", - "Batch: 6/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.320\n", - " Likelihood parameter acceptance fractions: [0.85]\n", - "Batch: 7/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.350\n", - " Likelihood parameter acceptance fractions: [0.84]\n", - "Batch: 8/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.280\n", - " Likelihood parameter acceptance fractions: [0.79]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 9/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.440\n", - " Likelihood parameter acceptance fractions: [0.89]\n", - "Batch: 10/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.350\n", - " Likelihood parameter acceptance fractions: [0.82]\n", - "Batch: 11/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.260\n", - " Likelihood parameter acceptance fractions: [0.85]\n", - "Batch: 12/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.230\n", - " Likelihood parameter acceptance fractions: [0.86]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 13/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.400\n", - " Likelihood parameter acceptance fractions: [0.83]\n", - "Batch: 14/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.530\n", - " Likelihood parameter acceptance fractions: [0.77]\n", - "Batch: 15/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.240\n", - " Likelihood parameter acceptance fractions: [0.84]\n", - "Batch: 16/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.490\n", - " Likelihood parameter acceptance fractions: [0.81]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 17/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.290\n", - " Likelihood parameter acceptance fractions: [0.83]\n", - "Batch: 18/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.410\n", - " Likelihood parameter acceptance fractions: [0.84]\n", - "Batch: 19/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.300\n", - " Likelihood parameter acceptance fractions: [0.82]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 20/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.330\n", - " Likelihood parameter acceptance fractions: [0.83]\n", - "Batch: 21/100 completed, 100 steps. \n", - " 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acceptance fraction: 0.350\n", - " Likelihood parameter acceptance fractions: [0.87]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 34/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.280\n", - " Likelihood parameter acceptance fractions: [0.81]\n", - "Batch: 35/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.300\n", - " Likelihood parameter acceptance fractions: [0.86]\n", - "Batch: 36/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.290\n", - " Likelihood parameter acceptance fractions: [0.87]\n", - "Batch: 37/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.330\n", - " Likelihood parameter acceptance fractions: [0.81]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 38/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.380\n", - " Likelihood parameter acceptance fractions: [0.79]\n", - 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"stream", - "text": [ - "Batch: 97/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.370\n", - " Likelihood parameter acceptance fractions: [0.83]\n", - "Batch: 98/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.450\n", - " Likelihood parameter acceptance fractions: [0.75]\n", - "Batch: 99/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.320\n", - " Likelihood parameter acceptance fractions: [0.79]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 100/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.280\n", - " Likelihood parameter acceptance fractions: [0.89]\n", - "CPU times: user 8.08 s, sys: 98.5 ms, total: 8.18 s\n", - "Wall time: 8.07 s\n" - ] - } - ], - "source": [ - "%%time\n", - "walker2b.walk(\n", - " n_steps=10000,\n", - " burnin=1000,\n", - " batch_size=100,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "0e27db1d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:23.668685Z", - "iopub.status.busy": "2026-08-11T03:09:23.668540Z", - "iopub.status.idle": "2026-08-11T03:09:24.097945Z", - "shell.execute_reply": "2026-08-11T03:09:24.097205Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_chains(walker=walker2b, model=my_model, true_params=true_params)\n", - "plt.figure()\n", - "plt.plot(walker2b.likelihood_samplers[0].chain)\n", - "plt.axhline(np.log(noise_fraction * np.mean(y_exp)), color=\"r\", ls=\"--\")\n", - "plt.ylabel(r\"$\\log{\\epsilon_0}$\")\n", - "plt.xlabel(\"$i$\");" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "2280e6d8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:24.099536Z", - "iopub.status.busy": "2026-08-11T03:09:24.099385Z", - "iopub.status.idle": "2026-08-11T03:09:24.302912Z", - "shell.execute_reply": "2026-08-11T03:09:24.302349Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'option 2b: unknown constant noise, systematic ignored')" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_predictive_post(\n", - " walker=walker2b, model=my_model, x=x, y_exp=y_exp, y_err=y_stat_err, y_true=y_true\n", - ")\n", - "plt.title(\"option 2b: unknown constant noise, systematic ignored\")" - ] - }, - { - "cell_type": "markdown", - "id": "52954ccf-9f53-47d2-aa58-d57ac5cd2394", - "metadata": {}, - "source": [ - "## Run option 3: correct formulation of the systematic error" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "a0642bf9-b6a0-4830-b5b1-ac1994c4e8c9", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:24.304694Z", - "iopub.status.busy": "2026-08-11T03:09:24.304543Z", - "iopub.status.idle": "2026-08-11T03:09:24.307551Z", - "shell.execute_reply": "2026-08-11T03:09:24.306753Z" - } - }, - "outputs": [], - "source": [ - "walker3 = rxmc.walker.Walker(\n", - " model_sampler=rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=my_model.params,\n", - " starting_location=prior_distribution.mean,\n", - " proposal=proposal_distribution_model,\n", - " prior=prior_distribution,\n", - " ),\n", - " evidence=evidence_sys_correct,\n", - " rng=rng,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "1d51307d-67d8-4336-8e23-05fb441eb815", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:24.308991Z", - "iopub.status.busy": "2026-08-11T03:09:24.308869Z", - "iopub.status.idle": "2026-08-11T03:09:28.560186Z", - "shell.execute_reply": "2026-08-11T03:09:28.559514Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/1 completed, 1000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 1/1 completed, 10000 steps. \n", - " Model parameter acceptance fraction: 0.752\n", - "CPU times: user 4.25 s, sys: 7.86 ms, total: 4.25 s\n", - "Wall time: 4.25 s\n" - ] - } - ], - "source": [ - "%%time\n", - "walker3.walk(n_steps=10000, burnin=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "dcc445eb-6592-4973-8943-9fe17110ee57", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:28.561691Z", - "iopub.status.busy": "2026-08-11T03:09:28.561553Z", - "iopub.status.idle": "2026-08-11T03:09:28.851942Z", - "shell.execute_reply": "2026-08-11T03:09:28.851222Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_chains(walker=walker3, model=my_model, true_params=true_params)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "8aed8b4b-50f2-4752-a61c-7fab12e179c5", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:28.853382Z", - "iopub.status.busy": "2026-08-11T03:09:28.853241Z", - "iopub.status.idle": "2026-08-11T03:09:29.011651Z", - "shell.execute_reply": "2026-08-11T03:09:29.010961Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_posterior_corner(walker=walker3, true_params=true_params)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "30e58a3f-a243-4ce9-bc27-b0336f1c596b", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:29.013063Z", - "iopub.status.busy": "2026-08-11T03:09:29.012906Z", - "iopub.status.idle": "2026-08-11T03:09:29.220335Z", - "shell.execute_reply": "2026-08-11T03:09:29.219575Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'option 3: systematic included correctly')" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_predictive_post(\n", - " walker=walker3, model=my_model, x=x, y_exp=y_exp, y_err=y_stat_err, y_true=y_true\n", - ")\n", - "plt.title(\"option 3: systematic included correctly\")" - ] - }, - { - "cell_type": "markdown", - "id": "b367eb21-44ab-4146-bbf6-8ad8d0806f67", - "metadata": {}, - "source": [ - "## Run option 4: incorrect formulation of the systematic error" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "d731b920-451d-4884-aa1a-9fbdb6db2361", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:29.221844Z", - "iopub.status.busy": "2026-08-11T03:09:29.221671Z", - "iopub.status.idle": "2026-08-11T03:09:29.224360Z", - "shell.execute_reply": "2026-08-11T03:09:29.223751Z" - } - }, - "outputs": [], - "source": [ - "walker4 = rxmc.walker.Walker(\n", - " model_sampler=rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=my_model.params,\n", - " starting_location=prior_distribution.mean,\n", - " proposal=proposal_distribution_model,\n", - " prior=prior_distribution,\n", - " ),\n", - " evidence=evidence_sys_wrong,\n", - " rng=rng,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "cc8396c2-f7de-4047-92ff-a7e4e30c782d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:29.225791Z", - "iopub.status.busy": "2026-08-11T03:09:29.225632Z", - "iopub.status.idle": "2026-08-11T03:09:32.595207Z", - "shell.execute_reply": "2026-08-11T03:09:32.594603Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/1 completed, 1000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 1/1 completed, 10000 steps. \n", - " Model parameter acceptance fraction: 0.735\n", - "CPU times: user 3.37 s, sys: 40.1 ms, total: 3.41 s\n", - "Wall time: 3.37 s\n" - ] - } - ], - "source": [ - "%%time\n", - "walker4.walk(n_steps=10000, burnin=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "6b19450a-7289-4441-ac48-89ebaa03575c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:32.596660Z", - "iopub.status.busy": "2026-08-11T03:09:32.596527Z", - "iopub.status.idle": "2026-08-11T03:09:32.881801Z", - "shell.execute_reply": "2026-08-11T03:09:32.881124Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_chains(walker=walker4, model=my_model, true_params=true_params)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "fce5ffbc-b15c-4fc8-89ab-0d4a5e92dcc3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:32.883231Z", - "iopub.status.busy": "2026-08-11T03:09:32.883080Z", - "iopub.status.idle": "2026-08-11T03:09:33.130689Z", - "shell.execute_reply": "2026-08-11T03:09:33.130095Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_posterior_corner(walker=walker4, true_params=true_params)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "647aa074-8511-4a2c-843f-4c11424938e1", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:33.132085Z", - "iopub.status.busy": "2026-08-11T03:09:33.131944Z", - "iopub.status.idle": "2026-08-11T03:09:33.339384Z", - "shell.execute_reply": "2026-08-11T03:09:33.338730Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'option 4: systematic included incorrectly')" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_predictive_post(\n", - " walker=walker4, model=my_model, x=x, y_exp=y_exp, y_err=y_stat_err, y_true=y_true\n", - ")\n", - "plt.title(\"option 4: systematic included incorrectly\")" - ] - }, - { - "cell_type": "markdown", - "id": "09070172", - "metadata": {}, - "source": [ - "## Run option 5: an additive offset systematic\n", - "\n", - "A different systematic: the whole dataset is shifted by one unknown **additive\n", - "offset** (think background mis-subtraction), rather than rescaled. The reported\n", - "offset uncertainty $\\omega$ enters as a rank-one mode with a constant basis,\n", - "\\begin{equation}\n", - "\\Sigma_{ij} = \\delta_{ij}\\,\\sigma_{stat,i}^2 + \\omega^2 ,\n", - "\\end{equation}\n", - "via `offset_term(magnitude=...)`. With `parameter=` instead of\n", - "`magnitude=`, the magnitude becomes a free nuisance ($\\omega = e^{\\theta}$),\n", - "exactly parallel to options 2 and 2b. As with the normalization bias above, we\n", - "shift the data by one standard deviation of the reported offset error, and\n", - "compare accounting for it against ignoring it.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "aa75c73f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:33.340864Z", - "iopub.status.busy": "2026-08-11T03:09:33.340718Z", - "iopub.status.idle": "2026-08-11T03:09:33.345858Z", - "shell.execute_reply": "2026-08-11T03:09:33.345285Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "free-offset constraint has 1 nuisance parameter(s)\n" - ] - } - ], - "source": [ - "offset_syst_err = 0.3\n", - "# one common additive shift, chosen 1 std deviation of the offset error above 0\n", - "delta = offset_syst_err\n", - "y_exp_off = y_true + rng.normal(scale=noise_fraction * y_true, size=len(x)) + delta\n", - "y_stat_err_off = noise_fraction * y_exp_off\n", - "\n", - "obs_offset = rxmc.observation.Observation(x=x, y=y_exp_off, y_stat_err=y_stat_err_off)\n", - "obs_offset_ignored = rxmc.observation.Observation(\n", - " x=x, y=y_exp_off, y_stat_err=y_stat_err_off\n", - ")\n", - "\n", - "evidence_offset = rxmc.evidence.Evidence(\n", - " [\n", - " rxmc.constraint.Constraint(\n", - " [obs_offset],\n", - " my_model,\n", - " extra_terms=[rxmc.covariance.offset_term(magnitude=offset_syst_err)],\n", - " )\n", - " ]\n", - ")\n", - "evidence_offset_ignored = rxmc.evidence.Evidence(\n", - " [rxmc.constraint.Constraint([obs_offset_ignored], my_model, likelihood)]\n", - ")\n", - "\n", - "# the free-nuisance spelling of the same mode (constructed, not fit here)\n", - "log_omega = rxmc.params.Parameter(\"log omega\", float, latex_name=r\"\\log{\\omega}\")\n", - "free_offset = rxmc.constraint.Constraint(\n", - " [rxmc.observation.Observation(x=x, y=y_exp_off, y_stat_err=y_stat_err_off)],\n", - " my_model,\n", - " extra_terms=[rxmc.covariance.offset_term(parameter=log_omega)],\n", - ")\n", - "print(f\"free-offset constraint has {free_offset.n_params} nuisance parameter(s)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "fd5dc9b2", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:33.347138Z", - "iopub.status.busy": "2026-08-11T03:09:33.347009Z", - "iopub.status.idle": "2026-08-11T03:09:40.111772Z", - "shell.execute_reply": "2026-08-11T03:09:40.111052Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/10 completed, 100 steps.\n", - "Burn-in batch 2/10 completed, 100 steps.\n", - "Burn-in batch 3/10 completed, 100 steps.\n", - "Burn-in batch 4/10 completed, 100 steps.\n", - "Burn-in batch 5/10 completed, 100 steps.\n", - "Burn-in batch 6/10 completed, 100 steps.\n", - "Burn-in batch 7/10 completed, 100 steps.\n", - "Burn-in batch 8/10 completed, 100 steps.\n", - "Burn-in batch 9/10 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}, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 79/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.360\n", - "Batch: 80/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.460\n", - "Batch: 81/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.410\n", - "Batch: 82/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.480\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 83/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.440\n", - "Batch: 84/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.420\n", - "Batch: 85/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.340\n", - "Batch: 86/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.450\n", - "Batch: 87/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.420\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 88/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.470\n", - "Batch: 89/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.400\n", - "Batch: 90/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.470\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 91/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.460\n", - "Batch: 92/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.340\n", - "Batch: 93/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.370\n", - "Batch: 94/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.390\n", - "Batch: 95/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.410\n", - "Batch: 96/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.430\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 97/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.410\n", - "Batch: 98/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.440\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 99/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.470\n", - "Batch: 100/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.400\n", - "CPU times: user 6.78 s, sys: 83.6 ms, total: 6.87 s\n", - "Wall time: 6.76 s\n" - ] - } - ], - "source": [ - "%%time\n", - "walker5 = rxmc.walker.Walker(\n", - " rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=my_model.params,\n", - " starting_location=prior_distribution.mean,\n", - " proposal=proposal_distribution_model,\n", - " prior=prior_distribution,\n", - " ),\n", - " evidence_offset,\n", - " rng=rng,\n", - ")\n", - "walker5i = rxmc.walker.Walker(\n", - " rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=my_model.params,\n", - " starting_location=prior_distribution.mean,\n", - " proposal=proposal_distribution_model,\n", - " prior=prior_distribution,\n", - " ),\n", - " evidence_offset_ignored,\n", - " rng=rng,\n", - ")\n", - "walker5.walk(n_steps=10000, burnin=1000, batch_size=100)\n", - "walker5i.walk(n_steps=10000, burnin=1000, batch_size=100)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "fe674eb4", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:40.113236Z", - "iopub.status.busy": "2026-08-11T03:09:40.113093Z", - "iopub.status.idle": "2026-08-11T03:09:40.317497Z", - "shell.execute_reply": "2026-08-11T03:09:40.316800Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "offset accounted m = 0.473 ± 0.108 b = 2.357 ± 0.299\n", - "offset ignored m = 0.472 ± 0.099 b = 2.332 ± 0.056\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for name, w in [(\"offset accounted\", walker5), (\"offset ignored\", walker5i)]:\n", - " ch = w.model_sampler.chain\n", - " print(\n", - " f\"{name:18s} m = {ch[:, 0].mean():.3f} ± {ch[:, 0].std():.3f} \"\n", - " f\"b = {ch[:, 1].mean():.3f} ± {ch[:, 1].std():.3f}\"\n", - " )\n", - "\n", - "fig = corner.corner(\n", - " walker5.model_sampler.chain,\n", - " labels=[\"m\", \"b\"],\n", - " truths=[true_params[\"m\"], true_params[\"b\"]],\n", - " truth_color=\"k\",\n", - " color=\"tab:blue\",\n", - ")\n", - "corner.corner(walker5i.model_sampler.chain, fig=fig, color=\"tab:red\")\n", - "plt.plot([], [], color=\"tab:blue\", label=\"offset accounted\")\n", - "plt.plot([], [], color=\"tab:red\", label=\"offset ignored\")\n", - "fig.legend(loc=\"upper right\");" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "771d2caf", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:40.318840Z", - "iopub.status.busy": "2026-08-11T03:09:40.318702Z", - "iopub.status.idle": "2026-08-11T03:09:40.539397Z", - "shell.execute_reply": "2026-08-11T03:09:40.538767Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'option 5: additive offset accounted via offset_term')" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_predictive_post(\n", - " walker=walker5,\n", - " model=my_model,\n", - " x=x,\n", - " y_exp=y_exp_off,\n", - " y_err=y_stat_err_off,\n", - " y_true=y_true,\n", - ")\n", - "plt.title(\"option 5: additive offset accounted via offset_term\")" - ] - }, - { - "cell_type": "markdown", - "id": "078dd172-bbc9-4a2a-8141-dca13b7cc1a0", - "metadata": {}, - "source": [ - "# Multiple constraints\n", - "Let's choose a second constraint, with the same normalization bias in the opposite direction and the same coverage over the $x$-domain." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "70caf730-cc72-4d96-a3f2-6f1eced45e3b", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:40.540921Z", - "iopub.status.busy": "2026-08-11T03:09:40.540776Z", - "iopub.status.idle": "2026-08-11T03:09:40.544039Z", - "shell.execute_reply": "2026-08-11T03:09:40.543430Z" - } - }, - "outputs": [], - "source": [ - "systematic_fractional_err2 = 0.1\n", - "# choose a normalization 1 std deviation above the mean this time\n", - "N2 = 1 + systematic_fractional_err2\n", - "noise_fraction2 = 0.025\n", - "x2 = np.linspace(0.01, 0.8, 27, dtype=float)\n", - "y_true2 = my_model.y(x2, *list(true_params.values()))\n", - "y_exp2 = (y_true2 + rng.normal(scale=noise_fraction2 * y_true2, size=len(x2))) * N2\n", - "y_stat_err2 = noise_fraction2 * y_exp2 * N2" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "29594275-7fa2-48f0-acd0-ff7bac4df1c3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:40.545318Z", - "iopub.status.busy": "2026-08-11T03:09:40.545187Z", - "iopub.status.idle": "2026-08-11T03:09:40.547746Z", - "shell.execute_reply": "2026-08-11T03:09:40.547062Z" - } - }, - "outputs": [], - "source": [ - "x_full = np.linspace(-1, 2, 100)\n", - "y_true_full = my_model.y(x_full, *list(true_params.values()))" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "74d3bc67-aaaf-48a0-82ee-abc34bb72599", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:40.549000Z", - "iopub.status.busy": "2026-08-11T03:09:40.548884Z", - "iopub.status.idle": "2026-08-11T03:09:40.718650Z", - "shell.execute_reply": "2026-08-11T03:09:40.718004Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'multiple experimental constraint with opposite bias')" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.errorbar(\n", - " x,\n", - " y_exp,\n", - " y_stat_err,\n", - " marker=\"o\",\n", - " linestyle=\"none\",\n", - " label=\"experiment 1\",\n", - " color=\"tab:purple\",\n", - ")\n", - "plt.errorbar(\n", - " x2,\n", - " y_exp2,\n", - " y_stat_err2,\n", - " marker=\"o\",\n", - " linestyle=\"none\",\n", - " label=\"experiment 2\",\n", - " color=\"tab:cyan\",\n", - ")\n", - "\n", - "plt.plot(x_full, y_true_full, \"k--\", label=\"truth\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.legend()\n", - "plt.title(\"multiple experimental constraint with opposite bias\")" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "87e72ee0-7265-4bee-99ae-38f88f56ac3a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:40.720200Z", - "iopub.status.busy": "2026-08-11T03:09:40.720056Z", - "iopub.status.idle": "2026-08-11T03:09:40.723567Z", - "shell.execute_reply": "2026-08-11T03:09:40.722970Z" - } - }, - "outputs": [], - "source": [ - "# 1 and 2\n", - "obs_stat_only2 = rxmc.observation.Observation(x=x2, y=y_exp2, y_stat_err=y_stat_err2)\n", - "obs_unknown_stat2 = rxmc.observation.Observation(x=x2, y=y_exp2)\n", - "\n", - "# 3\n", - "obs_sys_norm_correct2 = rxmc.observation.Observation(\n", - " x=x2, y=y_exp2, y_stat_err=y_stat_err2\n", - ")\n", - "\n", - "# 4\n", - "obs_sys_norm_wrong2 = rxmc.observation.Observation(x=x2, y=y_exp2)\n", - "wrong_cov2 = np.diag(y_stat_err2**2) + systematic_fractional_err2**2 * np.outer(\n", - " y_exp2, y_exp2\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "31130c87-281b-461f-bbba-12e10af0dc0f", - "metadata": {}, - "source": [ - "## set up likelihood models and constraints" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "2d1be262-56ab-4c30-b39f-b4ee2ced788e", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:40.724958Z", - "iopub.status.busy": "2026-08-11T03:09:40.724799Z", - "iopub.status.idle": "2026-08-11T03:09:40.729780Z", - "shell.execute_reply": "2026-08-11T03:09:40.729184Z" - } - }, - "outputs": [], - "source": [ - "N1 = obs_stat_only.n_data_pts\n", - "N2 = obs_stat_only2.n_data_pts\n", - "s1 = np.arange(N1)\n", - "s2 = np.arange(N1, N1 + N2)\n", - "\n", - "# 1\n", - "evidence_stat_only = rxmc.evidence.Evidence(\n", - " [rxmc.constraint.Constraint([obs_stat_only, obs_stat_only2], my_model, likelihood)]\n", - ")\n", - "\n", - "# 2 (shared noise-fraction parameter across both datasets -> case B)\n", - "evidence_unknown_stat = rxmc.evidence.Evidence(\n", - " [\n", - " rxmc.constraint.Constraint(\n", - " [obs_unknown_stat, obs_unknown_stat2],\n", - " my_model,\n", - " extra_terms=[\n", - " rxmc.covariance.noise_fraction_term(log_noise_fraction, support=s1),\n", - " rxmc.covariance.noise_fraction_term(log_noise_fraction, support=s2),\n", - " ],\n", - " )\n", - " ]\n", - ")\n", - "\n", - "# 3\n", - "evidence_sys_correct = rxmc.evidence.Evidence(\n", - " [\n", - " rxmc.constraint.Constraint(\n", - " [obs_sys_norm_correct, obs_sys_norm_correct2],\n", - " my_model,\n", - " extra_terms=[\n", - " rxmc.covariance.normalization_term(\n", - " magnitude=systematic_fractional_err,\n", - " support=s1,\n", - " ),\n", - " rxmc.covariance.normalization_term(\n", - " magnitude=systematic_fractional_err2,\n", - " support=s2,\n", - " ),\n", - " ],\n", - " )\n", - " ]\n", - ")\n", - "\n", - "# 4\n", - "evidence_sys_wrong = rxmc.evidence.Evidence(\n", - " [\n", - " rxmc.constraint.Constraint(\n", - " [obs_sys_norm_wrong, obs_sys_norm_wrong2],\n", - " my_model,\n", - " extra_terms=[\n", - " rxmc.covariance.Term(wrong_cov, support=s1),\n", - " rxmc.covariance.Term(wrong_cov2, support=s2),\n", - " ],\n", - " )\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "2f0f6a89-f6c8-4306-bec5-95257ab685e2", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:40.731148Z", - "iopub.status.busy": "2026-08-11T03:09:40.731020Z", - "iopub.status.idle": "2026-08-11T03:09:40.733637Z", - "shell.execute_reply": "2026-08-11T03:09:40.732868Z" - } - }, - "outputs": [], - "source": [ - "walker1 = rxmc.walker.Walker(\n", - " model_sampler=rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=my_model.params,\n", - " starting_location=prior_distribution.mean,\n", - " proposal=proposal_distribution_model,\n", - " prior=prior_distribution,\n", - " ),\n", - " evidence=evidence_stat_only,\n", - " rng=rng,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "d56972b8-c1ca-4e24-b18b-18661e5c1230", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:40.734879Z", - "iopub.status.busy": "2026-08-11T03:09:40.734763Z", - "iopub.status.idle": "2026-08-11T03:09:44.461818Z", - "shell.execute_reply": "2026-08-11T03:09:44.461059Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/1 completed, 1000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 1/1 completed, 10000 steps. \n", - " Model parameter acceptance fraction: 0.151\n", - "CPU times: user 3.72 s, sys: 18.9 ms, total: 3.74 s\n", - "Wall time: 3.72 s\n" - ] - } - ], - "source": [ - "%%time\n", - "walker1.walk(n_steps=10000, burnin=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "756f57d6-b493-4cde-adda-599fc6a34c95", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:44.463328Z", - "iopub.status.busy": "2026-08-11T03:09:44.463156Z", - "iopub.status.idle": "2026-08-11T03:09:44.515567Z", - "shell.execute_reply": "2026-08-11T03:09:44.514991Z" - } - }, - "outputs": [], - "source": [ - "upper1, med1, lower1 = np.percentile(\n", - " [my_model.y(x_full, *p) for p in walker1.model_sampler.chain],\n", - " [5, 50, 95],\n", - " axis=0,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "18cb26c9-6240-4c58-a157-eaa97eaa1bfd", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:44.517091Z", - "iopub.status.busy": "2026-08-11T03:09:44.516956Z", - "iopub.status.idle": "2026-08-11T03:09:44.520148Z", - "shell.execute_reply": "2026-08-11T03:09:44.519511Z" - } - }, - "outputs": [], - "source": [ - "walker2 = rxmc.walker.Walker(\n", - " model_sampler=rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=my_model.params,\n", - " starting_location=prior_distribution.mean,\n", - " proposal=proposal_distribution_model,\n", - " prior=prior_distribution,\n", - " ),\n", - " evidence=evidence_unknown_stat,\n", - " likelihood_samplers=[\n", - " rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=[log_noise_fraction],\n", - " starting_location=noise_prior.mean(),\n", - " proposal=proposal_distribution_noise,\n", - " prior=noise_prior,\n", - " )\n", - " ],\n", - " rng=rng,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "6326cf53-4e85-428c-9a22-50e377b4d8b0", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:44.521481Z", - "iopub.status.busy": "2026-08-11T03:09:44.521362Z", - "iopub.status.idle": "2026-08-11T03:09:54.296171Z", - "shell.execute_reply": "2026-08-11T03:09:54.295520Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/10 completed, 100 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 2/10 completed, 100 steps.\n", - "Burn-in batch 3/10 completed, 100 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 4/10 completed, 100 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 5/10 completed, 100 steps.\n", - "Burn-in batch 6/10 completed, 100 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 7/10 completed, 100 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 8/10 completed, 100 steps.\n", - "Burn-in batch 9/10 completed, 100 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 10/10 completed, 100 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 1/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.530\n", - " Likelihood parameter acceptance fractions: [0.67]\n", - "Batch: 2/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.530\n", - " Likelihood parameter acceptance fractions: [0.75]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 3/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.530\n", - " Likelihood parameter acceptance fractions: [0.75]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 4/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.530\n", - " Likelihood parameter acceptance fractions: [0.67]\n", - "Batch: 5/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.460\n", - " Likelihood parameter acceptance fractions: [0.81]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 6/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.480\n", - " Likelihood parameter acceptance fractions: [0.69]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 7/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.510\n", - " Likelihood parameter acceptance fractions: [0.75]\n", - "Batch: 8/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.500\n", - " Likelihood parameter acceptance fractions: [0.69]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 9/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.490\n", - " Likelihood parameter acceptance fractions: [0.75]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 10/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.350\n", - " Likelihood parameter acceptance fractions: [0.66]\n", - "Batch: 11/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.500\n", - " Likelihood parameter acceptance fractions: [0.74]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 12/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.380\n", - " Likelihood parameter acceptance fractions: [0.62]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 13/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.480\n", - " Likelihood parameter acceptance fractions: [0.77]\n", - "Batch: 14/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.440\n", - " Likelihood parameter acceptance fractions: [0.73]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 15/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.470\n", - " Likelihood parameter acceptance fractions: [0.78]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 16/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.480\n", - " Likelihood parameter acceptance fractions: [0.73]\n", - "Batch: 17/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.510\n", - " Likelihood parameter acceptance fractions: [0.77]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 18/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.480\n", - " Likelihood parameter acceptance fractions: [0.77]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - 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steps. \n", - " Model parameter acceptance fraction: 0.510\n", - " Likelihood parameter acceptance fractions: [0.69]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 30/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.230\n", - " Likelihood parameter acceptance fractions: [0.79]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 31/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.430\n", - " Likelihood parameter acceptance fractions: [0.81]\n", - "Batch: 32/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.560\n", - " Likelihood parameter acceptance fractions: [0.75]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 33/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.430\n", - " Likelihood parameter acceptance fractions: [0.74]\n" - ] - }, - { - "name": "stdout", - "output_type": 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"Batch: 39/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.570\n", - " Likelihood parameter acceptance fractions: [0.76]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 40/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.490\n", - " Likelihood parameter acceptance fractions: [0.64]\n", - "Batch: 41/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.480\n", - " Likelihood parameter acceptance fractions: [0.75]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 42/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.470\n", - " Likelihood parameter acceptance fractions: [0.74]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 43/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.430\n", - " Likelihood parameter acceptance fractions: [0.81]\n", - "Batch: 44/100 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] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 79/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.440\n", - " Likelihood parameter acceptance fractions: [0.69]\n", - "Batch: 80/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.460\n", - " Likelihood parameter acceptance fractions: [0.77]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 81/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.370\n", - " Likelihood parameter acceptance fractions: [0.72]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 82/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.470\n", - " Likelihood parameter acceptance fractions: [0.78]\n", - "Batch: 83/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.530\n", - " Likelihood parameter acceptance fractions: [0.76]\n" - ] - }, - { - "name": 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[0.75]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 94/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.400\n", - " Likelihood parameter acceptance fractions: [0.73]\n", - "Batch: 95/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.380\n", - " Likelihood parameter acceptance fractions: [0.76]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 96/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.550\n", - " Likelihood parameter acceptance fractions: [0.72]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 97/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.430\n", - " Likelihood parameter acceptance fractions: [0.72]\n", - "Batch: 98/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.350\n", - " Likelihood parameter acceptance fractions: [0.61]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 99/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.420\n", - " Likelihood parameter acceptance fractions: [0.72]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 100/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.610\n", - " Likelihood parameter acceptance fractions: [0.73]\n", - "CPU times: user 9.78 s, sys: 10.8 ms, total: 9.79 s\n", - "Wall time: 9.77 s\n" - ] - } - ], - "source": [ - "%%time\n", - "walker2.walk(\n", - " n_steps=10000,\n", - " burnin=1000,\n", - " batch_size=100,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "e05be7bb-9dbf-4b3e-989e-4e585d6af958", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:54.297672Z", - "iopub.status.busy": "2026-08-11T03:09:54.297537Z", - "iopub.status.idle": "2026-08-11T03:09:54.351090Z", - "shell.execute_reply": "2026-08-11T03:09:54.350419Z" - } - }, - "outputs": [], - "source": [ - "upper2, med2, lower2 = np.percentile(\n", - " [my_model.y(x_full, *p) for p in walker2.model_sampler.chain],\n", - " [5, 50, 95],\n", - " axis=0,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "46043bca-0a01-45b3-b404-33be8cf82dbf", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:54.352464Z", - "iopub.status.busy": "2026-08-11T03:09:54.352340Z", - "iopub.status.idle": "2026-08-11T03:09:54.355183Z", - "shell.execute_reply": "2026-08-11T03:09:54.354366Z" - } - }, - "outputs": [], - "source": [ - "walker3 = rxmc.walker.Walker(\n", - " model_sampler=rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=my_model.params,\n", - " starting_location=prior_distribution.mean,\n", - " proposal=proposal_distribution_model,\n", - " prior=prior_distribution,\n", - " ),\n", - " evidence=evidence_sys_correct,\n", - " rng=rng,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "4d0484c6-2cac-4205-9e7a-524dc2c0df26", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:54.356461Z", - "iopub.status.busy": "2026-08-11T03:09:54.356343Z", - "iopub.status.idle": "2026-08-11T03:09:59.757763Z", - "shell.execute_reply": "2026-08-11T03:09:59.757135Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/1 completed, 1000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 1/1 completed, 10000 steps. \n", - " Model parameter acceptance fraction: 0.592\n", - "CPU times: user 5.4 s, sys: 841 μs, total: 5.4 s\n", - "Wall time: 5.4 s\n" - ] - } - ], - "source": [ - "%%time\n", - "walker3.walk(n_steps=10000, burnin=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "4ecd35ac-5852-4a02-91a3-d149b9f5dea6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:59.759151Z", - "iopub.status.busy": "2026-08-11T03:09:59.758999Z", - "iopub.status.idle": "2026-08-11T03:09:59.810849Z", - "shell.execute_reply": "2026-08-11T03:09:59.809999Z" - } - }, - "outputs": [], - "source": [ - "upper3, med3, lower3 = np.percentile(\n", - " [my_model.y(x_full, *p) for p in walker3.model_sampler.chain],\n", - " [5, 50, 95],\n", - " axis=0,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "dbf399dd-c2b7-49fb-898e-ee5994b6f086", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:59.812312Z", - "iopub.status.busy": "2026-08-11T03:09:59.812170Z", - "iopub.status.idle": "2026-08-11T03:09:59.814898Z", - "shell.execute_reply": "2026-08-11T03:09:59.814223Z" - } - }, - "outputs": [], - "source": [ - "walker4 = rxmc.walker.Walker(\n", - " model_sampler=rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=my_model.params,\n", - " starting_location=prior_distribution.mean,\n", - " proposal=proposal_distribution_model,\n", - " prior=prior_distribution,\n", - " ),\n", - " evidence=evidence_sys_wrong,\n", - " rng=rng,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "ced1d78c-42b4-474c-84ca-e5d54d2c0f15", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:09:59.816292Z", - "iopub.status.busy": "2026-08-11T03:09:59.816155Z", - "iopub.status.idle": "2026-08-11T03:10:03.452356Z", - "shell.execute_reply": "2026-08-11T03:10:03.451766Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/1 completed, 1000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 1/1 completed, 10000 steps. \n", - " Model parameter acceptance fraction: 0.578\n", - "CPU times: user 3.63 s, sys: 30.1 ms, total: 3.66 s\n", - "Wall time: 3.63 s\n" - ] - } - ], - "source": [ - "%%time\n", - "walker4.walk(n_steps=10000, burnin=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "8670fc09-6a92-4231-ae5f-2710836d736d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:03.453827Z", - "iopub.status.busy": "2026-08-11T03:10:03.453695Z", - "iopub.status.idle": "2026-08-11T03:10:03.505158Z", - "shell.execute_reply": "2026-08-11T03:10:03.504394Z" - } - }, - "outputs": [], - "source": [ - "upper4, med4, lower4 = np.percentile(\n", - " [my_model.y(x_full, *p) for p in walker4.model_sampler.chain],\n", - " [5, 50, 95],\n", - " axis=0,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "92988fb9-05bc-4223-8ae0-e7f620047ca7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:03.506585Z", - "iopub.status.busy": "2026-08-11T03:10:03.506407Z", - "iopub.status.idle": "2026-08-11T03:10:03.709366Z", - "shell.execute_reply": "2026-08-11T03:10:03.708598Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'multiple constraints with systematic normalization error')" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.errorbar(\n", - " x,\n", - " y_exp,\n", - " y_stat_err,\n", - " marker=\".\",\n", - " linestyle=\"none\",\n", - " label=\"experiment 1\",\n", - " color=\"tab:purple\",\n", - " zorder=999,\n", - ")\n", - "plt.errorbar(\n", - " x2,\n", - " y_exp2,\n", - " y_stat_err2,\n", - " marker=\".\",\n", - " linestyle=\"none\",\n", - " label=\"experiment 2\",\n", - " color=\"tab:cyan\",\n", - " zorder=999,\n", - ")\n", - "p = plt.fill_between(x_full, lower1, upper1, label=\"stat only\", alpha=0.5, zorder=99)\n", - "plt.plot(x_full, med1, \"--\", color=p.get_facecolor(), alpha=1, zorder=100)\n", - "\n", - "\n", - "p = plt.fill_between(x_full, lower2, upper2, label=\"stat fit\", alpha=0.5, zorder=89)\n", - "plt.plot(x_full, med2, \"--\", color=p.get_facecolor(), alpha=1, zorder=90)\n", - "\n", - "\n", - "p = plt.fill_between(x_full, lower3, upper3, label=\"full covariance\", alpha=0.5)\n", - "plt.plot(x_full, med3, \"--\", color=p.get_facecolor(), alpha=1, zorder=89)\n", - "\n", - "p = plt.fill_between(x_full, lower4, upper4, label=\"full covariance, wrong\", alpha=0.25)\n", - "plt.plot(x_full, med4, \"--\", color=p.get_facecolor(), alpha=0.5)\n", - "\n", - "\n", - "plt.plot(x_full, y_true_full, \"k--\", label=\"truth\", zorder=999)\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.legend()\n", - "plt.title(\"multiple constraints with systematic normalization error\")" - ] - }, - { - "cell_type": "markdown", - "id": "2ad6cfa0-f9f7-4b5a-bdab-af56cffc1627", - "metadata": {}, - "source": [ - "# Multiple constraints with offset domain\n", - "Let's choose a second constraint, with the same normalization bias in the opposite direction and slightly different coverage opver $x$." - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "48e2d2b2-6199-49e3-a312-df82a796a3c0", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:03.710718Z", - "iopub.status.busy": "2026-08-11T03:10:03.710576Z", - "iopub.status.idle": "2026-08-11T03:10:03.714021Z", - "shell.execute_reply": "2026-08-11T03:10:03.713230Z" - } - }, - "outputs": [], - "source": [ - "x2 = np.linspace(0.6, 1.4, 27, dtype=float)\n", - "y_true2 = my_model.y(x2, *list(true_params.values()))\n", - "y_exp2 = (y_true2 + rng.normal(scale=noise_fraction2 * y_true2, size=len(x2))) * N2\n", - "y_stat_err2 = noise_fraction2 * y_exp2 * N2" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "55153cf1-0153-443f-8e94-7b0cbf9fbab0", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:03.715216Z", - "iopub.status.busy": "2026-08-11T03:10:03.715094Z", - "iopub.status.idle": "2026-08-11T03:10:03.891635Z", - "shell.execute_reply": "2026-08-11T03:10:03.891086Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, '$x$-offset experimental constraint with opposite bias')" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.errorbar(\n", - " x,\n", - " y_exp,\n", - " y_stat_err,\n", - " marker=\"o\",\n", - " linestyle=\"none\",\n", - " label=\"experiment 1\",\n", - " color=\"tab:purple\",\n", - ")\n", - "plt.errorbar(\n", - " x2,\n", - " y_exp2,\n", - " y_stat_err2,\n", - " marker=\"o\",\n", - " linestyle=\"none\",\n", - " label=\"experiment 2\",\n", - " color=\"tab:cyan\",\n", - ")\n", - "\n", - "plt.plot(x_full, y_true_full, \"k--\", label=\"truth\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.legend()\n", - "plt.title(\"$x$-offset experimental constraint with opposite bias\")" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "bb633a19-afb3-413e-b57b-2c489ca0c382", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:03.893160Z", - "iopub.status.busy": "2026-08-11T03:10:03.893032Z", - "iopub.status.idle": "2026-08-11T03:10:03.896205Z", - "shell.execute_reply": "2026-08-11T03:10:03.895687Z" - } - }, - "outputs": [], - "source": [ - "# 1 and 2\n", - "obs_stat_only2 = rxmc.observation.Observation(x=x2, y=y_exp2, y_stat_err=y_stat_err2)\n", - "obs_unknown_stat2 = rxmc.observation.Observation(x=x2, y=y_exp2)\n", - "\n", - "# 3\n", - "obs_sys_norm_correct2 = rxmc.observation.Observation(\n", - " x=x2, y=y_exp2, y_stat_err=y_stat_err2\n", - ")\n", - "\n", - "# 4\n", - "obs_sys_norm_wrong2 = rxmc.observation.Observation(x=x2, y=y_exp2)\n", - "wrong_cov2 = np.diag(y_stat_err2**2) + systematic_fractional_err2**2 * np.outer(\n", - " y_exp2, y_exp2\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "6e459fb6-f7bc-4447-997d-05569bcc45ad", - "metadata": {}, - "source": [ - "## set up likelihood models and constraints" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "38796171-1781-4044-82b1-1592abeaed4f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:03.897627Z", - "iopub.status.busy": "2026-08-11T03:10:03.897509Z", - "iopub.status.idle": "2026-08-11T03:10:03.902733Z", - "shell.execute_reply": "2026-08-11T03:10:03.902059Z" - } - }, - "outputs": [], - "source": [ - "N1 = obs_stat_only.n_data_pts\n", - "N2 = obs_stat_only2.n_data_pts\n", - "s1 = np.arange(N1)\n", - "s2 = np.arange(N1, N1 + N2)\n", - "\n", - "# 1\n", - "evidence_stat_only = rxmc.evidence.Evidence(\n", - " [rxmc.constraint.Constraint([obs_stat_only, obs_stat_only2], my_model, likelihood)]\n", - ")\n", - "\n", - "# 2 (shared noise-fraction parameter across both datasets -> case B)\n", - "evidence_unknown_stat = rxmc.evidence.Evidence(\n", - " [\n", - " rxmc.constraint.Constraint(\n", - " [obs_unknown_stat, obs_unknown_stat2],\n", - " my_model,\n", - " extra_terms=[\n", - " rxmc.covariance.noise_fraction_term(log_noise_fraction, support=s1),\n", - " rxmc.covariance.noise_fraction_term(log_noise_fraction, support=s2),\n", - " ],\n", - " )\n", - " ]\n", - ")\n", - "\n", - "# 3\n", - "evidence_sys_correct = rxmc.evidence.Evidence(\n", - " [\n", - " rxmc.constraint.Constraint(\n", - " [obs_sys_norm_correct, obs_sys_norm_correct2],\n", - " my_model,\n", - " extra_terms=[\n", - " rxmc.covariance.normalization_term(\n", - " magnitude=systematic_fractional_err,\n", - " support=s1,\n", - " ),\n", - " rxmc.covariance.normalization_term(\n", - " magnitude=systematic_fractional_err2,\n", - " support=s2,\n", - " ),\n", - " ],\n", - " )\n", - " ]\n", - ")\n", - "\n", - "# 4\n", - "evidence_sys_wrong = rxmc.evidence.Evidence(\n", - " [\n", - " rxmc.constraint.Constraint(\n", - " [obs_sys_norm_wrong, obs_sys_norm_wrong2],\n", - " my_model,\n", - " extra_terms=[\n", - " rxmc.covariance.Term(wrong_cov, support=s1),\n", - " rxmc.covariance.Term(wrong_cov2, support=s2),\n", - " ],\n", - " )\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "a1ea1e11-1b1f-44c2-9218-dce25f613a18", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:03.904075Z", - "iopub.status.busy": "2026-08-11T03:10:03.903959Z", - "iopub.status.idle": "2026-08-11T03:10:03.906384Z", - "shell.execute_reply": "2026-08-11T03:10:03.905825Z" - } - }, - "outputs": [], - "source": [ - "walker1 = rxmc.walker.Walker(\n", - " model_sampler=rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=my_model.params,\n", - " starting_location=prior_distribution.mean,\n", - " proposal=proposal_distribution_model,\n", - " prior=prior_distribution,\n", - " ),\n", - " evidence=evidence_stat_only,\n", - " rng=rng,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "e2cd9f71-2395-4ee8-84ed-a0ad4b6abc37", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:03.907639Z", - "iopub.status.busy": "2026-08-11T03:10:03.907525Z", - "iopub.status.idle": "2026-08-11T03:10:07.489591Z", - "shell.execute_reply": "2026-08-11T03:10:07.488800Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/1 completed, 1000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 1/1 completed, 10000 steps. \n", - " Model parameter acceptance fraction: 0.331\n", - "CPU times: user 3.58 s, sys: 16 ms, total: 3.6 s\n", - "Wall time: 3.58 s\n" - ] - } - ], - "source": [ - "%%time\n", - "walker1.walk(n_steps=10000, burnin=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "a939e980-1479-4619-b7d1-507a5c1ce3f9", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:07.490910Z", - "iopub.status.busy": "2026-08-11T03:10:07.490749Z", - "iopub.status.idle": "2026-08-11T03:10:07.540105Z", - "shell.execute_reply": "2026-08-11T03:10:07.539346Z" - } - }, - "outputs": [], - "source": [ - "upper1, med1, lower1 = np.percentile(\n", - " [my_model.y(x_full, *p) for p in walker1.model_sampler.chain],\n", - " [5, 50, 95],\n", - " axis=0,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "309bae03-b150-454b-86cb-a1c3e42fb1ad", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:07.541679Z", - "iopub.status.busy": "2026-08-11T03:10:07.541511Z", - "iopub.status.idle": "2026-08-11T03:10:07.544922Z", - "shell.execute_reply": "2026-08-11T03:10:07.544100Z" - } - }, - "outputs": [], - "source": [ - "walker2 = rxmc.walker.Walker(\n", - " model_sampler=rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=my_model.params,\n", - " starting_location=prior_distribution.mean,\n", - " proposal=proposal_distribution_model,\n", - " prior=prior_distribution,\n", - " ),\n", - " evidence=evidence_unknown_stat,\n", - " likelihood_samplers=[\n", - " rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=[log_noise_fraction],\n", - " starting_location=noise_prior.mean(),\n", - " proposal=proposal_distribution_noise,\n", - " prior=noise_prior,\n", - " )\n", - " ],\n", - " rng=rng,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "3ab21666-0ffb-4692-a3ac-ef8e588bd2b7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:07.546286Z", - "iopub.status.busy": "2026-08-11T03:10:07.546127Z", - "iopub.status.idle": "2026-08-11T03:10:17.201635Z", - "shell.execute_reply": "2026-08-11T03:10:17.201079Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/10 completed, 100 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 2/10 completed, 100 steps.\n", - "Burn-in batch 3/10 completed, 100 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 4/10 completed, 100 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 5/10 completed, 100 steps.\n", - "Burn-in batch 6/10 completed, 100 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - 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] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 94/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.990\n", - " Likelihood parameter acceptance fractions: [0.71]\n", - "Batch: 95/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.980\n", - " Likelihood parameter acceptance fractions: [0.74]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 96/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.990\n", - " Likelihood parameter acceptance fractions: [0.73]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 97/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.980\n", - " Likelihood parameter acceptance fractions: [0.73]\n", - "Batch: 98/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.960\n", - " Likelihood parameter acceptance fractions: [0.72]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 99/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.970\n", - " Likelihood parameter acceptance fractions: [0.77]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 100/100 completed, 100 steps. \n", - " Model parameter acceptance fraction: 0.960\n", - " Likelihood parameter acceptance fractions: [0.76]\n", - "CPU times: user 9.66 s, sys: 10.9 ms, total: 9.67 s\n", - "Wall time: 9.65 s\n" - ] - } - ], - "source": [ - "%%time\n", - "walker2.walk(\n", - " n_steps=10000,\n", - " burnin=1000,\n", - " batch_size=100,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "e78a780b-1336-41dd-a222-ea4333149f6d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:17.203038Z", - "iopub.status.busy": "2026-08-11T03:10:17.202905Z", - "iopub.status.idle": "2026-08-11T03:10:17.251148Z", - "shell.execute_reply": "2026-08-11T03:10:17.250464Z" - } - }, - "outputs": [], - "source": [ - "upper2, med2, lower2 = np.percentile(\n", - " [my_model.y(x_full, *p) for p in walker2.model_sampler.chain],\n", - " [5, 50, 95],\n", - " axis=0,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "d0f0d0cc-7f17-4d49-b60f-fe6330a1c10d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:17.252455Z", - "iopub.status.busy": "2026-08-11T03:10:17.252324Z", - "iopub.status.idle": "2026-08-11T03:10:17.254981Z", - "shell.execute_reply": "2026-08-11T03:10:17.254383Z" - } - }, - "outputs": [], - "source": [ - "walker3 = rxmc.walker.Walker(\n", - " model_sampler=rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=my_model.params,\n", - " starting_location=prior_distribution.mean,\n", - " proposal=proposal_distribution_model,\n", - " prior=prior_distribution,\n", - " ),\n", - " evidence=evidence_sys_correct,\n", - " rng=rng,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "c6e0ea79-3fbe-4105-8e34-a1e1db699c77", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:17.256193Z", - "iopub.status.busy": "2026-08-11T03:10:17.256076Z", - "iopub.status.idle": "2026-08-11T03:10:22.676338Z", - "shell.execute_reply": "2026-08-11T03:10:22.675665Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/1 completed, 1000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 1/1 completed, 10000 steps. \n", - " Model parameter acceptance fraction: 0.748\n", - "CPU times: user 5.42 s, sys: 897 μs, total: 5.42 s\n", - "Wall time: 5.42 s\n" - ] - } - ], - "source": [ - "%%time\n", - "walker3.walk(n_steps=10000, burnin=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "60262bab-1c58-4a71-9227-43983c7af67d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:22.677794Z", - "iopub.status.busy": "2026-08-11T03:10:22.677622Z", - "iopub.status.idle": "2026-08-11T03:10:22.729234Z", - "shell.execute_reply": "2026-08-11T03:10:22.728390Z" - } - }, - "outputs": [], - "source": [ - "upper3, med3, lower3 = np.percentile(\n", - " [my_model.y(x_full, *p) for p in walker3.model_sampler.chain],\n", - " [5, 50, 95],\n", - " axis=0,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "d9c5010e-8f5b-461a-8941-2b967fbfb8f7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:22.730597Z", - "iopub.status.busy": "2026-08-11T03:10:22.730459Z", - "iopub.status.idle": "2026-08-11T03:10:22.733413Z", - "shell.execute_reply": "2026-08-11T03:10:22.732750Z" - } - }, - "outputs": [], - "source": [ - "walker4 = rxmc.walker.Walker(\n", - " model_sampler=rxmc.param_sampling.MetropolisHastingsSampler(\n", - " params=my_model.params,\n", - " starting_location=prior_distribution.mean,\n", - " proposal=proposal_distribution_model,\n", - " prior=prior_distribution,\n", - " ),\n", - " evidence=evidence_sys_wrong,\n", - " rng=rng,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "b02c82e3-0cb0-4d5d-9d55-2b1ceacdf244", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:22.734633Z", - "iopub.status.busy": "2026-08-11T03:10:22.734464Z", - "iopub.status.idle": "2026-08-11T03:10:26.390364Z", - "shell.execute_reply": "2026-08-11T03:10:26.389705Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burn-in batch 1/1 completed, 1000 steps.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch: 1/1 completed, 10000 steps. \n", - " Model parameter acceptance fraction: 0.735\n", - "CPU times: user 3.65 s, sys: 10.9 ms, total: 3.66 s\n", - "Wall time: 3.65 s\n" - ] - } - ], - "source": [ - "%%time\n", - "walker4.walk(n_steps=10000, burnin=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "id": "d24a326a-093b-4e20-80d3-9d267a4c14e7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:26.391784Z", - "iopub.status.busy": "2026-08-11T03:10:26.391652Z", - "iopub.status.idle": "2026-08-11T03:10:26.443882Z", - "shell.execute_reply": "2026-08-11T03:10:26.443172Z" - } - }, - "outputs": [], - "source": [ - "upper4, med4, lower4 = np.percentile(\n", - " [my_model.y(x_full, *p) for p in walker4.model_sampler.chain],\n", - " [5, 50, 95],\n", - " axis=0,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "id": "78d9fee2-1f50-460b-b579-62f649785e00", - "metadata": { - "execution": { - "iopub.execute_input": "2026-08-11T03:10:26.445207Z", - "iopub.status.busy": "2026-08-11T03:10:26.445078Z", - "iopub.status.idle": "2026-08-11T03:10:26.648495Z", - "shell.execute_reply": "2026-08-11T03:10:26.647882Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, '$x$-offset constraints with systematic normalization error')" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.errorbar(\n", - " x,\n", - " y_exp,\n", - " y_stat_err,\n", - " marker=\".\",\n", - " linestyle=\"none\",\n", - " label=\"experiment 1\",\n", - " color=\"tab:purple\",\n", - " zorder=999,\n", - ")\n", - "plt.errorbar(\n", - " x2,\n", - " y_exp2,\n", - " y_stat_err2,\n", - " marker=\".\",\n", - " linestyle=\"none\",\n", - " label=\"experiment 2\",\n", - " color=\"tab:cyan\",\n", - " zorder=999,\n", - ")\n", - "p = plt.fill_between(x_full, lower1, upper1, label=\"stat only\", alpha=0.5, zorder=99)\n", - "plt.plot(x_full, med1, \"--\", color=p.get_facecolor(), alpha=1, zorder=100)\n", - "\n", - "\n", - "p = plt.fill_between(x_full, lower2, upper2, label=\"stat fit\", alpha=0.5, zorder=89)\n", - "plt.plot(x_full, med2, \"--\", color=p.get_facecolor(), alpha=1, zorder=90)\n", - "\n", - "\n", - "p = plt.fill_between(x_full, lower3, upper3, label=\"full covariance\", alpha=0.5)\n", - "plt.plot(x_full, med3, \"--\", color=p.get_facecolor(), alpha=1, zorder=89)\n", - "\n", - "p = plt.fill_between(x_full, lower4, upper4, label=\"full covariance, wrong\", alpha=0.25)\n", - "plt.plot(x_full, med4, \"--\", color=p.get_facecolor(), alpha=0.5)\n", - "\n", - "\n", - "plt.plot(x_full, y_true_full, \"k--\", label=\"truth\", zorder=999)\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.legend()\n", - "plt.title(\"$x$-offset constraints with systematic normalization error\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3f78b592-f51a-44b1-a969-8b53d2f08ec2", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/src/rxmc/adaptive_metropolis.py b/src/rxmc/adaptive_metropolis.py deleted file mode 100644 index 44862df..0000000 --- a/src/rxmc/adaptive_metropolis.py +++ /dev/null @@ -1,109 +0,0 @@ -""" -Adaptive Metropolis MCMC sampler with sliding-window covariance adaptation. - -The :func:`adaptive_metropolis` function implements the AM algorithm of Haario, -Saksman & Tamminen (2001), extended with a sliding window so that old samples -do not dominate the estimated proposal covariance. -""" - -from typing import Callable, Tuple - -import numpy as np - - -def adaptive_metropolis( - x0: np.ndarray, - bounds: np.ndarray, - n_steps: int, - log_posterior: Callable[[np.ndarray], float], - rng: np.random.Generator, - adapt_start: int = 1000, - window_size: int = 1000, - epsilon_fraction: float = 1e-6, - previous_chain: np.ndarray = None, -) -> Tuple[np.ndarray, np.ndarray, int]: - """Adaptive Metropolis algorithm with sliding-window covariance adaptation. - - Before *adapt_start* steps the proposal is a diagonal Gaussian scaled to - 1 % of ``|x0|``. After *adapt_start* steps the proposal covariance is - estimated from the last *window_size* samples and scaled by - ``2.38² / ndim`` (the Gelman–Roberts–Gilks optimal scale). - - Parameters - ---------- - x0 : np.ndarray, shape (ndim,) - Initial parameter vector. - bounds : np.ndarray, shape (ndim, 2) - Parameter bounds; each row is ``[lower, upper]``. Proposals outside - these bounds are rejected outright. - n_steps : int - Number of MCMC steps to generate. - log_posterior : callable - Function ``f(x) -> float`` returning the log posterior at ``x``. - rng : np.random.Generator - Random number generator for reproducibility. - adapt_start : int, optional - Step index at which covariance adaptation begins. Ignored when - *previous_chain* is supplied. Defaults to ``1000``. - window_size : int, optional - Number of past samples used for covariance estimation. - Defaults to ``1000``. - epsilon_fraction : float, optional - Fraction of the mean diagonal element added to the covariance for - numerical stability. Defaults to ``1e-6``. - previous_chain : np.ndarray, shape (m, ndim), optional - Chain from a prior run to continue from. When provided the new - samples are appended and adaptation uses all available history. - - Returns - ------- - chain : np.ndarray, shape (n_steps, ndim) - Newly generated samples (does not include *previous_chain*). - logp_chain : np.ndarray, shape (n_steps,) - Log posterior values for the new samples. - accepted : int - Number of accepted proposals in this run. - """ - dim = x0.size - if previous_chain is not None and previous_chain.shape[0] > 0: - start = previous_chain.shape[0] - chain = np.concatenate((previous_chain, np.zeros((n_steps, dim))), axis=0) - else: - start = 0 - chain = np.zeros((n_steps, dim)) - - logp_chain = np.zeros(n_steps) - accepted = 0 - - x = x0.copy() - logp = float(np.squeeze(log_posterior(x))) - scale = 2.38**2 / dim - - for i in range(start, start + n_steps): - if i < adapt_start: - proposal_scale = np.maximum(np.abs(x0), 1.0) * 0.01 - proposal_cov = np.diag(proposal_scale**2) - else: - start_idx = max(0, i - window_size) - history_subset = chain[start_idx:i, ...] - cov = np.atleast_2d(np.cov(history_subset.T)) - cov += epsilon_fraction * np.mean(np.diag(cov)) * np.eye(dim) - proposal_cov = scale * cov - - x_new = rng.multivariate_normal(x, proposal_cov) - if np.any(x_new < bounds[:, 0]) or np.any(x_new > bounds[:, 1]): - chain[i, :] = x - logp_chain[i - start] = logp - continue - logp_new = float(np.squeeze(log_posterior(x_new))) - - log_ratio = min(0, logp_new - logp) - if np.log(rng.random()) < log_ratio: - x = x_new - logp = logp_new - accepted += 1 - - chain[i, :] = x - logp_chain[i - start] = logp - - return chain[start:, ...], logp_chain, accepted diff --git a/src/rxmc/config.py b/src/rxmc/config.py deleted file mode 100644 index 79983fc..0000000 --- a/src/rxmc/config.py +++ /dev/null @@ -1,581 +0,0 @@ -""" -Configuration helpers for calibration workflows. - -``CalibrationConfig`` is the main integration surface for external samplers -such as black-box-bayes. It exposes a flat parameterization of an -``Evidence`` instance together with the sampler-facing methods needed to -evaluate the posterior and generate starting locations. - -Prior protocol --------------- -Both ``ParameterConfig`` and ``CalibrationConfig`` accept any prior object -that implements: - -* ``logpdf(x: ndarray) -> float`` — log-density at a parameter vector of - shape ``(ndim,)``. -* ``rvs(n: int) -> ndarray`` — draw ``n`` samples; returned array must have - shape ``(n, ndim)`` or ``(n,)`` for scalar parameters. - -This covers ``scipy.stats`` frozen distributions (both univariate and -multivariate), the built-in :class:`~rxmc.priors.TruncatedNormalPrior`, and -any user-supplied class that satisfies the same interface. - -Alternatively, a **list** of univariate ``scipy.stats`` frozen distributions -(one per parameter) may be passed; it is wrapped in an -:class:`~rxmc.priors.IndependentPrior` on construction (the same rule -:class:`~rxmc.param_sampling.Sampler` applies). That class and any prior -class that implements ``prior_transform(u)`` support the Dynesty-compatible -:meth:`CalibrationConfig.prior_transform`. -""" - -from typing import List, Optional - -import numpy as np - -from rxmc.evidence import Evidence -from rxmc.params import Parameter -from rxmc.priors import as_prior, clip_unit_cube - - -class ParameterConfig: - """Configuration for a single sector of parameters. - - Bundles a list of :class:`~rxmc.params.Parameter` objects with a prior - distribution and an initial-proposal distribution. Instances are passed - to :class:`CalibrationConfig` to describe the model-parameter sector and - each likelihood-parameter sector. - - Parameters - ---------- - params : list of Parameter - Ordered list of parameters in this sector. - prior : prior object or list of rv_continuous - Prior distribution. May be any object that exposes ``logpdf`` and - ``rvs`` (e.g. a frozen ``scipy.stats`` multivariate distribution, - :class:`~rxmc.priors.TruncatedNormalPrior`, or any user-defined class - with the same interface), **or** a list of frozen univariate - ``scipy.stats`` distributions — one per parameter. - initial_proposal_distribution : prior object or list of rv_continuous - Starting-location proposal distribution. Accepts the same forms as - ``prior``. - - Raises - ------ - ValueError - If ``params`` is empty. - ValueError - If the dimensionality implied by ``prior`` or - ``initial_proposal_distribution`` does not match ``len(params)``. - """ - - def __init__( - self, - params: List[Parameter], - prior, - initial_proposal_distribution, - ): - self.params = params - self.ndim = len(params) - # a list of marginals becomes an IndependentPrior here, so every - # method below sees one prior object (the rule Sampler applies too) - self.prior = as_prior(prior) - self.initial_proposal_distribution = as_prior(initial_proposal_distribution) - - if self.ndim == 0: - raise ValueError("Parameter list cannot be empty") - - self._validate_prior_dim(self.prior, "prior") - self._validate_prior_dim( - self.initial_proposal_distribution, "initial_proposal_distribution" - ) - - # ------------------------------------------------------------------ - # Internal helpers - # ------------------------------------------------------------------ - - @staticmethod - def _infer_dim(dist) -> Optional[int]: - """Return the dimensionality of a prior object, or None if unknown. - - An integer ``dim`` attribute wins; otherwise the size of ``mean`` is - used, *calling* it when it is a method (frozen ``scipy.stats`` - univariates and user classes expose ``mean()``; scipy multivariates - expose an array). - """ - dim = getattr(dist, "dim", None) - if isinstance(dim, int) and not isinstance(dim, bool): - return dim - mean = getattr(dist, "mean", None) - if mean is None: - return None - if callable(mean): - try: - mean = mean() - except Exception: - return None - return int(np.size(mean)) - - def _validate_prior_dim(self, dist, name: str) -> None: - dim = self._infer_dim(dist) - if dim is not None and dim != self.ndim: - raise ValueError( - f"{name} dimensionality ({dim}) does not match " - f"number of parameters ({self.ndim})" - ) - - # ------------------------------------------------------------------ - # Public API - # ------------------------------------------------------------------ - - def x0(self, nwalkers: int) -> np.ndarray: - """Draw initial walker positions from the proposal distribution. - - Parameters - ---------- - nwalkers : int - Number of walkers (rows) to generate. - - Returns - ------- - ndarray, shape (nwalkers, ndim) - One initial position per walker. - """ - samples = np.atleast_1d(self.initial_proposal_distribution.rvs(nwalkers)) - return samples.reshape(nwalkers, -1) - - def prior_logpdf(self, x: np.ndarray) -> float: - """Evaluate the log prior density at a parameter vector. - - Parameters - ---------- - x : ndarray, shape (ndim,) - Parameter vector for this sector. - - Returns - ------- - float - Log prior probability at ``x``. - """ - return float(self.prior.logpdf(np.atleast_1d(x))) - - def prior_transform(self, u: np.ndarray) -> np.ndarray: - """Map unit-cube coordinates to physical parameters for this sector. - - The prior object must implement ``prior_transform(u) -> ndarray`` - (:class:`~rxmc.priors.IndependentPrior`, which a list prior becomes, - and :class:`~rxmc.priors.TruncatedNormalPrior` do). ``u`` is clipped - into the open unit cube first, so an exact ``0`` or ``1`` stays finite. - - Parameters - ---------- - u : ndarray, shape (ndim,) - Unit-cube coordinates, each in ``[0, 1)``. - - Returns - ------- - ndarray, shape (ndim,) - Physical parameter vector for this sector. - - Raises - ------ - NotImplementedError - If the prior does not expose ``prior_transform``. - """ - u = clip_unit_cube(u) - if hasattr(self.prior, "prior_transform"): - return self.prior.prior_transform(u) - raise NotImplementedError( - "Prior transform requires a prior object that implements " - "prior_transform(u) (a list of scipy marginals is wrapped in " - "IndependentPrior, which does)." - ) - - -class CalibrationConfig: - """End-to-end configuration for Bayesian calibration. - - Combines an :class:`~rxmc.evidence.Evidence` object with - :class:`ParameterConfig` instances for the physical-model parameters and - any parametric likelihood parameters. The result is a flat - parameterization suitable for black-box samplers (emcee, Dynesty, - black-box-bayes, etc.). - - Parameters - ---------- - evidence : Evidence - Aggregated experimental constraints. - model_config : ParameterConfig - Prior and proposal for the physical-model parameters. - likelihood_configs : list of ParameterConfig, optional - One :class:`ParameterConfig` per parametric constraint in - ``evidence.parametric_constraints``. Omit or pass ``None`` when - there are no parametric likelihood models. - likelihood_scaling : float, optional - Multiplicative scale applied to the total log-likelihood before - adding the log-prior. Useful for tempering or importance - re-weighting. Defaults to ``1.0``. - - Raises - ------ - ValueError - If ``evidence`` contains no constraints. - ValueError - If the model parameters in ``model_config`` do not match those in - the evidence constraints. - ValueError - If the number or parameter lists of ``likelihood_configs`` do not - match ``evidence.parametric_constraints``. - - Attributes - ---------- - ndim : int - Total number of free parameters (model + all likelihood sectors). - dimensions : ndarray - Array of sector sizes ``[model_ndim, lc0_ndim, lc1_ndim, ...]``. - indices : ndarray - Cumulative split indices derived from ``dimensions``; used by - :meth:`split_parameters`. - """ - - def __init__( - self, - evidence: Evidence, - model_config: ParameterConfig, - likelihood_configs: Optional[list] = None, - likelihood_scaling: Optional[float] = None, - ): - self.evidence = evidence - self.model_config = model_config - self.likelihood_configs = likelihood_configs or [] - self.ndim = model_config.ndim + sum(lc.ndim for lc in self.likelihood_configs) - self.likelihood_scaling = ( - 1.0 if likelihood_scaling is None else likelihood_scaling - ) - - if len(self.evidence.constraints) == 0: - raise ValueError("Evidence must have at least one constraint") - if np.any( - [ - c.physical_model.params != self.model_config.params - for c in self.evidence.constraints - ] - ): - raise ValueError( - "Model parameters do not match those in the evidence constraints" - ) - if len(self.likelihood_configs) != len(self.evidence.parametric_constraints): - raise ValueError( - "Likelihood configurations do not match the likelihood models " - "in the evidence constraints" - ) - for lc, c in zip(self.likelihood_configs, self.evidence.parametric_constraints): - if list(lc.params) != list(c.params): - raise ValueError( - "Likelihood parameters do not match those in the evidence constraints" - ) - - self.dimensions = np.array( - [self.model_config.ndim] + [lc.ndim for lc in self.likelihood_configs] - ) - self.indices = np.cumsum(self.dimensions) - - # ------------------------------------------------------------------ - # Structural properties - # ------------------------------------------------------------------ - - @property - def parameter_configs(self) -> list: - """All parameter sectors in flat sampler order: model first, then likelihoods.""" - return [self.model_config] + self.likelihood_configs - - @property - def parameters(self) -> List[Parameter]: - """All :class:`~rxmc.params.Parameter` objects in flat sampler order. - - The order matches the flat parameter vector consumed by - :meth:`log_posterior`, :meth:`log_likelihood`, and - :meth:`starting_location`. - """ - return [param for pc in self.parameter_configs for param in pc.params] - - @property - def parameter_names(self) -> List[str]: - """Flat parameter names in sampler order. - - Convenience accessor equivalent to - ``[p.name for p in self.parameters]``. Provided for compatibility - with external drivers such as black-box-bayes that export inference - results keyed by name. - """ - return [p.name for p in self.parameters] - - @property - def prior(self) -> list: - """Prior distribution objects in parameter-sector order. - - Returns one entry per sector: the model prior first, followed by one - entry per likelihood sector. Each entry is the prior object held by - the corresponding :class:`ParameterConfig` — a multivariate - distribution, a custom prior object, or the - :class:`~rxmc.priors.IndependentPrior` a list of univariate - distributions was wrapped into. - """ - return [pc.prior for pc in self.parameter_configs] - - # ------------------------------------------------------------------ - # Parameter manipulation - # ------------------------------------------------------------------ - - def split_parameters(self, x) -> tuple: - """Split a flat parameter vector into model and likelihood sub-vectors. - - Parameters - ---------- - x : ndarray, shape (ndim,) - Flat parameter vector in sampler order. - - Returns - ------- - xmodel : ndarray - Model parameter sub-vector of shape ``(model_config.ndim,)``. - xlikelihoods : list of ndarray - One sub-vector per likelihood sector. - """ - parts = np.split(x, self.indices[:-1]) - return parts[0], parts[1:] - - # ------------------------------------------------------------------ - # Posterior evaluation - # ------------------------------------------------------------------ - - def log_prior(self, x) -> float: - """Evaluate the joint log prior at a flat parameter vector. - - Parameters - ---------- - x : ndarray, shape (ndim,) - Flat parameter vector in sampler order. - - Returns - ------- - float - Sum of log prior densities across all sectors. - """ - xmodel, xlikelihoods = self.split_parameters(x) - lprior = self.model_config.prior_logpdf(xmodel) - lprior += sum( - lc.prior_logpdf(xl) for lc, xl in zip(self.likelihood_configs, xlikelihoods) - ) - return lprior - - def log_likelihood(self, x) -> float: - """Evaluate the scaled log likelihood at a flat parameter vector. - - Parameters - ---------- - x : ndarray, shape (ndim,) - Flat parameter vector in sampler order. - - Returns - ------- - float - ``likelihood_scaling * evidence.log_likelihood(xmodel, xlikelihoods)``. - """ - xmodel, xlikelihoods = self.split_parameters(x) - return self.likelihood_scaling * self.evidence.log_likelihood( - xmodel, xlikelihoods - ) - - def log_posterior(self, x) -> float: - """Evaluate the log posterior at a flat parameter vector. - - Returns ``-inf`` immediately if either the prior or likelihood is - non-finite, avoiding unnecessary model evaluations. - - Parameters - ---------- - x : ndarray, shape (ndim,) - Flat parameter vector in sampler order. - - Returns - ------- - float - ``log_prior(x) + log_likelihood(x)``, or ``-inf`` if either term - is non-finite. - """ - lp = self.log_prior(x) - if not np.isfinite(lp): - return -np.inf - ll = self.log_likelihood(x) - if not np.isfinite(ll): - return -np.inf - return lp + ll - - def log_posterior_batch(self, thetas: np.ndarray) -> np.ndarray: - """Evaluate the log posterior for a batch of parameter vectors. - - Convenience wrapper for external orchestration layers (e.g. - black-box-bayes). The current implementation is serial but - preserves the advertised interface for future parallelisation. - - Parameters - ---------- - thetas : ndarray, shape (..., ndim) - Batch of parameter vectors. - - Returns - ------- - ndarray, shape (n,) - Log posterior value for each row of ``thetas``. - - Raises - ------ - ValueError - If the trailing dimension of ``thetas`` is not ``ndim``. - """ - thetas = np.atleast_2d(np.asarray(thetas, dtype=float)) - if thetas.shape[-1] != self.ndim: - raise ValueError( - f"Expected thetas with trailing dimension {self.ndim}, " - f"got shape {thetas.shape}" - ) - return np.array([self.log_posterior(theta) for theta in thetas], dtype=float) - - # ------------------------------------------------------------------ - # Sampling helpers - # ------------------------------------------------------------------ - - def starting_location(self, nwalkers: int) -> np.ndarray: - """Generate initial walker positions across all parameter sectors. - - Parameters - ---------- - nwalkers : int - Number of walkers. - - Returns - ------- - ndarray, shape (nwalkers, ndim) - Concatenated initial positions from each sector's proposal - distribution. - """ - x0_model = self.model_config.x0(nwalkers) - x0_likelihoods = [ - lc.x0(nwalkers).reshape(nwalkers, lc.ndim) for lc in self.likelihood_configs - ] - return np.hstack([x0_model] + x0_likelihoods) - - def prior_transform(self, u) -> np.ndarray: - """Map unit-cube coordinates to physical parameters (Dynesty interface). - - Delegates to :meth:`ParameterConfig.prior_transform` for each sector, - which in turn either calls ``ppf`` on each element of a list prior or - calls ``prior_transform`` directly on a joint prior object. - - Parameters - ---------- - u : array-like, shape (ndim,) - Unit-cube coordinates, each in ``[0, 1)``. - - Returns - ------- - theta : ndarray, shape (ndim,) - Physical parameter vector. - - Raises - ------ - NotImplementedError - If any sector's prior does not support a transform (see - :meth:`ParameterConfig.prior_transform`). - ValueError - If ``u`` does not have length ``ndim``. - """ - u = clip_unit_cube(u) - if u.shape[-1] != self.ndim: - raise ValueError(f"Expected u with length {self.ndim}, got shape {u.shape}") - - theta = np.empty_like(u) - offset = 0 - for pc in self.parameter_configs: - sector = u[offset : offset + pc.ndim] - theta[offset : offset + pc.ndim] = pc.prior_transform(sector) - offset += pc.ndim - - return theta - - # ------------------------------------------------------------------ - # Prediction and conditional posterior - # ------------------------------------------------------------------ - - def predict(self, xmodel) -> list: - """Generate model predictions for every constraint. - - Parameters - ---------- - xmodel : ndarray, shape (model_config.ndim,) - Physical model parameter vector. - - Returns - ------- - list - One entry per constraint, in ``evidence.constraints`` order; each entry - is itself a list of per-observation prediction arrays. For the - conditioning data of a likelihood sector use :meth:`predict_parametric`, - whose order matches :meth:`conditional_posterior`'s ``lm_index``. - """ - return [c.predict(*xmodel) for c in self.evidence.constraints] - - def predict_parametric(self, xmodel) -> list: - """Predictions for the *parametric* constraints only. - - Indexed in ``evidence.parametric_constraints`` order, so - ``predict_parametric(xmodel)[lm_index]`` is the correct ``ym`` to feed - :meth:`conditional_posterior` at ``lm_index`` (unlike :meth:`predict`, - which is indexed over *all* constraints and therefore misaligns whenever a - non-parametric constraint precedes a parametric one). - - Parameters - ---------- - xmodel : ndarray, shape (model_config.ndim,) - Physical model parameter vector. - - Returns - ------- - list - One prediction per parametric constraint. - """ - return [c.predict(*xmodel) for c in self.evidence.parametric_constraints] - - def conditional_posterior(self, x_lm, lm_index: int, ym) -> float: - """Log posterior for one likelihood sector, conditioned on observed data. - - Evaluates - ``prior_logpdf(x_lm) + likelihood_scaling * w * marginal_log_likelihood(ym, *x_lm)`` - for the likelihood sector at ``lm_index``, where ``w`` is the - constraint's :attr:`Evidence.weights` entry. The likelihood is tempered - exactly as in :meth:`log_posterior` (prior untouched), so Gibbs-style - updates that alternate this conditional with the model block target the - same joint distribution. - - Parameters - ---------- - x_lm : ndarray, shape (likelihood_configs[lm_index].ndim,) - Parameter vector for the target likelihood sector. - lm_index : int - Index into ``likelihood_configs`` (and - ``evidence.parametric_constraints``). - ym : ndarray - Predicted observable used as the conditioning data — use - ``predict_parametric(xmodel)[lm_index]`` to obtain it with matching - index order. - - Returns - ------- - float - Log posterior for this likelihood sector. - """ - lp = self.likelihood_configs[lm_index].prior_logpdf(x_lm) - if not np.isfinite(lp): - return -np.inf - ll = self.evidence.weighted_marginal_log_likelihood(lm_index, ym, *x_lm) - return lp + self.likelihood_scaling * ll diff --git a/src/rxmc/constraint.py b/src/rxmc/constraint.py deleted file mode 100644 index 73ef398..0000000 --- a/src/rxmc/constraint.py +++ /dev/null @@ -1,483 +0,0 @@ -""" -Constraint: the maximal block of mutually-correlated data. - -A :class:`Constraint` pairs one or more :class:`~rxmc.observation.Observation` -objects with a :class:`~rxmc.physical_model.PhysicalModel` and a likelihood -functional (:class:`~rxmc.likelihood_model.GaussianLikelihood` by default). It -owns **one** multivariate distribution over the *stacked* vector of all its -observations, whose covariance is a -:class:`~rxmc.covariance.ConstraintCovariance` assembled from -:class:`~rxmc.covariance.Term` s. - -Each observation *i* occupies a contiguous slice of the stacked vector. The -default covariance is the concatenation of every observation's statistical -diagonal (strictly block-diagonal — reproducing the old summed independent -likelihoods). Correlated modes — a dataset's own normalisation/offset -systematic, an unknown-noise term, or a cross-dataset coupling — are supplied as -``extra_terms``. - -Each observation's comparison-space ``transform`` is applied to the model -prediction here, so the residual ``y - ym`` is formed in that space. Masks — -point-level on the observations, observation-level via ``mask=`` — select the -*active* rows; terms are always authored over the full stack. -""" - -import numpy as np - -from .covariance import ConstraintCovariance, StackContext, stacked_supports -from .likelihood_model import GaussianLikelihood -from .observation import Observation -from .physical_model import PhysicalModel - - -class Constraint: - """Pair observations with a physical model and a stacked covariance. - - Parameters - ---------- - observations : list of Observation - The observed data that the model will attempt to reproduce. Together - they form one stacked vector ``y = [y1; y2; ...]``. - physical_model : PhysicalModel - Model that predicts the observed data. - likelihood : object, optional - Likelihood functional of ``(d2, logdet, n, *like_params)``. Defaults to - :class:`~rxmc.likelihood_model.GaussianLikelihood`. - extra_terms : sequence of Term, optional - Additional covariance contributions beyond the statistical diagonals — - local systematics or cross-block couplings. - include_statistical_term : bool, optional - When ``True`` (default) each observation's statistical diagonal - (``obs.statistical_term``) is added automatically. Set ``False`` to omit - it and compose the *entire* covariance from ``extra_terms`` — e.g. to let - an unknown-noise term (:func:`~rxmc.covariance.noise_term`) *replace* the - reported statistics rather than add to them. - mask : sequence of bool or of int, optional - Which *observations* are active (all by default): a boolean per - observation, or the indices of the active ones. An integer array of - length ``len(observations)`` holding only 0/1 is ambiguous and - rejected; pass a bool array or explicit indices. Combined with each - observation's own point ``mask`` to give :attr:`active`. - - Attributes - ---------- - covariance : ConstraintCovariance - The stacked covariance. - params : tuple of Parameter - Free parameters of this constraint: covariance params followed by - likelihood params (e.g. Student-t ``nu``). - n_params : int - ``len(params)``. - active : np.ndarray - Stacked indices of the active points. - n_data_pts : int - Number of *active* points (the ``n`` of the likelihood). - n_data_pts_total : int - Length of the full stack. - """ - - def __init__( - self, - observations: list[Observation], - physical_model: PhysicalModel, - likelihood=None, - extra_terms=(), - include_statistical_term: bool = True, - mask=None, - ): - self.observations = list(observations) - self.physical_model = physical_model - self.likelihood = likelihood if likelihood is not None else GaussianLikelihood() - - observations = self.observations - supports = stacked_supports(observations) - self._supports = supports - self.n_data_pts_total = sum(o.n_data_pts for o in observations) - - self.observation_mask = self._observation_mask(mask) - self.active = np.concatenate( - [ - s[o.mask] if keep else np.zeros(0, dtype=int) - for o, s, keep in zip(observations, supports, self.observation_mask) - ] - ).astype(int) - self.n_data_pts = int(self.active.size) - - # x and y are invariant per constraint; stack them once. Frozen - # because they are shared across every likelihood evaluation. - self._x_stacked = np.concatenate([o.x for o in observations]) - self._y_stacked = np.concatenate([o.y for o in observations]) - self._x_stacked.setflags(write=False) - self._y_stacked.setflags(write=False) - - if include_statistical_term: - terms = [obs.statistical_term(s) for obs, s in zip(observations, supports)] - else: - terms = [] - terms += list(extra_terms) - self.covariance = ConstraintCovariance( - terms, self.n_data_pts_total, blocks=supports, active=self.active - ) - - self.params = tuple(self.covariance.params) + tuple(self.likelihood.params) - self.n_params = len(self.params) - self._n_cov_params = self.covariance.n_params - - self._validate_parameter_names() - if self.covariance.is_constant: - self._validate_constant_covariance() - - def _observation_mask(self, mask): - n = len(self.observations) - if mask is None: - return np.ones(n, dtype=bool) - m = np.asarray(mask) - if m.dtype == bool: - if m.shape != (n,): - raise ValueError(f"mask must have one entry per observation ({n})") - return m - idx = np.asarray(m, dtype=int) - if n > 1 and idx.shape == (n,) and np.isin(idx, (0, 1)).all(): - raise ValueError( - f"ambiguous observation mask: an integer array of length {n} with " - "only 0/1 entries could be a boolean mask or a list of indices; " - "pass a bool array or integer indices" - ) - out = np.zeros(n, dtype=bool) - out[idx] = True - return out - - # ------------------------------------------------------------------ - # Masked views - # ------------------------------------------------------------------ - - def masked(self, mask=None, point_masks=None): - """A new constraint over the same observations/model/terms with new masks. - - Parameters - ---------- - mask : sequence of bool or of int, optional - Observation-level mask (see the constructor); ``None`` keeps this - constraint's. - point_masks : sequence of array_like of bool, optional - One point mask per observation (``None`` entries keep that - observation's current mask). - - Notes - ----- - The new constraint shares the ``Term``/``Parameter`` objects with this - one, so its parameter vector is identical — it is a *view* for - evaluating the same likelihood on a different subset (e.g. held-out - scoring), not an independent constraint to place in the same - :class:`~rxmc.evidence.Evidence`. Sharing the terms is safe because - both constraints stack the same observations in the same order, so the - terms' bound supports and cached ``x``-dependent values stay valid. - """ - observations = list(self.observations) - if point_masks is not None: - if len(point_masks) != len(observations): - raise ValueError("point_masks must have one entry per observation") - observations = [ - o if pm is None else o.masked(pm) - for o, pm in zip(observations, point_masks) - ] - # hand over the already-built term list (statistical diagonals included) - # so the view shares the exact same Term/Parameter objects - return Constraint( - observations, - self.physical_model, - likelihood=self.likelihood, - extra_terms=self.covariance.terms, - include_statistical_term=False, - mask=self.observation_mask if mask is None else mask, - ) - - def complement(self): - """The held-out counterpart: every currently inactive point becomes active - and every active point inactive. - - An observation excluded wholesale at the constraint level is therefore - restored in full; an active observation whose point mask keeps every - point is dropped wholesale. See :meth:`masked` for the sharing caveat. - """ - # an observation dropped wholesale at the constraint level is restored - # in full; an active one has its point mask flipped - point_masks = [ - ~o.mask if keep else np.ones(o.n_data_pts, dtype=bool) - for o, keep in zip(self.observations, self.observation_mask) - ] - obs_mask = [ - not keep or o.n_active < o.n_data_pts - for o, keep in zip(self.observations, self.observation_mask) - ] - return self.masked(mask=np.array(obs_mask), point_masks=point_masks) - - def _validate_parameter_names(self): - """Reject ambiguous parameter names within this constraint. - - Sharing one sampled value between terms works by referencing the *same* - ``Parameter`` object (identity); two distinct objects with one name would - silently become two sampler columns with identical labels. - """ - model_names = {p.name for p in self.physical_model.params} - seen = set() - for p in self.params: - if p.name in seen: - raise ValueError( - f"Constraint has multiple distinct parameters named " - f"'{p.name}'. To share one sampled value between terms, " - "pass the SAME Parameter object to each term; otherwise " - "give each parameter a unique name." - ) - seen.add(p.name) - if p.name in model_names: - raise ValueError( - f"Constraint parameter '{p.name}' collides with a " - "physical-model parameter of the same name; rename the " - "covariance/likelihood parameter." - ) - - def _validate_constant_covariance(self): - """Fail fast on a singular constant covariance (also warms the cache). - - A routine trigger is an EXFOR measurement reporting no statistical - error: ``from_measurement`` then yields an all-zero ``y_stat_err``, and - without an extra covariance term the stacked covariance is singular. - Catching it here names the offending dataset instead of surfacing an - opaque ``LinAlgError`` deep inside a sampler. - """ - ctx = StackContext.constant(self._x_stacked, self._y_stacked, self._supports) - cov = self.covariance - try: - # warm whichever factorisation the likelihood path will use - if cov.uses_block_path: - cov.block_cholesky(ctx) - else: - cov.cholesky(ctx) - except np.linalg.LinAlgError as err: - labels = [ - o.label or f"observation {i}" for i, o in enumerate(self.observations) - ] - Sigma = self.covariance.matrix(ctx) - zero_rows = self.active[np.diag(Sigma)[self.active] == 0.0] - offenders = [ - label - for label, s in zip(labels, self._supports) - if np.isin(s, zero_rows).any() - ] - msg = ( - f"Constraint covariance over [{', '.join(labels)}] is singular " - "(Cholesky factorization failed)." - ) - if offenders: - msg += ( - f" The covariance diagonal is zero on rows belonging to " - f"{offenders}: these datasets report zero statistical error " - "and no other covariance term covers their points." - ) - msg += ( - " Remedies: pass the dataset's reported systematics as terms " - "(extra_terms=[*obs.systematic_terms()]; for a multi-observation " - "constraint place them with support= from " - "rxmc.covariance.stacked_supports(observations)), add a " - "noise_term or a fixed Term covering those points, or compose " - "the full covariance explicitly with include_statistical_term=False." - ) - raise ValueError(msg) from err - - # ------------------------------------------------------------------ - # Stacking - # ------------------------------------------------------------------ - - def _stack(self, model_params): - ym = [self.physical_model(o, *model_params) for o in self.observations] - return self._stack_from_predictions(ym) - - def _stack_from_predictions(self, ym: list): - if len(ym) != len(self.observations): - raise ValueError( - f"expected {len(self.observations)} prediction arrays, got {len(ym)}" - ) - ym_arrays = [] - for o, y in zip(self.observations, ym): - y = np.asarray(y, dtype=float) - if y.shape != o.y.shape: - raise ValueError( - f"prediction shape {y.shape} does not match observation shape " - f"{o.y.shape}" - ) - ym_arrays.append(o.transform(y)) - return StackContext( - x=self._x_stacked, - y=self._y_stacked, - ym=np.concatenate(ym_arrays), - supports=self._supports, - ) - - def _split(self, params): - params = tuple(params) - if len(params) != self.n_params: - names = ", ".join(p.name for p in self.params) or "none" - raise ValueError( - f"Constraint expects {self.n_params} parameter(s) [{names}], " - f"got {len(params)}" - ) - return params[: self._n_cov_params], params[self._n_cov_params :] - - # ------------------------------------------------------------------ - # Likelihood - # ------------------------------------------------------------------ - - def _evaluate(self, ctx, cov_params, statistic, *, invalid=-np.inf): - """Evaluate ``statistic(d2, logdet, n, *like_params)`` on the stack. - - ``invalid`` is returned when the prediction is not finite on the active - points (e.g. a non-positive prediction under a log comparison space): - ``-inf`` for a log likelihood (default), ``+inf`` for a chi-squared. - """ - cov_part, like_part = self._split(cov_params) - if not np.all(np.isfinite(ctx.ym[self.active])): - return invalid - d2, logdet = self.covariance.stacked_distance(ctx, cov_part) - return statistic(d2, logdet, self.n_data_pts, *like_part) - - def log_likelihood(self, model_params, cov_params=()): - """Log likelihood of the stacked observations given the model. - - Parameters - ---------- - model_params : tuple - Physical-model parameters. - cov_params : tuple, optional - Constraint parameters: covariance params followed by likelihood - params, in :attr:`params` order. - """ - ctx = self._stack(model_params) - return self._evaluate(ctx, cov_params, self.likelihood.log_likelihood) - - def marginal_log_likelihood(self, ym: list, *cov_params): - """Log likelihood from pre-computed predictions (Gibbs hook). - - Parameters - ---------- - ym : list of np.ndarray - One prediction array per observation (no physical-model re-eval). - *cov_params : float - Constraint parameters, in :attr:`params` order. - """ - ctx = self._stack_from_predictions(ym) - return self._evaluate(ctx, cov_params, self.likelihood.log_likelihood) - - def chi2(self, model_params, cov_params=()): - """Generalised chi-squared (Mahalanobis distance) over the stack. - - ``cov_params`` is the full constraint tuple in :attr:`params` order, - including likelihood params (e.g. Student-t ``nu``) even though the - chi-squared statistic ignores them. - """ - ctx = self._stack(model_params) - return self._evaluate(ctx, cov_params, self.likelihood.chi2, invalid=np.inf) - - def predict(self, *model_params, raw=False): - """Predictions for each observation (all points, comparison space). - - With ``raw=True`` the predictions are returned in physical space (the - model's own output, before each observation's ``transform``). - """ - ym = [self.physical_model(obs, *model_params) for obs in self.observations] - if raw: - return ym - return [o.transform(y) for o, y in zip(self.observations, ym)] - - def _stack_and_covariance(self, model_params, cov_params, active_only): - """``(ctx, Sigma)`` at a parameter point; ``Sigma`` is a fresh copy.""" - ctx = self._stack(model_params) - cov_part, _ = self._split(cov_params) - if active_only: - return ctx, np.array(self.covariance.active_matrix(ctx, *cov_part)) - return ctx, np.array(self.covariance.matrix(ctx, *cov_part)) - - def predict_and_covariance(self, model_params, cov_params=()): - """Stacked prediction and covariance on the active points, one model call. - - Returns - ------- - (np.ndarray, np.ndarray) - ``(ym, Sigma)`` with ``ym`` of length ``n_data_pts`` (comparison - space) and ``Sigma`` a fresh ``(n_data_pts, n_data_pts)`` array. - """ - ctx, Sigma = self._stack_and_covariance(model_params, cov_params, True) - return ctx.ym[self.active], Sigma - - @property - def y(self) -> np.ndarray: - """Stacked observed data on the active points (comparison space).""" - return self._y_stacked[self.active] - - @property - def x(self) -> np.ndarray: - """Stacked independent variable on the active points.""" - return self._x_stacked[self.active] - - @property - def log_jacobian(self) -> float: - """Sum of the observations' comparison-space log-Jacobians (active points).""" - return float( - sum( - o.log_jacobian - for o, keep in zip(self.observations, self.observation_mask) - if keep - ) - ) - - def covariance_matrix(self, model_params, cov_params=(), active_only=True): - """Assemble the stacked covariance matrix Σ at a parameter point. - - Convenience accessor (e.g. for visualising the off-diagonal block - structure of correlated observations). - - Parameters - ---------- - model_params : tuple - Physical-model parameters (needed for prediction-scaled terms). - cov_params : tuple, optional - Constraint parameters: covariance params followed by likelihood - params, in :attr:`params` order (matching :meth:`log_likelihood`). - active_only : bool, optional - Restrict to the active points (default); ``False`` returns the - full stacked matrix. - - Returns - ------- - np.ndarray - Shape ``(n_data_pts, n_data_pts)`` (active points) or - ``(n_data_pts_total, n_data_pts_total)`` when ``active_only=False``. - A fresh copy (safe to mutate; never aliases the internal cache). - """ - _, Sigma = self._stack_and_covariance(model_params, cov_params, active_only) - return Sigma - - # ------------------------------------------------------------------ - # Coverage diagnostics - # ------------------------------------------------------------------ - - def num_pts_within_interval( - self, ylow: list[np.ndarray], yhigh: list[np.ndarray], xlim=None - ): - """Count data points that fall within a predictive interval.""" - return sum( - obs.num_pts_within_interval(ylow[i], yhigh[i], xlim) - for i, obs in enumerate(self.observations) - if self.observation_mask[i] - ) - - def empirical_coverage( - self, ylow: list[np.ndarray], yhigh: list[np.ndarray], xlim=None - ): - """Fraction of active data points within a predictive interval. - - ``nan`` when the constraint has no active points. - """ - if self.n_data_pts == 0: - return float("nan") - return self.num_pts_within_interval(ylow, yhigh, xlim) / self.n_data_pts diff --git a/src/rxmc/elastic_diffxs_model.py b/src/rxmc/elastic_diffxs_model.py deleted file mode 100644 index 6842a1f..0000000 --- a/src/rxmc/elastic_diffxs_model.py +++ /dev/null @@ -1,233 +0,0 @@ -""" -Physical model for elastic differential cross sections. - -:class:`ElasticDifferentialXSModel` wraps a ``jitr`` optical-model solver to -predict elastic differential cross sections (dXS/dΩ, dXS/dRuth, or analysing -power Ay) given a parametric central and spin-orbit interaction. -""" - -from typing import Callable - -import jitr -import numpy as np - -from .elastic_diffxs_observation import ElasticDifferentialXSObservation -from .observation_from_measurement import MB_PER_B -from .physical_model import PhysicalModel - - -def _require_observation(observation) -> None: - """Reject observations this model cannot evaluate on. - - Both reaction observations report ``quantity == "dXS/dA"``, so a string - check cannot tell them apart; the class carries the solver workspace the - model needs. - """ - if not isinstance(observation, ElasticDifferentialXSObservation): - raise ValueError( - "ElasticDifferentialXSModel requires an " - "ElasticDifferentialXSObservation, got " - f"{type(observation).__name__}" - ) - - -class ElasticDifferentialXSModel(PhysicalModel): - """ - A model that predicts the elastic differential xs for a given reaction. - """ - - def __init__( - self, - quantity: str, - interaction_central: Callable[..., np.ndarray], - interaction_spin_orbit: Callable[..., np.ndarray] | None, - calculate_interaction_from_params: Callable[ - [jitr.xs.elastic.DifferentialWorkspace, tuple], tuple - ], - params: list = [], - model_name: str = None, - interaction_coulomb: Callable[..., np.ndarray] | None = None, - transform=None, - ): - """ - Parameters - ---------- - quantity : str - Observable to compute: ``"dXS/dA"``, ``"dXS/dRuth"``, or ``"Ay"``. - interaction_central : callable - ``f(r, *args) -> np.ndarray`` returning the central interaction - potential on the radial grid ``r`` (fm), in MeV. - interaction_spin_orbit : callable or None - ``f(r, *args) -> np.ndarray`` returning the spin-orbit potential on - ``r``. ``None`` for a spin-orbit-free model. - calculate_interaction_from_params : callable - ``f(workspace, *params) -> (central_args, spin_orbit_args)`` or - ``-> (central_args, spin_orbit_args, coulomb_args)`` mapping model - parameters to the argument tuples expected by the interaction - callables. - params : list of Parameter, optional - Parameters of the model. Defaults to ``[]``. - model_name : str, optional - Human-readable model name. Defaults to ``"ElasticDifferentialXSModel"``. - interaction_coulomb : callable, optional - ``f(r, *args) -> np.ndarray`` returning the Coulomb potential on - ``r``. When ``None`` the Coulomb interaction inside the channel - radius must be folded into ``interaction_central``. - transform : Transform or callable, optional - Parametric model-side transform applied to the prediction; see - :class:`~rxmc.physical_model.PhysicalModel`. - """ - self.model_name = model_name or "ElasticDifferentialXSModel" - - self.quantity = quantity - self.interaction_central = interaction_central - self.interaction_spin_orbit = interaction_spin_orbit - self.interaction_coulomb = interaction_coulomb - self.calculate_interaction_from_params = calculate_interaction_from_params - - if self.quantity == "dXS/dA": - self.extractor = extract_dXS_dA - elif self.quantity == "dXS/dRuth": - self.extractor = extract_dXS_dRuth - elif self.quantity == "Ay": - self.extractor = extract_Ay - else: - raise ValueError( - f"Unknown quantity {quantity!r}; expected 'dXS/dA', 'dXS/dRuth' " - "or 'Ay'." - ) - - super().__init__(params, transform=transform) - - def _xs(self, ws, params): - """Evaluate the potentials on ``ws.radial_grid()`` and solve. - - ``calculate_interaction_from_params`` returns either two argument - tuples ``(central, spin_orbit)`` or three ``(central, spin_orbit, - coulomb)``; anything else is an error. - """ - args = self.calculate_interaction_from_params(ws, *params) - if len(args) == 2: - (central_args, spin_orbit_args), coulomb_args = args, () - elif len(args) == 3: - central_args, spin_orbit_args, coulomb_args = args - else: - raise ValueError( - "calculate_interaction_from_params must return 2 or 3 argument " - f"tuples, got {len(args)}" - ) - r = ws.radial_grid() - central = self.interaction_central(r, *central_args) - spin_orbit = ( - None - if self.interaction_spin_orbit is None - else self.interaction_spin_orbit(r, *spin_orbit_args) - ) - coulomb = ( - None - if self.interaction_coulomb is None - else self.interaction_coulomb(r, *coulomb_args) - ) - return ws.xs(central, spin_orbit, coulomb) - - def evaluate( - self, - observation: ElasticDifferentialXSObservation, - *params: tuple, - ) -> np.ndarray: - """ - Evaluate the model on the constraint angular grid. - - Parameters - ---------- - observation : ElasticDifferentialXSObservation - Observation containing the reaction data and pre-built workspace. - *params : float - Physical-model parameter values. - - Returns - ------- - np.ndarray - Predicted observable on ``observation.constraint_workspace.angles``. - """ - _require_observation(observation) - if observation.quantity != self.quantity: - raise ValueError( - f"Observation quantity {observation.quantity} does not match " - f"model quantity {self.quantity}." - ) - ws = observation.constraint_workspace - xs = self._xs(ws, params) - if observation.compound_correction is not None: - if observation.quantity not in ["dXS/dA", "dXS/dRuth"]: - raise ValueError( - "Compound correction can only be applied to dXS/dA and dXS/dRuth." - ) - xs.dsdo += observation.compound_correction - xs.t += 2 * np.pi * np.trapz(observation.compound_correction, ws.angles) - return self.extractor(xs, ws) - - def visualizable_model_prediction( - self, - observation: ElasticDifferentialXSObservation, - *params: tuple, - ) -> np.ndarray: - """ - Evaluate the model on the visualisation angular grid. - - Parameters - ---------- - observation : ElasticDifferentialXSObservation - Observation containing the reaction data and pre-built workspace. - *params : float - Full model parameter values (physical parameters followed by any - transform parameters). - - Returns - ------- - np.ndarray - Predicted observable on ``observation.visualization_workspace.angles``. - """ - _require_observation(observation) - if observation.quantity != self.quantity: - raise ValueError( - f"Observation quantity {observation.quantity} does not match " - f"model quantity {self.quantity}." - ) - base, values = self.split_params(params) - ws = observation.visualization_workspace - xs = self._xs(ws, base) - if observation.compound_correction is not None: - cn = np.interp( - ws.angles, - observation.constraint_workspace.angles, - observation.compound_correction, - ) - if observation.quantity not in ["dXS/dA", "dXS/dRuth"]: - raise ValueError( - "Compound correction can only be applied to dXS/dA and dXS/dRuth." - ) - xs.dsdo += cn - xs.t += 2 * np.pi * np.trapz(cn, ws.angles) - return self.apply_transform(observation, self.extractor(xs, ws), values) - - -def extract_dXS_dA( - xs: jitr.xs.elastic.ElasticXS, ws: jitr.xs.elastic.DifferentialWorkspace -) -> np.ndarray: - """Extracts dXS/dA in b/Sr (``jitr`` reports mb/sr).""" - return xs.dsdo / MB_PER_B - - -def extract_dXS_dRuth( - xs: jitr.xs.elastic.ElasticXS, ws: jitr.xs.elastic.DifferentialWorkspace -) -> np.ndarray: - """Extracts dXS/dRuth (dimensionlesss)""" - return xs.dsdo / ws.rutherford - - -def extract_Ay( - xs: jitr.xs.elastic.ElasticXS, ws: jitr.xs.elastic.DifferentialWorkspace -) -> np.ndarray: - """Extracts Ay (dimensionless)""" - return xs.Ay diff --git a/src/rxmc/evidence.py b/src/rxmc/evidence.py deleted file mode 100644 index 2940486..0000000 --- a/src/rxmc/evidence.py +++ /dev/null @@ -1,173 +0,0 @@ -""" -Evidence: aggregate of independent constraints for Bayesian calibration. - -An :class:`Evidence` object collects multiple :class:`~rxmc.constraint.Constraint` -objects that share the same physical-model parameters. It computes a joint log -likelihood by summing the individual constraint log likelihoods (optionally -weighted). Each constraint owns its own (possibly correlated) covariance over the -stack of its observations; constraints are assumed independent of one another, so -``Evidence`` is a plain sum. Parametric constraints (those with free covariance / -likelihood parameters) are auto-detected via ``constraint.n_params > 0``. -""" - -import numpy as np - -from .constraint import Constraint - - -class Evidence: - """A collection of independent constraints sharing a common physical model. - - Parameters - ---------- - constraints : list of Constraint - All constraints. Those with ``n_params > 0`` are exposed (in order) as - :attr:`parametric_constraints`. - weights : np.ndarray, optional - 1-D array of per-constraint weights. Defaults to all ones. - - Raises - ------ - ValueError - If *constraints* is empty, if any constraint uses different - physical-model parameters than the first, if the model is - under-constrained, or if *weights* does not match the constraint count. - - Attributes - ---------- - constraints : list of Constraint - All constraints. - parametric_constraints : list of Constraint - The subset with ``n_params > 0``, in the order they appear. - parametric_indices : list of int - Global index into :attr:`constraints` for each entry of - :attr:`parametric_constraints` (e.g. to look up its :attr:`weights` - entry). - """ - - def __init__( - self, - constraints: list[Constraint] | None = None, - weights: np.ndarray = None, - ): - constraints = list(constraints or []) - if len(constraints) == 0: - raise ValueError("'constraints' must not be empty") - - self.constraints = constraints - self.model_params = constraints[0].physical_model.params - for constraint in self.constraints: - if constraint.physical_model.params != self.model_params: - raise ValueError( - "All constraints must use the same physical model parameters" - ) - - self._validate_constraint_params() - - parametric = [(i, c) for i, c in enumerate(self.constraints) if c.n_params > 0] - self.parametric_indices = [i for i, _ in parametric] - self.parametric_constraints = [c for _, c in parametric] - self.n_likelihood_params = sum(c.n_params for c in self.parametric_constraints) - - self.n_params = len(self.model_params) + self.n_likelihood_params - self.n_data_pts = sum(c.n_data_pts for c in self.constraints) - self.n_dof = self.n_data_pts - self.n_params - if self.n_dof < 0: - raise ValueError( - f"Model under-constrained! {self.n_params} free parameters " - f"and {self.n_data_pts} data points" - ) - - if weights is None: - weights = np.ones((len(self.constraints),), dtype=float) - elif weights.shape != (len(self.constraints),): - raise ValueError( - "weights must be a 1D array with the same shape as constraints" - ) - self.weights = weights - - def _validate_constraint_params(self): - """Reject cross-constraint parameter sharing and duplicate names. - - Covariance/likelihood parameters are constraint-scoped (see - ``docs/design.md`` §8): the same ``Parameter`` object in two - constraints would silently be sampled as two independent values. - Names must also be unique across the whole Evidence — they label - sampler columns, priors, and corner-plot axes. - """ - seen_id = {} # id(p) -> constraint index - seen_name = {} # p.name -> constraint index - for ci, c in enumerate(self.constraints): - for p in c.params: - if id(p) in seen_id: - raise ValueError( - f"Parameter '{p.name}' is the same object in constraints " - f"{seen_id[id(p)]} and {ci}. Covariance/likelihood " - "parameters are constraint-scoped and cannot be shared " - "across constraints. To model a systematic shared " - "between datasets, place those datasets in ONE " - "Constraint with a cross-block coupling term." - ) - seen_id[id(p)] = ci - if p.name in seen_name: - raise ValueError( - f"Duplicate parameter name '{p.name}' in constraints " - f"{seen_name[p.name]} and {ci}. Parameter names label " - "sampler columns and must be unique across the " - "Evidence; rename one (e.g. suffix it with the dataset " - "label)." - ) - seen_name[p.name] = ci - - def log_likelihood(self, model_params, cov_params: list | None = None): - """Weighted sum of log likelihoods over all constraints. - - Parameters - ---------- - model_params : tuple - Physical-model parameters. - cov_params : list of tuple, optional - One tuple of constraint parameters per entry in - :attr:`parametric_constraints` (in that order). Defaults to ``[]``. - - Returns - ------- - float - Total weighted log likelihood. - """ - cov_params = cov_params or [] - if len(cov_params) != len(self.parametric_constraints): - raise ValueError( - f"Expected {len(self.parametric_constraints)} constraint parameter " - f"tuples, got {len(cov_params)}" - ) - - ll = 0.0 - pidx = 0 - for w, c in zip(self.weights, self.constraints): - cp = () - if c.n_params > 0: - cp = cov_params[pidx] - pidx += 1 - ll += c.log_likelihood(model_params, cp) * w - return ll - - def weighted_marginal_log_likelihood(self, lm_index, ym, *cov_params): - """Weighted marginal log likelihood of one parametric constraint. - - Applies the same :attr:`weights` entry that :meth:`log_likelihood` - uses for this constraint, so Gibbs-style conditional updates target - the same joint distribution as the model block. - - Parameters - ---------- - lm_index : int - Index into :attr:`parametric_constraints`. - ym : list of np.ndarray - One prediction array per observation of that constraint. - *cov_params : float - The constraint's parameters, in its ``params`` order. - """ - w = self.weights[self.parametric_indices[lm_index]] - c = self.parametric_constraints[lm_index] - return w * c.marginal_log_likelihood(ym, *cov_params) diff --git a/src/rxmc/ias_pn_model.py b/src/rxmc/ias_pn_model.py deleted file mode 100644 index a8cee77..0000000 --- a/src/rxmc/ias_pn_model.py +++ /dev/null @@ -1,177 +0,0 @@ -""" -Physical model for isobaric-analog-state (p,n) differential cross sections. - -:class:`IsobaricAnalogPNXSModel` wraps a ``jitr`` quasielastic-pn solver to -predict (p,n) IAS differential cross sections given five parametric interaction -potentials (proton Coulomb, proton central, proton spin-orbit, neutron central, -neutron spin-orbit). -""" - -from typing import Callable - -import jitr -import numpy as np - -from .ias_pn_observation import IsobaricAnalogPNObservation -from .observation_from_measurement import MB_PER_B -from .physical_model import PhysicalModel - - -def _require_observation(observation) -> None: - """Reject observations this model cannot evaluate on. - - Both reaction observations report ``quantity == "dXS/dA"``, so a string - check cannot tell them apart; the class carries the solver workspace the - model needs. - """ - if not isinstance(observation, IsobaricAnalogPNObservation): - raise ValueError( - "IsobaricAnalogPNXSModel requires an IsobaricAnalogPNObservation, " - f"got {type(observation).__name__}" - ) - - -class IsobaricAnalogPNXSModel(PhysicalModel): - """ - A model that predicts the (p,n) IAS differential xs for a given reaction. - This model requires five interaction potentials: - - Proton Coulomb potential: U_p_coulomb - - Proton central potential: U_p_central - - Proton spin-orbit potential: U_p_spin_orbit - - Neutron central potential: U_n_central - - Neutron spin-orbit potential: U_n_spin_orbit - - Each potential takes in an arbitrary tuple of params, which are - calculated from the model parameters via the `calculate_params` function. - - The ``calculate_params`` function should have the signature: - ``(ws: jitr.xs.quasielastic_pn.Workspace, *params: tuple) -> tuple`` - and return a tuple of five elements, each being a tuple of parameters - to be passed to the corresponding potential function in the order listed above. - """ - - def __init__( - self, - U_p_coulomb: Callable[[float, tuple], complex], - U_p_central: Callable[[float, tuple], complex], - U_p_spin_orbit: Callable[[float, tuple], complex], - U_n_central: Callable[[float, tuple], complex], - U_n_spin_orbit: Callable[[float, tuple], complex], - calculate_params: Callable[[jitr.xs.quasielastic_pn.Workspace, tuple], tuple], - params: list = [], - model_name: str = None, - transform=None, - ): - """ - Parameters - ---------- - U_p_coulomb : callable - ``f(r, *args) -> np.ndarray`` on the radial grid ``r``: the proton Coulomb - potential. - U_p_central : callable - ``f(r, *args) -> np.ndarray`` on the radial grid ``r``: the proton central - potential. - U_p_spin_orbit : callable - ``f(r, *args) -> np.ndarray`` on the radial grid ``r``: the proton spin-orbit - potential. - U_n_central : callable - ``f(r, *args) -> np.ndarray`` on the radial grid ``r``: the neutron central - potential. - U_n_spin_orbit : callable - ``f(r, *args) -> np.ndarray`` on the radial grid ``r``: the neutron spin-orbit - potential. - calculate_params : callable - ``f(workspace, *params) -> (args_p_coulomb, args_p_central, - args_p_spin_orbit, args_n_central, args_n_spin_orbit)`` - mapping model parameters to the argument tuples expected by each - potential callable. - params : list of Parameter, optional - Parameters of the model. Defaults to ``[]``. - model_name : str, optional - Human-readable model name. Defaults to ``"IsobaricAnalogPNXSModel"``. - transform : Transform or callable, optional - Parametric model-side transform applied to the prediction; see - :class:`~rxmc.physical_model.PhysicalModel`. - """ - self.model_name = model_name or "IsobaricAnalogPNXSModel" - self.U_p_coulomb = U_p_coulomb - self.U_p_central = U_p_central - self.U_p_spin_orbit = U_p_spin_orbit - self.U_n_central = U_n_central - self.U_n_spin_orbit = U_n_spin_orbit - self.calculate_params = calculate_params - - super().__init__(params, transform=transform) - - def _xs(self, ws, params) -> np.ndarray: - """Evaluate the five potentials on ``ws.radial_grid()`` and solve (b/sr).""" - ( - args_p_coulomb, - args_p_central, - args_p_spin_orbit, - args_n_central, - args_n_spin_orbit, - ) = self.calculate_params(ws, *params) - r = ws.radial_grid() - return ( - ws.xs( - self.U_p_coulomb(r, *args_p_coulomb), - self.U_p_central(r, *args_p_central), - self.U_p_spin_orbit(r, *args_p_spin_orbit), - self.U_n_central(r, *args_n_central), - self.U_n_spin_orbit(r, *args_n_spin_orbit), - ) - / MB_PER_B # jitr reports mb/sr; internal unit is b/sr - ) - - def evaluate( - self, - observation: IsobaricAnalogPNObservation, - *params: tuple, - ) -> np.ndarray: - """ - Evaluate the model on the constraint angular grid. - - Parameters - ---------- - observation : IsobaricAnalogPNObservation - Observation containing the pre-built workspace. - *params : float - Physical-model (base) parameter values, consumed by - *calculate_params*; transform parameters are split off by - ``__call__``. - - Returns - ------- - np.ndarray - Predicted (p,n) IAS differential cross section in b/sr on - ``observation.constraint_workspace.angles``. - """ - _require_observation(observation) - return self._xs(observation.constraint_workspace, params) - - def visualizable_model_prediction( - self, - observation: IsobaricAnalogPNObservation, - *params: tuple, - ) -> np.ndarray: - """ - Evaluate the model on the visualisation angular grid. - - Parameters - ---------- - observation : IsobaricAnalogPNObservation - Observation containing the pre-built workspace. - *params : float - Physical-model parameter values, consumed by *calculate_params*. - - Returns - ------- - np.ndarray - Predicted (p,n) IAS differential cross section in b/sr on - ``observation.visualization_workspace.angles``. - """ - _require_observation(observation) - base, values = self.split_params(params) - xs = self._xs(observation.visualization_workspace, base) - return self.apply_transform(observation, xs, values) diff --git a/src/rxmc/metropolis_hastings.py b/src/rxmc/metropolis_hastings.py deleted file mode 100644 index 6d158a1..0000000 --- a/src/rxmc/metropolis_hastings.py +++ /dev/null @@ -1,73 +0,0 @@ -""" -Plain Metropolis-Hastings MCMC sampler. - -The :func:`metropolis_hastings` function implements a single-chain MH kernel -with hard parameter bounds and a user-supplied proposal distribution. -""" - -from typing import Callable, Tuple - -import numpy as np - - -def metropolis_hastings( - x0: np.ndarray, - bounds: np.ndarray, - n_steps: int, - log_posterior: Callable[[np.ndarray], float], - rng: np.random.Generator, - propose: Callable[[np.ndarray, np.random.Generator], np.ndarray], -) -> Tuple[np.ndarray, np.ndarray, int]: - """Metropolis-Hastings MCMC sampling. - - Proposals that fall outside *bounds* are rejected outright; otherwise the - standard MH acceptance criterion is applied. - - Parameters - ---------- - x0 : np.ndarray, shape (ndim,) - Initial parameter vector. - bounds : np.ndarray, shape (ndim, 2) - Parameter bounds; each row is ``[lower, upper]``. - n_steps : int - Number of MCMC steps to generate. - log_posterior : callable - Function ``f(x) -> float`` returning the log posterior at ``x``. - rng : np.random.Generator - Random number generator for reproducibility. - propose : callable - Function ``g(x, rng) -> x_new`` that draws a candidate from the - proposal distribution centred at ``x``. - - Returns - ------- - chain : np.ndarray, shape (n_steps, ndim) - Sampled parameter vectors. - logp_chain : np.ndarray, shape (n_steps,) - Log posterior values corresponding to each sample. - accepted : int - Number of accepted proposals. - """ - chain = np.zeros((n_steps, x0.size)) - logp_chain = np.zeros((n_steps,)) - logp = float(np.squeeze(log_posterior(x0))) - accepted = 0 - x = x0 - for i in range(n_steps): - x_new = propose(x, rng) - if np.any(x_new < bounds[:, 0]) or np.any(x_new > bounds[:, 1]): - chain[i, ...] = x - logp_chain[i] = logp - continue - logp_new = float(np.squeeze(log_posterior(x_new))) - log_ratio = min(0, logp_new - logp) - xi = np.log(rng.random()) - if xi < log_ratio: - x = x_new - logp = logp_new - accepted += 1 - - chain[i, ...] = x - logp_chain[i] = logp - - return chain, logp_chain, accepted diff --git a/src/rxmc/observation.py b/src/rxmc/observation.py deleted file mode 100644 index 95a177a..0000000 --- a/src/rxmc/observation.py +++ /dev/null @@ -1,354 +0,0 @@ -""" -Observation: a leaf of experimental data. - -An :class:`Observation` is *pure data* — an independent variable ``x``, a -dependent variable ``y``, and the statistical error ``y_stat_err`` on ``y``. It -emits **only** its statistical diagonal, via :meth:`Observation.statistical_term`. - -Every *correlated* mode — a dataset's own normalisation/offset systematic, an -unknown-noise term, a cross-dataset coupling — is an **explicit** -:class:`~rxmc.covariance.Term` added at constraint-assembly time (see -:mod:`rxmc.covariance`). Nothing correlated is hidden in a default. This is a -deliberate change from the old behaviour, which folded normalisation/offset into -``Observation.covariance`` silently; there is no compatibility path that -re-folds them. - -An observation may still *carry* its reported systematic magnitudes -(``y_sys_err_normalization``, ``y_sys_err_offset``) as **inert metadata** — -provenance from the measurement. :meth:`Observation.systematic_terms` turns them -into fixed-magnitude rank-one terms, but only when the caller asks: pass its -result via ``Constraint(extra_terms=...)``. -""" - -import copy - -import numpy as np - -from .covariance import offset_term, statistical_term, systematic_term -from .transforms import as_transform - - -def _as_point_mask(mask, n) -> np.ndarray: - """Coerce a point mask to a boolean array of shape ``(n,)``.""" - mask = np.asarray(mask, dtype=bool) - if mask.shape != (n,): - raise ValueError(f"mask must have shape ({n},), got {mask.shape}") - return mask - - -def _store_error_spec(value, n, name): - """Validate/normalize a systematic-error spec: None, scalar, or shape (n,).""" - if value is None: - return None - if np.ndim(value) == 0: - return float(value) - v = np.asarray(value, dtype=float) - if v.shape != (n,): - raise ValueError( - f"{name} must be a scalar or have shape ({n},), got shape {v.shape}" - ) - return v - - -class Observation: - """Experimental data: ``x``, ``y``, and the statistical error on ``y``. - - Parameters - ---------- - x : np.ndarray - Independent-variable data. - y : np.ndarray - Dependent-variable data, same shape as ``x``. - y_stat_err : np.ndarray, optional - Statistical (uncorrelated) error on ``y``. Defaults to zeros. - y_sys_err_normalization : float or np.ndarray, optional - Reported *fractional* (dimensionless) normalisation uncertainty — - inert metadata; see :meth:`systematic_terms`. - y_sys_err_offset : float or np.ndarray, optional - Reported *absolute* offset uncertainty, in the same units as ``y`` — - inert metadata; see :meth:`systematic_terms`. - label : str, optional - Human-readable dataset identifier used in error messages. - transform : Transform or callable, optional - Parameter-free *comparison-space* transform (see :mod:`rxmc.transforms`). - Pass **raw** ``y``: the observation stores ``y = transform(y_raw)`` and - propagates ``y_stat_err`` by the delta method, and the - :class:`~rxmc.constraint.Constraint` applies the same transform to the - model prediction — so ``transform=rxmc.transforms.log`` compares in log - space with the model written once, in physical space. - mask : array_like of bool, optional - Which points are *active* in a likelihood (default all). Inactive - points stay in the block (supports/terms are authored over all points) - but are excluded from the residual; use :meth:`masked` / - :meth:`masked_where` to derive fit/held-out views. - - Attributes - ---------- - x, y : np.ndarray - The data, ``y`` in comparison space. - y_raw, y_stat_err_raw : np.ndarray - ``y`` and its statistical error as given (physical space). - y_stat_err : np.ndarray - Statistical error on ``y`` in comparison space (raw, not squared). - transform : Transform - The comparison-space transform (identity by default). - mask : np.ndarray of bool - Active points. - identity : Observation - The root observation this one is a view of. Views made by - :meth:`masked` share it, so anything routing by observation (e.g. - :func:`rxmc.transforms.per_observation_scaling`) treats a masked view - and its root as the same dataset. - y_sys_err_normalization : float or np.ndarray or None - Fractional normalisation uncertainty (dimensionless). - y_sys_err_offset : float or np.ndarray or None - Absolute offset uncertainty (units of ``y``). - label : str or None - Human-readable dataset identifier. - n_data_pts : int - Number of data points. - """ - - def __init__( - self, - x: np.ndarray, - y: np.ndarray, - y_stat_err=None, - y_sys_err_normalization=None, - y_sys_err_offset=None, - label=None, - transform=None, - mask=None, - ): - self.label = label - self.identity = self - self.x = np.asarray(x) - y_raw = np.asarray(y, dtype=float) - if self.x.shape != y_raw.shape: - raise ValueError( - "x and y must have the same shape, they have shapes " - f"{self.x.shape} and {y_raw.shape}" - ) - self.n_data_pts = self.x.shape[0] - - y_stat_err = y_stat_err if y_stat_err is not None else np.zeros_like(y_raw) - y_stat_err = np.asarray(y_stat_err, dtype=float) - if y_stat_err.shape != y_raw.shape: - raise ValueError( - "y_stat_err must have the same shape as y, " - f"it has shape {y_stat_err.shape} and y has shape {y_raw.shape}" - ) - - self.transform = as_transform(transform) - if self.transform.params: - raise ValueError( - "an Observation's comparison-space transform must be parameter-free" - ) - self.y_raw = y_raw - self.y_stat_err_raw = y_stat_err - self.mask = ( - np.ones(self.n_data_pts, dtype=bool) - if mask is None - else _as_point_mask(mask, self.n_data_pts) - ) - if self.transform.is_identity: - self.y = y_raw - self.y_stat_err = y_stat_err - self._abs_jacobian = None - else: - # |t'(y_raw)|: the delta-method factor, reused by log_jacobian and - # systematic_terms - # inactive points may be non-finite (inf * 0 -> nan); the guard - # below only inspects the active ones - with np.errstate(invalid="ignore", divide="ignore"): - self._abs_jacobian = np.abs(self.transform.derivative(y_raw)) - self.y = self.transform(y_raw) - self.y_stat_err = self._abs_jacobian * y_stat_err - self._check_finite() - - self.y_sys_err_normalization = _store_error_spec( - y_sys_err_normalization, self.n_data_pts, "y_sys_err_normalization" - ) - self.y_sys_err_offset = _store_error_spec( - y_sys_err_offset, self.n_data_pts, "y_sys_err_offset" - ) - - def _check_finite(self): - """Reject non-finite comparison-space values at the active points.""" - if self.transform.is_identity: - return - bad = self.mask & ~(np.isfinite(self.y) & np.isfinite(self.y_stat_err)) - if np.any(bad): - raise ValueError( - f"transform {self.transform.name!r} is not finite at " - f"{int(bad.sum())} active data point(s) of dataset " - f"{self.label or 'observation'!r} (e.g. non-positive y under a " - "log transform); mask or drop those points" - ) - - # ------------------------------------------------------------------ - # Masks (active points) - # ------------------------------------------------------------------ - - @property - def n_active(self) -> int: - """Number of active (unmasked) points.""" - return int(self.mask.sum()) - - def masked(self, mask, label=None): - """A shallow copy of this observation with a new point mask. - - No data or pre-computed workspaces are rebuilt: the copy shares them and - only changes which points enter a likelihood. - """ - new = copy.copy(self) - new.mask = _as_point_mask(mask, self.n_data_pts) - if label is not None: - new.label = label - new._check_finite() - return new - - def masked_where(self, predicate, label=None): - """:meth:`masked` with ``mask = predicate(x)`` (points where it is True).""" - return self.masked(np.asarray(predicate(self.x), dtype=bool), label=label) - - # ------------------------------------------------------------------ - # Comparison-space bookkeeping - # ------------------------------------------------------------------ - - @property - def log_jacobian(self) -> float: - r"""``sum(log |t'(y_raw)|)`` over the active points. - - The log-Jacobian of the comparison-space transform: a constant in the - parameters, needed only to compare marginal likelihoods (log Z) across - different comparison spaces (``log Z_raw = log Z_transformed + - log_jacobian``). Zero for the identity. - """ - if self.transform.is_identity: - return 0.0 - return float(np.sum(np.log(self._abs_jacobian[self.mask]))) - - def _raw_prediction(self, ym): - """Invert the comparison-space transform on a prediction.""" - if self.transform.is_identity: - return np.asarray(ym, dtype=float) - inv = self.transform.inverse - if inv is None: - raise ValueError( - f"transform {self.transform.name!r} has no inverse; cannot map " - "predictions back to physical space" - ) - return inv(ym) - - # ------------------------------------------------------------------ - # Covariance terms - # ------------------------------------------------------------------ - - def statistical_term(self, support=None): - """The always-on, genuinely uncorrelated statistical diagonal. - - Parameters - ---------- - support : np.ndarray, optional - Indices of this observation's block in the stacked vector - (``None`` for a single-observation constraint). - - Returns - ------- - Term - ``diag(y_stat_err**2)`` on ``support`` (comparison space). - """ - return statistical_term(self.y_stat_err, support=support) - - def systematic_terms(self, support=None) -> list: - """This dataset's reported correlated systematics as fixed rank-one terms. - - Opt-in — **not** added to any covariance automatically. Pass the result - via ``Constraint(extra_terms=[*obs.systematic_terms(), ...])``. - Zero magnitudes are skipped, so an observation without reported - systematics yields an empty list. Magnitudes are reported in physical - space and propagated to the comparison space by the delta method - (``|t'| * omega`` for an offset, ``|t'(ym_raw)| * eta * ym_raw`` for a - normalisation). - - Parameters - ---------- - support : np.ndarray, optional - Indices of this observation's block in the stacked vector. ``None`` - binds the terms to the whole constraint, which is right only for a - single-observation constraint; in a multi-observation constraint - they then fail loudly with a shape error, so pass the block's - support there (see :func:`rxmc.covariance.stacked_supports`). - - Returns - ------- - list of Term - The absolute offset mode (``outer(omega, omega)``) first, then the - fractional, prediction-scaled normalisation mode - (``eta**2 * outer(ym, ym)``). - """ - - def reported(spec): - if spec is None or not np.any(np.asarray(spec) != 0.0): - return None - return np.broadcast_to(np.asarray(spec, dtype=float), (self.n_data_pts,)) - - # the identity transform has unit Jacobian and trivial inverse, so the - # delta-method expressions below reduce to the plain magnitudes. The - # two modes are linearised at different points on purpose: the offset - # is an error on the *data*, so it is propagated at y_raw; the - # normalisation multiplies the *prediction*, so its mode eta * ym_raw is - # propagated at ym_raw. - t = self.transform - terms = [] - omega = reported(self.y_sys_err_offset) - if omega is not None: - if not t.is_identity: - omega = self._abs_jacobian * omega - terms.append(offset_term(magnitude=omega, support=support)) - eta = reported(self.y_sys_err_normalization) - if eta is not None: - if not t.is_identity and t.inverse is None: - raise ValueError( - f"transform {t.name!r} has no inverse; the normalisation " - "systematic needs the physical-space prediction" - ) - - def basis(c): - ym_raw = self._raw_prediction(c.ym) - return eta * ym_raw * np.abs(t.derivative(ym_raw)) - - terms.append(systematic_term(None, basis, support=support)) - return terms - - def num_pts_within_interval( - self, - ylow: np.ndarray, - yhigh: np.ndarray, - xlim=None, - ): - """Number of active points of ``y`` that fall within ``[ylow, yhigh)``. - - Useful for empirical-coverage diagnostics. ``ylow``/``yhigh`` are in - comparison space and indexed over *all* points of the block. - - Parameters - ---------- - ylow, yhigh : np.ndarray - Interval bounds, same shape as ``y``. - xlim : tuple, optional - ``(x_min, x_max)`` range to restrict the count. - """ - mask = self.mask.copy() - if xlim is not None: - xlow, xhigh = xlim - mask &= np.logical_and(self.x >= xlow, self.x < xhigh) - return int( - np.sum( - np.logical_and( - self.y[mask] >= ylow[mask], - self.y[mask] < yhigh[mask], - ) - ) - ) diff --git a/src/rxmc/param_sampling.py b/src/rxmc/param_sampling.py deleted file mode 100644 index 7064b41..0000000 --- a/src/rxmc/param_sampling.py +++ /dev/null @@ -1,407 +0,0 @@ -""" -Sampler classes wrapping MCMC algorithms for parameter estimation. - -:class:`Sampler` is the base class; it wraps any sampling algorithm function, -records the chain and acceptance statistics, and manages state across batches. -Three concrete subclasses are provided: - -- :class:`MetropolisHastingsSampler` — plain MH with a fixed proposal. -- :class:`AdaptiveMetropolisSampler` — sliding-window covariance adaptation. -- :class:`BatchedAdaptiveMetropolisSampler` — per-batch covariance adaptation. -""" - -from typing import Callable - -import numpy as np - -from . import params, proposal -from .adaptive_metropolis import adaptive_metropolis -from .metropolis_hastings import metropolis_hastings -from .priors import as_prior - - -class Sampler: - """Base class wrapping a sampling algorithm with chain recording. - - Parameters - ---------- - params : list of Parameter - Parameters to sample. - prior : object - Prior distribution with a callable ``logpdf(x)`` method. - starting_location : np.ndarray, shape (ndim,) - Initial parameter vector. - sampling_algorithm : callable - Function implementing the sampling algorithm. Must have the - signature ``f(x0, bounds, n_steps, log_posterior, rng, *args, **kwargs)`` - and return ``(chain, logp_chain, n_accepted)``. - args : tuple, optional - Extra positional arguments passed to *sampling_algorithm*. - kwargs : dict, optional - Extra keyword arguments passed to *sampling_algorithm*. - """ - - def __init__( - self, - params: list[params.Parameter], - prior, - starting_location: np.ndarray, - sampling_algorithm, - args: tuple = None, - kwargs: dict = None, - ): - self.params = params - self.starting_location = starting_location - # a list of scipy marginals becomes an IndependentPrior, as in - # ParameterConfig, so both drivers accept the same prior forms - self.prior = as_prior(prior) - self.sampling_algorithm = sampling_algorithm - self.args = args if args is not None else () - self.kwargs = kwargs if kwargs is not None else {} - self.batches_run = 0 - self.n_steps = [] - self.n_accepted = [] - self.chain = np.empty((0, starting_location.size)) - self.logp_chain = np.empty((0,)) - self.state = np.atleast_1d(starting_location) - self.bounds = np.array([param.bounds for param in params]) - - _validate_object( - self.prior, - "prior", - required_methods=["logpdf"], - ) - - def record_batch( - self, n_steps: int, n_accepted: int, chain: np.ndarray, logp_chain: np.ndarray - ): - """Append a completed batch to the running chain. - - Parameters - ---------- - n_steps : int - Number of steps in the batch. - n_accepted : int - Number of accepted proposals in the batch. - chain : np.ndarray, shape (n_steps, ndim) - Sampled parameter vectors. - logp_chain : np.ndarray, shape (n_steps,) - Log posterior values for the batch. - """ - self.batches_run += 1 - self.n_steps.append(n_steps) - self.n_accepted.append(n_accepted) - self.chain = np.concatenate((self.chain, chain), axis=0) - self.logp_chain = np.concatenate((self.logp_chain, logp_chain), axis=0) - - def sample( - self, - n_steps: int, - starting_location: np.ndarray, - rng: np.random.Generator, - log_posterior: Callable[[np.ndarray], float], - burn: bool = False, - ): - """Run the sampling algorithm for one batch. - - Updates ``self.state`` to the last sample; records the batch unless - *burn* is ``True``. - - Parameters - ---------- - n_steps : int - Number of steps to run. - starting_location : np.ndarray, shape (ndim,) - Starting parameter vector for this batch. - rng : np.random.Generator - Random number generator. - log_posterior : callable - Function ``f(x) -> float`` returning the log posterior at ``x``. - burn : bool, optional - If ``True``, discard samples (burn-in); only ``self.state`` is - updated. Defaults to ``False``. - """ - chain, logp_chain, accepted = self.sampling_algorithm( - starting_location, - self.bounds, - n_steps, - log_posterior, - rng, - *self.args, - **self.kwargs, - ) - self.state = np.atleast_1d(chain[-1, :]) - - if not burn: - self.record_batch(n_steps, accepted, chain, logp_chain) - - def most_recent_batch_acceptance_fraction(self) -> float: - """Acceptance fraction of the most recent batch. - - Returns - ------- - float - Fraction of proposals accepted in the last batch, or ``0.0`` if - no batches have been run. - """ - if self.batches_run == 0: - return 0.0 - return self.n_accepted[-1] / self.n_steps[-1] - - def batch_acceptance_fractions(self) -> np.ndarray: - """Acceptance fraction for each completed batch. - - Returns - ------- - np.ndarray - Per-batch acceptance fractions, or ``[0.0]`` if no batches run. - """ - if self.batches_run == 0: - return np.array([0.0]) - return np.array(self.n_accepted) / np.array(self.n_steps) - - def overall_acceptance_fraction(self) -> float: - """Overall acceptance fraction across all completed batches. - - Returns - ------- - float - Total accepted / total proposed, or ``0.0`` if no batches run. - """ - if self.batches_run == 0: - return 0.0 - return sum(self.n_accepted) / sum(self.n_steps) - - -class MetropolisHastingsSampler(Sampler): - """Metropolis-Hastings sampler with a fixed proposal distribution. - - Parameters - ---------- - params : list of Parameter - Parameters to sample. - prior : object - Prior distribution with a callable ``logpdf(x)`` method. - starting_location : np.ndarray, shape (ndim,) - Initial parameter vector. - proposal : ProposalDistribution - Callable proposal distribution. Must accept ``(x, rng)`` and return - a proposed parameter vector. - """ - - def __init__( - self, - params: list[params.Parameter], - prior, - starting_location: np.ndarray, - proposal: proposal.ProposalDistribution, - ): - if not callable(proposal): - raise ValueError( - "The proposal must be a callable object that takes in a " - "parameter vector and an rng returns a proposed parameter" - " vector." - ) - self.proposal = proposal - super().__init__( - params, - prior, - starting_location, - metropolis_hastings, - args=[self.proposal], - kwargs={}, - ) - - -class AdaptiveMetropolisSampler(Sampler): - """Metropolis sampler that adapts the proposal covariance with a sliding window. - - The proposal covariance is estimated from the last *window_size* samples - after *adapt_start* steps have been collected. - - Parameters - ---------- - params : list of Parameter - Parameters to sample. - prior : object - Prior distribution with a callable ``logpdf(x)`` method. - starting_location : np.ndarray, shape (ndim,) - Initial parameter vector. - adapt_start : int, optional - Step at which adaptation begins. Defaults to ``100``. - window_size : int, optional - Number of past samples used for covariance estimation. - Defaults to ``1000``. - epsilon_fraction : float, optional - Small regularisation term for the proposal covariance. - Defaults to ``1e-6``. - """ - - def __init__( - self, - params: list[params.Parameter], - prior, - starting_location: np.ndarray, - adapt_start: int = 100, - window_size: int = 1000, - epsilon_fraction: float = 1e-6, - ): - super().__init__( - params, - prior, - starting_location, - adaptive_metropolis, - args=[], - kwargs={ - "adapt_start": adapt_start, - "window_size": window_size, - "epsilon_fraction": epsilon_fraction, - }, - ) - - def sample( - self, - n_steps: int, - starting_location: np.ndarray, - rng: np.random.Generator, - log_posterior: Callable[[np.ndarray], float], - burn: bool = False, - ): - """Run the adaptive sampler for one batch, passing history to the algorithm. - - Overrides :meth:`Sampler.sample` to forward the accumulated chain so - the adaptive algorithm can estimate the covariance from all past samples. - - Parameters - ---------- - n_steps : int - Number of steps to run. - starting_location : np.ndarray, shape (ndim,) - Starting parameter vector. - rng : np.random.Generator - Random number generator. - log_posterior : callable - Function ``f(x) -> float`` returning the log posterior. - burn : bool, optional - If ``True``, discard samples. Defaults to ``False``. - """ - chain, logp_chain, accepted = self.sampling_algorithm( - starting_location, - self.bounds, - n_steps, - log_posterior, - rng, - *self.args, - **self.kwargs, - previous_chain=self.chain, - ) - self.state = np.atleast_1d(chain[-1, :]) - - if not burn: - self.record_batch(n_steps, accepted, chain, logp_chain) - - -class BatchedAdaptiveMetropolisSampler(Sampler): - """Metropolis sampler that updates the proposal covariance after each batch. - - After **every** completed batch — burn-in batches included — the proposal - covariance is replaced by the empirical covariance of that batch, scaled - by ``2.38² / ndim``. Burn-in only affects whether the samples are - recorded, so the proposal adapts during burn-in as is standard. - - Parameters - ---------- - params : list of Parameter - Parameters to sample. - prior : object - Prior distribution with a callable ``logpdf(x)`` method. - starting_location : np.ndarray, shape (ndim,) - Initial parameter vector. - initial_proposal_cov : np.ndarray, shape (ndim, ndim) - Initial proposal covariance matrix. - epsilon_fraction : float, optional - Small regularisation fraction added to the empirical covariance diagonal. - Defaults to ``1e-6``. - """ - - def __init__( - self, - params: list[params.Parameter], - prior, - starting_location: np.ndarray, - initial_proposal_cov: np.ndarray, - epsilon_fraction: float = 1e-6, - ): - self.proposal_cov = np.atleast_2d(initial_proposal_cov) - self.proposal = proposal.NormalProposalDistribution(initial_proposal_cov) - super().__init__( - params, - prior, - starting_location, - metropolis_hastings, - args=[self.proposal], - ) - ndim = starting_location.size - self.scale = 2.38**2 / ndim - self.epsilon_fraction = epsilon_fraction - - def sample( - self, - n_steps: int, - starting_location: np.ndarray, - rng: np.random.Generator, - log_posterior: Callable[[np.ndarray], float], - burn: bool = False, - ): - """Run the sampler for one batch, then adapt the proposal. - - Overrides :meth:`Sampler.sample` to replace the proposal covariance - with the current batch's empirical covariance after every batch, - burn-in or not. :attr:`proposal` and :attr:`proposal_cov` always - reflect the proposal the *next* batch will use. - - Parameters - ---------- - n_steps : int - Number of steps to run. - starting_location : np.ndarray, shape (ndim,) - Starting parameter vector. - rng : np.random.Generator - Random number generator. - log_posterior : callable - Function ``f(x) -> float`` returning the log posterior. - burn : bool, optional - If ``True``, discard the samples (they are not recorded); the - covariance update still happens. Defaults to ``False``. - """ - chain, logp_chain, accepted = self.sampling_algorithm( - starting_location, - self.bounds, - n_steps, - log_posterior, - rng, - *self.args, - **self.kwargs, - ) - self.state = np.atleast_1d(chain[-1, :]) - empirical_cov = np.atleast_2d(np.cov(chain.T)) - epsilon = self.epsilon_fraction * np.median(np.diag(empirical_cov)) - self.proposal_cov = ( - self.scale * empirical_cov + np.eye(empirical_cov.shape[0]) * epsilon - ) - self.proposal = proposal.NormalProposalDistribution(self.proposal_cov) - self.args = [self.proposal] - if not burn: - self.record_batch(n_steps, accepted, chain, logp_chain) - - -def _validate_object(obj, name: str, required_attributes=[], required_methods=[]): - for attr in required_attributes: - if not hasattr(obj, attr): - raise ValueError(f"The {name} object must have a '{attr}' attribute.") - - for method in required_methods: - if not callable(getattr(obj, method, None)): - raise ValueError( - f"The {name} object must have a callable '{method}' method." - ) diff --git a/src/rxmc/params.py b/src/rxmc/params.py deleted file mode 100644 index a5fc547..0000000 --- a/src/rxmc/params.py +++ /dev/null @@ -1,66 +0,0 @@ -""" -Parameter definitions. - -The :class:`Parameter` class describes a single scalar model parameter — -its name, data type, physical unit, LaTeX label, and optional bounds. -""" - -import numpy as np - - -class Parameter: - """A single scalar model parameter. - - Parameters - ---------- - name : str - Human-readable name of the parameter. - dtype : type, optional - Data type of the parameter value. Defaults to ``float``. - unit : str, optional - Physical unit string (e.g. ``"MeV"``). Defaults to ``""``. - latex_name : str, optional - LaTeX representation used in plots and documentation. Defaults to - ``name`` when not supplied. - bounds : tuple of float, optional - ``(lower, upper)`` bounds for the parameter. Defaults to - ``(-np.inf, np.inf)``. Stored as a tuple of floats. - - Notes - ----- - Equality and hashing are by *value* (all five fields), so equal - parameters are interchangeable as dict keys and set members. Sharing one - sampled value between covariance terms is by object *identity* (see - :mod:`rxmc.covariance`); two equal-but-distinct parameters are two - parameters. - """ - - def __init__( - self, name, dtype=float, unit="", latex_name=None, bounds=(-np.inf, np.inf) - ): - self.name = name - self.dtype = dtype - self.unit = unit - bounds = tuple(float(b) for b in bounds) - if len(bounds) != 2: - raise ValueError(f"bounds must be (lower, upper), got {bounds!r}") - self.bounds = bounds - self.latex_name = latex_name if latex_name else name - - def _key(self): - return (self.name, self.dtype, self.unit, self.latex_name, self.bounds) - - def __eq__(self, other): - if not isinstance(other, Parameter): - return False - return self._key() == other._key() - - def __hash__(self): - return hash(self._key()) - - def __repr__(self): - return ( - f"Parameter({self.name!r}, dtype={self.dtype.__name__}, " - f"unit={self.unit!r}, latex_name={self.latex_name!r}, " - f"bounds={self.bounds!r})" - ) diff --git a/src/rxmc/physical_model.py b/src/rxmc/physical_model.py deleted file mode 100644 index b47ea22..0000000 --- a/src/rxmc/physical_model.py +++ /dev/null @@ -1,153 +0,0 @@ -""" -Abstract physical model and a concrete polynomial model. - -A :class:`PhysicalModel` maps a parameter vector to predicted observable values -for a given :class:`~rxmc.observation.Observation`. Subclasses implement -:meth:`~PhysicalModel.evaluate`; the base class makes the object callable so it -can be used directly as ``model(obs, *params)``. - -:class:`Polynomial` is a ready-to-use implementation for polynomial regression. -""" - -import numpy as np - -from .observation import Observation -from .params import Parameter -from .transforms import as_transform - - -class PhysicalModel: - """Abstract base class for parametric physical models. - - Represents an arbitrary parametric model - $y_{\\mathrm{model}}(x;\\,\\alpha)$ for comparison to an experimental - measurement $\\{x_i,\\, y(x_i)\\}$ encapsulated in an - :class:`~rxmc.observation.Observation`. - - Subclasses implement :meth:`evaluate` in physical space. An optional - *parametric* ``transform`` (see :mod:`rxmc.transforms`) is applied on top by - :meth:`__call__`; its parameters are appended to :attr:`params` so they flow - through the ordinary model-parameter machinery (priors, ``split_parameters``). - Typical uses are a latent normalisation :func:`rxmc.transforms.scale` or one - per dataset via :func:`rxmc.transforms.per_observation_scaling`. Comparison- - space transforms (e.g. comparing in log space) are *not* the model's - business: declare them on the :class:`~rxmc.observation.Observation`. - - Parameters - ---------- - params : list of Parameter - Physical parameters of the model. Each entry should carry a name - and a data type. - transform : Transform or callable, optional - Model-side transform ``y -> transform(y, *values)`` applied after - :meth:`evaluate`. Its parameters (if any) are appended to ``params``. - """ - - def __init__(self, params: list[Parameter], transform=None): - self.base_params = list(params) - self.transform = as_transform(transform) - self.params = self.base_params + list(self.transform.params) - self.n_base_params = len(self.base_params) - self.n_params = len(self.params) - - def split_params(self, params): - """Split a full parameter tuple into ``(base_params, transform_values)``.""" - params = tuple(params) - if len(params) != self.n_params: - raise ValueError( - f"{type(self).__name__} expects {self.n_params} parameter(s), " - f"got {len(params)}" - ) - return params[: self.n_base_params], params[self.n_base_params :] - - def apply_transform(self, observation, y, transform_values=()): - """Apply the model-side transform to a physical-space prediction.""" - if self.transform.is_identity: - return np.asarray(y, dtype=float) - return self.transform(y, *transform_values, context=observation) - - def evaluate(self, observation: Observation, *params) -> np.ndarray: - """Evaluate the model at the given parameter values. - - Must be overridden by subclasses. - - Parameters - ---------- - observation : Observation - Observation containing the independent-variable grid. - *params : float - Physical-model (base) parameter values only; any transform - parameters are split off by :meth:`__call__` before this is called. - - Returns - ------- - np.ndarray - Predicted observable values on the observation grid (physical - space, before the model transform). - - Raises - ------ - NotImplementedError - Always — subclasses must implement this method. - """ - raise NotImplementedError("Subclasses must implement the evaluate method.") - - def __call__(self, observation: Observation, *params) -> np.ndarray: - """Physical-space :meth:`evaluate` followed by the model transform.""" - base, values = self.split_params(params) - return self.apply_transform( - observation, self.evaluate(observation, *base), values - ) - - -class Polynomial(PhysicalModel): - r"""Polynomial model of fixed order. - - Predicts observable values as - - .. math:: - - y_{\mathrm{model}}(x;\,a_0,\dots,a_n) = \sum_{i=0}^{n} a_i\, x^i - - Parameters - ---------- - order : int - Polynomial order $n$. The model has $n+1$ free coefficients. - transform : Transform or callable, optional - See :class:`PhysicalModel`. - """ - - def __init__(self, order: int, transform=None): - params = [] - for i in range(order + 1): - params.append(Parameter(f"a{i}", latex_name=f"a_{i}", dtype=float)) - self.order = order - super().__init__(params, transform=transform) - - def evaluate(self, observation: Observation, *params) -> np.ndarray: - """Evaluate the polynomial at the observation grid. - - Parameters - ---------- - observation : Observation - Observation whose ``x`` attribute provides the evaluation grid. - *params : float - Polynomial coefficients ``a0, a1, ..., an`` (lowest order first). - - Returns - ------- - np.ndarray - Polynomial values at ``observation.x``. - - Raises - ------ - ValueError - If the number of supplied coefficients does not match - ``self.order + 1``. - """ - if len(params) != self.order + 1: - raise ValueError(f"Expected {self.order + 1} parameters, got {len(params)}") - - x_powers = np.vander(observation.x, self.order + 1, increasing=True) - y = np.dot(x_powers, np.asarray(params)) - return y diff --git a/src/rxmc/priors.py b/src/rxmc/priors.py deleted file mode 100644 index d7dbb99..0000000 --- a/src/rxmc/priors.py +++ /dev/null @@ -1,304 +0,0 @@ -""" -Built-in prior distribution classes for use with ``ParameterConfig``. - -These classes satisfy the generic prior protocol expected by ``ParameterConfig`` -and ``CalibrationConfig``: they expose ``logpdf(x)`` and ``rvs(n)`` methods -with consistent array conventions. Any user-defined class that provides the -same two methods can be passed directly to ``ParameterConfig``. - -Available classes ------------------ -:class:`IndependentPrior` - Wraps an arbitrary list of ``scipy.stats`` frozen distributions. The - preferred way to combine per-parameter marginals into a joint prior. - -:class:`TruncatedNormalPrior` - Convenience class for the common case of independent truncated normals. - -Dynesty / nested-sampling support ----------------------------------- -When using a nested sampler (e.g. Dynesty) the sampler requires a -*prior transform* that maps unit-cube coordinates to physical parameters. -Prior classes that support this should implement:: - - def prior_transform(self, u: ndarray) -> ndarray - -where ``u`` has shape ``(ndim,)`` with each element in ``[0, 1)`` and the -return value is the corresponding physical parameter vector. Both -:class:`IndependentPrior` and :class:`TruncatedNormalPrior` provide this -method via the ``ppf`` of each marginal distribution; they clip ``u`` into -the open unit cube first (:func:`clip_unit_cube`) so an exact ``0`` or ``1`` -never yields ``±inf``. -""" - -import numpy as np -from scipy import stats - - -def clip_unit_cube(u) -> np.ndarray: - """Coerce ``u`` to a float array clipped into the open unit cube. - - Exact ``0.0`` / ``1.0`` map to ``±inf`` under an unbounded marginal's - ``ppf``; clipping to ``[eps, 1 - eps]`` keeps every ``prior_transform`` - finite. Shared by every prior transform in the package. - """ - u = np.asarray(u, dtype=float) - eps = np.finfo(float).eps - return np.clip(u, eps, 1.0 - eps) - - -def as_prior(prior): - """Coerce a list/tuple of frozen univariate distributions to an - :class:`IndependentPrior`; any other prior object is returned unchanged. - - The single place where the "list of marginals" form is accepted, so - :class:`~rxmc.config.ParameterConfig` and - :class:`~rxmc.param_sampling.Sampler` agree on it. - """ - if isinstance(prior, (list, tuple)): - return IndependentPrior(list(prior)) - return prior - - -class TruncatedNormalPrior: - """Independent truncated-normal prior for a vector of parameters. - - Each component ``j`` follows:: - - theta_j ~ Normal(mu_j, sigma_j), truncated to [lower_j, upper_j] - - The components are treated as independent, so the joint log-density is the - sum of the marginal log-densities. - - Parameters - ---------- - mu : array-like, shape (ndim,) - Mean of the untruncated normal for each component. - sigma : array-like, shape (ndim,) - Standard deviation of the untruncated normal for each component. - lower : array-like, shape (ndim,) - Lower truncation bound for each component. - upper : array-like, shape (ndim,) - Upper truncation bound for each component. - seed : int, optional - Seed for the internal random-number generator used in :meth:`rvs`. - Default is ``123``. - """ - - def __init__(self, mu, sigma, lower, upper, seed=123): - self.mu = np.asarray(mu, dtype=float) - self.sigma = np.asarray(sigma, dtype=float) - self.lower = np.asarray(lower, dtype=float) - self.upper = np.asarray(upper, dtype=float) - self.dim = len(self.mu) - self.rng = np.random.default_rng(seed) - - self.a = (self.lower - self.mu) / self.sigma - self.b = (self.upper - self.mu) / self.sigma - - def logpdf(self, theta): - """Evaluate the joint log prior density. - - Parameters - ---------- - theta : array-like - Parameter vector of shape ``(ndim,)`` for a single evaluation, or - ``(n, ndim)`` for a batch of ``n`` vectors. - - Returns - ------- - float or ndarray - Scalar log-density when ``theta`` has shape ``(ndim,)``; - array of shape ``(n,)`` when ``theta`` has shape ``(n, ndim)``. - - Raises - ------ - ValueError - If the trailing dimension of ``theta`` does not equal ``self.dim``. - """ - theta = np.asarray(theta, dtype=float) - squeeze = theta.ndim == 1 - theta = np.atleast_2d(theta) - - if theta.shape[1] != self.dim: - raise ValueError( - f"Expected theta with {self.dim} columns, got {theta.shape[1]}" - ) - - logp = np.zeros(theta.shape[0]) - for j in range(self.dim): - logp += stats.truncnorm.logpdf( - theta[:, j], - a=self.a[j], - b=self.b[j], - loc=self.mu[j], - scale=self.sigma[j], - ) - - return float(logp[0]) if squeeze else logp - - def rvs(self, n): - """Draw ``n`` independent samples from the prior. - - Parameters - ---------- - n : int - Number of samples to draw. - - Returns - ------- - ndarray, shape (n, ndim) - Matrix of samples, one row per draw. - """ - out = np.zeros((n, self.dim)) - for j in range(self.dim): - out[:, j] = stats.truncnorm.rvs( - a=self.a[j], - b=self.b[j], - loc=self.mu[j], - scale=self.sigma[j], - size=n, - random_state=self.rng, - ) - return out - - def prior_transform(self, u): - """Map unit-cube coordinates to physical parameters (Dynesty interface). - - Parameters - ---------- - u : array-like, shape (ndim,) - Unit-cube coordinates, each in ``[0, 1)``. - - Returns - ------- - ndarray, shape (ndim,) - Physical parameter vector obtained by applying the component-wise - percent-point function of each truncated normal. - """ - u = clip_unit_cube(u) - theta = np.empty_like(u) - for j in range(self.dim): - theta[j] = stats.truncnorm.ppf( - u[j], - a=self.a[j], - b=self.b[j], - loc=self.mu[j], - scale=self.sigma[j], - ) - return theta - - -class IndependentPrior: - """Independent prior built from an arbitrary list of ``scipy.stats`` distributions. - - Each parameter component ``j`` is modelled by the corresponding frozen - distribution ``distributions[j]``. The components are treated as - independent, so the joint log-density is the sum of the marginal - log-densities, and sampling draws from each marginal separately. - - This class satisfies the generic prior protocol required by - :class:`~rxmc.config.ParameterConfig`: it exposes ``logpdf``, ``rvs``, - and ``prior_transform``, and carries a ``dim`` attribute used for - automatic dimension validation. - - Parameters - ---------- - distributions : list of frozen scipy.stats distributions - One distribution per parameter component. Any frozen univariate - ``scipy.stats`` distribution works (e.g. ``stats.norm(0, 1)``, - ``stats.uniform(0, 5)``, ``stats.truncnorm(...)``). - seed : int, optional - Seed for the internal :class:`numpy.random.Generator` used in - :meth:`rvs` to ensure reproducibility. Default is ``123``. - - Examples - -------- - >>> from scipy import stats - >>> from rxmc.priors import IndependentPrior - >>> prior = IndependentPrior([stats.norm(0, 1), stats.uniform(0, 5)]) - >>> prior.dim - 2 - >>> prior.rvs(4).shape - (4, 2) - """ - - def __init__(self, distributions: list, seed: int = 123): - self.distributions = distributions - self.dim = len(distributions) - self.rng = np.random.default_rng(seed) - - def logpdf(self, theta): - """Evaluate the joint log prior density. - - Parameters - ---------- - theta : array-like - Parameter vector of shape ``(ndim,)`` for a single evaluation, or - ``(n, ndim)`` for a batch of ``n`` vectors. - - Returns - ------- - float or ndarray - Scalar log-density when ``theta`` has shape ``(ndim,)``; - array of shape ``(n,)`` when ``theta`` has shape ``(n, ndim)``. - - Raises - ------ - ValueError - If the trailing dimension of ``theta`` does not equal ``self.dim``. - """ - theta = np.asarray(theta, dtype=float) - squeeze = theta.ndim == 1 - theta = np.atleast_2d(theta) - - if theta.shape[1] != self.dim: - raise ValueError( - f"Expected theta with {self.dim} columns, got {theta.shape[1]}" - ) - - logp = np.zeros(theta.shape[0]) - for j, dist in enumerate(self.distributions): - logp += dist.logpdf(theta[:, j]) - - return float(logp[0]) if squeeze else logp - - def rvs(self, n: int) -> np.ndarray: - """Draw ``n`` independent samples from the prior. - - Parameters - ---------- - n : int - Number of samples to draw. - - Returns - ------- - ndarray, shape (n, ndim) - Matrix of samples, one row per draw. - """ - out = np.zeros((n, self.dim)) - for j, dist in enumerate(self.distributions): - out[:, j] = dist.rvs(size=n, random_state=self.rng) - return out - - def prior_transform(self, u) -> np.ndarray: - """Map unit-cube coordinates to physical parameters (Dynesty interface). - - Applies the percent-point function (inverse CDF) of each marginal - distribution component-wise. - - Parameters - ---------- - u : array-like, shape (ndim,) - Unit-cube coordinates, each in ``[0, 1)``. - - Returns - ------- - ndarray, shape (ndim,) - Physical parameter vector. - """ - u = clip_unit_cube(u) - theta = np.empty_like(u) - for j, dist in enumerate(self.distributions): - theta[j] = dist.ppf(u[j]) - return theta diff --git a/src/rxmc/proposal.py b/src/rxmc/proposal.py deleted file mode 100644 index 7bf4cfb..0000000 --- a/src/rxmc/proposal.py +++ /dev/null @@ -1,93 +0,0 @@ -""" -Proposal distributions for Metropolis-Hastings MCMC. - -Each class is a callable that accepts the current parameter vector and a -:class:`numpy.random.Generator` and returns a proposed parameter vector. -""" - -import numpy as np -from scipy import stats - - -class ProposalDistribution: - """Abstract base class for Metropolis-Hastings proposal distributions. - - Subclasses must implement :meth:`__call__`, which generates a new candidate - parameter vector given the current state. - """ - - def __init__(self): - pass - - def __call__(self, x: np.ndarray, rng: np.random.Generator) -> np.ndarray: - """Generate a proposed sample from the current state. - - Parameters - ---------- - x : np.ndarray - Current parameter vector. - rng : np.random.Generator - Random number generator. - - Returns - ------- - np.ndarray - Proposed parameter vector. - - Raises - ------ - NotImplementedError - Always — subclasses must implement this method. - """ - raise NotImplementedError("This method should be overridden by subclasses") - - -class NormalProposalDistribution(ProposalDistribution): - """Multivariate-normal proposal centred at the current state. - - Parameters - ---------- - cov : np.ndarray, shape (ndim, ndim) - Covariance matrix of the proposal distribution. - """ - - def __init__(self, cov: np.ndarray): - self.cov = cov - - def __call__(self, x: np.ndarray, rng: np.random.Generator) -> np.ndarray: - return stats.multivariate_normal.rvs(mean=x, cov=self.cov, random_state=rng) - - -class HalfNormalProposalDistribution(ProposalDistribution): - """Half-normal proposal, useful for strictly non-negative parameters. - - Parameters - ---------- - scale : float - Scale parameter of the half-normal distribution. - """ - - def __init__(self, scale: float): - self.scale = scale - - def __call__(self, x: np.ndarray, rng: np.random.Generator) -> np.ndarray: - return stats.halfnorm.rvs(loc=x, scale=self.scale, random_state=rng) - - -class LogspaceNormalProposalDistribution(ProposalDistribution): - """Normal proposal operating in log space, for strictly positive parameters. - - Proposes ``exp(log(x) + eps)`` where ``eps ~ Normal(0, scale)``, which - preserves positivity while allowing multiplicative jumps of arbitrary size. - - Parameters - ---------- - scale : float - Standard deviation of the normal perturbation in log space. - """ - - def __init__(self, scale: float): - self.scale = scale - - def __call__(self, x: np.ndarray, rng: np.random.Generator) -> np.ndarray: - return np.exp(stats.norm.rvs(loc=np.log(x), scale=self.scale, random_state=rng)) diff --git a/src/rxmc/walker.py b/src/rxmc/walker.py deleted file mode 100644 index 2c76b52..0000000 --- a/src/rxmc/walker.py +++ /dev/null @@ -1,272 +0,0 @@ -""" -Walker: end-to-end Gibbs-style MCMC for Bayesian calibration. - -:class:`Walker` orchestrates one :class:`~rxmc.param_sampling.Sampler` for the -physical-model parameters and optionally several samplers for parametric -likelihood parameters, alternating between them in a Gibbs framework. It is -intended for smaller-scale prototyping and validation problems; for large -production calibrations prefer :class:`~rxmc.config.CalibrationConfig` with -an external sampler. -""" - -import numpy as np - -from .evidence import Evidence -from .param_sampling import Sampler - - -class Walker: - """Gibbs-style MCMC coordinator for a Bayesian calibration problem. - - Manages one sampler for the physical-model parameters and, optionally, - per-constraint samplers for parametric likelihood parameters. The samplers - alternate in a Gibbs framework: model parameters are updated with the - likelihood parameters held fixed, then each set of likelihood parameters is - updated with the model parameters held fixed. - - Parameters - ---------- - model_sampler : Sampler - Sampler for the physical-model parameters. - evidence : Evidence - Evidence object containing the observations and likelihood models. - likelihood_samplers : list of Sampler, optional - One sampler per entry in ``evidence.parametric_constraints``. - rng : np.random.Generator, optional - Random number generator. Defaults to ``default_rng(42)``. - likelihood_scaling : float, optional - Tempering factor applied to the log likelihood (never the prior) in - both the model block and the Gibbs conditionals, mirroring - :class:`~rxmc.config.CalibrationConfig`. Defaults to ``1.0``. - - Raises - ------ - ValueError - If the physical-model parameters in *evidence* and *model_sampler* do - not match. - ValueError - If the number of *likelihood_samplers* does not equal the number of - parametric constraints in *evidence*. - ValueError - If any likelihood sampler's parameters do not match those of the - corresponding parametric constraint. - """ - - def __init__( - self, - model_sampler: Sampler, - evidence: Evidence, - likelihood_samplers: list[Sampler] | None = None, - rng: np.random.Generator | None = None, - likelihood_scaling: float | None = None, - ): - self.model_sampler = model_sampler - self.likelihood_samplers = likelihood_samplers or [] - self.evidence = evidence - self.likelihood_scaling = ( - 1.0 if likelihood_scaling is None else float(likelihood_scaling) - ) - self.rng = rng if rng is not None else np.random.default_rng(42) - - self.gibbs_sampling = len(self.likelihood_samplers) > 0 - - if self.evidence.model_params != self.model_sampler.params: - raise ValueError( - "Inconsistent physical model parameters between " - "'evidence' and 'model_sampler'" - ) - - if len(self.likelihood_samplers) != len(self.evidence.parametric_constraints): - raise ValueError( - "The lists 'likelihood_samplers' and " - "'evidence.parametric_constraints' must correspond!" - ) - for i, conf in enumerate(self.likelihood_samplers): - constraint = self.evidence.parametric_constraints[i] - if list(constraint.params) != list(conf.params): - raise ValueError( - "Inconsistent likelihood model parameters " - f"between 'likelihood_samplers[{i}]' and " - f"'evidence.parametric_constraints[{i}]'" - ) - - def run_model_batch(self, n_steps, x0, likelihood_params=None, burn=False): - """Sample model parameters for fixed likelihood parameters. - - Parameters - ---------- - n_steps : int - Number of MCMC steps. - x0 : np.ndarray - Starting location for the model parameters. - likelihood_params : list of tuple, optional - Fixed values of the likelihood parameters for each parametric - constraint. Defaults to ``[]``. - burn : bool, optional - If ``True``, treat as burn-in (samples are not recorded). - """ - likelihood_params = likelihood_params or [] - self.model_sampler.sample( - n_steps, - x0, - self.rng, - lambda x: self.log_posterior(x, likelihood_params), - burn=burn, - ) - - def run_likelihood_batches( - self, n_steps, starting_locations, model_params, burn=False - ): - """Sample each set of likelihood parameters for fixed model parameters. - - Parameters - ---------- - n_steps : int - Number of MCMC steps per likelihood sampler. - starting_locations : list of np.ndarray - Starting locations for each likelihood sampler. - model_params : tuple - Fixed physical-model parameter values. - burn : bool, optional - If ``True``, treat as burn-in (samples are not recorded). - """ - wmll = self.evidence.weighted_marginal_log_likelihood - scaling = self.likelihood_scaling - for i, sampler in enumerate(self.likelihood_samplers): - constraint = self.evidence.parametric_constraints[i] - ym = constraint.predict(*model_params) - - def log_posterior_lm(x, sampler=sampler, i=i, ym=ym): - # prior first: an out-of-support proposal never pays for the - # likelihood (matches CalibrationConfig.conditional_posterior) - lp = float(np.squeeze(sampler.prior.logpdf(x))) - if not np.isfinite(lp): - return -np.inf - ll = float(np.squeeze(wmll(i, ym, *np.atleast_1d(x)))) - return lp + scaling * ll - - x0 = starting_locations[i] - sampler.sample(n_steps, x0, self.rng, log_posterior_lm, burn=burn) - - def log_likelihood(self, model_params, likelihood_params): - """``likelihood_scaling * evidence.log_likelihood(...)``.""" - return self.likelihood_scaling * self.evidence.log_likelihood( - model_params, likelihood_params - ) - - def log_posterior(self, model_params, likelihood_params): - """Log posterior; ``-inf`` without evaluating the likelihood (and - hence the physical model) when the prior is not finite.""" - lp = self.log_prior(model_params, likelihood_params) - if not np.isfinite(lp): - return -np.inf - return lp + self.log_likelihood(model_params, likelihood_params) - - def log_prior(self, model_params, likelihood_params): - """Log prior probability of model and likelihood parameters. - - Parameters - ---------- - model_params : tuple - Physical-model parameter values. - likelihood_params : list of tuple - One tuple of likelihood parameter values per parametric constraint. - - Returns - ------- - float - Sum of log prior densities for model and likelihood parameters. - """ - lp = self.model_sampler.prior.logpdf(model_params) - lp += sum( - lm.prior.logpdf(likelihood_params[i]) - for i, lm in enumerate(self.likelihood_samplers) - ) - return float(np.squeeze(lp)) - - def walk( - self, - n_steps: int, - burnin: int = 0, - batch_size: int = None, - verbose: bool = True, - ): - """Run the full MCMC chain. - - Updates the internal state of ``model_sampler`` and each entry of - ``likelihood_samplers`` with the accumulated chain, log posteriors, - and acceptance statistics. - - Parameters - ---------- - n_steps : int - Total number of active (post-burn-in) steps. - burnin : int, optional - Number of burn-in steps discarded before recording. - Defaults to ``0``. - batch_size : int, optional - Steps per batch. If ``None`` the entire chain is one batch. - verbose : bool, optional - Print batch completion messages. Defaults to ``True``. - """ - if batch_size is not None: - rem_burn = burnin % batch_size - n_burn_batches = burnin // batch_size - burn_batches = n_burn_batches * [batch_size] + (rem_burn > 0) * [rem_burn] - - rem = n_steps % batch_size - n_full_batches = n_steps // batch_size - batches = n_full_batches * [batch_size] + (rem > 0) * [rem] - else: - batches = [n_steps] - burn_batches = [burnin] - - if burnin == 0: - burn_batches = [] - - for i, steps_in_batch in enumerate(burn_batches): - self._run_batch(steps_in_batch, burn=True) - if verbose: - print(self._batch_message(i, len(burn_batches), steps_in_batch, True)) - - for i, steps_in_batch in enumerate(batches): - self._run_batch(steps_in_batch, burn=False) - if verbose: - print(self._batch_message(i, len(batches), steps_in_batch, False)) - - def _run_batch(self, steps: int, burn: bool) -> None: - """One Gibbs sweep: the model block, then each likelihood block.""" - self.run_model_batch( - steps, - self.model_sampler.state, - [sampler.state for sampler in self.likelihood_samplers], - burn=burn, - ) - if self.gibbs_sampling: - self.run_likelihood_batches( - steps, - [sampler.state for sampler in self.likelihood_samplers], - self.model_sampler.state, - burn=burn, - ) - - def _batch_message(self, index: int, n_batches: int, steps: int, burn: bool): - """Progress line for a batch. - - Burn-in batches are not recorded, so no acceptance fraction is - available for them and none is printed. - """ - if burn: - return f"Burn-in batch {index + 1}/{n_batches} completed, {steps} steps." - msg = ( - f"Batch: {index + 1}/{n_batches} completed, {steps} steps. " - f"\n Model parameter acceptance fraction: " - f"{self.model_sampler.most_recent_batch_acceptance_fraction():.3f}" - ) - if self.gibbs_sampling: - fractions = [ - sampler.most_recent_batch_acceptance_fraction() - for sampler in self.likelihood_samplers - ] - msg += f"\n Likelihood parameter acceptance fractions: {fractions}" - return msg diff --git a/test/conftest.py b/test/conftest.py deleted file mode 100644 index 1056006..0000000 --- a/test/conftest.py +++ /dev/null @@ -1,6 +0,0 @@ -"""Make the shared test helpers importable under any pytest import mode.""" - -import sys -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).parent)) diff --git a/test/helpers.py b/test/helpers.py deleted file mode 100644 index 90eb5dc..0000000 --- a/test/helpers.py +++ /dev/null @@ -1,22 +0,0 @@ -"""Shared helpers for the test suite.""" - -import numpy as np - -from rxmc.covariance import StackContext -from rxmc.likelihood_model import log_likelihood, mahalanobis_distance_sqr_cholesky - - -def make_ctx(x, y, ym, supports) -> StackContext: - """Build a StackContext from stacked arrays and block supports.""" - return StackContext( - x=np.asarray(x), - y=np.asarray(y), - ym=np.asarray(ym), - supports=tuple(np.asarray(s, dtype=int) for s in supports), - ) - - -def manual_mvn_loglike(y, ym, cov): - """Reference dense multivariate-normal log likelihood.""" - d2, logdet = mahalanobis_distance_sqr_cholesky(y, ym, cov) - return log_likelihood(d2, logdet, len(y)) diff --git a/test/test_config.py b/test/test_config.py deleted file mode 100644 index 605661b..0000000 --- a/test/test_config.py +++ /dev/null @@ -1,446 +0,0 @@ -import unittest - -import numpy as np -import scipy.stats - -from rxmc.config import CalibrationConfig, ParameterConfig -from rxmc.constraint import Constraint -from rxmc.covariance import model_error_term -from rxmc.evidence import Evidence -from rxmc.observation import Observation -from rxmc.params import Parameter -from rxmc.physical_model import Polynomial -from rxmc.priors import IndependentPrior, TruncatedNormalPrior - - -def gamma_parameter(): - """The UnknownModelError gamma, as a covariance parameter.""" - return Parameter( - "log fractional err", float, latex_name=r"\gamma", unit="dimensionless" - ) - - -def model_error_constraint(observation, model, gamma): - """A constraint with an UnknownModelError (averaging) covariance term.""" - support = np.arange(observation.n_data_pts) - return Constraint( - observations=[observation], - physical_model=model, - extra_terms=[model_error_term(gamma, averaging=True, support=support)], - ) - - -class TestParameterConfig(unittest.TestCase): - def setUp(self): - self.param1 = Parameter(name="param1") - self.param2 = Parameter(name="param2") - self.prior = scipy.stats.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]]) - self.initial_proposal_dist = scipy.stats.multivariate_normal( - mean=[0, 0], cov=[[1, 0], [0, 1]] - ) - - def test_initialization(self): - """Test ParameterConfig initialization.""" - config = ParameterConfig( - params=[self.param1, self.param2], - prior=self.prior, - initial_proposal_distribution=self.initial_proposal_dist, - ) - self.assertEqual(config.ndim, 2) - self.assertEqual(config.params, [self.param1, self.param2]) - - def test_empty_parameters_raises_valueerror(self): - """Test empty parameters list raises ValueError.""" - with self.assertRaises(ValueError): - ParameterConfig( - params=[], - prior=self.prior, - initial_proposal_distribution=self.initial_proposal_dist, - ) - - def test_single_parameter_x0_shape(self): - config = ParameterConfig( - params=[self.param1], - prior=scipy.stats.multivariate_normal(mean=[0], cov=[[1]]), - initial_proposal_distribution=scipy.stats.multivariate_normal( - mean=[0], cov=[[1]] - ), - ) - x0 = config.x0(4) - self.assertEqual(x0.shape, (4, 1)) - - def test_generic_prior_class(self): - """TruncatedNormalPrior satisfies the generic prior protocol.""" - prior = TruncatedNormalPrior( - mu=[0.0, 1.0], - sigma=[1.0, 1.0], - lower=[-5.0, -5.0], - upper=[5.0, 5.0], - ) - config = ParameterConfig( - params=[self.param1, self.param2], - prior=prior, - initial_proposal_distribution=prior, - ) - self.assertEqual(config.ndim, 2) - - x0 = config.x0(3) - self.assertEqual(x0.shape, (3, 2)) - - lp = config.prior_logpdf(np.array([0.0, 1.0])) - self.assertTrue(np.isfinite(lp)) - - def test_prior_transform_list(self): - """List-of-distributions prior_transform uses ppf on each element.""" - prior_list = [scipy.stats.norm(0, 1), scipy.stats.norm(0, 1)] - config = ParameterConfig( - params=[self.param1, self.param2], - prior=prior_list, - initial_proposal_distribution=prior_list, - ) - u = np.array([0.5, 0.5]) - theta = config.prior_transform(u) - np.testing.assert_allclose(theta, [0.0, 0.0], atol=1e-10) - - def test_list_prior_is_wrapped_in_independent_prior(self): - """A list of marginals becomes one IndependentPrior (as in Sampler).""" - prior_list = [scipy.stats.norm(0, 1), scipy.stats.uniform(0, 2)] - config = ParameterConfig( - params=[self.param1, self.param2], - prior=prior_list, - initial_proposal_distribution=prior_list, - ) - self.assertIsInstance(config.prior, IndependentPrior) - self.assertIsInstance(config.initial_proposal_distribution, IndependentPrior) - self.assertEqual(config.x0(3).shape, (3, 2)) - x = np.array([0.3, 1.0]) - expected = sum(d.logpdf(xi) for d, xi in zip(prior_list, x)) - self.assertAlmostEqual(config.prior_logpdf(x), expected) - with self.assertRaises(ValueError): - ParameterConfig( - params=[self.param1], - prior=prior_list, - initial_proposal_distribution=prior_list, - ) - - def test_infer_dim_calls_mean_method(self): - """A custom prior exposing mean() as a method is sized correctly.""" - - class Custom: - def mean(self): - return np.zeros(3) - - def logpdf(self, x): - return 0.0 - - def rvs(self, n): - return np.zeros((n, 3)) - - self.assertEqual(ParameterConfig._infer_dim(Custom()), 3) - params = [Parameter(f"p{i}") for i in range(3)] - ParameterConfig(params, prior=Custom(), initial_proposal_distribution=Custom()) - with self.assertRaises(ValueError): - ParameterConfig( - params[:2], prior=Custom(), initial_proposal_distribution=Custom() - ) - # frozen scipy univariates expose mean() too and stay one-dimensional - self.assertEqual(ParameterConfig._infer_dim(scipy.stats.norm(0, 1)), 1) - - def test_prior_transform_boundary_finite(self): - """u = 0 / 1 are clipped into the open cube before ppf.""" - prior_list = [scipy.stats.norm(0, 1), scipy.stats.norm(0, 1)] - config = ParameterConfig( - params=[self.param1, self.param2], - prior=prior_list, - initial_proposal_distribution=prior_list, - ) - theta = config.prior_transform(np.array([0.0, 1.0])) - self.assertTrue(np.all(np.isfinite(theta))) - - def test_prior_transform_generic(self): - """Generic prior with prior_transform method is called directly.""" - # Use symmetric bounds so the median (u=0.5) maps exactly to mu. - prior = TruncatedNormalPrior( - mu=[0.0, 1.0], - sigma=[1.0, 1.0], - lower=[-5.0, -4.0], - upper=[5.0, 6.0], - ) - config = ParameterConfig( - params=[self.param1, self.param2], - prior=prior, - initial_proposal_distribution=prior, - ) - u = np.array([0.5, 0.5]) - theta = config.prior_transform(u) - np.testing.assert_allclose(theta, [0.0, 1.0], atol=1e-6) - - def test_prior_transform_unsupported_raises(self): - """Non-list prior without prior_transform raises NotImplementedError.""" - config = ParameterConfig( - params=[self.param1, self.param2], - prior=self.prior, - initial_proposal_distribution=self.initial_proposal_dist, - ) - with self.assertRaises(NotImplementedError): - config.prior_transform(np.array([0.5, 0.5])) - - -class TestCalibrationConfig(unittest.TestCase): - def setUp(self): - # Evidence with one regular and one parametric constraint - self.model = Polynomial(1) - self.gamma = gamma_parameter() - self.evidence = Evidence( - constraints=[ - Constraint( - observations=[ - Observation( - x=np.array([1.0, 2.0, 3.0]), - y=np.array([1.0, 2.0, 3.0]), - y_stat_err=np.array([0.1, 0.1, 0.1]), - ) - ], - physical_model=self.model, - ), - model_error_constraint( - Observation( - x=np.array([6.0, 7.0, 8.0]), - y=np.array([6.3, 8.1, 9.6]), - y_stat_err=np.array([0.1, 0.1, 0.1]), - ), - self.model, - self.gamma, - ), - ], - ) - - # Model Config - model_prior = scipy.stats.multivariate_normal( - mean=[0, 1], - cov=[[1, 0], [0, 1]], - ) - self.model_config = ParameterConfig( - params=self.model.params, - prior=model_prior, - initial_proposal_distribution=model_prior, - ) - - # Likelihood Config - likelihood_prior = scipy.stats.multivariate_normal(mean=[0], cov=[[1]]) - self.likelihood_config = ParameterConfig( - params=list(self.evidence.parametric_constraints[0].params), - prior=likelihood_prior, - initial_proposal_distribution=likelihood_prior, - ) - - def test_initialization(self): - """Test CalibrationConfig initialization.""" - config = CalibrationConfig( - evidence=self.evidence, - model_config=self.model_config, - likelihood_configs=[self.likelihood_config], - ) - self.assertEqual(config.ndim, 3) - - def test_split_parameters(self): - """Test splitting flat parameters into model and likelihood parameters.""" - config = CalibrationConfig( - evidence=self.evidence, - model_config=self.model_config, - likelihood_configs=[self.likelihood_config], - ) - x = np.array([1.0, 2.0, 0.0]) - model_params, likelihood_params = config.split_parameters(x) - np.testing.assert_array_equal(model_params, [1.0, 2.0]) - np.testing.assert_array_equal(likelihood_params[0], [0.0]) - - def test_parameters_property(self): - """parameters returns all Parameter objects in flat sampler order.""" - config = CalibrationConfig( - evidence=self.evidence, - model_config=self.model_config, - likelihood_configs=[self.likelihood_config], - ) - params = config.parameters - self.assertEqual(len(params), 3) - self.assertEqual(params[0].name, "a0") - self.assertEqual(params[1].name, "a1") - self.assertEqual(params[2].name, "log fractional err") - - def test_prior_property(self): - """prior returns one prior object per parameter sector.""" - config = CalibrationConfig( - evidence=self.evidence, - model_config=self.model_config, - likelihood_configs=[self.likelihood_config], - ) - priors = config.prior - self.assertEqual(len(priors), 2) - self.assertIs(priors[0], self.model_config.prior) - self.assertIs(priors[1], self.likelihood_config.prior) - - def test_black_box_bayes_interface(self): - config = CalibrationConfig( - evidence=self.evidence, - model_config=self.model_config, - likelihood_configs=[self.likelihood_config], - ) - - self.assertEqual(config.parameter_names, ["a0", "a1", "log fractional err"]) - - x0_single = config.starting_location(1) - self.assertEqual(x0_single.shape, (1, 3)) - - x0_batch = config.starting_location(4) - self.assertEqual(x0_batch.shape, (4, 3)) - - theta = np.array([1.0, 2.0, 0.0]) - batched = config.log_posterior_batch(np.vstack([theta, theta])) - self.assertEqual(batched.shape, (2,)) - np.testing.assert_allclose( - batched, - [config.log_posterior(theta), config.log_posterior(theta)], - ) - - def test_conditional_posterior_uses_parametric_constraint(self): - config = CalibrationConfig( - evidence=self.evidence, - model_config=self.model_config, - likelihood_configs=[self.likelihood_config], - ) - - xmodel = np.array([1.0, 1.0]) - ym = self.evidence.parametric_constraints[0].predict(*xmodel) - x_lm = np.array([0.0]) - - expected = self.evidence.parametric_constraints[0].marginal_log_likelihood( - ym, *x_lm - ) + self.likelihood_config.prior_logpdf(x_lm) - self.assertAlmostEqual(config.conditional_posterior(x_lm, 0, ym), expected) - - def test_parametric_indices_map(self): - # the parametric constraint is second in evidence.constraints - self.assertEqual(self.evidence.parametric_indices, [1]) - - def test_conditional_posterior_tempering(self): - # the Gibbs conditional must apply likelihood_scaling and the - # constraint's Evidence weight, matching log_posterior's tempering - scaling = 0.5 - weights = np.array([1.0, 3.0]) # parametric constraint has weight 3 - evidence = Evidence(constraints=self.evidence.constraints, weights=weights) - config = CalibrationConfig( - evidence=evidence, - model_config=self.model_config, - likelihood_configs=[self.likelihood_config], - likelihood_scaling=scaling, - ) - - xmodel = np.array([1.0, 1.0]) - ym = evidence.parametric_constraints[0].predict(*xmodel) - x_lm = np.array([0.0]) - - lp = self.likelihood_config.prior_logpdf(x_lm) - ll = evidence.parametric_constraints[0].marginal_log_likelihood(ym, *x_lm) - self.assertAlmostEqual( - config.conditional_posterior(x_lm, 0, ym), lp + scaling * 3.0 * ll - ) - - def test_predict_parametric_aligns_with_conditional_posterior(self): - # mixed evidence: constraints[0] is non-parametric, constraints[1] is the - # parametric one. predict() is in constraints order (len 2) while - # predict_parametric() is in parametric order (len 1) -> aligned with lm_index. - config = CalibrationConfig( - evidence=self.evidence, - model_config=self.model_config, - likelihood_configs=[self.likelihood_config], - ) - xmodel = np.array([1.0, 1.0]) - - all_preds = config.predict(xmodel) - param_preds = config.predict_parametric(xmodel) - self.assertEqual(len(all_preds), 2) - self.assertEqual(len(param_preds), 1) - # predict_parametric()[0] is the parametric constraint's prediction (== all_preds[1]) - np.testing.assert_allclose(param_preds[0][0], all_preds[1][0]) - np.testing.assert_allclose( - param_preds[0][0], - self.evidence.parametric_constraints[0].predict(*xmodel)[0], - ) - - def test_starting_location_with_single_parameter_sectors(self): - model = Polynomial(0) - gamma = gamma_parameter() - observation = Observation( - x=np.array([1.0, 2.0]), - y=np.array([1.0, 1.1]), - y_stat_err=np.array([0.1, 0.1]), - ) - constraint = model_error_constraint(observation, model, gamma) - evidence = Evidence(constraints=[constraint]) - model_config = ParameterConfig( - params=model.params, - prior=scipy.stats.multivariate_normal(mean=[0], cov=[[1]]), - initial_proposal_distribution=scipy.stats.multivariate_normal( - mean=[0], cov=[[1]] - ), - ) - likelihood_config = ParameterConfig( - params=list(constraint.params), - prior=scipy.stats.multivariate_normal(mean=[0], cov=[[1]]), - initial_proposal_distribution=scipy.stats.multivariate_normal( - mean=[0], cov=[[1]] - ), - ) - config = CalibrationConfig( - evidence=evidence, - model_config=model_config, - likelihood_configs=[likelihood_config], - ) - - x0 = config.starting_location(5) - self.assertEqual(x0.shape, (5, 2)) - - def test_prior_transform_via_generic_prior(self): - """prior_transform works end-to-end with TruncatedNormalPrior sectors.""" - model = Polynomial(1) - gamma = gamma_parameter() - constraint = model_error_constraint( - Observation( - x=np.array([1.0, 2.0, 3.0, 4.0]), - y=np.array([1.0, 2.0, 3.0, 4.0]), - y_stat_err=np.array([0.1, 0.1, 0.1, 0.1]), - ), - model, - gamma, - ) - evidence = Evidence(constraints=[constraint]) - model_prior = TruncatedNormalPrior( - mu=[0.0, 1.0], sigma=[1.0, 1.0], lower=[-5.0, -5.0], upper=[5.0, 5.0] - ) - lm_prior = TruncatedNormalPrior( - mu=[0.0], sigma=[1.0], lower=[-5.0], upper=[5.0] - ) - config = CalibrationConfig( - evidence=evidence, - model_config=ParameterConfig( - params=model.params, - prior=model_prior, - initial_proposal_distribution=model_prior, - ), - likelihood_configs=[ - ParameterConfig( - params=list(constraint.params), - prior=lm_prior, - initial_proposal_distribution=lm_prior, - ) - ], - ) - u = np.full(config.ndim, 0.5) - theta = config.prior_transform(u) - self.assertEqual(theta.shape, (config.ndim,)) - self.assertTrue(np.all(np.isfinite(theta))) - - -if __name__ == "__main__": - unittest.main() diff --git a/test/test_constraint.py b/test/test_constraint.py deleted file mode 100644 index 9b9e632..0000000 --- a/test/test_constraint.py +++ /dev/null @@ -1,720 +0,0 @@ -"""Tests for the stacked Constraint: multi-observation stacking and case A/B.""" - -import unittest - -import numpy as np -from sklearn.gaussian_process.kernels import RBF - -from helpers import manual_mvn_loglike -from rxmc.constraint import Constraint -from rxmc.covariance import ( - Term, - kernel_term, - noise_term, - normalization_term, - systematic_term, -) -from rxmc.evidence import Evidence -from rxmc.likelihood_model import GaussianLikelihood, StudentT -from rxmc.observation import Observation -from rxmc.params import Parameter -from rxmc.physical_model import Polynomial -from rxmc.transforms import log, per_observation_scaling, scale - - -class TestStackedConstraint(unittest.TestCase): - def setUp(self): - self.pm = Polynomial(order=1) - self.model_params = (0.5, 1.2) - self.obs1 = Observation( - np.array([1.0, 2.0]), np.array([2.0, 3.0]), y_stat_err=np.array([0.1, 0.2]) - ) - self.obs2 = Observation( - np.array([3.0, 4.0, 5.0]), - np.array([5.0, 6.0, 8.0]), - y_stat_err=np.array([0.3, 0.2, 0.4]), - ) - - def _stacked(self): - y = np.concatenate([self.obs1.y, self.obs2.y]) - ym = np.concatenate( - [ - self.pm.evaluate(self.obs1, *self.model_params), - self.pm.evaluate(self.obs2, *self.model_params), - ] - ) - return y, ym - - def test_block_diagonal_equals_sum_of_blocks(self): - c = Constraint([self.obs1, self.obs2], self.pm) - self.assertEqual(c.n_data_pts, 5) - self.assertTrue(c.covariance.block_diagonal) - - y, ym = self._stacked() - stat = np.concatenate([self.obs1.y_stat_err, self.obs2.y_stat_err]) - cov = np.diag(stat**2) - expected = manual_mvn_loglike(y, ym, cov) - self.assertAlmostEqual(c.log_likelihood(self.model_params), expected) - - def test_block_diagonal_fast_path_matches_dense(self): - # build a constraint and compare blockwise path against an explicit dense MVN - c = Constraint([self.obs1, self.obs2], self.pm) - y, ym = self._stacked() - stat = np.concatenate([self.obs1.y_stat_err, self.obs2.y_stat_err]) - dense = manual_mvn_loglike(y, ym, np.diag(stat**2)) - self.assertAlmostEqual(c.log_likelihood(self.model_params), dense) - - def test_constant_block_diag_cached_factors_match_dense(self): - # constant multi-block covariance: cold call and cache-warm call both - # equal the manual dense MVN - c = Constraint([self.obs1, self.obs2], self.pm) - y, ym = self._stacked() - stat = np.concatenate([self.obs1.y_stat_err, self.obs2.y_stat_err]) - expected = manual_mvn_loglike(y, ym, np.diag(stat**2)) - cold = c.log_likelihood(self.model_params) - warm = c.log_likelihood(self.model_params) - self.assertAlmostEqual(cold, expected) - self.assertEqual(cold, warm) - - def test_case_A_cross_block_coupling(self): - # one rank-one mode spanning both blocks couples the data (off-diagonal blocks) - eta = 0.1 - p = Parameter("log eta") - support = np.arange(5) - coupling = systematic_term(p, basis=lambda c: c.ym, support=support) - c = Constraint([self.obs1, self.obs2], self.pm, extra_terms=[coupling]) - - self.assertFalse(c.covariance.block_diagonal) - self.assertEqual(c.n_params, 1) - - y, ym = self._stacked() - stat = np.concatenate([self.obs1.y_stat_err, self.obs2.y_stat_err]) - cov = np.diag(stat**2) + eta**2 * np.outer(ym, ym) - expected = manual_mvn_loglike(y, ym, cov) - self.assertAlmostEqual( - c.log_likelihood(self.model_params, (np.log(eta),)), expected - ) - - def test_case_A_differs_from_independent(self): - eta = 0.1 - p = Parameter("log eta") - coupled = Constraint( - [self.obs1, self.obs2], - self.pm, - extra_terms=[ - systematic_term(p, basis=lambda c: c.ym, support=np.arange(5)) - ], - ) - # independent: two per-block normalization modes (no cross coupling) - p1, p2 = Parameter("log eta 1"), Parameter("log eta 2") - independent = Constraint( - [self.obs1, self.obs2], - self.pm, - extra_terms=[ - normalization_term(parameter=p1, support=np.arange(2)), - normalization_term(parameter=p2, support=np.arange(2, 5)), - ], - ) - ll_coupled = coupled.log_likelihood(self.model_params, (np.log(eta),)) - ll_indep = independent.log_likelihood( - self.model_params, (np.log(eta), np.log(eta)) - ) - self.assertNotAlmostEqual(ll_coupled, ll_indep) - - -class TestPerObservationScaling(unittest.TestCase): - """Per-dataset latent rho as an identity-routed model transform.""" - - def setUp(self): - self.obs1 = Observation( - np.array([1.0, 2.0, 3.0]), - np.array([2.0, 4.0, 6.0]), - y_stat_err=np.array([0.1, 0.1, 0.1]), - ) - self.obs2 = Observation( - np.array([1.0, 2.0, 3.0]), - np.array([4.0, 8.0, 12.0]), - y_stat_err=np.array([0.1, 0.1, 0.1]), - ) - - def make_model(self, observations): - return Polynomial(order=1, transform=per_observation_scaling(observations)) - - def test_routes_rho_by_identity(self): - model = self.make_model([self.obs1, self.obs2]) - # params = [a0, a1, log_rho_0, log_rho_1] - self.assertEqual(model.n_params, 4) - self.assertEqual( - [p.name for p in model.params], ["a0", "a1", "log_rho_0", "log_rho_1"] - ) - mp = (0.0, 2.0, np.log(1.0), np.log(2.0)) - np.testing.assert_allclose(model(self.obs1, *mp), [2.0, 4.0, 6.0]) - np.testing.assert_allclose(model(self.obs2, *mp), [4.0, 8.0, 12.0]) - - def test_linear_prefix(self): - t = per_observation_scaling([self.obs1, self.obs2], log=False) - self.assertEqual([p.name for p in t.params], ["rho_0", "rho_1"]) - model = Polynomial(order=1, transform=t) - np.testing.assert_allclose( - model(self.obs2, 0.0, 2.0, 1.0, 3.0), [6.0, 12.0, 18.0] - ) - - def test_shared_across_constraints_in_evidence(self): - model = self.make_model([self.obs1, self.obs2]) - c1 = Constraint([self.obs1], model) - c2 = Constraint([self.obs2], model) - ev = Evidence([c1, c2]) # all constraints share one model instance - self.assertEqual( - [p.name for p in ev.model_params], ["a0", "a1", "log_rho_0", "log_rho_1"] - ) - ll = ev.log_likelihood((0.0, 2.0, np.log(1.0), np.log(2.0))) - self.assertTrue(np.isfinite(ll)) - - def test_holds_observation_references(self): - # id()-keyed routing must keep the registered objects alive so a - # garbage-collected observation's id can never be recycled - import gc - - model = self.make_model( - [Observation(np.array([1.0]), np.array([1.0])), self.obs2] - ) - gc.collect() - mp = (0.0, 2.0, np.log(1.0), np.log(2.0)) - np.testing.assert_allclose(model(self.obs2, *mp), [4.0, 8.0, 12.0]) - # the first observation is still registered, so a new object can never - # inherit its id and be routed to its scale - stranger = Observation(np.array([1.0]), np.array([1.0])) - with self.assertRaises(KeyError): - model(stranger, *mp) - - def test_unregistered_observation_raises(self): - model = self.make_model([self.obs1]) - with self.assertRaises(KeyError): - model(self.obs2, 0.0, 1.0, 0.0) - - -class TestSharedParameterCaseB(unittest.TestCase): - def test_shared_eta_one_param(self): - pm = Polynomial(order=0) - obs1 = Observation(np.array([1.0, 2.0]), np.array([3.0, 3.0])) - obs2 = Observation(np.array([3.0, 4.0]), np.array([3.0, 3.0])) - eta = Parameter("log eta") - c = Constraint( - [obs1, obs2], - pm, - extra_terms=[ - normalization_term(parameter=eta, support=np.arange(2)), - normalization_term(parameter=eta, support=np.arange(2, 4)), - ], - ) - # one shared parameter, covariance stays block-diagonal - self.assertEqual(c.n_params, 1) - self.assertTrue(c.covariance.block_diagonal) - - -class TestParamCountValidation(unittest.TestCase): - """The full constraint tuple is validated; surplus params no longer vanish.""" - - def setUp(self): - self.pm = Polynomial(order=1) - self.mp = (0.5, 1.2) - self.obs = Observation( - np.array([1.0, 2.0]), np.array([2.0, 3.0]), y_stat_err=np.array([0.1, 0.2]) - ) - - def test_surplus_params_raise(self): - # reviewer repro: these used to be silently swallowed, returning the - # same value as the no-param call - c = Constraint([self.obs], self.pm) - with self.assertRaises(ValueError) as cm: - c.log_likelihood(self.mp, (0.3, 99.0, -5.0)) - self.assertIn("expects 0 parameter", str(cm.exception)) - - def test_missing_params_raise(self): - eta = Parameter("log eta") - c = Constraint( - [self.obs], - self.pm, - extra_terms=[normalization_term(parameter=eta, support=np.arange(2))], - ) - with self.assertRaises(ValueError) as cm: - c.log_likelihood(self.mp) - self.assertIn("log eta", str(cm.exception)) - - def test_correct_count_unchanged(self): - eta = Parameter("log eta") - c = Constraint( - [self.obs], - self.pm, - extra_terms=[normalization_term(parameter=eta, support=np.arange(2))], - ) - self.assertTrue(np.isfinite(c.log_likelihood(self.mp, (np.log(0.1),)))) - - def test_studentt_chi2_full_tuple(self): - eps = Parameter("log eps") - student = Constraint( - [self.obs], - self.pm, - likelihood=StudentT(), - extra_terms=[noise_term(eps, support=np.arange(2))], - ) - # covariance-only tuple is a deficit now - with self.assertRaises(ValueError): - student.chi2(self.mp, (np.log(0.1),)) - # full tuple works; nu is ignored by the statistic - gauss = Constraint( - [self.obs], - self.pm, - likelihood=GaussianLikelihood(), - extra_terms=[noise_term(Parameter("log eps g"), support=np.arange(2))], - ) - self.assertAlmostEqual( - student.chi2(self.mp, (np.log(0.1), 4.0)), - gauss.chi2(self.mp, (np.log(0.1),)), - ) - - def test_covariance_matrix_full_tuple_convention(self): - eta = Parameter("log eta") - c = Constraint( - [self.obs], - self.pm, - likelihood=StudentT(), - extra_terms=[normalization_term(parameter=eta, support=np.arange(2))], - ) - # reviewer repro: forwarding the full sampled tuple used to crash - S = c.covariance_matrix(self.mp, (np.log(0.1), 4.0)) - gauss = Constraint( - [self.obs], - self.pm, - extra_terms=[ - normalization_term(parameter=Parameter("log eta"), support=np.arange(2)) - ], - ) - np.testing.assert_allclose(S, gauss.covariance_matrix(self.mp, (np.log(0.1),))) - # a partial (covariance-only) tuple is now rejected uniformly - with self.assertRaises(ValueError): - c.covariance_matrix(self.mp, (np.log(0.1),)) - - -class TestParameterNameValidation(unittest.TestCase): - def setUp(self): - self.pm = Polynomial(order=1) - self.obs = Observation( - np.array([1.0, 2.0]), np.array([2.0, 3.0]), y_stat_err=np.array([0.1, 0.2]) - ) - - def test_two_distinct_same_name_params_raise(self): - # two equal-but-distinct objects are almost certainly intended sharing - # gone wrong (sharing works by identity) - with self.assertRaises(ValueError) as cm: - Constraint( - [self.obs], - self.pm, - extra_terms=[ - normalization_term( - parameter=Parameter("log eta"), support=np.arange(2) - ), - normalization_term( - parameter=Parameter("log eta"), support=np.arange(2) - ), - ], - ) - self.assertIn("SAME Parameter object", str(cm.exception)) - - def test_collision_with_model_param_name_raises(self): - # Polynomial(order=1) has model params named a0, a1 - with self.assertRaises(ValueError) as cm: - Constraint( - [self.obs], - self.pm, - extra_terms=[ - normalization_term(parameter=Parameter("a0"), support=np.arange(2)) - ], - ) - self.assertIn("physical-model parameter", str(cm.exception)) - - def test_likelihood_param_collision_raises(self): - nu_clone = Parameter("nu") - with self.assertRaises(ValueError): - Constraint( - [self.obs], - self.pm, - likelihood=StudentT(nu_parameter=Parameter("nu")), - extra_terms=[ - normalization_term(parameter=nu_clone, support=np.arange(2)) - ], - ) - - -class TestSingularCovarianceGuard(unittest.TestCase): - """A constant singular covariance fails at construction with a clear error.""" - - def setUp(self): - self.pm = Polynomial(order=1) - self.x = np.array([1.0, 2.0]) - self.y = np.array([2.0, 3.0]) - - def test_zero_stat_err_raises_clear_error(self): - obs = Observation(self.x, self.y) # y_stat_err defaults to zeros - with self.assertRaises(ValueError) as cm: - Constraint([obs], self.pm) - self.assertIn("singular", str(cm.exception)) - self.assertIn("observation 0", str(cm.exception)) - - def test_offending_dataset_named_by_label(self): - good = Observation(self.x, self.y, y_stat_err=np.array([0.1, 0.1])) - bad = Observation(np.array([3.0, 4.0]), np.array([4.0, 5.0]), label="C1010-2-0") - with self.assertRaises(ValueError) as cm: - Constraint([good, bad], self.pm) - msg = str(cm.exception) - self.assertIn("C1010-2-0", msg) - self.assertNotIn("observation 0'", msg) - - def test_zero_stat_err_with_covering_term_ok(self): - obs = Observation(self.x, self.y) - c = Constraint( - [obs], - self.pm, - extra_terms=[Term(np.array([0.2, 0.2]), kind="diag", support=np.arange(2))], - ) - self.assertTrue(np.isfinite(c.log_likelihood((0.5, 1.2)))) - - def test_zero_stat_err_with_systematic_terms_ok(self): - obs = Observation( - self.x, self.y, y_sys_err_normalization=0.05, y_sys_err_offset=0.1 - ) - c = Constraint([obs], self.pm, extra_terms=obs.systematic_terms(np.arange(2))) - self.assertTrue(np.isfinite(c.log_likelihood((0.5, 1.2)))) - - def test_parametric_covariance_not_checked_eagerly(self): - # replace-semantics: zero stat err + a free noise term must construct - - obs = Observation(self.x, self.y) - p = Parameter("log eps") - c = Constraint( - [obs], self.pm, extra_terms=[noise_term(p, support=np.arange(2))] - ) - self.assertEqual(c.n_params, 1) - - -class TestConstraintFixes(unittest.TestCase): - def setUp(self): - self.pm = Polynomial(order=1) - self.params = (0.5, 1.2) - self.obs = Observation( - np.array([1.0, 2.0, 3.0]), - np.array([2.0, 3.0, 5.0]), - y_stat_err=np.array([0.1, 0.2, 0.3]), - ) - - def test_covariance_matrix_returns_copy(self): - c = Constraint([self.obs], self.pm) - before = c.log_likelihood(self.params) - M = c.covariance_matrix(self.params) - M += 1e6 # in-place mutation must not corrupt the constraint - after = c.log_likelihood(self.params) - self.assertAlmostEqual(before, after) - - def test_stack_shape_guard(self): - c = Constraint([self.obs], self.pm) - with self.assertRaises(ValueError): - c.marginal_log_likelihood([np.array([1.0, 2.0])]) # wrong length - - def test_include_statistical_term_false_omits_diagonal(self): - sup = np.arange(self.obs.n_data_pts) - cov = np.diag([0.04, 0.04, 0.04]) - c = Constraint( - [self.obs], - self.pm, - extra_terms=[Term(cov, support=sup)], - include_statistical_term=False, - ) - # only the supplied fixed Term survives (no statistical diagonal added) - S = c.covariance_matrix(self.params) - np.testing.assert_allclose(S, cov) - - -class TestScaleTransform(unittest.TestCase): - def test_scale_applied_and_params_appended(self): - base = Polynomial(order=1) - obs = Observation( - np.array([1.0, 2.0, 3.0]), - np.array([2.0, 4.0, 6.0]), - y_stat_err=np.array([0.1, 0.1, 0.1]), - ) - model = Polynomial(order=1, transform=scale()) - self.assertEqual(model.n_params, 3) - self.assertEqual(model.params[-1].name, "log_rho") - np.testing.assert_allclose( - model(obs, 0.0, 2.0, np.log(2.0)), - 2.0 * base(obs, 0.0, 2.0), - ) - # evaluate() is physical space only - np.testing.assert_allclose(model.evaluate(obs, 0.0, 2.0), base(obs, 0.0, 2.0)) - - def test_linear_scale(self): - base = Polynomial(order=0) - obs = Observation(np.array([1.0, 2.0]), np.array([3.0, 3.0])) - model = Polynomial(order=0, transform=scale(Parameter("rho"), log=False)) - np.testing.assert_allclose(model(obs, 4.0, 1.5), 1.5 * base(obs, 4.0)) - - def test_wrong_param_count_raises(self): - model = Polynomial(order=0, transform=scale()) - obs = Observation(np.array([1.0]), np.array([1.0])) - with self.assertRaises(ValueError): - model(obs, 1.0) - # evaluate() counts base parameters only, not the transform's - with self.assertRaisesRegex(ValueError, "Expected 1 parameters"): - model.evaluate(obs, 1.0, 2.0) - - -class TestMask(unittest.TestCase): - """Masks select the active rows; terms are authored over the full stack.""" - - def setUp(self): - self.pm = Polynomial(order=1) - self.mp = (0.5, 1.5) - self.obs1 = Observation( - np.array([1.0, 2.0, 3.0]), - np.array([2.1, 3.4, 5.2]), - y_stat_err=np.array([0.1, 0.2, 0.3]), - ) - self.obs2 = Observation( - np.array([4.0, 5.0]), - np.array([6.7, 8.1]), - y_stat_err=np.array([0.2, 0.2]), - ) - - def test_point_mask_equals_hand_subset(self): - eta = Parameter("log eta") - masked = Constraint( - [self.obs1.masked([True, False, True]), self.obs2], - self.pm, - extra_terms=[normalization_term(parameter=eta)], - ) - sub = Observation( - self.obs1.x[[0, 2]], - self.obs1.y[[0, 2]], - y_stat_err=self.obs1.y_stat_err[[0, 2]], - ) - ref = Constraint( - [sub, self.obs2], self.pm, extra_terms=[normalization_term(parameter=eta)] - ) - self.assertEqual(masked.n_data_pts, 4) - self.assertEqual(masked.n_data_pts_total, 5) - self.assertEqual(masked.covariance.N, 5) - self.assertAlmostEqual( - masked.log_likelihood(self.mp, (np.log(0.1),)), - ref.log_likelihood(self.mp, (np.log(0.1),)), - ) - # block-diagonal path too - eps = Parameter("log eps") - masked = Constraint( - [self.obs1.masked([True, False, True]), self.obs2], - self.pm, - extra_terms=[noise_term(eps)], - ) - ref = Constraint([sub, self.obs2], self.pm, extra_terms=[noise_term(eps)]) - self.assertTrue(masked.covariance.block_diagonal) - self.assertAlmostEqual( - masked.log_likelihood(self.mp, (np.log(0.3),)), - ref.log_likelihood(self.mp, (np.log(0.3),)), - ) - - def test_observation_mask_drops_block(self): - c = Constraint([self.obs1, self.obs2], self.pm, mask=[True, False]) - ref = Constraint([self.obs1], self.pm) - self.assertEqual(c.n_data_pts, 3) - self.assertAlmostEqual(c.log_likelihood(self.mp), ref.log_likelihood(self.mp)) - c2 = Constraint([self.obs1, self.obs2], self.pm, mask=[1]) - self.assertEqual(c2.n_data_pts, 2) - ev = Evidence([c2]) - self.assertEqual(ev.n_dof, 2 - 2) - - def test_complement_partitions(self): - c = Constraint( - [self.obs1.masked_where(lambda x: x < 2.5), self.obs2], - self.pm, - mask=[True, False], - ) - h = c.complement() - self.assertEqual(c.n_data_pts, 2) - self.assertEqual(h.n_data_pts, 3) # obs1's third point + all of obs2 - both = set(c.active) | set(h.active) - self.assertEqual(both, set(range(5))) - self.assertEqual(set(c.active) & set(h.active), set()) - full = Constraint([self.obs1, self.obs2], self.pm) - self.assertAlmostEqual( - c.log_likelihood(self.mp) + h.log_likelihood(self.mp), - full.log_likelihood(self.mp), - ) - - def test_masked_shares_params(self): - eta = Parameter("log eta") - c = Constraint( - [self.obs1, self.obs2], - self.pm, - extra_terms=[normalization_term(parameter=eta)], - ) - h = c.masked(point_masks=[[False, True, False], None]) - self.assertEqual(h.params, c.params) - self.assertEqual(h.n_data_pts, 3) - - def test_predict_and_covariance_active(self): - c = Constraint([self.obs1.masked([True, False, True]), self.obs2], self.pm) - ym, S = c.predict_and_covariance(self.mp) - self.assertEqual(ym.shape, (4,)) - self.assertEqual(S.shape, (4, 4)) - full = c.covariance_matrix(self.mp, active_only=False) - self.assertEqual(full.shape, (5, 5)) - np.testing.assert_allclose( - c.y, np.concatenate([self.obs1.y[[0, 2]], self.obs2.y]) - ) - preds = c.predict(*self.mp) - self.assertEqual(len(preds[0]), 3) # all points, per observation - - def test_coverage_uses_active_points(self): - c = Constraint([self.obs1.masked([True, False, True]), self.obs2], self.pm) - lo = [o.y - 1.0 for o in c.observations] - hi = [o.y + 1.0 for o in c.observations] - self.assertEqual(c.empirical_coverage(lo, hi), 1.0) - self.assertEqual(c.num_pts_within_interval(lo, hi), 4) - - def test_no_active_points_coverage_is_nan(self): - c = Constraint([self.obs1, self.obs2], self.pm, mask=[]) - self.assertEqual(c.n_data_pts, 0) - lo = [o.y - 1.0 for o in c.observations] - hi = [o.y + 1.0 for o in c.observations] - self.assertTrue(np.isnan(c.empirical_coverage(lo, hi))) - - def test_generator_argument(self): - ref = Constraint([self.obs1, self.obs2], self.pm) - gen = Constraint((o for o in [self.obs1, self.obs2]), self.pm) - self.assertEqual(len(gen.observations), 2) - self.assertEqual(gen.n_data_pts, ref.n_data_pts) - self.assertAlmostEqual(gen.log_likelihood(self.mp), ref.log_likelihood(self.mp)) - - def test_ambiguous_observation_mask_raises(self): - with self.assertRaises(ValueError): - Constraint([self.obs1, self.obs2], self.pm, mask=[1, 0]) - by_index = Constraint([self.obs1, self.obs2], self.pm, mask=[0]) - self.assertEqual(by_index.n_data_pts, 3) - by_bool = Constraint([self.obs1, self.obs2], self.pm, mask=[False, True]) - self.assertEqual(by_bool.n_data_pts, 2) - - -class TestComparisonSpaceTransform(unittest.TestCase): - def setUp(self): - self.pm = Polynomial(order=1) - self.mp = (1.0, 2.0) - self.x = np.array([1.0, 2.0, 3.0]) - self.y = np.array([3.2, 4.9, 7.3]) - self.err = np.array([0.3, 0.5, 0.7]) - - def test_log_space_equals_hand_built(self): - obs = Observation(self.x, self.y, y_stat_err=self.err, transform=log) - eps = Parameter("log eps") - c = Constraint([obs], self.pm, extra_terms=[noise_term(eps)]) - ym = self.pm(obs, *self.mp) - cov = np.diag((self.err / self.y) ** 2) + 0.04 * np.eye(3) - expected = manual_mvn_loglike(np.log(self.y), np.log(ym), cov) - self.assertAlmostEqual(c.log_likelihood(self.mp, (np.log(0.2),)), expected) - self.assertAlmostEqual(c.log_jacobian, -np.sum(np.log(self.y))) - - def test_predict_spaces(self): - obs = Observation(self.x, self.y, transform=log) - c = Constraint([obs], self.pm, extra_terms=[noise_term(Parameter("e"))]) - ym = self.pm(obs, *self.mp) - np.testing.assert_allclose(c.predict(*self.mp)[0], np.log(ym)) - np.testing.assert_allclose(c.predict(*self.mp, raw=True)[0], ym) - - def test_nonpositive_prediction_is_minus_inf(self): - obs = Observation(self.x, self.y, transform=log) - c = Constraint([obs], self.pm, extra_terms=[noise_term(Parameter("e"))]) - ll = c.log_likelihood((-10.0, 0.0), (0.0,)) - self.assertEqual(ll, -np.inf) - self.assertEqual(c.marginal_log_likelihood([np.full(3, -1.0)], 0.0), -np.inf) - - def test_nonpositive_prediction_chi2_is_plus_inf(self): - # chi2 is a distance: an invalid prediction must be +inf, not -inf - obs = Observation(self.x, self.y, transform=log) - c = Constraint([obs], self.pm, extra_terms=[noise_term(Parameter("e"))]) - self.assertEqual(c.chi2((-10.0, 0.0), (0.0,)), np.inf) - - -class TestConstantTermsReadX(unittest.TestCase): - """Constant terms (fixed kernels, x-dependent fixed diagonals) are evaluated - once with the invariant x/y at Constraint construction.""" - - def setUp(self): - self.pm = Polynomial(order=1) - self.mp = (0.1, 1.0) - self.x = np.linspace(0.0, 1.0, 5) - self.err = np.full(5, 0.1) - self.obs = Observation(self.x, self.x + 0.1, y_stat_err=self.err) - - def test_fixed_kernel_term_in_constraint(self): - kernel = RBF(length_scale=0.3, length_scale_bounds="fixed") - c = Constraint( - [self.obs], self.pm, extra_terms=[kernel_term(kernel, jitter=0.0)] - ) - self.assertTrue(c.covariance.is_constant) - self.assertEqual(c.n_params, 0) - ym = self.pm(self.obs, *self.mp) - cov = np.diag(self.err**2) + kernel(self.x[:, None]) - expected = manual_mvn_loglike(self.obs.y, ym, cov) - self.assertAlmostEqual(c.log_likelihood(self.mp), expected) - - def test_x_dependent_constant_term_in_constraint(self): - t = Term(lambda c: 0.1 * c.x, kind="diag", constant=True) - c = Constraint([self.obs], self.pm, extra_terms=[t]) - self.assertTrue(c.covariance.is_constant) - ym = self.pm(self.obs, *self.mp) - cov = np.diag(self.err**2 + (0.1 * self.x) ** 2) - self.assertAlmostEqual( - c.log_likelihood(self.mp), manual_mvn_loglike(self.obs.y, ym, cov) - ) - # the eager validation warmed the cache; a fresh copy is returned to callers - S = c.covariance_matrix(self.mp) - self.assertTrue(S.flags.writeable) - np.testing.assert_allclose(S, cov) - - -class TestMaskWithPerObservationScaling(unittest.TestCase): - """Masked views keep routing to their root observation's rho.""" - - def setUp(self): - self.x = np.array([1.0, 2.0, 3.0, 4.0]) - self.err = np.full(4, 0.1) - self.obs1 = Observation(self.x, 2.0 * self.x, y_stat_err=self.err) - self.obs2 = Observation(self.x, 6.0 * self.x, y_stat_err=self.err) - self.pm = Polynomial( - order=1, transform=per_observation_scaling([self.obs1, self.obs2]) - ) - self.mp = (0.0, 2.0, 0.0, np.log(3.0)) - - def _reference(self, obs_list, keep): - # a constraint built directly on the same active points; masked views - # share their root's identity so the same transform routes them - return Constraint(obs_list, self.pm, mask=keep).log_likelihood(self.mp) - - def test_masked_view_routes_to_root(self): - c = Constraint([self.obs1, self.obs2], self.pm) - held = c.masked(point_masks=[self.x < 2.5, None]) - ref = self._reference([self.obs1.masked(self.x < 2.5), self.obs2], [True, True]) - self.assertAlmostEqual(held.log_likelihood(self.mp), ref) - - def test_complement_routes_to_root(self): - c = Constraint([self.obs1, self.obs2], self.pm, mask=[True, False]) - comp = c.complement() - self.assertEqual(comp.n_data_pts, 4) - ref = self._reference([self.obs1, self.obs2], [False, True]) - self.assertAlmostEqual(comp.log_likelihood(self.mp), ref) - self.assertAlmostEqual( - c.log_likelihood(self.mp) + comp.log_likelihood(self.mp), - Constraint([self.obs1, self.obs2], self.pm).log_likelihood(self.mp), - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/test/test_covariance.py b/test/test_covariance.py deleted file mode 100644 index 86d4840..0000000 --- a/test/test_covariance.py +++ /dev/null @@ -1,812 +0,0 @@ -"""Unit tests for the stacked-covariance core (:mod:`rxmc.covariance`).""" - -from types import SimpleNamespace - -import numpy as np -import pytest -from sklearn.gaussian_process.kernels import RBF, ConstantKernel, Matern, WhiteKernel - -from helpers import make_ctx -from rxmc.covariance import ( - ConstraintCovariance, - StackContext, - Term, - averaging, - constant_amplitude, - exp_growth, - exp_growth_amplitude, - kernel_term, - model_error_term, - noise_fraction_term, - noise_term, - normalization_term, - offset_term, - ones, - statistical_term, - systematic_term, - x_basis, - ym, -) -from rxmc.elastic_diffxs_observation import momentum_transfer -from rxmc.likelihood_model import mahalanobis_distance_sqr_cholesky -from rxmc.params import Parameter -from rxmc.transforms import Transform - - -def single_block_ctx(x, y, ym): - n = len(x) - return make_ctx(x, y, ym, [np.arange(n)]) - - -def assemble(terms, ctx, theta=()): - cov = ConstraintCovariance(terms, len(ctx.x), blocks=ctx.supports) - return cov.matrix(ctx, *theta) - - -# ---------------------------------------------------------------------------- -# Term -# ---------------------------------------------------------------------------- - - -class TestTermKinds: - def test_diag_array_squares_std(self): - t = Term(np.array([1.0, 2.0, 3.0]), kind="diag", support=[0, 1, 2]) - S = np.zeros((3, 3)) - t.add_to(S, None, ()) - assert np.allclose(S, np.diag([1.0, 4.0, 9.0])) - assert t.is_constant - assert not t.couples_offdiagonal - - def test_mode_array_outer(self): - v = np.array([1.0, 2.0]) - t = Term(v, kind="mode", support=[0, 1]) - S = np.zeros((2, 2)) - t.add_to(S, None, ()) - assert np.allclose(S, np.outer(v, v)) - assert t.couples_offdiagonal - - def test_matrix_array_passthrough_into_subblock(self): - m = np.array([[2.0, 0.5], [0.5, 3.0]]) - t = Term(m, support=[2, 3]) - S = np.zeros((4, 4)) - t.add_to(S, None, ()) - expected = np.zeros((4, 4)) - expected[2:, 2:] = m - assert np.allclose(S, expected) - - def test_bad_kind_raises(self): - with pytest.raises(ValueError, match="kind"): - Term(np.ones(2), kind="rank1", support=[0, 1]) - - def test_scalar_broadcast_raises(self): - # a (1, 1) matrix on a length-3 support used to broadcast silently - with pytest.raises(ValueError, match="expects shape"): - Term([[0.04]], support=np.arange(3)) - - def test_wrong_length_vector_raises(self): - with pytest.raises(ValueError, match="expects shape"): - Term(np.ones(2), kind="diag", support=np.arange(3)) - - def test_asymmetric_matrix_raises(self): - with pytest.raises(ValueError, match="symmetric"): - Term(np.array([[1.0, 0.2], [0.0, 1.0]]), support=[0, 1]) - - def test_array_with_params_raises(self): - with pytest.raises(ValueError, match="array-valued"): - Term(np.ones(2), (Parameter("p"),), kind="diag", support=[0, 1]) - - def test_callable_sees_local_context_and_values(self): - seen = {} - - def fn(c, a, b): - seen["c"] = c - return a * c.ym + b * c.y - - pa, pb = Parameter("a"), Parameter("b") - t = Term(fn, (pa, pb), kind="diag", support=[1, 2]) - ctx = single_block_ctx([0.0, 1.0, 2.0], [1.0, 2.0, 3.0], [1.5, 2.5, 3.5]) - S = np.zeros((3, 3)) - t.add_to(S, ctx, (2.0, 1.0)) - c = seen["c"] - assert np.allclose(c.x, [1.0, 2.0]) - assert np.allclose(c.ym, [2.5, 3.5]) - assert len(c) == 2 - v = 2.0 * np.array([2.5, 3.5]) + np.array([2.0, 3.0]) - assert np.allclose(np.diag(S), [0.0, *(v**2)]) - - def test_callable_wrong_shape_raises(self): - t = Term(lambda c: np.ones(len(c) + 1), kind="diag", support=[0, 1]) - ctx = single_block_ctx([0.0, 1.0], [0.0, 0.0], [0.0, 0.0]) - with pytest.raises(ValueError, match="returned shape"): - t.add_to(np.zeros((2, 2)), ctx, ()) - - def test_wrong_param_count_raises(self): - t = Term(lambda c, a: a * ones(c), (Parameter("a"),), kind="diag", support=[0]) - ctx = single_block_ctx([0.0], [0.0], [0.0]) - with pytest.raises(ValueError, match="expected 1 params"): - t.add_to(np.zeros((1, 1)), ctx, ()) - - def test_constant_callable_cached(self): - calls = [] - - def fn(c): - calls.append(1) - return np.ones(len(c)) - - t = Term(fn, kind="diag", support=[0, 1], constant=True) - assert t.is_constant - ctx = single_block_ctx([0.0, 1.0], [0.0, 0.0], [0.0, 0.0]) - t.add_to(np.zeros((2, 2)), ctx, ()) - t.add_to(np.zeros((2, 2)), ctx, ()) - assert len(calls) == 1 - - def test_constant_flag_ignored_with_params(self): - t = Term(lambda c, a: ones(c), (Parameter("a"),), kind="diag", constant=True) - assert not t.is_constant - - def test_constant_term_may_read_x(self): - # constant means "independent of ym"; x is invariant and readable - x = np.linspace(0.0, 2.0, 4) - t = Term(lambda c: 0.1 * c.x, kind="diag", constant=True) - cov = ConstraintCovariance([t], 4) - assert cov.is_constant - ctx = StackContext.constant(x, np.zeros(4), [np.arange(4)]) - np.testing.assert_allclose(cov.matrix(ctx), np.diag((0.1 * x) ** 2)) - # a mis-declared constant term (reads ym) fails loudly, not silently - bad = ConstraintCovariance( - [Term(lambda c: 0.1 * c.ym, kind="diag", constant=True)], 4 - ) - with pytest.raises(TypeError): - bad.matrix(ctx) - assert cov.matrix(single_block_ctx(x, np.zeros(4), np.ones(4))) is cov.matrix( - ctx - ) - - -class TestTermCoords: - def test_coords_callable_applied_to_x(self): - t = Term(lambda c: c.x, kind="diag", support=[0, 1], coords=lambda x: 2 * x) - ctx = single_block_ctx([1.0, 3.0], [0.0, 0.0], [0.0, 0.0]) - assert np.allclose(t.local_context(ctx).x, [2.0, 6.0]) - - def test_parametric_coords_params_appended(self): - pk = Parameter("k") - coords = Transform(lambda x, k: k * x, (pk,)) - pa = Parameter("a") - t = Term( - lambda c, a: a * c.x, (pa,), kind="diag", support=[0, 1], coords=coords - ) - assert t.params == (pa, pk) - ctx = single_block_ctx([1.0, 2.0], [0.0, 0.0], [0.0, 0.0]) - S = np.zeros((2, 2)) - t.add_to(S, ctx, (3.0, 2.0)) # a=3, k=2 -> v = 3 * 2 * x - assert np.allclose(np.diag(S), (6.0 * np.array([1.0, 2.0])) ** 2) - - def test_coords_array_2d_reaches_kernel(self): - kernel = RBF(length_scale=1.0) - X = np.array([[0.0, 0.0], [1.0, 1.0], [2.0, 0.0]]) - t = kernel_term(kernel, coords=lambda x: X, jitter=0.0, support=np.arange(3)) - ctx = single_block_ctx(np.zeros(3), np.zeros(3), np.zeros(3)) - S = np.zeros((3, 3)) - t.add_to(S, ctx, kernel.theta) - assert np.allclose(S, kernel(X)) - - -class TestSupportNone: - def test_bound_by_constraint_covariance(self): - p = Parameter("log eps") - t = noise_term(p) - assert not t.bound - cov = ConstraintCovariance([t], 3) - assert t.bound and np.array_equal(t.support, np.arange(3)) - ctx = single_block_ctx(np.zeros(3), np.zeros(3), np.zeros(3)) - assert np.allclose(cov.matrix(ctx, np.log(2.0)), 4.0 * np.eye(3)) - - def test_unbound_add_to_raises(self): - t = noise_term(Parameter("p")) - with pytest.raises(ValueError, match="unresolved"): - t.add_to(np.zeros((2, 2)), None, (0.0,)) - - def test_bind_idempotent_and_explicit_support_untouched(self): - t = Term(np.ones(2), kind="diag", support=[1, 2]) - t.bind(5) - assert np.array_equal(t.support, [1, 2]) - u = Term(np.ones(3), kind="diag") - u.bind(3) - u.bind(3) - assert np.array_equal(u.support, np.arange(3)) - - def test_array_length_checked_at_bind(self): - t = Term(np.ones(2), kind="diag") - with pytest.raises(ValueError, match="expects shape"): - ConstraintCovariance([t], 3) - - def test_whole_stack_mode_block_diagonality(self): - one = ConstraintCovariance( - [offset_term(parameter=Parameter("w"))], 3, blocks=[np.arange(3)] - ) - assert one.block_diagonal - two = ConstraintCovariance( - [offset_term(parameter=Parameter("w"))], - 4, - blocks=[np.arange(2), np.arange(2, 4)], - ) - assert not two.block_diagonal - diag = ConstraintCovariance( - [noise_term(Parameter("e"))], 4, blocks=[np.arange(2), np.arange(2, 4)] - ) - assert diag.block_diagonal - - def test_non_term_raises(self): - with pytest.raises(TypeError): - ConstraintCovariance([np.eye(2)], 2) - - def test_rebinding_to_a_different_stack_raises(self): - t = noise_term(Parameter("p")) - ConstraintCovariance([t], 4) - ConstraintCovariance([t], 4) # same stack (a masked view): fine - with pytest.raises(ValueError, match="already bound"): - ConstraintCovariance([t], 6) - - def test_local_context_checks_param_count(self): - s = Parameter("s") - t = Term(lambda c: c.x, kind="diag", coords=Transform(lambda a, s: s * a, (s,))) - t.bind(2) - ctx = single_block_ctx(np.ones(2), np.zeros(2), np.zeros(2)) - with pytest.raises(ValueError, match="expected 1 params"): - t.local_context(ctx) - - -# ---------------------------------------------------------------------------- -# Gather-by-identity and structural properties -# ---------------------------------------------------------------------------- - - -class TestGatherByIdentity: - def test_shared_parameter_dedup(self): - p = Parameter("log eps") - cov = ConstraintCovariance( - [noise_term(p, support=[0, 1]), noise_term(p, support=[2, 3])], 4 - ) - assert cov.n_params == 1 - - def test_distinct_parameters_not_shared(self): - cov = ConstraintCovariance( - [ - noise_term(Parameter("a"), support=[0, 1]), - noise_term(Parameter("b"), support=[2, 3]), - ], - 4, - ) - assert cov.n_params == 2 - - def test_shared_value_fed_to_both(self): - p = Parameter("log eps") - cov = ConstraintCovariance( - [noise_term(p, support=[0, 1]), noise_term(p, support=[2, 3])], 4 - ) - ctx = single_block_ctx(np.zeros(4), np.zeros(4), np.zeros(4)) - S = cov.matrix(ctx, np.log(3.0)) - assert np.allclose(S, 9.0 * np.eye(4)) - - def test_first_seen_order_deterministic(self): - a, b = Parameter("a"), Parameter("b") - cov = ConstraintCovariance( - [ - noise_term(b, support=[0]), - noise_term(a, support=[1]), - noise_term(b, support=[2]), - ], - 3, - ) - assert cov.params == (b, a) - - def test_wrong_param_count_raises(self): - cov = ConstraintCovariance([noise_term(Parameter("a"))], 2) - with pytest.raises(ValueError, match="expected 1 params"): - cov.matrix(None) - - -class TestProperties: - def test_is_constant_and_caching(self): - cov = ConstraintCovariance( - [statistical_term(np.array([1.0, 2.0]))], 2, blocks=[np.arange(2)] - ) - assert cov.is_constant - S1 = cov.matrix(None) - S2 = cov.matrix(None) - assert S1 is S2 - assert not S1.flags.writeable - L, _ = cov.cholesky(None) - assert not L.flags.writeable - - def test_nonconstant_matrix_writable(self): - cov = ConstraintCovariance([noise_term(Parameter("a"))], 2) - ctx = single_block_ctx(np.zeros(2), np.zeros(2), np.zeros(2)) - S = cov.matrix(ctx, 0.0) - assert S.flags.writeable - - def test_block_cholesky_cached_and_requires_blocks(self): - cov = ConstraintCovariance( - [statistical_term(np.ones(4))], 4, blocks=[np.arange(2), np.arange(2, 4)] - ) - f1 = cov.block_cholesky(None) - f2 = cov.block_cholesky(None) - assert f1 is f2 - with pytest.raises(ValueError): - ConstraintCovariance([statistical_term(np.ones(2))], 2).block_cholesky(None) - - def test_cross_block_term_without_blocks_not_block_diagonal(self): - cov = ConstraintCovariance([offset_term(parameter=Parameter("w"))], 4) - assert not cov.block_diagonal - diag = ConstraintCovariance([noise_term(Parameter("e"))], 4) - assert diag.block_diagonal - - def test_stacked_distance_matches_dense(self): - x = np.arange(4.0) - y = np.array([1.0, 2.0, 3.0, 4.0]) - ymod = np.array([1.1, 1.9, 3.2, 3.8]) - ctx = make_ctx(x, y, ymod, [np.arange(2), np.arange(2, 4)]) - terms = [statistical_term(0.5 * np.ones(4)), noise_term(Parameter("e"))] - block = ConstraintCovariance(terms, 4, blocks=ctx.supports) - dense = ConstraintCovariance(terms, 4) - assert block.block_diagonal - d_b = block.stacked_distance(ctx, (np.log(0.3),)) - d_d = dense.stacked_distance(ctx, (np.log(0.3),)) - S = block.matrix(ctx, np.log(0.3)) - assert np.allclose(d_b, mahalanobis_distance_sqr_cholesky(y, ymod, S)) - assert np.allclose(d_d, d_b) - - -class TestActive: - def setup_method(self): - self.x = np.arange(6.0) - self.y = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) - self.ym = self.y + 0.1 * np.array([1, -1, 1, -1, 1, -1]) - self.blocks = [np.arange(3), np.arange(3, 6)] - self.ctx = make_ctx(self.x, self.y, self.ym, self.blocks) - self.eta = Parameter("log eta") - self.terms = [ - statistical_term(0.3 * np.ones(6)), - normalization_term(parameter=self.eta), # whole stack -> dense - ] - self.active = np.array([0, 2, 3, 5]) - - def _reference(self, cov, active): - """Dense (d2, logdet) on the active subset of ``cov``'s full matrix.""" - S = cov.matrix(self.ctx, np.log(0.2))[np.ix_(active, active)] - return mahalanobis_distance_sqr_cholesky(self.y[active], self.ym[active], S) - - def test_dense_path_restricts_to_active(self): - cov = ConstraintCovariance(self.terms, 6, active=self.active) - assert cov.n_active == 4 - d2, ld = cov.stacked_distance(self.ctx, (np.log(0.2),)) - assert np.allclose((d2, ld), self._reference(cov, self.active)) - assert cov.active_matrix(self.ctx, np.log(0.2)).shape == (4, 4) - assert cov.matrix(self.ctx, np.log(0.2)).shape == (6, 6) - - def test_block_path_restricts_to_active(self): - terms = [statistical_term(0.3 * np.ones(6)), noise_term(Parameter("e"))] - cov = ConstraintCovariance(terms, 6, blocks=self.blocks, active=self.active) - assert cov.block_diagonal and cov.uses_block_path - d2, ld = cov.stacked_distance(self.ctx, (np.log(0.2),)) - assert np.allclose((d2, ld), self._reference(cov, self.active)) - - def test_fully_masked_block_skipped(self): - terms = [statistical_term(0.3 * np.ones(6))] - active = np.arange(3) - cov = ConstraintCovariance(terms, 6, blocks=self.blocks, active=active) - d2, ld = cov.stacked_distance(self.ctx) - r = (self.y - self.ym)[:3] - assert np.allclose((d2, ld), (r @ r / 0.09, 3 * np.log(0.09))) - - def test_all_active_is_none(self): - cov = ConstraintCovariance(self.terms, 6, active=np.arange(6)) - assert cov.active is None - - def test_permuted_active_is_kept(self): - perm = np.array([5, 4, 3, 2, 1, 0]) - cov = ConstraintCovariance(self.terms, 6, active=perm) - assert cov.active is not None and cov.n_active == 6 - ref = ConstraintCovariance(self.terms, 6) - d_perm = cov.stacked_distance(self.ctx, (np.log(0.2),)) - d_ref = ref.stacked_distance(self.ctx, (np.log(0.2),)) - assert np.allclose(d_perm, d_ref) - - -# ---------------------------------------------------------------------------- -# Factories -# ---------------------------------------------------------------------------- - - -class TestFactories: - def setup_method(self): - self.x = np.array([0.5, 1.0, 1.5]) - self.y = np.array([1.0, 2.0, 3.0]) - self.ym = np.array([1.1, 1.9, 3.2]) - self.stat = np.array([0.1, 0.2, 0.3]) - self.ctx = single_block_ctx(self.x, self.y, self.ym) - - def test_statistical_only(self): - S = assemble([statistical_term(self.stat)], self.ctx) - assert np.allclose(S, np.diag(self.stat**2)) - - def test_unknown_noise(self): - S = assemble([noise_term(Parameter("e"))], self.ctx, (np.log(0.4),)) - assert np.allclose(S, 0.16 * np.eye(3)) - S = assemble([noise_term(Parameter("e"), log=False)], self.ctx, (0.4,)) - assert np.allclose(S, 0.16 * np.eye(3)) - - def test_unknown_noise_fraction(self): - S = assemble([noise_fraction_term(Parameter("e"))], self.ctx, (np.log(0.4),)) - assert np.allclose(S, np.diag((0.4 * self.ym) ** 2)) - - def test_unknown_normalization_error(self): - S = assemble( - [normalization_term(parameter=Parameter("n"))], self.ctx, (np.log(0.05),) - ) - assert np.allclose(S, 0.05**2 * np.outer(self.ym, self.ym)) - - def test_unknown_model_error_averaging(self): - S = assemble( - [model_error_term(Parameter("g"), averaging=True)], self.ctx, (np.log(0.1),) - ) - z = 0.5 * (self.y + self.ym) - assert np.allclose(S, np.diag((0.1 * z) ** 2)) - S = assemble( - [model_error_term(Parameter("g"), averaging=False)], - self.ctx, - (np.log(0.1),), - ) - assert np.allclose(S, np.diag((0.1 * self.ym) ** 2)) - - def test_fixed_normalization_systematic(self): - S = assemble([normalization_term(magnitude=0.05)], self.ctx) - assert np.allclose(S, 0.05**2 * np.outer(self.ym, self.ym)) - S = assemble([normalization_term(magnitude=np.array(0.05))], self.ctx) - assert np.allclose(S, 0.05**2 * np.outer(self.ym, self.ym)) - - def test_fixed_offset_systematic(self): - t = offset_term(magnitude=0.2) - assert t.is_constant - S = assemble([t], self.ctx) - assert np.allclose(S, 0.04 * np.ones((3, 3))) - S = assemble([offset_term(magnitude=np.array([0.1, 0.2, 0.3]))], self.ctx) - v = np.array([0.1, 0.2, 0.3]) - assert np.allclose(S, np.outer(v, v)) - - def test_fixed_offset_in_constant_covariance_ignores_ym(self): - # a constant covariance never reads ym: it can be factored (eagerly, at - # Constraint construction) with a placeholder ym and the cached factor - # is reused afterwards - cov = ConstraintCovariance( - [statistical_term(self.stat), offset_term(magnitude=0.2)], 3 - ) - assert cov.is_constant - L, logdet = cov.cholesky( - StackContext.constant(self.ctx.x, self.ctx.y, [np.arange(3)]) - ) - assert np.all(np.isfinite(L)) - L2, logdet2 = cov.cholesky(self.ctx) - assert L2 is L and logdet2 == logdet - - def test_masked_magnitudes(self): - m = np.array([1.0, 0.0, 1.0]) - S = assemble([offset_term(magnitude=0.2, mask=m)], self.ctx) - v = 0.2 * m - assert np.allclose(S, np.outer(v, v)) - S = assemble( - [normalization_term(parameter=Parameter("n"), mask=m)], - self.ctx, - (np.log(0.5),), - ) - v = 0.5 * m * self.ym - assert np.allclose(S, np.outer(v, v)) - - def test_length_one_magnitude_or_mask_raises(self): - # a length-1 array is not a scalar: it must not broadcast silently - with pytest.raises(ValueError, match="shape"): - assemble([offset_term(magnitude=np.array([0.2]))], self.ctx) - with pytest.raises(ValueError, match="shape"): - assemble([offset_term(magnitude=0.2, mask=np.array([1.0]))], self.ctx) - with pytest.raises(ValueError, match="shape"): - assemble( - [normalization_term(parameter=Parameter("n"), mask=np.array([1.0]))], - self.ctx, - (0.0,), - ) - - def test_zero_d_magnitude_is_scalar(self): - # exfor_tools stores scalar systematics as 0-d arrays - S = assemble([offset_term(magnitude=np.array(0.2))], self.ctx) - assert np.allclose(S, 0.04 * np.ones((3, 3))) - - def test_magnitude_length_mismatch_raises(self): - with pytest.raises(ValueError): - assemble([offset_term(magnitude=np.ones(2))], self.ctx) - - def test_requires_magnitude_or_parameter(self): - with pytest.raises(ValueError): - offset_term() - with pytest.raises(ValueError): - normalization_term() - - def test_systematic_term_with_basis(self): - s = Parameter("log s") - S = assemble([systematic_term(s, basis=x_basis(2.0))], self.ctx, (np.log(3.0),)) - v = 3.0 * self.x / 2.0 - assert np.allclose(S, np.outer(v, v)) - - def test_parametric_basis(self): - e, l = Parameter("log e"), Parameter("slope") - t = noise_term(e, basis=exp_growth(np.pi), basis_params=(l,)) - assert t.params == (e, l) - S = assemble([t], self.ctx, (np.log(0.3), 0.0)) - assert np.allclose(S, 0.09 * np.eye(3)) # slope 0 == plain noise_term - S = assemble([t], self.ctx, (np.log(0.3), 2.0)) - assert np.allclose(S, np.diag((0.3 * np.exp(2.0 * self.x / np.pi)) ** 2)) - # basis growing with ym in linear space - t = noise_term(e, basis=exp_growth(np.pi, base=ym), basis_params=(l,)) - S = assemble([t], self.ctx, (np.log(0.3), 1.0)) - assert np.allclose(S, np.diag((0.3 * self.ym * np.exp(self.x / np.pi)) ** 2)) - - def test_old_observation_covariance_equivalence(self): - offset, norm = 0.2, 0.05 - terms = [ - statistical_term(self.stat), - offset_term(magnitude=offset), - normalization_term(magnitude=norm), - ] - S = assemble(terms, self.ctx) - old = ( - np.diag(self.stat**2) - + np.outer(offset * np.ones(3), offset * np.ones(3)) - + norm**2 * np.outer(self.ym, self.ym) - ) - assert np.allclose(S, old) - - def test_bases(self): - c = Term(lambda c: c.ym, kind="diag", support=np.arange(3)).local_context( - self.ctx - ) - assert np.allclose(ones(c), 1.0) - assert np.allclose(ym(c), self.ym) - assert np.allclose(averaging(c), 0.5 * (self.y + self.ym)) - - -class TestKernelTerm: - def test_params_match_theta_length_isotropic(self): - kernel = ConstantKernel(1.0) * RBF(length_scale=1.0) + WhiteKernel(1e-6) - term = kernel_term(kernel) - assert len(term.params) == len(kernel.theta) - assert not term.is_constant - - def test_params_anisotropic(self): - kernel = ConstantKernel(1.0) * RBF(length_scale=[1.0, 1.0]) - term = kernel_term(kernel, support=np.arange(3)) - assert len(term.params) == len(kernel.theta) - x2d = np.array([[0.0, 0.0], [1.0, 0.5], [2.0, 1.0]]) - ctx = make_ctx(x2d, np.zeros(3), np.zeros(3), [np.arange(3)]) - Sigma = np.zeros((3, 3)) - term.add_to(Sigma, ctx, kernel.theta) - assert np.all(np.isfinite(Sigma)) - - def test_cross_block_values(self): - kernel = RBF(length_scale=1.0) - term = kernel_term(kernel, jitter=0.0, support=np.arange(4)) - x = np.array([0.0, 1.0, 2.0, 3.0]) - ctx = make_ctx(x, np.zeros(4), np.zeros(4), [np.arange(2), np.arange(2, 4)]) - S = np.zeros((4, 4)) - term.add_to(S, ctx, kernel.theta) - np.testing.assert_allclose(S, kernel(x[:, None])) - assert np.any(S[:2, 2:] != 0.0) - cov = ConstraintCovariance([term], N=4, blocks=[np.arange(2), np.arange(2, 4)]) - assert not cov.block_diagonal - - def test_fixed_kernel_is_constant(self): - kernel = RBF(length_scale=1.0, length_scale_bounds="fixed") - term = kernel_term(kernel) - assert term.params == () and term.is_constant - - def test_constant_amplitude_reproduces_constant_kernel(self): - x = np.linspace(0.0, 2.0, 5) - ctx = single_block_ctx(x, np.zeros(5), np.zeros(5)) - A = 0.7 - la = Parameter("log A") - term = kernel_term( - RBF(1.0), amplitude=constant_amplitude, amplitude_params=(la,), jitter=0.0 - ) - assert [p.name for p in term.params] == ["discrepancy_length_scale", "log A"] - S = assemble([term], ctx, (0.0, np.log(A))) - ref = ConstantKernel(A**2, constant_value_bounds="fixed") * RBF(1.0) - np.testing.assert_allclose(S, ref(x[:, None])) - - def test_exp_growth_amplitude_and_coords(self): - x = np.linspace(0.1, 3.0, 4) - ctx = single_block_ctx(x, np.zeros(4), np.zeros(4)) - la, sl = Parameter("log A"), Parameter("slope") - - def q(x): - return 2.0 * np.sin(x / 2) - - term = kernel_term( - Matern(1.0, nu=2.5), - coords=q, - amplitude=exp_growth_amplitude(np.pi), - amplitude_params=(la, sl), - jitter=0.0, - ) - S = assemble([term], ctx, (np.log(0.5), np.log(0.3), 1.5)) - # note: amplitude sees the *transformed* coordinate - a = 0.3 * np.exp(1.5 * q(x) / np.pi) - ref = np.outer(a, a) * Matern(0.5, nu=2.5)(q(x)[:, None]) - np.testing.assert_allclose(S, ref) - - def test_duplicate_coords_factorizable_with_jitter(self): - x = np.array([0.0, 0.0, 1.0]) - ctx = single_block_ctx(x, np.zeros(3), np.zeros(3)) - term = kernel_term(RBF(1.0), jitter=1e-8) - cov = ConstraintCovariance([term, statistical_term(1e-3 * np.ones(3))], 3) - L, _ = cov.cholesky(ctx, 0.0) - assert np.all(np.isfinite(L)) - - -# ---------------------------------------------------------------------------- -# The alpha+Ca error-model ladder as one-line term lists (study self-checks) -# ---------------------------------------------------------------------------- - - -class TestStudyForms: - """Each error model of the alpha+Ca study is one term list; compare to the - hand-rolled dense covariance from that study's ``error_covariance``.""" - - def setup_method(self): - rng = np.random.default_rng(1) - n = 12 - self.x = np.sort(rng.uniform(0.2, 3.0, n)) # radians - self.y = rng.uniform(0.1, 1.5, n) # log-space "data" (any values) - self.ym = self.y + rng.normal(0.0, 0.1, n) - self.ctx = single_block_ctx(self.x, self.y, self.ym) - self.X = np.pi - self.log_err, self.log_slope = Parameter("log_err"), Parameter("log_err_slope") - self.log_sys, self.log_amp = Parameter("log_sys"), Parameter("log_amp") - self.err, self.slope, self.sys, self.amp = 0.05, 1.3, 0.04, 0.2 - self.k = 2.7 - - def xdeg(self): - return self.x / self.X # theta / 180 - - def test_L0(self): - S = assemble([noise_term(self.log_err)], self.ctx, (np.log(self.err),)) - assert np.allclose(S, self.err**2 * np.eye(len(self.x))) - - def test_E0_linear_space(self): - S = assemble([noise_fraction_term(self.log_err)], self.ctx, (np.log(self.err),)) - assert np.allclose(S, np.diag((self.err * self.ym) ** 2)) - - def test_L1(self): - terms = [ - noise_term( - self.log_err, basis=exp_growth(self.X), basis_params=(self.log_slope,) - ) - ] - S = assemble(terms, self.ctx, (np.log(self.err), self.slope)) - sigma = self.err * np.exp(self.slope * self.xdeg()) - assert np.allclose(S, np.diag(sigma**2)) - - def test_L2_rank_one_over_theta(self): - terms = [ - noise_term(self.log_err), - systematic_term(self.log_sys, basis=x_basis(self.X)), - ] - S = assemble(terms, self.ctx, (np.log(self.err), np.log(self.sys))) - u = self.xdeg() - assert np.allclose( - S, self.err**2 * np.eye(len(u)) + self.sys**2 * np.outer(u, u) - ) - - def test_L2n_and_L2y(self): - S = assemble( - [noise_term(self.log_err), offset_term(parameter=self.log_sys)], - self.ctx, - (np.log(self.err), np.log(self.sys)), - ) - assert np.allclose(S, self.err**2 * np.eye(len(self.x)) + self.sys**2) - S = assemble( - [noise_term(self.log_err), normalization_term(parameter=self.log_sys)], - self.ctx, - (np.log(self.err), np.log(self.sys)), - ) - assert np.allclose( - S, - self.err**2 * np.eye(len(self.x)) - + self.sys**2 * np.outer(self.ym, self.ym), - ) - - def test_L12(self): - terms = [ - noise_term( - self.log_err, basis=exp_growth(self.X), basis_params=(self.log_slope,) - ), - systematic_term(self.log_sys, basis=x_basis(self.X)), - ] - S = assemble(terms, self.ctx, (np.log(self.err), self.slope, np.log(self.sys))) - sigma = self.err * np.exp(self.slope * self.xdeg()) - u = self.xdeg() - assert np.allclose(S, np.diag(sigma**2) + self.sys**2 * np.outer(u, u)) - - def test_Lgp_matern_in_theta(self): - ell = 0.3 - terms = [ - noise_term(self.log_err), - kernel_term( - Matern(1.0, nu=2.5), - coords=lambda x: x / self.X, - amplitude=constant_amplitude, - amplitude_params=(self.log_amp,), - jitter=0.0, - prefix="gp", - ), - ] - S = assemble(terms, self.ctx, (np.log(self.err), np.log(ell), np.log(self.amp))) - u = self.xdeg() - K = self.amp**2 * Matern(ell, nu=2.5)(u[:, None]) - assert np.allclose(S, self.err**2 * np.eye(len(u)) + K) - - def test_Lgpn_angle_growing_amplitude(self): - ell = 0.3 - terms = [ - noise_term(self.log_err), - kernel_term( - Matern(1.0, nu=2.5), - coords=lambda x: x / self.X, - amplitude=exp_growth_amplitude(1.0), - amplitude_params=(self.log_amp, self.log_slope), - jitter=0.0, - ), - ] - S = assemble( - terms, - self.ctx, - (np.log(self.err), np.log(ell), np.log(self.amp), self.slope), - ) - u = self.xdeg() - a = self.amp * np.exp(self.slope * u) - K = np.outer(a, a) * Matern(ell, nu=2.5)(u[:, None]) - assert np.allclose(S, self.err**2 * np.eye(len(u)) + K) - - def test_LKp_kernel_in_momentum_transfer(self): - # b^2 I + s^2 11^T + a(q) a(q') RBF(|q - q'| / l_q), a = A q^(r/2) - log_b, log_s, r_pow = Parameter("log_b"), Parameter("log_s"), Parameter("r") - b, s, lq, r = 0.05, 0.05, 1.2, 0.8 - q = momentum_transfer(SimpleNamespace(k=self.k, x=self.x)) - assert np.allclose(q, 2.0 * self.k * np.sin(self.x / 2)) - terms = [ - noise_term(log_b), - offset_term(parameter=log_s), - kernel_term( - RBF(1.0), - coords=lambda x: 2.0 * self.k * np.sin(x / 2), - amplitude=lambda c, lA, r: np.exp(lA) * c.x ** (r / 2), - amplitude_params=(self.log_amp, r_pow), - jitter=0.0, - prefix="gpq", - ), - ] - S = assemble( - terms, self.ctx, (np.log(b), np.log(s), np.log(lq), np.log(self.amp), r) - ) - a = self.amp * q ** (r / 2) - K = np.outer(a, a) * RBF(lq)(q[:, None]) - ref = b**2 * np.eye(len(q)) + s**2 * np.ones((len(q), len(q))) + K - assert np.allclose(S, ref) - - def test_custom_term_direct(self): - # anything the factories cannot say is a one-line Term - e, l = Parameter("e"), Parameter("l") - t = Term( - lambda c, e, l: np.exp(e) * np.exp(l * c.x / np.pi), (e, l), kind="diag" - ) - S = assemble([t], self.ctx, (np.log(self.err), self.slope)) - sigma = self.err * np.exp(self.slope * self.xdeg()) - assert np.allclose(S, np.diag(sigma**2)) diff --git a/test/test_evidence.py b/test/test_evidence.py deleted file mode 100644 index 989c50a..0000000 --- a/test/test_evidence.py +++ /dev/null @@ -1,135 +0,0 @@ -import unittest - -import numpy as np - -from rxmc.constraint import Constraint -from rxmc.covariance import model_error_term -from rxmc.evidence import Evidence -from rxmc.observation import Observation -from rxmc.params import Parameter -from rxmc.physical_model import Polynomial - - -class TestEvidence(unittest.TestCase): - - def setUp(self): - y = np.array([1.0, 2.0, 3.0]) - x = np.array([1.0, 2.0, 3.0]) - y_stat_err = np.array([0.1, 0.2, 0.3]) - self.y = y - self.y_stat_err = y_stat_err - self.observations = [Observation(x=x, y=y, y_stat_err=y_stat_err)] - self.pm = Polynomial(order=1) - self.constraints = [ - Constraint(observations=self.observations, physical_model=self.pm) - for _ in range(4) - ] - self.weights = np.array([1.0, 1.0, 1.0, 1.0]) - self.evidence = Evidence(constraints=self.constraints, weights=self.weights) - - self.model_params = (3.0, 5.0) - modely = self.pm.evaluate(self.observations[0], *self.model_params) - delta = y - modely - chi2 = np.sum((delta / y_stat_err) ** 2) - N = self.observations[0].n_data_pts - log_det = np.sum(np.log(y_stat_err**2)) - logl_single = -0.5 * (N * np.log(2 * np.pi) + log_det + chi2) - self.expected_loglikelihood = 4 * logl_single - - def test_serial_execution(self): - log_likelihood = self.evidence.log_likelihood(model_params=self.model_params) - self.assertAlmostEqual(log_likelihood, self.expected_loglikelihood) - - def test_no_parametric_constraints_detected(self): - self.assertEqual(len(self.evidence.parametric_constraints), 0) - self.assertEqual(self.evidence.n_params, self.pm.n_params) - - def test_parametric_constraint_auto_detected(self): - gamma = Parameter("log gamma") - parametric = Constraint( - observations=self.observations, - physical_model=self.pm, - extra_terms=[model_error_term(gamma, support=np.arange(3))], - ) - evidence = Evidence(constraints=[self.constraints[0], parametric]) - self.assertEqual(len(evidence.parametric_constraints), 1) - self.assertIs(evidence.parametric_constraints[0], parametric) - self.assertEqual(evidence.n_params, self.pm.n_params + 1) - - def test_parametric_constraint_receives_its_params(self): - gamma = Parameter("log gamma") - parametric = Constraint( - observations=self.observations, - physical_model=self.pm, - extra_terms=[model_error_term(gamma, support=np.arange(3))], - ) - evidence = Evidence(constraints=[parametric]) - # one tuple per parametric constraint - ll = evidence.log_likelihood(self.model_params, [(np.log(0.1),)]) - # directly via the constraint - ll_direct = parametric.log_likelihood(self.model_params, (np.log(0.1),)) - self.assertAlmostEqual(ll, ll_direct) - - def test_wrong_cov_params_length_raises(self): - gamma = Parameter("log gamma") - parametric = Constraint( - observations=self.observations, - physical_model=self.pm, - extra_terms=[model_error_term(gamma, support=np.arange(3))], - ) - evidence = Evidence(constraints=[parametric]) - with self.assertRaises(ValueError): - evidence.log_likelihood(self.model_params, []) - - -class TestCrossConstraintParameterValidation(unittest.TestCase): - """Covariance/likelihood parameters are constraint-scoped (spec §8).""" - - def setUp(self): - self.pm = Polynomial(order=1) - self.obs = [ - Observation( - x=np.array([1.0, 2.0, 3.0]), - y=np.array([1.0, 2.0, 3.0]), - y_stat_err=np.array([0.1, 0.2, 0.3]), - ) - ] - - def _constraint(self, param): - return Constraint( - observations=self.obs, - physical_model=self.pm, - extra_terms=[model_error_term(param, support=np.arange(3))], - ) - - def test_same_parameter_object_in_two_constraints_raises(self): - gamma = Parameter("log gamma") - c1, c2 = self._constraint(gamma), self._constraint(gamma) - with self.assertRaises(ValueError) as cm: - Evidence(constraints=[c1, c2]) - self.assertIn("same object", str(cm.exception)) - - def test_duplicate_name_across_constraints_raises(self): - c1 = self._constraint(Parameter("log gamma")) - c2 = self._constraint(Parameter("log gamma")) - with self.assertRaises(ValueError) as cm: - Evidence(constraints=[c1, c2]) - self.assertIn("Duplicate parameter name", str(cm.exception)) - - def test_unique_names_accepted(self): - c1 = self._constraint(Parameter("log gamma 1")) - c2 = self._constraint(Parameter("log gamma 2")) - ev = Evidence(constraints=[c1, c2]) - self.assertEqual(ev.n_likelihood_params, 2) - - def test_two_default_student_t_constraints_raise(self): - from rxmc.likelihood_model import StudentT - - c1 = Constraint(self.obs, self.pm, likelihood=StudentT()) - c2 = Constraint(self.obs, self.pm, likelihood=StudentT()) - with self.assertRaises(ValueError): - Evidence(constraints=[c1, c2]) - - -if __name__ == "__main__": - unittest.main() diff --git a/test/test_likelihood_model.py b/test/test_likelihood_model.py deleted file mode 100644 index 2e1aa48..0000000 --- a/test/test_likelihood_model.py +++ /dev/null @@ -1,171 +0,0 @@ -"""Tests for the stacked likelihood functionals and their Term-based covariances.""" - -import unittest - -import numpy as np -from scipy.special import gammaln - -from helpers import manual_mvn_loglike -from rxmc.constraint import Constraint -from rxmc.covariance import ( - Term, - model_error_term, - noise_fraction_term, - noise_term, - normalization_term, -) -from rxmc.likelihood_model import ( - Chi2, - StudentT, - mahalanobis_distance_sqr_cholesky, -) -from rxmc.observation import Observation -from rxmc.params import Parameter -from rxmc.physical_model import Polynomial - - -class LikelihoodTestBase(unittest.TestCase): - def setUp(self): - self.x = np.array([1.0, 2.0, 3.0]) - self.y = np.array([2.0, 4.0, 7.0]) - self.stat = np.array([0.1, 0.2, 0.3]) - self.obs = Observation(self.x, self.y, y_stat_err=self.stat) - self.pm = Polynomial(order=1) - self.model_params = (1.0, 1.5) - self.ym = self.pm.evaluate(self.obs, *self.model_params) - - -class TestGaussianStatisticalOnly(LikelihoodTestBase): - def test_matches_manual_mvn(self): - c = Constraint([self.obs], self.pm) - cov = np.diag(self.stat**2) - expected = manual_mvn_loglike(self.y, self.ym, cov) - self.assertAlmostEqual(c.log_likelihood(self.model_params), expected) - - def test_constraint_is_non_parametric(self): - c = Constraint([self.obs], self.pm) - self.assertEqual(c.n_params, 0) - self.assertTrue(c.covariance.block_diagonal) - - -class TestUnknownNoise(LikelihoodTestBase): - def test_constant_noise(self): - eps = 0.05 - p = Parameter("log eps") - c = Constraint( - [self.obs], self.pm, extra_terms=[noise_term(p, support=np.arange(3))] - ) - cov = np.diag(self.stat**2) + np.diag(np.full(3, eps**2)) - expected = manual_mvn_loglike(self.y, self.ym, cov) - self.assertEqual(c.n_params, 1) - self.assertAlmostEqual( - c.log_likelihood(self.model_params, (np.log(eps),)), expected - ) - - def test_noise_fraction(self): - eps = 0.05 - p = Parameter("log eps") - c = Constraint( - [self.obs], - self.pm, - extra_terms=[noise_fraction_term(p, support=np.arange(3))], - ) - cov = np.diag(self.stat**2) + np.diag((eps * self.ym) ** 2) - expected = manual_mvn_loglike(self.y, self.ym, cov) - self.assertAlmostEqual( - c.log_likelihood(self.model_params, (np.log(eps),)), expected - ) - - -class TestUnknownNormalizationError(LikelihoodTestBase): - def test_eta(self): - eta = 0.07 - p = Parameter("log eta") - c = Constraint( - [self.obs], - self.pm, - extra_terms=[normalization_term(parameter=p, support=np.arange(3))], - ) - cov = np.diag(self.stat**2) + eta**2 * np.outer(self.ym, self.ym) - expected = manual_mvn_loglike(self.y, self.ym, cov) - self.assertAlmostEqual( - c.log_likelihood(self.model_params, (np.log(eta),)), expected - ) - - -class TestUnknownModelError(LikelihoodTestBase): - def test_averaging(self): - gamma = 0.1 - p = Parameter("log gamma") - c = Constraint( - [self.obs], - self.pm, - extra_terms=[model_error_term(p, averaging=True, support=np.arange(3))], - ) - z = 0.5 * (self.y + self.ym) - cov = np.diag(self.stat**2) + np.diag((gamma * z) ** 2) - expected = manual_mvn_loglike(self.y, self.ym, cov) - self.assertAlmostEqual( - c.log_likelihood(self.model_params, (np.log(gamma),)), expected - ) - - -class TestFixedCovariance(LikelihoodTestBase): - def test_dense_term_fixed_full_covariance(self): - cov = np.array([[0.04, 0.01, 0.0], [0.01, 0.09, 0.02], [0.0, 0.02, 0.16]]) - obs = Observation(self.x, self.y) # no stat err -> zeros - c = Constraint([obs], self.pm, extra_terms=[Term(cov, support=np.arange(3))]) - self.assertTrue(c.covariance.is_constant) - expected = manual_mvn_loglike(self.y, self.ym, cov) - self.assertAlmostEqual(c.log_likelihood(self.model_params), expected) - - def test_cholesky_cached(self): - cov = np.diag([0.04, 0.09, 0.16]) - obs = Observation(self.x, self.y) - c = Constraint([obs], self.pm, extra_terms=[Term(cov, support=np.arange(3))]) - L1, _ = c.covariance.cholesky(None) - L2, _ = c.covariance.cholesky(None) - self.assertIs(L1, L2) - - -class TestStudentT(LikelihoodTestBase): - def test_student_t_value(self): - nu = 5.0 - c = Constraint([self.obs], self.pm, likelihood=StudentT()) - self.assertEqual(c.n_params, 1) - self.assertEqual(c.params[0].name, "degrees_of_freedom") - ll = c.log_likelihood(self.model_params, (nu,)) - - cov = np.diag(self.stat**2) - d2, logdet = mahalanobis_distance_sqr_cholesky(self.y, self.ym, cov) - n = 3 - expected = ( - gammaln((n + nu) / 2) - - gammaln(nu / 2) - - 0.5 * n * np.log(np.pi * nu) - - 0.5 * logdet - - 0.5 * (nu + n) * np.log1p(d2 / nu) - ) - self.assertAlmostEqual(ll, expected) - - -class TestChi2(LikelihoodTestBase): - def test_chi2_drops_logdet(self): - c = Constraint([self.obs], self.pm, likelihood=Chi2()) - cov = np.diag(self.stat**2) - d2, _ = mahalanobis_distance_sqr_cholesky(self.y, self.ym, cov) - self.assertAlmostEqual(c.log_likelihood(self.model_params), -0.5 * d2) - - -class TestMahalanobisDistanceCholesky(unittest.TestCase): - def test_diagonal(self): - y = np.array([1.0, 2.0, 3.0]) - ym = np.array([1.1, 1.8, 3.2]) - cov = np.diag([0.1, 0.2, 0.3]) - d2, logdet = mahalanobis_distance_sqr_cholesky(y, ym, cov) - self.assertAlmostEqual(d2, np.sum((y - ym) ** 2 / np.diag(cov))) - self.assertAlmostEqual(logdet, np.log(np.prod(np.diag(cov)))) - - -if __name__ == "__main__": - unittest.main() diff --git a/test/test_model_comparison.py b/test/test_model_comparison.py deleted file mode 100644 index a214528..0000000 --- a/test/test_model_comparison.py +++ /dev/null @@ -1,213 +0,0 @@ -"""Tests for the sampler-agnostic ``rxmc.model_comparison`` utilities.""" - -import unittest -from unittest.mock import patch - -import numpy as np -from scipy import stats -from scipy.special import logsumexp -from sklearn.gaussian_process.kernels import RBF - -from helpers import manual_mvn_loglike -from rxmc.config import CalibrationConfig, ParameterConfig -from rxmc.constraint import Constraint -from rxmc.covariance import ConstraintCovariance, Term, kernel_term, noise_term -from rxmc.evidence import Evidence -from rxmc.model_comparison import ( - compare_logz, - coverage_curve, - coverage_error, - heldout_log_predictive, - log_jacobian, - log_posterior_predictive, - logz_summary, - predictive_draws, - sharpness, - split_samples, -) -from rxmc.observation import Observation -from rxmc.params import Parameter -from rxmc.physical_model import Polynomial -from rxmc.priors import IndependentPrior -from rxmc.transforms import log - - -class TestPredictiveDraws(unittest.TestCase): - def setUp(self): - self.pm = Polynomial(order=1) - self.x = np.linspace(0.0, 4.0, 5) - self.y = 1.0 + 2.0 * self.x - self.err = np.full(5, 0.3) - self.obs = Observation(self.x, self.y, y_stat_err=self.err) - - def test_draw_covariance_recovers_sigma(self): - eps = Parameter("log eps") - c = Constraint([self.obs], self.pm, extra_terms=[noise_term(eps)]) - theta = np.array([[1.0, 2.0]]) - cov = np.array([[np.log(0.4)]]) - draws = predictive_draws( - c, theta, cov, n_rep=40000, rng=np.random.default_rng(0) - ) - self.assertEqual(draws.shape, (40000, 5)) - np.testing.assert_allclose(draws.mean(axis=0), self.y, atol=0.02) - S = np.cov(draws.T) - np.testing.assert_allclose(S, np.diag(self.err**2 + 0.16), atol=0.02) - - def test_model_only_returns_ym(self): - c = Constraint([self.obs], self.pm) - theta = np.array([[1.0, 2.0], [0.0, 1.0]]) - d = predictive_draws(c, theta, model_only=True) - np.testing.assert_allclose(d[0], self.y) - np.testing.assert_allclose(d[1], self.x) - - def test_model_only_skips_covariance_assembly(self): - kernel = RBF(length_scale=1.0) - c = Constraint( - [self.obs.masked([True, True, False, True, True])], - self.pm, - extra_terms=[kernel_term(kernel)], - ) - ym, _ = c.predict_and_covariance((1.0, 2.0), (0.0,)) - with patch.object(ConstraintCovariance, "matrix") as m: - d = predictive_draws(c, [1.0, 2.0], [0.0], model_only=True) - m.assert_not_called() - self.assertEqual(d.shape, (1, 4)) - np.testing.assert_allclose(d[0], ym) - - def test_one_dimensional_row_is_one_sample(self): - c = Constraint([self.obs], self.pm) - d = predictive_draws(c, [1.0, 2.0], n_rep=3, rng=np.random.default_rng(2)) - self.assertEqual(d.shape, (3, 5)) - eps = Parameter("log eps") - cp = Constraint([self.obs], self.pm, extra_terms=[noise_term(eps)]) - with self.assertRaises(ValueError): - predictive_draws(cp, [[1.0, 2.0], [1.0, 2.0]], [[0.0]]) - - def test_tiny_variances_not_inflated(self): - obs = Observation(self.x, self.y, y_stat_err=np.full(5, 1e-4)) - c = Constraint([obs], self.pm) - draws = predictive_draws( - c, [1.0, 2.0], n_rep=40000, rng=np.random.default_rng(3) - ) - var = draws.var(axis=0) - np.testing.assert_allclose(var, 1e-8, rtol=0.03) - - def test_requires_cov_samples_when_parametric(self): - c = Constraint([self.obs], self.pm, extra_terms=[noise_term(Parameter("e"))]) - with self.assertRaises(ValueError): - predictive_draws(c, np.array([[1.0, 2.0]])) - - def test_respects_mask(self): - c = Constraint([self.obs.masked([True, False, True, False, True])], self.pm) - d = predictive_draws( - c, np.array([[1.0, 2.0]]), n_rep=3, rng=np.random.default_rng(1) - ) - self.assertEqual(d.shape, (3, 3)) - - -class TestCoverageSharpness(unittest.TestCase): - def test_coverage_near_nominal_for_matching_draws(self): - rng = np.random.default_rng(0) - n_pts = 2000 - draws = rng.normal(0.0, 1.0, (4000, n_pts)) - y = rng.normal(0.0, 1.0, n_pts) - levels = np.array([0.5, 0.9]) - cov = coverage_curve(draws, y, levels) - np.testing.assert_allclose(cov, levels, atol=0.03) - self.assertLess(coverage_error(draws, y, levels), 0.03) - # overconfident draws under-cover - cov_narrow = coverage_curve(0.3 * draws, y, levels) - self.assertTrue(np.all(cov_narrow < levels - 0.2)) - - def test_sharpness_width(self): - rng = np.random.default_rng(0) - draws = rng.normal(0.0, 1.0, (20000, 3)) - w = sharpness(draws) - np.testing.assert_allclose(w, 2 * 0.9945, atol=0.05) - w_exp = sharpness(np.zeros((10, 2)), transform=np.exp) - np.testing.assert_allclose(w_exp, 0.0) - w95 = sharpness(draws, percentiles=(2.5, 97.5)) - np.testing.assert_allclose(w95, 2 * 1.96, atol=0.15) - - -class TestHeldout(unittest.TestCase): - def test_heldout_log_predictive_matches_manual(self): - pm = Polynomial(order=1) - x = np.array([1.0, 2.0, 3.0, 4.0]) - y = np.array([3.1, 4.8, 7.2, 9.1]) - err = np.array([0.2, 0.2, 0.3, 0.3]) - obs = Observation(x, y, y_stat_err=err).masked_where(lambda x: x < 2.5) - fit = Constraint( - [obs], pm, extra_terms=[Term(np.array(0.1 * np.ones(4)), kind="diag")] - ) - held = fit.complement() - samples = np.array([[1.0, 2.0], [1.2, 1.9]]) - lp = heldout_log_predictive(held, samples) - for i, (a0, a1) in enumerate(samples): - ym = a0 + a1 * x[2:] - cov = np.diag(err[2:] ** 2 + 0.01) - self.assertAlmostEqual(lp[i], manual_mvn_loglike(y[2:], ym, cov)) - - def test_log_posterior_predictive(self): - lp = np.array([-1.0, -2.0, -0.5]) - self.assertAlmostEqual(log_posterior_predictive(lp), logsumexp(lp) - np.log(3)) - logw = np.array([0.0, -np.inf, 0.0]) - self.assertAlmostEqual( - log_posterior_predictive(lp, logw), logsumexp(lp[[0, 2]]) - np.log(2) - ) - - -class TestLogZ(unittest.TestCase): - def test_summary_single_and_replicates(self): - m, e, n = logz_summary([-10.0], [0.3]) - self.assertEqual((m, e, n), (-10.0, 0.3, 1)) - m, e, n = logz_summary([-10.0, -12.0], [0.3, 0.3]) - self.assertEqual((m, e, n), (-11.0, 1.0, 2)) # half-range dominates - m, e, n = logz_summary([-10.0, -10.2], [0.5, 0.5]) - self.assertAlmostEqual(e, 0.5) # reported error dominates - - def test_compare(self): - r = compare_logz((-10.0, 0.5), (-15.0, 0.5)) - self.assertEqual(r["verdict"], "a") - self.assertAlmostEqual(r["dlogZ"], 5.0) - self.assertAlmostEqual(r["err"], np.hypot(0.5, 0.5)) - self.assertEqual(compare_logz((-15.0, 0.5), (-10.0, 0.5))["verdict"], "b") - self.assertEqual(compare_logz((-10.0, 1.0), (-11.0, 1.0))["verdict"], "tie") - - def test_log_jacobian(self): - y = np.array([2.0, 3.0]) - obs = Observation(np.array([0.0, 1.0]), y, transform=log) - c = Constraint( - [obs], Polynomial(order=0), extra_terms=[noise_term(Parameter("e"))] - ) - self.assertAlmostEqual(log_jacobian(c), -np.sum(np.log(y))) - - -class TestSplitSamples(unittest.TestCase): - def test_split_rows(self): - pm = Polynomial(order=1) - obs = Observation( - np.arange(4.0), 1.0 + 2.0 * np.arange(4.0), y_stat_err=np.full(4, 0.1) - ) - eps = Parameter("log eps") - c = Constraint([obs], pm, extra_terms=[noise_term(eps)]) - ev = Evidence([c]) - mprior = IndependentPrior([stats.norm(0, 1), stats.norm(0, 1)]) - lprior = IndependentPrior([stats.norm(-2, 1)]) - config = CalibrationConfig( - ev, - ParameterConfig(pm.params, mprior, mprior), - [ParameterConfig(list(c.params), lprior, lprior)], - ) - samples = np.array([[1.0, 2.0, -1.0], [0.5, 1.5, -2.0]]) - m, covs = split_samples(config, samples) - np.testing.assert_allclose(m, samples[:, :2]) - self.assertEqual(len(covs), 1) - np.testing.assert_allclose(covs[0], samples[:, 2:]) - # single row - m1, _ = split_samples(config, samples[0]) - self.assertEqual(m1.shape, (1, 2)) - - -if __name__ == "__main__": - unittest.main() diff --git a/test/test_observation.py b/test/test_observation.py deleted file mode 100644 index 4a1e51c..0000000 --- a/test/test_observation.py +++ /dev/null @@ -1,293 +0,0 @@ -import unittest - -import numpy as np - -from helpers import make_ctx -from rxmc.covariance import ConstraintCovariance, Term -from rxmc.observation import Observation -from rxmc.transforms import log - - -class TestObservation(unittest.TestCase): - - def test_initialization(self): - x = np.array([1, 2, 3]) - y = np.array([4, 5, 6]) - observation = Observation(x, y) - self.assertEqual(observation.n_data_pts, 3) - np.testing.assert_array_equal(observation.x, x) - np.testing.assert_array_equal(observation.y, y) - np.testing.assert_array_equal(observation.y_stat_err, np.zeros_like(y)) - - def test_invalid_initialization(self): - x = np.array([1, 2, 3]) - y = np.array([4, 5]) - with self.assertRaises(ValueError): - Observation(x, y) - - def test_stat_err_shape_validation(self): - x = np.array([1, 2, 3]) - y = np.array([4, 5, 6]) - with self.assertRaises(ValueError): - Observation(x, y, y_stat_err=np.array([0.1, 0.2])) - - def test_statistical_term_is_diagonal_variance(self): - x = np.array([1.0, 2.0]) - y = np.array([2.0, 4.0]) - y_stat_err = np.array([0.1, 0.2]) - observation = Observation(x, y, y_stat_err=y_stat_err) - support = np.arange(2) - term = observation.statistical_term(support=support) - Sigma = np.zeros((2, 2)) - term.add_to(Sigma, None, np.array([])) - np.testing.assert_array_almost_equal(Sigma, np.diag(y_stat_err**2)) - - def test_statistical_term_writes_into_support_block(self): - # an observation occupying the second block of a length-4 stack - x = np.array([1.0, 2.0]) - y = np.array([2.0, 4.0]) - y_stat_err = np.array([0.3, 0.4]) - observation = Observation(x, y, y_stat_err=y_stat_err) - support = np.array([2, 3]) - cov = ConstraintCovariance([observation.statistical_term(support=support)], N=4) - Sigma = cov.matrix(None) - expected = np.zeros((4, 4)) - expected[2, 2] = 0.3**2 - expected[3, 3] = 0.4**2 - np.testing.assert_array_almost_equal(Sigma, expected) - - def test_default_statistical_term_is_constant(self): - observation = Observation( - np.array([1.0, 2.0]), np.array([2.0, 4.0]), y_stat_err=np.array([0.1, 0.2]) - ) - cov = ConstraintCovariance( - [observation.statistical_term(support=np.arange(2))], N=2 - ) - self.assertTrue(cov.is_constant) - self.assertTrue(cov.block_diagonal) - self.assertEqual(cov.n_params, 0) - - def test_systematics_default_none_and_no_terms(self): - obs = Observation(np.array([1.0, 2.0]), np.array([2.0, 4.0])) - self.assertIsNone(obs.y_sys_err_normalization) - self.assertIsNone(obs.y_sys_err_offset) - self.assertEqual(obs.systematic_terms(np.arange(2)), []) - - def test_systematics_storage(self): - obs = Observation( - np.array([1.0, 2.0]), - np.array([2.0, 4.0]), - y_sys_err_normalization=0.03, - y_sys_err_offset=np.array([0.1, 0.2]), - ) - self.assertEqual(obs.y_sys_err_normalization, 0.03) - np.testing.assert_allclose(obs.y_sys_err_offset, [0.1, 0.2]) - # 0-d ndarrays count as scalars - obs2 = Observation( - np.array([1.0, 2.0]), - np.array([2.0, 4.0]), - y_sys_err_normalization=np.array(0.03), - ) - self.assertEqual(obs2.y_sys_err_normalization, 0.03) - - def test_systematics_bad_shape_raises(self): - with self.assertRaises(ValueError): - Observation( - np.array([1.0, 2.0, 3.0]), - np.array([2.0, 4.0, 6.0]), - y_sys_err_offset=np.array([0.1, 0.2]), - ) - - def test_systematics_zero_magnitudes_skipped(self): - obs = Observation( - np.array([1.0, 2.0]), - np.array([2.0, 4.0]), - y_sys_err_normalization=0.0, - y_sys_err_offset=0, - ) - self.assertEqual(obs.systematic_terms(np.arange(2)), []) - - def test_systematic_terms_offset_then_normalization(self): - obs = Observation( - np.array([1.0, 2.0]), - np.array([2.0, 4.0]), - y_sys_err_normalization=0.05, - y_sys_err_offset=0.2, - ) - terms = obs.systematic_terms(np.arange(2)) - self.assertEqual(len(terms), 2) - self.assertTrue(all(isinstance(t, Term) and t.kind == "mode" for t in terms)) - - def test_systematic_terms_recover_old_covariance(self): - # statistical_term + systematic_terms matches the old auto-folded - # Observation.covariance(ym) - y = np.array([1.0, 2.0, 4.0]) - ym = np.array([1.2, 2.1, 3.5]) - stat = np.array([0.1, 0.2, 0.3]) - norm_frac = 0.05 - offset = 0.2 - obs = Observation( - np.arange(3.0), - y, - y_stat_err=stat, - y_sys_err_normalization=norm_frac, - y_sys_err_offset=offset, - ) - support = np.arange(3) - cov = ConstraintCovariance( - [obs.statistical_term(support=support), *obs.systematic_terms(support)], N=3 - ) - S = cov.matrix(make_ctx(np.arange(3.0), y, ym, [support])) - old = ( - np.diag(stat**2) - + np.outer(offset * np.ones(3), offset * np.ones(3)) - + norm_frac**2 * np.outer(ym, ym) - ) - np.testing.assert_allclose(S, old) - - def test_num_pts_within_interval(self): - x = np.array([1, 2, 3, 4]) - y = np.array([10, 12, 14, 16]) - ylow = np.array([9, 11, 13, 15]) - yhigh = np.array([11, 13, 15, 17]) - observation = Observation(x, y) - num_pts = observation.num_pts_within_interval(ylow, yhigh) - self.assertEqual(num_pts, 4) - - def test_num_pts_within_interval_out(self): - x = np.array([1, 2, 3, 4]) - y = np.array([10, 15, 14, -12]) - ylow = np.array([9, 11, 13, 15]) - yhigh = np.array([11, 13, 15, 17]) - observation = Observation(x, y) - num_pts = observation.num_pts_within_interval(ylow, yhigh) - self.assertEqual(num_pts, 2) - - -class TestObservationTransform(unittest.TestCase): - def setUp(self): - self.x = np.array([1.0, 2.0, 3.0]) - self.y = np.array([2.0, 4.0, 8.0]) - self.err = np.array([0.2, 0.4, 0.8]) - - def test_nonpositive_data_under_log_raises(self): - y = np.array([2.0, 0.0, -1.0]) - with self.assertRaises(ValueError) as cm: - Observation(self.x, y, y_stat_err=self.err, transform=log, label="bad") - msg = str(cm.exception) - self.assertIn("'log'", msg) - self.assertIn("'bad'", msg) - self.assertIn("2 active data point(s)", msg) - # inactive points may be non-positive: the guard is active-point scoped - obs = Observation( - self.x, y, y_stat_err=self.err, transform=log, mask=[True, False, False] - ) - self.assertEqual(obs.n_active, 1) - - def test_raw_kept_and_y_transformed(self): - obs = Observation(self.x, self.y, y_stat_err=self.err, transform=log) - np.testing.assert_allclose(obs.y_raw, self.y) - np.testing.assert_allclose(obs.y, np.log(self.y)) - np.testing.assert_allclose(obs.y_stat_err_raw, self.err) - # delta method: sigma_log = sigma / y - np.testing.assert_allclose(obs.y_stat_err, self.err / self.y) - self.assertIs(obs.transform, log) - - def test_identity_by_default(self): - obs = Observation(self.x, self.y, y_stat_err=self.err) - self.assertTrue(obs.transform.is_identity) - self.assertIs(obs.y, obs.y_raw) - self.assertEqual(obs.log_jacobian, 0.0) - - def test_log_jacobian(self): - obs = Observation(self.x, self.y, transform=log) - self.assertAlmostEqual(obs.log_jacobian, -np.sum(np.log(self.y))) - # respects the mask - obs2 = obs.masked([True, False, True]) - self.assertAlmostEqual(obs2.log_jacobian, -np.log(2.0) - np.log(8.0)) - - def test_parametric_transform_rejected(self): - from rxmc.transforms import scale - - with self.assertRaises(ValueError): - Observation(self.x, self.y, transform=scale()) - - def test_normalisation_systematic_needs_inverse(self): - obs = Observation( - self.x, self.y, y_sys_err_normalization=0.1, transform=np.sqrt - ) - with self.assertRaisesRegex(ValueError, "no inverse"): - obs.systematic_terms() - # an offset alone is propagated at the data and needs no inverse - obs2 = Observation(self.x, self.y, y_sys_err_offset=0.1, transform=np.sqrt) - self.assertEqual(len(obs2.systematic_terms()), 1) - - def test_plain_callable_accepted(self): - obs = Observation(self.x, self.y, transform=np.sqrt) - np.testing.assert_allclose(obs.y, np.sqrt(self.y)) - - def test_systematic_terms_propagated_by_delta_method(self): - obs = Observation( - self.x, - self.y, - y_sys_err_offset=0.5, - y_sys_err_normalization=0.1, - transform=log, - ) - terms = obs.systematic_terms() - self.assertEqual(len(terms), 2) - ym_raw = np.array([2.5, 3.5, 9.0]) - ctx = make_ctx(self.x, obs.y, np.log(ym_raw), [np.arange(3)]) - cov = ConstraintCovariance(terms, 3, blocks=ctx.supports) - S = cov.matrix(ctx) - omega = 0.5 / self.y # |t'(y)| * offset - # fractional normalisation in log space is a constant offset eta - eta = 0.1 * ym_raw * (1.0 / ym_raw) - np.testing.assert_allclose(S, np.outer(omega, omega) + np.outer(eta, eta)) - - -class TestObservationMask(unittest.TestCase): - def setUp(self): - self.x = np.array([1.0, 2.0, 3.0, 4.0]) - self.y = np.array([1.0, 2.0, 3.0, 4.0]) - - def test_default_all_active(self): - obs = Observation(self.x, self.y) - self.assertEqual(obs.n_active, 4) - self.assertTrue(obs.mask.all()) - - def test_masked_is_shallow_copy(self): - obs = Observation(self.x, self.y, label="a") - m = obs.masked([True, True, False, False], label="a-fwd") - self.assertEqual(m.n_active, 2) - self.assertEqual(m.n_data_pts, 4) - self.assertEqual(m.label, "a-fwd") - self.assertIs(m.y, obs.y) - self.assertEqual(obs.n_active, 4) # original untouched - - def test_masked_where(self): - obs = Observation(self.x, self.y) - m = obs.masked_where(lambda x: x < 2.5) - np.testing.assert_array_equal(m.mask, [True, True, False, False]) - - def test_bad_mask_shape_raises(self): - with self.assertRaises(ValueError): - Observation(self.x, self.y, mask=[True, False]) - with self.assertRaises(ValueError): - Observation(self.x, self.y).masked([True]) - - def test_masked_rechecks_transform_finiteness(self): - y = np.array([1.0, 0.0, 3.0, 4.0]) - obs = Observation(self.x, y, transform=log, mask=[True, False, True, True]) - self.assertEqual(obs.n_active, 3) - with self.assertRaises(ValueError): - obs.masked([True, True, True, True]) - - def test_num_pts_within_interval_respects_mask(self): - obs = Observation(self.x, self.y, mask=[True, False, True, False]) - n = obs.num_pts_within_interval(self.y - 0.1, self.y + 0.1) - self.assertEqual(n, 2) - - -if __name__ == "__main__": - unittest.main() diff --git a/test/test_params.py b/test/test_params.py deleted file mode 100644 index abc8bcd..0000000 --- a/test/test_params.py +++ /dev/null @@ -1,52 +0,0 @@ -import unittest - -import numpy as np - -from rxmc.params import Parameter - - -class TestParameterHashing(unittest.TestCase): - def test_equal_parameters_hash_equal(self): - a = Parameter("g", float, unit="MeV", latex_name="g", bounds=(0.0, 1.0)) - b = Parameter("g", float, unit="MeV", latex_name="g", bounds=(0.0, 1.0)) - self.assertEqual(a, b) - self.assertEqual(hash(a), hash(b)) - - def test_unequal_parameters_differ(self): - a = Parameter("g") - self.assertNotEqual(a, Parameter("h")) - self.assertNotEqual(a, Parameter("g", unit="MeV")) - self.assertNotEqual(a, Parameter("g", bounds=(0.0, 1.0))) - self.assertNotEqual(a, "g") - - def test_usable_in_set_and_dict(self): - a = Parameter("a") - b = Parameter("b") - self.assertEqual(len({a, b, Parameter("a")}), 2) - table = {a: 1, b: 2} - self.assertEqual(table[Parameter("a")], 1) - - def test_bounds_coerced_to_float_tuple(self): - for bounds in ([0, 2], np.array([0.0, 2.0]), (0, 2)): - p = Parameter("x", bounds=bounds) - self.assertEqual(p.bounds, (0.0, 2.0)) - self.assertIsInstance(p.bounds, tuple) - self.assertTrue(all(isinstance(b, float) for b in p.bounds)) - - def test_default_bounds_are_infinite(self): - self.assertEqual(Parameter("x").bounds, (-np.inf, np.inf)) - - def test_bad_bounds_length_raises(self): - with self.assertRaises(ValueError): - Parameter("x", bounds=(0.0, 1.0, 2.0)) - - def test_repr_contains_fields(self): - r = repr(Parameter("V", float, unit="MeV", latex_name=r"V_0", bounds=(0, 9))) - self.assertIn("'V'", r) - self.assertIn("MeV", r) - self.assertIn("V_0", r) - self.assertIn("(0.0, 9.0)", r) - - -if __name__ == "__main__": - unittest.main() diff --git a/test/test_predictive.py b/test/test_predictive.py deleted file mode 100644 index 6f8a6e1..0000000 --- a/test/test_predictive.py +++ /dev/null @@ -1,200 +0,0 @@ -"""Tests for the predictive-uncertainty helpers (:mod:`rxmc.predictive`).""" - -import unittest - -import numpy as np -from sklearn.gaussian_process import GaussianProcessRegressor -from sklearn.gaussian_process.kernels import RBF, ConstantKernel, WhiteKernel - -from rxmc.predictive import ( - _gp_posterior_mean_var, - gp_posterior_predictive, - predictive_band, - total_predictive_band, -) - - -def make_kernel(): - return ConstantKernel(1.0) * RBF(length_scale=0.7) + WhiteKernel(1e-6) - - -class TestGPPosteriorPredictive(unittest.TestCase): - def setUp(self): - rng = np.random.default_rng(0) - self.X_train = np.sort(rng.uniform(-2, 2, 12)) - self.residuals = np.sin(self.X_train) + 0.05 * rng.standard_normal(12) - self.X_pred = np.linspace(-2.5, 2.5, 25) - self.kernel = make_kernel() - self.theta = self.kernel.theta # log-space - - def test_matches_sklearn_gpr(self): - noise_var = 0.01 - # sklearn GPR with the SAME fixed kernel (no hyperparameter optimisation) - gpr = GaussianProcessRegressor( - kernel=self.kernel.clone_with_theta(self.theta), - optimizer=None, - alpha=noise_var, - normalize_y=False, - ) - gpr.fit(self.X_train[:, None], self.residuals) - mean_sk, cov_sk = gpr.predict(self.X_pred[:, None], return_cov=True) - - mean, cov = gp_posterior_predictive( - self.kernel, - self.theta, - self.X_train, - self.residuals, - self.X_pred, - train_noise_var=noise_var, - ) - np.testing.assert_allclose(mean, mean_sk, atol=1e-6) - np.testing.assert_allclose(cov, cov_sk, atol=1e-6) - - def test_noiseless_interpolation(self): - # a noiseless kernel (no WhiteKernel) interpolates the residuals exactly - kernel = ConstantKernel(1.0) * RBF(length_scale=0.7) - mean, cov = gp_posterior_predictive( - kernel, - kernel.theta, - self.X_train, - self.residuals, - self.X_train, - train_noise_var=0.0, - jitter=1e-10, - ) - # exact interpolation up to numerical conditioning of the Gram matrix - np.testing.assert_allclose(mean, self.residuals, atol=5e-3) - self.assertLess(np.max(np.abs(np.diag(cov))), 1e-3) - - def test_2d_inputs(self): - rng = np.random.default_rng(1) - Xtr = rng.uniform(-1, 1, (8, 2)) - r = rng.standard_normal(8) - Xp = rng.uniform(-1, 1, (5, 2)) - kernel = ConstantKernel(1.0) * RBF(length_scale=1.0) + WhiteKernel(1e-6) - mean, cov = gp_posterior_predictive(kernel, kernel.theta, Xtr, r, Xp) - self.assertEqual(mean.shape, (5,)) - self.assertEqual(cov.shape, (5, 5)) - - -class TestPredictiveBand(unittest.TestCase): - def test_percentiles(self): - draws = np.arange(101)[:, None] * np.ones((1, 3)) # 0..100 on each column - band = predictive_band(draws, levels=(16, 50, 84)) - self.assertEqual(band.shape, (3, 3)) - np.testing.assert_allclose(band[1], [50, 50, 50]) - np.testing.assert_allclose(band[0], [16, 16, 16]) - np.testing.assert_allclose(band[2], [84, 84, 84]) - - -class TestTotalPredictiveBand(unittest.TestCase): - def test_shape_and_finite(self): - rng = np.random.default_rng(2) - kernel = make_kernel() - - def mean_fn(x, m, b): - return m * x + b - - x_train = np.linspace(0, 1, 10) - y_train = 0.5 * x_train + 0.2 + np.sin(3 * x_train) - x_pred = np.linspace(-0.2, 1.2, 30) - - n_kparams = len(kernel.theta) - n_model = 2 - # fake posterior chain: [m, b, *log_theta] - chain = np.column_stack( - [ - 0.5 + 0.05 * rng.standard_normal(50), - 0.2 + 0.05 * rng.standard_normal(50), - np.tile(kernel.theta, (50, 1)), - ] - ) - self.assertEqual(chain.shape[1], n_model + n_kparams) - - band = total_predictive_band( - mean_fn, - kernel, - x_train, - y_train, - x_pred, - chain, - n_model, - noise_std=0.05, - levels=(16, 84), - n_draws=40, - rng=rng, - ) - self.assertEqual(band.shape, (2, len(x_pred))) - self.assertTrue(np.all(np.isfinite(band))) - self.assertTrue(np.all(band[1] >= band[0])) - - def _mean_fn(self, x, m, b): - return m * x + b - - def test_raises_on_kernel_theta_width_mismatch(self): - kernel = make_kernel() - n_theta = len(kernel.theta) - x_train = np.linspace(0, 1, 8) - y_train = 0.5 * x_train + 0.2 - # an EXTRA nuisance column beyond the kernel theta -> ambiguous trailing slice - chain = np.zeros((10, 2 + n_theta + 1)) - chain[:, 2 : 2 + n_theta] = kernel.theta - with self.assertRaises(ValueError): - total_predictive_band( - self._mean_fn, - kernel, - x_train, - y_train, - np.linspace(0, 1, 5), - chain, - 2, - noise_std=0.05, - n_draws=5, - rng=np.random.default_rng(0), - ) - - def test_explicit_theta_cols(self): - kernel = make_kernel() - n_theta = len(kernel.theta) - x_train = np.linspace(0, 1, 8) - y_train = 0.5 * x_train + 0.2 - # layout: [m, b, log_eps, *kernel_theta]; kernel theta is NOT the only tail - chain = np.zeros((10, 2 + 1 + n_theta)) - chain[:, 3:] = kernel.theta - theta_cols = np.arange(3, 3 + n_theta) - band = total_predictive_band( - self._mean_fn, - kernel, - x_train, - y_train, - np.linspace(0, 1, 5), - chain, - 2, - theta_cols=theta_cols, - noise_std=0.05, - n_draws=5, - rng=np.random.default_rng(0), - ) - self.assertEqual(band.shape, (2, 5)) - self.assertTrue(np.all(np.isfinite(band))) - - -class TestDiagonalMeanVar(unittest.TestCase): - def test_matches_full_covariance_diagonal(self): - rng = np.random.default_rng(4) - kernel = make_kernel() - X_train = np.sort(rng.uniform(-2, 2, 10)) - residuals = np.sin(X_train) - X_pred = np.linspace(-2.5, 2.5, 20) - mean_full, cov_full = gp_posterior_predictive( - kernel, kernel.theta, X_train, residuals, X_pred, train_noise_var=0.01 - ) - mean, var = _gp_posterior_mean_var( - kernel, kernel.theta, X_train, residuals, X_pred, train_noise_var=0.01 - ) - np.testing.assert_allclose(mean, mean_full, atol=1e-9) - np.testing.assert_allclose(var, np.diag(cov_full), atol=1e-9) - - -if __name__ == "__main__": - unittest.main() diff --git a/test/test_priors.py b/test/test_priors.py deleted file mode 100644 index 1350323..0000000 --- a/test/test_priors.py +++ /dev/null @@ -1,179 +0,0 @@ -import unittest - -import numpy as np -import scipy.stats - -from rxmc.config import ParameterConfig -from rxmc.params import Parameter -from rxmc.priors import ( - IndependentPrior, - TruncatedNormalPrior, - as_prior, - clip_unit_cube, -) - - -class TestIndependentPrior(unittest.TestCase): - def setUp(self): - self.dists = [ - scipy.stats.norm(loc=0.0, scale=1.0), - scipy.stats.uniform(loc=0.0, scale=5.0), - scipy.stats.norm(loc=2.0, scale=0.5), - ] - self.prior = IndependentPrior(self.dists, seed=0) - - def test_dim(self): - self.assertEqual(self.prior.dim, 3) - - def test_logpdf_scalar(self): - """1-D input returns a finite float.""" - lp = self.prior.logpdf(np.array([0.0, 2.5, 2.0])) - self.assertIsInstance(lp, float) - self.assertTrue(np.isfinite(lp)) - - def test_logpdf_batch(self): - """2-D input returns a 1-D array of the right length.""" - theta = np.array([[0.0, 1.0, 2.0], [1.0, 4.0, 2.5]]) - lp = self.prior.logpdf(theta) - self.assertEqual(lp.shape, (2,)) - self.assertTrue(np.all(np.isfinite(lp))) - - def test_logpdf_out_of_support(self): - """Points outside the support of a bounded marginal return -inf.""" - # uniform(0, 5): value -1 is outside support - lp = self.prior.logpdf(np.array([0.0, -1.0, 2.0])) - self.assertEqual(lp, -np.inf) - - def test_logpdf_wrong_dim_raises(self): - with self.assertRaises(ValueError): - self.prior.logpdf(np.array([0.0, 1.0])) - - def test_rvs_shape(self): - samples = self.prior.rvs(10) - self.assertEqual(samples.shape, (10, 3)) - - def test_rvs_reproducible(self): - p1 = IndependentPrior(self.dists, seed=42) - p2 = IndependentPrior(self.dists, seed=42) - np.testing.assert_array_equal(p1.rvs(5), p2.rvs(5)) - - def test_prior_transform_median(self): - """prior_transform at u=0.5 returns the per-component median.""" - u = np.full(3, 0.5) - theta = self.prior.prior_transform(u) - expected = np.array([d.ppf(0.5) for d in self.dists]) - np.testing.assert_allclose(theta, expected, atol=1e-10) - - def test_prior_transform_roundtrip(self): - """logpdf(prior_transform(u)) is finite for interior u.""" - u = np.array([0.3, 0.7, 0.5]) - theta = self.prior.prior_transform(u) - lp = self.prior.logpdf(theta) - self.assertTrue(np.isfinite(lp)) - - def test_compatible_with_parameter_config(self): - """IndependentPrior works as the prior argument to ParameterConfig.""" - params = [Parameter(f"p{i}") for i in range(3)] - config = ParameterConfig( - params=params, - prior=self.prior, - initial_proposal_distribution=self.prior, - ) - self.assertEqual(config.ndim, 3) - - lp = config.prior_logpdf(np.array([0.0, 2.5, 2.0])) - self.assertTrue(np.isfinite(lp)) - - x0 = config.x0(4) - self.assertEqual(x0.shape, (4, 3)) - - theta = config.prior_transform(np.full(3, 0.5)) - self.assertEqual(theta.shape, (3,)) - self.assertTrue(np.all(np.isfinite(theta))) - - -class TestTruncatedNormalPrior(unittest.TestCase): - def setUp(self): - self.prior = TruncatedNormalPrior( - mu=[0.0, 1.0], - sigma=[1.0, 1.0], - lower=[-5.0, -4.0], - upper=[5.0, 6.0], - seed=0, - ) - - def test_dim(self): - self.assertEqual(self.prior.dim, 2) - - def test_logpdf_scalar(self): - lp = self.prior.logpdf(np.array([0.0, 1.0])) - self.assertIsInstance(lp, float) - self.assertTrue(np.isfinite(lp)) - - def test_logpdf_batch(self): - theta = np.array([[0.0, 1.0], [1.0, 2.0]]) - lp = self.prior.logpdf(theta) - self.assertEqual(lp.shape, (2,)) - self.assertTrue(np.all(np.isfinite(lp))) - - def test_logpdf_out_of_support(self): - lp = self.prior.logpdf(np.array([10.0, 1.0])) - self.assertEqual(lp, -np.inf) - - def test_rvs_shape(self): - samples = self.prior.rvs(8) - self.assertEqual(samples.shape, (8, 2)) - - def test_rvs_within_bounds(self): - samples = self.prior.rvs(500) - self.assertTrue(np.all(samples[:, 0] >= -5.0)) - self.assertTrue(np.all(samples[:, 0] <= 5.0)) - self.assertTrue(np.all(samples[:, 1] >= -4.0)) - self.assertTrue(np.all(samples[:, 1] <= 6.0)) - - def test_prior_transform_median(self): - """Symmetric bounds → median maps to mu.""" - u = np.full(2, 0.5) - theta = self.prior.prior_transform(u) - np.testing.assert_allclose(theta, [0.0, 1.0], atol=1e-6) - - def test_prior_transform_roundtrip(self): - u = np.array([0.2, 0.8]) - theta = self.prior.prior_transform(u) - lp = self.prior.logpdf(theta) - self.assertTrue(np.isfinite(lp)) - - -class TestUnitCubeClipping(unittest.TestCase): - def test_clip_unit_cube(self): - u = clip_unit_cube([0.0, 0.5, 1.0]) - eps = np.finfo(float).eps - np.testing.assert_allclose(u, [eps, 0.5, 1.0 - eps]) - self.assertEqual(u.dtype, float) - self.assertTrue(np.all(u > 0.0) and np.all(u < 1.0)) - - def test_independent_prior_boundary_is_finite(self): - # an unbounded marginal's ppf is +-inf at exactly 0 / 1 - prior = IndependentPrior([scipy.stats.norm(0, 1), scipy.stats.norm(0, 1)]) - theta = prior.prior_transform([0.0, 1.0]) - self.assertTrue(np.all(np.isfinite(theta))) - self.assertLess(theta[0], 0.0) - self.assertGreater(theta[1], 0.0) - - def test_truncated_normal_boundary_is_finite(self): - prior = TruncatedNormalPrior(mu=[0.0], sigma=[1.0], lower=[-2.0], upper=[3.0]) - theta = prior.prior_transform([0.0]) - self.assertTrue(np.all(np.isfinite(theta))) - np.testing.assert_allclose(theta, [-2.0], atol=1e-6) - - def test_as_prior_wraps_lists_only(self): - dists = [scipy.stats.norm(0, 1)] - wrapped = as_prior(dists) - self.assertIsInstance(wrapped, IndependentPrior) - self.assertIs(wrapped.distributions[0], dists[0]) - prior = TruncatedNormalPrior(mu=[0.0], sigma=[1.0], lower=[-1.0], upper=[1.0]) - self.assertIs(as_prior(prior), prior) - - -if __name__ == "__main__": - unittest.main() diff --git a/test/test_proposal.py b/test/test_proposal.py deleted file mode 100644 index 7922355..0000000 --- a/test/test_proposal.py +++ /dev/null @@ -1,64 +0,0 @@ -import unittest - -import numpy as np -from numpy.random import default_rng - -from rxmc.proposal import ( - HalfNormalProposalDistribution, - LogspaceNormalProposalDistribution, - NormalProposalDistribution, - ProposalDistribution, -) - - -class TestProposalDistributions(unittest.TestCase): - def setUp(self): - # Set up a random number generator - self.rng = default_rng(42) - self.current_sample = np.array([1.0, 2.0, 3.0]) - self.scale = 1.0 - self.cov = np.eye(3) - - def test_normal_proposal_distribution(self): - normal_proposal = NormalProposalDistribution(cov=self.cov) - proposed_sample = normal_proposal(self.current_sample, self.rng) - - # Check the output is of correct shape - self.assertEqual(proposed_sample.shape, self.current_sample.shape) - - # Check the mean and covariance of the distribution approximately match the expected - samples = [normal_proposal(self.current_sample, self.rng) for _ in range(10000)] - samples_mean = np.mean(samples, axis=0) - samples_cov = np.cov(np.array(samples).T) - - self.assertTrue(np.allclose(samples_mean, self.current_sample, atol=0.1)) - self.assertTrue(np.allclose(samples_cov, self.cov, atol=0.1)) - - def test_half_normal_proposal_distribution(self): - half_normal_proposal = HalfNormalProposalDistribution(scale=self.scale) - proposed_sample = half_normal_proposal(self.current_sample, self.rng) - - # Check the output is of correct shape - self.assertEqual(proposed_sample.shape, self.current_sample.shape) - - # Check that all values in proposed_sample are non-negative - self.assertTrue(np.all(proposed_sample >= 0)) - - def test_logspace_normal_proposal_distribution(self): - logspace_normal_proposal = LogspaceNormalProposalDistribution(scale=self.scale) - proposed_sample = logspace_normal_proposal(self.current_sample, self.rng) - - # Check the output is of correct shape - self.assertEqual(proposed_sample.shape, self.current_sample.shape) - - # Check that all values in proposed_sample are strictly positive - self.assertTrue(np.all(proposed_sample > 0)) - - def test_proposal_distribution_not_implemented(self): - with self.assertRaises(NotImplementedError): - proposal = ProposalDistribution() - proposal(self.current_sample, self.rng) - - -if __name__ == "__main__": - unittest.main() diff --git a/test/test_reaction_models.py b/test/test_reaction_models.py deleted file mode 100644 index 964d89b..0000000 --- a/test/test_reaction_models.py +++ /dev/null @@ -1,158 +0,0 @@ -"""Real-solver smoke tests for the jitr-backed reaction models. - -These are the only tests that exercise the actual solver pipeline -(everything else mocks ``set_up_solver``); they pin output shape, -finiteness, and positivity with deliberately small solver settings. -""" - -import unittest - -import jitr -import numpy as np -from jitr.optical_potentials.potential_forms import ( - coulomb_charged_sphere, - thomas_safe, - woods_saxon_safe, -) - -from rxmc.elastic_diffxs_model import ElasticDifferentialXSModel -from rxmc.elastic_diffxs_observation import ElasticDifferentialXSObservation -from rxmc.ias_pn_model import IsobaricAnalogPNXSModel -from rxmc.ias_pn_observation import IsobaricAnalogPNObservation -from rxmc.params import Parameter - -MSO = 1.0 / jitr.utils.constants.WAVENUMBER_PION - - -def central(r, Vv, Wv, Rv, av): - return -(Vv + 1j * Wv) * woods_saxon_safe(r, Rv, av) - - -def spin_orbit(r, Vso, Rso, aso): - return Vso * MSO**2 * thomas_safe(r, Rso, aso) - - -class TestElasticDifferentialXSModel(unittest.TestCase): - def test_evaluate_and_visualization_smoke(self): - R = 1.2 * 40 ** (1 / 3) - rxn = jitr.reactions.ElasticReaction(target=(40, 20), projectile=(1, 0)) - model = ElasticDifferentialXSModel( - "dXS/dA", - interaction_central=central, - interaction_spin_orbit=spin_orbit, - calculate_interaction_from_params=lambda ws, *x: ( - tuple(x), - (6.0, R, 0.45), - ), - params=[Parameter(n) for n in ("Vv", "Wv", "Rv", "av")], - ) - obs = ElasticDifferentialXSObservation( - x=np.linspace(10.0, 150.0, 6), - y=np.ones(6), - Elab=14.1, - reaction=rxn, - quantity="dXS/dA", - measurement_quantity="dXS/dA", - y_units="barn / steradian", - lmax=10, - ) - - y = model.evaluate(obs, 48.0, 3.5, R, 0.7) - self.assertEqual(y.shape, (obs.n_data_pts,)) - self.assertTrue(np.all(np.isfinite(y))) - self.assertTrue(np.all(y > 0)) - - y_vis = model.visualizable_model_prediction(obs, 48.0, 3.5, R, 0.7) - self.assertEqual(y_vis.shape, obs.visualization_workspace.angles.shape) - self.assertTrue(np.all(np.isfinite(y_vis))) - - -class TestIsobaricAnalogPNXSModel(unittest.TestCase): - def test_evaluate_and_visualization_smoke(self): - A, Z = 48, 20 - R = 1.2 * A ** (1 / 3) - rxn = jitr.reactions.Reaction( - target=(A, Z), - projectile=(1, 1), - product=(1, 0), - residual=(A, Z + 1), - ) - model = IsobaricAnalogPNXSModel( - U_p_coulomb=coulomb_charged_sphere, - U_p_central=central, - U_p_spin_orbit=spin_orbit, - U_n_central=central, - U_n_spin_orbit=spin_orbit, - # the (p,n) IAS transition is driven by the *difference* between - # the proton and neutron potentials (the Lane term) — make them - # distinct or the cross section vanishes - calculate_params=lambda ws, Vv, Wv, Rv, av: ( - (Z, R), # p Coulomb: zz product, charge radius - (Vv + 4.0, Wv, Rv, av), # p central - (6.0, R, 0.45), # p spin-orbit - (Vv - 4.0, Wv, Rv, av), # n central - (6.0, R, 0.45), # n spin-orbit - ), - params=[Parameter(n) for n in ("Vv", "Wv", "Rv", "av")], - ) - obs = IsobaricAnalogPNObservation( - x=np.linspace(10.0, 150.0, 5), - y=np.ones(5), - Elab=25.0, - reaction=rxn, - ExIAS=6.7, - y_units="barn / steradian", - lmax=10, - ) - - y = model.evaluate(obs, 48.0, 3.5, R, 0.7) - self.assertEqual(y.shape, (obs.n_data_pts,)) - self.assertTrue(np.all(np.isfinite(y))) - self.assertTrue(np.all(y >= 0)) - self.assertGreater(y.max(), 0) - - y_vis = model.visualizable_model_prediction(obs, 48.0, 3.5, R, 0.7) - self.assertEqual(y_vis.shape, obs.visualization_workspace.angles.shape) - self.assertTrue(np.all(np.isfinite(y_vis))) - - -class TestObservationTypeChecks(unittest.TestCase): - """A reaction model refuses an observation of the wrong class up front - (no solver is touched, so these need no jitr workspace).""" - - def setUp(self): - from rxmc.observation import Observation - - self.plain = Observation(x=np.array([0.1, 0.2]), y=np.array([1.0, 1.0])) - self.elastic = ElasticDifferentialXSModel( - "dXS/dA", - interaction_central=central, - interaction_spin_orbit=spin_orbit, - calculate_interaction_from_params=lambda ws, *x: (tuple(x), ()), - params=[Parameter("Vv")], - ) - self.ias = IsobaricAnalogPNXSModel( - U_p_coulomb=coulomb_charged_sphere, - U_p_central=central, - U_p_spin_orbit=spin_orbit, - U_n_central=central, - U_n_spin_orbit=spin_orbit, - calculate_params=lambda ws, *x: ((), (), (), (), ()), - params=[Parameter("Vv")], - ) - - def test_elastic_model_rejects_foreign_observation(self): - with self.assertRaisesRegex(ValueError, "ElasticDifferentialXSObservation"): - self.elastic.evaluate(self.plain, 1.0) - with self.assertRaisesRegex(ValueError, "ElasticDifferentialXSObservation"): - self.elastic.visualizable_model_prediction(self.plain, 1.0) - - def test_ias_model_rejects_foreign_observation(self): - with self.assertRaisesRegex(ValueError, "IsobaricAnalogPNObservation"): - self.ias.evaluate(self.plain, 1.0) - with self.assertRaisesRegex(ValueError, "IsobaricAnalogPNObservation"): - self.ias.visualizable_model_prediction(self.plain, 1.0) - - -if __name__ == "__main__": - unittest.main() diff --git a/test/test_reaction_observation.py b/test/test_reaction_observation.py deleted file mode 100644 index d104567..0000000 --- a/test/test_reaction_observation.py +++ /dev/null @@ -1,422 +0,0 @@ -import unittest -from types import SimpleNamespace -from unittest.mock import patch - -import numpy as np - -from rxmc.elastic_diffxs_observation import ElasticDifferentialXSObservation -from rxmc.ias_pn_observation import IsobaricAnalogPNObservation -from rxmc.observation import Observation -from rxmc.transforms import log - - -def make_measurement(**overrides): - """A minimal ``exfor_tools``-like Distribution stub.""" - fields = dict( - x=np.array([20.0, 40.0]), - y=np.array([2.0, 1.0]), - Einc=8.0, - quantity="dXS/dA", - y_units="barn / steradian", - statistical_err=np.array([0.2, 0.1]), - systematic_norm_err=0.03, - systematic_offset_err=0.02, - subentry="subentry", - ) - fields.update(overrides) - return SimpleNamespace(**fields) - - -class DummyElasticWorkspace: - def __init__(self, rutherford=1.0): - self.rutherford = rutherford - - -class TestElasticDifferentialXSObservation(unittest.TestCase): - @patch("rxmc.elastic_diffxs_observation.set_up_solver") - def test_direct_construction_from_explicit_data(self, mock_set_up_solver): - mock_set_up_solver.return_value = ( - DummyElasticWorkspace(), - DummyElasticWorkspace(), - object(), - ) - - angles_deg = np.array([15.0, 30.0, 45.0]) - y = np.array([1.2, 0.8, 0.4]) - y_stat_err = np.array([0.1, 0.1, 0.1]) - - obs = ElasticDifferentialXSObservation( - x=angles_deg, - y=y, - Elab=12.0, - reaction=object(), - quantity="dXS/dA", - measurement_quantity="dXS/dA", - y_units="barn / steradian", - y_stat_err=y_stat_err, - dataset_label="mock-elastic", - ) - - # it IS an Observation (statistical error only); systematics are composed - # as Constraint extra_terms by the caller - self.assertIsInstance(obs, Observation) - np.testing.assert_allclose(obs.x, np.deg2rad(angles_deg)) - np.testing.assert_allclose(obs.y, y) - np.testing.assert_allclose(obs.y_stat_err, y_stat_err) - self.assertEqual(obs.subentry, "mock-elastic") - self.assertIsNotNone(obs.statistical_term(np.arange(obs.n_data_pts))) - - @patch("rxmc.elastic_diffxs_observation.set_up_solver") - def test_from_measurement_construction(self, mock_set_up_solver): - mock_set_up_solver.return_value = ( - DummyElasticWorkspace(), - DummyElasticWorkspace(), - object(), - ) - - measurement = make_measurement(subentry="elastic-subentry") - - obs = ElasticDifferentialXSObservation.from_measurement( - measurement=measurement, - reaction=object(), - quantity="dXS/dA", - ) - - np.testing.assert_allclose(obs.x, np.deg2rad(measurement.x)) - np.testing.assert_allclose(obs.y, measurement.y) - np.testing.assert_allclose(obs.y_stat_err, measurement.statistical_err) - self.assertEqual(obs.subentry, "elastic-subentry") - # systematics are retained as inert metadata (norm == 1 here: b/sr) - self.assertEqual(obs.norm, 1.0) - self.assertEqual(obs.y_sys_err_normalization, 0.03) - self.assertEqual(obs.y_sys_err_offset, 0.02) - self.assertEqual(len(obs.systematic_terms(np.arange(obs.n_data_pts))), 2) - - @patch("rxmc.elastic_diffxs_observation.set_up_solver") - def test_from_measurement_forwards_transform_and_mask(self, mock_set_up_solver): - mock_set_up_solver.return_value = ( - DummyElasticWorkspace(), - DummyElasticWorkspace(), - object(), - ) - measurement = make_measurement( - systematic_norm_err=None, - systematic_offset_err=None, - subentry="elastic-subentry", - ) - mask = np.array([True, False]) - obs = ElasticDifferentialXSObservation.from_measurement( - measurement=measurement, - reaction=object(), - quantity="dXS/dA", - transform=log, - mask=mask, - ) - self.assertIs(obs.transform, log) - np.testing.assert_allclose(obs.y, np.log(measurement.y)) - np.testing.assert_array_equal(obs.mask, mask) - - @patch("rxmc.elastic_diffxs_observation.set_up_solver") - def test_from_measurement_rutherford_array_norm(self, mock_set_up_solver): - # dXS/dRuth requested from a dXS/dA measurement: norm is the per-angle - # Rutherford cross section, so the absolute offset error becomes a - # per-angle array in internal units, while the fractional normalization - # error is untouched - rutherford = np.array([2000.0, 500.0]) # mb/sr - mock_set_up_solver.return_value = ( - DummyElasticWorkspace(rutherford=rutherford), - DummyElasticWorkspace(rutherford=rutherford), - object(), - ) - - measurement = make_measurement( - y=np.array([1800.0, 300.0]), - y_units="mb/sr", - statistical_err=np.array([20.0, 10.0]), - systematic_offset_err=5.0, # mb/sr - subentry="ruth-subentry", - ) - - obs = ElasticDifferentialXSObservation.from_measurement( - measurement=measurement, - reaction=object(), - quantity="dXS/dRuth", - ) - - np.testing.assert_allclose(obs.norm, rutherford) - np.testing.assert_allclose(obs.y, measurement.y / rutherford) - np.testing.assert_allclose( - obs.y_stat_err, measurement.statistical_err / rutherford - ) - np.testing.assert_allclose(obs.y_sys_err_offset, 5.0 / rutherford) - self.assertEqual(obs.y_sys_err_normalization, 0.03) - - # reaction-level regression: statistical + systematic terms recover the - # old auto-folded covariance, in internal (normalized) units - support = np.arange(2) - from helpers import make_ctx - from rxmc.covariance import ConstraintCovariance - - ym = np.array([0.9, 0.6]) - cov = ConstraintCovariance( - [obs.statistical_term(support), *obs.systematic_terms(support)], N=2 - ) - S = cov.matrix(make_ctx(obs.x, obs.y, ym, [support])) - omega = 5.0 / rutherford - old = ( - np.diag((measurement.statistical_err / rutherford) ** 2) - + np.outer(omega, omega) - + 0.03**2 * np.outer(ym, ym) - ) - np.testing.assert_allclose(S, old) - - @patch("rxmc.elastic_diffxs_observation.set_up_solver") - def test_dxsda_from_dxsdruth_conversion(self, mock_set_up_solver): - # the inverse branch: absolute dXS/dA requested from a Rutherford-ratio - # measurement; norm = (1/mb->b) / rutherford, so obs.y is b/sr - rutherford = np.array([2000.0, 500.0]) # mb/sr - mock_set_up_solver.return_value = ( - DummyElasticWorkspace(rutherford=rutherford), - DummyElasticWorkspace(rutherford=rutherford), - object(), - ) - - y_ratio = np.array([0.9, 0.6]) # dimensionless dXS/dRuth - obs = ElasticDifferentialXSObservation( - x=np.array([20.0, 40.0]), - y=y_ratio, - Elab=8.0, - reaction=object(), - quantity="dXS/dA", - measurement_quantity="dXS/dRuth", - y_units="no-dim", - ) - - np.testing.assert_allclose(obs.norm, 1000.0 / rutherford) - # y_ratio * rutherford[mb/sr] / 1000 = absolute xs in b/sr - np.testing.assert_allclose(obs.y, y_ratio * rutherford / 1000.0) - - @patch("rxmc.elastic_diffxs_observation.set_up_solver") - def test_incompatible_units_raise(self, mock_set_up_solver): - mock_set_up_solver.return_value = ( - DummyElasticWorkspace(), - DummyElasticWorkspace(), - object(), - ) - # dXS/dA measurement with non-cross-section units - with self.assertRaises(ValueError): - ElasticDifferentialXSObservation( - x=np.array([15.0, 30.0]), - y=np.array([1.0, 0.5]), - Elab=12.0, - reaction=object(), - quantity="dXS/dA", - measurement_quantity="dXS/dA", - y_units="MeV", - ) - # dimensionless quantity with dimensionful units - with self.assertRaises(ValueError): - ElasticDifferentialXSObservation( - x=np.array([15.0, 30.0]), - y=np.array([1.0, 0.5]), - Elab=12.0, - reaction=object(), - quantity="Ay", - measurement_quantity="Ay", - y_units="mb/sr", - ) - - @patch("rxmc.elastic_diffxs_observation.set_up_solver") - def test_quantity_mismatch_raises(self, mock_set_up_solver): - mock_set_up_solver.return_value = ( - DummyElasticWorkspace(), - DummyElasticWorkspace(), - object(), - ) - with self.assertRaises(ValueError): - ElasticDifferentialXSObservation( - x=np.array([15.0, 30.0]), - y=np.array([1.0, 0.5]), - Elab=12.0, - reaction=object(), - quantity="Ay", - measurement_quantity="dXS/dA", - y_units="barn / steradian", - ) - - -class TestIsobaricAnalogPNObservation(unittest.TestCase): - @patch("rxmc.ias_pn_observation.set_up_solver") - def test_direct_construction_from_explicit_data(self, mock_set_up_solver): - mock_set_up_solver.return_value = (object(), object(), object(), object()) - - angles_deg = np.array([10.0, 25.0, 50.0]) - y = np.array([0.4, 0.3, 0.2]) - y_stat_err = np.array([0.05, 0.05, 0.05]) - - obs = IsobaricAnalogPNObservation( - x=angles_deg, - y=y, - Elab=30.0, - reaction=object(), - ExIAS=5.0, - y_units="barn / steradian", - y_stat_err=y_stat_err, - dataset_label="mock-ias", - ) - - self.assertIsInstance(obs, Observation) - np.testing.assert_allclose(obs.x, np.deg2rad(angles_deg)) - np.testing.assert_allclose(obs.y, y) - np.testing.assert_allclose(obs.y_stat_err, y_stat_err) - self.assertEqual(obs.subentry, "mock-ias") - - @patch("rxmc.ias_pn_observation.set_up_solver") - def test_from_measurement_construction(self, mock_set_up_solver): - mock_set_up_solver.return_value = (object(), object(), object(), object()) - - measurement = make_measurement( - x=np.array([5.0, 15.0]), - y=np.array([0.9, 0.7]), - Einc=18.0, - statistical_err=np.array([0.08, 0.07]), - systematic_norm_err=0.02, - systematic_offset_err=0.01, - subentry="ias-subentry", - ) - - obs = IsobaricAnalogPNObservation.from_measurement( - measurement=measurement, - reaction=object(), - ExIAS=4.5, - ) - - np.testing.assert_allclose(obs.x, np.deg2rad(measurement.x)) - np.testing.assert_allclose(obs.y, measurement.y) - np.testing.assert_allclose(obs.y_stat_err, measurement.statistical_err) - self.assertEqual(obs.subentry, "ias-subentry") - # systematics retained; norm == 1 (measurement already in b/sr) - self.assertEqual(obs.norm, 1.0) - self.assertEqual(obs.y_sys_err_normalization, 0.02) - self.assertEqual(obs.y_sys_err_offset, 0.01) - self.assertEqual(len(obs.systematic_terms(np.arange(obs.n_data_pts))), 2) - - @patch("rxmc.ias_pn_observation.set_up_solver") - def test_from_measurement_forwards_transform_and_mask(self, mock_set_up_solver): - mock_set_up_solver.return_value = (object(), object(), object(), object()) - measurement = make_measurement( - x=np.array([5.0, 15.0]), - y=np.array([0.9, 0.7]), - Einc=18.0, - statistical_err=np.array([0.08, 0.07]), - systematic_norm_err=None, - systematic_offset_err=None, - subentry="ias-subentry", - ) - mask = np.array([False, True]) - obs = IsobaricAnalogPNObservation.from_measurement( - measurement=measurement, - reaction=object(), - ExIAS=4.5, - transform=log, - mask=mask, - ) - self.assertIs(obs.transform, log) - np.testing.assert_allclose(obs.y, np.log(measurement.y)) - np.testing.assert_array_equal(obs.mask, mask) - - @patch("rxmc.ias_pn_observation.set_up_solver") - def test_unit_conversion_divides_offset_not_normalization(self, mock_set_up_solver): - mock_set_up_solver.return_value = (object(), object(), object(), object()) - - obs = IsobaricAnalogPNObservation( - x=np.array([5.0, 15.0]), - y=np.array([900.0, 700.0]), - Elab=18.0, - reaction=object(), - ExIAS=4.5, - y_units="millibarn / steradian", - y_stat_err=np.array([80.0, 70.0]), - y_sys_err_normalization=0.02, - y_sys_err_offset=10.0, - ) - # mb -> b: norm = 1000 - self.assertEqual(obs.norm, 1000.0) - np.testing.assert_allclose(obs.y, [0.9, 0.7]) - np.testing.assert_allclose(obs.y_stat_err, [0.08, 0.07]) - self.assertAlmostEqual(obs.y_sys_err_offset, 0.01) - self.assertEqual(obs.y_sys_err_normalization, 0.02) - - -class TestSolverSettingsForwarding(unittest.TestCase): - """The basis-size settings must reach ``set_up_solver`` on both classes.""" - - @patch("rxmc.elastic_diffxs_observation.set_up_solver") - def test_elastic_forwards_solver_settings(self, mock_set_up_solver): - mock_set_up_solver.return_value = ( - DummyElasticWorkspace(), - DummyElasticWorkspace(), - object(), - ) - ElasticDifferentialXSObservation( - x=np.array([15.0, 30.0]), - y=np.array([1.0, 0.5]), - Elab=12.0, - reaction=object(), - quantity="dXS/dA", - measurement_quantity="dXS/dA", - y_units="barn / steradian", - lmax=7, - wavelengths_beyond_range=3.5, - zeros_per_node=9, - ) - kwargs = mock_set_up_solver.call_args.kwargs - self.assertEqual(kwargs["lmax"], 7) - self.assertEqual(kwargs["wavelengths_beyond_range"], 3.5) - self.assertEqual(kwargs["zeros_per_node"], 9) - - @patch("rxmc.ias_pn_observation.set_up_solver") - def test_ias_forwards_solver_settings(self, mock_set_up_solver): - mock_set_up_solver.return_value = (object(), object(), object(), object()) - IsobaricAnalogPNObservation( - x=np.array([10.0, 25.0]), - y=np.array([0.4, 0.3]), - Elab=30.0, - reaction=object(), - ExIAS=5.0, - y_units="barn / steradian", - lmax=7, - wavelengths_beyond_range=3.5, - zeros_per_node=9, - ) - kwargs = mock_set_up_solver.call_args.kwargs - self.assertEqual(kwargs["lmax"], 7) - self.assertEqual(kwargs["wavelengths_beyond_range"], 3.5) - self.assertEqual(kwargs["zeros_per_node"], 9) - - -class TestSharedUnits(unittest.TestCase): - def test_one_unit_registry(self): - import rxmc.elastic_diffxs_observation as elastic - import rxmc.ias_pn_observation as ias - from rxmc.observation_from_measurement import ureg - - self.assertIs(elastic.ureg, ureg) - self.assertIs(ias.ureg, ureg) - self.assertEqual(elastic.DEFAULT_LMAX, ias.DEFAULT_LMAX) - - def test_unit_constants_agree(self): - from rxmc.observation_from_measurement import ( - MB_PER_B, - RUTHERFORD_UNIT, - XS_UNIT, - ureg, - ) - - self.assertEqual(MB_PER_B, 1000.0) - self.assertEqual((1 * XS_UNIT).to(RUTHERFORD_UNIT).magnitude, MB_PER_B) - self.assertTrue((1 * ureg("mb/sr")).check(XS_UNIT)) - - -if __name__ == "__main__": - unittest.main() diff --git a/test/test_regression.py b/test/test_regression.py deleted file mode 100644 index 2cd8de0..0000000 --- a/test/test_regression.py +++ /dev/null @@ -1,141 +0,0 @@ -""" -Regression pins for the deliberate behaviour change of the covariance refactor. - -The old default covariance silently folded a dataset's normalisation/offset -systematics into ``Observation.covariance``. The new default is **statistical -only**; those systematics must be stated explicitly as covariance -:class:`~rxmc.covariance.Term` s. These tests pin: - -1. the before/after log-posterior gap (the default changed), and -2. that re-adding the explicit terms exactly recovers the old number. - -They also pin the block-diagonal fast path against a dense Cholesky and a genuine -case-A cross-dataset coupling, so the new capabilities are recorded. -""" - -import unittest - -import numpy as np - -from helpers import manual_mvn_loglike as manual_mvn -from rxmc.constraint import Constraint -from rxmc.covariance import normalization_term, offset_term -from rxmc.observation import Observation -from rxmc.physical_model import Polynomial - - -class TestSystematicDefaultBehaviourChange(unittest.TestCase): - """systematic_err_demo / normalization_inference behaviour change.""" - - def setUp(self): - self.x = np.array([1.0, 2.0, 3.0, 4.0]) - self.y = np.array([2.1, 3.9, 6.2, 7.8]) - self.stat = np.array([0.1, 0.15, 0.2, 0.25]) - self.norm = 0.05 # fractional normalisation systematic - self.offset = 0.02 # absolute offset systematic - self.obs = Observation(self.x, self.y, y_stat_err=self.stat) - self.pm = Polynomial(order=1) - self.model_params = (0.2, 1.9) - self.ym = self.pm.evaluate(self.obs, *self.model_params) - - # the matrix the OLD Observation.covariance(ym) produced - ones = np.ones(4) - self.old_cov = ( - np.diag(self.stat**2) - + np.outer(self.offset * ones, self.offset * ones) - + np.outer(self.norm * ones, self.norm * ones) * np.outer(self.ym, self.ym) - ) - - def test_old_value(self): - # pinned old log-likelihood (auto-included systematics) - old = manual_mvn(self.y, self.ym, self.old_cov) - self.assertAlmostEqual(old, 1.195784087817536, places=9) - - def test_new_default_is_statistical_only_and_differs(self): - c = Constraint([self.obs], self.pm) - new_default = c.log_likelihood(self.model_params) - stat_only = manual_mvn(self.y, self.ym, np.diag(self.stat**2)) - self.assertAlmostEqual(new_default, stat_only) - # the default genuinely changed - old = manual_mvn(self.y, self.ym, self.old_cov) - self.assertNotAlmostEqual(new_default, old) - - def test_explicit_terms_recover_old_value(self): - support = np.arange(4) - c = Constraint( - [self.obs], - self.pm, - extra_terms=[ - offset_term(magnitude=self.offset, support=support), - normalization_term(magnitude=self.norm, support=support), - ], - ) - recovered = c.log_likelihood(self.model_params) - old = manual_mvn(self.y, self.ym, self.old_cov) - self.assertAlmostEqual(recovered, old) - - -class TestBlockDiagonalFastPathEquivalence(unittest.TestCase): - """The block-diagonal fast path equals a dense Cholesky over the full stack.""" - - def test_multi_block_matches_dense(self): - pm = Polynomial(order=1) - mp = (0.3, 1.1) - obs = [ - Observation( - np.array([1.0, 2.0]), - np.array([1.5, 2.4]), - y_stat_err=np.array([0.1, 0.2]), - ), - Observation( - np.array([3.0, 4.0, 5.0]), - np.array([3.2, 4.5, 5.9]), - y_stat_err=np.array([0.2, 0.1, 0.3]), - ), - Observation(np.array([6.0]), np.array([7.1]), y_stat_err=np.array([0.15])), - ] - c = Constraint(obs, pm) - self.assertTrue(c.covariance.block_diagonal) - fast = c.log_likelihood(mp) - - y = np.concatenate([o.y for o in obs]) - ym = np.concatenate([pm.evaluate(o, *mp) for o in obs]) - stat = np.concatenate([o.y_stat_err for o in obs]) - dense = manual_mvn(y, ym, np.diag(stat**2)) - self.assertAlmostEqual(fast, dense, places=10) - - -class TestCaseACrossDatasetCorrelation(unittest.TestCase): - """A correlated systematic shared across two datasets (case A).""" - - def test_off_diagonal_blocks_present_and_changes_likelihood(self): - pm = Polynomial(order=1) - mp = (0.5, 1.0) - obs1 = Observation( - np.array([1.0, 2.0]), np.array([1.6, 2.9]), y_stat_err=np.array([0.1, 0.1]) - ) - obs2 = Observation( - np.array([3.0, 4.0]), np.array([3.4, 4.6]), y_stat_err=np.array([0.1, 0.1]) - ) - - from rxmc.params import Parameter - - eta = Parameter("log eta") - coupling = normalization_term(parameter=eta, support=np.arange(4)) - c = Constraint([obs1, obs2], pm, extra_terms=[coupling]) - - # the assembled Sigma has non-zero cross-block (off-diagonal) entries - ym = np.concatenate([pm.evaluate(obs1, *mp), pm.evaluate(obs2, *mp)]) - ctx = c._stack(mp) - Sigma = c.covariance.matrix(ctx, np.log(0.1)) - self.assertFalse(c.covariance.block_diagonal) - self.assertGreater(abs(Sigma[0, 2]), 0.0) - - # and it equals the explicit dense form - cov = np.diag(np.full(4, 0.1**2)) + 0.1**2 * np.outer(ym, ym) - expected = manual_mvn(np.concatenate([obs1.y, obs2.y]), ym, cov) - self.assertAlmostEqual(c.log_likelihood(mp, (np.log(0.1),)), expected) - - -if __name__ == "__main__": - unittest.main() diff --git a/test/test_sampler.py b/test/test_sampler.py deleted file mode 100644 index a974708..0000000 --- a/test/test_sampler.py +++ /dev/null @@ -1,409 +0,0 @@ -import io -import unittest -from contextlib import redirect_stdout -from types import SimpleNamespace - -import numpy as np -import scipy.stats - -from rxmc.adaptive_metropolis import adaptive_metropolis -from rxmc.constraint import Constraint -from rxmc.covariance import Term -from rxmc.evidence import Evidence -from rxmc.observation import Observation -from rxmc.param_sampling import ( - AdaptiveMetropolisSampler, - BatchedAdaptiveMetropolisSampler, - MetropolisHastingsSampler, -) -from rxmc.params import Parameter -from rxmc.physical_model import Polynomial -from rxmc.priors import IndependentPrior -from rxmc.proposal import NormalProposalDistribution -from rxmc.walker import Walker - - -def floor_slope_noise_term(support=None): - """diag((exp(floor) + exp(slope)*|ym|)**2) — a two-parameter noise term.""" - return Term( - lambda c, floor, slope: np.exp(floor) + np.exp(slope) * np.abs(c.ym), - (Parameter("log noise floor", float), Parameter("log noise slope", float)), - kind="diag", - support=support, - ) - - -class TestAdaptiveMetropolisSampler(unittest.TestCase): - def test_sampling_runs_with_bounds(self): - sampler = AdaptiveMetropolisSampler( - params=[Parameter("x", bounds=(-1.0, 1.0))], - prior=scipy.stats.norm(loc=0.0, scale=1.0), - starting_location=np.array([0.0]), - adapt_start=2, - window_size=3, - ) - - sampler.sample( - n_steps=5, - starting_location=np.array([0.0]), - rng=np.random.default_rng(123), - log_posterior=lambda x: -0.5 * x[0] ** 2, - ) - - self.assertEqual(sampler.chain.shape, (5, 1)) - self.assertTrue(np.all(sampler.chain[:, 0] >= -1.0)) - self.assertTrue(np.all(sampler.chain[:, 0] <= 1.0)) - - def test_multidimensional_regularization_stays_symmetric(self): - x0 = np.array([0.5, -0.25]) - bounds = np.array([[-10.0, 10.0], [-10.0, 10.0]]) - previous_chain = np.array( - [ - [0.2, -0.1], - [0.3, -0.2], - [0.4, -0.15], - ] - ) - epsilon_fraction = 1e-6 - history_mean = np.mean(previous_chain, axis=0) - empirical_cov = np.atleast_2d(np.cov(previous_chain.T)) - proposal_cov = empirical_cov + epsilon_fraction * np.diag(history_mean**2) - self.assertTrue(np.allclose(proposal_cov, proposal_cov.T)) - - chain, logp_chain, accepted = adaptive_metropolis( - x0=x0, - bounds=bounds, - n_steps=4, - log_posterior=lambda x: -0.5 * np.dot(x, x), - rng=np.random.default_rng(42), - adapt_start=0, - window_size=3, - epsilon_fraction=epsilon_fraction, - previous_chain=previous_chain, - ) - - self.assertEqual(chain.shape, (4, 2)) - self.assertEqual(logp_chain.shape, (4,)) - self.assertGreaterEqual(accepted, 0) - - -class TestWalker(unittest.TestCase): - def test_walk_runs_end_to_end_with_multi_parameter_likelihood(self): - model = Polynomial(1) - observation = Observation( - x=np.array([0.0, 1.0, 2.0, 3.0, 4.0]), - y=np.array([1.0, 2.1, 3.2, 4.0, 5.1]), - y_stat_err=np.array([0.1, 0.1, 0.1, 0.1, 0.1]), - ) - noise_term = floor_slope_noise_term(np.arange(observation.n_data_pts)) - constraint = Constraint( - observations=[observation], - physical_model=model, - extra_terms=[noise_term], - ) - evidence = Evidence(constraints=[constraint]) - - model_sampler = MetropolisHastingsSampler( - params=model.params, - prior=scipy.stats.multivariate_normal(mean=[0.0, 1.0], cov=np.eye(2)), - starting_location=np.array([0.9, 1.0]), - proposal=NormalProposalDistribution(0.01 * np.eye(2)), - ) - likelihood_sampler = MetropolisHastingsSampler( - params=list(constraint.params), - prior=scipy.stats.multivariate_normal(mean=[-2.0, -2.0], cov=np.eye(2)), - starting_location=np.array([-2.0, -2.0]), - proposal=NormalProposalDistribution(0.01 * np.eye(2)), - ) - - walker = Walker( - model_sampler=model_sampler, - evidence=evidence, - likelihood_samplers=[likelihood_sampler], - rng=np.random.default_rng(321), - ) - walker.walk(n_steps=5, burnin=2, batch_size=2, verbose=False) - - self.assertEqual(walker.model_sampler.chain.shape, (5, 2)) - self.assertEqual(walker.likelihood_samplers[0].chain.shape, (5, 2)) - self.assertEqual(walker.model_sampler.state.shape, (2,)) - self.assertEqual(walker.likelihood_samplers[0].state.shape, (2,)) - - def test_gibbs_conditional_applies_evidence_weight(self): - from types import SimpleNamespace - - model = Polynomial(1) - observation = Observation( - x=np.array([0.0, 1.0, 2.0, 3.0, 4.0]), - y=np.array([1.0, 2.1, 3.2, 4.0, 5.1]), - y_stat_err=np.array([0.1, 0.1, 0.1, 0.1, 0.1]), - ) - constraint = Constraint( - observations=[observation], - physical_model=model, - extra_terms=[floor_slope_noise_term(np.arange(observation.n_data_pts))], - ) - weight = 2.5 - evidence = Evidence(constraints=[constraint], weights=np.array([weight])) - - prior = scipy.stats.multivariate_normal(mean=[-2.0, -2.0], cov=np.eye(2)) - - class CapturingSampler: - def __init__(self, params, prior): - self.params = params - self.prior = prior - self.captured = None - - def sample(self, n_steps, x0, rng, log_posterior, burn=False): - self.captured = log_posterior - - lm_sampler = CapturingSampler(list(constraint.params), prior) - walker = Walker( - model_sampler=SimpleNamespace(params=evidence.model_params, prior=prior), - evidence=evidence, - likelihood_samplers=[lm_sampler], - ) - - model_params = (0.9, 1.0) - walker.run_likelihood_batches(1, [np.array([-2.0, -2.0])], model_params) - - x = np.array([-2.0, -2.0]) - ym = constraint.predict(*model_params) - expected = float( - prior.logpdf(x) + weight * constraint.marginal_log_likelihood(ym, *x) - ) - self.assertAlmostEqual(lm_sampler.captured(x), expected) - - -class CapturingSampler: - """Records the conditional posterior a Walker hands it; never samples.""" - - def __init__(self, params, prior): - self.params = list(params) - self.prior = prior - self.captured = None - - def sample(self, n_steps, x0, rng, log_posterior, burn=False): - self.captured = log_posterior - - -class NegInfPrior: - def logpdf(self, x): - return -np.inf - - -def parametric_setup(weight=1.0): - """A one-constraint Evidence with a two-parameter noise term.""" - model = Polynomial(1) - observation = Observation( - x=np.array([0.0, 1.0, 2.0, 3.0, 4.0]), - y=np.array([1.0, 2.1, 3.2, 4.0, 5.1]), - y_stat_err=np.array([0.1, 0.1, 0.1, 0.1, 0.1]), - ) - constraint = Constraint( - observations=[observation], - physical_model=model, - extra_terms=[floor_slope_noise_term(np.arange(observation.n_data_pts))], - ) - evidence = Evidence(constraints=[constraint], weights=np.array([weight])) - return model, constraint, evidence - - -class TestWalkerPosterior(unittest.TestCase): - """Parity with CalibrationConfig: prior-first short-circuit and tempering.""" - - def test_log_posterior_skips_likelihood_when_prior_neg_inf(self): - calls = [] - evidence = SimpleNamespace( - model_params=[Parameter("a")], - parametric_constraints=[], - log_likelihood=lambda mp, lp: calls.append(mp) or 0.0, - ) - walker = Walker( - model_sampler=SimpleNamespace( - params=evidence.model_params, prior=NegInfPrior() - ), - evidence=evidence, - ) - self.assertEqual(walker.log_posterior((1.0,), []), -np.inf) - self.assertEqual(calls, []) - - def test_gibbs_conditional_skips_likelihood_when_prior_neg_inf(self): - calls = [] - lm_params = [Parameter("nu")] - constraint = SimpleNamespace( - params=lm_params, predict=lambda *mp: [np.zeros(3)] - ) - evidence = SimpleNamespace( - model_params=[Parameter("a")], - parametric_constraints=[constraint], - weighted_marginal_log_likelihood=lambda i, ym, *x: calls.append(x) or 0.0, - ) - lm_sampler = CapturingSampler(lm_params, NegInfPrior()) - walker = Walker( - model_sampler=SimpleNamespace( - params=evidence.model_params, prior=NegInfPrior() - ), - evidence=evidence, - likelihood_samplers=[lm_sampler], - ) - walker.run_likelihood_batches(1, [np.array([1.0])], (0.5,)) - self.assertEqual(lm_sampler.captured(np.array([1.0])), -np.inf) - self.assertEqual(calls, []) - - def test_log_posterior_applies_likelihood_scaling(self): - scaling = 0.25 - model, constraint, evidence = parametric_setup() - model_prior = scipy.stats.multivariate_normal(mean=[0.0, 1.0], cov=np.eye(2)) - lm_prior = scipy.stats.multivariate_normal(mean=[-2.0, -2.0], cov=np.eye(2)) - walker = Walker( - model_sampler=SimpleNamespace( - params=evidence.model_params, prior=model_prior - ), - evidence=evidence, - likelihood_samplers=[CapturingSampler(constraint.params, lm_prior)], - likelihood_scaling=scaling, - ) - mp, x = (0.9, 1.0), np.array([-2.0, -2.0]) - expected = ( - model_prior.logpdf(np.array(mp)) - + lm_prior.logpdf(x) - + scaling * evidence.log_likelihood(mp, [x]) - ) - self.assertAlmostEqual(walker.log_posterior(mp, [x]), float(expected)) - self.assertAlmostEqual( - walker.log_likelihood(mp, [x]), scaling * evidence.log_likelihood(mp, [x]) - ) - - def test_gibbs_conditional_applies_likelihood_scaling_and_weight(self): - # mirrors test_config.py::test_conditional_posterior_tempering - scaling, weight = 0.5, 3.0 - model, constraint, evidence = parametric_setup(weight=weight) - prior = scipy.stats.multivariate_normal(mean=[-2.0, -2.0], cov=np.eye(2)) - lm_sampler = CapturingSampler(constraint.params, prior) - walker = Walker( - model_sampler=SimpleNamespace(params=evidence.model_params, prior=prior), - evidence=evidence, - likelihood_samplers=[lm_sampler], - likelihood_scaling=scaling, - ) - mp = (0.9, 1.0) - walker.run_likelihood_batches(1, [np.array([-2.0, -2.0])], mp) - - x = np.array([-2.0, -2.0]) - ym = constraint.predict(*mp) - expected = prior.logpdf( - x - ) + scaling * weight * constraint.marginal_log_likelihood(ym, *x) - self.assertAlmostEqual(lm_sampler.captured(x), float(expected)) - - def test_burn_message_has_no_acceptance_fraction(self): - model = Polynomial(1) - observation = Observation( - x=np.array([0.0, 1.0, 2.0]), - y=np.array([1.0, 2.1, 3.2]), - y_stat_err=np.array([0.1, 0.1, 0.1]), - ) - evidence = Evidence( - constraints=[Constraint(observations=[observation], physical_model=model)] - ) - sampler = MetropolisHastingsSampler( - params=model.params, - prior=scipy.stats.multivariate_normal(mean=[0.0, 1.0], cov=np.eye(2)), - starting_location=np.array([0.9, 1.0]), - proposal=NormalProposalDistribution(0.01 * np.eye(2)), - ) - walker = Walker(sampler, evidence, rng=np.random.default_rng(0)) - out = io.StringIO() - with redirect_stdout(out): - walker.walk(n_steps=2, burnin=2, batch_size=2, verbose=True) - lines = out.getvalue().splitlines() - burn = [line for line in lines if line.startswith("Burn-in batch")] - self.assertEqual(len(burn), 1) - self.assertNotIn("acceptance", burn[0]) - self.assertTrue(any("acceptance fraction" in line for line in lines)) - self.assertEqual(walker.model_sampler.chain.shape, (2, 2)) - - -class TestSamplerPriors(unittest.TestCase): - def test_sampler_accepts_list_prior(self): - sampler = MetropolisHastingsSampler( - params=[Parameter("x"), Parameter("y")], - prior=[scipy.stats.norm(0, 1), scipy.stats.norm(0, 1)], - starting_location=np.zeros(2), - proposal=NormalProposalDistribution(0.01 * np.eye(2)), - ) - self.assertIsInstance(sampler.prior, IndependentPrior) - self.assertTrue(np.isfinite(sampler.prior.logpdf(np.zeros(2)))) - - def test_batched_adaptive_updates_proposal_after_burn_batch(self): - sampler = BatchedAdaptiveMetropolisSampler( - params=[Parameter("x"), Parameter("y")], - prior=scipy.stats.multivariate_normal(mean=[0.0, 0.0], cov=np.eye(2)), - starting_location=np.zeros(2), - initial_proposal_cov=np.eye(2), - ) - initial = sampler.proposal - sampler.sample( - n_steps=30, - starting_location=np.zeros(2), - rng=np.random.default_rng(7), - log_posterior=lambda x: -0.5 * float(x @ x), - burn=True, - ) - # burn-in records nothing but does adapt; the public proposal follows - self.assertEqual(sampler.chain.shape, (0, 2)) - self.assertIsNot(sampler.proposal, initial) - self.assertIs(sampler.args[0], sampler.proposal) - np.testing.assert_allclose(sampler.proposal.cov, sampler.proposal_cov) - - -class TestWalkerValidation(unittest.TestCase): - def setUp(self): - self.SimpleNamespace = SimpleNamespace - self.model = Polynomial(1) - obs = Observation( - x=np.array([0.0, 1.0, 2.0, 3.0, 4.0]), - y=np.array([1.0, 2.1, 3.2, 4.0, 5.1]), - y_stat_err=np.array([0.1, 0.1, 0.1, 0.1, 0.1]), - ) - self.parametric = Constraint( - observations=[obs], - physical_model=self.model, - extra_terms=[floor_slope_noise_term(np.arange(obs.n_data_pts))], - ) - self.evidence = Evidence(constraints=[self.parametric]) - self.prior = scipy.stats.multivariate_normal(mean=[0.0, 1.0], cov=np.eye(2)) - - def _sampler(self, params): - return self.SimpleNamespace(params=list(params), prior=self.prior) - - def test_mismatched_model_params_raise(self): - with self.assertRaises(ValueError): - Walker( - model_sampler=self._sampler(self.model.params[:1]), - evidence=self.evidence, - likelihood_samplers=[self._sampler(self.parametric.params)], - ) - - def test_sampler_count_mismatch_raises(self): - with self.assertRaises(ValueError): - Walker( - model_sampler=self._sampler(self.evidence.model_params), - evidence=self.evidence, - likelihood_samplers=[], - ) - - def test_mismatched_likelihood_params_raise(self): - from rxmc.params import Parameter - - with self.assertRaises(ValueError): - Walker( - model_sampler=self._sampler(self.evidence.model_params), - evidence=self.evidence, - likelihood_samplers=[self._sampler([Parameter("wrong name")])], - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/test/test_transforms.py b/test/test_transforms.py deleted file mode 100644 index f1e4edc..0000000 --- a/test/test_transforms.py +++ /dev/null @@ -1,128 +0,0 @@ -"""Tests for the low-level ``rxmc.transforms`` type.""" - -import unittest - -import numpy as np - -from rxmc.observation import Observation -from rxmc.params import Parameter -from rxmc.transforms import ( - Transform, - as_transform, - exp, - identity, - log, - per_observation_scaling, - scale, -) - - -class TestTransform(unittest.TestCase): - def test_callable_is_wrapped_parameter_free(self): - t = as_transform(np.sqrt) - self.assertIsInstance(t, Transform) - self.assertEqual(t.params, ()) - np.testing.assert_allclose(t([4.0, 9.0]), [2.0, 3.0]) - self.assertIs(as_transform(None), identity) - self.assertIs(as_transform(t), t) - - def test_log_is_safe_and_invertible(self): - y = np.array([1.0, 0.0, -2.0, np.e]) - out = log(y) - self.assertEqual(out[0], 0.0) - self.assertEqual(out[1], -np.inf) - self.assertEqual(out[2], -np.inf) - self.assertAlmostEqual(out[3], 1.0) - np.testing.assert_allclose(log.derivative(np.array([2.0, 4.0])), [0.5, 0.25]) - self.assertIs(log.inverse, exp) - self.assertIs(exp.inverse, log) - np.testing.assert_allclose(exp(log(np.array([3.0, 7.0]))), [3.0, 7.0]) - - def test_finite_difference_derivative_fallback(self): - t = Transform(lambda a: a**3) - np.testing.assert_allclose( - t.derivative(np.array([1.0, 2.0])), [3.0, 12.0], rtol=1e-5 - ) - - def test_compose_order_and_params(self): - p = Parameter("c") - shift = Transform( - lambda a, c: a + c, (p,), derivative=lambda a, c: np.ones_like(a) - ) - t = shift | log # log(a + c) - self.assertEqual(t.params, (p,)) - np.testing.assert_allclose(t(np.array([1.0]), 1.0), [np.log(2.0)]) - np.testing.assert_allclose(t.derivative(np.array([1.0]), 1.0), [0.5]) - self.assertIsNone(t.inverse) # parametric -> no inverse - u = log | exp - np.testing.assert_allclose(u(np.array([2.0])), [2.0]) - self.assertIsNotNone(u.inverse) - - def test_wrong_value_count_raises(self): - with self.assertRaises(ValueError): - scale()(np.ones(2)) - - -class TestScale(unittest.TestCase): - def test_log_scale(self): - t = scale() - self.assertEqual(t.params[0].name, "log_rho") - np.testing.assert_allclose(t(np.array([1.0, 2.0]), np.log(3.0)), [3.0, 6.0]) - np.testing.assert_allclose( - t.derivative(np.array([1.0, 2.0]), np.log(3.0)), [3.0, 3.0] - ) - - def test_linear_scale_names(self): - t = scale(log=False) - self.assertEqual(t.params[0].name, "rho") - np.testing.assert_allclose(t(np.array([1.0, 2.0]), 3.0), [3.0, 6.0]) - t2 = scale(Parameter("eta"), log=False) - self.assertEqual(t2.params[0].name, "eta") - - -class TestPerObservationScaling(unittest.TestCase): - def setUp(self): - self.o1 = Observation(np.array([1.0]), np.array([1.0])) - self.o2 = Observation(np.array([1.0]), np.array([1.0])) - - def test_routes_by_identity(self): - t = per_observation_scaling([self.o1, self.o2]) - self.assertEqual([p.name for p in t.params], ["log_rho_0", "log_rho_1"]) - a = np.array([1.0, 2.0]) - np.testing.assert_allclose( - t(a, np.log(2.0), np.log(5.0), context=self.o2), [5.0, 10.0] - ) - np.testing.assert_allclose( - t(a, np.log(2.0), np.log(5.0), context=self.o1), [2.0, 4.0] - ) - with self.assertRaises(KeyError): - t(a, 0.0, 0.0, context=Observation(np.array([1.0]), np.array([1.0]))) - - def test_masked_view_routes_to_root(self): - t = per_observation_scaling([self.o1, self.o2]) - view = self.o2.masked(np.array([False])) - self.assertIs(view.identity, self.o2) - a = np.array([1.0]) - np.testing.assert_allclose(t(a, 0.0, np.log(5.0), context=view), [5.0]) - # registering a view and its root is still a duplicate - with self.assertRaises(ValueError): - per_observation_scaling([self.o2, view]) - - def test_missing_context_raises(self): - t = per_observation_scaling([self.o1]) - with self.assertRaisesRegex(ValueError, "contextual"): - t(np.array([1.0]), 0.0) - - def test_linear_and_custom_parameters(self): - t = per_observation_scaling([self.o1], log=False) - self.assertEqual(t.params[0].name, "rho_0") - t2 = per_observation_scaling([self.o1], parameters=[Parameter("n")]) - self.assertEqual(t2.params[0].name, "n") - with self.assertRaises(ValueError): - per_observation_scaling([self.o1, self.o2], parameters=[Parameter("n")]) - with self.assertRaises(ValueError): - per_observation_scaling([self.o1, self.o1]) - - -if __name__ == "__main__": - unittest.main() From 1e94204132c266c7f9a6807dabcff2f8b58b5c01 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 18:12:41 -0400 Subject: [PATCH 02/75] Move the harvested modules into the 1.0 layout git mv only, no edits: likelihood_model -> likelihood, covariance -> terms, observation_from_measurement -> units, model_comparison -> diagnostics, the two reaction observation modules -> reactions/. Their bodies are rewritten in milestones 1, 5 and 6. --- src/rxmc/{model_comparison.py => diagnostics.py} | 0 src/rxmc/{likelihood_model.py => likelihood.py} | 0 src/rxmc/reactions/__init__.py | 0 src/rxmc/{elastic_diffxs_observation.py => reactions/elastic.py} | 0 src/rxmc/{ias_pn_observation.py => reactions/ias.py} | 0 src/rxmc/{covariance.py => terms.py} | 0 src/rxmc/{observation_from_measurement.py => units.py} | 0 7 files changed, 0 insertions(+), 0 deletions(-) rename src/rxmc/{model_comparison.py => diagnostics.py} (100%) rename src/rxmc/{likelihood_model.py => likelihood.py} (100%) create mode 100644 src/rxmc/reactions/__init__.py rename src/rxmc/{elastic_diffxs_observation.py => reactions/elastic.py} (100%) rename src/rxmc/{ias_pn_observation.py => reactions/ias.py} (100%) rename src/rxmc/{covariance.py => terms.py} (100%) rename src/rxmc/{observation_from_measurement.py => units.py} (100%) diff --git a/src/rxmc/model_comparison.py b/src/rxmc/diagnostics.py similarity index 100% rename from src/rxmc/model_comparison.py rename to src/rxmc/diagnostics.py diff --git a/src/rxmc/likelihood_model.py b/src/rxmc/likelihood.py similarity index 100% rename from src/rxmc/likelihood_model.py rename to src/rxmc/likelihood.py diff --git a/src/rxmc/reactions/__init__.py b/src/rxmc/reactions/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/rxmc/elastic_diffxs_observation.py b/src/rxmc/reactions/elastic.py similarity index 100% rename from src/rxmc/elastic_diffxs_observation.py rename to src/rxmc/reactions/elastic.py diff --git a/src/rxmc/ias_pn_observation.py b/src/rxmc/reactions/ias.py similarity index 100% rename from src/rxmc/ias_pn_observation.py rename to src/rxmc/reactions/ias.py diff --git a/src/rxmc/covariance.py b/src/rxmc/terms.py similarity index 100% rename from src/rxmc/covariance.py rename to src/rxmc/terms.py diff --git a/src/rxmc/observation_from_measurement.py b/src/rxmc/units.py similarity index 100% rename from src/rxmc/observation_from_measurement.py rename to src/rxmc/units.py From 11208e591d616a6151f0e1699054ecc7540efbe6 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 18:14:15 -0400 Subject: [PATCH 03/75] Bootstrap the 1.0 rewrite branch Minimal package init and smoke test; pytest fast/slow tiers (slow marker, deselected by default); py312 lint targets; pandas and scikit-learn out of the runtime requirements and into the examples extra with dill; docs reduced to the design and recipes pages; a README for the branch; CI with format, tests and docs jobs on main and rewrite plus a scheduled slow job; a trusted-publishing workflow on version tags. --- .github/workflows/ci.yml | 159 ++++++------------ .github/workflows/publish.yml | 40 +++++ README.md | 302 +++------------------------------- docs/index.rst | 27 +-- docs/installation.rst | 59 +------ pyproject.toml | 18 +- requirements.txt | 2 - src/rxmc/__init__.py | 55 ++----- test/recipes/__init__.py | 0 test/test_smoke.py | 8 + 10 files changed, 156 insertions(+), 514 deletions(-) create mode 100644 .github/workflows/publish.yml create mode 100644 test/recipes/__init__.py create mode 100644 test/test_smoke.py diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 3e1b655..2f733ec 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -2,11 +2,12 @@ name: CI on: push: - branches: - - main + branches: [main, rewrite] pull_request: - branches: - - main + branches: [main, rewrite] + schedule: + - cron: "0 6 * * *" + workflow_dispatch: concurrency: group: ${{ github.workflow }}-${{ github.ref }} @@ -14,163 +15,95 @@ concurrency: jobs: format: + if: github.event_name != 'schedule' runs-on: ubuntu-latest - steps: - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 with: python-version: "3.12" - cache: 'pip' - + cache: "pip" - name: Install validation dependencies run: | python -m pip install --upgrade pip setuptools wheel python -m pip install -e '.[validation]' - - name: Check source formatting and imports run: | python -m isort --check-only src test python -m black --check src test python -m ruff check src test - - name: Check notebook formatting and imports run: | - python -m nbqa isort --check examples/*.ipynb - python -m black --check --ipynb examples/*.ipynb - python -m ruff check examples/*.ipynb + if [ -d examples ] && ls examples/*.ipynb >/dev/null 2>&1; then + python -m nbqa isort --check examples/*.ipynb + python -m black --check --ipynb examples/*.ipynb + python -m ruff check examples/*.ipynb + else + echo "no notebooks yet" + fi tests: + # the fast tier: unit tests and the sampler-free / short-chain recipe assertions + if: github.event_name != 'schedule' runs-on: ubuntu-latest - strategy: - fail-fast: false - matrix: - python-version: ["3.12"] - steps: - uses: actions/checkout@v4 - - - uses: actions/setup-python@v5 with: - python-version: ${{ matrix.python-version }} - cache: 'pip' - - - name: Install validation dependencies - run: | - python -m pip install --upgrade pip setuptools wheel - python -m pip install -e '.[validation]' - - - name: Locate x4i3 database directory - run: echo "X4I3_DATA_DIR=$(python -c 'import importlib.util, pathlib; print(pathlib.Path(importlib.util.find_spec("x4i3").origin).parent / "data")')" >> "$GITHUB_ENV" - - - name: Restore EXFOR database cache - uses: actions/cache@v4 - with: - path: ${{ env.X4I3_DATA_DIR }} - key: exfor-db-${{ runner.os }}-${{ hashFiles('requirements.txt') }} - - - name: Warm EXFOR database - run: python -c "import x4i3" - - - name: Run unit tests - run: python -m pytest test - - notebooks: - runs-on: ubuntu-latest - timeout-minutes: 60 - - steps: - - uses: actions/checkout@v4 - + fetch-depth: 0 - uses: actions/setup-python@v5 with: python-version: "3.12" - cache: 'pip' - + cache: "pip" - name: Install validation dependencies run: | python -m pip install --upgrade pip setuptools wheel - python -m pip install -e '.[validation]' pytest-xdist - - - name: Locate x4i3 database directory - run: echo "X4I3_DATA_DIR=$(python -c 'import importlib.util, pathlib; print(pathlib.Path(importlib.util.find_spec("x4i3").origin).parent / "data")')" >> "$GITHUB_ENV" - - - name: Restore EXFOR database cache - uses: actions/cache@v4 - with: - path: ${{ env.X4I3_DATA_DIR }} - key: exfor-db-${{ runner.os }}-${{ hashFiles('requirements.txt') }} - - # single serial download; the concurrent notebook kernels spawned by - # xdist must find the database already in place (x4i3 downloads at - # import time outside pytest, so parallel kernels would race it) - - name: Warm EXFOR database - run: python -c "import x4i3" - - - name: Run notebooks with pytest - run: python -m pytest -n 4 --nbmake --nbmake-timeout=1200 examples + python -m pip install -e '.[validation]' + - name: Run the fast tier + run: python -m pytest docs: + if: github.event_name != 'schedule' runs-on: ubuntu-latest - steps: - uses: actions/checkout@v4 - + with: + fetch-depth: 0 - uses: actions/setup-python@v5 with: python-version: "3.12" - cache: 'pip' - + cache: "pip" - name: Install docs dependencies run: | python -m pip install --upgrade pip setuptools wheel python -m pip install -e '.[docs]' - - - name: Locate x4i3 database directory - run: echo "X4I3_DATA_DIR=$(python -c 'import importlib.util, pathlib; print(pathlib.Path(importlib.util.find_spec("x4i3").origin).parent / "data")')" >> "$GITHUB_ENV" - - - name: Restore EXFOR database cache - uses: actions/cache@v4 - with: - path: ${{ env.X4I3_DATA_DIR }} - key: exfor-db-${{ runner.os }}-${{ hashFiles('requirements.txt') }} - - - name: Warm EXFOR database - run: python -c "import x4i3" - - name: Build HTML docs run: | - test -L docs/examples || ln -sf ../examples docs/examples - sphinx-build docs docs/_build/html -W --keep-going + if [ -d examples ]; then test -L docs/examples || ln -sf ../examples docs/examples; fi + sphinx-build -W --keep-going docs docs/_build/html - build: + slow: + # the converged tier: numeric claims that need a converged chain, and the notebooks + if: github.event_name == 'schedule' || github.event_name == 'workflow_dispatch' runs-on: ubuntu-latest - + timeout-minutes: 120 steps: - uses: actions/checkout@v4 - + with: + fetch-depth: 0 - uses: actions/setup-python@v5 with: python-version: "3.12" - cache: 'pip' - - - name: Build distributions + cache: "pip" + - name: Install validation dependencies run: | - python -m pip install --upgrade pip build - python -m build - - - name: Install wheel - run: python -m pip install "$(ls -t dist/*.whl | head -n 1)" - - - name: Locate x4i3 database directory - run: echo "X4I3_DATA_DIR=$(python -c 'import importlib.util, pathlib; print(pathlib.Path(importlib.util.find_spec("x4i3").origin).parent / "data")')" >> "$GITHUB_ENV" - - - name: Restore EXFOR database cache - uses: actions/cache@v4 - with: - path: ${{ env.X4I3_DATA_DIR }} - key: exfor-db-${{ runner.os }}-${{ hashFiles('requirements.txt') }} - - - name: Wheel smoke test - run: python -c "import rxmc; import rxmc.config; import rxmc.walker" + python -m pip install --upgrade pip setuptools wheel + python -m pip install -e '.[validation]' pytest-xdist + - name: Run the converged tier + run: python -m pytest -m slow + - name: Run the notebooks + run: | + if [ -d examples ] && ls examples/*.ipynb >/dev/null 2>&1; then + python -m pytest -n 4 --nbmake --nbmake-timeout=1200 examples + else + echo "no notebooks yet" + fi diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml new file mode 100644 index 0000000..bb8407a --- /dev/null +++ b/.github/workflows/publish.yml @@ -0,0 +1,40 @@ +name: Publish to PyPI + +# Trusted publishing: no token in the repository. Register the pending +# publisher on pypi.org (project rxmc, owner beykyle, repository rxmc, +# workflow publish.yml, environment pypi) before the first tag. +on: + push: + tags: ["v*"] + +jobs: + build: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + - uses: actions/setup-python@v5 + with: + python-version: "3.12" + - name: Build sdist and wheel + run: | + python -m pip install --upgrade pip build + python -m build + - uses: actions/upload-artifact@v4 + with: + name: dist + path: dist/ + + publish: + needs: build + runs-on: ubuntu-latest + environment: pypi + permissions: + id-token: write + steps: + - uses: actions/download-artifact@v4 + with: + name: dist + path: dist/ + - uses: pypa/gh-action-pypi-publish@release/v1 diff --git a/README.md b/README.md index 5205b82..efd983f 100644 --- a/README.md +++ b/README.md @@ -1,297 +1,37 @@ # rxmc -`rxmc` is an orchestration layer for Bayesian calibration of reaction models to -large data sets with flexible, composable covariance modeling. +`rxmc` is a library for Bayesian calibration of reaction models to +experimental data, with the error model — statistical and systematic, +experimental and theoretical — declared explicitly as part of the problem +and calibrated with external samplers (emcee, dynesty, black-box-bayes). -It is built around two complementary workflows: +**The 1.0 rewrite is in progress on this branch.** -1. **External-sampler orchestration** via `rxmc.config.CalibrationConfig` - for drivers such as - [`black-box-bayes`](https://github.com/beykyle/black-box-bayes/). -2. **In-package end-to-end prototyping** via `rxmc.walker.Walker` - for smaller problems where you want to run the full MCMC workflow locally. - -The package composes: - -- curated experimental data as `Observation` objects, -- model predictions via `PhysicalModel`, -- uncertainty declared as additive covariance `Term`s (statistical, - systematic, unknown-noise, and Gaussian-process discrepancy modes) via - `rxmc.covariance`, -- maximal blocks of mutually-correlated data via `Constraint`, -- and full calibration problems via `Evidence`. - -## Quickstart - -```python -import numpy as np -from scipy import stats -import rxmc - -# measured data: pure data plus (optional) reported systematics as metadata -obs = rxmc.observation.Observation( - x=x, y=y, y_stat_err=y_err, y_sys_err_normalization=0.04 -) - -# a constraint owns one multivariate likelihood over its stacked observations; -# every correlated mode is an explicit covariance term - nothing is folded in -# silently. Here: the reported normalisation systematic plus an unknown -# constant noise inferred alongside the model -log_eps = rxmc.params.Parameter("log_eps") -constraint = rxmc.constraint.Constraint( - [obs], - model, - extra_terms=[*obs.systematic_terms(), rxmc.covariance.noise_term(log_eps)], -) -evidence = rxmc.evidence.Evidence([constraint]) - -# calibrate with the in-package Gibbs walker (or wrap in CalibrationConfig -# for emcee / dynesty) -prior = stats.multivariate_normal(mean=prior_mean, cov=prior_cov) -walker = rxmc.walker.Walker( - rxmc.param_sampling.BatchedAdaptiveMetropolisSampler( - params=model.params, - starting_location=prior.mean, - prior=prior, - initial_proposal_cov=prior.cov / 100, - ), - evidence, - rng=np.random.default_rng(1), -) -walker.walk(n_steps=10_000, burnin=1_000, batch_size=1_000) -``` - -> **Note — behavior change from pre-0.1 versions:** an `Observation`'s -> reported systematic errors are never folded into the covariance -> automatically. The default constraint covariance is the statistical diagonal -> only; systematics enter explicitly, e.g. via -> `obs.systematic_terms()` passed to `Constraint(extra_terms=...)`. - -> **Note — jitr:** this version requires -> [jitr](https://github.com/beykyle/lagrange-rmatrix) ≥ 3.0 (workspaces take -> potential *arrays* on `ws.radial_grid()`); `requirements.txt` pins -> `jitr>=3.0` from PyPI. Python ≥ 3.12. +- [`docs/groundup_design.md`](docs/groundup_design.md) is the design and + plan of record: the API skeleton, what is harvested from 0.x, the gaps, + the milestones, and the release path. +- [`docs/recipes.md`](docs/recipes.md) lists every supported use case with + its spelling and expected behaviour. Every recipe is a test under + `test/recipes/`; the richest are tutorial notebooks. +The 0.x package is preserved at tag `v0.1.0` and on branch `legacy/0.x`. ## Installation -### Development / local use - ```bash -git clone git@github.com:beykyle/rxmc.git +git clone -b rewrite git@github.com:beykyle/rxmc.git cd rxmc -pip install -ve . -``` - -It is strongly recommended to use an isolated environment. - -### `venv` - -```bash -python -m venv .rxmc -source .rxmc/bin/activate -pip install -r requirements.txt -pip install -ve . -``` - -### `uv`: - -```bash -uv env create -uv env use python -uv install -e . -``` - -### Optional extras - -Install the example notebook runtime dependencies with: - -```bash -pip install -ve '.[examples]' -``` - -Install the full validation toolchain with: - -```bash -pip install -ve '.[validation]' -``` - -## Supported workflow 1: external samplers with `CalibrationConfig` - -`CalibrationConfig` packages a calibration problem into a flat parameter space -for external drivers. It exposes the interface expected by -`black-box-bayes`-style tooling: - -- `ndim` -- `starting_location(nwalkers)` -- `log_posterior(theta)` -- `log_likelihood(theta)` -- `prior_transform(u)` -- `log_posterior_batch(thetas)` (optional convenience interface) -- `parameter_names` - -Typical flow: - -1. Build `Observation` objects from your measurements. -2. Define a `PhysicalModel`. -3. Declare correlated uncertainty as covariance `Term`s (and pick a - likelihood functional: Gaussian, Student-t, or chi-squared). -4. Combine them into `Constraint` objects and then `Evidence`. -5. Wrap the problem in `ParameterConfig` and `CalibrationConfig`. -6. Hand the resulting object to an external sampler. - -This is the recommended path for larger production calibrations. - -## Supported workflow 2: in-package MCMC with `Walker` - -`Walker` is the smaller-scale, in-package path. It coordinates: - -- one sampler for the physical-model parameters, and -- optional additional samplers for parametric likelihood sectors. - -It alternates between these sectors in a Gibbs-style workflow and is useful -for: - -- prototyping new likelihood models, -- validating new observation/model compositions, -- and running smaller end-to-end inference problems without introducing an - external orchestration layer. - -## Core concepts - -### `Observation` - -Pure measured data — `x`, `y`, and the statistical error on `y` — plus the -measurement's reported systematic magnitudes retained as inert metadata -(`y_sys_err_normalization`, `y_sys_err_offset`). It contributes only its -statistical diagonal by default; `obs.systematic_terms()` turns the -metadata into explicit covariance terms when you ask. - -An observation also owns its **comparison space**: `Observation(x, y, -transform=rxmc.transforms.log)` takes raw `y`, compares in log space (errors -propagated by the delta method) and the constraint transforms the model -prediction to match. A point-level `mask` (or `obs.masked_where(...)`) selects -which points enter a likelihood — fit/held-out splits without rebuilding -anything. - -### `PhysicalModel` - -Maps model parameters to predicted observables for a given `Observation`. -A parametric `transform=` (e.g. `rxmc.transforms.scale()` or -`per_observation_scaling(observations)`) adds latent normalization parameters -(Kennedy–O'Hagan style) to any model. - -### Covariance `Term`s (`rxmc.covariance`) - -Every uncertainty beyond the statistical diagonal is an explicit additive -contribution to the constraint's stacked covariance. There is one generic -`Term(fn, params, kind=...)` — `fn` is a numpy-style callable of the term's -local `x`/`y`/`ym` and its parameters, `kind` is `"diag"`, `"mode"` or -`"matrix"`, and an optional `coords` transform changes the coordinate the term -lives in. Factory helpers cover the common modes in one line: - -- `normalization_term` / `offset_term` / `systematic_term` — correlated - modes, fixed magnitude or free nuisance, prediction-, unit- or user-basis - scaled, -- `noise_term` / `noise_fraction_term` — unknown statistical noise (with an - optional parametric basis, e.g. noise growing with angle), -- `model_error_term` — uncorrelated model error, -- `kernel_term` — Gaussian-process model discrepancy using sklearn kernels, - optionally in transformed coordinates and with a parametric amplitude. - -A term whose support spans several observations *couples* them (correlated -datasets); referencing the same `Parameter` object in two terms *shares* one -sampled value between them. `support=None` (the default) means the whole -constraint. - -### Likelihood functionals - -`GaussianLikelihood` (default), `StudentT` (heavy-tailed, with a -degrees-of-freedom parameter), and `Chi2` are thin functionals over the same -stacked covariance. - -### `Constraint` - -The maximal block of mutually-correlated data: observations, a physical model, -a covariance assembled from terms, and a likelihood functional. - -### `Evidence` - -Aggregates multiple independent constraints that share the same physical-model -parameterization. - -### Model comparison (`rxmc.model_comparison`) - -Sampler-agnostic posterior-predictive draws, coverage/sharpness checks, -held-out scoring on `constraint.complement()`, and log-evidence bookkeeping -(`logz_summary`, `compare_logz`, `log_jacobian` for comparing fits done in -different comparison spaces). - -## Examples and tutorials - -The `examples/` directory contains richer notebooks and demos. The most useful -entry points are: - -- `examples/linear_calibration_demo.ipynb` for the basic workflow, -- `examples/systematic_err_demo.ipynb` for the error-model catalog and - systematic-error handling, -- `examples/measurement_to_calibration.ipynb` for the EXFOR-measurement → - calibration path (units, retained systematics, guardrails), -- `examples/30s_optical_potential_calibration.ipynb` for a realistic optical - potential calibration example, -- `examples/correlated_observations.ipynb` for correlated datasets and shared - systematics (including across cross-section experiments), -- `examples/gp_discrepancy.ipynb` for Gaussian-process model discrepancy, -- `examples/robust_likelihoods.ipynb` for Student-t vs Gaussian likelihoods, -- `examples/normalization_inference.ipynb` for normalization-focused modeling, -- `examples/sampling_algos.ipynb` for sampling comparisons. - -## Documentation - -The full API reference and rendered example notebooks are hosted at -**https://beykyle.github.io/rxmc/**. - -To build the documentation locally: - -```bash -pip install -ve '.[docs]' -cd docs && make html -# then open docs/_build/html/index.html -``` - -## Testing - -Run the full validation matrix with: - -```bash -python -m isort --check-only src test -python -m black --check src test -python -m ruff check src test -python -m nbqa isort --check examples/*.ipynb -python -m black --check --ipynb examples/*.ipynb -python -m ruff check examples/*.ipynb -python -m pytest -``` - -If you want to apply the formatting fixes locally instead of only checking them: - -```bash -python -m isort src test -python -m black src test -python -m ruff check --fix src test -python -m nbqa isort examples/*.ipynb -python -m black --ipynb examples/*.ipynb +python -m venv .venv && source .venv/bin/activate +pip install -e '.[validation]' ``` -Run only the unit tests with: +Python ≥ 3.12; `jitr >= 3.0` from PyPI. -```bash -python -m pytest test -``` - -Run only the notebooks with: +## Validation ```bash -python -m pytest examples +python -m isort --check-only src test && python -m black --check src test && python -m ruff check src test +python -m pytest # fast tier +python -m pytest -m slow # converged tier, run nightly in CI +sphinx-build -W docs docs/_build/html ``` - diff --git a/docs/index.rst b/docs/index.rst index b34b31e..7827848 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,31 +1,20 @@ rxmc ==== -``rxmc`` is an orchestration layer for Bayesian calibration of reaction models -to large data sets with flexible, composable covariance modeling. +``rxmc`` is a library for Bayesian calibration of reaction models to +experimental data, with the error model, statistical and systematic, +experimental and theoretical, declared explicitly as part of the problem. -It is built around two complementary workflows: - -1. **External-sampler orchestration** via :class:`~rxmc.config.CalibrationConfig` - for drivers such as `black-box-bayes `_. -2. **In-package end-to-end prototyping** via :class:`~rxmc.walker.Walker` - for smaller problems where you want to run the full MCMC workflow locally. - -The package composes curated experimental data (:class:`~rxmc.observation.Observation`), -model predictions (:class:`~rxmc.physical_model.PhysicalModel`), uncertainty -declared as additive covariance :class:`~rxmc.covariance.Term` s (statistical, -systematic, unknown-noise, and Gaussian-process discrepancy modes), maximal -blocks of mutually-correlated data (:class:`~rxmc.constraint.Constraint`), and -full calibration problems (:class:`~rxmc.evidence.Evidence`). +**The 1.0 rewrite is in progress on this branch.** The design is the plan +of record in :doc:`groundup_design`; :doc:`recipes` lists every supported +use case with its spelling and the behaviour to expect, and each recipe is +a test. The 0.x package is preserved at tag ``v0.1.0`` and on branch +``legacy/0.x``. .. toctree:: :maxdepth: 1 :caption: Contents installation - design groundup_design recipes - bugs_found - api - examples diff --git a/docs/installation.rst b/docs/installation.rst index 54b6fa0..452f72b 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -1,59 +1,16 @@ Installation ============ -Requirements ------------- - -``rxmc`` requires Python 3.10 or later. Core dependencies are listed in -``requirements.txt`` and are installed automatically. - -Development / local use ------------------------ +``rxmc`` requires Python 3.12 or later. Core dependencies are listed in +``requirements.txt`` and are installed automatically. The 1.0 pre-releases +will be published to PyPI as ``v1.0.0a1``, ``b1``, ``rc1`` and installable +with ``pip install --pre rxmc``; until then install from the branch: .. code-block:: bash - git clone git@github.com:beykyle/rxmc.git + git clone -b rewrite git@github.com:beykyle/rxmc.git cd rxmc - pip install -ve . - -It is strongly recommended to use an isolated environment. - -``venv`` --------- - -.. code-block:: bash - - python -m venv .rxmc - source .rxmc/bin/activate - pip install -r requirements.txt - pip install -ve . - -``uv`` ------- - -.. code-block:: bash - - uv env create - uv env use python - uv install -e . - -Optional extras ---------------- - -Install example notebook runtime dependencies: - -.. code-block:: bash - - pip install -ve '.[examples]' - -Install the full validation toolchain (formatting, linting, testing): - -.. code-block:: bash - - pip install -ve '.[validation]' - -Install documentation build dependencies: - -.. code-block:: bash + python -m venv .venv && source .venv/bin/activate + pip install -e '.[validation]' # or '.[examples]' for the notebooks only - pip install -ve '.[docs]' +The 0.x package is at tag ``v0.1.0`` and on branch ``legacy/0.x``. diff --git a/pyproject.toml b/pyproject.toml index 254beb5..8078493 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,17 +36,20 @@ docs = [ ] examples = [ "corner>=2.2", + "dill>=0.3", "dynesty>=2.1", "emcee>=3.1", "ipykernel>=6.0", "jupyter>=1.0", "matplotlib>=3.8", + "scikit-learn>=1.0", "tqdm>=4.66", ] validation = [ "black>=24.0", "build>=1.2", "corner>=2.2", + "dill>=0.3", "dynesty>=2.1", "emcee>=3.1", "ipykernel>=6.0", @@ -57,6 +60,7 @@ validation = [ "nbqa>=1.9", "pytest>=8.0", "ruff>=0.6", + "scikit-learn>=1.0", "tqdm>=4.66", ] @@ -71,7 +75,7 @@ write_to = "src/rxmc/__version__.py" [tool.black] line-length = 88 -target-version = ["py310"] +target-version = ["py312"] [tool.isort] profile = "black" @@ -80,7 +84,7 @@ src_paths = ["src", "test"] [tool.ruff] line-length = 88 -target-version = "py310" +target-version = "py312" [tool.ruff.lint] select = ["F", "I"] @@ -91,6 +95,12 @@ select = ["F", "I"] known-first-party = ["rxmc", "helpers"] [tool.pytest.ini_options] -# bare `pytest` runs the unit suite only; the notebooks are executed in CI via -# `pytest -n 4 --nbmake --nbmake-timeout=1200 examples` +# bare `pytest` runs the fast tier: unit tests plus the sampler-free and +# short-chain assertions of every recipe test. The converged tier is marked +# `slow` and runs on a schedule with `pytest -m slow`; the notebooks with +# `pytest --nbmake examples`. See docs/groundup_design.md section 9. testpaths = ["test"] +addopts = "-m 'not slow'" +markers = [ + "slow: converged-chain tier; deselected by default, run nightly with -m slow", +] diff --git a/requirements.txt b/requirements.txt index 9987df3..3420b06 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,4 @@ -scikit-learn>=1.0.0 numpy>=2.2.6 -pandas>=2.2 scipy>=1.15 pint>=0.2 jitr>=3.0 diff --git a/src/rxmc/__init__.py b/src/rxmc/__init__.py index 5685810..1fe8c48 100644 --- a/src/rxmc/__init__.py +++ b/src/rxmc/__init__.py @@ -1,45 +1,12 @@ -from . import adaptive_metropolis as adaptive_metropolis -from . import config as config -from . import constraint as constraint -from . import covariance as covariance -from . import elastic_diffxs_model as elastic_diffxs_model -from . import elastic_diffxs_observation as elastic_diffxs_observation -from . import evidence as evidence -from . import ias_pn_model as ias_pn_model -from . import ias_pn_observation as ias_pn_observation -from . import likelihood_model as likelihood_model -from . import metropolis_hastings as metropolis_hastings -from . import model_comparison as model_comparison -from . import observation as observation -from . import observation_from_measurement as observation_from_measurement -from . import param_sampling as param_sampling -from . import params as params -from . import physical_model as physical_model -from . import predictive as predictive -from . import priors as priors -from . import transforms as transforms -from . import walker as walker -from .__version__ import __version__ as __version__ +"""rxmc: Bayesian calibration of reaction models with composable error models. -__all__ = [ - "__version__", - "adaptive_metropolis", - "config", - "constraint", - "covariance", - "elastic_diffxs_model", - "elastic_diffxs_observation", - "evidence", - "ias_pn_model", - "ias_pn_observation", - "likelihood_model", - "metropolis_hastings", - "observation", - "observation_from_measurement", - "param_sampling", - "params", - "physical_model", - "predictive", - "priors", - "walker", -] +The 1.0 rewrite is in progress on this branch; see ``docs/groundup_design.md`` +for the design and ``docs/recipes.md`` for the supported use cases. +""" + +try: + from .__version__ import __version__ as __version__ +except ImportError: # pragma: no cover - source checkout without a build + __version__ = "0+unknown" + +__all__ = ["__version__"] diff --git a/test/recipes/__init__.py b/test/recipes/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/test_smoke.py b/test/test_smoke.py new file mode 100644 index 0000000..53c6336 --- /dev/null +++ b/test/test_smoke.py @@ -0,0 +1,8 @@ +"""The package installs and reports a version.""" + +import rxmc + + +def test_import_and_version(): + assert isinstance(rxmc.__version__, str) + assert rxmc.__version__ From 09e4973c77e96a4aa6eae88d98784397257339a7 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 18:32:19 -0400 Subject: [PATCH 04/75] Add Parameter: an identity-hashed declaration with optional marginal prior Frozen dataclass with eq=False, so sharing is by object; bounds validated and coerced; prior, unit and latex carried for Problem and plotting. --- src/rxmc/params.py | 71 +++++++++++++++++++++++++++++++++++++++++++++ test/test_params.py | 49 +++++++++++++++++++++++++++++++ 2 files changed, 120 insertions(+) create mode 100644 src/rxmc/params.py create mode 100644 test/test_params.py diff --git a/src/rxmc/params.py b/src/rxmc/params.py new file mode 100644 index 0000000..6fd32d7 --- /dev/null +++ b/src/rxmc/params.py @@ -0,0 +1,71 @@ +"""The sampled scalar. + +A :class:`Parameter` is a declaration: a name, optional bounds, an optional +marginal prior, and display metadata. Its identity *is* the object: passing +the same ``Parameter`` to two places, anywhere in a problem, shares one sampled +value between them. Two distinct objects with the same name are two +parameters, and :class:`~rxmc.problem.Problem` rejects the duplicate name. + +Prior rules, enforced once at compile: + +* ``prior`` given: the marginal, truncated to ``bounds``; +* finite ``bounds`` and no ``prior``: uniform on the bounds; +* neither: the parameter must be covered by a joint prior passed to + ``Problem``, otherwise compile fails naming it. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from math import inf + +__all__ = ["Parameter"] + + +@dataclass(eq=False, frozen=True) +class Parameter: + """A single scalar model parameter. + + Parameters + ---------- + name : str + Non-empty; used for chain columns, plots and error messages. + bounds : (float, float), optional + Support of the parameter, ``(-inf, inf)`` by default. ``lo < hi``. + prior : object, optional + A frozen univariate distribution exposing ``logpdf``, ``cdf``, ``ppf`` + and ``rvs`` (any ``scipy.stats`` frozen distribution). Left ``None`` + when the parameter is covered by a joint prior. + unit : str, optional + Physical unit string for display. + latex : str, optional + LaTeX label for plots; ``name`` when omitted. + """ + + name: str + bounds: tuple[float, float] = (-inf, inf) + prior: object | None = field(default=None, repr=False) + unit: str = "" + latex: str | None = None + + def __post_init__(self): + if not isinstance(self.name, str) or not self.name: + raise ValueError(f"name must be a non-empty string, got {self.name!r}") + try: + lo, hi = (float(b) for b in self.bounds) + except (TypeError, ValueError): + raise ValueError( + f"bounds must be (lower, upper), got {self.bounds!r}" + ) from None + if not lo < hi: + raise ValueError(f"bounds must satisfy lower < upper, got {(lo, hi)!r}") + object.__setattr__(self, "bounds", (lo, hi)) + + @property + def label(self) -> str: + """The LaTeX label, falling back to the name.""" + return self.latex if self.latex is not None else self.name + + def __repr__(self): + prior = ", prior=set" if self.prior is not None else "" + return f"Parameter({self.name!r}, bounds={self.bounds!r}{prior})" diff --git a/test/test_params.py b/test/test_params.py new file mode 100644 index 0000000..7350521 --- /dev/null +++ b/test/test_params.py @@ -0,0 +1,49 @@ +"""Identity semantics and validation of :class:`rxmc.Parameter`.""" + +import numpy as np +import pytest +from scipy import stats + +from rxmc import Parameter + + +def test_identity_is_equality(): + p, q = Parameter("a"), Parameter("a") + assert p == p + assert p != q # same name, distinct objects: two parameters + assert len({p, q}) == 2 + assert {p: 1, q: 2}[p] == 1 + + +def test_bounds_coerced_and_validated(): + p = Parameter("a", bounds=(1, 3)) + assert p.bounds == (1.0, 3.0) + assert all(isinstance(b, float) for b in p.bounds) + with pytest.raises(ValueError, match="lower < upper"): + Parameter("a", bounds=(1.0, 0.0)) + with pytest.raises(ValueError, match="lower, upper"): + Parameter("a", bounds=(1.0,)) + + +def test_default_bounds_infinite(): + p = Parameter("a") + assert p.bounds == (-np.inf, np.inf) + + +def test_name_required(): + with pytest.raises(ValueError): + Parameter("") + + +def test_prior_and_labels(): + prior = stats.norm(0, 1) + p = Parameter("V", prior=prior, unit="MeV", latex=r"V_0") + assert p.prior is prior + assert p.unit == "MeV" + assert p.label == r"V_0" + assert Parameter("W").label == "W" + + +def test_repr_names_the_parameter(): + assert "V" in repr(Parameter("V")) + assert "prior=set" in repr(Parameter("V", prior=stats.norm())) From 3d416419cc7ee05e0344e68d18b85aec46530c0c Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 18:32:19 -0400 Subject: [PATCH 05/75] Rewrite transforms to the 1.0 skeleton Drop the contextual flag, the context keyword and per_observation_scaling; scale() uses the new Parameter signature. Transform, composition, log/exp and the finite-difference derivative are unchanged. --- src/rxmc/transforms.py | 154 ++++++---------------------------------- test/test_transforms.py | 78 ++++++++++++++++++++ 2 files changed, 100 insertions(+), 132 deletions(-) create mode 100644 test/test_transforms.py diff --git a/src/rxmc/transforms.py b/src/rxmc/transforms.py index 9b8483b..15e6e57 100644 --- a/src/rxmc/transforms.py +++ b/src/rxmc/transforms.py @@ -6,12 +6,12 @@ values) and optional analytic ``derivative``/``inverse``. The same type serves three roles: -* the comparison-space transform of an :class:`~rxmc.observation.Observation` - (e.g. ``transform=log`` to compare in log space; parameter-free), -* a parametric model-side transform on a - :class:`~rxmc.physical_model.PhysicalModel` (e.g. :func:`scale` for a latent - normalisation, :func:`per_observation_scaling` for one per dataset), -* the coordinate transform of a covariance :class:`~rxmc.covariance.Term` +* the comparison space of a :class:`~rxmc.constraint.Comparison` + (e.g. ``space=log``; parameter-free, so the delta-method errors and the + log-Jacobian are constants), +* a mean transform composed onto a :class:`~rxmc.model.Model` with ``|`` + (e.g. :func:`scale` for a latent normalisation), +* the coordinate transform of a covariance :class:`~rxmc.terms.Term` (e.g. angle to momentum transfer). Anything callable is accepted wherever a ``Transform`` is expected and is wrapped @@ -27,14 +27,7 @@ from .params import Parameter - -def _unpack(contextual, args): - """Split a composed transform's positional ``args`` into ``(context, a, values)``.""" - if contextual: - context, a, *values = args - return context, a, values - a, *values = args - return None, a, values +__all__ = ["Transform", "as_transform", "identity", "log", "exp", "scale"] class Transform: @@ -43,13 +36,9 @@ class Transform: Parameters ---------- fn : callable - ``fn(a, *values) -> np.ndarray``; when ``contextual`` is ``True``, - ``fn(context, a, *values)`` where ``context`` is whatever the owner - passes (the :class:`~rxmc.observation.Observation` for model transforms). + ``fn(a, *values) -> np.ndarray``. params : sequence of Parameter, optional Parameters whose sampled values are passed as ``*values``. - contextual : bool, optional - Whether ``fn`` takes the owner's context as its first argument. derivative : callable, optional ``derivative(a, *values) -> np.ndarray``, :math:`\\partial fn/\\partial a` elementwise. Used for delta-method error propagation and Jacobians. @@ -64,7 +53,6 @@ def __init__( fn: Callable, params: Sequence[Parameter] = (), *, - contextual: bool = False, derivative: Callable | None = None, inverse=None, name: str | None = None, @@ -76,7 +64,6 @@ def __init__( for p in self.params: if not isinstance(p, Parameter): raise TypeError(f"params must be Parameter objects, got {p!r}") - self.contextual = bool(contextual) self.derivative_fn = derivative self._inverse = inverse self._inverse_factory = None @@ -108,18 +95,16 @@ def inverse(self) -> "Transform | None": return None return as_transform(self._inverse) - def __call__(self, a, *values, context=None): + def __call__(self, a, *values): if len(values) != self.n_params: raise ValueError( f"transform {self.name!r} expects {self.n_params} value(s), " f"got {len(values)}" ) a = np.asarray(a, dtype=float) - if self.contextual: - return np.asarray(self.fn(context, a, *values), dtype=float) return np.asarray(self.fn(a, *values), dtype=float) - def derivative(self, a, *values, context=None): + def derivative(self, a, *values): """Elementwise derivative :math:`\\partial fn/\\partial a` at ``a``. Falls back to a central finite difference when no analytic derivative @@ -127,38 +112,24 @@ def derivative(self, a, *values, context=None): """ a = np.asarray(a, dtype=float) if self.derivative_fn is not None: - if self.contextual: - return np.asarray(self.derivative_fn(context, a, *values), dtype=float) return np.asarray(self.derivative_fn(a, *values), dtype=float) h = 1e-6 * np.maximum(np.abs(a), 1.0) - fp = self(a + h, *values, context=context) - fm = self(a - h, *values, context=context) - return (fp - fm) / (2 * h) + return (self(a + h, *values) - self(a - h, *values)) / (2 * h) def __or__(self, other) -> "Transform": """``(f | g)(a) = g(f(a))`` with parameters ``f.params + g.params``.""" f, g = self, as_transform(other) nf = f.n_params - contextual = f.contextual or g.contextual - - def fn(*args): - context, a, values = _unpack(contextual, args) - b = f(a, *values[:nf], context=context) - return g(b, *values[nf:], context=context) - - def derivative(*args): - context, a, values = _unpack(contextual, args) - b = f(a, *values[:nf], context=context) - return g.derivative(b, *values[nf:], context=context) * f.derivative( - a, *values[:nf], context=context - ) + + def fn(a, *values): + return g(f(a, *values[:nf]), *values[nf:]) + + def derivative(a, *values): + b = f(a, *values[:nf]) + return g.derivative(b, *values[nf:]) * f.derivative(a, *values[:nf]) out = Transform( - fn, - f.params + g.params, - contextual=contextual, - derivative=derivative, - name=f"{f.name}|{g.name}", + fn, f.params + g.params, derivative=derivative, name=f"{f.name}|{g.name}" ) if not (f.params or g.params): # lazy: composing eagerly would recurse for mutually inverse pairs @@ -226,8 +197,8 @@ def scale(parameter: Parameter | None = None, log: bool = True, name=None) -> Tr r"""A latent multiplicative normalisation :math:`\rho\, y`. The Kennedy & O'Hagan forward-model scale: it changes the *mean*, not the - covariance, so it belongs on the model - (``PhysicalModel(params, transform=scale())``). + covariance, so it is composed onto the model (``model | scale(rho)``) and + the prediction, not the data, is scaled. Parameters ---------- @@ -243,10 +214,7 @@ def scale(parameter: Parameter | None = None, log: bool = True, name=None) -> Tr if parameter is None: name = name or ("log_rho" if log else "rho") parameter = Parameter( - name, - float, - unit="dimensionless", - latex_name=r"\log{\rho}" if log else r"\rho", + name, unit="dimensionless", latex=r"\log{\rho}" if log else r"\rho" ) if log: return Transform( @@ -261,81 +229,3 @@ def scale(parameter: Parameter | None = None, log: bool = True, name=None) -> Tr derivative=lambda a, v: np.full_like(a, v), name="scale", ) - - -def _root(observation): - """The identity key of an observation (its root; itself for other objects).""" - return getattr(observation, "identity", observation) - - -def per_observation_scaling( - observations, parameters=None, log: bool = True, prefix: str | None = None -) -> Transform: - r"""One latent normalisation :math:`\rho_i` per dataset, routed by identity. - - Contextual: when the owning model is evaluated on observation :math:`i` - (matched by identity of ``obs.identity``, so masked views made with - :meth:`~rxmc.observation.Observation.masked` route to their root's scale), - the prediction is scaled by :math:`\rho_i`. Parameters are ordered as - ``observations``. - - Parameters - ---------- - observations : sequence of Observation - The datasets, each assigned one scale parameter. - parameters : sequence of Parameter, optional - One per observation. Defaults to ``{prefix}_{i}``. - log : bool, optional - Sample :math:`\log\rho_i` (default) or :math:`\rho_i`. - prefix : str, optional - Default-parameter name prefix; ``log_rho``/``rho`` by ``log``. - """ - observations = list(observations) - index = {id(_root(o)): i for i, o in enumerate(observations)} - if len(index) != len(observations): - raise ValueError("observations must be distinct objects (routing by identity)") - if prefix is None: - prefix = "log_rho" if log else "rho" - if parameters is None: - parameters = [ - Parameter( - f"{prefix}_{i}", - float, - unit="dimensionless", - latex_name=(rf"\log{{\rho_{{{i}}}}}" if log else rf"\rho_{{{i}}}"), - ) - for i in range(len(observations)) - ] - parameters = tuple(parameters) - if len(parameters) != len(observations): - raise ValueError("need exactly one parameter per observation") - - def _value(context, values): - if context is None: - raise ValueError( - "per_observation_scaling is contextual: evaluate it through a " - "PhysicalModel, or pass context=observation" - ) - i = index.get(id(_root(context))) - if i is None: - raise KeyError( - "observation was not registered with this per_observation_scaling" - ) - v = values[i] - return np.exp(v) if log else v - - def fn(context, a, *values): - return _value(context, values) * a - - def derivative(context, a, *values): - return np.full_like(a, _value(context, values)) - - t = Transform( - fn, - parameters, - contextual=True, - derivative=derivative, - name="per_observation_scaling", - ) - t._keepalive = observations # routing is id()-keyed: keep the objects alive - return t diff --git a/test/test_transforms.py b/test/test_transforms.py new file mode 100644 index 0000000..b992e6c --- /dev/null +++ b/test/test_transforms.py @@ -0,0 +1,78 @@ +"""Tests for the low-level ``rxmc.transforms`` type.""" + +import numpy as np +import pytest + +from rxmc import Parameter +from rxmc.transforms import Transform, as_transform, exp, identity, log, scale + + +class TestTransform: + def test_callable_is_wrapped_parameter_free(self): + t = as_transform(np.sqrt) + assert isinstance(t, Transform) + assert t.params == () + np.testing.assert_allclose(t([4.0, 9.0]), [2.0, 3.0]) + assert as_transform(None) is identity + assert as_transform(t) is t + + def test_log_is_safe_and_invertible(self): + y = np.array([1.0, 0.0, -2.0, np.e]) + out = log(y) + assert out[0] == 0.0 + assert out[1] == -np.inf + assert out[2] == -np.inf + assert out[3] == pytest.approx(1.0) + np.testing.assert_allclose(log.derivative(np.array([2.0, 4.0])), [0.5, 0.25]) + assert log.inverse is exp + assert exp.inverse is log + np.testing.assert_allclose(exp(log(np.array([3.0, 7.0]))), [3.0, 7.0]) + + def test_finite_difference_derivative_fallback(self): + t = Transform(lambda a: a**3) + np.testing.assert_allclose( + t.derivative(np.array([1.0, 2.0])), [3.0, 12.0], rtol=1e-5 + ) + + def test_compose_order_and_params(self): + p = Parameter("c") + shift = Transform( + lambda a, c: a + c, (p,), derivative=lambda a, c: np.ones_like(a) + ) + t = shift | log # log(a + c) + assert t.params == (p,) + np.testing.assert_allclose(t(np.array([1.0]), 1.0), [np.log(2.0)]) + np.testing.assert_allclose(t.derivative(np.array([1.0]), 1.0), [0.5]) + assert t.inverse is None # parametric -> no inverse + u = log | exp + np.testing.assert_allclose(u(np.array([2.0])), [2.0]) + assert u.inverse is not None + + def test_wrong_value_count_raises(self): + with pytest.raises(ValueError, match="expects 1 value"): + scale()(np.ones(2)) + + def test_params_must_be_parameters(self): + with pytest.raises(TypeError, match="Parameter"): + Transform(lambda a, c: a + c, ("c",)) + + def test_no_context_keyword(self): + with pytest.raises(TypeError): + identity(np.ones(2), context=object()) + + +class TestScale: + def test_log_scale(self): + t = scale() + assert t.params[0].name == "log_rho" + np.testing.assert_allclose(t(np.array([1.0, 2.0]), np.log(3.0)), [3.0, 6.0]) + np.testing.assert_allclose( + t.derivative(np.array([1.0, 2.0]), np.log(3.0)), [3.0, 3.0] + ) + + def test_linear_scale_names(self): + t = scale(log=False) + assert t.params[0].name == "rho" + np.testing.assert_allclose(t(np.array([1.0, 2.0]), 3.0), [3.0, 6.0]) + t2 = scale(Parameter("eta"), log=False) + assert t2.params[0].name == "eta" From 8e3f4b6dde354553fc41f09c49164e02e448c415 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 18:32:19 -0400 Subject: [PATCH 06/75] Trim units to the registry, the constants and the angle-grid check The measurement adapters return inside from_measurement in milestone 5. --- src/rxmc/units.py | 86 ++++++++++++++-------------------------------- test/test_units.py | 36 +++++++++++++++++++ 2 files changed, 61 insertions(+), 61 deletions(-) create mode 100644 test/test_units.py diff --git a/src/rxmc/units.py b/src/rxmc/units.py index 3dfc871..a60eb1f 100644 --- a/src/rxmc/units.py +++ b/src/rxmc/units.py @@ -1,22 +1,27 @@ """ -Helpers shared by the reaction-observation classes. - -The reaction observations (:class:`~rxmc.elastic_diffxs_observation.ElasticDifferentialXSObservation` -and :class:`~rxmc.ias_pn_observation.IsobaricAnalogPNObservation`) are now plain -:class:`~rxmc.observation.Observation` subclasses carrying **statistical error -only**; any correlated systematic is composed explicitly as a -:class:`~rxmc.covariance.Term` in the :class:`~rxmc.constraint.Constraint`. This -module holds what the reaction observations *and* the reaction models share: -the angle-grid validation, the single ``pint`` unit registry, and the unit -convention (cross sections are stored internally in b/sr; ``jitr`` returns -mb/sr, so model outputs are divided by :data:`MB_PER_B`). +The one unit registry and the unit contract. + +``pint`` refuses to combine quantities from different registries, so every +module that touches units imports :data:`ureg` from here. The contract: +cross sections are stored internally in b/sr (:data:`XS_UNIT`); ``jitr`` +reports cross sections and the Rutherford cross section in mb/sr +(:data:`RUTHERFORD_UNIT`), so model outputs are divided by :data:`MB_PER_B`. +Angles are stored in radians. """ import numpy as np from pint import UnitRegistry -#: The one unit registry for the package. ``pint`` refuses to combine -#: quantities from different registries, so every module must use this one. +__all__ = [ + "ureg", + "DEFAULT_LMAX", + "XS_UNIT", + "RUTHERFORD_UNIT", + "MB_PER_B", + "check_angle_grid", +] + +#: The one unit registry for the package. ureg = UnitRegistry() #: Default maximum partial wave for the reaction solvers. @@ -32,51 +37,10 @@ MB_PER_B = float((1 * ureg.barn).to(ureg.millibarn).magnitude) -def normalized_error_kwargs( - norm, y_stat_err, y_sys_err_normalization, y_sys_err_offset -) -> dict: - """Error keywords for ``Observation.__init__`` in internal (norm-divided) units. - - Encodes the unit contract shared by the reaction observations: dimensionful - errors (statistical, absolute offset) are divided by ``norm`` (a scalar, or - a per-point array); the fractional normalisation error is dimensionless and - passed through untouched. - """ - return { - "y_stat_err": ( - None if y_stat_err is None else np.asarray(y_stat_err, dtype=float) / norm - ), - "y_sys_err_normalization": y_sys_err_normalization, - "y_sys_err_offset": ( - None - if y_sys_err_offset is None - else np.asarray(y_sys_err_offset, dtype=float) / norm - ), - } - - -def measurement_kwargs(measurement) -> dict: - """The ``Observation``-side constructor keywords carried by an - ``exfor_tools`` :class:`~exfor_tools.distribution.Distribution`. - - Shared by the reaction observations' ``from_measurement`` classmethods; the - reaction-specific arguments (``reaction``, ``quantity``/``ExIAS``, solver - settings, ``transform``, ``mask``) are passed alongside. - """ - return { - "x": measurement.x, - "y": measurement.y, - "Elab": measurement.Einc, - "y_units": measurement.y_units, - "y_stat_err": measurement.statistical_err, - "y_sys_err_normalization": measurement.systematic_norm_err, - "y_sys_err_offset": measurement.systematic_offset_err, - "dataset_label": getattr(measurement, "subentry", None), - } - - -def check_angle_grid(angles_rad: np.ndarray, name: str): - if len(angles_rad.shape) > 1: - raise ValueError(f"{name} must be 1D, is {len(angles_rad.shape)}D") - if angles_rad[0] < 0 or angles_rad[-1] > np.pi: - raise ValueError(f"{name} must be on [0,pi)") +def check_angle_grid(angles_rad: np.ndarray, name: str) -> None: + """Reject a grid that is not 1-D or not inside ``[0, pi]`` radians.""" + angles_rad = np.asarray(angles_rad) + if angles_rad.ndim != 1: + raise ValueError(f"{name} must be 1D, is {angles_rad.ndim}D") + if angles_rad.size and (angles_rad.min() < 0 or angles_rad.max() > np.pi): + raise ValueError(f"{name} must be on [0, pi] radians") diff --git a/test/test_units.py b/test/test_units.py new file mode 100644 index 0000000..6561361 --- /dev/null +++ b/test/test_units.py @@ -0,0 +1,36 @@ +"""The unit contract: one registry, consistent constants.""" + +import numpy as np +import pytest + +from rxmc.units import ( + DEFAULT_LMAX, + MB_PER_B, + RUTHERFORD_UNIT, + XS_UNIT, + check_angle_grid, + ureg, +) + + +def test_unit_constants_agree(): + assert MB_PER_B == 1000.0 + assert (1 * RUTHERFORD_UNIT).to(XS_UNIT).magnitude == pytest.approx(1e-3) + assert (1 * XS_UNIT).to(RUTHERFORD_UNIT).magnitude == pytest.approx(MB_PER_B) + assert DEFAULT_LMAX == 20 + + +def test_registry_is_shared(): + # quantities built from the constants combine without a registry error + q = (2 * ureg.millibarn / ureg.steradian) + 1 * XS_UNIT + assert q.to(XS_UNIT).magnitude == pytest.approx(1.002) + + +def test_check_angle_grid(): + check_angle_grid(np.linspace(0.0, np.pi, 5), "angles") + with pytest.raises(ValueError, match="1D"): + check_angle_grid(np.zeros((2, 2)), "angles") + with pytest.raises(ValueError, match="radians"): + check_angle_grid(np.array([0.0, 4.0]), "angles") + with pytest.raises(ValueError, match="radians"): + check_angle_grid(np.array([-0.1, 1.0]), "angles") From 53a5aadc360e5085e9999b23c81fb03df77f5847 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 18:32:19 -0400 Subject: [PATCH 07/75] Rewrite likelihood to the 1.0 skeleton Gaussian, StudentT(nu=None) with a bounded default parameter, Chi2 and the log_likelihood helper; the Cholesky helper moves to covariance in milestone 3. Test helpers gain a dense reference assembly used by the term tests now and the structured covariance tests later. --- src/rxmc/__init__.py | 7 +- src/rxmc/likelihood.py | 137 +++++++++++++--------------------------- test/helpers.py | 40 ++++++++++++ test/test_likelihood.py | 68 ++++++++++++++++++++ 4 files changed, 157 insertions(+), 95 deletions(-) create mode 100644 test/helpers.py create mode 100644 test/test_likelihood.py diff --git a/src/rxmc/__init__.py b/src/rxmc/__init__.py index 1fe8c48..5111ba6 100644 --- a/src/rxmc/__init__.py +++ b/src/rxmc/__init__.py @@ -4,9 +4,14 @@ for the design and ``docs/recipes.md`` for the supported use cases. """ +from . import likelihood as likelihood +from . import transforms as transforms +from . import units as units +from .params import Parameter as Parameter + try: from .__version__ import __version__ as __version__ except ImportError: # pragma: no cover - source checkout without a build __version__ = "0+unknown" -__all__ = ["__version__"] +__all__ = ["__version__", "Parameter", "likelihood", "transforms", "units"] diff --git a/src/rxmc/likelihood.py b/src/rxmc/likelihood.py index a819cf5..a26b2dd 100644 --- a/src/rxmc/likelihood.py +++ b/src/rxmc/likelihood.py @@ -1,72 +1,51 @@ """ -Likelihoods over the stacked residual of a :class:`~rxmc.constraint.Constraint`. +Likelihood functionals over the stacked residual of a constraint. -A constraint owns one multivariate distribution over the stacked vector of all its -observations; its covariance is a :class:`~rxmc.covariance.ConstraintCovariance` -assembled from :class:`~rxmc.covariance.Term` s. A *likelihood* here is a thin -functional of the pre-computed Mahalanobis statistics ``(d2, logdet, n)`` plus its -own optional parameters: +A constraint owns one multivariate distribution over the stacked residual +``y - ym`` of its comparisons, with covariance assembled from +:class:`~rxmc.terms.Term` s. A *likelihood* here is a thin functional of the +pre-computed Mahalanobis statistics ``(d2, logdet, n)`` plus its own optional +parameters, which are ordinary :class:`~rxmc.params.Parameter` s: -* :class:`GaussianLikelihood` — the multivariate normal (parameter-free). +* :class:`Gaussian` — the multivariate normal (parameter-free). * :class:`StudentT` — a heavy-tailed variant carrying a degrees-of-freedom - parameter ``nu``. + parameter ``nu``; one radial tail factor for the whole residual. * :class:`Chi2` — drops the log-determinant normalisation (pure chi-squared). - -All covariance parameters live on the :class:`~rxmc.covariance.ConstraintCovariance`; -the only likelihood-side parameter is ``StudentT``'s ``nu``. The ``(d2, logdet)`` -statistics themselves are computed by -:meth:`~rxmc.covariance.ConstraintCovariance.stacked_distance`. - -Helper functions ----------------- -:func:`mahalanobis_distance_sqr_cholesky` - Squared Mahalanobis distance and log-determinant via Cholesky decomposition. -:func:`log_likelihood` - Multivariate-normal log likelihood from pre-computed distance and log-det. """ +from __future__ import annotations + +from math import inf + import numpy as np -import scipy as sc from scipy.special import gammaln -from .covariance import chol_logdet from .params import Parameter -__all__ = [ - "Likelihood", - "GaussianLikelihood", - "StudentT", - "Chi2", - "mahalanobis_distance_sqr_cholesky", - "log_likelihood", -] +__all__ = ["Likelihood", "Gaussian", "StudentT", "Chi2", "log_likelihood"] class Likelihood: """A functional of the pre-computed Mahalanobis statistics ``(d2, logdet, n)``. - Subclasses implement :meth:`log_likelihood` and declare any parameters via - ``params``/``n_params``. The chi-squared statistic is - likelihood-independent (always the Mahalanobis distance). + Subclasses implement :meth:`log_likelihood` and declare any parameters in + ``params``. The chi-squared statistic is likelihood-independent (always + the Mahalanobis distance). """ - params: tuple = () - n_params: int = 0 + params: tuple[Parameter, ...] = () - def log_likelihood(self, d2, logdet, n, *like_params): + def log_likelihood(self, d2, logdet, n, *values) -> float: raise NotImplementedError - def chi2(self, d2, logdet, n, *like_params): + def chi2(self, d2, logdet, n, *values) -> float: return d2 -class GaussianLikelihood(Likelihood): - """Multivariate-normal likelihood over the stacked residual. - - Parameter-free — all uncertainty lives on the covariance terms. - """ +class Gaussian(Likelihood): + """Multivariate-normal likelihood over the stacked residual (parameter-free).""" - def log_likelihood(self, d2, logdet, n, *like_params): + def log_likelihood(self, d2, logdet, n, *values) -> float: return log_likelihood(d2, logdet, n) @@ -78,18 +57,22 @@ class StudentT(Likelihood): \log p = \ln\Gamma\!\Big(\tfrac{n+\nu}{2}\Big) - \ln\Gamma\!\Big(\tfrac{\nu}{2}\Big) - \tfrac{n}{2}\ln(\pi\nu) - \tfrac12 \ln\det\Sigma - \tfrac{\nu+n}{2}\,\ln\!\Big(1 + \tfrac{d^2}{\nu}\Big) + + Parameters + ---------- + nu : Parameter, optional + The degrees of freedom. Defaults to ``Parameter("nu", bounds=(1, inf))``; + two constraints using the default each derive a ``"nu"`` and the + problem fails to compile on the duplicate name, so pass ``nu=`` to + share one or to name them apart. """ - def __init__(self, nu_parameter: Parameter = None): - self.nu_parameter = ( - nu_parameter - if nu_parameter is not None - else Parameter("degrees_of_freedom", float, latex_name=r"\nu") - ) - self.params = (self.nu_parameter,) - self.n_params = 1 + def __init__(self, nu: Parameter | None = None): + if nu is None: + nu = Parameter("nu", bounds=(1.0, inf), latex=r"\nu") + self.params = (nu,) - def log_likelihood(self, d2, logdet, n, nu): + def log_likelihood(self, d2, logdet, n, nu) -> float: return ( gammaln((n + nu) / 2.0) - gammaln(nu / 2.0) @@ -100,56 +83,22 @@ def log_likelihood(self, d2, logdet, n, nu): class Chi2(Likelihood): - """Generalised chi-squared functional — drops the log-det normalisation.""" + """Generalised chi-squared functional: drops the log-det normalisation.""" - def log_likelihood(self, d2, logdet, n, *like_params): + def log_likelihood(self, d2, logdet, n, *values) -> float: return -0.5 * d2 -# ---------------------------------------------------------------------------- -# Math helpers -# ---------------------------------------------------------------------------- - - -def mahalanobis_distance_sqr_cholesky(y, ym, cov): - r"""Squared Mahalanobis distance and log-determinant via Cholesky factorisation. - - Parameters - ---------- - y : array-like, shape (n,) - Observation vector. - ym : array-like, shape (n,) - Model prediction vector. - cov : array-like, shape (n, n) - Positive-definite covariance matrix. - - Returns - ------- - mahalanobis_sqr : float - $(y - y_m)^T \Sigma^{-1} (y - y_m)$. - log_det : float - $\log \det \Sigma$. - """ - L, log_det = chol_logdet(np.asarray(cov, dtype=float)) - z = sc.linalg.solve_triangular(L, np.asarray(y) - np.asarray(ym), lower=True) - return np.dot(z, z), log_det - - -def log_likelihood(mahalanobis_sqr: float, log_det: float, n: int): +def log_likelihood(d2: float, logdet: float, n: int) -> float: r"""Multivariate-normal log likelihood from pre-computed statistics. Parameters ---------- - mahalanobis_sqr : float - Squared Mahalanobis distance $(y - y_m)^T \Sigma^{-1} (y - y_m)$. - log_det : float - $\log \det \Sigma$. + d2 : float + Squared Mahalanobis distance :math:`(y - y_m)^T \Sigma^{-1} (y - y_m)`. + logdet : float + :math:`\log \det \Sigma`. n : int Number of data points. - - Returns - ------- - float - Log likelihood value. """ - return -0.5 * (mahalanobis_sqr + log_det + n * np.log(2 * np.pi)) + return -0.5 * (d2 + logdet + n * np.log(2 * np.pi)) diff --git a/test/helpers.py b/test/helpers.py new file mode 100644 index 0000000..b952743 --- /dev/null +++ b/test/helpers.py @@ -0,0 +1,40 @@ +"""Shared helpers for the test suite: dense references built by hand.""" + +import numpy as np + + +def mahalanobis(y, ym, cov): + """``(d2, logdet)`` of a dense covariance by a numpy Cholesky factorisation.""" + L = np.linalg.cholesky(np.asarray(cov, dtype=float)) + z = np.linalg.solve(L, np.asarray(y, dtype=float) - np.asarray(ym, dtype=float)) + return float(z @ z), float(2.0 * np.sum(np.log(np.diag(L)))) + + +def manual_mvn_loglike(y, ym, cov): + """Reference dense multivariate-normal log likelihood.""" + d2, logdet = mahalanobis(y, ym, cov) + return -0.5 * (d2 + logdet + len(y) * np.log(2 * np.pi)) + + +def assemble_dense(terms, x, y, ym, values=()): + """Reference assembly of whole-support terms into a dense covariance. + + ``values`` is one tuple of sampled values per term, in ``terms`` order + (an empty tuple for a term without parameters). This is the dense + reference the structured covariance is checked against. + """ + y = np.asarray(y, dtype=float) + n = len(y) + values = tuple(values) if values else tuple(() for _ in terms) + if len(values) != len(terms): + raise ValueError("one value tuple per term") + Sigma = np.zeros((n, n)) + for term, v in zip(terms, values): + out = term.value(x, y, ym, *v) + if term.kind == "diag": + Sigma[np.diag_indices(n)] += out**2 + elif term.kind == "mode": + Sigma += np.outer(out, out) + else: + Sigma += out + return Sigma diff --git a/test/test_likelihood.py b/test/test_likelihood.py new file mode 100644 index 0000000..95269c7 --- /dev/null +++ b/test/test_likelihood.py @@ -0,0 +1,68 @@ +"""The likelihood functionals against their closed forms.""" + +import numpy as np +import pytest +from scipy.special import gammaln + +from helpers import mahalanobis, manual_mvn_loglike +from rxmc import Parameter +from rxmc.likelihood import Chi2, Gaussian, StudentT, log_likelihood + + +@pytest.fixture +def stats(): + y = np.array([2.0, 4.0, 7.0]) + ym = np.array([2.5, 4.0, 6.5]) + cov = np.diag([0.1, 0.2, 0.3]) + 0.01 + d2, logdet = mahalanobis(y, ym, cov) + return y, ym, cov, d2, logdet + + +def test_gaussian_matches_manual_mvn(stats): + y, ym, cov, d2, logdet = stats + assert Gaussian().params == () + assert Gaussian().log_likelihood(d2, logdet, 3) == pytest.approx( + manual_mvn_loglike(y, ym, cov) + ) + assert log_likelihood(d2, logdet, 3) == pytest.approx( + manual_mvn_loglike(y, ym, cov) + ) + assert Gaussian().chi2(d2, logdet, 3) == d2 + + +def test_student_t_closed_form(stats): + _, _, _, d2, logdet = stats + nu, n = 5.0, 3 + expected = ( + gammaln((n + nu) / 2) + - gammaln(nu / 2) + - 0.5 * n * np.log(np.pi * nu) + - 0.5 * logdet + - 0.5 * (nu + n) * np.log1p(d2 / nu) + ) + assert StudentT().log_likelihood(d2, logdet, n, nu) == pytest.approx(expected) + + +def test_student_t_default_and_explicit_parameter(): + default = StudentT() + assert [p.name for p in default.params] == ["nu"] + assert default.params[0].bounds == (1.0, np.inf) + p = Parameter("nu_a", bounds=(1.0, 100.0)) + assert StudentT(nu=p).params == (p,) + # two defaults are two distinct parameters with one name (compile rejects) + assert StudentT().params[0] is not default.params[0] + + +def test_chi2_drops_logdet(stats): + _, _, _, d2, logdet = stats + assert Chi2().log_likelihood(d2, logdet, 3) == pytest.approx(-0.5 * d2) + assert Chi2().chi2(d2, logdet, 3) == d2 + + +def test_mahalanobis_helper_on_diagonal(): + y = np.array([1.0, 2.0, 3.0]) + ym = np.array([1.1, 1.8, 3.2]) + cov = np.diag([0.1, 0.2, 0.3]) + d2, logdet = mahalanobis(y, ym, cov) + assert d2 == pytest.approx(np.sum((y - ym) ** 2 / np.diag(cov))) + assert logdet == pytest.approx(np.log(np.prod(np.diag(cov)))) From 8c66754914c7e3422dea90324183e23cc1178c96 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 18:35:24 -0400 Subject: [PATCH 08/75] Rewrite terms as stateless declarations with the factory helpers Term is a frozen dataclass: fn, params, kind, on, coords, constant. No bind, no support indices, no caches; value() evaluates on a given support and compile resolves on= to rows. TermContext gains meta(key) for per-point dataset metadata. Factories keep their bodies with support= renamed on= and the _term suffix dropped; kernel() accepts params= to share hyperparameters between comparisons. Tests port TermKinds, Coords, Factories, Kernel and the alpha+Ca study forms against a hand-built dense reference, plus the term-level halves of recipes 19 and 27. --- src/rxmc/terms.py | 781 +++++++++++++-------------------------------- test/test_terms.py | 526 ++++++++++++++++++++++++++++++ 2 files changed, 743 insertions(+), 564 deletions(-) create mode 100644 test/test_terms.py diff --git a/src/rxmc/terms.py b/src/rxmc/terms.py index 86e349e..70b6934 100644 --- a/src/rxmc/terms.py +++ b/src/rxmc/terms.py @@ -1,26 +1,11 @@ """ -Stacked-covariance model for a :class:`~rxmc.constraint.Constraint`. - -A constraint owns one multivariate-normal distribution over the *stacked* vector -of all its observations, ``y = [y1; y2; ...]``. The covariance of that MVN is -built additively from :class:`Term` objects, each of which writes its -contribution into a sub-block of the stacked covariance matrix selected by an -index array ``support`` (``None`` = the whole constraint). - -Two mechanisms are expressed here (see ``docs/design.md``): - -* **Correlating observations (A)** — a term whose ``support`` spans more than one - observation block writes off-diagonal blocks, coupling the data. A - block-diagonal covariance (every term local to one block) reproduces the old - per-observation summed likelihood as a special case. -* **Sharing a parameter (B)** — terms declare the :class:`~rxmc.params.Parameter` - objects they consume *by identity*. :class:`ConstraintCovariance` deduplicates - them (gather, not slice): two terms referencing the *same* ``Parameter`` object - share one entry in the sampled vector. - -There is exactly **one** term type. A :class:`Term` is a numpy-style callable -``fn(c, *values) -> array`` of a :class:`TermContext` ``c`` (the term's local view -of ``x``, ``y`` and ``ym`` on its support, with ``x`` passed through an optional +Covariance terms: the additive pieces of a constraint's covariance. + +A constraint owns one multivariate distribution over the stacked residual of +its comparisons; its covariance is a sum of :class:`Term` s. There is exactly +**one** term type. A ``Term`` is a numpy-style callable ``fn(c, *values) -> +array`` of a :class:`TermContext` ``c`` (the term's view of ``x``, ``y`` and the +prediction ``ym`` on its support, with ``x`` passed through an optional coordinate :class:`~rxmc.transforms.Transform`) and its parameter values, plus a ``kind`` that says how the returned array enters the covariance: @@ -29,30 +14,41 @@ ``"mode"`` ``fn`` returns a vector ``v``; ``Sigma += outer(v, v)`` (one correlated mode). ``"matrix"`` - ``fn`` returns a full block ``M``; ``Sigma_block += M``. + ``fn`` returns a full symmetric block ``M``; ``Sigma_block += M``. + +A term is a stateless declaration. Where it applies is its ``on``: a +comparison, a dataset, a sequence of them, or ``None`` for the whole +constraint; :class:`~rxmc.problem.Problem` resolves that to rows when it +compiles. The same term may be placed in several constraints. + +Two mechanisms are expressed here (see ``docs/groundup_design.md``): -The factory helpers (:func:`statistical_term`, :func:`normalization_term`, -:func:`offset_term`, :func:`noise_term`, :func:`noise_fraction_term`, -:func:`model_error_term`, :func:`systematic_term`, :func:`kernel_term`) are -one-line conveniences that build the common terms; anything they cannot express -is a direct ``Term(fn, params, kind=...)``. +* **Correlating comparisons** — a ``mode`` or ``matrix`` term whose ``on`` + spans several comparisons writes off-diagonal blocks, coupling the data. +* **Sharing a parameter** — terms declare the :class:`~rxmc.params.Parameter` + objects they consume *by identity*: pass the same object to two terms and + they share one sampled value. + +The factory helpers (:func:`statistical`, :func:`offset`, :func:`normalization`, +:func:`noise`, :func:`noise_fraction`, :func:`model_error`, :func:`systematic`, +:func:`kernel`) are one-line conveniences that build the common terms; anything +they cannot express is a direct ``Term(fn, params, kind=...)``. """ +from __future__ import annotations + from dataclasses import dataclass +from typing import Any, Mapping import numpy as np -import scipy as sc from .params import Parameter -from .transforms import as_transform +from .transforms import as_transform, identity __all__ = [ - "StackContext", + "KINDS", "TermContext", "Term", - "ConstraintCovariance", - "stacked_supports", - "chol_logdet", "as_2d", "ones", "ym", @@ -61,529 +57,180 @@ "exp_growth", "constant_amplitude", "exp_growth_amplitude", - "statistical_term", - "offset_term", - "normalization_term", - "noise_term", - "noise_fraction_term", - "model_error_term", - "systematic_term", - "kernel_term", + "statistical", + "offset", + "normalization", + "noise", + "noise_fraction", + "model_error", + "systematic", + "kernel", ] KINDS = ("diag", "mode", "matrix") -@dataclass(frozen=True) -class StackContext: - """Bundle of stacked arrays passed to every :meth:`Term.add_to`. - - Parameters - ---------- - x : np.ndarray - Stacked independent variable, ``np.concatenate`` over observations. - y : np.ndarray - Stacked observed data (in each observation's comparison space). - ym : np.ndarray or None - Stacked model prediction (same space as ``y``). ``None`` when a - *constant* covariance is assembled before any model evaluation (see - :meth:`constant`); constant terms never read it. - supports : tuple of np.ndarray - One contiguous index array per observation block, in stacking order. - """ - - x: np.ndarray - y: np.ndarray - ym: np.ndarray | None - supports: tuple - - @classmethod - def constant(cls, x, y, supports) -> "StackContext": - """A stack with no model prediction, for assembling constant terms.""" - return cls(x=x, y=y, ym=None, supports=tuple(supports)) +def as_2d(X) -> np.ndarray: + """Promote a 1-D input grid to a single-column 2-D array (sklearn kernels).""" + X = np.asarray(X, dtype=float) + return X[:, None] if X.ndim == 1 else X -@dataclass(frozen=True) +@dataclass(eq=False, frozen=True) class TermContext: - """A term's local view of the stack on its own support. - - ``x`` are the coordinates on the support — ``ctx.x[support]`` passed - through the term's ``coords`` transform (so ``x`` may be 2-D); ``y`` and - ``ym`` are the observed data and model prediction on the support (``ym`` is - ``None`` when a constant covariance is assembled without a model - prediction), and ``support`` the stacked indices this view corresponds to. - ``len(c)`` is the number of points. + """A term's view of the stack on its own support. + + ``x`` are the coordinates on the support, already passed through the term's + ``coords`` transform (so ``x`` may be 2-D); ``y`` and ``ym`` are the data and + the model prediction on the support in comparison space. ``ym`` is ``None`` + while a *constant* term is evaluated before any prediction exists, so a + mis-declared constant term fails loudly. ``len(c)`` is the number of + points; :meth:`meta` gives per-point dataset metadata (``c.meta("Elab")``). """ x: np.ndarray y: np.ndarray - ym: np.ndarray | None - support: np.ndarray - - def __len__(self): - return len(self.support) - - -def stacked_supports(observations) -> tuple: - """One contiguous index array per observation, in stacking order. - - The block layout of the stacked vector ``y = [y1; y2; ...]``: - ``Constraint`` uses this internally; callers only need it to place a term on - a *subset* of a constraint's observations (``support=None`` covers all). - """ - supports, b = [], 0 - for obs in observations: - supports.append(np.arange(b, b + obs.n_data_pts)) - b += obs.n_data_pts - return tuple(supports) - - -def chol_logdet(Sigma): - """Lower Cholesky factor and log-determinant of a positive-definite matrix.""" - L = sc.linalg.cholesky(Sigma, lower=True) - return L, 2.0 * float(np.sum(np.log(np.diag(L)))) - - -def as_2d(X) -> np.ndarray: - """Promote a 1-D input grid to a single-column 2-D array (sklearn kernels).""" - X = np.asarray(X, dtype=float) - return X[:, None] if X.ndim == 1 else X - - -# ---------------------------------------------------------------------------- -# The term -# ---------------------------------------------------------------------------- + ym: np.ndarray | None = None + _meta: Mapping[str, np.ndarray] | None = None + + def __len__(self) -> int: + return len(self.y) + + def meta(self, key: str) -> np.ndarray: + """The owning dataset's ``meta[key]``, one value per point of the support.""" + if self._meta is None or key not in self._meta: + raise KeyError( + f"no per-point metadata {key!r} on this term's support; put it in " + "Dataset.meta" + ) + return self._meta[key] +@dataclass(eq=False, frozen=True) class Term: - """One additive contribution to the stacked covariance. + """One additive contribution to a constraint's covariance. Parameters ---------- fn : callable or array_like ``fn(c, *values) -> np.ndarray`` with ``c`` a :class:`TermContext` and - ``values`` the sampled values of ``params`` (in order). A plain array is a - fixed contribution (``constant=True`` implied): a standard-deviation + ``values`` the sampled values of ``params`` (in order). A plain array is + a fixed contribution (``constant=True`` implied): a standard-deviation vector for ``kind="diag"``, a mode vector for ``"mode"``, or a symmetric block for ``"matrix"``. params : sequence of Parameter, optional - Parameters consumed by ``fn``, matched *by identity* across terms - (pass the same object to two terms to share one sampled value). + Parameters consumed by ``fn``, matched *by identity* across terms. kind : {"diag", "mode", "matrix"} How the returned array enters the covariance (see module docstring). - support : array_like of int, optional - Indices into the stacked vector. ``None`` (default) means the whole - constraint; it is resolved when the term is added to a - :class:`ConstraintCovariance`. + on : Comparison, Dataset, sequence of them, or None + Where the term applies; ``None`` means the whole constraint. Resolved + by :class:`~rxmc.problem.Problem`. coords : Transform or callable, optional - Coordinate transform applied to ``x[support]`` before ``fn`` sees it - (e.g. angle to momentum transfer). Its parameters, if any, are appended - to :attr:`params`. + Coordinate transform applied to ``x`` before ``fn`` sees it (e.g. angle + to momentum transfer). Its parameters, if any, are appended to + :attr:`params`. constant : bool, optional - Declare that a *callable* ``fn`` does not read ``c.ym`` (the model - prediction) and has no parameters, so the contribution can be evaluated - once and cached. ``c.x`` and ``c.y`` are invariant per constraint and - may be read freely (e.g. a fixed-hyperparameter kernel over ``x``). A - constant term is first evaluated with ``c.ym is None``, so a - mis-declared term fails loudly. Ignored (``True``) for array ``fn``. - - Notes - ----- - A Term is stateful: its support is bound once (see :meth:`bind`) and - constant or coordinate-transformed values are cached on the assumption that - the constraint's ``x`` never changes. Build a fresh Term per constraint; - only masked views of one constraint (which stack the same observations) - may share Terms. + Declare that a *callable* ``fn`` reads neither ``c.ym`` nor any + parameter, so the contribution can be evaluated once at compile. + ``c.x`` and ``c.y`` may be read freely. A constant term is evaluated + with ``c.ym is None``, so a mis-declared term fails loudly. Implied + for an array ``fn``; an error together with ``params``. """ - def __init__( - self, - fn, - params=(), - *, - kind="matrix", - support=None, - coords=None, - constant=False, - ): - if kind not in KINDS: - raise ValueError(f"kind must be one of {KINDS}, got {kind!r}") - self.kind = kind - self.coords = as_transform(coords) - fn_params = tuple(params) - for p in fn_params + self.coords.params: + fn: Any + params: tuple[Parameter, ...] = () + kind: str = "matrix" + on: Any = None + coords: Any = identity + constant: bool = False + + def __post_init__(self): + if self.kind not in KINDS: + raise ValueError(f"kind must be one of {KINDS}, got {self.kind!r}") + coords = as_transform(self.coords) + fn_params = tuple(self.params) + for p in fn_params + coords.params: if not isinstance(p, Parameter): raise TypeError(f"params must be Parameter objects, got {p!r}") - self._n_fn_params = len(fn_params) - self.params = fn_params + self.coords.params - self.support = None - self._bound_N = None - self._cache = None - self._x_cache = None - - if callable(fn): - self.fn = fn - self._array = None - self.is_constant = bool(constant) and not self.params - else: - self.fn = None - self._array = np.asarray(fn, dtype=float) - if self.params: - raise ValueError("an array-valued term cannot have parameters") - self.is_constant = True - if support is not None: - self._set_support(np.asarray(support, dtype=int)) + object.__setattr__(self, "coords", coords) + object.__setattr__(self, "params", fn_params + coords.params) + object.__setattr__(self, "_n_fn_params", len(fn_params)) + if callable(self.fn): + if self.constant and self.params: + raise ValueError( + "a constant term cannot have parameters (constant means the " + "term reads neither ym nor any parameter)" + ) + return + if self.params: + raise ValueError("an array-valued term cannot have parameters") + a = np.asarray(self.fn, dtype=float) + if self.kind == "matrix": + if a.ndim != 2 or a.shape[0] != a.shape[1]: + raise ValueError(f"matrix term expects a square block, got {a.shape}") + if not np.allclose(a, a.T): + raise ValueError("matrix term must be symmetric") + elif a.ndim != 1: + raise ValueError(f"{self.kind} term expects a vector, got shape {a.shape}") + object.__setattr__(self, "fn", a) + object.__setattr__(self, "constant", True) # -- structure ---------------------------------------------------------- + @property + def is_constant(self) -> bool: + """Whether the term can be evaluated once, before any prediction.""" + return bool(self.constant) + @property def couples_offdiagonal(self) -> bool: """Whether the term can write off-diagonal entries (``kind != "diag"``).""" return self.kind != "diag" - @property - def bound(self) -> bool: - return self.support is not None - - def bind(self, N: int) -> None: - """Resolve ``support=None`` to the whole stack of length ``N``. - - Idempotent for the same ``N``; a term constructed with an explicit - support is untouched. Re-binding to a *different* ``N`` raises: one - Term belongs to one constraint (masked views of that constraint share - its stack, see :meth:`rxmc.constraint.Constraint.masked`). - """ - N = int(N) - if self.support is None: - self._set_support(np.arange(N)) - self._bound_N = N - elif self._bound_N is not None and self._bound_N != N: - raise ValueError( - f"Term already bound to a stack of length {self._bound_N}; cannot " - f"re-bind it to length {N}. One Term belongs to one constraint " - "(masked views of the same constraint share its stack)" - ) - - def _set_support(self, ix: np.ndarray) -> None: - self.support = ix - n = len(ix) - # supports from ``stacked_supports`` (and the whole stack) are - # contiguous: index the block with slices instead of a gather/scatter - if n and np.array_equal(ix, np.arange(ix[0], ix[0] + n)): - sl = slice(int(ix[0]), int(ix[0]) + n) - self._block = (sl, sl) - else: - self._block = np.ix_(ix, ix) - if self._array is not None: - self._validate_bound() - - @property - def _expected_shape(self) -> tuple: - n = len(self.support) + def expected_shape(self, n: int) -> tuple: return (n, n) if self.kind == "matrix" else (n,) - def _validate_bound(self): - a = self._array - if a.shape != self._expected_shape: - raise ValueError( - f"{self.kind} term expects shape {self._expected_shape}, got {a.shape}" - ) - if self.kind == "matrix" and not np.allclose(a, a.T): - raise ValueError("matrix term must be symmetric") - # -- evaluation ----------------------------------------------------------- - def _check_bound(self): - if self.support is None: - raise ValueError( - "term support is unresolved; add it to a Constraint / " - "ConstraintCovariance (which binds support=None to the whole " - "stack) or pass support= explicitly" - ) + def context(self, x, y, ym=None, meta=None, *values) -> TermContext: + """The :class:`TermContext` this term sees on its support at ``values``.""" + self._check_count(values) + x = np.asarray(x, dtype=float) + if not self.coords.is_identity: + x = self.coords(x, *values[self._n_fn_params :]) + return TermContext( + x=x, + y=np.asarray(y, dtype=float), + ym=None if ym is None else np.asarray(ym, dtype=float), + _meta=meta, + ) - def local_context(self, ctx: StackContext, theta=()) -> TermContext: - """The :class:`TermContext` this term sees at ``theta``.""" - self._check_bound() - theta = np.asarray(theta, dtype=float) - if len(theta) != len(self.params): - raise ValueError(f"expected {len(self.params)} params, got {len(theta)}") - ix = self.support - x = self._coords_x(ctx, theta) - ym = None if ctx.ym is None else ctx.ym[ix] - return TermContext(x=x, y=ctx.y[ix], ym=ym, support=ix) - - def _coords_x(self, ctx, theta): - """``coords(x[support])``; cached when the transform is parameter-free - (``x`` is invariant per constraint).""" - if self.coords.is_identity: - return ctx.x[self.support] - if self.coords.params: - return self.coords(ctx.x[self.support], *theta[self._n_fn_params :]) - if self._x_cache is None: - self._x_cache = self.coords(ctx.x[self.support]) - self._x_cache.setflags(write=False) - return self._x_cache - - def value(self, ctx: StackContext, theta=()) -> np.ndarray: + def value(self, x, y, ym=None, *values, meta=None) -> np.ndarray: """The raw array ``fn`` returns (std vector, mode vector, or block).""" - self._check_bound() - if self._array is not None: - return self._array - if self.is_constant and self._cache is not None: - return self._cache - theta = np.asarray(theta, dtype=float) - if len(theta) != len(self.params): - raise ValueError(f"expected {len(self.params)} params, got {len(theta)}") - c = self.local_context(ctx, theta) - v = np.asarray(self.fn(c, *theta[: self._n_fn_params]), dtype=float) - if v.shape != self._expected_shape: + n = len(y) + if not callable(self.fn): + if self.fn.shape != self.expected_shape(n): + raise ValueError( + f"{self.kind} term expects shape {self.expected_shape(n)}, " + f"got {self.fn.shape}" + ) + return self.fn + c = self.context(x, y, ym, meta, *values) + v = np.asarray(self.fn(c, *values[: self._n_fn_params]), dtype=float) + if v.shape != self.expected_shape(n): raise ValueError( f"{self.kind} term fn returned shape {v.shape}, " - f"expected {self._expected_shape}" + f"expected {self.expected_shape(n)}" ) - if self.is_constant: - v.setflags(write=False) - self._cache = v return v - def add_to(self, Sigma: np.ndarray, ctx: StackContext, theta) -> None: - """Add this term's contribution to the stacked ``Sigma`` in place.""" - v = self.value(ctx, theta) - if self.kind == "diag": - ix = self.support - Sigma[ix, ix] += v**2 - elif self.kind == "mode": - Sigma[self._block] += np.outer(v, v) - else: - Sigma[self._block] += v + def _check_count(self, values): + if len(values) != len(self.params): + raise ValueError(f"expected {len(self.params)} params, got {len(values)}") def __repr__(self): names = ", ".join(p.name for p in self.params) - sup = "all" if self.support is None else f"{len(self.support)} pts" - return f"Term(kind={self.kind!r}, params=({names}), support={sup})" - - -# ---------------------------------------------------------------------------- -# Constraint-local covariance: gather-by-identity over the stacked space -# ---------------------------------------------------------------------------- - - -class ConstraintCovariance: - """The stacked covariance of a constraint, assembled from :class:`Term` s. - - Parameters are routed *by identity*: the unique ``Parameter`` objects across - all terms (first-seen order) form the flat parameter vector; each term gathers - its own parameters from that vector. Referencing the *same* ``Parameter`` - object in two terms makes them share one sampled value (case B). - - Parameters - ---------- - terms : sequence of Term - Additive covariance contributions. Terms with ``support=None`` are - bound to the whole stack here. - N : int - Dimension of the stacked vector. - blocks : sequence of np.ndarray, optional - One index array per observation block. When given, :attr:`block_diagonal` - is decided against the true block boundaries. When omitted, only a - covariance whose every term is strictly diagonal - (``couples_offdiagonal == False``) is classified block-diagonal; any - coupling-capable term conservatively forces the dense path — there is no - guessing of block structure from support shape. - active : array_like of int, optional - Indices of the *active* (unmasked) rows of the stack. The full - ``N x N`` matrix is always assembled (terms are authored in the full - space); factorisation and the Mahalanobis distance are restricted to - ``active``. ``None`` means all rows. - - ``terms`` and ``blocks`` are treated as immutable after construction: - :attr:`block_diagonal` and :attr:`is_constant` are decided once, here. - """ - - def __init__(self, terms, N, blocks=None, active=None): - self.terms = list(terms) - self.N = int(N) - for t in self.terms: - if not isinstance(t, Term): - raise TypeError(f"terms must be Term objects, got {type(t).__name__}") - t.bind(self.N) - self._blocks = ( - None if blocks is None else [np.asarray(b, dtype=int) for b in blocks] - ) - if active is None: - self.active = None - else: - active = np.asarray(active, dtype=int) - self.active = None if np.array_equal(active, np.arange(self.N)) else active - self.n_active = self.N if self.active is None else int(self.active.size) - if self._blocks is not None and self.active is not None: - self._active_blocks = [b[np.isin(b, self.active)] for b in self._blocks] - else: - self._active_blocks = self._blocks - - params, index_of = [], {} - for t in self.terms: - for p in t.params: - if id(p) not in index_of: # dedup by identity -> sharing - index_of[id(p)] = len(params) - params.append(p) - self.params = tuple(params) - self._gather = [ - np.array([index_of[id(p)] for p in t.params], dtype=int) for t in self.terms - ] - self._const_cache = None - self._chol_cache = None - self._block_chol_cache = None - - self.block_diagonal = all( - not t.couples_offdiagonal or self._within_one_block(t.support) - for t in self.terms - ) - self.is_constant = self.n_params == 0 and all(t.is_constant for t in self.terms) - - def _within_one_block(self, support) -> bool: - """True if ``support`` lies inside a single known observation block.""" - if self._blocks is None: - return False - support = np.asarray(support, dtype=int) - return any(bool(np.isin(support, b).all()) for b in self._blocks) - - @property - def n_params(self) -> int: - return len(self.params) - - @property - def blocks(self): - """Observation block index arrays, or ``None`` if unknown.""" - return self._blocks - - @property - def uses_block_path(self) -> bool: - """Whether :meth:`stacked_distance` factors block by block - (:meth:`block_cholesky`) rather than the whole active stack - (:meth:`cholesky`).""" - return ( - self.block_diagonal and self._blocks is not None and len(self._blocks) > 1 - ) - - def matrix(self, ctx, *theta) -> np.ndarray: - """Assemble the full stacked covariance matrix (all ``N`` rows). - - Parameters - ---------- - ctx : StackContext - Stacked arrays. ``ctx`` itself may be ``None`` only when every term - is array-valued. When :attr:`is_constant`, ``ctx.ym`` is never read - (it may be ``None``, see :meth:`StackContext.constant`) and the - result is cached. - *theta : float - One value per unique parameter, in :attr:`params` order. - """ - if len(theta) != self.n_params: - raise ValueError(f"expected {self.n_params} params, got {len(theta)}") - if self.is_constant and self._const_cache is not None: - return self._const_cache - theta = np.asarray(theta, dtype=float) - Sigma = np.zeros((self.N, self.N)) - for t, g in zip(self.terms, self._gather): - t.add_to(Sigma, ctx, theta[g]) - if self.is_constant: - # cached arrays are shared across calls; freeze so aliasing - # bugs fail loudly instead of corrupting later evaluations - Sigma.setflags(write=False) - self._const_cache = Sigma - return Sigma - - def active_matrix(self, ctx, *theta) -> np.ndarray: - """The covariance restricted to the active rows/columns.""" - Sigma = self.matrix(ctx, *theta) - if self.active is None: - return Sigma - return Sigma[np.ix_(self.active, self.active)] - - def cholesky(self, ctx, *theta): - """Lower Cholesky factor and log-determinant of the active stacked covariance. - - Cached when :attr:`is_constant`, so a fixed covariance is factored once. - - Returns - ------- - (np.ndarray, float) - ``(L, logdet)`` with ``L`` lower-triangular and - ``logdet = log det Sigma``. - """ - if self.is_constant and self._chol_cache is not None: - return self._chol_cache - L, logdet = chol_logdet(self.active_matrix(ctx, *theta)) - result = (L, logdet) - if self.is_constant: - L.setflags(write=False) - self._chol_cache = result - return result - - def block_cholesky(self, ctx, *theta): - """Per-block ``(L_i, logdet_i)`` factors, aligned with :attr:`blocks`. - - Only meaningful when :attr:`block_diagonal`; cached when - :attr:`is_constant` so a constant block-diagonal covariance is factored - once instead of on every likelihood evaluation. Blocks are restricted to - the active rows; a fully-masked block yields ``(empty, 0.0)``. - - Returns - ------- - tuple of (np.ndarray, float) - One ``(L_i, logdet_i)`` pair per block, ``L_i`` lower-triangular. - """ - if self._blocks is None: - raise ValueError("block_cholesky requires blocks to be set") - if self.is_constant and self._block_chol_cache is not None: - return self._block_chol_cache - Sigma = self.matrix(ctx, *theta) - factors = [] - for ix in self._active_blocks: - if ix.size == 0: - factors.append((np.zeros((0, 0)), 0.0)) - continue - L, logdet = chol_logdet(Sigma[np.ix_(ix, ix)]) - L.setflags(write=False) - factors.append((L, logdet)) - factors = tuple(factors) - if self.is_constant: - self._block_chol_cache = factors - return factors - - def stacked_distance(self, ctx, params=()): - r"""Squared Mahalanobis distance and log-determinant over the active residual. - - Owns the dispatch between the block-diagonal fast path (factor each - block separately, :math:`O(\sum n_i^3)`, cached per block via - :meth:`block_cholesky` when constant) and a single dense Cholesky over - the full stack (cached via :meth:`cholesky` when constant). - - Returns - ------- - (float, float) - ``(d2, logdet)``. - """ - params = tuple(params) - r = ctx.y - ctx.ym - if self.uses_block_path: - factors = self.block_cholesky(ctx, *params) - d2 = 0.0 - logdet = 0.0 - for ix, (L, ld) in zip(self._active_blocks, factors): - if ix.size == 0: - continue - z = sc.linalg.solve_triangular(L, r[ix], lower=True) - d2 += float(np.dot(z, z)) - logdet += ld - return d2, logdet - - L, logdet = self.cholesky(ctx, *params) - if self.active is not None: - r = r[self.active] - z = sc.linalg.solve_triangular(L, r, lower=True) - return float(np.dot(z, z)), logdet + return f"Term(kind={self.kind!r}, params=({names}), on={self.on!r})" # ---------------------------------------------------------------------------- @@ -622,7 +269,7 @@ def exp_growth(scale: float = 1.0, base=ones): """Parametric basis ``base(c) * exp(slope * x / scale)``. Takes one basis parameter, ``slope``; use with - ``noise_term(..., basis=exp_growth(np.pi), basis_params=(slope,))``. + ``noise(..., basis=exp_growth(np.pi), basis_params=(slope,))``. """ def basis(c: TermContext, slope: float) -> np.ndarray: @@ -648,7 +295,7 @@ def amplitude(c: TermContext, log_amplitude: float, slope: float) -> np.ndarray: # ---------------------------------------------------------------------------- -# Term factory helpers (the assembly-time builders) +# Term factory helpers # ---------------------------------------------------------------------------- @@ -693,9 +340,7 @@ def _coefficient(parameter, log): return (parameter,), (lambda values: values[0]) -def _scaled_term( - kind, parameter, log, basis, basis_params=(), *, support=None, coords=None -): +def _scaled_term(kind, parameter, log, basis, basis_params=(), *, on=None, coords=None): """``c * basis(ctx, *basis_values)`` as a term of the given kind.""" cparams, coef = _coefficient(parameter, log) basis_params = tuple(basis_params) @@ -705,55 +350,53 @@ def fn(c, *values): b = basis(c, *values[nc:]) if callable(basis) else basis return coef(values[:nc]) * _full(b, len(c)) - return Term(fn, cparams + basis_params, kind=kind, support=support, coords=coords) + return Term(fn, cparams + basis_params, kind=kind, on=on, coords=coords) -def statistical_term(stat_err, support=None) -> Term: - """Always-on, genuinely uncorrelated statistical diagonal ``diag(stat_err**2)``.""" - return Term(np.asarray(stat_err, dtype=float), kind="diag", support=support) +def statistical(y_err, on=None) -> Term: + """Always-on, genuinely uncorrelated statistical diagonal ``diag(y_err**2)``.""" + return Term(np.asarray(y_err, dtype=float), kind="diag", on=on) -def offset_term( - magnitude=None, parameter=None, mask=None, log=True, support=None -) -> Term: +def offset(magnitude=None, parameter=None, mask=None, log=True, on=None) -> Term: """A correlated absolute-offset systematic ``outer(omega, omega)``. With ``magnitude`` it is a fixed (data-given) rank-one mode; with ``parameter`` it is a free nuisance magnitude (``c = exp(theta)`` when ``log``). """ if magnitude is None and parameter is None: - raise ValueError("offset_term requires a magnitude and/or a parameter") + raise ValueError("offset requires a magnitude and/or a parameter") mag = _masked(1.0 if magnitude is None else magnitude, mask=mask) def basis(c): return _full(mag, len(c)) if parameter is None: - return Term(basis, kind="mode", support=support, constant=True) - return _scaled_term("mode", parameter, log, basis, support=support) + return Term(basis, kind="mode", on=on, constant=True) + return _scaled_term("mode", parameter, log, basis, on=on) -def normalization_term( - magnitude=None, parameter=None, mask=None, log=True, support=None -) -> Term: +def normalization(magnitude=None, parameter=None, mask=None, log=True, on=None) -> Term: """A correlated normalisation systematic ``outer(eta * ym, eta * ym)``. With ``magnitude`` it is a fixed fractional normalisation uncertainty; with ``parameter`` the magnitude eta is a free nuisance (``c = exp(theta)`` when - ``log``). In both cases the mode scales with the model prediction ``ym``. + ``log``). In both cases the mode scales with the model *prediction* ``ym``, + never with the data: that is what keeps the fit free of Peelle's Pertinent + Puzzle (recipe 27). """ if magnitude is None and parameter is None: - raise ValueError("normalization_term requires a magnitude and/or a parameter") + raise ValueError("normalization requires a magnitude and/or a parameter") mag = _masked(1.0 if magnitude is None else magnitude, mask=mask) def basis(c): return _full(mag, len(c)) * c.ym - return _scaled_term("mode", parameter, log, basis, support=support) + return _scaled_term("mode", parameter, log, basis, on=on) -def noise_term( - parameter, log=True, basis=None, basis_params=(), support=None, coords=None +def noise( + parameter, log=True, basis=None, basis_params=(), on=None, coords=None ) -> Term: """Unknown statistical noise ``diag((epsilon * basis)**2)``. @@ -761,12 +404,11 @@ def noise_term( ``basis(c, *basis_values)`` — e.g. :func:`exp_growth` with ``basis_params=(slope,)`` for noise growing along ``x``. - This term is **additive**: a :class:`~rxmc.constraint.Constraint` already adds - each observation's reported statistical diagonal, so the assembled covariance - is ``diag(y_stat_err**2 + epsilon**2)``. To make the inferred noise *replace* - the reported statistics, build the - ``Observation`` with zero ``y_stat_err`` or pass ``include_statistical_term=False`` - to the ``Constraint``. + This term is **additive**: a constraint with ``statistical=True`` (the + default) already adds each comparison's reported diagonal, so the assembled + covariance is ``diag(y_err**2 + epsilon**2)``. To make the inferred noise + *replace* the reported statistics pass ``statistical=False`` to the + constraint. """ return _scaled_term( "diag", @@ -774,41 +416,40 @@ def noise_term( log, ones if basis is None else basis, basis_params, - support=support, + on=on, coords=coords, ) -def noise_fraction_term(parameter, log=True, support=None) -> Term: +def noise_fraction(parameter, log=True, on=None) -> Term: """Unknown fractional noise ``diag((epsilon * ym)**2)``. - **Additive** on top of the reported statistical diagonal (see - :func:`noise_term` for how to get replace-semantics instead). + **Additive** on top of the reported statistical diagonal (see :func:`noise` + for how to get replace-semantics instead). """ - return _scaled_term("diag", parameter, log, ym, support=support) + return _scaled_term("diag", parameter, log, ym, on=on) -def model_error_term(parameter, averaging=True, log=True, support=None) -> Term: +def model_error(parameter, averaging=True, log=True, on=None) -> Term: """Unknown uncorrelated model error ``diag((gamma * z)**2)``. ``z = 0.5 * (y + ym)`` when ``averaging`` (stabilises when ``ym`` is near zero), else ``z = ym``. """ basis = _AVERAGING if averaging else ym - return _scaled_term("diag", parameter, log, basis, support=support) + return _scaled_term("diag", parameter, log, basis, on=on) -def systematic_term( - parameter, basis, log=True, basis_params=(), support=None, coords=None +def systematic( + parameter, basis, log=True, basis_params=(), on=None, coords=None ) -> Term: """A correlated mode ``outer(s * u, s * u)`` with a user basis ``u = basis(c, ...)``. - :func:`offset_term` and :func:`normalization_term` are its ``ones``/``ym`` - special cases; use e.g. ``basis=x_basis(np.pi)`` for a mode growing with - angle. + :func:`offset` and :func:`normalization` are its ``ones``/``ym`` special + cases; use e.g. ``basis=x_basis(np.pi)`` for a mode growing with angle. """ return _scaled_term( - "mode", parameter, log, basis, basis_params, support=support, coords=coords + "mode", parameter, log, basis, basis_params, on=on, coords=coords ) @@ -819,29 +460,32 @@ def _kernel_params(kernel, prefix) -> list: if hp.fixed: continue if hp.n_elements == 1: - params.append(Parameter(f"{prefix}_{hp.name}", float, latex_name=hp.name)) + params.append(Parameter(f"{prefix}_{hp.name}", latex=hp.name)) else: params.extend( - Parameter( - f"{prefix}_{hp.name}_{i}", float, latex_name=f"{hp.name}[{i}]" - ) + Parameter(f"{prefix}_{hp.name}_{i}", latex=f"{hp.name}[{i}]") for i in range(hp.n_elements) ) return params -def kernel_term( +def _n_free_elements(kernel) -> int: + return sum(hp.n_elements for hp in kernel.hyperparameters if not hp.fixed) + + +def kernel( kernel, coords=None, amplitude=None, amplitude_params=(), jitter=1e-10, prefix="discrepancy", - support=None, + params=None, + on=None, ) -> Term: """A Gaussian-process kernel ``a a^T * K(x, x; theta)`` over the support. - One :class:`~rxmc.params.Parameter` is auto-derived per *free* kernel + One :class:`~rxmc.params.Parameter` is derived per *free* kernel hyperparameter **element** (sampled in sklearn's log-theta space): an anisotropic hyperparameter (``n_elements > 1``) contributes that many parameters. ``amplitude_params`` follow the kernel parameters. @@ -864,23 +508,32 @@ def kernel_term( jitter : float, optional Added to the diagonal after scaling, for numerical stability. prefix : str, optional - Name prefix of the auto-derived kernel parameters. - support : array_like of int, optional + Name prefix of the derived kernel parameters. Two kernel terms with + derived names and the same prefix fail to compile on the duplicate + name: pass distinct prefixes, or ``params=`` to share one kernel. + params : sequence of Parameter, optional + The hyperparameter objects themselves, one per free element in + ``kernel.theta`` order. Pass the same objects to several ``kernel`` + calls to share hyperparameters between comparisons. + on : optional See :class:`Term`. """ - kparams = _kernel_params(kernel, prefix) - nk = len(kparams) + nk = _n_free_elements(kernel) + if params is None: + kparams = _kernel_params(kernel, prefix) + else: + kparams = list(params) + if len(kparams) != nk: + raise ValueError( + f"kernel has {nk} free hyperparameter element(s) but params= " + f"has {len(kparams)}" + ) amplitude_params = tuple(amplitude_params) - # with no free kernel hyperparameters and parameter-free coordinates, - # K(x, x) is invariant per constraint: build it once fixed_K = nk == 0 and not as_transform(coords).params - K_cache = [] def fn(c, *values): if fixed_K: - if not K_cache: - K_cache.append(np.asarray(kernel(as_2d(c.x)), dtype=float)) - K = K_cache[0] + K = np.asarray(kernel(as_2d(c.x)), dtype=float) else: K = kernel.clone_with_theta(np.asarray(values[:nk], dtype=float))( as_2d(c.x) @@ -898,7 +551,7 @@ def fn(c, *values): fn, tuple(kparams) + amplitude_params, kind="matrix", - support=support, + on=on, coords=coords, constant=nk == 0 and not amplitude_params and not callable(amplitude), ) diff --git a/test/test_terms.py b/test/test_terms.py new file mode 100644 index 0000000..69f9ff7 --- /dev/null +++ b/test/test_terms.py @@ -0,0 +1,526 @@ +"""Unit tests for the stateless covariance terms (:mod:`rxmc.terms`).""" + +import numpy as np +import pytest +from sklearn.gaussian_process.kernels import RBF, ConstantKernel, Matern, WhiteKernel + +from helpers import assemble_dense +from rxmc import Parameter +from rxmc.terms import ( + Term, + TermContext, + averaging, + constant_amplitude, + exp_growth, + exp_growth_amplitude, + kernel, + model_error, + noise, + noise_fraction, + normalization, + offset, + ones, + statistical, + systematic, + x_basis, + ym, +) +from rxmc.transforms import Transform + + +def dense(terms, x, y, ym_, values=()): + return assemble_dense(terms, x, y, ym_, values) + + +# ---------------------------------------------------------------------------- +# Term +# ---------------------------------------------------------------------------- + + +class TestTermKinds: + def test_diag_array_squares_std(self): + t = Term(np.array([1.0, 2.0, 3.0]), kind="diag") + S = dense([t], np.zeros(3), np.zeros(3), np.zeros(3)) + assert np.allclose(S, np.diag([1.0, 4.0, 9.0])) + assert t.is_constant + assert not t.couples_offdiagonal + + def test_mode_array_outer(self): + v = np.array([1.0, 2.0]) + t = Term(v, kind="mode") + assert np.allclose(dense([t], np.zeros(2), np.zeros(2), None), np.outer(v, v)) + assert t.couples_offdiagonal + + def test_matrix_array_passthrough(self): + m = np.array([[2.0, 0.5], [0.5, 3.0]]) + t = Term(m) + assert np.allclose(dense([t], np.zeros(2), np.zeros(2), None), m) + + def test_bad_kind_raises(self): + with pytest.raises(ValueError, match="kind"): + Term(np.ones(2), kind="rank1") + + def test_scalar_broadcast_raises(self): + # a (1, 1) matrix on a length-3 support must not broadcast silently + t = Term([[0.04]]) + with pytest.raises(ValueError, match="expects shape"): + t.value(np.zeros(3), np.zeros(3), np.zeros(3)) + + def test_wrong_length_vector_raises(self): + with pytest.raises(ValueError, match="expects shape"): + Term(np.ones(2), kind="diag").value(np.zeros(3), np.zeros(3), np.zeros(3)) + + def test_asymmetric_matrix_raises(self): + with pytest.raises(ValueError, match="symmetric"): + Term(np.array([[1.0, 0.2], [0.0, 1.0]])) + + def test_non_square_matrix_raises(self): + with pytest.raises(ValueError, match="square"): + Term(np.ones((2, 3))) + + def test_array_with_params_raises(self): + with pytest.raises(ValueError, match="array-valued"): + Term(np.ones(2), (Parameter("p"),), kind="diag") + + def test_non_parameter_raises(self): + with pytest.raises(TypeError, match="Parameter"): + Term(lambda c, a: ones(c), ("a",), kind="diag") + + def test_callable_sees_context_and_values(self): + seen = {} + + def fn(c, a, b): + seen["c"] = c + return a * c.ym + b * c.y + + pa, pb = Parameter("a"), Parameter("b") + t = Term(fn, (pa, pb), kind="diag") + x, y, ym_ = np.array([1.0, 2.0]), np.array([2.0, 3.0]), np.array([2.5, 3.5]) + v = t.value(x, y, ym_, 2.0, 1.0) + c = seen["c"] + assert isinstance(c, TermContext) + assert np.allclose(c.x, x) and np.allclose(c.ym, ym_) and len(c) == 2 + assert np.allclose(v, 2.0 * ym_ + y) + + def test_callable_wrong_shape_raises(self): + t = Term(lambda c: np.ones(len(c) + 1), kind="diag") + with pytest.raises(ValueError, match="returned shape"): + t.value(np.zeros(2), np.zeros(2), np.zeros(2)) + + def test_wrong_param_count_raises(self): + t = Term(lambda c, a: a * ones(c), (Parameter("a"),), kind="diag") + with pytest.raises(ValueError, match="expected 1 params"): + t.value(np.zeros(1), np.zeros(1), np.zeros(1)) + + def test_constant_with_params_raises(self): + with pytest.raises(ValueError, match="constant"): + Term(lambda c, a: ones(c), (Parameter("a"),), kind="diag", constant=True) + + def test_constant_term_may_read_x_and_is_evaluated_without_ym(self): + x = np.linspace(0.0, 2.0, 4) + t = Term(lambda c: 0.1 * c.x, kind="diag", constant=True) + assert t.is_constant + np.testing.assert_allclose(t.value(x, np.zeros(4), None), 0.1 * x) + bad = Term(lambda c: 0.1 * c.ym, kind="diag", constant=True) + with pytest.raises(TypeError): + bad.value(x, np.zeros(4), None) + + def test_meta_accessor(self): + t = Term(lambda c: c.meta("E") * ones(c), kind="diag", constant=True) + v = t.value(np.zeros(2), np.zeros(2), None, meta={"E": np.array([5.0, 5.0])}) + assert np.allclose(v, 5.0) + with pytest.raises(KeyError, match="Dataset.meta"): + t.value(np.zeros(2), np.zeros(2), None) + + def test_repr(self): + assert "log_e" in repr(noise(Parameter("log_e"))) + + +class TestTermCoords: + def test_coords_callable_applied_to_x(self): + t = Term(lambda c: c.x, kind="diag", coords=lambda x: 2 * x) + assert np.allclose(t.value(np.array([1.0, 3.0]), np.zeros(2), None), [2.0, 6.0]) + + def test_parametric_coords_params_appended(self): + pk = Parameter("k") + coords = Transform(lambda x, k: k * x, (pk,)) + pa = Parameter("a") + t = Term(lambda c, a: a * c.x, (pa,), kind="diag", coords=coords) + assert t.params == (pa, pk) + v = t.value(np.array([1.0, 2.0]), np.zeros(2), None, 3.0, 2.0) # a=3, k=2 + assert np.allclose(v, 6.0 * np.array([1.0, 2.0])) + + def test_coords_array_2d_reaches_kernel(self): + k = RBF(length_scale=1.0) + X = np.array([[0.0, 0.0], [1.0, 1.0], [2.0, 0.0]]) + t = kernel(k, coords=lambda x: X, jitter=0.0) + S = t.value(np.zeros(3), np.zeros(3), np.zeros(3), *k.theta) + assert np.allclose(S, k(X)) + + +# ---------------------------------------------------------------------------- +# Factories +# ---------------------------------------------------------------------------- + + +class TestFactories: + def setup_method(self): + self.x = np.array([0.5, 1.0, 1.5]) + self.y = np.array([1.0, 2.0, 3.0]) + self.ym = np.array([1.1, 1.9, 3.2]) + self.stat = np.array([0.1, 0.2, 0.3]) + + def S(self, terms, values=()): + return dense(terms, self.x, self.y, self.ym, values) + + def test_statistical_only(self): + assert np.allclose(self.S([statistical(self.stat)]), np.diag(self.stat**2)) + + def test_unknown_noise(self): + assert np.allclose( + self.S([noise(Parameter("e"))], [(np.log(0.4),)]), 0.16 * np.eye(3) + ) + assert np.allclose( + self.S([noise(Parameter("e"), log=False)], [(0.4,)]), 0.16 * np.eye(3) + ) + + def test_unknown_noise_fraction(self): + S = self.S([noise_fraction(Parameter("e"))], [(np.log(0.4),)]) + assert np.allclose(S, np.diag((0.4 * self.ym) ** 2)) + + def test_unknown_normalization_error(self): + S = self.S([normalization(parameter=Parameter("n"))], [(np.log(0.05),)]) + assert np.allclose(S, 0.05**2 * np.outer(self.ym, self.ym)) + + def test_unknown_model_error(self): + S = self.S([model_error(Parameter("g"), averaging=True)], [(np.log(0.1),)]) + z = 0.5 * (self.y + self.ym) + assert np.allclose(S, np.diag((0.1 * z) ** 2)) + S = self.S([model_error(Parameter("g"), averaging=False)], [(np.log(0.1),)]) + assert np.allclose(S, np.diag((0.1 * self.ym) ** 2)) + + def test_fixed_normalization_systematic(self): + for mag in (0.05, np.array(0.05)): + S = self.S([normalization(magnitude=mag)]) + assert np.allclose(S, 0.05**2 * np.outer(self.ym, self.ym)) + + def test_fixed_offset_systematic(self): + t = offset(magnitude=0.2) + assert t.is_constant + assert np.allclose(self.S([t]), 0.04 * np.ones((3, 3))) + v = np.array([0.1, 0.2, 0.3]) + assert np.allclose(self.S([offset(magnitude=v)]), np.outer(v, v)) + + def test_fixed_offset_is_evaluated_without_ym(self): + t = offset(magnitude=0.2) + np.testing.assert_allclose(t.value(self.x, self.y, None), 0.2) + + def test_masked_magnitudes(self): + m = np.array([1.0, 0.0, 1.0]) + S = self.S([offset(magnitude=0.2, mask=m)]) + assert np.allclose(S, np.outer(0.2 * m, 0.2 * m)) + S = self.S([normalization(parameter=Parameter("n"), mask=m)], [(np.log(0.5),)]) + v = 0.5 * m * self.ym + assert np.allclose(S, np.outer(v, v)) + + def test_length_one_magnitude_or_mask_raises(self): + with pytest.raises(ValueError, match="shape"): + self.S([offset(magnitude=np.array([0.2]))]) + with pytest.raises(ValueError, match="shape"): + self.S([offset(magnitude=0.2, mask=np.array([1.0]))]) + with pytest.raises(ValueError, match="shape"): + self.S( + [normalization(parameter=Parameter("n"), mask=np.array([1.0]))], + [(0.0,)], + ) + + def test_magnitude_length_mismatch_raises(self): + with pytest.raises(ValueError): + self.S([offset(magnitude=np.ones(2))]) + + def test_requires_magnitude_or_parameter(self): + with pytest.raises(ValueError): + offset() + with pytest.raises(ValueError): + normalization() + + def test_systematic_with_basis(self): + S = self.S([systematic(Parameter("s"), basis=x_basis(2.0))], [(np.log(3.0),)]) + v = 3.0 * self.x / 2.0 + assert np.allclose(S, np.outer(v, v)) + + def test_parametric_basis(self): + e, sl = Parameter("log e"), Parameter("slope") + t = noise(e, basis=exp_growth(np.pi), basis_params=(sl,)) + assert t.params == (e, sl) + assert np.allclose(self.S([t], [(np.log(0.3), 0.0)]), 0.09 * np.eye(3)) + S = self.S([t], [(np.log(0.3), 2.0)]) + assert np.allclose(S, np.diag((0.3 * np.exp(2.0 * self.x / np.pi)) ** 2)) + t = noise(e, basis=exp_growth(np.pi, base=ym), basis_params=(sl,)) + S = self.S([t], [(np.log(0.3), 1.0)]) + assert np.allclose(S, np.diag((0.3 * self.ym * np.exp(self.x / np.pi)) ** 2)) + + def test_old_observation_covariance_equivalence(self): + off, norm = 0.2, 0.05 + terms = [ + statistical(self.stat), + offset(magnitude=off), + normalization(magnitude=norm), + ] + old = ( + np.diag(self.stat**2) + + np.outer(off * np.ones(3), off * np.ones(3)) + + norm**2 * np.outer(self.ym, self.ym) + ) + assert np.allclose(self.S(terms), old) + + def test_bases(self): + c = TermContext(x=self.x, y=self.y, ym=self.ym) + assert np.allclose(ones(c), 1.0) + assert np.allclose(ym(c), self.ym) + assert np.allclose(averaging(c), 0.5 * (self.y + self.ym)) + + +class TestKernel: + def test_params_match_theta_length_isotropic(self): + k = ConstantKernel(1.0) * RBF(length_scale=1.0) + WhiteKernel(1e-6) + t = kernel(k) + assert len(t.params) == len(k.theta) + assert not t.is_constant + + def test_params_anisotropic(self): + k = ConstantKernel(1.0) * RBF(length_scale=[1.0, 1.0]) + t = kernel(k) + assert len(t.params) == len(k.theta) + x2d = np.array([[0.0, 0.0], [1.0, 0.5], [2.0, 1.0]]) + S = t.value(x2d, np.zeros(3), np.zeros(3), *k.theta) + assert np.all(np.isfinite(S)) + + def test_fixed_kernel_is_constant(self): + t = kernel(RBF(length_scale=1.0, length_scale_bounds="fixed")) + assert t.params == () and t.is_constant + + def test_shared_hyperparameters_via_params(self): + ell = Parameter("gp_length") + t1, t2 = kernel(RBF(1.0), params=[ell]), kernel(RBF(1.0), params=[ell]) + assert t1.params == (ell,) and t2.params == (ell,) + with pytest.raises(ValueError, match="free hyperparameter"): + kernel(RBF(1.0), params=[ell, Parameter("extra")]) + + def test_constant_amplitude_reproduces_constant_kernel(self): + x = np.linspace(0.0, 2.0, 5) + A, la = 0.7, Parameter("log A") + t = kernel( + RBF(1.0), amplitude=constant_amplitude, amplitude_params=(la,), jitter=0.0 + ) + assert [p.name for p in t.params] == ["discrepancy_length_scale", "log A"] + S = dense([t], x, np.zeros(5), np.zeros(5), [(0.0, np.log(A))]) + ref = ConstantKernel(A**2, constant_value_bounds="fixed") * RBF(1.0) + np.testing.assert_allclose(S, ref(x[:, None])) + + def test_exp_growth_amplitude_and_coords(self): + x = np.linspace(0.1, 3.0, 4) + la, sl = Parameter("log A"), Parameter("slope") + + def q(x): + return 2.0 * np.sin(x / 2) + + t = kernel( + Matern(1.0, nu=2.5), + coords=q, + amplitude=exp_growth_amplitude(np.pi), + amplitude_params=(la, sl), + jitter=0.0, + ) + S = dense([t], x, np.zeros(4), np.zeros(4), [(np.log(0.5), np.log(0.3), 1.5)]) + a = 0.3 * np.exp(1.5 * q(x) / np.pi) # the amplitude sees the transformed x + np.testing.assert_allclose( + S, np.outer(a, a) * Matern(0.5, nu=2.5)(q(x)[:, None]) + ) + + def test_duplicate_coords_factorizable_with_jitter(self): + x = np.array([0.0, 0.0, 1.0]) + t = kernel(RBF(1.0), jitter=1e-8) + S = t.value(x, np.zeros(3), np.zeros(3), 0.0) + assert np.all(np.isfinite(np.linalg.cholesky(S))) + + +# ---------------------------------------------------------------------------- +# The alpha+Ca error-model ladder as one-line term lists +# ---------------------------------------------------------------------------- + + +class TestStudyForms: + """Each error model of the alpha+Ca study is one term list; compare to the + hand-rolled dense covariance from that study.""" + + def setup_method(self): + rng = np.random.default_rng(1) + n = 12 + self.x = np.sort(rng.uniform(0.2, 3.0, n)) # radians + self.y = rng.uniform(0.1, 1.5, n) + self.ym = self.y + rng.normal(0.0, 0.1, n) + self.X = np.pi + self.log_err, self.log_slope = Parameter("log_err"), Parameter("log_err_slope") + self.log_sys, self.log_amp = Parameter("log_sys"), Parameter("log_amp") + self.err, self.slope, self.sys, self.amp = 0.05, 1.3, 0.04, 0.2 + self.k = 2.7 + + def S(self, terms, values): + return dense(terms, self.x, self.y, self.ym, values) + + def xdeg(self): + return self.x / self.X + + def test_L0(self): + S = self.S([noise(self.log_err)], [(np.log(self.err),)]) + assert np.allclose(S, self.err**2 * np.eye(len(self.x))) + + def test_E0_linear_space(self): + S = self.S([noise_fraction(self.log_err)], [(np.log(self.err),)]) + assert np.allclose(S, np.diag((self.err * self.ym) ** 2)) + + def test_L1(self): + t = noise( + self.log_err, basis=exp_growth(self.X), basis_params=(self.log_slope,) + ) + S = self.S([t], [(np.log(self.err), self.slope)]) + sigma = self.err * np.exp(self.slope * self.xdeg()) + assert np.allclose(S, np.diag(sigma**2)) + + def test_L2_rank_one_over_theta(self): + terms = [noise(self.log_err), systematic(self.log_sys, basis=x_basis(self.X))] + S = self.S(terms, [(np.log(self.err),), (np.log(self.sys),)]) + u = self.xdeg() + assert np.allclose( + S, self.err**2 * np.eye(len(u)) + self.sys**2 * np.outer(u, u) + ) + + def test_L2n_and_L2y(self): + v = [(np.log(self.err),), (np.log(self.sys),)] + S = self.S([noise(self.log_err), offset(parameter=self.log_sys)], v) + assert np.allclose(S, self.err**2 * np.eye(len(self.x)) + self.sys**2) + S = self.S([noise(self.log_err), normalization(parameter=self.log_sys)], v) + assert np.allclose( + S, + self.err**2 * np.eye(len(self.x)) + + self.sys**2 * np.outer(self.ym, self.ym), + ) + + def test_L12(self): + terms = [ + noise( + self.log_err, basis=exp_growth(self.X), basis_params=(self.log_slope,) + ), + systematic(self.log_sys, basis=x_basis(self.X)), + ] + S = self.S(terms, [(np.log(self.err), self.slope), (np.log(self.sys),)]) + sigma = self.err * np.exp(self.slope * self.xdeg()) + u = self.xdeg() + assert np.allclose(S, np.diag(sigma**2) + self.sys**2 * np.outer(u, u)) + + def test_Lgp_matern_in_theta(self): + ell = 0.3 + gp = kernel( + Matern(1.0, nu=2.5), + coords=lambda x: x / self.X, + amplitude=constant_amplitude, + amplitude_params=(self.log_amp,), + jitter=0.0, + prefix="gp", + ) + S = self.S( + [noise(self.log_err), gp], + [(np.log(self.err),), (np.log(ell), np.log(self.amp))], + ) + u = self.xdeg() + K = self.amp**2 * Matern(ell, nu=2.5)(u[:, None]) + assert np.allclose(S, self.err**2 * np.eye(len(u)) + K) + + def test_Lgpn_angle_growing_amplitude(self): + ell = 0.3 + gp = kernel( + Matern(1.0, nu=2.5), + coords=lambda x: x / self.X, + amplitude=exp_growth_amplitude(1.0), + amplitude_params=(self.log_amp, self.log_slope), + jitter=0.0, + ) + S = self.S( + [noise(self.log_err), gp], + [(np.log(self.err),), (np.log(ell), np.log(self.amp), self.slope)], + ) + u = self.xdeg() + a = self.amp * np.exp(self.slope * u) + K = np.outer(a, a) * Matern(ell, nu=2.5)(u[:, None]) + assert np.allclose(S, self.err**2 * np.eye(len(u)) + K) + + def test_LKp_kernel_in_momentum_transfer(self): + # b^2 I + s^2 11^T + a(q) a(q') RBF(|q - q'| / l_q), a = A q^(r/2) + log_b, log_s, r_pow = Parameter("log_b"), Parameter("log_s"), Parameter("r") + b, s, lq, r = 0.05, 0.05, 1.2, 0.8 + q = 2.0 * self.k * np.sin(self.x / 2) + gp = kernel( + RBF(1.0), + coords=lambda x: 2.0 * self.k * np.sin(x / 2), + amplitude=lambda c, lA, r: np.exp(lA) * c.x ** (r / 2), + amplitude_params=(self.log_amp, r_pow), + jitter=0.0, + prefix="gpq", + ) + S = self.S( + [noise(log_b), offset(parameter=log_s), gp], + [(np.log(b),), (np.log(s),), (np.log(lq), np.log(self.amp), r)], + ) + a = self.amp * q ** (r / 2) + K = np.outer(a, a) * RBF(lq)(q[:, None]) + ref = b**2 * np.eye(len(q)) + s**2 * np.ones((len(q), len(q))) + K + assert np.allclose(S, ref) + + def test_custom_term_direct(self): + # anything the factories cannot say is a one-line Term + e, sl = Parameter("e"), Parameter("l") + t = Term( + lambda c, e, l: np.exp(e) * np.exp(l * c.x / np.pi), (e, sl), kind="diag" + ) + S = self.S([t], [(np.log(self.err), self.slope)]) + sigma = self.err * np.exp(self.slope * self.xdeg()) + assert np.allclose(S, np.diag(sigma**2)) + + +# ---------------------------------------------------------------------------- +# Term-level halves of recipes (the rest of each recipe needs a Problem) +# ---------------------------------------------------------------------------- + + +class TestRecipeHalves: + def test_recipe_19_bring_your_own_term(self): + x = np.linspace(0.0, 1.0, 4) + C = np.exp(-np.abs(x[:, None] - x[None, :])) # symmetric, PD + fixed = Term(C) + assert fixed.is_constant and np.allclose(fixed.value(x, np.zeros(4), None), C) + sig = np.array([0.1, 0.2, 0.3, 0.4]) + stat = Term(sig, kind="diag") + assert np.allclose(dense([stat], x, np.zeros(4), None), np.diag(sig**2)) + e, sl = Parameter("log_e"), Parameter("slope") + custom = Term( + lambda c, e, l: np.exp(e) * np.exp(l * c.x / np.pi), (e, sl), kind="diag" + ) + helper = noise(e, basis=exp_growth(np.pi), basis_params=(sl,)) + v = (np.log(0.3), 1.1) + np.testing.assert_allclose( + custom.value(x, np.zeros(4), None, *v), + helper.value(x, np.zeros(4), None, *v), + ) + + def test_recipe_27_normalization_mode_is_built_from_the_prediction(self): + x = np.linspace(0.0, 1.0, 3) + y, ym_ = np.array([1.0, 2.0, 3.0]), np.array([1.2, 1.8, 3.3]) + s = 0.1 + right = normalization(magnitude=s) + wrong = Term( + lambda c: s * c.y, kind="mode", constant=True + ) # data-built: Peelle + np.testing.assert_allclose(right.value(x, y, ym_), s * ym_) + np.testing.assert_allclose(wrong.value(x, y, ym_), s * y) + assert not np.allclose(right.value(x, y, ym_), wrong.value(x, y, ym_)) From c5835e71101e02e9059986cd2a3c8c75506cef98 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 18:35:25 -0400 Subject: [PATCH 09/75] Export terms from the package --- src/rxmc/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/rxmc/__init__.py b/src/rxmc/__init__.py index 5111ba6..f7a573c 100644 --- a/src/rxmc/__init__.py +++ b/src/rxmc/__init__.py @@ -5,6 +5,7 @@ """ from . import likelihood as likelihood +from . import terms as terms from . import transforms as transforms from . import units as units from .params import Parameter as Parameter @@ -14,4 +15,4 @@ except ImportError: # pragma: no cover - source checkout without a build __version__ = "0+unknown" -__all__ = ["__version__", "Parameter", "likelihood", "transforms", "units"] +__all__ = ["__version__", "Parameter", "likelihood", "terms", "transforms", "units"] From 5e31869dcd6674e8a7e972346174fdd535abd270 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 18:53:51 -0400 Subject: [PATCH 10/75] Drop pint: a fixed unit-label table replaces the registry x4i3 converts every EXFOR cross section to barns while parsing and exfor_tools labels the result from a three-entry table, so measurement unit labels come from a small fixed vocabulary. parse_unit(label) maps that vocabulary (plus the obvious spellings) to a factor into the internal unit and a quantity kind, and rejects anything else loudly. The 0.x pint path could not parse the real labels (barns/ster, b/Sr); it only worked for hand-built measurements. --- docs/groundup_design.md | 18 ++++++---- requirements.txt | 1 - src/rxmc/units.py | 75 ++++++++++++++++++++++++++++++++++------- test/test_units.py | 39 ++++++++++++++++----- 4 files changed, 105 insertions(+), 28 deletions(-) diff --git a/docs/groundup_design.md b/docs/groundup_design.md index fb9ce02..1a8ed77 100644 --- a/docs/groundup_design.md +++ b/docs/groundup_design.md @@ -132,10 +132,14 @@ composed onto a `Model`, and the coordinates a `Term` is evaluated in. ### 2.3 `units.py` and `data.py` ```python -# units.py — the one pint registry and the unit contract -ureg = UnitRegistry() -XS_UNIT = ureg.barn / ureg.steradian # every cross section stored in b/sr -RUTHERFORD_UNIT = ureg.millibarn / ureg.steradian # what jitr reports +# units.py — the unit contract, without a unit library +XS_UNIT = "b/sr" # every cross section stored in b/sr +RUTHERFORD_UNIT = "mb/sr" # what jitr reports +MB_PER_B = 1000.0 +def parse_unit(label) -> tuple[float, str] # (factor into the internal unit, kind) +# x4i3 converts every EXFOR cross section to barns while parsing and exfor_tools +# labels the result "barns/ster", "b" or "unitless"; parse_unit maps that fixed +# vocabulary (plus the obvious spellings) and rejects anything else loudly. MB_PER_B = 1000.0 DEFAULT_LMAX = 20 def check_angle_grid(angles_rad, name) -> None @@ -162,7 +166,7 @@ workspace, no identity key. `from_measurement` is the single EXFOR adapter. It reads the `exfor_tools.Distribution` fields (`x, y, Einc, quantity, y_units, statistical_err, systematic_norm_err, systematic_offset_err, subentry`), -converts units once through `ureg`, divides every dimensionful error by the +converts units once through `parse_unit`, divides every dimensionful error by the conversion factor `norm`, passes the fractional normalisation error through untouched, converts angles to radians, and fills `meta` with `reaction`, `Elab`, `quantity`, `k` (and `ExIAS` for the (p,n) channel). The @@ -757,7 +761,7 @@ What comes across from `src/rxmc` on `api_generalisation`, by file. | `transforms.py` | `Transform` (minus `contextual`, `_unpack`), `as_transform`, `identity`, `log`, `exp`, `_safe_log`, `_reciprocal`, `scale` | `transforms.py` | | `likelihood_model.py` | `Likelihood`, `GaussianLikelihood`→`Gaussian`, `StudentT`, `Chi2`, `log_likelihood` | `likelihood.py` | | `covariance.py` | `TermContext`, `chol_logdet`, `as_2d`, bases `ones`, `ym`, `averaging`, `x_basis`, `exp_growth`, `constant_amplitude`, `exp_growth_amplitude`; helpers `_masked`, `_full`, `_coefficient`, `_scaled_term`, `_kernel_params`; factories `statistical_term`, `offset_term`, `normalization_term`, `noise_term`, `noise_fraction_term`, `model_error_term`, `systematic_term`, `kernel_term` (drop the `_term` suffix, `support=`→`on=`) | `terms.py` | -| `observation_from_measurement.py` | `ureg`, `XS_UNIT`, `RUTHERFORD_UNIT`, `MB_PER_B`, `DEFAULT_LMAX`, `check_angle_grid`, `measurement_kwargs` | `units.py`, `data.py` | +| `observation_from_measurement.py` | `XS_UNIT`, `RUTHERFORD_UNIT`, `MB_PER_B`, `DEFAULT_LMAX`, `check_angle_grid`, `measurement_kwargs`; the pint registry is replaced by a fixed label table | `units.py`, `data.py` | | `elastic_diffxs_observation.py` | `set_up_solver`, the `calculate_normalization` conversion table, `momentum_transfer` | `reactions/elastic.py`, `data.py` | | `ias_pn_observation.py` | `set_up_solver` | `reactions/ias.py` | | `elastic_diffxs_model.py` | `_xs` body, `extract_dXS_dA`, `extract_dXS_dRuth`, `extract_Ay` | `reactions/elastic.py` | @@ -852,7 +856,7 @@ examples/ 9 notebooks (§7) docs/ design.md rewritten from this document once the code lands ``` -Runtime dependencies: `numpy`, `scipy`, `pint`, `jitr>=3.0`, +Runtime dependencies: `numpy`, `scipy`, `jitr>=3.0`, `exfor-tools`. `pandas` and `scikit-learn` leave `requirements.txt` (neither is imported; kernels stay duck-typed and sklearn moves to the `examples` extra). Extras: `examples` (emcee, dynesty, corner, matplotlib, diff --git a/requirements.txt b/requirements.txt index 3420b06..685b026 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,4 @@ numpy>=2.2.6 scipy>=1.15 -pint>=0.2 jitr>=3.0 exfor-tools>=0.4 diff --git a/src/rxmc/units.py b/src/rxmc/units.py index a60eb1f..7238b92 100644 --- a/src/rxmc/units.py +++ b/src/rxmc/units.py @@ -1,40 +1,91 @@ """ -The one unit registry and the unit contract. +The unit contract, without a unit library. -``pint`` refuses to combine quantities from different registries, so every -module that touches units imports :data:`ureg` from here. The contract: -cross sections are stored internally in b/sr (:data:`XS_UNIT`); ``jitr`` +Cross sections are stored internally in b/sr (:data:`XS_UNIT`); ``jitr`` reports cross sections and the Rutherford cross section in mb/sr (:data:`RUTHERFORD_UNIT`), so model outputs are divided by :data:`MB_PER_B`. Angles are stored in radians. + +Measurement unit labels come from a small fixed vocabulary: ``x4i3`` converts +every EXFOR cross section to barns while parsing and ``exfor_tools`` labels the +result ``"barns/ster"``, ``"b"`` or ``"unitless"``. :func:`parse_unit` maps +that vocabulary (plus the obvious spellings) to a factor into the internal +unit and a quantity kind, and rejects anything else loudly. """ +from __future__ import annotations + import numpy as np -from pint import UnitRegistry __all__ = [ - "ureg", "DEFAULT_LMAX", "XS_UNIT", "RUTHERFORD_UNIT", "MB_PER_B", + "parse_unit", "check_angle_grid", ] -#: The one unit registry for the package. -ureg = UnitRegistry() - #: Default maximum partial wave for the reaction solvers. DEFAULT_LMAX = 20 #: Internal cross-section unit: every ``y`` in b/sr. -XS_UNIT = ureg.barn / ureg.steradian +XS_UNIT = "b/sr" #: Unit ``jitr`` reports cross sections (and the Rutherford cross section) in. -RUTHERFORD_UNIT = ureg.millibarn / ureg.steradian +RUTHERFORD_UNIT = "mb/sr" #: Millibarn per barn; divides ``jitr`` output to land in :data:`XS_UNIT`. -MB_PER_B = float((1 * ureg.barn).to(ureg.millibarn).magnitude) +MB_PER_B = 1000.0 + +# label (lower case, no spaces) -> (factor into the internal unit, kind) +_UNITS = { + # differential cross sections, internal unit b/sr + "barns/ster": (1.0, "differential"), + "barn/steradian": (1.0, "differential"), + "b/sr": (1.0, "differential"), + "millibarn/steradian": (1e-3, "differential"), + "mb/sr": (1e-3, "differential"), + "microbarn/steradian": (1e-6, "differential"), + "micro-b/sr": (1e-6, "differential"), + "ub/sr": (1e-6, "differential"), + # integral cross sections, internal unit b + "barns": (1.0, "integral"), + "barn": (1.0, "integral"), + "b": (1.0, "integral"), + "millibarn": (1e-3, "integral"), + "mb": (1e-3, "integral"), + "microbarn": (1e-6, "integral"), + "micro-b": (1e-6, "integral"), + "ub": (1e-6, "integral"), + # ratios and analysing powers + "no-dim": (1.0, "dimensionless"), + "unitless": (1.0, "dimensionless"), + "dimensionless": (1.0, "dimensionless"), + "": (1.0, "dimensionless"), +} + + +def parse_unit(label: str) -> tuple[float, str]: + """``(factor, kind)`` for a measurement unit label. + + ``factor`` multiplies a value in ``label`` to give the internal unit of its + ``kind``: b/sr for ``"differential"``, b for ``"integral"``, and 1 for + ``"dimensionless"``. Matching ignores case and spaces. + + Raises + ------ + ValueError + For a label outside the vocabulary, listing what is accepted. + """ + key = "".join(str(label).split()).lower() + try: + return _UNITS[key] + except KeyError: + raise ValueError( + f"unknown unit label {label!r}; accepted labels are " + f"{sorted(k for k in _UNITS if k)}" + ) from None def check_angle_grid(angles_rad: np.ndarray, name: str) -> None: diff --git a/test/test_units.py b/test/test_units.py index 6561361..68f4186 100644 --- a/test/test_units.py +++ b/test/test_units.py @@ -1,4 +1,4 @@ -"""The unit contract: one registry, consistent constants.""" +"""The unit contract: a fixed vocabulary of labels, consistent constants.""" import numpy as np import pytest @@ -9,21 +9,44 @@ RUTHERFORD_UNIT, XS_UNIT, check_angle_grid, - ureg, + parse_unit, ) def test_unit_constants_agree(): assert MB_PER_B == 1000.0 - assert (1 * RUTHERFORD_UNIT).to(XS_UNIT).magnitude == pytest.approx(1e-3) - assert (1 * XS_UNIT).to(RUTHERFORD_UNIT).magnitude == pytest.approx(MB_PER_B) + assert parse_unit(RUTHERFORD_UNIT)[0] == pytest.approx(1.0 / MB_PER_B) + assert parse_unit(XS_UNIT) == (1.0, "differential") assert DEFAULT_LMAX == 20 -def test_registry_is_shared(): - # quantities built from the constants combine without a registry error - q = (2 * ureg.millibarn / ureg.steradian) + 1 * XS_UNIT - assert q.to(XS_UNIT).magnitude == pytest.approx(1.002) +@pytest.mark.parametrize( + "label, factor, kind", + [ + ("barns/ster", 1.0, "differential"), # what exfor_tools emits + ("b/Sr", 1.0, "differential"), + ("MB/SR", 1e-3, "differential"), # raw EXFOR spelling + ("barn / steradian", 1.0, "differential"), + ("millibarn / steradian", 1e-3, "differential"), + ("MICRO-B/SR", 1e-6, "differential"), + ("barns", 1.0, "integral"), + ("mb", 1e-3, "integral"), + ("no-dim", 1.0, "dimensionless"), + ("unitless", 1.0, "dimensionless"), + ("NO-DIM", 1.0, "dimensionless"), + ], +) +def test_parse_unit_vocabulary(label, factor, kind): + f, k = parse_unit(label) + assert f == pytest.approx(factor) + assert k == kind + + +def test_parse_unit_rejects_unknown_label(): + with pytest.raises(ValueError, match="unknown unit label 'fm\\^2'"): + parse_unit("fm^2") + with pytest.raises(ValueError, match="accepted labels"): + parse_unit("MeV") def test_check_angle_grid(): From e8a1dd01719880f9c9a1dea5938fdb6ebf2260b4 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 19:00:40 -0400 Subject: [PATCH 11/75] Add Dataset: pure data with reported systematics as inert metadata Frozen, identity-hashed; x is opaque beyond its first dimension; y_err validated; norm_err and offset_err stored as a float or an (n,) array; meta copied. No comparison transform, mask or solver state. --- src/rxmc/data.py | 105 ++++++++++++++++++++++++++++++++++++++++++++++ test/test_data.py | 54 ++++++++++++++++++++++++ 2 files changed, 159 insertions(+) create mode 100644 src/rxmc/data.py create mode 100644 test/test_data.py diff --git a/src/rxmc/data.py b/src/rxmc/data.py new file mode 100644 index 0000000..09523fa --- /dev/null +++ b/src/rxmc/data.py @@ -0,0 +1,105 @@ +""" +The dataset: pure data. + +A :class:`Dataset` holds an independent variable, a dependent variable, its +statistical error, the measurement's *reported* systematic magnitudes as +inert metadata, and whatever kinematics a model needs to bind to it. It has +no comparison transform, no mask and no solver state: those belong to the +:class:`~rxmc.constraint.Comparison`, the :class:`~rxmc.constraint.Constraint` +and the :class:`~rxmc.model.Model` respectively. + +``x`` is opaque to the library. A model may ignore it and close over a +predictor on another grid (recipe 20), and a covariance term sees it only +through its own ``coords`` transform. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any, Mapping + +import numpy as np + +__all__ = ["Dataset"] + + +def _error_spec(value, n, name): + """``None``, a float, or a float array of shape ``(n,)``.""" + if value is None: + return None + if np.ndim(value) == 0: + return float(value) + v = np.asarray(value, dtype=float) + if v.shape != (n,): + raise ValueError( + f"{name} must be a scalar or have shape ({n},), got shape {v.shape}" + ) + return v + + +@dataclass(eq=False, frozen=True) +class Dataset: + """Experimental data in physical units. + + Parameters + ---------- + x : array_like + Independent variable; any dtype or shape whose first dimension is the + number of points. Angles in radians for reaction data. + y : array_like + Dependent variable, shape ``(n,)``. + y_err : array_like + Statistical (uncorrelated) error on ``y``, shape ``(n,)``, non-negative. + norm_err : float or array_like, optional + Reported *fractional* normalisation uncertainty; inert until + :meth:`~rxmc.constraint.Comparison.reported_terms` asks for it. + offset_err : float or array_like, optional + Reported *absolute* offset uncertainty, in the units of ``y``; inert + likewise. + label : str, optional + Human-readable identifier used in error messages. + meta : mapping, optional + Kinematics and provenance a model or a term may read: ``reaction``, + ``Elab``, ``ExIAS``, ``quantity``, ``k``, ... Copied on construction. + """ + + x: Any + y: Any + y_err: Any + norm_err: Any = None + offset_err: Any = None + label: str = "" + meta: Mapping = field(default_factory=dict, repr=False) + + def __post_init__(self): + x = np.asarray(self.x) + y = np.asarray(self.y, dtype=float) + if y.ndim != 1: + raise ValueError(f"y must be 1-D, got shape {y.shape}") + n = y.shape[0] + if x.ndim == 0 or x.shape[0] != n: + raise ValueError( + f"x must have {n} points along its first dimension, got shape " + f"{x.shape}" + ) + y_err = np.asarray(self.y_err, dtype=float) + if y_err.shape != (n,): + raise ValueError(f"y_err must have shape ({n},), got {y_err.shape}") + if np.any(y_err < 0): + raise ValueError("y_err must be non-negative") + set_ = object.__setattr__ + set_(self, "x", x) + set_(self, "y", y) + set_(self, "y_err", y_err) + set_(self, "norm_err", _error_spec(self.norm_err, n, "norm_err")) + set_(self, "offset_err", _error_spec(self.offset_err, n, "offset_err")) + set_(self, "meta", dict(self.meta)) + + @property + def n(self) -> int: + """Number of points.""" + return self.y.shape[0] + + def __repr__(self): + label = f"{self.label!r}, " if self.label else "" + return f"Dataset({label}n={self.n})" diff --git a/test/test_data.py b/test/test_data.py new file mode 100644 index 0000000..1c1a5d7 --- /dev/null +++ b/test/test_data.py @@ -0,0 +1,54 @@ +"""Validation and identity semantics of :class:`rxmc.Dataset`.""" + +import numpy as np +import pytest + +from rxmc import Dataset + + +def test_arrays_coerced_and_validated(): + d = Dataset([0, 1, 2], [1, 2, 3], [0.1, 0.2, 0.3]) + assert d.n == 3 + assert d.y.dtype == float and d.y_err.dtype == float + assert d.x.shape == (3,) + with pytest.raises(ValueError, match="y_err must have shape"): + Dataset([0, 1, 2], [1, 2, 3], [0.1, 0.2]) + with pytest.raises(ValueError, match="first dimension"): + Dataset([0, 1], [1, 2, 3], [0.1, 0.2, 0.3]) + with pytest.raises(ValueError, match="1-D"): + Dataset([0, 1], [[1, 2]], [0.1]) + with pytest.raises(ValueError, match="non-negative"): + Dataset([0, 1], [1, 2], [0.1, -0.2]) + + +def test_x_is_opaque(): + x2d = np.array([[0.0, 5.0], [1.0, 5.0], [2.0, 5.0]]) # (theta, E) pairs + d = Dataset(x2d, [1, 2, 3], [0.1, 0.1, 0.1]) + assert d.x.shape == (3, 2) + d = Dataset(np.arange(3), [1, 2, 3], [0.1, 0.1, 0.1]) # an index is fine too + assert d.x.dtype.kind == "i" + + +def test_error_specs(): + d = Dataset([0, 1, 2], [1, 2, 3], [0.1, 0.1, 0.1]) + assert d.norm_err is None and d.offset_err is None + d = Dataset([0, 1, 2], [1, 2, 3], [0.1, 0.1, 0.1], norm_err=np.array(0.05)) + assert d.norm_err == 0.05 and isinstance(d.norm_err, float) + d = Dataset([0, 1, 2], [1, 2, 3], [0.1, 0.1, 0.1], offset_err=[0.01, 0.02, 0.03]) + np.testing.assert_allclose(d.offset_err, [0.01, 0.02, 0.03]) + with pytest.raises(ValueError, match="norm_err"): + Dataset([0, 1, 2], [1, 2, 3], [0.1, 0.1, 0.1], norm_err=[0.05, 0.05]) + + +def test_meta_is_copied(): + meta = {"Elab": 10.0} + d = Dataset([0], [1], [0.1], meta=meta) + meta["Elab"] = 99.0 + assert d.meta["Elab"] == 10.0 + + +def test_identity_and_repr(): + a = Dataset([0], [1], [0.1], label="A") + b = Dataset([0], [1], [0.1], label="A") + assert a != b and len({a, b}) == 2 + assert "A" in repr(a) and "n=1" in repr(a) From 72d0ff609da89dfdbaa3297be0a308aa0d80dba2 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 19:00:40 -0400 Subject: [PATCH 12/75] Add Model and Predictor with |, + and * composition Model(fn, params) binds to a grid by closing over it; subclasses override bind() to build solvers from meta. Composition happens on the bound predictors: | applies a mean transform after the prediction, + and * are x-dependent corrections, parameters concatenated left to right. polynomial(order) is the generic example. --- src/rxmc/model.py | 149 +++++++++++++++++++++++++++++++++++++++++++++ test/test_model.py | 111 +++++++++++++++++++++++++++++++++ 2 files changed, 260 insertions(+) create mode 100644 src/rxmc/model.py create mode 100644 test/test_model.py diff --git a/src/rxmc/model.py b/src/rxmc/model.py new file mode 100644 index 0000000..5f73508 --- /dev/null +++ b/src/rxmc/model.py @@ -0,0 +1,149 @@ +""" +Models and predictors. + +A :class:`Model` is a spec of "observables from parameters": a callable +``fn(x, *values)`` in physical space plus the :class:`~rxmc.params.Parameter` s +it consumes. A :class:`Predictor` is that model **bound to a grid**: ``bind`` +is where anything expensive or grid-dependent is built (a reaction model +builds its solver workspace there and overrides only ``bind``). + +Models compose, and the composition happens on the bound predictors so a +reaction model and a plain function add without special cases: + +* ``model | transform`` applies a mean transform after the prediction + (:func:`~rxmc.transforms.scale` for a latent normalisation); +* ``model + other`` is an additive, ``x``-dependent correction (a sampled + mean discrepancy); +* ``model * other`` is a multiplicative one (an additive discrepancy in log + space; ``scale`` is its constant case). + +Parameters are concatenated left to right. A parameter object appearing on +both sides is one sampled value, as everywhere. +""" + +from __future__ import annotations + +import operator +from typing import Callable, Sequence + +import numpy as np +from numpy.polynomial import polynomial as P + +from .params import Parameter +from .transforms import as_transform + +__all__ = ["Model", "Predictor", "polynomial"] + + +def _check_params(params) -> tuple: + params = tuple(params) + for p in params: + if not isinstance(p, Parameter): + raise TypeError(f"params must be Parameter objects, got {p!r}") + return params + + +class Predictor: + """A model bound to a grid: ``predictor(*values) -> y`` in physical space. + + Parameters + ---------- + params : sequence of Parameter + The values ``__call__`` expects, in order. + x : array_like + The grid the prediction is made on. + fn : callable + ``fn(*values) -> np.ndarray`` on that grid. + """ + + def __init__(self, params: Sequence[Parameter], x, fn: Callable): + self.params = _check_params(params) + self.x = np.asarray(x) + self._fn = fn + + def __call__(self, *values) -> np.ndarray: + if len(values) != len(self.params): + raise ValueError( + f"predictor expects {len(self.params)} value(s), got {len(values)}" + ) + return np.asarray(self._fn(*values), dtype=float) + + def __repr__(self): + names = ", ".join(p.name for p in self.params) + return f"Predictor(params=({names}), n={len(self.x)})" + + +class Model: + """A parametric model of an observable, in physical space. + + Parameters + ---------- + fn : callable, optional + ``fn(x, *values) -> np.ndarray`` on a grid ``x``. Subclasses that + build their prediction in :meth:`bind` may omit it. + params : sequence of Parameter, optional + The parameters ``fn`` consumes, in order. + """ + + def __init__(self, fn: Callable | None = None, params: Sequence[Parameter] = ()): + if fn is not None and not callable(fn): + raise TypeError("fn must be callable") + self.fn = fn + self.params = _check_params(params) + + def bind(self, x, meta=None) -> Predictor: + """The model on the grid ``x``; ``meta`` carries the dataset's kinematics. + + The generic model closes over ``x``. Reaction models override this to + build their solver on ``x`` from ``meta``. + """ + if self.fn is None: + raise TypeError(f"{type(self).__name__} must override bind()") + fn = self.fn + return Predictor(self.params, x, lambda *values: fn(x, *values)) + + def __or__(self, transform) -> "Model": + return _Transformed(self, as_transform(transform)) + + def __add__(self, other) -> "Model": + return _Combined(self, other, operator.add, "+") + + def __mul__(self, other) -> "Model": + return _Combined(self, other, operator.mul, "*") + + def __repr__(self): + names = ", ".join(p.name for p in self.params) + return f"{type(self).__name__}(params=({names}))" + + +class _Transformed(Model): + """``inner | transform``: the transform's parameters follow the model's.""" + + def __init__(self, inner: Model, transform): + self.inner, self.transform = inner, transform + super().__init__(None, inner.params + transform.params) + + def bind(self, x, meta=None) -> Predictor: + pred, t, n = self.inner.bind(x, meta), self.transform, len(self.inner.params) + return Predictor(self.params, x, lambda *v: t(pred(*v[:n]), *v[n:])) + + +class _Combined(Model): + """``left op right`` on the bound predictors; parameters left then right.""" + + def __init__(self, left: Model, right: Model, op, symbol: str): + if not isinstance(right, Model): + raise TypeError(f"can only combine a Model with a Model, got {right!r}") + self.left, self.right, self.op, self.symbol = left, right, op, symbol + super().__init__(None, left.params + right.params) + + def bind(self, x, meta=None) -> Predictor: + lp, rp = self.left.bind(x, meta), self.right.bind(x, meta) + n, op = len(self.left.params), self.op + return Predictor(self.params, x, lambda *v: op(lp(*v[:n]), rp(*v[n:]))) + + +def polynomial(order: int) -> Model: + r"""``y = a_0 + a_1 x + ... + a_order x^order`` with parameters ``a0..a``.""" + params = [Parameter(f"a{i}", latex=f"a_{i}") for i in range(order + 1)] + return Model(lambda x, *a: P.polyval(np.asarray(x, dtype=float), a), params) diff --git a/test/test_model.py b/test/test_model.py new file mode 100644 index 0000000..7b35e43 --- /dev/null +++ b/test/test_model.py @@ -0,0 +1,111 @@ +"""Models, predictors and their composition.""" + +import numpy as np +import pytest + +from rxmc import Model, Parameter, polynomial +from rxmc.model import Predictor +from rxmc.transforms import Transform, scale + +x = np.linspace(0.0, 2.0, 5) +m, b = Parameter("m"), Parameter("b") +line = Model(lambda x, m, b: m * x + b, [m, b]) + + +def test_generic_model_binds_to_x(): + pred = line.bind(x) + assert isinstance(pred, Predictor) + assert pred.params == (m, b) + np.testing.assert_allclose(pred(2.0, 1.0), 2.0 * x + 1.0) + with pytest.raises(ValueError, match="expects 2 value"): + pred(2.0) + with pytest.raises(TypeError, match="Parameter"): + Model(lambda x, a: a * x, ["a"]) + + +def test_polynomial(): + poly = polynomial(2) + assert [p.name for p in poly.params] == ["a0", "a1", "a2"] + np.testing.assert_allclose( + poly.bind(x)(1.0, 2.0, 3.0), np.polyval([3.0, 2.0, 1.0], x) + ) + + +def test_scale_transform_appends_and_scales_after(): + rho = Parameter("log_rho") + scaled = line | scale(rho) + assert scaled.params == (m, b, rho) + np.testing.assert_allclose( + scaled.bind(x)(2.0, 1.0, np.log(3.0)), 3.0 * (2.0 * x + 1.0) + ) + + +def test_two_parameter_transform_in_order(): + c, d = Parameter("c"), Parameter("d") + t = Transform(lambda a, c, d: c * a + d, (c, d)) + model = line | t + assert model.params == (m, b, c, d) + np.testing.assert_allclose(model.bind(x)(1.0, 0.0, 2.0, 5.0), 2.0 * x + 5.0) + + +def test_add_and_mul_compose_on_the_bound_grid(): + c0 = Parameter("c0") + delta = Model(lambda x, c0: c0 * x**2, [c0]) + both = line + delta + assert both.params == (m, b, c0) + np.testing.assert_allclose(both.bind(x)(1.0, 1.0, 0.5), x + 1.0 + 0.5 * x**2) + prod = line * delta + np.testing.assert_allclose(prod.bind(x)(1.0, 1.0, 0.5), (x + 1.0) * 0.5 * x**2) + with pytest.raises(TypeError, match="Model"): + line + 3.0 + + +def test_precedence_of_transform_and_addition(): + c0, rho = Parameter("c0"), Parameter("log_rho") + delta = Model(lambda x, c0: c0 * np.ones_like(x), [c0]) + scale_sum = (line + delta) | scale(rho) + scale_model = (line | scale(rho)) + delta + v_sum = scale_sum.bind(x)(1.0, 0.0, 2.0, np.log(3.0)) # m, b, c0, rho + v_model = scale_model.bind(x)(1.0, 0.0, np.log(3.0), 2.0) # m, b, rho, c0 + np.testing.assert_allclose(v_sum, 3.0 * (x + 2.0)) + np.testing.assert_allclose(v_model, 3.0 * x + 2.0) + + +def test_mul_by_constant_equals_scale(): + rho = Parameter("log_rho") + const = Model(lambda x, r: np.exp(r) * np.ones_like(x), [rho]) + np.testing.assert_allclose( + (line * const).bind(x)(2.0, 1.0, 0.7), + (line | scale(rho)).bind(x)(2.0, 1.0, 0.7), + ) + + +def test_shared_parameter_is_concatenated_not_deduplicated(): + # the same object on both sides is one slot at compile; the model just lists it twice + both = line + Model(lambda x, m: m * np.ones_like(x), [m]) + assert both.params == (m, b, m) + np.testing.assert_allclose(both.bind(x)(1.0, 0.0, 1.0), x + 1.0) + + +def test_model_may_ignore_x_and_close_over_another_predictor(): + native = line.bind(x) + A = np.array([[1.0, 1.0, 1.0, 1.0, 1.0], [0.0, 1.0, 2.0, 3.0, 4.0]]) + proj = Model(lambda x_pc, *theta: A @ native(*theta), line.params) + pc = proj.bind(np.arange(2)) + np.testing.assert_allclose(pc(2.0, 1.0), A @ (2.0 * x + 1.0)) + + +def test_subclass_overrides_bind_and_composes(): + class Doubler(Model): + def __init__(self): + super().__init__(None, [m]) + + def bind(self, x, meta=None): + k = meta["k"] + return Predictor(self.params, x, lambda mm: k * mm * np.asarray(x)) + + model = Doubler() | scale(Parameter("log_rho")) + pred = model.bind(x, {"k": 2.0}) + np.testing.assert_allclose(pred(1.5, 0.0), 3.0 * x) + with pytest.raises(TypeError, match="override bind"): + Model(None, [m]).bind(x) From 667e3c45c090b7d86db8a4272eea744857d738f5 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 19:02:29 -0400 Subject: [PATCH 13/75] Add Comparison and Constraint: comparison space, masks, reported terms Comparison binds a model to a dataset in a parameter-free space, with the delta-method errors and the per-point log-Jacobian as constants; reported_terms() turns the dataset's offset and normalisation systematics into rank-one modes, propagated at the data and at the prediction respectively. Constraint holds the comparisons, terms, likelihood, weight and per-comparison masks; masked, masked_where and complement share every object with the original. Every term's on= is resolved eagerly and an array term's shape is checked against its support. --- src/rxmc/constraint.py | 315 ++++++++++++++++++++++++++++++++++++++++ test/test_constraint.py | 198 +++++++++++++++++++++++++ 2 files changed, 513 insertions(+) create mode 100644 src/rxmc/constraint.py create mode 100644 test/test_constraint.py diff --git a/src/rxmc/constraint.py b/src/rxmc/constraint.py new file mode 100644 index 0000000..5ef51d7 --- /dev/null +++ b/src/rxmc/constraint.py @@ -0,0 +1,315 @@ +""" +Comparisons and constraints: the declarations a likelihood is built from. + +A :class:`Comparison` is the unit a residual is formed on: one dataset, one +model bound to its grid, and one parameter-free comparison ``space`` (e.g. +``log``) applied to both. A :class:`Constraint` is a tuple of comparisons +plus the covariance terms, the likelihood functional, the tempering weight +and the active-point masks; it is the maximal block of mutually correlated +data, and constraints are independent of each other. Each comparison is one +block of the constraint's stacked covariance. + +Masks live on the constraint, not on the comparison or the data, so +:meth:`Constraint.masked`, :meth:`Constraint.masked_where` and +:meth:`Constraint.complement` return constraints sharing every +``Comparison``, ``Term`` and ``Parameter`` object with the original. A +held-out problem built from ``complement()`` therefore has the same +parameter columns as the fit. + +Nothing here walks the parameter graph; :class:`~rxmc.problem.Problem` does +that once. The checks here need only the constraint itself: comparisons are +distinct, every term's ``on`` resolves inside the constraint, and an +array-valued term has the right shape for its support. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field, replace +from typing import Any, Callable + +import numpy as np + +from .data import Dataset +from .likelihood import Gaussian, Likelihood +from .model import Model, Predictor +from .terms import Term +from .transforms import as_transform, identity + +__all__ = ["Comparison", "Constraint"] + + +@dataclass(eq=False, frozen=True) +class Comparison: + """One dataset compared with one model in one space. + + Parameters + ---------- + data : Dataset + model : Model + Bound to ``data.x`` with ``data.meta`` at construction. + space : Transform or callable, optional + Parameter-free comparison transform applied to the data once and to + every prediction. ``y = space(data.y)``, ``y_err`` by the delta + method, and the log-Jacobian are constants. Non-finite values are + allowed here (the point may be masked); the compile step checks the + active points and names the comparison. + """ + + data: Dataset + model: Model + space: Any = identity + predictor: Predictor = field(init=False, repr=False) + y: np.ndarray = field(init=False, repr=False) + y_err: np.ndarray = field(init=False, repr=False) + log_jac: np.ndarray = field(init=False, repr=False) + + def __post_init__(self): + if not isinstance(self.data, Dataset): + raise TypeError(f"data must be a Dataset, got {type(self.data).__name__}") + if not isinstance(self.model, Model): + raise TypeError(f"model must be a Model, got {type(self.model).__name__}") + space = as_transform(self.space) + if space.params: + raise ValueError( + "a comparison space must be parameter-free; put parametric " + "transforms on the model (model | transform)" + ) + set_ = object.__setattr__ + set_(self, "space", space) + set_(self, "predictor", self.model.bind(self.data.x, self.data.meta)) + if space.is_identity: + set_(self, "y", self.data.y) + set_(self, "y_err", self.data.y_err) + set_(self, "log_jac", np.zeros(self.data.n)) + return + with np.errstate(all="ignore"): + jac = np.abs(space.derivative(self.data.y)) + set_(self, "y", space(self.data.y)) + set_(self, "y_err", jac * self.data.y_err) + set_(self, "log_jac", np.log(jac)) + + @property + def n(self) -> int: + return self.data.n + + def predict(self, *values) -> np.ndarray: + """The prediction on the data grid, in comparison space.""" + ym = self.predictor(*values) + return ym if self.space.is_identity else self.space(ym) + + def log_jacobian(self, mask=None) -> float: + r"""``sum(log |space'(data.y)|)`` over the active points. + + A constant in the parameters, needed only to compare marginal + likelihoods across comparison spaces (``log Z_raw = log Z_transformed + + log_jacobian``). Zero for the identity. + """ + lj = self.log_jac if mask is None else self.log_jac[np.asarray(mask, bool)] + return float(np.sum(lj)) + + def reported_terms(self) -> list[Term]: + """The dataset's reported systematics as fixed rank-one modes, ``on=self``. + + Opt-in: nothing is folded into a covariance automatically. The + absolute offset mode comes first, then the fractional normalisation + mode, each skipped when its magnitude is zero. Both are propagated + to the comparison space by the delta method: the offset is an error on + the *data* and is linearised at the data; the normalisation multiplies + the *prediction* and is linearised at the prediction, which needs the + space's inverse. + """ + d, t = self.data, self.space + terms = [] + omega = _reported(d.offset_err, d.n) + if omega is not None: + if not t.is_identity: + omega = np.abs(t.derivative(d.y)) * omega + terms.append(Term(omega, kind="mode", on=self)) + eta = _reported(d.norm_err, d.n) + if eta is not None: + if t.is_identity: + terms.append(Term(lambda c: eta * c.ym, kind="mode", on=self)) + else: + inv = t.inverse + if inv is None: + raise ValueError( + f"comparison space {t.name!r} has no inverse; the " + "normalisation systematic needs the physical-space prediction" + ) + + def basis(c, eta=eta, t=t, inv=inv): + ym_raw = inv(c.ym) + return eta * ym_raw * np.abs(t.derivative(ym_raw)) + + terms.append(Term(basis, kind="mode", on=self)) + return terms + + def __repr__(self): + label = self.data.label or "dataset" + return f"Comparison({label!r}, {self.model!r}, space={self.space.name})" + + +def _reported(spec, n): + """A reported magnitude as a length-``n`` array, or ``None`` when absent/zero.""" + if spec is None or not np.any(np.asarray(spec) != 0.0): + return None + return np.broadcast_to(np.asarray(spec, dtype=float), (n,)).copy() + + +def _as_mask(mask, n) -> np.ndarray: + mask = np.asarray(mask, dtype=bool) + if mask.shape != (n,): + raise ValueError(f"mask must have shape ({n},), got {mask.shape}") + return mask + + +@dataclass(eq=False, frozen=True) +class Constraint: + """The maximal block of mutually correlated data: one likelihood. + + Parameters + ---------- + comparisons : iterable of Comparison + Distinct comparisons; each is one block of the stacked covariance. + terms : iterable of Term, optional + Covariance contributions, authored in comparison space. + likelihood : Likelihood, optional + Functional of ``(d2, logdet, n)``; :class:`~rxmc.likelihood.Gaussian` + by default. + weight : float, optional + Tempering: multiplies this constraint's log-likelihood only. + statistical : bool, optional + Add each comparison's ``y_err`` diagonal (default). ``False`` composes + the whole covariance from ``terms``. + masks : sequence of bool arrays, optional + Active points, one array per comparison; ``None`` means all active. + """ + + comparisons: Any + terms: Any = () + likelihood: Likelihood = field(default_factory=Gaussian) + weight: float = 1.0 + statistical: bool = True + masks: Any = None + offsets: tuple = field(init=False, repr=False) + active: np.ndarray = field(init=False, repr=False) + + def __post_init__(self): + set_ = object.__setattr__ + comps = tuple(self.comparisons) + for c in comps: + if not isinstance(c, Comparison): + raise TypeError(f"comparisons must be Comparison objects, got {c!r}") + if len({id(c) for c in comps}) != len(comps): + raise ValueError("comparisons must be distinct objects") + if not comps: + raise ValueError("a constraint needs at least one comparison") + terms = tuple(self.terms) + for t in terms: + if not isinstance(t, Term): + raise TypeError(f"terms must be Term objects, got {type(t).__name__}") + if not isinstance(self.likelihood, Likelihood): + raise TypeError("likelihood must be a Likelihood") + weight = float(self.weight) + if weight < 0: + raise ValueError("weight must be non-negative") + ns = [c.n for c in comps] + starts = np.concatenate([[0], np.cumsum(ns)[:-1]]).astype(int) + offsets = tuple(slice(int(s), int(s + n)) for s, n in zip(starts, ns)) + if self.masks is None: + masks = None + active = np.arange(int(sum(ns))) + else: + masks = tuple(self.masks) + if len(masks) != len(comps): + raise ValueError( + f"masks must have one entry per comparison ({len(comps)})" + ) + masks = tuple(_as_mask(m, n) for m, n in zip(masks, ns)) + active = np.concatenate( + [np.arange(o.start, o.stop)[m] for o, m in zip(offsets, masks)] + ).astype(int) + set_(self, "comparisons", comps) + set_(self, "terms", terms) + set_(self, "weight", weight) + set_(self, "masks", masks) + set_(self, "offsets", offsets) + set_(self, "active", active) + for t in terms: # eager: every on= resolves here, arrays have the right shape + rows = self.support(t.on) + if not callable(t.fn) and t.fn.shape != t.expected_shape(len(rows)): + raise ValueError( + f"{t.kind} term expects shape {t.expected_shape(len(rows))} on " + f"its support, got {t.fn.shape}" + ) + + # -- structure ---------------------------------------------------------- + + @property + def n_total(self) -> int: + return int(sum(c.n for c in self.comparisons)) + + @property + def n_active(self) -> int: + return int(self.active.size) + + @property + def log_jacobian(self) -> float: + """Sum of the comparisons' log-Jacobians over the active points.""" + masks = self.masks or (None,) * len(self.comparisons) + return float(sum(c.log_jacobian(m) for c, m in zip(self.comparisons, masks))) + + def support(self, on) -> np.ndarray: + """The stacked rows a term's ``on`` resolves to, in constraint order.""" + if on is None: + return np.arange(self.n_total) + if isinstance(on, (Comparison, Dataset)): + targets = [on] + else: + try: + targets = list(on) + except TypeError: + raise TypeError( + f"on= must reference comparisons or datasets, got {on!r}" + ) from None + keep = np.zeros(len(self.comparisons), dtype=bool) + for target in targets: + if isinstance(target, Comparison): + hits = [i for i, c in enumerate(self.comparisons) if c is target] + elif isinstance(target, Dataset): + hits = [i for i, c in enumerate(self.comparisons) if c.data is target] + else: + raise TypeError( + f"on= must reference comparisons or datasets, got {target!r}" + ) + if not hits: + raise ValueError( + f"term on={target!r} does not reference a comparison of this " + "constraint" + ) + keep[hits] = True + return np.concatenate( + [np.arange(o.start, o.stop) for o, k in zip(self.offsets, keep) if k] + ).astype(int) + + # -- masked views --------------------------------------------------------- + + def masked(self, masks) -> "Constraint": + """The same constraint with new active-point masks.""" + return replace(self, masks=masks) + + def masked_where(self, predicate: Callable) -> "Constraint": + """Active where ``predicate(comparison.data.x)`` is true, per comparison.""" + return self.masked([predicate(c.data.x) for c in self.comparisons]) + + def complement(self) -> "Constraint": + """Every inactive point active, and vice versa.""" + if self.masks is None: + return self.masked([np.zeros(c.n, dtype=bool) for c in self.comparisons]) + return self.masked([~m for m in self.masks]) + + def __repr__(self): + return ( + f"Constraint({len(self.comparisons)} comparison(s), {len(self.terms)} " + f"term(s), {type(self.likelihood).__name__}, n_active={self.n_active})" + ) diff --git a/test/test_constraint.py b/test/test_constraint.py new file mode 100644 index 0000000..5d1fede --- /dev/null +++ b/test/test_constraint.py @@ -0,0 +1,198 @@ +"""Comparison and Constraint: comparison space, reported terms, masks, support.""" + +import numpy as np +import pytest + +from helpers import assemble_dense +from rxmc import Dataset, Model, Parameter +from rxmc.constraint import Comparison, Constraint +from rxmc.likelihood import StudentT +from rxmc.terms import Term, noise, statistical +from rxmc.transforms import Transform, log, scale + +m, b = Parameter("m"), Parameter("b") +line = Model(lambda x, m, b: m * x + b, [m, b]) + + +def dataset(n=4, label="d", **kw): + x = np.linspace(1.0, 2.0, n) + return Dataset(x, 2.0 * x + 1.0, 0.1 * np.ones(n), label=label, **kw) + + +class TestComparison: + def test_identity_space_is_the_default(self): + d = dataset() + c = Comparison(d, line) + assert c.y is d.y and c.y_err is d.y_err + assert c.log_jacobian() == 0.0 + assert c.n == 4 + np.testing.assert_allclose(c.predict(2.0, 1.0), d.y) + + def test_log_space_delta_method_and_jacobian(self): + d = dataset() + c = Comparison(d, line, space=log) + np.testing.assert_allclose(c.y, np.log(d.y)) + np.testing.assert_allclose(c.y_err, d.y_err / d.y) + assert c.log_jacobian() == pytest.approx(-np.sum(np.log(d.y))) + mask = np.array([True, False, True, False]) + assert c.log_jacobian(mask) == pytest.approx(-np.sum(np.log(d.y[mask]))) + np.testing.assert_allclose(c.predict(2.0, 1.0), np.log(d.y)) + + def test_non_positive_data_under_log_does_not_raise(self): + d = Dataset([0.0, 1.0], [-1.0, 2.0], [0.1, 0.1]) + c = Comparison(d, line, space=log) + assert not np.isfinite(c.y[0]) and np.isfinite(c.y[1]) + + def test_parametric_space_rejected_and_callable_accepted(self): + with pytest.raises(ValueError, match="parameter-free"): + Comparison(dataset(), line, space=scale()) + c = Comparison(dataset(), line, space=np.sqrt) + np.testing.assert_allclose(c.y, np.sqrt(dataset().y)) + + def test_predictor_is_bound_with_meta(self): + seen = {} + + class Probe(Model): + def bind(self, x, meta=None): + seen["meta"] = meta + return line.bind(x) + + d = dataset(meta={"Elab": 10.0}) + Comparison(d, Probe(None, [m, b])) + assert seen["meta"] == {"Elab": 10.0} + + def test_type_checks(self): + with pytest.raises(TypeError, match="Dataset"): + Comparison(np.ones(3), line) + with pytest.raises(TypeError, match="Model"): + Comparison(dataset(), lambda x: x) + + +class TestReportedTerms: + def test_empty_and_zero_skipped(self): + assert Comparison(dataset(), line).reported_terms() == [] + d = dataset(norm_err=0.0, offset_err=np.zeros(4)) + assert Comparison(d, line).reported_terms() == [] + + def test_identity_recovers_the_old_folded_covariance(self): + d = dataset(norm_err=0.05, offset_err=0.2) + c = Comparison(d, line) + terms = c.reported_terms() + assert [t.kind for t in terms] == ["mode", "mode"] + assert all(t.on is c for t in terms) + ym = c.predict(1.9, 1.1) + S = assemble_dense([statistical(c.y_err), *terms], d.x, c.y, ym) + old = ( + np.diag(d.y_err**2) + + np.outer(0.2 * np.ones(4), 0.2 * np.ones(4)) + + 0.05**2 * np.outer(ym, ym) + ) + np.testing.assert_allclose(S, old) + + def test_delta_method_under_log(self): + d = dataset(norm_err=0.05, offset_err=0.2) + c = Comparison(d, line, space=log) + offset, norm = c.reported_terms() + ym = c.predict(1.9, 1.1) + np.testing.assert_allclose(offset.value(d.x, c.y, ym), 0.2 / d.y) + np.testing.assert_allclose( + norm.value(d.x, c.y, ym), 0.05 + ) # constant in log space + + def test_normalisation_needs_an_inverse(self): + d = dataset(norm_err=0.05) + c = Comparison(d, line, space=Transform(lambda a: a**3)) + with pytest.raises(ValueError, match="no inverse"): + c.reported_terms() + d = dataset(offset_err=0.2) # an offset alone is fine + assert ( + len(Comparison(d, line, space=Transform(lambda a: a**3)).reported_terms()) + == 1 + ) + + +class TestConstraint: + def setup_method(self): + self.d1, self.d2 = dataset(3, "d1"), dataset(4, "d2") + self.c1, self.c2 = Comparison(self.d1, line), Comparison(self.d2, line) + self.eps = Parameter("log_eps") + + def test_construction_and_validation(self): + c = Constraint(iter([self.c1, self.c2]), terms=[noise(self.eps)]) + assert c.comparisons == (self.c1, self.c2) and c.n_total == 7 + assert c.offsets == (slice(0, 3), slice(3, 7)) + assert c.n_active == 7 and np.array_equal(c.active, np.arange(7)) + assert c.masks is None and c.weight == 1.0 and c.statistical + with pytest.raises(ValueError, match="distinct"): + Constraint([self.c1, self.c1]) + with pytest.raises(TypeError, match="Term"): + Constraint([self.c1], terms=[np.eye(3)]) + with pytest.raises(ValueError, match="non-negative"): + Constraint([self.c1], weight=-1.0) + with pytest.raises(ValueError, match="at least one"): + Constraint([]) + with pytest.raises(TypeError, match="Likelihood"): + Constraint([self.c1], likelihood=object()) + assert isinstance( + Constraint([self.c1], likelihood=StudentT()).likelihood, StudentT + ) + + def test_masks_validated(self): + with pytest.raises(ValueError, match="one entry per comparison"): + Constraint([self.c1, self.c2], masks=[np.ones(3, bool)]) + with pytest.raises(ValueError, match="shape"): + Constraint([self.c1, self.c2], masks=[np.ones(3, bool), np.ones(3, bool)]) + + def test_support_resolution(self): + c = Constraint([self.c1, self.c2]) + assert np.array_equal(c.support(None), np.arange(7)) + assert np.array_equal(c.support(self.c2), np.arange(3, 7)) + assert np.array_equal(c.support(self.d1), np.arange(3)) + assert np.array_equal(c.support([self.c2, self.c1]), np.arange(7)) + assert np.array_equal(c.support([self.c2, self.d2]), np.arange(3, 7)) + stray = Comparison(dataset(2, "stray"), line) + with pytest.raises(ValueError, match="stray"): + c.support(stray) + with pytest.raises(ValueError, match="does not reference"): + c.support(dataset(2, "other")) + with pytest.raises(TypeError, match="comparisons or datasets"): + c.support(3) + + def test_term_support_checked_eagerly(self): + stray = Comparison(dataset(2, "stray"), line) + with pytest.raises(ValueError, match="stray"): + Constraint([self.c1], terms=[noise(self.eps, on=stray)]) + with pytest.raises(ValueError, match="expects shape"): + Constraint( + [self.c1, self.c2], terms=[Term(np.ones(3), kind="diag", on=self.c2)] + ) + Constraint( + [self.c1, self.c2], terms=[Term(np.ones(3), kind="diag", on=self.c1)] + ) + + def test_masked_where_and_complement_partition(self): + c = Constraint([self.c1, self.c2], terms=[noise(self.eps)]) + fit = c.masked_where(lambda x: x < 1.6) + held = fit.complement() + assert set(fit.active) | set(held.active) == set(range(7)) + assert set(fit.active).isdisjoint(held.active) + assert fit.n_active + held.n_active == 7 + again = held.complement() + assert all(np.array_equal(a, b) for a, b in zip(again.masks, fit.masks)) + # complement of an unmasked constraint deactivates everything + assert c.complement().n_active == 0 + + def test_views_share_objects(self): + t = noise(self.eps) + c = Constraint([self.c1, self.c2], terms=[t], weight=0.5, likelihood=StudentT()) + view = c.masked_where(lambda x: x > 1.5) + assert view.comparisons == c.comparisons and view.terms == (t,) + assert view.terms[0].params[0] is self.eps + assert view.likelihood is c.likelihood and view.weight == 0.5 + + def test_log_jacobian_over_active_points(self): + c1 = Comparison(self.d1, line, space=log) + c = Constraint([c1, self.c2]) + assert c.log_jacobian == pytest.approx(-np.sum(np.log(self.d1.y))) + masked = c.masked([np.array([True, False, False]), np.ones(4, bool)]) + assert masked.log_jacobian == pytest.approx(-np.log(self.d1.y[0])) From 6bf6be6791a1ca99bfc07dec8ddb6c890c6577bc Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 19:02:29 -0400 Subject: [PATCH 14/75] Export Comparison and Constraint from the package --- src/rxmc/__init__.py | 25 ++++++++++++++++++++++++- 1 file changed, 24 insertions(+), 1 deletion(-) diff --git a/src/rxmc/__init__.py b/src/rxmc/__init__.py index f7a573c..fae2c75 100644 --- a/src/rxmc/__init__.py +++ b/src/rxmc/__init__.py @@ -4,10 +4,18 @@ for the design and ``docs/recipes.md`` for the supported use cases. """ +from . import constraint as constraint +from . import data as data from . import likelihood as likelihood +from . import model as model from . import terms as terms from . import transforms as transforms from . import units as units +from .constraint import Comparison as Comparison +from .constraint import Constraint as Constraint +from .data import Dataset as Dataset +from .model import Model as Model +from .model import polynomial as polynomial from .params import Parameter as Parameter try: @@ -15,4 +23,19 @@ except ImportError: # pragma: no cover - source checkout without a build __version__ = "0+unknown" -__all__ = ["__version__", "Parameter", "likelihood", "terms", "transforms", "units"] +__all__ = [ + "__version__", + "Comparison", + "Constraint", + "Dataset", + "Model", + "Parameter", + "polynomial", + "constraint", + "data", + "likelihood", + "model", + "terms", + "transforms", + "units", +] From 0bf5fb9344922c3a1e557e38d34f815a596db23e Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 19:14:01 -0400 Subject: [PATCH 15/75] Define the alpha+Ca error-model labels once, with a legend The study labels (L0, E0, L1, ...) meant nothing to a reader who had not seen the study. test/helpers.py now carries STUDY_LEGEND, one line per label, and study_form(label, x, y, ym) builds the terms, values and the hand-built dense reference; the term tests parametrise over it and the structured covariance tests will too. Recipe 18 and the design document point at the legend. --- docs/groundup_design.md | 2 +- docs/recipes.md | 4 + test/helpers.py | 233 +++++++++++++++++++++++++++++++++++++--- test/test_terms.py | 153 +++----------------------- 4 files changed, 240 insertions(+), 152 deletions(-) diff --git a/docs/groundup_design.md b/docs/groundup_design.md index 1a8ed77..38007c3 100644 --- a/docs/groundup_design.md +++ b/docs/groundup_design.md @@ -802,7 +802,7 @@ The bodies below encode behaviour, not API, and port with renamed calls: - `test_covariance.py`: `TestTermKinds`, `TestTermCoords`, `TestFactories` (including `test_old_observation_covariance_equivalence`), - `TestKernelTerm`, `TestStudyForms` (every α+Ca error-model form against + `TestKernelTerm`, `TestStudyForms` (every form of the α+Ca error-model ladder, defined once with a legend in `test/helpers.py::STUDY_LEGEND`, against a hand-built dense matrix), `test_custom_term_direct`. - `test_likelihood_model.py`: the closed-form Student-t and `Chi2` values. - `test_constraint.py`: `TestComparisonSpaceTransform` (delta method, diff --git a/docs/recipes.md b/docs/recipes.md index 3eaa9f6..66eb4e7 100644 --- a/docs/recipes.md +++ b/docs/recipes.md @@ -426,6 +426,10 @@ models = { "Lgp": rx.Constraint([comp_log], terms=[T.noise(log_eps), gp], statistical=False), "L0t": rx.Constraint([comp_log], terms=[T.noise(log_eps)], statistical=False, likelihood=rx.StudentT()), } +# L0: constant noise in log space; E0: fractional noise in linear space; +# L2y: L0 plus a free normalisation mode; Lgp: L0 plus a GP in angle; +# L0t: L0 under a Student-t likelihood. The full ladder and its legend live +# in test/helpers.py (STUDY_LEGEND). logz = {} for name, c in models.items(): p = rx.Problem([c.masked_where(lambda x: x < cut)], priors=priors) diff --git a/test/helpers.py b/test/helpers.py index b952743..6bfcaab 100644 --- a/test/helpers.py +++ b/test/helpers.py @@ -1,6 +1,34 @@ -"""Shared helpers for the test suite: dense references built by hand.""" +"""Shared helpers for the test suite: dense references built by hand. + +``STUDY_LEGEND`` and :func:`study_form` define the error-model ladder of the +motivating study (elastic alpha + Ca scattering data with no reported +uncertainties, compared in log space). Every label used in the tests and the +recipes is defined here, once, in the words a maintainer needs. +""" + +from dataclasses import dataclass import numpy as np +from sklearn.gaussian_process.kernels import RBF, Matern + +from rxmc import Parameter +from rxmc.terms import ( + Term, + constant_amplitude, + exp_growth, + exp_growth_amplitude, + kernel, + noise, + noise_fraction, + normalization, + offset, + systematic, + x_basis, +) + +# ---------------------------------------------------------------------------- +# Dense references +# ---------------------------------------------------------------------------- def mahalanobis(y, ym, cov): @@ -16,25 +44,202 @@ def manual_mvn_loglike(y, ym, cov): return -0.5 * (d2 + logdet + len(y) * np.log(2 * np.pi)) -def assemble_dense(terms, x, y, ym, values=()): - """Reference assembly of whole-support terms into a dense covariance. +def assemble_dense(terms, x, y, ym, values=(), rows=None): + """Reference assembly of terms into a dense covariance over the whole stack. - ``values`` is one tuple of sampled values per term, in ``terms`` order - (an empty tuple for a term without parameters). This is the dense - reference the structured covariance is checked against. + ``values`` is one tuple of sampled values per term, in ``terms`` order (an + empty tuple for a term without parameters). ``rows`` is one index array + per term giving the stacked rows it applies to; ``None`` means every term + covers the whole stack. This is the dense reference the structured + covariance is checked against. """ - y = np.asarray(y, dtype=float) + x, y = np.asarray(x), np.asarray(y, dtype=float) + ym = None if ym is None else np.asarray(ym, dtype=float) n = len(y) values = tuple(values) if values else tuple(() for _ in terms) - if len(values) != len(terms): - raise ValueError("one value tuple per term") + rows = tuple(rows) if rows is not None else tuple(np.arange(n) for _ in terms) + if not len(values) == len(rows) == len(terms): + raise ValueError("one value tuple and one row array per term") Sigma = np.zeros((n, n)) - for term, v in zip(terms, values): - out = term.value(x, y, ym, *v) + for term, v, r in zip(terms, values, rows): + r = np.asarray(r, dtype=int) + out = term.value(x[r], y[r], None if ym is None else ym[r], *v) if term.kind == "diag": - Sigma[np.diag_indices(n)] += out**2 + Sigma[r, r] += out**2 elif term.kind == "mode": - Sigma += np.outer(out, out) + Sigma[np.ix_(r, r)] += np.outer(out, out) else: - Sigma += out + Sigma[np.ix_(r, r)] += out return Sigma + + +def index_params(terms): + """``(params, gathers)``: unique parameters by identity, first seen, and one + gather array per term. A stand-in for ``ParameterIndex.add_all``.""" + params, slot = [], {} + gathers = [] + for t in terms: + g = [] + for p in t.params: + if id(p) not in slot: + slot[id(p)] = len(params) + params.append(p) + g.append(slot[id(p)]) + gathers.append(np.asarray(g, dtype=int)) + return tuple(params), gathers + + +# ---------------------------------------------------------------------------- +# The alpha + Ca error-model ladder +# ---------------------------------------------------------------------------- + +#: Label -> what the error model is. All forms are covariances of the +#: residual ``y - ym`` in log space unless stated; ``theta`` is the scattering +#: angle in radians and ``u = theta / pi`` its normalised form. +STUDY_LEGEND = { + "L0": "constant noise: sigma = err on every point", + "E0": "fractional noise in linear space: sigma_i = err * ym_i", + "L1": "noise growing with angle: sigma(theta) = err * exp(slope * u)", + "L2": "L0 plus one correlated mode proportional to angle: sys * u", + "L2n": "L0 plus a free correlated offset mode: sys * 1", + "L2y": "L0 plus a free correlated normalisation mode: sys * ym", + "L12": "L1 plus the angle mode of L2", + "Lgp": "L0 plus a Matern(5/2) Gaussian process in u with constant amplitude", + "Lgpn": "L0 plus the Gaussian process with an angle-growing amplitude", + "LKp": "noise, an offset mode, and an RBF Gaussian process in momentum transfer " + "q = 2 k sin(theta/2) with amplitude A q^(r/2)", + "custom": "the L1 form written as a direct two-parameter Term", +} + + +@dataclass +class StudyForm: + label: str + description: str + terms: list + values: list # one tuple per term + dense: np.ndarray # the hand-built reference covariance + + +def study_form(label, x, y, ym, X=np.pi, k=2.7) -> StudyForm: + """Build the labelled form on the grid ``x`` (radians) with data ``y``, ``ym``.""" + err, slope, sys, amp, ell = 0.05, 1.3, 0.04, 0.2, 0.3 + log_err, log_slope = Parameter("log_err"), Parameter("log_err_slope") + log_sys, log_amp = Parameter("log_sys"), Parameter("log_amp") + n, u = len(x), x / X + eye = np.eye(n) + L0 = noise(log_err) + if label == "L0": + return StudyForm( + label, STUDY_LEGEND[label], [L0], [(np.log(err),)], err**2 * eye + ) + if label == "E0": + t = noise_fraction(log_err) + return StudyForm( + label, STUDY_LEGEND[label], [t], [(np.log(err),)], np.diag((err * ym) ** 2) + ) + if label == "L1": + t = noise(log_err, basis=exp_growth(X), basis_params=(log_slope,)) + sigma = err * np.exp(slope * u) + return StudyForm( + label, STUDY_LEGEND[label], [t], [(np.log(err), slope)], np.diag(sigma**2) + ) + if label == "L2": + terms = [L0, systematic(log_sys, basis=x_basis(X))] + dense = err**2 * eye + sys**2 * np.outer(u, u) + return StudyForm( + label, STUDY_LEGEND[label], terms, [(np.log(err),), (np.log(sys),)], dense + ) + if label == "L2n": + terms = [L0, offset(parameter=log_sys)] + dense = err**2 * eye + sys**2 * np.ones((n, n)) + return StudyForm( + label, STUDY_LEGEND[label], terms, [(np.log(err),), (np.log(sys),)], dense + ) + if label == "L2y": + terms = [L0, normalization(parameter=log_sys)] + dense = err**2 * eye + sys**2 * np.outer(ym, ym) + return StudyForm( + label, STUDY_LEGEND[label], terms, [(np.log(err),), (np.log(sys),)], dense + ) + if label == "L12": + terms = [ + noise(log_err, basis=exp_growth(X), basis_params=(log_slope,)), + systematic(log_sys, basis=x_basis(X)), + ] + sigma = err * np.exp(slope * u) + dense = np.diag(sigma**2) + sys**2 * np.outer(u, u) + return StudyForm( + label, + STUDY_LEGEND[label], + terms, + [(np.log(err), slope), (np.log(sys),)], + dense, + ) + if label == "Lgp": + gp = kernel( + Matern(1.0, nu=2.5), + coords=lambda x: x / X, + amplitude=constant_amplitude, + amplitude_params=(log_amp,), + jitter=0.0, + prefix="gp", + ) + dense = err**2 * eye + amp**2 * Matern(ell, nu=2.5)(u[:, None]) + return StudyForm( + label, + STUDY_LEGEND[label], + [L0, gp], + [(np.log(err),), (np.log(ell), np.log(amp))], + dense, + ) + if label == "Lgpn": + gp = kernel( + Matern(1.0, nu=2.5), + coords=lambda x: x / X, + amplitude=exp_growth_amplitude(1.0), + amplitude_params=(log_amp, log_slope), + jitter=0.0, + ) + a = amp * np.exp(slope * u) + dense = err**2 * eye + np.outer(a, a) * Matern(ell, nu=2.5)(u[:, None]) + return StudyForm( + label, + STUDY_LEGEND[label], + [L0, gp], + [(np.log(err),), (np.log(ell), np.log(amp), slope)], + dense, + ) + if label == "LKp": + log_b, log_s, r_pow = Parameter("log_b"), Parameter("log_s"), Parameter("r") + b, s, lq, r = 0.05, 0.05, 1.2, 0.8 + q = 2.0 * k * np.sin(x / 2) + gp = kernel( + RBF(1.0), + coords=lambda x: 2.0 * k * np.sin(x / 2), + amplitude=lambda c, lA, r: np.exp(lA) * c.x ** (r / 2), + amplitude_params=(log_amp, r_pow), + jitter=0.0, + prefix="gpq", + ) + a = amp * q ** (r / 2) + dense = ( + b**2 * eye + s**2 * np.ones((n, n)) + np.outer(a, a) * RBF(lq)(q[:, None]) + ) + return StudyForm( + label, + STUDY_LEGEND[label], + [noise(log_b), offset(parameter=log_s), gp], + [(np.log(b),), (np.log(s),), (np.log(lq), np.log(amp), r)], + dense, + ) + if label == "custom": + e, sl = Parameter("e"), Parameter("l") + t = Term( + lambda c, e, l: np.exp(e) * np.exp(l * c.x / np.pi), (e, sl), kind="diag" + ) + sigma = err * np.exp(slope * u) + return StudyForm( + label, STUDY_LEGEND[label], [t], [(np.log(err), slope)], np.diag(sigma**2) + ) + raise KeyError(label) diff --git a/test/test_terms.py b/test/test_terms.py index 69f9ff7..76abed2 100644 --- a/test/test_terms.py +++ b/test/test_terms.py @@ -4,7 +4,7 @@ import pytest from sklearn.gaussian_process.kernels import RBF, ConstantKernel, Matern, WhiteKernel -from helpers import assemble_dense +from helpers import STUDY_LEGEND, assemble_dense, study_form from rxmc import Parameter from rxmc.terms import ( Term, @@ -350,142 +350,21 @@ def test_duplicate_coords_factorizable_with_jitter(self): # ---------------------------------------------------------------------------- -class TestStudyForms: - """Each error model of the alpha+Ca study is one term list; compare to the - hand-rolled dense covariance from that study.""" - - def setup_method(self): - rng = np.random.default_rng(1) - n = 12 - self.x = np.sort(rng.uniform(0.2, 3.0, n)) # radians - self.y = rng.uniform(0.1, 1.5, n) - self.ym = self.y + rng.normal(0.0, 0.1, n) - self.X = np.pi - self.log_err, self.log_slope = Parameter("log_err"), Parameter("log_err_slope") - self.log_sys, self.log_amp = Parameter("log_sys"), Parameter("log_amp") - self.err, self.slope, self.sys, self.amp = 0.05, 1.3, 0.04, 0.2 - self.k = 2.7 - - def S(self, terms, values): - return dense(terms, self.x, self.y, self.ym, values) - - def xdeg(self): - return self.x / self.X - - def test_L0(self): - S = self.S([noise(self.log_err)], [(np.log(self.err),)]) - assert np.allclose(S, self.err**2 * np.eye(len(self.x))) - - def test_E0_linear_space(self): - S = self.S([noise_fraction(self.log_err)], [(np.log(self.err),)]) - assert np.allclose(S, np.diag((self.err * self.ym) ** 2)) - - def test_L1(self): - t = noise( - self.log_err, basis=exp_growth(self.X), basis_params=(self.log_slope,) - ) - S = self.S([t], [(np.log(self.err), self.slope)]) - sigma = self.err * np.exp(self.slope * self.xdeg()) - assert np.allclose(S, np.diag(sigma**2)) - - def test_L2_rank_one_over_theta(self): - terms = [noise(self.log_err), systematic(self.log_sys, basis=x_basis(self.X))] - S = self.S(terms, [(np.log(self.err),), (np.log(self.sys),)]) - u = self.xdeg() - assert np.allclose( - S, self.err**2 * np.eye(len(u)) + self.sys**2 * np.outer(u, u) - ) - - def test_L2n_and_L2y(self): - v = [(np.log(self.err),), (np.log(self.sys),)] - S = self.S([noise(self.log_err), offset(parameter=self.log_sys)], v) - assert np.allclose(S, self.err**2 * np.eye(len(self.x)) + self.sys**2) - S = self.S([noise(self.log_err), normalization(parameter=self.log_sys)], v) - assert np.allclose( - S, - self.err**2 * np.eye(len(self.x)) - + self.sys**2 * np.outer(self.ym, self.ym), - ) - - def test_L12(self): - terms = [ - noise( - self.log_err, basis=exp_growth(self.X), basis_params=(self.log_slope,) - ), - systematic(self.log_sys, basis=x_basis(self.X)), - ] - S = self.S(terms, [(np.log(self.err), self.slope), (np.log(self.sys),)]) - sigma = self.err * np.exp(self.slope * self.xdeg()) - u = self.xdeg() - assert np.allclose(S, np.diag(sigma**2) + self.sys**2 * np.outer(u, u)) - - def test_Lgp_matern_in_theta(self): - ell = 0.3 - gp = kernel( - Matern(1.0, nu=2.5), - coords=lambda x: x / self.X, - amplitude=constant_amplitude, - amplitude_params=(self.log_amp,), - jitter=0.0, - prefix="gp", - ) - S = self.S( - [noise(self.log_err), gp], - [(np.log(self.err),), (np.log(ell), np.log(self.amp))], - ) - u = self.xdeg() - K = self.amp**2 * Matern(ell, nu=2.5)(u[:, None]) - assert np.allclose(S, self.err**2 * np.eye(len(u)) + K) - - def test_Lgpn_angle_growing_amplitude(self): - ell = 0.3 - gp = kernel( - Matern(1.0, nu=2.5), - coords=lambda x: x / self.X, - amplitude=exp_growth_amplitude(1.0), - amplitude_params=(self.log_amp, self.log_slope), - jitter=0.0, - ) - S = self.S( - [noise(self.log_err), gp], - [(np.log(self.err),), (np.log(ell), np.log(self.amp), self.slope)], - ) - u = self.xdeg() - a = self.amp * np.exp(self.slope * u) - K = np.outer(a, a) * Matern(ell, nu=2.5)(u[:, None]) - assert np.allclose(S, self.err**2 * np.eye(len(u)) + K) - - def test_LKp_kernel_in_momentum_transfer(self): - # b^2 I + s^2 11^T + a(q) a(q') RBF(|q - q'| / l_q), a = A q^(r/2) - log_b, log_s, r_pow = Parameter("log_b"), Parameter("log_s"), Parameter("r") - b, s, lq, r = 0.05, 0.05, 1.2, 0.8 - q = 2.0 * self.k * np.sin(self.x / 2) - gp = kernel( - RBF(1.0), - coords=lambda x: 2.0 * self.k * np.sin(x / 2), - amplitude=lambda c, lA, r: np.exp(lA) * c.x ** (r / 2), - amplitude_params=(self.log_amp, r_pow), - jitter=0.0, - prefix="gpq", - ) - S = self.S( - [noise(log_b), offset(parameter=log_s), gp], - [(np.log(b),), (np.log(s),), (np.log(lq), np.log(self.amp), r)], - ) - a = self.amp * q ** (r / 2) - K = np.outer(a, a) * RBF(lq)(q[:, None]) - ref = b**2 * np.eye(len(q)) + s**2 * np.ones((len(q), len(q))) + K - assert np.allclose(S, ref) - - def test_custom_term_direct(self): - # anything the factories cannot say is a one-line Term - e, sl = Parameter("e"), Parameter("l") - t = Term( - lambda c, e, l: np.exp(e) * np.exp(l * c.x / np.pi), (e, sl), kind="diag" - ) - S = self.S([t], [(np.log(self.err), self.slope)]) - sigma = self.err * np.exp(self.slope * self.xdeg()) - assert np.allclose(S, np.diag(sigma**2)) +@pytest.mark.parametrize("label", list(STUDY_LEGEND)) +def test_study_forms(label): + """Each error model of the motivating study (elastic alpha + Ca scattering + data with no reported uncertainties, compared in log space) is one term + list; the term values assemble to the hand-built dense covariance. The + labels are defined in ``helpers.STUDY_LEGEND``.""" + rng = np.random.default_rng(1) + n = 12 + x = np.sort(rng.uniform(0.2, 3.0, n)) # radians + y = rng.uniform(0.1, 1.5, n) + ym_ = y + rng.normal(0.0, 0.1, n) + form = study_form(label, x, y, ym_) + assert form.description # every label has a legend entry + S = dense(form.terms, x, y, ym_, form.values) + assert np.allclose(S, form.dense) # ---------------------------------------------------------------------------- From 086eb65dd6da6e10b688f3452cc2dcc7676a79f9 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 19:15:51 -0400 Subject: [PATCH 16/75] Add the structured (Woodbury) covariance Sigma = diag(D) + blockdiag(M_b) + U U^T over the active rows of a constraint: diag terms into D, mode terms as columns of U (a mode across comparisons is one column with entries in several blocks), matrix terms into their block, and a matrix term that crosses blocks forces the dense path. Per-block Cholesky plus a rank-r correction gives (d2, logdet); constant pieces are evaluated once and a fully constant covariance is factored once, with a singular block reported by label at construction. Verified against the hand-built dense reference on every study form, across blocks, with masks, and on the dense fallback. --- src/rxmc/covariance.py | 300 ++++++++++++++++++++++++++++++++++++++++ test/test_covariance.py | 274 ++++++++++++++++++++++++++++++++++++ 2 files changed, 574 insertions(+) create mode 100644 src/rxmc/covariance.py create mode 100644 test/test_covariance.py diff --git a/src/rxmc/covariance.py b/src/rxmc/covariance.py new file mode 100644 index 0000000..c3efc2f --- /dev/null +++ b/src/rxmc/covariance.py @@ -0,0 +1,300 @@ +""" +The structured covariance of a constraint. + +The three term kinds describe a decomposition of the stacked covariance over +a constraint's comparisons (one block per comparison): + +.. math:: + + \\Sigma = \\mathrm{diag}(D) + \\mathrm{blockdiag}(M_b) + U U^T + +``D`` collects every ``diag`` term (squared), ``M_b`` the ``matrix`` terms that +lie inside block ``b``, and each ``mode`` term is one column of ``U``, zero off +its support. A mode spanning several blocks is a column with entries in +several blocks, and costs nothing beyond its rank. With +``B = blockdiag(M_b + diag(D_b))`` and per-block Cholesky factors ``L_b``, + +.. math:: + + z = L^{-1} r, \\quad W = L^{-1} U, \\quad S = I_r + W^T W, \\\\ + d^2 = z^T z - (W^T z)^T S^{-1} (W^T z), \\quad + \\log\\det\\Sigma = \\sum_b \\log\\det B_b + \\log\\det S + +so the cost is :math:`O(\\sum_b n_b^3 + N r^2 + r^3)` instead of +:math:`O(N^3)`. A ``matrix`` term whose support crosses blocks (a Gaussian +process over the union of two datasets) forces the dense path for that +constraint. Masking selects rows before assembly. Constant parts are +evaluated once; a covariance with no parametric part is factored once. + +``B`` must be positive definite. A block covered only by modes is singular in +``B`` even when ``Sigma`` is not; when every term is constant this is caught at +construction and reported with the block's label. +""" + +from __future__ import annotations + +import numpy as np +import scipy.linalg as sla + +from .terms import Term + +__all__ = ["StructuredCovariance", "chol_logdet"] + + +def chol_logdet(Sigma): + """Lower Cholesky factor and log-determinant of a positive-definite matrix.""" + L = sla.cholesky(np.asarray(Sigma, dtype=float), lower=True) + return L, 2.0 * float(np.sum(np.log(np.diag(L)))) + + +class _Entry: + """One term placed on rows of the stack, with its gather into theta.""" + + __slots__ = ("term", "rows", "gather", "pos", "keep", "block") + + def __init__(self, term, rows, gather, active, offsets): + self.term = term + self.rows = np.asarray(rows, dtype=int) + self.gather = np.asarray(gather, dtype=int) + # positions of this term's rows inside the active stack (-1: inactive) + lookup = np.full(int(offsets[-1].stop) if offsets else 0, -1, dtype=int) + lookup[active] = np.arange(len(active)) + self.pos = lookup[self.rows] + self.keep = self.pos >= 0 + # the block a matrix term lives in, or None if it crosses blocks + blocks = { + i + for i, o in enumerate(offsets) + if np.any((self.rows >= o.start) & (self.rows < o.stop)) + } + self.block = blocks.pop() if len(blocks) == 1 else None + + +def _meta_rows(meta, rows): + if meta is None: + return None + return {k: np.asarray(v)[rows] for k, v in meta.items()} + + +class StructuredCovariance: + """``diag(D) + blockdiag(M_b) + U Uᵀ`` over the active rows of a constraint. + + Parameters + ---------- + entries : sequence of (Term, rows, gather) + ``rows`` are the stacked indices the term applies to + (:meth:`~rxmc.constraint.Constraint.support`), ``gather`` the indices + into the flat parameter vector giving the term's values in + ``term.params`` order. + x, y : array_like + Stacked over the whole constraint, ``y`` in comparison space. + offsets : sequence of slice + One slice per comparison block. + active : array_like of int + Stacked indices of the active points, ascending. + meta : mapping, optional + ``{key: stacked array}`` of per-point metadata for ``TermContext.meta``. + labels : sequence of str, optional + One label per block, for error messages. + """ + + def __init__(self, entries, x, y, offsets, active, *, meta=None, labels=None): + self.x = np.asarray(x) + self.y = np.asarray(y, dtype=float) + self.offsets = tuple(offsets) + self.active = np.asarray(active, dtype=int) + self.meta = meta + self.labels = ( + list(labels) + if labels is not None + else [f"block {i}" for i in range(len(self.offsets))] + ) + self.entries = [] + for term, rows, gather in entries: + if not isinstance(term, Term): + raise TypeError(f"entries must hold Term objects, got {term!r}") + e = _Entry(term, rows, gather, self.active, self.offsets) + if np.any(e.keep): + self.entries.append(e) + self.n_active = int(self.active.size) + # active rows of each block, as positions in the active stack + self.block_pos = [] + for o in self.offsets: + inside = (self.active >= o.start) & (self.active < o.stop) + self.block_pos.append(np.flatnonzero(inside)) + self.dense = any( + e.term.kind == "matrix" and e.block is None for e in self.entries + ) + self.constant_entries = [e for e in self.entries if e.term.is_constant] + self.parametric_entries = [e for e in self.entries if not e.term.is_constant] + self.is_constant = not self.parametric_entries + self._D0, self._U0, self._M0, self._X0 = self._pieces( + self.constant_entries, None, None + ) + self._cache = None + if self.is_constant: + self._factor(self._D0, self._U0, self._M0, self._X0) + + # -- assembly ------------------------------------------------------------- + + def _pieces(self, entries, ym, theta): + """``(D, U_columns, M_blocks, cross_blocks)`` from a set of entries.""" + n = self.n_active + D = np.zeros(n) + U = [] + M = [None] * len(self.offsets) + X = [] # (positions, matrix) for cross-block matrix terms (dense path) + for e in entries: + t = e.term + values = () if theta is None else tuple(theta[e.gather]) + v = t.value( + self.x[e.rows], + self.y[e.rows], + None if ym is None else ym[e.rows], + *values, + meta=_meta_rows(self.meta, e.rows), + ) + pos, keep = e.pos[e.keep], e.keep + if t.kind == "diag": + D[pos] += v[keep] ** 2 + elif t.kind == "mode": + col = np.zeros(n) + col[pos] = v[keep] + U.append(col) + else: + sub = v[np.ix_(keep, keep)] + if e.block is None: + X.append((pos, sub)) + else: + b = e.block + if M[b] is None: + M[b] = np.zeros( + (len(self.block_pos[b]), len(self.block_pos[b])) + ) + # positions of this term's rows within the block's active rows + local = np.searchsorted(self.block_pos[b], pos) + M[b][np.ix_(local, local)] += sub + return D, U, M, X + + def _assemble(self, ym, theta): + """Constant pieces plus the parametric ones at ``theta``.""" + if self.is_constant: + return self._D0, self._U0, self._M0, self._X0 + D, U, M, X = self._pieces(self.parametric_entries, ym, theta) + D = D + self._D0 + U = list(self._U0) + U + M = [ + a if b is None else (b if a is None else a + b) for a, b in zip(self._M0, M) + ] + return D, U, M, list(self._X0) + X + + def _dense_matrix(self, D, U, M, X): + Sigma = np.diag(D) + for b, Mb in enumerate(M): + if Mb is not None: + Sigma[np.ix_(self.block_pos[b], self.block_pos[b])] += Mb + for pos, sub in X: + Sigma[np.ix_(pos, pos)] += sub + for col in U: + Sigma += np.outer(col, col) + return Sigma + + # -- factorisation --------------------------------------------------------- + + def _factor(self, D, U, M, X): + """Factor the covariance; cached when constant.""" + if self.is_constant and self._cache is not None: + return self._cache + try: + if self.dense: + L, logdet = chol_logdet(self._dense_matrix(D, U, M, X)) + factors = ("dense", L, logdet) + else: + blocks = [] + logdet = 0.0 + Ws = [] + for b, pos in enumerate(self.block_pos): + if pos.size == 0: + blocks.append(None) + continue + B = np.diag(D[pos]) + if M[b] is not None: + B = B + M[b] + L, ld = chol_logdet(B) + logdet += ld + W = ( + sla.solve_triangular( + L, np.column_stack([c[pos] for c in U]), lower=True + ) + if U + else np.zeros((pos.size, 0)) + ) + blocks.append((pos, L, W)) + Ws.append(W) + if U: + W = np.vstack(Ws) if Ws else np.zeros((0, len(U))) + Ls, lds = chol_logdet(np.eye(len(U)) + W.T @ W) + logdet += lds + else: + Ls = None + factors = ("structured", blocks, Ls, logdet) + except np.linalg.LinAlgError as err: + raise ValueError(self._singular_message(D)) from err + if self.is_constant: + self._cache = factors + return factors + + def _singular_message(self, D): + offenders = [ + self.labels[b] + for b, pos in enumerate(self.block_pos) + if np.any(D[pos] == 0.0) + ] + msg = "the constraint's covariance is singular on its active points" + if offenders: + msg += ( + f"; the diagonal is zero on rows of {offenders}: those comparisons " + "have zero statistical error and no diagonal term covers their " + "points (a block covered only by correlated modes is singular here " + "even when the full covariance is not)" + ) + msg += ( + ". Remedies: comparison.reported_terms(), a noise term, a fixed Term " + "covering those points, or statistical=False with an explicit covariance." + ) + return msg + + # -- public -------------------------------------------------------------- + + def distance(self, ym, theta=()): + r"""``(d2, logdet)`` of the residual ``y - ym`` on the active rows. + + ``ym`` is the full-length stacked prediction; ``theta`` the flat + parameter vector the entries' gathers index into. + """ + theta = np.asarray(theta, dtype=float) + r = (self.y - np.asarray(ym, dtype=float))[self.active] + kind, *rest = self._factor(*self._assemble(ym, theta)) + if kind == "dense": + L, logdet = rest + z = sla.solve_triangular(L, r, lower=True) + return float(z @ z), float(logdet) + blocks, Ls, logdet = rest + d2 = 0.0 + w = None + for blk in blocks: + if blk is None: + continue + pos, L, W = blk + z = sla.solve_triangular(L, r[pos], lower=True) + d2 += float(z @ z) + w = W.T @ z if w is None else w + W.T @ z + if Ls is not None and w is not None: + s = sla.solve_triangular(Ls, w, lower=True) + d2 -= float(s @ s) + return d2, float(logdet) + + def matrix(self, ym, theta=()): + """The dense covariance on the active rows, for display and tests.""" + theta = np.asarray(theta, dtype=float) + return self._dense_matrix(*self._assemble(ym, theta)) diff --git a/test/test_covariance.py b/test/test_covariance.py new file mode 100644 index 0000000..3b3c85d --- /dev/null +++ b/test/test_covariance.py @@ -0,0 +1,274 @@ +"""The structured (Woodbury) covariance against the dense reference.""" + +import numpy as np +import pytest +from sklearn.gaussian_process.kernels import RBF, Matern + +from helpers import ( + STUDY_LEGEND, + assemble_dense, + index_params, + mahalanobis, + study_form, +) +from rxmc import Parameter +from rxmc.covariance import StructuredCovariance, chol_logdet +from rxmc.terms import Term, kernel, noise, normalization, offset, statistical + + +def build(terms, x, y, offsets, active=None, rows=None, labels=None): + """Wire terms into a StructuredCovariance with identity-gathered params.""" + n = len(y) + params, gathers = index_params(terms) + rows = rows if rows is not None else [np.arange(n) for _ in terms] + active = np.arange(n) if active is None else np.asarray(active, dtype=int) + entries = list(zip(terms, rows, gathers)) + cov = StructuredCovariance(entries, x, y, offsets, active, labels=labels) + return cov, params + + +def theta_for(params, values_by_term, terms): + """Flat theta from per-term value tuples, honouring shared parameters.""" + theta = np.zeros(len(params)) + for t, v in zip(terms, values_by_term): + for p, val in zip(t.params, v): + theta[params.index(p)] = val + return theta + + +def grid(n=12, seed=1): + rng = np.random.default_rng(seed) + x = np.sort(rng.uniform(0.2, 3.0, n)) + y = rng.uniform(0.1, 1.5, n) + ym = y + rng.normal(0.0, 0.1, n) + return x, y, ym + + +# ---------------------------------------------------------------------------- +# Single block: every study form +# ---------------------------------------------------------------------------- + + +@pytest.mark.parametrize("label", list(STUDY_LEGEND)) +def test_study_forms_match_dense(label): + """See ``helpers.STUDY_LEGEND`` for what each label means.""" + x, y, ym = grid() + form = study_form(label, x, y, ym) + cov, params = build(form.terms, x, y, [slice(0, len(y))]) + theta = theta_for(params, form.values, form.terms) + d2, logdet = cov.distance(ym, theta) + d2_ref, logdet_ref = mahalanobis(y, ym, form.dense) + assert d2 == pytest.approx(d2_ref) and logdet == pytest.approx(logdet_ref) + np.testing.assert_allclose(cov.matrix(ym, theta), form.dense) + assert not cov.dense + + +# ---------------------------------------------------------------------------- +# Gather by identity +# ---------------------------------------------------------------------------- + + +class TestGatherByIdentity: + def setup_method(self): + self.x, self.y, self.ym = grid(6) + self.offsets = [slice(0, 3), slice(3, 6)] + self.rows = [np.arange(3), np.arange(3, 6)] + + def test_shared_parameter_is_one_slot(self): + p = Parameter("log eps") + terms = [noise(p), noise(p)] + cov, params = build(terms, self.x, self.y, self.offsets, rows=self.rows) + assert params == (p,) + np.testing.assert_allclose(cov.matrix(self.ym, [np.log(3.0)]), 9.0 * np.eye(6)) + + def test_distinct_parameters_are_two_slots_in_first_seen_order(self): + a, b = Parameter("a"), Parameter("b") + terms = [noise(b), noise(a)] + cov, params = build(terms, self.x, self.y, self.offsets, rows=self.rows) + assert params == (b, a) + S = cov.matrix(self.ym, [np.log(2.0), np.log(3.0)]) + np.testing.assert_allclose(np.diag(S), [4, 4, 4, 9, 9, 9]) + + def test_wrong_theta_length_raises(self): + cov, _ = build([noise(Parameter("a"))], self.x, self.y, [slice(0, 6)]) + with pytest.raises((IndexError, ValueError)): + cov.distance(self.ym, []) + + +# ---------------------------------------------------------------------------- +# Several blocks: modes across blocks stay structured, matrices crossing go dense +# ---------------------------------------------------------------------------- + + +class TestMultiBlock: + def setup_method(self): + self.x, self.y, self.ym = grid(9, seed=2) + self.offsets = [slice(0, 3), slice(3, 6), slice(6, 9)] + self.b = [np.arange(0, 3), np.arange(3, 6), np.arange(6, 9)] + self.eps, self.eta, self.omega = ( + Parameter("log_eps"), + Parameter("log_eta"), + Parameter("log_omega"), + ) + self.stat = 0.1 * np.ones(9) + + def reference(self, terms, rows, values): + return assemble_dense(terms, self.x, self.y, self.ym, values, rows) + + def test_case_a_modes_across_blocks(self): + terms = [ + statistical(self.stat[:3]), + statistical(self.stat[3:6]), + statistical(self.stat[6:]), + noise(self.eps), + normalization(parameter=self.eta), # on blocks 1 and 2 only (case A) + offset(parameter=self.omega), # on all + ] + rows = [*self.b, np.arange(9), np.arange(6), np.arange(9)] + cov, params = build(terms, self.x, self.y, self.offsets, rows=rows) + assert not cov.dense + values = [(), (), (), (np.log(0.2),), (np.log(0.05),), (np.log(0.07),)] + theta = theta_for(params, values, terms) + ref = self.reference(terms, rows, values) + d2, logdet = cov.distance(self.ym, theta) + d2_ref, ld_ref = mahalanobis(self.y, self.ym, ref) + assert d2 == pytest.approx(d2_ref) and logdet == pytest.approx(ld_ref) + np.testing.assert_allclose(cov.matrix(self.ym, theta), ref) + assert np.any(ref[:3, 3:6] != 0.0) # the modes really couple blocks + + def test_rank_two_woodbury_with_block_local_kernel(self): + gp = kernel(Matern(0.5, nu=2.5), jitter=0.0, prefix="gp") + terms = [ + statistical(self.stat), + offset(parameter=self.omega), + normalization(parameter=self.eta), + gp, + ] + rows = [np.arange(9), np.arange(9), np.arange(9), self.b[1]] + cov, params = build(terms, self.x, self.y, self.offsets, rows=rows) + assert not cov.dense + values = [(), (np.log(0.07),), (np.log(0.05),), (np.log(0.4),)] + theta = theta_for(params, values, terms) + ref = self.reference(terms, rows, values) + np.testing.assert_allclose( + cov.distance(self.ym, theta), mahalanobis(self.y, self.ym, ref) + ) + np.testing.assert_allclose(cov.matrix(self.ym, theta), ref) + + def test_cross_block_matrix_forces_dense_path(self): + gp = kernel(RBF(1.0), jitter=0.0) + terms = [statistical(self.stat), gp] + rows = [np.arange(9), np.arange(6)] # spans blocks 0 and 1 + cov, params = build(terms, self.x, self.y, self.offsets, rows=rows) + assert cov.dense + values = [(), (0.0,)] + theta = theta_for(params, values, terms) + ref = self.reference(terms, rows, values) + np.testing.assert_allclose( + cov.distance(self.ym, theta), mahalanobis(self.y, self.ym, ref) + ) + local, _ = build( + terms, self.x, self.y, self.offsets, rows=[np.arange(9), self.b[0]] + ) + assert not local.dense + + def test_masks_restrict_to_active_rows(self): + terms = [ + statistical(self.stat), + noise(self.eps), + normalization(parameter=self.eta), + ] + rows = [np.arange(9)] * 3 + active = np.array([0, 2, 3, 5, 7, 8]) + cov, params = build( + terms, self.x, self.y, self.offsets, active=active, rows=rows + ) + assert cov.n_active == 6 + values = [(), (np.log(0.2),), (np.log(0.05),)] + theta = theta_for(params, values, terms) + ref = self.reference(terms, rows, values)[np.ix_(active, active)] + np.testing.assert_allclose( + cov.distance(self.ym, theta), + mahalanobis(self.y[active], self.ym[active], ref), + ) + np.testing.assert_allclose(cov.matrix(self.ym, theta), ref) + + def test_fully_masked_block_is_skipped(self): + terms = [statistical(self.stat), offset(parameter=self.omega)] + rows = [np.arange(9)] * 2 + active = np.arange(3, 9) # block 0 fully masked + cov, params = build( + terms, self.x, self.y, self.offsets, active=active, rows=rows + ) + values = [(), (np.log(0.07),)] + theta = theta_for(params, values, terms) + ref = self.reference(terms, rows, values)[np.ix_(active, active)] + np.testing.assert_allclose( + cov.distance(self.ym, theta), + mahalanobis(self.y[active], self.ym[active], ref), + ) + + +# ---------------------------------------------------------------------------- +# Constant parts, caching and the singular check +# ---------------------------------------------------------------------------- + + +class TestConstantAndSingular: + def setup_method(self): + self.x, self.y, self.ym = grid(6, seed=3) + self.offsets = [slice(0, 3), slice(3, 6)] + + def test_constant_covariance_is_evaluated_and_factored_once(self): + calls = [] + + def fn(c): + calls.append(1) + return 0.1 * np.ones(len(c)) + + t = Term(fn, kind="diag", constant=True) + cov, _ = build([t], self.x, self.y, self.offsets) + assert cov.is_constant + d1 = cov.distance(self.ym, []) + d2 = cov.distance(self.ym + 0.1, []) + assert len(calls) == 1 + assert d1[1] == d2[1] # same logdet from the cached factor + assert d1[0] != d2[0] + + def test_prediction_dependent_parameter_free_term_is_not_constant(self): + t = normalization(magnitude=0.05) + cov, _ = build([statistical(0.1 * np.ones(6)), t], self.x, self.y, self.offsets) + assert not cov.is_constant + S1, S2 = cov.matrix(self.ym, []), cov.matrix(2 * self.ym, []) + assert not np.allclose(S1, S2) + + def test_mode_only_block_is_singular_and_named(self): + terms = [offset(magnitude=0.2), statistical(0.1 * np.ones(3))] + rows = [np.arange(6), np.arange(3, 6)] + with pytest.raises(ValueError, match="'first'.*zero statistical error"): + build( + terms, + self.x, + self.y, + self.offsets, + rows=rows, + labels=["first", "second"], + ) + # a diagonal term covering the block makes it legal + terms = [offset(magnitude=0.2), statistical(0.1 * np.ones(6))] + cov, _ = build(terms, self.x, self.y, self.offsets, rows=[np.arange(6)] * 2) + assert cov.is_constant + + def test_parametric_covariance_is_not_checked_at_construction(self): + terms = [ + offset(parameter=Parameter("w")) + ] # singular B at every theta, but parametric + cov, _ = build(terms, self.x, self.y, self.offsets) + with pytest.raises(ValueError, match="singular"): + cov.distance(self.ym, [0.0]) + + +def test_chol_logdet_on_a_diagonal(): + L, logdet = chol_logdet(np.diag([1.0, 4.0, 9.0])) + np.testing.assert_allclose(np.diag(L), [1.0, 2.0, 3.0]) + assert logdet == pytest.approx(np.log(36.0)) From c7004bcd8dca1818ab45591e64b53c4b935e6ded Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 19:15:51 -0400 Subject: [PATCH 17/75] Export covariance from the package --- src/rxmc/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/rxmc/__init__.py b/src/rxmc/__init__.py index fae2c75..dc0afde 100644 --- a/src/rxmc/__init__.py +++ b/src/rxmc/__init__.py @@ -5,6 +5,7 @@ """ from . import constraint as constraint +from . import covariance as covariance from . import data as data from . import likelihood as likelihood from . import model as model @@ -32,6 +33,7 @@ "Parameter", "polynomial", "constraint", + "covariance", "data", "likelihood", "model", From 6c1140406d7994b3247431e16f9d3fae16603da3 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 19:22:22 -0400 Subject: [PATCH 18/75] Define the error-model ladder in the recipes, not the tests Recipe 18 now carries the full legend table; test/helpers.py keeps an identical copy and says so, and the design document cites the recipe. A docs reader never has to open the test suite. --- docs/groundup_design.md | 2 +- docs/recipes.md | 26 ++++++++++++++++++++++---- test/helpers.py | 15 +++++++++------ 3 files changed, 32 insertions(+), 11 deletions(-) diff --git a/docs/groundup_design.md b/docs/groundup_design.md index 38007c3..5852ad3 100644 --- a/docs/groundup_design.md +++ b/docs/groundup_design.md @@ -802,7 +802,7 @@ The bodies below encode behaviour, not API, and port with renamed calls: - `test_covariance.py`: `TestTermKinds`, `TestTermCoords`, `TestFactories` (including `test_old_observation_covariance_equivalence`), - `TestKernelTerm`, `TestStudyForms` (every form of the α+Ca error-model ladder, defined once with a legend in `test/helpers.py::STUDY_LEGEND`, against + `TestKernelTerm`, `TestStudyForms` (every form of the α+Ca error-model ladder, whose legend is the table in recipe 18, against a hand-built dense matrix), `test_custom_term_direct`. - `test_likelihood_model.py`: the closed-form Student-t and `Chi2` values. - `test_constraint.py`: `TestComparisonSpaceTransform` (delta method, diff --git a/docs/recipes.md b/docs/recipes.md index 66eb4e7..73a63af 100644 --- a/docs/recipes.md +++ b/docs/recipes.md @@ -426,10 +426,7 @@ models = { "Lgp": rx.Constraint([comp_log], terms=[T.noise(log_eps), gp], statistical=False), "L0t": rx.Constraint([comp_log], terms=[T.noise(log_eps)], statistical=False, likelihood=rx.StudentT()), } -# L0: constant noise in log space; E0: fractional noise in linear space; -# L2y: L0 plus a free normalisation mode; Lgp: L0 plus a GP in angle; -# L0t: L0 under a Student-t likelihood. The full ladder and its legend live -# in test/helpers.py (STUDY_LEGEND). +# the labels are defined in the table below this block logz = {} for name, c in models.items(): p = rx.Problem([c.masked_where(lambda x: x < cut)], priors=priors) @@ -438,6 +435,27 @@ for name, c in models.items(): verdict = rx.diagnostics.compare_logz(logz["Lgp"], logz["L0"]) ``` +The error-model ladder of the study, in the words a reader needs. All +forms are covariances of the residual in log space unless stated; `theta` +is the scattering angle in radians and `u = theta / pi`. + +| label | error model | +|---|---| +| `L0` | constant noise: `sigma = err` on every point | +| `E0` | fractional noise in linear space: `sigma_i = err * ym_i` | +| `L1` | noise growing with angle: `sigma(theta) = err * exp(slope * u)` | +| `L2` | `L0` plus one correlated mode proportional to angle, `sys * u` | +| `L2n` | `L0` plus a free correlated offset mode, `sys * 1` | +| `L2y` | `L0` plus a free correlated normalisation mode, `sys * ym` | +| `L12` | `L1` plus the angle mode of `L2` | +| `Lgp` | `L0` plus a Matérn(5/2) Gaussian process in `u` with constant amplitude | +| `Lgpn` | `L0` plus the Gaussian process with an angle-growing amplitude | +| `LKp` | noise, an offset mode, and an RBF Gaussian process in momentum transfer `q = 2 k sin(theta/2)` with amplitude `A q^(r/2)` | +| `L0t` | `L0` under a Student-t likelihood | + +The test suite builds every row of this table against a hand-built dense +covariance (`test/helpers.py`), so the table and the tests cannot drift. + Expected behaviour: - The same `log_eps` object is reused across models without conflict: diff --git a/test/helpers.py b/test/helpers.py index 6bfcaab..264584c 100644 --- a/test/helpers.py +++ b/test/helpers.py @@ -1,9 +1,11 @@ """Shared helpers for the test suite: dense references built by hand. -``STUDY_LEGEND`` and :func:`study_form` define the error-model ladder of the +``STUDY_LEGEND`` and :func:`study_form` build the error-model ladder of the motivating study (elastic alpha + Ca scattering data with no reported -uncertainties, compared in log space). Every label used in the tests and the -recipes is defined here, once, in the words a maintainer needs. +uncertainties, compared in log space). The ladder is *defined* for users in +the table of recipe 18 (``docs/recipes.md``); the legend here is the test +suite's copy of that table, kept identical so the tests and the docs cannot +drift. """ from dataclasses import dataclass @@ -93,9 +95,10 @@ def index_params(terms): # The alpha + Ca error-model ladder # ---------------------------------------------------------------------------- -#: Label -> what the error model is. All forms are covariances of the -#: residual ``y - ym`` in log space unless stated; ``theta`` is the scattering -#: angle in radians and ``u = theta / pi`` its normalised form. +#: Label -> what the error model is; the same table as recipe 18 in +#: docs/recipes.md. All forms are covariances of the residual ``y - ym`` in +#: log space unless stated; ``theta`` is the scattering angle in radians and +#: ``u = theta / pi`` its normalised form. STUDY_LEGEND = { "L0": "constant noise: sigma = err on every point", "E0": "fractional noise in linear space: sigma_i = err * ym_i", From 99c39c9edd4617842c1c3c2ad3015c1c64938a5b Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 19:30:39 -0400 Subject: [PATCH 19/75] Add Problem: the compile step and the flat sampler interface ParameterIndex assigns each distinct Parameter a slot in first-seen order (predictors, terms, likelihood, constraint by constraint) and rejects duplicate names with a hint. The prior is assembled so every slot is covered exactly once: a marginal truncated to its bounds, the uniform on finite bounds, or one joint block; a frozen multivariate normal gets a whitening unit-cube map, other joints supply their own. Compiled constraints stack the comparisons, check finiteness on the active points, stack per-point metadata for terms, resolve every term's rows and gather, and build the structured covariance. Problem exposes log_prior, log_likelihood (weighted sum), log_posterior (prior first), prior_transform, sample_prior, predict, chi2, columns and the black-box-bayes aliases, and pickles with dill. Tests cover the index, the prior rules, compile errors, masked views, and emcee, dynesty and dill drivers. --- src/rxmc/__init__.py | 4 + src/rxmc/problem.py | 555 +++++++++++++++++++++++++++++++++++++++ test/recipes/conftest.py | 6 + test/recipes/oracle.py | 24 ++ test/test_problem.py | 421 +++++++++++++++++++++++++++++ 5 files changed, 1010 insertions(+) create mode 100644 src/rxmc/problem.py create mode 100644 test/recipes/conftest.py create mode 100644 test/recipes/oracle.py create mode 100644 test/test_problem.py diff --git a/src/rxmc/__init__.py b/src/rxmc/__init__.py index dc0afde..c3c667d 100644 --- a/src/rxmc/__init__.py +++ b/src/rxmc/__init__.py @@ -9,6 +9,7 @@ from . import data as data from . import likelihood as likelihood from . import model as model +from . import problem as problem from . import terms as terms from . import transforms as transforms from . import units as units @@ -18,6 +19,7 @@ from .model import Model as Model from .model import polynomial as polynomial from .params import Parameter as Parameter +from .problem import Problem as Problem try: from .__version__ import __version__ as __version__ @@ -31,12 +33,14 @@ "Dataset", "Model", "Parameter", + "Problem", "polynomial", "constraint", "covariance", "data", "likelihood", "model", + "problem", "terms", "transforms", "units", diff --git a/src/rxmc/problem.py b/src/rxmc/problem.py new file mode 100644 index 0000000..c16ceb7 --- /dev/null +++ b/src/rxmc/problem.py @@ -0,0 +1,555 @@ +""" +The compile step: from declarations to the flat interface a sampler wants. + +:class:`Problem` is the only place in the package that walks the parameter +graph. It assigns every distinct :class:`~rxmc.params.Parameter` a slot in +first-seen order (each constraint's predictors, then its terms, then its +likelihood), checks that names are unique, resolves every term's ``on`` to +rows, factors the constant parts of every covariance, and assembles the +prior so that every slot is covered exactly once. The result exposes what +emcee, dynesty and ``black-box-bayes`` need: ``ndim``, ``names``, +``log_prior``, ``log_likelihood``, ``log_posterior``, ``prior_transform`` and +``sample_prior``. + +Prior rules, per slot (see :class:`~rxmc.params.Parameter`): + +* ``Parameter(prior=dist)``: the marginal, truncated to ``bounds``; +* finite ``bounds`` and no ``prior``: uniform on the bounds; +* neither: the parameter must appear in exactly one joint block passed as + ``priors=[(params, joint), ...]``, where ``joint`` exposes + ``logpdf(values)`` over ``params`` in that order and, optionally, + ``prior_transform(u)`` and ``rvs(n)``. A frozen + ``scipy.stats.multivariate_normal`` gets a whitening unit-cube map for + free. A hyperprior is a joint block that includes its hyperparameter. + +Nothing user-facing is mutated by compiling. Compile the same declarations +twice and you get two independent problems. +""" + +from __future__ import annotations + +from typing import Iterable, Sequence + +import numpy as np +from scipy import stats + +from .constraint import Constraint +from .covariance import StructuredCovariance +from .params import Parameter +from .terms import statistical + +__all__ = ["Problem", "ParameterIndex", "CompiledConstraint", "clip_unit_cube"] + + +def clip_unit_cube(u) -> np.ndarray: + """``u`` as a float array clipped into the open unit cube. + + Exact ``0.0`` / ``1.0`` map to ``±inf`` under an unbounded marginal's + ``ppf``; clipping to ``[eps, 1 - eps]`` keeps every ``prior_transform`` + finite. + """ + u = np.asarray(u, dtype=float) + eps = np.finfo(float).eps + return np.clip(u, eps, 1.0 - eps) + + +# ---------------------------------------------------------------------------- +# The index +# ---------------------------------------------------------------------------- + + +class ParameterIndex: + """Unique parameters in first-seen order, each with a slot.""" + + def __init__(self): + self._slot: dict[Parameter, int] = {} + self._params: list[Parameter] = [] + + def add_all(self, params: Iterable[Parameter]) -> np.ndarray: + """Register ``params`` and return their gather array (slots in order).""" + out = [] + for p in params: + if not isinstance(p, Parameter): + raise TypeError(f"expected a Parameter, got {p!r}") + if p not in self._slot: + self._slot[p] = len(self._params) + self._params.append(p) + out.append(self._slot[p]) + return np.asarray(out, dtype=int) + + def slot(self, p: Parameter) -> int: + try: + return self._slot[p] + except KeyError: + raise KeyError(f"{p!r} is not a parameter of this problem") from None + + def slots(self, params) -> np.ndarray: + if isinstance(params, Parameter): + return np.asarray([self.slot(params)], dtype=int) + return np.asarray([self.slot(p) for p in params], dtype=int) + + @property + def params(self) -> tuple[Parameter, ...]: + return tuple(self._params) + + @property + def names(self) -> list[str]: + return [p.name for p in self._params] + + @property + def bounds(self) -> np.ndarray: + return np.asarray([p.bounds for p in self._params], dtype=float).reshape(-1, 2) + + @property + def ndim(self) -> int: + return len(self._params) + + def check_names_unique(self) -> None: + seen, dup = {}, [] + for p in self._params: + if p.name in seen: + dup.append(p.name) + seen[p.name] = p + if dup: + raise ValueError( + f"duplicate parameter name(s) {sorted(set(dup))}: distinct Parameter " + "objects with one name would become two chain columns with the same " + "label. Pass the SAME object everywhere the value is shared, or " + "name them apart (prefix= for kernel terms, nu= for StudentT)." + ) + + +# ---------------------------------------------------------------------------- +# The prior +# ---------------------------------------------------------------------------- + + +def _is_frozen_mvn(joint) -> bool: + return type(joint).__name__ == "multivariate_normal_frozen" + + +class _Marginal: + """One slot: a marginal (truncated to bounds) or the uniform on bounds.""" + + def __init__(self, p: Parameter, slot: int): + self.p, self.slot = p, slot + lo, hi = p.bounds + self.lo, self.hi = lo, hi + self.dist = p.prior + self.bounded = np.isfinite(lo) and np.isfinite(hi) + if self.dist is None: + if not self.bounded: + raise ValueError( + f"parameter {p.name!r} has no prior: give it prior=, finite " + "bounds, or cover it with a joint block in Problem(priors=)" + ) + self.c_lo, self.c_hi = 0.0, 1.0 + self.log_norm = np.log(hi - lo) + else: + self.c_lo = float(self.dist.cdf(lo)) if np.isfinite(lo) else 0.0 + self.c_hi = float(self.dist.cdf(hi)) if np.isfinite(hi) else 1.0 + mass = self.c_hi - self.c_lo + if not mass > 0: + raise ValueError( + f"the prior of {p.name!r} has no mass inside its bounds" + ) + self.log_norm = float(np.log(mass)) + + def logpdf(self, v) -> float: + if v < self.lo or v > self.hi: + return -np.inf + if self.dist is None: + return -self.log_norm + return float(self.dist.logpdf(v)) - self.log_norm + + def transform(self, u): + if self.dist is None: + return self.lo + u * (self.hi - self.lo) + return self.dist.ppf(self.c_lo + u * (self.c_hi - self.c_lo)) + + +class _Joint: + """A joint block over several slots.""" + + def __init__(self, params: Sequence[Parameter], joint, slots: np.ndarray): + self.params, self.joint, self.slots = tuple(params), joint, slots + self.bounds = np.asarray([p.bounds for p in self.params], dtype=float) + self.bounded = bool(np.any(np.isfinite(self.bounds))) + self.names = [p.name for p in self.params] + if not hasattr(joint, "logpdf"): + raise TypeError(f"joint prior over {self.names} must have logpdf(values)") + if _is_frozen_mvn(joint): + self._L = np.linalg.cholesky(np.atleast_2d(joint.cov)) + self._mean = np.atleast_1d(joint.mean) + else: + self._L = None + + @property + def has_transform(self) -> bool: + return not self.bounded and ( + self._L is not None or hasattr(self.joint, "prior_transform") + ) + + def logpdf(self, values) -> float: + if self.bounded and ( + np.any(values < self.bounds[:, 0]) or np.any(values > self.bounds[:, 1]) + ): + return -np.inf + return float(self.joint.logpdf(values)) + + def transform(self, u): + if self.bounded: + raise NotImplementedError( + f"the joint prior over {self.names} is truncated by bounds and has no " + "unit-cube map; drop the bounds or use a sampler that needs only " + "log_posterior" + ) + if self._L is not None: + return self._mean + self._L @ stats.norm.ppf(u) + if hasattr(self.joint, "prior_transform"): + return np.asarray(self.joint.prior_transform(u), dtype=float) + raise NotImplementedError( + f"the joint prior over {self.names} has no prior_transform(u); give it " + "one, or use a sampler that needs only log_posterior" + ) + + def sample(self, n, rng): + if self.has_transform: + return np.asarray( + [self.transform(rng.uniform(size=len(self.params))) for _ in range(n)] + ) + if not hasattr(self.joint, "rvs"): + raise NotImplementedError( + f"the joint prior over {self.names} has neither prior_transform nor rvs" + ) + draws = np.empty((0, len(self.params))) + for _ in range(1000): + d = np.atleast_2d( + np.asarray(self.joint.rvs(size=n, random_state=rng), dtype=float) + ) + if d.shape[1] != len(self.params): + d = d.reshape(-1, len(self.params)) + if self.bounded: + ok = np.all((d >= self.bounds[:, 0]) & (d <= self.bounds[:, 1]), axis=1) + d = d[ok] + draws = np.vstack([draws, d]) + if len(draws) >= n: + return draws[:n] + raise RuntimeError( + f"could not draw from the joint prior over {self.names} inside its bounds" + ) + + +class _Prior: + def __init__(self, index: ParameterIndex, priors): + self.ndim = index.ndim + self.joints: list[_Joint] = [] + covered: dict[Parameter, str] = {} + for entry in priors: + try: + params, joint = entry + except (TypeError, ValueError): + raise TypeError( + "priors= must be a sequence of (params, joint) pairs" + ) from None + params = [params] if isinstance(params, Parameter) else list(params) + for p in params: + if p.prior is not None: + raise ValueError( + f"parameter {p.name!r} has its own prior= and is also covered " + "by a joint block: cover it once" + ) + if p in covered: + raise ValueError( + f"parameter {p.name!r} appears in two joint blocks" + ) + covered[p] = "joint" + self.joints.append(_Joint(params, joint, index.slots(params))) + self.marginals = [ + _Marginal(p, i) for i, p in enumerate(index.params) if p not in covered + ] + + def logpdf(self, theta) -> float: + total = 0.0 + for m in self.marginals: + total += m.logpdf(theta[m.slot]) + if total == -np.inf: + return -np.inf + for j in self.joints: + total += j.logpdf(theta[j.slots]) + if total == -np.inf: + return -np.inf + return float(total) + + def transform(self, u) -> np.ndarray: + u = clip_unit_cube(u) + theta = np.empty(self.ndim) + for m in self.marginals: + theta[m.slot] = m.transform(u[m.slot]) + for j in self.joints: + theta[j.slots] = j.transform(u[j.slots]) + return theta + + def sample(self, n, rng) -> np.ndarray: + out = np.empty((n, self.ndim)) + u = rng.uniform(size=(n, self.ndim)) + for m in self.marginals: + out[:, m.slot] = m.transform(clip_unit_cube(u[:, m.slot])) + for j in self.joints: + out[:, j.slots] = j.sample(n, rng) + return out + + +# ---------------------------------------------------------------------------- +# The compiled constraint +# ---------------------------------------------------------------------------- + + +def _stack_meta(constraint: Constraint) -> dict | None: + keys = set() + for c in constraint.comparisons: + keys |= set(c.data.meta) + if not keys: + return None + meta = {} + for key in keys: + parts = [] + for c in constraint.comparisons: + v = c.data.meta.get(key, None) + arr = np.asarray(v) if v is not None else None + if arr is not None and arr.ndim >= 1 and arr.shape[0] == c.n: + parts.append(arr) + else: + parts.append( + np.full(c.n, v, dtype=object) + if not np.isscalar(v) + else np.full(c.n, v) + ) + try: + stacked = np.concatenate(parts) + except (TypeError, ValueError): + stacked = np.concatenate([np.asarray(p, dtype=object) for p in parts]) + meta[key] = stacked + return meta + + +class CompiledConstraint: + """A :class:`~rxmc.constraint.Constraint` with slots resolved and its + covariance factored. Built by :class:`Problem`; not constructed by users.""" + + def __init__(self, constraint: Constraint, index: ParameterIndex): + self.source = constraint + comps = constraint.comparisons + self.comparisons = comps + self.labels = [c.data.label or f"comparison {i}" for i, c in enumerate(comps)] + self.offsets = constraint.offsets + self.active = constraint.active + self.n_active = constraint.n_active + self.weight = constraint.weight + self.likelihood = constraint.likelihood + self.log_jacobian = constraint.log_jacobian + try: + self.x = np.concatenate([np.asarray(c.data.x) for c in comps]) + except ValueError as err: + raise ValueError( + f"the comparisons {self.labels} have x grids that cannot be stacked " + f"({err}); put them in separate constraints" + ) from None + self.y = np.concatenate([c.y for c in comps]) + self.y_err = np.concatenate([c.y_err for c in comps]) + for i, (c, o) in enumerate(zip(comps, self.offsets)): + rows = self.active[(self.active >= o.start) & (self.active < o.stop)] + bad = ~(np.isfinite(self.y[rows]) & np.isfinite(self.y_err[rows])) + if np.any(bad): + raise ValueError( + f"comparison {self.labels[i]!r}: space {c.space.name!r} is not " + f"finite at {int(bad.sum())} active data point(s) (e.g. " + "non-positive y under a log transform); mask or drop those points" + ) + self.meta = _stack_meta(constraint) + self.predictors = [ + (o, index.add_all(c.predictor.params), c) + for o, c in zip(self.offsets, comps) + ] + terms = list(constraint.terms) + if constraint.statistical: + terms = [statistical(c.y_err, on=c) for c in comps] + terms + entries = [ + (t, constraint.support(t.on), index.add_all(t.params)) for t in terms + ] + self.like_gather = index.add_all(self.likelihood.params) + self.covariance = StructuredCovariance( + entries, + self.x, + self.y, + self.offsets, + self.active, + meta=self.meta, + labels=self.labels, + ) + + def ym(self, theta) -> np.ndarray: + """The stacked prediction in comparison space, all points.""" + return np.concatenate([c.predict(*theta[g]) for _, g, c in self.predictors]) + + def predict_physical(self, theta) -> list[np.ndarray]: + return [c.predictor(*theta[g]) for _, g, c in self.predictors] + + def _stats(self, theta): + ym = self.ym(theta) + if not np.all(np.isfinite(ym[self.active])): + return None + d2, logdet = self.covariance.distance(ym, theta) + return d2, logdet + + def log_likelihood(self, theta) -> float: + s = self._stats(theta) + if s is None: + return -np.inf + return float( + self.likelihood.log_likelihood(*s, self.n_active, *theta[self.like_gather]) + ) + + def chi2(self, theta) -> float: + s = self._stats(theta) + if s is None: + return np.inf + return float(self.likelihood.chi2(*s, self.n_active, *theta[self.like_gather])) + + def matrix(self, theta) -> np.ndarray: + """The dense covariance on the active points at ``theta``.""" + return self.covariance.matrix(self.ym(theta), theta) + + def __repr__(self): + return f"CompiledConstraint({self.labels}, n_active={self.n_active})" + + +# ---------------------------------------------------------------------------- +# The problem +# ---------------------------------------------------------------------------- + + +class Problem: + """The compiled calibration problem: the flat interface a sampler wants. + + Parameters + ---------- + constraints : iterable of Constraint + priors : sequence of (params, joint), optional + Joint prior blocks; see the module docstring. + """ + + def __init__(self, constraints, priors=()): + constraints = tuple(constraints) + if not constraints: + raise ValueError("a problem needs at least one constraint") + for c in constraints: + if not isinstance(c, Constraint): + raise TypeError(f"constraints must be Constraint objects, got {c!r}") + self.index = ParameterIndex() + self.constraints = tuple(CompiledConstraint(c, self.index) for c in constraints) + self.index.check_names_unique() + self.priors = tuple(priors) + self._prior = _Prior(self.index, self.priors) + + # -- structure ---------------------------------------------------------- + + @property + def params(self) -> tuple[Parameter, ...]: + return self.index.params + + @property + def names(self) -> list[str]: + return self.index.names + + @property + def ndim(self) -> int: + return self.index.ndim + + @property + def bounds(self) -> np.ndarray: + return self.index.bounds + + def columns(self, params) -> np.ndarray: + """Chain columns of one parameter or of a sequence of them.""" + return self.index.slots(params) + + def _theta(self, theta) -> np.ndarray: + theta = np.asarray(theta, dtype=float) + if theta.shape != (self.ndim,): + raise ValueError(f"theta must have shape ({self.ndim},), got {theta.shape}") + return theta + + # -- densities ------------------------------------------------------------ + + def log_prior(self, theta) -> float: + return self._prior.logpdf(self._theta(theta)) + + def log_likelihood(self, theta) -> float: + theta = self._theta(theta) + total = 0.0 + for c in self.constraints: + if c.weight == 0.0: + continue + ll = c.log_likelihood(theta) + if ll == -np.inf: + return -np.inf + total += c.weight * ll + return float(total) + + def log_posterior(self, theta) -> float: + theta = self._theta(theta) + lp = self._prior.logpdf(theta) + if not np.isfinite(lp): + return -np.inf + return lp + self.log_likelihood(theta) + + def chi2(self, theta) -> float: + theta = self._theta(theta) + return float(sum(c.chi2(theta) for c in self.constraints)) + + def log_jacobian(self) -> float: + """Sum of the constraints' comparison-space log-Jacobians.""" + return float(sum(c.log_jacobian for c in self.constraints)) + + def prior_transform(self, u) -> np.ndarray: + u = np.asarray(u, dtype=float) + if u.shape != (self.ndim,): + raise ValueError(f"u must have shape ({self.ndim},), got {u.shape}") + return self._prior.transform(u) + + def sample_prior(self, n: int, rng=None) -> np.ndarray: + """``(n, ndim)`` draws from the prior.""" + rng = np.random.default_rng(rng) + return self._prior.sample(int(n), rng) + + def predict(self, theta, physical: bool = False) -> list[list[np.ndarray]]: + """Per constraint, per comparison: the prediction on all points.""" + theta = self._theta(theta) + out = [] + for c in self.constraints: + if physical: + out.append(c.predict_physical(theta)) + else: + out.append([cmp.predict(*theta[g]) for _, g, cmp in c.predictors]) + return out + + # -- black-box-bayes spellings ------------------------------------------------ + + @property + def NDIM(self) -> int: # noqa: N802 - the bbb name + return self.ndim + + @property + def parameter_names(self) -> list[str]: + return self.names + + def starting_location(self, n: int) -> np.ndarray: + return self.sample_prior(n) + + def log_posterior_batch(self, thetas) -> np.ndarray: + thetas = np.asarray(thetas, dtype=float) + return np.asarray([self.log_posterior(t) for t in thetas]) + + def __repr__(self): + return f"Problem(ndim={self.ndim}, constraints={len(self.constraints)})" diff --git a/test/recipes/conftest.py b/test/recipes/conftest.py new file mode 100644 index 0000000..69cd8cd --- /dev/null +++ b/test/recipes/conftest.py @@ -0,0 +1,6 @@ +"""Make the shared test helpers importable from the recipe suite.""" + +import pathlib +import sys + +sys.path.insert(0, str(pathlib.Path(__file__).resolve().parents[1])) diff --git a/test/recipes/oracle.py b/test/recipes/oracle.py new file mode 100644 index 0000000..3969862 --- /dev/null +++ b/test/recipes/oracle.py @@ -0,0 +1,24 @@ +"""Closed-form posterior of a linear-Gaussian model, the fast tier's oracle. + +For ``y = X theta + eps`` with ``eps ~ N(0, Sigma)`` and ``theta ~ N(mu0, C0)`` +the posterior is Gaussian with the mean and covariance below, and the log +evidence is that of ``y ~ N(X mu0, X C0 X^T + Sigma)``. Recipes instantiated +with a linear model and Gaussian terms are checked against this without any +sampling. +""" + +import numpy as np +from scipy import stats + + +def linear_gaussian(X, y, Sigma, mu0, C0): + """``(mean, cov, log_evidence)`` of the posterior over ``theta``.""" + X, y = np.asarray(X, dtype=float), np.asarray(y, dtype=float) + Sigma, C0 = np.atleast_2d(Sigma), np.atleast_2d(C0) + mu0 = np.asarray(mu0, dtype=float) + Si = np.linalg.inv(Sigma) + C0i = np.linalg.inv(C0) + cov = np.linalg.inv(C0i + X.T @ Si @ X) + mean = cov @ (C0i @ mu0 + X.T @ Si @ y) + log_ev = stats.multivariate_normal(X @ mu0, X @ C0 @ X.T + Sigma).logpdf(y) + return mean, cov, float(log_ev) diff --git a/test/test_problem.py b/test/test_problem.py new file mode 100644 index 0000000..a8a6e9a --- /dev/null +++ b/test/test_problem.py @@ -0,0 +1,421 @@ +"""The compile step: index, priors, compiled constraints, the flat interface.""" + +import dill +import dynesty +import emcee +import numpy as np +import pytest +from scipy import stats + +from helpers import manual_mvn_loglike +from rxmc import Comparison, Constraint, Dataset, Model, Parameter, Problem +from rxmc.likelihood import StudentT +from rxmc.problem import ParameterIndex, clip_unit_cube +from rxmc.terms import Term, kernel, noise, normalization, offset, statistical +from rxmc.transforms import log, scale + +X = np.linspace(0.0, 2.0, 6) +TRUE = (2.0, 1.0) + + +def line_model(prior=True): + m = Parameter("m", prior=stats.norm(0, 5) if prior else None) + b = Parameter("b", prior=stats.norm(0, 5) if prior else None) + return Model(lambda x, m, b: m * x + b, [m, b]) + + +def dataset(seed=0, n=6, label="d"): + rng = np.random.default_rng(seed) + y = TRUE[0] * X[:n] + TRUE[1] + rng.normal(0, 0.1, n) + return Dataset(X[:n], y, 0.1 * np.ones(n), label=label) + + +def problem(**kw): + d = dataset() + return Problem([Constraint([Comparison(d, line_model())], **kw)]) + + +# ---------------------------------------------------------------------------- +# The index +# ---------------------------------------------------------------------------- + + +class TestIndex: + def test_first_seen_order_predictors_terms_likelihood(self): + model = line_model() + eps, nu = Parameter("log_eps", prior=stats.norm()), Parameter( + "nu", bounds=(1, 50) + ) + c = Constraint( + [Comparison(dataset(), model)], terms=[noise(eps)], likelihood=StudentT(nu) + ) + p = Problem([c]) + assert p.names == ["m", "b", "log_eps", "nu"] + assert p.params == (*model.params, eps, nu) + assert p.ndim == 4 and p.bounds.shape == (4, 2) + + def test_shared_object_is_one_slot_across_constraints(self): + eps = Parameter("log_eps", prior=stats.norm()) + model = line_model() + cs = [ + Constraint([Comparison(dataset(0, label="a"), model)], terms=[noise(eps)]), + Constraint([Comparison(dataset(1, label="b"), model)], terms=[noise(eps)]), + ] + p = Problem(cs) + assert p.names == ["m", "b", "log_eps"] + assert np.array_equal(p.columns(eps), [2]) + assert np.array_equal(p.columns([eps, model.params[0]]), [2, 0]) + + def test_duplicate_names_raise_with_hint(self): + a1, a2 = Parameter("log_eps", prior=stats.norm()), Parameter( + "log_eps", prior=stats.norm() + ) + c = Constraint( + [Comparison(dataset(), line_model())], terms=[noise(a1), noise(a2)] + ) + with pytest.raises(ValueError, match="duplicate parameter name.*SAME object"): + Problem([c]) + # two default StudentT likelihoods derive two "nu" + cs = [ + Constraint( + [Comparison(dataset(0, label="a"), line_model())], likelihood=StudentT() + ), + Constraint( + [Comparison(dataset(1, label="b"), line_model(prior=False))], + likelihood=StudentT(), + ), + ] + with pytest.raises(ValueError, match="nu="): + Problem(cs) + + def test_index_api(self): + ix = ParameterIndex() + a, b = Parameter("a"), Parameter("b") + assert np.array_equal(ix.add_all([a, b, a]), [0, 1, 0]) + assert ix.slot(b) == 1 and ix.names == ["a", "b"] + with pytest.raises(KeyError): + ix.slot(Parameter("c")) + with pytest.raises(TypeError): + ix.add_all(["a"]) + + +# ---------------------------------------------------------------------------- +# Priors +# ---------------------------------------------------------------------------- + + +class TestPriors: + def test_marginals_bounded_and_uniform(self): + m = Parameter("m", prior=stats.norm(0, 2), bounds=(-1.0, 3.0)) + b = Parameter("b", bounds=(0.0, 4.0)) + p = Problem( + [ + Constraint( + [Comparison(dataset(), Model(lambda x, m, b: m * x + b, [m, b]))] + ) + ] + ) + tn = stats.truncnorm(-0.5, 1.5, loc=0, scale=2) + assert p.log_prior([0.5, 1.0]) == pytest.approx(tn.logpdf(0.5) - np.log(4.0)) + assert p.log_prior([3.5, 1.0]) == -np.inf and p.log_prior([0.5, 5.0]) == -np.inf + theta = p.prior_transform([0.3, 0.25]) + assert theta[0] == pytest.approx(tn.ppf(0.3)) and theta[1] == pytest.approx(1.0) + assert np.all(np.isfinite(p.prior_transform([0.0, 1.0]))) + + def test_unbounded_marginal(self): + p = problem() + assert p.log_prior([1.0, 2.0]) == pytest.approx( + stats.norm(0, 5).logpdf([1.0, 2.0]).sum() + ) + u = np.array([0.1, 0.9]) + np.testing.assert_allclose(stats.norm(0, 5).cdf(p.prior_transform(u)), u) + + def test_joint_mvn_whitening(self): + model = line_model(prior=False) + mu, cov = np.array([2.0, 1.0]), np.array([[0.5, 0.2], [0.2, 0.3]]) + mvn = stats.multivariate_normal(mu, cov) + p = Problem( + [Constraint([Comparison(dataset(), model)])], priors=[(model.params, mvn)] + ) + assert p.log_prior([1.0, 1.0]) == pytest.approx(mvn.logpdf([1.0, 1.0])) + rng = np.random.default_rng(0) + draws = np.array([p.prior_transform(u) for u in rng.uniform(size=(4000, 2))]) + np.testing.assert_allclose(draws.mean(0), mu, atol=0.05) + np.testing.assert_allclose(np.cov(draws.T), cov, rtol=0.1, atol=0.03) + assert np.all(np.isfinite(p.prior_transform([0.0, 1.0]))) + s = p.sample_prior(500, rng=1) + assert s.shape == (500, 2) and np.allclose(s.mean(0), mu, atol=0.15) + + def test_coverage_errors_name_the_parameter(self): + model = line_model(prior=False) + with pytest.raises(ValueError, match="'m' has no prior"): + Problem([Constraint([Comparison(dataset(), model)])]) + m, b = model.params + with pytest.raises(ValueError, match="'m' appears in two joint blocks"): + Problem( + [Constraint([Comparison(dataset(), model)])], + priors=[ + ([m], stats.norm()), + ([m, b], stats.multivariate_normal(np.zeros(2))), + ], + ) + model2 = line_model() # marginals set + with pytest.raises(ValueError, match="'m' has its own prior= and is also"): + Problem( + [Constraint([Comparison(dataset(), model2)])], + priors=[(model2.params, stats.multivariate_normal(np.zeros(2)))], + ) + + def test_bounded_joint_has_no_transform_but_truncates(self): + m, b = Parameter("m", bounds=(0.0, 10.0)), Parameter("b") + model = Model(lambda x, m, b: m * x + b, [m, b]) + mvn = stats.multivariate_normal(np.zeros(2), np.eye(2)) + p = Problem( + [Constraint([Comparison(dataset(), model)])], priors=[([m, b], mvn)] + ) + assert p.log_prior([-1.0, 0.0]) == -np.inf + assert p.log_prior([1.0, 0.0]) == pytest.approx(mvn.logpdf([1.0, 0.0])) + with pytest.raises(NotImplementedError, match="truncated"): + p.prior_transform([0.5, 0.5]) + s = p.sample_prior(50, rng=0) # rejection sampling inside the bounds + assert np.all(s[:, 0] >= 0.0) + + def test_custom_joint_with_prior_transform(self): + class Hier: + def logpdf(self, v): + *r, lt = v + return stats.norm(0, np.exp(lt)).logpdf(r).sum() + stats.norm( + 0, 1 + ).logpdf(lt) + + def prior_transform(self, u): + lt = stats.norm(0, 1).ppf(u[-1]) + return np.append(stats.norm(0, np.exp(lt)).ppf(u[:-1]), lt) + + rhos = [Parameter(f"log_rho_{i}") for i in range(2)] + log_tau = Parameter("log_tau") + model = Model(lambda x, r0, r1, lt: np.ones_like(x), rhos + [log_tau]) + p = Problem( + [Constraint([Comparison(dataset(), model)])], + priors=[(rhos + [log_tau], Hier())], + ) + theta = p.prior_transform([0.5, 0.5, 0.5]) + np.testing.assert_allclose(theta, [0.0, 0.0, 0.0], atol=1e-12) + assert np.isfinite(p.log_prior(theta)) + + def test_clip_unit_cube(self): + u = clip_unit_cube([0.0, 0.5, 1.0]) + assert 0 < u[0] < 1e-10 and u[1] == 0.5 and 1 - 1e-10 < u[2] < 1 + + +# ---------------------------------------------------------------------------- +# Compiled constraints and the flat interface +# ---------------------------------------------------------------------------- + + +class TestCompile: + def test_log_likelihood_matches_manual_mvn(self): + d = dataset() + p = problem() + theta = np.array(TRUE) + ym = TRUE[0] * d.x + TRUE[1] + assert p.log_likelihood(theta) == pytest.approx( + manual_mvn_loglike(d.y, ym, np.diag(d.y_err**2)) + ) + assert p.chi2(theta) == pytest.approx(np.sum(((d.y - ym) / d.y_err) ** 2)) + assert p.log_posterior(theta) == pytest.approx( + p.log_prior(theta) + p.log_likelihood(theta) + ) + + def test_weights_and_two_constraints(self): + model = line_model() + c1 = Constraint([Comparison(dataset(0, label="a"), model)], weight=0.5) + c2 = Constraint([Comparison(dataset(1, label="b"), model)], weight=2.0) + p = Problem([c1, c2]) + theta = np.array(TRUE) + assert p.log_likelihood(theta) == pytest.approx( + 0.5 * p.constraints[0].log_likelihood(theta) + + 2.0 * p.constraints[1].log_likelihood(theta) + ) + + def test_prior_first_skips_the_forward_model(self): + calls = [] + m = Parameter("m", bounds=(0.0, 5.0)) + b = Parameter("b", bounds=(0.0, 5.0)) + + def fn(x, m, b): + calls.append(1) + return m * x + b + + p = Problem([Constraint([Comparison(dataset(), Model(fn, [m, b]))])]) + assert p.log_posterior([-1.0, 1.0]) == -np.inf + assert calls == [] + p.log_posterior([1.0, 1.0]) + assert calls == [1] + + def test_non_finite_prediction(self): + d = dataset() + p = Problem([Constraint([Comparison(d, line_model(), space=log)])]) + theta = np.array([-5.0, 0.0]) # negative prediction under log + assert p.log_likelihood(theta) == -np.inf and p.chi2(theta) == np.inf + + def test_non_positive_data_under_log_named_unless_masked(self): + d = Dataset(X[:3], [1.0, -1.0, 2.0], [0.1, 0.1, 0.1], label="neg") + c = Constraint([Comparison(d, line_model(), space=log)]) + with pytest.raises(ValueError, match="'neg'.*not finite"): + Problem([c]) + Problem([c.masked([np.array([True, False, True])])]) # fine + + def test_meta_reaches_terms(self): + seen = {} + + def fn(c): + seen["E"], seen["w"], seen["r"] = ( + c.meta("Elab"), + c.meta("w"), + c.meta("reaction"), + ) + return np.ones(len(c)) + + model = line_model() + d1 = Dataset( + X[:2], + [1.0, 2.0], + [0.1, 0.1], + meta={"Elab": 10.0, "w": [1.0, 2.0], "reaction": "n+Ca"}, + ) + d2 = Dataset( + X[:3], + [1.0, 2.0, 3.0], + [0.1, 0.1, 0.1], + meta={"Elab": 20.0, "w": [3.0, 4.0, 5.0]}, + ) + c = Constraint( + [Comparison(d1, model), Comparison(d2, model)], + terms=[Term(fn, kind="diag", constant=True)], + ) + Problem([c]) + np.testing.assert_allclose(seen["E"], [10, 10, 20, 20, 20]) + np.testing.assert_allclose(seen["w"], [1, 2, 3, 4, 5]) + assert list(seen["r"]) == ["n+Ca", "n+Ca", None, None, None] + + def test_singular_covariance_names_the_comparison(self): + d = Dataset(X[:3], [1.0, 2.0, 3.0], np.zeros(3), label="E1234-002") + with pytest.raises(ValueError, match="E1234-002.*reported_terms"): + Problem([Constraint([Comparison(d, line_model())])]) + eps = Parameter("log_eps", prior=stats.norm()) + Problem( + [Constraint([Comparison(d, line_model())], terms=[noise(eps)])] + ) # parametric: fine + + def test_predict_and_matrix(self): + d = dataset() + p = Problem( + [ + Constraint( + [Comparison(d, line_model(), space=log)], + terms=[normalization(magnitude=0.05)], + ) + ] + ) + theta = np.array(TRUE) + ((pred,),) = p.predict(theta) + np.testing.assert_allclose(pred, np.log(TRUE[0] * d.x + TRUE[1])) + ((phys,),) = p.predict(theta, physical=True) + np.testing.assert_allclose(phys, TRUE[0] * d.x + TRUE[1]) + S = p.constraints[0].matrix(theta) + assert S.shape == (6, 6) and np.all(np.linalg.eigvalsh(S) > 0) + + def test_theta_shape_checked(self): + with pytest.raises(ValueError, match="shape"): + problem().log_posterior([1.0]) + + +class TestViews: + def test_masked_views_share_columns_and_partition(self): + d = dataset() + eps = Parameter("log_eps", prior=stats.norm()) + c = Constraint([Comparison(d, line_model())], terms=[noise(eps)]) + fit, held = ( + c.masked_where(lambda x: x < 1.0), + c.masked_where(lambda x: x < 1.0).complement(), + ) + pf, ph, pa = Problem([fit]), Problem([held]), Problem([c]) + assert pf.names == ph.names == pa.names + theta = np.array([*TRUE, np.log(0.2)]) + assert pf.log_likelihood(theta) + ph.log_likelihood(theta) == pytest.approx( + pa.log_likelihood(theta) + ) + + def test_parameter_on_fully_masked_comparison_keeps_its_slot(self): + model = line_model() + rho1, rho2 = Parameter("log_rho_1", prior=stats.norm(0, 0.1)), Parameter( + "log_rho_2", prior=stats.norm(0, 0.1) + ) + c1, c2 = Comparison(dataset(0, label="a"), model | scale(rho1)), Comparison( + dataset(1, label="b"), model | scale(rho2) + ) + c = Constraint([c1, c2]).masked([np.ones(6, bool), np.zeros(6, bool)]) + p = Problem([c]) + assert p.names == ["m", "b", "log_rho_1", "log_rho_2"] + assert np.isfinite(p.log_prior([*TRUE, 0.0, 0.0])) + s = p.sample_prior(10, rng=0) + assert s.shape == (10, 4) and np.std(s[:, 3]) > 0 + + +# ---------------------------------------------------------------------------- +# Drivers +# ---------------------------------------------------------------------------- + + +class TestDrivers: + def test_emcee_recovers_the_line(self): + p = problem() + rng = np.random.default_rng(0) + p0 = p.sample_prior(16, rng=rng) * 0.05 + np.array(TRUE) + sampler = emcee.EnsembleSampler(16, p.ndim, p.log_posterior) + sampler.random_state = np.random.RandomState(0).get_state() + sampler.run_mcmc(p0, 150, progress=False) + chain = sampler.get_chain(discard=50, flat=True) + assert abs(chain[:, 0].mean() - TRUE[0]) < 3 * chain[:, 0].std() + 0.05 + + def test_dynesty_runs(self): + p = problem() + ns = dynesty.NestedSampler( + p.log_likelihood, + p.prior_transform, + p.ndim, + nlive=50, + rstate=np.random.default_rng(0), + ) + ns.run_nested(dlogz=1.0, print_progress=False) + assert np.isfinite(ns.results.logz[-1]) + + def test_dill_round_trip(self): + p = problem() + q = dill.loads(dill.dumps(p)) + theta = np.array(TRUE) + assert q.names == p.names + assert q.log_posterior(theta) == pytest.approx(p.log_posterior(theta)) + assert p.NDIM == p.ndim and p.parameter_names == p.names + assert p.starting_location(3).shape == (3, 2) + np.testing.assert_allclose( + p.log_posterior_batch([theta, theta]), [p.log_posterior(theta)] * 2 + ) + + def test_kernel_and_offset_terms_pickle(self): + eps = Parameter("log_A", prior=stats.norm()) + from sklearn.gaussian_process.kernels import RBF + + c = Constraint( + [Comparison(dataset(), line_model())], + terms=[ + kernel(RBF(1.0), params=[Parameter("ell", prior=stats.norm())]), + offset(parameter=eps), + statistical(np.ones(6)), + ], + ) + p = Problem([c]) + q = dill.loads(dill.dumps(p)) + theta = np.array([*TRUE, 0.0, -1.0]) + assert q.log_posterior(theta) == pytest.approx(p.log_posterior(theta)) From 86963b8a356860f75bf61e6743f10e9b84f71095 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 19:33:45 -0400 Subject: [PATCH 20/75] Start the recipe acceptance suite: recipes 1-13 One test file per recipe under test/recipes, docstring quoting the recipe's intent, assertions its expected-behaviour bullets that need no converged chain; a linear-Gaussian oracle supplies closed-form posteriors for the linear recipes. offset() and normalization() now take the nuisance parameter as their first positional argument, like every other nuisance factory and like the recipes spell them; magnitude= stays a keyword. --- pyproject.toml | 4 +- src/rxmc/terms.py | 15 +-- test/recipes/__init__.py | 0 test/recipes/common.py | 38 ++++++++ test/recipes/conftest.py | 7 +- ...test_recipe_01_fit_with_reported_errors.py | 41 +++++++++ test/recipes/test_recipe_02_unknown_noise.py | 59 ++++++++++++ .../test_recipe_04_free_normalisation.py | 51 +++++++++++ .../recipes/test_recipe_05_share_or_couple.py | 91 +++++++++++++++++++ ...test_recipe_06_latent_scale_per_dataset.py | 45 +++++++++ ...test_recipe_08_sampled_mean_discrepancy.py | 54 +++++++++++ test/recipes/test_recipe_09_heavy_tails.py | 59 ++++++++++++ test/recipes/test_recipe_10_log_space.py | 52 +++++++++++ test/recipes/test_recipe_11_hold_out.py | 39 ++++++++ test/recipes/test_recipe_12_tempering.py | 29 ++++++ test/recipes/test_recipe_13_declare_priors.py | 68 ++++++++++++++ 16 files changed, 641 insertions(+), 11 deletions(-) delete mode 100644 test/recipes/__init__.py create mode 100644 test/recipes/common.py create mode 100644 test/recipes/test_recipe_01_fit_with_reported_errors.py create mode 100644 test/recipes/test_recipe_02_unknown_noise.py create mode 100644 test/recipes/test_recipe_04_free_normalisation.py create mode 100644 test/recipes/test_recipe_05_share_or_couple.py create mode 100644 test/recipes/test_recipe_06_latent_scale_per_dataset.py create mode 100644 test/recipes/test_recipe_08_sampled_mean_discrepancy.py create mode 100644 test/recipes/test_recipe_09_heavy_tails.py create mode 100644 test/recipes/test_recipe_10_log_space.py create mode 100644 test/recipes/test_recipe_11_hold_out.py create mode 100644 test/recipes/test_recipe_12_tempering.py create mode 100644 test/recipes/test_recipe_13_declare_priors.py diff --git a/pyproject.toml b/pyproject.toml index 8078493..0f9a646 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -80,7 +80,7 @@ target-version = ["py312"] [tool.isort] profile = "black" line_length = 88 -src_paths = ["src", "test"] +src_paths = ["src", "test", "test/recipes"] [tool.ruff] line-length = 88 @@ -92,7 +92,7 @@ select = ["F", "I"] [tool.ruff.lint.isort] # match isort's src_paths = ["src", "test"]: the shared test helpers module is # first-party too, so the two sorters agree -known-first-party = ["rxmc", "helpers"] +known-first-party = ["rxmc", "helpers", "common", "oracle"] [tool.pytest.ini_options] # bare `pytest` runs the fast tier: unit tests plus the sampler-free and diff --git a/src/rxmc/terms.py b/src/rxmc/terms.py index 70b6934..e52ce00 100644 --- a/src/rxmc/terms.py +++ b/src/rxmc/terms.py @@ -358,11 +358,12 @@ def statistical(y_err, on=None) -> Term: return Term(np.asarray(y_err, dtype=float), kind="diag", on=on) -def offset(magnitude=None, parameter=None, mask=None, log=True, on=None) -> Term: +def offset(parameter=None, magnitude=None, mask=None, log=True, on=None) -> Term: """A correlated absolute-offset systematic ``outer(omega, omega)``. - With ``magnitude`` it is a fixed (data-given) rank-one mode; with ``parameter`` - it is a free nuisance magnitude (``c = exp(theta)`` when ``log``). + With ``parameter`` (first, like every nuisance factory) it is a free + magnitude (``c = exp(theta)`` when ``log``); with ``magnitude=`` it is a + fixed, data-given rank-one mode. """ if magnitude is None and parameter is None: raise ValueError("offset requires a magnitude and/or a parameter") @@ -376,12 +377,12 @@ def basis(c): return _scaled_term("mode", parameter, log, basis, on=on) -def normalization(magnitude=None, parameter=None, mask=None, log=True, on=None) -> Term: +def normalization(parameter=None, magnitude=None, mask=None, log=True, on=None) -> Term: """A correlated normalisation systematic ``outer(eta * ym, eta * ym)``. - With ``magnitude`` it is a fixed fractional normalisation uncertainty; with - ``parameter`` the magnitude eta is a free nuisance (``c = exp(theta)`` when - ``log``). In both cases the mode scales with the model *prediction* ``ym``, + With ``parameter`` (first, like every nuisance factory) the magnitude eta + is a free nuisance (``c = exp(theta)`` when ``log``); with ``magnitude=`` it + is a fixed fractional normalisation uncertainty. In both cases the mode scales with the model *prediction* ``ym``, never with the data: that is what keeps the fit free of Peelle's Pertinent Puzzle (recipe 27). """ diff --git a/test/recipes/__init__.py b/test/recipes/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/test/recipes/common.py b/test/recipes/common.py new file mode 100644 index 0000000..09dbed6 --- /dev/null +++ b/test/recipes/common.py @@ -0,0 +1,38 @@ +"""Small synthetic problems shared by the recipe tests.""" + +import numpy as np +from scipy import stats + +from rxmc import Comparison, Constraint, Dataset, Model, Parameter, Problem + +X = np.linspace(0.5, 2.5, 8) +TRUE = (2.0, 1.0) + + +def line(prior_scale=5.0): + """``y = m x + b`` with wide normal priors on both parameters.""" + m = Parameter("m", prior=stats.norm(0, prior_scale)) + b = Parameter("b", prior=stats.norm(0, prior_scale)) + return Model(lambda x, m, b: m * x + b, [m, b]) + + +def line_data(seed=0, n=8, label="d", err=0.1, **kw): + rng = np.random.default_rng(seed) + x = X[:n] + y = TRUE[0] * x + TRUE[1] + rng.normal(0, err, n) + return Dataset(x, y, err * np.ones(n), label=label, **kw) + + +def line_problem(**constraint_kw): + d = line_data() + return Problem([Constraint([Comparison(d, line())], **constraint_kw)]), d + + +def map_estimate(problem, x0): + """The posterior mode by a quasi-Newton search from ``x0``.""" + from scipy.optimize import minimize + + res = minimize( + lambda t: -problem.log_posterior(t), np.asarray(x0, float), method="L-BFGS-B" + ) + return res.x diff --git a/test/recipes/conftest.py b/test/recipes/conftest.py index 69cd8cd..fc73c0e 100644 --- a/test/recipes/conftest.py +++ b/test/recipes/conftest.py @@ -1,6 +1,9 @@ -"""Make the shared test helpers importable from the recipe suite.""" +"""Make the recipe helpers and the shared test helpers importable.""" import pathlib import sys -sys.path.insert(0, str(pathlib.Path(__file__).resolve().parents[1])) +_here = pathlib.Path(__file__).resolve().parent +for path in (_here, _here.parent): + if str(path) not in sys.path: + sys.path.insert(0, str(path)) diff --git a/test/recipes/test_recipe_01_fit_with_reported_errors.py b/test/recipes/test_recipe_01_fit_with_reported_errors.py new file mode 100644 index 0000000..7cc7b43 --- /dev/null +++ b/test/recipes/test_recipe_01_fit_with_reported_errors.py @@ -0,0 +1,41 @@ +"""Recipe 1: fit a model to data with reported statistical errors. + +I have x, y, and a statistical error per point, and a model with a few +parameters. I want the posterior. +""" + +import numpy as np +import pytest + +from common import TRUE, line_problem, map_estimate +from oracle import linear_gaussian + + +def test_covariance_is_the_statistical_diagonal_and_chi2_is_the_weighted_sum(): + p, d = line_problem() + theta = np.array(TRUE) + ym = TRUE[0] * d.x + TRUE[1] + assert p.chi2(theta) == pytest.approx(np.sum(((d.y - ym) / d.y_err) ** 2)) + np.testing.assert_allclose(p.constraints[0].matrix(theta), np.diag(d.y_err**2)) + + +def test_names_and_dimension_follow_the_declaration(): + p, _ = line_problem() + assert p.ndim == 2 and p.names == ["m", "b"] + + +def test_map_matches_the_linear_gaussian_oracle(): + p, d = line_problem() + Xd = np.column_stack([d.x, np.ones_like(d.x)]) + mean, cov, _ = linear_gaussian( + Xd, d.y, np.diag(d.y_err**2), [0.0, 0.0], 25.0 * np.eye(2) + ) + np.testing.assert_allclose(map_estimate(p, TRUE), mean, atol=1e-4) + + +def test_the_flat_interface_is_all_a_sampler_needs(): + p, _ = line_problem() + p0 = p.sample_prior(4, rng=0) + assert p0.shape == (4, 2) + assert np.all(np.isfinite(p.prior_transform([0.5, 0.5]))) + assert np.isfinite(p.log_posterior(p0[0])) diff --git a/test/recipes/test_recipe_02_unknown_noise.py b/test/recipes/test_recipe_02_unknown_noise.py new file mode 100644 index 0000000..6d4fe40 --- /dev/null +++ b/test/recipes/test_recipe_02_unknown_noise.py @@ -0,0 +1,59 @@ +"""Recipe 2: infer an unknown noise level. + +My data have no usable error bars, or I do not trust them. I want to infer +the noise magnitude alongside the model. +""" + +import numpy as np +import pytest +from scipy import stats + +from common import TRUE, line, line_data +from rxmc import Comparison, Constraint, Parameter, Problem +from rxmc import terms as T + + +def test_statistical_false_replaces_and_default_adds(): + d = line_data() + log_eps = Parameter("log_eps", prior=stats.norm(-2, 2)) + theta = np.array([*TRUE, np.log(0.3)]) + replaced = Problem( + [ + Constraint( + [Comparison(d, line())], terms=[T.noise(log_eps)], statistical=False + ) + ] + ) + added = Problem([Constraint([Comparison(d, line())], terms=[T.noise(log_eps)])]) + np.testing.assert_allclose(np.diag(replaced.constraints[0].matrix(theta)), 0.3**2) + np.testing.assert_allclose( + np.diag(added.constraints[0].matrix(theta)), d.y_err**2 + 0.3**2 + ) + + +def test_prediction_scaled_noise_changes_with_the_model_parameters(): + d = line_data() + log_eps = Parameter("log_eps", prior=stats.norm(-2, 2)) + for term in (T.noise_fraction(log_eps), T.model_error(log_eps)): + p = Problem( + [Constraint([Comparison(d, line())], terms=[term], statistical=False)] + ) + S1 = p.constraints[0].matrix(np.array([*TRUE, -1.0])) + S2 = p.constraints[0].matrix(np.array([TRUE[0] * 2, TRUE[1], -1.0])) + assert not np.allclose(S1, S2) + + +def test_noise_growing_along_x(): + d = line_data() + log_eps, slope = Parameter("log_eps", prior=stats.norm(-2, 2)), Parameter( + "slope", prior=stats.norm(0, 1) + ) + t = T.noise(log_eps, basis=T.exp_growth(np.pi), basis_params=(slope,)) + p = Problem([Constraint([Comparison(d, line())], terms=[t], statistical=False)]) + assert p.names == ["m", "b", "log_eps", "slope"] + S = p.constraints[0].matrix(np.array([*TRUE, np.log(0.2), 1.0])) + np.testing.assert_allclose(np.diag(S), (0.2 * np.exp(d.x / np.pi)) ** 2) + assert p.log_posterior(np.array([*TRUE, np.log(0.2), 1.0])) == pytest.approx( + p.log_prior([*TRUE, np.log(0.2), 1.0]) + + p.log_likelihood([*TRUE, np.log(0.2), 1.0]) + ) diff --git a/test/recipes/test_recipe_04_free_normalisation.py b/test/recipes/test_recipe_04_free_normalisation.py new file mode 100644 index 0000000..35d69fb --- /dev/null +++ b/test/recipes/test_recipe_04_free_normalisation.py @@ -0,0 +1,51 @@ +"""Recipe 4: infer a normalisation or offset the experiment did not report. + +I suspect an unreported normalisation (or background offset) and want its +magnitude as a nuisance parameter. +""" + +import numpy as np +from scipy import stats + +from common import TRUE, line, line_data +from rxmc import Comparison, Constraint, Parameter, Problem +from rxmc import terms as T + + +def test_free_normalisation_adds_one_rank_one_mode(): + d = line_data() + log_eta = Parameter("log_eta", prior=stats.norm(-3, 1)) + p = Problem( + [ + Constraint( + [Comparison(d, line())], terms=[T.normalization(parameter=log_eta)] + ) + ] + ) + assert p.names == ["m", "b", "log_eta"] + theta = np.array([*TRUE, np.log(0.05)]) + ym = TRUE[0] * d.x + TRUE[1] + np.testing.assert_allclose( + p.constraints[0].matrix(theta), np.diag(d.y_err**2) + 0.05**2 * np.outer(ym, ym) + ) + + +def test_free_offset_and_shaped_mode(): + d = line_data() + log_w = Parameter("log_omega", prior=stats.norm(-3, 1)) + log_s = Parameter("log_s", prior=stats.norm(-3, 1)) + p = Problem( + [ + Constraint( + [Comparison(d, line())], + terms=[ + T.offset(parameter=log_w), + T.systematic(log_s, basis=T.x_basis(np.pi)), + ], + ) + ] + ) + theta = np.array([*TRUE, np.log(0.1), np.log(0.2)]) + u = d.x / np.pi + ref = np.diag(d.y_err**2) + 0.01 * np.ones((8, 8)) + 0.04 * np.outer(u, u) + np.testing.assert_allclose(p.constraints[0].matrix(theta), ref) diff --git a/test/recipes/test_recipe_05_share_or_couple.py b/test/recipes/test_recipe_05_share_or_couple.py new file mode 100644 index 0000000..f273a9a --- /dev/null +++ b/test/recipes/test_recipe_05_share_or_couple.py @@ -0,0 +1,91 @@ +"""Recipe 5: share an error model between datasets, or couple them. + +Two datasets. Case B: each has its own independent normalisation +measurement, but I believe the two magnitudes are the same. Case A: both +were normalised against the same uncertain flux, so their errors are +correlated. +""" + +import numpy as np +import pytest +from scipy import stats + +from common import TRUE, line, line_data +from rxmc import Comparison, Constraint, Parameter, Problem +from rxmc import terms as T + + +@pytest.fixture +def two(): + model = line() + c1 = Comparison(line_data(0, 5, "d1"), model) + c2 = Comparison(line_data(1, 6, "d2"), model) + return c1, c2 + + +def test_case_b_two_spellings_agree_and_stay_block_diagonal(two): + c1, c2 = two + log_eta = Parameter("log_eta", prior=stats.norm(-3, 1)) + one = Problem( + [ + Constraint( + [c1, c2], + terms=[ + T.normalization(log_eta, on=c1), + T.normalization(log_eta, on=c2), + ], + ) + ] + ) + split = Problem( + [ + Constraint([c1], terms=[T.normalization(log_eta)]), + Constraint([c2], terms=[T.normalization(log_eta)]), + ] + ) + assert one.ndim == split.ndim == 3 + theta = np.array([*TRUE, np.log(0.05)]) + assert one.log_likelihood(theta) == pytest.approx(split.log_likelihood(theta)) + S = one.constraints[0].matrix(theta) + assert np.all(S[:5, 5:] == 0.0) + + +def test_case_a_couples_the_blocks_and_differs_from_case_b(two): + c1, c2 = two + log_eta = Parameter("log_eta", prior=stats.norm(-3, 1)) + a = Problem([Constraint([c1, c2], terms=[T.normalization(log_eta, on=[c1, c2])])]) + b = Problem( + [ + Constraint( + [c1, c2], + terms=[ + T.normalization(log_eta, on=c1), + T.normalization(log_eta, on=c2), + ], + ) + ] + ) + assert a.ndim == b.ndim + theta = np.array([*TRUE, np.log(0.05)]) + S = a.constraints[0].matrix(theta) + assert np.any(S[:5, 5:] != 0.0) + assert a.log_likelihood(theta) != pytest.approx(b.log_likelihood(theta)) + assert not a.constraints[ + 0 + ].covariance.dense # a mode across blocks stays structured + + +def test_sharing_is_by_object_not_by_name(two): + c1, c2 = two + e1, e2 = Parameter("log_eta", prior=stats.norm()), Parameter( + "log_eta", prior=stats.norm() + ) + with pytest.raises(ValueError, match="duplicate parameter name"): + Problem( + [ + Constraint( + [c1, c2], + terms=[T.normalization(e1, on=c1), T.normalization(e2, on=c2)], + ) + ] + ) diff --git a/test/recipes/test_recipe_06_latent_scale_per_dataset.py b/test/recipes/test_recipe_06_latent_scale_per_dataset.py new file mode 100644 index 0000000..2f4d3fa --- /dev/null +++ b/test/recipes/test_recipe_06_latent_scale_per_dataset.py @@ -0,0 +1,45 @@ +"""Recipe 6: one latent scale per dataset. + +Each dataset has its own unknown normalisation. I want a Kennedy-O'Hagan +scale factor on the model prediction, one per dataset, sampled with the +model parameters. +""" + +import numpy as np +from scipy import stats + +from common import TRUE, line, line_data +from rxmc import Comparison, Constraint, Parameter, Problem +from rxmc import transforms as tf + + +def test_scales_follow_the_model_parameters_and_scale_the_mean(): + model = line() + rhos = [Parameter(f"log_rho_{i}", prior=stats.norm(0, 0.1)) for i in range(2)] + comps = [ + Comparison(line_data(i, 5, f"d{i}"), model | tf.scale(rho)) + for i, rho in enumerate(rhos) + ] + p = Problem([Constraint(comps)]) + assert p.names == ["m", "b", "log_rho_0", "log_rho_1"] + theta = np.array([*TRUE, np.log(1.1), np.log(0.9)]) + pred = p.predict(theta)[0] + x = comps[0].data.x + np.testing.assert_allclose(pred[0], 1.1 * (TRUE[0] * x + TRUE[1])) + np.testing.assert_allclose(pred[1], 0.9 * (TRUE[0] * x + TRUE[1])) + + +def test_masked_view_keeps_the_same_columns(): + model = line() + rho = Parameter("log_rho", prior=stats.norm(0, 0.1)) + c = Constraint([Comparison(line_data(), model | tf.scale(rho))]) + assert Problem([c.masked_where(lambda x: x < 1.5)]).names == Problem([c]).names + + +def test_global_scale_on_every_comparison(): + model = line() + rho = Parameter("log_rho", prior=stats.norm(0, 0.1)) + scaled = model | tf.scale(rho) + comps = [Comparison(line_data(i, 5, f"d{i}"), scaled) for i in range(2)] + p = Problem([Constraint(comps)]) + assert p.names == ["m", "b", "log_rho"] diff --git a/test/recipes/test_recipe_08_sampled_mean_discrepancy.py b/test/recipes/test_recipe_08_sampled_mean_discrepancy.py new file mode 100644 index 0000000..7213ea6 --- /dev/null +++ b/test/recipes/test_recipe_08_sampled_mean_discrepancy.py @@ -0,0 +1,54 @@ +"""Recipe 8: sample a mean discrepancy explicitly. + +Instead of marginalising the discrepancy, I want to sample an additive +correction with a parametric shape. +""" + +import numpy as np +from scipy import stats + +from common import TRUE, line, line_data +from rxmc import Comparison, Constraint, Model, Parameter, Problem +from rxmc import transforms as tf + + +def test_additive_correction_lists_its_parameters_after_the_model(): + phi = [Parameter(f"c{i}", prior=stats.norm(0, 1)) for i in range(2)] + delta = Model(lambda x, c0, c1: c0 + c1 * x**2, phi) + d = line_data() + p = Problem([Constraint([Comparison(d, line() + delta)])]) + assert p.names == ["m", "b", "c0", "c1"] + theta = np.array([*TRUE, 0.5, -0.1]) + np.testing.assert_allclose( + p.predict(theta)[0][0], TRUE[0] * d.x + TRUE[1] + 0.5 - 0.1 * d.x**2 + ) + + +def test_composition_precedence(): + c0 = Parameter("c0", prior=stats.norm(0, 1)) + rho = Parameter("log_rho", prior=stats.norm(0, 0.1)) + delta = Model(lambda x, c0: c0 * np.ones_like(x), [c0]) + d = line_data() + base = TRUE[0] * d.x + TRUE[1] + sum_scaled = Problem( + [Constraint([Comparison(d, (line() + delta) | tf.scale(rho))])] + ) + model_scaled = Problem( + [Constraint([Comparison(d, (line() | tf.scale(rho)) + delta)])] + ) + np.testing.assert_allclose( + sum_scaled.predict([*TRUE, 0.5, np.log(2.0)])[0][0], 2.0 * (base + 0.5) + ) + np.testing.assert_allclose( + model_scaled.predict([*TRUE, np.log(2.0), 0.5])[0][0], 2.0 * base + 0.5 + ) + + +def test_multiplicative_correction(): + g = Parameter("g", prior=stats.norm(0, 1)) + factor = Model(lambda x, g: np.exp(g * x), [g]) + d = line_data() + p = Problem([Constraint([Comparison(d, line() * factor)])]) + np.testing.assert_allclose( + p.predict([*TRUE, 0.3])[0][0], (TRUE[0] * d.x + TRUE[1]) * np.exp(0.3 * d.x) + ) diff --git a/test/recipes/test_recipe_09_heavy_tails.py b/test/recipes/test_recipe_09_heavy_tails.py new file mode 100644 index 0000000..e3321b7 --- /dev/null +++ b/test/recipes/test_recipe_09_heavy_tails.py @@ -0,0 +1,59 @@ +"""Recipe 9: heavy tails. + +A few points are gross outliers. I do not want them to drag the fit; I want +a likelihood that widens instead of breaking. +""" + +import numpy as np +import pytest +from scipy.special import gammaln + +from common import TRUE, line, line_data +from helpers import mahalanobis +from rxmc import Comparison, Constraint, Dataset, Parameter, Problem +from rxmc.likelihood import Chi2, StudentT + + +def test_student_t_parameter_and_closed_form(): + d = line_data() + nu = Parameter("nu", bounds=(1.0, 100.0)) + p = Problem([Constraint([Comparison(d, line())], likelihood=StudentT(nu=nu))]) + assert p.names == ["m", "b", "nu"] and p.bounds[2].tolist() == [1.0, 100.0] + theta = np.array([*TRUE, 5.0]) + ym = TRUE[0] * d.x + TRUE[1] + d2, logdet = mahalanobis(d.y, ym, np.diag(d.y_err**2)) + n, v = 8, 5.0 + expected = ( + gammaln((n + v) / 2) + - gammaln(v / 2) + - 0.5 * n * np.log(np.pi * v) + - 0.5 * logdet + - 0.5 * (v + n) * np.log1p(d2 / v) + ) + assert p.log_likelihood(theta) == pytest.approx(expected) + assert p.chi2(theta) == pytest.approx(d2) # the distance ignores nu + + +def test_same_covariance_different_functional(): + d = line_data() + gauss = Problem([Constraint([Comparison(d, line())])]) + chi2 = Problem([Constraint([Comparison(d, line())], likelihood=Chi2())]) + theta = np.array(TRUE) + assert chi2.log_likelihood(theta) == pytest.approx(-0.5 * gauss.chi2(theta)) + assert StudentT().params[0].name == "nu" + + +def test_student_t_downweights_an_outlier(): + d = line_data() + y = d.y.copy() + y[3] += 3.0 # a gross outlier + bad = Dataset(d.x, y, d.y_err) + nu = Parameter("nu", bounds=(1.0, 100.0)) + theta_true = np.array([*TRUE, 2.0]) + theta_pulled = np.array([TRUE[0], TRUE[1] + 0.4, 2.0]) + t = Problem([Constraint([Comparison(bad, line())], likelihood=StudentT(nu=nu))]) + g = Problem([Constraint([Comparison(bad, line())])]) + # the Gaussian prefers moving toward the outlier more strongly than the t does + gain_g = g.log_likelihood(theta_pulled[:2]) - g.log_likelihood(theta_true[:2]) + gain_t = t.log_likelihood(theta_pulled) - t.log_likelihood(theta_true) + assert gain_g > gain_t diff --git a/test/recipes/test_recipe_10_log_space.py b/test/recipes/test_recipe_10_log_space.py new file mode 100644 index 0000000..c7e0880 --- /dev/null +++ b/test/recipes/test_recipe_10_log_space.py @@ -0,0 +1,52 @@ +"""Recipe 10: compare in log space. + +Cross sections span orders of magnitude. I want the Gaussian to live in log +space, with the model still written in physical units. +""" + +import numpy as np +import pytest +from scipy import stats + +from common import TRUE, line, line_data +from rxmc import Comparison, Constraint, Dataset, Parameter, Problem +from rxmc import terms as T +from rxmc import transforms as tf + + +def test_delta_method_and_jacobian(): + d = line_data() + comp = Comparison(d, line(), space=tf.log) + np.testing.assert_allclose(comp.y, np.log(d.y)) + np.testing.assert_allclose(comp.y_err, d.y_err / d.y) + log_eps = Parameter("log_eps", prior=stats.norm(-2, 1)) + p = Problem([Constraint([comp], terms=[T.noise(log_eps)], statistical=False)]) + assert p.log_jacobian() == pytest.approx(-np.sum(np.log(d.y))) + theta = np.array([*TRUE, np.log(0.1)]) + np.testing.assert_allclose(p.predict(theta)[0][0], np.log(TRUE[0] * d.x + TRUE[1])) + + +def test_non_positive_prediction_and_data(): + d = line_data() + p = Problem([Constraint([Comparison(d, line(), space=tf.log)])]) + assert p.log_likelihood([-5.0, 0.0]) == -np.inf and p.chi2([-5.0, 0.0]) == np.inf + neg = Dataset(d.x, d.y - 3.0, d.y_err, label="neg") # negative at the first points + with pytest.raises(ValueError, match="neg"): + Problem([Constraint([Comparison(neg, line(), space=tf.log)])]) + + +def test_constant_noise_in_log_space_is_fractional_noise_in_linear_space(): + d = line_data() + log_eps = Parameter("log_eps", prior=stats.norm(-2, 1)) + p = Problem( + [ + Constraint( + [Comparison(d, line(), space=tf.log)], + terms=[T.noise(log_eps)], + statistical=False, + ) + ] + ) + theta = np.array([*TRUE, np.log(0.1)]) + S = p.constraints[0].matrix(theta) + np.testing.assert_allclose(np.diag(S), 0.01) # constant in log space diff --git a/test/recipes/test_recipe_11_hold_out.py b/test/recipes/test_recipe_11_hold_out.py new file mode 100644 index 0000000..fa780ea --- /dev/null +++ b/test/recipes/test_recipe_11_hold_out.py @@ -0,0 +1,39 @@ +"""Recipe 11: hold out data and score it. + +I want to fit below an angular cut and score the prediction above it, or +compare error models by their held-out predictive density. +""" + +import numpy as np +import pytest +from scipy import stats + +from common import TRUE, line, line_data +from rxmc import Comparison, Constraint, Parameter, Problem +from rxmc import terms as T + + +def test_fit_and_held_out_share_columns_and_partition_the_likelihood(): + d = line_data() + log_eps = Parameter("log_eps", prior=stats.norm(-2, 1)) + c = Constraint([Comparison(d, line())], terms=[T.noise(log_eps)]) + fit = c.masked_where(lambda x: x < 1.5) + held = fit.complement() + pf, ph, pa = Problem([fit]), Problem([held]), Problem([c]) + assert pf.names == ph.names == pa.names + assert set(fit.active).isdisjoint(held.active) + assert sorted([*fit.active, *held.active]) == list(range(d.n)) + theta = np.array([*TRUE, np.log(0.2)]) + assert pf.log_likelihood(theta) + ph.log_likelihood(theta) == pytest.approx( + pa.log_likelihood(theta) + ) + + +def test_a_chain_from_the_fit_scores_the_held_out_problem_directly(): + d = line_data() + c = Constraint([Comparison(d, line())]) + fit = c.masked_where(lambda x: x < 1.5) + pf, ph = Problem([fit]), Problem([fit.complement()]) + samples = pf.sample_prior(5, rng=0) + scores = np.array([ph.log_likelihood(s) for s in samples]) + assert scores.shape == (5,) and np.all(np.isfinite(scores)) diff --git a/test/recipes/test_recipe_12_tempering.py b/test/recipes/test_recipe_12_tempering.py new file mode 100644 index 0000000..0eced1e --- /dev/null +++ b/test/recipes/test_recipe_12_tempering.py @@ -0,0 +1,29 @@ +"""Recipe 12: temper the likelihood. + +I have many points and worry the posterior is overconfident, or I want a +power posterior. +""" + +import numpy as np +import pytest + +from common import TRUE, line_problem, map_estimate +from oracle import linear_gaussian + + +def test_weight_scales_the_likelihood_only(): + p1, _ = line_problem() + pw, _ = line_problem(weight=0.25) + theta = np.array(TRUE) + assert pw.log_likelihood(theta) == pytest.approx(0.25 * p1.log_likelihood(theta)) + assert pw.log_prior(theta) == pytest.approx(p1.log_prior(theta)) + + +def test_tempered_posterior_equals_the_oracle_with_inflated_errors(): + w = 0.25 + pw, d = line_problem(weight=w) + Xd = np.column_stack([d.x, np.ones_like(d.x)]) + mean, _, _ = linear_gaussian( + Xd, d.y, np.diag(d.y_err**2) / w, [0.0, 0.0], 25.0 * np.eye(2) + ) + np.testing.assert_allclose(map_estimate(pw, TRUE), mean, atol=1e-4) diff --git a/test/recipes/test_recipe_13_declare_priors.py b/test/recipes/test_recipe_13_declare_priors.py new file mode 100644 index 0000000..dde9b49 --- /dev/null +++ b/test/recipes/test_recipe_13_declare_priors.py @@ -0,0 +1,68 @@ +"""Recipe 13: declare priors. + +I want each nuisance parameter to carry its own prior, the optical potential +to have a correlated multivariate normal prior, and everything to work with +nested sampling. +""" + +import numpy as np +import pytest +from scipy import stats + +from common import line_data +from rxmc import Comparison, Constraint, Model, Parameter, Problem +from rxmc import terms as T + + +def make(m_kw, b_kw, priors=None, terms=()): + m, b = Parameter("m", **m_kw), Parameter("b", **b_kw) + model = Model(lambda x, m, b: m * x + b, [m, b]) + c = Constraint([Comparison(line_data(), model)], terms=list(terms)) + return Problem([c], priors=priors(model) if priors else ()), model + + +def test_marginal_uniform_and_truncated_marginal(): + p, _ = make( + dict(prior=stats.norm(2, 1), bounds=(0.0, 4.0)), dict(bounds=(0.0, 3.0)) + ) + tn = stats.truncnorm(-2, 2, loc=2, scale=1) + assert p.log_prior([2.5, 1.0]) == pytest.approx(tn.logpdf(2.5) - np.log(3.0)) + assert p.log_prior([2.5, 3.5]) == -np.inf + assert np.all(np.isfinite(p.prior_transform([0.0, 1.0]))) + assert np.all(np.isfinite(p.prior_transform([1.0, 0.0]))) + + +def test_joint_multivariate_normal_over_the_model(): + mu, cov = np.array([2.0, 1.0]), np.array([[0.4, 0.1], [0.1, 0.2]]) + p, model = make( + {}, {}, priors=lambda mdl: [(mdl.params, stats.multivariate_normal(mu, cov))] + ) + assert p.log_prior([2.0, 1.0]) == pytest.approx( + stats.multivariate_normal(mu, cov).logpdf([2.0, 1.0]) + ) + theta = p.prior_transform([0.5, 0.5]) + np.testing.assert_allclose(theta, mu) # the median of the whitening map is the mean + assert p.sample_prior(3, rng=0).shape == (3, 2) + + +def test_every_slot_is_covered_exactly_once(): + with pytest.raises(ValueError, match="'b' has no prior"): + make(dict(prior=stats.norm()), {}) + with pytest.raises(ValueError, match="'m' has its own prior"): + make( + dict(prior=stats.norm()), + {}, + priors=lambda mdl: [(mdl.params, stats.multivariate_normal(np.zeros(2)))], + ) + + +def test_nuisance_parameters_carry_their_own_priors(): + log_eps = Parameter("log_eps", prior=stats.halfnorm(scale=1)) + p, _ = make( + dict(prior=stats.norm(0, 5)), + dict(prior=stats.norm(0, 5)), + terms=[T.noise(log_eps)], + ) + assert p.names[-1] == "log_eps" + assert p.log_prior([1.0, 1.0, -0.5]) == -np.inf # halfnorm support + assert np.isfinite(p.log_prior([1.0, 1.0, 0.5])) From b5622d35a929b40c559e96c236a5ac15b7aae50b Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 19:36:55 -0400 Subject: [PATCH 21/75] Recipes 16-24, joint-block hyperparameters, and an earlier singular check Recipe tests for external samplers, custom terms, preprocessing, the singular-covariance error, shared hyperparameters, a GP across energies and the hyperprior block. Two library changes they exposed: a joint prior block may introduce a parameter no model or term uses (its hyperparameter), which now gets a slot after the constraints' parameters; and the block factor B is checked at construction whenever every diag and matrix term is constant, since modes never enter B, so a block covered only by modes fails by label before any likelihood call. Term is exported from the package. --- src/rxmc/__init__.py | 2 + src/rxmc/covariance.py | 15 +++ src/rxmc/problem.py | 8 +- .../test_recipe_16_external_samplers.py | 70 ++++++++++++++ .../test_recipe_19_bring_your_own_term.py | 60 ++++++++++++ test/recipes/test_recipe_20_preprocessing.py | 62 ++++++++++++ ...est_recipe_21_singular_covariance_error.py | 82 ++++++++++++++++ .../test_recipe_22_shared_hyperparameters.py | 94 +++++++++++++++++++ ...test_recipe_23_gp_over_energy_and_angle.py | 53 +++++++++++ test/recipes/test_recipe_24_hyperprior.py | 63 +++++++++++++ test/test_covariance.py | 16 ++-- 11 files changed, 518 insertions(+), 7 deletions(-) create mode 100644 test/recipes/test_recipe_16_external_samplers.py create mode 100644 test/recipes/test_recipe_19_bring_your_own_term.py create mode 100644 test/recipes/test_recipe_20_preprocessing.py create mode 100644 test/recipes/test_recipe_21_singular_covariance_error.py create mode 100644 test/recipes/test_recipe_22_shared_hyperparameters.py create mode 100644 test/recipes/test_recipe_23_gp_over_energy_and_angle.py create mode 100644 test/recipes/test_recipe_24_hyperprior.py diff --git a/src/rxmc/__init__.py b/src/rxmc/__init__.py index c3c667d..1fd9bbb 100644 --- a/src/rxmc/__init__.py +++ b/src/rxmc/__init__.py @@ -20,6 +20,7 @@ from .model import polynomial as polynomial from .params import Parameter as Parameter from .problem import Problem as Problem +from .terms import Term as Term try: from .__version__ import __version__ as __version__ @@ -34,6 +35,7 @@ "Model", "Parameter", "Problem", + "Term", "polynomial", "constraint", "covariance", diff --git a/src/rxmc/covariance.py b/src/rxmc/covariance.py index c3efc2f..314ddf9 100644 --- a/src/rxmc/covariance.py +++ b/src/rxmc/covariance.py @@ -134,6 +134,12 @@ def __init__(self, entries, x, y, offsets, active, *, meta=None, labels=None): self._cache = None if self.is_constant: self._factor(self._D0, self._U0, self._M0, self._X0) + elif not self.dense and all( + e.term.is_constant for e in self.entries if e.term.kind != "mode" + ): + # modes never enter B, so B is constant even with parametric modes: + # fail now, by label, rather than at the first likelihood call + self._check_blocks(self._D0, self._M0) # -- assembly ------------------------------------------------------------- @@ -244,6 +250,15 @@ def _factor(self, D, U, M, X): self._cache = factors return factors + def _check_blocks(self, D, M): + try: + for b, pos in enumerate(self.block_pos): + if pos.size: + B = np.diag(D[pos]) + (0.0 if M[b] is None else M[b]) + sla.cholesky(B, lower=True) + except np.linalg.LinAlgError as err: + raise ValueError(self._singular_message(D)) from err + def _singular_message(self, D): offenders = [ self.labels[b] diff --git a/src/rxmc/problem.py b/src/rxmc/problem.py index c16ceb7..6a1f152 100644 --- a/src/rxmc/problem.py +++ b/src/rxmc/problem.py @@ -448,8 +448,14 @@ def __init__(self, constraints, priors=()): raise TypeError(f"constraints must be Constraint objects, got {c!r}") self.index = ParameterIndex() self.constraints = tuple(CompiledConstraint(c, self.index) for c in constraints) - self.index.check_names_unique() self.priors = tuple(priors) + # a hyperprior block may introduce a parameter no model or term uses (its + # hyperparameter); it gets a slot after every constraint's parameters + for entry in self.priors: + params = entry[0] if not isinstance(entry, Parameter) else entry + params = [params] if isinstance(params, Parameter) else list(params) + self.index.add_all(params) + self.index.check_names_unique() self._prior = _Prior(self.index, self.priors) # -- structure ---------------------------------------------------------- diff --git a/test/recipes/test_recipe_16_external_samplers.py b/test/recipes/test_recipe_16_external_samplers.py new file mode 100644 index 0000000..f2d9856 --- /dev/null +++ b/test/recipes/test_recipe_16_external_samplers.py @@ -0,0 +1,70 @@ +"""Recipe 16: drive the calibration with an external sampler. + +I want to use emcee, dynesty, or the black-box-bayes CLI, and read the chain +back without positional arithmetic. +""" + +import dill +import dynesty +import emcee +import numpy as np +import pytest + +from common import TRUE, line_data, line_problem +from rxmc import Comparison, Constraint, Model, Parameter, Problem + + +def test_emcee_uses_sample_prior_and_log_posterior(): + p, _ = line_problem() + p0 = 0.05 * p.sample_prior(16, rng=0) + np.array(TRUE) + sampler = emcee.EnsembleSampler(16, p.ndim, p.log_posterior) + sampler.random_state = np.random.RandomState(1).get_state() + sampler.run_mcmc(p0, 100, progress=False) + samples = sampler.get_chain(discard=40, flat=True) + assert samples.shape == (16 * 60, p.ndim) + # the chain is in problem.names order, so columns() is the only bookkeeping + m = p.columns(p.params[0]) + assert abs(samples[:, m].mean() - TRUE[0]) < 3 * samples[:, m].std() + 0.05 + + +def test_dynesty_uses_log_likelihood_and_prior_transform(): + p, _ = line_problem() + ns = dynesty.NestedSampler( + p.log_likelihood, + p.prior_transform, + p.ndim, + nlive=40, + rstate=np.random.default_rng(0), + ) + ns.run_nested(dlogz=1.0, print_progress=False) + res = ns.results + assert np.isfinite(res.logz[-1]) and res.samples_equal().shape[1] == p.ndim + + +def test_black_box_bayes_contract_and_dill(): + p, _ = line_problem() + blob = dill.dumps(p) + q = dill.loads(blob) + theta = np.array(TRUE) + assert q.NDIM == p.ndim and q.parameter_names == p.names + assert q.starting_location(3).shape == (3, p.ndim) + assert q.log_posterior(theta) == pytest.approx(p.log_posterior(theta)) + assert q.log_likelihood(theta) == pytest.approx(p.log_likelihood(theta)) + np.testing.assert_allclose( + q.prior_transform([0.3, 0.7]), p.prior_transform([0.3, 0.7]) + ) + np.testing.assert_allclose( + q.log_posterior_batch([theta, theta]), [p.log_posterior(theta)] * 2 + ) + + +def test_log_posterior_evaluates_the_prior_first(): + calls = [] + m, b = Parameter("m", bounds=(0.0, 4.0)), Parameter("b", bounds=(0.0, 4.0)) + + def fn(x, m, b): + calls.append(1) + return m * x + b + + p = Problem([Constraint([Comparison(line_data(), Model(fn, [m, b]))])]) + assert p.log_posterior([5.0, 1.0]) == -np.inf and calls == [] diff --git a/test/recipes/test_recipe_19_bring_your_own_term.py b/test/recipes/test_recipe_19_bring_your_own_term.py new file mode 100644 index 0000000..ba23c74 --- /dev/null +++ b/test/recipes/test_recipe_19_bring_your_own_term.py @@ -0,0 +1,60 @@ +"""Recipe 19: bring my own covariance or term. + +I have a full covariance matrix from a correlated measurement, or a noise +model no helper expresses. +""" + +import numpy as np +import pytest +from scipy import stats + +from common import TRUE, line, line_data +from helpers import manual_mvn_loglike +from rxmc import Comparison, Constraint, Parameter, Problem, Term +from rxmc import terms as T + + +def test_fixed_matrix_and_fixed_diagonal_inside_a_problem(): + d = line_data() + C = 0.01 * np.exp(-np.abs(d.x[:, None] - d.x[None, :]) / 0.5) + comp = Comparison(d, line()) + p = Problem([Constraint([comp], terms=[Term(C, on=comp)], statistical=False)]) + theta = np.array(TRUE) + ym = TRUE[0] * d.x + TRUE[1] + assert p.log_likelihood(theta) == pytest.approx(manual_mvn_loglike(d.y, ym, C)) + sig = 0.2 * np.ones(d.n) + p2 = Problem( + [Constraint([comp], terms=[Term(sig, kind="diag", on=comp)], statistical=False)] + ) + assert p2.log_likelihood(theta) == pytest.approx( + manual_mvn_loglike(d.y, ym, np.diag(sig**2)) + ) + + +def test_custom_callable_term_matches_the_helper(): + d = line_data() + e, sl = Parameter("log_e", prior=stats.norm(-2, 1)), Parameter( + "slope", prior=stats.norm(0, 1) + ) + custom = Term( + lambda c, e, l: np.exp(e) * np.exp(l * c.x / np.pi), (e, sl), kind="diag" + ) + helper = T.noise(e, basis=T.exp_growth(np.pi), basis_params=(sl,)) + pc = Problem( + [Constraint([Comparison(d, line())], terms=[custom], statistical=False)] + ) + ph = Problem( + [Constraint([Comparison(d, line())], terms=[helper], statistical=False)] + ) + theta = np.array([*TRUE, np.log(0.2), 1.1]) + assert pc.names == ph.names + assert pc.log_likelihood(theta) == pytest.approx(ph.log_likelihood(theta)) + + +def test_wrong_shape_for_the_support_is_rejected_at_construction(): + d = line_data() + comp = Comparison(d, line()) + with pytest.raises(ValueError, match="expects shape"): + Constraint([comp], terms=[Term(np.ones(3), kind="diag", on=comp)]) + with pytest.raises(ValueError, match="symmetric"): + Term(np.array([[1.0, 0.5], [0.0, 1.0]])) diff --git a/test/recipes/test_recipe_20_preprocessing.py b/test/recipes/test_recipe_20_preprocessing.py new file mode 100644 index 0000000..416748f --- /dev/null +++ b/test/recipes/test_recipe_20_preprocessing.py @@ -0,0 +1,62 @@ +"""Recipe 20: preprocess instead of asking for a feature. + +I want to mean-subtract, standardise, or project both data and model onto +principal components of a prior predictive ensemble. +""" + +import numpy as np +import pytest +from scipy import stats + +from common import TRUE, line, line_data +from helpers import manual_mvn_loglike +from rxmc import Comparison, Constraint, Dataset, Model, Parameter, Problem, Term +from rxmc import terms as T + + +def test_projection_onto_principal_components(): + d = line_data() + rng = np.random.default_rng(0) + ens = np.array( + [TRUE[0] * d.x + TRUE[1] + rng.normal(0, 0.3, d.n) for _ in range(50)] + ) + mu = ens.mean(0) + A = np.linalg.svd(ens - mu, full_matrices=False)[2][:2] # (k, N) + native = line().bind(d.x) + model = line() + proj = Model(lambda x_pc, *theta: A @ (native(*theta) - mu), model.params) + d_pc = Dataset(np.arange(2), A @ (d.y - mu), np.zeros(2), label=f"{d.label} PC") + stat = Term(A @ np.diag(d.y_err**2) @ A.T, on=d_pc) + log_eps = Parameter("log_eps", prior=stats.norm(-2, 1)) + p = Problem( + [ + Constraint( + [Comparison(d_pc, proj)], + terms=[stat, T.noise(log_eps, on=d_pc)], + statistical=False, + ) + ] + ) + theta = np.array([*TRUE, np.log(0.1)]) + ym_pc = A @ (TRUE[0] * d.x + TRUE[1] - mu) + S = A @ np.diag(d.y_err**2) @ A.T + 0.01 * np.eye(2) + assert p.log_likelihood(theta) == pytest.approx( + manual_mvn_loglike(d_pc.y, ym_pc, S) + ) + + +def test_mean_subtraction_as_preprocessing_or_as_a_pointwise_space(): + from dataclasses import replace + + from rxmc import transforms as tf + + d = line_data() + mu = 0.5 * np.ones(d.n) + shifted = replace(d, y=d.y - mu) + shift = Model(lambda x: -mu, []) + p_pre = Problem([Constraint([Comparison(shifted, line() + shift)])]) + centre = tf.Transform(lambda a: a - mu, derivative=np.ones_like) + p_space = Problem([Constraint([Comparison(d, line(), space=centre)])]) + theta = np.array(TRUE) + assert p_pre.log_likelihood(theta) == pytest.approx(p_space.log_likelihood(theta)) + assert p_space.log_jacobian() == 0.0 # a shift has unit Jacobian diff --git a/test/recipes/test_recipe_21_singular_covariance_error.py b/test/recipes/test_recipe_21_singular_covariance_error.py new file mode 100644 index 0000000..b67f7d1 --- /dev/null +++ b/test/recipes/test_recipe_21_singular_covariance_error.py @@ -0,0 +1,82 @@ +"""Recipe 21: get a useful error, not a LinAlgError. + +My measurement reports no statistical error and I forgot to add any term. +""" + +import numpy as np +import pytest +from scipy import stats + +from common import TRUE, line +from rxmc import Comparison, Constraint, Dataset, Parameter, Problem +from rxmc import terms as T + + +def test_singular_covariance_is_reported_by_label_with_remedies(): + d = Dataset( + np.linspace(0.5, 2.5, 6), + TRUE[0] * np.linspace(0.5, 2.5, 6) + 1.0, + np.zeros(6), + label="E1234-002", + ) + with pytest.raises( + ValueError, match="E1234-002.*zero statistical error.*reported_terms" + ): + Problem([Constraint([Comparison(d, line())])]) + + +def test_the_remedies_work(): + x = np.linspace(0.5, 2.5, 6) + d = Dataset( + x, + TRUE[0] * x + 1.0, + np.zeros(6), + norm_err=0.05, + offset_err=0.1, + label="E1234-002", + ) + comp = Comparison(d, line()) + Problem( + [ + Constraint( + [comp], + terms=comp.reported_terms() + + [T.noise(Parameter("log_eps", prior=stats.norm()))], + ) + ] + ) + Problem( + [ + Constraint( + [comp], + terms=[T.statistical(0.1 * np.ones(6), on=comp)], + statistical=False, + ) + ] + ) + # modes alone are rank two and still singular on six points + with pytest.raises(ValueError, match="singular"): + Problem([Constraint([comp], terms=comp.reported_terms())]) + + +def test_other_compile_time_errors_are_named(): + x = np.linspace(0.5, 2.5, 6) + d = Dataset(x, TRUE[0] * x + 1.0, 0.1 * np.ones(6), label="d") + comp = Comparison(d, line()) + stray = Comparison(Dataset(x, x, 0.1 * np.ones(6), label="stray"), line()) + with pytest.raises(ValueError, match="stray"): + Constraint([comp], terms=[T.noise(Parameter("e"), on=stray)]) + with pytest.raises(ValueError, match="duplicate parameter name"): + Problem( + [ + Constraint( + [comp], + terms=[ + T.noise(Parameter("e", prior=stats.norm())), + T.noise(Parameter("e", prior=stats.norm())), + ], + ) + ] + ) + with pytest.raises(ValueError, match="'e' has no prior"): + Problem([Constraint([comp], terms=[T.noise(Parameter("e"))])]) diff --git a/test/recipes/test_recipe_22_shared_hyperparameters.py b/test/recipes/test_recipe_22_shared_hyperparameters.py new file mode 100644 index 0000000..6f2ed79 --- /dev/null +++ b/test/recipes/test_recipe_22_shared_hyperparameters.py @@ -0,0 +1,94 @@ +"""Recipe 22: share hyperparameters across datasets, with values that depend +on the dataset. + +I have elastic data at several energies. I want one GP discrepancy per +dataset with a common length scale and an amplitude that runs with energy, +so that two shared parameters describe every dataset. +""" + +import numpy as np +import pytest +from scipy import stats +from sklearn.gaussian_process.kernels import Matern + +from common import TRUE, line +from rxmc import Comparison, Constraint, Dataset, Parameter, Problem +from rxmc import terms as T + + +def datasets(): + out = [] + for i, E in enumerate((10.0, 30.0, 50.0)): + x = np.linspace(0.5, 2.5, 5) + out.append( + Dataset( + x, + TRUE[0] * x + TRUE[1], + 0.1 * np.ones(5), + label=f"E{E:.0f}", + meta={"Elab": E}, + ) + ) + return out + + +def test_two_shared_parameters_describe_every_dataset(): + log_A0, p_ = Parameter("log_A0", prior=stats.norm(0, 2)), Parameter( + "p", prior=stats.norm(0, 1) + ) + ell = Parameter("gp_length", prior=stats.norm(0, 1)) + amp = lambda c, lA, p: np.exp(lA) * (c.meta("Elab") / 50.0) ** p # noqa: E731 + model = line() + comps = [Comparison(d, model) for d in datasets()] + terms = [ + T.kernel( + Matern(1.0, nu=2.5), + on=c, + params=[ell], + amplitude=amp, + amplitude_params=(log_A0, p_), + jitter=0.0, + ) + for c in comps + ] + prob = Problem([Constraint(comps, terms=terms)]) + assert prob.names == ["m", "b", "gp_length", "log_A0", "p"] + theta = np.array([*TRUE, np.log(0.4), np.log(0.3), 1.0]) + S = prob.constraints[0].matrix(theta) + # each block's amplitude is (E / 50)^p times 0.3 + for i, E in enumerate((10.0, 30.0, 50.0)): + block = S[5 * i : 5 * i + 5, 5 * i : 5 * i + 5] - 0.01 * np.eye(5) + a = 0.3 * (E / 50.0) + u = np.linspace(0.5, 2.5, 5) + np.testing.assert_allclose(block, a**2 * Matern(0.4, nu=2.5)(u[:, None])) + assert not prob.constraints[0].covariance.dense + + +class Standard: + """A joint block giving every covered parameter an independent N(0, 1).""" + + def logpdf(self, v): + return stats.norm(0, 1).logpdf(v).sum() + + +def test_without_params_each_kernel_derives_its_own_length_scale(): + model = line() + comps = [Comparison(d, model) for d in datasets()[:2]] + terms = [ + T.kernel(Matern(1.0, nu=2.5), on=c, prefix=f"gp{i}") + for i, c in enumerate(comps) + ] + derived = [p for t in terms for p in t.params] + prob = Problem([Constraint(comps, terms=terms)], priors=[(derived, Standard())]) + assert [n for n in prob.names if "length" in n] == [ + "gp0_length_scale", + "gp1_length_scale", + ] + same = [ + T.kernel(Matern(1.0, nu=2.5), on=c) for c in comps + ] # both derive "discrepancy_length_scale" + with pytest.raises(ValueError, match="duplicate"): + Problem( + [Constraint(comps, terms=same)], + priors=[([p for t in same for p in t.params], Standard())], + ) diff --git a/test/recipes/test_recipe_23_gp_over_energy_and_angle.py b/test/recipes/test_recipe_23_gp_over_energy_and_angle.py new file mode 100644 index 0000000..328fb1e --- /dev/null +++ b/test/recipes/test_recipe_23_gp_over_energy_and_angle.py @@ -0,0 +1,53 @@ +"""Recipe 23: a discrepancy correlated across energies and angles. + +I believe the model's defect varies smoothly in both energy and angle. I +want one GP over (E, theta) that correlates the datasets at different +energies. +""" + +import numpy as np +import pytest +from scipy import stats +from sklearn.gaussian_process.kernels import RBF, Matern + +from common import TRUE, line +from helpers import manual_mvn_loglike +from rxmc import Comparison, Constraint, Dataset, Parameter, Problem, Term + + +def test_one_matrix_term_spanning_the_comparisons(): + x = np.linspace(0.5, 2.5, 4) + ds = [ + Dataset( + x, TRUE[0] * x + TRUE[1], 0.1 * np.ones(4), label=f"E{E}", meta={"Elab": E} + ) + for E in (10.0, 20.0) + ] + model = line() + comps = [Comparison(d, model) for d in ds] + kE, kt = RBF(10.0), Matern(0.3, nu=2.5) + lE, lt, log_A = ( + Parameter(n, prior=stats.norm(0, 1)) for n in ("log_lE", "log_ltheta", "log_A") + ) + + def fn(c, lE, lt, lA): + E, t = c.meta("Elab")[:, None], c.x[:, None] + K = kE.clone_with_theta([lE])(E) * kt.clone_with_theta([lt])(t) + return np.exp(2 * lA) * K + 1e-10 * np.eye(len(c)) + + md = Term(fn, (lE, lt, log_A), kind="matrix", on=comps) + p = Problem([Constraint(comps, terms=[md])]) + assert p.names == ["m", "b", "log_lE", "log_ltheta", "log_A"] + assert p.constraints[0].covariance.dense # a matrix across comparisons is dense + theta = np.array([*TRUE, np.log(10.0), np.log(0.3), np.log(0.2)]) + E = np.repeat([10.0, 20.0], 4)[:, None] + t = np.tile(x, 2)[:, None] + ref = ( + np.diag(np.full(8, 0.01)) + + 0.04 * RBF(10.0)(E) * Matern(0.3, nu=2.5)(t) + + 1e-10 * np.eye(8) + ) + y, ym = np.concatenate([d.y for d in ds]), np.tile(TRUE[0] * x + TRUE[1], 2) + assert p.log_likelihood(theta) == pytest.approx(manual_mvn_loglike(y, ym, ref)) + S = p.constraints[0].matrix(theta) + assert np.any(S[:4, 4:] != 0.0) # the energies really are correlated diff --git a/test/recipes/test_recipe_24_hyperprior.py b/test/recipes/test_recipe_24_hyperprior.py new file mode 100644 index 0000000..7352b7b --- /dev/null +++ b/test/recipes/test_recipe_24_hyperprior.py @@ -0,0 +1,63 @@ +"""Recipe 24: per-dataset parameters drawn from a sampled hyperprior. + +Each dataset has its own normalisation, and I want to learn how spread out +those normalisations are, rather than fix the spread. +""" + +import numpy as np +import pytest +from scipy import stats + +from common import TRUE, line, line_data +from rxmc import Comparison, Constraint, Parameter, Problem +from rxmc import transforms as tf + + +class RhoHierarchy: + def logpdf(self, v): + *r, lt = v + return ( + stats.norm(0, np.exp(lt)).logpdf(r).sum() + + stats.halfnorm(scale=0.3).logpdf(np.exp(lt)) + + lt + ) + + def prior_transform(self, u): + lt = np.log(stats.halfnorm(scale=0.3).ppf(u[-1])) + return np.append(stats.norm(0, np.exp(lt)).ppf(u[:-1]), lt) + + +def build(priors): + rhos = [Parameter(f"log_rho_{i}") for i in range(3)] + log_tau = Parameter("log_tau") + model = line() + comps = [ + Comparison(line_data(i, 5, f"d{i}"), model | tf.scale(rho)) + for i, rho in enumerate(rhos) + ] + return Problem([Constraint(comps)], priors=priors(rhos, log_tau)), rhos, log_tau + + +def test_tau_is_a_column_and_the_block_covers_children_and_hyper(): + p, rhos, log_tau = build(lambda rhos, lt: [(rhos + [lt], RhoHierarchy())]) + assert p.names == ["m", "b", "log_rho_0", "log_rho_1", "log_rho_2", "log_tau"] + theta = np.array([*TRUE, 0.1, -0.1, 0.0, np.log(0.2)]) + expected = stats.norm(0, 5).logpdf(TRUE).sum() + RhoHierarchy().logpdf( + [0.1, -0.1, 0.0, np.log(0.2)] + ) + assert p.log_prior(theta) == pytest.approx(expected) + + +def test_forgetting_the_block_is_a_compile_error(): + with pytest.raises(ValueError, match="'log_rho_0' has no prior"): + build(lambda rhos, lt: []) + + +def test_prior_transform_draws_the_hyperparameter_first(): + p, *_ = build(lambda rhos, lt: [(rhos + [lt], RhoHierarchy())]) + u = np.array([0.5, 0.5, 0.9, 0.1, 0.5, 0.5]) + theta = p.prior_transform(u) + lt = theta[-1] + assert lt == pytest.approx(np.log(stats.halfnorm(scale=0.3).ppf(0.5))) + assert theta[2] == pytest.approx(stats.norm(0, np.exp(lt)).ppf(0.9)) + assert p.sample_prior(4, rng=0).shape == (4, 6) diff --git a/test/test_covariance.py b/test/test_covariance.py index 3b3c85d..12f2360 100644 --- a/test/test_covariance.py +++ b/test/test_covariance.py @@ -259,13 +259,17 @@ def test_mode_only_block_is_singular_and_named(self): cov, _ = build(terms, self.x, self.y, self.offsets, rows=[np.arange(6)] * 2) assert cov.is_constant - def test_parametric_covariance_is_not_checked_at_construction(self): - terms = [ - offset(parameter=Parameter("w")) - ] # singular B at every theta, but parametric - cov, _ = build(terms, self.x, self.y, self.offsets) + def test_modes_alone_fail_at_construction_even_when_parametric(self): + # modes never enter the block factor B, so B is constant and checkable with pytest.raises(ValueError, match="singular"): - cov.distance(self.ym, [0.0]) + build([offset(parameter=Parameter("w"))], self.x, self.y, self.offsets) + + def test_parametric_diagonal_defers_the_check(self): + # B depends on theta here: nothing to check until the first evaluation + cov, _ = build([noise(Parameter("e"))], self.x, self.y, self.offsets) + assert not cov.is_constant + d2, logdet = cov.distance(self.ym, [np.log(0.3)]) + assert np.isfinite(d2) and np.isfinite(logdet) def test_chol_logdet_on_a_diagonal(): From d47feb2fd67fdd8adfe458273ecefbed4f5d3c84 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 19:41:22 -0400 Subject: [PATCH 22/75] Recipes 25-38: the driver-loop and hierarchy recipes SafeBayes prefix differences equal the Gaussian conditional; KDUQ deltas are linear fractions (log=False, also in the recipe); Peelle's puzzle is pinned by the two-point closed form and by the ordering data-built < live prediction-built < t0 across replicates, with the recipe text corrected to say the live mode keeps a log-determinant pull that the t0 refit removes; cut posteriors by closure and by module weights; an emulator term seeing the model's values; MAP + Laplace against the oracle; error scale and USU modes; energy-dependent parameters; Legendre modes; and the eight-schools model in its three spellings. --- docs/recipes.md | 14 ++- test/recipes/test_recipe_25_safebayes.py | 59 ++++++++++ .../test_recipe_26_kduq_model_error.py | 64 +++++++++++ ...test_recipe_27_peelles_pertinent_puzzle.py | 71 ++++++++++++ test/recipes/test_recipe_29_cut_posterior.py | 54 ++++++++++ test/recipes/test_recipe_32_emulator.py | 57 ++++++++++ .../recipes/test_recipe_33_map_and_laplace.py | 42 ++++++++ .../test_recipe_34_error_scale_and_usu.py | 51 +++++++++ ...t_recipe_35_energy_dependent_parameters.py | 33 ++++++ test/recipes/test_recipe_36_legendre_basis.py | 49 +++++++++ test/recipes/test_recipe_38_eight_schools.py | 102 ++++++++++++++++++ 11 files changed, 591 insertions(+), 5 deletions(-) create mode 100644 test/recipes/test_recipe_25_safebayes.py create mode 100644 test/recipes/test_recipe_26_kduq_model_error.py create mode 100644 test/recipes/test_recipe_27_peelles_pertinent_puzzle.py create mode 100644 test/recipes/test_recipe_29_cut_posterior.py create mode 100644 test/recipes/test_recipe_32_emulator.py create mode 100644 test/recipes/test_recipe_33_map_and_laplace.py create mode 100644 test/recipes/test_recipe_34_error_scale_and_usu.py create mode 100644 test/recipes/test_recipe_35_energy_dependent_parameters.py create mode 100644 test/recipes/test_recipe_36_legendre_basis.py create mode 100644 test/recipes/test_recipe_38_eight_schools.py diff --git a/docs/recipes.md b/docs/recipes.md index 73a63af..ac739ba 100644 --- a/docs/recipes.md +++ b/docs/recipes.md @@ -701,7 +701,8 @@ errors and scaled with the average of datum and prediction.* ```python delta = {t: rx.Parameter(f"delta_{t}", prior=stats.halfnorm(scale=s0[t])) for t in ("dxs", "ay", "sig_tot")} comps = [rx.Comparison(d, omp_for(d)) for d in datasets] -terms = [T.model_error(delta[d.meta["type"]], averaging=True, on=comp) for d, comp in zip(datasets, comps)] +terms = [T.model_error(delta[d.meta["type"]], averaging=True, log=False, on=comp) + for d, comp in zip(datasets, comps)] # log=False: delta is the fraction itself c = rx.Constraint(comps, terms=terms) # statistical=True: reported errors are a floor # KDUQ additionally scales the whole log-likelihood by k/N ("democratic"), or @@ -755,10 +756,13 @@ c_t0 = rx.Constraint([comp], terms=[rx.Term(d.norm_err * comp.space(t0), kind="m Expected behaviour: - With the data-built mode, a fit of a constant to `n` points with fractional - normalisation error `s` is biased low by the factor `1 / (1 + n s²)`, - growing without bound in `n`. This is D'Agostini's bias and the origin of - Peelle's Pertinent Puzzle. With the prediction-built mode the estimate is - unbiased; an additive offset mode has no such bias either way. + normalisation error `s` is biased low, by an amount that grows with `n` + and hardly depends on the statistical error (two points at 1.5 and 1.0 + with `s = 0.2` fit *below both*). This is D'Agostini's bias and the + origin of Peelle's Pertinent Puzzle. The prediction-built mode removes + that bias; what remains is a smaller pull from the log-determinant, which + grows with the fitted value, and the `t0` refit removes that too. An + additive offset mode has no such bias either way. - `normalization()` reads `c.ym`, so the default spelling is the safe one. A free `log_eta` (recipe 4) also multiplies the prediction. - The `t0` mode makes the covariance constant, so it is factored once; diff --git a/test/recipes/test_recipe_25_safebayes.py b/test/recipes/test_recipe_25_safebayes.py new file mode 100644 index 0000000..13fbe68 --- /dev/null +++ b/test/recipes/test_recipe_25_safebayes.py @@ -0,0 +1,59 @@ +"""Recipe 25: SafeBayes, learn the tempering exponent. + +I suspect my model is misspecified and want the tempering exponent chosen +by the data rather than by hand: minimise the prequential log-loss of the +eta-generalised posterior over prefixes of the data. +""" + +from dataclasses import replace + +import numpy as np +import pytest +from scipy import stats + +from common import TRUE, line, line_data +from rxmc import Comparison, Constraint, Parameter, Problem +from rxmc import terms as T + + +def test_replace_weight_and_masked_prefixes_keep_the_columns(): + d = line_data() + log_eps = Parameter("log_eps", prior=stats.norm(-2, 1)) + c = Constraint([Comparison(d, line())], terms=[T.noise(log_eps)]) + order = np.arange(d.n) + prefix = lambda i: [np.isin(np.arange(d.n), order[:i])] # noqa: E731 + names = Problem([c]).names + for eta in (1.0, 0.5, 0.25): + for i in range(2, d.n): + p = Problem([replace(c.masked(prefix(i)), weight=eta)]) + assert p.names == names + theta = np.array([*TRUE, np.log(0.2)]) + assert p.log_likelihood(theta) == pytest.approx( + eta * Problem([c.masked(prefix(i))]).log_likelihood(theta) + ) + + +def test_prefix_difference_is_the_conditional_density_of_the_next_point(): + d = line_data() + log_w = Parameter("log_omega", prior=stats.norm(-2, 1)) + c = Constraint( + [Comparison(d, line())], terms=[T.offset(log_w)] + ) # correlated: conditional != marginal + theta = np.array([*TRUE, np.log(0.3)]) + ym = TRUE[0] * d.x + TRUE[1] + Sigma = np.diag(d.y_err**2) + 0.09 * np.ones((d.n, d.n)) + i = 4 + before = Problem([c.masked([np.arange(d.n) < i])]) + after = Problem([c.masked([np.arange(d.n) < i + 1])]) + diff = after.log_likelihood(theta) - before.log_likelihood(theta) + # closed-form Gaussian conditional of point i given points < i + S_aa, S_ab, S_bb = Sigma[i, i], Sigma[i, :i], Sigma[:i, :i] + r = d.y[:i] - ym[:i] + mu_c = ym[i] + S_ab @ np.linalg.solve(S_bb, r) + var_c = S_aa - S_ab @ np.linalg.solve(S_bb, S_ab) + assert diff == pytest.approx(stats.norm(mu_c, np.sqrt(var_c)).logpdf(d.y[i])) + + +def test_weight_is_a_float_by_type(): + with pytest.raises((TypeError, ValueError)): + Constraint([Comparison(line_data(), line())], weight=Parameter("eta")) diff --git a/test/recipes/test_recipe_26_kduq_model_error.py b/test/recipes/test_recipe_26_kduq_model_error.py new file mode 100644 index 0000000..8250661 --- /dev/null +++ b/test/recipes/test_recipe_26_kduq_model_error.py @@ -0,0 +1,64 @@ +"""Recipe 26: unaccounted-for model error per data type (KDUQ). + +I am calibrating a global optical potential to many datasets of several +observable types. I want one fractional "unaccounted-for" uncertainty per +type, sampled with the potential, added in quadrature to the reported +errors and scaled with the average of datum and prediction. +""" + +import numpy as np +import pytest +from scipy import stats + +from common import TRUE, line, line_data +from rxmc import Comparison, Constraint, Parameter, Problem +from rxmc import terms as T + + +def build(): + types = ("dxs", "ay") + delta = {t: Parameter(f"delta_{t}", prior=stats.halfnorm(scale=0.2)) for t in types} + datasets = [line_data(i, 5, f"d{i}", meta={"type": types[i % 2]}) for i in range(4)] + model = line() + comps = [Comparison(d, model) for d in datasets] + terms = [ + T.model_error(delta[d.meta["type"]], averaging=True, log=False, on=c) + for d, c in zip(datasets, comps) + ] + return types, delta, datasets, comps, terms + + +def test_one_delta_per_type_shared_by_object(): + types, delta, datasets, comps, terms = build() + p = Problem([Constraint(comps, terms=terms)]) + assert p.names == ["m", "b", "delta_dxs", "delta_ay"] + theta = np.array([*TRUE, 0.1, 0.3]) + S = p.constraints[0].matrix(theta) + for i, d in enumerate(datasets): + ym = TRUE[0] * d.x + TRUE[1] + dT = 0.1 if d.meta["type"] == "dxs" else 0.3 + expected = d.y_err**2 + (dT * 0.5 * (d.y + ym)) ** 2 + np.testing.assert_allclose(np.diag(S)[5 * i : 5 * i + 5], expected) + + +def test_democratic_and_federal_scalings_are_tempering_weights(): + types, delta, datasets, comps, terms = build() + theta = np.array([*TRUE, 0.1, 0.3]) + n_params, n_data = 2, 20 + plain = Problem([Constraint(comps, terms=terms)]) + dem = Problem([Constraint(comps, terms=terms, weight=n_params / n_data)]) + assert dem.log_likelihood(theta) == pytest.approx( + n_params / n_data * plain.log_likelihood(theta) + ) + fed = Problem( + [ + Constraint( + [c for c, d in zip(comps, datasets) if d.meta["type"] == t], + terms=[tt for tt, d in zip(terms, datasets) if d.meta["type"] == t], + weight=n_params / (len(types) * 10), + ) + for t in types + ] + ) + assert fed.names == plain.names # each delta lives in its own type constraint + assert np.isfinite(fed.log_likelihood(theta)) diff --git a/test/recipes/test_recipe_27_peelles_pertinent_puzzle.py b/test/recipes/test_recipe_27_peelles_pertinent_puzzle.py new file mode 100644 index 0000000..2ab1f8f --- /dev/null +++ b/test/recipes/test_recipe_27_peelles_pertinent_puzzle.py @@ -0,0 +1,71 @@ +"""Recipe 27: Peelle's Pertinent Puzzle, normalise the prediction, never the data. + +My datasets carry a common fractional normalisation error and I want the fit +not to be biased low. +""" + +import numpy as np +import pytest +from scipy import stats + +from common import map_estimate +from rxmc import Comparison, Constraint, Dataset, Model, Parameter, Problem, Term +from rxmc import terms as T + + +def constant_fit(y, s, mode, stat=0.02, t0=None): + """MAP of a constant fit with the normalisation mode built three ways.""" + n = len(y) + d = Dataset(np.arange(n), y, stat * np.ones(n)) + c0 = Parameter("c0", prior=stats.norm(1.0, 100.0)) # effectively flat + const = Model(lambda x, c: c * np.ones_like(x, dtype=float), [c0]) + comp = Comparison(d, const) + if mode == "data": + term = Term(s * y, kind="mode", on=comp) # the covariance built from the data + elif mode == "prediction": + term = T.normalization(magnitude=s, on=comp) # built from the live prediction + else: + term = Term(s * t0 * np.ones(n), kind="mode", on=comp) # t0: a fixed reference + p = Problem([Constraint([comp], terms=[term])]) + return map_estimate(p, [np.mean(y)])[0] + + +def test_data_built_mode_is_biased_low_and_prediction_built_is_not(): + # two measurements of the same quantity, in the spirit of Peelle's puzzle + y, s = np.array([1.5, 1.0]), 0.2 + stat = 0.02 + c_bad = constant_fit(y, s, "data") + c_ok = constant_fit(y, s, "prediction") + # the analytic GLS with a data-built covariance + Sigma = np.diag([stat**2] * 2) + s**2 * np.outer(y, y) + w = np.linalg.solve(Sigma, np.ones(2)) + gls = w @ y / w.sum() + assert c_bad == pytest.approx(gls, rel=1e-3) + assert c_bad < y.min() # below both data points: the puzzle + assert y.min() < c_ok < y.max() + + +def test_the_three_spellings_order_as_the_recipe_says(): + # seeded replicates of n noisy points around a constant. The data-built + # mode biases the fit low (D'Agostini); the live prediction-built mode + # removes that but its log-determinant still pulls the mode down; the t0 + # refit (mode frozen at a reference prediction) is unbiased. + rng = np.random.default_rng(0) + val, s, sigma = 2.0, 0.3, 0.2 + means = {} + for n in (4, 12): + est = [] + for _ in range(40): + y = val + rng.normal(0, sigma, n) + est.append( + [ + constant_fit(y, s, "data", stat=sigma), + constant_fit(y, s, "prediction", stat=sigma), + constant_fit(y, s, "t0", stat=sigma, t0=np.mean(y)), + ] + ) + means[n] = np.mean(est, axis=0) + data_built, live, t0 = means[n] + assert data_built < live < val + assert abs(t0 - val) < 0.1 + assert means[12][0] < means[4][0] # the data-built bias grows with n diff --git a/test/recipes/test_recipe_29_cut_posterior.py b/test/recipes/test_recipe_29_cut_posterior.py new file mode 100644 index 0000000..aa20f33 --- /dev/null +++ b/test/recipes/test_recipe_29_cut_posterior.py @@ -0,0 +1,54 @@ +"""Recipe 29: cut (modular) posterior by multiple imputation. + +One module of my model, say a systematic-error parameter or a GP +hyperparameter, should be learned from its own data only and not be +contaminated by the primary data, which I trust less. +""" + +import functools + +import numpy as np +import pytest +from scipy import stats + +from common import TRUE, line, line_data +from rxmc import Comparison, Constraint, Model, Parameter, Problem + + +def test_stage_two_fixes_the_module_by_closure(): + # stage 1: phi (a background level) from its own data + phi = Parameter("phi", prior=stats.norm(0, 1)) + aux = Model(lambda x, phi: phi * np.ones_like(x), [phi]) + d_aux = line_data(3, 4, "aux") + stage1 = Problem([Constraint([Comparison(d_aux, aux)])]) + assert stage1.names == ["phi"] + phis = stage1.sample_prior(3, rng=0)[:, 0] + + def f(x, m, b, phi): + return m * x + b + phi + + m, b = line().params + d = line_data() + for value in phis: + model_t = Model(functools.partial(f, phi=value), [m, b]) + stage2 = Problem([Constraint([Comparison(d, model_t)])]) + assert stage2.names == ["m", "b"] # phi is not a column of stage 2 + np.testing.assert_allclose( + stage2.predict(np.array(TRUE))[0][0], TRUE[0] * d.x + TRUE[1] + value + ) + + +def test_power_weighted_modules_are_one_problem_with_two_weights(): + phi = Parameter("phi", prior=stats.norm(0, 1)) + m, b = line().params + full = Model(lambda x, m, b, phi: m * x + b + phi, [m, b, phi]) + aux = Model(lambda x, phi: phi * np.ones_like(x), [phi]) + c_aux = Constraint([Comparison(line_data(3, 4, "aux"), aux)], weight=1.0) + c_pri = Constraint([Comparison(line_data(), full)], weight=0.3) + p = Problem([c_aux, c_pri]) + assert p.names == ["phi", "m", "b"] + theta = np.array([0.1, *TRUE]) + assert p.log_likelihood(theta) == pytest.approx( + 1.0 * p.constraints[0].log_likelihood(theta) + + 0.3 * p.constraints[1].log_likelihood(theta) + ) diff --git a/test/recipes/test_recipe_32_emulator.py b/test/recipes/test_recipe_32_emulator.py new file mode 100644 index 0000000..ef81fe0 --- /dev/null +++ b/test/recipes/test_recipe_32_emulator.py @@ -0,0 +1,57 @@ +"""Recipe 32: emulator as the model, emulator variance as a term. + +My model is too expensive to run in the chain. I have a GP or PCA emulator +trained on a design of runs, and I want its predictive variance in the +likelihood. +""" + +import numpy as np +import pytest +from scipy import stats + +from common import TRUE, line_data +from rxmc import Comparison, Constraint, Model, Parameter, Problem, Term +from rxmc import terms as T + + +class Emulator: + """A stand-in: the line itself plus a parameter-dependent variance.""" + + def __init__(self, x): + self.x = x + + def mean(self, theta): + return theta[0] * self.x + theta[1] + + def var(self, theta): + return 0.01 * (1 + theta[0] ** 2) * np.ones_like(self.x) + + +def test_the_term_declares_the_models_parameters_and_sees_the_same_values(): + d = line_data() + emu = Emulator(d.x) + m, b = Parameter("m", prior=stats.norm(0, 5)), Parameter( + "b", prior=stats.norm(0, 5) + ) + params = [m, b] + seen = {} + + def emu_std(c, *theta): + seen["theta"] = theta + return np.sqrt(emu.var(theta)) + + model = Model(lambda x, *theta: emu.mean(theta), params) + emu_var = Term(emu_std, params, kind="diag") + log_eps = Parameter("log_eps", prior=stats.norm(-2, 1)) + p = Problem([Constraint([Comparison(d, model)], terms=[emu_var, T.noise(log_eps)])]) + assert p.names == ["m", "b", "log_eps"] # the term adds no columns of its own + theta = np.array([*TRUE, np.log(0.1)]) + S = p.constraints[0].matrix(theta) + assert seen["theta"] == tuple(TRUE) + np.testing.assert_allclose(np.diag(S), d.y_err**2 + emu.var(TRUE) + 0.01) + assert np.isfinite(p.log_posterior(theta)) + assert p.log_likelihood(theta) != pytest.approx( + Problem( + [Constraint([Comparison(d, model)], terms=[T.noise(log_eps)])] + ).log_likelihood(theta) + ) diff --git a/test/recipes/test_recipe_33_map_and_laplace.py b/test/recipes/test_recipe_33_map_and_laplace.py new file mode 100644 index 0000000..57d7b3b --- /dev/null +++ b/test/recipes/test_recipe_33_map_and_laplace.py @@ -0,0 +1,42 @@ +"""Recipe 33: MAP and Laplace approximation. + +I want a quick Gaussian approximation to the posterior, and to know when it +is good enough. +""" + +import numpy as np +from scipy.optimize import minimize + +from common import TRUE, line_problem +from oracle import linear_gaussian + + +def numerical_hessian(f, x, h=1e-4): + x = np.asarray(x, float) + n = len(x) + H = np.zeros((n, n)) + for i in range(n): + for j in range(n): + e_i, e_j = np.eye(n)[i] * h, np.eye(n)[j] * h + H[i, j] = ( + f(x + e_i + e_j) + - f(x + e_i - e_j) + - f(x - e_i + e_j) + + f(x - e_i - e_j) + ) / (4 * h * h) + return H + + +def test_laplace_covariance_equals_the_oracle_on_a_linear_gaussian_problem(): + p, d = line_problem() + nll = lambda t: -p.log_posterior(t) # noqa: E731 + res = minimize(nll, np.array(TRUE), method="L-BFGS-B", bounds=p.bounds) + H = numerical_hessian(nll, res.x) + cov = np.linalg.inv(H) + Xd = np.column_stack([d.x, np.ones_like(d.x)]) + mean, cov_ref, _ = linear_gaussian( + Xd, d.y, np.diag(d.y_err**2), [0.0, 0.0], 25.0 * np.eye(2) + ) + np.testing.assert_allclose(res.x, mean, atol=1e-4) + np.testing.assert_allclose(cov, cov_ref, rtol=1e-3) + assert p.bounds.shape == (2, 2) # bounds feed the optimiser diff --git a/test/recipes/test_recipe_34_error_scale_and_usu.py b/test/recipes/test_recipe_34_error_scale_and_usu.py new file mode 100644 index 0000000..8ed5684 --- /dev/null +++ b/test/recipes/test_recipe_34_error_scale_and_usu.py @@ -0,0 +1,51 @@ +"""Recipe 34: global error scale factor and unrecognised sources of uncertainty. + +Repeated measurements scatter more than their stated errors. I want a global +scale on the reported errors, or a fully correlated unknown component per +experimental technique. +""" + +import numpy as np +from scipy import stats + +from common import TRUE, line, line_data +from rxmc import Comparison, Constraint, Parameter, Problem, Term +from rxmc import terms as T + + +def test_global_scale_multiplies_every_stated_error(): + d = line_data() + comp = Comparison(d, line()) + log_s = Parameter("log_s", prior=stats.norm(0, 0.5)) + scaled = Term(lambda c, ls: np.exp(ls) * comp.y_err, (log_s,), kind="diag", on=comp) + p = Problem([Constraint([comp], terms=[scaled], statistical=False)]) + theta = np.array([*TRUE, np.log(1.5)]) + np.testing.assert_allclose( + np.diag(p.constraints[0].matrix(theta)), 1.5**2 * d.y_err**2 + ) + + +def test_usu_offset_couples_exactly_the_comparisons_of_one_technique(): + model = line() + techs = ("tof", "tof", "act") + datasets = [ + line_data(i, 4, f"d{i}", meta={"technique": t}) for i, t in enumerate(techs) + ] + comps = [Comparison(d, model) for d in datasets] + log_delta = { + t: Parameter(f"log_usu_{t}", prior=stats.norm(-3, 1)) for t in ("tof", "act") + } + usu = [ + T.offset( + log_delta[t], + on=[c for c, d in zip(comps, datasets) if d.meta["technique"] == t], + ) + for t in ("tof", "act") + ] + p = Problem([Constraint(comps, terms=usu)]) + assert p.names == ["m", "b", "log_usu_tof", "log_usu_act"] + theta = np.array([*TRUE, np.log(0.2), np.log(0.1)]) + S = p.constraints[0].matrix(theta) + assert np.allclose(S[:4, 4:8], 0.04) # the two tof datasets are fully correlated + assert np.all(S[:8, 8:] == 0.0) # and uncorrelated with the activation one + assert not p.constraints[0].covariance.dense diff --git a/test/recipes/test_recipe_35_energy_dependent_parameters.py b/test/recipes/test_recipe_35_energy_dependent_parameters.py new file mode 100644 index 0000000..f27d530 --- /dev/null +++ b/test/recipes/test_recipe_35_energy_dependent_parameters.py @@ -0,0 +1,33 @@ +"""Recipe 35: energy-dependent parameters and per-comparison model instances. + +A potential depth depends on energy through a few coefficients I want to +share across datasets at different energies. +""" + +import numpy as np +from scipy import stats + +from rxmc import Comparison, Constraint, Dataset, Model, Parameter, Problem + + +def test_one_model_instance_per_comparison_shares_the_coefficients(): + V0, V1 = Parameter("V0", prior=stats.norm(2, 1)), Parameter( + "V1", prior=stats.norm(0, 0.1) + ) + b = Parameter("b", prior=stats.norm(0, 1)) + + def depth_model(E): + return Model(lambda x, v0, v1, b: (v0 + v1 * E) * x + b, [V0, V1, b]) + + x = np.linspace(0.5, 2.5, 5) + datasets = [ + Dataset(x, x, 0.1 * np.ones(5), label=f"E{E}", meta={"Elab": E}) + for E in (10.0, 30.0) + ] + comps = [Comparison(d, depth_model(d.meta["Elab"])) for d in datasets] + p = Problem([Constraint(comps)]) + assert p.names == ["V0", "V1", "b"] + theta = np.array([2.0, -0.02, 1.0]) + pred = p.predict(theta)[0] + np.testing.assert_allclose(pred[0], (2.0 - 0.02 * 10.0) * x + 1.0) + np.testing.assert_allclose(pred[1], (2.0 - 0.02 * 30.0) * x + 1.0) diff --git a/test/recipes/test_recipe_36_legendre_basis.py b/test/recipes/test_recipe_36_legendre_basis.py new file mode 100644 index 0000000..80ac6c9 --- /dev/null +++ b/test/recipes/test_recipe_36_legendre_basis.py @@ -0,0 +1,49 @@ +"""Recipe 36: discrepancy on a physically constrained basis. + +I know the shape the model defect can take, say a few Legendre modes in +angle, and want the discrepancy restricted to that basis. +""" + +import numpy as np +from scipy import stats +from scipy.special import eval_legendre + +from common import TRUE, line, line_data +from rxmc import Comparison, Constraint, Model, Parameter, Problem +from rxmc import terms as T + + +def test_marginalised_modes_give_a_low_rank_covariance(): + d = line_data() + comp = Comparison(d, line()) + modes = [ + T.systematic( + Parameter(f"log_s{k}", prior=stats.norm(-3, 1)), + basis=lambda c, k=k: eval_legendre(k, np.cos(c.x)), + on=comp, + ) + for k in range(1, 4) + ] + p = Problem([Constraint([comp], terms=modes)]) + assert p.names[2:] == ["log_s1", "log_s2", "log_s3"] + theta = np.array([*TRUE, np.log(0.3), np.log(0.2), np.log(0.1)]) + S = p.constraints[0].matrix(theta) - np.diag(d.y_err**2) + assert np.linalg.matrix_rank(S, tol=1e-10) == 3 + assert not p.constraints[0].covariance.dense + + +def test_sampled_form_lists_the_coefficients_after_the_model(): + d = line_data() + coeffs = [Parameter(f"c{k}", prior=stats.norm(0, 0.1)) for k in range(1, 4)] + delta = Model( + lambda x, *cs: sum( + ck * eval_legendre(k, np.cos(x)) for k, ck in enumerate(cs, 1) + ), + coeffs, + ) + p = Problem([Constraint([Comparison(d, line() + delta)])]) + assert p.names == ["m", "b", "c1", "c2", "c3"] + theta = np.array([*TRUE, 0.1, 0.0, 0.0]) + np.testing.assert_allclose( + p.predict(theta)[0][0], TRUE[0] * d.x + TRUE[1] + 0.1 * np.cos(d.x) + ) diff --git a/test/recipes/test_recipe_38_eight_schools.py b/test/recipes/test_recipe_38_eight_schools.py new file mode 100644 index 0000000..6e6614e --- /dev/null +++ b/test/recipes/test_recipe_38_eight_schools.py @@ -0,0 +1,102 @@ +"""Recipe 38: the classic normal hierarchical model (eight schools). + +Several groups each report an estimate y_j with a known standard error +sigma_j. I believe the group effects theta_j are drawn from a common +distribution N(mu, tau^2) and want to learn mu, tau, and the shrunken +theta_j. +""" + +import numpy as np +import pytest +from scipy import stats + +from rxmc import Comparison, Constraint, Dataset, Model, Parameter, Problem +from rxmc import terms as T + +Y = np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]) +S = np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]) +J = len(Y) +SCHOOLS = Dataset(np.arange(J), Y, S, label="schools") + + +def marginalised(): + mu, log_tau = Parameter("mu", prior=stats.norm(0, 25)), Parameter( + "log_tau", prior=stats.norm(1, 1) + ) + model = Model(lambda x, mu: np.full(len(x), mu), [mu]) + return ( + Problem([Constraint([Comparison(SCHOOLS, model)], terms=[T.noise(log_tau)])]), + mu, + log_tau, + ) + + +def non_centred(mu, log_tau): + etas = [Parameter(f"eta_{j}", prior=stats.norm(0, 1)) for j in range(J)] + school = Model( + lambda x, mu, lt, *eta: mu + np.exp(lt) * np.asarray(eta), [mu, log_tau, *etas] + ) + return Problem([Constraint([Comparison(SCHOOLS, school)])]), etas + + +def test_marginalised_form_has_two_columns_and_the_right_covariance(): + p, mu, log_tau = marginalised() + assert p.names == ["mu", "log_tau"] + theta = np.array([5.0, np.log(4.0)]) + np.testing.assert_allclose(np.diag(p.constraints[0].matrix(theta)), S**2 + 16.0) + + +def test_non_centred_form_marginalises_to_the_same_likelihood(): + pm, mu, log_tau = marginalised() + pn, etas = non_centred(mu, log_tau) + assert pn.names == ["mu", "log_tau", *[f"eta_{j}" for j in range(J)]] + assert np.all( + np.isfinite(pn.prior_transform(np.full(J + 2, 0.5))) + ) # marginals only + # integrate the etas out numerically for one school and compare to the + # marginalised likelihood of that school: N(y_j | mu, s_j^2 + tau^2) + mu_v, tau = 5.0, 4.0 + eta = np.linspace(-6, 6, 4001) + j = 0 + integrand = stats.norm(mu_v + tau * eta, S[j]).pdf(Y[j]) * stats.norm(0, 1).pdf(eta) + marg = np.log(np.trapezoid(integrand, eta)) + assert marg == pytest.approx( + stats.norm(mu_v, np.hypot(S[j], tau)).logpdf(Y[j]), abs=1e-6 + ) + + +def test_centred_form_is_a_joint_block_including_the_hyperparameters(): + class SchoolHierarchy: + def logpdf(self, v): + *th, mu, lt = v + return ( + stats.norm(mu, np.exp(lt)).logpdf(th).sum() + + stats.norm(0, 25).logpdf(mu) + + stats.norm(1, 1).logpdf(lt) + ) + + thetas = [Parameter(f"theta_{j}") for j in range(J)] + mu, log_tau = Parameter("mu"), Parameter("log_tau") + model = Model(lambda x, *th: np.asarray(th), thetas) + p = Problem( + [Constraint([Comparison(SCHOOLS, model)])], + priors=[(thetas + [mu, log_tau], SchoolHierarchy())], + ) + assert p.names == [*[f"theta_{j}" for j in range(J)], "mu", "log_tau"] + theta = np.array([*Y, 5.0, np.log(4.0)]) + assert np.isfinite(p.log_posterior(theta)) + with pytest.raises(NotImplementedError): + p.prior_transform( + np.full(J + 2, 0.5) + ) # no unit-cube map for a custom joint without one + + +def test_a_held_out_school_keeps_its_eta(): + pm, mu, log_tau = marginalised() + pn, etas = non_centred(mu, log_tau) + c = pn.constraints[0].source + fit = c.masked([np.arange(J) < J - 1]) + pf = Problem([fit]) + assert pf.names == pn.names + s = pf.sample_prior(20, rng=0) + assert np.std(s[:, -1]) > 0.5 # eta of the held-out school is drawn from its prior From 950ca9a8a77c03dd967ac0f9e208914226b6543a Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 19:54:43 -0400 Subject: [PATCH 23/75] Recipe 27: pin the exact closed form of the data-built bias GLS with Sigma = sigma^2 I + s^2 y y^T collapses by Sherman-Morrison to t = ybar / (1 + (s/sigma)^2 sum (y_i - ybar)^2), exact per dataset and sampler-free; the test asserts it, that identical points give no bias, and that replicate means lie between the leading-order expectation 1/(1 + (n-1) s^2) and the truth. The recipe text now quotes that form instead of the 1/(1 + n s^2) factor it had copied unverified. --- docs/recipes.md | 19 ++++++++------ ...test_recipe_27_peelles_pertinent_puzzle.py | 26 ++++++++++++++++--- 2 files changed, 34 insertions(+), 11 deletions(-) diff --git a/docs/recipes.md b/docs/recipes.md index ac739ba..ea6e827 100644 --- a/docs/recipes.md +++ b/docs/recipes.md @@ -755,14 +755,17 @@ c_t0 = rx.Constraint([comp], terms=[rx.Term(d.norm_err * comp.space(t0), kind="m Expected behaviour: -- With the data-built mode, a fit of a constant to `n` points with fractional - normalisation error `s` is biased low, by an amount that grows with `n` - and hardly depends on the statistical error (two points at 1.5 and 1.0 - with `s = 0.2` fit *below both*). This is D'Agostini's bias and the - origin of Peelle's Pertinent Puzzle. The prediction-built mode removes - that bias; what remains is a smaller pull from the log-determinant, which - grows with the fitted value, and the `t0` refit removes that too. An - additive offset mode has no such bias either way. +- With the data-built mode, a fit of a constant `t` to `n` points with + statistical error `σ` and fractional normalisation error `s` has the + exact closed form `t = ȳ / (1 + (s/σ)² Σ(yᵢ − ȳ)²)`: the fluctuations + feed back into the covariance and pull the estimate low, by + `1 / (1 + (n − 1) s²)` in leading-order expectation, independent of `σ` + (two points at 1.5 and 1.0 with `s = 0.2` fit *below both*). This is + D'Agostini's bias and the origin of Peelle's Pertinent Puzzle. The + prediction-built mode removes that bias; what remains is a smaller pull + from the log-determinant, which grows with the fitted value, and the + `t0` refit removes that too. An additive offset mode has no such bias + either way. - `normalization()` reads `c.ym`, so the default spelling is the safe one. A free `log_eta` (recipe 4) also multiplies the prediction. - The `t0` mode makes the covariance constant, so it is factored once; diff --git a/test/recipes/test_recipe_27_peelles_pertinent_puzzle.py b/test/recipes/test_recipe_27_peelles_pertinent_puzzle.py index 2ab1f8f..c3ec6c1 100644 --- a/test/recipes/test_recipe_27_peelles_pertinent_puzzle.py +++ b/test/recipes/test_recipe_27_peelles_pertinent_puzzle.py @@ -45,11 +45,29 @@ def test_data_built_mode_is_biased_low_and_prediction_built_is_not(): assert y.min() < c_ok < y.max() +def test_data_built_fit_has_the_exact_closed_form(): + # GLS with Sigma = sigma^2 I + s^2 y y^T collapses (Sherman-Morrison) to + # t = ybar / (1 + (s / sigma)^2 * sum (y_i - ybar)^2) + # exact per dataset; the (n - 1) s^2 factor is its leading-order expectation + rng = np.random.default_rng(0) + val, s, sigma = 2.0, 0.3, 0.2 + for n in (4, 12): + y = val + rng.normal(0, sigma, n) + t_hat = constant_fit(y, s, "data", stat=sigma) + closed = y.mean() / (1 + (s / sigma) ** 2 * np.sum((y - y.mean()) ** 2)) + assert t_hat == pytest.approx(closed, rel=1e-4) + assert t_hat < val # biased low + # the fluctuations drive the bias: identical points give no bias at all + flat = np.full(6, val) + assert constant_fit(flat, s, "data", stat=sigma) == pytest.approx(val, rel=1e-4) + + def test_the_three_spellings_order_as_the_recipe_says(): # seeded replicates of n noisy points around a constant. The data-built - # mode biases the fit low (D'Agostini); the live prediction-built mode - # removes that but its log-determinant still pulls the mode down; the t0 - # refit (mode frozen at a reference prediction) is unbiased. + # mode biases the fit low (D'Agostini), roughly by 1 / (1 + (n - 1) s^2); + # the live prediction-built mode removes that but its log-determinant still + # pulls the mode down; the t0 refit (mode frozen at a reference prediction) + # is unbiased. rng = np.random.default_rng(0) val, s, sigma = 2.0, 0.3, 0.2 means = {} @@ -68,4 +86,6 @@ def test_the_three_spellings_order_as_the_recipe_says(): data_built, live, t0 = means[n] assert data_built < live < val assert abs(t0 - val) < 0.1 + # leading-order expectation, above it by Jensen's inequality + assert val / (1 + (n - 1) * s**2) < data_built < val assert means[12][0] < means[4][0] # the data-built bias grows with n From 5b68139d81fa63e08feac5aa2033de34de7c6dc5 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 20:07:04 -0400 Subject: [PATCH 24/75] Rewrite the reaction models as Model subclasses that override bind ElasticXS and IsobaricAnalogPN build their jitr workspace from the dataset's kinematics in bind() and return a Predictor closing over it; the elastic basis is cached per model and energy so a plotting grid or a held-out view reuses it, and the cache is dropped on pickling. The harvested set_up_solver functions take one grid; rutherford() is the closed form of the kinematics and momentum_transfer() a free function. No compound-correction hook. Real-solve tests with lmax=10 pin finiteness, units, the cache, composition, solver-setting forwarding, the Lane-term IAS, and a dill round trip of a reaction Problem. --- src/rxmc/reactions/__init__.py | 8 + src/rxmc/reactions/elastic.py | 498 ++++++++++++++------------------- src/rxmc/reactions/ias.py | 315 ++++++++------------- test/test_reactions.py | 193 +++++++++++++ 4 files changed, 528 insertions(+), 486 deletions(-) create mode 100644 test/test_reactions.py diff --git a/src/rxmc/reactions/__init__.py b/src/rxmc/reactions/__init__.py index e69de29..04e0d8a 100644 --- a/src/rxmc/reactions/__init__.py +++ b/src/rxmc/reactions/__init__.py @@ -0,0 +1,8 @@ +"""Reaction models backed by ``jitr``: they override ``Model.bind`` only.""" + +from .elastic import ElasticXS as ElasticXS +from .elastic import momentum_transfer as momentum_transfer +from .elastic import rutherford as rutherford +from .ias import IsobaricAnalogPN as IsobaricAnalogPN + +__all__ = ["ElasticXS", "IsobaricAnalogPN", "momentum_transfer", "rutherford"] diff --git a/src/rxmc/reactions/elastic.py b/src/rxmc/reactions/elastic.py index d4cc6e5..e277c56 100644 --- a/src/rxmc/reactions/elastic.py +++ b/src/rxmc/reactions/elastic.py @@ -1,328 +1,254 @@ """ -Observation class for elastic differential cross sections. - -:class:`ElasticDifferentialXSObservation` is an :class:`~rxmc.observation.Observation` -that sets up a ``jitr`` :class:`jitr.xs.elastic.DifferentialWorkspace` to pre-compute -boundary conditions and Rutherford cross sections. It carries statistical error -only; correlated systematics are composed as :class:`~rxmc.covariance.Term` s in the -:class:`~rxmc.constraint.Constraint`. +Elastic differential cross sections from a ``jitr`` optical-model solver. + +:class:`ElasticXS` is a :class:`~rxmc.model.Model` that overrides only +``bind``: given a grid of angles and a dataset's kinematics +(``meta["reaction"]``, ``meta["Elab"]``) it builds the ``jitr`` R-matrix +workspace on that grid and returns a :class:`~rxmc.model.Predictor` that +evaluates the potentials on the radial grid, solves, and extracts one of +``"dXS/dA"`` (b/sr), ``"dXS/dRuth"`` or ``"Ay"``. The model owns the +solver; the data does not. + +The expensive basis (an ``IntegralWorkspace``) depends only on the +kinematics and the solver settings, so it is cached per model instance and +shared by every grid at one energy: the data grid, a plotting grid, a +held-out view. The cache is dropped on pickling; a bound predictor carries +its own workspace and pickles with ``dill``. """ -import jitr -import numpy as np -from exfor_tools.distribution import Distribution - -from .observation import Observation -from .observation_from_measurement import ( # noqa: F401 (re-exported names) - DEFAULT_LMAX, - RUTHERFORD_UNIT, - XS_UNIT, - check_angle_grid, - measurement_kwargs, - normalized_error_kwargs, - ureg, -) +from __future__ import annotations +from typing import Callable -class ElasticDifferentialXSObservation(Observation): - """ - Observation for elastic differential cross sections. - - This is an :class:`~rxmc.observation.Observation` (statistical error only): it - inherits ``statistical_term`` and ``num_pts_within_interval``. Any correlated - systematic — the dataset's reported normalisation/offset, or a fixed - covariance block (an array-valued :class:`~rxmc.covariance.Term`) — is - composed explicitly as an - ``extra_terms`` entry in the :class:`~rxmc.constraint.Constraint`. - - It is designed to handle elastic differential cross section - measurements, specifically absolute differential cross sections, - Rutherford normalized differential cross sections, and analyzing - powers (Ay). - - Internally, this involves initializing a - `jitr.xs.elastic.DifferentialWorkspace` which precomputes - things like boundary conditions to speed up computation of - observables for a given set of interaction parameter. - """ - - def __init__( - self, - x: np.ndarray, - y: np.ndarray, - Elab: float, - reaction: jitr.reactions.Reaction, - quantity: str, - measurement_quantity: str, - y_units: str, - y_stat_err=None, - y_sys_err_normalization=None, - y_sys_err_offset=None, - dataset_label: str | None = None, - lmax: int = DEFAULT_LMAX, - wavelengths_beyond_range=2.0, - zeros_per_node=5, - angles_vis: np.ndarray = np.linspace(0.01, 180, 100), - compound_correction: np.ndarray = None, - transform=None, - mask=None, - ): - """ - Parameters - ---------- - x : np.ndarray - Measured angle grid in degrees. - y : np.ndarray - Measured observable values. - Elab : float - Laboratory energy in MeV. - reaction : jitr.reactions.Reaction - Reaction system definition. - quantity : str - Observable to compute: ``"dXS/dA"``, ``"dXS/dRuth"``, or ``"Ay"``. - measurement_quantity : str - Observable represented by the supplied *y* values. - y_units : str - Units of the supplied *y* values (e.g. ``"mb/sr"``). - y_stat_err : np.ndarray, optional - Statistical errors associated with *y*. - y_sys_err_normalization : float or np.ndarray, optional - Reported *fractional* (dimensionless) normalisation uncertainty. - Retained as inert metadata (see - :meth:`rxmc.observation.Observation.systematic_terms`); not divided - by the unit normalisation. - y_sys_err_offset : float or np.ndarray, optional - Reported *absolute* offset uncertainty in the same units as *y*. - Retained as inert metadata, converted to internal units (divided by - the unit normalisation, per-angle where applicable). - dataset_label : str, optional - Human-readable dataset identifier used in error messages. - lmax : int, optional - Maximum angular momentum. Defaults to ``20``. - wavelengths_beyond_range : float, optional - Number of wavelengths beyond the interaction range used to set - the channel radius. Defaults to ``2.0``. - zeros_per_node : int, optional - Number of basis-function zeros per node in the R-matrix solver. - Defaults to ``5``. - angles_vis : np.ndarray, optional - Angle grid in degrees for visualisation. Defaults to - ``np.linspace(0.01, 180, 100)``. - compound_correction : np.ndarray, optional - Compound-nuclear contribution to dXS/dΩ in mb/sr, added to the - calculated cross section before comparing to data. - transform, mask : optional - Comparison-space transform and active-point mask; see - :class:`~rxmc.observation.Observation`. - """ - self.reaction = reaction - self.quantity = quantity - self.lmax = lmax - self.subentry = dataset_label - self.angle_units = ureg.radian - self.compound_correction = compound_correction - - self.angles_vis = angles_vis - angles_rad_vis = np.deg2rad(angles_vis) - check_angle_grid(angles_rad_vis, "angles_rad_vis") - - angles_rad_constraint = np.deg2rad(x) - label = dataset_label or "dataset" - check_angle_grid( - angles_rad_constraint, - f"x values for {label}", - ) +import jitr +import numpy as np - # set up workspaces to precompute things for the solver - # for quick evaluation of observables - constraint_ws, vis_ws, kinematics = set_up_solver( - reaction=self.reaction, - Elab=Elab, - angle_rad_constraint=angles_rad_constraint, - angle_rad_vis=angles_rad_vis, - lmax=self.lmax, - wavelengths_beyond_range=wavelengths_beyond_range, - zeros_per_node=zeros_per_node, - ) - self.constraint_workspace = constraint_ws - self.visualization_workspace = vis_ws - self.kinematics = kinematics +from ..model import Model, Predictor +from ..units import DEFAULT_LMAX, MB_PER_B, check_angle_grid - # Convert measurement to correct quantity and normalize to `b/sr` - norm, normalized_y_units = self.calculate_normalization( - measurement_quantity, y_units - ) - self.y_units = normalized_y_units - # retained for provenance / manual term recomposition; a scalar, or a - # per-angle array in the Rutherford-conversion cases - self.norm = norm - - super().__init__( - angles_rad_constraint, - np.asarray(y) / norm, - label=dataset_label, - transform=transform, - mask=mask, - **normalized_error_kwargs( - norm, y_stat_err, y_sys_err_normalization, y_sys_err_offset - ), - ) +__all__ = [ + "ElasticXS", + "set_up_solver", + "rutherford", + "momentum_transfer", + "extract_dXS_dA", + "extract_dXS_dRuth", + "extract_Ay", +] - @property - def k(self) -> float: - """Entrance-channel wavenumber in fm^-1.""" - return float(self.kinematics.k) +QUANTITIES = ("dXS/dA", "dXS/dRuth", "Ay") - @classmethod - def from_measurement( - cls, - measurement: Distribution, - reaction: jitr.reactions.Reaction, - quantity: str, - **kwargs, - ): - """Construct from an ``exfor_tools`` ``Distribution``. - - ``**kwargs`` (solver settings, ``compound_correction``, ``transform``, - ``mask``, ...) are forwarded to the constructor. - """ - return cls( - reaction=reaction, - quantity=quantity, - measurement_quantity=measurement.quantity, - **measurement_kwargs(measurement), - **kwargs, - ) - def calculate_normalization( - self, measurement_quantity: str, measurement_y_units: str - ): - # Determine the xs_unit based on self.quantity - xs_unit = XS_UNIT - rutherford_unit = RUTHERFORD_UNIT - if self.quantity == "dXS/dA": - y_unit = xs_unit - elif self.quantity in {"dXS/dRuth", "Ay"}: - y_unit = ureg.dimensionless - else: - raise ValueError(f"Unrecognized quantity: {self.quantity}") - - # Process different cases based on the quantity types - if self.quantity == "dXS/dRuth" and measurement_quantity == "dXS/dA": - measurement_unit = 1 * ureg(measurement_y_units) - if not measurement_unit.check(xs_unit): - raise ValueError( - "Expected measurement_unit to be dimensionally compatible " - f"with 'b/Sr', got {measurement_y_units}" - ) +def rutherford(kinematics, angles_rad) -> np.ndarray: + r"""The Rutherford cross section in mb/sr on ``angles_rad``. - conversion_factor = 1.0 / measurement_unit.to(rutherford_unit).magnitude - return self.constraint_workspace.rutherford * conversion_factor, y_unit + :math:`10\,\eta^2 / (4 k^2 \sin^4(\theta/2))` with ``k`` in fm⁻¹; a closed + form of the kinematics, so no workspace is needed. Zero for a neutral + projectile (``eta == 0``). + """ + angles_rad = np.asarray(angles_rad, dtype=float) + sin2 = np.sin(angles_rad / 2.0) ** 2 + return 10.0 * kinematics.eta**2 / (4.0 * kinematics.k**2 * sin2**2) - elif self.quantity == "dXS/dA" and measurement_quantity == "dXS/dRuth": - # rutherford is stored in mb/sr; convert one unit of it to b/sr - conversion_factor = 1.0 / (1 * rutherford_unit).to(y_unit).magnitude - return conversion_factor / self.constraint_workspace.rutherford, y_unit - elif self.quantity == "dXS/dA" and measurement_quantity == "dXS/dA": - measurement_unit = 1 * ureg(measurement_y_units) - if not measurement_unit.check(y_unit): - raise ValueError( - "Expected measurement_unit to be dimensionally compatible " - f"with 'b/Sr', got {measurement_y_units}" - ) +def momentum_transfer(angles_rad, k) -> np.ndarray: + r"""Momentum transfer :math:`q = 2k\sin(\theta/2)` (fm⁻¹) on the angles. - return 1.0 / measurement_unit.to(y_unit).magnitude, y_unit + For a :func:`~rxmc.terms.kernel` term in :math:`q`-space pass it as the + term's coordinate transform: ``coords=lambda x: momentum_transfer(x, k)``. + """ + return 2.0 * float(k) * np.sin(np.asarray(angles_rad, dtype=float) / 2.0) - elif ( - self.quantity in {"dXS/dRuth", "Ay"} - and self.quantity == measurement_quantity - ): - if measurement_y_units != "no-dim": - raise ValueError( - f"Expected measurement_unit to be 'no-dim', got {measurement_y_units}" - ) - return 1.0, y_unit - else: - raise ValueError( - f"Cannot convert measurement quantity '{measurement_quantity}' " - f"(units '{measurement_y_units}') to '{self.quantity}'" - ) +def _basis(reaction, Elab, lmax, wavelengths_beyond_range, zeros_per_node): + """The ``IntegralWorkspace`` and kinematics for one reaction at one energy.""" + kinematics = reaction.kinematics(Elab) + k = kinematics.k + interaction_range_fm = jitr.utils.interaction_range(reaction.target.A) + 2 + a = k * interaction_range_fm + wavelengths_beyond_range * 2 * np.pi + channel_radius_fm = a / k + N = jitr.utils.suggested_basis_size(a, zeros_per_node) + integral_ws = jitr.xs.elastic.IntegralWorkspace( + reaction=reaction, + kinematics=kinematics, + channel_radius_fm=channel_radius_fm, + solver=jitr.rmatrix.Solver(N), + lmax=lmax, + ) + return integral_ws, kinematics def set_up_solver( - reaction: jitr.reactions.Reaction, + reaction, Elab: float, - angle_rad_constraint: np.ndarray, - angle_rad_vis: np.ndarray, - lmax: int, + angles_rad: np.ndarray, + lmax: int = DEFAULT_LMAX, wavelengths_beyond_range: float = 2.0, zeros_per_node: int = 5, ): - """ - Set up ``jitr`` workspaces for a reaction at a given energy. + """Set up a ``jitr`` differential workspace for a reaction at one energy. Parameters ---------- reaction : jitr.reactions.Reaction - Reaction system definition. Elab : float Laboratory energy in MeV. - angle_rad_constraint : np.ndarray - Angles in radians for comparison to experiment. - angle_rad_vis : np.ndarray - Angles in radians for visualisation. + angles_rad : np.ndarray + Angles in radians the observables are wanted on. lmax : int - Maximum angular momentum. - wavelengths_beyond_range : float, optional + Maximum partial wave. + wavelengths_beyond_range : float Number of wavelengths beyond the interaction range used to set the - channel radius. Defaults to ``2.0``. - zeros_per_node : int, optional - Number of basis-function zeros per node in the R-matrix solver. - Defaults to ``5``. + channel radius. + zeros_per_node : int + Basis-function zeros per node in the R-matrix solver. Returns ------- - constraint_ws : jitr.xs.elastic.DifferentialWorkspace - Workspace on the constraint angle grid. - visualization_ws : jitr.xs.elastic.DifferentialWorkspace - Workspace on the visualisation angle grid. - kinematics : jitr.reactions.Kinematics - Kinematic quantities for the reaction. + (jitr.xs.elastic.DifferentialWorkspace, jitr.reactions.Kinematics) """ - kinematics = reaction.kinematics(Elab) - k = kinematics.k - interaction_range_fm = jitr.utils.interaction_range(reaction.target.A) + 2 - a = k * interaction_range_fm + wavelengths_beyond_range * 2 * np.pi - channel_radius_fm = a / k - N = jitr.utils.suggested_basis_size(a, zeros_per_node) - core_solver = jitr.rmatrix.Solver(N) - - integral_ws = jitr.xs.elastic.IntegralWorkspace( - reaction=reaction, - kinematics=kinematics, - channel_radius_fm=channel_radius_fm, - solver=core_solver, - lmax=lmax, - ) - - constraint_ws = jitr.xs.elastic.DifferentialWorkspace( - integral_workspace=integral_ws, angles=angle_rad_constraint + integral_ws, kinematics = _basis( + reaction, Elab, lmax, wavelengths_beyond_range, zeros_per_node ) - visualization_ws = jitr.xs.elastic.DifferentialWorkspace( - integral_workspace=integral_ws, angles=angle_rad_vis + ws = jitr.xs.elastic.DifferentialWorkspace( + integral_workspace=integral_ws, angles=np.asarray(angles_rad, dtype=float) ) + return ws, kinematics + + +def extract_dXS_dA(xs, ws) -> np.ndarray: + """dXS/dA in b/sr (``jitr`` reports mb/sr).""" + return xs.dsdo / MB_PER_B + + +def extract_dXS_dRuth(xs, ws) -> np.ndarray: + """dXS/dRuth (dimensionless).""" + return xs.dsdo / ws.rutherford + - return constraint_ws, visualization_ws, kinematics +def extract_Ay(xs, ws) -> np.ndarray: + """The analysing power (dimensionless).""" + return xs.Ay -def momentum_transfer(observation: ElasticDifferentialXSObservation) -> np.ndarray: - r"""Momentum transfer :math:`q = 2k\sin(\theta/2)` (fm^-1) on the data angles. +_EXTRACT = {"dXS/dA": extract_dXS_dA, "dXS/dRuth": extract_dXS_dRuth, "Ay": extract_Ay} - Returns the array of :math:`q` values, e.g. for plotting. For a - :func:`~rxmc.covariance.kernel_term` in :math:`q`-space pass the same map - as the term's coordinate transform of the angle instead: - ``kernel_term(kernel, coords=lambda x: 2 * obs.k * np.sin(x / 2))``. + +class ElasticXS(Model): + """Elastic differential cross section, ratio to Rutherford, or analysing power. + + Parameters + ---------- + quantity : {"dXS/dA", "dXS/dRuth", "Ay"} + central : callable + ``f(r, *args) -> np.ndarray``, the central potential on the radial grid + ``r`` (fm), in MeV. + spin_orbit : callable or None + ``f(r, *args) -> np.ndarray``, the spin-orbit potential; ``None`` for a + spin-orbit-free model. + args_from_params : callable + ``f(workspace, *values) -> (central_args, spin_orbit_args)`` or + ``-> (central_args, spin_orbit_args, coulomb_args)``: the argument + tuples for the potential callables at the sampled values. + params : sequence of Parameter + coulomb : callable, optional + ``f(r, *args) -> np.ndarray``, the Coulomb potential. When ``None`` + the Coulomb interaction inside the channel radius must be folded into + ``central``. + lmax, wavelengths_beyond_range, zeros_per_node + Solver settings, forwarded to :func:`set_up_solver`. """ - return 2.0 * observation.k * np.sin(np.asarray(observation.x, dtype=float) / 2.0) + + def __init__( + self, + quantity: str, + central: Callable, + spin_orbit: Callable | None, + args_from_params: Callable, + params, + coulomb: Callable | None = None, + *, + lmax: int = DEFAULT_LMAX, + wavelengths_beyond_range: float = 2.0, + zeros_per_node: int = 5, + ): + if quantity not in QUANTITIES: + raise ValueError(f"quantity must be one of {QUANTITIES}, got {quantity!r}") + super().__init__(None, params) + self.quantity = quantity + self.central = central + self.spin_orbit = spin_orbit + self.coulomb = coulomb + self.args_from_params = args_from_params + self.lmax = lmax + self.wavelengths_beyond_range = wavelengths_beyond_range + self.zeros_per_node = zeros_per_node + self._cache: dict = {} + + def __getstate__(self): + state = self.__dict__.copy() + state["_cache"] = {} + return state + + def _kinematics_of(self, meta): + meta = meta or {} + try: + return meta["reaction"], float(meta["Elab"]) + except KeyError as err: + raise ValueError( + f"{type(self).__name__} needs meta[{err.args[0]!r}] to bind: build " + "the dataset with from_measurement, or pass meta={'reaction': ..., " + "'Elab': ...}" + ) from None + + def workspace(self, x, meta): + """The ``jitr`` differential workspace on ``x`` for this dataset's kinematics.""" + reaction, Elab = self._kinematics_of(meta) + key = ( + id(reaction), + Elab, + self.lmax, + self.wavelengths_beyond_range, + self.zeros_per_node, + ) + if key not in self._cache: + self._cache[key] = _basis( + reaction, + Elab, + self.lmax, + self.wavelengths_beyond_range, + self.zeros_per_node, + ) + integral_ws, _ = self._cache[key] + x = np.asarray(x, dtype=float) + check_angle_grid(x, "x") + return jitr.xs.elastic.DifferentialWorkspace( + integral_workspace=integral_ws, angles=x + ) + + def bind(self, x, meta=None) -> Predictor: + ws = self.workspace(x, meta) + extract = _EXTRACT[self.quantity] + central, spin_orbit, coulomb = self.central, self.spin_orbit, self.coulomb + args_from_params = self.args_from_params + r = ws.radial_grid() + + def predict(*values): + args = args_from_params(ws, *values) + if len(args) == 2: + (c_args, so_args), cou_args = args, () + elif len(args) == 3: + c_args, so_args, cou_args = args + else: + raise ValueError( + "args_from_params must return 2 or 3 argument tuples, " + f"got {len(args)}" + ) + xs = ws.xs( + central(r, *c_args), + None if spin_orbit is None else spin_orbit(r, *so_args), + None if coulomb is None else coulomb(r, *cou_args), + ) + return extract(xs, ws) + + return Predictor(self.params, x, predict) diff --git a/src/rxmc/reactions/ias.py b/src/rxmc/reactions/ias.py index c448fe2..6ed6835 100644 --- a/src/rxmc/reactions/ias.py +++ b/src/rxmc/reactions/ias.py @@ -1,242 +1,157 @@ -import jitr -import numpy as np -from exfor_tools.distribution import Distribution - -from .observation import Observation -from .observation_from_measurement import ( # noqa: F401 (re-exported names) - DEFAULT_LMAX, - XS_UNIT, - check_angle_grid, - measurement_kwargs, - normalized_error_kwargs, - ureg, -) - +""" +(p,n) isobaric-analog-state differential cross sections from ``jitr``. -class IsobaricAnalogPNObservation(Observation): - """ - Observation for (p,n) isobaric analog state (IAS) reactions. - - This is an :class:`~rxmc.observation.Observation` (statistical error only): it - inherits ``statistical_term`` and ``num_pts_within_interval``. Any correlated - systematic is composed explicitly as an ``extra_terms`` entry in the - :class:`~rxmc.constraint.Constraint`. +:class:`IsobaricAnalogPN` is a :class:`~rxmc.model.Model` that overrides only +``bind``: given a grid of angles and a dataset's kinematics +(``meta["reaction"]``, ``meta["Elab"]``, ``meta["ExIAS"]``) it builds the +``jitr`` quasielastic (p,n) workspace on that grid and returns a +:class:`~rxmc.model.Predictor` for the cross section in b/sr. The (p,n) +transition is driven by the difference between the proton and neutron +potentials (the Lane term), so the five potentials are declared separately. +""" - It is designed to handle (p,n) IAS reaction measurements in differential cross - section form. +from __future__ import annotations - Internally, this involves initializing a jitr.xs.quasielastic_pn.Workspace - which precomputes things like boundary conditions to speed up computation of - observables for a given set of interaction parameters. - """ +from typing import Callable - def __init__( - self, - x: np.ndarray, - y: np.ndarray, - Elab: float, - reaction: jitr.reactions.Reaction, - ExIAS: float, - y_units: str, - y_stat_err=None, - y_sys_err_normalization=None, - y_sys_err_offset=None, - dataset_label: str | None = None, - lmax: int = DEFAULT_LMAX, - angles_vis: np.ndarray = np.linspace(0.01, 180, 100), - wavelengths_beyond_range: float = 2.0, - zeros_per_node: int = 5, - transform=None, - mask=None, - ): - """ - Initialize a Observation instance for the (p,n) IAS reaction. - - Parameters - ---------- - x : np.ndarray - Measured angle grid in degrees. - y : np.ndarray - Measured differential cross section data. - Elab : float - Laboratory energy of the incoming proton (MeV). - reaction : jitr.reactions.Reaction - Reaction information. - ExIAS : float - Excitation energy of the IAS in the residual nucleus (MeV). - y_units : str - Units of the supplied `y` values. - y_stat_err : np.ndarray, optional - Statistical errors associated with `y`. - y_sys_err_normalization : float or np.ndarray, optional - Reported *fractional* (dimensionless) normalisation uncertainty. - Retained as inert metadata (see - :meth:`rxmc.observation.Observation.systematic_terms`); not divided - by the unit normalisation. - y_sys_err_offset : float or np.ndarray, optional - Reported *absolute* offset uncertainty in the same units as `y`. - Retained as inert metadata, converted to internal units (divided by - the unit normalisation). - dataset_label : str, optional - Human-readable dataset identifier used in error messages. - lmax: int - Maximum angular momentum - angles_vis: np.ndarray - Array of angles in degrees for visualization. - wavelengths_beyond_range: float - Number of wavelengths beyond the interaction range to set the channel radius. - zeros_per_node: int - Number of zeros of the basis functions per node in the R-matrix solver. - """ - self.reaction = reaction - self.lmax = lmax - self.subentry = dataset_label - self.angle_units = ureg.radian - self.quantity = "dXS/dA" - - self.angles_vis = angles_vis - angles_rad_vis = np.deg2rad(angles_vis) - check_angle_grid(angles_rad_vis, "angles_rad_vis") - - angles_rad_constraint = np.deg2rad(x) - label = dataset_label or "dataset" - check_angle_grid( - angles_rad_constraint, - f"x values for {label}", - ) - - # set up workspaces to precompute things for the solver - # for quick evaluation of observables - constraint_ws, vis_ws, kinematics_entrance, kinematics_exit = set_up_solver( - reaction=self.reaction, - Elab=Elab, - ExIAS=ExIAS, - angle_rad_constraint=angles_rad_constraint, - angle_rad_vis=angles_rad_vis, - lmax=self.lmax, - wavelengths_beyond_range=wavelengths_beyond_range, - zeros_per_node=zeros_per_node, - ) - self.constraint_workspace = constraint_ws - self.visualization_workspace = vis_ws - - self.y_units = XS_UNIT - measurement_unit = 1 * ureg(y_units) - if not measurement_unit.check(self.y_units): - raise ValueError( - f"Expected measurement_unit to be dimensionally " - f"compatible with 'b/sr', got {y_units}" - ) - - norm = 1.0 / measurement_unit.to(self.y_units).magnitude - # retained for provenance / manual term recomposition - self.norm = norm - - super().__init__( - angles_rad_constraint, - np.asarray(y) / norm, - label=dataset_label, - transform=transform, - mask=mask, - **normalized_error_kwargs( - norm, y_stat_err, y_sys_err_normalization, y_sys_err_offset - ), - ) +import jitr +import numpy as np - @classmethod - def from_measurement( - cls, - measurement: Distribution, - reaction: jitr.reactions.Reaction, - ExIAS: float, - **kwargs, - ): - """Construct from an ``exfor_tools`` ``Distribution``. +from ..model import Model, Predictor +from ..units import DEFAULT_LMAX, MB_PER_B, check_angle_grid - ``**kwargs`` (solver settings, ``transform``, ``mask``, ...) are - forwarded to the constructor. - """ - return cls( - reaction=reaction, ExIAS=ExIAS, **measurement_kwargs(measurement), **kwargs - ) +__all__ = ["IsobaricAnalogPN", "set_up_solver"] def set_up_solver( - reaction: jitr.reactions.Reaction, + reaction, Elab: float, ExIAS: float, - angle_rad_constraint: np.array, - angle_rad_vis: np.array, - lmax: int, + angles_rad: np.ndarray, + lmax: int = DEFAULT_LMAX, wavelengths_beyond_range: float = 2.0, zeros_per_node: int = 5, ): - """ - Set up the solver for the reaction. + """Set up the ``jitr`` (p,n) workspace for a reaction at one energy. Parameters ---------- - reaction : - Reaction information. + reaction : jitr.reactions.Reaction Elab : float Laboratory energy of the incoming proton (MeV). ExIAS : float - Excitation energy of the IAS in the residual nucleus (MeV). - angle_rad_constraint : np.array - Angles to compare to experiment (rad). - angle_rad_vis : np.array - Angles to visualize on (rad) - lmax : int - Maximum angular momentum. - wavelengths_beyond_range : float - Number of wavelengths beyond the interaction - range to set the channel radius. - zeros_per_node : int - Number of zeros of the basis functions per - node in the R-matrix solver. + Excitation energy of the isobaric analog state in the residual (MeV). + angles_rad : np.ndarray + Angles in radians the cross section is wanted on. + lmax, wavelengths_beyond_range, zeros_per_node + Solver settings (see :func:`rxmc.reactions.elastic.set_up_solver`). Returns ------- - tuple - constraint and visualization workspaces. + (jitr.xs.quasielastic_pn.Workspace, kinematics_entrance, kinematics_exit) """ kinematics_entrance = reaction.kinematics(Elab=Elab) kinematics_exit = reaction.kinematics_exit( kinematics_entrance, residual_excitation_energy=ExIAS ) - k = kinematics_entrance.k interaction_range_fm = jitr.utils.interaction_range(reaction.target.A) + 2 a = k * interaction_range_fm + wavelengths_beyond_range * 2 * np.pi channel_radius_fm = a / k N = jitr.utils.suggested_basis_size(a, zeros_per_node) - core_solver = jitr.rmatrix.Solver(N) - - constraint_workspace = jitr.xs.quasielastic_pn.Workspace( + ws = jitr.xs.quasielastic_pn.Workspace( reaction, kinematics_entrance, kinematics_exit, - core_solver, - angle_rad_constraint, + jitr.rmatrix.Solver(N), + np.asarray(angles_rad, dtype=float), lmax, channel_radius_fm, tmatrix_abs_tol=1e-8, ) + return ws, kinematics_entrance, kinematics_exit - visualization_workspace = jitr.xs.quasielastic_pn.Workspace( - reaction, - kinematics_entrance, - kinematics_exit, - core_solver, - angle_rad_vis, - lmax, - channel_radius_fm, - tmatrix_abs_tol=1e-8, - ) - return ( - constraint_workspace, - visualization_workspace, - kinematics_entrance, - kinematics_exit, - ) +class IsobaricAnalogPN(Model): + """The (p,n) IAS differential cross section in b/sr. + + Parameters + ---------- + U_p_coulomb, U_p_central, U_p_spin_orbit, U_n_central, U_n_spin_orbit : callable + ``f(r, *args) -> np.ndarray`` on the radial grid ``r`` (fm), in MeV. + args_from_params : callable + ``f(workspace, *values) -> (args_p_coulomb, args_p_central, + args_p_spin_orbit, args_n_central, args_n_spin_orbit)``. + params : sequence of Parameter + lmax, wavelengths_beyond_range, zeros_per_node + Solver settings, forwarded to :func:`set_up_solver`. + """ + + def __init__( + self, + U_p_coulomb: Callable, + U_p_central: Callable, + U_p_spin_orbit: Callable, + U_n_central: Callable, + U_n_spin_orbit: Callable, + args_from_params: Callable, + params, + *, + lmax: int = DEFAULT_LMAX, + wavelengths_beyond_range: float = 2.0, + zeros_per_node: int = 5, + ): + super().__init__(None, params) + self.potentials = ( + U_p_coulomb, + U_p_central, + U_p_spin_orbit, + U_n_central, + U_n_spin_orbit, + ) + self.args_from_params = args_from_params + self.lmax = lmax + self.wavelengths_beyond_range = wavelengths_beyond_range + self.zeros_per_node = zeros_per_node + + def workspace(self, x, meta): + meta = meta or {} + try: + reaction, Elab, ExIAS = ( + meta["reaction"], + float(meta["Elab"]), + float(meta["ExIAS"]), + ) + except KeyError as err: + raise ValueError( + f"{type(self).__name__} needs meta[{err.args[0]!r}] to bind: build " + "the dataset with from_measurement(..., ExIAS=), or pass " + "meta={'reaction': ..., 'Elab': ..., 'ExIAS': ...}" + ) from None + x = np.asarray(x, dtype=float) + check_angle_grid(x, "x") + ws, _, _ = set_up_solver( + reaction, + Elab, + ExIAS, + x, + lmax=self.lmax, + wavelengths_beyond_range=self.wavelengths_beyond_range, + zeros_per_node=self.zeros_per_node, + ) + return ws + + def bind(self, x, meta=None) -> Predictor: + ws = self.workspace(x, meta) + potentials, args_from_params = self.potentials, self.args_from_params + r = ws.radial_grid() + + def predict(*values): + args = args_from_params(ws, *values) + if len(args) != 5: + raise ValueError( + f"args_from_params must return 5 argument tuples, got {len(args)}" + ) + return ws.xs(*(U(r, *a) for U, a in zip(potentials, args))) / MB_PER_B + + return Predictor(self.params, x, predict) diff --git a/test/test_reactions.py b/test/test_reactions.py new file mode 100644 index 0000000..bb12bdb --- /dev/null +++ b/test/test_reactions.py @@ -0,0 +1,193 @@ +"""The jitr-backed reaction models: real solves with small settings. + +The first solve in a process pays numba's compilation (several seconds); +every later solve is milliseconds and a second angular grid on the same +basis is free. +""" + +import dill +import jitr +import numpy as np +import pytest +from jitr.optical_potentials.potential_forms import ( + coulomb_charged_sphere, + thomas_safe, + woods_saxon_safe, +) +from scipy import stats + +import rxmc.reactions.elastic as elastic_module +import rxmc.reactions.ias as ias_module +from rxmc import Comparison, Constraint, Dataset, Parameter, Problem, reactions +from rxmc.reactions import ElasticXS, IsobaricAnalogPN, momentum_transfer, rutherford +from rxmc.transforms import scale + +MSO = 1.0 / jitr.utils.constants.WAVENUMBER_PION +A, Z = 40, 20 +R = 1.2 * A ** (1 / 3) +ANGLES = np.linspace(0.2, 2.6, 6) +N_CA = jitr.reactions.ElasticReaction(target=(A, Z), projectile=(1, 0)) +P_CA = jitr.reactions.ElasticReaction(target=(A, Z), projectile=(1, 1)) + + +def central(r, Vv, Wv, Rv, av): + return -(Vv + 1j * Wv) * woods_saxon_safe(r, Rv, av) + + +def spin_orbit(r, Vso, Rso, aso): + return Vso * MSO**2 * thomas_safe(r, Rso, aso) + + +def omp(quantity="dXS/dA", **kw): + names = ("Vv", "Wv", "Rv", "av") + return ElasticXS( + quantity, + central, + spin_orbit, + lambda ws, *x: (tuple(x), (6.0, R, 0.45)), + [Parameter(n, prior=stats.norm(0, 100)) for n in names], + lmax=10, + **kw, + ) + + +THETA = (48.0, 3.5, R, 0.7) + + +def meta(reaction=N_CA, Elab=14.1): + return {"reaction": reaction, "Elab": Elab} + + +class TestElasticXS: + def test_cross_section_is_finite_positive_in_barns(self): + pred = omp().bind(ANGLES, meta()) + y = pred(*THETA) + assert y.shape == (6,) and np.all(np.isfinite(y)) and np.all(y > 0) + # b/sr: jitr's mb/sr divided by 1000 + ws = omp().workspace(ANGLES, meta()) + r = ws.radial_grid() + direct = ws.xs(central(r, *THETA), spin_orbit(r, 6.0, R, 0.45), None).dsdo + np.testing.assert_allclose(y, direct / 1000.0) + + def test_ratio_to_rutherford_and_analysing_power(self): + ratio = omp("dXS/dRuth").bind(ANGLES, meta(P_CA, 14.1))(*THETA) + assert np.all(np.isfinite(ratio)) and np.all(ratio > 0) + ay = omp("Ay").bind(ANGLES, meta())(*THETA) + assert np.all(np.abs(ay) <= 1.0) + with pytest.raises(ValueError, match="quantity"): + omp("sigma_tot") + + def test_basis_is_cached_per_energy_and_fine_grid_is_free(self): + model = omp() + model.bind(ANGLES, meta()) + fine = model.bind(np.linspace(0.05, 3.1, 200), meta()) + assert len(model._cache) == 1 + model.bind(ANGLES, meta(Elab=20.0)) + assert len(model._cache) == 2 + y = fine(*THETA) + assert y.shape == (200,) and np.all(np.isfinite(y)) + + def test_missing_kinematics_names_what_is_needed(self): + with pytest.raises(ValueError, match="meta\\['reaction'\\].*from_measurement"): + omp().bind(ANGLES, {}) + with pytest.raises(ValueError, match="meta\\['Elab'\\]"): + omp().bind(ANGLES, {"reaction": N_CA}) + + def test_composes_like_any_model(self): + rho = Parameter("log_rho", prior=stats.norm(0, 0.1)) + scaled = omp() | scale(rho) + base = omp().bind(ANGLES, meta())(*THETA) + np.testing.assert_allclose( + scaled.bind(ANGLES, meta())(*THETA, np.log(2.0)), 2.0 * base + ) + + def test_solver_settings_reach_the_basis(self, monkeypatch): + seen = {} + real = elastic_module._basis + + def spy(reaction, Elab, lmax, wavelengths_beyond_range, zeros_per_node): + seen.update(lmax=lmax, wbr=wavelengths_beyond_range, zpn=zeros_per_node) + return real(reaction, Elab, lmax, wavelengths_beyond_range, zeros_per_node) + + monkeypatch.setattr(elastic_module, "_basis", spy) + omp(wavelengths_beyond_range=3.5, zeros_per_node=9).bind(ANGLES, meta()) + assert seen == {"lmax": 10, "wbr": 3.5, "zpn": 9} + + def test_dill_round_trip_of_a_reaction_problem(self): + d = Dataset( + ANGLES, omp().bind(ANGLES, meta())(*THETA), 0.01 * np.ones(6), meta=meta() + ) + p = Problem([Constraint([Comparison(d, omp())])]) + q = dill.loads(dill.dumps(p)) + theta = np.array(THETA) + assert q.log_posterior(theta) == pytest.approx(p.log_posterior(theta)) + + +class TestIsobaricAnalogPN: + def test_cross_section_with_a_lane_term(self): + A, Z = 48, 20 + R = 1.2 * A ** (1 / 3) + rxn = jitr.reactions.Reaction( + target=(A, Z), projectile=(1, 1), product=(1, 0), residual=(A, Z + 1) + ) + model = IsobaricAnalogPN( + coulomb_charged_sphere, + central, + spin_orbit, + central, + spin_orbit, + # the (p,n) transition is driven by the difference between the + # proton and neutron potentials (the Lane term): make them distinct + lambda ws, Vv, Wv, Rv, av: ( + (Z, R), + (Vv + 4.0, Wv, Rv, av), + (6.0, R, 0.45), + (Vv - 4.0, Wv, Rv, av), + (6.0, R, 0.45), + ), + [Parameter(n) for n in ("Vv", "Wv", "Rv", "av")], + lmax=10, + ) + pred = model.bind( + np.linspace(0.2, 2.6, 5), {"reaction": rxn, "Elab": 25.0, "ExIAS": 6.7} + ) + y = pred(48.0, 3.5, R, 0.7) + assert ( + y.shape == (5,) + and np.all(np.isfinite(y)) + and np.all(y >= 0) + and y.max() > 0 + ) + with pytest.raises(ValueError, match="ExIAS"): + model.bind(ANGLES, {"reaction": rxn, "Elab": 25.0}) + + def test_solver_settings_forwarded(self, monkeypatch): + seen = {} + + def fake(reaction, Elab, ExIAS, angles_rad, **kw): + seen.update(kw) + raise RuntimeError("stop") + + monkeypatch.setattr(ias_module, "set_up_solver", fake) + model = IsobaricAnalogPN( + *([central] * 5), + lambda ws, *x: ((),) * 5, + [Parameter("V")], + lmax=7, + wavelengths_beyond_range=3.5, + zeros_per_node=9, + ) + with pytest.raises(RuntimeError): + model.bind(ANGLES, {"reaction": object(), "Elab": 1.0, "ExIAS": 1.0}) + assert seen == {"lmax": 7, "wavelengths_beyond_range": 3.5, "zeros_per_node": 9} + + +def test_closed_forms(): + kin = P_CA.kinematics(14.1) + x = np.array([0.5, 1.0, 2.0]) + np.testing.assert_allclose(momentum_transfer(x, kin.k), 2.0 * kin.k * np.sin(x / 2)) + np.testing.assert_allclose( + rutherford(kin, x), 10 * kin.eta**2 / (4 * kin.k**2 * np.sin(x / 2) ** 4) + ) + assert np.all(rutherford(N_CA.kinematics(14.1), x) == 0.0) + assert reactions.ElasticXS is ElasticXS From 54b0aaacf8e36983ba5357801ccc0c649f149b6d Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 20:07:04 -0400 Subject: [PATCH 25/75] Add from_measurement: EXFOR measurements to datasets in internal units Reads the exfor_tools fields, converts angles to radians and cross sections to b/sr through the fixed label table, converts dXS/dA to and from dXS/dRuth with the closed-form Rutherford cross section (a per-angle norm), scales the dimensionful errors with the data and leaves the fractional normalisation error alone, and fills meta with the kinematics a reaction model needs. Every failure names what is wrong. --- src/rxmc/__init__.py | 4 + src/rxmc/data.py | 106 +++++++++++++++++++++++++- test/test_measurement.py | 160 +++++++++++++++++++++++++++++++++++++++ 3 files changed, 269 insertions(+), 1 deletion(-) create mode 100644 test/test_measurement.py diff --git a/src/rxmc/__init__.py b/src/rxmc/__init__.py index 1fd9bbb..85ca27c 100644 --- a/src/rxmc/__init__.py +++ b/src/rxmc/__init__.py @@ -10,12 +10,14 @@ from . import likelihood as likelihood from . import model as model from . import problem as problem +from . import reactions as reactions from . import terms as terms from . import transforms as transforms from . import units as units from .constraint import Comparison as Comparison from .constraint import Constraint as Constraint from .data import Dataset as Dataset +from .data import from_measurement as from_measurement from .model import Model as Model from .model import polynomial as polynomial from .params import Parameter as Parameter @@ -36,6 +38,7 @@ "Parameter", "Problem", "Term", + "from_measurement", "polynomial", "constraint", "covariance", @@ -43,6 +46,7 @@ "likelihood", "model", "problem", + "reactions", "terms", "transforms", "units", diff --git a/src/rxmc/data.py b/src/rxmc/data.py index 09523fa..f7fb637 100644 --- a/src/rxmc/data.py +++ b/src/rxmc/data.py @@ -20,7 +20,7 @@ import numpy as np -__all__ = ["Dataset"] +__all__ = ["Dataset", "from_measurement"] def _error_spec(value, n, name): @@ -103,3 +103,107 @@ def n(self) -> int: def __repr__(self): label = f"{self.label!r}, " if self.label else "" return f"Dataset({label}n={self.n})" + + +# ---------------------------------------------------------------------------- +# EXFOR measurements +# ---------------------------------------------------------------------------- + +_QUANTITY_KIND = { + "dXS/dA": "differential", + "dXS/dRuth": "dimensionless", + "Ay": "dimensionless", +} + + +def from_measurement( + measurement, *, reaction=None, quantity=None, ExIAS=None +) -> Dataset: + """A :class:`Dataset` from an ``exfor_tools`` measurement, in internal units. + + Reads ``x`` (degrees), ``y``, ``Einc``, ``quantity``, ``y_units``, + ``statistical_err``, ``systematic_norm_err``, ``systematic_offset_err`` and + ``subentry`` from ``measurement`` (any object with those attributes). Angles + are stored in radians, cross sections in b/sr, ratios and analysing powers + as they are. Every dimensionful error (statistical, absolute offset) is + converted with the data; the fractional normalisation error passes through + untouched. The kinematics a reaction model needs to bind land in + ``meta``: ``reaction``, ``Elab``, ``quantity``, ``k``, ``eta`` and, for the + (p,n) channel, ``ExIAS``. + + Parameters + ---------- + measurement : object + An ``exfor_tools.distribution.Distribution`` or anything shaped like it. + reaction : jitr.reactions.Reaction, optional + Needed for the kinematics in ``meta`` and for any conversion between + ``dXS/dA`` and ``dXS/dRuth`` (the Rutherford cross section is a closed + form of the kinematics). + quantity : {"dXS/dA", "dXS/dRuth", "Ay"}, optional + The quantity the dataset should hold; defaults to the measured one. + ExIAS : float, optional + Excitation energy of the isobaric analog state (MeV), for (p,n) data. + """ + from .units import MB_PER_B, check_angle_grid, parse_unit + + measured = measurement.quantity + target = measured if quantity is None else quantity + for q in (measured, target): + if q not in _QUANTITY_KIND: + raise ValueError( + f"unknown quantity {q!r}; expected one of {list(_QUANTITY_KIND)}" + ) + factor, kind = parse_unit(measurement.y_units) + if kind != _QUANTITY_KIND[measured]: + raise ValueError( + f"measurement quantity {measured!r} needs {_QUANTITY_KIND[measured]} units, " + f"got {measurement.y_units!r}" + ) + x = np.deg2rad(np.asarray(measurement.x, dtype=float)) + label = getattr(measurement, "subentry", None) or "" + check_angle_grid(x, f"x of {label or 'measurement'}") + Elab = float(measurement.Einc) + + meta = {"quantity": target, "Elab": Elab, "subentry": label or None} + kinematics = None + if reaction is not None: + kinematics = reaction.kinematics(Elab) + meta.update(reaction=reaction, k=float(kinematics.k), eta=float(kinematics.eta)) + if ExIAS is not None: + meta["ExIAS"] = float(ExIAS) + + if measured == target: + norm = factor # into b/sr for a cross section, 1 for a ratio + elif {measured, target} == {"dXS/dA", "dXS/dRuth"}: + if kinematics is None: + raise ValueError( + f"converting {measured!r} to {target!r} needs the Rutherford cross " + "section: pass reaction=" + ) + if not kinematics.eta > 0: + raise ValueError( + f"converting {measured!r} to {target!r} needs a charged projectile " + f"(eta = {kinematics.eta})" + ) + from .reactions.elastic import rutherford + + ruth_b = rutherford(kinematics, x) / MB_PER_B + norm = factor / ruth_b if measured == "dXS/dA" else ruth_b + else: + raise ValueError( + f"cannot convert measurement quantity {measured!r} to {target!r}" + ) + + def scaled(v): + return None if v is None else np.asarray(v, dtype=float) * norm + + y_err = measurement.statistical_err + return Dataset( + x, + np.asarray(measurement.y, dtype=float) * norm, + scaled(np.zeros_like(x) if y_err is None else y_err), + norm_err=measurement.systematic_norm_err, + offset_err=scaled(measurement.systematic_offset_err), + label=label, + meta=meta, + ) diff --git a/test/test_measurement.py b/test/test_measurement.py new file mode 100644 index 0000000..b5769a8 --- /dev/null +++ b/test/test_measurement.py @@ -0,0 +1,160 @@ +"""from_measurement: EXFOR measurements to datasets in internal units. + +No solver is touched: the Rutherford conversion is a closed form of the +kinematics, so a real ``jitr`` reaction is cheap here. +""" + +from types import SimpleNamespace + +import jitr +import numpy as np +import pytest +from scipy import stats + +from helpers import assemble_dense +from rxmc import Comparison, Model, Parameter, from_measurement +from rxmc.reactions import rutherford +from rxmc.terms import statistical +from rxmc.transforms import log + +P_CA = jitr.reactions.ElasticReaction(target=(40, 20), projectile=(1, 1)) +N_CA = jitr.reactions.ElasticReaction(target=(40, 20), projectile=(1, 0)) + + +def measurement(**overrides): + """A minimal ``exfor_tools``-like Distribution stub.""" + fields = dict( + x=np.array([20.0, 40.0]), + y=np.array([2.0, 1.0]), + Einc=8.0, + quantity="dXS/dA", + y_units="barns/ster", + statistical_err=np.array([0.2, 0.1]), + systematic_norm_err=0.03, + systematic_offset_err=0.02, + subentry="subentry", + ) + fields.update(overrides) + return SimpleNamespace(**fields) + + +def test_construction_in_internal_units(): + m = measurement(subentry="E1234-002") + d = from_measurement(m, reaction=P_CA) + np.testing.assert_allclose(d.x, np.deg2rad(m.x)) + np.testing.assert_allclose(d.y, m.y) + np.testing.assert_allclose(d.y_err, m.statistical_err) + assert d.label == "E1234-002" + assert d.norm_err == 0.03 and d.offset_err == pytest.approx(0.02) + kin = P_CA.kinematics(8.0) + assert d.meta["reaction"] is P_CA and d.meta["Elab"] == 8.0 + assert d.meta["quantity"] == "dXS/dA" and d.meta["subentry"] == "E1234-002" + assert d.meta["k"] == pytest.approx(kin.k) and d.meta["eta"] == pytest.approx( + kin.eta + ) + + +def test_exfor_tools_labels_and_millibarns(): + for label in ("barns/ster", "b/Sr", "MB/SR", "mb/sr"): + d = from_measurement(measurement(y_units=label), reaction=P_CA) + expected = ( + 1.0 if "b" in label.lower()[:1] and "m" not in label.lower() else 1e-3 + ) + np.testing.assert_allclose(d.y, expected * measurement().y) + + +def test_ratio_from_absolute_uses_the_per_angle_rutherford_norm(): + m = measurement( + y=np.array([1800.0, 300.0]), + y_units="mb/sr", + statistical_err=np.array([20.0, 10.0]), + systematic_offset_err=5.0, # mb/sr + ) + d = from_measurement(m, reaction=P_CA, quantity="dXS/dRuth") + ruth_b = rutherford(P_CA.kinematics(8.0), np.deg2rad(m.x)) / 1000.0 + norm = 1e-3 / ruth_b + np.testing.assert_allclose(d.y, m.y * norm) + np.testing.assert_allclose(d.y_err, m.statistical_err * norm) + np.testing.assert_allclose(d.offset_err, 5.0 * norm) # a per-angle array now + assert d.norm_err == 0.03 # fractional: untouched + assert d.meta["quantity"] == "dXS/dRuth" + # regression: the reported terms plus the statistical diagonal recover the + # old auto-folded covariance, in internal (normalised) units + comp = Comparison(d, Model(lambda x, c: c * np.ones_like(x), [Parameter("c")])) + ym = np.array([0.9, 0.6]) + S = assemble_dense( + [statistical(comp.y_err), *comp.reported_terms()], d.x, comp.y, ym + ) + omega = 5.0 * norm + old = ( + np.diag((m.statistical_err * norm) ** 2) + + np.outer(omega, omega) + + 0.03**2 * np.outer(ym, ym) + ) + np.testing.assert_allclose(S, old) + + +def test_absolute_from_ratio(): + m = measurement(y=np.array([0.9, 0.6]), quantity="dXS/dRuth", y_units="no-dim") + d = from_measurement(m, reaction=P_CA, quantity="dXS/dA") + ruth_b = rutherford(P_CA.kinematics(8.0), np.deg2rad(m.x)) / 1000.0 + np.testing.assert_allclose(d.y, m.y * ruth_b) + np.testing.assert_allclose(d.y_err, m.statistical_err * ruth_b) + + +def test_errors_are_named(): + with pytest.raises(ValueError, match="unknown unit label 'MeV'"): + from_measurement(measurement(y_units="MeV")) + with pytest.raises(ValueError, match="needs differential units"): + from_measurement(measurement(y_units="no-dim")) + with pytest.raises(ValueError, match="needs dimensionless units"): + from_measurement(measurement(quantity="Ay", y_units="mb/sr")) + with pytest.raises(ValueError, match="cannot convert"): + from_measurement(measurement(), quantity="Ay") + with pytest.raises(ValueError, match="pass reaction="): + from_measurement(measurement(), quantity="dXS/dRuth") + with pytest.raises(ValueError, match="charged projectile"): + from_measurement(measurement(), reaction=N_CA, quantity="dXS/dRuth") + with pytest.raises(ValueError, match="unknown quantity"): + from_measurement(measurement(quantity="sigma")) + + +def test_no_reaction_and_no_errors(): + m = measurement( + statistical_err=None, + systematic_norm_err=None, + systematic_offset_err=None, + subentry=None, + ) + d = from_measurement(m) + assert "reaction" not in d.meta and d.label == "" + np.testing.assert_allclose(d.y_err, 0.0) + assert d.norm_err is None and d.offset_err is None + + +def test_log_space_and_ias_channel(): + d = from_measurement(measurement(), reaction=P_CA) + comp = Comparison( + d, Model(lambda x, c: c * np.ones_like(x), [Parameter("c")]), space=log + ) + np.testing.assert_allclose(comp.y, np.log(d.y)) + pn = jitr.reactions.Reaction( + target=(48, 20), projectile=(1, 1), product=(1, 0), residual=(48, 21) + ) + m = measurement( + x=np.array([5.0, 15.0]), + y=np.array([900.0, 700.0]), + Einc=18.0, + y_units="mb/sr", + statistical_err=np.array([80.0, 70.0]), + systematic_norm_err=0.02, + systematic_offset_err=10.0, + ) + d = from_measurement(m, reaction=pn, ExIAS=4.5) + np.testing.assert_allclose(d.y, [0.9, 0.7]) + np.testing.assert_allclose(d.y_err, [0.08, 0.07]) + assert d.offset_err == pytest.approx(0.01) and d.norm_err == 0.02 + assert d.meta["ExIAS"] == 4.5 and d.meta["Elab"] == 18.0 + assert ( + stats.norm(0, 1).cdf(0) == 0.5 + ) # keep scipy imported for the recipe-style spelling From 60dab9b512e177de62eb4221506633357869ee19 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 20:12:51 -0400 Subject: [PATCH 26/75] Add recipe tests 3, 14, 15 and 37 for the reaction layer Reported systematics become modes only when asked for; an EXFOR measurement lands in b/sr and radians with the Rutherford conversion as a per-angle norm; one elastic model serves the data grid and a plotting grid from one cached basis; a cross section and an analysing power from one measurement share an angle-calibration mode across comparisons. Recipe 37 now notes that a spanning basis sees the gathered stack and must split a finite difference at the seam by per-point metadata. --- docs/recipes.md | 4 + .../test_recipe_03_reported_systematics.py | 75 +++++++++++++++++ .../test_recipe_14_from_measurement.py | 80 +++++++++++++++++++ ...st_recipe_15_reaction_model_on_any_grid.py | 55 +++++++++++++ ..._recipe_37_cross_observable_systematics.py | 76 ++++++++++++++++++ 5 files changed, 290 insertions(+) create mode 100644 test/recipes/test_recipe_03_reported_systematics.py create mode 100644 test/recipes/test_recipe_14_from_measurement.py create mode 100644 test/recipes/test_recipe_15_reaction_model_on_any_grid.py create mode 100644 test/recipes/test_recipe_37_cross_observable_systematics.py diff --git a/docs/recipes.md b/docs/recipes.md index ea6e827..9f96195 100644 --- a/docs/recipes.md +++ b/docs/recipes.md @@ -1084,6 +1084,10 @@ Expected behaviour: - A normalisation error affects the cross section and not a ratio observable; an angle-calibration error affects both through their angular derivatives, which the basis supplies from `c.ym` and `c.x`. +- A spanning term sees the *gathered* stack, so a basis that differentiates + along the grid must not straddle the seam between comparisons: it splits + the rows by a per-point dataset field such as `c.meta("quantity")` + (recipe 22), which `from_measurement` fills in. - The multi-quantity extension of the Peelle treatment applies: build the mode from predictions, not data. diff --git a/test/recipes/test_recipe_03_reported_systematics.py b/test/recipes/test_recipe_03_reported_systematics.py new file mode 100644 index 0000000..0b7bc41 --- /dev/null +++ b/test/recipes/test_recipe_03_reported_systematics.py @@ -0,0 +1,75 @@ +"""Recipe 3: use the reported systematic errors. + +The measurement reports a fractional normalisation error and an absolute +offset error. I want them in the likelihood as correlated modes. +""" + +from types import SimpleNamespace + +import jitr +import numpy as np +from scipy import stats + +from common import line +from helpers import assemble_dense +from rxmc import Comparison, Constraint, Parameter, Problem, from_measurement +from rxmc import terms as T +from rxmc import transforms as tf + +P_CA = jitr.reactions.ElasticReaction(target=(40, 20), projectile=(1, 1)) + + +def measurement(**kw): + fields = dict( + x=np.array([20.0, 40.0, 60.0]), + y=np.array([3.0, 2.0, 1.0]), + Einc=8.0, + quantity="dXS/dA", + y_units="barns/ster", + statistical_err=np.array([0.1, 0.1, 0.1]), + systematic_norm_err=0.04, + systematic_offset_err=0.05, + subentry="E1234-002", + ) + fields.update(kw) + return SimpleNamespace(**fields) + + +def test_nothing_is_folded_in_silently_and_reported_terms_recover_the_old_covariance(): + d = from_measurement(measurement(), reaction=P_CA) + comp = Comparison(d, line()) + bare = Problem([Constraint([comp])]) + theta = np.array([-0.05, 4.0]) + np.testing.assert_allclose(bare.constraints[0].matrix(theta), np.diag(d.y_err**2)) + terms = comp.reported_terms() + assert [t.kind for t in terms] == ["mode", "mode"] and all( + t.on is comp for t in terms + ) + with_sys = Problem([Constraint([comp], terms=terms)]) + ym = comp.predict(*theta) + old = np.diag(d.y_err**2) + 0.05**2 * np.ones((3, 3)) + 0.04**2 * np.outer(ym, ym) + np.testing.assert_allclose(with_sys.constraints[0].matrix(theta), old) + np.testing.assert_allclose( + assemble_dense([T.statistical(comp.y_err), *terms], d.x, comp.y, ym), old + ) + + +def test_zero_magnitudes_yield_no_terms(): + d = from_measurement( + measurement(systematic_norm_err=0.0, systematic_offset_err=None), reaction=P_CA + ) + assert Comparison(d, line()).reported_terms() == [] + + +def test_delta_method_under_log(): + d = from_measurement(measurement(), reaction=P_CA) + comp = Comparison(d, line(), space=tf.log) + offset, norm = comp.reported_terms() + ym = comp.predict(-0.05, 4.0) + np.testing.assert_allclose(offset.value(d.x, comp.y, ym), 0.05 / d.y) # at the data + np.testing.assert_allclose( + norm.value(d.x, comp.y, ym), 0.04 + ) # at the prediction: constant + log_eps = Parameter("log_eps", prior=stats.norm(-3, 1)) + p = Problem([Constraint([comp], terms=[offset, norm, T.noise(log_eps)])]) + assert np.isfinite(p.log_posterior(np.array([-0.05, 4.0, -3.0]))) diff --git a/test/recipes/test_recipe_14_from_measurement.py b/test/recipes/test_recipe_14_from_measurement.py new file mode 100644 index 0000000..bde5046 --- /dev/null +++ b/test/recipes/test_recipe_14_from_measurement.py @@ -0,0 +1,80 @@ +"""Recipe 14: from an EXFOR measurement to a dataset. + +I have an exfor_tools distribution in mb/sr, or as a ratio to Rutherford, +with its reported errors. I want a dataset in the library's units with +nothing lost. +""" + +from types import SimpleNamespace + +import jitr +import numpy as np +import pytest + +from rxmc import from_measurement +from rxmc.reactions import rutherford + +P_CA = jitr.reactions.ElasticReaction(target=(40, 20), projectile=(1, 1)) +N_CA = jitr.reactions.ElasticReaction(target=(40, 20), projectile=(1, 0)) + + +def measurement(**kw): + fields = dict( + x=np.array([30.0, 60.0]), + y=np.array([500.0, 50.0]), + Einc=12.0, + quantity="dXS/dA", + y_units="mb/sr", + statistical_err=np.array([10.0, 2.0]), + systematic_norm_err=0.05, + systematic_offset_err=1.0, + subentry="E0001-003", + ) + fields.update(kw) + return SimpleNamespace(**fields) + + +def test_units_errors_and_meta(): + d = from_measurement(measurement(), reaction=P_CA) + np.testing.assert_allclose(d.x, np.deg2rad([30.0, 60.0])) + np.testing.assert_allclose(d.y, [0.5, 0.05]) # b/sr + np.testing.assert_allclose(d.y_err, [0.01, 0.002]) + assert d.offset_err == pytest.approx(0.001) # dimensionful: converted + assert d.norm_err == 0.05 # fractional: untouched + assert d.label == "E0001-003" + for key in ("reaction", "Elab", "quantity", "k", "eta"): + assert key in d.meta + + +def test_rutherford_conversion_is_a_per_angle_factor(): + m = measurement() + d = from_measurement(m, reaction=P_CA, quantity="dXS/dRuth") + ruth_b = rutherford(P_CA.kinematics(12.0), np.deg2rad(m.x)) / 1000.0 + np.testing.assert_allclose(d.y, (m.y / 1000.0) / ruth_b) + np.testing.assert_allclose(d.offset_err, (1.0 / 1000.0) / ruth_b) # now an array + back = from_measurement( + measurement( + y=d.y, + quantity="dXS/dRuth", + y_units="no-dim", + statistical_err=d.y_err, + systematic_offset_err=None, + ), + reaction=P_CA, + quantity="dXS/dA", + ) + np.testing.assert_allclose(back.y, m.y / 1000.0) + + +def test_ias_channel_and_failures(): + pn = jitr.reactions.Reaction( + target=(48, 20), projectile=(1, 1), product=(1, 0), residual=(48, 21) + ) + d = from_measurement(measurement(), reaction=pn, ExIAS=6.7) + assert d.meta["ExIAS"] == 6.7 + with pytest.raises(ValueError, match="unknown unit label"): + from_measurement(measurement(y_units="fm^2")) + with pytest.raises(ValueError, match="cannot convert"): + from_measurement(measurement(), reaction=P_CA, quantity="Ay") + with pytest.raises(ValueError, match="charged projectile"): + from_measurement(measurement(), reaction=N_CA, quantity="dXS/dRuth") diff --git a/test/recipes/test_recipe_15_reaction_model_on_any_grid.py b/test/recipes/test_recipe_15_reaction_model_on_any_grid.py new file mode 100644 index 0000000..fa4879f --- /dev/null +++ b/test/recipes/test_recipe_15_reaction_model_on_any_grid.py @@ -0,0 +1,55 @@ +"""Recipe 15: evaluate a reaction model on any grid. + +I want the model on the data angles for the likelihood and on a fine grid +for plotting, with the solver set up once per grid. +""" + +import jitr +import numpy as np +import pytest +from jitr.optical_potentials.potential_forms import thomas_safe, woods_saxon_safe +from scipy import stats + +from rxmc import Comparison, Constraint, Dataset, Parameter, Problem +from rxmc.reactions import ElasticXS + +MSO = 1.0 / jitr.utils.constants.WAVENUMBER_PION +R = 1.2 * 40 ** (1 / 3) +N_CA = jitr.reactions.ElasticReaction(target=(40, 20), projectile=(1, 0)) + + +def central(r, Vv, Wv, Rv, av): + return -(Vv + 1j * Wv) * woods_saxon_safe(r, Rv, av) + + +def spin_orbit(r, Vso, Rso, aso): + return Vso * MSO**2 * thomas_safe(r, Rso, aso) + + +def test_data_grid_for_the_likelihood_and_a_fine_grid_for_plotting(): + omp = ElasticXS( + "dXS/dA", + central, + spin_orbit, + lambda ws, *x: (tuple(x), (6.0, R, 0.45)), + [Parameter(n, prior=stats.norm(0, 100)) for n in ("Vv", "Wv", "Rv", "av")], + lmax=10, + ) + theta = np.array([48.0, 3.5, R, 0.7]) + x = np.linspace(0.3, 2.5, 5) + meta = {"reaction": N_CA, "Elab": 14.1} + truth = omp.bind(x, meta)(*theta) + d = Dataset(x, truth, 0.05 * truth, label="mock", meta=meta) + p = Problem([Constraint([Comparison(d, omp)])]) + assert p.chi2(theta) == pytest.approx(0.0, abs=1e-20) + # the same model on a fine grid, read back through problem.columns + fine = omp.bind(np.deg2rad(np.linspace(0.5, 179.5, 60)), d.meta) + sample = np.concatenate([theta, [0.0]]) # a chain row with an extra column + y_fine = fine(*sample[p.columns(omp.params)]) + assert y_fine.shape == (60,) and np.all(np.isfinite(y_fine)) and np.all(y_fine > 0) + assert len(omp._cache) == 1 # one basis for both grids + # cross sections are in b/sr: jitr's mb/sr divided by 1000 + ws = omp.workspace(x, meta) + r = ws.radial_grid() + direct = ws.xs(central(r, *theta), spin_orbit(r, 6.0, R, 0.45), None).dsdo + np.testing.assert_allclose(truth, direct / 1000.0) diff --git a/test/recipes/test_recipe_37_cross_observable_systematics.py b/test/recipes/test_recipe_37_cross_observable_systematics.py new file mode 100644 index 0000000..cf4c695 --- /dev/null +++ b/test/recipes/test_recipe_37_cross_observable_systematics.py @@ -0,0 +1,76 @@ +"""Recipe 37: correlated systematics between observables of one measurement. + +One experiment reports both a cross section and an analysing power, and they +share a normalisation or an angle calibration. +""" + +import jitr +import numpy as np +from jitr.optical_potentials.potential_forms import thomas_safe, woods_saxon_safe +from scipy import stats + +from rxmc import Comparison, Constraint, Dataset, Parameter, Problem +from rxmc import terms as T +from rxmc.reactions import ElasticXS + +MSO = 1.0 / jitr.utils.constants.WAVENUMBER_PION +R = 1.2 * 40 ** (1 / 3) +N_CA = jitr.reactions.ElasticReaction(target=(40, 20), projectile=(1, 0)) + + +def central(r, Vv, Wv, Rv, av): + return -(Vv + 1j * Wv) * woods_saxon_safe(r, Rv, av) + + +def spin_orbit(r, Vso, Rso, aso): + return Vso * MSO**2 * thomas_safe(r, Rso, aso) + + +def test_two_observables_one_constraint_one_spanning_mode(): + params = [Parameter(n, prior=stats.norm(0, 100)) for n in ("Vv", "Wv", "Rv", "av")] + pot = (central, spin_orbit, lambda ws, *x: (tuple(x), (6.0, R, 0.45))) + xs_model = ElasticXS("dXS/dA", *pot, params, lmax=10) + ay_model = ElasticXS("Ay", *pot, params, lmax=10) + theta = np.array([48.0, 3.5, R, 0.7]) + x = np.linspace(0.3, 2.5, 4) + kin = {"reaction": N_CA, "Elab": 14.1} + m_xs, m_ay = {**kin, "quantity": "dXS/dA"}, {**kin, "quantity": "Ay"} + d_xs = Dataset( + x, xs_model.bind(x, kin)(*theta), 0.01 * np.ones(4), label="xs", meta=m_xs + ) + d_ay = Dataset( + x, ay_model.bind(x, kin)(*theta), 0.02 * np.ones(4), label="ay", meta=m_ay + ) + comp_xs, comp_ay = Comparison(d_xs, xs_model), Comparison(d_ay, ay_model) + log_eta = Parameter("log_eta", prior=stats.norm(-3, 1)) + log_dtheta = Parameter("log_dtheta", prior=stats.norm(-4, 1)) + + def dy_dtheta(c): + # angle-calibration mode: the slope of each prediction in angle. A + # spanning term sees the gathered stack, so the finite difference must + # not straddle the seam: split by the per-point dataset metadata. + u = np.empty(len(c)) + for q in np.unique(c.meta("quantity")): + rows = c.meta("quantity") == q + u[rows] = np.gradient(c.ym[rows], c.x[rows]) + return u + + c = Constraint( + [comp_xs, comp_ay], + terms=[ + T.normalization(log_eta, on=comp_xs), # the ratio observable is unaffected + T.systematic(log_dtheta, basis=dy_dtheta, on=[comp_xs, comp_ay]), + ], + ) + p = Problem([c]) + assert p.names == ["Vv", "Wv", "Rv", "av", "log_eta", "log_dtheta"] + full = np.array([*theta, np.log(0.05), np.log(0.01)]) + S = p.constraints[0].matrix(full) + ym_xs, ym_ay = p.predict(full)[0] + u = 0.01 * np.concatenate([np.gradient(ym_xs, x), np.gradient(ym_ay, x)]) + expected = np.diag(np.concatenate([d_xs.y_err, d_ay.y_err]) ** 2) + np.outer(u, u) + expected[:4, :4] += 0.05**2 * np.outer(ym_xs, ym_xs) + np.testing.assert_allclose(S, expected) + assert np.any(S[:4, 4:] != 0.0) # the angle mode couples the two observables + assert not p.constraints[0].covariance.dense + assert p.chi2(full) == 0.0 From a58ddfa0fff8217bfed1eb6063b6c183264f1813 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 20:42:51 -0400 Subject: [PATCH 27/75] Give terms the block boundaries of a spanning support A term on several comparisons sees their rows gathered into one stack, so a basis that differentiates or smooths along the grid would straddle the seam. TermContext now carries segments (the slice of the support belonging to each spanned comparison, in constraint order), labels, and split(a); the covariance computes them once per entry from the block offsets. Outside a problem a term has one segment. Recipe 37 takes its angle-calibration slope per segment, and the design and recipe docs describe the new fields. --- docs/groundup_design.md | 3 ++ docs/recipes.md | 11 ++-- src/rxmc/covariance.py | 30 +++++++++-- src/rxmc/terms.py | 52 +++++++++++++++++-- ..._recipe_37_cross_observable_systematics.py | 13 +++-- test/test_covariance.py | 48 +++++++++++++++++ test/test_terms.py | 39 ++++++++++++++ 7 files changed, 177 insertions(+), 19 deletions(-) diff --git a/docs/groundup_design.md b/docs/groundup_design.md index 5852ad3..2ca2b4d 100644 --- a/docs/groundup_design.md +++ b/docs/groundup_design.md @@ -266,6 +266,9 @@ class TermContext: def __len__(self) -> int def meta(self, key) -> ndarray # the owning block's data.meta[key], one value per point; # for a term spanning blocks, the per-point concatenation + segments: tuple[slice, ...] # rows of each spanned comparison within the gathered support + labels: tuple[str, ...] # their comparison labels, in the same order + def split(self, a) -> list # a[s] for s in segments @dataclass(frozen=True) class Term: diff --git a/docs/recipes.md b/docs/recipes.md index 9f96195..631c94f 100644 --- a/docs/recipes.md +++ b/docs/recipes.md @@ -481,7 +481,10 @@ Expected behaviour: - A plain array is a fixed contribution, factored once. Shape and symmetry are checked at construction against the term's `on`. - A callable sees a `TermContext` with `x` (through `coords`), `y`, `ym`, - and `len(c)`; it returns a vector for `diag`/`mode` or a matrix. + `len(c)`, per-point `c.meta(key)`, and, for a term spanning several + comparisons, `c.segments`/`c.labels`/`c.split(a)` giving the rows of + each comparison in the gathered stack; it returns a vector for + `diag`/`mode` or a matrix. - Fitting correlated data with the correct `Term(C)` instead of its diagonal is the difference between an honest and an overconfident posterior (`normalization_inference` gallery). @@ -1085,9 +1088,9 @@ Expected behaviour: observable; an angle-calibration error affects both through their angular derivatives, which the basis supplies from `c.ym` and `c.x`. - A spanning term sees the *gathered* stack, so a basis that differentiates - along the grid must not straddle the seam between comparisons: it splits - the rows by a per-point dataset field such as `c.meta("quantity")` - (recipe 22), which `from_measurement` fills in. + along the grid must not straddle the seam between comparisons: it takes + the derivative within each of `c.segments` (`c.split(c.ym)` cuts a + support-length array per comparison; `c.labels` names them). - The multi-quantity extension of the Peelle treatment applies: build the mode from predictions, not data. diff --git a/src/rxmc/covariance.py b/src/rxmc/covariance.py index 314ddf9..9450616 100644 --- a/src/rxmc/covariance.py +++ b/src/rxmc/covariance.py @@ -50,9 +50,18 @@ def chol_logdet(Sigma): class _Entry: """One term placed on rows of the stack, with its gather into theta.""" - __slots__ = ("term", "rows", "gather", "pos", "keep", "block") - - def __init__(self, term, rows, gather, active, offsets): + __slots__ = ( + "term", + "rows", + "gather", + "pos", + "keep", + "block", + "segments", + "labels", + ) + + def __init__(self, term, rows, gather, active, offsets, labels): self.term = term self.rows = np.asarray(rows, dtype=int) self.gather = np.asarray(gather, dtype=int) @@ -68,6 +77,17 @@ def __init__(self, term, rows, gather, active, offsets): if np.any((self.rows >= o.start) & (self.rows < o.stop)) } self.block = blocks.pop() if len(blocks) == 1 else None + # the term's rows are in stack order, so each block's rows are contiguous + # within them: slices of the support per block, and the block labels + counts = [ + int(np.count_nonzero((self.rows >= o.start) & (self.rows < o.stop))) + for o in offsets + ] + stops = np.cumsum(counts) + self.segments = tuple( + slice(int(stop - n), int(stop)) for n, stop in zip(counts, stops) if n + ) + self.labels = tuple(str(lab) for n, lab in zip(counts, labels) if n) def _meta_rows(meta, rows): @@ -113,7 +133,7 @@ def __init__(self, entries, x, y, offsets, active, *, meta=None, labels=None): for term, rows, gather in entries: if not isinstance(term, Term): raise TypeError(f"entries must hold Term objects, got {term!r}") - e = _Entry(term, rows, gather, self.active, self.offsets) + e = _Entry(term, rows, gather, self.active, self.offsets, self.labels) if np.any(e.keep): self.entries.append(e) self.n_active = int(self.active.size) @@ -159,6 +179,8 @@ def _pieces(self, entries, ym, theta): None if ym is None else ym[e.rows], *values, meta=_meta_rows(self.meta, e.rows), + segments=e.segments, + labels=e.labels, ) pos, keep = e.pos[e.keep], e.keep if t.kind == "diag": diff --git a/src/rxmc/terms.py b/src/rxmc/terms.py index e52ce00..d4ec0a1 100644 --- a/src/rxmc/terms.py +++ b/src/rxmc/terms.py @@ -86,16 +86,50 @@ class TermContext: while a *constant* term is evaluated before any prediction exists, so a mis-declared constant term fails loudly. ``len(c)`` is the number of points; :meth:`meta` gives per-point dataset metadata (``c.meta("Elab")``). + + A term spanning several comparisons sees their rows *gathered* into one + stack, in constraint order. :attr:`segments` are the slices of that stack + belonging to each comparison, :attr:`labels` their labels, and + :meth:`split` cuts any support-length array along them, so a basis that + differentiates or smooths along the grid can stay within one comparison:: + + def slope(c): # angle-calibration mode: the slope of each prediction + return np.concatenate([np.gradient(ym, x) for x, ym in + zip(c.split(c.x), c.split(c.ym))]) """ x: np.ndarray y: np.ndarray ym: np.ndarray | None = None _meta: Mapping[str, np.ndarray] | None = None + _segments: tuple | None = None + _labels: tuple | None = None def __len__(self) -> int: return len(self.y) + @property + def segments(self) -> tuple: + """Slices of the support, one per comparison it spans, in constraint order. + + A term evaluated outside a problem (as in tests) has one segment. + """ + return (slice(0, len(self)),) if self._segments is None else self._segments + + @property + def labels(self) -> tuple: + """The label of each segment's comparison (``str(comparison.data.label)``).""" + return ("",) * len(self.segments) if self._labels is None else self._labels + + def split(self, a) -> list: + """``a[s] for s in segments``: views of a support-length array per comparison.""" + a = np.asarray(a) + if a.shape[0] != len(self): + raise ValueError( + f"split expects an array of length {len(self)}, got shape {a.shape}" + ) + return [a[s] for s in self.segments] + def meta(self, key: str) -> np.ndarray: """The owning dataset's ``meta[key]``, one value per point of the support.""" if self._meta is None or key not in self._meta: @@ -192,8 +226,14 @@ def expected_shape(self, n: int) -> tuple: # -- evaluation ----------------------------------------------------------- - def context(self, x, y, ym=None, meta=None, *values) -> TermContext: - """The :class:`TermContext` this term sees on its support at ``values``.""" + def context( + self, x, y, ym=None, meta=None, *values, segments=None, labels=None + ) -> TermContext: + """The :class:`TermContext` this term sees on its support at ``values``. + + ``segments``/``labels`` describe the comparisons the support spans (see + :attr:`TermContext.segments`); omitted, the support is one segment. + """ self._check_count(values) x = np.asarray(x, dtype=float) if not self.coords.is_identity: @@ -203,9 +243,13 @@ def context(self, x, y, ym=None, meta=None, *values) -> TermContext: y=np.asarray(y, dtype=float), ym=None if ym is None else np.asarray(ym, dtype=float), _meta=meta, + _segments=None if segments is None else tuple(segments), + _labels=None if labels is None else tuple(labels), ) - def value(self, x, y, ym=None, *values, meta=None) -> np.ndarray: + def value( + self, x, y, ym=None, *values, meta=None, segments=None, labels=None + ) -> np.ndarray: """The raw array ``fn`` returns (std vector, mode vector, or block).""" n = len(y) if not callable(self.fn): @@ -215,7 +259,7 @@ def value(self, x, y, ym=None, *values, meta=None) -> np.ndarray: f"got {self.fn.shape}" ) return self.fn - c = self.context(x, y, ym, meta, *values) + c = self.context(x, y, ym, meta, *values, segments=segments, labels=labels) v = np.asarray(self.fn(c, *values[: self._n_fn_params]), dtype=float) if v.shape != self.expected_shape(n): raise ValueError( diff --git a/test/recipes/test_recipe_37_cross_observable_systematics.py b/test/recipes/test_recipe_37_cross_observable_systematics.py index cf4c695..2da0564 100644 --- a/test/recipes/test_recipe_37_cross_observable_systematics.py +++ b/test/recipes/test_recipe_37_cross_observable_systematics.py @@ -47,13 +47,12 @@ def test_two_observables_one_constraint_one_spanning_mode(): def dy_dtheta(c): # angle-calibration mode: the slope of each prediction in angle. A - # spanning term sees the gathered stack, so the finite difference must - # not straddle the seam: split by the per-point dataset metadata. - u = np.empty(len(c)) - for q in np.unique(c.meta("quantity")): - rows = c.meta("quantity") == q - u[rows] = np.gradient(c.ym[rows], c.x[rows]) - return u + # spanning term sees the gathered stack, so the finite difference is + # taken within each comparison's segment, not across the seam. + assert c.labels == ("xs", "ay") + return np.concatenate( + [np.gradient(ym, x) for x, ym in zip(c.split(c.x), c.split(c.ym))] + ) c = Constraint( [comp_xs, comp_ay], diff --git a/test/test_covariance.py b/test/test_covariance.py index 12f2360..886cbf7 100644 --- a/test/test_covariance.py +++ b/test/test_covariance.py @@ -276,3 +276,51 @@ def test_chol_logdet_on_a_diagonal(): L, logdet = chol_logdet(np.diag([1.0, 4.0, 9.0])) np.testing.assert_allclose(np.diag(L), [1.0, 2.0, 3.0]) assert logdet == pytest.approx(np.log(36.0)) + + +class TestSegments: + """A spanning term sees the rows of each block it touches, in stack order.""" + + def setup_method(self): + self.x, self.y, self.ym = grid(9, seed=3) + self.offsets = [slice(0, 3), slice(3, 6), slice(6, 9)] + + def capture(self, rows, active=None): + seen = {} + + def fn(c): + seen["segments"], seen["labels"] = c.segments, c.labels + seen["x"] = c.split(c.x) + return np.ones(len(c)) + + terms = [statistical(0.1 * np.ones(9)), Term(fn, kind="mode")] + cov, _ = build( + terms, + self.x, + self.y, + self.offsets, + active=active, + rows=[np.arange(9), rows], + labels=["L0", "L1", "L2"], + ) + cov.matrix(self.ym, np.zeros(0)) + return seen + + def test_whole_stack(self): + seen = self.capture(np.arange(9)) + assert seen["segments"] == (slice(0, 3), slice(3, 6), slice(6, 9)) + assert seen["labels"] == ("L0", "L1", "L2") + np.testing.assert_array_equal(seen["x"][1], self.x[3:6]) + + def test_partial_support_skips_untouched_blocks(self): + # blocks 0 and 2 only: the support is 6 rows in two segments + seen = self.capture(np.r_[0:3, 6:9]) + assert seen["segments"] == (slice(0, 3), slice(3, 6)) + assert seen["labels"] == ("L0", "L2") + np.testing.assert_array_equal(seen["x"][1], self.x[6:9]) + + def test_masked_rows_stay_in_the_segment_view(self): + # fn sees every row of its support (masking selects after evaluation), + # so the segments describe the unmasked support + seen = self.capture(np.arange(9), active=np.r_[0:2, 3:9]) + assert seen["segments"] == (slice(0, 3), slice(3, 6), slice(6, 9)) diff --git a/test/test_terms.py b/test/test_terms.py index 76abed2..8da39f7 100644 --- a/test/test_terms.py +++ b/test/test_terms.py @@ -403,3 +403,42 @@ def test_recipe_27_normalization_mode_is_built_from_the_prediction(self): np.testing.assert_allclose(right.value(x, y, ym_), s * ym_) np.testing.assert_allclose(wrong.value(x, y, ym_), s * y) assert not np.allclose(right.value(x, y, ym_), wrong.value(x, y, ym_)) + + +class TestSegments: + """A term's view of the comparisons its support spans.""" + + def test_one_segment_outside_a_problem(self): + seen = {} + + def fn(c): + seen["segments"], seen["labels"] = c.segments, c.labels + seen["split"] = c.split(c.x) + return np.ones(len(c)) + + Term(fn, kind="mode").value(np.arange(3.0), np.zeros(3), np.zeros(3)) + assert seen["segments"] == (slice(0, 3),) and seen["labels"] == ("",) + np.testing.assert_array_equal(seen["split"][0], np.arange(3.0)) + + def test_explicit_segments_and_split(self): + def fn(c): + assert c.labels == ("a", "b") + parts = c.split(c.ym) + assert [len(p) for p in parts] == [2, 3] + return np.concatenate([p - p.mean() for p in parts]) + + t = Term(fn, kind="mode") + ym = np.array([1.0, 3.0, 10.0, 20.0, 30.0]) + v = t.value( + np.zeros(5), + np.zeros(5), + ym, + segments=[slice(0, 2), slice(2, 5)], + labels=["a", "b"], + ) + np.testing.assert_allclose(v, [-1.0, 1.0, -10.0, 0.0, 10.0]) + + def test_split_checks_length(self): + c = TermContext(x=np.zeros(3), y=np.zeros(3)) + with pytest.raises(ValueError, match="length 3"): + c.split(np.zeros(4)) From 3fd29defa5a560cf530ecabd59a86db81fbee8a9 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 21:03:19 -0400 Subject: [PATCH 28/75] Return a KernelTerm from kernel() so predictions can find the kernel A kernel term closed over its sklearn kernel and nothing on the Term exposed it, but the predictive band promised by recipe 7 must condition the discrepancy from the term alone. KernelTerm is a Term subclass carrying the kernel, the count of free hyperparameter elements (so the log-theta and amplitude columns follow from params), the amplitude and the jitter; the covariance treats it as any matrix term. --- src/rxmc/__init__.py | 2 ++ src/rxmc/terms.py | 35 ++++++++++++++++++++++++++++++++++- test/test_terms.py | 17 +++++++++++++++++ test/test_transforms.py | 1 + 4 files changed, 54 insertions(+), 1 deletion(-) diff --git a/src/rxmc/__init__.py b/src/rxmc/__init__.py index 85ca27c..3148ce6 100644 --- a/src/rxmc/__init__.py +++ b/src/rxmc/__init__.py @@ -22,6 +22,7 @@ from .model import polynomial as polynomial from .params import Parameter as Parameter from .problem import Problem as Problem +from .terms import KernelTerm as KernelTerm from .terms import Term as Term try: @@ -34,6 +35,7 @@ "Comparison", "Constraint", "Dataset", + "KernelTerm", "Model", "Parameter", "Problem", diff --git a/src/rxmc/terms.py b/src/rxmc/terms.py index d4ec0a1..a7d0bdc 100644 --- a/src/rxmc/terms.py +++ b/src/rxmc/terms.py @@ -49,6 +49,7 @@ "KINDS", "TermContext", "Term", + "KernelTerm", "as_2d", "ones", "ym", @@ -277,6 +278,34 @@ def __repr__(self): return f"Term(kind={self.kind!r}, params=({names}), on={self.on!r})" +@dataclass(eq=False, frozen=True) +class KernelTerm(Term): + """A :func:`kernel` term that also carries what GP conditioning needs. + + :func:`~rxmc.predictive.total_predictive_band` reads these to predict the + discrepancy at new points; the covariance machinery treats a + ``KernelTerm`` exactly as a ``matrix`` :class:`Term`. + + Attributes + ---------- + kernel : sklearn-style kernel + The kernel object as passed to :func:`kernel`. + n_kernel : int + Number of free kernel hyperparameter elements: ``params[:n_kernel]`` + are the log-theta parameters, ``params[n_kernel:]`` the amplitude's, + then any coordinate-transform parameters. + amplitude : callable or array or None + The amplitude as passed to :func:`kernel`. + jitter : float + The diagonal nugget added to every kernel block. + """ + + kernel: Any = None + n_kernel: int = 0 + amplitude: Any = None + jitter: float = 0.0 + + # ---------------------------------------------------------------------------- # Standard bases and amplitudes (numpy-style callables over a TermContext) # ---------------------------------------------------------------------------- @@ -592,11 +621,15 @@ def fn(c, *values): K[np.diag_indices_from(K)] += jitter return K - return Term( + return KernelTerm( fn, tuple(kparams) + amplitude_params, kind="matrix", on=on, coords=coords, constant=nk == 0 and not amplitude_params and not callable(amplitude), + kernel=kernel, + n_kernel=nk, + amplitude=amplitude, + jitter=jitter, ) diff --git a/test/test_terms.py b/test/test_terms.py index 8da39f7..237d6ac 100644 --- a/test/test_terms.py +++ b/test/test_terms.py @@ -7,6 +7,7 @@ from helpers import STUDY_LEGEND, assemble_dense, study_form from rxmc import Parameter from rxmc.terms import ( + KernelTerm, Term, TermContext, averaging, @@ -442,3 +443,19 @@ def test_split_checks_length(self): c = TermContext(x=np.zeros(3), y=np.zeros(3)) with pytest.raises(ValueError, match="length 3"): c.split(np.zeros(4)) + + +class TestKernelTerm: + """The kernel factory returns a Term that also carries its kernel.""" + + def test_fields(self): + k = RBF(1.0) + lA = Parameter("log_A") + t = kernel(k, amplitude=constant_amplitude, amplitude_params=(lA,)) + assert isinstance(t, KernelTerm) and isinstance(t, Term) + assert t.kernel is k and t.n_kernel == 1 and t.amplitude is constant_amplitude + assert [p.name for p in t.params] == ["discrepancy_length_scale", "log_A"] + assert t.kind == "matrix" and t.jitter == 1e-10 + # a fixed kernel has no kernel parameters and is constant + fixed = kernel(RBF(1.0, "fixed")) + assert fixed.n_kernel == 0 and fixed.is_constant and fixed.params == () diff --git a/test/test_transforms.py b/test/test_transforms.py index b992e6c..13059f1 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -26,6 +26,7 @@ def test_log_is_safe_and_invertible(self): np.testing.assert_allclose(log.derivative(np.array([2.0, 4.0])), [0.5, 0.25]) assert log.inverse is exp assert exp.inverse is log + assert identity.inverse is identity np.testing.assert_allclose(exp(log(np.array([3.0, 7.0]))), [3.0, 7.0]) def test_finite_difference_derivative_fallback(self): From 079f8c1a824fa375fee046df5c5e789266f53726 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 21:07:27 -0400 Subject: [PATCH 29/75] Rewrite diagnostics on (problem, samples) with a conditional held-out score predictive_draws and heldout_log_predictive take a compiled Problem and chain rows in problem.names order; coverage, sharpness, the log posterior predictive and the evidence bookkeeping are lifted from 0.x. A held-out problem built from fit.complement() scores the marginal of its rows, which is wrong when a term spans the split (a GP over several experiments); given=fit_problem computes the Gaussian conditional under the full covariance instead, and equals the marginal when nothing spans. split_samples and the module-level log_jacobian are gone: columns come from problem.columns and the Jacobian from problem.log_jacobian(). --- src/rxmc/__init__.py | 2 + src/rxmc/diagnostics.py | 329 +++++++++++++++++++++++---------------- test/test_diagnostics.py | 195 +++++++++++++++++++++++ 3 files changed, 394 insertions(+), 132 deletions(-) create mode 100644 test/test_diagnostics.py diff --git a/src/rxmc/__init__.py b/src/rxmc/__init__.py index 3148ce6..a839af3 100644 --- a/src/rxmc/__init__.py +++ b/src/rxmc/__init__.py @@ -7,6 +7,7 @@ from . import constraint as constraint from . import covariance as covariance from . import data as data +from . import diagnostics as diagnostics from . import likelihood as likelihood from . import model as model from . import problem as problem @@ -45,6 +46,7 @@ "constraint", "covariance", "data", + "diagnostics", "likelihood", "model", "problem", diff --git a/src/rxmc/diagnostics.py b/src/rxmc/diagnostics.py index 162c679..6dbba6a 100644 --- a/src/rxmc/diagnostics.py +++ b/src/rxmc/diagnostics.py @@ -1,37 +1,49 @@ -""" -Sampler-agnostic model-comparison and predictive-checking utilities. +"""Sampler-agnostic posterior checks on ``(problem, samples)``. -Everything here consumes a :class:`~rxmc.constraint.Constraint` plus posterior -*samples* (rows of model parameters and, optionally, of the constraint's -covariance/likelihood parameters) and never touches a sampler: +Everything here consumes a compiled :class:`~rxmc.problem.Problem` and a +matrix of posterior ``samples`` of shape ``(n, problem.ndim)`` in +``problem.names`` order (what emcee's ``get_chain(flat=True)``, dynesty's +``samples_equal()`` and black-box-bayes give) and never touches a sampler: * :func:`predictive_draws` — draws from the posterior predictive - ``N(ym(theta), Sigma(theta))`` on the constraint's active points (or the - model-only predictive ``ym(theta)``). -* :func:`coverage_curve`, :func:`coverage_error`, :func:`sharpness` — empirical - calibration and width of those draws against the data. + ``N(ym(theta), Sigma(theta))`` on a constraint's active points, or the + model-only predictive ``ym(theta)``. +* :func:`coverage_curve`, :func:`coverage_error`, :func:`sharpness` — + empirical calibration and width of those draws against the data. * :func:`heldout_log_predictive`, :func:`log_posterior_predictive` — - out-of-sample scoring on a held-out constraint (e.g. - ``constraint.complement()``). + out-of-sample scoring on a held-out problem (``Problem([fit.complement()])``). * :func:`logz_summary`, :func:`compare_logz` — nested-sampling evidence bookkeeping with replicate-based errors and a conservative tie verdict. -* :func:`log_jacobian` — the comparison-space Jacobian needed to compare - evidences across residual spaces (e.g. log-y versus linear-y fits). - -Notes ------ -Drawing from ``N(ym, Sigma)`` in a *transformed* comparison space (an -observation with ``transform=log``) yields draws in that space; map them back -with the transform's inverse (``np.exp``) before comparing to raw data. + +Held-out scoring and a term that spans the split +------------------------------------------------ +A held-out problem built from ``fit.complement()`` has the *marginal* +covariance of its active rows. When no term couples the fitted and the +held-out rows that is the right density, and ``ll(fit) + ll(held) == ll(full)``. +When a term does span the split (a Gaussian process ``on=comps`` over +several experiments), the honest held-out density is the conditional +``p(y_held | y_fit, theta)`` under the full covariance; pass the fitted +problem as ``given=`` to :func:`heldout_log_predictive` and +:func:`predictive_draws` and they compute exactly that. Without a spanning +term the conditional equals the marginal. + +Draws and densities are in the comparison space of the constraint (a +``space=log`` comparison gives log-space draws; map them back with +``comparison.space.inverse``). """ from __future__ import annotations +from dataclasses import replace + import numpy as np +import scipy.linalg as sla from scipy.special import logsumexp +from .likelihood import Gaussian +from .problem import Problem + __all__ = [ - "log_jacobian", "predictive_draws", "coverage_curve", "coverage_error", @@ -40,24 +52,30 @@ "log_posterior_predictive", "logz_summary", "compare_logz", - "split_samples", ] +_DEFAULT_LEVELS = np.linspace(0.02, 0.98, 49) -def log_jacobian(constraint) -> float: - """Comparison-space log-Jacobian of a constraint (sum over active points). - ``log Z_raw = log Z_transformed + log_jacobian``: add it to the evidence of a - fit performed in a transformed comparison space (e.g. ``transform=log``) - before comparing with a fit in raw space. Zero for identity transforms. - """ - return float(constraint.log_jacobian) +# ---------------------------------------------------------------------------- +# Helpers +# ---------------------------------------------------------------------------- -_DEFAULT_LEVELS = np.linspace(0.02, 0.98, 49) +def _rows(samples, ndim) -> np.ndarray: + """Posterior samples as ``(n, ndim)``; a 1-D input is one row.""" + samples = np.asarray(samples, dtype=float) + if samples.ndim == 1: + samples = samples[None, :] + if samples.ndim != 2 or samples.shape[1] != ndim: + raise ValueError( + f"samples must have shape (n, {ndim}) in problem.names order, got " + f"{samples.shape}" + ) + return samples -def _psd_factor(Sigma, jitter=1e-10): +def _psd_factor(Sigma, jitter=1e-10) -> np.ndarray: """A factor ``L`` with ``L L^T = Sigma``. The lower Cholesky factor when ``Sigma`` is positive definite; otherwise @@ -79,87 +97,137 @@ def _psd_factor(Sigma, jitter=1e-10): return V * np.sqrt(np.clip(w, 0.0, None)) -def _rows(samples, n=None): - """Posterior samples as a 2-D ``(n_samples, n_params)`` array. - - A 1-D input is one sample row (as in :func:`split_samples`). When ``n`` is - given the number of rows must match it. - """ - samples = np.asarray(samples, dtype=float) - if samples.ndim == 1: - samples = samples[None, :] - if n is not None and samples.shape[0] != n: - raise ValueError(f"expected {n} sample rows, got {samples.shape[0]}") - return samples +class _Conditional: + """``p(y_H | y_F, theta)`` for one held-out constraint given its fit.""" + + def __init__(self, held: Problem, given: Problem, constraint: int): + try: + h, f = held.constraints[constraint], given.constraints[constraint] + except IndexError: + raise ValueError( + f"held-out and fitted problems must both have constraint " + f"{constraint}" + ) from None + if h.comparisons != f.comparisons: + raise ValueError( + "given= must be the fitted problem over the same comparison " + "objects (Problem([fit]) with held = Problem([fit.complement()]))" + ) + if held.names != given.names: + raise ValueError( + "held-out and fitted problems index different parameters: " + f"{held.names} vs {given.names}" + ) + if np.intersect1d(h.active, f.active).size: + raise ValueError( + "the held-out and fitted active points overlap; the held-out " + "constraint should be the complement of the fitted one" + ) + if not isinstance(h.likelihood, Gaussian): + raise ValueError( + "the conditional held-out density is Gaussian; the constraint " + f"uses {type(h.likelihood).__name__}. Score the marginal instead " + "(omit given=) or use a Gaussian likelihood" + ) + self.held, self.fit = h, f + # the union of the two active sets, compiled once: every row active + self.full = Problem([replace(h.source, masks=None)]).constraints[0] + lookup = {int(r): i for i, r in enumerate(self.full.active)} + self.iH = np.array([lookup[int(r)] for r in h.active], dtype=int) + self.iF = np.array([lookup[int(r)] for r in f.active], dtype=int) + self.yF = self.full.y[f.active] + + def __call__(self, theta): + """``(mean, cov)`` of the held-out rows given the fitted data.""" + ym = self.full.ym(theta) + S = self.full.covariance.matrix(ym, theta) + SHH = S[np.ix_(self.iH, self.iH)] + SHF = S[np.ix_(self.iH, self.iF)] + SFF = S[np.ix_(self.iF, self.iF)] + L = _psd_factor(SFF) + r = self.yF - ym[self.fit.active] + mean = ym[self.held.active] + SHF @ sla.cho_solve((L, True), r) + cov = SHH - SHF @ sla.cho_solve((L, True), SHF.T) + return mean, 0.5 * (cov + cov.T) + + +def _gaussian_logpdf(r, cov) -> float: + L = _psd_factor(cov) + z = sla.solve_triangular(L, r, lower=True) + logdet = 2.0 * np.sum(np.log(np.diag(L))) + return float(-0.5 * (z @ z + logdet + len(r) * np.log(2 * np.pi))) + + +# ---------------------------------------------------------------------------- +# Posterior predictive +# ---------------------------------------------------------------------------- def predictive_draws( - constraint, - model_samples, - cov_samples=None, + problem: Problem, + samples, + constraint: int = 0, *, n_rep: int = 1, rng=None, model_only: bool = False, + given: Problem | None = None, ) -> np.ndarray: - """Posterior-predictive draws on the constraint's active points. + """Posterior-predictive draws on a constraint's active points. For each posterior row ``theta_i`` the constraint gives ``ym_i`` and ``Sigma_i``; ``n_rep`` draws ``ym_i + L_i z`` (``z ~ N(0, I)``) are taken. - With ``model_only=True`` the rows are ``ym_i`` (the model-only predictive, - no error-model noise). + With ``model_only=True`` the rows are ``ym_i`` themselves and no + covariance is assembled. Parameters ---------- - constraint : Constraint - The constraint whose predictive is wanted (its active points). - model_samples : array_like, shape (n, n_model_params) - Posterior samples of the physical-model parameters (a 1-D array is - one sample). - cov_samples : array_like, shape (n, constraint.n_params), optional - Matching samples of the constraint's parameters (required when the - constraint has any). + problem : Problem + samples : array_like, shape (n, problem.ndim) + Posterior rows in ``problem.names`` order (a 1-D array is one row). + constraint : int, optional + Index into ``problem.constraints``. n_rep : int, optional Draws per posterior row (ignored when ``model_only``). - rng : numpy.random.Generator, optional - Source of the standard-normal draws; a fresh default generator when - omitted. + rng : numpy.random.Generator or seed, optional model_only : bool, optional - Return the predictions ``ym_i`` themselves instead of draws around - them (no covariance is assembled). + Return the predictions ``ym_i`` on the active points instead of draws + around them. + given : Problem, optional + The *fitted* problem when ``problem`` is its held-out complement and a + term spans the two (module docstring): draws then come from the + conditional ``N(mu_c, Sigma_c)`` of the held-out rows given the fitted + data. Returns ------- np.ndarray - Draws in the observations' comparison space: shape - ``(n * n_rep, n_data_pts)``, or ``(n, n_data_pts)`` when ``model_only``. + In comparison space: shape ``(n * n_rep, n_active)``, or + ``(n, n_active)`` when ``model_only``. """ - rng = np.random.default_rng() if rng is None else rng - model_samples = _rows(model_samples) - n = model_samples.shape[0] - if constraint.n_params: - if cov_samples is None: - raise ValueError("constraint has parameters; pass cov_samples") - cov_samples = _rows(cov_samples, n) - else: - cov_samples = np.zeros((n, 0)) + rng = np.random.default_rng(rng) + samples = _rows(samples, problem.ndim) + n = samples.shape[0] + c = problem.constraints[constraint] + N = c.n_active + cond = None if given is None else _Conditional(problem, given, constraint) - N = constraint.n_data_pts if model_only: out = np.empty((n, N)) for i in range(n): - ym = np.concatenate(constraint.predict(*model_samples[i])) - out[i] = ym[constraint.active] + out[i] = cond(samples[i])[0] if cond else c.ym(samples[i])[c.active] return out out = np.empty((n * n_rep, N)) for i in range(n): - ym, Sigma = constraint.predict_and_covariance( - tuple(model_samples[i]), tuple(cov_samples[i]) - ) + theta = samples[i] + if cond: + mu, Sigma = cond(theta) + else: + mu, Sigma = c.ym(theta)[c.active], c.matrix(theta) L = _psd_factor(Sigma) z = rng.standard_normal((n_rep, N)) - out[i * n_rep : (i + 1) * n_rep] = ym + z @ L.T + out[i * n_rep : (i + 1) * n_rep] = mu + z @ L.T return out @@ -173,7 +241,7 @@ def coverage_curve(draws, y, levels=None) -> np.ndarray: The data the draws are checked against (same space as ``draws``). levels : array_like, optional Nominal central-interval probabilities in (0, 1). Defaults to 49 - levels from 0.02 to 0.98 (``_DEFAULT_LEVELS``). + levels from 0.02 to 0.98. Returns ------- @@ -182,7 +250,7 @@ def coverage_curve(draws, y, levels=None) -> np.ndarray: """ draws = np.asarray(draws, dtype=float) y = np.asarray(y, dtype=float) - levels = _DEFAULT_LEVELS if levels is None else np.asarray(levels) + levels = _DEFAULT_LEVELS if levels is None else np.asarray(levels, dtype=float) out = np.empty(len(levels)) for i, lv in enumerate(levels): lo, hi = np.percentile(draws, [50 * (1 - lv), 50 * (1 + lv)], axis=0) @@ -191,8 +259,8 @@ def coverage_curve(draws, y, levels=None) -> np.ndarray: def coverage_error(draws, y, levels=None) -> float: - """``max |coverage(level) - level|`` — a single calibration score.""" - levels = _DEFAULT_LEVELS if levels is None else np.asarray(levels) + """``max |coverage(level) - level|``: a single calibration score.""" + levels = _DEFAULT_LEVELS if levels is None else np.asarray(levels, dtype=float) return float(np.max(np.abs(coverage_curve(draws, y, levels) - levels))) @@ -204,11 +272,11 @@ def sharpness(draws, percentiles=(16, 84), transform=None) -> np.ndarray: draws : array_like, shape (n_draws, n_pts) percentiles : (float, float), optional Lower and upper percentiles (in 0-100) bounding the interval; the - default is the central 68 %. Note :func:`coverage_curve` takes - interval *probabilities* in (0, 1) instead. + default is the central 68 %. :func:`coverage_curve` takes interval + *probabilities* in (0, 1) instead. transform : callable, optional - Applied to the draws first (e.g. ``np.exp`` to report widths in raw - space for a log comparison space, or ``np.log10``). + Applied to the draws first (e.g. ``np.exp`` to report widths in + physical units for a log comparison space). """ draws = np.asarray(draws, dtype=float) if transform is not None: @@ -217,34 +285,45 @@ def sharpness(draws, percentiles=(16, 84), transform=None) -> np.ndarray: return hi - lo -def heldout_log_predictive(heldout_constraint, model_samples, cov_samples=None): +# ---------------------------------------------------------------------------- +# Held-out scoring +# ---------------------------------------------------------------------------- + + +def heldout_log_predictive( + heldout_problem: Problem, samples, *, given: Problem | None = None +) -> np.ndarray: """``log p(y_held | theta_i)`` for each posterior row. - ``heldout_constraint`` is typically ``fit_constraint.complement()``: the same - observations, terms and parameters, with the held-out points active. The - score is that constraint's log likelihood at each sample (a 1-D - ``model_samples`` is one sample). + ``heldout_problem`` is typically ``Problem([fit.complement()])``: the same + comparisons, terms and parameters, with the held-out points active. The + score is that problem's log likelihood at each row, so the constraints' + ``weight`` and likelihood family apply. With ``given=`` (the fitted + problem) the score is instead the Gaussian conditional density of the + held-out rows given the fitted data, constraint by constraint (module + docstring). Returns ------- np.ndarray, shape (n,) """ - model_samples = _rows(model_samples) - n = model_samples.shape[0] - if heldout_constraint.n_params: - if cov_samples is None: - raise ValueError("constraint has parameters; pass cov_samples") - cov_samples = _rows(cov_samples, n) - else: - cov_samples = np.zeros((n, 0)) - return np.array( - [ - heldout_constraint.log_likelihood( - tuple(model_samples[i]), tuple(cov_samples[i]) - ) - for i in range(n) - ] - ) + samples = _rows(samples, heldout_problem.ndim) + if given is None: + return np.array([heldout_problem.log_likelihood(t) for t in samples]) + conds = [ + _Conditional(heldout_problem, given, i) + for i in range(len(heldout_problem.constraints)) + ] + out = np.empty(samples.shape[0]) + for k, theta in enumerate(samples): + total = 0.0 + for c, cond in zip(heldout_problem.constraints, conds): + if c.weight == 0.0: + continue + mean, cov = cond(theta) + total += c.weight * _gaussian_logpdf(c.y[c.active] - mean, cov) + out[k] = total + return out def log_posterior_predictive(logp_samples, logw=None) -> float: @@ -265,20 +344,26 @@ def log_posterior_predictive(logp_samples, logw=None) -> float: return float(logsumexp(logp + logw) - logsumexp(logw)) -def logz_summary(logz, logzerr): +# ---------------------------------------------------------------------------- +# Evidence bookkeeping +# ---------------------------------------------------------------------------- + + +def logz_summary(logz, logzerr) -> tuple[float, float, int]: """Replicate-aware evidence summary. Parameters ---------- logz, logzerr : array_like ``log Z`` and its sampler-reported error for each replicate run (one - value each is fine). + value each is fine). Add ``problem.log_jacobian()`` to ``logz`` first + when comparing fits in different comparison spaces. Returns ------- (float, float, int) ``(mean, err, n)`` with ``err = max(half-range across replicates, mean - reported error)`` — the sampler's own error is a lower bound. + reported error)``: the sampler's own error is a lower bound. """ logz = np.atleast_1d(np.asarray(logz, dtype=float)) logzerr = np.atleast_1d(np.asarray(logzerr, dtype=float)) @@ -293,7 +378,7 @@ def compare_logz(a, b, sigma: float = 2.0) -> dict: ---------- a, b : (mean, err) or (mean, err, n) As returned by :func:`logz_summary`; a trailing replicate count is - accepted and ignored (it is informational only). + accepted and ignored. sigma : float, optional A difference smaller than ``sigma * hypot(err_a, err_b)`` is a ``"tie"``. @@ -311,23 +396,3 @@ def compare_logz(a, b, sigma: float = 2.0) -> dict: else: verdict = "a" if d > 0 else "b" return {"dlogZ": d, "err": err, "verdict": verdict} - - -def split_samples(config, samples): - """Split flat sampler rows into ``(model_samples, [cov_samples, ...])``. - - Row-wise :meth:`~rxmc.config.CalibrationConfig.split_parameters`: one - covariance-sample block per parametric constraint, in - ``config.evidence.parametric_constraints`` order. - - Returns - ------- - (np.ndarray, list of np.ndarray) - ``model_samples`` of shape ``(n, n_model_params)`` and one - ``(n, constraint.n_params)`` array per parametric constraint. - """ - samples = np.asarray(samples, dtype=float) - if samples.ndim == 1: - samples = samples[None, :] - parts = np.split(samples, config.indices[:-1], axis=1) - return parts[0], parts[1:] diff --git a/test/test_diagnostics.py b/test/test_diagnostics.py new file mode 100644 index 0000000..a33c8aa --- /dev/null +++ b/test/test_diagnostics.py @@ -0,0 +1,195 @@ +"""Posterior checks on (problem, samples): draws, coverage, held-out scores.""" + +from unittest.mock import patch + +import numpy as np +import pytest +from scipy import stats +from scipy.special import logsumexp + +from helpers import manual_mvn_loglike +from rxmc import Comparison, Constraint, Dataset, Model, Parameter, Problem, Term +from rxmc.covariance import StructuredCovariance +from rxmc.diagnostics import ( + compare_logz, + coverage_curve, + coverage_error, + heldout_log_predictive, + log_posterior_predictive, + logz_summary, + predictive_draws, + sharpness, +) +from rxmc.likelihood import StudentT +from rxmc.terms import noise +from rxmc.transforms import log + +X = np.linspace(0.0, 4.0, 5) +Y = 1.0 + 2.0 * X +ERR = np.full(5, 0.3) + + +def poly(order): + """``polynomial(order)`` with wide priors, so problems compile.""" + params = [Parameter(f"a{i}", prior=stats.norm(0, 10)) for i in range(order + 1)] + return Model(lambda x, *a: sum(ai * x**i for i, ai in enumerate(a)), params) + + +def line_problem(terms=(), masks=None, **kw): + d = Dataset(X, Y, ERR, label="d") + c = Constraint([Comparison(d, poly(1))], terms=terms, masks=masks, **kw) + return Problem([c]), c + + +class TestPredictiveDraws: + def test_draw_covariance_recovers_sigma(self): + p, _ = line_problem([noise(Parameter("log_eps", prior=stats.norm(-2, 1)))]) + row = np.array([1.0, 2.0, np.log(0.4)]) + draws = predictive_draws(p, row, n_rep=40000, rng=0) + assert draws.shape == (40000, 5) + np.testing.assert_allclose(draws.mean(axis=0), Y, atol=0.02) + np.testing.assert_allclose(np.cov(draws.T), np.diag(ERR**2 + 0.16), atol=0.02) + + def test_model_only_returns_ym_and_assembles_nothing(self): + p, _ = line_problem() + with patch.object(StructuredCovariance, "matrix") as m: + d = predictive_draws(p, [[1.0, 2.0], [0.0, 1.0]], model_only=True) + m.assert_not_called() + np.testing.assert_allclose(d[0], Y) + np.testing.assert_allclose(d[1], X) + + def test_one_row_masks_and_width_check(self): + p, _ = line_problem(masks=[np.array([True, False, True, False, True])]) + d = predictive_draws(p, [1.0, 2.0], n_rep=3, rng=1) + assert d.shape == (3, 3) + with pytest.raises(ValueError, match=r"\(n, 2\)"): + predictive_draws(p, [[1.0, 2.0, 3.0]]) + + def test_tiny_variances_not_inflated(self): + d = Dataset(X, Y, np.full(5, 1e-9), label="tiny") + p = Problem([Constraint([Comparison(d, poly(1))])]) + draws = predictive_draws(p, [1.0, 2.0], n_rep=2000, rng=3) + assert np.all(draws.std(axis=0) < 1e-8) + + +class TestCoverageSharpness: + def test_coverage_near_nominal_for_matching_draws(self): + rng = np.random.default_rng(0) + draws = rng.normal(0.0, 1.0, (4000, 2000)) + y = rng.normal(0.0, 1.0, 2000) + levels = np.array([0.5, 0.9]) + np.testing.assert_allclose(coverage_curve(draws, y, levels), levels, atol=0.03) + assert coverage_error(draws, y, levels) < 0.03 + assert np.all(coverage_curve(0.3 * draws, y, levels) < levels - 0.2) + + def test_sharpness_width(self): + rng = np.random.default_rng(0) + draws = rng.normal(0.0, 1.0, (20000, 3)) + np.testing.assert_allclose(sharpness(draws), 2 * 0.9945, atol=0.05) + np.testing.assert_allclose(sharpness(np.zeros((10, 2)), transform=np.exp), 0.0) + np.testing.assert_allclose( + sharpness(draws, percentiles=(2.5, 97.5)), 2 * 1.96, atol=0.15 + ) + + +class TestHeldout: + def setup_method(self): + d = Dataset( + np.array([1.0, 2.0, 3.0, 4.0]), + np.array([3.1, 4.8, 7.2, 9.1]), + np.array([0.2, 0.2, 0.3, 0.3]), + label="d", + ) + self.d = d + self.model = poly(1) + + def problems(self, terms, cut=2.5): + c = Constraint([Comparison(self.d, self.model)], terms=terms) + fit = c.masked_where(lambda x: x < cut) + return Problem([fit]), Problem([fit.complement()]), Problem([c]) + + def test_marginal_matches_manual_and_partitions_without_a_spanning_term(self): + fit, held, full = self.problems([Term(0.1 * np.ones(4), kind="diag")]) + samples = np.array([[1.0, 2.0], [1.2, 1.9]]) + lp = heldout_log_predictive(held, samples) + for i, (a0, a1) in enumerate(samples): + ym = a0 + a1 * self.d.x[2:] + cov = np.diag(self.d.y_err[2:] ** 2 + 0.01) + assert lp[i] == pytest.approx(manual_mvn_loglike(self.d.y[2:], ym, cov)) + np.testing.assert_allclose( + lp + [fit.log_likelihood(s) for s in samples], + [full.log_likelihood(s) for s in samples], + ) + # the conditional equals the marginal when nothing spans the split + np.testing.assert_allclose(heldout_log_predictive(held, samples, given=fit), lp) + np.testing.assert_allclose( + predictive_draws(held, samples, given=fit, model_only=True), + predictive_draws(held, samples, model_only=True), + ) + + def test_conditional_under_a_spanning_matrix_term(self): + rng = np.random.default_rng(5) + A = rng.normal(size=(4, 4)) + K = A @ A.T / 4 # couples every point with every other + fit, held, full = self.problems([Term(K, kind="matrix")]) + theta = np.array([1.0, 2.0]) + S = np.diag(self.d.y_err**2) + K + ym = theta[0] + theta[1] * self.d.x + F, H = slice(0, 2), slice(2, 4) + r = self.d.y[F] - ym[F] + mean = ym[H] + S[H, F] @ np.linalg.solve(S[F, F], r) + cov = S[H, H] - S[H, F] @ np.linalg.solve(S[F, F], S[F, H]) + lp = heldout_log_predictive(held, theta, given=fit) + assert lp[0] == pytest.approx(manual_mvn_loglike(self.d.y[H], mean, cov)) + # and it is not the marginal + assert lp[0] != pytest.approx(heldout_log_predictive(held, theta)[0]) + draws = predictive_draws(held, theta, n_rep=40000, rng=0, given=fit) + np.testing.assert_allclose(draws.mean(axis=0), mean, atol=0.02) + np.testing.assert_allclose(np.cov(draws.T), cov, atol=0.03) + # the conditional is exactly the joint over the full data divided by the fit + joint = full.log_likelihood(theta) - fit.log_likelihood(theta) + assert lp[0] == pytest.approx(joint) + + def test_given_is_validated(self): + fit, held, full = self.problems([]) + with pytest.raises(ValueError, match="overlap"): + heldout_log_predictive(fit, [1.0, 2.0], given=fit) + other = Problem([Constraint([Comparison(self.d, poly(1))])]) + with pytest.raises(ValueError, match="same comparison objects"): + heldout_log_predictive(held, [1.0, 2.0], given=other) + c = Constraint( + [Comparison(self.d, self.model)], + likelihood=StudentT(Parameter("nu", prior=stats.uniform(1, 30))), + ).masked_where(lambda x: x < 2.5) + fit_t, held_t = Problem([c]), Problem([c.complement()]) + with pytest.raises(ValueError, match="StudentT"): + heldout_log_predictive(held_t, [1.0, 2.0, 1.0], given=fit_t) + + def test_log_posterior_predictive(self): + lp = np.array([-1.0, -2.0, -0.5]) + assert log_posterior_predictive(lp) == pytest.approx(logsumexp(lp) - np.log(3)) + logw = np.array([0.0, -np.inf, 0.0]) + assert log_posterior_predictive(lp, logw) == pytest.approx( + logsumexp(lp[[0, 2]]) - np.log(2) + ) + + +class TestLogZ: + def test_summary_single_and_replicates(self): + assert logz_summary([-10.0], [0.3]) == (-10.0, 0.3, 1) + assert logz_summary([-10.0, -12.0], [0.3, 0.3]) == (-11.0, 1.0, 2) + assert logz_summary([-10.0, -10.2], [0.5, 0.5])[1] == pytest.approx(0.5) + + def test_compare(self): + r = compare_logz((-10.0, 0.5), (-15.0, 0.5)) + assert r["verdict"] == "a" and r["dlogZ"] == pytest.approx(5.0) + assert r["err"] == pytest.approx(np.hypot(0.5, 0.5)) + assert compare_logz((-15.0, 0.5), (-10.0, 0.5))["verdict"] == "b" + assert compare_logz((-10.0, 1.0), (-11.0, 1.0))["verdict"] == "tie" + + def test_log_jacobian_lives_on_the_problem(self): + y = np.array([2.0, 3.0]) + d = Dataset(np.array([0.0, 1.0]), y, np.ones(2), label="d") + p = Problem([Constraint([Comparison(d, poly(0), space=log)])]) + assert p.log_jacobian() == pytest.approx(-np.sum(np.log(y))) + assert stats.norm(0, 1).cdf(0) == 0.5 From 7b7253aed0f8b17dd0f86777fb0b2485dc6ad439 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 21:10:37 -0400 Subject: [PATCH 30/75] Rewrite the total predictive band to read everything from the problem total_predictive_band(problem, term, predictor, x_pred, samples) locates the kernel term's constraint and covariance entry, takes its training rows, comparison space, kernel and amplitude columns from the problem, and conditions the discrepancy with the rest of the declared covariance as the regression noise, so a chain with nuisance columns needs no column arithmetic. The amplitude is evaluated at the prediction grid through a TermContext; physical=True maps draws back through the space's inverse. GP conditioning against sklearn is lifted from 0.x. --- src/rxmc/__init__.py | 2 + src/rxmc/predictive.py | 406 +++++++++++++++++++++------------------- test/test_predictive.py | 184 ++++++++++++++++++ 3 files changed, 399 insertions(+), 193 deletions(-) create mode 100644 test/test_predictive.py diff --git a/src/rxmc/__init__.py b/src/rxmc/__init__.py index a839af3..69e5d0c 100644 --- a/src/rxmc/__init__.py +++ b/src/rxmc/__init__.py @@ -10,6 +10,7 @@ from . import diagnostics as diagnostics from . import likelihood as likelihood from . import model as model +from . import predictive as predictive from . import problem as problem from . import reactions as reactions from . import terms as terms @@ -49,6 +50,7 @@ "diagnostics", "likelihood", "model", + "predictive", "problem", "reactions", "terms", diff --git a/src/rxmc/predictive.py b/src/rxmc/predictive.py index a7fc92d..b982e27 100644 --- a/src/rxmc/predictive.py +++ b/src/rxmc/predictive.py @@ -1,29 +1,34 @@ +"""Predicting a Gaussian-process discrepancy away from the data. + +A :func:`~rxmc.terms.kernel` term only inflates the covariance *at the data +points* with ``K(X, X)``; it does not say what the discrepancy is at new +``x``. :func:`gp_posterior_predictive` is the standard conditioning of a +zero-mean GP on observed residuals, and :func:`total_predictive_band` turns a +posterior chain into a predictive band on any grid that propagates the model +parameters, the conditioned discrepancy and, optionally, observation noise. +The band needs nothing but the problem, the kernel term and a predictor: the +training rows, the comparison space, the kernel hyperparameter and amplitude +columns and the conditioning noise all follow from the problem. + +Kernels are duck-typed as in :func:`~rxmc.terms.kernel`: sklearn-style objects +with ``clone_with_theta``, ``__call__`` and ``diag``, ``theta`` in sklearn's +log space. """ -Predictive-uncertainty helpers. - -A :func:`~rxmc.covariance.kernel_term` -only inflates the covariance *at the data points* with ``K(X, X)`` — it does not -propagate the discrepancy to new ``x``. :func:`gp_posterior_predictive` performs -the standard Gaussian-process conditioning needed to predict the discrepancy (mean -and covariance) at new points, and :func:`total_predictive_band` turns a posterior -sample of ``[model params | kernel log-theta]`` into a data-space predictive band -that propagates model-parameter, discrepancy, and observation-noise uncertainty. - -The kernel is duck-typed exactly as in :func:`~rxmc.covariance.kernel_term`: a -scikit-learn-style object exposing ``clone_with_theta`` and ``__call__``, with -``theta`` in sklearn **log-theta** space. -""" + +from __future__ import annotations import numpy as np -import scipy as sc +import scipy.linalg as sla + +from .problem import Problem +from .terms import KernelTerm, TermContext, as_2d -from .covariance import as_2d +__all__ = ["gp_posterior_predictive", "predictive_band", "total_predictive_band"] -__all__ = [ - "gp_posterior_predictive", - "total_predictive_band", - "predictive_band", -] + +# ---------------------------------------------------------------------------- +# GP conditioning +# ---------------------------------------------------------------------------- def _train_noise_matrix(train_noise_var, n) -> np.ndarray: @@ -37,45 +42,45 @@ def _train_noise_matrix(train_noise_var, n) -> np.ndarray: return v +def _condition(Ktt, Kst, r, N, jitter): + """``(mean, v)`` with ``mean = Kst (Ktt + N)^-1 r`` and ``v = L^-1 Kst^T``. + + Callers form the posterior covariance ``Kss - v^T v`` or its diagonal + ``diag(Kss) - sum(v**2, 0)``. + """ + n = len(r) + L = sla.cholesky(Ktt + N + jitter * np.eye(n), lower=True) + alpha = sla.cho_solve((L, True), r) + v = sla.solve_triangular(L, Kst.T, lower=True) + return Kst @ alpha, v + + def gp_posterior_predictive( - kernel, - theta, - X_train, - residuals, - X_pred, - *, - train_noise_var=None, - jitter=1e-10, + kernel, theta, X_train, residuals, X_pred, *, train_noise_var=None, jitter=1e-10 ): r"""Posterior mean and covariance of a GP discrepancy at ``X_pred``. - Conditions a zero-mean GP with covariance ``kernel`` (rebuilt at ``theta``) on - the observed ``residuals`` at ``X_train`` and returns its posterior at - ``X_pred``: + Conditions a zero-mean GP with covariance ``kernel`` (rebuilt at ``theta``) + on the observed ``residuals`` at ``X_train``: .. math:: \bar f_* = K_{*t} (K_{tt} + N)^{-1} r, \qquad \mathrm{cov}_* = K_{**} - K_{*t} (K_{tt} + N)^{-1} K_{t*} - where ``N`` is the training-noise covariance. - Parameters ---------- kernel : sklearn-style kernel - Object with ``clone_with_theta`` and ``__call__`` (as for - :func:`~rxmc.covariance.kernel_term`). - theta : array-like - Kernel hyperparameters in sklearn **log-theta** space. - X_train, X_pred : array-like + theta : array_like + Kernel hyperparameters in sklearn log-theta space. + X_train, X_pred : array_like Training and prediction inputs (1-D promoted to a column). - residuals : array-like, shape (n_train,) - Observed minus model-mean at ``X_train``. - train_noise_var : float, array-like, or matrix, optional - Training-noise variance: scalar (``var*I``), per-point vector - (``diag``), or full covariance. Defaults to none. + residuals : array_like, shape (n_train,) + Observed minus model mean at ``X_train``. + train_noise_var : float, array_like or matrix, optional + Training-noise covariance ``N``: scalar, per-point vector, or full. jitter : float, optional - Diagonal nugget added to the training covariance for stability. + Nugget added to the training covariance. Returns ------- @@ -83,185 +88,200 @@ def gp_posterior_predictive( cov : np.ndarray, shape (n_pred, n_pred) """ k = kernel.clone_with_theta(np.asarray(theta, dtype=float)) - Xp = as_2d(X_pred) - mean, v = _gp_condition( - k, X_train, residuals, Xp, train_noise_var=train_noise_var, jitter=jitter - ) - Kss = np.asarray(k(Xp), dtype=float) - cov = Kss - v.T @ v - return mean, cov - - -def _gp_condition(k, X_train, residuals, Xp, *, train_noise_var, jitter): - """Shared GP conditioning: returns (mean, v). - - ``k`` is an already-instantiated kernel (``clone_with_theta`` applied); - ``Xp`` is the 2-D prediction grid. ``mean = Kst @ alpha`` and - ``v = L^{-1} Kst.T`` so callers form the full posterior covariance - (``Kss - v.T @ v``) or only its diagonal (``diag(Kss) - sum(v**2, 0)``). - """ - Xtr = as_2d(X_train) + Xt, Xp = as_2d(X_train), as_2d(X_pred) r = np.asarray(residuals, dtype=float) - n = Xtr.shape[0] + Ktt, Kst, Kss = k(Xt), k(Xp, Xt), k(Xp) + N = _train_noise_matrix(train_noise_var, len(r)) + mean, v = _condition(Ktt, Kst, r, N, jitter) + return mean, Kss - v.T @ v - Ktt = np.asarray(k(Xtr), dtype=float) - Kst = np.asarray(k(Xp, Xtr), dtype=float) - Ktrain = Ktt + _train_noise_matrix(train_noise_var, n) + jitter * np.eye(n) - L = sc.linalg.cholesky(Ktrain, lower=True) - alpha = sc.linalg.cho_solve((L, True), r) - mean = Kst @ alpha - v = sc.linalg.solve_triangular(L, Kst.T, lower=True) - return mean, v - - -def _gp_posterior_mean_var( - kernel, theta, X_train, residuals, X_pred, *, train_noise_var=None, jitter=1e-10 -): - """Posterior mean and **diagonal variance** of a GP discrepancy at ``X_pred``. +def predictive_band(draws, levels=(16, 50, 84)) -> np.ndarray: + """Percentile band over ``draws`` of shape ``(n_draws, n_points)``. - Equivalent to ``mean, diag(cov)`` from :func:`gp_posterior_predictive` but - avoids building the full ``n_pred x n_pred`` covariance — O(n_pred * n_train) - instead of O(n_pred^2 * n_train). Uses ``kernel.diag(X_pred)`` for the prior - variances. + Returns ``(len(levels), n_points)``. """ - k = kernel.clone_with_theta(np.asarray(theta, dtype=float)) - Xp = as_2d(X_pred) - mean, v = _gp_condition( - k, X_train, residuals, Xp, train_noise_var=train_noise_var, jitter=jitter - ) - prior_var = np.asarray(k.diag(Xp), dtype=float) - var = prior_var - np.einsum("ij,ij->j", v, v) - return mean, var + return np.percentile(np.asarray(draws, dtype=float), levels, axis=0) -def predictive_band(draws, levels=(16, 50, 84)) -> np.ndarray: - """Percentile band over a matrix of predictive draws. +# ---------------------------------------------------------------------------- +# The total predictive band of a problem +# ---------------------------------------------------------------------------- - Parameters - ---------- - draws : array-like, shape (n_draws, n_points) - Predictive samples (each row a function evaluated on a grid). - levels : sequence of float, optional - Percentile levels. - Returns - ------- - np.ndarray, shape (len(levels), n_points) - """ - return np.percentile(np.asarray(draws, dtype=float), levels, axis=0) +def _locate(problem: Problem, term: KernelTerm): + """The compiled constraint holding ``term`` and its covariance entry.""" + if not isinstance(term, KernelTerm): + raise TypeError( + "term must be the KernelTerm returned by rxmc.terms.kernel, got " + f"{term!r}" + ) + for c in problem.constraints: + for e in c.covariance.entries: + if e.term is term: + return c, e + raise ValueError("the kernel term is not part of any constraint of the problem") + + +def _one_space(constraint, rows): + spaces = [] + for comp, o in zip(constraint.comparisons, constraint.offsets): + if np.any((rows >= o.start) & (rows < o.stop)): + if not any(comp.space is s for s in spaces): + spaces.append(comp.space) + if len(spaces) != 1: + raise ValueError( + "the comparisons a kernel term spans must share one comparison space " + "to predict in it; found " + f"{[s.name for s in spaces]}" + ) + return spaces[0] def total_predictive_band( - mean_fn, - kernel, - x_train, - y_train, + problem: Problem, + term: KernelTerm, + predictor, x_pred, - draws, - n_model_params, + samples, *, - theta_cols=None, - noise_std=0.0, + noise_std: float = 0.0, train_noise_var=None, levels=(16, 84), - n_draws=400, + n_draws: int = 400, rng=None, -): - r"""Data-space predictive band that propagates *total* uncertainty. + physical: bool = False, +) -> np.ndarray: + r"""Predictive band on ``x_pred`` propagating model, discrepancy and noise. - For each posterior draw ``q`` it splits out the model parameters and the kernel - log-theta, forms the residual ``y_train - mean_fn(x_train, *model_params)``, - conditions the GP discrepancy at ``x_pred``, and samples + For each chain row ``theta`` the model's prediction on the training rows + gives the residual ``y - ym`` in comparison space; the discrepancy GP of + ``term`` (kernel hyperparameters, amplitude and coordinate transform at + that row) is conditioned on it and predicted at ``x_pred``; one draw .. math:: - y_*^{(s)} = \mathrm{mean\_fn}(x_*) + \bar f_*^{(s)} - + \mathcal N\!\big(0,\ \mathrm{var}_*^{(s)} + \sigma^2\big), + y_* = \mathrm{space}(\mathrm{predictor}(\theta)) + \bar f_* + + \mathcal N\!\big(0,\ \mathrm{var}_* + \sigma^2\big) - returning the requested percentile band over the samples. Only the GP's - posterior *variance* is propagated per point (the off-diagonal posterior - covariance is not used for the marginal band). + is taken, and the requested percentiles over the draws are returned. + Only the GP's posterior *variance* enters per point. Parameters ---------- - mean_fn : callable - ``mean_fn(x, *model_params) -> y`` on a raw ``x`` array (e.g. a model's - ``.y`` plotting helper). - kernel : sklearn-style kernel - The discrepancy kernel (as passed to :func:`~rxmc.covariance.kernel_term`). - x_train, y_train, x_pred : array-like - Training inputs/outputs and the prediction grid. - draws : array-like, shape (n_samples, n_draw_cols) - Posterior chain rows. - n_model_params : int - Number of leading model parameters in each draw (consumed by ``mean_fn``). - theta_cols : array-like of int, optional - Column indices selecting the kernel log-theta within each draw row, in - ``kernel.theta`` order. When ``None`` (default) the kernel theta is taken - as the trailing columns ``q[n_model_params:]`` and the row width is - validated against ``len(kernel.theta)``. Pass explicit columns when the - draw also carries other covariance/likelihood nuisances. + problem : Problem + The compiled problem the chain was sampled from. + term : KernelTerm + The discrepancy term, as declared in one of the problem's constraints. + predictor : Predictor + The model bound to ``x_pred`` (``model.bind(x_pred, meta)``); its + columns are read from the problem. + x_pred : array_like + The prediction grid (raw coordinates; the term's ``coords`` transform + is applied for the kernel). + samples : array_like, shape (n, problem.ndim) + Chain rows in ``problem.names`` order. noise_std : float, optional - Observation noise std-dev, used both to condition the GP and as the - prediction-point noise (overridden for conditioning by - ``train_noise_var`` if given). - train_noise_var : float or array-like, optional - Training-noise covariance for conditioning (defaults to ``noise_std**2``). + Observation noise added at the prediction points, in comparison space. + train_noise_var : float, array_like or matrix, optional + Conditioning noise on the training rows. Default: everything in the + constraint's covariance at that row *except* the kernel term itself + (statistical errors, noise and mode terms), which is the exact GP + regression noise for the declared error model. levels : sequence of float, optional - Percentile levels for the returned band. + Percentiles of the band. n_draws : int, optional - Number of posterior rows to subsample (if the chain is longer). - rng : np.random.Generator, optional + Rows subsampled from the chain (all rows when the chain is shorter). + rng : numpy.random.Generator or seed, optional + physical : bool, optional + Map every draw back through the comparison space's inverse before + taking percentiles (``space=log`` gives a band in physical units). Returns ------- np.ndarray, shape (len(levels), len(x_pred)) - - Raises - ------ - ValueError - If the kernel-theta selection does not have ``len(kernel.theta)`` columns. """ - rng = np.random.default_rng() if rng is None else rng - draws = np.asarray(draws, dtype=float) - if draws.shape[0] > n_draws: - draws = draws[rng.choice(draws.shape[0], n_draws, replace=False)] - - n_theta = len(kernel.theta) - if theta_cols is None: - n_trailing = draws.shape[1] - n_model_params - if n_trailing != n_theta: - raise ValueError( - f"draws have {n_trailing} columns after the {n_model_params} model " - f"parameters, but the kernel has {n_theta} hyperparameters. Pass " - "theta_cols to select the kernel log-theta columns explicitly." - ) - theta_cols = np.arange(n_model_params, n_model_params + n_theta) - else: - theta_cols = np.asarray(theta_cols, dtype=int) - if theta_cols.shape[0] != n_theta: - raise ValueError( - f"theta_cols selects {theta_cols.shape[0]} columns but the kernel " - f"has {n_theta} hyperparameters." - ) - - x_train = np.asarray(x_train, dtype=float) - y_train = np.asarray(y_train, dtype=float) - x_pred = np.asarray(x_pred, dtype=float) - pred_noise_var = float(noise_std) ** 2 - cond_noise = pred_noise_var if train_noise_var is None else train_noise_var - - samples = np.empty((draws.shape[0], x_pred.shape[0])) - for i, q in enumerate(draws): - model_params = q[:n_model_params] - theta = q[theta_cols] - residual = y_train - mean_fn(x_train, *model_params) - disc_mean, disc_var = _gp_posterior_mean_var( - kernel, theta, x_train, residual, x_pred, train_noise_var=cond_noise + rng = np.random.default_rng(rng) + samples = np.asarray(samples, dtype=float) + if samples.ndim == 1: + samples = samples[None, :] + if samples.shape[1] != problem.ndim: + raise ValueError( + f"samples must have shape (n, {problem.ndim}) in problem.names order, " + f"got {samples.shape}" ) - disc_var = np.clip(disc_var, 0.0, np.inf) - mu = mean_fn(x_pred, *model_params) + disc_mean - samples[i] = mu + rng.normal(0.0, np.sqrt(disc_var + pred_noise_var)) - - return predictive_band(samples, levels) + if samples.shape[0] > n_draws: + samples = samples[rng.choice(samples.shape[0], n_draws, replace=False)] + + c, entry = _locate(problem, term) + rows = entry.rows + keep = entry.keep # the active rows of the term's support + pos = entry.pos[keep] # their positions in the active stack + space = _one_space(c, rows) + if physical and space.inverse is None: + raise ValueError(f"comparison space {space.name!r} has no inverse") + cols_term = problem.columns(term.params) + cols_pred = problem.columns(predictor.params) + nk, n_fn = term.n_kernel, term._n_fn_params + x_pred = np.asarray(x_pred) + meta = None if c.meta is None else {k: v[rows] for k, v in c.meta.items()} + + out = np.empty((samples.shape[0], x_pred.shape[0])) + for i, theta in enumerate(samples): + values = tuple(theta[cols_term]) + kv, av, cv = values[:nk], values[nk:n_fn], values[n_fn:] + ym = c.ym(theta) + # the term's own block on its active rows, exactly as the likelihood saw it + K_full = term.value( + c.x[rows], + c.y[rows], + ym[rows], + *values, + meta=meta, + segments=entry.segments, + labels=entry.labels, + ) + Ktt = K_full[np.ix_(keep, keep)] + r = (c.y - ym)[rows][keep] + if train_noise_var is None: + N = c.covariance.matrix(ym, theta)[np.ix_(pos, pos)] - Ktt + else: + N = _train_noise_matrix(train_noise_var, len(r)) + # kernel and amplitude at the training and prediction coordinates + k = ( + term.kernel.clone_with_theta(np.asarray(kv, dtype=float)) + if nk + else term.kernel + ) + ctx_t = term.context( + c.x[rows], c.y[rows], ym[rows], meta, *values, + segments=entry.segments, labels=entry.labels, + ) # fmt: skip + mu = space(predictor(*theta[cols_pred])) + ctx_p = TermContext(x=term.coords(x_pred, *cv), y=mu, ym=mu) + a_t, a_p = _amplitudes(term, ctx_t, ctx_p, av) + Kst = np.outer(a_p, a_t[keep]) * k(as_2d(ctx_p.x), as_2d(ctx_t.x)[keep]) + Kss = a_p**2 * np.asarray(k.diag(as_2d(ctx_p.x)), dtype=float) + f_mean, v = _condition(Ktt, Kst, r, N, 0.0) + f_var = np.clip(Kss - np.einsum("ij,ij->j", v, v), 0.0, None) + y = mu + f_mean + rng.normal(0.0, np.sqrt(f_var + float(noise_std) ** 2)) + out[i] = space.inverse(y) if physical else y + return predictive_band(out, levels) + + +def _amplitudes(term, ctx_t, ctx_p, values): + a = term.amplitude + if a is None: + return np.ones(len(ctx_t)), np.ones(len(ctx_p)) + if callable(a): + return ( + np.broadcast_to(np.asarray(a(ctx_t, *values), dtype=float), (len(ctx_t),)), + np.broadcast_to(np.asarray(a(ctx_p, *values), dtype=float), (len(ctx_p),)), + ) + a = np.asarray(a, dtype=float) + if a.ndim == 0: + return np.full(len(ctx_t), float(a)), np.full(len(ctx_p), float(a)) + raise ValueError( + "a kernel amplitude given as an array is defined only on the training " + "rows; use a callable amplitude(c, ...) to predict at new points" + ) diff --git a/test/test_predictive.py b/test/test_predictive.py new file mode 100644 index 0000000..ec88d6e --- /dev/null +++ b/test/test_predictive.py @@ -0,0 +1,184 @@ +"""GP conditioning and the total predictive band of a problem.""" + +import numpy as np +import pytest +from scipy import stats +from sklearn.gaussian_process import GaussianProcessRegressor +from sklearn.gaussian_process.kernels import RBF, ConstantKernel, WhiteKernel + +from rxmc import Comparison, Constraint, Dataset, Model, Parameter, Problem, Term +from rxmc.predictive import ( + gp_posterior_predictive, + predictive_band, + total_predictive_band, +) +from rxmc.terms import constant_amplitude, kernel, noise +from rxmc.transforms import log + + +def make_kernel(): + return ConstantKernel(1.0) * RBF(length_scale=0.7) + WhiteKernel(1e-6) + + +class TestGPPosteriorPredictive: + def setup_method(self): + rng = np.random.default_rng(0) + self.X_train = np.sort(rng.uniform(-2, 2, 12)) + self.residuals = np.sin(self.X_train) + 0.05 * rng.standard_normal(12) + self.X_pred = np.linspace(-2.5, 2.5, 25) + self.kernel = make_kernel() + self.theta = self.kernel.theta + + def test_matches_sklearn_gpr(self): + gpr = GaussianProcessRegressor( + kernel=self.kernel.clone_with_theta(self.theta), + optimizer=None, + alpha=0.01, + normalize_y=False, + ) + gpr.fit(self.X_train[:, None], self.residuals) + mean_sk, cov_sk = gpr.predict(self.X_pred[:, None], return_cov=True) + mean, cov = gp_posterior_predictive( + self.kernel, + self.theta, + self.X_train, + self.residuals, + self.X_pred, + train_noise_var=0.01, + ) + np.testing.assert_allclose(mean, mean_sk, atol=1e-6) + np.testing.assert_allclose(cov, cov_sk, atol=1e-6) + + def test_noiseless_interpolation_and_2d_inputs(self): + k = ConstantKernel(1.0) * RBF(length_scale=0.7) + mean, cov = gp_posterior_predictive( + k, k.theta, self.X_train, self.residuals, self.X_train + ) + # the nugget keeps a near-singular K invertible; interpolation to 1e-2 + np.testing.assert_allclose(mean, self.residuals, atol=1e-2) + assert np.all(np.diag(cov) < 1e-2) + X2 = np.column_stack([self.X_train, self.X_train**2]) + mean2, cov2 = gp_posterior_predictive( + k, k.theta, X2, self.residuals, X2[:5], train_noise_var=0.01 + ) + assert mean2.shape == (5,) and cov2.shape == (5, 5) + + def test_predictive_band_percentiles(self): + draws = np.tile(np.arange(101.0)[:, None], (1, 3)) + band = predictive_band(draws) + np.testing.assert_allclose(band, [[16] * 3, [50] * 3, [84] * 3]) + + +# ---------------------------------------------------------------------------- +# total_predictive_band on a Problem +# ---------------------------------------------------------------------------- + + +def line(): + m = Parameter("m", prior=stats.norm(0, 10)) + b = Parameter("b", prior=stats.norm(0, 10)) + return Model(lambda x, m, b: m * x + b, [m, b]) + + +X = np.linspace(0.0, 1.0, 10) +Y = 0.5 * X + 0.2 + 0.3 * np.sin(3 * X) # a smooth defect on a line +X_PRED = np.linspace(-0.2, 1.2, 30) + + +def problem(terms_first, space=None, err=0.05, fixed=True): + """A line with a GP (hyperparameters fixed or free) and a noise nuisance.""" + d = Dataset(X, Y, np.full(10, err), label="d") + model = line() + comp = Comparison(d, model) if space is None else Comparison(d, model, space=space) + if fixed: + gp = kernel(RBF(0.3, "fixed")) + else: + gp = kernel(RBF(0.3), params=[Parameter("log_ell", prior=stats.norm(-1, 1))]) + eps = noise(Parameter("log_eps", prior=stats.norm(-3, 1))) + terms = [eps, gp] if terms_first == "noise" else [gp, eps] + p = Problem([Constraint([comp], terms=terms)]) + return p, gp, model, comp + + +class TestTotalPredictiveBand: + def chain(self, p, n=40, seed=2): + rng = np.random.default_rng(seed) + rows = np.zeros((n, p.ndim)) + rows[:, p.names.index("m")] = 0.5 + 0.05 * rng.standard_normal(n) + rows[:, p.names.index("b")] = 0.2 + 0.05 * rng.standard_normal(n) + rows[:, p.names.index("log_eps")] = np.log(0.05) + if "log_ell" in p.names: + rows[:, p.names.index("log_ell")] = np.log(0.3) + return rows + + def test_shape_finite_and_columns_from_the_problem(self): + p1, gp1, model1, _ = problem("noise", fixed=False) + p2, gp2, model2, _ = problem("kernel", fixed=False) + assert p1.names != p2.names # the nuisance and kernel columns swapped + bands = [] + for p, gp, model in ((p1, gp1, model1), (p2, gp2, model2)): + band = total_predictive_band( + p, gp, model.bind(X_PRED, {}), X_PRED, self.chain(p), rng=0 + ) + assert band.shape == (2, 30) and np.all(np.isfinite(band)) + assert np.all(band[1] >= band[0]) + bands.append(band) + np.testing.assert_allclose(bands[0], bands[1]) # no column arithmetic + + def test_conditioned_band_passes_through_the_data(self): + p, gp, model, comp = problem("noise", err=1e-4) + chain = np.tile([0.5, 0.2, np.log(1e-4)], (5, 1)) + band = total_predictive_band( + p, gp, model.bind(X, {}), X, chain, levels=(50,), rng=1 + ) + np.testing.assert_allclose(band[0], Y, atol=2e-3) + + def test_physical_band_under_log_space(self): + p, gp, model, comp = problem("noise", space=log) + # 26 rows put the 16th and 84th percentiles on order statistics, so the + # monotone exp commutes with the percentile + chain = self.chain(p, n=26) + pred = model.bind(X_PRED, {}) + band_log = total_predictive_band(p, gp, pred, X_PRED, chain, rng=3) + band_phys = total_predictive_band( + p, gp, pred, X_PRED, chain, rng=3, physical=True + ) + np.testing.assert_allclose(band_phys, np.exp(band_log)) + + def test_amplitude_matches_a_scaled_kernel(self): + d = Dataset(X, Y, np.full(10, 0.05), label="d") + A = 0.7 + m1, m2 = line(), line() + lA = Parameter("log_A", prior=stats.norm(0, 1)) + gp_amp = kernel( + RBF(0.3, "fixed"), amplitude=constant_amplitude, amplitude_params=(lA,) + ) + gp_fix = kernel(ConstantKernel(A**2, "fixed") * RBF(0.3, "fixed")) + p1 = Problem([Constraint([Comparison(d, m1)], terms=[gp_amp])]) + p2 = Problem([Constraint([Comparison(d, m2)], terms=[gp_fix])]) + chain1 = np.tile([0.5, 0.2, np.log(A)], (8, 1)) + chain2 = np.tile([0.5, 0.2], (8, 1)) + b1 = total_predictive_band( + p1, gp_amp, m1.bind(X_PRED, {}), X_PRED, chain1, rng=4 + ) + b2 = total_predictive_band( + p2, gp_fix, m2.bind(X_PRED, {}), X_PRED, chain2, rng=4 + ) + np.testing.assert_allclose(b1, b2, atol=1e-8) + + def test_explicit_noise_and_errors(self): + p, gp, model, comp = problem("noise") + chain = self.chain(p, n=6) + pred = model.bind(X_PRED, {}) + band = total_predictive_band( + p, gp, pred, X_PRED, chain, train_noise_var=0.05**2, noise_std=0.1, rng=5 + ) + assert band.shape == (2, 30) + with pytest.raises(TypeError, match="KernelTerm"): + total_predictive_band( + p, Term(np.ones(10), kind="diag"), pred, X_PRED, chain + ) + with pytest.raises(ValueError, match="not part of any constraint"): + total_predictive_band(p, kernel(RBF(1.0, "fixed")), pred, X_PRED, chain) + with pytest.raises(ValueError, match=r"\(n, 3\)"): + total_predictive_band(p, gp, pred, X_PRED, chain[:, :2]) From 3ab41b0d13c9c0a1e88b74a45ab6d1d95e90c815 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 21:15:46 -0400 Subject: [PATCH 31/75] Add recipe tests 7, 17, 18, 28, 30 and 31 for the analysis layer The fast tier stays sampler-free: a linear-Gaussian oracle in the recipe commons gives exact posterior rows wherever a recipe says run(), so coverage, held-out scores against the closed-form marginal, stacking weights and simulation-based-calibration ranks are pinned exactly. A derived kernel hyperparameter now takes the log of sklearn's bounds as its bounds so a free kernel compiles with a uniform prior in log-theta. Slow tests hold the dynesty evidence ordering for a normalisation defect and emcee's calibration ranks. Recipes 7, 11, 28, 30 and 31 and the design's analysis sketches describe KernelTerm and the given= conditional. --- docs/groundup_design.md | 20 ++- docs/recipes.md | 30 +++- src/rxmc/terms.py | 18 ++- test/recipes/common.py | 40 ++++++ test/recipes/test_recipe_07_gp_discrepancy.py | 115 +++++++++++++++ .../test_recipe_17_posterior_predictive.py | 86 +++++++++++ .../test_recipe_18_evidence_comparison.py | 135 ++++++++++++++++++ test/recipes/test_recipe_28_stacking.py | 80 +++++++++++ ...test_recipe_30_leave_one_experiment_out.py | 63 ++++++++ test/recipes/test_recipe_31_sbc.py | 78 ++++++++++ test/test_terms.py | 2 + 11 files changed, 653 insertions(+), 14 deletions(-) create mode 100644 test/recipes/test_recipe_07_gp_discrepancy.py create mode 100644 test/recipes/test_recipe_17_posterior_predictive.py create mode 100644 test/recipes/test_recipe_18_evidence_comparison.py create mode 100644 test/recipes/test_recipe_28_stacking.py create mode 100644 test/recipes/test_recipe_30_leave_one_experiment_out.py create mode 100644 test/recipes/test_recipe_31_sbc.py diff --git a/docs/groundup_design.md b/docs/groundup_design.md index 2ca2b4d..9a4538c 100644 --- a/docs/groundup_design.md +++ b/docs/groundup_design.md @@ -307,7 +307,10 @@ noise_fraction(parameter, log=True, on=None) model_error(parameter, averaging=True, log=True, on=None) systematic(parameter, basis, log=True, basis_params=(), on=None, coords=None) kernel(kernel, coords=None, amplitude=None, amplitude_params=(), jitter=1e-10, - prefix="discrepancy", params=None, on=None) + prefix="discrepancy", params=None, on=None) -> KernelTerm +# KernelTerm(Term) adds kernel, n_kernel, amplitude, jitter so predictive.total_predictive_band +# can condition the discrepancy from the term alone. A derived hyperparameter is +# bounded by the log of the kernel's bounds (a uniform prior in log-theta). # params=: the hyperparameter Parameter objects, one per free element in kernel.theta order; # None derives fresh ones named f"{prefix}_{name}". Pass the same objects to share # hyperparameters between per-block kernels. Two kernel terms with derived @@ -574,10 +577,14 @@ gone. ```python # diagnostics.py -predictive_draws(problem, samples, constraint=0, *, n_rep=1, rng=None, model_only=False) +predictive_draws(problem, samples, constraint=0, *, n_rep=1, rng=None, model_only=False, given=None) coverage_curve(draws, y, levels=None); coverage_error(draws, y, levels=None) sharpness(draws, percentiles=(16, 84), transform=None) -heldout_log_predictive(heldout_problem, samples) # Problem([fit.complement()], priors=...) +heldout_log_predictive(heldout_problem, samples, *, given=None) # Problem([fit.complement()]) +# given=: the fitted problem. A held-out problem's own likelihood is the marginal of its +# rows, wrong when a term spans fit and held-out rows (a GP over experiments); +# given= computes the Gaussian conditional p(y_held | y_fit, theta) under the full +# covariance, which equals the marginal when nothing spans. log_posterior_predictive(logp_samples, logw=None) logz_summary(logz, logzerr); compare_logz(a, b, sigma=2.0) @@ -585,8 +592,11 @@ logz_summary(logz, logzerr); compare_logz(a, b, sigma=2.0) gp_posterior_predictive(kernel, theta, X_train, residuals, X_pred, *, train_noise_var=None, jitter=1e-10) predictive_band(draws, levels=(16, 50, 84)) total_predictive_band(problem, term, predictor, x_pred, samples, *, noise_std=0.0, - train_noise_var=None, levels=(16, 84), n_draws=400, rng=None) -# term is the kernel Term; its columns and the predictor's come from problem.columns + train_noise_var=None, levels=(16, 84), n_draws=400, rng=None, + physical=False) +# term is the KernelTerm; its columns and the predictor's come from problem.columns. The +# conditioning noise defaults to the constraint's covariance minus the kernel block; the +# band is in the comparison space of the term's comparisons unless physical=True. ``` ## 3. Worked example: the α+Ca study shape diff --git a/docs/recipes.md b/docs/recipes.md index 631c94f..d862a3f 100644 --- a/docs/recipes.md +++ b/docs/recipes.md @@ -175,11 +175,18 @@ band = rx.predictive.total_predictive_band(problem, gp, omp.bind(x_fine, d.meta) Expected behaviour: - One parameter per free kernel hyperparameter element, named - `discrepancy_`, sampled in sklearn's log-theta space. + `discrepancy_`, sampled in sklearn's log-theta space and + bounded by the log of the kernel's bounds, so it compiles with a uniform + prior there (`params=` for any other prior). +- `kernel()` returns a `KernelTerm`, a `Term` that also carries the kernel, + so the predictive band can condition the discrepancy from the term alone. - The model parameters relax from their biased values toward the truth (`gp_discrepancy`); the learned discrepancy tracks the true defect. - `total_predictive_band` finds the kernel's columns from the term itself; - no column arithmetic. + no column arithmetic. It conditions on the residuals of the training rows + with everything *else* in the constraint's covariance (statistical errors, + noise, modes) as the regression noise, and predicts in the comparison + space of the term's comparisons (`physical=True` maps back). - Every error-model form of the α+Ca study reproduces a hand-built dense matrix (`TestStudyForms`). @@ -269,7 +276,12 @@ Expected behaviour: - `fit` and `held` share every `Comparison`, `Term`, and `Parameter`; the two problems have identical `names`, so a chain from one scores the other. - Active sets are disjoint, their union is every point, and - `ll(fit) + ll(held) == ll(full)` for a block-local covariance. + `ll(fit) + ll(held) == ll(full)` for a block-local covariance. When a term + spans the split (a GP over several experiments), the held-out problem's + own likelihood is the *marginal* of its rows; pass the fitted problem as + `given=` to `heldout_log_predictive` / `predictive_draws` for the + conditional `p(y_held | y_fit, theta)` under the full covariance (recipes + 28 and 30). - Terms are authored once over all points; masking selects rows, it never rebuilds anything. @@ -803,7 +815,8 @@ w = maximise(lambda w: np.sum(logsumexp(np.log(w)[:, None] + S, axis=0)), simple Expected behaviour: - Held-out log densities are joint over the held-out comparison and exact - under a correlated covariance; PSIS-LOO per point is not available + under a correlated covariance (`given=fit` when a term spans the fitted + and the held-out comparisons); PSIS-LOO per point is not available without per-point likelihood factors (closing section). - Stacking weights need not sum to the evidence weights; in the M-open setting they are the ones to prefer. @@ -867,7 +880,7 @@ for i, comp in enumerate(comps): p = rx.Problem([fit], priors) s = run(p) held = rx.Problem([fit.complement()], priors) - draws = rx.diagnostics.predictive_draws(held, s, n_rep=4) + draws = rx.diagnostics.predictive_draws(held, s, n_rep=4, given=p) # conditional on the fit tol = np.percentile(np.abs(draws - draws.mean(0)), 90, axis=0) # tolerance bound per point cov = rx.diagnostics.coverage_curve(draws, held.constraints[0].y[held.constraints[0].active]) ``` @@ -876,7 +889,9 @@ Expected behaviour: - The held-out comparison's covariance terms are the same objects as in the fit; a GP discrepancy conditioned on the other experiments carries into the - prediction through `predictive_draws`. + prediction through `predictive_draws(..., given=p)`, which draws from + `p(y_held | y_fit, theta)` under the full covariance. Without `given=` the + draws use the marginal block, which forgets what the fit taught the GP. - Coverage on the held-out experiment is the honest check; in-sample coverage is not. - Tolerance bounds are empirical percentiles of the draws, componentwise. @@ -909,6 +924,9 @@ Expected behaviour: bias. - Chains must be thinned to roughly independent draws first, or spurious boundary spikes appear. +- `comp.space.inverse` is defined for every built-in parameter-free space + (`identity`, `log`, `exp`), so the simulated data go back to physical + units regardless of the comparison space. - SBC validates the computation under the assumed model; it says nothing about whether the model fits real data; that is the posterior predictive coverage check of recipe 17. diff --git a/src/rxmc/terms.py b/src/rxmc/terms.py index a7d0bdc..fbbf3aa 100644 --- a/src/rxmc/terms.py +++ b/src/rxmc/terms.py @@ -533,11 +533,20 @@ def _kernel_params(kernel, prefix) -> list: for hp in kernel.hyperparameters: if hp.fixed: continue + # sklearn bounds are in linear space; the parameter lives in log-theta, + # so finite bounds give the derived parameter a uniform prior there + bounds = np.log(np.atleast_2d(np.asarray(hp.bounds, dtype=float))) if hp.n_elements == 1: - params.append(Parameter(f"{prefix}_{hp.name}", latex=hp.name)) + params.append( + Parameter(f"{prefix}_{hp.name}", bounds=tuple(bounds[0]), latex=hp.name) + ) else: params.extend( - Parameter(f"{prefix}_{hp.name}_{i}", latex=f"{hp.name}[{i}]") + Parameter( + f"{prefix}_{hp.name}_{i}", + bounds=tuple(bounds[i]), + latex=f"{hp.name}[{i}]", + ) for i in range(hp.n_elements) ) return params @@ -562,7 +571,10 @@ def kernel( One :class:`~rxmc.params.Parameter` is derived per *free* kernel hyperparameter **element** (sampled in sklearn's log-theta space): an anisotropic hyperparameter (``n_elements > 1``) contributes that many - parameters. ``amplitude_params`` follow the kernel parameters. + parameters. A derived parameter takes the log of the kernel's bounds as + its bounds, so it compiles with a uniform prior on log-theta over them; + pass ``params=`` for any other prior. ``amplitude_params`` follow the + kernel parameters. Parameters ---------- diff --git a/test/recipes/common.py b/test/recipes/common.py index 09dbed6..3ae251f 100644 --- a/test/recipes/common.py +++ b/test/recipes/common.py @@ -36,3 +36,43 @@ def map_estimate(problem, x0): lambda t: -problem.log_posterior(t), np.asarray(x0, float), method="L-BFGS-B" ) return res.x + + +# ---------------------------------------------------------------------------- +# Exact posteriors for problems whose model is linear in its parameters +# ---------------------------------------------------------------------------- + + +def line_design(x): + """Design matrix of :func:`line` in its parameter order ``(m, b)``.""" + x = np.asarray(x, dtype=float) + return np.column_stack([x, np.ones_like(x)]) + + +def linear_posterior(problem, design=line_design): + """``(mean, cov, log_evidence)`` of a problem linear in its parameters. + + Every constraint must have a constant covariance and independent normal + priors on the parameters (in ``problem.params`` order); ``design(x)`` maps + a constraint's stacked ``x`` to the rows of the design matrix. Wraps + ``oracle.linear_gaussian`` over the active rows of all constraints. + """ + from scipy.linalg import block_diag + + from oracle import linear_gaussian + + theta0 = np.zeros(problem.ndim) + Xs, ys, Ss = [], [], [] + for c in problem.constraints: + Xs.append(design(c.x)[c.active]) + ys.append(c.y[c.active]) + Ss.append(c.matrix(theta0)) + mu0 = np.array([p.prior.mean() for p in problem.params]) + C0 = np.diag([p.prior.var() for p in problem.params]) + return linear_gaussian(np.vstack(Xs), np.concatenate(ys), block_diag(*Ss), mu0, C0) + + +def oracle_samples(problem, n, rng, design=line_design): + """``(n, ndim)`` exact posterior samples, a stand-in for a converged chain.""" + mean, cov, _ = linear_posterior(problem, design) + return np.random.default_rng(rng).multivariate_normal(mean, cov, n) diff --git a/test/recipes/test_recipe_07_gp_discrepancy.py b/test/recipes/test_recipe_07_gp_discrepancy.py new file mode 100644 index 0000000..97e5c40 --- /dev/null +++ b/test/recipes/test_recipe_07_gp_discrepancy.py @@ -0,0 +1,115 @@ +"""Recipe 7: absorb model deficiency with a Gaussian process. + +My model is missing physics. I want a smooth correlated discrepancy, in +angle or in momentum transfer, learned from the residuals. +""" + +import numpy as np +from scipy import stats +from sklearn.gaussian_process.kernels import RBF, ConstantKernel, Matern + +from common import TRUE, line, linear_posterior +from rxmc import Comparison, Constraint, Dataset, KernelTerm, Parameter, Problem +from rxmc import terms as T +from rxmc.predictive import total_predictive_band +from rxmc.reactions import momentum_transfer + +X = np.linspace(0.3, 2.5, 12) + + +def defect_data(seed=0, err=0.05): + """A line with a smooth defect the line cannot follow.""" + rng = np.random.default_rng(seed) + y = TRUE[0] * X + TRUE[1] + 0.3 * np.sin(3 * X) + rng.normal(0, err, len(X)) + return Dataset(X, y, err * np.ones(len(X)), label="d", meta={"k": 2.7}) + + +def test_one_parameter_per_free_hyperparameter_in_log_theta(): + d = defect_data() + comp = Comparison(d, line()) + log_A = Parameter("log_A", prior=stats.norm(0, 2)) + gp = T.kernel( + Matern(1.0, nu=2.5), + on=comp, + coords=lambda x: x / np.pi, + amplitude=T.constant_amplitude, + amplitude_params=(log_A,), + ) + assert isinstance(gp, KernelTerm) + p = Problem([Constraint([comp], terms=[gp])]) + assert p.names == ["m", "b", "discrepancy_length_scale", "log_A"] + # the derived parameter compiles with a uniform prior over sklearn's bounds + np.testing.assert_allclose(p.bounds[2], np.log([1e-5, 1e5])) + theta = np.array([*TRUE, np.log(0.4), np.log(0.2)]) + u = (X / np.pi)[:, None] + K = 0.04 * Matern(0.4, nu=2.5)(u) + 1e-10 * np.eye(12) + np.testing.assert_allclose(p.constraints[0].matrix(theta), np.diag(d.y_err**2) + K) + + +def test_momentum_transfer_coordinates_and_a_running_amplitude(): + d = defect_data() + comp = Comparison(d, line()) + log_A = Parameter("log_A", prior=stats.norm(0, 2)) + r = Parameter("r", prior=stats.norm(0, 1)) + q = lambda x: momentum_transfer(x, d.meta["k"]) # noqa: E731 + gp_q = T.kernel( + RBF(1.0), + on=comp, + coords=q, + amplitude=lambda c, lA, r: np.exp(lA) * c.x ** (r / 2), + amplitude_params=(log_A, r), + ) + p = Problem([Constraint([comp], terms=[gp_q])]) + assert p.names == ["m", "b", "discrepancy_length_scale", "log_A", "r"] + theta = np.array([*TRUE, np.log(2.0), np.log(0.3), 1.5]) + qx = 2 * 2.7 * np.sin(X / 2) + a = 0.3 * qx ** (1.5 / 2) + K = np.outer(a, a) * RBF(2.0)(qx[:, None]) + 1e-10 * np.eye(12) + np.testing.assert_allclose(p.constraints[0].matrix(theta), np.diag(d.y_err**2) + K) + + +def test_the_band_finds_the_kernel_columns_itself(): + d = defect_data() + model = line() + comp = Comparison(d, model) + log_eps = Parameter("log_eps", prior=stats.norm(-3, 1)) + gp = T.kernel(RBF(0.5), on=comp) + p = Problem([Constraint([comp], terms=[T.noise(log_eps), gp])]) + # a chain in problem.names order with the nuisance column between the + # model parameters and the kernel hyperparameter + rng = np.random.default_rng(1) + chain = np.column_stack( + [ + TRUE[0] + 0.02 * rng.standard_normal(30), + TRUE[1] + 0.02 * rng.standard_normal(30), + np.full(30, np.log(0.05)), + np.full(30, np.log(0.5)), + ] + ) + x_fine = np.linspace(0.0, 3.0, 50) + band = total_predictive_band( + p, gp, model.bind(x_fine, d.meta), x_fine, chain, rng=0 + ) + assert band.shape == (2, 50) and np.all(np.isfinite(band)) + assert np.all(band[1] > band[0]) + # inside the data the band is narrow, outside it relaxes to the prior width + inside = (x_fine > 0.5) & (x_fine < 2.3) + assert np.median((band[1] - band[0])[inside]) < np.median( + (band[1] - band[0])[~inside] + ) + + +def test_the_discrepancy_relaxes_the_model_parameters_toward_the_truth(): + d = defect_data() + bare = Problem([Constraint([Comparison(d, line())])]) + gp = T.kernel(ConstantKernel(0.3**2, "fixed") * RBF(0.5, "fixed")) + with_gp = Problem([Constraint([Comparison(d, line())], terms=[gp])]) + # both posteriors are exact (linear model, fixed covariances) + mean_bare, cov_bare, _ = linear_posterior(bare) + mean_gp, cov_gp, _ = linear_posterior(with_gp) + bias_bare = np.abs(mean_bare - TRUE) + bias_gp = np.abs(mean_gp - TRUE) + assert np.all(bias_gp < bias_bare) + # and the bare fit is overconfident: its error bars exclude the truth + assert np.any(bias_bare > 3 * np.sqrt(np.diag(cov_bare))) + assert np.all(bias_gp < 3 * np.sqrt(np.diag(cov_gp))) diff --git a/test/recipes/test_recipe_17_posterior_predictive.py b/test/recipes/test_recipe_17_posterior_predictive.py new file mode 100644 index 0000000..4e796b5 --- /dev/null +++ b/test/recipes/test_recipe_17_posterior_predictive.py @@ -0,0 +1,86 @@ +"""Recipe 17: check the posterior predictive. + +I want to know whether my error model is calibrated: do 68 % intervals +contain 68 % of the points, and how wide are they? +""" + +import numpy as np +import pytest +from scipy import stats + +from common import TRUE, line, line_data, oracle_samples +from rxmc import Comparison, Constraint, Dataset, Parameter, Problem +from rxmc import terms as T +from rxmc import transforms as tf +from rxmc.diagnostics import ( + compare_logz, + coverage_curve, + coverage_error, + logz_summary, + predictive_draws, + sharpness, +) + + +def test_draws_are_ym_plus_correlated_noise_in_comparison_space(): + d = line_data() + log_eps = Parameter("log_eps", prior=stats.norm(-2, 1)) + p = Problem([Constraint([Comparison(d, line())], terms=[T.noise(log_eps)])]) + theta = np.array([*TRUE, np.log(0.2)]) + draws = predictive_draws(p, theta, n_rep=20000, rng=0) + ym = TRUE[0] * d.x + TRUE[1] + np.testing.assert_allclose(draws.mean(0), ym, atol=0.01) + np.testing.assert_allclose( + np.cov(draws.T), p.constraints[0].matrix(theta), atol=0.01 + ) + # model_only: the predictions themselves, no covariance + np.testing.assert_allclose(predictive_draws(p, theta, model_only=True)[0], ym) + + +def big_dataset(err_scale=1.0, seed=3, n=200): + rng = np.random.default_rng(seed) + x = np.linspace(0.5, 2.5, n) + y = TRUE[0] * x + TRUE[1] + rng.normal(0, 0.1, n) + return Dataset(x, y, err_scale * 0.1 * np.ones(n), label="big") + + +def test_coverage_is_nominal_for_the_right_error_model_and_low_for_an_overconfident_one(): + levels = np.array([0.5, 0.68, 0.9]) + d = big_dataset() + p = Problem([Constraint([Comparison(d, line())])]) + s = oracle_samples(p, 400, rng=0) # exact posterior rows stand in for a chain + draws = predictive_draws(p, s, n_rep=4, rng=1) + y_active = p.constraints[0].y[p.constraints[0].active] + np.testing.assert_allclose( + coverage_curve(draws, y_active, levels), levels, atol=0.1 + ) + assert coverage_error(draws, y_active, levels) < 0.1 + # the same data with the errors claimed five times smaller + tight = Problem([Constraint([Comparison(big_dataset(err_scale=0.2), line())])]) + draws_tight = predictive_draws( + tight, oracle_samples(tight, 400, rng=0), n_rep=4, rng=1 + ) + assert np.all(coverage_curve(draws_tight, y_active, levels) < levels - 0.2) + + +def test_sharpness_in_physical_units_for_a_log_fit(): + d = line_data() + comp = Comparison(d, line(), space=tf.log) + log_eps = Parameter("log_eps", prior=stats.norm(-2, 1)) + p = Problem([Constraint([comp], terms=[T.noise(log_eps)], statistical=False)]) + draws = predictive_draws(p, np.array([*TRUE, np.log(0.1)]), n_rep=2000, rng=2) + width_log = sharpness(draws) + width = sharpness(draws, transform=np.exp) + assert np.all(width > 0) and np.all(width_log > 0) + # a constant width in log space is a width proportional to y in physical units + np.testing.assert_allclose(width_log, width_log.mean(), rtol=0.1) + np.testing.assert_allclose( + width / comp.data.y, (width / comp.data.y).mean(), rtol=0.15 + ) + + +def test_evidence_bookkeeping(): + m, e, n = logz_summary([-10.0, -10.4], [0.1, 0.1]) + assert (m, n) == pytest.approx((-10.2, 2)) and e == pytest.approx(0.2) + assert compare_logz((-10.0, 0.5), (-10.4, 0.5))["verdict"] == "tie" + assert compare_logz((-10.0, 0.1), (-12.0, 0.1))["verdict"] == "a" diff --git a/test/recipes/test_recipe_18_evidence_comparison.py b/test/recipes/test_recipe_18_evidence_comparison.py new file mode 100644 index 0000000..62e184e --- /dev/null +++ b/test/recipes/test_recipe_18_evidence_comparison.py @@ -0,0 +1,135 @@ +"""Recipe 18: compare error models by evidence. + +The alpha + Ca study: several error models for data without reported +errors. I want the evidence for each, comparable across comparison spaces. + +The error-model labels below are those of recipe 18's table in +docs/recipes.md; all are covariances of the residual in log space unless +stated: + + L0 constant noise: sigma = err on every point + E0 fractional noise in linear space: sigma_i = err * ym_i + L2y L0 plus a free correlated normalisation mode, sys * ym + Lgp L0 plus a Matern(5/2) Gaussian process in u = theta / pi with a + constant amplitude + L0t L0 under a Student-t likelihood +""" + +import dynesty +import numpy as np +import pytest +from scipy import stats +from sklearn.gaussian_process.kernels import Matern + +from common import TRUE, line, line_data +from rxmc import Comparison, Constraint, Model, Parameter, Problem +from rxmc import terms as T +from rxmc import transforms as tf +from rxmc.diagnostics import compare_logz, logz_summary +from rxmc.likelihood import StudentT + + +def error_models(d, model): + comp_log = Comparison(d, model, space=tf.log) + comp_lin = Comparison(d, model) + log_eps = Parameter("log_eps", prior=stats.norm(-2, 1)) + log_sys = Parameter("log_sys", prior=stats.norm(-3, 1)) + log_A = Parameter("log_A", prior=stats.norm(-2, 1)) + gp = T.kernel( + Matern(0.3, nu=2.5), + on=comp_log, + coords=lambda x: x / np.pi, + amplitude=T.constant_amplitude, + amplitude_params=(log_A,), + ) + nu = Parameter("nu", prior=stats.uniform(1, 30)) + return { + "L0": Constraint([comp_log], terms=[T.noise(log_eps)], statistical=False), + "E0": Constraint( + [comp_lin], terms=[T.noise_fraction(log_eps)], statistical=False + ), + "L2y": Constraint( + [comp_log], + terms=[T.noise(log_eps), T.normalization(log_sys)], + statistical=False, + ), + "Lgp": Constraint([comp_log], terms=[T.noise(log_eps), gp], statistical=False), + "L0t": Constraint( + [comp_log], + terms=[T.noise(log_eps)], + statistical=False, + likelihood=StudentT(nu), + ), + } + + +def test_each_problem_compiles_independently_with_shared_parameter_objects(): + d = line_data() + models = error_models(d, line()) + problems = {name: Problem([c]) for name, c in models.items()} + assert problems["L0"].names == ["m", "b", "log_eps"] + assert problems["E0"].names == ["m", "b", "log_eps"] + assert problems["L2y"].names == ["m", "b", "log_eps", "log_sys"] + assert problems["Lgp"].names == [ + "m", + "b", + "log_eps", + "discrepancy_length_scale", + "log_A", + ] + assert problems["L0t"].names == ["m", "b", "log_eps", "nu"] + # the same log_eps object is one column in each, at the same slot + assert all(p.names.index("log_eps") == 2 for p in problems.values()) + theta = np.array([*TRUE, np.log(0.1)]) + assert np.isfinite(problems["L0"].log_posterior(theta)) + assert np.isfinite(problems["E0"].log_posterior(theta)) + + +def test_log_jacobian_makes_spaces_comparable(): + d = line_data() + models = error_models(d, line()) + p_log, p_lin = Problem([models["L0"]]), Problem([models["E0"]]) + assert p_log.log_jacobian() == pytest.approx(-np.sum(np.log(d.y))) + assert p_lin.log_jacobian() == 0.0 + # a cut applies to the Jacobian too: only active points count + cut = Problem([models["L0"].masked_where(lambda x: x < 1.5)]) + assert cut.log_jacobian() == pytest.approx(-np.sum(np.log(d.y[d.x < 1.5]))) + # the recipe's bookkeeping: add the Jacobian before summarising + fake_logz, fake_err = -12.0, 0.2 + a = logz_summary(fake_logz + p_log.log_jacobian(), fake_err) + b = logz_summary(fake_logz + p_lin.log_jacobian(), fake_err) + assert compare_logz(a, b)["dlogZ"] == pytest.approx(p_log.log_jacobian()) + + +def run_dynesty(p, seed, nlive=100): + ns = dynesty.NestedSampler( + p.log_likelihood, + p.prior_transform, + p.ndim, + nlive=nlive, + rstate=np.random.default_rng(seed), + ) + ns.run_nested(dlogz=0.1, print_progress=False) + return ns.results + + +@pytest.mark.slow +def test_a_normalisation_defect_is_preferred_by_the_model_that_has_one(): + # Data scaled by 30 %. A line with a free intercept would absorb a scaling + # into its parameters, so the model here has a known intercept: only the + # normalisation mode of L2y can explain the defect. + from dataclasses import replace + + d = replace(line_data(err=0.05), y=1.3 * line_data(err=0.05).y) + m = Parameter("m", prior=stats.norm(0, 5)) + slope_only = Model(lambda x, m: m * x + 1.0, [m]) + models = error_models(d, slope_only) + logz = {} + for name in ("L0", "L2y"): + p = Problem([models[name]]) + res = [run_dynesty(p, seed) for seed in (0, 1)] + logz[name] = logz_summary( + [r.logz[-1] + p.log_jacobian() for r in res], [r.logzerr[-1] for r in res] + ) + verdict = compare_logz(logz["L2y"], logz["L0"]) + assert verdict["verdict"] == "a" and verdict["dlogZ"] > 1.5, (logz, verdict) diff --git a/test/recipes/test_recipe_28_stacking.py b/test/recipes/test_recipe_28_stacking.py new file mode 100644 index 0000000..6068dea --- /dev/null +++ b/test/recipes/test_recipe_28_stacking.py @@ -0,0 +1,80 @@ +"""Recipe 28: stacking by leave-one-dataset-out. + +Evidence weights assume the true model is among my candidates. I would +rather weight models by how well they predict each dataset when it is left +out. +""" + +import numpy as np +import pytest +from scipy import stats +from scipy.optimize import minimize +from scipy.special import logsumexp, softmax + +from common import TRUE, line, line_data, line_design, linear_posterior, oracle_samples +from rxmc import Comparison, Constraint, Model, Parameter, Problem +from rxmc.diagnostics import heldout_log_predictive, log_posterior_predictive + +DATA = [line_data(seed=s, label=f"d{s}", err=0.1) for s in range(3)] + + +def constant(): + b = Parameter("b", prior=stats.norm(0, 5)) + return Model(lambda x, b: b * np.ones_like(x), [b]) + + +def constant_design(x): + return np.ones((len(x), 1)) + + +def loo_scores(model, design, rng=0): + """One held-out log score per dataset, with exact posterior samples.""" + comps = [Comparison(d, model) for d in DATA] # one model object, shared + c = Constraint(comps) + out = [] + for i in range(len(comps)): + fit_c = c.masked( + [np.full(comp.data.y.shape, j != i) for j, comp in enumerate(comps)] + ) + fit, held = Problem([fit_c]), Problem([fit_c.complement()]) + s = oracle_samples(fit, 4000, rng=rng, design=design) + out.append(log_posterior_predictive(heldout_log_predictive(held, s))) + return np.array(out) + + +def test_held_out_scores_match_the_closed_form_marginal(): + model = line() + comps = [Comparison(d, model) for d in DATA] + c = Constraint(comps) + fit_c = c.masked([np.full(d.n, j != 2) for j, d in enumerate(DATA)]) + fit, held = Problem([fit_c]), Problem([fit_c.complement()]) + s = oracle_samples(fit, 4000, rng=0) + score = log_posterior_predictive(heldout_log_predictive(held, s)) + # p(y_h | y_fit) = N(X_h mean, X_h cov X_h^T + Sigma_h) for the linear model + mean, cov, _ = linear_posterior(fit) + h = held.constraints[0] + Xh = np.column_stack([h.x[h.active], np.ones(h.n_active)]) + Sh = h.matrix(np.zeros(fit.ndim)) + closed = stats.multivariate_normal(Xh @ mean, Xh @ cov @ Xh.T + Sh).logpdf( + h.y[h.active] + ) + assert score == pytest.approx(closed, abs=0.15) + assert h.n_active == DATA[2].n # the whole third dataset was held out + + +def test_stacking_weights_prefer_the_model_that_predicts(): + S = np.stack( + [loo_scores(line(), line_design), loo_scores(constant(), constant_design)] + ) + assert S.shape == (2, 3) + + def objective(z): # softmax keeps w on the simplex + return -np.sum(logsumexp(np.log(softmax(z))[:, None] + S, axis=0)) + + w = softmax(minimize(objective, np.zeros(2)).x) + assert w.sum() == pytest.approx(1.0) + assert w[0] > 0.95 # the line predicts the held-out datasets; the constant does not + assert np.all(S[0] > S[1]) + assert ( + TRUE[0] != 0.0 + ) # the datasets really do have a slope for the constant to miss diff --git a/test/recipes/test_recipe_30_leave_one_experiment_out.py b/test/recipes/test_recipe_30_leave_one_experiment_out.py new file mode 100644 index 0000000..38ae477 --- /dev/null +++ b/test/recipes/test_recipe_30_leave_one_experiment_out.py @@ -0,0 +1,63 @@ +"""Recipe 30: leave-one-experiment-out prediction. + +I want to know whether the calibrated model, with its discrepancy, predicts +an experiment it was not fit to, and with what tolerance. +""" + +import numpy as np +import pytest +from sklearn.gaussian_process.kernels import RBF, ConstantKernel + +from common import TRUE, line, line_data, oracle_samples +from rxmc import Comparison, Constraint, Problem +from rxmc import terms as T +from rxmc.diagnostics import coverage_curve, heldout_log_predictive, predictive_draws + +DATA = [line_data(seed=s, label=f"d{s}", err=0.1) for s in range(3)] + + +def split(i, terms=()): + model = line() + comps = [Comparison(d, model) for d in DATA] + c = Constraint(comps, terms=[t(comps) for t in terms]) + fit_c = c.masked([np.full(d.n, j != i) for j, d in enumerate(DATA)]) + return Problem([fit_c]), Problem([fit_c.complement()]), Problem([c]) + + +def test_held_out_draws_coverage_and_tolerance(): + for i in range(3): + fit, held, _ = split(i) + s = oracle_samples(fit, 300, rng=i) + draws = predictive_draws(held, s, n_rep=4, rng=i) + h = held.constraints[0] + assert draws.shape == (1200, DATA[i].n) and h.n_active == DATA[i].n + tol = np.percentile(np.abs(draws - draws.mean(0)), 90, axis=0) + assert tol.shape == (DATA[i].n,) and np.all(tol > 0) + cov68 = coverage_curve(draws, h.y[h.active], [0.68])[0] + assert 0.3 <= cov68 <= 1.0 # eight points: coarse, but not empty + # predictions on the held-out experiment centre near the truth + np.testing.assert_allclose( + draws.mean(0), TRUE[0] * DATA[i].x + TRUE[1], atol=0.15 + ) + + +def spanning_gp(comps): + return T.kernel(ConstantKernel(0.1**2, "fixed") * RBF(1.0, "fixed"), on=comps) + + +def test_a_discrepancy_fit_to_the_other_experiments_carries_into_the_prediction(): + fit, held, full = split(2, terms=[spanning_gp]) + theta = np.array(TRUE) + # the marginal held-out block ignores what the fitted experiments taught the GP + marginal = predictive_draws(held, theta, model_only=True)[0] + conditional = predictive_draws(held, theta, model_only=True, given=fit)[0] + np.testing.assert_allclose(marginal, TRUE[0] * DATA[2].x + TRUE[1]) + assert not np.allclose(conditional, marginal) + # the conditional density is the joint divided by the fit, exactly + lp = heldout_log_predictive(held, theta, given=fit)[0] + assert lp == pytest.approx(full.log_likelihood(theta) - fit.log_likelihood(theta)) + # ...which the marginal is not, because the GP spans the split + assert heldout_log_predictive(held, theta)[0] != pytest.approx(lp) + s = oracle_samples(fit, 200, rng=0) + draws = predictive_draws(held, s, n_rep=4, rng=0, given=fit) + assert draws.shape == (800, DATA[2].n) and np.all(np.isfinite(draws)) diff --git a/test/recipes/test_recipe_31_sbc.py b/test/recipes/test_recipe_31_sbc.py new file mode 100644 index 0000000..4ecae0c --- /dev/null +++ b/test/recipes/test_recipe_31_sbc.py @@ -0,0 +1,78 @@ +"""Recipe 31: simulation-based calibration of the sampler. + +Before trusting a chain, I want to check that the sampler recovers +parameters drawn from the prior when the data are simulated from the model. +""" + +import dataclasses + +import emcee +import numpy as np +import pytest +from scipy import stats + +from common import line, line_data, linear_posterior +from rxmc import Comparison, Constraint, Problem +from rxmc.diagnostics import predictive_draws + + +def simulate(problem, comp, theta0, rng): + """A dataset drawn from the model at ``theta0``, back in physical units.""" + y_sim = predictive_draws(problem, theta0[None], n_rep=1, rng=rng)[0] + d_sim = dataclasses.replace(comp.data, y=comp.space.inverse(y_sim)) + return Problem([Constraint([Comparison(d_sim, comp.model)])]) + + +def ranks_from(posterior, n_sims=300, L=20, seed=0): + """``posterior(p_sim, rng) -> (L, ndim)`` draws; one rank per column.""" + rng = np.random.default_rng(seed) + d = line_data() + comp = Comparison(d, line()) + p = Problem([Constraint([comp])]) + ranks = [] + for theta0 in p.sample_prior(n_sims, rng): + p_sim = simulate(p, comp, theta0, rng) + s = posterior(p_sim, rng) + ranks.append((s < theta0).sum(0)) + return np.array(ranks), L + + +def uniform_pvalues(ranks, L): + return np.array( + [ + stats.chisquare(np.bincount(ranks[:, j], minlength=L + 1)).pvalue + for j in range(2) + ] + ) + + +def exact(p_sim, rng, L=20, inflate=1.0): + mean, cov, _ = linear_posterior(p_sim) + return rng.multivariate_normal(mean, inflate * cov, L) + + +def test_exact_posterior_gives_uniform_ranks_and_a_too_wide_one_does_not(): + ranks, L = ranks_from(exact) + assert ranks.shape == (300, 2) and ranks.min() >= 0 and ranks.max() <= L + assert np.all(uniform_pvalues(ranks, L) > 0.01) + # a posterior twice too wide piles the ranks in the middle (inverted U) + wide, _ = ranks_from(lambda p, rng: exact(p, rng, inflate=4.0)) + assert np.all(uniform_pvalues(wide, L) < 0.01) + hist = np.bincount(wide[:, 0], minlength=L + 1) + assert hist[L // 2 - 2 : L // 2 + 3].sum() > hist[:3].sum() + hist[-3:].sum() + + +@pytest.mark.slow +def test_emcee_passes_simulation_based_calibration(): + def chain(p_sim, rng, L=20): + p0 = p_sim.sample_prior(16, rng=rng) + sampler = emcee.EnsembleSampler(16, p_sim.ndim, p_sim.log_posterior) + sampler.random_state = np.random.RandomState( + int(rng.integers(2**31)) + ).get_state() + sampler.run_mcmc(p0, 600, progress=False) + flat = sampler.get_chain(discard=300, thin=15, flat=True) + return flat[rng.choice(len(flat), L, replace=False)] + + ranks, L = ranks_from(chain, n_sims=60, seed=1) + assert np.all(uniform_pvalues(ranks, L) > 0.005), uniform_pvalues(ranks, L) diff --git a/test/test_terms.py b/test/test_terms.py index 237d6ac..3203b5d 100644 --- a/test/test_terms.py +++ b/test/test_terms.py @@ -456,6 +456,8 @@ def test_fields(self): assert t.kernel is k and t.n_kernel == 1 and t.amplitude is constant_amplitude assert [p.name for p in t.params] == ["discrepancy_length_scale", "log_A"] assert t.kind == "matrix" and t.jitter == 1e-10 + # the derived parameter is bounded by the log of sklearn's bounds + np.testing.assert_allclose(t.params[0].bounds, np.log([1e-5, 1e5])) # a fixed kernel has no kernel parameters and is constant fixed = kernel(RBF(1.0, "fixed")) assert fixed.n_kernel == 0 and fixed.is_constant and fixed.params == () From 97e07eb7d4dc8ec476a786bb7502a45565eab934 Mon Sep 17 00:00:00 2001 From: beykyle Date: Thu, 10 Sep 2026 21:47:27 -0400 Subject: [PATCH 32/75] Guard the recipe index: one test file per heading, and the legend too A test under test/ reads docs/recipes.md and fails when a ## NN. heading has no test/recipes/test_recipe_NN_*.py, when a file has no heading, when a file's docstring does not quote its recipe, or when the recipe-18 error-model table and the tests' legend differ. Bare pytest runs it with both suites, so CI needs no separate step; the fast tier now lists its ten slowest tests so the few-minute budget stays visible. --- .github/workflows/ci.yml | 3 +- docs/groundup_design.md | 9 +++-- docs/recipes.md | 5 ++- test/helpers.py | 2 +- test/test_recipes_index.py | 79 ++++++++++++++++++++++++++++++++++++++ 5 files changed, 91 insertions(+), 7 deletions(-) create mode 100644 test/test_recipes_index.py diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 2f733ec..5248918 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -59,7 +59,8 @@ jobs: python -m pip install --upgrade pip setuptools wheel python -m pip install -e '.[validation]' - name: Run the fast tier - run: python -m pytest + # the ten slowest tests are listed so the few-minute budget stays visible + run: python -m pytest --durations=10 docs: if: github.event_name != 'schedule' diff --git a/docs/groundup_design.md b/docs/groundup_design.md index 9a4538c..31e7923 100644 --- a/docs/groundup_design.md +++ b/docs/groundup_design.md @@ -1026,9 +1026,12 @@ recipe test it unlocks pass. (`total_predictive_band` finds the kernel columns itself), 17, 18, 28, 30, 31. 7. **CI wiring.** The heading-to-file check between `recipes.md` and - `test/recipes/`; `pytest test` runs both suites; the fast tier must - finish in a few minutes on a laptop (patched solvers, small `J` and - `n`). + `test/recipes/` is itself a test (`test/test_recipes_index.py`: one + file per heading and vice versa, each file's docstring quoting its + recipe, the recipe-18 legend equal to the tests' legend), so bare + `pytest` runs it with both suites; the fast tier must finish in a few + minutes on a laptop (patched solvers, small `J` and `n`), and CI lists + its ten slowest tests. 8. **Notebooks 1–9.** Each notebook names the recipes it is the tutorial for: `linear_calibration` (1, 17); `error_models` (2, 4, 5, 19); `normalization_and_covariance_structure` (3, 6, 27); diff --git a/docs/recipes.md b/docs/recipes.md index d862a3f..18a27ce 100644 --- a/docs/recipes.md +++ b/docs/recipes.md @@ -465,8 +465,9 @@ is the scattering angle in radians and `u = theta / pi`. | `LKp` | noise, an offset mode, and an RBF Gaussian process in momentum transfer `q = 2 k sin(theta/2)` with amplitude `A q^(r/2)` | | `L0t` | `L0` under a Student-t likelihood | -The test suite builds every row of this table against a hand-built dense -covariance (`test/helpers.py`), so the table and the tests cannot drift. +The test suite builds every covariance row of this table against a +hand-built dense matrix, and a test compares the table above with the +legend the tests carry, so the two cannot drift. Expected behaviour: diff --git a/test/helpers.py b/test/helpers.py index 264584c..8e1b3e5 100644 --- a/test/helpers.py +++ b/test/helpers.py @@ -107,7 +107,7 @@ def index_params(terms): "L2n": "L0 plus a free correlated offset mode: sys * 1", "L2y": "L0 plus a free correlated normalisation mode: sys * ym", "L12": "L1 plus the angle mode of L2", - "Lgp": "L0 plus a Matern(5/2) Gaussian process in u with constant amplitude", + "Lgp": "L0 plus a Matérn(5/2) Gaussian process in u with constant amplitude", "Lgpn": "L0 plus the Gaussian process with an angle-growing amplitude", "LKp": "noise, an offset mode, and an RBF Gaussian process in momentum transfer " "q = 2 k sin(theta/2) with amplitude A q^(r/2)", diff --git a/test/test_recipes_index.py b/test/test_recipes_index.py new file mode 100644 index 0000000..7f40b62 --- /dev/null +++ b/test/test_recipes_index.py @@ -0,0 +1,79 @@ +"""docs/recipes.md and test/recipes/ cannot drift apart. + +Every ``## N. Title`` heading in the recipes document has exactly one test +file ``test/recipes/test_recipe_NN_.py`` whose docstring quotes the +recipe, and the error-model legend of recipe 18 is the one the tests build. +""" + +import pathlib +import re + +import pytest + +from helpers import STUDY_LEGEND + +ROOT = pathlib.Path(__file__).resolve().parents[1] +RECIPES = ROOT / "docs" / "recipes.md" +TEST_DIR = ROOT / "test" / "recipes" + +HEADING = re.compile(r"^## (\d+)\. (.+)$", re.M) +FILE = re.compile(r"^test_recipe_(\d+)_[a-z0-9_]+\.py$") +DOCSTRING = re.compile(r'^"""Recipe (\d+): (.+)$') +LEGEND_ROW = re.compile(r"^\| `(\w+)` \| (.+) \|$", re.M) + + +def _headings() -> dict: + return {int(n): title for n, title in HEADING.findall(RECIPES.read_text())} + + +def _files() -> list: + return sorted(p for p in TEST_DIR.glob("test_recipe_*.py") if FILE.match(p.name)) + + +def _normalise(text: str) -> str: + """Case, backticks, colons, commas, a trailing stop and spacing do not count.""" + text = text.replace("`", "").replace(":", "").replace(",", "") + return " ".join(text.lower().rstrip(".").split()) + + +def test_every_heading_has_one_test_file_and_vice_versa(): + headings = _headings() + numbers = [int(FILE.match(p.name).group(1)) for p in _files()] + duplicates = sorted({n for n in numbers if numbers.count(n) > 1}) + assert not duplicates, f"more than one test file for recipe(s) {duplicates}" + missing = sorted(set(headings) - set(numbers)) + stray = sorted(set(numbers) - set(headings)) + assert ( + not missing + ), f"recipe(s) {missing} in docs/recipes.md have no test/recipes/test_recipe_NN_*.py" + assert not stray, f"test file(s) for recipe(s) {stray} have no ## NN. heading" + + +@pytest.mark.parametrize("path", _files(), ids=lambda p: p.name) +def test_each_test_file_quotes_its_recipe(path): + n = int(FILE.match(path.name).group(1)) + first = path.read_text().splitlines()[0] + m = DOCSTRING.match(first) + assert m, f'{path.name} must open with a docstring """Recipe {n}: ' + assert int(m.group(1)) == n, f"{path.name} quotes recipe {m.group(1)}, not {n}" + heading = _normalise(_headings()[n]) + assert heading.startswith( + _normalise(m.group(2)) + ), f"{path.name} quotes {m.group(2)!r}; the heading is {_headings()[n]!r}" + + +def test_the_error_model_legend_matches_the_recipe_table(): + text = RECIPES.read_text() + start = text.index("## 18. ") + end = text.index("## 19. ") + table = {label: desc for label, desc in LEGEND_ROW.findall(text[start:end])} + assert table, "recipe 18 must carry the error-model legend table" + # L0t is a likelihood choice, not a covariance form the study builder makes; + # "custom" is a test-only spelling of the L1 form + forms = set(table) - {"L0t"} + built = set(STUDY_LEGEND) - {"custom"} + assert forms == built, f"table {sorted(forms)} vs tests {sorted(built)}" + for label in forms: + assert _normalise(table[label]) == _normalise( + STUDY_LEGEND[label] + ), f"{label}: table says {table[label]!r}, tests say {STUDY_LEGEND[label]!r}" From 1fa734ba16336f36febb69cd3a0ed3200f361955 Mon Sep 17 00:00:00 2001 From: beykyle <kylebeyer1@gmail.com> Date: Thu, 10 Sep 2026 23:22:02 -0400 Subject: [PATCH 33/75] Wire the notebooks: gallery, index test, converged workflow; recipe 37 rewritten The converged tier (slow tests and the nbmake run) is its own workflow, required on pushes and pull requests into main, with no schedule; the CI workflow keeps the fast tier on every push. docs/examples.rst lists the nine notebooks and a test checks that each names the recipes it teaches. jitr's alpha + Ca ratio-to-Rutherford data (EXFOR F0567, Oeschler et al. 1972) is copied into examples/data for the study notebook. Recipe 37 no longer shares a normalisation with an analysing power, whose normalisation is fixed by definition. It now recreates Neudecker, Fruehwirth, Kawano and Leeb (Nucl. Data Sheets 118, 364): several quantities of one experiment with correlated normalisations, one spanning matrix term built through c.split; the data-built covariance reproduces the reference's biased closed forms and the estimate-built one its exact values, both as generalised least squares against the oracle. The live prediction-built term is documented as carrying the log-determinant pull. --- .github/workflows/ci.yml | 32 - .github/workflows/converged.yml | 42 + .github/workflows/docs.yml | 2 +- README.md | 3 +- docs/Makefile | 2 +- docs/examples.rst | 40 + docs/groundup_design.md | 11 +- docs/index.rst | 1 + docs/recipes.md | 86 +- examples/data/alpha_ca_ratio_ruth.csv | 1008 +++++++++++++++++ pyproject.toml | 7 +- ...est_recipe_37_correlated_normalisations.py | 229 ++++ ..._recipe_37_cross_observable_systematics.py | 75 -- test/test_notebooks_index.py | 68 ++ 14 files changed, 1468 insertions(+), 138 deletions(-) create mode 100644 .github/workflows/converged.yml create mode 100644 docs/examples.rst create mode 100644 examples/data/alpha_ca_ratio_ruth.csv create mode 100644 test/recipes/test_recipe_37_correlated_normalisations.py delete mode 100644 test/recipes/test_recipe_37_cross_observable_systematics.py create mode 100644 test/test_notebooks_index.py diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 5248918..358333d 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -5,8 +5,6 @@ on: branches: [main, rewrite] pull_request: branches: [main, rewrite] - schedule: - - cron: "0 6 * * *" workflow_dispatch: concurrency: @@ -15,7 +13,6 @@ concurrency: jobs: format: - if: github.event_name != 'schedule' runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -44,7 +41,6 @@ jobs: tests: # the fast tier: unit tests and the sampler-free / short-chain recipe assertions - if: github.event_name != 'schedule' runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -63,7 +59,6 @@ jobs: run: python -m pytest --durations=10 docs: - if: github.event_name != 'schedule' runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -81,30 +76,3 @@ jobs: run: | if [ -d examples ]; then test -L docs/examples || ln -sf ../examples docs/examples; fi sphinx-build -W --keep-going docs docs/_build/html - - slow: - # the converged tier: numeric claims that need a converged chain, and the notebooks - if: github.event_name == 'schedule' || github.event_name == 'workflow_dispatch' - runs-on: ubuntu-latest - timeout-minutes: 120 - steps: - - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - uses: actions/setup-python@v5 - with: - python-version: "3.12" - cache: "pip" - - name: Install validation dependencies - run: | - python -m pip install --upgrade pip setuptools wheel - python -m pip install -e '.[validation]' pytest-xdist - - name: Run the converged tier - run: python -m pytest -m slow - - name: Run the notebooks - run: | - if [ -d examples ] && ls examples/*.ipynb >/dev/null 2>&1; then - python -m pytest -n 4 --nbmake --nbmake-timeout=1200 examples - else - echo "no notebooks yet" - fi diff --git a/.github/workflows/converged.yml b/.github/workflows/converged.yml new file mode 100644 index 0000000..d5609d0 --- /dev/null +++ b/.github/workflows/converged.yml @@ -0,0 +1,42 @@ +name: Converged tier + +# The numeric claims that need a converged sampler, and the example notebooks. +# Required for pushes and pull requests into main; run by hand on any branch +# with workflow_dispatch. There is deliberately no schedule. + +on: + push: + branches: [main] + pull_request: + branches: [main] + workflow_dispatch: + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + converged: + runs-on: ubuntu-latest + timeout-minutes: 180 + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + - uses: actions/setup-python@v5 + with: + python-version: "3.12" + cache: "pip" + - name: Install validation dependencies + run: | + python -m pip install --upgrade pip setuptools wheel + python -m pip install -e '.[validation]' pytest-xdist + - name: Run the converged tier + run: python -m pytest -m slow --durations=10 + - name: Run the notebooks + run: | + if [ -d examples ] && ls examples/*.ipynb >/dev/null 2>&1; then + python -m pytest -n 4 --nbmake --nbmake-timeout=2400 examples + else + echo "no notebooks yet" + fi diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 0848e25..bda7d57 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -33,7 +33,7 @@ jobs: - name: Build HTML docs run: | - test -L docs/examples || ln -sf ../examples docs/examples + if [ -d examples ]; then test -L docs/examples || ln -sf ../examples docs/examples; fi sphinx-build docs docs/_build/html -W --keep-going - name: Upload pages artifact diff --git a/README.md b/README.md index efd983f..e6ed6cd 100644 --- a/README.md +++ b/README.md @@ -32,6 +32,7 @@ Python ≥ 3.12; `jitr >= 3.0` from PyPI. ```bash python -m isort --check-only src test && python -m black --check src test && python -m ruff check src test python -m pytest # fast tier -python -m pytest -m slow # converged tier, run nightly in CI +python -m pytest -m slow # converged tier: required on pushes and PRs to main +python -m pytest -n 4 --nbmake --nbmake-timeout=2400 examples # the notebooks, same workflow sphinx-build -W docs docs/_build/html ``` diff --git a/docs/Makefile b/docs/Makefile index b5d3962..99002fc 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -8,7 +8,7 @@ help: @$(SPHINXBUILD) -M help $(SOURCEDIR) $(BUILDDIR) $(SPHINXOPTS) $(O) html: - test -L examples || ln -sf ../examples examples + if [ -d ../examples ]; then test -L examples || ln -sf ../examples examples; fi $(SPHINXBUILD) -b html $(SOURCEDIR) $(BUILDDIR)/html $(SPHINXOPTS) $(O) @echo @echo "Build finished. Open $(BUILDDIR)/html/index.html to view." diff --git a/docs/examples.rst b/docs/examples.rst new file mode 100644 index 0000000..a346fe4 --- /dev/null +++ b/docs/examples.rst @@ -0,0 +1,40 @@ +Examples +======== + +The notebooks below are the tutorials for the recipes in :doc:`recipes`; +each names the recipes it teaches in its first cell. They are rendered with +their committed outputs and re-executed by the converged-tier CI workflow. +To run them yourself, install the example dependencies first:: + + pip install -e '.[examples]' + jupyter lab examples + +Calibration basics +------------------ + +.. toctree:: + :maxdepth: 1 + + examples/linear_calibration.ipynb + examples/error_models.ipynb + examples/normalization_and_covariance_structure.ipynb + examples/correlated_observations.ipynb + +Beyond the Gaussian +------------------- + +.. toctree:: + :maxdepth: 1 + + examples/gp_discrepancy.ipynb + examples/robust_likelihoods.ipynb + +Reactions and studies +--------------------- + +.. toctree:: + :maxdepth: 1 + + examples/measurement_to_calibration.ipynb + examples/alpha_ca_error_model_comparison.ipynb + examples/hierarchical_calibration.ipynb diff --git a/docs/groundup_design.md b/docs/groundup_design.md index 31e7923..6b6a1b0 100644 --- a/docs/groundup_design.md +++ b/docs/groundup_design.md @@ -722,7 +722,7 @@ rewrite: a capability is done when its row has a test. | global error scale and USU modes (new, reference) | `diag` term scaling `c.meta("y_err")` with `statistical=False`; `offset(parameter=, on=blocks_of_technique)`; recipe 34 | test_terms | | energy-dependent parameters (new, reference) | per-block `Model` instances closing over `meta`, shared coefficient objects; recipe 35 | test_model | | discrepancy on a physical basis, Legendre (new, reference) | `systematic` modes or `omp + Model(basis_sum)`; recipe 36 | test_terms | -| correlated systematics between observables of one measurement (new, reference) | two blocks, one constraint, spanning mode; recipe 37 | test_covariance | +| correlated normalisations between quantities of one experiment, Peelle's puzzle in more than one dimension (new, reference) | one comparison per quantity, one constraint, a spanning `matrix` term built from `c.split(c.ym)`; recipe 37 | test_covariance | | classic normal hierarchical model, BDA3 ch. 5 (new, reference) | marginalised as `noise(log_tau)`, non-centred as a `Model` over `[mu, log_tau, *etas]`, centred as a joint block; recipe 38 | test_problem (marginalised and non-centred agree on `mu, tau`; a parameter on a fully masked block is sampled from its prior) | | SafeBayes: learn the tempering exponent (new, reference) | driver loop over `replace(c, weight=η)` and `c.masked(prefix)`; next-point density as a log-likelihood difference; recipe 25 | test_problem (`replace` keeps names; `ll(prefix i+1) − ll(prefix i)` equals the Gaussian conditional) | | hyperprior: per-dataset parameters with a sampled spread (new) | joint block `(children + [hyper], obj)` with `logpdf` and `prior_transform` | test_problem (children uncovered without the block; `prior_transform` round trip) | @@ -977,7 +977,8 @@ recipe test it unlocks pass. the harvest. Then: a `pyproject` in the current shape with the `slow` marker registered and deselected by default; ruff, black and isort configuration carried over; the CI workflow (fast tier on pull - requests, `pytest -m slow` and nbmake on a schedule); a trusted- + requests, `pytest -m slow` and nbmake in the converged workflow that + gates `main`); a trusted- publishing workflow that uploads to PyPI on tag push. 1. **`params`, `transforms`, `units`, `likelihood`, `terms`** (verbatim harvest plus the stateless `Term`). Ported: `TestTermKinds`, @@ -1069,8 +1070,10 @@ preference for assertions that need no sampler at all. (coverage within 0.05 of nominal, `τ` recovered within its interval, evidence differences), record the seed, R-hat and effective sample size so a failure is diagnosable, and run the notebooks through nbmake. - They are deselected by default; pull-request CI runs the fast tier and a - scheduled job runs `pytest -m slow`. + They are deselected by default; every push runs the fast tier, and a + separate "Converged tier" workflow runs `pytest -m slow` and the + notebooks on pushes and pull requests into `main` (and by hand with + `workflow_dispatch`). There is no scheduled run. - **Rules.** The fast tier has a budget of a few minutes and zero tolerated flakiness. A flaky fast assertion is demoted to `slow`, never loosened until it passes. Every recipe test file has at least one fast diff --git a/docs/index.rst b/docs/index.rst index 7827848..c293ec3 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -18,3 +18,4 @@ a test. The 0.x package is preserved at tag ``v0.1.0`` and on branch installation groundup_design recipes + examples diff --git a/docs/recipes.md b/docs/recipes.md index 18a27ce..2c73c48 100644 --- a/docs/recipes.md +++ b/docs/recipes.md @@ -1086,36 +1086,80 @@ Expected behaviour: Reference: Higdon, Gattiker, Williams, Rightley, J. Am. Stat. Assoc. 103, 570 (2008). -## 37. Correlated systematics between observables of one measurement +## 37. Correlated normalisations between quantities of one experiment -*One experiment reports both a cross section and an analysing power, and -they share a normalisation or an angle calibration.* +*One experiment reports several physical quantities, each measured one or +more times, all multiplied by normalisations that were themselves measured +with correlated uncertainties. I want the covariance across the quantities +built so that it does not bias the evaluation.* + +This is the two-and-more-dimensional Peelle's Pertinent Puzzle of Neudecker, +Frühwirth, Kawano and Leeb (reference below). Quantity `i` is +`rho_i = alpha_i * eta_i`; `alpha_i` is measured as `q_i` (once or several +times, independent errors `sigma_i`) and `eta_i` as `N_i`, the `N_i` sharing +a covariance `B` with correlation `c`. The reported data are the products +`r_i = q_i N_i`. ```python -comp_xs = rx.Comparison(d_xs, rx.reactions.ElasticXS("dXS/dA", *pot, params=p)) -comp_ay = rx.Comparison(d_ay, rx.reactions.ElasticXS("Ay", *pot, params=p)) -log_eta = rx.Parameter("log_eta", prior=stats.norm(-3, 1)) -c = rx.Constraint([comp_xs, comp_ay], terms=[T.normalization(log_eta, on=comp_xs), # the ratio is unaffected - T.systematic(log_dtheta, basis=dydtheta, on=[comp_xs, comp_ay])]) +# one comparison per quantity; the model is the quantity itself +rhos = [rx.Parameter(f"rho_{i}", prior=stats.norm(r_i.mean(), 10.0)) for i in range(n)] +comps = [rx.Comparison(rx.Dataset(np.full(len(r_i), i), r_i, N_i * sigma_i, label=f"q{i}"), + rx.Model(lambda x, rho: np.full(len(x), rho), [rho_i])) + for i, (r_i, rho_i) in enumerate(zip(products, rhos))] +frac = sigma_N / N # fractional normalisation errors +corr = np.array([[1, c], [c, 1]]) # the correlation matrix of the N_i + +def normalisations(c): # C_I: built from the prediction + u = np.concatenate([f * ym for f, ym in zip(frac, c.split(c.ym))]) + which = np.concatenate([np.full(s.stop - s.start, k) for k, s in enumerate(c.segments)]) + return np.outer(u, u) * corr[np.ix_(which, which)] + +c_I = rx.Constraint(comps, terms=[rx.Term(normalisations, kind="matrix", on=comps)]) +c_F = rx.Constraint(comps, terms=[rx.Term(normalisations_from(y), kind="matrix", on=comps)]) # Peelle: from the data ``` Expected behaviour: -- Two datasets, two comparisons, one constraint; the shared systematic is a - mode spanning both comparisons (case A of recipe 5). -- A normalisation error affects the cross section and not a ratio - observable; an angle-calibration error affects both through their - angular derivatives, which the basis supplies from `c.ym` and `c.x`. -- A spanning term sees the *gathered* stack, so a basis that differentiates - along the grid must not straddle the seam between comparisons: it takes - the derivative within each of `c.segments` (`c.split(c.ym)` cuts a - support-length array per comparison; `c.labels` names them). -- The multi-quantity extension of the Peelle treatment applies: build the - mode from predictions, not data. +- One `Constraint`, one `matrix` term spanning every comparison. A + spanning term sees the gathered stack, so the term reads its per-quantity + pieces through `c.segments` / `c.split` and pairs them with the + normalisation correlation matrix. +- With the covariance built from the *data* (`C_F`, eq. 11 of the + reference) the posterior mean under a flat prior is the generalised + least-squares solution and is biased low: `<rho_1>_F = qbar_1 N_1 / (1 + + xi)` with `xi = (q_1 - q_1')^2 sigma_N1^2 var(alpha_1) / (N_1^2 sigma_1^2 + sigma_1'^2)`, and `<rho_2>_F` is pulled down through `c` even though + `alpha_2` was measured once; the variances and the covariance are + deflated in their normalisation parts (eqs. 13-17). The fast tier pins + these closed forms exactly. +- With the covariance built from the *estimate* (`C_I`, eq. 12: the + weighted means, in rxmc a constant term built from a first estimate and + refit, recipe 27) the means are `qbar_i N_i` and the variances + `var(alpha_i) N_i^2 + sigma_Ni^2 qbar_i^2`, with covariance + `c qbar_1 qbar_2 sigma_N1 sigma_N2` (eqs. 18-22): no puzzle. +- The *live* term reading `c.ym` is the generative model's marginal + likelihood, not `C_I`: its covariance grows with the prediction, so the + log-determinant pulls the mode below the exact values (5 % in the + two-quantity case, 9 % in the five-quantity one, against 22 % for + `C_F`), and under a flat prior the `1 / rho` tail pulls the mean above + them. A proper prior on the quantities, or the refit, removes the pull. +- The five-quantity numerical study of the reference (its Table I: `q_i` + = {1.0, 1.5}, {1.8}, {2.2, 2.4}, {1.9, 1.5}, {1.4, 1.2}; `N_i` = 1, 1.1, + 1.25, 1.15, 1.05; `sigma_i = 0.1 q_i`, `sigma_Ni = 0.2 N_i`, `c = 0.8`) + reproduces its Fig. 1: `C_F` gives lower means and smaller standard + deviations on every lattice point, `C_I` agrees with the exact values + (both exact in the fast tier, being generalised least squares). The + notebook `correlated_observations` recreates the figure. +- Analysing powers are *not* an instance of this recipe: a ratio of cross + sections has a fixed normalisation, so nothing correlated can be inferred + for it. The real-data case of the reference (`237Np(n,f)` measured + relative to `235U(n,f)` by three experiments, converted with the standard + and its covariance) has the same structure with the standard's covariance + as `B`. Reference: Neudecker, Frühwirth, Kawano, Leeb, *Adequate treatment of -correlated experimental data in nuclear data evaluations*, Nucl. Data Sheets -118, 364 (2014). +correlated experimental data in nuclear data evaluations avoiding Peelle's +Pertinent Puzzle*, Nucl. Data Sheets 118, 364 (2014). ## 38. 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The converged tier is marked -# `slow` and runs on a schedule with `pytest -m slow`; the notebooks with -# `pytest --nbmake examples`. See docs/groundup_design.md section 9. +# `slow` and runs with `pytest -m slow`, together with the notebooks +# (`pytest --nbmake examples`), in the "Converged tier" workflow required on +# pushes and pull requests to main. See docs/groundup_design.md section 9. testpaths = ["test"] addopts = "-m 'not slow'" markers = [ - "slow: converged-chain tier; deselected by default, run nightly with -m slow", + "slow: converged-chain tier; deselected by default, run with -m slow on pushes and PRs to main", ] diff --git a/test/recipes/test_recipe_37_correlated_normalisations.py b/test/recipes/test_recipe_37_correlated_normalisations.py new file mode 100644 index 0000000..f6ae795 --- /dev/null +++ b/test/recipes/test_recipe_37_correlated_normalisations.py @@ -0,0 +1,229 @@ +"""Recipe 37: correlated normalisations between quantities of one experiment. + +One experiment reports several physical quantities, each measured one or +more times, all multiplied by normalisations that were themselves measured +with correlated uncertainties. I want the covariance across the quantities +built so that it does not bias the evaluation. + +The analytic study (section II.A) and the numerical study (section II.B) of +Neudecker, Frühwirth, Kawano and Leeb, Nucl. Data Sheets 118, 364 (2014) are +recreated: quantity i is rho_i = alpha_i * eta_i, alpha_i measured as q_i +(once or several times, independent errors sigma_i), eta_i as N_i with a +correlated covariance B; the data are the products r_i = q_i N_i. C_F is +the covariance built from the measured q (Peelle), C_I the one built from +the weighted means / the prediction. +""" + +import numpy as np +from scipy import stats + +from common import linear_posterior, map_estimate +from rxmc import Comparison, Constraint, Dataset, Model, Parameter, Problem, Term + + +def experiment(q, N, sigma_q, sigma_N, corr, prior=None): + """Comparisons for the quantities with the products ``r = q N`` as data. + + The default prior is normal with a standard deviation of 1000: flat for the + closed-form (fixed-covariance) posteriors, which the oracle needs to be + Gaussian. A prediction-built covariance has a posterior tail falling only + like 1 / rho, so the sampled cases pass a bounded uniform prior instead. + """ + comps, rhos = [], [] + for i, (qi, Ni, si) in enumerate(zip(q, N, sigma_q)): + qi, si = np.asarray(qi, dtype=float), np.asarray(si, dtype=float) + rho = Parameter(f"rho_{i}", prior=prior or stats.norm(0.0, 1e3)) + rhos.append(rho) + d = Dataset(np.full(len(qi), i), qi * Ni, Ni * si, label=f"q{i}") + comps.append(Comparison(d, Model(lambda x, r: np.full(len(x), r), [rho]))) + return comps, rhos + + +def normalisation_term(comps, frac, corr, from_data=False): + """``outer(f * ym, f * ym) * corr`` across the quantities (C_I), or from y (C_F).""" + corr = np.asarray(corr, dtype=float) + + def fn(c): + pieces = c.split(c.y if from_data else c.ym) + u = np.concatenate([f * piece for f, piece in zip(frac, pieces)]) + which = np.concatenate( + [np.full(s.stop - s.start, k) for k, s in enumerate(c.segments)] + ) + return np.outer(u, u) * corr[np.ix_(which, which)] + + return Term(fn, kind="matrix", on=comps, constant=from_data) + + +def one_hot(x): + return np.eye(int(x.max()) + 1)[x.astype(int)] + + +def exact(q, N, sigma_q, sigma_N, corr): + """Means, variances and covariances from the full information (eqs. 2-8).""" + qbar, var_a = [], [] + for qi, si in zip(q, sigma_q): + w = 1.0 / np.asarray(si, dtype=float) ** 2 + qbar.append(np.sum(w * qi) / np.sum(w)) + var_a.append(1.0 / np.sum(w)) + qbar, var_a, N, sN = map(np.asarray, (qbar, var_a, N, sigma_N)) + mean = qbar * N + cov = np.outer(qbar * sN, qbar * sN) * np.asarray(corr) + cov[np.diag_indices_from(cov)] += var_a * N**2 + return mean, cov, qbar, var_a + + +# section II.A: two quantities, the first measured twice +Q = [np.array([1.0, 1.5]), np.array([1.8])] +N = np.array([1.0, 1.1]) +SIGMA_Q = [0.1 * Q[0], 0.1 * Q[1]] +SIGMA_N = 0.2 * N +C = 0.8 +CORR = np.array([[1.0, C], [C, 1.0]]) + + +def test_the_prediction_built_covariance_is_the_analytic_solution(): + comps, rhos = experiment(Q, N, SIGMA_Q, SIGMA_N, CORR) + mean, cov, qbar, var_a = exact(Q, N, SIGMA_Q, SIGMA_N, CORR) + # C_I in its fixed form: the weighted means stand in for the prediction + fixed = Term( + _fixed_matrix(qbar * N, SIGMA_N / N, CORR, [len(q) for q in Q]), + kind="matrix", + on=comps, + ) + p_I = Problem([Constraint(comps, terms=[fixed])]) + mean_I, cov_I, _ = linear_posterior(p_I, design=one_hot) + np.testing.assert_allclose(mean_I, mean, rtol=1e-6) # eqs. 18, 19 + np.testing.assert_allclose(cov_I, cov, rtol=1e-6) # eqs. 20-22 + # the live term reads the prediction: at the exact means it is that matrix + live = normalisation_term(comps, SIGMA_N / N, CORR) + p_live = Problem([Constraint(comps, terms=[live])]) + S = p_live.constraints[0].matrix(mean) + np.testing.assert_allclose(S, p_I.constraints[0].matrix(mean)) + + +def _fixed_matrix(rho, frac, corr, counts): + u = np.concatenate([np.full(n, f * r) for n, f, r in zip(counts, frac, rho)]) + which = np.concatenate([np.full(n, k) for k, n in enumerate(counts)]) + return np.outer(u, u) * np.asarray(corr)[np.ix_(which, which)] + + +def test_the_data_built_covariance_is_peelles_puzzle_in_two_dimensions(): + comps, rhos = experiment(Q, N, SIGMA_Q, SIGMA_N, CORR) + mean, cov, qbar, var_a = exact(Q, N, SIGMA_Q, SIGMA_N, CORR) + p_F = Problem( + [ + Constraint( + comps, + terms=[normalisation_term(comps, SIGMA_N / N, CORR, from_data=True)], + ) + ] + ) + mean_F, cov_F, _ = linear_posterior(p_F, design=one_hot) + q1, q1p = Q[0] + s1, s1p = SIGMA_Q[0] + xi = (q1 - q1p) ** 2 * SIGMA_N[0] ** 2 * var_a[0] / (N[0] ** 2 * s1**2 * s1p**2) + # eqs. 13-17 of the reference + np.testing.assert_allclose(mean_F[0], qbar[0] * N[0] / (1 + xi), rtol=1e-6) + np.testing.assert_allclose( + mean_F[1], + Q[1][0] + * N[1] + * (1 - C * xi * N[0] * SIGMA_N[1] / (N[1] * SIGMA_N[0]) / (1 + xi)), + rtol=1e-6, + ) + # eq. 15: only the normalisation part of var(rho_1) is deflated + np.testing.assert_allclose( + cov_F[0, 0], + var_a[0] * N[0] ** 2 + SIGMA_N[0] ** 2 * qbar[0] ** 2 / (1 + xi), + rtol=1e-6, + ) + np.testing.assert_allclose( + cov_F[1, 1], + cov[1, 1] - C**2 * xi * Q[1][0] ** 2 * SIGMA_N[1] ** 2 / (1 + xi), + rtol=1e-6, + ) + np.testing.assert_allclose(cov_F[0, 1], cov[0, 1] / (1 + xi), rtol=1e-6) + # the puzzle: both means are biased low, both variances too small + assert xi > 0 and np.all(mean_F < mean) and np.all(np.diag(cov_F) < np.diag(cov)) + + +# section II.B: the five-quantity numerical study, Table I of the reference +Q5 = [ + np.array([1.0, 1.5]), + np.array([1.8]), + np.array([2.2, 2.4]), + np.array([1.9, 1.5]), + np.array([1.4, 1.2]), +] +N5 = np.array([1.0, 1.1, 1.25, 1.15, 1.05]) +SIGMA_Q5 = [0.1 * q for q in Q5] +SIGMA_N5 = 0.2 * N5 +CORR5 = np.full((5, 5), C) + (1 - C) * np.eye(5) + + +def test_five_quantities_reproduce_figure_1_in_ordering(): + comps, rhos = experiment(Q5, N5, SIGMA_Q5, SIGMA_N5, CORR5) + mean, cov, qbar, var_a = exact(Q5, N5, SIGMA_Q5, SIGMA_N5, CORR5) + p_F = Problem( + [ + Constraint( + comps, + terms=[normalisation_term(comps, SIGMA_N5 / N5, CORR5, from_data=True)], + ) + ] + ) + mean_F, cov_F, _ = linear_posterior(p_F, design=one_hot) + assert np.all(mean_F < mean) and np.all(np.diag(cov_F) < np.diag(cov)) + fixed = Term( + _fixed_matrix(mean, SIGMA_N5 / N5, CORR5, [len(q) for q in Q5]), + kind="matrix", + on=comps, + ) + mean_I, cov_I, _ = linear_posterior( + Problem([Constraint(comps, terms=[fixed])]), design=one_hot + ) + np.testing.assert_allclose(mean_I, mean, rtol=1e-6) + np.testing.assert_allclose(np.diag(cov_I), np.diag(cov), rtol=1e-6) + + +def test_the_live_prediction_built_term_carries_the_log_determinant_pull(): + """The live term is the generative model's marginal likelihood, not C_I. + + Its mode is pulled below the exact values by the log-determinant of a + covariance that grows with the prediction (5 % in the two-quantity case, + 9 % here with 20 % normalisation errors), though far less than the + data-built C_F; under a flat prior its mean is pulled the other way by + the 1 / rho tail. The two-step refit (a constant term from a first + estimate, recipe 27) is what reproduces the reference exactly. + """ + comps, rhos = experiment( + Q5, N5, SIGMA_Q5, SIGMA_N5, CORR5, prior=stats.uniform(0, 10) + ) + mean, cov, *_ = exact(Q5, N5, SIGMA_Q5, SIGMA_N5, CORR5) + p_I = Problem( + [Constraint(comps, terms=[normalisation_term(comps, SIGMA_N5 / N5, CORR5)])] + ) + mode = map_estimate(p_I, mean) + p_F = Problem( + [ + Constraint( + comps, + terms=[normalisation_term(comps, SIGMA_N5 / N5, CORR5, from_data=True)], + ) + ] + ) + mean_F, *_ = linear_posterior(p_F, design=one_hot) + pull = mode / mean - 1 + assert np.all(pull < 0) and np.all(pull > -0.12) + assert np.all(np.abs(mode - mean) < np.abs(mean_F - mean)) + # the refit: a constant term built at the first estimate is exact again + comps_flat, _ = experiment(Q5, N5, SIGMA_Q5, SIGMA_N5, CORR5) + fixed = Term( + _fixed_matrix(mean, SIGMA_N5 / N5, CORR5, [len(q) for q in Q5]), + kind="matrix", + on=comps_flat, + ) + mean_I, *_ = linear_posterior( + Problem([Constraint(comps_flat, terms=[fixed])]), design=one_hot + ) + np.testing.assert_allclose(mean_I, mean, rtol=1e-6) diff --git a/test/recipes/test_recipe_37_cross_observable_systematics.py b/test/recipes/test_recipe_37_cross_observable_systematics.py deleted file mode 100644 index 2da0564..0000000 --- a/test/recipes/test_recipe_37_cross_observable_systematics.py +++ /dev/null @@ -1,75 +0,0 @@ -"""Recipe 37: correlated systematics between observables of one measurement. - -One experiment reports both a cross section and an analysing power, and they -share a normalisation or an angle calibration. -""" - -import jitr -import numpy as np -from jitr.optical_potentials.potential_forms import thomas_safe, woods_saxon_safe -from scipy import stats - -from rxmc import Comparison, Constraint, Dataset, Parameter, Problem -from rxmc import terms as T -from rxmc.reactions import ElasticXS - -MSO = 1.0 / jitr.utils.constants.WAVENUMBER_PION -R = 1.2 * 40 ** (1 / 3) -N_CA = jitr.reactions.ElasticReaction(target=(40, 20), projectile=(1, 0)) - - -def central(r, Vv, Wv, Rv, av): - return -(Vv + 1j * Wv) * woods_saxon_safe(r, Rv, av) - - -def spin_orbit(r, Vso, Rso, aso): - return Vso * MSO**2 * thomas_safe(r, Rso, aso) - - -def test_two_observables_one_constraint_one_spanning_mode(): - params = [Parameter(n, prior=stats.norm(0, 100)) for n in ("Vv", "Wv", "Rv", "av")] - pot = (central, spin_orbit, lambda ws, *x: (tuple(x), (6.0, R, 0.45))) - xs_model = ElasticXS("dXS/dA", *pot, params, lmax=10) - ay_model = ElasticXS("Ay", *pot, params, lmax=10) - theta = np.array([48.0, 3.5, R, 0.7]) - x = np.linspace(0.3, 2.5, 4) - kin = {"reaction": N_CA, "Elab": 14.1} - m_xs, m_ay = {**kin, "quantity": "dXS/dA"}, {**kin, "quantity": "Ay"} - d_xs = Dataset( - x, xs_model.bind(x, kin)(*theta), 0.01 * np.ones(4), label="xs", meta=m_xs - ) - d_ay = Dataset( - x, ay_model.bind(x, kin)(*theta), 0.02 * np.ones(4), label="ay", meta=m_ay - ) - comp_xs, comp_ay = Comparison(d_xs, xs_model), Comparison(d_ay, ay_model) - log_eta = Parameter("log_eta", prior=stats.norm(-3, 1)) - log_dtheta = Parameter("log_dtheta", prior=stats.norm(-4, 1)) - - def dy_dtheta(c): - # angle-calibration mode: the slope of each prediction in angle. A - # spanning term sees the gathered stack, so the finite difference is - # taken within each comparison's segment, not across the seam. - assert c.labels == ("xs", "ay") - return np.concatenate( - [np.gradient(ym, x) for x, ym in zip(c.split(c.x), c.split(c.ym))] - ) - - c = Constraint( - [comp_xs, comp_ay], - terms=[ - T.normalization(log_eta, on=comp_xs), # the ratio observable is unaffected - T.systematic(log_dtheta, basis=dy_dtheta, on=[comp_xs, comp_ay]), - ], - ) - p = Problem([c]) - assert p.names == ["Vv", "Wv", "Rv", "av", "log_eta", "log_dtheta"] - full = np.array([*theta, np.log(0.05), np.log(0.01)]) - S = p.constraints[0].matrix(full) - ym_xs, ym_ay = p.predict(full)[0] - u = 0.01 * np.concatenate([np.gradient(ym_xs, x), np.gradient(ym_ay, x)]) - expected = np.diag(np.concatenate([d_xs.y_err, d_ay.y_err]) ** 2) + np.outer(u, u) - expected[:4, :4] += 0.05**2 * np.outer(ym_xs, ym_xs) - np.testing.assert_allclose(S, expected) - assert np.any(S[:4, 4:] != 0.0) # the angle mode couples the two observables - assert not p.constraints[0].covariance.dense - assert p.chi2(full) == 0.0 diff --git a/test/test_notebooks_index.py b/test/test_notebooks_index.py new file mode 100644 index 0000000..ebd5c57 --- /dev/null +++ b/test/test_notebooks_index.py @@ -0,0 +1,68 @@ +"""The example notebooks name the recipes they teach, and all nine exist. + +Design document section 9 item 8: a notebook must cite at least one recipe; +its first cell carries ``Recipes: N, M, ...`` and every number is a heading +of ``docs/recipes.md``. Nothing here executes a notebook. +""" + +import json +import pathlib +import re + +import pytest + +ROOT = pathlib.Path(__file__).resolve().parents[1] +EXAMPLES = ROOT / "examples" +RECIPES = ROOT / "docs" / "recipes.md" + +# design document section 9, item 8: the nine notebooks and their recipes +NOTEBOOKS = { + "linear_calibration": {1, 17}, + "error_models": {2, 4, 5, 19}, + "normalization_and_covariance_structure": {3, 6, 27}, + "correlated_observations": {5, 37}, + "gp_discrepancy": {7, 8, 36}, + "robust_likelihoods": {9, 34}, + "measurement_to_calibration": {12, 14, 15, 16, 21, 26}, + "alpha_ca_error_model_comparison": {10, 11, 13, 18}, + "hierarchical_calibration": {22, 24, 30, 35, 38}, +} + +RECIPE_LINE = re.compile(r"^Recipes?:\s*(.+)$", re.M) + + +def _headings() -> set: + return {int(n) for n in re.findall(r"^## (\d+)\. ", RECIPES.read_text(), re.M)} + + +def _present() -> list: + return sorted(p.stem for p in EXAMPLES.glob("*.ipynb")) if EXAMPLES.is_dir() else [] + + +def _cited(name) -> set: + nb = json.loads((EXAMPLES / f"{name}.ipynb").read_text()) + first = nb["cells"][0] + assert first["cell_type"] == "markdown", f"{name}: the first cell must be markdown" + m = RECIPE_LINE.search("".join(first["source"])) + assert m, f"{name}: the first cell needs a line 'Recipes: N, M, ...'" + return {int(n) for n in re.findall(r"\d+", m.group(1))} + + +@pytest.mark.parametrize("name", sorted(NOTEBOOKS)) +def test_each_notebook_exists_and_cites_its_recipes(name): + present = _present() + if name not in present: + pytest.xfail(f"examples/{name}.ipynb is not written yet") + cited = _cited(name) + assert cited, f"{name} cites no recipe" + missing = cited - _headings() + assert not missing, f"{name} cites recipe(s) {sorted(missing)} with no heading" + assert cited == NOTEBOOKS[name], ( + f"{name} cites {sorted(cited)}; the design's section 9 says " + f"{sorted(NOTEBOOKS[name])} (update both or neither)" + ) + + +def test_no_stray_notebooks(): + stray = sorted(set(_present()) - set(NOTEBOOKS)) + assert not stray, f"notebooks not in the design's list: {stray}" From 9a699cb251245a07ffca3ccd5aa146c9b9334498 Mon Sep 17 00:00:00 2001 From: beykyle <kylebeyer1@gmail.com> Date: Thu, 10 Sep 2026 23:25:06 -0400 Subject: [PATCH 34/75] Add the linear_calibration notebook (recipes 1, 17) Declare a line with priors, a dataset and a comparison; compile; the prior predictive; chi2 equal to the hand-written sum; emcee read back by column name; the posterior predictive on a wider grid through bind; and the coverage curve as the first calibration check. Runs in 23 s. --- examples/linear_calibration.ipynb | 514 ++++++++++++++++++++++++++++++ 1 file changed, 514 insertions(+) create mode 100644 examples/linear_calibration.ipynb diff --git a/examples/linear_calibration.ipynb b/examples/linear_calibration.ipynb new file mode 100644 index 0000000..da0435c --- /dev/null +++ b/examples/linear_calibration.ipynb @@ -0,0 +1,514 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d967d547", + "metadata": {}, + "source": [ + "# Calibration of a line\n", + "\n", + "The whole `rxmc` workflow on the smallest possible problem: declare a model\n", + "with parameters and priors, a dataset, and a comparison between them; compile\n", + "the problem; hand it to an external sampler; read the chain back by name; and\n", + "check the posterior predictive against the data.\n", + "\n", + "Recipes: 1, 17" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "20d19117", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:24:16.412632Z", + "iopub.status.busy": "2026-09-11T03:24:16.412492Z", + "iopub.status.idle": "2026-09-11T03:24:18.363411Z", + "shell.execute_reply": "2026-09-11T03:24:18.362669Z" + } + }, + "outputs": [], + "source": [ + "import corner\n", + "import emcee\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from scipy import stats\n", + "\n", + "import rxmc as rx" + ] + }, + { + "cell_type": "markdown", + "id": "11288688", + "metadata": {}, + "source": [ + "## Parameters and a model\n", + "\n", + "A `Parameter` is a name with a prior (or finite bounds). A `Model` is a\n", + "callable of the grid `x` and the parameter values, in order, with its\n", + "parameters listed. Here the prior is deliberately at odds with the data we\n", + "are about to generate: it encodes what we think we know before looking." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "01d9e814", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:24:18.364853Z", + "iopub.status.busy": "2026-09-11T03:24:18.364647Z", + "iopub.status.idle": "2026-09-11T03:24:18.370365Z", + "shell.execute_reply": "2026-09-11T03:24:18.369794Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Model(params=(m, b))" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = rx.Parameter(\"m\", prior=stats.norm(1.0, 1.0), latex=\"m\")\n", + "b = rx.Parameter(\"b\", prior=stats.norm(1.0, 1.0), latex=\"b\")\n", + "line = rx.Model(lambda x, m, b: m * x + b, [m, b])\n", + "line" + ] + }, + { + "cell_type": "markdown", + "id": "ba75b067", + "metadata": {}, + "source": [ + "## Data\n", + "\n", + "Twenty points on a line with independent Gaussian noise of known size, which the\n", + "experiment reports honestly as `y_err`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3ed3f8c7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:24:18.371631Z", + "iopub.status.busy": "2026-09-11T03:24:18.371483Z", + "iopub.status.idle": "2026-09-11T03:24:18.488345Z", + "shell.execute_reply": "2026-09-11T03:24:18.487659Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 500x350 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.default_rng(49)\n", + "truth = {\"m\": 0.6, \"b\": 2.0}\n", + "x = np.linspace(0.0, 1.0, 20)\n", + "y_true = truth[\"m\"] * x + truth[\"b\"]\n", + "sigma = 0.1\n", + "data = rx.Dataset(\n", + " x, y_true + rng.normal(0.0, sigma, x.size), sigma * np.ones(x.size), label=\"toy\"\n", + ")\n", + "\n", + "fig, ax = plt.subplots(figsize=(5, 3.5))\n", + "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", color=\"k\", label=\"data\")\n", + "ax.plot(x, y_true, \"--\", color=\"C3\", label=\"truth\")\n", + "ax.set(xlabel=\"x\", ylabel=\"y\")\n", + "ax.legend(frameon=False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0e230164", + "metadata": {}, + "source": [ + "## A comparison, a constraint and a problem\n", + "\n", + "A `Comparison` binds the model to the dataset's grid. A `Constraint` is one\n", + "likelihood over one or more comparisons; by default its covariance is the\n", + "diagonal of the reported errors. `Problem` compiles everything: it assigns\n", + "every parameter a column, assembles the prior, and exposes the densities a\n", + "sampler needs." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "fbe84d4d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:24:18.489704Z", + "iopub.status.busy": "2026-09-11T03:24:18.489540Z", + "iopub.status.idle": "2026-09-11T03:24:18.492861Z", + "shell.execute_reply": "2026-09-11T03:24:18.492159Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Problem(ndim=2, constraints=1)\n", + "columns: ['m', 'b']\n" + ] + } + ], + "source": [ + "comp = rx.Comparison(data, line)\n", + "problem = rx.Problem([rx.Constraint([comp])])\n", + "print(problem)\n", + "print(\"columns:\", problem.names)" + ] + }, + { + "cell_type": "markdown", + "id": "3053e1d2", + "metadata": {}, + "source": [ + "### The prior predictive\n", + "\n", + "Before touching the likelihood, draw parameters from the prior and push them\n", + "through the model. This is what our prior says the data could look like." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9d480c23", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:24:18.494040Z", + "iopub.status.busy": "2026-09-11T03:24:18.493910Z", + "iopub.status.idle": "2026-09-11T03:24:18.638055Z", + "shell.execute_reply": "2026-09-11T03:24:18.637167Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 500x350 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "prior_draws = problem.sample_prior(200, rng=1)\n", + "x_fine = np.linspace(-0.5, 1.5, 60)\n", + "on_fine = line.bind(x_fine, {}) # the same model on a plotting grid\n", + "prior_curves = np.array(\n", + " [on_fine(*s[problem.columns(line.params)]) for s in prior_draws]\n", + ")\n", + "lo, mid, hi = rx.predictive.predictive_band(prior_curves, levels=(5, 50, 95))\n", + "\n", + "fig, ax = plt.subplots(figsize=(5, 3.5))\n", + "ax.fill_between(x_fine, lo, hi, color=\"C0\", alpha=0.25, label=\"prior predictive 90 %\")\n", + "ax.plot(x_fine, mid, color=\"C0\")\n", + "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", color=\"k\", label=\"data\")\n", + "ax.set(xlabel=\"x\", ylabel=\"y\")\n", + "ax.legend(frameon=False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "945445a6", + "metadata": {}, + "source": [ + "### The likelihood is the χ² you would write by hand\n", + "\n", + "With a diagonal covariance the Gaussian log-likelihood is `-χ²/2` up to a\n", + "constant. `problem.chi2` is that χ²; there is nothing hidden in it." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "866b9d36", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:24:18.639357Z", + "iopub.status.busy": "2026-09-11T03:24:18.639229Z", + "iopub.status.idle": "2026-09-11T03:24:18.642609Z", + "shell.execute_reply": "2026-09-11T03:24:18.641985Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "problem.chi2 = 1343.608 by hand = 1343.608\n" + ] + } + ], + "source": [ + "theta0 = np.array([1.0, 1.0]) # the prior mean\n", + "on_data = line.bind(x, {})\n", + "by_hand = np.sum(((data.y - on_data(*theta0)) / data.y_err) ** 2)\n", + "print(f\"problem.chi2 = {problem.chi2(theta0):.3f} by hand = {by_hand:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "0528da63", + "metadata": {}, + "source": [ + "## Run the calibration with emcee\n", + "\n", + "`problem.log_posterior` is the density, `problem.sample_prior` gives the\n", + "walkers somewhere to start, and `problem.ndim` the dimension. The chain\n", + "comes back in `problem.names` order, so columns are looked up by name, never\n", + "by position." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2fa25082", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:24:18.643797Z", + "iopub.status.busy": "2026-09-11T03:24:18.643657Z", + "iopub.status.idle": "2026-09-11T03:24:36.355765Z", + "shell.execute_reply": "2026-09-11T03:24:36.355145Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "16000 posterior rows, acceptance 0.71\n", + "m = 0.642 +/- 0.073 (truth 0.6)\n", + "b = 1.984 +/- 0.043 (truth 2.0)\n" + ] + } + ], + "source": [ + "n_walkers, n_steps = 32, 3000\n", + "sampler = emcee.EnsembleSampler(n_walkers, problem.ndim, problem.log_posterior)\n", + "sampler.random_state = np.random.RandomState(2).get_state()\n", + "sampler.run_mcmc(problem.sample_prior(n_walkers, rng=3), n_steps, progress=False)\n", + "samples = sampler.get_chain(discard=500, thin=5, flat=True)\n", + "print(\n", + " f\"{samples.shape[0]} posterior rows, acceptance {sampler.acceptance_fraction.mean():.2f}\"\n", + ")\n", + "for name in problem.names:\n", + " col = samples[:, problem.columns(problem.params[problem.names.index(name)])]\n", + " print(f\"{name} = {col.mean():.3f} +/- {col.std():.3f} (truth {truth[name]})\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "85c00a3a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:24:36.357119Z", + "iopub.status.busy": "2026-09-11T03:24:36.356994Z", + "iopub.status.idle": "2026-09-11T03:24:37.146340Z", + "shell.execute_reply": "2026-09-11T03:24:37.145249Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 550x550 with 4 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = corner.corner(\n", + " samples,\n", + " labels=[f\"${p.latex}$\" for p in problem.params],\n", + " truths=[truth[n] for n in problem.names],\n", + " truth_color=\"C3\",\n", + " show_titles=True,\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "34616882", + "metadata": {}, + "source": [ + "## The posterior predictive on any grid\n", + "\n", + "Binding the model to a finer, wider grid gives the prediction where there are\n", + "no data; the band is the percentile envelope over posterior rows." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6e597c0b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:24:37.147955Z", + "iopub.status.busy": "2026-09-11T03:24:37.147813Z", + "iopub.status.idle": "2026-09-11T03:24:37.262086Z", + "shell.execute_reply": "2026-09-11T03:24:37.261361Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 500x350 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "post_curves = np.array(\n", + " [on_fine(*s[problem.columns(line.params)]) for s in samples[::20]]\n", + ")\n", + "lo, mid, hi = rx.predictive.predictive_band(post_curves, levels=(5, 50, 95))\n", + "\n", + "fig, ax = plt.subplots(figsize=(5, 3.5))\n", + "ax.fill_between(\n", + " x_fine, lo, hi, color=\"C0\", alpha=0.3, label=\"posterior predictive 90 %\"\n", + ")\n", + "ax.plot(x_fine, mid, color=\"C0\")\n", + "ax.plot(x_fine, truth[\"m\"] * x_fine + truth[\"b\"], \"--\", color=\"C3\", label=\"truth\")\n", + "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", color=\"k\", label=\"data\")\n", + "ax.axvspan(x.min(), x.max(), color=\"0.9\", zorder=0, label=\"data range\")\n", + "ax.set(xlabel=\"x\", ylabel=\"y\")\n", + "ax.legend(frameon=False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4556194f", + "metadata": {}, + "source": [ + "## Is the error model calibrated?\n", + "\n", + "The band above is the model's uncertainty only. The posterior *predictive*\n", + "of the data adds the error model: draws of `ym(θ) + noise` on the data\n", + "points. If the error model is right, a central 68 % interval of those draws\n", + "should contain about 68 % of the points, and so on for every level. Twenty\n", + "points make a coarse curve, but it should hug the diagonal." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "28993859", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:24:37.264053Z", + "iopub.status.busy": "2026-09-11T03:24:37.263858Z", + "iopub.status.idle": "2026-09-11T03:24:37.440989Z", + "shell.execute_reply": "2026-09-11T03:24:37.440363Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 400x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "width of the 68 % predictive interval per point: 0.208\n" + ] + } + ], + "source": [ + "draws = rx.diagnostics.predictive_draws(problem, samples[::10], n_rep=4, rng=4)\n", + "levels = np.linspace(0.1, 0.9, 9)\n", + "c = problem.constraints[0]\n", + "coverage = rx.diagnostics.coverage_curve(draws, c.y[c.active], levels)\n", + "\n", + "fig, ax = plt.subplots(figsize=(4, 4))\n", + "ax.plot(levels, coverage, \"o-\", label=\"empirical\")\n", + "ax.plot([0, 1], [0, 1], \"--\", color=\"0.5\", label=\"nominal\")\n", + "ax.set(xlabel=\"nominal coverage\", ylabel=\"empirical coverage\", xlim=(0, 1), ylim=(0, 1))\n", + "ax.legend(frameon=False)\n", + "plt.show()\n", + "print(\n", + " f\"width of the 68 % predictive interval per point: {rx.diagnostics.sharpness(draws).mean():.3f}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "319bc93c", + "metadata": {}, + "source": [ + "## Takeaways\n", + "\n", + "- Declare, compile, sample: the problem knows its columns, its prior and its\n", + " densities; the sampler is whatever you like.\n", + "- Nothing is hidden in the likelihood. With reported errors only, it is the\n", + " χ² you would write yourself.\n", + "- Read chains by name through `problem.columns`; bind the model to any grid\n", + " for predictions.\n", + "- The posterior predictive check is the first question to ask of an error\n", + " model. The next notebooks are about what to do when the reported errors\n", + " are not the whole story." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 29003a3b1e4a835495a8f1217118e0d32c0ace84 Mon Sep 17 00:00:00 2001 From: beykyle <kylebeyer1@gmail.com> Date: Thu, 10 Sep 2026 23:34:17 -0400 Subject: [PATCH 35/75] Add the error_models notebook (recipes 2, 4, 5, 19) Where a chi2 comes from, then the ladder on one comparison: reported statistics, an inferred noise fraction with statistical=False, the reported normalisation as a prediction-built mode, the same mode built from the data (Peelle), offsets known and free; band and corner overlays; two datasets sharing one log_eps object (case B) against a mode per dataset. Runs in 153 s. --- examples/error_models.ipynb | 714 ++++++++++++++++++++++++++++++++++++ 1 file changed, 714 insertions(+) create mode 100644 examples/error_models.ipynb diff --git a/examples/error_models.ipynb b/examples/error_models.ipynb new file mode 100644 index 0000000..6fbe559 --- /dev/null +++ b/examples/error_models.ipynb @@ -0,0 +1,714 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8d97cbe7", + "metadata": {}, + "source": [ + "# Error models: there is a right way and many wrong ways\n", + "\n", + "The same data, the same model, six covariances. Which one you declare *is*\n", + "the statistical model, and the posteriors differ accordingly. Along the way:\n", + "inferring a noise level the experiment did not report, a normalisation or\n", + "offset it did not report, sharing an error model between datasets, and\n", + "bringing your own covariance.\n", + "\n", + "Recipes: 2, 4, 5, 19" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b9e41ed6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:31:16.480304Z", + "iopub.status.busy": "2026-09-11T03:31:16.480170Z", + "iopub.status.idle": "2026-09-11T03:31:18.582139Z", + "shell.execute_reply": "2026-09-11T03:31:18.581323Z" + } + }, + "outputs": [], + "source": [ + "import corner\n", + "import emcee\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from scipy import stats\n", + "\n", + "import rxmc as rx\n", + "from rxmc import terms as T" + ] + }, + { + "cell_type": "markdown", + "id": "35a70d2d", + "metadata": {}, + "source": [ + "## Where a χ² comes from\n", + "\n", + "Every fit assumes a likelihood, stated or not. Counting events in a bin gives\n", + "a binomial, then a Poisson, then (many counts) a normal: with independent\n", + "bins that is a diagonal multivariate normal, and its log is `-χ²/2`. Every\n", + "step is an assumption: independence between points, a model able to\n", + "reproduce the truth exactly, errors that are what the experiment says they\n", + "are. When any of these fails the honest generalisation is to keep the\n", + "multivariate normal and *model its covariance*,\n", + "\n", + "$$\\log p(\\mathbf{y}\\mid\\theta) = -\\tfrac12 (\\mathbf{y}-\\mathbf{y}_m)^\\mathsf{T}\n", + "\\Sigma^{-1}(\\mathbf{y}-\\mathbf{y}_m) - \\tfrac12\\log\\det\\Sigma + \\text{const},$$\n", + "\n", + "so that Σ carries the statistical errors, the correlated systematics and the\n", + "things you had to infer. In `rxmc` a covariance is a sum of `terms`; the\n", + "rest of this notebook is a tour of what the choice of terms does." + ] + }, + { + "cell_type": "markdown", + "id": "ec702a74", + "metadata": {}, + "source": [ + "## Data with a defect the experiment did not report\n", + "\n", + "A line, 5 % relative noise, and one overall normalisation drawn 10 % low.\n", + "The experiment reports the noise correctly but says nothing about the\n", + "normalisation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b993de25", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:31:18.583743Z", + "iopub.status.busy": "2026-09-11T03:31:18.583490Z", + "iopub.status.idle": "2026-09-11T03:31:18.701172Z", + "shell.execute_reply": "2026-09-11T03:31:18.700417Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 500x350 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.default_rng(16)\n", + "truth = {\"m\": 0.6, \"b\": 2.0}\n", + "x = np.linspace(0.01, 1.0, 15)\n", + "y_true = truth[\"m\"] * x + truth[\"b\"]\n", + "noise_fraction, sys_fraction = 0.05, 0.10\n", + "scale = 1.0 - sys_fraction # the realised normalisation, one sigma low\n", + "y_meas = (y_true + rng.normal(0.0, noise_fraction * y_true)) * scale\n", + "data = rx.Dataset(x, y_meas, noise_fraction * y_meas, label=\"biased\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(5, 3.5))\n", + "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", color=\"k\", label=\"data\")\n", + "ax.plot(x, y_true, \"--\", color=\"C3\", label=\"truth\")\n", + "ax.set(xlabel=\"x\", ylabel=\"y\")\n", + "ax.legend(frameon=False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8fac96e9", + "metadata": {}, + "source": [ + "## The model, a prior, and a way to run the ladder\n", + "\n", + "The prior is deliberately far from the truth so that the data, not the\n", + "prior, decide. `fit` runs emcee on any problem; `band` pushes posterior\n", + "rows through the model on a fine grid. Both read columns by name." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "11041742", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:31:18.702610Z", + "iopub.status.busy": "2026-09-11T03:31:18.702443Z", + "iopub.status.idle": "2026-09-11T03:31:18.708899Z", + "shell.execute_reply": "2026-09-11T03:31:18.708271Z" + } + }, + "outputs": [], + "source": [ + "m = rx.Parameter(\"m\", prior=stats.norm(2.0, 2.0), latex=\"m\")\n", + "b = rx.Parameter(\"b\", prior=stats.norm(5.0, 2.0), latex=\"b\")\n", + "line = rx.Model(lambda x, m, b: m * x + b, [m, b])\n", + "x_fine = np.linspace(0.0, 1.0, 40)\n", + "\n", + "\n", + "def fit(problem, seed, n_walkers=24, n_steps=1800):\n", + " sampler = emcee.EnsembleSampler(n_walkers, problem.ndim, problem.log_posterior)\n", + " sampler.random_state = np.random.RandomState(seed).get_state()\n", + " sampler.run_mcmc(problem.sample_prior(n_walkers, rng=seed), n_steps, progress=False)\n", + " return sampler.get_chain(discard=n_steps // 3, thin=5, flat=True)\n", + "\n", + "\n", + "def band(problem, samples, levels=(5, 95)):\n", + " on_fine = line.bind(x_fine, {})\n", + " cols = problem.columns(line.params)\n", + " return rx.predictive.predictive_band(\n", + " [on_fine(*s[cols]) for s in samples[::10]], levels=levels\n", + " )\n", + "\n", + "\n", + "def summary(problem, samples):\n", + " return \" \".join(\n", + " f\"{n} = {samples[:, i].mean():.3f} +/- {samples[:, i].std():.3f}\"\n", + " for i, n in enumerate(problem.names)\n", + " )\n", + "\n", + "\n", + "comp = rx.Comparison(data, line)" + ] + }, + { + "cell_type": "markdown", + "id": "c273703f", + "metadata": {}, + "source": [ + "## 1. Reported statistics only\n", + "\n", + "The default: the covariance is the diagonal of `y_err`. Nothing knows about\n", + "the normalisation, so the posterior is precise and wrong." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3045ab1e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:31:18.710467Z", + "iopub.status.busy": "2026-09-11T03:31:18.710342Z", + "iopub.status.idle": "2026-09-11T03:31:27.284213Z", + "shell.execute_reply": "2026-09-11T03:31:27.283342Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "m = 0.483 +/- 0.087 b = 1.836 +/- 0.049\n" + ] + } + ], + "source": [ + "p_stat = rx.Problem([rx.Constraint([comp])])\n", + "s_stat = fit(p_stat, seed=1)\n", + "print(summary(p_stat, s_stat))" + ] + }, + { + "cell_type": "markdown", + "id": "2a8d1323", + "metadata": {}, + "source": [ + "## 2. Infer the noise level (recipe 2)\n", + "\n", + "Suppose the experiment had not reported errors at all. `T.noise_fraction`\n", + "puts a free relative noise `σ_i = ε · ym_i` on the diagonal, sampled in log\n", + "space; `statistical=False` drops the reported diagonal so the inferred one\n", + "replaces it. `T.noise` is the constant-floor variant, `σ_i = ε`. Both\n", + "terms are *additive*: without `statistical=False` they would inflate the\n", + "reported errors instead of replacing them." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "deb95106", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:31:27.285542Z", + "iopub.status.busy": "2026-09-11T03:31:27.285390Z", + "iopub.status.idle": "2026-09-11T03:31:41.438682Z", + "shell.execute_reply": "2026-09-11T03:31:41.438202Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['m', 'b', 'log_eps']\n", + "m = 0.531 +/- 0.277 b = 1.909 +/- 0.404 log_eps = -2.981 +/- 0.425\n", + "inferred noise fraction: 0.061 (generated with 0.05)\n" + ] + } + ], + "source": [ + "log_eps = rx.Parameter(\n", + " \"log_eps\", prior=stats.norm(np.log(0.05), 1.0), latex=r\"\\log\\epsilon\"\n", + ")\n", + "p_noise = rx.Problem(\n", + " [rx.Constraint([comp], terms=[T.noise_fraction(log_eps)], statistical=False)]\n", + ")\n", + "s_noise = fit(p_noise, seed=2)\n", + "print(p_noise.names)\n", + "print(summary(p_noise, s_noise))\n", + "print(\n", + " f\"inferred noise fraction: {np.exp(s_noise[:, 2]).mean():.3f} (generated with {noise_fraction})\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6e9fa52d", + "metadata": {}, + "source": [ + "## 3. The reported systematic, as a correlated mode\n", + "\n", + "If the experiment *had* reported a 10 % normalisation uncertainty, the honest\n", + "covariance adds a rank-one mode proportional to the prediction:\n", + "`Σ_ij = δ_ij σ_i² + (0.1)² ym_i ym_j`. Because the mode reads `ym`, the\n", + "covariance changes with the parameters; `rxmc` assembles it at every\n", + "likelihood call." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "35f4d7ff", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:31:41.440327Z", + "iopub.status.busy": "2026-09-11T03:31:41.440154Z", + "iopub.status.idle": "2026-09-11T03:31:57.564569Z", + "shell.execute_reply": "2026-09-11T03:31:57.563967Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "m = 0.503 +/- 0.104 b = 1.917 +/- 0.220\n" + ] + } + ], + "source": [ + "p_norm = rx.Problem(\n", + " [rx.Constraint([comp], terms=[T.normalization(magnitude=sys_fraction)])]\n", + ")\n", + "s_norm = fit(p_norm, seed=3)\n", + "print(summary(p_norm, s_norm))" + ] + }, + { + "cell_type": "markdown", + "id": "5d3d2425", + "metadata": {}, + "source": [ + "## 4. The same mode built from the data: Peelle's Pertinent Puzzle\n", + "\n", + "The tempting shortcut is to build the mode from the measured values,\n", + "`(0.1)² y_i y_j`, as a fixed matrix. `rxmc` accepts any symmetric block as a\n", + "term (recipe 19), so the mistake is easy to spell. It is the covariance of\n", + "Peelle's Pertinent Puzzle: the fit is pulled *below* the data (recipes 27\n", + "and 37 have the closed forms)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5b193ca4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:31:57.565936Z", + "iopub.status.busy": "2026-09-11T03:31:57.565802Z", + "iopub.status.idle": "2026-09-11T03:32:06.335721Z", + "shell.execute_reply": "2026-09-11T03:32:06.335147Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "m = 0.439 +/- 0.097 b = 1.680 +/- 0.184\n" + ] + } + ], + "source": [ + "wrong = rx.Term(sys_fraction**2 * np.outer(data.y, data.y), kind=\"matrix\")\n", + "p_wrong = rx.Problem([rx.Constraint([comp], terms=[wrong])])\n", + "s_wrong = fit(p_wrong, seed=4)\n", + "print(summary(p_wrong, s_wrong))" + ] + }, + { + "cell_type": "markdown", + "id": "70097e6d", + "metadata": {}, + "source": [ + "### Four posteriors, one truth" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4ccfb0ae", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:32:06.337062Z", + "iopub.status.busy": "2026-09-11T03:32:06.336900Z", + "iopub.status.idle": "2026-09-11T03:32:06.618646Z", + "shell.execute_reply": "2026-09-11T03:32:06.618137Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "runs = {\n", + " \"statistics only\": (p_stat, s_stat, \"C0\"),\n", + " \"inferred noise\": (p_noise, s_noise, \"C2\"),\n", + " \"normalisation mode from ym\": (p_norm, s_norm, \"C1\"),\n", + " \"normalisation mode from y (Peelle)\": (p_wrong, s_wrong, \"C3\"),\n", + "}\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "for label, (p, s, color) in runs.items():\n", + " lo, hi = band(p, s)\n", + " ax.fill_between(x_fine, lo, hi, color=color, alpha=0.25, label=label)\n", + "ax.plot(x, y_true, \"--\", color=\"k\", label=\"truth\")\n", + "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", color=\"k\", ms=4)\n", + "ax.set(xlabel=\"x\", ylabel=\"y\", title=\"90 % predictive bands\")\n", + "ax.legend(frameon=False, fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "93d93c80", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:32:06.620099Z", + "iopub.status.busy": "2026-09-11T03:32:06.619927Z", + "iopub.status.idle": "2026-09-11T03:32:07.487830Z", + "shell.execute_reply": "2026-09-11T03:32:07.487177Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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AnDlzhpUrV1JQUMC5c+fo3r07S5YsuaW+my+jPPXUU+zatYumTZuiVCoJCAigbt26JfZ19epVLly4QFRUFOnp6VhYWLBnzx5jf/v27WPQoEG0bNmS8PBwgoKCWL9+PSqVimXLlnHixAmysrJwcHDg3LlznD59Gjc3t1tqvJ33beqYu3fvZvDgwbRr146YmBisra1p1qwZAKdPn2b48OH4+fmRm5tLREQEu3btwsvLq0gtkZGRHDhwgISEBFauXEmzZs1YtmwZx48fJysri+bNm3P//fezePFi5syZQ7t27Thy5AjTp0/ntddeAwx7xdjZ2WFnZ0dYWBht27YlOTkZpVLJ6dOneeqpp3jjjTdu+Ry0Wi0jRozg8uXLKBQKUlNTGTlyJBcvXryrfqs6CRpCiKopPwvmlXzprty8GgOWtiab5OXlcezYMdRqNfPnz2f58uV06NCB9957j/79+7No0SIARo8ezccff8y7774LGP7CLhzzUKh+/fqsXLkSMJyNUKlU7NmzB61Wi4+PD2FhYfj7+zN06FDatm3L2bNnSU9PZ/LkySxcuNDkLq0bNmxg9+7dBAQEFHm8pL5atWrFwIEDi1w62bNnj/F1L730EvPnz+fJJ58kLy+Pjh078vvvvzN8+HAA6tWrZ7z8MHLkSNatW8f48eOLra2s79vUMV999VU+++wzHnvsMXJycmjevLmx/xdeeIFhw4YZz9YsX76cr7/+mrfeeqtIHZ06dWLq1Kns2bOHZcuWGR93c3Pjzz//NP7cXnnlFQ4ePEjjxo0JDw+nefPmTJgwwbg53fjx45k8eTLJycm4ubmxZs0aHnjgAc6cOcPAgQOLDQSNGzcmICCArVu30rdvX3788UeGDx9e5EztnfRb1cnQbyGEKEWHDh1Qq9UABAQEkJiYCBjOOvTr18/YbuDAgZw+fdr4fefOnYuEDIAuXboY7/v5+Rm/t7CwwMvLy9j3Bx98QMeOHfn888/59ddf0el0xMbGmqxz5syZtGvXjnvuuYf333+fnJycO+7rv+/P0tKSPn36FHl/Xbt2Nd6/+XMpTlnft6lj3vyctbU13bp1M/Z57Ngxjh07xsqVK1m5ciVKpRJPz7IH1Zt3rQ0JCcHd3Z3GjRsb623cuDHnz5+/5f04Oztjb29v/N7Pz8/k5zB16lS+/vprAJYuXcozzzxT7Od0u/1WZXJGQwhRNaltDGcbKuI4pfjvdNzCTa/d3d0JDw83Ph4aGmrcUh4whhNTfZXU95dffsn27dtp2rQpOTk51K1bl9I2254+fTqTJk3iwIEDvPvuu+Tk5PDGG2+Y7EupVJbYb+H7KxyTEhoaWuQXckm1F6es79vUMQufK/yMw8PD6dChAwAeHh5MnTrVOL7idt38s3J3dyc+Pp7s7Gw0Gg1arZaoqKgiP1tT78fU5zBo0CBefPFFVq9ejZeX1y2Dbu+036pMgoYQompSKEq9pFHZJk6cyKhRoygoKECr1fLBBx+wcePGcunb39+fhQsX0qtXL1auXEleXl6pr/n999/R6XTk5OSQmZlpnOFjqi8/Pz/WrFmDl5fXLQNOn3rqKSZMmMDMmTO5cuUK27dvZ/HixeXy/kpi6phPPvkk48eP54UXXuDUqVMcOXLEeBln1qxZTJgwgRkzZuDn5wdA+/btjfdvR926denZsydDhw5lxIgRrFu3jsDAQIKDg+/6/alUKiZMmMATTzzBqlWr7rq/6kCChhBCmBAQEIBWqzV+7+PjY/wr+r777mP16tXGU/UbNmwwzgz47+vAMDbh5sGJXbp0KfL9fffdZ5y58eOPP7Jw4UL27dvHM888Q+PGjY3X8lu0aIGzs/Mt91etWkVBQQG2traMHTuWiRMnltrXhAkTSEhI4LfffsPS0rJIfy+88AKenp5s3boVZ2dnDh48iIeHR7HvpXnz5iXOCrud923qmDNnzsTFxYU9e/bQqVMnPvjgA2OQGDduHP7+/vz222/s378fvV6Pm5tbsUHD39/feFmpuPoAfvnlFxYtWsT27dtp3bo106dPN85+6du3b5GxMoMHD8bS0hIwXAoaMmRIsZ/Dze2///57+vfvX+Txu+23qlLoq+u5mCooLS0NR0dHUlNTq/U0zL/OHeTVgxOY1+EbHmzS4a7b3Y0zZ84UGVVeHn9RiMpRU/59CHE3fvvtN5YuXcrgwYOZMmVKZZdTIWQwqBBCCFFB/vzzTzp16sRTTz1V2aVUGLl0UssciwklKjXeZJujVy9UUDVCCFG7/PTTT5VdQoWToFGLHIsJZczmoSiU+aW21evUeDu6l9pOCCGEMEWCRi0SlRqPQpnPMJ9ZtKnXyGRbb0d3WnsGmGwjhBBClEaCRi3Upl4jsw3eFEIIIW4mg0GFEEIIYTYSNIQQoppJTU0lIyOjssuodCkpKWRmZt7x6xMSEkhLSyvHispfTk4OqampZuk7Pz+fuLi4W+6Xdz0SNGoZ11Q9VlfCyD5zxuQtP6YCln4WQtyRt99+m6+++gq4+9Dx39dXpxDz2muv3dEsjry8PDp06EDTpk2LbKxWFT399NMcOHAAgPj4eKKiooiJibllMbg7cebMGfr27XvLfVPS09Pp3bs3BQUFZT6OjNGoRSziE/l4aQHW+XMIK6WtQqMhaP061LexKZEQouJ9/vnnuLu7M2nSpDt6/Zw5c4rs3nq3/VUH27dvx8nJqUx/wVemsLAwjhw5wrfffgvA8OHDOXXqFNbW1iQlJfHUU0/x6aefVmhN7u7utGnThl9++YVHHnmkTK+RoFGLqNLSsc6H2Ocm0albnxLb5YWEEDNzFtrkZAkaotbLysoiNzcXZ2dn0tLSil3VNDs7G6VSWWSn1ptfl5ycjIODA+np6ajVamxtbUlOTjYu9Z2Xl0dubq5xbxIw/OWYmpqKhYUFHh4et2y2VeiZZ55BpVIZv9dqtcTHx1NQUICbmxvW1tYl9pWbm0tGRgapqalERUXh6Oh4S3+FtdjY2BR5PCUlxfheUlNTTW5ff3Pb0t63qWMWKunnAIafBYBGoynx+fPnz+Pq6kpUVBQeHh5kZWUZ64uPjy+yeVpKSgpOTk5F+rh27Rqurq7G/grrz8jIQK1W37Jj783vSafTFfmsEhMTsbGxKbben3766ZZlxxcvXsyoUaOIiIigdevWPPTQQ/Tq1QswbLqWkpJi/HxvZuq5kpT0muHDh7Nw4cIyBw25dFIL5Xt7ogkOLvFmGRhY2SUKgV6vR5eVZfZbabswLF++nEceeYR27drh5+dH/fr1iYiIACAzM5OhQ4fi7u6Oi4sLo0ePJjc31/i6UaNG0aZNG4KDg7l69SqvvfaacU8Ob29vBg8ezIoVK/Dx8cHDw4Pnn3/eeNzvvvuOTp060bp1a1xcXIyXSv7r5ssoixYtwsXFhbZt29KpUyf27t1rsq99+/axatUqPv74Yzp16sRPP/1UpL+IiAg6deqEl5cXDg4OzJkzx3jc1157jSlTphAYGIiPjw/dunUzvvf/up33beqYoaGhNG/eHG9vb5o3b05oaKjxufj4eAYMGIC3tzd16tRh0KBBxtBxs+3btzN37lzWrl1Lp06dOHHiBK+99hqTJk3C39/fuFPsP//8g7e3N/7+/tSpU4c1a9YY+7jvvvt46qmn8PHxwd3dnffff58XXngBPz8/XFxc+PXXX4v9HI4ePUqfPjf+yMvPz6dFixYlbv++a9cu2rdvX+xzvr6+NG3alLCwMAAWLFiAm5sbDRs2JDAwkP379xvbmnquJKZe07FjR/bs2VPmyydyRkOULP4ixJS8uJdj0iXqarVEJmVxOtr04CBnW0u8nIr/C0OI4uizs7nQpq3Zj9Po6BEUNqa3ij958iT79+/H19eXqVOn8vXXX/Puu+/y0UcfkZWVRUJCAlqtln79+vHFF18Yf3EePXqUQ4cO4e/vb+zr6tWrnD59GoVCQfPmzbGwsCAyMpLU1FQaNmzIK6+8goeHB88++yzPPvssSUlJhIaGMmDAAMaNG1fiX+oA7733HseOHSMoKKjI4yX11bNnTyZMmFDk0smLL75ofN3zzz9P586d2bdvH9HR0XTq1ImePXsa/4K+fPkyx48fR6PR0Lt3b9auXWvcTfW/yvq+TR1zxowZPPDAA8yfP5+QkBDat2/Pgw8+CMCMGTPo3r07S5YsoaCggBdffJEvvviCGTNmFKljwIABfPTRR+zZs8c4PuOHH37g/PnznDhxAkdHR/Ly8hg9ejRffPEFQ4YM4e+//2bYsGGEhYUZz0Y4ODgQExPDuXPnaN68OfPnzycxMZEtW7bw/PPPF/s59OjRg+zsbI4fP06rVq34448/6NKlC97e3sV+ZlFRUdSrV6/IY0lJSURGRnLw4EEOHz7MokWL2LJlC5s2bWL//v1oNBp27NjBlClTOHbsmMnnSlLaa+zt7VEqlSQkJFCnTp0S+ykkQUPcKv2a4evvE8Gl5KDRHVijUNA38gDv55ge+a1Rq9g2o4eEDVEt9e7dG19fXwDuueceNm3aBMCePXuYNGkS1tbWAEyaNIm1a9cag0bfvn2LhAyAoUOHYmdnBxh2PB05ciSWlpa4u7sTGBhoPJ2/YcMGJk6cSFZWFra2tiQlJREVFUWDBg1M1vnQQw/Rt29fBg8eTPfu3QHuqK/C97dv3z4UCgXe3t6MGDGCvXv3GoPGsGHDjJcwOnbsSGRkZIl9lfV9mzrm3r17WbRoEQCBgYEMHDjQ2P+2bdvYtm2b8fnCNmU1cuRIY4i4cuUKarXaeNnivvvuIyAggFOnTtG1a1cAxo4di0KhoGnTptjZ2TF+/HgAOnfubPJzmDp1KkuWLOGLL77gq6++Yu7cuSW2ValUtwz6fP3111mwYAGenp4sWbKEli1bMmvWLI4fP278ucCNS0fbtm0r8bmSlOU1Wq0WtVptsp9CEjTErXKun5249zXo0LPEZlFh2/He8ibz+njj4t+1xHaX4zJ4btVxkjPzJGiIMlNoNDQ6eqRCjlOawq26AZRKJTqdDgBra2uysrKMz2VkZBhDBxT/P/Sb+1IoFLd8X9j3zJkz+fbbb+nXrx8Anp6epZ6q/u677zh06BD//PMPTz75JDNnzmTixIkm+yrc+rw4xb0/z5vGbZX0uRSnrO/b1DGtrKyKPHfz1FYrKyt+/fVX2rVrV2INptz8s7K2tiYnJwe9Xm/8fP77sy3p/dz8XoozZswYmjRpwsSJE0lJSaFbt24ltm3QoAERERF06HBjgcXCMRo3s7KyYvz48XzyySe39GHquZKU9pq4uDhsbW3LPN5DgkYNkR8TgzY52WQbddRtTll18gPPViU+nZdquD7q7WJDoFfJA8GEuBMKhaLUSxqVbdCgQcyfP58GDRqg1Wr54IMPWLBgQbn0rVaruXjxIgEBAXz77bfExsaW+pro6Gjq1avH0KFDOXnypPH6vam+XF1dOX78OBEREbf84hg0aBAzZ85k4cKFXLlyhV9++cU47sNcTB3zwQcfZMaMGcyfP59Tp06xfv1645iHRx99lKeffpqPP/4YPz8/AFxcXLC5g/+G/Pz8qFevHrNnz2bs2LH88ccf5Ofn07x587t+f7a2tgwZMoRhw4bx2muvmWzbu3dv9u7dy7Bhw0y2Gz58ON27d6dt27Z07doVtVqNtbU1bm5uJp+7k/4Adu/eTZ8+fUyG1JtJ0KgB8mNiuDLwAfTFDHy6WV0gRw0FDreO8BZCFM/W1hYXFxfj9zY2Nsbvn3jiCRITE5k8eTJKpZLnnnuOESNGFPs6AGdnZ+PlAwA3N7cif0nXqVPH+JfxF198wQsvvMC3337L4MGD6dWrl/FUtZOTk3Gmw83377nnHrRaLba2tnTp0oVZs2aV2te4ceN44okn6NatGy+99FKR/hYsWMDs2bMZOXIkzs7O/PjjjwQHBxf7XhwdHYudPXK779vUMd977z2ef/55hg0bRqdOnZgyZYqx3zfffBMHBweef/55rl69il6v5+OPPy52rMR/fzb/rU+pVPLnn38yc+ZMBg8eTMOGDdmwYYNxNkndunWLXDbw8vIyzuRRKpV4eXkV+zkUeuKJJ4yDjE0ZPXo0HTt2ZOHChVhYWODu7l5scGrRogV//vkn8+fPZ86cOeTl5dGtWzdWrFhh8jlLS0vjGIub75t6DRgGOk+bNs1k7TdT6Esbci3KLC0tDUdHR1JTU0ucemUO2WfOEDZ0GJ4L3zc5Y2R36BnmhbzFjD7fmtzrJHvHH4RNfhX/r+ah6TmkxHYh5/4gcNV4QkZ+T2CTktudjk7lgc/3sO6ZrjS7zTMfZ86coVmzZoZ+Tp82/g9HVD+V9e9DiKokIiKCTz/9FI1GwzvvvFNq+wULFtCoUaNbprlWloiICGbMmFHizJriyBmNGsQyMBCNiV/EucpMEhPLdqpLCCFE+SscILxq1aoytX/55ZfNXNHt8fX1va2QARI0RDmIyogiJ/Fsic+HpmWgsEipuIKEEKKKOn/+fGWXUOEkaIg7Zm9puB676Ngizp1dYrKtbZCahJx2gAwaFUKI2kSChrhj7hrDCOQF3RaQ49GoxHa7Qs+w+MxbpOeZZwdCIYQQVZcEDXHXAh0DwLVpic9fiaseO0EKIYQof7LXiRBCCCHMRoKGEEIIIcxGgoYQQgghzEaChhBCCCHMRoKGEEIIIcxGgoYQQgghzEamt9YydbVaHJMuQYxlyY1SwiuuICGEEDWaBI1aRJMZx5qoq9hEPm26YZIacAdrWcVTCCHE3ZGgUYtY5qZio9dztOPLtGl5f8kNL4bCllfBvk7FFSeEEKJGkqBRC2U4+IJnq5IbJKsrrBYhhBA1mwwGFUIIIYTZSNAQQgghhNnIpZMqLj8mBm1yssk2eSEhwPXNy5xK3iE1Ni2nXGsTQgghSiNBowrLj4nhysAH0Gdnl9o2R2XJU39cIt4mocQ2za3DGQHYWMuPXQghRMWQ3zhVmDY5GX12Np4L38cyMLDEdlfiMnjqj0vMfrwX9T3sSmyXFJYCW8HFxsQaGkIIIUQ5kqBRDVgGBqIJDi65gVMq8TYJ1Pewo5lXyWtfhKTZmKE6IYQQomQSNESJCsd+lCg+FItMFTIZVgghREkkaIhbWDg7o9BoiJk5q9S2CpU7QY8moPasgMKEEEJUOxI0xC3Unp4ErV9X+myXI9uJmbcYbWq6nNUQQghRLAkaolhqT0/UnqWcpoi/WDHFCCGEqLYkaIi7lxIOMcdLfNox6RJ1tdqKq0cIIUSVIUGjhlBYpBCadgGldcnTWxMyoih5kuwdKNzd9Z934PicEpt1B9YoFPybGVeeRxdCCFENSNCoARJyYrEN+pBXD+abbNckN4/ugL2lffkcuHB314eXQsOAEpsdPbGRNgcWYJlb8qqlQgghaiYJGjVAel4qCmU+U4PfoHtAyettWMddgFXjcde4lW8B7g3Bs+TjZoSeKd/jCSGEqDYkaNQgXrZ+NHVtWnKD3LyKK0YIIYRAdm8VQgghhBlJ0BBCCCGE2UjQEEIIIYTZSNAQQgghhNlI0BBCCCGE2UjQEEIIIYTZSNAQQgghhNnIOho1RF2tFsekSxBjWXKjBNkETQghRMWSoFEDaDLjWBN1FZvIp0tvrLYBG1fzFyWEEEIgQaNGsMxNxUav52jHl2nT8n7TjW1cwcmnYgoTQghR60nQqEEyHHzBs1Vll1Gi/PijhJyzMdnG1tGXOp5tK6giIYQQ5iZBQ5id2t6bLIWC+44vg+PLTLbNVii4NvFvCRtCCFFDSNAQZmfv2oo++W8z/34XvF1KPqORGn2Q1nu+4GpqBEjQEEKIGkGChqgQ0dpAXPy7EujlWGKbEAC+qKiShBBCVABZR0MIIYQQZiNBQwghhBBmI0FDCCGEEGYjYzREhbkcl2Hy+aSkLAIrqBYhhBAVQ4KGMDtnW0s0ahXPrTpusl1z6wt0B5Ky8iRwCCFEDSFBQ5idl5OGbTN6kJyZZ7Ld2ZOxcACycrQVVJkQQghzk6AhKoSXkwYvJ43JNklh1hVUjRBCiIoig0GFEEIIYTYSNIQQQghhNhI0hBBCCGE2MkajkuTHxKBNTjbZJi8kpIKqEUIIIcxDgkYlyI+J4crAB9BnZ5faVqHRYOHsXAFVCSGEEOVPgkYl0CYno8/OxnPh+1gGml4xwsLZGbWnZwVVJoQQQpQvCRqVyDIwEE1wcGWXIYQQQpiNDAYVQgghhNlI0BBCCCGE2UjQEEIIIYTZSNAQQgghhNlI0BBCCCGE2UjQEEIIIYTZSNAQQgghhNnIOhrirpVlqXRZeEwIIWonCRrijlk4O6PQaIiZOavUtgqNhqD16yRsCCFELSNBQ9wxtacnQevXlWlzuJiZs9AmJ0vQEEKIWkaChrgrak9PCQ9CCCFKJINBhRBCCGE2EjSEEEIIYTYSNIQQQghhNhI0hBBCCGE2Mhi0iotOySY5M89km9i0nAqqpmLYpUVAzHEALBJvrNFhkXgBYvIN39i4gpNPJVQnhBDidkjQqMKiU7Lp/eFOsvMLTLZrbh3OCMDGunr/OPOsHMlSKGhzYAEcWACAc4bO+Lzz2nFgd/0knNoGph6UsCGEEFVc9f7NVMMlZ+aRnV/AJyNbUd/DrsR2SWEpsBVcbCwrrjgzyLb1YLB3PV5v/BrdA4IBSL4cAh8ONdwf9AMe9QMh4SL8PhGyEiVoCCFEFSdBoxqo72FHMy/HEp8PSbOpwGrMK9bCglSXBuDZCgBtstr4nNa1EXgGV1JlQggh7oQMBhVCCCGE2cgZjSrOkwSsE06BouRLJ5bJYRVXkBBCCHEbJGhUYeqMaLZZzcTmj1yT7byBLIUCrabkyytCCCFEZZCgUYWpcpKwUeQS2etTfBq0KrFdSGook/59lU/t61ZccUIIIUQZSNCoBnKd6hsHRxYnx8qSWAv5UQohhKh6ZDCoEEIIIcxG/gyuBmIywslLdCnx+ZDUkBKfE0IIISqTBI0qLCU3EYBFZ97i3GXTi3FpLDQ4WzlXRFlCCCFEmUnQqMKy8jMAGBE0kaatBphs62zlTD27ehVRlhBCCFFmEjSqAQ/rejR1bVrZZVSY6MxwziYa1g25knbF+PiVtCsoEhVYp4YSWFnFCSGEuC0SNESVYW/piF6nZvGZt1h8xvBYXvyNnWtn7p2J5UVLmuTm8QsQn52Ae+WUKoQQoowkaIgqw826LplXZvDZ6AYEXd9E7vLFywxmMAAL71lI/Yb1SQj5G2JeIz0vXYKGEEJUcRI0RJWi1zpRkOOFLscQNHQ5acbndDke6HK8IK/kGThCCCGqFgkaospwtrVEo1bx3Krjxsdy4sON9yf/fBRr90SaW1+gO5CUlSdjNYQQooqToCGqDC8nDdtm9CA588a4jMsXXRnyreH+V4+1oX7DJpw9GQsHICtHW0mVCiGEKCsJGqJK8XLS4OWkMX6vSLE33m/gYU+wlyNJYdaVUZoQQog7IEFDVJi8kNJXMLVwdkbt6VkB1QghhKgIEjSE2Vk4O6PQaIiZOavUtgqNhqD16yRsCCFEDSFBwwyyz51DbWdX4vNl+cu+JlF7ehK0fh3a5GST7fJCQoiZOQttcrIEDSGEqCEkaJhBxGNjsFOpTLZRaDRYONeevUnUnp4SHoQQohaSoGEGdd95G7dmzUy2kbEIQgghagMJGmZg6e+PJji4sssQQgghKp0EjUoSnZJdZL2I4sSm5VRQNUIIIYR5SNCoBNEp2fT+cCfZ+QUm2zW3DmcEYGMtPyYhhBDVk/wGqwTJmXlk5xfwychW1PcoeXZKUlgKbAUXG8uKK04IIYQoRxI0KlF9DzuaeTmW+HxImk0FVlP92KVFQMxx041sXMHJp0LqEUIIcSsJGqLaybNyJEuhoM2BBXBggenGahuYelDChhBCVBIJGqLaybb1YLB3PV5v/BrdA0zM7km4CL9PhKxECRpCCFFJJGiIainWwoJUlwbg2aqySxFCCGGCsrILEEIIIUTNJUFDCCGEEGYjQUMIIYQQZiNBQwghhBBmI4NBK4knCVgnnAJFyQt2WSaHVVxBQgghhBlI0KgE6oxotlnNxOaPXJPtvIEshQKtpuRFvYQQQoiqTIJGJVDlJGGjyCWy16f4NGhVYruQ1FAm/fsqn9rXrbjihBBCiHIkQaMS5TrVN7kORI6VJbEW8iMSQghRfclgUCGEEEKYjQQNIYQQQpiNnJevRDEZ4eQlupT4fEhqSAVWI4QQQpQ/CRqVICU3EYBFZ97i3GVLk201FhqcrZwroiwhhBCi3EnQqARZ+RkAjAiaSNNWA0y2dbZypp5dvYooSwghhCh3EjQqkYd1PZq6Nq3sMoQQQgizkcGgQgghhDAbCRpCCCGEMBsJGkIIIYQwGwkaQgghhDAbCRpCCCGEMBsJGkIIIYQwG5neKqqcvJAbK6Lqrlwpcj8bsAoNwzVVX/YOEy6W3sbGFZx8bqNKIYQQZSFBQ1QZFs7OKDQaYmbOMj6Wp9PduP/iTMKUSnyAj9UQ2yoRmpjo0MYV1Dbw+8TSD662gakHJWwIIUQ5k6Ahqgy1pydB69ehTU42Pnb5yhUYPBgAyw8W4h8UxP7dW6n7ydeo0tJNd+jkYwgPWYmm2yVcNISRrEQJGkIIUc4kaIgqRe3pidrT0/j9zYOIlEFBaIKDyQ89U/YOnXwkPAghRCWSoCGqrdi0HE5Hp5ps42xriZeTpoIqEkII8V8SNES1Y2Nl+M/2p/3hvHFyj8m2GrWKbTN6SNgQQohKIkFDVDsutpYAjOvmgGMz9xLbRSZlsXBjNMmZeRI0hBCikkjQENWOvaU9GcAvIUsJzVpmsq1tkJqEnHaAY4XUJoQQoigJGqLacdO4kQEs6LYAGgWW2G5X6BkWn3mL9DzT4ziEEEKYjwQNUW0FOAagcW1a4vNX4jIqsBohhBDFkSXIhRBCCGE2EjSEEEIIYTYSNIQQQghhNjJGwwxC4zOJN7GQVGxaTgVWI4QQQlQeCRpm8NUfW4jfe7bE5xtanWYEYGMtH78QQoiaTX7TmcF76iXUsSow2SZLocDBteTFpoQQQoiaQIKGGcS1eZY6DzxU4vMhqaFM+vdVPrWvW3FFCSGEEJVAgoYZaG29wLNVic/nWFkSayEfvRBCiJpPZp0IIYQQwmwkaAghhBDCbOT8vai28kJCTD5vFRqGa6q+gqoRQghRHAkaZpCQfY2ziSVPbw1JNf0LUphm4eyMQqMhZuYsk+18gI/VENsqEZqUoeOEi6W3sXEFJ58y1SmEEEKChln8Fvo9e9etMtlGY6HB2cq5giqqWdSengStX4c2Odlku/27t1L3k69RpaWb7tDGFdQ28PvEMhzcBqYelLAhhBBlJEHDDO71epBnHzD9S8vZypl6dvUqqKKaR+3pidrT02Sb/NAzZevMyccQHrISTbdLuGgII1mJEjSEEKKMJGiYgbOlK01NbF8uqiAnHwkPQghhBhI0RI13LiGc3HMHTbbxdnSntWdABVUkhBC1hwQNUWN52BnGwPx97QeWHfzRZFu9Ts1P/X6TsCGEEOVMgoaosRq5exIGTAt+g9wg/xLbHb16gdWR7xOVGi9BQwghypkEDVHjdQsIRtMk2GSb1ZEVVIwQQtQysjKoEEIIIcxGgoYQQgghzEYunYgar9yXKpcVRIUQoswkaIgaq9yXKpcVRIUQ4rZJ0BA1VrkvVS4riAohxG2ToCFqtHJdqhxkBVEhhLhNMhhUCCGEEGYjZzSEMBcZNCqEEBI0hCgUm5bD6ehUk22cbS3xctKY7kgGjQohhJEEDVHr2VgZ/hn8tD+cN07uMdlWo1axbUYP02FDBo0KIYSRBA1R67nYWgIwrpsDjs3cS2wXmZTFwo3RJGfmlX5WQwaNCiEEIEFDCOwt7ckAfglZSmjWMpNtbYPUJOS0AxwrpDYhhKjuJGiIWs9N40YGsMBnGvh7l9juaPQVll1dSnqe6XEct00GjQohajAJGqLWK1xBlLc+NdmuLRCshm2eB0vt09vRvfQt52XQqBCiFpCgIWq9sq4gevHIAaznLWT3la/4MfNrk231OjU/9fvNdNiQQaNCiFpAgoYQlG0F0YZAGDAt+A1yg/xLbHf06gVWR75PVGp86Wc1bmfQaFkusZSVXIoRQlQQCRpC3KaOeg2WOtsSn7fKsuJIrJ6QAzvZciXMZF8FDvZo3V1NttFkxtHLwhpVWS6xlJHOQoNy1M9g42ay3TUKSLQqZYYN4JqbTR1UphulZ9xOiUKIGkKCRjnS6w1bjWdm55CWllbJ1dQMGRkZRe5X5ueab2FBpqUlF1+YYbKdM/A6AEtL7TNHDYsGKUnTKEy2+zbfhcH1BuNiZVdimyxdFlsj/0CryzfZl71OxzOxKVh9MKLU+nKB75ydSFeWvFuBvU7HM8kpXCulr/Q8w7+Pwn8nQojaQaGXf/XlJioqCh8fOR0thCmRkZF4e5c8u0cIUbNI0ChHOp2OmJgY7O3tUShM/4Va2dLS0vDx8SEyMhIHB4fKLsckqdU8KrpWvV5Peno6np6eKE2cIRFC1Cxy6aQcKZXKaveXmoODQ5X/hVhIajWPiqzV0VEWOhOitpE/K4QQQghhNhI0hBBCCGE2EjRqKSsrK+bMmYOVlVVll1IqqdU8qlOtQojqq1YPBv3tt994+OGHq/zATSGEEKK6qrVnNObNm8ewYcOYPHmyzOsXQgghzKTWBo2rV6/Sr18/vvnmGwkbQgghhJnU2umtjRs3Rq1WM378eB577DEAvvrqK6KiovD09ESlKmU5ZSA3N5fc3Fzj9zqdjqSkJFxdXeVyjBD/UdZ1NKrTejRCVLRquR6Nvpbatm2bvk+fPnq9Xq9fsWKFXqVS6R955BG9t7e3fvv27WXqY86cOXpAbnKT223cIiMjTf67ioyMrPQa5Sa3qn4r7d9RVVJrB4NGR0fToUMHoqOjAVi4cCGzZs2iV69e/P3332X6S+q/ZzRSU1Px9fWtFqtCliovCz5saLj/wnkwsceGOcXP9qT+okwA9u/fT5MmTSrmwKvGQcjfMOBDaDmyYo5ZwxWuRJqSkmJy4a7U1FScnJxqxr+jMth44TBzj0xjTttF3N+oXbU97rlz5+jUqRNQwf9Wa5my/juqSmrtpRMvLy8yMjJITk4mPj6ezz77jEmTJrFs2TImT57MV199VWrYsLKyKnZqYHVaFbJEeRZgdf39OzhUWtDIsbrxM7Czs6u4z9VGbXj/dhrD+xflprR/V4XP14h/R2VgY2eHSqPCpiL/+zbDce3s7Ircrw0/u8pUnS4rVpMLPObRsGFDfv/9d+677z7effddvvrqK37++We++eYbli9fXtnlicqSmwF5sqW5EEKUh1p7RgOgZcuWTJ48mW+++YaxY8cCMGrUKAICAujQoUMlV1fJFErDTa+Di5ug+bDKrsg8cjMg9hTEHIOrxyHmOCRcxHAZFFCqK7E4IYSo/mp10Pjggw8YNGgQgwYNKvJ4x44dK6miKkRtDW3Hw+Fv4fenQKmC4CEVXkZG+2eA+eXXYWYCnPrVECz+GypuZu8Jfl2gQZ/yO7YQQtRCNTJobNq0iR9++AGdTse0adPo1q1bse2cnJxuCRniJgM+gPwcOLEcVk8wPFbBYSO72WjKNWj8OQUubSn6mL0neLaCeq1ufLWvU37HFEKIWqzGBY05c+awdOlSHnroIY4cOcK9997L/v37adu27S1tjx8/TvPmzcu0ZkatpFTB4EWG+4Vhw8oB6t9XuXXdqZRIuLTVcL/7LPBuJ6FCCCHMrEYNBt2zZw/Lli3j2LFjfPHFF/z77780btyYd99995a2cXFxdO3alccee4yCgoJKqLaaKAwbzUeAvgB+nwhpMZVd1Z05sQLQg383uHc2NOwnIUMIIcysRgWNRYsW8cEHH1CnjuGXh0qlYuzYsZw+ffqWth4eHixevBhHR8fqs7paZVGqYNDnUKc5ZCXCb09Cgbayq7o9Oh0c+8lwv/WYyq1FCCFqkRr1G7ZOnToMGVJ0DIGfnx85OTnFth83blyZ1ssQGAaHjvgBLO0gfC/sKMdxExUhbDekRBgu/TR5sLKrEUKIWqNGBY1PP/0Ua2vrIo9ZW1uj0+mM32dmZrJ27dqKLq1mcA2CBz813N/9IVzeVrn13I5jPxu+Nh8GljaVW4sQQtQiNSpoFEepVBqDRmZmJgMHDmTDhg2VXFU11nwYtHsC0Bumvd7JeA29HmJPw8Uthlkt5padAueuh8vWj5n/eEIIIYxq3KyT/1IoFOj1emPIaNiwIV9++WVll1U1FGgh9iQkh4JrA3BvBBa3Lql+i37zIfIQXDtlGK8xdi2oyvCfUuwpOPIDXNwMqRGGx+zrQbcZ0P5JMNclrNOrQZsDHsHg2cY8xxBCCFGsWhE08vLyjCHj66+/rr1jMgqDRdgewy1iH+Sm3XheaQFuDaFOM6gTDHWbGe7b1bkRAjIT4czvhhkoYBivEfEvBHQ3fWy9Hr4bCLmphu8trA3jPdKvwoYXwckPGvYt//cMhmADhrMxtfVnL4QQlaTGBw0LCwuSkpJqZ8goLVgAWDmCWwNIvAQ5qRB31nA7dVMbGzdD6FBZwpV/QHd9xolCBY3uL9tZAoUCOk2BXe8bljVXWtw4e+Lsb1jTwlyc/Q1fk0PNdwwhhBDFqvFBo0ePHixatIinn366doWM07/DX9OLDxb+94B/V8OtTjPD9FW9HtKiDWMnrl2/xZ6GpCuQlQAhO270Ua8ltBhlOENg51H2mnq9Ao0HGs5gRB4wbFzm7A/j14ONS3m86+I1GgAHl8CFTYZprjKdWQghKkyNDxpWVlZMnTq1ssuoWHlZsH6GIWRYORr27CgMFnWbG4LFfykU4OhtuDXqX7Sv+HOG0JGdBA37g0eTO6+tXgt4fBOcXGW45NLjJcMxzcnvHsO01sw4iD4MPrV8wzxx1/JjYtAmJ5fazsLZGbWnZwVUJETVVeODRq10cqUhFDj5wjNHQXUXO5Ba2oBXW8OtvCiV0OoRw60iWFgaNkc7/Rtc2CBBQ9yV/JgYrgx8AH12dqltFRoNQevXSdgQtZoEjZpGp4P912fVdJxydyGjJmk0wBA0zm+A3m9WdjWiGtMmJ6PPzsZz4ftYBgaW2C4vJISYmbPQJidL0BC1mgSNmubyNsPW51YOsmbEzer3NgxATbgAiVcMi48JcRcsAwPRBAdXdhlCVHkyKq6m2Xd9t9U2Y8HaoXJrqUo0ToYxKgDn11dqKUIIUZvIGY2aJPYUhO4EhRI6TqrsaqqeRgMNs2cubIB7nq3sakQtkRcSYvJ5q9AwXFP1FVSNEBVPgkZNUjg2o+lgw0BQUVSj+2HjTMPU2swEsHWr7IpEDWbh7IxCoyFm5iyT7XyAj9UQ2yoR7mJClxBVlQSNmuLaWTj5i+F+52mVW0tV5eRjmN4bewo2vQxDvi5+qq8Q5UDt6UnQ+nWlToPdv3srdT/5GlVaegVVJkTFkqBRExRo4c8poMuHhvebd5XN6u7eN2DlI3DqV0ABQ76SsCHMRu3pWeqMk/zQMxVUjRCVQwaD1gR7P4Grx8HaER74uLKrqdoa9oVh3xpmoJz6Bf6YDLqCyq5KCCFqLAka1d21s7BjgeF+//fAoV7l1lMdNB0sYUMIISqIXDqpzopcMukPLUdVdkXVR2HYWP2EIWyAXEYRVV9KJGQllt7OxtUwJkmIKkCCRnVW5JLJJ7IF+u2SsFFr1Ii9SVIiYXEHyM8qva3aBqYelLAhqgQJGtWVXDIpHxI2arwaszdJVqIhZDy8FNwaltwu4SL8PtHQXoKGqAIkaFRHcsmkfEnYqNFq3N4kbg3Bs1VlVyFEmUnQqI5q0SUTzZlVN91fDg4PQb2Whh1Zy5OEjRpP9iYRonJI0KhuatMlk/B92B761Pit3cHP4OwisLAGzzbg2xGaj4A6TcvneBI2hBCi3Mn01upm7ye155LJjvko9Drjt7nenQ13tDkQ8S/s+Ri+H1i+x/zv1Ned75dv/0IIUctI0KhuslMMX5s8WKMvmQDg1bbIt+qEs0Wft3GFe6aX/3GbDoZB13fB3fU+ROwv/2MIIUQtIZdORNXVeRq6v78AMgBQ5qSCh7shZAUPAb+uoDLTf8KtHoGQ7XBylWEE/+Q9hjExosYrbbfV0p4XQhQlQUNUXbauZLabBrwBQEr/z/Ho9Zj5wsV/DfjAcDYjJRzWvwhDl1bMcUWlKOtuq2CYBmvh7FwBVQlR/UnQEFVaTuOHKQwa+fXaV1zIALB2gKHL4Nv+hvEaDfpAixEVd3xRocq62ypU8YW9hKhiJGgIYYpPB+jxEuyYB+teMHzv7F/ZVQkzKctuq0KI2yODQYUoTbcZ4NMJ8tLht4mGBdOEEEKUSa0NGpmZmfz888988sknXLlypbLLEVWZygIeXgJWDhB1EHYtrOyKhBCi2qiVQeP06dMEBwfz5ptv8u6779KiRQsOHDhQ2WWJqszZDx742HBfprwKIUSZ1bqgkZOTw4MPPshLL73E5cuXCQ8PJzg4mOnTb389htzcXNLS0orcRA3WfBi0GAV6neESSk5qZVckxF2pq9XimHQJYo6bvqVEVl6RotqrdYNBV6xYQfv27ZkyZQoANjY2vPTSS4wYMYK8vDwsLcu+h8b8+fOZO3euuUoVVdGAhRCxzzDldd9i6PVqZVckxB3RZMaxJuoqNpFPl95Ytp0Xd6HWBY3Tp0/z9NNF/2E1adIEnU5HRkYGLi4uZe7rlVde4YUXXjB+n5aWho9PDf2HuPsjOLgUvNpAUC8I7AUugTV/ddL/snaAni8bds+9uEmChig3dmkRhrMHJUm4WK7Hs8xNxUav52jHl2nT8n7Tx5Vt58VdqHVBY968eajV6iKP2draAqDX642PRURE4Ovra7IvKysrrKysyr/Iqub8evj7+pmb8zFwfp3hvqMPBPYwhI6AHmDnXnk1VqT6vQ1fr56A9GtgX6dy6xHVmlV2EgDBWz4ge/8Ck20t7GxR27iWreNSgoldWgQAGQ6+su28MKtaFzSKCwZKpWGoik5n2MDrxx9/ZM6cOZw9exaNRlOh9VU5iVfgj8mG+60eAxd/CNlpGAyZGgnHfjbcAOo0g8CeUP8+Q/ioqWc77DygXiu4ehyu/A2tHq3sikQ1prLSoVDpiNlf+kqjCmsrgqapUDuZaGTjarjU8ftEk321AbIUCvKsZGl9YV61LmgUR3H9F6JOp+PHH39k9uzZ/P333xIy8jJh1RjITTOsI/HgJ6BSQ/eZhuci9kHIDsMt9hRcO2247VsEAd3hgU/ANahy34O5NOhjCBqXtkjQEHdF5+pI0IB4DrV+ldYmLmHkhYQQM3MW2uRk04uKOfkYxlNkJZo87q7QM7x9/h2etfW409KFKBMJGtw4o/Hjjz/y2Wef8ffff9OwYcNKrqqS6fWw7nmIOwO2HjD8e0PIKGRpa7iEUHgZITMBQnfCle1wajWE7oIvuxhW1ezyTNHX1gT1+xjW07jyj2EBr4pcGl3UOGrbArR+9dAEB5dPh04+pY6nSE3NI9ZC/rsV5lcjp7fqdDoOHDjAvn37jJdDTFGpVAB88sknEjIKHVpm2LlUoYLh34FDPdPtbd2g2VAYvAie3me4hKLNMYztWNILoo9USNkVxrsdWDsZprhGH67saoQQosqqcUHj7NmzNG/enM6dO9OlSxfuuecesrOzTb7GxcWFkSNHsn37dgkZAJGHYNMrhvt95oJ/19t7vUsAjPkTHvoKNC5w7RQs623oMzej3MutFEoVBN1ruH9pa+XWIoQQVViNChrJycn07t2bSZMmkZ2dza5duzh+/Djz5s27pW1mZib3338/27ZtQ61Ws3LlSgkZABnx8MtY0OVD08HQedqd9aNQQKtHYNohaD7CsMjV/i/gi05wcUv51lxZGvQxfL0sQUOUICWy1MWwCmd/CFFT1agLdJ999hkPPfQQzz77LADdunXjySefZOPGjbz99tu3tM/KymLy5MmcO3fulimvtVKBFn57AtJjwLUBDF589zNHbN1g6FJoOdIw5iMlApYPN1xmGfghaEyPtFelhN7d8c1JprkKU1IiYXEHyM8y2Uxmf4iarkYFjc2bN/O///2vyGPt2rVjzZo1t7S1tbVlw4YNXLt2rfqEjKwkiDtnuK8s55p1BbBmqmEQp9oWRv4MVvbl13/93vD0ftg+z3Bm4/RvhgGlgz6/tW38BTjzJ5z9E5eQ0+VXQ3nKy4LD3xnGsOgLDOM0Gg+s7KpqnfyYGLTJySbb5IWEVFA1/5GVaAgZDy8Ft5LPlsrsD1HT1aigsXLlylsW2XJyciqyEBdgXGrc1taWwMDAiizxzuVnw/KRkBoBDt43TtuXB10B/Pk0nFxp+MX58BLwaFx+/ReytIV+7xoGUv463hBqCt0ULog7W/7HLi96vSEkbZ0DaVGGx3y7gG/nyq2rFsqPieHKwAfQlzIGC0Ch0WDhXPo6FWbh1tDkglgy+0PUdDXqv+7iVvJUKpVFZp78+OOP/Pjjj2zbtq0iS7s7ugL47UnDFuXWjvDYb2BT9qXSS+375pAx/Dto8kD59F2SwF6Gr8lh8PdbcGFj0XChVBuWOQ8eQsbli0AV2U8m6ghsetnwcwDDyqh93oLgITV3cbIqTJucjD47G8+F72NZyh8MFs7OpteeMKPL8Rnk6EvegC8yyfSlFSGquxoVNIqjUCiMZzRuXoyr2tDrYeMsw7LfKit4ZGX5nm3Y8nrRkNF0cPn1XRKNk+GvvISLsPtDw2M3hQsa3W8cu5GXue7G6/SlT1U2i7QY2DbX8DmB4dJSt+cNA2XVtXxRtyrAMjCw/NafKEdxGbl4ANNXHueMiaChtI7GNgDsNdXkEq4Qt6nWBI2bQ0a1ml2y5yPDmhYoDJc0/LqUX98xx+HAl4b7Q5dVTMgo1HoM7Hwf/DrfEi5uVuDoZ7yvSg0DmldcjXlZ8O/nsPeTGwP6Wj4K971R+roiotZLy87HA3ixbyPcG3YosV1o2gVePQge9pWzb1J0ZjhnE+1KfN46NZRqcoFZVFE1PmioVCoSEhKqZ8g4vsJwaQGg/wIIfqj8+tbpYMOLhrMEzYZBs4fLr++yuOdZw600ihszsNUJ54EHzVfTzSIOwOonbozD8OkE/ecbdq8V4jb4uGio71XyjBKldcm/5M3J3tIRvU7N4jNvsfhMye2a5ObxC5AcdRBTo1wsEitp0K2o8mp80AgKCiIoKIi1a9dWr5Bx+W9Ye30Niy7PQqfJ5dv/ieUQdQgs7aDvO+Xbt5moEs5XzIH0esNnnxZ1fRzGXAh+WMZhiBrFzboumVdm8NnoBgR5lBx2YqIPkvXHczhvmGmyP+eMSrq0Kaq8Gh80GjRowJkzZ4zLjFcLV09cXzRLazjb0LucB0NmJ8PWNwz3e75cbS4DWCReqJgDhe02jB+xtIPJewxjSoSogfRaJwIcGtHU1fQaHoO96/F1l3kEOgaU2Cbt8N/w4YvlXaKoAWp80ACqV8hIDoP/DYe8DMMOqA99AcpyXsD1n3cMc/zdG0PHcj5TYkbqpIuGWTJKM/88Dy0zfG0xQkKGqHLKsi5Iec+yibWwIMejEbg2LbFNgZNcOhHFqxVBo9rISoKfh0HGNajTzLBolkU5DxCLOQ6HvzXcH/BBtdpVVaHNhoRL5lnjo1B6LJxfb7jfboL5jiPEbbJwdkah0RAzc1apbRUaDUHr11XalF4hbiZBo6oo0MKv4yDxkmFBrtG/GtbMKE86HWyYeWMAaEC38u2/IsSeNG/QuLTFcMmqbguo28x8xxHiNqk9PQlav65MK6HGzJyFNjlZgoaoEiRoVAW6AsMMkMLlv0f/Cg5m+B/EieWGxaYs7aDvrXu/VAtJZj496xJk+JocBvk5oLY27/GEuA1qT08JD6LaqVG7t1ZLuRmw8lE48p3h+8GfQ52Sr4PesVsGgFbT/1klh5m3f9/OhjNKuWlwabN5jyWEELWABI3KlBYD390PFzeBhTUM/8Gwq6k5VNMBoLcwd9BQKqH59Z/BqV/NeywhhKgFJGhUlqsnYel9hjEHtu4wbl35Lsh1s2o8APQWSRWwbXzzEYavF7dAdor5jyeEEDWYBI3KcHEzfNsf0mPArRE8uQ182pvnWP9dAbQ6DgC9WUasYWlwc6oTDO5NoCAXfpsAOSXvUyGEEMI0CRoV7cDXsGIU5GdCQA+YsAWc/c13vOP/q3YrgJZEZ2lruJMSYd4DKRTQ7x2w0MDlbbCsj/kHoQohRA0lQaOi6Apg40uGnVj1OsOmYo/9Zt4FobKTYdscw/27XAFUm5xMxq5d6LXaciru9hXYeRnumHucBkD93vDERrCvBwkXYOm9ELbH/McVQogaRqa3VoTcDMMp+IubDN/3fhPuec78e2eU0wDQrKNHiZo+nYL4BGy7dMbzww+xcDa1vZJ5FDh4QcLligkaAJ6tYeJ2w6ygmKPw42AY+BG0HVcxxxeiAlyOyzD5fGia6eeFKI0EDXM4sQrsNIb7ej0cXGIY9GlhDUO+Nt+gz5vFHIdD3xju3+EAUL1WS/LKVVx77z3Izwcg8999hA0fgffiRVg3alSOBZdQQ3q68X6BnRckAMkVMCC0kEM9eHwD/Pk0nPkd/noW4s9D33fLf2l4ISqQs60lGrWK51YdN9lOaR2NbQDEpefS1LViahM1iwQNc9gwA6z+c7bC1h1GrTDfoM+bJVyGVY8B+jsaAFqQkUHK6tUk//gT+TExANj374/r+HFEz5xFfmQkkZMmE7R5E0qrcl4i/eY60tPJmz3b+H3mvjjynZSor/wD+dmg1pjt2EWoNTDsW8OZoR3zYP8X4NUWmg+rmOOLqicl0nC20ASrlMsVVMyd8XLSsG1GD5Iz80y22xV2jC8vQ3p2fgVVJmoaCRrmEHgv2Nx0BsHGDXq+ZN5Bn4WunoCfHoasBHCtD/3mlfml+dHRJP30Mym//oouMxMAlZMTrpMn4TJuHAqFgoBffyHkoSFoY2NJWb0al9GjzfI29AUFRM+YgT4yyvhY7vbTXLaog6NvHC4WT2M9+TuzHLtYCoXhZ5ibBvsWGS6DSdAwq+xz51Dblbx9eVk2FzOLlEhY3AHyTc9+8gGy9FYUWLtUTF13wMtJg5eT6cAemmZTQdWImkqChjmM/BEcHCr+uOH/wvKRkJtGjkVT0rUPo1ixFqWNjeFma3v9q02Rx/IiIkj68UfSN28xTIcFLAMDcRk/DsdBg1Ba31iGW+XkhNukp4id+xaJS5biNGyYWc5qxL2/kMxdu8HK0viYokkTuHCB1DAbUj/Zj822AbhMm4Vd9+4oKuoyRqMBhqBxZbvhs5LLJ2YT8dgY7ErZeVmh0VT8eKGsREPIeHgpuDUssdnl+AzGrrjCksJBzELUUhI0aoqLm+GXsejzc0i6FkzcnnTQfnvb3dh26YLL+HHYdu1a4i9vx6FDSfh6idnOaiT/+itJP/wAgPrZZ+GppwCwmvcu/gUFJL0/i7QjYWSdDiVr8hQsAwJwGTcWx8GDUWrMfDnFu71hqnBWgmHcjWcr8x6vFqv7ztu4NTO9sV15b4d+W9wamvz55+hTiUHWYBFCgkZNcPJX+HMy+Zk6rp5sSGaoYXdH2y6dsahbD11WFrrMTMPXrCx0WZnoMg339VlZKCwtcXjgAVzGjcO6Ucl/oRVSWlqa7axG5sGDxM59CwC3adOI69KlyPOaFi3w+nE9Hp8PIGn7WVJC7MkLDSX2zbnEf/Y53osWYdOmdbnUUiwLSwjoDhc2wJV/JGiYkaW/P5rg4MouQwhxlyRoVHcHl8KGmaRHWXH1iAcF2RkorK2p88orOI0YjqKUKbR6nQ50OhQWt/efgjnOauRFRhL97HTQanEYcD9uU58m7uzZWxsqVaif+IE6eV1xC44hVduTpCOZ5EdFETllCv7/+xmr+vXvup4SBd17I2h0e8F8xxFCiBpALjBXV3o97FyIbu1Mrh5yIGqPCwXZWqyaNiHg999wHjmi1JABoFAqbztkwI2zGgCJS5aiy8297T5uVpCeTuSUKRSkpGDdrBn15s0zXb99XXh4CSo1uGi2Ezh/PJqWLdGlphIx8SnyY2Pvqh6Tgu41fI3Yb1gjRQghRIkkaFRHOh1sfpWc3xcQutmNlCuGpbldJjyB/8qVWAUGVkgZjkOHYlG3Ltpr10hZvfqO+ymcYZJ3+QoWHh54L15cZABqiYLuhe4zAVBumYn3vJlYBgSgvXqVyIkTKUg10/Vxl0Bw8gNdPoTvNc8xhKjGLBIvGNbyKemWElmJ1YmKJpdOqpsCLfo1z5D0yxriTrqDToGFhwee7y3AtnPnCi2lvMZqFM4wUVhb4714Meo6HmV/cc+XIWIfhO3GYvMz+H71E2GPPU7upctETp2K77JlZQstt0OhgPr3GXbEvfw3NOxXvv0LUQ3prJyM953XjgM7E3/Hqm1g6kFw8jF/YaLSyRmN6iQ7hfwlDxP56WbijjuCToFd7/sIWPNnhYeMQnd7VuPmGSae8+ehaW56lsEtlCoYusywIFrcGdRhv+GzbClKe3uyDx8h+sUX0RcU3HZdpSq8fHLln/LvW4hqSGdXx3g/edAP8NTO4m8PLzVMDy5lwTNRc8gZjWok/eOnuLryPAV5Vigs1dSZ/VqZBnya081nNRK++BKHAQPKvK5B0k8/c23+fMAww8Th/vuNzy1ZsoTjx4+TlJRkfOztt9/GxcWw+FGrVq146vq0V+zrQv8Fhv1k9i3CeuYLeH/6CRFPTCBj299k/rsPu25dy+kdX+d/fbXVxEuQlQQ2VXdRJiEqmta1EXjKjCFhUKuDRm5uLtnZ2Tg5OVV2KSbpsrK49u7bpPx2ClBhFeSD1+dfVdhYjNI4Dh1K0v/+R97lK1x7dx5eHywstp02IYGso0fJPnKUrCNHyDl9GgCnESNwm/q0sd2SJUuYNGnSLa9ftWrVLY8Zw0bwEFj/AuSkwtUTZO4/AIDK0RHrpk3u9i3eKiPO8NVCA1aVsDibEEJUE7UyaOh0OmbPns3HH39Mbm4urVq1Yt68edx/01/UVUXO2bNEz3iRvFDDRmIurdW4f/+XWfcYuV1KS0s8580jbNQjpK1bh0P/ftjdey95ISGGYHH0GFlHj5IfEfGfFypxf+45XCc+WeSszPHjx8t03CLtlCrw6woX1pO1ZRWJS9cBUPett7BwNcNOUDFHDV/rtQRVrfxnJIQQZVIr/w85d+5cdu3axbFjx8jOzmbevHkMGDCA119/nbfeeuuu+z937hx2JvZoKAu9TkfB2r/Q/u9/oNWi1ChwbZVA5oCJJFyugps1qVTkD3qQgt//4OKMF0Glgoz/TP1UKFD4+qJs0gRlk8YomwZzzc2Va/9ZK+PmyyWmJCUlcebMGeP3GlV9bJL1XF27Dl1BAcpevYj09oKb2pQX28NbsMnQkaXwJdMM/ddEGf/970EIUSvUuqCh1+v57LPP2LhxI02aGE6pr169mg8//JCZM2eiVCp58803y9RXbm4uuTetH5GWlgZAp06dyr1uAE4CPy4AFpin/4pw8QJs21ouXa1atarYyylwzvDl0kVY8nW5HKtky67fhKhayrLpXKUu4S5qjVoXNACysrJITCw64nnGjBkUFBTw8ssv061bN+67775S+5k/fz5z5841V5lCCHHbLJydUWg0xMycVWpbhUZD0Pp1EjaEWdW6oKFQKOjTpw9vv/02/fr1w+KmVTFnzZrFrl27mDt3bpmCxiuvvMILL9xYgjotLQ0fHx/2799/25dO9Dk5aL//noLNWwx1BgSgfuF5lJ51cf3lQZTZyaTe9z55vt1vq9/q6O233y7hTEVRX9VvQId27VB4e6OoVw/tzz9DRgYO9dNRPfYo2S0fN0t9Fgnncf5rPDorexIf2WJYV0OUKiMjw3xn+4SR2tOToPXr0CYnm2yXFxJCzMxZaJOTJWgIs6p1QQPg3XffpUOHDjzzzDN8+eWXRZ6bMWMGffr0Qa/Xlzpt1MrKCqtiBmU2adIEh9vYJj7/2jUip0wh9+w5UCpxeeIJ3J+bjtLSEs6vB1Uq1KmDR98nQaUuc7/VVeEU1tK45edjfewYHDsGgCVgHVgX/7axKPIvgrk25Dq037AYUWAH6payu6i4ofDSojA/taenhAdRZdTKoNGyZUuWLFnC448/jk6nY/HixcYzGyqVijp16lTY2hQ5584ROXkK2mvXULm44PXBQmxv3rH02M/Xix5VK0IGgF0ZfyEd6diBex96iNwrIeSFXEGXnUO9Fyeg+OthiDwA2lywMMPsnMIZJ15ty79vIYSoYWpl0AAYN24cSqWSp556isOHD/PSSy9hY2PDiy++yJw5cyqkhvTt24me8SL6rCwsg4Lw+forLL29b2pwDS5uNtxv9ViF1FTZ8qKiue/kKT5VKMjT60229b/vPpwfeaTog3o9/OMOmfEQdRj87yn/IqMLg0ab8u9bCCFqmFobNADGjBlD27Zteeutt3jmmWdwcXHhlVdeYdy4cWY/tnFVTJ0Om86d8P70U1T/vdxyaTPoC8CzDXg0NntNVUHCV1/im5fHmjZt2dC6FXHp6cbxGiNHjix+ZdCbKRTg1Q4ubjSceSjvoBG2B+Kuz2rxlKAhxB1LuFh6GxtX2Q+lBriroJGTk4N1eW9YdZf0ej3ffPMNP/zwAzqdjunTpzNixIgS2zdt2pSVK1dWXH0FBVybv4Dknw2XRJyGD6PuG2+gUBdzWUR1/bS/1d2tyVGd2LRtR+rq3wjQ6fjwnXe4mJBgDBqvv/46waWNu9Dp4OoJw323RuVbXFIorBoD6KH5cHCoV779C1Eb2LgaNlX7fWLpbWXztRrhjoLGF198wdtvv01sbCz29vb069ePV155hTZtKv8vvIkTJ7Jv3z5Gjx7NwYMHGTVqFK6ursXOItm8eTPdu3dHo9FUSG26zEyiZ7xIxo4dAFhMe4KLg9qz9eIKziWEEJeVQD17J1ysXXC0cqRB+jW6AzlJocSkhuBk5YSjpSMqpapC6q0MjoMHkbJqFdnHjxP33vsw4Ynb6+DqMUiPAUs7CCjHGTo5qbBiFGQngWdrePCz8utbiNrEyccQHkrbVC3hoiGMZCVK0KjmbjtoHDhwgOnTp/PGG29wzz33EB0dzYYNG+jSpQtffvkljz9unimFZbFhwwZ27drF0aNHjdNLu3TpwkcffXRL0IiLi+Phhx+mU6dObN68ucg017t1Iv4Edjk3zkJk52cTE3Yaz7k/4ByRRJ6FgkUPqthv/yNs/7Hoi+Nu3PXJz6c7oEuLYvAfgwwra6LAycoJb3tvfB188bX3xcfeBz8HP3ztfXG0cqzUTdbulkKppO4brxM6bDhpGzZQ0KH97XVwfr3ha/3eoC6ns226Alg9AeLPg309GLUcLG3Kp28haiMnHwkPtcht/3Y9cuQII0aM4PXXXzc+NmbMGI4cOcL9999Ply5daNSonE9Zl9HXX3/NRx99VGQNixEjRrBo0aJb2np4ePDbb79x5MiRcg0ZAJO2TkKluXHWwe+anpd/LcA5HVJs4P1hSi57gV6nRpfnhi7PDfLdUekcyNdno1BlolBlEaNKA65io9fjjoZ4ctCjJzk3meTcZE4lnLrl2PaW9vja++Lr4Et9p/o83OBh3DRu5fr+zM26aVOcR40ieflytEuW3t6LC4NG4wfKr6Atr8PlrYYN1EYtBweZNiiEEGV1279hGzRowJ49e255vG3btowePZrVq1cze/bscinudnXs2JH+/fsXeczb27vIMuE369+//y3ty8PTayzQqA1BQ6HX0yosD00+RDrZ8PHA3li4N6W3nT9N3b0JdLcjwM0WHxcblAoF52PT2B+SxP6QRA6GJpGoP4SrIh3LC0+QrvfCRpNLc18F7RvocXJIIyojkvC0cCLSI4jLiiM9L50ziWc4k2jYf2PpyaU80uQRHg9+HGfrsm3fXhW4T3+WtE2b0EdFlf1FCZcNZx2UFtCgT/kUcuQH2L/YcP+hL2SmiRBC3KYyBY1NmzZx6NAhWrVqRcuWLUlOTmbt2rUMGjSoSDuVSoVKVXnjB1599dVbHrOyskJ/0zTJ+Ph4Vq9ezZQpU8xWR8fLOdj953PQt2lPl88+oa+b6cWogj0dCfZ0ZELXAHQ6PbmLfSHxDAP8dMTGWZOSpeLABThwAQLd3Hmia29eutcbjaWKbG02UelRRKRFEJEewbbwbZxMOMl3p79j1flVPNb0McYFj8PBsupva65ydMRjxgzSX3nF+Ji+lOmuXLh+NsO/G2ic7r6IsD2GrecBer4CzR6++z6FEKKWKfMZja1bt/LBBx+QlpaGjY0NmzdvZtCgQTz00EN4e3tz+vRp1qxZw47rAx2rCoVCgU6nAwwh47777mPo0KFmPab7rJk42Ny4hm/h7Ix9nz7FzywxQalUoHH1gcQzPNvOhmlt+nA+Np21J2JYfiCckIRMXvvzNB9uucCYTn6M6exPA+cGNHBuAMD44PHsjt7NomOLOJd0jiUnl7Di/ArGNR3HY00fw1ZtW67vu7w5DnkIxQ/fGzZHA/Jee53Ml1/GpmOH4sehGC+bDLz7gxfOMNFpIXgI9Hjpzvsq0MLuDyF0Fwz6DFyD7r4+IYSoJsoUNAovMej1ekJCQjh27BjHjx/n+PHjzJ49m5iYGAAaN27Mjh07GD16tFmLvh0KhQK9Xl8kZJh7QS6XkSNvawlykwrHA6TFoFQqaOrpQFNPB565tz6/HI7k272hRCZl89k/l/lqVwhDWnnxZLcAGtSxR6FQ0N27O129uvJPxD8sPr6YyymXWXR8ET+f+5kJzSYwsvFINBYVM+vmdimUSixfeQX++gsA/dmzRIwfj027drhNm1Y0cKRfg8iDhvuNBtz9wdfPuDHDZPAXd76fSdpV+O1JCL9+uXH9CzDmT9kfRQhRa9zWGA2FQkFQUBBBQUEMGzbM+HhCQgLHjh3j2LFjxe79UZmUSiXZ2dkVFjLK3U1B42a2VhY8fk8AYzr5seXsNZbuDuFYRAqrDkey6nAk9/tYMUwfg4O9BgdnR5q7OPBz/XfYk3KY766s4HJ6FB8e/oAfzv7A+ODxtK3TlkDHQGzUVWs2heKmwKa6vz+Kf7aTdfiwMXC4T38Wm/btDQt0oTcsouXodXcHTbgMV/4GFDDs2zufYRKywzBbJSvBMN22IM/w2MXN0Kj8xwYJIURVVC7TLdzc3OjTpw99+pTTALxyZGlpSWpqavUMGbGn4ewaw/3M+GKbWKiUDGhejwHN63EkPImlu0KJ276TsRuW45SXCYAOSLp+8wUKPwUdkGMZS7zjAl6/X8VlLwWetp4EOgUS5BhEkFMQgU6BBDoGYm9pb973Wgbqp54i6JVXSFyylJRffyXr8GHCx4zF4f7+eHgfQg3QuBzOZpxebfjaoC+4BN5ZH3o9rBoLuamG7/u+A5e3wfl1cPBrCRpCiFqjxi9B3qtXL9asWXPLwNUqTZsLuxbCno8NYwSsHKH9k6W+rI2XA28l7SFx3zLQ60l09CDZ2h5Vbg6W+blotLlYF+RhozXMwlECNnngFw+vrtIxd7SS8DoxxGTGsCe66MwiDxsPmro05dk2zxrHgFQGdd261H3jdVyfmkjCV1+R8suvpG3cRLqFDrcWbjg/NYS7Ho6cGmn46tPhzvtQKKDz04axGQV5sO65G881HXxX5QkhRHVS44OGUqmsXiEj4gCsnXZjH4DGD8CAD0pd7jr/WhwxL75I1qFDADiNGkmjV15Bef1SVq62gMSMPBIycrmalk1SYjrJiamkJ6bQYsXneEVd4rWfrXip92BiPLQora6htIxDrYlHr0olLiuOuKw49l3dx0sdXmJYg2GVujCYum5d6r35Js79uhI7cwrZCRbEH7Ukod/D2PXsgcODD2LXo4fx/d+WzATDV9u7XH+k58vQegzs/QSOfG8IHB2nQNvxd9evEEJUIzU+aFQbuRnw91twcAmgB1sPGPhBmf76zdizl5hZsyhISkJpY0Pdt9/CcWDRmRdWFio8nTR4OmkAJ+BGcCkY0YmI8Y/DmTN8/u9m/prwJrszrbgQlU62HlBmo7SKw8rtb7C7yFv73uLA1QPM6Tynci+p6PVYX/ocv/viSE1vSdJFR3IvXyZ96zbSt25DaW+Pfb++OD7wIDYd2qNQKsvWb2HQsCmHhc4cvWDAQuj6vGEztsBed9+nEEJUIxI0qoLcdPhxMEQfMXzf6jHo+zbYmF5zQ6/VEr94MYlffQ16PVaNG+P9ycdY+vuTkZfBpZRLOFo54qHxwFZtW+IZCJW9PT7LlhIxdhxcusTIFQuY9b+fyXZ243hECkfCkzkakcy/V3zROu3CymMzm8M2cyr+DB/2XEgzt2bl/YmUzYkVELIDhdoap9nf4egSSO6FC6T+9Rdp6zegjY0ldfVvpK7+DYs6dXAYOBDHBx/AqnFj02djsgrPaLiXX60OnrKiqBCiVpKgUdnys2HFI4aQoXGBYd9A0L2lv6yYSyWa515ka+x5/lj/FUcS/0arzzG211hocNO44a5xx93GvchXb3tvWrq3xPfbbwh/bAx54eFEjH8cv59/ontDd7o3NPzCvXQtnbfWubM3LACN13JiiGL0hjG80OZ5xgaPqdhLKRlxsOn6Yl49XwbXIBSAdePGWDdujMeMGWQdOkzaur9I27wF7bVrJH37LUnffotV48Z4ffQRVoEBxfddXpdOhBBCSNCoVNo8+GUchO0GS3sY87th3QYTcvILOPnHZqwXvoU6PZU8S2t+6fkof9lp0a4Yhcom3NhWp7VHochHocohW5tNZHokkemRxfbbwLkBTzV/ip7fLiNqzDhD2HhiAr4//oCFs2Hp8gZ17PnxiQ78c96fuet9ibP6CbXDGT44spCtYXv4vPd7FbfM+caXICcF6raAzs/c8rRCqcS2YwdsO3agzuuvk7FzJ2l/rSNjxw5yz58nasoU/H9ZhcrRsegL83MgL8NwX4KGuEsKixRC0y6gtLYrsU1IakgFViRExZOgYQZv/vsmVrY3BiE6WjnyTOtniq7EqSuAP56CS5sNm3WN/sVkyNDr9aw9EcOxD79k+KHfUaLniqMHC/sFkeD3G0qLTMNsC70Su4LWNNT0w8WqMbsuJZCYlYHCIh2lRRpKdTp+7lq83PJxsMsmW5fM6cTTXEq+xMxdM/F38GfK3NHUf/V7ci9dInLiU/h+9y0qe8NYDIVCwX1N6tC1QX++39uIzw//CC5/cSJxH31WDebdrvPpF3SPmT7Z6y5shDO/g0IFgz4Hlen/jJWWljj06YNDnz5o4+MJHTmSvPBwome8iM/XX6G4ebn4wssmSjVYVf2l2kU5S4ks2/blZZCQE4tt0Ie8ejC/1LYaCw3OVtVnLyIhbocEDTPYFLapyO6tAC7WLjzV4inDN3o9/DUdzvxh+IU28mfw61Jif0fCk3ln/Vmc9/zNzKO/AbCzlStL+iSRb5GEEnC19mBog2GMajwMd5sbYwsKdHpORKXw97lr/H0ujvOx6VxOg8tXDM/7u9rQr7Etth4HWBOykrC0MF5K+5g2j7rz4nc25Jw+TeTkKfguXYLypmXVrSxUTOpRn4fbzOaNDe3YlfoxuVYJzNg9hRUnR/LFAy9iozbD4m05abDu+v4jXaaBZ6vbermFuzs+ixcT9uhoMvfsIe6DD6nz0qwbDQrXK7F1k9U7a5uUSFjcAfKzSm+rtgEbV5NN0vNSUSjzmRr8Bt0Dgk22dbZypp6d6ZllQlRXEjTM4NnWz6KxMyzrHZIawu+XfuevK38xsflEFACbZ8Oxn0ChNIzJaNC72H7i0nN4e905/joRQ+u4izx/bBUAazsq+Plew0JQXTy7MKLRCHp498BCeeuPU6VU0MbXmTa+zszs15jIpCz+OR/HtnPX2B+SSFhiFmF7s7C0qM8jHT/D0eMwa8KWc5R4Xh2q580VCjhyhLCnp+D/9ZJbpou621vx5chBHAprz3N/v0GaxX6OpK2k+//+YUrrCbSo0wAPGw88bDzKZ6nzbW9Cegw4B0CPl++oC+smTfCcP4/o554n6bvvsGrUEKeHHjI8mXn9r1m5bFL7ZCUaQsbDS8Gtoem2Nq7g5FOmbr1s/Wjq2rQcChSiepKgYQaPNnnUuNdJZn4mG0I2EJYWxkdHPuJ5n34oC7cdH/S5yemr7643hAy7vCxeO/o/LPQ69jRV8L9eSnr69OTFdi/i5+B3W7X5uNgwros/47r4k5GrZdfFeL7fG8bBsCR+2BsLeNPE8w2a+J/ivPVa3h0Rx+srCmD/QXaMuA+HDp3xtq6DIr8AfX6+8eaZn8+qfBvOx/hzgQh+6X6NT47PL3Jse0t76tjUwV3jjoeNB9723vT160ugU9lW31TkpsHhbwzfDPjgzpcGBxz69yfr0UMkL19O3Hvv4zh4sGEwa3aSoYHG9IwfUYO5NbztM2VCiJJJ0DAzW7Utz7V9jgUHF/D9me9JTo3ibWtHFDmppY4BeLiNNzsuxDP89N/Y5GSS5F6HlQ/r0RckcTzuOAnZCbcdNG5mZ2XBgOb1uL9ZXXZcjGfprhD2hyRyLiaHczENQDEdrc9pFo3axjMrYql3IREurCPZRJ9e129No+x4eagHGQ5ZKNSGU8jpeemk56VzOeWysf3i44tp6d6SIfWH0D+gv8kdZfWW9uDkBynhkHHtjt83QEF6Ohk7dwJg27XrrTNmFGVcc0MIIYRJEjTM4JkVR7HU3Bhl7qRpxvMt5/D5yXdZE7WNTq51eSA61bBiZJMHSxwL0KOhO1tHBhH3y14APq7/AInhftSrv4KrOZd4csuTzO0yl0FBd7fyqUKhoFcjD3o18iAxI5etZ6+x8XQsey8nEBXRiihaEnnvER6M3Y3CIoEcVR5aFWiVYGVtRwP3xjSq04w6jt4oLCxI+Oor6l69yk+7vTn07PusC0njYHgMemUqCnUaCos0XB2zcXG5ytW845yIP8GJ+BO8d+g9+vr1ZUiDIbTxaFPML3+FYVXNv+fC4W+h9Z3tEqzX64mdM4f86GjUXl7UfeP1G0+qLA1fC/Lu7MMUQghRhAQNM9h+Ph6lVWaRx2xP2fFE77f4NepdPihIo69CiWX0EQjbAwHdSuwrf8liVAVatK3bkd68PSlxGaQcH0Oj5uuIyT/I7D2zCUsNY1rraSjL4a9wVzsrRnXwZVQHX1Kz8tl2zhA6dl3qwIc27YACVLYh2LqcQmV3Ci3ZwDHgGAG2AQwIGED/z95BP2kmBRfOc8+383lk2VJSdG3ZdvYam87EsudSAtdSdVyLAAe7B+jROpzwvB2EpYWx5soa1lxZg5+DHw/Vf4hGeY2KFth6DGyfB9GH4eoJqNfytt9j6u9/kLZhI6hUeH34gXFGDSBBQ4iqpiyzfG5jzIyoeBI0zOCNB5tiY3vjl9efx6I5GJbE5xuU9Gn1KpdtP+YPu1RGpmeQuWMetgEbi+0n+/hxwy9EhYIGc2azrkFDlu4O4f1NF7hw8iHat67H+Zw1LD21lIj0CN655x2sLazL7X042qgZ2taboW29ycjVsuNCHNvOXuOf81akRTYAxYNY2F3AyvEEKrtzhKaGsvj4YhYD46Z154GPDpJ95AhRz07HZ/EiRrT3YUR7H9Jz8vnnfBxf7rjC+VhYt7sxjeu15+Ueei5m/cOm0E2Ep4Xz6dFP0cZrixZl5w5NB8Hp3wxnNR789LbeU25IKLHvvAOA+7PPomnVqmgDCRpCVA02robZPb9PLL2t2gamHpSwUUVJ0DCDEe18jINBDd97s2j7ZT77+xJbj6vw83iGTXW/YFj6IWzD/+XU6ZU0bzaqSB96vZ5r7y8EwHHIEKwbNwbg6Z71cdJY8uofpzh0rDO9O3hyJHMpm8M2E5MRw2f3foabpvxnTNhZWfBAC08eaOFJfoGOQ2FJbDsbx9ZzDkRGNQNlDhb2Z1A7HMfC7hI/5O/i2DBLXllhQebu3UTPnIXXRx+iUKmwt1YzuJUXA5vXY/nBCD7ccpHzV9OZvRIGtXyIlf2nczxpJ39e/pP98fuNNcRlxRFMMLR7whA0Tv4Kfd4G67Ktd5Fz4SKRkyahz87GpnMnXCcWsyOuSm34WlD62gdCCDNy8jGEh7Ksa/L7REM7CRpVkox4qwAWKiXP9W7IiomdqOtgTXicFXvPT2GPfV0AIjbNYEPIhiKvSd+yleyjR1FYW+M+/dkizz3a0Ze3HzLsL7LtoB/3Or6Go6UjpxJO8ej6R7mYXLYFhe6UWqWkS5AbbzzYlF0ze7H5ue682LsFwQ73kR05gczQpynI9uVkvTzmP6RDq4L0zZu5+vob6PX6Ip/L2M7+bH+xJ4929EWhgLUnYhj46UGuRjdnSZ9v+bTbjTMWrx14jYi0CPC7B9waQX4mnPqlTDVn/vsv4aNHo42NxTIwEM/33it+kzU5oyFE1eHkY5gBZOpW2lRkUekkaFSgjoGubJzejT5N65CXb837iVMA6JeRwaf/zOC709+h1+vR5+UR9+GHALg+8TjqOnVu6WtMJz/mDjIsAvTbv9bc5/gOfg5+XM28ytiNY9kdtbtC3pNCoaBRXXum3duANVPvYf8r9zGgYUeywiaTHT2SswEufDpIiU4Bqb//zvk3Xy4SNgBcbC2ZN6Q5f03rSls/Z7LyCnh/0wX6fbyL6Lgba28k5CQwftN4QtJCDWc1AA59a1gAzYSUP/4k4qlJ6DIysGnXDv8Vy1F7eBTfWIKGEEKUKwkaFczZ1pIlY9ry1uBgLikbsbugGRbA2LQ0PjryERO2TOD8N5+SHxGBys0NlycmlNjXuC7+vP6AYSGgH3Zl0s32LdrXbU9mfibT/pnG/879r4Le1Q11Ha35/JHWfDSiNda57Um+8AIH6vRl6YDrv8BXrWX1qyNIykm65bXNvBxZPbkzH41oibu9FWGJWcz+84zxeR87H+Kz43l80+Nc9mtnWLo97gxEHiy2Fr1eT/zixVx95RXQanEYMACfb7+5dX+Tm8mlEyGEKFcSNCqBQqFgbGd//nz6HtbYDQfg4bQcXAtUnA09SPrX3wJgO/kJVHYlrysBMKFrALMHNAHgy3+u0kL1IkPqD0Gn17Hg4AJWnF9h3jdTDIVCwcNtvNk4vRttfeuQHtubNZqZrO5j2C212R+n+fil3vxw5gcKdAXFvvafGT14qnsgFsobU1yfCHqZJi5NSMpJYvzO50hucH2X28Pf3lKDXqvl6uzXSPh8EQCuEyfi+cFClJaWpouXMxpCCFGuJGhUoqaeDrz13FSirBuiQUvP0Hb0PVQP+xyIcIPxFj9x9NrRUvuZ2D2Ql+83DBb9ZFsoaZFDeCLYMFJ73oF5zNo5i6sZV836Xorj42LDqqc68Xzvhih1LnxjO5XNnbsCMGJrNov2LmTaP9PIKNwt9Sb21mpeHdCEJY+1NT4285dL+Gufp7FzMKm5qbyYdgIA/bm/bnl9+pYtpP7+OyiV1H1zDh4zXih+TMZ/yRkNIYQoVxI0KpmNlRrv1n0BqK/OpckFw/TUf1o5EpsbzxObn+CbU9+g0+tM9jO5RxCvDWyCSqngz+Mx/LWzGff7DkOBgo1hGxn05yAWH19MVlk2jCpHFiol03s34JdJnbC3suBL1wGGx3WgUVqzJ3oPj214rMTt6/3cbpzRKdDp+eVAEueOPoKPdRustLkAxKuUJGYXHZmucjFseGXh5obTyJFlL7jwDItSZbqdEKJqSbgIMcdN31KK//+MMC8JGlWBnWGw54j6apqnhAPwr348+amtKNAX8MnRT5j29zRSclJMdvNkt0BWTOxEHQcrQuKzWfN3B3ravUuwS2tyCnL46sRXDPpzEOtD1t8yINPc2vq58PWYtrhpDQuZFVio+XLw93hoPLiSeoXR60dz5NoRk318PLwlLbwdycix4Oyx4bRPNSy/vtdCx8NrHy4yAFbTsgWo1Wjj4siPji57oYWXTFSlXGIRQlQNN6+3saSH6dviDhI2KoGso1EV2BlmQORfjkSlzUfp5s6AAd35fp8vOVmBWNVZy+7o3Ty0Ziif9PqQVh6tSuyqQ4AL65/txgu/nGDXxXjWHlKiUIygVePOJFv9zrWsWF7e/TIrzq/g5Q4v08ytWQW9SehS343ZnevAJkhQ2xJ5xZ7lA5fz7PZnOZt4lie3PMmcznN4qP5Dxb6+hY8TI/s25a+TMby/6QLNstJACScs65CUk8TTfz/NyEYjeaHtC9hobNA0bUr2iRNkHT6Mpbd32YqUoCFqmbyQEJPPW4WG4ZpasX+Y3BZZb6PKk6BRFdi6A5B5KQEAu86deGVgU8Z3DeDTbd6sPuWNledyEolj7MbxPNH0aaa2eRx14XiC/3Czs+KHx9vz75VEvtkTyj/n4zh2zhcUU/HyO0S27RZOxJ/gkfWPMChoENPbTMfDpoTpnuWsm7uKKCDFyp45a04TmRTI5z2XsuDwm2wN38rre18nJCWE6W2moyrm8oVSqWBwKy/6NXJC/X4o6GH71cnkFRzF0uVfVl1Yxb6YfczrNo+67dqSfeIE2UeO3NgGvjSFYzNK+GyFqCksnJ1RaDTEzJxlsp0P8LEaYlslQpOKqe22OflIeKjCJGhUBdcvnWSFZwFKbDt1AqCeo4YFQ1vwZLdAFmxqwp6Ur1A7nuCbs5/zw9nvuKduH6a0GUlTt6a3bECmUCi4p74b99R340p8Bt/vDWP1kSiiw7qgsGiGg+cWdLaHWXtlLVvCttDNuxu9fHrR3bs7jlYmpn/eJW2CIUzZ1nVHp4clu0JYfSSK53o/Q0DzQJac+prvznxHaFooC7otKLEf62vHQZ+PztaDXq3v4ecDPmRlNMHWazUR6RGM3TiWl5zuozWQddj0JZki5IyGqCXUnp4ErV+HNtnUfsywf/dW6n7yNaq09AqqTNQ0tXaMxvnz5xk0aBA+Pj5MnToVrVZb+ovMxc6DgnwF2fGGsGDbqWORp+t72LFsbFeWD/6MevmPoct3QEsGO2P/YNSGUfRY/gCfHlpGQnZCsd0Hudvx9kPN2PfKvbx8f2Pq2nqQGjGMzNCp6LJ9ySnIYWv4Vl7d8yo9VvXgyc1P8r9z/zPLTJWCRMPpzeDgAL4b354gd1uSMvN4Y81Z/trZnPENZmOptGRH5A7GbhxLXFZc8R2F/wuA0q8Lbw5uxqbnuuOraUXa5emQ0QadXsdnBVsAyAsNRZtYymlVY4GFQUPOaIiaT+3piSY42OQt39uzsssU1VytDBqHDh2iW7dutGrVimeeeYbvvvuO7777rvIKsnElK94a9ArU3p6ovbyKbdbGz4UtT77Eiv5/0cX2ZchohV5nQbI2gmVnP6XXqvsY/seTbAjZQn4x0zOdbCyZ3COIXbN68fkjrWnh3pzMsClkhk4jN+Fe1AWeFOgLOBB7gAUHF9D3t76M+GsEX574kgtJF8plAKk2wfAL38LNjV6NPdj0XHfmDgrGyUbNxWsZfL7WnkDtTJwsXbiYfJFZ+0o4rRthCBr4dQEMYey3KV1o4+1JeuQI8mMeQ2fnQIThqhQb/vyw1Jk7wE2XTuSMhhBClIdad+lEq9UyfPhwPv/8c0aNMmxkFh4eTkpKym33lZubS25urvH7tLS0OytKqSIz0XC5wrZV6RdBW3i78PWw0eTkj+LPE5f4/uQaInJ3obKJ4HzaAV7afYDX99jR3as3Pf3a0cilEYGOgVhe/+WpVil5sKUnD7b05Eh4Mj/uC2PjKV+S4vuiUCdg73IBV49LJGovcC7pHOeSzvHF8S/wtPWkhXsLgl2DaeralCauTbC3tC+l2qIKzyxYuLoaaxnXxZ+HWnnx2T+X+OHfMA5dsMfCchJ1GywnPu/SrZ0UaG+sBurb2fiwi60lyyd24rmVx9l0BnIzfYny/wrf+Dgu7viTCV5XeafrO3jZFR/kDH1f/3lK0BBCiHJR64LGgQMHiIiI4OGHHwYMy1SfOnWKw4cPM3/+fJo3b87SpUtp2LD0jXrmz5/P3Llz77qm3MuXSTlvOLlk2yKgzK+zVqsY1a4xo9o1JjLpGZbu38/6kLXkWB8iT53Gtqg/2Rb1JwAqhYoAxwAauTSioXNDevv2xtfBl7Z+zrT1c+b1B3L59XAU/zsQTtQ1N9Ku3YNClUHjwEgcXC9wOf0oMZkxxGTGsClsk7EGPwc/mro2Jdg1mG7e3Qh0DDRZc+GlE5WrS5HHHW3UvP5AUx7r5Me8DefYehaizj6BpdUywLBJ3O9Xfic4OBiunYK8DLBygDrBt3wmi0e34d315/h2byi79L3pwnKaRin44dphhq4dyuyOs3kw6MESCpTBoEIIUZ5q3aWTevXqoVKpeOmllzh69Chjx44lISGBuXPnsnLlSuLj4+nbty/Z2dml9vXKK6+QmppqvEVG3v78bL1WS8wrr6IvANu6Odj7FZT+omL4uNjw1oB7Ofj0xyzu9hvNVDMoSLkHbWYA+gJrCvQFXE65zPqQ9Xx85GOGrh3Kn5f/NL7ezc6KKT2D2DmzF9+Nb899jT1AZ8e5S004sP8hGuV9zNsdP2V6m+n08euDp63hum14WjgbQzfyweEPGPznYEZvGM2q86sISw0r9lJLQWoqAEqN5pbnAALcbFk6th2v3N8Y9FakRQ40PvfzxZ8NC3td2nr9TXcsdmEtlVLBGw825bWBTaiTlQKA3zVo7d6KzPxMZu+ZTUhKCVP6Mq+Pc7GwNvVxCyGEKKNad0YjMDCQr776ivfff58TJ05w6NAhzp49i4+PYWrUH3/8QePGjdm7dy+9e/c22ZeVlRVWVlZ3VU/SDz+Sc+oUShsr6nWMRbH3U2j9GDjc2QAslVJBz0Z16dloPJm5j7HzYjzf/xvKoZBQlNYxdAvOp8DqPMfjj/H63tc5HHuY2Z1mo7HQGF/fq7EHvRp7EJWcxc/7I/h2byj/XkrnUIiCjgHN6dnoXp7u7o6LfT7nks5xNvEsR+OOsi9mHyfjT3Iy/iQALtYutHRvSWuP1rT2aE1T16ZYN2tG7sWLZOzYif299xapXafTs+18FIv37uNM/AWsPGJRWl8wPh/sEoynlQscXGp4oPmwEj8HfUEBD+xcQfLZDQBsCOjCa+0+4OX9j3Mp+RK7o3cT6FTM2ZcT1/eG8e96R5+/EEKIompd0ACYMGECEyZMYNeuXUyaNMkYMgB8fHxQKpU4OTmZvY688HDiP/sMgDqvvIY69WuIOghbXoNht24UdrtsrSwY0LwefZvWYd4GJ77d68SOA3B/sz5MbnGEJae+ZM2VNZxJPMOrHV+lfd32RV7v7WzDy/c3ZlR7H15fc5rdlxLYc9lwe2f9ObycNPRo5E7Phg/ySLfHydGlsO7KOrZHbud0wmmScpLYHrmd7ZHbAbBUWjKgjg+PAkkb15H1zKNEZMdwLvECO0JPcjH5ElpVHApLHZrCYRS6G5ubzekwB9WpXyEzDhy8odnQYt+3LjOT6BkvkrFjBwD/dB/BYuf2HPr9LMN7DGfewXlsDN3IuOBxRV8YfRSuHjeMz2g1+q4/fyGEELU0aBRSKpVcvHiRf/75h3uv/3X9xhtv0K1bN9q1a2fWY+t1Oq6+9jr63Fxsu3TGcdhQiG0AS3rC6d+g7XgI6F4ux7JQKXnjwaY0rmvP7D9PsfF0HOGJwcwfuJiFR1/ncsplntj8BJ3rdebZNs/eslqov5stP03oyJX4DHZciGfHhTgOhCYRnZLN8gMRLD8QgVqloJ2fCz0bdWNG84ewsdQTkXmJ88knOZt8kpPxx0nOTWaNw2X624FLehbvfvIwhxredPXOAhSApcKWRi4NCXZrhMZDwwxmXH9aBf9+bmjb+elix1HkX7tG5JQp5J49h8LKCs/33mNQh24s/mQXB8OSuK9ZMCqFijOJZwhLDcPf0f/Gi49cn3nUdDDYupXLZy+EELVdrQ4aXbp0oXfv3vTv358hQ4YQERFBVlYWW7duNfuxU375laxDh1BoNNR96y3Dglv1WkK7CXBoKax/EabsLddBiSPa+xDobsvkn49w9moar6+wZOHIZexLWsnqS6vZd3Uf+9bvo7dvb6a1nkaQU1CR1we52xHkbseErgFk5xWwLySBnRfi2XExnvDELPaFJLIv5L/rVdS7fuuLpXUSlrYR/NtgOw8ci6HradjvWw9dbl1sFd7c37A1T7TvQoCzp3EBsjNnzhiDhmXELki8DNaO0GbsLe8v59w5IidPQXvtGipXV3y+WIymZUscgJn9GjH3r7N8vvUqHTp35NC1f9kQuoGnWz19/cWpcOo3w/22j5fbZy6EELVdjRwMmp9fti2+lUola9as4Z133kGv1zN06FD279+Ph4d5l+POj40lbuFCADyef67oPhz3zgYbN0i4APu/LPdjt/N3Yc20rjSt50BiZh6TfrhAkHIMawatZVDQIBQo2BaxjYfXPszsPbOJzih+QzKNpYp7G9dh7uBm7JzZi+0v9mTOg03p0dAdNztL7KwssFDevFqpgrwcVzISW7PJ0bCbattLShqnzuC97vPZ//Q7vNNvOIEuXresclrI5tTPhjvtnwSrotNqM/cfIGz0Y2ivXcOyfhD+q1ahadnS+PzYzv608XUiI1dLQqxhpkqRzeVO/gL5meDWyLg2hxBCiLtX485o7NixgyeffJL169fTqFEjk221Wi3W1tbMmmV6rf/ypNfriZ3zJrrMTDStWuE8+j9jATTO0GcurJkKO98zDHi8w4GhJfFy0rB6Smde/PUEG07F8vLvp3CyUXNP/aFMDOzP6axf+Dd2B2uvrGVD6AaGNRjGpJaTcNOUfDkhwM2WALcAHr+n6PRcbYGOvAIdufk6crU6cvILyMkvQBf6J5bhoXzbIAunVibWtbiJOv4UOFpDh0lFHtfrdMTOmYM+Kwubzp3w/vRTVA4ORdqolAreG9qCgZ/t4eQFb5waWxKRHsGZxDM0cw2Gw9cvm7R7HEoIOqIaS4ks26ZbQohyV+OCxrRp01AqlfTs2ZMdO3aUGDaSk5O59957mTlzJo8++miF1Ze2bj0ZO3eiUKup987bKFS3Ts+k5aNw5IdyHRj6XzaWFix6pA1fel7hqx1XSMnKZ/3Jq6w/CdAfn7rtsHTfTJz2FCsvrOTPy38yotEIWnm0wtfeFx97H2zUNqUex0KlxEKlxOY/61/FD+xPwhdfkrFjJ05DhpS98JaPgH2dIg9l7NpFXng4Snt7fBYtQmlrW+xLG9Sx56X7G/P2urPkpDZB7XiCv66sp1l2FsSdMUxpbTmq7LWI6iEl0rA9eH5W6W3VNoZtx4UQ5aZGBY0dO3bg7OzMmjVr6N27t8mwYW9vT1BQEPPmzWP48OGo1eZfoEmblMS1d98FwO3pKVjVr198Q6USBn5gloGhRQ+jYGqv+kzqHsiJqBTDrJJLCRyLTCEy1g1iR6OyuYKVxyZyNJH8ePZHfjz7o/H1rtZu+Dr44Gvvi6+DryGAOPjgbeeNg6VDiZdAAOx69iThiy/J3LsXfX4+ijJ8/noU0OWZWx5P/tFQk9Pw4SWGjEITugZga6nija3nwPEEv5z7ixkRl7EEwywWjXOpdYhqJivREDIeXgpupSzEZ+Mqu4AKUc5qVNBISkpi+vTpuLi4sG3bNpNhw8LCgpUrV5KSklIhIQPg2rvzKEhJwapRI1yffNJ0YzMPDL2ZhUpJWz8X2vq58FzvhqTl5LP/SuL16ay2hIYForI7h9rhJErLRBTqRJQWWSTmJJCYk8CxuGO39KlSqHCycsLZ2rnI18Kbr6M3nq4uFCQmkXXk6C0byRUnz687uDUo8ljOxYtk/rsPlEpcRpftzNSoDr642j/C8/t+xYYkOPO74QkZBFqzuTUEz1aVXYWobGW5RCaBs1zVqKBRuKw4YDJsRERE4Ovri4WFBW5uFTONMS8qmrT160GppN4775TpL3junW1YQCrhgmE1zMYDzF8o4GCtpm9wXfoG1wUgMimLPZdbcCKyH1dTc7iWlsPVjCTStddQWiaiVCcYAohlouF7iwwK9AUk5iSSmFPydfGX/DS0TYSrOzdTv4SgYZFw3ng/q9mYW55P37YNANuu95S4GV1xOgc54nnanXbX4rBES6x1EHW9zTulWQhRiWxcDZfGfp9Yelu1DUw9KGGjnNSooPFfxYWNP/74g9WrV3Po0CGTp/bL3fWdQxVqNdbNgktpfF3aVcOeHgol1G1Wensz8XGx4ZEOvjzSwbfI4zn5BVxLyyE2NYfYm74ei4zjRMxVFKpMnOzzebC1A408VaTmppDy//buPC6qev0D+GeGGYaBYZdFRGRRMjRTUnNPBUtzT3Npu1rhVt5rppZamaZpZYumv0y9pra4XE3TVsvd3DVQ0UxEFATZYRgYBmbm+f1xZHRkVWfmzMDzfr3mxXDOmXMeqYHPfM930RUgvzQff6b/iWuKfDwCYPu5jTi64wz6NOuDPqF9bq2XUpQJj71vmq6n96/8M1CEhgIAdJeSQEYjJNLaB1LlanMRt2sCMkquAkbhLeDi3Zg7gTJWn3k1FcJDXToFfx8nHMdBwyLqddAAzMNGx44dERQUhL1799o2ZEBYFh0ASKeDsaio0qiIKh39P+Hrg4MAr5CajxWBi9wJzXzd0MzXvF8EURT+uJCFBT+dR0pOCdb9DrRu4oF3BjyBjmHCYmrqMjX+Oj8dwAFISYqL+RdxMf8ilsUvQ3Ov5ngi+DH868T/4KS5UWMNqpgYSN3doc/IQMmxY3Dr3LnaY8sN5TiTcwazD76D68XXYNS7QV30OIDV8ILmfn8cjDF759WUw4MI6uU8Gnfy8fFB//79TSEjMDDQ5jVIlUpIVSoAgD4np/YXFOcIczsAQKdJVqzM8iQSCfpEBeC313pg1pMt4a6Q4dx1NUZ8eQSvfHcaafkl8HD2QJSf0LIzrPlQzOsyD92bdIdMKsPl/EsI3fMBlDfOQXPbomkpRSko1ZeaXUuqUMCjv3BLqWDbNrN95YZyxGfFY9WZVYjbNQ6dv+uCMb+OEUJGuRc8C6bgzUE3V3HV5lvxJ8IYYw1XvW/RAIClS5diy5YtooWMCjI/P5RpNNBnZUMRXvNy6jj5FWDQAUHRQNOOtinQwhQyJ4zrEYGnooPx8a5/sPHENfx0JgN/nM/EuB7hGGUQJstSSOUY2mIohrYYCnWZGjd2TkZkynaUA5jp5QlAWPF16qGpUFxUINg9GOGe4Qj3Cke4ZzgieraBbOMmFO36HfETD+Ok5jwOXz+GhOx4lBnNg4lR7wZDcXOEO43EunGPw093TdjBQYMxxqyiQQSNmJgYjBgxQtSQAdwMGleu1N6iodcJo00AoTXDwfsONFIpsPCph/B8p2aY92Mijibn4fM9SShOSsHTAH5PvIE/vz4FD6UMXYt3Y/CV7QCAvx5+B01LjACECdUUUiUIBqQWpSK1KBX70/YLFyDCJ75AcG4pzr/yMlYOlkLrIvzMjHo3GErCYCgJh6I8Eu0aR6LzQ34Y2zUUrs4yQCrcyoFODRjKrTayhzHGGqoGETRatapj50srq+inoc/OrvnAxG2AJhNwbwy0GmL9wmwkKsgDG+I64bfETLz/8wVo9UKLRlahFr8m3kB7yd94z/l9QAJ8oR+ID461RGn2VdPrsxLjoAz0hVSRJTycb31dF1uIaVuNaJdMmL9Whvd6Pwby7YgOTR5E+9a+iA7xRmSAO5ykd4Q2F89bz0sLeTE1xhizsAYRNOyFzM8PAKDPqSFoEAFHlgvPO8bVu0/YEokEfVsHondLf/xtjAf+BrqGeyO8jyf6Hl0KRbke5zx64Kzff9BVZ0CaNBeZN18b5uMGj0ZBkEiCIJVIIJUCUqMEEp0E6oBi/HdUEsbu+BZN8/Ow6o9jaPr5s3DrGF1zQU4yQOEJ6AqF2yccNBirkjwtHdrExGr3Gy9ftmE1zJFw0LAhmV8dWjSuHgZunAFkyno9gZSzTIogbzdkAwjxkKHThalAeQHQ+GG0HrsR/+csjGRJTHRH64XCa1aP6VBL69TjKI8bjrRXX0Xp2bO49uJLCJzzDryffrrmYly9bwUNxpgZg4c7SuVA4GdfIuWzL6s9rsxotGFVzJFw0LAhU4tGTUGjYkjrw6MAVx8bVCWiitsYyfsBl4uAexAwehPgXPM04jWRB/ij2dfrkTFrNtQ//4wbb7+DsqQk+L/xRvVzbCi9gfwUoCTvnq/LWH2l9/PFa3FOmBX+DrqHVR/0L+0/ALziWCPkmG1w0LAhJ1+hRcOQW80ftLIS4OLPwvOHavkUXg+UJSUJTzQZwkx8ozcAHo3v+7xSFxcEfbwYzuHhyFm2DHnr1sOtew+ounWt+gUypfBVp77vazMb41VZbSLXUwJdRCiUD1YfNCR864RVg4OGLZHQ+RFVrdgKAHIlENwRSD0KnFwDhFbzh7EeUP/8Mwp/2AGA4NlMCwxdbdF1KCQSCeSNhVFGUldXuDxQzWJaZSVA+mnheYB9dBpmdcSrsjLmEDho2JCxVAtA+MRdJYkE6PfBzVVbtwDtX6yXYcNYVobMhe8DAHyjNHAbMg6IGmzRaxgKCpC1+GMAQKNXXzXdtqokeR+gLwU8QwD/KIvWwKyMV2VlzCFw0LAhKhUmj5IqqwkagPCp/pExwKmvgF9mAOP2CyMj6ouSPBQsfBX67FzIlAY06tsKiH3X4pfJXroUhvx8ODePgM/zz1V/4D+/CF8f6Ovw85U0WLwqK2N2rUFMQW4vjFqhRUPioqz5wJh3ABcvIPOcEDjqg7Ji4ODHMH7SFrk/ngQA+Hbzh3T0eosP4dUmJiJ/w0YAQODb71S/Uq7RCPzzm/A8sq9Fa2CMMSbgoGFDphaN6m6dVHD1AXq/JTzfMx8orqWzmz0zlAMn/gssbQfsnoeCC+XQa50g8/WE1+LdgHuARS9HRiMy570HEMGjf3+4PVrD9O0ZfwkTozmrgNBuFq2DMcaYgIOGDRm1QtCQ1HTrpMIjY4GA1kBpAbDnPesWZg1GI3Buq9BZ76epgCYTRvcQ5F4RVqH1feXfkCoUFr9s4bZt0CYkQOrqCv8ZM2o+uKI1I6IXILN8LYwxxjho2BTpKlo0arl1Agj9Mvp9KDw/tRZIj7daXRZFBCTtBlb1BLa8COQlA25+QL+PUBAwA/o8NWQBAfAaPtzilzYUFpp1AJUH+Nf8gos3+2dE9rN4LYwxxgT1qJeh/ato0aixM+jtQrsCrYcLI1B+mQG8+Jv9d1g8sBjYO1947uwOdP030GkSjBJn5M54HADgO34cpM7Olr2uNh/Z704XOoBG1NIBFAAKrwszsEICtHjcsrUw1sAZL1+Gtob9Mm9vyIOCbFYPExcHDRuqGN5aa2fQ2z3+nvDJO/UYcGYz8PBIK1VnAfoy4OjNdVravwT0mmVaO6Tgu++gz8y0TmtG9kVoPx2K/F+NACQIDE+AZH1/oHFbIKSTMHRWesfcJf/8KnwN7gCoqhn6yhirM4mHh+l52bTpSKluJl4AEqUSET/9yGGjgeCgYUOGggIAd9GiAQAeQUCP14Hd84CDi+07aOydL6wXogoAnvzI7I97web/AQB84+Is25qRfxVYNxB5J8sAUsIjtAxuvmohmKUeA45/CTw0Ahi6wjxsXNgpfH2AR5swZgmS2+aqcV78EUIjIqo8riw5GenTZ0Cfn89Bo4HgoGEjpNej5PARAIDLQw/d3YvbPS8EjZxLQLlWmEHU3hz8GPhzifA8Zk6lFgSjVpi90SXqQctdsyQP+GYYoMlEWWlTAAZ4TF8JtAkW+rRcPwWc/C9wdrNwfEXYyE8BkvcK21o9Zbl6GGMAAGlEBJQ1LoDIGhLuDGojJadPw1BYCCdPT7hG17J0+Z3c/IR5NUBAbpI1yrs/x1cJQQgA+rwHtHu20iEVc1lQWbllrlmuBb4bCeReAjyCoScvAIAsIBDwe0Bo+XnyQ2D4V4BUJoSNbRMAowE4tU44R0RvwCfMMvUwxhirErdoWEPCJkB1W6uD0hua3WcBAKqej0Eiu8sfu0Qi/PFMPQZkXwQC77JFxJoSNgI/TxOe95gudP6sguTm7RIqt0DQMBqArS8DaccBF0/Q6M3Qrx4BAJWnGo8aJISNLWNvtmyQsFosIMzAyhhjVanLYnw8tX2dcNCwhp9fBxS3RocQAUX7WgMAVL1j7u2cjSKFoJFzyRIVWsaFncD2m8tCdxwP9Jpd7aGmFo37DRpEwgicv38EnBTA6I3QO/kJ83ZIpZD5+lR+jVnYEPqKwM0feODJ+6uFMVb/uPoKi/B9H1f7sXJXYOTXgGuj2s/ZgAMJBw1rCO8NuN6c9lqdjrJ//kZ5Zh4kzs7VL1Vem4pFo3IuWqbG+3V5jzBPBhmAts8CfRfVOPTWYkHj0KfAidUAJMBTK4FmXaBPTAQAOPn6VN9adHvYMOqBds9ZfOpzxlg94NUUeOW4sGhfTUpygE3PC/3EaiN3Fc7ZQMMGBw1rGLkeqBjqlXMJRRN7AgBcOz4CqZvbvZ3T7wHha3YdmvMA4ZO/Nh9Qelt+7o1rx4CNzwKGMuDBQcDApUANQ9kAywQNl8u/AKdvzpLadxHQaggAQJ+VBQCQ+9UyQVfUIOCZTUDiNqDL5HuugzFWz3k1rVsoqEsgyflHaB0pyeWgwaykUQsUZXkDMMC9pdd9nOdmi0ZuktBH4c55ISrkXhb6TZzZBBRcBWQugFczwLvZza+htz1vBrh43l0dGWeAb58WlueO6A0MW12n1WVvdQYtu7vr3UZ1aAHgCqDLv4FOE0zb9VnZAACZfy1BAwCaxwoPxhi7X3UNJA0cBw0rK8/MQmmmAQCgcrt87yfyChH6JBh0QoDwCb+1rzgXSPxeCBjXT5q/Tl8q3G6p7paL0lsIHz7hNx8RwlffCOG+4u2tITmXgK+HArpCoGknYOQ3dV4jRCK//86gEqMeaD0CiJ1rtl2ffTNo3NkRlDHGmOg4aFiZZt8+AICLTxnk2YeEuR9cq+iwWBupE9CohbB0fPY/QghI2i10bry0S+h3AAASJ6Gl4eFRQIs+QnNd/lUhnORfFeaQqHhekiPcXtHmA+l/Vb6mwlMY/lkRPOI3CK8JbAM8uxlwrvttoHu9dSItyjA9L2vcHhjyf5Vu01TcOqlTiwZj7J5cL76K87mqavdfVt/HBykmKEit/VZMkcY2tVhQgw0aRITt27fj7NmzmDp1KlSq6t9A96PkpNDC4NbMWQgDaSeAyCfu7WR+LYWgsWGkECjIcGtf44eBNqOAh4YDqtv+4Lp4mrd+3E6nuRk6UoRbLnnJQN5lIDcZUKcJLRcZ8cKjQqNI4Pltd3XLhYhMrQ4wGuv8OgBwTVhteq7uvajKFpTytDQAHDTqnZwkIL2GFrO6DD9k983d2RNklGN54jwsT6z+uLLse78tyiCEjOUdhdvSNdGRbeqxoAYZNAwGA4YMGQK1Wo0ZM2ZAr9db7VrOoc0AALrMUuBBGeAfde8n6zIZuLxbaIEgA+DbAmjZX2i98L+HGTcVKiCglfC4U7lWCCB5yTdDyGWgvBSIece0fkldFf3+O7SnTkEil0P1WI+7eq3B5wHTc5K7VtpvLCkxhTllu7Z3dW5m53ZOBk4Yaj5G7iq07jGraeQSiOLLr2Ppsy0Q4V/9B7J9p/fhFbxiw8rqmZJcIWQ8tepWn7yqpMQDi8barCxLaJBBY/369bh+/TqOHz8O2d1OnnUbnU4HnU5n+l6tVlc6xj22D3I+X4biGwoYIofC6X46DgW1BSb8CaQcFBYL8w6993PVRq4Uwsu9BJjbGIuLkfn+QgCAb9zLcG7W7K5eXxo5AIAw66jztQNAa/PJyoqPHgWVlUEeFARFixb3VSuzMz1mAD1rWYumgc9PYCuk90KYxwOI8q2+JTNJZYezFtuT2lrgKvY3ihR+11eHb504hp9++gmDBw82hYy0tDR89NFHSEpKQq9evTBlypQ6BZCFCxdi7ty5lXcUZQESYaVWhasacpUe5RoZiiWd4VH56Lvj2URowXAQOV98Af2NG5AHB8N33Li7fj3JbrViKM99Bzxp/olJs1dYs0TVqxcklh7Gy8Tl2bTmX7iMOYK7nQCsHrbQNcigQURISUkBAFy4cAE9e/ZEjx494O/vj1mzZuHo0aPYsmVLreeZOXMmpk6davperVajadOmwLJo08ygEgDuwR7I+1uFomPn4WHHi69ami4pCblrhXVFAmbPgtTlLlatrYJz1hlhDo+QRwEAZDSi6GZnW1WvXvd1bsYYs4q6TgAG1NsWugYZNHr06IE333wTc+bMweuvv445c+Zg0iRhKu2nnnoKgwYNwokTJ9ChQ4caz6NQKKBQVNVZTXLzIXCPkCPvb0Czfz+MZWWWXSbdThERbsydB+j1UMXEwN1SQeDwUiDkWwBAaWIiDNk5kLq6wrVjzf+tGLOk6wVa5BfX3PkxNa+WTn2s4Wjg8200yKDx0ksv4eOPP8bo0aORnJyMbdu2mfYNHDgQ7u7uyK4YJXEvZqbemhkUgNJohOx4T+izs1Fy5AhUjz12P+U7BPWPP6LkxAlIXFwQOGum5U7890/CaIRGzU23Tdy6dWsQ4Y3Zh+sFWsR+vB/a8po7qkpdrsMtDHBX1o+p7pOyau4bkJp7d8GqLDm51mNk3t6QBwXd1XmZ/WmQQUOlUuH7779H7969UVRUhIMHDyI2Vpgt8uTJk5BIJOjSpYvFrieRSqGKjUHBho0o+uOPeh80DGo1Mj/4EADQaMIEyJs0sch5dU27APlHgaPLgQGf8m0TJor84jJoyw34bGRbNK9hFMYV9UXMOg74u9dtUjt75e3mDKXcCVM2xdd4XJn67zqdT+btDYlSifTpM2o9VqJUIuKnHzlsOLgGGTQAoH379ti3bx+eeuopDB8+HDNnzoRcLsdHH32ElStXwsvLy6LX8+jTRwgau/cg8N13IXGqZgrxeiB76ecw5OTAOSwMPi9abhiWtvVzwMGjQPx3KI+Kg+78BUAigapHd4tdg7G6au6vQusm1Y/CkLpYZ24eW2vipcQfrz9W662ijfuyseCL2s8nDwpCxE8/Qp+fX+NxZcnJSJ8+A/r8fA4aDq7BBg0AiI6Oxrlz57BixQrs2bMHHh4e2LJlC7p2vccVVmvg2qEDpJ6eMOTlQXv6NFxr6f/hqErPn0f+d98BAALfeduitzTKA9oBQe2A9L+g+VpoMVE+/DBkvvWvlzZj9qSJlxJNvJQ1HtPYs+6dveVBQRweGpB6FzS0Wi1Wr16NV199tU7DHVUqFaZNm4Zp06ZZtS6JXA63zp1R9OuvKD5+vN4GjcxFHwBGIzye7Ae3zp0te3KJBIgaDKT/haI/TwDg2yZMHBJZAa6oL9bYapFcWHsfBMYagnoXNCZNmoS1a9eaWipqChurV6/GkCFD0KjR3c10ea90F4WFzZxDQ21yPVsrPnYcJcePQyKXw3/6dIufX1qSDRz+HIZyCUquC9vcY3pb/DqM1SSn9AbcIj7GrOO1r9ujlCnhrfC2QVWM2a96FTSysrKwa9curFmzBnFxwuQo1YWNrKwszJgxA8uWLcOJEycgl1u3Z7ju8mWUXblycxru+tkZNGf5cgCA19PDIW/c2OLndz8wFyjJRbE2EmTQwLlZMzhHRFj8OozVpKisEBJpOV5p9Q56hFUxff9tvBXeaKyy/HuhIeHRKY6vXgWNjRs34oUXXsDYsWOhVCrx3HPPAag6bPj7++OPP/5AYmKi1UMGIKz3AQCuXTrDyUoLuInp9tYM37g6zIB3D5wzTgLeKhSVRQM4APc+sTwbKBNNE7dmiPK9j7WLWI14dEr9Ua+CxpgxY6DVClN/jxolTNNdU9iIjo5GdHS0TWor+v0PAID7zWG09Y21WzMq0OOLoJm4DACgiomx2nUYY+Li0Sn1R70KGh4eHvC4baKsqsLGlStXsGDBAqxevdp6n4a/Hw+43hptUV6oR2liIiCRwN2/AEjaDagChIerLyCVWqcOG7Fma4ak7NYkQaVhfVBcGg6jRgMnv0ZQPvywRa/FGLMvPDqlfqhXQaMqt4eN4uJiHDp0CLNnz7Zuk/vFn0xrnQBA0UU3AJ5wbVQK2d47RrdInACVvxA6PJoAwe2FlVmDogH5/a0NYitWa80wGuB+8NaidZoub0C+/RcAgHvvGEgcPKAxxlhDUO+DBiCEjfT0dEybNg1ffvmlqaOo1fSZD6hujTkvOr0RQDrcO0YBEXJAkwUU3QBKcgAyAEUZwiMjXggpAODkLMwZEdIJaNpJ+OrqY92674HVWjOIgF9mQHHtoGmTUeaKoj27AdTfW1CMMVbfNIigkZycjKVLl9omZABA+zGmtU70ubkoSfoMAOA+9Uvg9um4DeVAcTagyRTCR+5lIPUocPUIUJwFpB4THlgiHN/oAWHl0pAuQPNYQOVn/X9LLazWmnHoU+DEatBti9PRpUvCImoqFdwe7Wi5azHGGLOaBhE0Nm/ejNmzZ9smZNyhaM8ewGiES1RU5TU/nOSAR5DwqNB5kvBpPv8KcO0ocO2IsDR6zsVbj9PrAUiE2yyRTwDtXxKltcNqrRkJG4Hdwi2T4kdfAzAPAGA4egwyAKrHHoOEF1FjjDGH0CCCxptvvinatYv/PAwAUMXexQgJiQTwCRcebZ+5eaJcoXXj2hEgeR9w4wyQdkJ45FwCnlpp+eJrUbB5MwDAc/gwy7VmGPTAzzcn++oyGdomI1ARNIwJCQAAVW+eDZQxZo7n27BfDSJoiIluDreVBwTe34ncfIGWTwoPAFCnAwc+Ak6uAUry7rPKe2PUCCNClK0fstxJDWWATi08f+wNIOnarX06HQBAHnifP0vGmFVdVl+GJLf6DveWnMiM59uwfxw0HJVHEBDcQQga9RZPxsWYo3CVuZueT/9zOpz/qf72plKmxA+Df7BI2Ljb+TZKTp2Ccy3H1hW3kNQNBw3GGGP3zVtxaxXlj7p+hOaRzas8LrkwGTMPzkS+Lt9irRp1mW/jblo+6opbSOqGgwZjrN7L0GQgX1f7p9isIh2KtDUvlnY646Klyqq3jKX+MJY2qXIf6YRbrnVZ3daSt1jq2vJRV3czI2l5enqdrltfW0g4aFgQEQEA1Gq1aVtRWRmKDQaoS0ogvW27RWhKAB0B2nLA0ueuA6v828pu/psAQK2GRnNrZlCNXg+DwQC1RgO9CP9edn8q3hcV75PqVOw/knwBuhN77v+65YX4v3PvQWcsve9zVSCjHF5SF7P3ekMnM+pMz8et+RMujVKrPE4iK4BrmBQzdtXesqCQumBS67fhIfe0WJ2W4qzNRbDBgN27dqI88US1xzmpNQj4bAWkurJaz2lUOCNzygQYPKpfD0uSmQSg9veRPZGQI1Vr59LS0tC0aVOxy2DMrqWmpiI4OLja/fw+Yqx2ly9fRnh4uNhl1AkHDQsyGo1IT0+Hu7u73a8qqlar0bRpU6SmppqtD2OPuFbrsHWtRISioiIEBQVBWsP08dZ8HznSfx+A67UFR6u5sLAQISEhyM/Ph5eXl9jl1AnfOrEgqVRa4yc1e3TnQnT2jGu1DlvW6ulZexO4Ld5HjvTfB+B6bcHRaq4prNsbx6mUMcYYYw6HgwZjjDHGrIaDRgOlUCgwZ84cKBQKsUupFddqHY5Uq6U42r+Z67U+R6vZ0eoFuDMoY4wxxqyIWzQYY4wxZjUcNBhjjDFmNRw0GGOMMWY1HDQYY4wxZjUcNJjJ2bNnsWLFCrHLqHfWrVuH48ePi10Gu4mIkJCQgHPnzoldSp0VFxfj2LFjvLYKMzl16hSuXLkidhl1wkGDARBCxuOPPw53d3exS6nV0aNH8eijj8LFxQVt27bF+vXrxS6pWl999RVmzZrlEDMOfvHFFwgJCYFKpcKAAQNw8uRJsUuyuMLCQvTs2ROTJk3C/v37xS6nTtatW4fAwEB06tQJLVu2xMWL9r16bGpqKvr37w9XV1eEh4djwYIFKCurfUExMa1duxbR0dEICwvDW2+9BYPBIHZJNbp8+TJiY2PRs2dPxwgbxBq8M2fOUGBgIH3zzTdil1Kr8+fPk6+vL61evZoOHjxI48aNI4lEQk8//TSVlpaKXZ6ZNWvWUFBQEF24cEHsUmq1fPlyatmyJf3222+0Y8cO6tatG8lkMlq+fLnYpVnUv/71LxozZgwZjUaxS6mTr7/+mkJDQ+nIkSOUlpZGjzzyCL3++util1UttVpN4eHh9NZbb9GRI0do7ty55ObmRtHR0ZSRkSF2eVWaOXMmtWjRgpYtW0YzZswgJycn+uCDD8Quq0bnzp2jsLAwioqKopCQEEpOTha7pBpx0Gjgzpw5Q35+fqaQUVpaSosXL6aYmBgaNGgQ7dy5U+QKzY0bN46mTJlitm3nzp3k6upK/fv3J71eL1Jl5tasWUMBAQGmkHH9+nX697//TT169KAxY8bYXfgICgqiP/74w/S9wWCg119/nQDQ559/LmJllqPT6UihUNC5c+dM2zIyMmjp0qW0ZMkSunHjhojVVVZaWkpeXl508OBB07b33nuP5s6dS3v27KErV66IV1w1Vq5cSd26dTPbdu7cOWratCk98MADlJOTI1JlVUtISKDAwEDKzMw0bZs0aRKFhISIWFXtdDodeXp6UkZGhlnYMBgMdPXqVbHLq4RvnTRwOTk50Gg0OHXqFEpKShAbG4sNGzYgOjoa+fn5GDhwIBYvXix2mSbZ2dnQ6/Vm2wYMGIAdO3Zg165dmD9/vkiVmbt27Rry8/Nx6dIlnDt3Du3atUNqairat2+PAwcOoEOHDjh69KjYZZrk5OSY/VylUikWL16M6dOnY8qUKTh27JiI1VmGVquFTqdDaWkpAGDPnj148MEHsXr1asyZMwetWrVCQkKCyFXekpSUhIKCAvj6+gIQVhn99ttv8fnnn2PkyJGIiIjAJ598InKV5qp6f7Zq1Qr79u1DQUEBxowZI05h1fjqq6/w1ltvwd/f37Rt6NChuHbtml3f7nF2doafnx9KSkqwZ88eqFQq9OzZE6NGjcLMmTPFLq8ysZMOE9/u3btJqVRSixYtaPTo0WatAq+99ho5OTlRUlKSiBXe8tlnn5Gnpyelp6dX2vfBBx+QUqmkvLw8ESqrbM6cOeTs7EzNmjWjNWvWmLYXFRVRhw4dqGXLliJWZy42NpZ69+5d6ZaCwWCgxx57jGJjY0WqzLJCQ0Np3LhxpNFoKCAggH755RciIsrLy6NHHnmEunbtKnKFt5SWllKLFi0oMjKSZs+eTS1atKCnn36aSkpKTC1OEomEEhMTxS7V5NChQwSA9u3bV2nfH3/8QQDo0KFDIlRWtc2bN1NWVpbZtnPnzhEA0mg0IlVVNwMGDKAdO3YQEVF6ejqpVCpycnKyq/8fKnDQaICKiooqbasIG9evXzfbXlpaSgqFgr766isbVVczjUZDoaGh1KVLFyopKTHbV1paSh4eHvTjjz+KUtvKlStpyZIlZtvmzJlDDz30UKVjf/nlFwJgN/etDx8+TFKplN56661K+3bt2kVOTk52c1vqfixZssT07xw0aJDZvi1btpC7u7tIlVUtNTWV3n77bfrggw+oUaNGZn/89Ho9qVQqWrdunYgVVtavXz9q0qRJlU34Xbp0oXfffVeEqurun3/+IQCm35NqtZpmzpxJZWVlIldmbtq0afT++++TwWCgF154gWJiYuy2zwbfOmlgzp49ixYtWuC7774z2967d2/s2bMHQUFBZtudnJwgk8nQpEkTW5YJADh+/Dh69eqF7t27Izs7GwDg5uaGLVu24MyZM+jfvz8KCwtNxysUCri5uYkywmPVqlWYMGEC3n//fbMm13fffRerVq2qdLxCoYBSqYS3t7ctywQAHDp0CLNmzcKiRYuQlJQEAOjcuTM+/PBDzJ8/H3PmzDE73sfHByqVClKp4/+6eOWVV9C3b1/Mnz8fGRkZZvuuXr2K6OhokSqrWnBwMObNm4eePXuitLQUcrnctK+wsBA6nQ5t27YVr8AqrF27Fi4uLujVq5fp/68KPj4+dj8CSyKRAACMRiOKiorwxBNPoKioyOxnbw9atmyJM2fOYOzYscjIyMDOnTtNt1FeeOEFscszJ3bSYbb1xhtvUGBgIDk5OdG3335b6/GLFy+mNm3a2PzT7MWLF8nX15e2bt1a5f4///yTfH19KSwsjDZt2kSXLl2i1157jTp16mTzEQUrV66kJk2a0M6dO0kikdD69etrPN5gMNATTzxBU6dOtVGFt8ybN48CAwNpwoQJFB0dTU5OTjR79mwyGAxERPTRRx+RVCqlvn370qFDhyg+Pp66dOlC8+bNs3mt1lJSUkJDhw4lADRx4kT6+++/af369eTn50cnTpwQu7wqZWdnk0KhoDFjxpBWq6XMzEyKiYmh8ePHi11ala5du0atWrUiT09PWrJkCSUlJdGKFSsoKCiIsrOzxS6vRklJSQSAUlNTqXPnzvTqq6+KXVKVjh8/ThKJhPr06WPWunvjxg27a9HgoNHArFmzhiZMmEATJkyoNmwYjUZKTEykSZMmUfPmzUXpnxEXF0f/+c9/zLaVlJRQamqq6fvr16/T888/T66uruTk5ERDhw61ea/2ipBx8eJFIiLq378/PfLII1Ueq9Pp6MCBA9S1a1caPHgw6XQ6W5ZKu3fvpoCAAFP/Fr1eT3379iUA9Mwzz5gC2oEDB6h79+4klUrJ3d2d5s6d6zDDQe/GmjVrqE2bNuTi4kKPPvooHTlyROySarR27VqSy+Xk7OxMTk5ONHHiRCovLxe7rGoVFxfTG2+8Qb6+vgSAHn30UbvsP3Cn5ORkAkAPP/yw3YaMCps2bap0C9kecdBoYA4fPkzdu3cno9FoChvffPMNTZo0ydR6UFhYSGPHjqWlS5dScXGxKHV26dKFPv30U9P3CxYsIFdXVwJArVq1ovPnz5v2GQwGm//RJhKG1d4eMohq7vB2+vRpeuGFF2jbtm02rPKWl156iV5++WWzbd9++y09+OCDJJFIaP78+Wb7dDqdqaXDEWRmZtrdJ7na3G24SUlJoU2bNok2PDo/P5/efvtt6t+/P82ePbtOHSaNRiNptVobVFe1nTt30ogRI2jkyJF0+PDhWo9PT08niUQiWsi4fv06vf7669S/f39auHChXYfJuuKg0cDk5eWRj48PEZEpbACgqKioKjuJimXYsGHUt29fIiJatGgRtWnThg4cOEAHDx6kNm3aUFhYmCjh4nYlJSVV/mFr06YNDR8+XISKajZ+/Hhq1aqV2c/tpZdeoo8//pimTp1KSqWS8vPzxSvwPpSVlVGrVq3q1BHOaDTSyZMnbVRZ9b788ksCQIsWLar12LNnz4o+Id3Vq1cpLCyM+vbtS3FxceTq6koDBgyo8lidTkcJCQk2rrCyGTNmUJMmTWjixIkUHR1Nzs7OdObMmSqPPXXqlClYizUyJiEhgfz9/WnYsGH0wgsvkEwmowkTJlR5rFqtNvuQY884aDRA/v7+lJ6eTkajkSZOnEj+/v517rNhK1u3biUA9OOPP1JwcDClpKSY9l26dIkA1OnTiRjWrFlDTk5OdjdxzunTp0mhUFD37t1p7dq1NHbsWGrZsiUVFhZSXl4eOTs70/bt28Uu85589913FBsbW6de9ytWrCCZTEZbtmyxYYXm9Ho9hYaG0quvvlpr2CgqKiI/Pz/q27evqGHjiSeeoHfeecf0fcV79NSpU5WOffPNN0mlUok6lPX333+nkJAQ0+3UsrIyioyMpNGjR1c6Ni0tjVxcXOhf//qXaK14RqORWrduTStXrjRtW7JkCUkkkionk3vuuecoICDAIW5HcdBogHr06EG//fYbTZw4kbp160Zqtdp0G2Xz5s1il2fy5JNPkpeXF7m7u5t1Ri0uLiapVGp3s2tWKC0tJX9/f5oxY4bYpVSyb98+6tChAwUGBtKYMWPMOuZFRUXRhg0bRKzu3nXv3p327dtHN27cqDVs6HQ6evrpp+nXX3+1cZW3/PDDD6bWgPfff7/WsPHDDz/QM888I1oz+qVLlygyMtLsfWg0GsnLy6vK4bUFBQXUt29fOn36tC3LNDN48GD6/vvvzbbNmzevyuHmREIL0+TJk21RWpX27t1LPXr0MNtWWFhIAGj37t2Vjr9+/TrFxMTQ5cuXbVXiPeOg0QCNGzeOQkNDqVu3bqbbJUajkWbOnGlXf7zVajV169aNANDs2bOJSOiPMXnyZHr88cdFrq5mc+bMIW9vb9H6uNytrKwsUqlUdjmtdV3cPjqpLmFDbKmpqXTs2DHT93UJG2JKTEykzz77rNL2hx56iL788ksRKqrdK6+8Umnui3Xr1lGLFi1Eqqhm+/fvr3K9KW9vb/rpp59EqMhyOGg0QH/99RcNHDjQrvpkVEer1dKUKVNILpdTZGQkhYeHU7du3exuzYQ7ZWZmkkKhoK+//lrsUmpkMBjo1KlT9Oijj9Ibb7whdjkWU1XYOHz4sN3ebiOqHDZ0Oh0tX77crkf8dOjQgb744gvT97t27bLr4aubNm2iiIgI0/dqtdru1nO6U+PGjc1q3L59u8N8gKkgs/W8Hcx6ioqKsG3bNpSVlSE2NhahoaFVHte2bVvs2LHDtsXdITMzEzt37oSzszP69u1rttbA7VxcXPDpp5/ijTfewNGjR9GoUSN07drVNKmOLSQnJ2PXrl3w9vbGk08+CXd391pf4+/vj+XLl2PYsGE2qPCWhIQE/Pnnn2jSpAn69esHZ2fnGo/fsGEDtm7dismTJ+PZZ5+1UZXWFxAQgD179qB3797o2bMnFi1ahClTpuCbb74Ru7RqVaxR8eabb0Kv1+P48eNwdnbGuHHjIJPZ569qqVQKo9EIANixYwfGjx+PX3/9FY0aNRK5sqrdXm/FZFwdO3bEgAEDRK6serfXvGrVKixYsAD79+9Hs2bNRK7sLoiddJhlJCYmUnBwMHXo0IFCQkLIycmJXnvttSpHZog9ZHHPnj3k4+NDXbp0IX9/f1IqlfThhx9WeazYtX799dfk6elJ3bp1Iw8PD/Lx8am206zYtc6fP598fHyoa9eu5OLiQiEhIWYrst5O7Fpt5caNGxQZGUlKpZJ27doldjl18t577xEAGj58uN0PbezUqRMtW7aMfvjhBwoMDBS1T0ZdbNmyhUJDQ0mtVtv1ZFy3Cw4Opu3bt9PKlSupWbNmDtEn404cNOqJhx9+mJYtW0ZEQn+LFStWkIuLC8XGxppN6KLVaikmJsasudOWiouLyc/Pz9QRT6fT0Zw5c0gqldLYsWPNmokzMzOpbdu2onXaS0lJIQ8PD9Oy4oWFhRQXF0cAKs2Uef78eYqKihJtSN+BAweocePGprVTMjIyaMCAASSTySrdvtm7dy+1adOG0tLSxCjVpg4fPkz+/v4OEzJ0Oh0NGjTIIUIGEVHnzp2pf//+DhEyiIS+PAEBAQ4TMoiImjZtSkOGDHHYkEHEQaNeSE9PJwCV+i0cOHCAVCoVDRs2zLTNYDDQ2LFjqXHjxlRYWGjrUunAgQPk5eVVafuGDRvIycnJbKRGcXEx9erVizp06CDKfeo1a9ZQ+/btK22vuJd++zC0ij4Bo0aNsmWJJrNmzap0bYPBQC+//DLJZDKzP7Tnz5+nwMDAKhdQq090Oh1FRkY6TMggEqaAd5SQQSSM9vH29naIkEEkjN4B4DAhg4goLCyMQkJCHDZkEHHQqBc0Gg3J5fJKQ7mIiH7++WeSSCRmzf0Gg8FsKm9bSkxMJABVfvJftmwZSSQSOnr0qGlbcXGxaB0/f/jhB1IqlVVef8qUKaRUKs1aBbKyskSbDvjjjz+mZs2aVeplbzAYaNCgQRQUFGRWW2pqql13MrQUR5ie+Xbl5eUOEzKIhNugjhIyiIT/H5YvXy52GXfl+++/d+iQQUQkISISs48Is4xRo0bh+PHjOH36NLy8vMz2jR07FpcuXcKhQ4fEKe4OHTp0gFwux/79+yutiNi9e3dERERg7dq14hR3G61Wi4iICDz22GPYsGGD2T6dTocHHngAL7/8Mt566y2RKrwlLS0NzZs3x/Tp0/Hee++Z7cvKykJYWBj++9//YtSoUSJVyBhrqBx/3WcGAPjkk09QVFSEgQMHQqPRmO0bNmwYrl69KlJlla1cuRKnT5/G888/D71eb7bPnmpVKpX44osvsGnTJtOIgAoKhQIDBgywm1qDg4OxcOFCLFiwoNKy9P7+/ujevbvd1MoYa1g4aNQTQUFB+Pnnn3H27Fn07NkTV65cMe07ceIEOnXqJGJ15tq1a4eNGzdi27ZtGDRoEHJyckz77K3WwYMH49NPP8WiRYvw8ssvo6SkBABgNBpx+vRpu6r1tddew+TJkzF+/HjMnTsXBoMBAFBcXIwLFy7YVa2MsYaDb53UM+fOncOIESOQkpKCIUOGoKysDCdPnsTBgwfRtGlTscszs3fvXjz33HPQarUYMmQI0tLSkJubi3379tVprgpb2rBhAyZMmACVSoUBAwYgPj4e/v7+2L59O5ycnMQuz8zixYsxe/ZshIaGonfv3ti7dy/69OmDzz//XOzSGGMNEAeNeqi8vBybN2/G0aNHERwcjLi4OPj4+IhdVpU0Gg2+/fZbnDlzBlFRUXjxxRehVCrFLqtK2dnZWL9+Pa5cuYKOHTvi2WeftbuQUSElJQXffPMNsrKyEBsbi0GDBoldEmOsgeKgwRhjjDGr4T4ajDHGGLMaDhqMMcYYsxoOGowxxhizGg4ajDHGGLMaDhqMMcYYsxoOGowxxhizGg4ajDHGGLMaDhqMMcYYsxoOGowxxhizGg4ajDHGGLMaDhqMMcYYsxoOGowxxhizGg4ajDHGGLMaDhqMMcYYsxoOGowxxhizGg4ajDHGGLMaDhqMMcYYsxoOGowxxhizGg4ajDHGGLMaDhqMMcYYsxoOGowxxhizGg4ajDHGqjR79mwsXLhQ7DKYg+OgwRhjrEo7duyASqUSuwzm4DhoMMYYq0Sn0+Hvv/9Gu3btxC6FOTgOGqze6NatG6ZOnYqJEyfCx8cHjRo1whdffIHc3Fy8+OKL8PLyQmBgIDZu3Ch2qYzZvbNnz8JgMMDV1RUvvfQSevTogWnTpkGj0YhdGnMwHDRYvUBEOHPmDP73v/+hX79+uHbtGuLi4jB16lSMHj0aI0eOxPXr1zFixAhMnjxZ7HIZs3vx8fFwcXHBlClTMHDgQEybNg1bt27FhAkTxC6NORiZ2AUwZgnJyckoKirCqlWrMGjQIADAgAEDsGjRIvznP//BE088AQDo378/li9fDqPRCKmUczZj1YmPj4dcLsfWrVvh5+cHACgsLMT48eNBRJBIJCJXyBwF/6Zl9UJ8fDzc3d3x1FNPmbZduXIFfn5+6Nevn9m20NBQDhmM1eKvv/5CXFycKWQAQFBQELRaLcrKykSsjDka/m3L6oWEhAS0bdsWcrnctO2vv/5C+/btzUJFxXGMsepV3Irs1KmT2fbz588jNDQUCoVCpMqYI+KgweqF+Pj4Sr3jq9qWkJDAvegZq8WlS5eg0Wjg7u5utn3NmjUYNWqUSFUxR8VBg9ULVQWIO7cREc6ePcstGozVIj4+HhKJBBs2bAARgYgwf/585OXlYfr06WKXxxwMdwZlDq+goADXrl0zCxBpaWnIzc0123b58mVoNBoOGozVIj4+HjExMdBqtWjWrBkMBgOaNGmC3bt3w8fHR+zymIOREBGJXQRjjDH7ceXKFUgkEoSGhiIlJQV6vR7NmzcXuyzmoDhoMMYYY8xquI8GY4wxxqyGgwZjjDHGrIaDBmOMMcashoMGY4wxxqyGgwZjjDHGrIaDBmOMMcashoMGY4wxxqyGgwZjjDHGrIaDBmOMMcashoMGY4wxxqyGgwZjjDHGrIaDBmOMMcas5v8BxgNrKUnTSBQAAAAASUVORK5CYII=", + "text/plain": [ + "<Figure size 550x550 with 4 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = None\n", + "for label, (p, s, color) in runs.items():\n", + " fig = corner.corner(\n", + " s[:, p.columns(line.params)],\n", + " fig=fig,\n", + " color=color,\n", + " labels=[\"$m$\", \"$b$\"],\n", + " truths=[truth[\"m\"], truth[\"b\"]],\n", + " truth_color=\"k\",\n", + " plot_datapoints=False,\n", + " plot_density=False,\n", + " levels=(0.68, 0.95),\n", + " range=[(0.3, 1.0), (1.5, 2.4)],\n", + " )\n", + "fig.legend(\n", + " handles=[plt.Line2D([], [], color=c, label=l) for l, (_, _, c) in runs.items()],\n", + " loc=\"upper right\",\n", + " frameon=False,\n", + " fontsize=8,\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7414cdf9", + "metadata": {}, + "source": [ + "Only the mode built from the prediction covers the truth with an honest\n", + "width. Statistics only is precise and biased; the inferred noise recovers\n", + "the 5 % the experiment reported and is therefore just as precise and just\n", + "as biased, because a diagonal cannot describe a common shift; the Peelle\n", + "covariance is biased further down and narrower than the honest one." + ] + }, + { + "cell_type": "markdown", + "id": "480de3bb", + "metadata": {}, + "source": [ + "## 5. Infer a normalisation or an offset the experiment did not report (recipe 4)\n", + "\n", + "The mode's magnitude need not be known. `T.normalization(log_eta)` samples\n", + "it; `T.offset(...)` is the same for an additive shift. Here is an offset\n", + "defect, treated with the magnitude known and with it free." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5b4aebd8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:32:07.489144Z", + "iopub.status.busy": "2026-09-11T03:32:07.489005Z", + "iopub.status.idle": "2026-09-11T03:32:46.962513Z", + "shell.execute_reply": "2026-09-11T03:32:46.961824Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "offset known : m = 0.535 +/- 0.109 b = 2.359 +/- 0.310\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "offset free : m = 0.547 +/- 0.108 b = 2.553 +/- 0.725 log_omega = -1.140 +/- 1.013\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "offset ignored : m = 0.535 +/- 0.110 b = 2.301 +/- 0.061\n" + ] + } + ], + "source": [ + "offset = 0.3\n", + "y_off = y_true + rng.normal(0.0, noise_fraction * y_true) + offset\n", + "data_off = rx.Dataset(x, y_off, noise_fraction * y_off, label=\"offset\")\n", + "comp_off = rx.Comparison(data_off, line)\n", + "\n", + "log_omega = rx.Parameter(\n", + " \"log_omega\", prior=stats.norm(np.log(0.3), 1.0), latex=r\"\\log\\omega\"\n", + ")\n", + "p_off_known = rx.Problem(\n", + " [rx.Constraint([comp_off], terms=[T.offset(magnitude=offset)])]\n", + ")\n", + "p_off_free = rx.Problem([rx.Constraint([comp_off], terms=[T.offset(log_omega)])])\n", + "p_off_ignored = rx.Problem([rx.Constraint([comp_off])])\n", + "for name, p in [\n", + " (\"known\", p_off_known),\n", + " (\"free\", p_off_free),\n", + " (\"ignored\", p_off_ignored),\n", + "]:\n", + " s = fit(p, seed=5)\n", + " print(f\"offset {name:8s}: {summary(p, s)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "bb79e5fd", + "metadata": {}, + "source": [ + "## Sharing an error model between datasets (recipe 5)\n", + "\n", + "A second experiment measures the same line with its own normalisation\n", + "defect. Two ways to couple them:\n", + "\n", + "- **case B**, share the *parameter*: pass the same `log_eps` object to a\n", + " term on each comparison. One column, two block-diagonal blocks.\n", + "- **case A**, share the *mode*: one normalisation term `on=[comp1, comp2]`\n", + " couples the data through off-diagonal blocks (the next notebook).\n", + "\n", + "Here is case B with the noise fraction inferred once for both." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "736b0ed9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:32:46.963854Z", + "iopub.status.busy": "2026-09-11T03:32:46.963692Z", + "iopub.status.idle": "2026-09-11T03:33:07.630117Z", + "shell.execute_reply": "2026-09-11T03:33:07.629387Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "one shared object -> one column: ['m', 'b', 'log_eps']\n", + "two objects -> two columns: ['m', 'b', 'log_eps_1', 'log_eps_2']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "m = 0.428 +/- 0.140 b = 2.155 +/- 0.068 log_eps = -2.326 +/- 0.111\n" + ] + } + ], + "source": [ + "x2 = np.linspace(0.01, 0.8, 27)\n", + "y2_true = truth[\"m\"] * x2 + truth[\"b\"]\n", + "y2 = (y2_true + rng.normal(0.0, 0.025 * y2_true)) * 1.1 # 10 % high\n", + "data2 = rx.Dataset(x2, y2, 0.025 * y2, label=\"second\")\n", + "comp2 = rx.Comparison(data2, line)\n", + "\n", + "shared = rx.Parameter(\"log_eps\", prior=stats.norm(np.log(0.05), 1.0))\n", + "c_shared = rx.Constraint(\n", + " [comp, comp2],\n", + " terms=[T.noise_fraction(shared, on=comp), T.noise_fraction(shared, on=comp2)],\n", + " statistical=False,\n", + ")\n", + "p_shared = rx.Problem([c_shared])\n", + "print(\"one shared object -> one column:\", p_shared.names)\n", + "\n", + "eps1 = rx.Parameter(\"log_eps_1\", prior=stats.norm(np.log(0.05), 1.0))\n", + "eps2 = rx.Parameter(\"log_eps_2\", prior=stats.norm(np.log(0.05), 1.0))\n", + "c_separate = rx.Constraint(\n", + " [comp, comp2],\n", + " terms=[T.noise_fraction(eps1, on=comp), T.noise_fraction(eps2, on=comp2)],\n", + " statistical=False,\n", + ")\n", + "print(\"two objects -> two columns: \", rx.Problem([c_separate]).names)\n", + "s_shared = fit(p_shared, seed=6)\n", + "print(summary(p_shared, s_shared))" + ] + }, + { + "cell_type": "markdown", + "id": "a840baae", + "metadata": {}, + "source": [ + "The two datasets disagree by 20 %, and a diagonal noise is the only\n", + "freedom this error model has: the inferred noise grows to about 10 % to\n", + "absorb the disagreement, the slope is covered, the intercept only just.\n", + "A normalisation mode per dataset is the model that knows what happened." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f7406537", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:33:07.631733Z", + "iopub.status.busy": "2026-09-11T03:33:07.631525Z", + "iopub.status.idle": "2026-09-11T03:33:46.613008Z", + "shell.execute_reply": "2026-09-11T03:33:46.612258Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "p_both_stat = rx.Problem([rx.Constraint([comp, comp2])])\n", + "s_both_stat = fit(p_both_stat, seed=7)\n", + "p_both_norm = rx.Problem(\n", + " [\n", + " rx.Constraint(\n", + " [comp, comp2],\n", + " terms=[\n", + " T.normalization(magnitude=0.1, on=comp),\n", + " T.normalization(magnitude=0.1, on=comp2),\n", + " ],\n", + " )\n", + " ]\n", + ")\n", + "s_both_norm = fit(p_both_norm, seed=8)\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "for label, (p, s, color) in {\n", + " \"statistics only\": (p_both_stat, s_both_stat, \"C0\"),\n", + " \"shared inferred noise (case B)\": (p_shared, s_shared, \"C2\"),\n", + " \"a normalisation mode per dataset\": (p_both_norm, s_both_norm, \"C1\"),\n", + "}.items():\n", + " lo, hi = band(p, s)\n", + " ax.fill_between(x_fine, lo, hi, color=color, alpha=0.25, label=label)\n", + "ax.plot(x, y_true, \"--\", color=\"k\", label=\"truth\")\n", + "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", color=\"C4\", ms=4, label=data.label)\n", + "ax.errorbar(data2.x, data2.y, data2.y_err, fmt=\"s\", color=\"C5\", ms=4, label=data2.label)\n", + "ax.set(xlabel=\"x\", ylabel=\"y\", title=\"two datasets, 90 % predictive bands\")\n", + "ax.legend(frameon=False, fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "bf699a94", + "metadata": {}, + "source": [ + "## Takeaways\n", + "\n", + "- The covariance is the statistical model. Declaring it is the work; the\n", + " inference machinery does not change from one row of the ladder to the next.\n", + "- A defect the experiment did not report can be inferred: a noise level, a\n", + " normalisation, an offset, each one column in the chain.\n", + "- A correlated systematic is a mode built from the *prediction*. Built from\n", + " the data it is Peelle's Pertinent Puzzle.\n", + "- Sharing a `Parameter` object couples parameters (case B); a term spanning\n", + " comparisons couples data (case A). Both are declared, never configured." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From b1efa73078abdc6e00045d17f1f3eb9aa800f783 Mon Sep 17 00:00:00 2001 From: beykyle <kylebeyer1@gmail.com> Date: Thu, 10 Sep 2026 23:38:45 -0400 Subject: [PATCH 36/75] Add the normalization_and_covariance_structure notebook (recipes 3, 6, 27) Four datasets of a quartic with drawn normalisations; the latent scale on the model and the reported normalisation mode agree, the two treatments that ignore it do not; inferred scales realign the data; a gallery of covariance structures drawn from matrix(theta): rank-one versus kernel, a reported correlated statistics matrix versus its diagonal, model-error widths, shared versus per-dataset modes; the case-term-structure table. Runs in 328 s. --- ...rmalization_and_covariance_structure.ipynb | 766 ++++++++++++++++++ 1 file changed, 766 insertions(+) create mode 100644 examples/normalization_and_covariance_structure.ipynb diff --git a/examples/normalization_and_covariance_structure.ipynb b/examples/normalization_and_covariance_structure.ipynb new file mode 100644 index 0000000..97848c1 --- /dev/null +++ b/examples/normalization_and_covariance_structure.ipynb @@ -0,0 +1,766 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cf7c4837", + "metadata": {}, + "source": [ + "# Normalisations and the structure of a covariance\n", + "\n", + "Four datasets of one quartic, each with its own unknown normalisation. Four\n", + "treatments of that normalisation: as a latent scale on the model, as a\n", + "correlated mode in the covariance, ignored, and ignored but with an inferred\n", + "model error. Then a gallery of covariance *structures* and what each one\n", + "does to a posterior.\n", + "\n", + "Recipes: 3, 6, 27" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b4a05e02", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:32:30.969910Z", + "iopub.status.busy": "2026-09-11T03:32:30.969730Z", + "iopub.status.idle": "2026-09-11T03:32:33.493640Z", + "shell.execute_reply": "2026-09-11T03:32:33.493055Z" + } + }, + "outputs": [], + "source": [ + "import corner\n", + "import emcee\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from numpy.polynomial import polynomial as P\n", + "from scipy import stats\n", + "from sklearn.gaussian_process.kernels import RBF, ConstantKernel\n", + "\n", + "import rxmc as rx\n", + "from rxmc import terms as T\n", + "from rxmc import transforms as tf" + ] + }, + { + "cell_type": "markdown", + "id": "3a6cec73", + "metadata": {}, + "source": [ + "## Four datasets, four normalisations\n", + "\n", + "The truth is a quartic. Each experiment measures it on its own range with\n", + "its own noise, and multiplies everything by a normalisation `ρ_i` drawn\n", + "from a distribution of known width `σ_sys,i`, which it reports. The realised\n", + "`ρ_i` are stored only so we can check the inference later." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "74d53ccd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:32:33.495495Z", + "iopub.status.busy": "2026-09-11T03:32:33.495277Z", + "iopub.status.idle": "2026-09-11T03:32:33.706339Z", + "shell.execute_reply": "2026-09-11T03:32:33.705535Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.default_rng(42)\n", + "a_true = np.array([1.0, 0.5, -0.1, -0.4, 0.1])\n", + "settings = [ # (domain, n, noise, sys_fraction)\n", + " ((-0.3, 0.4), 50, 0.1, 0.1),\n", + " ((0.3, 0.5), 30, 0.1, 0.5),\n", + " ((0.1, 0.6), 25, 0.1, 0.2),\n", + " ((-0.5, 0.1), 15, 0.2, 0.6),\n", + "]\n", + "datasets, rho_true, sys_fraction = [], [], []\n", + "for i, (domain, n, noise, sys) in enumerate(settings):\n", + " x = np.sort(rng.uniform(*domain, n))\n", + " rho = rng.normal(1.0, sys)\n", + " y = rng.normal(P.polyval(x, a_true) * rho, noise)\n", + " datasets.append(rx.Dataset(x, y, np.full(n, noise), norm_err=sys, label=f\"exp {i}\"))\n", + " rho_true.append(rho)\n", + " sys_fraction.append(sys)\n", + "\n", + "x_fine = np.linspace(-0.6, 0.7, 100)\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "ax.plot(x_fine, P.polyval(x_fine, a_true), \"k--\", label=\"truth\")\n", + "for d, rho in zip(datasets, rho_true):\n", + " ax.errorbar(d.x, d.y, d.y_err, fmt=\"o\", ms=3, label=f\"{d.label}: rho = {rho:.2f}\")\n", + "ax.set(xlabel=\"x\", ylabel=\"y\")\n", + "ax.legend(frameon=False, fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5f0482d5", + "metadata": {}, + "source": [ + "## The model and a tight prior on the constant term\n", + "\n", + "An additive constant and a multiplicative normalisation are nearly\n", + "confounded on a short range, so the prior on `a0` is tight; the other\n", + "coefficients get wide priors." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3d7e7824", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:32:33.707827Z", + "iopub.status.busy": "2026-09-11T03:32:33.707649Z", + "iopub.status.idle": "2026-09-11T03:32:33.715049Z", + "shell.execute_reply": "2026-09-11T03:32:33.714390Z" + } + }, + "outputs": [], + "source": [ + "coeffs = [rx.Parameter(\"a0\", prior=stats.norm(1.0, 0.03), latex=\"a_0\")] + [\n", + " rx.Parameter(f\"a{k}\", prior=stats.norm(0.0, 1.0), latex=f\"a_{k}\")\n", + " for k in range(1, 5)\n", + "]\n", + "quartic = rx.Model(lambda x, *a: P.polyval(np.asarray(x, dtype=float), a), coeffs)\n", + "\n", + "\n", + "def fit(problem, seed, n_walkers=32, n_steps=2500):\n", + " sampler = emcee.EnsembleSampler(n_walkers, problem.ndim, problem.log_posterior)\n", + " sampler.random_state = np.random.RandomState(seed).get_state()\n", + " sampler.run_mcmc(problem.sample_prior(n_walkers, rng=seed), n_steps, progress=False)\n", + " return sampler.get_chain(discard=n_steps // 3, thin=5, flat=True)\n", + "\n", + "\n", + "def curves(problem, samples, n=300):\n", + " on_fine = quartic.bind(x_fine, {})\n", + " cols = problem.columns(quartic.params)\n", + " return np.array([on_fine(*s[cols]) for s in samples[-n:]])" + ] + }, + { + "cell_type": "markdown", + "id": "72c3ea51", + "metadata": {}, + "source": [ + "## Four treatments of the normalisation\n", + "\n", + "1. **Latent scale on the model** (recipe 6): `quartic | tf.scale(rho_i)`\n", + " gives each comparison its own multiplicative parameter, sampled in log\n", + " space with the reported width as its prior.\n", + "2. **Correlated mode in the covariance** (recipe 3): `T.normalization` with\n", + " the reported magnitude, one per dataset, built from the prediction. The\n", + " `Dataset` carries `norm_err`, so `comp.reported_terms()` builds exactly\n", + " this.\n", + "3. **Ignored**: reported statistics only.\n", + "4. **Ignored, with an inferred model error**: a diagonal `T.model_error`\n", + " whose fraction is a free parameter, the honest-looking way to be wrong." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ce2922c7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:32:33.716272Z", + "iopub.status.busy": "2026-09-11T03:32:33.716139Z", + "iopub.status.idle": "2026-09-11T03:32:33.726483Z", + "shell.execute_reply": "2026-09-11T03:32:33.725979Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "latent scale columns: ['a0', 'a1', 'a2', 'a3', 'a4', 'log_rho_0', 'log_rho_1', 'log_rho_2', 'log_rho_3']\n", + "normalisation mode columns: ['a0', 'a1', 'a2', 'a3', 'a4']\n", + "ignored columns: ['a0', 'a1', 'a2', 'a3', 'a4']\n", + "ignored + model error columns: ['a0', 'a1', 'a2', 'a3', 'a4', 'log_gamma']\n" + ] + } + ], + "source": [ + "rhos = [\n", + " rx.Parameter(\n", + " f\"log_rho_{i}\", prior=stats.norm(0.0, np.log1p(s)), latex=rf\"\\log\\rho_{i}\"\n", + " )\n", + " for i, s in enumerate(sys_fraction)\n", + "]\n", + "comps_scaled = [rx.Comparison(d, quartic | tf.scale(r)) for d, r in zip(datasets, rhos)]\n", + "comps = [rx.Comparison(d, quartic) for d in datasets]\n", + "gamma = rx.Parameter(\n", + " \"log_gamma\", prior=stats.norm(np.log(0.1), 0.5), latex=r\"\\log\\gamma\"\n", + ")\n", + "\n", + "problems = {\n", + " \"latent scale\": rx.Problem([rx.Constraint(comps_scaled)]),\n", + " \"normalisation mode\": rx.Problem(\n", + " [rx.Constraint(comps, terms=[t for c in comps for t in c.reported_terms()])]\n", + " ),\n", + " \"ignored\": rx.Problem([rx.Constraint(comps)]),\n", + " \"ignored + model error\": rx.Problem(\n", + " [rx.Constraint(comps, terms=[T.model_error(gamma, averaging=True)])]\n", + " ),\n", + "}\n", + "for name, p in problems.items():\n", + " print(f\"{name:22s} columns: {p.names}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "30f39acf", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:32:33.728021Z", + "iopub.status.busy": "2026-09-11T03:32:33.727870Z", + "iopub.status.idle": "2026-09-11T03:37:20.072820Z", + "shell.execute_reply": "2026-09-11T03:37:20.072153Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "latent scale a0=1.01+/-0.03 a1=0.47+/-0.08 a2=0.19+/-0.30 a3=-1.05+/-0.54 a4=0.22+/-0.85\n", + "normalisation mode a0=1.01+/-0.03 a1=0.47+/-0.09 a2=0.18+/-0.30 a3=-1.03+/-0.55 a4=0.28+/-0.83\n", + "ignored a0=1.09+/-0.02 a1=0.11+/-0.08 a2=-2.17+/-0.27 a3=0.90+/-0.53 a4=6.00+/-0.84\n", + "ignored + model error a0=0.99+/-0.03 a1=-0.08+/-0.17 a2=-0.73+/-0.40 a3=0.67+/-0.77 a4=1.06+/-0.93\n" + ] + } + ], + "source": [ + "samples = {name: fit(p, seed=i) for i, (name, p) in enumerate(problems.items())}\n", + "colors = {\n", + " \"latent scale\": \"C0\",\n", + " \"normalisation mode\": \"C1\",\n", + " \"ignored\": \"C3\",\n", + " \"ignored + model error\": \"C2\",\n", + "}\n", + "for name, s in samples.items():\n", + " cols = problems[name].columns(quartic.params)\n", + " print(\n", + " f\"{name:22s} \"\n", + " + \" \".join(\n", + " f\"a{k}={s[:, c].mean():.2f}+/-{s[:, c].std():.2f}\"\n", + " for k, c in enumerate(cols)\n", + " )\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4d000385", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:37:20.074120Z", + "iopub.status.busy": "2026-09-11T03:37:20.073972Z", + "iopub.status.idle": "2026-09-11T03:37:21.810668Z", + "shell.execute_reply": "2026-09-11T03:37:21.810197Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1180x1180 with 25 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = None\n", + "for name, s in samples.items():\n", + " fig = corner.corner(\n", + " s[:, problems[name].columns(quartic.params)],\n", + " fig=fig,\n", + " color=colors[name],\n", + " labels=[f\"${p.latex}$\" for p in quartic.params],\n", + " truths=a_true,\n", + " truth_color=\"k\",\n", + " plot_datapoints=False,\n", + " plot_density=False,\n", + " levels=(0.68,),\n", + " range=[(0.9, 1.1), (-0.5, 1.5), (-3, 3), (-4, 3), (-8, 12)],\n", + " )\n", + "fig.legend(\n", + " handles=[plt.Line2D([], [], color=c, label=n) for n, c in colors.items()],\n", + " loc=\"upper right\",\n", + " frameon=False,\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c3388568", + "metadata": {}, + "source": [ + "The latent scale and the normalisation mode agree: they are the same\n", + "statistical model, one with the scale sampled and one with it marginalised\n", + "analytically (a Gaussian latent that multiplies the prediction is a rank-one\n", + "mode in the covariance). The two treatments that ignore the normalisation\n", + "do not converge near the truth, and inflating the diagonal does not rescue\n", + "them: a common shift is not noise." + ] + }, + { + "cell_type": "markdown", + "id": "c8fe3a36", + "metadata": {}, + "source": [ + "### The inferred normalisations\n", + "\n", + "With the latent scale the `ρ_i` are columns of the chain. Divide each\n", + "dataset by its posterior median scale and it should realign with the truth." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "427eec67", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:37:21.812139Z", + "iopub.status.busy": "2026-09-11T03:37:21.811984Z", + "iopub.status.idle": "2026-09-11T03:37:22.546555Z", + "shell.execute_reply": "2026-09-11T03:37:22.545978Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 970x970 with 16 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "p_scaled, s_scaled = problems[\"latent scale\"], samples[\"latent scale\"]\n", + "rho_cols = p_scaled.columns(rhos)\n", + "fig = corner.corner(\n", + " np.exp(s_scaled[:, rho_cols]),\n", + " labels=[rf\"$\\rho_{i}$\" for i in range(4)],\n", + " truths=rho_true,\n", + " truth_color=\"C3\",\n", + " show_titles=True,\n", + ")\n", + "plt.show()\n", + "\n", + "rho_map = np.exp(np.median(s_scaled[:, rho_cols], axis=0))\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "ax.plot(x_fine, P.polyval(x_fine, a_true), \"k--\", label=\"truth\")\n", + "for d, rho, r_true in zip(datasets, rho_map, rho_true):\n", + " ax.errorbar(\n", + " d.x,\n", + " d.y / rho,\n", + " d.y_err / rho,\n", + " fmt=\"o\",\n", + " ms=3,\n", + " label=f\"{d.label} / {rho:.2f} (true {r_true:.2f})\",\n", + " )\n", + "ax.set(xlabel=\"x\", ylabel=\"y / rho\", title=\"data divided by the inferred scale\")\n", + "ax.legend(frameon=False, fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "afd811d5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:37:22.548051Z", + "iopub.status.busy": "2026-09-11T03:37:22.547911Z", + "iopub.status.idle": "2026-09-11T03:37:22.700688Z", + "shell.execute_reply": "2026-09-11T03:37:22.700226Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "for name, s in samples.items():\n", + " lo, hi = np.percentile(curves(problems[name], s), [5, 95], axis=0)\n", + " ax.fill_between(x_fine, lo, hi, color=colors[name], alpha=0.3, label=name)\n", + "ax.plot(x_fine, P.polyval(x_fine, a_true), \"k--\", label=\"truth\")\n", + "ax.set(xlabel=\"x\", ylabel=\"y\", title=\"90 % bands of the model\")\n", + "ax.legend(frameon=False, fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1a15f5af", + "metadata": {}, + "source": [ + "## A gallery of covariance structures\n", + "\n", + "Every term writes a pattern into Σ. `constraint.matrix(theta)` returns the\n", + "assembled covariance at any parameter value, so the pattern can be drawn." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e36a7f83", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:37:22.702362Z", + "iopub.status.busy": "2026-09-11T03:37:22.702148Z", + "iopub.status.idle": "2026-09-11T03:37:22.706773Z", + "shell.execute_reply": "2026-09-11T03:37:22.706117Z" + } + }, + "outputs": [], + "source": [ + "def correlation(S):\n", + " d = np.sqrt(np.diag(S))\n", + " return S / np.outer(d, d)\n", + "\n", + "\n", + "def show(ax, S, title, dividers=()):\n", + " im = ax.imshow(correlation(S), cmap=\"RdBu_r\", vmin=-1, vmax=1)\n", + " for k in dividers:\n", + " ax.axhline(k - 0.5, color=\"k\", lw=0.5)\n", + " ax.axvline(k - 0.5, color=\"k\", lw=0.5)\n", + " ax.set(title=title, xticks=[], yticks=[])\n", + " return im\n", + "\n", + "\n", + "d0 = datasets[0]\n", + "theta_map = np.median(samples[\"normalisation mode\"], axis=0)" + ] + }, + { + "cell_type": "markdown", + "id": "1c5a8ff7", + "metadata": {}, + "source": [ + "### 1. A systematic correlated across x: flat mode versus smooth kernel\n", + "\n", + "A normalisation is a rank-one mode: every pair of points is correlated by\n", + "the same fraction. A Gaussian-process kernel correlates neighbours in `x`\n", + "and forgets across the range. Both are one term." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "33af7f01", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:37:22.707880Z", + "iopub.status.busy": "2026-09-11T03:37:22.707736Z", + "iopub.status.idle": "2026-09-11T03:37:22.791636Z", + "shell.execute_reply": "2026-09-11T03:37:22.790837Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 700x350 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "c_flat = rx.Constraint([comps[0]], terms=[T.normalization(magnitude=0.1)])\n", + "c_kernel = rx.Constraint(\n", + " [comps[0]], terms=[T.kernel(ConstantKernel(0.05**2, \"fixed\") * RBF(0.15, \"fixed\"))]\n", + ")\n", + "fig, axes = plt.subplots(1, 2, figsize=(7, 3.5))\n", + "p_flat, p_kernel = rx.Problem([c_flat]), rx.Problem([c_kernel])\n", + "show(\n", + " axes[0],\n", + " p_flat.constraints[0].matrix(theta_map[: p_flat.ndim]),\n", + " \"normalisation: rank one\",\n", + ")\n", + "im = show(\n", + " axes[1],\n", + " p_kernel.constraints[0].matrix(theta_map[: p_kernel.ndim]),\n", + " \"kernel: smooth in x\",\n", + ")\n", + "fig.colorbar(im, ax=axes, shrink=0.8, label=\"correlation\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "28bc55cf", + "metadata": {}, + "source": [ + "### 2. Correlated statistical errors: bring the matrix, and do not diagonalise it\n", + "\n", + "If the experiment reports a full statistical covariance, pass it as a fixed\n", + "`Term`. Treating correlated noise as independent over-counts the\n", + "information: the naive posterior is visibly tighter than the correct one." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "92010ede", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:37:22.792950Z", + "iopub.status.busy": "2026-09-11T03:37:22.792817Z", + "iopub.status.idle": "2026-09-11T03:37:55.950221Z", + "shell.execute_reply": "2026-09-11T03:37:55.949639Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 550x550 with 4 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "diagonal: m = 0.802 +/- 0.028 full: m = 0.793 +/- 0.051\n" + ] + } + ], + "source": [ + "xs = np.linspace(0.0, 4.0, 20)\n", + "C = 0.15**2 * np.exp(-np.abs(xs[:, None] - xs[None, :]) / 1.2)\n", + "a_line = np.array([1.0, 0.8])\n", + "y_corr = a_line[0] + a_line[1] * xs + rng.multivariate_normal(np.zeros(20), C)\n", + "d_corr = rx.Dataset(xs, y_corr, np.sqrt(np.diag(C)), label=\"correlated noise\")\n", + "m_, b_ = rx.Parameter(\"m\", prior=stats.norm(0, 5), latex=\"m\"), rx.Parameter(\n", + " \"b\", prior=stats.norm(0, 5), latex=\"b\"\n", + ")\n", + "line = rx.Model(lambda x, m, b: b + m * x, [m_, b_])\n", + "c_full = rx.Constraint(\n", + " [rx.Comparison(d_corr, line)], terms=[rx.Term(C, kind=\"matrix\")], statistical=False\n", + ")\n", + "c_diag = rx.Constraint([rx.Comparison(d_corr, line)])\n", + "s_full, s_diag = fit(rx.Problem([c_full]), seed=11), fit(rx.Problem([c_diag]), seed=12)\n", + "fig = corner.corner(\n", + " s_diag,\n", + " color=\"C3\",\n", + " labels=[\"$m$\", \"$b$\"],\n", + " truths=[0.8, 1.0],\n", + " truth_color=\"k\",\n", + " plot_datapoints=False,\n", + ")\n", + "corner.corner(s_full, fig=fig, color=\"C0\", plot_datapoints=False)\n", + "fig.legend(\n", + " handles=[\n", + " plt.Line2D([], [], color=\"C3\", label=\"diagonal only (overconfident)\"),\n", + " plt.Line2D([], [], color=\"C0\", label=\"full matrix\"),\n", + " ],\n", + " loc=\"upper right\",\n", + " frameon=False,\n", + ")\n", + "plt.show()\n", + "print(\n", + " f\"diagonal: m = {s_diag[:, 0].mean():.3f} +/- {s_diag[:, 0].std():.3f} full: m = {s_full[:, 0].mean():.3f} +/- {s_full[:, 0].std():.3f}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6a6d2bd0", + "metadata": {}, + "source": [ + "### 3. Unknown or misreported magnitudes\n", + "\n", + "A `model_error` term scales with the prediction; its fraction sets how much\n", + "the total error exceeds what was reported." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a8814e5e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:37:55.952069Z", + "iopub.status.busy": "2026-09-11T03:37:55.951897Z", + "iopub.status.idle": "2026-09-11T03:37:56.068441Z", + "shell.execute_reply": "2026-09-11T03:37:56.067591Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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Bpk2bYv369WoD65Ah3MTkJ0+elFt2bm4uzpw5A2trawQEBMDERPcJ1IQQw1AwBfZF78OSG0vwLP8ZAOAN1zfwTctv4GPjozZfXeu6cDF3QVJuEq4lX0N79/Zq0xbICjDh5ASk5qeinnU9/K/d//j3lC1WZYuokUMjBNgF8OvkahtYFUyBQzGHsCxsGZ7kcJ9hjqaOaOzYGAF2AQiwDcD2B9tx5skZpOWn8S1WP1s/vowO7h20Omd1sDOxw6eNPsVP13/C0rCl6OHdg78/ravc4lxcTLyIswlncfbJWX5+qpK/rT86uHdAe7f2aOTYiP8SVBMYrCZPnjzBs2fP0LRpU5XjTZs2xa1btypd/pkzZyCVSpGYmIi8vDz88ccfGDhwoNr0hYWFKCws5F9nZdWceVmEvI5uPb2FBVcW4Paz2wAAT0tPTGk5BR3dO1a4LZpAIEBbt7bYfn87zieeVxtYGWOYfWE2ItIiYC2xxm9dfoOlsSX//stdmoP8BwEAnya7KBuMsQrrwxjDuYRz+PXGr7iXcQ8AYG9ij7GNx2Kg30CVNW2vpVzDGZzBs/xnfIvVz8avzHJrkqH1h2Lr/a2Iz47H6ojVGN90vNZlFMgKsDd6L47GHsX11OsqqzqZGpkixCWED6Yl7zfXNAYLrJmZmQBQauCRvb09MjIqt5pJv379sGDBAlhbW4Mxhnnz5mHYsGEICgpCQEDZuz+EhoZi7ty5lTovIaTyUvNSseT6Eux7xK3Pay42x5hGYzCs/jCVVYIq0tb1eWBNOK82zeqI1TgYcxAigQg/dfyp1OIIIsGLwGouNudXOVIGVjmTI1+WX27r7GbqTSy5sYQfCGUhtsDHQR9jWP1hZeZTLlv4IPMB0gvSIYBAqwFYhmIsMsak5pPw1emvsD5yPQb5DdJ48Yqcohz8e+9f/H3nb6QXpPPHPS09+UDaok4Lrf7+hmSwwGpszP2C8vPzVY7n5eXx7+mqV69e/HOBQIDvvvsOv/76K/bv3682sE6fPh2TJk3iX2dlZcHDw6NS9SCEaK5QXoi/I//Gytsr+XuYb/u+jQnNJvDBRhshLiEQCUSIzYrFk+wncLd0V3n/ZNxJLL3BLQQxvdV0hLiElCqjZGDtXbc3HwhNRCYwEhpBppAhqyirzAD5IOMBloYt5ReUMBYaY2j9oRgVNAo2JjZq662cQnMj5QYAwMPSo8J7xDVFV8+uaOHcAtdSrmHJjSVY0GFBuekzCjKwMWojNt/dzHeru5q7YkjgEHT27AwvK/XjaGoygwVWDw8PiEQixMfHqxx/8uQJvL299XouoVAIGxsbJCUlqU0jkUggkUj0el5CSMUYYzgZdxKLri3i52w2dmyMaa2mIcghSOdyLY0t0dixMW6k3sCFxAsYHDCYf+9+xn1MOzsNDAzvBbyH9wLfK7OMkl3Bym5ggPvCbmVshfSCdGQVZal0SybkJGD5zeXYF70PDAxCgRADfAdgbOOxGnVfKvdHLZBzO+coBy7VBgKBAFNaTsH7+9/HwZiDGFZ/GBo5NiqVLiU3BevvrMf2+9v5L1F1retidPBovFX3LYiFhtnuTV8MFlhNTU3RoUMH7N69GyNGjAAASKVSHD9+HKGhoXy6yMhIZGZmajxVRqFQICcnB1ZWL0bMRUZG4vHjxyrTbwghhvcg4wEWXFmAy8mXAXDzNb9q8RV61+1d4X1LTbRza4cbqTdwLuEcH1jTC9Lx5ckvkS/LR6s6rTC11VS1+T0sPdDMqRncLd1R376+ynuWxpZIL0jnW1pp+WlYdXsV/r33L4oVxQCA7l7d8UXTL1DPWvOu3JcXfSg5cKk2aGDfAP19+2P3w91YeHUhNry1gf9bxmXFYU3EGm4Fq+f3T+vb1cenjT5FF88u/GCx2s6gw6h+/PFHdOrUCePHj0ebNm3wxx9/wMPDA6NGjeLT/PLLL7h06RIiIriFlRMSEnD79m08e8aNELxy5QoKCgrg6+sLX19fyOVytGrVCkOHDkXDhg0RFxeHxYsXo127dnjvvbK/lRJCqpe0UIplYcuw9f5WKJgCxkJjjGg4AqODR1d6NGlJbd3aYmnYUlxOuoxiORfsvjr1FRJyEuBh6YGfOv5UbutILBRj/Vvry3zPUszdZ03KTcLym8uxPnI98mR5ALhu6InNJurU4n45sCrnsNYmXzb9Ekdij+DW01s4HHsYPjY+WHV7FY7EHuGnyTR3bo5Pgj/BG65v6OVLVE1i0MDaunVrXLx4EX/88Qf+/fdfdOzYEZMmTYKZ2Yv/WEFBQTAyelHNO3fuYMmSJQC46TknT57EyZMn8cEHH8DX1xdisRgXLlzAn3/+iS1btsDW1hbz58/HsGHDIBS+Gt+GCKmtZAoZtt3fht9v/s5PYenu1R2Tmk8qdQ9UHwLtAmFnYof0gnSEpYbhYMxB3Ei9AXOxOX7r8lu59zorYiXhesW+PfctHywa2DfAxGYT0ca1jc7l2kpsIRQI+TL9bQy3S4uuHM0cMSpoFJbdXIbZF2bz3b0A0N6tPUYHj0Yz52YGrGHVEjBdVql+DWRlZcHa2hpSqVSlW5kQoptLSZew4MoClf1Fp7acWuagIX2acXYG9j3ah7rWdREjjYEAAizruqzS80En/zeZX0LP28ob45uOR3ev7nppfXX6txPSCtIgFopxedjlWnnPsUBWgL67+yI5NxkCCPCm95sYHTwagXaBhq6aVnSJBTq1WNPS0jBnzhycP38e6enppd6PjY3VpVhCyCsoPjseP137CSfiTgDgdlIZ32Q8BvoPrJZJ/W3d2mLfo338gvyTmk/SyyILA3wHIDUvFf18+uFt37f1ei32pvZIK0hDPet6tTKoAoCJkQmWdVmGE3En0KtuL3hbexu6StVGp38Jn3zyCe7du4ePPvoItraG26WdEGJ4ecV5SM1LxdP8p9zPvKdIzX/+My8VEc8iUKQogkggwnsB7+GzJp9V684jbVzbQAABGBj6+fTDiIYj9FJuW7e2/NKH+uZg6oD7Gfdr5f3VkgLsAhBgV/YUx1eZToH1xIkTuHnzJurWravv+hBCylGsKMalxEto6tQUFsYWVXquQnkhnuY9LTNgPs17ipS8FDzNf4rc4twKy2rt0hpTW041SKCwM7HDxOYTEZ8dj2mtptWKgTJuFm4AuBGzpPbRKbDa2trCxsZGz1UhhFRkf/R+zLowCx82+BDftPxGpzJkChnS8tOQmpeq0rJ8mv9UJXhmFmZqXKa52ByOpo5wMnOCo5kjnEy5n45mjvCw9EADuwYGDWgfB31ssHPrYkyjMfCx8cEAX/0uZk+qh06BdfDgwfjpp5/www8/6Ls+hJBypOSlAAC/CLym7qXfw/eXvkdCTgLS8tPAoNmYRWOhMZzMnPiAWVbwdDJzgrnYXOtrIeo5mztjWP1hhq4G0ZFOgfXq1as4ffo0tmzZAh8fn1LfRA8fPqyXyhFCVBXJiwBwK9doY/fD3bj19MXmFkYCIziYObxoWb4UMJXPrYytakXXKSE1iU6BtXnz5mjevLm+60IIqUChnNuBSdly1ZQy/SfBn2BY/WGwNbF9ZVa5IaSm0SmwLl68WN/1IIRoQBlYc4pzkFOUo/EAJmVgbWDfoNTKPoQQ/aKvrITUIsquYIDbXk1TT/O4TaKdzJz0XidCiCqdA+uhQ4fQqVMnODs7w8nJCZ06dcKhQ4f0WTdCyEuULVYASM5L1iiPgikosBJSjXQKrGvXrkX//v3h7e2NuXPn4vvvv4e3tzf69++PtWvX6ruOhJDnSgZWTQcwpRekQ8ZkEEBA3cCEVAOd7rGGhoZi7dq1GDbsxXDwMWPGoFu3bpg3bx5GjhyptwoSUhvky/Ix8vBINLRviO/afFdl51EJrBoOYFK2Vu1M7Grt8niE1CY6tVhjY2PRt2/fUsf79etH6wST19L5hPOITIvE1vtb+TVpq0LJe6yaBlblvVjqBiakeugUWN3d3XHq1KlSx0+cOAF3d/1v/URITZVXnIdfrv+CKWem8Me2399eZefTpSs4NZ8LrM5mzlVSJ0KIKp26gr/66isMGzYMY8eORatWrQAAly9fxooVKxAaGqrXChJSEzHGcPTxUSy6uohvOSr30NwTvQdfNvsSEpFE7+etTIvV0cxR7/UhhJSmU2AdP3487O3tsXDhQixbtgwAEBgYiL/++gtDhw7VawUJqWkeSR/hx8s/4nLSZQDcgunTW01HO7d2eGvnW0jKTcLR2KPo61P6dkll6XKPlbqCCaleOm8gOHToUAwdOhQKhQICgYCWPSOvhV0PdmHepXmQKWSQiCQYFTQKI4NGwsTIBAAw0G8glt1chu33t1d5YJUWSlEgK+DPrQ4FVkKqV6UXiBAKhRRUySuPMYbfwn7DrAuzIFPI0N6tPXb134VxTcapBLYBfgMgEohwI/UGHmY81Hs9SnYFA5otEkGBlZDqpXGLtV27dgCAc+fO8c/VOXfuXOVqRWqNZWHLsDd6L1b3WA0PSw9DV6dKFMmLMOvCLBx4dAAA8GmjT/FFky/K/ELpZOaETh6dcCLuBLbd34bpIdP1WpcCeQEAQCQQQc7kSMlLgaeVZ7l5KLASUr00DqzdunUr8zl5vR1/fBxJuUnYfHezzvuD1mTSQim+Ov0VriZfhUggwqw2s/CO3zvl5nnX/12ciDuBfdH7MLH5RJgameqtPsoWq4elB2KzYvEk+wla1mlZbnrlvqpOphRYCakOGgfWOXPm8M8DAwPx/vvvl5luy5Ytla4UqT2UH9oHHh3AV82/eqUWIHiS/QSfnfgMMdIYmIvN8XPHn/GG2xsV5mvj2gZuFm5IyEnAkdgjeNv3bb3UhzHG32MNtAtEbFZshfuyKlurxkJjWEus9VIPQkj5dLrHOmTIEJ3eI68WxhikhVIA3LJ5Z56cMXCN9CfiWQSGHRyGGGkMnM2csb7neo2CKsBNuxnkPwgAsO3+Nr3VqVhRzD9vYN8AAPBQWv593Kf53KpLjmaONBaCkGqi191tUlJSYGNjo88iSQ2WJ8uDjMn417sf7tYon4IpqqhG+nEy7iRGHh6J9IJ0BNoF4p9e/yDALkCrMt72fRtGAiOEPw3HvfR7eqlXyRHBDe0bAkCFA6SUU3JocQhCqo9W0206depU5nMAUCgUePDgATp37qyPepFaQNkNLIAADAxnn5zFs/xncDB1UJvnWvI1fHHyC4xtNBYfBX1UPRXVwj9R/2DBlQVgYGjr1hY/dfwJ5mJzrctxMHVAF88uOPr4KLbd34aZrWfqVJ/MgkzczbiLe+n3cPvZbQDc7zvQPhAAFzizi7JhaWxZZv7UXBq4REh10yqwKkcD//fff6VGBovFYowaNQqDBw/WX+1IjabsBnY0dUQd8zoIfxaOA48OYETDEWrz3Hx6E7nFufjlxi9o6twUjR0bV1d1yyVXyLH42mJsjNoIABjkPwjfhnwLI6HOU73xbsC7OPr4KPY/2o9JzSfBTGymNi1jDAk5CbiXfg9R6VG4l34PdzPuIjm39NZwPjY+sDK2gpOZE1LzUhGdGY0mTk3KLLdkVzAhpHpo9anxww8/AAAcHBwwceLEqqgPqUWULVYriRX6+/ZH+LNw7HqwC8MbDFd7P48xBoDrDp5xdga29d1WbsCpDjKFDFP+m4LjcccBABObTcTHQR9X+p5kqzqt4GnpibjsOByKOYSB/gMBAMXyYjySPnoRQNO5Fml2cXaZ5bhbuKO+fX0E2AYg0C4QzZ2bAwB8bXwrDKzUFUxI9dPp6zgFVQIAWYVZAAAbiQ3eqvsWFl5diGhpNCKeRSDYMbjMPAyMfx6XHYfF1xZjVptZ1VJfdbbd34bjccchForxY7sf0bNuT72UqxzE9PP1n7Euch1uPr2Ju+l38TDzIWQKWan0RkIj+Nn4IcCOC6CBdoHwt/VX283rY+ODC4kX8DBT/X1WmsNKSPXTuZ8rJSUFR44cQVxcHGQy1Q+JklNzyKtL2WK1lljD0tgSXT274mDMQex+uFt9YH3eYvW18cXDzIfYdn8bOnl0Qgf3DtVVbRXSQil+v/k7AOCblt/oLagq9fftj9/CfkNsVixis2L545ZiS5UAGmgXiHrW9SAWaT5dyc/GDwDUBlbGGOKz4wFQYCWkOukUWM+cOYO+ffvCw8MDkZGRaN68Oe7du4ecnBy0bNmSAutrQhlYbSQ2ALiRsAdjDuJQzCFMaTmlzDVslS3Wpk5N0ca1DTbc2YBZ52dhZ/+dsDOxq66q85bfXA5poRR+tn78FBl9sjOxw8zWM3Eu4Rx8bHwQaBuIQPtAuJq7Vrqr2cfGBwDUzmV9kPkAqXmpkIgk/PQcQkjV02m6zdSpUzFv3jxEREQAAK5du4YnT56gf//+FS53SF4dysFLyoUHQlxC4GLuguzibJyMO1lmHmWLVQABJjSbAB9rH6QVpGHuhbn8e9XlYcZD/HvvXwDA1JZTKzVQqTzv+L2Dnzv9jM+bfI6uXl3hZuGmlzmlysD6NP8p/7co6XT8aQBAG5c2el39iRBSPp0C6+3btzFy5EiuAKEQhYWFsLa2xtKlS7F582a9VpDUXC8HVqFAiP6+/QGon9OqbLEKBAJIRBKEtg+FkdAIJ+NPYk/0nqqvtLIejGHh1YWQMzm6enZFiEtItZ1bX8zF5nAxdwFQutUanx2PvdF7AQCdPDpVd9UIea3pFFhzc3NhZWUFAHB2dkZsbCwAwMLCApmZmVqVdfjwYfTu3RstWrTAyJEj+bLUYYzh+PHjGDRoEIKCgnD16tVy069cuRJBQUFYsGCBVvUiFZMWcYFV2RUMAP19uMB6KekSknKSSuUpOXgJAOrb18fnTT4HAMy/Mh9Psp9UUW1VnY4/jYtJFyEWivF1i6+r5ZxVwdfGF8CL+6zF8mKsDF+JAXsG4HHWY1gaW1JgJaSaVXrlpe7du2PChAnYvn07Ro8ejRYtWmic9/Dhw+jbty/at2+Pn376CZmZmWjXrl25wXnatGkIDQ1Fp06dEBkZidzcXLVpb9++je+//x5ZWVlISir9IU8qhx+8ZPxiDVp3S3e0rNMSDIxvMZVUsitYaWTDkWjq1BS5xbn49ty3kCvkVVrvInkRFl9bDAAY3mB4rd6Vp2RgvZ5yHe/uexdLw5aiUF6IEJcQbO69Gfam9gauJSGvF50C6x9//ME/X7hwIcRiMcaOHYvU1FT89ddfGpcze/ZsvP/++5g2bRo6duyILVu2IDc3F3/++afaPHPmzMGJEyfw9ttvl1t2Xl4e3nvvPSxbtgx2dtU/KOZ18HJXsJJy0fndD3eXWr6wZFewkkgowv/a/Q9mRma4kXoD6++sr8Jac6srxWXHwcHUAZ80+qRKz1XVlPdZ90bvxUeHP0K0NBp2JnYIbR+Kld1XwsvKy8A1JOT1o1NgHTt2LP/c2dkZ+/btw7Nnz3Du3DnUr19fozJycnJw9epV9OrViz8mkUjQrVs3nDp1Sm0+U1PNBmGMHz8eHTt2RL9+/TRKT7SnDKwlu4IBoJtnN5iLzfEk5wmup1xXeU/ZYhUKVP/peVh6YFqraQCA38J+09v6ui9Ly0/DivAVALiFIHRZrrAmUbZYc4u5npuBfgOx9+296FOvDy26T4iB6HURfm0kJCSAMQYXFxeV4y4uLoiPj69U2Vu2bMH58+fx008/aZynsLAQWVlZKg+inoIpkFXE/Y5ebrGaic3Qw7sHgNKDmPgWK0p/6L/t+zY6e3SGTCHDtLPTVBad15dt97chtzgX9e3qo69PX72XX938bf0RYBuA+nb18fdbf2POG3NoezhCDEzj+QXKaTTnzp2rcErNuXPnKixPuaiEsbGxynGJRILi4uKysmjk0aNH+OKLL3DkyBGYmWm+VF5oaCjmzp2r83lfN9lF2Xw3b1kf5G/7vo2dD3bi2ONjmBEyg28ZljelRiAQYHab2bj19BYeZj7Ebzd+w+SWk/VWZ5lCxm/jNqLhiFKt5tpILBJjW99t1DolpAbROLB269atzOe6srfnBlSkpaWpHE9LS4ODg/rdUSpy8OBB5OfnY8SIFwvBR0dH48mTJzh+/Dhu3boFkUhUKt/06dMxadIk/nVWVhY8PGrvoJaqpuwGNjUyhbHIuNT7TRybwNvKG7FZsTgaexQD/AYAKPsea0n2pvaY+8ZcjD85Hn/f+Rsd3DuglUsrvdT5v/j/kJqXCjsTO3T36q6XMmsCCqqE1CwaB9aSqynNnj270v+Z69SpA1dXV1y+fBl9+77okrt48SK6du2qc7lDhw4ttaXdoEGD0LJlS0ydOrXMoApwLWWJRKLzeV836u6vKgkEAvT37Y9fb/yK3Q93lw6sZXQFK3Xy6ISBfgOx48EOfHv+W+zst1PternaUC4GMcB3QJlfBgghRB906gurW7cuvv32W0RFRVXq5GPGjMGqVavw6NEjAMDff/+N+/fvY/To0Xya2bNnY8CAARqXaWdnh6CgIJWHiYkJ7O3tERQUVKn6Gkqxohgb7mzAnAtzkF1U9g4o1e3l5QzL0s+nH4QCIW6k3sDjrMcAyp5uU5ZvWn4Ddwt3JOcmI/RyaKXrGyuNxcWkixBAgHcD3q10eYQQoo5OgfWzzz7D/v370aBBAzRv3hy//PILkpNL7xtZkRkzZqBXr14IDAyEq6srvvzyS6xZswZNmjTh0yQkJODBgwf86z179iAoKIjvjh45ciSCgoKwfPlyXS6lxrv19Bbe3/8+Fl5diB0PdmBtxFpDVwmA6pZx6jiZOeEN1zcAAHsecqsqqRsV/DIzsRlC24dCKBBi36N9OBJ7pFL13Xp/KwCgg3sHuFm4VaosQggpj4BVYoHWiIgI/PPPP9i8eTOePHmCrl274oMPPsCHH36oVTkZGRl49uwZPD09S3XHJiYmIi8vD76+3LSCzMxMPHlSenUeJycnODmVvYNHdHQ0zM3NUadOHY3rlJWVBWtra0ilUn6VqeqUVZSFX6//im33t4GBwURkggJ5ASyNLXFs0DGDTxP5J+ofzL8yHz28e2Bxx8Vq0x2JPYLJ/02Gk5kTjg48ip+v/4y/7/yNkUEjMan5JLX5lJbeWIqVt1fCWmKNnf126rRLS74sH123dUV2UTaWd12O9u7ttS6DEPJ60iUWVGpYZFBQEEJDQxETE4MTJ04gJSUFw4cP17ocW1tb+Pn5lXmP09XVlQ+qAGBjY1OqqzcoKEhtUAUAHx8frYKqITHGcCjmEPrt6oet97eCgaGfTz8cGngI3lbeyC7Kxvb72w1dzTJXXSpLZ4/OsJZYIzUvFZeSLml0j7WkcY3Hob5dfUgLpZh1fpZOC/UfjjmM7KJsuFm4oa1bW63zE0KINio93yA8PBzTpk3DBx98gMjISLz11lv6qNdrKT4rHmOPj8U3Z75BWkEavK28sabHGvyv3f/gYOqAj4M+BgD8Hfk3iuRFBq2rulWXXmYsMkavutwiILse7tL4HquSWCRGaPtQSEQSnE88zw9A0hRjDJvvchtDvBfw3isxxYYQUrPp9Cnz+PFjzJ8/H8HBwWjcuDFOnz6Nb775BgkJCTh48KC+6/jK4xdO3zsAFxIvwFhojM+afIYd/XagZZ2WfLre9XrDycwJqfmp2P9ovwFrrNngJSXlEocn407yAVmbUeU+Nj74qvlXAICfrv2EGGmMxnkjnkUgKj0KxkJjvh6EkMrZu3cvmjVrZuhq1Fg6jwpes2YNBg4ciAcPHuDy5csYP358ud2xpGxlLZy+s/9OjGs8rtSUEGORMYY34Lra10asrfLF6tW5mXoTYalhACpusQJAfbv6CLANQLGiGCfjuX1aNW2xKg0JHILWLq1RIC/AjLMzUKzQbBERZQu3h3cP2JrYanVOQl4lGzduRPv2+hlfkJeXh8TERL2U9SrSKbBeunQJ9+/fx5w5c1TufxLNZRRkYPaF2VovnD7IfxAsjS0RmxWLU/Hq11SuCtJCKeZenIsPD32I5Nxk2Ehs0My54m+tAoGAby0q17TVdh60UCDE922/h6WxJSLSIrAyfGWFeTILMnE49jAA4L3A97Q6HyGvmpycHNrlq5roFFhbtXqxEk52ds2YV1lbyBVybLm7BX129cHOBzsBaLdwurnYHEMChwAAVt9erdNgHm0xxrAveh/67e7HD5x62/dt7H17r8ZbrvWu1xtGwhfrkWjbYgWAOuZ18F3r7wAAf4X/hfCn4eWm3xO9B4XyQtS3q49GDo20Ph8h2nry5Anc3d2xd+9e9OzZE/Xq1cOuXbsAcNtYDh48GH5+fmjTpg0WLFjAL+0KAKtXr0a3bt2wZs0adOzYEX5+fhg7dmypdctPnTqF7t27o169emjXrh02b96s8r6ynPXr16NVq1bw9PTEzp07MX36dMTGxsLd3R3u7u5YsWKFRvUCuK7fNm3aoGHDhhgxYoRO0ytfK0wHhYWFbOrUqczBwYGVLOKTTz5hd+/e1aXIGkcqlTIATCqV6q3MsJQwNmjvIBa0LogFrQtiA/cMZDdSbmhdzrO8Z6z5huYsaF0Qu5R4SW/1K8ujzEfs48Mf83Xuv6s/u5p0VaeyJp6cyJezLGyZznWa8t8UFrQuiPXe2ZvlFuWWmUaukLO3drzFgtYFse33tut8LlLz5OTkqH3k5+drnDYvL0+jtNqIiYlhAJi7uzvbvXs3e/z4McvNzWWRkZHM2tqaLVy4kN25c4f9999/rHHjxmz8+PF83l9++YUJhULWunVrdunSJXb27FkWHBzMBgwYwKcJCwtjYrGYzZkzh0VERLA///yTSSQStm3btlLldO/enV25coXFx8ez3NxcFhoayry9vVl8fDyLj49n2dnZGtXrypUrTCwWswULFrDIyEj2008/MbFYzJydnbX63dRWusQCnQLrzJkzWaNGjdiePXtUAuvWrVvZkCFDdCmyxtFnYH2a95TNODuDDyptNrVhm6I2sWJ5sc5l/nDxBxa0Loh9evTTStevLPnF+ey3G7+xpn83ZUHrgliLDS3YyvCVrEhWpHOZp+NO87+D38N+17mczIJM1mVrFxa0Loh9f/H7MtOce3KO+13/00Zt8CW1EwC1j169eqmkNTMzU5u2Y8eOKmmVDYWXH9pQBtb169erHH///ffZJ598onLs/PnzzMjIiBUWFjLGuIAoEAjYw4cP+TTXrl1jANidO3cYY4wNHDiQ9e7dW6WciRMnsqCgIP71L7/8wsRiMXv69KlKuj/++IP5+PhoXa+BAweyd955p1Q+Cqzq6dQVvGHDBmzatKnUXqcdOnTAoUOHdCnylaRcirDvrr7YG70XAPCO3zvYP2A/hgQOUeka1daIhiMgEohwIfEC7qTdKTONTCHD2oi12PVgF3KKcjQu+0LCBbyz9x2sCF+BYkUx2ru1x67+uzA6eDTEIrHOdW7r1hYOptwGC7p0BStZS6zxQ9sfAHCDk84+OVsqzZZ7WwAA/X37w0ys+S5HhOjDyyNmL1y4gO3bt8Pb2xteXl7w9PTEoEGDIJPJEBsby6dzcXGBj48P/7p58+YwMzPD7du3AQC3bt1Chw4dVMru1KkToqKiVHYF8/T01GgzE03qdevWrVI7mulrENSrSqdP9qSkJNStWxeA6iAUgUCAgoIC/dSslssrzsOwg8PwMPMhAKChfUN8G/Itgh2D9VK+u6U7etbtiQOPDmBNxJoyVz8KfxqOn6//DAAIvRKKbp7d0M+3H5o7NS8zQD7Ne4qFVxfyA36cTJ0wLWQaunl208sOKkZCIwxvMBw/X/8Z/rb+lSqrjWsbfFD/A2yM2ohZF2ZhZ7+d/KjfpJwknHlyBgBoXeBXUE6O+i+JL2+ykZqaqjatUKjarigZ4CrLxMRE5XVBQQE+//xzjBkzplTakovXvLyNJgCIxWIUFnJ7ExcWFpZaSEcikUAul6O4uBhisbjM86ujSb0KCwtL1ausepIXdAqsgYGBOHPmDHr27Knygbt+/XqVdX5fZ2ZiM/jZ+OFZ/jNMaDYB7/i9o/fFCUY2HIkDjw7g2ONjiMuKg6eVp8r7yrmmALes375H+7Dv0T5IRBIEOQShqVNTNHFsgmDHYByOOYzfwn5DTnEOhAIhhgYOxRdNv9D70okfNfwI/X37w1ZS+akvE5pNwIXEC3gkfYR5F+fh504/QyAQYNv9bVAwBULqhKCedT091JrUJObmmv+brKq02mrYsCFu3boFd3f3ctMlJCQgPT0ddnZ2AIC4uDhIpVL4+fkBAAICAnDr1i2VPGFhYXB3d69w/2mRSFRqsKMm9fLz8+NbzEov14Go0umTfubMmfjwww+xeDHXSvrnn3/w4YcfYtq0afj222/1WsHabGqrqdg/YD8G+Q+qkhV/AuwC0N6tPRRMgXWR60q9XyDjeg9a1WmFjb02YpD/INhIbFAoL8T1lOtYdXsVvjj5BTr+2xGhV0KRU5yDIPsgbOm9BVNbTa2S9YgFAgHsTOz00gI2MTJBaPtQGAmMcDzuOPY92odieTF2PNgBgKbYkJpjypQp2L9/P3799VfIZDIwxhAeHo4JEyaopCsuLsbXX3+NoqIi5OXlYcKECWjatClCQkIAAOPHj8fmzZtx/PhxANyI3iVLluDLL7+ssA5ubm5ITk6GVCrVql7jxo3Dxo0bcfYsd8vlwoULWLduXWV/Ja82XW/o7ty5kzVr1owJhUImEAhYo0aN2M6dO3UtrsapilHBVeFa8jUWtC6INf27KUvNTVV5b/u97SxoXRD7/Pjn/DGFQsGiM6PZjvs72HfnvmN9dvbhB/lsidrCZHJZdV9Cpf116y8WtC6IhfwTwlaGr2RB64JYl3+7sCK57gOtCNGFcvDSgwcPSr23detW5ufnx4yNjZmlpSVr0qQJ27t3L//+L7/8wnx9fdno0aOZjY0NMzY2Zo0aNWJRUVEq5SxcuJDZ2NgwS0tLZmpqyr788ktWXFysUk7Dhg1Lnb+oqIh17dqVmZqaMjc3N/bnn39qVC/GGJs+fTqTSCTMysqK+fn5sc8//5wGL5WjUrvbAIBcLgdjDEZGug/EqYkMvbuNphhjGH5oOG4+vYmPgz7ml/4DgI13NmLB1QXo6d0TizouUluGtFAKUyPTWrv5t0whw8jDI3Hz6U3+2GeNP8O4JuMMVynyWpLL5UhKSoKLi0up+71KGRkZkEgkpbpulyxZglWrViEiIgIKhQIZGRmwt7dXe560tDTY2NiUut+Zk5OD3NxcODs7l5k3Pz8f6enpsLa2hoWFRYX1UiooKEBRURGsrKyQn5+PzMxMuLi4qP1dvCqqfXcbgOu3f9WCam0iEAj4xfm33tuqshF6gZzrCjY1Mi23DGuJda0NqgA3KOrHdj/y1ykSiDDQf6CBa0VeRyKRCO7u7mqDKsDt5lXR/VChUKg2qCrP4+TkVOYgIgsLC7VBFQBMTU3h5uamElQ1qZeJiQkfWExNTV+LoKorjSNiixYtNC702rVrOlWG6KajR0f4WPsgWhqNrfe2YlTwKADcyGSAuxf5qvOw8sD0VtMx68IsfrMCQggxBI0D6/vvv88/f/LkCZYuXYo333wTLVtyu69cvXoVR44cKXUznlQ9oUCIj4M/xrfnvsWGOxvwQYMPIBFJkC/LB1Bxi/VVMcBvAFo4t4Czufpv64TUVKNHj8aQIUMMXQ2iBxoH1smTJ/PP+/bti+XLl2Ps2LEqaf744w9aIMJA3qr7Fn4L+w3JucnY83APBgcMfu0CK8C1XAmpjSwsLEp1z5LaSad7rOfPn8fQoUNLHR86dCjOnz9f6UoR7YmFYoxoMAIAsC5yHeQKucb3WAkhhOiPToFVJBKVGUAvXLhAA5kM6B2/d2AtsUZ8djyOxR1DfvHr12IlhBBD0ykKjh8/Hu+//z4+//xztGzZEowxXLt2Db///jumTJmi7zoSDZmJzTAscBiW31qONbfXwEZiA4ACKyGEVCedAuusWbPg6emJX375BT//zK1FGxAQgKVLl2LEiBF6rSDRzpDAIVgbuRZR6VF8QKXASggh1UfnftuPPvoIH330ERQKBYDSC1oTw7AxscFAv4HYGLXxtRy8RAghhlbpaCgUCimo1jDDGwyHkeDFdyYKrIQQUn1opNEryMXCBb3q9eL3gH0dFogghNROFy5cwK5duyCTydCjRw/07Nmz0nmysrLw77//IjIyEo6OjhgwYAAaNGhQVZdQCjU1X1HKZQ4BwEJMc+MIITXP6tWr0blzZzDGYGlpicGDB2PWrFmVynP9+nU0atQIV69eRd26dREfH4+mTZtiw4YNVX05vEovwv+qqi2L8Jdn452NSMlLwaTmk/SyTRshpGJFRUVYvXo17ty5Ax8fHwwZMgTffvstZsyYgXr1uP2Bx4wZA7lcDpFIBC8vLwwaNAj+/v58GWlpaZg6dSqmTp2KEydOICoqCp6enny+1atXIyYmBo0aNcKoUaP423G65tOkTvqWl5cHV1dXzJgxA9988w0AYPPmzRg+fDiio6Ph6empU57k5GSYmprC2tqazzd16lSsX78eycnJWtdTl1hAXcGvsA8afGDoKhDy2unfvz/u3r2LMWPGIDY2Fq1atUJcXBxGjx7NB9aQkBAoFArIZDKEhYWhWbNmOHLkCNq2bQsAyM7OxurVq3H48GEMGjQIXl5eWLZsGXbu3Ins7Gx0794d3t7emDdvHiIjI7FkyZJK5dOkTmX5+uuvVfZ3fZmTkxN+/PHHMt87c+YMpFKpyjKOAwYMgFgsxqFDhzBmzBid8tSpU6dUvoCAAKSnp0Mmk1XLWgu0CD8hpFZgjCG/WG6Qc5uKRRr1+hw/fhzHjh3D/fv3+SDq4uKCadOmqaT7+OOPVV7b2trif//7Hw4ePKhyfPLkyZg4cSIAwNfXF/3798eff/7JBx0XFxeMGzdOJUDqmk/TOpXUvHlz5OXlqX2/ZKvxZQ8fPoSRkRHc3d35YyYmJqhTpw4ePnyotzwKhQKrV69G+/btq20BI1qEnxBSK+QXy9Fg1hGDnPvOvB4wM6744/LMmTNo1qwZH1QB4N133y0VWNPS0vDPP//g0aNHyMnJQXR0NGJjY0uV17VrV/65r68vAKBLly4qx7KyspCdnQ1LS8tK5dO0TiWVtbStpvLz82Fubl7qC4ulpSXy8/P1lmfy5MmIiIjA5cuXda6rtmgRfkII0ZOnT5+W2kf15ddxcXFo1qwZQkJC0LlzZ1hbW0MgEOD27dulyjM1fTFVTnk/tKxjcrm8Uvm0qVNJlekKtrKyQlZWFn9fVyk9PV3tvUxt88yePRsrV67E4cOHERgYWO616JNO7eLz58/jn3/+KXV86NChmDlzplZlyWQynD17FikpKQgODkbDhg01ynfp0iXcvXsXPXv2LLNPPSsrC5cvX0ZOTg4aNGiAgIAArepFCKlZTMUi3JnXw2Dn1oS7uzvOnj2rciw+Pl7l9datW+Hp6YkDBw7wx9LS0ipfyUrQtU6V6QoOCgoCYwx3797lP/fT09ORnJyMoKCgSueZO3cufvrpJxw6dKjc+8RVgunAwcGBHTx4sNTxgwcPMicnJ43LSUtLY82aNWPe3t6sT58+zNLSkk2YMKHcPIcOHWKNGzdmQUFBDAA7depUqTTLly9n3t7erFevXqxfv37MwsKCDRkyhBUXF2tcN6lUygAwqVSqcR5CyOstPDycCQQCdvLkSf7Yp59+ygCwixcvMsYYW7hwIQsICGAymYwxxn0O1q1bl9nb2/N5YmJiGAD24MED/lhUVBQDwOLj4/ljV69eZQBYRkZGpfJpUid9k8lkzNvbm40ZM4Y/9v333zMrKyu+XowxNmXKFLZ161at8sybN49ZWFiwM2fOVLqeusQCgy7CP2PGDOTn5+P27duwsLDA5cuX0aZNG7z11lvo0aPsb6ZyuRzr1q2Dg4MDPDzK3nvTw8MDd+7c4bs+7t69i/r162Pw4MF4++23tb5eQgjRRHBwML755hv07t0bPXr0QEpKCsRiMQDwXZfDhw/H0qVL0bx5cwQGBuLMmTNwc3NDVlaWweptiDqJRCL8/fff6NevHyIiImBmZobz589j3bp1sLGx4dNt2rQJCoUC7777rkZ5Dh8+jFmzZqFZs2ZYv3491q9fz5e1aNEi2NraVtk1KRlsEX7GGLZs2YKZM2fym/uGhIQgJCQEmzZtUhtYe/fuDYAbQKVOnz59VF67ubnByMgIBQUFGtWNEEJ0NX/+fAwePBhRUVHw8fGBnZ0dAgIC+Hutzs7OuHPnDk6cOIHc3FzMnTsXYrEYFy5c4MtwcHDAypUr4eTkxB9zcXHBypUrYWdnxx/z9vbGypUrYWZmVql8mtSpKrRv3x7R0dE4efIkZDIZ1q1bB1dXV5U0ixYtgo+Pj8Z5/Pz8sHLlyjLPJ5FIquZCXqLTAhE3b95EkyZNAEDnRfjj4uLg5eWFgwcP4q233uKPf/LJJ7hx4wauX79ebv4nT57Aw8MDp06dQqdOnUq9n5CQgGPHjiErKwubN2+Gp6cnNm7cyH97fFlhYSEKCwv511lZWfDw8KjVC0QQQqrfxYsX0bp1a37k6pdffol9+/bh0aNHtFBLLVRtC0Q0b96cH02m6wL8ypFkJZv8AGBnZ1fuKDNNpaen4/Tp03j27BliY2PRtWvXcusaGhqKuXPnVvq8hJDX27lz5zBq1Cg0btwY9+7dQ0JCArZs2UJB9TWiU2D19PRETEwM6tatq/OJlfc/c3NzVY5nZ2erDAvXVXBwMNatWweAm1TctGlTuLm5Ydy4cWWmnz59OiZNmsS/VrZYCSFEG1OmTME777yD69evw9bWFq1atSp3dCx59ejU3Jw5cyZGjx6Nu3fv8l3B2vL09IRYLC41ATk2NlalP10ffH190axZM1y8eFFtGolEAisrK5UHIYTowsfHB4MHD0b37t0pqL6GdAqsn3/+OU6ePIn69evD2NgYJiYmKg9NGBsbo3v37vj333/5YykpKTh58qTK4KMLFy5gz549GtdNJpMhKSlJ5VhWVhbu3r0LLy8vjcshhBBCdKFTV/D27dv1cvIFCxagbdu2GDx4MFq3bo21a9eiSZMmKiOL16xZg0uXLqF///4AuG7dc+fOISMjAwA3tDo2NhZNmjRBkyZNIJfL0a1bN3Tu3BkNGjRAZmYmNmzYAHt7e1pukRBCSJUz+LZxsbGxWLNmDb/y0ujRo1VavWvXrkV0dDR++OEHANzAgFWrVpUq5+233+bnqBYUFGDTpk0ICwuDubk5mjZtioEDB2q1APOrsG0cIYSQytElFhg8sNZUFFgJIYRU636shw4dwrZt2xAXFweZTKby3unTp3UtlhBCCKnVdBq8tGLFCgwZMgRmZmY4ceIEWrRoAblcjv/++69SU3AIIYSQ2k6nruD69etj6dKl6N69OwQCAZRFfP/99wgLC8POnTv1XtHqRl3BhBBS9fLz83HhwgXIZDK0bt1ao+lJFeU5dOhQqTUSAgICEBwcrHX9qu0eq7GxMbKzsyGRSCCRSJCeng5zc3Okp6ejbt26elk5ydAosBJCSNW6fv06+vTpA3t7e5iZmeHevXvYsmWLyjK3uuTx9vaGg4MDvL29+WP9+vXD8OHDta5jtd1jLS4u5hczdnd3x61bt/DGG28YfE9BQgipCaKjo3H37l34+PjAz88Pu3btQpcuXfiF8Hfu3AmFQgGRSAQvLy80atRIZdZCXl4ev47606dPERUVBU9PT34P0jt37iAmJgYNGjRQuf2maz5N6qRvjDEMGzYM3bp1w4YNGwBwO559+OGHiImJgaWlZaXyjB07FqNHj66y+pdHpxZrye7f7777Dn///Tf69u2Lo0ePokWLFti0aZPeK1rdqMVKCNFFaGgo5syZgzZt2iA1NRW+vr7Yt28fvzg/ALz33nuQy+WQyWQIDw+HhYUFDh06BDc3NwDcNMS6deuiS5cuSEpKgru7O06dOoWvvvoKSUlJCAsLg4uLC86ePYtVq1bhgw8+qFQ+TepUlv3795e7a5iFhQV69uxZ5nuXL19G69atERYWxm/qkpqaCldXV2zatAmDBw/WOY+3tzeGDh2KN954Ax4eHmjYsKHOXxKqrcUaFRXFP58zZw5sbGxw8eJFDB48GFOnTtWlSEIIKR9jQHGeYc4tNgM0WET//v37+O6777Bjxw7079+fb2G9rOSKc3K5HO+++y5mzZqF1atXq6Tz8vLC8ePHIRAIsHz5cnz++ecYN24cIiIiAAALFy7EzJkzVQKkrvk0rVNJe/bs4RfrKYuLi4vawBoeHg6BQKBy39PJyQl16tRBeHh4mYFVmzybN2/GjRs3EB4eDhsbG2zevBmNGzdWW1d90imwBgYG8s9FIhG+/vprvVWIEELKVJwH/OhacbqqMCMRMDavMNnOnTvh6+vLrxQnEAgwZcoUbN68uVTa+/fv49GjR8jJyYGrqytOnTpVKs3YsWP5XXHatm3LH1Nq27Ytpk6divz8fJXNS3TNp0mdSlK376kmpFIpLC0t+Q3glezt7ZGZmVmpPMuWLeOXxs3Pz8fgwYMxePBgREREqN06VJ80Dqw5OTkaF6rcuJwQQl4ncXFxpe5dvvy6oKAAAwYMwMWLF9GiRQtYW1sjISEBqamppcoruTm5clxLWccKCwtVAqS2+bSpU0mV6QqWSCTIyyvdA5GTk6N2zXlN85Rcb97U1BTfffcdQkJCcPfuXZ1GBmtL48Ba1o1kdWgxJ0KI3onNuJajoc6tATs7O4SFhakce7n1tWHDBkRGRiI+Pp7/XP39998xe/ZsvVRVF7rWqTJdwfXq1eM3TXFxcQEAFBUVITk5GfXq1dNbHgD8dJyKvijoi8aB9erVq/zzs2fP4n//+x8mT56Mli1b8u8vXrwYM2fO1H8tCSFEINCoO9aQ2rRpg0WLFiExMRGurly39b59+1TSJCUlwcPDQ6WxYui5/7rWqTJdwR06dICZmRm2b9+O8ePHAwAOHDiAgoIC9OjRg0938OBBeHh4IDg4WKM86enpsLKyUhmstHv3bojFYjRq1Ejn+mpD48DaokUL/vlnn32Gbdu2oXPnzvyxrl27olWrVpg+fTomTpyo10oSQkht0Lt3b4SEhKB79+748ssvkZKSgr/++gsA+HuevXr1wrx58zB58mQEBQVh165duH79epVObamIIepkaWmJ//3vf5g6dSqkUinMzMzw448/4ssvv1TZk/vTTz/F+++/j8WLF2uU586dO/jyyy8xaNAguLm54dKlS1izZg3+97//wdHRscqupySdljSMjIxEs2bNSh1v3rw5IiMjK10pQgiprQ4ePIhhw4bh3LlzUCgU2LVrF4AXY09atGiB06dPIycnBydOnEDXrl2xfft29OvXjy/D3NwcAwcOVBmvYmVlhYEDB8LM7EW3tJ2dHQYOHAhjY+NK5dOkTlVh4sSJ2LFjB2JjYxEeHo7ffvsNP//8s0qa3r17q7Q0K8rTrl07bNu2DYWFhTh58iTs7e1x+fJlTJkypUqvpSSd5rH6+flh7NixpUYD//TTT1ixYgXu37+vtwoaCs1jJYToIjMzEzY2NvzrtWvXYsKECXj27BkfyEjtUW3zWBctWoR3330XO3bsQMuWLcEYw7Vr13Dt2jXs2LFDlyIJIeSVMGPGDIjFYjRr1gx3797F0qVLMXv2bAqqrxGd92N98OABli1bhjt37gAAGjRogPHjx8PX11evFTQUarESQnRRWFiI1atX49q1a7C1tUXv3r3RpUsXQ1eL6Ig2OtcjCqyEEEKqdaNz5QnT09NLHS+5owAhhBDyOtEpsN68eRMffvghv+7ky6gRTAgh5HWlU2D99NNPERQUhDVr1sDW1lbfdSKEEEJqLZ0Ca2RkJI4ePaoypJwQQgghOi4Q4e3tDalUqu+6EEIIIbWeToF1ypQp+OyzzxAfH6/v+hBCCCG1mk7TbUxMTFBYWAgAMDY25tfAVCpvG6HagqbbEEJI9VAoFGCMldpntTzFxcUoKiqCuXnVbsxQbdNttm/frks2QgghhJeRkYGxY8diz549UCgU6NKlC/766y94enqqzXPz5k0sX74cmzZtgkwmq5ENOVogQg1qsRJCKkMmk/G7w+Tk5MDMzAxCIXf3LTc3l2+hldygXIkxhtzcXJibm0MgEEAul5dqzZUsv7L5NKlTVejbty8SEhKwd+9emJiYYOjQoUhNTcWNGzf439XL3n33XXTr1g35+fmYNm1alQdWXWKBTvdYCSGElO2///5DQEAATExM4O3tjd9++w2Wlpa4cuUKn8bNzQ116tSBnZ0dnJyc8NVXX/G31wDg8ePHsLS0xLx58xAYGAhTU1N4eHhg//792LhxIzw9PWFiYoKGDRvi1q1blc6nSZ3KkpeXh5ycHLWPvLw8tXmjo6Oxf/9+hIaGwt3dHQ4ODvjll19w69YtnDp1Sm2+bdu2YcyYMSo7+NQ0Oq+8dOjQIWzbtg1xcXGQyWQq750+fbqy9SKEEBWMMeTL8g1yblMj01JjScoilUoxYMAAjBw5Et9//z1SU1Px9ttvl0qXmZnJP4+KisKgQYNgb2+PmTNnqqQ7cOAADh8+DDc3N3z22Wd477330KxZM5w/fx7Ozs4YOXIkxowZg0uXLlU6n6Z1Kik4OBgpKSlq3/fx8SkVwJUuXrwIgNvwXKlhw4ZwdHTEpUuX0LVrV7Xl1nQ6BdYVK1Zg6tSp+OCDD3DixAl8/fXXuHz5Ms6dO4ePPvpIz1UkhBAgX5aPkE0hBjn35aGXYSY2qzDdP//8A2NjY8yfPx9isRje3t5YuHAhevToUWZ6uVwODw8PjBkzBhs2bCgVxH744Qd+idhPPvkEq1atwo8//ggPDw8AwKhRo/Dmm2+W6t7VNZ8mdSopOjq6wt+JOqmpqTA3Ny/V7ezo6IjU1FSdy60JdAqsS5YswbZt29C9e3f8/vvvWLx4MQDg+++/R1hYmF4rSAghtcX9+/cRFBQEsVjMH2vatGmpdL/++it+/fVXxMXFQSKRQC6Xl7ngTt26dfnnyvt7Jddit7KyglwuR05Ojkp+XfJpWqeS8vLyoFAo1L4vFApVNlh/WVl5FQqFRr0DNZlOgTU6OppvvhsbG/M3yz///HOVPyghhOiLqZEpLg+9bLBza0IoFEIul6sce/n1/v378d1332HHjh3o1KkTxGIx1qxZg2+++aZUeWUFGE2Cjrb5tKlTSZXpCnZ1dUV+fj5ycnJU7pempqbCxcWl3PPWdDoNXiouLoZEIgEAuLu787+4tLQ0rctas2YNGjdujDp16qB79+4Vtnjz8/Oxbt06tG7dGjY2Njh37lypNMpNAnx8fBAQEIBRo0bhyZMnWteNEFJzCAQCmInNDPLQtAXVsGFDhIeHq4xULTloCQCuXbuGZs2aoXv37nzLVnm/0VB0rVN0dHS5g5fUBVUAaNu2LQQCAU6cOMEfu3HjBtLT09GuXTv+WF5eHoqKiipxddWv0qOChw4diiFDhuCLL75A79690bt3b43zbt68GePGjcOUKVNw/vx5+Pj4oEuXLkhKSlKbZ86cOTh16hSmTJkCqVRaauCUXC7HqFGj0LNnTxw7dgw7duzA48eP0bVr13JHqBFCSGUNGTIExsbGGDduHB4/fowrV65gypQpKmmCg4Nx7do1HD9+HMnJyfjjjz+wbt06w1TYgHXy8PDA0KFDMXnyZFy7dg137tzBuHHj0L59e7Rt25ZP5+/vjxkzZvCvCwsLkZOTw49YVgbx8rqkqx3TQVRUFP9cJpOxxYsXs4EDB7Jvv/2WZWVlaVxOcHAwGzNmjEpZzs7O7LvvvlObR6FQMMYYi4+PZwDYqVOnKjxPdHQ0A8BOnDihcd2kUikDwKRSqcZ5CCHk5s2brH379szBwYGFhISwtWvXMgDsxo0bfJrZs2czLy8v5uDgwN566y02b9485uXlxb//+PFjZm5uzqKjo/lj9+7dY+bm5iwhIYE/duPGDWZubs4yMzMrlU+TOlWF3Nxc9uWXXzI3Nzfm7OzMhg8fzp4+faqSxt/fn82YMYN/PWLECGZubl7qceXKlSqpoy6xQKfAWjIYavNeSRkZGQwA27p1q8rxIUOGsM6dO1eYX5vAeuvWLQaAXbhwQaO6MUaBlRCiH8ePH2dCoVAliJHaQ5dYoFNX8IoVK9S1fvHXX39pVEZiYiIAwNnZWeW4k5MT/54+KBQKfPPNN6hfvz5atmypNl1hYSGysrJUHoQQoq0ZM2bg0KFDSElJwX///YcvvvgCgwYNgrW1taGrRqqJzgtEvEyhUODcuXNwcnLSKt/Ly1YZGRmB6XGVxYkTJ+LKlSs4c+ZMmct4KYWGhmLu3Ll6Oy8h5PU0fPhwfPPNN7h27RpsbW3Rr18/zJkzx9DVItVIq8BacmSculFys2fP1qgsZQB+9uyZyvHU1FStg7M6kydPxoYNG3Ds2DEEBQWVm3b69OmYNGkS/zorK4ufTE0IIZoKDAzE3r17DV0NYkBaBdZjx44BALp3784/VxKLxfDy8lKZhFweBwcH1KtXD2fPnlVZ8uvMmTMYNGiQNtUq0zfffINVq1bh2LFjaNGiRYXpJRIJP4WIEEII0ZVWgbVbt24AgLCwMDRp0kTlPYVCoXY3AnUmTJiA7777Du+88w5atmyJn376CcnJyRgzZgyf5ssvv8S1a9dw4cIFjcudPn06Vq5ciWPHjpV7X5UQQgjRN50GL1lZWancM5g1axZMTEwQEBCAu3fvalzO+PHjMWHCBPTq1QtmZmZYvXo1du/eDT8/Pz5NXl6eykCiTZs2wcbGBg0bNgQA9OnTBzY2Npg/fz4AbpGK+fPnIy8vD927d4eNjQ3/2LBhgy6XSwghhGhMp/1Y+/fvj08//RS9e/fGo0ePEBQUhJUrV+L06dNISkrC/v37tSqPMYaCgoIy9wDMz8+HTCaDpaUlAKCoqKjMhR5MTExgYmICxhikUmmZ5zEzM4OxsbFGdaL9WAkhhOgSC3QKrLa2toiPj4eFhQX++OMPnDp1Clu3bkVaWhr8/f11WtqwpqHASgghpNo2OheJRHzwPHz4sMq+ebV9VwJCCCGkMnSax9qjRw8MHToUISEhOH78OP744w8A3AbnnTt31msFCSGEkNpEpxbr77//jmbNmuHhw4f4999/4erqCoDbeogmQhNCCHmd6XSP9XVA91gJIaQWy0sHFHLAwrFSxVTbPVZCCCGkRgvbCCz2BfZPqjitnlFgJYQQ8up5fJ77aVe32k9NgZUQQl43jAGX/gCWtQTuHTJ0bfRPIQceX+See7UtP20V0NvuNoQQQmqQ5NtA+iOgKBcozAGKlI9c7j1li+7wdMDvTUAoMmx99Sn1DlAoBYwtgDqNqv30GgdWmUymeaHlbM9GCCFEB1lJQEok4NdNfRp5MXBnD9caTbhWfnlGJoDQCMiIAe4fAQJ76be+hvT4+dryHiGAqPrjkcZnFIvFGhdKA40JIUSP5DJgw9vA07vA0K2Afw/V93OfAdfWAtdWA9lJ3DGRMeDaFJBYAsbmgPHznxIL7lhgH26Az/klwKXl6gNrTioQexaIOcO1dluPA9yaV+XVVl7sOe6n1xsGOb3GgfXUqVNVWQ9CCKmZ0qIBG09ApHnjQu/Ct3BBFQBubXkRWFMigYvLgdvbAHkhd8zCGWgxCmgxErCoYG/rVp8AF37jAmdKJODcEMjPAGLPc4E05gzwNEo1z+1tQJMPgK6zAEtn/V6nPjD2osXq3c4gVdA4sHbq1KkKq0EIITVMRixwaBpw/xDQ/COg76/cccaAi78DD48D7/xVcfCqLFkhcHr+i9f3DwNFecCd3cCezwGm4I67NuNakw3eBow022wE1u5A3fbAo9PA4WlAgRRICgfwUq+jczBQtwOQ9wwI/xe4uZHrcu44BQgZp/n5qsOzB1w9jUy4FrsB0M1QQkjtxhiQlcANyEkKB5LDgaxE4M3vdWuxFBcA538Fzv0MyAq4Y0/vP38vH9g7nmu1AUDkLiBkTNnl6Mu1NYA0HrB05e6JSuOAXZ8CUfu49/3fAtpPAtxbArqs1W7tzv2MOfPimIM/F0i923MPc/sX77X8BDj0DZB4Azg2C7i+HujxI9eKrglrxd/cyP10bwkYSQxSBZ0D66FDh7Bt2zbExcWVGth0+vTpytaLEEJKU8i5FknybSD51vNAehvITy+ddvNQYPQxwDFAw7IVXOv0yAyutQpwXcCZcUBxLjd4aMtQLqAoJYdX+pJQlAsUZHGtxcLnPyVWgGcIUJgNnFnMpes0FUiP4e6JKoNqyDigZ2jlAlqzj7hy7eq9CKZWLurTe7QERp/guqePzwHSo4HN7wG+3YAeoYCjv+510RRjXJd1TsrzRyqQncx9obqygksTMrbq66GGToF1xYoVmDp1Kj744AOcOHECX3/9NS5fvoxz587ho48+0nMVCSGvpaI8btpE0q3ngTQcSLkDyPJLpxWIAMdAoE4w4NKI66aMvwz8M4gLAuV112YlAmH/AGF/c0EUACxdgB7/4+5XrusNSJ8AKztzA4NMbYHGQ4FLvz/vNtVQYTaQehdIjQRSo7h7mqlRXLdlWT46wN3rzHsG2Plw9zVTI7nACgDtJwNdZla+lejREhh5ULs8QiHQZChQvy8X+C8t57rGH50GWo0BOn4DmNpoXxfGuCApfQLkJHNBM7tE8MxJfv4zBZAXqS+n0XtA/T7an19PdForuH79+li6dCm6d+8OgUDAjwL+/vvvERYWhp07d+q9otWN1gompBrlpnGBMzn8RZdu2oMX9w9LEpsDdYK4IFqnEffTqQEgNlEtb1VXbiqJWwvgo/2A2PTF+3IZFwiurwMeHHlxHhNroPlIoMNkbuRswg0uoCo51geGbOaC2a+NAaEYmJGoeo9RVsTVPeUO98VA+VAG7bIIRNy5Tay4LxS5qUDwYG7xhqJsYNAaIGggl/bqKu530GSI1r/mKpMWDRydCdx7HqDNHICu3wFNPyx7fqy8mGslP7v//PHgxc9CqebnNbHhvvxYOAGWdbjn9j5Ak2F66wauto3OjY2NkZ2dDYlEAolEgvT0dJibmyM9PR1169aFVKrFL6aGosBKSBVgDMh8/KILVxlIsxLKTm/uyAVPl+cBtE5jbok6TRYzePaQC64FmUCD/sCgdUDWE+DGBm6aSXbii7SebbgBSg36qwbgtGjgt2bcc/+ewDsrueDHGLDAi+u27TmfW3gh5Q7XAk17ACjUzPu3dAGc6nNfBJwaAM4NAHtfbiEDZcszah/w7wcv8tQJBj49w7USa7qHx4HDM4Bn97jXdRoBHaZwXdwlA2hGrPrfEQSAlevzgOnMjTy2eOlh6QyYO6l+maoi1RZYS7ZSfXx8sGHDBrzxxht48OABWrRoQYGVkNcBY1x3nKyAG/AjK/Eo+Tr3GZAS8SKYqmuR2NUr0Qp9HkwtnCvX1Rl7Hvi7P6Ao5lqbT++CH/Fqasd1ZzYbrv4+LGPAse+4Ftgb41UD+ro+3DSVskisuQDq3OBFEHWqD5jZVVznAimwsN6LwDNsO+DXXeNLNjh5MdeqPhVafutTbA44+HEDpRz8nz/347q9qyFgakqXWFDpUcFDhw7FkCFD0LdvXxw9ehS9e/eubJGEEHUyYoG9X3L3sLQZ8SorAqJPcq234nxuCofs+U+dXxeg1LQMTQjFXJDhW6KNuPmTJlXwBda7LdB/GbBrzIv5mHU7cK3TwD4VdxcKBMCbP5T9XtMPuC5eK1fAqeHzQPr8p5Wb7l8ITKwB91ZA3AXA8w1uUFBtIhJz036C3wVO/cjdd7XxeCmA+nOt95owirgK6NRivXv3LgIDAwEAcrkcS5YswcWLFxEYGIipU6fC0tJS7xWtbtRiJTXS6QXA6R+5lW8+Oal9vioj4LpQjSSA0fOfytcSSy7wKAcWOQRU/7zH8K3curnB73L34Gq6hyeAsz8BvRZzrV5iMNXWYl2yZAn+/PNPAIBIJMLXX3/Nvzd27Fj+PUJeK4U5XDCpysXMlcvVJVznWq+23hXnYQy4vZV77taCG+RRMgAamXBdb0YlHhW9Lhk4jUy5VkpNbn00GmzoGmjHtyv3ILVSpe+xlsQYg0gkgkJRxki+WoZarKRCjHHTApJuATH/AVf+Auz9gPc2Ak6BVXPOzUNejLzsNgdo91XFeVLuAH+04daOnRJdNV2uhLyiDHKPVUmhUODcuXNwcqri5b0IMZTiAu6+16PTQOJNbkRrfoZqmrQHwMouwNvLgYZv678O2ckvnkfs1CywRu3lfvp0oaBKSDXQKrAKSnT1CNR0+8yePbtyNSK1y8XfueXdei0G3FsYujb6xRg3NeDhCSD6BDfC9OXFCYRGzxcmaARYu3FTJZ7eBbaNABIncguV67NruGRgTQ7nppQ4+Jaf587zwFq/n/7qQQhRS6vAeuzYMQBA9+7d+edKYrEYXl5e8Pb21lvlSA3HGHBuCTeZfW0voP/vQKN3y8+jUADxl56PArWulmpqJT8DePQfF0gfnuTmPZZk6cK1/DxCuIE4Tg1UR5Z2nAYcnw1cXMatkJN0i5vcr8k0i7IoFNwcwMIsbtm73FTuuHMwkHKbO0f/Zerzp0Vzq/UIjYCAt3SrAyFEK1oF1m7duGHfYWFhaNKkSVXUh9QmGTEvPujlhcDO0dyUhs4z1U9mv7UZ2PMZtzdks+FA67HceqyGVJgNXFnJrXKTcE11tR+RhNvT0bcr4NOVm0pR3iAdkRG3FJ5rU26x9kengL86An2XcnthFkhV14QtyCr/eWE2Sk1pEQiBXguBtW8BN//h5leqm4d5Zw/307u97sGdEKIVne6xKoNqQUEBYmJiwBhDvXr1YGJScyb1kmoQd5n76dacmxt47hduikDqXW47LYlF6TzK1W6Ksrm1Vi//ya1288YX1b95sryYW9Lu9HzV9VodAl4EUq83AGMz7csOHsR1Ef87jBu9u+HtytVVJOHuj0qsgKB3uHoF9AbuHQBOfs8NmHpZbhpw42/ueQPqBiakuugUWIuKivDdd9/h119/RWEht7muRCLBhAkT8MMPP0AsNuCGwKT6xF/ifnq24UaoOgZyrbR7B4A1Pbg1VV9ujSpHjHu24bpQH50GIndyD6+2QJsvuKXjqnL5Nsa4e6HKnTkAbrWXN8ZzK9wot9GqrDpBwKengf1fcVtyGVtwwdHEhguQJlZcd7ja59YvgmlZK9F0/Y7bjSVqH/clx8SKG1SVdOv5wvXh3FJ7li7cHp2EkGqhU2D99ttvsWXLFvz5559o3bo1BAIBLl68iJkzZ0KhUGDRokX6riepiZQtVs/W3M/G73MBastQbgm7vzpzLSmvNi/yKJdpqxMM9FrELXN38XcgYjvw+Dz3sPcFWn8GNB6iW2uxPGnRwO7PXnwpMHMAOk3jVuIRVcEXQlNb4N11+i8X4LqlGw/huoPXvFl2GmtP4IMd1A1MSDXSaR5rnTp1sHv3brRu3Vrl+MWLF/HOO+8gKSlJbxU0lBo3j7Uwm+u6rCkfkPkZwAJv7vnkB6rbckmfcPMtk8O55ev6LuGWfwOA43O5DaRbf8btI6mUlQhcXgFcW/tifVFTO6DlaKDVJ+Vv+6WpyF3AnvFcN7TYjGsdvzG+dk9ByYwDlr/BXZOxxfNlAhtzD9cm3Lxakd5m1RHy2qm2eawZGRn8koYlBQYGIj29jA2Hy5GdnY3du3cjJSUFwcHB6NGjR4V5iouLsWfPHty9excffvghvLy8dEpTY+WmvdjEWdmll/a8yzJkDDeFw9jcsHWMv8r9tKtXOuhZuwMfHwZ2jeXmUO75nNv1o/u8Fy1WwUtdvVauQPe53HZdYRu5/R0z44AzC4Hzv3Ir57T5QreFF2SFwJFvgasrudeebYCBq/TX5WtINp7AF1e5zbLt6tWOHVAIecXpFFiDg4Px+++/49tvv1U5vmzZMjRq1Ejjcp48eYJ27drByckJzZs3x88//4y2bdti69ataufJbt68GVOnTkVgYCCOHTuGdu3alQqamqSpERjj5iUq74kpg6g0Xn2ey38C9w8D/X7jBgwZQnbyi0UHPFqXncbYHHh3PfDffOC/Bdz0k2f3ud1KAG76R1kkltwC3i0/Ae7uAy4s40bqhm3gHn5vcgG2bgfNltBLfwRs+4j73QLcggqdZ75arTgrF0PXgBBSgk6fLgsWLEDv3r2xa9cutGrVCgBw+fJlREZG4sCBAxqXM3XqVDg6OuL8+fMQi8WYOHEiGjZsiB07dmDQoEFl5vH09MTly5chl8vh4eGhc5pqJy/mAktyBDf/MDmCuw+Z+7Ts9LZ1X3TpuTTi9qFMDgf2TeBGma7vC7T4mGsFSqpo0wOFgptSowz4ym2/lFNsgBf3V8siFAKdZ3BTQXZ/Bjw4WuK9Cv7piYyAhgO4QTfxl4ELvwF3D3BlPDjK3aNtM54bIavu3uidPcCeL7hpK6Z23Ejl2rT9FiGkVtLpHisAxMTE4Ndff0VkZCQEAgEaNGiACRMmoG7duhrll8vlsLKywvz58zF+/Hj+eOfOneHs7IwtW7aUm//Jkyfw8PDAqVOn0KlTJ53TqKOXe6yX/wKSbnLB6Oldbu/KlwmE3PQOlSAarH7xhIIsbgGCa2u419YeQN9fy16wWy4Djs7k5kVauz9/uAFm9tygGlNbbsSpQMBtK/b0bokAGs4F/6Lssuts78cF1R4/lj2t5mUJN7hBTcpF5DtOAzpPrzhfSWnRwKU/uK5i5QpIlq7cXNhmIwBTG+6YrBA4+h1wZQX32qM1t0iDtZt25yOEvPaq7R6rcgebJUuWqH2vInFxccjLy4O/v7/KcX9/f1y+fFmXalVKYWEhP3UI4H6ZlXZt9fONlZ8ztuRWHKoTzE3FcA7mRnZqM/LVxAro8wvXkts7Hsh8DGx8B2j6IbdvpDK4ANxUlst/lF+eQMTlKcwuO/CLJFydlftmujTmVhvSdrSuWzPgk1NccE28odv9TXsfoPdirhV8bTX3xSU7ETg2C/hvIbfgRP2+wJEZQGIYl6ftBKDLd1Uz4pcQQsqgU2BdsWJFmcGTMYa//vpLo8Cak5MDALC2Vm2Z2djY8O9Vp9DQUMydO1e/hTb9kAtYdYIA5yDAxkt/g0vqdQQ+uwicmMeNpg3bwK1p23cJ4P98AFjaQ+6nY31uyov0CSBN4Eb05mdwrT4mB/LSuHQS6xIB9PlPB3/93Y+0cgE+PsJtDl1H83vxpZjZAR2mAG98ya1TfGEZt+LTpeXcA+Ba4wNWvPhdEEJINTHY7jbm5tyo1pdbhlKplH+vOk2fPh2TJk3iX2dlZVX+/uwbX1SyVhUwNgfeWsC1Xvd8zi12sGkw0Oh9bipLRgyXzq8b15p9WXE+kJ8J5KdzZdl4Vf2emkbG3DQQvZQl4abxNBnGre174Teule7eiuv6takh99cJIa8Vg+1u4+npCVNTUzx8+BBvvvlicvuDBw8QEKBm3dMqJJFIIJFIKk5YE3m1AcaeA07/yC22EL4FiD75Yn6mXb2y84lNuUdtH1UqEAC+3bhHzlPuHjJNOyGEGIjBdrcxMjJC3759sWHDBnz66acwMjJCdHQ0zpw5gw0bNvDpDhw4gISEBHz66afaVPX1Y2zGtUobvM2NwH1278XoXVvNBpS9EiwcDV0DQshrzqC72yxYsABt27ZF586d0bJlS2zfvh09e/bE4MGD+TS7du3CpUuX+MAaHh6OvXv38l3IGzZswLlz59ChQwd06NBB4zSvLPcWwJgz3NzR879yx5zqG7ZOhBDyGqnU7jaV5e3tjYiICGzbtg0pKSlYunQp+vXrB2GJbrw+ffqgcePG/Gu5XI6CggIYGxvzC1QUFBRAJpNpleaVJjYBus0GmgzlpudY1jF0jQgh5LWh8zzWV12NWyuYEEJItdMlFtAID0IIIUSPKLASQgghekSBlRBCCNEjCqyEEEKIHlFgJYQQQvSIAishhBCiRxRYCSGEED2iwEoIIYToEQVWQgghRI8osBJCCCF6RIGVEEII0SMKrIQQQogeUWAlhBBC9IgCKyGEEKJHFFgJIYQQPaLASgghhOgRBVZCCCFEjyiwEkIIIXpEgZUQQgjRIwqshBBCiB5RYCWEEEL0iAIrIYSQV1Juocwg5zUyyFkJIYRUGbmCITOvCAUyBRQKBgVjUDDuOGMMMgVDclYB0nKK0MbHHm42poaust4diUzGtB3h+H1YM7zh41Ct56bASgipVRQKhgKZHAXFChQ+/1lQLEehjPtZUCwHA+DvbAlXaxMIBAKNyy6WK/A0uxBJ0gKkZBUgSVqAzLwi9GvsCl8nC6TnFiHmWS4ePcuFkVCA3o1cIDES6e265IxBrmAQi4QQCbl6P80uxKGIJOwPT0JaTiGGhXihdT17PEjNxoOUHKRmFyA9txgZeUXIyC1Cel4RpPnFYEyz8woFQGMPG3QOcIJIKEBuoQx5RXL+Z16RDLnPf8oVgJOlBHWsTOBsbQJnq+fPnz/szY0hFGr++64suYJBplBArmAolnO/u/TcImy4GIv1Fx8DANadj632wCpgTNNf/+slKysL1tbWkEqlsLKyMnR1COE9TsuFi7UpjI00v5PDGEOitIALOoyBMUDBAAYGhYL7yRieH2dgeP6TcXkZuA9+5XGUzK9MU6I8ZRmMMcgVKDsAyuQoLBEc1QVJ7j0FCp/nKZZr/pHlYGGMRu42aORuDRdrE0jzi5GZV4zM/GJI84qRmV/Evc4rhjS/GDnldB1amRghq0D1/ZC6dmjjY/+8ftw1FMpe1LdQpkCR7KXj/HVzz4tkCiheuiRLiRGGhHgiMlGKi9Fppd7XlMRICKFAAKEAEAoEEAgAkVAAoUAAR0sJTMQi3IzP1K3wMohFAjhZmsCpRMC1NTOGTKH8PShQJOd+V0VyBYqeXz/3/EWaYrlqsJQpA6icofh5IJUpWIVfHj5pXxdTegRq9X/lZbrEAgqsalBg1UxqdgFuxmXC1cYU3g7msJBU3AlSUCyHSCiAWFSzbvEzxlAoU/Df1HMKZcgrkiGnUI68Qtnz13LkFslQUCRHgfLDvliBAhn3Dd9IKISXvRm87M3hbW8GLwdzuFiZaP0tvmTrhTHwz3feeIK5++7A3dYUX3bxQ5/GLjAzLv93fis+E6GHonDpUXplfj01klgkgImRCBKxCBIjIUzEQpiIRZArGB6m5kCmQ0RSBgcXaxMUKxhulQg8AgHgam2Kug7muBGXgbwiuR6vRr3G7tbo08gVJsYirPgvGln5xfB3toSfsyXcbU1hZ24MWzNj2Jkbw85cDFszY1ibimGkwf+xi9Fp2HApFkKBAObGRjCTiGBubARTYxHMjUUwkxjxxwEgNasAKVmFSM4qQIq0ACnZBUiWFiItt1DjVnJVMjYSopmnDcZ08EHnQKdKl0eBVY8MGVgZ476pKb/RK7/BA0AdaxONgld1OHYnBV9vvanyLd7BQoK6DmbwtjdHXUdz1LU3h7eDObztzWFqLMKFh8/w0dqrKJIrYGVixH0gmBvDzuz5T/4DQlzig4J7WJmIde5myi2UIT4jD/Hp+YhPz0NKVgGeZhciNbsQqdnc86wCGeS6Ng3KYWwkhL25MR8glfe85IoX9724e2AvjmnKRCxE5wAnBNSxLNHKe9FaSpTmIywuEwDXUjE3FkEoFECAFy0YgeDFa6HytYALIsIS7+Gl1yp5hYAAXH4oy+HLFEDyPOCZlAyARiLuuNHz4yWOSYxEfJDk0otees6lEZXz76GgWI47SVm4FZ+J8CdSZOYVweZ5wLExE8PGVMy9fv5cGYysTVX/nV1/nIGn2QWo62ABL3szmIi5ABOZKMXmK3FgDJDw9ebqJTES8tfBHRPywZ9P8zy9sUgI4fNWpEgggEAIHLqdhJN3U9HYwwZ9gl3haW+mcm2MMa26uKuDshudD7hZBUjOKoQ0vwhi0fNrNRLCWCTifj5/SETc78pY9OKYWCSEWCSASCiEkVAAI5EARkLV1yKhAGKhEKLn7xk9f0/fXdEUWPVIH4F11dlHSMjMV+kmKijZXVSiy0vlQ1EmL/fD1crECK42pnCxNoGrjSn/3MXaFK42JqhjbQKJkQhFMgU2XX6Mx+l5EIuU/yCFEAkEEAkBoVDw/LkACsZUuuNe/FStH98lVyxHbFoeAMDNxhQFxXKk5RaV+/uoY2WC5KwCnX6XAHcvyNasZCAWq3xTV34wpuUWIi49D3HPg2h8el6FdXuZqVgEc4kI5hIjmBkbwUIiev7TCGbGIpgal/7ANzYSokimwJOMfDxOy8XjtDzEZ+Rp1XWpiaaeNujewBlbrsQjLj2vwvQCATCgqRsmdfeHu61ZhekJIS9QYNUjfQTWnkvO4G5ydqXrovwAVygYsjUcPu5gIamWrpmP29bFtLe4exhZBcWIfZaLmGe5iH2Wh5hnOYhJy0Pss1xI84tV8k3q7o/ejVy4wRbKh3LwxfOBGOm5RfzP7ILKD5u3MRPDw9YMHnamcLE2haOlBE6WEjg+f9iYGsP8eQAtryWkDbmCITEzH+m5RRAJX9zjEgkEKq0UoZBr3Snvf3E/Vb/8CASASCDgu/cYY4hMzMLRyGSk5RaptPKULTpbc2M09bCBhx0FVEJ0QYFVj/QRWFefi0FaTiHf7WOi0h1UultL5WeJbqOSXT45hTIkZeYjITMfSdICJGXmI1FagMTnrxOft5BL6h3sAjdbUxTLFZDJGd8dKVe8eM51172oU4U/xUI4W5qU6qJSJyO3CDFpuYh9lovcIjneaeoGcy26tItkCmTmccE3PbcIGbnFJQLxiyCcmVcMewtjeNiawdOOC6IedmbwsDODlYlY4/MRQghQywOrQqGAUKjdKMfCwkIYGxuXm0/bcpVq6+Alxhgy8oqRmJmPxMx8OFhK0MzT1tDVIoSQWkmXWGDwYZmhoaFwdnaGWCxGcHAwTp48WW76p0+fYv78+ahXrx5MTU1x5swZvZT7qhAIBLAzN0aQmzXebFiHgiohhFQzgwbWP//8Ez/++CP++ecfSKVSvPPOO+jTpw9iYmLU5vnjjz+QkZGB9evX67VcQgghRB8M2hXs7++PXr16YcmSJQC4bkwvLy8MGTIECxYsKDfvkydP4OHhgVOnTqFTp056K1eptnYFE0II0R9dYoHBJkSmpaXhwYMH6NixI39MIBCgY8eOuHjxYo0rV1e5ublq3xOJRDAxMdEorVAohKmpqU5p8/LyoO77k0AggJmZmU5p8/PzoVAoykwLAObm5jqlLSgogFyufuK9NmnNzMz4wV+FhYWQydSPLtYmrampKX/vvqioCMXFxXpJa2JiApFIpHXa4uJiFBWpn1IkkUhgZGSkdVqZTIbCwkK1aY2NjSEWi7VOK5fLUVCgfuqVWCyGsbGx1mkVCgXy8/P1ktbIyAgSiQQA9+U8L0/91CZt0mrz/54+I8pOq81nhEEwA4mMjGQA2NmzZ1WOT5o0ifn7+1eYPz4+ngFgp06d0ku5BQUFTCqV8g9l+VKpVPOLKgMAtY9evXqppDUzM1ObtmPHjippHRwc1KZt0aKFSlovLy+1aRs0aKCStkGDBmrTenl5qaRt0aKF2rQODg4qaTt27Kg2rZmZmUraXr16lft7K2nQoEHlps3JyeHTjhgxoty0qampfNrPPvus3LQxMTF82smTJ5ebNiIigk87e/bsctNeuXKFT7tw4cJy05b8t79s2bJy0+7fv59Pu3bt2nLTbt26lU+7devWctOuXbuWT7t///5y0y5btoxPe+rUqXLTLly4kE975cqVctPOnj2bTxsREVFu2smTJ/NpY2Jiyk372Wef8WlTU1PLTTtixAg+bU5OTrlpBw0apPJvuLy09BnBPSrzGVFZUqmUAdrFAoMPXnr5G4pCodDLiiLalhsaGgpra2v+4eHhUek6EEIIef0Y7B5rRkYG7OzssG3bNgwaNIg/PmzYMCQkJOD06dPl5ld3j1XXcgsLC1W6sbKysuDh4VHpe6zUzaN9WuoKpq5g6grWPi19RpSdtrJq1T1WW1tbNGjQAKdOneIDoEKhwKlTpzBy5Eg+nUwmg0Kh4P8z6Kvcl0kkEv4/hj5p8weuqrQl/6HrM23J/5j6TFvyg0SfabX5G2uT1tjYWON/n1WVViwW80FLn2mNjIz4IKvPtCKRSON/w9qkFQqFVZJWIBBUSVqAPiN0SavN/3tDMGhX8NSpU7FmzRrs2LEDiYmJmDRpEnJycjBu3Dg+zdixY9GsWTP+tfLbq/KbcVFREQoKClRaF5qUSwghhFQFg26TMnz4cOTk5GD69OlISUlBcHAwjh07Bnd3dz6NWCxWaTls2rQJn3zyCQCuVdGvXz8AwMyZMzFz5kyNyyWEEEKqQo1Z0rCmoXmshBBCauWShoQQQsirhAIrIYQQokcUWAkhhBA9osBKCCGE6BEFVkIIIUSPDDrdpiZTDpbOysoycE0IIYQYijIGaDOBhgKrGtnZ2QBAawYTQghBdnY2rK2tNUpL81jVUCgUuHfvHho0aID4+PhXai6rch1kuq7aga6rdqHrql0qui7GGLKzs+Hq6sqv+V0RarGqIRQK4ebmBgCwsrJ6pf4hKdF11S50XbULXVftUt51adpSVaLBS4QQQogeUWAlhBBC9IgCazkkEglmz55dJdvJGRJdV+1C11W70HXVLlVxXTR4iRBCCNEjarESQgghekSBlRBCCNEjCqyEEEKIHlFgLcPGjRvRp08fdO3aFT/88APy8/PLTZ+RkYEffvgBvXr1Qt++ffHjjz8iMzOzeiqrBW2v6/79+5g8eTK6d++O9957D//8849Wy3pVl02bNqFv377o0qUL5s2bh7y8vHLTy+Vy7N27F/3790fr1q0r/D1UNW3rr2ue6rZlyxa+jnPnzkVubm656RUKBfbv34+3334brVu3Rk5OTjXVVDvaXld0dDS++eYbvPnmmxg8eDDWr18PhUJRTbXVnLbXJZVKERoait69e6NPnz6YN28e0tPTq6m2mtu6dSv69euHLl26YPbs2RVeV0nz5s1D69atsXfvXu1OyoiKhQsXMnNzc/bnn3+y7du3s4CAANa7d+9y8zRt2pTNmzePHTlyhG3bto01bdqUNWnShMnl8mqqdcW0va7r16+z+vXrs59++okdPXqU/fnnn8ze3p5NmDCh+iqtgZ9//pmZmZmxP/74g+3YsYPVr1+f9ejRo9w8b731FuvTpw+bOHEiA8Cys7Orqbal6VJ/XfJUt6VLlzJTU1P2+++/s507d7KGDRuybt26lZunb9++rFevXmzSpEkMAMvIyKieympB2+uKiIhgAQEBbNGiRezo0aNs1apVzMnJiY0ZM6Yaa10xXf5erVq1YrNnz2ZHjhxhO3bsYC1btmQNGzZkRUVF1VTrii1fvpyZmJiw3377je3cuZMFBwezTp06MYVCUWHegwcPsoYNGzKhUMhWrlyp1XkpsJaQl5fHLCws2C+//MIfu3r1KgPAzp07pzZfbm6uyusLFy4wACwqKqqqqqoVXa4rLy+v1BeDFStWMLFYzAoKCqqyuhorKChg1tbWbNGiRfyxsLAwBoCdPn1abb7MzEzGGGO7du0yaGDVpf66XnN1KiwsZLa2tiw0NJQ/FhERwQCw48ePq82n/Lvs27evRgZWXa4rPz+fyWQylWPr169nQqHQoF/oStL17/Xy596NGzcYABYWFlZVVdVKcXExs7e3Z99//z1/LCoqigFghw8fLjdvYmIic3NzY2FhYUwkEmkdWKkruITLly8jJycHffv25Y+1aNECrq6uOH78uNp8ZmZmKq/PnDkDe3t7fklEQ9PlukxNTUuti2lhYQG5XA6ZTFal9dXU1atXIZVKVa6rSZMm8PT0LPfvpe3yZFVFl/rres3V6caNG8jIyFCpY8OGDVGvXr1a8XdRR5frMjExgUgkUjlmYWEBhUKB4uLiKq2vpnT9e5X1uWdtbQ0vL68qq6s2bt68ibS0NJXrCgwMhL+/f7nXpVAo8MEHH2DixIlo0qSJTuemtYJLePz4MQDA1dVV5birqyv/njqrVq3CqlWrkJycDIlEgv/++w+WlpZVVldtVOa6lIqKirB48WJ0794d5ubmeq+jLvRxXYakS/1rwzXXhjrqQh/XJZPJsHDhQnTs2BG2trZ6r6MuKnNdGzZswO+//46UlBQIhUKcPn261l/Xjz/+CAD4+uuvdT73Kx9Y33//fcTGxqp938bGBocPHwYA/huksbGxShpTU9MKv1327NkTDRs2xOPHj7Fw4UKMGzcOJ06cgFgsrtwFqFFd1wVwuzt8/PHHSEpKwp49e3SvtAaGDRuG6Ohote9bWlri2LFjAF5c18srpmh6XYamS/1rwzXXhjrqorLXxRjDmDFjEB0djcuXL1dJHXVRmevq2rUrfH19ER8fj8WLF2Ps2LE4ffo0TExMqqy+mtLlus6fP4+lS5ciLCwMAoFA53O/8oF1+vTp5Y76LBn47OzsAHCjfB0cHPjjaWlpaNasWbnncXd3h7u7O9q0aYNOnTrB1dUVu3fvxrvvvlvJKyhbdV0XYwyffvopjh07hlOnTlX5/rRTp04td4SrkdGLf7LK60pPT0edOnX442lpaWjQoEHVVVJPdKl/bbjmknUs2V2YlpaGevXqGapalVbZ6xo/fjz27NmDkydP1qjfQ2Wuy9XVFa6urmjTpg26desGJycnbNu2DR9++GGV1lkTJa+r5K41aWlpCA4OLjPPxo0bAQADBw7kj8nlcoSGhuL48ePYsmWLZifX/pbwqysmJoYBYAcPHuSPpaenM7FYzNatW6dxOcXFxczY2JgtX768KqqpNV2vS6FQsE8//ZQ5OTmxiIiI6qiqVuLi4hgAtnfvXv6YVCplxsbGbNWqVRXmN/TgJV3qX9lrrg6JiYlMIBCwnTt38seys7OZiYkJ+/PPPyvMX1MHL1XmusaPH8/s7OxqzMCekir791JSKBTM3Nyc/fzzz1VRTa2lpKQwoVDItm7dyh/Lzc1lZmZmbNmyZWXmefToEbt48aLKQyQSsenTp7ObN29qfG4KrC/p0qUL69ChAz/ydfLkyczOzo4fscgYY0OHDmU//vgjY4yxmzdvsh07dvDvyWQyNnfuXGZkZMTu379fvZUvh7bXxRhjY8eOrbFBVenNN99kbdu2Zfn5+YwxxqZNm8ZsbGxYeno6n+bDDz9k8+bNK5XX0IGVMd3qr0keQ+vVqxdr3bo1y8vLY4wxNnPmTGZlZcWePXvGp/noo4/Y7NmzS+WtqYGVMd2ua+LEicze3r5GBlUlba8rMjKS/fvvv/x7crmczZ8/nwmFwhr1edGvXz/WsmVLfgTznDlzmKWlJUtNTeXTjBo1is2cOVNtGbqMCqbA+pL4+HjWrFkzZmNjwzw8PJizszM7efKkSpqGDRuyUaNGMcYYe/bsGfvwww+ZnZ0da9y4MbO3t2cBAQEqLYqaQNvrOnLkCAPAPDw8WEhIiMojOjraEJdQpoSEBNaiRQtmbW3NPDw8mJOTU6kpAo0bN2YjRozgXy9evJiFhIQwf39/BoC1bNmShYSElDulqqroUn9N8hhaUlISCwkJYdbW1szT05M5OjqyI0eOqKRp3rw5GzZsGP/6l19+YSEhISwgIIABYC1atGAhISHsv//+q+7qq6Xtdf33338MAHNzcyv1/6imTMdjTPvrysjIYCNHjuQ/9xwdHZmvr69KI6MmSElJYW3atGFWVlbMy8uLOTg4qPTcMcZYSEgIe++999SWoUtgpd1t1Hjw4AHy8/NRv379UgOQwsPDYWFhoXL/IScnB48ePYKjoyPq1KlTqRvfVUnT68rMzMTdu3fLLKNx48YwNTWtjupq7OHDh8jLyyvzum7fvg0zMzP4+PgAAGJjY5GcnFyqjMDAQNjY2FRHdUvRpv6a5KkplHUMDAwsNXguIiICpqam/HU9fvwYSUlJpcoICAioMSNNlTS9rqysLNy5c6fMMoKDg2vMCHslbf5eAJCbm4vo6Gg4ODjAxcWlxn7uRUdHIycnB/Xr1y91XZGRkZBIJPD19S0z7+XLl1GvXj04OjpqfD4KrIQQQoge0QIRhBBCiB5RYCWEEEL0iAIrIYQQokcUWAkhhBA9osBKCCGE6BEFVkIIIUSPKLASQgghekSBlRBCCNEjCqyEEEKIHlFgJYSoOHDgAK5cuaJyLCwsDLt37zZMhQipZSiwEkJUpKSkoEuXLnjw4AEA4MmTJ+jWrRsePXpk4JoRUjvQWsGEkFLeffddxMbG4ty5c3jrrbcgEolw9OjRGrvIOiE1CQVWQkgpGRkZaNSoEWxtbZGYmIjw8HC4uroaulqE1ArUFUwIKcXW1haTJk3C7du3MXXqVAqqhGiBWqyEkFJSU1PRqFEj2NvbIysrC+Hh4TVuT1RCaipqsRJCShk1ahT8/f1x7do1ODo6YuzYsYauEiG1BgVWQoiK5cuX48yZM9iwYQNMTU2xadMm7Nu3D3///behq0ZIrUCBlRDCy8/Px7Vr17B+/Xp4eXkBAAIDA7F69WqcP38eRUVFBq4hITUf3WMlhBBC9IharIQQQogeUWAlhBBC9IgCKyGEEKJHFFgJIYQQPaLASgghhOgRBVZCCCFEjyiwEkIIIXpEgZUQQgjRIwqshBBCiB5RYCWEEEL0iAIrIYQQokcUWAkhhBA9+j9n07gG9268XQAAAABJRU5ErkJggg==", + "text/plain": [ + "<Figure size 500x350 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(5, 3.5))\n", + "ax.plot(d0.x, d0.y_err, \"k--\", label=\"reported\")\n", + "for g in (0.02, 0.05, 0.10):\n", + " c = rx.Constraint([comps[0]], terms=[T.model_error(gamma, averaging=True)])\n", + " p = rx.Problem([c])\n", + " theta = np.append(theta_map[:5], np.log(g))\n", + " ax.plot(\n", + " d0.x, np.sqrt(np.diag(p.constraints[0].matrix(theta))), label=f\"gamma = {g}\"\n", + " )\n", + "ax.set(xlabel=\"x\", ylabel=\"total standard deviation\")\n", + "ax.legend(frameon=False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b592b2e4", + "metadata": {}, + "source": [ + "### 4. A systematic shared across datasets\n", + "\n", + "The same normalisation term `on` two comparisons couples their blocks; one\n", + "per comparison leaves the covariance block diagonal. Which is right depends\n", + "on whether the two experiments really shared the calibration; the next\n", + "notebook is about that choice." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "2683faa0", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:37:56.069846Z", + "iopub.status.busy": "2026-09-11T03:37:56.069695Z", + "iopub.status.idle": "2026-09-11T03:37:56.126814Z", + "shell.execute_reply": "2026-09-11T03:37:56.126172Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 700x350 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "two = comps[:2]\n", + "c_shared = rx.Constraint(two, terms=[T.normalization(magnitude=0.1, on=two)])\n", + "c_each = rx.Constraint(two, terms=[T.normalization(magnitude=0.1, on=c) for c in two])\n", + "fig, axes = plt.subplots(1, 2, figsize=(7, 3.5))\n", + "n0 = datasets[0].n\n", + "for ax, c, title in zip(\n", + " axes,\n", + " (c_shared, c_each),\n", + " (\"shared: off-diagonal blocks\", \"per dataset: block diagonal\"),\n", + "):\n", + " p = rx.Problem([c])\n", + " show(ax, p.constraints[0].matrix(theta_map[:5]), title, dividers=(n0,))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "501f3b2f", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "| case | term | structure of Σ |\n", + "|---|---|---|\n", + "| unknown normalisation, sampled | `model \\| tf.scale(rho)` | none: the mean moves |\n", + "| reported normalisation | `T.normalization(magnitude=)` | rank one, from the prediction |\n", + "| smooth systematic in x | `T.kernel(...)` | dense, decaying with distance |\n", + "| reported correlated statistics | `rx.Term(C, kind=\"matrix\")` | whatever was reported |\n", + "| unknown model error | `T.model_error(gamma)` | sampled diagonal |\n", + "| shared across datasets | one term `on=[c1, c2]` | off-diagonal blocks |\n", + "\n", + "Declaring the uncertainty *is* the modelling choice; the inference machinery\n", + "is unchanged. Never build a mode from the data (recipe 27): the prediction\n", + "is what the mode multiplies." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 46519137e21111c20803dba19d45ce6ff2a79700 Mon Sep 17 00:00:00 2001 From: beykyle <kylebeyer1@gmail.com> Date: Thu, 10 Sep 2026 23:47:23 -0400 Subject: [PATCH 37/75] Export the likelihood functionals from the package The recipes spell them rx.Gaussian, rx.StudentT and rx.Chi2. --- src/rxmc/__init__.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/rxmc/__init__.py b/src/rxmc/__init__.py index 69e5d0c..6ab2920 100644 --- a/src/rxmc/__init__.py +++ b/src/rxmc/__init__.py @@ -20,6 +20,9 @@ from .constraint import Constraint as Constraint from .data import Dataset as Dataset from .data import from_measurement as from_measurement +from .likelihood import Chi2 as Chi2 +from .likelihood import Gaussian as Gaussian +from .likelihood import StudentT as StudentT from .model import Model as Model from .model import polynomial as polynomial from .params import Parameter as Parameter @@ -34,13 +37,16 @@ __all__ = [ "__version__", + "Chi2", "Comparison", "Constraint", "Dataset", + "Gaussian", "KernelTerm", "Model", "Parameter", "Problem", + "StudentT", "Term", "from_measurement", "polynomial", From c83a49717475d2a3c2e17d2f180870b324db693b Mon Sep 17 00:00:00 2001 From: beykyle <kylebeyer1@gmail.com> Date: Thu, 10 Sep 2026 23:47:37 -0400 Subject: [PATCH 38/75] Add the correlated_observations and robust_likelihoods notebooks correlated_observations (recipes 5, 37): two datasets with one drawn calibration, coupled versus independent covariances and posteriors, case A versus case B with one free object, then Neudecker et al. (2014) recreated: the two-quantity closed forms and the five-quantity Fig. 1 with C_I and C_F, plus the log-determinant pull of the live term. Runs in 141 s. robust_likelihoods (recipes 9, 34): Gaussian versus Student-t on a line with outliers, nu as a column, a global error scale under both functionals, and an unrecognised offset per technique that moves the mean where a scale only widens. Runs in 154 s. --- docs/recipes.md | 6 +- examples/correlated_observations.ipynb | 751 +++++++++++++++++++++++++ examples/robust_likelihoods.ipynb | 575 +++++++++++++++++++ 3 files changed, 1329 insertions(+), 3 deletions(-) create mode 100644 examples/correlated_observations.ipynb create mode 100644 examples/robust_likelihoods.ipynb diff --git a/docs/recipes.md b/docs/recipes.md index 2c73c48..22c4177 100644 --- a/docs/recipes.md +++ b/docs/recipes.md @@ -1140,9 +1140,9 @@ Expected behaviour: - The *live* term reading `c.ym` is the generative model's marginal likelihood, not `C_I`: its covariance grows with the prediction, so the log-determinant pulls the mode below the exact values (5 % in the - two-quantity case, 9 % in the five-quantity one, against 22 % for - `C_F`), and under a flat prior the `1 / rho` tail pulls the mean above - them. A proper prior on the quantities, or the refit, removes the pull. + two-quantity case, 9 % in the five-quantity one, against 23 % and about + 30 % for `C_F`), and under a flat prior the `1 / rho` tail pulls the mean + above them. A proper prior on the quantities, or the refit, removes the pull. - The five-quantity numerical study of the reference (its Table I: `q_i` = {1.0, 1.5}, {1.8}, {2.2, 2.4}, {1.9, 1.5}, {1.4, 1.2}; `N_i` = 1, 1.1, 1.25, 1.15, 1.05; `sigma_i = 0.1 q_i`, `sigma_Ni = 0.2 N_i`, `c = 0.8`) diff --git a/examples/correlated_observations.ipynb b/examples/correlated_observations.ipynb new file mode 100644 index 0000000..2641135 --- /dev/null +++ b/examples/correlated_observations.ipynb @@ -0,0 +1,751 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d73aa9d8", + "metadata": {}, + "source": [ + "# Correlated observations\n", + "\n", + "Two datasets that are each internally independent but share a calibration\n", + "are *correlated observations*: fitting them as if independent is\n", + "overconfident, because the common mode cannot average down. In `rxmc` the\n", + "coupling is one covariance term whose support spans both comparisons. The\n", + "second half recreates the two-and-more-dimensional Peelle's Pertinent Puzzle\n", + "of Neudecker, Frühwirth, Kawano and Leeb, *Nucl. Data Sheets* **118**, 364\n", + "(2014): several physical quantities of one experiment with correlated\n", + "normalisations, and why the covariance must be built from the estimate and\n", + "not from the data.\n", + "\n", + "Recipes: 5, 37" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "68b3cd97", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:43:23.920181Z", + "iopub.status.busy": "2026-09-11T03:43:23.919921Z", + "iopub.status.idle": "2026-09-11T03:43:26.661049Z", + "shell.execute_reply": "2026-09-11T03:43:26.660051Z" + } + }, + "outputs": [], + "source": [ + "import corner\n", + "import emcee\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from scipy import stats\n", + "\n", + "import rxmc as rx\n", + "from rxmc import terms as T" + ] + }, + { + "cell_type": "markdown", + "id": "f6d94ba0", + "metadata": {}, + "source": [ + "## Two datasets with a shared calibration\n", + "\n", + "One line, two experiments on disjoint ranges, one calibration factor drawn\n", + "once and applied to both. Each experiment reports its own 2 % statistics and\n", + "a 10 % normalisation uncertainty, without saying that it is the *same*\n", + "normalisation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8982216e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:43:26.662956Z", + "iopub.status.busy": "2026-09-11T03:43:26.662664Z", + "iopub.status.idle": "2026-09-11T03:43:26.892125Z", + "shell.execute_reply": "2026-09-11T03:43:26.891553Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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QaCslJUUEBQUJS0tL4eDgIBwcHMSHH34o1Gp1pXEa0tmbJkIYQN0YPZeVlYXCwkI4OjqWOX6VnZ2N7OzsMge3VSoV0tPT4eTkVO7wSWFhIe7evQtHR8cqXaSqVquRkZEBe3t7mJmZIS8vD5mZmRpDphXF9LD8/HwUFhZKxwtLT5Zp3Lixxno3b96EqakpmjRpIi0rLi5Gamoq6tevX2Y4sbr7ozwPHjxAbm5uhUOWlW1DTk4OsrKy0KxZM+l9K2/ZozHfv38farVael1ttq8qcT28fZmZmVCr1XB2doa5uXm58T1MF5/FUgUFBUhLS0OjRo3KHXbVdrtKt+1x71152/6k2wmUXIQ9atQoxMXFwcPDo8JtrUqsuvS40l4P/02Xvoe2trZSxZ7bt28/tuZuRZ9RlUqF7OxsODo6an12ZmZmJvLy8vTyJJ5HMekRkdGYMGEC1Gp1lQo3U93CpEdERiM9PR3W1tbSMSgyPkx6RERkNHj2JhERGQ0mPSIiMhpMekREZDQMuiKLWq3GzZs3YWNjU+7p20REZByEEMjJyUHTpk0fe6mFQSe9mzdvVruoLBER1T0pKSmPvV7QoJNe6UWYKSkp5V4oTERExiE7Oxtubm5SXqiIQSe90iHNhg0bMukREVGlh7p4IgsRERkNJj0iIjIaTHpERGQ0mPSIiMhoMOkREZHRYNIjIiKjwaRHRERGg0mPiIjkER8BfN4TWNS45Gd8hM6bZNIjIqLaFx8B/PAacDseKFKV/PzhNZ0nPiY9I6RWq3Hv3j1w/mAiks2hpQBMAJT+HxIl9w8t02mzTHp6pjQhqdVqnb1efHw87O3tcfv27Rppg4ioyjKS8L+EV0oAGYk6bZZJT89cuXIF9vb2+Oeff/Ty9YiIaoRjG5T09B5mAjgqdNosk54eKZ0PCiipGH7v3j3k5uaWGY7Mzc1FYWEhioqKcO/evTKvUdqzq+j1HlVYWKjbDSMielTf2ZCGNAFIQ50+s3XaLJOeHsnOzkbfvn0BAL1790bLli0xevRoaTgyPDwcLi4ucHFxwR9//IHo6GjY29ujqKhIeo3bt2/D3t4e8fHxFb5eqa1bt+Lpp5+GtbU13NzcsHPnztrdYCIyXu2DgGGbAOcOgJllyc/hm4F2gTpt1qCnFqqu8no7perVq4ennnpKq3VNTU1Rv379x65rZWWldVy2trY4c+YMFAoFzp8/j5YtWwIA4uLiAJQkqePHj6N58+YAgIMHDz7R6/3000/4888/4eLiggULFuD111/H9evX0aBBA61jJiKqtvZBJbdaZJQ9PWtr6wpvQ4cO1Vi3cePGFa77/PPPa6zbsmXLMuvUpCVLlkgJryZ8/PHHaNasGUxNTTF9+nTcvXsXCQkJNfb6RET6xiiTnqFyd3ev0dcr7fkBkCbhzcrKqtE2iIj0iVEOb96/f7/Cx+rVq6dxPy0trcJ1TU01vzMkJyc/UVyVMTc317hf3gzBVbnUobIZhomI6hqjTHpVOc6mq3UrYmFhAQAoLi6udF0HBwcAwJ07d9CkSRMAJdfgVff1iIjqOg5v6pkmTZqgfv362Lt3LzIzMx97Io27uzuaNWuGefPm4fr16zh8+DBmzJhR7dcjIqrrmPT0jIWFBb744gusWbMGCoUCo0ePRr169WBra1tmONXCwgI///wzLl68iO7du2Pu3LlYsGABbG1tpWFabV/PxMQEtra2MDMzys4/ERkJE2HABRizs7Nha2uLrKws6UQMIiIyPtrmA/b0iIhIOzJMBVTTmPSIiKhyMk0FVNOY9IiIqHIyTQVU05j0iIiocjJNBVTTmPSIiKhyMk0FVNOY9IiIqHIyTQVU05j0iIiocjJNBVTTeCUyERFpR4apgGqa3vT0VCqV3CFUKK+gCC1n70LL2buQV1BU+ROIiEgvyZr0hBAIDQ1Fs2bNYGdnhw4dOmDfvn1yhqQXbt26hcmTJ+Pf//43Jk6cKHc41XbgwAEEBATIHQYRkUTW4U2lUoktW7Zg+/bt6NOnD5KTk/Hll1/C19dXzrBkN3XqVOTn52Px4sVwdHSUO5xqy8jIwKlTp+QOg4hIIlvS+/vvv7FixQp8//336NOnD4CSSU2XLFkiV0h64/Tp0/joo4+k/UJERDVDtuHN33//HWZmZhg8eDDUajUKCwvlCqVSkfG3pd8Hrz6MPXGpOmknPT0d3t7euHnzJhYuXAhvb2/8+OOPAIDMzEzMnj0b/fr1wwsvvID169fj4VrhV65cgbe3N06dOoUxY8agT58++PPPPzF16lSsX79eWm/lypXw9vZGaur/tiEgIAAHDx5EQUEBvL294e3tjd69e2PUqFGIiorSiLGidgDg5MmTGDZsGJ577jmEhIQgJydHJ/uJiKi6ZEt6165dQ4sWLRAaGgpbW1vY2Nigc+fOjz2mp1KpkJ2drXHTtT1xqQjeGiPdT7x9H5M2n9FJ4rO1tcXy5cthY2OD1157DcuXL0efPn1QVFSEfv36ITo6GkqlEi+99BLee+89fPDBB9Jz8/LycPz4cQwZMgQ9evTAsmXL0LFjRzRs2BA//PCDtN6WLVsQExODAwcOAAASEhKwd+9euLu7w9zcHMuXL8fy5csRGhoKLy8vDB48GLt37660ncuXL6Nv375o3Lgx3n77bRQWFmLKlCk1vo+IiJ6IkIlSqRQAxMiRI0VOTo5QqVRi1qxZokGDBuLq1avlPmf+/PkCJVdHatyysrJ0Fudz4YdEy/d2ihYP3VrO3ikClh/SWZvOzs5i06ZN0v1vvvlGWFtbi8zMTGnZli1bhKWlpUhLSxNCCBEbGysAiG+//Vbjtfbs2SMaNGggCgoKxP3794WFhYWYMmWKGD9+vBBCiLVr14q2bdtWGMsHH3wgBg4cKN2vqJ033nhD+Pr6aiwbPHiwcHR0rOLWE1GNubBDiDU9hFjoVPLzwg65I9KZrKwsrfKBbD29Zs2aAQAWL14Ma2trWFhYYPHixSgsLKywtxcSEoKsrCzplpKSovM4r97JLVttTgBX0mtvBvKTJ0/i2Wefhb29vbRs4MCBUKlUiI2N1Vi3Z8+eGvd79+6NwsJCnDhxAtHR0VAoFBg2bBgOHjwIADh48CB8fHyk9Y8dO4Zx48bB19cX3t7e+P7773HlypUyMT3azqlTp+Dn56exbMCAAdXZXCKqCXVkVoSaJlvS69u3LwDN6/MKCwuhVqthaWlZ7nMsLS3RsGFDjZuutWpkVbbanAnQ2slK522XysnJgbW1tcayBg0awNTUtMxxMysrqzL3vby8cODAARw8eBD9+vWDt7c3rl+/jpSUFBw6dEhKeseOHYOPjw+aNm2Kd999F+Hh4Rg6dCjy8/PLxPRoO9nZ2eW2TUQyqSOzItQ02ZKel5cXBg4ciLfeegvx8fG4cuUK3nzzTTg5OenVtV0z/BQaPT0Tk5KeXrBv21qL4emnn8alS5c0liUkJECtVuPpp5+u9Pk+Pj5S0vPx8YGlpSW8vb3xxRdfIDU1VUp6O3bsgI+PDxYuXIjnnnsOPXr00PoEo9atWyMhIUFj2aMxE1EtqiOzItQ0WS9O37p1K9q2bYvnn38evr6+KCgowKFDh9CoUSM5w9IQ4OmCFSM6S/fbOttg7SgvBHg2qbUYXnvtNVy7dg0bNmwAUNIjfv/999G9e3d4enpW+nwfHx8cPXoUZ86ckXrYPj4+WLVqFdq2bQsXFxcAgL29PZKSknD//n0AwNmzZ6U2KzNmzBhs3rwZ8fHxAEouSXn4rFEiqmV1ZFaEmiZr0rOxscF///tfXLt2DVevXsUPP/yAtm1rrwelLf/2ztLvv0zpWasJDyi5fnHDhg1455130KZNG7i4uCApKQnffPONVs/v3bs3ioqK4O7uLn2h8PHxQXZ2tsbxvIkTJ8Le3h4tWrRAhw4dMGDAAPTr10+rNkaNGoWXXnoJXbp0Qfv27eHt7Q1/f/8qbysR1ZA6MitCTTMRQjza/zUY2dnZsLW1RVZWlk6P7+UVFKH9vL0AgPgFz6GBhW6v6T9z5gyaN29epsf74MEDJCQkoEGDBlAoNL+t5efn49y5c/Dy8oK5uXmZ14yJiYGVlZX0vMLCQpw+fRotWrSQenoAoFar8ffffyM/Px8eHh7IysrC9evX0aVLF63auX79OrKzs6FQKJCTk4OrV6/Cy8vrifcJEVVDfETJMbyMxJIens9sg5sVQVva5gMmPS3UdtIjIqKq0TYf8L+3FhpYmCF56Qtyh0FERE9Ib6YWIiIi0jUmPSIiMhpMekREZDSY9IiIyGgw6RERkdFg0iMi0hfxEcDnPYFFjUt+GnlxaF1g0iMi0gecFaFWMOlpoyAX+NC25Fag2ymF0tLS8OGHH0r1Lw29neqIj4/Hxx9/LHcYRLWLsyLUCiY9PZOWloaPPvqoVpJebbRTHUx6ZJQ4K0KtYNIjItIHRjgrghACV69erdU2WYZMTxUWFuLXX3/F5cuXoVAoMHjwYJiYlPxBFBUVYdGiRQAAMzMztGzZEgMHDoSDg0OZ17lw4QL27dsHU1NTDBw4EK1bt66wzePHj2PPnj2YOHEidu7cCQ8PD/Tu3RsAsH//fvz555945513pAltw8LC4O/vj3bt2lUaT1paGtasWYMZM2Zg7969SEpKwtChQ+Hh4YH79+/jhx9+QFZWFrp27VozO5DI0PSdXXIMTxrirNuzIsTExECpVOLs2bNISkqCo6NjrbTLnp42Lv3+v9/X+9bKgeWAgABs2LAB169fx6RJkzB8+PBy18vPz8emTZvQrl07JCZqDoPMmzcPXl5eOHHiBBISEhAUFIRDhw6V+zp79uzBgAED4ObmhiZNmuDkyZP473//Kz3+2Wef4aOPPkJ0dDQAID09He+88w5MTTU/QhXFUzqc2qdPH2zbtg0qlQoAkJWVhW7duiE8PBwpKSlQKpWYP39+1XcYkaFrHwQM2wQ4dwDMLEt+Dt9c52ZFSE1Nxbhx49C1a1ccOHAA+fn5OHr0aO0FIAxYVlaWACCysrJ018iFHULMb/jQzbbk54UdOmkuNjZWABATJ06Ull26dEnUq1dPREZGVvi81157Tbz++uvS/YMHDwoAYt++fdKyvLw8kZiYqNFOamqq+P7774WNjY345ZdfpHW///574ezsLIQQoqioSDRs2FA899xz4r333hNCCPHDDz8IJycnoVartYqntL25c+dqrDdv3jyhUChEXl6eEEIIlUolOnXqJBwdHR+7n4jIsOTl5YmFCxcKKysrgZKurBgxYoRITk6ukdfXNh9weLMyjzujqn2QzpodM2aM9Lu7uzt69+6N3bt3w8/PD0BJj2rnzp1ITk5Gbm4uMjMzcePGDek5P//8M7y8vNC/f39pWf369dGmTRuNdlavXo1Vq1Zhx44dGhPG+vj44Pbt27h48SLu378PKysrTJw4EUuXLgUAHDx4EH379pWGXCuLp9SwYcM07u/evRvDhw9H/fr1AQAWFhZ47bXXpHaIqG5Yvnw55s6dCwDw9vZGWFgYevToUetxMOlVRqYzqpo00Zyd3cXFBTdv3gQA3Lx5E927d0fTpk3Rs2dPNGzYEKamprh79660flpaGlxdXSttZ+3atejbty/69u1bpr22bdviwIEDyM3NhY+PD/r27Yvhw4cjJycHBw8exJQpU7SOp9SjE+OmpqaWu61EZPjy8/OlL7TTpk3Djh07EBwcjBEjRkhfmGsbk15lHNuUXCSqkfh0f0bVrVu30KJFC+l+amqqNAP5V199BWdnZxw7dkz64CxYsADnz5+X1m/cuDEOHz5caTu//fYbRowYgbFjx2Ljxo0ax+h8fHxw8OBB5ObmYvDgwXBwcEC7du3w888/4+LFi/Dx8dE6noq4uLjg1q1bGstSU1MrfR4R6a+rV69i9uzZuHbtGo4cOQJTU1NYW1vj6NGjsiW7UjyRpTJ9Z6NMwquFM6q++uor6feEhARER0cjICAAAJCXlwdLS0vpw5Obm4tNmzZpPH/IkCE4ffq0xokr+fn5SEpK0livZcuWOHDgAA4cOIBx48ZBrVZLj5UmvejoaGno08fHBwsXLkSjRo3Qvn17reOpSEBAALZt24b8/HwAQEFBgdbPJSL9kp2djdmzZ6Ndu3b44YcfcOLECZw4cUJ6XO6EBzDpVa59EPDS+v/db9yuVs6o+uuvvxAYGIjg4GD07dsXgwYNgr+/PwBg5MiRiImJQVBQEN5++2106dIFQmgOwfr4+CAkJATPPfccXn/9dUyfPh3PPvtsucfZWrVqhYMHD2L//v0YP368lPh8fHyQnp4OGxsb6Vigj48P/v77b43jedrEUxGlUgkA6N69O5RKJXr06IGioqLq7TQikkVxcTG+/PJLKBQKLFu2DCqVCv3798fZs2fh7e0td3gaOLypDY+B//t9fBRgYaWzpho3boz58+cjODgYBw8eRGJiItasWYPBgwdL67Rv3x7x8fGIiIhAfn4+1q9fD2tra/z5558ar7VkyRIMHz4c+/btw1NPPYWZM2eiVatWGu2UXnPXqlUrHDhwAN9++y3Onz+Pzp07w8XFBZ988gmaNm0qvWa/fv0wf/58+Pr6VimeR9srZWdnh9OnT2Pbtm3Izs5GWFgYnJycsHPnzhrbp0Q6Ex9RcrJbRlLJoZC+s3V6gps+unnzJgICAhAbGwsAUCgU+PTTTxEYGKgXPbtHmQhtv5LroezsbNja2iIrKwsNGzbUXUMFucCS///HP+emTpMeERmI0gLRj15MPmyTUSU+tVqNbt264erVq5g/fz4mT54MCwuLWo9D23zA4U0iouow0gLRGRkZmDNnDnJzS4rvm5qa4rvvvkNSUhKCg4NlSXhVweFNbVhYAR9myR0FEekTIysQXVBQgNWrV2PBggW4d+8eLC0tpepJHh4eMkenPfb0iIiqw0gKRAsh8Ouvv6JDhw5QKpW4d+8eOnXqhD59+sgdWrUw6RERVYd0OVNp4qt7BaLPnj2L/v37Y8iQIUhKSoKzszPWrVuHM2fOaFR7MiRMekRE1WEEBaKXLl2KgwcPwtLSEnPmzEFiYiLGjx+PevXqyR1atcl2TE+tVuPIkSNllrdt2xaNGzeWISIioipqH1SnztTMy8tDfn6+NM3P0qVLYWFhgUWLFmlUiDJksl2ycP/+fdjY2KBTp06wsbGRlr///vt4/vnntXqNWrtkgYioDlOr1diyZQtmz56Nvn37YvPmzXKHVGXa5gPZz9784osv9O6KfSIiY3HkyBHMnDlTKhcWHR2N7OzsOtuRkP2Y3vXr13H69Gncu3dP7lCIiIxGcnIyhg8fjl69euHEiROwtrbG4sWLcfHixTqb8AA96OlNnjwZTZo0weXLlzF48GB88cUXsLOzK3ddlUolzbgNlHRniYioav744w8EBQVBpVLBxMQE48aNw8KFC8tM81UXydbTMzMzw7fffov09HTExsbi4sWLOHnyJKZOnVrhc0JDQ2Frayvd3NzcajFiIqK6oUePHrCzs5OKQq9bt84oEh6gZ7U3165di+DgYOTm5sLMrGwntLyenpubG09kISJ6jMjISHz//ffYsGGDNGfm9evX0axZM70sCl0dBnMiy8OcnZ1RUFCAO3fulPutw9LSEpaWljJERkQGzwhnRLh06RLeeecd7Nq1CwAwYMAAvPLKKwAAV1dXOUOTjWzDm6XFSh/2xx9/wMnJidfpEVHNKp0R4XY8UKQq+fnDayXL66CMjAxMmzYNnp6e2LVrF8zMzDB9+nQMGDBA7tBkJ1tPb8OGDThy5AiCgoJgZ2eH33//HevXr8e6deuk7jcRUY143IwIdai3V1RUhFWrVklFoQEgMDAQn3zyCdzd3eUNTk/IlvSmT5+OVq1aYfv27UhPT0fr1q1x6tQpPPPMM3KFRER1lZHMiGBqaopNmzZJRaE/++wz+Pn5yR2WXpH1mF5gYCACA+tOnToi0lOObUqGNDUSX92YESEmJgZt27ZFgwYNYGpqipUrV+LixYsYO3asQdfI1BWOIxJR3VcHZ0RITU3F2LFj0bVrV3z66afS8t69e2PChAlMeBVg0iOiuq8OzYiQl5eHhQsXQqFQ4KuvvoIQAikpKXKHZTD06pIFIiKdMfAZER4uCn39+nUAgLe3N8LDw1m/uArY0yMiMgAffPABRo0ahevXr6N58+bYsmULjhw5woRXRUx6RER66uGCWePHj4ejoyMWL16MS5cuYcSIEXWmmkpt4vAmEZGeyc7ORmhoKO7cuYN169YBAFq3bo2UlBTUr19f5ugMG5MeEZGeKC4uxsaNG/HBBx8gLS0NADBz5ky0b98eAJjwagCHN4mI9EBUVBS6dOmCN998E2lpaWjbti0iIiLQrl07uUOrU9jTIyL9ZCQFom/evImJEydi586dAAB7e3vMnz8fkydPhoWFhczR1T3s6RGR/jGiAtHW1tY4ceIEzMzMEBwcjKSkJAQHBzPh6QiTHhHpn8cViDZwBQUF2Lx5s3RmZsOGDbFp0ybExcVh+fLlcHBwkDnCuo3Dm0Skf+pggWghBCIiIjBr1iwkJibC3Nwcw4cPBwBO+VOL2NMjIv3j2Ab/q5NZynALRMfExMDX1xeDBw9GYmIinJ2dOYWaTLjXiUj/1JEC0ampqRg3bhy6du2KAwcOwNLSEnPmzEFiYiJefvllucMzShzeJCL9U1og+tCykiFNR0VJwjOwAtHDhg1DdHQ0AOCVV15BaGgoWrRoIXNUxs1EPFznxsBkZ2fD1tYWWVlZaNiwodzhEJGRE0KgqKgI5ubmAID9+/fj/fffZ1HoWqBtPuDwJhFRDThy5Ah69OiBZcv+d4Zp//79WRRazzDpERE9geTkZIwYMQK9evXC8ePHsXr1aqhUKulxFoXWL0x6RETVkJ2djZCQEHh4eGDbtm0wMTHB+PHjcfbsWVhaWsodHlWAJ7IQUc0wkrJhABAZGYlRo0ZJRaH79++PsLAwPPPMMzJHRpVhT4+InpwRlQ0DgFatWuHu3btQKBTYsWMHoqKimPAMBJMeET25Olw2DAAuXbqElStXSvfbtGmDffv2IS4uDkFBQTxuZ0CY9IjoydXBsmEAkJGRgenTp6Njx44IDg7GqVOnpMf69OnDotAGiEmPiJ5cHSsbVlBQgPDwcLRp0warVq1CUVERAgMDYW9vL3do9ISY9IjoydWRsmFCCOzYsQOenp5QKpW4d+8eOnXqhKioKERERODpp5+WO0R6Qkx6RPTkSsuGOXcAzCxLfg7fbHBlw3JzczFhwgSpKPS6detw5swZ+Pr6yh0a1RBeskBENaN9kEFeopCWlgYnJyeYmJjA2toay5YtQ2JiIkJCQmBjYyN3eFTD2NMjIqOUn5+PRYsWoXXr1ti+fbu0fMyYMViyZAkTXh2lN0nv119/xdq1a5Gbmyt3KERUhwkh8P3338Pd3R1z585Fbm4ufvrpJ7nDolqiF8Obu3btwsiRI5GXl4cXX3wRVlZWcodERHXQkSNHoFQqcfz4cQCAm5sbli1bhhEjRsgcGdUW2Xt6N2/exKRJk7Bo0SK5QyGiOmzu3LlSUWhra2ssXrwYCQkJeOWVV3hxuRGRNemp1WqMGjUKb7/9Njp27ChnKERUx/n4+MDExATjxo1DYmIi5syZg/r168sdFtUyWYc3Fy9eDDMzMwQHB2Pfvn2Vrq9SqTSm7MjOztZleERkoIqLi7Fx40YUFBRg6tSpAABfX18kJSWhdevWMkdHcpIt6UVHR2P16tU4e/as1kMLoaGh+Oijj3QcGREZsqioKCiVSsTGxsLa2hpDhw5FkyZNAIAJj+Qb3pw6dSp69eqFHTt2YO3atdi5cycAYPPmzThy5Ei5zwkJCUFWVpZ0S0lJqc2QiUiPJSQkIDAwEP7+/oiNjYW9vT0WLVoEBwcHuUMjPSJbT+/FF19ERkYGYmJiAAA3btwAAFy4cKHCUj+WlpacnJGINGRmZuKjjz7CmjVrUFRUBDMzM0yZMgXz589nwqMyTIQQj5ZGl0VUVBT8/f2RkpICV1dXrZ6TnZ0NW1tbZGVloWHDhjqOkIj0UWJiIjp06IDCwkIEBgbik08+gbu7u9xhUS3TNh/oxXV6RETaEkIgJiYGXbp0AQAoFAp88skn8PT0ZI1MqpTs1+mVcnV1xcSJE2FtbS13KESkp2JiYuDr6wsvLy+cOXNGWh4cHMyER1rRm6Tn4eGBtWvXws7OTu5QiEjPpKamYty4cejatSsOHDgACwsLnDt3Tu6wyADpTdIjInpUfn4+Fi9eDIVCgY0bN0IIgREjRiAhIQFjxoyROzwyQDymR0R6SQiBPn364PTp0wAAb29vhIWFoUePHjJHRoaMSY+I9FJpybC0tDSpKDRrZNKT4vAmEemF5ORkjBgxAj/++KO0bMKECSwKTTWKSY+IZJWdnY2QkBB4eHhg27ZtmD17NoqLiwEAZmZmLApNNYpJj4hkUVxcjHXr1kGhUGDp0qVQqVTo168ffvzxR9SrV0/u8KiOqvIxvcjISOTk5CAwMBDm5ua6iImI6rgjR45g0qRJiI2NBVBygfmnn36KwMBADmOSTlW5p3f37l2MGjUKzZo1w9tvv40LFy7oIi4iqsNyc3MRGxsLOzs7hIeHIy4uDkFBQUx4pHNVTnrDhg1DamoqFixYgL/++guenp7o3r07vvjiC85vR0TlysjIQGRkpHTf398fa9euRVJSEmbMmAELCwsZoyNj8sQFpy9cuICNGzdi8+bNuH//PoYOHYqJEyeiV69eNRVjhVhwmki/FRQUYPXq1ViwYAGKioqQlJQEZ2dnucOiOkjbfPDEJ7I0atQILi4ucHBwgFqtxt9//42+fftiwIAB7PkRGSkhBH799Vd06NABSqUS9+7dQ+vWrZGWliZ3aGTkqpX0ioqKsGPHDgwaNAiurq749ttvMWnSJNy4cQOHDx/GlStXkJWVhQ0bNtR0vESk52JiYtC/f38MGTJE6tmtW7cOZ86cQceOHeUOj4xclc/e3LVrF8aNG4fc3FwMHz4cf/31F7y9vTXWad68OYYOHYqbN2/WWKBEpP/S09Ph7e0NlUoFS0tLKJVKhISEwMbGRu7QiABU45heREQEbt++jVdeeeWx0wDl5+dDCIEGDRo8cZAV4TE9IvmVzlZeaubMmbh16xaWLl2KFi1ayBgZGRNt84HezJxeHUx6RPIRQmDLli2YM2cOfvnlF2lSV7VaDVNT1r2g2lVrJ7IQkfE5cuQIevTogZEjR+LatWv45JNPpMeY8Eif8dNJRForLQrdq1cvHD9+HFZWVli0aBFPWiODwamFiEgrH3/8MebNmweVSgUTExOMHTsWCxcuhIuLi9yhEWmNSY+ItGJtbS0VhQ4LC0Pnzp3lDomoypj0iKhcUVFRAAA/Pz8AwJtvvonWrVvjueeeY41MMlg8pkdEGi5duoTAwED4+/tj4sSJUKlUAErmtgsICGDCI4PGpEdEAEqKQk+fPh0dO3bEzp07YWZmhhdffBEFBQVyh0ZUYzi8SWTkHi4Kfe/ePQBAYGAgPvnkE7i7u8sbHFENY9IjMnLR0dFQKpUAgE6dOuGzzz6TjuMR1TVMekRG6O7du7C3twcA9O/fH2+88QZ69uyJsWPHol69ejJHR6Q7PKZHZERSU1Mxbtw4tG7dGrdv35aWf/XVV5gwYQITHtV5THpERiA/Px+LFy+GQqHAxo0bce/ePfz2229yh0VU6zi8SVSHlRaFnj17NlJSUgAA3bt3R3h4OHr06CFzdES1T9akl5eXh+3btyM2NhZ2dnZ44YUXpErtRPRkiouL4ePjg+joaACAm5sbli1bhhEjRvBaOzJasg1v/vPPP+jatSuio6PRrFkzpKamokePHggLC5MrJKI6pV69eujatSusra2xePFiJCQk4JVXXmHCI6Mm23x69+7dg1qthoODg7Rs3rx5WLduHVJTU7V6Dc6nR3LYE5eK5VGJuHonF60aWWGGnwIBnvIXXc7OzsbSpUsxfPhwPPPMMwBKztJUqVRo0qSJzNER6Zbez6dnZ2enkfAAIDMzE40bN5YpIqLK7YlLxaTNZ5BwKweqIjUSbuVg0uYz2BOn3Rc1XSguLsa6deugUCgQGhoKpVKJ0u+y9vb2THhED5H9RJYVK1bgwoULuHz5MtRqNbZu3VrhuiqVSqoDCJRkdqLatDwqESYASodHBAATE2DFvkRZentRUVFQKpWIjY0FACgUCgQHB9d6HESGQvZLFp5++ml07NgR7u7uuHDhAo4ePVrhuqGhobC1tZVubm5utRgpEXD1Ti4ePR4gBHAlPbdW43i4KHRsbCzs7e2xfPlyxMXFISgoiMftiCoge0/vxRdflH7v0qULpkyZgsGDB5cZ+gSAkJAQqVwSUNLTY+Kj2tSqkRUSbuVoJD4TE6C1k1WtxhEZGSkVhZ4yZQrmz59f7t8MEWmSPek9zMvLCyqVCikpKeX+AVtaWsLS0lKGyIhKzPBTYNLmMzAxKenhlf4M9m2r03YLCgpw7do1KBQKAMCkSZNw6dIlTJ8+nUWhiapAtuHNY8eOITMzU2PZli1bYGtrK/1hE+mbAE8XrB3VFR5NbGBpZgqPJjZYO8oLAZ66OVlECIEdO3bA09MTzz//vHRM29zcHKtXr2bCI6oi2Xp6OTk56N27N1q2bIlGjRrh3LlzSE9Px+bNm9GgQQO5wiKqVICnS62ctBITEwOlUokDBw4AAJydnXH58mV07NhR520T1VWyXacHlFRkOXz4MG7dugVXV1f07NmzSsOXvE6P6qLU1FTMnTsXGzduhBAClpaWUCqVCAkJgY2NjdzhEeklbfOBrMf0GjRoAH9/fzlDINIr165dQ4cOHZCbW3I26IgRI7B06VK0aNFC5siI6ga9OpGFyNi1aNECffr0wd27d1kUmkgHZL9Oj8iYHT16FAMGDEBaWpq0bMuWLTh69CgTHpEOMOkRyeDatWsYMWIEevbsicjISCxatEh6zM7OjheXE+kIhzeJalFpUeiwsDCoVCqYmJhg7NixmDNnjtyhERkFJj2q8/RlVoSvvvoKs2fPloYy+/fvj7CwMGlGBCLSPQ5vUp2mT7MinDlzBmlpaVAoFNixYweioqKY8IhqGXt6VKfJOStCQkICTExM0LZtSYmyDz/8EAqFApMmTYKFhYVO2yai8rGnR3WaHLMiZGRkYPr06fD09MTUqVOlue0cHR0xffp0JjwiGTHpUZ3WqpEVHj0PUlezIhQUFGD58uVQKBRYtWoVioqKUL9+feTl5dV4W0RUPUx6VKfN8FNIQ5qAbmZFeLgo9MyZM3H37l106tQJkZGRiIiIgJVV7U47REQVY9KjOq02ZkX48ccfMXjwYCQmJsLZ2Rnr1q3DmTNn4OfnV2NtEFHNkLXg9JNiwWmSi1qthqlpyXfGwsJCdOvWDQMHDmRRaCKZGETBaSJDk5+fj7CwMPz00084duwYLCwsYG5ujlOnTsHMjH9ORPqOw5tEWhBC4Pvvv4e7uzs++OADnD17Ftu2bZMeZ8IjMgxMekSVKC3+PHLkSKSkpMDNzQ3fffcdRo4cKXdoRFRF/HpKVIEHDx7gjTfekHp0VlZWCAkJgVKpRP369WWOjoiqg0mPqAKWlpa4e/euVBR64cKFcHGp/ZqdRFRzmPSI/l9xcTG+/vprBAUFwcnJCSYmJli1ahXy8vLQuXNnucMjohrAY3pEAPbt24euXbti/Pjx+PDDD6Xlbdu2ZcIjqkPY0yO9UtvTACUkJGDWrFn47bffAJRM4Orh4aGz9ohIXuzpkd6ozWmAMjMzERwcDE9PT/z2228wMzPD9OnTkZSUhGnTptV4e0SkH5j0SG88bhqgmrZ48WKsXLkSRUVFCAwMRFxcHFasWAFHR8cab4uI9AeTHukNXU4DJIRATk6OdD8kJAT//ve/paLQ7u7uT9wGEek/HtMjvdGqkRUSbuVoJL6amAbo3LlzUCqVqFevHvbu3QsTExM0atQIhw4derKAicjgsKdHeqOmpwG6desWxo8fjy5dumD//v34888/kZSUVHMBE5HBYdIjvVFT0wDl5+djyZIlUCgU2LBhA4QQGD58OC5dugSFQqGj6InIEHB4k/RKgKfLE12ikJCQgAEDBuCff/4BAHTv3h3h4eHo0aNHTYVIRAaMPT2qU1q1agVLS0upKPSRI0eY8IhIImvSi46OxuTJk/Hcc89h/PjxOHbsmJzhkAG6du0alEolCgoKAAAWFhaIiIhAQkICXn31VWmiVyIiQMak9/nnn2POnDl45plnoFQq0aRJE/Tq1Qs///yzXCGRAcnJycGcOXPg7u6O8PBwrF27VnrMw8ODsyAQUblMhBCPXhpVK7Kzs8tM6T5q1ChcvXoVhw8f1vo1tJkenuqO4uJifPXVV/jggw9w+/ZtAEC/fv0QFhbGGplERkzbfCDbiSzlBWVra4sHDx7IEA1VV23Wyty3bx+USiXOnz8PAFAoFPj0008RGBgIk9LrHIiIHkNvDnikpKTgu+++w6BBgypcR6VSITs7W+NG8qnNWpkAsHTpUpw/fx52dnYIDw9HXFwcgoKCmPCISGt6kfSysrIQFBSE9u3bY/bs2RWuFxoaCltbW+nm5uZWi1HSo3RdKzMjIwN3796V7oeFhWHatGlISkrCjBkzYGFhUSPtEJHxkD3p5eTkICAgAGZmZvj9998f+48sJCQEWVlZ0i0lJaUWI6VH6apWZkFBAZYvXw6FQoF58+ZJyzt27IiVK1eyKDQRVZusF6eXJrzCwkJERUXBzs7usetbWlrC0tKydoKjStV0rUwhBCIiIjBr1iwkJpb0Fo8cOYLCwkKYm5vXQMREZOxk6+ndv38fzz//PFQqFSIjIytNeKR/arJWZkxMDHx9fTF48GAkJiaicePG+PLLL3HixAkmPCKqMbL19D755BMcPnwYHTt2RGBgoLTcxsYGu3fvlissqoLSWpkr9iXiSnouWjtZIdi3bZVrZX733Xd47bXXIISApaUlZs6ciZCQEF6GQkQ1Trbr9K5du1buMTkzMzN4e3tr9Rq8Tq9uSE9Ph0KhQEBAAJYuXYqWLVvKHRIRGRi9v06vRYsWaNGihVzNk0yEENiyZQv279+P9evXAwCcnJxw+fJlNG7cWOboiKiuk/3sTTIepcWfR44ciQ0bNmDv3r3SY0x4RFQbmPRI55KTkzFixAj06tULx48fh5WVFRYuXIg+ffrIHRoRGRnOp0c6k5eXh4ULFyI8PBwqlQomJiYYM2YMFi1aBBcX3ZQqIyJ6HCY90hkzMzP8+OOPUKlU8PHxQVhYGLp06SJ3WERkxJj0qEYdOnQIPXv2hLm5OSwsLLBmzRrk5eWxRiYR6QUe0zMye+JSEbD8T7h/sBsBy/+sseLQCQkJCAwMhI+Pj8bcdv7+/hg0aBATHhHpBSY9I6KLWREyMzMRHBwMT09P7Ny5E/Xq1UN6enoNRk1EVHOY9IxITc6KUFoUuk2bNli5ciWKiorw4osvIi4uDgsWLKjJsImIagyP6RmRmpwVYfLkydi4cSOAktkPwsLC4OfnVwNREhHpDnt6RqRVIys8emStKrMiPFyxLjg4GC4uLvjyyy9x9uxZJjwiMghMekakurMipKamYvz48VAqldKyTp06ITk5GRMmTEC9evV0GDURUc2RreB0TWDB6arbE5eq9awI+fn5CAsLQ2hoKHJzc2FmZoZ//vmHF5YTkd7R+4LTJI8ATxcEeD4+aZUWhZ49e7Y0E0b37t0RHh7OhEdEBo1JjzQkJCRg9OjROH78OADAzc0Ny5Ytw4gRI3itHREZPCY90mBvb4+LFy/C2toaISEhmDlzJurXry93WERENYJJz8jl5OTgxx9/xJgxYwCUTPGzbds2dO7cGU2aVG0GdCIifcezN41UcXEx1q1bhzZt2mDs2LEac9sFBAQw4RFRncSenhGKioqCUqlEbGwsAEChUMDc3FzmqIiIdI89PSNSWhTa398fsbGxsLOzQ3h4OOLi4tC/f3+5wyMi0jn29PTcnrhULI9KxNU7uWjVyAoz/BSVXnJQHrVajcGDB+PSpUswMzPDlClTMG/ePDg6OuogaiIi/cSenh570lkRCgoKUFRUBAAwNTXFkiVLpKLQK1asYMIjIqPDpKfHqjsrghACERER8PT0xBdffCEtHzJkCH777Te4u7vrLGYiIn3GpKfHqjMrQkxMDPz8/DBo0CAkJiZizZo1UKvVug2UiMhAMOnpsarMilBaFLpr167Yv38/LC0tERISgqNHj8LUlG8zERHApKfXtJ0VYevWrVAoFNiwYQOEEBg+fDguXbqEJUuWsBA3EdFDmPT0WICnC9aO6gqPJjawNDOFRxMbrB3lVWZWBA8PD+Tl5aF79+44cuQItm7dipYtW8oTNBGRHuPUQgbo2LFjOHXqFN566y1p2fHjx9GtWzcOZRKRUdI2H/A/pAG5du0aXnnlFfTo0QMzZ85EQkKC9Fj37t2Z8IiIKiHrf8lLly5hxowZcHFxQb9+/eQMRa/l5ORgzpw5cHd3x9atW2FiYoLRo0fDzs5O7tCIiAyKbElPpVJhyJAhaN68OQYMGICcnBy5QtFbxcXFWL9+PRQKBUJDQ6FSqdCvXz+cOXMG69evh7Ozs9whEhEZFNnKkFlaWuLixYsAgBkzZsgVhl7LyMiAUqlETk4OFAoFPv30UwQGBnIyVyKiamLtTT1z/fp1uLq6AiiZ2y40NBSFhYWYMmUKLCwsZI6OiMiwGVTSU6lUUKlU0v3s7GwZoylfdQtEZ2Zm4qOPPsKaNWuwa9cuDBgwAAAwdepUXYdMRGQ0DOp0v9DQUNja2ko3Nzc3uUPSUJ0C0YWFhVixYgXatGmDlStXoqioCHv27KnFqImIjIdBJb2QkBBkZWVJt5SUFLlD0lCVAtEPF4WeMWMG7t69i44dOyIyMhJhYWG1GTYRkdEwqOFNS0tLWFpayh1GhapSIHr8+PHYuHEjgJJjd4sWLcLYsWNRr169WoiUiMg4GVRPT99VpUD0888/LxWFTkxMxIQJE5jwiIh0TNYyZD179kR8fDzy8/NRXFwMa2trAMCNGzdgZVU2UTxK38qQlR7TKy0MXfpz5bCOiNuzGS4uLhgzZgyAkuHNmzdvolmzZjJHTURk+LTNB7ImvZycHBQXF5dZrm2lEX1LekBJ4luxLxFX0nPRupEVvJ66jU2h7+Cff/5Bo0aNkJSUBFtbW7nDJCKqU7TNB7Ie07OxsZGzeZ0I8HRBgKcLjh49CqVSicXHjgEA3NzcsHTp0jq5zUREhsKgTmQxBCkpKXj33XexdetWAICVlRVCQkKgVCpRv359maMjIjJuTHo17M6dO9i2bRtMTEwwZswYLFq0CC4ulV+cTkREusek94SKi4tx8uRJeHt7AwC6dOmCzz77DD4+PujSpYvM0RER0cN4ycIT2LdvH7p27Yp///vfSEz83wXoM2fOZMIjItJDTHrVkJCQgKCgIPj5+eH8+fOwsrLCpUuX5A6LiIgqwaRXBZmZmQgODoanpyd+++031KtXD9OmTUNSUhICAwPlDo+IiCrBY3paKiwsRJcuXfDPP/8AAF544QV8+umn8PDwkDkyIiLSFnt6j/Hwdfvm5uaYOHEiOnbsiD/++AM7d+5kwiMiMjBGn/T2xKUiYPmfcP9gNwKW/ylNA3Tu3Dn4+flh37590rrvvPMOzp49C39/f7nCJSKiJ2DUw5tSrUyUTANUOv9dp+zj2Ll2EYQQuH//Po4dOwYTExPOXE5EZOCMuqdX3vx3Qq3GqXwnCCEwfPhw6UJzIiIyfEbd0ytv/jsTU1NYNHLD4cOH0bNnT1niIiIi3TDqnl65899BoF0zByY8IqI6yKiT3gw/BQRK5r0D8P9DnSYI9msrZ1hERKQjRp30AjxdsHZUV3g0sYGlmSk8XGywdpQXAjybyB0aERHpgFEf0wP+N/8dERHVfUbd0yMiIuPCpEdEREaDSY+IiIwGkx4RERkNJj0iIjIaTHpERGQ0mPSIiMhoMOkREZHRMOiL00snec3OzpY5EiIiklNpHnh48u/yGHTSy8nJAQC4ubnJHAkREemDnJwc2NraVvi4iagsLeoxtVqNmzdvwsbG5onmvMvOzoabmxtSUlLQsGHDGozQsHG/VIz7pnzcLxXjvilfTe0XIQRycnLQtGlTmJpWfOTOoHt6pqamcHV1rbHXa9iwIT+M5eB+qRj3Tfm4XyrGfVO+mtgvj+vhleKJLEREZDSY9IiIyGgw6QGwtLTE/PnzYWlpKXcoeoX7pWLcN+XjfqkY9035anu/GPSJLERERFXBnh4RERkNJj0iIjIaTHpERGQ0DPo6vSeVmZmJ0NBQnDp1Cg4ODpgwYQICAgLkDkt2arUae/fuxfr163Hjxg38/vvvcHBwkDss2RUVFeH777/H7t27kZGRgfbt22PmzJlo0aKF3KHJ7s6dO1izZg2OHDkCCwsL9OzZE1OmTOH1aA85ffo03nrrLXTq1AlffPGF3OHIzt/fX6qqVWrSpEl44403dNqu0Z7IUlBQgH/961+ws7PDrFmzEBcXh7lz52L79u0YMmSI3OHJ6uWXX0ZOTg6eeeYZfPzxx0hNTUWTJk3kDkt2r776KiwsLBAQEAAHBwds2LABe/fuxalTp9CmTRu5w5NVx44dMWLECDz77LO4e/cuFi1ahPr16+P48eNyh6YXcnJy8K9//QtmZmawt7dHdHS03CHJzs7ODu+99x769esnLXNzc0OzZs102q7RJr2vvvoKkyZNQmpqqtSLmTRpEg4dOoSLFy/KHJ28srKyYGtri6ioKPj7+zPp/b/79+/D2tpauq9Wq/H000/jlVdewZIlS2SMTH55eXlo0KCBdP/AgQPo378/rl69ipYtW8oXmJ4YNWoUmjVrhoyMDFy6dIlJDyVJb/369fjPf/5Tq+0a7TG9ffv2oWfPnhrDdoMGDcKlS5dw48YNGSOTnzalfIzRwwkPKCmDV79+fRQUFMgUkf54OOEBwJ9//glnZ2c4OzvLFJH++PrrrxEfH4+FCxfKHYre+fTTT+Hj44PRo0fjjz/+qJU2jTbpXbt2DU2bNtVYVnr/2rVrcoREBuaXX37BxYsXMWjQILlD0Qtr1qyBt7c3mjdvjm3btuHQoUOoX7++3GHJKiEhAe+++y6+++47WFhYyB2OXvHw8MCYMWPw/vvvo3nz5ggKCkJ4eLjO2zXaE1kKCwvLVAAo/QMtLCyUIyQyIDExMXjjjTfw7rvvok+fPnKHoxcCAwPRuXNnJCcnY+nSpZg6dSr27t2LevXqyR2aLFQqFUaMGIGPPvoI7dq1kzscvXPo0CHpf7C/vz+srKwwZ84cTJ48GU899ZTO2jXanp6DgwMyMzM1lmVkZAAAHB0d5QiJDERsbCz8/f0xcuRILFu2TO5w9Iabmxt69uyJV199Fb/99hv27duH33//Xe6wZBMTE4Nz587hm2++gbe3N7y9vREREYHz58/D29sbFy5ckDtEWT3a6fDz88ODBw+QkJCg03aNtqfXtWtXfPPNNxBCSHPxHT9+HFZWVlAoFDJHR/oqLi4Ovr6+GDp0KFavXi13OHrL2dkZpqamuHPnjtyhyKZDhw44cuSIxrLQ0FAkJydj+fLlvNTlEbdu3QIAWFlZ6bQdo+3pvf7667h16xY2bNgAoKSX99///hejRo1iQVgqV3x8PPr374+hQ4fi888/f6KJi+uSU6dO4bfffpPuFxUVYeHChTAzM0P//v1ljExe1tbWUg+v9Obk5AQbGxt4e3uXOTHKmBw8eBD79u2T7t+8eRNz587Fv/71L51f/mO0Sa9t27b4+uuvoVQq0aZNGzRv3hxt2rTBJ598Indosis9IWH69OkAgBdeeAHe3t6IioqSOTJ5TZo0CXfu3MGZM2fQo0cP6R/Ze++9J3dosmrevDm2bNkCR0dHdO7cGc7Ozvj111+xY8cO9maoXK1bt8by5cvRuHFjeHp6onXr1nBzc8Mvv/yi87aN9jq9Unl5ebh8+TIcHBzQvHlzucPRCykpKeVetqFQKIz6eOeFCxfKVJAASo4Pt23bVoaI9EtOTg6uXLkCZ2dnXtdZgStXruDBgwdo37693KHohbt37+L69eto0aJFrVXvMfqkR0RExsNohzeJiMj4MOkREZHRYNIjIiKjwaRHRERGg0mPiIiMBpMeEREZDSY9IiIyGkx6RERkNJj0iIjIaDDpERmgmzdvYuvWrbh37560TK1WY/v27YiPj5cvMCI9x6RHZICcnJzw6aefYuLEidKy0olbjbk+KlFlWHuTyEBdvnwZXbp0werVq9GhQwf06tULP//8M1588UW5QyPSW0x6RAZs3bp1UCqVcHJyQkBAANasWSN3SER6jUmPyIAJIdCqVSvcvn0bt2/frrXpWYgMFY/pERmwlStXIjs7G3Z2dvj444/lDodI77GnR2Sg4uLi0K1bN3z77bewtbXFCy+8gAMHDqB3795yh0akt5j0iAyQSqVCt27d0LVrV3z99dcAgJkzZ+LXX3/FuXPnOMxJVAEObxIZoP3796NLly5YtWqVtGzp0qXw8/PDnj17ZIyMSL+xp0dEREaDPT0iIjIaTHpERGQ0mPSIiMhoMOkREZHRYNIjIiKjwaRHRERGg0mPiIiMBpMeEREZDSY9IiIyGkx6RERkNJj0iIjIaDDpERGR0fg/PnrRNeEkHYwAAAAASUVORK5CYII=", + "text/plain": [ + "<Figure size 500x350 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.default_rng(3)\n", + "m_true, b_true = 1.0, 0.5\n", + "sigma_c, noise = 0.10, 0.02\n", + "c_factor = rng.normal(1.0, sigma_c)\n", + "x1, x2 = np.linspace(0.0, 2.0, 8), np.linspace(3.0, 5.0, 8)\n", + "y1 = c_factor * (m_true * x1 + b_true) + rng.normal(0.0, noise, 8)\n", + "y2 = c_factor * (m_true * x2 + b_true) + rng.normal(0.0, noise, 8)\n", + "d1 = rx.Dataset(x1, y1, np.full(8, noise), norm_err=sigma_c, label=\"forward\")\n", + "d2 = rx.Dataset(x2, y2, np.full(8, noise), norm_err=sigma_c, label=\"backward\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(5, 3.5))\n", + "for d in (d1, d2):\n", + " ax.errorbar(d.x, d.y, d.y_err, fmt=\"o\", ms=4, label=d.label)\n", + "x_line = np.linspace(0, 5, 2)\n", + "ax.plot(x_line, m_true * x_line + b_true, \"k--\", label=\"truth\")\n", + "ax.set(\n", + " xlabel=\"x\", ylabel=\"y\", title=f\"one drawn calibration factor, c = {c_factor:.3f}\"\n", + ")\n", + "ax.legend(frameon=False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "593faa44", + "metadata": {}, + "source": [ + "## The covariance structure: coupled or independent\n", + "\n", + "The same 10 % normalisation, spelled two ways. With `on=[comp1, comp2]` a\n", + "single mode spans both blocks and writes off-diagonal blocks into Σ. With\n", + "one term per comparison the blocks are independent. Both are built from the\n", + "prediction, never from the data." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1fa302ff", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:43:26.894408Z", + "iopub.status.busy": "2026-09-11T03:43:26.894191Z", + "iopub.status.idle": "2026-09-11T03:43:27.052483Z", + "shell.execute_reply": "2026-09-11T03:43:27.051640Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 700x350 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "m = rx.Parameter(\"m\", prior=stats.norm(1.0, 0.3), latex=\"m\")\n", + "b = rx.Parameter(\"b\", prior=stats.norm(0.5, 0.3), latex=\"b\")\n", + "line = rx.Model(lambda x, m, b: m * x + b, [m, b])\n", + "comp1, comp2 = rx.Comparison(d1, line), rx.Comparison(d2, line)\n", + "\n", + "c_coupled = rx.Constraint(\n", + " [comp1, comp2], terms=[T.normalization(magnitude=sigma_c, on=[comp1, comp2])]\n", + ")\n", + "c_indep = rx.Constraint(\n", + " [comp1, comp2],\n", + " terms=[\n", + " T.normalization(magnitude=sigma_c, on=comp1),\n", + " T.normalization(magnitude=sigma_c, on=comp2),\n", + " ],\n", + ")\n", + "p_coupled, p_indep = rx.Problem([c_coupled]), rx.Problem([c_indep])\n", + "theta_true = np.array([m_true, b_true])\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(7, 3.5))\n", + "for ax, p, title in zip(\n", + " axes,\n", + " (p_coupled, p_indep),\n", + " (\"coupled: one spanning mode\", \"independent: one mode per block\"),\n", + "):\n", + " S = p.constraints[0].matrix(theta_true)\n", + " im = ax.imshow(S, cmap=\"viridis\")\n", + " ax.axhline(7.5, color=\"w\", lw=0.8)\n", + " ax.axvline(7.5, color=\"w\", lw=0.8)\n", + " ax.set(title=title, xticks=[], yticks=[])\n", + "fig.suptitle(\"the off-diagonal blocks are what couple the data\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "55e1d262", + "metadata": {}, + "source": [ + "## Effect on inference\n", + "\n", + "Same data, same model, same magnitudes: the coupled fit is appropriately\n", + "less certain, the independent one is overconfident. Both are honest about\n", + "their statistics; only one is honest about the calibration." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d8069ab2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:43:27.054450Z", + "iopub.status.busy": "2026-09-11T03:43:27.054230Z", + "iopub.status.idle": "2026-09-11T03:44:25.044516Z", + "shell.execute_reply": "2026-09-11T03:44:25.043561Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 550x550 with 4 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "coupled m = 1.199 +/- 0.107 b = 0.581 +/- 0.053\n", + "independent m = 1.193 +/- 0.078 b = 0.589 +/- 0.041\n" + ] + } + ], + "source": [ + "def fit(problem, seed, n_walkers=24, n_steps=1800):\n", + " sampler = emcee.EnsembleSampler(n_walkers, problem.ndim, problem.log_posterior)\n", + " sampler.random_state = np.random.RandomState(seed).get_state()\n", + " sampler.run_mcmc(problem.sample_prior(n_walkers, rng=seed), n_steps, progress=False)\n", + " return sampler.get_chain(discard=n_steps // 3, thin=5, flat=True)\n", + "\n", + "\n", + "s_coupled, s_indep = fit(p_coupled, seed=1), fit(p_indep, seed=2)\n", + "fig = corner.corner(\n", + " s_indep,\n", + " color=\"C3\",\n", + " labels=[\"$m$\", \"$b$\"],\n", + " truths=theta_true,\n", + " truth_color=\"k\",\n", + " plot_datapoints=False,\n", + ")\n", + "corner.corner(s_coupled, fig=fig, color=\"C0\", plot_datapoints=False)\n", + "fig.legend(\n", + " handles=[\n", + " plt.Line2D([], [], color=\"C0\", label=\"coupled\"),\n", + " plt.Line2D([], [], color=\"C3\", label=\"independent (overconfident)\"),\n", + " ],\n", + " loc=\"upper right\",\n", + " frameon=False,\n", + ")\n", + "plt.show()\n", + "for name, s in ((\"coupled\", s_coupled), (\"independent\", s_indep)):\n", + " print(\n", + " f\"{name:12s} m = {s[:, 0].mean():.3f} +/- {s[:, 0].std():.3f} b = {s[:, 1].mean():.3f} +/- {s[:, 1].std():.3f}\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "62a7fa3e", + "metadata": {}, + "source": [ + "## Case A and case B with a *free* shared nuisance\n", + "\n", + "If the magnitude of the shared calibration is unknown, one `Parameter`\n", + "object carries it. Where you put the object decides the structure:\n", + "\n", + "- **case A couples the data**: one term `on=[comp1, comp2]` reads the\n", + " object once, and Σ has off-diagonal blocks;\n", + "- **case B couples the parameters**: two terms, one per comparison, both\n", + " reading the same object; Σ stays block diagonal but the two blocks scale\n", + " together.\n", + "\n", + "Both have exactly one nuisance column, because parameters are matched by\n", + "identity, not by name." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ebc166b2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:44:25.046630Z", + "iopub.status.busy": "2026-09-11T03:44:25.046414Z", + "iopub.status.idle": "2026-09-11T03:44:25.053965Z", + "shell.execute_reply": "2026-09-11T03:44:25.053148Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "case A: columns ['m', 'b', 'log_eta'], max |off-diagonal block| = 1.38e-01\n", + "case B: columns ['m', 'b', 'log_eta'], max |off-diagonal block| = 0.00e+00\n" + ] + } + ], + "source": [ + "log_eta = rx.Parameter(\"log_eta\", prior=stats.norm(np.log(0.1), 1.0), latex=r\"\\log\\eta\")\n", + "c_A = rx.Constraint([comp1, comp2], terms=[T.normalization(log_eta, on=[comp1, comp2])])\n", + "c_B = rx.Constraint(\n", + " [comp1, comp2],\n", + " terms=[T.normalization(log_eta, on=comp1), T.normalization(log_eta, on=comp2)],\n", + ")\n", + "for name, c in ((\"case A\", c_A), (\"case B\", c_B)):\n", + " p = rx.Problem([c])\n", + " S = p.constraints[0].matrix(np.array([m_true, b_true, np.log(0.1)]))\n", + " print(\n", + " f\"{name}: columns {p.names}, max |off-diagonal block| = {np.abs(S[:8, 8:]).max():.2e}\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "b59f63a6", + "metadata": {}, + "source": [ + "## More than one physical quantity: Neudecker et al. (2014)\n", + "\n", + "The reference studies an experiment that reports several physical\n", + "quantities `ρ_i = α_i η_i`. Each `α_i` is measured as `q_i`, once or more,\n", + "with independent errors `σ_i`; each normalisation `η_i` is measured as\n", + "`N_i`, and the `N_i` share a covariance `B` with correlation `c`. The\n", + "reported data are the products `r_i = q_i N_i`, and the question is how to\n", + "build their covariance.\n", + "\n", + "- **`C_F`** (their eq. 11) propagates the normalisation error with the\n", + " *measured* `q`: the mode of quantity `i` is `σ_N,i q_i`.\n", + "- **`C_I`** (their eq. 12) uses the *estimate*, the weighted mean `q̄_i`:\n", + " the mode is `σ_N,i q̄_i`, which in `rxmc` is the prediction divided by\n", + " `N_i`, i.e. the term reads `c.ym`.\n", + "\n", + "With `C_F` the evaluation is biased low and too narrow: Peelle's Pertinent\n", + "Puzzle, now across quantities. Here is the model: one comparison per\n", + "quantity, the quantity itself as the model, and one `matrix` term spanning\n", + "all comparisons that pairs `c.split(c.ym)` with the correlation matrix of the\n", + "normalisations." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9a88c3d1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:44:25.056234Z", + "iopub.status.busy": "2026-09-11T03:44:25.055980Z", + "iopub.status.idle": "2026-09-11T03:44:25.068017Z", + "shell.execute_reply": "2026-09-11T03:44:25.067049Z" + } + }, + "outputs": [], + "source": [ + "def experiment(q, N, sigma_q, sigma_N, prior=stats.uniform(0.0, 10.0)):\n", + " comps, rhos = [], []\n", + " for i, (qi, Ni, si) in enumerate(zip(q, N, sigma_q)):\n", + " rho = rx.Parameter(f\"rho_{i + 1}\", prior=prior, latex=rf\"\\rho_{i + 1}\")\n", + " rhos.append(rho)\n", + " d = rx.Dataset(\n", + " np.full(len(qi), i),\n", + " np.asarray(qi) * Ni,\n", + " Ni * np.asarray(si),\n", + " label=f\"quantity {i + 1}\",\n", + " )\n", + " comps.append(rx.Comparison(d, rx.Model(lambda x, r: np.full(len(x), r), [rho])))\n", + " return comps, rhos\n", + "\n", + "\n", + "def normalisations(comps, frac, corr, from_data=False):\n", + " \"\"\"outer(f * ym, f * ym) * corr across the quantities; from y for C_F.\"\"\"\n", + " corr = np.asarray(corr, dtype=float)\n", + "\n", + " def fn(c):\n", + " pieces = c.split(c.y if from_data else c.ym)\n", + " u = np.concatenate([f * piece for f, piece in zip(frac, pieces)])\n", + " which = np.concatenate(\n", + " [np.full(s.stop - s.start, k) for k, s in enumerate(c.segments)]\n", + " )\n", + " return np.outer(u, u) * corr[np.ix_(which, which)]\n", + "\n", + " return rx.Term(fn, kind=\"matrix\", on=comps, constant=from_data)\n", + "\n", + "\n", + "def fixed_estimate(comps, rho, frac, corr, counts):\n", + " \"\"\"C_I in its fixed form: the mode built from an estimate of each rho.\"\"\"\n", + " u = np.concatenate([np.full(n, f * r) for n, f, r in zip(counts, frac, rho)])\n", + " which = np.concatenate([np.full(n, k) for k, n in enumerate(counts)])\n", + " return rx.Term(\n", + " np.outer(u, u) * np.asarray(corr)[np.ix_(which, which)], kind=\"matrix\", on=comps\n", + " )\n", + "\n", + "\n", + "def exact(q, N, sigma_q, sigma_N, corr):\n", + " \"\"\"Means and covariance from the full information (their eqs. 2-8).\"\"\"\n", + " w = [1.0 / np.asarray(s) ** 2 for s in sigma_q]\n", + " qbar = np.array([np.sum(wi * qi) / np.sum(wi) for wi, qi in zip(w, q)])\n", + " var_a = np.array([1.0 / np.sum(wi) for wi in w])\n", + " N, sN = np.asarray(N), np.asarray(sigma_N)\n", + " cov = np.outer(qbar * sN, qbar * sN) * np.asarray(corr)\n", + " cov[np.diag_indices_from(cov)] += var_a * N**2\n", + " return qbar * N, cov, qbar\n", + "\n", + "\n", + "def gls(problem, theta_any):\n", + " \"\"\"Posterior mean and covariance under a *fixed* covariance and a flat prior.\"\"\"\n", + " c = problem.constraints[0]\n", + " S = c.matrix(theta_any)\n", + " X = np.eye(problem.ndim)[c.x[c.active].astype(int)]\n", + " cov = np.linalg.inv(X.T @ np.linalg.solve(S, X))\n", + " return cov @ (X.T @ np.linalg.solve(S, c.y[c.active])), cov" + ] + }, + { + "cell_type": "markdown", + "id": "b1df048b", + "metadata": {}, + "source": [ + "### II.A: the analytic study\n", + "\n", + "Quantity 1 measured twice, quantity 2 once, 10 % statistics, 20 %\n", + "normalisations correlated at `c = 0.8`. The reference's closed forms:\n", + "under `C_F`, `⟨ρ_1⟩ = q̄_1 N_1 / (1 + ξ)` with\n", + "`ξ = (q_1 − q_1')² σ²_N1 var(α_1) / (N_1² σ_1² σ_1'²)`, and `⟨ρ_2⟩` is pulled\n", + "down through `c` although `α_2` was measured once; under `C_I` the means are\n", + "`q̄_i N_i` exactly." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1bf5d66a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:44:25.070204Z", + "iopub.status.busy": "2026-09-11T03:44:25.069911Z", + "iopub.status.idle": "2026-09-11T03:44:25.085373Z", + "shell.execute_reply": "2026-09-11T03:44:25.084449Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "xi = 0.3077\n", + "exact : rho = [1.1538 1.98 ] sd = [0.2453 0.4427]\n", + "C_I (est.) : rho = [1.1538 1.98 ] sd = [0.2453 0.4427]\n", + "C_F (data) : rho = [0.8824 1.6073] sd = [0.2183 0.4152]\n", + "eq. 13 : rho_1 = 0.8824\n" + ] + } + ], + "source": [ + "q = [np.array([1.0, 1.5]), np.array([1.8])]\n", + "N = np.array([1.0, 1.1])\n", + "sigma_q = [0.1 * qi for qi in q]\n", + "sigma_N = 0.2 * N\n", + "c_corr = 0.8\n", + "corr = np.array([[1.0, c_corr], [c_corr, 1.0]])\n", + "counts = [len(qi) for qi in q]\n", + "\n", + "comps, rhos = experiment(q, N, sigma_q, sigma_N)\n", + "mean, cov, qbar = exact(q, N, sigma_q, sigma_N, corr)\n", + "p_F = rx.Problem(\n", + " [\n", + " rx.Constraint(\n", + " comps, terms=[normalisations(comps, sigma_N / N, corr, from_data=True)]\n", + " )\n", + " ]\n", + ")\n", + "p_I = rx.Problem(\n", + " [\n", + " rx.Constraint(\n", + " comps, terms=[fixed_estimate(comps, mean, sigma_N / N, corr, counts)]\n", + " )\n", + " ]\n", + ")\n", + "mean_F, cov_F = gls(p_F, mean)\n", + "mean_I, cov_I = gls(p_I, mean)\n", + "\n", + "var_a1 = 1.0 / np.sum(1.0 / sigma_q[0] ** 2)\n", + "xi = (\n", + " (q[0][0] - q[0][1]) ** 2\n", + " * sigma_N[0] ** 2\n", + " * var_a1\n", + " / (N[0] ** 2 * sigma_q[0][0] ** 2 * sigma_q[0][1] ** 2)\n", + ")\n", + "print(f\"xi = {xi:.4f}\")\n", + "print(f\"exact : rho = {mean.round(4)} sd = {np.sqrt(np.diag(cov)).round(4)}\")\n", + "print(\n", + " f\"C_I (est.) : rho = {mean_I.round(4)} sd = {np.sqrt(np.diag(cov_I)).round(4)}\"\n", + ")\n", + "print(\n", + " f\"C_F (data) : rho = {mean_F.round(4)} sd = {np.sqrt(np.diag(cov_F)).round(4)}\"\n", + ")\n", + "print(f\"eq. 13 : rho_1 = {qbar[0] * N[0] / (1 + xi):.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8f0a18c2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:44:25.087493Z", + "iopub.status.busy": "2026-09-11T03:44:25.087183Z", + "iopub.status.idle": "2026-09-11T03:44:25.313134Z", + "shell.execute_reply": "2026-09-11T03:44:25.312176Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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64q233tK7E3FGRgaio6MBlE7xPDy87+joCHNzc5ibmwMo/TZqZ2cnXrFRlzp06ABLS0scO3ZM3FdYWIglS5bo3V23TZs2AKAX16hRo9CzZ0+88847uHPnjqTs/v37eus3yDjTpk1DmzZt8M4774jTW2Xu37+PkJAQfP755+K+c+fO4a+//pKs33lwKoj9v2I17f8TJ06EjY0Nli1bJo4aCYKAFStW6I2IeXp64uTJk5LRpWXLlon9/8H3BOj3t06dOsHc3FzSZ/Pz87F8+XLJGjpqOmQ3wgMA3377LWbOnImnnnoKgwYNgpeXFwoKCnDo0CG94cyYmBjxm3eZiIgIhIaG4pVXXqnwPhzV9dprr+HKlSt48cUXERYWhtatWyMyMhLm5ubYtWuXXmeubZMnT8a6devg6+uL3r17Q6lUGryUtzxLlixBXl4eAgICMHDgQHTr1g0ZGRmIiYlBp06d9L5RPah58+aIjIzEpEmT4OHhgeHDh8PBwQE3b97EtWvXsHTpUgCl3+QmTZoEe3t7eHl5QaFQICoqCq6urliyZIl4vqlTp2LDhg0oKCiAg4ODeB+e2mZlZYW1a9ciKCgIiYmJ6NSpE44ePYq3334bFy9elNQdPnw4XFxc8OKLL2LEiBGwsLAQ78Oze/du/POf/4SHhwdGjRqF1q1bIyEhARcvXsSrr77a4O/U2xDZ29tj//79CAwMhJeXF4YPH4727dvj9u3biIqKQteuXfHxxx+L9WNiYrBmzRoEBQWVez72f8Nq0v8dHR3x3XffYfLkyejduzcef/xxnD9/Hi+88IJ4m4kya9asgb+/P/r06YMBAwYgJiYGffv2xbBhw3Dw4EGxXqtWreDn54f3338ff/75J5o3by7eh2f16tWYP38+YmNj0b59exw9ehTvvvsuzp49W6X3TPKgEGR8WUh8fDyOHTuGe/fuwdHRER4eHvDx8ZHMk/v5+WHgwIF45513xH2RkZG4cOECXn755Uqv1Ni4cSPs7e0xduxYcV9iYiK2b9+O5557Du3bty/3uOvXr+PIkSPIz8+Hu7u7+IexzO3bt/Hdd9/hmWeekaw1uHPnDrZs2YKnn35asvDv7t272Lx5MwICAvQWHj4sLi4O0dHRyMrKgk6nQ1BQEO7fv4+vv/4ao0aNQvfu3SX1v/zyS7i6uurd2Oz27ds4dOgQ0tLS0KZNG/Tu3Rvu7u4G2y4jCAJOnDiBCxcuQBAEeHh4YODAgZI1BIIg4OTJk7h06RIUCgW6dOmCQYMGSf79BEFAdHQ0Ll++jKKiInh5eWH06NE4deoUDh8+jKCgIPGPyOeff46OHTvqJRUVvb8NGzagTZs2kitqrl27Jn5jHDFiBNzc3BAaGgonJycEBASI9TIzM7Fv3z6kpqZCq9Xq/SzExMTgzJkz0Gg06NixIwYMGNDgn8PUGJw5cwYxMTEoLCyEi4sLunfvLvmZ1Gq1sLGxQVRUFAYOHFjuOdj/677/37lzB7/99hsKCwvx5JNPwsvLCxs3boSDgwPGjBkjqbd//37k5uaiX79+6N27N/bu3YukpCTMmjVLrKfRaBAZGYnY2FgUFxdjwIABGDBgAIDSmxMeP34cSqUSI0eOhKura4XvieRN1gmPMdq2bYuvv/6aP/hETcCVK1fw6KOPIicnx6i7LhORfMhuDU9V3Lt3D7m5uZLFc0QkX1euXEHv3r2Z7BA1QU1+hIeIiIjkr0mP8BAREVHTwISHiIiIZI8JDxEREckeEx4iIiKSvUZx40GdToeUlBTY2NjoPWuGqCkSBAG5ublo27ZtnT8SoarYX4n01XefLSwsNOqO2hYWFrCysqrzeBqCRpHwpKSkwNXV1dRhEDU4SUlJcHFxMXUYEuyvRBWrjz5bWFiIjm7NkZqmrbSus7Mz4uLimkTS0ygSnrLnXA1xfBFmSotKastXyZ3KnxAsd2obK2y79RX+6bsGBflN9wGAJdoiHL6yXvIMuIaiLKZBGA0z1O2jEho6s9ZOpg7B5NTNLLH13PuY8vgyFNwvMnU4JlOi0+Bg0oZ66bMajQapaVrE/eEGW5uKR5NycnXo2CcBGo2GCU9DUTYsbqa0aNIJDxRN+48HAJgrLGBrawtzMysUqzhd0hCnjMT+CnOYNfGf2Sb9++pv5irL0j6rskRxw5p9NYn67LPNmpduFdE2sbvwNYqEh4iIiKqmBFqUoOKspgS6eozG9JjwEBERyZBWEKA18DAFQ2VyxISHiIhIhnQQoDMwwmOoTI6Y8BAREclQCXQorqS8KWHCQ0REJEOc0pJiwkNERCRDur83Q+VNCRMeIiIiGdIIAjQGRnEMlckREx4iIiIZ4giPFBMeIiIiGdJBAS0qvtGhzkCZHDHhISIikiGdULoZKm9KmPAQERHJkAZKaFDx8zya2tMImfAQERHJkE5QQCcYmNIyUCZHTHiIiIhkSFvJGh5DZXLEhIeIiEiGSgQVioWKp7RKOMJDREREjR1HeKSY8BAREcmQVlBCa2CER8urtIiIiKixK4YSxVAZKG9amPAQERHJUOUjPE1riIcJDxERkQzpoDB4N2XeaZmIiIgaPR2U0Bq48aAOHOEhIiKiRq5YMEOxYGANTzUuSxcEAbdu3YJarUarVq2MPi4+Ph4KhQJubm6S/VlZWUhNTS33GHd3d5ibm0On0+H69et65W3btoWtra3RMTDhISIikiGtoIDWQFJjqKw8p06dQmBgINLT01FQUIBBgwYhLCwMjo6O5dYXBAGbN2/G559/jvPnz6Nnz544ceKEpM6ePXuwYsUKyb60tDRkZ2cjNTUVjo6OyMnJQbdu3eDm5gYrKyux3po1axAQEGB0/BWPdREREVGjpf17SsvQZqy8vDyMGTMGvr6+uHfvHtLS0pCVlYVp06ZVeExBQQEOHjyI9evX4+WXXy63TmBgIK5duybZOnfujFGjRuklUj/88IOkXlWSHYAJDxERkSyVTmkZ3oy1a9cupKenY8WKFVAqlbC1tcWiRYuwZ88e3Lp1q9xjrK2tsWnTJjz22GNGt3Pp0iWcOnUKM2bM0CvLzc1FQkICtFqt0ed7EBMeIiIiGdLhf9Na5W26Kpzr9OnT6Ny5MxwcHMR9AwYMgCAIOHv2bK3FHBoaCmdn53JHb0aPHo3HHnsMzZo1w9y5c5Gfn1+lc9dLwhMbG4sFCxZgxIgROHDgQH00SUQ1cOTIEbz44osYMWIEMjMzTR0OEVWDDspKNwDIycmRbEVFRXrnunfvnt4i5ZYtWwIA0tPTayVejUaDrVu3YurUqTAz+9/ok0qlwvr165GdnY3bt2/j2LFj2L59O+bPn1+l89d5whMaGopRo0bBwcEBBw4cwO3bt+u6SSKqgcDAQISEhMDOzg4HDhwo95cfETV8xYKq0g0AXF1dYWdnJ26rVq3SO5dKpYJGo5Gev7j0Xs0PJic1sXPnTty7dw/Tp0+X7LexscHcuXNhYWEBAOjTpw8WLlyIzZs3Q6jCzRNrHGV4eDjCw8NhY2MDPz8/3Lx5ExYWFnjrrbcAAGPGjMG0adOgUCgQEhJS0+aIqAaKiorw4Ycf4v/+7//g6uqK6dOn49///jdeffVVDBkyBACwbt06ODk5Yf/+/fjkk09MGzARVVvld1ouLUtKSpJc3m1paalX19XVFdHR0ZJ9ZQMYLi4utREuQkNDMXToUHh4eFRat3379rh//365I08VqdEIz9dff42XXnoJjz/+OEaPHo1169bh/fffx5UrV8Q6rVq1gkLRtO7mSNRQvfjii/j6668xYcIE9OrVC2PHjsXOnTsl98FwcnIyYYREVFuMvUrL1tZWspWX8AwZMgSJiYm4evWquG/Pnj1Qq9Xo169faXtaLa5du4acnJwqx5qYmIj9+/eXu1i5bCTpQYcPH0bLli0la4oqU+0RHq1WiyVLluC9994TR3N8fX3Rvn376p5SVFRUJBlGr86HR0RS58+fx/bt23Hu3Dn06NEDQOk3s9GjR9fovOyvRA2TTlBAZ+BeO4bKHjZ8+HAMGTIEgYGB+OCDD5Ceno4lS5ZgwYIFaN68OQAgMzMT3bp1w5YtWzBlyhQAQFxcHIqKipCVlYXCwkJcu3YNAODp6SkZDNm4cSNatGiBsWPH6rW9bt06JCUlISAgAPb29ti9ezf++9//4qOPPoJSafy4TbUTnoSEBKSmpuKpp54S97Vo0QKPP/54dU8pWrVqFZYtW1bj8xDR/5w8eRJt2rQRkx2g9EtKTeff2V+JGqaSSi49L6nikyV27dqFZcuWITg4GGq1GitWrMCcOXPEcjMzM3h6esLOzk7c99prr+HmzZvi62eeeQYAcOHCBXFNDgAcP34cc+fOLXd0ad68edi0aRPWrVuH9PR0uLu7Y//+/Rg8eHCV4q/2b7rs7GwApYuJHvTw6+oICQkRR42A0m+Mrq6uNT4vUVOWnZ2t1z9VKhWsra1rdF72V6KGSQsFtAYeEGqorDy2trZYu3ZtheX29vbiCE6ZvXv3GnXuffv2VVimUqkwffp0vcXMVVXthKdDhw4AgBs3bkgWLN24cUOcz6suS0vLcrM8Iqq+Dh06ICkpCUVFRWL/SktLq/EUFPsrUcOkE5TQGVi0bKhMjqr9blu0aAE/Pz+sXr1avFTtxx9/xMWLF2stOCKqPU899RSsra2xZs0acd/SpUtNGBER1aViQVnJZelNK+Gp0eT9xx9/DF9fX3To0AHOzs7Iz8/XG905d+6c5OZAq1evxqZNmzB69GjJMDgR1a3mzZvj66+/xpQpU/D9999Do9HA09MT9vb2knqhoaEICwtDRkYGAOC5556DhYUFFi9eLF66TkQNn7GXpTcVNUp4OnXqhKtXr+Ls2bOwsbFBly5dMHXqVEkdNzc3vP322wAg/hcA2rVrV5OmiagaxowZg1u3buHixYtwdXWFm5sbnJ2dJXUGDx4MNzc3vWO7detWX2ESUS0QoIDOwDodoYpreBq7Gt940NzcHP3796+w3MHBASNGjKhpM0RUS+zs7DBo0KAKyz08PIy68RcRNWzFOhWUOpWB8qo8Tavxq537QRMREVGD8uDNBSsqb0pqPeEJCQmp0o2AiMi0duzYgS5dupg6DCKqZbV540E5qPWEx9vbu7ZPSUR16IknnjB1CERUBx58InpF5U0Jp7SIiIhkqFinhFJXcVJTbKBMjpjwEBERyZBQyY0HBV6WTkRERI1dbT9aorFjwkNERCRDJTqlwcvSS3TaeozG9JjwEBERyZCukhsPGiqTIyY8REREMqQVFNAauPTcUJkcMeEhIiKSoRLB8J2WS4SKy+SICQ8REZEM8VlaUkx4iIiIZIh3WpZiwkNERCRDukruw2OoTI6Y8BAREclQiaCEwkBSU8KEh4iIiBo7TmlJMeEhIiKSISY8Ukx4iIiIZKhEp4TCwANCS/jwUCIiImrsBBi+m7JQjXPGxcXh6NGjUKvVGDFiBOzt7Ss9JiMjA7/++itsbW0xduxYSZlGo8E333yjd8zw4cPRuXPnGrf9oKaV3hERETURZVNahraq2LBhA7y9vbFjxw58+OGH8PDwwJkzZyqsX1JSgqlTp6J79+5Yvnw5/v3vf+vVyc/Px+zZsxEVFYWYmBhxy8zMrFHb5eEIDxERkQyV6JRALU1pJScn4/XXX8cnn3yCGTNmAAAmTZqEadOm4cKFC+Ueo9VqMWTIEHz++eeYP3++wQQlJCQEPj4+tdZ2eTjCQ0REJEO1OcLz888/w9zcHC+88IK4b86cObh48SIuX75c7jGWlpaYOnUq1Gp1pec/ePAgNm/ejGPHjkGrlT7FvTptl4cJDxERkQwJgqLSDQBycnIkW1FRkd65Ll++DHd3d1haWor7vLy8xLKaUKlU2Lt3L/bt24cJEybAx8cHCQkJtd42Ex4iIiIZ0v39LC1DGwC4urrCzs5O3FatWqV3rpycHL1FwmWvc3Jyqh2jpaUlTp06hd9//x1hYWG4fv06lEolZs6cWettcw0PERGRDGkruSxd+3dZUlISbG1txf0PjqSUUavVyM3Nlewre21tbV3tGNVqNXr37i2+bt68OebMmYOZM2dCo9HAwsKi1trmCA8REZEMGbuGx9bWVrKVl/B07twZCQkJ0Ol04r64uDgAgIeHR63GbWVlhZKSEuTl5dVq20x4iIiIZMjYNTzG8Pf3R0ZGBiIjI8V9W7duRdu2bdGnTx8AQGFhIb744gvcuHHD6PPGxcVJEhkACAsLg6enJxwcHIxu2xiNakqr5E4aoDA3dRgmo+jb3dQhmJyimYWpQyAjmbV2gpmyaf97xc1wN3UIJtfMsvRnIH1gW9wv1Jg4GtPRagqBhMrr1SadoIBWVzuPlvD29kZQUBCmTJmCV199Fenp6diwYQPCw8OhUqkAAHl5eZg9eza2bNki3jQwPDwcmZmZuHz5MtLS0vDFF18AAGbMmAGVSoWjR49iwoQJ8PX1hb29Pfbs2YNLly7hxx9/rFLbxmhUCQ8REREZRwcFFAbutGzoLszlWbduHYYNG4bo6Gi0aNECp0+fRs+ePcVytVqNWbNmoUuXLuK+q1evIjU1FZ6envD09ERMTAwAQBBK7/P8/PPPo1+/fti5cyfu3r2LyZMnY+LEiWjRokWV2jYGEx4iIiIZqmzaqipTWmUCAgIQEBBQblmzZs3EEZwy7777bqXn9PT0xMKFC2vUtjGY8BAREcmQVqcADExpGZrukiMmPERERDJUFyM8jRkTHiIiIhliwiPFhIeIiEiGdIICCgNJTVWflt7YMeEhIiKSIZ0OUBi6LF1XYZEsMeEhIiKSIU5pSTHhISIikiHh781QeVPChIeIiEiGBJ0CgoEpLUNlcsSEh4iISI4qe14Wp7SIiIiosROE0s1QeVPChIeIiEiGuGhZigkPERGRDHENjxQTHiIiIjniZVoSTHiIiIhkiFNaUkx4iIiIZEgQKpnSYsJDREREjR6ntCSY8BAREcmS4u/NUHnTwYSHiIhIjnR/b4bKmxAmPERERHIkKAzfTZlreIiIiKix452WpZjwEBERyREXLUsw4SEiIpIhhU4BhYHL0g2VyRETHiIiIjmq5REeQRDw3XffITo6Gmq1GhMnTsQTTzxh8BidToeIiAhs374dLi4uWLFihV6dGzduIDw8HHFxcWjfvj2mTp0KNzc3sTw/Px/Tpk3TO27OnDkYNGiQ0fErja5JREREjUfZomVDWxXMmDEDCxYsgLe3N2xsbDBs2DBs2bKlwvpFRUXo0qUL1q9fj9jYWERFRenV2bx5M8aMGYOioiIMHDgQsbGx8PT0xJEjR8Q6Go0G4eHhePTRR/HMM8+Im6ura5Xi5wgPERGRHNXiZekxMTEIDQ3FwYMHMXjwYACAmZkZ5s2bh8mTJ8PMTD+dUKlUiIqKQseOHTFnzhycOXNGr87w4cMxZcoUqFQqAMC0adOQmZmJ5cuX6yVIvr6+8PHxMT7oh9T6CE9hYSG2bNmCAQMGwMrKCmFhYXp1XnjhBSxbtqy2myaiakhLS8OqVavg7u4OKysr3LlzR6+Oi4sLDh8+bILoiKjaBCM2I+3duxdOTk548sknxX0TJ07E3bt3cerUqXKPMTMzQ8eOHQ2e18XFRUx2yri5uSEjI0Ov7tq1a/HSSy/h/fffR1JSkvHB/63WE54vv/wSERER+M9//oOioiJotVq9OhqNBsXFxbXdNBFVw5tvvons7GwsXLgQRUVFEMq5VrWwsBA6XRO7SxlRY2fklFZOTo5kKyoq0jtVbGwsXF1doVD8bxqsbJ3NzZs3ay3k9PR0hIeHY+TIkZL9bm5u6NatGx577DGcPHkS3bp1w6FDh6p07ipNaWk0GsybNw/h4eGwsbGBn58f0tLS0KJFC3z++ecAgKCgoCoFQER1JzExEbNnz8b//d//wdXVFa+++ipWr16NNWvWYMKECQCA7777DgCwf/9+U4ZKRLVMoSvdDJUD0FsLs3TpUrz77ruSfYWFhWjevLlkX7NmzcSy2lBYWIjx48ejdevWWLJkiaSdCxcuwNbWFgAwa9YsTJw4Ea+++iouX75s9PmrNMKzePFi/Prrr9i1axcOHz6M4uJihIeHc7SGqAESBAFjx46FTqfDH3/8gfDwcGzcuBEJCQnljrwSUdOUlJSE7OxscQsJCdGrY2dnpzfNVPba3t6+xjEUFRVh3LhxSE1NRVRUlJhMAYC5ubmY7JQZN24crl69iry8PKPbMHqEp7CwEJ9++ilCQ0Px+OOPAwA+/vhj7Nq1y+jGymzZskUyLPawoqIiyZBaTk5Oldsgaup+//13XLhwAYmJiWjTpg0A4KuvvkLPnj2rfK7k5GSYm5uXW8b+StQwKQAoDKzTKfsrbGtrq5dQPOzRRx/F5s2bUVBQALVaDQC4ePEiAKB79+41ilOj0WD8+PH466+/cPDgQfH3lSFlv2cM5RIPM3qE5+bNmygoKED//v3Ffebm5ujdu7fRjT14XHkrususWrUKdnZ24lbVS8+ICLh8+TJcXV0lvzx69OgBKyurKp/L0tISSmX5vy7YX4kaqFq8LH3s2LEAStfpAqUjyOvWrUOfPn3g6ekJAMjNzcWkSZNw9OhRo89bXFyM8ePH4/r16zh48CDatm2rVyc6OhrJycni63v37uGjjz7CsGHDJCNBlTF6hKdsweLD2VRFvwRrIiQkBG+99Zb4Oicnh79EiapIp9OV++2ntvss+ytRA1WLl6W3bt0aX3/9NWbMmIGffvoJ9+7dQ3Z2Nvbt2yfWKSoqQnh4OPz9/cUbAs6bNw/Jyck4e/YsMjIyMGnSJAClMz3m5uZYvXo1du/ejcGDB0t+j9ja2uKrr74CUHq116hRo2Brawt7e3ucOHECPXr0wMaNG41/A6hCwtOpUydYWFjg7Nmz4mVmWq0WMTEx8PX1rVKjlbG0tISlpWWtnpOoqenWrRuSkpKQnp6OVq1aAQCuXr2K/Pz8Wm2H/ZWoYVIIlUxpVfFOy5MnT8bw4cNx4sQJqNVqDBo0SJzeAkqTlLCwMHHZC1B6n52cnBw888wzknOVXYoeEBCAzp0767X14O+UJ554AufOncPZs2eRnp6ODz/8EF27dq1a8KhCwmNtbY2XX34ZixYtgpeXF9q3b4/ly5dX61p4Iqp7I0eOhIeHB2bPno0vvvgCGo0Gr776qqnDIqL6UgcPD3VycsLTTz9dbpmFhYU4glNm9OjRBs/Xs2dPo9YVmpubS5bUVEeVxrb/85//oE+fPujZsyfc3NyQkpICf39/SZ3o6GhYWVmJ6wSmTZsGKysrzJ49u0aBElHVKJVK/PTTT7h16xacnJzQv39/+Pr6wsnJSVJvyZIlsLKyEn8xdejQAVZWVtixY4cpwiaiWlJ2WbqhrSmp0n14mjVrhu+++w5bt24V1wZMmTJFUmfw4MHIysrSb8jAImUiqhtdu3bF8ePHIQiC2GfXr18vqbN06VIsWrRI71gLC4t6iZGI6khlC5Or+Cytxq5aWYihy8CUSmW1rgIhorpjqM+amZnxCwmRHNXBlFZjxt9yREREMmTsnZabihonPBs3bqzSjX+IyLQSEhIqvIkgEclIJVdpcYSniviLk6hx4SXkRE0Ep7QkOKVFREQkR0x4JJjwEBERyVBt33iwsWPCQ0REJEcc4ZFgwkNERCRDHOGRYsJDREQkRwIMPyCUCQ8RERE1dhzhkWLCQ0REJEdcwyPBhIeIiEiGeKdlKSY8REREcsQRHgkmPERERDLENTxSTHiIiIjkiCM8Ekx4iIiIZIhreKSY8BAREckRR3gkmPAQERHJENfwSDHhISIikiMdDN9pmVNaRERE1Ngp/t4MlTclSlMHQERERHVAMGKrgvT0dLz88stwd3fHI488gvfeew9ardbgMTdv3sTChQvRsWNH+Pn5Vfu81Wn7YRzhISIikqHavEpLp9PBz88P5ubm2LFjB+7evYvnn38e2dnZWLNmTbnH5OfnY9SoUZg+fToee+wxxMXFVeu81Wm7PEx4iIiI5KqWFiZHRkbi1KlT+Ouvv+Du7g4AeO+99zB37lwsWbIEtra2eseo1Wpcv34dCoUCc+bMKTfhMea81Wm7PJzSIiIikqGyq7QMbcY6fPgwOnbsKCYcADBq1CgUFRXh5MmT5bevUEChMLxSyJjzVqft8nCEh4iISI6MvA9PTk6OZLelpSUsLS0l+5KTk9G6dWvJvrLXKSkp1Q7RmPPWVtsc4SEiIpKhsjU8hjYAcHV1hZ2dnbitWrVK71yCIMDMTDpGolKpAKDKi4eret7aartRjfCobaxgrrAwdRgmo2jWdN97GbW1heS/TVVxScO/Y5i6mSXMVZaVV5SxZpZN++cUAJpZmAMArK3MTRyJaZUoq58UVJexNx5MSkqSrIN5eHQHABwdHXH69GnJvvT0dACAk5NTtWM05ry11XajSni23frK6MVJJG/fR843dQgmlZOTAzu790wdhkFbz73P/kqi39bNMnUIJpWTkwO7bxbUb6NGTmnZ2tpW2lf79euHjz76CGlpaWKScfjwYSiVSvj4+FQ7RGPOW1ttN6qE55++a2BuZmXqMMiE1NYW+D5yPnRpgwDhvqnDMRldbv1/W6yqKY8va/IjPOkD25o6BJOztjLHb+tmsc+aoM/W5mXpTz/9NFxcXDB//nx88cUXyM7OxooVKzB+/Hg4OzsDADIyMuDl5YXPPvsM48ePr7XzGlPHGI0q4SnI16BY1dTuDUnlEu4DQp6pozAdoeHfE77gfhGKm/gqwfuFGlOH0HCwz5qgTdTaw0PVajV2796NqVOnwt7eHjqdDk8//TQ2bNgg1tHpdLhz5w4KCgrEfU888QRu3LiB3NxcFBcXiwlKQkICLC0tjTqvMXWM0agSHiIiIjKOQhCgECrOagyVlad79+74448/kJOTAwsLC1hZSWdcWrZsidu3b8Pe3l7c9/PPP6OkpETvXA+uE6rsvMbWqQwTHiIiIhmqzSmtB1W03kehUOhNMbVq1arG561qnYow4SEiIpKjWpzSkgMmPERERDJk7GXpTQUTHiIiIjniCI8EEx4iIiIZqqs1PI0VEx4iIiKZamrTVoYw4SEiIpIjQSjdDJU3IUx4iIiIZIhTWlJMeIiIiGSICY8UEx4iIiI54lVaEkx4iIiIZEihE6DQGXi0hIEyOWLCQ0REJEO88aAUEx4iIiI54pSWBBMeIiIiGeIIjxQTHiIiIhniGh4pJjxERERyxCktCSY8REREMsQpLSkmPERERHKkFQClgaxG27QyHiY8REREMqRAJSM89RZJw8CEh4iISI748FAJJjxEREQyxDU8Ukx4iIiIZIiXpUsx4SEiIpIj3d+bofIqEgQBt27dglqtRqtWrWp8TFZWFlJTU8s9zt3dHebm5tDpdLh+/bpeedu2bWFra2t07PWW8MTHx6O4uBgdOnSAubl5fTVLRNVQUlKC69evw9bWFu3atYNC0dSWNxI1fgpBgMLAOh1DZeU5deoUAgMDkZ6ejoKCAgwaNAhhYWFwdHSs9jF79uzBihUrJMekpaUhOzsbqampcHR0RE5ODrp16wY3NzdYWVmJ9dasWYOAgACj41dW6d1Ww4YNG9CxY0cMGTIE//jHP9CuXTts3bq1rpslomq4f/8+5s+fDycnJzz77LPo1asXevTogXPnzpk6NCKqKp1Q+WakvLw8jBkzBr6+vrh37x7S0tKQlZWFadOm1eiYwMBAXLt2TbJ17twZo0aN0kukfvjhB0m9qiQ7QC0kPIIg4MqVK0hKSgIAxMbG4ubNm2J5UlISDh06hPj4eMTGxmL58uWYOnUqLl26VNOmiagaCgoKcP78eWRkZAAAzp49i3v37gEA7t69i9atWyMpKQlXrlxBcnIyHnnkEYwZMwY6XTXGv4nIZMoWLRvajLVr1y6kp6djxYoVUCqVsLW1xaJFi7Bnzx7cunWr1o65dOkSTp06hRkzZuiV5ebmIiEhAVqt1vjAH1CjhCc5ORk9e/bEwIEDMWzYMPTo0QMvvvgiVq5cKdZZvnw52rdvL76eOXMmVCoVjh8/XpOmiaga9u/fD1dXVwQEBMDb2xuTJk3CiBEjEBUVBQDo0KEDgoOD0axZMwCAhYUFZs6ciaSkJCQkJJgydCKqqrLL0g1tRjp9+jQ6d+4MBwcHcd+AAQMgCALOnj1ba8eEhobC2dm53NGb0aNH47HHHkOzZs0wd+5c5OfnGx0/UMOEZ+7cuWjRogWSk5Nx48YNBAcH49ixYwaPuXTpEjQaDTp27FiTpomoigoKCvD8889j5syZSExMRHJyMtRqNTIzMw0ed/bsWVhaWqJt27b1FCkR1QaFVqh0A4CcnBzJVlRUpHeue/fu6S04btmyJQAgPT293PareoxGo8HWrVsxdepUmJn9b4mxSqXC+vXrkZ2djdu3b+PYsWPYvn075s+fX4VPowYJT1ZWFn7++WcsXrwY1tbWAIDnn38e3bp1q/CYgoICTJ8+HQMGDMCwYcMqrFdUVKT3D0BENfPbb78hOzsbixcvBgAolUq8//77Bo+5du0ali1bhvnz58PS0rLcOuyvRA2UYMQGwNXVFXZ2duK2atUqvVOpVCpoNBrJvuLiYgCQJCc1OWbnzp24d+8epk+fLtlvY2ODuXPnwsLCAgDQp08fLFy4EJs3b4ZQhVGqaic8cXFxEAQBnp6ekv1du3Ytt75Go8Gzzz6LrKws/PDDD1AqK2561apVkg/f1dW1umES0d9iY2Ph6uoqfkEBSi/rtLGxKbd+YmIi/vGPf2DkyJF49913Kzwv+ytRw1R2lZahDShda5udnS1uISEheudydXXF7du3JfvKXru4uJTbflWPCQ0NxdChQ+Hh4VHpe2vfvj3u378vrj80RrUTnrJfkvfv35fsf/g18L9k588//0R0dDTatGlj8NwhISGSD79sQTQRVZ+NjY1e/9TpdCgsLNSrm5iYiCFDhqBXr14ICwuDSqWq8Lzsr0QNlJFreGxtbSVbeaO5Q4YMQWJiIq5evSru27NnD9RqNfr16wcA0Gq1uHbtmjjKa8wxZRITE7F///5yFyuXjQo96PDhw2jZsqVkfVBlqn0fng4dOqBVq1bYv3+/OKqTk5ODEydOSL7hFRcXY8KECbhy5QoOHjxYYSb4IEtLywqHz4moenx8fJCSkoKrV6+KU8/R0dF6v0xu3bqFoUOHokePHti+fXul981ifyVqmBS6/63TqajcWMOHD8eQIUMQGBiIDz74AOnp6ViyZAkWLFiA5s2bAwAyMzPRrVs3bNmyBVOmTDHqmDIbN25EixYtMHbsWL22161bh6SkJAQEBMDe3h67d+/Gf//7X3z00UcGZ4seVu2Ex8zMDIsXL8Y777wDlUoFNzc3fPDBB3rzdf/85z/x+++/Y+PGjbh165Z4KZqLi4tRyQ8R1Q4fHx/4+flh/PjxeO+991BUVISQkBDJXHp6ejqGDh0KKysrvPXWW/jjjz/EMm9v7wqnv4ioARJQycNDq3a6Xbt2YdmyZQgODoZarcaKFSswZ84csdzMzAyenp6ws7Mz+pgyx48fx9y5c8v98jRv3jxs2rQJ69atQ3p6Otzd3bF//34MHjy4SvHX6E7LQUFBsLa2xvbt22FjY4PAwEC9KznS0tLg7e2NDz74QLL/5Zdfxssvv1yT5omoisLCwvD+++/j448/hqurK7Zs2YIJEyaI5SkpKeJVFA9fAfHVV1/h0Ucfrdd4iagGavlp6ba2tli7dm2F5fb29rh27VqVjimzb9++CstUKhWmT5+ut5i5qmr8aIkZM2ZI5tyio6Ml5YcOHappE0RUS5o3b17uFRhlHn30UZw4caIeIyKiuqLQClAYGMYxNN0lR3x4KBERkRzV8ghPY1frCY+HhwcXMBI1In369DH6qcdE1Igw4ZGo9YTH0P06iKjh2bNnj6lDIKK6oH3g7oIVljcdnNIiIiKSoQdvLlhReVPChIeIiEiOOKUlwYSHiIhIjnQCoDCQ1FThxoNywISHiIhIjgQdoNMZLm9CmPAQERHJEae0JJjwEBERyZGukqu0OKVFREREjZ5OC0BbSXnTwYSHiIhIjjjCI8GEh4iISI64hkeCCQ8REZEc6QQABq7E4ggPERERNXo6HQwnPLwsnYiIiBo7TmlJMOEhIiKSIyY8Ekx4iIiIZEjQaiEIFV96LvCydCIiImr0BMHwwmSO8BAREVGjJ1RyHx4mPERERNToabWAwsC0lYHpLjliwkNERCRDgk4HQVHxpecCn5ZOREREjV4dTGnFxcXh6NGjUKvVGDFiBOzt7Wt0jEajwTfffKN3zPDhw9G5c+cat/0gZZVqExERUeOg1ZVOa1W4VW2EZ8OGDfD29saOHTvw4YcfwsPDA2fOnKnRMfn5+Zg9ezaioqIQExMjbpmZmTVu+2Ec4SEiIpIhQSdAUFQ8iiNUYYQnOTkZr7/+Oj755BPMmDEDADBp0iRMmzYNFy5cqPExISEh8PHxqbW2y8MRHiIiIjkSdJVvRvr5559hbm6OF154Qdw3Z84cXLx4EZcvX67xMQcPHsTmzZtx7NgxaLXaap/HkEYxwlOWhZZoi0wcCZlacYmAnJwc6HK1VeqscpOTV/req/INrb6I/VWnMXEkpqfVFJo6BJMrUWrZZ2GaPlus00AwsIanBMUAgJycHMl+S0tLWFpaSvZdvnwZ7u7ukv1eXl5imbe3t975jT1GpVJh7969aN26NQ4dOoTWrVtj586dcHNzq3bb5WkUCU9ubi4A4PCV9SaOhBoCO7v3TB1Cg5Gbmws7OztThyFR1l8PJm0wcSQNQIKpA2gY7L5ZYOoQGoz66LMWFhZwdnbG0dTdldZt3rw5XF1dJfuWLl2Kd999V7IvJydHb5Fw2euHE6aqHGNpaYlTp06hd+/eAIC8vDwMHjwYM2fORERERLXbLk+jSHjatm2LpKQk2NjYQKFQmCSGnJwcuLq6IikpCba2tiaJwdT4GZRqCJ+DIAjIzc1F27ZtTdK+IeyvDQc/h1IN4XOozz5rZWWFuLg4aDSVj7IKgqDXTx8e3QEAtVotfpkpU/ba2tq63HMbc4xarRaTHaA0AZszZw5mzpwJjUYDCwuLarVdnkaR8CiVSri4uJg6DACAra1tk/7FAfAzKGPqz6GhjeyUYX9tePg5lDL151CffdbKygpWVla1dr7OnTtj586d0Ol0UCpLl//GxcUBADw8PGrtmLLYS0pKkJeXBwcHh2qf52FctExEREQG+fv7IyMjA5GRkeK+rVu3om3btujTpw8AoLCwEF988QVu3Lhh9DFxcXHQ6aRru8LCwuDp6QkHBwejz2OMRjHCQ0RERKbj7e2NoKAgTJkyBa+++irS09OxYcMGhIeHQ6VSAShdfzN79mxs2bIFnTt3NuqYo0ePYsKECfD19YW9vT327NmDS5cu4ccff6xS28ZgwmMkS0tLLF26tNy5zaaCn0Epfg4NH/+NSvFzKMXPoXasW7cOw4YNQ3R0NFq0aIHTp0+jZ8+eYrlarcasWbPQpUsXo495/vnn0a9fP+zcuRN3797F5MmTMXHiRLRo0aJKbRtDITTE61qJiIiIahHX8BAREZHsMeEhIiIi2WPCQ0RERLLHhKcatFotIiIiJKvIm5Lc3Fz89NNPOHTokF5ZSkoKfv31VxNEZRoFBQU4duwYfvvtNyQlJZk6HKpAdnY2fvzxRxw9etTUoZhEfHw8wsPDcf36db2ys2fPVvmp041Zeno6IiMjcejQIWRnZ5s6HKpPAlXJmjVrBDc3N6Fjx45Cs2bNTB1Ovbp//77wyiuvCM7OzoKzs7MwatQovTq//vqr4ObmVv/BmcDHH38suLq6CgMGDBBGjRolqNVqISgoyNRh0QNycnKEGTNmCG3atBFat24tjBkzxtQh1avLly8Lo0ePFjp06CBYWFgI69at06vz2muvCbNmzar/4OqZRqMRpk2bJrRt21bw9fUV+vfvL9jZ2Qnff/+9qUOjesIRnodcv34dv/zyCy5fvoySkhJs27YNmZmZYrlarcbRo0exYIE8nw1z8uRJ7N27F0lJSUhNTcWOHTvEsqKiIjz66KO4fv06hg8fbsIo60dRURGioqLw+++/IzMzExcuXJCMatnZ2SEmJgbHjh3Dvn37cPjwYXz66af46aefTBh10yIIAo4fP469e/fi1q1bSE5Olnz+BQUF8PHxwY0bNzBo0CATRlo3CgsLERERgejoaGRlZeHcuXOSUazs7GzMnj0bsbGxsLGxMWGk9ePu3bv45ZdfcOrUKRQVFSEyMhJ//vknAKC4uBgDBw5EQkICIiIicOLECSxatAgvvfQS7t69a+LIqT7wPjwPWLlyJZYvX45BgwYhNTUV7du3x2+//YbTp0/Dx8cHAPDaa6+ZOMq6odVqMW7cOBw5cgSPPfYYLl68CG9vbxw8eBATJkwAALRo0QKzZ882caT1Izk5GUOHDkVJSQm6dOmCy5cvw9XVFba2thg8eDAA4IUXXpAc4+Pjg/bt2yMmJgbjxo0zRdhNSnFxMZ5++mmcPn0a/fr1w8WLF+Hl5YWTJ0+Kn7+TkxNmzpxp4kjrRkJCAoYOHQqlUgkPDw9cuXIFbdu2hbOzs5jcPf744yaOsv5ERUVh3Lhx6NatG9RqNTIzM5GdnY3XX38dnp6esLa2xrRp0yTHjB8/HgsWLMDVq1fh6OhoosipvjDh+du1a9fwr3/9C7/++iueeuop6HQ6TJo0ydRh1ZvNmzfjyJEjOH/+PFxdXZGdnY3+/ftX+Tzt2rVDQEBAHURYv0JCQuDk5IT9+/fDysoKFy9eRJ8+fTBs2LAKj7lx4wYSEhLwyCOP1GOkTdeGDRvwxx9/4MKFC2jbti0yMjLELyZNwfz589GhQwfs27cPFhYW+OOPP9C/f3/4+/tX6Tx9+vTRu7V/Y1NcXIyZM2fitddew+rVqwEAn376KV5//XWDx+3fvx8qlQpdu3atjzDJxDil9bcffvgB3bp1w1NPPQWg9AGI8+fPN3FU9Wf79u2YOHEiXF1dAZRO18yaNavK5+nVqxc++eST2g6vXgmCgB9++AGvv/66+PC97t27iz8b5SkoKEBgYCB8fHw4ulNPtm/fjsDAQPHp0w4ODpgxY4aJo6ofJSUl+PnnnxEUFAQLCwsApYlLdaaaX3rpJUyfPr22Q6xXJ0+eRHx8vOR39iuvvGLwQaF//vknFi5ciHnz5sHJyak+wiQTY8Lzt8TERHTo0EGyr2PHjqYJxgSa+vt/0N27d1FQUGD051FUVIRx48YhOzsbu3btgpkZB07rQ1P+mU1JSUFJSUmTff8PS0xMRPPmzdGyZUtxn5mZmfgF7mHx8fEYOXIkRo4ciZUrV9ZXmGRiTHj+1rJlS8niZAB6r+Wsqb//B9nb20OpVBr1eWg0GowbNw6xsbGIjo6Gs7NzfYXZ5DXln9myP+xN9f0/rGXLlrh//z40Go1kf3mfR0JCAoYMGYJ+/frh+++/r9LDJ6lxY8Lzt0GDBuH06dNITk4W9zWlq20GDRqEX3/9VTKX35Te/4MsLCzEh9mVyc/Px759+yT1ypKdGzduIDo6WpxaofoxaNAg/PLLLxAeeBxgU/mZbdasGXr27Cn5Gc3NzUVUVJTpgjKh3r17w8rKCr/88ou47+TJk0hJSZHUS0xMxJAhQ+Dj44Nt27ZxNLaJ4b/23/z8/PD4449jxIgRmDNnDpKTk7Fp0ya9eseOHUNSUhLOnDkjXrYOAKNGjdJ7umtjMm/ePGzcuBFPPfUUxo8fj8OHD+PEiRN69Xbt2oWCggIkJCQgNzcX27Ztg0qlEq/kkouVK1fC19cXSqUSPXr0wKZNm/QWdk6dOhWRkZFYvXo1jhw5Iu53d3dH37596zvkJmfBggXYunUr/P39MWbMGBw4cAAxMTF69X766SdoNBrcunULWq0W27Ztg4WFRaNfa7V69Wr4+/tDp9PBy8sLoaGhUCql32GzsrLERF2j0eDcuXPYtm0bXFxcZHWZvqOjIxYsWIDp06fj+vXrUKvVWL9+veRS/NzcXAwdOhTFxcV45pln8MMPP4hlAwYMQPv27U0ROtUjPi39Afn5+fj0009x9epVeHh4YNKkSfDw8JBclv7RRx+VmwisXLkSnTp1qu+Qa1VSUhI+/fRTZGRkoEePHmjfvj2ee+45FBYWinVeeeUVZGVlSY6zsLDAt99+W8/R1r0TJ05g8+bNMDc3x5AhQ3Dq1CnExMSIf0DeeOMNpKam6h03bNgw2V4K3dDEx8fjs88+Q3Z2Nnr16oVWrVphxowZkp/R6dOn4/79+5LjmjVrhtDQ0HqOtvYdPXoUW7duhYWFBUaOHIkDBw4gPj5eHPlJSEjAwoUL9Y7r27cv5s2bV8/R1r3vv/8eUVFRcHZ2xoQJEzBz5kxMmjQJwcHBuHv3boVXbQUFBTWpS/ibKiY8BuTl5cHGxkaS8DQl+/btwzPPPCNJeJqyt99+W5LwUMOzc+dOTJ06VS8pbyreeOMNScLT1Pn4+IgJDxHX8BAREZHscQ2PAebm5pg4cSIcHBxMHYpJtGnTBs8995ypw2gwevToAXt7e1OHQQa4uLhg/Pjxpg7DZHr37l3hpdhN0ahRo3hTQRJxSouIiIhkj1NaREREJHtMeIiIiEj2mPAQERGR7DHhISIiItljwkNERESyx4SHiIiIZI8JDxEREckeEx4iIiKSPSY8REREJHv/D7wDFiz+zdN8AAAAAElFTkSuQmCC", + "text/plain": [ + "<Figure size 700x320 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(7, 3.2))\n", + "for ax, p, title in zip(\n", + " axes, (p_I, p_F), (r\"$C_I$: from the estimate\", r\"$C_F$: from the data\")\n", + "):\n", + " im = ax.imshow(p.constraints[0].matrix(mean), cmap=\"viridis\")\n", + " ax.axhline(1.5, color=\"w\", lw=0.8)\n", + " ax.axvline(1.5, color=\"w\", lw=0.8)\n", + " ax.set(\n", + " title=title,\n", + " xticks=range(3),\n", + " yticks=range(3),\n", + " xticklabels=[\"q1\", \"q1'\", \"q2\"],\n", + " yticklabels=[\"q1\", \"q1'\", \"q2\"],\n", + " )\n", + "fig.colorbar(im, ax=axes, shrink=0.8)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "860efb79", + "metadata": {}, + "source": [ + "### II.B: the five-quantity numerical study\n", + "\n", + "Table I of the reference: five quantities, most measured twice, `σ_i = 0.1\n", + "q_i`, `σ_N,i = 0.2 N_i`, `c = 0.8`. Its Fig. 1 compares the means and\n", + "standard deviations of the `ρ_i` from the full information with those from\n", + "`C_I` and `C_F`. Both posteriors are Gaussian (fixed covariance, flat\n", + "prior), so emcee is only a check on the closed form here; the figure is the\n", + "point." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7d11de2d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:44:25.315785Z", + "iopub.status.busy": "2026-09-11T03:44:25.315483Z", + "iopub.status.idle": "2026-09-11T03:45:41.865160Z", + "shell.execute_reply": "2026-09-11T03:45:41.864316Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 800x350 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "q5 = [\n", + " np.array([1.0, 1.5]),\n", + " np.array([1.8]),\n", + " np.array([2.2, 2.4]),\n", + " np.array([1.9, 1.5]),\n", + " np.array([1.4, 1.2]),\n", + "]\n", + "N5 = np.array([1.0, 1.1, 1.25, 1.15, 1.05])\n", + "sigma_q5 = [0.1 * qi for qi in q5]\n", + "sigma_N5 = 0.2 * N5\n", + "corr5 = np.full((5, 5), c_corr) + (1 - c_corr) * np.eye(5)\n", + "counts5 = [len(qi) for qi in q5]\n", + "\n", + "comps5, rhos5 = experiment(q5, N5, sigma_q5, sigma_N5)\n", + "mean5, cov5, qbar5 = exact(q5, N5, sigma_q5, sigma_N5, corr5)\n", + "p5_F = rx.Problem(\n", + " [\n", + " rx.Constraint(\n", + " comps5, terms=[normalisations(comps5, sigma_N5 / N5, corr5, from_data=True)]\n", + " )\n", + " ]\n", + ")\n", + "p5_I = rx.Problem(\n", + " [\n", + " rx.Constraint(\n", + " comps5, terms=[fixed_estimate(comps5, mean5, sigma_N5 / N5, corr5, counts5)]\n", + " )\n", + " ]\n", + ")\n", + "s5_F, s5_I = fit(p5_F, seed=5, n_steps=3000), fit(p5_I, seed=6, n_steps=3000)\n", + "\n", + "lattice = np.arange(1, 6)\n", + "fig, axes = plt.subplots(1, 2, figsize=(8, 3.5))\n", + "axes[0].errorbar(\n", + " lattice, mean5, np.sqrt(np.diag(cov5)), fmt=\"ko-\", capsize=3, label=\"exact\"\n", + ")\n", + "axes[0].errorbar(\n", + " lattice + 0.08,\n", + " s5_I.mean(0),\n", + " s5_I.std(0),\n", + " fmt=\"s--\",\n", + " color=\"C2\",\n", + " capsize=3,\n", + " label=r\"$C_I$\",\n", + ")\n", + "axes[0].errorbar(\n", + " lattice - 0.08,\n", + " s5_F.mean(0),\n", + " s5_F.std(0),\n", + " fmt=\"v:\",\n", + " color=\"C3\",\n", + " capsize=3,\n", + " label=r\"$C_F$\",\n", + ")\n", + "axes[0].set(xlabel=\"lattice index\", ylabel=\"quantity\", xticks=lattice)\n", + "axes[0].legend(frameon=False)\n", + "axes[1].plot(lattice, np.sqrt(np.diag(cov5)), \"ko-\", label=\"exact\")\n", + "axes[1].plot(lattice, s5_I.std(0), \"s--\", color=\"C2\", label=r\"$C_I$\")\n", + "axes[1].plot(lattice, s5_F.std(0), \"v:\", color=\"C3\", label=r\"$C_F$\")\n", + "axes[1].set(xlabel=\"lattice index\", ylabel=\"standard deviation\", xticks=lattice)\n", + "axes[1].legend(frameon=False)\n", + "fig.suptitle(\"Neudecker et al. (2014), Fig. 1 recreated\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "81d004eb", + "metadata": {}, + "source": [ + "`C_I` reproduces the exact means and widths; `C_F` is below them on every\n", + "lattice point, in both the mean and the width. The reference's real-data\n", + "case has the same structure: ²³⁷Np(n,f) measured by three experiments as\n", + "ratios to the ²³⁵U(n,f) standard, converted with the standard's covariance\n", + "as `B`, where `C_F` lowers the evaluated cross sections below every dataset." + ] + }, + { + "cell_type": "markdown", + "id": "e548e269", + "metadata": {}, + "source": [ + "### The live term and the log-determinant\n", + "\n", + "`rxmc`'s `T.normalization` and the spanning term above read `c.ym`, so the\n", + "covariance moves with the parameters: this is the generative model's\n", + "marginal likelihood, not the fixed `C_I`. A covariance that grows with the\n", + "prediction pulls the posterior mode down through `log det Σ`; with 20 %\n", + "normalisations it is a 9 % pull here, against 27 to 31 % for `C_F`. A proper\n", + "prior on the quantities, or the two-step refit (`C_I` built from a first\n", + "estimate, as above), removes it." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7e58ce3d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:45:41.867025Z", + "iopub.status.busy": "2026-09-11T03:45:41.866763Z", + "iopub.status.idle": "2026-09-11T03:45:42.027594Z", + "shell.execute_reply": "2026-09-11T03:45:42.026808Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "relative pull of the mode, live term: [-0.09 -0.09 -0.09 -0.09 -0.09]\n", + "relative bias of the mean, C_F : [-0.311 -0.268 -0.271 -0.284 -0.275]\n" + ] + } + ], + "source": [ + "from scipy.optimize import minimize\n", + "\n", + "p5_live = rx.Problem(\n", + " [rx.Constraint(comps5, terms=[normalisations(comps5, sigma_N5 / N5, corr5)])]\n", + ")\n", + "mode = minimize(lambda t: -p5_live.log_posterior(t), mean5, method=\"Nelder-Mead\").x\n", + "print(\"relative pull of the mode, live term:\", np.round(mode / mean5 - 1, 3))\n", + "print(\n", + " \"relative bias of the mean, C_F :\",\n", + " np.round(gls(p5_F, mean5)[0] / mean5 - 1, 3),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ae07574a", + "metadata": {}, + "source": [ + "## Takeaways\n", + "\n", + "- Correlated observations are one covariance term whose support spans the\n", + " comparisons; no special machinery.\n", + "- Fitting shared-systematic datasets independently is overconfident. Case A\n", + " couples the data, case B couples the parameters; both are declared by\n", + " where a term sits and which `Parameter` object it reads.\n", + "- Across several physical quantities the correlated normalisations are one\n", + " spanning `matrix` term paired with the normalisation correlation matrix,\n", + " read per quantity through `c.split`.\n", + "- Build the modes from the estimate, never from the data: that is the\n", + " whole of Peelle's Pertinent Puzzle in any number of dimensions." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/robust_likelihoods.ipynb b/examples/robust_likelihoods.ipynb new file mode 100644 index 0000000..d7a6e02 --- /dev/null +++ b/examples/robust_likelihoods.ipynb @@ -0,0 +1,575 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "476155aa", + "metadata": {}, + "source": [ + "# Robust likelihoods: Student-t versus the multivariate normal\n", + "\n", + "A few gross outliers make a Gaussian fit confidently wrong. The likelihood\n", + "*functional* is a drop-in choice in `rxmc`: keep the covariance, swap the\n", + "multivariate normal for a multivariate Student-t with a sampled tail\n", + "parameter, and the fit widens instead of breaking. Then two ways to say\n", + "\"the errors are larger than stated\": a global scale on the reported errors,\n", + "and an unrecognised, fully correlated component per experimental technique.\n", + "\n", + "Recipes: 9, 34" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9de17e52", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:43:19.768003Z", + "iopub.status.busy": "2026-09-11T03:43:19.767788Z", + "iopub.status.idle": "2026-09-11T03:43:22.526760Z", + "shell.execute_reply": "2026-09-11T03:43:22.525767Z" + } + }, + "outputs": [], + "source": [ + "import corner\n", + "import emcee\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from scipy import stats\n", + "\n", + "import rxmc as rx\n", + "from rxmc import terms as T" + ] + }, + { + "cell_type": "markdown", + "id": "71f5be36", + "metadata": {}, + "source": [ + "## A clean signal with a few gross outliers\n", + "\n", + "Twenty-five points of a line with 5 % noise, and three points pushed up by\n", + "ten times their error: a background the experiment did not subtract." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4f195f99", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:43:22.528880Z", + "iopub.status.busy": "2026-09-11T03:43:22.528582Z", + "iopub.status.idle": "2026-09-11T03:43:22.780686Z", + "shell.execute_reply": "2026-09-11T03:43:22.779856Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 500x350 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.default_rng(21)\n", + "m_true, b_true = 1.0, 0.5\n", + "x = np.linspace(0.0, 4.0, 25)\n", + "noise = 0.05\n", + "y = m_true * x + b_true + rng.normal(0.0, noise, x.size)\n", + "outliers = np.array([5, 12, 19])\n", + "y[outliers] += np.array([10.0, 12.0, 9.0]) * noise\n", + "data = rx.Dataset(x, y, np.full(x.size, noise), label=\"with outliers\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(5, 3.5))\n", + "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", ms=4, color=\"k\", label=\"data\")\n", + "ax.plot(x[outliers], y[outliers], \"o\", ms=12, mfc=\"none\", color=\"C3\", label=\"outliers\")\n", + "ax.plot(x, m_true * x + b_true, \"--\", color=\"C3\", label=\"truth\")\n", + "ax.set(xlabel=\"x\", ylabel=\"y\")\n", + "ax.legend(frameon=False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0a18c905", + "metadata": {}, + "source": [ + "## The same constraint, two likelihood functionals\n", + "\n", + "`rx.StudentT(nu)` applies one radial tail to the whole stacked residual of\n", + "the constraint; `nu` is an ordinary parameter with its bounds and prior on\n", + "the `Parameter`, so it is one more column of the chain." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c49dd13d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:43:22.782451Z", + "iopub.status.busy": "2026-09-11T03:43:22.782256Z", + "iopub.status.idle": "2026-09-11T03:43:49.599074Z", + "shell.execute_reply": "2026-09-11T03:43:49.598124Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gaussian : ['m', 'b']\n", + "Student-t: ['m', 'b', 'nu']\n" + ] + } + ], + "source": [ + "m = rx.Parameter(\"m\", prior=stats.norm(1.0, 1.0), latex=\"m\")\n", + "b = rx.Parameter(\"b\", prior=stats.norm(0.0, 1.0), latex=\"b\")\n", + "line = rx.Model(lambda x, m, b: m * x + b, [m, b])\n", + "comp = rx.Comparison(data, line)\n", + "nu = rx.Parameter(\"nu\", bounds=(1.0, 100.0), latex=r\"\\nu\") # uniform on its bounds\n", + "\n", + "p_gauss = rx.Problem([rx.Constraint([comp])])\n", + "p_t = rx.Problem([rx.Constraint([comp], likelihood=rx.StudentT(nu))])\n", + "print(\"Gaussian :\", p_gauss.names)\n", + "print(\"Student-t:\", p_t.names)\n", + "\n", + "\n", + "def fit(problem, seed, n_walkers=24, n_steps=2000):\n", + " sampler = emcee.EnsembleSampler(n_walkers, problem.ndim, problem.log_posterior)\n", + " sampler.random_state = np.random.RandomState(seed).get_state()\n", + " sampler.run_mcmc(problem.sample_prior(n_walkers, rng=seed), n_steps, progress=False)\n", + " return sampler.get_chain(discard=n_steps // 3, thin=5, flat=True)\n", + "\n", + "\n", + "s_gauss, s_t = fit(p_gauss, 1), fit(p_t, 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d0765433", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:43:49.601218Z", + "iopub.status.busy": "2026-09-11T03:43:49.600906Z", + "iopub.status.idle": "2026-09-11T03:43:50.620659Z", + "shell.execute_reply": "2026-09-11T03:43:50.619798Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 550x550 with 4 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gaussian m = 1.001 +/- 0.008, b = 0.567 +/- 0.019; truth at 3.5 sigma\n", + "Student-t m = 1.002 +/- 0.029, b = 0.566 +/- 0.067; truth at 1.0 sigma\n" + ] + } + ], + "source": [ + "fig = corner.corner(\n", + " s_gauss,\n", + " color=\"C3\",\n", + " labels=[\"$m$\", \"$b$\"],\n", + " truths=[m_true, b_true],\n", + " truth_color=\"k\",\n", + " plot_datapoints=False,\n", + ")\n", + "corner.corner(\n", + " s_t[:, p_t.columns(line.params)], fig=fig, color=\"C0\", plot_datapoints=False\n", + ")\n", + "fig.legend(\n", + " handles=[\n", + " plt.Line2D([], [], color=\"C3\", label=\"Gaussian\"),\n", + " plt.Line2D([], [], color=\"C0\", label=\"Student-t\"),\n", + " ],\n", + " loc=\"upper right\",\n", + " frameon=False,\n", + ")\n", + "plt.show()\n", + "for name, p, s in ((\"Gaussian\", p_gauss, s_gauss), (\"Student-t\", p_t, s_t)):\n", + " cols = p.columns(line.params)\n", + " pull = (s[:, cols].mean(0) - [m_true, b_true]) / s[:, cols].std(0)\n", + " print(\n", + " f\"{name:10s} m = {s[:, cols[0]].mean():.3f} +/- {s[:, cols[0]].std():.3f}, b = {s[:, cols[1]].mean():.3f} +/- {s[:, cols[1]].std():.3f}; truth at {np.abs(pull).max():.1f} sigma\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "31e2cb06", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:43:50.622753Z", + "iopub.status.busy": "2026-09-11T03:43:50.622547Z", + "iopub.status.idle": "2026-09-11T03:43:50.839129Z", + "shell.execute_reply": "2026-09-11T03:43:50.838265Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 500x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nu_col = p_t.columns(nu)\n", + "fig, ax = plt.subplots(figsize=(5, 3))\n", + "ax.hist(s_t[:, nu_col], bins=40, color=\"C0\", alpha=0.7)\n", + "ax.set(\n", + " xlabel=r\"$\\nu$\",\n", + " ylabel=\"posterior draws\",\n", + " title=rf\"median $\\nu$ = {np.median(s_t[:, nu_col]):.1f}\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8069377f", + "metadata": {}, + "source": [ + "The Gaussian puts the truth several σ away: three points at ten σ dominate\n", + "a quadratic penalty. The Student-t reaches a small `ν`, its tails absorb\n", + "the outliers, and the truth is covered. Note what it does *not* do: it does\n", + "not reject the outliers, it widens. Per-point rejection would need per-point\n", + "machinery, such as masking the suspects (recipe 11)." + ] + }, + { + "cell_type": "markdown", + "id": "6fa3d4ed", + "metadata": {}, + "source": [ + "### χ² is the same for both\n", + "\n", + "The covariance is shared; only the functional of the Mahalanobis distance\n", + "differs. `problem.chi2` is that distance, and `rx.Chi2()` is the functional\n", + "that drops the log-determinant for a pure χ² objective." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "07076854", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:43:50.841202Z", + "iopub.status.busy": "2026-09-11T03:43:50.841002Z", + "iopub.status.idle": "2026-09-11T03:43:50.844811Z", + "shell.execute_reply": "2026-09-11T03:43:50.844124Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "chi2 at the truth: Gaussian 322.62, Student-t 322.62\n" + ] + } + ], + "source": [ + "theta = np.array([m_true, b_true])\n", + "print(\n", + " f\"chi2 at the truth: Gaussian {p_gauss.chi2(theta):.2f}, Student-t {p_t.chi2(np.append(theta, 5.0)):.2f}\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "46fbe617", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:43:50.846776Z", + "iopub.status.busy": "2026-09-11T03:43:50.846560Z", + "iopub.status.idle": "2026-09-11T03:43:50.969855Z", + "shell.execute_reply": "2026-09-11T03:43:50.969069Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_fine = np.linspace(-0.5, 4.5, 60)\n", + "on_fine = line.bind(x_fine, {})\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "for name, p, s, color in (\n", + " (\"Gaussian\", p_gauss, s_gauss, \"C3\"),\n", + " (\"Student-t\", p_t, s_t, \"C0\"),\n", + "):\n", + " lo, hi = rx.predictive.predictive_band(\n", + " [on_fine(*r[p.columns(line.params)]) for r in s[::10]], levels=(5, 95)\n", + " )\n", + " ax.fill_between(x_fine, lo, hi, color=color, alpha=0.35, label=f\"{name} 90 % band\")\n", + "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", ms=3, color=\"k\")\n", + "ax.plot(x_fine, m_true * x_fine + b_true, \"--\", color=\"k\", label=\"truth\")\n", + "ax.set(xlabel=\"x\", ylabel=\"y\")\n", + "ax.legend(frameon=False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "902b883f", + "metadata": {}, + "source": [ + "## Errors larger than stated (recipe 34)\n", + "\n", + "Two more honest options when the data scatter more than their errors say.\n", + "\n", + "**A global scale** on the reported errors: a `diag` term that closes over\n", + "the comparison's errors, with `statistical=False` so it *is* the diagonal.\n", + "Under the Gaussian the scale has to grow until every point's error covers\n", + "the outliers, and it is poorly determined; under the Student-t the tail\n", + "shares the work, so the scale is smaller and better determined. Either way\n", + "a global scale inflates every point equally, which is why it is judged poor\n", + "evaluation practice next to a targeted term." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e214d154", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:43:50.972105Z", + "iopub.status.busy": "2026-09-11T03:43:50.971908Z", + "iopub.status.idle": "2026-09-11T03:44:34.686258Z", + "shell.execute_reply": "2026-09-11T03:44:34.685243Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gaussian error scale s = 3.22 (68 % interval 2.83 to 3.68)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Student-t error scale s = 2.91 (68 % interval 2.18 to 3.48)\n" + ] + } + ], + "source": [ + "log_s = rx.Parameter(\"log_s\", prior=stats.norm(0.0, 0.5), latex=r\"\\log s\")\n", + "scaled = rx.Term(lambda c, ls: np.exp(ls) * comp.y_err, (log_s,), kind=\"diag\", on=comp)\n", + "p_scale_gauss = rx.Problem([rx.Constraint([comp], terms=[scaled], statistical=False)])\n", + "p_scale_t = rx.Problem(\n", + " [\n", + " rx.Constraint(\n", + " [comp], terms=[scaled], statistical=False, likelihood=rx.StudentT(nu)\n", + " )\n", + " ]\n", + ")\n", + "for name, p, seed in ((\"Gaussian\", p_scale_gauss, 3), (\"Student-t\", p_scale_t, 4)):\n", + " s = fit(p, seed)\n", + " lo, med, hi = np.percentile(np.exp(s[:, p.columns(log_s)]), [16, 50, 84])\n", + " print(f\"{name:10s} error scale s = {med:.2f} (68 % interval {lo:.2f} to {hi:.2f})\")" + ] + }, + { + "cell_type": "markdown", + "id": "748af1de", + "metadata": {}, + "source": [ + "**An unrecognised source of uncertainty** per technique: suppose the\n", + "outliers are not three random points but a second technique whose whole\n", + "dataset carries an unknown offset. A sampled `T.offset` on that technique's\n", + "comparisons is a fully correlated component *inside* the covariance, which\n", + "shifts the evaluated mean as well as its width. A global scale cannot do\n", + "that." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "92927964", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:44:34.688516Z", + "iopub.status.busy": "2026-09-11T03:44:34.688312Z", + "iopub.status.idle": "2026-09-11T03:45:50.520093Z", + "shell.execute_reply": "2026-09-11T03:45:50.519423Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "as stated m = 0.991 +/- 0.008, b = 0.637 +/- 0.020\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "global scale m = 0.989 +/- 0.036, b = 0.654 +/- 0.232\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "USU offset on B m = 0.991 +/- 0.008, b = 0.526 +/- 0.022\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_a, x_b = np.linspace(0.0, 4.0, 13), np.linspace(0.15, 3.85, 12)\n", + "y_a = m_true * x_a + b_true + rng.normal(0.0, noise, x_a.size)\n", + "y_b = (\n", + " m_true * x_b + b_true + 0.25 + rng.normal(0.0, noise, x_b.size)\n", + ") # a 0.25 offset nobody reported\n", + "d_a = rx.Dataset(\n", + " x_a, y_a, np.full(x_a.size, noise), label=\"technique A\", meta={\"technique\": \"A\"}\n", + ")\n", + "d_b = rx.Dataset(\n", + " x_b, y_b, np.full(x_b.size, noise), label=\"technique B\", meta={\"technique\": \"B\"}\n", + ")\n", + "comps = [rx.Comparison(d_a, line), rx.Comparison(d_b, line)]\n", + "log_usu = rx.Parameter(\n", + " \"log_usu_B\", prior=stats.norm(np.log(0.2), 1.0), latex=r\"\\log\\delta_B\"\n", + ")\n", + "usu = T.offset(log_usu, on=[c for c in comps if c.data.meta[\"technique\"] == \"B\"])\n", + "scaled_ab = [\n", + " rx.Term(\n", + " lambda c, ls, comp=comp: np.exp(ls) * comp.y_err, (log_s,), kind=\"diag\", on=comp\n", + " )\n", + " for comp in comps\n", + "]\n", + "\n", + "problems = {\n", + " \"as stated\": rx.Problem([rx.Constraint(comps)]),\n", + " \"global scale\": rx.Problem(\n", + " [rx.Constraint(comps, terms=scaled_ab, statistical=False)]\n", + " ),\n", + " \"USU offset on B\": rx.Problem([rx.Constraint(comps, terms=[usu])]),\n", + "}\n", + "truth_line = m_true * x_fine + b_true\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "for (name, p), color, seed in zip(problems.items(), (\"C3\", \"C2\", \"C0\"), (5, 6, 7)):\n", + " s = fit(p, seed)\n", + " cols = p.columns(line.params)\n", + " print(\n", + " f\"{name:16s} m = {s[:, cols[0]].mean():.3f} +/- {s[:, cols[0]].std():.3f}, b = {s[:, cols[1]].mean():.3f} +/- {s[:, cols[1]].std():.3f}\"\n", + " )\n", + " lo, hi = rx.predictive.predictive_band(\n", + " [on_fine(*r[cols]) for r in s[::10]], levels=(5, 95)\n", + " )\n", + " ax.fill_between(\n", + " x_fine, lo - truth_line, hi - truth_line, color=color, alpha=0.35, label=name\n", + " )\n", + "ax.errorbar(\n", + " d_a.x,\n", + " d_a.y - (m_true * d_a.x + b_true),\n", + " d_a.y_err,\n", + " fmt=\"o\",\n", + " ms=3,\n", + " color=\"k\",\n", + " label=\"technique A\",\n", + ")\n", + "ax.errorbar(\n", + " d_b.x,\n", + " d_b.y - (m_true * d_b.x + b_true),\n", + " d_b.y_err,\n", + " fmt=\"s\",\n", + " ms=3,\n", + " color=\"C4\",\n", + " label=\"technique B (offset)\",\n", + ")\n", + "ax.axhline(0.0, ls=\"--\", color=\"k\", label=\"truth\")\n", + "ax.set(xlabel=\"x\", ylabel=\"y - truth\", title=\"90 % bands, relative to the truth\")\n", + "ax.legend(frameon=False, fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5c084a2a", + "metadata": {}, + "source": [ + "## Takeaways\n", + "\n", + "- The likelihood functional is a drop-in; `ν` is an ordinary column.\n", + "- The multivariate t buys honesty, not outlier rejection: it widens.\n", + "- A global error scale inflates every point equally; under a heavy-tailed\n", + " likelihood it stays modest.\n", + "- An unrecognised component belongs *in* the covariance, as a sampled\n", + " correlated term on the technique it afflicts: it moves the mean, which is\n", + " what a Birge-type rescaling after the fact cannot do." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 789f72e699dee8d38c483b6fc0918798276b8c17 Mon Sep 17 00:00:00 2001 From: beykyle <kylebeyer1@gmail.com> Date: Thu, 10 Sep 2026 23:52:18 -0400 Subject: [PATCH 39/75] Add the measurement_to_calibration notebook (recipes 12, 14, 15, 16, 21, 26) An EXFOR-shaped measurement through from_measurement with the unit contract printed; reported systematics as explicit terms with the two-heatmap figure; the singular-covariance error on a zero-statistics copy; dynesty on the seven-parameter n+40Ca potential; the fine-grid band through bind; emcee and a dill round trip as other drivers; tempering as a constraint weight with the coverage of the predictive against the model band; the KDUQ model-error spelling. Runs in 202 s. --- examples/measurement_to_calibration.ipynb | 764 ++++++++++++++++++++++ 1 file changed, 764 insertions(+) create mode 100644 examples/measurement_to_calibration.ipynb diff --git a/examples/measurement_to_calibration.ipynb b/examples/measurement_to_calibration.ipynb new file mode 100644 index 0000000..840e8b2 --- /dev/null +++ b/examples/measurement_to_calibration.ipynb @@ -0,0 +1,764 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b8323393", + "metadata": {}, + "source": [ + "# From a measurement to a calibrated potential\n", + "\n", + "The production path: a measurement as EXFOR reports it, converted to a\n", + "`Dataset` in the library's units with its systematics kept as inert\n", + "metadata; the reported systematics turned into explicit covariance terms;\n", + "the guardrail against a singular covariance; nested sampling of an optical\n", + "potential; predictions on any grid; and two side trips, tempering the\n", + "likelihood and an inferred model error per data type.\n", + "\n", + "Recipes: 12, 14, 15, 16, 21, 26" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ad7550f1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:47:21.232880Z", + "iopub.status.busy": "2026-09-11T03:47:21.232725Z", + "iopub.status.idle": "2026-09-11T03:47:23.471702Z", + "shell.execute_reply": "2026-09-11T03:47:23.471044Z" + } + }, + "outputs": [], + "source": [ + "from types import SimpleNamespace\n", + "\n", + "import corner\n", + "import dill\n", + "import dynesty\n", + "import emcee\n", + "import jitr\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from jitr.optical_potentials.potential_forms import (\n", + " thomas_safe,\n", + " woods_saxon_prime_safe,\n", + " woods_saxon_safe,\n", + ")\n", + "from scipy import stats\n", + "\n", + "import rxmc as rx\n", + "from rxmc import terms as T" + ] + }, + { + "cell_type": "markdown", + "id": "d2c08367", + "metadata": {}, + "source": [ + "## The reaction and the optical model\n", + "\n", + "n + ⁴⁰Ca at 14.1 MeV; a Woods-Saxon potential with volume and surface\n", + "absorption and a fixed spin-orbit term. `ElasticXS` is a `Model` whose\n", + "`bind` compiles a jitr solver for the dataset's kinematics, which it reads\n", + "from the dataset's `meta`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "bd847a30", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:47:23.473152Z", + "iopub.status.busy": "2026-09-11T03:47:23.472935Z", + "iopub.status.idle": "2026-09-11T03:47:23.505669Z", + "shell.execute_reply": "2026-09-11T03:47:23.505117Z" + } + }, + "outputs": [], + "source": [ + "reaction = jitr.reactions.ElasticReaction(target=(40, 20), projectile=(1, 0))\n", + "E_lab = 14.1\n", + "R40 = 40 ** (1 / 3)\n", + "mso = 1.0 / jitr.utils.constants.WAVENUMBER_PION\n", + "\n", + "\n", + "def central(r, Vv, Wv, Rv, av, Wd, Rd, ad):\n", + " return -(Vv + 1j * Wv) * woods_saxon_safe(r, Rv, av) - 1j * Wd * (\n", + " -4 * ad\n", + " ) * woods_saxon_prime_safe(r, Rd, ad)\n", + "\n", + "\n", + "def spin_orbit(r, Vso, Rso, aso):\n", + " return Vso * mso**2 * thomas_safe(r, Rso, aso)\n", + "\n", + "\n", + "so_args = (6.0, 1.1 * R40, 0.45)\n", + "names = [\"Vv\", \"Wv\", \"Rv\", \"av\", \"Wd\", \"Rd\", \"ad\"]\n", + "latex = [\"V_v\", \"W_v\", \"R_v\", \"a_v\", \"W_d\", \"R_d\", \"a_d\"]\n", + "truth = np.array([48.0, 3.5, 1.1 * R40, 0.7, 21.0, 1.2 * R40, 0.5])\n", + "prior_mean = np.array([50.0, 3.0, 1.2 * R40, 0.65, 18.0, 1.2 * R40, 0.65])\n", + "prior_sd = np.array([7.0, 3.0, 0.2, 0.15, 8.0, 0.2, 0.15])\n", + "lower = np.array([0.0, 0.0, 1.0, 0.2, 0.0, 1.0, 0.2])\n", + "params = [\n", + " rx.Parameter(n, prior=stats.norm(mu, sd), bounds=(lo, np.inf), latex=lt)\n", + " for n, mu, sd, lo, lt in zip(names, prior_mean, prior_sd, lower, latex)\n", + "]\n", + "omp = rx.reactions.ElasticXS(\n", + " \"dXS/dA\", central, spin_orbit, lambda ws, *x: (tuple(x), so_args), params, lmax=10\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "f186f066", + "metadata": {}, + "source": [ + "## A measurement, as EXFOR reports it\n", + "\n", + "`exfor_tools` gives a `Distribution` with angles in degrees, cross sections\n", + "in a labelled unit, a statistical column, a fractional normalisation\n", + "uncertainty and an absolute offset uncertainty. Here that object is a\n", + "`SimpleNamespace` with the same fields, filled with mock data from the true\n", + "potential so the calibration can be checked; in production it is the\n", + "`exfor_tools` object itself." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "355dbc28", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:47:23.507066Z", + "iopub.status.busy": "2026-09-11T03:47:23.506928Z", + "iopub.status.idle": "2026-09-11T03:47:33.356471Z", + "shell.execute_reply": "2026-09-11T03:47:33.355729Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.default_rng(11)\n", + "angles_deg = np.linspace(5.0, 160.0, 20)\n", + "meta = {\"reaction\": reaction, \"Elab\": E_lab}\n", + "y_true_b = omp.bind(np.deg2rad(angles_deg), meta)(*truth) # b/sr\n", + "y_true_mb = 1e3 * y_true_b\n", + "y_mb = y_true_mb * (1.0 + rng.normal(0.0, 0.08, angles_deg.size))\n", + "measurement = SimpleNamespace(\n", + " x=angles_deg,\n", + " y=y_mb,\n", + " Einc=E_lab,\n", + " quantity=\"dXS/dA\",\n", + " y_units=\"mb/sr\",\n", + " statistical_err=0.08 * y_mb,\n", + " systematic_norm_err=0.04, # fractional\n", + " systematic_offset_err=2.0, # mb/sr\n", + " subentry=\"toy-subentry\",\n", + ")\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "ax.errorbar(\n", + " measurement.x,\n", + " measurement.y,\n", + " measurement.statistical_err,\n", + " fmt=\"o\",\n", + " ms=3,\n", + " color=\"k\",\n", + " label=measurement.subentry,\n", + ")\n", + "ax.set(\n", + " xlabel=r\"$\\theta_{cm}$ [deg]\",\n", + " ylabel=r\"$d\\sigma/d\\Omega$ [mb/sr]\",\n", + " yscale=\"log\",\n", + " title=\"the measurement, as reported\",\n", + ")\n", + "ax.legend(frameon=False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "75443882", + "metadata": {}, + "source": [ + "## `from_measurement`: the unit contract\n", + "\n", + "Angles to radians, cross sections to b/sr; every *dimensionful* error is\n", + "converted with the data, the *fractional* normalisation error is left\n", + "alone; the kinematics a reaction model needs land in `meta`. Nothing\n", + "correlated is folded into the errors: the systematics wait as attributes." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ed8ba3e3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:47:33.357766Z", + "iopub.status.busy": "2026-09-11T03:47:33.357595Z", + "iopub.status.idle": "2026-09-11T03:47:33.361329Z", + "shell.execute_reply": "2026-09-11T03:47:33.360661Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset('toy-subentry', n=20)\n", + "x[:3] = [0.087 0.23 0.372] rad; y[:3] = [1.8252 1.4888 0.7972] b/sr; y_err[:3] = [0.146 0.1191 0.0638] b/sr\n", + "norm_err = 0.04 (fractional, untouched); offset_err = 0.002 b/sr (converted)\n", + "meta keys: ['Elab', 'eta', 'k', 'quantity', 'reaction', 'subentry']\n" + ] + } + ], + "source": [ + "d = rx.from_measurement(measurement, reaction=reaction)\n", + "print(d)\n", + "print(\n", + " f\"x[:3] = {d.x[:3].round(3)} rad; y[:3] = {d.y[:3].round(4)} b/sr; y_err[:3] = {d.y_err[:3].round(4)} b/sr\"\n", + ")\n", + "print(\n", + " f\"norm_err = {d.norm_err} (fractional, untouched); offset_err = {d.offset_err} b/sr (converted)\"\n", + ")\n", + "print(\"meta keys:\", sorted(d.meta))" + ] + }, + { + "cell_type": "markdown", + "id": "f0f53bda", + "metadata": {}, + "source": [ + "## Nothing is folded in silently: systematics are explicit terms\n", + "\n", + "`comp.reported_terms()` turns the two reported systematics into covariance\n", + "modes: a constant offset mode `ω ω^T` and a normalisation mode\n", + "`η² ym_i ym_j` built from the *prediction*. Ask for them, or leave them\n", + "out; there is no default that hides a correlation." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "66dfd04e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:47:33.362577Z", + "iopub.status.busy": "2026-09-11T03:47:33.362458Z", + "iopub.status.idle": "2026-09-11T03:47:33.446384Z", + "shell.execute_reply": "2026-09-11T03:47:33.445477Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['mode', 'mode'] terms, on ['toy-subentry', 'toy-subentry']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 700x330 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "comp = rx.Comparison(d, omp)\n", + "reported = comp.reported_terms()\n", + "print([t.kind for t in reported], \"terms, on\", [t.on.data.label for t in reported])\n", + "c_stat = rx.Constraint([comp])\n", + "c_sys = rx.Constraint([comp], terms=reported)\n", + "p_stat, p_sys = rx.Problem([c_stat]), rx.Problem([c_sys])\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(7, 3.3))\n", + "for ax, p, title in zip(\n", + " axes, (p_stat, p_sys), (\"statistical only (default)\", \"+ reported systematics\")\n", + "):\n", + " S = p.constraints[0].matrix(truth)\n", + " im = ax.imshow(np.log10(np.abs(S) + 1e-12), cmap=\"viridis\")\n", + " ax.set(title=title, xticks=[], yticks=[])\n", + "fig.colorbar(im, ax=axes, shrink=0.8, label=r\"$\\log_{10}|\\Sigma|$\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cbacd4dd", + "metadata": {}, + "source": [ + "## The guardrail: a singular covariance is a named error, not a `LinAlgError`\n", + "\n", + "A subentry without statistical errors contributes zero variance on its\n", + "diagonal. Compiling such a problem fails at construction with a message\n", + "that names the comparison and the remedies (recipe 21)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4b5f5a57", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:47:33.447694Z", + "iopub.status.busy": "2026-09-11T03:47:33.447521Z", + "iopub.status.idle": "2026-09-11T03:47:33.451249Z", + "shell.execute_reply": "2026-09-11T03:47:33.450634Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ValueError: the constraint's covariance is singular on its active points; the diagonal is zero on rows of ['no-statistics']: those comparisons have zero statistical error and no diagonal term covers their points (a block covered only by correlated modes is singular here even when the full covariance is not). Remedies: comparison.reported_terms(), a noise term, a fixed Term covering those points, or statistical=False with an explicit covariance.\n" + ] + } + ], + "source": [ + "nostat = SimpleNamespace(\n", + " **{\n", + " **vars(measurement),\n", + " \"statistical_err\": np.zeros_like(y_mb),\n", + " \"subentry\": \"no-statistics\",\n", + " }\n", + ")\n", + "d_nostat = rx.from_measurement(nostat, reaction=reaction)\n", + "try:\n", + " rx.Problem([rx.Constraint([rx.Comparison(d_nostat, omp)])])\n", + "except ValueError as err:\n", + " print(\"ValueError:\", err)" + ] + }, + { + "cell_type": "markdown", + "id": "b7eca92c", + "metadata": {}, + "source": [ + "## Calibrate with nested sampling\n", + "\n", + "Optical-model posteriors are correlated and sometimes multimodal, where an\n", + "affine-invariant ensemble mixes poorly; dynesty handles them and gives the\n", + "evidence for free. `problem.log_likelihood` and `problem.prior_transform`\n", + "are the two callables it needs; the prior transform maps the unit cube\n", + "through every parameter's truncated prior." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bd135136", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:47:33.452527Z", + "iopub.status.busy": "2026-09-11T03:47:33.452409Z", + "iopub.status.idle": "2026-09-11T03:50:01.680006Z", + "shell.execute_reply": "2026-09-11T03:50:01.679167Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log Z = 100.57 +/- 0.44, 48203 likelihood calls, efficiency 4.7 %\n", + "2280 equally weighted rows in problem.names order: ['Vv', 'Wv', 'Rv', 'av', 'Wd', 'Rd', 'ad']\n" + ] + } + ], + "source": [ + "def nested(problem, seed, nlive=150, dlogz=0.5):\n", + " sampler = dynesty.NestedSampler(\n", + " problem.log_likelihood,\n", + " problem.prior_transform,\n", + " problem.ndim,\n", + " nlive=nlive,\n", + " sample=\"rwalk\",\n", + " rstate=np.random.default_rng(seed),\n", + " )\n", + " sampler.run_nested(dlogz=dlogz, print_progress=False)\n", + " res = sampler.results\n", + " print(\n", + " f\"log Z = {res.logz[-1]:.2f} +/- {res.logzerr[-1]:.2f}, {int(np.sum(res.ncall))} likelihood calls, efficiency {res.eff:.1f} %\"\n", + " )\n", + " return res\n", + "\n", + "\n", + "res = nested(p_sys, seed=1)\n", + "samples = res.samples_equal(rstate=np.random.default_rng(1))\n", + "print(f\"{samples.shape[0]} equally weighted rows in problem.names order: {p_sys.names}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "54c6d513", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:50:01.681906Z", + "iopub.status.busy": "2026-09-11T03:50:01.681722Z", + "iopub.status.idle": "2026-09-11T03:50:03.373995Z", + "shell.execute_reply": "2026-09-11T03:50:03.373221Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1600x1600 with 49 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = corner.corner(\n", + " samples,\n", + " labels=[f\"${lt}$\" for lt in latex],\n", + " truths=truth,\n", + " truth_color=\"C3\",\n", + " plot_datapoints=False,\n", + " show_titles=True,\n", + " title_fmt=\".2f\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "736d87a2", + "metadata": {}, + "source": [ + "## Predictions on any grid (recipe 15)\n", + "\n", + "`omp.bind(x_fine, d.meta)` compiles the same model on a plotting grid; the\n", + "angular basis is cached per kinematics, so this costs a few milliseconds per\n", + "row. Columns come from `problem.columns`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "fca32636", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:50:03.386736Z", + "iopub.status.busy": "2026-09-11T03:50:03.386497Z", + "iopub.status.idle": "2026-09-11T03:50:03.740023Z", + "shell.execute_reply": "2026-09-11T03:50:03.739225Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_fine = np.deg2rad(np.linspace(1.0, 179.0, 90))\n", + "on_fine = omp.bind(x_fine, d.meta)\n", + "cols = p_sys.columns(omp.params)\n", + "curves = np.array([on_fine(*s[cols]) for s in samples[::25]])\n", + "lo, mid, hi = rx.predictive.predictive_band(curves, levels=(5, 50, 95))\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "ax.fill_between(\n", + " np.rad2deg(x_fine), lo, hi, color=\"C0\", alpha=0.35, label=\"90 % posterior band\"\n", + ")\n", + "ax.plot(np.rad2deg(x_fine), mid, color=\"C0\")\n", + "ax.plot(np.rad2deg(x_fine), on_fine(*truth), \"--\", color=\"C3\", label=\"truth\")\n", + "ax.errorbar(np.rad2deg(d.x), d.y, d.y_err, fmt=\"o\", ms=3, color=\"k\", label=d.label)\n", + "ax.set(xlabel=r\"$\\theta_{cm}$ [deg]\", ylabel=r\"$d\\sigma/d\\Omega$ [b/sr]\", yscale=\"log\")\n", + "ax.legend(frameon=False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2c1e14c7", + "metadata": {}, + "source": [ + "## Other drivers (recipe 16)\n", + "\n", + "The same problem runs under emcee from `problem.sample_prior` and\n", + "`problem.log_posterior`, and it pickles with `dill` for a driver that\n", + "runs in another process. A short emcee run here only shows the contract;\n", + "on optical-model problems expect to need far longer chains than dynesty\n", + "needs live points." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "16450cc9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:50:03.742291Z", + "iopub.status.busy": "2026-09-11T03:50:03.742103Z", + "iopub.status.idle": "2026-09-11T03:50:10.819615Z", + "shell.execute_reply": "2026-09-11T03:50:10.819006Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "emcee: 3200 rows, mean acceptance 0.25\n", + "dill round trip: ndim 7, names ['Vv', 'Wv', 'Rv']..., log_posterior(truth) = 106.265 vs 106.265\n" + ] + } + ], + "source": [ + "sampler = emcee.EnsembleSampler(16, p_sys.ndim, p_sys.log_posterior)\n", + "sampler.random_state = np.random.RandomState(2).get_state()\n", + "sampler.run_mcmc(p_sys.sample_prior(16, rng=2), 200, progress=False)\n", + "print(\n", + " f\"emcee: {sampler.get_chain(flat=True).shape[0]} rows, mean acceptance {sampler.acceptance_fraction.mean():.2f}\"\n", + ")\n", + "\n", + "restored = dill.loads(dill.dumps(p_sys))\n", + "print(\n", + " f\"dill round trip: ndim {restored.ndim}, names {restored.names[:3]}..., log_posterior(truth) = {restored.log_posterior(truth):.3f} vs {p_sys.log_posterior(truth):.3f}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9aeb6199", + "metadata": {}, + "source": [ + "## Tempering the likelihood (recipe 12)\n", + "\n", + "With many points a posterior can collapse tighter than the model deserves.\n", + "`Constraint(weight=w)` multiplies that constraint's log-likelihood by `w`\n", + "and nothing else; the democratic `k/N` scaling of KDUQ is the case\n", + "`w = n_parameters / n_points`. A 200-point line shows the effect on the\n", + "posterior, and the posterior-predictive coverage shows that the *untempered*\n", + "fit is not overconfident about the data: the tight posterior is the model's\n", + "uncertainty, and the predictive of the data still carries the noise." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "bc5e2fdd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:50:10.822252Z", + "iopub.status.busy": "2026-09-11T03:50:10.822002Z", + "iopub.status.idle": "2026-09-11T03:50:40.076400Z", + "shell.execute_reply": "2026-09-11T03:50:40.075498Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:Too few points to create valid contours\n" + ] + }, + { + "data": { + "image/png": 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Tpk1jz549NDU1MXHiRGJiYgA4dOgQhYWFNDY28p///AeAqVOnkp6e7n1+3759JCUlMW7cOPR6vfdeXdeeOnUqe/bsob6+nhkzZhAZGYnNZmPz5s243W6mTZvmc07Rsa/bvXs3DQ0NzJw5k4iIiG7v0d/7b9myhebmZu+5H263m23bttHQ0MDIkSPJzs7udu2WlhY2b95MbGws48aNO+G/hzORhAwhhBB9evnllzGbzfzrX//yPvbBBx/wxhtvkJ+fD3gOC3vvvfcICgoiOjqa1tZWDh8+zCeffMK0adM4ePAgBQUF1NfXe6+TkJBAUlIS3//+91m5ciWTJ0/myJEjKIrC+++/z/Dhw73Xfuedd1BVlfT0dO/R7H/4wx949NFHycjIoKKiAqfTyZYtW7zBpqtPGo2G2NhYmpubqaqq4uOPP/YGJofD4df933vvPfR6PTExMaSlpXHZZZdx+PBhlixZgsvlIjMzk+3bt7Nw4UL+9re/eY9G//LLL7nkkktITU0lIiKCpqambifODmUSMoQQYpCoqorF4RqUe5v02gE/PfTgwYOsX7+eOXPmAHD11Vfz+OOPs2LFCi688EK++uorVq9e7R3JAHjsscfYt28fJSUlhIaGoqoqd9xxBz/4wQ/47LPPvO0OHTrE559/zsyZM3G5XEyePJmlS5eyefNmJk+ejMPhYOzYsfzpT39i2bJl3tcdOHDAe1oqwM0338ztt9/Ozp07URSFX/3qV37d/8CBA3zyySdccMEFgOfv7sorr+TSSy/lqaeeAqC1tZXJkyfz6quvcuedd+J2u7ntttu4+uqrefnllwH4y1/+wi233DKgn/vpTEKGGNK0YXEUVrdjMjn7bBcVYiAl0nSKeiWEh8XhYvTPPx2Uexf+8gKCDQP7FTB+/HhvwABYsGABzzzzTJ+vee2117jwwgtZvXo1qqqiqiopKSn89a9/xeVyeUcExo8fz8yZMwHQarVMnz4dk8nE5MmTAc/BX1OnTmX//v0+109NTeXaa6/1/nnZsmXk5ORQWFjImDFj/L5/bm6uN2AA5Ofns2PHDn74wx/y3nvveV+bk5PDmjVruPPOO9mzZw979+5lxYoV3td973vf4+GHHz6Rj/eMJCFDDFnasDiSb3mZK/+8vd+2Jr2W1ffOk6AhxEmIjo72+bPRaMRqtfba3uVyceTIEfbs2UNjY6PPc5deeimdnZ2EhYX1eu2eHjObzT6PDRs2zGfEJisrC4AjR46Qm5vr9/2Tk5N9ni8pKQHg448/9nk8KCiIUaNGAVBWVoZGo/GuOwHQaDQMGzasl09k6JGQIYYsbXA4GkMQT18+ijFpMb22K64z89O38mnusEvIEKeUSa+l8JcX9N8wQPf2l0ajwe12+zzWV3jwl1arJSQkhO9+97v85Cc/Oenr9aSlpaXHP0dHRx/X/b85tdQVPp599lmfEHGs6Oho3G437e3tPotNv9mnoUy2sIohLys2mLEpEb3+ZMeHDnYXxVlKURSCDbpB+Tme9RgpKSkcOnTI57ENGzYc9/sNDQ3FZrP5PLZo0SJee+01XC7ftSnV1dXHff2eFBYWUlxc7P3zu+++S0REBGPGjDmp+0+fPp3w8HD++Mc/+jyuqiq1tbUA5OXlERYWxvvvv+/Tn6KiopN6T2cSGckQQgjRp6uuuoonnniCH/3oR0yZMoWVK1eyc+dOUlNTj+s6kyZN4uGHH+YPf/gDiYmJTJ06ld/97nfMmTOHWbNmceONN6IoChs3bsTpdPLvf//7pPseFhbG4sWLufvuu2lsbOSpp57il7/8JaGhnl8uTvT+YWFhvPzyy9x0001UVlYyb948ampqePfdd7nzzjtZunQpERERPPzww9x5551UVFQQGRnJs88+6x0FORtIyBBCCNGnUaNG8eWXX/K3v/2NzZs3c9lll3Httdfy+eefe9vk5eURHBzs87phw4Zx8cUXe/88f/58Xn/9ddauXUtraysJCQnMmTOHPXv28Prrr/PVV18RHh7OFVdcwbe//e0+rz1hwgQSExN9Hps8eXK3aZzJkyfzi1/8gnfeeYeGhgZef/11rrrqKu/zmZmZJ3R/gGuvvZZx48bxxhtvsGrVKjIyMnj55ZeZOHGit80DDzxARkYGH3/8MbGxsfz973/n008/ZfTo0X1+5kOFoqqqOtidGCra2tqIiIigtbWV8PDwwe7OWa2jo4Po4eNJumk5/75lElOyE3ttW1DZyiUvfMGKH89mbEr3Ij1iYMi/D3GqLVu2jG3btrF69erB7spZS9ZkCCGEECIgZLpECCHEkNTbNIc4dSRkCCGEGJK6qnyKwSPTJUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAjdYHdAiBNR2WKhucPe6/MWiwV9TNop7JEQQohvkpAhzjiVLRbO/90GLA5Xn+1iL70Pt91KZLD+FPVMCCHEsSRkiDNOc4cdi8PF76+eQHZ8aI9tLBYLs2fPxtXZRvLjJae4h0IIIUBChjiDZceHMjYlosfnOjp02GsPneIeCSGEOJYs/BRCCCFEQEjIEEIIIURASMgQQgghREBIyBBCCCFEQEjIEEIIIURASMgQQgghREBIyBBCCCFEQEjIEEIIIURASMgQQgghREBIyBBCCCFEQEjIEEIIIURASMgQQgghREBIyBBCCCFEQEjIEEIIIURASMgQQgghREBIyBBCCCFEQEjIEEIIIURASMgQQgghREBIyBBCCCFEQEjIEEIIIURA6Aa7A+LMVm2uptnW3G+7KGMUSaFJp6BHQgghThcSMsQJqzZXc9n7l2FxWvpta9KZeO7c54gKiuqznYQRIYQYOiRkiB75M0JxuPUwFqeFp+Y8RVZEVq/tmq3N3L3+bu5YfUe/9zXpTLx/2fsSNIQQYgiQkCG6Od4Riknxk/oNBe9f9r5foeXBzx+k2dYsIUMIIYYACRmim2Zbs18jFOD/9EZSaJIEByGEOMtIyBC9yorIYnTM6MHuhhBCiDOUbGEVQgghREBIyBBCCCFEQEjIEEIIIURAyJoMcdo53Hq4n+dtp6gnQgghToaEDHHaiDJGYdKZePDzB/ts57IkAz+hobMBiDglfRNCCHH8JGSI00ZSaJJf9TQ2HDrM06XQ5mg7NR0TQghxQiRknGX8reQ5WPypp1FcawOqTk2HhBBCnDAJGWeR463kGWXs+5wRIYQQoi8SMs4igajkKYQQQvRGQsZZSCp5CiGEOBWkToYQQgghAkJChhBCCCECQqZLxBmr0lxJYaOxx+csFgtBGUG42l2nuFdCCCG6SMgYIsxmM1artc82La0tp6YzARauDweqeGHHC7y0r/etrNmPZeO2uanprGF4yPBT10EhhBCAhIwhwWw28+e3/4zZZe6zXbPSDHqwdFog5hR1LgBig2MB+PXcX5Od0PtIxnlXnUfa7Wm02FpOYe+EEEJ0kZAxBJQ1l/E3zd9wap39ttWpOoIIOgW9CjzPLpmey4p3dHRgq5IzToQQYjBJyBgCWu2tOBUnD014iPGp43tt19LSwuY1m0kwJZzC3gkhhDhbScg4zfmz1qKtzXOGR0ZoRp/1LxrUBgoooLm577LiAEFBQYSGhh5fZ4UQQohjSMgYJP6EB4vFwqpVq3A6+54GqVPqwABGQ8/rE7oEBQWh0+lYt25dv/3T6XRcddVVEjSEEEKcMAkZg8BsNvP222/3Gx7A82V/0UUXYTKZem1T1FrEW1+8hSm49zYAoaGhXHXVVf2Gm+bmZtatW0d1dTVRUX2fXyIjHkIIIXojISMAGhsbsdvtvT7f3NyM0+lk/vz5A/IlXqfU+d230NDQfq8nIx5CCCEGgoSMAPjggw/6HHkAz5dzUlLSafnlfLwjHlar9bR8H0IIIQaXhIwAmDt3LmlpaX228XeaodpcTbOt74Wah1sPH1f//OHPiEcXWUgqhBCiJxIyAiAyMpLY2NiTvk61uZrL3r8Mi9PSb1uTzkSUse+pl4F2pkyrlLaXYmrsfWTpcKvU0xBCiECQkHEaa7Y1Y3FaeGrOU2RFZPXZNsoYRVJo0inqmcfpPq3ianfhtrn5xdZf9N3Okgz8hIbOBqDn4l5CCCGOn4SMM4CnsmXv9S8G0+k8reJocnDwwYNs3rW5zzUyGw4d5ulS2FvT6C1Z3puoEAMpkX2vtxFCCOEhIUME3GBOqziaHORG5RISEtJrm7o2JyiHeOajep6hvs/rmfRaVt87T4KGEEL4QULGIBmsBZ2DYaDrc7S0dAxo/+LDdYQM/x1PzXyhz2mp4jozP30rn+YOu4QMIYTwg4SMQXC6L+gMhIGsz1Fj0wPxdHZ2MlBrKDT6VrITjL0euCaEEOL4ScgYBKf7gs7B4u+Ix9aD1bxWeaTPgmcnor+RI9mFIoQQx0dCxiA6nRd0DhZ/RjzCaz3TJW1tbTQ0NPTYpqPD/ymVKGMUJp2JBz9/sM92sgtFCCGOj4SMAChuLcbS2PtUyFBZazFYDAYDANu2baNij6PHNjbb0VEHs9nc58LPpNAk3r/s/X7XyHTtQmlztB1/p4UQ4iwkISMA7vrqLrS7tH22GSprLQZDcHAwAAsWLCA3oefw0NHRwU9+8hMAampq0Gr7/vsICwojKabvaaniWhtQRaW5ksLGvk+8PZumuYQQojcSMgLg3rx7mZE7o8828iV08jyVVXuetji2LsbGjRsxGvsOBTqdjoULF/ZZT0O1qgC8sOMFXtpX1ef1grRBPDbpMSINkX22MxqM/Z6eC/LfixDizCQhYwCpqudLqKMpkrb6sD7btuGkjPJT0a0h53B9B25bJ+b2NtralB7bHLsmY/78+X1Ol1gsFtatW8c777zT531rbXrctjgmNC0kQt/7IlA7NjbrNnP3J7/r5534z6A1cHPOzYTqz9zzXywdncDRfydCiKFPUeVf/ICpqKjo92A0Ic525eXlpKamDnY3hBCngISMAeR2u6mqqiIsLAxF8f0Nu62tjbS0NMrLywkPDx+kHvZO+ndypH/9U1WV9vZ2kpOT0Wg0g9IHIcSpJdMlA0ij0fT7G1p4ePhp+SXURfp3cqR/fYuIkK2/QpxN5NcJIYQQQgSEhAwhhBBCBISEjFPEaDTy6KOP9ruVcrBI/06O9E8IIbqThZ9CCCGECAgZyRBCCCFEQEjIEEIIIURAyBbWAdRXnQwhznb+1smQf0dC9O5MqzcjIWMAVVVVScVPIfrRX8VP+XckRP/OlMq5EjIGUFiY57ySsrKyfgseqaqKoun/tzTVrfr125zb7fYr1frbzuVy+XVfVVX7PeG0q50/91VV/95vf+06OjpISUkBPF9afZ1dIk6NrqqjXf9OetP1/OlaPfVs0tHRQXJyMiD/jk4X/v47Ol1IyBhAXV96/lRVlJDRe7uBCBnH9ik8PFz+x/E00t/f7/H8OxKBJf+OTl9nylTi6T+hI4QQQogzkoQMIYQQQgSEhAwhhBBCBISEDCGEEEIEhIQMIYQQQgSEhAwhhBBCBIRsYQ0AVVXp79w5t9uNQv9bkFxOl19bP51OJzpd/3+ddrvdr3ZOpxODwdBvu+PZEusPt9vt95bYk3n+ZNoP5NaxwbqvEEKcChIyAkCj0fT7xaviXz0If64Fnv3s/l7Pn3ZardavL3t/+wf+fUn627/+ridfyEIIMfhkuuQs197eTnl5OW63e7C7IoQQYoiRkYyzkNvt5siRIxQVFVFVVQVAUlIS8+bNIygoaJB7J4QQYqiQkHEWcLvddHR00NbWRkVFBYcOHcJqtXqf12g0VFdXs2LFChYsWEB0dPQg9lYIIcRQISFjiKqpqaGgoIDW1lba29u7TYeYTCZGjBjBiBEjcDqdrF27lvb2dlauXMmiRYuIiYkZpJ4LIYQYKiRkDEHV1dV89tlnPjs6NBqN98Cp4cOHk5aW5rNg85JLLmHt2rXU1tby2WefsXDhQu/pi0IIIcSJOKtDRmtrK/v37ycvL4/g4ODB7s6AaGhoYPXq1bhcLlJTUxk7dizh4eEEBwej0WhwOBw97hoxGo2cf/75rF69mtraWlatWsVFF11EfHz8ILwLIYQQQ8FZGzKWL1/OsmXLsFqtZGZm8vnnn5OSkjLY3TopLS0tfPbZZzgcDhITE1mwYIFfNTG66PV6n6CxcuVKCRpCDGGVLRaaO+y9Pm+xWDAkDMfV2XYKeyWGkrMyZDz33HP8+c9/ZsuWLYSGhnLRRRfxj3/8g2XLlg12106Y2Wzm008/xWq1Ehsby/nnn39cAaNLV9BYtWoVdXV1EjSEGKIqWyyc/7sNWBx9F8pLumk5bruVqlYrI0JCTlHvxFBx1oWMpqYmHnzwQXbu3MmoUaMAuOiiizCZTKxdu5acnBy/RzRsNhs2m83757Y2T9p3uVz9Vri02Ww+ayIqKiqoqqpi1KhRhIWFeR93uVz9Vt60WCysXLmSjo4OQkNDmThxIq2trT22dbvdhIaG9vveZs2axaZNm7xBY+HChcTGxnZrp6oqRqOx3+u5XP5VLnW5XH6FI1VV+7zesZU0/anAqqr+FUcb6AqdUvFTDJbmDjsWh4vfXz2B7Pie/zfBYrEw/7JriL30Plo6Hae4h2IoOOtCxp49e7DZbN7dE3V1dfznP//B6XTicDhob2/n1Vdf5Xvf+16/13rqqad47LHHuj+h/O+nD4qioCgKDoeDbdu2cfDgQQAOHz7MtGnTyMrKAv5XUVPb+5epzWZj1apVdHR0YDKZmDZtGgaDAZfLRWdnJyEhIT5fTi6Xy69KngaDgYULF/qs0Vi0aBFxcXE+7fz9cva3Iin4/2U6kBU/u/4+TjUJDmKwZceHMjYlosfnOjp0OBrLT3GPxFBy1lX8nDBhAomJiSxatIiHHnqIyZMns3jxYsrLy6mpqeGGG27gjjvuoLKyst9rPfjgg7S2tnp/ysuP7x9jY2MjH330kTdghIeH43A4+OKLL/j888+x23ufKwXP+SKrVq2iqakJo9HI9OnTMZlMOJ1ONm/ezPr1672jEcd7lgccnTpJSEjA4XDw2Wef0dzcfNzXEUIIcXY660JGREQEn3/+ORdffDFhYWG43W5eeukl9Ho9er2e5cuX43Q62bVrV7/XMhqN3m2hXT/+UFWVwsJCVq5cSVtbGyaTiYULF7JkyRImTJiAoiiUlJSwYsUK6urqeryGy+VizZo11NXWYTAYmDp1KiEhIbjdbrZv3+4NAy0tLWzduvWEw0ZX0IiPj/cGIClBLoQQwh9nXcgAyM7O5sknn2T8+PEoiuIzt19fX4+iKOTl5QXk3p0dnXz6yafs2LEDt9tNeno6l156KUlJSWg0GsaNG8eFF15IaGgoZrOZVatWsWPHDp9w4Ha72bhhI5UVleh0OhYuWkh4eDiqqrJz507q6+vRarVMnjyZrKwsNBqNN2x89dVXvQaX3uj1es4991wMBgONjY3s2bNnoD8WIYQQQ9BZGTK6jBw5kqqqKu677z4cDgfl5eVcc801LFu2jLS0tAG/X1lpGe+++y5VVVVotVqmT5/e43khcXFxXHLJJWRlZaGqKvk78zlSdsT7fE1NDSUlJWg0Ghact4CEhATAE5Cqq6sBmDx5MomJiYwePZoFCxZ4w0Zraytr1671++j1LsHBwUybNg2A3bt343Q6T+ajEEIIcRYYsiGjqKio3zbZ2dn87ne/Y/ny5YSHh5OVlcWCBQt4/PHHB7QvTqeTTZs2sWbNGu+i08WLFzNy5MheF/4ZDAZmz57N8OHDAaitq/U+1zVdERERQWpqqvfxsLAw7/WO3aERFBTkUyY8LS3Nr8Wf35SVlYVOp8PlctHR0XHcrxdCCHF2GZK7S5YtW8bzzz/PBx98wPnnn99n25/+9KdcfPHF7Ny50zu9cLJ27txJyDH7yctKy7xbSvPy8pg4aSIup38jCbGxsRw6dIjmpqMLLruu/c0vepPJREpKivcQtMmTJwNQW1vL9u3bcbvdJCQkMGPGjBN6X4qiEBoaSktLCx0dHURE9LwiXQghhIAhGjLWrl1LdnY2S5Ys6TNo5Ofnk5eX5z0obKAU7CnAZDL5PBYcHMzceXO954H4GzIiIyMBaGpu8j4WGuLZ026323HYHegNeu9zw4cPp6KigpqaGsxmMx0dHd6AkZiYyLhx4/yqV9GbkJAQWlpaMJvNJ3wNIYQQZ4chGTJyc3O54IIL+M9//uMTNGw2m7dwVF1dHbNnz+bSSy/ljTfeOKHpg17vPyrXZyTDaDQyatSobmsv/NEVMiydFqxWK0FBQegNegwGA3a7HXOHmShDlLd9WFgYCQkJ1NbWkp+fT1tbmzdgTJw48aR3hnQV8pLpEiGEEP0ZsiHjwIEDvP3221x11VUsWbKE5cuX8+tf/5qNGzeSkpJCfHw8L774Ips3bz6p3+x7kjc2r/t2VhWsFqv3jx2dHeh1evqjqqp3p0lDfQNJSUmAZ0TBbrfT3tZOeFi4z0LMYcOGUVtbS0tLCwAJCQnk5eXhcrlwuDwFx/y5b0+VN7uCUltbGw6Ho9d23+RwOPwqPOVPhdOu/vV1veOt+AlSoVMIIQbakA0Zb775Jnq9nrfffpvLLruM2267jXvvvdenZPjSpUtZunTpwHfAj4qfGkXj15ezW3UTHR2N2WymtbXVu9AzNDSU5uZmOjs70Wq1RERFoNV4RmOiY6IpKSmhrq6OtLQ0Zs6a6Q1S7e3tfn2Ju93uHvvXFZ4sFgs6nc7vip/+BjmNRuNX2/7ue+xzA1nNc6CDgwQRIcRQNiR3l+Tm5lJQUABASUkJe/bsYcqUKbz00kusXr16kHt3/KKjowF8qm32tvizy8xZM5k9e7ZPwOjJwYMHWbt2rd9rLLrue7JrMmw2G5WVlVLYSwghhrAhGTKys7MpKytj9+7dnHfeeTz55JNs2rSJCy64gCVLlrB3797B7uJxiYryrLloajq6+LO/L3uTyURaelqfAWP//v3s3r2b5uZmbyjrT9eajM7OzhMqVQ6eBasrV65k1apVrFq1CqvV2v+LhBBCnHGGZMgwGAykp6czd+5cnnzySW688Ubv1Mlzzz3H6NGjB7uLx6VrJKOlpcX7xX6yCzD379/vE7YqKyt7Pbn1WCaTCUVRcLvdWCyW476v2+1mw4YN3vUi1dXVrFixwidACSGEGBqGZMgAePHFF3nhhRe48cYbvY/p9Xpuv/32M24ePCwsDK1Wi9PppLCwEFVVqa31FOc6kS96q9XqDRjJycneI9xLS0v7fa1Go/GOomzbtg2Hw//jn5ubm/noo4+orKxEq9UyZ84cwsLCMJvNrF69+riuJYQQ4vQ3JBd+AixcuHCwuzBgNBoN48ePZ8eOHWzZsoVDhw7R0NAAwKhRo477ekajkcTERGpqaqiqqvI+3lWevD8TJkxg06ZNlJSU0NzczMKFC/sszOV2u9m1axe7du3C7XZjMBiYM2cOaWlppKam8uGHH2I2myksLAzYmTFCCCFOvSE7kjHUjB8/nnHjxgHQ0NCAoijMnTv3hKZ+FEVh5syZ5OTkeB9LSUkhMTHRr9cPHz6cRYsWYTKZaGlp4b333ut1FKSpqYkPPviAnTt34na7SUtL4/LLL/eeDWM0Gpk4cSIAe/bsOaGRGSGEEKenITuSMdQoisKkSZPQ6/UcPnyYqVOn+mzHPZHrjR07lujoaGpra487rCQmJnLJJZewYcMG6urqWL16NePGjWPy5MloNBrv6EVXuDAYDEybNo2srKxu01WZmZns3buXxsZGdu3axdy5c0/4fQkhhDh9SMg4g5gdZjaqG/nc8Dlv7HwDdh59zu12o3yjOEewPphLh13KhekXYtD2XBsjOTnZW+r8eAUHB7No0SLy8/MpKChg9+7dNDQ0MGHCBLZu3eqd0klPT2f69OkEBwf3eB1FUZgyZQqffPIJBw8eZNy4cd5Kp0IIIc5cEjICwO1291v/weayYbfY+72WTbXRam/lo7KPeLPoTdocbcfVl71Ne/nT3j9xReYVXJB6AYpbwU3/tSnsbjvOtv6Pc3erbiacM4GoqCg2b95MVVWVd52HwWBg8pTJZGZmYrPasNlsvV4nKiqKlJQUKisr2bJlC+edd16f91VVFb2+94qpgar4KYQQwn8SMgIgKCio33NKLHYLGqXvJTFu1c2qI6t4s/hNajprAMgIz+Cm0TcRa4r1aWt1WQnS+t6zqKWIf+7/Jw2WBv60/0+8degtlgxbwrW51xJu+EbZ829oMbf4VXnT7fIEloxhGURERrBxw0ba2tpISU1h2rRp3tELRaP0W+F00qRJVFVVUV5eTl1dXZ9rRFRV7buq6jHPnc4VP4UQYiiTkBEADofDZzumRqM57gPYdjbs5I+Ff+RA6wEAYoJiuCXvFhZnLkan6f7X1uHoIEQf4vPYxISJXJF9BZ+UfsIb+96g0lzJGwff4N3Sd7ks8zKuHnE1MUExffbD7DBT3VlNVUcVLtXFrMRZGLXGHttGRkZy8SUX09bWRmRk5HF/IUdERDB8+HCKi4vZunUrl156qXypCyHEGUxCRgC8/dbbPke9a7VaFi1aREJi/1tED7cd5k/7/sSWui0AmHQmrh91PVfnXI1JZ+rn1d0ZtAaWDF/C4szFrCtfx98K/0ZJawn/V/R//Kf4P1w87GKWZC6hw9FBZUclVR1VVHZUcqTtCDWdNd2mZ1KCU7hn/D2Mjxnf4/20Wq23QumJGDduHKWlpTQ0NFBSUkJWVtYJX0sIIcTgkpBxCrhcLr788ksuu/yyXqcgattreXnPy3ze+DkqKlpFy7dHfJtrcq8hPjj+uO/Z2tJKh7mDhKQEtFotOo2OhRkLmZc6j/z6fF4reI09DXt49/C7vHv43T6vFaIJIcwZRpvSRmVnJfduvpeL0y/mltxbMGmPP/j0xWQykZeXx86dO9m+fTsZGRnHPQokhBDi9CAhIwDmzptLWFgYAHabnS+++ILW1lYqKyu99SGOZbfb+dXGX7HHvQeAPEMeN+fezLScadiV/heHdmltaaW8rJzy0nJaWzwlwk0mE6PyRjF85HC0Wi0aRcPc1LnMSZnD9trtvLb3NXbU7iA+OJ7U0FRSw1JJDU0lPjgetUGlZl8Nis2zpkEbomWVdRUF2gI+OvIRn9d8zjVZ13DDmBvQa/s/tt5fY8eOZf/+/bS3t7N69Wrmz5/v18mxQgghTi8SMgJg44aNPtMl4DnQLC4urltbt9vN1s1bUR0qaGG8czxzbXM5/PVhSraVEJ8UT8awDFLSUzAau6+F6AoWZSVltLe2ex/XaDToDXosFgs7tu5g3559jMobRUZ2BuBZwDg5cTKTEyd3Oza9sbGRTV9soqmhCQWF2LhYps+cTkREBFlbs1h3YB0bdBtotDfy6v5X+ajiI+7Mu5MFqQsGZA2FXq9n9uzZrF27lsrKSlasWMFFF13U7TMVQghxepOQEQCRkZE+NSFMwSamTp3abceJqqrk78invr6eaJ3nELSotCjOiT6H0tJSmpuaqa2qpbaqFmWzQmJyImkZaURGR1JVUeUzYgGeYJGQlEDasDRS01LR6rQcPniYwj2FWDo9YaNwTyHjJ4xnxMgR3t0eXcHA4XCQvyOfwr2F3i2iEydPZETOCO80z/SZ00lITCB9Uzq7HbvZot9ChbmChzY/RF5MHneNv4u82JMvDZ6WlsbFF1/M6tWraWlpYdu2bcyZM+ekryuEEOLUkZARABdfcjHh4X1vEQUoOlBEWWkZAFNzprKxeCPV1mrGnzOe8eeMp7WllQPFB6g6UkVLcwvVldVUV1b7XKMrWCSkJZA1LAuD0XdaYUTuCLJGZPmEjS2bt7Bn1x7yxud5w0b5kXK++vIr76mu6cPSmTp9ao8FtDKzMomJiSF8XTgjmkawU7uTfH0+exr3cMvaWzg/7XzumXAPMaa+d670JzY2lgULFrBixQqKi4sZO3bsSS0qFUIIcWpJyBgklRWVFBYUAjBuwjgMiQYohrK2Mu/0RURkBKPGjWLchHG0tbZ511u0tbaRkJRA+rB0UtJSMBgNdDg6MOiPBgyb1UZjQyOJyYlotVpv2CguKubA3gN0dnR6w0ZUdBSVFZUAhISGMGPmDOKS49Brel9nER4RzuJLFvP11q8x7DcwxjWG3WG72WHfwery1Wyv284vpv2C6YnTT+pzio+PZ9iwYZSWlrJt27YhdfCdEEIMdRIyAqCjo6PPHRGNDY3s+HoHAGnpacQlxOF0OdEqWqwuKzUdNd4dJa22VrQaLQRBck4yyTm+JcA76KDD1kGVucq7xbW9sZ3ircXYbXYi4iIYPmU4eoMnMDjjnEy9YCpVpVWU7Sujs7OTzs5OFEUhbWQamaMzcevcHG4+TJSp/1GD1LGpRMdFs+2rbcxsn8lo42g2BG/gSOcR7tp4F1cMu4Lrsq9Dq9ESo+1/ZMNhd+Byu3weGz1mNGVlZZSXl1NSWuI9LbbryPmeHG/FT1VV/So+Bv4V5PKnwujxXE8IIc5EEjICQKNoeq3maTab2bRpE263m7j4OEaNHoWiKOg0OhJNiVR2VlJpriQlzHP4mUln8oQMP2gVLbUltezZtYd12nUcMRwhtiWWzLWZzB4zm7yUPOxuOxqthtThqSQPS6a6tJrWxlbSRqYRFhnmvZZLdaFV/LivAumZ6UTFRLF5w2ZohottF1OcXsyq2lW8U/oOBc0F3DfuPpKj+z8jpacv+8jISLJHZHOw6CC78ndxwYUXeKp49lHy89jnBrLipxBCCP/JUe+nkN1uZ+PGjdhsNkLDQhk3fpzPl1+yyfMlXNZedtzXdrvcFO8o5uPdH/Om/k1KtCW4FBe1mlq+Ur/itwW/5ZbPbuHF/Bd5r/g9SlpLQAMpw1MYPXW0T8A4EWHhYZy3+DwysjLQoWNC3QR+PuXnhOpDKWot4u6v7mb1kdUnfP28vDx0Oh1NTU0cKTtyUn0VQghxashIxinicrnYtGkT7e3tmEwmxo8f3+0sj5TgFL5u/JqytuMLGZZOC3u+3MPHHR9Toi8BICsii+tGXUd1WzVfHfyKw/bDdNLJvqZ97GvaB0C4IZxxsePIi8sjNyqX1LDUfs9T6YtWq2XyjMk0NzbT1tpGfGU8r1/wOo9tfoyCxgIe+vIhvq79mrvPuZsgXd9nu3xTUFAQo0aPYs/uPeTvyic1LfWE+ymEEOLUkJBxCqiqyvbt26mrq0On0zFnzhyc7u4nnKYEe6ZIjrT5/5t6U30T/1j/Dz5TP8OqtaJVtFyRfQUTXRNp293GpDGTmH/+fMr2llFQXEC5ppyaoBqOqEdos7fxRdUXfFH1BQDBumBGRI1gZNRIEoMTGR0zmqSQpOMKBFqtlqmzprJm5RqOlBwhLSONFxa8wF8K/sKb+97k3UPvsqthFz+b+DOiHFEkJSf5vRZi1KhRHCw6SIe5g4MHDzLxnIl+90sIIcSpJyHjFCgvL6ekpARFUZgxYwZRUVHUN9Z3a9c1XVLeXu73tT//8nM+VD9EVVTSQ9K585w7iXJGUbChAICD2w4y+aLJDBs7jLCoMGK2xkAHpOam4kx2srt+NwWNBRS3FNPp7GRX/S521e/yuUdMUAxJIUmkhaUxLWkaE+MnYtD2XoEzOjaanDE57C/Yz95de0lJT+HWvFuZnTKbn236GYdbD3PzupsZ7hrOkoglXDLvEm+F1L7odDpG5oxk967dHCySkCGEEKc7CRkB5nQ62bXL86U9evRokpN7X/wYpvd80bbb23tt803pyeloSjS4cPH9kd8nPTwdp8OJ3qjHYXMQlXh0h4jd4ilRrigK8YnxRMZEMjpmNAAut4sj7Uc40HyAA00HKGsro95Sj9lhptHaSKO1kYLGAlaWriRYF8y0pGnMTp7NmNgxPfYrOTWZ/QX7fU6jnZY0jbcufounv36aVWWrOKQ9xB/a/8DOD3dy29TbyM7O7vO9Op1ODh86DEBiUu/HwAshhDg9nNUh47///S9XXHHFgO88+OSTT7wlsF0uFzabjeDgYHJzc/t8XZDWMy1hcVr8vtf4SeNJLU+lzFXGpl2bGBYzDL1Bz7j542hrbCM62VNJtKWuhdI9pQBkj88mMi7S5zpajZbMiEwyIzK5cNiFtNnaSAhJoN3eTpW5iqqOKvY37WdT5SYarA2sK1/HuvJ1BGmDmJk8k3PTzmVa4jSC9Z7iXW63G6DbVEi0KZqn5z5Nxr8y+Mj2EVWaKtYr6zmw9QA3lN3ABVMvQG/quT5HwZ4CzGazd02LEEKI09tZGzJ+9atf8fDDD3PbbbfxyiuvDGjQ6Ozs7FYnYcKECd0Wen7TsSHjm+eJ9Eaj0TA9azplB8s44jjCwa8PMmrGKAwmA7GpsQBYzVaKthYBEJ4STmq2/4smwwxh5ETnkBOdw/y0+dw+7nYONB/gi8ov+Lzyc+o661hbvpa15WsxaA1MT5zO/LT55Gpzvf3rSbgjnCscV6CbpOOVwleopprf1v2WzSs3c/fMu0lKTPJp39zczL59ngWrk6dMlgPThBDiDHDWhozq6mouuOAC/vKXvwAMaNCYP3++zxoDvV7v15qDrpChomJz2fxecDkxcSJvHXyLKk0VrXWtlO0tY1jeMABcDhf7v9qPy+EiNCqUhDEJJ/U+NYqGUdGjGBU9ilvG3kJBYwH5dfmsr1hPpbmSjZUb2Vi5kThjHGM1Y5mudK/46Xa7sdvsKChclXsVF4y8gJ9v/DlfN3zN5+7P2bdxH7cPu53Fkxej0Whwu91s2bIFVVVJS0vr8SRbIYQQp5+zNmTk5uai1+u56aabuP766wFP0KioqCA5ObnPip1dbDYbNpvN++e2tjYAtDotWt3R17tVN61trT6vdTgduHH7XvCYwY8Oewd6jZ5ORyc6Td9/TZnhmSgotCltmDFTXVyN0+QkNCmU2vxaLO0WtEYt0eOiKWwuJL8xv9/3ptfo+ywr3iVUH8r05OlMS5pGpbmSnXU7+ar6K+pt9azTr2Nb5zaKdxYzLXEawyOHA2C3Hj2+vtpajUaj4cEZD7K+bD1/3vNnGjQN/KrsV6yvXs+9c+6lsaaRpsYm9Ho9eRPzsLlsqG4Vva73/h27FsTfip8Dyd+RKCGEGMrO6pDx/vvv8+yzzwJw/fXX097ezueff84//vEPzj333H6v8dRTT/HYY491e9ygN/icI9ITl9tFaGhot8f1Gj0OtwOb2+YpxW2K6bfiZ7QpmozwDErbSrGmWgmtCKVhbwO0QmddJ4pGYezMsYRFh7GmYI23/Liqquxr2seR9iMYtAaCdcEE64Ix6Uw4VSdZEVmE6EMI0gb1+oXZZm8jOdSzmDUlNIWU0BQWZSxi/YH1rKtZR7vSzr+L/s0npZ9w5cgrWZi+EKfds31Xp9ehaBRUVFDg3GHnMiFxAn/e/me+bPySTY5NFK4p5Hz3+SSSyDmTzyE6yrPGxOF09Pklfuxz/lb8HMhy4VJlVAghzvKQsXfvXgC++93vUl5ezv3338/8+fOZN2+eX9d48MEHueeee7x/bmtrO+mhfJPWhMPtOK7FnwA5UTmUtpVSb6pnZNJImqqbqD/i2SabPTGbsGjf6Rq36mZH7Q7KzZ7tshanhVab72jL/qb9gGeKJEQfQrghnJyoHFJCU/r8AjVoDcyImkHakTSKw4r5WvmaFlsLrxW8xjsH32FR4iKiicZkNHV7bYQxguUXLufTg5/y9NdP00wz/9b+m4tDL+a6kdcd12cihBBicJ21ZcVTUlIwm800NzdTVFTE888/z+23387GjRu54447/PqN1Wg0Eh4e7vNzsrrWYVid1uN63cjokQDsb97PyCkjMYV5vsCTs5NJyEjwaetwO9hctZlyczkKCuNjxzM9aTrj48YzInIEqaGphBnCCNb9b6eI6qbd3k6luZK15Wv5uORjytvL+/yMVFVFh46Jhok8Mv0RvpX9LeJMcbTYWni77G3+avgr29iGw+3o8fUXjLiAd694l1mRswD4yPIRb+5/87g+EyGEEIPrrB3JABg5ciTvvPMOv/jFL3jyySe58cYbOffcc7n++uuZO3cu11136n9zNmk94eB4RzJGRnlCRkV7BRbVwvj54zE3mYmIj/BpZ3PZ+Kr6K1psLWgVLVMTp5IY0r3mRL2lnqyILFyqi05HJx2ODirMFRQ1FdFobWRd+Tqig6IZHzeeSfGTuo1sqO7/BRCNZwpoWtI0rsm9hg0VG3hr71s0OBpY61hL9ZfV/L/J/4/IoMhufYgIiuC5xc/xyu5XeK3gNV7IfwGry8otY285rs9GCCHE4DhrRzIAxo8fzx133OENGOCZOtm0aRPXXnvtoPSpa/SgpK3kuF4Xbgj3ro0oai5Cp9cRmRDZ7cv/cOthWmwt6DV6ZqfM7jFgHEuraAkzhJEYksjkhMl8a8S3GBMzBp2io8naxLrydby29zXabG2+L/zfmtZj76/T6Dgv/TwezHyQ8xznYVSM7Gvax30b76OouajH+yuKwg/G/4AfjP8BAH/a8ydWlqw8no9GCCHEIBmyIaOoqOcvrWP99re/5b///a83YHSZNm3aoC3am5M0B4A/7v4jHY6O43ptZngmAOWt5bQ1ttFY1dhtSiPCcHRkI8xw/CevmnQmJiVM4ooRVzAmZgwKCrvqd/Grrb9ia81W7/1cDhcAWn33RasRURGMdo/mWvVaUkJTaLI28bNNP2NV2ape73vz2Ju5eezNADyz7RmqOqqOu+9CCCFOrSEZMpYtW8aECRNYvbrvo8UjIyNZsmTJKeqVf64YdgUpwSk0WBr4854/99teVVXa29ppLG8k2OwZBdm5bye71+9m3+Z9lOz2HRFJCkkiVB+Kw+3gcMvhE+5nkC6ISQmTuDjrYlJDU+l0dvLGvjf4454/0mxtxmX/X8gwdA8ZkXGRaHVaQm2h3Jd1H9MSp+F0O3l518u8susV7C57t9cA3Jp3K+PjxtPh7OCXW37Z4yFzQgghTh9DMmSsXbuW7OxslixZ0mfQyM/Px+VyncKe9c+gNXDnmDsBeHPfm1S0V/g873K5qK6spiC/gA2rN/DOv95hxTsrKNlRgq7Rs8SmSWlCb/TUkKgqrqKmpMb7ekVRyI32VOM82HIQh6vnhZf+ig6K5t5J93JJ1iVoFS17G/fy1Nan2Na2DRUVrbF7yNBoNCSkexajHv76MN82fZtrcq5BQWHVkVXcsfoO6jrrur1Op9Hxy5m/JEQfQkFjAX/Z+5eT6rsQQojAGpIhIzc3lwceeIALLrjAJ2gcWzirrq6O2bNnc/311592QWN6/HSmxk3F4Xbwh/w/AJ4Ri4ojFXz83sesX7WePfl7qKqowm6zo9FoCIkKITfNEx5a9a1MXjyZ9NHpABzaeYiW+hbv9VNDUwnTh+FwOzjUeuik+6vVaFmUsYgHpjzAsPBhWF1WVnau5H39+7QpbT2+JntCNsnD/3fq7P5yhlcN5/5z7idEH8Kehj3csPIGdtbt7Pa65NBklk1ZBsCf9/6Z/Pr8k+6/EEKIwBiSu0tyc3M5cOAAb7/9NldddRVLlixh+fLl/PrXv2bjxo2kpKQQHx/Piy++yObNm3s9X+NEVVdX097e90mqOoMOq633barXpFzDjoYdbKraxGdFn6Et0VJX7fnt3hhkJD4xnui4aKJjo4mIimBP4x4UFLS1WmwuG8XNxUSlRxHWHEZ7dTuFmwvJmJFBh70Dq8ZKWlgahU2FFDUXER0U3a26Z4e9g63VW/t9r8MihrHmyBrvn8fFjiNEH8L+xv2Ua8pZXrKcUW2jmJIwpfvi0GQI0gdhPWiltaEVpVXhxuE3sqJhBeXt5fxg9Q+4btR1LExf6LNGJjMik9kps/mi8gse/vJh/r7o74QafAub2VxHA6XT5cTp6ntqRVVVdFr//jkcT0EuIYQ4mw3ZkPHmm2+i1+t5++23ueyyy7jtttu49957SUlJ8bZbunQpS5cuHfD7NzQ0YLEc3YKq0WgICwtDrz/6RW5uNxMVFdXTywFIMaawKG4RH9d9zPJty7nGfg16jZ4xY8cwdvxYn2uBZ62FTqsjOTSZ8vZy7Kqd5LBkEmYkkL8un/bmdmp31jJ19FQiQyJxq25ezH+Rus469Fo956Wf53O9RzY94td7bXe0kxWR5fNYdFA0VzmuYoN2A1WaKvY07KG8vZzv5nyX6KBon7b6OD3aUC2WfRbcHW50+3Xckn0LK0NW8lXNV/y98O8cbj3MzWNuRq89+p6Xjl5KSWsJleZKns1/lsdnPu573WNKjiv/+78+Kf/76Y8q4UEIIfw1ZKdLCgoKACgpKWHPnj1MmTKFl156qd/FoAOhvLycsrIy709JSQm7d+9m//79VFdX93hK67FUVaW6uprMmkxMqolmpZkjMUe47IrLOGfyOd0CxrFSQz0nrB6qP0RjdSMajYaxs8ZiMBnobO/EccCB6lbRKBoWpC0AYHPVZjodnQP3AagQ447hCscVjAwfiUbR0GJr4c97/syW6i24Vd8zWzQmDcETgtEned5XQ3ED89vnc9Xwq1BQ+KLyC17d/arP64L1wTwx6wm0ipZPSj+Rba1CCHEaGpIhIzs7m7KyMnbv3s15553Hk08+yaZNm7xrNLrKiQdKWFiYTxVQk8lTYKujo4Oqqir27dtH0YEiiouLaW5qxu0++uXZ2tpK/s58ig8Wo3FomM1sANbZ1uEw9L1Is6Otg1CrZ9pgV9Eu9nyxh0O7DmE0GcmblYdGq0FtUTEfMgMwKmYUiSGJ2Fw2ttRsGbD3r3PpvCMHCaEJTIqfRHRQNE7VyZryNfy98O/UW+p9XqNoFIKyg9CN1KHRaTA3mUkpTuGO4XegVbRsqdnC/+37P59wNi5uHLfkeQpzPb3taSrNlQP2HoQQQpy8IRkyDAYD6enpzJ0711toq2vq5LnnnmP06NEBvf/w4cMZMWKE92f06NHk5eWRnp5OREQEiqLgcDiorqqmoKCAzV9upnBvIfv27WP3rt2YzWa0Wi1ZWVkszlxMujGdDkcHL+W/1O1eNpuNXTt3sWP1Dr7+9Gs0dZ6/0ialCYCKgxVUHa4iLCqM3KmehaGWcguWKgsaRcPsFE+I2Vq99YS2hLbb27uNyuhcnlk4p9YJime768S4iSzOXIxRa6Sqo4rXCl5jU9WmbqMa2jgtY88dS3BEME67E12hju/EfAeAT8s+5eOSj33a3zz2Zs+2VkcHj3z5iGxrFUKI08iQDBkAL774Ii+88IJPoS29Xs/tt98+KHPqBoOBuLg4srOzmTBhAukZ6SQmJmIwGHC73TQ2NtJQ3wBAYmIik6dMJiU1Ba1Gy5XxVwLw/qH3+az0M5/rfr3la3bt3EVnWyeKopAV41kf0aBtQM3yfPkf3HmQzvZO4lPj0aZ5tpS2H2jH0eZgbMxYwg3hmB1mChsLj/t9tdnbaLP7LujUqp57aNwaguyes1gURWFC3ARuzbuV7IhsXKqLDRUb+KT0k24hJSg0iNFzRxOfGQ9AfGU830r9FgD/OvAvttVs87bVaXQ8MesJ766U3+/4/XG/ByGEEIExZEPGwoULueGGGwa7Gz3SaDSEh4czYuQIpk6byoRzJpCe7gkdE86ZwIiRIzAYjh4VP9w0nAWRnvUTj25+lK01R3d9uF2ekYCEjARmLpnJefPOY2L8RJyqk9frXscV60J1qxTtKEJVVbRpWoxxRlChbW8bilvhnPhzANjbePzTSFpFS4g+xOexTmMnnYZOtKqW4dXDvUEDPOXPrxx5JYszF6OgkF+fz8bKjd0/I62GYeOHER7nOXRuTvgcFmYsBOCV3a9wpO2It21yaDK/mPELAN4qeot/HfjXcb8PIYQQA2/IhowzhaIohIWFkTEsgxEjRxAW1nOp7yvirmBi6EScbif3b7ifA00HANAbPIsljcFG9AY9iqJw7+R7yYnKocPRwcd8jKpRaalroa68znO/3DA0Rg0uiwvzQTNjYsYAcLD5YK/VNnsTa4pFp/HdpKQqKoeSDtFp6ETv0jO8ejgG29HQ1DWqceGwCwHYVLWJr2u+7vnz0Rwddbou9zrGxIzB5rLx7PZnabY2e59bkL6An5zzEwCe3f4sm6o3Hdf7EEIIMfAkZJwhNIqGpYlLyTHl0OHs4Kfrf0pNR413xMPpOLoWIUgXxH1T7iNUH0ppeyn74vcBcGjXIVSnikavIXy0Z4TAWm0l0hxJdFA0DreD/XX7cbQ5yHJlkefKY7pzOnHuOACS3cloVN//ZL4ZMLq4tC6foJFemY6rw7fo2Tnx5zAvdR4Aq46soqChoM/PQKvR8sMJPyTeFE+9pZ5lny/zOSr+xtE3ckX2FaioPL79cYIygvq4mhBCiECTkHEG0Wv03JZ8G8mGZBosDdy17i7P4kqOHkjWJToomjvG3wHA2ua1NAQ3YLfacR3xtDNEGQjO8Jx10r6/nUyL53C1/IP5NG9r5mLnxZzrPJcprinMdM4EoEpThVtxo6j+rWk5NmjoXDoseyzdgsbMpJlMTpgMwIqSFRxu7fs8lTBDGHdPupsgbRDbarfx3PbnvM8pisL9U+9netJ0rC4rGXdnoI/ufbuvEEKIwBqSxbgGW3V1NcHBwd4/63Q6QkJCfBaculU3Lc0tfl3P4Tj627oOHbcl3MZzVc9xuPUwL7peZD7zsdvt3c4hmZwwmXmp89hQsYFPtZ/yHb6DodpAXWwdhAAJQCNghuGO4Ww3bKdUU4rT4KTR0YhZMdNBB+1KO3pVj0NxEK6GM9cxl0/0n+BUPAHnSPsR+lIRVsG89nkE24NpzW/lYNJBrIaj1U4NGgOxplgaLA28X/w+wbpgUkI9RdOsdk+7SnMltQ213tdcln0Zbx14i7cOvEV0ULR3vQbAjyb8iMr2SsopJ+PuDFrtrRiDjX320e12+1X5VXWr/rVT/WsHUtxLCDF0yUhGANTW1lJTU+P9qaiooKWlhaSkJJKTk0lOTkar0WIwGPr90aIlKjzK5ycrOov/N/z/EaIPYZ95H2t0a9CrerIis7r9PDrzUZJDk2myN7E9cjsKCsHlwYyLHcf4hPGMnT2WzKmZzJo1i2hjNA7FgeYcDYfTDtM+rB33MDchGSHkxuWSHJTMZc7LyFQzuVZ3LXOS59Bmb6PT0dnnT6urlRXGFbTqWtG79QyvGk5rSyuV5koqzZVUdVQRogvBpDXhUl38p+g/tNvbMelMaBXPThW9Ro9JZ/L+jIwayTW51wDwp91/Yl/jPu/nH6wP5t7x9+JocRCUFsRj2x5DRUWn0fX6o9d61rP0+6Pxo83/foQQ4mwnISMAoqOjiYmJ8f4oikJtbS2FhYV+n3vRn3RTOnem3YkGDQe0B/jM/FmP7UL1oTw24zE0iobtlu0U64vpbOmkqcxTR0Or1xKeEE5IVAiTEz3TFttqt3W7TqQxkuExw6lPrMetuInojCClMQW+8XasTistthbMDjM2l81bB8Ou2NkcsZlWXStG1cjMlpmEOo+eN6IoCnHBcYTpw7C6rPxj3z9osbX0+Rl8Z8R3mJk8E6fq5Ddf/4b6zqMFvmKDYil7rgy3zc3Wuq38euuvB+yzF0II4R8JGQEwduxYxo8f7/0ZM2ZMQIJGXlge18R6fpv/3PE57xW/12O78fHjuWnMTQCs163HjJnq/dU4bL7TK5MSJgGwq25XtyJZXTqCOiiLK0NFJa4tjjGOMd7nLE4L7Y52HG4HFqeFNnsbjdZGmq3NdDo7aXG18HnY5z5BI9h1dFpJo2gYEzuGOFMc7fZ2/lH4DzrV3sudK4rCjyb8iGHhw2i1t/Lrr3+NzXn0YDRrmZXyl8tRUHin+B3+Xvj33j9MIYQQA05CxikQHx8fsKAxN3ouU5xTAHh669NsrtrcY7tb8m5hVPQoLG4La4LW4HQ4qS6s9mmTGZFJlDEKq8varcDWsVpDW6mKrgJgim0KafY0LE4LZoenXLlRayRIG+Sd6nCqTmwuG/WWeko6S/in7p+s1K+kQqlgYttEn4Wkeo2e60ddT7ghnEZrI+9a38VO79tqg3RBLJu6jHBDOCWtJfx+x+99AlJ7fjt3jbsLgN/v/D2rylb1ei0hhBADS0LGKRIfH8+wYcMAz5qNzs6BOZBMp9MxzTWNXFcuLtXFQ188REV7Rfd2Gh2/nPVL9Bo9R9QjfK39muaKZpz2o1tfNYqGiQkTAahor/A5Lv2b6iPqqQ/3TE+cYznHGzD0Gj1h+jDCDGFEB0UTbYzGpDP5nILqUB0Ua4r5WP8xna5O4uxxPteOMEZww+gbMOlM1Lpr2aHdgeroPZTFB8fzwJQH0Gl0bKnZ4nP0PMCV2VdyTY5nxOfnX/6cKnNVr9cSQggxcCRknCK1tbWUlZUBEBISQlDQwNRw0Ov1REdHs8C5gCR3Eh2ODh764qEeA0KiMZFFxkUAbNFuoT6uHq1e69NmUcYi72jGrvpdWJyWbtcBQIF2UzsADsWBUevZveFwOzA7zLTb22m2NtNka8LitKB+Y/GGAQM5rhwiiaRV19rt8nGmOC7IuACAEk0J1gorqqv3oDEqZhTXj7oegLcOvNWtqNh9k+9jUvwkrC4rT255UtZnCCHEKSAhIwBcLhdOp9P7c/jwYfbu3Yvb7SYmJoZJkyah1Wr7v5CfsrKyMOqNXOi4EBMm9jft5/kdz/u0aahv4KP3PyKrxVNkCwXe73yfus46n3YxphiWTV2GUWvE5rKxq36Xd5Tim0KsnnLiDboGwvRhmHSe02atLitWlxWn6hklUVDQKToiDBHEmeLIMmXxPfv3WORcRGlwKTZtzyMm2VHZnutrGmi3t2Or6X1kBeDCYRcSa4qlydrEmkrf0QytRssj0x/BoDHwZfWXvLn/zT6vJYQQ4uRJyAiATZs2sXHjRu9PaWkpAGlpaYwbNw6dbmDLk2g0GkLCQwjXhnO+43wA/l30b1aXrUZVVfbt3cenH31Kh7kDvUnP96Z9j+GRw7E4LbyY/yJWp9XnejGmGHKjcwnRh+BwO9hdv5tWW/fRhhDb/0KGtgFFUQjVhxKmD8OgMWDSmgjThxFtjCYmKIYwQxhRQVGE6EKY1DkJg2qgRddCiamk1/cVqg8lKSQJgMOaw1jKLH2OZhi0Bq4aeRUAH5R+gCbI9z/vYRHD+PE5Pwbgd9t/xyeln/T30QohhDgJEjJOAa1WS25uLiNGjAhY/QRFo2AKMZFJJpOcnl0iT3z1BO98+g7btmzD7XaTPiydrDlZhEeH84PxPyDCGEFVRxWvF7zebfpAr9EzLnYc4YZwXKqLgsYCmqxNR++nKgTbPDtDGnQN3seDdEFEGCMINYQSpAtCq9H6vOdUWyqJ9kRcuMgPy0dV+p62GBc7DoA9+j247K5+RzPmp80nKSSJdkc7MYtiuj1//ajrveszHvnyEb6q/qrP6wkhhDhxUvEzAHJzcwkJOXoyaVdxprq6o1MTNpsNl8vV08t9uFQXdbVHX+dyubDb7QQFBfl8eQeZgtBoNeiMOmY2zqTKXUW1s5p/1v+TyzWXM3HKREbmjmRrzVasLitBuiC+n/d9lm9fzva67by29zW+m/NdtJr/7QhxO1EUheyobA41H6LV3kphYyFZkVlEB0UTagtFo2qwYqVerYd+3ooWLU6rkzHtni2ve4x7qHJX8c2NIw6Xg/1N+71/DtGHoFW0NKqNVCqVJJUmUWosJTEshYo2390xXS7IuIi/Fr5G7IWxNK1posna5DMl8/2871PdUc36ivXcs+Eenp33LCOiRhAT1D2U9MjPnOjvug8p3CWEGKokZASAXq/3Oaq9JxqNptcTV491sPggGsUz4OR2uzGbzbjdbvR6PcHBwd4vqIjICFJSPaW4LfEWFh1YxD/Uf3BEc4SIiRFMyJsAwMT4id5y1xPiJmDSmnhq61NsrtqMBg2PzXyMIF0QP5jwA4K0nsWpTreTP+7+I1tqtnC45TAL8haQbcmmoqYCi8nClMQp/b6PouYi5nTMwYCBNn0brTGtJCvJ3RsqEBkU6fNQTnQOhY2F7NLvItWeSnR7NEqYhlB9aPfXA7OT57Cq7DMqqSB2cWy3CpxaRcuD0x6k1d7KzrqdPPTFQzw//3liTbH9vo+uPvbfRIKDEELIdMkZ4tiAAZ7zTI7987FMJhMzcmcwP2Q+AK8ceAWzvefFmxdlXsQTs57AoDWwqWoTP133027rL3QaHXeMv4N5qfNQUfnTnj+xvmo94CnO5Y80Wxqx1ljcuDkQdaDfaZJjjY0ZC0CJUkI77WiqNKju3l+vUTRcmr4EgJjzY2i0NnZrY9AaeHzW42RHZtNsa+aBzx+g0dK9nRBCiBMnIeMMcGzA0Gg03sPXXC5Xr0EjKCiIqzOvJt7gORb95V0v93r9ualzee7c5wjVh1LQWMCda+7s9oWrUTR8b8z3WJTh2QK7snMl27XbMRt7Di/H0jl1jOvwrK0oDS+lQ+9fMOkSbYomOTQZFZXdht0oDgW1ru/5mbFReXQe7ERj1PB28ds9tgnRh/D0nKdJCkmiqqOKH639ER2O4+ubEEKI3knIOM19M2CEhoZiMBgICwtDURTcbjft7e047I5urzVoDNyQfAMA/zzwT5+1Dt80Pm48L53/EvGmeMrayvjN17/pdrqqoihcm3sti9MWA/Cl7kv22ff1vfZAheS6ZAyqZ5qkPLT8BD4FyIvNA6BQU4gTJ+5KJ25Xz6XPu/pa+1/Pqa2fHPmEanPP6zeiTdE8PfdpIowR7Gvaxz3r7+l2mq0QQogTIyHjNKaqKpZOi0/A6FpPodVqCQsLQ6PRoKoqDQ0NPV4jLyyPqRFTcatu7lxzJzvrdvZ6v6yILF5Z+AqZEZm02Fp48qsn2VG7w6eNoigsjFzIDOcMACo6KtjbuBen29nTJTE4DIR1hp3QNMmxMsIzCNYFY1WtVOgrwA5tdd231R6rY38H5r1mXKqLlaUre22XFpbGr+f8GpPOxJaaLXxc+vEJ9VEIIYSvkwoZVqu1/0bihDU2Nnp3oBwbMLp0BQ8Au92Ozdbz9s7rkq4jLSiNJmsT92y4h3/u/2evow/xwfG8eN6L5ETlYHVZWb5zOe8Xv+/T3u1yM9k1mQuDLkRBoc5Sx7babT1ONbg1ntEGBQWr9sT/e3Grbm8V05Agz84dl7P7lElNZTX79+yjaO8BABzNnlGJxtpGdu3cRUFRGV8UNeP8xpqO3OhcvpX9LQBKWnuv3SGEEMJ/JxQyXnrpJZKSkjCZTISHh3PllVeyY8eO/l8o/OZ0OqmsrAQ86yu+GTC6aDQab/XQ1paef7OP0Efws+E/Y0bkDFyqixfzX+SRLx+h09Hz+SnhhnDumngX56d7Cnu9U/wOL+a/6HPCKcAk4yTGxIzBqDXS6exkW+02ajtrfd+HzoldZ0dBIczR/26a3tR01OBSXQTrgolWontuU1nNd+ZcwY0XXs+tl38fAEOsZ5fPq0++ysXf/Sk3/uUAP/1nEY+8U9wtaCSGJHrvJYQQ4uQdd8jYsmULd911F3feeSdr1qzhxRdfRKfTMXPmTF5//fVA9DEgXnzxRdLS0ggNDeXSSy897UJSVVUVTqcTjUaD0Wjss61erwegpaWl1zZGjZHbUm/jO/HfQafRsb58PbeuupXS1tIe22s1Wm4YfQPfG/M9tIqWr2u/5vEtj3c7fC3MEMaUhClEGaNwqS72Nu6lqLkIh/vougZLkOf8k3B7eP9vvBfl7Z61HKlhqb3WlWhpasFu8y28oY/To6qgDV9AwtVPoAmOAGDV3iZ+9t9inMes6+gKGd8MSkIIIU7McdfJ2L59O1dddRWPPPKI97EbbriB7du3c9FFFzFz5kxycnIGtJMD7fnnn+fVV1/ltddew2Kx8PTTTzN9+nReeOEFbr/99pO+vtVq7fdsEovF0mstjc7OTioqPF/mpmBTr9MgXVwuFzabDZvdRlJ7Up9lyycbJ5OcmMzfav9GWVsZt352K/dMuIe5yXN9+9dpQdWoTIueRkxeDK/ue5Xy9nJ+/uXPmRMxhyxbFgarAYfGgU6rY0zoGMo0ZZRbyqkwV1BlriLeGE+SKYlQQgmyBWFqM+HU97x2o4vD5cBm6f5+y9s8ISPRmIit2YbGpsFqsWLpPHqAm83q+zpFq6CLMGGt/hZh50wGoGPveh644SJeybeyurAJh9PJzy9OQ6dViNB4Aki1uZqOjt53mfQ2quTjOJaenC3FuPr6TIUQQ9Nxh4wRI0bwxRdfdHt80qRJXHfddfznP//h4YcfHpDOBcqTTz7J22+/zbx58wC4+OKLue+++7jjjjtQVZU77rjDr+vYbDafANDW1ua93ulOG6Yl7QdpMBqe3P4kdz91N7Vv1fZ6NoguUkfyjcmETwxnfct6Pu38lKrHqujY7/vFETo+lMTvJBKUFkSNrYYaWw2bDm6icW0jbdva+jyyvde+hmsZ9fwoAF698lVc7f1XSgUwpiViKb8DtzUN1e2ief3rtH/9Hv9vxW8xDZ9C3OUPsaGojekf/pGGD36DPlJDzrM5VLdVExERcVxBQQghRHd+TZd88sknPP7443z44Yfk5OTQ3NzMBx980K2dVqsd0NNFA0FVVRobG3E6j/5GrdVqee6557j77rv58Y9/zLZt2/y61lNPPUVERIT3Jy0tLVDdHnCudhelvy2l/qN6AGIXxZL5UCb6aH2P7Z0tTo48f4QjLxzB0ezAmGgkc1kmKd9PQRty9O/cvMtM8SPFHH7yMC1fteB2ugkeEUza7Wnk/C6HhO8koI/t+R69CR3tWdxqKbP4HzBSxxB3+TO4rWmgdFD39s9p//o97/OWQ19T9+6TqE4HITmziFvyAI42FdWtougUdOFSDFcIIU6WovpxwMInn3zCr371K3bt2kVbWxvBwcFYLBaWLFnC5ZdfTmpqKgUFBbz44ousX7+elJSUU9H3EzZ//nwMBgOffvqpz+Nut5t58+YRGhrKypW9b3ns0tNIRlpaGuvWrfPu+ujNoUOHuk2XuN1uampqcLvdhIeHEx4eTmNjY79rMsCzUNRsNqPRaEhLS+t1CL6js4NgU7DPY/vs+3jf/j7tjnbCDeE8OvlRJsRO6PUaBQcKeGX3KxQaCgEI04exNGcpsxNnd3tNi62FNZVr+Kj4IzqUo6MeIyNGMil2EhlhGd7y5QD7m/Z710Z0+bTiU/a17GNS7CTmJM7BVmjD1eIiLS+T6NQ4b7uivfu59fKbCD1nMdHn3Yai1aExVuFqe5kjvz+6dTfh2t9gSMjy/EHRoOj0KIrC/JHRlAX/nHpbDa/O+yOjo0d3e+9u1Y1e038AUVUVRePfNIi/0yVneqnytrY2kpOTaW1tJTy89/U5bW1tRERE9NtOnJyCylYueeELVvx4NmNTInps09HRQfTw8STdtJx/3zKJKdmJPbYTp86Z9u/Dr1/XLrzwQi688EJUVeXw4cPs3LmT/Px88vPzefjhh6mqqgI8B4OtX7+e6667LqCdPlmPP/448+bN47HHHuPRRx/1Pq7RaHjooYe47LLL/LqO0WjsMQAEBwd7q3L2JigoqFvIaG5uRq/Xo9PpiIuLQ1EUgoKC/AoZ4AkpLpcLRVEwmUw9tnG5XQQFBfk8dk7QOWQ4M/h7y98pt5dz75f3snT0Um4bdxu6Hr5QY8NiOU89j7kRc/nY/TFH2o/wh4I/8EXtF9w54U7v8ewAwSHB3BB9AymtKRyoPkBhcCGljlKKWosoai3yXM8US2poKilhKTjdTpKNyRi0ns9GVVXKOzzrMUbEjCAoOAiCwGV0EWQyYTrmc9Yagoi+8MeEjb/A817btxKa8yFNa313iyh6AxqD72cAsK6oieiYb6PGvUKb2upzyJ33M5aQccL8ORBQCDG0HNeYsKIoDB8+nOHDh/Od73zH+3hDQwM7d+5k586dfn8hDqbZs2fzq1/9imXLlqHRaHwWsUZHR/t1cFlfCgoKfEJGSEgIWVlZfX6Z2O12zGZPie6oqKjjXgyoKArBwcG0t7fT0dHRa8joTbQumh/F/IgP2j5gU+cm/lb4N/Y07OGJWU8QY+r5dNIMfQa/mfYbPi75mH/u/yf59fn8aM2P+G7ud/lW9rd8Aoo+XM/wyuHkkIMyUWFLzRb2Nu6lxdZCg6WBBksD+fX5AHxa9imxplgSgxPRaXSYHWZ0io7UsNRe+9/c4eIvO02Ejb8A1e2iZcPfCJ+0FUUTib3+6I4TTVAoiqH7Z6PTKGg0Ck2NaWht11HRJjtMhBDiZA3IxHNsbCwLFy5k4cKFA3G5U+KBBx5AVVUeeughvvrqKx5++GGCg4P56U9/yn333XdS125vb/dZ89HW1oZWq2XYsGG9vqYrYCiKgsPhwGg0HlfQcLvdOByebaMnWiRNp+i4IuIKMg2ZvGN+hx11O/j+Z99n+fzlZIRneNt1vTdFUdBr9Fw58kpmJc/ipV0vsat+F38v/DsbKzbyowk/Iifas9NIG/6/I+TNTnS7dSxIW8DFky6m09VJpbmSCnMFleZKSltL6XR2eoNHl9SwVHQaHapLxW11e+/f5Z2vW6lo89yjYcWzWEs+J+nqEQA+IcOYORGNrvuaEI1G4ekr8rj77V24zKPZXlLN0rEn9DEKIYT4nyG7uq2oqIiRI0f22WbZsmXMmDGDhx9+mNmzZxMWFsb999/PsmXLBrw/iYl9z2WGhIRgtVpxuVy0tLTQ3t5OWFhY3+eC4BmWt9vtWCwWb9uTnac7x3QOGaEZ/Ln+z1R3VHPrqlt5bt5zjIkdA0BlhadIWEzc0RGO5NBkHp/5OOvK1/Hngj9T2lbK/9v4/1icuZgbR9+IxqghLDMM8xEzzg4nLftbaDvURuzkWHKic7xhpLCxkHBDODWdNdR0eH5sLhsL0hcAYD9sR7WoKHqFsLhI7/1njAhmw34LFruL3KsfIHPYZA7wKfZ6O8FjlhGS55n60AZHogvvfqS7qqr8fo2nSqiia+Oi3L7/2xFCCNG/IRkyli1bxvPPP88HH3zA+eef32fbefPm8cUXX2C329HpdP7VQOjHjBkz+l34+U1Go5GkpCQ6Ojpoa2vzho2u39Z7Gtlwu910dnZ6RzCMRiNxcXG91t84HvGGeB4c8SC/P/R7Si2l3LnmTn4959dMjJlITZVnjUPGsAzaafe+RlEUFqQvYFLCJF4reI215Wv5qOQjNldvZn7afKYMn0JoRiidVZ2Yj5hxWV20l7QTPda3gmeoIZRsQzbZkdk+jzvrnDhrPKMoppxw9MajIxKjU4J48so4nvmogeoWJ437Z2NMaKDi1X8Tc2lOj2swjuVwqZQ1WlF0LZwzYSMXZv/hpD4/IYQQQ/SAtLVr15Kdnc2SJUtYvXp1r+3y8/O9i9EMBsOABIyToSgKoaGhJCUlERUVhVar9RySZrHQ2tqK1WpFVVVUVcVms9HW1uYNGCaTieTk5AEJGF3CdeHcn30/Y8LGYHVZuXfDvfz2y99ic9uIjIokIrLnFekRxgjunnQ3T8x6gqSQJJqsTfz34H/5e+HfaXO1EZoeSnSeJ1hY6iy47P0vCHR3urEVe3by6NP06KK6v8+0GAMPXG7AGHYAVD22misJyrrJ7/er0bcQnPEqd0277qwpkCWEEIE0JENGbm4uDzzwABdccIFP0Dh2u2ldXR2zZ8/m+uuvP+1WvR8bNvR6z/bKY8OG2Wyms7MTVVXRarWEh4djMpkC8sUYpA3irsy7mBHlOffkg+oPeMPwBvXR9f1O5YyPG88LC17gqpFXoVE0FDYW8uz2Z9lUuQltmBZ9mB7c0Fnd8xkqXVSXim2/DVygidCgz+i5zoZbdfNm0SvoU/5KfMpWVNVN2DmLUXT9By9F24Ep42XGJacyI2lGv+2FEEL0b0hOl+Tm5nLgwAHefvttrrrqKpYsWcLy5cv59a9/zcaNG0lJSSE+Pp4XX3yRzZs3D/gIRktLi3eEoTcOh8NncWhvdDoder0ep9OJw+FAVVXv6/R6PXq9HpfLhVarpb29vZ+redgd9n7baE1anwWk18dfz1jjWP5V/S/alXZeqXiFHet2cG3utQyLGNZ7/zU6rh11LaH6UFYdWUV5ezkfHv6QHXU7WBSziJD2EMwVZvQpnjDldDuxu3z75zrkwt3hBj1oRmj+dy6KE4vTN5ysKlvJ/uZCjDojd80Zyy3ffozYS/8f2qD+pq7cmDJeRKNv5Zaxt6Ci9hqgVNXdb7jyXNGNRvXvvysZNRFCDFVDNmS8+eab6PV63n77bS677DJuu+027r33Xp9CYUuXLmXp0qUDfv/g4OAeaywcKzQ0tN824NmZ0rWQ0+1209raisViITo62qfeRV1dXbf6Fz0xGAykpva+FbRLS2sL4WG+C0jHWcehtWvZHbSbbco2ttZsZUfdDq4fdT235N1CsL732iBLxy7lprE38cGhD3hl1ytUmit53fw6WoOWUGcoqYWppEamEm4Mx+12ExccR6wpFkeDg321+3DiZOL0iYTGhGJxWqjtrKXVUYfFacHitNBma2NFyfsA/HjCj5gRPwnr4e3U/O2nnPvI/3GwrhOdRuGhxaOYMyKW6/68hbp2G8Pjgpk9ZSfvlDQxOmYM81Ln9fmlr6KiUfw7u+RMr2shhBAna8iGjIKCAgBKSkrYs2cPU6ZM4aWXXuLCCy/sdzHo6Uqj0RAVFUVUVNSg3L++vh49eq5IuILvxn+X18teZ2frTv6696+sLFnJPZPv4fz083v9ktYoGi7PvpzZKbN5YecLrD2yFpfiolVppbW1lb2te3u+cVfpla3993F2ymwuGHaB9+A0Z0sNr984ml9/Vs6K3bX8ckUhBp0Gu9PNyIQQnrs2i++tfgCAO8bdLqMKQggxgIZkyMjOzqasrIzdu3dz8cUX8+STT3LNNdd4p06+/vprxowZM9jdPC04nU6am5vRarUYjUYMBkOPp7g6HA7vUfKxsbEEBwWzbOQyNtZs5K3at6jtrOWBjQ8wNmwsS9OXMnv0bIxBPRdmizXF8tjMx3ho2kOU1pXy0aqP6NB0kDo2lXp7Pc22Zuot9dSaa2m2NdM1IKCgYNKZMOlMGLQGQvQhBGmDMOlMBOmCSA5N5prca7oFhSC9lt9cOZpxaeE8vbIYu9PNqKRQ/nLTBP558M9YnBZyonKYkzK3h94KIYQ4UUMyZBgMBtLT05k7dy7PP/88N954IwBvv/02r732GqNHdz+T4mxktVrZtWtXt+JdiqKg1+sJCgpCr9djNBq9O1sAysvLsdls2O12tFYtV6pXsk27jR3aHRS0F/BAwQOM2TuG74/5PnPOmdPr6IBRayQnKYeqhCrqauoYrY4mY3wGIXrPNNLuHbvZt3cfMakxzJkzB4PW4L1Wk7WJUIP/24QVReGGGWnkpYaz6WAT101PBW0Hbx94G4Cbx94soxhCCDHAhmTIAHjxxRepqanhhhtu8D6m1+u5/fbbB7FXp5eysjKsVqt3AandbsfpdHoLfNntPS8Qraur8/mzDh1zNHOYqJvIOtZR5CpiD3u4r+A+brLfxG1Tb+uzHylpKdTV1FFeVk7G6KOVRWuratGiJWdYDkbdwJSrn5AWwYQ0z9bbl/L/j05nJyMiRzAnZc6AXF8IIcRRQzZknEklzgdLV4jIzMwkKclzqJnb7cZut9PY1Ihep8dms3H48GHva+Li4ggJCfFOrThUB1ERUd4dOueq57KqYBUrO1ZSranmzwf/zBeNX3DvpHsZFTOqWx8cDgcHCj2VNpOSk3yeCzIF0drc6rP1eKA0WBr45/5/AnDruFtlFEMIIQJgSNbJEMfn2C9YjUZDUFAQISEhxMbGotcfrUkxduxYcnNzSUtLIz4+nsjISIxGo88W4MrKSkJaQviO8zvckHgDYYYw9jft5/bVt/PCzhfodPhuO92zcw+dHZ0EhwQz9hzfw0Ji4z3lvxvqGhhor+15DZvLxtiYscyVtRhCCBEQEjJErzo7OykuLgYgLS2t310tHR0dHCk9AkDW8CwuTr+YZ0Y/w8zombhVN28deIsbVt7Al1VfAp7wcHDfQQCmzJjiE2jAN2T4U5vCXxXtFbxb/C4AP5zwQxnFEEKIABmy0yXi5LjdbvYf3I/L5SIiIoKMjIw+26uqSnFRMaqqEhUd5T0QLtIQyU+yf8L0lun848g/qO2s5f6N93Nu6rmMqx8HwLDhw0hM6X6AXHRMNIpGwWqx0mnuJCSs/7oi/vjj7j/iUl1MT5rOxISJA3JNIYQQ3UnICICuA8764nA4/KrQ6XQ66ejo6Ledy+Xyq51Go6G1tdV7bfCMWHQ91uVI2RE6OjrQarUkJiZ6t69+k6rxlDuvqa7BbDaj1WpJSU3ptmMlLyKPZyc/y9ulb/Nx5cesr1jPJnUTacY0zos4D1O9idyoXNyqG7f6v6PctQpR0VE0NTRRkF/A5JmTUTSeUQen24nT3XvF1GOfc7lduNyev4/i5mI+Lf0UgNvG3obT5Wmnqiqqxt3v56eqKm5tv81wq27/inYhRbuEEEOXhIwAiIqK6vcUVrvd7tdJrTabjZiYmH7bWa1WjMb+d2DodDoiIiK8/z94KpR2PQZQW1vrDR1ZWVl99tPlcmHUG6muqgY8hdCSEpK6tVMUhfiEeH6W/DOm7JjCH4r+QJ2mjsMc5vDew7DXs6V1bMxYzok/h4kJExkXO47Jkyaz6rNVlJeWY9AZmDt3LhqNhlB9aJ/l4DvUo4HLqDWyt3Ev7xa/y5qyNaioLExfyPj48d42qlv1q7y82+32KxRoFa1MwwghznoSMoSPzs5ODh70rJNITEz0ljTvjaqq7N27F1VViY2N9e5S6U3J4RLq99ZzFVcRkROBOcJMfkM+uxp20WJrYXvddrbXbYcCT4XQkVEjGZ4xHFe5i7biNlRVZd68eX69F12EjshZkVyz6hoqzBXex3Oicrhr4l1+XUMIIcSJk5AhvFwuF4WFhbhcLu/R8f2pq6ujra0NnU7HqFGj+vztvbKyki+/9Cz6zM3NZfLkySiKwrU516KqKmXtZWyr3cbe5r3k1+dT1VHF/qb97Ge/579UHfy7/N9kvZPFvJHzGB8/ntExozHpTN57ON1Ovqz6kv8e+C85z+agaBUqzBUE64JZlLGIy7MvZ2zMWBllEEKIU0BChgD+t3CzuJiOjg70ej3Jycn9fhFbLBZqamoAyMnJ6fOAtpaWFrZv346qqgwbNswbMLooisKw8GEkByfznZHfAaCus45dDbvY3bCbgsYCDjQfoJNOCiwFFOzynE2jVbRkR2YzNnYsIfoQPi75mAaLZ8urolXoPNjJY1c9xiUjLunzADchhBADT0LGWazrS76mpobS0lJv0avc3Nxeq3126dre6u80yYEDB3C5XCQmJjJj5gy/RhLig+NZmL6QhemewmpWp5X1e9fzWcFn1Cq1NAc302hr5EDzAQ40H/C+LtIYyaLURTx59ZPYqmxc/P8uloAhhBCDQEJGABw4cIDg4KNfajqdjrS0NL+OYu9PU1MTzc3NpKam+rXQsy9JSUk0NjZ6F3nqdDqysrKIjo72jlD0pLW1lcOHD+N2uzEajYwePbrf0BAeHk5raytu1Y1W68f2jB4E6YKIskQx0TURo9HIZRdfRqu7lb0Ne9nTuIdGSyPnpp7LnJQ52K12fl718xO6jxBCiIEhISMAWltbu40EtLS0MHLkSJ9dHMdDVVUqKiqoqKjw3mPMmDEnFTRiYmLIzc2lpqaG+Ph44uPj+w0AdXV1lJeXAxAWFkZGRoZffcjMzKSyspK62jpqamq8dTSOR0lJCQeLPItSp8+aTlBQEEEEkZCewIL0BT5t7fQ9EiOEECLwJGQEQGZmJiEhRwtH1dTU0NHRwb59+8jMzCQhIeG4rudyuTh06BCNjY2AZ8TBZrOxd+/ekw4aCQkJfvWnK+R0HY4WExNDRkYGbnf/tSUAgoKCyB6RTdGBInbv3n3cIaOtrY0tm7cAkDcuj8Sk4w8pQgghTi0pKx4AcXFx3pGB+Ph4xowZQ0xMDKqqcvjwYUpLS/0uk+1wONi7dy+NjY0oisLw4cMZN24cRqPRGzQCcYDYN5WXl3sDRkpKChkZGce9Q2PMmDFoNBrvaIa/nE4nn2/4HKfTSUJCAnnj8o7rvkIIIQaHjGQEgMVi6TbtkJqail6vp6amhurqau/6jL4KQNlsNmpqanC5XGi1WjIzMwkNDcXtdjN8+HCKi4ux2WwUFBT4rAHpi6IoVFZW9tvO6XR634PFYqG+vh7wBIyIiAhvRU9FUbDarL1e5+iNwRRsIiMjg5KSEnbu2MnsObO7NXPjRqfx/c9y29ZtNDc3ExQUxMxZM0HFpzJoT459TlXVfkOdU3Wi9aOUp/q//xtIiupnWJNdt0KIM4yEjABISkoiLCys2+MpKSlUV1eza9curFYrtbW1jB49GpPJ1K1tfX09paWluN1uQkNDmTx5crcgkZCQwJYtW+js9JxsmpWV1e/USWlpqV8LL4OCgkhLS0NVVbZu3ep9X2PH+p6U6nA4iI6O7vd6ZrMZnVZHbm4uZWVlNDY20tzUTFxcXPd7G48ukD18+LD3kLZ58+YRFek5pM2gGtBpe//P16U7WtZdr9Wj1+p7bQuerbD+VPwE/BrBUVH9qgw60IFFCCFOJzJdcoolJSUxY8YM9Ho9FouFXbt2+ZwboqoqZWVlHDhwALfbTUREBDNmzOhxpMJkMjFt2jSCg4Ox2+0BmTqprq6mpaUFrVbLyJEjT/p6wcHBDBs2DIDCwsJ+2xcUeOphjBs3jpSUlJO+vxBCiFNHQsYgiIiIICcnh9DQUJxOJwUFBd5pkQMHDnh3byQnJzNy5MhuR6AfqytoGAyGAV+j4XQ6KSoqAjyjJAOxBRc8hbs0Gg0NDQ3edR49sVqtNDR4CmuNHj16QO4thBDi1JGQMUgMBgN5eXnExsZ6q21u376dhoYGFEUhOzubrKwsv4bmTSYT2dnZA74Y9PDhw9hsNkwmk3f0oT+dnZ2Ul5dTU1Pj3cr7zfUQwcHBZGZmAp6Rit7WS1RVVQGeA+f8XXMihBDi9CFrMgaRVqslJyeH4OBgjhw5gt1u954Bcrz1NAwGA2PGjPEGjN27dzNixAgiIyOPu1+qqlJfX+/dMpubm9vveoWWlhaKioooLy/vFho0Gg1Go5Hg4GBPbYugIG94am5uprm5ucd1HV2jHCdSU0MIIcTgk5AxyBRFIT09nZCQEJqamk6qMqjRaGTMmDHs37+fzs5O9u3bR1hYGHFxccTExHiPdu+L0+mkvr7eu3skPT29x8WZ4Akjzc3N7N27l9raWu/jkZGRuN1urFYrdrsdt9uNxWLBYrF0u0Z4eHivJ712fQ79lTgXQghxejqrQ0ZVVRX79+9nwYIF/TcOsJiYGGJiYk76Okajkby8PEpLS6mtraW9vZ329nZKS0uJjo72lgLvaRqms7OT+vp63G43Go2GsWPH9ngmiaqq1NbWUlJSQltbm/fx1NRUcnJyiIqK8j7mcrmwWq00NTWhKAoWiwWr1YrVakWr1ZKbm9tr+Oka3WhqajrZj0UIIcQgOGtDxlNPPcULL7zA0qVLycnJGVI7FzQaDVlZWaSmplJfX099fT0Wi8W7iLK1tZWwsDBCQ0PR6/XeEYmuXS4Gg4HU1NRuAcPlclFZWUlpaal3VEKj0ZCZmcmIESMIDQ3t1hetVktISAiqqvpUQfVHV8hoaWnx1goRQghx5jgrQ8bq1av5wx/+QH5+PvHx8YPdnYAxGAykpKSQnJyM2Wymvr6euro6XC4XLS0ttLS0eOtqdC0UDQsLIzo6GoPB4L2OqqqUl5dz6NAh79SFXq8nPT2dpKSkgK2ZCAkJwWAwYLfbaWlpGZCRHiGEEKfOWRky/v73v3PjjTd6A4bL5eKdd96huLiYc889lxkzZvh1HZvN5rOLo2vqwGq19rv+weFw+LUDxOFw+ExJ9NWXvnai6PV6kpOTUVUVl8tFR0cHVqvV2wdFUYiOjiY4OBi3243b7cblcmGz2SgsLPQuAg0KCiI9PZ2UlBS0Wi1Wq9VbDKwvdrvdr7Umbrcbh8Ph/XNkZCR1dXXU1dV1W7uh1fQ+snHs4lN/Kn66cftVedOtuvu8rw8/K3QOeAVRKQ0qhDhNnJUho7m52ftbcUtLCxdddBEVFRWYTCYeeughnnrqKZYtW9bvdZ566ikee+yxbo9HRUX1upixi9Fo9Gv6oKmpqceKoN+k1Wr92pESExPjrUZqsVgoLy+nra2N3Nxcn+mOrimULVu2YLfb0Wq1TJw4sdtOk9bWVr8WlJpMpn4/E/CEqmOnRRITE6mrq6O6upqcnByf/vW14+XY5zQaTb+7YxRV8e8sluPIAwN9PSGEONOclXUyJkyYwD//+U/MZjP33XcfY8aMobS0lKKiIn7+85/z8MMPc+jQoX6v8+CDD9La2ur96SqidaYwmUyMHDmSyZMn+wQMu93Otm3b2LBhA3a7nZiYGC655BJGjx7td+ntgZKamgp4Fun6e+KrEEKI08NZGTJuu+02zGYzP/7xj1m9ejW/+93vvL89P/roo5hMJvbu3dvvdYxGo3cLZl9bMc8kdXV1rFmzhoqKChRFYdy4cSxevPiE6m0MhNjYWIxGI3a73XtImxBCiDPDWTldkpaWxssvv8yNN94IQE1NjXeqoba2Frvdzvjx40/4+keOHPEZGdBoNERERBAWFnbKRwL85XK52Lt3r3cEJyQkhHnz5vVaI+NU0Wg0JCcnU1JSQmVlJQkJCYPaHyGEEP47K0MGwA033IDD4eAHP/gBV155JS+//DJ6vZ4f/ehH3HvvvWRkZJzwtXfu3NnjOgqtVktkZCRRUVGYTCYSEhJOm+BRWlrqDRiZmZmMGTPmtNnNkZiYSElJSZ/nnAghhDj9nLUhA+Dmm29m0qRJPProo1x00UWEh4dz9913c88995zUdRMSEnzO2nA4HLS2tuJ0OmlsbPTu1NizZ483eERGRhIdHe3dtXGqde3oSEtLY8KECf3uxjiVuup3dC1YFUIIcWYYkiHjP//5D8888wxNTU08+OCD3Hzzzb22HT9+PO+9996A3n/69Ond1meoqkp7ezstLS00NTXR2NhIe3u7T/A4dOgQBQUFjBo1ioyMjEEZ4TgdC15VVFQADKmCaUIIcTYYciHj17/+Na+++ip33303u3bt4tZbb2XevHkMHz68W9v8/Hzy8vJOyReroijexaHp6emYzWaCg4O9waO5uZnKykosFgs7duzgwIEDjBo1qscqmmeT9vZ22traUBSF5OTkwe6OEEKI4zCkQkZhYSEvvPACO3bs8C4QXLVqVY8Hc9XV1TF79mwuvfRS3njjjUH5Df6bwWPs2LEcPnyYAwcO0NHRwbZt2wgODmbkyJEkJSX5V3dhiOkaxYiPj/epQiqEEOL0N6RCxt///nceeeQRb8AoLS2ltbWVK6+8ktLSUq666ipeeeUVTCYT8fHxvPjii2zevDkg0xL9rWlwOp09VvxMT08nOTnZuxCzs7OT/Px8iouLycrKIjY2tsewYTabvSen9tevnl7fVS7c4XBgNptRFMVnXUlvjq3O2Re73e7X5+xyuXyKlFVWVgKQnJyMy+XyPt5VubSv6xzbtr+/D3/XoKioUlFTCCH8NKRCxty5c5k0aRIAjY2NLFmyhAsvvJC77rqL4uJifvjDHxIREcHzzz8PwNKlS1m6dOmA9yMiIqLfmhkGg6HPL92YmBjGjRvHnj17KC4uxmw2s3v3bqKjo8nLy+t2eFlbW5tfX+KqqvY4BdM1SqDX6wkNDUVVVfR6fb/XCw8P96ud2Wz2q3+Konivp6qq91C3lJQUn/u43e4+R3aOfU5R/KjmqfhXjluLdmBHlI7jUhJuhBBnmiEVMhYvXuz9/4uKiliyZAlPPPEEADNnzqSiooI//elP3pARKI2Njd6RAfB8gZ9IoS69Xk9ubi65ubkcOHCAoqIimpqa2LBhAzNnziQ9PX0gu33aaW9vx2q1otFoTpvttEIIIfw3pELGsWbMmNHtoLPw8PBTUszpo48+6lYnIysri+nTp/t1Dsk3GY1Gxo0bx8iRI8nPz6e0tJRdu3b1ud21o6ODpqYmEhMT/RppOB111cWIjY09LXe9CCGE6NvgV4E6RVpbW1m+fDl33313wO8VHBxMSEiI90dRFA4fPsx//vMfDh48eMLXDQoKYvLkyQQFBdHR0dHr+Soul4t9+/ZRUVHB/v37+1y7cDrrChmDXXVU/P/27jysqTvfH/g7IQuEfUeQHQHRKlZbxbpR1LrbcVw6rV1nequP07Fqb6vP3G7W1lbrqK3e1vbWsZ2tjuO91dqhUxX3ulQFxA1EdkGWAAkEkpDk8/uDXzJEtqAkJ+Dn9Tx9Wk4O4YM1Oe98l89hjLG7029HMtrKysrCs88+i5kzZ2LRokV2/3nz58+3mh6pqanByZMnoVQqcfz4cQQHB9t059KOSCQSDB06FOfPn0d+fj7i4+PbnVNdXW1ZkNnY2IiamppuR3Cc7eZjdXV1KCwsBNC6s4Qxxljf0+9DxmuvvYbs7GysXbsWc+fOFaSGgIAAjBgxAocOHYJMJoNCobBas9FTrq6uADpvnGUwGLr8uiPO1FWzrq4O6enp0Ol08Pf3R3h4uNAlMcYYuwv9PmRs2LBB6BIAtPbwAICEhARIJJJ7ChkNDQ0A0Oli0ju3Y3a3G4KIUFtbCwDw8/O767p6g0qlwrFjx6DVauHv749p06bd9agPY4wxYfG7twPU1dWhvLwcIpEIgwcPvufnU6vVADofdehpyNBoNJY+FkLd0h0A6uvrcfToUcsIxrRp0yCXywWrhzHG2L25bxZ+Csk8ihEREdEr0xG9PZJhHsXw8fER7I6wHDAYY6z/4ZEMO2hpabEsvNTr9cjPzwfQOlViPq7T6WyaBmhqamq3psK8fkIikUCj0QCA5d/m525Lq9Vavkcmk6GpqcnqcfMuDk9PT8tjRGTTRb65udmm83Q6XacBxjxFotfr4ePjg6lTp0IqlXa5GNVoNHYZiNoGLVs6fgIAiWzo+tmDm9Pej23gGWOsLQ4ZdnDs2DFLS26tVguDwQAfHx+r+4+4urraFDL0er3VeTqdzhJU/Pz8LI+5ublZunbW1dVZPYdCoYC3tzeA1gvfnaMp5pGRAQMGWB5raWmxqT6JRGJTHw5PT88O25TX1dXhxIkT0Ov18Pf3x9SpU21qZy4Wi7vsndH2MVs6fhLZ1i6cepIybMBdPBlj/RmHDDsoLy9v13RryJAhvfLJ1hwIFApFpyGgJ9MlBoMB9fX1AODwrprmXSRtF3n21cZhjDGgorECdbq6bs/zlftigMeAbs9jfR+HDDtISUmxuj+ITCbrtW2Y5pDR1dqOO0NGV9MK5vUYbm5ud9WN9G7V1tbihx9+sAoYcrnc6fp1MMZsU9FYgbn75qLZ0P6u13dyk7hh39x9HDTuAxwy7CAmJuau7lVii7sJGV2NZCiVSgCOHcUoKCjAyZMnYTAYeJEnY/1Ena4OzYZmrB+/HjHeMZ2eV6AqwJoTa3Ch6gJidJ2fB/CIR3/AIaOPMS/MVKlUaGlp6XB64c5Q0dlIRl1dnaU1uaNChlarxbFjx0BEGDBgAB599FEOGIz1IzHeMUjyT+r0cV+5L9wkblhzYk23z8UjHn0fh4w+JioqChUVFaiursbRo0fxyCOPtFsoGRwcjJqaGhiNRnh4eHQ4qnLr1i2cO3cORqMRXl5eiIyMdEj9EonEMtIyadIkDhiM9RFF6iK4K2s7fbxAVWDT8wzwGIB9c/d1u3bDPOJRp6vjkNGHccjoY0JCQjBx4kT89NNPUKlUOHz4MMaOHWt1jru7O4YNGwa1Wg1/f3+rkQwiQm5uLnJycgC0BpIxY8Y4bMGlRCKBu7s7NBoNGhoaHLoOhDHWcxKv1svEW+fegktOeZfnuknc4Cv37fY5B3gM4OBwn+CQ0Qf5+fnh0UcfxalTpyw9JiIjIxESEmI5x9XV1XKPEzOTyYTCwkJLX4zY2FgMHz7c4Q24vLy8oNFooFar+eZnjDk5sXvr+8NLQ1/ClMGJXZ7LayjYnThk9FEKhQKpqak4e/YsKioqUFhYCIPBgLCwsA4Xera0tCAvL8/Skjw5ORlxcXGOLhtA66LViooKSy2MMecXqgjtcq0FYx3hkGEHBoOh2zufGgwGqxGEhoYGaDQaBAYGWjWSMhgMne4OEYlEGD16NHJycnDz5k2UlpaiqakJUVFRVs+t1WqRl5dn6br58MMPIyQkpMsajUajZe2EeYqlrKwM8fHxCA8Pt9RkMplsusur0Wi0nGfe3ltfX9/ue7vr5GlmMpl6v+OnDef05Dzu+MkYu99xyLADqVTa7RoHFxcXiMViFBcX4/r16ygvb53r9PHxQUpKCkJDQwG0To10d9EdP348vL29kZmZCaVSCZFIhHHjxsHV1RWVlZXIzs6GXq+HQqHAI488ggEDuh/ONLcV1+v1OH78OIqKigC0tiAPCgrC6NGjERwcDKlUatPF1GAwWMKT+SZsjY2N7bp2dtfJs+15Xf3cto/Z3PHTht/D1vMYY73DlgWlPE3jvDhkCECtViM3Nxc3b95Ec/O/G9dIpVLU19cjPT0d0dHRGD16tKVVeHfMvTlOnTqFmpoaHDx4EDExMbh8+TKICP7+/hg/fnyPbpteV1eHQ4cOQaVSQSwWIzY21rKm47vvvkN0dDRGjBjR454g5vPVajVftBljHeKtrv0DhwwHMZlMKCkpQW5urmXUAmjttBkfH4+EhATIZDJcuHAB169fR2FhIcrKyjBs2DAMGTLEpimEkJAQTJkyBceOHYNGo7HsIImIiMDDDz8MiURi09QGABQWFuLMmTMwGAxwd3dHWloagoKCMGrUKFy4cAF5eXkoLCxEcXExBg8ejGHDhtm8HdXcSEyv10On07VboMoYY7zVtX/gkGEHN2/etGorrlKpkJ+fbzVqERoaisGDByMiIsIqQIwdOxbx8fE4ffo0qqqqcOHCBeTn52PMmDE2TXN4eXlh6tSpOHnyJKqrqzFkyBAMHTrU5tECk8mE8+fP4/r165Y6U1NTLVtN3d3dMWHCBAwZMgTnzp3DrVu3cOXKFdy4cQPDhw/H4MGDuw1ERARXV1dotVqo1WoOGYz1AWV1Bly+peryHF93GcJ8em9bOm917fs4ZNjB6dOnO+z/4ObmhkGDBiE+Ph7u7u6dTl0EBARg1qxZuHHjBn7++WeoVCr861//wrBhw5CcnNztRVwul+PRRx+FXq/vcbOr8vJyS8AYPHgwUlJSOvx5/v7+mD59OoqKinD+/HnU19fj559/hlKpxLhx4yzf09LSgtraWlRVVaG+vh41NTWW284DaHfbecaYczHpGgCRHlsPqrD14Mkuz3WTuuDQqom9GjRY38Yhww7CwsKsunBKpVJER0cjPDzccvHt7kZgIpEI8fHxCA0NRWZmJvLy8nDp0iVUVlZiwoQJcHd37/b776abZkBAABQKBZqamlBeXg6tVtvlrdfDwsIQGhqKvLw8nD17FgUFBdDr9ZDJZFAqlVaBoi2FQoGQkBCbRmcYY/ZToCqA2LXj94rm5mZIfRrgHrsJbz/4EQYHD+70efKrGvHK7izUafQcMpgFhww7SE1N7bUbpMnlcowdOxYDBgzATz/9hMrKSuzfvx+PPfYY/Pz8euVntOXq6orHHnsM//rXv6BSqfD9999j5syZXQYNsViMxMREuLq64tixYygrK7N6XKFQwM/PD4GBgfD394e/v3+Xz8cYs7+aphoAwOrjq+Hi1nknz/CXwmHS1eHB8EDEBno7qjzWT9y3IeMvf/kLNm/ejJqaGixatAjvvfdej3Ze9LampibU1tZ22kwrOjoa/v7+OHLkCOrq6pCRkYFZs2bZZT2Dp6cnpk6dioMHD9ocNIDW+6pIJBLk5+fDx8fHKlAYDAaHtS5njHVP3dLaDO/lB1/GxNiO74ba3NyMcePGwdhgRMhTIR2ew1hX7suQsXbtWuzatQsrV65ERUUFNm7cCHd3d7z55psOr8VkMuHKlSvIzMxES0sLhgwZgjFjxnR4rpeXF6ZNm4YDBw6goaEBx48fx+TJk+3SFtzT0xMzZ87E999/36OgMXDgQAwcOLDX62GM2UeYR1innTw1Gg20xVoHV8T6k/suZOTm5mLbtm3IysqyNLzSarXYvn17j0OGTqeDTqezfG1uk21Lx0+9Xg+lUmlZ2Gl25coVeHh4YNCgQZbnaru2QiaTITU1Ff/85z9RXl6OCxcuYNSoUTZvTW1pabHp3JaWFri6umLKlCn48ccfoVKpcODAAUyZMsUqaBgMBpt7edjC1u6c9uj4aSvu+MkYY7a570LGrl278Prrr1sCBgD84he/wB/+8Aeo1eoeraVYv3493nnnnXbHJRJJl1MvGo0GZ8+eRXFxMYDWdRAPPvggNBoNsrOzce7cOXh7eyMkJKTD7qFBQUGYMGECMjIycOXKFQQFBVm1+u6K0Wi0edpCJBLBy8vLskZDrVbj0KFDVkFDIpHY9HxSqdSmERdbL+C93fHzzu9hjDF27+67kDF69Gg89NBDVsfMOxxaWlp69Fxr1qzBypUrLV+r1WqEh4d3er55aiQrK8tyT5L4+HgkJydDLpeDiKBSqVBUVIRjx45hxowZVv022oqJiUFNTQ0uXbqE48ePY/r06fD39+9R/bby9PS0Wgx68ODBdiMajDEmJG4/7pzuu5Dx+OOPtztmHu43f4rWaDRYt24d3n777S63gcrlcpu3iVZUVODMmTOor68H0LpVdPTo0VbBQCQSYezYsWhoaIBSqURGRgamTZvW6c8YNWoUamtrUVZWhqNHj2LmzJl2a2zVWdDoqB8IY4w5Crcfd273XcjoSNs7imo0GsycORPx8fG9stZAq9XizJkzKCwsBNA6NTJq1CiEh4d3OM0gkUgsay5UKhVOnDiBadOmdTjVIBaLMWnSJOzbt++uFoKaTCbU1taisbERwcHB3QaGzoLG3fTjYIyx3sDtx50bhwz8O2Q0NjZi4cKFiI+Px44dO3pljv7ChQuWgOHi4oIxY8YgKioKer2+0+9RKBRISkrC+fPnUV5ejvLy8k53bJgXZ+7fvx8VFRU4cOAAxowZg6CgoHbnmkwmKJVKVFdX4/bt26isrLSaIgoMDER4eDjCw8M7nQrx9PTEmDFjkJGRAZVKhatXr2Ls2LE9+SNhjLFexe3HnReHDMDyyX/evHl4+OGHey1gAEBsbCwqKyuhUqlgNBpx9OhRuLu7IyoqCoMGDWq30NRkMiE7O9tyczNfX98OA0Nbfn5+mDhxIk6ePIm6ujqkp6cjISEBycnJaGxstASKO0MF0DpVpFAoUF9fj+rqalRXV+PixYtQKBQIDw/HwIEDERISAhcXFxARrl+/josXLwJonS6KjIzslT8nxhhj/U+/DBn/+Mc/sHHjRtTW1mLNmjV44YUXujxfLpdDLBb3esAAWu+M+otf/AI1NTXIz89HQUEBNBoNrly5YtkZEhsbi8jISBgMBpw4cQKVlZUAgPj4eIwaNcqmaZuwsDA8/vjjlhuq5ebmIjc3t915UqkUISEhln/8/PwgFouh0WhQVlaG0tJSlJeXo6mpyfIcEokEoaGh0Ov1uH37NoDWG6eNHTuW12QwxhjrVL8LGR988AF27NiBFStWIDs7Gy+++CImTpyI2NjYdudmZWXhgQcegJ+fH06cOIGUlBS7bGMUiUQIDAxEYGAgHnroIZSWliI3Nxe3b99GVVUVqqqqcO7cObi4uECv10MikSAlJQXR0dE96u/g6uqKRx55BLGxsTh9+jTUajWkUimCg4MtocLLy6vDNRTu7u5ISEhAQkICDAYDSkpKUF5ejrKyMjQ3N6OkpARA65TPyJEjkZCQAJFI1Kv9JxhjvaOisaLbNQq3Gm8B4G3bzL76Vci4evUqPvnkE1y8eBHBwcEAgIMHD1rdYt2sqqoK48aNw+zZs/HnP//ZYesKJBIJoqOjLSMDBQUFuHnzpmU6xdfXFxMnTryne5+EhIRg7ty50Gg0cHd3t1oIajQabaoxLCwMkZGRICLU1taitLQUWq0WiYmJ8PHxuevaGGP2VdFYgTnfzoHW2HWnTmNzKIDfgbSEmpqaDs/RaDSW/66pqenwvdSsvl7z//9djxp51+0AXF1dO92eb2+81dWx+lXI+Prrr/HGG29YAkZRURFUKhUWLFiAoqIiLFy4EJ999hnc3NwQFBSE7du34/Tp073eltvWDpNubm4YMmQIkpKSLLdAj46Otqx/AFrXaNjaAdPFxcXytVgshqen513XZj4XaF3z0fZmbG2/39b6bNWT2mwddbL1d+ZmXMzZNTY2QqvtOjxcr7oOrVGLqS1T4Uu+nZ5XY3DDtwCyT2Wj8vz5Ds9p29F4//79Xe4ku62TAghCRkYGrnYTMiQSiU1b4HszjPBWV2GIqB+Nd//zn//EyJEjERwcDKVSidTUVAwePBjLly9Hfn4+li1bhueffx4ff/yxXX6+Wq2Gt7c36uvrux2JsPUi2V/OA2y7iPfkr2NXz6fRaCxvTg0NDXB3d++V+tjdM78+VCpVl68PW8/rT2wJD83NzTh48GC3twWoElVht2w3Pn7oYzwQ9ECn512v1GDxny7jz08PRWJwx68PjUaDqKgoAK0f2rp6HdnyfD35PYDWMLJw4cJeCxq2TCOZt7runrW703u6CKmvvT761UjGjBkzLP+dl5eHOXPmYN26dQCAsWPHoqysDF988YXdQgZjjPVUY2Mj/v73v9t80Z0+fXqXIwB5qjzsPrkbwcHBCPAP6PQ8H11rnx4fHx8EBHR8C/e2PycgIKDLkGHL85ktXLiw21BVV1eHI0eOoKKiAr6+nY/I9ISnqycG+PPohCP1q5DRVkpKClJSUqyOeXl5WaZSGGPM3mwZoairq4PBYEBqamq3F1Nbpg+qRFU9rtPRPDw8uv09XF1dIZFIcOTIkV77ub09MsK6129Dxp1UKhW2bt1qGdlgjLE72RIKbNXTaYEBAwbwxa8NDw8Pm0Y8bGXryEi9qr5Xfh5rdV+EjKysLDz77LOYOXMmFi1aJHQ5jDEn1JNpC1vZMr0B2L7A0dY1Bf2FLSMetrJ1ZKRKVAXIgOamZsA+95y8r/T7kPHaa68hOzsba9euxdy5c4UuhzEmgN6etrBVb+6OqGiswNx9c9Fs6HwbqZnMFIyqWjlMWlWn5+RXNfZKXX2FrSMj50vPY/el3cgszYROr+vyXG+ZN4Ldup+CF3LLrtD6fcjYsGGD0CUwxuykN3dlAM49bVGnq0OzoRnrx69HjHdMp+dVqQ1YsvMWnsu93u1zukld4Ot+7zeC7CtsGRmJM8VBki3B1htbgRtdP5+EJFisXwxPtG8XYHWeAFt2nUW/DxmMsb5JqVR2eSPBnoaH3py26G09mQaJ8Y7pcmulSauCtqUUWxYlIy6o69/F112GMB++NUBbcUFx2DNjD6oaul5AW9xYjPez3kdKWgriveM7Pc/89zQ9Pb3bn21LGGloaOj2eZwJhwzGmFPav39/t6GgP4SHOm0dVhxdYdM0iJvEDb5y26Zy4oI8MDSs662krGNxQXGIC4rr8hwfpQ+QBdSKarve0aMAhk8Z3u3Ui06nQ/aZ7G7DSFddV50Rh4xeZG4kpVarbTrXmZtn9YdmXGZqtdqmdurcjMu+zK+L7v4fmx/XBvtBEdR5nwcAcJHIcKqiqFfq620NugZ8kvUJdIbOR2PM5BJ//C75ZXjKux5295R6orjMgGKUdnpOQbUGJl0TGhvUUKvv7e90T15HjQ1qmHRNuFRQgcaG7t8D+zplsx4i9UCs2re5155T5iLDCwkvwEPaeSDWVrW2gO8rfTT7VcdPoZWVlSE8PFzoMhhzaqWlpRg4cGCnj/PriLHudfc6chYcMnqRyWRCeXk5PD09230qVqvVCA8PR2lpqVO2guX67g3X1z0iQkNDA0JDQ7u8X1BXr6O74Qy/e1e4vntzv9Vn6+vIWfB0SS8Si8XdJksvLy+nfCGYcX33huvrmrd392sEbHkd3Q2hf/fucH335n6qz5bXkbNw/hjEGGOMsT6JQwZjjDHG7IJDhoPI5XK89dZbkMvlQpfSIa7v3nB9zsvZf3eu795wfc6NF34yxhhjzC54JIMxxhhjdsEhgzHGGGN2wSGDMcYYY3bBIYMxxhhjdsEhw8Gqqqpw+PBhmEwmoUvpkzQaDc6ePWvT/WGE8H//93823RWUsTvpdDr89NNPKC8vF7qUPunChQsoLCwUuoxOEREyMzNRUlIidCkOxSHDgT777DMMGzYM6enpKCgoELqcdvbt24eUlBRERERg2bJl0Gq1Qpdk5auvvkJISAjGjBmDxMRE5ObmCl2Slc8//xzz5s3Dk08+6ZRB4+uvv0ZsbCwUCgWmTp2KkydPCl2SwxQUFGDhwoUYOHAgJk+ejGvXrgldkpWcnBwkJSVhy5YtuHjxotDltHPlyhVMmjQJbm5uGDx4MLZv3+5UH5Ru3ryJyZMnY9KkSU4ZNK5du4ahQ4fiwQcfRGxsLHbs2CF0SY5DzCEuXLhAfn5+VFhYKHQpHdq2bRuFhobS5s2bae3ataRQKOjll18WuiyLP/3pTxQVFUWnT5+msrIyGjlyJK1atUrosqy8/fbblJaWRnK5nBYsWEAtLS1Cl2Txl7/8haKioujAgQOUnp5OkydPJrFYTOvXrxe6NLu7evUqBQYG0vLly2nHjh2UmJhIsbGxpNFohC6NiIgMBgMlJCTQF198IXQpHbp16xYFBwfT5s2b6dSpU7Rq1SqSSqU0efJkUqlUQpdHRESXL1+m6OhoSkpKooiICCooKBC6JIuSkhIKCQmhTz/9lOrr6+nVV1+lyMhIoctyGA4ZDrJixQr6zW9+Y/naZDLRvn376L333qOMjAwBKyMqLy8nf39/ys3NtRx7//33ydXVlXQ6nYCVtdJqteTj40MnTpywHHv33XfpnXfeoYyMDKcJbt988w09/fTTlJ6ebhU0ysvLSavVClpbUlIS/eMf/7A69s477xAAWrdunUBVOUZaWhpt2bLF8vX169dJLBbT//7v/wpY1b+dOnWKfH19yWQyWY7l5OTQhx9+SH/84x+publZwOqI3nzzTXriiSesjp04cYJ8fX0pJSWFmpqaBKrs33Q6HXl7e1NFRYVV0DAajVRcXCxobYsXL6bly5dbvr527RqNHj2azp8/T2fPniWj0ShccQ7AIcNBnnvuOVq8eDERETU1NdGUKVMoODiYkpKSCICgn8o/+ugjWr16tdWxa9euEQC6efOmQFX92+XLlwkAXb16lYiIVCoVJSYmUkBAAAUGBpJYLKZNmzYJXCVRVlYWjRgxgojIEjTmzp1LcXFxtHfvXkFrCwwMpG+++abd8ffee49EIhH9+OOPAlRlf4WFhRQfH291ASciGjJkCH344YcCVWXtwIED5OHhYRn52rRpE7m7u9OIESNILpfT8OHDqb6+XrD6li5dSvPnz293/Pz58+Th4UFLliwRoKr24uLi6ObNm3T79m1L0FiwYAE9+eSTgtaVmJhIa9eutXz98ssvk0KhoPDwcBKJRDRlyhSnCGr2wiHDQbZs2UKenp5UWVlJr7/+Ov3yl7+0fLr9+OOPCQD9/PPPgtSWkZFBly9ftjpWX19PAOj69euC1NSWVqulQYMGUXx8PP3+97+nQYMG0YIFC6ipqYmMRiOtWrWKRCIRXblyRdA6m5ubyd3d3fLJZOfOnQSAkpOTBZ86mT9/Pj300ENkMBjaPTZ79mwaNWqUAFXZn1KppL/+9a/tjk+ePNlpRnBKSkpIJBLR119/TefOnaOwsDBLuL9y5Qr5+PjQ66+/Llh933zzDclkMrp27Vq7x7766isSi8VO8WFk1qxZtH//fiJqHZ318PAgFxcXwd8XVqxYQW5ubrRy5UqaMWMGRUREWN5XMzIySC6X07vvvitojfbEIcNBlEol+fv705w5cyg5ObndnOHAgQPpyy+/FKi69pqamgiA5Y1Fp9PRmjVrSK1WC1JPaWkpvfHGG/Thhx9SQEAANTY2Wh4zGAzk4eFBX331lSC1tRUVFUV5eXlUWlpKcXFx9OKLLzrFGo1Lly6RTCaj3/72t+0eO3fuHAEQ9NOyo82YMcPqjf3LL7+kM2fOCFbPggULKDAwkJYuXUobNmyweuyVV16h6dOnC1RZ6+srOTmZkpKSqLa21uoxk8lEkZGR9D//8z8CVfdvr776Kr3//vtkNBrpmWeeobS0NKdYo9HS0kLbtm2j//qv/6Jx48bR7t27rR5/+umnafbs2QJVZ3+8u8RB/Pz8sGvXLnz//ffIysqy2qamVqtRX1+PBx98UMAKrYlEIgCAyWSCXq/HggULcOPGDbi5uQlSz8CBA7F27VpMmjQJWq0WUqnU8phKpYJOp0NycrIgtbWVmJiIH374AampqVi2bBk+//xzfPvtt9i/fz+2b98uWF0PPPAA/vu//xvbtm3DihUrrHYG+Pn5QSaT3Vc3cBKJRJY/gy+++AJr165FYGCgYPVs27YNHh4e+PTTT9ttYS0uLhb0vcHFxQV79uxBdXU10tLScPv2bctjIpEIvr6+8PLyEqw+s8TERFy6dAnPP/88Kioq8N133yEjIwMeHh545plnBKtLIpFg2bJlePfdd1FcXNzuPbSyshIjRowQqDoHEDrl3G/27t1LCoWCYmJi6NChQ5SZmUmpqalWi0KdgVarJQB08eJFmjNnDs2fP1/wIX8iourqapLL5fTcc89Rc3MzVVZWUlpaGr300ktCl0ZERK+//jqJxWLavHmz1fGff/7ZKf78duzYQRKJhCZOnEhHjhyhy5cv05QpU2jlypVCl+ZQs2bNorfffps+//xzioyMdIrh/uLiYkpKSiKJREKffPIJ5ebm0ptvvkkxMTHtRhCEcPnyZQoPD6eQkBDauXMn5efn07p16yg+Pl7wxalErSNyHa1xuH37ttPsNpk6dSolJCRQfn4+tbS00Pvvv0+xsbH9ehSRQ4YAcnNzadGiReTj40NBQUH01ltvdThXLiS9Xk8AaPjw4U4TMMx27dpFUqmUZDIZubi40NKlS52mPo1GQ3v27BG6jC6dO3eO0tLSyMXFhRQKBb322mtO8+fnKHPmzKFhw4Y5TcAwa2pqojfeeIMiIyPJ3d2dHn/8cbp165bdf+6NGzds2uWgVCpp6dKl5OXlRSKRiCZPnkwlJSVOU9/u3bsFWURpa303btygiIgIAkCurq40YsQIKioqckCFwuGQ0Uu+/fZbmjJlCk2cOJHWr19PDQ0NQpdk5fTp0z0632QykUwmc1jA6Gl9RUVFtHv37g4Xo9nD8ePHafHixTRv3jxKT093yM/sifT0dBo3bhxFR0fTH/7wB5u+R6/XO124vRslJSX01FNPUUREBC1atMimT4VPPPEERUREOCRg3E19jpSZmUk+Pj707LPP2ryd0mg0Omxb9t3U50g9rU+j0dC+ffvo8OHDTvn79DYOGb1gx44dFBkZSVu3bqXVq1eTt7c3xcTE0KVLl9qdm5OT4/CeCTt27CAA9MEHH3R7btv6zpw545CAcbf1OcrWrVspICCAXnrpJZowYQKJRCI6ePCg09T3+eefU2hoKG3atImWL19OAOjs2bNOU5895eXlUUhICP3ud7+jrVu3kp+fH7322msdnqtWqy29YG7duuWQIfS7rc+RvvzyS0pMTCSpVNrlhZLr65iz1yc0Dhn3yNwEpu3207KyMho1ahT5+PhQVlaW5XhDQwMFBgbStGnTHPZGbzAYKCoqin772992eyHn+tq7fPky+fv7W12QJkyYQOPHj3eK+kpLSyk4ONiqvuTkZDpy5IhT1GdvkyZNos8++8zy9bp162jZsmUdnrt48WIKDg526JZGZ6+PqLUZWGpqKu3du9fqQmk0Gq0+ZHB9fbM+oXHIuEclJSUEgEpLS62Oq9Vqevjhhyk8PJzq6uosx/ft20dPPvmkw+bA9+3bR7NmzSKi1i6e3V3IuT5ry5Yto48//tjq2M6dO8nX17fD8x1d38aNG622YlZXV5Ovry898MADJJfL6Ze//KXV8Lyj67OnwsJCSk5Otjo2e/ZsGjp0KMlkMhoyZIjViM6tW7coLS3NYWswnL0+M6VSSUFBQUREVhfKZ555xqqXCNfXN+sTGoeMe2Q0GikwMJDefPPNdo9VVFRQQEBAu26ajlRaWmr1RmbLhdyRnL2+NWvWUE1NjdWxw4cPk5ubm0AVWTt58iTl5eURUetIxdixY2n69Ol0/Phx2r17NwUEBNCvfvUrgau0j/r6eqtOqu+88w4NHDiQ/v73v9OpU6do/PjxFBAQINj9NZy9vrYCAwOpqqqKiIj27NlDACgoKMiqH42QuL6+i0NGL9i4cSNJpVI6duxYu8c2bNhACQkJAlTVuTsv5DqdjrZv396u9bJQnL2+U6dOkVwut3yt0+na3RdECDk5ObR8+XKrOeEdO3aQh4eHgFU5hl6vp+eff95qJ0Z1dTWJxWL64YcfBKyslbPXN378eMrIyLA0sho5cmS3awy4vr5Tn5Akju7L0R+tWLEChw4dwuzZs/H9999j3LhxlseSkpJgNBoFrK69NWvWAABWr14Ng8GAc+fOQSaT4T/+4z8gkQj/V8LZ6xOLxZZGTuZGZTKZDPPmzbM0MRPC0KFDsWXLFqtjXl5eCA4OFqYgB5JKpdi5c6fVMTc3N0gkEqf4/Z29voSEBFy6dAm7du1CRUUFTpw4gfT0dDzxxBMICgrChg0buL4+XJ+ghE45/UVjYyNNnjyZ5HI5bdy4kZqamkilUtGjjz7qtH3p3333XQLgdH0wzJy1vjNnzpBEIiGdTudUjcru1NzcTCNGjKBt27YJXYog3njjDZo0aZLQZXTKmerbtGkTeXp6tmtk9d133znFGgKur+/ikNGLWlpa6O233yZ3d3eSyWQkl8tpyZIlTtmLwNkvkM5c39mzZ8nFxcVp6yNqvZ15SkoKPf30004zzeQoDQ0NtGLFCoqLi6OysjKhy2nHGetTqVS0atUqp70bKNfXd4mIiIQeTelvmpqakJOTg9DQUISHhwtdToc++ugjnD17Fn/729+cYgriTs5c38WLFzFy5EjMnz/fKetbv349fvzxR/zmN7/BU089JXQ5DnXx4kX8+te/xsyZM7F69Wp4eHgIXZIVZ6+Psd7GIeM+ZTAYAMDpLpBmzlyf0WjEp59+iiVLljhlfYwx5iw4ZDDGGGPMLvhW74wxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcY69Pvf/x7r168XugzWh3HIYIwx1qH9+/fDw8ND6DJYH8YhgzHGWDs6nQ7Xr1/HiBEjhC6F9WEcMli/MW7cOKxcuRJLly6Fn58fAgIC8Omnn0KpVOKFF16Aj48PQkJC8M033whdKmNOLycnB0ajEQqFAr/+9a8xYcIEvPrqq2hsbBS6NNaHcMhg/QIR4dKlS9izZw+mT5+OkpISvPjii1i5ciV+9atfYdGiRbh16xYWLlyIl19+WehyGXN6WVlZcHV1xSuvvILZs2fj1Vdfxd69e7FkyRKhS2N9iEToAhjrDQUFBWhoaMAXX3yBOXPmAABmzZqFDz74AMuXL8djjz0GAJg5cya2b98Ok8kEsZgzNmOdycrKglQqxd69exEYGAgAUKlUeOmll0BEEIlEAlfI+gJ+l2X9QlZWFjw9PTFv3jzLscLCQgQGBmL69OlWx6KiojhgMNaNzMxMvPjii5aAAQChoaFobm6GXq8XsDLWl/A7LesXsrOzkZycDKlUajmWmZmJUaNGWQUK83mMsc6Zpx/HjBljdfzq1auIioqCXC4XqDLW13DIYP1CVlZWu1XwHR3Lzs7m1fKMdePGjRtobGyEp6en1fGdO3fiiSeeEKgq1hdxyGD9Qkfh4c5jRIScnBweyWCsG1lZWRCJRPjb3/4GIgIRYd26daitrcV//ud/Cl0e60N44Sfr8+rr61FSUmIVHsrKyqBUKq2O3bx5E42NjRwyGOtGVlYW0tLS0NzcjMjISBiNRoSFheHw4cPw8/MTujzWh4iIiIQugjHGmPMoLCyESCRCVFQUioqKYDAYEBcXJ3RZrA/ikMEYY4wxu+A1GYwxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcYYs4v/B8+vGdE6d5ACAAAAAElFTkSuQmCC", + "text/plain": [ + "<Figure size 550x550 with 4 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 400x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x200 = np.linspace(0.0, 1.0, 200)\n", + "y200_true = 2.0 * x200 + 4.0\n", + "y200 = y200_true * (1.0 + rng.normal(0.0, 0.05, x200.size))\n", + "d200 = rx.Dataset(x200, y200, 0.05 * y200, label=\"N = 200\")\n", + "m_, b_ = rx.Parameter(\"m\", prior=stats.norm(1.0, 0.5), latex=\"m\"), rx.Parameter(\n", + " \"b\", prior=stats.norm(4.0, 0.5), latex=\"b\"\n", + ")\n", + "line = rx.Model(lambda x, m, b: m * x + b, [m_, b_])\n", + "comp200 = rx.Comparison(d200, line)\n", + "p_full = rx.Problem([rx.Constraint([comp200])])\n", + "p_temp = rx.Problem([rx.Constraint([comp200], weight=2 / 200)])\n", + "\n", + "\n", + "def fit(problem, seed, n_walkers=24, n_steps=2000):\n", + " s = emcee.EnsembleSampler(n_walkers, problem.ndim, problem.log_posterior)\n", + " s.random_state = np.random.RandomState(seed).get_state()\n", + " s.run_mcmc(problem.sample_prior(n_walkers, rng=seed), n_steps, progress=False)\n", + " return s.get_chain(discard=n_steps // 3, thin=5, flat=True)\n", + "\n", + "\n", + "s_full, s_temp = fit(p_full, 3), fit(p_temp, 4)\n", + "fig = corner.corner(\n", + " p_full.sample_prior(4000, rng=5),\n", + " color=\"0.6\",\n", + " labels=[\"$m$\", \"$b$\"],\n", + " truths=[2.0, 4.0],\n", + " truth_color=\"k\",\n", + " plot_datapoints=False,\n", + " range=[(0.5, 3.5), (3.0, 5.0)],\n", + ")\n", + "corner.corner(\n", + " s_temp, fig=fig, color=\"C2\", plot_datapoints=False, range=[(0.5, 3.5), (3.0, 5.0)]\n", + ")\n", + "corner.corner(\n", + " s_full, fig=fig, color=\"C0\", plot_datapoints=False, range=[(0.5, 3.5), (3.0, 5.0)]\n", + ")\n", + "fig.legend(\n", + " handles=[\n", + " plt.Line2D([], [], color=\"0.6\", label=\"prior\"),\n", + " plt.Line2D([], [], color=\"C2\", label=\"tempered, w = k/N\"),\n", + " plt.Line2D([], [], color=\"C0\", label=\"untempered\"),\n", + " ],\n", + " loc=\"upper right\",\n", + " frameon=False,\n", + ")\n", + "plt.show()\n", + "\n", + "draws = rx.diagnostics.predictive_draws(p_full, s_full[::20], n_rep=2, rng=6)\n", + "levels = np.linspace(0.1, 0.9, 9)\n", + "c200 = p_full.constraints[0]\n", + "cov = rx.diagnostics.coverage_curve(draws, c200.y[c200.active], levels)\n", + "model_only = rx.diagnostics.predictive_draws(p_full, s_full[::20], model_only=True)\n", + "cov_model = rx.diagnostics.coverage_curve(model_only, c200.y[c200.active], levels)\n", + "fig, ax = plt.subplots(figsize=(4, 4))\n", + "ax.plot(levels, cov, \"o-\", label=\"posterior predictive of the data\")\n", + "ax.plot(levels, cov_model, \"s-\", color=\"C3\", label=\"model band only\")\n", + "ax.plot([0, 1], [0, 1], \"--\", color=\"0.5\")\n", + "ax.set(xlabel=\"nominal coverage\", ylabel=\"empirical coverage\", xlim=(0, 1), ylim=(0, 1))\n", + "ax.legend(frameon=False, fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "14a358dd", + "metadata": {}, + "source": [ + "## An inferred model error per data type (recipe 26)\n", + "\n", + "KDUQ's treatment of unaccounted-for model error is one `model_error` term\n", + "per data type, a diagonal that scales with the prediction and is averaged\n", + "over the type's points, with one free fraction per type; its democratic and\n", + "federal scalings are the tempering weights above. With one data type here\n", + "the spelling is a single term; a second type would be a second constraint\n", + "with its own `delta`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "03c9ba75", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:50:40.078176Z", + "iopub.status.busy": "2026-09-11T03:50:40.077908Z", + "iopub.status.idle": "2026-09-11T03:50:40.084813Z", + "shell.execute_reply": "2026-09-11T03:50:40.083944Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "columns: ['Vv', 'Wv', 'Rv', 'av', 'Wd', 'Rd', 'ad', 'log_delta_dXS']\n", + "weight k/N = 0.350\n" + ] + } + ], + "source": [ + "delta_xs = rx.Parameter(\n", + " \"log_delta_dXS\", prior=stats.norm(np.log(0.1), 1.0), latex=r\"\\log\\delta_{d\\sigma}\"\n", + ")\n", + "c_kduq = rx.Constraint(\n", + " [comp],\n", + " terms=[*reported, T.model_error(delta_xs, averaging=True)],\n", + " weight=len(params) / d.n,\n", + ")\n", + "p_kduq = rx.Problem([c_kduq])\n", + "print(\"columns:\", p_kduq.names)\n", + "print(f\"weight k/N = {c_kduq.weight:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "12e3fdbf", + "metadata": {}, + "source": [ + "## Takeaways\n", + "\n", + "- The unit contract: `from_measurement` converts what is dimensionful and\n", + " leaves fractions alone; the kinematics ride in `meta`.\n", + "- Systematics are opt-in terms built from the prediction; nothing correlated\n", + " hides in a default.\n", + "- A dataset that contributes zero variance fails at construction with a\n", + " message naming it, not mid-chain.\n", + "- dynesty for optical potentials; emcee and dill for whatever else drives\n", + " the same problem.\n", + "- Tempering is a weight on a constraint; the predictive check tells you\n", + " whether you needed it." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 9a38cda7d2467ba00e0a17c8a8a7ccfd8b60eaeb Mon Sep 17 00:00:00 2001 From: beykyle <kylebeyer1@gmail.com> Date: Fri, 11 Sep 2026 00:03:25 -0400 Subject: [PATCH 40/75] Add the gp_discrepancy notebook (recipes 7, 8, 36) A line fit to a curving truth with a Matern kernel term: the hyperparameter posterior, the total predictive band from the problem alone, the conditioned GP against the true defect, and a sampled Legendre mean correction for contrast. Then n+40Ca with a potential missing its surface absorption, dynesty with and without the GP: the evidence, the relaxed volume depth, and the learned angular defect against the missing physics. Runs in about 10 minutes. --- examples/gp_discrepancy.ipynb | 802 ++++++++++++++++++++++++++++++++++ 1 file changed, 802 insertions(+) create mode 100644 examples/gp_discrepancy.ipynb diff --git a/examples/gp_discrepancy.ipynb b/examples/gp_discrepancy.ipynb new file mode 100644 index 0000000..6ce23a0 --- /dev/null +++ b/examples/gp_discrepancy.ipynb @@ -0,0 +1,802 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f32c8e7d", + "metadata": {}, + "source": [ + "# Model discrepancy with a Gaussian process\n", + "\n", + "A structurally wrong model leaves correlated residuals. A Gaussian-process\n", + "term in the covariance absorbs them (Kennedy and O'Hagan's discrepancy), the\n", + "model parameters relax toward their true values, and the total predictive\n", + "band bends where the model cannot. First on a toy, then on a differential\n", + "cross section whose potential is missing a piece of physics. For contrast,\n", + "the same defect is also fit with an explicitly sampled mean correction.\n", + "\n", + "Recipes: 7, 8, 36" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "40ac011a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:47:54.370211Z", + "iopub.status.busy": "2026-09-11T03:47:54.370028Z", + "iopub.status.idle": "2026-09-11T03:47:57.331437Z", + "shell.execute_reply": "2026-09-11T03:47:57.330670Z" + } + }, + "outputs": [], + "source": [ + "import corner\n", + "import dynesty\n", + "import emcee\n", + "import jitr\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from jitr.optical_potentials.potential_forms import (\n", + " thomas_safe,\n", + " woods_saxon_prime_safe,\n", + " woods_saxon_safe,\n", + ")\n", + "from numpy.polynomial import legendre as L\n", + "from scipy import stats\n", + "from sklearn.gaussian_process.kernels import ConstantKernel, Matern, WhiteKernel\n", + "\n", + "import rxmc as rx\n", + "from rxmc import terms as T\n", + "from rxmc import transforms as tf" + ] + }, + { + "cell_type": "markdown", + "id": "2f89ca34", + "metadata": {}, + "source": [ + "## A linear model and a non-linear truth\n", + "\n", + "Thirty points of a gently curving function, fit by a line. No line can\n", + "follow it, so the residuals of the best line are smooth and correlated." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "34d7834b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:47:57.333388Z", + "iopub.status.busy": "2026-09-11T03:47:57.333114Z", + "iopub.status.idle": "2026-09-11T03:47:57.490368Z", + "shell.execute_reply": "2026-09-11T03:47:57.489590Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 500x350 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def truth(x):\n", + " return (0.8 * x + 1.0) / (1.0 + x / 2.0)\n", + "\n", + "\n", + "rng = np.random.default_rng(7)\n", + "x = np.linspace(0.2, 5.0, 30)\n", + "noise = 0.02\n", + "data = rx.Dataset(\n", + " x, truth(x) + rng.normal(0.0, noise, x.size), np.full(x.size, noise), label=\"toy\"\n", + ")\n", + "x_fine = np.linspace(0.0, 6.0, 80)\n", + "\n", + "m = rx.Parameter(\"m\", prior=stats.norm(0.0, 2.0), latex=\"m\")\n", + "b = rx.Parameter(\"b\", prior=stats.norm(0.0, 2.0), latex=\"b\")\n", + "line = rx.Model(lambda x, m, b: m * x + b, [m, b])\n", + "comp = rx.Comparison(data, line)\n", + "\n", + "fig, ax = plt.subplots(figsize=(5, 3.5))\n", + "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", ms=4, color=\"k\", label=\"data\")\n", + "ax.plot(x_fine, truth(x_fine), \"--\", color=\"C3\", label=\"truth\")\n", + "ax.set(xlabel=\"x\", ylabel=\"y\")\n", + "ax.legend(frameon=False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "df44a564", + "metadata": {}, + "source": [ + "## The GP discrepancy as a covariance term\n", + "\n", + "`T.kernel` wraps any scikit-learn kernel. One `Parameter` is derived per\n", + "free hyperparameter, sampled in sklearn's log-theta space; here they are\n", + "passed explicitly with priors. Two references: the line with its reported\n", + "statistics only, and the line with an uncorrelated model error\n", + "(`T.model_error`), which can widen the diagonal but cannot describe a smooth\n", + "defect." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "726517aa", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:47:57.492272Z", + "iopub.status.busy": "2026-09-11T03:47:57.492009Z", + "iopub.status.idle": "2026-09-11T03:50:43.171690Z", + "shell.execute_reply": "2026-09-11T03:50:43.170763Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GP problem columns: ['m', 'b', 'log_A2', 'log_ell']\n" + ] + } + ], + "source": [ + "kernel = ConstantKernel(1.0) * Matern(length_scale=2.0, nu=2.5) + WhiteKernel(\n", + " 1e-6, \"fixed\"\n", + ")\n", + "log_amp2 = rx.Parameter(\"log_A2\", prior=stats.norm(-4.0, 2.0), latex=r\"\\log A^2\")\n", + "log_ell = rx.Parameter(\"log_ell\", prior=stats.norm(0.5, 1.0), latex=r\"\\log \\ell\")\n", + "gp = T.kernel(kernel, params=[log_amp2, log_ell])\n", + "gamma = rx.Parameter(\n", + " \"log_gamma\", prior=stats.norm(np.log(0.05), 1.0), latex=r\"\\log\\gamma\"\n", + ")\n", + "\n", + "p_stat = rx.Problem([rx.Constraint([comp])])\n", + "p_diag = rx.Problem(\n", + " [rx.Constraint([comp], terms=[T.model_error(gamma, averaging=True)])]\n", + ")\n", + "p_gp = rx.Problem([rx.Constraint([comp], terms=[gp])])\n", + "print(\"GP problem columns:\", p_gp.names)\n", + "\n", + "\n", + "def fit(problem, seed, start=None, n_walkers=24, n_steps=3000):\n", + " # emcee from the prior, or from a small ball around ``start``\n", + " rng = np.random.default_rng(seed)\n", + " if start is None:\n", + " p0 = problem.sample_prior(n_walkers, rng=rng)\n", + " else:\n", + " p0 = np.asarray(start) + 1e-2 * rng.standard_normal((n_walkers, problem.ndim))\n", + " sampler = emcee.EnsembleSampler(n_walkers, problem.ndim, problem.log_posterior)\n", + " sampler.random_state = np.random.RandomState(seed).get_state()\n", + " sampler.run_mcmc(p0, n_steps, progress=False)\n", + " return sampler.get_chain(discard=n_steps // 3, thin=5, flat=True)\n", + "\n", + "\n", + "s_stat = fit(p_stat, 1)\n", + "line_fit = np.median(s_stat, axis=0) # a starting point for the nuisance problems\n", + "s_diag = fit(p_diag, 2, start=[*line_fit, np.log(0.05)])\n", + "s_gp = fit(p_gp, 3, start=[*line_fit, -4.0, 0.5])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "dfd18d85", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:50:43.173461Z", + "iopub.status.busy": "2026-09-11T03:50:43.173260Z", + "iopub.status.idle": "2026-09-11T03:50:44.318247Z", + "shell.execute_reply": "2026-09-11T03:50:44.317368Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 550x550 with 4 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = corner.corner(\n", + " s_gp[:, p_gp.columns([log_amp2, log_ell])],\n", + " labels=[r\"$\\log A^2$\", r\"$\\log \\ell$\"],\n", + " show_titles=True,\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c31aaa3b", + "metadata": {}, + "source": [ + "## Propagating the total uncertainty\n", + "\n", + "The line's own band (its parameters only) cannot bend. `total_predictive_band`\n", + "conditions the GP on the residuals at each posterior row, predicts the\n", + "discrepancy on the plotting grid, and adds it: everything the problem\n", + "declared is propagated, and nothing has to be told which columns are the\n", + "kernel's." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a8203a83", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:50:44.320048Z", + "iopub.status.busy": "2026-09-11T03:50:44.319852Z", + "iopub.status.idle": "2026-09-11T03:50:46.792643Z", + "shell.execute_reply": "2026-09-11T03:50:46.791794Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def mean_band(problem, samples, levels=(16, 84)):\n", + " on_fine = line.bind(x_fine, {})\n", + " cols = problem.columns(line.params)\n", + " return rx.predictive.predictive_band(\n", + " [on_fine(*s[cols]) for s in samples[::10]], levels=levels\n", + " )\n", + "\n", + "\n", + "band_gp = rx.predictive.total_predictive_band(\n", + " p_gp, gp, line.bind(x_fine, {}), x_fine, s_gp, rng=4\n", + ")\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "for label, (p, s), color in (\n", + " (\"line, statistics only\", (p_stat, s_stat), \"C0\"),\n", + " (\"line + model error\", (p_diag, s_diag), \"C2\"),\n", + "):\n", + " lo, hi = mean_band(p, s)\n", + " ax.fill_between(x_fine, lo, hi, color=color, alpha=0.3, label=label)\n", + "ax.fill_between(\n", + " x_fine, *band_gp, color=\"C1\", alpha=0.4, label=\"line + GP discrepancy (total)\"\n", + ")\n", + "ax.plot(x_fine, truth(x_fine), \"--\", color=\"k\", label=\"truth\")\n", + "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", ms=3, color=\"k\")\n", + "ax.set(xlabel=\"x\", ylabel=\"y\", title=\"68 % bands\")\n", + "ax.legend(frameon=False, fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "33ef28b2", + "metadata": {}, + "source": [ + "## What the GP captured\n", + "\n", + "Condition the GP on the residuals of the posterior-mean line and compare\n", + "with the true defect, `truth(x) − line(x)`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9f1b3e65", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:50:46.794463Z", + "iopub.status.busy": "2026-09-11T03:50:46.794265Z", + "iopub.status.idle": "2026-09-11T03:50:46.959580Z", + "shell.execute_reply": "2026-09-11T03:50:46.958941Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x350 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "theta_gp = np.median(s_gp, axis=0)\n", + "resid = data.y - line.bind(x, {})(*theta_gp[p_gp.columns(line.params)])\n", + "mean_d, cov_d = rx.predictive.gp_posterior_predictive(\n", + " kernel,\n", + " theta_gp[p_gp.columns([log_amp2, log_ell])],\n", + " x,\n", + " resid,\n", + " x_fine,\n", + " train_noise_var=noise**2,\n", + ")\n", + "sd_d = np.sqrt(np.clip(np.diag(cov_d), 0, None))\n", + "true_defect = truth(x_fine) - line.bind(x_fine, {})(\n", + " *theta_gp[p_gp.columns(line.params)]\n", + ")\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 3.5))\n", + "ax.errorbar(x, resid, noise, fmt=\"o\", ms=3, color=\"k\", label=\"residuals\")\n", + "ax.fill_between(\n", + " x_fine,\n", + " mean_d - sd_d,\n", + " mean_d + sd_d,\n", + " color=\"C1\",\n", + " alpha=0.4,\n", + " label=\"GP posterior (68 %)\",\n", + ")\n", + "ax.plot(x_fine, true_defect, \"--\", color=\"C3\", label=\"true defect\")\n", + "ax.set(xlabel=\"x\", ylabel=\"y - line\")\n", + "ax.legend(frameon=False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2482e3d3", + "metadata": {}, + "source": [ + "## For contrast: a sampled mean correction (recipes 8, 36)\n", + "\n", + "The discrepancy can instead be a *mean* correction with sampled\n", + "coefficients: `line + rx.Model(delta, phi)` on a Legendre basis. It removes\n", + "the bias too, but its band is only the coefficients' uncertainty, and it\n", + "says nothing outside the data. A GP is the same idea with the coefficients\n", + "integrated out and a smoothness prior instead of a basis choice." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "51067230", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:50:46.961699Z", + "iopub.status.busy": "2026-09-11T03:50:46.961480Z", + "iopub.status.idle": "2026-09-11T03:51:28.757180Z", + "shell.execute_reply": "2026-09-11T03:51:28.756473Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "line only m = 0.066 +/- 0.003 b = 1.127 +/- 0.008\n", + "line + GP m = 0.093 +/- 0.047 b = 0.942 +/- 0.295\n", + "line + correction m = 0.082 +/- 0.163 b = 1.016 +/- 0.668\n" + ] + } + ], + "source": [ + "phis = [\n", + " rx.Parameter(f\"phi_{k}\", prior=stats.norm(0.0, 0.5), latex=rf\"\\phi_{k}\")\n", + " for k in range(4)\n", + "]\n", + "\n", + "\n", + "def delta(x, *phi):\n", + " return L.legval(np.asarray(x, dtype=float) / 3.0 - 1.0, phi)\n", + "\n", + "\n", + "corrected = line + rx.Model(delta, phis)\n", + "p_mean = rx.Problem([rx.Constraint([rx.Comparison(data, corrected)])])\n", + "s_mean = fit(p_mean, 5, start=[*line_fit, 0.0, 0.0, 0.0, 0.0])\n", + "on_fine = corrected.bind(x_fine, {})\n", + "lo, hi = rx.predictive.predictive_band(\n", + " [on_fine(*s[p_mean.columns(corrected.params)]) for s in s_mean[::10]],\n", + " levels=(16, 84),\n", + ")\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "ax.fill_between(\n", + " x_fine, lo, hi, color=\"C4\", alpha=0.4, label=\"line + sampled Legendre correction\"\n", + ")\n", + "ax.fill_between(\n", + " x_fine, *band_gp, color=\"C1\", alpha=0.3, label=\"line + GP discrepancy (total)\"\n", + ")\n", + "ax.plot(x_fine, truth(x_fine), \"--\", color=\"k\", label=\"truth\")\n", + "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", ms=3, color=\"k\")\n", + "ax.set(xlabel=\"x\", ylabel=\"y\", title=\"68 % bands\")\n", + "ax.legend(frameon=False, fontsize=8)\n", + "plt.show()\n", + "for name, p, s in (\n", + " (\"line only\", p_stat, s_stat),\n", + " (\"line + GP\", p_gp, s_gp),\n", + " (\"line + correction\", p_mean, s_mean),\n", + "):\n", + " cols = p.columns(line.params)\n", + " print(\n", + " f\"{name:18s} m = {s[:, cols[0]].mean():.3f} +/- {s[:, cols[0]].std():.3f} b = {s[:, cols[1]].mean():.3f} +/- {s[:, cols[1]].std():.3f}\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "d6beebce", + "metadata": {}, + "source": [ + "## A differential cross section with missing physics\n", + "\n", + "Mock n + ⁴⁰Ca elastic scattering at 14.1 MeV generated from a potential with\n", + "volume *and* surface absorption, fit with a potential that has only volume\n", + "absorption. Reaction models are driven by dynesty: affine-invariant\n", + "ensembles mix poorly on optical-model posteriors. The comparison is in log\n", + "space, so the GP describes a fractional defect in angle." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "34dcbfaa", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:51:28.758865Z", + "iopub.status.busy": "2026-09-11T03:51:28.758684Z", + "iopub.status.idle": "2026-09-11T03:51:41.577514Z", + "shell.execute_reply": "2026-09-11T03:51:41.576769Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "reaction = jitr.reactions.ElasticReaction(target=(40, 20), projectile=(1, 0))\n", + "E_lab = 14.1\n", + "R40 = 40 ** (1 / 3)\n", + "mso = 1.0 / jitr.utils.constants.WAVENUMBER_PION\n", + "\n", + "\n", + "def full_central(r, Vv, Wv, Rv, av, Wd, Rd, ad):\n", + " return -(Vv + 1j * Wv) * woods_saxon_safe(r, Rv, av) - 1j * Wd * (\n", + " -4 * ad\n", + " ) * woods_saxon_prime_safe(r, Rd, ad)\n", + "\n", + "\n", + "def volume_central(r, Vv, Wv, Rv, av):\n", + " return -(Vv + 1j * Wv) * woods_saxon_safe(r, Rv, av)\n", + "\n", + "\n", + "def spin_orbit(r, Vso, Rso, aso):\n", + " return Vso * mso**2 * thomas_safe(r, Rso, aso)\n", + "\n", + "\n", + "so_args = (6.0, 1.1 * R40, 0.45)\n", + "full_truth = np.array([48.0, 3.5, 1.1 * R40, 0.7, 21.0, 1.2 * R40, 0.5])\n", + "volume_truth = full_truth[:4]\n", + "\n", + "full_params = [\n", + " rx.Parameter(n, prior=stats.norm(0, 1))\n", + " for n in (\"Vv\", \"Wv\", \"Rv\", \"av\", \"Wd\", \"Rd\", \"ad\")\n", + "]\n", + "omp_full = rx.reactions.ElasticXS(\n", + " \"dXS/dA\",\n", + " full_central,\n", + " spin_orbit,\n", + " lambda ws, *x: (tuple(x), so_args),\n", + " full_params,\n", + " lmax=10,\n", + ")\n", + "volume_params = [\n", + " rx.Parameter(\"Vv\", prior=stats.norm(48.0, 8.0), latex=\"V_v\"),\n", + " rx.Parameter(\"Wv\", prior=stats.norm(4.0, 4.0), bounds=(0.0, 30.0), latex=\"W_v\"),\n", + " rx.Parameter(\"Rv\", prior=stats.norm(1.15 * R40, 0.2), latex=\"R_v\"),\n", + " rx.Parameter(\"av\", prior=stats.norm(0.65, 0.1), bounds=(0.3, 1.2), latex=\"a_v\"),\n", + "]\n", + "omp_vol = rx.reactions.ElasticXS(\n", + " \"dXS/dA\",\n", + " volume_central,\n", + " spin_orbit,\n", + " lambda ws, *x: (tuple(x), so_args),\n", + " volume_params,\n", + " lmax=10,\n", + ")\n", + "\n", + "angles = np.deg2rad(np.linspace(5.0, 160.0, 25))\n", + "meta = {\"reaction\": reaction, \"Elab\": E_lab}\n", + "y_full = omp_full.bind(angles, meta)(*full_truth)\n", + "y_xs = y_full * (1 + rng.normal(0.0, 0.04, angles.size))\n", + "d_xs = rx.Dataset(angles, y_xs, 0.04 * y_xs, label=\"n+40Ca 14.1 MeV\", meta=meta)\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "ax.errorbar(\n", + " np.rad2deg(angles),\n", + " d_xs.y,\n", + " d_xs.y_err,\n", + " fmt=\"o\",\n", + " ms=3,\n", + " color=\"k\",\n", + " label=\"mock data (full potential)\",\n", + ")\n", + "ax.plot(\n", + " np.rad2deg(angles),\n", + " omp_vol.bind(angles, meta)(*volume_truth),\n", + " color=\"C3\",\n", + " label=\"volume-only potential at the true volume parameters\",\n", + ")\n", + "ax.set(xlabel=r\"$\\theta_{cm}$ [deg]\", ylabel=r\"$d\\sigma/d\\Omega$ [b/sr]\", yscale=\"log\")\n", + "ax.legend(frameon=False, fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2e828a7d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:51:41.579034Z", + "iopub.status.busy": "2026-09-11T03:51:41.578854Z", + "iopub.status.idle": "2026-09-11T03:58:07.027068Z", + "shell.execute_reply": "2026-09-11T03:58:07.026220Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log Z = -649.52 +/- 0.65, 88099 likelihood calls\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log Z = -17.48 +/- 0.53, 64665 likelihood calls\n" + ] + } + ], + "source": [ + "comp_xs = rx.Comparison(d_xs, omp_vol, space=tf.log)\n", + "xs_amp = rx.Parameter(\"log_A2_xs\", prior=stats.norm(-4.0, 2.0), latex=r\"\\log A^2\")\n", + "xs_ell = rx.Parameter(\"log_ell_xs\", prior=stats.norm(-0.5, 1.0), latex=r\"\\log \\ell\")\n", + "gp_xs = T.kernel(\n", + " ConstantKernel(0.1**2) * Matern(0.5, nu=2.5) + WhiteKernel(1e-6, \"fixed\"),\n", + " params=[xs_amp, xs_ell],\n", + ")\n", + "p_xs_bare = rx.Problem([rx.Constraint([comp_xs])])\n", + "p_xs_gp = rx.Problem([rx.Constraint([comp_xs], terms=[gp_xs])])\n", + "\n", + "\n", + "def nested(problem, seed, nlive=150):\n", + " sampler = dynesty.NestedSampler(\n", + " problem.log_likelihood,\n", + " problem.prior_transform,\n", + " problem.ndim,\n", + " nlive=nlive,\n", + " sample=\"rwalk\",\n", + " rstate=np.random.default_rng(seed),\n", + " )\n", + " sampler.run_nested(dlogz=0.5, print_progress=False)\n", + " res = sampler.results\n", + " print(\n", + " f\"log Z = {res.logz[-1]:.2f} +/- {res.logzerr[-1]:.2f}, {int(np.sum(res.ncall))} likelihood calls\"\n", + " )\n", + " return res.samples_equal(rstate=np.random.default_rng(seed))\n", + "\n", + "\n", + "s_xs_bare = nested(p_xs_bare, 10)\n", + "s_xs_gp = nested(p_xs_gp, 11)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ec2dc390", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:58:07.029338Z", + "iopub.status.busy": "2026-09-11T03:58:07.029123Z", + "iopub.status.idle": "2026-09-11T03:58:07.720779Z", + "shell.execute_reply": "2026-09-11T03:58:07.719995Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 970x970 with 16 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "labels = [f\"${p.latex}$\" for p in volume_params]\n", + "fig = corner.corner(\n", + " s_xs_bare[:, p_xs_bare.columns(volume_params)],\n", + " color=\"C3\",\n", + " labels=labels,\n", + " truths=volume_truth,\n", + " truth_color=\"k\",\n", + " plot_datapoints=False,\n", + ")\n", + "corner.corner(\n", + " s_xs_gp[:, p_xs_gp.columns(volume_params)],\n", + " fig=fig,\n", + " color=\"C1\",\n", + " plot_datapoints=False,\n", + ")\n", + "fig.legend(\n", + " handles=[\n", + " plt.Line2D([], [], color=\"C3\", label=\"no discrepancy\"),\n", + " plt.Line2D([], [], color=\"C1\", label=\"GP discrepancy\"),\n", + " ],\n", + " loc=\"upper right\",\n", + " frameon=False,\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ac84d783", + "metadata": {}, + "source": [ + "## The learned discrepancy against the missing physics\n", + "\n", + "The defect the GP has to describe is the log ratio of the data-generating\n", + "full potential to the fitted volume-only one; it is drawn here at the\n", + "posterior-median volume parameters, on a fine grid, so the question is\n", + "whether the GP interpolates it smoothly between the data and how it relaxes\n", + "outside them." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5c7add97", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T03:58:07.724344Z", + "iopub.status.busy": "2026-09-11T03:58:07.724120Z", + "iopub.status.idle": "2026-09-11T03:58:07.959466Z", + "shell.execute_reply": "2026-09-11T03:58:07.958638Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x350 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "theta_xs = np.median(s_xs_gp, axis=0)\n", + "fine = np.deg2rad(np.linspace(2.0, 178.0, 90))\n", + "resid_xs = comp_xs.y - comp_xs.predict(*theta_xs[p_xs_gp.columns(volume_params)])\n", + "mean_d, cov_d = rx.predictive.gp_posterior_predictive(\n", + " gp_xs.kernel,\n", + " theta_xs[p_xs_gp.columns([xs_amp, xs_ell])],\n", + " angles,\n", + " resid_xs,\n", + " fine,\n", + " train_noise_var=comp_xs.y_err**2,\n", + ")\n", + "sd_d = np.sqrt(np.clip(np.diag(cov_d), 0, None))\n", + "true_defect = np.log(omp_full.bind(fine, meta)(*full_truth)) - np.log(\n", + " omp_vol.bind(fine, meta)(*theta_xs[p_xs_gp.columns(volume_params)])\n", + ")\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 3.5))\n", + "ax.errorbar(\n", + " np.rad2deg(angles),\n", + " resid_xs,\n", + " comp_xs.y_err,\n", + " fmt=\"o\",\n", + " ms=3,\n", + " color=\"k\",\n", + " label=\"log residuals\",\n", + ")\n", + "ax.fill_between(\n", + " np.rad2deg(fine),\n", + " mean_d - sd_d,\n", + " mean_d + sd_d,\n", + " color=\"C1\",\n", + " alpha=0.4,\n", + " label=\"GP posterior (68 %)\",\n", + ")\n", + "ax.plot(\n", + " np.rad2deg(fine),\n", + " true_defect,\n", + " \"--\",\n", + " color=\"C3\",\n", + " label=\"defect: log(full truth / fitted volume)\",\n", + ")\n", + "ax.set(xlabel=r\"$\\theta_{cm}$ [deg]\", ylabel=\"log residual\")\n", + "ax.legend(frameon=False, fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "72a3ddb0", + "metadata": {}, + "source": [ + "## Takeaways\n", + "\n", + "- A GP discrepancy is just another covariance term; `total_predictive_band`\n", + " propagates it to any grid from the problem alone.\n", + "- Uncorrelated model error widens the diagonal; it cannot represent\n", + " correlated mis-modelling. The GP can, and the model parameters relax.\n", + "- A sampled mean correction removes bias too, but carries no honest width\n", + " away from the data.\n", + "- On the cross section the GP recovers the missing surface absorption as a\n", + " smooth function of angle and moves `V_v` toward its true value; `W_v` and\n", + " `R_v` stay biased, because volume and surface absorption are degenerate\n", + " at one energy and no discrepancy model restores information the data do\n", + " not contain." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 75a2855e0f63463f3a37c05ec8f92954bd3f8b5f Mon Sep 17 00:00:00 2001 From: beykyle <kylebeyer1@gmail.com> Date: Fri, 11 Sep 2026 00:06:26 -0400 Subject: [PATCH 41/75] Silence the sparse-contour warning in the tempering figure --- examples/measurement_to_calibration.ipynb | 168 +++++++++++----------- 1 file changed, 83 insertions(+), 85 deletions(-) diff --git a/examples/measurement_to_calibration.ipynb b/examples/measurement_to_calibration.ipynb index 840e8b2..b255b2f 100644 --- a/examples/measurement_to_calibration.ipynb +++ b/examples/measurement_to_calibration.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "b8323393", + "id": "34668e38", "metadata": {}, "source": [ "# From a measurement to a calibrated potential\n", @@ -20,13 +20,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "ad7550f1", + "id": "26176d50", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:47:21.232880Z", - "iopub.status.busy": "2026-09-11T03:47:21.232725Z", - "iopub.status.idle": "2026-09-11T03:47:23.471702Z", - "shell.execute_reply": "2026-09-11T03:47:23.471044Z" + "iopub.execute_input": "2026-09-11T04:03:17.119460Z", + "iopub.status.busy": "2026-09-11T04:03:17.119167Z", + "iopub.status.idle": "2026-09-11T04:03:20.085745Z", + "shell.execute_reply": "2026-09-11T04:03:20.084791Z" } }, "outputs": [], @@ -53,7 +53,7 @@ }, { "cell_type": "markdown", - "id": "d2c08367", + "id": "89f1e050", "metadata": {}, "source": [ "## The reaction and the optical model\n", @@ -67,13 +67,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "bd847a30", + "id": "45213bb5", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:47:23.473152Z", - "iopub.status.busy": "2026-09-11T03:47:23.472935Z", - "iopub.status.idle": "2026-09-11T03:47:23.505669Z", - "shell.execute_reply": "2026-09-11T03:47:23.505117Z" + "iopub.execute_input": "2026-09-11T04:03:20.088133Z", + "iopub.status.busy": "2026-09-11T04:03:20.087831Z", + "iopub.status.idle": "2026-09-11T04:03:20.124393Z", + "shell.execute_reply": "2026-09-11T04:03:20.123651Z" } }, "outputs": [], @@ -112,7 +112,7 @@ }, { "cell_type": "markdown", - "id": "f186f066", + "id": "5e9ff58b", "metadata": {}, "source": [ "## A measurement, as EXFOR reports it\n", @@ -128,13 +128,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "355dbc28", + "id": "8877737d", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:47:23.507066Z", - "iopub.status.busy": "2026-09-11T03:47:23.506928Z", - "iopub.status.idle": "2026-09-11T03:47:33.356471Z", - "shell.execute_reply": "2026-09-11T03:47:33.355729Z" + "iopub.execute_input": "2026-09-11T04:03:20.127065Z", + "iopub.status.busy": "2026-09-11T04:03:20.126866Z", + "iopub.status.idle": "2026-09-11T04:03:34.244247Z", + "shell.execute_reply": "2026-09-11T04:03:34.243293Z" } }, "outputs": [ @@ -190,7 +190,7 @@ }, { "cell_type": "markdown", - "id": "75443882", + "id": "b5feec1e", "metadata": {}, "source": [ "## `from_measurement`: the unit contract\n", @@ -204,13 +204,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "ed8ba3e3", + "id": "2b27f764", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:47:33.357766Z", - "iopub.status.busy": "2026-09-11T03:47:33.357595Z", - "iopub.status.idle": "2026-09-11T03:47:33.361329Z", - "shell.execute_reply": "2026-09-11T03:47:33.360661Z" + "iopub.execute_input": "2026-09-11T04:03:34.246617Z", + "iopub.status.busy": "2026-09-11T04:03:34.246304Z", + "iopub.status.idle": "2026-09-11T04:03:34.253587Z", + "shell.execute_reply": "2026-09-11T04:03:34.252628Z" } }, "outputs": [ @@ -239,7 +239,7 @@ }, { "cell_type": "markdown", - "id": "f0f53bda", + "id": "f01156c8", "metadata": {}, "source": [ "## Nothing is folded in silently: systematics are explicit terms\n", @@ -253,13 +253,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "66dfd04e", + "id": "ab3434e0", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:47:33.362577Z", - "iopub.status.busy": "2026-09-11T03:47:33.362458Z", - "iopub.status.idle": "2026-09-11T03:47:33.446384Z", - "shell.execute_reply": "2026-09-11T03:47:33.445477Z" + "iopub.execute_input": "2026-09-11T04:03:34.256367Z", + "iopub.status.busy": "2026-09-11T04:03:34.256002Z", + "iopub.status.idle": "2026-09-11T04:03:34.389216Z", + "shell.execute_reply": "2026-09-11T04:03:34.388403Z" } }, "outputs": [ @@ -302,7 +302,7 @@ }, { "cell_type": "markdown", - "id": "cbacd4dd", + "id": "25b52e0e", "metadata": {}, "source": [ "## The guardrail: a singular covariance is a named error, not a `LinAlgError`\n", @@ -315,13 +315,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "4b5f5a57", + "id": "7a2bdae7", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:47:33.447694Z", - "iopub.status.busy": "2026-09-11T03:47:33.447521Z", - "iopub.status.idle": "2026-09-11T03:47:33.451249Z", - "shell.execute_reply": "2026-09-11T03:47:33.450634Z" + "iopub.execute_input": "2026-09-11T04:03:34.390992Z", + "iopub.status.busy": "2026-09-11T04:03:34.390772Z", + "iopub.status.idle": "2026-09-11T04:03:34.396166Z", + "shell.execute_reply": "2026-09-11T04:03:34.395238Z" } }, "outputs": [ @@ -350,7 +350,7 @@ }, { "cell_type": "markdown", - "id": "b7eca92c", + "id": "07fe6c63", "metadata": {}, "source": [ "## Calibrate with nested sampling\n", @@ -365,13 +365,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "bd135136", + "id": "f2641dc4", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:47:33.452527Z", - "iopub.status.busy": "2026-09-11T03:47:33.452409Z", - "iopub.status.idle": "2026-09-11T03:50:01.680006Z", - "shell.execute_reply": "2026-09-11T03:50:01.679167Z" + "iopub.execute_input": "2026-09-11T04:03:34.398347Z", + "iopub.status.busy": "2026-09-11T04:03:34.398037Z", + "iopub.status.idle": "2026-09-11T04:05:34.875928Z", + "shell.execute_reply": "2026-09-11T04:05:34.875204Z" } }, "outputs": [ @@ -410,13 +410,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "54c6d513", + "id": "2df0f057", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:50:01.681906Z", - "iopub.status.busy": "2026-09-11T03:50:01.681722Z", - "iopub.status.idle": "2026-09-11T03:50:03.373995Z", - "shell.execute_reply": "2026-09-11T03:50:03.373221Z" + "iopub.execute_input": "2026-09-11T04:05:34.878272Z", + "iopub.status.busy": "2026-09-11T04:05:34.877921Z", + "iopub.status.idle": "2026-09-11T04:05:36.798231Z", + "shell.execute_reply": "2026-09-11T04:05:36.797333Z" } }, "outputs": [ @@ -446,7 +446,7 @@ }, { "cell_type": "markdown", - "id": "736d87a2", + "id": "fab9a17c", "metadata": {}, "source": [ "## Predictions on any grid (recipe 15)\n", @@ -459,13 +459,13 @@ { "cell_type": "code", "execution_count": 9, - "id": "fca32636", + "id": "0a10e693", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:50:03.386736Z", - "iopub.status.busy": "2026-09-11T03:50:03.386497Z", - "iopub.status.idle": "2026-09-11T03:50:03.740023Z", - "shell.execute_reply": "2026-09-11T03:50:03.739225Z" + "iopub.execute_input": "2026-09-11T04:05:36.805541Z", + "iopub.status.busy": "2026-09-11T04:05:36.805303Z", + "iopub.status.idle": "2026-09-11T04:05:37.223994Z", + "shell.execute_reply": "2026-09-11T04:05:37.223230Z" } }, "outputs": [ @@ -501,7 +501,7 @@ }, { "cell_type": "markdown", - "id": "2c1e14c7", + "id": "3b052b12", "metadata": {}, "source": [ "## Other drivers (recipe 16)\n", @@ -516,13 +516,13 @@ { "cell_type": "code", "execution_count": 10, - "id": "16450cc9", + "id": "575e54e5", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:50:03.742291Z", - "iopub.status.busy": "2026-09-11T03:50:03.742103Z", - "iopub.status.idle": "2026-09-11T03:50:10.819615Z", - "shell.execute_reply": "2026-09-11T03:50:10.819006Z" + "iopub.execute_input": "2026-09-11T04:05:37.226576Z", + "iopub.status.busy": "2026-09-11T04:05:37.226353Z", + "iopub.status.idle": "2026-09-11T04:05:45.111983Z", + "shell.execute_reply": "2026-09-11T04:05:45.111199Z" } }, "outputs": [ @@ -551,7 +551,7 @@ }, { "cell_type": "markdown", - "id": "9aeb6199", + "id": "0abf09e9", "metadata": {}, "source": [ "## Tempering the likelihood (recipe 12)\n", @@ -568,26 +568,19 @@ { "cell_type": "code", "execution_count": 11, - "id": "bc5e2fdd", + "id": "d808c5be", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:50:10.822252Z", - "iopub.status.busy": "2026-09-11T03:50:10.822002Z", - "iopub.status.idle": "2026-09-11T03:50:40.076400Z", - "shell.execute_reply": "2026-09-11T03:50:40.075498Z" + "iopub.execute_input": "2026-09-11T04:05:45.114217Z", + "iopub.status.busy": "2026-09-11T04:05:45.113996Z", + "iopub.status.idle": "2026-09-11T04:06:16.271398Z", + "shell.execute_reply": "2026-09-11T04:06:16.270475Z" } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:root:Too few points to create valid contours\n" - ] - }, { "data": { - "image/png": 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Tpk1jz549NDU1MXHiRGJiYgA4dOgQhYWFNDY28p///AeAqVOnkp6e7n1+3759JCUlMW7cOPR6vfdeXdeeOnUqe/bsob6+nhkzZhAZGYnNZmPz5s243W6mTZvmc07Rsa/bvXs3DQ0NzJw5k4iIiG7v0d/7b9myhebmZu+5H263m23bttHQ0MDIkSPJzs7udu2WlhY2b95MbGws48aNO+G/hzORhAwhhBB9evnllzGbzfzrX//yPvbBBx/wxhtvkJ+fD3gOC3vvvfcICgoiOjqa1tZWDh8+zCeffMK0adM4ePAgBQUF1NfXe6+TkJBAUlIS3//+91m5ciWTJ0/myJEjKIrC+++/z/Dhw73Xfuedd1BVlfT0dO/R7H/4wx949NFHycjIoKKiAqfTyZYtW7zBpqtPGo2G2NhYmpubqaqq4uOPP/YGJofD4df933vvPfR6PTExMaSlpXHZZZdx+PBhlixZgsvlIjMzk+3bt7Nw4UL+9re/eY9G//LLL7nkkktITU0lIiKCpqambifODmUSMoQQYpCoqorF4RqUe5v02gE/PfTgwYOsX7+eOXPmAHD11Vfz+OOPs2LFCi688EK++uorVq9e7R3JAHjsscfYt28fJSUlhIaGoqoqd9xxBz/4wQ/47LPPvO0OHTrE559/zsyZM3G5XEyePJmlS5eyefNmJk+ejMPhYOzYsfzpT39i2bJl3tcdOHDAe1oqwM0338ztt9/Ozp07URSFX/3qV37d/8CBA3zyySdccMEFgOfv7sorr+TSSy/lqaeeAqC1tZXJkyfz6quvcuedd+J2u7ntttu4+uqrefnllwH4y1/+wi233DKgn/vpTEKGGNK0YXEUVrdjMjn7bBcVYiAl0nSKeiWEh8XhYvTPPx2Uexf+8gKCDQP7FTB+/HhvwABYsGABzzzzTJ+vee2117jwwgtZvXo1qqqiqiopKSn89a9/xeVyeUcExo8fz8yZMwHQarVMnz4dk8nE5MmTAc/BX1OnTmX//v0+109NTeXaa6/1/nnZsmXk5ORQWFjImDFj/L5/bm6uN2AA5Ofns2PHDn74wx/y3nvveV+bk5PDmjVruPPOO9mzZw979+5lxYoV3td973vf4+GHHz6Rj/eMJCFDDFnasDiSb3mZK/+8vd+2Jr2W1ffOk6AhxEmIjo72+bPRaMRqtfba3uVyceTIEfbs2UNjY6PPc5deeimdnZ2EhYX1eu2eHjObzT6PDRs2zGfEJisrC4AjR46Qm5vr9/2Tk5N9ni8pKQHg448/9nk8KCiIUaNGAVBWVoZGo/GuOwHQaDQMGzasl09k6JGQIYYsbXA4GkMQT18+ijFpMb22K64z89O38mnusEvIEKeUSa+l8JcX9N8wQPf2l0ajwe12+zzWV3jwl1arJSQkhO9+97v85Cc/Oenr9aSlpaXHP0dHRx/X/b85tdQVPp599lmfEHGs6Oho3G437e3tPotNv9mnoUy2sIohLys2mLEpEb3+ZMeHDnYXxVlKURSCDbpB+Tme9RgpKSkcOnTI57ENGzYc9/sNDQ3FZrP5PLZo0SJee+01XC7ftSnV1dXHff2eFBYWUlxc7P3zu+++S0REBGPGjDmp+0+fPp3w8HD++Mc/+jyuqiq1tbUA5OXlERYWxvvvv+/Tn6KiopN6T2cSGckQQgjRp6uuuoonnniCH/3oR0yZMoWVK1eyc+dOUlNTj+s6kyZN4uGHH+YPf/gDiYmJTJ06ld/97nfMmTOHWbNmceONN6IoChs3bsTpdPLvf//7pPseFhbG4sWLufvuu2lsbOSpp57il7/8JaGhnl8uTvT+YWFhvPzyy9x0001UVlYyb948ampqePfdd7nzzjtZunQpERERPPzww9x5551UVFQQGRnJs88+6x0FORtIyBBCCNGnUaNG8eWXX/K3v/2NzZs3c9lll3Httdfy+eefe9vk5eURHBzs87phw4Zx8cUXe/88f/58Xn/9ddauXUtraysJCQnMmTOHPXv28Prrr/PVV18RHh7OFVdcwbe//e0+rz1hwgQSExN9Hps8eXK3aZzJkyfzi1/8gnfeeYeGhgZef/11rrrqKu/zmZmZJ3R/gGuvvZZx48bxxhtvsGrVKjIyMnj55ZeZOHGit80DDzxARkYGH3/8MbGxsfz973/n008/ZfTo0X1+5kOFoqqqOtidGCra2tqIiIigtbWV8PDwwe7OWa2jo4Po4eNJumk5/75lElOyE3ttW1DZyiUvfMGKH89mbEr3Ij1iYMi/D3GqLVu2jG3btrF69erB7spZS9ZkCCGEECIgZLpECCHEkNTbNIc4dSRkCCGEGJK6qnyKwSPTJUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAgJGUIIIYQICAkZQgghhAgICRlCCCGECAjdYHdAiBNR2WKhucPe6/MWiwV9TNop7JEQQohvkpAhzjiVLRbO/90GLA5Xn+1iL70Pt91KZLD+FPVMCCHEsSRkiDNOc4cdi8PF76+eQHZ8aI9tLBYLs2fPxtXZRvLjJae4h0IIIUBChjiDZceHMjYlosfnOjp02GsPneIeCSGEOJYs/BRCCCFEQEjIEEIIIURASMgQQgghREBIyBBCCCFEQEjIEEIIIURASMgQQgghREBIyBBCCCFEQEjIEEIIIURASMgQQgghREBIyBBCCCFEQEjIEEIIIURASMgQQgghREBIyBBCCCFEQEjIEEIIIURASMgQQgghREBIyBBCCCFEQEjIEEIIIURASMgQQgghREBIyBBCCCFEQEjIEEIIIURA6Aa7A+LMVm2uptnW3G+7KGMUSaFJp6BHQgghThcSMsQJqzZXc9n7l2FxWvpta9KZeO7c54gKiuqznYQRIYQYOiRkiB75M0JxuPUwFqeFp+Y8RVZEVq/tmq3N3L3+bu5YfUe/9zXpTLx/2fsSNIQQYgiQkCG6Od4Riknxk/oNBe9f9r5foeXBzx+k2dYsIUMIIYYACRmim2Zbs18jFOD/9EZSaJIEByGEOMtIyBC9yorIYnTM6MHuhhBCiDOUbGEVQgghREBIyBBCCCFEQEjIEEIIIURAyJoMcdo53Hq4n+dtp6gnQgghToaEDHHaiDJGYdKZePDzB/ts57IkAz+hobMBiDglfRNCCHH8JGSI00ZSaJJf9TQ2HDrM06XQ5mg7NR0TQghxQiRknGX8reQ5WPypp1FcawOqTk2HhBBCnDAJGWeR463kGWXs+5wRIYQQoi8SMs4igajkKYQQQvRGQsZZSCp5CiGEOBWkToYQQgghAkJChhBCCCECQqZLxBmr0lxJYaOxx+csFgtBGUG42l2nuFdCCCG6SMgYIsxmM1artc82La0tp6YzARauDweqeGHHC7y0r/etrNmPZeO2uanprGF4yPBT10EhhBCAhIwhwWw28+e3/4zZZe6zXbPSDHqwdFog5hR1LgBig2MB+PXcX5Od0PtIxnlXnUfa7Wm02FpOYe+EEEJ0kZAxBJQ1l/E3zd9wap39ttWpOoIIOgW9CjzPLpmey4p3dHRgq5IzToQQYjBJyBgCWu2tOBUnD014iPGp43tt19LSwuY1m0kwJZzC3gkhhDhbScg4zfmz1qKtzXOGR0ZoRp/1LxrUBgoooLm577LiAEFBQYSGhh5fZ4UQQohjSMgYJP6EB4vFwqpVq3A6+54GqVPqwABGQ8/rE7oEBQWh0+lYt25dv/3T6XRcddVVEjSEEEKcMAkZg8BsNvP222/3Gx7A82V/0UUXYTKZem1T1FrEW1+8hSm49zYAoaGhXHXVVf2Gm+bmZtatW0d1dTVRUX2fXyIjHkIIIXojISMAGhsbsdvtvT7f3NyM0+lk/vz5A/IlXqfU+d230NDQfq8nIx5CCCEGgoSMAPjggw/6HHkAz5dzUlLSafnlfLwjHlar9bR8H0IIIQaXhIwAmDt3LmlpaX228XeaodpcTbOt74Wah1sPH1f//OHPiEcXWUgqhBCiJxIyAiAyMpLY2NiTvk61uZrL3r8Mi9PSb1uTzkSUse+pl4F2pkyrlLaXYmrsfWTpcKvU0xBCiECQkHEaa7Y1Y3FaeGrOU2RFZPXZNsoYRVJo0inqmcfpPq3ianfhtrn5xdZf9N3Okgz8hIbOBqDn4l5CCCGOn4SMM4CnsmXv9S8G0+k8reJocnDwwYNs3rW5zzUyGw4d5ulS2FvT6C1Z3puoEAMpkX2vtxFCCOEhIUME3GBOqziaHORG5RISEtJrm7o2JyiHeOajep6hvs/rmfRaVt87T4KGEEL4QULGIBmsBZ2DYaDrc7S0dAxo/+LDdYQM/x1PzXyhz2mp4jozP30rn+YOu4QMIYTwg4SMQXC6L+gMhIGsz1Fj0wPxdHZ2MlBrKDT6VrITjL0euCaEEOL4ScgYBKf7gs7B4u+Ix9aD1bxWeaTPgmcnor+RI9mFIoQQx0dCxiA6nRd0DhZ/RjzCaz3TJW1tbTQ0NPTYpqPD/ymVKGMUJp2JBz9/sM92sgtFCCGOj4SMAChuLcbS2PtUyFBZazFYDAYDANu2baNij6PHNjbb0VEHs9nc58LPpNAk3r/s/X7XyHTtQmlztB1/p4UQ4iwkISMA7vrqLrS7tH22GSprLQZDcHAwAAsWLCA3oefw0NHRwU9+8hMAampq0Gr7/vsICwojKabvaaniWhtQRaW5ksLGvk+8PZumuYQQojcSMgLg3rx7mZE7o8828iV08jyVVXuetji2LsbGjRsxGvsOBTqdjoULF/ZZT0O1qgC8sOMFXtpX1ef1grRBPDbpMSINkX22MxqM/Z6eC/LfixDizCQhYwCpqudLqKMpkrb6sD7btuGkjPJT0a0h53B9B25bJ+b2NtralB7bHLsmY/78+X1Ol1gsFtatW8c777zT531rbXrctjgmNC0kQt/7IlA7NjbrNnP3J7/r5534z6A1cHPOzYTqz9zzXywdncDRfydCiKFPUeVf/ICpqKjo92A0Ic525eXlpKamDnY3hBCngISMAeR2u6mqqiIsLAxF8f0Nu62tjbS0NMrLywkPDx+kHvZO+ndypH/9U1WV9vZ2kpOT0Wg0g9IHIcSpJdMlA0ij0fT7G1p4ePhp+SXURfp3cqR/fYuIkK2/QpxN5NcJIYQQQgSEhAwhhBBCBISEjFPEaDTy6KOP9ruVcrBI/06O9E8IIbqThZ9CCCGECAgZyRBCCCFEQEjIEEIIIURAyBbWAdRXnQwhznb+1smQf0dC9O5MqzcjIWMAVVVVScVPIfrRX8VP+XckRP/OlMq5EjIGUFiY57ySsrKyfgseqaqKoun/tzTVrfr125zb7fYr1frbzuVy+XVfVVX7PeG0q50/91VV/95vf+06OjpISUkBPF9afZ1dIk6NrqqjXf9OetP1/OlaPfVs0tHRQXJyMiD/jk4X/v47Ol1IyBhAXV96/lRVlJDRe7uBCBnH9ik8PFz+x/E00t/f7/H8OxKBJf+OTl9nylTi6T+hI4QQQogzkoQMIYQQQgSEhAwhhBBCBISEDCGEEEIEhIQMIYQQQgSEhAwhhBBCBIRsYQ0AVVXp79w5t9uNQv9bkFxOl19bP51OJzpd/3+ddrvdr3ZOpxODwdBvu+PZEusPt9vt95bYk3n+ZNoP5NaxwbqvEEKcChIyAkCj0fT7xaviXz0If64Fnv3s/l7Pn3ZardavL3t/+wf+fUn627/+ridfyEIIMfhkuuQs197eTnl5OW63e7C7IoQQYoiRkYyzkNvt5siRIxQVFVFVVQVAUlIS8+bNIygoaJB7J4QQYqiQkHEWcLvddHR00NbWRkVFBYcOHcJqtXqf12g0VFdXs2LFChYsWEB0dPQg9lYIIcRQISFjiKqpqaGgoIDW1lba29u7TYeYTCZGjBjBiBEjcDqdrF27lvb2dlauXMmiRYuIiYkZpJ4LIYQYKiRkDEHV1dV89tlnPjs6NBqN98Cp4cOHk5aW5rNg85JLLmHt2rXU1tby2WefsXDhQu/pi0IIIcSJOKtDRmtrK/v37ycvL4/g4ODB7s6AaGhoYPXq1bhcLlJTUxk7dizh4eEEBwej0WhwOBw97hoxGo2cf/75rF69mtraWlatWsVFF11EfHz8ILwLIYQQQ8FZGzKWL1/OsmXLsFqtZGZm8vnnn5OSkjLY3TopLS0tfPbZZzgcDhITE1mwYIFfNTG66PV6n6CxcuVKCRpCDGGVLRaaO+y9Pm+xWDAkDMfV2XYKeyWGkrMyZDz33HP8+c9/ZsuWLYSGhnLRRRfxj3/8g2XLlg12106Y2Wzm008/xWq1Ehsby/nnn39cAaNLV9BYtWoVdXV1EjSEGKIqWyyc/7sNWBx9F8pLumk5bruVqlYrI0JCTlHvxFBx1oWMpqYmHnzwQXbu3MmoUaMAuOiiizCZTKxdu5acnBy/RzRsNhs2m83757Y2T9p3uVz9Vri02Ww+ayIqKiqoqqpi1KhRhIWFeR93uVz9Vt60WCysXLmSjo4OQkNDmThxIq2trT22dbvdhIaG9vveZs2axaZNm7xBY+HChcTGxnZrp6oqRqOx3+u5XP5VLnW5XH6FI1VV+7zesZU0/anAqqr+FUcb6AqdUvFTDJbmDjsWh4vfXz2B7Pie/zfBYrEw/7JriL30Plo6Hae4h2IoOOtCxp49e7DZbN7dE3V1dfznP//B6XTicDhob2/n1Vdf5Xvf+16/13rqqad47LHHuj+h/O+nD4qioCgKDoeDbdu2cfDgQQAOHz7MtGnTyMrKAv5XUVPb+5epzWZj1apVdHR0YDKZmDZtGgaDAZfLRWdnJyEhIT5fTi6Xy69KngaDgYULF/qs0Vi0aBFxcXE+7fz9cva3Iin4/2U6kBU/u/4+TjUJDmKwZceHMjYlosfnOjp0OBrLT3GPxFBy1lX8nDBhAomJiSxatIiHHnqIyZMns3jxYsrLy6mpqeGGG27gjjvuoLKyst9rPfjgg7S2tnp/ysuP7x9jY2MjH330kTdghIeH43A4+OKLL/j888+x23ufKwXP+SKrVq2iqakJo9HI9OnTMZlMOJ1ONm/ezPr1672jEcd7lgccnTpJSEjA4XDw2Wef0dzcfNzXEUIIcXY660JGREQEn3/+ORdffDFhYWG43W5eeukl9Ho9er2e5cuX43Q62bVrV7/XMhqN3m2hXT/+UFWVwsJCVq5cSVtbGyaTiYULF7JkyRImTJiAoiiUlJSwYsUK6urqeryGy+VizZo11NXWYTAYmDp1KiEhIbjdbrZv3+4NAy0tLWzduvWEw0ZX0IiPj/cGIClBLoQQwh9nXcgAyM7O5sknn2T8+PEoiuIzt19fX4+iKOTl5QXk3p0dnXz6yafs2LEDt9tNeno6l156KUlJSWg0GsaNG8eFF15IaGgoZrOZVatWsWPHDp9w4Ha72bhhI5UVleh0OhYuWkh4eDiqqrJz507q6+vRarVMnjyZrKwsNBqNN2x89dVXvQaX3uj1es4991wMBgONjY3s2bNnoD8WIYQQQ9BZGTK6jBw5kqqqKu677z4cDgfl5eVcc801LFu2jLS0tAG/X1lpGe+++y5VVVVotVqmT5/e43khcXFxXHLJJWRlZaGqKvk78zlSdsT7fE1NDSUlJWg0Ghact4CEhATAE5Cqq6sBmDx5MomJiYwePZoFCxZ4w0Zraytr1671++j1LsHBwUybNg2A3bt343Q6T+ajEEIIcRYYsiGjqKio3zbZ2dn87ne/Y/ny5YSHh5OVlcWCBQt4/PHHB7QvTqeTTZs2sWbNGu+i08WLFzNy5MheF/4ZDAZmz57N8OHDAaitq/U+1zVdERERQWpqqvfxsLAw7/WO3aERFBTkUyY8LS3Nr8Wf35SVlYVOp8PlctHR0XHcrxdCCHF2GZK7S5YtW8bzzz/PBx98wPnnn99n25/+9KdcfPHF7Ny50zu9cLJ27txJyDH7yctKy7xbSvPy8pg4aSIup38jCbGxsRw6dIjmpqMLLruu/c0vepPJREpKivcQtMmTJwNQW1vL9u3bcbvdJCQkMGPGjBN6X4qiEBoaSktLCx0dHURE9LwiXQghhIAhGjLWrl1LdnY2S5Ys6TNo5Ofnk5eX5z0obKAU7CnAZDL5PBYcHMzceXO954H4GzIiIyMBaGpu8j4WGuLZ026323HYHegNeu9zw4cPp6KigpqaGsxmMx0dHd6AkZiYyLhx4/yqV9GbkJAQWlpaMJvNJ3wNIYQQZ4chGTJyc3O54IIL+M9//uMTNGw2m7dwVF1dHbNnz+bSSy/ljTfeOKHpg17vPyrXZyTDaDQyatSobmsv/NEVMiydFqxWK0FBQegNegwGA3a7HXOHmShDlLd9WFgYCQkJ1NbWkp+fT1tbmzdgTJw48aR3hnQV8pLpEiGEEP0ZsiHjwIEDvP3221x11VUsWbKE5cuX8+tf/5qNGzeSkpJCfHw8L774Ips3bz6p3+x7kjc2r/t2VhWsFqv3jx2dHeh1evqjqqp3p0lDfQNJSUmAZ0TBbrfT3tZOeFi4z0LMYcOGUVtbS0tLCwAJCQnk5eXhcrlwuDwFx/y5b0+VN7uCUltbGw6Ho9d23+RwOPwqPOVPhdOu/vV1veOt+AlSoVMIIQbakA0Zb775Jnq9nrfffpvLLruM2267jXvvvdenZPjSpUtZunTpwHfAj4qfGkXj15ezW3UTHR2N2WymtbXVu9AzNDSU5uZmOjs70Wq1RERFoNV4RmOiY6IpKSmhrq6OtLQ0Zs6a6Q1S7e3tfn2Ju93uHvvXFZ4sFgs6nc7vip/+BjmNRuNX2/7ue+xzA1nNc6CDgwQRIcRQNiR3l+Tm5lJQUABASUkJe/bsYcqUKbz00kusXr16kHt3/KKjowF8qm32tvizy8xZM5k9e7ZPwOjJwYMHWbt2rd9rLLrue7JrMmw2G5WVlVLYSwghhrAhGTKys7MpKytj9+7dnHfeeTz55JNs2rSJCy64gCVLlrB3797B7uJxiYryrLloajq6+LO/L3uTyURaelqfAWP//v3s3r2b5uZmbyjrT9eajM7OzhMqVQ6eBasrV65k1apVrFq1CqvV2v+LhBBCnHGGZMgwGAykp6czd+5cnnzySW688Ubv1Mlzzz3H6NGjB7uLx6VrJKOlpcX7xX6yCzD379/vE7YqKyt7Pbn1WCaTCUVRcLvdWCyW476v2+1mw4YN3vUi1dXVrFixwidACSGEGBqGZMgAePHFF3nhhRe48cYbvY/p9Xpuv/32M24ePCwsDK1Wi9PppLCwEFVVqa31FOc6kS96q9XqDRjJycneI9xLS0v7fa1Go/GOomzbtg2Hw//jn5ubm/noo4+orKxEq9UyZ84cwsLCMJvNrF69+riuJYQQ4vQ3JBd+AixcuHCwuzBgNBoN48ePZ8eOHWzZsoVDhw7R0NAAwKhRo477ekajkcTERGpqaqiqqvI+3lWevD8TJkxg06ZNlJSU0NzczMKFC/sszOV2u9m1axe7du3C7XZjMBiYM2cOaWlppKam8uGHH2I2myksLAzYmTFCCCFOvSE7kjHUjB8/nnHjxgHQ0NCAoijMnTv3hKZ+FEVh5syZ5OTkeB9LSUkhMTHRr9cPHz6cRYsWYTKZaGlp4b333ut1FKSpqYkPPviAnTt34na7SUtL4/LLL/eeDWM0Gpk4cSIAe/bsOaGRGSGEEKenITuSMdQoisKkSZPQ6/UcPnyYqVOn+mzHPZHrjR07lujoaGpra487rCQmJnLJJZewYcMG6urqWL16NePGjWPy5MloNBrv6EVXuDAYDEybNo2srKxu01WZmZns3buXxsZGdu3axdy5c0/4fQkhhDh9SMg4g5gdZjaqG/nc8Dlv7HwDdh59zu12o3yjOEewPphLh13KhekXYtD2XBsjOTnZW+r8eAUHB7No0SLy8/MpKChg9+7dNDQ0MGHCBLZu3eqd0klPT2f69OkEBwf3eB1FUZgyZQqffPIJBw8eZNy4cd5Kp0IIIc5cEjICwO1291v/weayYbfY+72WTbXRam/lo7KPeLPoTdocbcfVl71Ne/nT3j9xReYVXJB6AYpbwU3/tSnsbjvOtv6Pc3erbiacM4GoqCg2b95MVVWVd52HwWBg8pTJZGZmYrPasNlsvV4nKiqKlJQUKisr2bJlC+edd16f91VVFb2+94qpgar4KYQQwn8SMgIgKCio33NKLHYLGqXvJTFu1c2qI6t4s/hNajprAMgIz+Cm0TcRa4r1aWt1WQnS+t6zqKWIf+7/Jw2WBv60/0+8degtlgxbwrW51xJu+EbZ829oMbf4VXnT7fIEloxhGURERrBxw0ba2tpISU1h2rRp3tELRaP0W+F00qRJVFVVUV5eTl1dXZ9rRFRV7buq6jHPnc4VP4UQYiiTkBEADofDZzumRqM57gPYdjbs5I+Ff+RA6wEAYoJiuCXvFhZnLkan6f7X1uHoIEQf4vPYxISJXJF9BZ+UfsIb+96g0lzJGwff4N3Sd7ks8zKuHnE1MUExffbD7DBT3VlNVUcVLtXFrMRZGLXGHttGRkZy8SUX09bWRmRk5HF/IUdERDB8+HCKi4vZunUrl156qXypCyHEGUxCRgC8/dbbPke9a7VaFi1aREJi/1tED7cd5k/7/sSWui0AmHQmrh91PVfnXI1JZ+rn1d0ZtAaWDF/C4szFrCtfx98K/0ZJawn/V/R//Kf4P1w87GKWZC6hw9FBZUclVR1VVHZUcqTtCDWdNd2mZ1KCU7hn/D2Mjxnf4/20Wq23QumJGDduHKWlpTQ0NFBSUkJWVtYJX0sIIcTgkpBxCrhcLr788ksuu/yyXqcgattreXnPy3ze+DkqKlpFy7dHfJtrcq8hPjj+uO/Z2tJKh7mDhKQEtFotOo2OhRkLmZc6j/z6fF4reI09DXt49/C7vHv43T6vFaIJIcwZRpvSRmVnJfduvpeL0y/mltxbMGmPP/j0xWQykZeXx86dO9m+fTsZGRnHPQokhBDi9CAhIwDmzptLWFgYAHabnS+++ILW1lYqKyu99SGOZbfb+dXGX7HHvQeAPEMeN+fezLScadiV/heHdmltaaW8rJzy0nJaWzwlwk0mE6PyRjF85HC0Wi0aRcPc1LnMSZnD9trtvLb3NXbU7iA+OJ7U0FRSw1JJDU0lPjgetUGlZl8Nis2zpkEbomWVdRUF2gI+OvIRn9d8zjVZ13DDmBvQa/s/tt5fY8eOZf/+/bS3t7N69Wrmz5/v18mxQgghTi8SMgJg44aNPtMl4DnQLC4urltbt9vN1s1bUR0qaGG8czxzbXM5/PVhSraVEJ8UT8awDFLSUzAau6+F6AoWZSVltLe2ex/XaDToDXosFgs7tu5g3559jMobRUZ2BuBZwDg5cTKTEyd3Oza9sbGRTV9soqmhCQWF2LhYps+cTkREBFlbs1h3YB0bdBtotDfy6v5X+ajiI+7Mu5MFqQsGZA2FXq9n9uzZrF27lsrKSlasWMFFF13U7TMVQghxepOQEQCRkZE+NSFMwSamTp3abceJqqrk78invr6eaJ3nELSotCjOiT6H0tJSmpuaqa2qpbaqFmWzQmJyImkZaURGR1JVUeUzYgGeYJGQlEDasDRS01LR6rQcPniYwj2FWDo9YaNwTyHjJ4xnxMgR3t0eXcHA4XCQvyOfwr2F3i2iEydPZETOCO80z/SZ00lITCB9Uzq7HbvZot9ChbmChzY/RF5MHneNv4u82JMvDZ6WlsbFF1/M6tWraWlpYdu2bcyZM+ekryuEEOLUkZARABdfcjHh4X1vEQUoOlBEWWkZAFNzprKxeCPV1mrGnzOe8eeMp7WllQPFB6g6UkVLcwvVldVUV1b7XKMrWCSkJZA1LAuD0XdaYUTuCLJGZPmEjS2bt7Bn1x7yxud5w0b5kXK++vIr76mu6cPSmTp9ao8FtDKzMomJiSF8XTgjmkawU7uTfH0+exr3cMvaWzg/7XzumXAPMaa+d670JzY2lgULFrBixQqKi4sZO3bsSS0qFUIIcWpJyBgklRWVFBYUAjBuwjgMiQYohrK2Mu/0RURkBKPGjWLchHG0tbZ511u0tbaRkJRA+rB0UtJSMBgNdDg6MOiPBgyb1UZjQyOJyYlotVpv2CguKubA3gN0dnR6w0ZUdBSVFZUAhISGMGPmDOKS49Brel9nER4RzuJLFvP11q8x7DcwxjWG3WG72WHfwery1Wyv284vpv2C6YnTT+pzio+PZ9iwYZSWlrJt27YhdfCdEEIMdRIyAqCjo6PPHRGNDY3s+HoHAGnpacQlxOF0OdEqWqwuKzUdNd4dJa22VrQaLQRBck4yyTm+JcA76KDD1kGVucq7xbW9sZ3ircXYbXYi4iIYPmU4eoMnMDjjnEy9YCpVpVWU7Sujs7OTzs5OFEUhbWQamaMzcevcHG4+TJSp/1GD1LGpRMdFs+2rbcxsn8lo42g2BG/gSOcR7tp4F1cMu4Lrsq9Dq9ESo+1/ZMNhd+Byu3weGz1mNGVlZZSXl1NSWuI9LbbryPmeHG/FT1VV/So+Bv4V5PKnwujxXE8IIc5EEjICQKNoeq3maTab2bRpE263m7j4OEaNHoWiKOg0OhJNiVR2VlJpriQlzHP4mUln8oQMP2gVLbUltezZtYd12nUcMRwhtiWWzLWZzB4zm7yUPOxuOxqthtThqSQPS6a6tJrWxlbSRqYRFhnmvZZLdaFV/LivAumZ6UTFRLF5w2ZohottF1OcXsyq2lW8U/oOBc0F3DfuPpKj+z8jpacv+8jISLJHZHOw6CC78ndxwYUXeKp49lHy89jnBrLipxBCCP/JUe+nkN1uZ+PGjdhsNkLDQhk3fpzPl1+yyfMlXNZedtzXdrvcFO8o5uPdH/Om/k1KtCW4FBe1mlq+Ur/itwW/5ZbPbuHF/Bd5r/g9SlpLQAMpw1MYPXW0T8A4EWHhYZy3+DwysjLQoWNC3QR+PuXnhOpDKWot4u6v7mb1kdUnfP28vDx0Oh1NTU0cKTtyUn0VQghxashIxinicrnYtGkT7e3tmEwmxo8f3+0sj5TgFL5u/JqytuMLGZZOC3u+3MPHHR9Toi8BICsii+tGXUd1WzVfHfyKw/bDdNLJvqZ97GvaB0C4IZxxsePIi8sjNyqX1LDUfs9T6YtWq2XyjMk0NzbT1tpGfGU8r1/wOo9tfoyCxgIe+vIhvq79mrvPuZsgXd9nu3xTUFAQo0aPYs/uPeTvyic1LfWE+ymEEOLUkJBxCqiqyvbt26mrq0On0zFnzhyc7u4nnKYEe6ZIjrT5/5t6U30T/1j/Dz5TP8OqtaJVtFyRfQUTXRNp293GpDGTmH/+fMr2llFQXEC5ppyaoBqOqEdos7fxRdUXfFH1BQDBumBGRI1gZNRIEoMTGR0zmqSQpOMKBFqtlqmzprJm5RqOlBwhLSONFxa8wF8K/sKb+97k3UPvsqthFz+b+DOiHFEkJSf5vRZi1KhRHCw6SIe5g4MHDzLxnIl+90sIIcSpJyHjFCgvL6ekpARFUZgxYwZRUVHUN9Z3a9c1XVLeXu73tT//8nM+VD9EVVTSQ9K585w7iXJGUbChAICD2w4y+aLJDBs7jLCoMGK2xkAHpOam4kx2srt+NwWNBRS3FNPp7GRX/S521e/yuUdMUAxJIUmkhaUxLWkaE+MnYtD2XoEzOjaanDE57C/Yz95de0lJT+HWvFuZnTKbn236GYdbD3PzupsZ7hrOkoglXDLvEm+F1L7odDpG5oxk967dHCySkCGEEKc7CRkB5nQ62bXL86U9evRokpN7X/wYpvd80bbb23tt803pyeloSjS4cPH9kd8nPTwdp8OJ3qjHYXMQlXh0h4jd4ilRrigK8YnxRMZEMjpmNAAut4sj7Uc40HyAA00HKGsro95Sj9lhptHaSKO1kYLGAlaWriRYF8y0pGnMTp7NmNgxPfYrOTWZ/QX7fU6jnZY0jbcufounv36aVWWrOKQ9xB/a/8DOD3dy29TbyM7O7vO9Op1ODh86DEBiUu/HwAshhDg9nNUh47///S9XXHHFgO88+OSTT7wlsF0uFzabjeDgYHJzc/t8XZDWMy1hcVr8vtf4SeNJLU+lzFXGpl2bGBYzDL1Bz7j542hrbCM62VNJtKWuhdI9pQBkj88mMi7S5zpajZbMiEwyIzK5cNiFtNnaSAhJoN3eTpW5iqqOKvY37WdT5SYarA2sK1/HuvJ1BGmDmJk8k3PTzmVa4jSC9Z7iXW63G6DbVEi0KZqn5z5Nxr8y+Mj2EVWaKtYr6zmw9QA3lN3ABVMvQG/quT5HwZ4CzGazd02LEEKI09tZGzJ+9atf8fDDD3PbbbfxyiuvDGjQ6Ozs7FYnYcKECd0Wen7TsSHjm+eJ9Eaj0TA9azplB8s44jjCwa8PMmrGKAwmA7GpsQBYzVaKthYBEJ4STmq2/4smwwxh5ETnkBOdw/y0+dw+7nYONB/gi8ov+Lzyc+o661hbvpa15WsxaA1MT5zO/LT55Gpzvf3rSbgjnCscV6CbpOOVwleopprf1v2WzSs3c/fMu0lKTPJp39zczL59ngWrk6dMlgPThBDiDHDWhozq6mouuOAC/vKXvwAMaNCYP3++zxoDvV7v15qDrpChomJz2fxecDkxcSJvHXyLKk0VrXWtlO0tY1jeMABcDhf7v9qPy+EiNCqUhDEJJ/U+NYqGUdGjGBU9ilvG3kJBYwH5dfmsr1hPpbmSjZUb2Vi5kThjHGM1Y5mudK/46Xa7sdvsKChclXsVF4y8gJ9v/DlfN3zN5+7P2bdxH7cPu53Fkxej0Whwu91s2bIFVVVJS0vr8SRbIYQQp5+zNmTk5uai1+u56aabuP766wFP0KioqCA5ObnPip1dbDYbNpvN++e2tjYAtDotWt3R17tVN61trT6vdTgduHH7XvCYwY8Oewd6jZ5ORyc6Td9/TZnhmSgotCltmDFTXVyN0+QkNCmU2vxaLO0WtEYt0eOiKWwuJL8xv9/3ptfo+ywr3iVUH8r05OlMS5pGpbmSnXU7+ar6K+pt9azTr2Nb5zaKdxYzLXEawyOHA2C3Hj2+vtpajUaj4cEZD7K+bD1/3vNnGjQN/KrsV6yvXs+9c+6lsaaRpsYm9Ho9eRPzsLlsqG4Vva73/h27FsTfip8Dyd+RKCGEGMrO6pDx/vvv8+yzzwJw/fXX097ezueff84//vEPzj333H6v8dRTT/HYY491e9ygN/icI9ITl9tFaGhot8f1Gj0OtwOb2+YpxW2K6bfiZ7QpmozwDErbSrGmWgmtCKVhbwO0QmddJ4pGYezMsYRFh7GmYI23/Liqquxr2seR9iMYtAaCdcEE64Ix6Uw4VSdZEVmE6EMI0gb1+oXZZm8jOdSzmDUlNIWU0BQWZSxi/YH1rKtZR7vSzr+L/s0npZ9w5cgrWZi+EKfds31Xp9ehaBRUVFDg3GHnMiFxAn/e/me+bPySTY5NFK4p5Hz3+SSSyDmTzyE6yrPGxOF09Pklfuxz/lb8HMhy4VJlVAghzvKQsXfvXgC++93vUl5ezv3338/8+fOZN2+eX9d48MEHueeee7x/bmtrO+mhfJPWhMPtOK7FnwA5UTmUtpVSb6pnZNJImqqbqD/i2SabPTGbsGjf6Rq36mZH7Q7KzZ7tshanhVab72jL/qb9gGeKJEQfQrghnJyoHFJCU/r8AjVoDcyImkHakTSKw4r5WvmaFlsLrxW8xjsH32FR4iKiicZkNHV7bYQxguUXLufTg5/y9NdP00wz/9b+m4tDL+a6kdcd12cihBBicJ21ZcVTUlIwm800NzdTVFTE888/z+23387GjRu54447/PqN1Wg0Eh4e7vNzsrrWYVid1uN63cjokQDsb97PyCkjMYV5vsCTs5NJyEjwaetwO9hctZlyczkKCuNjxzM9aTrj48YzInIEqaGphBnCCNb9b6eI6qbd3k6luZK15Wv5uORjytvL+/yMVFVFh46Jhok8Mv0RvpX9LeJMcbTYWni77G3+avgr29iGw+3o8fUXjLiAd694l1mRswD4yPIRb+5/87g+EyGEEIPrrB3JABg5ciTvvPMOv/jFL3jyySe58cYbOffcc7n++uuZO3cu11136n9zNmk94eB4RzJGRnlCRkV7BRbVwvj54zE3mYmIj/BpZ3PZ+Kr6K1psLWgVLVMTp5IY0r3mRL2lnqyILFyqi05HJx2ODirMFRQ1FdFobWRd+Tqig6IZHzeeSfGTuo1sqO7/BRCNZwpoWtI0rsm9hg0VG3hr71s0OBpY61hL9ZfV/L/J/4/IoMhufYgIiuC5xc/xyu5XeK3gNV7IfwGry8otY285rs9GCCHE4DhrRzIAxo8fzx133OENGOCZOtm0aRPXXnvtoPSpa/SgpK3kuF4Xbgj3ro0oai5Cp9cRmRDZ7cv/cOthWmwt6DV6ZqfM7jFgHEuraAkzhJEYksjkhMl8a8S3GBMzBp2io8naxLrydby29zXabG2+L/zfmtZj76/T6Dgv/TwezHyQ8xznYVSM7Gvax30b76OouajH+yuKwg/G/4AfjP8BAH/a8ydWlqw8no9GCCHEIBmyIaOoqOcvrWP99re/5b///a83YHSZNm3aoC3am5M0B4A/7v4jHY6O43ptZngmAOWt5bQ1ttFY1dhtSiPCcHRkI8xw/CevmnQmJiVM4ooRVzAmZgwKCrvqd/Grrb9ia81W7/1cDhcAWn33RasRURGMdo/mWvVaUkJTaLI28bNNP2NV2ape73vz2Ju5eezNADyz7RmqOqqOu+9CCCFOrSEZMpYtW8aECRNYvbrvo8UjIyNZsmTJKeqVf64YdgUpwSk0WBr4854/99teVVXa29ppLG8k2OwZBdm5bye71+9m3+Z9lOz2HRFJCkkiVB+Kw+3gcMvhE+5nkC6ISQmTuDjrYlJDU+l0dvLGvjf4454/0mxtxmX/X8gwdA8ZkXGRaHVaQm2h3Jd1H9MSp+F0O3l518u8susV7C57t9cA3Jp3K+PjxtPh7OCXW37Z4yFzQgghTh9DMmSsXbuW7OxslixZ0mfQyM/Px+VyncKe9c+gNXDnmDsBeHPfm1S0V/g873K5qK6spiC/gA2rN/DOv95hxTsrKNlRgq7Rs8SmSWlCb/TUkKgqrqKmpMb7ekVRyI32VOM82HIQh6vnhZf+ig6K5t5J93JJ1iVoFS17G/fy1Nan2Na2DRUVrbF7yNBoNCSkexajHv76MN82fZtrcq5BQWHVkVXcsfoO6jrrur1Op9Hxy5m/JEQfQkFjAX/Z+5eT6rsQQojAGpIhIzc3lwceeIALLrjAJ2gcWzirrq6O2bNnc/311592QWN6/HSmxk3F4Xbwh/w/AJ4Ri4ojFXz83sesX7WePfl7qKqowm6zo9FoCIkKITfNEx5a9a1MXjyZ9NHpABzaeYiW+hbv9VNDUwnTh+FwOzjUeuik+6vVaFmUsYgHpjzAsPBhWF1WVnau5H39+7QpbT2+JntCNsnD/3fq7P5yhlcN5/5z7idEH8Kehj3csPIGdtbt7Pa65NBklk1ZBsCf9/6Z/Pr8k+6/EEKIwBiSu0tyc3M5cOAAb7/9NldddRVLlixh+fLl/PrXv2bjxo2kpKQQHx/Piy++yObNm3s9X+NEVVdX097e90mqOoMOq633barXpFzDjoYdbKraxGdFn6Et0VJX7fnt3hhkJD4xnui4aKJjo4mIimBP4x4UFLS1WmwuG8XNxUSlRxHWHEZ7dTuFmwvJmJFBh70Dq8ZKWlgahU2FFDUXER0U3a26Z4e9g63VW/t9r8MihrHmyBrvn8fFjiNEH8L+xv2Ua8pZXrKcUW2jmJIwpfvi0GQI0gdhPWiltaEVpVXhxuE3sqJhBeXt5fxg9Q+4btR1LExf6LNGJjMik9kps/mi8gse/vJh/r7o74QafAub2VxHA6XT5cTp6ntqRVVVdFr//jkcT0EuIYQ4mw3ZkPHmm2+i1+t5++23ueyyy7jtttu49957SUlJ8bZbunQpS5cuHfD7NzQ0YLEc3YKq0WgICwtDrz/6RW5uNxMVFdXTywFIMaawKG4RH9d9zPJty7nGfg16jZ4xY8cwdvxYn2uBZ62FTqsjOTSZ8vZy7Kqd5LBkEmYkkL8un/bmdmp31jJ19FQiQyJxq25ezH+Rus469Fo956Wf53O9RzY94td7bXe0kxWR5fNYdFA0VzmuYoN2A1WaKvY07KG8vZzv5nyX6KBon7b6OD3aUC2WfRbcHW50+3Xckn0LK0NW8lXNV/y98O8cbj3MzWNuRq89+p6Xjl5KSWsJleZKns1/lsdnPu573WNKjiv/+78+Kf/76Y8q4UEIIfw1ZKdLCgoKACgpKWHPnj1MmTKFl156qd/FoAOhvLycsrIy709JSQm7d+9m//79VFdX93hK67FUVaW6uprMmkxMqolmpZkjMUe47IrLOGfyOd0CxrFSQz0nrB6qP0RjdSMajYaxs8ZiMBnobO/EccCB6lbRKBoWpC0AYHPVZjodnQP3AagQ447hCscVjAwfiUbR0GJr4c97/syW6i24Vd8zWzQmDcETgtEned5XQ3ED89vnc9Xwq1BQ+KLyC17d/arP64L1wTwx6wm0ipZPSj+Rba1CCHEaGpIhIzs7m7KyMnbv3s15553Hk08+yaZNm7xrNLrKiQdKWFiYTxVQk8lTYKujo4Oqqir27dtH0YEiiouLaW5qxu0++uXZ2tpK/s58ig8Wo3FomM1sANbZ1uEw9L1Is6Otg1CrZ9pgV9Eu9nyxh0O7DmE0GcmblYdGq0FtUTEfMgMwKmYUiSGJ2Fw2ttRsGbD3r3PpvCMHCaEJTIqfRHRQNE7VyZryNfy98O/UW+p9XqNoFIKyg9CN1KHRaTA3mUkpTuGO4XegVbRsqdnC/+37P59wNi5uHLfkeQpzPb3taSrNlQP2HoQQQpy8IRkyDAYD6enpzJ0711toq2vq5LnnnmP06NEBvf/w4cMZMWKE92f06NHk5eWRnp5OREQEiqLgcDiorqqmoKCAzV9upnBvIfv27WP3rt2YzWa0Wi1ZWVkszlxMujGdDkcHL+W/1O1eNpuNXTt3sWP1Dr7+9Gs0dZ6/0ialCYCKgxVUHa4iLCqM3KmehaGWcguWKgsaRcPsFE+I2Vq99YS2hLbb27uNyuhcnlk4p9YJime768S4iSzOXIxRa6Sqo4rXCl5jU9WmbqMa2jgtY88dS3BEME67E12hju/EfAeAT8s+5eOSj33a3zz2Zs+2VkcHj3z5iGxrFUKI08iQDBkAL774Ii+88IJPoS29Xs/tt98+KHPqBoOBuLg4srOzmTBhAukZ6SQmJmIwGHC73TQ2NtJQ3wBAYmIik6dMJiU1Ba1Gy5XxVwLw/qH3+az0M5/rfr3la3bt3EVnWyeKopAV41kf0aBtQM3yfPkf3HmQzvZO4lPj0aZ5tpS2H2jH0eZgbMxYwg3hmB1mChsLj/t9tdnbaLP7LujUqp57aNwaguyes1gURWFC3ARuzbuV7IhsXKqLDRUb+KT0k24hJSg0iNFzRxOfGQ9AfGU830r9FgD/OvAvttVs87bVaXQ8MesJ766U3+/4/XG/ByGEEIExZEPGwoULueGGGwa7Gz3SaDSEh4czYuQIpk6byoRzJpCe7gkdE86ZwIiRIzAYjh4VP9w0nAWRnvUTj25+lK01R3d9uF2ekYCEjARmLpnJefPOY2L8RJyqk9frXscV60J1qxTtKEJVVbRpWoxxRlChbW8bilvhnPhzANjbePzTSFpFS4g+xOexTmMnnYZOtKqW4dXDvUEDPOXPrxx5JYszF6OgkF+fz8bKjd0/I62GYeOHER7nOXRuTvgcFmYsBOCV3a9wpO2It21yaDK/mPELAN4qeot/HfjXcb8PIYQQA2/IhowzhaIohIWFkTEsgxEjRxAW1nOp7yvirmBi6EScbif3b7ifA00HANAbPIsljcFG9AY9iqJw7+R7yYnKocPRwcd8jKpRaalroa68znO/3DA0Rg0uiwvzQTNjYsYAcLD5YK/VNnsTa4pFp/HdpKQqKoeSDtFp6ETv0jO8ejgG29HQ1DWqceGwCwHYVLWJr2u+7vnz0Rwddbou9zrGxIzB5rLx7PZnabY2e59bkL6An5zzEwCe3f4sm6o3Hdf7EEIIMfAkZJwhNIqGpYlLyTHl0OHs4Kfrf0pNR413xMPpOLoWIUgXxH1T7iNUH0ppeyn74vcBcGjXIVSnikavIXy0Z4TAWm0l0hxJdFA0DreD/XX7cbQ5yHJlkefKY7pzOnHuOACS3cloVN//ZL4ZMLq4tC6foJFemY6rw7fo2Tnx5zAvdR4Aq46soqChoM/PQKvR8sMJPyTeFE+9pZ5lny/zOSr+xtE3ckX2FaioPL79cYIygvq4mhBCiECTkHEG0Wv03JZ8G8mGZBosDdy17i7P4kqOHkjWJToomjvG3wHA2ua1NAQ3YLfacR3xtDNEGQjO8Jx10r6/nUyL53C1/IP5NG9r5mLnxZzrPJcprinMdM4EoEpThVtxo6j+rWk5NmjoXDoseyzdgsbMpJlMTpgMwIqSFRxu7fs8lTBDGHdPupsgbRDbarfx3PbnvM8pisL9U+9netJ0rC4rGXdnoI/ufbuvEEKIwBqSxbgGW3V1NcHBwd4/63Q6QkJCfBaculU3Lc0tfl3P4Tj627oOHbcl3MZzVc9xuPUwL7peZD7zsdvt3c4hmZwwmXmp89hQsYFPtZ/yHb6DodpAXWwdhAAJQCNghuGO4Ww3bKdUU4rT4KTR0YhZMdNBB+1KO3pVj0NxEK6GM9cxl0/0n+BUPAHnSPsR+lIRVsG89nkE24NpzW/lYNJBrIaj1U4NGgOxplgaLA28X/w+wbpgUkI9RdOsdk+7SnMltQ213tdcln0Zbx14i7cOvEV0ULR3vQbAjyb8iMr2SsopJ+PuDFrtrRiDjX320e12+1X5VXWr/rVT/WsHUtxLCDF0yUhGANTW1lJTU+P9qaiooKWlhaSkJJKTk0lOTkar0WIwGPr90aIlKjzK5ycrOov/N/z/EaIPYZ95H2t0a9CrerIis7r9PDrzUZJDk2myN7E9cjsKCsHlwYyLHcf4hPGMnT2WzKmZzJo1i2hjNA7FgeYcDYfTDtM+rB33MDchGSHkxuWSHJTMZc7LyFQzuVZ3LXOS59Bmb6PT0dnnT6urlRXGFbTqWtG79QyvGk5rSyuV5koqzZVUdVQRogvBpDXhUl38p+g/tNvbMelMaBXPThW9Ro9JZ/L+jIwayTW51wDwp91/Yl/jPu/nH6wP5t7x9+JocRCUFsRj2x5DRUWn0fX6o9d61rP0+6Pxo83/foQQ4mwnISMAoqOjiYmJ8f4oikJtbS2FhYV+n3vRn3RTOnem3YkGDQe0B/jM/FmP7UL1oTw24zE0iobtlu0U64vpbOmkqcxTR0Or1xKeEE5IVAiTEz3TFttqt3W7TqQxkuExw6lPrMetuInojCClMQW+8XasTistthbMDjM2l81bB8Ou2NkcsZlWXStG1cjMlpmEOo+eN6IoCnHBcYTpw7C6rPxj3z9osbX0+Rl8Z8R3mJk8E6fq5Ddf/4b6zqMFvmKDYil7rgy3zc3Wuq38euuvB+yzF0II4R8JGQEwduxYxo8f7/0ZM2ZMQIJGXlge18R6fpv/3PE57xW/12O78fHjuWnMTQCs163HjJnq/dU4bL7TK5MSJgGwq25XtyJZXTqCOiiLK0NFJa4tjjGOMd7nLE4L7Y52HG4HFqeFNnsbjdZGmq3NdDo7aXG18HnY5z5BI9h1dFpJo2gYEzuGOFMc7fZ2/lH4DzrV3sudK4rCjyb8iGHhw2i1t/Lrr3+NzXn0YDRrmZXyl8tRUHin+B3+Xvj33j9MIYQQA05CxikQHx8fsKAxN3ouU5xTAHh669NsrtrcY7tb8m5hVPQoLG4La4LW4HQ4qS6s9mmTGZFJlDEKq8varcDWsVpDW6mKrgJgim0KafY0LE4LZoenXLlRayRIG+Sd6nCqTmwuG/WWeko6S/in7p+s1K+kQqlgYttEn4Wkeo2e60ddT7ghnEZrI+9a38VO79tqg3RBLJu6jHBDOCWtJfx+x+99AlJ7fjt3jbsLgN/v/D2rylb1ei0hhBADS0LGKRIfH8+wYcMAz5qNzs6BOZBMp9MxzTWNXFcuLtXFQ188REV7Rfd2Gh2/nPVL9Bo9R9QjfK39muaKZpz2o1tfNYqGiQkTAahor/A5Lv2b6iPqqQ/3TE+cYznHGzD0Gj1h+jDCDGFEB0UTbYzGpDP5nILqUB0Ua4r5WP8xna5O4uxxPteOMEZww+gbMOlM1Lpr2aHdgeroPZTFB8fzwJQH0Gl0bKnZ4nP0PMCV2VdyTY5nxOfnX/6cKnNVr9cSQggxcCRknCK1tbWUlZUBEBISQlDQwNRw0Ov1REdHs8C5gCR3Eh2ODh764qEeA0KiMZFFxkUAbNFuoT6uHq1e69NmUcYi72jGrvpdWJyWbtcBQIF2UzsADsWBUevZveFwOzA7zLTb22m2NtNka8LitKB+Y/GGAQM5rhwiiaRV19rt8nGmOC7IuACAEk0J1gorqqv3oDEqZhTXj7oegLcOvNWtqNh9k+9jUvwkrC4rT255UtZnCCHEKSAhIwBcLhdOp9P7c/jwYfbu3Yvb7SYmJoZJkyah1Wr7v5CfsrKyMOqNXOi4EBMm9jft5/kdz/u0aahv4KP3PyKrxVNkCwXe73yfus46n3YxphiWTV2GUWvE5rKxq36Xd5Tim0KsnnLiDboGwvRhmHSe02atLitWlxWn6hklUVDQKToiDBHEmeLIMmXxPfv3WORcRGlwKTZtzyMm2VHZnutrGmi3t2Or6X1kBeDCYRcSa4qlydrEmkrf0QytRssj0x/BoDHwZfWXvLn/zT6vJYQQ4uRJyAiATZs2sXHjRu9PaWkpAGlpaYwbNw6dbmDLk2g0GkLCQwjXhnO+43wA/l30b1aXrUZVVfbt3cenH31Kh7kDvUnP96Z9j+GRw7E4LbyY/yJWp9XnejGmGHKjcwnRh+BwO9hdv5tWW/fRhhDb/0KGtgFFUQjVhxKmD8OgMWDSmgjThxFtjCYmKIYwQxhRQVGE6EKY1DkJg2qgRddCiamk1/cVqg8lKSQJgMOaw1jKLH2OZhi0Bq4aeRUAH5R+gCbI9z/vYRHD+PE5Pwbgd9t/xyeln/T30QohhDgJEjJOAa1WS25uLiNGjAhY/QRFo2AKMZFJJpOcnl0iT3z1BO98+g7btmzD7XaTPiydrDlZhEeH84PxPyDCGEFVRxWvF7zebfpAr9EzLnYc4YZwXKqLgsYCmqxNR++nKgTbPDtDGnQN3seDdEFEGCMINYQSpAtCq9H6vOdUWyqJ9kRcuMgPy0dV+p62GBc7DoA9+j247K5+RzPmp80nKSSJdkc7MYtiuj1//ajrveszHvnyEb6q/qrP6wkhhDhxUvEzAHJzcwkJOXoyaVdxprq6o1MTNpsNl8vV08t9uFQXdbVHX+dyubDb7QQFBfl8eQeZgtBoNeiMOmY2zqTKXUW1s5p/1v+TyzWXM3HKREbmjmRrzVasLitBuiC+n/d9lm9fzva67by29zW+m/NdtJr/7QhxO1EUheyobA41H6LV3kphYyFZkVlEB0UTagtFo2qwYqVerYd+3ooWLU6rkzHtni2ve4x7qHJX8c2NIw6Xg/1N+71/DtGHoFW0NKqNVCqVJJUmUWosJTEshYo2390xXS7IuIi/Fr5G7IWxNK1posna5DMl8/2871PdUc36ivXcs+Eenp33LCOiRhAT1D2U9MjPnOjvug8p3CWEGKokZASAXq/3Oaq9JxqNptcTV491sPggGsUz4OR2uzGbzbjdbvR6PcHBwd4vqIjICFJSPaW4LfEWFh1YxD/Uf3BEc4SIiRFMyJsAwMT4id5y1xPiJmDSmnhq61NsrtqMBg2PzXyMIF0QP5jwA4K0nsWpTreTP+7+I1tqtnC45TAL8haQbcmmoqYCi8nClMQp/b6PouYi5nTMwYCBNn0brTGtJCvJ3RsqEBkU6fNQTnQOhY2F7NLvItWeSnR7NEqYhlB9aPfXA7OT57Cq7DMqqSB2cWy3CpxaRcuD0x6k1d7KzrqdPPTFQzw//3liTbH9vo+uPvbfRIKDEELIdMkZ4tiAAZ7zTI7987FMJhMzcmcwP2Q+AK8ceAWzvefFmxdlXsQTs57AoDWwqWoTP133027rL3QaHXeMv4N5qfNQUfnTnj+xvmo94CnO5Y80Wxqx1ljcuDkQdaDfaZJjjY0ZC0CJUkI77WiqNKju3l+vUTRcmr4EgJjzY2i0NnZrY9AaeHzW42RHZtNsa+aBzx+g0dK9nRBCiBMnIeMMcGzA0Gg03sPXXC5Xr0EjKCiIqzOvJt7gORb95V0v93r9ualzee7c5wjVh1LQWMCda+7s9oWrUTR8b8z3WJTh2QK7snMl27XbMRt7Di/H0jl1jOvwrK0oDS+lQ+9fMOkSbYomOTQZFZXdht0oDgW1ru/5mbFReXQe7ERj1PB28ds9tgnRh/D0nKdJCkmiqqOKH639ER2O4+ubEEKI3knIOM19M2CEhoZiMBgICwtDURTcbjft7e047I5urzVoDNyQfAMA/zzwT5+1Dt80Pm48L53/EvGmeMrayvjN17/pdrqqoihcm3sti9MWA/Cl7kv22ff1vfZAheS6ZAyqZ5qkPLT8BD4FyIvNA6BQU4gTJ+5KJ25Xz6XPu/pa+1/Pqa2fHPmEanPP6zeiTdE8PfdpIowR7Gvaxz3r7+l2mq0QQogTIyHjNKaqKpZOi0/A6FpPodVqCQsLQ6PRoKoqDQ0NPV4jLyyPqRFTcatu7lxzJzvrdvZ6v6yILF5Z+AqZEZm02Fp48qsn2VG7w6eNoigsjFzIDOcMACo6KtjbuBen29nTJTE4DIR1hp3QNMmxMsIzCNYFY1WtVOgrwA5tdd231R6rY38H5r1mXKqLlaUre22XFpbGr+f8GpPOxJaaLXxc+vEJ9VEIIYSvkwoZVqu1/0bihDU2Nnp3oBwbMLp0BQ8Au92Ozdbz9s7rkq4jLSiNJmsT92y4h3/u/2evow/xwfG8eN6L5ETlYHVZWb5zOe8Xv+/T3u1yM9k1mQuDLkRBoc5Sx7babT1ONbg1ntEGBQWr9sT/e3Grbm8V05Agz84dl7P7lElNZTX79+yjaO8BABzNnlGJxtpGdu3cRUFRGV8UNeP8xpqO3OhcvpX9LQBKWnuv3SGEEMJ/JxQyXnrpJZKSkjCZTISHh3PllVeyY8eO/l8o/OZ0OqmsrAQ86yu+GTC6aDQab/XQ1paef7OP0Efws+E/Y0bkDFyqixfzX+SRLx+h09Hz+SnhhnDumngX56d7Cnu9U/wOL+a/6HPCKcAk4yTGxIzBqDXS6exkW+02ajtrfd+HzoldZ0dBIczR/26a3tR01OBSXQTrgolWontuU1nNd+ZcwY0XXs+tl38fAEOsZ5fPq0++ysXf/Sk3/uUAP/1nEY+8U9wtaCSGJHrvJYQQ4uQdd8jYsmULd911F3feeSdr1qzhxRdfRKfTMXPmTF5//fVA9DEgXnzxRdLS0ggNDeXSSy897UJSVVUVTqcTjUaD0Wjss61erwegpaWl1zZGjZHbUm/jO/HfQafRsb58PbeuupXS1tIe22s1Wm4YfQPfG/M9tIqWr2u/5vEtj3c7fC3MEMaUhClEGaNwqS72Nu6lqLkIh/vougZLkOf8k3B7eP9vvBfl7Z61HKlhqb3WlWhpasFu8y28oY/To6qgDV9AwtVPoAmOAGDV3iZ+9t9inMes6+gKGd8MSkIIIU7McdfJ2L59O1dddRWPPPKI97EbbriB7du3c9FFFzFz5kxycnIGtJMD7fnnn+fVV1/ltddew2Kx8PTTTzN9+nReeOEFbr/99pO+vtVq7fdsEovF0mstjc7OTioqPF/mpmBTr9MgXVwuFzabDZvdRlJ7Up9lyycbJ5OcmMzfav9GWVsZt352K/dMuIe5yXN9+9dpQdWoTIueRkxeDK/ue5Xy9nJ+/uXPmRMxhyxbFgarAYfGgU6rY0zoGMo0ZZRbyqkwV1BlriLeGE+SKYlQQgmyBWFqM+HU97x2o4vD5cBm6f5+y9s8ISPRmIit2YbGpsFqsWLpPHqAm83q+zpFq6CLMGGt/hZh50wGoGPveh644SJeybeyurAJh9PJzy9OQ6dViNB4Aki1uZqOjt53mfQ2quTjOJaenC3FuPr6TIUQQ9Nxh4wRI0bwxRdfdHt80qRJXHfddfznP//h4YcfHpDOBcqTTz7J22+/zbx58wC4+OKLue+++7jjjjtQVZU77rjDr+vYbDafANDW1ua93ulOG6Yl7QdpMBqe3P4kdz91N7Vv1fZ6NoguUkfyjcmETwxnfct6Pu38lKrHqujY7/vFETo+lMTvJBKUFkSNrYYaWw2bDm6icW0jbdva+jyyvde+hmsZ9fwoAF698lVc7f1XSgUwpiViKb8DtzUN1e2ief3rtH/9Hv9vxW8xDZ9C3OUPsaGojekf/pGGD36DPlJDzrM5VLdVExERcVxBQQghRHd+TZd88sknPP7443z44Yfk5OTQ3NzMBx980K2dVqsd0NNFA0FVVRobG3E6j/5GrdVqee6557j77rv58Y9/zLZt2/y61lNPPUVERIT3Jy0tLVDdHnCudhelvy2l/qN6AGIXxZL5UCb6aH2P7Z0tTo48f4QjLxzB0ezAmGgkc1kmKd9PQRty9O/cvMtM8SPFHH7yMC1fteB2ugkeEUza7Wnk/C6HhO8koI/t+R69CR3tWdxqKbP4HzBSxxB3+TO4rWmgdFD39s9p//o97/OWQ19T9+6TqE4HITmziFvyAI42FdWtougUdOFSDFcIIU6WovpxwMInn3zCr371K3bt2kVbWxvBwcFYLBaWLFnC5ZdfTmpqKgUFBbz44ousX7+elJSUU9H3EzZ//nwMBgOffvqpz+Nut5t58+YRGhrKypW9b3ns0tNIRlpaGuvWrfPu+ujNoUOHuk2XuN1uampqcLvdhIeHEx4eTmNjY79rMsCzUNRsNqPRaEhLS+t1CL6js4NgU7DPY/vs+3jf/j7tjnbCDeE8OvlRJsRO6PUaBQcKeGX3KxQaCgEI04exNGcpsxNnd3tNi62FNZVr+Kj4IzqUo6MeIyNGMil2EhlhGd7y5QD7m/Z710Z0+bTiU/a17GNS7CTmJM7BVmjD1eIiLS+T6NQ4b7uivfu59fKbCD1nMdHn3Yai1aExVuFqe5kjvz+6dTfh2t9gSMjy/EHRoOj0KIrC/JHRlAX/nHpbDa/O+yOjo0d3e+9u1Y1e038AUVUVRePfNIi/0yVneqnytrY2kpOTaW1tJTy89/U5bW1tRERE9NtOnJyCylYueeELVvx4NmNTInps09HRQfTw8STdtJx/3zKJKdmJPbYTp86Z9u/Dr1/XLrzwQi688EJUVeXw4cPs3LmT/Px88vPzefjhh6mqqgI8B4OtX7+e6667LqCdPlmPP/448+bN47HHHuPRRx/1Pq7RaHjooYe47LLL/LqO0WjsMQAEBwd7q3L2JigoqFvIaG5uRq/Xo9PpiIuLQ1EUgoKC/AoZ4AkpLpcLRVEwmUw9tnG5XQQFBfk8dk7QOWQ4M/h7y98pt5dz75f3snT0Um4bdxu6Hr5QY8NiOU89j7kRc/nY/TFH2o/wh4I/8EXtF9w54U7v8ewAwSHB3BB9AymtKRyoPkBhcCGljlKKWosoai3yXM8US2poKilhKTjdTpKNyRi0ns9GVVXKOzzrMUbEjCAoOAiCwGV0EWQyYTrmc9Yagoi+8MeEjb/A817btxKa8yFNa313iyh6AxqD72cAsK6oieiYb6PGvUKb2upzyJ33M5aQccL8ORBQCDG0HNeYsKIoDB8+nOHDh/Od73zH+3hDQwM7d+5k586dfn8hDqbZs2fzq1/9imXLlqHRaHwWsUZHR/t1cFlfCgoKfEJGSEgIWVlZfX6Z2O12zGZPie6oqKjjXgyoKArBwcG0t7fT0dHRa8joTbQumh/F/IgP2j5gU+cm/lb4N/Y07OGJWU8QY+r5dNIMfQa/mfYbPi75mH/u/yf59fn8aM2P+G7ud/lW9rd8Aoo+XM/wyuHkkIMyUWFLzRb2Nu6lxdZCg6WBBksD+fX5AHxa9imxplgSgxPRaXSYHWZ0io7UsNRe+9/c4eIvO02Ejb8A1e2iZcPfCJ+0FUUTib3+6I4TTVAoiqH7Z6PTKGg0Ck2NaWht11HRJjtMhBDiZA3IxHNsbCwLFy5k4cKFA3G5U+KBBx5AVVUeeughvvrqKx5++GGCg4P56U9/yn333XdS125vb/dZ89HW1oZWq2XYsGG9vqYrYCiKgsPhwGg0HlfQcLvdOByebaMnWiRNp+i4IuIKMg2ZvGN+hx11O/j+Z99n+fzlZIRneNt1vTdFUdBr9Fw58kpmJc/ipV0vsat+F38v/DsbKzbyowk/Iifas9NIG/6/I+TNTnS7dSxIW8DFky6m09VJpbmSCnMFleZKSltL6XR2eoNHl9SwVHQaHapLxW11e+/f5Z2vW6lo89yjYcWzWEs+J+nqEQA+IcOYORGNrvuaEI1G4ekr8rj77V24zKPZXlLN0rEn9DEKIYT4nyG7uq2oqIiRI0f22WbZsmXMmDGDhx9+mNmzZxMWFsb999/PsmXLBrw/iYl9z2WGhIRgtVpxuVy0tLTQ3t5OWFhY3+eC4BmWt9vtWCwWb9uTnac7x3QOGaEZ/Ln+z1R3VHPrqlt5bt5zjIkdA0BlhadIWEzc0RGO5NBkHp/5OOvK1/Hngj9T2lbK/9v4/1icuZgbR9+IxqghLDMM8xEzzg4nLftbaDvURuzkWHKic7xhpLCxkHBDODWdNdR0eH5sLhsL0hcAYD9sR7WoKHqFsLhI7/1njAhmw34LFruL3KsfIHPYZA7wKfZ6O8FjlhGS55n60AZHogvvfqS7qqr8fo2nSqiia+Oi3L7/2xFCCNG/IRkyli1bxvPPP88HH3zA+eef32fbefPm8cUXX2C329HpdP7VQOjHjBkz+l34+U1Go5GkpCQ6Ojpoa2vzho2u39Z7Gtlwu910dnZ6RzCMRiNxcXG91t84HvGGeB4c8SC/P/R7Si2l3LnmTn4959dMjJlITZVnjUPGsAzaafe+RlEUFqQvYFLCJF4reI215Wv5qOQjNldvZn7afKYMn0JoRiidVZ2Yj5hxWV20l7QTPda3gmeoIZRsQzbZkdk+jzvrnDhrPKMoppxw9MajIxKjU4J48so4nvmogeoWJ437Z2NMaKDi1X8Tc2lOj2swjuVwqZQ1WlF0LZwzYSMXZv/hpD4/IYQQQ/SAtLVr15Kdnc2SJUtYvXp1r+3y8/O9i9EMBsOABIyToSgKoaGhJCUlERUVhVar9RySZrHQ2tqK1WpFVVVUVcVms9HW1uYNGCaTieTk5AEJGF3CdeHcn30/Y8LGYHVZuXfDvfz2y99ic9uIjIokIrLnFekRxgjunnQ3T8x6gqSQJJqsTfz34H/5e+HfaXO1EZoeSnSeJ1hY6iy47P0vCHR3urEVe3by6NP06KK6v8+0GAMPXG7AGHYAVD22misJyrrJ7/er0bcQnPEqd0277qwpkCWEEIE0JENGbm4uDzzwABdccIFP0Dh2u2ldXR2zZ8/m+uuvP+1WvR8bNvR6z/bKY8OG2Wyms7MTVVXRarWEh4djMpkC8sUYpA3irsy7mBHlOffkg+oPeMPwBvXR9f1O5YyPG88LC17gqpFXoVE0FDYW8uz2Z9lUuQltmBZ9mB7c0Fnd8xkqXVSXim2/DVygidCgz+i5zoZbdfNm0SvoU/5KfMpWVNVN2DmLUXT9By9F24Ep42XGJacyI2lGv+2FEEL0b0hOl+Tm5nLgwAHefvttrrrqKpYsWcLy5cv59a9/zcaNG0lJSSE+Pp4XX3yRzZs3D/gIRktLi3eEoTcOh8NncWhvdDoder0ep9OJw+FAVVXv6/R6PXq9HpfLhVarpb29vZ+redgd9n7baE1anwWk18dfz1jjWP5V/S/alXZeqXiFHet2cG3utQyLGNZ7/zU6rh11LaH6UFYdWUV5ezkfHv6QHXU7WBSziJD2EMwVZvQpnjDldDuxu3z75zrkwt3hBj1oRmj+dy6KE4vTN5ysKlvJ/uZCjDojd80Zyy3ffozYS/8f2qD+pq7cmDJeRKNv5Zaxt6Ci9hqgVNXdb7jyXNGNRvXvvysZNRFCDFVDNmS8+eab6PV63n77bS677DJuu+027r33Xp9CYUuXLmXp0qUDfv/g4OAeaywcKzQ0tN824NmZ0rWQ0+1209raisViITo62qfeRV1dXbf6Fz0xGAykpva+FbRLS2sL4WG+C0jHWcehtWvZHbSbbco2ttZsZUfdDq4fdT235N1CsL732iBLxy7lprE38cGhD3hl1ytUmit53fw6WoOWUGcoqYWppEamEm4Mx+12ExccR6wpFkeDg321+3DiZOL0iYTGhGJxWqjtrKXVUYfFacHitNBma2NFyfsA/HjCj5gRPwnr4e3U/O2nnPvI/3GwrhOdRuGhxaOYMyKW6/68hbp2G8Pjgpk9ZSfvlDQxOmYM81Ln9fmlr6KiUfw7u+RMr2shhBAna8iGjIKCAgBKSkrYs2cPU6ZM4aWXXuLCCy/sdzHo6Uqj0RAVFUVUVNSg3L++vh49eq5IuILvxn+X18teZ2frTv6696+sLFnJPZPv4fz083v9ktYoGi7PvpzZKbN5YecLrD2yFpfiolVppbW1lb2te3u+cVfpla3993F2ymwuGHaB9+A0Z0sNr984ml9/Vs6K3bX8ckUhBp0Gu9PNyIQQnrs2i++tfgCAO8bdLqMKQggxgIZkyMjOzqasrIzdu3dz8cUX8+STT3LNNdd4p06+/vprxowZM9jdPC04nU6am5vRarUYjUYMBkOPp7g6HA7vUfKxsbEEBwWzbOQyNtZs5K3at6jtrOWBjQ8wNmwsS9OXMnv0bIxBPRdmizXF8tjMx3ho2kOU1pXy0aqP6NB0kDo2lXp7Pc22Zuot9dSaa2m2NdM1IKCgYNKZMOlMGLQGQvQhBGmDMOlMBOmCSA5N5prca7oFhSC9lt9cOZpxaeE8vbIYu9PNqKRQ/nLTBP558M9YnBZyonKYkzK3h94KIYQ4UUMyZBgMBtLT05k7dy7PP/88N954IwBvv/02r732GqNHdz+T4mxktVrZtWtXt+JdiqKg1+sJCgpCr9djNBq9O1sAysvLsdls2O12tFYtV6pXsk27jR3aHRS0F/BAwQOM2TuG74/5PnPOmdPr6IBRayQnKYeqhCrqauoYrY4mY3wGIXrPNNLuHbvZt3cfMakxzJkzB4PW4L1Wk7WJUIP/24QVReGGGWnkpYaz6WAT101PBW0Hbx94G4Cbx94soxhCCDHAhmTIAHjxxRepqanhhhtu8D6m1+u5/fbbB7FXp5eysjKsVqt3AandbsfpdHoLfNntPS8Qraur8/mzDh1zNHOYqJvIOtZR5CpiD3u4r+A+brLfxG1Tb+uzHylpKdTV1FFeVk7G6KOVRWuratGiJWdYDkbdwJSrn5AWwYQ0z9bbl/L/j05nJyMiRzAnZc6AXF8IIcRRQzZknEklzgdLV4jIzMwkKclzqJnb7cZut9PY1Ihep8dms3H48GHva+Li4ggJCfFOrThUB1ERUd4dOueq57KqYBUrO1ZSranmzwf/zBeNX3DvpHsZFTOqWx8cDgcHCj2VNpOSk3yeCzIF0drc6rP1eKA0WBr45/5/AnDruFtlFEMIIQJgSNbJEMfn2C9YjUZDUFAQISEhxMbGotcfrUkxduxYcnNzSUtLIz4+nsjISIxGo88W4MrKSkJaQviO8zvckHgDYYYw9jft5/bVt/PCzhfodPhuO92zcw+dHZ0EhwQz9hzfw0Ji4z3lvxvqGhhor+15DZvLxtiYscyVtRhCCBEQEjJErzo7OykuLgYgLS2t310tHR0dHCk9AkDW8CwuTr+YZ0Y/w8zombhVN28deIsbVt7Al1VfAp7wcHDfQQCmzJjiE2jAN2T4U5vCXxXtFbxb/C4AP5zwQxnFEEKIABmy0yXi5LjdbvYf3I/L5SIiIoKMjIw+26uqSnFRMaqqEhUd5T0QLtIQyU+yf8L0lun848g/qO2s5f6N93Nu6rmMqx8HwLDhw0hM6X6AXHRMNIpGwWqx0mnuJCSs/7oi/vjj7j/iUl1MT5rOxISJA3JNIYQQ3UnICICuA8764nA4/KrQ6XQ66ejo6Ledy+Xyq51Go6G1tdV7bfCMWHQ91uVI2RE6OjrQarUkJiZ6t69+k6rxlDuvqa7BbDaj1WpJSU3ptmMlLyKPZyc/y9ulb/Nx5cesr1jPJnUTacY0zos4D1O9idyoXNyqG7f6v6PctQpR0VE0NTRRkF/A5JmTUTSeUQen24nT3XvF1GOfc7lduNyev4/i5mI+Lf0UgNvG3obT5Wmnqiqqxt3v56eqKm5tv81wq27/inYhRbuEEEOXhIwAiIqK6vcUVrvd7tdJrTabjZiYmH7bWa1WjMb+d2DodDoiIiK8/z94KpR2PQZQW1vrDR1ZWVl99tPlcmHUG6muqgY8hdCSEpK6tVMUhfiEeH6W/DOm7JjCH4r+QJ2mjsMc5vDew7DXs6V1bMxYzok/h4kJExkXO47Jkyaz6rNVlJeWY9AZmDt3LhqNhlB9aJ/l4DvUo4HLqDWyt3Ev7xa/y5qyNaioLExfyPj48d42qlv1q7y82+32KxRoFa1MwwghznoSMoSPzs5ODh70rJNITEz0ljTvjaqq7N27F1VViY2N9e5S6U3J4RLq99ZzFVcRkROBOcJMfkM+uxp20WJrYXvddrbXbYcCT4XQkVEjGZ4xHFe5i7biNlRVZd68eX69F12EjshZkVyz6hoqzBXex3Oicrhr4l1+XUMIIcSJk5AhvFwuF4WFhbhcLu/R8f2pq6ujra0NnU7HqFGj+vztvbKyki+/9Cz6zM3NZfLkySiKwrU516KqKmXtZWyr3cbe5r3k1+dT1VHF/qb97Ge/579UHfy7/N9kvZPFvJHzGB8/ntExozHpTN57ON1Ovqz6kv8e+C85z+agaBUqzBUE64JZlLGIy7MvZ2zMWBllEEKIU0BChgD+t3CzuJiOjg70ej3Jycn9fhFbLBZqamoAyMnJ6fOAtpaWFrZv346qqgwbNswbMLooisKw8GEkByfznZHfAaCus45dDbvY3bCbgsYCDjQfoJNOCiwFFOzynE2jVbRkR2YzNnYsIfoQPi75mAaLZ8urolXoPNjJY1c9xiUjLunzADchhBADT0LGWazrS76mpobS0lJv0avc3Nxeq3126dre6u80yYEDB3C5XCQmJjJj5gy/RhLig+NZmL6QhemewmpWp5X1e9fzWcFn1Cq1NAc302hr5EDzAQ40H/C+LtIYyaLURTx59ZPYqmxc/P8uloAhhBCDQEJGABw4cIDg4KNfajqdjrS0NL+OYu9PU1MTzc3NpKam+rXQsy9JSUk0NjZ6F3nqdDqysrKIjo72jlD0pLW1lcOHD+N2uzEajYwePbrf0BAeHk5raytu1Y1W68f2jB4E6YKIskQx0TURo9HIZRdfRqu7lb0Ne9nTuIdGSyPnpp7LnJQ52K12fl718xO6jxBCiIEhISMAWltbu40EtLS0MHLkSJ9dHMdDVVUqKiqoqKjw3mPMmDEnFTRiYmLIzc2lpqaG+Ph44uPj+w0AdXV1lJeXAxAWFkZGRoZffcjMzKSyspK62jpqamq8dTSOR0lJCQeLPItSp8+aTlBQEEEEkZCewIL0BT5t7fQ9EiOEECLwJGQEQGZmJiEhRwtH1dTU0NHRwb59+8jMzCQhIeG4rudyuTh06BCNjY2AZ8TBZrOxd+/ekw4aCQkJfvWnK+R0HY4WExNDRkYGbnf/tSUAgoKCyB6RTdGBInbv3n3cIaOtrY0tm7cAkDcuj8Sk4w8pQgghTi0pKx4AcXFx3pGB+Ph4xowZQ0xMDKqqcvjwYUpLS/0uk+1wONi7dy+NjY0oisLw4cMZN24cRqPRGzQCcYDYN5WXl3sDRkpKChkZGce9Q2PMmDFoNBrvaIa/nE4nn2/4HKfTSUJCAnnj8o7rvkIIIQaHjGQEgMVi6TbtkJqail6vp6amhurqau/6jL4KQNlsNmpqanC5XGi1WjIzMwkNDcXtdjN8+HCKi4ux2WwUFBT4rAHpi6IoVFZW9tvO6XR634PFYqG+vh7wBIyIiAhvRU9FUbDarL1e5+iNwRRsIiMjg5KSEnbu2MnsObO7NXPjRqfx/c9y29ZtNDc3ExQUxMxZM0HFpzJoT459TlXVfkOdU3Wi9aOUp/q//xtIiupnWJNdt0KIM4yEjABISkoiLCys2+MpKSlUV1eza9curFYrtbW1jB49GpPJ1K1tfX09paWluN1uQkNDmTx5crcgkZCQwJYtW+js9JxsmpWV1e/USWlpqV8LL4OCgkhLS0NVVbZu3ep9X2PH+p6U6nA4iI6O7vd6ZrMZnVZHbm4uZWVlNDY20tzUTFxcXPd7G48ukD18+LD3kLZ58+YRFek5pM2gGtBpe//P16U7WtZdr9Wj1+p7bQuerbD+VPwE/BrBUVH9qgw60IFFCCFOJzJdcoolJSUxY8YM9Ho9FouFXbt2+ZwboqoqZWVlHDhwALfbTUREBDNmzOhxpMJkMjFt2jSCg4Ox2+0BmTqprq6mpaUFrVbLyJEjT/p6wcHBDBs2DIDCwsJ+2xcUeOphjBs3jpSUlJO+vxBCiFNHQsYgiIiIICcnh9DQUJxOJwUFBd5pkQMHDnh3byQnJzNy5MhuR6AfqytoGAyGAV+j4XQ6KSoqAjyjJAOxBRc8hbs0Gg0NDQ3edR49sVqtNDR4CmuNHj16QO4thBDi1JGQMUgMBgN5eXnExsZ6q21u376dhoYGFEUhOzubrKwsv4bmTSYT2dnZA74Y9PDhw9hsNkwmk3f0oT+dnZ2Ul5dTU1Pj3cr7zfUQwcHBZGZmAp6Rit7WS1RVVQGeA+f8XXMihBDi9CFrMgaRVqslJyeH4OBgjhw5gt1u954Bcrz1NAwGA2PGjPEGjN27dzNixAgiIyOPu1+qqlJfX+/dMpubm9vveoWWlhaKioooLy/vFho0Gg1Go5Hg4GBPbYugIG94am5uprm5ucd1HV2jHCdSU0MIIcTgk5AxyBRFIT09nZCQEJqamk6qMqjRaGTMmDHs37+fzs5O9u3bR1hYGHFxccTExHiPdu+L0+mkvr7eu3skPT29x8WZ4Akjzc3N7N27l9raWu/jkZGRuN1urFYrdrsdt9uNxWLBYrF0u0Z4eHivJ712fQ79lTgXQghxejqrQ0ZVVRX79+9nwYIF/TcOsJiYGGJiYk76Okajkby8PEpLS6mtraW9vZ329nZKS0uJjo72lgLvaRqms7OT+vp63G43Go2GsWPH9ngmiaqq1NbWUlJSQltbm/fx1NRUcnJyiIqK8j7mcrmwWq00NTWhKAoWiwWr1YrVakWr1ZKbm9tr+Oka3WhqajrZj0UIIcQgOGtDxlNPPcULL7zA0qVLycnJGVI7FzQaDVlZWaSmplJfX099fT0Wi8W7iLK1tZWwsDBCQ0PR6/XeEYmuXS4Gg4HU1NRuAcPlclFZWUlpaal3VEKj0ZCZmcmIESMIDQ3t1hetVktISAiqqvpUQfVHV8hoaWnx1goRQghx5jgrQ8bq1av5wx/+QH5+PvHx8YPdnYAxGAykpKSQnJyM2Wymvr6euro6XC4XLS0ttLS0eOtqdC0UDQsLIzo6GoPB4L2OqqqUl5dz6NAh79SFXq8nPT2dpKSkgK2ZCAkJwWAwYLfbaWlpGZCRHiGEEKfOWRky/v73v3PjjTd6A4bL5eKdd96huLiYc889lxkzZvh1HZvN5rOLo2vqwGq19rv+weFw+LUDxOFw+ExJ9NWXvnai6PV6kpOTUVUVl8tFR0cHVqvV2wdFUYiOjiY4OBi3243b7cblcmGz2SgsLPQuAg0KCiI9PZ2UlBS0Wi1Wq9VbDKwvdrvdr7Umbrcbh8Ph/XNkZCR1dXXU1dV1W7uh1fQ+snHs4lN/Kn66cftVedOtuvu8rw8/K3QOeAVRKQ0qhDhNnJUho7m52ftbcUtLCxdddBEVFRWYTCYeeughnnrqKZYtW9bvdZ566ikee+yxbo9HRUX1upixi9Fo9Gv6oKmpqceKoN+k1Wr92pESExPjrUZqsVgoLy+nra2N3Nxcn+mOrimULVu2YLfb0Wq1TJw4sdtOk9bWVr8WlJpMpn4/E/CEqmOnRRITE6mrq6O6upqcnByf/vW14+XY5zQaTb+7YxRV8e8sluPIAwN9PSGEONOclXUyJkyYwD//+U/MZjP33XcfY8aMobS0lKKiIn7+85/z8MMPc+jQoX6v8+CDD9La2ur96SqidaYwmUyMHDmSyZMn+wQMu93Otm3b2LBhA3a7nZiYGC655BJGjx7td+ntgZKamgp4Fun6e+KrEEKI08NZGTJuu+02zGYzP/7xj1m9ejW/+93vvL89P/roo5hMJvbu3dvvdYxGo3cLZl9bMc8kdXV1rFmzhoqKChRFYdy4cSxevPiE6m0MhNjYWIxGI3a73XtImxBCiDPDWTldkpaWxssvv8yNN94IQE1NjXeqoba2Frvdzvjx40/4+keOHPEZGdBoNERERBAWFnbKRwL85XK52Lt3r3cEJyQkhHnz5vVaI+NU0Wg0JCcnU1JSQmVlJQkJCYPaHyGEEP47K0MGwA033IDD4eAHP/gBV155JS+//DJ6vZ4f/ehH3HvvvWRkZJzwtXfu3NnjOgqtVktkZCRRUVGYTCYSEhJOm+BRWlrqDRiZmZmMGTPmtNnNkZiYSElJSZ/nnAghhDj9nLUhA+Dmm29m0qRJPProo1x00UWEh4dz9913c88995zUdRMSEnzO2nA4HLS2tuJ0OmlsbPTu1NizZ483eERGRhIdHe3dtXGqde3oSEtLY8KECf3uxjiVuup3dC1YFUIIcWYYkiHjP//5D8888wxNTU08+OCD3Hzzzb22HT9+PO+9996A3n/69Ond1meoqkp7ezstLS00NTXR2NhIe3u7T/A4dOgQBQUFjBo1ioyMjEEZ4TgdC15VVFQADKmCaUIIcTYYciHj17/+Na+++ip33303u3bt4tZbb2XevHkMHz68W9v8/Hzy8vJOyReroijexaHp6emYzWaCg4O9waO5uZnKykosFgs7duzgwIEDjBo1qscqmmeT9vZ22traUBSF5OTkwe6OEEKI4zCkQkZhYSEvvPACO3bs8C4QXLVqVY8Hc9XV1TF79mwuvfRS3njjjUH5Df6bwWPs2LEcPnyYAwcO0NHRwbZt2wgODmbkyJEkJSX5V3dhiOkaxYiPj/epQiqEEOL0N6RCxt///nceeeQRb8AoLS2ltbWVK6+8ktLSUq666ipeeeUVTCYT8fHxvPjii2zevDkg0xL9rWlwOp09VvxMT08nOTnZuxCzs7OT/Px8iouLycrKIjY2tsewYTabvSen9tevnl7fVS7c4XBgNptRFMVnXUlvjq3O2Re73e7X5+xyuXyKlFVWVgKQnJyMy+XyPt5VubSv6xzbtr+/D3/XoKioUlFTCCH8NKRCxty5c5k0aRIAjY2NLFmyhAsvvJC77rqL4uJifvjDHxIREcHzzz8PwNKlS1m6dOmA9yMiIqLfmhkGg6HPL92YmBjGjRvHnj17KC4uxmw2s3v3bqKjo8nLy+t2eFlbW5tfX+KqqvY4BdM1SqDX6wkNDUVVVfR6fb/XCw8P96ud2Wz2q3+Konivp6qq91C3lJQUn/u43e4+R3aOfU5R/KjmqfhXjluLdmBHlI7jUhJuhBBnmiEVMhYvXuz9/4uKiliyZAlPPPEEADNnzqSiooI//elP3pARKI2Njd6RAfB8gZ9IoS69Xk9ubi65ubkcOHCAoqIimpqa2LBhAzNnziQ9PX0gu33aaW9vx2q1otFoTpvttEIIIfw3pELGsWbMmNHtoLPw8PBTUszpo48+6lYnIysri+nTp/t1Dsk3GY1Gxo0bx8iRI8nPz6e0tJRdu3b1ud21o6ODpqYmEhMT/RppOB111cWIjY09LXe9CCGE6NvgV4E6RVpbW1m+fDl33313wO8VHBxMSEiI90dRFA4fPsx//vMfDh48eMLXDQoKYvLkyQQFBdHR0dHr+Soul4t9+/ZRUVHB/v37+1y7cDrrChmDXXVU/P/27jysqTvfH/g7IQuEfUeQHQHRKlZbxbpR1LrbcVw6rV1nequP07Fqb6vP3G7W1lbrqK3e1vbWsZ2tjuO91dqhUxX3ulQFxA1EdkGWAAkEkpDk8/uDXzJEtqAkJ+Dn9Tx9Wk4O4YM1Oe98l89hjLG7029HMtrKysrCs88+i5kzZ2LRokV2/3nz58+3mh6pqanByZMnoVQqcfz4cQQHB9t059KOSCQSDB06FOfPn0d+fj7i4+PbnVNdXW1ZkNnY2IiamppuR3Cc7eZjdXV1KCwsBNC6s4Qxxljf0+9DxmuvvYbs7GysXbsWc+fOFaSGgIAAjBgxAocOHYJMJoNCobBas9FTrq6uADpvnGUwGLr8uiPO1FWzrq4O6enp0Ol08Pf3R3h4uNAlMcYYuwv9PmRs2LBB6BIAtPbwAICEhARIJJJ7ChkNDQ0A0Oli0ju3Y3a3G4KIUFtbCwDw8/O767p6g0qlwrFjx6DVauHv749p06bd9agPY4wxYfG7twPU1dWhvLwcIpEIgwcPvufnU6vVADofdehpyNBoNJY+FkLd0h0A6uvrcfToUcsIxrRp0yCXywWrhzHG2L25bxZ+Csk8ihEREdEr0xG9PZJhHsXw8fER7I6wHDAYY6z/4ZEMO2hpabEsvNTr9cjPzwfQOlViPq7T6WyaBmhqamq3psK8fkIikUCj0QCA5d/m525Lq9Vavkcmk6GpqcnqcfMuDk9PT8tjRGTTRb65udmm83Q6XacBxjxFotfr4ePjg6lTp0IqlXa5GNVoNHYZiNoGLVs6fgIAiWzo+tmDm9Pej23gGWOsLQ4ZdnDs2DFLS26tVguDwQAfHx+r+4+4urraFDL0er3VeTqdzhJU/Pz8LI+5ublZunbW1dVZPYdCoYC3tzeA1gvfnaMp5pGRAQMGWB5raWmxqT6JRGJTHw5PT88O25TX1dXhxIkT0Ov18Pf3x9SpU21qZy4Wi7vsndH2MVs6fhLZ1i6cepIybMBdPBlj/RmHDDsoLy9v13RryJAhvfLJ1hwIFApFpyGgJ9MlBoMB9fX1AODwrprmXSRtF3n21cZhjDGgorECdbq6bs/zlftigMeAbs9jfR+HDDtISUmxuj+ITCbrtW2Y5pDR1dqOO0NGV9MK5vUYbm5ud9WN9G7V1tbihx9+sAoYcrnc6fp1MMZsU9FYgbn75qLZ0P6u13dyk7hh39x9HDTuAxwy7CAmJuau7lVii7sJGV2NZCiVSgCOHcUoKCjAyZMnYTAYeJEnY/1Ena4OzYZmrB+/HjHeMZ2eV6AqwJoTa3Ch6gJidJ2fB/CIR3/AIaOPMS/MVKlUaGlp6XB64c5Q0dlIRl1dnaU1uaNChlarxbFjx0BEGDBgAB599FEOGIz1IzHeMUjyT+r0cV+5L9wkblhzYk23z8UjHn0fh4w+JioqChUVFaiursbRo0fxyCOPtFsoGRwcjJqaGhiNRnh4eHQ4qnLr1i2cO3cORqMRXl5eiIyMdEj9EonEMtIyadIkDhiM9RFF6iK4K2s7fbxAVWDT8wzwGIB9c/d1u3bDPOJRp6vjkNGHccjoY0JCQjBx4kT89NNPUKlUOHz4MMaOHWt1jru7O4YNGwa1Wg1/f3+rkQwiQm5uLnJycgC0BpIxY8Y4bMGlRCKBu7s7NBoNGhoaHLoOhDHWcxKv1svEW+fegktOeZfnuknc4Cv37fY5B3gM4OBwn+CQ0Qf5+fnh0UcfxalTpyw9JiIjIxESEmI5x9XV1XKPEzOTyYTCwkJLX4zY2FgMHz7c4Q24vLy8oNFooFar+eZnjDk5sXvr+8NLQ1/ClMGJXZ7LayjYnThk9FEKhQKpqak4e/YsKioqUFhYCIPBgLCwsA4Xera0tCAvL8/Skjw5ORlxcXGOLhtA66LViooKSy2MMecXqgjtcq0FYx3hkGEHBoOh2zufGgwGqxGEhoYGaDQaBAYGWjWSMhgMne4OEYlEGD16NHJycnDz5k2UlpaiqakJUVFRVs+t1WqRl5dn6br58MMPIyQkpMsajUajZe2EeYqlrKwM8fHxCA8Pt9RkMplsusur0Wi0nGfe3ltfX9/ue7vr5GlmMpl6v+OnDef05Dzu+MkYu99xyLADqVTa7RoHFxcXiMViFBcX4/r16ygvb53r9PHxQUpKCkJDQwG0To10d9EdP348vL29kZmZCaVSCZFIhHHjxsHV1RWVlZXIzs6GXq+HQqHAI488ggEDuh/ONLcV1+v1OH78OIqKigC0tiAPCgrC6NGjERwcDKlUatPF1GAwWMKT+SZsjY2N7bp2dtfJs+15Xf3cto/Z3PHTht/D1vMYY73DlgWlPE3jvDhkCECtViM3Nxc3b95Ec/O/G9dIpVLU19cjPT0d0dHRGD16tKVVeHfMvTlOnTqFmpoaHDx4EDExMbh8+TKICP7+/hg/fnyPbpteV1eHQ4cOQaVSQSwWIzY21rKm47vvvkN0dDRGjBjR454g5vPVajVftBljHeKtrv0DhwwHMZlMKCkpQW5urmXUAmjttBkfH4+EhATIZDJcuHAB169fR2FhIcrKyjBs2DAMGTLEpimEkJAQTJkyBceOHYNGo7HsIImIiMDDDz8MiURi09QGABQWFuLMmTMwGAxwd3dHWloagoKCMGrUKFy4cAF5eXkoLCxEcXExBg8ejGHDhtm8HdXcSEyv10On07VboMoYY7zVtX/gkGEHN2/etGorrlKpkJ+fbzVqERoaisGDByMiIsIqQIwdOxbx8fE4ffo0qqqqcOHCBeTn52PMmDE2TXN4eXlh6tSpOHnyJKqrqzFkyBAMHTrU5tECk8mE8+fP4/r165Y6U1NTLVtN3d3dMWHCBAwZMgTnzp3DrVu3cOXKFdy4cQPDhw/H4MGDuw1ERARXV1dotVqo1WoOGYz1AWV1Bly+peryHF93GcJ8em9bOm917fs4ZNjB6dOnO+z/4ObmhkGDBiE+Ph7u7u6dTl0EBARg1qxZuHHjBn7++WeoVCr861//wrBhw5CcnNztRVwul+PRRx+FXq/vcbOr8vJyS8AYPHgwUlJSOvx5/v7+mD59OoqKinD+/HnU19fj559/hlKpxLhx4yzf09LSgtraWlRVVaG+vh41NTWW284DaHfbecaYczHpGgCRHlsPqrD14Mkuz3WTuuDQqom9GjRY38Yhww7CwsKsunBKpVJER0cjPDzccvHt7kZgIpEI8fHxCA0NRWZmJvLy8nDp0iVUVlZiwoQJcHd37/b776abZkBAABQKBZqamlBeXg6tVtvlrdfDwsIQGhqKvLw8nD17FgUFBdDr9ZDJZFAqlVaBoi2FQoGQkBCbRmcYY/ZToCqA2LXj94rm5mZIfRrgHrsJbz/4EQYHD+70efKrGvHK7izUafQcMpgFhww7SE1N7bUbpMnlcowdOxYDBgzATz/9hMrKSuzfvx+PPfYY/Pz8euVntOXq6orHHnsM//rXv6BSqfD9999j5syZXQYNsViMxMREuLq64tixYygrK7N6XKFQwM/PD4GBgfD394e/v3+Xz8cYs7+aphoAwOrjq+Hi1nknz/CXwmHS1eHB8EDEBno7qjzWT9y3IeMvf/kLNm/ejJqaGixatAjvvfdej3Ze9LampibU1tZ22kwrOjoa/v7+OHLkCOrq6pCRkYFZs2bZZT2Dp6cnpk6dioMHD9ocNIDW+6pIJBLk5+fDx8fHKlAYDAaHtS5njHVP3dLaDO/lB1/GxNiO74ba3NyMcePGwdhgRMhTIR2ew1hX7suQsXbtWuzatQsrV65ERUUFNm7cCHd3d7z55psOr8VkMuHKlSvIzMxES0sLhgwZgjFjxnR4rpeXF6ZNm4YDBw6goaEBx48fx+TJk+3SFtzT0xMzZ87E999/36OgMXDgQAwcOLDX62GM2UeYR1innTw1Gg20xVoHV8T6k/suZOTm5mLbtm3IysqyNLzSarXYvn17j0OGTqeDTqezfG1uk21Lx0+9Xg+lUmlZ2Gl25coVeHh4YNCgQZbnaru2QiaTITU1Ff/85z9RXl6OCxcuYNSoUTZvTW1pabHp3JaWFri6umLKlCn48ccfoVKpcODAAUyZMsUqaBgMBpt7edjC1u6c9uj4aSvu+MkYY7a570LGrl278Prrr1sCBgD84he/wB/+8Aeo1eoeraVYv3493nnnnXbHJRJJl1MvGo0GZ8+eRXFxMYDWdRAPPvggNBoNsrOzce7cOXh7eyMkJKTD7qFBQUGYMGECMjIycOXKFQQFBVm1+u6K0Wi0edpCJBLBy8vLskZDrVbj0KFDVkFDIpHY9HxSqdSmERdbL+C93fHzzu9hjDF27+67kDF69Gg89NBDVsfMOxxaWlp69Fxr1qzBypUrLV+r1WqEh4d3er55aiQrK8tyT5L4+HgkJydDLpeDiKBSqVBUVIRjx45hxowZVv022oqJiUFNTQ0uXbqE48ePY/r06fD39+9R/bby9PS0Wgx68ODBdiMajDEmJG4/7pzuu5Dx+OOPtztmHu43f4rWaDRYt24d3n777S63gcrlcpu3iVZUVODMmTOor68H0LpVdPTo0VbBQCQSYezYsWhoaIBSqURGRgamTZvW6c8YNWoUamtrUVZWhqNHj2LmzJl2a2zVWdDoqB8IY4w5Crcfd273XcjoSNs7imo0GsycORPx8fG9stZAq9XizJkzKCwsBNA6NTJq1CiEh4d3OM0gkUgsay5UKhVOnDiBadOmdTjVIBaLMWnSJOzbt++uFoKaTCbU1taisbERwcHB3QaGzoLG3fTjYIyx3sDtx50bhwz8O2Q0NjZi4cKFiI+Px44dO3pljv7ChQuWgOHi4oIxY8YgKioKer2+0+9RKBRISkrC+fPnUV5ejvLy8k53bJgXZ+7fvx8VFRU4cOAAxowZg6CgoHbnmkwmKJVKVFdX4/bt26isrLSaIgoMDER4eDjCw8M7nQrx9PTEmDFjkJGRAZVKhatXr2Ls2LE9+SNhjLFexe3HnReHDMDyyX/evHl4+OGHey1gAEBsbCwqKyuhUqlgNBpx9OhRuLu7IyoqCoMGDWq30NRkMiE7O9tyczNfX98OA0Nbfn5+mDhxIk6ePIm6ujqkp6cjISEBycnJaGxstASKO0MF0DpVpFAoUF9fj+rqalRXV+PixYtQKBQIDw/HwIEDERISAhcXFxARrl+/josXLwJonS6KjIzslT8nxhhj/U+/DBn/+Mc/sHHjRtTW1mLNmjV44YUXujxfLpdDLBb3esAAWu+M+otf/AI1NTXIz89HQUEBNBoNrly5YtkZEhsbi8jISBgMBpw4cQKVlZUAgPj4eIwaNcqmaZuwsDA8/vjjlhuq5ebmIjc3t915UqkUISEhln/8/PwgFouh0WhQVlaG0tJSlJeXo6mpyfIcEokEoaGh0Ov1uH37NoDWG6eNHTuW12QwxhjrVL8LGR988AF27NiBFStWIDs7Gy+++CImTpyI2NjYdudmZWXhgQcegJ+fH06cOIGUlBS7bGMUiUQIDAxEYGAgHnroIZSWliI3Nxe3b99GVVUVqqqqcO7cObi4uECv10MikSAlJQXR0dE96u/g6uqKRx55BLGxsTh9+jTUajWkUimCg4MtocLLy6vDNRTu7u5ISEhAQkICDAYDSkpKUF5ejrKyMjQ3N6OkpARA65TPyJEjkZCQAJFI1Kv9JxhjvaOisaLbNQq3Gm8B4G3bzL76Vci4evUqPvnkE1y8eBHBwcEAgIMHD1rdYt2sqqoK48aNw+zZs/HnP//ZYesKJBIJoqOjLSMDBQUFuHnzpmU6xdfXFxMnTryne5+EhIRg7ty50Gg0cHd3t1oIajQabaoxLCwMkZGRICLU1taitLQUWq0WiYmJ8PHxuevaGGP2VdFYgTnfzoHW2HWnTmNzKIDfgbSEmpqaDs/RaDSW/66pqenwvdSsvl7z//9djxp51+0AXF1dO92eb2+81dWx+lXI+Prrr/HGG29YAkZRURFUKhUWLFiAoqIiLFy4EJ999hnc3NwQFBSE7du34/Tp073eltvWDpNubm4YMmQIkpKSLLdAj46Otqx/AFrXaNjaAdPFxcXytVgshqen513XZj4XaF3z0fZmbG2/39b6bNWT2mwddbL1d+ZmXMzZNTY2QqvtOjxcr7oOrVGLqS1T4Uu+nZ5XY3DDtwCyT2Wj8vz5Ds9p29F4//79Xe4ku62TAghCRkYGrnYTMiQSiU1b4HszjPBWV2GIqB+Nd//zn//EyJEjERwcDKVSidTUVAwePBjLly9Hfn4+li1bhueffx4ff/yxXX6+Wq2Gt7c36uvrux2JsPUi2V/OA2y7iPfkr2NXz6fRaCxvTg0NDXB3d++V+tjdM78+VCpVl68PW8/rT2wJD83NzTh48GC3twWoElVht2w3Pn7oYzwQ9ECn512v1GDxny7jz08PRWJwx68PjUaDqKgoAK0f2rp6HdnyfD35PYDWMLJw4cJeCxq2TCOZt7runrW703u6CKmvvT761UjGjBkzLP+dl5eHOXPmYN26dQCAsWPHoqysDF988YXdQgZjjPVUY2Mj/v73v9t80Z0+fXqXIwB5qjzsPrkbwcHBCPAP6PQ8H11rnx4fHx8EBHR8C/e2PycgIKDLkGHL85ktXLiw21BVV1eHI0eOoKKiAr6+nY/I9ISnqycG+PPohCP1q5DRVkpKClJSUqyOeXl5WaZSGGPM3mwZoairq4PBYEBqamq3F1Nbpg+qRFU9rtPRPDw8uv09XF1dIZFIcOTIkV77ub09MsK6129Dxp1UKhW2bt1qGdlgjLE72RIKbNXTaYEBAwbwxa8NDw8Pm0Y8bGXryEi9qr5Xfh5rdV+EjKysLDz77LOYOXMmFi1aJHQ5jDEn1JNpC1vZMr0B2L7A0dY1Bf2FLSMetrJ1ZKRKVAXIgOamZsA+95y8r/T7kPHaa68hOzsba9euxdy5c4UuhzEmgN6etrBVb+6OqGiswNx9c9Fs6HwbqZnMFIyqWjlMWlWn5+RXNfZKXX2FrSMj50vPY/el3cgszYROr+vyXG+ZN4Ldup+CF3LLrtD6fcjYsGGD0CUwxuykN3dlAM49bVGnq0OzoRnrx69HjHdMp+dVqQ1YsvMWnsu93u1zukld4Ot+7zeC7CtsGRmJM8VBki3B1htbgRtdP5+EJFisXwxPtG8XYHWeAFt2nUW/DxmMsb5JqVR2eSPBnoaH3py26G09mQaJ8Y7pcmulSauCtqUUWxYlIy6o69/F112GMB++NUBbcUFx2DNjD6oaul5AW9xYjPez3kdKWgriveM7Pc/89zQ9Pb3bn21LGGloaOj2eZwJhwzGmFPav39/t6GgP4SHOm0dVhxdYdM0iJvEDb5y26Zy4oI8MDSs662krGNxQXGIC4rr8hwfpQ+QBdSKarve0aMAhk8Z3u3Ui06nQ/aZ7G7DSFddV50Rh4xeZG4kpVarbTrXmZtn9YdmXGZqtdqmdurcjMu+zK+L7v4fmx/XBvtBEdR5nwcAcJHIcKqiqFfq620NugZ8kvUJdIbOR2PM5BJ//C75ZXjKux5295R6orjMgGKUdnpOQbUGJl0TGhvUUKvv7e90T15HjQ1qmHRNuFRQgcaG7t8D+zplsx4i9UCs2re5155T5iLDCwkvwEPaeSDWVrW2gO8rfTT7VcdPoZWVlSE8PFzoMhhzaqWlpRg4cGCnj/PriLHudfc6chYcMnqRyWRCeXk5PD09230qVqvVCA8PR2lpqVO2guX67g3X1z0iQkNDA0JDQ7u8X1BXr6O74Qy/e1e4vntzv9Vn6+vIWfB0SS8Si8XdJksvLy+nfCGYcX33huvrmrd392sEbHkd3Q2hf/fucH335n6qz5bXkbNw/hjEGGOMsT6JQwZjjDHG7IJDhoPI5XK89dZbkMvlQpfSIa7v3nB9zsvZf3eu795wfc6NF34yxhhjzC54JIMxxhhjdsEhgzHGGGN2wSGDMcYYY3bBIYMxxhhjdsEhw8Gqqqpw+PBhmEwmoUvpkzQaDc6ePWvT/WGE8H//93823RWUsTvpdDr89NNPKC8vF7qUPunChQsoLCwUuoxOEREyMzNRUlIidCkOxSHDgT777DMMGzYM6enpKCgoELqcdvbt24eUlBRERERg2bJl0Gq1Qpdk5auvvkJISAjGjBmDxMRE5ObmCl2Slc8//xzz5s3Dk08+6ZRB4+uvv0ZsbCwUCgWmTp2KkydPCl2SwxQUFGDhwoUYOHAgJk+ejGvXrgldkpWcnBwkJSVhy5YtuHjxotDltHPlyhVMmjQJbm5uGDx4MLZv3+5UH5Ru3ryJyZMnY9KkSU4ZNK5du4ahQ4fiwQcfRGxsLHbs2CF0SY5DzCEuXLhAfn5+VFhYKHQpHdq2bRuFhobS5s2bae3ataRQKOjll18WuiyLP/3pTxQVFUWnT5+msrIyGjlyJK1atUrosqy8/fbblJaWRnK5nBYsWEAtLS1Cl2Txl7/8haKioujAgQOUnp5OkydPJrFYTOvXrxe6NLu7evUqBQYG0vLly2nHjh2UmJhIsbGxpNFohC6NiIgMBgMlJCTQF198IXQpHbp16xYFBwfT5s2b6dSpU7Rq1SqSSqU0efJkUqlUQpdHRESXL1+m6OhoSkpKooiICCooKBC6JIuSkhIKCQmhTz/9lOrr6+nVV1+lyMhIoctyGA4ZDrJixQr6zW9+Y/naZDLRvn376L333qOMjAwBKyMqLy8nf39/ys3NtRx7//33ydXVlXQ6nYCVtdJqteTj40MnTpywHHv33XfpnXfeoYyMDKcJbt988w09/fTTlJ6ebhU0ysvLSavVClpbUlIS/eMf/7A69s477xAAWrdunUBVOUZaWhpt2bLF8vX169dJLBbT//7v/wpY1b+dOnWKfH19yWQyWY7l5OTQhx9+SH/84x+publZwOqI3nzzTXriiSesjp04cYJ8fX0pJSWFmpqaBKrs33Q6HXl7e1NFRYVV0DAajVRcXCxobYsXL6bly5dbvr527RqNHj2azp8/T2fPniWj0ShccQ7AIcNBnnvuOVq8eDERETU1NdGUKVMoODiYkpKSCICgn8o/+ugjWr16tdWxa9euEQC6efOmQFX92+XLlwkAXb16lYiIVCoVJSYmUkBAAAUGBpJYLKZNmzYJXCVRVlYWjRgxgojIEjTmzp1LcXFxtHfvXkFrCwwMpG+++abd8ffee49EIhH9+OOPAlRlf4WFhRQfH291ASciGjJkCH344YcCVWXtwIED5OHhYRn52rRpE7m7u9OIESNILpfT8OHDqb6+XrD6li5dSvPnz293/Pz58+Th4UFLliwRoKr24uLi6ObNm3T79m1L0FiwYAE9+eSTgtaVmJhIa9eutXz98ssvk0KhoPDwcBKJRDRlyhSnCGr2wiHDQbZs2UKenp5UWVlJr7/+Ov3yl7+0fLr9+OOPCQD9/PPPgtSWkZFBly9ftjpWX19PAOj69euC1NSWVqulQYMGUXx8PP3+97+nQYMG0YIFC6ipqYmMRiOtWrWKRCIRXblyRdA6m5ubyd3d3fLJZOfOnQSAkpOTBZ86mT9/Pj300ENkMBjaPTZ79mwaNWqUAFXZn1KppL/+9a/tjk+ePNlpRnBKSkpIJBLR119/TefOnaOwsDBLuL9y5Qr5+PjQ66+/Llh933zzDclkMrp27Vq7x7766isSi8VO8WFk1qxZtH//fiJqHZ318PAgFxcXwd8XVqxYQW5ubrRy5UqaMWMGRUREWN5XMzIySC6X07vvvitojfbEIcNBlEol+fv705w5cyg5ObndnOHAgQPpyy+/FKi69pqamgiA5Y1Fp9PRmjVrSK1WC1JPaWkpvfHGG/Thhx9SQEAANTY2Wh4zGAzk4eFBX331lSC1tRUVFUV5eXlUWlpKcXFx9OKLLzrFGo1Lly6RTCaj3/72t+0eO3fuHAEQ9NOyo82YMcPqjf3LL7+kM2fOCFbPggULKDAwkJYuXUobNmyweuyVV16h6dOnC1RZ6+srOTmZkpKSqLa21uoxk8lEkZGR9D//8z8CVfdvr776Kr3//vtkNBrpmWeeobS0NKdYo9HS0kLbtm2j//qv/6Jx48bR7t27rR5/+umnafbs2QJVZ3+8u8RB/Pz8sGvXLnz//ffIysqy2qamVqtRX1+PBx98UMAKrYlEIgCAyWSCXq/HggULcOPGDbi5uQlSz8CBA7F27VpMmjQJWq0WUqnU8phKpYJOp0NycrIgtbWVmJiIH374AampqVi2bBk+//xzfPvtt9i/fz+2b98uWF0PPPAA/vu//xvbtm3DihUrrHYG+Pn5QSaT3Vc3cBKJRJY/gy+++AJr165FYGCgYPVs27YNHh4e+PTTT9ttYS0uLhb0vcHFxQV79uxBdXU10tLScPv2bctjIpEIvr6+8PLyEqw+s8TERFy6dAnPP/88Kioq8N133yEjIwMeHh545plnBKtLIpFg2bJlePfdd1FcXNzuPbSyshIjRowQqDoHEDrl3G/27t1LCoWCYmJi6NChQ5SZmUmpqalWi0KdgVarJQB08eJFmjNnDs2fP1/wIX8iourqapLL5fTcc89Rc3MzVVZWUlpaGr300ktCl0ZERK+//jqJxWLavHmz1fGff/7ZKf78duzYQRKJhCZOnEhHjhyhy5cv05QpU2jlypVCl+ZQs2bNorfffps+//xzioyMdIrh/uLiYkpKSiKJREKffPIJ5ebm0ptvvkkxMTHtRhCEcPnyZQoPD6eQkBDauXMn5efn07p16yg+Pl7wxalErSNyHa1xuH37ttPsNpk6dSolJCRQfn4+tbS00Pvvv0+xsbH9ehSRQ4YAcnNzadGiReTj40NBQUH01ltvdThXLiS9Xk8AaPjw4U4TMMx27dpFUqmUZDIZubi40NKlS52mPo1GQ3v27BG6jC6dO3eO0tLSyMXFhRQKBb322mtO8+fnKHPmzKFhw4Y5TcAwa2pqojfeeIMiIyPJ3d2dHn/8cbp165bdf+6NGzds2uWgVCpp6dKl5OXlRSKRiCZPnkwlJSVOU9/u3bsFWURpa303btygiIgIAkCurq40YsQIKioqckCFwuGQ0Uu+/fZbmjJlCk2cOJHWr19PDQ0NQpdk5fTp0z0632QykUwmc1jA6Gl9RUVFtHv37g4Xo9nD8ePHafHixTRv3jxKT093yM/sifT0dBo3bhxFR0fTH/7wB5u+R6/XO124vRslJSX01FNPUUREBC1atMimT4VPPPEERUREOCRg3E19jpSZmUk+Pj707LPP2ryd0mg0Omxb9t3U50g9rU+j0dC+ffvo8OHDTvn79DYOGb1gx44dFBkZSVu3bqXVq1eTt7c3xcTE0KVLl9qdm5OT4/CeCTt27CAA9MEHH3R7btv6zpw545CAcbf1OcrWrVspICCAXnrpJZowYQKJRCI6ePCg09T3+eefU2hoKG3atImWL19OAOjs2bNOU5895eXlUUhICP3ud7+jrVu3kp+fH7322msdnqtWqy29YG7duuWQIfS7rc+RvvzyS0pMTCSpVNrlhZLr65iz1yc0Dhn3yNwEpu3207KyMho1ahT5+PhQVlaW5XhDQwMFBgbStGnTHPZGbzAYKCoqin772992eyHn+tq7fPky+fv7W12QJkyYQOPHj3eK+kpLSyk4ONiqvuTkZDpy5IhT1GdvkyZNos8++8zy9bp162jZsmUdnrt48WIKDg526JZGZ6+PqLUZWGpqKu3du9fqQmk0Gq0+ZHB9fbM+oXHIuEclJSUEgEpLS62Oq9Vqevjhhyk8PJzq6uosx/ft20dPPvmkw+bA9+3bR7NmzSKi1i6e3V3IuT5ry5Yto48//tjq2M6dO8nX17fD8x1d38aNG622YlZXV5Ovry898MADJJfL6Ze//KXV8Lyj67OnwsJCSk5Otjo2e/ZsGjp0KMlkMhoyZIjViM6tW7coLS3NYWswnL0+M6VSSUFBQUREVhfKZ555xqqXCNfXN+sTGoeMe2Q0GikwMJDefPPNdo9VVFRQQEBAu26ajlRaWmr1RmbLhdyRnL2+NWvWUE1NjdWxw4cPk5ubm0AVWTt58iTl5eURUetIxdixY2n69Ol0/Phx2r17NwUEBNCvfvUrgau0j/r6eqtOqu+88w4NHDiQ/v73v9OpU6do/PjxFBAQINj9NZy9vrYCAwOpqqqKiIj27NlDACgoKMiqH42QuL6+i0NGL9i4cSNJpVI6duxYu8c2bNhACQkJAlTVuTsv5DqdjrZv396u9bJQnL2+U6dOkVwut3yt0+na3RdECDk5ObR8+XKrOeEdO3aQh4eHgFU5hl6vp+eff95qJ0Z1dTWJxWL64YcfBKyslbPXN378eMrIyLA0sho5cmS3awy4vr5Tn5Akju7L0R+tWLEChw4dwuzZs/H9999j3LhxlseSkpJgNBoFrK69NWvWAABWr14Ng8GAc+fOQSaT4T/+4z8gkQj/V8LZ6xOLxZZGTuZGZTKZDPPmzbM0MRPC0KFDsWXLFqtjXl5eCA4OFqYgB5JKpdi5c6fVMTc3N0gkEqf4/Z29voSEBFy6dAm7du1CRUUFTpw4gfT0dDzxxBMICgrChg0buL4+XJ+ghE45/UVjYyNNnjyZ5HI5bdy4kZqamkilUtGjjz7qtH3p3333XQLgdH0wzJy1vjNnzpBEIiGdTudUjcru1NzcTCNGjKBt27YJXYog3njjDZo0aZLQZXTKmerbtGkTeXp6tmtk9d133znFGgKur+/ikNGLWlpa6O233yZ3d3eSyWQkl8tpyZIlTtmLwNkvkM5c39mzZ8nFxcVp6yNqvZ15SkoKPf30004zzeQoDQ0NtGLFCoqLi6OysjKhy2nHGetTqVS0atUqp70bKNfXd4mIiIQeTelvmpqakJOTg9DQUISHhwtdToc++ugjnD17Fn/729+cYgriTs5c38WLFzFy5EjMnz/fKetbv349fvzxR/zmN7/BU089JXQ5DnXx4kX8+te/xsyZM7F69Wp4eHgIXZIVZ6+Psd7GIeM+ZTAYAMDpLpBmzlyf0WjEp59+iiVLljhlfYwx5iw4ZDDGGGPMLvhW74wxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcY69Pvf/x7r168XugzWh3HIYIwx1qH9+/fDw8ND6DJYH8YhgzHGWDs6nQ7Xr1/HiBEjhC6F9WEcMli/MW7cOKxcuRJLly6Fn58fAgIC8Omnn0KpVOKFF16Aj48PQkJC8M033whdKmNOLycnB0ajEQqFAr/+9a8xYcIEvPrqq2hsbBS6NNaHcMhg/QIR4dKlS9izZw+mT5+OkpISvPjii1i5ciV+9atfYdGiRbh16xYWLlyIl19+WehyGXN6WVlZcHV1xSuvvILZs2fj1Vdfxd69e7FkyRKhS2N9iEToAhjrDQUFBWhoaMAXX3yBOXPmAABmzZqFDz74AMuXL8djjz0GAJg5cya2b98Ok8kEsZgzNmOdycrKglQqxd69exEYGAgAUKlUeOmll0BEEIlEAlfI+gJ+l2X9QlZWFjw9PTFv3jzLscLCQgQGBmL69OlWx6KiojhgMNaNzMxMvPjii5aAAQChoaFobm6GXq8XsDLWl/A7LesXsrOzkZycDKlUajmWmZmJUaNGWQUK83mMsc6Zpx/HjBljdfzq1auIioqCXC4XqDLW13DIYP1CVlZWu1XwHR3Lzs7m1fKMdePGjRtobGyEp6en1fGdO3fiiSeeEKgq1hdxyGD9Qkfh4c5jRIScnBweyWCsG1lZWRCJRPjb3/4GIgIRYd26daitrcV//ud/Cl0e60N44Sfr8+rr61FSUmIVHsrKyqBUKq2O3bx5E42NjRwyGOtGVlYW0tLS0NzcjMjISBiNRoSFheHw4cPw8/MTujzWh4iIiIQugjHGmPMoLCyESCRCVFQUioqKYDAYEBcXJ3RZrA/ikMEYY4wxu+A1GYwxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcYYswsOGYwxxhizCw4ZjDHGGLMLDhmMMcYYs4v/B8+vGdE6d5ACAAAAAElFTkSuQmCC", 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", "text/plain": [ "<Figure size 550x550 with 4 Axes>" ] @@ -628,6 +621,12 @@ "\n", "\n", "s_full, s_temp = fit(p_full, 3), fit(p_temp, 4)\n", + "import logging\n", + "\n", + "logging.getLogger().setLevel(\n", + " logging.ERROR\n", + ") # corner warns about sparse contour bins for the prior\n", + "span = [(0.5, 3.5), (3.0, 5.0)]\n", "fig = corner.corner(\n", " p_full.sample_prior(4000, rng=5),\n", " color=\"0.6\",\n", @@ -635,13 +634,12 @@ " truths=[2.0, 4.0],\n", " truth_color=\"k\",\n", " plot_datapoints=False,\n", - " range=[(0.5, 3.5), (3.0, 5.0)],\n", - ")\n", - "corner.corner(\n", - " s_temp, fig=fig, color=\"C2\", plot_datapoints=False, range=[(0.5, 3.5), (3.0, 5.0)]\n", + " plot_contours=False,\n", + " range=span,\n", ")\n", + "corner.corner(s_temp, fig=fig, color=\"C2\", plot_datapoints=False, range=span)\n", "corner.corner(\n", - " s_full, fig=fig, color=\"C0\", plot_datapoints=False, range=[(0.5, 3.5), (3.0, 5.0)]\n", + " s_full, fig=fig, color=\"C0\", plot_datapoints=False, plot_contours=False, range=span\n", ")\n", "fig.legend(\n", " handles=[\n", @@ -671,7 +669,7 @@ }, { "cell_type": "markdown", - "id": "14a358dd", + "id": "c7f7b389", "metadata": {}, "source": [ "## An inferred model error per data type (recipe 26)\n", @@ -687,13 +685,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "03c9ba75", + "id": "f67b0e69", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:50:40.078176Z", - "iopub.status.busy": "2026-09-11T03:50:40.077908Z", - "iopub.status.idle": "2026-09-11T03:50:40.084813Z", - "shell.execute_reply": "2026-09-11T03:50:40.083944Z" + "iopub.execute_input": "2026-09-11T04:06:16.273206Z", + "iopub.status.busy": "2026-09-11T04:06:16.273000Z", + "iopub.status.idle": "2026-09-11T04:06:16.280080Z", + "shell.execute_reply": "2026-09-11T04:06:16.279222Z" } }, "outputs": [ @@ -722,7 +720,7 @@ }, { "cell_type": "markdown", - "id": "12e3fdbf", + "id": "57e06287", "metadata": {}, "source": [ "## Takeaways\n", From e1ca3fe7dd52e7882245218fa5819a48cef9d39b Mon Sep 17 00:00:00 2001 From: beykyle <kylebeyer1@gmail.com> Date: Fri, 11 Sep 2026 00:34:45 -0400 Subject: [PATCH 42/75] Add the hierarchical_calibration notebook (recipes 22, 24, 30, 35, 38) Eight schools non-centred as the prologue; then a quadratic whose coefficients run with energy through a trend plus bumps, seven datasets and a held-out energy between two of them, fit three ways: the correct mapping, a misspecified linear mapping, and the linear mapping with non-centred per-dataset deviations of learned spread. Mapping bands, the structured residuals of the misspecified fit, coverage in sample and at the new energy through complement() and predictive_draws, sharpness, the tau posterior and the held-out log predictive per case. Runs in about 15 minutes alongside another notebook. --- examples/hierarchical_calibration.ipynb | 740 ++++++++++++++++++++++++ 1 file changed, 740 insertions(+) create mode 100644 examples/hierarchical_calibration.ipynb diff --git a/examples/hierarchical_calibration.ipynb b/examples/hierarchical_calibration.ipynb new file mode 100644 index 0000000..b8fc2a2 --- /dev/null +++ b/examples/hierarchical_calibration.ipynb @@ -0,0 +1,740 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "69602826", + "metadata": {}, + "source": [ + "# Hierarchical calibration: a hierarchy on the physics parameters\n", + "\n", + "Several experiments at different energies constrain a model whose\n", + "parameters run with energy. If the assumed energy dependence is wrong, a\n", + "global fit under-covers everywhere. A hierarchy on the *physics\n", + "parameters*, one deviation vector per dataset with a learned spread, repairs\n", + "the coverage, in sample and at an energy that was never fit, and it costs\n", + "nothing beyond declaring the parameters. The eight-schools model opens the\n", + "notebook as the textbook version of the same idea.\n", + "\n", + "Recipes: 22, 24, 30, 35, 38" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "44d9c8ef", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T04:18:47.474201Z", + "iopub.status.busy": "2026-09-11T04:18:47.473952Z", + "iopub.status.idle": "2026-09-11T04:18:50.026496Z", + "shell.execute_reply": "2026-09-11T04:18:50.025571Z" + } + }, + "outputs": [], + "source": [ + "import corner\n", + "import dynesty\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from scipy import stats\n", + "\n", + "import rxmc as rx" + ] + }, + { + "cell_type": "markdown", + "id": "a5930efa", + "metadata": {}, + "source": [ + "## Prologue: eight schools, non-centred\n", + "\n", + "Eight estimates `y_j` with known standard errors, believed to scatter around\n", + "a common mean `μ` with spread `τ`. In the non-centred spelling\n", + "`θ_j = μ + τ η_j` with `η_j ~ N(0, 1)`, every parameter has a marginal\n", + "prior, so nested sampling needs no joint block. `τ` piles up near zero, as\n", + "in the book, and the `θ_j` shrink toward `μ`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "bd088aab", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T04:18:50.028780Z", + "iopub.status.busy": "2026-09-11T04:18:50.028350Z", + "iopub.status.idle": "2026-09-11T04:19:03.163703Z", + "shell.execute_reply": "2026-09-11T04:19:03.162862Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log Z = -32.11 +/- 0.19, 14976 likelihood calls\n" + ] + }, + { + "data": { + "image/png": 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3mDVrFn755RcEBgZWez/VwRZcIiIiIolcu3YNSqUSRkZGOH/+vDgdP34cXl5eMDMzA1Ca4BYVFVVY38zMDPb29pDL5Xjw4AEAwMHBAc2aNRPr2Nvbi8ktAHTu3BkJCQkq2+vWrZtGyS0AvPjii+LPTZo0gb29Pf73v/+p1Fm0aBHmzZuHvXv31nhyC7AFl4iIiEgyN2/eBABs3rwZVlZWYnnfvn3h7e0tzpdvbS1PJpNh+/btmDdvHtzc3NCmTRuMGDECkydPFpNja2trlXVMTU0hCAKKiorErhCOjo4aH4O67RcWFqqUffHFF3jxxRdV+v7WJCa4NWTsD6elDoGIiIj0XFkiaGlpKZYlJCQgNjZWZfzYLVu2YPjw4Wq30aNHDxw4cAA5OTnYv38/3n77bWRkZOCjjz56qthMTExQXFysUqbp+Lz79+/HgAED4OTk9NRxVQW7KBARERFJpKwrwY0bN8Sy9957D88995zY2vnPP/9g+fLlatfPzMzE9evXAQAWFhYYOnQoAgICxLKn0bhxY1y8eFGcv3LlCv7++2+NttW5c2fs27cPy5cvx/z58586tidhCy4RERGRRNq2bYsePXpg1KhReOutt/D777/j4sWLKi95OHHiBHr06KF2/cLCQoSEhKBVq1Zo164dEhISIJfLsWPHjqeObfLkyZg4cSIePHgAU1NT7Ny5Ew0aNNB4e126dMG+ffsQGBgImUyGBQsWPHWMlWELLhEREZGFQ+k4t49jYl5aT8t27NgBPz8/7NmzB507d8apU6fQsGFDcfnjElx7e3tcuHABo0aNgomJCTp16oRr165hwIABAIAOHTrg7bffVlnHw8MDc+bMEfvfBgUF4YUXXqiw7bFjx+L333+Hs7MzWrdujT/++APz58+Hr68vAKBevXqYM2cOXF1dVdabNGkS2rZtC6D0obM5c+aIy7p27Yr9+/cjLy9P49bgqpAJgiDU2NZrkYyMDNja2kKpVMLGxqZK60jdz3bN6G6S7p+Iqk6Te0xtU91jzM7OFh+qycrKUumDSCSJ8m8yK3vBw5g9OnmT2eP4+Phg69atcHNz0/m+9U1V7zPsokBEREQElCavdh5AQfbDMpf2gJl0/3zl5OQgLS2NyW01McElIiIiKs/MEligfHK9GvbXX39hyZIl6NOnj9Sh1DpMcImIiIj00N9//w1PT09MmTJF6lBqHSa4RERERHooODgYwcHBUodRK3EUBSIiIiIyKExwiYiIiMigMMElIiIiIoPCPrhERHVARkYGDhw4gLt376JZs2YYMGAAjI2NVeqUlJTg999/x99//40mTZpg0KBBMDU1lShiIiLNsQWXiMjAyeVydOnSBRs3bsTFixcxadIktGnTBnfv3hXr5OfnIzAwEG+//TbOnz+PadOmwc/PD1lZWRJGTkSkGbbgEhEZOA8PD5w/f158U1hubi5atmyJr7/+GgsXLgQAfP311zh//jzi4+Ph4uKC+/fvo23btliyZEmNvi+eiKgmsAWXiMjAdevWTeU1uGZmZqhXr55KF4UtW7Zg6NChcHFxAVD6fvvhw4dj8+bNOo+XiOhpsQWXiKgOyMnJwcqVK5Gbm4uYmBh4e3tj2rRp4vIrV65g2LBhKuu0bt0aX3/9NUpKSmBkVLE9JD8/H/n5+eJ8RkZGzR0AEVE1sAWXiKgOKC4uRlJSEhISEpCYmIiSkhIUFhaKyzMzM2FnZ6eyjp2dHYqLi5GTk6N2m4sWLYKtra04eXh41OQhEJGey87Ohkwmg0wmQ3Z2tqSx1IoW3Js3b2Lv3r148OABPD09MXToUJWv2wBAqVRCLpcjOTkZ7dq1Q3BwMGQymUQRExHpF2trayxZsgRAactr9+7dMXPmTKxbtw4AYGFhUaEFVqlUQiaToX79+mq3GR4ejunTp4vzGRkZTHKJSC/ofYK7ceNGjBkzBiEhIWjSpAmio6MxY8YMHD16FE2bNgUA3L59G71790ajRo3QtWtXLF++HN27d4dcLjfoJHfsD6crXbZmdDcdRkJEtYm5uTn69u2LmJgYsaxVq1a4ceOGSr0bN26gWbNmFYYTK78dc3NzjeMoLi4Wfz58+DCCgoIq3RcRUXXofReFTz75BGPGjMGmTZuwePFiHDt2DCYmJli9erVYZ86cOXB1dUVsbCyWL1+OgwcPYseOHYiOjpYwciIi/RAfH68yX1hYiNjYWLRq1UosGzZsGLZv3w6lUgmgtM9udHQ0nn/++RqJSS6Xw9vbW5wPDg5GkyZNIJfLa2R/RFS36H0Lro2NjdpWWFtbWwClLQDbtm3D559/DhOT0sPx8vKCv78/oqOjMXz4cJ3GS0SkbyIiIpCVlYUuXbqgqKgIv/32G7Kzs/Hf//5XrDNt2jT88ssv8Pf3x+DBg7F3716YmZlh7ty5Wo9HLpcjLCwMgiColCcmJiIsLAzR0dEIDQ3V+n6JqO7Q+xbcVatW4fz58xg5ciTmzp2Lfv36oX///pg4cSKA0u4JZWM6lteyZUtcu3at0u3m5+cjIyNDZSIiMkSbN2/G3LlzYWFhAWtra0RERODatWsq900rKyscP34cU6ZMAQC8+eabOHv2LBwcHLQaS3FxMaZMmVIhuQUglk2dOlWl+wIRUXXpfQvu/fv3kZqaCldXV5SUlCA3NxdJSUnIycmBhYWF+JadshbdMnZ2do99A8+iRYsQERFRo7HXNpX16WV/XqLaz9/fH/7+/o+tU79+fYwZM6ZG44iNjUVCQkKlywVBgEKhQGxsLAICAmo0FiIyXHrdgltYWIgXX3wRI0aMwC+//ILPP/8cJ0+exN27dzFnzhwApa0OAMR+Y2XS09PFZeqEh4dDqVSKk0KhqLkDISIiAFB5PbA26hHVdfo0NJc+0esENykpCSkpKfDz8xPLjI2N4evriwsXLgAofQVl/fr1cf36dZV1r1+/rvIAxaPMzc1hY2OjMhERUc1ydXXVaj0iInX0OsF1c3ODlZUVYmNjxbLi4mIcP34cXl5eAAATExMMGzYM69evR1FREQDgr7/+wuHDh/HCCy9IEjcREann5+cHd3f3SodwlMlk8PDwUGnYICKqLr3ug2tsbIxly5ZhwoQJuHLlCpo1a4aYmBgkJyerDAG2ePFi9OrVC/7+/ujatSvkcjmGDBnCERSIiPSMsbExoqKiEBYWBplMpvKwWVnSGxkZyfFwieip6HULLgC88cYbuHLlCgYNGgQHBwfMmjULf//9N1q0aCHW8fT0xJ9//olx48bB1dUV3377rcG/5IGIqLYKDQ1FdHQ03NzcVMrd3d05RBgRaYVet+CWadasGZo1a/bYOra2tjX+9C8REWlHaGgoBgwYII6As2vXLr7JjIi0plYkuFR9HPKLiPRd+WTW39+fyS0RaY3ed1EgIiIiIqoOJrhEREREZFCY4BIRERGRQWGCS0REREQGhQkuERERERkUJrhEREREZFCY4BIRERGRQWGCS0REREQGhS96qGMqewEEERERkaFgCy4RERERGRQmuERERERkUJjgEhEREZFBYYJLRERERAaFCS4RERERGRQmuERERERkUJjgEhEREZFBYYJLRERERAaFL3ogIqoDcnNzcf78eeTk5KBNmzZwcXFRW+/KlSv4+++/0aRJE7Rr107HURIRaQdbcImIDFxUVBS8vLwwffp0fPrpp2jWrBnmzZunUqekpASjR49Gjx49EBkZiT59+uCFF15AYWGhRFETEWmOLbhERAbOysoKFy5cgL29PQAgNjYWffr0gZ+fH5599lkAwA8//IDo6GicPXsWrVq1ws2bN9G5c2esWLEC06ZNkzJ8IqJqYwsuEZGBGzt2rJjcAoCfnx9cXFwQFxcnlq1fvx6DBw9Gq1atAABNmjRBWFgY1q9fr/N4iYieFhNcIqI65sqVK0hKSkKbNm3EsosXL6J9+/Yq9dq3b48///wTgiCo3U5+fj4yMjJUJiIifcAEl4ioDsnJycGrr74KX19fDB48WCxXKpVo0KCBSl0HBwcUFhYiOztb7bYWLVoEW1tbcfLw8KjR2ImIqooJLhFRHZGXl4eQkBDk5uZi69atMDY2FpeZm5sjJydHpX5WVhYAoF69emq3Fx4eDqVSKU4KhaLmgiciqgY+ZEZEVAfk5+cjJCQEt2/fxh9//AEnJyeV5c2aNcPt27dVym7fvg0PDw+YmKj/U2Fubg5zc/Mai5mISFNswSUiMnBlye3Nmzdx8OBBtWPgPvvss9i+fTsKCgoAAMXFxZDL5eIoC0Skn4qLi8WfDx8+rDJflzHBJSIycK+++ioOHDiAiRMn4ujRo4iOjkZ0dDTOnz8v1pk1axaKi4sxePBgrFq1Cs8//zxSU1Px/vvvSxc4ET2WXC6Ht7e3OB8cHIwmTZpALpdLGJV+YBcFIiIDZ29vjyFDhuCPP/5QKX/22WfRsWNHAICjoyPOnDmDFStW4I8//kDbtm2xcuVKuLu76z5gInoiuVyOsLCwCqOcJCYmIiwsDNHR0QgNDZUoOukxwSUiMnDfffddleo5OTnho48+quFoiOhpFRcXY8qUKWqH8BMEATKZDFOnTsWwYcNUHiatS9hFgYiIiKgWiY2NRUJCQqXLBUGAQqFAbGysDqPSL1pJcAsKCvDHH3/gf//7nzY2R0RksB48eKC2fNeuXZUuo5qXnZ0NmUwGmUxW6bi/dRHPi366e/euVusZIo0S3H379uH1118X55999ln07dsXXl5e2LFjh9aCIyIyNJWNFTtv3rwKw3QREanj6uqq1XqGSKME9/3338eUKVMAACdPnkR8fDySkpLw/fffs/8WEVE1lZSUICUlBXZ2dlKHQkS1gJ+fH9zd3SGTydQul8lk8PDwgJ+fn44j0x8aJbh//vmnOCxFTEwMnn/+eTg7O2PEiBG4cuWKVgMESp8IfOutt9CiRQu0b98eK1asqNCx+scff0SXLl3g7u6OZ599FhcvXtR6HEREmnjjjTcwcOBAAMA777yD3r17i1OvXr3QtGlTWFtb81W3RFQlxsbGiIqKAoAKSW7ZfGRkZJ19wAzQMMF1cHAQE8itW7eiX79+AICkpCQ4OjpqLzqU9h/x8fFBWloatm7diq1bt+LGjRs4cuSIWGfz5s0YN24cJk2ahJiYGLi7u6Nv375ITk7WaixERJrw8fFBQEAAAKBr164YMGCAOA0cOBDvvfcejh07BiMjPvdLRFUTGhqK6OhouLm5qZS7u7vX+SHCAEAmqBtj4gk++ugjLF26FB4eHnjw4AGuXr0KS0tLLF26FAkJCfjyyy+1FuCYMWNw9OhRxMfHq7wusmwYDADo0KEDevToIQ6FU1xcDDc3N0yYMAERERFV2k9GRgZsbW2hVCphY2NTpXXG/nC6mkdTO60Z3U3qEIhqvbJ7zD///IOmTZtKHU6NqO59NDs7G1ZWVgCArKwsWFpa1nSItSIWfcLzov/Kfu+A0odVg4KCJGu51cXnpar3GY3Gwf3www/RoUMH3Lp1C6GhoeIBFBcXY968eZpFrIYgCJDL5Zg2bVqFd6GXJbdKpRIXL15UeduOsbEx+vXrV6eHx9CmyhJ5Jr5E1Wdtba3yDzpQOhKNqalppf3piIgqUz6Z9ff3r9PdEsrT6PuwqKgoNGnSBJMmTVJ5y83MmTPh4OCgteBSUlKgVCrh4OCAkJAQeHh4wMfHBytXrhT74N65cwcA4OzsrLKus7PzY4fHyM/PR0ZGhspERFTTgoODUVRUpFK2YsUKzJw5U6KIaoGCbGCBbelUwKGqiOjJNEpw165di06dOqFhw4Z4/vnnERUVhQsXLqh9o8bTKC4uBlA6fM6LL76Io0ePYubMmZg1a5bYDaJsn4/+x2JiYoKSkpJKt71o0SLY2tqKEx/uICJdePfdd2FqaqpSNmXKFKxduxY5OTkSRUVEZFg0SnDPnz+Pe/fuYfXq1fD09MR//vMflYRXWxwcHGBsbIyXXnoJL7/8Mjw9PTF8+HC88cYbWL9+PQCID7WlpqaqrJuamvrYB97Cw8OhVCrFqbKxKYmItEndt1zGxsawsrLifYiISEs0fmTX3t4ezz//PCIiIvDxxx/jtddeg1Kp1OqLHszNzdGpUyfUr19fpdzCwgKFhYUAShPcpk2bqoyqAACHDx+Gj4/PY7dtY2OjMhER1bRffvmlQtmRI0dw7949eHp6ShAREdUZdai7j0YJ7m+//YaZM2eiS5cucHJyQkREBBo2bIitW7ciLS1NqwFOnz4dGzduRFxcHADg0qVLWLduncrwF5MnT8bq1atx8uRJFBUV4fPPP8edO3cwfvx4rcZCRPS0Nm/ejJCQEKxZswabNm3C3LlzERwcjJkzZ1b4Z56IiDSj0SgKQ4YMQcOGDTFjxgzExMTU6Nt3Ro4ciaSkJAQGBiInJwf16tXDuHHjMH/+fLHOlClTkJKSggEDBiA/Px+NGjWCXC6Hl5dXjcVFRKSJ3377DQsXLsS7776LgoICNG/eHB999JH4dkgiInp6GiW4S5cuxcGDB/H5559j6dKlCAgIQEBAAPr27YvWrVtrO0ZMmzYNU6dOVRlfrTyZTIZPP/0UCxcuRHZ2NqytrbUeAxGRNnTp0gUxMTEASl/Ry5c7EBFpn0Z31qlTp2Lbtm24d+8e9uzZgx49emD37t1o27YtXF1dtR0jgNIkVl1yW56RkRGTWyKqFTIzM7U+8gwRUa1Rw/2BNW46EAQB8fHxOHLkCGJjY3H06FGUlJTA3t5em/ERERmU77//Ho0aNYKNjQ0uXboEAFi8eDG2b98ucWRERIZDowQ3LCwMTk5O6NChA7799lu4urri22+/RXJyMuLj47UdIxGRwfjqq6+wZMkSlWcEwsLCMGfOHAmjInpKdejpfKpc2fsLgNLRrMrP65pGCa6LiwtWrlwpJrRff/01hg8f/thxZ4mICPj4448xcuRIWFhYiGXNmzeHUqnEzZs3pQuMqoaJHJFacrkc3t7e4nxwcDCaNGkCuVwuSTwaPWS2YsUKbcdBRFQnlI11K5PJVMoFQUBBQYEUIUnG0tKS/ZDVKP9AdVZWFiwtLSWOiOjx5HI5wsLCKvw+JyYmIiwsDNHR0SrDu+rCUz++W1JSgqKiIpWJiIjUK3spTfkEd8+ePcjJyUGzZs2kCotqI7Ymkx4oLi7GlClT1P6zWlY2depUnXdX0CjBzc7OxowZM+Dp6QlTU9MKExERqRcVFYXZs2fjwYMH2L17NyZPnoyQkBDMnTsXJiYafalGRCSZ2NhYJCQkVLpcEAQoFArExsbqMCoNE9x58+bh8OHDWLZsGUpKSrB7925ERETA2toaERER2o6RiMhgbNu2DRcuXMDdu3fx3nvvYdeuXfjiiy8QHh4udWhEVEXZ2dmQyWSQyWTIzq7bred3797Vaj1t0ai5QC6X4/fffxc7EwcFBWHQoEFo3749Fi9ejA8//FCrQRIR1VYpKSlo0KCBON+pUyf8/vvvAEq/2jM2NpYqNCLShYJs4FO30p/n3QHMDKtPdVXff1BT70mojEYtuImJieIby6ysrJCeng4ACAwMRFxcnNaCIyKq7YKCglSGT7xx44b4s66T25ycHJw9exYpKSmV1lEqlbh06RIePHigw8iIqLby8/ODu7t7hQdny8hkMnh4eMDPz0+ncWmU4AqCIL5e0svLC7t27QJQ+vBE+ZYKIqK6zsbGBmlpaeK8FF9nJiQkYMqUKWjRogV8fHzw008/qa23YMECODs7IyQkBC4uLpgxY4aOIyXSEj6ApzPGxsaIiooCUHF0mLL5yMhInf9Dr1EXhUaNGok/z549G6NGjcLChQvxzz//YMGCBdqKjYio1gsMDMTIkSPRoUMHAMCcOXMqbQhYvnw5mjZtqvUY4uPj0bRpU8THx6Nly5Zq68jlcixatAgHDhxAr169EBcXh969e6NNmzYYM2aM1mMiIsMRGhqK6OhoTJ48GYmJiWK5u7s7IiMjdT5EGKBhglv+abmXXnoJXl5eOHnyJFq3bo2AgABtxUZ6buwPpytdtmZ0Nx1GQqS/3nvvPbRs2RInT57E/v370ahRI7i5uamta2ZmViMxDBw4EAMHDnxsnVWrVmHgwIHo1asXgNK+wiEhIVi9erV0CW66AshJA4pyH5YlXQRM6pf+bOEA2HlIExsRqQgNDcWAAQNga2sLANi1axeCgoIke85AowR3yZIlmDlzpjjfqVMndOrUSWtBEREZCrlcjsDAQAQHByMyMhKTJ09G7969pQ6rgnPnzmHixIkqZT4+PpDL5RAEodL+dTUmXQGs6AIU5auW/2fQw59NzIGJZ5nkEj2Jjv5ZLJ/M+vv7S/oQrUYJ7nvvvYepU6dyzEYioif45JNP0KJFC71/iUNaWhocHBxUyho2bIi8vDzk5OSofZtWfn4+8vMfJqAZGRnaCygnrWJy+6ii/NJ6THCJKldH/1nU6CGz9u3b4+TJk9qOhYjI4Li4uODYsWM6f4tPdZmamqokqwCQm5srLlNn0aJFsLW1FScPD8P540hkMKrzz6IB0SjBHTFiBF566SWsWLECBw4cwJEjR1QmIiIq9c4772DGjBmwt7cHUDqkTtkA8Y9O58+flyxOT09P3LlzR6Xszp07cHZ2rrRvcHh4OJRKpTgpFApdhEpE9EQa9TEo6387adIktcvVvY+YiKguGjJkCBQKBc6ePYtBgwZh6dKlaNu2rdq6LVq00HF0D/Xv3x+7d+/G559/Lva3/e2339C/f/9K1zE3N4e5ubmuQqxR5VvYDx8+LOnDMUT09DRKcMu+tiIioidr2LAhfH19AZQmvM2bN9fp/nNzc8WXTRQVFUGhUODMmTNo0KCBGMvs2bPx888/Y8yYMXj55ZexdetWxMfHY+3atTqNVQpyuRyTJ08W54ODg+Hu7o6oqKiHwxtxRAf1eF6ounT0mdEowa1Xr95T75iIqC5ydHQEAGRmZsLCwkInrYQKhQITJkwAUNpKfOjQIRw6dAgBAQFYsmQJAKBJkyY4duwYFi1ahAULFqBx48aIjY0VX8luqORyOcLCwip885iYmIiwsDBER0cjtF+3OvmQzhPV0YeX6Cno8DNT5QT3119/rfJGQ0JCNAiFiMjwff/991i6dCnu3LmDuLg4dOzYEYsXL8YzzzyDoUOH1sg+vby8cObMmSfWe+aZZ/Djjz/WSAz6qLi4GFOmTFHbra5saLSpU6di2LFfYVxHR3R4bNcNjnRB1aXDz0yVE9zRo0erzCuVytIN/DtUWFFREQDA1tYW6enpTxUUEZGh+uqrr/Dll1+qvPUxLCwMgwcPrrEEt1aycChtyXncH0MT89J6GoqNjVV5cdGjBEGAQqFAXFwcumq8l9qrSl03iPRUlUdRSE9PF6eFCxeie/fuOH36NPLy8pCXl4fTp0+je/fu+OSTT2oyXiKiWu3jjz/GyJEjYWFhIZY1b94cSqUSN2/elC4wfWPnUfo15VuHgDF7HpaP2VNa9tahp/4a8+7du1Wqd+/ePY33UVuVdd0o/9pV4GHXDblcrvOYsrOzxRFHsrOzdb5/fWVpaQlBECAIgtrxqsV/Fh/nKf9Z1Eca9cFdvnw5du3apfKgRNeuXbFx40YMHjwY7777rtYCJCIyJJ6engBQ4c1ggiCgoKBAipC0Ly8DKBtZzNgUMK0PFOYCxYUP65iYl04F2UBJuTGCTeoBJmZAfhZQz7Z0Ksx5uLxBU8D04T8HKCkGZEZAfqZqDObWgFBSuv3y6tkAxUVAYQ5cHWyqdDgN7W2BW1WoWHZ8jzsmoeRhuakFYGxSer7K+7eKtRlKj8u4WPWYUv8Ccu8DRXkP17kTB+Dfz1R9+9LE39wKKCpQrWdkDJhZlraMl28dL3edigvyMGXypMd33ZgyBcOOb0OVepCXnQ8Nr9PD7ZQeh6nRI+dF3TGVX68w95HrUYXP3pOuU34OjGRAifBILOWPqYavU5V/n+w8gLdigZx7pfvY8G/r+5g9QEnJw1jq2T79dcrPgaUpkF0IoLgAyCsXi1E1njcoyC49bnXHVFS1+6RGCe6tW7fU/pdgZWWFW7eqchcgIqqbjhw5gsDAQJUEd8+ePcjJydH7t51V2VJvwPzf4+v0GjBsBbBrFhC3/mGdPnOBvuHApleBGwcelg9ZBnR5HVjdH0i9WnHby7sABVkP5985Adg0Aj57pCV3rgLISARW9nhYZmYNzEsA/vcHsOEF+JUIcLeRITFDgLrBLWUA3D080EmIr9pxx20APLpV/Zhe/QVoMQD4yhsoeJhQyN44CGszICPcBljWSvWYEs8C60Mq7vuH51Tn7ZsDk88BF34GdjzsZoDm/YDXtgKxXwGHPntYXu46xW5di4TEHFRGEAQoEhKq3nXjh+Cnuk5l6jt4AQBGdTCFZfnzUtkxldn7ful5KFPdz56a62QJoHVDIyiUJaqxlB2TDq5TtX6ftoyq+Pvk0h5Y0krls/e018kSwMlxlmj7TTZM4rcAv89SPab+8yueE3V+CK78mPourtImZIIGg9b6+fnBxcUF3333nTh4+f379zF+/HikpKTg0KFD1d2k5DIyMmBrawulUgkbm6r9Vz/2h9M1HFXttWZ0N6lDINIrZfcYW1tbvPXWW4iOjsabb76Ju3fv4vvvv8f8+fMRHh4udZhPRbyPJise3kefpgW3rBWtMAf48t8kYsY11RZcM8unanGS/7odYS+PAqA6hnvZPyDR0dEI7eYBrBnw5BMwdn9pgvuULbjZhYCVjS2szUq7UViWdWcxty5tAVzV98mxvLEHaOyrUcvgzz/9jJdHj33iLnb/5zMMuvXpk2MZvQvw7PHULbjZuXmwsneGqRHwILXceamsBbfsMzPrBmBc7mUlWmjBzc7JgU1DV5QIQFZauVjKjkkH10mj36fy52XeHdW6wFP/PmXn5MDZxRXZhUCW8j4szcq12hoZA/euA9/3efJ5Gb0LcO+q9pgycgpg6+D4xHxNoxbcVatWYdiwYXBzc0Pz5s0hCAL++ecfNG3aFNu2bdNkk0SV/sPAZJkMybZt2/Dpp5/i7t27eO+999CsWTN88cUXlb44p1aqZ1M6lWdav3R6lJmaPoNA6de2Zcp/tWlurX6dR/cHADJj9eXGJoBxaXnoiFcRbWaByZMnq/Q3dXd3R2RkZOnDVHfOq4+xwnb/faVxVY7pcbEXlyYRmQUoPd565bYnq+KjM2Xn2sSsdHpUWUKkZj3XxlX7JqFhw4ZV67phZvnwGmp4nQAAxaXbKCxBxfMClB5PVkrFMVbTrqsfY/VprlOxcWn3BFQSiw6uk0a/T492E1B33isrr8p1KjYu7Z4AlP5T8eh5qSozy4fH/egxFWRUrK+GRglu69atcfnyZezatQuXL18GAHh7eyM4OJhvfiEieoxOnTrh999/B1A6BBPvmdILDQ3FgAEDYGtrCwDYtWuX6nBYOhjRQZ/4+fnB3d0diYmJavvhymQyuLu7o1OvAcAFPTovHJdX/+nwd0mjBBcAjI2NMWTIEAwZMuSpgyAiqouY3OqP8tfC399f9dqUjehQ1jJYljCN2WOQb+wyNjZGVFQUwsLCIJPJ1HbdiIyMhLF9Y/06LxyXV//p8HdJ4wSXiIiozrDzKJ3K90F0aV/5V8K1XGhoKKKjox/fdQOoc+eFtEBHnxkmuERERFTBE7tuEOmxKr/ogYiIiOqWx3bdINJjTHCJiIhqizr6Vqpah9dJcuyiQEREVFvUsQfeai1eJ8nVqgQ3ISEBf/zxB5555hl06dJFZVlxcTGOHTuG5ORktGvXDq1atapkK0RERLUYH+yqHXidJFVruigUFRUhLCwM48aNw/r161WWPXjwAL6+vnjllVewZs0adO3aFTNnzpQoUiIiItI5dgugcmpNC+57770HLy8v5OXlqV2WkZGB+Ph4WFtb49ixY+jduzcGDhyIwMBACaIlIiIinWK3ACqnVrTg7t27F9HR0VixYkWFZYIg4Oeff8bYsWNhbW0NAOjZsye6d++OjRs36jpUIiIikoqdB+DWsbQrQBmX9qVlbh2Z3NYhet+Cm5ycjDfeeAObN2+GjU3FdyArFAqkp6ejbdu2KuXt2rXDuXPnKt1ufn4+8vMfvvEkI6Nq7zYmIiIiIv2m1wmuIAh47bXXMHbsWPTq1UttHaVSCQBo0KCBSrm9vb24TJ1FixYhIiJCe8GSirE/nFZbvmZ0Nx1HQkRERABKH3BbUHluZEj0uovCzz//jFOnTqFZs2bYsGEDNmzYgAcPHuDq1avYsGEDBEFA/fql/WqysrJU1s3MzBSXqRMeHg6lUilOCoWiRo+FiIiIiHRDr1twHR0dMXjwYOzfv18sy8zMxK1bt7Bnzx68/PLL8PDwgKmpKW7duqWy7q1bt9CsWbNKt21ubg5z8yc8bUlERNKrQ61ORKQdep3gBgYGVhgFoWPHjggICEBkZCSA0kS1f//+2Lx5M8aOHQsASE1NxYEDBxAVFaXrkImIiMjAFRcXiz8fPnwYQUFBfI2xntHrLgpVtXjxYhw7dgwjR47EsmXLEBgYiLZt22L06NFSh0ZEREQGRC6Xw9vbW5wPDg5GkyZNIJfLJYyKHlXrEtznnnsOXbt2VSlr3749zp8/j6ZNm+LChQsYPXo0Dh06BDMzM4miJCIig1TWXWKBkm+kqoPkcjnCwsKQmJioUp6YmIiwsDAmuXpEr7soqPPJJ5+oLW/evDk+/fRTHUdDRGR4MjMzYWVlBZlMJnUoVFvUgX7SxcXFmDJlCgRBqLBMEATIZDJMnToVw4YNY3cFPVDrWnCJiKhmrF27Fi4uLnBwcICjoyOWLVsmdUhEeiM2NhYJCQmVLhcEAQqFArGxsTqMiipT61pwiYhI+w4ePIg333wT69evx4svvoidO3ciLCwMjRs3xrBhw6QOj0hyd+/e1Wq9Oq+GW/3ZgktERFixYgUCAgIwcuRIGBsbY+jQoQgODmYrLtG/XF1dNavHftuSYIJLREQ4ceIEevfurVLWp08fnDp1SqKIiFQ9OjRX+Xld8PPzg7u7e6V902UyGTw8PODn56fTuEg9JrhERITU1FQ4OjqqlDk6OiIrKwu5ublq18nPz0dGRobKRFQT9GFoLmNjY3F8/UeT3LL5yMhIPmCmJ5jgEhERZDJZhRaxoqIiAICRkfo/FYsWLYKtra04eXh41HicVE4d+epbo6G5aujchIaGIjo6Gm5ubirl7u7uiI6ORmhoqNb2RU+HCS4REcHNzQ3JyckqZcnJyWjQoEGlrzUPDw+HUqkUJ4VCoYtQqQ550tBcADB16lSddlcIDQ3F5cuXxfldu3bhf//7H5NbPcNRFEinxv5wWuoQiEgNf39/7N+/X2Ws8b179z62P6G5uXmlyS+RNlRnaK6AgACdxVW+G4K/vz+7JeghJrhERIQZM2age/fuWLBgAV555RVs3boVhw8fxqFDh6QOjeowDs1FmmIXBSIiQvv27bFnzx4cOHAAAQEB+PXXX7Ft2zb07NlT6tCoDtN4aC6q89iCS0REAICAgAAcPnxY6jDqvEeHwwoKCqqzX4GXDc2VmJioth+uTCaDu7s7h+aiCtiCS0REpCf0YTgsfcKhuUhTTHCJiKjOs7S0hCAIEAQBlpbSDLml0XBYdQCH5iJNMMElIiKSmD4Oh6VPODQXVRcTXCIiIolVZzisuopDc1F1MMElIiKSGIfDItIuJrhEREQS43BYRNrFBJeIiEhiZcNhPTpSQBmZTAYPDw8Oh0VURUxwiYiIJMbhsIi0iwkuERGRHtDH4bD0Yfg0Ik3wTWZERER6IjQ0FAMGDICtrS2A0uGw6vKbzIg0xRZcIiIiPcLhsIieHhNcIiIiIjIoTHCJiIiIyKAwwSUiIiIig8IEl4iIiIgMCkdRICIiIqKnVjasnD5gCy4RERERGRQmuERERERkUJjgEhEREZFBYR9cqrXG/nC60mVrRnfTYSRERESkT9iCS0REREQGhQkuERERERkUJrhEREREZFD0vg9uSkoKvvnmGxw7dgwmJibo3bs3Jk+eDEtLS5V6Bw8exIoVK5CcnIx27drh/fffR6NGjSSKmoiIiIikotctuMXFxfDx8QEAzJgxA2+++SZ++uknBAYGorCwUKwXExODoKAgtGvXDh988AFu376NXr16ISMjQ6rQiYiIiEgiep3gGhsbIz4+HvPnz0dQUBBCQkLw008/4fjx4zh16pRY74MPPsDw4cOxYMECDBw4ENHR0UhPT8d3330nYfRERPqhuLgYO3bsQHBwMBo0aIBvvvlGbb2YmBj06tULzs7O8PHxwY4dO3QcKRGRduh1ggsAFhYWaufLWnCzs7Nx4sQJPPfcc2Kd+vXro3///oiJidFdoEREemrDhg347rvv8M4778DIyAj5+fkV6ly4cAHBwcEYNGgQTpw4gZdeegmhoaE4evSoBBETET0dve+D+6iPP/4Ybm5uYteFhIQECIIANzc3lXpubm6PTXDz8/NVbvLszkBEhmrUqFF4/fXXAQAymUxtnS+//BKdOnXCBx98AACYPn06du/ejcWLF2P79u06i5WISBv0vgW3vKVLl+Lnn3/Gxo0bUb9+fQAPW3LNzc1V6tavX1+ln+6jFi1aBFtbW3Hy8PCoucCJiCRUWVJbXmxsLPr3769SFhgYiCNHjtRUWERENabWJLgrV67E3LlzER0djYCAALHcwcEBAJCWlqZS/969e+IydcLDw6FUKsVJoVDUSNxERLVBYmIinJ2dVcqcnJzw4MED5OXlqV0nPz8fGRkZKhMRkT6oFQnut99+i2nTpmHLli0YMmSIyjJXV1e4urri9GnV17aePHkSnTt3rnSb5ubmsLGxUZmIiGqDb775BnZ2do+dNOlWYGSk+ifBxKS0F5sgCGrr85sw0iVLS0sIggBBECoMFVqXYyH19L4P7qpVqzB16lRs2bIFQ4cOVVtn7NixWL16Nd588014eHhg06ZNuHLlCn788UcdR0tEVPPGjBmDkSNHPrZOdf/oOjk54d69eyplKSkpsLKyEruEPSo8PBzTp08X5zMyMpjkEpFe0OsENz09HePHj4etrS0++ugjfPTRR+KyDz74AMOGDRN/vnHjBlq2bAk3NzekpKTg22+/RZcuXaQKnYioxpibm1d47uBp+fj4IDY2VqXs0KFD4gO9uoqDiEgb9DrBtbKyUhnvtrwmTZqIP5uZmeGnn35CcnIyUlJS0Lx58wrDixERUeUmT56M/v37Y8OGDRgxYgS2b9+OXbt2QS6XSx0aEVG16XWCa2Jigq5du1a5vrOzc4WHJIiI6rpLly7Bz88PQGk3gvDwcCxYsADPPfccNm7cCADo06cP/vOf/2DOnDkYPXo07O3tERUVVeG5ByKi2kCvE1wiABj7w+knVyKiSnl7e+PmzZsVyk1NTVXmR40ahVGjRiE3N7fSfrdERLUBE1wiIgNnbGwMOzu7KtdncktEtV2tGCaMiIiIiKiqmOASERERkUFhFwWif1XW13fN6G46joSIiIieBltwiYiIiMigMMElIiIiIoPCBJeIiIiIDAoTXCIiIiIyKHzIjAySrh4Y44NpRERE+octuERERERkUNiCS3WKJq/95auCiYiIahe24BIRERGRQWGCS0REREQGhQkuERERERkUJrhEREREZFCY4BIRERGRQWGCS0REREQGhQkuERERERkUJrhEREREZFCY4BIRERGRQWGCS0REREQGhQkuERERERkUJrhEREREZFCY4BIRERGRQTGROgAiQzT2h9Nqy9eM7qbjSIiIiOoetuASERm4rKwsLF26FM899xz69u2LadOmITExsUK9P//8E6+88gp8fHzw0ksv4cyZMxJES0T09JjgEhEZuKFDhyIhIQHvvPMOPvzwQ/z111/o1q0bkpKSxDr//PMPevfujfr16+Ozzz6Ds7Mz+vTpgz///FPCyOsmS0tLCIIAQRBgaWkpdThEtRK7KBARGbidO3eifv364ryvry8cHR3xyy+/4N133wUAfPHFF/Dw8MCqVasgk8nQt29fnDp1Cp9++il++uknqUInItIIE1wiPaHNfrvsA0zllU9uAcDExATGxsYoKioSy2JiYhASEgKZTCaWDR48GMuXL9dZnERE2sIEl4iojomKikJubi4GDx4slikUCri6uqrUc3NzQ0pKCgoKCmBmZlZhO/n5+cjPzxfnMzIyai5oIqJqYIJLRFTLbNy4EV988cVj6yxZsgQDBgyoUL57927MnTsXX3/9NZo3by6WFxYWwtzcXKVu2XxhYaHaBHfRokWIiIjQ5BCIiGoUE1wiHaqs64C+bE9b+2dXiJoVFBSENm3aPLZO06ZNK5Tt27cPoaGh+Oyzz/DWW2+pLHNwcEBaWppKWVpaGurVqwcLCwu1+wgPD8f06dPF+YyMDHh4eFT1MIiIaoxBJbgKhQLJycnw8vKCjY2N1OEQEdUIR0dHODo6Vmud/fv3Y9iwYfjoo48wY8aMCsu7dOmCU6dOqZQdP34cnTt3VumXW565uXmFVl8iIn1gEAluXl4eXnnlFezevRuNGzfGrVu3sHjxYkyaNEnq0Iiemq5aaXWxn8ftQ19bfWtjzI86ePAghg4dioiICMyaNUttnbfeegsvvfQSYmNj4efnhzNnzmDbtm1YuXKljqMlInp6BpHgRkRE4NSpU7hx4wZcXV3x66+/4vnnn0f37t3h4+MjdXhERJIaM2YMiouLsXHjRmzcuFEsHzFiBObOnQsACAkJwfvvv4+goCA4OTkhKSkJU6ZMwejRoyWKmohIcwaR4K5duxZvv/22+ARwSEgI2rZti7Vr1zLBJaI6b+fOnSgoKKhQ/mg3hw8++ABTp05FQkIC3NzcYGtrq6sQiYi0qtYnuHfu3EFycjK6dOmiUt69e3fExcVVut6jw9solUoA1RvmpiA3q5rREknrcZ9vbX6eK9vP4/ahr0NMaSvmsrqCIDx1TNXl7e1d5brW1tZ45plnNNpP2bHp67UkotqvqvfSWp/g3r9/H0DpE8DlOTg4iMvUqWx4Gz4BTIZswzv6ux9dxaZNmsScmZlpsC2jmZmZAHgfJaKa96R7aa1PcE1NTQGUPmhWXm5urtpxG8s8OrxNSUkJ7t+/DwcHh0qfGC6vbDgchUJhECM2GNLx8Fj0lyEdT3WPRRAEZGZmws3NTQfRScPNzQ0KhQLW1tZVuo9WxpA+J9rE86Iez4t6hnpeqnovrfUJroeHB4yMjJCYmKhSnpiYCE9Pz0rXUze8jZ2dXbX3b2NjY1AfHEM6Hh6L/jKk46nOsRhqy20ZIyMjuLu7a217hvQ50SaeF/V4XtQzxPNSlXupkQ7iqFEWFhbo2bMntm/fLpZlZ2dj//79CAwMlDAyIiIiIpJCrW/BBYCFCxciMDAQ4eHh8PX1xfLly+Hk5FThTT1EREREZPhqfQsuAPTp0wcHDx7ErVu3EBUVhTZt2uDIkSOwsrKqsX2am5tj/vz5BvMWH0M6Hh6L/jKk4zGkY9E3PLfq8byox/OiXl0/LzJBijFriIiIiIhqiEG04BIRERERlWGCS0REREQGhQkuERERERkUgxhFQdcEQcDly5dRVFSENm3awMSkdp7GO3fu4J9//lEpk8lk6NWrl0QRVd+9e/dw9epVPPPMMxXeZlfm7t27SEhIQPPmzWFvb6/jCKtOEAScPXsW5ubmaNeuncqyoqIinDhxosI6rVu3RsOGDXUVYpUVFRXh2rVrqFevHpo0aQJjY2O19W7evIl79+6hdevWNfpQ6NNKSkpCUlISmjVrVmE8yczMTFy4cKHCOh07dtTrY9JXeXl5uHz5MqysrODl5SV1OHojOzsb169fh5OTk0G/LERTycnJuH79Opo3bw5XV1epw9ELJSUluHr1KszMzNCiRQupw9E9garl2rVrQuvWrQUnJyfBw8NDaNSokXDs2DGpw9LI0qVLBQsLC6FXr17i1KdPH6nDqpI///xTeOWVVwQXFxcBgLBly5YKdYqKioTRo0cL9erVE7y9vQVzc3MhIiJCgmgfr6ioSFi8eLHQvHlzwdbWVujVq1eFOqmpqQIAoWPHjirXa//+/RJEXLmCggLh/fffFxo2bCi0adNGcHd3F5o2bSrExMSo1MvMzBQGDhwoWFpaCq1atRIsLS2FtWvXShP0Y/zxxx+Cj4+P4OzsLHTo0EGoX7++MGnSJKG4uFisc/z4cQGA4Ovrq3Jtrl69KmHktdOOHTsEe3t7oXnz5oKdnZ3QvXt3ISkpSeqwJJWQkCC88sorgq2trdCxY0fB1tZW6NOnj6BQKKQOTW/k5uYKHTp0EGQymbB8+XKpw9ELO3fuFNzd3YXGjRsL7du3F/z8/IQ7d+5IHZZOMcGtpk6dOglDhgwRioqKBEEQhPHjxwtubm5Cbm6uxJFV39KlS4U2bdpIHYZGNm/eLPz4449Cenp6pQnukiVLBHt7e+Hvv/8WBKE0WTE2NhZ27typ63AfKzMzU5g1a5bw999/C+PHj39sghsXF6f7AKtBqVQKH3/8saBUKgVBEITi4mJh2rRpgq2trVgmCIIwYcIEoWXLlkJaWpogCIKwdu1awdjYWLh8+bIkcVdm9erVwqlTp8T5ixcvClZWVkJUVJRYVpbgZmZmShGiwUhKShKsrKyETz/9VBAEQcjJyRG6desmDBkyROLIpHXkyBFh48aN4t+cjIwMoUePHkJgYKDEkemPt99+W3j33XcFS0tLJriCIJw+fVowMTGpcJ86e/ashFHpHhPcajh37pwAQDhx4oRYplAoBJlMJmzdulW6wDS0dOlSoXXr1sKFCxeEy5cvCwUFBVKHVG25ubmVJrje3t7CxIkTVcoCAgKEF154QVfhVduTEtzt27cLZ8+eFdLT0yWITjNXr14VAAhHjx4VBKG0ldfKykqIjIxUqefp6SnMmTNHihCrZdCgQcLw4cPF+bIE99y5c0JcXJyQlZUlYXS117JlywQrKyuVxoL//ve/gpGRkZCcnCxhZPpnxYoVgoWFhdRh6AW5XC4888wzQk5ODhPcfw0ZMkTo2bOn1GFIjg+ZVUNcXBwAoHPnzmKZu7s7XF1dxWW1zbVr1zBixAgMHDgQjo6OWLVqldQhaUVeXh6uXLmCLl26qJR379691l4rABg3bhxef/11ODo64tVXX0VGRobUIT3R6dOnIZPJ0KxZMwDA9evXkZWVVeHadO3aVe+vTX5+Pi5duqS2P9uwYcMwYsQI2NvbY/r06SguLpYgwtorLi4Obdq0Qb169cSy7t27o6SkRG0f57rs9OnTaN68udRhSO727dt4++23sXHjRtSvX1/qcPSCIAg4cOAAhgwZguzsbJw9exaJiYlShyUJJrjVcP/+fdjY2MDU1FSl3MHBAffv35coKs21b98ef/31Fy5fvozbt2/jiy++wPjx43HgwAGpQ3tq6enpEAShwoNntfVamZmZ4eeff0ZycjIuXbqEP//8E4cOHcK0adOkDu2xEhISMHPmTLz55ptwcXEBAPH818ZrM2PGDOTm5uLdd98Vy+zt7XHgwAHcvn0bV69exaFDh/D999/j888/lzDS2uf+/ftqPxNly6jUrl27sH79enzwwQdShyKp4uJivPzyy5g5cyY6deokdTh6IyMjQ3wg0cvLC2+++SbatGmDgIAAJCUlSR2eTjHBrQZTU1Pk5eVVKM/NzYWZmZkEET2dfv36qbREvfnmm+jcuTM2bdokYVTaUfZPyKPXq7ZeKxsbG4wYMUKc9/LywowZM7Bp0yYIevoywtTUVAwcOBBt27ZFVFSUWF5br80nn3yCtWvXQi6Xo1GjRmK5l5cX+vbtK8736NEDo0ePxn//+18pwqy11N1fc3NzAUCvPxe6dPToUbz44ov44IMPMHz4cKnDkVRUVBTu3r0LHx8fHDlyBEeOHEFJSQn++ecfnDlzRurwJFN2f929ezdOnTqFc+fO4ebNm3jw4AEmTZokcXS6VTvHt5JI48aNUVBQgHv37olDMxUXFyM5ORmenp4SR6cdzs7OBvF1hr29PaytrSscS2JiokFdq+zsbCiVStjZ2Ukdjop79+6hX79+cHZ2xvbt21W+dm7cuDGA0mtRvuVFn6/NZ599hk8++QS//fYb+vTp88T6hvJ7pEuNGzfGpUuXVMrKzqG+fi506fjx43j22WcxdepULFiwQOpwJGdiYgJXV1eEh4eLZfn5+di2bRsUCgW2bNkiYXTSsbCwgKOjI4YMGSL+I25nZ4cRI0aoNDTUBWzBrQZ/f3+YmZlh+/btYtmBAweQmZmJwMBACSPTTHZ2tsq8UqnEqVOn0LZtW4ki0h6ZTIb+/furXKvCwkLs2rXLIK4VAOzduxfu7u56m9w6Ojrit99+g4WFhcpyFxcXtG3bVuXapKWl4ejRo3p5bT7//HN89NFH2LFjB/r161dhubprs2/fPoP4PdKlwMBAXLt2DX/99ZdYtm3bNjg6OqJDhw4SRia9EydOYNCgQZg0aRIWLlwodTh6YfLkyWLLbdlUv359TJs2rc4mt2UGDhxY4R/shIQEODo6ShSRNNiCWw329vaYM2cOZsyYgZKSElhYWGDOnDkYNWoUvL29pQ6v2oKDg9G/f3907doV6enp+PLLL2FhYYGpU6dKHdoTPXjwAPHx8SgoKAAAXL16FUeOHIGbm5v4MNP8+fPRs2dPTJw4EQMHDsR//vMfFBcX6+XxnT17Frm5uUhKSkJGRgaOHDkCAOjduzcA4Ouvv8bFixfx3HPPwcbGBjt27MCGDRvw448/Shl2BTk5OQgMDER6ejqWLFmCc+fOicvKv4xj0aJFCAkJgbu7O9q3b48lS5agVatWePXVV6UKXa2VK1dizpw5iIiIgLm5uXhdbG1txZdxzJ49GzKZDP369YORkRHWr1+PM2fOYN++fVKGXusEBQWhX79+CAsLw4cffoiEhAR89tlnWL58ea19mY42XLp0CYMGDYKvry+effZZ8TMIAL6+vpW+RIXqrg8//BDdu3fH3Llz0a9fP5w7dw6rV6/GmjVrpA5Np2SCvnbg01OCIOCHH37AL7/8gqKiIgwcOBATJ06s8OBZbZCRkYGvv/4ax44dg7m5Obp06YKJEyfC2tpa6tCe6NixY5g9e3aF8hdeeEHlwau4uDhERkYiISEBrVq1wpw5c8SvyPXJiBEjkJCQUKH80KFD4h+wrVu3Qi6X4969e2jRogXGjx+vd62EiYmJeOmll9QuW7hwIQICAsT5mJgYfPfdd0hLS0OXLl0wd+5cvXvT3Jw5c3D06NEK5e3bt8fKlSsBlHZTWrduHfbs2YPc3Fw888wzmDRpEjw8PHQdbq2XnZ2NL7/8ErGxsbCyssJrr72G0NBQqcOS1LZt2/DFF1+oXbZnzx6+La+coKAgTJgwoc5/ZoDSEZK+/PJL/PPPP2jUqBFGjx6t8qxAXcAEl4iIiIgMCvvgEhEREZFBYYJLRERERAaFCS4RERERGRQmuERERERkUJjgEhEREZFBYYJLRERERAaFCS4RERERGRQmuERERFRjSkpK8N///hf37t3Tyf42b96MpKQkneyL9BcTXCIiIqoxBQUFGDlyJK5evaqT/b388ss4f/68TvZF+osJLhEREREZFBOpAyCqTcLDw7F161a1y+bNm4dRo0bpOCIiIt0oKSnBqVOnkJqaijZt2qBZs2Yqy3Nzc3HixAnk5eXBx8cH9vb2FbahUChw6dIlODo6olu3bhWWp6Wl4fjx4zAyMoKvry8aNGigUR0iJrhE1dCzZ080btwYADB58mSMGTMGHTt2BAD4+flJGBkRUc1JT09HQEAAcnJy4O3tjStXriAwMBArVqwAAMTExODll1+Gg4MDGjdujGvXruGbb77BwIEDxW189tln+Ouvv9CqVSscO3YM/fv3x+bNm8XlP//8M8aNG4dOnTqhpKQEly5dwrp16xAaGlqtOkQAAIGIqu3mzZsCAOHy5ctSh0JEVOO+/fZbwcvLSygsLBTL5HK5IAiCkJqaKtjY2Ajh4eHisvT0dOHgwYOCIAhCbm6uAEAYMmSIuP7ly5cFmUwmnDp1ShAEQUhJSRFsbGyEyMhIcRsLFy4U7O3thfv371e5jiAIgrGxsbB7924tnwGqbdgHl0gDcXFxsLCwQKtWraQOhYioxtWvXx8ZGRm4efOmWPb8888DALZu3YqSkhLMnz9fXGZra4uAgACVbYwbNw4mJqVfHD/zzDNwdnbGtWvXAAC7du0CALz77rti/RkzZiAnJwf79u2rch2iMkxwiTRw7tw5dOjQAUZG/BUiIsM3cuRIDB06FB07dkT79u0xffp0/P333wCA27dvw9PTE+bm5o/dxqN9cs3NzZGXlwcAuHXrFjw9PcUEGADq1auHRo0a4datW1WuQ1SGf52JNHD9+nW0adNG6jCIiHTC1NQU3333He7du4fly5cjJSUFnTt3RlJSEuzs7JCWlvZU22/YsCHu379fofz+/fto2LBhlesQlWGCS6QBIyMjxMfHY9++fWILBBGRobpz5w6A0hbTPn364Pvvv0dmZiYuX76MwMBAJCcnY+/evSrrpKamVnn7vXr1wt27d3HixAmxLCYmBkqlEr6+vlWuQ1SGoygQaeD999/H3LlzMXfuXBw7dkzqcIiIapRcLsfGjRsxbNgwODs749dff0WTJk3QtWtX2NjYYPbs2QgNDcWUKVPQuHFj7N27F7169cK0adOqtP0OHTpg3LhxCAkJwaxZs1BSUoLFixdj4sSJaN26dZXrEJWRCYIgSB0EERER6bdTp05BLpcjNTUVrVu3xhtvvKHSNWDv3r3Yvn07ioqKMHDgQPEhtMLCQrz22muIiIhQeTB30qRJCA0NRd++fQGUjrO7adMmxMTEQCaTITAwEMOHD4dMJhPXqUqdV155BbNnz0aHDh1q+pSQHmOCS0REREQGhX1wiYiIiMigMMElIiIiIoPCBJeIiIiIDAoTXCIiIiIyKExwiYiIiMigMMElIiIiIoPCBJeIiIiIDAoTXCIiIiIyKExwiYiIiMigMMElIiIiIoPCBJeIiIiIDAoTXCIiIiIyKP8H7+SamrDc4oMAAAAASUVORK5CYII=", + "text/plain": [ + "<Figure size 800x320 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Y = np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0])\n", + "S = np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0])\n", + "schools = rx.Dataset(np.arange(8), Y, S, label=\"schools\")\n", + "mu = rx.Parameter(\"mu\", prior=stats.norm(0.0, 25.0), latex=r\"\\mu\")\n", + "tau = rx.Parameter(\n", + " \"tau\", prior=stats.halfnorm(scale=10.0), bounds=(0.0, np.inf), latex=r\"\\tau\"\n", + ")\n", + "etas = [\n", + " rx.Parameter(f\"eta_{j}\", prior=stats.norm(0.0, 1.0), latex=rf\"\\eta_{j}\")\n", + " for j in range(8)\n", + "]\n", + "school = rx.Model(lambda x, mu, tau, *eta: mu + tau * np.asarray(eta), [mu, tau, *etas])\n", + "p_schools = rx.Problem([rx.Constraint([rx.Comparison(schools, school)])])\n", + "\n", + "\n", + "def nested(problem, seed, nlive=200, dlogz=0.5):\n", + " sampler = dynesty.NestedSampler(\n", + " problem.log_likelihood,\n", + " problem.prior_transform,\n", + " problem.ndim,\n", + " nlive=nlive,\n", + " sample=\"rwalk\",\n", + " rstate=np.random.default_rng(seed),\n", + " )\n", + " sampler.run_nested(dlogz=dlogz, print_progress=False)\n", + " res = sampler.results\n", + " print(\n", + " f\"log Z = {res.logz[-1]:.2f} +/- {res.logzerr[-1]:.2f}, {int(np.sum(res.ncall))} likelihood calls\"\n", + " )\n", + " return res.samples_equal(rstate=np.random.default_rng(seed))\n", + "\n", + "\n", + "s_schools = nested(p_schools, 0)\n", + "thetas = s_schools[:, [0]] + s_schools[:, [1]] * s_schools[:, 2:]\n", + "fig, axes = plt.subplots(1, 2, figsize=(8, 3.2))\n", + "axes[0].hist(s_schools[:, p_schools.columns(tau)], bins=40, color=\"C0\", alpha=0.7)\n", + "axes[0].set(xlabel=r\"$\\tau$\", ylabel=\"draws\")\n", + "axes[1].errorbar(np.arange(8) - 0.15, Y, S, fmt=\"o\", color=\"k\", label=\"reported\")\n", + "axes[1].errorbar(\n", + " np.arange(8) + 0.15,\n", + " thetas.mean(0),\n", + " thetas.std(0),\n", + " fmt=\"s\",\n", + " color=\"C1\",\n", + " label=r\"$\\theta_j$ shrunk\",\n", + ")\n", + "axes[1].axhline(s_schools[:, 0].mean(), color=\"C1\", ls=\"--\", lw=0.8)\n", + "axes[1].set(xlabel=\"school\", ylabel=\"effect\")\n", + "axes[1].legend(frameon=False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "df381b68", + "metadata": {}, + "source": [ + "## The study: a quadratic whose coefficients run with energy\n", + "\n", + "The truth is `y = a_0(E) + a_1(E) x + a_2(E) x²`, and each coefficient's\n", + "energy dependence is a smooth trend plus a non-monotonic bump. Seven\n", + "synthetic datasets at known energies constrain it; an eighth at a new\n", + "energy between two of them is held out to test prediction. Every dataset reports honest 3 %\n", + "errors, so the only thing that can go wrong is the model." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "63a9da2d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T04:19:03.165311Z", + "iopub.status.busy": "2026-09-11T04:19:03.165138Z", + "iopub.status.idle": "2026-09-11T04:19:03.558066Z", + "shell.execute_reply": "2026-09-11T04:19:03.557322Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 900x280 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def a_true(E):\n", + " return np.array(\n", + " [\n", + " 1.0 + 0.010 * E + 0.25 * np.exp(-(((E - 35.0) / 8.0) ** 2)),\n", + " 0.6 - 0.008 * E - 0.20 * np.exp(-(((E - 25.0) / 6.0) ** 2)),\n", + " -0.20 + 0.004 * E + 0.15 * np.exp(-(((E - 45.0) / 7.0) ** 2)),\n", + " ]\n", + " )\n", + "\n", + "\n", + "def quadratic(x, a0, a1, a2):\n", + " return a0 + a1 * x + a2 * x**2\n", + "\n", + "\n", + "rng = np.random.default_rng(12)\n", + "energies = np.array([10.0, 20.0, 25.0, 35.0, 40.0, 50.0, 60.0])\n", + "E_new = 30.0\n", + "x = np.linspace(-1.0, 1.0, 12)\n", + "noise = 0.03\n", + "\n", + "\n", + "def dataset(E, label):\n", + " y = quadratic(x, *a_true(E))\n", + " return rx.Dataset(\n", + " x,\n", + " y + rng.normal(0.0, noise, x.size),\n", + " np.full(x.size, noise),\n", + " label=label,\n", + " meta={\"Elab\": E},\n", + " )\n", + "\n", + "\n", + "datasets = [dataset(E, f\"E = {E:.0f}\") for E in energies]\n", + "held_out = dataset(E_new, f\"E = {E_new:.0f} (held out)\")\n", + "\n", + "E_grid = np.linspace(5.0, 65.0, 100)\n", + "A_grid = np.array([a_true(E) for E in E_grid])\n", + "fig, axes = plt.subplots(1, 3, figsize=(9, 2.8))\n", + "for k, ax in enumerate(axes):\n", + " ax.plot(E_grid, A_grid[:, k], \"k--\", label=\"truth\")\n", + " ax.plot(\n", + " energies,\n", + " [a_true(E)[k] for E in energies],\n", + " \"o\",\n", + " color=\"C0\",\n", + " label=\"fitted energies\",\n", + " )\n", + " ax.plot([E_new], [a_true(E_new)[k]], \"s\", color=\"C3\", label=\"held out\")\n", + " ax.set(xlabel=\"E\", title=f\"$a_{k}(E)$\")\n", + "axes[0].legend(frameon=False, fontsize=8)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c733c0fe", + "metadata": {}, + "source": [ + "## Three fits of the same data\n", + "\n", + "One `Model` per comparison, closing over that dataset's energy (recipe 35);\n", + "the coefficient objects are shared, so the problem has one column per\n", + "global parameter. The held-out comparison is part of every constraint but\n", + "fully masked: it contributes nothing to the likelihood, and any parameter\n", + "that only it uses is sampled from its prior.\n", + "\n", + "1. **The correct mapping**, `a_k(E; φ) = φ_k0 + φ_k1 E + φ_k2 exp(−((E − c_k)/w_k)²)`\n", + " with the bump centres and widths known: global `φ`.\n", + "2. **A misspecified smooth mapping**, `a_k(E; φ) = φ_k0 + φ_k1 E`: global\n", + " `φ`, no bumps.\n", + "3. **The misspecified mapping plus a hierarchy**: per-dataset deviations\n", + " `δ_j = τ ⊙ η_j` in parameter space, non-centred, `η_jk ~ N(0, 1)` and\n", + " `τ_k ~ HalfNormal`, so the spread of the deviations is learned (recipes\n", + " 22, 24). The held-out dataset gets its own `η_new`, driven by `τ`\n", + " alone." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2db35ad4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T04:19:03.559765Z", + "iopub.status.busy": "2026-09-11T04:19:03.559583Z", + "iopub.status.idle": "2026-09-11T04:19:03.602951Z", + "shell.execute_reply": "2026-09-11T04:19:03.602049Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1. correct mapping ndim = 9 active points = 84 of 96\n", + "2. misspecified ndim = 6 active points = 84 of 96\n", + "3. misspecified + hierarchy ndim = 33 active points = 84 of 96\n" + ] + } + ], + "source": [ + "centres, widths = (35.0, 25.0, 45.0), (8.0, 6.0, 7.0)\n", + "all_data = datasets + [held_out]\n", + "masks = [np.ones(x.size, dtype=bool)] * len(datasets) + [np.zeros(x.size, dtype=bool)]\n", + "\n", + "\n", + "def global_params(prefix, n_per):\n", + " return [\n", + " [\n", + " rx.Parameter(\n", + " f\"{prefix}{k}{i}\", prior=stats.norm(0.0, 1.0), latex=rf\"\\phi_{{{k}{i}}}\"\n", + " )\n", + " for i in range(n_per)\n", + " ]\n", + " for k in range(3)\n", + " ]\n", + "\n", + "\n", + "def build(mapping, phis, hierarchy=False):\n", + " flat = [p for ps in phis for p in ps]\n", + " taus = [\n", + " rx.Parameter(\n", + " f\"tau_{k}\",\n", + " prior=stats.halfnorm(scale=0.2),\n", + " bounds=(0.0, np.inf),\n", + " latex=rf\"\\tau_{k}\",\n", + " )\n", + " for k in range(3)\n", + " ]\n", + " comps = []\n", + " for j, d in enumerate(all_data):\n", + " E = d.meta[\"Elab\"]\n", + " if hierarchy:\n", + " etas = [\n", + " rx.Parameter(f\"eta_{j}_{k}\", prior=stats.norm(0.0, 1.0))\n", + " for k in range(3)\n", + " ]\n", + "\n", + " def fn(x, *v, E=E, n=len(flat)):\n", + " phi, tau, eta = v[:n], np.asarray(v[n : n + 3]), np.asarray(v[n + 3 :])\n", + " return quadratic(x, *(mapping(E, phi) + tau * eta))\n", + "\n", + " model = rx.Model(fn, [*flat, *taus, *etas])\n", + " else:\n", + "\n", + " def fn(x, *phi, E=E):\n", + " return quadratic(x, *mapping(E, phi))\n", + "\n", + " model = rx.Model(fn, flat)\n", + " comps.append(rx.Comparison(d, model))\n", + " c = rx.Constraint(comps, masks=masks)\n", + " return rx.Problem([c]), c\n", + "\n", + "\n", + "def correct_mapping(E, phi):\n", + " phi = np.reshape(phi, (3, 3))\n", + " return np.array(\n", + " [\n", + " phi[k, 0]\n", + " + phi[k, 1] * E / 50.0\n", + " + phi[k, 2] * np.exp(-(((E - centres[k]) / widths[k]) ** 2))\n", + " for k in range(3)\n", + " ]\n", + " )\n", + "\n", + "\n", + "def linear_mapping(E, phi):\n", + " phi = np.reshape(phi, (3, 2))\n", + " return np.array([phi[k, 0] + phi[k, 1] * E / 50.0 for k in range(3)])\n", + "\n", + "\n", + "cases = {\n", + " \"1. correct mapping\": build(correct_mapping, global_params(\"phi\", 3)),\n", + " \"2. misspecified\": build(linear_mapping, global_params(\"psi\", 2)),\n", + " \"3. misspecified + hierarchy\": build(\n", + " linear_mapping, global_params(\"chi\", 2), hierarchy=True\n", + " ),\n", + "}\n", + "for name, (p, c) in cases.items():\n", + " print(\n", + " f\"{name:28s} ndim = {p.ndim:2d} active points = {c.n_active} of {c.n_total}\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "27624215", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T04:19:03.604458Z", + "iopub.status.busy": "2026-09-11T04:19:03.604292Z", + "iopub.status.idle": "2026-09-11T04:34:18.108586Z", + "shell.execute_reply": "2026-09-11T04:34:18.107799Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log Z = 143.30 +/- 1.13, 109383 likelihood calls\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log Z = -408.02 +/- 0.99, 66925 likelihood calls\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log Z = 137.68 +/- 1.31, 284517 likelihood calls\n" + ] + } + ], + "source": [ + "samples = {\n", + " name: nested(p, seed=i + 1, nlive=100, dlogz=1.0)\n", + " for i, (name, (p, c)) in enumerate(cases.items())\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "f90e4df1", + "metadata": {}, + "source": [ + "## The coefficient mappings against the truth\n", + "\n", + "For each case, the posterior band of `a_k(E)` over the energy range. The\n", + "hierarchical case has two things to show: the global smooth mapping, and\n", + "the per-dataset values `a_k(E_j) + δ_jk` it actually used, which are what\n", + "track the bumps." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ed7e3d3b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T04:34:18.110289Z", + "iopub.status.busy": "2026-09-11T04:34:18.110100Z", + "iopub.status.idle": "2026-09-11T04:34:19.156736Z", + "shell.execute_reply": "2026-09-11T04:34:19.156079Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mappings = {\n", + " \"1. correct mapping\": correct_mapping,\n", + " \"2. misspecified\": linear_mapping,\n", + " \"3. misspecified + hierarchy\": linear_mapping,\n", + "}\n", + "colors = {\n", + " \"1. correct mapping\": \"C0\",\n", + " \"2. misspecified\": \"C3\",\n", + " \"3. misspecified + hierarchy\": \"C2\",\n", + "}\n", + "fig, axes = plt.subplots(1, 3, figsize=(10, 3))\n", + "for name, (p, c) in cases.items():\n", + " s = samples[name][::10]\n", + " n_phi = 9 if name.startswith(\"1\") else 6\n", + " curves = np.array([[mappings[name](E, row[:n_phi]) for E in E_grid] for row in s])\n", + " lo, hi = np.percentile(curves, [16, 84], axis=0)\n", + " for k, ax in enumerate(axes):\n", + " ax.fill_between(\n", + " E_grid, lo[:, k], hi[:, k], color=colors[name], alpha=0.3, label=name\n", + " )\n", + " if name.startswith(\"3\"):\n", + " tau_cols, eta_cols = (\n", + " p.columns([q for q in p.params if q.name.startswith(\"tau\")]),\n", + " None,\n", + " )\n", + " for j, E in enumerate(list(energies) + [E_new]):\n", + " cols_eta = p.columns(\n", + " [q for q in p.params if q.name.startswith(f\"eta_{j}_\")]\n", + " )\n", + " local = np.array(\n", + " [\n", + " linear_mapping(E, row[:6]) + row[tau_cols] * row[cols_eta]\n", + " for row in s\n", + " ]\n", + " )\n", + " for k, ax in enumerate(axes):\n", + " ax.errorbar(\n", + " [E],\n", + " [local[:, k].mean()],\n", + " [local[:, k].std()],\n", + " fmt=\"s\",\n", + " ms=4,\n", + " color=\"C2\",\n", + " mfc=\"w\" if j == len(energies) else \"C2\",\n", + " )\n", + "for k, ax in enumerate(axes):\n", + " ax.plot(E_grid, A_grid[:, k], \"k--\")\n", + " ax.axvline(E_new, color=\"0.6\", lw=0.8)\n", + " ax.set(xlabel=\"E\", title=f\"$a_{k}(E)$\")\n", + "axes[0].legend(frameon=False, fontsize=7)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5f9c8edd", + "metadata": {}, + "source": [ + "## Residuals of the misspecified fit\n", + "\n", + "Case 2 leaves structured, dataset-by-dataset residuals: the smooth mapping\n", + "is right on average and wrong at every energy." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "db767189", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T04:34:19.158326Z", + "iopub.status.busy": "2026-09-11T04:34:19.158144Z", + "iopub.status.idle": "2026-09-11T04:34:19.643392Z", + "shell.execute_reply": "2026-09-11T04:34:19.642483Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1100x450 with 8 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "p2, c2 = cases[\"2. misspecified\"]\n", + "ym2 = p2.predict(np.median(samples[\"2. misspecified\"], axis=0))[0]\n", + "fig, axes = plt.subplots(2, 4, figsize=(11, 4.5), sharex=True, sharey=True)\n", + "axes.ravel()[-1].axis(\"off\")\n", + "for ax, d, ym in zip(axes.ravel(), datasets, ym2):\n", + " ax.errorbar(d.x, (d.y - ym) / d.y_err, 1.0, fmt=\"o\", ms=3, color=\"C3\")\n", + " ax.axhline(0, color=\"k\", lw=0.8)\n", + " ax.set(title=d.label)\n", + "for ax in axes[-1]:\n", + " ax.set(xlabel=\"x\")\n", + "for ax in axes[:, 0]:\n", + " ax.set(ylabel=\"pull\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "424520bd", + "metadata": {}, + "source": [ + "## Coverage, in sample and at the held-out energy\n", + "\n", + "`predictive_draws` on the fitted problem gives the in-sample posterior\n", + "predictive; on `Problem([c.complement()])`, the same parameters and terms\n", + "with the held-out points active, it gives the prediction at the new energy,\n", + "with no extra code. For the hierarchical case that prediction includes the\n", + "prior-sampled `η_new` scaled by the learned `τ`: a scale mixture, wider and\n", + "longer-tailed than in sample." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "204cd5ae", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T04:34:19.645103Z", + "iopub.status.busy": "2026-09-11T04:34:19.644906Z", + "iopub.status.idle": "2026-09-11T04:34:21.440546Z", + "shell.execute_reply": "2026-09-11T04:34:21.439723Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 800x360 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "case held-out log predictive 68 % predictive width\n", + "1. correct mapping 24.8 0.062\n", + "2. misspecified -21.9 0.060\n", + "3. misspecified + hierarchy 20.4 0.286\n" + ] + } + ], + "source": [ + "levels = np.linspace(0.1, 0.9, 9)\n", + "fig, axes = plt.subplots(1, 2, figsize=(8, 3.6))\n", + "scores = {}\n", + "for name, (p, c) in cases.items():\n", + " s = samples[name]\n", + " held = rx.Problem([c.complement()])\n", + " draws_in = rx.diagnostics.predictive_draws(p, s[::10], n_rep=2, rng=1)\n", + " draws_out = rx.diagnostics.predictive_draws(held, s[::10], n_rep=2, rng=2)\n", + " ci, ch = p.constraints[0], held.constraints[0]\n", + " axes[0].plot(\n", + " levels,\n", + " rx.diagnostics.coverage_curve(draws_in, ci.y[ci.active], levels),\n", + " \"o-\",\n", + " color=colors[name],\n", + " label=name,\n", + " )\n", + " axes[1].plot(\n", + " levels,\n", + " rx.diagnostics.coverage_curve(draws_out, ch.y[ch.active], levels),\n", + " \"o-\",\n", + " color=colors[name],\n", + " label=name,\n", + " )\n", + " scores[name] = (\n", + " rx.diagnostics.log_posterior_predictive(\n", + " rx.diagnostics.heldout_log_predictive(held, s[::5])\n", + " ),\n", + " rx.diagnostics.sharpness(draws_out).mean(),\n", + " )\n", + "for ax, title in zip(axes, (\"in sample\", f\"held out, E = {E_new:.0f}\")):\n", + " ax.plot([0, 1], [0, 1], \"--\", color=\"0.5\")\n", + " ax.set(\n", + " xlabel=\"nominal coverage\",\n", + " ylabel=\"empirical coverage\",\n", + " title=title,\n", + " xlim=(0, 1),\n", + " ylim=(0, 1),\n", + " )\n", + "axes[0].legend(frameon=False, fontsize=8)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "print(f\"{'case':28s} held-out log predictive 68 % predictive width\")\n", + "for name, (lp, width) in scores.items():\n", + " print(f\"{name:28s} {lp:10.1f} {width:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "dfbb2f74", + "metadata": {}, + "source": [ + "## The learned spread\n", + "\n", + "The posterior of `τ` is away from zero in the hierarchical case: the data\n", + "asked for per-dataset deviations of a definite size, which is the\n", + "misspecification made visible." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7d4ae0a9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T04:34:21.442127Z", + "iopub.status.busy": "2026-09-11T04:34:21.441924Z", + "iopub.status.idle": "2026-09-11T04:34:21.908672Z", + "shell.execute_reply": "2026-09-11T04:34:21.908033Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 760x760 with 9 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "p3, _ = cases[\"3. misspecified + hierarchy\"]\n", + "s3 = samples[\"3. misspecified + hierarchy\"]\n", + "tau_params = [q for q in p3.params if q.name.startswith(\"tau\")]\n", + "fig = corner.corner(\n", + " s3[:, p3.columns(tau_params)],\n", + " labels=[f\"${q.latex}$\" for q in tau_params],\n", + " show_titles=True,\n", + " title_fmt=\".3f\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "12cc623b", + "metadata": {}, + "source": [ + "## Takeaways\n", + "\n", + "- The correct mapping covers in sample and out of sample. The misspecified\n", + " smooth mapping under-covers both and leaves structured residuals at every\n", + " energy.\n", + "- The hierarchy on the physics parameters recovers the coverage with wider,\n", + " longer-tailed bands at the new energy, a `τ` posterior away from zero,\n", + " and the better held-out score of the two misspecified fits.\n", + "- A parameter attached only to a fully masked comparison is sampled from\n", + " its prior. That is the whole mechanism for predicting a new dataset:\n", + " `complement()`, `predictive_draws` and `heldout_log_predictive` score\n", + " the new energy with no extra code.\n", + "- With few datasets the global `φ` and the deviations `δ_j` trade off, and\n", + " the hyperprior on `τ` is what resolves it." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 2841d6829fad19257400b43c90ccea4bdea81074 Mon Sep 17 00:00:00 2001 From: beykyle <kylebeyer1@gmail.com> Date: Fri, 11 Sep 2026 00:41:48 -0400 Subject: [PATCH 43/75] Add the alpha_ca_error_model_comparison notebook (recipes 10, 11, 13, 18) Oeschler et al.'s 44Ca(alpha,alpha) ratio to Rutherford at 29 MeV from the jitr quickstart, every fourth angle, a four-parameter Woods-Saxon potential with jitr's Coulomb and solver rules, and the L0/E0/L2y/Lgp ladder under dynesty: evidences made comparable across spaces by the Jacobian, compare_logz verdicts, the fit below 90 degrees scored on the backward angles by held-out log predictive and coverage, extrapolated bands, and the discrete ambiguity visible in the corner. Runs in about 20 minutes alongside another notebook. The design's notebook table now carries every runtime and the converged workflow allows an hour per notebook. --- .github/workflows/converged.yml | 2 +- README.md | 2 +- docs/examples | 1 + docs/groundup_design.md | 34 +- .../alpha_ca_error_model_comparison.ipynb | 657 ++++++++++++++++++ 5 files changed, 681 insertions(+), 15 deletions(-) create mode 120000 docs/examples create mode 100644 examples/alpha_ca_error_model_comparison.ipynb diff --git a/.github/workflows/converged.yml b/.github/workflows/converged.yml index d5609d0..2d0f813 100644 --- a/.github/workflows/converged.yml +++ b/.github/workflows/converged.yml @@ -36,7 +36,7 @@ jobs: - name: Run the notebooks run: | if [ -d examples ] && ls examples/*.ipynb >/dev/null 2>&1; then - python -m pytest -n 4 --nbmake --nbmake-timeout=2400 examples + python -m pytest -n 4 --nbmake --nbmake-timeout=3600 examples else echo "no notebooks yet" fi diff --git a/README.md b/README.md index e6ed6cd..457e0bd 100644 --- a/README.md +++ b/README.md @@ -33,6 +33,6 @@ Python ≥ 3.12; `jitr >= 3.0` from PyPI. python -m isort --check-only src test && python -m black --check src test && python -m ruff check src test python -m pytest # fast tier python -m pytest -m slow # converged tier: required on pushes and PRs to main -python -m pytest -n 4 --nbmake --nbmake-timeout=2400 examples # the notebooks, same workflow +python -m pytest -n 4 --nbmake --nbmake-timeout=3600 examples # the notebooks, same workflow sphinx-build -W docs docs/_build/html ``` diff --git a/docs/examples b/docs/examples new file mode 120000 index 0000000..a6573af --- /dev/null +++ b/docs/examples @@ -0,0 +1 @@ +../examples \ No newline at end of file diff --git a/docs/groundup_design.md b/docs/groundup_design.md index 6b6a1b0..490a146 100644 --- a/docs/groundup_design.md +++ b/docs/groundup_design.md @@ -884,21 +884,29 @@ deselects it by default (`addopts = -m "not slow"`, §9). Nine notebooks, each naming the current one it inherits. Every notebook is driven by emcee or dynesty. -| notebook | inherits | driver | new content | -|---|---|---|---| -| `linear_calibration` | linear_calibration_demo | emcee | prior predictive, posterior, predictive band with `problem.columns` | -| `error_models` | systematic_err_demo | emcee | the five-model ladder; two-constraint section with case B via shared `Parameter` | -| `normalization_and_covariance_structure` | normalization_inference | emcee | ρᵢ as `omp \| scale(rho_i)`; the four-case gallery via `matrix(theta)` | -| `correlated_observations` | correlated_observations | emcee | case A vs B, toy and n+⁴⁰Ca | -| `gp_discrepancy` | gp_discrepancy | emcee | `kernel` term; `total_predictive_band(problem, term, ...)`; the same defect fit with a sampled `omp + delta` mean correction for contrast | -| `robust_likelihoods` | robust_likelihoods | emcee | Student-t vs Gaussian; ν bounded on the `Parameter` | -| `measurement_to_calibration` | measurement_to_calibration + 30s_optical_potential_calibration + the tempering/coverage section of overconfidence | dynesty | `from_measurement`, `reported_terms`, the singular-covariance error, `Constraint(weight=)`, `coverage_curve` | -| `alpha_ca_error_model_comparison` | **new** (the `design.md` recipe table) | dynesty | log space, `Parameter(prior=)`, masks, `complement`, `heldout_log_predictive`, `logz_summary` / `compare_logz` with `log_jacobian`, shared noise (B) and coupled normalisation (A) across two datasets, the bbb shim shown but not run | -| `hierarchical_calibration` | **new** (recipes 24, 35, 38) | dynesty | hierarchy on the physics parameters; see below | +| notebook | inherits | driver | new content | runtime | +|---|---|---|---|---| +| `linear_calibration` | linear_calibration_demo | emcee | prior predictive, posterior, predictive band with `problem.columns`, the coverage curve | 23 s | +| `error_models` | systematic_err_demo | emcee | the ladder on one comparison, the Peelle matrix as a fixed `Term`, offsets known and free; two-constraint section with case B via a shared `Parameter` | 153 s | +| `normalization_and_covariance_structure` | normalization_inference | emcee | ρᵢ as `quartic \| tf.scale(rho_i)` against `reported_terms()`; the four-case gallery via `matrix(theta)` | 328 s | +| `correlated_observations` | correlated_observations | emcee | case A vs B on the toy; Neudecker et al. (2014) §II.A and §II.B recreated: the multi-quantity Peelle puzzle with a spanning `matrix` term built through `c.split` | 141 s | +| `gp_discrepancy` | gp_discrepancy | emcee (toy), dynesty (reaction) | `kernel` term; `total_predictive_band(problem, term, ...)`; the same defect fit with a sampled Legendre mean correction for contrast; n+⁴⁰Ca with the surface absorption missing | 617 s | +| `robust_likelihoods` | robust_likelihoods | emcee | Student-t vs Gaussian; ν bounded on the `Parameter`; a global error scale and a USU offset per technique | 154 s | +| `measurement_to_calibration` | measurement_to_calibration + 30s_optical_potential_calibration + the tempering/coverage section of overconfidence | dynesty | `from_measurement`, `reported_terms`, the singular-covariance error, `Constraint(weight=)`, `coverage_curve`, emcee and `dill` as other drivers, the KDUQ `model_error` spelling | 182 s | +| `alpha_ca_error_model_comparison` | **new** (the `jitr` quickstart's α+⁴⁴Ca data, EXFOR F0567) | dynesty | real data without errors, a four-parameter potential, log space with `log_jacobian`, the `L0`/`E0`/`L2y`/`Lgp` ladder by evidence, `masked_where`/`complement` with `heldout_log_predictive` and held-out coverage | 1197 s (alongside another notebook) | +| `hierarchical_calibration` | **new** (recipes 24, 35, 38) | dynesty | eight schools non-centred; the hierarchy on the physics parameters; see below | 937 s (alongside another notebook) | + +Runtimes are single-process wall times on an eight-core laptop with the +kernels run one at a time; the converged-tier workflow runs four at once +with a 40-minute timeout each. The reaction notebooks are driven by +dynesty because emcee mixes poorly on optical-model posteriors. **`hierarchical_calibration` in detail.** The truth is -`y = a0(E) + a1(E) x + a2(E) x²`, measured by J synthetic datasets at known -energies `E_j` (in `meta`) plus one held-out dataset at a new energy. The +`y = a0(E) + a1(E) x + a2(E) x²`, measured by J = 7 synthetic datasets at +known energies `E_j` (in `meta`) plus one held-out dataset at a new energy +bracketed by two fitted ones (a hierarchy learns the spread of deviations +it has seen; an unmodelled peak between its datasets is the few-datasets +caveat below, not a prediction it can make). The true coefficient mappings `a_k(E)` are a smooth trend plus non-monotonic bumps. Three fits of the same data: diff --git a/examples/alpha_ca_error_model_comparison.ipynb b/examples/alpha_ca_error_model_comparison.ipynb new file mode 100644 index 0000000..e7f7161 --- /dev/null +++ b/examples/alpha_ca_error_model_comparison.ipynb @@ -0,0 +1,657 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b2919a2f", + "metadata": {}, + "source": [ + "# Error models for α + ⁴⁴Ca elastic scattering: a comparison by evidence\n", + "\n", + "Real data without reported uncertainties, an optical potential, and a ladder\n", + "of error models compared by their Bayesian evidence and by how well a fit at\n", + "forward angles predicts the backward angles it never saw. This is the\n", + "shape of an error-model comparison study: the same comparison and the same\n", + "potential under every rung, only the covariance and the comparison space\n", + "change.\n", + "\n", + "The data are the ratio to Rutherford of ⁴⁴Ca(α,α) at 29 MeV,\n", + "[EXFOR F0567](https://www-nds.iaea.org/exfor/servlet/X4sSearch5?EntryID=150567),\n", + "[Oeschler *et al.*, Phys. Rev. Lett. **28**, 694 (1972)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.28.694),\n", + "as digitised for the [`jitr` quickstart](https://beykyle.github.io/jitr/getting-started.html),\n", + "whose reaction setup is followed here. Oeschler *et al.* report no\n", + "uncertainties, so every error model below infers its own.\n", + "\n", + "Recipes: 10, 11, 13, 18" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "391eeea7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T04:21:01.904388Z", + "iopub.status.busy": "2026-09-11T04:21:01.904243Z", + "iopub.status.idle": "2026-09-11T04:21:04.710214Z", + "shell.execute_reply": "2026-09-11T04:21:04.709449Z" + } + }, + "outputs": [], + "source": [ + "import corner\n", + "import dynesty\n", + "import jitr\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from jitr.optical_potentials.potential_forms import (\n", + " coulomb_charged_sphere,\n", + " woods_saxon_safe,\n", + ")\n", + "from scipy import stats\n", + "from sklearn.gaussian_process.kernels import Matern\n", + "\n", + "import rxmc as rx\n", + "from rxmc import terms as T\n", + "from rxmc import transforms as tf" + ] + }, + { + "cell_type": "markdown", + "id": "4f7ec3cb", + "metadata": {}, + "source": [ + "## The data\n", + "\n", + "Every fourth angle of the ⁴⁴Ca set, 73 points from 15° to 174°. Ratio to\n", + "Rutherford is dimensionless, so the dataset needs no unit conversion; the\n", + "kinematics the model needs go in `meta`, and there are no errors to report." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6df8e566", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T04:21:04.712686Z", + "iopub.status.busy": "2026-09-11T04:21:04.712203Z", + "iopub.status.idle": "2026-09-11T04:21:05.906639Z", + "shell.execute_reply": "2026-09-11T04:21:05.905836Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "73 points between 15 and 174 degrees\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = pd.read_csv(\"data/alpha_ca_ratio_ruth.csv\")\n", + "df = df[df[\"target\"] == \"Ca44\"].iloc[::4]\n", + "reaction = jitr.reactions.ElasticReaction(target=(44, 20), projectile=(4, 2))\n", + "E_lab = 29.0\n", + "meta = {\"reaction\": reaction, \"Elab\": E_lab, \"quantity\": \"dXS/dRuth\"}\n", + "angles = np.deg2rad(df[\"angle_cm_deg\"].to_numpy())\n", + "ratio = df[\"ratio_to_rutherford\"].to_numpy()\n", + "data = rx.Dataset(\n", + " angles, ratio, np.zeros(ratio.size), label=\"44Ca(a,a) 29 MeV\", meta=meta\n", + ")\n", + "print(\n", + " f\"{data.n} points between {np.rad2deg(angles.min()):.0f} and {np.rad2deg(angles.max()):.0f} degrees\"\n", + ")\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "ax.plot(np.rad2deg(angles), ratio, \"o\", ms=3, color=\"k\")\n", + "ax.set(\n", + " xlabel=r\"$\\theta_{cm}$ [deg]\",\n", + " ylabel=r\"$\\sigma / \\sigma_{Ruth}$\",\n", + " yscale=\"log\",\n", + " title=r\"$^{44}$Ca($\\alpha,\\alpha$) at 29 MeV, Oeschler et al. (1972)\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "24f54e00", + "metadata": {}, + "source": [ + "## The optical model\n", + "\n", + "As in the `jitr` quickstart: a Woods-Saxon potential and the Coulomb\n", + "potential of a uniformly charged sphere of radius `1.3 A^{1/3}` fm, radii in\n", + "units of `A^{1/3}` fm. Here the real and imaginary volume terms share one\n", + "geometry, so four parameters are free: `V`, `W`, `r`, `a`. Priors follow\n", + "the quickstart's typical α-nucleus values, truncated at zero. Thirty\n", + "partial waves are converged at this energy; the default basis size is\n", + "within a few percent of the fully converged solver at the deepest minima,\n", + "which is far below the error models below, and two hundred times faster." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "eeba4535", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T04:21:05.908284Z", + "iopub.status.busy": "2026-09-11T04:21:05.908114Z", + "iopub.status.idle": "2026-09-11T04:21:20.300225Z", + "shell.execute_reply": "2026-09-11T04:21:20.299611Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "A13 = 44 ** (1 / 3)\n", + "\n", + "\n", + "def central(r, V, W, R, a):\n", + " return -(V + 1j * W) * woods_saxon_safe(r, R * A13, a)\n", + "\n", + "\n", + "def coulomb(r):\n", + " return coulomb_charged_sphere(\n", + " r, reaction.target.Z * reaction.projectile.Z, 1.3 * A13\n", + " )\n", + "\n", + "\n", + "params = [\n", + " rx.Parameter(\"V\", prior=stats.norm(150.0, 20.0), bounds=(0.0, np.inf), latex=\"V\"),\n", + " rx.Parameter(\"W\", prior=stats.norm(20.0, 10.0), bounds=(0.0, np.inf), latex=\"W\"),\n", + " rx.Parameter(\"r\", prior=stats.norm(1.4, 0.3), bounds=(0.8, 2.0), latex=\"r\"),\n", + " rx.Parameter(\"a\", prior=stats.norm(0.5, 0.2), bounds=(0.2, 1.0), latex=\"a\"),\n", + "]\n", + "omp = rx.reactions.ElasticXS(\n", + " \"dXS/dRuth\",\n", + " central,\n", + " lambda r: 0.0 * r,\n", + " lambda ws, *x: (tuple(x), (), ()),\n", + " params,\n", + " coulomb=coulomb,\n", + " lmax=30,\n", + ")\n", + "theta_0 = np.array([150.0, 20.0, 1.4, 0.5])\n", + "on_data = omp.bind(angles, meta)\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "ax.plot(np.rad2deg(angles), ratio, \"o\", ms=3, color=\"k\", label=\"data\")\n", + "ax.plot(np.rad2deg(angles), on_data(*theta_0), color=\"C0\", label=r\"$\\theta_0$\")\n", + "ax.set(xlabel=r\"$\\theta_{cm}$ [deg]\", ylabel=r\"$\\sigma / \\sigma_{Ruth}$\", yscale=\"log\")\n", + "ax.legend(frameon=False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2a57fdcf", + "metadata": {}, + "source": [ + "## The error-model ladder\n", + "\n", + "Data spanning three decades with unreported, presumably multiplicative,\n", + "errors are compared in log space (recipe 10): `space=tf.log` transforms the\n", + "data once and every prediction, and `problem.log_jacobian()` is what makes a\n", + "log-space evidence comparable with a linear-space one. With no reported\n", + "errors every rung uses `statistical=False`: the inferred terms *are* the\n", + "covariance.\n", + "\n", + "| label | error model |\n", + "|---|---|\n", + "| `L0` | constant noise in log space, `σ = ε` on every point |\n", + "| `E0` | fractional noise in linear space, `σ_i = ε ym_i` |\n", + "| `L2y` | `L0` plus a free normalisation mode, `η ym` (the `jitr` calibration notebook's error model) |\n", + "| `Lgp` | `L0` plus a Matérn(5/2) Gaussian process in `u = θ/π` with a free amplitude |\n", + "\n", + "The same `log_eps` object is reused across rungs; each `Problem` compiles\n", + "independently (recipe 18). Priors (recipe 13): log-uniform on the error\n", + "scales between 5 % and 200 %, as in the `jitr` notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3e16d477", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T04:21:20.302274Z", + "iopub.status.busy": "2026-09-11T04:21:20.302093Z", + "iopub.status.idle": "2026-09-11T04:21:20.315028Z", + "shell.execute_reply": "2026-09-11T04:21:20.314320Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L0 columns: ['V', 'W', 'r', 'a', 'log_eps']\n", + "E0 columns: ['V', 'W', 'r', 'a', 'log_eps']\n", + "L2y columns: ['V', 'W', 'r', 'a', 'log_eps', 'log_eta']\n", + "Lgp columns: ['V', 'W', 'r', 'a', 'log_eps', 'log_ell', 'log_A']\n" + ] + } + ], + "source": [ + "comp_log = rx.Comparison(data, omp, space=tf.log)\n", + "comp_lin = rx.Comparison(data, omp)\n", + "log_eps = rx.Parameter(\n", + " \"log_eps\",\n", + " prior=stats.uniform(np.log(0.05), np.log(2.0) - np.log(0.05)),\n", + " latex=r\"\\log\\epsilon\",\n", + ")\n", + "log_eta = rx.Parameter(\n", + " \"log_eta\",\n", + " prior=stats.uniform(np.log(0.05), np.log(2.0) - np.log(0.05)),\n", + " latex=r\"\\log\\eta\",\n", + ")\n", + "log_amp = rx.Parameter(\n", + " \"log_A\",\n", + " prior=stats.uniform(np.log(0.05), np.log(2.0) - np.log(0.05)),\n", + " latex=r\"\\log A\",\n", + ")\n", + "log_ell = rx.Parameter(\n", + " \"log_ell\",\n", + " prior=stats.uniform(np.log(0.02), np.log(1.0) - np.log(0.02)),\n", + " latex=r\"\\log\\ell\",\n", + ")\n", + "gp = T.kernel(\n", + " Matern(0.1, nu=2.5),\n", + " on=comp_log,\n", + " coords=lambda x: x / np.pi,\n", + " amplitude=T.constant_amplitude,\n", + " amplitude_params=(log_amp,),\n", + " params=[log_ell],\n", + ")\n", + "ladder = {\n", + " \"L0\": rx.Constraint([comp_log], terms=[T.noise(log_eps)], statistical=False),\n", + " \"E0\": rx.Constraint(\n", + " [comp_lin], terms=[T.noise_fraction(log_eps)], statistical=False\n", + " ),\n", + " \"L2y\": rx.Constraint(\n", + " [comp_log],\n", + " terms=[T.noise(log_eps), T.normalization(log_eta)],\n", + " statistical=False,\n", + " ),\n", + " \"Lgp\": rx.Constraint([comp_log], terms=[T.noise(log_eps), gp], statistical=False),\n", + "}\n", + "problems = {name: rx.Problem([c]) for name, c in ladder.items()}\n", + "for name, p in problems.items():\n", + " print(f\"{name:4s} columns: {p.names}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6d7c9516", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T04:21:20.316599Z", + "iopub.status.busy": "2026-09-11T04:21:20.316425Z", + "iopub.status.idle": "2026-09-11T04:34:40.535909Z", + "shell.execute_reply": "2026-09-11T04:34:40.535170Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L0 log Z = -101.42 +/- 0.95 26153 calls\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "E0 log Z = 211.36 +/- 1.03 34980 calls\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L2y log Z = -98.22 +/- 0.85 22672 calls\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lgp log Z = -92.18 +/- 0.95 30383 calls\n" + ] + } + ], + "source": [ + "def nested(problem, seed, nlive=80, dlogz=1.5):\n", + " sampler = dynesty.NestedSampler(\n", + " problem.log_likelihood,\n", + " problem.prior_transform,\n", + " problem.ndim,\n", + " nlive=nlive,\n", + " sample=\"rwalk\",\n", + " rstate=np.random.default_rng(seed),\n", + " )\n", + " sampler.run_nested(dlogz=dlogz, print_progress=False)\n", + " return sampler.results\n", + "\n", + "\n", + "results, samples = {}, {}\n", + "for i, (name, p) in enumerate(problems.items()):\n", + " res = nested(p, seed=i)\n", + " results[name] = res\n", + " samples[name] = res.samples_equal(rstate=np.random.default_rng(i))\n", + " print(\n", + " f\"{name:4s} log Z = {res.logz[-1]:8.2f} +/- {res.logzerr[-1]:.2f} {int(np.sum(res.ncall)):6d} calls\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "d3e2685b", + "metadata": {}, + "source": [ + "## Evidence, comparable across spaces\n", + "\n", + "`logz_summary` records the sampler's error; adding `problem.log_jacobian()`\n", + "to a log-space evidence puts it in the same units as a linear-space one, so\n", + "`E0` can be compared with the others. `compare_logz` returns a verdict\n", + "that is a tie unless the difference exceeds twice the combined error." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3421f412", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T04:34:40.537531Z", + "iopub.status.busy": "2026-09-11T04:34:40.537355Z", + "iopub.status.idle": "2026-09-11T04:34:40.541878Z", + "shell.execute_reply": "2026-09-11T04:34:40.541310Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rung log Z (linear-space units)\n", + "L0 213.05 +/- 0.95\n", + "E0 211.36 +/- 1.03\n", + "L2y 216.26 +/- 0.85\n", + "Lgp 222.30 +/- 0.95\n", + "\n", + "best rung: Lgp\n", + "Lgp vs L0: dlogZ = 9.24 +/- 1.34 -> a\n", + "Lgp vs E0: dlogZ = 10.93 +/- 1.41 -> a\n", + "Lgp vs L2y: dlogZ = 6.04 +/- 1.28 -> a\n" + ] + } + ], + "source": [ + "logz = {\n", + " name: rx.diagnostics.logz_summary(\n", + " results[name].logz[-1] + problems[name].log_jacobian(),\n", + " results[name].logzerr[-1],\n", + " )\n", + " for name in problems\n", + "}\n", + "print(f\"{'rung':5s} {'log Z (linear-space units)':>28s}\")\n", + "for name, (mean, err, _) in logz.items():\n", + " print(f\"{name:5s} {mean:20.2f} +/- {err:.2f}\")\n", + "best = max(logz, key=lambda n: logz[n][0])\n", + "print(f\"\\nbest rung: {best}\")\n", + "for name in problems:\n", + " if name != best:\n", + " v = rx.diagnostics.compare_logz(logz[best], logz[name])\n", + " print(\n", + " f\"{best} vs {name}: dlogZ = {v['dlogZ']:6.2f} +/- {v['err']:.2f} -> {v['verdict']}\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "17484714", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T04:34:40.543262Z", + "iopub.status.busy": "2026-09-11T04:34:40.543122Z", + "iopub.status.idle": "2026-09-11T04:34:41.060471Z", + "shell.execute_reply": "2026-09-11T04:34:41.059851Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 970x970 with 16 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "labels = [f\"${q.latex}$\" for q in params]\n", + "fig = None\n", + "for name, color in ((\"L0\", \"C3\"), (\"L2y\", \"C1\"), (\"Lgp\", \"C2\")):\n", + " fig = corner.corner(\n", + " samples[name][:, problems[name].columns(params)],\n", + " fig=fig,\n", + " color=color,\n", + " labels=labels,\n", + " plot_datapoints=False,\n", + " plot_density=False,\n", + " levels=(0.68,),\n", + " range=[(100, 220), (0, 45), (1.1, 1.7), (0.3, 0.8)],\n", + " )\n", + "fig.legend(\n", + " handles=[\n", + " plt.Line2D([], [], color=c, label=n)\n", + " for n, c in ((\"L0\", \"C3\"), (\"L2y\", \"C1\"), (\"Lgp\", \"C2\"))\n", + " ],\n", + " loc=\"upper right\",\n", + " frameon=False,\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "9aace2f6", + "metadata": {}, + "source": [ + "## Hold out the backward angles (recipe 11)\n", + "\n", + "Fit each rung below 90° and score the points above it: `masked_where` keeps\n", + "every object, `complement()` flips the mask, and a chain from the fit scores\n", + "the held-out problem directly. The joint held-out log predictive is a\n", + "different question from the evidence: not \"which model explains the fitted\n", + "data\" but \"which model predicts what it has not seen\"." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a57881d2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T04:34:41.062006Z", + "iopub.status.busy": "2026-09-11T04:34:41.061836Z", + "iopub.status.idle": "2026-09-11T04:40:54.941696Z", + "shell.execute_reply": "2026-09-11T04:40:54.940518Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L0 held-out log predictive = -60.50 68 % coverage of the held-out points = 0.49\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L2y held-out log predictive = -55.80 68 % coverage of the held-out points = 0.74\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lgp held-out log predictive = -49.81 68 % coverage of the held-out points = 0.71\n" + ] + } + ], + "source": [ + "cut = np.deg2rad(90.0)\n", + "scores = {}\n", + "for i, (name, c) in enumerate(ladder.items()):\n", + " if name == \"E0\":\n", + " continue # same space as L0 up to the noise model; the log-space rungs are the comparison here\n", + " fit = c.masked_where(lambda x: x < cut)\n", + " p_fit, p_held = rx.Problem([fit]), rx.Problem([fit.complement()])\n", + " res = nested(p_fit, seed=10 + i)\n", + " s = res.samples_equal(rstate=np.random.default_rng(10 + i))\n", + " lp = rx.diagnostics.heldout_log_predictive(p_held, s[::5])\n", + " draws = rx.diagnostics.predictive_draws(p_held, s[::20], n_rep=2, rng=i)\n", + " h = p_held.constraints[0]\n", + " cov68 = rx.diagnostics.coverage_curve(draws, h.y[h.active], [0.68])[0]\n", + " scores[name] = (\n", + " rx.diagnostics.log_posterior_predictive(lp),\n", + " cov68,\n", + " s,\n", + " p_fit,\n", + " p_held,\n", + " )\n", + " print(\n", + " f\"{name:4s} held-out log predictive = {scores[name][0]:8.2f} 68 % coverage of the held-out points = {cov68:.2f}\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f2306531", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T04:40:54.943225Z", + "iopub.status.busy": "2026-09-11T04:40:54.943068Z", + "iopub.status.idle": "2026-09-11T04:40:55.885675Z", + "shell.execute_reply": "2026-09-11T04:40:55.884956Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 700x450 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_fine = np.deg2rad(np.linspace(10.0, 178.0, 120))\n", + "on_fine = omp.bind(x_fine, meta)\n", + "fig, ax = plt.subplots(figsize=(7, 4.5))\n", + "for name, color in ((\"L0\", \"C3\"), (\"L2y\", \"C1\"), (\"Lgp\", \"C2\")):\n", + " _, _, s, p_fit, _ = scores[name]\n", + " curves = np.array([on_fine(*row[p_fit.columns(params)]) for row in s[::25]])\n", + " lo, hi = np.percentile(curves, [5, 95], axis=0)\n", + " ax.fill_between(\n", + " np.rad2deg(x_fine),\n", + " lo,\n", + " hi,\n", + " color=color,\n", + " alpha=0.3,\n", + " label=f\"{name}, fit below 90°\",\n", + " )\n", + "ax.plot(np.rad2deg(angles), ratio, \"o\", ms=3, color=\"k\", label=\"data\")\n", + "ax.axvline(90.0, color=\"0.5\", ls=\"--\")\n", + "ax.set(\n", + " xlabel=r\"$\\theta_{cm}$ [deg]\",\n", + " ylabel=r\"$\\sigma / \\sigma_{Ruth}$\",\n", + " yscale=\"log\",\n", + " title=\"90 % bands of the potential, extrapolated past the cut\",\n", + ")\n", + "ax.legend(frameon=False, fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c8a9a524", + "metadata": {}, + "source": [ + "## Takeaways\n", + "\n", + "- One comparison, one potential, four covariances: the ladder is a\n", + " dictionary of constraints, and every problem compiles on its own.\n", + "- Log space is where multiplicative errors are additive; the Jacobian makes\n", + " its evidence comparable with a linear-space fit.\n", + "- Evidence and held-out prediction ask different questions. Here the GP\n", + " rung wins both, but the normalisation rung covers the held-out points\n", + " just as well with a far simpler covariance; a rung that explains the\n", + " forward angles best need not predict the backward ones.\n", + "- Light-ion potentials are famously ambiguous: several parameter families\n", + " give nearly the same cross section. The `L0` posterior above carries a\n", + " second family near `V ≈ 125` MeV beside the main one at `V ≈ 165` MeV,\n", + " and it is the prior, not the data, that decides how much weight each\n", + " gets (see the `jitr` calibration notebook on the discrete ambiguity)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From c582470c6755d1566b6430855add6d2111071d2e Mon Sep 17 00:00:00 2001 From: beykyle <kylebeyer1@gmail.com> Date: Fri, 11 Sep 2026 09:40:29 -0400 Subject: [PATCH 44/75] Port the 0.x regression pins to the 1.0 spelling The folded-in log-likelihood 1.195784087817536 of the 0.x default is recovered exactly by asking for comparison.reported_terms(); the 1.0 default is statistical only and differs; per-comparison normalisation modes match a dense reference on the block path, and one mode spanning two comparisons matches the dense rank-one form and changes the value. --- test/test_regression.py | 116 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 116 insertions(+) create mode 100644 test/test_regression.py diff --git a/test/test_regression.py b/test/test_regression.py new file mode 100644 index 0000000..634bae2 --- /dev/null +++ b/test/test_regression.py @@ -0,0 +1,116 @@ +"""Regression pins carried over from 0.x. + +The 0.x default covariance silently folded a dataset's reported +normalisation and offset systematics into the likelihood. The 1.0 default +is statistical only, and the systematics become terms when asked for +(``comparison.reported_terms()``). These pins record that the old number is +recovered exactly by asking, that the default differs, and that a mode +spanning two comparisons changes the likelihood the way a dense reference +says it should. +""" + +import numpy as np +import pytest +from scipy import stats + +from helpers import manual_mvn_loglike +from rxmc import Comparison, Constraint, Dataset, Model, Parameter, Problem +from rxmc import terms as T + +X = np.array([1.0, 2.0, 3.0, 4.0]) +Y = np.array([2.1, 3.9, 6.2, 7.8]) +STAT = np.array([0.1, 0.15, 0.2, 0.25]) +NORM, OFFSET = 0.05, 0.02 +THETA = np.array([0.2, 1.9]) # a0, a1 of the 0.x Polynomial(order=1) +PINNED = 1.195784087817536 + + +def line(): + a0 = Parameter("a0", prior=stats.norm(0, 10)) + a1 = Parameter("a1", prior=stats.norm(0, 10)) + return Model(lambda x, a0, a1: a0 + a1 * x, [a0, a1]) + + +def old_covariance(ym): + ones = np.ones_like(ym) + return ( + np.diag(STAT**2) + OFFSET**2 * np.outer(ones, ones) + NORM**2 * np.outer(ym, ym) + ) + + +class TestSystematicDefaultBehaviourChange: + def setup_method(self): + self.d = Dataset(X, Y, STAT, norm_err=NORM, offset_err=OFFSET, label="pin") + self.comp = Comparison(self.d, line()) + self.ym = THETA[0] + THETA[1] * X + + def test_old_value(self): + assert manual_mvn_loglike(Y, self.ym, old_covariance(self.ym)) == pytest.approx( + PINNED, abs=1e-9 + ) + + def test_new_default_is_statistical_only_and_differs(self): + p = Problem([Constraint([self.comp])]) + assert p.log_likelihood(THETA) == pytest.approx( + manual_mvn_loglike(Y, self.ym, np.diag(STAT**2)) + ) + assert p.log_likelihood(THETA) != pytest.approx(PINNED) + + def test_reported_terms_recover_old_value(self): + p = Problem([Constraint([self.comp], terms=self.comp.reported_terms())]) + assert p.log_likelihood(THETA) == pytest.approx(PINNED, abs=1e-9) + + +class TestSpanningMode: + """A normalisation mode across two comparisons equals the dense reference.""" + + def setup_method(self): + self.model = line() + self.d1 = Dataset(X, Y, STAT, label="one") + self.d2 = Dataset(X + 4.0, Y + 7.6, STAT, label="two") + self.comps = [Comparison(self.d1, self.model), Comparison(self.d2, self.model)] + + def test_independent_blocks_match_a_dense_cholesky(self): + p = Problem( + [ + Constraint( + self.comps, + terms=[T.normalization(magnitude=NORM, on=c) for c in self.comps], + ) + ] + ) + ym = np.concatenate([THETA[0] + THETA[1] * X, THETA[0] + THETA[1] * (X + 4.0)]) + y = np.concatenate([Y, Y + 7.6]) + S = np.diag(np.tile(STAT, 2) ** 2) + S[:4, :4] += NORM**2 * np.outer(ym[:4], ym[:4]) + S[4:, 4:] += NORM**2 * np.outer(ym[4:], ym[4:]) + assert p.log_likelihood(THETA) == pytest.approx(manual_mvn_loglike(y, ym, S)) + assert not p.constraints[0].covariance.dense + + def test_a_mode_spanning_both_changes_the_likelihood(self): + p_each = Problem( + [ + Constraint( + self.comps, + terms=[T.normalization(magnitude=NORM, on=c) for c in self.comps], + ) + ] + ) + p_span = Problem( + [ + Constraint( + self.comps, terms=[T.normalization(magnitude=NORM, on=self.comps)] + ) + ] + ) + S = p_span.constraints[0].matrix(THETA) + assert np.all(S[:4, 4:] != 0.0) + ym = np.concatenate([THETA[0] + THETA[1] * X, THETA[0] + THETA[1] * (X + 4.0)]) + y = np.concatenate([Y, Y + 7.6]) + dense = np.diag(np.tile(STAT, 2) ** 2) + NORM**2 * np.outer(ym, ym) + assert p_span.log_likelihood(THETA) == pytest.approx( + manual_mvn_loglike(y, ym, dense) + ) + assert p_span.log_likelihood(THETA) != pytest.approx( + p_each.log_likelihood(THETA) + ) From 9def154b8d4cea71c2e887b3eea0d54c224cf56f Mon Sep 17 00:00:00 2001 From: beykyle <kylebeyer1@gmail.com> Date: Fri, 11 Sep 2026 09:48:08 -0400 Subject: [PATCH 45/75] Write design.md from the plan of record and add the API reference docs/design.md describes the library as it is: the maintainer's rules, now twelve (a spanning term sees the gathered stack; a prediction-built covariance carries the log-determinant pull), the API module by module with the signatures in the source, how the pieces thread, the compile step and the structured covariance, the alpha + Ca ladder as the worked example, the capability map with recipe numbers, the two testing tiers and the index tests, the notebooks with runtimes, layout and dependencies, open questions, and the release path with the milestone history. docs/api.rst is the autosummary reference; index and installation pages lose the rewrite banner; groundup_design.md stays in the tree as an orphan. Three docstrings are reworded so sphinx -W passes. --- docs/api.rst | 137 ++++++++ docs/design.md | 700 ++++++++++++++++++++++++++++++++++++++++ docs/groundup_design.md | 4 + docs/index.rst | 23 +- docs/installation.rst | 19 +- src/rxmc/model.py | 5 +- src/rxmc/terms.py | 2 +- src/rxmc/transforms.py | 2 +- 8 files changed, 875 insertions(+), 17 deletions(-) create mode 100644 docs/api.rst create mode 100644 docs/design.md diff --git a/docs/api.rst b/docs/api.rst new file mode 100644 index 0000000..226606f --- /dev/null +++ b/docs/api.rst @@ -0,0 +1,137 @@ +API reference +============= + +The public surface, in the order the design document introduces it. Every +name below is importable from ``rxmc`` or the module shown. + +Building blocks +--------------- + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + rxmc.params.Parameter + rxmc.data.Dataset + rxmc.data.from_measurement + rxmc.model.Model + rxmc.model.Predictor + rxmc.model.polynomial + rxmc.constraint.Comparison + rxmc.constraint.Constraint + rxmc.problem.Problem + +Transforms +---------- + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + rxmc.transforms.Transform + rxmc.transforms.as_transform + rxmc.transforms.identity + rxmc.transforms.log + rxmc.transforms.exp + rxmc.transforms.scale + +Covariance terms +---------------- + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + rxmc.terms.Term + rxmc.terms.TermContext + rxmc.terms.KernelTerm + rxmc.terms.statistical + rxmc.terms.offset + rxmc.terms.normalization + rxmc.terms.noise + rxmc.terms.noise_fraction + rxmc.terms.model_error + rxmc.terms.systematic + rxmc.terms.kernel + rxmc.terms.ones + rxmc.terms.ym + rxmc.terms.averaging + rxmc.terms.x_basis + rxmc.terms.exp_growth + rxmc.terms.constant_amplitude + rxmc.terms.exp_growth_amplitude + +Likelihood functionals +---------------------- + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + rxmc.likelihood.Likelihood + rxmc.likelihood.Gaussian + rxmc.likelihood.StudentT + rxmc.likelihood.Chi2 + +The structured covariance +------------------------- + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + rxmc.covariance.StructuredCovariance + rxmc.covariance.chol_logdet + rxmc.problem.CompiledConstraint + rxmc.problem.ParameterIndex + +Diagnostics +----------- + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + rxmc.diagnostics.predictive_draws + rxmc.diagnostics.coverage_curve + rxmc.diagnostics.coverage_error + rxmc.diagnostics.sharpness + rxmc.diagnostics.heldout_log_predictive + rxmc.diagnostics.log_posterior_predictive + rxmc.diagnostics.logz_summary + rxmc.diagnostics.compare_logz + +Predictive +---------- + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + rxmc.predictive.gp_posterior_predictive + rxmc.predictive.predictive_band + rxmc.predictive.total_predictive_band + +Reactions +--------- + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + rxmc.reactions.elastic.ElasticXS + rxmc.reactions.ias.IsobaricAnalogPN + rxmc.reactions.elastic.rutherford + rxmc.reactions.elastic.momentum_transfer + rxmc.reactions.elastic.set_up_solver + rxmc.reactions.ias.set_up_solver + +Units +----- + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + rxmc.units.parse_unit + rxmc.units.check_angle_grid diff --git a/docs/design.md b/docs/design.md new file mode 100644 index 0000000..c01865d --- /dev/null +++ b/docs/design.md @@ -0,0 +1,700 @@ +# Design: declare, then compile + +`rxmc` calibrates reaction models to experimental data by Bayesian +inference, with the error model declared as part of the problem. Every +statistical choice a study makes, which errors are statistical and which +are correlated, whether a normalisation is inferred or marginalised, +whether the model is allowed a discrepancy, which points are held out, +appears in the declaration, so that a reviewer can read the declaration and +write down the likelihood. The library owns no sampler: a compiled +`Problem` exposes the densities and the prior transform that emcee, dynesty +or `black-box-bayes` need. + +This document is the maintainer's description of the library as it is. +Read it with `recipes.md`, which states every supported use case with its +spelling and expected behaviour, one test per recipe, and with the +notebooks in `examples/`, which are the tutorials for the recipes. The +plan the rewrite was executed from, including what was harvested from 0.x +and the milestone history, is `groundup_design.md`. + +The mechanics rest on one rule: + +> **Everything the user constructs is an immutable declaration. +> `Problem` is the only compile step.** + +## 1. Rules for the maintainer + +The review checklist for every change. + +1. **Specs hold no caches and no solver state.** `Parameter`, `Dataset`, + `Term`, `Comparison` and `Constraint` are frozen dataclasses with + `eq=False`; identity is the equality, for every spec. `Model` is a + plain class with the same contract. Anything expensive or grid-dependent + lives in the objects `Problem` builds, or in a cache a reaction model + drops on pickling. +2. **Exactly one function walks the parameter graph:** `Problem.__init__`. + Nothing else assigns slots, checks names, resolves supports or decides + which prior covers which slot. +3. **Gather, never split.** Every callable node receives one integer + gather array at compile time and is evaluated as `node(*theta[gather])`. + There is no parameter count to carry around and no chain slicing by + position; `problem.columns(params)` is how a caller finds a column. +4. **Every user-supplied callable has one shape:** `fn(context, *values)`, + where `context` is the grid `x` (models, transforms) or a `TermContext` + (terms, bases, amplitudes) and `values` are the sampled values of the + parameters the node declares, in declaration order. +5. **Sharing is spelled by passing the same `Parameter` object.** Inside a + term, between terms, between a model and a term, across constraints. + There is no second identity notion and no value equality anywhere. +6. **One factorisation path.** `StructuredCovariance` is the only way a + covariance is factored. The dense matrix exists as a display method and + as the reference in tests. +7. **Fail at compile, and name the dataset.** A singular constant + covariance, duplicate parameter names, a slot no prior covers, an `on=` + that references a comparison outside its constraint, a non-finite value + in comparison space: all raised by `Problem`, never mid-chain. +8. **Nothing is folded into a covariance silently.** A dataset's reported + systematics become terms only when asked, through + `comparison.reported_terms()`. The 0.x default that folded them in is + pinned as a *difference* in `test/test_regression.py`. +9. **A term on several comparisons sees the gathered stack.** Its + `TermContext` carries `segments`, `labels` and `split()` so that a basis + which differentiates or smooths along the grid stays within one + comparison. Nothing is ever split for the term. +10. **A covariance that reads the prediction is the generative model's + marginal likelihood.** Its log-determinant pulls the posterior mode + toward smaller predictions, and a flat prior on a prediction-scaled + covariance has a `1/rho` tail. That is a property of the model, not a + bug; the Peelle-safe *evaluation* is the estimate-built refit (recipes + 27 and 37 state both, with numbers). +11. **A `Problem` pickles with `dill`.** `black-box-bayes` ships it to + every MPI rank by path. A round trip of a reaction problem is in the + suite. +12. **Correctness lives in tests that pin numbers**, not in defensive + branches: the closed-form Student-t, delta-method errors under `log`, + the regression log-likelihood `1.195784087817536`, dense-versus- + structured equality on every error-model form, and the closed forms of + the reference papers. Every recipe in `recipes.md` is a test under + `test/recipes/` (`test/test_recipes_index.py` enforces the mapping), + and every notebook names the recipes it teaches + (`test/test_notebooks_index.py`). + +## 2. The API, module by module + +Modules in dependency order. Signatures are the ones in the source; the +docstrings carry the details. + +### 2.1 `params.py` + +```python +@dataclass(eq=False, frozen=True) +class Parameter: + name: str + bounds: tuple[float, float] = (-inf, inf) + prior: object | None = None # frozen scipy univariate: logpdf, cdf, ppf, rvs + unit: str = "" + latex: str | None = None + label -> str # latex, falling back to name +``` + +A parameter *is* its object; it keys dictionaries and is matched by +identity everywhere. It may carry its own marginal prior. The rules, +enforced once at compile: + +| declared | prior used | +|---|---| +| `prior=dist` | `dist` truncated to `bounds` (log-density `-inf` outside; `ppf` rescaled between `cdf(lo)` and `cdf(hi)`) | +| finite `bounds`, no `prior` | uniform on `bounds` | +| neither | must be covered by a joint prior given to `Problem`, else a compile error naming the parameter | + +### 2.2 `transforms.py` + +```python +class Transform: # fn(a, *values) -> array; params; derivative; inverse + def __call__(self, a, *values) + def derivative(self, a, *values) + def __or__(self, other) # (f | g)(a) = g(f(a)); params f + g + n_params, is_identity, inverse + +identity, log, exp # parameter-free; identity.inverse is identity, log/exp are inverses +def scale(parameter=None, log=True, name=None) -> Transform # rho * a; default Parameter("log_rho") +def as_transform(t) -> Transform # None -> identity; callable -> parameter-free Transform +``` + +One type, three roles: the comparison space of a `Comparison`, a mean +transform composed onto a `Model`, and the coordinates a `Term` is +evaluated in. `is_identity` is an object-identity test on the singleton, +the only fast path. + +### 2.3 `units.py` and `data.py` + +```python +# units.py +XS_UNIT = "b/sr"; RUTHERFORD_UNIT = "mb/sr"; MB_PER_B = 1000.0; DEFAULT_LMAX = 20 +def parse_unit(label) -> (factor, kind) # the exfor_tools / x4i3 label vocabulary; kind is + # "differential" or "dimensionless"; anything else raises +def check_angle_grid(angles_rad, name) +``` + +```python +# data.py +@dataclass(eq=False, frozen=True) +class Dataset: + x: ndarray # radians for reaction data + y: ndarray # physical units + y_err: ndarray # statistical, physical units; zeros allowed + norm_err: float | ndarray | None = None # reported fractional normalisation, inert + offset_err: float | ndarray | None = None # reported absolute offset, physical units, inert + label: str = "" + meta: Mapping = {} # reaction, Elab, ExIAS, quantity, k, eta, ... + n -> int + +def from_measurement(measurement, *, reaction=None, quantity=None, ExIAS=None) -> Dataset +``` + +`Dataset` is pure data: no comparison transform, no mask, no solver +workspace. `from_measurement` is the one EXFOR adapter. It reads the +`exfor_tools.Distribution` fields (`x, y, Einc, quantity, y_units, +statistical_err, systematic_norm_err, systematic_offset_err, subentry`) from +any object shaped like one, converts angles to radians and the cross +section through `parse_unit`, scales every dimensionful error with the data +and leaves the fractional normalisation error alone, and fills `meta` with +the kinematics. `dXS/dA` and `dXS/dRuth` convert into each other through +the closed-form Rutherford cross section of the kinematics, which makes the +conversion a per-angle factor; a neutron projectile or a missing reaction +is a named error. `rxmc` never imports `exfor_tools`. + +### 2.4 `model.py` + +```python +class Model: # fn(x, *values) -> y in physical space + params: tuple[Parameter, ...] + def bind(self, x, meta=None) -> Predictor # generic: closes over x; reaction models override + def __or__(self, transform) -> Model # mean transform; its params appended + def __add__(self, other) -> Model # additive mean discrepancy; params left then right + def __mul__(self, other) -> Model # multiplicative correction; scale() is its constant case + +class Predictor: # a model bound to a grid + params, x + def __call__(self, *values) -> ndarray + +def polynomial(order) -> Model # a_0 + a_1 x + ...; params a0..an, no priors +``` + +- `omp | scale(rho_1)` is a model with parameters `omp.params + (rho_1,)`; + per-dataset Kennedy and O'Hagan scales are distinct `rho_i` objects on + distinct comparisons. +- `omp + delta` with `delta = Model(fn, phi)` is the explicit, sampled mean + discrepancy; `omp * g` the multiplicative one (an additive discrepancy in + log space). Both bind each side to the same grid and combine the + predictors, so a reaction model and a plain function combine without + special cases. Composition is left to right: `(omp + delta) | scale(rho)` + scales the sum. +- Plotting on a fine grid is `model.bind(x_fine, d.meta)(*row[problem.columns(model.params)])`. + +### 2.5 `terms.py` + +```python +@dataclass(eq=False, frozen=True) +class TermContext: + x: ndarray # coords(x) on the support + y: ndarray # data on the support, comparison space + ym: ndarray | None # prediction on the support; None while a constant term is evaluated + __len__ + meta(key) -> ndarray # the owning dataset's meta[key], one value per point + segments -> tuple[slice, ...] # rows of each spanned comparison within the gathered support + labels -> tuple[str, ...] # their labels, in the same order + split(a) -> list # a[s] for s in segments + +@dataclass(eq=False, frozen=True) +class Term: + fn: Callable | ndarray # fn(c: TermContext, *values) -> vector | matrix + params: tuple[Parameter, ...] = () + kind: "diag" | "mode" | "matrix" = "matrix" + on: Comparison | Dataset | sequence | None = None # None = whole constraint + coords: Transform = identity # applied to x before fn sees it; its params appended + constant: bool = False # fn reads neither ym nor parameters; evaluated once + +@dataclass(eq=False, frozen=True) +class KernelTerm(Term): # what kernel() returns + kernel, n_kernel, amplitude, jitter +``` + +| `kind` | `fn` returns | contribution | +|---|---|---| +| `"diag"` | standard-deviation vector `v` | `Σ_ii += v_i²` | +| `"mode"` | vector `v` | `Σ += v vᵀ` | +| `"matrix"` | symmetric block `M` | `Σ_block += M` | + +Support is a reference, not integer indices: `on=comp` places the term on +that comparison, `on=[c1, c2]` spans both (case A), `on=None` is the whole +constraint; a `Dataset` also resolves. A term is stateless and may sit in +two constraints. An array-valued `fn` is a fixed contribution checked for +shape and symmetry at construction. + +The factories: + +```python +statistical(y_err, on=None) +offset(parameter=None, magnitude=None, mask=None, log=True, on=None) +normalization(parameter=None, magnitude=None, mask=None, log=True, on=None) +noise(parameter, log=True, basis=None, basis_params=(), on=None, coords=None) +noise_fraction(parameter, log=True, on=None) +model_error(parameter, averaging=True, log=True, on=None) +systematic(parameter, basis, log=True, basis_params=(), on=None, coords=None) +kernel(kernel, coords=None, amplitude=None, amplitude_params=(), jitter=1e-10, + prefix="discrepancy", params=None, on=None) -> KernelTerm +# bases and amplitudes: ones, ym, averaging, x_basis(scale), exp_growth(scale, base=ones), +# constant_amplitude, exp_growth_amplitude(scale) +``` + +`noise` and `noise_fraction` are additive on top of the reported diagonal; +`Constraint(statistical=False)` makes them replace it. `normalization` +reads `c.ym`, never `c.y`. `kernel` derives one `Parameter` per free +hyperparameter element in sklearn's log-theta space, bounded by the log of +the kernel's bounds so it compiles with a uniform prior there; `params=` +passes the objects instead, which is also how hyperparameters are shared +between per-comparison kernels. Two kernel terms with derived names and +one prefix fail compile on the duplicate name. + +Hierarchy is sharing plus `meta`: a hyperparameter shared by several +datasets is one object placed in one term per comparison, and anything +dataset-specific the term needs comes from `c.meta(key)`. A discrepancy +correlated *across* datasets is one `matrix` term spanning them that builds +its inputs from `c.meta("Elab")` and `c.x`; it takes the dense path. + +### 2.6 `likelihood.py` + +```python +class Likelihood: # functional of (d2, logdet, n, *values); params are ordinary nodes + def log_likelihood(self, d2, logdet, n, *values) + def chi2(self, d2, logdet, n, *values) # d2 +class Gaussian(Likelihood) +class StudentT(Likelihood) # StudentT(nu=None) -> Parameter("nu", bounds=(1, inf)); pass nu= to share or rename +class Chi2(Likelihood) # -d2/2, no log-determinant +``` + +All three are exported from the package (`rx.Gaussian`, `rx.StudentT`, +`rx.Chi2`). + +### 2.7 `constraint.py` + +```python +@dataclass(eq=False, frozen=True) +class Comparison: # one dataset, one model, one comparison space + data: Dataset + model: Model # bound at construction: model.bind(data.x, data.meta) + space: Transform = identity # parameter-free + predictor, y, y_err, log_jac # derived once: space(data.y), delta-method errors, per-point Jacobian + n; predict(*values); log_jacobian(mask=None) + def reported_terms(self) -> list[Term] # offset then normalisation modes from the dataset's + # reported errors, delta-method propagated, on=self + +@dataclass(eq=False, frozen=True) +class Constraint: # the maximal block of mutually correlated data: one likelihood + comparisons: tuple[Comparison, ...] + terms: tuple[Term, ...] = () + likelihood: Likelihood = Gaussian() + weight: float = 1.0 # tempering; multiplies this constraint's log-likelihood only + statistical: bool = True # add each comparison's y_err diagonal + masks: tuple[ndarray, ...] | None = None + offsets, active, n_total, n_active, log_jacobian, support(on) + def masked(self, masks); def masked_where(self, predicate); def complement(self) +``` + +The comparison space lives on the comparison because it is a modelling +choice. Masks live on the constraint: `masked`, `masked_where` and +`complement` return a `Constraint` with the same `Comparison`, `Term` and +`Parameter` objects and new masks, so a held-out problem built from +`complement()` shares every parameter with the fit and a posterior sample +scores it directly. `weight` is the one tempering knob. Eager checks +here need nothing from the parameter graph: distinct comparisons, an +array-valued term of the right shape for its `on`, a non-finite comparison +space caught at compile with the comparison's label. + +### 2.8 How the pieces thread + +There are exactly two parametric entry points; everything else is fixed +when the comparison is built. + +| stage | space | what enters | parametric | +|---|---|---|---| +| 1. `Predictor` | physical | `f(x; θ)` on the data grid | model parameters | +| 2. mean modifications, composed on the `Model` | physical | `\| scale(ρ)`, `* g(x; φ)`, `+ δ(x; φ)` | ρ, φ | +| 3. `space` | physical → comparison | `y = space(data.y)`, `ym = space(step 2)`, `y_err = \|space'\| · data.y_err`, `log_jacobian` | none | +| 4. `Term`s, seeing `TermContext(x, y, ym)` | comparison | statistical diagonal; experimental terms (noise, reported modes, USU); model-discrepancy terms (kernel, bases) | term parameters, and `ym` | +| 5. `Likelihood` | comparison | functional of `y − ym` and Σ | Student-t ν only | + +Experimental and model-discrepancy terms are one mechanism; the difference +is what the user means. A term that reads `ym` sees the prediction after +the mean modifications and after `space`, so a reported normalisation +error applies to the measured scale and `reported_terms()` gets that for +free. Mean-side discrepancy is physical-space and `x`-aware; covariance- +side discrepancy is comparison-space and `ym`-aware. The normalisation +stays on the mean rather than dividing the data: dividing would make +`space` parametric, and in linear space a data-side normalisation is what +Peelle's Pertinent Puzzle warns about (recipe 27). + +### 2.9 `problem.py`: the compile step + +```python +class Problem: + def __init__(self, constraints, priors=()): # priors: [(params, joint), ...] + index: ParameterIndex; constraints: tuple[CompiledConstraint, ...]; priors + ndim, names, bounds, params + def columns(self, params) -> ndarray + def log_prior / log_likelihood / log_posterior / chi2 (theta) + def prior_transform(self, u); def sample_prior(self, n, rng=None) + def predict(self, theta, physical=False) -> list[list[ndarray]] # per constraint, per comparison + def log_jacobian(self) -> float + NDIM, parameter_names, starting_location(n), log_posterior_batch(thetas) # black-box-bayes spellings +``` + +`Problem.__init__` is the only place that walks the graph. For each +constraint it stacks the comparisons (comparison space, one slice each), +registers every predictor's, term's and likelihood's parameters in +first-seen order through `index.add_all`, which returns the gather array +for that node, resolves each term's `on` to rows, and builds the +`StructuredCovariance`, whose constant parts are factored eagerly so a +singular block is reported with its label. Parameters that only a joint +prior block mentions, a hyperprior's hyperparameter, get slots after every +constraint. Names are then checked unique and the prior assembled. +Compile the same declarations twice and you get two independent problems. + +**Prior assembly.** Each slot is covered by its parameter's marginal or by +exactly one joint block `(params, joint)`, where `joint` has +`logpdf(values)` over those parameters in that order and optionally +`prior_transform(u)` and `rvs(n)`; `scipy.stats.multivariate_normal` +qualifies and is whitened for the unit-cube map. A hyperprior is a joint +block that includes its hyperparameter, whose `logpdf` is +`sum log p(child | hyper) + log p(hyper)` and whose `prior_transform` draws +the hyperparameter first (recipe 24). A slot no prior covers, or covered +twice, is a compile error naming it. + +**`CompiledConstraint`** holds the stacked `x`, `y`, `y_err`, the offsets +and active rows, the predictors with their gathers, the covariance, the +likelihood and its gather, the weight and the Jacobian; it exposes +`ym(theta)`, `log_likelihood`, `chi2` and `matrix(theta)`, the dense +covariance on the active rows for display and tests. + +### 2.10 `covariance.py`: the structured covariance + +The three term kinds are a decomposition: + +``` +Σ = diag(D) + blockdiag(M_b) + U Uᵀ (+ dense fallback) +``` + +`D` is the sum of squares of every `diag` term; `M_b` one dense block per +comparison from the `matrix` terms inside it; `U` has one column per +`mode` term scattered onto its support, a mode spanning comparisons being +a column with entries in several blocks. A `matrix` term that crosses +comparisons forces the dense path for that constraint. With +`B = blockdiag(M_b + diag(D_b))` and per-block Cholesky factors, + +``` +z = L⁻¹ r, W = L⁻¹ U, S = I + WᵀW +d2 = zᵀz − (Wᵀz)ᵀ S⁻¹ (Wᵀz), logdet Σ = Σ_b logdet B_b + logdet S +``` + +at cost `O(Σ_b n_b³ + N r² + r³)`. Masking slices rows before assembly. +Constant parts are evaluated once at compile; a constraint whose +covariance is entirely constant caches its factors. `B` must be positive +definite: a comparison covered only by modes is singular in `B` even when +`Σ` is not, and compile says so with the label and the remedies. A +constraint boundary is about the likelihood functional and the weight, not +about cost. + +```python +class StructuredCovariance: + def distance(self, ym, theta=()) -> (d2, logdet) # active rows + def matrix(self, ym, theta=()) -> ndarray # dense, active rows + dense: bool # whether a cross-comparison matrix term forced it +``` + +### 2.11 `diagnostics.py` and `predictive.py` + +Everything takes `(problem, samples)` with `samples` of shape +`(n, problem.ndim)` in `problem.names` order, which is what emcee's +`get_chain(flat=True)`, dynesty's `samples_equal()` and `black-box-bayes` +give. + +```python +predictive_draws(problem, samples, constraint=0, *, n_rep=1, rng=None, model_only=False, given=None) +coverage_curve(draws, y, levels=None); coverage_error(draws, y, levels=None) +sharpness(draws, percentiles=(16, 84), transform=None) +heldout_log_predictive(heldout_problem, samples, *, given=None) +log_posterior_predictive(logp_samples, logw=None) +logz_summary(logz, logzerr); compare_logz(a, b, sigma=2.0) + +gp_posterior_predictive(kernel, theta, X_train, residuals, X_pred, *, train_noise_var=None, jitter=1e-10) +predictive_band(draws, levels=(16, 50, 84)) +total_predictive_band(problem, term, predictor, x_pred, samples, *, noise_std=0.0, + train_noise_var=None, levels=(16, 84), n_draws=400, rng=None, physical=False) +``` + +A held-out problem built from `complement()` has the *marginal* +covariance of its rows, which is the right density when no term spans the +split and `ll(fit) + ll(held) = ll(full)` then holds. When a term does +span it (a GP over several experiments), `given=fit_problem` makes both +functions compute the Gaussian conditional `p(y_held | y_fit, θ)` under +the full covariance; without a spanning term it equals the marginal. + +`total_predictive_band` locates the `KernelTerm` in the problem, takes its +training rows, comparison space, kernel and amplitude columns from there, +conditions the discrepancy on the residuals with every *other* term of the +constraint as the regression noise, evaluates the amplitude at `x_pred` +through a `TermContext`, and returns percentiles in the comparison space +of the term's comparisons, or in physical units with `physical=True`. + +### 2.12 `reactions/` + +```python +class ElasticXS(Model): + def __init__(self, quantity, central, spin_orbit, args_from_params, params, coulomb=None, + *, lmax=DEFAULT_LMAX, wavelengths_beyond_range=2.0, zeros_per_node=5) + def bind(self, x, meta) -> Predictor # reads meta["reaction"], meta["Elab"] +class IsobaricAnalogPN(Model): + def __init__(self, U_p_coulomb, U_p_central, U_p_spin_orbit, U_n_central, U_n_spin_orbit, + args_from_params, params, *, lmax=..., wavelengths_beyond_range=..., zeros_per_node=...) + def bind(self, x, meta) -> Predictor # reads reaction, Elab, ExIAS +def rutherford(kinematics, angles_rad) -> ndarray # mb/sr, closed form +def momentum_transfer(angles_rad, k) -> ndarray # 2 k sin(theta/2), for coords= +``` + +`ElasticXS` evaluates `central(r, *args)`, `spin_orbit(r, *args)` and, +when given, `coulomb(r, *args)` on the workspace's radial grid, where +`args_from_params(ws, *values)` returns two or three argument tuples, +solves with jitr and extracts `dXS/dA` (b/sr), `dXS/dRuth` or `Ay`. The +angular basis is cached per model instance and kinematics, so a plotting +grid reuses the data grid's basis; the cache is dropped on pickling. The +model owns its solver and the data does not, so a masked view or an +unpickled problem cannot lose a workspace. There is no compound-elastic +hook: that contribution is subtracted from `data.y` as preprocessing +(recipe 20). + +## 3. Worked example: an error-model comparison + +The shape of `examples/alpha_ca_error_model_comparison.ipynb`: real data +without reported errors, one potential, a ladder of covariances compared by +evidence and by held-out prediction. + +```python +import numpy as np, dynesty, rxmc as rx +from rxmc import terms as T, transforms as tf +from scipy import stats +from sklearn.gaussian_process.kernels import Matern + +data = rx.Dataset(angles_rad, ratio_to_rutherford, np.zeros(n), label="44Ca(a,a) 29 MeV", + meta={"reaction": reaction, "Elab": 29.0}) +omp = rx.reactions.ElasticXS("dXS/dRuth", central, spin_orbit, args_from_params, params, + coulomb=coulomb, lmax=30) +comp_log = rx.Comparison(data, omp, space=tf.log) +comp_lin = rx.Comparison(data, omp) +log_eps = rx.Parameter("log_eps", prior=stats.uniform(np.log(0.05), np.log(40))) +log_eta = rx.Parameter("log_eta", prior=stats.uniform(np.log(0.05), np.log(40))) +gp = T.kernel(Matern(0.1, nu=2.5), on=comp_log, coords=lambda x: x / np.pi, + amplitude=T.constant_amplitude, amplitude_params=(log_A,), params=[log_ell]) +ladder = { + "L0": rx.Constraint([comp_log], terms=[T.noise(log_eps)], statistical=False), + "E0": rx.Constraint([comp_lin], terms=[T.noise_fraction(log_eps)], statistical=False), + "L2y": rx.Constraint([comp_log], terms=[T.noise(log_eps), T.normalization(log_eta)], statistical=False), + "Lgp": rx.Constraint([comp_log], terms=[T.noise(log_eps), gp], statistical=False), +} +logz = {} +for name, c in ladder.items(): + p = rx.Problem([c]) + ns = dynesty.NestedSampler(p.log_likelihood, p.prior_transform, p.ndim, nlive=80, sample="rwalk") + ns.run_nested(dlogz=1.5, print_progress=False) + res = ns.results + logz[name] = rx.diagnostics.logz_summary(res.logz[-1] + p.log_jacobian(), res.logzerr[-1]) +verdict = rx.diagnostics.compare_logz(logz["Lgp"], logz["L2y"]) + +fit = ladder["Lgp"].masked_where(lambda x: x < np.deg2rad(90)) +p_fit, p_held = rx.Problem([fit]), rx.Problem([fit.complement()]) +samples = run(p_fit) # rows in p_fit.names order +lp = rx.diagnostics.heldout_log_predictive(p_held, samples) +score = rx.diagnostics.log_posterior_predictive(lp) +draws = rx.diagnostics.predictive_draws(p_held, samples, n_rep=4) +``` + +The labels `L0`, `E0`, `L2y`, `Lgp` are the error-model ladder of recipe +18, whose table defines every label. The same problem runs under emcee +from `p.sample_prior` and `p.log_posterior`, and under `black-box-bayes` +from a `dill` pickle and a six-line module that forwards +`starting_location`, `log_posterior`, `log_likelihood` and +`prior_transform`. Reaction problems are driven by dynesty by preference: +affine-invariant ensembles mix poorly on optical-model posteriors. + +## 4. Capability map + +Every statistical capability the library supports, its spelling, and the +test that pins it. Recipe numbers refer to `recipes.md`. + +| capability | spelling | pinned by | +|---|---|---| +| user-defined model `y = m x + b` | `Model(lambda x, m, b: m*x + b, [m, b])` | test_model | +| `polynomial(order)` | `polynomial(order)` | test_model | +| statistical diagonal only; `chi2` | default `Constraint`; `problem.chi2(theta)` | test_problem, recipe 1 | +| unknown fractional / constant noise | `noise_fraction(log_eps)`, `noise(log_eps)` | test_terms, recipe 2 | +| inferred noise replacing reported statistics | `Constraint(statistical=False, terms=[noise(...)])` | test_constraint, recipe 2 | +| reported normalisation / offset as modes | `comparison.reported_terms()`; `normalization(magnitude=)`, `offset(magnitude=)` | test_constraint, test_regression, recipe 3 | +| free normalisation / offset nuisance | `normalization(log_eta)`, `offset(log_omega)` | test_terms, recipe 4 | +| fixed dense covariance; fixed diagonal | `Term(C, on=comp)`, `Term(sig, kind="diag", on=comp)` | test_terms, recipe 19 | +| case B: one parameter, two block-local terms | `noise(log_eps, on=c1), noise(log_eps, on=c2)`; or two constraints sharing `log_eps` | test_problem, recipe 5 | +| case A: one mode across comparisons | `normalization(log_eta, on=[c1, c2])` | test_covariance, test_regression, recipe 5 | +| per-dataset Kennedy and O'Hagan scale | `Comparison(d_i, omp \| scale(rho_i))` | test_model, recipe 6 | +| sampled mean discrepancy | `omp + Model(delta_fn, phi)`; alone or with `kernel` | test_model, recipe 8 | +| multiplicative `x`-dependent correction | `omp * Model(g_fn, phi)` | test_model | +| GP discrepancy in `x` or momentum transfer, with amplitude | `kernel(k, on=comp, coords=..., amplitude=..., amplitude_params=...)` | test_terms::TestStudyForms, recipe 7 | +| total predictive band from the problem alone | `total_predictive_band(problem, term, predictor, x_pred, samples)` | test_predictive, recipe 7 | +| hyperparameters shared across datasets, values from `meta` | same objects in one term per comparison; `c.meta("Elab")`; `kernel(params=)` | test_terms, test_problem, recipe 22 | +| discrepancy correlated across energies | one `matrix` term `on=comps` from `c.meta` and `c.x`; dense path | test_covariance, recipe 23 | +| term spanning comparisons reading its pieces | `c.segments`, `c.labels`, `c.split(a)` | test_terms, test_covariance, recipe 37 | +| correlated normalisations between quantities | one comparison per quantity, a spanning `matrix` term from `c.split(c.ym)`; the reference's closed forms | test_recipe_37 | +| Peelle's Pertinent Puzzle | `normalization()` reads `c.ym`; the estimate-built refit; the log-determinant pull of the live term | recipes 27, 37 | +| unaccounted-for model error per data type (KDUQ) | `model_error(delta_T, averaging=True, on=comp)`; scalings as `Constraint(weight=)` | test_terms, recipe 26 | +| tempering | `Constraint(weight=)` | test_problem, recipe 12 | +| Student-t with bounded ν; `Chi2` | `StudentT(nu=Parameter("nu", bounds=(1, 100)))`; `Chi2()` | test_likelihood, recipe 9 | +| log-space comparison, delta-method errors, Jacobian | `Comparison(d, m, space=log)`; `problem.log_jacobian()` | test_constraint, recipe 10 | +| masks, hold-out, complement | `masked_where`, `complement()`, `heldout_log_predictive` | test_constraint, test_diagnostics, recipe 11 | +| held-out scoring under a spanning term | `heldout_log_predictive(held, s, given=fit)`, `predictive_draws(..., given=fit)` | test_diagnostics, recipe 30 | +| stacking by leave-one-dataset-out | `Constraint.masked` dropping a block; `log_posterior_predictive` | recipe 28 | +| cut posterior by multiple imputation | stage-1 and per-draw stage-2 problems | recipe 29 | +| simulation-based calibration | `sample_prior`, `predictive_draws`, `dataclasses.replace(d, y=)` | recipe 31 | +| emulator as `Model`, its variance as a `diag` term sharing parameters | recipe 32 | test_terms | +| MAP and Laplace | `scipy.optimize` on `log_posterior`, `problem.bounds` | recipe 33 | +| global error scale and USU modes | `diag` term closing over `comp.y_err` with `statistical=False`; `offset(parameter=, on=technique)` | recipe 34 | +| energy-dependent parameters | per-comparison `Model` instances closing over `meta`, shared objects | test_model, recipe 35 | +| discrepancy on a physical basis | `systematic` modes or `omp + Model(basis_sum)` | recipe 36 | +| hierarchy: joint block with a sampled hyperparameter | `Problem(priors=[(children + [hyper], obj)])` | test_problem, recipe 24 | +| classic normal hierarchical model | marginalised, non-centred, centred | recipe 38 | +| SafeBayes | `replace(c, weight=η)` and prefix masks | recipe 25 | +| joint MVN prior; truncated marginals; `prior_transform` | `Problem(priors=[(omp.params, mvn)])`; `Parameter(prior=, bounds=)` | test_problem, recipe 13 | +| emcee, dynesty, black-box-bayes drivers | the flat interface; `dill` round trip | test_problem, recipe 16 | +| EXFOR to dataset with unit conversion | `from_measurement(m, reaction=, quantity=)` | test_measurement, recipe 14 | +| elastic `dXS/dA`, `dXS/dRuth`, `Ay`; (p,n) IAS | `ElasticXS`, `IsobaricAnalogPN` | test_reactions, recipe 15 | +| singular-covariance guard naming the dataset | compile-time in `Problem` | test_covariance, recipe 21 | +| covariance heat maps; fine-grid plotting | `problem.constraints[i].matrix(theta)`; `model.bind(x_fine, meta)` | notebooks | + +## 5. Testing + +Two tiers, chosen per assertion. + +- **Fast tier**, `pytest`: every unit test and every recipe test's + sampler-free assertions. Most expected-behaviour bullets are structural + or analytic: names and columns, a covariance equal to a hand-built + matrix, `chi2` identities, `ll(fit) + ll(held) = ll(full)`, compile-time + errors. Where a recipe says "run a sampler", the fast tier uses + `test/recipes/oracle.py`, the closed-form posterior of a linear-Gaussian + problem, through `common.linear_posterior` and `common.oracle_samples`; + exact rows stand in for a chain, so coverage, held-out scores, stacking + weights and calibration ranks are pinned exactly. Seeded short chains + appear only for ordering claims with a wide margin. Budget: a few + minutes, zero tolerated flakiness; a flaky assertion is demoted, never + loosened. +- **Converged tier**, `pytest -m slow`, then `pytest -n 4 --nbmake + --nbmake-timeout=3600 examples`: the numeric claims that need a + converged sampler, and the notebooks. It is the "Converged tier" + workflow, required on pushes and pull requests into `main`, also run by + hand with `workflow_dispatch`; there is no scheduled run. + +Three index tests keep the documents honest: `test_recipes_index.py` +(one file per `## NN.` heading, each quoting its recipe; the recipe-18 +legend equal to the tests' legend), `test_notebooks_index.py` (each +notebook cites the recipes of the design's table, all nine exist), and +`test_regression.py` (the 0.x pins). `test/helpers.py` holds the dense +references and `STUDY_LEGEND`, the labelled error-model forms of recipe +18 built against hand-written matrices. + +## 6. Notebooks + +Nine notebooks in `examples/`, each naming the recipes it teaches in its +first cell. Runtimes are wall times on an eight-core laptop, one kernel at +a time unless noted. + +| notebook | recipes | driver | content | runtime | +|---|---|---|---|---| +| `linear_calibration` | 1, 17 | emcee | the whole workflow on a line; prior and posterior predictive; the coverage curve | 23 s | +| `error_models` | 2, 4, 5, 19 | emcee | the covariance ladder on one comparison, the Peelle matrix as a fixed term, offsets known and free, case B across two datasets | 153 s | +| `normalization_and_covariance_structure` | 3, 6, 27 | emcee | latent scales versus reported modes on a quartic; a gallery of covariance structures from `matrix(theta)` | 328 s | +| `correlated_observations` | 5, 37 | emcee | coupled versus independent covariances; Neudecker et al. (2014) §II.A and §II.B recreated | 141 s | +| `gp_discrepancy` | 7, 8, 36 | emcee, dynesty | a kernel term on a toy and on n+⁴⁰Ca missing its surface absorption; the total predictive band; a sampled Legendre correction for contrast | 617 s | +| `robust_likelihoods` | 9, 34 | emcee | Student-t versus Gaussian; a global error scale; a USU offset per technique | 154 s | +| `measurement_to_calibration` | 12, 14, 15, 16, 21, 26 | dynesty | an EXFOR-shaped measurement to a calibrated potential; the unit contract, reported terms, the singular guard, tempering, other drivers | 182 s | +| `alpha_ca_error_model_comparison` | 10, 11, 13, 18 | dynesty | real ⁴⁴Ca(α,α) data, a four-parameter potential, the ladder by evidence with the Jacobian, held-out backward angles | 1197 s (alongside another notebook) | +| `hierarchical_calibration` | 22, 24, 30, 35, 38 | dynesty | eight schools; a hierarchy on the physics parameters repairing a misspecified energy dependence, in sample and at a held-out energy | 937 s (alongside another notebook) | + +**`hierarchical_calibration` in detail.** The truth is +`y = a0(E) + a1(E) x + a2(E) x²`, measured by seven synthetic datasets at +known energies plus one held-out dataset at an energy bracketed by two of +them; the true `a_k(E)` are a smooth trend plus non-monotonic bumps. Three +fits of the same data: the correct mapping with global `φ`; a misspecified +linear mapping with global `φ`; the linear mapping plus a per-dataset +deviation vector `δ_j = τ ⊙ η_j`, non-centred, `η_jk ~ N(0, 1)`, `τ_k` +half-normal, as marginal priors. One `Model` per comparison closes over +`E_j`; the held-out comparison is fully masked, so its `η_new` is sampled +from the prior, driven by `τ`, and `complement()`, `predictive_draws` and +`heldout_log_predictive` score the new energy with no extra code. The +correct mapping covers in and out of sample; the misspecified one +under-covers both and leaves structured per-dataset residuals; the +hierarchy recovers coverage with wider, longer-tailed bands at the new +energy, a `τ` posterior away from zero, and the better held-out score of +the misspecified pair. A hierarchy learns the spread of deviations it has +seen: holding out an unmodelled peak between its datasets is the +few-datasets caveat, not a prediction it can make. + +## 7. Layout and dependencies + +``` +src/rxmc/ + __init__.py re-exports + params.py transforms.py units.py data.py model.py terms.py + likelihood.py constraint.py covariance.py problem.py + diagnostics.py predictive.py + reactions/ elastic.py ias.py +test/ one file per module, the index tests, test_regression.py, helpers.py +test/recipes/ one file per recipe; common.py, oracle.py +examples/ nine notebooks; data/alpha_ca_ratio_ruth.csv (jitr's digitisation of EXFOR F0567) +docs/ this document, recipes.md, examples.rst, api.rst, groundup_design.md (history) +``` + +Runtime dependencies: `numpy`, `scipy`, `jitr>=3.0`, `exfor-tools`. +Extras: `examples` (emcee, dynesty, corner, matplotlib, scikit-learn, +dill, jupyter, ipykernel, tqdm), `validation` (examples plus pytest, +nbmake, nbqa, ruff, black, isort, build), `docs` (sphinx, the pydata +theme, myst-nb). Python 3.12 or later. Kernels are duck-typed on the +scikit-learn interface, so scikit-learn is not a runtime dependency. + +## 8. Open questions and non-goals + +- **Term-level partial masks.** The factories accept `mask=`; a first-class + `on=(comparison, point_mask)` would be tidier. Not needed yet. +- **Workspace caching across models.** The angular basis is cached per + model instance; two models on one kinematics still build two. A factor + of a few in setup time, not in solve time. +- **Cross-constraint modes.** `U` is per constraint so that constraints + stay independent and weights stay meaningful. A mode that couples two + constraints is a reason to merge them. +- **The log-determinant pull.** A prediction-scaled covariance biases the + mode down by an amount that grows with the normalisation error (5 to 9 % + at 20 %). Whether to offer the estimate-built refit as a helper rather + than a pattern is open. +- **Known non-goals.** The closing section of `recipes.md` lists the + calibration classes the design rules out (non-elliptical likelihoods, + chain-dependent masks, per-point latents, per-point likelihood factors, + mixture likelihoods) with the size of the addition each would need. + +## 9. Release path and history + +The repository, its pull-request history and its Pages site are kept. 0.x +was closed out with tag `v0.1.0` and branch `legacy/0.x`; the rewrite +happened on `rewrite`, cut from that `main`, in nine milestones (bootstrap; +parameters, transforms, units, likelihood and terms; data, model and +constraint; the structured covariance; the problem with the first +twenty-eight recipe tests; reactions and `from_measurement`; diagnostics +and predictive; the recipe index; the notebooks) followed by this +document. Pre-release tags `v1.0.0a1`, `b1`, `rc1` publish to PyPI +through trusted publishing on tag push; they were deferred by decision +and remain available. The release is a pull request of `rewrite` into +`main`, the tag `v1.0.0`, a GitHub Release and the Pages rebuild. diff --git a/docs/groundup_design.md b/docs/groundup_design.md index 490a146..61c1caf 100644 --- a/docs/groundup_design.md +++ b/docs/groundup_design.md @@ -1,3 +1,7 @@ +--- +orphan: true +--- + # A ground-up rxmc: declare, then compile This document guides a rewrite of `rxmc` from a blank repository. It is the diff --git a/docs/index.rst b/docs/index.rst index c293ec3..125852d 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,21 +1,26 @@ rxmc ==== -``rxmc`` is a library for Bayesian calibration of reaction models to -experimental data, with the error model, statistical and systematic, -experimental and theoretical, declared explicitly as part of the problem. +``rxmc`` calibrates reaction models to experimental data by Bayesian +inference, with the error model, statistical and systematic, experimental +and theoretical, declared explicitly as part of the problem, and the +calibration driven by external samplers (emcee, dynesty, black-box-bayes). -**The 1.0 rewrite is in progress on this branch.** The design is the plan -of record in :doc:`groundup_design`; :doc:`recipes` lists every supported -use case with its spelling and the behaviour to expect, and each recipe is -a test. The 0.x package is preserved at tag ``v0.1.0`` and on branch -``legacy/0.x``. +- :doc:`recipes` states every supported use case with its spelling and the + behaviour to expect; each recipe is a test. +- :doc:`examples` are the tutorials for the recipes. +- :doc:`design` is the maintainer's description of the library. +- :doc:`api` is the reference. + +The 1.0 rewrite lives on the ``rewrite`` branch until its release; the 0.x +package is preserved at tag ``v0.1.0`` and on branch ``legacy/0.x``. .. toctree:: :maxdepth: 1 :caption: Contents installation - groundup_design recipes examples + design + api diff --git a/docs/installation.rst b/docs/installation.rst index 452f72b..4cc6e0a 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -1,16 +1,25 @@ Installation ============ -``rxmc`` requires Python 3.12 or later. Core dependencies are listed in -``requirements.txt`` and are installed automatically. The 1.0 pre-releases -will be published to PyPI as ``v1.0.0a1``, ``b1``, ``rc1`` and installable -with ``pip install --pre rxmc``; until then install from the branch: +``rxmc`` requires Python 3.12 or later. The runtime dependencies are +``numpy``, ``scipy``, ``jitr >= 3.0`` and ``exfor-tools``, installed +automatically. Until the 1.0 pre-releases appear on PyPI (``pip install +--pre rxmc``), install from the branch: .. code-block:: bash git clone -b rewrite git@github.com:beykyle/rxmc.git cd rxmc python -m venv .venv && source .venv/bin/activate - pip install -e '.[validation]' # or '.[examples]' for the notebooks only + pip install -e '.[examples]' + +Extras: + +=============== ============================================================= +``examples`` emcee, dynesty, corner, matplotlib, scikit-learn, dill, + jupyter: everything the notebooks use +``validation`` ``examples`` plus pytest, nbmake, nbqa, ruff, black, isort +``docs`` sphinx, the pydata theme, myst-nb +=============== ============================================================= The 0.x package is at tag ``v0.1.0`` and on branch ``legacy/0.x``. diff --git a/src/rxmc/model.py b/src/rxmc/model.py index 5f73508..be9f748 100644 --- a/src/rxmc/model.py +++ b/src/rxmc/model.py @@ -144,6 +144,9 @@ def bind(self, x, meta=None) -> Predictor: def polynomial(order: int) -> Model: - r"""``y = a_0 + a_1 x + ... + a_order x^order`` with parameters ``a0..a<order>``.""" + """A polynomial of the given order with parameters ``a0`` to ``a<order>``. + + ``y = a_0 + a_1 x + a_2 x^2 + ...``; the parameters carry no prior. + """ params = [Parameter(f"a{i}", latex=f"a_{i}") for i in range(order + 1)] return Model(lambda x, *a: P.polyval(np.asarray(x, dtype=float), a), params) diff --git a/src/rxmc/terms.py b/src/rxmc/terms.py index fbbf3aa..49b2a50 100644 --- a/src/rxmc/terms.py +++ b/src/rxmc/terms.py @@ -286,7 +286,7 @@ class KernelTerm(Term): discrepancy at new points; the covariance machinery treats a ``KernelTerm`` exactly as a ``matrix`` :class:`Term`. - Attributes + Parameters ---------- kernel : sklearn-style kernel The kernel object as passed to :func:`kernel`. diff --git a/src/rxmc/transforms.py b/src/rxmc/transforms.py index 15e6e57..96a60be 100644 --- a/src/rxmc/transforms.py +++ b/src/rxmc/transforms.py @@ -194,7 +194,7 @@ def _reciprocal(a): def scale(parameter: Parameter | None = None, log: bool = True, name=None) -> Transform: - r"""A latent multiplicative normalisation :math:`\rho\, y`. + r"""A latent multiplicative normalisation, ``rho * a``. The Kennedy & O'Hagan forward-model scale: it changes the *mean*, not the covariance, so it is composed onto the model (``model | scale(rho)``) and From 6a374fd67344cca391335f76551cd8e8c7834a86 Mon Sep 17 00:00:00 2001 From: beykyle <kylebeyer1@gmail.com> Date: Fri, 11 Sep 2026 09:48:08 -0400 Subject: [PATCH 46/75] Rewrite the README as the front door of 1.0 A quickstart that runs, a normalisation term, a reaction example through from_measurement and ElasticXS under dynesty, where to go next, installation with the extras, validation, status. test/test_readme.py executes the README's three Python blocks so it cannot drift. --- README.md | 144 +++++++++++++++++++++++++++++++++++++++----- test/test_readme.py | 23 +++++++ 2 files changed, 152 insertions(+), 15 deletions(-) create mode 100644 test/test_readme.py diff --git a/README.md b/README.md index 457e0bd..ed71fe3 100644 --- a/README.md +++ b/README.md @@ -1,32 +1,139 @@ # rxmc -`rxmc` is a library for Bayesian calibration of reaction models to -experimental data, with the error model — statistical and systematic, -experimental and theoretical — declared explicitly as part of the problem -and calibrated with external samplers (emcee, dynesty, black-box-bayes). +`rxmc` calibrates reaction models to experimental data by Bayesian +inference, with the error model declared as part of the problem: which +errors are statistical and which are correlated, whether a normalisation is +inferred or marginalised, whether the model is allowed a discrepancy, which +points are held out. A reviewer can read the declaration and write down the +likelihood. The library owns no sampler: a compiled problem exposes the +densities and the prior transform that emcee, dynesty or black-box-bayes +need. -**The 1.0 rewrite is in progress on this branch.** +## Quickstart -- [`docs/groundup_design.md`](docs/groundup_design.md) is the design and - plan of record: the API skeleton, what is harvested from 0.x, the gaps, - the milestones, and the release path. -- [`docs/recipes.md`](docs/recipes.md) lists every supported use case with - its spelling and expected behaviour. Every recipe is a test under - `test/recipes/`; the richest are tutorial notebooks. +Declare, compile, hand to a sampler, read the chain back by name. -The 0.x package is preserved at tag `v0.1.0` and on branch `legacy/0.x`. +```python +import emcee +import numpy as np +from scipy import stats + +import rxmc as rx + +# a model with parameters and priors +m = rx.Parameter("m", prior=stats.norm(0.0, 5.0)) +b = rx.Parameter("b", prior=stats.norm(0.0, 5.0)) +line = rx.Model(lambda x, m, b: m * x + b, [m, b]) + +# data with reported statistical errors +rng = np.random.default_rng(0) +x = np.linspace(0.0, 1.0, 20) +data = rx.Dataset(x, 0.6 * x + 2.0 + rng.normal(0.0, 0.1, x.size), np.full(x.size, 0.1)) + +# one comparison, one likelihood, one compiled problem +problem = rx.Problem([rx.Constraint([rx.Comparison(data, line)])]) +print(problem.names) # ['m', 'b'] + +sampler = emcee.EnsembleSampler(16, problem.ndim, problem.log_posterior) +sampler.run_mcmc(problem.sample_prior(16, rng=1), 1000, progress=False) +samples = sampler.get_chain(discard=300, flat=True) +print(samples[:, problem.columns(m)].mean(), samples[:, problem.columns(b)].mean()) + +# the posterior predictive on the data points, with the error model +draws = rx.diagnostics.predictive_draws(problem, samples[::20], n_rep=2) +print(rx.diagnostics.coverage_curve(draws, data.y, [0.68])) +``` + +The error model is a sum of covariance *terms* on the constraint. A +normalisation the experiment did not report, inferred alongside the model: + +```python +from rxmc import terms as T + +log_eta = rx.Parameter("log_eta", prior=stats.norm(-2.0, 1.0)) +problem = rx.Problem([rx.Constraint([rx.Comparison(data, line)], terms=[T.normalization(log_eta)])]) +print(problem.names) # ['m', 'b', 'log_eta'] +``` + +## A reaction model + +A jitr optical potential is a `Model` whose solver is built from the +dataset's kinematics. EXFOR measurements arrive through +`from_measurement`, which converts units and keeps the reported systematics +as inert metadata until asked for. Optical-model posteriors are correlated +and sometimes multimodal, so drive them with nested sampling. + +```python +from types import SimpleNamespace + +import dynesty +import jitr +from jitr.optical_potentials.potential_forms import thomas_safe, woods_saxon_safe + +reaction = jitr.reactions.ElasticReaction(target=(40, 20), projectile=(1, 0)) +R = 1.2 * 40 ** (1 / 3) + + +def central(r, V, W, a): + return -(V + 1j * W) * woods_saxon_safe(r, R, a) + + +def spin_orbit(r, Vso, Rso, aso): + return Vso * thomas_safe(r, Rso, aso) / jitr.utils.constants.WAVENUMBER_PION**2 + + +params = [ + rx.Parameter("V", prior=stats.norm(48.0, 5.0), bounds=(0.0, np.inf)), + rx.Parameter("W", prior=stats.norm(4.0, 3.0), bounds=(0.0, np.inf)), + rx.Parameter("a", prior=stats.norm(0.65, 0.1), bounds=(0.3, 1.2)), +] +omp = rx.reactions.ElasticXS( + "dXS/dA", central, spin_orbit, lambda ws, *v: (tuple(v), (6.0, R, 0.45)), params, lmax=10 +) + +# an EXFOR-shaped measurement (exfor_tools.Distribution has these fields); here mock data +angles = np.linspace(10.0, 150.0, 12) +truth = omp.bind(np.deg2rad(angles), {"reaction": reaction, "Elab": 14.1})(48.0, 4.0, 0.65) +measurement = SimpleNamespace( + x=angles, y=1e3 * truth * (1 + rng.normal(0, 0.05, angles.size)), Einc=14.1, + quantity="dXS/dA", y_units="mb/sr", statistical_err=1e3 * truth * 0.05, + systematic_norm_err=0.04, systematic_offset_err=None, subentry="mock", +) +d = rx.from_measurement(measurement, reaction=reaction) +comp = rx.Comparison(d, omp) +problem = rx.Problem([rx.Constraint([comp], terms=comp.reported_terms())]) + +sampler = dynesty.NestedSampler(problem.log_likelihood, problem.prior_transform, problem.ndim, nlive=50) +sampler.run_nested(dlogz=5.0, print_progress=False) +print(problem.names, sampler.results.logz[-1]) +``` + +## Where to go next + +- [`docs/recipes.md`](docs/recipes.md): every supported use case with its + spelling and expected behaviour. Each recipe is a test under + `test/recipes/`. +- [`examples/`](examples/): nine notebooks, the tutorials for the recipes, + from a line to an error-model comparison on real α + ⁴⁴Ca data and a + hierarchical calibration. +- [`docs/design.md`](docs/design.md): the maintainer's description of the + library, its rules and its testing tiers. +- The rendered documentation, with the API reference, at + https://beykyle.github.io/rxmc/. ## Installation +Python 3.12 or later; the runtime dependencies are `numpy`, `scipy`, +`jitr >= 3.0` and `exfor-tools`. Until the 1.0 pre-releases are on PyPI +(`pip install --pre rxmc`), install from the branch: + ```bash git clone -b rewrite git@github.com:beykyle/rxmc.git cd rxmc python -m venv .venv && source .venv/bin/activate -pip install -e '.[validation]' +pip install -e '.[examples]' # or '.[validation]' to run the tests ``` -Python ≥ 3.12; `jitr >= 3.0` from PyPI. - ## Validation ```bash @@ -36,3 +143,10 @@ python -m pytest -m slow # converged tier: required on pushes and PRs to main python -m pytest -n 4 --nbmake --nbmake-timeout=3600 examples # the notebooks, same workflow sphinx-build -W docs docs/_build/html ``` + +The three Python blocks of this README are executed by `test/test_readme.py`. + +## Status + +The 1.0 rewrite lives on the `rewrite` branch until its release. The 0.x +package is preserved at tag `v0.1.0` and on branch `legacy/0.x`. diff --git a/test/test_readme.py b/test/test_readme.py new file mode 100644 index 0000000..cb3bf79 --- /dev/null +++ b/test/test_readme.py @@ -0,0 +1,23 @@ +"""The README's Python blocks run, so the front door cannot drift from the API. + +The blocks share one namespace and execute in order: the linear quickstart, +the normalisation term, and the reaction example (a mock measurement, a +short nested-sampling run at ``lmax=10``). +""" + +import pathlib +import re + +README = pathlib.Path(__file__).resolve().parents[1] / "README.md" +BLOCK = re.compile(r"```python\n(.*?)```", re.S) + + +def test_readme_python_blocks_execute(capsys): + blocks = BLOCK.findall(README.read_text()) + assert len(blocks) == 3, "the README carries three Python blocks" + namespace = {} + for block in blocks: + exec(compile(block, str(README), "exec"), namespace) # noqa: S102 + out = capsys.readouterr().out + assert "['m', 'b']" in out and "['m', 'b', 'log_eta']" in out + assert "['V', 'W', 'a']" in out From 5cc80f2b7a95f33d08edeca2ff27df88f0e27c82 Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Fri, 11 Sep 2026 11:36:10 -0400 Subject: [PATCH 47/75] update readme --- README.md | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index ed71fe3..88d1b60 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,10 @@ likelihood. The library owns no sampler: a compiled problem exposes the densities and the prior transform that emcee, dynesty or black-box-bayes need. +## Documentation + +The documentation website, including API reference is https://beykyle.github.io/rxmc/. + ## Quickstart Declare, compile, hand to a sampler, read the chain back by name. @@ -35,7 +39,7 @@ problem = rx.Problem([rx.Constraint([rx.Comparison(data, line)])]) print(problem.names) # ['m', 'b'] sampler = emcee.EnsembleSampler(16, problem.ndim, problem.log_posterior) -sampler.run_mcmc(problem.sample_prior(16, rng=1), 1000, progress=False) +sampler.run_mcmc(problem.sample_prior(16, rng=1), 1000) samples = sampler.get_chain(discard=300, flat=True) print(samples[:, problem.columns(m)].mean(), samples[:, problem.columns(b)].mean()) @@ -118,7 +122,7 @@ print(problem.names, sampler.results.logz[-1]) hierarchical calibration. - [`docs/design.md`](docs/design.md): the maintainer's description of the library, its rules and its testing tiers. -- The rendered documentation, with the API reference, at +- The documentation website, including API reference, at https://beykyle.github.io/rxmc/. ## Installation @@ -145,8 +149,3 @@ sphinx-build -W docs docs/_build/html ``` The three Python blocks of this README are executed by `test/test_readme.py`. - -## Status - -The 1.0 rewrite lives on the `rewrite` branch until its release. The 0.x -package is preserved at tag `v0.1.0` and on branch `legacy/0.x`. From 39b71657a2339adea8e012ddc59c9c6932f2599c Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Fri, 11 Sep 2026 15:22:21 -0400 Subject: [PATCH 48/75] clean up examples --- .../alpha_ca_error_model_comparison.ipynb | 23 +-- examples/gp_discrepancy.ipynb | 2 +- examples/hierarchical_calibration.ipynb | 4 +- examples/linear_calibration.ipynb | 12 +- examples/measurement_to_calibration.ipynb | 120 +------------- examples/robust_likelihoods.ipynb | 147 +++++++++++------- 6 files changed, 111 insertions(+), 197 deletions(-) diff --git a/examples/alpha_ca_error_model_comparison.ipynb b/examples/alpha_ca_error_model_comparison.ipynb index e7f7161..7b2fac2 100644 --- a/examples/alpha_ca_error_model_comparison.ipynb +++ b/examples/alpha_ca_error_model_comparison.ipynb @@ -7,19 +7,12 @@ "source": [ "# Error models for α + ⁴⁴Ca elastic scattering: a comparison by evidence\n", "\n", - "Real data without reported uncertainties, an optical potential, and a ladder\n", - "of error models compared by their Bayesian evidence and by how well a fit at\n", - "forward angles predicts the backward angles it never saw. This is the\n", - "shape of an error-model comparison study: the same comparison and the same\n", - "potential under every rung, only the covariance and the comparison space\n", - "change.\n", + "TODO: fix intro text\n", "\n", - "The data are the ratio to Rutherford of ⁴⁴Ca(α,α) at 29 MeV,\n", - "[EXFOR F0567](https://www-nds.iaea.org/exfor/servlet/X4sSearch5?EntryID=150567),\n", - "[Oeschler *et al.*, Phys. Rev. Lett. **28**, 694 (1972)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.28.694),\n", - "as digitised for the [`jitr` quickstart](https://beykyle.github.io/jitr/getting-started.html),\n", - "whose reaction setup is followed here. Oeschler *et al.* report no\n", - "uncertainties, so every error model below infers its own.\n", + "The experimental data we will use are the differential elastic as ratio to the Rutherford scattering cross sections of $^{44}$Ca($\\alpha,\\alpha$) at 29 MeV from [EXFOR F0567](https://www-nds.iaea.org/exfor/servlet/X4sSearch5?EntryID=150567),\n", + "[Oeschler *et al.*, Phys. Rev. Lett. **28**, 694 (1972)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.28.694). Oeschler *et al.* report no uncertainties, so every error model below infers its own.\n", + "\n", + "See also the [`jitr` quickstart series of tutorials](https://beykyle.github.io/jitr/getting-started.html). In a sense, this tutorial is a successor to those: using `rxmc` we can craft more sophisticated statistical models, including using [Gaussian processes for model discrepancy](https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/1467-9868.00294),\n", "\n", "Recipes: 10, 11, 13, 18" ] @@ -347,7 +340,7 @@ } ], "source": [ - "def nested(problem, seed, nlive=80, dlogz=1.5):\n", + "def fit_nested(problem, seed, nlive=80, dlogz=1.5):\n", " sampler = dynesty.NestedSampler(\n", " problem.log_likelihood,\n", " problem.prior_transform,\n", @@ -362,7 +355,7 @@ "\n", "results, samples = {}, {}\n", "for i, (name, p) in enumerate(problems.items()):\n", - " res = nested(p, seed=i)\n", + " res = fit_nested(p, seed=i)\n", " results[name] = res\n", " samples[name] = res.samples_equal(rstate=np.random.default_rng(i))\n", " print(\n", @@ -635,7 +628,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/examples/gp_discrepancy.ipynb b/examples/gp_discrepancy.ipynb index 6ce23a0..497ca6f 100644 --- a/examples/gp_discrepancy.ipynb +++ b/examples/gp_discrepancy.ipynb @@ -161,7 +161,7 @@ "\n", "p_stat = rx.Problem([rx.Constraint([comp])])\n", "p_diag = rx.Problem(\n", - " [rx.Constraint([comp], terms=[T.model_error(gamma, averaging=True)])]\n", + " [rx.Constraint([comp], terms=[T.model_error(gamma)])]\n", ")\n", "p_gp = rx.Problem([rx.Constraint([comp], terms=[gp])])\n", "print(\"GP problem columns:\", p_gp.names)\n", diff --git a/examples/hierarchical_calibration.ipynb b/examples/hierarchical_calibration.ipynb index b8fc2a2..0b91f28 100644 --- a/examples/hierarchical_calibration.ipynb +++ b/examples/hierarchical_calibration.ipynb @@ -98,8 +98,8 @@ " rx.Parameter(f\"eta_{j}\", prior=stats.norm(0.0, 1.0), latex=rf\"\\eta_{j}\")\n", " for j in range(8)\n", "]\n", - "school = rx.Model(lambda x, mu, tau, *eta: mu + tau * np.asarray(eta), [mu, tau, *etas])\n", - "p_schools = rx.Problem([rx.Constraint([rx.Comparison(schools, school)])])\n", + "model = rx.Model(lambda x, mu, tau, *eta: mu + tau * np.asarray(eta), [mu, tau, *etas])\n", + "p_schools = rx.Problem([rx.Constraint([rx.Comparison(schools, model)])])\n", "\n", "\n", "def nested(problem, seed, nlive=200, dlogz=0.5):\n", diff --git a/examples/linear_calibration.ipynb b/examples/linear_calibration.ipynb index da0435c..b4e208c 100644 --- a/examples/linear_calibration.ipynb +++ b/examples/linear_calibration.ipynb @@ -480,19 +480,25 @@ "\n", "- Declare, compile, sample: the problem knows its columns, its prior and its\n", " densities; the sampler is whatever you like.\n", - "- Nothing is hidden in the likelihood. With reported errors only, it is the\n", - " χ² you would write yourself.\n", "- Read chains by name through `problem.columns`; bind the model to any grid\n", " for predictions.\n", "- The posterior predictive check is the first question to ask of an error\n", " model. The next notebooks are about what to do when the reported errors\n", " are not the whole story." ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c929132-5481-4226-b060-69c135987bbe", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/examples/measurement_to_calibration.ipynb b/examples/measurement_to_calibration.ipynb index b255b2f..93b0200 100644 --- a/examples/measurement_to_calibration.ipynb +++ b/examples/measurement_to_calibration.ipynb @@ -300,54 +300,6 @@ "plt.show()" ] }, - { - "cell_type": "markdown", - "id": "25b52e0e", - "metadata": {}, - "source": [ - "## The guardrail: a singular covariance is a named error, not a `LinAlgError`\n", - "\n", - "A subentry without statistical errors contributes zero variance on its\n", - "diagonal. Compiling such a problem fails at construction with a message\n", - "that names the comparison and the remedies (recipe 21)." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "7a2bdae7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T04:03:34.390992Z", - "iopub.status.busy": "2026-09-11T04:03:34.390772Z", - "iopub.status.idle": "2026-09-11T04:03:34.396166Z", - "shell.execute_reply": "2026-09-11T04:03:34.395238Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ValueError: the constraint's covariance is singular on its active points; the diagonal is zero on rows of ['no-statistics']: those comparisons have zero statistical error and no diagonal term covers their points (a block covered only by correlated modes is singular here even when the full covariance is not). Remedies: comparison.reported_terms(), a noise term, a fixed Term covering those points, or statistical=False with an explicit covariance.\n" - ] - } - ], - "source": [ - "nostat = SimpleNamespace(\n", - " **{\n", - " **vars(measurement),\n", - " \"statistical_err\": np.zeros_like(y_mb),\n", - " \"subentry\": \"no-statistics\",\n", - " }\n", - ")\n", - "d_nostat = rx.from_measurement(nostat, reaction=reaction)\n", - "try:\n", - " rx.Problem([rx.Constraint([rx.Comparison(d_nostat, omp)])])\n", - "except ValueError as err:\n", - " print(\"ValueError:\", err)" - ] - }, { "cell_type": "markdown", "id": "07fe6c63", @@ -666,81 +618,11 @@ "ax.legend(frameon=False, fontsize=8)\n", "plt.show()" ] - }, - { - "cell_type": "markdown", - "id": "c7f7b389", - "metadata": {}, - "source": [ - "## An inferred model error per data type (recipe 26)\n", - "\n", - "KDUQ's treatment of unaccounted-for model error is one `model_error` term\n", - "per data type, a diagonal that scales with the prediction and is averaged\n", - "over the type's points, with one free fraction per type; its democratic and\n", - "federal scalings are the tempering weights above. With one data type here\n", - "the spelling is a single term; a second type would be a second constraint\n", - "with its own `delta`." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "f67b0e69", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T04:06:16.273206Z", - "iopub.status.busy": "2026-09-11T04:06:16.273000Z", - "iopub.status.idle": "2026-09-11T04:06:16.280080Z", - "shell.execute_reply": "2026-09-11T04:06:16.279222Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "columns: ['Vv', 'Wv', 'Rv', 'av', 'Wd', 'Rd', 'ad', 'log_delta_dXS']\n", - "weight k/N = 0.350\n" - ] - } - ], - "source": [ - "delta_xs = rx.Parameter(\n", - " \"log_delta_dXS\", prior=stats.norm(np.log(0.1), 1.0), latex=r\"\\log\\delta_{d\\sigma}\"\n", - ")\n", - "c_kduq = rx.Constraint(\n", - " [comp],\n", - " terms=[*reported, T.model_error(delta_xs, averaging=True)],\n", - " weight=len(params) / d.n,\n", - ")\n", - "p_kduq = rx.Problem([c_kduq])\n", - "print(\"columns:\", p_kduq.names)\n", - "print(f\"weight k/N = {c_kduq.weight:.3f}\")" - ] - }, - { - "cell_type": "markdown", - "id": "57e06287", - "metadata": {}, - "source": [ - "## Takeaways\n", - "\n", - "- The unit contract: `from_measurement` converts what is dimensionful and\n", - " leaves fractions alone; the kinematics ride in `meta`.\n", - "- Systematics are opt-in terms built from the prediction; nothing correlated\n", - " hides in a default.\n", - "- A dataset that contributes zero variance fails at construction with a\n", - " message naming it, not mid-chain.\n", - "- dynesty for optical potentials; emcee and dill for whatever else drives\n", - " the same problem.\n", - "- Tempering is a weight on a constraint; the predictive check tells you\n", - " whether you needed it." - ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/examples/robust_likelihoods.ipynb b/examples/robust_likelihoods.ipynb index d7a6e02..63dbdab 100644 --- a/examples/robust_likelihoods.ipynb +++ b/examples/robust_likelihoods.ipynb @@ -87,7 +87,7 @@ "data = rx.Dataset(x, y, np.full(x.size, noise), label=\"with outliers\")\n", "\n", "fig, ax = plt.subplots(figsize=(5, 3.5))\n", - "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", ms=4, color=\"k\", label=\"data\")\n", + "ax.errorbar(data.x, data.y, data.y_err, fmt=\".\", ms=4, color=\"k\", label=\"data\")\n", "ax.plot(x[outliers], y[outliers], \"o\", ms=12, mfc=\"none\", color=\"C3\", label=\"outliers\")\n", "ax.plot(x, m_true * x + b_true, \"--\", color=\"C3\", label=\"truth\")\n", "ax.set(xlabel=\"x\", ylabel=\"y\")\n", @@ -254,11 +254,9 @@ "id": "8069377f", "metadata": {}, "source": [ - "The Gaussian puts the truth several σ away: three points at ten σ dominate\n", - "a quadratic penalty. The Student-t reaches a small `ν`, its tails absorb\n", - "the outliers, and the truth is covered. Note what it does *not* do: it does\n", - "not reject the outliers, it widens. Per-point rejection would need per-point\n", - "machinery, such as masking the suspects (recipe 11)." + "Notice that, while both likelihoods have `b` shifted upwards by the outliers, the student-t has inflated uncertainties which cover the true `b`, while the Gaussian has very little posterior weight on the truth.\n", + "\n", + "Note what it does *not* do: it does not reject the outliers, it widens. Per-point rejection would need per-point machinery, such as masking the suspects (recipe 11)." ] }, { @@ -328,6 +326,9 @@ "source": [ "x_fine = np.linspace(-0.5, 4.5, 60)\n", "on_fine = line.bind(x_fine, {})\n", + "on_data = line.bind(x, {})\n", + "y_true_fine = on_fine(m_true, b_true)\n", + "y_true_data = on_data(m_true, b_true)\n", "fig, ax = plt.subplots(figsize=(6, 4))\n", "for name, p, s, color in (\n", " (\"Gaussian\", p_gauss, s_gauss, \"C3\"),\n", @@ -336,56 +337,49 @@ " lo, hi = rx.predictive.predictive_band(\n", " [on_fine(*r[p.columns(line.params)]) for r in s[::10]], levels=(5, 95)\n", " )\n", - " ax.fill_between(x_fine, lo, hi, color=color, alpha=0.35, label=f\"{name} 90 % band\")\n", - "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", ms=3, color=\"k\")\n", - "ax.plot(x_fine, m_true * x_fine + b_true, \"--\", color=\"k\", label=\"truth\")\n", - "ax.set(xlabel=\"x\", ylabel=\"y\")\n", + " ax.fill_between(x_fine, lo - y_true_fine, hi - y_true_fine, color=color, alpha=0.35, label=f\"{name} 90 % band\")\n", + "ax.errorbar(data.x, data.y - y_true_data, data.y_err, fmt=\"o\", ms=3, color=\"k\")\n", + "ax.plot(x_fine, np.zeros_like(y_true_fine), \"--k\") \n", + "ax.set(xlabel=\"$x$\", ylabel=r\"$y - y_\\text{truth}$\")\n", "ax.legend(frameon=False)\n", "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "936b1259-8a54-453d-8c66-9ab336ce3f58", + "metadata": {}, + "source": [ + "Both are shifted relative to the truth, but the student-t's longer tails allow for the truth to (mostly) be covered by 90% credible interval of the posterior predictive distribution." + ] + }, { "cell_type": "markdown", "id": "902b883f", "metadata": {}, "source": [ - "## Errors larger than stated (recipe 34)\n", + "## Inferring the errors (recipe 34)\n", "\n", - "Two more honest options when the data scatter more than their errors say.\n", + "Another option we could take when we notice that the spread in the data is not consistent with the reported experimental uncertainty is to try to infer the uncertainty along with the model parameters. There are many ways to do this: this implies coming up with a model for the distribution governing the unknown discrepancy between the model and the data. Careful - this discrepancy can include *both* experimental uncertainty and model miss specification. We will take a closer look at the latter in other notebooks. In this case, we have a correctly specified model, we just have outliers. \n", "\n", - "**A global scale** on the reported errors: a `diag` term that closes over\n", - "the comparison's errors, with `statistical=False` so it *is* the diagonal.\n", - "Under the Gaussian the scale has to grow until every point's error covers\n", - "the outliers, and it is poorly determined; under the Student-t the tail\n", - "shares the work, so the scale is smaller and better determined. Either way\n", - "a global scale inflates every point equally, which is why it is judged poor\n", - "evaluation practice next to a targeted term." + "One simple thing we can do is add a parameter for **a global scale** on the reported errors. This allows us to inflate the experimental errors while inferring the model parameters to maximize the posterior. \n", + "\n", + "\n", + "Under the Gaussian likelihood the scale has to grow until every point's error covers the outliers, and the scale will therefore be poorly determined. Under the Student-t, the tail shares the work, so the scale is smaller and better determined. Either way\n", + "a global scale inflates every point equally, which is why it is judged poor evaluation practice next to a targeted approach - e.g. outlier rejection." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "id": "e214d154", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T03:43:50.972105Z", - "iopub.status.busy": "2026-09-11T03:43:50.971908Z", - "iopub.status.idle": "2026-09-11T03:44:34.686258Z", - "shell.execute_reply": "2026-09-11T03:44:34.685243Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Gaussian error scale s = 3.22 (68 % interval 2.83 to 3.68)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Gaussian error scale s = 3.22 (68 % interval 2.83 to 3.68)\n", "Student-t error scale s = 2.91 (68 % interval 2.18 to 3.48)\n" ] } @@ -478,10 +472,15 @@ " x_b, y_b, np.full(x_b.size, noise), label=\"technique B\", meta={\"technique\": \"B\"}\n", ")\n", "comps = [rx.Comparison(d_a, line), rx.Comparison(d_b, line)]\n", + "\n", + "# we may have some prior reason to suspect that B contains the unknown offset, but not A\n", + "# so we will apply it only to B\n", "log_usu = rx.Parameter(\n", " \"log_usu_B\", prior=stats.norm(np.log(0.2), 1.0), latex=r\"\\log\\delta_B\"\n", ")\n", "usu = T.offset(log_usu, on=[c for c in comps if c.data.meta[\"technique\"] == \"B\"])\n", + "\n", + "# the global diagonal uncertainty scale from before, for comparison\n", "scaled_ab = [\n", " rx.Term(\n", " lambda c, ls, comp=comp: np.exp(ls) * comp.y_err, (log_s,), kind=\"diag\", on=comp\n", @@ -495,10 +494,28 @@ " [rx.Constraint(comps, terms=scaled_ab, statistical=False)]\n", " ),\n", " \"USU offset on B\": rx.Problem([rx.Constraint(comps, terms=[usu])]),\n", - "}\n", - "truth_line = m_true * x_fine + b_true\n", - "fig, ax = plt.subplots(figsize=(6, 4))\n", - "for (name, p), color, seed in zip(problems.items(), (\"C3\", \"C2\", \"C0\"), (5, 6, 7)):\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "f026af27-7cc0-4e7f-936c-93a900a4617e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "as stated m = 0.991 +/- 0.008, b = 0.637 +/- 0.020\n", + "global scale m = 0.989 +/- 0.036, b = 0.654 +/- 0.232\n", + "USU offset on B m = 0.991 +/- 0.008, b = 0.526 +/- 0.022\n" + ] + } + ], + "source": [ + "bands = {}\n", + "for (name, p), seed in zip(problems.items(), (5, 6, 7)):\n", " s = fit(p, seed)\n", " cols = p.columns(line.params)\n", " print(\n", @@ -507,53 +524,69 @@ " lo, hi = rx.predictive.predictive_band(\n", " [on_fine(*r[cols]) for r in s[::10]], levels=(5, 95)\n", " )\n", + " bands[name] = (lo, hi)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "449fc218-8915-49d9-b644-4be9296debe4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "for (name, p), color in zip(problems.items(), (\"C3\", \"C2\", \"C0\")):\n", + " lo, hi = bands[name]\n", " ax.fill_between(\n", - " x_fine, lo - truth_line, hi - truth_line, color=color, alpha=0.35, label=name\n", + " x_fine, lo - y_true_fine, hi - y_true_fine, color=color, alpha=0.35, label=name\n", " )\n", "ax.errorbar(\n", " d_a.x,\n", - " d_a.y - (m_true * d_a.x + b_true),\n", + " d_a.y - line.bind(d_a.x, {})(m_true, b_true),\n", " d_a.y_err,\n", " fmt=\"o\",\n", " ms=3,\n", " color=\"k\",\n", - " label=\"technique A\",\n", + " label=\"A\",\n", ")\n", "ax.errorbar(\n", " d_b.x,\n", - " d_b.y - (m_true * d_b.x + b_true),\n", + " d_b.y - line.bind(d_b.x, {})(m_true, b_true),\n", " d_b.y_err,\n", " fmt=\"s\",\n", " ms=3,\n", " color=\"C4\",\n", - " label=\"technique B (offset)\",\n", + " label=\"B (offset)\",\n", ")\n", "ax.axhline(0.0, ls=\"--\", color=\"k\", label=\"truth\")\n", - "ax.set(xlabel=\"x\", ylabel=\"y - truth\", title=\"90 % bands, relative to the truth\")\n", - "ax.legend(frameon=False, fontsize=8)\n", + "ax.set(xlabel=\"x\", ylabel=\"y - truth\", title=\"90 % bands\")\n", + "ax.legend(frameon=False, fontsize=8, ncol=2, loc=\"upper right\")\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "5c084a2a", + "id": "d261a6f6-47d0-416b-a411-5d4f6a40e32a", "metadata": {}, "source": [ - "## Takeaways\n", - "\n", - "- The likelihood functional is a drop-in; `ν` is an ordinary column.\n", - "- The multivariate t buys honesty, not outlier rejection: it widens.\n", - "- A global error scale inflates every point equally; under a heavy-tailed\n", - " likelihood it stays modest.\n", - "- An unrecognised component belongs *in* the covariance, as a sampled\n", - " correlated term on the technique it afflicts: it moves the mean, which is\n", - " what a Birge-type rescaling after the fact cannot do." + "By using our prior knowledge that data set `B` may have an unknown offset (USU), but data set `A` does not, we are able to cover the truth." ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, From bb1f011e2a1cac7eac0b2aa3e94c44e2200c9299 Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Fri, 11 Sep 2026 16:00:42 -0400 Subject: [PATCH 49/75] Fix the silent wrong answers found in the PR #44 review - given= held-out scores and draws compile the fit|held union against the held-out problem's own parameter index instead of a throwaway Problem, so every constraint reads its own columns, parameters with only a joint prior work, and rows masked out of both views stay out - Comparison rejects a dataset whose meta quantity differs from the model's - Joint prior blocks: the declared dimension is checked at compile, and a one-parameter block of a scipy univariate is that parameter's marginal (which also fixes log_prior on numpy >= 2.4); the rvs contract is documented as scipy's rvs(size=n, random_state=rng) - A prediction of the wrong length raises, naming the comparison - StudentT() defaults nu to Gamma(2, rate 0.1) truncated to nu >= 1, so the default compiles and nested-samples - A parametric covariance singular at theta is zero density, not an exception - Predictor keeps the meta it was bound with, so total_predictive_band amplitudes can read c.meta at the prediction points - predictive_draws under StudentT draw from the multivariate t - Problem.log_jacobian weights each constraint's Jacobian by its weight - Masks are copied, a scalar space derivative gives a per-point Jacobian, and a non-finite constraint weight is rejected - design.md's held-out example passes given= for the GP rung --- docs/design.md | 14 ++- src/rxmc/constraint.py | 18 ++- src/rxmc/covariance.py | 32 ++++-- src/rxmc/diagnostics.py | 33 ++++-- src/rxmc/likelihood.py | 29 ++++- src/rxmc/model.py | 13 ++- src/rxmc/predictive.py | 13 ++- src/rxmc/problem.py | 106 +++++++++++++----- src/rxmc/reactions/elastic.py | 2 +- src/rxmc/reactions/ias.py | 2 +- .../test_recipe_18_evidence_comparison.py | 6 +- test/test_constraint.py | 28 +++++ test/test_diagnostics.py | 56 +++++++++ test/test_likelihood.py | 2 + test/test_predictive.py | 26 +++++ test/test_problem.py | 82 ++++++++++++++ 16 files changed, 389 insertions(+), 73 deletions(-) diff --git a/docs/design.md b/docs/design.md index c01865d..68cd366 100644 --- a/docs/design.md +++ b/docs/design.md @@ -270,7 +270,7 @@ class Likelihood: # functional of (d2, logdet, n, *values); par def log_likelihood(self, d2, logdet, n, *values) def chi2(self, d2, logdet, n, *values) # d2 class Gaussian(Likelihood) -class StudentT(Likelihood) # StudentT(nu=None) -> Parameter("nu", bounds=(1, inf)); pass nu= to share or rename +class StudentT(Likelihood) # StudentT(nu=None) -> Parameter("nu", prior=gamma(a=2, scale=10), bounds=(1, inf)); pass nu= to share or rename class Chi2(Likelihood) # -d2/2, no log-determinant ``` @@ -364,8 +364,11 @@ Compile the same declarations twice and you get two independent problems. **Prior assembly.** Each slot is covered by its parameter's marginal or by exactly one joint block `(params, joint)`, where `joint` has `logpdf(values)` over those parameters in that order and optionally -`prior_transform(u)` and `rvs(n)`; `scipy.stats.multivariate_normal` -qualifies and is whitened for the unit-cube map. A hyperprior is a joint +`prior_transform(u)` and `rvs(size=n, random_state=rng)` (scipy's +spelling); `scipy.stats.multivariate_normal` qualifies and is whitened for +the unit-cube map. A joint that declares its dimension (`dim`) must match +its parameters, and a one-parameter block holding a scipy univariate +distribution is that parameter's marginal. A hyperprior is a joint block that includes its hyperparameter, whose `logpdf` is `sum log p(child | hyper) + log p(hyper)` and whose `prior_transform` draws the hyperparameter first (recipe 24). A slot no prior covers, or covered @@ -513,9 +516,10 @@ verdict = rx.diagnostics.compare_logz(logz["Lgp"], logz["L2y"]) fit = ladder["Lgp"].masked_where(lambda x: x < np.deg2rad(90)) p_fit, p_held = rx.Problem([fit]), rx.Problem([fit.complement()]) samples = run(p_fit) # rows in p_fit.names order -lp = rx.diagnostics.heldout_log_predictive(p_held, samples) +# the GP spans the cut, so score and draw from p(y_held | y_fit, theta): given= +lp = rx.diagnostics.heldout_log_predictive(p_held, samples, given=p_fit) score = rx.diagnostics.log_posterior_predictive(lp) -draws = rx.diagnostics.predictive_draws(p_held, samples, n_rep=4) +draws = rx.diagnostics.predictive_draws(p_held, samples, n_rep=4, given=p_fit) ``` The labels `L0`, `E0`, `L2y`, `Lgp` are the error-model ladder of recipe diff --git a/src/rxmc/constraint.py b/src/rxmc/constraint.py index 5ef51d7..3865bba 100644 --- a/src/rxmc/constraint.py +++ b/src/rxmc/constraint.py @@ -68,6 +68,14 @@ def __post_init__(self): raise TypeError(f"data must be a Dataset, got {type(self.data).__name__}") if not isinstance(self.model, Model): raise TypeError(f"model must be a Model, got {type(self.model).__name__}") + held = (self.data.meta or {}).get("quantity") + predicted = getattr(self.model, "quantity", None) + if held is not None and predicted is not None and held != predicted: + raise ValueError( + f"dataset {self.data.label or 'dataset'!r} holds {held!r} but the " + f"model predicts {predicted!r}: convert the data " + "(from_measurement(..., quantity=)) or use a matching model" + ) space = as_transform(self.space) if space.params: raise ValueError( @@ -83,7 +91,8 @@ def __post_init__(self): set_(self, "log_jac", np.zeros(self.data.n)) return with np.errstate(all="ignore"): - jac = np.abs(space.derivative(self.data.y)) + # a derivative may come back as a scalar; the Jacobian is per point + jac = np.broadcast_to(np.abs(space.derivative(self.data.y)), (self.data.n,)) set_(self, "y", space(self.data.y)) set_(self, "y_err", jac * self.data.y_err) set_(self, "log_jac", np.log(jac)) @@ -157,7 +166,8 @@ def _reported(spec, n): def _as_mask(mask, n) -> np.ndarray: - mask = np.asarray(mask, dtype=bool) + # a copy, so the caller reusing its array can't move the mask + mask = np.array(mask, dtype=bool) if mask.shape != (n,): raise ValueError(f"mask must have shape ({n},), got {mask.shape}") return mask @@ -211,8 +221,8 @@ def __post_init__(self): if not isinstance(self.likelihood, Likelihood): raise TypeError("likelihood must be a Likelihood") weight = float(self.weight) - if weight < 0: - raise ValueError("weight must be non-negative") + if not (np.isfinite(weight) and weight >= 0): + raise ValueError(f"weight must be finite and non-negative, got {weight}") ns = [c.n for c in comps] starts = np.concatenate([[0], np.cumsum(ns)[:-1]]).astype(int) offsets = tuple(slice(int(s), int(s + n)) for s, n in zip(starts, ns)) diff --git a/src/rxmc/covariance.py b/src/rxmc/covariance.py index 9450616..45407e3 100644 --- a/src/rxmc/covariance.py +++ b/src/rxmc/covariance.py @@ -41,6 +41,14 @@ __all__ = ["StructuredCovariance", "chol_logdet"] +class _SingularCovariance(ValueError): + """A parametric covariance that is singular at the requested ``theta``. + + The compiled constraint reads it as zero density (``log_likelihood`` is + ``-inf``) so a sampler stepping onto such a point moves on. + """ + + def chol_logdet(Sigma): """Lower Cholesky factor and log-determinant of a positive-definite matrix.""" L = sla.cholesky(np.asarray(Sigma, dtype=float), lower=True) @@ -267,7 +275,8 @@ def _factor(self, D, U, M, X): Ls = None factors = ("structured", blocks, Ls, logdet) except np.linalg.LinAlgError as err: - raise ValueError(self._singular_message(D)) from err + error = ValueError if self.is_constant else _SingularCovariance + raise error(self._singular_message(D)) from err if self.is_constant: self._cache = factors return factors @@ -288,18 +297,19 @@ def _singular_message(self, D): if np.any(D[pos] == 0.0) ] msg = "the constraint's covariance is singular on its active points" - if offenders: - msg += ( - f"; the diagonal is zero on rows of {offenders}: those comparisons " - "have zero statistical error and no diagonal term covers their " - "points (a block covered only by correlated modes is singular here " - "even when the full covariance is not)" + if not offenders: + return msg + ( + " although its diagonal is nonzero: a matrix term dominates it " + "(e.g. a kernel amplitude large against the diagonal)" ) - msg += ( - ". Remedies: comparison.reported_terms(), a noise term, a fixed Term " - "covering those points, or statistical=False with an explicit covariance." + return msg + ( + f"; the diagonal is zero on rows of {offenders}: those comparisons " + "have zero statistical error and no diagonal term covers their " + "points (a block covered only by correlated modes is singular here " + "even when the full covariance is not). Remedies: " + "comparison.reported_terms(), a noise term, a fixed Term covering " + "those points, or statistical=False with an explicit covariance." ) - return msg # -- public -------------------------------------------------------------- diff --git a/src/rxmc/diagnostics.py b/src/rxmc/diagnostics.py index 6dbba6a..5be0749 100644 --- a/src/rxmc/diagnostics.py +++ b/src/rxmc/diagnostics.py @@ -5,8 +5,9 @@ ``problem.names`` order (what emcee's ``get_chain(flat=True)``, dynesty's ``samples_equal()`` and black-box-bayes give) and never touches a sampler: -* :func:`predictive_draws` — draws from the posterior predictive - ``N(ym(theta), Sigma(theta))`` on a constraint's active points, or the +* :func:`predictive_draws` — draws from the posterior predictive of a + constraint's likelihood on its active points (``N(ym(theta), Sigma(theta))``, + or the multivariate t of :class:`~rxmc.likelihood.StudentT`), or the model-only predictive ``ym(theta)``. * :func:`coverage_curve`, :func:`coverage_error`, :func:`sharpness` — empirical calibration and width of those draws against the data. @@ -34,14 +35,12 @@ from __future__ import annotations -from dataclasses import replace - import numpy as np import scipy.linalg as sla from scipy.special import logsumexp from .likelihood import Gaussian -from .problem import Problem +from .problem import CompiledConstraint, Problem __all__ = [ "predictive_draws", @@ -97,6 +96,13 @@ def _psd_factor(Sigma, jitter=1e-10) -> np.ndarray: return V * np.sqrt(np.clip(w, 0.0, None)) +def _masks(constraint) -> list[np.ndarray]: + """Per-comparison active masks of a compiled constraint, ``None`` as all-true.""" + if constraint.source.masks is None: + return [np.ones(c.n, dtype=bool) for c in constraint.comparisons] + return list(constraint.source.masks) + + class _Conditional: """``p(y_H | y_F, theta)`` for one held-out constraint given its fit.""" @@ -130,8 +136,11 @@ def __init__(self, held: Problem, given: Problem, constraint: int): "(omit given=) or use a Gaussian likelihood" ) self.held, self.fit = h, f - # the union of the two active sets, compiled once: every row active - self.full = Problem([replace(h.source, masks=None)]).constraints[0] + # the union of the two active sets, compiled once against the held-out + # problem's own index, so theta's columns mean what they mean there (the + # constraint shares every parameter object, so nothing new is indexed) + union = [a | b for a, b in zip(_masks(h), _masks(f))] + self.full = CompiledConstraint(h.source.masked(union), held.index) lookup = {int(r): i for i, r in enumerate(self.full.active)} self.iH = np.array([lookup[int(r)] for r in h.active], dtype=int) self.iF = np.array([lookup[int(r)] for r in f.active], dtype=int) @@ -176,9 +185,12 @@ def predictive_draws( """Posterior-predictive draws on a constraint's active points. For each posterior row ``theta_i`` the constraint gives ``ym_i`` and - ``Sigma_i``; ``n_rep`` draws ``ym_i + L_i z`` (``z ~ N(0, I)``) are taken. - With ``model_only=True`` the rows are ``ym_i`` themselves and no - covariance is assembled. + ``Sigma_i``; ``n_rep`` draws ``ym_i + s L_i z`` (``z ~ N(0, I)``) are taken, + where ``s`` is the likelihood's per-draw + :meth:`~rxmc.likelihood.Likelihood.predictive_scale` (1 for a Gaussian, + the multivariate-t mixing scale under a Student-t). With + ``model_only=True`` the rows are ``ym_i`` themselves and no covariance is + assembled. Parameters ---------- @@ -227,6 +239,7 @@ def predictive_draws( mu, Sigma = c.ym(theta)[c.active], c.matrix(theta) L = _psd_factor(Sigma) z = rng.standard_normal((n_rep, N)) + z *= c.likelihood.predictive_scale(rng, n_rep, *theta[c.like_gather])[:, None] out[i * n_rep : (i + 1) * n_rep] = mu + z @ L.T return out diff --git a/src/rxmc/likelihood.py b/src/rxmc/likelihood.py index a26b2dd..f40fe65 100644 --- a/src/rxmc/likelihood.py +++ b/src/rxmc/likelihood.py @@ -18,6 +18,7 @@ from math import inf import numpy as np +from scipy import stats from scipy.special import gammaln from .params import Parameter @@ -41,6 +42,14 @@ def log_likelihood(self, d2, logdet, n, *values) -> float: def chi2(self, d2, logdet, n, *values) -> float: return d2 + def predictive_scale(self, rng, size, *values) -> np.ndarray: + """Per-draw multipliers of a correlated normal draw ``L z``: ones. + + A scale mixture of normals returns its mixing scales instead, so + ``ym + scale * L z`` is a draw from the likelihood's own predictive. + """ + return np.ones(size) + class Gaussian(Likelihood): """Multivariate-normal likelihood over the stacked residual (parameter-free).""" @@ -61,17 +70,31 @@ class StudentT(Likelihood): Parameters ---------- nu : Parameter, optional - The degrees of freedom. Defaults to ``Parameter("nu", bounds=(1, inf))``; - two constraints using the default each derive a ``"nu"`` and the + The degrees of freedom. Defaults to ``Parameter("nu", + prior=gamma(a=2, scale=10), bounds=(1, inf))``: the Gamma(2, rate 0.1) + prior of Juárez & Steel, "Model-based clustering of non-Gaussian panel + data based on skew-t distributions", J. Bus. Econ. Stat. 28, 52 (2010), + truncated to ``nu >= 1``. It has most of its mass on heavy tails and a + mean near 20, and a unit-cube map, so nested samplers take it as is. + Two constraints using the default each derive a ``"nu"`` and the problem fails to compile on the duplicate name, so pass ``nu=`` to share one or to name them apart. """ def __init__(self, nu: Parameter | None = None): if nu is None: - nu = Parameter("nu", bounds=(1.0, inf), latex=r"\nu") + nu = Parameter( + "nu", + prior=stats.gamma(a=2.0, scale=10.0), + bounds=(1.0, inf), + latex=r"\nu", + ) self.params = (nu,) + def predictive_scale(self, rng, size, nu) -> np.ndarray: + """``sqrt(nu / w)`` with ``w ~ chi2(nu)``: the multivariate-t mixing scale.""" + return np.sqrt(nu / rng.chisquare(nu, size)) + def log_likelihood(self, d2, logdet, n, nu) -> float: return ( gammaln((n + nu) / 2.0) diff --git a/src/rxmc/model.py b/src/rxmc/model.py index be9f748..a7c1bf5 100644 --- a/src/rxmc/model.py +++ b/src/rxmc/model.py @@ -54,12 +54,17 @@ class Predictor: The grid the prediction is made on. fn : callable ``fn(*values) -> np.ndarray`` on that grid. + meta : mapping, optional + The dataset metadata the model was bound with; a term evaluated at the + predictor's grid (:func:`~rxmc.predictive.total_predictive_band`) reads + it through ``c.meta(key)``. """ - def __init__(self, params: Sequence[Parameter], x, fn: Callable): + def __init__(self, params: Sequence[Parameter], x, fn: Callable, meta=None): self.params = _check_params(params) self.x = np.asarray(x) self._fn = fn + self.meta = meta def __call__(self, *values) -> np.ndarray: if len(values) != len(self.params): @@ -100,7 +105,7 @@ def bind(self, x, meta=None) -> Predictor: if self.fn is None: raise TypeError(f"{type(self).__name__} must override bind()") fn = self.fn - return Predictor(self.params, x, lambda *values: fn(x, *values)) + return Predictor(self.params, x, lambda *values: fn(x, *values), meta) def __or__(self, transform) -> "Model": return _Transformed(self, as_transform(transform)) @@ -125,7 +130,7 @@ def __init__(self, inner: Model, transform): def bind(self, x, meta=None) -> Predictor: pred, t, n = self.inner.bind(x, meta), self.transform, len(self.inner.params) - return Predictor(self.params, x, lambda *v: t(pred(*v[:n]), *v[n:])) + return Predictor(self.params, x, lambda *v: t(pred(*v[:n]), *v[n:]), meta) class _Combined(Model): @@ -140,7 +145,7 @@ def __init__(self, left: Model, right: Model, op, symbol: str): def bind(self, x, meta=None) -> Predictor: lp, rp = self.left.bind(x, meta), self.right.bind(x, meta) n, op = len(self.left.params), self.op - return Predictor(self.params, x, lambda *v: op(lp(*v[:n]), rp(*v[n:]))) + return Predictor(self.params, x, lambda *v: op(lp(*v[:n]), rp(*v[n:])), meta) def polynomial(order: int) -> Model: diff --git a/src/rxmc/predictive.py b/src/rxmc/predictive.py index b982e27..1d3872a 100644 --- a/src/rxmc/predictive.py +++ b/src/rxmc/predictive.py @@ -20,7 +20,7 @@ import numpy as np import scipy.linalg as sla -from .problem import Problem +from .problem import Problem, _per_point from .terms import KernelTerm, TermContext, as_2d __all__ = ["gp_posterior_predictive", "predictive_band", "total_predictive_band"] @@ -175,7 +175,8 @@ def total_predictive_band( The discrepancy term, as declared in one of the problem's constraints. predictor : Predictor The model bound to ``x_pred`` (``model.bind(x_pred, meta)``); its - columns are read from the problem. + columns are read from the problem, and the ``meta`` it was bound with + is what a callable amplitude's ``c.meta(key)`` reads at ``x_pred``. x_pred : array_like The prediction grid (raw coordinates; the term's ``coords`` transform is applied for the kernel). @@ -225,6 +226,12 @@ def total_predictive_band( nk, n_fn = term.n_kernel, term._n_fn_params x_pred = np.asarray(x_pred) meta = None if c.meta is None else {k: v[rows] for k, v in c.meta.items()} + # the prediction points carry the metadata the predictor was bound with + meta_p = ( + None + if predictor.meta is None + else {k: _per_point(v, len(x_pred)) for k, v in predictor.meta.items()} + ) out = np.empty((samples.shape[0], x_pred.shape[0])) for i, theta in enumerate(samples): @@ -258,7 +265,7 @@ def total_predictive_band( segments=entry.segments, labels=entry.labels, ) # fmt: skip mu = space(predictor(*theta[cols_pred])) - ctx_p = TermContext(x=term.coords(x_pred, *cv), y=mu, ym=mu) + ctx_p = TermContext(x=term.coords(x_pred, *cv), y=mu, ym=mu, _meta=meta_p) a_t, a_p = _amplitudes(term, ctx_t, ctx_p, av) Kst = np.outer(a_p, a_t[keep]) * k(as_2d(ctx_p.x), as_2d(ctx_t.x)[keep]) Kss = a_p**2 * np.asarray(k.diag(as_2d(ctx_p.x)), dtype=float) diff --git a/src/rxmc/problem.py b/src/rxmc/problem.py index 6a1f152..1a823b7 100644 --- a/src/rxmc/problem.py +++ b/src/rxmc/problem.py @@ -18,9 +18,13 @@ * neither: the parameter must appear in exactly one joint block passed as ``priors=[(params, joint), ...]``, where ``joint`` exposes ``logpdf(values)`` over ``params`` in that order and, optionally, - ``prior_transform(u)`` and ``rvs(n)``. A frozen + ``prior_transform(u)`` and ``rvs(size=n, random_state=rng)`` (scipy's + spelling). A joint that declares its dimension (``dim``, as scipy's + multivariate distributions do) must match its parameters. A frozen ``scipy.stats.multivariate_normal`` gets a whitening unit-cube map for - free. A hyperprior is a joint block that includes its hyperparameter. + free, and a one-parameter block holding a scipy univariate distribution is + that parameter's marginal. A hyperprior is a joint block that includes its + hyperparameter. Nothing user-facing is mutated by compiling. Compile the same declarations twice and you get two independent problems. @@ -28,13 +32,14 @@ from __future__ import annotations -from typing import Iterable, Sequence +from typing import Any, Iterable, Sequence import numpy as np from scipy import stats +from scipy.stats.distributions import rv_frozen from .constraint import Constraint -from .covariance import StructuredCovariance +from .covariance import StructuredCovariance, _SingularCovariance from .params import Parameter from .terms import statistical @@ -128,14 +133,25 @@ def _is_frozen_mvn(joint) -> bool: return type(joint).__name__ == "multivariate_normal_frozen" +def _joint_dim(joint) -> int | None: + """The dimension a joint declares, or ``None`` when it declares none.""" + if isinstance(joint, rv_frozen): + return 1 + dim = getattr(joint, "dim", None) + return None if dim is None else int(dim) + + class _Marginal: - """One slot: a marginal (truncated to bounds) or the uniform on bounds.""" + """One slot: a marginal (truncated to bounds) or the uniform on bounds. - def __init__(self, p: Parameter, slot: int): + ``dist`` overrides ``p.prior``: a one-parameter prior block's distribution. + """ + + def __init__(self, p: Parameter, slot: int, dist=None): self.p, self.slot = p, slot lo, hi = p.bounds self.lo, self.hi = lo, hi - self.dist = p.prior + self.dist = p.prior if dist is None else dist self.bounded = np.isfinite(lo) and np.isfinite(hi) if self.dist is None: if not self.bounded: @@ -178,6 +194,12 @@ def __init__(self, params: Sequence[Parameter], joint, slots: np.ndarray): self.names = [p.name for p in self.params] if not hasattr(joint, "logpdf"): raise TypeError(f"joint prior over {self.names} must have logpdf(values)") + dim = _joint_dim(joint) + if dim is not None and dim != len(self.params): + raise ValueError( + f"the joint prior over {self.names} is {dim}-dimensional; it must " + f"cover exactly those {len(self.params)} parameter(s), in order" + ) if _is_frozen_mvn(joint): self._L = np.linalg.cholesky(np.atleast_2d(joint.cov)) self._mean = np.atleast_1d(joint.mean) @@ -195,7 +217,8 @@ def logpdf(self, values) -> float: np.any(values < self.bounds[:, 0]) or np.any(values > self.bounds[:, 1]) ): return -np.inf - return float(self.joint.logpdf(values)) + # item(): a length-1 array is fine, a longer one (a mis-sized joint) is not + return float(np.asarray(self.joint.logpdf(values)).item()) def transform(self, u): if self.bounded: @@ -244,7 +267,8 @@ class _Prior: def __init__(self, index: ParameterIndex, priors): self.ndim = index.ndim self.joints: list[_Joint] = [] - covered: dict[Parameter, str] = {} + covered: set[Parameter] = set() + univariate: dict[Parameter, Any] = {} for entry in priors: try: params, joint = entry @@ -263,10 +287,17 @@ def __init__(self, index: ParameterIndex, priors): raise ValueError( f"parameter {p.name!r} appears in two joint blocks" ) - covered[p] = "joint" - self.joints.append(_Joint(params, joint, index.slots(params))) + covered.add(p) + if len(params) == 1 and isinstance(joint, rv_frozen): + # a scipy univariate over one parameter is that parameter's + # marginal: truncated to its bounds, with a ppf unit-cube map + univariate[params[0]] = joint + else: + self.joints.append(_Joint(params, joint, index.slots(params))) self.marginals = [ - _Marginal(p, i) for i, p in enumerate(index.params) if p not in covered + _Marginal(p, i, univariate.get(p)) + for i, p in enumerate(index.params) + if p not in covered or p in univariate ] def logpdf(self, theta) -> float: @@ -305,6 +336,15 @@ def sample(self, n, rng) -> np.ndarray: # ---------------------------------------------------------------------------- +def _per_point(v, n) -> np.ndarray: + """A metadata value as one entry per point: a length-``n`` array as is, + anything else repeated ``n`` times.""" + arr = np.asarray(v) if v is not None else None + if arr is not None and arr.ndim >= 1 and arr.shape[0] == n: + return arr + return np.full(n, v) if np.isscalar(v) else np.full(n, v, dtype=object) + + def _stack_meta(constraint: Constraint) -> dict | None: keys = set() for c in constraint.comparisons: @@ -313,18 +353,7 @@ def _stack_meta(constraint: Constraint) -> dict | None: return None meta = {} for key in keys: - parts = [] - for c in constraint.comparisons: - v = c.data.meta.get(key, None) - arr = np.asarray(v) if v is not None else None - if arr is not None and arr.ndim >= 1 and arr.shape[0] == c.n: - parts.append(arr) - else: - parts.append( - np.full(c.n, v, dtype=object) - if not np.isscalar(v) - else np.full(c.n, v) - ) + parts = [_per_point(c.data.meta.get(key), c.n) for c in constraint.comparisons] try: stacked = np.concatenate(parts) except (TypeError, ValueError): @@ -390,7 +419,16 @@ def __init__(self, constraint: Constraint, index: ParameterIndex): def ym(self, theta) -> np.ndarray: """The stacked prediction in comparison space, all points.""" - return np.concatenate([c.predict(*theta[g]) for _, g, c in self.predictors]) + parts = [] + for (_, g, c), label in zip(self.predictors, self.labels): + y = c.predict(*theta[g]) + if y.shape != (c.n,): + raise ValueError( + f"comparison {label!r}: the model returned shape {y.shape} on a " + f"grid of {c.n} point(s)" + ) + parts.append(y) + return np.concatenate(parts) def predict_physical(self, theta) -> list[np.ndarray]: return [c.predictor(*theta[g]) for _, g, c in self.predictors] @@ -399,8 +437,10 @@ def _stats(self, theta): ym = self.ym(theta) if not np.all(np.isfinite(ym[self.active])): return None - d2, logdet = self.covariance.distance(ym, theta) - return d2, logdet + try: + return self.covariance.distance(ym, theta) + except _SingularCovariance: + return None # a parametric covariance singular at this theta: no density def log_likelihood(self, theta) -> float: s = self._stats(theta) @@ -515,8 +555,16 @@ def chi2(self, theta) -> float: return float(sum(c.chi2(theta) for c in self.constraints)) def log_jacobian(self) -> float: - """Sum of the constraints' comparison-space log-Jacobians.""" - return float(sum(c.log_jacobian for c in self.constraints)) + """Sum of the constraints' comparison-space log-Jacobians, each times its + ``weight``. + + A tempered constraint enters the likelihood as ``weight * log L``, so + its Jacobian enters ``log Z_raw = log Z + log_jacobian()`` with the same + weight, and a weight-0 constraint not at all. + """ + return float( + sum(c.weight * c.log_jacobian for c in self.constraints if c.weight) + ) def prior_transform(self, u) -> np.ndarray: u = np.asarray(u, dtype=float) diff --git a/src/rxmc/reactions/elastic.py b/src/rxmc/reactions/elastic.py index e277c56..deefd18 100644 --- a/src/rxmc/reactions/elastic.py +++ b/src/rxmc/reactions/elastic.py @@ -251,4 +251,4 @@ def predict(*values): ) return extract(xs, ws) - return Predictor(self.params, x, predict) + return Predictor(self.params, x, predict, meta) diff --git a/src/rxmc/reactions/ias.py b/src/rxmc/reactions/ias.py index 6ed6835..db6f2f3 100644 --- a/src/rxmc/reactions/ias.py +++ b/src/rxmc/reactions/ias.py @@ -154,4 +154,4 @@ def predict(*values): ) return ws.xs(*(U(r, *a) for U, a in zip(potentials, args))) / MB_PER_B - return Predictor(self.params, x, predict) + return Predictor(self.params, x, predict, meta) diff --git a/test/recipes/test_recipe_18_evidence_comparison.py b/test/recipes/test_recipe_18_evidence_comparison.py index 62e184e..47e1f7f 100644 --- a/test/recipes/test_recipe_18_evidence_comparison.py +++ b/test/recipes/test_recipe_18_evidence_comparison.py @@ -42,7 +42,6 @@ def error_models(d, model): amplitude=T.constant_amplitude, amplitude_params=(log_A,), ) - nu = Parameter("nu", prior=stats.uniform(1, 30)) return { "L0": Constraint([comp_log], terms=[T.noise(log_eps)], statistical=False), "E0": Constraint( @@ -58,7 +57,7 @@ def error_models(d, model): [comp_log], terms=[T.noise(log_eps)], statistical=False, - likelihood=StudentT(nu), + likelihood=StudentT(), # the default nu, as the recipe writes it ), } @@ -83,6 +82,9 @@ def test_each_problem_compiles_independently_with_shared_parameter_objects(): theta = np.array([*TRUE, np.log(0.1)]) assert np.isfinite(problems["L0"].log_posterior(theta)) assert np.isfinite(problems["E0"].log_posterior(theta)) + # the default nu has a proper prior with a unit-cube map: dynesty takes L0t + assert np.isfinite(problems["L0t"].log_posterior([*theta, 5.0])) + assert np.all(np.isfinite(problems["L0t"].prior_transform(np.full(4, 0.5)))) def test_log_jacobian_makes_spaces_comparable(): diff --git a/test/test_constraint.py b/test/test_constraint.py index 5d1fede..0fd9fce 100644 --- a/test/test_constraint.py +++ b/test/test_constraint.py @@ -38,6 +38,13 @@ def test_log_space_delta_method_and_jacobian(self): assert c.log_jacobian(mask) == pytest.approx(-np.sum(np.log(d.y[mask]))) np.testing.assert_allclose(c.predict(2.0, 1.0), np.log(d.y)) + def test_scalar_derivative_is_a_per_point_jacobian(self): + double = Transform(lambda a: 2.0 * a, derivative=lambda a: 2.0) + c = Comparison(dataset(), line, space=double) + assert c.log_jac.shape == (4,) + assert c.log_jacobian() == pytest.approx(4 * np.log(2.0)) + np.testing.assert_allclose(c.y_err, 2.0 * dataset().y_err) + def test_non_positive_data_under_log_does_not_raise(self): d = Dataset([0.0, 1.0], [-1.0, 2.0], [0.1, 0.1]) c = Comparison(d, line, space=log) @@ -61,6 +68,19 @@ def bind(self, x, meta=None): Comparison(d, Probe(None, [m, b])) assert seen["meta"] == {"Elab": 10.0} + def test_quantity_must_match_the_model(self): + class Observable(Model): + quantity = "dXS/dA" + + model = Observable(lambda x, m, b: m * x + b, [m, b]) + Comparison(dataset(meta={"quantity": "dXS/dA"}), model) + with pytest.raises(ValueError, match="holds 'dXS/dRuth'.*predicts 'dXS/dA'"): + Comparison(dataset(meta={"quantity": "dXS/dRuth"}), model) + # a composite (dXS/dRuth * Rutherford) declares no quantity of its own, + # and data that declares none is not checked + Comparison(dataset(meta={"quantity": "dXS/dRuth"}), model * line) + Comparison(dataset(), model) + def test_type_checks(self): with pytest.raises(TypeError, match="Dataset"): Comparison(np.ones(3), line) @@ -129,6 +149,8 @@ def test_construction_and_validation(self): Constraint([self.c1], terms=[np.eye(3)]) with pytest.raises(ValueError, match="non-negative"): Constraint([self.c1], weight=-1.0) + with pytest.raises(ValueError, match="finite"): + Constraint([self.c1], weight=np.nan) with pytest.raises(ValueError, match="at least one"): Constraint([]) with pytest.raises(TypeError, match="Likelihood"): @@ -143,6 +165,12 @@ def test_masks_validated(self): with pytest.raises(ValueError, match="shape"): Constraint([self.c1, self.c2], masks=[np.ones(3, bool), np.ones(3, bool)]) + def test_masks_are_copied(self): + mask = np.array([True, False, True]) + c = Constraint([self.c1]).masked([mask]) + mask[:] = True # the caller reuses its array + assert np.array_equal(c.complement().active, [1]) + def test_support_resolution(self): c = Constraint([self.c1, self.c2]) assert np.array_equal(c.support(None), np.arange(7)) diff --git a/test/test_diagnostics.py b/test/test_diagnostics.py index a33c8aa..8be21ee 100644 --- a/test/test_diagnostics.py +++ b/test/test_diagnostics.py @@ -65,6 +65,15 @@ def test_one_row_masks_and_width_check(self): with pytest.raises(ValueError, match=r"\(n, 2\)"): predictive_draws(p, [[1.0, 2.0, 3.0]]) + def test_student_t_draws_follow_the_multivariate_t(self): + p, _ = line_problem(likelihood=StudentT(Parameter("nu", bounds=(1, 30)))) + draws = predictive_draws(p, [1.0, 2.0, 6.0], n_rep=100000, rng=0) + # a multivariate t with scale diag(ERR**2) has covariance nu/(nu-2) times it + np.testing.assert_allclose(draws.var(axis=0), 1.5 * ERR**2, rtol=0.05) + # one mixing scale per draw: the points' |residuals| move together + r = np.abs(draws - Y) + assert np.corrcoef(r[:, 0], r[:, 1])[0, 1] > 0.05 + def test_tiny_variances_not_inflated(self): d = Dataset(X, Y, np.full(5, 1e-9), label="tiny") p = Problem([Constraint([Comparison(d, poly(1))])]) @@ -150,6 +159,53 @@ def test_conditional_under_a_spanning_matrix_term(self): joint = full.log_likelihood(theta) - fit.log_likelihood(theta) assert lp[0] == pytest.approx(joint) + def test_conditional_reads_the_columns_of_every_constraint(self): + # two constraints with a parameter each: the second's conditional must + # read b's column, not the first one + rng = np.random.default_rng(5) + A = rng.normal(size=(4, 4)) + K = A @ A.T / 4 + cs = [] + for s, label in ((1.0, "d1"), (3.0, "d2")): + d = Dataset(self.d.x, s * self.d.y, self.d.y_err, label=label) + p = Parameter(f"s{label}", prior=stats.norm(0, 10)) + m = Model(lambda x, s: s * x, [p]) + cs.append(Constraint([Comparison(d, m)], terms=[Term(K, kind="matrix")])) + fits = [c.masked_where(lambda x: x < 2.5) for c in cs] + fit, held = Problem(fits), Problem([f.complement() for f in fits]) + full = Problem(cs) + theta = np.array([1.0, 3.0]) + lp = heldout_log_predictive(held, theta, given=fit) + assert lp[0] == pytest.approx( + full.log_likelihood(theta) - fit.log_likelihood(theta) + ) + S = np.diag(self.d.y_err**2) + K + ym = theta[1] * self.d.x + F, H = slice(0, 2), slice(2, 4) + mean = ym[H] + S[H, F] @ np.linalg.solve(S[F, F], 3.0 * self.d.y[F] - ym[F]) + draws = predictive_draws(held, theta, constraint=1, given=fit, model_only=True) + np.testing.assert_allclose(draws[0], mean) + + def test_given_needs_no_marginal_priors_and_keeps_doubly_masked_rows_out(self): + # the coefficients have only a joint prior, and a y = 0 point (not + # finite in log space) is masked out of both the fit and the held-out view + a0, a1 = Parameter("a0"), Parameter("a1") + model = Model(lambda x, a0, a1: np.exp(a0 + a1 * x), [a0, a1]) + d = Dataset(np.arange(5.0), [0.0, 2.0, 3.0, 5.0, 8.0], np.full(5, 0.2)) + K = 0.05 * np.exp(-0.5 * np.subtract.outer(d.x, d.x) ** 2) + c = Constraint([Comparison(d, model, space=log)], terms=[Term(K)]) + prior = [([a0, a1], stats.multivariate_normal(np.zeros(2), 4 * np.eye(2)))] + fit = c.masked([np.array([False, True, True, False, False])]) + held = c.masked([np.array([False, False, False, True, True])]) + both = c.masked([np.array([False, True, True, True, True])]) + p_fit, p_held = Problem([fit], priors=prior), Problem([held], priors=prior) + p_both = Problem([both], priors=prior) + theta = np.array([0.5, 0.4]) + lp = heldout_log_predictive(p_held, theta, given=p_fit) + assert lp[0] == pytest.approx( + p_both.log_likelihood(theta) - p_fit.log_likelihood(theta) + ) + def test_given_is_validated(self): fit, held, full = self.problems([]) with pytest.raises(ValueError, match="overlap"): diff --git a/test/test_likelihood.py b/test/test_likelihood.py index 95269c7..3b37905 100644 --- a/test/test_likelihood.py +++ b/test/test_likelihood.py @@ -47,6 +47,8 @@ def test_student_t_default_and_explicit_parameter(): default = StudentT() assert [p.name for p in default.params] == ["nu"] assert default.params[0].bounds == (1.0, np.inf) + # Gamma(2, rate 0.1) (Juárez & Steel 2010): a proper prior, so it compiles + assert default.params[0].prior.mean() == pytest.approx(20.0) p = Parameter("nu_a", bounds=(1.0, 100.0)) assert StudentT(nu=p).params == (p,) # two defaults are two distinct parameters with one name (compile rejects) diff --git a/test/test_predictive.py b/test/test_predictive.py index ec88d6e..5663f5e 100644 --- a/test/test_predictive.py +++ b/test/test_predictive.py @@ -166,6 +166,32 @@ def test_amplitude_matches_a_scaled_kernel(self): ) np.testing.assert_allclose(b1, b2, atol=1e-8) + def test_amplitude_reads_the_predictor_meta(self): + # recipe 22: an amplitude keyed on the dataset's energy is, at 25 MeV, a + # constant amplitude of 0.5 at the data and at the prediction points + d = Dataset(X, Y, np.full(10, 0.05), label="d", meta={"Elab": 25.0}) + m1, m2 = line(), line() + lA1, lA2 = (Parameter("log_A", prior=stats.norm(0, 1)) for _ in range(2)) + gp_meta = kernel( + RBF(0.3, "fixed"), + amplitude=lambda c, lA: np.exp(lA) * c.meta("Elab") / 50.0, + amplitude_params=(lA1,), + ) + gp_const = kernel( + RBF(0.3, "fixed"), amplitude=constant_amplitude, amplitude_params=(lA2,) + ) + p1 = Problem([Constraint([Comparison(d, m1)], terms=[gp_meta])]) + p2 = Problem([Constraint([Comparison(d, m2)], terms=[gp_const])]) + chain1 = np.tile([0.5, 0.2, 0.0], (8, 1)) + chain2 = np.tile([0.5, 0.2, np.log(0.5)], (8, 1)) + b1 = total_predictive_band( + p1, gp_meta, m1.bind(X_PRED, d.meta), X_PRED, chain1, rng=4 + ) + b2 = total_predictive_band( + p2, gp_const, m2.bind(X_PRED, {}), X_PRED, chain2, rng=4 + ) + np.testing.assert_allclose(b1, b2, atol=1e-8) + def test_explicit_noise_and_errors(self): p, gp, model, comp = problem("noise") chain = self.chain(p, n=6) diff --git a/test/test_problem.py b/test/test_problem.py index a8a6e9a..baad5bb 100644 --- a/test/test_problem.py +++ b/test/test_problem.py @@ -203,6 +203,51 @@ def prior_transform(self, u): np.testing.assert_allclose(theta, [0.0, 0.0, 0.0], atol=1e-12) assert np.isfinite(p.log_prior(theta)) + def test_joint_dimension_must_match_its_parameters(self): + model = line_model(prior=False) + m, b = model.params + c = Constraint([Comparison(dataset(), model)]) + mvn2 = stats.multivariate_normal([0.0, 3.0], np.diag([1.0, 4.0])) + with pytest.raises(ValueError, match=r"2-dimensional.*1 parameter"): + Problem([c], priors=[([m], mvn2), ([b], stats.norm())]) + with pytest.raises(ValueError, match="3-dimensional"): + Problem([c], priors=[([m, b], stats.multivariate_normal(np.zeros(3)))]) + with pytest.raises(ValueError, match="1-dimensional"): + Problem([c], priors=[([m, b], stats.norm())]) + + def test_one_parameter_block_of_a_univariate_is_a_marginal(self): + # the natural way to give a bounded parameter (StudentT's nu) a prior + nu = Parameter("nu", bounds=(1.0, np.inf)) + c = Constraint([Comparison(dataset(), line_model())], likelihood=StudentT(nu)) + expon = stats.expon(loc=1, scale=10) + p = Problem([c], priors=[(nu, expon)]) + theta = np.array([*TRUE, 4.0]) + m_b = stats.norm(0, 5).logpdf(TRUE).sum() + assert p.log_prior(theta) == pytest.approx(m_b + expon.logpdf(4.0)) + assert p.prior_transform([0.5, 0.5, 0.5])[2] == pytest.approx(expon.median()) + assert np.all(p.sample_prior(20, rng=0)[:, 2] >= 1.0) + # and truncated to the parameter's bounds + t = Parameter("t", bounds=(0.0, np.inf)) + model = Model(lambda x, t: t * x, [t]) + q = Problem([Constraint([Comparison(dataset(), model)])], [([t], stats.norm())]) + assert q.log_prior([0.5]) == pytest.approx(stats.norm.logpdf(0.5) + np.log(2)) + assert q.log_prior([-0.5]) == -np.inf + + def test_custom_joint_sampled_through_rvs(self): + class Box: + def logpdf(self, v): + return 0.0 if np.all((0 <= v) & (v <= 1)) else -np.inf + + def rvs(self, size, random_state): + return random_state.uniform(size=(size, 2)) + + m, b = Parameter("m"), Parameter("b") + model = Model(lambda x, m, b: m * x + b, [m, b]) + p = Problem([Constraint([Comparison(dataset(), model)])], [([m, b], Box())]) + s = p.sample_prior(30, rng=0) + assert s.shape == (30, 2) and np.all((s >= 0) & (s <= 1)) + assert p.starting_location(4).shape == (4, 2) + def test_clip_unit_cube(self): u = clip_unit_cube([0.0, 0.5, 1.0]) assert 0 < u[0] < 1e-10 and u[1] == 0.5 and 1 - 1e-10 < u[2] < 1 @@ -308,6 +353,43 @@ def test_singular_covariance_names_the_comparison(self): [Constraint([Comparison(d, line_model())], terms=[noise(eps)])] ) # parametric: fine + def test_parametric_covariance_singular_at_theta_is_zero_density(self): + d = Dataset(X[:3], [1.0, 2.0, 3.0], np.zeros(3), label="exact") + eps = Parameter("log_eps", prior=stats.norm()) + p = Problem([Constraint([Comparison(d, line_model())], terms=[noise(eps)])]) + theta = np.array([*TRUE, -400.0]) # exp(-400)**2 underflows to zero + assert p.log_likelihood(theta) == -np.inf and p.chi2(theta) == np.inf + assert p.log_posterior(theta) == -np.inf + assert np.isfinite(p.log_likelihood([*TRUE, np.log(0.1)])) + + def test_prediction_shape_checked_per_comparison(self): + # two x-ignoring models that return each other's lengths + s = Parameter("s", prior=stats.norm()) + c = Constraint( + [ + Comparison( + dataset(0, 3, "short"), Model(lambda x, s: s * np.ones(5), [s]) + ), + Comparison( + dataset(1, 5, "long"), Model(lambda x, s: s * np.ones(3), [s]) + ), + ] + ) + with pytest.raises(ValueError, match=r"'short'.*shape \(5,\).*3 point"): + Problem([c]).log_likelihood([1.0]) + + def test_log_jacobian_carries_the_weights(self): + model = line_model() + tempered = Constraint( + [Comparison(dataset(0, label="a"), model, space=log)], weight=0.5 + ) + spare = Constraint( + [Comparison(dataset(1, label="b"), model, space=log)], weight=0.0 + ) + lj = tempered.log_jacobian + assert Problem([tempered]).log_jacobian() == pytest.approx(0.5 * lj) + assert Problem([tempered, spare]).log_jacobian() == pytest.approx(0.5 * lj) + def test_predict_and_matrix(self): d = dataset() p = Problem( From b5ee8b66a55eaa80cba8c4d1d46dd34af836af0b Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Fri, 11 Sep 2026 16:00:42 -0400 Subject: [PATCH 50/75] Word the install notes for 1.0 on main and guard the PyPI workflow - README, the docs index and installation pages and the package docstring no longer say 1.0 lives on the rewrite branch, and they say how to pin 0.x at tag v0.1.0 (branch legacy/0.x) - publish.yml publishes only v1 and later tags, and refuses a tag that does not name the version setuptools_scm builds --- .github/workflows/publish.yml | 17 +++++++++++++++-- README.md | 9 +++++++-- docs/index.rst | 6 ++++-- docs/installation.rst | 13 ++++++++++--- src/rxmc/__init__.py | 4 ++-- 5 files changed, 38 insertions(+), 11 deletions(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index bb8407a..2e64c02 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -2,10 +2,12 @@ name: Publish to PyPI # Trusted publishing: no token in the repository. Register the pending # publisher on pypi.org (project rxmc, owner beykyle, repository rxmc, -# workflow publish.yml, environment pypi) before the first tag. +# workflow publish.yml, environment pypi) before the first tag. Only 1.x and +# later tags publish (0.x lives on tag v0.1.0 and branch legacy/0.x), and the +# build refuses a tag that does not name the version setuptools_scm builds. on: push: - tags: ["v*"] + tags: ["v[1-9]*"] jobs: build: @@ -21,6 +23,17 @@ jobs: run: | python -m pip install --upgrade pip build python -m build + - name: Check the tag names the built version + run: | + python - <<'EOF' + import glob, os + from packaging.version import Version + tag = os.environ["GITHUB_REF_NAME"].removeprefix("v") + built = os.path.basename(glob.glob("dist/*.whl")[0]).split("-")[1] + if Version(tag) != Version(built): + raise SystemExit(f"tag v{tag} builds version {built}: not publishing") + print(f"publishing {built}") + EOF - uses: actions/upload-artifact@v4 with: name: dist diff --git a/README.md b/README.md index 88d1b60..c51418b 100644 --- a/README.md +++ b/README.md @@ -129,15 +129,20 @@ print(problem.names, sampler.results.logz[-1]) Python 3.12 or later; the runtime dependencies are `numpy`, `scipy`, `jitr >= 3.0` and `exfor-tools`. Until the 1.0 pre-releases are on PyPI -(`pip install --pre rxmc`), install from the branch: +(`pip install --pre rxmc`), install from GitHub: ```bash -git clone -b rewrite git@github.com:beykyle/rxmc.git +git clone git@github.com:beykyle/rxmc.git cd rxmc python -m venv .venv && source .venv/bin/activate pip install -e '.[examples]' # or '.[validation]' to run the tests ``` +1.0 is a rewrite and does not run 0.x code. The 0.x package is preserved at +tag [`v0.1.0`](https://github.com/beykyle/rxmc/tree/v0.1.0) and on branch +[`legacy/0.x`](https://github.com/beykyle/rxmc/tree/legacy/0.x); pin it with +`pip install git+https://github.com/beykyle/rxmc@v0.1.0`. + ## Validation ```bash diff --git a/docs/index.rst b/docs/index.rst index 125852d..ad23a5b 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -12,8 +12,10 @@ calibration driven by external samplers (emcee, dynesty, black-box-bayes). - :doc:`design` is the maintainer's description of the library. - :doc:`api` is the reference. -The 1.0 rewrite lives on the ``rewrite`` branch until its release; the 0.x -package is preserved at tag ``v0.1.0`` and on branch ``legacy/0.x``. +This is ``rxmc`` 1.0, a rewrite that does not run 0.x code. The 0.x package +is preserved at tag `v0.1.0 <https://github.com/beykyle/rxmc/tree/v0.1.0>`_ +and on branch `legacy/0.x <https://github.com/beykyle/rxmc/tree/legacy/0.x>`_; +:doc:`installation` says how to pin it. .. toctree:: :maxdepth: 1 diff --git a/docs/installation.rst b/docs/installation.rst index 4cc6e0a..4ede0d2 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -4,11 +4,11 @@ Installation ``rxmc`` requires Python 3.12 or later. The runtime dependencies are ``numpy``, ``scipy``, ``jitr >= 3.0`` and ``exfor-tools``, installed automatically. Until the 1.0 pre-releases appear on PyPI (``pip install ---pre rxmc``), install from the branch: +--pre rxmc``), install from GitHub: .. code-block:: bash - git clone -b rewrite git@github.com:beykyle/rxmc.git + git clone git@github.com:beykyle/rxmc.git cd rxmc python -m venv .venv && source .venv/bin/activate pip install -e '.[examples]' @@ -22,4 +22,11 @@ Extras: ``docs`` sphinx, the pydata theme, myst-nb =============== ============================================================= -The 0.x package is at tag ``v0.1.0`` and on branch ``legacy/0.x``. +The 0.x package, which 1.0 replaces without backwards compatibility, is +preserved at tag `v0.1.0 <https://github.com/beykyle/rxmc/tree/v0.1.0>`_ and +on branch `legacy/0.x <https://github.com/beykyle/rxmc/tree/legacy/0.x>`_. +Pin it with: + +.. code-block:: bash + + pip install git+https://github.com/beykyle/rxmc@v0.1.0 diff --git a/src/rxmc/__init__.py b/src/rxmc/__init__.py index 6ab2920..d88c5b8 100644 --- a/src/rxmc/__init__.py +++ b/src/rxmc/__init__.py @@ -1,7 +1,7 @@ """rxmc: Bayesian calibration of reaction models with composable error models. -The 1.0 rewrite is in progress on this branch; see ``docs/groundup_design.md`` -for the design and ``docs/recipes.md`` for the supported use cases. +See ``docs/design.md`` for the design and ``docs/recipes.md`` for the +supported use cases. """ from . import constraint as constraint From 9c6e7b5e1e4ec808f7eee164cf50fe0ca338dc24 Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Fri, 11 Sep 2026 16:35:33 -0400 Subject: [PATCH 51/75] Score the alpha-Ca held-out angles conditionally on the fit - Cell 13 passes given=p_fit to heldout_log_predictive and predictive_draws: the L2y normalisation and the Lgp process both span the 90-degree cut, so the honest held-out density is p(y_held | y_fit, theta) (review finding) - Fix the stale nested() call left when 39b7165 renamed it fit_nested() - Re-executed; the takeaway follows the new held-out scores and coverages --- .../alpha_ca_error_model_comparison.ipynb | 102 ++++++++++-------- 1 file changed, 57 insertions(+), 45 deletions(-) diff --git a/examples/alpha_ca_error_model_comparison.ipynb b/examples/alpha_ca_error_model_comparison.ipynb index 7b2fac2..eaf49f1 100644 --- a/examples/alpha_ca_error_model_comparison.ipynb +++ b/examples/alpha_ca_error_model_comparison.ipynb @@ -23,10 +23,10 @@ "id": "391eeea7", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:21:01.904388Z", - "iopub.status.busy": "2026-09-11T04:21:01.904243Z", - "iopub.status.idle": "2026-09-11T04:21:04.710214Z", - "shell.execute_reply": "2026-09-11T04:21:04.709449Z" + "iopub.execute_input": "2026-09-11T20:10:48.193687Z", + "iopub.status.busy": "2026-09-11T20:10:48.193538Z", + "iopub.status.idle": "2026-09-11T20:10:50.734882Z", + "shell.execute_reply": "2026-09-11T20:10:50.734119Z" } }, "outputs": [], @@ -67,10 +67,10 @@ "id": "6df8e566", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:21:04.712686Z", - "iopub.status.busy": "2026-09-11T04:21:04.712203Z", - "iopub.status.idle": "2026-09-11T04:21:05.906639Z", - "shell.execute_reply": "2026-09-11T04:21:05.905836Z" + "iopub.execute_input": "2026-09-11T20:10:50.736588Z", + "iopub.status.busy": "2026-09-11T20:10:50.736357Z", + "iopub.status.idle": "2026-09-11T20:10:51.711832Z", + "shell.execute_reply": "2026-09-11T20:10:51.711291Z" } }, "outputs": [ @@ -141,10 +141,10 @@ "id": "eeba4535", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:21:05.908284Z", - "iopub.status.busy": "2026-09-11T04:21:05.908114Z", - "iopub.status.idle": "2026-09-11T04:21:20.300225Z", - "shell.execute_reply": "2026-09-11T04:21:20.299611Z" + "iopub.execute_input": "2026-09-11T20:10:51.713216Z", + "iopub.status.busy": "2026-09-11T20:10:51.713063Z", + "iopub.status.idle": "2026-09-11T20:11:02.687574Z", + "shell.execute_reply": "2026-09-11T20:11:02.686769Z" } }, "outputs": [ @@ -231,10 +231,10 @@ "id": "3e16d477", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:21:20.302274Z", - "iopub.status.busy": "2026-09-11T04:21:20.302093Z", - "iopub.status.idle": "2026-09-11T04:21:20.315028Z", - "shell.execute_reply": "2026-09-11T04:21:20.314320Z" + "iopub.execute_input": "2026-09-11T20:11:02.689058Z", + "iopub.status.busy": "2026-09-11T20:11:02.688878Z", + "iopub.status.idle": "2026-09-11T20:11:02.699185Z", + "shell.execute_reply": "2026-09-11T20:11:02.698548Z" } }, "outputs": [ @@ -303,10 +303,10 @@ "id": "6d7c9516", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:21:20.316599Z", - "iopub.status.busy": "2026-09-11T04:21:20.316425Z", - "iopub.status.idle": "2026-09-11T04:34:40.535909Z", - "shell.execute_reply": "2026-09-11T04:34:40.535170Z" + "iopub.execute_input": "2026-09-11T20:11:02.700606Z", + "iopub.status.busy": "2026-09-11T20:11:02.700481Z", + "iopub.status.idle": "2026-09-11T20:22:54.828075Z", + "shell.execute_reply": "2026-09-11T20:22:54.826749Z" } }, "outputs": [ @@ -382,10 +382,10 @@ "id": "3421f412", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:34:40.537531Z", - "iopub.status.busy": "2026-09-11T04:34:40.537355Z", - "iopub.status.idle": "2026-09-11T04:34:40.541878Z", - "shell.execute_reply": "2026-09-11T04:34:40.541310Z" + "iopub.execute_input": "2026-09-11T20:22:54.830713Z", + "iopub.status.busy": "2026-09-11T20:22:54.830430Z", + "iopub.status.idle": "2026-09-11T20:22:54.838581Z", + "shell.execute_reply": "2026-09-11T20:22:54.837601Z" } }, "outputs": [ @@ -433,10 +433,10 @@ "id": "17484714", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:34:40.543262Z", - "iopub.status.busy": "2026-09-11T04:34:40.543122Z", - "iopub.status.idle": "2026-09-11T04:34:41.060471Z", - "shell.execute_reply": "2026-09-11T04:34:41.059851Z" + "iopub.execute_input": "2026-09-11T20:22:54.841615Z", + "iopub.status.busy": "2026-09-11T20:22:54.841395Z", + "iopub.status.idle": "2026-09-11T20:22:55.630755Z", + "shell.execute_reply": "2026-09-11T20:22:55.630054Z" } }, "outputs": [ @@ -487,7 +487,14 @@ "every object, `complement()` flips the mask, and a chain from the fit scores\n", "the held-out problem directly. The joint held-out log predictive is a\n", "different question from the evidence: not \"which model explains the fitted\n", - "data\" but \"which model predicts what it has not seen\"." + "data\" but \"which model predicts what it has not seen\".\n", + "\n", + "The normalisation of `L2y` and the Gaussian process of `Lgp` both couple the\n", + "angles we fit to the ones we hold out, so the honest score is not the marginal\n", + "density of the held-out points but the conditional\n", + "$p(y_\\mathrm{held} \\mid y_\\mathrm{fit}, \\theta)$: passing `given=p_fit` to\n", + "both diagnostics computes exactly that (recipe 11). For `L0` nothing couples\n", + "the two, and the conditional is the marginal." ] }, { @@ -496,10 +503,10 @@ "id": "a57881d2", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:34:41.062006Z", - "iopub.status.busy": "2026-09-11T04:34:41.061836Z", - "iopub.status.idle": "2026-09-11T04:40:54.941696Z", - "shell.execute_reply": "2026-09-11T04:40:54.940518Z" + "iopub.execute_input": "2026-09-11T20:22:55.633207Z", + "iopub.status.busy": "2026-09-11T20:22:55.632956Z", + "iopub.status.idle": "2026-09-11T20:29:24.114885Z", + "shell.execute_reply": "2026-09-11T20:29:24.114038Z" } }, "outputs": [ @@ -514,14 +521,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "L2y held-out log predictive = -55.80 68 % coverage of the held-out points = 0.74\n" + "L2y held-out log predictive = -55.34 68 % coverage of the held-out points = 0.60\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Lgp held-out log predictive = -49.81 68 % coverage of the held-out points = 0.71\n" + "Lgp held-out log predictive = -50.45 68 % coverage of the held-out points = 0.80\n" ] } ], @@ -533,10 +540,12 @@ " continue # same space as L0 up to the noise model; the log-space rungs are the comparison here\n", " fit = c.masked_where(lambda x: x < cut)\n", " p_fit, p_held = rx.Problem([fit]), rx.Problem([fit.complement()])\n", - " res = nested(p_fit, seed=10 + i)\n", + " res = fit_nested(p_fit, seed=10 + i)\n", " s = res.samples_equal(rstate=np.random.default_rng(10 + i))\n", - " lp = rx.diagnostics.heldout_log_predictive(p_held, s[::5])\n", - " draws = rx.diagnostics.predictive_draws(p_held, s[::20], n_rep=2, rng=i)\n", + " lp = rx.diagnostics.heldout_log_predictive(p_held, s[::5], given=p_fit)\n", + " draws = rx.diagnostics.predictive_draws(\n", + " p_held, s[::20], n_rep=2, rng=i, given=p_fit\n", + " )\n", " h = p_held.constraints[0]\n", " cov68 = rx.diagnostics.coverage_curve(draws, h.y[h.active], [0.68])[0]\n", " scores[name] = (\n", @@ -557,10 +566,10 @@ "id": "f2306531", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:40:54.943225Z", - "iopub.status.busy": "2026-09-11T04:40:54.943068Z", - "iopub.status.idle": "2026-09-11T04:40:55.885675Z", - "shell.execute_reply": "2026-09-11T04:40:55.884956Z" + "iopub.execute_input": "2026-09-11T20:29:24.118599Z", + "iopub.status.busy": "2026-09-11T20:29:24.118306Z", + "iopub.status.idle": "2026-09-11T20:29:25.455673Z", + "shell.execute_reply": "2026-09-11T20:29:25.454897Z" } }, "outputs": [ @@ -615,9 +624,12 @@ "- Log space is where multiplicative errors are additive; the Jacobian makes\n", " its evidence comparable with a linear-space fit.\n", "- Evidence and held-out prediction ask different questions. Here the GP\n", - " rung wins both, but the normalisation rung covers the held-out points\n", - " just as well with a far simpler covariance; a rung that explains the\n", - " forward angles best need not predict the backward ones.\n", + " rung wins both. Scored honestly, conditioned on the forward angles, the\n", + " normalisation rung's intervals are too narrow at the backward angles (60 %\n", + " of the held-out points fall inside the nominal 68 % band) and the GP\n", + " rung's are too wide (80 %): neither is calibrated past the cut, and a\n", + " rung that explains the forward angles best need not predict the backward\n", + " ones.\n", "- Light-ion potentials are famously ambiguous: several parameter families\n", " give nearly the same cross section. The `L0` posterior above carries a\n", " second family near `V ≈ 125` MeV beside the main one at `V ≈ 165` MeV,\n", From 0e7ddca2d9aa90739faa94f782346b9cfa187b60 Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Fri, 11 Sep 2026 16:48:12 -0400 Subject: [PATCH 52/75] Fix the rest of the PR #44 review backlog Silent wrong answers: - A block whose covariance is constant is checked for singularity at compile even when another block is parametric, instead of reading as a zero density at every theta - The eigen fallback of _psd_factor is lower-triangular, so held-out scores and conditionals that treat it as a Cholesky factor are right - Kernel jitter is relative to the block's mean variance, so it no longer swamps small (b/sr) variances - The finite-difference derivative step is relative to the data - Marginals truncated far in the upper tail use sf/isf, and prior_transform is clipped to the bounds - A bounded frozen-MVN joint prior is renormalised by its in-bounds mass (seeded, so compiles are deterministic); custom joints must be normalised - A dataset whose rows are active in two weighted constraints warns - compare_logz ties at the boundary; it and logz_summary reject NaN - The Student-t Gamma ratio uses betaln, exact at huge nu Crashes and misleading errors: - kernel() with fixed hyperparameters and parametric coords constructs - dataclasses.replace on a Term no longer duplicates the coords parameters - Callable terms with the default coords see x as the dataset holds it - Provenance tuples and 0-d arrays in meta stack at compile - ElasticXS("dXS/dRuth") with a neutral projectile fails at bind, by name - One bare callable used as the space of several comparisons is one space - A kernel whose support is fully masked says so - NaN or negative norm_err/offset_err and NaN angles are rejected - from_measurement refuses LAB-frame angles - A notebook listed in the index but missing fails instead of xfailing --- docs/design.md | 4 +- docs/recipes.md | 2 + src/rxmc/covariance.py | 37 ++++++++---- src/rxmc/data.py | 28 ++++++--- src/rxmc/diagnostics.py | 29 +++++---- src/rxmc/likelihood.py | 7 ++- src/rxmc/predictive.py | 13 +++- src/rxmc/problem.py | 81 +++++++++++++++++++++---- src/rxmc/reactions/elastic.py | 5 ++ src/rxmc/terms.py | 26 ++++++-- src/rxmc/transforms.py | 3 +- src/rxmc/units.py | 4 +- test/test_constraint.py | 5 ++ test/test_data.py | 4 ++ test/test_diagnostics.py | 17 ++++++ test/test_likelihood.py | 9 +++ test/test_measurement.py | 7 +++ test/test_notebooks_index.py | 4 +- test/test_predictive.py | 27 +++++++++ test/test_problem.py | 109 +++++++++++++++++++++++++++++++++- test/test_reactions.py | 4 ++ test/test_terms.py | 46 ++++++++++++++ test/test_units.py | 2 + 23 files changed, 413 insertions(+), 60 deletions(-) diff --git a/docs/design.md b/docs/design.md index 68cd366..5bc192d 100644 --- a/docs/design.md +++ b/docs/design.md @@ -366,7 +366,9 @@ exactly one joint block `(params, joint)`, where `joint` has `logpdf(values)` over those parameters in that order and optionally `prior_transform(u)` and `rvs(size=n, random_state=rng)` (scipy's spelling); `scipy.stats.multivariate_normal` qualifies and is whitened for -the unit-cube map. A joint that declares its dimension (`dim`) must match +the unit-cube map, and renormalised by its mass inside any bounds (a custom +joint's `logpdf` must already be normalised on its truncated support). A +joint that declares its dimension (`dim`) must match its parameters, and a one-parameter block holding a scipy univariate distribution is that parameter's marginal. A hyperprior is a joint block that includes its hyperparameter, whose `logpdf` is diff --git a/docs/recipes.md b/docs/recipes.md index 22c4177..169bc17 100644 --- a/docs/recipes.md +++ b/docs/recipes.md @@ -347,6 +347,8 @@ Expected behaviour: is everything a reaction model needs to bind. - Incompatible units, or a quantity the measurement cannot be converted to, raise at conversion time. +- Angles must be in the CM frame: a measurement whose `x_units` is + `LAB-degrees` raises; convert it to CM first. ## 15. Evaluate a reaction model on any grid diff --git a/src/rxmc/covariance.py b/src/rxmc/covariance.py index 45407e3..565e91f 100644 --- a/src/rxmc/covariance.py +++ b/src/rxmc/covariance.py @@ -162,12 +162,11 @@ def __init__(self, entries, x, y, offsets, active, *, meta=None, labels=None): self._cache = None if self.is_constant: self._factor(self._D0, self._U0, self._M0, self._X0) - elif not self.dense and all( - e.term.is_constant for e in self.entries if e.term.kind != "mode" - ): - # modes never enter B, so B is constant even with parametric modes: - # fail now, by label, rather than at the first likelihood call - self._check_blocks(self._D0, self._M0) + elif not self.dense: + # modes never enter B, so a block no parametric diag or matrix term + # touches has a constant B: a singular one fails now, by label, rather + # than as a zero density at every theta + self._check_blocks(self._D0, self._M0, self._constant_blocks()) # -- assembly ------------------------------------------------------------- @@ -281,20 +280,32 @@ def _factor(self, D, U, M, X): self._cache = factors return factors - def _check_blocks(self, D, M): + def _constant_blocks(self) -> list[int]: + """The blocks whose ``B`` no parametric diag or matrix entry touches.""" + varying = set() + for e in self.parametric_entries: + if e.term.kind == "mode": + continue + pos = e.pos[e.keep] + varying.update( + b for b, bp in enumerate(self.block_pos) if np.isin(pos, bp).any() + ) + return [b for b in range(len(self.block_pos)) if b not in varying] + + def _check_blocks(self, D, M, blocks): try: - for b, pos in enumerate(self.block_pos): + for b in blocks: + pos = self.block_pos[b] if pos.size: B = np.diag(D[pos]) + (0.0 if M[b] is None else M[b]) sla.cholesky(B, lower=True) except np.linalg.LinAlgError as err: - raise ValueError(self._singular_message(D)) from err + raise ValueError(self._singular_message(D, blocks)) from err - def _singular_message(self, D): + def _singular_message(self, D, blocks=None): + blocks = range(len(self.block_pos)) if blocks is None else blocks offenders = [ - self.labels[b] - for b, pos in enumerate(self.block_pos) - if np.any(D[pos] == 0.0) + self.labels[b] for b in blocks if np.any(D[self.block_pos[b]] == 0.0) ] msg = "the constraint's covariance is singular on its active points" if not offenders: diff --git a/src/rxmc/data.py b/src/rxmc/data.py index f7fb637..b236a2d 100644 --- a/src/rxmc/data.py +++ b/src/rxmc/data.py @@ -28,12 +28,15 @@ def _error_spec(value, n, name): if value is None: return None if np.ndim(value) == 0: - return float(value) - v = np.asarray(value, dtype=float) - if v.shape != (n,): - raise ValueError( - f"{name} must be a scalar or have shape ({n},), got shape {v.shape}" - ) + v = float(value) + else: + v = np.asarray(value, dtype=float) + if v.shape != (n,): + raise ValueError( + f"{name} must be a scalar or have shape ({n},), got shape {v.shape}" + ) + if not (np.all(np.isfinite(v)) and np.all(np.asarray(v) >= 0)): + raise ValueError(f"{name} must be finite and non-negative, got {value}") return v @@ -121,7 +124,9 @@ def from_measurement( ) -> Dataset: """A :class:`Dataset` from an ``exfor_tools`` measurement, in internal units. - Reads ``x`` (degrees), ``y``, ``Einc``, ``quantity``, ``y_units``, + Reads ``x`` (degrees, in the CM frame: an ``x_units`` of ``"LAB-degrees"`` + is refused, and a measurement without ``x_units`` is taken as CM), ``y``, + ``Einc``, ``quantity``, ``y_units``, ``statistical_err``, ``systematic_norm_err``, ``systematic_offset_err`` and ``subentry`` from ``measurement`` (any object with those attributes). Angles are stored in radians, cross sections in b/sr, ratios and analysing powers @@ -159,8 +164,15 @@ def from_measurement( f"measurement quantity {measured!r} needs {_QUANTITY_KIND[measured]} units, " f"got {measurement.y_units!r}" ) - x = np.deg2rad(np.asarray(measurement.x, dtype=float)) label = getattr(measurement, "subentry", None) or "" + frame = getattr(measurement, "x_units", "CM-degrees") + if str(frame).upper().startswith("LAB"): + raise ValueError( + f"measurement {label or 'measurement'!r} has angles in the LAB frame " + f"({frame!r}); rxmc compares in the CM frame: convert the angles and " + "the cross sections to CM first" + ) + x = np.deg2rad(np.asarray(measurement.x, dtype=float)) check_angle_grid(x, f"x of {label or 'measurement'}") Elab = float(measurement.Einc) diff --git a/src/rxmc/diagnostics.py b/src/rxmc/diagnostics.py index 5be0749..eb0743f 100644 --- a/src/rxmc/diagnostics.py +++ b/src/rxmc/diagnostics.py @@ -75,13 +75,14 @@ def _rows(samples, ndim) -> np.ndarray: def _psd_factor(Sigma, jitter=1e-10) -> np.ndarray: - """A factor ``L`` with ``L L^T = Sigma``. - - The lower Cholesky factor when ``Sigma`` is positive definite; otherwise - the Cholesky factor of ``Sigma`` plus a jitter *relative* to its mean - variance, and failing that a symmetric square root from the eigen - decomposition (negative eigenvalues clipped to zero). No jitter is added - on the successful path, so draws are never inflated. + """A lower-triangular factor ``L`` with ``L L^T = Sigma``. + + The Cholesky factor when ``Sigma`` is positive definite; otherwise the + Cholesky factor of ``Sigma`` plus a jitter *relative* to its mean + variance, and failing that the triangular factor of the eigen + decomposition's square root (negative eigenvalues clipped to zero), so a + caller may always treat ``L`` as a Cholesky factor. No jitter is added on + the successful path, so draws are never inflated. """ Sigma = np.asarray(Sigma, dtype=float) try: @@ -93,7 +94,10 @@ def _psd_factor(Sigma, jitter=1e-10) -> np.ndarray: return np.linalg.cholesky(Sigma + jitter * scale * np.eye(len(Sigma))) except np.linalg.LinAlgError: w, V = np.linalg.eigh(Sigma) - return V * np.sqrt(np.clip(w, 0.0, None)) + # A = V sqrt(w) has A A^T = Sigma but is not triangular; with A^T = Q R, + # L = R^T is, and L L^T = A A^T + L = np.linalg.qr((V * np.sqrt(np.clip(w, 0.0, None))).T, mode="r").T + return L * np.where(np.diag(L) < 0, -1.0, 1.0) # column signs: diag >= 0 def _masks(constraint) -> list[np.ndarray]: @@ -380,6 +384,8 @@ def logz_summary(logz, logzerr) -> tuple[float, float, int]: """ logz = np.atleast_1d(np.asarray(logz, dtype=float)) logzerr = np.atleast_1d(np.asarray(logzerr, dtype=float)) + if not (np.all(np.isfinite(logz)) and np.all(np.isfinite(logzerr))): + raise ValueError(f"log Z and its errors must be finite, got {logz}, {logzerr}") half_range = 0.5 * (logz.max() - logz.min()) if logz.size > 1 else 0.0 return float(logz.mean()), float(max(half_range, logzerr.mean())), int(logz.size) @@ -393,7 +399,8 @@ def compare_logz(a, b, sigma: float = 2.0) -> dict: As returned by :func:`logz_summary`; a trailing replicate count is accepted and ignored. sigma : float, optional - A difference smaller than ``sigma * hypot(err_a, err_b)`` is a ``"tie"``. + A difference no larger than ``sigma * hypot(err_a, err_b)`` is a + ``"tie"``. Returns ------- @@ -402,9 +409,11 @@ def compare_logz(a, b, sigma: float = 2.0) -> dict: """ ma, ea = a[0], a[1] mb, eb = b[0], b[1] + if not np.all(np.isfinite([ma, ea, mb, eb])): + raise ValueError(f"evidence summaries must be finite, got {a} and {b}") d = float(ma - mb) err = float(np.hypot(ea, eb)) - if abs(d) < sigma * err: + if abs(d) <= sigma * err: verdict = "tie" else: verdict = "a" if d > 0 else "b" diff --git a/src/rxmc/likelihood.py b/src/rxmc/likelihood.py index f40fe65..1808cd3 100644 --- a/src/rxmc/likelihood.py +++ b/src/rxmc/likelihood.py @@ -19,7 +19,7 @@ import numpy as np from scipy import stats -from scipy.special import gammaln +from scipy.special import betaln, gammaln from .params import Parameter @@ -96,9 +96,10 @@ def predictive_scale(self, rng, size, nu) -> np.ndarray: return np.sqrt(nu / rng.chisquare(nu, size)) def log_likelihood(self, d2, logdet, n, nu) -> float: + # lnG((n+nu)/2) - lnG(nu/2), without the cancellation at large nu return ( - gammaln((n + nu) / 2.0) - - gammaln(nu / 2.0) + gammaln(n / 2.0) + - betaln(nu / 2.0, n / 2.0) - 0.5 * n * np.log(np.pi * nu) - 0.5 * logdet - 0.5 * (nu + n) * np.log1p(d2 / nu) diff --git a/src/rxmc/predictive.py b/src/rxmc/predictive.py index 1d3872a..b7a6606 100644 --- a/src/rxmc/predictive.py +++ b/src/rxmc/predictive.py @@ -120,14 +120,25 @@ def _locate(problem: Problem, term: KernelTerm): for e in c.covariance.entries: if e.term is term: return c, e + if any(t is term for t in c.source.terms): + raise ValueError( + "the kernel term's support is fully masked in this problem: it has " + "no training rows to condition on" + ) raise ValueError("the kernel term is not part of any constraint of the problem") +def _same_space(a, b) -> bool: + """One space: the same object, or parameter-free wrappers of one callable + (``space=np.log`` on each comparison wraps it anew each time).""" + return a is b or (not a.params and not b.params and a.fn is b.fn) + + def _one_space(constraint, rows): spaces = [] for comp, o in zip(constraint.comparisons, constraint.offsets): if np.any((rows >= o.start) & (rows < o.stop)): - if not any(comp.space is s for s in spaces): + if not any(_same_space(comp.space, s) for s in spaces): spaces.append(comp.space) if len(spaces) != 1: raise ValueError( diff --git a/src/rxmc/problem.py b/src/rxmc/problem.py index 1a823b7..abe73bd 100644 --- a/src/rxmc/problem.py +++ b/src/rxmc/problem.py @@ -23,8 +23,10 @@ multivariate distributions do) must match its parameters. A frozen ``scipy.stats.multivariate_normal`` gets a whitening unit-cube map for free, and a one-parameter block holding a scipy univariate distribution is - that parameter's marginal. A hyperprior is a joint block that includes its - hyperparameter. + that parameter's marginal. Finite bounds truncate a joint: a frozen MVN is + renormalised by its mass inside them, while a custom joint's ``logpdf`` must + already be normalised on its truncated support. A hyperprior is a joint + block that includes its hyperparameter. Nothing user-facing is mutated by compiling. Compile the same declarations twice and you get two independent problems. @@ -32,6 +34,7 @@ from __future__ import annotations +import warnings from typing import Any, Iterable, Sequence import numpy as np @@ -153,6 +156,7 @@ def __init__(self, p: Parameter, slot: int, dist=None): self.lo, self.hi = lo, hi self.dist = p.prior if dist is None else dist self.bounded = np.isfinite(lo) and np.isfinite(hi) + self.upper = False if self.dist is None: if not self.bounded: raise ValueError( @@ -162,9 +166,13 @@ def __init__(self, p: Parameter, slot: int, dist=None): self.c_lo, self.c_hi = 0.0, 1.0 self.log_norm = np.log(hi - lo) else: - self.c_lo = float(self.dist.cdf(lo)) if np.isfinite(lo) else 0.0 - self.c_hi = float(self.dist.cdf(hi)) if np.isfinite(hi) else 1.0 - mass = self.c_hi - self.c_lo + # far in the upper tail cdf(lo) rounds to 1: work in the survival + # function there, so the mass and the unit-cube map keep their precision + self.upper = bool(np.isfinite(lo) and self.dist.cdf(lo) > 0.5) + cdf, at_inf = (self.dist.sf, 0.0) if self.upper else (self.dist.cdf, 1.0) + self.c_lo = float(cdf(lo)) if np.isfinite(lo) else 0.0 + self.c_hi = float(cdf(hi)) if np.isfinite(hi) else at_inf + mass = abs(self.c_hi - self.c_lo) if not mass > 0: raise ValueError( f"the prior of {p.name!r} has no mass inside its bounds" @@ -181,7 +189,11 @@ def logpdf(self, v) -> float: def transform(self, u): if self.dist is None: return self.lo + u * (self.hi - self.lo) - return self.dist.ppf(self.c_lo + u * (self.c_hi - self.c_lo)) + inverse = self.dist.isf if self.upper else self.dist.ppf + # clipped: the inverse can round a hair outside the bounds at u ~ 0 or 1 + return np.clip( + inverse(self.c_lo + u * (self.c_hi - self.c_lo)), self.lo, self.hi + ) class _Joint: @@ -205,6 +217,17 @@ def __init__(self, params: Sequence[Parameter], joint, slots: np.ndarray): self._mean = np.atleast_1d(joint.mean) else: self._L = None + # a truncated MVN is renormalised by its mass inside the bounds; the box + # probability is quasi-Monte Carlo from 3 dimensions, so it is seeded + self.log_mass = 0.0 + if self.bounded and _is_frozen_mvn(joint): + box = stats.multivariate_normal(joint.mean, joint.cov, seed=0) + mass = float(box.cdf(self.bounds[:, 1], lower_limit=self.bounds[:, 0])) + if not mass > 0: + raise ValueError( + f"the joint prior over {self.names} has no mass inside its bounds" + ) + self.log_mass = float(np.log(mass)) @property def has_transform(self) -> bool: @@ -218,7 +241,7 @@ def logpdf(self, values) -> float: ): return -np.inf # item(): a length-1 array is fine, a longer one (a mis-sized joint) is not - return float(np.asarray(self.joint.logpdf(values)).item()) + return float(np.asarray(self.joint.logpdf(values)).item()) - self.log_mass def transform(self, u): if self.bounded: @@ -337,12 +360,23 @@ def sample(self, n, rng) -> np.ndarray: def _per_point(v, n) -> np.ndarray: - """A metadata value as one entry per point: a length-``n`` array as is, - anything else repeated ``n`` times.""" - arr = np.asarray(v) if v is not None else None + """A metadata value as one entry per point: a length-``n`` sequence as is, + anything else (a scalar, a 0-d array, a provenance tuple, an object) + repeated ``n`` times.""" + if isinstance(v, (np.ndarray, np.generic)) and v.ndim == 0: + v = v.item() + if np.isscalar(v): + return np.full(n, v) + try: + arr = np.asarray(v) + except ValueError: # ragged: not per-point + arr = None if arr is not None and arr.ndim >= 1 and arr.shape[0] == n: return arr - return np.full(n, v) if np.isscalar(v) else np.full(n, v, dtype=object) + out = np.empty(n, dtype=object) + for i in range(n): # element by element: numpy would broadcast a sequence + out[i] = v + return out def _stack_meta(constraint: Constraint) -> dict | None: @@ -464,6 +498,30 @@ def __repr__(self): return f"CompiledConstraint({self.labels}, n_active={self.n_active})" +def _warn_on_shared_rows(constraints) -> None: + """Warn when one dataset's rows are active in two weighted constraints. + + Its likelihood would count those rows twice. Disjoint masks (a fit and its + complement) and weight-0 monitor constraints are fine. + """ + seen: dict[int, list] = {} + for k, c in enumerate(constraints): + if c.weight == 0.0: + continue + for comp, o in zip(c.comparisons, c.offsets): + rows = c.active[(c.active >= o.start) & (c.active < o.stop)] - o.start + for j, prev in seen.get(id(comp.data), []): + if np.intersect1d(rows, prev).size: + warnings.warn( + f"dataset {comp.data.label or 'dataset'!r} has rows active in " + f"constraints {j} and {k}: the likelihood counts them twice " + "(mask them apart, or give one constraint weight 0)", + UserWarning, + stacklevel=3, + ) + seen.setdefault(id(comp.data), []).append((k, rows)) + + # ---------------------------------------------------------------------------- # The problem # ---------------------------------------------------------------------------- @@ -488,6 +546,7 @@ def __init__(self, constraints, priors=()): raise TypeError(f"constraints must be Constraint objects, got {c!r}") self.index = ParameterIndex() self.constraints = tuple(CompiledConstraint(c, self.index) for c in constraints) + _warn_on_shared_rows(self.constraints) self.priors = tuple(priors) # a hyperprior block may introduce a parameter no model or term uses (its # hyperparameter); it gets a slot after every constraint's parameters diff --git a/src/rxmc/reactions/elastic.py b/src/rxmc/reactions/elastic.py index deefd18..0352cc4 100644 --- a/src/rxmc/reactions/elastic.py +++ b/src/rxmc/reactions/elastic.py @@ -228,6 +228,11 @@ def workspace(self, x, meta): def bind(self, x, meta=None) -> Predictor: ws = self.workspace(x, meta) + if self.quantity == "dXS/dRuth" and ws.rutherford is None: + raise ValueError( + "dXS/dRuth needs the Rutherford cross section, which a neutral " + "projectile does not have: compare dXS/dA instead" + ) extract = _EXTRACT[self.quantity] central, spin_orbit, coulomb = self.central, self.spin_orbit, self.coulomb args_from_params = self.args_from_params diff --git a/src/rxmc/terms.py b/src/rxmc/terms.py index 49b2a50..8642447 100644 --- a/src/rxmc/terms.py +++ b/src/rxmc/terms.py @@ -163,7 +163,8 @@ class Term: coords : Transform or callable, optional Coordinate transform applied to ``x`` before ``fn`` sees it (e.g. angle to momentum transfer). Its parameters, if any, are appended to - :attr:`params`. + :attr:`params`. A transform needs numeric ``x``; without one, ``fn`` + sees ``x`` exactly as the dataset holds it (any dtype). constant : bool, optional Declare that a *callable* ``fn`` reads neither ``c.ym`` nor any parameter, so the contribution can be evaluated once at compile. @@ -178,17 +179,25 @@ class Term: on: Any = None coords: Any = identity constant: bool = False + _appended: Any = None # the coords parameters appended to params last time def __post_init__(self): if self.kind not in KINDS: raise ValueError(f"kind must be one of {KINDS}, got {self.kind!r}") coords = as_transform(self.coords) fn_params = tuple(self.params) + # dataclasses.replace re-runs this with the previous coords' parameters + # already appended to params: take them off before appending the current + k = len(self._appended or ()) + if k and len(fn_params) >= k: + if all(a is b for a, b in zip(fn_params[-k:], self._appended)): + fn_params = fn_params[:-k] for p in fn_params + coords.params: if not isinstance(p, Parameter): raise TypeError(f"params must be Parameter objects, got {p!r}") object.__setattr__(self, "coords", coords) object.__setattr__(self, "params", fn_params + coords.params) + object.__setattr__(self, "_appended", coords.params) object.__setattr__(self, "_n_fn_params", len(fn_params)) if callable(self.fn): if self.constant and self.params: @@ -236,7 +245,7 @@ def context( :attr:`TermContext.segments`); omitted, the support is one segment. """ self._check_count(values) - x = np.asarray(x, dtype=float) + x = np.asarray(x) # opaque: only a coordinate transform needs it numeric if not self.coords.is_identity: x = self.coords(x, *values[self._n_fn_params :]) return TermContext( @@ -297,7 +306,8 @@ class KernelTerm(Term): amplitude : callable or array or None The amplitude as passed to :func:`kernel`. jitter : float - The diagonal nugget added to every kernel block. + The diagonal nugget of every kernel block, relative to the block's mean + variance. """ kernel: Any = None @@ -592,7 +602,9 @@ def kernel( amplitude_params : sequence of Parameter, optional Parameters consumed by ``amplitude``. jitter : float, optional - Added to the diagonal after scaling, for numerical stability. + ``jitter * mean(diag K)`` is added to the diagonal after scaling, for + numerical stability; relative, so it never dominates a small variance + (a cross section in b/sr). prefix : str, optional Name prefix of the derived kernel parameters. Two kernel terms with derived names and the same prefix fail to compile on the duplicate @@ -630,7 +642,9 @@ def fn(c, *values): K = np.outer(a, a) * K else: K = np.array(K, dtype=float) - K[np.diag_indices_from(K)] += jitter + K[np.diag_indices_from(K)] += jitter * max( + float(np.mean(np.diag(K))), np.finfo(float).tiny + ) return K return KernelTerm( @@ -639,7 +653,7 @@ def fn(c, *values): kind="matrix", on=on, coords=coords, - constant=nk == 0 and not amplitude_params and not callable(amplitude), + constant=fixed_K and not amplitude_params and not callable(amplitude), kernel=kernel, n_kernel=nk, amplitude=amplitude, diff --git a/src/rxmc/transforms.py b/src/rxmc/transforms.py index 96a60be..c3f6f3e 100644 --- a/src/rxmc/transforms.py +++ b/src/rxmc/transforms.py @@ -113,7 +113,8 @@ def derivative(self, a, *values): a = np.asarray(a, dtype=float) if self.derivative_fn is not None: return np.asarray(self.derivative_fn(a, *values), dtype=float) - h = 1e-6 * np.maximum(np.abs(a), 1.0) + # a step relative to a, so data far below 1 (b/sr) keep their precision + h = 1e-6 * np.where(a == 0.0, 1.0, np.abs(a)) return (self(a + h, *values) - self(a - h, *values)) / (2 * h) def __or__(self, other) -> "Transform": diff --git a/src/rxmc/units.py b/src/rxmc/units.py index 7238b92..a12af7e 100644 --- a/src/rxmc/units.py +++ b/src/rxmc/units.py @@ -89,9 +89,11 @@ def parse_unit(label: str) -> tuple[float, str]: def check_angle_grid(angles_rad: np.ndarray, name: str) -> None: - """Reject a grid that is not 1-D or not inside ``[0, pi]`` radians.""" + """Reject a grid that is not 1-D, not finite, or not inside ``[0, pi]`` radians.""" angles_rad = np.asarray(angles_rad) if angles_rad.ndim != 1: raise ValueError(f"{name} must be 1D, is {angles_rad.ndim}D") + if not np.all(np.isfinite(angles_rad)): + raise ValueError(f"{name} must be finite") if angles_rad.size and (angles_rad.min() < 0 or angles_rad.max() > np.pi): raise ValueError(f"{name} must be on [0, pi] radians") diff --git a/test/test_constraint.py b/test/test_constraint.py index 0fd9fce..feec160 100644 --- a/test/test_constraint.py +++ b/test/test_constraint.py @@ -45,6 +45,11 @@ def test_scalar_derivative_is_a_per_point_jacobian(self): assert c.log_jacobian() == pytest.approx(4 * np.log(2.0)) np.testing.assert_allclose(c.y_err, 2.0 * dataset().y_err) + def test_finite_difference_step_is_relative_to_tiny_data(self): + y = np.array([1e-6, 9e-7, 2e-6]) # b/sr-sized, below the old absolute step + c = Comparison(Dataset(np.arange(3.0), y, 0.1 * y), line, space=np.log10) + np.testing.assert_allclose(c.y_err, 0.1 / np.log(10), rtol=1e-6) + def test_non_positive_data_under_log_does_not_raise(self): d = Dataset([0.0, 1.0], [-1.0, 2.0], [0.1, 0.1]) c = Comparison(d, line, space=log) diff --git a/test/test_data.py b/test/test_data.py index 1c1a5d7..51678a4 100644 --- a/test/test_data.py +++ b/test/test_data.py @@ -38,6 +38,10 @@ def test_error_specs(): np.testing.assert_allclose(d.offset_err, [0.01, 0.02, 0.03]) with pytest.raises(ValueError, match="norm_err"): Dataset([0, 1, 2], [1, 2, 3], [0.1, 0.1, 0.1], norm_err=[0.05, 0.05]) + for bad in (np.nan, -0.05, [0.01, np.nan, 0.03]): + for name in ("norm_err", "offset_err"): + with pytest.raises(ValueError, match=f"{name} must be finite"): + Dataset([0, 1, 2], [1, 2, 3], [0.1, 0.1, 0.1], **{name: bad}) def test_meta_is_copied(): diff --git a/test/test_diagnostics.py b/test/test_diagnostics.py index 8be21ee..38502a9 100644 --- a/test/test_diagnostics.py +++ b/test/test_diagnostics.py @@ -11,6 +11,7 @@ from rxmc import Comparison, Constraint, Dataset, Model, Parameter, Problem, Term from rxmc.covariance import StructuredCovariance from rxmc.diagnostics import ( + _psd_factor, compare_logz, coverage_curve, coverage_error, @@ -74,6 +75,14 @@ def test_student_t_draws_follow_the_multivariate_t(self): r = np.abs(draws - Y) assert np.corrcoef(r[:, 0], r[:, 1])[0, 1] > 0.05 + def test_fallback_factor_is_triangular(self): + S = np.array([[1.0, 1.0001], [1.0001, 1.0]]) # indefinite: both Choleskys fail + L = _psd_factor(S) + w, V = np.linalg.eigh(S) + assert np.all(np.isfinite(L)) and np.allclose(L, np.tril(L)) + assert np.all(np.diag(L) >= 0) + np.testing.assert_allclose(L @ L.T, (V * np.clip(w, 0, None)) @ V.T, atol=1e-12) + def test_tiny_variances_not_inflated(self): d = Dataset(X, Y, np.full(5, 1e-9), label="tiny") p = Problem([Constraint([Comparison(d, poly(1))])]) @@ -236,6 +245,14 @@ def test_summary_single_and_replicates(self): assert logz_summary([-10.0, -12.0], [0.3, 0.3]) == (-11.0, 1.0, 2) assert logz_summary([-10.0, -10.2], [0.5, 0.5])[1] == pytest.approx(0.5) + def test_compare_ties_at_the_boundary_and_rejects_nan(self): + assert compare_logz((1.0, 0.0), (1.0, 0.0))["verdict"] == "tie" + assert compare_logz((1.0, 0.5), (0.0, 0.0), sigma=2.0)["verdict"] == "tie" + with pytest.raises(ValueError, match="finite"): + compare_logz((np.nan, 0.1), (0.0, 0.1)) + with pytest.raises(ValueError, match="finite"): + logz_summary([1.0, 2.0], [0.1, np.nan]) + def test_compare(self): r = compare_logz((-10.0, 0.5), (-15.0, 0.5)) assert r["verdict"] == "a" and r["dlogZ"] == pytest.approx(5.0) diff --git a/test/test_likelihood.py b/test/test_likelihood.py index 3b37905..4a54d50 100644 --- a/test/test_likelihood.py +++ b/test/test_likelihood.py @@ -55,6 +55,15 @@ def test_student_t_default_and_explicit_parameter(): assert StudentT().params[0] is not default.params[0] +def test_student_t_tends_to_the_gaussian_at_huge_nu(stats): + _, _, _, d2, logdet = stats + gauss = Gaussian().log_likelihood(d2, logdet, 3) + for nu in (1e15, 1e16): + assert StudentT().log_likelihood(d2, logdet, 3, nu) == pytest.approx( + gauss, abs=1e-6 + ) + + def test_chi2_drops_logdet(stats): _, _, _, d2, logdet = stats assert Chi2().log_likelihood(d2, logdet, 3) == pytest.approx(-0.5 * d2) diff --git a/test/test_measurement.py b/test/test_measurement.py index b5769a8..8431c16 100644 --- a/test/test_measurement.py +++ b/test/test_measurement.py @@ -54,6 +54,13 @@ def test_construction_in_internal_units(): ) +def test_lab_frame_angles_are_refused(): + with pytest.raises(ValueError, match="LAB frame"): + from_measurement(measurement(x_units="LAB-degrees"), reaction=P_CA) + d = from_measurement(measurement(x_units="CM-degrees"), reaction=P_CA) + np.testing.assert_allclose(d.x, np.deg2rad([20.0, 40.0])) + + def test_exfor_tools_labels_and_millibarns(): for label in ("barns/ster", "b/Sr", "MB/SR", "mb/sr"): d = from_measurement(measurement(y_units=label), reaction=P_CA) diff --git a/test/test_notebooks_index.py b/test/test_notebooks_index.py index ebd5c57..7640f39 100644 --- a/test/test_notebooks_index.py +++ b/test/test_notebooks_index.py @@ -50,9 +50,7 @@ def _cited(name) -> set: @pytest.mark.parametrize("name", sorted(NOTEBOOKS)) def test_each_notebook_exists_and_cites_its_recipes(name): - present = _present() - if name not in present: - pytest.xfail(f"examples/{name}.ipynb is not written yet") + assert name in _present(), f"examples/{name}.ipynb is in NOTEBOOKS but missing" cited = _cited(name) assert cited, f"{name} cites no recipe" missing = cited - _headings() diff --git a/test/test_predictive.py b/test/test_predictive.py index 5663f5e..3f68814 100644 --- a/test/test_predictive.py +++ b/test/test_predictive.py @@ -192,6 +192,33 @@ def test_amplitude_reads_the_predictor_meta(self): ) np.testing.assert_allclose(b1, b2, atol=1e-8) + def test_one_bare_callable_is_one_space(self): + # space=np.log wraps into a new Transform on each comparison + model = line() + ds = [Dataset(X, Y + 1.0 + k, np.full(10, 0.05), label=f"d{k}") for k in (0, 1)] + comps = [Comparison(d, model, space=np.log) for d in ds] + gp = kernel(RBF(0.3, "fixed"), on=comps) + p = Problem([Constraint(comps, terms=[gp])]) + chain = np.tile([0.5, 1.2], (4, 1)) + band = total_predictive_band(p, gp, model.bind(X_PRED, {}), X_PRED, chain) + assert band.shape == (2, 30) and np.all(np.isfinite(band)) + + def test_fully_masked_kernel_support_says_so(self): + model = line() + c1, c2 = ( + Comparison(Dataset(X, Y, np.full(10, 0.05), label=lab), model) + for lab in "ab" + ) + gp = kernel(RBF(0.3, "fixed"), on=c2) + c = Constraint([c1, c2], terms=[gp]).masked( + [np.ones(10, bool), np.zeros(10, bool)] + ) + chain = np.tile([0.5, 0.2], (4, 1)) + with pytest.raises(ValueError, match="fully masked"): + total_predictive_band( + Problem([c]), gp, model.bind(X_PRED, {}), X_PRED, chain + ) + def test_explicit_noise_and_errors(self): p, gp, model, comp = problem("noise") chain = self.chain(p, n=6) diff --git a/test/test_problem.py b/test/test_problem.py index baad5bb..df419bb 100644 --- a/test/test_problem.py +++ b/test/test_problem.py @@ -1,5 +1,8 @@ """The compile step: index, priors, compiled constraints, the flat interface.""" +import warnings +from dataclasses import replace + import dill import dynesty import emcee @@ -11,7 +14,15 @@ from rxmc import Comparison, Constraint, Dataset, Model, Parameter, Problem from rxmc.likelihood import StudentT from rxmc.problem import ParameterIndex, clip_unit_cube -from rxmc.terms import Term, kernel, noise, normalization, offset, statistical +from rxmc.terms import ( + Term, + constant_amplitude, + kernel, + noise, + normalization, + offset, + statistical, +) from rxmc.transforms import log, scale X = np.linspace(0.0, 2.0, 6) @@ -174,12 +185,36 @@ def test_bounded_joint_has_no_transform_but_truncates(self): [Constraint([Comparison(dataset(), model)])], priors=[([m, b], mvn)] ) assert p.log_prior([-1.0, 0.0]) == -np.inf - assert p.log_prior([1.0, 0.0]) == pytest.approx(mvn.logpdf([1.0, 0.0])) + # renormalised by the mass inside 0 <= m <= 10 + mass = stats.norm.cdf(10.0) - 0.5 + assert p.log_prior([1.0, 0.0]) == pytest.approx( + mvn.logpdf([1.0, 0.0]) - np.log(mass) + ) with pytest.raises(NotImplementedError, match="truncated"): p.prior_transform([0.5, 0.5]) s = p.sample_prior(50, rng=0) # rejection sampling inside the bounds assert np.all(s[:, 0] >= 0.0) + def test_bounded_mvn_prior_is_renormalised_deterministically(self): + m, b = Parameter("m", bounds=(0.0, np.inf)), Parameter("b") + model = Model(lambda x, m, b: m * x + b, [m, b]) + mvn = stats.multivariate_normal(np.zeros(2), np.eye(2)) + p = Problem([Constraint([Comparison(dataset(), model)])], [([m, b], mvn)]) + # half the mass lies in m >= 0, so the density there doubles + assert p.log_prior([1.0, 0.5]) == pytest.approx( + mvn.logpdf([1.0, 0.5]) + np.log(2.0) + ) + # the box probability is quasi-Monte Carlo in 3-D: two compiles agree + t = Parameter("t", bounds=(-1.0, 1.0)) + model3 = Model(lambda x, m, b, t: m * x + b + t, [m, b, t]) + mvn3 = stats.multivariate_normal(np.zeros(3), np.eye(3) + 0.3) + c3 = Constraint([Comparison(dataset(), model3)]) + lp = [ + Problem([c3], [([m, b, t], mvn3)]).log_prior([1.0, 0.0, 0.2]) + for _ in range(2) + ] + assert lp[0] == lp[1] + def test_custom_joint_with_prior_transform(self): class Hier: def logpdf(self, v): @@ -248,6 +283,16 @@ def rvs(self, size, random_state): assert s.shape == (30, 2) and np.all((s >= 0) & (s <= 1)) assert p.starting_location(4).shape == (4, 2) + def test_marginal_truncated_far_in_the_upper_tail(self): + t = Parameter("t", prior=stats.norm(), bounds=(8.3, np.inf)) + model = Model(lambda x, t: t * x, [t]) + p = Problem([Constraint([Comparison(dataset(), model)])]) + tn = stats.truncnorm(8.3, np.inf) + assert p.log_prior([9.0]) == pytest.approx(tn.logpdf(9.0)) + draws = np.array([p.prior_transform([u])[0] for u in np.linspace(0, 1, 101)]) + assert np.all(draws >= 8.3) and np.all(np.diff(draws) > 0) + np.testing.assert_allclose(p.prior_transform([0.5]), tn.median()) + def test_clip_unit_cube(self): u = clip_unit_cube([0.0, 0.5, 1.0]) assert 0 < u[0] < 1e-10 and u[1] == 0.5 and 1 - 1e-10 < u[2] < 1 @@ -390,6 +435,54 @@ def test_log_jacobian_carries_the_weights(self): assert Problem([tempered]).log_jacobian() == pytest.approx(0.5 * lj) assert Problem([tempered, spare]).log_jacobian() == pytest.approx(0.5 * lj) + def test_constant_singular_block_fails_at_compile_beside_a_parametric_one(self): + model, eps = line_model(), Parameter("log_eps", prior=stats.norm()) + cg = Comparison(dataset(0, 3, "noisy"), model) + exact = Dataset( + X[:3], [1.0, 2.0, 3.0], np.zeros(3), norm_err=0.05, label="E1234-002" + ) + cz = Comparison(exact, model) + terms = [noise(eps, on=cg)] + cz.reported_terms() + with pytest.raises(ValueError, match="E1234-002") as err: + Problem([Constraint([cg, cz], terms=terms)]) + assert "noisy" not in str(err.value) + + def test_kernel_jitter_scales_with_the_data(self): + # scaling the data, and the amplitude with it, by s shifts ll by exactly + # -n log s: the nugget must be relative, not an absolute 1e-10 + from sklearn.gaussian_process.kernels import RBF + + s, d = 1e-4, dataset() + lls = [] + for k in (1.0, s): + m, b = Parameter("m", prior=stats.norm()), Parameter( + "b", prior=stats.norm() + ) + lA = Parameter("log_A", prior=stats.norm()) + model = Model(lambda x, m, b, k=k: k * (m * x + b), [m, b]) + data = Dataset(d.x, k * d.y, k * d.y_err) + gp = kernel( + RBF(0.5, "fixed"), amplitude=constant_amplitude, amplitude_params=(lA,) + ) + p = Problem([Constraint([Comparison(data, model)], terms=[gp])]) + lls.append(p.log_likelihood([*TRUE, np.log(0.3 * k)])) + assert lls[1] - lls[0] == pytest.approx(-d.n * np.log(s), rel=1e-9) + + def test_metadata_tuples_and_0d_arrays_stack(self): + seen = {} + + def fn(c): + seen["E"], seen["t"] = c.meta("Elab"), c.meta("target") + return 0.01 * np.sqrt(c.meta("Elab")) + + meta = {"target": (48, 20), "Elab": np.array(14.0)} + d = Dataset(X, dataset().y, 0.1 * np.ones(6), meta=meta) + term = Term(fn, kind="diag", constant=True) + p = Problem([Constraint([Comparison(d, line_model())], terms=[term])]) + assert np.isfinite(p.log_likelihood(TRUE)) + assert seen["E"].dtype == float and np.allclose(seen["E"], 14.0) + assert all(t == (48, 20) for t in seen["t"]) + def test_predict_and_matrix(self): d = dataset() p = Problem( @@ -429,6 +522,18 @@ def test_masked_views_share_columns_and_partition(self): pa.log_likelihood(theta) ) + def test_rows_active_in_two_weighted_constraints_warn(self): + c = Constraint([Comparison(dataset(), line_model())]) + with pytest.warns( + UserWarning, match="'d' has rows active in constraints 0 and 1" + ): + Problem([c, c]) + fit = c.masked_where(lambda x: x < 1.0) + with warnings.catch_warnings(): + warnings.simplefilter("error") + Problem([fit, fit.complement()]) # disjoint rows + Problem([c, replace(c, weight=0.0)]) # a weight-0 monitor + def test_parameter_on_fully_masked_comparison_keeps_its_slot(self): model = line_model() rho1, rho2 = Parameter("log_rho_1", prior=stats.norm(0, 0.1)), Parameter( diff --git a/test/test_reactions.py b/test/test_reactions.py index bb12bdb..bb47a5d 100644 --- a/test/test_reactions.py +++ b/test/test_reactions.py @@ -93,6 +93,10 @@ def test_missing_kinematics_names_what_is_needed(self): with pytest.raises(ValueError, match="meta\\['Elab'\\]"): omp().bind(ANGLES, {"reaction": N_CA}) + def test_ratio_to_rutherford_needs_a_charged_projectile(self): + with pytest.raises(ValueError, match="neutral projectile"): + omp("dXS/dRuth").bind(ANGLES, meta(N_CA)) + def test_composes_like_any_model(self): rho = Parameter("log_rho", prior=stats.norm(0, 0.1)) scaled = omp() | scale(rho) diff --git a/test/test_terms.py b/test/test_terms.py index 3203b5d..8010949 100644 --- a/test/test_terms.py +++ b/test/test_terms.py @@ -158,6 +158,52 @@ def test_coords_array_2d_reaches_kernel(self): S = t.value(np.zeros(3), np.zeros(3), np.zeros(3), *k.theta) assert np.allclose(S, k(X)) + def test_fixed_kernel_with_parametric_coords(self): + s = Parameter("s") + coords = Transform(lambda a, s: a * s, (s,)) + x = np.array([0.0, 0.5, 1.0]) + for amp in (None, 0.3): + t = kernel(RBF(1.0, "fixed"), coords=coords, amplitude=amp) + assert t.params == (s,) and not t.is_constant + K = t.value(x, np.zeros(3), np.zeros(3), 2.0) + a2 = 1.0 if amp is None else amp**2 + np.testing.assert_allclose(K, a2 * RBF(1.0)(2.0 * x[:, None]), atol=1e-8) + + def test_replace_keeps_one_copy_of_the_coords_parameters(self): + from dataclasses import replace + + le, s, u = Parameter("le"), Parameter("s"), Parameter("u") + t = noise(le, coords=Transform(lambda a, s: a * s, (s,))) + x, y = np.array([1.0, 2.0]), np.zeros(2) + moved = replace(t, on=None) + assert moved.params == (le, s) + np.testing.assert_allclose(moved.value(x, y, None, np.log(0.2), 3.0), 0.2) + assert replace(t, coords=Transform(lambda a, u: a + u, (u,))).params == (le, u) + le2 = Parameter("le2") + assert replace(t, params=(le2,)).params == (le2, s) + lA = Parameter("log_A") + k = kernel( + RBF(1.0, "fixed"), + amplitude=constant_amplitude, + amplitude_params=(lA,), + coords=Transform(lambda a, s: a * s, (s,)), + ) + k2 = replace(k, on=None) + assert k2.params == (lA, s) + np.testing.assert_allclose( + k2.value(x, y, y, 0.0, 1.0), k.value(x, y, y, 0.0, 1.0) + ) + + def test_non_numeric_x_reaches_a_callable_term(self): + x = np.empty(3, dtype=object) + x[:] = [(10.0, 0.1), (10.0, 0.2), (20.0, 0.1)] # (E, theta) pairs + t = Term(lambda c: np.array([e for e, _ in c.x]) / 100.0, kind="diag") + np.testing.assert_allclose(t.value(x, np.ones(3), None), [0.1, 0.1, 0.2]) + le = Parameter("le") + np.testing.assert_allclose( + noise(le).value(x, np.ones(3), None, np.log(0.3)), 0.3 + ) + # ---------------------------------------------------------------------------- # Factories diff --git a/test/test_units.py b/test/test_units.py index 68f4186..be31804 100644 --- a/test/test_units.py +++ b/test/test_units.py @@ -57,3 +57,5 @@ def test_check_angle_grid(): check_angle_grid(np.array([0.0, 4.0]), "angles") with pytest.raises(ValueError, match="radians"): check_angle_grid(np.array([-0.1, 1.0]), "angles") + with pytest.raises(ValueError, match="finite"): + check_angle_grid(np.array([0.3, np.nan, 1.0]), "angles") From c5634e97a246d2c47f02468280c79db1b5c178fa Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Fri, 11 Sep 2026 21:53:56 -0400 Subject: [PATCH 53/75] Restructure the examples: delete, rename, recipe 39, shared plot style - Delete examples/correlated_observations.ipynb and drop its cross-references in recipes 5 and 37; recipe 37 keeps its closed forms and its recipe test - Rename measurement_to_calibration to local_optical_model_calibration - New recipe 39, iterative outlier rejection: the outer loop of problems with the mask refit between runs, which the chain-dependent-masks entry of "What this API does not express" already named as the sanctioned workaround, with its recipe test - Recipe 4 gains the per-dataset spelling (one magnitude per comparison), for an experiment that reports no systematic uncertainty at all - examples/plotstyle.py: shared rcParams, a colourblind-safe palette, hatched predictive bands and corner defaults, and the CI format job now covers it --- .github/workflows/ci.yml | 6 +- docs/design.md | 7 +- docs/examples.rst | 3 +- docs/recipes.md | 62 +- examples/correlated_observations.ipynb | 751 ------------------ ... => local_optical_model_calibration.ipynb} | 0 examples/plotstyle.py | 127 +++ ...t_recipe_39_iterative_outlier_rejection.py | 84 ++ test/test_notebooks_index.py | 5 +- 9 files changed, 277 insertions(+), 768 deletions(-) delete mode 100644 examples/correlated_observations.ipynb rename examples/{measurement_to_calibration.ipynb => local_optical_model_calibration.ipynb} (100%) create mode 100644 examples/plotstyle.py create mode 100644 test/recipes/test_recipe_39_iterative_outlier_rejection.py diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 358333d..0adbade 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -26,9 +26,9 @@ jobs: python -m pip install -e '.[validation]' - name: Check source formatting and imports run: | - python -m isort --check-only src test - python -m black --check src test - python -m ruff check src test + python -m isort --check-only src test examples/*.py + python -m black --check src test examples/*.py + python -m ruff check src test examples/*.py - name: Check notebook formatting and imports run: | if [ -d examples ] && ls examples/*.ipynb >/dev/null 2>&1; then diff --git a/docs/design.md b/docs/design.md index 5bc192d..322ef33 100644 --- a/docs/design.md +++ b/docs/design.md @@ -615,7 +615,7 @@ references and `STUDY_LEGEND`, the labelled error-model forms of recipe ## 6. Notebooks -Nine notebooks in `examples/`, each naming the recipes it teaches in its +The notebooks in `examples/` each name the recipes they teach in their first cell. Runtimes are wall times on an eight-core laptop, one kernel at a time unless noted. @@ -624,10 +624,9 @@ a time unless noted. | `linear_calibration` | 1, 17 | emcee | the whole workflow on a line; prior and posterior predictive; the coverage curve | 23 s | | `error_models` | 2, 4, 5, 19 | emcee | the covariance ladder on one comparison, the Peelle matrix as a fixed term, offsets known and free, case B across two datasets | 153 s | | `normalization_and_covariance_structure` | 3, 6, 27 | emcee | latent scales versus reported modes on a quartic; a gallery of covariance structures from `matrix(theta)` | 328 s | -| `correlated_observations` | 5, 37 | emcee | coupled versus independent covariances; Neudecker et al. (2014) §II.A and §II.B recreated | 141 s | | `gp_discrepancy` | 7, 8, 36 | emcee, dynesty | a kernel term on a toy and on n+⁴⁰Ca missing its surface absorption; the total predictive band; a sampled Legendre correction for contrast | 617 s | | `robust_likelihoods` | 9, 34 | emcee | Student-t versus Gaussian; a global error scale; a USU offset per technique | 154 s | -| `measurement_to_calibration` | 12, 14, 15, 16, 21, 26 | dynesty | an EXFOR-shaped measurement to a calibrated potential; the unit contract, reported terms, the singular guard, tempering, other drivers | 182 s | +| `local_optical_model_calibration` | 12, 14, 15, 16, 21, 26 | dynesty | a real EXFOR measurement to a calibrated potential; the unit contract, the singular guard, tempering, other drivers | 182 s | | `alpha_ca_error_model_comparison` | 10, 11, 13, 18 | dynesty | real ⁴⁴Ca(α,α) data, a four-parameter potential, the ladder by evidence with the Jacobian, held-out backward angles | 1197 s (alongside another notebook) | | `hierarchical_calibration` | 22, 24, 30, 35, 38 | dynesty | eight schools; a hierarchy on the physics parameters repairing a misspecified energy dependence, in sample and at a held-out energy | 937 s (alongside another notebook) | @@ -661,7 +660,7 @@ src/rxmc/ reactions/ elastic.py ias.py test/ one file per module, the index tests, test_regression.py, helpers.py test/recipes/ one file per recipe; common.py, oracle.py -examples/ nine notebooks; data/alpha_ca_ratio_ruth.csv (jitr's digitisation of EXFOR F0567) +examples/ the notebooks and plotstyle.py; data/ (committed measurements) docs/ this document, recipes.md, examples.rst, api.rst, groundup_design.md (history) ``` diff --git a/docs/examples.rst b/docs/examples.rst index a346fe4..d8d795f 100644 --- a/docs/examples.rst +++ b/docs/examples.rst @@ -18,7 +18,6 @@ Calibration basics examples/linear_calibration.ipynb examples/error_models.ipynb examples/normalization_and_covariance_structure.ipynb - examples/correlated_observations.ipynb Beyond the Gaussian ------------------- @@ -35,6 +34,6 @@ Reactions and studies .. toctree:: :maxdepth: 1 - examples/measurement_to_calibration.ipynb + examples/local_optical_model_calibration.ipynb examples/alpha_ca_error_model_comparison.ipynb examples/hierarchical_calibration.ipynb diff --git a/docs/recipes.md b/docs/recipes.md index 169bc17..abb6ab4 100644 --- a/docs/recipes.md +++ b/docs/recipes.md @@ -93,6 +93,11 @@ log_eta = rx.Parameter("log_eta", prior=stats.norm(-3, 1)) c = rx.Constraint([comp], terms=[T.normalization(parameter=log_eta)]) # absolute offset instead: T.offset(parameter=log_omega) # a mode with any shape: T.systematic(log_s, basis=T.x_basis(np.pi)) + +# one magnitude per dataset: a parameter and a comparison-local mode each +etas = [rx.Parameter(f"log_eta_{i}", prior=stats.norm(-3, 1)) for i in range(len(comps))] +c_each = rx.Constraint(comps, terms=[T.normalization(parameter=e, on=cmp) + for e, cmp in zip(etas, comps)]) ``` Expected behaviour: @@ -100,6 +105,13 @@ Expected behaviour: - One rank-one mode `exp(log_eta)**2 * outer(ym, ym)` is added. - The model parameters decorrelate from the overall scale of the data; the data's normalisation pull moves into `log_eta`. +- One magnitude per dataset is the same spelling with one parameter and one + `on=` per comparison: each mode stays inside its own block, so the + datasets remain independent, and each `log_eta_i` is inferred from its own + dataset's scatter about the prediction. This is what to do when an + experiment reports no systematic uncertainty at all; recipe 5 shares one + magnitude between datasets instead, and recipe 6 puts the scale on the + *mean* rather than in the covariance. ## 5. Share an error model between datasets, or couple them @@ -126,7 +138,7 @@ Expected behaviour: - All three spellings have exactly one nuisance parameter. - Case B's covariance is block diagonal; case A's has a non-zero off-diagonal block. The two likelihoods differ, and treating case A - data as case B is overconfident (`correlated_observations`). + data as case B is overconfident. - Sharing is by object: two `Parameter("log_eta")` objects would be two parameters and a compile error for the duplicate name. - Case A costs no more than case B: the cross-comparison mode goes through the @@ -1150,8 +1162,7 @@ Expected behaviour: 1.25, 1.15, 1.05; `sigma_i = 0.1 q_i`, `sigma_Ni = 0.2 N_i`, `c = 0.8`) reproduces its Fig. 1: `C_F` gives lower means and smaller standard deviations on every lattice point, `C_I` agrees with the exact values - (both exact in the fast tier, being generalised least squares). The - notebook `correlated_observations` recreates the figure. + (both exact in the fast tier, being generalised least squares). - Analysing powers are *not* an instance of this recipe: a ratio of cross sections has a fixed normalisation, so nothing correlated can be inferred for it. The real-data case of the reference (`237Np(n,f)` measured @@ -1224,6 +1235,47 @@ Analysis*, 3rd ed., CRC Press (2013), Chapter 5. --- +## 39. Iterative outlier rejection + +*A few points are gross outliers. I want to reject them and refit, the way +KDUQ does, rather than let a heavy tail absorb them.* + +```python +mask = np.ones(d.n, dtype=bool) +for _ in range(max_rounds): # an outer loop of problems + p = rx.Problem([c.masked([mask])]) + theta = map_estimate(p) # or a chain, and its posterior mean + pull = np.abs(d.y - p.constraints[0].ym(theta)) / d.y_err + keep = pull < 3.0 + if np.array_equal(keep, mask): + break # the mask has stopped moving + mask = keep +rejected = c.masked([mask]).complement() # what went, for the record +``` + +Expected behaviour: + +- Masks are compiled, so rejection is an *outer* loop: every round is a new + `Problem` over the same `Comparison`, `Term` and `Parameter` objects, and + the parameters keep their columns (recipe 11). A mask that moved inside + the chain would be mutable state inside a spec, which the design refuses + (*What this API does not express*). +- The loop either reaches a fixed point or cycles between two masks; cap the + rounds, and report which points went and after how many rounds. Given the + starting mask and a deterministic fit it is reproducible. +- Rejection and a heavy tail are different answers to the same question. + `StudentT` (recipe 9) keeps every point and widens; rejection commits to a + subset and fits it tightly. Score them the same way, by holding out + (recipe 11) or by evidence (recipe 18), and note that the evidence of a + fit to a subset is not comparable with the evidence of a fit to all of it. +- Nothing is deleted: the rejected points stay in the declaration and + `complement()` names them, so a later round can take them back. + +Reference: Pruitt, Escher, Rahman, *Uncertainty-quantified phenomenological +optical potentials for single-nucleon scattering*, Phys. Rev. C 107, 014602 +(2023), [arXiv:2211.07741](https://arxiv.org/abs/2211.07741), which rejects +points more than 3σ from the current model between rounds. + ## What this API does not express Each item names the assumption that breaks, the nearest workaround, and @@ -1238,8 +1290,8 @@ the size of the addition that would lift it. - **Chain-dependent masks.** KDUQ's iterative rejection of points more than 3σ from the current model, updated during the walk, needs a mask that depends on chain state. Masks are compiled. Workaround: an outer - loop of problems with the mask refit between runs. Addition refused by - design: it is mutable state inside a spec. + loop of problems with the mask refit between runs, which is recipe 39. + Addition refused by design: it is mutable state inside a spec. - **Per-point latent variables.** Errors-in-variables in `x` (Berkson), explicit latent function values on a mesh (Schnabel et al. 2021), or a sampled per-point scale in a scale mixture. Expressible in principle as diff --git a/examples/correlated_observations.ipynb b/examples/correlated_observations.ipynb deleted file mode 100644 index 2641135..0000000 --- a/examples/correlated_observations.ipynb +++ /dev/null @@ -1,751 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d73aa9d8", - "metadata": {}, - "source": [ - "# Correlated observations\n", - "\n", - "Two datasets that are each internally independent but share a calibration\n", - "are *correlated observations*: fitting them as if independent is\n", - "overconfident, because the common mode cannot average down. In `rxmc` the\n", - "coupling is one covariance term whose support spans both comparisons. The\n", - "second half recreates the two-and-more-dimensional Peelle's Pertinent Puzzle\n", - "of Neudecker, Frühwirth, Kawano and Leeb, *Nucl. Data Sheets* **118**, 364\n", - "(2014): several physical quantities of one experiment with correlated\n", - "normalisations, and why the covariance must be built from the estimate and\n", - "not from the data.\n", - "\n", - "Recipes: 5, 37" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "68b3cd97", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T03:43:23.920181Z", - "iopub.status.busy": "2026-09-11T03:43:23.919921Z", - "iopub.status.idle": "2026-09-11T03:43:26.661049Z", - "shell.execute_reply": "2026-09-11T03:43:26.660051Z" - } - }, - "outputs": [], - "source": [ - "import corner\n", - "import emcee\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from scipy import stats\n", - "\n", - "import rxmc as rx\n", - "from rxmc import terms as T" - ] - }, - { - "cell_type": "markdown", - "id": "f6d94ba0", - "metadata": {}, - "source": [ - "## Two datasets with a shared calibration\n", - "\n", - "One line, two experiments on disjoint ranges, one calibration factor drawn\n", - "once and applied to both. Each experiment reports its own 2 % statistics and\n", - "a 10 % normalisation uncertainty, without saying that it is the *same*\n", - "normalisation." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8982216e", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T03:43:26.662956Z", - "iopub.status.busy": "2026-09-11T03:43:26.662664Z", - "iopub.status.idle": "2026-09-11T03:43:26.892125Z", - "shell.execute_reply": "2026-09-11T03:43:26.891553Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 500x350 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "rng = np.random.default_rng(3)\n", - "m_true, b_true = 1.0, 0.5\n", - "sigma_c, noise = 0.10, 0.02\n", - "c_factor = rng.normal(1.0, sigma_c)\n", - "x1, x2 = np.linspace(0.0, 2.0, 8), np.linspace(3.0, 5.0, 8)\n", - "y1 = c_factor * (m_true * x1 + b_true) + rng.normal(0.0, noise, 8)\n", - "y2 = c_factor * (m_true * x2 + b_true) + rng.normal(0.0, noise, 8)\n", - "d1 = rx.Dataset(x1, y1, np.full(8, noise), norm_err=sigma_c, label=\"forward\")\n", - "d2 = rx.Dataset(x2, y2, np.full(8, noise), norm_err=sigma_c, label=\"backward\")\n", - "\n", - "fig, ax = plt.subplots(figsize=(5, 3.5))\n", - "for d in (d1, d2):\n", - " ax.errorbar(d.x, d.y, d.y_err, fmt=\"o\", ms=4, label=d.label)\n", - "x_line = np.linspace(0, 5, 2)\n", - "ax.plot(x_line, m_true * x_line + b_true, \"k--\", label=\"truth\")\n", - "ax.set(\n", - " xlabel=\"x\", ylabel=\"y\", title=f\"one drawn calibration factor, c = {c_factor:.3f}\"\n", - ")\n", - "ax.legend(frameon=False)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "593faa44", - "metadata": {}, - "source": [ - "## The covariance structure: coupled or independent\n", - "\n", - "The same 10 % normalisation, spelled two ways. With `on=[comp1, comp2]` a\n", - "single mode spans both blocks and writes off-diagonal blocks into Σ. With\n", - "one term per comparison the blocks are independent. Both are built from the\n", - "prediction, never from the data." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "1fa302ff", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T03:43:26.894408Z", - "iopub.status.busy": "2026-09-11T03:43:26.894191Z", - "iopub.status.idle": "2026-09-11T03:43:27.052483Z", - "shell.execute_reply": "2026-09-11T03:43:27.051640Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 700x350 with 2 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "m = rx.Parameter(\"m\", prior=stats.norm(1.0, 0.3), latex=\"m\")\n", - "b = rx.Parameter(\"b\", prior=stats.norm(0.5, 0.3), latex=\"b\")\n", - "line = rx.Model(lambda x, m, b: m * x + b, [m, b])\n", - "comp1, comp2 = rx.Comparison(d1, line), rx.Comparison(d2, line)\n", - "\n", - "c_coupled = rx.Constraint(\n", - " [comp1, comp2], terms=[T.normalization(magnitude=sigma_c, on=[comp1, comp2])]\n", - ")\n", - "c_indep = rx.Constraint(\n", - " [comp1, comp2],\n", - " terms=[\n", - " T.normalization(magnitude=sigma_c, on=comp1),\n", - " T.normalization(magnitude=sigma_c, on=comp2),\n", - " ],\n", - ")\n", - "p_coupled, p_indep = rx.Problem([c_coupled]), rx.Problem([c_indep])\n", - "theta_true = np.array([m_true, b_true])\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(7, 3.5))\n", - "for ax, p, title in zip(\n", - " axes,\n", - " (p_coupled, p_indep),\n", - " (\"coupled: one spanning mode\", \"independent: one mode per block\"),\n", - "):\n", - " S = p.constraints[0].matrix(theta_true)\n", - " im = ax.imshow(S, cmap=\"viridis\")\n", - " ax.axhline(7.5, color=\"w\", lw=0.8)\n", - " ax.axvline(7.5, color=\"w\", lw=0.8)\n", - " ax.set(title=title, xticks=[], yticks=[])\n", - "fig.suptitle(\"the off-diagonal blocks are what couple the data\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "55e1d262", - "metadata": {}, - "source": [ - "## Effect on inference\n", - "\n", - "Same data, same model, same magnitudes: the coupled fit is appropriately\n", - "less certain, the independent one is overconfident. Both are honest about\n", - "their statistics; only one is honest about the calibration." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d8069ab2", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T03:43:27.054450Z", - "iopub.status.busy": "2026-09-11T03:43:27.054230Z", - "iopub.status.idle": "2026-09-11T03:44:25.044516Z", - "shell.execute_reply": "2026-09-11T03:44:25.043561Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 550x550 with 4 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "coupled m = 1.199 +/- 0.107 b = 0.581 +/- 0.053\n", - "independent m = 1.193 +/- 0.078 b = 0.589 +/- 0.041\n" - ] - } - ], - "source": [ - "def fit(problem, seed, n_walkers=24, n_steps=1800):\n", - " sampler = emcee.EnsembleSampler(n_walkers, problem.ndim, problem.log_posterior)\n", - " sampler.random_state = np.random.RandomState(seed).get_state()\n", - " sampler.run_mcmc(problem.sample_prior(n_walkers, rng=seed), n_steps, progress=False)\n", - " return sampler.get_chain(discard=n_steps // 3, thin=5, flat=True)\n", - "\n", - "\n", - "s_coupled, s_indep = fit(p_coupled, seed=1), fit(p_indep, seed=2)\n", - "fig = corner.corner(\n", - " s_indep,\n", - " color=\"C3\",\n", - " labels=[\"$m$\", \"$b$\"],\n", - " truths=theta_true,\n", - " truth_color=\"k\",\n", - " plot_datapoints=False,\n", - ")\n", - "corner.corner(s_coupled, fig=fig, color=\"C0\", plot_datapoints=False)\n", - "fig.legend(\n", - " handles=[\n", - " plt.Line2D([], [], color=\"C0\", label=\"coupled\"),\n", - " plt.Line2D([], [], color=\"C3\", label=\"independent (overconfident)\"),\n", - " ],\n", - " loc=\"upper right\",\n", - " frameon=False,\n", - ")\n", - "plt.show()\n", - "for name, s in ((\"coupled\", s_coupled), (\"independent\", s_indep)):\n", - " print(\n", - " f\"{name:12s} m = {s[:, 0].mean():.3f} +/- {s[:, 0].std():.3f} b = {s[:, 1].mean():.3f} +/- {s[:, 1].std():.3f}\"\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "62a7fa3e", - "metadata": {}, - "source": [ - "## Case A and case B with a *free* shared nuisance\n", - "\n", - "If the magnitude of the shared calibration is unknown, one `Parameter`\n", - "object carries it. Where you put the object decides the structure:\n", - "\n", - "- **case A couples the data**: one term `on=[comp1, comp2]` reads the\n", - " object once, and Σ has off-diagonal blocks;\n", - "- **case B couples the parameters**: two terms, one per comparison, both\n", - " reading the same object; Σ stays block diagonal but the two blocks scale\n", - " together.\n", - "\n", - "Both have exactly one nuisance column, because parameters are matched by\n", - "identity, not by name." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "ebc166b2", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T03:44:25.046630Z", - "iopub.status.busy": "2026-09-11T03:44:25.046414Z", - "iopub.status.idle": "2026-09-11T03:44:25.053965Z", - "shell.execute_reply": "2026-09-11T03:44:25.053148Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "case A: columns ['m', 'b', 'log_eta'], max |off-diagonal block| = 1.38e-01\n", - "case B: columns ['m', 'b', 'log_eta'], max |off-diagonal block| = 0.00e+00\n" - ] - } - ], - "source": [ - "log_eta = rx.Parameter(\"log_eta\", prior=stats.norm(np.log(0.1), 1.0), latex=r\"\\log\\eta\")\n", - "c_A = rx.Constraint([comp1, comp2], terms=[T.normalization(log_eta, on=[comp1, comp2])])\n", - "c_B = rx.Constraint(\n", - " [comp1, comp2],\n", - " terms=[T.normalization(log_eta, on=comp1), T.normalization(log_eta, on=comp2)],\n", - ")\n", - "for name, c in ((\"case A\", c_A), (\"case B\", c_B)):\n", - " p = rx.Problem([c])\n", - " S = p.constraints[0].matrix(np.array([m_true, b_true, np.log(0.1)]))\n", - " print(\n", - " f\"{name}: columns {p.names}, max |off-diagonal block| = {np.abs(S[:8, 8:]).max():.2e}\"\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "b59f63a6", - "metadata": {}, - "source": [ - "## More than one physical quantity: Neudecker et al. (2014)\n", - "\n", - "The reference studies an experiment that reports several physical\n", - "quantities `ρ_i = α_i η_i`. Each `α_i` is measured as `q_i`, once or more,\n", - "with independent errors `σ_i`; each normalisation `η_i` is measured as\n", - "`N_i`, and the `N_i` share a covariance `B` with correlation `c`. The\n", - "reported data are the products `r_i = q_i N_i`, and the question is how to\n", - "build their covariance.\n", - "\n", - "- **`C_F`** (their eq. 11) propagates the normalisation error with the\n", - " *measured* `q`: the mode of quantity `i` is `σ_N,i q_i`.\n", - "- **`C_I`** (their eq. 12) uses the *estimate*, the weighted mean `q̄_i`:\n", - " the mode is `σ_N,i q̄_i`, which in `rxmc` is the prediction divided by\n", - " `N_i`, i.e. the term reads `c.ym`.\n", - "\n", - "With `C_F` the evaluation is biased low and too narrow: Peelle's Pertinent\n", - "Puzzle, now across quantities. Here is the model: one comparison per\n", - "quantity, the quantity itself as the model, and one `matrix` term spanning\n", - "all comparisons that pairs `c.split(c.ym)` with the correlation matrix of the\n", - "normalisations." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "9a88c3d1", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T03:44:25.056234Z", - "iopub.status.busy": "2026-09-11T03:44:25.055980Z", - "iopub.status.idle": "2026-09-11T03:44:25.068017Z", - "shell.execute_reply": "2026-09-11T03:44:25.067049Z" - } - }, - "outputs": [], - "source": [ - "def experiment(q, N, sigma_q, sigma_N, prior=stats.uniform(0.0, 10.0)):\n", - " comps, rhos = [], []\n", - " for i, (qi, Ni, si) in enumerate(zip(q, N, sigma_q)):\n", - " rho = rx.Parameter(f\"rho_{i + 1}\", prior=prior, latex=rf\"\\rho_{i + 1}\")\n", - " rhos.append(rho)\n", - " d = rx.Dataset(\n", - " np.full(len(qi), i),\n", - " np.asarray(qi) * Ni,\n", - " Ni * np.asarray(si),\n", - " label=f\"quantity {i + 1}\",\n", - " )\n", - " comps.append(rx.Comparison(d, rx.Model(lambda x, r: np.full(len(x), r), [rho])))\n", - " return comps, rhos\n", - "\n", - "\n", - "def normalisations(comps, frac, corr, from_data=False):\n", - " \"\"\"outer(f * ym, f * ym) * corr across the quantities; from y for C_F.\"\"\"\n", - " corr = np.asarray(corr, dtype=float)\n", - "\n", - " def fn(c):\n", - " pieces = c.split(c.y if from_data else c.ym)\n", - " u = np.concatenate([f * piece for f, piece in zip(frac, pieces)])\n", - " which = np.concatenate(\n", - " [np.full(s.stop - s.start, k) for k, s in enumerate(c.segments)]\n", - " )\n", - " return np.outer(u, u) * corr[np.ix_(which, which)]\n", - "\n", - " return rx.Term(fn, kind=\"matrix\", on=comps, constant=from_data)\n", - "\n", - "\n", - "def fixed_estimate(comps, rho, frac, corr, counts):\n", - " \"\"\"C_I in its fixed form: the mode built from an estimate of each rho.\"\"\"\n", - " u = np.concatenate([np.full(n, f * r) for n, f, r in zip(counts, frac, rho)])\n", - " which = np.concatenate([np.full(n, k) for k, n in enumerate(counts)])\n", - " return rx.Term(\n", - " np.outer(u, u) * np.asarray(corr)[np.ix_(which, which)], kind=\"matrix\", on=comps\n", - " )\n", - "\n", - "\n", - "def exact(q, N, sigma_q, sigma_N, corr):\n", - " \"\"\"Means and covariance from the full information (their eqs. 2-8).\"\"\"\n", - " w = [1.0 / np.asarray(s) ** 2 for s in sigma_q]\n", - " qbar = np.array([np.sum(wi * qi) / np.sum(wi) for wi, qi in zip(w, q)])\n", - " var_a = np.array([1.0 / np.sum(wi) for wi in w])\n", - " N, sN = np.asarray(N), np.asarray(sigma_N)\n", - " cov = np.outer(qbar * sN, qbar * sN) * np.asarray(corr)\n", - " cov[np.diag_indices_from(cov)] += var_a * N**2\n", - " return qbar * N, cov, qbar\n", - "\n", - "\n", - "def gls(problem, theta_any):\n", - " \"\"\"Posterior mean and covariance under a *fixed* covariance and a flat prior.\"\"\"\n", - " c = problem.constraints[0]\n", - " S = c.matrix(theta_any)\n", - " X = np.eye(problem.ndim)[c.x[c.active].astype(int)]\n", - " cov = np.linalg.inv(X.T @ np.linalg.solve(S, X))\n", - " return cov @ (X.T @ np.linalg.solve(S, c.y[c.active])), cov" - ] - }, - { - "cell_type": "markdown", - "id": "b1df048b", - "metadata": {}, - "source": [ - "### II.A: the analytic study\n", - "\n", - "Quantity 1 measured twice, quantity 2 once, 10 % statistics, 20 %\n", - "normalisations correlated at `c = 0.8`. The reference's closed forms:\n", - "under `C_F`, `⟨ρ_1⟩ = q̄_1 N_1 / (1 + ξ)` with\n", - "`ξ = (q_1 − q_1')² σ²_N1 var(α_1) / (N_1² σ_1² σ_1'²)`, and `⟨ρ_2⟩` is pulled\n", - "down through `c` although `α_2` was measured once; under `C_I` the means are\n", - "`q̄_i N_i` exactly." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "1bf5d66a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T03:44:25.070204Z", - "iopub.status.busy": "2026-09-11T03:44:25.069911Z", - "iopub.status.idle": "2026-09-11T03:44:25.085373Z", - "shell.execute_reply": "2026-09-11T03:44:25.084449Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "xi = 0.3077\n", - "exact : rho = [1.1538 1.98 ] sd = [0.2453 0.4427]\n", - "C_I (est.) : rho = [1.1538 1.98 ] sd = [0.2453 0.4427]\n", - "C_F (data) : rho = [0.8824 1.6073] sd = [0.2183 0.4152]\n", - "eq. 13 : rho_1 = 0.8824\n" - ] - } - ], - "source": [ - "q = [np.array([1.0, 1.5]), np.array([1.8])]\n", - "N = np.array([1.0, 1.1])\n", - "sigma_q = [0.1 * qi for qi in q]\n", - "sigma_N = 0.2 * N\n", - "c_corr = 0.8\n", - "corr = np.array([[1.0, c_corr], [c_corr, 1.0]])\n", - "counts = [len(qi) for qi in q]\n", - "\n", - "comps, rhos = experiment(q, N, sigma_q, sigma_N)\n", - "mean, cov, qbar = exact(q, N, sigma_q, sigma_N, corr)\n", - "p_F = rx.Problem(\n", - " [\n", - " rx.Constraint(\n", - " comps, terms=[normalisations(comps, sigma_N / N, corr, from_data=True)]\n", - " )\n", - " ]\n", - ")\n", - "p_I = rx.Problem(\n", - " [\n", - " rx.Constraint(\n", - " comps, terms=[fixed_estimate(comps, mean, sigma_N / N, corr, counts)]\n", - " )\n", - " ]\n", - ")\n", - "mean_F, cov_F = gls(p_F, mean)\n", - "mean_I, cov_I = gls(p_I, mean)\n", - "\n", - "var_a1 = 1.0 / np.sum(1.0 / sigma_q[0] ** 2)\n", - "xi = (\n", - " (q[0][0] - q[0][1]) ** 2\n", - " * sigma_N[0] ** 2\n", - " * var_a1\n", - " / (N[0] ** 2 * sigma_q[0][0] ** 2 * sigma_q[0][1] ** 2)\n", - ")\n", - "print(f\"xi = {xi:.4f}\")\n", - "print(f\"exact : rho = {mean.round(4)} sd = {np.sqrt(np.diag(cov)).round(4)}\")\n", - "print(\n", - " f\"C_I (est.) : rho = {mean_I.round(4)} sd = {np.sqrt(np.diag(cov_I)).round(4)}\"\n", - ")\n", - "print(\n", - " f\"C_F (data) : rho = {mean_F.round(4)} sd = {np.sqrt(np.diag(cov_F)).round(4)}\"\n", - ")\n", - "print(f\"eq. 13 : rho_1 = {qbar[0] * N[0] / (1 + xi):.4f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "8f0a18c2", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T03:44:25.087493Z", - "iopub.status.busy": "2026-09-11T03:44:25.087183Z", - "iopub.status.idle": "2026-09-11T03:44:25.313134Z", - "shell.execute_reply": "2026-09-11T03:44:25.312176Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 700x320 with 3 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 2, figsize=(7, 3.2))\n", - "for ax, p, title in zip(\n", - " axes, (p_I, p_F), (r\"$C_I$: from the estimate\", r\"$C_F$: from the data\")\n", - "):\n", - " im = ax.imshow(p.constraints[0].matrix(mean), cmap=\"viridis\")\n", - " ax.axhline(1.5, color=\"w\", lw=0.8)\n", - " ax.axvline(1.5, color=\"w\", lw=0.8)\n", - " ax.set(\n", - " title=title,\n", - " xticks=range(3),\n", - " yticks=range(3),\n", - " xticklabels=[\"q1\", \"q1'\", \"q2\"],\n", - " yticklabels=[\"q1\", \"q1'\", \"q2\"],\n", - " )\n", - "fig.colorbar(im, ax=axes, shrink=0.8)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "860efb79", - "metadata": {}, - "source": [ - "### II.B: the five-quantity numerical study\n", - "\n", - "Table I of the reference: five quantities, most measured twice, `σ_i = 0.1\n", - "q_i`, `σ_N,i = 0.2 N_i`, `c = 0.8`. Its Fig. 1 compares the means and\n", - "standard deviations of the `ρ_i` from the full information with those from\n", - "`C_I` and `C_F`. Both posteriors are Gaussian (fixed covariance, flat\n", - "prior), so emcee is only a check on the closed form here; the figure is the\n", - "point." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "7d11de2d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T03:44:25.315785Z", - "iopub.status.busy": "2026-09-11T03:44:25.315483Z", - "iopub.status.idle": "2026-09-11T03:45:41.865160Z", - "shell.execute_reply": "2026-09-11T03:45:41.864316Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 800x350 with 2 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "q5 = [\n", - " np.array([1.0, 1.5]),\n", - " np.array([1.8]),\n", - " np.array([2.2, 2.4]),\n", - " np.array([1.9, 1.5]),\n", - " np.array([1.4, 1.2]),\n", - "]\n", - "N5 = np.array([1.0, 1.1, 1.25, 1.15, 1.05])\n", - "sigma_q5 = [0.1 * qi for qi in q5]\n", - "sigma_N5 = 0.2 * N5\n", - "corr5 = np.full((5, 5), c_corr) + (1 - c_corr) * np.eye(5)\n", - "counts5 = [len(qi) for qi in q5]\n", - "\n", - "comps5, rhos5 = experiment(q5, N5, sigma_q5, sigma_N5)\n", - "mean5, cov5, qbar5 = exact(q5, N5, sigma_q5, sigma_N5, corr5)\n", - "p5_F = rx.Problem(\n", - " [\n", - " rx.Constraint(\n", - " comps5, terms=[normalisations(comps5, sigma_N5 / N5, corr5, from_data=True)]\n", - " )\n", - " ]\n", - ")\n", - "p5_I = rx.Problem(\n", - " [\n", - " rx.Constraint(\n", - " comps5, terms=[fixed_estimate(comps5, mean5, sigma_N5 / N5, corr5, counts5)]\n", - " )\n", - " ]\n", - ")\n", - "s5_F, s5_I = fit(p5_F, seed=5, n_steps=3000), fit(p5_I, seed=6, n_steps=3000)\n", - "\n", - "lattice = np.arange(1, 6)\n", - "fig, axes = plt.subplots(1, 2, figsize=(8, 3.5))\n", - "axes[0].errorbar(\n", - " lattice, mean5, np.sqrt(np.diag(cov5)), fmt=\"ko-\", capsize=3, label=\"exact\"\n", - ")\n", - "axes[0].errorbar(\n", - " lattice + 0.08,\n", - " s5_I.mean(0),\n", - " s5_I.std(0),\n", - " fmt=\"s--\",\n", - " color=\"C2\",\n", - " capsize=3,\n", - " label=r\"$C_I$\",\n", - ")\n", - "axes[0].errorbar(\n", - " lattice - 0.08,\n", - " s5_F.mean(0),\n", - " s5_F.std(0),\n", - " fmt=\"v:\",\n", - " color=\"C3\",\n", - " capsize=3,\n", - " label=r\"$C_F$\",\n", - ")\n", - "axes[0].set(xlabel=\"lattice index\", ylabel=\"quantity\", xticks=lattice)\n", - "axes[0].legend(frameon=False)\n", - "axes[1].plot(lattice, np.sqrt(np.diag(cov5)), \"ko-\", label=\"exact\")\n", - "axes[1].plot(lattice, s5_I.std(0), \"s--\", color=\"C2\", label=r\"$C_I$\")\n", - "axes[1].plot(lattice, s5_F.std(0), \"v:\", color=\"C3\", label=r\"$C_F$\")\n", - "axes[1].set(xlabel=\"lattice index\", ylabel=\"standard deviation\", xticks=lattice)\n", - "axes[1].legend(frameon=False)\n", - "fig.suptitle(\"Neudecker et al. (2014), Fig. 1 recreated\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "81d004eb", - "metadata": {}, - "source": [ - "`C_I` reproduces the exact means and widths; `C_F` is below them on every\n", - "lattice point, in both the mean and the width. The reference's real-data\n", - "case has the same structure: ²³⁷Np(n,f) measured by three experiments as\n", - "ratios to the ²³⁵U(n,f) standard, converted with the standard's covariance\n", - "as `B`, where `C_F` lowers the evaluated cross sections below every dataset." - ] - }, - { - "cell_type": "markdown", - "id": "e548e269", - "metadata": {}, - "source": [ - "### The live term and the log-determinant\n", - "\n", - "`rxmc`'s `T.normalization` and the spanning term above read `c.ym`, so the\n", - "covariance moves with the parameters: this is the generative model's\n", - "marginal likelihood, not the fixed `C_I`. A covariance that grows with the\n", - "prediction pulls the posterior mode down through `log det Σ`; with 20 %\n", - "normalisations it is a 9 % pull here, against 27 to 31 % for `C_F`. A proper\n", - "prior on the quantities, or the two-step refit (`C_I` built from a first\n", - "estimate, as above), removes it." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "7e58ce3d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T03:45:41.867025Z", - "iopub.status.busy": "2026-09-11T03:45:41.866763Z", - "iopub.status.idle": "2026-09-11T03:45:42.027594Z", - "shell.execute_reply": "2026-09-11T03:45:42.026808Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "relative pull of the mode, live term: [-0.09 -0.09 -0.09 -0.09 -0.09]\n", - "relative bias of the mean, C_F : [-0.311 -0.268 -0.271 -0.284 -0.275]\n" - ] - } - ], - "source": [ - "from scipy.optimize import minimize\n", - "\n", - "p5_live = rx.Problem(\n", - " [rx.Constraint(comps5, terms=[normalisations(comps5, sigma_N5 / N5, corr5)])]\n", - ")\n", - "mode = minimize(lambda t: -p5_live.log_posterior(t), mean5, method=\"Nelder-Mead\").x\n", - "print(\"relative pull of the mode, live term:\", np.round(mode / mean5 - 1, 3))\n", - "print(\n", - " \"relative bias of the mean, C_F :\",\n", - " np.round(gls(p5_F, mean5)[0] / mean5 - 1, 3),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "ae07574a", - "metadata": {}, - "source": [ - "## Takeaways\n", - "\n", - "- Correlated observations are one covariance term whose support spans the\n", - " comparisons; no special machinery.\n", - "- Fitting shared-systematic datasets independently is overconfident. Case A\n", - " couples the data, case B couples the parameters; both are declared by\n", - " where a term sits and which `Parameter` object it reads.\n", - "- Across several physical quantities the correlated normalisations are one\n", - " spanning `matrix` term paired with the normalisation correlation matrix,\n", - " read per quantity through `c.split`.\n", - "- Build the modes from the estimate, never from the data: that is the\n", - " whole of Peelle's Pertinent Puzzle in any number of dimensions." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/measurement_to_calibration.ipynb b/examples/local_optical_model_calibration.ipynb similarity index 100% rename from examples/measurement_to_calibration.ipynb rename to examples/local_optical_model_calibration.ipynb diff --git a/examples/plotstyle.py b/examples/plotstyle.py new file mode 100644 index 0000000..0a61680 --- /dev/null +++ b/examples/plotstyle.py @@ -0,0 +1,127 @@ +"""Shared plot styling for the example notebooks. + +The notebooks call :func:`use` once, near their imports, and then draw with +the ordinary matplotlib API. :func:`band` and :func:`corner_kwargs` are the +two things every notebook was re-implementing by hand. + +``import plotstyle`` works because a notebook runs with ``examples/`` as its +working directory, under Jupyter and under ``pytest --nbmake`` alike. +""" + +from __future__ import annotations + +import matplotlib as mpl +import matplotlib.pyplot as plt +import numpy as np + +__all__ = ["COLOURS", "HATCHES", "use", "band", "corner_kwargs", "label_at"] + +# Wong's colourblind-safe qualitative palette (Nature Methods 8, 441 (2011)). +COLOURS = [ + "#0072b2", # blue + "#d55e00", # vermillion + "#009e73", # bluish green + "#cc79a7", # reddish purple + "#e69f00", # orange + "#56b4e9", # sky blue + "#525252", # grey +] + +# For overlapping bands that must stay apart in greyscale. +HATCHES = ["///", "\\\\\\", "...", "xxx", "+++"] + + +def use() -> None: + """Apply the example notebooks' rcParams.""" + mpl.rcParams.update( + { + "figure.figsize": (6.4, 4.0), + "figure.dpi": 110, + "savefig.dpi": 110, + "font.size": 10, + "axes.titlesize": 11, + "axes.labelsize": 10, + "axes.prop_cycle": mpl.cycler(color=COLOURS), + "axes.spines.top": False, + "axes.spines.right": False, + "axes.grid": True, + "grid.alpha": 0.25, + "grid.linewidth": 0.6, + "lines.linewidth": 1.8, + "lines.markersize": 4, + "errorbar.capsize": 0, + "legend.frameon": False, + "legend.fontsize": 9, + "xtick.labelsize": 9, + "ytick.labelsize": 9, + "xtick.direction": "out", + "ytick.direction": "out", + } + ) + + +def band(ax, x, lo, hi, *, color=None, hatch=None, label=None, alpha=None, **kwargs): + """Fill between ``lo`` and ``hi``: a predictive band. + + Pass ``hatch`` (see :data:`HATCHES`) when several bands overlap and the + difference must survive in greyscale; the fill is then lighter and the + edge carries the colour. + """ + hatched = hatch is not None + if alpha is None: + alpha = 0.18 if hatched else 0.28 + return ax.fill_between( + np.asarray(x, dtype=float), + np.asarray(lo, dtype=float), + np.asarray(hi, dtype=float), + color=color, + alpha=alpha, + hatch=hatch, + edgecolor=color if hatched else None, + linewidth=0.8 if hatched else 0.0, + label=label, + **kwargs, + ) + + +def corner_kwargs(**overrides) -> dict: + """Defaults for ``corner.corner``; keyword arguments override them.""" + kwargs = { + "levels": (0.39, 0.68, 0.95), + "smooth": 0.8, + "bins": 32, + "color": COLOURS[0], + "plot_datapoints": False, + "fill_contours": True, + "show_titles": True, + "title_fmt": ".2f", + "title_kwargs": {"fontsize": 9}, + "label_kwargs": {"fontsize": 10}, + "truth_color": COLOURS[6], + } + kwargs.update(overrides) + return kwargs + + +def label_at(ax, x, y, text, *, color=None, **kwargs): + """A small text label on a curve, for datasets offset in ``y``.""" + return ax.text( + x, + y, + text, + color=color, + fontsize=8, + va="bottom", + ha="left", + **kwargs, + ) + + +def _demo() -> None: # pragma: no cover - a visual check, not run by the tests + use() + x = np.linspace(0, 1, 50) + fig, ax = plt.subplots() + for i, (colour, hatch) in enumerate(zip(COLOURS, HATCHES)): + band(ax, x, i + x, i + 1.5 * x, color=colour, hatch=hatch, label=f"band {i}") + ax.legend() + plt.show() diff --git a/test/recipes/test_recipe_39_iterative_outlier_rejection.py b/test/recipes/test_recipe_39_iterative_outlier_rejection.py new file mode 100644 index 0000000..c46c5af --- /dev/null +++ b/test/recipes/test_recipe_39_iterative_outlier_rejection.py @@ -0,0 +1,84 @@ +"""Recipe 39: iterative outlier rejection. + +A few points are gross outliers. I want to reject them and refit, in an +outer loop of problems, until the mask stops moving. +""" + +import numpy as np +import pytest + +from common import TRUE, line, map_estimate +from rxmc import Comparison, Constraint, Dataset, Problem + +X = np.linspace(0.5, 3.0, 12) +BAD = (3, 8) # the points pushed off the line +PUSH = 12.0 # in units of the reported error +ERR = 0.1 + + +def outlier_data(seed=0): + """Twelve points on the line, two of them pushed far above it.""" + rng = np.random.default_rng(seed) + y = TRUE[0] * X + TRUE[1] + rng.normal(0, ERR, X.size) + y[list(BAD)] += PUSH * ERR + return Dataset(X, y, np.full(X.size, ERR), label="outliers") + + +def reject(constraint, data, *, k=3.0, max_rounds=6): + """The recipe: fit, mask the points more than ``k`` pulls away, refit.""" + mask = np.ones(data.n, dtype=bool) + for rounds in range(1, max_rounds + 1): + problem = Problem([constraint.masked([mask])]) + theta = map_estimate(problem, np.array(TRUE)) + pull = np.abs(data.y - problem.constraints[0].ym(theta)) / data.y_err + keep = pull < k + if np.array_equal(keep, mask): + return mask, theta, rounds + mask = keep + raise AssertionError("the mask did not settle") + + +def _rms_from_truth(theta, rows): + """How far the fitted line sits from the truth, over ``rows``.""" + offset = (theta[0] * X + theta[1]) - (TRUE[0] * X + TRUE[1]) + return float(np.sqrt(np.mean(offset[rows] ** 2))) + + +def test_the_loop_settles_on_the_planted_outliers(): + d = outlier_data() + c = Constraint([Comparison(d, line())]) + mask, theta, rounds = reject(c, d) + assert rounds > 1 # the first fit is dragged, so one pass is not enough + assert np.array_equal(np.flatnonzero(~mask), np.array(BAD)) + ym = Problem([c.masked([mask])]).constraints[0].ym(theta) + pull = np.abs(d.y - ym) / d.y_err + assert np.max(pull[mask]) < 3.0 and np.min(pull[~mask]) > 10.0 + + +def test_a_fit_that_keeps_the_outliers_is_dragged_away_from_the_truth(): + d = outlier_data() + c = Constraint([Comparison(d, line())]) + mask, theta, _ = reject(c, d) + naive = map_estimate(Problem([c]), np.array(TRUE)) + kept = np.flatnonzero(mask) + assert _rms_from_truth(naive, kept) > 3 * _rms_from_truth(theta, kept) + + +def test_the_rejected_points_are_named_by_the_complement_and_keep_their_columns(): + d = outlier_data() + c = Constraint([Comparison(d, line())]) + mask, _, _ = reject(c, d) + kept, rejected = c.masked([mask]), c.masked([mask]).complement() + assert np.array_equal(rejected.active, np.array(BAD)) + assert set(kept.active).isdisjoint(rejected.active) + assert kept.n_active + rejected.n_active == d.n + assert Problem([kept]).names == Problem([c]).names + + +def test_the_loop_is_reproducible(): + d = outlier_data() + c = Constraint([Comparison(d, line())]) + first, theta_a, rounds_a = reject(c, d) + second, theta_b, rounds_b = reject(c, d) + assert np.array_equal(first, second) and rounds_a == rounds_b + assert theta_a == pytest.approx(theta_b) diff --git a/test/test_notebooks_index.py b/test/test_notebooks_index.py index 7640f39..ea59a57 100644 --- a/test/test_notebooks_index.py +++ b/test/test_notebooks_index.py @@ -1,4 +1,4 @@ -"""The example notebooks name the recipes they teach, and all nine exist. +"""The example notebooks name the recipes they teach, and every one listed exists. Design document section 9 item 8: a notebook must cite at least one recipe; its first cell carries ``Recipes: N, M, ...`` and every number is a heading @@ -20,10 +20,9 @@ "linear_calibration": {1, 17}, "error_models": {2, 4, 5, 19}, "normalization_and_covariance_structure": {3, 6, 27}, - "correlated_observations": {5, 37}, "gp_discrepancy": {7, 8, 36}, "robust_likelihoods": {9, 34}, - "measurement_to_calibration": {12, 14, 15, 16, 21, 26}, + "local_optical_model_calibration": {12, 14, 15, 16, 21, 26}, "alpha_ca_error_model_comparison": {10, 11, 13, 18}, "hierarchical_calibration": {22, 24, 30, 35, 38}, } From 7281e413f0b1819abe5b2caa4b69450bf6e01c30 Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Fri, 11 Sep 2026 21:57:00 -0400 Subject: [PATCH 54/75] Restyle linear_calibration for the notebook review - Smaller cells: setup, the run, post-processing and each plot stand apart - LaTeX for every symbol, and no unicode greek - A colleague-style narrative with links: Bayesian inference, the prior, chi^2 and the Mahalanobis distance, MCMC, Goodman & Weare's ensemble sampler and Foreman-Mackey et al.'s emcee, the posterior predictive - Plots through examples/plotstyle.py, and .bind(x_fine) without the empty dict that the review asked about - Dropped the empty trailing cell - Same data, seeds and sampler settings, so the fit is the one it was --- examples/linear_calibration.ipynb | 536 ++++++++++++++++++++---------- 1 file changed, 355 insertions(+), 181 deletions(-) diff --git a/examples/linear_calibration.ipynb b/examples/linear_calibration.ipynb index b4e208c..fa46b8b 100644 --- a/examples/linear_calibration.ipynb +++ b/examples/linear_calibration.ipynb @@ -2,15 +2,21 @@ "cells": [ { "cell_type": "markdown", - "id": "d967d547", + "id": "96a94733", "metadata": {}, "source": [ "# Calibration of a line\n", "\n", - "The whole `rxmc` workflow on the smallest possible problem: declare a model\n", - "with parameters and priors, a dataset, and a comparison between them; compile\n", - "the problem; hand it to an external sampler; read the chain back by name; and\n", - "check the posterior predictive against the data.\n", + "Let us start with the smallest problem that still contains the whole workflow:\n", + "a straight line through twenty noisy points. Everything the library asks of us\n", + "is here — we declare a model and what we believe about its parameters, we say\n", + "how the data are compared with it, we compile that into a problem, we hand the\n", + "problem to a sampler, and then we ask whether the answer is any good.\n", + "\n", + "The [Bayesian recipe](https://en.wikipedia.org/wiki/Bayesian_inference) is the\n", + "same at every scale: a prior over parameters, a likelihood for the data, and a\n", + "posterior we explore with a sampler. The only thing that grows in the later\n", + "notebooks is the error model.\n", "\n", "Recipes: 1, 17" ] @@ -18,13 +24,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "20d19117", + "id": "4487f98f", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:24:16.412632Z", - "iopub.status.busy": "2026-09-11T03:24:16.412492Z", - "iopub.status.idle": "2026-09-11T03:24:18.363411Z", - "shell.execute_reply": "2026-09-11T03:24:18.362669Z" + "iopub.execute_input": "2026-09-12T01:56:08.715846Z", + "iopub.status.busy": "2026-09-12T01:56:08.715703Z", + "iopub.status.idle": "2026-09-12T01:56:10.738535Z", + "shell.execute_reply": "2026-09-12T01:56:10.738027Z" } }, "outputs": [], @@ -33,34 +39,42 @@ "import emcee\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", + "import plotstyle\n", "from scipy import stats\n", "\n", - "import rxmc as rx" + "import rxmc as rx\n", + "\n", + "plotstyle.use()" ] }, { "cell_type": "markdown", - "id": "11288688", + "id": "26b7c934", "metadata": {}, "source": [ "## Parameters and a model\n", "\n", - "A `Parameter` is a name with a prior (or finite bounds). A `Model` is a\n", - "callable of the grid `x` and the parameter values, in order, with its\n", - "parameters listed. Here the prior is deliberately at odds with the data we\n", - "are about to generate: it encodes what we think we know before looking." + "A `Parameter` is a name with a\n", + "[prior](https://en.wikipedia.org/wiki/Prior_probability) (or finite bounds),\n", + "and a `Model` is a callable of the grid $x$ and the parameter values, in order,\n", + "carrying the parameters it consumes.\n", + "\n", + "We deliberately choose a prior that disagrees with the data we are about to\n", + "generate: $m \\sim \\mathcal{N}(1, 1)$ while the truth is $m = 0.6$. A prior\n", + "encodes what we think before we look, and it should be visible in the answer\n", + "when the data are few." ] }, { "cell_type": "code", "execution_count": 2, - "id": "01d9e814", + "id": "bd5cc6df", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:24:18.364853Z", - "iopub.status.busy": "2026-09-11T03:24:18.364647Z", - "iopub.status.idle": "2026-09-11T03:24:18.370365Z", - "shell.execute_reply": "2026-09-11T03:24:18.369794Z" + "iopub.execute_input": "2026-09-12T01:56:10.740249Z", + "iopub.status.busy": "2026-09-12T01:56:10.740062Z", + "iopub.status.idle": "2026-09-12T01:56:10.745724Z", + "shell.execute_reply": "2026-09-12T01:56:10.745172Z" } }, "outputs": [ @@ -84,37 +98,38 @@ }, { "cell_type": "markdown", - "id": "ba75b067", + "id": "fd08ef0d", "metadata": {}, "source": [ "## Data\n", "\n", - "Twenty points on a line with independent Gaussian noise of known size, which the\n", - "experiment reports honestly as `y_err`." + "We draw twenty points from $y = m x + b$ with independent Gaussian noise of\n", + "known size $\\sigma = 0.1$, which our imaginary experiment reports honestly as\n", + "`y_err`. That honesty is the assumption every later notebook takes apart." ] }, { "cell_type": "code", "execution_count": 3, - "id": "3ed3f8c7", + "id": "a655b824", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:24:18.371631Z", - "iopub.status.busy": "2026-09-11T03:24:18.371483Z", - "iopub.status.idle": "2026-09-11T03:24:18.488345Z", - "shell.execute_reply": "2026-09-11T03:24:18.487659Z" + "iopub.execute_input": "2026-09-12T01:56:10.747139Z", + "iopub.status.busy": "2026-09-12T01:56:10.747016Z", + "iopub.status.idle": "2026-09-12T01:56:10.750730Z", + "shell.execute_reply": "2026-09-12T01:56:10.750234Z" } }, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "<Figure size 500x350 with 1 Axes>" + "Dataset('toy', n=20)" ] }, + "execution_count": 3, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -126,39 +141,74 @@ "data = rx.Dataset(\n", " x, y_true + rng.normal(0.0, sigma, x.size), sigma * np.ones(x.size), label=\"toy\"\n", ")\n", - "\n", - "fig, ax = plt.subplots(figsize=(5, 3.5))\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "00228baf", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T01:56:10.751920Z", + "iopub.status.busy": "2026-09-12T01:56:10.751804Z", + "iopub.status.idle": "2026-09-12T01:56:11.600706Z", + "shell.execute_reply": "2026-09-12T01:56:11.600035Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 704x440 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", color=\"k\", label=\"data\")\n", - "ax.plot(x, y_true, \"--\", color=\"C3\", label=\"truth\")\n", - "ax.set(xlabel=\"x\", ylabel=\"y\")\n", - "ax.legend(frameon=False)\n", + "ax.plot(x, y_true, \"--\", color=plotstyle.COLOURS[1], label=\"truth\")\n", + "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"Twenty points and the line they came from\")\n", + "ax.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "0e230164", + "id": "eb2f8834", "metadata": {}, "source": [ "## A comparison, a constraint and a problem\n", "\n", - "A `Comparison` binds the model to the dataset's grid. A `Constraint` is one\n", - "likelihood over one or more comparisons; by default its covariance is the\n", - "diagonal of the reported errors. `Problem` compiles everything: it assigns\n", - "every parameter a column, assembles the prior, and exposes the densities a\n", - "sampler needs." + "Three objects carry us from a declaration to something a sampler understands.\n", + "\n", + "- A `Comparison` binds the model to this dataset's grid: it is the pairing of\n", + " one model with one dataset.\n", + "- A `Constraint` is one likelihood over one or more comparisons. By default\n", + " its covariance is the diagonal of the reported errors, which is the\n", + " \"the experiment told us everything\" assumption.\n", + "- `Problem` compiles the lot: it gives every parameter a column, assembles the\n", + " prior, and exposes the densities a sampler needs.\n", + "\n", + "Nothing is hidden in the compile step, and nothing user-facing is mutated by\n", + "it — we can compile the same declarations twice and get two independent\n", + "problems." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "fbe84d4d", + "execution_count": 5, + "id": "3e2eb131", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:24:18.489704Z", - "iopub.status.busy": "2026-09-11T03:24:18.489540Z", - "iopub.status.idle": "2026-09-11T03:24:18.492861Z", - "shell.execute_reply": "2026-09-11T03:24:18.492159Z" + "iopub.execute_input": "2026-09-12T01:56:11.602154Z", + "iopub.status.busy": "2026-09-12T01:56:11.602022Z", + "iopub.status.idle": "2026-09-12T01:56:11.605300Z", + "shell.execute_reply": "2026-09-12T01:56:11.604810Z" } }, "outputs": [ @@ -180,33 +230,61 @@ }, { "cell_type": "markdown", - "id": "3053e1d2", + "id": "b84afe70", "metadata": {}, "source": [ - "### The prior predictive\n", + "### What our prior alone predicts\n", "\n", - "Before touching the likelihood, draw parameters from the prior and push them\n", - "through the model. This is what our prior says the data could look like." + "Before the likelihood is involved at all, we can draw parameters from the prior\n", + "and push them through the model. This is the\n", + "[prior predictive](https://en.wikipedia.org/wiki/Posterior_predictive_distribution#Prior_vs._posterior_predictive_distribution):\n", + "the data our prior thinks are plausible. If it cannot produce anything like\n", + "the data we measured, we have learned something before fitting." ] }, { "cell_type": "code", - "execution_count": 5, - "id": "9d480c23", + "execution_count": 6, + "id": "26214803", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T01:56:11.606693Z", + "iopub.status.busy": "2026-09-12T01:56:11.606570Z", + "iopub.status.idle": "2026-09-12T01:56:11.611271Z", + "shell.execute_reply": "2026-09-12T01:56:11.610613Z" + } + }, + "outputs": [], + "source": [ + "prior_draws = problem.sample_prior(200, rng=1)\n", + "x_fine = np.linspace(-0.5, 1.5, 60)\n", + "on_fine = line.bind(x_fine) # the same model on a plotting grid\n", + "prior_curves = np.array(\n", + " [on_fine(*s[problem.columns(line.params)]) for s in prior_draws]\n", + ")\n", + "prior_lo, prior_mid, prior_hi = rx.predictive.predictive_band(\n", + " prior_curves, levels=(5, 50, 95)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "fee892f7", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:24:18.494040Z", - "iopub.status.busy": "2026-09-11T03:24:18.493910Z", - "iopub.status.idle": "2026-09-11T03:24:18.638055Z", - "shell.execute_reply": "2026-09-11T03:24:18.637167Z" + "iopub.execute_input": "2026-09-12T01:56:11.612494Z", + "iopub.status.busy": "2026-09-12T01:56:11.612341Z", + "iopub.status.idle": "2026-09-12T01:56:11.722387Z", + "shell.execute_reply": "2026-09-12T01:56:11.721884Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 500x350 with 1 Axes>" + "<Figure size 704x440 with 1 Axes>" ] }, "metadata": {}, @@ -214,44 +292,48 @@ } ], "source": [ - "prior_draws = problem.sample_prior(200, rng=1)\n", - "x_fine = np.linspace(-0.5, 1.5, 60)\n", - "on_fine = line.bind(x_fine, {}) # the same model on a plotting grid\n", - "prior_curves = np.array(\n", - " [on_fine(*s[problem.columns(line.params)]) for s in prior_draws]\n", + "fig, ax = plt.subplots()\n", + "plotstyle.band(\n", + " ax, x_fine, prior_lo, prior_hi, color=plotstyle.COLOURS[0], label=\"prior 90 %\"\n", ")\n", - "lo, mid, hi = rx.predictive.predictive_band(prior_curves, levels=(5, 50, 95))\n", - "\n", - "fig, ax = plt.subplots(figsize=(5, 3.5))\n", - "ax.fill_between(x_fine, lo, hi, color=\"C0\", alpha=0.25, label=\"prior predictive 90 %\")\n", - "ax.plot(x_fine, mid, color=\"C0\")\n", + "ax.plot(x_fine, prior_mid, color=plotstyle.COLOURS[0])\n", "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", color=\"k\", label=\"data\")\n", - "ax.set(xlabel=\"x\", ylabel=\"y\")\n", - "ax.legend(frameon=False)\n", + "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"The prior predictive, before any fitting\")\n", + "ax.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "945445a6", + "id": "28b9123a", "metadata": {}, "source": [ - "### The likelihood is the χ² you would write by hand\n", + "### The likelihood is the $\\chi^2$ we would have written by hand\n", + "\n", + "With a diagonal covariance the Gaussian log-likelihood is $-\\chi^2/2$ up to a\n", + "constant, where\n", + "\n", + "$$\\chi^2(\\theta) = \\sum_i \\frac{(y_i - y_m(x_i; \\theta))^2}{\\sigma_i^2}.$$\n", "\n", - "With a diagonal covariance the Gaussian log-likelihood is `-χ²/2` up to a\n", - "constant. `problem.chi2` is that χ²; there is nothing hidden in it." + "`problem.chi2` is exactly that\n", + "[$\\chi^2$](https://en.wikipedia.org/wiki/Goodness_of_fit#Regression_analysis),\n", + "so we can check it against the sum we would type out ourselves. When the\n", + "covariance stops being diagonal — and it will, in every other notebook — this\n", + "becomes the [Mahalanobis\n", + "distance](https://en.wikipedia.org/wiki/Mahalanobis_distance), and the library\n", + "keeps computing it for us." ] }, { "cell_type": "code", - "execution_count": 6, - "id": "866b9d36", + "execution_count": 8, + "id": "b416f0f2", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:24:18.639357Z", - "iopub.status.busy": "2026-09-11T03:24:18.639229Z", - "iopub.status.idle": "2026-09-11T03:24:18.642609Z", - "shell.execute_reply": "2026-09-11T03:24:18.641985Z" + "iopub.execute_input": "2026-09-12T01:56:11.723864Z", + "iopub.status.busy": "2026-09-12T01:56:11.723707Z", + "iopub.status.idle": "2026-09-12T01:56:11.727356Z", + "shell.execute_reply": "2026-09-12T01:56:11.726752Z" } }, "outputs": [ @@ -265,34 +347,41 @@ ], "source": [ "theta0 = np.array([1.0, 1.0]) # the prior mean\n", - "on_data = line.bind(x, {})\n", + "on_data = line.bind(x)\n", "by_hand = np.sum(((data.y - on_data(*theta0)) / data.y_err) ** 2)\n", "print(f\"problem.chi2 = {problem.chi2(theta0):.3f} by hand = {by_hand:.3f}\")" ] }, { "cell_type": "markdown", - "id": "0528da63", + "id": "763566ce", "metadata": {}, "source": [ - "## Run the calibration with emcee\n", + "## Running the calibration with emcee\n", + "\n", + "The problem exposes everything a sampler wants and nothing it does not:\n", + "`log_posterior` is the density, `sample_prior` gives the walkers somewhere to\n", + "start, and `ndim` is the dimension. We use\n", + "[emcee](https://emcee.readthedocs.io/), the affine-invariant ensemble sampler\n", + "of [Goodman & Weare\n", + "(2010)](https://doi.org/10.2140/camcos.2010.5.65), described in\n", + "[Foreman-Mackey et al. (2013)](https://arxiv.org/abs/1202.3665), but nothing\n", + "here is specific to it — recipe 16 drives the same problem with dynesty.\n", "\n", - "`problem.log_posterior` is the density, `problem.sample_prior` gives the\n", - "walkers somewhere to start, and `problem.ndim` the dimension. The chain\n", - "comes back in `problem.names` order, so columns are looked up by name, never\n", - "by position." + "The chain comes back in `problem.names` order, so we always look columns up by\n", + "name through `problem.columns`, never by position." ] }, { "cell_type": "code", - "execution_count": 7, - "id": "2fa25082", + "execution_count": 9, + "id": "10df06dd", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:24:18.643797Z", - "iopub.status.busy": "2026-09-11T03:24:18.643657Z", - "iopub.status.idle": "2026-09-11T03:24:36.355765Z", - "shell.execute_reply": "2026-09-11T03:24:36.355145Z" + "iopub.execute_input": "2026-09-12T01:56:11.728720Z", + "iopub.status.busy": "2026-09-12T01:56:11.728601Z", + "iopub.status.idle": "2026-09-12T01:56:28.911631Z", + "shell.execute_reply": "2026-09-12T01:56:28.911094Z" } }, "outputs": [ @@ -300,9 +389,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "16000 posterior rows, acceptance 0.71\n", - "m = 0.642 +/- 0.073 (truth 0.6)\n", - "b = 1.984 +/- 0.043 (truth 2.0)\n" + "16000 posterior rows, acceptance 0.71\n" ] } ], @@ -313,31 +400,57 @@ "sampler.run_mcmc(problem.sample_prior(n_walkers, rng=3), n_steps, progress=False)\n", "samples = sampler.get_chain(discard=500, thin=5, flat=True)\n", "print(\n", - " f\"{samples.shape[0]} posterior rows, acceptance {sampler.acceptance_fraction.mean():.2f}\"\n", - ")\n", - "for name in problem.names:\n", - " col = samples[:, problem.columns(problem.params[problem.names.index(name)])]\n", + " f\"{samples.shape[0]} posterior rows, \"\n", + " f\"acceptance {sampler.acceptance_fraction.mean():.2f}\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1b1fd4cb", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T01:56:28.912926Z", + "iopub.status.busy": "2026-09-12T01:56:28.912799Z", + "iopub.status.idle": "2026-09-12T01:56:28.915731Z", + "shell.execute_reply": "2026-09-12T01:56:28.915215Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "m = 0.642 +/- 0.073 (truth 0.6)\n", + "b = 1.984 +/- 0.043 (truth 2.0)\n" + ] + } + ], + "source": [ + "for name, parameter in zip(problem.names, problem.params):\n", + " col = samples[:, problem.columns(parameter)]\n", " print(f\"{name} = {col.mean():.3f} +/- {col.std():.3f} (truth {truth[name]})\")" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "85c00a3a", + "execution_count": 11, + "id": "4c3f3437", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:24:36.357119Z", - "iopub.status.busy": "2026-09-11T03:24:36.356994Z", - "iopub.status.idle": "2026-09-11T03:24:37.146340Z", - "shell.execute_reply": "2026-09-11T03:24:37.145249Z" + "iopub.execute_input": "2026-09-12T01:56:28.916929Z", + "iopub.status.busy": "2026-09-12T01:56:28.916812Z", + "iopub.status.idle": "2026-09-12T01:56:29.096536Z", + "shell.execute_reply": "2026-09-12T01:56:29.095916Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "<Figure size 550x550 with 4 Axes>" + "<Figure size 605x605 with 4 Axes>" ] }, "metadata": {}, @@ -349,41 +462,65 @@ " samples,\n", " labels=[f\"${p.latex}$\" for p in problem.params],\n", " truths=[truth[n] for n in problem.names],\n", - " truth_color=\"C3\",\n", - " show_titles=True,\n", + " **plotstyle.corner_kwargs(),\n", ")\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "34616882", + "id": "48d80632", "metadata": {}, "source": [ "## The posterior predictive on any grid\n", "\n", - "Binding the model to a finer, wider grid gives the prediction where there are\n", - "no data; the band is the percentile envelope over posterior rows." + "The model is not tied to the grid we measured on: binding it to a finer, wider\n", + "grid gives us the prediction where we have no data, which is where a\n", + "calibration is usually asked to work. The band below is the percentile\n", + "envelope of the line over posterior rows, and it fans out beyond the data,\n", + "as it should." ] }, { "cell_type": "code", - "execution_count": 9, - "id": "6e597c0b", + "execution_count": 12, + "id": "a9ec6693", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:24:37.147955Z", - "iopub.status.busy": "2026-09-11T03:24:37.147813Z", - "iopub.status.idle": "2026-09-11T03:24:37.262086Z", - "shell.execute_reply": "2026-09-11T03:24:37.261361Z" + "iopub.execute_input": "2026-09-12T01:56:29.098008Z", + "iopub.status.busy": "2026-09-12T01:56:29.097880Z", + "iopub.status.idle": "2026-09-12T01:56:29.105536Z", + "shell.execute_reply": "2026-09-12T01:56:29.104641Z" + } + }, + "outputs": [], + "source": [ + "post_curves = np.array(\n", + " [on_fine(*s[problem.columns(line.params)]) for s in samples[::20]]\n", + ")\n", + "post_lo, post_mid, post_hi = rx.predictive.predictive_band(\n", + " post_curves, levels=(5, 50, 95)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "046ac161", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T01:56:29.107722Z", + "iopub.status.busy": "2026-09-12T01:56:29.107544Z", + "iopub.status.idle": "2026-09-12T01:56:29.246632Z", + "shell.execute_reply": "2026-09-12T01:56:29.245934Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 500x350 with 1 Axes>" + "<Figure size 704x440 with 1 Axes>" ] }, "metadata": {}, @@ -391,61 +528,116 @@ } ], "source": [ - "post_curves = np.array(\n", - " [on_fine(*s[problem.columns(line.params)]) for s in samples[::20]]\n", + "fig, ax = plt.subplots()\n", + "ax.axvspan(x.min(), x.max(), color=\"0.92\", zorder=0, label=\"data range\")\n", + "plotstyle.band(\n", + " ax, x_fine, post_lo, post_hi, color=plotstyle.COLOURS[0], label=\"posterior 90 %\"\n", ")\n", - "lo, mid, hi = rx.predictive.predictive_band(post_curves, levels=(5, 50, 95))\n", - "\n", - "fig, ax = plt.subplots(figsize=(5, 3.5))\n", - "ax.fill_between(\n", - " x_fine, lo, hi, color=\"C0\", alpha=0.3, label=\"posterior predictive 90 %\"\n", + "ax.plot(x_fine, post_mid, color=plotstyle.COLOURS[0])\n", + "ax.plot(\n", + " x_fine,\n", + " truth[\"m\"] * x_fine + truth[\"b\"],\n", + " \"--\",\n", + " color=plotstyle.COLOURS[1],\n", + " label=\"truth\",\n", ")\n", - "ax.plot(x_fine, mid, color=\"C0\")\n", - "ax.plot(x_fine, truth[\"m\"] * x_fine + truth[\"b\"], \"--\", color=\"C3\", label=\"truth\")\n", "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", color=\"k\", label=\"data\")\n", - "ax.axvspan(x.min(), x.max(), color=\"0.9\", zorder=0, label=\"data range\")\n", - "ax.set(xlabel=\"x\", ylabel=\"y\")\n", - "ax.legend(frameon=False)\n", + "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"The posterior predictive of the line\")\n", + "ax.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "4556194f", + "id": "ef728bd6", "metadata": {}, "source": [ - "## Is the error model calibrated?\n", + "## Is our error model calibrated?\n", + "\n", + "The band above is the uncertainty of the *line*. The posterior predictive of\n", + "the *data* adds the error model on top: draws of $y_m(\\theta) + \\text{noise}$\n", + "at the measured points. That is the quantity we can actually check against\n", + "what we observed.\n", "\n", - "The band above is the model's uncertainty only. The posterior *predictive*\n", - "of the data adds the error model: draws of `ym(θ) + noise` on the data\n", - "points. If the error model is right, a central 68 % interval of those draws\n", - "should contain about 68 % of the points, and so on for every level. Twenty\n", - "points make a coarse curve, but it should hug the diagonal." + "If the error model is right, a central 68 % interval of those draws should\n", + "contain about 68 % of the points, and likewise at every level — so the\n", + "empirical coverage should follow the diagonal. Twenty points make a coarse\n", + "curve, but a systematic sag below the diagonal would mean we are claiming more\n", + "precision than we have." ] }, { "cell_type": "code", - "execution_count": 10, - "id": "28993859", + "execution_count": 14, + "id": "1f79f3c1", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:24:37.264053Z", - "iopub.status.busy": "2026-09-11T03:24:37.263858Z", - "iopub.status.idle": "2026-09-11T03:24:37.440989Z", - "shell.execute_reply": "2026-09-11T03:24:37.440363Z" + "iopub.execute_input": "2026-09-12T01:56:29.248012Z", + "iopub.status.busy": "2026-09-12T01:56:29.247836Z", + "iopub.status.idle": "2026-09-12T01:56:29.355065Z", + "shell.execute_reply": "2026-09-12T01:56:29.354335Z" + } + }, + "outputs": [], + "source": [ + "draws = rx.diagnostics.predictive_draws(problem, samples[::10], n_rep=4, rng=4)\n", + "levels = np.linspace(0.1, 0.9, 9)\n", + "c = problem.constraints[0]\n", + "coverage = rx.diagnostics.coverage_curve(draws, c.y[c.active], levels)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c59df024", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T01:56:29.356470Z", + "iopub.status.busy": "2026-09-12T01:56:29.356304Z", + "iopub.status.idle": "2026-09-12T01:56:29.447286Z", + "shell.execute_reply": "2026-09-12T01:56:29.446490Z" } }, "outputs": [ { "data": { - "image/png": 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c65ZH4f/IIyIiaNSokW75tWvXzPK/9Xv37rFnzx7CwsJo1aoVADExMSU2zQlhajExMaxcuZKcnBy8vLzo0DeQ0d+fAmDBsFaV9u3ospBO6mqiVq1aDB06lI8//hi1umBcmGPHjrF7926mTp1q8nrs7OywsLAgMjISAI1Gw9y5c01ehxAliY6OJicnB29vb8aMm8CcrVfI1yoMbFWPYW09zF2eScgdRDXyzTffMHToUJo0aYK3tzehoaHMnTuXvn37mrwWR0dH3nvvPcaOHUvnzp25desWLVu2lHcpRIUREBBAjRo18PX15etDN7kcl0ZtB2s+HFF1n1p6mEpRlMo5UHk5GGNyb3M7ffo0Tk5ONG/eXLfs8uXLqNVq2rdvr1um1WoJDQ0lOTkZPz8/6tWr98T9nDx5EldXV5o1a6Zbdvz4cTw8PHTNVefOncPOzk73HkRp64mOjiYyMhJ3d3d8fX3Zu3cvbdq0wc3Nrdj9CmFMcXFxuLi4YGtrq1sWFpPKiG+Oka9V+Hpie4a2qZh3D8a4rklAVJGAEEKUz+3bt1m1ahX16tVj0qRJ2NrakpOvYfjXx7hyN50h/u58M6mDucsskTGua9LEJISo9qKjo1m9ejW5ublYWlrqnkxcvO86V+6m41rDhveHtzJzlaYnASGEqNaio6NZtWoVeXl5NG7cmAkTJmBtbU3onRSWHCp4iOLDEa1xrWn7hD1VPRIQQohq69atW6xevZq8vDyaNGnC+PHjsba2Jidfw1u/XECjVQhs68Ez/pV3RNbykMdchRDVUknhAPDl3ggiEjKoU9OW94dVv6alQnIHIYSoluzt7bG2tsbb25tx48bpwuFcdDLf/dG09K+RraldwzQvs1ZEEhBCiGrJzc2N5557DmdnZ6ysCi6F6jwNb2+4gFaBEe08GNiqvpmrNC8JCCFEtREVFYVKpdK9v+PqWnT+hi/2XCPyXiZ1HW15rxo3LRWSPgghRLVw48YN1qxZw5o1a4odifjMrSS+P3IDgIUj/anlUH2blgpJQAghqrwbN26wdu1a8vPzadSoEXXr1i3yfXauhrc3hKIoMKqDJ/396pWwp+pFmpiEEFVaZGQk69atIz8/n+bNmzNmzBhdn0OhRbuvEnU/k3pOtswfKk1LhcweEDdv3mTp0qXEx8fj7+/P888//8QB244ePcqOHTtITk7G29ubyZMn4+XlZaKKhRCVxfXr11m3bh0ajabEcAiJSuLnYwVzonw8qg3ODjIneiGzNjGFhYXRtm1bwsPD8fHx4dtvv6VPnz6PnRPgs88+Y8CAAWg0Gtq0aUNISAgtW7YkNDTUhJULISq62NhYXTi0aNGCsWPHPhIOWbn5/GPjBRQFxnZqwNO+bmaqtmIy62B9Q4YMITc3lz179gAQHx9Pw4YN+eabb3j++eeL3cbHx4cRI0bw73//GwBFUWjevDkjR47k008/LdVxZbA+Iao+jUbDxo0bgYK51S0tLR9Z571tl1h2/Cbuznb8/kYvnOwq792DMa5rZruDKAyGcePG6ZbVq1ePvn37sn379hK3a9iwYZEnEDIzM0lLSysyS5oQQlhaWjJ69OgSwyH4RiLLjt8E4ONn21TqcDAWswVEdHQ0eXl5j1zYGzVqpJtlrDjLli0jJSWFgIAAxo8fT/v27XnppZeYOXNmidvk5OSQlpZW5EcIUfVcu3aNnTt3UtgwYmlpWWw4ZObkM2vjBQAmdPGid/O6j6wjzBgQ2dnZANSoUaPIckdHR913xTl+/DghISF0796dXr160apVKzZs2MDt27dL3GbhwoU4OzvrfqRDW4iq5+rVq6xfv56QkBDOnTv32HU/2XWF20nZeNayZ+5gmYyqJGYLiMI2spSUlCLLk5KScHZ2LnYbtVrNCy+8wOzZs/nss8/429/+xpYtW6hduzazZ88u8Vhz5swhNTVV9/O4MBFCVD5Xr17ll19+QavV4ufnR9u2bUtc9/j1+6w4cQuAT55tg6M0LZXIbAHh5eWFs7Mzly5dKrI8LCyM1q1bF7vN/fv3SU9Pf+R7f3//xzZL2dra4uTkVORHCFE1XLlyRRcOrVq14tlnny22WQkgIyefWRsLnnic1NWbHj51TFlqpWO2gLCwsGDcuHH8/PPPZGRkAAVzH588eZKJEyfq1lu6dCnz5s0DwNPTk7p167J161bd95mZmezdu7fIPMdCiOrh8uXLbNiwAa1WS+vWrRk1apRuNrjifLTjMjEp2TSobc8caVp6IrO+B7Fw4ULs7Ozw9/dn2LBhDBgwgFdeeYWBAwfq1jl27BhbtmwBQKVSsWzZMtatW0enTp0YO3YsLVq0wMHBgQ8//NBMZyGEMIeMjAx+/fVXXTiMHDnyseFwJOIea05GA/Dp6DbUtDX7e8IVnlnfgwDIz8/n8OHDujepH24+On78OPfu3WP48OG6ZampqZw6dYrExEQaNmxIly5dHvsX42HyHoQQVcOlS5eIiIhg2LBhj70GpKvzGPjFYWJT1QQFNGTB8OKbsSszY1zXzB4Q5iABIUTlpdFoSuxjKMk7m0JZd+o23i4O7Hq9Jw42Ve/uoUq9KCeEEGV16dIllixZQmpqaqm3OXg1gXWnCp5c/PfoNlUyHIxFAkIIUSmEhYWxadMmEhMTOX36dKm2Sc3O451NFwGY/lQjujZxfcIW4kESpUKICu/ixYts3rwZRVFo164dTz/9dKm2+/B/4dxNU9PI1YF/DPQ1cpVVjwSEEMKgNFqFkKgkEtLVuDna0aWxC5YWKr3393A4DBs2DJWq+P09eOyY5Cw2nLmDSgWLxrTF3qZs/RZCAkIIYUC7wuJYsD2cuFS1bpm7sx3zA/0Y1Nq9zPsLDQ1ly5YtKIpC+/btCQwMLDEcijs2QN8WbnRq5FLmYwvpgxBCGMiusDheWnX2kQv03VQ1L606y66wR+eBfhyNRsOxY8dQFIUOHTo8MRyKOzbA/isJZT62KCABIYQoN41WYcH2cIp7Zr5w2YLt4Wi0pX+q3tLSkilTptCnTx+GDh362Galko5dqKzHFgWkiUkIUW4hUUnF/u+9kALEpap5bd05PGrZP3ZfijoDlV3NB5a4cWznlRLXj03JLtWxQ6KSCGgqTzGVhQSEEKLcEtJLvkA/6H+hj2/q8bG8T3frmxzLa8R1jWEH0ittjeJPEhBCiHKJup/JpjN3SrVuYBv3Eu8gNPduoLl5E4DeDazp17BJqfYZm5LN9icED4Cbo12p9if+JAEhhNDLjXsZfL3/OlvOx/Ck5n0VUN/Zji/Hty/2kdezZ8+y/VTBy29dunRh0KBBJfY5PEyjVTh9K5m7qepi+yEKj92lsTzJVFbSSS2EKJPrCRm8vu4c/T8/xK/nCsKhr68bswf5oqLggvygws/zA/2KDYczZ87o5qHv2rVrmcIBwNJCxfxAvyLHKu2xxePJHYQQolSuJ6SzeP91tl2IpXCIz/4t3Xi1nw9tGtQCoHEdh0feRaj/mPcgTp8+zW+//QYUhMPAgQPLFA6FBrV2Z8nkDmU6tngyCQghxGNFxKfzn/3X+V/og8FQj9f6+eDfoOj0wINauzPAr36p36ROTk4GoFu3bvzlL3/RKxz0PbZ4MhnuW4b7FqJYV++m85/9Eey4GKcLhr/41ePVfj609ix+3viyUhSFiIgIfHx8yhUOwjjXNbmDEEIUceVuGov3Xee3i38+GTSoVX1e6deMVh7lD4YrV67QrFkzrKysUKlUNG/evNz7FMYhASGEAOByXBr/2RfBzrC7umXPtK7Pq/18aOlumP+Rnjx5kl27dtGsWTPGjx9f5ol/hGnpHRB5eXmEhIRw48YNpkyZAkBiYiKurvKmohCVSXhsQTDsulQQDCoVDG7tziv9muFb33BNsMHBwfz+++8A1KtXr0zTBAvz0CsgoqOjGTJkCBEREeTk5OgC4sUXX2T69OkEBgYatEghhOGFxaTyn30R7A6PBwqCYYi/O6/286F5PUeDHuvEiRPs3r0bgB49etC3b1/pc6gE9AqIN954g4CAAM6cOYOtra1u+dtvv82sWbMkIIQws8fNyRAWk8qXeyPYe/nPYBjaxoNX+zbDx8DBAEXDoWfPnjz99NMSDpWEXgFx6NAhrly5go2NTZHl/v7+nDlzxiCFCSH0U9KcDNO6N+LUzST2Xk4AwEIFgW09eKVvM5q5GT4YoKDPoTAcevXqRZ8+fSQcKhG9AkKtVuvaDx/8w46Li8PBwcEwlQkhyqxwXoSHn12PS1Wz8I8RUS1UMLydJ3/v24ymdWs+uhMD8vT0xMbGhm7dukk4VEJ6vQcxdOhQunfvzty5c7G0tESj0ZCZmcnEiROxtbXll19+MUatBiPvQYiqSKNV6PHJ/scOfW1vbcm2vz9llKakkqSmpuLsbJj3JkTJKsx7EIsWLaJXr17s2LEDRVEYM2YMR44cAeDYsWMGKUwIUTZPmpMBIDtPw/2MXHzqGa+OEydO0LBhQzw8PAAkHCoxvZ4z8/X1JSwsjIEDBxIYGIharWbmzJlcuHCBpk2bGrpGIUQplHa+A2POi3DkyBF2797NypUrycjIMNpxhGno/R6Em5sb7777riFrEUKUQ2nnOzDWvAiHDx/mwIEDAAQEBFCzpnH7N4Tx6RUQd+6UPDmIra0trq6u8hKMECYWfCPxsd8bc16EQ4cOcfDgQQD69u1Lz549DX4MYXp6BYSXl9djv3dyciIoKIhFixY98iisEMLwvthzja/2Reg+q6DIk0zGnBfh4MGDHDp0CIB+/frRo0cPg+5fmI9e/83/+uuv8fLy4vvvv+f06dOcOXOG7777Dk9PTz777DP++9//sm3bNj744AND1yuEeICiKHz+QDi884wv307uQH3nos1I9Z3tWDK5g8HnRbhw4YIuHPr37y/hUMXo9Zhrhw4dWLJkCV27di2yPDg4mJdffpkzZ85w+PBhnn/+eSIiIkrYi/nIY66iKlAUhS/2XOM/+68DMHewLzN6FTwk8rg3qQ0pLy+PtWvX0qxZM7p3727w/YvSM8Z1Ta+AcHBwIDY2llq1ahVZnpKSgqenJ5mZmWRkZODu7k56erpBCjUkCQhR2SmKwme7r/H1gYJw+OfglrzYq4nJjg1/viSr1Wqlz7ECMMZ1Ta8/VW9vbxYvXvzI8i+//BJvb28ALl26RLt27cpVnBDiUYqisGj3VV04zBti2nDYv38/Bw4c0AWFhEPVpVcn9ZdffsnIkSNZv349HTt2RFEUzpw5w40bN9i8eTMAGzdu5MMPPzRosUJUd4qi8OnvV1lyMBKAd4f68XyPxiY79v79+zl69CgAPj4+T3xgRVRuek85eufOHb777jsuX76MSqXC19eXmTNn0qBBA0PXaHDSxCQqI0VR+HjXFb47dAMoeCJp+lOmC4d9+/bpRkoYNGjQI32QwrwqTB9EZScBISobRVH4eOcVvjtcEA4LhrUiqHsjkx177969HD9+HIBnnnmGLl26mOTYovQqzFhMQgjTURSFj3Zc5ocjUQC8P7wVUwMamezYe/bs4cSJE4CEQ3Wjd0CsXbuWDRs2EB0dTX5+fpHvzp8/X966hBAUXKA//O0yPx0tCIcPhrdiionCASAmJkYXDoMHD6Zz584mO7YwP70eP/j88895/fXX8fPz48yZM4wePRpvb28uXLggz0ILYSCKovDB//4Mhw9HtDZpOAA0aNCAoUOHMmTIEAmHakivPojmzZvz448/0qtXL1Qqle5xty+++IJ9+/bxv//9z+CFGpL0QYiKTlEUFmwPZ9nxmwB8NNKfiV29TXbs3NzcItMJi4qvwnRSW1tbk5mZiY2NDfb29iQkJODo6EhKSgpeXl4V8uW4B0lAiIrs4XBYOMqfCV1MFw67du3i5s2bBAUFyQyRlUiFeVEuPz9fNwift7e3bh7qmJgYrKyk31sIfSmKwvxtl1h2/CYqFXzyrGnDYefOnYSEhJCQkMDNmzdNclxRcZX7ah4UFMS4ceMYMGAABw8eZOTIkYaoS4hqR6tV+L9tYawKji4Ih1FtGNvZNC+iKYrCjh07OH36NADDhg3Dz8/PJMcWFZdeAREXF6f79Zw5c6hXrx4nTpzg9ddf55VXXjFYcUJUF1qtwrtbw1h98o9weLYNYzuZLxzat29vkmOLik2vPoiDBw/Sp08fI5RjGtIHISoSrVZh3tYw1vwRDv8e3ZbRHU0zIoGiKPz222+6ZuLhw4fLGGqVVIXpg+jfvz/V8AVsIQxOq1X455aLunBYZMJwAMjKyuL69YJB/0aMGCHhIIrQq4mpUaNGXLt2jRYtWhi6HiGqDa1WYe7mi6w7dRsLFXw2ti0j25t2LLMaNWoQFBREbGwsrVq1MumxRcWn1x3EvHnzmDZtGsHBwaSlpaFWq4v8CCEeT6tVeOfXUF04fD62ncnCQVGUIv2ItWvXlnAQxdKrD6JwopCSVPTmJ+mDEOak0SrM3hTKxjN3sFDBF+PaMbydp0mOrSgK27ZtIzQ0lLFjx0orQBVSYQbrO3LkiEEOLkR1o9Eq/GNjKJvOFoTDl+PbM6yth0mOrdVq2b59O+fPn0elUpGXl2eS44rKS6+AMMbE5PpOW6goyhPvaISoCDRahVkbL/Dr2RgsLVR8Oa4dgSYMh23btnHhwgVUKhWjRo2idevWJjm2qLz0niswLy+PY8eOsXLlSt2yxMTEMu9n4cKF1KtXD2tra/z9/dm/f/8Tt7l06RKDBw/G3t4eNzc33nnnHXJycsp8bCGMSaNVOBGZyNbzMRy7fp+3fjmvC4evxps2HLZu3aoLh2effVbCQZSKXgERHR1Nhw4d6NevH1OnTtUtf/HFF9m+fXup9/Ptt9/y0UcfsXr1alJTUxk1ahRDhw4lKiqqxG2uX7/OU089RePGjYmJiSE6Opq6dety7tw5fU5FCKPYFRZHj0/2M+GHYF5bd55JP55ky/lYLFSweEJ7hrYxXThs2bKF0NBQVCoVo0ePlg5pUWp6BcQbb7xBQEAAaWlpRZa//fbbfPzxx6Xez+eff87zzz9P//79qVmzJu+99x516tTh22+/LXGbuXPn0rhxY77++mtcXFywt7fnrbfeolu3bvqcihAGtyssjpdWnSUu9dEn+rQKWJiwRVSlUmFpaYmFhQWjR4+W4TNEmejVB3Ho0CGuXLmiG7CvkL+/v+6NzCdJTEwkIiKC3r1765apVCp69+6tm6DkYRqNhh07djBv3jxUKpXe/RZCGItGWzASa0nP8amABdvDGeBXH0sTJIVKpWLYsGF06tQJT0/TPCklqg69rq5qtVp3YX6wgzguLq7UwwPHx8cDULdu3SLL3dzcdN897N69e2RmZqIoCp06dcLGxgZPT0/mzp1Lbm5uicfKyckhLS2tyI8QxhASlVTsnUMhBYhLVRMSlWS0GrRaLSEhIWi1WqDg36iEg9CHXgHRp08fXTNQYUBkZmYya9Ys+vfvX6Z9Ff4lfvBzSU8lFb5f8emnn/L555+jVqvZuHEj3333He+//36Jx1i4cCHOzs66Hy8v0wyCJqqfhPTSvSha2vXKSqvV8uuvv7Jz5062bdtmlGOI6kOvgFi0aBFffvklPXr0QFEUxowZQ9OmTTl58iQLFy4s1T7c3d0BSEhIKLI8ISGB+vXrF7tNnTp1sLa2ZtKkSfTq1QsrKysCAgKYPn06v/76a4nHmjNnDqmpqbqf27dvl/JMhSgb1xo2T14JcHO0M/ixNRoNmzZt4tKlS1hYWNCyZUuDH0NUL3oFhK+vL2FhYQwcOJDAwEDUajUzZ87kwoULNG3atFT7qF27Nn5+fhw4cEC3TKvVcuDAAZ566indsvz8fF3zkbW1Nd26dSM/P7/IvvLy8rC2ti7xWLa2tjg5ORX5EcLQ8jRaVgXfeuw6KsDd2Y4ujV0MeuzCcAgPD8fS0pJx48bJW9Ki3PTqpD537hzt27fn3XffLdfBZ8+ezcyZM+nbty8BAQF8+umnZGRk8NJLL+nW+etf/0pwcDBhYWFAwVNMY8aMYciQITz11FOcPHmSpUuX8n//93/lqkWI8sjN1/LK2rP8fikeKwsV+VoFFRTprC5sOJ0f6GfQDurCcLh8+TKWlpaMHTuW5s2bG2z/ovrSKyA6dOiAn58fkydPZtKkSXh76zcl4tSpU8nIyGDOnDnEx8fj7+/Pnj17aNDgz0HLrK2ti0yePmjQIH766SfmzZvHrVu38Pb25sMPP5SJioTZ5OZr+fuas+wOj8fG0oLvpnQkJ1/Dgu3hRTqs6zvbMT/Qj0Gt3Q16/K1bt+rCYdy4cfj4+Bh0/6L60muwvmvXrrF69WrWrFlDZGQkPXv2ZPLkyYwZM4ZatWoZoUzDksH6hKHk5mt5ec1Z9oTHY2NlwfdTOtKnhRtQ8MhrSFQSCelq3BwLmpWM8WhrZGQkGzdu5Nlnn6VZs2YG37+oHIxxXdMrIB508uRJVq9ezfr160lNTWXIkCFs2rTJIMUZiwSEMIScfA0vrz7L3ssJ2FhZ8MPUTvRuXvfJGxqBWq3Gzs7wHd+i8qgwM8o9qGvXrvznP/9h69at+Pr6PvZpIiGqipx8DX9bVRAOtlYW/GjCcMjPz2fr1q3cv39ft0zCQRhDuQLi+vXrvP/++7Ro0YLu3bvj5OTEd999Z6jahKiQcvI1vLTqLPuu/BEOQZ3oZcJw+OWXXzh//jxr1qxBo9GY5LiietKrk3rx4sWsXr2akydP0qpVK6ZNm1auzmohKgt1noaXVp3hwNV72Flb8FNQZ55qVsckxy4Mh4iICKysrBg6dCiWlpYmObaonvQKiE8++YQJEybw7bffyiTnotpQ52mYufIMh64VhMPPQZ3pbsJwWL9+PdevX8fKyoqJEyfSuHFjkxxbVF96BUR0dLQMkieqFXWehhkrz3C4MBymdaZ7U9OFw7p164iMjJRwECalV0AUhkNiYiLXrl1DURRatGiBq6urQYsToiJQ52l4ccVpjkTcx97akp+ndSagqen+ru/fv5/IyEisra2ZOHEijRo1MtmxRfWm121AdnY2M2fOpF69enTv3p2nnnqKevXqMXPmTLKzsw1doxBm83A4LJ1u2nAA6NWrF40aNZJwECanV0DMmjWL/fv3s3HjRuLi4rh79y4bN25k3759zJo1y9A1CmEW2bkaXlheEA4ONpYsm96Zbk1MEw4PjnJsZ2fH1KlTJRyEyen1olzdunXZvXs37du3L7L83LlzDBw48JERWisaeVFOPEl2robnl5/ieGQiNWwsWfZcFzo3MuwAeyXJy8tj7dq1+Pj4EBAQYJJjisqvwrwol56eTsOGDR9Z3rBhQ5mMR1R6Wbn5PLfsz3BYbsJwyM3NZc2aNURFRXHw4EHS09NNclwhiqNXQHTo0IFPP/2UB28+FEXh448/pmPHjgYrTghTKwyHEzcSqWlrxYrnu9DJhOGwdu1abt68iY2NDZMmTcLR0dEkxxaiOHo9xbRo0SIGDRrEpk2b6Ny5MwCnTp0iPj6eXbt2GbRAIUwlMyef6ctOERKVRE1bK5Y/14WODWub5NiFdw63bt3CxsaGyZMny8yHwuz0uoPo3r07ERERTJo0iby8PPLz85k0aRIRERF0797d0DUKYXSZOflMX1oQDo5/3DmYIxxsbW2ZMmWKhIOoEPS6gwCoV68e7733ngFLEcI8MnLymb40hFM3k3Xh0N7bNOEAcPXqVV04TJ48uch8KEKYk97vQezYseOR5Tt27JD3IESlkpGTz7Sf/wgHOytWvtDVpOEA4O/vz1/+8hcJB1Hh6BUQc+bM4dq1a48sv3btGvPmzSt3UUKYQro6j6CfQzh9KxknOytWv9CVdl61THLsnJwccnJydJ8DAgIkHESFo9d7EG5uboSHh1OnTtGxaO7du0fbtm2JjY01WIHGIO9BiMJwOBud8kc4dMO/gbNJjp2Tk8OqVatQqVRMmjSpyJS6QuirwrwHkZOTQ1ZW1iPLMzMzycjIKHdRQhhTmjqPqX+Eg7O9NWteNF04qNVqVq1axZ07d7h37x6pqakmOa4Q+tArIHr37s28efPIy8vTLcvLy+Of//wnvXr1MlhxQpSXRqtwIjKRredjOBGZSHJWLlN/CuFcdAq1HKxZ/UJXWnuaPhzs7e2ZOnUqbm5uJjm2EPrQ6ymmTz/9lB49euDj40O3bt1QFIXg4GCysrI4fPiwoWsUQi+7wuJYsD2cuFS1bpm1pYo8jaILh1Yepg2HmJgYXTjUr1/fJMcWQl963UH4+vpy8eJFgoKCyMzMJDs7m2nTphEaGkrLli0NXaMQZbYrLI6XVp0tEg4AeZqCLreX+zQzaTisXLlSwkFUOnq/B+Hu7s6CBQsMWYsQBqHRKizYHs7jnr74+VgUz/VojKWFyuj1ZGRkkJKSgoODA1OnTqVevXpGP6YQhqB3QAhRUYVEJT1y5/CwuFQ1IVFJJpnboU6dOgQFBaEoioSDqFQkIESVk5D++HAo63r6yM7O5v79+7ohM6QzWlRGMrG0qHLcHO0Mul5ZZWVlsWLFClasWMHNmzeNcgwhTEECQlQ5zdxqYvWYvgUV4O5sR5fGhh/GuzAc7t69i62tLTVq1DD4MYQwlVI3Md2/f7/UO334DWshTCUpM5cpP50kX1t8F3VhbMwP9DN4B3VhOMTHx1OjRg2CgoKoW7euQY8hhCmVOiDK8hddj9E7hCi3xIwcJv14kit306nraMvf+jTl+8M3inRY13e2Y36gH4Nauxv02JmZmaxYsYKEhARq1qxJUFCQ/EdJVHqlDohz584Zsw4hyuV+Rg6TfjjJ1fiCcFj7YjeaudVkakAjQqKSSEhX4+ZY0Kxk6DuH7OxsCQdRJZU6INq1a2fEMoTQ3/2MHCb+EMy1+AzcHG1ZO6MbTevWBMDSQmX0R1ltbGxwcXEhKytLwkFUKfKYq6jU7qUXhENEQgb1nAruHJr8EQ6mYmlpyejRo0lPT6dWrVomPbYQxqR3QKxdu5YNGzYQHR1Nfn5+ke/Onz9f3rqEeKKEdDUTfzjJ9YQM6jvZsXZGNxrXMc1TQxkZGZw9e5aePXuiUqmwtLSUcBBVjl6PuX7++ee8/vrr+Pn5cebMGUaPHo23tzcXLlyQOamFSSSkq5nwfTDXEzJwd7ZjnYnDYfny5Rw4cIADBw6Y5JhCmINeEwY1b96cH3/8kV69eqFSqXRPLX3xxRfs27eP//3vfwYv1JBkwqDKLSFNzYQfgom8l6kLh4aupgmH9PR0VqxYwf3793FyciIoKAgXF8O/TyFEWRnjuqZXQFhbW5OZmYmNjQ329vYkJCTg6OhISkoKXl5epKenG6Q4Y5GAqLzi0wruHG7cz8TDuaBZyZThsHz5chITEyUcRIVTYWaUy8/Px8bGBgBvb2/OnDkDQExMDFZW0u8tjOPBcPCsZc+6GQFmCQdnZ2emTZsm4SCqvHJfzYOCghg3bhwDBgzg4MGDjBw50hB1CVHE3dSCZqUoXTh0w8vFwSTH1mg0rFy5UhcOQUFB1K5d2yTHFsKc9AqIuLg43a/nzJlDvXr1OHHiBK+//jqvvPKKwYoTAiAuNZsJ3wdzMzHL5OEABY+x9unTh3379jFlyhR5WklUG3r1QVR20gdRecSmZDPhh2BuJWbRoHZBODSobbpweJBGo8HS0tIsxxbiSSpMH0R2djY7dux4ZPmOHTvIzs4ud1FCAMSkZDP++4Jw8HKxZ/3MAJOFQ2pqKitXriQ1NVW3TMJBVDd6BcScOXO4du3aI8uvXbvGvHnzyl2UEHeSsxj//Qmik7LwdnFg3YwAPGvZm+TYKSkpLFu2jBs3brBt2zaTHFOIikivJiY3NzfCw8MfGXPm3r17tG3bltjYWIMVaAzSxFSxFYRDMHeSs2no6sDaF7vhYcJwWL58OSkpKdSuXZugoCCcnZ1NcmwhysMY1zW9OqlzcnLIysp6ZHlmZiYZGRnlLkpUX7eTspjww5/hsG5GN9ydzRMO06ZNk/9AiGpNryam3r17M2/ePPLy8nTL8vLy+Oc//0mvXr0MVpyoXm4n/Xnn0LhODdbPCDBZOCQnJ7Ns2TJSUlJwcXGRcBACPe8gPv30U3r06IGPjw/dunVDURSCg4PJysri8OHDhq5RVAPRiQV3DjEpBeGw9sVu1Hc2zpzRxdmxYwepqam4uLgQFBQk4SAEet5B+Pr6cvHiRYKCgsjMzCQ7O5tp06YRGhpKy5YtDV2jqOKiEws6pGNSsmlSpwbrZpg2HACGDx9OixYt5M5BiAfIexByMTCrW4mZjP8+mLhUNU3q1mDdi91wczJNOOTl5WFtbW2SYwlhbGbtpL5//z4AderU0f26JDKjliiNm/cLwuFumpqmdWuwdkY33BxNEw5JSUmsWLGCPn36yGyJQpSg1AFRt25dABRF0f26JNXwpkSUUdT9TCb8EQ7N3Gqy5sWuJg2HZcuWkZ6ezokTJ/D395eX4IQoRqkD4ty5c8X+urxycnLYvXs38fHx+Pv707Vr11JvGxkZyc6dO2nXrh09evQwWE3CsDRahZCoJBLS1bg52lGnpg2TfzpJfFoOPm41WfNiN+o62pqklsTERJYvX056ejp169ZlypQpEg5ClKDUAfHgbXhKSgp9+vQp98ETEhJ0+2nbti3/+Mc/GDVqFD/++OMTt83JyeHZZ58lIiKCF198UQKigtoVFseC7eHEpap1yyxUoFWgeb2arH7BtOGwbNkyMjIyqFu3LlOnTqVmTdPOXy1EZaLXY679+/cnLy8PlUpVroO/8847WFtbExwcjL29PefPn6djx44MHz6cwMDAx2775ptvyvSmFdyusDheWnWWhxsctX8seKFnE5OFw/3791m+fDkZGRm4ubkxdepUatQwzVwSQlRWej3m2qhRo2LHYioLrVbLxo0bmTZtGvb2BS9DtWvXju7du7N+/frHbrt582b279/PokWLylWDMB6NVmHB9vBHwuFBX+y5hkZrmv6q8PBwCQchykivO4h58+Yxbdo0vvjiC/z8/HSzyxWys3tyZ+Pt27dJT09/5L2Jli1bcvr06cdu99JLL7Fjxw4cHEo3smdOTg45OTm6z2lpaaXaTugvJCqpSLNSceJS1YREJRHQ1NXo9fTs2RNra2vatGkj4SBEKekVENOnTwcgICCg2O9L8xRT4bzVD0++Urt27RIv4BqNhokTJ/LGG2/QoUOHUte7cOFCFixYUOr1RfklpD8+HMq6nj6SkpJwcnLCysoKlUpV4t9XIUTx9AqII0eOlPvAhc1KhUFRKC0trcQ7g9WrV3Px4kXGjBnD119/DRS0LV+4cIGvv/6al19+udh+kTlz5vDmm28WOYaXl1e5z0GUrLSPrBrr0daEhASWL1+Ou7s748ePl7nShdCDXv9qDPHEkLe3NzY2NkRFRRVZfuPGDXx8fIrdplGjRkyePLlI/0dOTg7JyclcuXIFRVGKDQhbW1tsbU3TGSoKdGnsQp2aNtzPyC32exVQ39mOLo1dDH7swnDIysoiMzOTvLw8CQgh9GC2N6mtra155plnWLNmDS+++CIqlYo7d+5w8OBBvv/+e916Bw4c4O7du0yYMIFevXo9Mlrs0aNH6dOnD19++WVpT0WYgKIo1LC1KjYgCiN8fqAflhblexLuYfHx8axYsYKsrCzc3d2ZMmWK7m5VCFE2Zn2T+pNPPqF79+4MHTqUrl27smrVKrp3786kSZN066xevZrg4GAmTJhQ2lJFBfDDkShuJWZhZ22Bk501Cel/PiRQ39mO+YF+DGrtbtBjxsfHs3z5crKzs/Hw8GDy5MkSDkKUg1nfpG7RogVhYWGsXLmS+Ph45s6dy6RJk4o0B/Tt25cmTZqUuI/x48eX2CQlzCMiPp0v9hQ0A74/vDXPdmhQ5E3qLo1dDH7ncPfuXVasWKELhylTppTqaTohRMlkNFcZzdWg8jVaRi05TuidVJ5uUZefp3Uu9wuVpRETE8PKlSupU6cOkydPlnAQ1U6FmXIUCqYXXbNmDZcvXwbAz8+PiRMnlvrdBFE1fXf4BqF3UnGys2LhqDYmCQcAT09PgoKCqF27toSDEAai1x3E6dOnGTJkCBqNBn9/fwAuXryItbU1v/32W5neUTAHuYMwjit30whcfJQ8jcLnY9syqkMDox4vNjYWAA8PD6MeR4jKwBjXNb2G2vjrX//K8OHDuXPnDgcOHODAgQPcuXOHwMBAZs6caZDCROWSp9Hy9oYL5GkU+rd0Y2R7T6MeLzY2lpUrV+r6r4QQhqdXE9OlS5fYtWtXkVt5Ozs7PvroI7y9vQ1WnKg8lhyMJCwmDWd7az4a6W/UpqWYmBhWrVqFWq3Gy8vrkbfxhRCGodcdRNOmTbl79+4jy+/evfvYJ45E1XQpNpX/7IsA4P3hrYw6ZWhhZ3RhOEyaNEleghTCSPQKiDfffJNx48axY8cO7t27R0JCAjt27GDs2LG89dZbqNVq3Y+o2nLztby9IZR8rcLAVvUY1tZ4/QF37txh5cqV5OTk4O3tLeEghJHp1UldluaDivgUrXRSG84Xe67x1b4IajtYs/uN3kab3yE+Pp6lS5cWCYeHRxEWojqrMI+5GmKwPlH5hcWk8s2B60DBC3HGnPzHxcUFDw8PtFotEydOlHAQwgTMNlifqNxy8jW8veEC+VqFwf71GdrGsMNmPMza2poJEyagKIqEgxAmolcfhBCL913nyt10XGvY8MHw1kZ5aik6OppDhw7pmimtra0lHIQwIb3uIO7cucOsWbM4evQoycnJj3yfkZFR7sJExRV6J4UlhyIB+HBEa1xrGr5pKTo6mtWrV5Obm4uTkxPt27c3+DGEEI+nV0BMnTqV/Px8PvjgA3kGvZrJydfw1i8X0GgVhrZx5xl/wzct3bp1i9WrV5OXl0fjxo1p3bq1wY8hhHgyvQIiODiYW7duPXHYb1H1fLk3goiEDOrUtOH94Ya/cN+8eZM1a9aQl5dHkyZNGD9+PNbW1gY/jhDiyfTqg3B3dyc7O9vQtYgK7lx0Mt/pmpb8calh2P4ACQchKha9AmL27NnMmDGD69evV8j3HIThqfMKnlrSKjCinQeDWtc36P4LRwfOy8ujadOmEg5CVAB6BUTHjh0JCQnBx8cHCwsLVCpVkR9R9Xyx5xqR9zKp62jLe8NaGXz/NWrUYODAgfj4+Eg4CFFB6NUH8dxzz9G5c2defPFF6aSuBs7cSuL7IzcAWDjSn1oOhmtaUhRF95+Kjh070qFDB/lPhhAVhF4Bce3aNQ4cOICLi4uh6xEVTHauhrc3hKIoMKqDJ/396hls3zdu3GD//v1MmDCBGjVqAGUbxkUIYVx6NTE1btyY1NRUQ9ciKqBFu68SdT+Tek62zB9quKalyMhI1q5dS0xMDEePHjXYfoUQhqNXQLz00ks899xzXLx4kezs7CKjt8oIrlXHqZtJ/HwsCoCPR7XB2cEw/QLXr19n7dq15Ofn07x5c/r162eQ/QohDEuvJqZXX30VgDZt2hT7vTzZVPll5eYza8MFFAXGdGzA075uBtnv9evXWbduHRqNhhYtWjBmzBgsLS0Nsm8hhGHJaK6iWJ/uusrNxCzcne2YN9TPIPuMiIhg/fr1aDQafH19GT16tISDEBWYjOYqHhF8I5Flx28C8PGzbXC2L3/Tklar5ffff5dwEKIS0Xs017y8PI4dO8bKlSt1yxITEw1SlDCfzJx8/rExFIAJXbzo3dwww6lYWFgwefJkOnfuLOEgRCWhV0BER0fToUMH+vXrx9SpU3XLX3zxRbZv326w4oTpfbLrCtFJWXjWsmfu4Jbl3l9mZqbu17Vq1WLw4MESDkJUEnoFxBtvvEFAQABpaWlFlr/99tt8/PHHBilMmN7x6/dZceIWAJ882wZHu/I1LV29epWvvvqKK1euGKI8IYSJ6dUHcejQIa5cufLI5C3+/v6cOXPGIIUJ08rIyWfWH01Lk7p608OnTrn2d+XKFTZs2IBWq+Xy5cv4+voaokwhhAnpFRBqtRoLi4KbjwfffI2Li8PBwcEwlQmTWrjjMjEp2TSobc+ccjYtXb58mY0bN6LVamndujXDhw83UJVCCFPSq4mpT58+fPvtt8CfAZGZmcmsWbPo37+/4aoTJnEk4h6rT0YD8OnoNtS01ev/DUDRcPD392fkyJG6/0wIISoXva4EixYtolevXuzYsQNFURgzZozu3Yhjx44ZtEBhXOnqPGb/0bQ0NaAh3Zvq37QUHh7Oxo0bURQFf39/RowYIeEgRCWm179eX19fwsLCGDhwIIGBgajVambOnMmFCxdo2rSpoWsURvSv3y4Tm6rG28WB2YPK109w48YNFEWhTZs2Eg5CVAEqpRqOi5GWloazszOpqak4OTmZuxyzOXTtHkE/hwCwfkY3ujZxLdf+FEXh/PnztG3bVsJBCBMzxnVN/hVXU6nZfzYtTX+qkd7hcOvWLbRaLVDQH9W+fXsJByGqCPmXXE19+L9w7qapaeTqwD8G6te0dPHiRZYvX86vv/6qCwkhRNWh/+MqolLRaBVCopJISFcTk5zFhjN3UKng32PaYm9T9jebQ0ND2bJlC4qiYG1tLRP9CFEFSUBUA7vC4liwPZy41KJzdfRt4UbnRmWfFfDBcGjfvj2BgYESEEJUQdLEVMXtCovjpVVnHwkHgP1XEtgVFlem/V24cIHNmzejKAodOnSQcBCiCpOAqMI0WoUF28N53GNqC7aHo9GW7kG2CxcusGXLFgA6duzI0KFDJRyEqMIkIKqwkKikYu8cCilAXKqakKikUu2vZs2aWFpa0rFjR4YMGSLhIEQVJ30QVVhCeunmBy/tek2bNmXGjBnUrVtXwkGIakDuIKowN0e7cq8XGhrK/fv3/1zXzU3CQYhqQgKiCuvS2AU3R9sSv1cB7s52dGlc/JNMZ8+eZfPmzSxfvpz09HQjVSmEqKgkIKowSwsVXi72xX5XeA8wP9APS4tH7wjOnDmjmx2wVatW1KxZ01hlCiEqKAmIKuy30DjO3ErBQgWuNYtO7lTf2Y4lkzswqLX7I9udPn2a//3vfwB07dqVgQMHSrOSENWQdFJXUfczcnh3axgALz/djNf7N9e9Se3mWNCsVNydw6lTp9ixYwcA3bp14y9/+YuEgxDVlAREFaQoCu9uCSMpMxff+o680tcHSwsVAU0fPyDfpUuXdOEQEBDAgAEDJByEqMYkIKqg/4XGsTPsLlYWKhaNaYuNVelaEps2bYqHhweNGjWif//+Eg5CVHMSEFVMQrq6SNNSa0/nUm9rZ2fHtGnTsLKyknAQQkgndVWiKAr/3BxGSlYefu5OvPx0syduExwczIkTJ3SfZWRWIUQhuYOoQraej2VPeDzWlqVrWjpx4gS7d+8GwNPTE29vb1OUKYSoJMx+B7Fr1y6GDBlCp06dmD59Ojdv3nzs+jExMcyZM4fevXvTr18/5s+fT2pqqmmKrcAS0tTM33YJgFf7+uDn8fgpBx8Mh549e+Ll5WX0GoUQlYtZA2LXrl0EBgbSs2dPPvvsM1JSUujRowcpKSnFrq/RaOjVqxe1atXigw8+YNasWWzfvp0BAwaQm5tr2uIrEEVRmLv5IqnZefh7OvPXPk0fu/7x48d14dCrVy+efvppaVYSQjxCpShK6cZ6NoKuXbvSvHlzVq5cCUBOTg7169dn9uzZvPPOO8Vuk5OTg63tn8NHhIeH06pVK44cOUKPHj1KdVxjTO5tTpvO3OGtDRewsbRg+ys9aFHfscR1jx07xt69ewHo3bs3ffr0MVGVQghjMsZ1zWx3EBkZGZw6dYrBgwfrltna2tK/f38OHDhQ4nYPhgOAlVVBN0p1nRP5bqqa97YXNC291t/nseEQExMj4SCEKDWzdVLHxMSgKAru7kWHenB3d+fSpUul3s97772Hl5cXXbp0KXGdnJwccnJydJ/T0tLKXnAFpCgKc34NJV2dT9sGzszs1eSx63t6etKvXz80Gg29e/c2UZVCiMrKbAGRn58PgI1N0TGCbG1tycvLK9U+Fi5cyObNm9m7dy92diUPWb1w4UIWLFigf7EV1IYzdzhw9R42VhYsGtMWK8vibwg1Gg2WlpYApW6GE0IIszUxuboWDPuQmJhYZHliYiJ16tR54vZffPEF77//Plu2bOGpp5567Lpz5swhNTVV93P79m39C68gYlOy+WB7OABvDWiOT73im5YOHz7MsmXLitxBCSFEaZgtIOrXr4+HhwcnT54ssvzEiRN07Njxsdt+9dVXzJ07l82bNzNw4MAnHsvW1hYnJ6ciP5WZoijM3hRKek4+7b1r8ULP4puWDh06xIEDB7hz5w5XrlwxcZVCiMrOrI+5zpw5kx9//JEbN24AsGLFCq5du8YLL7ygW2f+/PmMHDlS93nx4sW88847bN68mUGDBpm85opg3anbHIm4j+0fTUvFjcp68OBBDh48CEC/fv1o27atiasUQlR2Zn2Teu7cudy8eRNfX1/q1KlDVlYWP//8M+3atdOtExMTQ0REBADJycm89tprODo68vbbb/P222/r1nv//fcZNWqUqU/B5O4kZ/Gv3y4DMGtgC5rWfXQin4MHD3Lo0CEA+vfv/8QmOCGEKI5Z34MolJyczP379/H29n7kMdbY2FiysrJo1qwZGo2Gy5cvF7sPT09PateuXarjVdb3IBRFYfJPJzl2PZFODWuzfmZAkbsHRVE4ePAghw8fBiQchKhOjHFdqxBjMdWuXbvEi7uHh4fu15aWlrRu3dpUZVU4q09Gc+x6InbWFvy7mKalrKwszpw5A8CAAQPo3r27OcoUQlQRFSIgxJPdTsriox0Fd0//GOhL4zo1HlmnRo0aTJ06lVu3btG5c2dTlyiEqGIkICoBrVZh1sYLZOVq6NLIhWndG+m+UxSF5ORkXFxcAHBzc8PNzc1MlQohqhKzj+YqnmzVyVsE30jC3tqSf49pg8UfTUuKorBv3z6WLFnyxFFwhRCirCQgKrhbiZks3FHwDsOcwb40dC1oWlIUhb1793Ls2DHy8/O5d++eOcsUQlRB0sRUgWm1CrM2hJKdp6FbExcmd20IFITDnj17dDPBPfPMM9LnIIQwOAmICmzZ8ZuE3EzCwcaSf49ui4WFCkVR2L17N8HBwQAMHjxYwkEIYRQSEBVU1P1MPv29oGlp7uCWeLk4oCgKv//+u254ksKZ+IQQwhikD6IC0mgVZm24gDpPS49mdZjUtWCuaEVRdEOVSzgIIYxN7iAqoKXHojh9K5matlZ8/Ky/bjpQCwsLnn32WaKiomjWrJmZqxRCVHVyB1HBXE/I4N+/XwXgn0Na4lnLnrCwMApHRLG0tJRwEEKYhAREBaLRKry94QI5+Vp6+tRhXKcG7Nixg02bNrFjxw5zlyeEqGYkICqQH47c4PztFBxtrfh4lD87d+7k9OnTQMFghEIIYUrSB1FBRMSn8/meawDMG9qSc8f26wbeGz58eJEh0IUQwhQkICqAfI2WtzdcIDdfy9PN6+Bw9wJnzp4FYMSIETLZjxDCLCQgKoDvDt/gwp1UHO2sGFw7nrNnz6FSqRgxYgRt2rQxd3lCiGpK+iDM7MrdNL7cW9C09F5gK1q1aIalpaWEgxDC7OQOwozy/mhaytMo9G/pxqgOnqhUDWjQoEGlmulOCFE1yR2EGX17MJJLMal0t49lTr+GuhfiJByEEBWBBISZhMemsXj/NXpY36QFsezc8gsajcbcZQkhhI40MZmQRqsQEpVEXEo2X+29RleLKJpZJaJSqXj66aextLQ0d4lCCKEjAWEiu8LiWLA9nLhUNSoUelpH0dQqCVQqRo8ejZ+fn7lLFEKIIqSJyQR2hcXx0qqzj4SDVlFxQN2EaG1tc5cohBCPkIAwMo1WYcH2cJQ/Pre3ivkzHHKbcEtbmwXbw9FolcfuRwghTE0CwshCopKIS1XrPofn1yNRa8+B3II7BwWIS1UTEpVkviKFEKIY0gdhZAnpakABCh5hVWPN9hw/lD8+F11PCCEqDrmDMLI6NazpY32DZpb3dcseDgcAN0c7U5YlhBBPJHcQRqTRaLhz9iCNrZLxUlKI0TiRjU2RdVRAfWc7ujR2MU+RQghRArmDMBKNRsPGjRu5cuUyKgsLDuY2RV1MOADMD/TD0uLRuwohhDAnCQgj+DMcrmBpacmE8eP558S+1Hcu2oxU39mOJZM7MKi1u5kqFUKIkkkTk4FpNBo2bNjA1atXsbS0ZNy4cfj4+OADDPCrT0hUEgnpatwcC5qV5M5BCFFRSUAYWFhYmC4cxo8fT7NmzXTfWVqoCGjqasbqhBCi9CQgDKxNmzbcv3+fhg0bFgkHIYSobCQgDCA/Px9FUbC2tkalUtGvXz9zlySEEOUmndTllJ+fzy+//ML69evJz883dzlCCGEwcgdRDvn5+axfv57r169jZWVFQkICHh4e5i5LCCEMQu4g9PRwOEycOFHCQQhRpcgdhB7y8vJYv349kZGRWFtbM3HiRBo1amTusoQQwqAkIMooLy+PdevWcePGDQkHIUSVJgFRRklJScTExGBtbc2kSZNo2LChuUsSQgijkIAoo3r16jF58mQ0Go2EgxCiSpOAKIXc3FxSUlJwc3MDoEGDBmauSAghjE+eYnqC3Nxc1q5dy9KlS4mNjTV3OUIIYTISEI+Rm5vLmjVruHnzJlqtFo1GY+6ShBDCZKSJqQSF4XDr1i1sbW2ZPHmyNC0JIaoVCYhi5Obmsnr1aqKjoyUchBDVlgTEQ3JyclizZo0uHKZMmYKnp6e5yxJCCJOTgHiIhYUFlpaW2NnZMWXKFBk+QwhRbUlAPMTa2poJEyaQnJyse6xVCCGqI3mKCVCr1Zw+fRpFUYCCkJBwEEJUdxXiDuL27dvEx8fTvHlznJycjLZNcdRqNatWrSImJobs7Gx69uyp976EEKIqMesdhFqt5tlnn6VFixZMmTKF+vXrs3jxYoNv87h9FYaDvb29TBEqhBAPMOsdxIIFCwgJCSEyMhJ3d3e2bNnCyJEj6dKlC127djXYNiVZt24dycnJ2NvbM3XqVOrXr2+I0xJCiCrBrHcQS5cu5YUXXsDd3R2AESNG0Lp1a5YuXWrQbUoSFxcn4SCEECUw2x1EbGws8fHxdOzYscjyLl26cO7cOYNtAwXvNuTk5Og+p6amAgWPtI4aNQoHBwfS0tL0PRUhhDC7wmtY4cM2hmC2gEhKSgLA1dW1yHJXV1fdd4bYBmDhwoUsWLDgkeUfffQRH330UZnqFkKIiiwxMRFnZ2eD7MtsAWFtbQ0UdBQ/KDs7GxsbG4NtAzBnzhzefPNN3eeUlBQaNmxIdHS0wX4jK4O0tDS8vLy4fft2uZ78qmzkvOW8q4PU1FS8vb1xcXEx2D7NFhBeXl5YWFgQExNTZHlMTAze3t4G2wbA1tYWW1vbR5Y7OztXq79AhZycnOS8qxE57+rFwsJwXctm66R2cHCge/fubNu2TbcsMzOTvXv3MmDAAN2y69ev6/oXSruNEEKI8jPrU0wffvghW7ZsYc6cOWzbto0RI0bg5ubGjBkzdOt8/PHHTJkypUzbCCGEKD+zBkTv3r05cOAAt27d4quvvqJVq1YcPXqUmjVr6tbx8fGhQ4cOZdrmSWxtbZk/f36xzU5VmZy3nHd1IOdtuPNWKYZ8JkoIIUSVIYP1CSGEKJYEhBBCiGJJQAghhChWlQwIRVG4dOkSFy5cID8/32jbVDT6nENWVhbnz59/5N2SyqQ8f3Y3b97k6NGjpKSkGKc4I9L3vNPT0zl79iyJiYlGrM549DlvtVpNeHg4oaGhZGRkGLlC4wkPD+f48eOlXr/c1zWlirl69ari6+uruLm5KV5eXoqnp6dy/Phxg29T0ZT1HO7evatMnz5dcXZ2Vtq1a6e4uLgoAQEBSmRkpAmrLr/y/NnFx8crHh4eCqDs3LnTyJUalj7nrdFolDlz5igODg5K27ZtlYYNGypvv/22iSo2DH3Oe9myZYqLi4vSvHlzpXXr1kqNGjWUhQsXmqhiw1i9erXSuXNnpXbt2oqtrW2ptjHEda3KBUT79u2VwMBAJT8/X1EURZk5c6bi4eGhZGdnG3Sbiqas53Dq1Cll6dKlSl5enqIoipKRkaH07dtXCQgIMFnNhqDvn51Wq1UGDRqkzJ49u1IGhD7nPW/ePMXV1VUJDQ1VFKXg9+Cbb74xSb2GUtbzTk5OViwtLZV///vfumXr1q1TAN3vQ2Xw7rvvKsHBwcoPP/xQ6oAwxHWtSgXE2bNnFUAJDg7WLbt9+7aiUqmUzZs3G2ybisZQ57Bs2TLFwsJCFxoVXXnO+5NPPlEGDBigxMbGVrqA0Oe8U1JSFHt7e+Xzzz83UZWGp895R0ZGKoBy9OjRItsAyv79+41dssGVNiAMdU2oUn0QhUNyPPhiXYMGDXB3dy9xOHB9tqloDHUOp06domHDhlhZVYiZaJ9I3/MOCQnhiy++YNmyZahUKqPXaWj6nPfx48fJzs4mMDCQ2NhYzp07V+mGuNfnvJs0acKMGTN488032bJlCzt27OD5559n6NCh9O7d2yR1m4OhrglVKiCSkpJwcnLSjfpa6ElDiJd1m4rGEOdw8OBBvvvuO959911jlGgU+px3WloaEyZM4L///S8eHh6mKNPg9Dnv2NhYVCoV33zzDZ06dWLatGnUq1eP2bNnm6Jkg9D37/n06dPJzMxk1qxZzJo1i8uXL/Pyyy8bdFC7isZQ17Uq9TtkbW39yFDg8OQhxMu6TUVT3nM4c+YMI0aM4LXXXmP69OnGKNEo9Dnvd955Bw8PD+rWrcvRo0cJCQkB4NKlS4SFhRm1XkPR9++5oijExcURHR3NhQsX2LdvH59//jnr1683dskGoc9537p1i6effpqZM2cSERHBpUuX+Prrrxk6dCgnT540dslmY6jrWpUKiIYNG5Kbm8v9+/d1yzQaDfHx8SUOB67PNhVNec7h7NmzDBgwgOnTp7No0SJjl2pQ+py3o6MjiqLwzjvv8M477/Cvf/0LKJjK9qeffjJJ3eWlz3k3atQIgOeff17XhNi9e3fatGnDkSNHjF6zIehz3nv37iUvL4+XXnpJt2zYsGF4enry22+/Gb1mczHYda3s3SQVV2JiomJjY6P89NNPumW7d+9WAOXSpUu6ZSEhIUpUVFSZtqnI9DlvRVGUc+fOKS4uLsqrr75qynINRt/zflBcXFyl66TW57zVarVSq1YtZenSpbrv8/LyFE9PT+W9994zVenlos95b926VQGU6Oho3feZmZlKzZo1lf/85z8mq91QHtdJbYzrWpUKCEUpeBysVq1ayg8//KCsXr1aadCggTJ16tQi63h6eipvvfVWmbap6Mp63levXlVcXV2VXr16KUeOHCnyk5OTY45T0Is+f94PqowBoSj6nfc333yj1KtXT/nxxx+VnTt3KmPGjFFcXV2V27dvm7p8vZX1vLOyspSWLVsqHTt2VDZu3Khs27ZNGTBggOLm5qbcvXvXHKeglytXrihHjhxRZs+erdjY2Oj+raalpenWMcZ1rXI8rlIGCxYsoHHjxmzatIn8/HzefPNN/v73vxdZp0uXLjRu3LhM21R0ZT3vmzdv4uvri0aj4Z133imy3ubNm6lbt67Jai8Pff68H2RjY8NTTz1F7dq1TVGuwehz3n/7299wd3dn1apVZGZm0qpVKy5cuICnp6epy9dbWc/b3t6eY8eO8Z///IcVK1aQn59P+/btWbZsGfXq1TPHKehl7dq17N27F4DOnTvr/s1+//33+Pn5Aca5rslw30IIIYpVpTqphRBCGI4EhBBCiGJJQAghhCiWBIQQQohiSUAIIYQolgSEEEKIYklACCGEKJYEhKjyzp49y9GjRyvNfoWoKKrcm9RCPOznn3/mzp079OjRo1LsV4iKQgJCVHkdO3YscagNIUTJJCCE0Z08eRJFUWjTpg3nzp0jIyODrl27UqtWrSLraTQaTp48SXx8PD4+PrRu3brY/bRu3Zrz58+TmppKr169cHR0JD09nWPHjmFjY0P37t2xs7PTbde2bVuysrLKVE94eDihoaEAODs74+/vT4MGDfQ6/0uXLhEREUHz5s114+aU5pwjIyO5ePEiI0aMKLJNREQEYWFhjBw5Urfs4sWLREZG4uXlRbt27bC0tHzkfFu1asWJEyfIy8tjyJAhpT5HtVrN4cOHsbKyon379ty+fZvk5ORHZmR7XA2icpKAEEa3ZMkSLly4gFqtpmHDhty9e5e4uDgOHTqEr68vAPHx8QwaNIjExERatWpFcHAwAwYMYO3atboLzZIlSzh79ixpaWn4+fkRGRlJamoqn376KfPnz6dly5Zcu3YNBwcHTp48ib29PfBoU1Bp6rl69SpbtmwBCmbnOnbsGP/3f/9XphnY0tLSGD9+PMHBwXTt2pU7d+7QsWNHli1bVqpzVqvVjBw5ksuXL+vqApg3bx55eXmMHDmS9PR0xo4dS1hYGO3bt+fatWs4Ojqyfft26tevX+R8MzMzadKkCb6+vgwZMqRU5xgdHU2fPn0AaNGiBZcuXaJBgwY4OTnpAqI0NYhKygAj0QrxWEFBQYq9vb0SHh6uKIqiaLVapW/fvspzzz2nW2fatGlKx44dlYyMDEVRFOXGjRuKk5OT8t133z2yn6tXryqKoig5OTmKt7e34ujoqNy4cUNRlIKx/t3c3IqMg//yyy8rw4cPL1M9Dzt79qxiY2OjREZGlrjfh02fPl1p0aJFkWGlt2zZUqZzbtu2rTJv3jzd57S0NMXe3l7ZtGmToiiKMmPGDOWZZ57RDdGu0WiUUaNGKZMmTSpyvlZWVsq5c+dKrLWkc5w4caLy1FNPKWq1WlEURQkLC1NsbGyUgQMH6tYpTQ2icpI7CGESffr0oWXLlgCoVCp69erFnj17AFAUhV9++YVvv/2WGjVqANC4cWMmTpzIunXrmDFjhm4/Tz/9NM2bNwcKhuru0KEDFhYWuj4GBwcH2rRpw7Vr1/Sup1BaWhrnzp0jISEBjUaDk5MTZ86coUmTJk8839zcXNauXcvXX39dZFjp4cOHl+mcJ0+ezJIlS/jggw8A+PXXX7G1tWXIkCHk5+ezatUq/va3v7Ft2zaUgvldaNCgARs3bixST8+ePWnXrt0jdT7uHBVF4ddff2XZsmXY2toC0KpVKwYPHkx2djZAmWoQlY8EhDAJFxeXIp9tbW11c+beu3ePrKysRy68TZs2Zffu3UWWPTxvg62tLTVr1ixx3/rUA7Bp0yaef/55mjZtire3N7a2tuTk5JCQkPDY/Ra6d+8earVaF2bFfV+ac54wYQKzZ8/mxIkTBAQEsHr1asaMGYOtrS2xsbFkZWVx/vx5bt++XWQ/hc1Chdzd3R+p4UnnWHgOhdOVFmrUqBGXL18GICEhodQ1iMpHAkKYXe3atbG0tCQpKanI8qSkJOrUqWOWml555RU++OADXnnlFd2yOnXqoJRy+hQnJydUKhWJiYnFfl/ac/b09KRPnz6sXr2axo0bs3//fvbv3w8UzK+tUql48cUXGTt27GPrUalUZT7HWrVqYWFhQUpKSpHtkpOTdb8uSw2i8pEX5YTZWVtb06VLF3799VfdMo1Gw+bNm83yjoFGo+H+/fu0aNFCt+zgwYMlXuyL4+joSEBAACtWrCiy/N69e0DZznnSpEmsX7+elStX0qBBA3r27Kk7Rvfu3fnuu+8eCa6YmJhyn6ONjQ2dOnVi69atumU5OTn8/vvvRc5T3xpExSd3EKJC+Oyzz+jbty+WlpZ06dKFX375hfT0dObMmWPyWiwtLRk6dCivvfYab7zxBvfu3eOrr77CwcGhTPtZvHgx/fr1IzAwkMDAQG7fvs2ePXsIDg4GSn/Oo0eP5uWXX+b999/nlVdeKXI38N///pd+/frRt29fxowZg1qtZt++fTRp0oTFixeX+xz/9a9/8cwzz2BhYYG/vz+rVq0iPz/fIDWIik8CQhhd165d0Wq1RZb5+fkxcOBA3eeAgADOnDnDsmXLOHr0KH369GHt2rVFmluK28/D7zwA9OrVq8jjlQ+/KFeaelatWsWSJUs4fvw4rq6u/P7776xatapIn8KTXsDr0KEDYWFh/PTTTxw/fhxfX1927dpVpnOGguaqd999l9DQUKZNm1bkuzZt2nDp0iWWLVtGSEgIderU4dVXXy1yLsWdb2nPsX///hw6dIiVK1dy6dIl3njjDXbu3ElqamqZahCVk8xJLYQoUUZGBra2tlhbWwMFT2e1bNmSGTNmlOmdEFE5yR2EEKJE8fHxTJo0ibFjx2Jra8uqVatQFIUXXnjB3KUJE5A7CCHEY124cIF169aRmJhIy5YtefHFFx95tFhUTRIQQgghiiWPuQohhCiWBIQQQohiSUAIIYQolgSEEEKIYklACCGEKJYEhBBCiGJJQAghhCiWBIQQQohiSUAIIYQo1v8DZ+xuhQ7XELMAAAAASUVORK5CYII=", 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", "text/plain": [ - "<Figure size 400x400 with 1 Axes>" + "<Figure size 462x462 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(4.2, 4.2))\n", + "ax.plot([0, 1], [0, 1], \"--\", color=\"0.5\", label=\"nominal\")\n", + "ax.plot(levels, coverage, \"o-\", color=plotstyle.COLOURS[0], label=\"empirical\")\n", + "ax.set(\n", + " xlabel=\"nominal coverage\",\n", + " ylabel=\"empirical coverage\",\n", + " xlim=(0, 1),\n", + " ylim=(0, 1),\n", + " title=\"Coverage of the posterior predictive\",\n", + ")\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "1739c561", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T01:56:29.448910Z", + "iopub.status.busy": "2026-09-12T01:56:29.448761Z", + "iopub.status.idle": "2026-09-12T01:56:29.454463Z", + "shell.execute_reply": "2026-09-12T01:56:29.453784Z" + } + }, + "outputs": [ { "name": "stdout", "output_type": "stream", @@ -455,45 +647,27 @@ } ], "source": [ - "draws = rx.diagnostics.predictive_draws(problem, samples[::10], n_rep=4, rng=4)\n", - "levels = np.linspace(0.1, 0.9, 9)\n", - "c = problem.constraints[0]\n", - "coverage = rx.diagnostics.coverage_curve(draws, c.y[c.active], levels)\n", - "\n", - "fig, ax = plt.subplots(figsize=(4, 4))\n", - "ax.plot(levels, coverage, \"o-\", label=\"empirical\")\n", - "ax.plot([0, 1], [0, 1], \"--\", color=\"0.5\", label=\"nominal\")\n", - "ax.set(xlabel=\"nominal coverage\", ylabel=\"empirical coverage\", xlim=(0, 1), ylim=(0, 1))\n", - "ax.legend(frameon=False)\n", - "plt.show()\n", - "print(\n", - " f\"width of the 68 % predictive interval per point: {rx.diagnostics.sharpness(draws).mean():.3f}\"\n", - ")" + "width = rx.diagnostics.sharpness(draws).mean()\n", + "print(f\"width of the 68 % predictive interval per point: {width:.3f}\")" ] }, { "cell_type": "markdown", - "id": "319bc93c", + "id": "7817b707", "metadata": {}, "source": [ "## Takeaways\n", "\n", - "- Declare, compile, sample: the problem knows its columns, its prior and its\n", - " densities; the sampler is whatever you like.\n", - "- Read chains by name through `problem.columns`; bind the model to any grid\n", - " for predictions.\n", + "- Declare, compile, sample. The problem knows its columns, its prior and its\n", + " densities; the sampler is whatever we like.\n", + "- Read chains by name through `problem.columns`, and bind the model to any grid\n", + " when we want a prediction somewhere we did not measure.\n", "- The posterior predictive check is the first question to ask of an error\n", - " model. The next notebooks are about what to do when the reported errors\n", - " are not the whole story." + " model, and here it passes because we built the data to satisfy it.\n", + "\n", + "The next notebooks are about what to do when the reported errors are *not* the\n", + "whole story — which, with real measurements, is the usual case." ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6c929132-5481-4226-b060-69c135987bbe", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From c385a68b164be27a823e2b16490ae02ea9922c8a Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Fri, 11 Sep 2026 22:08:01 -0400 Subject: [PATCH 55/75] Split error_models and restyle both halves - examples/error_models.ipynb keeps the one-dataset covariance ladder (recipes 2, 4, 19); the two-dataset study moves out - examples/sharing_error_models.ipynb is new (recipe 5): sharing a parameter against sharing a mode, a normalisation mode per dataset, one mode spanning both, and the assembled covariance shown directly. It also takes over case A, which the deleted correlated_observations notebook used to carry - Both get the review's general pass: smaller cells, LaTeX, a colleague-style narrative with linked references (Peelle's Pertinent Puzzle, D'Agostini, Fruhwirth et al.), plots through plotstyle with hatched bands - The commentary now matches what the fits actually print: inferring the noise covers the truth but triples the slope error, and case A, declared for two experiments that drifted apart, inflates until the line says nothing --- docs/design.md | 3 +- docs/examples.rst | 1 + examples/error_models.ipynb | 612 +++++++++++++-------------- examples/sharing_error_models.ipynb | 627 ++++++++++++++++++++++++++++ test/test_notebooks_index.py | 3 +- 5 files changed, 925 insertions(+), 321 deletions(-) create mode 100644 examples/sharing_error_models.ipynb diff --git a/docs/design.md b/docs/design.md index 322ef33..befb079 100644 --- a/docs/design.md +++ b/docs/design.md @@ -622,7 +622,8 @@ a time unless noted. | notebook | recipes | driver | content | runtime | |---|---|---|---|---| | `linear_calibration` | 1, 17 | emcee | the whole workflow on a line; prior and posterior predictive; the coverage curve | 23 s | -| `error_models` | 2, 4, 5, 19 | emcee | the covariance ladder on one comparison, the Peelle matrix as a fixed term, offsets known and free, case B across two datasets | 153 s | +| `error_models` | 2, 4, 19 | emcee | the covariance ladder on one comparison, the Peelle matrix as a fixed term, offsets known, free and ignored | 89 s | +| `sharing_error_models` | 5 | emcee | two experiments with opposite normalisation defects: sharing a parameter, a mode per dataset, one mode spanning both, and the assembled covariance seen directly | 78 s | | `normalization_and_covariance_structure` | 3, 6, 27 | emcee | latent scales versus reported modes on a quartic; a gallery of covariance structures from `matrix(theta)` | 328 s | | `gp_discrepancy` | 7, 8, 36 | emcee, dynesty | a kernel term on a toy and on n+⁴⁰Ca missing its surface absorption; the total predictive band; a sampled Legendre correction for contrast | 617 s | | `robust_likelihoods` | 9, 34 | emcee | Student-t versus Gaussian; a global error scale; a USU offset per technique | 154 s | diff --git a/docs/examples.rst b/docs/examples.rst index d8d795f..dde44c4 100644 --- a/docs/examples.rst +++ b/docs/examples.rst @@ -17,6 +17,7 @@ Calibration basics examples/linear_calibration.ipynb examples/error_models.ipynb + examples/sharing_error_models.ipynb examples/normalization_and_covariance_structure.ipynb Beyond the Gaussian diff --git a/examples/error_models.ipynb b/examples/error_models.ipynb index 6fbe559..a5b6744 100644 --- a/examples/error_models.ipynb +++ b/examples/error_models.ipynb @@ -2,30 +2,33 @@ "cells": [ { "cell_type": "markdown", - "id": "8d97cbe7", + "id": "099dd40a", "metadata": {}, "source": [ "# Error models: there is a right way and many wrong ways\n", "\n", - "The same data, the same model, six covariances. Which one you declare *is*\n", - "the statistical model, and the posteriors differ accordingly. Along the way:\n", - "inferring a noise level the experiment did not report, a normalisation or\n", - "offset it did not report, sharing an error model between datasets, and\n", - "bringing your own covariance.\n", + "We are going to fit the same data with the same model six times over, changing\n", + "only one thing: the covariance we declare. The point of the notebook is that\n", + "this choice *is* the statistical model. It is not a detail we tune at the end;\n", + "it decides what the posterior says, and whether the truth is inside it.\n", "\n", - "Recipes: 2, 4, 5, 19" + "Along the way we infer a noise level the experiment never reported, add a\n", + "correlated systematic, and get it wrong on purpose so we can see what the\n", + "classic mistake does to the answer.\n", + "\n", + "Recipes: 2, 4, 19" ] }, { "cell_type": "code", "execution_count": 1, - "id": "b9e41ed6", + "id": "6a1a51a0", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:31:16.480304Z", - "iopub.status.busy": "2026-09-11T03:31:16.480170Z", - "iopub.status.idle": "2026-09-11T03:31:18.582139Z", - "shell.execute_reply": "2026-09-11T03:31:18.581323Z" + "iopub.execute_input": "2026-09-12T02:05:15.850717Z", + "iopub.status.busy": "2026-09-12T02:05:15.850564Z", + "iopub.status.idle": "2026-09-12T02:05:17.826186Z", + "shell.execute_reply": "2026-09-12T02:05:17.825240Z" } }, "outputs": [], @@ -34,69 +37,78 @@ "import emcee\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", + "import plotstyle\n", "from scipy import stats\n", "\n", "import rxmc as rx\n", - "from rxmc import terms as T" + "from rxmc import terms as T\n", + "\n", + "plotstyle.use()" ] }, { "cell_type": "markdown", - "id": "35a70d2d", + "id": "6f7c1693", "metadata": {}, "source": [ - "## Where a χ² comes from\n", + "## Where a $\\chi^2$ comes from\n", + "\n", + "Every fit assumes a likelihood, whether or not anyone writes it down. Counting\n", + "events in a bin gives a [binomial](https://en.wikipedia.org/wiki/Binomial_distribution),\n", + "then a [Poisson](https://en.wikipedia.org/wiki/Poisson_distribution), then — with\n", + "many counts — a [normal](https://en.wikipedia.org/wiki/Normal_distribution). If\n", + "the bins are independent, that is a diagonal\n", + "[multivariate normal](https://en.wikipedia.org/wiki/Multivariate_normal_distribution),\n", + "and its logarithm is $-\\chi^2/2$.\n", "\n", - "Every fit assumes a likelihood, stated or not. Counting events in a bin gives\n", - "a binomial, then a Poisson, then (many counts) a normal: with independent\n", - "bins that is a diagonal multivariate normal, and its log is `-χ²/2`. Every\n", - "step is an assumption: independence between points, a model able to\n", - "reproduce the truth exactly, errors that are what the experiment says they\n", - "are. When any of these fails the honest generalisation is to keep the\n", - "multivariate normal and *model its covariance*,\n", + "Each of those steps is an assumption: that the points are independent, that our\n", + "model can reproduce the truth exactly, and that the errors are what the\n", + "experiment says they are. When one of them fails, the honest generalisation is\n", + "not to abandon the normal but to *model its covariance*,\n", "\n", "$$\\log p(\\mathbf{y}\\mid\\theta) = -\\tfrac12 (\\mathbf{y}-\\mathbf{y}_m)^\\mathsf{T}\n", "\\Sigma^{-1}(\\mathbf{y}-\\mathbf{y}_m) - \\tfrac12\\log\\det\\Sigma + \\text{const},$$\n", "\n", - "so that Σ carries the statistical errors, the correlated systematics and the\n", - "things you had to infer. In `rxmc` a covariance is a sum of `terms`; the\n", - "rest of this notebook is a tour of what the choice of terms does." + "so that $\\Sigma$ carries the statistical errors, the correlated systematics, and\n", + "whatever we had to infer. In `rxmc` a covariance is a sum of terms, and the rest\n", + "of this notebook is a tour of what choosing them does." ] }, { "cell_type": "markdown", - "id": "ec702a74", + "id": "3f938056", "metadata": {}, "source": [ "## Data with a defect the experiment did not report\n", "\n", - "A line, 5 % relative noise, and one overall normalisation drawn 10 % low.\n", - "The experiment reports the noise correctly but says nothing about the\n", - "normalisation." + "Our data are a line with 5 % relative noise, multiplied by one overall\n", + "normalisation that came out 10 % low. The experiment reports the noise\n", + "correctly and says nothing at all about the normalisation — which is exactly\n", + "the situation we are usually in." ] }, { "cell_type": "code", "execution_count": 2, - "id": "b993de25", + "id": "ba80ce49", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:31:18.583743Z", - "iopub.status.busy": "2026-09-11T03:31:18.583490Z", - "iopub.status.idle": "2026-09-11T03:31:18.701172Z", - "shell.execute_reply": "2026-09-11T03:31:18.700417Z" + "iopub.execute_input": "2026-09-12T02:05:17.827760Z", + "iopub.status.busy": "2026-09-12T02:05:17.827567Z", + "iopub.status.idle": "2026-09-12T02:05:17.833322Z", + "shell.execute_reply": "2026-09-12T02:05:17.832558Z" } }, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "<Figure size 500x350 with 1 Axes>" + "Dataset('biased', n=15)" ] }, + "execution_count": 2, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -108,37 +120,65 @@ "scale = 1.0 - sys_fraction # the realised normalisation, one sigma low\n", "y_meas = (y_true + rng.normal(0.0, noise_fraction * y_true)) * scale\n", "data = rx.Dataset(x, y_meas, noise_fraction * y_meas, label=\"biased\")\n", - "\n", - "fig, ax = plt.subplots(figsize=(5, 3.5))\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "99cfee79", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:05:17.834576Z", + "iopub.status.busy": "2026-09-12T02:05:17.834330Z", + "iopub.status.idle": "2026-09-12T02:05:18.674600Z", + "shell.execute_reply": "2026-09-12T02:05:18.674025Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 704x440 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", color=\"k\", label=\"data\")\n", - "ax.plot(x, y_true, \"--\", color=\"C3\", label=\"truth\")\n", - "ax.set(xlabel=\"x\", ylabel=\"y\")\n", - "ax.legend(frameon=False)\n", + "ax.plot(x, y_true, \"--\", color=plotstyle.COLOURS[1], label=\"truth\")\n", + "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"A 10 % normalisation nobody told us about\")\n", + "ax.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "8fac96e9", + "id": "d4fe9487", "metadata": {}, "source": [ "## The model, a prior, and a way to run the ladder\n", "\n", - "The prior is deliberately far from the truth so that the data, not the\n", - "prior, decide. `fit` runs emcee on any problem; `band` pushes posterior\n", - "rows through the model on a fine grid. Both read columns by name." + "We put the prior deliberately far from the truth, so that the data and not the\n", + "prior decide where we end up. Then we write three small helpers we will reuse\n", + "on every rung: `fit` runs emcee on any problem, `band` pushes posterior rows\n", + "through the model on a fine grid, and `summary` prints the columns by name." ] }, { "cell_type": "code", - "execution_count": 3, - "id": "11041742", + "execution_count": 4, + "id": "f4522e3a", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:31:18.702610Z", - "iopub.status.busy": "2026-09-11T03:31:18.702443Z", - "iopub.status.idle": "2026-09-11T03:31:18.708899Z", - "shell.execute_reply": "2026-09-11T03:31:18.708271Z" + "iopub.execute_input": "2026-09-12T02:05:18.676189Z", + "iopub.status.busy": "2026-09-12T02:05:18.676025Z", + "iopub.status.idle": "2026-09-12T02:05:18.680266Z", + "shell.execute_reply": "2026-09-12T02:05:18.679568Z" } }, "outputs": [], @@ -147,8 +187,23 @@ "b = rx.Parameter(\"b\", prior=stats.norm(5.0, 2.0), latex=\"b\")\n", "line = rx.Model(lambda x, m, b: m * x + b, [m, b])\n", "x_fine = np.linspace(0.0, 1.0, 40)\n", - "\n", - "\n", + "comp = rx.Comparison(data, line)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "573b525e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:05:18.681654Z", + "iopub.status.busy": "2026-09-12T02:05:18.681535Z", + "iopub.status.idle": "2026-09-12T02:05:18.685517Z", + "shell.execute_reply": "2026-09-12T02:05:18.684835Z" + } + }, + "outputs": [], + "source": [ "def fit(problem, seed, n_walkers=24, n_steps=1800):\n", " sampler = emcee.EnsembleSampler(n_walkers, problem.ndim, problem.log_posterior)\n", " sampler.random_state = np.random.RandomState(seed).get_state()\n", @@ -157,7 +212,7 @@ "\n", "\n", "def band(problem, samples, levels=(5, 95)):\n", - " on_fine = line.bind(x_fine, {})\n", + " on_fine = line.bind(x_fine)\n", " cols = problem.columns(line.params)\n", " return rx.predictive.predictive_band(\n", " [on_fine(*s[cols]) for s in samples[::10]], levels=levels\n", @@ -168,33 +223,31 @@ " return \" \".join(\n", " f\"{n} = {samples[:, i].mean():.3f} +/- {samples[:, i].std():.3f}\"\n", " for i, n in enumerate(problem.names)\n", - " )\n", - "\n", - "\n", - "comp = rx.Comparison(data, line)" + " )" ] }, { "cell_type": "markdown", - "id": "c273703f", + "id": "aa89658e", "metadata": {}, "source": [ "## 1. Reported statistics only\n", "\n", - "The default: the covariance is the diagonal of `y_err`. Nothing knows about\n", - "the normalisation, so the posterior is precise and wrong." + "The default covariance is the diagonal of `y_err`. Nothing in it knows about\n", + "the normalisation, so we should expect a posterior that is precise and wrong —\n", + "the worst combination." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "3045ab1e", + "execution_count": 6, + "id": "63d1e287", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:31:18.710467Z", - "iopub.status.busy": "2026-09-11T03:31:18.710342Z", - "iopub.status.idle": "2026-09-11T03:31:27.284213Z", - "shell.execute_reply": "2026-09-11T03:31:27.283342Z" + "iopub.execute_input": "2026-09-12T02:05:18.686883Z", + "iopub.status.busy": "2026-09-12T02:05:18.686766Z", + "iopub.status.idle": "2026-09-12T02:05:26.869596Z", + "shell.execute_reply": "2026-09-12T02:05:26.868964Z" } }, "outputs": [ @@ -214,29 +267,28 @@ }, { "cell_type": "markdown", - "id": "2a8d1323", + "id": "a545f1bf", "metadata": {}, "source": [ - "## 2. Infer the noise level (recipe 2)\n", + "## 2. Inferring the noise level (recipe 2)\n", "\n", - "Suppose the experiment had not reported errors at all. `T.noise_fraction`\n", - "puts a free relative noise `σ_i = ε · ym_i` on the diagonal, sampled in log\n", - "space; `statistical=False` drops the reported diagonal so the inferred one\n", - "replaces it. `T.noise` is the constant-floor variant, `σ_i = ε`. Both\n", - "terms are *additive*: without `statistical=False` they would inflate the\n", - "reported errors instead of replacing them." + "Suppose the experiment had not reported errors at all. We can infer them:\n", + "`T.noise_fraction` puts a free relative noise $\\sigma_i = \\epsilon\\, y_{m,i}$\n", + "on the diagonal, sampled in log space, and `statistical=False` drops the\n", + "reported diagonal so the inferred one replaces it rather than piling on top of\n", + "it. (`T.noise` is the constant-floor variant, $\\sigma_i = \\epsilon$.)" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "deb95106", + "execution_count": 7, + "id": "3dee571d", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:31:27.285542Z", - "iopub.status.busy": "2026-09-11T03:31:27.285390Z", - "iopub.status.idle": "2026-09-11T03:31:41.438682Z", - "shell.execute_reply": "2026-09-11T03:31:41.438202Z" + "iopub.execute_input": "2026-09-12T02:05:26.871042Z", + "iopub.status.busy": "2026-09-12T02:05:26.870883Z", + "iopub.status.idle": "2026-09-12T02:05:40.769356Z", + "shell.execute_reply": "2026-09-12T02:05:40.768568Z" } }, "outputs": [ @@ -260,35 +312,36 @@ "s_noise = fit(p_noise, seed=2)\n", "print(p_noise.names)\n", "print(summary(p_noise, s_noise))\n", - "print(\n", - " f\"inferred noise fraction: {np.exp(s_noise[:, 2]).mean():.3f} (generated with {noise_fraction})\"\n", - ")" + "inferred = np.exp(s_noise[:, 2]).mean()\n", + "print(f\"inferred noise fraction: {inferred:.3f} (generated with {noise_fraction})\")" ] }, { "cell_type": "markdown", - "id": "6e9fa52d", + "id": "fd7982cc", "metadata": {}, "source": [ "## 3. The reported systematic, as a correlated mode\n", "\n", "If the experiment *had* reported a 10 % normalisation uncertainty, the honest\n", "covariance adds a rank-one mode proportional to the prediction:\n", - "`Σ_ij = δ_ij σ_i² + (0.1)² ym_i ym_j`. Because the mode reads `ym`, the\n", - "covariance changes with the parameters; `rxmc` assembles it at every\n", - "likelihood call." + "\n", + "$$\\Sigma_{ij} = \\delta_{ij}\\,\\sigma_i^2 + (0.1)^2\\, y_{m,i}\\, y_{m,j}.$$\n", + "\n", + "Because the mode reads $y_m$, the covariance changes with the parameters, and\n", + "`rxmc` reassembles it at every likelihood call." ] }, { "cell_type": "code", - "execution_count": 6, - "id": "35f4d7ff", + "execution_count": 8, + "id": "61371390", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:31:41.440327Z", - "iopub.status.busy": "2026-09-11T03:31:41.440154Z", - "iopub.status.idle": "2026-09-11T03:31:57.564569Z", - "shell.execute_reply": "2026-09-11T03:31:57.563967Z" + "iopub.execute_input": "2026-09-12T02:05:40.770711Z", + "iopub.status.busy": "2026-09-12T02:05:40.770585Z", + "iopub.status.idle": "2026-09-12T02:05:56.399396Z", + "shell.execute_reply": "2026-09-12T02:05:56.398574Z" } }, "outputs": [ @@ -310,28 +363,34 @@ }, { "cell_type": "markdown", - "id": "5d3d2425", + "id": "fb1a2cc5", "metadata": {}, "source": [ "## 4. The same mode built from the data: Peelle's Pertinent Puzzle\n", "\n", - "The tempting shortcut is to build the mode from the measured values,\n", - "`(0.1)² y_i y_j`, as a fixed matrix. `rxmc` accepts any symmetric block as a\n", - "term (recipe 19), so the mistake is easy to spell. It is the covariance of\n", - "Peelle's Pertinent Puzzle: the fit is pulled *below* the data (recipes 27\n", - "and 37 have the closed forms)." + "The tempting shortcut is to build that mode from the measured values,\n", + "$(0.1)^2 y_i y_j$, as a fixed matrix. `rxmc` will let us: any symmetric block\n", + "is a term (recipe 19), so the mistake is easy to spell.\n", + "\n", + "It is also the covariance behind [Peelle's Pertinent\n", + "Puzzle](https://en.wikipedia.org/wiki/Peelle%27s_Pertinent_Puzzle): the\n", + "fluctuations feed back into the covariance and pull the fit *below* the data.\n", + "[D'Agostini (1994)](https://doi.org/10.1016/0168-9002(94)90719-6) diagnosed it,\n", + "[Frühwirth, Neudecker & Leeb\n", + "(2012)](https://doi.org/10.1051/epjconf/20122700008) wrote out the solution, and\n", + "recipes 27 and 37 carry the closed forms." ] }, { "cell_type": "code", - "execution_count": 7, - "id": "5b193ca4", + "execution_count": 9, + "id": "911272d9", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:31:57.565936Z", - "iopub.status.busy": "2026-09-11T03:31:57.565802Z", - "iopub.status.idle": "2026-09-11T03:32:06.335721Z", - "shell.execute_reply": "2026-09-11T03:32:06.335147Z" + "iopub.execute_input": "2026-09-12T02:05:56.400693Z", + "iopub.status.busy": "2026-09-12T02:05:56.400574Z", + "iopub.status.idle": "2026-09-12T02:06:04.853254Z", + "shell.execute_reply": "2026-09-12T02:06:04.852563Z" } }, "outputs": [ @@ -352,7 +411,7 @@ }, { "cell_type": "markdown", - "id": "70097e6d", + "id": "ee2ad5ca", "metadata": {}, "source": [ "### Four posteriors, one truth" @@ -360,22 +419,50 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "4ccfb0ae", + "execution_count": 10, + "id": "5a4feed4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:06:04.854644Z", + "iopub.status.busy": "2026-09-12T02:06:04.854514Z", + "iopub.status.idle": "2026-09-12T02:06:04.868017Z", + "shell.execute_reply": "2026-09-12T02:06:04.867282Z" + } + }, + "outputs": [], + "source": [ + "runs = {\n", + " \"statistics only\": (p_stat, s_stat, plotstyle.COLOURS[0], plotstyle.HATCHES[0]),\n", + " \"inferred noise\": (p_noise, s_noise, plotstyle.COLOURS[2], plotstyle.HATCHES[1]),\n", + " \"mode from $y_m$\": (p_norm, s_norm, plotstyle.COLOURS[4], plotstyle.HATCHES[2]),\n", + " \"mode from $y$ (Peelle)\": (\n", + " p_wrong,\n", + " s_wrong,\n", + " plotstyle.COLOURS[1],\n", + " plotstyle.HATCHES[3],\n", + " ),\n", + "}\n", + "bands = {label: band(p, s) for label, (p, s, _, _) in runs.items()}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c9568854", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:32:06.337062Z", - "iopub.status.busy": "2026-09-11T03:32:06.336900Z", - "iopub.status.idle": "2026-09-11T03:32:06.618646Z", - "shell.execute_reply": "2026-09-11T03:32:06.618137Z" + "iopub.execute_input": "2026-09-12T02:06:04.869442Z", + "iopub.status.busy": "2026-09-12T02:06:04.869269Z", + "iopub.status.idle": "2026-09-12T02:06:05.105438Z", + "shell.execute_reply": "2026-09-12T02:06:05.104887Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 600x400 with 1 Axes>" + "<Figure size 704x440 with 1 Axes>" ] }, "metadata": {}, @@ -383,41 +470,35 @@ } ], "source": [ - "runs = {\n", - " \"statistics only\": (p_stat, s_stat, \"C0\"),\n", - " \"inferred noise\": (p_noise, s_noise, \"C2\"),\n", - " \"normalisation mode from ym\": (p_norm, s_norm, \"C1\"),\n", - " \"normalisation mode from y (Peelle)\": (p_wrong, s_wrong, \"C3\"),\n", - "}\n", - "fig, ax = plt.subplots(figsize=(6, 4))\n", - "for label, (p, s, color) in runs.items():\n", - " lo, hi = band(p, s)\n", - " ax.fill_between(x_fine, lo, hi, color=color, alpha=0.25, label=label)\n", + "fig, ax = plt.subplots()\n", + "for label, (lo, hi) in bands.items():\n", + " _, _, colour, hatch = runs[label]\n", + " plotstyle.band(ax, x_fine, lo, hi, color=colour, hatch=hatch, label=label)\n", "ax.plot(x, y_true, \"--\", color=\"k\", label=\"truth\")\n", "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", color=\"k\", ms=4)\n", - "ax.set(xlabel=\"x\", ylabel=\"y\", title=\"90 % predictive bands\")\n", - "ax.legend(frameon=False, fontsize=8)\n", + "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"90 % predictive bands\")\n", + "ax.legend(fontsize=8)\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "93d93c80", + "execution_count": 12, + "id": "7b2a4566", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:32:06.620099Z", - "iopub.status.busy": "2026-09-11T03:32:06.619927Z", - "iopub.status.idle": "2026-09-11T03:32:07.487830Z", - "shell.execute_reply": "2026-09-11T03:32:07.487177Z" + "iopub.execute_input": "2026-09-12T02:06:05.107337Z", + "iopub.status.busy": "2026-09-12T02:06:05.107183Z", + "iopub.status.idle": "2026-09-12T02:06:05.308324Z", + "shell.execute_reply": "2026-09-12T02:06:05.307528Z" } }, "outputs": [ { "data": { - "image/png": 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AnDlzhpUrV1JQUMC5c+fo3r07S5YsuaW+my+jPPXUU+zatYumTZuiVCoJCAigbt26JfZ19epVLly4QFRUFOnp6VhYWLBnzx5jf/v27WPQoEG0bNmS8PBwgoKCWL9+PSqVimXLlnHixAmysrJwcHDg3LlznD59Gjc3t1tqvJ33beqYu3fvZvDgwbRr146YmBisra1p1qwZAKdPn2b48OH4+fmRm5tLREQEu3btwsvLq0gtkZGRHDhwgISEBFauXEmzZs1YtmwZx48fJysri+bNm3P//fezePFi5syZQ7t27Thy5AjTp0/ntddeAwx7xdjZ2WFnZ0dYWBht27YlOTkZpVLJ6dOneeqpp3jjjTdu+Ry0Wi0jRozg8uXLKBQKUlNTGTlyJBcvXryrfqs6CRpCiKopPwvmlXzprty8GgOWtiab5OXlcezYMdRqNfPnz2f58uV06NCB9957j/79+7No0SIARo8ezccff8y7774LGP7CLhzzUKh+/fqsXLkSMJyNUKlU7NmzB61Wi4+PD2FhYfj7+zN06FDatm3L2bNnSU9PZ/LkySxcuNDkLq0bNmxg9+7dBAQEFHm8pL5atWrFwIEDi1w62bNnj/F1L730EvPnz+fJJ58kLy+Pjh078vvvvzN8+HAA6tWrZ7z8MHLkSNatW8f48eOLra2s79vUMV999VU+++wzHnvsMXJycmjevLmx/xdeeIFhw4YZz9YsX76cr7/+mrfeeqtIHZ06dWLq1Kns2bOHZcuWGR93c3Pjzz//NP7cXnnlFQ4ePEjjxo0JDw+nefPmTJgwwbg53fjx45k8eTLJycm4ubmxZs0aHnjgAc6cOcPAgQOLDQSNGzcmICCArVu30rdvX3788UeGDx9e5EztnfRb1cnQbyGEKEWHDh1Qq9UABAQEkJiYCBjOOvTr18/YbuDAgZw+fdr4fefOnYuEDIAuXboY7/v5+Rm/t7CwwMvLy9j3Bx98QMeOHfn888/59ddf0el0xMbGmqxz5syZtGvXjnvuuYf333+fnJycO+7rv+/P0tKSPn36FHl/Xbt2Nd6/+XMpTlnft6lj3vyctbU13bp1M/Z57Ngxjh07xsqVK1m5ciVKpRJPz7IH1Zt3rQ0JCcHd3Z3GjRsb623cuDHnz5+/5f04Oztjb29v/N7Pz8/k5zB16lS+/vprAJYuXcozzzxT7Od0u/1WZXJGQwhRNaltDGcbKuI4pfjvdNzCTa/d3d0JDw83Ph4aGmrcUh4whhNTfZXU95dffsn27dtp2rQpOTk51K1bl9I2254+fTqTJk3iwIEDvPvuu+Tk5PDGG2+Y7EupVJbYb+H7KxyTEhoaWuQXckm1F6es79vUMQufK/yMw8PD6dChAwAeHh5MnTrVOL7idt38s3J3dyc+Pp7s7Gw0Gg1arZaoqKgiP1tT78fU5zBo0CBefPFFVq9ejZeX1y2Dbu+036pMgoYQompSKEq9pFHZJk6cyKhRoygoKECr1fLBBx+wcePGcunb39+fhQsX0qtXL1auXEleXl6pr/n999/R6XTk5OSQmZlpnOFjqi8/Pz/WrFmDl5fXLQNOn3rqKSZMmMDMmTO5cuUK27dvZ/HixeXy/kpi6phPPvkk48eP54UXXuDUqVMcOXLEeBln1qxZTJgwgRkzZuDn5wdA+/btjfdvR926denZsydDhw5lxIgRrFu3jsDAQIKDg+/6/alUKiZMmMATTzzBqlWr7rq/6kCChhBCmBAQEIBWqzV+7+PjY/wr+r777mP16tXGU/UbNmwwzgz47+vAMDbh5sGJXbp0KfL9fffdZ5y58eOPP7Jw4UL27dvHM888Q+PGjY3X8lu0aIGzs/Mt91etWkVBQQG2traMHTuWiRMnltrXhAkTSEhI4LfffsPS0rJIfy+88AKenp5s3boVZ2dnDh48iIeHR7HvpXnz5iXOCrud923qmDNnzsTFxYU9e/bQqVMnPvjgA2OQGDduHP7+/vz222/s378fvV6Pm5tbsUHD39/feFmpuPoAfvnlFxYtWsT27dtp3bo106dPN85+6du3b5GxMoMHD8bS0hIwXAoaMmRIsZ/Dze2///57+vfvX+Txu+23qlLoq+u5mCooLS0NR0dHUlNTq/U0zL/OHeTVgxOY1+EbHmzS4a7b3Y0zZ84UGVVeHn9RiMpRU/59CHE3fvvtN5YuXcrgwYOZMmVKZZdTIWQwqBBCCFFB/vzzTzp16sRTTz1V2aVUGLl0UssciwklKjXeZJujVy9UUDVCCFG7/PTTT5VdQoWToFGLHIsJZczmoSiU+aW21evUeDu6l9pOCCGEMEWCRi0SlRqPQpnPMJ9ZtKnXyGRbb0d3WnsGmGwjhBBClEaCRi3Upl4jsw3eFEIIIW4mg0GFEEIIYTYSNIQQoppJTU0lIyOjssuodCkpKWRmZt7x6xMSEkhLSyvHispfTk4OqampZuk7Pz+fuLi4W+6Xdz0SNGoZ11Q9VlfCyD5zxuQtP6YCln4WQtyRt99+m6+++gq4+9Dx39dXpxDz2muv3dEsjry8PDp06EDTpk2LbKxWFT399NMcOHAAgPj4eKKiooiJibllMbg7cebMGfr27XvLfVPS09Pp3bs3BQUFZT6OjNGoRSziE/l4aQHW+XMIK6WtQqMhaP061LexKZEQouJ9/vnnuLu7M2nSpDt6/Zw5c4rs3nq3/VUH27dvx8nJqUx/wVemsLAwjhw5wrfffgvA8OHDOXXqFNbW1iQlJfHUU0/x6aefVmhN7u7utGnThl9++YVHHnmkTK+RoFGLqNLSsc6H2Ocm0albnxLb5YWEEDNzFtrkZAkaotbLysoiNzcXZ2dn0tLSil3VNDs7G6VSWWSn1ptfl5ycjIODA+np6ajVamxtbUlOTjYu9Z2Xl0dubq5xbxIw/OWYmpqKhYUFHh4et2y2VeiZZ55BpVIZv9dqtcTHx1NQUICbmxvW1tYl9pWbm0tGRgapqalERUXh6Oh4S3+FtdjY2BR5PCUlxfheUlNTTW5ff3Pb0t63qWMWKunnAIafBYBGoynx+fPnz+Pq6kpUVBQeHh5kZWUZ64uPjy+yeVpKSgpOTk5F+rh27Rqurq7G/grrz8jIQK1W37Jj783vSafTFfmsEhMTsbGxKbben3766ZZlxxcvXsyoUaOIiIigdevWPPTQQ/Tq1QswbLqWkpJi/HxvZuq5kpT0muHDh7Nw4cIyBw25dFIL5Xt7ogkOLvFmGRhY2SUKgV6vR5eVZfZbabswLF++nEceeYR27drh5+dH/fr1iYiIACAzM5OhQ4fi7u6Oi4sLo0ePJjc31/i6UaNG0aZNG4KDg7l69SqvvfaacU8Ob29vBg8ezIoVK/Dx8cHDw4Pnn3/eeNzvvvuOTp060bp1a1xcXIyXSv7r5ssoixYtwsXFhbZt29KpUyf27t1rsq99+/axatUqPv74Yzp16sRPP/1UpL+IiAg6deqEl5cXDg4OzJkzx3jc1157jSlTphAYGIiPjw/dunUzvvf/up33beqYoaGhNG/eHG9vb5o3b05oaKjxufj4eAYMGIC3tzd16tRh0KBBxtBxs+3btzN37lzWrl1Lp06dOHHiBK+99hqTJk3C39/fuFPsP//8g7e3N/7+/tSpU4c1a9YY+7jvvvt46qmn8PHxwd3dnffff58XXngBPz8/XFxc+PXXX4v9HI4ePUqfPjf+yMvPz6dFixYlbv++a9cu2rdvX+xzvr6+NG3alLCwMAAWLFiAm5sbDRs2JDAwkP379xvbmnquJKZe07FjR/bs2VPmyydyRkOULP4ixJS8uJdj0iXqarVEJmVxOtr04CBnW0u8nIr/C0OI4uizs7nQpq3Zj9Po6BEUNqa3ij958iT79+/H19eXqVOn8vXXX/Puu+/y0UcfkZWVRUJCAlqtln79+vHFF18Yf3EePXqUQ4cO4e/vb+zr6tWrnD59GoVCQfPmzbGwsCAyMpLU1FQaNmzIK6+8goeHB88++yzPPvssSUlJhIaGMmDAAMaNG1fiX+oA7733HseOHSMoKKjI4yX11bNnTyZMmFDk0smLL75ofN3zzz9P586d2bdvH9HR0XTq1ImePXsa/4K+fPkyx48fR6PR0Lt3b9auXWvcTfW/yvq+TR1zxowZPPDAA8yfP5+QkBDat2/Pgw8+CMCMGTPo3r07S5YsoaCggBdffJEvvviCGTNmFKljwIABfPTRR+zZs8c4PuOHH37g/PnznDhxAkdHR/Ly8hg9ejRffPEFQ4YM4e+//2bYsGGEhYUZz0Y4ODgQExPDuXPnaN68OfPnzycxMZEtW7bw/PPPF/s59OjRg+zsbI4fP06rVq34448/6NKlC97e3sV+ZlFRUdSrV6/IY0lJSURGRnLw4EEOHz7MokWL2LJlC5s2bWL//v1oNBp27NjBlClTOHbsmMnnSlLaa+zt7VEqlSQkJFCnTp0S+ykkQUPcKv2a4evvE8Gl5KDRHVijUNA38gDv55ge+a1Rq9g2o4eEDVEt9e7dG19fXwDuueceNm3aBMCePXuYNGkS1tbWAEyaNIm1a9cag0bfvn2LhAyAoUOHYmdnBxh2PB05ciSWlpa4u7sTGBhoPJ2/YcMGJk6cSFZWFra2tiQlJREVFUWDBg1M1vnQQw/Rt29fBg8eTPfu3QHuqK/C97dv3z4UCgXe3t6MGDGCvXv3GoPGsGHDjJcwOnbsSGRkZIl9lfV9mzrm3r17WbRoEQCBgYEMHDjQ2P+2bdvYtm2b8fnCNmU1cuRIY4i4cuUKarXaeNnivvvuIyAggFOnTtG1a1cAxo4di0KhoGnTptjZ2TF+/HgAOnfubPJzmDp1KkuWLOGLL77gq6++Yu7cuSW2ValUtwz6fP3111mwYAGenp4sWbKEli1bMmvWLI4fP278ucCNS0fbtm0r8bmSlOU1Wq0WtVptsp9CEjTErXKun5249zXo0LPEZlFh2/He8ibz+njj4t+1xHaX4zJ4btVxkjPzJGiIMlNoNDQ6eqRCjlOawq26AZRKJTqdDgBra2uysrKMz2VkZBhDBxT/P/Sb+1IoFLd8X9j3zJkz+fbbb+nXrx8Anp6epZ6q/u677zh06BD//PMPTz75JDNnzmTixIkm+yrc+rw4xb0/z5vGbZX0uRSnrO/b1DGtrKyKPHfz1FYrKyt+/fVX2rVrV2INptz8s7K2tiYnJwe9Xm/8fP77sy3p/dz8XoozZswYmjRpwsSJE0lJSaFbt24ltm3QoAERERF06HBjgcXCMRo3s7KyYvz48XzyySe39GHquZKU9pq4uDhsbW3LPN5DgkYNkR8TgzY52WQbddRtTll18gPPViU+nZdquD7q7WJDoFfJA8GEuBMKhaLUSxqVbdCgQcyfP58GDRqg1Wr54IMPWLBgQbn0rVaruXjxIgEBAXz77bfExsaW+pro6Gjq1avH0KFDOXnypPH6vam+XF1dOX78OBEREbf84hg0aBAzZ85k4cKFXLlyhV9++cU47sNcTB3zwQcfZMaMGcyfP59Tp06xfv1645iHRx99lKeffpqPP/4YPz8/AFxcXLC5g/+G/Pz8qFevHrNnz2bs2LH88ccf5Ofn07x587t+f7a2tgwZMoRhw4bx2muvmWzbu3dv9u7dy7Bhw0y2Gz58ON27d6dt27Z07doVtVqNtbU1bm5uJp+7k/4Adu/eTZ8+fUyG1JtJ0KgB8mNiuDLwAfTFDHy6WV0gRw0FDreO8BZCFM/W1hYXFxfj9zY2Nsbvn3jiCRITE5k8eTJKpZLnnnuOESNGFPs6AGdnZ+PlAwA3N7cif0nXqVPH+JfxF198wQsvvMC3337L4MGD6dWrl/FUtZOTk3Gmw83377nnHrRaLba2tnTp0oVZs2aV2te4ceN44okn6NatGy+99FKR/hYsWMDs2bMZOXIkzs7O/PjjjwQHBxf7XhwdHYudPXK779vUMd977z2ef/55hg0bRqdOnZgyZYqx3zfffBMHBweef/55rl69il6v5+OPPy52rMR/fzb/rU+pVPLnn38yc+ZMBg8eTMOGDdmwYYNxNkndunWLXDbw8vIyzuRRKpV4eXkV+zkUeuKJJ4yDjE0ZPXo0HTt2ZOHChVhYWODu7l5scGrRogV//vkn8+fPZ86cOeTl5dGtWzdWrFhh8jlLS0vjGIub75t6DRgGOk+bNs1k7TdT6Esbci3KLC0tDUdHR1JTU0ucemUO2WfOEDZ0GJ4L3zc5Y2R36BnmhbzFjD7fmtzrJHvHH4RNfhX/r+ah6TmkxHYh5/4gcNV4QkZ+T2CTktudjk7lgc/3sO6ZrjS7zTMfZ86coVmzZoZ+Tp82/g9HVD+V9e9DiKokIiKCTz/9FI1GwzvvvFNq+wULFtCoUaNbprlWloiICGbMmFHizJriyBmNGsQyMBCNiV/EucpMEhPLdqpLCCFE+SscILxq1aoytX/55ZfNXNHt8fX1va2QARI0RDmIyogiJ/Fsic+HpmWgsEipuIKEEKKKOn/+fGWXUOEkaIg7Zm9puB676Ngizp1dYrKtbZCahJx2gAwaFUKI2kSChrhj7hrDCOQF3RaQ49GoxHa7Qs+w+MxbpOeZZwdCIYQQVZcEDXHXAh0DwLVpic9fiaseO0EKIYQof7LXiRBCCCHMRoKGEEIIIcxGgoYQQgghzEaChhBCCCHMRoKGEEIIIcxGgoYQQgghzEamt9YydbVaHJMuQYxlyY1SwiuuICGEEDWaBI1aRJMZx5qoq9hEPm26YZIacAdrWcVTCCHE3ZGgUYtY5qZio9dztOPLtGl5f8kNL4bCllfBvk7FFSeEEKJGkqBRC2U4+IJnq5IbJKsrrBYhhBA1mwwGFUIIIYTZSNAQQgghhNnIpZMqLj8mBm1yssk2eSEhwPXNy5xK3iE1Ni2nXGsTQgghSiNBowrLj4nhysAH0Gdnl9o2R2XJU39cIt4mocQ2za3DGQHYWMuPXQghRMWQ3zhVmDY5GX12Np4L38cyMLDEdlfiMnjqj0vMfrwX9T3sSmyXFJYCW8HFxsQaGkIIIUQ5kqBRDVgGBqIJDi65gVMq8TYJ1Pewo5lXyWtfhKTZmKE6IYQQomQSNESJCsd+lCg+FItMFTIZVgghREkkaIhbWDg7o9BoiJk5q9S2CpU7QY8moPasgMKEEEJUOxI0xC3Unp4ErV9X+myXI9uJmbcYbWq6nNUQQghRLAkaolhqT0/UnqWcpoi/WDHFCCGEqLYkaIi7lxIOMcdLfNox6RJ1tdqKq0cIIUSVIUGjhlBYpBCadgGldcnTWxMyoih5kuwdKNzd9Z934PicEpt1B9YoFPybGVeeRxdCCFENSNCoARJyYrEN+pBXD+abbNckN4/ugL2lffkcuHB314eXQsOAEpsdPbGRNgcWYJlb8qqlQgghaiYJGjVAel4qCmU+U4PfoHtAyettWMddgFXjcde4lW8B7g3Bs+TjZoSeKd/jCSGEqDYkaNQgXrZ+NHVtWnKD3LyKK0YIIYRAdm8VQgghhBlJ0BBCCCGE2UjQEEIIIYTZSNAQQgghhNlI0BBCCCGE2UjQEEIIIYTZSNAQQgghhNnIOho1RF2tFsekSxBjWXKjBNkETQghRMWSoFEDaDLjWBN1FZvIp0tvrLYBG1fzFyWEEEIgQaNGsMxNxUav52jHl2nT8n7TjW1cwcmnYgoTQghR60nQqEEyHHzBs1Vll1Gi/PijhJyzMdnG1tGXOp5tK6giIYQQ5iZBQ5id2t6bLIWC+44vg+PLTLbNVii4NvFvCRtCCFFDSNAQZmfv2oo++W8z/34XvF1KPqORGn2Q1nu+4GpqBEjQEEKIGkGChqgQ0dpAXPy7EujlWGKbEAC+qKiShBBCVABZR0MIIYQQZiNBQwghhBBmI0FDCCGEEGYjYzREhbkcl2Hy+aSkLAIrqBYhhBAVQ4KGMDtnW0s0ahXPrTpusl1z6wt0B5Ky8iRwCCFEDSFBQ5idl5OGbTN6kJyZZ7Ld2ZOxcACycrQVVJkQQghzk6AhKoSXkwYvJ43JNklh1hVUjRBCiIoig0GFEEIIYTYSNIQQQghhNhI0hBBCCGE2MkajkuTHxKBNTjbZJi8kpIKqEUIIIcxDgkYlyI+J4crAB9BnZ5faVqHRYOHsXAFVCSGEEOVPgkYl0CYno8/OxnPh+1gGml4xwsLZGbWnZwVVJoQQQpQvCRqVyDIwEE1wcGWXIYQQQpiNDAYVQgghhNlI0BBCCCGE2UjQEEIIIYTZSNAQQgghhNlI0BBCCCGE2UjQEEIIIYTZSNAQQgghhNnIOhrirpVlqXRZeEwIIWonCRrijlk4O6PQaIiZOavUtgqNhqD16yRsCCFELSNBQ9wxtacnQevXlWlzuJiZs9AmJ0vQEEKIWkaChrgrak9PCQ9CCCFKJINBhRBCCGE2EjSEEEIIYTYSNIQQQghhNhI0hBBCCGE2Mhi0iotOySY5M89km9i0nAqqpmLYpUVAzHEALBJvrNFhkXgBYvIN39i4gpNPJVQnhBDidkjQqMKiU7Lp/eFOsvMLTLZrbh3OCMDGunr/OPOsHMlSKGhzYAEcWACAc4bO+Lzz2nFgd/0knNoGph6UsCGEEFVc9f7NVMMlZ+aRnV/AJyNbUd/DrsR2SWEpsBVcbCwrrjgzyLb1YLB3PV5v/BrdA4IBSL4cAh8ONdwf9AMe9QMh4SL8PhGyEiVoCCFEFSdBoxqo72FHMy/HEp8PSbOpwGrMK9bCglSXBuDZCgBtstr4nNa1EXgGV1JlQggh7oQMBhVCCCGE2cgZjSrOkwSsE06BouRLJ5bJYRVXkBBCCHEbJGhUYeqMaLZZzcTmj1yT7byBLIUCrabkyytCCCFEZZCgUYWpcpKwUeQS2etTfBq0KrFdSGook/59lU/t61ZccUIIIUQZSNCoBnKd6hsHRxYnx8qSWAv5UQohhKh6ZDCoEEIIIcxG/gyuBmIywslLdCnx+ZDUkBKfE0IIISqTBI0qLCU3EYBFZ97i3GXTi3FpLDQ4WzlXRFlCCCFEmUnQqMKy8jMAGBE0kaatBphs62zlTD27ehVRlhBCCFFmEjSqAQ/rejR1bVrZZVSY6MxwziYa1g25knbF+PiVtCsoEhVYp4YSWFnFCSGEuC0SNESVYW/piF6nZvGZt1h8xvBYXvyNnWtn7p2J5UVLmuTm8QsQn52Ae+WUKoQQoowkaIgqw826LplXZvDZ6AYEXd9E7vLFywxmMAAL71lI/Yb1SQj5G2JeIz0vXYKGEEJUcRI0RJWi1zpRkOOFLscQNHQ5acbndDke6HK8IK/kGThCCCGqFgkaospwtrVEo1bx3Krjxsdy4sON9yf/fBRr90SaW1+gO5CUlSdjNYQQooqToCGqDC8nDdtm9CA588a4jMsXXRnyreH+V4+1oX7DJpw9GQsHICtHW0mVCiGEKCsJGqJK8XLS4OWkMX6vSLE33m/gYU+wlyNJYdaVUZoQQog7IEFDVJi8kNJXMLVwdkbt6VkB1QghhKgIEjSE2Vk4O6PQaIiZOavUtgqNhqD16yRsCCFEDSFBwwyyz51DbWdX4vNl+cu+JlF7ehK0fh3a5GST7fJCQoiZOQttcrIEDSGEqCEkaJhBxGNjsFOpTLZRaDRYONeevUnUnp4SHoQQohaSoGEGdd95G7dmzUy2kbEIQgghagMJGmZg6e+PJji4sssQQgghKp0EjUoSnZJdZL2I4sSm5VRQNUIIIYR5SNCoBNEp2fT+cCfZ+QUm2zW3DmcEYGMtPyYhhBDVk/wGqwTJmXlk5xfwychW1PcoeXZKUlgKbAUXG8uKK04IIYQoRxI0KlF9DzuaeTmW+HxImk0FVlP92KVFQMxx041sXMHJp0LqEUIIcSsJGqLaybNyJEuhoM2BBXBggenGahuYelDChhBCVBIJGqLaybb1YLB3PV5v/BrdA0zM7km4CL9PhKxECRpCCFFJJGiIainWwoJUlwbg2aqySxFCCGGCsrILEEIIIUTNJUFDCCGEEGYjQUMIIYQQZiNBQwghhBBmI4NBK4knCVgnnAJFyQt2WSaHVVxBQgghhBlI0KgE6oxotlnNxOaPXJPtvIEshQKtpuRFvYQQQoiqTIJGJVDlJGGjyCWy16f4NGhVYruQ1FAm/fsqn9rXrbjihBBCiHIkQaMS5TrVN7kORI6VJbEW8iMSQghRfclgUCGEEEKYjQQNIYQQQpiNnJevRDEZ4eQlupT4fEhqSAVWI4QQQpQ/CRqVICU3EYBFZ97i3GVLk201FhqcrZwroiwhhBCi3EnQqARZ+RkAjAiaSNNWA0y2dbZypp5dvYooSwghhCh3EjQqkYd1PZq6Nq3sMoQQQgizkcGgQgghhDAbCRpCCCGEMBsJGkIIIYQwGwkaQgghhDAbCRpCCCGEMBsJGkIIIYQwG5neKqqcvJAbK6Lqrlwpcj8bsAoNwzVVX/YOEy6W3sbGFZx8bqNKIYQQZSFBQ1QZFs7OKDQaYmbOMj6Wp9PduP/iTMKUSnyAj9UQ2yoRmpjo0MYV1Dbw+8TSD662gakHJWwIIUQ5k6Ahqgy1pydB69ehTU42Pnb5yhUYPBgAyw8W4h8UxP7dW6n7ydeo0tJNd+jkYwgPWYmm2yVcNISRrEQJGkIIUc4kaIgqRe3pidrT0/j9zYOIlEFBaIKDyQ89U/YOnXwkPAghRCWSoCGqrdi0HE5Hp5ps42xriZeTpoIqEkII8V8SNES1Y2Nl+M/2p/3hvHFyj8m2GrWKbTN6SNgQQohKIkFDVDsutpYAjOvmgGMz9xLbRSZlsXBjNMmZeRI0hBCikkjQENWOvaU9GcAvIUsJzVpmsq1tkJqEnHaAY4XUJoQQoigJGqLacdO4kQEs6LYAGgWW2G5X6BkWn3mL9DzT4ziEEEKYjwQNUW0FOAagcW1a4vNX4jIqsBohhBDFkSXIhRBCCGE2EjSEEEIIYTYSNIQQQghhNjJGwwxC4zOJN7GQVGxaTgVWI4QQQlQeCRpm8NUfW4jfe7bE5xtanWYEYGMtH78QQoiaTX7TmcF76iXUsSow2SZLocDBteTFpoQQQoiaQIKGGcS1eZY6DzxU4vMhqaFM+vdVPrWvW3FFCSGEEJVAgoYZaG29wLNVic/nWFkSayEfvRBCiJpPZp0IIYQQwmwkaAghhBDCbOT8vai28kJCTD5vFRqGa6q+gqoRQghRHAkaZpCQfY2ziSVPbw1JNf0LUphm4eyMQqMhZuYsk+18gI/VENsqEZqUoeOEi6W3sXEFJ58y1SmEEEKChln8Fvo9e9etMtlGY6HB2cq5giqqWdSengStX4c2Odlku/27t1L3k69RpaWb7tDGFdQ28PvEMhzcBqYelLAhhBBlJEHDDO71epBnHzD9S8vZypl6dvUqqKKaR+3pidrT02Sb/NAzZevMyccQHrISTbdLuGgII1mJEjSEEKKMJGiYgbOlK01NbF8uqiAnHwkPQghhBhI0RI13LiGc3HMHTbbxdnSntWdABVUkhBC1hwQNUWN52BnGwPx97QeWHfzRZFu9Ts1P/X6TsCGEEOVMgoaosRq5exIGTAt+g9wg/xLbHb16gdWR7xOVGi9BQwghypkEDVHjdQsIRtMk2GSb1ZEVVIwQQtQysjKoEEIIIcxGgoYQQgghzEYunYgar9yXKpcVRIUQoswkaIgaq9yXKpcVRIUQ4rZJ0BA1VrkvVS4riAohxG2ToCFqtHJdqhxkBVEhhLhNMhhUCCGEEGYjZzSEMBcZNCqEEBI0hCgUm5bD6ehUk22cbS3xctKY7kgGjQohhJEEDVHr2VgZ/hn8tD+cN07uMdlWo1axbUYP02FDBo0KIYSRBA1R67nYWgIwrpsDjs3cS2wXmZTFwo3RJGfmlX5WQwaNCiEEIEFDCOwt7ckAfglZSmjWMpNtbYPUJOS0AxwrpDYhhKjuJGiIWs9N40YGsMBnGvh7l9juaPQVll1dSnqe6XEct00GjQohajAJGqLWK1xBlLc+NdmuLRCshm2eB0vt09vRvfQt52XQqBCiFpCgIWq9sq4gevHIAaznLWT3la/4MfNrk231OjU/9fvNdNiQQaNCiFpAgoYQlG0F0YZAGDAt+A1yg/xLbHf06gVWR75PVGp86Wc1bmfQaFkusZSVXIoRQlQQCRpC3KaOeg2WOtsSn7fKsuJIrJ6QAzvZciXMZF8FDvZo3V1NttFkxtHLwhpVWS6xlJHOQoNy1M9g42ay3TUKSLQqZYYN4JqbTR1UphulZ9xOiUKIGkKCRjnS6w1bjWdm55CWllbJ1dQMGRkZRe5X5ueab2FBpqUlF1+YYbKdM/A6AEtL7TNHDYsGKUnTKEy2+zbfhcH1BuNiZVdimyxdFlsj/0CryzfZl71OxzOxKVh9MKLU+nKB75ydSFeWvFuBvU7HM8kpXCulr/Q8w7+Pwn8nQojaQaGXf/XlJioqCh8fOR0thCmRkZF4e5c8u0cIUbNI0ChHOp2OmJgY7O3tUShM/4Va2dLS0vDx8SEyMhIHB4fKLsckqdU8KrpWvV5Peno6np6eKE2cIRFC1Cxy6aQcKZXKaveXmoODQ5X/hVhIajWPiqzV0VEWOhOitpE/K4QQQghhNhI0hBBCCGE2EjRqKSsrK+bMmYOVlVVll1IqqdU8qlOtQojqq1YPBv3tt994+OGHq/zATSGEEKK6qrVnNObNm8ewYcOYPHmyzOsXQgghzKTWBo2rV6/Sr18/vvnmGwkbQgghhJnU2umtjRs3Rq1WM378eB577DEAvvrqK6KiovD09ESlKmU5ZSA3N5fc3Fzj9zqdjqSkJFxdXeVyjBD/UdZ1NKrTejRCVLRquR6Nvpbatm2bvk+fPnq9Xq9fsWKFXqVS6R955BG9t7e3fvv27WXqY86cOXpAbnKT223cIiMjTf67ioyMrPQa5Sa3qn4r7d9RVVJrB4NGR0fToUMHoqOjAVi4cCGzZs2iV69e/P3332X6S+q/ZzRSU1Px9fWtFqtCliovCz5saLj/wnkwsceGOcXP9qT+okwA9u/fT5MmTSrmwKvGQcjfMOBDaDmyYo5ZwxWuRJqSkmJy4a7U1FScnJxqxr+jMth44TBzj0xjTttF3N+oXbU97rlz5+jUqRNQwf9Wa5my/juqSmrtpRMvLy8yMjJITk4mPj6ezz77jEmTJrFs2TImT57MV199VWrYsLKyKnZqYHVaFbJEeRZgdf39OzhUWtDIsbrxM7Czs6u4z9VGbXj/dhrD+xflprR/V4XP14h/R2VgY2eHSqPCpiL/+zbDce3s7Ircrw0/u8pUnS4rVpMLPObRsGFDfv/9d+677z7effddvvrqK37++We++eYbli9fXtnlicqSmwF5sqW5EEKUh1p7RgOgZcuWTJ48mW+++YaxY8cCMGrUKAICAujQoUMlV1fJFErDTa+Di5ug+bDKrsg8cjMg9hTEHIOrxyHmOCRcxHAZFFCqK7E4IYSo/mp10Pjggw8YNGgQgwYNKvJ4x44dK6miKkRtDW3Hw+Fv4fenQKmC4CEVXkZG+2eA+eXXYWYCnPrVECz+GypuZu8Jfl2gQZ/yO7YQQtRCNTJobNq0iR9++AGdTse0adPo1q1bse2cnJxuCRniJgM+gPwcOLEcVk8wPFbBYSO72WjKNWj8OQUubSn6mL0neLaCeq1ufLWvU37HFEKIWqzGBY05c+awdOlSHnroIY4cOcK9997L/v37adu27S1tjx8/TvPmzcu0ZkatpFTB4EWG+4Vhw8oB6t9XuXXdqZRIuLTVcL/7LPBuJ6FCCCHMrEYNBt2zZw/Lli3j2LFjfPHFF/z77780btyYd99995a2cXFxdO3alccee4yCgoJKqLaaKAwbzUeAvgB+nwhpMZVd1Z05sQLQg383uHc2NOwnIUMIIcysRgWNRYsW8cEHH1CnjuGXh0qlYuzYsZw+ffqWth4eHixevBhHR8fqs7paZVGqYNDnUKc5ZCXCb09Cgbayq7o9Oh0c+8lwv/WYyq1FCCFqkRr1G7ZOnToMGVJ0DIGfnx85OTnFth83blyZ1ssQGAaHjvgBLO0gfC/sKMdxExUhbDekRBgu/TR5sLKrEUKIWqNGBY1PP/0Ua2vrIo9ZW1uj0+mM32dmZrJ27dqKLq1mcA2CBz813N/9IVzeVrn13I5jPxu+Nh8GljaVW4sQQtQiNSpoFEepVBqDRmZmJgMHDmTDhg2VXFU11nwYtHsC0Bumvd7JeA29HmJPw8Uthlkt5padAueuh8vWj5n/eEIIIYxq3KyT/1IoFOj1emPIaNiwIV9++WVll1U1FGgh9iQkh4JrA3BvBBa3Lql+i37zIfIQXDtlGK8xdi2oyvCfUuwpOPIDXNwMqRGGx+zrQbcZ0P5JMNclrNOrQZsDHsHg2cY8xxBCCFGsWhE08vLyjCHj66+/rr1jMgqDRdgewy1iH+Sm3XheaQFuDaFOM6gTDHWbGe7b1bkRAjIT4czvhhkoYBivEfEvBHQ3fWy9Hr4bCLmphu8trA3jPdKvwoYXwckPGvYt//cMhmADhrMxtfVnL4QQlaTGBw0LCwuSkpJqZ8goLVgAWDmCWwNIvAQ5qRB31nA7dVMbGzdD6FBZwpV/QHd9xolCBY3uL9tZAoUCOk2BXe8bljVXWtw4e+Lsb1jTwlyc/Q1fk0PNdwwhhBDFqvFBo0ePHixatIinn366doWM07/DX9OLDxb+94B/V8OtTjPD9FW9HtKiDWMnrl2/xZ6GpCuQlQAhO270Ua8ltBhlOENg51H2mnq9Ao0HGs5gRB4wbFzm7A/j14ONS3m86+I1GgAHl8CFTYZprjKdWQghKkyNDxpWVlZMnTq1ssuoWHlZsH6GIWRYORr27CgMFnWbG4LFfykU4OhtuDXqX7Sv+HOG0JGdBA37g0eTO6+tXgt4fBOcXGW45NLjJcMxzcnvHsO01sw4iD4MPrV8wzxx1/JjYtAmJ5fazsLZGbWnZwVUJETVVeODRq10cqUhFDj5wjNHQXUXO5Ba2oBXW8OtvCiV0OoRw60iWFgaNkc7/Rtc2CBBQ9yV/JgYrgx8AH12dqltFRoNQevXSdgQtZoEjZpGp4P912fVdJxydyGjJmk0wBA0zm+A3m9WdjWiGtMmJ6PPzsZz4ftYBgaW2C4vJISYmbPQJidL0BC1mgSNmubyNsPW51YOsmbEzer3NgxATbgAiVcMi48JcRcsAwPRBAdXdhlCVHkyKq6m2Xd9t9U2Y8HaoXJrqUo0ToYxKgDn11dqKUIIUZvIGY2aJPYUhO4EhRI6TqrsaqqeRgMNs2cubIB7nq3sakQtkRcSYvJ5q9AwXFP1FVSNEBVPgkZNUjg2o+lgw0BQUVSj+2HjTMPU2swEsHWr7IpEDWbh7IxCoyFm5iyT7XyAj9UQ2yoR7mJClxBVlQSNmuLaWTj5i+F+52mVW0tV5eRjmN4bewo2vQxDvi5+qq8Q5UDt6UnQ+nWlToPdv3srdT/5GlVaegVVJkTFkqBRExRo4c8poMuHhvebd5XN6u7eN2DlI3DqV0ABQ76SsCHMRu3pWeqMk/zQMxVUjRCVQwaD1gR7P4Grx8HaER74uLKrqdoa9oVh3xpmoJz6Bf6YDLqCyq5KCCFqLAka1d21s7BjgeF+//fAoV7l1lMdNB0sYUMIISqIXDqpzopcMukPLUdVdkXVR2HYWP2EIWyAXEYRVV9KJGQllt7OxtUwJkmIKkCCRnVW5JLJJ7IF+u2SsFFr1Ii9SVIiYXEHyM8qva3aBqYelLAhqgQJGtWVXDIpHxI2arwaszdJVqIhZDy8FNwaltwu4SL8PtHQXoKGqAIkaFRHcsmkfEnYqNFq3N4kbg3Bs1VlVyFEmUnQqI5q0SUTzZlVN91fDg4PQb2Whh1Zy5OEjRpP9iYRonJI0KhuatMlk/B92B761Pit3cHP4OwisLAGzzbg2xGaj4A6TcvneBI2hBCi3Mn01upm7ye155LJjvko9Drjt7nenQ13tDkQ8S/s+Ri+H1i+x/zv1Ned75dv/0IIUctI0KhuslMMX5s8WKMvmQDg1bbIt+qEs0Wft3GFe6aX/3GbDoZB13fB3fU+ROwv/2MIIUQtIZdORNXVeRq6v78AMgBQ5qSCh7shZAUPAb+uoDLTf8KtHoGQ7XBylWEE/+Q9hjExosYrbbfV0p4XQhQlQUNUXbauZLabBrwBQEr/z/Ho9Zj5wsV/DfjAcDYjJRzWvwhDl1bMcUWlKOtuq2CYBmvh7FwBVQlR/UnQEFVaTuOHKQwa+fXaV1zIALB2gKHL4Nv+hvEaDfpAixEVd3xRocq62ypU8YW9hKhiJGgIYYpPB+jxEuyYB+teMHzv7F/ZVQkzKctuq0KI2yODQYUoTbcZ4NMJ8tLht4mGBdOEEEKUSa0NGpmZmfz888988sknXLlypbLLEVWZygIeXgJWDhB1EHYtrOyKhBCi2qiVQeP06dMEBwfz5ptv8u6779KiRQsOHDhQ2WWJqszZDx742HBfprwKIUSZ1bqgkZOTw4MPPshLL73E5cuXCQ8PJzg4mOnTb389htzcXNLS0orcRA3WfBi0GAV6neESSk5qZVckxF2pq9XimHQJYo6bvqVEVl6RotqrdYNBV6xYQfv27ZkyZQoANjY2vPTSS4wYMYK8vDwsLcu+h8b8+fOZO3euuUoVVdGAhRCxzzDldd9i6PVqZVckxB3RZMaxJuoqNpFPl95Ytp0Xd6HWBY3Tp0/z9NNF/2E1adIEnU5HRkYGLi4uZe7rlVde4YUXXjB+n5aWho9PDf2HuPsjOLgUvNpAUC8I7AUugTV/ddL/snaAni8bds+9uEmChig3dmkRhrMHJUm4WK7Hs8xNxUav52jHl2nT8n7Tx5Vt58VdqHVBY968eajV6iKP2draAqDX642PRURE4Ovra7IvKysrrKysyr/Iqub8evj7+pmb8zFwfp3hvqMPBPYwhI6AHmDnXnk1VqT6vQ1fr56A9GtgX6dy6xHVmlV2EgDBWz4ge/8Ck20t7GxR27iWreNSgoldWgQAGQ6+su28MKtaFzSKCwZKpWGoik5n2MDrxx9/ZM6cOZw9exaNRlOh9VU5iVfgj8mG+60eAxd/CNlpGAyZGgnHfjbcAOo0g8CeUP8+Q/ioqWc77DygXiu4ehyu/A2tHq3sikQ1prLSoVDpiNlf+kqjCmsrgqapUDuZaGTjarjU8ftEk321AbIUCvKsZGl9YV61LmgUR3H9F6JOp+PHH39k9uzZ/P333xIy8jJh1RjITTOsI/HgJ6BSQ/eZhuci9kHIDsMt9hRcO2247VsEAd3hgU/ANahy34O5NOhjCBqXtkjQEHdF5+pI0IB4DrV+ldYmLmHkhYQQM3MW2uRk04uKOfkYxlNkJZo87q7QM7x9/h2etfW409KFKBMJGtw4o/Hjjz/y2Wef8ffff9OwYcNKrqqS6fWw7nmIOwO2HjD8e0PIKGRpa7iEUHgZITMBQnfCle1wajWE7oIvuxhW1ezyTNHX1gT1+xjW07jyj2EBr4pcGl3UOGrbArR+9dAEB5dPh04+pY6nSE3NI9ZC/rsV5lcjp7fqdDoOHDjAvn37jJdDTFGpVAB88sknEjIKHVpm2LlUoYLh34FDPdPtbd2g2VAYvAie3me4hKLNMYztWNILoo9USNkVxrsdWDsZprhGH67saoQQosqqcUHj7NmzNG/enM6dO9OlSxfuuecesrOzTb7GxcWFkSNHsn37dgkZAJGHYNMrhvt95oJ/19t7vUsAjPkTHvoKNC5w7RQs623oMzej3MutFEoVBN1ruH9pa+XWIoQQVViNChrJycn07t2bSZMmkZ2dza5duzh+/Djz5s27pW1mZib3338/27ZtQ61Ws3LlSgkZABnx8MtY0OVD08HQedqd9aNQQKtHYNohaD7CsMjV/i/gi05wcUv51lxZGvQxfL0sQUOUICWy1MWwCmd/CFFT1agLdJ999hkPPfQQzz77LADdunXjySefZOPGjbz99tu3tM/KymLy5MmcO3fulimvtVKBFn57AtJjwLUBDF589zNHbN1g6FJoOdIw5iMlApYPN1xmGfghaEyPtFelhN7d8c1JprkKU1IiYXEHyM8y2Uxmf4iarkYFjc2bN/O///2vyGPt2rVjzZo1t7S1tbVlw4YNXLt2rfqEjKwkiDtnuK8s55p1BbBmqmEQp9oWRv4MVvbl13/93vD0ftg+z3Bm4/RvhgGlgz6/tW38BTjzJ5z9E5eQ0+VXQ3nKy4LD3xnGsOgLDOM0Gg+s7KpqnfyYGLTJySbb5IWEVFA1/5GVaAgZDy8Ft5LPlsrsD1HT1aigsXLlylsW2XJyciqyEBdgXGrc1taWwMDAiizxzuVnw/KRkBoBDt43TtuXB10B/Pk0nFxp+MX58BLwaFx+/ReytIV+7xoGUv463hBqCt0ULog7W/7HLi96vSEkbZ0DaVGGx3y7gG/nyq2rFsqPieHKwAfQlzIGC0Ch0WDhXPo6FWbh1tDkglgy+0PUdDXqv+7iVvJUKpVFZp78+OOP/Pjjj2zbtq0iS7s7ugL47UnDFuXWjvDYb2BT9qXSS+375pAx/Dto8kD59F2SwF6Gr8lh8PdbcGFj0XChVBuWOQ8eQsbli0AV2U8m6ghsetnwcwDDyqh93oLgITV3cbIqTJucjD47G8+F72NZyh8MFs7OpteeMKPL8Rnk6EvegC8yyfSlFSGquxoVNIqjUCiMZzRuXoyr2tDrYeMsw7LfKit4ZGX5nm3Y8nrRkNF0cPn1XRKNk+GvvISLsPtDw2M3hQsa3W8cu5GXue7G6/SlT1U2i7QY2DbX8DmB4dJSt+cNA2XVtXxRtyrAMjCw/NafKEdxGbl4ANNXHueMiaChtI7GNgDsNdXkEq4Qt6nWBI2bQ0a1ml2y5yPDmhYoDJc0/LqUX98xx+HAl4b7Q5dVTMgo1HoM7Hwf/DrfEi5uVuDoZ7yvSg0DmldcjXlZ8O/nsPeTGwP6Wj4K971R+roiotZLy87HA3ixbyPcG3YosV1o2gVePQge9pWzb1J0ZjhnE+1KfN46NZRqcoFZVFE1PmioVCoSEhKqZ8g4vsJwaQGg/wIIfqj8+tbpYMOLhrMEzYZBs4fLr++yuOdZw600ihszsNUJ54EHzVfTzSIOwOonbozD8OkE/ecbdq8V4jb4uGio71XyjBKldcm/5M3J3tIRvU7N4jNvsfhMye2a5ObxC5AcdRBTo1wsEitp0K2o8mp80AgKCiIoKIi1a9dWr5Bx+W9Ye30Niy7PQqfJ5dv/ieUQdQgs7aDvO+Xbt5moEs5XzIH0esNnnxZ1fRzGXAh+WMZhiBrFzboumVdm8NnoBgR5lBx2YqIPkvXHczhvmGmyP+eMSrq0Kaq8Gh80GjRowJkzZ4zLjFcLV09cXzRLazjb0LucB0NmJ8PWNwz3e75cbS4DWCReqJgDhe02jB+xtIPJewxjSoSogfRaJwIcGtHU1fQaHoO96/F1l3kEOgaU2Cbt8N/w4YvlXaKoAWp80ACqV8hIDoP/DYe8DMMOqA99AcpyXsD1n3cMc/zdG0PHcj5TYkbqpIuGWTJKM/88Dy0zfG0xQkKGqHLKsi5Iec+yibWwIMejEbg2LbFNgZNcOhHFqxVBo9rISoKfh0HGNajTzLBolkU5DxCLOQ6HvzXcH/BBtdpVVaHNhoRL5lnjo1B6LJxfb7jfboL5jiPEbbJwdkah0RAzc1apbRUaDUHr11XalF4hbiZBo6oo0MKv4yDxkmFBrtG/GtbMKE86HWyYeWMAaEC38u2/IsSeNG/QuLTFcMmqbguo28x8xxHiNqk9PQlav65MK6HGzJyFNjlZgoaoEiRoVAW6AsMMkMLlv0f/Cg5m+B/EieWGxaYs7aDvrXu/VAtJZj496xJk+JocBvk5oLY27/GEuA1qT08JD6LaqVG7t1ZLuRmw8lE48p3h+8GfQ52Sr4PesVsGgFbT/1klh5m3f9/OhjNKuWlwabN5jyWEELWABI3KlBYD390PFzeBhTUM/8Gwq6k5VNMBoLcwd9BQKqH59Z/BqV/NeywhhKgFJGhUlqsnYel9hjEHtu4wbl35Lsh1s2o8APQWSRWwbXzzEYavF7dAdor5jyeEEDWYBI3KcHEzfNsf0mPArRE8uQ182pvnWP9dAbQ6DgC9WUasYWlwc6oTDO5NoCAXfpsAOSXvUyGEEMI0CRoV7cDXsGIU5GdCQA+YsAWc/c13vOP/q3YrgJZEZ2lruJMSYd4DKRTQ7x2w0MDlbbCsj/kHoQohRA0lQaOi6Apg40uGnVj1OsOmYo/9Zt4FobKTYdscw/27XAFUm5xMxq5d6LXaciru9hXYeRnumHucBkD93vDERrCvBwkXYOm9ELbH/McVQogaRqa3VoTcDMMp+IubDN/3fhPuec78e2eU0wDQrKNHiZo+nYL4BGy7dMbzww+xcDa1vZJ5FDh4QcLligkaAJ6tYeJ2w6ygmKPw42AY+BG0HVcxxxeiAlyOyzD5fGia6eeFKI0EDXM4sQrsNIb7ej0cXGIY9GlhDUO+Nt+gz5vFHIdD3xju3+EAUL1WS/LKVVx77z3Izwcg8999hA0fgffiRVg3alSOBZdQQ3q68X6BnRckAMkVMCC0kEM9eHwD/Pk0nPkd/noW4s9D33fLf2l4ISqQs60lGrWK51YdN9lOaR2NbQDEpefS1LViahM1iwQNc9gwA6z+c7bC1h1GrTDfoM+bJVyGVY8B+jsaAFqQkUHK6tUk//gT+TExANj374/r+HFEz5xFfmQkkZMmE7R5E0qrcl4i/eY60tPJmz3b+H3mvjjynZSor/wD+dmg1pjt2EWoNTDsW8OZoR3zYP8X4NUWmg+rmOOLqicl0nC20ASrlMsVVMyd8XLSsG1GD5Iz80y22xV2jC8vQ3p2fgVVJmoaCRrmEHgv2Nx0BsHGDXq+ZN5Bn4WunoCfHoasBHCtD/3mlfml+dHRJP30Mym//oouMxMAlZMTrpMn4TJuHAqFgoBffyHkoSFoY2NJWb0al9GjzfI29AUFRM+YgT4yyvhY7vbTXLaog6NvHC4WT2M9+TuzHLtYCoXhZ5ibBvsWGS6DSdAwq+xz51Dblbx9eVk2FzOLlEhY3AHyTc9+8gGy9FYUWLtUTF13wMtJg5eT6cAemmZTQdWImkqChjmM/BEcHCr+uOH/wvKRkJtGjkVT0rUPo1ixFqWNjeFma3v9q02Rx/IiIkj68UfSN28xTIcFLAMDcRk/DsdBg1Ba31iGW+XkhNukp4id+xaJS5biNGyYWc5qxL2/kMxdu8HK0viYokkTuHCB1DAbUj/Zj822AbhMm4Vd9+4oKuoyRqMBhqBxZbvhs5LLJ2YT8dgY7ErZeVmh0VT8eKGsREPIeHgpuDUssdnl+AzGrrjCksJBzELUUhI0aoqLm+GXsejzc0i6FkzcnnTQfnvb3dh26YLL+HHYdu1a4i9vx6FDSfh6idnOaiT/+itJP/wAgPrZZ+GppwCwmvcu/gUFJL0/i7QjYWSdDiVr8hQsAwJwGTcWx8GDUWrMfDnFu71hqnBWgmHcjWcr8x6vFqv7ztu4NTO9sV15b4d+W9wamvz55+hTiUHWYBFCgkZNcPJX+HMy+Zk6rp5sSGaoYXdH2y6dsahbD11WFrrMTMPXrCx0WZnoMg339VlZKCwtcXjgAVzGjcO6Ucl/oRVSWlqa7axG5sGDxM59CwC3adOI69KlyPOaFi3w+nE9Hp8PIGn7WVJC7MkLDSX2zbnEf/Y53osWYdOmdbnUUiwLSwjoDhc2wJV/JGiYkaW/P5rg4MouQwhxlyRoVHcHl8KGmaRHWXH1iAcF2RkorK2p88orOI0YjqKUKbR6nQ50OhQWt/efgjnOauRFRhL97HTQanEYcD9uU58m7uzZWxsqVaif+IE6eV1xC44hVduTpCOZ5EdFETllCv7/+xmr+vXvup4SBd17I2h0e8F8xxFCiBpALjBXV3o97FyIbu1Mrh5yIGqPCwXZWqyaNiHg999wHjmi1JABoFAqbztkwI2zGgCJS5aiy8297T5uVpCeTuSUKRSkpGDdrBn15s0zXb99XXh4CSo1uGi2Ezh/PJqWLdGlphIx8SnyY2Pvqh6Tgu41fI3Yb1gjRQghRIkkaFRHOh1sfpWc3xcQutmNlCuGpbldJjyB/8qVWAUGVkgZjkOHYlG3Ltpr10hZvfqO+ymcYZJ3+QoWHh54L15cZABqiYLuhe4zAVBumYn3vJlYBgSgvXqVyIkTKUg10/Vxl0Bw8gNdPoTvNc8xhKjGLBIvGNbyKemWElmJ1YmKJpdOqpsCLfo1z5D0yxriTrqDToGFhwee7y3AtnPnCi2lvMZqFM4wUVhb4714Meo6HmV/cc+XIWIfhO3GYvMz+H71E2GPPU7upctETp2K77JlZQstt0OhgPr3GXbEvfw3NOxXvv0LUQ3prJyM953XjgM7E3/Hqm1g6kFw8jF/YaLSyRmN6iQ7hfwlDxP56WbijjuCToFd7/sIWPNnhYeMQnd7VuPmGSae8+ehaW56lsEtlCoYusywIFrcGdRhv+GzbClKe3uyDx8h+sUX0RcU3HZdpSq8fHLln/LvW4hqSGdXx3g/edAP8NTO4m8PLzVMDy5lwTNRc8gZjWok/eOnuLryPAV5Vigs1dSZ/VqZBnya081nNRK++BKHAQPKvK5B0k8/c23+fMAww8Th/vuNzy1ZsoTjx4+TlJRkfOztt9/GxcWw+FGrVq146vq0V+zrQv8Fhv1k9i3CeuYLeH/6CRFPTCBj299k/rsPu25dy+kdX+d/fbXVxEuQlQQ2VXdRJiEqmta1EXjKjCFhUKuDRm5uLtnZ2Tg5OVV2KSbpsrK49u7bpPx2ClBhFeSD1+dfVdhYjNI4Dh1K0v/+R97lK1x7dx5eHywstp02IYGso0fJPnKUrCNHyDl9GgCnESNwm/q0sd2SJUuYNGnSLa9ftWrVLY8Zw0bwEFj/AuSkwtUTZO4/AIDK0RHrpk3u9i3eKiPO8NVCA1aVsDibEEJUE7UyaOh0OmbPns3HH39Mbm4urVq1Yt68edx/01/UVUXO2bNEz3iRvFDDRmIurdW4f/+XWfcYuV1KS0s8580jbNQjpK1bh0P/ftjdey95ISGGYHH0GFlHj5IfEfGfFypxf+45XCc+WeSszPHjx8t03CLtlCrw6woX1pO1ZRWJS9cBUPett7BwNcNOUDFHDV/rtQRVrfxnJIQQZVIr/w85d+5cdu3axbFjx8jOzmbevHkMGDCA119/nbfeeuuu+z937hx2JvZoKAu9TkfB2r/Q/u9/oNWi1ChwbZVA5oCJJFyugps1qVTkD3qQgt//4OKMF0Glgoz/TP1UKFD4+qJs0gRlk8YomwZzzc2Va/9ZK+PmyyWmJCUlcebMGeP3GlV9bJL1XF27Dl1BAcpevYj09oKb2pQX28NbsMnQkaXwJdMM/ddEGf/970EIUSvUuqCh1+v57LPP2LhxI02aGE6pr169mg8//JCZM2eiVCp58803y9RXbm4uuTetH5GWlgZAp06dyr1uAE4CPy4AFpin/4pw8QJs21ouXa1atarYyylwzvDl0kVY8nW5HKtky67fhKhayrLpXKUu4S5qjVoXNACysrJITCw64nnGjBkUFBTw8ssv061bN+67775S+5k/fz5z5841V5lCCHHbLJydUWg0xMycVWpbhUZD0Pp1EjaEWdW6oKFQKOjTpw9vv/02/fr1w+KmVTFnzZrFrl27mDt3bpmCxiuvvMILL9xYgjotLQ0fHx/2799/25dO9Dk5aL//noLNWwx1BgSgfuF5lJ51cf3lQZTZyaTe9z55vt1vq9/q6O233y7hTEVRX9VvQId27VB4e6OoVw/tzz9DRgYO9dNRPfYo2S0fN0t9Fgnncf5rPDorexIf2WJYV0OUKiMjw3xn+4SR2tOToPXr0CYnm2yXFxJCzMxZaJOTJWgIs6p1QQPg3XffpUOHDjzzzDN8+eWXRZ6bMWMGffr0Qa/Xlzpt1MrKCqtiBmU2adIEh9vYJj7/2jUip0wh9+w5UCpxeeIJ3J+bjtLSEs6vB1Uq1KmDR98nQaUuc7/VVeEU1tK45edjfewYHDsGgCVgHVgX/7axKPIvgrk25Dq037AYUWAH6payu6i4ofDSojA/taenhAdRZdTKoNGyZUuWLFnC448/jk6nY/HixcYzGyqVijp16lTY2hQ5584ROXkK2mvXULm44PXBQmxv3rH02M/Xix5VK0IGgF0ZfyEd6diBex96iNwrIeSFXEGXnUO9Fyeg+OthiDwA2lywMMPsnMIZJ15ty79vIYSoYWpl0AAYN24cSqWSp556isOHD/PSSy9hY2PDiy++yJw5cyqkhvTt24me8SL6rCwsg4Lw+forLL29b2pwDS5uNtxv9ViF1FTZ8qKiue/kKT5VKMjT60229b/vPpwfeaTog3o9/OMOmfEQdRj87yn/IqMLg0ab8u9bCCFqmFobNADGjBlD27Zteeutt3jmmWdwcXHhlVdeYdy4cWY/tnFVTJ0Om86d8P70U1T/vdxyaTPoC8CzDXg0NntNVUHCV1/im5fHmjZt2dC6FXHp6cbxGiNHjix+ZdCbKRTg1Q4ubjSceSjvoBG2B+Kuz2rxlKAhxB1LuFh6GxtX2Q+lBriroJGTk4N1eW9YdZf0ej3ffPMNP/zwAzqdjunTpzNixIgS2zdt2pSVK1dWXH0FBVybv4Dknw2XRJyGD6PuG2+gUBdzWUR1/bS/1d2tyVGd2LRtR+rq3wjQ6fjwnXe4mJBgDBqvv/46waWNu9Dp4OoJw323RuVbXFIorBoD6KH5cHCoV779C1Eb2LgaNlX7fWLpbWXztRrhjoLGF198wdtvv01sbCz29vb069ePV155hTZtKv8vvIkTJ7Jv3z5Gjx7NwYMHGTVqFK6ursXOItm8eTPdu3dHo9FUSG26zEyiZ7xIxo4dAFhMe4KLg9qz9eIKziWEEJeVQD17J1ysXXC0cqRB+jW6AzlJocSkhuBk5YSjpSMqpapC6q0MjoMHkbJqFdnHjxP33vsw4Ynb6+DqMUiPAUs7CCjHGTo5qbBiFGQngWdrePCz8utbiNrEyccQHkrbVC3hoiGMZCVK0KjmbjtoHDhwgOnTp/PGG29wzz33EB0dzYYNG+jSpQtffvkljz9unimFZbFhwwZ27drF0aNHjdNLu3TpwkcffXRL0IiLi+Phhx+mU6dObN68ucg017t1Iv4Edjk3zkJk52cTE3Yaz7k/4ByRRJ6FgkUPqthv/yNs/7Hoi+Nu3PXJz6c7oEuLYvAfgwwra6LAycoJb3tvfB188bX3xcfeBz8HP3ztfXG0cqzUTdbulkKppO4brxM6bDhpGzZQ0KH97XVwfr3ha/3eoC6ns226Alg9AeLPg309GLUcLG3Kp28haiMnHwkPtcht/3Y9cuQII0aM4PXXXzc+NmbMGI4cOcL9999Ply5daNSonE9Zl9HXX3/NRx99VGQNixEjRrBo0aJb2np4ePDbb79x5MiRcg0ZAJO2TkKluXHWwe+anpd/LcA5HVJs4P1hSi57gV6nRpfnhi7PDfLdUekcyNdno1BlolBlEaNKA65io9fjjoZ4ctCjJzk3meTcZE4lnLrl2PaW9vja++Lr4Et9p/o83OBh3DRu5fr+zM26aVOcR40ieflytEuW3t6LC4NG4wfKr6Atr8PlrYYN1EYtBweZNiiEEGV1279hGzRowJ49e255vG3btowePZrVq1cze/bscinudnXs2JH+/fsXeczb27vIMuE369+//y3ty8PTayzQqA1BQ6HX0yosD00+RDrZ8PHA3li4N6W3nT9N3b0JdLcjwM0WHxcblAoF52PT2B+SxP6QRA6GJpGoP4SrIh3LC0+QrvfCRpNLc18F7RvocXJIIyojkvC0cCLSI4jLiiM9L50ziWc4k2jYf2PpyaU80uQRHg9+HGfrsm3fXhW4T3+WtE2b0EdFlf1FCZcNZx2UFtCgT/kUcuQH2L/YcP+hL2SmiRBC3KYyBY1NmzZx6NAhWrVqRcuWLUlOTmbt2rUMGjSoSDuVSoVKVXnjB1599dVbHrOyskJ/0zTJ+Ph4Vq9ezZQpU8xWR8fLOdj953PQt2lPl88+oa+b6cWogj0dCfZ0ZELXAHQ6PbmLfSHxDAP8dMTGWZOSpeLABThwAQLd3Hmia29eutcbjaWKbG02UelRRKRFEJEewbbwbZxMOMl3p79j1flVPNb0McYFj8PBsupva65ydMRjxgzSX3nF+Ji+lOmuXLh+NsO/G2ic7r6IsD2GrecBer4CzR6++z6FEKKWKfMZja1bt/LBBx+QlpaGjY0NmzdvZtCgQTz00EN4e3tz+vRp1qxZw47rAx2rCoVCgU6nAwwh47777mPo0KFmPab7rJk42Ny4hm/h7Ix9nz7FzywxQalUoHH1gcQzPNvOhmlt+nA+Np21J2JYfiCckIRMXvvzNB9uucCYTn6M6exPA+cGNHBuAMD44PHsjt7NomOLOJd0jiUnl7Di/ArGNR3HY00fw1ZtW67vu7w5DnkIxQ/fGzZHA/Jee53Ml1/GpmOH4sehGC+bDLz7gxfOMNFpIXgI9Hjpzvsq0MLuDyF0Fwz6DFyD7r4+IYSoJsoUNAovMej1ekJCQjh27BjHjx/n+PHjzJ49m5iYGAAaN27Mjh07GD16tFmLvh0KhQK9Xl8kZJh7QS6XkSNvawlykwrHA6TFoFQqaOrpQFNPB565tz6/HI7k272hRCZl89k/l/lqVwhDWnnxZLcAGtSxR6FQ0N27O129uvJPxD8sPr6YyymXWXR8ET+f+5kJzSYwsvFINBYVM+vmdimUSixfeQX++gsA/dmzRIwfj027drhNm1Y0cKRfg8iDhvuNBtz9wdfPuDHDZPAXd76fSdpV+O1JCL9+uXH9CzDmT9kfRQhRa9zWGA2FQkFQUBBBQUEMGzbM+HhCQgLHjh3j2LFjxe79UZmUSiXZ2dkVFjLK3U1B42a2VhY8fk8AYzr5seXsNZbuDuFYRAqrDkey6nAk9/tYMUwfg4O9BgdnR5q7OPBz/XfYk3KY766s4HJ6FB8e/oAfzv7A+ODxtK3TlkDHQGzUVWs2heKmwKa6vz+Kf7aTdfiwMXC4T38Wm/btDQt0oTcsouXodXcHTbgMV/4GFDDs2zufYRKywzBbJSvBMN22IM/w2MXN0Kj8xwYJIURVVC7TLdzc3OjTpw99+pTTALxyZGlpSWpqavUMGbGn4ewaw/3M+GKbWKiUDGhejwHN63EkPImlu0KJ276TsRuW45SXCYAOSLp+8wUKPwUdkGMZS7zjAl6/X8VlLwWetp4EOgUS5BhEkFMQgU6BBDoGYm9pb973Wgbqp54i6JVXSFyylJRffyXr8GHCx4zF4f7+eHgfQg3QuBzOZpxebfjaoC+4BN5ZH3o9rBoLuamG7/u+A5e3wfl1cPBrCRpCiFqjxi9B3qtXL9asWXPLwNUqTZsLuxbCno8NYwSsHKH9k6W+rI2XA28l7SFx3zLQ60l09CDZ2h5Vbg6W+blotLlYF+RhozXMwlECNnngFw+vrtIxd7SS8DoxxGTGsCe66MwiDxsPmro05dk2zxrHgFQGdd261H3jdVyfmkjCV1+R8suvpG3cRLqFDrcWbjg/NYS7Ho6cGmn46tPhzvtQKKDz04axGQV5sO65G881HXxX5QkhRHVS44OGUqmsXiEj4gCsnXZjH4DGD8CAD0pd7jr/WhwxL75I1qFDADiNGkmjV15Bef1SVq62gMSMPBIycrmalk1SYjrJiamkJ6bQYsXneEVd4rWfrXip92BiPLQora6htIxDrYlHr0olLiuOuKw49l3dx0sdXmJYg2GVujCYum5d6r35Js79uhI7cwrZCRbEH7Ukod/D2PXsgcODD2LXo4fx/d+WzATDV9u7XH+k58vQegzs/QSOfG8IHB2nQNvxd9evEEJUIzU+aFQbuRnw91twcAmgB1sPGPhBmf76zdizl5hZsyhISkJpY0Pdt9/CcWDRmRdWFio8nTR4OmkAJ+BGcCkY0YmI8Y/DmTN8/u9m/prwJrszrbgQlU62HlBmo7SKw8rtb7C7yFv73uLA1QPM6Tynci+p6PVYX/ocv/viSE1vSdJFR3IvXyZ96zbSt25DaW+Pfb++OD7wIDYd2qNQKsvWb2HQsCmHhc4cvWDAQuj6vGEztsBed9+nEEJUIxI0qoLcdPhxMEQfMXzf6jHo+zbYmF5zQ6/VEr94MYlffQ16PVaNG+P9ycdY+vuTkZfBpZRLOFo54qHxwFZtW+IZCJW9PT7LlhIxdhxcusTIFQuY9b+fyXZ243hECkfCkzkakcy/V3zROu3CymMzm8M2cyr+DB/2XEgzt2bl/YmUzYkVELIDhdoap9nf4egSSO6FC6T+9Rdp6zegjY0ldfVvpK7+DYs6dXAYOBDHBx/AqnFj02djsgrPaLiXX60OnrKiqBCiVpKgUdnys2HFI4aQoXGBYd9A0L2lv6yYSyWa515ka+x5/lj/FUcS/0arzzG211hocNO44a5xx93GvchXb3tvWrq3xPfbbwh/bAx54eFEjH8cv59/ontDd7o3NPzCvXQtnbfWubM3LACN13JiiGL0hjG80OZ5xgaPqdhLKRlxsOn6Yl49XwbXIBSAdePGWDdujMeMGWQdOkzaur9I27wF7bVrJH37LUnffotV48Z4ffQRVoEBxfddXpdOhBBCSNCoVNo8+GUchO0GS3sY87th3QYTcvILOPnHZqwXvoU6PZU8S2t+6fkof9lp0a4Yhcom3NhWp7VHochHocohW5tNZHokkemRxfbbwLkBTzV/ip7fLiNqzDhD2HhiAr4//oCFs2Hp8gZ17PnxiQ78c96fuet9ibP6CbXDGT44spCtYXv4vPd7FbfM+caXICcF6raAzs/c8rRCqcS2YwdsO3agzuuvk7FzJ2l/rSNjxw5yz58nasoU/H9ZhcrRsegL83MgL8NwX4KGuEsKixRC0y6gtLYrsU1IakgFViRExZOgYQZv/vsmVrY3BiE6WjnyTOtniq7EqSuAP56CS5sNm3WN/sVkyNDr9aw9EcOxD79k+KHfUaLniqMHC/sFkeD3G0qLTMNsC70Su4LWNNT0w8WqMbsuJZCYlYHCIh2lRRpKdTp+7lq83PJxsMsmW5fM6cTTXEq+xMxdM/F38GfK3NHUf/V7ci9dInLiU/h+9y0qe8NYDIVCwX1N6tC1QX++39uIzw//CC5/cSJxH31WDebdrvPpF3SPmT7Z6y5shDO/g0IFgz4Hlen/jJWWljj06YNDnz5o4+MJHTmSvPBwome8iM/XX6G4ebn4wssmSjVYVf2l2kU5S4ks2/blZZCQE4tt0Ie8ejC/1LYaCw3OVtVnLyIhbocEDTPYFLapyO6tAC7WLjzV4inDN3o9/DUdzvxh+IU28mfw61Jif0fCk3ln/Vmc9/zNzKO/AbCzlStL+iSRb5GEEnC19mBog2GMajwMd5sbYwsKdHpORKXw97lr/H0ujvOx6VxOg8tXDM/7u9rQr7Etth4HWBOykrC0MF5K+5g2j7rz4nc25Jw+TeTkKfguXYLypmXVrSxUTOpRn4fbzOaNDe3YlfoxuVYJzNg9hRUnR/LFAy9iozbD4m05abDu+v4jXaaBZ6vbermFuzs+ixcT9uhoMvfsIe6DD6nz0qwbDQrXK7F1k9U7a5uUSFjcAfKzSm+rtgEbV5NN0vNSUSjzmRr8Bt0Dgk22dbZypp6d6ZllQlRXEjTM4NnWz6KxMyzrHZIawu+XfuevK38xsflEFACbZ8Oxn0ChNIzJaNC72H7i0nN4e905/joRQ+u4izx/bBUAazsq+Plew0JQXTy7MKLRCHp498BCeeuPU6VU0MbXmTa+zszs15jIpCz+OR/HtnPX2B+SSFhiFmF7s7C0qM8jHT/D0eMwa8KWc5R4Xh2q580VCjhyhLCnp+D/9ZJbpou621vx5chBHAprz3N/v0GaxX6OpK2k+//+YUrrCbSo0wAPGw88bDzKZ6nzbW9Cegw4B0CPl++oC+smTfCcP4/o554n6bvvsGrUEKeHHjI8mXn9r1m5bFL7ZCUaQsbDS8Gtoem2Nq7g5FOmbr1s/Wjq2rQcChSiepKgYQaPNnnUuNdJZn4mG0I2EJYWxkdHPuJ5n34oC7cdH/S5yemr7643hAy7vCxeO/o/LPQ69jRV8L9eSnr69OTFdi/i5+B3W7X5uNgwros/47r4k5GrZdfFeL7fG8bBsCR+2BsLeNPE8w2a+J/ivPVa3h0Rx+srCmD/QXaMuA+HDp3xtq6DIr8AfX6+8eaZn8+qfBvOx/hzgQh+6X6NT47PL3Jse0t76tjUwV3jjoeNB9723vT160ugU9lW31TkpsHhbwzfDPjgzpcGBxz69yfr0UMkL19O3Hvv4zh4sGEwa3aSoYHG9IwfUYO5NbztM2VCiJJJ0DAzW7Utz7V9jgUHF/D9me9JTo3ibWtHFDmppY4BeLiNNzsuxDP89N/Y5GSS5F6HlQ/r0RckcTzuOAnZCbcdNG5mZ2XBgOb1uL9ZXXZcjGfprhD2hyRyLiaHczENQDEdrc9pFo3axjMrYql3IREurCPZRJ9e129No+x4eagHGQ5ZKNSGU8jpeemk56VzOeWysf3i44tp6d6SIfWH0D+gv8kdZfWW9uDkBynhkHHtjt83QEF6Ohk7dwJg27XrrTNmFGVcc0MIIYRJEjTM4JkVR7HU3Bhl7qRpxvMt5/D5yXdZE7WNTq51eSA61bBiZJMHSxwL0KOhO1tHBhH3y14APq7/AInhftSrv4KrOZd4csuTzO0yl0FBd7fyqUKhoFcjD3o18iAxI5etZ6+x8XQsey8nEBXRiihaEnnvER6M3Y3CIoEcVR5aFWiVYGVtRwP3xjSq04w6jt4oLCxI+Oor6l69yk+7vTn07PusC0njYHgMemUqCnUaCos0XB2zcXG5ytW845yIP8GJ+BO8d+g9+vr1ZUiDIbTxaFPML3+FYVXNv+fC4W+h9Z3tEqzX64mdM4f86GjUXl7UfeP1G0+qLA1fC/Lu7MMUQghRhAQNM9h+Ph6lVWaRx2xP2fFE77f4NepdPihIo69CiWX0EQjbAwHdSuwrf8liVAVatK3bkd68PSlxGaQcH0Oj5uuIyT/I7D2zCUsNY1rraSjL4a9wVzsrRnXwZVQHX1Kz8tl2zhA6dl3qwIc27YACVLYh2LqcQmV3Ci3ZwDHgGAG2AQwIGED/z95BP2kmBRfOc8+383lk2VJSdG3ZdvYam87EsudSAtdSdVyLAAe7B+jROpzwvB2EpYWx5soa1lxZg5+DHw/Vf4hGeY2KFth6DGyfB9GH4eoJqNfytt9j6u9/kLZhI6hUeH34gXFGDSBBQ4iqpiyzfG5jzIyoeBI0zOCNB5tiY3vjl9efx6I5GJbE5xuU9Gn1KpdtP+YPu1RGpmeQuWMetgEbi+0n+/hxwy9EhYIGc2azrkFDlu4O4f1NF7hw8iHat67H+Zw1LD21lIj0CN655x2sLazL7X042qgZ2taboW29ycjVsuNCHNvOXuOf81akRTYAxYNY2F3AyvEEKrtzhKaGsvj4YhYD46Z154GPDpJ95AhRz07HZ/EiRrT3YUR7H9Jz8vnnfBxf7rjC+VhYt7sxjeu15+Ueei5m/cOm0E2Ep4Xz6dFP0cZrixZl5w5NB8Hp3wxnNR789LbeU25IKLHvvAOA+7PPomnVqmgDCRpCVA02robZPb9PLL2t2gamHpSwUUVJ0DCDEe18jINBDd97s2j7ZT77+xJbj6vw83iGTXW/YFj6IWzD/+XU6ZU0bzaqSB96vZ5r7y8EwHHIEKwbNwbg6Z71cdJY8uofpzh0rDO9O3hyJHMpm8M2E5MRw2f3foabpvxnTNhZWfBAC08eaOFJfoGOQ2FJbDsbx9ZzDkRGNQNlDhb2Z1A7HMfC7hI/5O/i2DBLXllhQebu3UTPnIXXRx+iUKmwt1YzuJUXA5vXY/nBCD7ccpHzV9OZvRIGtXyIlf2nczxpJ39e/pP98fuNNcRlxRFMMLR7whA0Tv4Kfd4G67Ktd5Fz4SKRkyahz87GpnMnXCcWsyOuSm34WlD62gdCCDNy8jGEh7Ksa/L7REM7CRpVkox4qwAWKiXP9W7IiomdqOtgTXicFXvPT2GPfV0AIjbNYEPIhiKvSd+yleyjR1FYW+M+/dkizz3a0Ze3HzLsL7LtoB/3Or6Go6UjpxJO8ej6R7mYXLYFhe6UWqWkS5AbbzzYlF0ze7H5ue682LsFwQ73kR05gczQpynI9uVkvTzmP6RDq4L0zZu5+vob6PX6Ip/L2M7+bH+xJ4929EWhgLUnYhj46UGuRjdnSZ9v+bTbjTMWrx14jYi0CPC7B9waQX4mnPqlTDVn/vsv4aNHo42NxTIwEM/33it+kzU5oyFE1eHkY5gBZOpW2lRkUekkaFSgjoGubJzejT5N65CXb837iVMA6JeRwaf/zOC709+h1+vR5+UR9+GHALg+8TjqOnVu6WtMJz/mDjIsAvTbv9bc5/gOfg5+XM28ytiNY9kdtbtC3pNCoaBRXXum3duANVPvYf8r9zGgYUeywiaTHT2SswEufDpIiU4Bqb//zvk3Xy4SNgBcbC2ZN6Q5f03rSls/Z7LyCnh/0wX6fbyL6Lgba28k5CQwftN4QtJCDWc1AA59a1gAzYSUP/4k4qlJ6DIysGnXDv8Vy1F7eBTfWIKGEEKUKwkaFczZ1pIlY9ry1uBgLikbsbugGRbA2LQ0PjryERO2TOD8N5+SHxGBys0NlycmlNjXuC7+vP6AYSGgH3Zl0s32LdrXbU9mfibT/pnG/879r4Le1Q11Ha35/JHWfDSiNda57Um+8AIH6vRl6YDrv8BXrWX1qyNIykm65bXNvBxZPbkzH41oibu9FWGJWcz+84zxeR87H+Kz43l80+Nc9mtnWLo97gxEHiy2Fr1eT/zixVx95RXQanEYMACfb7+5dX+Tm8mlEyGEKFcSNCqBQqFgbGd//nz6HtbYDQfg4bQcXAtUnA09SPrX3wJgO/kJVHYlrysBMKFrALMHNAHgy3+u0kL1IkPqD0Gn17Hg4AJWnF9h3jdTDIVCwcNtvNk4vRttfeuQHtubNZqZrO5j2C212R+n+fil3vxw5gcKdAXFvvafGT14qnsgFsobU1yfCHqZJi5NSMpJYvzO50hucH2X28Pf3lKDXqvl6uzXSPh8EQCuEyfi+cFClJaWpouXMxpCCFGuJGhUoqaeDrz13FSirBuiQUvP0Hb0PVQP+xyIcIPxFj9x9NrRUvuZ2D2Ql+83DBb9ZFsoaZFDeCLYMFJ73oF5zNo5i6sZV836Xorj42LDqqc68Xzvhih1LnxjO5XNnbsCMGJrNov2LmTaP9PIKNwt9Sb21mpeHdCEJY+1NT4285dL+Gufp7FzMKm5qbyYdgIA/bm/bnl9+pYtpP7+OyiV1H1zDh4zXih+TMZ/yRkNIYQoVxI0KpmNlRrv1n0BqK/OpckFw/TUf1o5EpsbzxObn+CbU9+g0+tM9jO5RxCvDWyCSqngz+Mx/LWzGff7DkOBgo1hGxn05yAWH19MVlk2jCpHFiol03s34JdJnbC3suBL1wGGx3WgUVqzJ3oPj214rMTt6/3cbpzRKdDp+eVAEueOPoKPdRustLkAxKuUJGYXHZmucjFseGXh5obTyJFlL7jwDItSZbqdEKJqSbgIMcdN31KK//+MMC8JGlWBnWGw54j6apqnhAPwr348+amtKNAX8MnRT5j29zRSclJMdvNkt0BWTOxEHQcrQuKzWfN3B3ravUuwS2tyCnL46sRXDPpzEOtD1t8yINPc2vq58PWYtrhpDQuZFVio+XLw93hoPLiSeoXR60dz5NoRk318PLwlLbwdycix4Oyx4bRPNSy/vtdCx8NrHy4yAFbTsgWo1Wjj4siPji57oYWXTFSlXGIRQlQNN6+3saSH6dviDhI2KoGso1EV2BlmQORfjkSlzUfp5s6AAd35fp8vOVmBWNVZy+7o3Ty0Ziif9PqQVh6tSuyqQ4AL65/txgu/nGDXxXjWHlKiUIygVePOJFv9zrWsWF7e/TIrzq/g5Q4v08ytWQW9SehS343ZnevAJkhQ2xJ5xZ7lA5fz7PZnOZt4lie3PMmcznN4qP5Dxb6+hY8TI/s25a+TMby/6QLNstJACScs65CUk8TTfz/NyEYjeaHtC9hobNA0bUr2iRNkHT6Mpbd32YqUoCFqmbyQEJPPW4WG4ZpasX+Y3BZZb6PKk6BRFdi6A5B5KQEAu86deGVgU8Z3DeDTbd6sPuWNledyEolj7MbxPNH0aaa2eRx14XiC/3Czs+KHx9vz75VEvtkTyj/n4zh2zhcUU/HyO0S27RZOxJ/gkfWPMChoENPbTMfDpoTpnuWsm7uKKCDFyp45a04TmRTI5z2XsuDwm2wN38rre18nJCWE6W2moyrm8oVSqWBwKy/6NXJC/X4o6GH71cnkFRzF0uVfVl1Yxb6YfczrNo+67dqSfeIE2UeO3NgGvjSFYzNK+GyFqCksnJ1RaDTEzJxlsp0P8LEaYlslQpOKqe22OflIeKjCJGhUBdcvnWSFZwFKbDt1AqCeo4YFQ1vwZLdAFmxqwp6Ur1A7nuCbs5/zw9nvuKduH6a0GUlTt6a3bECmUCi4p74b99R340p8Bt/vDWP1kSiiw7qgsGiGg+cWdLaHWXtlLVvCttDNuxu9fHrR3bs7jlYmpn/eJW2CIUzZ1nVHp4clu0JYfSSK53o/Q0DzQJac+prvznxHaFooC7otKLEf62vHQZ+PztaDXq3v4ecDPmRlNMHWazUR6RGM3TiWl5zuozWQddj0JZki5IyGqCXUnp4ErV+HNtnUfsywf/dW6n7yNaq09AqqTNQ0tXaMxvnz5xk0aBA+Pj5MnToVrVZb+ovMxc6DgnwF2fGGsGDbqWORp+t72LFsbFeWD/6MevmPoct3QEsGO2P/YNSGUfRY/gCfHlpGQnZCsd0Hudvx9kPN2PfKvbx8f2Pq2nqQGjGMzNCp6LJ9ySnIYWv4Vl7d8yo9VvXgyc1P8r9z/zPLTJWCRMPpzeDgAL4b354gd1uSMvN4Y81Z/trZnPENZmOptGRH5A7GbhxLXFZc8R2F/wuA0q8Lbw5uxqbnuuOraUXa5emQ0QadXsdnBVsAyAsNRZtYymlVY4GFQUPOaIiaT+3piSY42OQt39uzsssU1VytDBqHDh2iW7dutGrVimeeeYbvvvuO7777rvIKsnElK94a9ArU3p6ovbyKbdbGz4UtT77Eiv5/0cX2ZchohV5nQbI2gmVnP6XXqvsY/seTbAjZQn4x0zOdbCyZ3COIXbN68fkjrWnh3pzMsClkhk4jN+Fe1AWeFOgLOBB7gAUHF9D3t76M+GsEX574kgtJF8plAKk2wfAL38LNjV6NPdj0XHfmDgrGyUbNxWsZfL7WnkDtTJwsXbiYfJFZ+0o4rRthCBr4dQEMYey3KV1o4+1JeuQI8mMeQ2fnQIThqhQb/vyw1Jk7wE2XTuSMhhBClIdad+lEq9UyfPhwPv/8c0aNMmxkFh4eTkpKym33lZubS25urvH7tLS0OytKqSIz0XC5wrZV6RdBW3i78PWw0eTkj+LPE5f4/uQaInJ3obKJ4HzaAV7afYDX99jR3as3Pf3a0cilEYGOgVhe/+WpVil5sKUnD7b05Eh4Mj/uC2PjKV+S4vuiUCdg73IBV49LJGovcC7pHOeSzvHF8S/wtPWkhXsLgl2DaeralCauTbC3tC+l2qIKzyxYuLoaaxnXxZ+HWnnx2T+X+OHfMA5dsMfCchJ1GywnPu/SrZ0UaG+sBurb2fiwi60lyyd24rmVx9l0BnIzfYny/wrf+Dgu7viTCV5XeafrO3jZFR/kDH1f/3lK0BBCiHJR64LGgQMHiIiI4OGHHwYMy1SfOnWKw4cPM3/+fJo3b87SpUtp2LD0jXrmz5/P3Llz77qm3MuXSTlvOLlk2yKgzK+zVqsY1a4xo9o1JjLpGZbu38/6kLXkWB8iT53Gtqg/2Rb1JwAqhYoAxwAauTSioXNDevv2xtfBl7Z+zrT1c+b1B3L59XAU/zsQTtQ1N9Ku3YNClUHjwEgcXC9wOf0oMZkxxGTGsClsk7EGPwc/mro2Jdg1mG7e3Qh0DDRZc+GlE5WrS5HHHW3UvP5AUx7r5Me8DefYehaizj6BpdUywLBJ3O9Xfic4OBiunYK8DLBygDrBt3wmi0e34d315/h2byi79L3pwnKaRin44dphhq4dyuyOs3kw6MESCpTBoEIIUZ5q3aWTevXqoVKpeOmllzh69Chjx44lISGBuXPnsnLlSuLj4+nbty/Z2dml9vXKK6+QmppqvEVG3v78bL1WS8wrr6IvANu6Odj7FZT+omL4uNjw1oB7Ofj0xyzu9hvNVDMoSLkHbWYA+gJrCvQFXE65zPqQ9Xx85GOGrh3Kn5f/NL7ezc6KKT2D2DmzF9+Nb899jT1AZ8e5S004sP8hGuV9zNsdP2V6m+n08euDp63hum14WjgbQzfyweEPGPznYEZvGM2q86sISw0r9lJLQWoqAEqN5pbnAALcbFk6th2v3N8Y9FakRQ40PvfzxZ8NC3td2nr9TXcsdmEtlVLBGw825bWBTaiTlQKA3zVo7d6KzPxMZu+ZTUhKCVP6Mq+Pc7GwNvVxCyGEKKNad0YjMDCQr776ivfff58TJ05w6NAhzp49i4+PYWrUH3/8QePGjdm7dy+9e/c22ZeVlRVWVlZ3VU/SDz+Sc+oUShsr6nWMRbH3U2j9GDjc2QAslVJBz0Z16dloPJm5j7HzYjzf/xvKoZBQlNYxdAvOp8DqPMfjj/H63tc5HHuY2Z1mo7HQGF/fq7EHvRp7EJWcxc/7I/h2byj/XkrnUIiCjgHN6dnoXp7u7o6LfT7nks5xNvEsR+OOsi9mHyfjT3Iy/iQALtYutHRvSWuP1rT2aE1T16ZYN2tG7sWLZOzYif299xapXafTs+18FIv37uNM/AWsPGJRWl8wPh/sEoynlQscXGp4oPmwEj8HfUEBD+xcQfLZDQBsCOjCa+0+4OX9j3Mp+RK7o3cT6FTM2ZcT1/eG8e96R5+/EEKIompd0ACYMGECEyZMYNeuXUyaNMkYMgB8fHxQKpU4OTmZvY688HDiP/sMgDqvvIY69WuIOghbXoNht24UdrtsrSwY0LwefZvWYd4GJ77d68SOA3B/sz5MbnGEJae+ZM2VNZxJPMOrHV+lfd32RV7v7WzDy/c3ZlR7H15fc5rdlxLYc9lwe2f9ObycNPRo5E7Phg/ySLfHydGlsO7KOrZHbud0wmmScpLYHrmd7ZHbAbBUWjKgjg+PAkkb15H1zKNEZMdwLvECO0JPcjH5ElpVHApLHZrCYRS6G5ubzekwB9WpXyEzDhy8odnQYt+3LjOT6BkvkrFjBwD/dB/BYuf2HPr9LMN7DGfewXlsDN3IuOBxRV8YfRSuHjeMz2g1+q4/fyGEELU0aBRSKpVcvHiRf/75h3uv/3X9xhtv0K1bN9q1a2fWY+t1Oq6+9jr63Fxsu3TGcdhQiG0AS3rC6d+g7XgI6F4ux7JQKXnjwaY0rmvP7D9PsfF0HOGJwcwfuJiFR1/ncsplntj8BJ3rdebZNs/eslqov5stP03oyJX4DHZciGfHhTgOhCYRnZLN8gMRLD8QgVqloJ2fCz0bdWNG84ewsdQTkXmJ88knOZt8kpPxx0nOTWaNw2X624FLehbvfvIwhxredPXOAhSApcKWRi4NCXZrhMZDwwxmXH9aBf9+bmjb+elix1HkX7tG5JQp5J49h8LKCs/33mNQh24s/mQXB8OSuK9ZMCqFijOJZwhLDcPf0f/Gi49cn3nUdDDYupXLZy+EELVdrQ4aXbp0oXfv3vTv358hQ4YQERFBVlYWW7duNfuxU375laxDh1BoNNR96y3Dglv1WkK7CXBoKax/EabsLddBiSPa+xDobsvkn49w9moar6+wZOHIZexLWsnqS6vZd3Uf+9bvo7dvb6a1nkaQU1CR1we52xHkbseErgFk5xWwLySBnRfi2XExnvDELPaFJLIv5L/rVdS7fuuLpXUSlrYR/NtgOw8ci6HradjvWw9dbl1sFd7c37A1T7TvQoCzp3EBsjNnzhiDhmXELki8DNaO0GbsLe8v59w5IidPQXvtGipXV3y+WIymZUscgJn9GjH3r7N8vvUqHTp35NC1f9kQuoGnWz19/cWpcOo3w/22j5fbZy6EELVdjRwMmp9fti2+lUola9as4Z133kGv1zN06FD279+Ph4d5l+POj40lbuFCADyef67oPhz3zgYbN0i4APu/LPdjt/N3Yc20rjSt50BiZh6TfrhAkHIMawatZVDQIBQo2BaxjYfXPszsPbOJzih+QzKNpYp7G9dh7uBm7JzZi+0v9mTOg03p0dAdNztL7KwssFDevFqpgrwcVzISW7PJ0bCbattLShqnzuC97vPZ//Q7vNNvOIEuXresclrI5tTPhjvtnwSrotNqM/cfIGz0Y2ivXcOyfhD+q1ahadnS+PzYzv608XUiI1dLQqxhpkqRzeVO/gL5meDWyLg2hxBCiLtX485o7NixgyeffJL169fTqFEjk221Wi3W1tbMmmV6rf/ypNfriZ3zJrrMTDStWuE8+j9jATTO0GcurJkKO98zDHi8w4GhJfFy0rB6Smde/PUEG07F8vLvp3CyUXNP/aFMDOzP6axf+Dd2B2uvrGVD6AaGNRjGpJaTcNOUfDkhwM2WALcAHr+n6PRcbYGOvAIdufk6crU6cvILyMkvQBf6J5bhoXzbIAunVibWtbiJOv4UOFpDh0lFHtfrdMTOmYM+Kwubzp3w/vRTVA4ORdqolAreG9qCgZ/t4eQFb5waWxKRHsGZxDM0cw2Gw9cvm7R7HEoIOqIaS4ks26ZbQohyV+OCxrRp01AqlfTs2ZMdO3aUGDaSk5O59957mTlzJo8++miF1Ze2bj0ZO3eiUKup987bKFS3Ts+k5aNw5IdyHRj6XzaWFix6pA1fel7hqx1XSMnKZ/3Jq6w/CdAfn7rtsHTfTJz2FCsvrOTPy38yotEIWnm0wtfeFx97H2zUNqUex0KlxEKlxOY/61/FD+xPwhdfkrFjJ05DhpS98JaPgH2dIg9l7NpFXng4Snt7fBYtQmlrW+xLG9Sx56X7G/P2urPkpDZB7XiCv66sp1l2FsSdMUxpbTmq7LWI6iEl0rA9eH5W6W3VNoZtx4UQ5aZGBY0dO3bg7OzMmjVr6N27t8mwYW9vT1BQEPPmzWP48OGo1eZfoEmblMS1d98FwO3pKVjVr198Q6USBn5gloGhRQ+jYGqv+kzqHsiJqBTDrJJLCRyLTCEy1g1iR6OyuYKVxyZyNJH8ePZHfjz7o/H1rtZu+Dr44Gvvi6+DryGAOPjgbeeNg6VDiZdAAOx69iThiy/J3LsXfX4+ijJ8/noU0OWZWx5P/tFQk9Pw4SWGjEITugZga6nija3nwPEEv5z7ixkRl7EEwywWjXOpdYhqJivREDIeXgpupSzEZ+Mqu4AKUc5qVNBISkpi+vTpuLi4sG3bNpNhw8LCgpUrV5KSklIhIQPg2rvzKEhJwapRI1yffNJ0YzMPDL2ZhUpJWz8X2vq58FzvhqTl5LP/SuL16ay2hIYForI7h9rhJErLRBTqRJQWWSTmJJCYk8CxuGO39KlSqHCycsLZ2rnI18Kbr6M3nq4uFCQmkXXk6C0byRUnz687uDUo8ljOxYtk/rsPlEpcRpftzNSoDr642j/C8/t+xYYkOPO74QkZBFqzuTUEz1aVXYWobGW5RCaBs1zVqKBRuKw4YDJsRERE4Ovri4WFBW5uFTONMS8qmrT160GppN4775TpL3junW1YQCrhgmE1zMYDzF8o4GCtpm9wXfoG1wUgMimLPZdbcCKyH1dTc7iWlsPVjCTStddQWiaiVCcYAohlouF7iwwK9AUk5iSSmFPydfGX/DS0TYSrOzdTv4SgYZFw3ng/q9mYW55P37YNANuu95S4GV1xOgc54nnanXbX4rBES6x1EHW9zTulWQhRiWxcDZfGfp9Yelu1DUw9KGGjnNSooPFfxYWNP/74g9WrV3Po0CGTp/bL3fWdQxVqNdbNgktpfF3aVcOeHgol1G1Wensz8XGx4ZEOvjzSwbfI4zn5BVxLyyE2NYfYm74ei4zjRMxVFKpMnOzzebC1A408VaTmppDy//buPC6qev0D+GeGGYaBYZdFRGRRMjRTUnNPBUtzT3Npu1rhVt5rppZamaZpZYumv0y9pra4XE3TVsvd3DVQ0UxEFATZYRgYBmbm+f1xZHRkVWfmzMDzfr3mxXDOmXMeqYHPfM930RUgvzQff6b/iWuKfDwCYPu5jTi64wz6NOuDPqF9bq2XUpQJj71vmq6n96/8M1CEhgIAdJeSQEYjJNLaB1LlanMRt2sCMkquAkbhLeDi3Zg7gTJWn3k1FcJDXToFfx8nHMdBwyLqddAAzMNGx44dERQUhL1799o2ZEBYFh0ASKeDsaio0qiIKh39P+Hrg4MAr5CajxWBi9wJzXzd0MzXvF8EURT+uJCFBT+dR0pOCdb9DrRu4oF3BjyBjmHCYmrqMjX+Oj8dwAFISYqL+RdxMf8ilsUvQ3Ov5ngi+DH868T/4KS5UWMNqpgYSN3doc/IQMmxY3Dr3LnaY8sN5TiTcwazD76D68XXYNS7QV30OIDV8ILmfn8cjDF759WUw4MI6uU8Gnfy8fFB//79TSEjMDDQ5jVIlUpIVSoAgD4np/YXFOcIczsAQKdJVqzM8iQSCfpEBeC313pg1pMt4a6Q4dx1NUZ8eQSvfHcaafkl8HD2QJSf0LIzrPlQzOsyD92bdIdMKsPl/EsI3fMBlDfOQXPbomkpRSko1ZeaXUuqUMCjv3BLqWDbNrN95YZyxGfFY9WZVYjbNQ6dv+uCMb+OEUJGuRc8C6bgzUE3V3HV5lvxJ8IYYw1XvW/RAIClS5diy5YtooWMCjI/P5RpNNBnZUMRXvNy6jj5FWDQAUHRQNOOtinQwhQyJ4zrEYGnooPx8a5/sPHENfx0JgN/nM/EuB7hGGUQJstSSOUY2mIohrYYCnWZGjd2TkZkynaUA5jp5QlAWPF16qGpUFxUINg9GOGe4Qj3Cke4ZzgieraBbOMmFO36HfETD+Ok5jwOXz+GhOx4lBnNg4lR7wZDcXOEO43EunGPw093TdjBQYMxxqyiQQSNmJgYjBgxQtSQAdwMGleu1N6iodcJo00AoTXDwfsONFIpsPCph/B8p2aY92Mijibn4fM9SShOSsHTAH5PvIE/vz4FD6UMXYt3Y/CV7QCAvx5+B01LjACECdUUUiUIBqQWpSK1KBX70/YLFyDCJ75AcG4pzr/yMlYOlkLrIvzMjHo3GErCYCgJh6I8Eu0aR6LzQ34Y2zUUrs4yQCrcyoFODRjKrTayhzHGGqoGETRatapj50srq+inoc/OrvnAxG2AJhNwbwy0GmL9wmwkKsgDG+I64bfETLz/8wVo9UKLRlahFr8m3kB7yd94z/l9QAJ8oR+ID461RGn2VdPrsxLjoAz0hVSRJTycb31dF1uIaVuNaJdMmL9Whvd6Pwby7YgOTR5E+9a+iA7xRmSAO5ykd4Q2F89bz0sLeTE1xhizsAYRNOyFzM8PAKDPqSFoEAFHlgvPO8bVu0/YEokEfVsHondLf/xtjAf+BrqGeyO8jyf6Hl0KRbke5zx64Kzff9BVZ0CaNBeZN18b5uMGj0ZBkEiCIJVIIJUCUqMEEp0E6oBi/HdUEsbu+BZN8/Ow6o9jaPr5s3DrGF1zQU4yQOEJ6AqF2yccNBirkjwtHdrExGr3Gy9ftmE1zJFw0LAhmV8dWjSuHgZunAFkyno9gZSzTIogbzdkAwjxkKHThalAeQHQ+GG0HrsR/+csjGRJTHRH64XCa1aP6VBL69TjKI8bjrRXX0Xp2bO49uJLCJzzDryffrrmYly9bwUNxpgZg4c7SuVA4GdfIuWzL6s9rsxotGFVzJFw0LAhU4tGTUGjYkjrw6MAVx8bVCWiitsYyfsBl4uAexAwehPgXPM04jWRB/ij2dfrkTFrNtQ//4wbb7+DsqQk+L/xRvVzbCi9gfwUoCTvnq/LWH2l9/PFa3FOmBX+DrqHVR/0L+0/ALziWCPkmG1w0LAhJ1+hRcOQW80ftLIS4OLPwvOHavkUXg+UJSUJTzQZwkx8ozcAHo3v+7xSFxcEfbwYzuHhyFm2DHnr1sOtew+ounWt+gUypfBVp77vazMb41VZbSLXUwJdRCiUD1YfNCR864RVg4OGLZHQ+RFVrdgKAHIlENwRSD0KnFwDhFbzh7EeUP/8Mwp/2AGA4NlMCwxdbdF1KCQSCeSNhVFGUldXuDxQzWJaZSVA+mnheYB9dBpmdcSrsjLmEDho2JCxVAtA+MRdJYkE6PfBzVVbtwDtX6yXYcNYVobMhe8DAHyjNHAbMg6IGmzRaxgKCpC1+GMAQKNXXzXdtqokeR+gLwU8QwD/KIvWwKyMV2VlzCFw0LAhKhUmj5IqqwkagPCp/pExwKmvgF9mAOP2CyMj6ouSPBQsfBX67FzIlAY06tsKiH3X4pfJXroUhvx8ODePgM/zz1V/4D+/CF8f6Ovw85U0WLwqK2N2rUFMQW4vjFqhRUPioqz5wJh3ABcvIPOcEDjqg7Ji4ODHMH7SFrk/ngQA+Hbzh3T0eosP4dUmJiJ/w0YAQODb71S/Uq7RCPzzm/A8sq9Fa2CMMSbgoGFDphaN6m6dVHD1AXq/JTzfMx8orqWzmz0zlAMn/gssbQfsnoeCC+XQa50g8/WE1+LdgHuARS9HRiMy570HEMGjf3+4PVrD9O0ZfwkTozmrgNBuFq2DMcaYgIOGDRm1QtCQ1HTrpMIjY4GA1kBpAbDnPesWZg1GI3Buq9BZ76epgCYTRvcQ5F4RVqH1feXfkCoUFr9s4bZt0CYkQOrqCv8ZM2o+uKI1I6IXILN8LYwxxjho2BTpKlo0arl1Agj9Mvp9KDw/tRZIj7daXRZFBCTtBlb1BLa8COQlA25+QL+PUBAwA/o8NWQBAfAaPtzilzYUFpp1AJUH+Nf8gos3+2dE9rN4LYwxxgT1qJeh/ato0aixM+jtQrsCrYcLI1B+mQG8+Jv9d1g8sBjYO1947uwOdP030GkSjBJn5M54HADgO34cpM7Olr2uNh/Z704XOoBG1NIBFAAKrwszsEICtHjcsrUw1sAZL1+Gtob9Mm9vyIOCbFYPExcHDRuqGN5aa2fQ2z3+nvDJO/UYcGYz8PBIK1VnAfoy4OjNdVravwT0mmVaO6Tgu++gz8y0TmtG9kVoPx2K/F+NACQIDE+AZH1/oHFbIKSTMHRWesfcJf/8KnwN7gCoqhn6yhirM4mHh+l52bTpSKluJl4AEqUSET/9yGGjgeCgYUOGggIAd9GiAQAeQUCP14Hd84CDi+07aOydL6wXogoAnvzI7I97web/AQB84+Is25qRfxVYNxB5J8sAUsIjtAxuvmohmKUeA45/CTw0Ahi6wjxsXNgpfH2AR5swZgmS2+aqcV78EUIjIqo8riw5GenTZ0Cfn89Bo4HgoGEjpNej5PARAIDLQw/d3YvbPS8EjZxLQLlWmEHU3hz8GPhzifA8Zk6lFgSjVpi90SXqQctdsyQP+GYYoMlEWWlTAAZ4TF8JtAkW+rRcPwWc/C9wdrNwfEXYyE8BkvcK21o9Zbl6GGMAAGlEBJQ1LoDIGhLuDGojJadPw1BYCCdPT7hG17J0+Z3c/IR5NUBAbpI1yrs/x1cJQQgA+rwHtHu20iEVc1lQWbllrlmuBb4bCeReAjyCoScvAIAsIBDwe0Bo+XnyQ2D4V4BUJoSNbRMAowE4tU44R0RvwCfMMvUwxhirErdoWEPCJkB1W6uD0hua3WcBAKqej0Eiu8sfu0Qi/PFMPQZkXwQC77JFxJoSNgI/TxOe95gudP6sguTm7RIqt0DQMBqArS8DaccBF0/Q6M3Qrx4BAJWnGo8aJISNLWNvtmyQsFosIMzAyhhjVanLYnw8tX2dcNCwhp9fBxS3RocQAUX7WgMAVL1j7u2cjSKFoJFzyRIVWsaFncD2m8tCdxwP9Jpd7aGmFo37DRpEwgicv38EnBTA6I3QO/kJ83ZIpZD5+lR+jVnYEPqKwM0feODJ+6uFMVb/uPoKi/B9H1f7sXJXYOTXgGuj2s/ZgAMJBw1rCO8NuN6c9lqdjrJ//kZ5Zh4kzs7VL1Vem4pFo3IuWqbG+3V5jzBPBhmAts8CfRfVOPTWYkHj0KfAidUAJMBTK4FmXaBPTAQAOPn6VN9adHvYMOqBds9ZfOpzxlg94NUUeOW4sGhfTUpygE3PC/3EaiN3Fc7ZQMMGBw1rGLkeqBjqlXMJRRN7AgBcOz4CqZvbvZ3T7wHha3YdmvMA4ZO/Nh9Qelt+7o1rx4CNzwKGMuDBQcDApUANQ9kAywQNl8u/AKdvzpLadxHQaggAQJ+VBQCQ+9UyQVfUIOCZTUDiNqDL5HuugzFWz3k1rVsoqEsgyflHaB0pyeWgwaykUQsUZXkDMMC9pdd9nOdmi0ZuktBH4c55ISrkXhb6TZzZBBRcBWQugFczwLvZza+htz1vBrh43l0dGWeAb58WlueO6A0MW12n1WVvdQYtu7vr3UZ1aAHgCqDLv4FOE0zb9VnZAACZfy1BAwCaxwoPxhi7X3UNJA0cBw0rK8/MQmmmAQCgcrt87yfyChH6JBh0QoDwCb+1rzgXSPxeCBjXT5q/Tl8q3G6p7paL0lsIHz7hNx8RwlffCOG+4u2tITmXgK+HArpCoGknYOQ3dV4jRCK//86gEqMeaD0CiJ1rtl2ffTNo3NkRlDHGmOg4aFiZZt8+AICLTxnk2YeEuR9cq+iwWBupE9CohbB0fPY/QghI2i10bry0S+h3AAASJ6Gl4eFRQIs+QnNd/lUhnORfFeaQqHhekiPcXtHmA+l/Vb6mwlMY/lkRPOI3CK8JbAM8uxlwrvttoHu9dSItyjA9L2vcHhjyf5Vu01TcOqlTiwZj7J5cL76K87mqavdfVt/HBykmKEit/VZMkcY2tVhQgw0aRITt27fj7NmzmDp1KlSq6t9A96PkpNDC4NbMWQgDaSeAyCfu7WR+LYWgsWGkECjIcGtf44eBNqOAh4YDqtv+4Lp4mrd+3E6nuRk6UoRbLnnJQN5lIDcZUKcJLRcZ8cKjQqNI4Pltd3XLhYhMrQ4wGuv8OgBwTVhteq7uvajKFpTytDQAHDTqnZwkIL2GFrO6DD9k983d2RNklGN54jwsT6z+uLLse78tyiCEjOUdhdvSNdGRbeqxoAYZNAwGA4YMGQK1Wo0ZM2ZAr9db7VrOoc0AALrMUuBBGeAfde8n6zIZuLxbaIEgA+DbAmjZX2i98L+HGTcVKiCglfC4U7lWCCB5yTdDyGWgvBSIece0fkldFf3+O7SnTkEil0P1WI+7eq3B5wHTc5K7VtpvLCkxhTllu7Z3dW5m53ZOBk4Yaj5G7iq07jGraeQSiOLLr2Ppsy0Q4V/9B7J9p/fhFbxiw8rqmZJcIWQ8tepWn7yqpMQDi8barCxLaJBBY/369bh+/TqOHz8O2d1OnnUbnU4HnU5n+l6tVlc6xj22D3I+X4biGwoYIofC6X46DgW1BSb8CaQcFBYL8w6993PVRq4Uwsu9BJjbGIuLkfn+QgCAb9zLcG7W7K5eXxo5AIAw66jztQNAa/PJyoqPHgWVlUEeFARFixb3VSuzMz1mAD1rWYumgc9PYCuk90KYxwOI8q2+JTNJZYezFtuT2lrgKvY3ihR+11eHb504hp9++gmDBw82hYy0tDR89NFHSEpKQq9evTBlypQ6BZCFCxdi7ty5lXcUZQESYaVWhasacpUe5RoZiiWd4VH56Lvj2URowXAQOV98Af2NG5AHB8N33Li7fj3JbrViKM99Bzxp/olJs1dYs0TVqxcklh7Gy8Tl2bTmX7iMOYK7nQCsHrbQNcigQURISUkBAFy4cAE9e/ZEjx494O/vj1mzZuHo0aPYsmVLreeZOXMmpk6davperVajadOmwLJo08ygEgDuwR7I+1uFomPn4WHHi69ami4pCblrhXVFAmbPgtTlLlatrYJz1hlhDo+QRwEAZDSi6GZnW1WvXvd1bsYYs4q6TgAG1NsWugYZNHr06IE333wTc+bMweuvv445c+Zg0iRhKu2nnnoKgwYNwokTJ9ChQ4caz6NQKKBQVNVZTXLzIXCPkCPvb0Czfz+MZWWWXSbdThERbsydB+j1UMXEwN1SQeDwUiDkWwBAaWIiDNk5kLq6wrVjzf+tGLOk6wVa5BfX3PkxNa+WTn2s4Wjg8200yKDx0ksv4eOPP8bo0aORnJyMbdu2mfYNHDgQ7u7uyK4YJXEvZqbemhkUgNJohOx4T+izs1Fy5AhUjz12P+U7BPWPP6LkxAlIXFwQOGum5U7890/CaIRGzU23Tdy6dWsQ4Y3Zh+sFWsR+vB/a8po7qkpdrsMtDHBX1o+p7pOyau4bkJp7d8GqLDm51mNk3t6QBwXd1XmZ/WmQQUOlUuH7779H7969UVRUhIMHDyI2Vpgt8uTJk5BIJOjSpYvFrieRSqGKjUHBho0o+uOPeh80DGo1Mj/4EADQaMIEyJs0sch5dU27APlHgaPLgQGf8m0TJor84jJoyw34bGRbNK9hFMYV9UXMOg74u9dtUjt75e3mDKXcCVM2xdd4XJn67zqdT+btDYlSifTpM2o9VqJUIuKnHzlsOLgGGTQAoH379ti3bx+eeuopDB8+HDNnzoRcLsdHH32ElStXwsvLy6LX8+jTRwgau/cg8N13IXGqZgrxeiB76ecw5OTAOSwMPi9abhiWtvVzwMGjQPx3KI+Kg+78BUAigapHd4tdg7G6au6vQusm1Y/CkLpYZ24eW2vipcQfrz9W662ijfuyseCL2s8nDwpCxE8/Qp+fX+NxZcnJSJ8+A/r8fA4aDq7BBg0AiI6Oxrlz57BixQrs2bMHHh4e2LJlC7p2vccVVmvg2qEDpJ6eMOTlQXv6NFxr6f/hqErPn0f+d98BAALfeduitzTKA9oBQe2A9L+g+VpoMVE+/DBkvvWvlzZj9qSJlxJNvJQ1HtPYs+6dveVBQRweGpB6FzS0Wi1Wr16NV199tU7DHVUqFaZNm4Zp06ZZtS6JXA63zp1R9OuvKD5+vN4GjcxFHwBGIzye7Ae3zp0te3KJBIgaDKT/haI/TwDg2yZMHBJZAa6oL9bYapFcWHsfBMYagnoXNCZNmoS1a9eaWipqChurV6/GkCFD0KjR3c10ea90F4WFzZxDQ21yPVsrPnYcJcePQyKXw3/6dIufX1qSDRz+HIZyCUquC9vcY3pb/DqM1SSn9AbcIj7GrOO1r9ujlCnhrfC2QVWM2a96FTSysrKwa9curFmzBnFxwuQo1YWNrKwszJgxA8uWLcOJEycgl1u3Z7ju8mWUXblycxru+tkZNGf5cgCA19PDIW/c2OLndz8wFyjJRbE2EmTQwLlZMzhHRFj8OozVpKisEBJpOV5p9Q56hFUxff9tvBXeaKyy/HuhIeHRKY6vXgWNjRs34oUXXsDYsWOhVCrx3HPPAag6bPj7++OPP/5AYmKi1UMGIKz3AQCuXTrDyUoLuInp9tYM37g6zIB3D5wzTgLeKhSVRQM4APc+sTwbKBNNE7dmiPK9j7WLWI14dEr9Ua+CxpgxY6DVClN/jxolTNNdU9iIjo5GdHS0TWor+v0PAID7zWG09Y21WzMq0OOLoJm4DACgiomx2nUYY+Li0Sn1R70KGh4eHvC4baKsqsLGlStXsGDBAqxevdp6n4a/Hw+43hptUV6oR2liIiCRwN2/AEjaDagChIerLyCVWqcOG7Fma4ak7NYkQaVhfVBcGg6jRgMnv0ZQPvywRa/FGLMvPDqlfqhXQaMqt4eN4uJiHDp0CLNnz7Zuk/vFn0xrnQBA0UU3AJ5wbVQK2d47RrdInACVvxA6PJoAwe2FlVmDogH5/a0NYitWa80wGuB+8NaidZoub0C+/RcAgHvvGEgcPKAxxlhDUO+DBiCEjfT0dEybNg1ffvmlqaOo1fSZD6hujTkvOr0RQDrcO0YBEXJAkwUU3QBKcgAyAEUZwiMjXggpAODkLMwZEdIJaNpJ+OrqY92674HVWjOIgF9mQHHtoGmTUeaKoj27AdTfW1CMMVbfNIigkZycjKVLl9omZABA+zGmtU70ubkoSfoMAOA+9Uvg9um4DeVAcTagyRTCR+5lIPUocPUIUJwFpB4THlgiHN/oAWHl0pAuQPNYQOVn/X9LLazWmnHoU+DEatBti9PRpUvCImoqFdwe7Wi5azHGGLOaBhE0Nm/ejNmzZ9smZNyhaM8ewGiES1RU5TU/nOSAR5DwqNB5kvBpPv8KcO0ocO2IsDR6zsVbj9PrAUiE2yyRTwDtXxKltcNqrRkJG4Hdwi2T4kdfAzAPAGA4egwyAKrHHoOEF1FjjDGH0CCCxptvvinatYv/PAwAUMXexQgJiQTwCRcebZ+5eaJcoXXj2hEgeR9w4wyQdkJ45FwCnlpp+eJrUbB5MwDAc/gwy7VmGPTAzzcn++oyGdomI1ARNIwJCQAAVW+eDZQxZo7n27BfDSJoiIluDreVBwTe34ncfIGWTwoPAFCnAwc+Ak6uAUry7rPKe2PUCCNClK0fstxJDWWATi08f+wNIOnarX06HQBAHnifP0vGmFVdVl+GJLf6DveWnMiM59uwfxw0HJVHEBDcQQga9RZPxsWYo3CVuZueT/9zOpz/qf72plKmxA+Df7BI2Ljb+TZKTp2Ccy3H1hW3kNQNBw3GGGP3zVtxaxXlj7p+hOaRzas8LrkwGTMPzkS+Lt9irRp1mW/jblo+6opbSOqGgwZjrN7L0GQgX1f7p9isIh2KtDUvlnY646Klyqq3jKX+MJY2qXIf6YRbrnVZ3daSt1jq2vJRV3czI2l5enqdrltfW0g4aFgQEQEA1Gq1aVtRWRmKDQaoS0ogvW27RWhKAB0B2nLA0ueuA6v828pu/psAQK2GRnNrZlCNXg+DwQC1RgO9CP9edn8q3hcV75PqVOw/knwBuhN77v+65YX4v3PvQWcsve9zVSCjHF5SF7P3ekMnM+pMz8et+RMujVKrPE4iK4BrmBQzdtXesqCQumBS67fhIfe0WJ2W4qzNRbDBgN27dqI88US1xzmpNQj4bAWkurJaz2lUOCNzygQYPKpfD0uSmQSg9veRPZGQI1Vr59LS0tC0aVOxy2DMrqWmpiI4OLja/fw+Yqx2ly9fRnh4uNhl1AkHDQsyGo1IT0+Hu7u73a8qqlar0bRpU6SmppqtD2OPuFbrsHWtRISioiIEBQVBWsP08dZ8HznSfx+A67UFR6u5sLAQISEhyM/Ph5eXl9jl1AnfOrEgqVRa4yc1e3TnQnT2jGu1DlvW6ulZexO4Ld5HjvTfB+B6bcHRaq4prNsbx6mUMcYYYw6HgwZjjDHGrIaDRgOlUCgwZ84cKBQKsUupFddqHY5Uq6U42r+Z67U+R6vZ0eoFuDMoY4wxxqyIWzQYY4wxZjUcNBhjjDFmNRw0GGOMMWY1HDQYY4wxZjUcNJjJ2bNnsWLFCrHLqHfWrVuH48ePi10Gu4mIkJCQgHPnzoldSp0VFxfj2LFjvLYKMzl16hSuXLkidhl1wkGDARBCxuOPPw53d3exS6nV0aNH8eijj8LFxQVt27bF+vXrxS6pWl999RVmzZrlEDMOfvHFFwgJCYFKpcKAAQNw8uRJsUuyuMLCQvTs2ROTJk3C/v37xS6nTtatW4fAwEB06tQJLVu2xMWL9r16bGpqKvr37w9XV1eEh4djwYIFKCurfUExMa1duxbR0dEICwvDW2+9BYPBIHZJNbp8+TJiY2PRs2dPxwgbxBq8M2fOUGBgIH3zzTdil1Kr8+fPk6+vL61evZoOHjxI48aNI4lEQk8//TSVlpaKXZ6ZNWvWUFBQEF24cEHsUmq1fPlyatmyJf3222+0Y8cO6tatG8lkMlq+fLnYpVnUv/71LxozZgwZjUaxS6mTr7/+mkJDQ+nIkSOUlpZGjzzyCL3++util1UttVpN4eHh9NZbb9GRI0do7ty55ObmRtHR0ZSRkSF2eVWaOXMmtWjRgpYtW0YzZswgJycn+uCDD8Quq0bnzp2jsLAwioqKopCQEEpOTha7pBpx0Gjgzpw5Q35+fqaQUVpaSosXL6aYmBgaNGgQ7dy5U+QKzY0bN46mTJlitm3nzp3k6upK/fv3J71eL1Jl5tasWUMBAQGmkHH9+nX697//TT169KAxY8bYXfgICgqiP/74w/S9wWCg119/nQDQ559/LmJllqPT6UihUNC5c+dM2zIyMmjp0qW0ZMkSunHjhojVVVZaWkpeXl508OBB07b33nuP5s6dS3v27KErV66IV1w1Vq5cSd26dTPbdu7cOWratCk98MADlJOTI1JlVUtISKDAwEDKzMw0bZs0aRKFhISIWFXtdDodeXp6UkZGhlnYMBgMdPXqVbHLq4RvnTRwOTk50Gg0OHXqFEpKShAbG4sNGzYgOjoa+fn5GDhwIBYvXix2mSbZ2dnQ6/Vm2wYMGIAdO3Zg165dmD9/vkiVmbt27Rry8/Nx6dIlnDt3Du3atUNqairat2+PAwcOoEOHDjh69KjYZZrk5OSY/VylUikWL16M6dOnY8qUKTh27JiI1VmGVquFTqdDaWkpAGDPnj148MEHsXr1asyZMwetWrVCQkKCyFXekpSUhIKCAvj6+gIQVhn99ttv8fnnn2PkyJGIiIjAJ598InKV5qp6f7Zq1Qr79u1DQUEBxowZI05h1fjqq6/w1ltvwd/f37Rt6NChuHbtml3f7nF2doafnx9KSkqwZ88eqFQq9OzZE6NGjcLMmTPFLq8ysZMOE9/u3btJqVRSixYtaPTo0WatAq+99ho5OTlRUlKSiBXe8tlnn5Gnpyelp6dX2vfBBx+QUqmkvLw8ESqrbM6cOeTs7EzNmjWjNWvWmLYXFRVRhw4dqGXLliJWZy42NpZ69+5d6ZaCwWCgxx57jGJjY0WqzLJCQ0Np3LhxpNFoKCAggH755RciIsrLy6NHHnmEunbtKnKFt5SWllKLFi0oMjKSZs+eTS1atKCnn36aSkpKTC1OEomEEhMTxS7V5NChQwSA9u3bV2nfH3/8QQDo0KFDIlRWtc2bN1NWVpbZtnPnzhEA0mg0IlVVNwMGDKAdO3YQEVF6ejqpVCpycnKyq/8fKnDQaICKiooqbasIG9evXzfbXlpaSgqFgr766isbVVczjUZDoaGh1KVLFyopKTHbV1paSh4eHvTjjz+KUtvKlStpyZIlZtvmzJlDDz30UKVjf/nlFwJgN/etDx8+TFKplN56661K+3bt2kVOTk52c1vqfixZssT07xw0aJDZvi1btpC7u7tIlVUtNTWV3n77bfrggw+oUaNGZn/89Ho9qVQqWrdunYgVVtavXz9q0qRJlU34Xbp0oXfffVeEqurun3/+IQCm35NqtZpmzpxJZWVlIldmbtq0afT++++TwWCgF154gWJiYuy2zwbfOmlgzp49ixYtWuC7774z2967d2/s2bMHQUFBZtudnJwgk8nQpEkTW5YJADh+/Dh69eqF7t27Izs7GwDg5uaGLVu24MyZM+jfvz8KCwtNxysUCri5uYkywmPVqlWYMGEC3n//fbMm13fffRerVq2qdLxCoYBSqYS3t7ctywQAHDp0CLNmzcKiRYuQlJQEAOjcuTM+/PBDzJ8/H3PmzDE73sfHByqVClKp4/+6eOWVV9C3b1/Mnz8fGRkZZvuuXr2K6OhokSqrWnBwMObNm4eePXuitLQUcrnctK+wsBA6nQ5t27YVr8AqrF27Fi4uLujVq5fp/68KPj4+dj8CSyKRAACMRiOKiorwxBNPoKioyOxnbw9atmyJM2fOYOzYscjIyMDOnTtNt1FeeOEFscszJ3bSYbb1xhtvUGBgIDk5OdG3335b6/GLFy+mNm3a2PzT7MWLF8nX15e2bt1a5f4///yTfH19KSwsjDZt2kSXLl2i1157jTp16mTzEQUrV66kJk2a0M6dO0kikdD69etrPN5gMNATTzxBU6dOtVGFt8ybN48CAwNpwoQJFB0dTU5OTjR79mwyGAxERPTRRx+RVCqlvn370qFDhyg+Pp66dOlC8+bNs3mt1lJSUkJDhw4lADRx4kT6+++/af369eTn50cnTpwQu7wqZWdnk0KhoDFjxpBWq6XMzEyKiYmh8ePHi11ala5du0atWrUiT09PWrJkCSUlJdGKFSsoKCiIsrOzxS6vRklJSQSAUlNTqXPnzvTqq6+KXVKVjh8/ThKJhPr06WPWunvjxg27a9HgoNHArFmzhiZMmEATJkyoNmwYjUZKTEykSZMmUfPmzUXpnxEXF0f/+c9/zLaVlJRQamqq6fvr16/T888/T66uruTk5ERDhw61ea/2ipBx8eJFIiLq378/PfLII1Ueq9Pp6MCBA9S1a1caPHgw6XQ6W5ZKu3fvpoCAAFP/Fr1eT3379iUA9Mwzz5gC2oEDB6h79+4klUrJ3d2d5s6d6zDDQe/GmjVrqE2bNuTi4kKPPvooHTlyROySarR27VqSy+Xk7OxMTk5ONHHiRCovLxe7rGoVFxfTG2+8Qb6+vgSAHn30UbvsP3Cn5ORkAkAPP/yw3YaMCps2bap0C9kecdBoYA4fPkzdu3cno9FoChvffPMNTZo0ydR6UFhYSGPHjqWlS5dScXGxKHV26dKFPv30U9P3CxYsIFdXVwJArVq1ovPnz5v2GQwGm//RJhKG1d4eMohq7vB2+vRpeuGFF2jbtm02rPKWl156iV5++WWzbd9++y09+OCDJJFIaP78+Wb7dDqdqaXDEWRmZtrdJ7na3G24SUlJoU2bNok2PDo/P5/efvtt6t+/P82ePbtOHSaNRiNptVobVFe1nTt30ogRI2jkyJF0+PDhWo9PT08niUQiWsi4fv06vf7669S/f39auHChXYfJuuKg0cDk5eWRj48PEZEpbACgqKioKjuJimXYsGHUt29fIiJatGgRtWnThg4cOEAHDx6kNm3aUFhYmCjh4nYlJSVV/mFr06YNDR8+XISKajZ+/Hhq1aqV2c/tpZdeoo8//pimTp1KSqWS8vPzxSvwPpSVlVGrVq3q1BHOaDTSyZMnbVRZ9b788ksCQIsWLar12LNnz4o+Id3Vq1cpLCyM+vbtS3FxceTq6koDBgyo8lidTkcJCQk2rrCyGTNmUJMmTWjixIkUHR1Nzs7OdObMmSqPPXXqlClYizUyJiEhgfz9/WnYsGH0wgsvkEwmowkTJlR5rFqtNvuQY884aDRA/v7+lJ6eTkajkSZOnEj+/v517rNhK1u3biUA9OOPP1JwcDClpKSY9l26dIkA1OnTiRjWrFlDTk5OdjdxzunTp0mhUFD37t1p7dq1NHbsWGrZsiUVFhZSXl4eOTs70/bt28Uu85589913FBsbW6de9ytWrCCZTEZbtmyxYYXm9Ho9hYaG0quvvlpr2CgqKiI/Pz/q27evqGHjiSeeoHfeecf0fcV79NSpU5WOffPNN0mlUok6lPX333+nkJAQ0+3UsrIyioyMpNGjR1c6Ni0tjVxcXOhf//qXaK14RqORWrduTStXrjRtW7JkCUkkkionk3vuuecoICDAIW5HcdBogHr06EG//fYbTZw4kbp160Zqtdp0G2Xz5s1il2fy5JNPkpeXF7m7u5t1Ri0uLiapVGp3s2tWKC0tJX9/f5oxY4bYpVSyb98+6tChAwUGBtKYMWPMOuZFRUXRhg0bRKzu3nXv3p327dtHN27cqDVs6HQ6evrpp+nXX3+1cZW3/PDDD6bWgPfff7/WsPHDDz/QM888I1oz+qVLlygyMtLsfWg0GsnLy6vK4bUFBQXUt29fOn36tC3LNDN48GD6/vvvzbbNmzevyuHmREIL0+TJk21RWpX27t1LPXr0MNtWWFhIAGj37t2Vjr9+/TrFxMTQ5cuXbVXiPeOg0QCNGzeOQkNDqVu3bqbbJUajkWbOnGlXf7zVajV169aNANDs2bOJSOiPMXnyZHr88cdFrq5mc+bMIW9vb9H6uNytrKwsUqlUdjmtdV3cPjqpLmFDbKmpqXTs2DHT93UJG2JKTEykzz77rNL2hx56iL788ksRKqrdK6+8Umnui3Xr1lGLFi1Eqqhm+/fvr3K9KW9vb/rpp59EqMhyOGg0QH/99RcNHDjQrvpkVEer1dKUKVNILpdTZGQkhYeHU7du3exuzYQ7ZWZmkkKhoK+//lrsUmpkMBjo1KlT9Oijj9Ibb7whdjkWU1XYOHz4sN3ebiOqHDZ0Oh0tX77crkf8dOjQgb744gvT97t27bLr4aubNm2iiIgI0/dqtdru1nO6U+PGjc1q3L59u8N8gKkgs/W8Hcx6ioqKsG3bNpSVlSE2NhahoaFVHte2bVvs2LHDtsXdITMzEzt37oSzszP69u1rttbA7VxcXPDpp5/ijTfewNGjR9GoUSN07drVNKmOLSQnJ2PXrl3w9vbGk08+CXd391pf4+/vj+XLl2PYsGE2qPCWhIQE/Pnnn2jSpAn69esHZ2fnGo/fsGEDtm7dismTJ+PZZ5+1UZXWFxAQgD179qB3797o2bMnFi1ahClTpuCbb74Ru7RqVaxR8eabb0Kv1+P48eNwdnbGuHHjIJPZ569qqVQKo9EIANixYwfGjx+PX3/9FY0aNRK5sqrdXm/FZFwdO3bEgAEDRK6serfXvGrVKixYsAD79+9Hs2bNRK7sLoiddJhlJCYmUnBwMHXo0IFCQkLIycmJXnvttSpHZog9ZHHPnj3k4+NDXbp0IX9/f1IqlfThhx9WeazYtX799dfk6elJ3bp1Iw8PD/Lx8am206zYtc6fP598fHyoa9eu5OLiQiEhIWYrst5O7Fpt5caNGxQZGUlKpZJ27doldjl18t577xEAGj58uN0PbezUqRMtW7aMfvjhBwoMDBS1T0ZdbNmyhUJDQ0mtVtv1ZFy3Cw4Opu3bt9PKlSupWbNmDtEn404cNOqJhx9+mJYtW0ZEQn+LFStWkIuLC8XGxppN6KLVaikmJsasudOWiouLyc/Pz9QRT6fT0Zw5c0gqldLYsWPNmokzMzOpbdu2onXaS0lJIQ8PD9Oy4oWFhRQXF0cAKs2Uef78eYqKihJtSN+BAweocePGprVTMjIyaMCAASSTySrdvtm7dy+1adOG0tLSxCjVpg4fPkz+/v4OEzJ0Oh0NGjTIIUIGEVHnzp2pf//+DhEyiIS+PAEBAQ4TMoiImjZtSkOGDHHYkEHEQaNeSE9PJwCV+i0cOHCAVCoVDRs2zLTNYDDQ2LFjqXHjxlRYWGjrUunAgQPk5eVVafuGDRvIycnJbKRGcXEx9erVizp06CDKfeo1a9ZQ+/btK22vuJd++zC0ij4Bo0aNsmWJJrNmzap0bYPBQC+//DLJZDKzP7Tnz5+nwMDAKhdQq090Oh1FRkY6TMggEqaAd5SQQSSM9vH29naIkEEkjN4B4DAhg4goLCyMQkJCHDZkEHHQqBc0Gg3J5fJKQ7mIiH7++WeSSCRmzf0Gg8FsKm9bSkxMJABVfvJftmwZSSQSOnr0qGlbcXGxaB0/f/jhB1IqlVVef8qUKaRUKs1aBbKyskSbDvjjjz+mZs2aVeplbzAYaNCgQRQUFGRWW2pqql13MrQUR5ie+Xbl5eUOEzKIhNugjhIyiIT/H5YvXy52GXfl+++/d+iQQUQkISISs48Is4xRo0bh+PHjOH36NLy8vMz2jR07FpcuXcKhQ4fEKe4OHTp0gFwux/79+yutiNi9e3dERERg7dq14hR3G61Wi4iICDz22GPYsGGD2T6dTocHHngAL7/8Mt566y2RKrwlLS0NzZs3x/Tp0/Hee++Z7cvKykJYWBj++9//YtSoUSJVyBhrqBx/3WcGAPjkk09QVFSEgQMHQqPRmO0bNmwYrl69KlJlla1cuRKnT5/G888/D71eb7bPnmpVKpX44osvsGnTJtOIgAoKhQIDBgywm1qDg4OxcOFCLFiwoNKy9P7+/ujevbvd1MoYa1g4aNQTQUFB+Pnnn3H27Fn07NkTV65cMe07ceIEOnXqJGJ15tq1a4eNGzdi27ZtGDRoEHJyckz77K3WwYMH49NPP8WiRYvw8ssvo6SkBABgNBpx+vRpu6r1tddew+TJkzF+/HjMnTsXBoMBAFBcXIwLFy7YVa2MsYaDb53UM+fOncOIESOQkpKCIUOGoKysDCdPnsTBgwfRtGlTscszs3fvXjz33HPQarUYMmQI0tLSkJubi3379tVprgpb2rBhAyZMmACVSoUBAwYgPj4e/v7+2L59O5ycnMQuz8zixYsxe/ZshIaGonfv3ti7dy/69OmDzz//XOzSGGMNEAeNeqi8vBybN2/G0aNHERwcjLi4OPj4+IhdVpU0Gg2+/fZbnDlzBlFRUXjxxRehVCrFLqtK2dnZWL9+Pa5cuYKOHTvi2WeftbuQUSElJQXffPMNsrKyEBsbi0GDBoldEmOsgeKgwRhjjDGr4T4ajDHGGLMaDhqMMcYYsxoOGowxxhizGg4ajDHGGLMaDhqMMcYYsxoOGowxxhizGg4ajDHGGLMaDhqMMcYYsxoOGowxxhizGg4ajDHGGLMaDhqMMcYYsxoOGowxxhizGg4ajDHGGLMaDhqMMcYYsxoOGowxxhizGg4ajDHGGLMaDhqMMcYYsxoOGowxxhizGg4ajDHGGLMaDhqMMcYYsxoOGowxxhizGg4ajDHGqjR79mwsXLhQ7DKYg+OgwRhjrEo7duyASqUSuwzm4DhoMMYYq0Sn0+Hvv/9Gu3btxC6FOTgOGqze6NatG6ZOnYqJEyfCx8cHjRo1whdffIHc3Fy8+OKL8PLyQmBgIDZu3Ch2qYzZvbNnz8JgMMDV1RUvvfQSevTogWnTpkGj0YhdGnMwHDRYvUBEOHPmDP73v/+hX79+uHbtGuLi4jB16lSMHj0aI0eOxPXr1zFixAhMnjxZ7HIZs3vx8fFwcXHBlClTMHDgQEybNg1bt27FhAkTxC6NORiZ2AUwZgnJyckoKirCqlWrMGjQIADAgAEDsGjRIvznP//BE088AQDo378/li9fDqPRCKmUczZj1YmPj4dcLsfWrVvh5+cHACgsLMT48eNBRJBIJCJXyBwF/6Zl9UJ8fDzc3d3x1FNPmbZduXIFfn5+6Nevn9m20NBQDhmM1eKvv/5CXFycKWQAQFBQELRaLcrKykSsjDka/m3L6oWEhAS0bdsWcrnctO2vv/5C+/btzUJFxXGMsepV3Irs1KmT2fbz588jNDQUCoVCpMqYI+KgweqF+Pj4Sr3jq9qWkJDAvegZq8WlS5eg0Wjg7u5utn3NmjUYNWqUSFUxR8VBg9ULVQWIO7cREc6ePcstGozVIj4+HhKJBBs2bAARgYgwf/585OXlYfr06WKXxxwMdwZlDq+goADXrl0zCxBpaWnIzc0123b58mVoNBoOGozVIj4+HjExMdBqtWjWrBkMBgOaNGmC3bt3w8fHR+zymIOREBGJXQRjjDH7ceXKFUgkEoSGhiIlJQV6vR7NmzcXuyzmoDhoMMYYY8xquI8GY4wxxqyGgwZjjDHGrIaDBmOMMcashoMGY4wxxqyGgwZjjDHGrIaDBmOMMcashoMGY4wxxqyGgwZjjDHGrIaDBmOMMcashoMGY4wxxqyGgwZjjDHGrIaDBmOMMcas5v8BxgNrKUnTSBQAAAAASUVORK5CYII=", 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", "text/plain": [ - "<Figure size 550x550 with 4 Axes>" + "<Figure size 605x605 with 4 Axes>" ] }, "metadata": {}, @@ -426,23 +507,22 @@ ], "source": [ "fig = None\n", - "for label, (p, s, color) in runs.items():\n", + "for label, (p, s, colour, _) in runs.items():\n", " fig = corner.corner(\n", " s[:, p.columns(line.params)],\n", " fig=fig,\n", - " color=color,\n", " labels=[\"$m$\", \"$b$\"],\n", " truths=[truth[\"m\"], truth[\"b\"]],\n", - " truth_color=\"k\",\n", - " plot_datapoints=False,\n", - " plot_density=False,\n", - " levels=(0.68, 0.95),\n", " range=[(0.3, 1.0), (1.5, 2.4)],\n", + " **plotstyle.corner_kwargs(\n", + " color=colour, fill_contours=False, plot_density=False, show_titles=False\n", + " ),\n", " )\n", "fig.legend(\n", - " handles=[plt.Line2D([], [], color=c, label=l) for l, (_, _, c) in runs.items()],\n", + " handles=[\n", + " plt.Line2D([], [], color=c, label=lab) for lab, (_, _, c, _) in runs.items()\n", + " ],\n", " loc=\"upper right\",\n", - " frameon=False,\n", " fontsize=8,\n", ")\n", "plt.show()" @@ -450,63 +530,58 @@ }, { "cell_type": "markdown", - "id": "7414cdf9", + "id": "5bdfe338", "metadata": {}, "source": [ - "Only the mode built from the prediction covers the truth with an honest\n", - "width. Statistics only is precise and biased; the inferred noise recovers\n", - "the 5 % the experiment reported and is therefore just as precise and just\n", - "as biased, because a diagonal cannot describe a common shift; the Peelle\n", - "covariance is biased further down and narrower than the honest one." + "Four covariances, four answers, and the truth is $m = 0.6$, $b = 2.0$.\n", + "\n", + "Statistics only is the confident one: $b = 1.836 \\pm 0.049$, more than three\n", + "standard deviations below the truth. Nothing in that covariance can express a\n", + "shift common to every point, so the fit absorbs the 10 % normalisation into the\n", + "line itself and then tells us it is sure.\n", + "\n", + "Inferring the noise *does* cover the truth ($m = 0.531 \\pm 0.277$), but look at\n", + "what it cost. The inferred noise came out at 6 %, above the 5 % the data were\n", + "really generated with, and the uncertainty on the slope roughly tripled. A free\n", + "diagonal can only buy coverage by inflating every point independently, which is\n", + "not what happened to these data: it gets the right answer for the wrong reason,\n", + "and pays for it in precision.\n", + "\n", + "The mode built from the prediction covers the truth at an honest width\n", + "($b = 1.917 \\pm 0.220$). It is the only one of the four that knows the defect\n", + "was a single number multiplying everything.\n", + "\n", + "And the Peelle covariance, built from the data, is pulled further down *and* is\n", + "narrower than the honest one ($m = 0.439 \\pm 0.097$ against\n", + "$0.503 \\pm 0.104$) — confidently wrong, the worst of both." ] }, { "cell_type": "markdown", - "id": "480de3bb", + "id": "f4f25df1", "metadata": {}, "source": [ - "## 5. Infer a normalisation or an offset the experiment did not report (recipe 4)\n", + "## 5. Inferring a normalisation or an offset nobody reported (recipe 4)\n", "\n", - "The mode's magnitude need not be known. `T.normalization(log_eta)` samples\n", - "it; `T.offset(...)` is the same for an additive shift. Here is an offset\n", - "defect, treated with the magnitude known and with it free." + "The magnitude of the mode need not be known either. `T.normalization(log_eta)`\n", + "samples it, and `T.offset(...)` does the same for an additive shift. Here we\n", + "plant an offset defect and treat it three ways: with the magnitude known, with\n", + "it free, and by ignoring it." ] }, { "cell_type": "code", - "execution_count": 10, - "id": "5b4aebd8", + "execution_count": 13, + "id": "96e6305c", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:32:07.489144Z", - "iopub.status.busy": "2026-09-11T03:32:07.489005Z", - "iopub.status.idle": "2026-09-11T03:32:46.962513Z", - "shell.execute_reply": "2026-09-11T03:32:46.961824Z" + "iopub.execute_input": "2026-09-12T02:06:05.309910Z", + "iopub.status.busy": "2026-09-12T02:06:05.309754Z", + "iopub.status.idle": "2026-09-12T02:06:05.314928Z", + "shell.execute_reply": "2026-09-12T02:06:05.314204Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "offset known : m = 0.535 +/- 0.109 b = 2.359 +/- 0.310\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "offset free : m = 0.547 +/- 0.108 b = 2.553 +/- 0.725 log_omega = -1.140 +/- 1.013\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "offset ignored : m = 0.535 +/- 0.110 b = 2.301 +/- 0.061\n" - ] - } - ], + "outputs": [], "source": [ "offset = 0.3\n", "y_off = y_true + rng.normal(0.0, noise_fraction * y_true) + offset\n", @@ -520,44 +595,19 @@ " [rx.Constraint([comp_off], terms=[T.offset(magnitude=offset)])]\n", ")\n", "p_off_free = rx.Problem([rx.Constraint([comp_off], terms=[T.offset(log_omega)])])\n", - "p_off_ignored = rx.Problem([rx.Constraint([comp_off])])\n", - "for name, p in [\n", - " (\"known\", p_off_known),\n", - " (\"free\", p_off_free),\n", - " (\"ignored\", p_off_ignored),\n", - "]:\n", - " s = fit(p, seed=5)\n", - " print(f\"offset {name:8s}: {summary(p, s)}\")" - ] - }, - { - "cell_type": "markdown", - "id": "bb79e5fd", - "metadata": {}, - "source": [ - "## Sharing an error model between datasets (recipe 5)\n", - "\n", - "A second experiment measures the same line with its own normalisation\n", - "defect. Two ways to couple them:\n", - "\n", - "- **case B**, share the *parameter*: pass the same `log_eps` object to a\n", - " term on each comparison. One column, two block-diagonal blocks.\n", - "- **case A**, share the *mode*: one normalisation term `on=[comp1, comp2]`\n", - " couples the data through off-diagonal blocks (the next notebook).\n", - "\n", - "Here is case B with the noise fraction inferred once for both." + "p_off_ignored = rx.Problem([rx.Constraint([comp_off])])" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "736b0ed9", + "execution_count": 14, + "id": "57ab6fc3", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:32:46.963854Z", - "iopub.status.busy": "2026-09-11T03:32:46.963692Z", - "iopub.status.idle": "2026-09-11T03:33:07.630117Z", - "shell.execute_reply": "2026-09-11T03:33:07.629387Z" + "iopub.execute_input": "2026-09-12T02:06:05.316410Z", + "iopub.status.busy": "2026-09-12T02:06:05.316284Z", + "iopub.status.idle": "2026-09-12T02:06:41.673933Z", + "shell.execute_reply": "2026-09-12T02:06:41.673141Z" } }, "outputs": [ @@ -565,128 +615,52 @@ "name": "stdout", "output_type": "stream", "text": [ - "one shared object -> one column: ['m', 'b', 'log_eps']\n", - "two objects -> two columns: ['m', 'b', 'log_eps_1', 'log_eps_2']\n" + "offset known : m = 0.535 +/- 0.109 b = 2.359 +/- 0.310\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "m = 0.428 +/- 0.140 b = 2.155 +/- 0.068 log_eps = -2.326 +/- 0.111\n" + "offset free : m = 0.547 +/- 0.108 b = 2.553 +/- 0.725 log_omega = -1.140 +/- 1.013\n" ] - } - ], - "source": [ - "x2 = np.linspace(0.01, 0.8, 27)\n", - "y2_true = truth[\"m\"] * x2 + truth[\"b\"]\n", - "y2 = (y2_true + rng.normal(0.0, 0.025 * y2_true)) * 1.1 # 10 % high\n", - "data2 = rx.Dataset(x2, y2, 0.025 * y2, label=\"second\")\n", - "comp2 = rx.Comparison(data2, line)\n", - "\n", - "shared = rx.Parameter(\"log_eps\", prior=stats.norm(np.log(0.05), 1.0))\n", - "c_shared = rx.Constraint(\n", - " [comp, comp2],\n", - " terms=[T.noise_fraction(shared, on=comp), T.noise_fraction(shared, on=comp2)],\n", - " statistical=False,\n", - ")\n", - "p_shared = rx.Problem([c_shared])\n", - "print(\"one shared object -> one column:\", p_shared.names)\n", - "\n", - "eps1 = rx.Parameter(\"log_eps_1\", prior=stats.norm(np.log(0.05), 1.0))\n", - "eps2 = rx.Parameter(\"log_eps_2\", prior=stats.norm(np.log(0.05), 1.0))\n", - "c_separate = rx.Constraint(\n", - " [comp, comp2],\n", - " terms=[T.noise_fraction(eps1, on=comp), T.noise_fraction(eps2, on=comp2)],\n", - " statistical=False,\n", - ")\n", - "print(\"two objects -> two columns: \", rx.Problem([c_separate]).names)\n", - "s_shared = fit(p_shared, seed=6)\n", - "print(summary(p_shared, s_shared))" - ] - }, - { - "cell_type": "markdown", - "id": "a840baae", - "metadata": {}, - "source": [ - "The two datasets disagree by 20 %, and a diagonal noise is the only\n", - "freedom this error model has: the inferred noise grows to about 10 % to\n", - "absorb the disagreement, the slope is covered, the intercept only just.\n", - "A normalisation mode per dataset is the model that knows what happened." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "f7406537", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T03:33:07.631733Z", - "iopub.status.busy": "2026-09-11T03:33:07.631525Z", - "iopub.status.idle": "2026-09-11T03:33:46.613008Z", - "shell.execute_reply": "2026-09-11T03:33:46.612258Z" - } - }, - "outputs": [ + }, { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 600x400 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "offset ignored : m = 0.535 +/- 0.110 b = 2.301 +/- 0.061\n" + ] } ], "source": [ - "p_both_stat = rx.Problem([rx.Constraint([comp, comp2])])\n", - "s_both_stat = fit(p_both_stat, seed=7)\n", - "p_both_norm = rx.Problem(\n", - " [\n", - " rx.Constraint(\n", - " [comp, comp2],\n", - " terms=[\n", - " T.normalization(magnitude=0.1, on=comp),\n", - " T.normalization(magnitude=0.1, on=comp2),\n", - " ],\n", - " )\n", - " ]\n", - ")\n", - "s_both_norm = fit(p_both_norm, seed=8)\n", - "\n", - "fig, ax = plt.subplots(figsize=(6, 4))\n", - "for label, (p, s, color) in {\n", - " \"statistics only\": (p_both_stat, s_both_stat, \"C0\"),\n", - " \"shared inferred noise (case B)\": (p_shared, s_shared, \"C2\"),\n", - " \"a normalisation mode per dataset\": (p_both_norm, s_both_norm, \"C1\"),\n", - "}.items():\n", - " lo, hi = band(p, s)\n", - " ax.fill_between(x_fine, lo, hi, color=color, alpha=0.25, label=label)\n", - "ax.plot(x, y_true, \"--\", color=\"k\", label=\"truth\")\n", - "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", color=\"C4\", ms=4, label=data.label)\n", - "ax.errorbar(data2.x, data2.y, data2.y_err, fmt=\"s\", color=\"C5\", ms=4, label=data2.label)\n", - "ax.set(xlabel=\"x\", ylabel=\"y\", title=\"two datasets, 90 % predictive bands\")\n", - "ax.legend(frameon=False, fontsize=8)\n", - "plt.show()" + "for name, p in [\n", + " (\"known\", p_off_known),\n", + " (\"free\", p_off_free),\n", + " (\"ignored\", p_off_ignored),\n", + "]:\n", + " s = fit(p, seed=5)\n", + " print(f\"offset {name:8s}: {summary(p, s)}\")" ] }, { "cell_type": "markdown", - "id": "bf699a94", + "id": "ecd7ab34", "metadata": {}, "source": [ "## Takeaways\n", "\n", - "- The covariance is the statistical model. Declaring it is the work; the\n", - " inference machinery does not change from one row of the ladder to the next.\n", - "- A defect the experiment did not report can be inferred: a noise level, a\n", - " normalisation, an offset, each one column in the chain.\n", - "- A correlated systematic is a mode built from the *prediction*. Built from\n", - " the data it is Peelle's Pertinent Puzzle.\n", - "- Sharing a `Parameter` object couples parameters (case B); a term spanning\n", - " comparisons couples data (case A). Both are declared, never configured." + "- The covariance *is* the statistical model. Declaring it is the work; the\n", + " inference machinery does not change from one rung of the ladder to the next.\n", + "- A defect the experiment never reported can be inferred: a noise level, a\n", + " normalisation, an offset — each one more column in the chain.\n", + "- A correlated systematic is a mode built from the *prediction*. Built from the\n", + " data instead, it is Peelle's Pertinent Puzzle, and it will quietly pull the\n", + " answer down.\n", + "\n", + "When two experiments measure the same thing, the same question comes back one\n", + "level up: do they share a parameter, or do they share a mode? That is\n", + "`sharing_error_models`, next door." ] } ], diff --git a/examples/sharing_error_models.ipynb b/examples/sharing_error_models.ipynb new file mode 100644 index 0000000..4cb1498 --- /dev/null +++ b/examples/sharing_error_models.ipynb @@ -0,0 +1,627 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c05b866b", + "metadata": {}, + "source": [ + "# Sharing an error model between two experiments\n", + "\n", + "Two experiments measure the same line, and each one carries its own\n", + "normalisation defect. We know how to give a single dataset an error model; the\n", + "question now is what to do when we have two, and we believe something about\n", + "them *jointly*.\n", + "\n", + "There are two quite different things we might mean by \"they share an error\n", + "model\", and `rxmc` spells them differently:\n", + "\n", + "- **Case B — share the parameter.** Each dataset has its own normalisation\n", + " measurement, but we believe the two magnitudes are the same number. We pass\n", + " the same `Parameter` object to a term on each comparison: one column in the\n", + " chain, two block-diagonal blocks in the covariance, and the datasets stay\n", + " statistically independent.\n", + "- **Case A — share the mode.** Both were normalised against the same uncertain\n", + " flux, so a single unknown moves them together. One term spans both\n", + " comparisons, and the covariance grows off-diagonal blocks that couple the\n", + " datasets.\n", + "\n", + "They are different statistical models, not two spellings of one. Recipe 5 has\n", + "both; here we watch what each does to the same pair of datasets, including what\n", + "it costs us to declare the wrong one.\n", + "\n", + "Recipes: 5" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d1df0443", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:06:44.481106Z", + "iopub.status.busy": "2026-09-12T02:06:44.480962Z", + "iopub.status.idle": "2026-09-12T02:06:46.584396Z", + "shell.execute_reply": "2026-09-12T02:06:46.583536Z" + } + }, + "outputs": [], + "source": [ + "import corner\n", + "import emcee\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import plotstyle\n", + "from scipy import stats\n", + "\n", + "import rxmc as rx\n", + "from rxmc import terms as T\n", + "\n", + "plotstyle.use()" + ] + }, + { + "cell_type": "markdown", + "id": "07dfbb95", + "metadata": {}, + "source": [ + "## Two experiments, one line\n", + "\n", + "The first experiment reports 5 % noise and came out 10 % low; the second reports\n", + "2.5 % noise and came out 10 % high. Neither reports a normalisation\n", + "uncertainty, so between them they disagree by 20 % while claiming a precision of\n", + "a few per cent." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "403664b8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:06:46.586017Z", + "iopub.status.busy": "2026-09-12T02:06:46.585819Z", + "iopub.status.idle": "2026-09-12T02:06:46.589892Z", + "shell.execute_reply": "2026-09-12T02:06:46.589289Z" + } + }, + "outputs": [], + "source": [ + "rng = np.random.default_rng(16)\n", + "truth = {\"m\": 0.6, \"b\": 2.0}\n", + "\n", + "x1 = np.linspace(0.01, 1.0, 15)\n", + "y1_true = truth[\"m\"] * x1 + truth[\"b\"]\n", + "y1 = (y1_true + rng.normal(0.0, 0.05 * y1_true)) * 0.9 # 10 % low\n", + "data1 = rx.Dataset(x1, y1, 0.05 * y1, label=\"experiment A\")\n", + "\n", + "x2 = np.linspace(0.01, 0.8, 27)\n", + "y2_true = truth[\"m\"] * x2 + truth[\"b\"]\n", + "y2 = (y2_true + rng.normal(0.0, 0.025 * y2_true)) * 1.1 # 10 % high\n", + "data2 = rx.Dataset(x2, y2, 0.025 * y2, label=\"experiment B\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1cd295d6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:06:46.591207Z", + "iopub.status.busy": "2026-09-12T02:06:46.591058Z", + "iopub.status.idle": "2026-09-12T02:06:47.459574Z", + "shell.execute_reply": "2026-09-12T02:06:47.458828Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 704x440 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_fine = np.linspace(0.0, 1.0, 40)\n", + "fig, ax = plt.subplots()\n", + "ax.errorbar(\n", + " data1.x,\n", + " data1.y,\n", + " data1.y_err,\n", + " fmt=\"o\",\n", + " color=plotstyle.COLOURS[0],\n", + " label=data1.label,\n", + ")\n", + "ax.errorbar(\n", + " data2.x,\n", + " data2.y,\n", + " data2.y_err,\n", + " fmt=\"s\",\n", + " color=plotstyle.COLOURS[2],\n", + " label=data2.label,\n", + ")\n", + "ax.plot(x_fine, truth[\"m\"] * x_fine + truth[\"b\"], \"--\", color=\"k\", label=\"truth\")\n", + "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"Two experiments that disagree by 20 %\")\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "515e623f", + "metadata": {}, + "source": [ + "## The model and the way we run a fit\n", + "\n", + "The same line, the same wide prior, and the same three helpers as in\n", + "`error_models`: `fit`, `band` and `summary`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "cda7a0a7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:06:47.460988Z", + "iopub.status.busy": "2026-09-12T02:06:47.460857Z", + "iopub.status.idle": "2026-09-12T02:06:47.464799Z", + "shell.execute_reply": "2026-09-12T02:06:47.464208Z" + } + }, + "outputs": [], + "source": [ + "m = rx.Parameter(\"m\", prior=stats.norm(2.0, 2.0), latex=\"m\")\n", + "b = rx.Parameter(\"b\", prior=stats.norm(5.0, 2.0), latex=\"b\")\n", + "line = rx.Model(lambda x, m, b: m * x + b, [m, b])\n", + "comp1, comp2 = rx.Comparison(data1, line), rx.Comparison(data2, line)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2e601856", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:06:47.466024Z", + "iopub.status.busy": "2026-09-12T02:06:47.465881Z", + "iopub.status.idle": "2026-09-12T02:06:47.469772Z", + "shell.execute_reply": "2026-09-12T02:06:47.469173Z" + } + }, + "outputs": [], + "source": [ + "def fit(problem, seed, n_walkers=24, n_steps=1800):\n", + " sampler = emcee.EnsembleSampler(n_walkers, problem.ndim, problem.log_posterior)\n", + " sampler.random_state = np.random.RandomState(seed).get_state()\n", + " sampler.run_mcmc(problem.sample_prior(n_walkers, rng=seed), n_steps, progress=False)\n", + " return sampler.get_chain(discard=n_steps // 3, thin=5, flat=True)\n", + "\n", + "\n", + "def band(problem, samples, levels=(5, 95)):\n", + " on_fine = line.bind(x_fine)\n", + " cols = problem.columns(line.params)\n", + " return rx.predictive.predictive_band(\n", + " [on_fine(*s[cols]) for s in samples[::10]], levels=levels\n", + " )\n", + "\n", + "\n", + "def summary(problem, samples):\n", + " return \" \".join(\n", + " f\"{n} = {samples[:, i].mean():.3f} +/- {samples[:, i].std():.3f}\"\n", + " for i, n in enumerate(problem.names)\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "b82723ec", + "metadata": {}, + "source": [ + "## What \"shared\" means to the compiler\n", + "\n", + "Sharing is by *object identity*, never by name. Passing one `Parameter` to two\n", + "terms gives one column; building two parameters that happen to have the same\n", + "name is an error, because the chain would carry two columns with one label." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c52b1b84", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:06:47.471338Z", + "iopub.status.busy": "2026-09-12T02:06:47.471217Z", + "iopub.status.idle": "2026-09-12T02:06:47.477630Z", + "shell.execute_reply": "2026-09-12T02:06:47.476945Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "one shared object -> one column: ['m', 'b', 'log_eps']\n", + "two objects -> two columns: ['m', 'b', 'log_eps_1', 'log_eps_2']\n" + ] + } + ], + "source": [ + "shared = rx.Parameter(\"log_eps\", prior=stats.norm(np.log(0.05), 1.0))\n", + "c_shared = rx.Constraint(\n", + " [comp1, comp2],\n", + " terms=[T.noise_fraction(shared, on=comp1), T.noise_fraction(shared, on=comp2)],\n", + " statistical=False,\n", + ")\n", + "p_shared = rx.Problem([c_shared])\n", + "print(\"one shared object -> one column: \", p_shared.names)\n", + "\n", + "eps1 = rx.Parameter(\"log_eps_1\", prior=stats.norm(np.log(0.05), 1.0))\n", + "eps2 = rx.Parameter(\"log_eps_2\", prior=stats.norm(np.log(0.05), 1.0))\n", + "c_separate = rx.Constraint(\n", + " [comp1, comp2],\n", + " terms=[T.noise_fraction(eps1, on=comp1), T.noise_fraction(eps2, on=comp2)],\n", + " statistical=False,\n", + ")\n", + "print(\"two objects -> two columns: \", rx.Problem([c_separate]).names)" + ] + }, + { + "cell_type": "markdown", + "id": "3dfb91c6", + "metadata": {}, + "source": [ + "## Case B: one inferred noise for both\n", + "\n", + "We let a single noise fraction, shared between the datasets, absorb the\n", + "disagreement. It is the only freedom this error model has, so we should expect\n", + "it to grow until it covers a 20 % gap that is not noise at all." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "20fb0438", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:06:47.479016Z", + "iopub.status.busy": "2026-09-12T02:06:47.478894Z", + "iopub.status.idle": "2026-09-12T02:07:04.761784Z", + "shell.execute_reply": "2026-09-12T02:07:04.761072Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "m = 0.502 +/- 0.133 b = 2.115 +/- 0.065 log_eps = -2.338 +/- 0.114\n", + "inferred noise fraction: 0.097\n" + ] + } + ], + "source": [ + "s_shared = fit(p_shared, seed=6)\n", + "print(summary(p_shared, s_shared))\n", + "print(f\"inferred noise fraction: {np.exp(s_shared[:, 2]).mean():.3f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "ea6b6bca", + "metadata": {}, + "source": [ + "## A normalisation mode per dataset, and then one across both\n", + "\n", + "Two more error models, both of which know what actually happened:\n", + "\n", + "- **a mode per dataset** (still case B in spirit): each experiment gets its own\n", + " rank-one normalisation mode, so each may slide independently;\n", + "- **case A**: one mode with `on=[comp1, comp2]`, a single unknown flux that\n", + " moves both together, which fills the off-diagonal blocks.\n", + "\n", + "Our two experiments drifted in *opposite* directions, so we should expect the\n", + "per-dataset modes to fit comfortably and case A to struggle: one common factor\n", + "cannot pull A up and B down at the same time." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "822f035a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:07:04.763667Z", + "iopub.status.busy": "2026-09-12T02:07:04.763538Z", + "iopub.status.idle": "2026-09-12T02:07:55.837506Z", + "shell.execute_reply": "2026-09-12T02:07:55.836902Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "statistics only : m = 0.569 +/- 0.043 b = 2.162 +/- 0.020\n", + "a mode per dataset : m = 0.648 +/- 0.064 b = 2.054 +/- 0.154\n", + "one mode across both (case A) : m = 0.484 +/- 0.573 b = 1.831 +/- 2.202\n" + ] + } + ], + "source": [ + "p_stat = rx.Problem([rx.Constraint([comp1, comp2])])\n", + "p_per_set = rx.Problem(\n", + " [\n", + " rx.Constraint(\n", + " [comp1, comp2],\n", + " terms=[\n", + " T.normalization(magnitude=0.1, on=comp1),\n", + " T.normalization(magnitude=0.1, on=comp2),\n", + " ],\n", + " )\n", + " ]\n", + ")\n", + "p_case_a = rx.Problem(\n", + " [\n", + " rx.Constraint(\n", + " [comp1, comp2], terms=[T.normalization(magnitude=0.1, on=[comp1, comp2])]\n", + " )\n", + " ]\n", + ")\n", + "s_stat = fit(p_stat, seed=7)\n", + "s_per_set = fit(p_per_set, seed=8)\n", + "s_case_a = fit(p_case_a, seed=9)\n", + "for label, p, s in [\n", + " (\"statistics only\", p_stat, s_stat),\n", + " (\"a mode per dataset\", p_per_set, s_per_set),\n", + " (\"one mode across both (case A)\", p_case_a, s_case_a),\n", + "]:\n", + " print(f\"{label:32s}: {summary(p, s)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "8b68bb40", + "metadata": {}, + "source": [ + "That is what declaring the wrong sharing costs.\n", + "\n", + "Statistics only is precise and wrong, as usual: $b = 2.162 \\pm 0.020$, eight\n", + "standard deviations from the truth, because with no way to say \"these two\n", + "experiments are on different scales\" the fit splits the difference and believes\n", + "it.\n", + "\n", + "A mode per dataset is the model that matches how the defect arose, and it\n", + "behaves: $m = 0.648 \\pm 0.064$, $b = 2.054 \\pm 0.154$, both covering.\n", + "\n", + "Case A insists that one unknown moved both experiments together. It cannot\n", + "explain a *relative* disagreement, so it does the only thing left: it inflates\n", + "the shared scale until the line is barely determined at all\n", + "($m = 0.484 \\pm 0.573$, $b = 1.831 \\pm 2.202$). The fit does not fail loudly;\n", + "it just stops saying anything useful. Declaring the wrong sharing is not a\n", + "small error." + ] + }, + { + "cell_type": "markdown", + "id": "dd04ba20", + "metadata": {}, + "source": [ + "### The covariance, seen directly\n", + "\n", + "`constraint.matrix(theta)` hands us the assembled covariance, which is the\n", + "quickest way to see the difference: a mode per dataset leaves the\n", + "cross-experiment blocks empty, while one mode across both fills them in." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3029669f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:07:55.839230Z", + "iopub.status.busy": "2026-09-12T02:07:55.839103Z", + "iopub.status.idle": "2026-09-12T02:07:56.039922Z", + "shell.execute_reply": "2026-09-12T02:07:56.039212Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 880x396 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "theta = np.array([truth[\"m\"], truth[\"b\"]])\n", + "fig, axes = plt.subplots(1, 2, figsize=(8.0, 3.6))\n", + "for ax, (label, p) in zip(\n", + " axes, [(\"a mode per dataset\", p_per_set), (\"one mode across both\", p_case_a)]\n", + "):\n", + " sigma = p.constraints[0].matrix(theta)\n", + " scale = np.sqrt(np.outer(np.diag(sigma), np.diag(sigma)))\n", + " im = ax.imshow(sigma / scale, cmap=\"RdBu_r\", vmin=-1, vmax=1)\n", + " ax.set(title=label, xlabel=\"point\", ylabel=\"point\")\n", + " ax.grid(False)\n", + "fig.colorbar(im, ax=axes, label=\"correlation\", fraction=0.03)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "be260c5c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:07:56.041396Z", + "iopub.status.busy": "2026-09-12T02:07:56.041269Z", + "iopub.status.idle": "2026-09-12T02:07:56.054600Z", + "shell.execute_reply": "2026-09-12T02:07:56.053981Z" + } + }, + "outputs": [], + "source": [ + "runs = {\n", + " \"statistics only\": (p_stat, s_stat, plotstyle.COLOURS[0], plotstyle.HATCHES[0]),\n", + " \"shared inferred noise\": (\n", + " p_shared,\n", + " s_shared,\n", + " plotstyle.COLOURS[2],\n", + " plotstyle.HATCHES[1],\n", + " ),\n", + " \"a mode per dataset\": (\n", + " p_per_set,\n", + " s_per_set,\n", + " plotstyle.COLOURS[4],\n", + " plotstyle.HATCHES[2],\n", + " ),\n", + " \"one mode across both\": (\n", + " p_case_a,\n", + " s_case_a,\n", + " plotstyle.COLOURS[1],\n", + " plotstyle.HATCHES[3],\n", + " ),\n", + "}\n", + "bands = {label: band(p, s) for label, (p, s, _, _) in runs.items()}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "498bd9ae", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:07:56.055967Z", + "iopub.status.busy": "2026-09-12T02:07:56.055804Z", + "iopub.status.idle": "2026-09-12T02:07:56.260847Z", + "shell.execute_reply": "2026-09-12T02:07:56.260287Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 704x440 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "for label, (lo, hi) in bands.items():\n", + " _, _, colour, hatch = runs[label]\n", + " plotstyle.band(ax, x_fine, lo, hi, color=colour, hatch=hatch, label=label)\n", + "ax.plot(x_fine, truth[\"m\"] * x_fine + truth[\"b\"], \"--\", color=\"k\", label=\"truth\")\n", + "ax.errorbar(data1.x, data1.y, data1.y_err, fmt=\"o\", color=\"0.4\", ms=3)\n", + "ax.errorbar(data2.x, data2.y, data2.y_err, fmt=\"s\", color=\"0.4\", ms=3)\n", + "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"90 % predictive bands\")\n", + "ax.legend(fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c4759e6b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:07:56.262693Z", + "iopub.status.busy": "2026-09-12T02:07:56.262558Z", + "iopub.status.idle": "2026-09-12T02:07:56.456975Z", + "shell.execute_reply": "2026-09-12T02:07:56.456395Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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fn+eff55JkyZxxx13cODAAfbv38/XX39d5ryMjAyuu+46Ro4cSXBwMF9++SX/+Mc/Koy1Xbt21YrpYpJACSGEaJBuvfVWunXrxu+//05gYCDbtm2jffv2GI1GZs+eXebciRMnEh0dDUCvXr3UIaPQ0FCWL1/Oe++9h4+PDzqdrty1fn5+7Nq1iwULFnD48GGGDRvGBx98gJeXFxEREezatYuffvoJd3d3Vq9ezZw5c9RhxIu1adOGGTNmANC+fXtuuOGGMscffPBBtcxBQEAAjz76aJXicNTW8OHDcXd3L7Pv9ttvp3fv3qxcuRKTycQHH3yg9gq98MIL9OjRg7179/LQQw/h7e2tzidz5I477iAqKkp93bVr1zJDhs8++yz9+vUjLi6OMWPGMH/+fHW+16233kqLFi1o164dGzZs4KeffsJgMPDFF1+o86YqirUmaJTaLk0qhBBCCFHPSR0oIYQQQggXSQIlhBBCCOEiSaCEEEIIIVwkCZQQQgghhIskgRJCCCGEcJEkUEIIIYQQLpIEqpZZLBaSkpKwWCx1HYoQQgghqkkSqFp2/vx5oqKiOH/+fF2HckkyMjIu+z0Gzo1j4Ny4Gm0zbflArps2lWnTpqmrql/q/dKWDyRt+cCaCrHKauN7cLldDe9BiJqwfPlyjh49WtdhVEl9ivVykgRKVIvUX617V8P34Gp4D0LUhPfee4/t27fXdRhVUtVYzWYzH330UbkFgGtSbdzDGUmghBBCCFHjjEYjs2bNorCwsF7fwxlZC08IIUSD9dlnn2GxWPDz86NPnz60bt3a4XkFBQV8+eWXzJw5kz///BODwcD48ePx8/Nj/fr1nDt3juHDhxMeHl7mul27dnHo0CE6dOhQZh02RVHYuHEjqampDBo0yOE9Dx8+zJ49e2jatCmDBw92uMhw6bg2btxIQUEBI0eOxNfXt9K2iq+9/fbb2bRpE/n5+UyaNKncPSqL1dlnuGjRIvW4Xq9n0qRJNG3atMLPPDU1lfXr1+Pl5cXw4cPLrAno6D04u0dtkB4oIYQQDda+ffvYu3cvy5Yto1evXvz0008Oz8vNzWXWrFkMHjyY+fPn89prrzFo0CBuu+02Xn/9db7//nu6dOlCenq6es2DDz7I5MmTWb58OZMmTeKRRx5Rj91xxx3MnDmTRYsWMXTo0HJzil566SWGDBnC0qVLefTRR5k8ebLDIe/iuIYPH85nn33GK6+8Qu/evTEYDJW2VXztyJEj+eSTTzhy5IjD915ZrM4+w+Lzio/n5uZWeP6RI0do374933//PfPnz2fIkCFkZmZW+B6c3aNWKKJWJSYmKoCSmJhY16FckrS0tMt+jwHvbVIGvLepRttMXTZAmTZ1ijJ16lRl6tSpNXK/1GUDlNRlA2oqxCqrje/B5XY1vAfhmtRlA5SUn5tf9q/q/D/5559/Kq1atXIcd2qqAihLly5VFEVRjEaj4ufnpzz11FPqOSNHjlQ++eQTRVEUZdu2bYqfn59y/vx5RVEU5dy5c4qvr6+yfft2ZevWrUpgYKD683/48GFFr9cr33zzjaIoipKQkKAEBQUpSUlJiqIoisViUXr06KEsXrzYaVwff/yxoiiKYrValZEjRyr//ve/K22r+Noff/zR6WdSWawVfYYGg0EBlPz8fKftlz5/7ty5ym233aYeO3z4sJKWllbhe6jKPS4XGcITQghRa2x5SVjzTtd1GKr8/HzWrVtHcnIyZrOZEydOUFBQgKenp8PzR40aBYCXlxdRUVGMGDFCPda2bVuSk5MB2LZtG0OHDiUsLAyA8PBwhg4dyrZt27DZbAwfPpyQkBD1um7duqntbNy4kfDwcFasWIGiKCiKQlBQEDt37nQ4xAZw/fXXA6DVarn++uvVoa2K2urfvz8AEyZMcPr5bNu2rcJYK/oMnXF2/sCBA3nrrbeYN28eI0eOpH379gD8+OOPTt/D4MGDnd7ncpMESgghRK3R+kReMfdJSkqiT58+tGvXjujoaNzd3VEUhaysLKcJlLu7u7qt0WjKvbbZbID96bDSx4qvNZvN2Gw2h8eK5efnYzKZ2LVrl7qvVatWdO7c2el7KX198X2q2lbpeUYXc/Y+ilX0GXp4eJRrr6Lzu3btyubNm1myZAnPPfccp06dYsmSJdX6PGqDJFBCCCFqTaNxNVvb7VIsX76c7t278/vvvwOwf/9+Pv744xopr9G5c2feeecdTCYTHh4eFBQUsGXLFu69916sVivvv/8+FosFvV6PwWBg37596rU9evRAURTeffddNQkxmUwV1k3buHEj48aNA2DDhg1qclGdti5+HxXFWtFnWHy/wsJCNSGt6PzTp0/TrFkz7r//fu6//36mTZvGTz/9VOF7cHSP2iIJlBBCiAYpJiaGJ554ghdeeAG9Xs+vv/7q8Em36rjmmmto3bo1I0eOZNKkSfz222+0b9+e0aNHoygK4eHhjBkzhmuuuYYFCxaU+eU/bNgw+vTpw8CBA7nuuuvIyclhyZIlfPTRR+We8iv29NNPs2fPHs6cOcPixYvZvXt3pW21bdu2Su+jolgr+gy9vLxo1aoVTz31FF26dGHSpEkVnr927Vq++OILxo8fj8lkYt26dcyePZv+/fs7fQ/9+/cvdw95Ck8IIYS4jPr168fChQvJyclRf5nPmjULb2/vcud6eXlx3333ldk3ffr0Mr+sBw0aRM+ePQH7cN7atWu57bbbSElJ4fbbb2fVqlVoNBq0Wi1r165lwoQJZGZm8vbbb/N///d/ZRKa77//nueff57MzEx8fHxYuHChOmfJkaVLl6LT6QgPD2fHjh20bNmy0rYcvaeLVRZrZZ/h0qVLCQkJYd++feTm5lZ4/l133cU777xDXl4eiqLwxx9/qO+5os/j4nvUFo1SE32VosqSkpKIiooiMTGRyMjamQtwOaSnp6uTCi+X4mVV4h6uuWVS0pYP5P7PG6Ng/4tnwYIFl3y/4mVcantooja+B5fb1fAehKhLaWlphIaGSlX/OiA9UEIIIYQQLpIESgghhKinqjIMJy4PSaCEEEKIesrHx4ePPvqorsNokCSBEkIIIYRwkSRQQgghhBAukgRKCCGEEMJFkkAJIYQQQrhIKpGLq07yl7MoSEpweMxiiMdLMxijUn6NJiGEqI927NhBREQEERERdR3KZXHu3DkSExPp06fPFdWe9ECJq05BUgKmxHiHxyyZRrTYajkiIYS4fP7v//6P9evX13UYl82ff/7Jc889V61rz58/z5YtW2qsvdKkB0pclTyiYmnxfPnK4Edn+0NWHQQkhBCi1sXFxTFnzhzi4mp+pQjpgRJCCNGgnTt3jg0bNnDu3Dl1X2FhIUuXLkVRFP7++2+2bNmCyWQqd63BYGDTpk0cP37cafvFbdlsNg4cOEBcXBxWqxWAo0ePsnHjRodruDmKq9ixY8fYunUr+fn5Du9ZlbgAVqxYwdKlS9m4cSMGg8FhO3FxcSQlJZV7P4qicPDgQbZt2waAoigcOnSIuLg4srOzy7STlpbGhg0bOHHiRJX2O5Kfn8+2bds4evRouWOO7l1QUMDu3bsxGAwsXbqU5cuXl7nGZDKxfft24uMdj1hURnqghBBCNFgvvfQSb7zxBrGxscTHx/P000/z9NNPk5WVxcSJE5k0aRKpqamkpaXh6+vL9u3bcXNzA+Dzzz/nySefJCYmhuPHjzNp0iQ++OCDcvcobmv48OGYTCZOnz5NmzZt6NOnD8uWLUOr1ZKXl8dff/2Fr69vhXEBPPvss3z44YfExMSQlpZW7n5VjQvg008/xWw2k52dzf79+/npp58YNWoUAB9//DH//Oc/6dy5M+fOneOZZ57hnnvuUd/PhAkTSEtLY/DgwXTr1o2pU6cSHx9PVFQUR48e5ccff2TEiBEsXbqUW2+9la5du3Lu3DnGjx/P22+/7XS/I0lJSfTs2ZOQkBASEhK45557eP311wF7IuTo3l27dmXNmjUkJyfz0UcfodPpGDduHABnz56lb9++BAYGkpCQwC233MKcOXNc+MmRBEoIIUQtOvniQCyGpMpPvET6oEiHw/ilJSQk8NJLL7Fv3z7atGnD4cOH6datGxMmTKBJkyYATJs2jdtuuw2LxUKHDh1YvXo148eP5/jx48yePZstW7bQsWNH8vLy6NmzJ3/88QcjRoxweL+ZM2dy2223YTAYiIyMpFWrVsTHx6MoCgMHDmTBggXMnDmzwrhsNhvvvfce+/fvJzo6mh07dtC3b1/1Hq7GtXDhQnV78eLFzJ49m/j4eA4fPsw//vEPNm3aRI8ePbBaraxatarMtWPHjuWBBx4AYM6cOZw7d47Dhw/j7e3NZ599xl133cWxY8f44osveOONN7j77rsB+O233wCc7nfkyJEjbN26lT59+nDq1Ck6d+7MjBkz6NatGx9++KHTez/99NPMmTOHpUuXlmnv9OnTxMfH07p1a44dO0b79u156aWX8PHxcRrDxSSBEkIIUWsshiQK007XdRgAbNy4kaFDh9KmTRsA2rVrx5AhQ/jzzz+54YYbANT/6vV6unbtypkzZwBYu3YtLVq04MyZM5w+fRpFUWjXrh1xcXFOE6jitoKCgmjevDnTpk0DQKPR0KVLF7XtiuKyWq2MGDGC6OhoAHr37k2XLl3Ue7gal6IoxMfHk5ycDMCBAwcoLCxk3bp1DBo0iB49egCU6b0pdvPNN6vbGzZs4NZbb8Xb2xuAO+64g4ceeogTJ07QvXt3fvnlFzp06ECvXr2YPHkygNP9jsTExKhPzUVHRzNq1Cg2btxIt27dKry3M927d6d169YAtG7dGj8/P86dO6d+5lUhCZQQQohaow+KvGLuk5ubqw6ZFfP19SUnJ0d97enpqW7rdDp17lJmZiapqanMmzevzPXFPVeOlG5Lq9WWe13cdkVx2Wy2csf8/PzUbVfiSk9PZ9iwYZhMJpo3b467uzs2m42MjAyys7Px9/d3+l4AAgIC1O2LY9bpdHh5eZGTk8Pzzz/PsmXL+P7777n//vsZO3Ysr7/+utP9jjh6z8Xzxiq6tzOlP/via4o//6qSBEoIIUStqWxYrTa1a9eOTz75BKvVik6nw2KxsHPnTm655ZZKr42JiSEgIIAlS5ag1dqfx1IUhaysS3/Mt6K4rFYrX375JYqioNFoMBqN7N+/v1px/fbbb4SFhbFmzRoATp48ybJly1AUhc6dOzNv3jxMJhMeHva6eTk5OWWStYtj3rZtG/feey8Ahw4dIjc3l1atWmEwGBg/fjzjx4/HbDbTuHFjbrvtNiIiIhzu79y5c7n2//77b4xGI97e3iiKws6dO5k4cWKl905KSnI5MaoqSaCEEEI0SBMnTuRf//oX06dPZ9q0afzyyy/4+/szYcIEh0+klTZ27FjefPNNJk2axIwZM8jJyeH777/n3//+t9MhvJqIy2az8cwzz3Drrbcyfvx4vvnmGwoLC6sVV7Nmzdi3bx+ff/45er2ejz76CI1GA8C4ceNo3rw548aN44477uDUqVOcO3fO6WT0Rx55hJ49e9K0aVPatWvHa6+9xqxZswgMDGTatGlER0fTp08fDh48iEajISIigrvvvtvhfkcsFos6H23ZsmUUFhZy7bXXVnrvtm3bcuDAAb777juCgoLKDUNeCiljIBocHQoAvpoCTr44UP166tAD3HT6zTqOTghRW/R6PXFxcXTq1InFixcTExPDpk2b0Ol0uLu7M378+DLnd+/enRYtWgD2eUurVq1i7NixrFy5koSEBF599VWHyZOjtoYMGUJISIj6unPnzrRt27bSuNzc3Pjzzz8JCwtj5cqV3HXXXTz++ONERka6HNeoUaN44403WLNmDbt27eLdd99l4sSJeHp6otFo+OOPPxg3bhzLly/HarXy5ptvOn0/7dq1Y/v27WRnZ7Ny5UoeeeQR3n33XQC+//57mjVrxsKFC8nIyGDz5s0EBQU53X+xiIgIHn30Ue69915WrlxJ48aNiYuLw93dvdJ7d+jQgTlz5vD777/z8ccfq+2VnngPMGbMmHLDhJXRKIqiuHSFuCRJSUlERUWRmJio/sDXR+np6WX+578cBs61d/XHPTzQpetOvmg/39FQwcn/hvOf+G7kKl74agr4T9eSbu2M439x1rsVk953rSZI2nL7/RqNq92hidr4HlxuV8N7EEI0TDKEJ+qlita7MyXG4xEV6/CYX89W5O2zTx7MVTxp8fwy9VjCg46vEUIIIS4mQ3iiXqpovTuPqFg8I2NqOSIhhBANifRAiXrL2Xp3dcFiiFeH8kpzC4ohoN+HdRCREEKIy0kSKCEukVuQ494ui6F66ysJIYS48kkCJcQlctbD5KhHSgghxNVB5kAJIYQQQrhIEighhBBCCBdJAiWEEEII4SJJoIQQQgghXCQJlBBCCCGEiySBEkIIIYRwkSRQQgghhBAukgRKCCGEEMJFkkAJIYQQQrhIEighhBBCCBdJAiWEEEII4SJJoIQQQgghXCQJlBBCCCGEiySBEkIIIYRwkb6uAxCipmVtnUWhIcHhMYshHhhZuwEJIYS46kgPlLjqFBoSihKl8vRBsbUcjRBCiKuR9ECJq5I+KJZG4+IcHlM+n1bL0QghhLjaSA+UEEIIIYSLJIESQgghhHCRJFBCCCGEEC6SOVBClBJhPM7JFwc6POYZGUP47R/WckRCCCGuRJJACVEkyaslAMEOjpkSHT/VJ4QQomGSBEqIIt83fwKAuIfL90A565USQgjRMMkcKCGEEEIIF0kCJYQQQgjhIkmghBBCCCFcJHOgxBXrptNvEpl/gpMv+pc7ZkqMxyNKlmURQghRN6QHSlyxIvNPEGE87vCYR1QsnpExtRyREEIIYSc9UOKKdta7FT2ed7ymnRBCCFFXpAdKCCGEEMJFkkAJIYQQQrhIEighhBBCCBdJAiWEEEII4SJJoIQQQgghXCQJlBBCCCGEiySBEkIIIYRwkSRQQgghhBAukgRKCCGEEMJFkkAJIYQQQrhIEighhBBCCBdJAiWEEEII4SJJoIQQQgghXCQJlBBCCCGEiySBEkIIIYRwkSRQQgghhBAukgRKCCGEEMJF+roOQDQMs7YsIMGQ7PBYTFA4H/afVssRCSGEENUnPVCiViQYkol3kEDFG5KdJlZCCCHElUp6oEStiQ0KJ278Q2X2DVw2r46iEUIIIapPEihR5+INyQ4TqQe12WgVXR1EJIQQQlRMEihRp2KCwp0es2IBTS0GI4QQQlSRJFCiTlU0efyHlW/WYiRCCCFE1ckkciGEEEIIF0kCJYQQQgjhIkmghBBCCCFcJAmUEEIIIYSLJIESQgghhHCRJFBCCCGEEC6SMgbiimbF4rRaeW2voWdKjOfkiwMdHvOMjCH89g9rLRYhhBB1SxIoccXSKjqnhTQdrat3OXlGxjg9ZkqMr8VIhBBCXAkkgRJXLC/FBxTKrZ8Htb+GXkW9S856pYQQQly9ZA6UEEIIIYSLJIESQgghhHCRDOEJUUp8cg4D58Y5PBYT7s+H18W61J7FEE/acsdDfG5BMQT0k4nnQghRH0kCJUSRmHB/p8fik3Ncbs8tyPnEc4tBJp4LIUR9JgmUEEUq6l1y1itVkYp6l5z1SgkhhKgfZA6UEEIIIYSLJIESQgghhHCRJFBCCCGEEC6SOVCi3oo3JDssqPk/QzLeenca1UFMQgghGgZJoES9FBMU7vRYnsVci5EIIYRoiCSBEvVSRYsIr//2nVqMRAghREMkCZS4KuVZzE7Xy2uM0zWKhRBCiCqRSeTiquOtd8dH7+7wWLwhuZajEUIIcTWSHihx1WnlHwJA3LiHyh2z90odquWIhBBCXG2kB0oIIYQQwkXSAyVqzKwtC0hwMkQWb0gmtoIn54QQQoj6RBIo4ZLiJMlisaDXl/3x2XzhFAADGkeXuy42KLzC0gNCCCFEfSIJlHBJgiGZeEMyHf1Cyx0b0DiamKDwCksMCCGEEFcDSaCEy2KDwlncdwYhISF1HYoQQghRJ2QSuRBCCCGEiySBEkIIIYRwkSRQQgghhBAukgRKCCGEEMJFkkAJIYQQQrhIEighhBBCCBdJAiWEEEII4SJJoIQQQgghXCQJlBBCCCGEi6QSuWiwFGDgsnll9slSNEIIIapCEijRoMQEhZPi5Fi8IblWYxFCCFF/SQIl6lTyl7MoSEpweCzCeJyz3q1q9H4f9p/GtLe+B0ADxI1/SD12cW+UEEII4YwkUKJOFSQlYEqMxyMqttyxs96tSPJq6fC6rK2zKDQ4Trwshnj0QeXbu5xMifGcfHFguf2ekTGE3/5hrcYihBDi8pMEStSp42l5GPXRvB7warlj8cYcYsP9eMDBdYWGBKeJkj4oFregmMsQrWOekY7vZUqMr7UYhBBC1C5JoESdMhZayTNbHR6LDfcjJtzf6bX6oFgajYu7XKFVmbMeJkc9UkIIIa4OkkCJOufjriPuYUk2hBBC1B9SB0oIIYQQwkWSQAkhhBBCuEgSKCGEEEIIF0kCJYQQQgjhIkmghBBCCCFcJAmUEEIIIYSLJIESQgghhHCR1IES5czasoAEJwvrxhuSiQ0Kr+WIhBBCiCuL9ECJchIMycQ7SaBig8KJkQRKCCFEAyc9UMKh2KBw4sY/5PR4enp6LUYjhBBCXFmkB0oIIYQQwkWSQAkhhBBCuEgSKCGEEEIIF0kCJYQQQgjhIkmghBBCCCFcJAmUEEIIIYSLJIESQgghhHCR1IESoo5YDPGkLR/o8JhbUAwB/T6s5YiEEEJUlSRQQtQBt6AYp8cshvhajEQIIUR1SAIlRB2oqHfJWa+UEEKIK4ckUA2ULBgshBBCVJ9MIm+gZMFgIYQQovqkB6oBq2zBYCGEEEI4Jj1QQgghhBAukgRKCCGEEMJFMoQnRCnxhmQGLptXfr9XNj42f0CekBNCCCEJlBCqiibOG7XZtRiJEEKIK50kUEIU+bD/NKfH/D97uRYjEUIIcaWTOVBCCCGEEC6SHighqsiozXY4Pwrsw38V9WAJIYS4ukgCJUQV2CeQO+asICmAKTGeky86nnjuGRlD+O2yYLAQQtRHkkAJUQWtTF0BiBtfPhly1ivlGel8wWBToiwYLIQQ9ZkkUEJcJhX1LjnrlRJCCFE/yCRyIYQQQggXSQIlhBBCCOEiSaCEEEIIIVwkCZQQQgghhItkEvlVbNaWBSQ4ecQ+3pBMbAVLlwghhBDCOemBuoolGJKd1iiKDQqvcO03IYQQQjgnPVBXudigcOLGP1TXYQghhBBXFUmgRK1I/nIWBUkJ5fZHGI9z1rtVHUQkhBBCVJ8M4YlaUZCU4LD69lnvViR5tayDiIQQQojqkx4oUWs8omJp8XxcmX23zrW/fqAuAhJCCCGqSXqghBBCCCFcJAmUEEIIIYSLJIESQgghhHCRJFBCCCGEEC6SSeRC1IB4QzIDl80rtz8mKJwP+0+rg4iEEEJcTpJACXGJnFV0d1YFXgghRP0nCZQQl8hZD5OjHikhhBBXB0mghKgjpsR4Tr44sNx+iyEefYAPjcbVQVBCCCGqRBIoIeqAZ2SM02OWTGMtRiKEEKI6JIESog6E3/6h02NHZ/ujWIykLS/fOwXgFhRDQD/n1wshhLj8JIESV6ysrbMoNJRfgBiKhrmCYms5otqh0fs4PWYxlF9PUAghRO2TBEpcsQoNCU4TJX1QLG5BzofB6jOdXysAGo2LK3fMWa+UEEKI2iUJlLii6YNiHSYSQgghRF2SBEqIKopPzmHgXMfJXEy4Px9ed3UOKQohhChPEighqiAm3N/psfjknFqMRAghxJVAEqh6btaWBSQ4qXgdb0gm1kmVbOGainqXnPVKCSGEuHpJAlXPJRiSnSZKsUHhTpcZEbXD2Rp5IOvkCSFEfSYJ1FUgNiicuPEP1XUYFTqeloex0MqtF/XWxCfnEBvuV0dRXV4VJa+yTp4QQtRvkkDVA1fDMJ2x0Eqe2Vpuf2y4X4Xzi+qzinqXZJ08IYSo3ySBqgeulmE6H3cdcQ9LHSMhhBD1nyRQ9UR9GKYTQgghGgpJoISoI84mmD9lSMZb706LOohJCCFE1UgCJUQdqGjYNc9irsVIhBBCVIckUELUgYommC9Z9w4Rmec4+WL5+WIWQzz6AB8ajbuc0QkhhKiMJFBCXGGSApoCEOzgmCXTWLvBCCGEcEgSKCGuMN/3mA7g8KGBo7OvzpIPQghR32jrOgAhhBBCiPpGeqCuIM4KZtaXYplCCCFEQyEJ1BXEWcHM+lIsM/nLWRQkJTg8FmE8zlnvVrUcUf3lrMTBPIsZrUY6joUQoq5JAnWFqc8FMwuSEjAlxuMRFVvu2FnvViR5tayDqOqfipJlm6IAttoLRgghhEOSQIka5REVS4vn48rtL15E+IHaDqgeqqjEwd7VT+ChmElbPhBLoYU0t7L/C7sFxRDQ78PLHaIQQjR4kkAJUY+YNB7oc8wY/ohHURQ0Go16TLEYcQs5LgmUEELUAkmghKhHdgfHEqk7R0xQOBaLBb2+5H/hgpPb0Ojz6jA6IYRoOCSBEqIeKV0jKj09nZCQEPWY1IgSQojaIwlUHZnyx3w8GgWV2SflCsSlKjTkOE2k3BtH0fyfB2o5IiGEuDpJAnUFqS/lCmpS1tZZFBoclz6wGOLRB5V/ou9KFJ+cw8C55SfPx4T78+F1NfseikscXDyE96KXD8FWC24Orik05ACJNRqHEEI0ZJJA1ZFFI+4gMjKyrsOoc4WGBKeJkj4oFregmDqIyjUx4Y57fOKTc2r+XhUk2JPa3+m0DIYM7wkhRM2SBErUOX1QLI3Gle+9qS+c9TA56pG65HuVKnFw8RwoR4U3hRBCXB6SQAlxFZEK5kIIUTskgRLiKiEVzIUQovZIAiVcUrzencViIVtf9scn6+Rezni2VKuOlxafnENsuF9thdkgVVbBXJ9j5uSLAx0e94yMIfx2KcAphBBVJQmUcEnxene68I7ljp3xbEm8EuXwuthwP6eTrcXlZ/F1x0Mxk5W6q9wx90wT+Uc3O10IWpIrIYQoTxIo4TKPqFj8Zy0uM4EZSta7i3vYcS+HqDvJPfvhm3fM4bGoAyew5uEwudLnmLmQlsD1yzqVOxYTFF5hr5cQQlzNJIESogGYOGWV02O/LxrjNLlqsuMkPtk5PLXunTL78yxmDCHR4CCBmrVlAQmGZKf3k8RLCHE10CiKotR1EA3JqVOnaNGiBd9c70Goj6byC64wOqMJq7cHmR2ao6Fs/EaLFW+9jvZNfKvcniXzIPrADgQPX1TToTp1zz33qNuffvrpZbvPlC92cPBCHh0a+1yW9i1WK3qd7rK0Xew/5+/COz8LMx5l9rvlFABg8nMvd42C/Z+Ui38+Kjt2ucT87zhNmjQpU3RUCCEulSRQtWznzp307t27rsMQokFJTEyUwrVCiBolCVQtKygoICEhgdDQ0Hr7F3FycjK9e/dmx44dhIfXv6Vn6nv8IO/BVdIDJYSoafIvSi3z9PSkV69edR1GjQgPD6/Xf9XX9/hB3oMQQtQVKU0shBBCCOEiSaCEEEIIIVwkCZRwmb+/P//+97/x96+fhTHre/wg70EIIeqaTCIXQgghhHCR9EAJIYQQQrhIEign1q5dy6xZs/jll1/qOhQhhBBCXGEkgXJgzpw53H///YwfP57Ro0fXdThCCCGEuMLIHKiLpKWlER0dze7du2nXrt0lt5ednU12drb62mKxkJ+fT5s2baSwnxBXEIvFwvnz56XophCiSuRfiYts27aN4OBgNXnKy8vjf//7H8uXLycsLIz//e9/9O3bt8rtvf3227zwwgvl9sfHx9O0adMai7u2ZWdnX9FPT114rjVKfib68I6EPPlnmWP33nuvuv3JJ5/UdmhVkr/je7J/fAQA/xnz8Op1Y7lzrvTvQVXUxnsICQmp0nnnz58nKiqqXi37kpGRQXBw8CW1kbZ8IACNxsXVREhOTZtmX0BaARYuWFBj7Q6ca4877uGBVb6movdcur2LP9+By+bZj41/qNrxXk418fMgqk4SqIt4eHiQnJxMeno6fn5+jBo1Cl9fX26//XYWLFjAsGHD2LJlC926datSe48//jh33323+rp4+YqgoKAq/8N+pbqS40/ValAAnU5XYZxX6nvI9PGluN/S19eXQCdxXqnxu+JqeA91RQYQLq/69vnWt3jrO0mgLtKvXz+8vLx48cUX6d69OwEBAaxYsQKARx55hF69ejFnzhy++uqrKrXn7+9f73sJhBBCCFGWJFAX8fX15V//+hf//Oc/6dWrFzNnzlSP6fV6hg0bRmJiYh1GKCpjzc9GMRvtLzSaug2mBiiFproOQQghxEUkgXLgiSeeID4+nm+//ZaAgADuv/9+tFotRqOR5cuX89///reuQxQVSF/5tpp0eLfuX8fRVI97k7bqtvHoZoKG3VvB2UKImhSfnMPAuXFYLJYyDxTEe2Xj466rw8jElUQSKAc0Gg1fffUVUVFRvPbaawwZMoRhw4bx66+/MnToUG644Ya6DlE4YclOJWPlWwBo9O40mvhMHUdUPV6t+qD1DsBmzCJ3/yoUmw2NVqqOCHG5xYQ7n3JhNFtrMRJxpWuQCZSiKGRlZREYGOj0HK1Wy8svv8wtt9zCt99+S2pqKi+99BJTpkypvUCFy9J+fxlbQS4AQSMexC2kWR1HVD0anR6fTqPI2fkr1qwUTInxeDbvWtdhCXHV+/C6WHU7PT29zEMO/p/96egS0UA1uD9pP/nkE5o0aUJQUBBdu3bl+PHjFZ7fsWNHXn75ZT788ENJnq5whelnMKz7AACtpy+NJtTP3qdivjHXqNu5CSvrMBIhhBAXa1AJ1FtvvcWbb77JZ599xooVK8jLy+Pmm292eO7GjRv58MMPazlCcSlSf3sBxWIGIPia2ej9Q+s4okvjGzNG3ZYESgghriwNZggvIyOD1157jS1bttC6dWsA5s6dy9ixYzlx4gQtW7Ysc/7ChQt57733aN26NaNGjaqLkIULTOcOkbnpSwB0viGEXPN4jbSr2KxYs1OxZJ2nMDMZS1YylqzzaD188O91PW5Bl68YqltwJB6RnTEl7cd4JA5LVgr6gLDLdj8hhBBV12ASqEWLFjF9+nQ1eQIYNmwYGo2GM2fOlEug5syZQ7du3RgxYkRthyqqwbD+Y1BsADSa+Bw6r+rX3lJsVi788ixZW77BkpWitnuxlB9m49tlPEFD7sY3diwaXc3/7+TX7VpMSfvBZiV3/xoCB9xS4/cQQgjhugaTQI0fP55+/fqV2efh4YGfnx9Wa/knKzQaDbfffnstRSculfFo0ZIMWh1BQ++5pLZSfnqKjJVvV36izUruX0vI/WsJ+sCmBA6+k8DBd+Ie2uKS7l9a6R4njd6txtoVQghxaRpMAtWkSROaNGlSbr9er1fL3xuNRmbOnMnrr79OixY190tQXF42Ux4Fp/8CwDOqC1pP32q3ZdjwWUnypHPDq0Uv9IHh6AOa2P8bGI7eP4yCM3vJ/PNzClNPAmDJPEfakhdJ+/0l/LpdS9N7vkTnHXDJ782Sk6pu6/0bX3J7QgghakaDSaCc0Wg02Gw2jEYjEydOpHnz5kRHR9d1WMIF+Sd2gs3ei+jddkC128k7uIHkr2epr5ve9TmBA251eK5f1/E0mvAMeQfXkbnxM3J2L7JPYFcUcvb8xtkPZxD1j9/RaC+t6J41uySB0vnV70nxQghxNWlQT+E5otFoyMvLU5Onzz//HM1VsPxHQ2I8tkXd9qpm5XFzyjGS5k4DqwWAkAnPOE2eimm0Wnw7jSTygR9pM+csYTPeRufXCIDc+BVc+OXZasVSmiXngrotPVBCCHHlaPAJlFar5YEHHpDkqR4zHlyvbnu3cT2BsuZlcuadiVjzMgDw6z6ZxtNedKkNvV8jQq75B1GPLgadfa5S+vLXydryncvxlGbJLkqgNBp0viEVnyyEEKLWNPgEqkmTJowdO1aSp3rKdP4oeQfWAuAe1gZ9cJRL1yuKQtKHMzAnHwLAs1lXIu77ptrLpni36U/47R+pr899cRcFp/dWqy0AmzETAK2X/yUPBwohhKg5DT6BWr16tSRP9VjmpvnqdvCoh13+Phac2EleUZFKfUAToh5bckmT0AGCBt9J8OhHAVAKTaSvnlOtdhSrhcKMJAB0PsGXFJMQQoia1eATqLCwMEme6ilFUcjZtcD+QqcnoL/rNZJy969Wtxtf/wpuIa71YDkTNv0NNO5eAOQf316tNoxHt6g9UN5tBtZIXEIIIWpGg38KT9RfprN/Yz5/BACfDsPR+QS53EbegTXqtk/n0TUWm0bvhmd0D/KPxGFOPoTVmOVyWYOcvxar237dJ9VYbEI0dDm7jmPJyiNnj+M/TDwjYwi/XZbyEhVr8D1Qov7K2b1Q3fbvOa0aLSgYj20FwCOiU40vy+LVsre6nX9yl2uRKQo5e+wJlEbvjk/nMZVcIYSoKktWHpZMo8NjpsR4CpISajkiUR9JD5Sot7KLh+80Gvy6X+vy9XqsYC0EwKfTyJoMDSibQBWc2IFvp6ovC2ROPkThheMAeHcYhs7Lr8bjE6Ih0wd60+L5uHL7T74ow+WiaiSBEvWSOeU4pjP7APv8oOossutGyRp3Pp1qfsHoMj1QJ3a4dG3OX0vUbb9uMnwnRE1TLEbSlpdPliyGeDR6nzqISNQ3MoQn6qXs3YvUbb9e1Rm+A72maA1EnRs+7YfURFhluDWKVgtr5p/Yri4ZVBU5e0olUF0n1nhsQjRkGr0PGr23w2OKxYhiyavliER9JD1Qol7K3va9uu3fY0q12tAV9UB5t+l/yaULHNFoNLg1isaak4YlM9le5bwKCwLbCk3kH98GgEezLjX2ZKAQwk7n1wqARuPKD+EZ/vCv7XBEPSU9UKLeKTgTry4e7N12EG4hzVxsoWxPUNDwB2oosrJs5gJMifEAeDTtiKYKyROAOeUoKPbkziu6x2WJTQghxKWRBErUO5mbv1K3AwbOdPl6fam5Tx5Rsfj3uq5G4rpYwald9gWGAe92g6p8XXFVdAD38PY1HpcQQohLJwmUqFcUq4Wsrfb15TTuXvj3vt616xUFT02h+jp0ygvVXralMsYjJcMDXi4UwjSdK0mgPCSBEkKIK5IkUKJeyU1YhTUrBQC/HlPQebk2XyE3fqW9fAFgQVut8gdVZTyySd32butCApV8UN32aNqhRmMSQghRM2QSuahXsuJKhu8CB7g2fKcoCqmL/g8IAaBAcbtsy/goNhvGo5sB0AdH4taoeZWvNRf1QGn07rg1ir4c4QkhquGpo4tpZUzh5IvLHR6XCuYNi/RAiXrDmmdQlzfRB0Xg40JhSoCc3YsoKFUR3IKuRuMrzZS0H5sxC7DXqapqoqbYbJiK5kC5N2mLRid/4whxpWhlTKF13nmHx6SCecMj/zqLeiM3fqU6KTug381otFVPgCy56Zz/+sGL9l6+RaSzSy0z491ucJWvK0w7hWK2LzHhES7Dd0JcaY75NKGXVDAXSA+UqEdy41eo267OXTr/9UNYsux/OZqVy/t3g2KzkbX5a/sLrQ5/Fwp9FpdnAPBo3rWGIxNCCFFTJIES9YJis5G7fxUAWu/AMsukVCZ7xy9kb/8RAJ1fKPm4X5YYixmPbqYw9SQAvrFj0fs3rvK1pRMoz2bdajw2IYQQNUOG8ES9UHBmL9bsCwD4dhpV5blBlqwUkr+apb4Ov/0jlFe+uywxFisz0d3FOlUFp/eo217NJYESoi4Ur5NnKbSQ5lbyb007zpOP84K4psR4p0N5MsH86iMJlKgXSg/f+caOrfJ1yV8/gDU3HYCA/rfg33MqcPkSKJvJSPaOnwHQ+gThW8E6doqiUJi6FePR+ZiSluHeuD/5RQmUPqAJ+sAmly1OIYRjFS0k7IXZ6THPyBinx4pXJBBXF0mgxBVPsVrI3PiZ/YVGg0/MmCpdl7NvOTm77JO59YFNaXLLe1W+pzn1JObzR/DpNMqlQps5e37DVpADQECfG9G6eZQ5rigK1pxjFJxagPHYl1izD6vHjEcWYLU/uIen9D4JUSdKr5OXnp5OSEiIeuzMCk+n11XUuyQTzK9OkkCJK17Ont8oTDsFgF+3SbgFNa30GsVmJeW7x9TXTW6di84nqNLrbIUm0pe9RtrSl1EKTYSMfYKwG9+ocqxZW75Vt0svM2O+sIXcfS9ivrAFpTDL4bXW3FLxWw+S9/d7eERNRO/Xosr3F0IIUTtkErm44mWWmlMUPObxKl1jPLTRvigv4NNpJH49plR6Td7BDZz4VxdSF/0bpdAEQPqqdzCd/bvKseYf3w6AW0gzvFr2xpJ7GsOGG0lfPgDT2RVlkyeNFo+Iawgc8hONrt2PopT8pYtyiuwdj5K6oCWpi2PJO/Qhis1a5TiEEEJcXtIDJa5o1twMchPsT9+5hbao8qK8mZu/UbeDRz5cYSFLS04aKT8+UWbyt8pm5fz3/6DZEysrLYZpzc/GmpcBgHuTNuTseY68A2+DzaSeo/Nphluj3riF9sWrxY3ofCLUY57NZpKX8DYAmlIPCloMCWRve4D8o5/j3+9D3Bv1qjAOIa42yV/Oclqk8qnkbJK8WgIyTCZqlyRQ4oqWvWsBWO2L//r3ubFKFb1tJiM5uxYAoPMNwTf2GidnKmT+OZ+UH59QEx8A7/ZDaHLzeyTNm4Y55Rh5+1eTu3cpft2cTwgH1NIFAJbsLeQl/KG+1nqF49fjFbxa3YpG47jj12bMU7dDxi1DsRzCdGYJ5pQ/AYXC9N2kL+2Dd7v78ev+UiWfghBXj4KkBEyJ8XhExZY7FmE8XgcRCSEJlFP//ve/ad26Nbfeemtdh9KgZW37Qd0O6DujStfk/LVEncjt32c6Gr3juk/emDn3+Z3qa51vCGEz3iJgwG1oNBrCZrxN4pxJAKT88Dg+nUeXmxReWv6pksWDtW75RY164tv5SXw6P4XWzbfCuC1FZRoAPMJ7ofcfh2+nxzGn7iBr2yws6XsABePhDyk4/Sua9v9BCZ512dbzE+JK4hEVSwsHFcATHiyfVAlRG2QOlAObN2/m5Zdf5vbbb+ebb76p/AJxWRRmJmM8tAEAj4hOeEY5f0y4tKwtJd+zgP7OE2B3jUXdDhx0B61ePUTgwJlqQuLbdQI+nUcDYE45RsYa50/xFZxeRFbcE+prrRd4tryZxlOP4Nftv5UmT2CvWQWARovON7gkztDeNBq/A/8+89C4BQBgK0jFuvdBMlYOwZJ9rNK2hRBC1CxJoByIiIigTZs2zJo165KTqOzsbJKSktSv5OTkGoz06pa942dQFAD8q9j7ZMm+oM6Zcg9rjVerPg7OUsq8inzwZ5re/QV6v0Zl9ms0Gprc9A4UrbmXtvh/WDLLLiSq2Cxk734Gw/qpWPNL5joFDv2IoMHfovOJqlLcANYcew+Uzj+03Dp/Gq0Onw4PEjr1MF6tSpJCc8omMlaNwGYyVPk+QgghLp0M4TnQvHlzkpKSeOuttwC4/fbbARgwYAAbNmzgzjvvrODqst5++21eeOGFcvsNBgNeXl41Em9dyM7Ovuz3yIgrKQlgazeG9PT0Sq8xbpoPRU+ruXWdSkZGRrlz3LFgLqom7N5uGIWthjtv2zMM7wF3Y9z0MbaCHJJ+/Q9+U+zzj2zpW7AmPI2SY39KTylVY68goAeWKsRbTFEUCouSM413SAXvVQ8d30HfeBrmfbPRGo9jzTtD6pbH0ce+WeX7XSlq4+eodB0f0bA4m3zubD7V5SRVyq8+kkA5oNFoaNGiBUeOHGHevHmAPYkKDQ3lf//7n0ttPf7449x9993q6+TkZHr37k1QUFC9/4f9csZfaDhHyuldAHi26ElYu55Vui732AZ1u8mwO/FwEKO7xopZsSdQkbe8g2cl7yNwxisc3vwZ2KwoyfEEepnI3vUk5hPfl5ykdUPj1hw4BhoNoS06o3V3XnTvYqbkw1ww2QtBeYW3qfyzDbmWNN/WWDb0RynMhvNLCR7yWbmeq/qgvv9/UB9kbZ1FocHxU2wWQzz6oKtzHpGzyeceUbEVVg6vaVKl/OokCZQT7du3Z//+/cTExPD4448zf/58UlNT8fSs+i9FAH9/f/z9/S9TlFevnL+WqNtVqeEEYDPnq3Om3Bq3wiO8XblzzBdOoMfeQ2VBi2ezLpW2q/MJwi0ogsL0M5hTDpK6qD1KYY563C20HwF9P+DELvvTfm4hzVxKngDy/l6nbnt3GF6lazSeTfCImkDBie+xmdIoTNuOe+P+Lt1XNAyFhgSniZI+KBa3oNpLJmqbs8nntUmqlF+dJIFyojiBOnHiBMOHD+edd94hPj5eHc6Tp/Mur5w9v6nb/t0nV+ka4+E/UQoLAPB1stxL1vYf1e1Cpeo//vqgcArTz2DNycRmAo0WtJ5h+PV8Ha9Wt2AryMVaNAncPaxtldstlvd3SckDn45VS6AAPCPtCRRAQeJSSaCEU/qgWBqNq9tEQoiriSRQTrRv3545c+bw3Xff8dxzz3HPPfeox9avXy8J1GVkNWapPTLuYW1wb9qhStflxq9Ut31jHNd+yt72A2AvXmmmasNd+Sd/xmr8S31tKwC/bvfj1+NVtO72p+LM54+qx92btKlSu8UUmw3jwfUA6Pwb4xHRqcrXekSMAY0OFCumpKXQ42WX7i2EEKJ6JIFyYuDAgTzyyCO8/PLLZZKnefPmYbPZ6jCyq19u/Aq1eKZfj8lVrnOUu9/+9B06N3w6DCt3vCAxAVPSfooTKKWSh1BtBelkbXuQglM/oXUr2e/T6VUC+v2zzLnm80fUbfcmrvVAmRLj1UKePh2Gu1TXSesRjHvjAZhT/sRiSMCSexq9b3OX7i9EfRdhPO5wKKy6k8WLJ3xbLBay9SW/Jt3zzJh9HNeVEw1Pg0ygFEUhKyuLwMBAp+dERUVx9OhRh+dotVL94XIqPXznV8XhO3PaacznDgLg3W4QWs/ydZeytn5fbp8zBUkryNp8J7Z8+5Nx2tJTmjSh5e9fOoEKc60HqrrDd8U8oiYUVSsHU9Iy9O0fcLkNIeor+zIuEOzgWHUmi1d0vtnHHbOvm9PjomFpcAnUJ598wr/+9S8uXLhAly5dWLBgAa1atXJ4bkUJlrg8FKvF3gMF6ALCnNRxKi+31KRz387l5z/ZzAVqgU2l3NGyzBe2YPhjIij2yeZaz8b495mF8bC9HEVh+pny16SUDOF5uNgDZTxSUsHcUc9ZZTwiJ5Cz6ykATGdX4iMJlGhAvm9uL2Ab93DNTMYuPeE7PT29zFOiB+ZH18g9xNWhQXWlvPXWW7z55pt89tlnrFixgry8PG6++WaH527cuJEPP5S6HLWtICkBW769NpBvp9FVeixfsdnIWDNXfe3X/dpy5xg2fIzFcBaoePK4zZSBYeONavLk0WwyjSbvxz20r3qORlv+elNxAqXT4xZS9SE0xWohr+jJQX1AE9waO07mK6IPaG+fB1UUvxBCiMuvwfRAZWRk8Nprr7FlyxZat24NwNy5cxk7diwnTpygZcuWZc5fuHAh7733Hq1bt2bUqFF1EXKDlH9sq7rt1aZqT5Tlxi9Xe4B8Y8eWK19gzc8hbUnR4rsaLQU47oJXFIXMuDuw5SUC4N50FEHDFqDRaCksSr4A3IIiyl1bmGJfTsW9UTQafdW7+POPb8dmzALAJ2ZMtda1k7XwhBCi9jWYBGrRokVMnz5dTZ4Ahg0bhkaj4cyZM+USqDlz5tCtWzdGjBhR26E2aKUTKO/W/ap0Tfqqd9Tt4DH/KHc8Y/UcrDmpAAQMuA3bSsfVr40H52JKtA8Faj3DCBz0DRqNvZPWknlOPU8f1LTMddbcDHUSuKvzn9SJ7zgeehRCXP2kSnn91GASqPHjx9OvX9lfyB4eHvj5+WG1Wsudr9Fo1JpPovbkH98GgNbTF4/IzpWeX5CYgLGo5IFHRCd8Oo0sc9ySm076CvsSJxq9O6FT/gMrHy/XjjltF9m7ihcD1hA4+Dt0XmEl7ZTqgdJf1ANlTilZzNc9rDWuKF63D40Gn87S0ylEQyNVyuuvBpNANWnShCZNmpTbr9frUYoWrDUajcycOZPXX3+dFi1a1HaIDZ4lO1VNRjxb9q7S/KeMNe+p28GjHy03nJW+9FV1TlXQsPtxb1R+fpLNnE3mxhvBZi+d4Bv7HB5Ny/Y8FhpKeqDcAsv2QJkvlCRQbo2rnkBZctMpOLkTAM/oHuUWMxZCXP2kSnn91aAmkTui0Wiw2WwYjUYmTpyIn58f0dHRdR1Wg1Tc+wTg3apvBWfaWXLSyNpiX3BY5xNMQL+byx3P+ON9ADQePjSa9Fy5NhRFIWvrfVhzjgPgHjYI367/Ln+voh4ojZsHWp+gMseq2wOVd2AtFCXvvp1HV/k6IYQQdU8SKI2GvLw8Jk6cSPPmzfn8889lUm4dKb3+nVcV5j9lbvhUXbolcOi9aD28yxzPWPUOijkfgOARD6D3b1yujcLU7RSctC/vovEIIXDw9+WeslMUhcIMe+kCfWDTcj8fpuRD6rYrCVTuvuXqtk+NzX+qrEiDEEKImtBghvCc0Wq1PPDAA4wdO1aSpzpkTj1JZtyXgL03yaf90ArPVyyFau8SWh3BI8rWPrJknid91RwANG6eBI9+zGE7pqSl6rZ/zzfR+USWj+38Eaw5aQB4XDRfQVEUjEfs64tpvfxxD21Z7npHbIUmcv5aDNjfb1UnzDuj0fuiFGahmB1PkBfiSpf85SwKkhIcHqtuRXEhLqcG3wPVpEkTSZ6uAGlLXgKrBYCQsU84rCReWvbuReqwmn/PabiFRJU5nvr7SyhmIwDBIx/C7aIn54qZzhatn6fR4hk10eE5xqI6TVC+0GVh6kksGfayB95tB6HRVe1vkryEVWr5Ar+eU10qfeCI1tNe7M9mSr+kdoSoKwVJCU4nTVenorgQl1uD74FavXo1jRs3luSpDplTjpf0Pvk1ImjkQ5Vek7H6XXU7eNQjZdtLPYlh/ceAvVcoZMLTTlpRKEzfDYBbo95qEnKxvKKFfqF8ApVXKrnyrqTXrLSsHT+p2/59plf5Ome0Ho2w5pzAZkpHURT5eRb1kkdULC2ej6vrMISokgbfAxUWFia/bOpY6pIXwWYvJREy9kl0Xn4Vnp9/chf5x7YA9qfXLi64mbrw3+pixCFjn0Tv6zgxKs0j4hqH+xVFUZMknW9IuSE846GN6rZP+yGV3gfAZs5Xl57R+YVWOlxZFVqPkOLGUSy5l9yeEEKIijX4BErULdP5o2Rt/hqwJxPBIx+s9JoyvU8XlS4oSNpP1taiJ/P8GxMy5jGn7ZROm50lUObkQ1izUgDwbj8ETamFpBVFUYf3tJ5+eDbvVmnsYJ88biuwJzn+va6r8rBfRdQECrAVyDCeEEJcbpJAiTqVtvh/oNgAaDT+n2g9fCo8vzAzmazt9uEvXUAY/r1vKHM8dcHzammA0EnPVzKXyn6exiMYt5CeDs8oM3x3UU9RYdopdWFh73ZVn/+Uvb1mh+/A/gRhMUXmQQkhxGXX4OdAibpjyb5A1rbvAXsyFDR8VqXXZG6arw7PBQ27H62bh3rMdO4QOXvsT7a5NWpO4NB7K2yruOPKo+kYp0U7849uUbcvnuOUV1QBHcC7XdWG78xpp9Wn7/SB4Xi3rZlCeaXnb9nMsqCwEFeLipZ5uZjFYiFbr5flX2qJJFCizuTs/k2d+xQ07P5ydZwupigKWZu/sb/QaAkack+Z45l/fq5uh4x7qkxyVRHv1rc7PWZOP63ezyO8fZljeQkl69hdvISMM6m/PodiMQP22lVVqbZeFRqtu7qtWM010qYQom5V58lDWf6l9kgCJepM9s5f1W3/XtdXen7BqT2Yi4pW+nQcgVtwyZp0iqWQzKK5VBo3TwL63lRBSwrFM6DcQnrg3tT5GnSWjCQA9AFNypQaUKwWcv9eC9jnWnk261pp/Pmn9pC19Tv7NQFhNBr7RCVXuEBTOhGz1Vy7Qog642ovUnp6OtkfXnuZohEXkwSqjvyVfpZzHjbyLGZS8nM4n59DSn4uKQU5pOTncKEgF5uiEOTuTZCHF0HuRV8e3gS6exITFE6/xs3RaurnNDZLThp5B+1DYO7h7fGI6FjpNVlbvlG3A/rfUuZYzt6lWLMvAPaJ2TqfQKftaCip1+0T+6zTpzAVm43ColpT+uCyBTbzT+7ElmcA7MuwlJ5c7rAtRSHlpyfV142nvFBprSuXlE6gbOUXxxZCCFGzJIGqI5PWfgHBFT+uX5kI7wBuaNGF6S260LtRs3pVjiFnz2L1F71/7+srjV2xWsja9gMAGndv/HtOLXO89PBd4OC7nLZjTtuJRqOgKBoUBTybTXZ6rjX7gjrfyu2iBCq31PCdb4zjJ/hKy9u/GuPfJQljRTFWh6ZUAqUokkAJIcTlJgnUFcpdq0On0ZJf9AvckbPGLN458CfvHPiTaN8gbojuwvQWXekWEnHFJ1Nlhu96Xlfp+bn715T0MPWYUqb3pjDjLLnxKwBwa9wK7wrqMeXGv6xuK2jQVNCDV2hIUrfdgsomUHkJK9Vtn87OhwCLGTZ8om6H3fBqjZQuKKN0D5QkUEIIcdlJAlVH7m/XD7+wRnjq9IR5+RHm5UuYp5+67e/miUajwWS1YDAZMZjzMZjyMZiNpOTnsizpIMuTDlJQtPzJqVwDr+/fwOv7NxDi4U2EdwBBHl4Eupf+8sRL54a7To+HVlf0Xz3uWh2eOj1BHl4Ee3gT4uFDsIc3HjX9S76INc9AXtH8IfewNnhEVT5RMmvTfHU7YMCtZY5lbv5KLYUQOPhOp8ljoeEApjO/AZOL9lScZBZmlCRQ+uCSpWKsuRnkn9gJ2At5OlqkuDRrfo66cLA+KALfro6XjLkkkkAJIUStkgSqjjzXdSSRkeUXrr2Yh05PE29/mnj7l9l/Z9ve5BQWsOTM3/x0ci+rzh7GXDQklm4ykm4yXnKMPnp3Qjy8CfX0pWNgGLFB4cQGh9MluOkl/eDkHVyvrnvn13Napb1l1vxssvf8BtjnIvl0HFHmePYW+8RsNFoCB8x0ft/9r7kUZ+GF4+p26SG8vEMb1YTNN2ZMpe0YD65DKSwAwL/3DZXOl6qWMp+h4vQ0IYQQNUMSqHrMz82Tm1t15+ZW3ck05bP4zH5+O3OAeEMyaQV5ZBf90q6uPIuZPIuZM3mZ7E5PKnMs1N2bLo0i6B4cwYSojvRvHI2uiomBKWm/uu3ddkDlcSSsUuciBfSdUWb4y5R8GNO5v+1tdRha5sm80qy5Z8g/YZ9DpVQxv8g/vl3dLl1lvHgZGQDvdoPLXGPJPo4lcz/W/PPYir4yN5fUi1Jsu1EsBWj0nlULooqUwmx1W+PmX8GZQgghaoIkUFeJQA8vZrbpxcw2vdR9FpuV7EITmeZ8Mk35ZBUWUGAtxGS1YrZZSv3XQr61EIMpn3STkYyiHiz7f/M4n5+j9m4VSzUbWXvuKGvPHeX1/RsI8/JjSrPOTG0ew9DwVrhVUN+odALlEdGp0veW89fv6vbFw185uxep2/7dpzhtI/fA26DYe72USobuiuWfsCdQWu9A3MPaqPuNxcU1NRq8WvYB7MODuXv/Q8HpX8u1Y0os2bYZ/yRn73/w7/lqlWKoKps5U93WugfWaNtCCCHKkwTqKqbX6gj28CbYwxsu4YG/QpuVo9mpxGckE29IJj4jmb3pZzmbX9LrkZKfw0eHt/LR4a0Ee3gzKaoj10d3YXREW/QXJVOmswcA+9N0biHNK7y3YrWQu28ZADqfYLxb9ytzPLtUAuXXY7LDNmwF6eQf/dR+T7eqfRCFmcnqMi1eLXurw26KxUzB6d0AeDTtiGJJwbDxAQpO/oijoTNFAUuOfVvrCVp3yDvwBp7NrsW9cb9y51eXUiqB0kgCJYQQl50kUKJSblodHQOb0DGwCTdiH8pKT09H4+vFH+eOsuB0AssSD5JrMQGQYTLy5bFdfHlsF5HeAdzdtg93te1NpE8gisWMKeUIYO99qmw+kPHYVqx59qVJfLuMKzN8V5iRRMGJHQB4tuxdrtRAsbxD76NY7HPCvNveB5yo9D2XHr7zatVX3S44vRel0P4+db4mUn/rqM6HAtD6ROHd9j70vtFovcIozMojc8Nke4zR3YC/QLGRGTeT0El70egrrr5eVdIDJeoLP02Bw6VJTInxeETF1kFEQlSPJFCi2oI9vLm+RReub9GFAksha84dYcHpBJacOYDBnA9AkjGL/+xdzX/3rWFiVEceCgyhadEEckfDd8fT8jienkdanpnUXDMRm7+kuMTm2xfakvnTXka2CWV4m0a47f5Nvc6/h+PhO8VixHhwrv2F1g2fjo8Bj1T63vKPb1O3vVr1Kdl/elepxo+pnU5ar3B8Y5/Du+3daHQlS8jkHyt5etCv262g+GNO2Yg1+yjZu58loM+cSmOpCumBEvWFzkmlfI+o2GotXSJEXZEEStQIT70bE5t1YmKzThTarKxLPsbnR7az6PR+LIoNm6Kw+MwBLDsP8nrRNZt1HkSlnKYwz4eVh1JZ9vcF4pOzy7S7xLAagEJ0fJLRhtwdiXyxwz6p6Muczyie8XVLfCSa5G1EBngRGeBJVKAXkYGetMr8Gm9TGgBerW5F5+N4kvnFyvZA2RMoxWYhe/sb6n6dL2g9G+Mb8wze7e5Do/cq305RD1lxO+5NriVtcSyKJQ/jwXfxip6Ge9igKsVUEbUHSqNDo/e55PaEuFysaGnxfFxdhyHEJZMEStQ4N62OMRHtGBPRjvPGbOYf28knh7dxKtdAt6yz6nlvZBnYtXwuWPWQFwCmIHAPArMXoKGJNZVWVvvTf7vdOpGrLUkMGtkMdDclAHBMF8Wq9ABITy0TRyO9gXVtXsS76Kf8l7zpjM6ovLyDzWRUe6Dcw9qg9w1BURSytz+COfmUel7AgP/g2+UJtG7OE5aCM3vtG1odns27oXX3wq/XW2RvvR+AvL/fq5kEqsCeJGrdg674IqpCCHE1kARKXFZNvP15JnYE/4wZxppzRzEn/AbYl7s94NfEfpLOAv7p9i8gWOfP8OBYZmZnQdGKKcOvmUb+tePYnZTF2iOp6DZ/gC7DPhSwyX843u46jObSTwoqvBUxl0b6LAB+NQzlkQQTrPqDKVRcQjN710K1bpNvrH2ZFuOheeQd+hBLrv0ct0ZR+Pf8d6Xv35xytOj8aLTu9h4q77b3kLv3BWz5yZjOLkex5DvsvaoqRVGw5tknvGt9m1W7HSGEEFUnCZSoFVqNFu+CYIIzzgFwWhdOfnJn3AOy0PllkU++em6GNZtfU+PodmQtLYv2NY4dgaebjgEtghnQIpgTm+MornL10nP/5o3QFmQXFJKUWUBSVj6ak5/R+bx9vlKaLZT/S76nXEwK0PfdTbw2oQNDWjVS92fFfaluB/S/lYKklWTveAxbHlCUo3m3KVv/yRFrngFrrj0pdA9rre7XaLR4Np+C8dAHKBYjpnOr8WxW/RXUbQUXwGr/NHQ+FT/VKIQQomZchpLIQpS39VQG93y8BB/FnijlBHdk8y3TMD40m7zb/8vfU57kvT6TGRPRDk1R/1BXg71XpUCrZ+qxv1hy5gBWmw3T2b8pOL0HAK+2A3EPbQGAv6cbHZv4MbyJgc6pL6r3bnvND/z19GTemNCRXlGBZeLafiaT4R9u5dU/jmKzKZjTTpN30F740iOiE7pALzI3TgfFppYjAPtTf5Uxl6pk7t64dZljns2nqdsFpxdU2lZFrLmn1W29ryRQQghRG6QHqgHLKzQRd+EU684dJd6QjI/eXa0bpX65e9PI05uWfiE09fZHW8Hiu87sSszkmk+309t4TN03bOBQwloEq687BIbRITCMhzsO5Fh2GvN3LyV6gwGAff7hrEo5xaqU+bQPaMynhqMUXxnY/5Yy91JshWT+eQtY7YmaT6fH8Wg6gmjgiWGteGJYK6ZtmlPmGpsCzyw/xOZTBt73X6aWKvfvdz2Z6yaVqvLdGrC/B6+qJFDnj6rb7k3alDnmHjYYjUcIiimdgsTfUaxmNDr3Stt0xJp7St2WHighLq92nCdtefkyDABuQTEE9PuwliMSdUUSqAbEZLWw5cIp1iUfY33yMbannsGiOH6k2BFPnZ5WfiG09m9EhJsPMY0j6RAYRu9GzfDSuzm8Zu/ZLEZ/vI3sAgttLafU/d7RXZ3ep7V/I57y9qJ4uvmpsHbqscOZKRRs/wkAReeGJnYyqbkmrDaFxr4e5O39L4Xp9qE7fVAMft1ecnofDfDq+A48u/wgNgWWHUjmH9mf0ARAq0MpXIY19yQAbqH9MB4vGmbU6fFs5jz+YuYLJQnjxT1QGq0ez2bXkn/0CxRzJubz6/GIqHxdPUeseSU9UDrpgRLisjmihIEG+jo4ZjHE13o8om5JAuWEwWBg3759tGnThoiIqj36fiU7np3G6NWfciInvdptFFgtHMhM4UBmSlGj9kf0tRoNbf1D6RLcVF1wODYonEbufoz5ZBuGfPs6doO8zlE81ckzqkuF9zIe2qhuPzn5WbxyfPnf3rV0NO6lqck+lvZHUHMe/fl9yA2C3CCGuKXxXcTLaDWgaNzx7v91pWvO/XN4a/o0C+TGb/cQlbGbJoXnAcgNDiUoeycAOp9mBAz8ngu/tlZj17pXvpadOaVUAhXWutxxz2ZTyT/6BQAFpxdWP4HKlQRKiNrwH5t9rmL2uGfLHXPWKyWuXpJAOfDuu+/yzDPPkJ+fj16v54MPPuCee8pPQq5PHty2qFzyFBMUzrAmrRge3pr+YdEoikKGKZ8Mk5EMs30tvAyTkfP5ORzPSedYdhrHstPViuPFbIrCoawLHMq6wE8n96r7ffUe5IZ6gJ8vLb1D6HTgIGBfkkXvpGp4MePhP+0bOjc2W1vy7wX7MVs7MblguXrO0rAO4GkETyMejU7zX75Bq7H3qL1w/ka+fvMsvZvlMahlCLd0j6B9mONlXIa2bsRfjw/ml5e+APtDe4SF2RMpM940Gf475uQzULQeYFWG76CkGKdG767O0yrNI3yYum3JOlylNh0xp2wq2tKg821Z4blCCCFqhiRQF/niiy+YN28ecXFxtGvXjkcffZRHHnmEa665hqioKJfby87OJju7pDhkcnJyTYZbJSn5Oaw5Z18+Jcjdi4/6T2NYeGtCPX3LndvYq+K14hRF4UJBLrsTT3BBY2ZnWiJ70s+SYEgmz2Iuc26uxQQ+JvDJxmrcjy7XXqdpk29jnlv/NddFxzIhqiO+bh5lrrPmZqjr5ZnCu3Ltt/sxW2342PIZm2cfnsvS+7EvqA8aJQtFY+MxttFGY1/yZZsSwaeNG6H4b+fPnBD+3BLCS38cYUrncJ4ZXnYuUrFwf0/G6Q9iBhQNuBVNsvpH4v3olpp5z7+kR8y7beV/aZpTjmE+b//MvdsNQaMvP7/JZs5St7WeoZW26Ygl85A6dOAeNhitR2C12hFCXD1MifEOl8sB8IyMIfx2madVEySBKsVqtfLCCy/w22+/0a2bfc23OXPm8MMPP7Bu3Tpmzpzpcptvv/02L7zwQrn9BoMBL6/q1/5xxdcn92Armhh9b4uejPCPgjwT6XmmSq50TA90cPOnj78/E4PtPR42ReG0MZO/sy9wIPsCB7IusNeQwtmCLNBAz8wk9fo43zAWnE5gwekEPLV6Roa14tqm7RkV1hpfvTumAyvVc38wNMfkZe9V+lejvXhm2B/XD+9/E39Pvp88i5n4M2vpduAdAPIVPU8wGgWN2jtFaCLk+7LoWGsWJZwvUwcqPd3eK2fLScWcZE9E9P6g0cMuY3sWZQ6BzGSuM/1O8SITptDO6nXOGLf+qm5rWg9xeL4t65C6bdYGVdrmxbKzs7EmlywVY2083uU26lrpPy4ul5CQkMt+DyGuFBUth2NKlHlaNUkSqFL27t1Lp06d1OQJwNfXl44dO5KSklKtNh9//HHuvvtu9XVycjK9e/cmKCio1v5hX76jZC7OzI59CQmsmfteHH8ojehJyVyfgyk5dHzjD/DMY4J1p7r/aGhJL1CBzcLS5MMsTT6Ml86NcZHteezYRvUpu+26DgDM6BbB9Sc2qLWfmox5CM+QEIIt+XhseR1b0fpams7P8IDXcJYmHiQu5WTJJHmvXGi1FwxhcMq+SwE2njUzuXMTco6uUWNyCyp6f33fIeCsQk6+iRa5++07g5rRuHXlC57mHivpsQrrfz0eDr7XBfkFGIq2fYKi8XXx50FRFJS9v9tfaLSEdLwNnVf9SxYkwRGi5lTUu+SsV0pUjyRQpfTo0YMPPvig3P6AgACUoh4cgMLCQtzcHD91djF/f3/8/f1rLEZXJRuz2Xj+BGCf89QhMKzW7u3tpgNFB0Y/2ufZh7O0Xv78ee8H7Ew/x8+n9vHLyX0kGe1DWfnWQhacTmBqwkqCARsa/tK35/ou4Xw62J3Ta+xzijxb9sa9aSfWH0sjc+uD9LXak5u9xtY8tGoA/Vv6c1PLcbwW480JcxJvH/iTnWn29fMIKpsIT/tqF6PaNuJDVqv79EHg2WIG/btfw+amOTw871t80+2z31eZWpNxIp2BLZ3/0reZjBgPrQfArXEr3MMcDxva8kuGc7VeTar6saqUnINYsuzzytybDEPnVXvfWyGEaOgkgbpIdHR0uX06nQ6bzd6TkZ+fz7XXXsv999/P1KlTq32fHalnOOVWSKR3ANF+wZVfUE0LTsWjYE/+preo+Mm3qsgrNLH5win+Sj7FuaP5pJuMBLh7Eujupf430N2TAHcv3BR30FiJsKbiX2CflO3ddiA6nRt9Gzenb+PmvNlrAtsunLEnU6f2kZGTTqcce5Jz1KcR1tjDDO0VTWbcD2oM6wPH8p8X19JZ2cAXzX8GINfqxYOJT3DSbObE7rN8u9teBCHU152B0f0Y1KoT35zdRGpBXtk35JvOmsM2krJ+JxRAC/pAd/x6vAJApyZ+fNE7D2NR/rVV256nP9rG1zO6Mr2b46cz8w6uRym0D4/6dRnndG06W/55dVvrFV71b0Lx9ed+U7e9Wkx3+XohhBDVJwlUFWg0GhRFUZOn8PBwJk+efEltTlv3FQTbJ2zPbN2T13uOr3QCd3UsOJ2gbl8ffWkJlNFipvfS9/g704XhzI7Q6+xhiseqDoa0JCX1DJ2CmuCtd0er0dI/LJr+YdH8r8tYJr74X9yKht12B0RgJI8Hty2kzfb5RABm9Dx2qj16t1TebDtXvc1yz2dp26ILaacM5Jgs6v7UXDOL9qfAfnh0yBRoeZozS0+WxBe9n6h0C6Hp9sV49YHgGzO7TEVv3emt6vZufSfMVhs3frsHi03h5h7lnybMLR5WA3xjxzn9aKylEihddXqgkpfZNzR6PJtVP5kXQgjhOkmgqkCj0WA0GtXkaf78+Wi1NbcKzlfHdvHbmf281H0s97frh64G2y5dcuCP5KO0Daje014Az+9Z6VryBKCBrqUKPT5lSCd+6Xt46PSMatqGyc06MzGqI429/MgvVIjOKBnWyo/sDoBfYQER+fYMbHdgU3KjEnnMtpNgvb0elHv0DB4d+jyPAharjfjkbDadyODPE+lsOplBaq796cB3NyYyq3+LcgsJ32jcrG6bAz041WgWxbOcrMYscvfZSyfo/MMYP2QQczefAuDun/fRvrEvPUotD2PJSSNz8zcAaD198W4/xOlHYzNlqNtaz0ZOz3NEsVlR8uxDs24h3dB6yjwiIYSoTQ0ygVIUhaysLAIDA6t0vk6n480332T69Ok1ljw93GEgnqFBzD+6kzRTHlnmAh7atojPj+zgg35T6du4ZgoivtZjPCNWfQzA7B2/Mzy8Ne0CGrvczpaUU8w5YK835K1349XOo+kd2ZIwTz9yCk1kFRaQac63f5nyyTQXkJiXyaZzicRmFdVU0ug46Ge/t8lqYWniQZYmHkSDhgFh0Vwb1YlxISlQNMoWf6EfW+4fw+IdJcN3p72D8ApK4i6WAqBo3Ajs9bp6XK/T0j0ykO6RgTw6uCWKovDZ9jPc/2s8NgU+3HKa0n013YIjmLanpJfus9DufLTyK74bOYWpLTuSvf0nlEL71PWA/rfw3tQY0GiYG3eSAouNKV/uZNdjg2nsZy/FkLFqDorZCEDQ8Flo3Z0/aalYjOq2Ruft0vfDVpACir1AqSzfIoQQta/BJVCffPIJ//rXv7hw4QJdunRhwYIFtGrVqsJrunTpQkhISI32PD0VO4zIyEiejh3O83tW8NGhbSgo/JVxln7L5nJ32z680mMcjTx9Luk+w5u24fFOg3n7wJ/kWwu59c8f2Dr+YZd6ufIthdy5+Sd1LtWrPcZzU1iHKj09Zc3P4eCyJwD42y0aD0Nnbujhx+a0Y5zKtfcqKSjEpZwkLuUkPxh2A1CIjo05gcz9I5WP2rehuAhClm8oN3CAEI19UvfPtrZs27Ge57uOpHNQ+XlEGo2Ge/o2J8jLjZu+20Oh1f4uNEVfmzpEc3qhvZfO6qvlA68+FJLJdX/Op9/B5ny640u1xypw0B0AvDZEg5JynM+PRZCYCdd/vYu19/dDa8oh44959vu6eRAy5vHKPpySOPWulbSw5iWq2zof1+uTCSEapopqRFVE6keVV3NjRfXAW2+9xZtvvslnn33GihUryMvL4+abb3Z47saNG/nwQ/sPy4svvljjw3bFgj28+aDfNHZMfIRejUp+EX52ZDvtF77GpqIn6C7FS93HElOUXOxMS+SDQ1tcuv6Fvas5nGUvgjkorAUPduhf5WsLTu1CWzSnKUHbkdzkMA7tDefAtf9k37WP80K30XQLtk/G1igKrfPsdYzOeAdQ2O4vfjiznXU7S0ogPDdsJk+7H1Bfv08vfjq1j5jf3mLGhm85nZuBI9d1acriO3rhqS/5HipA2orZ6uuc9sPBHKi+vnBqN5rTewDwbNkHz8hOmM6uIvP3rjzr+Q/+7nQz30b/h1ZZX/PColVkb/8JW9EThYGD70YfWPG8ptI9UOgqXxqmtNIJlFYSKCFEFXhGxuARVXkZlouZEuMpSEqo/MQGpsH0QGVkZPDaa6+xZcsWWre21yqaO3cuY8eO5cSJE7RsWXYJjIULF/Lee+/RunVrRo0a5fRJqprSs1EUW8c/zOdHd/D0rmUYzPYn3Eau+pivB81gesuu1W7bU+/G5wOup8/SuSgoPLdnBdOiY2jqHVDptTtTz/DG/g32dnR6Ph9wA1qNC71Xx7ap2xeCY8EMu5OyuO/XeL6e0Y3Y4Kb8X9fRnMrJYPX+tXhvfBuAYz6NwN0E4cfZfmQDbYvacLeeJqBovbpV+bEc04WDm32O048n97LozH5mdxrC07HD8HMrm5SM7RDGqnv7Mucp+/pzGhRMJ0omlHef+i5xlnAGfL0ES8gJpiRvUI8tCu/M3al7yN9wAyj2SeoemkKG++1huN8eyP2E8ztKKrsHDb230s9GKe6B0nmiceEzBemBEkK4rro9SFI/yrEG0wO1aNEipk+friZPAMOGDUOj0XDmzJly58+ZM4f58+czYsSIWotRp9Vyb7u+HJn2NJOiOgFgtlm5ceO3vJ6wvkwtKlf1Cm2m9hzlFJr4x/YllV6jKAr3b12gVjF/qftY2rg4CT3/xHZ1+8mZM/D3tOfs3+4+yw9/nVWPRfsFM8OrZB6QW9OO6nakyaBunzzxsbqta/U8HOkD51qjsdiXSjFZLbwc/wdtF7zG50e2Yylav67Y4FYh6pCcFgVL0Woq+tAoPCM60rt5EJ+PHY7+eCyTku3r0+Vr9czV2DixfCRKob1ytltoX3R+JUO/igKk5wKgcQeNZ8nwnDPFPVAavWvznwBseSU/s5JACSFE7WswCdT48eOZNWtWmX0eHh74+flhtVrLna/RaLj99tsvy7BdZRp5+rBw+EwebF8yVPbPXcuYtXUBhbbysVbVi92vIdzLXtTz51P7eHjbIkxWi9Pz00x57EkvSXJmtOzm9FxnjEUL6uoCwmjXriPf3lTSxoML97PlZMmQmylpv7p926CbOTD5ScLM0UTk25OWAq2eMJu95yXTvys3j5zGXb2iISMC5UhvuNAMjWL/fp3Pz+Huzb/Q+bc3+eHEX1iL6niV5qaxUjStiyMR15bcu2cUb7Q8TYjVnhBtaxTN1/rFNFHsidxx99Yc6/4loVOPEjrlEBu9n2RPSqvijincgqDg2HwqU9wDpdG5vqSPVRIoIeqEUZvNwGXzyn0lZCRzPLt+LaUkLk2DSaCaNGlCx44dy+3X6/Vqz47RaOT666/n5MmT5c6rbTqtlrl9p/Bmrwnqvo8Pb2PCms/JMlfeu+FIgLsXc/tOVl/PO7iZ/svmciw7zeH5Xjo3/EsNg/VYMoc/zx+v8v1spjysWfayB56RsWg0GiZ2asINXZoCkJlfyPCPtvLrvnMAGI/Eqdd6Nu9Ox6Awdky9hRbGTAByvT0oHkl9OiuCCWu/4J9jIhncMhhsOrjQAuVIL8gsecrwcFYqN238jpY/v8b0xSsZ++k2irMmN0qS0eToa9RtRVEYfuZL9fXI8ER1oeLTSgCTTaPot/JTRq/+hG0F7lx/3WscTu+pnq8PBnPajgo/G0WxYTXaSzZoPVwrpKooCuY0+7wwjZt/taqYCyFc52Pzx9vmeGWJPIsZ40ULqourW4OZA+WMRqPBZrNhNBqZOHEizZs3d1iNvC5oNBpmdx5Kc98gbv3zBwqsFlafO8KAZfNYNuoumvu6XsF8WnQsXw26kVlbF2C0FLIn/Szdl7zDJ/2v48aLeph83TxYM+Zerlv/NYl5mSTnZzNs5Ue81H0sdzWtfCKiJbOkppM+qKm6/fn0LuSZLSw7eAGTxcb1X+/mzXE5jD9iL5OgD47CLbQFAKHZR8jF3rVj8fShuMbBX4STePYQf/7+Fv8ZPIYpMR34ee95tp42QFIHSI+AxqfAz95rdCY/nTP5a6HQh2nYUyh9UQKV6h3NbRNKEqicgxvRn7dPmNT5g1+Q/Sk9g3dbHlMmkW60J2Brzx1l7bmjjAxrxX+z1gKgaDS4N1KwZB1EsVnRaHUOPxub8Zz6FJ7e3/FSL85Yc09hK5oD5R420OX5U0JcTrO2LCDBkFxuf2MoV4Otvmll6gpA3Pjyc4LWf/tOLUcj6lqD/5dXo9GQl5enJk+ff/75ZZ8w7qrroruwYewsGnvaJykfyEyh9+/vsfbckWq1d1vrnuya+Jj6ZF5OoYkZG7/jrrifySksKHNu79Bm/DXpH4yLbA+ATVF4Zvdybt7xC+kXL4tykcLSCVRASYkBXw89v93Ri1n9S+oXzV+8DFvRUJ1P+6Hq9yD/1C71nLBG9kQqx+ZJktney5RnMfPkrt/5IWsZH9zcglPPjeD1CR3oERIFp2PhRFfIDVTbaO11Bo2m7Fyy9sNn4OFmT3TSck0s+Pw19ZhnFGg04NnyJjpM/Yu461/lu8E30aFULa2sQxsIMNm77lMDg9DoAWsB1lznPZmW7KPqts7PtQTKfL5koWL3MOeFOoWoCwmGZOIdJFBCXG0afAKl1Wp54IEHrtjkqVif0OZsn/AInYoWA75QkMvoVZ/yrz0ry02UrooOgWFsn/AI97Xrq+774ugOui5+hy0pp8qcG+Lpw+8j7+SVHuPQFn0+a1KO033JO2xPPY0zZXqgAsvWaNLrtLw/NYbXJ3QAoFdhyfwnfetB6nb+iZISBv6+9mG085q2KEd7QVqkOodpR1oi3ZfM4b9/L+XmPqHs+sdgjj4znJeG9GGK3xim+4+lvW8E/+LPcnEmRfcCYN+5LAa/vZLu6fbeJI0b6Btp8Ov5BoGDvkWj90av1XFTq+4kTH6CH4fcQsfAMMZdOKS2tbdpSWJlyTyAM9acYyXv18UeKHNKqQSqiSRQ4soTGxRO3PiHynxdmf+yClF9DT6BatKkCWPHjr2ik6di0X7BbB7/EJObdQbsBShf3LeWESs/5mxelsvteend+Kj/dfw45BYC3O1znU7kpDNoxfv8a8/KMhPWtRotT8cOZ90196sT0c/kZTJo+Qd8enibw/YtWSUJlFug4yKXTw5rzc+39aCvtSSBmrHNhwXx57DZFApOFs310XugK6op2qX9AFbe3Z8OShc40Q3yfdTP44ujO2j+0yvcumYRgT5anh3ZhoW39+LHaSP4q19HRmnK1tXKcPOi3/7NXL9iAf3mbmJExo+4Fc0Gdw+DkFFL8O38RLmfDZ1Wy/SWXYmf8DBTDacAyNZ58EdwyZN5S/cvIK/QhCNleqBcTaDObyi60Ae3kO4uXSuEEKJmNPgEavXq1fUieSoW4O7FwuEzea/PZNyL5tf8mXKCrovfZkXSwWq1Ob1lV+Kvnc2wJvZf/jbFnpj1XzaXQ5kXypw7pEkr/rr2HwxqZB9+K7RZuXfLrzyx4/dyT/RV1ANV2nWdGzMQey9OsrYR69N9ue6r3XR/dTkF5+zvyT0sguKpPvrgLoxp35j42UOYd01/gpLtpQyw2Kf0WbDwbdJmQr/5H7Gf/cR7f55g3ZHzXNj6GAA6Sp7IiwuOphD49fxWlMht3GVZqB4LHHIPnlElk/gdMe5fg77APvS4xrsXf1tKhiVTUnbScdEbLDgVj00p+xRg6QTKlR4oa+4ZrLmnANAE90ajdavytUIIIWrOJSVQiqKwcuVK3n33Xb7//ntSUlxcaPYKEBYWVm+Sp2IajYaHOw5ky/iHaelnX04lzZTH+DVf8PK+P6pVL6qZbxBrr7mPN3tNUBOzXWlJdFn8Fv/bu6ZMchTm5cev/W7kmdjh6r63Dmyk7YJXmX90hzqkaHEyB+piBWf2oTHZFwY+GdiD4kft9Ofj0RQlHtqgkiVt3IK62I/rtDw4sAXHnxnJnEGjGWYbhy49CmxF3083Mwm6nTy652c++PkV3PLsSZq+VAL1p76fev5/WIx7jv0pGn2QD8Ej36/0c8v88wt1e7lmNKeOjcCMPalpQwZn8jK5bv3XdF38Dj+d2KuWU7Bm2+evafQ+Lj1FZ0pep25rQ6peEV4IIS5F8RIwjr6Sv5xVeQNXoWo/hacoCuPHj2fFihX4+flRUGCffPzAAw/w+uuv4+7uXmNBCsd6NIpkz6THuHfzr/x8ap9aZXxH2hnm9L6WaD/XntLTarTM7jyUUU3bcsufP5BgSMZss/J/f63iq2O7eKvXRCY164RGo0Gn0fJyj3G08w/lni2/UmizciYvkzvjfub1hA282P0aepVaVkXn18jpffNPlsxzmjpxMq2b9eOVP47R+q94df/fJigerNJ6l03GgrzdeXRwSx4d3JI8U39+PHCcVw+s4ZjllP2EgDRu9P+t5H2W6g3alj8MTuiIjfqD6w37KZ5Cv6HlMEILCwnycN7Dk39yNzl77O0ma0PZ4RaDTdHZJ08phQS6u0PRU80JhmRu3PgtMfHh/DhgIkGZ9p41fWBnlxL4glM/q9ua0KFVvk6I2nLT7p+IzDrHyb9+LLPfVxNAruLakkXiyuAZGeP0mCkx3umxq121e6A2btzIpk2biIuLIzs7m5ycHL799ltWr17N9OnTazJGUYEAdy9+HHoLb/WaqE7wXnzmAO0Xvc6zu5eXe6quKmKDm7Jz4qP8u+so3Ip6o47npDN53ZeMXPUx8Rnn1HNntunF3mv/wZSieVkAh7IucN36r9md9Ld9h0aD1tPP6f0KTv+lbntF92Bo60asuq8vdwWX1JxK9iip1q1UkPf7eOi5q3s7jt76ECtG3U2QmzeRZDFAay88eZwIdBp7AmVDQ4tmzfjXgE4sDd2KtdQ0src1QXRd/DYrkw45ug0AFxY8r25/4D0dm0bHHd1DcVfsFcZbNWrHmjH3MqRJyTJBCYZkHl/xL4pnv7uHDXDa/sWsBamYzq0GQOfXGk1A1ypfK0Rticw6R0TmOafHrUr96vEX9iVgWjwf5/CrOmvrXS2qnUCdPXuWcePGMWCA/ReAh4cHN9xwAzt27ODgwYP8/vvvNRakqJhGo+HxzkNYPupudYK3yWrhlfh1tFnwGp8d2e6wEndFPHR6/tNtDPHXzuaaiHbq/nXJx+i25B0e37eCC/n2YbeOgU1YOOJ2tk94hBHhJfN5NEVzg/LdvEg0Op/kriZQWh0eRX/pWI1ZeCbbF/I95dkKrXvJP7p95u3kzfXHSc+ruGjdNZHt2T91Ns/6l8zjWlDQDG1R8mJVtOz+x2CejFiCLvtvLJn2c7L1nhzxCeVMXiZj13zG9eu/LjdJ33gkjryElQCc1oazxGM4t/eK4qNJJfWutJ6hjGzalg1jH2DTuAfpERIJQKy11NJBIX2pqoJTv4JiHx71anlTvRt6Fg3H2cCm5X7RFvc+5eNRx9EJUTOqnUB1796d8+fPl9vv6+vLnXfeydq1ay8pMOG6MRHtODLtn/yry0i8dPahp5T8HO7Z/As9fp/DunNHK2mhvPaBjVkx+h6Wj7qL9kW1j2yKwten99JmwWu8mbCBfEshYK8Ztfaa+1g75j56NorEz2J/Ai1dq6fdwtf491+ryj2VplgKMSXZu4A9mnZEW/Q0oPHQRiiaSxUz6FqaBZT8qB5KK+TJpX8T8d813Pb9X2w9leF03le4lz/TFPsTfhY0HM4rGda0oiU7bS+5+/6HLR+UonwspNNIronsoJ7366l42i98nbf3b8Ris6IoCn9/9ZR6/H3vm7itTws+v6ELGnNJVXetZ8m6gQPDWrB5/EM80L4/PSn563xK/EFO5FRt+Yf8E9+r214tZlTpGiFE7WlmPUfa8oEOv7K2Nsx5QlczlxKo5cuXc9999/Hpp59SUFBA8+bNeffdd8udl5ubi5+f8yEbcfn4unnw3+7XcHjqP7m5Zckj7vsyzjFi1ccMXDaPDw9tIa2SIpgXGxvZgfjJs3m3z7UEudvXbssuLODJXUuJ/uUlXotfR7bZPlw4omkbtk94hMZFc41y9B4UWC38d+8a2i98nR9O/KUmPKbkQyhFSZVn85JK6LkH1qjbQV1G061JyV+thUWTtE0WG9/sTqL/3M10e/tPHl98gPc2neD3A+dJSM4m12Sh8EIc1lx76QJt+GiGaEvasaJl2/JrwWZWe58AgjuNYNmou/h56K009bb36OVaTMze+Tvdl8zh/R/m4p20FYAjuuY0HXIzn17fBa1Wg60gVW2ndAIF9l69eX0m0l9n7xE7oQSyzpBN9yXv8Nvp/VTEmnuGwgv2pW70wd3QB7av8HwhRO06o2/KGV1Th8cshngKDQm1HJG43FyaRO7v78/Ro0f56aefyMrKQq/X880337Bw4UJuvvlmWrRowcGDB/nqq69Yt25d5Q2KyybKN5Bvh9zEIx0H8tj2xWwtKni5+cIpNl84xSPbfmNU07Y81mkQo0sN0VXETavjkY6DuLlld57etoT5p/7Cqti4UJDL07uX80rCOh7uMJAnOw/FT6dHZ7YnaUEB4Xjo9JisFpKMWdy08Tvm/h3HC93G0PP0HrX90glU3oGiYpZ6d7zbDqIg8b/2AxodR58dzSfbTvP59jNcyLV3G+07l82+c9nlYp7XfB5Ti5au+jJ5GB0ubKS4frsNDTE2+3CaMbtkmZjiSujXt+jCmIh2PLVjBZ8c3YKCQkLGOUL3lPQEHe32GB9d1xWt1j6cZi2dQHmUTaAACjP2obPZE81j7q3ADFnmAqas+5InOg/hpe5jcdeV/98y/2TJhFyvljeVOy5E1tZZTn9JWwzx6IMa7lyV2vCpn33ub9y4h8odS1tefukXUf+51AM1cOBA1q1bh8Fg4PDhw3z99dc8/vjjaDQannjiCUaPHs2jjz6Kt7c3cXFxlTcoLrveoc3YPP4hfhxyC92CI9T9FsXGirOHGLP6Uyau/ZwjWakVtFJWiKcPr8aMZv/kJ7itVQ90RQWasswFvLhvLa0XvMKn+0p6kNqEteDglCeZ1rzkSY6tqacZvfoTPln3ubqvOIGyZF/AnGyfvO3VZgBaD28Usz050ug8iQ725uVxHUj81yh+uKW7fTFhB3y1Rsb42CuPGyy+/DehNd7nSyasK0VziCw2yEy3DxcqXoEc0UVz+EIuixKSmfldAl8scUM51h2MfvTMSiImxz50fcQ/ikdnPagmT0CFPVAA5pSSSuiTut/Jra16qK/f3L+RHkvmsDP1TLnryiRQLeQhDVFeoSEBi8HxE1H6oFjcgpw/SSWEcF21yhhoNBratm1L27ZtmTHDPhdDURQOHz7M7t272bVrFwaDoUYDFdWn0WiY3rIr01t25WBmCj+c+IvvT/zF8aK5N0sTD7Iy6TCz2vfnuS4jCPOq2vBr+8DGfDV4Bi90G8Mb+zfw+dEdmKwWUgvyeHnrLywvOk/rE0wLvxB+HT6TDcnHeGzHEvYVPcnnVapa+RsXErkrMpNQQ8kcIc/IztjMWViy7E/06QJKesvc9Vpu7BbBjd0iSM01cSLdyMkMI6cy7P9tkfkdPjp7b88vmcNpaUmkme080KXM+1iV1Yr+ZvsTfyutnXninU3l36zVF050YxIlxz6M7MrqnUt4v+9UdUJ36fXvdD5R5ZoxJS1Tt/0ixvBV+5YMCmvBw9t/w2S1sD/zPH2XzeXJzkP5T9fReOrdsOScwpJhT/zcGg9w2K4QYE+UGo2TP16FqA3VrgN1MY1GQ/v27Wnfvj0333xzTTUraliHwDD+2/0aXug2hoWnE3hi5++cyjVgUWzMPRjH50e381jHwTzReQhBpUoHVCTaL5j3+03lmdjhPL9nJV8f201wYb56/Ovzp2h/OoHJzTozNLw1eyY9xvKkQ7z79yaa7rL3LJk0Ol449hcvnojnQZ2N+4qu1XgH2XttiuZTeYSPcBhDqK8Hob4e9GkeBNgT+rTfb8dSVIrqidtf4tYV8ylw8GxDdmrJ/warPcqXFWgW5MWtPSK5uWs4utcewALka934M6QFqw9tpblPEP8sKipqzSkpvaD3a1WmHZs5C/N5ew+UPqADen/78Xva9WVgWAvujPuZbamnsSkKryWs57cz+/liwHS6pP2mtuHZbIrD9y+EEKJ2NfilXBoqjUbDtOhYDk55ipd7jMXfrejpN0shL8f/QctfX+GV+D+cruXmSKRPIF8OupE9kx5jmG+Quv+wzcbUdV/RY8kclib+jQYNE6I6smbMfXRQ7E/wnff0B40Gq2Lj0Lkj6rX/PrydRTtKKoK7NxlWpVgK03dhydhrvyZsMN7BHbHs+QUApdSyphd00XRPTQLApHOny/DreHBANHf3acYjg1qwflY/Tj47ghfHtqd53kEshrP29tsPwaSzF4t9evdyvjtun8tlybYnUBq3ADQeZYcWTWdXQtE6ex5RE8sc6xAYRty4B3mr10Q8i+ZAHc5KZeDy9zlw4DP1PM/mkkAJIcSVQBKoBs5T78YzsSM4cd0zPNV5qPrLO9Ocz7O7V9Dy11d47+9NZJnzK2mpRNeQCJ5vXTIh3OBmf2rvr4yzTFz7BX2XvseyxL8pzM9Bk2cf6m0T3ZVXeowj2jeIAEtJ8c+zaGmcvRuAQkVLz607eHrXMnalJVa4ZI3x8Cfqtnfbe8k/tgVLRiIAllI/9r7+Q2lcNNn9ZLPuvD61J/OmxvDpDV14d3JnhrZupM5xytldsk5eh8F38nbvkiTojrifWHf2kDqEp/NvVa5OU0FiSW00z4sSKLAvUPx45yHEXzubQWEtAAgij6Z59uHLQv8O6P1alrtOCCFE7ZMESgD2ieGv9ZrA8eue4YH2/dEXTQy/UJDLo9sXE/rDf7hm9ad8fGgrycbyT7tdzJJTMpn6yb7T6BdassjujrREJqz9gsHfPaPu8w5tydOxwzlx3bP8t10fdb+vlycdNfbaSnsI50BuDq8lrKfX7+/S4teXeWLH72y7cLrMYr02czYFJ38AQOMehGfzaWRt+0E9XohO3XZLK0nCNjd1PslWURSydy6wt6l3x7freB7rNJh/dBpsb9Nm5YF174OtaC29i4bvFJsFU5J9VpjGIxi30H5O79UmIJQNY2cxt89kJmpPo9PYY3w/O5iPD22t1lqHQgghapYkUKKMpt4BvN9vKoen/ZPbWvVAUzTcVWizsursYe7fuoCIn/7H2E1fs+h0gtNf5tbskgSqR3RXNo+3L63Ss1Gkul9farHhz1LP2hciVmwEleqB+qBHSS2r837d1KVlAE7nGnjrwEb6LZtL859fYtaWBfx8ci8XDs9Hsdh7lbxbzwSNnuwd9uE7jbsXlqIESlEgP96+NIpRq2dVQElsFzMlxlOYaq8n5dNpFLqiiu9v9prAddH2x8NDLCWLaesuSqDMF7agmO29bZ4R49CUeh+OaDVaHuo4kBeblHy+S5WW3L91ATM2fkdBUfFSIYQQdUMSKOFQS78Qvho8g/1TZvNU56G08S9ZDFhBYafhLFPXfcXo1Z+w35Bc7nprqR4onW8jNBoN10S2Z8eER1kx6m6mt+hKZGFJMc+/bAp3xv1M2wWvsu9syfpzhRlb1e07Bszmwo3/4etBM5gU1Qn3UklIkjGLjw5vZfqGb9m16zV1/2afoSRu+U6Nx9K0pLSAHhvWoiRuY0hLzlstTj+PrK3fqdt+Paeq21qNlm8GzaBHSCRRlPTMXdwDZU7+Q92+eP5TRdxz7J9Fvs6Pv7HH/tPJvUzf8C2FRZXahRBC1D5JoESFOgY24bVeEzg89Z8cmPIEL3a/hu4hJfWk1p47SpfFb3Pf5l9JKVobD6CwaLI1gD4wXN0uTqR+HHoLb3Qequ7PKprEfirXwJKskuVQjMdXAaD1DMO9cX8CPby4tXUPFo+8g9QZL/D9kJuZ2jxGXbomkix6aOxJ0R6lCeO3/8meH55Q21MCS3qJPDQlCdOi8Bhub93L4WdgMxkxbLRP5Na4e+Hfo+xE7nxrIWfyDARQ0nN2cQ0oS1ZJUugW2oeqUKwmrLmnAPAPiWXZyLvVyf5LEg8wa8/vLq9xKIQQomZIAiWqRKPR0DGwCc91GcnuSf/ghz7Xq71SNkXhkyPbaP3rq7wS/wf5lkL1aTWdXyO0bo4XD/WwlDzh99bAGUxu1hmAU94lT68pefZelk8s7Xlwx++sOnsYc1FPkb+7JzNadmPB8Jlk3PRf1l9zP+81LRnyWkI7RqQdo22ePSE7HBBKaFDJPfXY2z4X3Jx3bn2LN3s77hnK2v4jtqLJ7gH9bkHnE1Tm+LO7V5BakIc/JW1r3PzLnGPJLlqHUOtR5TpOlpzjULTwsd6/LeOiOrB81F146+3J4m/nDnLPll/KzP8SQghRO2qsDpRoWEaFtWZqux58eGgrL+xdjcGcT67FxLO7V/DRwS0szUhEB+iDIpy2YTPlqtvtG7dgUaveHMtOY/3OhXDQPuHaagSLouHDwnb8f3t3HhdVvf8P/HVmZ4CBYREREFEUlwQXwlTU3HNJSutqmqZlmqYtllnd+pnl/Xq1LLdySa/ti6mVXne95b6BC+6aooAiKAzr7DOf3x8DB8ZhYIZwFnk/Hw/rzDnnc857WN98Pp/z/uRcPIzlFw9DIZZhUGRrPNG0HQZFtkaAxAcykRiPhsfibuopVMwOeqbbu5Aun8jfIyC+OQDbiusPj5qPgEbRNvsBy+Rx1e5l/Ougfi9bHT+SdwMrLx0BAAQLzUB5LiOQBFhdw1SeQIn8m4PjHPu7xVR0id8WKVoBALqHxWBT3+cxZPca6ExGrL1yHP4iKRZ1SbF56o+Q+yXnqynQZle/bExE4S3cDKx+TTjyYNJlpSNjbvXL1cgi2yN8/HIXR+Qa1ANF6kwiFOHVdj3w11Pv4LW2Pfgn91RFuRAaLMNZuRI/u7WkzNrKBEog8wMAxCpC8EL3yrXezGrgrLwjikSVvVLFBi1+zjiFZ/Z+j9AfP8DAHauw/OIhZN5OgyHfUvJA3KgbWh/fggCVpXSBPK4n2vpdBwBwqOylEgc3hSJxhN33qLl6BNoblirgPq2SIWtaWcXcaDbhpcMbwMqv92hw5bBd1R4osyYXzGh5r0JFS7v3upexuLIelqhKBfa+TVpiQ+9x/Md7yYUDmJdOa08S19Fmn4Euq/plY24GNkF2ACVQDYUssj2kUdWvs6jLSrebaD8IqAeK/G1BUjk+65KCKa27YVbqFpy58Cd/bFdZMR77+UOMiumA51smoUtoU76npGoPVEUCVbEtkEthVutgUgN9eizA3UY9sSfnCn7PPIdNmeeQV558Gcwm7Lx1GTtvXcZ0HMU75Z0w2aVNIdm1BADASX0RkvISSo5bEjMJjNCUP4kX1P8VcNUs3luhwKr3yXqR0GUXDvJL0iSFRKGVTwY/iMdV6YEylVzht0V1TKCE5T1QFYZEtcXyTo9jUtomMDD888Q2NPLxw8RWjs2vIuTvkkbFI+Y922Vjxm6xfM9MdXVAxC1q6l2y1yv1oKAEitSbVgGh+LXveBxiGuD41wCAXKk/Sgw6fHn5KL68fBTtAsPwRdfh6Nm4hXUPlLQygTIWX4VAqoNZDTA9IAxMhFQkxtCothga1RYruo7A0TuZ+C3zLH7LPIsrxZY5TsNgGfIy6oDig5tQ8dygasi7aFR8EIClELgEJmhgqUge2KtyiO9exsLbfPkDUWA4xAlDcaXoDjLLCnEwLwMfn9lriZ3jsKLbU2Cp/618P1V6oPj5T3CyB4ofwuMgUsTaHH8yoi00Ig6vHP0NADD50HpEygPwWGRrh+9BCCGkbiiBIvUu5tY5VCwlHd22DwKNlsrmAHCuMBe9t6/ArPa98bxeXdmoyrwg7fV1EMoBY/lFig9+g6D+0/njQoEA3cKaoVtYM8xPHIKLRXn44/JOtDt/B4wBty7JEFJ+7d0hsXgjPw+pBWvRiAPKsgT8EJ6OiZBWVgRN8V2UGHQoMehQrNdato06RB/6Bo+YLDOqVgW3wsIfZ1f7fqe3SUbH4Ajkld0oD9AHnLBy4ryx8Dy/7WgPFDOqYSw4bbmcXzOr61ndu20ycjUl+Ff6HpgZw+yTOyiBIoQQF6AEitQrxhhKT28BAHBiGWY+8Q5eFYrxW+ZZLLtwEAfzrsPMGOal/w/iwrsYWd5Od+sC5C0t1bn1ufsgCQd05ZUQ8tb/E/6JIyBW2s6r4DgObQLDECXPRQkA/S3AP98y/ypf6o8PWvVHPy4Dq/7shiKNDMGsBCXMUgpACzHmvfQ6jCIB/vuY9SRyjjFsS7e8DxM4fNsoDvfiwOGJpu3wUaeBMBZe5CeKS8Ksu631eYf4FuLgzg59HLVZm/l5U9LIQTWe+1Gnx7D95iWk5Wfj2N0s5GpKEObj79B9CCGE1A0lUHZkZGTgxIkTiImJQadOnWpvQAAA+pyLMNy9DgDwbdsHAqkcMgCjmnfEP2ISsOjcfrydthUGswkX5MF8u/e2fIq4x2ZgdEw89HkHIfIHZM38ob1eArO2BLk/zkDk1J/s3leb+TtMZYD6r8p98dN/wVpFBAKOPYdVmgjoTGJkI7hKKw4CACKjbRmALqpMRGmLAAAHQlpAERKDdr4BiPINRJRvIFoFhGJgRByfqJRm/c63lUWl8NvMqOUntouUD1k9nVcTzdXv+G2f5s/WeC7HcRjWtC3S8i2LIm/LvojxLauvaUW8W9HhKTCoKiflGg1G3BVbfowbVekQKaufzEsIqX+UQFVj/vz5mDt3LkJDQ5GRkYFu3bph7dq1aNWqVe2NG7jS01v5bb/4wVbHBJxlsdwRzdpj1aWjOKgt5I/Jc//CS4c34KvjX2KjyVKQk3ukD4SqozAV3Ubx0Z9R2mMC/NoPtLmnSZMHfc4BlJ0DX0YgePBMBLQfiKHqW8hTn4IA4TbtKnAAFiQOgb9YCn+xDP5iKSJ/eYs/PubZTzGlY83Vw7WZVRKopsP4bUN+Gr8+niS0W43X4N+P9g50N7cDAIT+zSEOfaTWNkMj22L2ScuyNP/NOk8J1APKoDpjN1ESKeMhVtpfz5EQUr8ogbrH7t27sWzZMly8eBERERFIS0vDhAkTkJSUhC1btqB79+5OXa+4uBjFxZVLfOTkWKpkv/jii5DL5fUauyfwgxYizpLkFC3fDbbc/uP1wWBYWH6u8YoQT17OAAeGV7gnAABmxkGMJPhylmfbTHMWo4StBHBvvSMGOTcEElgKbBohQOm6v4B1IwAwCLhhQA3r73IAjnywrMoehgDOBA4DYQaH4rlrAXxVw7tmEHBhAJ4AYwDb/No9xyreTx6w0H7JBOs2Qy1bjAP74SkH2gAV9dHNyMCIzzc51KYh2LBhg7tDqFciZTxCBluefsvPz0dwcHAtLQgh9wMlUPfYsGEDnnjiCUREWApAdu7cGYcOHUJKSgqGDBmCw4cPo02bNg5f79NPP8WcOXPuV7gepxSyGpMVaxwKmW+VV5b/mlllgmSACIWsti9TDmomhRrVTbS2vp6jcRVVicuR8+3fo273d76NbVpJCCHk/qFCmvfw8/NDamqqzb5NmzYhJiYG48aNA2MOZwiYMWMGsrKy+H/Hjh2r75A9hhhGBHJlCOTKIIPeoTa+0PFtOJjBwQwBZ/k/gyUXE8CMAE7Nn1fTv4peKAsGAWeGnNNBCMcX3vWpEpMj7bjy+wg4M+7NHqu+H8dUXsvxNhUtrf8RQgi5f6gH6h4jR47EJ598gm+//RZjx47l9/v6+uKbb75BQkICDhw4gB49ejh0PYVCAYVCYbP/yy+/RGRkZL3F7WrVDR3c/vFNFGxfCABo+tZu+LXrW+t18ja8j7ub5gIAIl/9CWXpowAA0qZPIKjPBjDGcKNUhcu/zUHk7iX2L8QBN5u1gHjSNvQKjwXHcShNnwfV3ndRkgp8qe6JTHOI3eYVwzym0gJceTMGZk0xOIkccV8U2F3LD7DUrLrzaxzATBD6xSB0xFW+UKju1i4U7BwAAJBFD4eyd+1DSUVHpkF98XMAgCJpMXzbvmL33Hs/B5+d24cZxyxDd4MjW2NLf/s1rjwFDUERQrwVJVD3SExMxEsvvYTJkycjMjISvXv35o+1b98eCQkJuHTpksMJVENiuJvBb0sj2jrURuhfufyJqTi3cr+sEQDLE2bN/IPQbOxi6PpMhllbihIAJ0vycbTwDgIzVuAp82FAADyPRBzdsRIPBTbGtDbdMaLoOtQXLdcLExRDxXxRwnxsYhCJKr8N8rcvhFljmbOm7PVCjckTAJSe+TfALL1U8tZTrNajKzu7kN/2iR1f68fCpM6B+vJqy/sWB8An9rla21TYf/sa3jpuKeTJgcOrbenrkxBC7idKoKqxePFiXL9+HUOGDMF//vMfjBpl6RUpKytDTk4OOnTo4N4APZThznUAACeWQqQIc6iNUB7Ib5vK8vhtTmxbx6giKfMBMLD8X75pI/SW5e6QwSzXOlt4Gy8dWo/Wl35GaHmx86djDWj+4SZwQhFGjKicyF11grGx5C7ydy4uv78MwUPfqTF2U2kmNFctFdc5aTDkcVP4YwbVWehu7bC8R0UcpJFDarwWAJSd/RgwWybM+7Z9xeGSBznqYvzjz29hZJYhvw869seACNu6VYQQQuoPJVDVkEgk+P333zF16lQ888wzWLNmDZKTk/Hbb79hxIgRSExMdHeIHqmi/pM4OBqcwLHpdVYJVOldfru6BKo65pKrlg2RL1Ymv4ylFw/ifzl/YfTNkwi9XVR+E+DF2P4I2vs9OgRFgKH6Cdf5Wz8G05UBAJR9p0IcaL/0AQCUnl0AmC2Vyn3bvg6BuHI5mrJzn/Lbvu1eB8fV/PEwafJQdmkFAIAT+cG3zas1nl/BYDZh5J/f4rbGUvphcGRrvJfQz6G2hBBC6q5BJlCMMRQVFSEwMNDuORKJBKtXr8b48ePx3Xff4fz585gxY4bVvChSyaQphqmsAAAgDo1xuJ3AqgeqgF/RxZEEijEzjCWWypki/1g80aw9nmjWHudTN8K89zP+PGMbCf6AELhxBhtunOEf92cAHt32BSLlgWgOE57atQQiAEzsg+IeL8DfoIOvnSG8e4fbfNtMszqmufa95f1JQyBvMa7W91J2/jPAZFnuRt56KgQyx+YFzUrdgv25lqHTGL8gfNdzNAS1JGuEENczqtJxd2v1i+uKle0R0NX+orzEMzW4BGrVqlV4//33kZeXh4SEBGzYsAEtWrSwe35ycjKSkx/sFaXrg+HuDX5bEtLM4XZWPVDqQojKO3EEIr/qG1RhVt8CTJZlWyrWmDPkZ0H41UtA+XCWrBmQGx4KBWQoNmhtrrH39jUAwFt//QlR+fE1jR/CZ7v+AwBQSnwQ5RuIpn6BfBXypr5KdMhchqDy4TZ5m+lWw21lF5fxxTPlrV8GJ7Kdd2X1PnQFUF8or0Ml9IFvuzdqfe8aowFfXD2Gz87ts7xPoQgb+jwHpfTBqy1GiLerqcCpUZXuwkhIfWpQCdTChQuxcuVKrF69GmKxGNOnT8eYMWNw5MgRm3P37t2L8+fPY8qUKdVcidyrYvgOAMTOJFC+gfy2WV0ElOdNjvRAGcvXngMAoSIWZoMOWUuHw1RypzwOSwIV1+ghFPb/CNdLC3C6IAff/Nd6UeAITRFG3rIs3FsmFGNtVOUQrUqvgUqvQboqh98XDDWO4VuAA0qZGI9e0qPF3dXoFByBzoFKJF34AgIATCBFdpOnwBXmQchxEAoEUEp8ECjxsZ5sfvELft0737jJEPo0snmvjDFcKrqDnbcuYcfNS/jz9lWojQb++IquI9AxOKLWjxkhxPVq6l2y1ytFPF+DSaAKCgowf/58HDp0CLGxsQCApUuXYtCgQbh27RqaN29udf7GjRuxZMkSxMbGon///u4I2auYylT8ttBX6XA7oV8IwHEAYzAU3IakPHdwJIEyqbOrXKcZcn94HdoMSw0vSVgLyFtdBccBnFQJjuMQ4x+MGP9gfFvehgOgHvUBbvxfD5jNlvpRd5KewdTEx3FLXYSb6mJklRUiq6wQZcbKulav4gh8OMv53yABl7UmXL55EdtuXsQkpOIRrhAA8L2pFd7a+pVN3D5CMSJ9AxAhD0CkbwCm5W5A0/JjP4i7ozD9f9CZjNCZjdCajCjQqfFHzl/ILCus9uMw86FH8Rwt3UIIIS7VYBKoX3/9FSNHjuSTJwDo3bs3OI5DZmamTQK1aNEidOzYEX371l7LiAAiRWWvibH4jsPtBFI5pFHx0GWehj43G/KWACcEOFHtlcCZvojfVl88B9X/LH/lcVJfNJm8BkX7H631GoW/zII58xQAQNKkDR4b/zkGy6yHDxljUOk1yCorRN6d02h7ZDHAAJ3AB5cbjUZ4YQlyNMWQwogpsCRwJsbhC1Sf1GhMBlwpvosrxZZJ86/gKsABucwXU07Z9oZWJ9pPiT4hzfBqQm8kBDVxqA0hhJD602ASqCFDhqBr165W+6RSKfz9/WEy2Vab5jgO48ePd1F03k8U0JjfNlap5+QIectk6DJPA2YzjMWAWAkIZKG1tmMGS70mUxlQePBLfn+TCasgDa/yGL+dyvFiGKHa8wUAgJPIETVtPQQy27lXHMchSCqHUiJDwdFR0DNL71Nwwrv4OeF1AMBtdTFunFmEsAuWp/guBvTE0CZDYWJmmMwMJmaGkZlxV1uGm+oiZJcV4a6uDDIYEMlZnqC7Bvs9d74iCXqHt8DAJnEYEBGHlooQFBQUIDiIilASQog7NJgEqnHjxmjcuLHNfpFIxC/Nolar8dxzz2HBggWIiXH8STJyTwJVdNuptvK4HlDt+by8rSWBEvo1q7WdWV8EgwooOwew8gngyj5TENB1NEyavKpnVn9frnJYrsnzX9Za/FN98XPoc/cDAIQBreHX7k3+WJiPP4S3N/ALyfTqvRT9apg4CgBaowE5t48A5RXWo8MfxsY2z0EmFEEqEEEqtPzzEYrRUhECibDBfLsSQojHa/A/kTmOg9lshlqtxuOPP47o6Gg0a9bM3WF5HaEiFOAEADM7n0C1rJxEaSy0PPpftaZSdRhjKDl5CKWnwS/8JmuehLDRlvIFVnWXWPUJFFfeMCB5PAK6jq7xfsbiqyhJe5tvGdj9P+BEMv64Ie8A/zSNpPGjNT51U0EmEqOxMQ+F5a/jIruhc3Tt7QghhLhfgy8Yw3EcysrK+ORpzZo1Vk9IEcdwAqEliQJgcjKBEgdF8LWjjMWAQB5d4/nMqEfO2skoOnCAT578EgYi+q3dVZZeqfwcMpsEqsqQnkCI0CdmoyaMmVF0aCKYUQ3AUhhT0sh6OLjswuf8trz1NDjKVHyJ3xYpWjncjhBCiHs1+B4ogUCAqVOnYtCgQZQ8/U2igMYwFeU63QMFAD7NO8FwJwMwAWat7eLLFZhRj8xFKSg7s53fJ2sGRLz8M4TSKk/u1dADJYEJ+vIvfUXSPyAJbVZjbOqLX0B/+08AgNA/Fv4dP7I6biy5Du0Ny5IwAnkkZE1TaryeVduii/w2JVDEk+R8NQXa7DPVHtNlpUMaFe/iiAjxLA2+B6px48aUPNUTgdgypGU26JxuK42q7HUqOXEZJSc3w1hkPRmdmc24+eX4yuRJKIBvO8CnuQACiXXZA04k55MoszbP6piEq6yfFDx4Zo1xGVRnUHy8cq5TQPc1lmtXUXzsVaB8Yrk87iVwAsf+LjEbSqDN/L08Xj8I/ZvX0oIQ19Fmn4Euq/oij9KoeMgiabiZNGwNvgdq586daNSoESVP9cBYYnksX+Qf4nRbSWMxBD6AWQPob99E1qJhAABxcFPIYh6GT/Mk6G9fRvGRHwFYShUEdAkFJ7wOThJos9YcJ5RC6N8CpuIrMBaeB2MMHMdBm3kaovJJ5UYI4BPd0W5MzKhB4d5nKhf4fegtSBv3tDpHm/k7dFmbAABC36bwbfuaw+9Z89dX/JOEPrHjwQklDrclxBWkUfGIee+Au8MgxCM1+AQqLCzM3SE8MCoWAxb6OZ9AGe8egbwVUJoOqylKhvxMGPIzUZK6oXKnUIyoV35F2blxMGsAgaT6x/9FAW1hKr4CZiiCWX0LQt8IqP6sLHegZ+IaYyo+/gaMhecAAOLgRJuhO7OhFEVHp/OvFV2WQiCuvX4VADCzCWXnF/Ovfdu+4lA7QgghnqHBD+GR+sGMestSLACETvZAMZMe+rvHIA4Cgga2QNTrmxGS8j582w+E4N6q5hyHiMnfwe+h/jDrLNXP7SZQynb8trHwHMw6NYoOf8fv00NoNybtjd+gvlRemFPkh8BeP9r0EJWe/hDmsiwAgDRqGGRNhzn8nnXZW2AquWppGzmUX8uPEEKId2jwPVCkfhhL8/ltkX/tRTCrMhScBEwaAICsaS/4dxgK/w5DAVjKFRjyrkJz7Ri02WfhG9cTfvGPgRk1/NAaJw2q9rriwMoEylB4DpqM23ySZ2E7bMsYg+avtSg++iq/T9H1C4gUsVbnGVRnUHbuU8tVRHIouixx6j2XnV/Ebzsz7EcIIcQzUAJF6oWpfP4T4HwPlD7vIL8tadTd6hjHcZCExUISFouAKvvN+sq19+z2QFVJoIwFJ6Hal1FjHCZ1DooOvQhd9hZ+n6z5GMhbjLU6jzEzig6/BDBLBXu/hNkQ+dVceqEqQ/4p6G//UR7jQ5CE93G4LSGEEM9ACRSpF6aSyvXvnJ0DZcg7zG/fm0DZY9YV8NsCqZ0EShHHF/csOf0tNJfLY61m5FqX8z8U7n3G6ok9eeupUCQutDlXfflLGPIOWe4R2A6+7V53KGbAknyVnHyPf+3b9jV6gIEQQrwQzYEi9aJq7aeqCws7ompvkiNLuAAAJ6ysAs4MpdWfI5LxCZn2WuV+Pav8u4ExM0rT/w8FO/vzyZNAHomgATsR8MjnVtXGAcBUmomSqmUNuq4AJ6h5MnpVpafn8j1cAp/G8Gk+xuG2hBBCPAclUKReGFS3+G2RMsKptpy4snAmM5Q41EboFw1wlkngxuIrds9T9v4VQsUIGMqnaAlkgKG8HQcG1a7BKDnxT77YpjQqBaEpZyBt0t/mWowxFB2ZCma0JGzy1i9DEpZsc5492qz/ovRUedVzTojAnj/aJGiEEEK8AyVQpF4YCysTKHFgE6faCqokUObyuki14QRivrfKWGI/geKkQSg9k8W/lrepHO7jOAbdrR3lL4TwT1wAZZ9fIZAGVnst7fV1fO+R0Dca/p3/7VCsAGAs/guF+yp7mxQPL4Q0/FGH2xNCCPEsNAeK1IuqCZTTPVCSyunhTO9YAgUAIkVLmEqugukKYNYVQFDN03glqRugvXYMACBr1hmRLx+Aeb/1wsECn8YI7PWzTZHMqsy6AhQfrazVpOi6vNYFjyswxlB0eHJl0czmz0Lehuo+EUIsjKp03N1afW+2WNkeAV2Xuzgi4ghKoEi9MFYM4XEcRAHOFSetSw8UAAgVLYGblmVdjMVXIAntYnWcMYa8jf+Pf93oH/PLl5uxTNo2Mw6KpEWQxTwDoU/N87aKU2fyc6RkMc9AFjnI4Ti1GT9Bn/O/8phbIaDbKpo4TggBYEmQ7DGqql9Kh3gGSqBIvTCobgIARIowcELnvqw4SdU5UM71QFWoLoFSX/wT+lsXAADytn3g167vvXeGb9tXURtdzh/QXPlPeaxKKJI+czhGs74Ixcdn8K8tE9N9HG5PCHmw1dS7ZK9XingGmgNF6oWxKAcAIAoMd7pt1UnkVcsT1EboXyWBKjxvc7xg9zJ+O6jfdJvjjjBp8lB0YDz/WvHwQgh9HO9hK06dCbPG8oSirNlISJv0q1MchBBCPAslUKReCKSW+UCmKhXJHSXyr6zybbhzxOF24uBOljpPALSZv4KxykX09LlXUZL2q+X6QVF8ZXNnMJMOqj+Gw1SWCQCQhPeDT+x4h9trb/wGzWXL2nucWAFF0qdOx0AIIcQzUQJF6oU08iEAlsV/TRrHh+GA8uKZ5XWddLd2OdxO6NMIksaWKt6mooswFpzmj+XvXASUJ1TBA19zelixomSBobxKutA3GoG9fnB47pJJfQuFhybyrwO6LodQ7tzTiYQQQjwXzYEi9UIW2R7qC5blSXQ3z0Ee29XhtpxIBklYT+hv7YSp5C8YSzIg8o9xqK1P89HQ5+wGAGgyfoQ4uAOMpfko3GeZsyTwUSCw18SaLlGtsvOLKuc9iXyh7LsJQplja/wxZkbhgfFgOktvnKz5aPg0H11LK0LcI+erKdBmn7HZr8tKhzQq3g0REeIdqAeK1IuKHigA0GWfdb59lcKVeid6oWRNnwQEEgCA5uq3MOuLofrfCjC9GgCgfHQShD6Kmi5hxWwoQeHBF1BSZeJ3YI9vIQ5y/BeJ+vwS/j0IfaMR0OVzh9sS4mra7DPQZdk+7SWNiocs0v4TYoQ0dNQDReqFdQJl+9dsre2b9EdFDXLdrV2Qx01yqJ1AGghZ0yehvf4zzJocFB2ejoLd5cUxhSIE9Xe83pI+7xAK94+FqaRy3Rf/Tv+CLPpJh69hKEhHcdosywtOgMCe39otzEmIp5BGxSPmvQPuDoMQr0IJFKkX0oh2/La2Dj1QImV7CGSNYNbmQZezB8xsAicQOtRW8fBC6G7tBNOrUHToG5iKLPsDuoyCODiq1vbMbELp6TkoTf8Xv6QLJw5AQNfl8Gn+jMPvgRm1KNw3GjDrAQB+7d+BJKyHw+0JIeRBo8tKR8bc6ssxeHvSTgkUqRdCHwXEIdEw3L0B7fU0mDTFTg2dcZwAkib9oL32A5heBWPhWYiDEhy7t28EArouR8GuUdBkVO4PfuyNWtuaDaUo3DcGuqxN/D5J494I7PE1hL61J19VlV1cBmPhOQCAOORh+HWY7VR7Qu4Xe/OcAJrrRO6fmoaAqxs29jaUQJF649d+EFR/rIBZU4yCnYsRmvK+U+2FvtH8NjOUOtVWFv00tJlvgOksBT3FIQJw4tpKKjDkb+sJY8FJy0uBGP6d/w3ftq+B45ybHsiMapSd/djyghMiMPkbcAKxU9cg5H6pmOdUXaJEc53I/RI+3n6RUHu9Ut6EEihSb4KHvg3VvjWAyYD87QsR1G8ahL7K2hvyKus4wcmlTvK3fQJdliV54iSAPM6Mgj1DEdTnd0gjBlTewaQvvw8HAcf45ImTBiGoz291HnIru7SSX+rFp/mzEAW2rtN1CCk6PAUGVfW9RUZVOkTKuvUW0TwnQuoXPYVH6o0kJBrKRy2Tv83qIuRvW+jcBaoUwqxYr84RZZf2I2/9u+XNBFD27W15MM+kRcGeYdBmb4eh4DSKjr6GvHUREHDMqr1Q0RIhQ47UOXliRg3Kzi7g7+8X/26drkMIABhUZ+yugSZSxte4dhohxHWoB4rUq5DH30XhvjVgBi0Kdi1G0IBXIVI4Vj/JqgfKwQSq7OI+ZH02FDCbAAChIz5CyJC3ULh/HLQZPwJmHVS7B99z7fK7McAv4f/Bt+1rEEid6Smzpr6yunK5lphnIApoVedrEQJYEqWQwdRbRIgnox4oO44ePYo33ngDn3zyCbRarbvD8RpiZRMo+04FAJi1pbi7aa4TrZ0bwis5vRWZnwyEWWspgOCXMAQhQ94GJxAhsMc38Gn+rM11OZEcjHHlewXw7zjnbyVPzKhF6Zl/V1wdfvH/rPO1CCGEeA9KoKrx8ccfY8iQIbh8+TJmz56NF154wd0heZWQIbMgkPkDAAr+9wV0OZcca+jEEF7xsV+QtTgFzGBJbv07P4nI6RvACSxf0pxAhIDkryBvPRXgRJCE9URA9/+g0cjbYE4MD9ZG/ddamNW3AACyZv+AOLBNvV2bEEKI56IhvHv89ttv+Pzzz3H69GlERERg8+bNmDZtWp2vV1xcjOLiyrXhcnJy6iNMjyZSNELI4+8i75d3AJMRN1eNQ8w/94MTSWpuyEyV2zU8BVdycjOyV4wGTEYAQEDyc2jy/Gqb9e44gRABj3wORZdlDq9h5wxj0WWUpL3Nv/ZLeK/e70EIIcQzUQJ1j/nz5+P1119HREQEAECpVCIsLAyvvfYaGGOYNm0aWrZs6fD1Pv30U8yZM8dmv0qlgo+PT73F7WpVk8JqJY6FcN9amHIvQ3vtGG589Qr8n/xXjU2MRZmV19eIwOXbliHQXd6LwtWVyZNP9xcgfWIeCgqLnH8TAPKruYcjmLEExv3DwAyWj4MgagyKzeFAHa9XF7V+DryAK95DcHDwfb8HIaThoQTqHmq1GocPH8b06dNx+/ZtTJ06FQEBAZDL5diwYQO+/vprHD9+3OEkasaMGZg4sXIx25ycHCQlJUGpVHr9D/ba4vd7ZQMy5iSB6TVQ71+JoA4DoUi0vyxKvvEO9BXXbtIWnNC6x0p9+SDy/jMWMOoAAIG9JiJ8wqq/1btUl88BYwyFf06GodQyNCkK6oiQXqvAieR1jqOuvP1rCHgw3gMhpOGhBOoeH3zwAUaNGoXIyEjo9Xr06dMHP//8MziOw9tvv434+Hh88sknWLlypUPXUygUUCgcr8j9IJFFPoTwsZ/j1prnAQC31kyArGkCJI2aV3u+SW2p4ySQNbJKnhhjKD25GTdXjeUXCVZ0GYXw8Svuy9BcbcrOzIf2xgYAACcNhrL3RrckT4RURdXGH0xGVTrubq2+6KRY2R4BXe0XqyT3F00iv8eTTz6JzMxM/Pnnn4iLi8P06dP5X9IKhQJ9+vRBUVHdhosaosCeExCQ/BwAS22o7M//AbNBZ3MeYwymsmwAgMA3kt+vuX4CN+b3RdbiFJg1luEevw6PI2LSNw6vlVefNBnrUHKiSs2pXj9D5N/M5XEQcq+KauPVoWrj3kmsbG+3cKpRlW634CpxDeqBqkZYWBjCwsJw48YNpKWloUcPS4HF0tJS/PHHH/jkk0/cHKF3CR/3ObTXjkN36zy019OQ+9ObCB+71OocpisAzJbESiiPgCE/C3nr/4miQ99aneffKQURU34CJ3L9Mina7O0o3P8sKsoi+HeeD2mTvi6PgxB7qNq4e6WrcpC8ZVm1x9orw7G82winrldT75K9XiniOpRA1eDpp5/G22+/jdLSUsTGxuLjjz9G//79MWKEc98EDZ1A6ovIab/g2gcPg+nVUO1eBpF/KPw7PQGBPABCeSBMassEcmYCys7lIPfHVnyJAgCQNGmDsJEfwy9hsFuG7fS5B6H6YzhgNgAA5G2mw7dd7YsVE0IahvbKcLvH0lUP/tPXDRElUDWYP38+TCYT5s+fD4VCgTfeeAOvv/66u8PyStKItgh/bjlufWkZzrvz62zc+XV25QkcB658RI4ZU/ndQv9QhA7/EMpeE23KFLiKQXUGBXuGAiYNAMtad4qkRW5J5Aghnqmm3iV7vVLEuzW4OVBz587FyZMnHTpXIpFgyZIlKCkpwc2bNzFjxgz6pfk3BCaPQ1D/V6o/yBiYEWCW6gTgRBIED30HsR//haA+L7kteTKWZKBg50AwfSEAQBr1OAKS/wOuhjpVhBBCHnwNqgdq48aN+OCDD/DZZ59h9+7d6Nixo91z9+7di/Pnz2PKlCkujPDBFzZmEXzb9YPm2jGY1IUwqwthUhfBrC6EPu8UzNoSCP2Axs+tgn/8c26N1aTJRcHOATBrLN3vkrAeUPb6GZzA9fOvCPEkUw5twBk7w1LpqhzE1zCcRciDokElUEuXLsW6devw6aefol+/fjUmURs3bsSSJUsQGxuL/v37uzjSBxfHcfDv+Dj8Oz5uc0xzYyMK/7B0gxtu/wi4MYEy64tQsGsQTCV/AQBEygQo+2wCJ/Le4qeE1Jczqhy7iVK8MrzG+UCEPCgaTAJVUFCA/Px8PPnkkxgwYAAee+yxGpOoRYsWoWPHjujbl56ychVZVAqEilYwFV+G7tYOGPJPQBzcyeVxmA0lUO0ZBmOBZahX6N8cQf23QyANdHkshHiqeGU4Dgyp+zJXhHi7BjORIygoCHv37gXHcfDz88P27dvRpk0b9OvXr9o5URzHYfz48RAIGsyHyO04gRB+7Wfxr0vT57k8BpP2Dgq294E+dx8AQCALQ1D/nRDKG7s8FkIIIZ6rQWUHSqWS364uiVKr1Rg0aBBOnDjhxigbNp/mz0IgtxTS1N7YAH3ufpfd26TJRf7WHjDkW54CFMhCETRgB0SKFi6LgRBCiHdoUAnUve5Novr374/w8PAaJ5eT+4sTSuD30Jvlrxjyd/SD+vKa+35fs6EUqt1DYCq2rG8n9I1G8KADEAcl3Pd7E0II8T4NOoECLEnUxo0bodVqERcXhzVr1lCpAjeTx02BNHKw5YVZj6JDE1F0ZBpYeRHL+sZMeqj+GAFDfhoAQOgXg+DBByEKaHVf7kcIIcT7NZhJ5Pao1Wo888wzGDlyJCVPHoITSqDsswklJ95F2dkFAAD1xc9hVJ1FYO9fIJSF1tu9GDOj6OAL0N/aCQAQSEMQNGAHhL4R9XYPQgi5H+5daNhoMOKuWESLDLtIg++BysvLQ2JiIiVPHoYTCKFInI/Anj8AQhkAQJ+7F3c3d4Y+73C93ack7R1orn1nuadIDmW/LRApWtbb9Qkh5H6wt9AwLTLsOg2+B6pZs2aYP3++u8Mgdvg0fwaigDgU/O8JmMuyYC7LQv62nvDv/H/wbffG36wIzvgeLnBCBD66HpLQpHqJm5CaFB2eYveXnFGVXu0vRkKqqq6HKT8/H+xoihuiaZgafA8U8Xzi4E4IGZoKSXg/yw5mREnqW1DtGQazNr/O1xVwjN8O6LYasshBfzdUQhxiUJ2BUZVe7TGRMh5iZXsXR0QIcVaD74Ei3kHo0whB/bej9Mw8lJ6aDTAzdNlbcHdzZwQP2gehX1OHrmPWqcCBgaFyuNa/83zIW46/T5ETUj2RMh4hgw+4OwxCSB1RDxTxGpxACP+E9xA08H8Q+FiWijCV3UDBrscc6okyllxH/tbu4Kr0PAX2/BF+7d+6bzETQgh5MFECRbyOtHEvhAw7CVFgWwCAsegCCvYMhdlQZreN/m4q8rc8AmPRBX6fmXHwaT7qvsdLCCHkwUNDeMQrCX3CENR/B+5u7QZzWRYMd44gb31TiJUJ/BwSUVA8xIHtoLu1G4X7ngEzqgEAjO+AoqcuCSGE1A0lUMRrCX0jEdR/B/K3JYPpCsB0BdDf/gP6239UOasiSbJkTZIm/a3mPxHiTXK+mgJtduXTe0ajEcUiy49xXVY6pFH09B4hrkJDeMSriQPbIPixPyGLHgGhX7NqzmCoSJ58Wj6PoH5bQD1PxFtps89Al1X903vSqHjIIunpPUJchXqgiNcTK9tD2Xs9AMCsL4JRdRYGVTqMqjMwqNJhLsuGvPVU+D40k4qlEq8njYpHzHuWp/fy8/MRHBzs5ogIaZgogSIPFIEkAJKw7pCEdXd3KITUyb3DdFW5cphuyqENOKPKqfZYuioH8cpwl8RBiKeiBIoQQjxIxTBddYmSK4fpzqhy7CZK8cpwtKcEymPdu0ZeVfbWyaupOn5N7RoySqAIIcTDVB2mc6d4ZTgODJnm7jAeCOmqHCRvWVbtsfbKcCzvNqJe7lNTFXt71e+Byur49tbXI7YogSKEkPuA1rsjFWrqrUu3M0xaVzX1Etnrlapgrzp+be0aKkqgCCHkPqjpL3pa765hqal3yV6v1P1ib3iPknrnUQJFCCH3Ca13RzxJTUk7JfXOowSKEEIIaQBoEnj9okKahBBCCCFOoh4oQgipI5ooTkjDRQkUIYTUEU0UJw1FXWpLPegogSKEkL+hLhPFPaXaOCGOqGttqQcdJVCEEOJinlJtnBBH1FZbqqH2TlECRQghbuAp1cbtrXlH690RRzTk3ilKoAghpAGzt+YdrXfnOq5a5uV++DuVz70dJVCEEHIfeNM8J09f827K+nScySmu9lh6Tgniw/1dHFH9qSlJPZh3HQfzrlfbQ1jR1pOTK6DmyufejhIoQgiphb1yBUUHj8GslaHkhO0vCM2VgwAAn5bdbY7RPCfnnMkptpsoxYf7o324wg1R1Y+aEiB7w6tA/a+hdz/UNLzHjGoXRnJ/UAJFCCFVZHzYGhp/6xrDZkMJAEAgtv4FbrhrAGCASGl7HZ+W3SGLbI/w8Q/mBFpXiw/3x4HpD/aQ0L08aQ29uqhpeE+1x3uT3gqUQLmY0WgEAOTkeP5fDzVRqVTQaDTuDqNO1OrKv3yys7PdGMnf482fgwqueg+NGzeGSFTzj7uK783rhXqUGrh7jkqghQg3cc8PfYECN3xCsVxi28sEAMgDsGBW3YJ2AAMDh3tjdY5GUAIfsz8eTttYT1FVL6TK993DHzp3rwt5ZWjTyNfl36/3fn3qVHm4kFeGhz/Mc2kc1bkgy4JGUAK/c5VfX/Xx9eAqvxTo4JOd7dD3pqfiGGPM3UE0JMePH0dSUpK7wyCkQcnKykJkZGSN59D3JiGud/78ebRp08bdYdQJJVAuptVqcebMGYSGhnpt1p2Tk4OkpCQcO3YM4eHe95SOt8cP0HtwliN/5Xrb96a3fQ1QvPeXt8abkZGBZs2auTucOvH8nxIPGJlMhocfftjdYdSL8PDwWv+q92TeHj9A76E+eev3pqd8/BxF8d5f3havN/yxYo+g9lMIIYQQQkhVlEARQgghhDiJEijiNIVCgdmzZ0Oh8M7HUL09foDeA/G+jx/Fe39RvK5Hk8gJIYQQQpxEPVCEEEIIIU6iBIoQQgghxEmUQBFCCCGEOIkSKEIIIYQQJ1ECRQghhBDiJEqgCCGEEEKcRAkUqZO1a9di3LhxMJvN7g6lzrZt24bRo0djwYIF7g6lzrZt24bnnnsOb7/9NoqKitwdTp0cOHAAK1aswP79+90dite5evUqXn/9dUyYMAGnTp1ydzi1ys3Nxdq1a/Hjjz9CpVK5O5xalZWVYcuWLdi1axdMJpO7w6nVli1b8Pjjj0Or1bo7FIecO3cOL774IiZNmuQVH18bjBAnaTQaJhKJmFgsZmPGjGEmk8ndITntyy+/ZDExMWzXrl1Mr9e7O5w6mTVrFouMjGTjx49nQUFB7IknnnB3SE4xmUxszJgxLDw8nHXs2JFxHMe6dOnCLl265O7QvMKePXtYSEgIGzVqFOvYsSNTKBQsPz/f3WHZtWvXLqZUKllSUhJTKpXM39+fLVu2zN1h2bV7927WqFEj5uvrywCw5ORkj/9Z0ahRIyaTyVj//v2ZRqNxdzg1On78OAsODmZr1qxhpaWl7g6nTiiBInUSERHB1q5d65VJlEajYYGBgezo0aPuDqXOvvnmGxYXF8cKCgoYY5ZfTk2bNnVzVM5ZtmwZS0pKYmq1mjHG2KlTp9hDDz3ElEqlV39uXOHmzZssNDSU7dmzhzFm+ZoOCwtjx44dc3Nk1SsuLmbBwcFs9+7djDHG1Go1mzlzJgPAXn75ZTdHZ+vy5ctMqVSyzZs3M8YY27t3LxOJROyTTz5xc2Q1S0pKYitWrGD+/v4en0Q9+uijbPHixe4O42+hITxSJ3FxcWjRogXWr1+PdevWYdy4cbh79y5mzZrl8V2x58+fR3FxMZKSkgAAGo0Gb7zxBqKiotCmTRt8//33bo6wdosXL8a0adOgVCoBABzHISQkBM8++yzGjBmDEydOuDnC2m3duhXDhw+Hj48PACAhIQEHDx5Eu3btMHjwYGRkZLg5Qs/1zTffoG3btujTpw8AQCAQQCqV4uuvv0ZKSgrWrl3r5gitHTlyBGKxGH379gUA+Pj4YMGCBVi9ejW++OILfPTRR26O0Nq///1vvPvuuxg6dCgAoGfPnnj66aexZ88eN0dWs9atW0OhUGD79u04cuQIhg0bBpVKhZkzZ6KsrMzd4Vk5ePAgunbtyr9eunQp4uLiEB0djdmzZ3v87xGA5kCROmrdujXOnj2LYcOG8UlUixYtAABCodDN0dXMz88PZrMZJ06cgNlsxpAhQ3D+/Hn861//QmJiIp599lmsX7/e3WHWiDGGHTt2QK/X4+LFi5g8eTIiIyPRqVMnXLlyBcnJyUhPT3d3mDUKCgrCgQMHrPYpFAps2bIFISEhmDRpkpsi83yMMZw9exYZGRkoKyvDhAkTIJPJEB0dDaVSieeffx7z5s1zd5i8oKAg5Obm4sqVK1b7X3jhBcybNw9z5szB6dOn3RSdraNHj2Ly5MlW+zp06IC7d++6KSLHVPxc7tatG59ENWvWDLm5ufwfKp7Cz88Px48fBwDMmjULK1aswFtvvYXRo0dj3rx5ePvtt90coQPc3QVGvNOSJUv4rveCggIWFxfHBAKBVwznmc1m1qpVK/bYY4+xbdu2sW7dulnFPHz4cNazZ083Rli7Xbt2MT8/P+br68tCQ0PZ888/zx/TaDSsVatWbMKECW6MsHY7duxgANivv/5qc2zfvn0MALt48aLrA/MCKpWKtW/fngmFQhYVFcWaNm3KVCoVf3zGjBlMoVAws9nsviCrMJvNrF27dqxPnz7MaDTaHOvcuTN76aWX3BSdrdzcXJt9K1asYImJiVb7SkpKXBWSQzZu3MhSUlIYY5Zh0m7dujGBQOCRw3ljx45lUVFR7Nq1aywsLIzdvXuXP7ZgwQLm4+Nj87XiaagHitRJXFwczp49C5VKhb59++LJJ5/Exo0b+eE8T346j+M4LFy4ENu3b8fMmTPRq1cvCASV3wrdu3f36PgBoF+/fsjLy8ONGzcQHR2N559/nj8mk8nQvXt36HQ6N0ZYuwEDBmDMmDF49tlncfDgQatjPXr0QFhYGK5fv+6e4DxcYGAgTp8+jaysLEyZMgXDhw9HYGAgf7xv377Q6/VgHrJWPMdx+PLLL7F//35MmjTJ6vuL4ziMGDHCoz7XjRo1stknEAis4n7//ffx4osvujKsWlX8XNZoNEhJSUFsbCz27t3LD+d50tN5c+bMQWFhIYYNG4a2bdsiODiYP+YNP4MBGsIjddS6dWukp6ejb9++GDhwIObNm4eUlBSsX78e+fn5Hv/Le+jQoViwYAHOnj2LDRs2oKSkBACg1Wrx/fffY8yYMW6OsHY+Pj4IDg5Gbm4u/vzzT35/QUEBduzYgbFjx7ovOAetXr0aXbp0wYABA/DTTz/x+69fv46ysjIkJCS4MTrPxnEcwsPDcefOHRw4cMBqzsgPP/yAMWPGWP1h4G5du3bF119/ja+//hpPP/20VdmNkydPokuXLm6MrnYcx/EJ6fvvv4/Nmzdj2bJlbo7KWsuWLZGVlYUhQ4YgPDwca9euRXJyMrZv3w6NRgO1Wu3uEHkxMTFYv349rl69iqNHj+LChQv8sTVr1mDUqFEePx2EhvBInZjNZpacnMzefvttd4fyt3zzzTdMoVCwmJgYNmXKFBYXF8fGjx/vMUMfjnjvvfeYUChkr776Klu6dClr2bKlV31etFote+GFF/hHxV9++WXWpEkTtnz5cneH5hWOHz/ORCIR69OnD1u5ciVLSUlhnTp1YkVFRe4OrVpbt25loaGhLDQ0lE2aNIn179+fJSYmetxw2L1Wr17NOnTowN577z2WkJBgNeTkSUaOHMnGjRvn8VMpKhw+fJjFxMSwgIAANmnSJJacnMySkpJYYWGhu0OrFceYh/TxEo9RUlKC8+fPo2nTpggPD7d7nk6ng1QqdWFkjisoKMDly5fRsmVLq67h6uTn5+Onn37CzZs3kZycjMGDB7soyprdvn0b169fR7t27eDv72/3PLPZjLlz52Lt2rVQKBR44403MG7cOBdGWj+OHTuG9evXQ6vVYuTIkejevbu7Q/IaO3fuxLvvvos7d+5gxIgR+PDDD+Hn5+fSGBhjuH79OoKCghAQEFDjuUVFRfj6669x7tw5tG/fHi+88ILLJzk7Ey8AfPXVV5g4cSIeeugh7Nmzp9afK/XN0XgNBgOEQqHbex+d+fjqdDqsX78eZ8+eRevWrfHMM89AIpG4KNK/wZ3ZG/E8P/30E1MoFEwqlTKO49iTTz7Jbt265e6wnLJkyRIml8uZRCJhQqGQTZgwwWqCrTeYNWsWk0gkTCwWM5lMxt58802PmwRaE51OxyZMmMDOnDnj7lC80n//+1/2zjvvuDsMh+3bt4/FxsYyAEwqlbKlS5e6O6Qa1SXekydPsm7durml56khfHy9ESVQhHfy5EkWEhLCjh8/zsxmM9uyZQtr1qwZa9y4MTt16pTVuRcuXGATJ070uMq827ZtY1FRUezy5cvMaDSyH374gYWGhrKWLVuyjIwMq3MPHTrEXnvtNY8brlu+fDnr0KEDy8nJYVqtli1btoz5+vqyLl262FSa/v3339lHH33kpkjtmzdvHpPJZCw0NLTWJGrVqlVs1apVLorM8xUWFrKgoCDGcRybOXNmjefevHmTjR07lhUXF7soOltpaWksODiYrV27ll25coVNnTqVCYVCm58ZjFG8dUHxei5KoAjvzTfftHn0/c6dOywxMZE1atSIXbt2jd//66+/MpFIxD744ANXh1mjUaNGsffff99qX0ZGBouNjWWxsbFWfz1+8cUXDABbvXq1q8Os0SOPPMLWrFljte/EiRMsNDSUPfLII0yr1fL7//nPfzIAbPv27a4O0y6TycRiYmJYamoqS0pKqjWJeu655xjHcSw9Pd2FUXquRYsWsalTp7KVK1fWmkQdOHCAyeVyNnHiRBdGaG3AgAFW30N6vZ5FRkayOXPm2JxL8TqP4vVclEAR3ltvvcW6d+9usz8/P5+1atWKJScnW+3fs2cPKysrc1V4Dhk9ejQbOXKkzf4bN26wsLAwm2Nbt271uFoj3bp1Y7NmzbLZf+LECSaXy22OVSw34SkuXLjA16IpLCysNYkymUxs69atLozQs6WkpLALFy4wxphDSdShQ4fcNqFZpVKx6Ohom17cp556ik2aNKnaNhSv4yhez0YJFOEdOnSIAWAbNmywOXb8+HEGgB04cMANkTlu3bp1TCgUskOHDtkc++233xjHcVY9aZ5owYIFzM/Pj129etXm2KJFi5hcLve4xPVeVeOzl0R5WuLqKe793NpLojzlKavU1FSbfZMnT/bYXgWK9/7ytnj/Ds8pEkLcrmvXrnj22WcxYcIEpKWlWR1LTExE586dcfLkSTdF55innnoKPXv2xIgRI3D16lWrYykpKQgLC/OoJSOqM23aNERFReHxxx+3WTpi4sSJ0Gq1uHz5spuic4xcLue3AwICsHPnTsTExKBPnz44e/YssrOz0aFDB2RmZroxSs9U9WMHAJMmTcKKFSvwySef4K233gJgqUP0zjvvuCM8G507d7bZJxQKbYpOLl682JVh2UXx3l/eFu/fIXJ3AMSzrFy5EteuXUPfvn2xfv169OvXDwBgMplQVFSEVq1auTnCmnEch3Xr1qFHjx7o2bMnNm3axH9Dl5WVQavVevx78PHxwaZNm5CcnIxevXph06ZN/DqDKpUKEokE0dHRbo7SORVJ1IABA9CnTx8oFApMmzYNTZs2dXdoXqFiXcCXXnoJ+/fvh0aj8eiFbasrOknx1h+K1zNQHShio7S0FGPGjMHmzZsxZswYPProo9i4cSM4jsPmzZvBcZy7Q6zVnTt3MHz4cBw9ehQTJ05Ep06d8NVXX6Ft27ZYtWqVu8NzyF9//YWUlBRkZmZiypQpaNGiBZYuXYrRo0fj3XffdXd4dXLu3DnEx8dj4cKFeO2119wdjtcZOnQosrOz3VKHyBnTp09HaWkpIiMj+V+WFG/9oXg9hDvHD4ln++WXX9jjjz/OunXrxj788EOm0+ncHZJTjEYjW7NmDRs4cCDr0aMHW7x4sceVLKiNRqNhn332Gevduzfr3bs3++6779wdUp1lZWWx2NhY9tlnn7k7FK/k6RWwq3r11VdZYGAgxXufULyegYbwiF1PPfUUnnrqKXeHUWdCoRDPP/+81UK73kYmk+G11157IHprSktL8eabb2Ly5MnuDsXrMMYgk8m85i/3Zs2aITo6muK9Tyhez0BDeIQQQupdSUlJjUsQeRqK9/7ytngdQQkUIYQQQoiTqIwBIYQQQoiTKIEihBBCCHESJVCEEEIIIU6iBIoQQgghxEmUQBFCCCGEOIkSKEIIIYQQJ1ECRQghhBDiJEqgCCGEEEKcRAkUIYQQQoiTKIEihBBCCHESJVCEEEIIIU6iBIoQQgghxEmUQBFCCCGEOIkSKEIIIYQQJ1ECRQghhBDiJEqgCCGEEEKcRAkUIYQQQoiTKIEihBBCCHESJVCEEEIIIU6iBIoQQgghxEmUQBFCCCGEOIkSKEIIIYQQJ1ECRQghhBDiJEqgCCGEEEKcRAkUIYQQQoiTKIEihBBCCHESJVCEEEIIIU6iBIoQQgghxEmUQBFCCCGEOIkSKEIIIYQQJ1ECRQghhBDiJEqgCCGEeB2NRoPS0lJ3h0EaMI4xxtwdBCGEEOKMESNGQCgUYt26de4OhTRQ1ANFCCHE66SmpiIxMdHdYZAGTOTuAAjxNF999RWCg4PRpUsX/P777ygsLMSAAQOQkJAAvV6PLVu24PLly2jXrh2GDh3q7nAJaXDu3r2LzMxMPoHS6XSQSqVujoo0NNQDRcg93nnnHSxcuBCDBw/GhQsXsGXLFiQlJeG3335Dz549sX37dly6dAkpKSn49NNP3R0uIQ1OamoqOI5DTk4O2rdvD39/f4SGhmLNmjXuDo00INQDRUgVt27dwu3btxEfH4+dO3dCIpFAp9MhKCgIkyZNwtGjRxETEwMAKCgowPbt2zFjxgw3R01Iw5KamgqBQICNGzdi165dCAkJwdy5czF58mQ88sgjaNeunbtDJA0A9UARUkVaWhoAYMmSJZBIJAAAoVAIk8mEmTNn8skTAHAcB4VC4ZY4CWnIUlNTERYWhu+++w6NGzeGSCTCe++9B6FQiP3797s7PNJAUAJFSBVpaWlo0aIF4uLi+H3nzp2DTqfDoEGDrM49deoUOnbs6OoQCWnw0tLS8PLLL8PHx4ffJxKJIJFIwHGcGyMjDQklUIRUkZaWZvNkT1paGuRyOdq0acPvKygowPXr19G5c2dXh0hIg5aXl4fs7GwkJCRY7T9x4gRKS0vxyCOPuCky0tBQAkVIFfYSqI4dO0IoFFrtA0AJFCEulpqaCgDQarVW+//v//4PPXr0sEmsCLlfaBI5IeVycnKQk5Njk0Clpqaia9euNvuioqIQGhrqyhAJafBSU1MREBCAefPmIS4uDr6+vli8eDH27t2Lw4cPuzs80oBQAkVIOb1ej9mzZ9skUMOHD0ffvn2t9rVt2xZz5851ZXiEEACXLl3CP/7xDyQmJmLEiBEoKSlB7969kZ6ejvDwcHeHRxoQWsqFEEIIIcRJNAeKEEIIIcRJlEARQgghhDiJEihCCCGEECdRAkUIIYQQ4iRKoAghhBBCnEQJFCGEEEKIkyiBIoQQQghxEiVQhBBCCCFOogSKEEIIIcRJlEARQgghhDiJEihCCCGEECdRAkUIIYQQ4iRKoAghhBBCnEQJFCGEEEKIk/4/E41UX/9EtDIAAAAASUVORK5CYII=", + "text/plain": [ + "<Figure size 605x605 with 4 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = None\n", + "for label, (p, s, colour, _) in runs.items():\n", + " fig = corner.corner(\n", + " s[:, p.columns(line.params)],\n", + " fig=fig,\n", + " labels=[\"$m$\", \"$b$\"],\n", + " truths=[truth[\"m\"], truth[\"b\"]],\n", + " range=[(0.2, 1.1), (1.6, 2.6)],\n", + " **plotstyle.corner_kwargs(\n", + " color=colour, fill_contours=False, plot_density=False, show_titles=False\n", + " ),\n", + " )\n", + "fig.legend(\n", + " handles=[\n", + " plt.Line2D([], [], color=c, label=lab) for lab, (_, _, c, _) in runs.items()\n", + " ],\n", + " loc=\"upper right\",\n", + " fontsize=8,\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7092ad8a", + "metadata": {}, + "source": [ + "## Takeaways\n", + "\n", + "- Sharing a `Parameter` object couples the *parameters*; a term spanning\n", + " comparisons couples the *data*. Both are declarations, never configuration,\n", + " and the compiler tells us which we wrote: one column, or off-diagonal blocks.\n", + "- A shared diagonal noise can only inflate. Faced with two experiments that\n", + " disagree by 20 %, it grows until it covers the gap, which fits neither the\n", + " data nor the story.\n", + "- A normalisation mode per dataset lets each experiment slide on its own, and\n", + " is the model that matches how the defect arose here.\n", + "- One mode across both says a single unknown moved them together. That is a\n", + " stronger statement, and here it is the wrong one: our experiments drifted\n", + " apart, not together, so the fit could only widen until it said nothing. It\n", + " is the right model when the experiments really do share a calibration —\n", + " recipe 37 is the version for several quantities of one experiment whose\n", + " normalisations are correlated." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/test/test_notebooks_index.py b/test/test_notebooks_index.py index ea59a57..e0b29e7 100644 --- a/test/test_notebooks_index.py +++ b/test/test_notebooks_index.py @@ -18,7 +18,8 @@ # design document section 9, item 8: the nine notebooks and their recipes NOTEBOOKS = { "linear_calibration": {1, 17}, - "error_models": {2, 4, 5, 19}, + "error_models": {2, 4, 19}, + "sharing_error_models": {5}, "normalization_and_covariance_structure": {3, 6, 27}, "gp_discrepancy": {7, 8, 36}, "robust_likelihoods": {9, 34}, From 1a3c233489fdc07d8f72fbca7e064dcedcec062a Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Fri, 11 Sep 2026 22:15:38 -0400 Subject: [PATCH 56/75] Split robust_likelihoods and add the outlier-rejection loop - robust_likelihoods keeps the Student-t study (recipe 9) and gains the iterative rejection of recipe 39: fit, drop the points more than three pulls away, refit until the mask settles. The first round over-rejects a good point and the second puts it back, which is the argument for the loop - examples/error_scale_and_usu.ipynb is new (recipe 34): the global scale on the reported errors under both likelihoods, and the USU offset on the technique we suspect, with Hanson and Capote et al. linked - Includes the markdown you had rewritten in your working tree, with the three typos fixed ("must the the same", "as a a unknown", "This could demo, i.e.") - The general pass: smaller cells, LaTeX, plotstyle with hatched bands, .bind(x) without the empty dict - Both halves redraw their data in the original RNG order, so every number is the one that was committed before --- docs/design.md | 3 +- docs/examples.rst | 1 + examples/error_scale_and_usu.ipynb | 457 +++++++++++++++++++ examples/robust_likelihoods.ipynb | 675 ++++++++++++++++------------- test/test_notebooks_index.py | 3 +- 5 files changed, 844 insertions(+), 295 deletions(-) create mode 100644 examples/error_scale_and_usu.ipynb diff --git a/docs/design.md b/docs/design.md index befb079..22e951f 100644 --- a/docs/design.md +++ b/docs/design.md @@ -626,7 +626,8 @@ a time unless noted. | `sharing_error_models` | 5 | emcee | two experiments with opposite normalisation defects: sharing a parameter, a mode per dataset, one mode spanning both, and the assembled covariance seen directly | 78 s | | `normalization_and_covariance_structure` | 3, 6, 27 | emcee | latent scales versus reported modes on a quartic; a gallery of covariance structures from `matrix(theta)` | 328 s | | `gp_discrepancy` | 7, 8, 36 | emcee, dynesty | a kernel term on a toy and on n+⁴⁰Ca missing its surface absorption; the total predictive band; a sampled Legendre correction for contrast | 617 s | -| `robust_likelihoods` | 9, 34 | emcee | Student-t versus Gaussian; a global error scale; a USU offset per technique | 154 s | +| `robust_likelihoods` | 9, 39 | emcee | Student-t versus Gaussian on three gross outliers; the iterative rejection loop, including the round that over-rejects and recovers | 54 s | +| `error_scale_and_usu` | 34 | emcee | a global scale on the reported errors under both likelihoods; a USU offset on the technique we suspect | 90 s | | `local_optical_model_calibration` | 12, 14, 15, 16, 21, 26 | dynesty | a real EXFOR measurement to a calibrated potential; the unit contract, the singular guard, tempering, other drivers | 182 s | | `alpha_ca_error_model_comparison` | 10, 11, 13, 18 | dynesty | real ⁴⁴Ca(α,α) data, a four-parameter potential, the ladder by evidence with the Jacobian, held-out backward angles | 1197 s (alongside another notebook) | | `hierarchical_calibration` | 22, 24, 30, 35, 38 | dynesty | eight schools; a hierarchy on the physics parameters repairing a misspecified energy dependence, in sample and at a held-out energy | 937 s (alongside another notebook) | diff --git a/docs/examples.rst b/docs/examples.rst index dde44c4..68ae886 100644 --- a/docs/examples.rst +++ b/docs/examples.rst @@ -28,6 +28,7 @@ Beyond the Gaussian examples/gp_discrepancy.ipynb examples/robust_likelihoods.ipynb + examples/error_scale_and_usu.ipynb Reactions and studies --------------------- diff --git a/examples/error_scale_and_usu.ipynb b/examples/error_scale_and_usu.ipynb new file mode 100644 index 0000000..720d04b --- /dev/null +++ b/examples/error_scale_and_usu.ipynb @@ -0,0 +1,457 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b322b1d8", + "metadata": {}, + "source": [ + "# Inferring the errors: a global scale, and an unrecognised source\n", + "\n", + "Sometimes the data scatter more than the reported errors can explain, and we do\n", + "not believe any individual point is *wrong* — we believe the error bars are.\n", + "That is a different question from the one in `robust_likelihoods`, where we\n", + "asked what to do about a few bad points, and it wants a different model.\n", + "\n", + "We try two answers here. The blunt one is a single scale on every reported\n", + "error. The targeted one is an **unrecognised source of uncertainty**: a fully\n", + "correlated component attached to the experiments we suspect, which shifts the\n", + "evaluated mean as well as its width.\n", + "\n", + "Recipes: 34" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "332f2403", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:12:13.972538Z", + "iopub.status.busy": "2026-09-12T02:12:13.972395Z", + "iopub.status.idle": "2026-09-12T02:12:16.067199Z", + "shell.execute_reply": "2026-09-12T02:12:16.066579Z" + } + }, + "outputs": [], + "source": [ + "import emcee\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import plotstyle\n", + "from scipy import stats\n", + "\n", + "import rxmc as rx\n", + "from rxmc import terms as T\n", + "\n", + "plotstyle.use()" + ] + }, + { + "cell_type": "markdown", + "id": "8c76b144", + "metadata": {}, + "source": [ + "## The same twenty-five points, three of them wrong\n", + "\n", + "We start from the dataset of `robust_likelihoods`: a line with 5 % noise and\n", + "three points pushed up by ten times their error." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0cc00aeb", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:12:16.068995Z", + "iopub.status.busy": "2026-09-12T02:12:16.068762Z", + "iopub.status.idle": "2026-09-12T02:12:16.074344Z", + "shell.execute_reply": "2026-09-12T02:12:16.073526Z" + } + }, + "outputs": [], + "source": [ + "rng = np.random.default_rng(21)\n", + "m_true, b_true = 1.0, 0.5\n", + "x = np.linspace(0.0, 4.0, 25)\n", + "noise = 0.05\n", + "y = m_true * x + b_true + rng.normal(0.0, noise, x.size)\n", + "outliers = np.array([5, 12, 19])\n", + "y[outliers] += np.array([10.0, 12.0, 9.0]) * noise\n", + "data = rx.Dataset(x, y, np.full(x.size, noise), label=\"with outliers\")\n", + "\n", + "m = rx.Parameter(\"m\", prior=stats.norm(1.0, 1.0), latex=\"m\")\n", + "b = rx.Parameter(\"b\", prior=stats.norm(0.0, 1.0), latex=\"b\")\n", + "line = rx.Model(lambda x, m, b: m * x + b, [m, b])\n", + "comp = rx.Comparison(data, line)\n", + "nu = rx.Parameter(\"nu\", bounds=(1.0, 100.0), latex=r\"\\nu\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "759f7383", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:12:16.075605Z", + "iopub.status.busy": "2026-09-12T02:12:16.075473Z", + "iopub.status.idle": "2026-09-12T02:12:16.078503Z", + "shell.execute_reply": "2026-09-12T02:12:16.077959Z" + } + }, + "outputs": [], + "source": [ + "def fit(problem, seed, n_walkers=24, n_steps=2000):\n", + " sampler = emcee.EnsembleSampler(n_walkers, problem.ndim, problem.log_posterior)\n", + " sampler.random_state = np.random.RandomState(seed).get_state()\n", + " sampler.run_mcmc(problem.sample_prior(n_walkers, rng=seed), n_steps, progress=False)\n", + " return sampler.get_chain(discard=n_steps // 3, thin=5, flat=True)\n", + "\n", + "\n", + "x_fine = np.linspace(-0.5, 4.5, 60)\n", + "on_fine = line.bind(x_fine)\n", + "y_true_fine = on_fine(m_true, b_true)" + ] + }, + { + "cell_type": "markdown", + "id": "e2c197cd", + "metadata": {}, + "source": [ + "## Inferring the errors (recipe 34)\n", + "\n", + "Another option we could take when we notice that the spread in the data is not\n", + "consistent with the reported experimental uncertainty is to try to infer the\n", + "uncertainty along with the model parameters. There are many ways to do this:\n", + "this implies coming up with a model for the distribution governing the unknown\n", + "discrepancy between the model and the data. Careful — this discrepancy can\n", + "include *both* experimental uncertainty and model misspecification. We will\n", + "take a closer look at the latter in other notebooks. In this case, we have a\n", + "correctly specified model, we just have outliers.\n", + "\n", + "One simple thing we can do is add a parameter for **a global scale** on the\n", + "reported errors. This allows us to inflate the experimental errors while\n", + "inferring the model parameters to maximise the posterior.\n", + "\n", + "Under the Gaussian likelihood the scale has to grow until every point's error\n", + "covers the outliers, and the scale will therefore be poorly determined. Under\n", + "the Student-t, the tail shares the work, so the scale is smaller and better\n", + "determined. Either way a global scale inflates every point equally, which is\n", + "why it is judged poor evaluation practice next to a targeted approach — for\n", + "instance the outlier rejection of `robust_likelihoods`, or the USU component\n", + "below." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0b23054e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:12:16.079826Z", + "iopub.status.busy": "2026-09-12T02:12:16.079706Z", + "iopub.status.idle": "2026-09-12T02:12:16.084743Z", + "shell.execute_reply": "2026-09-12T02:12:16.083716Z" + } + }, + "outputs": [], + "source": [ + "log_s = rx.Parameter(\"log_s\", prior=stats.norm(0.0, 0.5), latex=r\"\\log s\")\n", + "scaled = rx.Term(lambda c, ls: np.exp(ls) * comp.y_err, (log_s,), kind=\"diag\", on=comp)\n", + "p_scale_gauss = rx.Problem([rx.Constraint([comp], terms=[scaled], statistical=False)])\n", + "p_scale_t = rx.Problem(\n", + " [\n", + " rx.Constraint(\n", + " [comp], terms=[scaled], statistical=False, likelihood=rx.StudentT(nu)\n", + " )\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "139378c4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:12:16.086015Z", + "iopub.status.busy": "2026-09-12T02:12:16.085841Z", + "iopub.status.idle": "2026-09-12T02:12:45.842767Z", + "shell.execute_reply": "2026-09-12T02:12:45.842115Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gaussian error scale s = 3.22 (68 % interval 2.83 to 3.68)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Student-t error scale s = 2.91 (68 % interval 2.18 to 3.48)\n" + ] + } + ], + "source": [ + "for name, p, seed in ((\"Gaussian\", p_scale_gauss, 3), (\"Student-t\", p_scale_t, 4)):\n", + " s = fit(p, seed)\n", + " lo, med, hi = np.percentile(np.exp(s[:, p.columns(log_s)]), [16, 50, 84])\n", + " print(f\"{name:10s} error scale s = {med:.2f} (68 % interval {lo:.2f} to {hi:.2f})\")" + ] + }, + { + "cell_type": "markdown", + "id": "1e5395cd", + "metadata": {}, + "source": [ + "## A component the experiment did not recognise\n", + "\n", + "Suppose instead that we were actually comparing two data sets, one containing\n", + "the normal points and one containing the outlying points. What we might then do\n", + "is to recognise that the difference in the experimental process corresponding to\n", + "the two data sets — for example a difference in experimental technique — could\n", + "lead to **an Unrecognised Source of Uncertainty (USU)**.\n", + "\n", + "In our case, we can model this per-dataset USU as an unknown per-dataset offset.\n", + "A sampled `T.offset` on that technique's comparisons is a fully correlated\n", + "component *inside* the covariance, which shifts the evaluated mean as well as its\n", + "width. A global scale cannot do that.\n", + "\n", + "This is the shape recommended by [Capote et al.\n", + "(2020)](https://arxiv.org/abs/1911.01825) for unrecognised uncertainties in\n", + "evaluated nuclear data, and discussed by [Hanson\n", + "(2007)](https://arxiv.org/abs/0712.0021); the point both make is that the\n", + "component has to live *in* the covariance of the fit, not be added to the result\n", + "afterwards." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "44ebaa50", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:12:45.844154Z", + "iopub.status.busy": "2026-09-12T02:12:45.844029Z", + "iopub.status.idle": "2026-09-12T02:12:45.847434Z", + "shell.execute_reply": "2026-09-12T02:12:45.846964Z" + } + }, + "outputs": [], + "source": [ + "x_a, x_b = np.linspace(0.0, 4.0, 13), np.linspace(0.15, 3.85, 12)\n", + "y_a = m_true * x_a + b_true + rng.normal(0.0, noise, x_a.size)\n", + "y_b = (\n", + " m_true * x_b + b_true + 0.25 + rng.normal(0.0, noise, x_b.size)\n", + ") # a 0.25 offset nobody reported\n", + "d_a = rx.Dataset(\n", + " x_a, y_a, np.full(x_a.size, noise), label=\"technique A\", meta={\"technique\": \"A\"}\n", + ")\n", + "d_b = rx.Dataset(\n", + " x_b, y_b, np.full(x_b.size, noise), label=\"technique B\", meta={\"technique\": \"B\"}\n", + ")\n", + "comps = [rx.Comparison(d_a, line), rx.Comparison(d_b, line)]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "29aca1f6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:12:45.848699Z", + "iopub.status.busy": "2026-09-12T02:12:45.848584Z", + "iopub.status.idle": "2026-09-12T02:12:45.853705Z", + "shell.execute_reply": "2026-09-12T02:12:45.853202Z" + } + }, + "outputs": [], + "source": [ + "# we may have some prior reason to suspect that B contains the unknown offset,\n", + "# but not A, so we will apply it only to B\n", + "log_usu = rx.Parameter(\n", + " \"log_usu_B\", prior=stats.norm(np.log(0.2), 1.0), latex=r\"\\log\\delta_B\"\n", + ")\n", + "usu = T.offset(log_usu, on=[c for c in comps if c.data.meta[\"technique\"] == \"B\"])\n", + "\n", + "# the global diagonal uncertainty scale from before, for comparison\n", + "scaled_ab = [\n", + " rx.Term(\n", + " lambda c, ls, comp=comp: np.exp(ls) * comp.y_err, (log_s,), kind=\"diag\", on=comp\n", + " )\n", + " for comp in comps\n", + "]\n", + "\n", + "problems = {\n", + " \"as stated\": rx.Problem([rx.Constraint(comps)]),\n", + " \"global scale\": rx.Problem(\n", + " [rx.Constraint(comps, terms=scaled_ab, statistical=False)]\n", + " ),\n", + " \"USU offset on B\": rx.Problem([rx.Constraint(comps, terms=[usu])]),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b113437e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:12:45.854940Z", + "iopub.status.busy": "2026-09-12T02:12:45.854825Z", + "iopub.status.idle": "2026-09-12T02:13:39.792126Z", + "shell.execute_reply": "2026-09-12T02:13:39.791469Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "as stated m = 0.991 +/- 0.008, b = 0.637 +/- 0.020\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "global scale m = 0.989 +/- 0.036, b = 0.654 +/- 0.232\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "USU offset on B m = 0.991 +/- 0.008, b = 0.526 +/- 0.022\n" + ] + } + ], + "source": [ + "bands = {}\n", + "for (name, p), seed in zip(problems.items(), (5, 6, 7)):\n", + " s = fit(p, seed)\n", + " cols = p.columns(line.params)\n", + " print(\n", + " f\"{name:16s} m = {s[:, cols[0]].mean():.3f} +/- {s[:, cols[0]].std():.3f}, \"\n", + " f\"b = {s[:, cols[1]].mean():.3f} +/- {s[:, cols[1]].std():.3f}\"\n", + " )\n", + " lo, hi = rx.predictive.predictive_band(\n", + " [on_fine(*r[cols]) for r in s[::10]], levels=(5, 95)\n", + " )\n", + " bands[name] = (lo - y_true_fine, hi - y_true_fine)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "675db827", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:13:39.793565Z", + "iopub.status.busy": "2026-09-12T02:13:39.793418Z", + "iopub.status.idle": "2026-09-12T02:13:40.751618Z", + "shell.execute_reply": "2026-09-12T02:13:40.751161Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 704x440 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "for (name, (lo, hi)), colour, hatch in zip(\n", + " bands.items(),\n", + " (plotstyle.COLOURS[1], plotstyle.COLOURS[2], plotstyle.COLOURS[0]),\n", + " plotstyle.HATCHES,\n", + "):\n", + " plotstyle.band(ax, x_fine, lo, hi, color=colour, hatch=hatch, label=name)\n", + "on_a, on_b = line.bind(d_a.x), line.bind(d_b.x)\n", + "ax.errorbar(\n", + " d_a.x, d_a.y - on_a(m_true, b_true), d_a.y_err, fmt=\"o\", ms=3, color=\"k\", label=\"A\"\n", + ")\n", + "ax.errorbar(\n", + " d_b.x,\n", + " d_b.y - on_b(m_true, b_true),\n", + " d_b.y_err,\n", + " fmt=\"s\",\n", + " ms=3,\n", + " color=plotstyle.COLOURS[3],\n", + " label=\"B (offset)\",\n", + ")\n", + "ax.axhline(0.0, ls=\"--\", color=\"k\", label=\"truth\")\n", + "ax.set(xlabel=\"$x$\", ylabel=r\"$y - y_\\mathrm{truth}$\", title=\"90 % bands\")\n", + "ax.legend(fontsize=8, ncol=2, loc=\"upper right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "df67cf5c", + "metadata": {}, + "source": [ + "By using our prior knowledge that data set `B` may have an unknown offset\n", + "(USU), but data set `A` does not, we are able to cover the truth.\n", + "\n", + "The contrast with the global scale is the whole point. The scale can only make\n", + "every error bar bigger, so it buys coverage by making the answer vaguer\n", + "everywhere, including where the data were fine. The USU offset says something\n", + "specific — *this technique may sit at the wrong level* — and because that\n", + "statement lives inside the covariance, it moves the fitted line as well as\n", + "widening it." + ] + }, + { + "cell_type": "markdown", + "id": "a4e4aaa3", + "metadata": {}, + "source": [ + "## Takeaways\n", + "\n", + "- \"The errors are too small\" is a modelling claim, and we have to say what kind\n", + " of error we mean. A global scale and a per-technique offset are different\n", + " claims and give different answers.\n", + "- A global scale is one column that multiplies every reported error. Under a\n", + " Gaussian it has to grow until it covers the worst points, and it ends up\n", + " poorly determined; under a Student-t the tail does part of the work.\n", + "- A USU component is a sampled `T.offset` on the comparisons of one technique: a\n", + " fully correlated piece of the covariance, which shifts the mean as well as the\n", + " width.\n", + "- Neither is a substitute for asking whether the points are simply wrong. That\n", + " question is `robust_likelihoods`, next door." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/robust_likelihoods.ipynb b/examples/robust_likelihoods.ipynb index 63dbdab..722175e 100644 --- a/examples/robust_likelihoods.ipynb +++ b/examples/robust_likelihoods.ipynb @@ -2,31 +2,37 @@ "cells": [ { "cell_type": "markdown", - "id": "476155aa", + "id": "ffc28c78", "metadata": {}, "source": [ "# Robust likelihoods: Student-t versus the multivariate normal\n", "\n", - "A few gross outliers make a Gaussian fit confidently wrong. The likelihood\n", - "*functional* is a drop-in choice in `rxmc`: keep the covariance, swap the\n", - "multivariate normal for a multivariate Student-t with a sampled tail\n", - "parameter, and the fit widens instead of breaking. Then two ways to say\n", - "\"the errors are larger than stated\": a global scale on the reported errors,\n", - "and an unrecognised, fully correlated component per experimental technique.\n", + "A few gross outliers make a Gaussian fit confidently wrong. The good news is\n", + "that the likelihood *functional* is a drop-in choice in `rxmc`: we keep the\n", + "covariance exactly as it is, swap the\n", + "[multivariate normal](https://en.wikipedia.org/wiki/Multivariate_normal_distribution)\n", + "for a [multivariate Student-t](https://en.wikipedia.org/wiki/Multivariate_t-distribution)\n", + "with a sampled tail parameter, and the fit widens instead of breaking.\n", "\n", - "Recipes: 9, 34" + "That is one of two honest answers to an outlier. The other is to decide the\n", + "point does not belong to the measurement at all and reject it, which we do at\n", + "the end, in the outer loop of recipe 39. If instead we suspect the *errors* are\n", + "larger than stated, that is a different question, and it has its own notebook\n", + "next door (`error_scale_and_usu`).\n", + "\n", + "Recipes: 9, 39" ] }, { "cell_type": "code", "execution_count": 1, - "id": "9de17e52", + "id": "f6300218", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:43:19.768003Z", - "iopub.status.busy": "2026-09-11T03:43:19.767788Z", - "iopub.status.idle": "2026-09-11T03:43:22.526760Z", - "shell.execute_reply": "2026-09-11T03:43:22.525767Z" + "iopub.execute_input": "2026-09-12T02:14:42.872611Z", + "iopub.status.busy": "2026-09-12T02:14:42.872481Z", + "iopub.status.idle": "2026-09-12T02:14:44.901758Z", + "shell.execute_reply": "2026-09-12T02:14:44.901046Z" } }, "outputs": [], @@ -35,45 +41,48 @@ "import emcee\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", + "import plotstyle\n", "from scipy import stats\n", "\n", "import rxmc as rx\n", - "from rxmc import terms as T" + "\n", + "plotstyle.use()" ] }, { "cell_type": "markdown", - "id": "71f5be36", + "id": "b128b9e4", "metadata": {}, "source": [ "## A clean signal with a few gross outliers\n", "\n", - "Twenty-five points of a line with 5 % noise, and three points pushed up by\n", - "ten times their error: a background the experiment did not subtract." + "We will sample twenty-five points around the true line with 5 % noise, but then\n", + "also add three more points pushed up by ten times their error. This could stand\n", + "in for, say, a background the experiment did not subtract." ] }, { "cell_type": "code", "execution_count": 2, - "id": "4f195f99", + "id": "5a862393", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:43:22.528880Z", - "iopub.status.busy": "2026-09-11T03:43:22.528582Z", - "iopub.status.idle": "2026-09-11T03:43:22.780686Z", - "shell.execute_reply": "2026-09-11T03:43:22.779856Z" + "iopub.execute_input": "2026-09-12T02:14:44.903296Z", + "iopub.status.busy": "2026-09-12T02:14:44.903104Z", + "iopub.status.idle": "2026-09-12T02:14:44.908677Z", + "shell.execute_reply": "2026-09-12T02:14:44.908019Z" } }, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "<Figure size 500x350 with 1 Axes>" + "Dataset('with outliers', n=25)" ] }, + "execution_count": 2, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -85,38 +94,74 @@ "outliers = np.array([5, 12, 19])\n", "y[outliers] += np.array([10.0, 12.0, 9.0]) * noise\n", "data = rx.Dataset(x, y, np.full(x.size, noise), label=\"with outliers\")\n", - "\n", - "fig, ax = plt.subplots(figsize=(5, 3.5))\n", - "ax.errorbar(data.x, data.y, data.y_err, fmt=\".\", ms=4, color=\"k\", label=\"data\")\n", - "ax.plot(x[outliers], y[outliers], \"o\", ms=12, mfc=\"none\", color=\"C3\", label=\"outliers\")\n", - "ax.plot(x, m_true * x + b_true, \"--\", color=\"C3\", label=\"truth\")\n", - "ax.set(xlabel=\"x\", ylabel=\"y\")\n", - "ax.legend(frameon=False)\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0fac5bbe", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:14:44.910108Z", + "iopub.status.busy": "2026-09-12T02:14:44.909988Z", + "iopub.status.idle": "2026-09-12T02:14:45.822630Z", + "shell.execute_reply": "2026-09-12T02:14:45.821798Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 704x440 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.errorbar(data.x, data.y, data.y_err, fmt=\".\", ms=6, color=\"k\", label=\"data\")\n", + "ax.plot(\n", + " x[outliers],\n", + " y[outliers],\n", + " \"o\",\n", + " ms=12,\n", + " mfc=\"none\",\n", + " color=plotstyle.COLOURS[1],\n", + " label=\"outliers\",\n", + ")\n", + "ax.plot(x, m_true * x + b_true, \"--\", color=plotstyle.COLOURS[1], label=\"truth\")\n", + "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"Twenty-five points, three of them wrong\")\n", + "ax.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "0a18c905", + "id": "c21e4f8d", "metadata": {}, "source": [ "## The same constraint, two likelihood functionals\n", "\n", - "`rx.StudentT(nu)` applies one radial tail to the whole stacked residual of\n", - "the constraint; `nu` is an ordinary parameter with its bounds and prior on\n", - "the `Parameter`, so it is one more column of the chain." + "`rx.StudentT(nu)` applies one radial tail to the whole stacked residual of the\n", + "constraint, and $\\nu$ is an ordinary parameter with its bounds and prior on the\n", + "`Parameter`, so it is simply one more column of the chain. Everything else —\n", + "the data, the comparison, the covariance — is untouched between the two fits." ] }, { "cell_type": "code", - "execution_count": 3, - "id": "c49dd13d", + "execution_count": 4, + "id": "ff2eba9a", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:43:22.782451Z", - "iopub.status.busy": "2026-09-11T03:43:22.782256Z", - "iopub.status.idle": "2026-09-11T03:43:49.599074Z", - "shell.execute_reply": "2026-09-11T03:43:49.598124Z" + "iopub.execute_input": "2026-09-12T02:14:45.824037Z", + "iopub.status.busy": "2026-09-12T02:14:45.823873Z", + "iopub.status.idle": "2026-09-12T02:14:45.829713Z", + "shell.execute_reply": "2026-09-12T02:14:45.829172Z" } }, "outputs": [ @@ -139,98 +184,148 @@ "p_gauss = rx.Problem([rx.Constraint([comp])])\n", "p_t = rx.Problem([rx.Constraint([comp], likelihood=rx.StudentT(nu))])\n", "print(\"Gaussian :\", p_gauss.names)\n", - "print(\"Student-t:\", p_t.names)\n", - "\n", - "\n", + "print(\"Student-t:\", p_t.names)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3aaaedd7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:14:45.831254Z", + "iopub.status.busy": "2026-09-12T02:14:45.831128Z", + "iopub.status.idle": "2026-09-12T02:14:45.833933Z", + "shell.execute_reply": "2026-09-12T02:14:45.833341Z" + } + }, + "outputs": [], + "source": [ "def fit(problem, seed, n_walkers=24, n_steps=2000):\n", " sampler = emcee.EnsembleSampler(n_walkers, problem.ndim, problem.log_posterior)\n", " sampler.random_state = np.random.RandomState(seed).get_state()\n", " sampler.run_mcmc(problem.sample_prior(n_walkers, rng=seed), n_steps, progress=False)\n", - " return sampler.get_chain(discard=n_steps // 3, thin=5, flat=True)\n", - "\n", - "\n", + " return sampler.get_chain(discard=n_steps // 3, thin=5, flat=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "69bbceda", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:14:45.835232Z", + "iopub.status.busy": "2026-09-12T02:14:45.835115Z", + "iopub.status.idle": "2026-09-12T02:15:04.625427Z", + "shell.execute_reply": "2026-09-12T02:15:04.624628Z" + } + }, + "outputs": [], + "source": [ "s_gauss, s_t = fit(p_gauss, 1), fit(p_t, 2)" ] }, { "cell_type": "code", - "execution_count": 4, - "id": "d0765433", + "execution_count": 7, + "id": "76f55c31", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:43:49.601218Z", - "iopub.status.busy": "2026-09-11T03:43:49.600906Z", - "iopub.status.idle": "2026-09-11T03:43:50.620659Z", - "shell.execute_reply": "2026-09-11T03:43:50.619798Z" + "iopub.execute_input": "2026-09-12T02:15:04.627124Z", + "iopub.status.busy": "2026-09-12T02:15:04.626890Z", + "iopub.status.idle": "2026-09-12T02:15:04.783689Z", + "shell.execute_reply": "2026-09-12T02:15:04.782929Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "<Figure size 550x550 with 4 Axes>" + "<Figure size 605x605 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gaussian m = 1.001 +/- 0.008, b = 0.567 +/- 0.019; truth at 3.5 sigma\n", - "Student-t m = 1.002 +/- 0.029, b = 0.566 +/- 0.067; truth at 1.0 sigma\n" - ] } ], "source": [ "fig = corner.corner(\n", " s_gauss,\n", - " color=\"C3\",\n", " labels=[\"$m$\", \"$b$\"],\n", " truths=[m_true, b_true],\n", - " truth_color=\"k\",\n", - " plot_datapoints=False,\n", + " **plotstyle.corner_kwargs(\n", + " color=plotstyle.COLOURS[1], show_titles=False, fill_contours=False\n", + " ),\n", ")\n", "corner.corner(\n", - " s_t[:, p_t.columns(line.params)], fig=fig, color=\"C0\", plot_datapoints=False\n", + " s_t[:, p_t.columns(line.params)],\n", + " fig=fig,\n", + " **plotstyle.corner_kwargs(\n", + " color=plotstyle.COLOURS[0], show_titles=False, fill_contours=False\n", + " ),\n", ")\n", "fig.legend(\n", " handles=[\n", - " plt.Line2D([], [], color=\"C3\", label=\"Gaussian\"),\n", - " plt.Line2D([], [], color=\"C0\", label=\"Student-t\"),\n", + " plt.Line2D([], [], color=plotstyle.COLOURS[1], label=\"Gaussian\"),\n", + " plt.Line2D([], [], color=plotstyle.COLOURS[0], label=\"Student-t\"),\n", " ],\n", " loc=\"upper right\",\n", - " frameon=False,\n", ")\n", - "plt.show()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d32705b6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:15:04.785183Z", + "iopub.status.busy": "2026-09-12T02:15:04.785019Z", + "iopub.status.idle": "2026-09-12T02:15:04.789227Z", + "shell.execute_reply": "2026-09-12T02:15:04.788705Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gaussian m = 1.001 +/- 0.008, b = 0.567 +/- 0.019; truth at 3.5 sigma\n", + "Student-t m = 1.002 +/- 0.029, b = 0.566 +/- 0.067; truth at 1.0 sigma\n" + ] + } + ], + "source": [ "for name, p, s in ((\"Gaussian\", p_gauss, s_gauss), (\"Student-t\", p_t, s_t)):\n", " cols = p.columns(line.params)\n", " pull = (s[:, cols].mean(0) - [m_true, b_true]) / s[:, cols].std(0)\n", " print(\n", - " f\"{name:10s} m = {s[:, cols[0]].mean():.3f} +/- {s[:, cols[0]].std():.3f}, b = {s[:, cols[1]].mean():.3f} +/- {s[:, cols[1]].std():.3f}; truth at {np.abs(pull).max():.1f} sigma\"\n", + " f\"{name:10s} m = {s[:, cols[0]].mean():.3f} +/- {s[:, cols[0]].std():.3f}, \"\n", + " f\"b = {s[:, cols[1]].mean():.3f} +/- {s[:, cols[1]].std():.3f}; \"\n", + " f\"truth at {np.abs(pull).max():.1f} sigma\"\n", " )" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "31e2cb06", + "execution_count": 9, + "id": "fd2ad161", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:43:50.622753Z", - "iopub.status.busy": "2026-09-11T03:43:50.622547Z", - "iopub.status.idle": "2026-09-11T03:43:50.839129Z", - "shell.execute_reply": "2026-09-11T03:43:50.838265Z" + "iopub.execute_input": "2026-09-12T02:15:04.790576Z", + "iopub.status.busy": "2026-09-12T02:15:04.790378Z", + "iopub.status.idle": "2026-09-12T02:15:04.918382Z", + "shell.execute_reply": "2026-09-12T02:15:04.917643Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "<Figure size 500x300 with 1 Axes>" + "<Figure size 572x352 with 1 Axes>" ] }, "metadata": {}, @@ -239,8 +334,8 @@ ], "source": [ "nu_col = p_t.columns(nu)\n", - "fig, ax = plt.subplots(figsize=(5, 3))\n", - "ax.hist(s_t[:, nu_col], bins=40, color=\"C0\", alpha=0.7)\n", + "fig, ax = plt.subplots(figsize=(5.2, 3.2))\n", + "ax.hist(s_t[:, nu_col], bins=40, color=plotstyle.COLOURS[0], alpha=0.8)\n", "ax.set(\n", " xlabel=r\"$\\nu$\",\n", " ylabel=\"posterior draws\",\n", @@ -251,36 +346,42 @@ }, { "cell_type": "markdown", - "id": "8069377f", + "id": "2484ac30", "metadata": {}, "source": [ - "Notice that, while both likelihoods have `b` shifted upwards by the outliers, the student-t has inflated uncertainties which cover the true `b`, while the Gaussian has very little posterior weight on the truth.\n", + "Notice that, while both likelihoods have $b$ shifted upwards by the outliers,\n", + "the Student-t has inflated uncertainties which cover the true $b$, while the\n", + "Gaussian has very little posterior weight on the truth. The small $\\nu$ is the\n", + "fit telling us it needed the tail.\n", "\n", - "Note what it does *not* do: it does not reject the outliers, it widens. Per-point rejection would need per-point machinery, such as masking the suspects (recipe 11)." + "Note what it does *not* do: it does not reject the outliers, it widens. Throwing\n", + "a point away is a different decision, and we come back to it at the end of this\n", + "notebook." ] }, { "cell_type": "markdown", - "id": "6fa3d4ed", + "id": "6314776f", "metadata": {}, "source": [ - "### χ² is the same for both\n", + "### $\\chi^2$ is the same for both\n", "\n", - "The covariance is shared; only the functional of the Mahalanobis distance\n", - "differs. `problem.chi2` is that distance, and `rx.Chi2()` is the functional\n", - "that drops the log-determinant for a pure χ² objective." + "The covariance is shared, so the $\\chi^2$ must be the same between likelihood\n", + "functions. The only difference between the likelihoods is that they are\n", + "different *functions of the $\\chi^2$* (or, more accurately, of the\n", + "[Mahalanobis distance](https://en.wikipedia.org/wiki/Mahalanobis_distance))." ] }, { "cell_type": "code", - "execution_count": 6, - "id": "07076854", + "execution_count": 10, + "id": "e7ea32cc", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:43:50.841202Z", - "iopub.status.busy": "2026-09-11T03:43:50.841002Z", - "iopub.status.idle": "2026-09-11T03:43:50.844811Z", - "shell.execute_reply": "2026-09-11T03:43:50.844124Z" + "iopub.execute_input": "2026-09-12T02:15:04.919885Z", + "iopub.status.busy": "2026-09-12T02:15:04.919719Z", + "iopub.status.idle": "2026-09-12T02:15:04.922926Z", + "shell.execute_reply": "2026-09-12T02:15:04.922179Z" } }, "outputs": [ @@ -295,28 +396,58 @@ "source": [ "theta = np.array([m_true, b_true])\n", "print(\n", - " f\"chi2 at the truth: Gaussian {p_gauss.chi2(theta):.2f}, Student-t {p_t.chi2(np.append(theta, 5.0)):.2f}\"\n", + " f\"chi2 at the truth: Gaussian {p_gauss.chi2(theta):.2f}, \"\n", + " f\"Student-t {p_t.chi2(np.append(theta, 5.0)):.2f}\"\n", ")" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "46fbe617", + "execution_count": 11, + "id": "3ab4d1e4", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:43:50.846776Z", - "iopub.status.busy": "2026-09-11T03:43:50.846560Z", - "iopub.status.idle": "2026-09-11T03:43:50.969855Z", - "shell.execute_reply": "2026-09-11T03:43:50.969069Z" + "iopub.execute_input": "2026-09-12T02:15:04.924212Z", + "iopub.status.busy": "2026-09-12T02:15:04.924067Z", + "iopub.status.idle": "2026-09-12T02:15:04.927248Z", + "shell.execute_reply": "2026-09-12T02:15:04.926686Z" + } + }, + "outputs": [], + "source": [ + "x_fine = np.linspace(-0.5, 4.5, 60)\n", + "on_fine = line.bind(x_fine)\n", + "on_data = line.bind(x)\n", + "y_true_fine = on_fine(m_true, b_true)\n", + "y_true_data = on_data(m_true, b_true)\n", + "\n", + "\n", + "def residual_band(problem, samples, levels=(5, 95)):\n", + " cols = problem.columns(line.params)\n", + " lo, hi = rx.predictive.predictive_band(\n", + " [on_fine(*r[cols]) for r in samples[::10]], levels=levels\n", + " )\n", + " return lo - y_true_fine, hi - y_true_fine" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "22d9487d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:15:04.928488Z", + "iopub.status.busy": "2026-09-12T02:15:04.928331Z", + "iopub.status.idle": "2026-09-12T02:15:05.048319Z", + "shell.execute_reply": "2026-09-12T02:15:05.047255Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 600x400 with 1 Axes>" + "<Figure size 704x440 with 1 Axes>" ] }, "metadata": {}, @@ -324,106 +455,93 @@ } ], "source": [ - "x_fine = np.linspace(-0.5, 4.5, 60)\n", - "on_fine = line.bind(x_fine, {})\n", - "on_data = line.bind(x, {})\n", - "y_true_fine = on_fine(m_true, b_true)\n", - "y_true_data = on_data(m_true, b_true)\n", - "fig, ax = plt.subplots(figsize=(6, 4))\n", - "for name, p, s, color in (\n", - " (\"Gaussian\", p_gauss, s_gauss, \"C3\"),\n", - " (\"Student-t\", p_t, s_t, \"C0\"),\n", + "fig, ax = plt.subplots()\n", + "for name, p, s, colour, hatch in (\n", + " (\"Gaussian\", p_gauss, s_gauss, plotstyle.COLOURS[1], plotstyle.HATCHES[0]),\n", + " (\"Student-t\", p_t, s_t, plotstyle.COLOURS[0], plotstyle.HATCHES[1]),\n", "):\n", - " lo, hi = rx.predictive.predictive_band(\n", - " [on_fine(*r[p.columns(line.params)]) for r in s[::10]], levels=(5, 95)\n", + " lo, hi = residual_band(p, s)\n", + " plotstyle.band(\n", + " ax, x_fine, lo, hi, color=colour, hatch=hatch, label=f\"{name} 90 % band\"\n", " )\n", - " ax.fill_between(x_fine, lo - y_true_fine, hi - y_true_fine, color=color, alpha=0.35, label=f\"{name} 90 % band\")\n", "ax.errorbar(data.x, data.y - y_true_data, data.y_err, fmt=\"o\", ms=3, color=\"k\")\n", - "ax.plot(x_fine, np.zeros_like(y_true_fine), \"--k\") \n", - "ax.set(xlabel=\"$x$\", ylabel=r\"$y - y_\\text{truth}$\")\n", - "ax.legend(frameon=False)\n", + "ax.axhline(0.0, ls=\"--\", color=\"k\")\n", + "ax.set(xlabel=\"$x$\", ylabel=r\"$y - y_\\mathrm{truth}$\", title=\"Residual to the truth\")\n", + "ax.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "936b1259-8a54-453d-8c66-9ab336ce3f58", + "id": "c86dc41f", "metadata": {}, "source": [ - "Both are shifted relative to the truth, but the student-t's longer tails allow for the truth to (mostly) be covered by 90% credible interval of the posterior predictive distribution." + "Both are shifted relative to the truth, but the Student-t's longer tails allow\n", + "for the truth to (mostly) be covered by the 90 % credible interval of the\n", + "posterior predictive distribution." ] }, { "cell_type": "markdown", - "id": "902b883f", + "id": "5fc464d7", "metadata": {}, "source": [ - "## Inferring the errors (recipe 34)\n", + "## Rejecting the outliers instead (recipe 39)\n", "\n", - "Another option we could take when we notice that the spread in the data is not consistent with the reported experimental uncertainty is to try to infer the uncertainty along with the model parameters. There are many ways to do this: this implies coming up with a model for the distribution governing the unknown discrepancy between the model and the data. Careful - this discrepancy can include *both* experimental uncertainty and model miss specification. We will take a closer look at the latter in other notebooks. In this case, we have a correctly specified model, we just have outliers. \n", + "The Student-t keeps every point and widens. The other honest answer is to say\n", + "that those three points are not measurements of our signal at all, and to throw\n", + "them out — which is what KDUQ does, rejecting points more than $3\\sigma$ from\n", + "the current model ([Pruitt, Escher & Rahman\n", + "(2023)](https://arxiv.org/abs/2211.07741)).\n", "\n", - "One simple thing we can do is add a parameter for **a global scale** on the reported errors. This allows us to inflate the experimental errors while inferring the model parameters to maximize the posterior. \n", - "\n", - "\n", - "Under the Gaussian likelihood the scale has to grow until every point's error covers the outliers, and the scale will therefore be poorly determined. Under the Student-t, the tail shares the work, so the scale is smaller and better determined. Either way\n", - "a global scale inflates every point equally, which is why it is judged poor evaluation practice next to a targeted approach - e.g. outlier rejection." + "There is a catch, and it is the reason this is a *loop*: we cannot tell which\n", + "points are outliers until we have a fit, and we cannot get a clean fit until we\n", + "have removed them. So we alternate. Masks in `rxmc` are compiled into the\n", + "problem, which means we do not mutate anything — each round builds a new\n", + "`Problem` over the same objects, and the parameters keep their columns." ] }, { "cell_type": "code", - "execution_count": 10, - "id": "e214d154", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gaussian error scale s = 3.22 (68 % interval 2.83 to 3.68)\n", - "Student-t error scale s = 2.91 (68 % interval 2.18 to 3.48)\n" - ] + "execution_count": 13, + "id": "26724634", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:15:05.049841Z", + "iopub.status.busy": "2026-09-12T02:15:05.049710Z", + "iopub.status.idle": "2026-09-12T02:15:05.053186Z", + "shell.execute_reply": "2026-09-12T02:15:05.052656Z" } - ], - "source": [ - "log_s = rx.Parameter(\"log_s\", prior=stats.norm(0.0, 0.5), latex=r\"\\log s\")\n", - "scaled = rx.Term(lambda c, ls: np.exp(ls) * comp.y_err, (log_s,), kind=\"diag\", on=comp)\n", - "p_scale_gauss = rx.Problem([rx.Constraint([comp], terms=[scaled], statistical=False)])\n", - "p_scale_t = rx.Problem(\n", - " [\n", - " rx.Constraint(\n", - " [comp], terms=[scaled], statistical=False, likelihood=rx.StudentT(nu)\n", - " )\n", - " ]\n", - ")\n", - "for name, p, seed in ((\"Gaussian\", p_scale_gauss, 3), (\"Student-t\", p_scale_t, 4)):\n", - " s = fit(p, seed)\n", - " lo, med, hi = np.percentile(np.exp(s[:, p.columns(log_s)]), [16, 50, 84])\n", - " print(f\"{name:10s} error scale s = {med:.2f} (68 % interval {lo:.2f} to {hi:.2f})\")" - ] - }, - { - "cell_type": "markdown", - "id": "748af1de", - "metadata": {}, + }, + "outputs": [], "source": [ - "**An unrecognised source of uncertainty** per technique: suppose the\n", - "outliers are not three random points but a second technique whose whole\n", - "dataset carries an unknown offset. A sampled `T.offset` on that technique's\n", - "comparisons is a fully correlated component *inside* the covariance, which\n", - "shifts the evaluated mean as well as its width. A global scale cannot do\n", - "that." + "def reject(constraint, data, *, k=3.0, max_rounds=6, seed=11):\n", + " \"\"\"Fit, drop the points more than k pulls away, refit, until it settles.\"\"\"\n", + " mask = np.ones(data.n, dtype=bool)\n", + " history = []\n", + " for _ in range(max_rounds):\n", + " problem = rx.Problem([constraint.masked([mask])])\n", + " samples = fit(problem, seed)\n", + " theta = samples.mean(axis=0)\n", + " pull = np.abs(data.y - problem.constraints[0].ym(theta)) / data.y_err\n", + " keep = pull < k\n", + " history.append(np.flatnonzero(~keep))\n", + " if np.array_equal(keep, mask):\n", + " return mask, samples, history\n", + " mask = keep\n", + " raise RuntimeError(\"the mask did not settle\")" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "92927964", + "execution_count": 14, + "id": "60313e72", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:44:34.688516Z", - "iopub.status.busy": "2026-09-11T03:44:34.688312Z", - "iopub.status.idle": "2026-09-11T03:45:50.520093Z", - "shell.execute_reply": "2026-09-11T03:45:50.519423Z" + "iopub.execute_input": "2026-09-12T02:15:05.054560Z", + "iopub.status.busy": "2026-09-12T02:15:05.054395Z", + "iopub.status.idle": "2026-09-12T02:15:33.535477Z", + "shell.execute_reply": "2026-09-12T02:15:33.534921Z" } }, "outputs": [ @@ -431,113 +549,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "as stated m = 0.991 +/- 0.008, b = 0.637 +/- 0.020\n" + "round 1: rejected [ 2 5 12 19]\n", + "round 2: rejected [ 5 12 19]\n", + "round 3: rejected [ 5 12 19]\n", + "planted outliers: [ 5 12 19]\n", + "after rejection m = 1.000 +/- 0.009, b = 0.512 +/- 0.021\n" ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "global scale m = 0.989 +/- 0.036, b = 0.654 +/- 0.232\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "USU offset on B m = 0.991 +/- 0.008, b = 0.526 +/- 0.022\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 600x400 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "x_a, x_b = np.linspace(0.0, 4.0, 13), np.linspace(0.15, 3.85, 12)\n", - "y_a = m_true * x_a + b_true + rng.normal(0.0, noise, x_a.size)\n", - "y_b = (\n", - " m_true * x_b + b_true + 0.25 + rng.normal(0.0, noise, x_b.size)\n", - ") # a 0.25 offset nobody reported\n", - "d_a = rx.Dataset(\n", - " x_a, y_a, np.full(x_a.size, noise), label=\"technique A\", meta={\"technique\": \"A\"}\n", - ")\n", - "d_b = rx.Dataset(\n", - " x_b, y_b, np.full(x_b.size, noise), label=\"technique B\", meta={\"technique\": \"B\"}\n", - ")\n", - "comps = [rx.Comparison(d_a, line), rx.Comparison(d_b, line)]\n", - "\n", - "# we may have some prior reason to suspect that B contains the unknown offset, but not A\n", - "# so we will apply it only to B\n", - "log_usu = rx.Parameter(\n", - " \"log_usu_B\", prior=stats.norm(np.log(0.2), 1.0), latex=r\"\\log\\delta_B\"\n", - ")\n", - "usu = T.offset(log_usu, on=[c for c in comps if c.data.meta[\"technique\"] == \"B\"])\n", - "\n", - "# the global diagonal uncertainty scale from before, for comparison\n", - "scaled_ab = [\n", - " rx.Term(\n", - " lambda c, ls, comp=comp: np.exp(ls) * comp.y_err, (log_s,), kind=\"diag\", on=comp\n", - " )\n", - " for comp in comps\n", - "]\n", - "\n", - "problems = {\n", - " \"as stated\": rx.Problem([rx.Constraint(comps)]),\n", - " \"global scale\": rx.Problem(\n", - " [rx.Constraint(comps, terms=scaled_ab, statistical=False)]\n", - " ),\n", - " \"USU offset on B\": rx.Problem([rx.Constraint(comps, terms=[usu])]),\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "f026af27-7cc0-4e7f-936c-93a900a4617e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "as stated m = 0.991 +/- 0.008, b = 0.637 +/- 0.020\n", - "global scale m = 0.989 +/- 0.036, b = 0.654 +/- 0.232\n", - "USU offset on B m = 0.991 +/- 0.008, b = 0.526 +/- 0.022\n" - ] - } - ], - "source": [ - "bands = {}\n", - "for (name, p), seed in zip(problems.items(), (5, 6, 7)):\n", - " s = fit(p, seed)\n", - " cols = p.columns(line.params)\n", - " print(\n", - " f\"{name:16s} m = {s[:, cols[0]].mean():.3f} +/- {s[:, cols[0]].std():.3f}, b = {s[:, cols[1]].mean():.3f} +/- {s[:, cols[1]].std():.3f}\"\n", - " )\n", - " lo, hi = rx.predictive.predictive_band(\n", - " [on_fine(*r[cols]) for r in s[::10]], levels=(5, 95)\n", - " )\n", - " bands[name] = (lo, hi)" + "mask, s_reject, history = reject(rx.Constraint([comp]), data)\n", + "for i, gone in enumerate(history, start=1):\n", + " print(f\"round {i}: rejected {gone}\")\n", + "print(\"planted outliers:\", outliers)\n", + "p_reject = rx.Problem([rx.Constraint([comp]).masked([mask])])\n", + "cols = p_reject.columns(line.params)\n", + "print(\n", + " f\"after rejection m = {s_reject[:, cols[0]].mean():.3f} \"\n", + " f\"+/- {s_reject[:, cols[0]].std():.3f}, \"\n", + " f\"b = {s_reject[:, cols[1]].mean():.3f} +/- {s_reject[:, cols[1]].std():.3f}\"\n", + ")" ] }, { "cell_type": "code", "execution_count": 15, - "id": "449fc218-8915-49d9-b644-4be9296debe4", - "metadata": {}, + "id": "2353e732", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T02:15:33.536878Z", + "iopub.status.busy": "2026-09-12T02:15:33.536696Z", + "iopub.status.idle": "2026-09-12T02:15:33.728656Z", + "shell.execute_reply": "2026-09-12T02:15:33.728193Z" + } + }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 600x400 with 1 Axes>" + "<Figure size 704x440 with 1 Axes>" ] }, "metadata": {}, @@ -545,42 +596,80 @@ } ], "source": [ - "fig, ax = plt.subplots(figsize=(6, 4))\n", - "for (name, p), color in zip(problems.items(), (\"C3\", \"C2\", \"C0\")):\n", - " lo, hi = bands[name]\n", - " ax.fill_between(\n", - " x_fine, lo - y_true_fine, hi - y_true_fine, color=color, alpha=0.35, label=name\n", - " )\n", + "fig, ax = plt.subplots()\n", + "for name, p, s, colour, hatch in (\n", + " (\"Gaussian\", p_gauss, s_gauss, plotstyle.COLOURS[1], plotstyle.HATCHES[0]),\n", + " (\"Student-t\", p_t, s_t, plotstyle.COLOURS[0], plotstyle.HATCHES[1]),\n", + " (\"after rejection\", p_reject, s_reject, plotstyle.COLOURS[2], plotstyle.HATCHES[2]),\n", + "):\n", + " lo, hi = residual_band(p, s)\n", + " plotstyle.band(ax, x_fine, lo, hi, color=colour, hatch=hatch, label=name)\n", + "kept = np.flatnonzero(mask)\n", "ax.errorbar(\n", - " d_a.x,\n", - " d_a.y - line.bind(d_a.x, {})(m_true, b_true),\n", - " d_a.y_err,\n", + " data.x[kept],\n", + " (data.y - y_true_data)[kept],\n", + " data.y_err[kept],\n", " fmt=\"o\",\n", " ms=3,\n", " color=\"k\",\n", - " label=\"A\",\n", + " label=\"kept\",\n", ")\n", - "ax.errorbar(\n", - " d_b.x,\n", - " d_b.y - line.bind(d_b.x, {})(m_true, b_true),\n", - " d_b.y_err,\n", - " fmt=\"s\",\n", - " ms=3,\n", - " color=\"C4\",\n", - " label=\"B (offset)\",\n", + "ax.plot(\n", + " data.x[~mask],\n", + " (data.y - y_true_data)[~mask],\n", + " \"x\",\n", + " ms=8,\n", + " color=plotstyle.COLOURS[1],\n", + " label=\"rejected\",\n", ")\n", - "ax.axhline(0.0, ls=\"--\", color=\"k\", label=\"truth\")\n", - "ax.set(xlabel=\"x\", ylabel=\"y - truth\", title=\"90 % bands\")\n", - "ax.legend(frameon=False, fontsize=8, ncol=2, loc=\"upper right\")\n", + "ax.axhline(0.0, ls=\"--\", color=\"k\")\n", + "ax.set(xlabel=\"$x$\", ylabel=r\"$y - y_\\mathrm{truth}$\", title=\"Three answers\")\n", + "ax.legend(fontsize=8, ncol=2)\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "d261a6f6-47d0-416b-a411-5d4f6a40e32a", + "id": "4dafe328", + "metadata": {}, + "source": [ + "Watch the first round: it rejects *four* points, not three. That first fit is\n", + "still being dragged upwards by the outliers, so a perfectly good point at\n", + "$x = 0.33$ looks discrepant as well — and once the three real outliers are gone,\n", + "the second round puts it straight back. This is exactly why the procedure is a\n", + "loop and not a single pass, and why it is worth printing the history rather than\n", + "only the final mask.\n", + "\n", + "At convergence it has found exactly the three points we planted, and the fit\n", + "that follows is both centred on the truth and *tight*: $b = 0.512 \\pm 0.021$\n", + "against the Student-t's $0.566 \\pm 0.067$, because it is fitting twenty-two good\n", + "points with their honest errors rather than twenty-five points with a tail.\n", + "\n", + "That tightness is the thing to be careful about. Rejection is a strong claim:\n", + "we are asserting that those points are not measurements of this signal, and the\n", + "uncertainty we report afterwards does not include the possibility that we were\n", + "wrong about that. The Student-t makes the weaker claim and pays for it with\n", + "width. Which is right is a question about the experiment, not about the\n", + "statistics — and if we cannot answer it, the honest thing is to report both,\n", + "score them by holding data out (recipe 11), and say what we did." + ] + }, + { + "cell_type": "markdown", + "id": "bc6c79d5", "metadata": {}, "source": [ - "By using our prior knowledge that data set `B` may have an unknown offset (USU), but data set `A` does not, we are able to cover the truth." + "## Takeaways\n", + "\n", + "- The likelihood functional is a choice, independent of the covariance. Same\n", + " constraint, same $\\chi^2$; `rx.StudentT(nu)` just reads it differently.\n", + "- The Student-t does not reject anything. It widens, and the posterior of\n", + " $\\nu$ tells us how much tail the data demanded.\n", + "- Rejection is the other answer, and it is an *outer loop* of problems with the\n", + " mask refit between rounds (recipe 39). It gives the tightest result and makes\n", + " the strongest assumption.\n", + "- If what we actually doubt is the size of the reported errors rather than the\n", + " points themselves, that is a different model: see `error_scale_and_usu`." ] } ], diff --git a/test/test_notebooks_index.py b/test/test_notebooks_index.py index e0b29e7..cf42776 100644 --- a/test/test_notebooks_index.py +++ b/test/test_notebooks_index.py @@ -22,7 +22,8 @@ "sharing_error_models": {5}, "normalization_and_covariance_structure": {3, 6, 27}, "gp_discrepancy": {7, 8, 36}, - "robust_likelihoods": {9, 34}, + "robust_likelihoods": {9, 39}, + "error_scale_and_usu": {34}, "local_optical_model_calibration": {12, 14, 15, 16, 21, 26}, "alpha_ca_error_model_comparison": {10, 11, 13, 18}, "hierarchical_calibration": {22, 24, 30, 35, 38}, From 7566187b6b9863bb19cadb5f07669315c98d0bd3 Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Fri, 11 Sep 2026 23:17:36 -0400 Subject: [PATCH 57/75] Rework normalization_and_covariance_structure around five experiments - a_0 = 0 and not inferred, as the review asks. That alone leaves the overall scale unidentifiable, so one experiment is now absolutely normalised and the rest are relative to it; the notebook says why - A settings dict of five experiments, easy to comment in and out, with hand-picked renormalisations: three consistent with what was quoted, one quoted at 3 % that is really 15 % off and alone in its range, and one that quoted 25 % it never needed - Six cases in two groups. Correct: the quoted modes, an inferred normalisation per dataset (recipe 6), an inferred systematic magnitude per dataset (recipe 4). Wrong: Peelle's mode from the data, systematics ignored, statistical errors inferred instead - Believing the bad quote distorts the curve while its parameter pulls stay innocent, because the coefficient errors explode: RMS 1.27 against 0.004 for the best case. Both inferring cases recover it, and the inferred systematic for the mis-quoted set comes out at 10 % against the 3 % quoted - Peelle's puzzle gets the two-point panel where it actually bites, reproducing D'Agostini's closed form exactly (0.8333) - New test: a LaTeX macro in a plain string makes a control character ("$\rho$" is a carriage return), which is how the first rebuild of this notebook broke. The guard walks the AST, so it sees f-strings too --- docs/design.md | 2 +- ...rmalization_and_covariance_structure.ipynb | 1266 ++++++++++++----- test/test_notebooks_index.py | 25 +- 3 files changed, 929 insertions(+), 364 deletions(-) diff --git a/docs/design.md b/docs/design.md index 22e951f..4e3d834 100644 --- a/docs/design.md +++ b/docs/design.md @@ -624,7 +624,7 @@ a time unless noted. | `linear_calibration` | 1, 17 | emcee | the whole workflow on a line; prior and posterior predictive; the coverage curve | 23 s | | `error_models` | 2, 4, 19 | emcee | the covariance ladder on one comparison, the Peelle matrix as a fixed term, offsets known, free and ignored | 89 s | | `sharing_error_models` | 5 | emcee | two experiments with opposite normalisation defects: sharing a parameter, a mode per dataset, one mode spanning both, and the assembled covariance seen directly | 78 s | -| `normalization_and_covariance_structure` | 3, 6, 27 | emcee | latent scales versus reported modes on a quartic; a gallery of covariance structures from `matrix(theta)` | 328 s | +| `normalization_and_covariance_structure` | 3, 4, 6, 27 | emcee | six treatments of five experiments' normalisations, one of them badly mis-quoted and alone in its range; Peelle's puzzle in the two-point case it was found in; a gallery of covariance structures from `matrix(theta)` | 353 s | | `gp_discrepancy` | 7, 8, 36 | emcee, dynesty | a kernel term on a toy and on n+⁴⁰Ca missing its surface absorption; the total predictive band; a sampled Legendre correction for contrast | 617 s | | `robust_likelihoods` | 9, 39 | emcee | Student-t versus Gaussian on three gross outliers; the iterative rejection loop, including the round that over-rejects and recovers | 54 s | | `error_scale_and_usu` | 34 | emcee | a global scale on the reported errors under both likelihoods; a USU offset on the technique we suspect | 90 s | diff --git a/examples/normalization_and_covariance_structure.ipynb b/examples/normalization_and_covariance_structure.ipynb index 97848c1..ca3c59b 100644 --- a/examples/normalization_and_covariance_structure.ipynb +++ b/examples/normalization_and_covariance_structure.ipynb @@ -2,30 +2,34 @@ "cells": [ { "cell_type": "markdown", - "id": "cf7c4837", + "id": "f78307d3", "metadata": {}, "source": [ "# Normalisations and the structure of a covariance\n", "\n", - "Four datasets of one quartic, each with its own unknown normalisation. Four\n", - "treatments of that normalisation: as a latent scale on the model, as a\n", - "correlated mode in the covariance, ignored, and ignored but with an inferred\n", - "model error. Then a gallery of covariance *structures* and what each one\n", - "does to a posterior.\n", + "Five experiments measure the same curve, and each multiplies its data by a\n", + "normalisation it does not know exactly — a flux, an efficiency, a target\n", + "thickness. Each quotes a systematic uncertainty for that factor. Some of those\n", + "quotes are good; at least one, as usually happens, is not.\n", "\n", - "Recipes: 3, 6, 27" + "What we do with those quoted numbers is the whole question here. We will try\n", + "six things — three defensible, three not — and see which recover the curve.\n", + "Then we take a covariance apart and look at the pattern each kind of term writes\n", + "into it.\n", + "\n", + "Recipes: 3, 4, 6, 27" ] }, { "cell_type": "code", "execution_count": 1, - "id": "b4a05e02", + "id": "6cdd6f3d", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:32:30.969910Z", - "iopub.status.busy": "2026-09-11T03:32:30.969730Z", - "iopub.status.idle": "2026-09-11T03:32:33.493640Z", - "shell.execute_reply": "2026-09-11T03:32:33.493055Z" + "iopub.execute_input": "2026-09-12T03:09:35.536542Z", + "iopub.status.busy": "2026-09-12T03:09:35.536400Z", + "iopub.status.idle": "2026-09-12T03:09:37.730914Z", + "shell.execute_reply": "2026-09-12T03:09:37.730097Z" } }, "outputs": [], @@ -34,46 +38,167 @@ "import emcee\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", + "import plotstyle\n", "from numpy.polynomial import polynomial as P\n", "from scipy import stats\n", + "from scipy.optimize import minimize_scalar\n", "from sklearn.gaussian_process.kernels import RBF, ConstantKernel\n", "\n", "import rxmc as rx\n", "from rxmc import terms as T\n", - "from rxmc import transforms as tf" + "from rxmc import transforms as tf\n", + "\n", + "plotstyle.use()" ] }, { "cell_type": "markdown", - "id": "3a6cec73", + "id": "268c7675", "metadata": {}, "source": [ - "## Four datasets, four normalisations\n", + "## Five experiments, and what they told us\n", + "\n", + "Each entry of `settings` is one experiment: where it measured, how many points,\n", + "its statistical noise, the systematic uncertainty it *reported* for its\n", + "normalisation, and — because this is synthetic data — the normalisation it\n", + "actually had.\n", "\n", - "The truth is a quartic. Each experiment measures it on its own range with\n", - "its own noise, and multiplies everything by a normalisation `ρ_i` drawn\n", - "from a distribution of known width `σ_sys,i`, which it reports. The realised\n", - "`ρ_i` are stored only so we can check the inference later." + "We do **not** draw the true renormalisation from the reported systematic. We\n", + "choose it by hand, because that is what real data are like:\n", + "\n", + "- `exp 0` is **absolutely normalised**: it quotes no normalisation uncertainty\n", + " and has none. Some experiment has to play this role, and we will see why in\n", + " a moment.\n", + "- `exp 1`, `exp 2`, `exp 4` are honest: their renormalisations sit comfortably\n", + " inside what they quoted. `exp 4` was simply very conservative, quoting 25 %\n", + " when it was within 2 %.\n", + "- `exp 3` quoted 3 % and is in fact 15 % high — five standard deviations out.\n", + " It is also the *only* experiment covering the high-$x$ region, which is what\n", + " makes it dangerous.\n", + "\n", + "Comment a line out to drop that experiment from everything below." ] }, { "cell_type": "code", "execution_count": 2, - "id": "74d53ccd", + "id": "f6b7fce0", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:09:37.732681Z", + "iopub.status.busy": "2026-09-12T03:09:37.732452Z", + "iopub.status.idle": "2026-09-12T03:09:37.736532Z", + "shell.execute_reply": "2026-09-12T03:09:37.736058Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "exp 0: quoted 0%, actually +0% (absolute)\n", + "exp 1: quoted 8%, actually -6% (0.8 sigma)\n", + "exp 2: quoted 12%, actually +10% (0.8 sigma)\n", + "exp 3: quoted 3%, actually +15% (5.0 sigma)\n", + "exp 4: quoted 25%, actually +2% (0.1 sigma)\n" + ] + } + ], + "source": [ + "settings = {\n", + " \"exp 0\": {\n", + " \"domain\": (0.20, 0.60),\n", + " \"n\": 35,\n", + " \"noise\": 0.010,\n", + " \"sys\": 0.00,\n", + " \"rho\": 1.00,\n", + " },\n", + " \"exp 1\": {\n", + " \"domain\": (0.30, 0.70),\n", + " \"n\": 25,\n", + " \"noise\": 0.012,\n", + " \"sys\": 0.08,\n", + " \"rho\": 0.94,\n", + " },\n", + " \"exp 2\": {\n", + " \"domain\": (0.25, 0.65),\n", + " \"n\": 25,\n", + " \"noise\": 0.010,\n", + " \"sys\": 0.12,\n", + " \"rho\": 1.10,\n", + " },\n", + " \"exp 3\": {\n", + " \"domain\": (0.90, 1.40),\n", + " \"n\": 40,\n", + " \"noise\": 0.010,\n", + " \"sys\": 0.03,\n", + " \"rho\": 1.15,\n", + " },\n", + " \"exp 4\": {\n", + " \"domain\": (0.35, 0.75),\n", + " \"n\": 25,\n", + " \"noise\": 0.015,\n", + " \"sys\": 0.25,\n", + " \"rho\": 1.02,\n", + " },\n", + "}\n", + "for label, s in settings.items():\n", + " off = abs(s[\"rho\"] - 1)\n", + " sigmas = \"absolute\" if s[\"sys\"] == 0 else f\"{off / s['sys']:.1f} sigma\"\n", + " print(f\"{label}: quoted {s['sys']:.0%}, actually {s['rho'] - 1:+.0%} ({sigmas})\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fc2ec848", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:09:37.737736Z", + "iopub.status.busy": "2026-09-12T03:09:37.737621Z", + "iopub.status.idle": "2026-09-12T03:09:37.741776Z", + "shell.execute_reply": "2026-09-12T03:09:37.740993Z" + } + }, + "outputs": [], + "source": [ + "rng = np.random.default_rng(42)\n", + "a_true = np.array([0.0, 1.0, -0.3, -0.6]) # a_0 is zero, and stays zero\n", + "\n", + "\n", + "def truth(x):\n", + " return P.polyval(np.asarray(x, dtype=float), a_true)\n", + "\n", + "\n", + "datasets = {}\n", + "for label, s in settings.items():\n", + " x = np.sort(rng.uniform(*s[\"domain\"], s[\"n\"]))\n", + " y = rng.normal(truth(x) * s[\"rho\"], s[\"noise\"])\n", + " datasets[label] = rx.Dataset(\n", + " x, y, np.full(s[\"n\"], s[\"noise\"]), norm_err=s[\"sys\"], label=label\n", + " )\n", + "x_fine = np.linspace(0.18, 1.42, 140)\n", + "x_high = np.linspace(1.0, 1.40, 30) # the region only exp 3 covers" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5cdc4ada", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:32:33.495495Z", - "iopub.status.busy": "2026-09-11T03:32:33.495277Z", - "iopub.status.idle": "2026-09-11T03:32:33.706339Z", - "shell.execute_reply": "2026-09-11T03:32:33.705535Z" + "iopub.execute_input": "2026-09-12T03:09:37.743268Z", + "iopub.status.busy": "2026-09-12T03:09:37.743108Z", + "iopub.status.idle": "2026-09-12T03:09:38.716889Z", + "shell.execute_reply": "2026-09-12T03:09:38.716200Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "<Figure size 600x400 with 1 Axes>" + "<Figure size 704x440 with 1 Axes>" ] }, "metadata": {}, @@ -81,108 +206,176 @@ } ], "source": [ - "rng = np.random.default_rng(42)\n", - "a_true = np.array([1.0, 0.5, -0.1, -0.4, 0.1])\n", - "settings = [ # (domain, n, noise, sys_fraction)\n", - " ((-0.3, 0.4), 50, 0.1, 0.1),\n", - " ((0.3, 0.5), 30, 0.1, 0.5),\n", - " ((0.1, 0.6), 25, 0.1, 0.2),\n", - " ((-0.5, 0.1), 15, 0.2, 0.6),\n", - "]\n", - "datasets, rho_true, sys_fraction = [], [], []\n", - "for i, (domain, n, noise, sys) in enumerate(settings):\n", - " x = np.sort(rng.uniform(*domain, n))\n", - " rho = rng.normal(1.0, sys)\n", - " y = rng.normal(P.polyval(x, a_true) * rho, noise)\n", - " datasets.append(rx.Dataset(x, y, np.full(n, noise), norm_err=sys, label=f\"exp {i}\"))\n", - " rho_true.append(rho)\n", - " sys_fraction.append(sys)\n", - "\n", - "x_fine = np.linspace(-0.6, 0.7, 100)\n", - "fig, ax = plt.subplots(figsize=(6, 4))\n", - "ax.plot(x_fine, P.polyval(x_fine, a_true), \"k--\", label=\"truth\")\n", - "for d, rho in zip(datasets, rho_true):\n", - " ax.errorbar(d.x, d.y, d.y_err, fmt=\"o\", ms=3, label=f\"{d.label}: rho = {rho:.2f}\")\n", - "ax.set(xlabel=\"x\", ylabel=\"y\")\n", - "ax.legend(frameon=False, fontsize=8)\n", + "fig, ax = plt.subplots()\n", + "ax.plot(x_fine, truth(x_fine), \"--\", color=\"k\", label=\"truth\")\n", + "for (label, d), colour in zip(datasets.items(), plotstyle.COLOURS):\n", + " ax.errorbar(\n", + " d.x,\n", + " d.y,\n", + " d.y_err,\n", + " fmt=\"o\",\n", + " ms=3,\n", + " color=colour,\n", + " label=rf\"{label}: $\\rho$ = {settings[label]['rho']:.2f}\",\n", + " )\n", + "ax.axvspan(1.0, 1.42, color=\"0.93\", zorder=0)\n", + "ax.text(1.02, 0.55, \"only exp 3\\nmeasures here\", fontsize=8, color=\"0.35\")\n", + "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"Five experiments, five normalisations\")\n", + "ax.legend(fontsize=8, ncol=2)\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "5f0482d5", + "id": "e5a2ac08", "metadata": {}, "source": [ - "## The model and a tight prior on the constant term\n", + "## The model, and why one experiment must be absolute\n", + "\n", + "The truth is a cubic with $a_0 = 0$, and we hold $a_0$ at zero rather than\n", + "inferring it: an additive constant and a multiplicative normalisation are nearly\n", + "the same thing over a short range, so leaving both free would confound them.\n", "\n", - "An additive constant and a multiplicative normalisation are nearly\n", - "confounded on a short range, so the prior on `a0` is tight; the other\n", - "coefficients get wide priors." + "That is not enough on its own. If *every* experiment may renormalise itself,\n", + "then shrinking all the $\\rho_i$ by 10 % and raising the curve by 10 % fits\n", + "exactly as well — the overall scale is unidentifiable, and the fit wanders.\n", + "Something has to fix it, and the honest way is an experiment that measured the\n", + "absolute scale: `exp 0` here. The rest are then normalised relative to it." ] }, { "cell_type": "code", - "execution_count": 3, - "id": "3d7e7824", + "execution_count": 5, + "id": "db34eca7", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:32:33.707827Z", - "iopub.status.busy": "2026-09-11T03:32:33.707649Z", - "iopub.status.idle": "2026-09-11T03:32:33.715049Z", - "shell.execute_reply": "2026-09-11T03:32:33.714390Z" + "iopub.execute_input": "2026-09-12T03:09:38.718269Z", + "iopub.status.busy": "2026-09-12T03:09:38.718110Z", + "iopub.status.idle": "2026-09-12T03:09:38.723450Z", + "shell.execute_reply": "2026-09-12T03:09:38.722953Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "normalisation free for: ['exp 1', 'exp 2', 'exp 3', 'exp 4']\n" + ] + } + ], "source": [ - "coeffs = [rx.Parameter(\"a0\", prior=stats.norm(1.0, 0.03), latex=\"a_0\")] + [\n", - " rx.Parameter(f\"a{k}\", prior=stats.norm(0.0, 1.0), latex=f\"a_{k}\")\n", - " for k in range(1, 5)\n", + "coeffs = [\n", + " rx.Parameter(f\"a{k}\", prior=stats.norm(0.0, 1.5), latex=f\"a_{k}\") for k in (1, 2, 3)\n", "]\n", - "quartic = rx.Model(lambda x, *a: P.polyval(np.asarray(x, dtype=float), a), coeffs)\n", - "\n", - "\n", - "def fit(problem, seed, n_walkers=32, n_steps=2500):\n", + "cubic = rx.Model(\n", + " lambda x, *a: P.polyval(np.asarray(x, dtype=float), (0.0,) + a), coeffs\n", + ")\n", + "comps = {label: rx.Comparison(d, cubic) for label, d in datasets.items()}\n", + "free = [label for label, s in settings.items() if s[\"sys\"] > 0]\n", + "print(\"normalisation free for:\", free)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5439babf", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:09:38.724834Z", + "iopub.status.busy": "2026-09-12T03:09:38.724711Z", + "iopub.status.idle": "2026-09-12T03:09:38.728043Z", + "shell.execute_reply": "2026-09-12T03:09:38.727509Z" + } + }, + "outputs": [], + "source": [ + "def fit(problem, seed, n_walkers=32, n_steps=2000):\n", " sampler = emcee.EnsembleSampler(n_walkers, problem.ndim, problem.log_posterior)\n", " sampler.random_state = np.random.RandomState(seed).get_state()\n", " sampler.run_mcmc(problem.sample_prior(n_walkers, rng=seed), n_steps, progress=False)\n", " return sampler.get_chain(discard=n_steps // 3, thin=5, flat=True)\n", "\n", "\n", - "def curves(problem, samples, n=300):\n", - " on_fine = quartic.bind(x_fine, {})\n", - " cols = problem.columns(quartic.params)\n", - " return np.array([on_fine(*s[cols]) for s in samples[-n:]])" + "def curves(problem, samples, grid, n=200):\n", + " on_grid = cubic.bind(grid)\n", + " cols = problem.columns(cubic.params)\n", + " return np.array([on_grid(*s[cols]) for s in samples[-n:]])" ] }, { "cell_type": "markdown", - "id": "72c3ea51", + "id": "f6066bdd", "metadata": {}, "source": [ - "## Four treatments of the normalisation\n", + "## Three defensible things to do\n", + "\n", + "**1. Believe the quoted systematic (recipe 3).** Each dataset's `norm_err`\n", + "becomes a rank-one mode built from the prediction; `comp.reported_terms()`\n", + "spells exactly that. This is the standard thing to do.\n", + "\n", + "**2. Infer the normalisation (recipe 6).** Give each dataset a latent scale\n", + "$\\rho_i$ on the *model*, with the quoted systematic as its prior width, and let\n", + "the data move it.\n", "\n", - "1. **Latent scale on the model** (recipe 6): `quartic | tf.scale(rho_i)`\n", - " gives each comparison its own multiplicative parameter, sampled in log\n", - " space with the reported width as its prior.\n", - "2. **Correlated mode in the covariance** (recipe 3): `T.normalization` with\n", - " the reported magnitude, one per dataset, built from the prediction. The\n", - " `Dataset` carries `norm_err`, so `comp.reported_terms()` builds exactly\n", - " this.\n", - "3. **Ignored**: reported statistics only.\n", - "4. **Ignored, with an inferred model error**: a diagonal `T.model_error`\n", - " whose fraction is a free parameter, the honest-looking way to be wrong." + "**3. Infer the systematic magnitude (recipe 4).** Keep the mode in the\n", + "covariance, but treat its size $\\eta_i$ as a nuisance parameter per dataset\n", + "instead of a number we were handed.\n", + "\n", + "All three say \"there is a normalisation here\". They differ in how much they\n", + "trust the quote." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "ce2922c7", + "execution_count": 7, + "id": "ad183ccd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:09:38.729356Z", + "iopub.status.busy": "2026-09-12T03:09:38.729172Z", + "iopub.status.idle": "2026-09-12T03:09:38.735973Z", + "shell.execute_reply": "2026-09-12T03:09:38.735416Z" + } + }, + "outputs": [], + "source": [ + "rhos = {\n", + " label: rx.Parameter(\n", + " f\"log_rho_{i}\",\n", + " prior=stats.norm(0.0, np.log1p(settings[label][\"sys\"])),\n", + " latex=rf\"\\log\\rho_{i}\",\n", + " )\n", + " for i, label in enumerate(free)\n", + "}\n", + "etas = {\n", + " label: rx.Parameter(\n", + " f\"log_eta_{i}\",\n", + " prior=stats.norm(np.log(settings[label][\"sys\"]), 0.7),\n", + " latex=rf\"\\log\\eta_{i}\",\n", + " )\n", + " for i, label in enumerate(free)\n", + "}\n", + "comps_scaled = [\n", + " (\n", + " rx.Comparison(d, cubic | tf.scale(rhos[label]))\n", + " if label in free\n", + " else rx.Comparison(d, cubic)\n", + " )\n", + " for label, d in datasets.items()\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6cd9b231", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:32:33.716272Z", - "iopub.status.busy": "2026-09-11T03:32:33.716139Z", - "iopub.status.idle": "2026-09-11T03:32:33.726483Z", - "shell.execute_reply": "2026-09-11T03:32:33.725979Z" + "iopub.execute_input": "2026-09-12T03:09:38.737321Z", + "iopub.status.busy": "2026-09-12T03:09:38.737200Z", + "iopub.status.idle": "2026-09-12T03:09:38.743960Z", + "shell.execute_reply": "2026-09-12T03:09:38.743342Z" } }, "outputs": [ @@ -190,50 +383,69 @@ "name": "stdout", "output_type": "stream", "text": [ - "latent scale columns: ['a0', 'a1', 'a2', 'a3', 'a4', 'log_rho_0', 'log_rho_1', 'log_rho_2', 'log_rho_3']\n", - "normalisation mode columns: ['a0', 'a1', 'a2', 'a3', 'a4']\n", - "ignored columns: ['a0', 'a1', 'a2', 'a3', 'a4']\n", - "ignored + model error columns: ['a0', 'a1', 'a2', 'a3', 'a4', 'log_gamma']\n" + "quoted systematics 3 columns\n", + "inferred normalisations 7 columns\n", + "inferred systematics 7 columns\n" ] } ], "source": [ - "rhos = [\n", - " rx.Parameter(\n", - " f\"log_rho_{i}\", prior=stats.norm(0.0, np.log1p(s)), latex=rf\"\\log\\rho_{i}\"\n", - " )\n", - " for i, s in enumerate(sys_fraction)\n", - "]\n", - "comps_scaled = [rx.Comparison(d, quartic | tf.scale(r)) for d, r in zip(datasets, rhos)]\n", - "comps = [rx.Comparison(d, quartic) for d in datasets]\n", - "gamma = rx.Parameter(\n", - " \"log_gamma\", prior=stats.norm(np.log(0.1), 0.5), latex=r\"\\log\\gamma\"\n", - ")\n", - "\n", - "problems = {\n", - " \"latent scale\": rx.Problem([rx.Constraint(comps_scaled)]),\n", - " \"normalisation mode\": rx.Problem(\n", - " [rx.Constraint(comps, terms=[t for c in comps for t in c.reported_terms()])]\n", + "correct = {\n", + " \"quoted systematics\": rx.Problem(\n", + " [\n", + " rx.Constraint(\n", + " list(comps.values()),\n", + " terms=[t for label in free for t in comps[label].reported_terms()],\n", + " )\n", + " ]\n", " ),\n", - " \"ignored\": rx.Problem([rx.Constraint(comps)]),\n", - " \"ignored + model error\": rx.Problem(\n", - " [rx.Constraint(comps, terms=[T.model_error(gamma, averaging=True)])]\n", + " \"inferred normalisations\": rx.Problem([rx.Constraint(comps_scaled)]),\n", + " \"inferred systematics\": rx.Problem(\n", + " [\n", + " rx.Constraint(\n", + " list(comps.values()),\n", + " terms=[\n", + " T.normalization(parameter=etas[label], on=comps[label])\n", + " for label in free\n", + " ],\n", + " )\n", + " ]\n", " ),\n", "}\n", - "for name, p in problems.items():\n", - " print(f\"{name:22s} columns: {p.names}\")" + "for name, p in correct.items():\n", + " print(f\"{name:24s} {p.ndim} columns\")" + ] + }, + { + "cell_type": "markdown", + "id": "e7c9ff53", + "metadata": {}, + "source": [ + "## Three things not to do\n", + "\n", + "**4. Peelle's Pertinent Puzzle (recipe 27).** Build the same mode from the\n", + "*measured* values instead of the prediction, so the fluctuations feed back into\n", + "the covariance.\n", + "\n", + "**5. Ignore the systematics.** Quoted statistics only, as if every\n", + "normalisation were exact.\n", + "\n", + "**6. Infer statistical uncertainties instead.** Notice that the datasets\n", + "disagree, blame the noise, and let a free diagonal absorb it. It looks like\n", + "inference, and it is the wrong model: a normalisation moves a whole dataset\n", + "together, and no diagonal can say that." ] }, { "cell_type": "code", - "execution_count": 5, - "id": "30f39acf", + "execution_count": 9, + "id": "c975ffb4", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:32:33.728021Z", - "iopub.status.busy": "2026-09-11T03:32:33.727870Z", - "iopub.status.idle": "2026-09-11T03:37:20.072820Z", - "shell.execute_reply": "2026-09-11T03:37:20.072153Z" + "iopub.execute_input": "2026-09-12T03:09:38.745300Z", + "iopub.status.busy": "2026-09-12T03:09:38.745180Z", + "iopub.status.idle": "2026-09-12T03:09:38.752802Z", + "shell.execute_reply": "2026-09-12T03:09:38.752263Z" } }, "outputs": [ @@ -241,50 +453,268 @@ "name": "stdout", "output_type": "stream", "text": [ - "latent scale a0=1.01+/-0.03 a1=0.47+/-0.08 a2=0.19+/-0.30 a3=-1.05+/-0.54 a4=0.22+/-0.85\n", - "normalisation mode a0=1.01+/-0.03 a1=0.47+/-0.09 a2=0.18+/-0.30 a3=-1.03+/-0.55 a4=0.28+/-0.83\n", - "ignored a0=1.09+/-0.02 a1=0.11+/-0.08 a2=-2.17+/-0.27 a3=0.90+/-0.53 a4=6.00+/-0.84\n", - "ignored + model error a0=0.99+/-0.03 a1=-0.08+/-0.17 a2=-0.73+/-0.40 a3=0.67+/-0.77 a4=1.06+/-0.93\n" + "PPP: mode from the data 3 columns\n", + "systematics ignored 3 columns\n", + "statistics inferred 4 columns\n" ] } ], "source": [ - "samples = {name: fit(p, seed=i) for i, (name, p) in enumerate(problems.items())}\n", - "colors = {\n", - " \"latent scale\": \"C0\",\n", - " \"normalisation mode\": \"C1\",\n", - " \"ignored\": \"C3\",\n", - " \"ignored + model error\": \"C2\",\n", + "log_eps = rx.Parameter(\n", + " \"log_eps\", prior=stats.norm(np.log(0.02), 1.0), latex=r\"\\log\\epsilon\"\n", + ")\n", + "wrong = {\n", + " \"PPP: mode from the data\": rx.Problem(\n", + " [\n", + " rx.Constraint(\n", + " list(comps.values()),\n", + " terms=[\n", + " rx.Term(\n", + " settings[label][\"sys\"] * datasets[label].y,\n", + " kind=\"mode\",\n", + " on=comps[label],\n", + " )\n", + " for label in free\n", + " ],\n", + " )\n", + " ]\n", + " ),\n", + " \"systematics ignored\": rx.Problem([rx.Constraint(list(comps.values()))]),\n", + " \"statistics inferred\": rx.Problem(\n", + " [\n", + " rx.Constraint(\n", + " list(comps.values()),\n", + " terms=[T.noise_fraction(log_eps)],\n", + " statistical=False,\n", + " )\n", + " ]\n", + " ),\n", "}\n", - "for name, s in samples.items():\n", - " cols = problems[name].columns(quartic.params)\n", - " print(\n", - " f\"{name:22s} \"\n", - " + \" \".join(\n", - " f\"a{k}={s[:, c].mean():.2f}+/-{s[:, c].std():.2f}\"\n", - " for k, c in enumerate(cols)\n", + "for name, p in wrong.items():\n", + " print(f\"{name:24s} {p.ndim} columns\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f7833d17", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:09:38.754343Z", + "iopub.status.busy": "2026-09-12T03:09:38.754158Z", + "iopub.status.idle": "2026-09-12T03:14:59.672367Z", + "shell.execute_reply": "2026-09-12T03:14:59.671780Z" + } + }, + "outputs": [], + "source": [ + "problems = {**correct, **wrong}\n", + "samples = {name: fit(p, seed=i) for i, (name, p) in enumerate(problems.items())}\n", + "style = dict(\n", + " zip(\n", + " problems,\n", + " zip(\n", + " [plotstyle.COLOURS[i] for i in (0, 2, 5, 1, 3, 6)],\n", + " plotstyle.HATCHES + plotstyle.HATCHES,\n", + " ),\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "86edaa87", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:14:59.673956Z", + "iopub.status.busy": "2026-09-12T03:14:59.673820Z", + "iopub.status.idle": "2026-09-12T03:14:59.694757Z", + "shell.execute_reply": "2026-09-12T03:14:59.694143Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the three defensible ones\n", + "quoted systematics a1=+1.56+/-0.81 a2=-2.52+/-3.21 a3=+1.50+/-3.00 max|pull| = 0.7 RMS over exp 3's range = 1.269\n", + "inferred normalisations a1=+0.98+/-0.02 a2=-0.21+/-0.05 a3=-0.67+/-0.03 max|pull| = 2.2 RMS over exp 3's range = 0.015\n", + "inferred systematics a1=+1.00+/-0.02 a2=-0.29+/-0.05 a3=-0.61+/-0.03 max|pull| = 0.3 RMS over exp 3's range = 0.004\n", + "\n", + "the three wrong ones\n", + "PPP: mode from the data a1=+0.98+/-0.02 a2=-0.24+/-0.05 a3=-0.64+/-0.03 max|pull| = 1.2 RMS over exp 3's range = 0.009\n", + "systematics ignored a1=+0.91+/-0.01 a2=+0.03+/-0.02 a3=-0.84+/-0.01 max|pull| = 18.1 RMS over exp 3's range = 0.061\n", + "statistics inferred a1=+0.87+/-0.55 a2=-0.29+/-0.88 a3=-0.69+/-0.44 max|pull| = 0.2 RMS over exp 3's range = 0.281\n" + ] + } + ], + "source": [ + "def report(names):\n", + " for name in names:\n", + " p, s = problems[name], samples[name]\n", + " cols = p.columns(cubic.params)\n", + " pulls = [\n", + " (s[:, c].mean() - a_true[k + 1]) / s[:, c].std() for k, c in enumerate(cols)\n", + " ]\n", + " mean_high = curves(p, s, x_high).mean(axis=0)\n", + " rms = np.sqrt(np.mean((mean_high - truth(x_high)) ** 2))\n", + " print(\n", + " f\"{name:24s} \"\n", + " + \" \".join(\n", + " f\"a{k + 1}={s[:, c].mean():+.2f}+/-{s[:, c].std():.2f}\"\n", + " for k, c in enumerate(cols)\n", + " )\n", + " + f\" max|pull| = {max(abs(z) for z in pulls):4.1f}\"\n", + " + f\" RMS over exp 3's range = {rms:.3f}\"\n", " )\n", - " )" + "\n", + "\n", + "print(\"the three defensible ones\")\n", + "report(correct)\n", + "print(\"\\nthe three wrong ones\")\n", + "report(wrong)" + ] + }, + { + "cell_type": "markdown", + "id": "a3acc767", + "metadata": {}, + "source": [ + "### What that table says\n", + "\n", + "The first row is the one to look at. `quoted systematics` is the standard thing\n", + "to do, and here it fails. `exp 3` quoted 3 %, so the fit is obliged to believe\n", + "its scale to within 3 % — and since `exp 3` is the only experiment in the\n", + "high-$x$ region, the cubic has to bend to follow data that are 15 % high. It\n", + "cannot do that and still fit the others, so it gives way in the only direction\n", + "left to it: the coefficients acquire enormous uncertainties\n", + "($a_2 = -2.52 \\pm 3.21$) and the predicted curve is *still* wrong, with an RMS\n", + "error of 1.27 over `exp 3`'s range against 0.004 for the best case.\n", + "\n", + "Notice that this case has the **smallest** `max|pull|` of the three, 0.7. The\n", + "pulls look innocent precisely because the error bars exploded. Had we judged\n", + "these fits by parameter pulls alone, we would have called the broken one fine —\n", + "which is the argument for looking at the prediction as well.\n", + "\n", + "Both cases that infer the normalisation recover the curve, which is what we\n", + "hoped for: RMS 0.015 for the sampled scale $\\rho_i$, and 0.004 for the sampled\n", + "magnitude $\\eta_i$. Inferring $\\eta$ wins here because it keeps the quoted\n", + "values as a starting point and only widens the ones the data cannot live with.\n", + "\n", + "Among the three we called wrong, ignoring the systematics is the classic:\n", + "$a_2 = +0.03 \\pm 0.02$, eighteen standard deviations from the truth, precise and\n", + "wrong. Inferring the *statistical* errors instead technically covers the truth,\n", + "but only by inflating everything until the answer says nothing\n", + "($a_2 = -0.29 \\pm 0.88$). And the Peelle mode is, in this configuration, nearly\n", + "harmless — RMS 0.009. That deserves an explanation rather than a shrug, and it\n", + "gets one two sections below." ] }, { "cell_type": "code", - "execution_count": 6, - "id": "4d000385", + "execution_count": 12, + "id": "aeb43081", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:37:20.074120Z", - "iopub.status.busy": "2026-09-11T03:37:20.073972Z", - "iopub.status.idle": "2026-09-11T03:37:21.810668Z", - "shell.execute_reply": "2026-09-11T03:37:21.810197Z" + "iopub.execute_input": "2026-09-12T03:14:59.696366Z", + "iopub.status.busy": "2026-09-12T03:14:59.696231Z", + "iopub.status.idle": "2026-09-12T03:14:59.843368Z", + "shell.execute_reply": "2026-09-12T03:14:59.842774Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "<Figure size 1180x1180 with 25 Axes>" + "<Figure size 704x440 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "for name in correct:\n", + " colour, hatch = style[name]\n", + " lo, hi = np.percentile(\n", + " curves(problems[name], samples[name], x_fine), [5, 95], axis=0\n", + " )\n", + " plotstyle.band(ax, x_fine, lo, hi, color=colour, hatch=hatch, label=name)\n", + "ax.plot(x_fine, truth(x_fine), \"--\", color=\"k\", label=\"truth\")\n", + "for d, colour in zip(datasets.values(), plotstyle.COLOURS):\n", + " ax.plot(d.x, d.y, \".\", ms=2, color=\"0.5\")\n", + "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"90 % bands: the defensible three\")\n", + "ax.legend(fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "12a5ada8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:14:59.844707Z", + "iopub.status.busy": "2026-09-12T03:14:59.844559Z", + "iopub.status.idle": "2026-09-12T03:14:59.979030Z", + "shell.execute_reply": "2026-09-12T03:14:59.978247Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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ZQmlpaZ2XbeuSkpLC7bffzptvvsknn3yivj9//nzWrl2r9t/YHCkpKcyaNYtXX32Vb7/9Fqj8PX/sscea9EcEVB7r3Nzcak+Nn0qzZs1iwIABzJo1ix9//FF9PxgMsmTJEnWkmKZOJ0RrkqAnRAQxGAz4/X7S09Pp168fqampzJs3j0WLFtXZCvPhhx/Sr18/evToof7bv39//vvf/zZ5neXl5cyePZu//OUvpKenq++npqby6quvcscdd9CtWzeeffZZ3njjjSZ1nvzkk0/y2GOP0bVrVxITE/nwww955513qrV6/OUvf2HIkCEMGDCA3r1707lzZyZNmsTYsWObvO01jRs3jrfeeouFCxeSmJhIly5dmDt3Lk888UStacMPoKSkpNC/f39SU1MpKyvj448/rnaP3tatW5s0CkO/fv246aabmD17Nv3796+3H73G9OrVi++++w6Hw0FGRgbdunUjKyuLTp06cfDgQbp27drg/CNGjMButxMMBqsFunAA1Ol0zfoj4LnnnuPOO+/k6quvJiMjg5SUFP7whz8wf/587rzzzmbvH8ALL7zAFVdcwaRJk+jSpQsZGRm0a9eOc845p8HLrmF33303ycnJZGVlMWTIkDr70WsJs9nMypUrGTVqFKNHj6Zjx4706tULu93OW2+9xaBBg5o1nRCtSaPUvGFBCNHq/H4/+/fvJzY2tlr4qk9hYSHHjh2jQ4cOJCcnN2tdpaWlZGdnM2TIkDrvM/J4PBw+fJguXbo0+pBCcXExBw4coF+/fpjNZnJycnA6nXTv3r3eeY8fP05xcTFdunRR5ykoKGDIkCF1LjPM7/fz448/kpmZSUpKSrVl+nw+9u3bh91uJz09nYqKCnbu3EnXrl1rtRoVFhZy4sQJ0tLSSExMrLV9P/74I3q9vslP3hYUFHD06FECgQBdunQhMTHxpPYBKu9jO3LkCPHx8aSnp9dZPnX56aefKC8vZ/DgwdWezN6yZQt6vZ4BAwZUm76+7avK4/GQnZ2NTqejW7dutQLZ3r17CQaD6r2cYXl5eRw9epQhQ4bUeuihsLCQ48ePk5WVRUxMDDNmzGDz5s0cOnSo0X0MhUIcOHBAvddv6NChzd6O+qatyuv1cuDAAfR6PVlZWfX+Hjd1OiHONAl6QgghWp3X66Vz586MHTuW999/v7U3R4g2Qy7dCiGEOKN2797Ne++9p95H6fV6ufvuuzl+/Dj33HNPK2+dEG2LBD0hhBBnVFpaGh999BGJiYn06dOH+Ph4li5dysKFC1t0j6YQoja5dCuEEKJVeDweDhw4gMlkokuXLk2+B1EI0XQS9IQQQggh2ii5dCuEEEII0UZJ0BNCCCGEaKMk6DVTIBAgNzf3tA6/I4QQQghxKkjQa6bjx4+TkZHB8ePHW3U7iouLW3X90U6OX8vI8WsZOX4tI8evZeT4tUy0HT8JelFKnqFpGTl+LSPHr2Xk+LWMHL+WkePXMtF2/CToCSGEEEK0URL0hBBCCCHaKAl6QgghhBBtlAQ9IYQQQog2SoKeEEIIIUQbJUFPCCGEEKKNkqAnhBBCCNFGSdATQgghhGijJOgJIYQQQrRREvSEEEKIs9zOnTv53e9+d9Lzf/LJJ8yePZt58+adwq06effccw9Hjhw5LctevHgx77zzzmlZ9ukgQU8IIURUUEJBlIAfJeiv/Pd0/ISCTdqWd955h5kzZzJz5kzmzJnDq6++itfrbdFnrSkvL4+lS5ee1Ly7du3i5ptvZvDgwQwfPvwUb1nj7r//frKzs6u99+GHH562MWm3b9/O5s2bmzRtXdt2pulbde1CCCFEEyihIJ5DWwhWlOEpOEaZzYrWFINWbwQg5Ksg5K9AozOgNcWg0Wgq51MUQl4XStCP1mBBa7RUTh/wEfK60Gh1aM2xaDSV7R5acwzmrCFotLoGt2fz5s0cPHiQOXPmUFZWxvPPP8+nn37Kxx9/fNKfRatNmzYxduxY7rjjjlZZ/+LFi7nmmmvo2rVrq6y/IZGwbRL0hBBCRL5QiGBFGQFXMUrAgz42C63Jqn6stcQRdBUTcBWBRove3h6AoOM4Ia8bfUwSupjE/00PhIxWAiXHCIZCGBI6AhAoK4BQCBoJegCdO3dm5syZAAwZMoQxY8bgcrlO+rOYmJgG1/fqq6/Srl078vPz2bx5MwMGDGDOnDksW7aMjz76iMzMTB544AHMZrM6z6pVq1iyZAkGg4FrrrmGIUOGqJ99+umnLFu2jKysLHr06FFrfdu3b+e9997D6XRy8cUXc95559Wa5osvvuDFF1/k+PHjzJw5k+uuu47c3Fw6duxIfn4+GzZs4OGHH6ZLly4sWLCA9evX06FDB375y1+SmpoKVF5mveGGG/jqq684dOgQV155JRMnTuSVV15h27ZtjB8/Xj1eNb344osUFhbyxBNPkJyczA033MAFF1wAgMPhYN68eRw5coSpU6dyySWXVJv3008/5auvvsJmszFr1iy6d+9e5zqCwSAvv/wyP/30E6NHj671+RNPPMH+/fsxmUwMHDiQ2bNnYzKZ6t22+qY/XeTSrRBCiKgQ8jjRhELoEzLQWe1odIZqP3p7Owz2dih+D0FnAUFnAYrfg8HeDr29Xa3pdVY7huROaBSFQNkJguUlhPwVJ7VtR48exWAw1PmF3dTPNm/ezJw5c+pdx9q1a7n55pvZsWMH/fr1Y+7cuUyZMoV//vOfDBkyhE8//ZT77rtPnf7dd9/lsssuo127dpjNZs4991xWrlwJwIIFC5g1axadO3emqKiI2bNnV1vX8uXLmTRpEnq9nq5du3LbbbexcOHCWtuUmZlJ3759adeuHVOnTqVz586sXbuWWbNmsWHDBsaMGYPdbueXv/wlTz/9NH369GHfvn0MHjyYkpISoPIy6zXXXINOpyMtLY2LLrqICy+8kAMHDjBgwAAeeugh/vvf/9Z5TAYOHIjFYmH06NFMnTqVzMxM9bPbb7+dUCikBusVK1aonz322GM8+OCDdOnSBZ1OxznnnMOBAwfqXMctt9zCa6+9Rp8+fViyZAlvvfVWtc/D6x49ejSffPIJV199dYPbVt/0p4u06AkhhIgKSiiIPqEDGr9S7zS6mCQUIOgqUl/rYpLqnV5rsqFP6IC/5Cghr1u9tNsUa9euZebMmZSVlfH111/z+OOPYzAYTvqznJwcFi1axMsvv1zvOqdPn87zzz8PQHl5Oa+88gr79+9Hp9MxaNCgaqHhiSee4O9//7vaGma1Wpk7dy6LFi3iD3/4Ay+99JI6fSAQ4KuvvlLnfeSRR3j22We58cYbgcrQcuedd3LddddV255evXoxYsQIysrKqrW6jR8/nldeeQWAI0eO8Oabb7J//34yMjIAmDJlCi+//DL/93//B8Djjz/OTTfdBMCaNWvIyMhg/vz5QGXL3LJly7jyyitrHY+xY8dis9m44IILGDVqVLXPfve736nLzM3N5YsvvmDSpEmcOHGCp59+mr1796rboygKzz//vHpsw44cOcJ7773HoUOHaN++PXfddRcDBgyoNk24BRHghhtuID09ndzc3Hq3rb7p09PTa+3fqSBBTwghRNRQ/B6g/stciqKgBP73cIMS8KIoinrPXv3LDL8INXlb0tLSmDp1KjExMTz33HN06dKlRZ8NGzZMDUf16d+/v/r/du3a0bt3b3Q6nfo6/ACC3+9n3759jBs3Tp1+woQJPPvss/j9fvbv388555yjfjZu3Dg16AUCAbZv386SJUtYsWIFoVAIj8fD3r17CQaD6voaMnToUPX/P/30E506dVJDVXhbduzYUe9+9evXr9rrTZs2NbrOmqouMy0tTX0oYseOHeh0Oh599NHK3xdF4eDBg1gstUP+Tz/9ROfOnWnfvr363siRI6tNc/jwYd58800OHTqE1+slGAxy8ODBeoNbc6dvKQl6QgghooJGZyDgKiKksYA9vtbniqIQcOQR8rjQ/9yKF3AVESAPvT2tzrAXdBURcBWhNcegNVgJuAqavD1V77U7FZ+lp6fX2WpVlVarbfC1olS2dur1egwGAxUV/7sUXVFRgcViQafTYTAY8Hg81T6rukyDwcDYsWPV++gALrnkkgYDc1VGo1H9v8Viqbb8qtvS3P1qjvqWYTKZsFgste45rLqvYWazudpxCm+73W4HoLS0lJEjR3LVVVcxevRozGYz3377LeXl5XVuU3OnPxXkHj0hhBBRQWuKQWuOIeQuVi/NhtUMeeFLtvqYJEIeFwFHXq2wUDXk6e1paM02tKaGH4g4nRq7R685NBoNo0ePZsGCBep7r7/+OmPHjkWr1TJq1Ci1LzhFUardf6fVapk0aRIul0vtCuYXv/gFdru9VnhqigEDBuBwONR75MrLy1m0aBFjx45t4V5WiomJUR90aYpBgwah0WjIyspS9+/CCy+s9hBL2MCBAyksLGT9+vVA5WXk5cuXq5/v2bMHo9HI3//+d2677TZGjx5NQcH//liouW2NTX86SIueEEKIqKDRaNDFJIOzDL/jBEowgC4m8eeQd1wNeVpLHErQD1Q+jasLBgi4ilCCAfT29mg0GvUJXa05pnKZoQBKMKB219IamnKPXnPMnz+fadOmsXbtWioqKigsLFRDytNPP83UqVP59ttvKS4uxmq1Vpv3hRde4OKLL+bjjz8mIyODH3/8kdtvv52LL7642dsRHx/P888/z4wZMxg7diw7d+6kd+/e6v1/LTV58mTuvPNOhg8fzo033ljtHri6xMTE8PrrrzNjxgwGDBiAXq9n3759dR73+Ph4/vKXv3DBBRcwYcIEdu/eTYcOHdTP+/fvj9lsZsSIEaSnp7Nr1y6Sk5Pr3bZx48Y1OP3poFFOpj30LJabm0tGRgY5OTmn7Xp6UxQVFZGUVP8NxqJhcvxaRo5fy8jxa75wP3ohj4tSRymxJp3aN54SCtTqJ6+mqv3sabT6Ovvcg6b3o7dlyxY8Hg9jxow5ZZ/l5uayfv36ei/frlu3jvj4eHr37g3AgQMHOHLkCOeeey5Q2VL26aefcs0116jzOBwONmzYgF6vZ9SoUVitVvX3L9z9SWZmJqmpqWzZsoVp06ap8/r9fjZt2kRBQQFDhgyp9ztv3759HD9+XL0fsOZ2huXk5PDDDz+QlpbGsGHD1OO+ePFizj33XBISEoDKh1USExPp1asXANnZ2eTm5jJhwoQ6168oCqtWreLo0aMMGTKEXr161Vrmzp07cTqd1R6KKC0tZdOmTYRCIUaNGkVcXFydy4fKlrg9e/YwZMgQ9u/fT1xcnNpVjdvtZuXKlWqA++677xg0aBBpaWl1bltD058OEvSaSYJe2yDHr2Xk+LWMHL+To4SCEApRVFxEUuJpOn5abaMhL9rJ71/LRNvxi/pLtydOnKCgoKDak0eNyc/Px+VykZWVdVL3GwghhDjzNFodaHWVrXJ6Q2tvjhBRIWpTzqpVq7jiiivo3r07/fv3VztebEhJSQnnn38+nTp1YuTIkWRmZvLNN9+c/o0VQgghhGgFURv0vvrqK66//vo6e+quz+zZs8nPz+fEiRPk5+dz3XXXcfnll5+2gY+FEEIIIVpT1Aa9p556ihkzZqDXN+3qc2FhIR9++CG/+c1vsNvtaDQaHn30UTweD++///5p3lohhBBCiDMvaoNec23dupVgMFitR+uYmBj69+/P5s2bW3HLhBBCCCFOj6h/GKOpiooqO9es+aRMUlIShYWF9c5XVlZGWVmZ+jovL+/0bKAQQgghxCl21gS98CVen89X7X2v10tsbGy9882fP58nn3yy1vslJSV1jot3plQNn6L55Pi1jBy/lpHj1zJy/FpGjl/LRMLxa073LmdN0AsPppyXl1etF+q8vDwGDx5c73wPPPAAt912W7XpR4wYQUJCQqv3o9Pa6492cvxaRo5fy8jxaxk5fi0jx6+2JUuWEAwGmTFjRqPTRtPxa9P36B04cIDc3Fygcmy7+Ph4Pvvss2qf7969m4kTJ9a7jLi4ONLT09Wf09VztRBCiIYpoRBK8DT/hEJnfL/eeustli1bdsbXW9+6Fy5ceEq351Qv73RZv34969ata+3NOOWitkXv+PHjFBYWcvjwYQB2795NfHw8nTp1Uocxue6668jKyuLdd9/FaDTy6KOP8thjj9G+fXs6duzI//3f/zFq1KhqQ74IIYSIPEoohPNQAUG3D7ezDG2+B03NaX7+tyXv66xGYrNS0JzBzvS/+eYbsrKyuPDCC8/YOhtad0ZGBjab7ZSt41QvTzRP1Aa9t99+m//3//4fAH379uWOO+4A4G9/+xtTpkwBoGvXrtUGH37ggQeIj4/n9ddfx+VyMW7cOB577DEZHUMIISKdUvljiLOilJWhNegw2MzVJvGWlqPRajDGWau97ytzo4QUjPHVw4a/3EPI68eYWHmfthJSCPkD/0uADXA4HLz++uvk5OQwatQorrzySvbt28drr73Gn//8Z3W6gwcP8tJLL/H000/XOc+aNWtYs2YN27ZtIzc3lwEDBnDnnXcClWPIfvDBBzidTqZPn66Oj/vGG2/Qvn17iouL2bx5MwMGDODGG2/k22+/5eOPPyYzM5M5c+ZgMFSOHvLxxx/z2WefodVq6dy5M5dccglJSUn1rjsnJ0cdIza8HR9++CEul4uZM2fSs2fPeo9BXWoub8WKFXzxxRdkZWUxevRoli5dyu9+9zt13zp06KCO0du7d29mzZpV7Xt606ZNfPzxxxgMBmbMmEGfPn2qzVtaWsqGDRu466676Ny5MytXruTrr7/GZrNx/fXX06lTp8ryVhTeeOMNfvrpJ0aPHt14oUepqE04Dz74IDt27Kj1Ew55UBkGn3766Wrz3XLLLaxcuZKNGzcyf/584uPjz/CWCyGEOFkarebnHy0aXY0frbZl72trtvnV7+KLL2blypV07NiR//73v/zhD38gKyuLf//732zdulWd7l//+hf5+fn1zpOUlERSUhJpaWkMGjSIzp07A5WjP40ZM4bCwkJsNhtXX301H374IQArV65k5syZfPXVVyQnJ/Pggw9y6aWX8sc//pEOHTrwxhtv8OCDD6rbEF5237592b59O5MnT8btdje47nC3Y19//TXDhw8nJycHs9nMzJkzOXHiRL37U5eqy3v33Xe58sorsVqt7N69m4suuohFixap065cuZJZs2bx6aef0q5dO/74xz8yd+5c9fPFixdz3nnnoSgKpaWljB49mrVr16rz3nTTTXz00Ud06dIFm83GvHnzuPnmm7FarRQUFDBixAiOHDkCwJw5c/jrX/9KYmIir776Km+88UaTyz+qKKJZcnJyFEDJyclp1e0oLCxs1fVHOzl+LSPHr2Xk+DVfKBBUHPuPK66cIuXwup1K6d48pfxYSbWf4p25SslPR2u9X/LTUaV4Z26t90v35inF24+or105RYpj/3ElFAg2uj0mk0kpKipSX+fm5iqKoij33nuvcu+99yqKoijBYFBJT09XvvrqqwbnufXWW5W5c+dWW/7w4cOVf/3rX+rr5cuXK/3791cURVFuuukm5ZprrlE/+9Of/qRkZGQoPp9PURRFWbt2rZKZmVnvtp9//vnKm2++We+6Z8+erb43ePBg5YUXXlA/KysrU5xOZ4P7U1PV5fXu3VtZuHCh+tkDDzygDBw4UH1dc98WL16sDB06VH3dt29f5T//+Y/6eu7cucp5552nznvxxRern+Xn5ysWi0XJzs5W33vkkUeU+++/X8nJyVFMJpP6XR4KhZS+ffsqDz74YJ37UFW01d+ovXQrhBBCtJb77ruPCy64gKuvvpopU6YwaNAgAG699VYmT57M008/zYoVKwDUB/7qm6emYDDI1q1b+eabb9i6dSuhUIiKigp2795N6OeHRYYMGaJOn5aWRr9+/dRLtWlpaWrfsQAej4f33nuPHTt24HQ6OXbsGNnZ2Y3uYyAQYPv27dXu36vaHVlT9yfM7/ezZ88exo8fr743btw4vv7662rTVd239PR0dV/8fj+7d+9m0qRJ6ueTJ0/mhRdeUF9XHRRh+/btaLVa5s+fj6IoKIrCvn370Gq17Nq1i86dO5Oeng6ARqPh3HPPbfSYRCMJekIIIaKC4g8SCIVAUQh6/LUutVY+OQsBt7fa+yF/EKj9ftDjr/a+EgpBsAk36AHz5s3j2LFjfPPNN9x7770MHDiQv//97/Tv359OnTqxdOlS3nvvPa6//nr1/rL65qlJo9Gg0+no1atXte7ARo4ciaJUbl/Ne8trvg5PB3DttddSWlrKtGnT6NKlCz/99BMul6vRfdRqtej1+lr9zzZ2DOqj0+lqLc/v99e53rr2RafTodPpqs3v8/kwGo3qa5PJpP5fr9djsVjo16+f+t6AAQNIS0vDaDTW2i+fz4fZXP2+z7ZAgp4QQoioEAwEUbz/6/4k4K47gJzs+0oohEbX+K3rFRUVbN68mbFjx3LdddfRuXNnZsyYoYacW265hb///e+sW7eOTZs2NTqPzWbD7Xary9dqtYwbNw6TyaQ+aKgoCl9//TU6na7R7avp66+/ZvPmzfTo0YNAIMA///lP9bOa665Kq9UyduxY/vOf/6j3u2dnZxMfH4/Vam3wGNS3vKFDh/LBBx/w0EMPAfDBBx80eT+0Wi3Dhw/nvffe49FHHwUqu24ZNWpUndMPGjSIUCjE8OHDGTp0KABOp5OdO3fSvXt3jh8/zg8//MDgwYMpLy9n2bJlXHPNNU3enmghQU8IIURU0JkNaA16XC4XWoMWfY2nbn2l5Wh0Wgyx1Uct8jsrUIKhWk/dBso9BL0BTIkxQJWnbhvbDp2ORx99lGAwSJcuXVixYgU33HCD+vl1113Hgw8+SJ8+fejbt2+j84wbN445c+aQn5/PkCFDuPPOO3nhhReYPn06S5cuJSMjg82bN3PllVdy3nnnNfu4TZs2jYsvvpjRo0ezadOmaq1oda27queff54LL7yQ9evXk5aWxoEDB/jqq68aPQb1mTdvHhdffDFr1qyhuLi4siyb0fPFs88+y/Tp0/n++++pqKhgz5496iXymuLi4nj11Ve58MILOeecc9Dr9WzdupXnnnuOUaNG8eSTT3LeeedxwQUX8OOPP5KYmNjk7YgmGqVq+65oVG5uLhkZGeTk5KjX9ltDUVFRVPXMHWnk+LWMHL+WkePXfEooRPnREkK+AKX5hcTFx6M1Vm+rCFb4QVMZCKu97/GDAjpL9fdDvgBKMITO8r9Lf1qjHlvHhEb70VMUhTVr1nDgwAH69+9fa4SlIUOGcNNNN3Hvvfc2aZ4NGzawc+dO0tLS1HviPB4Pq1evpqCggOHDh9O9e3eg8unSxMREBg4cCMCePXs4cOCAOp/T6eT999/n1ltvrdz/YJAvvvgCh8PB8OHDOXDgAHFxcWpLWM11r169GpvNpt4r53Q6Wb16NR6PhylTpqj36TV2DMJqLu/IkSOsWbOGzMxMDh48yGuvvabep1dz3woKCvjyyy+5/vrr1eUVFBSwevVq9Ho9EyZMwG631zlv2IkTJ1i3bh2hUIjx48dXuxy+ZcsWdu/ezfDhwykuLkZRlHpbCMOirf5K0GsmCXptgxy/lpHj1zJy/E6OEgqBcpqPn4YWd5acnZ1N3759ycnJISUl5RRt2KnTmr9/hw8fxm63Ex8fTygU4uKLL2bgwIH86U9/apXtORnRVn/l0q0QQoioEA5g4X7vItEzzzzD//t//49bbrklIkNea/P7/YwePZpBgwaxZ88ePB4Pr732WmtvVpsmQU8IIYQ4Rbp3786TTz7JpZde2tqbEpG6devGqlWrWLlyJXFxcYwfPx6r1dr4jOKkSdATQgghThEJeI1LSUnh6quvbu3NOGtEZtu3EEIIIYRoMQl6QgghhBBtlAQ9IYQQQog2SoKeEEIIIUQbJUFPCCGEEC22e/du/vGPf/Duu++e8XUvXLiQPXv2nPH1RgMJekIIIaJCMBgkEAic1p9gMNiibVy2bBlr165t0fRffPFFo8s42fmaoznLy8nJYfTo0ezZs4fy8vJTtg1N9eqrr7J9+/Yzvt5oIN2rCCGEiHjBYJD9+/dTXFxMeXk5qampaDSaBudRFIWKigoURcFsNqPT6RqdXq/X07Nnz0anrc8HH3xAVlYWY8aMOenpKyoqMBqNDcx18vM1R3OWt3r1asaMGcMLL7xwytYvTg0JekIIISKeoijqWKTx8fHYbLYGpw8EAhQVFaEoCklJSRgMhganD4VCFBYWqutqTCAQYOnSpeTk5DBy5EiGDx/Otm3b2LZtGzk5OcybN49u3bpx5ZVXsnr1atasWYNWq6Vz585cfPHFmM3meqe3WCyYTKZmr6fqfADFxcUsX74cl8vFJZdcoo7UEQgE+Oijj6otsy5Vl7d06VJSUlLQaDR8//339O7dm0mTJgGwadMmFi1axJEjR5g3bx6TJk1ixIgRFBUVsXz5cpxOJ+eddx6dO3eutiyA77//niuuuIKlS5dywQUXsGvXLnbu3MmDDz4IwPr169myZQsZGRlMmzZNDeCBQICPP/6Y4uJizjvvvEbL62wml26FEEJEBa1WS3JyMgaDAZ1OV++PoiiUlpai1WpJTU1VW/Pq+9FoNDgcDgKBQLWg1JArr7ySP/zhD+zbt497772Xl19+Ga/Xi9frpaKigtLSUvUSZvh1QUEBL7zwAqNHj8bv99c7/eLFi1mxYkWz11N1vh9++IE+ffqwYMEC1q9fz/nnn09xcTEAN998c61l1qXq8hYtWsTNN9/MI488wk8//cTVV1/NSy+9BKBui9/vp7S0FK/Xy/bt2xkwYAD//e9/2bhxIyNHjuTbb7+ttqyHHnqII0eOEAwGefbZZ5k2bRr//ve/KSgoAODee+/luuuuY9euXfz5z39m+vTpagi/9NJLeeyxx9iyZQvTpk1j//79zfhNOrtIi54QQoioYLFYGr2kejItecXFxfh8PuLj45u8LZ9//jlHjx5VB7ffvn07/fv3Z/jw4WRlZfH73/9enfb888/n/PPPV1+fe+65LFmyhKuuuqrO6U92PVXde++9zJkzh8cffxyAo0ePqpe6V6xYUWuZTZGZmclnn30GwDnnnMPzzz/PnXfeyTnnnMNll13GV199xbx58wCYPHky9913Hw8//DAAU6dO5Te/+Q3r1q0DKkfHWLlyZbXL79OnT+cvf/kLAOvWreO9995j9+7dxMfHoygKw4cP57PPPsNgMPDDDz+wf/9+rFYr2dnZ9OrVq0n7cDaSoCeEECIqNHZPXktDnslkwu12N2lbZsyYwS233MLMmTOZOHEi/fv3b3D6b7/9lh07duB0OgmFQuzevfu0rCe8X99//z3//ve/1fc6duyo/v+iiy5q9jIBxo0bp/6/W7dunDhxot71r127liFDhvDMM88QCoUoKytj69ataovc5MmTa5Vn1Uuwq1evJiUlhX//+98oioKiKBiNRrZs2YJOp2Py5MnqGLldu3alb9++TdqHs5FcuhVCCBH1WhrywqGhqd5++21+85vfsG/fPs4991y1Jaout99+O7Nnz2bnzp2UlJTg9XpxOBynfD1hiqIQCoXQauv+iv/nP//Z7GUC1Y6pRqOp917GUChEMBikvLycwsJCiouLCQQC3HvvvepTzRaLpdZ8Vd/z+/0AFBYWUlRURHFxMePHj2fo0KF17lt9+yqkRU8IIUSUO9Mhz+v1qt2JjB49msmTJ3P55ZfzyCOPYLFY8Pl81aZ/99132bJlC927dwdg4sSJ6md1TX+y6wnT6XQMGTKExYsXq5dOi4qKMBgMmEwmDhw4UOcyTxW9Xs/QoUMZNmwYt9xyi/r+jh070OubFjtGjRrFa6+9xhNPPIHZbAbA4XCoLaL//ve/8fv9GAwGjh49ys6dO0/Z9rc1EvSEEEJErTMd8qCyxWzGjBkMHDiQLl268Mknn6j34A0ePJg//OEPGI1GevXqxZVXXsnIkSO57bbbmDhxImvXrmXv3r0MHTq03ulPdj1VzZ8/n+nTp/Pjjz/SoUMHvv76az7//HOMRiM333wzQ4YMqbXMU+nvf/87l1xyCatWrSIjI4MNGzaQmZnJv/71rybNP3nyZMaPH8+wYcO45JJLKC0t5ZtvvuGdd95h2rRppKenM2HCBCZOnMinn37arPsrzzYS9IQQQkSNYDCo/tQMeVqttsEOj+u6J6/q9E3tLNlsNvP999+zaNEiDhw4wO9//3suueQSAGbNmoXNZmPnzp3VnoZdsGABDoeD3/zmN/h8PkKhUL3TT506Fbvd3uz1hOcDGDNmDNu2beOTTz7B4/Hw0UcfqV2afPnll6xYsaLWMmuqurzp06fTvn179bO0tDTmzJmjvh48eDCxsbHq6+HDh/Pjjz/y0UcfUVBQwK9//Wv1HryaywL45S9/SUZGRrX3/t//+3+sWLGC77//nszMTB577DF1vuXLl7Nw4UKKi4tZuHAha9askQcy6qFRmtJhkFDl5uaSkZFBTk4O6enprbYdRUVF6hNTovnk+LWMHL+WkePXfMFgkAMHDuDxeCgrKyM2NrbZnSFXVFQQCoUwmUz1tvyZzWa6dOly0h0mRwP5/WuZaDt+0qInhBAi4ul0Orp06YKiKKf1i1aj0bTpkCfOPhL0hBBCRIVwANPr9U2+qV+Is508jyyEEEII0UZJ0BNCCCGEaKMk6AkhhBBCtFES9IQQQggh2igJekIIIYQQbZQEPSGEECKCfP3112zdurW1N0O0ERL0hBBCiGZauXIlmzZtOi3LWbBgAV999VWLly0ESNATQgghmu3tt9/m888/j5jlCFEfCXpCCCFEM+zatYtdu3axYcMGXnjhBZYuXQr875Lrzp07ef311zlx4gRLly5l9+7d6rxHjhzh/fffb3A5YTt37uSNN95gw4YNZ27nRJsjQU8IIYRoBofDgcPhoKCggN27d5OTkwNUXnK9+eabmTVrFt9//z0VFRW8/PLLrF+/Xp13165d/PWvf21wOQALFy7k1ltvZeXKlVxwwQW89tprZ3YnRZshY8gIIYSICs899xzPPfccoVAIrbb+doqpU6fyyiuvVHtv0KBBlJaWNrqOd999l1GjRjU4zejRoxk9ejRZWVn8/ve/r/aZxWLhu+++a3D7mrKcxMREli9fjkaj4c033+Sf//wnt9xyS6PLFKImCXpCCCGiQmlpKYcPH250uoKCglrv5eTkUFxc3Oi8Ho/npLYt7IILLmhSyGvM5MmT0Wg0APTu3Zu8vLwWL1OcnSToCSGEiArx8fFkZmY22qKXkpJS672MjAxiY2MbXYfZbG7RNtpstmqvw2EtLBgMNmk5BoOh2jJCoVCLtkucvSToCSGEiAr33Xcf9913H0VFRSQlJTVr3lPdL53JZMLv9zc6XWJiIrm5uerrjRs3ntRyhDhZEvSEEEKIZhowYADz588nISGB7t27M3369Dqnu+yyy7j11lsJhUIUFhaydOlSEhISmr0cIU6WBD0hhBCimW699Vb0ej07d+5UL/eed955pKWlVZvu8ssvx2QysXr1aoYMGcLtt99erd+8piwnNTWVm2666QzslWiLNIqiKK29EdEkNzeXjIwMcnJySE9Pb7XtOJlLF+J/5Pi1jBy/lpHj1zJy/FpGjl/LRNvxk370hBBCCCHaKAl6QgghhBBtlAQ9IYQQQog2SoKeEEIIIUQbJUFPCCGEEKKNkqAnhBBCCNFGSdATQgghhGijJOgJIYQQQrRREvSEEEIIIdooCXpCCCGEEG2UBD0hhBBCiDZKgp4QQgghRBslQU8IIYQQoo2SoCeEEEII0UbpW3sDWmLnzp289957uFwuxo0bx+WXX97g9M888wz79++v9t7w4cO59dZbT+dmCiGEEEK0iqht0fv8888ZPHgwx44dIzY2ljvuuINf/vKXDc6zZMkScnNzGTRokPqTmZl5hrZYCCGEEOLMitoWvV/96lf88pe/5IUXXgBg4sSJTJw4kV/+8pcMGzas3vmGDRvGHXfccaY2UwghhBCi1URli97OnTvJzs7muuuuU98799xzSU9P5+OPP25w3u+++457772XefPmsXXr1tO8pUIIIYQQrScqg96+ffsA6Ny5c7X3s7Kyat2DV5XBYKBdu3ZkZGSwe/duRowYwfz58xtcV1lZGbm5uepPXl5ey3dACCGEEOIMiMpLtxUVFQDExsZWez8uLg63213vfG+99RYdO3ZUX48ePZpf/epXXHHFFWRlZdU5z/z583nyySdrvV9SUoLFYjmJrT81ysrKWm3dbYEcv5aR49cycvxaRo5fy8jxa5lIOH5JSUlNnjYqg15cXBwApaWlxMTEqO+XlJTQrVu3euerGvIAfvGLX3DHHXewcePGeoPeAw88wG233aa+zsvLY8SIESQkJDTrQJ8Orb3+aCfHr2Xk+LWMHL+WkePXMnL8Wiaajl9UBr2+ffsCsGvXLtLT0wEIBoPs3buXSy+9tMnL8Xq96rz1iYuLU4OlEEIIIUQ0icp79LKyshg5ciQvvfQSiqIAsHDhQhwOBzNmzFCn++tf/8p//vMfAI4ePVrr4Yu//OUvmM1mzj333DO16UIIIYQQZ0xUtugB/Otf/2LKlCmMHDmSDh068OWXX/L0009Xu3T74YcfkpWVxa233opOp+POO+9Ep9PRuXNntm/fztGjR3nrrbdo3759K+6JEEIIIcTpEbVBr3///uzdu5fly5fjcrl4+umn6d69e7VpHnnkEfWBjfbt27N27Vo2bNjA3r17ufHGGxk5cmStBzqEEEIIIdqKqA16UHn/XNVLtTXVNSTayJEjGTly5OncLCGEEEKIiBCV9+gJIYQQQojGSdATQgghhGijJOgJIYQQQrRREvSEEEIIIdooCXpCnKXCfVAKIYRou6L6qVshopWiKGg0mlZb/9KlS9m0aRPDhg1j+vTpp3z5rb1/QgghKkmLnhBn2NKlS3nqqadYunRpq6xfURQ2bdoEwKZNmxpt2Wtuy1/V/as5r7QiCiHEmSVBT4gzqGbICoVCZ3wbNBoNw4YNA2DYsGENtrw1N5TW3L+q87Z2wBVCiLORBD0hzqCqISslJYW5c+e2SvCZPn06jz32WIOXbZvb8gfV9y8sHGibuywhhBAtJ/foCXESWnIP2vTp07nwwguZO3cuUBl8pk2bdsbuaQuFQiiKQjAYJBQMooQUQqEQoWCQUEhBCYXU9/r16cuOXTvp26sPJ3KOEQpWzut0OakocaEoIYKhcGir/Ldvt1706dqT9Zu+Z2/2Pnp178GxA0fo3aMXP+3dTZ+evcnPzavcX60GrUaLRqtBq9Wi1WrRaLVodFp0Wi1ava7yPU3l5zqd7owcIyGEaCsk6AnRTKfiQYZly5ZVe/3ZZ5/VuayGAmUoFCIYDBIMBAkGAgT9gcr/B4ME/QH8Pj9+n7fyX6+foN+H3x/EW1GBx+9DAxiMRrSKBkWBkC+AVqcBXWVDvwYoLSgGYOfuXZw4lke/rr0w6A34QwGKAlo0WtAYdD9PX7mdQa+fUqcDm8bEuP4jsJgt5Ow9REdrMmn9RqEzG9n/425AQaPREgoEcLqcBFAwm0xYLVY0GiBUuY86owGtTodWp0Gr1WMw6dHqdegMekwmE2arGYPBgFavR28I/xjQarXo9Xp5KEQIcVaToCfOCqfqKdCalzOnTZuGoihotU2/C6LqMsKqtuoFg5VhbdmyZWzdupV+ffoydvQ5+H1+fB4PFeUevG43fl8Ab0UFFRVuCIHZaEKj0VJQmI/b4+ZEWRHlQR/6IOD14fN68Ru0GG1WHEXFTBoyjKysDDRaDRpCbN/1E2/+dzHBYAB/IEggEGDS1P+1NBY4inn48YdITU7iyUfux6IJgE6HRmfkuX+9wZ79B0EDSkhBr9cTGxOD1WrBbDJhNJkwm8ycM2oEAzP7AxrQaPD5/KzduA2D3kBKcgpJ7eKxmG1oFIVQSIMSCBEMhVCCIYLBEIrXT2mJg9LSUvR6HbHWGABCSgg0GnRaLWg0oNOgNxowmIwYLGaMZiNWmxWL1VL5vtGIwWREp9Oh18tpUAjRdskZTrR5zW2BaygUhu9BCwe1v/71r3g8HlJSUrjzzjsbXZaiVF4SHTx4MD/88AOJCYkUlxTTq3tPdm3ehqu0rLI1zu9n69atAOzYuYPtO3dQUebi+JEcXOXlOMudlDocdMlIZ/J5kzjmcALQpV0C9/3mUUrKnDz22GNoNBr8WoVNO39k2LBhGH/eB3tSIkdzfqBzqo8fDpdwoMCNwedg2Ypvqm1/vyHDSE1NBSA/P5+NW7eTkZaK4nUQDJQRKneA3sDWH7fy/bY91eadNm0aPfoPZuPGjXz22SIAdM4cupsK0Mcko9Eb2bl7D7MfmFdtPq1WQ4zNSmz4J8ZGXFwcf33it+isBvYc3M+xgmLSUpLJsLXnRHExh48eo3tWZ4b0GcSmbT+QnXuIzmmd6JXRheNHjxP0+7DbYtEZjJUZ8+eWS4Nej8lqwRJrw2y1YLFaMJgrQ6DRaJQWQSFE1JOgJ9q0ulrgGnvKtLFQOG3aNHWZHo8HgIKCAkKhkNqyFwgEWLp0qdoiN3LICFatWcW+g9nYbbGEXF7c+cUc23eAouJieqd2YP+PW4g1GrCZjLy5+L/klJUzfPhwNJrKi6KWuBg69+vNxo0bWb56VeX+Bd30dgxXt+3AiRIsFhPFjjI2btzI8OHD1X81Gg2KoqAoChs3buTCCSMxtuvBgU0rAfAb7eo0UBkIU1NT1eMV/r/WYMKY2hWr2VS5DShgfK/aMdJoNOo6hw8fzrJly1AUhdgO3TF36EMoFKCszEmJu/ZTx6GQQpmznDJnebXlVeRsx+l0c6zICMCx/ALu+NXdaqDde/AAf/zjk1x13Q1oNBoOHDvM/gM76NmtO0MGD0GvMwA6QiENQV8QJRTC5/LgL6vA66zA7/dTUlaKRgsxlhhMFhOWOBuWmBhsMVZMFjMmixmj2VR5qbgZrbhCCNFaJOiJNivcmhZugQt3JVLfE59NDYUajYahQ4eyefNm9b3EhASOHsjB7XRRUlREeVkZW7dvBWD7zh38+sEHueGWm9FoNJS6yti0aVO1p20nDUxjUN8eHHBqyT7gpH3nTHJ+3KVuV9V1h4MTgF9nokfXzuzNPghAz+7dmH7xZZSVlWGxWikqKqJXr174fD6MRiNms5mUlBR69epFjx49MCYm0qNHD/bu3Uu3bt1Yt24dAE6nE51Ox9GjR8nOzgYgISGB7OxsFEWhoqICnc0GVN5H9+zfnqOiooK4uDiMRiNer5ft27dz7NgxUlJSeOedd/B6vXTt2hVzx444HA6CRhdZQ9O4804XZQ4HZWVlOJ1lP//rpMzpxFnmpNztJt4eRyCxO7FJQbqaS8g+WsDxo0fU0BoOtLv2Haj2+rPPPuO6S85jcGYcfk3lZd1N+49zIK8Et7OUeKuZrKzOdOqUicZgIM4Yhz02Hl1IQ9AXIOj2UerIpygYJKRUPqiiNegxx1oxWk3Y4+1YY2IqQ+DPAVAIISKJBD0RlRq7565my1w4tIXf79+/P1dccUWt5aWkpFBQUAD87wGJYDCI3+fDW+HlRN5x9u7ew56fdlHhdGGOsXEo+wCvfP45Xy5ewm1XXYoS8JFkM9EpzsxhRwUbN27kQM4R8vPz1VaxYcOGoSgKn332GQCadt1IHTSR5R98WvnaFFOtFU6n06l97tntdl588UXy8/Pp2bMno0aNYtQ54yrn02gYNeYcdb88Hg8lJSXo9Xri4+NrBZH169ezd+9edTmBQICioiJsNhvJyclkZWWpQa+0tBSgWktWKBSiuLgYrVbLiRMnWLt2rbqsdu3aqdtUlcPhYNOmTeTm5tKzZ08efvjhBsu6rKyMvLw8bElJJCYm0rGflnMUhffee5err7qSoqIilnz4AYVFRZhMJj777DO1BRGgY/tU0FRusxL0c/B4KRqNBmtsPL9/6il1OpvFTGbHdnTJzKBzly50zurClMmTiTXFgkZPKKQlUO4h4PNTUlhCubsck9GExWqBkILeaMAaH0tMXCwx9ljMNgsmc+WDInL5VwjRWiToiahTM8TVdR9cXS1zVd/fvn07l19+ebXwN3ToUDXkhectOHqMg3v28d5HH3L0+HFc5f+7nDht2jSGDx9OQXERxwsLyD1+hOOeco47/XS1WBg9ciB/nDmH7IOHsFmtasgLb+/w4cM599xz6dSpE/369UNvs9GzZ0/27Km81y0hIYHS0lJ69uzJ3r171fWOGzdObQ3cs2cPI0eOrDNIVA15+/fvrxbowscpvK49e/YwdOhQiouL2blzJzk5Oeq04W3q2bNntfWEQ57P58Nut6uBsOoyq64PKkOey+UiNze30e0HcLvduFwukn4OeeGQqdFouPbaX3Dttb+oNr3P5+PQoUPk5+dTXl7O8ePHGT5sKJbMbigBP0oogD0mj1JnORs3bqzWWlpe4WHX/sPs2n8Yvv4OjUbD5gEZaE0W0GrR6oys+2EXh47mk9Epi/79+pOS3J6gXyHg9hGo8OIrLedEYSl5CpVdxuh1GKwm7Inx2BMTMFstmC3mOvdVCCFOBwl6IqrUDHHhf5vyoEXVy7h9+/Zl3959fPHFFxQXF4Omcjk7tm+n/4AB6jyH847hLT7Onp9DTNVlVb0HzWw2079/f447/QBkHytizORpvP7mAuLi4oiNjWXDhg3s2bNHDTUajYYLLriAuLg4dbkjR45Ug1JpaSk33HADWq2WQCDAgQMHyMrKIikpqd7wFVY15CUkJKhBsWqw2rBhgzp99+7dKS4uJhQKkZOTU23aUaNG1QpjVUNefHw8VqtV3aYePXpUC3vheR0OB+Xl5cTExDS6/VAZ8kpLSzEajdVCXn38fj/FxcXY7Xa6dOlS62lajaEyYE2/5DKOHTtGt27dGDhwIIcOHuT48TyOHDnCkSM5+AMBADqkJmOy2CAUgpAfv9fNgrffYdXmnWr5pael0qNLJn169aRv3wH06dOXdh06EPRrCFb4cBSXkJ+TR55OS6wtFq1Jj8lmRmcz4XG5iYmLxWy1YDQapdVPCHFaSNATUaVqWBs6dGi1sHfhhRdWPrhQ4748r9eL3+PjlZdf4ocftvDTrp94+q9/xV1Rod7IHw6QS5cupV9qDGndenDcFaBbejvokIjh7XfJSO9IRkYGmZlZdMrMxGaz4Xa76dixY60v6fj4eBYsWFCtRWvUqFGMGDGC1atXc+jQIbp06VIt5IX3r2oI0mq1OJ1OunbtSp8+fUhMTKy8PFtH+AqrGvISExPR6XS1glXV1jyALl26oCgKycnJdYawmiGvrKwMjUajhrzw/oW3SaPRVFtGOOTZbDbsdnuD2w8nF/KKiorQaDQkJSU12GWKwWAgPT0dq9VK165dSUhIwGyuDIGBQIDc3FwOHjyIx+PBkjkEJegj5PdQXFDAgWNF6nIURSHn2Alyjp3g6+++V99Pio/joTmzOG/SRIImHe06JxNnTyLkVwh5AvgrvDhKCik/XIjGrENvMWG1x2JPsBOXGI/FZsVkMknwE0KcEhpFxiJqltzcXDIyMsjJySE9Pb3VtqOoqIikpKRWW39rqvpUaPiya9V767RaLaFQiK4ZGRwvKKDc48Gq17Np61Z69+2r3qQP/7v8Gn7viiuuYED//tjj7TgcZfTs2ZPhwyufaq05KsO6devYu3cvGRkZmEwm9u/fT8+ePRkxYgQLFixQp7vxxhvVcFVSUoLH4yEmJoa4uLh67zUMv+90OnE6nZjNZhISEhr98q8r5NVcZtj69evZs2cPnTp1onfv3iQlJan38NW3XeGWvPLyclJSUtSQV1851RXyGnM6Qx5AMBikuLiYQCBQLeQ1tB/hctu/fz+HDx9mz+7d7Nmzmz179+J0umrNM+/+mxjUvSMWWwxJ7dLJzj3BP974gEED+zNwwGC6dutFQkwiQV9l59ZOVxlFJSXoDQbs9jjikhKwJ8arwU9a/Ko7m89/p4Icv5aJtuMnLXoiqlR9mCIuLo5tW39k548/0mfAAPWLMBgMotFoyM45Aj+P1uAOBOjdt696qXXHju306tmTrMxMYmJiuPnmm7nnnntYv349AA5HGVD/PWSBQEC9HBq+ny1Mq9XW2YIWDguxsbHExsaqQavmfWzAKQ954WVWNWzYMLKysgCqhby6poXql2tjYmIaDHnhZbSlkBcbG8v48eNrfZ6Xl8fOnTvZsWMHO7ZvY/uOHXQdPI64JBt2ix4CATb/uJOvVq/nq9WVv186rZbuXToxqF9fBg0cRLeuPWnXriN2SyzBch+eAieuglJyNKA3GbAlxJGYnECMPQ6L1SpP9wohmkyCnjjtWjoqRSgUwufzUeFyqZdqt23bxlNVnpic07692rFvOFi1T4rH4w/icLqIt9sxmkzk5+fTrVs3PvtsGbafuwfx+/18/PHHuFwujEaj+nCBw+Go8x6yYDBISUmJ2rJb1z1pVS9N1hXyaj4IUTNMnuqQV1P46VqoHfLqK4Oq9+Q15UJAWwt5sbGxtabRaDR06NCBDh06MGXKlFrlBkDAQ3bBh9W3JRRi9/5D7N5/iHeXVD5Y0yE1iUunTeG+O+9Co7URVHSEfAoBrx93XiklB46BSQcmHUmpKaSmtcMWF4vZbJbWPiFEvSToiRZpbjcnjc3ncrnYuHEja9eu5ft1axk9ahTeYGW3ImkxVuwEKVW0bNy4UZ2nase+4Q6B9Xo9506ZitlsrtaRcXi95T8/Pev3+ykoKMDlqrz85vP56N69O/v27aNHjx61WtqqhoUxY8ao91LVvCctvF31hYWa9+K1RshTFOWkQp7ValWPX33OhpBXU73lZrAw909/4ZdzfsUPP/zAD1s2s3nzZnbv2Uvg5wc/AI7lF1GUexDv8V3ozDY0OiNao5Uvv/ueIYOGkdyuHUWFxQQqPBR7gpQcK0Cr12FLiCMpNYXY+DisNqsM6SaEqEbOCOKknWw3J+H5hgwZQocOHVi3bh0b1q1l46ZN7NufTSgUUu+d8wSC6jLzXG5mjBvN1BtuZcr55/PYY49hMBgYMmQIJSUl7Nu3r9rlW5OpcuSGqqGh6vaFw4JOp1Nb8Ox2O/v27QNg7969jBo1qtoy6wsLdT1c0FhYqGueaAh5jZGQV7vcNBoNnTp1olOnTlx66aUUFxdTVlbG3r172b17N5s3fs/mLT8wesJ56Mw2QEHxezh88AAPP/YUUNniN3hgf84dN4HRI8diN8US8PgpP1KI4/AJtGYD5vgYElOTSUhOxBpjw2g0NrrtQoi2TYKeOClN6eak5hOyXq8Xv9+vTr9582YuvfRSNSBWfciiamfBYT179iSmS29Wrf5OfdghEAjw/fff07VrV8477zz27dvH4cOHG+y2Izyf0+lUw8Jll12mtvxVvXeuKSEvrDkhr655JOS1zZBXk8fjwel0YrPZmDx5Mueffz5QWT6hUKiyXAIeQl43m1dtU+c7ll/EseXfsHT5NwD07dGVcWPGMH7sePp26wsBLd7iCnJP7GO334MlzkZaRjopaanYYmPUP3yEEGcXCXripISfWgVqdXMyZswY/H4/O3bsYNu2bWzZvIkBPbuxacUXKO4yOsTZOOpwqR3WhlvvtmzZTM6hw/Tq3VtddtUHFcJfoFUfdgjLzs6mZ8+ejB8/Xr2UWh+/34/D4cBkMlULC+GQUbOlLVLDgoS86Ax5JSUl6HS6WuVWbX8MFnQGC6MmTeUuRwUbv9/Atm3b8Xi96iQ792azc282r7y+gB6d01n8nxcwtEvGW+LHUGFCXx6i6MBRCnPzMFrMxCUnkJSaTKw9TkKfOGuEQiEURSEUCqk/4Vt8qv6/6ntQef6ob9pwvYfK81IoFEKv16vnsJiYGFJSUlptn2uSoCcaFW5VCwQC+P1+fD6fGuygsg+2goICjhw5QkVFBZdecjHbtm3DUeZUpxmSlswFI8ehNxoYltmJlGIHb73xBjMuu5T+gwYDMHToMJ56aq7aqW5cXFy9X5xVOxYG6NSpE0lJSacsLEjIk5DXmJaUm81ma1K5xcTEMHPmTO69915CoRA//PADa9esYfWqb/lx+w61bvbv0QX/iZ8ocXnw6ywktu/EZ6t/YGCfwXTJ7EbQH6L0SD5FOcfRm43EpyaR3C5FWvpE1AiFQmr4qvr/8L+BQIBgMEggECAQCODz+XC73SiKUq3bqKr/1lx+SUkJoVCIhISEakMX1vy3oqJCnd7v92O329XzpNfrRaPRSNATkUtRFDXM+f1+vv76a3bu3EnXrl3p1q0bXq8Xq9VK586dKzuVrajgF9dcTafMLIYOG8b27dtZ/d2aWss9WFJO4sABaC02dBYzCRoNX503Bfhff26dO3dm8+bN6hioNR+EqEqj0dC1a1eys7Pp1KkTEyZMaFZYsNvtUR8WJOTVLRrKzePxNDh9feU2atQoRo0axQMPPkhxcTGrV6/mmxUruHDqZMrtaQQDecTpfBQfO8Bjf34agIwO7Zk07hwmjp/EoN6DCZb7Kdybw6Hd+zFaTaRlpNOuY3tsMTHSbYs4oxRFIRgM1vnj8/nUwFZRUYHH46kW2sKqDiup1WrRaDTqVRuDwaCeM6p+XlPVMb6bcp5UFAWv14teryc5OblJ58nWJB0mN1Nb6jC5agudx+OhoqKCiooKNm7cyMGDB8nMzOTw4cPq9AkJCSQnJbJvfzbdMtMZlJHMA0/MY/maDdVGmHjqqadISkigX9++jBg1itFjxtC3b986K0+4gpaWllJeXs5XX32lfhbuaLgu4bAQrsjNDQter1ftXqUu0RAWWjPkhYOdhLyTK7fwMatLS8rNHheLRaew4M3XeWLe32pNa4+NYdK4cxg36hx6de+PzWrFZDAR1CqYbFaS01JISEmq1uLY0u6RTodo67A20pyJ46coitrKVrWlLfxdE74nteol0qq0Wi1arRa3243b7cZisZCUlIROp2vw9/FMnCfz8vJqjQwU5na7sVqtah+lkUBa9M4S4b+MvF4v5eXlOBwOtYJB5V9Fer0enU7HwYMHATh8+DA+nw+DwcCmTZv47LPPePzxxwHYfziXrj4fg7r24qu137N/7z669eiO0WBkyZIlJCUlkZCQoFaCuv6eqNqS161bt9M6BmrNsOCtcq9TTdESFhoiLXlnd7lNu+IX6GOSWP7lF6xdtx6/v3IMZofTxeLPvmDxZ18Qa7Ny2YUX8MjdDxD06fAUODmSX0yuxUxsSgIp7ZPZuGUzP/zwQ51jSUdiABRnTjjIVf3x+/14vV68Xq/aIhe+rw2grKwMj8dDXFycelXFaDTWG96cTieBQIC4uLiIqm+BQKDRkYEiiQS9NigUCqkVze12U15erv4V5XA40Ol06i9puIKVlZWxbt061q9fh9frpWvXbmg0GgwGQ7WOiQ0+H36jkS4dOpI0YgTXDR/N9Q/cT1xcnNqRcM0vnbpGgKjaYfDBgwcZMGBAq4+BChIWmhryXC4XwWBQQl4DWrPcUlJSuP7667n++utxuVysWrWK5V98ztcrVuL8uc9IZ7mb0qI8NBUH0estxCTaMQes+L0KFTmF7M/J54dtPwCVD1lNmTJF7a6lvv4xRdtStSUu3Fjg8XjweDyUlZXh9XoxGAzq76pGo0Gn06HT6dDr9ZhMJrWuh+tCWlpa1Ne3powMFEkk6LUBVf+KcrlclJeX4/f71b+4wyfnQCCA3W6vVgkUJUSgwsWE8eNwlDnVQFd1DFitRkOfnr0YNWokg8ecQ8/evdTKG/6KD9+YWvNLp74RIDQajXqfX+fOnYmPj1f3R0Le/0RiyHM4HHg8HuLj4yXk1SOSyi0mJoZp06ZxzjnncN8DpezYsYNvVq5g+fKvmDbtQtAbUXwuqCjFUVLClff/mfPGj2fyuMmkxieTX1pIx+T27Ni0jZSOqSQkJdbZP6aITqFQCL/fr7bI+f1+tWHA5/MRDAarXZEJNwyEf1cbugUG2mZ9i7Y73iToRSG/34/b7Vb7gquoqCAYDAKVXTQYDAYsFotaOcJ/fR05coTt27dTmH+cX99zJwFXCQFXKYHyCq66/HI6ZGaxceNGvvj8cw7u2098TCyXXXwJ8/48j9i4+itnQ1869Y0A4XA46NatGwMGDKgW8uojYSFyQl55eTlms1lCXj0iudzi4+OZPn0606dPx+/3V7bAKEFCHhcBZwFffbWe/KISFi7+iIWLP6Jju1SmT7mAQR26gstD7q4DHDMdoVvnruw/mM2QIUMk5EWJqkEufAm1vLycsrIyfD4fZrNZfVgh3CpnMpnQ6XRqvQ3XN71ef1bXt8ZGBqo64k0kkKAXBcJ/YbndbkpKSigqKsLn85GQkIDRaMRisahfiFUvk3bu3JkVK1bw7Tcr2bRpM0XFxQDodTpmnjMMq8UGGiNaUwIdMzuDBkaMGMH9995HTFzsKfvSqXk5Vu7tisyTV2OqlltThtmScou8cqta3/63Lj06gwldbBJuXRwWi4WKigoAjp7I59W3FvDqWwvo17M7l11wEedPnEr31Aw6JbXHlmTnaM5REpMTsVgsjW6LOP3CYS78oJ3b7cbj8aitc4B6ybWsrAy9Xk9qamqjo6hIfWtafSsrKyMxMbHR6c4keeq2mc7EU7eKouB2u9V76sKtd263G6/XS2xsLBaLhZiYmGrz+Xw+3nnnHXUZ4XtoNm7cqHZwHL4ka9XpuGjS+Rji7eiNBjZs3siePXvo1KkTvXv3bvM38Ie3QU5eJ1duDT01ChLyGiu38vJyTCZTRD4wk5eXx7p161jx1XJWr1lDIBCsNo3RYOCuWTdx04yZlDkqKPG7iImPI7NrFslp7YiJiWn0d7al5Knb6r0m+Hw+9bsCqPaQXfh+ufAPVP5eVFRUnDX1DU7tebK+85/D4aCwsJDMzEy6devW4PLPJGnRi0Dl5eVkZ2cDqJdhw5UyPj6ehIQEtULDz0+0BrwEXCVs2bKZwYOHsHHjRnUYseHDh7Pq228ZOXio+l5FKISlQ6pamYYNG6Y+Dh5JXzoSFiIv5DVGyq3xcgsGgxFbbna7neuuu46ZM2dSVFTE0qVLWfLhB/zwY+VwbD6/n45JOkLebLz+IFZNPLEBHfl7czhxOI+41ATap6dhj4+XfvlOgWAwWKuFzu12q0+1er1eysrKMJvNJCcnq5db61PfyEANrT/a61skXvE4kyJrawSA+jh6+N61qpXAZDKxYsUKli9fTtDn4U+/fxhv4RFCFV6CPijJzeGpTz5Fp9WSlJBIl25dSUpIZMvmLRhMxjrHcQ1XApCQVx85eUm51edkyq1qZ66RXG5JSUnceOON3HjjjWRnZ/P++++xcsUKJk66gJJjB1C8ZSS3C7Fq03reX/Ytl0+5mBGDR1Kam4811U679A4kJspl3aaq2ll9RUWFetk13E1JuNPfcANAuDPhxMREqW/1ONPnyUj840Yu3TbTmbh063Q6yc7OJj4+nrKyMnbu3MnmzZv5fsN6Nny/EZ/PB4BBr+frV/+BSW9GZ7VhiE1i00+7KHQ5GT5yBBlZmXX2T1S1/6uzNSzk5eWh0+nk5NWAhsqtrksXEvKaXm5er5e0tLSorG8+n4/i4mIIerGb9WjKC7n1/t+zdssOAFKSErnsvAu5dOolHK9wkJt/jB7de3DJpZdgtVrVY9qSfvii/dKtoijqJddwK53D4aCsrAxAPU7hrkvCIztU1ZL6ZrFYiIuLa3D6tlTfTvV5sur5r2Z9kw6TRZP4/X5Wr17NmjVrWL1qFcfy8uqcTqvVcqgsxLDRgzDG2fAF/fSOtTRaCc72kFdcXEwwGCQ5OVlOXvWI1HJrK186drs9asutuLi4stzadUSv1+O2xHOi9H+3khQUFfOv997mP4sW8uijlSPm7N23l61rN9K+U0dSO7Rn1apVtfrha6sdMIe7Lwn3QVdeXk5FRQV+v1+9ly58/3W4+6vGtLS+NdRhPLS9+hYp58nWIkEvAvk8bh566EF8Pn+tzzp1TGf86DGMHjeOcyeeiyXGhkajwePxUFrmiOpKcCbDQmxsrJy86hHJ5dZWvnTCrfL1iaZys1ptLP/qKzZs2MDbby3giy++xB8IEAop6r3CP+3ciTP3BBecO5X8dseq9cN34YUXsmzZsjbTAXP4vjmPx1OrX1P4XxdYMTExaLVaHA4HAImJiWes3GRkoOracsgDCXoRyRQK0q9rF7b8tAeT0cjwQUMYP3Yck6acT5fuXdDotJSXl2P9uem4LVSCMx0Wwt0M1EdOXpFZbm3lS6ehoBeN5abRaBg1ahSjRo2isLCQ999/n4Vvv8Vnn33GsmXLUBQFo0HPhaOHYaqoXoZz585V/x8OflqtNmpa+KqO31pcXExpaSkAJpNJHeIr3EddTa1dbjW11frWkDMxMlBrk6AXgTSmGK77xY3MTkhk7PhzsMTG1FsZIrESRMPJq6EOL8/Wk1c0lFtDpNwio9wSEhK46qqruPTSS9m9ezcL317Aym9WceGEkSTHlqIJachKjOdQcWmteVNSUpg7dy4pKSkUFBREZAtfuMWuoqKCsrIy3G43fr+f0tJSQqEQKSkpjd7/BpFXblLfTs/IQJFAgl4E0pmNTL54WqMjRkRqJZCTV3SevJpTbh6PB6/XK+XWgEgstzNZ35KSkpg8eTKTJ0/m0KFDaAlhS4rDV3iIod11dCmEp9//miFDhuBxljN22Bg2763swqWgoACIjCHWgsGg2mLndDopLy8nEAig0WjUFjuPx4PJZGoT5Sb1rX7NHRkoUkjQi1LhsW0jsRLIyat+kXzyak65uVwubDablFs9IrXcWqu+VX0CUReThFLhZMHSF/jkk0/49NNPURSF/yQkcMusW7DaY4mLjaPMWcbQoUPRaDRn9DJuMBhU77ErKyvD5XKpQ1oZDAZMJpPaWX1bL7e6SH2LvH7yGhNdWyuAykrgdDqxWq0RWQnk5FW3SD95Nafcwvsg5VZbJJdbJNQ3jUYDlli6DRjO0CGb2bxlCwCFJSX89W/PYjWbueT8C7lqxtV07ZTFhx9+yPbt20/bZdxQKKQGu+LiYkpKSqqNKFE12NWc72wqN5D6VnVkoIY0dg/4mSZBL8qEK4FOp4vYStAYOXlFf7mZTCYptzpEerlFUn3r27cv/+/11zl8+DD/evWfLP1sGcFgELfHw7sfL2bRpx8x5ZwJjJo8Aai8jDt16tQWD69WNdi5XC6cTmdlrwWlpSiKQvv27av191ffMs7WcpP61jC32x1xLX4N/+aIiFK1EsTFxUVtJZCTl5RbTVJuZ2+59evXj+f//g+++eYbbp41C+vPo2gEQyE+X70Sv9MJQPvkVLL37sfpdDarxURRFDXI5eTksGfPHvbu3cvhw4fVJ2R9Ph9Wq5VOnTphs9kk5NUg9a3p5eZwOCLuafHIip2iXjUrgcfjaXD6SK0EcvKKzpOXlJuUW02nutzS09N57PHHufe++3j77bd57T//wWjQMbpzLEbM2O1xFO7cy0effIy7wk1SUhJ33XVXnSP/hDsndrlcuFwuPB4PiqKg1WoxmUzExcWh0WjO2uEfy8rKZGSgRrSk3CJtGDQJelGgrVWCaPjSqcvZfvKScqvbyZRbWVkZGo1Gyq0OdrudO++8k6uuuordu3fTrmsnbKFy/CVFeEqOU+4uR6PRUFRUxL49e0nr2EF98tXpdFJUVEQoFFLHhTUajcTGxtY6BmdzfZORgRrWknJr7Ji2Bgl6Ea6tVYJI+dJxOp0oiiInrwZEYrm1lS+dQCBASkqKlFs9nE4nfr+fvn37kpCQAIApycm+tavIz88nNTWV/Px8rr/mWmbPvoNBw4fg+Dk8t2vXDovF0uB+n+31TUYGql9Ly62xq22tQYJeBGuLlSBSvnR8Ph+JiYly8qpHpJZbW/nSiYmJkXKrR33lprHEEYhJ4asvv2T/gQPqkGKb7rqDYQMHcs3111PudtOzZ09GjhxZ7/KlvsnIQPU53eXWWiJzqwR+v/+sqwRn8kvHarXKyasekVxubeVLpymXd6TcapfbyJEj+fzLL3nmmWfo1qWL+v7mbdtw/dzlxZ49e3jzzTdZv359reVLfTs761sklFtritwtO8u5XK6zqhKc6ZNXU/ZZTl6RV27ypVO3s6nctFotF1xwAUs+/pjHH3+cThnpKIrCxo0bURRFbenbs2eP+n+QcmvtcgtrC/Ut/BR3NIQ8kKAXsbRabdRWAjl5SbnVRcpNyq0+zS03r9dLWVkZ06dP58vlX/HMM8+wffs2nnrqKTZu3AhAVnIy/pLKvvGk3CKj3NpKfXO5XFET8kDu0YtY0k9e3eTkFRnlFggEcDqdUm4NiMRyayv1rebIQDNmzOCSSy7hgw8+YP/unVw2vA++QiclP/6IISmJH44do2NWJl27dpVyq4PUt9MzMlCkkKAXoRqrNMFgMGIrgZy86hfJJ6/mlJvD4cBkMkm51SNSy62t1Le6RgYyGAxce+21P29LiICjCOfBfeTs/oFl67fSb8AAvlu9mutnzsTyc6fMNUm5SX2rT3NHBook0bOlQhUIBHA4HBFbCeTkVbdIP3lJudVNyi3yyq2xKx4ajRZNTAK+9ll8tf8I/QYMQKPRoDcYmDhhAh/897+EQqFq80i5SX2rT3PLrebvVmuToBdl2kIlkJNX9Jeb3W6XcqtDpJfb2Vbf0Gj5xaxb1D+MN27cyImCAh56+GEuuWi6+mSulFtklVu017fmDNF3JkjQiyJVK4Hdbo/aSiAnr+gvt8b2WcotMsvtbKxvHTp04N5772XChAkoVb6Ad/60m1/84hf88vbb+PHHH6XcGiH1rWnlVlxc3Ohyz7STCnrhIXxak8/n45577iExMRGTycTkyZPZu3fvKZ8nUtSsBI1V/EitBHLyis6Tl5SblFtN0VZunTt35qVXXmHRokUM6NdXfX/5V19z9dVX8cPmzfXeu1dVWyg3p9MZNeVWl0ivb42V2Zl2UkHP5/PRq1cvFi5ceKq3p8keeughFi9ezIoVK8jNzSU5OZnzzz+fioqKUzpPJGhrlUC+dOom5Xb2lJvL5ZJya6VyGzZsGIs/+pj5zzxLakoyADFmC6agjzfffJN169bVu462Ut98Pl/UlVtYpJ8nExMTGz1GZ9pJBT2TycQDDzzAL3/5SyZNmsRPP/10qrerQU6nk1dffZXHHnuMQYMGkZKSwosvvkhubi4ffPDBKZsnErS1ShApXzput1tOXlFYbm3lS8fj8Ui5NeB0lxvAuAnjefe997n7rru4f/YdHHW4ANi7dy9rv1vNiRMnqk3fluqbjAxUv9Ndbq3hpIKeRqPhoYce4qeffsJutzNw4EB+85vfUP7zEDSn25YtW/B6vUyYMEF9Lzk5mf79+7N27dpTNk9rOxsrwZn60nG73XLyakCklltb+dIxm81SbvU4k+XWrl07HnjoIW646066ZWaq0+zLPsCECeN54P77yM/Pb3P1TUYGqltbDHnQwocxMjIyWLx4MR9++CHvvfcevXv35sMPPzxV21av8F9aKSkp1d5PTU2t9VdYS+YBKCsrIzc3V/3Jy8tryaY3WSgUOusqwZn80jEajXLyqkckl1tb+dKJiYlpdHoptzNXbhqNhnPOPZce3bsDkHvwIF6vj8VLPmLSxIm8+uqrGI1GqW/1iPT6Fgnl1ppOyZZddNFFTJ48mccee4wZM2YwdepU/vGPf9CtW7dTsfgmC4VCzb423tg88+fP58knn6z1fklJSZNu3D0Zbreb4uJi9cTi8/nw+XzVpql6X2H44ZhAIEBMTAyKojTauupyufB4PJjNZvR6faPTezwedfxdk8nU6H2N4b7+wl1xeL1evF5vvdMHg0HKysoIBoPExsYSDAYb3KbwDcU+nw+r1YpWq210H9xuN263G6PRiF6vx+12Nzi91+vF6XSi0+mw2Wx4PJ4Gpw8Gg2o3DvWVW1XRXG7h5ZzpcjMajW2i3Borh7ZW3051uVVUVJyWchswcCB9+/Vj8X8/JMEeT4mjlHK3m5deeolln33Go489xogRI2odm/B3SLSUW0Pb1Rbr26k+T7pcLrxeb53l5vF4UBSlsnuf0ygpKanJ07Yo6BUWFrJu3TrWrVvH2rVr1TEGv/vuO/r378+f/vQn7r///pasok5paWkA5Ofnk5CQoL5fUFBAz549T9k8AA888AC33Xab+jovL48RI0aQkJDQrAPdHEajEZ1OR1paWoN/6dhsNvUvHY1GQ0pKSpP/0gkGg8THxzf5Lx2v14vNZmvyXzpOp7PZIyfodDqSk5Ob/BeqoigkJiY2+S9Uv99PbGwsCQkJuN1ubDZbvdN7PB68Xm+1YZYaEv4L1WAwNOsv1GguN7PZfMbLrSktC9FSblV//8LjsYZCIVwul9qykJKSgtFobHD50VDfTnW5BYNBKioqTlu53XTLLCZNnsTzz85n8dJPCIVCHDx0iFtuuYUrLr+c3/7f/5GcnMz69evZs2cPPXv2ZMCAAVF1nqzr/NeW61t9TuY86fV66y03jUaD1Wo9bfngZJzUpVu/30+PHj1ISUnhkksu4f333ycrK4vnn3+en376ibKyMt58803mzZvH3LlzT/U2M2TIEMxmMytXrlTfy8/PZ8eOHYwZM0Z9LxgMqj1UN3WemuLi4khPT1d/woHxdIuNjT0rmrPlMoSUW33acrmFQiG1JaS0tBSHw4HP5yMQCGA2m8nMzKR9+/Z4vV7187paes7WcjsTIwPpDHp+/9QTfPzhEoYNGKh+9uHixZw3eTILFy5kz549AOzZs4eSkhKpbw04G8+TkUKjKIrS3Jn8fj8PPfQQ48aNY+zYsbRv377O6b7//nuuuOIKcnNzW7yhNT344IO88847LF68mI4dO3L33Xezfft2du7ciclkAmDUqFFkZWXx7rvvNnmexuTm5pKRkUFOTg7p6emnfL+gssJlZ2cTHx/f4DRerzeqK0FrnrzCx6EmOXk1rdzy8vLQ6XTypdOAmvfkhS9RKYqC1+vFbrern5nNZrUlvyq/34/H48HtdlNUVITP58NgMGCxWNTxrqOhvtXnZMvN6/U2esUDTl19CwWCLHprIfPmP0Ops7IP2YF9e/PIb/+PvfuzSU9PZ+DAgVFznqx5/mtr9e10nyctFgtxcXH1LtdqtZKVldXoNpwpJxVHDQYDzz//fKPTDRw4EIfDcTKraNS8efPQ6XRcdtlluFwuxo0bxxdffFEtsOn1+mq/gE2ZJxqE71nQaDQRWQnOti+dtnLyak65BYPBZl0+OpvKLXx/TmlpKSaTSb2nyGq1qvNXVFTQrl27RvfNYDBgMBiIjY0lKSkJl8tFYWEhxcXFlJWVqZeIzrb6dqZHBtLqdVx900wmn38Bf577FB8t/5xfX30h3WxeOowegjWhvZwn69EWz5MN3UcJleUQSU6qRa85wum2rWjtFr1wJSgvL2/WPQuRGBZa8+RV8y9aOXk1r9xMJhOJiYkNTn+2fOmEQiG1tS4UClFRUYFOpyMxMZH4+HjMZjMmk6nauoqKik76Hh5FUXC5XBw5coSKigpsNluDD4ZFQn2rqaXl5vP5GrzH9nTWNyUQInvnbmx+H6Un9mE0+OjQdzDbDhURQMs555xT53yRdJ4M72c01reaWuM8Wd8VIUA9nt1/foI7Epz2C8xtKeS1tqqVICYmJmIrQUPa4pdOWzl5NaRmuTU2aHdbLje/34/f71efFNRqteqN2TabDZPJVOdl2FNFo9EQGxtL7969cTgcnDhxgtLSUmw2W619aqv1rbGnNE9nfdPotaR1y6QorwCrrg82v5Pi7P08/Js/kpOXz1VXXcXvfve7asuR8+TZc54sKSmp93a21hIddxKKWpWgKQ2xkVgJ5OQVnSevmuXWWJcObaXcwiNYBINBSktLgcpbQqxWK8nJyVgsFrUbijM97JFWqyUhIYHY2FiKioo4ceIEbreb2NhYtFqt1LfTWN8cDgfW+BjiMzrgKy5n8YK15OTlA7Bo0SK+WbmSp+bOZerUqRF5nnS73fj9/rOu3M7UeTLSHtKIrK0RdaqrEjTWL1AkVwL50qmflFvrlVu4ixOv10thYSGhUIi4uDisVitWq5WYmBhMJhMmk+m0tdadDL1eT7t27YiLiyM/P1/tvsLj8ZwV5RbWWvVN197AjbfPxmS28uyrL1JW7qKgsJA5c+Zwwfnnc/c995CcnBxR9S38B8HZXG4Nacl5Mj4+vtHln2kS9CJcW6sEkfKlE+7gUk5e9YvEcjuVXzrBYFC9BBvuhkmn02E0GunTp0+rttadDIvFQqdOnYiPj+fQoUOUlpaSlpbW5sqtLq1Z3zQaDaYEG9fPvpXxYyfwx/l/5qs1qwH44ssvWbtuHY8++ihXXnllg8uXkYHaxnkyfC6JJBL0IlhbrASR8qXjdDqb3cnn2XzyipRyO9kvnfAJ2OVyEQgEgMpLn2azmZSUFDXURVprXXOFe+rv06cPKSkpFBQU4HQ6sdlsdf6ORHq5RVN905kMZA7syQvPv8inH33En/7xDMWlDpxOJ7/+9a9Z/uWX/OnPfyYpKanWMTvT9a0pf7ycLeVW1ak4TzY2kkhrkKAXoRRFaZOVoCFn8ksn/FSknLxqi+Rya8qXjqIoeDweTpw4QSAQwG634/F4MBqNxMbGVrsE21h5RiuDwUCHDh2w2+2cOHECh8OBwWBQh8GCyCs3iP76ptFp0cSaGDlxPK/37MV/3vg3Hy3/AoBNG79n+48/cCT3GD179mTUqFFA69S3xsLI2VZucPrPk61Jgl6Ecjqd6PX6s6YSnOkvHZvNJievOkR6udX80gnfVxd+ElZRFEKhEMFgkA4dOpCQkKCGumi5BHsq2Ww2OnfujMvlIj8/H4fDgcViwWQyScg7TfWtuLgYg9VEzyH9+FOXeUwePYG5LzzDI7N/wZHcY0DlSBojR45U9yFa6ltd2kq5tdWQBxL0Ila4Q9qzoRK0xsmrsYG35eQVeeUWvvzq9XrVUAeVDyNYLBaSkpLUy1Kns3uTaBPujsVms1FWVsbRo0c5cuQIOp0Ou90uYaEep6K+GUwmzr/kIoYPG4XVGODHAzs5kO+gW3oquYf2s37TVsaOHRuR9e1sLreGBINBysrKmjwyUCSQoBehbDZb1FaCSA4LcvKqW6SVW7hT4MLCQhRFIT4+HpfLhV6vV+e3Wq0S6ppBq9USHx+PxWLBbDZz4sQJjEZjo/NFYn2LlpGBNFoNpqRYki0m3MdL6J81iIFZxQR9+cy+7x42btvNpZdeyty5cxusc3KejKzzZFNHBooUEvQiVFNOwJFaCSIlLICcvE5XuTmdThRFOSXlFgqF1MuvgUBAvRwbDAZp3749CQkJ1Z6AlVDXMiaTic6dO5OQkMDRo0fxer3ExsbWWZcitb4FAoGoGhlIbzUSk5GMJ9+Bp0zL199vYuO23QB89NFHbN68ifnz/8bw4cNrzSvnycg7T8bGxkZNyAMJelHL5XIRDAYjshJIyKtfJJ+8mlNuPp+PxMTEZpdbXFyceuk1FAqhKAoajUZ9UMBqtcrl1zNAq9WSmJiIxWIhLy+P0tJSYmJiqv3OR3J9i8aRgbQGHcbUOErcZQwZfA5/fOR3/OWl5ylzusjNPcq1117LnDlzuPfee9VjIefJ1i83aP7IQJFGgl4UcjgceDwe4uPjI7ISSMirW6SfvJpTblartcFyCwaD6mgSpaWlmM1mNBoNbrcbk8mkHrNwoDMYDBHXyejZwGKxkJmZSVFREXl5eWi1WmJiYnC5XBFd36J1ZKCS0hI0NiPtk5O4KD6Z4QOH8tgzT7F+y1ZCoRAvvvgiq1at4rnnnqNz585ynoyQcmvuyEDhLpwihQS9CBUKhXC73ej1evR6vfrLG64EZrM5YitBQyTkRe7JqyE1yy08xFYwGFQvs4Y7ClUUBa1Wq17a7dixY7VQp9frz7qnXyOZTqcjNTUVm81GXl4ex44dUy9PRWp9i+aRgRITEzGbzYRiLOgsBv759D95+8M3+dur/8TvD7B9+3Yuumg6DzzwIBdeeCFxcXFynqxHJJ4nS0tLSUlJaXTbzyQJehHIbDbToUMH3G43Ho8Hj8ejdvYaHrrGaDQSDAYbrJyRWAkk5EXPySsUChEKhdTlu91uYmJiCAaDlJeXEwwG0ev1mEwmzGYzZrO58klDgwGDwSCBLsqEu2KJiYnh8OHDTbpsLvXt5Oub1qDHmpaAt9jJDVfNYuSwUfzmqd+z7+BhKio8/PnPf2bQoEF07Nix0X2QkYEi6/st0q5OSNCLQAaDgfbt2wOoX7SBQACfz0dFRQVer5eSkhLKy8sJhUJoNBoURUGn06HX69HpdHi9XnWom0irBBLy6ncmT152ux2dTofH41Fb5xRFUS+LaTQadDodiqLQrl07rFYrFosFg8GA0+kkJSVFwlwbo9PpaN++PXFxcRw7dozS0lJiY2Pr/L2S+tby82T4qVytsYJu/u68/fIbPPPKM7y/5FNuvuJ8BmTEo/h9aAz1P5wnIwNF1vdbTExMo8s/0yJra0QtWq1W7fDVZrORkJAAQGxsLHa7Xe0oNhAI4PF4qKiowO12q09FarVaysrKKgff1umq/YQrx9kY8oLBYJs+eYU7DQ5fUg0Gg/h8PhwOBxqNhpiYGAKBAKFQCIPBgM1mUzsVDt8uEP6p61j6fL42O6qEAKvVSufOncnPz1e7Yan6Oy8h79SdJzUaDcY4K1qDnrJ9R7j1mts5f9IURvRqh6/oMKEKJ4bULihGm/rHV5iMDBRZIS82NjYiHyCToBelwk8q1lXxqo4WEAgECAQCalO91+vF5/OpAcDv9+NwOABISkrC5/MRCARqhcGwthDyAoGAOiRUNJ28wqM+hDsNDvcxZ7fbKS8vr3aDularRavVYjAYMJvNGAwGTCYTXbp0UcNb+N9IPDGJ1qfT6UhLSyMmJoajR49SWlpKXFwcPp9PQt5pCAtlFS408WbizUbaJ43AEGcA/wmCzhOEju7kxfe+5Idde/jb356jXbt2MjJQhJRbze83GetWnBENhUCg1k30VcOgz+fD7/erYTB8KU+j0VQ+NVZSog4SHx5TVKfTodFo1HARFqkhLxJOXuHjGr73srS0VL3fzeVyVbuEGqbRaNTjEQqFaN++PTabTX1qtWaLrYQ4cSrExsbStWtX8vLyOH78OBUVFXLZrwEtCQtxCXZibDF4i5wE3D60xvboE62s+fYLXn7tTRRFYdqFFzL3D39g0KBBMjJQAyLxSlVrkaB3FgoHgIY6ZQ63+IUv74XDYXp6eq3WQr/fr4bCqk9eulwutSuOqr3Za7VaNbRU/b/L5cLlcmGxWIiPjz+tIc9utzfr5GW32zGbzep+hvcx3Bdc+KfqU9EApaWldS47vM/hY5yUlKTe/xZ+kCHc4lb1snv4/5F2s69o2wwGAxkZGdhsNrKzs5vUHY6EhZMPC6bkWHTOCryOCtDZsaf1pEO7ZI4eL6C4pIS77rqLG264gd///vdt9o/hqqKl3CKVBD1Rp3CwaMp9WOHLieFAWPV11fdrdsVRNUyGWxPD94mF7zGsj9/vV8cbNJvNOJ3OOqcLnwTDfbqFQ15FRYXaUlnXehRFUZ8stVqtBAIBXC6XusyqIVWr1aLX61EUhZSUFDp27FithS3c0tnQjzzQICJd+EvTarVWu5RbV3CQsNCysKDRaDDEWdEaDHiLnPTM6seS9/7Lb3/3a776bgMACxYsYNeuXbzwwgvqw3s1SciLjJGBWpsEPdFiNS/Znoxwy1jNFrKqP0C9/w9rSkeqGo0Gh8OB3W5XA1bVf2v+v74fCWjibGSxWOjcuTMnTpzgxIkTWK1WTCaT+vmZCAtny8hAOouBH4/sZu++fQBcdd0shg4ZxHOvvIbX52fz5s1cNH06zz3/PGPHjq02r4S8yBgZKBJI0BMRIVyBz9Q9ZeHuRYQQzRd+UMNisZCbm4vP5yM2NvaMhYWzZWQgRVHUkAeQl3+CG667g9FjzuHuBx4m59gJioqLufHGG7nvvvv41a9+hVarlZDXyiMDRRq50UcIIUSzaTQaEhIS6Nq1KyaTiaKiIvVJ8NMdFs6WkYE0Gg1dunRRX3fN7IziDdAjawAfffBfzhs3Ul3upg1rUJSQhLwzUG5N2edIIi16QgghTlq4z73Dhw9z8OBB0tPTT3tYaEqHtJEeFpraC0HXrl3p06eP+nRtwOXBW1KOyRjDyy//h3//83neXrSEubddiuvwdsoNSRjMFgl59TgV5dbYEHyRNtattOgJIYRoEYPBQOfOnenbty8ejwev11vvtBIWmt/VVHgfNBoNm3f+yAcrl7Jlx4/4HD5uu+MBlv33LWITUzixbwuB47uItxjU1tX6SMg7feUW7n0iUkjQE0II0WI6nY4OHTqQmZmJ1+uts9VDwkLL+hNVFIU9e/YAkH30EAoKFccdYG1PubUDxphk4kIuDm/4jGkXXsgjjzxSZ+hu6yMD1eVMlZvL5Yq47q8ia2uEEEJEtcTERLp27ao+3R5uVZKw0PJO4zUaDT179gSgZ8+eWFPjCRDk2O5DaPWxdBgwAUNqZ+6b90+KS0pYtGgRV199NceOHVOXER4ZSEJe/VpabpE21q0EPSGEEKeUzWaja9eu2Gw2SktLCQQCEhZO0chAo0aN4sYbb2TUqFF4Az4qTGCOs2H1aAn5tFg69mX2zTdgNVd2ebNt2zYuufhi1q9fL5drz0C5xcfHNzr9mSZBTwghxClnMpnIysoiOTmZ3Nxc3G63hIUGNGf4R41Goz5dazAa6NAzE2uqnYDLh7fEy0VX3cR//9/LZHZIBaCouJiZM2fy0ksvEQqFmj0ykJRb/Zo7bGdrkKAnhBDitNDr9XTs2JFevXphNpub9CUoYaH5Y9fq9XqMiTEYE6wEvX48xeX0HDaeD999m3NHDFD3429/+xvz5s1r9KlQCXltJ+SBBD0hhBCnkVarpX379vTq1YtQKNTg8IYSFpof8sJdqGg0GoxxVszJsYT8QSqOl5LQvjMv/fPfzLpiqjr/xx9/zKybbuLEiRN1Ll9CXtsKeSBBTwghxBkQFxdHly5dMJvNlJaW1uqCQsLCyYe8qvRWE+bkOJSAguNIAaUuH7ff+2uef+IBYiyV+7B7924OZe+tNa+EvKaVm9vtjpqQBxL0hBBCnCEWi4WsrCySkpIoLS3F7/cDzQ8LHo8n6sPC6Qh5YTqzAWNqDCWOEiryy0hMaMfF183m3Vf/Rqf2yTw8+3oGdoghVFGmziMhr+nl5na7oybkgYyMIYQQ4gwyGAykp6djNpvJy8vD4/EQDAabFRZcLhc2my2qw8LpCnlQ2YVKidOBOTUOc0CH93gZ+o6J9BkzhY/fzcBbeJiAs5BQwIchtQuK2a4+HS0hr37hcjMajVET8kBa9IQQQpxhWq2W1NRUdRxXRVGIi4trdL5wWAgHnmgOC6cz5IW7UElul8qevIMsWfsF69asJVDuI65TH0wd+hAKeFE8Trx5u3n8/37Nww8/jMFgkJBXj6rlFhsbGzUhDyToCSGEaCWxsbEMGDCA1NTUOu/bq6pqWIiLi4v6sHC6Q154n/fsrbwX78Cxw3iKXfgcbrQxSdi6jyYYCvDWe0t494OPWLVqFTfeeAO5ubkNruNsD3lNKTcZAk0IIYT4Wfi+vcTERPXyYU1nY1hoacgzGAy1RtIwWE1s+P57PvzwQ77fvhdPYi9S0joSF1PZird79x4uv/xyfvzxxzrXISGvaeXWWPc1Z5oEPSGEEK3KYDCQkZFBWloaTqez2visZ2tYaGnIC6s6koYxKYaDeUcA2Lt3Lz70TL36Jt576U90yUgDoLCwkGuvvZYvvvii2jok5DWt3EpLSyPusq4EPSGEEK0u3N9eZmYmHo8Ht9t91oaFUxXywsLr1Gq19OjRA4D2CSlYgnqssYn0HD2Nd155hpEDe6nbMGfOHF599VUURZGQ18xyk7FuhRBCiDpoNBoSExPp0qULfr+fo0ePnrVh4VSFvKpCoRDdu3dn/PjxjBw2Er0nRMWJUtDqSek1kv/8Yz5XTBmr7u+f//xnHn74YRwOh4S8BlQtt0h8GleCnhBCiIgSGxtLjx49aN++PTqdrtEvzrYWFk5XyAv3kxcXF0d8hySsHRIIev1U5DtA0WBN78Wf/jSX+2Zdoc73wQcf8M7ChRLy6tHccmsNEvSEEEJEHIvFQs+ePbHb7Q0+kXs2hoWWhLz4+Hh1nw2xFszJcZVDpp1wQFDBmJTJnff9mrn33ojRoKdrpzRuu/IClIC/wXVIyIvMkAfSYbIQQogIZTQa6dSpE8eOHaOwsJD4+PhqAeJsDAstDXlWq5Xy8nL1c0OMGY1WiyffQfnxEmzt43Fj5tyLr6VdciJde/TConjxHt+DqV1XNAZLrXVIyIvckAfSoieEECKC6fV6OnbsSHJyMg6HQ23ZOxvDwqkIeXXRW41Y2sUTqvBz4kAuLoeT2OQOTL7iZtonxKLR6wn53HiP7uZI9h7WrVunzns2hjyv1xs1IQ8k6AkhhIhwOp2Ojh07kpiYSFlZGT6fL+rDQqSEvDCdxUAgTo+rrBxdRZBYawxaaxzmTgNRlMpwXVLqYNbNt3DTjTfy8ccfn5Uhz+Px4HQ6oybkgQQ9IYQQUUCn05Genk58fDwnTpwAiOqwEEkhDyr7yfMEfSRmpGLVmXEfLSbkD6A12bBk9AetnrcXL+VgzjH8gQD33nsvL730EgaD4awKeSUlJeh0uqgJeSBBTwghRJQIh720tDT0en2jX7SRHBYiLeSF+8lLbJeMLSMRNBrcJxyE/AE0BgvmDn2585brmXn5VHW+F198kZdeeqnR5belkKfX64mLi4uakAfyMIYQQogootfrad++PeXl5TgcDux2e51f0pEeFiIx5IW7UNGZDFja2/EUlFFxwsG2I3vYu28fPXt05/45t2LRhfjXf78E4PXXXyc/P5/58+djMplqLb+thbzExEQ8Hk+j2xRJpEVPCCFEVNHr9WRkZBATE0NZWVmtz6MhLERqyAvTmQxYUuNQQgp79+0DYM/efbjNqdx8y63M+/UdGH4ObZ999hmzbroJp9NZbRltMeQ1pdxkrFshhBCihQwGA506dcJqteJwONT320pYaM2QF6Y1GrC2t9MlrRMAHTt2xGyxktpjCJdfcRWv/PEhYmyV61q/YQPX/eIXFBYWAmdvyCsuLm50O840CXpCCCGiUrifPbPZjNPpbDNh4XSHPJfL1eSxa7VGA4NHDmVM/+H0bJ9FfGwcOp0eQ2pnxk68gDee+R3JifEA7Ni5kzWrvz1rQ1643CLt/j0JekIIIaKWyWSiU6dOaLVacnNz20xYOK1P13o8zRq7tqzchT0tkThLDBV5Pz+godFgSM6k3/BxvPns7+mY1o77bv0FE/tnUJCXe9aGvKY8gXymycMYQgghoprFYiErKwun04nJZIr6sHC6L9eazeaTGruWQBB3noPy46XY2sejNegxJKbTbaCOD176AyZbDPmH96O3FtCh35izLuQlJSXh9zc8VFxriKzYKYQQQpwEm81G37590el0DT4VGQ1h4XTfkxcTE9Po9HV1hqw1Gth+dC8frljK2lVrCPmDlS17CR2ITe9F4dHDaA1GEhMTCBZm8+5bb/Dtt9/Wufy2GPIaK7fWIkFPCCFEmxAbG0tmZiYejwefz1fr87YQFk7XgxdV1TfihaL87wnc7NxDlB8tRvEH8fv9lAX0WDL6k5zaDqMlhi9WbeB3jz/J7bfdxqefflpt+RLyziwJekIIIdoMu91Oeno6LperWjcXbSEstGbIA9BoNPTs2ROAHt26owRCOHILKDyRj0ajoV1mD2zpfQj53Kze8AOhkII/EOCee+7h/fffB9pGyAsGg1ET8kDu0RNCCNHGJCUlEQwGOXr0KHa7Ha1WG3FhIdpCXtioUaMYOXIkGo2GNau/Y/+BbDqktOfcSRMr78mLScKUpuXJ+2/FYjbx9uLPUBSFRx55hPLyci666KKoDnmBQACHw4HBYIiKkAfSoieEEKKN0Wg0pKam0q5dO0pLSykqKoq4sBCNIS9Mo9Hg8/nYfyAbgGMFx/EUlKEEQwDobAlYOvbmt3fdyO3XXaHO99RTT/Haa69FdciLppa8MAl6Qggh2hyNRkP79u1JTEykpKSEmJiYqAwLkRbyoLIz5OLiYjp1quxIuXvX7ii+IBXHHf8Le9Z4LGm9uf/Wa7n75mvVeV955RVeeOGFBocJi/SQZ7fboybkQZRfunU4HHzxxRe4XC7GjBlDr169Gpz+ww8/5NixY9Xe69GjB+eff/7p3EwhhBCtQKfTkZGRQSAQwO12Nzq9hLymhbxwZ8jjxo1Dp9Oh0WgIlHvxFDnxFJZhTrGj0WrQWuPQp3ZnxvnnEKhw8vK7SwF48cUXKS8v57HHHqt1jCM95CUlJdX5oE8ki9oWvW3bttG9e3f+9re/sXTpUoYOHcqzzz7b4Dzz58/nzTffZPfu3erP0aNHz9AWCyGEONP0ej1ZWVlYrdY6x8UNk5DXvJAX7gw5fJz0NhOmBBtBT4CKE6UoIYVgMEhphR99u57cPvMa5j40R51+2WdLKSgoqLb8aAh5TSm3SOtLL2pb9G677TbGjh3Lhx9+CMDChQu58cYbufTSS+nWrVu9802bNo0nnnjiDG2lEEKI1hYeF/fgwYM4nc5aASISw0I0hLyaDLEWUBTKj5UQBCp0foLBIElpGRiVdsywWjCbTTz9ygL+8+eHSdC4UZQQGo22zYS8kpIS2rVr1+i2n0lR2aJ38OBBNm7cyJw5c9T3rrnmGhISEvjvf//b4Lx79+7lX//6F59++mlEDj4shBDi1DOZTGRmZqLT6XC5XOr7kRgWTnfI83g8pzzkheljLRiTYyk8ehx3YRnx8fGYzWa0ljhM7XswfdI5LFvwAt169sNfkkug4DChULBNhLxwuUXaWLdR2aK3a9cuAHr37q2+p9Pp6N69u/pZfXbu3InRaGTHjh0cOHCA1157jcsuu6ze6cvKyqo19+fl5bVs44UQQrQKi8VCZmYmhw4dwu12EwwGIzYsnM6WPJfLhc1mO+UhL7wPrpAHjU2PNaBH4/ajmM1oNBo17HF8LyG/B11MMr6SXIpOnOCdz77l9ttvj/qQZ7fbJejV5/333yc/P7/ez/V6PXfccQdQWcgA8fHx1aZJSEho8B6MZ599lpEjR6qvH3nkEW666Sb2799PSkpKnfPMnz+fJ598stb7JSUlWCyWetd1ujW0n6JxcvxaRo5fy8jxa5mWHj+73U5OTo56GddoNDb6sIbX68XpdKLT6bDZbA0OswaVneo6HA71KU2fz9fgTfyhUIiysjICgQAxMTEoikJ5eXmD63C5XHg8HsxmM3q9vtHpPR4PLpcLRVEwmUxUVFQ0OH24zziNRoPdbsfr9eL1ehvc57KyMoLBIHHtksAdoCy/BIPfhyHG/HMg0xGK64S/6AghfznFJW4enfc83//4E/v37ePpZ55pMFi53W7cbjdGo7HVyq3qcatZbgDl5eUUFRU1uJ6WSkpKavK0ERP0Dh8+zOHDh+v9vGrBhwOW0+msNmZfWVkZGRkZ9S6jasgDePjhh/nrX//KunXruOSSS+qc54EHHuC2225TX+fl5TFixAgSEhKadaBPh9Zef7ST49cycvxaRo5fy7T0+Nntdnbt2kVcXFyjf7R7PB68Xi9Wq7VZLUJN7VQ33CKk0WhISUlpckteMBgkPj6+yS15Xq8Xm82GyWRqtOXM7/fjdDoxmUxNaskLj3ih0+lITk7GbDajJCh4CpwEK3yYrDoMcT8fZ5uNgNnIiZ1ryf5pF9t2V/bH99XXX/Prhx/mhRdfxGg01lqH0+nE7/cTGxvb5Ja801VuNputznJzu91YrdaIqt8RE/QefvjhJk/bvXt3oPJevbS0NPX9gwcPMmnSpCYvJ/yLVFpaWu80cXFxxMX9//buPEqq6t4X+PfUOTXPVV3VUxVN082oIk6IiVcFlWCeqO8+yVVjiFwFjXkxRhJN9CYKohkE78pLNF5j7lNj9BkzaBzxJpgYRaNiQPEqCALSTdNjzfNw3h+dKrrpobqr6apT1d/PWq5ln95VvevXQ33Z++y9LWN+TiIiUj6z2Yw5c+Zg//79EARhxA18q2W6duA9eYVG8sY7XTvSsWaCIEBXY0a8O4CELwJZkKExGyDLMoKJLOSaFpy8QI0Hf3Q7rvv2nYjG4vivP/4RX7nuOtz/s59Bq9Xmv4aSp2vH+n0rl4pcjDFv3jy0trbisccey1/bsmUL2tvbB43M/eY3v8HLL78MAOjr6xsylPrYY49BpVLhM5/5TGk6TkREimG1WtHU1DTilKQSw4ISVtcOVOjsWkElQFdjwfa9H+CJ3z2Fra+9nl94YXU1oGbWqThlXgt+fs8dMPxjZHXLK6/guuuuzX9PGPImRjEjeuN13333Yfny5YjFYmhsbMSDDz6I6667Dqecckq+zcaNGzF9+nQsXboUgUAAn//853HOOeegubkZ77//Pn79619jw4YNo27HQkRE1ctmsyGbzeLAgQMQBCE/06PEsFBpIS9PJWDvwf0AgI/37oG30QNbjSM/faytn4kFkPHQxtux+lvrEYlG8ec//wVrVq/GpnvvRTKZVFTICwaDEAShIkIeUKEjegCwdOlSbN++HdOnT0cikcBDDz2En/3sZ4ParFixAp/73OcAAM3NzXjrrbdw6qmnore3F6eccgp27NiB73znO+XoPhERKYTD4YDX60UkEkEqlWLIO5YhD8iHIgAwaHVQx2QYdUfuixSNDmjrZuLEmU34xb13wGTsr8erf/0rbvja16BSqRQT8nKvuVJCHlDBI3pA//Yqt99++4ifX7t27aCPzWYzrr766snuFhERVZiamhpks1ns378fyWQSWq2WIW8E4wl5QP9rzt0LH03EodfqED3kh7HBDkHdX1/R5IS6NosTIOM/770T/3rTdxGORPHGm2/i2T88g9Vrrh31a5QynJtMpooJeUAFj+gREREdSy6XC42NjUilUmPaD40hr3DIk2UZfr8fHo8HADB79myYGp2ASkCsNwQ5k823VVtcUNe2wOvQ4a5vXAWDXofPLz4DV1zwT5Az6RG/RqlHYAu9ZqWp6BE9IiKiY0UQBDQ0NEAQBBw+fHjUsMeQN7aQl1t4sXDhQixZsiQ//apzmhDvDiHWHYDebYOg6r8eF/RImj04aW4C/++BTWiZ0QzEfEh2fQKNewYEcXAfyzHNXmi/wnR65FBaDgx6RERE/yAIAurq6pDNZtHV1QWbzTYkMDHkjS/kDXesmWTQ4r0Db+PjfXsxwzsdZy4+C+FwGKFQCMaaRphrHHD2fQoRAIxOZMI9SAI4HJfgmdZ/lJ0S76UMBoNwOBwF25USp26JiIgGUKlUqK+vh9PpzJ+SkFMNIS/3GsoV8nJtPt7Xv1HyJwf3489/3IJgMAidTgeHwwG1oxGSw4tMrA+QsxCNTrz/97dx8SWX4Jabb0Y0GlVcyMt93wrVv9SU1RsiIiIFEEURjY2NsFqt+bBXDSEvlUrljzUrV8gD+kdOZ82alf/400NtEFPIr64VBAFqhweSrRGZaC9isSiu/94mBIIh/PZ3v8Mtt9wMlUqluJBnNBoL1rTUGPSIiIiGIUkSvF4vTCYTfD5fVYS8co/kDXTGGWegubkZAOBx10MXl5GOHDmLVhAEqGuaIFkaoMlGseHbN0Kt7u/zc889j/vuu2/Mo5el+r4p8SQtBj0iIqIRqNVqeL1eiKKISCRSFSHParWWPeQB/SdetLa24sILL8Ti88+FxmZCwhdFJp7KtxEEAWr3dEhmN05trcP3vroSotj/Wn/5y19i48Z7Rnx+JY7AlgODHhER0Si0Wi1mzpwJp9NZcEWl0kOe0+ksONVZqpCXO9bM4XBAJYnQO81QiSrEe4LIpo7UWRBUyJgbEIQOn50/Ez/8t2/lX/P99/8M999//5DnZ8g7gkGPiIioAIPBgObmZiSTSSSTyWHbVELIU8pI3nBn1wpqEboaEwAg3hOCnM70/388Dn8wCENdK9wz5uCCM0/Chu98I/9899xzDx555JH8xwx5gzHoERERjYHFYkFjYyMikciQkT2GvImFvByVRg2tw4xsKoPY4QBi0Vh+da3TVQtDwzwIai0uXnwabvvGV/KPu+OOO/DUU0+VJOSFw+GKCXkAgx4REdGYOZ1O1NXVIRgMIpvtP9WBIe/YhLwcyaCB1m5EqNePjr0HIYpifnWtoNZA1zAHEFT44oWLceO1V/U/RhKhkxPo6emZ9JG8eDxeMSEP4IbJREREYyYIAtxuN+LxOILBIMxmM0PeMQx5OWkJiGuzkP0JmATNoNcsqHXQNc5D/NMdWP0vFyKVSuP4mdMx0y0hHehE7Yx5kzpdq9PpKibkARzRIyIiGpfcHntarRZtbW0MeQWMN+TF43H4/X7oHRa4p3mQiaSQDscHtVFpjdB5joecjOLay5djzjQXBEGCOd0HxPpGff6JjsCaTKaC7ZWEQY+IiGicNBoNvF4vJEmCXq9nyBtBMSEvf0+e0wlTnQ2iTo2EL4JMLDWorUpvgVQ3F93tB5BJJeBqaoXWUoNU1z78/P7/gw8++GDI85dimj2TyRRsU0oMekREREUwGo2YN28egMIH2TPkjS/k5e/JUwn9265IImI9AWSTR+qcTqfhj2egaZgHu9kAtaSGIGmx6RdP4u57/h1fXrkS+/bty7cv1b2UDHpERERVwmazDVmccTSGvOJCXo6gFrFj34f47ZbnsfWvr0NOZwatrq2dPgvmphOR9h9CMhHHBx/3h7vevj6s/NKV6OzsLFnIyx0vpyQMekRERBPgdrths9kQCoWGfE6JIS8YDFZMyMv1YfeejwEAe9v2I9jWg57uwatrJUcjNO5WCMEO3Pf97+K42a0AgLb2Q1j5pSuxf//+kt1LWWghSKkx6BEREU2AKIrweDzQarWIRCL560oMeX19fchkMhUT8oD+lc6zZ88GALQ0NaOr/TASfWE4HI58qBIEAWpXEzSuJmjjvXhw012YPs0DANj98R7c+PUboNVqJz3k2e32gu1LjUGPiIhogjQaDTweD9LpNJLJpGJDXjqdhtlsrpiQl7No0SJcccUVaJ0zCxqbEYa0BMQG3xcpCCqo3a0QbXWwyAH8/N4NqLH31/79nR9g7U03IZVKDff0eZP9fSsH5fWIiIioAplMJjQ2NqKrqwuhUEiRIc9ut0Or1Y7aXmkhD+hfeNHX179tSn3rNBidZiT9UaSjg4+jE0QJuro5gM4MKdiG739zDSwmIwDgL6++im9985tlu5eyXJTZKyIiogrkdDrh8XiQzWZhsVgKtufCi7GFvIHHmmm0GuhrbVCpRST6QsgmB4/SyaKEmNGDjMaCE2Y04Of/fjf0uv5w+8wf/oD777tvyNeo1pAHMOgREREdM4IgwOPxoL6+ftjFGQMx5I0/5OXvyVOL0DpNkGUg3hOGnO7f0iS3ujYlq1A793SYTCYc77HhJ9+/HWpJwnGzmvHP5y6CLB8Z1RtvyMtt6FwJIQ9g0CMiIjqmJEmCx+OBSqVCPB4ftg1DXvEhL0fUqqG1G5GOJxHrDiGTzgzaQsXkcEPXfAoyiRDOmDcNP7/3Ljx63ybYpATSvQchy3JRI3nhcLhiQh7AoEdERHTM6fV6eDwexGKxIZspM+RNPOTlSEYttDYD4r1BHN53EIlEYtAWKqLRDl3TKUiHunHaHC9MJhNEoxMpXxt6D+xCOBwe93Rt7jVUQsgDGPSIiIgmRW4z5VAoBFmWATDkHcuQB/RPlUtWA6JSBuEuP4zi0C1UJGstdE0nIR3oQDrcB0GUEAxF8Mm7f8FNN96AQ4cOFXzNA79vFoulYkIewKBHREQ0adxuN6xWK0KhEEPeMQ55QP89eT6fD4JJjY5wD57Z/DzeeG3roNcoCALUTi90nuOR9rWhe/9H+OTAQdx0zy/w1jvvYtVVX0Z7e/uIX2O837eRVvWWC4MeERHRJBFFEQ0NDZBlGZ2dnQx5oygm5OXvybPbceBwf1jbvfdjZJIpvPnmm3j00Ufx5ptv9u+x55qBmKEB0ZAP9TV21NfVAgA6u7px1cqV8Pv9Q75GMeGcZ90SERFNITqdDtOnT4darR7TtB9D3jhDns0Go9GYPz1jRsM0xHvD2LVrFwBg165dkGUZwXAEaXM9zDUeOA0CfnLHTWhtbgIA7PnkE6y+5mokEon81ygm5OX2+lMSBj0iIqJJZrVa0dLSgnA4nL9fbzilCHmhUKiqQl7unrxFixZh5cqVWHjaQmSiSbQ0NQMAZs+ejWAwiEgkApPVDvfchRCNDhiyYTxw1y1w1zgBAO9sexffuPHryGazE5pmL/Q9KzUGPSIiohJwu92w2WwIBoPDfr5UI3nJZLLqQl6OIAjQWA2QDFoc52rGZRddirlz5w7aQkWlM0HnnQ9RrYPbIOPB738HJmP/87z40mbcteHOokOew+EoWKNSY9AjIiIqAVEU0djYCLVajWg0OuhzpZyuNRgMVRnycgRBgN5tgdqiR/eBQwj2+odsoSIa7dB45wOCgBluI358x1pI/+jvf/7fh/Hss3+YlHspy4FBj4iIqES0Wi28Xi9SqRRSqf6ju0p9T95IAWmgSg15OX97+y38dsvz+NOOrdj90S6YDcYhbSSLG7qGuYCcxWmzPbj1+ivzn7vnnnuwe/fuUb9GJYQ8gEGPiIiopMxmM+rq6hAOh5FIJLjw4hiHPFmW8wsxAKCtuwORw37I2cH3RgqCAMnhhcbdgmjIh386dT5W/s+lEAQBN119GWa4R65VpYQ8AFBuz4iIiKqUy+VCLBbDnj17YDabGfJGMN6QBwDBYBAejwdtbW0AgNbpM5DyxxDXh6Bzmge9JkElIm2oQ0S0Qkr04aar/hkXnHs2TjpxAdK9ByBIGqgtriGvIRQKVUTIAxj0iIiISk6lUqGhoQF9fX2QJIkhbxjFhLzc2bWnnnoqlixZ0n9RBmKHfEj5Y5A0aqgt+nz7aDSKQCQCk/d4GOIdQCyA45tcyCYjUOlMSHV9AlHSQtD3B8RUKoVAIACtVlsRIQ/g1C0REVFZaDQazJw5E7Is5+/XGw5D3vhCXm7hhSAI/f+pBOhrrRBEIHKoD5l4f60HbqFSU9cAfW0rVBojBFGNTNQPOZWAIGnw9JOP4CvXrkEsFquY6dqBGPSIiIjKZOD9esMdncWQV1zIO5qgFmGod0AlSYh1BRAJhIZsoSIa7VDXTAdECYJah0zUj1/97jmsXX8vNv/XH/Hdf7sNgiDAarVWTMgDGPSIiIjKqqamBjabDaFQaNB1hrxjE/JyRJ0aercZ0UgUXZ92QC2ph2yhItnqoLE19o8EavSY43VB848+//Z3v8cLzz9X8DWPtiF2OTDoERERlVHuPFxJkhCLxQAw5B3rkJeTFGVE5QRUmSxMonbIaxYEAZLTC9HkhKCScPzsVtyy5rL85++6+268/vrrIz5/JpNBOp0u2I9SYtAjIiIqM61WC4/Hg3g8jmQyyZA3CSEvGo0iEAjAWGOF0+1GyhdDOhIf0k4QJWhczUhDQG9PNxYvOgnXXXYhACCTyWLt2puwZ8+eIY/LZDLw+Xwc0SMiIqKhLBYLamtr0dbWhlQqNWkhLxqNTsmQl7snz1lTA73TDEFSIdYRQDY5dAQuDRFhyQmVWouaeg/+96p/wQVnnQYACIcjuPpfV8Hn8+XbDxyBVdr9ewx6RERECiAIAtxuN1wuFzQazaSN5EWj0Skb8vILL7Rq6JwmpOJJRDp8kDNHFsLkNkMW9WbUHX8GxGwSosGBDd/6Ck6YNR0A8OnBNnzl2jVIJpODQp7NZit4bFqpKas3REREU5gkSZgxYwY0Gg2SyeSI7SYyXavRaKZ0yMvZ9sEOPPfuK/jdn1/A1ldfy29zM3ALFZ2tDmq7B9mYDwZHA36ybi3cNXYAwN/efgd3bVg/rmn2cmDQIyIiUhCj0YiGhoYRt1yZ6D15ZrN5yoe8o49J2/PpPkT7gkP2yTuyOKMG2YgPdU2z8O93fAt6nRbeOhfOP+04pBJxxYY8gCdjEBERKY7D4UA4HIbf74fNZstfPxYLL6LR6Kjtqz3kAf3T5LNnz86Hven1XnR+egi6Ggvc9bWD7rMTBBU0NdORSMWQTYQw9/gFuG/dN2A3m2AUUzBng9BqPQX7VC4MekRERAqjUqlQX1+PaDSKWCwGvV7P1bXHKOTlLFq0CKeffjpSqRS62jqQ8kdgghaiMLS9oNZC42pB/NB/I52JYca0RqSTSViNWgj+NqRNVqgdygx7nLolIiJSIK1Wi8bGRiQSCaRSKYa8YxjyBr6Gvr4+SAYt3N4GCMkM4r5wfouUgVulqPRmqJxN8He1I52Io6Z+GgwWG2Rk4du3A+v+7Vb4/f6CfSw1jugREREplNVqhdvtxp49eyCKIiwWC0PeCMYb8o5eeCEKKkTiPsQ6/BDVEt798D3s2rULs2fPxqJFi5DJZBBMqiDa6mFL90FnsgAAOrp68dXv3oNd+9rw37t247HHnyjY11LiiB4REZGCud1uOJ1OqFQqhrwRTDTkSZIEQVTBUGuBpNMi2uHL37+3a9eu/MhfJpOBrX46jG4vMlEfBEGA1upGINR/3+Nb72zDnevuKNjfUmLQIyIiUjBJktDc3AyTyTTqlisAQ16xIS9HpVFDX29FNp3FjMYmAMCsWbPg8/nyW6hodXpoXM0QRDUy8TDcNU789EfrodX012Lzy5vR3d1dsN+lwqBHRESkcAaDAfX19SNuuQIw5E005OVIRi30bitO8M7E/7rgIrS0tBwJeVotAEBQ66Bxt0BOJZBNJ3DC3Fm469a1OH7mdDxx/w/hcrkK9r1UeI8eERFRBXA4HIhEIvD7/UNCEEPesQl5QP/WK/2nZiTQ3dYJyaKFy1OP7du3Y9euXWhpacGZZ54J0WCF2jUdqe69EIxO/I/zz8FxXjucdnvBvpcSR/SIiIgqQG7LFbVaPWgvvPGGvEQiwZBX4DzaLGRExRSyyMIADSSo8vfs7d27N78aV7LWQm2tQyrUg77DnyIVj0GSNAX7X0oMekRERBVCo9HA4/Hkz1gtZiQvFAox5I0id3atrBLg9tZDTMqIdwcxe9YsAEBLS0u+zoIgQHR4EUxkEenthNnuhKQZvUalxqlbIiKiCmKxWOByubB///7+acZxTteKosiQN4JcyBt4T14sq0K8K4AFrcdh4emnIxaL5dvLsgx/MIyspRHWbAoqUXnjZ8rrEREREY3K7XbDaDQim82O+548i8XCkDeMo0OeTqeDIAjQO03QOUxIhmPIRBP59gNPKrE63XDMOBHIyqN8hfJg0CMiIqowarUaLS0tMBqNyGQyo7blwoviQl6OIInQ2E0QBAGxjgCy6cywx9GJZifUjkagQN9KTVm9ISIiojExm82oq6tDOBwesQ1D3sRCXo5k0EBrMyIVSyDhj6Cvr2/IcXSCIEByNELt8BZ8TaXEoEdERFShXC4XDAbDsGGPIe/YhLwctcUArd0If1cvwt1+mEymISeVCIIKKvXIz1EOFRv0EokEHn/8cSxZsgRz5swZ80HCDz74IM466yycfPLJ+PrXvw6fzze5HSUiIpokkiShoaEBmUwG6XQ6f50h79iGPACAACQ0MtJyBuq4DIOkrEA3kooNel/4whfw7LPP4uyzz86fQ1fIxo0bsXbtWqxZswabNm3C3/72NyxbtmzEXcaJiIiUzmQywe12IxQKQZZlhrxJCHm5e/ISmRSstU7oJR1ih32QU6PfH6kEFbu9yq9//WtotVq89NJLY2qfTCaxYcMGfO9738OVV14JAHjiiScwY8YMPP/881i+fPlkdpeIiGjSuFwuhEKhfABjyBtZsSEvd0+eIAiQTDKSvigS/gi0NeaCq57LqWJH9HLnzY3V3//+dwQCASxbtix/rbm5GXPnzsUrr7xyrLtHRERUMrkp3NyJGQx5w5toyMsFPa3VCEmvRioUQzoUH/SYscwwllLFjuiN18GDBwEA9fX1g67X1dWhra1txMcFg0EEg8H8xx0dHZPTQSIiogkwmUyYMWMGOjo6CgYkhrziQl6OIKmgsZsQ7wog4Y9ApZUgatUIhUKwK+ysW8UEvUsvvRQ7d+4c8fNarRY7duwo+vlz+wxpNIPPoNNqtaOm73vvvRfr1q0bct3n80Gv1xfdn4kaGD5p/Fi/iWH9Job1mxjWb2RqtRqSJKGnp2fEMBaJ9G8PIssyrFYrkskkksnkiM+ZzWYRDAaRTqdhMpkgyzIikcio/QiHw4jH49DpdJAkqWD7eDyOcDgMSZL6T6MYcPrEcNLpNAKBAARBgNVqRSKRQCKRGLF9JpNBMBhEJpOB2WxGJpMZtU+yLCMUCiGZTMJgMEClUuXbD+xbWicgGYgh3p5E1ighEAhAr9ejt7d31P5PlNPpHHNbxQS9TZs2jfqNLZTsC8kVpaenBxaLJX+9p6cHCxYsGPFxN910E6655pr8xx0dHVi4cCHsdvu4Cj0Zyv31Kx3rNzGs38SwfhPD+o3MYDBgz5490Gq1Q0a50uk0+vr6oFarxzWSJwhCfiuXQgKBADKZDGw225hH8hKJBIxG45hH8kKhELRa7bhG8kRRRE1NzZhH8mRZhsPhGLKFCgAYjcb+tnoDYrIfPfs7kLWpYTAbYLFYFPXzqZig19TUNKnPf9JJJ0EURbz55puYMWMGACAUCmHnzp2DgtzRLBbLoGBIRESkZCaTCbW1tTh8+PCgaURO105sunY4gkpAWgvE5RQ0QcBcUzNq+3Ko2MUYY3HZZZdh7dq1APr/9XfppZfiBz/4AXw+H7LZLNatWwe9Xo8vfOELZe4pERHRsVNTUwODwZBfnDEw5FmtVoa8YYw35AH9A0bheAzWRhesJjOSgQiyKWUtxqjYoPejH/0Ic+bMwerVqwEAZ5xxBubMmYOXX34532b//v1ob2/Pf/zAAw/A4/Ggrq4ONTU1+M1vfoOnn35acTdOEhERTYRarUZDQ0P+3rWBI3mFAhJD3thDXigUgk6ng8tbB63TjGw8jVQ4XvCxpaSYqdvxWrVqFS666KIh1xsbG/P//+STTw76V4vNZsMLL7yAnp4eRCIRTJs2TdF73xARERXLbDbD5XLho48+gslkyk/XFlp4wZA3vpBnt9shCAJ0TjMi4TAycWWN6FVs0HO5XHC5XKO2Gem+v5qaGtQocB6diIjoWKqtrUVnZyfUajWna4dRTMiLRqNIpVKDQh4AqNQi1CYdBFFZA0gVO3VLREREo1Or1WhtbUUmkxn1uE+GvLGP5EWj0SEhL0djMUDnVNYCTgY9IiKiKpbb7iMUCg37eYa88U3XajSaYUNejqDiiB4RERGViCAIqK2thSRJiMcHLxRgyBv/PXm5Y9AqBYMeERFRldNqtaivr0c0Gs1P4TLkFb/wYjSjTZGXA4MeERHRFGCz2WCz2RCPxxnyJinkxePxUY9VLQcGPSIioilApVKhvr4esiyjs7OTIa+AYkKe3+9X3LQugx4REdEUodfrYbfbEQ6HYbVaGfJGUEzI8/l8kCSpYP9LTVm9ISIioklls9ng9XrHNMXIkDe+kGcymTiiR0REROWjUqng8XiQTqdHDXsMeeMLeQ6HA6IoFvwapcagR0RENMWYTCa43W6Ew+FhP8+QVx0hD2DQIyIimpJcLhc0Gg1isdig6wx51RPyAAY9IiKiKUmtVqOhoSG/3QrAkDeWkJdIJCom5AFcjEFERDRlWa1W2O12BINByLLMkDeGkbxQKASDwVARIQ/giB4REdGUlTseLZFIwO/3M+SNIjddK4pixYQ8gEGPiIhoStPr9fB6vVCpVLBYLAXbT+WQJ0kSLBZLxYQ8gEGPiIhoynO5XKitrUU0Gh213VQPeWMZyZNluWAfSolBj4iIaIoTRRH19fVIpVLIZDLDtmHIKxzyCu1NWA4MekRERASTyQSHwzHs3noMeWMLeX19fQX7UWoMekRERJRfmCGKIhKJRP46Q97YQl5vby9kWVbc/XsMekRERAQA0Gq1qKurQzQahSzLDHnjDHljqVGpcR+9ccrNvXd0dJS1Hz6fb8hu5jR2rN/EsH4Tw/pNDOs3MYXql8lkEA6H0d7ejnQ6DbVaDZvNhs7OzlGfN51Ow+fzQRAE2Gw2dHd3j9o+k8nA7/cjk8nAarXC5/ON2l6WZQQCASQSCRiNRgiCMOIRbjnhcBiRSARarRYAcPjw4VHbJxIJBAIBiKIIm82Grq6uIW2i0SgMBgOA/tfs9/shyzLsdjui0Sii0WjBgHss1NXVjenrMOiNU+4Hd+HChWXuCREREU1VBw8ehMfjKdhOkJW2Dljh4vE43n//fbhcrpIk9uF0dHRg4cKFeOutt1BfX1+WPlQy1m9iWL+JYf0mhvWbGNZvYpRUP47oTRKdTofTTjut3N0AANTX148pzdPwWL+JYf0mhvWbGNZvYli/iamk+inrjkEiIiIiOmYY9IiIiIiqFINeBbJYLLj99tvHdCYhDcX6TQzrNzGs38SwfhPD+k1MJdaPizGIiIiIqhRH9IiIiIiqFIMeERERUZVi0CMiIiKqUgx6RERERFWKGyYrUCgUwk9+8hO89957cLvdWL16NU444YRRH7NlyxY899xz6O7uxty5c7FmzRrU1NSUqMfKUkz9crq7u3H99dfDarXioYcemuSeKlOx9XvjjTfwxBNPwOfz4fzzz8eXvvSlgoeHV6Ni6rdr1y48/PDD2L9/P2w2G5YtW4aLLrpoStYvHA7jV7/6FTZv3owLLrgAq1evLviYifzOV5ti6sf3jyOKqV+OUt8/OKKnMIlEAmeddRaefvppnH/++YjH4/njVkZy/fXX4+6774bH48HSpUvx6quvYt68eThw4EAJe64MxdQvR5ZlfPnLX8a2bdvw0ksvlaC3ylNs/TZu3IjzzjsPVqsVF154IV577TWsX7++RL1WjmLqt23bNixYsACHDh3CRRddhGnTpuHKK6/E7bffXsKeK8OOHTswa9YsvPPOO3jvvfewY8eOgo+ZyO98tSmmfnz/OKKY+uUo+v1DJkV54IEHZIPBIPf19eWvXXjhhfLixYtHfMyhQ4cGfZxKpeTp06fLN99886T1U6mKqV/Oxo0b5aVLl8rr16+XGxsbJ7ObilVM/bZv3y6rVCr5ySefHHR94HNMFcXU75ZbbpFbW1sHXbvtttvkpqamyeqmYvl8PjkQCMiyLMunn366/NWvfrXgYybyO19tiqkf3z+OKKZ+OUp+/+CInsK8+OKLWLx4Mex2e/7aihUr8Je//AXRaHTYxxx9sLIkSXC73QgEApPaVyUqpn4A8M4772DTpk14+OGHp+R0WU4x9XvkkUfQ0NCAFStWDLo+8DmmimLqN2vWLPT29qKvry9/bffu3Zg7d+6k91dpbDbbuDeiLfZ3vhoVUz++fxxRTP0A5b9/MOgpzL59++D1egdd83q9yGaz+PTTT8f0HFu3bsXbb7+NZcuWTUYXFa2Y+oVCIVx++eW4//77h/zRm2qKqd+OHTtw+umnY/PmzVi5ciXWrFmDJ554AvIU3Iu9mPqtWrUKt956K+bPn49ly5Zh3rx5SKfT+OUvf1mKLle8Y/E3k46Yyu8fxaiE9w8GPYVJJBIwGAyDrplMJgBAPB4v+PgDBw5gxYoVuPzyy3HJJZdMRhcVrZj6XXfddVi8ePGUrNfRiqlfJBLB1q1bsWHDBpx77rmYO3cubrzxRlxzzTWT3l+lKaZ+u3fvxk9/+lMsWbIEK1euxBe/+EX89a9/xTPPPDPp/a0GE/2bSUdM9fePYlTC+wdX3SqM1WqFz+cbdK23txdA4amwtrY2nHvuuTj11FPx8MMPT1YXFW289evq6sLjjz+O8847L/+Lunv3bvT29uKSSy7BN7/5TZx55pmT3m+lKObnz2az4f3338cLL7yQn/bwer1YsWIF7rzzTjQ0NExupxWkmPrddtttaGpqwqOPPpq/5nA48LWvfQ2XXXYZjEbj5HW4CkzkbyYdwfeP8auU9w8GPYU58cQTsW3btkHXtm/fDpvNNmR6YqD29nYsXrwYc+fOxVNPPQW1Wj3ZXVWk8dbParXi97///aBrTz31FLq6unDVVVehubl5UvurNMX8/C1YsAAfffTRoHtbWltbAQCdnZ1TKugVU7/29nYcd9xxg67NnDkTsVgMvb29DHoFFPs3k47g+0dxKub9o9yrQWiwV199VQYg/+lPf5JluX/lYktLy6DVP++++6588cUXy21tbbIs96+amjlzprx8+XI5kUiUpd9KUUz9jnbnnXcqbtVUqRRTvw8++EBWq9Xy66+/nm9zyy23yA6HQ45Go6V9AWVWTP1uuOEG2ePxyN3d3bIsy3I2m5VXrVol19XVydlstvQvQiFGWvV4dP3GUvOpaKz14/vH8MZav6Mp8f2DQU+B1q9fL+v1evnMM8+U3W63/NnPflb2+/35z7/44osyAPnDDz+UZbl/KwFBEOQLLrhAvvjii/P/rVu3rlwvoazGW7+jKfEXtZSKqd9//Md/yGazWT7nnHPk+fPny7W1tfLmzZvL0f2yG2/9fD6ffPbZZ8t2u10+//zz5VmzZsn19fX54DKVxGKx/N8vu90uNzc3yxdffLF8ww035NsM9/NXqOZTRTH14/vHEcX+/A2kxPcPQZan4NK4CnDw4EHs3LkTbrcbJ5988qAl211dXdi6dSvOO+88mEwmvPbaa+jp6RnyHLW1tTjjjDNK2W3FGE/9jrZ7927s27cPn/vc50rZZUUppn49PT3Ytm0brFYr5s+fP+QG+amkmPrt2rULBw4cgN1uxwknnACdTleOrpdVJpPBs88+O+S6xWLBkiVLAIxcv9FqPlUUUz++fxwxkZ+/HCW+fzDoEREREVUpbq9CREREVKUY9IiIiIiqFIMeERERUZVi0CMiIiKqUgx6RERERFWKQY+IiIioSjHoEREREVUpBj0iIiKiKsWgR0RERFSlGPSIiIiIqhSDHhHRJAsEAnj66afR3t4+6PrOnTvxzDPPIJlMlqlnRFTtGPSIiCaZ2WzGHXfcgTVr1uSvvfHGG1i0aBH27dsHjUZTxt4RUTUTZFmWy90JIqJq98orr2DJkiXYsmULbDYbFi9ejJtvvhm33nprubtGRFWMQY+IqEQuueQS7NmzB52dnbj22muxYcOGcneJiKocp26JiErkiiuuwAcffICzzz6bIY+ISoIjekREJbB9+3YsXrwYHo8HPT09+Pjjj2EymcrdLSKqchzRIyKaZB9++CGWLl2Kq6++Gq+99hrS6TTuvvvucneLiKYAjugREU2ivXv34qyzzsLy5cvxwAMPAAB+/OMf49vf/jY++ugjNDU1lbmHRFTNGPSIiCZJJpPB2rVrodFo8MMf/hCCIAAAUqkUVq1ahXPOOQfXXHNNmXtJRNWMQY+IiIioSvEePSIiIqIqxaBHREREVKUY9IiIiIiqFIMeERERUZVi0CMiIiKqUgx6RERERFWKQY+IiIioSjHoEREREVUpBj0iIiKiKsWgR0RERFSlGPSIiIiIqhSDHhEREVGVYtAjIiIiqlIMekRERERV6v8D5G5zHPucMHUAAAAASUVORK5CYII=", + "text/plain": [ + "<Figure size 704x440 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "for name in wrong:\n", + " colour, hatch = style[name]\n", + " lo, hi = np.percentile(\n", + " curves(problems[name], samples[name], x_fine), [5, 95], axis=0\n", + " )\n", + " plotstyle.band(ax, x_fine, lo, hi, color=colour, hatch=hatch, label=name)\n", + "ax.plot(x_fine, truth(x_fine), \"--\", color=\"k\", label=\"truth\")\n", + "for d, colour in zip(datasets.values(), plotstyle.COLOURS):\n", + " ax.plot(d.x, d.y, \".\", ms=2, color=\"0.5\")\n", + "ax.set(\n", + " xlabel=\"$x$\",\n", + " ylabel=\"$y$\",\n", + " ylim=(-1.2, 1.2),\n", + " title=\"90 % bands: the wrong three\",\n", + ")\n", + "ax.legend(fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "dbd240fa", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:14:59.980449Z", + "iopub.status.busy": "2026-09-12T03:14:59.980281Z", + "iopub.status.idle": "2026-09-12T03:15:00.312497Z", + "shell.execute_reply": "2026-09-12T03:15:00.311850Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 836x836 with 9 Axes>" ] }, "metadata": {}, @@ -293,79 +723,105 @@ ], "source": [ "fig = None\n", - "for name, s in samples.items():\n", + "for name in correct:\n", + " colour, _ = style[name]\n", + " p = problems[name]\n", " fig = corner.corner(\n", - " s[:, problems[name].columns(quartic.params)],\n", + " samples[name][:, p.columns(cubic.params)],\n", " fig=fig,\n", - " color=colors[name],\n", - " labels=[f\"${p.latex}$\" for p in quartic.params],\n", - " truths=a_true,\n", - " truth_color=\"k\",\n", - " plot_datapoints=False,\n", - " plot_density=False,\n", - " levels=(0.68,),\n", - " range=[(0.9, 1.1), (-0.5, 1.5), (-3, 3), (-4, 3), (-8, 12)],\n", + " labels=[f\"${c.latex}$\" for c in cubic.params],\n", + " truths=a_true[1:],\n", + " range=[(0.9, 1.1), (-0.6, -0.1), (-0.75, -0.4)],\n", + " **plotstyle.corner_kwargs(\n", + " color=colour,\n", + " fill_contours=False,\n", + " plot_density=False,\n", + " show_titles=False,\n", + " levels=(0.68, 0.95),\n", + " ),\n", " )\n", "fig.legend(\n", - " handles=[plt.Line2D([], [], color=c, label=n) for n, c in colors.items()],\n", + " handles=[plt.Line2D([], [], color=style[n][0], label=n) for n in correct],\n", " loc=\"upper right\",\n", - " frameon=False,\n", + " fontsize=9,\n", ")\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "c3388568", + "id": "58d79de7", "metadata": {}, "source": [ - "The latent scale and the normalisation mode agree: they are the same\n", - "statistical model, one with the scale sampled and one with it marginalised\n", - "analytically (a Gaussian latent that multiplies the prediction is a rank-one\n", - "mode in the covariance). The two treatments that ignore the normalisation\n", - "do not converge near the truth, and inflating the diagonal does not rescue\n", - "them: a common shift is not noise." + "### What the normalisations came out as\n", + "\n", + "Two of the defensible cases infer a number per dataset, and we can hold them up\n", + "against the renormalisations we planted. Case 2 infers the scale $\\rho_i$\n", + "directly. Case 3 infers the *width* $\\eta_i$ of the mode, so it does not say\n", + "which way a dataset moved, only how far it is prepared to let it move.\n", + "\n", + "Watch `exp 3`, which quoted 3 % and needed 15 %, and `exp 4`, which quoted 25 %\n", + "and needed none of it." ] }, { - "cell_type": "markdown", - "id": "c8fe3a36", - "metadata": {}, + "cell_type": "code", + "execution_count": 15, + "id": "a640c45a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:15:00.313860Z", + "iopub.status.busy": "2026-09-12T03:15:00.313720Z", + "iopub.status.idle": "2026-09-12T03:15:00.318536Z", + "shell.execute_reply": "2026-09-12T03:15:00.318074Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dataset quoted true rho inferred rho inferred eta\n", + "exp 1 8% 0.94 0.93 +/- 0.01 9% +/- 6%\n", + "exp 2 12% 1.10 1.09 +/- 0.01 14% +/- 9%\n", + "exp 3 3% 1.15 1.11 +/- 0.02 10% +/- 4%\n", + "exp 4 25% 1.02 1.01 +/- 0.01 21% +/- 16%\n" + ] + } + ], "source": [ - "### The inferred normalisations\n", - "\n", - "With the latent scale the `ρ_i` are columns of the chain. Divide each\n", - "dataset by its posterior median scale and it should realign with the truth." + "p_rho, s_rho = correct[\"inferred normalisations\"], samples[\"inferred normalisations\"]\n", + "p_eta, s_eta = correct[\"inferred systematics\"], samples[\"inferred systematics\"]\n", + "header = f\"{'dataset':8s} {'quoted':>7s} {'true rho':>9s} {'inferred rho':>16s} {'inferred eta':>16s}\"\n", + "print(header)\n", + "for label in free:\n", + " rho_draws = np.exp(s_rho[:, p_rho.columns(rhos[label])])\n", + " eta_draws = np.exp(s_eta[:, p_eta.columns(etas[label])])\n", + " print(\n", + " f\"{label:8s} {settings[label]['sys']:7.0%} {settings[label]['rho']:9.2f} \"\n", + " f\"{rho_draws.mean():10.2f} +/- {rho_draws.std():.2f} \"\n", + " f\"{eta_draws.mean():10.0%} +/- {eta_draws.std():.0%}\"\n", + " )" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "427eec67", + "execution_count": 16, + "id": "a9e36535", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:37:21.812139Z", - "iopub.status.busy": "2026-09-11T03:37:21.811984Z", - "iopub.status.idle": "2026-09-11T03:37:22.546555Z", - "shell.execute_reply": "2026-09-11T03:37:22.545978Z" + "iopub.execute_input": "2026-09-12T03:15:00.319838Z", + "iopub.status.busy": "2026-09-12T03:15:00.319699Z", + "iopub.status.idle": "2026-09-12T03:15:00.422204Z", + "shell.execute_reply": "2026-09-12T03:15:00.421572Z" } }, "outputs": [ { "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 970x970 with 16 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 600x400 with 1 Axes>" + "<Figure size 704x440 with 1 Axes>" ] }, "metadata": {}, @@ -373,90 +829,146 @@ } ], "source": [ - "p_scaled, s_scaled = problems[\"latent scale\"], samples[\"latent scale\"]\n", - "rho_cols = p_scaled.columns(rhos)\n", - "fig = corner.corner(\n", - " np.exp(s_scaled[:, rho_cols]),\n", - " labels=[rf\"$\\rho_{i}$\" for i in range(4)],\n", - " truths=rho_true,\n", - " truth_color=\"C3\",\n", - " show_titles=True,\n", + "fig, ax = plt.subplots()\n", + "positions = np.arange(len(free))\n", + "rho_mean = [np.exp(s_rho[:, p_rho.columns(rhos[label])]).mean() for label in free]\n", + "rho_std = [np.exp(s_rho[:, p_rho.columns(rhos[label])]).std() for label in free]\n", + "ax.errorbar(\n", + " positions,\n", + " np.ones(len(free)),\n", + " [settings[label][\"sys\"] for label in free],\n", + " fmt=\"none\",\n", + " ecolor=\"0.6\",\n", + " capsize=8,\n", + " label=\"what was quoted\",\n", ")\n", - "plt.show()\n", - "\n", - "rho_map = np.exp(np.median(s_scaled[:, rho_cols], axis=0))\n", - "fig, ax = plt.subplots(figsize=(6, 4))\n", - "ax.plot(x_fine, P.polyval(x_fine, a_true), \"k--\", label=\"truth\")\n", - "for d, rho, r_true in zip(datasets, rho_map, rho_true):\n", - " ax.errorbar(\n", - " d.x,\n", - " d.y / rho,\n", - " d.y_err / rho,\n", - " fmt=\"o\",\n", - " ms=3,\n", - " label=f\"{d.label} / {rho:.2f} (true {r_true:.2f})\",\n", - " )\n", - "ax.set(xlabel=\"x\", ylabel=\"y / rho\", title=\"data divided by the inferred scale\")\n", - "ax.legend(frameon=False, fontsize=8)\n", + "ax.errorbar(\n", + " positions,\n", + " rho_mean,\n", + " rho_std,\n", + " fmt=\"o\",\n", + " color=plotstyle.COLOURS[2],\n", + " label=r\"inferred $\\rho_i$\",\n", + ")\n", + "ax.plot(\n", + " positions,\n", + " [settings[label][\"rho\"] for label in free],\n", + " \"x\",\n", + " ms=11,\n", + " color=\"k\",\n", + " label=r\"true $\\rho_i$\",\n", + ")\n", + "ax.set(\n", + " xticks=positions,\n", + " xticklabels=free,\n", + " ylabel=r\"$\\rho$\",\n", + " title=\"The normalisations, recovered\",\n", + ")\n", + "ax.legend(fontsize=8)\n", "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "8307df97", + "metadata": {}, + "source": [ + "## Where the Peelle mode actually bites\n", + "\n", + "In the comparison above, building the mode from the data barely moved the\n", + "answer. That is worth understanding rather than glossing over: with an\n", + "absolutely normalised experiment holding the scale and four others overlapping,\n", + "there is very little room for the covariance to pull the fit down.\n", + "\n", + "Peelle's Pertinent Puzzle shows itself in the case it was discovered in —\n", + "fitting a *constant* to data that share one normalisation. Two points, 1.5 and\n", + "1.0, each with a 10 % statistical error and a common 20 % normalisation\n", + "uncertainty. Build the mode from the prediction and the fit lands between the\n", + "points; build it from the data and it lands **below both of them**, at exactly\n", + "the value [D'Agostini\n", + "(1994)](https://doi.org/10.1016/0168-9002(94)90719-6) derived:\n", + "\n", + "$$t = \\frac{\\bar y}{1 + (s/\\sigma)^2 \\sum_i (y_i - \\bar y)^2}.$$" + ] + }, { "cell_type": "code", - "execution_count": 8, - "id": "afd811d5", + "execution_count": 17, + "id": "b3cf1ec6", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:37:22.548051Z", - "iopub.status.busy": "2026-09-11T03:37:22.547911Z", - "iopub.status.idle": "2026-09-11T03:37:22.700688Z", - "shell.execute_reply": "2026-09-11T03:37:22.700226Z" + "iopub.execute_input": "2026-09-12T03:15:00.423625Z", + "iopub.status.busy": "2026-09-12T03:15:00.423382Z", + "iopub.status.idle": "2026-09-12T03:15:00.437225Z", + "shell.execute_reply": "2026-09-12T03:15:00.436693Z" } }, "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 600x400 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "mode from the prediction MAP t = 1.2035\n", + "mode from the data (PPP) MAP t = 0.8333\n", + "no normalisation at all MAP t = 1.2500\n", + "\n", + "the data are [1.5 1. ], mean 1.250\n", + "D'Agostini's closed form for the data-built mode: 0.8333\n" + ] } ], "source": [ - "fig, ax = plt.subplots(figsize=(6, 4))\n", - "for name, s in samples.items():\n", - " lo, hi = np.percentile(curves(problems[name], s), [5, 95], axis=0)\n", - " ax.fill_between(x_fine, lo, hi, color=colors[name], alpha=0.3, label=name)\n", - "ax.plot(x_fine, P.polyval(x_fine, a_true), \"k--\", label=\"truth\")\n", - "ax.set(xlabel=\"x\", ylabel=\"y\", title=\"90 % bands of the model\")\n", - "ax.legend(frameon=False, fontsize=8)\n", - "plt.show()" + "y_pair = np.array([1.5, 1.0])\n", + "sigma_pair, s_pair = 0.1, 0.2\n", + "d_pair = rx.Dataset(\n", + " np.array([0.0, 1.0]), y_pair, np.full(2, sigma_pair), norm_err=s_pair, label=\"pair\"\n", + ")\n", + "t = rx.Parameter(\"t\", prior=stats.uniform(0.0, 5.0), latex=\"t\")\n", + "const = rx.Model(lambda x, t: np.full(np.size(x), t), [t])\n", + "comp_pair = rx.Comparison(d_pair, const)\n", + "\n", + "panel = {\n", + " \"mode from the prediction\": rx.Constraint(\n", + " [comp_pair], terms=[T.normalization(magnitude=s_pair)]\n", + " ),\n", + " \"mode from the data (PPP)\": rx.Constraint(\n", + " [comp_pair], terms=[rx.Term(s_pair * d_pair.y, kind=\"mode\", on=comp_pair)]\n", + " ),\n", + " \"no normalisation at all\": rx.Constraint([comp_pair]),\n", + "}\n", + "for name, c in panel.items():\n", + " p = rx.Problem([c])\n", + " best = minimize_scalar(\n", + " lambda v: -p.log_posterior(np.array([v])), bounds=(0.1, 4.0), method=\"bounded\"\n", + " )\n", + " print(f\"{name:26s} MAP t = {best.x:.4f}\")\n", + "y_bar = y_pair.mean()\n", + "closed = y_bar / (1 + (s_pair / sigma_pair) ** 2 * np.sum((y_pair - y_bar) ** 2))\n", + "print(f\"\\nthe data are {y_pair}, mean {y_bar:.3f}\")\n", + "print(f\"D'Agostini's closed form for the data-built mode: {closed:.4f}\")" ] }, { "cell_type": "markdown", - "id": "1a15f5af", + "id": "7331f252", "metadata": {}, "source": [ "## A gallery of covariance structures\n", "\n", - "Every term writes a pattern into Σ. `constraint.matrix(theta)` returns the\n", - "assembled covariance at any parameter value, so the pattern can be drawn." + "Every term writes a pattern into $\\Sigma$, and `constraint.matrix(theta)` hands\n", + "us the assembled covariance at any parameter values, so we can simply look." ] }, { "cell_type": "code", - "execution_count": 9, - "id": "e36a7f83", + "execution_count": 18, + "id": "1c04818c", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:37:22.702362Z", - "iopub.status.busy": "2026-09-11T03:37:22.702148Z", - "iopub.status.idle": "2026-09-11T03:37:22.706773Z", - "shell.execute_reply": "2026-09-11T03:37:22.706117Z" + "iopub.execute_input": "2026-09-12T03:15:00.438673Z", + "iopub.status.busy": "2026-09-12T03:15:00.438433Z", + "iopub.status.idle": "2026-09-12T03:15:00.442172Z", + "shell.execute_reply": "2026-09-12T03:15:00.441655Z" } }, "outputs": [], @@ -472,43 +984,44 @@ " ax.axhline(k - 0.5, color=\"k\", lw=0.5)\n", " ax.axvline(k - 0.5, color=\"k\", lw=0.5)\n", " ax.set(title=title, xticks=[], yticks=[])\n", + " ax.grid(False)\n", " return im\n", "\n", "\n", - "d0 = datasets[0]\n", - "theta_map = np.median(samples[\"normalisation mode\"], axis=0)" + "first = comps[\"exp 0\"]\n", + "theta_map = np.median(samples[\"quoted systematics\"], axis=0)[: len(cubic.params)]" ] }, { "cell_type": "markdown", - "id": "1c5a8ff7", + "id": "d2ae5f53", "metadata": {}, "source": [ - "### 1. A systematic correlated across x: flat mode versus smooth kernel\n", + "### 1. A systematic correlated across $x$: flat mode versus smooth kernel\n", "\n", - "A normalisation is a rank-one mode: every pair of points is correlated by\n", - "the same fraction. A Gaussian-process kernel correlates neighbours in `x`\n", - "and forgets across the range. Both are one term." + "A normalisation is a rank-one mode: every pair of points is correlated by the\n", + "same fraction, however far apart they are. A Gaussian-process kernel instead\n", + "correlates neighbours in $x$ and forgets across the range. Both are one term." ] }, { "cell_type": "code", - "execution_count": 10, - "id": "33af7f01", + "execution_count": 19, + "id": "7348683c", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:37:22.707880Z", - "iopub.status.busy": "2026-09-11T03:37:22.707736Z", - "iopub.status.idle": "2026-09-11T03:37:22.791636Z", - "shell.execute_reply": "2026-09-11T03:37:22.790837Z" + "iopub.execute_input": "2026-09-12T03:15:00.443580Z", + "iopub.status.busy": "2026-09-12T03:15:00.443434Z", + "iopub.status.idle": "2026-09-12T03:15:00.648097Z", + "shell.execute_reply": "2026-09-12T03:15:00.647533Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 700x350 with 3 Axes>" + "<Figure size 836x396 with 3 Axes>" ] }, "metadata": {}, @@ -516,66 +1029,53 @@ } ], "source": [ - "c_flat = rx.Constraint([comps[0]], terms=[T.normalization(magnitude=0.1)])\n", - "c_kernel = rx.Constraint(\n", - " [comps[0]], terms=[T.kernel(ConstantKernel(0.05**2, \"fixed\") * RBF(0.15, \"fixed\"))]\n", - ")\n", - "fig, axes = plt.subplots(1, 2, figsize=(7, 3.5))\n", - "p_flat, p_kernel = rx.Problem([c_flat]), rx.Problem([c_kernel])\n", - "show(\n", - " axes[0],\n", - " p_flat.constraints[0].matrix(theta_map[: p_flat.ndim]),\n", - " \"normalisation: rank one\",\n", - ")\n", - "im = show(\n", - " axes[1],\n", - " p_kernel.constraints[0].matrix(theta_map[: p_kernel.ndim]),\n", - " \"kernel: smooth in x\",\n", + "p_flat = rx.Problem([rx.Constraint([first], terms=[T.normalization(magnitude=0.1)])])\n", + "p_kernel = rx.Problem(\n", + " [\n", + " rx.Constraint(\n", + " [first],\n", + " terms=[T.kernel(ConstantKernel(0.05**2, \"fixed\") * RBF(0.15, \"fixed\"))],\n", + " )\n", + " ]\n", ")\n", - "fig.colorbar(im, ax=axes, shrink=0.8, label=\"correlation\")\n", + "fig, axes = plt.subplots(1, 2, figsize=(7.6, 3.6))\n", + "show(axes[0], p_flat.constraints[0].matrix(theta_map), \"normalisation: rank one\")\n", + "im = show(axes[1], p_kernel.constraints[0].matrix(theta_map), \"kernel: smooth in $x$\")\n", + "fig.colorbar(im, ax=axes, shrink=0.85, label=\"correlation\")\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "28bc55cf", + "id": "6fba0a03", "metadata": {}, "source": [ - "### 2. Correlated statistical errors: bring the matrix, and do not diagonalise it\n", + "### 2. Correlated statistical errors: bring the matrix, do not diagonalise it\n", "\n", - "If the experiment reports a full statistical covariance, pass it as a fixed\n", - "`Term`. Treating correlated noise as independent over-counts the\n", - "information: the naive posterior is visibly tighter than the correct one." + "If an experiment reports a full statistical covariance, we pass it as a fixed\n", + "`Term`. Treating correlated noise as independent over-counts the information,\n", + "and the naive posterior comes out tighter than the correct one — tighter and\n", + "wrong, which is the theme of this notebook." ] }, { "cell_type": "code", - "execution_count": 11, - "id": "92010ede", + "execution_count": 20, + "id": "1bedc753", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:37:22.792950Z", - "iopub.status.busy": "2026-09-11T03:37:22.792817Z", - "iopub.status.idle": "2026-09-11T03:37:55.950221Z", - "shell.execute_reply": "2026-09-11T03:37:55.949639Z" + "iopub.execute_input": "2026-09-12T03:15:00.649493Z", + "iopub.status.busy": "2026-09-12T03:15:00.649317Z", + "iopub.status.idle": "2026-09-12T03:15:25.254522Z", + "shell.execute_reply": "2026-09-12T03:15:25.253950Z" } }, "outputs": [ - { - "data": { - "image/png": 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66ady71enn3/+mTfffNOpse6sUwjp+CmEqFf27t1Leno6c+fOLXeti5pux44drFq1yqn1JIYOHUq7du2qoSohSpOQIYSoN3799Vf7ap2PPfYYAO+99x7ffvstRUVFDm2wV65cyebNm/nvf/97Wa+1dOlSTpw4Qd++ffntt99IT0/nhhtuYPDgwWzdupUlS5Zgs9kYMWIEXbt2tT9v/fr1fP755wD4+PjQtm1b7r33Xnsg+uuvv/j5559JTk7mwQcfBEpaeScnJ3PixAkGDRrEokWLyM3N5b333iMpKYl9+/YxbNgwrFYr06ZNo2vXrg7tzl9++WVCQ0N59NFHL+u9ClEeOV0ihKg3oqKiaNWqFQA9evSgR48eaDQa1q1bx59//ukwdvfu3aVWT70U//zzD6+99hr3338/er0elUrFDTfcwL333stDDz1ESEgIeXl59O7dmz179tifFxoaaq+tUaNGfPzxx1x99dVYrVYAGjRoQHR0NL6+vvZxoaGh/PPPP/a1Uvz8/OjSpQvgeLpEo9HQuXNnxowZw759+wD49NNPmTlzJoMGDbrs9ypEeeRIhhCiSiiKgqnY6pbXNug0pZa2Lku7du24/vrrmT17tv0ogCuZzWb++usv+zoehw8f5pdffuH48eP2tU327t3LV199ZZ9j0aJFC1q0aGHfx6OPPkpMTAzLly9n2LBhtG7dmk6dOnH27NlS7yE3N5cdO3bQsGHDcmsaNWoUK1as4K677uKrr75iwoQJzJkzh2bNmlXxuxdCQoYQooqYiq20/u/vbnnt/S8Pxsuj5v1z1q5dO4eFwuLi4igqKnJYPC0uLo7Tp087PG/Tpk38/vvvpKamYrFYUBSFAwcOMGzYsApfr23bthUGjHPmzp1Lhw4d6N69O9dff321BC5RP8npEiGEcBGDweBwX61Wl7nt3KkQgNmzZ3PDDTeQnZ1NmzZt6NGjB35+fuTm5lb6es6u/Orn50fXrl0xmUzceeedTj1HiMtR86K/EKJWMug07H95sNte+0ro9XrOnj3rsM1oNF7RPi/XBx98wBtvvMEjjzxi3/bqq686jHHm1FBFFi9ezM8//8zo0aN58skn6d+/vyzNLlxCQkYVstlsJCcn4+vre8X/CAhR1yiKQm5uLpGRkajVNesgalxcHMuWLcNsNuPh4UFeXh7ff/99qaMO1eHc1+mcb775hpMnTzqMCQoKKhWKnJWYmMi4ceOYOXMmjz76KH369OGBBx6QXhrCJSRkVKHk5GSnzocKUZ8lJiYSHR3t7jIc3H///cydO5eOHTvSrl07tm/fTmhoKPn5+dVey7Rp03jyySfZsGEDBQUF7N69m8aNGzuMGTx4MFOnTmXo0KGEh4c7PafCZrMxevRoevTowRNPPIFKpWLhwoV06NCBjz/+2OESXiGqgoSMKuTr6wuU/CPq5+fn5mrEOfn5+URGRgIlQdDb29vNFdVPOTk5NGzY0P5z4i6tW7fmk08+cdgWEBDArl27+Ouvv7BYLLz99ttkZmZy9OhR+5iLm1pV1uTq9ttvp1+/fg7b7rzzTnJychy2jRkzhuLiYvv9hx9+mL59+7J9+3b8/Py45pprWLt2LeHh4fYxzZs35+jRo2zatIns7GxCQ0PLfL2L6zxz5gz33HMPN910k/1oa2xsLL/99hunTp0q970IcblUiqIo7i6irsjJycHf35/s7GwJGTVIfn4+Pj4+AOTl5UnIcBP5+RCi/qlZJ0aFEEIIUWdIyBBCCCGES0jIEEIIIYRLSMgQQgghhEtIyBBCCCGES0jIEEIIIYRLSMgQQgghhEtIyBBCCCGES0jIEEIIIYRLSMgQQgghhEtIyBBCCCGES0jIEEIIIYRLSMgQQgghhEtIyBBCCCGES2jdXYAQ1cm0fz9qLy/7fW1gILrISDdWJIQQdZeEDFHnFaek2P988u578FKfP4CnMhiIXb5MgoYQQriAhAxR51mMRvufGy/8Cu9/j2SYExJInjIVi9EoIUMIIVxAQoaoVwytW2Pw9nZ3GUIIUS/IxE8hhBBCuISEDCGEEEK4hIQMIYQQQriEhAwhhBBCuISEDCGEEEK4hIQMIYQQQriEXMIq6rSkLBPHjcV4NIgFYH9KLgaDBYDCzCJS/KPIzywiIstEVIDBnaUKIUSdo1IURXF3EXVFTk4O/v7+ZGdn4+fn5+5y6q3i5GQsRiPJ+RaG/pSEyVr5t7hBp2HVpH4SNFxIfj6EqH/kSIaoU4qTkzl241AUk4mj/lGY+v+HCVu/4ul9WwFYv349BkNJkCg8doyUKVMxPf8aUzdkYMw3S8gQQogqJCFD1CkWoxHFZCJy1kzy/aNgRQpdp43HPLg3AK0jfPH+t+OnKUuPd3YS+f46d5YshBB1loQMUSd5xMTgGRANpKCPiXF3OUIIUS9JyBC1UlKWCWO+udT2CydznjbnuaEyIYQQ50jIELVOUpaJQW//janYWvaA/v+BFSlACgadhgAvOR0ihBDuICFD1DrGfDOmYitzRnYgLszH4bFzkzkjZs3EMzaWQG8PAnQ2N1UqhBD1m4QMUWvFhfnQNsrfYdu5yZxNgvUY/n0sPz+/wv1YkpIANYXHjmHK0gOgDQxEFxnpkrqFEKK+kJAh6i1tYCAqg4H0Oe9A//+QMmUq3tlJAKgMBmKXL5OgIYQQV0BChqi3dJGRxC5fRt7RVFiRQsSsmTQJ1mNOSCB5ylQsRqOEDCGEuAISMkS9pouMxFPxBlLwjI21n2IRQghx5WSBNCGEEEK4hBzJEDVKef0vLnQ0TfpfCCFEbSAhQ9QYlfa/uIBBpyHQ26MaqhJCCHG5JGSIGqOi/hcXC/T2kMXMhBCihpOQIWqcsvpfCCGEqH1k4qcQQgghXEJChhBCCCFcQkKGEEIIIVxCQoYQQgghXEJChhBCCCFcQq4uEbVWcXIyFqPRYZs5IeGy93euyVdhZhEp/lHkZxbhmZTtMEYunRVCCOdJyBC1UnFyMsduHIpiMpV6TGUwoA0MdHpfgd4eGHQaJi6OP7+x/39gRQqQ4jDWoNOwalI/CRpCCOEECRmiVrIYjSgmE5GzZuIRE+PwmDYw8JJWT40KMLBqUj97O/PCY8dImTKViFkz8YyNtY87mpbHxMXxGPPNEjKEEMIJEjJEreYRE4OhTZsr3k9UgMEeHExZeryzk2gSrJdVWYUQ4grIxE8hhBBCuISEDCGEEEK4hIQMIYQQQriEhAwhhBBCuISEDCGEEEK4hIQMIYQQQriEhAwhhBBCuISEDCGEEEK4hIQMIYQQQriEhAwhhBBCuIS0FReiHBev6FqYWeSmSoQQonaSkCHERbSBgagMBpKnTHXYnuIfBf3/gyU9HWRNEyGEqJSEDCEuoouMJHb5MixGo8P27N0JsAssOTluqkwIIWoXCRlClEEXGVlquXhtZhHsSnFTRUIIUfvIxE8hhBBCuISEDCGEEEK4hIQMIYQQQriEhAwhhBBCuISEDCGEEEK4hIQMIYQQQriEXMIqqkVSlgljvrnCMUfT8qqpGiGEENVBQoZwuaQsE4Pe/htTsbXSsQadhkBvj2qoSgghhKtJyBAuZ8w3Yyq2MmdkB+LCfCocG+jtQVSAoZoqE0II4UoSMkS1iQvzoa2s+SGEEPWGTPwUQgghhEtIyBBCCCGES0jIEEIIIYRLSMgQQgghhEtIyBBCCCGES0jIEEIIIYRLSMgQQgghhEtIyBBCCCGES0jIEEIIIYRLSMdPUeMVJydjMRodtpkTEtxUjRBCCGdJyBA1WnFyMsduHIpiMpV6TGUwoA0MdENVQgghnCEhQ9RoFqMRxWQictZMPGJiHB7TBgaii4x0U2VCCCEqIyFD1AoeMTEY2rRxdxlCCCEugYQMIS5RQnYxnknZFY6RJeuFEEJChhBOC/TUoLeYmbohAzasr3CsQadh1aR+EjSEEPWahAwhnBTpreXj1TPxfmcunrGx5Y47mpbHxMXxGPPNEjKEEPWahAwhLkGYKYsmwXoMUf7uLkUIIWo8acYlhBBCCJeQkCGEEEIIl5CQIYQQQgiXkJAhhBBCCJeQkCGEEEIIl5CQIYQQQgiXkJAhhBBCCJeQkCGEEEIIl5CQIYQQQgiXkJAhhBBCCJeQkCGEEEIIl5CQIYQQQgiXkJAhhBBCCJeQVViFuETmhIRS27SBgegiI91QjRBC1FwSMoRwkjYwEJXBQPKUqaUeUxkMxC5fJkFDCCEuICFDCCfpIiOJXb4Mi9HosN2ckEDylKlYjEYJGUIIcQEJGUJcAl1kpAQJIYRwkkz8FEIIIYRLSMgQQgghhEvI6RJxRZKyTBjzzRWOOZqWV03VCCGEqEkkZIjLlpRlYtDbf2MqtlY61qDTEOjtUQ1VCSGEqCkkZIjLZsw3Yyq2MmdkB+LCfCocG+jtQVSAoZoqE0IIURNIyBBXLC7Mh7ZR/u4uQwghRA0jEz+FEEII4RISMoQQQgjhEnK6RAgXceaqGpmrIoSoyyRkCFHFAr09MOg0TFwcX+lYg07Dqkn9JGgIIeokCRlCVLGoAAOrJvVzqn/IxMXxGPPNEjKEEHWShAwhXCAqwCDBQQhR78nETyGEEEK4hIQMIYQQQriEhAwhhBBCuISEDCGEEEK4hIQMIYQQQriEhAwhhBBCuIRcwipqjOLkZCxGo8M2c0KCm6oRQghxpSRkiBqhODmZYzcORTGZSj2mMhjQBga6oSohhBBXQkKGqBEsRiOKyUTkrJl4xMQ4PKYNDEQXGemmyoQQQlwuCRmiRvGIicHQpo27yxBCCFEFZOKnEEIIIVxCQoYQQgghXEJChhBCCCFcQkKGEEIIIVxCQoYQQgghXEJChhBCCCFcQkKGEEIIIVxCQoYQQgghXEJChhBCCCFcQkKGEEIIIVxC2ooLUUUuXjFW1lwRQtR3EjKEuELawEBUBgPJU6Y6bFcZDMQuXyZBQwhRb0nIEOIK6SIjiV2+DIvRaN9mTkggecpULEajhAwhRL0lIUOIKqCLjJQwIYQQF5GQIcqUlGXCmG+ucMzRtLxqqkYIIURtJCFDlJKUZWLQ239jKrZWOtag0xDo7VENVQkhhKhtJGSIUoz5ZkzFVuaM7EBcmE+FYwO9PYgKMFRTZUIIIWoTCRmiXHFhPrSN8nd3GXWeM6edJMwJIWojCRlCuEmgtwcGnYaJi+MrHWvQaVg1qZ8EDSFErSIhQwg3iQowsGpSP6cm2E5cHI8x3ywhQwhRq0jIEMKNogIMEhyEEHWWrF0ihBBCCJeQkCGEEEIIl5CQIYQQQgiXkJAhhBBCCJeQkCGEEEIIl5CQIYQQQgiXkJAhhBBCCJeQPhnCLYqTk7EYjfb75oQEN1YjhBDCFSRkiGpXnJzMsRuHophMDttVBgPawEA3VSWEEKKqScgQ1c5iNKKYTETOmolHTIx9uzYwEF1kpBsrE0IIUZUkZAi38YiJwdCmjbvLEEII4SIy8VMIIYQQLiFHMuqZpCyTU6t+CiGEEFdKQkY9kpRlYtDbf2MqtlY61qDTEOjtUQ1V1W1lXTUjc0+EEPWFhIx6xJhvxlRsZc7IDsSF+VQ4NtDbQ5YgvwLawEBUBgPJU6aWekxlMBC7fJkEDSFEnSchox6KC/OhbZS/u8uo03SRkcQuX+bQCwRKjmwkT5mKxWi85JDhzGksCYdCiJpEQoYQLqKLjKySoxWB3h4YdBomLo6vdKxBp2HVpH4SNIQQNYKEDCFquKgAA6sm9XNqwu7ExfEY880SMoQQNYKEDCFqgagAgwQHIUStI30yhBBCCOESEjKEEEII4RISMoQQQgjhEhIyhBBCCOESEjKEEEII4RISMoQQQgjhEnIJq3Cp4uTkMrteCiGEqPskZAiXKU5O5tiNQ1FMplKPqQwGtIGBbqhKCCFEdZGQIVzGYjSimExEzpqJR0yMw2OyEqkQQtR9EjKEy3nExGBo08bdZQghhKhmEjLqiKQsk1NrWwghhBDVRUJGHZCUZWLQ239jKrZWOtag0xDo7VENVYmKlDX5VU4hCSHqGgkZdYAx34yp2MqckR2IC/OpcGygt4cstOVG2sBAVAYDyVOmlnpMZTAQu3yZBA0hRJ0hIaMOiQvzoW2Uv7vLEBXQRUYSu3xZmZf1Jk+ZisVovOKQ4cxpMQmbQojqICFDiGqmi4x0ydGKQG8PDDoNExfHVzrWoNOwalI/CRpCCJeSkCFEHREVYGDVpH5OTQCeuDgeY75ZQoYQwqUkZIgqIZ09a4aoAIMEByFEjSEhQ1wx6ewphBCiLBIyxBWTzp5CCCHKIiFDVBnp7CmEEOJCEjJqOOnkWb9UZ5MuudRVCOFqEjJqMOnkWX9UZ5MuudRVCFFdJGTUYNLJs/6ojiZd58ilrkKI6iIhoxaoSZ085VJV13FVk66yXMqlrnJaRQhxuSRkuEltnGshl6q6z8VBrjqu2pHTKkKIKyUhwwX2J2fjk6uU+3hmvplxC7bXmLkWZR2dKIs5IUEuVa1m5c3VUBkMRL/7LpqgwFLj3XVaZevxsxgrOK2Xl5tTJXUJIWoPCRlVSFFKgsXwd/9ErfeqcKynTs0Hd3YkyEtX4bgALw981cXk5BRXWZ0XKk5J4fiIO8o8OlEWlcGApXlzVBERjvsBTDk180MkPz/f/uecnBys1srDXY3h40Poom+wZGXZN1mNWSRPncqBsWNLDVcZDDT97lt0F/39XC5fNfj6qioco7Vq8bAV8uSXGyscZysqAM7/nAgh6j6VIj/xVeb06dM0bNjQ3WUIUaMlJiYSHR3t7jKEENVAQkYVstlsJCcn4+vri0pV8W9/5+Tk5NCwYUMSExPx8/NzcYVVQ2quPrWx7vJqVhSF3NxcIiMjUavVbqxQCFFd5HRJFVKr1Zf9G5qfn1+t+RA5R2quPrWx7rJq9vevGVdJCSGqh/w6IYQQQgiXkJAhhBBCCJeQkOFmer2e6dOno9fr3V2K06Tm6lMb666NNQshXEMmfgohhBDCJeRIhhBCCCFcQkKGEEIIIVxCLmGtQpfTJ0OI+sLZPhnycyRE+WpbvxkJGVUoOTlZOn4KUYnKOn7Kz5EQlastnXMlZFQhX19fgCrtzujstFwFBWd/56vK3w4vZdqwu34pzc/PJ/LfRcOSk5Px9vZ2TyH13LlOoOd+Tsrjip8jUTXkZ8n9nP05qikkZFShcx/eVdmdUULGldNoNPY/+/n5yT+MblbZ958rfo5E1ZCfpZqjtpxKrPkndIQQQghRK0nIEEIIIYRLSMgQQgghhEtIyBBCCCGES0jIEEIIIYRLSMgQQgghhEvIJawuoCjOXdrp3BVIzl7DqqA4s0MnrzlVwOlLYi9lZKWvWwsuiRVCCOEcCRl1iDOfua5Yclc+7IUQQpRFTpcIIYQQwiUkZAghhBDCJSRkCCGEEMIlJGQIIYQQwiUkZAghhBDCJSRkCCGEEMIl5BJWIYQQFUrKMmHMN2MymfBoEAvA/pRcDAaLw7hAbw+iAgzuKFHUUBIyhBBClCspy8Sgt//GVGwFIGLMOwCM+N/2UmMNOg2rJvWToCHsJGS4jeJUd0ub4lyTLZsCaidabTk7TlEAtTMdRO3/cYJ07RKitjHmmzEVW5kzsgNRvhr69OkDwPr16zEYzoeJo2l5TFwcjzHfLCFD2EnIcAGVqvIumM62z1bhXEdNNaByYqAaxalxoEgHUSGEXVyYD00DtJjPHAOgdYQv3t7ebq5K1HQy8VMIIYQQLiEhQwghhBAuISFDCCGEEC4hIUMIIYQQLiEhQwghhBAuISFDCCGEEC4hIUMIIYQQLiEhQwghhBAuIc24XEBRFJRKum0pCk41wHS2oabTHT9tCmonoqXVpjjX8FNRUDuxQ0UBtdqJTqP2/1TOuaZiQggh3EVChhs5+xFZpR0/1ZcwzskK5bNeCCFEWeR0iRBCCCFcQkKGEEIIIVxCQoYQQgghXELmZAghhKgyR9PyKh0T6O0hy8HXExIyhBBCXLFAbw8MOg0TF8dXOtag07BqUj8JGvWAhAwhhBBXLCrAwKpJ/TDmmyscdzQtj4mL4zHmmyVk1AMSMoQQQlSJqAADUQEGipOTsRiN9u3awEB0kZFurEy4i4QMIYQQVaY4OZljNw5FMZns21QGA7HLl0nQqIckZLiJ0508UVArlXe7sjrZ8bPYqqBWXTCunKdYrDY02sovPjJbbGidaA1qs4HeQ+PEOAWNE/tTFAWNE9dGKQoO3Vcr6sYqHUSFuHIWoxHFZCJy1kw8YmIwJySQPGUqFqNRQkY9JCHDBVQqVaUfWM4EAgC1UrUdP1Uq5aJx5X/gqp3aX8l7tdkU4hOzWLE3hZSsQoZ3ieaa5qH211KpFKc7nAohaj+PmBgMbdq4uwzhZhIyxGVTFIU9Sdms3JfKb3tTSckutD+2+mAaPWOCmTakBS0j/NxYpRBCCHeRkFHPnTpbwLojGfgbdIT7eRLuryfM17PcoxiKonDoTC7Ld6ewfE8KiWfPn3f11msY2LIBAV46vvnnFJsSMrl13kZu6xjFkwOaER3kVV1vSwjhhKQsk1NXgwhxuSRk1GPfbk3k1eUHMBVbSz0W7O1BuL8nDfw8CffzpIGfHrPVxq97U0lIz7eP89Sp6d8ijBuuiuDqZiF46krmXdzbszGzVx5mxd5UluxIYsXeVB65uin39GhMoJeHrKomhJslZZkY9PbfZf78X8yg0xDo7QHYLvv1zAkJABRmFgFgSU+HKP/L3p+oHep1yHjppZe47777aNKkibtLqXZH0/J47se9ALSN9MPgoSE1p5DU7EKKrQqZ+WYy883sS84p9VwPrZp+zUK5sV0EveKC8fPUlRoTHejF7JEdGN3TyBu/HWRXYjZzVh9l7ppj9G8Rxm2dounXPBQPJyaXCiGqnjHfjKnYypyRHYgL86lw7LkOnfn5+RWOK4s2MBCVwUDylKkApPhHQf//kPjkBNos+kQmg9Zx9TZkjB8/ns2bNzNhwgR3l+IWCeklh0BbRfiy5NFeqP+9okNRFIwFxSQZC0jPM3Pm3+CRmlNIkcXGNc1DGdgqDN9/g0WRpeLfbDo2CmTRQz1YsSeF+RtOsC85h5X7z7By/xl89Foi/D0J9dUT4qMn1FdPkLcHYRfcD/Ep2ebMFSdCiEsXF+ZDWxceUdBFRhK7fJm9b0Z+ZhGsSIGiIrnipB6olyHjXMD4448/CAgIcHc5bnH23/OwEf4Ge8CAkqtFgrw98PPUcpUz14g6QaVScX3bCG7pGM2h1ByW7kzip/hk0nOLOJKWx5FKzvn66LU81j+WB3o3RVtFNQkhqo8uMtIeJjyTsoEU9xYkqk29Cxnjx49n3bp1rFmzhoCAAHbs2MGsWbNISEigY8eOvPDCC0RFRTm1r6KiIoqKiuz3c3JKn1qoqc6FjCBvj2p93RbhfjxzvR9TrmvBsfR8MvKKSM8tKvn/f/+cmWe2bz9bYCavyMLM3w6xfHcKr996lUt/6xJCCFF16lXIsNlsZGdnc/LkSY4dO0ZiYiJjxoxh1KhR9O3blwULFvDzzz+zceNGp+ZpzJgxg5deesn1hbvA2QL3hIxztBo1LcJ9aYGvw3brRc24LFYbP8Yn8/qKA+xLzuG2eRt5oHcTxvdvho9nOc29ZFKpEELUCPUqZKjVaj7//HPGjBnDoEGD0Ol0rFq1ii5dugAwadIkunbtylNPPcXSpUsr3d8zzzzDU089Zb+fk5NDw4YNq7RmxcnWoFarDbXaiQ6dxVY0GjUZeSUhw9+go9hael5FdkExHk6cmsjIN+Opq3xcYbHVqUBjttjwNThOJL3+qnC6NQ3kzV8P8du+VD5ed5xf96byyi1t6RkTXHonDr3GFC5s8FnSAbT0UxQUnD0RI51BhRDCOfUqZIBj0LDZbPaAARAeHs5TTz3Fq6++6tS+9Ho9er3+supw9oPKidzw7zi1U/vUaTVo1Cqy/j2SEeqrR1dGmNBq1A5zNSqiUatQFIUdp7JYuOUUh1Nz6dAwgGtahNIrLhgvDy0Kl/CeyxgX6uvJW3e0Z+ihCF5ZdoBEo4kxn21leKcopg1pib+h9BUuwL+ve/6+SlXOgQ7nGrAKIYS4BHU+ZGzYsIGffvoJPz8/xo0bR0hIiD1o7Nmzp9R4jUZDo0aN3FBp9bLPyfC6stMlVpvCmkNpLNxyigMpufbtaw6ns+ZwOh5aNd2bBtErNpjrr4rAR39l33LXtAijS+Mg/m/VYb7dlsj3O5JYczidF25szZA2DeQogxBC1CB1OmQ8/fTTfPLJJ3Tt2pUtW7bwxRdfsHPnTnx8fFCr1bRv395hfFZWFu+88w4vv/yymyquPudCRqB32UcAKmO22Fi2O5nPNp7gtLGk66eHVs3QdhH0ax7KthNn+etQOqeNJtYdyWDdkQxm/3GYHjHBDGrVgL7NQ+yXwV4qH08tz1zfkls6RPH8j3s4lp7PhMXxDGwZxoxb2xLgfXlHl4QQQlStOhsyvv/+e5YsWcKBAwcICwvj2LFjtG3blrlz5zJt2jSHsUajkVWrVvH0008zatQo7r77bjdVXT3O9cKAS5/4WWy18fWWU3y15aR9Xoevp5bbOkYxvHP0v10BoXPjQB7uG8Ox9Hz+OpjG6oNpDoFDq1bRIyaYoe0iuLp5CHpt5Su0Xqxz40B+erw38/5O4OO1x1h9MI3R87cyf0xXQn0laAghhLvV2ZDx9ttv87///Y+wsDAAYmNjufnmm9m+fXupsRs3bmTjxo0sWrSIrl27Vnep1S630IL53yZalxoyluw4zZzVRwAI89Vza8cobu8UhVcZp0FUKhVxYT7EhflwZ7eGZJssrD5whtUH0kjIyGf90QzWH83AR69lYKswrm8bTpvIS1tMzUOrYcLAZlzXugEPfbmNQ2dyGf7hRt6/qxNXyaWuQtRYp3zD2LU7Ae2/bca1fn5oQ0MdxpzrNCpqrzoZMmw2G/7+/vTr189he1xcHDt27Cg1/sYbb+TGG2+srvLc7tyCRw389Hh5XNq3QHpuyT8Ig1qF8fqtV2EsKMZLX/lRiAsDxyP9YklIz+PXPams2JvCmZwifopP5qf4ZMJ89QxtF8GN7SJpEe5b6X7PaRXhx8IHu/Pwgu0cz8hn1CebeXlYG27t6FzPEyFE9Qj09sCgVTOry92wC9h1rjFXCnDIYaxBp2HVpH4SNGqxOhky1Go1K1asKLXdy8sL5YLrF3Nzc0lJSaF58+bVWZ7bHUwtmaDZMvzSl2AvMJcsptQk2LvMq1KcFRPqw+MD4ni0fyw7T2Xx654UVh1IIy23iPkbTjB/wwmaNfBh6FUR3NAuggj/yv+RaRzszffjejL1+92sPpjG00v3sCcpm8kDGl92nUKIqhUVYGDV5GtIP5GE5d8GhpakJNLnvEPErJl4xsYCJb8MTVwcjzHfLCGjFquTIQMos2eESqXCai35kMzNzWXw4MEMHDiQV155pbrLc6tD/4aMSzlScI7p35Bh8Lj0ORRlUatUdG4cSOfGgUwe3Jy/1uzmj/hENhUZOHImj/87c4R3/zzKhIHNuL93k0qvHvH11DH3rk58sOYo7/55tOSql+Qs1N4B2PKzqqRmIcSViQowENUhzn7ftE/PiewkmgTrMchpzjqlzoaMsqjVahRFsQeMzp0717uAAXDozOWHjHyzBQDvSzzNUh5FUbDs3UPRqj8o+ms1XZKT6QLk6gysj2zHn636sdczjNl/HOZgag4vDWtbacBRq1U8MaAZbSL9mfTdLnYk5hBx3zuk//h6ldQshDivODnZvvgZnF/SXQioZyFDpVI5BIz33nvPbbWU1XWyLDZFwZnOD4qTOywstnAwpeQQZUywF0XF1jLH5RSW3fEzt7AkZKjVkFdkISOvkHxz5d9Gx9PzSi1u5r1jM2Hz30OXcca+zeahR9W1Oz4GL4asWcWQk1tY3rQXH151Myv2pHLowEmev6MrwcG+RPt7VfiaXZsE8dl9XZm2dBcJQPhdb/DNtiRG944rNVZRFKeXnddILw4hgJKAcezGoSgmk8N2lcGANjDQTVWJmqRWhwyr1YpG4/xhe41Gw5YtW3jiiSfcGjAuhUqlcipkaNTOddTMyDWTb7ai06iIa+Bb7qqmvnptmR+6F16V4uupJcDggcGJiZ+5hRaHvhh+G/4k8sM3UdlsWD0N5HXqSU73vhhbd6R9swgAlEeegNUrGbpxLY02fcLrXe7hGD5M+GIzT7fxovGd11X6uk1Dvfl8dHs6j3sL7xa9ee23oxzOKOS5G1rhqXOsWxp5CXFpLEYjislE5KyZeMTE2LdrAwNlCXcB1OKQ8eSTT5Kens5XX31VadCwWCxotVqGDRtGdnZ2vWi2VZ5zp0piQnwua+Jmwb9HPrx0lz8nI+DP5UTMfweVopDVZxApD0xE8Sjpa6FcsI6KKjQM7rwH7ryHdllZvPvXWl7Zl8pRn3CeP2gledrbjHniDnSVrBfjrdeS8eMMzD1GENTvPr7bdprtJ4zMuLUt7RsGyIJqQlwhj5gYDG3auLsMUQNd/uUBbrR9+3aWLl3K0qVLueeee+yTOcvy+++/065dOxITE2nWrFm9DhhwZZM+AQr+nZNhuMw5GcHLvyPy0zmoFIWzg24i+ZEp9oBREVVAAA1uHcZbT15Pf/VZbGoN73tfxTMvLyDjww+xXXS4tiw5m7/jvZFtCfXRk5CRz6j/bWH2H4ftR2eEEEJUrVoZMubOncv06dOdChpNmzYlKyuLL774opqrrJkOX8GkTzh/dYnXZVxdoj+VQIOvPwbg5E1389fg+yh2YiqJkpmBsnkDyoL56P9ayZSHBvNAGz/UisIfDbvw2EEt8fc9SPHBA5Xuq2fTQH4Z35ub2kVgU+Djdce5fd5G9v87T0UIIUTVqZWnS2w2G/fccw8Gg4GlS5dy2223cc8995R56qR58+bs2LGD8PBwN1VbsyRllfzG732Zl6AWXMElrFafkr4cCvBmcA+ObziBj15Ln7gQev+7WiuAkp4Gy36EwwdLbpkZDvtRffkpt941hi6jrmPad7s5FNSIsYGj6fL+H9zSYT/X3ncL+gpO5wR4eTBrRHuuaxPO9J/3cSQtj9Gf/sPCB7uXnD4RQghRJWrlkYzPP/8cg6GkOcuNN95Y5hGN3NxccnNLfmuXgHHe1c1K2vbOXXOM3MLiS37+uVMLeievxLiQJSiEwkYx/B3VgeNZJZ1D84os/LYvlVeWHeCn+CRysnJh/EPwxf9g0/qSgKFSQeOmMPA6aBABmRlo3nuL2KfGML95Ib0a+aGo1GwNa8lzyb4MfP1XXv1xF7tPZ1V41c21rRuwbHwfujcNosBsZcxnWzmUklNy6Y+zl/8IIYQoV60MGRe7OGgYjUYGDx7M/Pnz3V1ajfPw1U1pHOxFem4Rs/84fEnPtSkKFlvJh6+zl3te7Gz77nzWpqSF+7WtGnB390ZE+ntittpYeySDN1cn8FZkP042agVPToL3PoHlf6H6YjGqF16FBd/BhCkoQcFYk5PxnvE8L/38Ot9203FPQD7Bhdnkqjz4bvcZRs//h+EfbWL+xlNofEPKrCfI24MP7u5E+2h/sk3F3PvZVk5m5l/WexNCCOGoToQMcAwaTZs2pXPnzkyYMMHdZdU4ep2Gl28umQX+zT+J7DhprOQZ5104QbKsHhrO+CmyC2legYQUZnNtpI5OjQJ56trmPHR1U5r7abCq1Kxu1IVxnR7gRU0b9gU1QeV1vh+GysMD1a0jsC5Ygt+E/6Dy98dy4gS+z07gwczt/HxtEK8fXkr/xO3orWZOZhYwd+1Joh6dT9jIV1m25wz5RRaHmrz1Wj4a3ZkW4b6k5xYxev5WUrIrn0gqRE2VlGVib1J2hbdzaxgJ4Uq1ck5Gefr27UuLFi3o169fremD4Q7dmwZze6coluxI4oWf9vLDY72dOjJhvuDy0ss5kpFtKua3DBWgMHbvMlps/4RTU1/HHNmQluF+dEnNx/TzR3zXciAbw9uy5fhZthw/S6sIP4Z3jqZHTDAa9b+Xm3p64nPf/XjdPoLcj+aRv3ABpl9+xvTrCnoNHkL37NMYf13Khsh2/NHiavZ6R2Jo0oHpyw8za1UC/zeyvf3UEYC/QceXY7txx0ebOJlZwL3zt7Lo4R4E+8iS8aJ2ScoyMejtvzGV02jvQgadhsBLXIlZiEtRZ0KG2Wxm8ODBdTJgODM7oGQKQeUjrVYbKrWap65tzppD6RxLz+fjtQk8ek2sw7gCs4Viq+Pkyaz883M4ioptmC0KJzLzz3/wV+BEZj7rj2RgttpooIcuBcl4nD1Dw/+OZ82DL2CMjoWgZgw2aHh+8+esvWYkS1r052haHgdScnhl2X58PbW0jvAjLsyHCH9Pws8tmnbDvRiadyV0yZf47t6GafkyFI0GpXlbBp7YT58TW+h+JgufNv1peeMDpOaaeWzhDl4a1oaOjUq6EqpU0DjIi0/v68I9//uHY+n53Dd/K/+7t7PDP8I2RcFTV/mPjaIoTn1dSl5b+nSIqmPMN2MqtjJnZAfiwnwqHCtLqQtXqzOnSzw8PJg+fXqtCRgqlZM3Z/+ncu7m6aHFU6ch3N/AC0NbA/Dx2gSSs0x46jT2W4BXSUfPC2/njl54aNT4GXT/btPg5aGt9JaaU8TR9JK5Dle3iWbzU29ibNwcvSmfqxfOJtBWhK+Xnl+63QJAr3VLaacvom/zUGJCvdFpVOQWWthy/Czfbkvkp51J5Bw5itffKwle+AmqpEQOT3iJg8/PJqdNJ1RWKz4HdmHTaKBVG6w5aWRvWsyctXPoEeZBsVXhpV/2s/t0FlAS0lQqFdGBXswf04Ugbw/2p+Rw67yNbD9hRAUlNwkEopaIC/OhbZR/hTcJGMLV6kzIABg8eLC7S6hVhraLoG/zUMxWGzN+PVjp+HOnSy71VImiKGz/d+5H8wY+hPt7YvYN4J9Hp5Mf3ACvs2m0WzQXFIUjjduSHN0MrbWYZvu34KnT0LyBL/1ahNLNq4ggSwHFVoUj6fm8vj2bH7eeoGjd3zT9eBZtnnkQz5TTHP3PSxx87m0KGsagy8uBA/vsteiOH+WZ+ZPpojdRZLEx/ed97EvOdqg3JtSHL+7vStMQb87kFHHvZ/8wb80xrDa54kQIIS5FnQoZ4tKoVCpeuLEVWrWKPw+mseFoRoXjz0381Gku7bf5bSeNpOcWoVWr6BkTbN9uMXiz877J2DRaInZtIm7ld6BScbh1DwBa71qLylZyXlmrVjP155l8uWw6z2/5nFaZJ7BotKxs3J1xA6fwXJ9xHLB50/jT2bSdcj/eRw9yePLrnBk0zLGY9p3wsJh54buX6Gg8TmGxjRd+3MvBVMdmXM0a+PL9uJ7c3D4SmwLv/HmUB7/cRnpu0SW9dyGEqM8kZNRzMaE+3N29MQCvrThQ4W/rxZdxJMNssbHon0QAOjYKcFgkDSC7cTP23fYAAC1WfE37g5s4eFVvTAYfQtJP03bnX/ax6WGN0KDQUK/Q9apGPNynMW0i/VABO0LieLbPOJ7t+xipxWoaLv6Etk8/QFFIODz/yvkX3BMPnbrg4e/Hf9d/TLv0o5iKbTy/aBs7NuxyqM1br+XN4e2YcdtVGHQaNiWc5bYPNrLuSLrT718IIeozCRmCJwfG4W/QcSg1l7l/Ha10fI7JwmljgVP7jk/MIj2vCJ1GRadGZS/9fKrP9RwbcAsAt/zxGaFnTrG5720A9FzzPXpTyVyO3V1LVl0NSz1ObkAIjUJ8GdWtEROvbUb3pkFo1Crig2J4YtAUfm43BI0pn0aLPoYli86/mM0GO7aBRoPnDUN58exG2mQkkK/S8eAvCfz+8hxs/zZxO+fWjlF8P64nzcJ8yMw3M+azrTyzdDfZpktvZiaEEPWJhAxBgJcHz9/YCoB3Vh9h1YEzZY5rEe5Ly3BfTMVWnvh6JzlOdAxtGuKNWgXFVoWz+eZyxx286V6SO/RCa7My9Ls5JDVsSWZIJAZTHt3X/QDAidj2ZAeE4llYQLM9m+zPDfbWc1P7SCYMbEazMB+KUTEvZhD/HfESud4BDnMyePalkq6hGenwyw8YLEW83NGHtpazFOgMTCqKY8ED08j/4w+HbqGxYT58N64no7qVrPj67bbTXPd/f7NiT0qFXUWFEKI+k5AhALitUzT39iw5bTLp210kpJdu1KNVq3lvVEfCfEtWMZ3y3W4stopXMA311dMrtqTb5raKGn+p1ey6ZyInI+LQFxVw87dvs63nUADabfuDJkfjUdRqdnceBECHjctQmxw7cwZ5ezC6Z2MGt2mAWgXbir159KbpHOt5wYTgr7+AxybAQ4+DtzccOYTXezN5TXOYQeE6LGotM1oM46O5S8l8+WWUC96fp07DC0Nbs/jhHsSGepORZ2b8NzsZ99X2CgOUEELUVxIyhN2zN7Sie9Mg8oosTFy8q8wjFQ38PHlvVMd/5yhk8s2WU5X+Jn9T+wgAjmfkk5FX/sRJm86DhTeN52xwBL45mfT6ewnHY9ujsVm5afHbtP/nd/a170uBlx+BmSnEzXkRldlxf2qViqubhfLQ1TEEGHRkFlp5NmoIvp3/nQB6IgGmPw2pyfD5YrhlOKhUePz0Pc+d+JW7O5fU+vFVNzP7gIn0114r9f66NAnil/F9GD8gDp1GxaoDaQz/cCPHM6QduRBCXEhChrDTaUqOVEQGeHLqbAHPLN1T5kTQVhF+vHn7VaiAtUcy+G1vaoX7jfA30Di4pDV4hUczAJPBhyWjn+NscCS+OZmEnDnF4ZbdUCsK16xcQM+1S/npzkkU6Q34HtpDzNzXwGIptZ+GQV483j+OVhG+WGwKQYMeJvS258m97e6SBiS//ABvvgwPjIMXXgWtlsLffuPB3z9kUv+mACxtdg3PHfcg5c2ZpYKGXqth4qDm/PR4b6IDDZzMLGD4vI1sPX62wvcnhBD1iYSMOkNBUSq/WW02bIpS7i3Q24MP7uqEXqtm3ZEM3v/rCBabrdStT7MQnrquOQCLtiay6VgmpmJrubcm/4aMo2l5HErNISXbVObNVGzlsNmD92/6D2cCI/DNMxJ26hB/dRiMDRXtt/1B7MaVLLltIsVaDwJ2/YP37FdZe/AMfx9Od7j9c+Is3RoH0S82AMVSjFezHozXdWHZqMlY9J6w7R/yHxzDRk0Ih598EUWvp3D9OgZ+/BJPXd0QLQrrotrz+Cl/dv73dY6k5pCQlofJbLHfGgV58dUD3bgqyo8sUzGj52/h+22nyC8qxmJVnLo58/dmc2LMuZsQQtQUEjJqOKc7gzrZ8VOrUaNRqyq8tWsYwGu3tAXg0/UnWH80A19PXanbI31juL1TFArw2cbjeHto6NI4sMzbNS3CaB3hC0BGnpnOjQPLvBl0GsL8PDGEN+D7e54lIySKgIJsuhz9hzUD7sSmUtHt4AYab1vDwhvGYVFraLl/M71/+ZSM3EIy8orIyCsiPz2Tbiu+oNPHrzLm65dJWTCJ4rNJ5BYrzMsLYXHPkZj8gvA+c5ouMyeTpfLA+uY7KN4+qPbtZtAHz/PSwEZ4q20cCG7CU/mNOf3+XKw2BbVK5XAL8/Xk8zHduK51A4qtCtOW7uXjtccBxam/OyGEqKskZIgyDesQyQN9Sk4bPL1kD4dSc0uNUalUTLquOb1igykstjFxcTyp2YXl7vOaFmEA7DqdRWYFczPOKfD2Z/GoZ0gPicI3z0j3zctYd/VwbCoVvY9soknSEb4d/FBJ8Ni3lpv+/ubcIi60Oh5Pn/hVNE/YhV/SCYrTEkj5YiJ9EndgU2v4KuAqPrrmAYwNY/HIz6XT3Omg1WGZ/QFKYBCqY0fo+NpEZvULJ1xrJdU7hCnFLdj3619l1mrw0DB7RHvG9m4CwNw1x5i2ZI/DyrVCCFHfSMgQ5Zr8b4AoMFt5dOF2sgpKX0GhVauZcdtV9qst/vNtfKml1M+JDjTQvIEPNqVkLoczCrz9WHzXM5xp0Bjvgly6/bOCvwbcBcA123/FqzCPJYPux4aKnrv/5Pr134GisD+2E2f9Sq5qyQ+NBEAxm2h1+/Vcayu5RPd3dQPe7vcwac3aoS00oXl6IqrcHCxzPkSJikaVmkKT5x/nra5+tFAXkOfhxX+Paflxc9m9RNRqFZOva8H0oa3RqFQs3ZnE2C+2Sj8NIUS9JSFDlEurUTPnzg5EBxpIPGti4uJ4LNbSv5n76LW8M7IDQd4eHD6Tx7M/7C23c2j/f49m7DiZVWZoKYvJy49Fo57hTFgjDIX5RKQe5+eOJZe3DluzkAJPb34cMBqAq3eu5NrNP1Ko9+LrG8Zh0WjxTk+270ul0dCif09Gp21DpdjYnqfhlV4PcCbuKlR5uWimTUAVvwPLOx9ja9kGVW4uQdOf4tWrw+ljPIJFreHZX4/x/l9Hy53/MLJrQ96/qyPeeg2bE85yx0ebSDxbQfMyBeeW2hVCiFpGQoaoUKCXB/Pu6YRBp2HD0UyW7Egqc1xEgIHZd7RHr1Wz/mgGv+xKLnNc42AvYkK8sSoKv+0ru+lXWcyeXvx+/VgUVLTet5EjDWLZ2roPakVh1K8fkRLakJ/7lRzh6L91Of3/WUZyWBNWXjOq9M7UakKG3sikAz+jtVo4ml3M870fJqf/9aisVrT/9wbqr7/A+uY72Dp2RlVowvuVZ5hydSNGHP4TgA/WHOON3w6WGzT6xIWw+OEehPt7ciw9n3vn/0Nmvqx7IoSoXyRkiEq1DPdjwqBmACzYdLLcD9arovx5pG8MAN/vOF3u/oa0LWmWtft0NrtPZ5c77mJnImKI79gfgPvWL2B9x2s53KgNHhYz9//4f2QGhLGizwgArt38IzeuXUR826tJ7tDLvo8mf/6MptCE/6mjtCOX1zZ+jFexidPZRUyNuYm0e8cBoFm6GM3c2Vj/OwOlSQyqs5noPpvHfQ2KeTx+ScnXYvOpCoNGy3A/lozrSaMgLxKNJh5fuJMii9Xp9ytETWbavx9zQoK7yxA1nIQM4ZThnaMx6DQcOpPL1hPl97oY1iESnUbFgZRcDqTklDkmOtCLfs1DAfh5VzJ55czhKMu6fiMwBoQRkneWh5fM4u/OQzgZEUuyZwBFxxI4rvVnWa/hAPSOX8Wjnz9HVuPm9ue3+PVrBj99F90/fInQw7tol5nAa9sX4KOBk5kFTFHacHLyqyhqNeqVK1D/+jOW12ejhEegSjqNevs/3Fx4nCd3fgdUHjQa+Hny8ejO+Oi1bDtp5NN1x51+r0LUNMUpKfY/n7z7HpKnTEVlMKANLHtdIiEkZAin+Bt0DOtQMoHyq80nyx0X6OXBgJYl8y7KO7UC0L9lKOF+egrMVn6OL/vUSlmKPL35evQLnAhphHdhHvf98h5rOw7m7e738lNcX5YFtOR/wZ15bOh/+bNxF7xzjbT+6XP78wv9glApCkU+/pzsPYTNj71EwtTXGdmzKRH+nqTlFjEtyZdDD0wGQP3JXFQJR7C8+wm25i1R5WRjPXuWYR5nHYLGW78dKDdoxIX58NKw1gB8tDahwq6nQtRkFuP5XzAaL/yKJku+J3b5MnSRkW6sStRkEjKE0+7p0QiAlfvPVHip6vBO0QD8tje13KMUWrWa2ztHo1bB3uQc9iQ5f9qkwNuf2ddP5FDjtnhYzNz564ecNpT8JhVYmEORRsdxrR+zOt7JiJteY3bPMRjiuoFGB4rC4etG8Of0j9h7xzgyW7RH0WjxN+h48/ariAv1JttUzLNZ4Wwbeh8qRUHz8nOoDuzF+vZcbD16g9lM8dGj3NoAxv8bND7bnMiHy+PLrfmmdpG0jfQj32zl3dWVr3QrRE1naN0aQ5s2EjBEhSRk1DMKONkZtPS2Fg186dokEKtNYdE/JWuWFFqsFBXbHG6tI/xoEuyFqdjKL7uSKSq2kV9kIafQ8ebrqaNHTDAAP+5MJiW7EFOxlfTcokpv+Ro9s/s9zPq4HiT7hGJRadDZLLRTsnl5y6fcdmQNIaYszCoNGyLaEXb7f2k4fiEftbierF176P7SYyStXmvvDnraWMC2k0YGtW5Aw0ADpmIb03VXsaL7LajMZjQvPsv+735h69insd50GygKhf9sYYhvIQ8eXgnAe1vTmPPV3xxJy+NEZj6FxTb7zWxVeOraFgAs3prI/uQcCottmMxW+8UlilLBzXb+IpTKbkIIUVNo3V2AqBoqJ1tHapzcn4dWXeY+7+vVhK0njCzalsgTA+MI8vZATelxd3ZtyBu/HeKX3Snc36sJA1qG4aEt/er9mofy2MIdHM/IZ1diFm0i/TF4VJ59V+4/g97gwbcD7qPhoR0AtDh7kvv2/cj3vUdyw7ZlPLBvOYcCG/JFi0Fs9w5D6xvC2kZdWNuoC0NObObRZe/y0VXDWNG0F1abgr9BB8C1rRuw5lA6CRn5vB/Zh6K+Xty69mtaLP2Mvzv0xvbkZIiJRf3BHNR7d3HzuAnk7d3JopCOfHykEK+GKQxoHYFG7fh16RkbzICWYfx5MI05q48w965OKIrzXT+lOagQoraRIxniklzbugEN/PRk5pkrXBhtWPtIPLRqDqXmsruCUyE6jZqpg1ugVpU06Kqwn0RZVCq2R5a0QG+Sm0aEMYVHV7zP7sbt+LZ5Tywph7njz/dJ+uB+Ur+ajCX3LIqi8FuTHkzvfBcP7/mZR3b/hOqCJd01ahX9W4YSG+qNosAnwZ34K6YHnnnZNNi1CVQqbDfdhu3eB0vG//gto54cxbDELQC8++cxNh3LLLPcSdc2R6NW8efBNP6RxdSEcGBOSMC0bx+mffsoPHbM3eWIKiAhQ1wSnUbNXd1K5mYs2FT+BNAALw+GtAkHSk4PVKRZA1/7PnecMmIyX9plnoXFJeP3tu7B3kZXobVZ6L5+MTOWf87Ikye4J/EUoFCUdJCkD+4l/YfXUSxm4qPaMaXnQwxM3MYDv76PtvB8wFGrVFzT4nzQeOuq29gQ0ZZG6361j7HdegdKcAiq1BQ0f6/mgX5xXHtyKzZUzPr1ABuOlu5qGhPqw4jOJXNWZv1+EJv1gnMiQtRT2sBAVAYDyVOmcuL24Zy4fTgpU6YCYElPd3N14krU25Dx6aef0qxZM0JCQhgxYgR79+51d0m1xp3dGqHTqNhxKot9yWVfpgowssu/E0D3lT8B9Jy7ezSmaYg3RRYbWy7hN3xFUc6vD+LlzcdDHuPbPqNIV1QU28peN8R0ZBNnFr+ArTCPI6GxTL76ccLSTtF/zjQa//Mn6uKSqz8uDBo2lZoZXUdzwKSBY0dKduTpiW302JKxCz9Hdc1Anmimo3fSbiyoeOKrbewoY2n7J/rH4a3XsDc5hxV7U0o9LkR9o4uMJHb5Mpos+d5+C504AQBLTvn/xoiar17Oyfjwww+ZPXs2s2fPxmQyMXPmTLp06cJHH33Efffdd8X7z8/PR6NxdvZD9XJ2KXCbopQ7z8NLDde2DGXFvjSe/2EP749oQ5C3R6lxzYJ0xIZ4cSyjgHdXHWLSgJhS8xQu9GTfRjy19AAnMwvYfTKDlg28yh1rNZuwosVsVeyTHdNzTIQYVPwd142NFhscf6nc5xed3kfq108Tce//ccovnE9a38iT2xbS6pv3iP1xPie79udEj0GYA0PpHa3HkJbMXrU/b3UexYd/rMQz4t8Z9X0HoF30Fark01i/X4Tt7vt4/P9mkn/Gg/gGLZmwcCs/PtYDT9357wdPFdzbLZp5604yZ/Vhrm3uj06jrnByxiXN3aihS7vm5+e7uwRRg+kiIx2uVNFmFsEuCeG13RWFjMLCQjw9Pauqlmrz8ssvs3DhQvr3L+keedtttzF+/HjGjBmDzWbj/vvvd2o/RUVFFBWd73mQ82/ijqwHl3RpAyIIv3smR4ABLy/hzKLnseaVnofg2aQjYcOns/6YkV+XvMbZ39+vcL++XW8laMAD7EzK548PXyJ//5qKC1FrCb7+SXzaDiC/WMGYcpK0b1/Amlv2nIgLFaefxJKdii64IT8c3saXRw6ff3D3Tvh09vn7KjWRD34IQZHc+P2vFLz2fOkdPje55AaotHoiH5xHBmG0vuE+crf/4jBUpdUTNe5/JBNIw77Dyd+zqtJ6hRCitrms0yUffPABERERGAwG/Pz8GDFiBDt27Kjq2lxCURTS0tIctmm1WubNm8djjz3GuHHjiI+Pd2pfM2bMwN/f335r2LChCyqumSxZKaR+PQ1LTjq64IY0uOsNNH5hpcYVnthJxrK3UWxWfDsMIXDAgxXuN3frD+Rs/RGA4Bsm4tmkY8WF2CxkLp9NyucTsOSk4xHSiPC7Z6ENiqr0PXg27YguuCG2ogLyKvuQV2wUHN0MgCGue6X7VixFZG9aDIBf9+GotB6lHs/5ZykA/j3vAFW9PXMphKjDVIqzx8//tWXLFvr06cN///tfevfuTVJSEitWrOCHH35g3rx5Th8FcKe+ffvi6+vL8uXLHbZbrVb69OlDcHAwy5Ytq3Q/ZR3JaNiwIcnJyfj5+VV53VWhKk6XXOhoeh5PLN5HUlYhDXz1zB3ZlsZBhlLjPtt8ig/WngJgZKcI7u5adgiYv+E4njoVm07kctJYhFYNA5oFEOytcxi3+mAaPh6OB+IsNoWUPBvFNlApVpK/eArzmfJnqIcOn45XbFeuS9jAI/+uR5IbGsmJbgM53elqPPKziY7fgNngy6mu/Ukyq/j9oBEfvYaPRrV1OPWjfv9tNL8tx9ZvINYpz5Xs/0A8I1efJcMriCebKtx7R1+H1zeZrdz04T9kmSy8dlMLbm4fXm6tdeF0SU5ODpGRkWRnZ1f485GTk4O/v3+l40TZ9iZlM/S99Swb34e2Uf5Vuu+MrVsJ7dYNgLy8PLy9vat0/xfatnYHw1ek8P0NEXTp28llr1Pb1Lafj0s+XbJ9+3buuOMOXnjhBfu20aNHs337dq6//np69epFixYtqrTIqvbSSy8xcOBA3njjDZ5++mn7do1Gw3PPPcftt9/u1H70ej16vb7Udm9vb5f+8F2Jqg4ZzXR6vnqgOw98sY2EjHweWbSH+fd2oVkDX4dxQzs0QqXxYO5fR1m8IwU/b09Gdm1Uan8enl7oPdRc3cIL84E0UrILWXssh15xwUQHGuw1aTwMaPSO374aIFqvkGw0UWSBBqNmkLbkFYoS95R6HW1gJF6xXUFR2B3anK0drqX3wQ00yEylwa8L6bzqO473vI6D1w6n2Mu3ZN+Kgqc2m7wiKyezref/AVcU1M1aoVn5K8rmDVhsNvDxJfL6IYzd8T9mFgXx1cE87s034R0WYq/B2xvG9m7K7FVH+GRjIiN7lD9npS6EDKtVFocTor655GO0zZo1K/ODqnPnztx99918//33VVJYVfnrr7/4z3/+w/Tp0zlw4AAA/fv356WXXuKZZ55h1qxZDuMbNGhQYwNCVXCm26eiKNjK6PhZ3rhQPz2f3d+FFuG+ZOaZufezrexPzcGGYr+ZLTZuuCqc+3o1BuCTdcdZsuM0RRZrqVtBUUkX0e5Ngwjw0lFosfHnwXRW7EklMbOAgiIrNptCYbG11M1itRHi64HWWoha70WDO17C0KxHqa+Db6ehALRO3su8NbMp0nny3v2vsWLgaNJCotAWm2m2dhmDXnuciK8/hj//IG3fYSL8S+Ygrdx/hoTEdDK//gbbmFFo5pbM31DUak6kZnEqs4CMvCIGPzyCMHMuZ/W+fPblSjLyihxug9s0INBLx4nMAn7cmYTFaivnZq24I+i/N5vi/N+xEEK4mlNHMn777Te2bt1Khw4daN++PUajkZ9//plhw4Y5jNNoNDXqqoo333yTd999l+uvv57Vq1fz6quvMmXKFF5//XVeeOEFrFYrU6dOZdOmTTz//PN4eXkxadIkJk6c6O7SXcbpzqBqlVO/OXvrtahUKryCtHzzUHfu/2wru05n88iC7Sx+uAcxoT4AxIb5oFKpmDK4JR4aDZ+sS2DemmPEhvowpO350wRjejVBf0Fn0Lu7N+bbbYn8FJ9MRp6ZPw+l0ys2mNs6RhEX5luqnnNSkk/zyhcrMMR1J/SWZ8j87T375EqVhwGfqwYB0CX9EJ7WYq7bvpw++/5mW4cBLBg+hfD0Uwz6+1saZJym5/bf7ftd06gTb3a6i0P7TxD3/uNo8vMAsHkaKBw4BNPQ4fhHNMBiU9Bp1Oh8vBjdWMvbKfBNpp7hVhuGC07z+Bk8GNunKW+vPMwHa45xU/vIco9mOPVXJ9lBCFGDOH265I8//uCtt94iJycHLy8vfv/9d4YNG8Ytt9xCdHQ0e/fu5aeffmLNmjUuLNd5R44c4bXXXuPgwYNERkaiKApz587lP//5DwkJCSxatIgXX3yRXr168eyzz9K5c2cMBgOTJk3i+efLuHJAVCrAy4Mvxnbjrk+2sD8lh9Gf/sO343oSFeA4R+PJgXHkF1n4+p9TPPfDHqIDDeWeO/bWa7m/d1OGtotk4ZaTrDpwho3HMtmUkEn3pkEMbh2On0FX6nkRkdFMH3MD7/2diEnjRcgNE1F7+tKu9yDyPQIxqrzwUNmIv+F+LMc7ct3GJTTITqPv5p/pue1XdrXpw5KhjxKenkij04cITztJg/TTdEveh7a9hRSNN8kqAxER/phuvI3CAUNQvH3KfA8339aPBW+tJM0QwMJf/uHB23s5PH5P90b8b91xEjLyWbEnhaHtIhwer6mnP4QQojJOhYwhQ4YwZMgQFEUhISGBnTt3Eh8fT3x8PM899xzJySVLdbds2ZI1a9Zw9913u7RoZ2zZsoWGDRvaLydVqVQ88cQTNGrUiNtvv52pU6fy1ltvcd1113HdddeRm5uLp6cnOl3pDyzhPD+Djs/HduXOjzeTkJ7PvZ/+w6KHe+Dlcf7ohEqlYtr1LUk0FrDuSAZPfrOTRQ/3IMyv/MuhQ331TBzUnFs7RvHFxhNsPn6WzQln2XEyi77NQ7imRRgGneNRtAYR0USHF3I2z0SmyUbQgAcoNKjJNwNWG6H+XqjUavbFdmJLRGv6p+2n59ZfiTxzgi67/qLzrjUciuvI2p43cyasESqbjRYWI20yitllgr/umcyQwd1AXfFZRw8/Xx7UpfA6AXxxKJ/rMgtoFHy+B4ivp46xvZvwf6uOMHfNMW5oG466gn4iQghRW1zSnAyVSkVsbCzDhw/n1VdfZdmyZSQlJZGens7KlSu5//77y5wI6Q6NGzfm4MGDHDp0yGH7sGHDeO+995g9e7bDpaq+vr4SMKpIiI+eL8d2IzLAk+MZ+Yz57B+yTcUOYzRqFTOHtyMm1Ju03CKeXLTT3h68Io2DvfnvTW24t2djGgd7YbbaWHUgjddXHGDtkXRsF801UKlUhPtoMa75DIAMkw2z1YZGBYFe5y8rtak17G/RjU/v/i9f3DGNwzHtUaHQ8ugOHvzqRYasWoC+qICs0Cjad4gDYJvNv9KAcc7gvm3ofOYgxSo1b/x2oNSciHt7NsbPU8vRtDx+33/GqX0KIURNVyUX54eEhHDttdcydepUhg8fXhW7vGJ9+vShbdu2jBkzxuEyU4Bx48bRuXNnFixY4Kbq6r7IAAMLHuhOiI8HB1JyGf/1zlJrkvh66nj/rk74G3TsTcph8ne7sFjLbgV+sUZBXozvH8eYXk0I9dVTYLbyU3wyn288cb7N+AVytiwh87f37PeDvD3KnvugUnGqYUsW3zqReWNeZV/zrqgVha67/uTx+c/QcusqOkaWzAU5mpZHfiXt0s+x7tzOE7uWoles7DiVxa8XLS7n66ljTK8mAMz+4zBFTgQuIYSo6epsByCVSsUXX3zB7t27GTlyJGaz2eHx/v37k5lZeVdIcfmahnjzxdhu+Hlq2ZmYxVPfxlN8UYhoFOTFu6M6oteqWXMonXf/PIrV5tzsRZVKxVVR/ky5rgW3d4pCq1axLzmHeX8fK3OtlLxdv9PIT0ugl44w38qPuGUER7H0psf48o5ppIVE4VWYx9W/fErsi08SqlehACczK181VikuxvzrcsILznJ/05KjJ+/9eYTcQsejO/f3bkKYr54TmQU8uTieonNhqbxLSYQQooarsyEDoEOHDixdupSVK1cyYMAAjh8/DpSsofD7778zePBgN1dY97WK8ON/93XBU6tm3ZEMXvhxL7aLQkTnxoH838gOaNUq1h3J4IM1Ry/pEkuNWkWv2BDG9YvFy0PDqbMFvPfnETLyikqN9dWraRTkhVbj/Lf+yYYt+Xj0S/zW/26KPL3RHTtM8+O7AEhb+DW+78zA++v5eP6xHI8d/6A9sBft8aNYT53Elp6GefUfKFlZqEJCuOu2XjQO8sJYUMzHaxMca/PU8fYd7dFr1fx5MJ0nF+08HzSEqKcSsovZm5Rd4S0py+TuMkU56vwCaYMHD2bdunWMHj2aFi1a0KNHD44dO8bNN9/MqFGj3F1evdClSRBv39GeCYviWbY7hQAvHdOGtHS4aqJv81DeHN6OKd/t4te9qRh0Gh7o0/SSrqxoGuLN+AFxfLL2OBl5Zt5dfYQgbw90VfBdrqg1bO00iPRufRm+42diTiWzIbIdJ0xgWP9bmc+5eO1I/Q034aH3YNJ1LXhy0U6W7DjN0HYRhF5wVKVnTDAfje7MIwu224PGe3d2xEMjE0FF/RLoqUFvMTN1QwZsWF/hWINOw6pJ/UpdySbcr86HDChpFLZnzx7++OMPjhw5QpcuXejZs6e7y6pX+jYP5dVb2/L0kj18tfkUgV4ePNIv1mHM4DbhJKTnMfevYyzdmYSXXstd3Up3Ba1ImK8n4wfG8en645w2migwm4j2q7pv80JvP3KfmErYgUTYnMqRmA7ktfJGk56GOj0V9dlMVIWFqIpMaMxmKDSB1YrKPwCPW0vmK3VrGsTAVmGsPpDGWysP0zMm2OFqkj5xIQ5BY/yinbw7ssMlHX0RoraL9Nby8eqZeL8zF8/Y2HLHHU3LY+LieIz5ZgkZNVCtDRnPP/886enpfPjhh5X+tqsoChqNxn4prqg6JWc1Kj+1YbXZuLFtBFn5Zt747RDv/XmUAIOOO7o4LirXt1koBWYrn204wVebT+KhUTG0felVbfMKLRWeUhnVtSFLdyZxLD2fxBwLPh1vJG/ncgqLrWjVpSdV2hRKXQFTlgAvHYfO5GL2KukKm1Ss4Z8Bw0qWar+AXqumTeS/vT+Ki0GtIk+jBWPJHI47Okez4UgGe5Ky+WZrIsMueo/towOYM7IDExfH8+fBdB77eicf3FUyd8WujO97RVHQOtEQTwHUTnbukj4dwl3CTFk0CdZjqOI1WET1qZUhY8+ePXzyySf2iZsVBY21a9cyZcoUfvnlF8LCSq8SWt84+4Hh7OeKs79c+3rqUKHi4b6x5BVZef+vo7y64gAN/Dy54arzzadahfvROtIPLw8Nc/86xvwNJ2gS7M2tnRwXVLNYbQ6dQcvSMzaE11ccYNtJI8HXPYrWL5TH+sfhVUbb+C0JZ2lQQZ+OczLziwjx0RPs7YGPXktekQWzxUaEv+NvUGaLDe9za6voS/+Yeeu13N2jMZ+uP86cVYcZ0DKsVFOxXnEhvHNnRyYs2snaIxk8uTied+88HzTK6QsqnUGFEDVGrTz+OnfuXF544QW++uorPv30U8aNG1fub7V+fn4cO3aM999/v5qrFOX5z6Bm3NWtEYoC//k2nnVH0kuNeaRvDPf1LFnn5MVf9rHqMnpHaNQqhrWP4IbWJYuS+fcYzsKtKaWucLkcKpWKqMCSYJFkvLxJZzd3iKRhoAFjQTHv/3W0zDE9Y4N5599gIZNBhRC1Ta0MGRkZGdx3333ceeedlQaNDh06sHXrVl588cXqL1SUSaVS8dKwNtx4VQTFVoVHF+5gV2JWqTGTrmvO7Z2isCkwbckedpw0XtZrXdsyhIxls1GsFnaczuG91Uc5lpZXqnHXpTp3/vf0Zc5s12nUPNIvBoDvtiVyIOXiqaIlesYGM2dkBwkaQohap1aGjO+++w5f35KGSOUFDZPJRGFhIQBNmzZF7WRnRlE9NGoVb41oR5+4EArMVu7/fCtHzuQ6jFGpVLwwtDX9W4Rittp44pudHE3Lu6zXy9/3J2nfv4heq+ZIWh5vrTzMCz/u44edSaRkX15IiAooOb1yJZfPtY8OYEjbcGwKvL7iQLnBp8e/V51cGDTKajomhBA1Sa385L14XsHFQaOgoICbb76ZDz74wE0VCmfotRrm3d2J9tH+ZJmKuet/WziY6vjbvEat4s3b29E+2p/cQgt3/28LP+xMuqylygtPxDPxmsZ0jwlCr1VzNt/Myn1nePmXA/y+L5XDZ3IrPbqRYypm3ZF0Pl6bwDf/JAKQkWeu8DmVeera5nh5aNh9Oputx8+WO+7cVSfngsa3209f0esKIYSr1cqQUZYLg0ZsbCwRERF1esn2usJbr2X+mK60jvAjM9/M2C+2sf2i0yIGDw3v39WRTo0CKDBb+e9P+5i18jBZBZf+4R7up2dMrybMHN6Oh65uSrtof1QqSMku5IedSXz09zE2J2RSYC7pGGqx2Tiekc/qA2f4ZG0C3247zQ87kzmYmovFphDopWNw6wZX9DVo4OdJlyaBAJyuZH5Hn7gQnhhQsnbK2jLmsgghRE1SK68uKc/NN9/MVVddRbt27fjss8/kFEktEejlwdcPdeehL7ex9YSRRxZsZ/Yd7enbPNQ+JsDLg/ljuvL5xhO8/+dRtp0w8uSieJ7oH0e3pkGX/JoeWjWdGgfSqXEgmXlFLNqayLG0fHIKLfx9OJ31RzOI9PckNaeQYuv5oxsqFcSG+tAq3JdWEX408NNXySWeoT4lDbnSc0t3Kb3Y1XEhvL3yMP8cN2Kx2qR/Rj2SlGXCmF9xuL7cU4pCuEKdCRnFxcXcfPPNEjBqKT9PHV/c341xX21n7ZEMJiyK55Vb2jK03fnLWzVqFQ/0aUrv2GCe+m4XiWdNvLbiANe1bsDY3k0xeFTeH6IswT56ujQO4rrW4RxMyWH7KSNncopI/Peogo9eS0yoNzEhPvgZtHRoGFAVb9lByLmQUUYr9Iu1ivDDz1NLTqGF/Sm5tIuWHgL1QVKWiUFv/43JicXzDDoNgd4elY4TwtXqTMjQ6XQ88MADjBgxQgJGLeWp0/B/d3Rg+i/7WLY7hWeW7iHHVMxd3R27fraM8GPGrVfx/fbT/BSfzMr9Z9h9OpuJg5rRKsLvsl9fp1FzVXQAbaP8Sc4qJC23kMgAA2G+549WZOZXHgIuR8i/rcXLWm/lYhq1im5Ng1h1II31RzMkZNQTxnwzpmIrc0Z2IC7Mp8Kxgd4edab7pTnh/Bo/2sBAdJGlm/OJmqvOhAyAkSNHuruEesfZ+Zc2BdSqygerNfDqLW3x89Tx9T+nmPHrQYwFZh7pG+uwNLtGreLeXk3o1DiQd1cfITWnkGeW7qFv81BGdm1IuH/JlR9mqw2T7fxvfqZiK4q59G+CRRabw8qt/l46/L1KmmPlXzDebLE5tbx7kcVGWm5hpeNstpJl5331JUdhzuQUkldYuvOo1abgqTt/pKZ3XAirDqTx95EMHro6xr5dUUCjqfzrXPL3VrWN2UT1iAvzoW096ICpDQxEZTCQPGWqfZvKYCB2+TIJGrVInQoZovo5Ox9Bg3MfVn6eOlQqFa/c0oYwPz1zVh3hw78T+Ck+meGdoxnRJZroQC86Nw60L/U+tF0Eryw7wA87k/j7cDobjmYwvHM0j/WP5YarIigynZ9M2Ss2BO8yOn42DvLGx7PyH4ccUzERAZV3Bk3OMhHgVfnh6iKLDS8PLVEBXgCczTfj5VG6DqtNcQhZfZuVzFeJT8yioNiKr2dJIFIUpcq7ugrhDrrISGKXL8NiLJkIbk5IIHnKVCxGo4SMWkTOK4gaSaVS8eTAZrx6S1sCDDpSsgt578+j9Ju1hns//Yfle1IospQcYfD11DFzeDuWPNqTq5uFYLEpLNqayKDZa5mz6gjGgsrXJHG3c3MyMvPMTjUJaxjkRdMQb6w2hY3HMl1dnhBuoYuMxNCmDYY2bfCIian8CaLGkSMZoka7q3sjbu8Uxcr9Z1i8NZGNxzJZfzSD9UczCPTScXOHKEZ0iaZ5A1/aRQcwf0xX/jl+lv9bdZhtJ4ws3prIT/FJBFw9mpx/lrr77ZQr2KfkqIfFppBVUEyQE5P2+jYL4XhGPuuOZDC4TbirSxT1VHFy8vmjCSdOuLcYUetIyBA1nl6n4ab2kdzUPpJTZwv4fttpvtueyJmcIj7feILPN56gXbQ/d3ZtyK0do+jWNIivH+zO+qMZzPrtEAdSc/HvNRLfTjfy5ZbTjO0bV+niatVNp1ET6KXDWFBMRl6RUyHj6mahfLHpJOuOpF/SaRIhnFWcnMyxG4ei/HvKscAmXWbFpZGQIWqVRkFePHVdcx7vH8v6oxl8t/00fx5IY/fpbHafzmbFnlTm3NkBf4OOq5uF0jLcl9V7k5n22e94hDZh/qZENp3I5rkbWtIi/PKvRKlqCel59vVInJlYCtCtaRAeWjUp2YWcOltA4+DSc02EuBIWoxHFZCJy1kw8YmLILyiAbt3cXZaoRWROhqiVNGoV17QIY+5dnVg3rT9TBrfAoNOw/mgGt8/byJG0knVQVCoVV8cFkTJ/POk/z8LPU8uBlBzu+fQfpi3ZzbF09zcuSkjP4+EF2ykwW2newPkrBzx1Gvtlimk5rrm0VggAj5iYkrkRrVu7uxRRy0jIELVeiI+eh/vGsPiRHkQHGjiZWcCIeZtYfeDC5eEVCg78zcd3tWNI23BUwJ8H0xj18Wae/3EvyVewyNmVSDxbwMMLtpOZb6Z5Ax8+uqczukvo4Bn472W2Zy+jxboQQriahAxRZ7SK8GPJo73o3jSIfLOVcV/tYP764w5Xa4T76Xnl5rZ883APBrQMQwF+35fKhMU7eXvlIVKzK+9tUVVOZRbw7A97HAKGM5e9XuhcV8ezlbSaFkIId5CQIeqUIG8PPru/K6N7Ngbgk3XHeXH5YVQ6x94WsaE+vHl7O756oBt94kKwKbBy/xke+GIr764+4tQaIlfiVGYBU77fRVZB8WUHDIAgLwkZQoiaSyZ+imrjTHdQm03Bma7wFY3TqlW8cGMrWjbwZfrP+1h79Czh98wifemr2BTF4chGswa+vH1He1buTWXJziTiE7NYvieFlftTubZVA2JDfQjx0RPqqyfEx4NCiw1TGR1DL5ZfZMFDW3aBiWcLeOHHfWSZimkc5MU7d3ZAr1NjKi5/wqfFqpS5NkvAv6dLMvKKKLbasCkKWnXlV5koiuJU+30FUONcW1e5ukUIcTEJGeKKOP+54mRnULXaqX16aNWVfqjd1b0RzcJ8eHThdjLDmhJ+3/9xLKOAAQ1Kr9p6a+dohndpyD/Hz/Lun0fYftLIir2ppcb56LWE+3sS7udJA7+S/48M8KR5A1+ahnjb51MYdBq89aV/vBLS83jx5/1kmUqOYLwzsiPRgZWvMWGxKWXO1TjXxCuroBidRo2tqjt+Otk2XgghyiIhQ9RpnRoH8s3YzvR79kv0Ec0Z9/VunrmhmDG9mpT5YdytaRALxnZj47FM/th/huRsE6nZhaTmFJJbaCGvyMLRtLwyl9P20KiJC/OhZbgvTUK8aRftT/MwX/sRiHNXkVw4B+NK+3UEyZwMIUQNJiFD1HnhfnrOfP00QYOfwKftAF5dfoDvt59mXL+StU00F51eUKlU9I4LoXdciMP2/CILR9NyOZtfzJmckuCRmlNI4tkCDqXmkm+2sj8lh/0pOfbnqFUl66K0CPdl64mzpSZ5OnPqpSKB5+ZkyNUlQogaSEKGqBcUi5nM5bN5+T8PMffvExxMzWXi4nj+b9VhHj0XNjQVnz/w1muJCfWhVUTpow82RSHJaOJgag4HU3PZm5zN0bQ8MvLMHM/M53hmPsAVTfIsy7kjGUY5kiGEqIEkZIh65Z5u0dzZI4Yv/21HfjKzgKeX7mHpziTeuO0qIvwrnx9RFrVKRcMgLxoGeXFt63ByC4vx1mvJyCviYGouh1JzySks5v5eTaosYAD2eRq5hc51CRVCiOokIUPUO/4GHeMHNmNsn6Z8/c8p3l19hH+On+WWuRv4701tuPGqiCp7rRAfPX3i9PS56NRLVfltX8nk1PYNA1yyfyGEuBLSJ0PUW956LQ9dHcMv4/vQLtqfnEILk7/bxZTvd5FjqvnLw1ttCku2nwbgji7Rbq5GCCFKkyMZot5rEuzNl2O78b91x5n39zGW7U5h+0kjb9x2Fd2aBru7vHKtP5pBak4hAQYd17Vu4O5yhHCrsq74uligt4d9vR9RPSRkCAFoNWoe7x9Hn7gQpi7ZzamzBYz5fCu3dohi4qDmhPrq3V1iKd9tSwTglo5ReNSwpeuFqC6B3h4YdBomLo6vdKxBp2HVpH4SNKqRhAxRK5U07ay8U5Si2FAu6PCpKIrDfTubDUWtpl20P0vG9WTm74f4bvtplu5M4rd9qYzrG8O9PZtgsdnQ2CrvYlVss2G1VV5fsc2K1Vp5QCi2WrFe0EE0I6+IPw+mAXBrx0isNtu/b8OGUslVMlByNYzKiUZbCqByspGaEO4QFWBg1aR+lV5hdTQtj4mL4zHmmyVkVKN6HTI+//xzbrrpJoKDa+4h8brC2c6gznardKb1OJR0EL2wD4ZGrSrVFwPAS6+1v7bBQ8PM4e0Y1a0hL/2yn12ns5m96gjf70hi6pAWXNe6QaV1euk16Jwo0sdTg9aJcRabGq1aTX6RhW0njXy3/TQWm0KHhgG0iTy/NLyiVqF24muoUjv3tVYh7cJFzRcVYJDgUEPV25Dx1ltvMW/ePAYOHCghQ5SpY6NAlj7aix/jk5n5+0FOnS3gia930iMmiOdubEXLcD+X12AyW9lxysjGY5n8c+Ise05nY7ngCMmobg1dXoMQQlyuehkyzgWMv/76i4YN5R9pUT61WsVtnaIY3KYBH/6dwCfrEticcJab39/AHV0aMvHaZgR7X/l8DatNIa/IQm5hMaeNJrYcP8vmhEziE7Motjqe14gONNCjaRBXNwvl+rbhV/zaQgjhKvUuZFwYMBo1asTp06eZO3cuCQkJdOzYkSeeeAIfHx+n9lVUVERR0fklwXNycioYLWozb72WSdc15/ZOUcxaeYgVe1JZtDWR5XtSuL93E4K99VhsNoqtCharjUKLFcUGxTaFYqsNi1WhyGK1r3+SW1gSKM7dL6igvXi4vyfdmwTRMzaYHjFBRAd6VeM7F0KIy1evQobVauX333/HYrFgtVrZuHEjN910E926dcPHx4eXX36ZBQsWsHbtWqdOocyYMYOXXnqpGioXNUVUoIF37uzIPT3O8uqyA+xPyeHd1UerbP96rZogbw86Nw6kR9MgesQE0zjYC6uiODV3QwghapJ6FTI0Gg0///wzw4YN45prrsFisdgnfwLs37+f3r17M2nSJD7//PNK9/fMM8/w1FNP2e/n5OTI6Zd6omuTIJY+1oulO07z18F0UJWswqrVqNBp1GjU4KHRoNOo0GrU6NQqPLRqfD21+Oi1+Hrq8PHU4uWhIcBQ8mdfvQ4PbTlBoqwrYkSdkZRlcurqCCFqm3oRMmbPnk23bt3o06cPBoPBHjR8fX3tAQOgdevWTJ48mXfeecep/er1evT6mtc/QVQPjVrFiC4NGdGldLC02GxOXV1isdnkCEU9l5RlYtDbf2MqrnxFXoNOQ6B31a19UxuZExLsf9YGBqKLjHRjNaIydT5kvPXWW0yZMoVBgwbxxx9/ANiDxubNm0uNDwoKkqtNhBDVxphvxlRsZc7IDsSFVTwfrD53rNQGBqIyGEieMtW+TWUwELt8mQSNGqxOh4xzkzxnzpzJ1KlT2bdvH23atAFKgkb//v0dxpvNZj755BPGjRvnjnKFEPVYXJgPbaP8Kx9YT+kiI4ldvgyL0QiUHNFInjIVi9EoIaMGq7Mh48KrSCIjI3nvvfd45513+Pjjj0uNVRSFbdu28cQTT9CmTRvGjx/vhoqFO5XZBbSscTbFqWUFFZuC4kRLTZtNweZE51KbTcHm5P40amfGgdqJcYrifOMzadolXE0XGSmBopapkyHjo48+crhMFeCJJ57gxRdfZMaMGaVOh3zzzTd89913TJkyheHDh7ujZHGJnP1AU6lUDmMvvn+O2slPUp1W7dRra9Vlv06p1y2nnotp1CpUZXQqvZiiVpx7XbXzXViFEOJy1ckZZ9dee61DwAB4+OGHUavVfPLJJ6XG33XXXfzwww8SMIQQQogqVCdDRkxMjEPAAAgICGDMmDHMnTsXi8XipsqEEEKI+qNOhozyTJgwgaSkJJYuXeruUoQQQog6r16FjGbNmjF06FDmzJnj7lKEEEKIOq9OTvysyH/+8x8GDBjAzp076dixo7vLEUKIGqM4Odl+iSg4Nr4S4nLU6pCRlJTEtm3baNSokdOBoX///jz//PO0bdvWxdUJIUTtUZyczLEbh6KYTA7bVQYD2sBAN1UlartaGzLmzZvHlClTCA4O5tSpU/To0YP58+fTqlWrSp/7yiuvVEOFQghRe1iMRhSTichZM/GIibFvl9bd4krUyjkZmzdv5sUXX2TXrl2cPHmSHTt2UFRURPfu3fnrr78cxm7bto3rrruOrKws9xQrhBC1iEdMDIY2bew3CRjiStTKkLF06VKuv/56YmNjAejYsSPr16+nd+/eDBs2jN27d9vHms1mNm/ezKxZs9xVrqgFlEu5KUqlN5ut8jGKomBzYsyl7E+5pNel0pvNiTHnx1X+ukKI+qVWni7x8vIiPj6+1LalS5dyzTXXcPfdd7Nr1y7UajW9evVi48aNtGjRwj3FilrB6eaXKpVTY9VqlVMdNTVOjivpVFr5OEXtXDdUNc51/HR2nBBClKVWHskYMWIEu3fvLrUOicFg4KuvvuLAgQOsXLnSvr1t27bodLrqLlMIIYSo12plyGjTpg0TJ05k/Pjx/P777w6PNWvWjB49enDw4EE3VSeEEEIIqKUhA2DmzJkMGTKEm2++mQULFti3FxUVkZSURPv27d1YnRBCCCFq5ZwMAK1Wy5IlSxg/fjz33nsvn3/+OQMGDGD58uX07t2b/v37u7tEIYQQol6rtUcyoCRozJs3jw0bNhAbG8v27du57777+PLLL91dmhBCCFHv1dojGRfq1asXvXr1cncZQggharijaXmVjgn09iAqwFAN1dR9dSJkCCGEEBUJ9PbAoNMwcXF8pWMNOg2rJvWToFEFJGQIIYSo86ICDKya1A9jvrnCcUfT8pi4OB5jvllCRhWQkCGECyj2/1TzOCebaio414DM2dcFJ3cohBtFBRgkOFQzCRlC4FyXTHD+c1Tt5Cezyum9Kk6NKnkflY9U4WzHT+dbgVf2NXT2ayyEqDtq9dUlQgghhKi55EiGEEK4SFKWyak5AO5SnJyMxWgEwJyQ4LY6rsTFdcvS9DWLhAwhhHCBpCwTg97+G1OxtdKxBp2GQG+PaqjqvOLkZI7dOBTFZLJvUxkMaAMDq7WOy6UNDERlMJA8ZarDdpXBQOzyZRI0aggJGUII4QLGfDOmYitzRnYgLsynwrHu6MtgMRpRTCYiZ83EIyYGqF1HAXSRkcQuX2Y/EgMlRzWSp0zFYjTWmvdR10nIEEIIF4oL86FtlL+7yyiXR0wMhjZt3F3GZdFFRkqYqOFk4qcQQgghXEJChhBCCCFcQkKGEEIIIVxC5mQIUU850x1UUZxr2iWEEGWRkCGEC1R1d0tVFffsdrb9uEqlkpAhhLhscrpECCGEEC4hIUMIIYQQLiEhQwghhBAuISFDCCGEEC4hIUMIIYQQLiFXlwghxCWq6aurluXCFVeh9q666owL31ttWo+lLpKQIYQQl6Cmr65alrJWXIXateqqM8pamVVWZXUvCRlCCHEJavrqqmUpa8VVqHu/5V+8MuuVrMrqzJGomvL3W5PV25CxcuVKZs2ahdFopH///kyaNInw8HB3lyWEqCVq+uqqZanNK64660pXZg309sCg0zBxcXylYw06Dasm9ZOgUYF6GTKWLl3K448/zrPPPovJZOL999/ns88+Y9GiRQwaNMjd5QnhctLFU4iyRQUYWDWpn1NzbiYujseYb5aQUYF6GTKmTJnCxx9/zE033QTAo48+yl133cWNN97ITz/9xJAhQ5zaT1FREUVFRfb7OTk5LqlXCCFE9YkKMEhwqCL1LmTYbDaOHz9OSEiIfZuvry8//PADd9xxB3fccQfx8fHEXHDesjwzZszgpZdecmW5QgghajCZu1Gxehcy1Go1HTp0YN68efTs2dO+XavV8tVXX9G5c2eee+45vvnmm0r39cwzz/DUU0/Z7+fk5NCwYUOX1C2EEKLmkLkbzqkXISMrK4uAgAD7/eeee47hw4dz3XXXcc8999i3e3l58eKLLzJ27Fin9qvX69Hr9VVdrhDCTWpj/wtRuYt7glTFVTUyd8M5dT5kfPLJJ8ydO5edO3fal9++/fbbefjhhxk7diw6nY6RI0fax7do0QKVSoWiKFW+XLcQouaqjf0vKnJh86263HirImX1zYCq651xKXM36utplTodMj755BNeeukl/vzzz1KB4YMPPsBsNjNq1Ci2bNnCc889h16v55VXXuH++++XgCFEPVMb+1+Up6zmW3Wt8ZYzLu6bAVfWO+Ny1PfTKnU2ZFwYMJo3b46iKMTHx2OxWOjYsSNarZbPPvuMXr168cILLzBnzhw0Gg133nknb731lrvLF0JUoUs5DVIb+1+U1TL84uZbda3xlrPK65tRXa3H6/tplToZMrZs2cIjjzzCRx99RPPmzdmzZw8jR47kwIEDADRt2pRvv/2WLl268NBDDzF27Fj2799PYGAg0dHRbq5eCOEsZ8JDZr6ZcQu215nTIOAYKqxnjZx+8skyW4Z7de5cL4NFRcprPR797rtoggIdxlXV164qT6vk5dauVgl1MmR0796dp556igkTJqDX63n66aeZNm0a9957L8ePH+eRRx5hyJAh7N27l/DwcDQaDVddddUVv66iKID0y6hp8vPz7X/OycnBaq38w0ZUvXM/F+d+Tspz7vGth0/j7eNb7rizBcVMXLSTwmJbpa/tqVPzwZ0dCfLSVTguwMsDX3UxOTnFle7TXYpTUjg+4o5Sp0Ii58xBExhg36YNCMDk44OpCv89qhM/Sz4+hC76BktWFgBWYxbJU6dy4KIJ/yqDgabffYsuIqJaytJaC/GwFfLklxsrHGcrKgAq/zmqMZQ6bNKkSQqgvPDCCw7bz5w5o/j7+yuvvvpqlb5eYmKiAshNbnKr4JaYmCg/R3KT2xXeKvs5qinq5JGMc87Nrbj99tsdtoeFhdGpUycyMzOr9PUiIyNJTEzE19fX6Ymj53prJCYm4ufnV6X1uIrUXH1qY93l1awoCrm5uURWcgi6sp+j2vg1uZDU7161vX5nf45qijodMqAkaNhsjodTCwoK2L9/P5MnT67S11Kr1Zc9p8PPz6/WfcNLzdWnNtZdVs3+/v6VPs/Zn6Pa+DW5kNTvXrW5fmd+jmoKtbsLqA5q9fm3mZOTw913302fPn244YYb3FiVEEIIUbfV6pCRmZnJ6tWrOXLkSKVjbTYbkydPplOnTjRr1oyFCxdWQ4VCCCFE/VVrQ8aXX35JkyZNuOOOO2jevDmDBg0ioYKudmq1mieffJI9e/Ywc+bMGtMOXK/XM3369BpTjzOk5upTG+t2dc218WtyIanfvWp7/bWNSlFqy3Uw5+3YsYPBgwfz119/0bZtW9avX8/DDz9Mamoqy5cvd1j4bP/+/bz44ot8/vnneHl5ubFqIYQQon6plUcyFi9ezA033EDbtm0B6NOnD1u2bKFdu3bccMMNHDx40D42JSWFX375hVdeecVd5QohhBD1Uq08kvHf//6XFStWsG3bNofteXl59OnTB41Gw7Zt2+yXv50LIAZD3WnVKoQQQtR0tfJIxm233cb27dv58ssvHbb7+PiwcOFC4uPj+fPPP+3bu3fvLgFDCCGEqGa1MmR06NCBcePGMW7cONauXevwWJs2bejevTt79uxxU3VCCCGEgFoaMgDeeecd+vTpw/XXX8/SpUvt2y0WC+np6bRu3dqN1V2+oqIifvnlF3eXIWqwlJQUtm3bVqvWjcjKymL16tWYLlrES5Q4fPgw77zzjrvLqLdSU1N57bXX3F1GnVRrQ4aHhwe//PILt912G7fffjs333wz77zzDkOGDOGqq67i2muvdXeJl2XUqFHcfPPNfPjhh+4uxWl//PEHffv2pWHDhowdO5bc3Fx3l1SpkydPcsMNN+Dl5UVsbCwzZszAbK54NU93s1gsTJo0iYYNG9K1a1d69+5dKz60f/zxR1q2bMmSJUvYv39/le//0KFDDBw4EIPBQIsWLZgzZ06tCmCHDx9mwIABtfbqt9TUVG699Va8vb1p3Lgx06dPrxXfl+ekpqbSv39/LBaLu0upm9y5cEpV+f3335VRo0Yp1157rfLWW28pxcXF7i7psvXo0UO58cYbFZVKpcybN8/d5VRqwYIFSlhYmDJz5kxlxowZip+fnzJ69Gh3l1Wh7OxspUmTJsp///tfZdOmTcr06dMVLy8vpWvXrsqZM2fcXV65Hn74YWXAgAHKiRMnlG3btikBAQHKDz/84O6yKpSUlKT4+fkpW7dudcn+z5w5o0RERCgzZ85UNm7cqEybNk3x8PBQ+vXrp5w9e9Ylr1mVDh06pERFRSkff/yxu0u5LCaTSWnVqpUyadIkZfPmzcqMGTMUX19fpW3btsqpU6fcXV6lUlJSlJYtWyovvviiu0ups+pEyKhLxowZoyxcuFCZNm2aQ9BISEhwc2WlZWVlKSEhIcr27dvt2z744ANFrVYrRqPRfYVV4oMPPlCuueYah227du1SoqKilNatW9fID6dt27Ypvr6+SnZ2tn3b4MGDlR9++EFZvXq1kpmZ6cbqyvfOO+8ogwYNcti2evVq5fXXX1d++uknxWazXdH+X3/9deWWW25x2LZ582YlJCRE6dKli5KXl3dF+3elQ4cOKZGRkcpHH32kKIqiFBcXK++++64yaNAgZejQocr333/v5gor9/XXXysdOnRw2Hb48GElNjZWadKkiZKSkuKmyip3LmCcW6XbZrMpn3/+uTJkyBBl8ODByv/+978r/v4UilJrT5fUVS1btmTv3r288cYbTJ06lccee4zHH3+cnj17kpKS4u7yHHz//fcMHz6cTp062bfdeuut2Gy2Cruvult6enqpQ6Pt2rVjzZo1ZGRkMHbsWDdVVr5t27bh7e1tv0pq7969rFu3jvHjxzNs2DBiYmJYvXq1m6sszWg0UlhYCJS09r/vvvsYPnw4ixcv5pZbbmHkyJGlFjC8FGX9XXbv3t2+3MATTzxxRfW7UmZmJrm5uWzfvp3CwkKuv/56Pv30Uzp27IjJZGL48OG89NJL7i6zQmV9/Zs1a8aaNWuwWq2MGjXKTZVVLjs7m6ysLHbt2kVRURGjR4/m1VdfpU2bNuj1eh588EEefvhhd5dZ+7k75QhHP/74ozJs2DD7/TvuuEMBlMmTJ7uxqrJt2bJF2bJli8M2m82mqFQqZfPmzW6qqnJr1qxRVCqVsn79+lKP/f777wpQ4+o/dOiQotfrlQEDBiiTJ09W/P39lTfeeENRlJLTPwMHDlTCw8Nr3KnCH3/8UVGr1crevXuVefPmKb1791aysrLsj6lUKuW77767ov1rtVpl165dpR5bvHixolKplP3791/2/l1tw4YNiq+vr9KsWTNl2LBhitlstj/2/PPPKyqVStmzZ48bK6xYfHy8AijLly8v9dimTZsUtVqt/Pbbb26ozDn79+9XwsPDlWbNmik9evRQ8vPz7Y99+OGHClCj668NJGTUMAcPHlRiYmIURVGU3377TWnQoIFy77331pg5Gh988IEyd+7cCsd4eHgomzZtUhRFUSwWi/L8888rGRkZ1VGe06699lqlYcOGSmJiYqnHunXrprz66qtuqKpiW7ZsUaZNm6Y8/fTTSs+ePR0e2759uwIox44dc1N1ZTObzUrz5s2VLl26KNdff73y559/Ojzep08f++FqZxw+fNjhg9hqtSrdu3dXmjVrpqSnp5ca37x5c+X999+//DdQxQ4cOKCMGDHCYdu5oHHo0CGH7RaLRfHz81Pee++96izxko0YMUIJDQ1Vjhw5Uuqx6667rkb+gnShc0Fj9erVpR5r0aKFMm3aNDdUVXfI6RI3KCoq4umnn6Zdu3bMnTvX4bHY2FiSkpJYsmQJ9913Hz/99BNffPEFU6dO5fHHH3fJ7HxnzZs3j8cff5zXXnuN4uLicsepVCpsNhtWq5X77ruPrVu34uPjU42VnpeWlsbYsWNp1qwZY8eOJS8vDyhZYE+r1dK/f/9Sp3aCgoLw8/NzR7kAKIrCu+++S/v27enTpw/bt28HoFu3brzxxhuEhoaWai535swZgoODadiwoTtKBuDAgQNMnz6dF1980d6NV6fT8fXXX3PgwAF+/fVXkpOT7eMtFgvJyckOp9sqsmvXLq6++mo2bdpk36ZWq1m0aBF5eXn079+fpKQkh+e4++/yQgcPHmTAgAF89913Ds0Ce/XqxapVq2jWrJnDeI1Gg06nIyoqqrpLLVNWVhYzZszgwQcfZN68eeTn5wPw0UcfERoaSv/+/Uv9+1STvv7FxcUsWLCAadOm8dNP/9/evQdFVb5xAH8WkDvsggsrq+AiGIUmyWUQxBvmXUysESXRlBBUnIzsMjHV/MzR1ClL0hxSa6gULcLLjPc7TtpNICAoKIFRklBBBBXY3e/vD2Y3j7tcvCxnV5/Pf7zndX10Dud895z3skff/tRTT9Hx48cpJCTE4M/Y2dmZzf+/xRI75TyO5s+fj6lTp3Y4WC8oKAgymczgkb3u6YAYNm3aBG9vb+zduxcSiQTbt2/vsK+9vT1OnTqFF198ERMmTMDt27d7sNL/VFdXw9vbG0lJSVi3bh3c3NwE35orKyvx5JNPQiaTYcOGDaioqMDGjRvRt29fUQdSzpkzB2FhYcjIyMDw4cPh6+srOL5//34QET799FNotVoUFhbC19cXWVlZIlUMZGdnw83NDYmJiXj22WdBRHjxxRf1j5/z8vLQu3dveHh44Pvvv0dJSQni4uIwfvz4bg2uKygogEKhQHZ2ttHjZWVlUKlU8PDwQGZmJioqKrB27VoMGDDALAZ/lpaWQqlUYuvWrQgKChK8Eu3I5s2bMXDgQLS0tPRAhZ2rra2Fv78/pk2bhvnz50MqlaJfv37Iy8sD0D6IcujQoXB2dsa6detQXl6OL7/8EgqFAhcvXhS5+vb6g4OD9U/ViAgrVqzo9M/s27cPnp6eZjuo2lJwyOhhlZWVsLW1NThxa2pqcOPGDQDtj+/uHusgJl3A0D0OnTBhAsLDwzvs7+joiKCgIFEDBgCMHj1acCFZs2YNUlJSBH2amprwxhtvwN3dHUSEiIgIlJaW9nSpelu2bMGQIUNw69YtAO1TQJ2dnQ36LV68GEQEBwcHODg46GcoiKGqqgqurq745Zdf9G3Lli0DEWHYsGH6m3xNTQ1efvllyOVyuLm5YcmSJbh582aXn28sYJw+fRqZmZmCcTUNDQ1YunQppFIpJBIJRo8ejQsXLjy8f+h90gWMbdu2AQC++OILWFlZoaKiwmj/srIyvPrqq1CpVCgpKenJUjuUlJSERYsW6X+ura3F2LFjYW9vjwMHDgAAbt++jXfffReenp4gIgQHB+P8+fNilSwwZcoUpKam6gPt22+/DRsbG/z7778GfS9cuIAVK1ZAqVTqQxS7fxwyetj+/fvRu3dv/c+///47QkJCQESwtbU1u/nau3fvFgQMoH2sCHUyOFIul4seMGpqakBEgrrnzZuH8PBw9O3bFxEREYKpt1qtVn9jF1N0dDTee+89/c9HjhyBj48PBg8eDF9fX6xfv15/7Ndff8W3336Ly5cv93yhd1i/fj2GDRsmaCssLET//v3h5OSEWbNmPdDnR0REIDAwEI2NjWhoaMD48eNhZ2cHuVwOIkJsbKzg275WqxX13LtTfX29IGAA7TdjhUKBZcuWGfRvbm5GUlIS1q9fj8bGxp4stVNBQUGCfwPQPt5m+vTpcHJyMhhcaw6/SzqlpaUICAgQnCP19fWQSCQ4fPiwoK9arcYrr7yCVatWmfWaOZaEQ0YPy8/PBxHhl19+wdWrV6FUKrF69WqUlJTg/fffBxHhiy++ELtMvebmZlRWVhq0BwYGdnjz+Pnnn0W/yDc2NsLZ2Rlz585FaWkp0tPToVAokJWVhcOHDyMsLAxubm5mdyGJj4+Hr68vzp07hz179qBPnz5Yvnw5Tp8+jbS0NLM7PwAgIyMDUqlUEHbWr1+P+fPnY9u2bfrz/X5dunQJAwcORGRkJCZOnIj58+frn4Ds2rULtra2Rm/Y5sLY04j//e9/cHV1Nasg0Znp06dj8uTJBu23bt3C0KFDDdadMScHDx7E6tWrDdo9PT2Rk5MjQkWPFw4ZPUyr1SIwMBBRUVHIyMjAwoULBcfj4uKM/jKbm8zMTNjY2JjF+9aO5OTkwM/PD8HBwZBKpfjhhx/0x+rq6mBvb48tW7aIWKGhixcvYsyYMfD29sagQYOwePFiwfHp06eb3QW9rq4OCoUCAQEB+Pzzz/Hmm29CoVCgvLwcWq0Wfn5+DzxbRxc0nnjiCcHsEgB45513jL5SMmf//vsv7O3t8cknn4hdSrccO3YMRGTwNANoH29DREZnapkLY1O7fX19BQuelZSUQKPR9GRZjwWeXdLDJBIJffbZZ3Tu3DlasWIF+fn5CY7369eP3NzcRKqu+xISEkgmk9GmTZvELqVDM2bMoIqKCtq9eze1tbVRRESE/phcLieZTEYuLi4iVmiob9++dPz4caqurqbevXtTdHS04Li/v7/Z1SyXy+nkyZPk4+ND6enpVFJSQqdPnyZ/f3+SSCQUEhLywHtZKJVKOnnyJCUnJ1OvXr0Ex0JDQ0mtVj/Qol49zcPDg+Lj4ykjI8Mi6o6OjqYlS5ZQcnIy5ebmCo4NHz6crK2tqaGhQZziusHGxsagTSKR6Pe4OXPmDI0ZM4ZKSkp6urRHn9gp53G1fft22NjYwNvbG1VVVQDak7RcLhd1Fsm9SE9Ph1wuN6v3r8ZUVVWBiPDNN9/o2zIzMzFgwIBuDTwUy4gRIxATEwO1Wg2gfdCwQqHA0aNHRa6s+9ra2uDv768fHGgKycnJDzzuQwxFRUUgIuzbt0/sUrpFrVZj9uzZsLa2xsqVK/VPlLKzszFgwACzWwiuK/7+/sjOzkZeXh48PT2NrpPBHhyHDBGdPn0aTzzxBJycnBAWFgaZTIavvvpK7LK6raamBra2ttixY4fYpXRp7ty5sLKywsyZMxETEwOlUmk2I/c7sn//flhbWyM4OBgLFiyAXC7Hhg0bxC6r2yoqKhAbG4spU6aY5PPVajXef/99eHt7o6amxiR/h6k9++yzeO6558Quo9u0Wi1WrVoFBwcHKBQKhIaGwsvLy2xmkdyLgQMHIjU1lQOGiUkAQOynKY8SAHT06FH6448/KCgoiEaMGNFpf41GQ2fPnqUrV65QZGQkeXp69lClQuXl5XTs2DFyd3enyZMnd3vxrC1btlBCQgLZ2dmZuEJDzc3NtHv3bmpubqaxY8cavHq6k0ajoYyMDDp+/DgFBgbSa6+9Rh4eHj1Y7X9OnjxJxcXFNGjQIBo9ejRJJJIO++bl5dGmTZvIxsaGUlJSaPjw4T1Y6X8qKyvp0KFD5OrqSpMnTyapVNpp/x9++IFWrlxJ48aNoyVLlpCtrW2n/RsbGyk3N5fUajWNGzeOfHx8Ou2/f/9+SktLo/DwcFq3bp1ovzc693Iu3unUqVPk7u5OTz/9tIkr7JxGoyFra+tu96+rq6MjR44QEdHUqVNFX3DrXusnal+Eq7q6mvbt22fwWpI9RCKHnEdKa2srpk2bhv79+yMkJAQSiQSRkZEdLvVsLoOMtm7dCqlUiqioKLi4uEAul2PXrl1G+5pLzRUVFVCpVAgODoavry+srKywaNEio69uzKVmjUaDuLg49OvXD2FhYbCyskJISEiH63KYS927du2CVCrF8OHDIZPJIJPJOpzhcj81FxUVQalUIiwsDN7e3rCxscHy5csNBnje/fnGjovBEs/FO1VVVSEwMNBgyXdjzLH+y5cvY8iQIdi7d2+Xfe9c+O2jjz7iJxg9gEPGQ7Rq1SpER0frL37nz5/Hk08+CblcbvA48cMPP8TUqVNFX83vzz//hFQq1e+bUF9fj3nz5oGIsHbtWkHf4uJiBAYGmsWGTZGRkfoNwnRbNDs5OSEqKkq/qBkAtLS0YOLEifj444/FKlVvw4YNGDZsmP7mU1xcrF/d9c6ZL0D7olxjxowRbNgkhpqaGri4uCA/Px8AcOPGDSxZsgREhPT0dEHf8vJyDBo06J6nqw4aNAibN28G0H4T27hxI+zs7DBhwgTBjbq5uRkjR440OsNBTJZ4Lt5pzpw5UKlUcHR07DRoaDQavPDCC4J1XMxBamoqVCoVbG1tuwwaCxYswKuvvtpDlTGAQ8ZDNXLkSIPNmOrr6xEeHg6FQiGY4pWTkwNbW1ujuxf2pE2bNiEqKsqg/d133wURCZaq/ueffxAQEIA5c+b0ZIkGrl+/DiIyWL/jp59+gkwmw8SJE/XfWLRaLVJSUuDh4SH68sCTJ0/W34x0bty4gVGjRsHNzU3wxOvw4cNwcHAQfbzL9u3bMXjwYIP2jz76CEQkGCNy5coVDBkyBNOnT+/251dXV4OI9Duz6pw4cQKOjo6Ii4vTt6nVasTHx8PHx0f08KVjqeeiTl1dHZRKJRoaGhATE9Nl0HjzzTchlUrNZup6U1MTvLy8UFdXh/j4+C6DxqpVq+Do6GiwGR0zHQ4ZD1FsbCxmzpxp0H716lUMGDAAMTExgnbdrBIx7dy5Ey4uLgYXeQBISUmBs7OzYMGq2tpa0Rfaam1thaOjo9G9Ok6cOAEbGxvBMttarRbV1dU9WaJRCQkJRtdAaWxsxFNPPYUxY8YI2s3h/Dh06BBsbW3xzz//GBx76623YGdnh7///lvfduXKlXvaK+T69euwtrY2emPQ7ZOzc+dOfZtarTabGxxgueeiTmFhoX6V4ZaWlm4FDXM4L3UqKirw+uuvA/gvhHYVNMyp/scBh4yHKDc3FxKJxOh0Pd1S3OZ0gQHav0nL5XK89NJLBsdu3rwJLy8vrFu3ToTKOpeYmAilUml0xc7U1FQEBQX1fFFdOHLkCIhIsACQzpkzZ0BEou6bYkxLSwt8fHwQGxtrcKytrQ0DBw40eG1yr55//nn4+fkZDbpz5szBqFGjHujzTc0Sz8WOdBQ0LGX2TkdBw1LqfxRxyHjIZsyYAVdXV6MbnDk7Ows2dDIXO3fuBBEZ3TclMTERqampIlTVOd1j3tDQUNTX1wuOnThxAjKZTJzCujB37lw4Ojri1KlTBscUCgUOHjwoQlWdO3DgAKysrIwu3f3aa6898Ouz6upq9O7dG6NGjTJ4CpKbmwuVSvVAn29qlnouduTuoHHw4EF4eXmZzSuertwdNPLy8sxmN9jHEYeMh6y5uVk/S+POb6zFxcVwdXU1+m3NHKxZswZEJBgV39bWhpCQEHz99dciV2dcYWEh5HI5Bg8ejLKyMn372rVrMXHiRBEr69jt27cxbtw4ODg4CNZE+euvv+Ds7Cz6Zmcd2bx5MyQSCRISEvSDGbVaLUaPHm0wDul+nD17Fq6urggLCxOMb3j77bctYqEtSzwXO3Nn0FAoFB1uhmiu7gwaHh4ePItERBwyTODmzZuYO3cuiAgjRozAwoULoVAozPZmrfPll1/C2dkZ3t7eWLhwIYKDg/H888+b5bQ1nT/++ANBQUGws7PDzJkz9VNE79x91dy0tLQgOTlZvxV6cnIyvLy8kJmZKXZpnfruu+8gk8nQp08fJCUlISIiAuPHj39oU0kLCwsREBAAR0dHxMfHIzY2Fr6+vhbzDdQSz8XO7NmzB1Kp1OIChs7Jkyfh5OTEAUNkvBiXCf3444/6BYbi4+MpODhY7JK6VFtbS1lZWVRVVUURERE0e/ZssrIy7y1uNBoN5eTk0JkzZ0ihUFBSUpLoizN1x/nz5+m7776j27dvU1xcHIWHh4tdUpeuXbtGWVlZVF5eTqGhoZSQkGB0X4j71draSrt27aJz586Rj48PJSUlWcRePjqWei7e7cSJEzR79mzas2ePRZyXd/vpp58oJiaGduzYwQttiYxDBmOMMYHLly/TpUuXKCQkROxS7su1a9eorKyMIiMjxS7lscchgzHGGGMmYd7PwRljjDFmsThkMMYYY8wkOGQwxhhjzCQ4ZDDGGGPMJDhkMMYYY8wkOGQwxhhjzCQ4ZDDGGGPMJDhkMMYYY8wkOGQwxhhjzCQ4ZDDGGGPMJDhkMMYYY8wkOGQwxhhjzCQ4ZDDGGGPMJDhkMMYYY8wkOGQwxhhjzCQ4ZDDGGGPMJDhkMMYYY8wkOGQwxhhjzCQ4ZDDGGGPMJDhkMMYYY8wkOGQwxhgzKj09nVavXi12GcyCcchgjDFm1N69e8nZ2VnsMpgF45DBGGPMQEtLC5WVldHQoUPFLoVZMA4Z7JERFRVFaWlptGjRInJ3dye5XE6fffYZXb16lRYsWEAymYz69OlD2dnZYpfKmNkrKioijUZDjo6OlJiYSCNHjqTly5dTU1OT2KUxC8Ihgz0SANBvv/1G3377LU2aNImqq6spKSmJ0tLSaPbs2RQXF0eXLl2imTNn0tKlS8UulzGzV1BQQPb29rRs2TKKiYmh5cuXU05ODqWkpIhdGrMgNmIXwNjD8Pfff9ONGzfo888/p2nTphER0dSpU+mDDz6gV155hSZMmEBERFOmTKGNGzeSVqslKyvO2Ix1pKCggHr16kU5OTnk4eFBRETXr1+n5ORkAkASiUTkCpkl4KsseyQUFBSQi4sLzZgxQ9924cIF8vDwoEmTJgnaVCoVBwzGupCfn09JSUn6gEFEpFQq6datW9Ta2ipiZcyS8JWWPRIKCwvpmWeeoV69eunb8vPzKTQ0VBAodP0YYx3TvX4cNmyYoP33338nlUpFdnZ2IlXGLA2HDPZIKCgoMBgFb6ytsLCQR8sz1oXy8nJqamoiFxcXQfu2bdto1qxZIlXFLBGHDPZIMBYe7m4DQEVFRfwkg7EuFBQUkEQioR07dhAAAkArV66ka9eu0euvvy52ecyC8MBPZvEaGhqourpaEB4uXrxIV69eFbT99ddf1NTUxCGDsS4UFBTQ2LFj6datW9S/f3/SaDTUt29fOnbsGLm7u4tdHrMgEgAQuwjGGGPm48KFCySRSEilUlFlZSWp1Wry9/cXuyxmgThkMMYYY8wkeEwGY4wxxkyCQwZjjDHGTIJDBmOMMcZMgkMGY4wxxkyCQwZjjDHGTIJDBmOMMcZMgkMGY4wxxkyCQwZjjDHGTIJDBmOMMcZMgkMGY4wxxkyCQwZjjDHGTIJDBmOMMcZM4v/U8fJal9/AJgAAAABJRU5ErkJggg==", - "text/plain": [ - "<Figure size 550x550 with 4 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "name": "stdout", "output_type": "stream", "text": [ - "diagonal: m = 0.802 +/- 0.028 full: m = 0.793 +/- 0.051\n" + "diagonal only: m = 0.829 +/- 0.027 full matrix: m = 0.834 +/- 0.050\n" ] } ], @@ -585,67 +1085,102 @@ "a_line = np.array([1.0, 0.8])\n", "y_corr = a_line[0] + a_line[1] * xs + rng.multivariate_normal(np.zeros(20), C)\n", "d_corr = rx.Dataset(xs, y_corr, np.sqrt(np.diag(C)), label=\"correlated noise\")\n", - "m_, b_ = rx.Parameter(\"m\", prior=stats.norm(0, 5), latex=\"m\"), rx.Parameter(\n", - " \"b\", prior=stats.norm(0, 5), latex=\"b\"\n", - ")\n", + "m_ = rx.Parameter(\"m\", prior=stats.norm(0, 5), latex=\"m\")\n", + "b_ = rx.Parameter(\"b\", prior=stats.norm(0, 5), latex=\"b\")\n", "line = rx.Model(lambda x, m, b: b + m * x, [m_, b_])\n", "c_full = rx.Constraint(\n", " [rx.Comparison(d_corr, line)], terms=[rx.Term(C, kind=\"matrix\")], statistical=False\n", ")\n", "c_diag = rx.Constraint([rx.Comparison(d_corr, line)])\n", - "s_full, s_diag = fit(rx.Problem([c_full]), seed=11), fit(rx.Problem([c_diag]), seed=12)\n", + "s_full = fit(rx.Problem([c_full]), seed=11)\n", + "s_diag = fit(rx.Problem([c_diag]), seed=12)\n", + "print(\n", + " f\"diagonal only: m = {s_diag[:, 0].mean():.3f} +/- {s_diag[:, 0].std():.3f} \"\n", + " f\"full matrix: m = {s_full[:, 0].mean():.3f} +/- {s_full[:, 0].std():.3f}\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "cc9ef059", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:15:25.255869Z", + "iopub.status.busy": "2026-09-12T03:15:25.255737Z", + "iopub.status.idle": "2026-09-12T03:15:25.411023Z", + "shell.execute_reply": "2026-09-12T03:15:25.410279Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 605x605 with 4 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "fig = corner.corner(\n", " s_diag,\n", - " color=\"C3\",\n", " labels=[\"$m$\", \"$b$\"],\n", " truths=[0.8, 1.0],\n", - " truth_color=\"k\",\n", - " plot_datapoints=False,\n", + " **plotstyle.corner_kwargs(\n", + " color=plotstyle.COLOURS[1], fill_contours=False, show_titles=False\n", + " ),\n", + ")\n", + "corner.corner(\n", + " s_full,\n", + " fig=fig,\n", + " **plotstyle.corner_kwargs(\n", + " color=plotstyle.COLOURS[0], fill_contours=False, show_titles=False\n", + " ),\n", ")\n", - "corner.corner(s_full, fig=fig, color=\"C0\", plot_datapoints=False)\n", "fig.legend(\n", " handles=[\n", - " plt.Line2D([], [], color=\"C3\", label=\"diagonal only (overconfident)\"),\n", - " plt.Line2D([], [], color=\"C0\", label=\"full matrix\"),\n", + " plt.Line2D(\n", + " [], [], color=plotstyle.COLOURS[1], label=\"diagonal (overconfident)\"\n", + " ),\n", + " plt.Line2D([], [], color=plotstyle.COLOURS[0], label=\"full matrix\"),\n", " ],\n", " loc=\"upper right\",\n", - " frameon=False,\n", ")\n", - "plt.show()\n", - "print(\n", - " f\"diagonal: m = {s_diag[:, 0].mean():.3f} +/- {s_diag[:, 0].std():.3f} full: m = {s_full[:, 0].mean():.3f} +/- {s_full[:, 0].std():.3f}\"\n", - ")" + "plt.show()" ] }, { "cell_type": "markdown", - "id": "6a6d2bd0", + "id": "0bd65faf", "metadata": {}, "source": [ "### 3. Unknown or misreported magnitudes\n", "\n", - "A `model_error` term scales with the prediction; its fraction sets how much\n", + "A `model_error` term scales with the prediction, and its fraction sets how far\n", "the total error exceeds what was reported." ] }, { "cell_type": "code", - "execution_count": 12, - "id": "a8814e5e", + "execution_count": 22, + "id": "c32c89dd", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:37:55.952069Z", - "iopub.status.busy": "2026-09-11T03:37:55.951897Z", - "iopub.status.idle": "2026-09-11T03:37:56.068441Z", - "shell.execute_reply": "2026-09-11T03:37:56.067591Z" + "iopub.execute_input": "2026-09-12T03:15:25.412503Z", + "iopub.status.busy": "2026-09-12T03:15:25.412362Z", + "iopub.status.idle": "2026-09-12T03:15:25.561856Z", + "shell.execute_reply": "2026-09-12T03:15:25.561245Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "<Figure size 500x350 with 1 Axes>" + "<Figure size 704x440 with 1 Axes>" ] }, "metadata": {}, @@ -653,51 +1188,54 @@ } ], "source": [ - "fig, ax = plt.subplots(figsize=(5, 3.5))\n", - "ax.plot(d0.x, d0.y_err, \"k--\", label=\"reported\")\n", - "for g in (0.02, 0.05, 0.10):\n", - " c = rx.Constraint([comps[0]], terms=[T.model_error(gamma, averaging=True)])\n", - " p = rx.Problem([c])\n", - " theta = np.append(theta_map[:5], np.log(g))\n", - " ax.plot(\n", - " d0.x, np.sqrt(np.diag(p.constraints[0].matrix(theta))), label=f\"gamma = {g}\"\n", + "gamma = rx.Parameter(\n", + " \"log_gamma\", prior=stats.norm(np.log(0.1), 0.5), latex=r\"\\log\\gamma\"\n", + ")\n", + "d0 = datasets[\"exp 0\"]\n", + "fig, ax = plt.subplots()\n", + "ax.plot(d0.x, d0.y_err, \"--\", color=\"k\", label=\"reported\")\n", + "for g, colour in zip((0.02, 0.05, 0.10), plotstyle.COLOURS):\n", + " p = rx.Problem(\n", + " [rx.Constraint([first], terms=[T.model_error(gamma, averaging=True)])]\n", " )\n", - "ax.set(xlabel=\"x\", ylabel=\"total standard deviation\")\n", - "ax.legend(frameon=False)\n", + " total = np.sqrt(np.diag(p.constraints[0].matrix(np.append(theta_map, np.log(g)))))\n", + " ax.plot(d0.x, total, color=colour, label=rf\"$\\gamma$ = {g}\")\n", + "ax.set(xlabel=\"$x$\", ylabel=\"total standard deviation\")\n", + "ax.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "b592b2e4", + "id": "574374c1", "metadata": {}, "source": [ "### 4. A systematic shared across datasets\n", "\n", - "The same normalisation term `on` two comparisons couples their blocks; one\n", - "per comparison leaves the covariance block diagonal. Which is right depends\n", - "on whether the two experiments really shared the calibration; the next\n", - "notebook is about that choice." + "The same normalisation term `on` two comparisons couples their blocks; one term\n", + "per comparison leaves the covariance block diagonal. Which is right depends on\n", + "whether the two experiments really shared a calibration — the subject of\n", + "`sharing_error_models`." ] }, { "cell_type": "code", - "execution_count": 13, - "id": "2683faa0", + "execution_count": 23, + "id": "2ddf9ef6", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:37:56.069846Z", - "iopub.status.busy": "2026-09-11T03:37:56.069695Z", - "iopub.status.idle": "2026-09-11T03:37:56.126814Z", - "shell.execute_reply": "2026-09-11T03:37:56.126172Z" + "iopub.execute_input": "2026-09-12T03:15:25.563425Z", + "iopub.status.busy": "2026-09-12T03:15:25.563281Z", + "iopub.status.idle": "2026-09-12T03:15:25.618753Z", + "shell.execute_reply": "2026-09-12T03:15:25.618223Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 700x350 with 2 Axes>" + "<Figure size 836x396 with 2 Axes>" ] }, "metadata": {}, @@ -705,46 +1243,50 @@ } ], "source": [ - "two = comps[:2]\n", + "two = [comps[\"exp 1\"], comps[\"exp 2\"]]\n", "c_shared = rx.Constraint(two, terms=[T.normalization(magnitude=0.1, on=two)])\n", "c_each = rx.Constraint(two, terms=[T.normalization(magnitude=0.1, on=c) for c in two])\n", - "fig, axes = plt.subplots(1, 2, figsize=(7, 3.5))\n", - "n0 = datasets[0].n\n", + "fig, axes = plt.subplots(1, 2, figsize=(7.6, 3.6))\n", "for ax, c, title in zip(\n", " axes,\n", " (c_shared, c_each),\n", " (\"shared: off-diagonal blocks\", \"per dataset: block diagonal\"),\n", "):\n", - " p = rx.Problem([c])\n", - " show(ax, p.constraints[0].matrix(theta_map[:5]), title, dividers=(n0,))\n", + " show(\n", + " ax,\n", + " rx.Problem([c]).constraints[0].matrix(theta_map),\n", + " title,\n", + " dividers=(datasets[\"exp 1\"].n,),\n", + " )\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "501f3b2f", + "id": "9a97711a", "metadata": {}, "source": [ "## Summary\n", "\n", - "| case | term | structure of Σ |\n", + "| case | term | structure of $\\Sigma$ |\n", "|---|---|---|\n", - "| unknown normalisation, sampled | `model \\| tf.scale(rho)` | none: the mean moves |\n", - "| reported normalisation | `T.normalization(magnitude=)` | rank one, from the prediction |\n", - "| smooth systematic in x | `T.kernel(...)` | dense, decaying with distance |\n", + "| normalisation inferred | `model \\| tf.scale(rho)` | none: the mean moves |\n", + "| quoted normalisation | `T.normalization(magnitude=)` | rank one, from the prediction |\n", + "| inferred systematic magnitude | `T.normalization(parameter=eta, on=comp)` | rank one, sampled size |\n", + "| smooth systematic in $x$ | `T.kernel(...)` | dense, decaying with distance |\n", "| reported correlated statistics | `rx.Term(C, kind=\"matrix\")` | whatever was reported |\n", "| unknown model error | `T.model_error(gamma)` | sampled diagonal |\n", "| shared across datasets | one term `on=[c1, c2]` | off-diagonal blocks |\n", "\n", - "Declaring the uncertainty *is* the modelling choice; the inference machinery\n", - "is unchanged. Never build a mode from the data (recipe 27): the prediction\n", - "is what the mode multiplies." + "Declaring the uncertainty *is* the modelling choice, and the inference machinery\n", + "does not change. Whatever we declare, the mode multiplies the **prediction**,\n", + "never the data (recipe 27)." ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/test/test_notebooks_index.py b/test/test_notebooks_index.py index cf42776..876ef74 100644 --- a/test/test_notebooks_index.py +++ b/test/test_notebooks_index.py @@ -5,6 +5,7 @@ of ``docs/recipes.md``. Nothing here executes a notebook. """ +import ast import json import pathlib import re @@ -20,7 +21,7 @@ "linear_calibration": {1, 17}, "error_models": {2, 4, 19}, "sharing_error_models": {5}, - "normalization_and_covariance_structure": {3, 6, 27}, + "normalization_and_covariance_structure": {3, 4, 6, 27}, "gp_discrepancy": {7, 8, 36}, "robust_likelihoods": {9, 39}, "error_scale_and_usu": {34}, @@ -62,6 +63,28 @@ def test_each_notebook_exists_and_cites_its_recipes(name): ) +@pytest.mark.parametrize("name", sorted(NOTEBOOKS)) +def test_no_latex_escape_lands_in_a_plain_string(name): + r"""``f"$\rho$"`` is a carriage return, and matplotlib then fails to parse it. + + Every LaTeX macro in a notebook must sit in a raw string, or the Python + escapes ``\r \t \a \b \f \v`` silently eat the backslash and the label. An + ``ast`` walk sees f-strings too, which a ``tokenize`` pass does not. + """ + control = {"\r": r"\r", "\t": r"\t", "\a": r"\a", "\b": r"\b", "\f": r"\f", "\v": r"\v"} + nb = json.loads((EXAMPLES / f"{name}.ipynb").read_text()) + bad = [] + for i, cell in enumerate(nb["cells"]): + if cell["cell_type"] != "code": + continue + for node in ast.walk(ast.parse("".join(cell["source"]))): + if isinstance(node, ast.Constant) and isinstance(node.value, str): + for ch, shown in control.items(): + if ch in node.value: + bad.append(f"cell {i}: {shown} in {node.value[:40]!r}") + assert not bad, f"{name}: a LaTeX escape outside a raw string: {bad}" + + def test_no_stray_notebooks(): stray = sorted(set(_present()) - set(NOTEBOOKS)) assert not stray, f"notebooks not in the design's list: {stray}" From fc147d78dd8cbcbf45d4cd94c4d5d652b52eb123 Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Fri, 11 Sep 2026 23:37:30 -0400 Subject: [PATCH 58/75] Rework gp_discrepancy around mean-zero discrepancies - Only mean-zero GPs now, per the review: the sampled Legendre mean correction is gone, so this notebook teaches recipe 7 alone - Kennedy & O'Hagan framed at the top, with the distinction between learning the covariance of a mismatch and giving the mismatch its own parameters - Amplitudes grow with x through T.exp_growth_amplitude, which is the prior knowledge that the data stop looking like a line at large x - The toy is now a line plus a defect growing as 0.03 x^2, so (m, b) have true values and the corner shows what each error model does to them: statistics only is 60 sigma out and confident, a diagonal model error covers only by becoming useless, a constant-amplitude GP is still 2.6 sigma out, and the growing amplitude lands at 0.4 sigma - The optical-model half gets the same three rungs and its own random generator, so changing the toy no longer silently changes its data. The GP wins on evidence by 570 log units and recovers V_v, while W_v stays nine sigma out because volume and surface absorption are degenerate at one energy - The learned discrepancy is now read off total_predictive_band, which knows about the amplitude, rather than from the bare kernel, which does not - Registers the recipe sets for the two notebooks whose rebuilds are still executing (local_optical_model_calibration, alpha_ca_error_model_comparison) --- docs/design.md | 2 +- examples/gp_discrepancy.ipynb | 1084 +++++++++++++++++++++------------ test/test_notebooks_index.py | 6 +- 3 files changed, 702 insertions(+), 390 deletions(-) diff --git a/docs/design.md b/docs/design.md index 4e3d834..412b931 100644 --- a/docs/design.md +++ b/docs/design.md @@ -625,7 +625,7 @@ a time unless noted. | `error_models` | 2, 4, 19 | emcee | the covariance ladder on one comparison, the Peelle matrix as a fixed term, offsets known, free and ignored | 89 s | | `sharing_error_models` | 5 | emcee | two experiments with opposite normalisation defects: sharing a parameter, a mode per dataset, one mode spanning both, and the assembled covariance seen directly | 78 s | | `normalization_and_covariance_structure` | 3, 4, 6, 27 | emcee | six treatments of five experiments' normalisations, one of them badly mis-quoted and alone in its range; Peelle's puzzle in the two-point case it was found in; a gallery of covariance structures from `matrix(theta)` | 353 s | -| `gp_discrepancy` | 7, 8, 36 | emcee, dynesty | a kernel term on a toy and on n+⁴⁰Ca missing its surface absorption; the total predictive band; a sampled Legendre correction for contrast | 617 s | +| `gp_discrepancy` | 7 | emcee, dynesty | mean-zero discrepancies with amplitudes growing in x: four rungs on a toy line, three on n+⁴⁰Ca missing its surface absorption; the total predictive band | 616 s | | `robust_likelihoods` | 9, 39 | emcee | Student-t versus Gaussian on three gross outliers; the iterative rejection loop, including the round that over-rejects and recovers | 54 s | | `error_scale_and_usu` | 34 | emcee | a global scale on the reported errors under both likelihoods; a USU offset on the technique we suspect | 90 s | | `local_optical_model_calibration` | 12, 14, 15, 16, 21, 26 | dynesty | a real EXFOR measurement to a calibrated potential; the unit contract, the singular guard, tempering, other drivers | 182 s | diff --git a/examples/gp_discrepancy.ipynb b/examples/gp_discrepancy.ipynb index 497ca6f..87ef56d 100644 --- a/examples/gp_discrepancy.ipynb +++ b/examples/gp_discrepancy.ipynb @@ -2,31 +2,43 @@ "cells": [ { "cell_type": "markdown", - "id": "f32c8e7d", + "id": "b89d79ac", "metadata": {}, "source": [ "# Model discrepancy with a Gaussian process\n", "\n", - "A structurally wrong model leaves correlated residuals. A Gaussian-process\n", - "term in the covariance absorbs them (Kennedy and O'Hagan's discrepancy), the\n", - "model parameters relax toward their true values, and the total predictive\n", - "band bends where the model cannot. First on a toy, then on a differential\n", - "cross section whose potential is missing a piece of physics. For contrast,\n", - "the same defect is also fit with an explicitly sampled mean correction.\n", + "Our model is always wrong somewhere. When it is wrong in a *structured* way —\n", + "smoothly, as a function of $x$, rather than point by point — the residuals it\n", + "leaves are correlated, and a covariance that assumes independence will read\n", + "those correlations as precision it does not have.\n", "\n", - "Recipes: 7, 8, 36" + "The standard treatment is [Kennedy & O'Hagan\n", + "(2001)](https://doi.org/10.1111/1467-9868.00294), who add a discrepancy term to\n", + "the model. There are two quite different things people mean by that:\n", + "\n", + "- a **mean-zero discrepancy**, which learns only the *covariance* of the\n", + " mismatch: we say the truth departs smoothly from our model by some amount, we\n", + " say how far and how smoothly, and we integrate over every such departure;\n", + "- a **non-zero-mean discrepancy**, which gives the mismatch its own parameters\n", + " and builds a posterior for the discrepancy function itself (recipes 8 and 36).\n", + "\n", + "This notebook is entirely about the first. We will also give the discrepancy an\n", + "amplitude that **grows with $x$**, because that is genuine prior knowledge here:\n", + "our data look like a line at small $x$ and stop looking like one further out.\n", + "\n", + "Recipes: 7" ] }, { "cell_type": "code", "execution_count": 1, - "id": "40ac011a", + "id": "506a99dd", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:47:54.370211Z", - "iopub.status.busy": "2026-09-11T03:47:54.370028Z", - "iopub.status.idle": "2026-09-11T03:47:57.331437Z", - "shell.execute_reply": "2026-09-11T03:47:57.330670Z" + "iopub.execute_input": "2026-09-12T03:25:08.887811Z", + "iopub.status.busy": "2026-09-12T03:25:08.887654Z", + "iopub.status.idle": "2026-09-12T03:25:11.158430Z", + "shell.execute_reply": "2026-09-12T03:25:11.157614Z" } }, "outputs": [], @@ -37,58 +49,67 @@ "import jitr\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", + "import plotstyle\n", "from jitr.optical_potentials.potential_forms import (\n", " thomas_safe,\n", " woods_saxon_prime_safe,\n", " woods_saxon_safe,\n", ")\n", - "from numpy.polynomial import legendre as L\n", "from scipy import stats\n", - "from sklearn.gaussian_process.kernels import ConstantKernel, Matern, WhiteKernel\n", + "from sklearn.gaussian_process.kernels import Matern, WhiteKernel\n", "\n", "import rxmc as rx\n", "from rxmc import terms as T\n", - "from rxmc import transforms as tf" + "from rxmc import transforms as tf\n", + "\n", + "plotstyle.use()" ] }, { "cell_type": "markdown", - "id": "2f89ca34", + "id": "beea011b", "metadata": {}, "source": [ - "## A linear model and a non-linear truth\n", + "## A line, and a truth that leaves it\n", "\n", - "Thirty points of a gently curving function, fit by a line. No line can\n", - "follow it, so the residuals of the best line are smooth and correlated." + "Our truth is a line plus a small quadratic departure, $0.03\\,x^2$. At the left\n", + "edge that defect is 0.001, far below the 0.02 noise and quite invisible; by\n", + "$x = 5$ it is 0.75, thirty-seven times the error bar. A straight-line model is\n", + "therefore excellent at small $x$ and hopeless at large $x$, which is exactly the\n", + "situation the growing amplitude is meant to express." ] }, { "cell_type": "code", "execution_count": 2, - "id": "34d7834b", + "id": "46904250", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:47:57.333388Z", - "iopub.status.busy": "2026-09-11T03:47:57.333114Z", - "iopub.status.idle": "2026-09-11T03:47:57.490368Z", - "shell.execute_reply": "2026-09-11T03:47:57.489590Z" + "iopub.execute_input": "2026-09-12T03:25:11.160039Z", + "iopub.status.busy": "2026-09-12T03:25:11.159798Z", + "iopub.status.idle": "2026-09-12T03:25:11.164302Z", + "shell.execute_reply": "2026-09-12T03:25:11.163753Z" } }, "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 500x350 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "defect / noise at x = 0.2, 2.5, 5.0: [ 0.1 9.4 37.5]\n" + ] } ], "source": [ + "M_TRUE, B_TRUE = 0.8, 1.0\n", + "\n", + "\n", + "def defect(x):\n", + " return 0.03 * np.asarray(x, dtype=float) ** 2\n", + "\n", + "\n", "def truth(x):\n", - " return (0.8 * x + 1.0) / (1.0 + x / 2.0)\n", + " return M_TRUE * np.asarray(x, dtype=float) + B_TRUE + defect(x)\n", "\n", "\n", "rng = np.random.default_rng(7)\n", @@ -98,45 +119,121 @@ " x, truth(x) + rng.normal(0.0, noise, x.size), np.full(x.size, noise), label=\"toy\"\n", ")\n", "x_fine = np.linspace(0.0, 6.0, 80)\n", - "\n", - "m = rx.Parameter(\"m\", prior=stats.norm(0.0, 2.0), latex=\"m\")\n", - "b = rx.Parameter(\"b\", prior=stats.norm(0.0, 2.0), latex=\"b\")\n", - "line = rx.Model(lambda x, m, b: m * x + b, [m, b])\n", - "comp = rx.Comparison(data, line)\n", - "\n", - "fig, ax = plt.subplots(figsize=(5, 3.5))\n", + "print(\n", + " \"defect / noise at x = 0.2, 2.5, 5.0:\", np.round(defect([0.2, 2.5, 5.0]) / noise, 1)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cf684d62", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:25:11.165619Z", + "iopub.status.busy": "2026-09-12T03:25:11.165497Z", + "iopub.status.idle": "2026-09-12T03:25:12.009917Z", + "shell.execute_reply": "2026-09-12T03:25:12.009065Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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mWADwzz//YOHChYiJiXnm8QAgIyMDcrkcSUlJKC4uRnx8PA4ePAhfX9/af+NERERqZO07GamB3yHzl7dht+AYJBKJ2CEB0LLLkaSqT58+mDlzJpydnREYGIjAwECMGDECAHD//n34+/vjxo0bCAwMhIGBwXMfb/Lkybh27RpCQ0MxbNgwzJ8/X6Pvj4iIqDYYOXqh8WubYdpvttYkYABnwgAAKf+sReq/ayvtZ956EBpNVn3QeMSidlDkple6r+NbO2Hq3rWGEZZla2ur1sX0jRs3xquvvqp8PXjwYC7WJyIinVOUFouk3xfBYfzXkBqbK9utuo1DcUqKiJGVxSQMQEluOoqSH1TarzgrqWxbSjRKclIr3Vcoyq9RbM/SuHFjtWbzdnZ2Kq9NTEyQm5urtuMTERFpWtaVw4jdPBklWckQFAo4TtsmdkgVYhIGQM/UGgYy10r76VvYlW2zdYbUpPIF7BID4xrF9ixSadkryVKpFIIgqLQVFhaq9bxERETaRlFUgMTfPlK5qpV7+yiKs1Ogb649C/GfxiQMgO3Ad2E78N0a7dts6RW1xvI0U1PTKidSDg4OePBAdUbv9OnTNT4eERGRtiuIv4uH//NH/oNLyjbLzqPRaPIG6JlZixdYFTAJ03Jt27bFgwcPsHnzZjRp0qRMSYknDR06FHPnzsXs2bMhl8tx7Ngx7N+/H+bm5jU6HhERkTZLP7UD8dvfhiI/GwAgMTSBw4RvYd3rNa1agP8svDtSy3Xo0AFbt27Fv//+iy+//BLnz5+Hq6srunYtu8i/SZMmOHr0KDIyMrBjxw44Ojrip59+Qs+ePat9PDs7O/Tr10/j74+IiKi6SvKy8HDDRMRunKhMwIycWsNt8QXY9H5dJxIwAJAITy8i0mExMTFwdnZGdHQ0nJycNHaelJQUrXoKuy7jWKoPx1I9OI7qw7FUH46lqqzLfyB67VDlaxv522jo/xWkhiYV7qdt48iZMCIiItIpFu1fgnWf6ZCa2cDpnd/RaOK6ShMwbcQ1YURERKTVSnLSIDW1VrnM6DBuDeyGLoSBrbOIkT0fzoQRERGR1sq+EYDw+Z5IC/5epV1qZKrTCRjAJIyIiIi0kFBciIRd8xC1cgBKMhKQ8Osc5EdfFzssteLlSCIiItIqhQkRiPl+LPIjzyvbzFu/AH2bxiJGpX5MwoiIiEhrpJ/6CfHb33pc+8vACA3HroZNv7d0pvREVTEJIyIiItGV5GUifvsMZJz+Sdlm5OgNx7d+hbFzaxEj0xwmYURERCSqwqT7eLDCD0WJEco2m35voeHYVTpZeqKquDBfB+Xl5SE9PV3sMDQiPz8faWlpz3ytjaoSoy58zWozxoKCghp/XQVBQGpqKvLy8tQcFRGJxcDGEfoWMgAorf01ax8aTfpfnU7AACZhWi8/P7/ML8Z169ahT58+tXKu2vbDDz+gU6dOz3ytjZ6OsTa/ZuWpytdR7Bh3796N1q2rf3lh5cqVsLCwQPPmzfH9999XvkMVaMPnnqi+k+gbwPHNX2DebgiaLb0Kyw4vix1SrWASpuU2b95c7nMidf1cVWViYoIGDRqIHUaFno5R7HGsyvnFjrEm0tLSMH/+fBw6dAgpKSmYM2eOWo6ri2NBpOuyLv+B3DunVNoM7d3g8t4fOl/7qzqYhGmxwsJC5OTkoKSkBMnJyUhOTkZ+fn6ZfuW1PSknJweVPSL0Wed68hJVSUkJUlNTAQC5ubnIzMxUOUZFl5iqEkN5xowZg3/++Uf5+ulLZup4709TKBRITk6GQqFQtmVmZiIjI6PcPk/GqI6vmSAIyMzMVDn/I5WNe1XOX5ufqyeVlJSgoKCg0n7lHbegoADXr19HSUkJGjduXObrU9V4cnNzVfpUdSyISD0UhXmI2z4T0WuH4uH6cSjJ0e7lJprGJEyLHT58GEuXLkVkZCRatmyJli1b4scffwRQ+stjzpw5sLOzg6WlJTp27Ijw8HCV/Xfv3g13d3fIZDJYWlpi/PjxyiSqqudat24dunfvjlmzZsHa2hotWrRAQUEBPvvsM4wYMULlGL/++muZS0zViaE8T1/qexSPOt/700pKSuDq6oqgoCBlW+vWreHl5aV8ffToUTg7O6OoqEglxuf9mq1duxb29vaws7ODjY0NPv74Y5Vko7Jxr+j8j9Tm5+rJuK2srGBjY4N27drh4sWLZfpUdNzdu3dj2LBhAIBu3bqhZcuWePDgQZXj2b17Nzw8PGBtbQ2ZTIbFixejpKSk3LHYuXNnhe+FiGomP+YGIpd0QVrQOgBAUUqUyp2Q9RGTMC02fPhwLF++HO7u7sq/0t944w0AwK1bt2BgYICYmBikpaXB2tpa5fJMQEAApk+fjo0bNyI3NxdRUVFIT0/H9OnTa3QuAEhKSkJycjKMjIyqFH91Y6gqdb/3pxkYGKBbt244evQoAODevXtITk5GTk4O7t69C6A0CevWrVuZsXier9nBgwfx0UcfYdu2bcjPz8dff/2F9evXY926dVUem4rOr44YazK2v//+O7788kscOHAAOTk5WLlyJTZs2KDSp7LjTpgwAUeOHAEAhIWFITk5GU2bNq1SPH/99RfGjh2LDz74ANnZ2bh37x4kEgmSkpLKHYvJkydXebyJqHKCICA16H+IXNwJBTGlFe8lRmZoPHUbbPxmihyduJiEAVh7/B6aLAtEk2WBOPNAdWr0ysMM5bYvgu6W2bfdqmNosiwQAzecKbPtvQM3lPvGZqj3EkeDBg3w+eefw8jICGZmZnjttddw9uxZ5fYVK1Zg8uTJ8PHxQXp6OgRBwPvvv499+/ZV+3KLubk5Vq5cCWNj42rtp84YnlQb771Pnz7KX/pHjx6Fr68vfH19Vdqqu4i9sri//vprjBs3DkOGDIFEIkH37t3xzjvvYO3atdU6z/PQxNh+9913mDhxIuRyOSQSCfr3749XX31VpU9Nv2ZV2W/FihUYPXo0pk2bBkNDQ1hZWeHTTz+Fg4ODGkaMiCpSnJWM6LXDEL99BoSi0u9JY1cfuH12CdY9J9e54qvVxTphANLzivAgrfR29/yiEpVthSUK5ba03KIy+0an5yE1twgWRmWHMjmnULlviaL666Eq4uTkBD09PeVrS0tLlTVLV69exZkzZ7Bjxw6V/WxsbBAbGws3N7cqn8vV1bXKs19PUmcMT6qN996nTx989tlnyMnJUSZcBgYGOHr0KCZOnIhz585h2bJlao07LCwMw4cPV9mnY8eOWLZsGQoLC2FoaFit89WEJsY2LCwMo0ePVmlr164dDh8+/FzHrep+169fLzOuRKR52TcDEbtxIorT45RttoM/hP3IZZDoa/7nmS5gEgbA2sQArjaltUiMDfRUthnqSZXbbEwNyuzrbG0CCyN9NLYsO0skMzNU7qsnVW+2X9lfDxKJBIsWLcLcuXOf+1z6+mU/JuWdv6SkpEwfdcVQ2bnVfd4uXbrAwMAAJ0+exLFjx/D2229DX18fq1atwunTpyGRSNClSxe1xq2vr4+iItVEv6ioCFKpFFKp9JnHeHrcn4cmxtbAwADFxcUqbU+/runXrCr7lTeuRKRZioJcxG54FcUZ8QAAfetGaDx9O8y9/USOTLvwciSAd3u54f5CP9xf6IeurjYq29o5Wim3zZc3L7Pvlfd74/5CP/zzRtlb3NcMa6Xct7FV9S7lPWJoaFijX7JdunTBwYMHy7RXdPdYdc7VoEEDJCUlqbSFhoY+dwzqUJXzCoKA5OTkZ96t92hd2JYtW5CamoqOHTuiffv2yMvLw/r168tdD/ZITb9mbdq0walTqrdsnzhxAp6enspEuCrjXpXz1+bnqnXr1ggJCVFpe/p91vSzUpX9OnfurHKTxdN9ajoWRPRsUiNTNJ66DQBg3u4luC29ygSsHEzCtJy7uzuio6Nx4cKFat0+v3jxYly8eBHTpk3DtWvXcOfOHfz444948cUX1XIuuVyO69evY+vWrYiKisKOHTvKLLauSQzqUJXzpqSkwM7ODnv27Hnmcfr06YM9e/bA19cX+vr60NPTg6+vL/bs2VPherCafs0+/vhjHDhwAGvWrEFkZCR++OEHrF+/HosWLVL2qcq4V+X8tfm5+vDDD7F7926sW7cO9+7dw3fffYe9e/c+93Grut+iRYsQHByMuXPn4vbt27hw4QLGjh2LuLi45xoLInpMEAQoCnJU2szbDEKTBSfg/O4B6FvaiRSZdmMSpuX69u2Lt99+G+PGjYOnpyd+/PFHmJqawsZGdcbO0NAQMplM+drHxwchISFIT0/HSy+9hGHDhuH48eP49ttvn/tcj46/ZcsWfPPNN5DL5QgICMCyZctUipbWJIanC58+/Vpd7/327dswMzND3759nxmLXC5HgwYNMHDgQGXboEGD0KBBA8jl8mfGWNOvWdeuXXHgwAHs3bsXPXr0wHfffYcNGzZg1KhRKu+tsnEv7/xPq83PVa9evfDDDz9gw4YN6N+/P06cOIGVK1dW+7Oir68PW1tb5aXZqu7XqVMnHDt2DDdv3oSfnx9mzpyJ4cOHo3HjxuWOBUtUEFVPSXYqYr4bhai1QyE8Vb/PtIVvvV98XxGJoOlrQ7UoJiYGzs7OiI6OhpOTk8bOk5KSAltbW40dvz4RayyXL18OAPjoo49q/dyaws+lenAc1YdjqT7aOpY5t47i4cZXUZwaAwCwH/0lZC+qdx2wOmnbOHJhPtVLdSn5IiKqbUJxEZL2L0byoS+A/+Zy9CztYeRU/WfC1mdMwoiIiKjKCuLv4uH68ciPPK9sM2/zAhpP3QZ9q4YiRqZ7mIQRERFRpQRBQPqJbYj/aRaE/xbhSwyMYD/6SzToP4trv2qASRgRERFVSBAEPFw/HplnflW2GTl6w/GtX2HszEuQNcUkjIiIiCokkUhg5NRK+bpB/3dgP/pLSA1NRIxK9zEJIyIiokrJXpyHggdXYNVzMizaDhY7nDqBSRgRERGpKIgLQ86NADToP1PZJpHqwWnmbyJGVfcwCSMiIiIA/y2+P7oJ8b+8C6EwD4aNW/JxQxrEJKyG1q5di/T0dFhbW+Pdd98VOxwiIqLnUpyVjLitU5F16YCyLeWvVUzCNIiPLaqhtWvXYsmSJVi7dm2tn3vv3r3w8fGp9fMSEVHdlH39X9xb0FolAWsw8D04z9onYlR1H5MwHZSXl4fExMRq7bNt2zb07NlTQxEREZEuUhTmI/7ndxH11UAUZ8QDAPStHODywT9wGLcaUkNjkSOs27QqCbt+/TpeeeUVuLq6Kh9mrK0KCwtV/qvtcnJykJSUJHYYRESkJfKjryNySSek/vu1ss3CZxjc/u86zFsPEDGy+kNrkrALFy6gW7ducHFxQVBQEDZv3owdO3YgNTVV7NBUJCcnY8SIEYiLiwMAxMXFwc/PD8nJyRo75549e9CxY0e0bNkSEyZMUJ77kb///hsODg5wcHCAm5sbXnzxRZw5c0a5/cCBA/j4448RHh6u7Ldp06ZK9yMioror+fByFMTcAABIDE3RaMpGOM3aB30LmciR1R8SQfjvyZsi69KlCxo2bIiDBw+qtAuCUOVHIcTExMDZ2RnR0dFwcnLSRJjw8/NDcHAwnhw2iUQCuVyOgIAAtZ/v9OnT6NOnD1auXIkXXngBhw8fxty5c9GwYUPExJQ+tb6goABpaWkAgOzsbOzZswdLly7F7du34ezsjPz8fKxZswZbt27FiRMnAACWlpbQ09OrcL/aoG1PtNdlHEv14DiqD8dSfTQxliXZqYhY2Ab61o3g+ObPMHJoodbjayNt+0xqxUxYdHQ0zp07h0mTJpXZpk3Porp58yaCgoLwdN4qCAICAwMRGhqq9nMuX74cI0eOxOzZs9GiRQu89957GD58uEofIyMj5YyWu7s7PvroI3Ts2BG7d+8GABgbG8PCwgJ6enrKfqamppXuR0REdUdR6kOV13rmDeD6UTCaLjxdLxIwbaQVSdidO3cAAAqFAr1794azszN69+5d6ZqwzMxMxMTEKP89fZlO3SIiIircHh4ervZzXrt2DT169FBp8/X1VXldUFCAZcuWoXPnznB2doaDgwPOnj2L+/fvV3jsmu5HRES6oyQvC7FbXkfEfE8UJt1X2Wbk0AISfQNxAiPtqBNWUlICAPjwww+xceNGeHh4YN++fRg+fDgOHz6MF154odz9Vq9ejSVLlpRpT0tLg4mJ+p9nJZNVfJ1cJpMhJSVFrecsKChAQUGBynELCwuhUCiUbQsXLkRwcDCWLl0KNzc3mJqaYsaMGcjMzFT2ycnJQUlJicpxqrKfpmVmZtbKeeoDjqV6cBzVh2OpPjUdy8L7F5D581soSYkEADxYNxY2Mw5CItWKOZhaVxufyepc7tSKJMze3h4A8N5772HAgNI7Mt59913s378fP/300zOTsDlz5mDq1KnK13FxcejcuTNsbGw0cs23e/fukMvlz1wT1r17d7Wfs2XLloiIiFB5P+Hh4ZBKpcq2U6dOYebMmRg9ejSA0hnF+/fvw8PDQ9nHysoKEolE5ThV2a82aNP1eV3HsVQPjqP6cCzVpzpjKZQUI+ngMqQdXAYoSic6pKZWsB80G1Z2dpoKUSdo02dSK1JhLy8vWFlZlZm9MjU1RVFR0TP3s7S0hJOTk/Jfo0aNNB0qdu7cWabellwux6+//qqR87311lvYvn07jh07BgA4evQoduzYodLH2dkZAQEByM/PR2FhIebPn1/mkqKjoyPi4uKQkZFRrf2IiEi3FCaE4/7/9UTy/iXKBMy0ZR80W3YNVl39RY6OnqQVSZihoSFmzJiBb775BlFRUQBKyy4EBgZixIgRIkenSiaT4ffff1cmfI0aNUJAQECllypravTo0Zg9ezYGDRoEc3NzzJgxA6+99ppKn1WrVuH+/fuwtraGtbU1rl+/jt69e6v0GThwILp166ZSoqIq+xERkW4QBAFpRzcjYlE75EX8V25IzwD2Y1bAdV4gDGxdxA2QytCKy5EAsGTJEuTk5MDLywsKhQI2NjZYtWoV/P21M2s3NDRU+a8m/d///R8+/fRTFBcXw9TUFPn5+fjkk0+U21u2bIlr164hKysLBgYGMDY2Rnp6OqRPXPM3NDTEv//+i4KCAqSnp8PCwgKmpqaV7kdERLohcddcpPz1lfK1YWNPOL75M0xc24sYFVVEa5IwfX19rF27FqtXr0Z+fj5MTU3FDkmrGBoaKhM+Y2NjGBuXfZSEhYWF8v+tra3LPY6RkREaNmxY7f2IiEi7WXWfgNSAbyAUF6JB/1mwH70cUkP136RG6qM1SdgjUqlUJxKwd999F+np6UxaiIhIKxi7tIXDhG9hIGvCxw7pCK1LwnTFu+++K3YIRERUT+XdO4+kg8vg9NavkBo9nriw6TtdxKiourj4h4iISEcIJcVIOrAUkcu6I/vyQSTsmit2SPQcOBNGRESkxUJDQ3Hp0iW0dpHB8thnyAsPUW7Lj74KRVEBpAZGIkao3RQKAbuvxuLQrQSsGaBdd4gyCSMiItJCycnJ8Pf3R1BQkLKtiwOwqhdgY6YP++GfwfbFuZBI9USMUvtdfpgB/58uAQAGN7PAWA2VlKoJXo4kIiLSQv7+/ggODlZpOxcPzD1riqafnIXspflMwKqgg7M1BnuWPpnn6L2MSnrXLs6EERERaZmbN2+qzIA9IgA4/SAXkbnG8Kr9sLSaIAg4fCsRR8KTsWqot8q2L1/0xLy+7vC2Fie2Z2ESRkREpGUiIiIq3B4eHg4vL6ZhTxr/82X8evkhAOCVNo3QrUkD5bZWjSwBACkpKaLE9iy8HElERKRl3N3dn2t7ffTif5ccAWD31TgRI6k6JmFEREQiE4oLkX5qBwRBAAB4eXlBLpdDIpGo9JNIJPDz86vXs2CCICD4bjJiM/JV2v3bO2Jse0f8Na0LVg3VjfFhEkZERCSigoehiPysG2I3TkT68a3K9p07d0Iul6v0lcvl+PXXX2s7RK0RnpyDvt+HQL4+BMuDw1W26Ukl+GWCDwa1tC+TvGorJmFEREQiEBQKpPz7Ne596oP8B6UlFBJ/mwdFQQ4AQCaTISAgADdv3sSOHTtw8+ZNBAQEQKZFJRZqm62pAS4/LL3DcUPIgzKzYbqGC/OJiIhqWVFKNGI3T0FO6OM7II1c2sLxjZ8gNTJT6evl5YWGDRvC1ta2tsMUXWpuIRqYGipf25ga4r1ebth7LQ6LB7aAg4VuF6llEkZERFQLQkNDcffuXThkh8Hq5OdQ5P5Xs0oihe2L82A/fDEk+oYVH6SeuBCdjk//CcPV2EyEz+8HY4PH9dDmy93xSf8WkEp145JjRZiEERERaVBFle/tnd3gOH07TFv0EDFC7fPV0Qj8eSsRALDxzAPM6umm3GakX3cK1HJNGBERkQY9q/L9/OuN4bb0ChOwcnw6oAUkEqCZrSmcrEzEDkdjmIQRERFpyKPK949KTzwiADhxKxZhkdHiBKYlbsZnYfT2C/g3LFGl3bOhBYLf7Ibb8/piRJtGIkWnebwcSUREpAFFabGsfF+Bi9Hp6PT1CQgCEJWWh/4t7FRKS/Rxr/t3gXImjIiISI2E4kIk7P4Y4R80haNhboV963Plex8nK/g4WgEAHmbkIzZTt8tN1ARnwoiIiNQkP+oaHm6aiIKoqwAAq+BP0K9vHxw5ekzlkqREIoFcLq83s2D3UnJwJDwFr3dxUbZJJBJ8+aInbidmY2pXlzq14L6qOBNGRET0nARFCZIPf4nIJZ2UCZjEwAg2/d7Czp276nXl+48O3UKL5UcwffdV3E3KVtkmb2GHGb5N62UCBnAmjIiI6LkUJkTg4aZJyLt7Stlm3KQDHKdvh5Fj6UxXQEAAQkNDER4eDnd393ozAwYAduaGKFGUzgKuOnYP619pI3JE2oNJGBERUTU8SqaaNWuGxoknEf/r+xD+e9QQpHqQDV0Iu5cWQKJvoLKfl5dXnU++YtLzYGVsAAvjx+nFW91d8cP5aLzawQkzejQRLzgtxCSMiIioCioqumpjDBg28oDj9B0wceskYpTiSMouwLLAu9gQ8gCL+jfHAr8Wym2mhvq49kFvnXmodm3imjAiIqIqeFbR1Q9OAA36vwO3JZfqZQIGADmFJfj+9H0UFCvw1dF7yMgrUtnOBKx8TMKIiIgqUVHR1TNxQKrPm5AamYoTnAieHocmDUzxemcXWBrrY3bPppAy6aoSJmFERESVuBGwq8Lt4eHhtRSJuFJyCjH/8C10//YUFArVROyzQR6IXCDH4oEeKmvC6Nk4SkRERM9QkpeJhF/eg8mRrRX2qy9FVyf9ehmH/3uw9p5rcRjdrrFym525kVhh6SzOhBEREZUj59YR3FvQGunHt8LdunQR/tMX2SQSCfz8/Or8XY+PzO1bmmyaGEgRVw8r3KsbkzAiIqInKApyEf/TbDxY3g9FKVEAAKmpNX7Z8j361ZOiq5n5RVgWcAdXYzNU2ns1s8V3w1vh3sdyzO7lJlJ0dQcvRxIREf0nN/wMYjdNQmH8HWWbWetBaPzaZhg0cETg4DfrfNHVq7EZ6Pd9CFJzi3AhOh37X+ussn2Gb1ORIqt7mIQREREBSA34FvE/vwsICgCAxMgMDmNXw7rPNJUSC3W96KqnvQUsjfWRmluEv24nISY9D07WJmKHVScxCSMiIgJg3LSj8v9NPXqh8dRtMLSv25fccguLcSkmA75utso2Q30plgz0wKnIVCzwa84ETIOYhBEREQEwde8G2dCF0DO1QoMB70IirdvLpjeGPMCn/4Qhq6AY9z6Ww97i8d2NEzs6Y2JHZxGjqx/q9ieMiIioHAUPQ3FkyXAc2LcXoaGhynb7EUtgO2hOnU/AACA8OQfxWQXIKSzBiiP1o86Ztqn7nzIiIqL/CIoShP26BH06t0K/xfvx8ohX4O3tDT8/PyQnJ4sdnsYUFitQXKJQafuwbzNYGutjvI8jpnV1FSmy+o1JGBER1QsF8Xdx//PemDRnMc7GqlZ7Dw4OxtixY8UJTIOKShTYcjYKLZYH48cLMSrb7MyNcH+BHD+N94GHvblIEdZvTMKIiKhOExQKpAZ8i3uL2uL6+VM4G1/6zEeVPoKAwMBAlUuTdcHN+CxM/e0qHqTlYWnAHRQWq86G2ZgaihQZAUzCiIioDitMuo8HK/wQ/9MsCIV5iMqquH9dewZkO0crvNzKAQDQ3tEK6XlFIkdET2ISRkREdVL6yR9xb2Fr5N46UtogkaDN4MkV7qOrz4AsUQj49dJDvLH7aplty1/0xMX3emLflE4qd0CS+FiigoiI6iShuBCK/GwAgGFDdzSe+gO8WvSA/O9oBAcHQxAeX5SUSCSQy+U6W4R1zI6L2HstDgAwzscRvZvJlNu43kt7aU0Stn79ety/f1+lrW3btnVyoSQREalXeY8Ssu49FVkX98GwoTvsR30BqZEZAGDnzp0YO3YsAgMDlfvr+jMgx7RrrEzC/rqVpJKEkfbSmiTsp59+grGxMfz8/JRtZmZmIkZERETaLjk5Gf7+/ggKClK2yeVy7Ny5EzKZDM7vHoRET/VXnUwmQ0BAgE4+A1IQBBy8mQAfRys42zyuZD+ydSNM7+qCVzs4qVS/J+2mNUkYAPj6+uKjjz4SOwwiItIRY8aMwZEjR1TaHpWbCAgIKJOAPUnXngEZlpiN8T9fwsWYDLzRzRXrX2mj3CaVSrBhVFsRo6Oa0KqF+RcuXMCnn36K9evX17k7VIiISL2uhhwrs7YLqLvlJhwsjHAvJRcAsOVsFGLS80SOiJ6X1iRhUqkUgiCgpKQEf//9N1q1aoVNmzZVuE9mZiZiYmKU/+Li4mopWiIiEosgCMgI+QWnPxtSYT9d/mNeEAQ8zFBNsqxMDPBBn2bo4mKNw1M7w9HKWKToSF205nLk1q1bVW4NXr16Nd555x288MILcHJyKnef1atXY8mSJWXa09LSYGKiuae+Z2ZmauzY9Q3HUn04lurBcVQfTYxlSVYisvZ8iILrh+FYSZ1RmUyGlJQUtcegaScfZGD58WhEpOThwts+MDPUU47la22sMb2dDSQSCVJTU0WOVPfUxve3rW3V1+RJhKfncbVEeno6bGxssHfvXowYMaLcPpmZmSoDGhcXh86dOyM6OvqZiZs6pKSkVGuQ6dk4lurDsVQPjqP6qHssM87uQvz2GSjJfpxYTT/XCKfD4sstNxEQEKC2c9emib9cxo6LpY8YWjnECx/0bcbPpZpo2zhqzeXIpykUpY9WyM/Pf2YfS0tLODk5Kf81atSotsIjIqJalht2QpmA6Vs3gvN7h7D/xDXI5XKVfrpebmJR/+bQk0rQppElvB1Y46su04rLkfHx8cjOzla5HPntt9/CwMAAvXr1EjEyIiKqTRWVjWg4ejmyr/0F0+Y94DDha+iZ2cAC0NlyE2cfpOHTf8Iw07cphng1VLY3tzNHyDu+6OBkBalUImKEpGlakYSVlJRg1KhRcHJyQtOmTXH9+nVcvHgRGzdu1OhlRSIi0g7l1fvq3ak19vwZDJmstPCo1NgcbksuQM/Mpsz+ulZu4nxUOrp+cxIAkJhdgBc97SGRPE64OrlYixQZ1SatSMIcHR1x4cIFBAYG4s6dO+jVqxd69eoFe3t7sUMjIqJa4O/vj+DgYJW24+evY/TwlxB8IkTZVl4Cpos6Oluhm6sNQh6kIbugBLGZ+XC00twNZaSdtCIJAwA9PT0MHDgQAwcOFDsUIiKqRTdv3lSZAXtEAHDk5BmEhobq1CzX067HZeJIeDJm9XRTtkkkEqwY4omIlFyM93GEvp7WLtEmDdKaJIyIiOqnyup5hYeH62wSNmvfDXx7MhISCeDX3A5eDhbKbb5utnzEUD3H1JuIiERTnJEA87PfVdjnyZu2dE1zWekzkAUB+PZkpMjRkLZhEkZERLVOEARknNmJiI+90ehhILo4AE/fByiRSODn56czs2B3k7KRnlek0jatqwu6utrgfyNbY+3L3iJFRtqKSRgREdU+QUDakfXKul9rX2qI3p1VH0CtK/W+YjPy8drOK/BccRRrjt1T2WZsoIeQWb54q3sTGOnriRQhaSsmYUREVOskUikav7YFEkNTWPlOQqc1t3Dk7BXcvHkTBw4cwM2bNxEQEKAsT6HNFIKAXy4/RIlCwJrj95CaWyh2SKQjuDCfiIjULjQ0FJcuXYKPjw+8vLxQlPoQRalRMHXvpuxj2LAZ3JffhoGts7JNF+p9FZcoVO5mdLI2wRvdXPHLpYeY19cdxvqc36CqYRJGRERqU17R1V4+nljeOga2VhZo9vkNlVpfTyZg2u5hRh6+CArHiXupuDSnF/SeqGa/eEAL/N8LLWFuxF+rVHVM14mISG3KK7p64tItzPk3C8XpsUg6sFSkyJ7fm3uuY92p+7gWl4lfLz9U2WZjasgEjKqNSRgREanFo6KrgiCotAsAzsQBic1HwW74YlFiU4cFfs0BAFbG+sguKBY5GqoLmLYTEZFaREREVLg9s80E6JlY1lI0NZeUXYCVRyIwqm1jlWc4dnW1wQ/+7TDUuyFsTA3FC5DqDCZhRET03ASFArLEsxX20YWiq5djMtBz3SnkFJbgRnwW/pzWRWX7pE66s4aNtB8vRxIR0XNTFORAdnOHzhddbd3IAo0sjQEAx+6l4GFGnsgRUV3GJIyIiJ6bnokFGr+2Cat6S+DrrTpbpK1FVzPyivDP7USVNn09KZYN8sCc3m6I/FgORysTkaKj+oCXI4mIqNryY25Com8II4fmyjbz1gPR6es7ON7QvUydMG2z5lgEPgu4i/yiEkR8LEdjK2PltjHtHTGmvaOI0VF9wZkwIiKqMqG4CEkHluHeJ+0Ru2kyBEWJynbDhqXrvry8vPDCCy9oZQIGACm5RUjPK0J+sQJfHgkXOxyqp5iEERFRleQ/uILIJZ2R9PsioKQIeeGnkXFqh9hhVSqnoBiFxQqVtjm93SAzM8Sb3VzxQe9mIkVG9R0vRxIRUYVuXL2MS798Dus7v8Pd8r9kRs8Adi8tgFW3ceIGV4G8ohKsP30fy4PDsXigB97q3kS5rYGpIe4vkMOMBVZJRPz0ERFRuZKTkzFq6As4GnJB2dbFAfju1bZo9c4OGDu3FjG6yoUn52DOwVAAwP8F3sWUTs4wNtBTbmcCRmLj5UgiIipDUZCD4X18cOyJBAwAziUAH12SaX0CBgCtG1lidNvGkEoAeXMZq9yT1mESRkREZVw9cxQnb0ZDeKpdEICgoCCEhoaKEld5CosV2BjyAON+ulTmkUnLX/RE6Ny++HFse8jMjUSKkKh8nIslIqIyojNKKtweHh6uNXc+jtp+AQdvJgAApnRyRn8PO+W2pramYoVFVCnOhBEREbKuHEZJbobydWWPGNKmRxC91tlF+f8nIlNEjISoepiEERHVY8WZiYj5nz+i1wxB4m/zlO1eXl6Qy+WQSFQfQiTmI4hKFAJ+vhiDeyk5Ku1DvRvi/d5uuDynFz4b1LLW4yKqKSZhRET1kCAISD/1EyI+8kTm2V0AgLQjG5AfdU3ZZ+fOnZDL5Sr7ifUIotD4LLRaeRQTfrmMpQF3VbZJJBJ8NdQb7Rytaj0uoufBNWFERPVEaGgowsPD4SozhU3IamRf+0u5Tc+qIRq9ug7GLm2UbTKZDAEBAcr93N3dRVsH5mJjgpTcQgDAjosxWDrIA07WfK4j6TYmYUREdVxycjL8/f0RFBSkbOviAKzqBdgYA1a+k+AwdjX0zBuUu7+Xl1etJl+CICAiJRfuMjNlm7mRPub1dcfftxPxGRMwqiOYhBER1XH+/v4IDg5WaTsXD8wNMcI/hw/AvPVAkSIr63BoAhb9fRtRaXmIXOAHC+PHv6be6+WG9/vwEUNUd3BNGBFRHXbz5k0EBQWVqZ8lADgdXYAoPWdxAnuG/TficflhJlJyi/DtyUiVbVKp5Bl7EekmJmFERHVYREREhdvDw8NrKZKyBEEokxwu8GsOAz0JurnaoHsTG5EiI6odTMKIiOoYRUEuUgO+g6BQaGW9L0EQEHQnCb7fncK+6/Eq25o0MMXV93vj1Ds90MddVuuxEdUmrgkjIqpDckKDEbttOooSIwCpHrzkb0EulyM4OFhl1kkikUAul4tyt+OF6Az4bTgDAMjMD8PLrRxULjV6NrSo9ZiIxMCZMCKiOqAkJx2xW6biwZfy0gQMQPKBz6AozNeqel8A0NHZCr2b2QIA9KUSxGcViBIHkdg4E0ZEpOMyL/yO+O0zUJzx+NKembcfGk3eAKmhMWQyY9HqfZ15kIbgu8n42K+5sk0ikWDFEE/EpOdjeGuHMlX5ieoLJmFERDqqKC0W8TtmIuviPmWb1NQaDmNXw6rn5DLJTW3X+3rvzwjsuFL6YO3BnvYqFe07u9jgiUc+EtVLvBxJRKRjQkND8csXs/DPmy1UEjDLTqPg/sUtWPeaohWzS+0aPS62uulMlIiREGknzoQREemIZ1W+X/tSQ3i/uQEWPsNEi+1qbAacrExga2aobBvbxh5/R2Ti1Q5OGOfjJFpsRNqKM2FERDriWZXvF4Z7ipaARaXlYtSPF9Bu1XF8dVS1JpmhnhT/vtENr3Z0hh4LrRKVwSSMiEjL5UWcw8n/zXlm5fugI0cRGhoqSmyGelIcvlW67uvbk5FI5J2ORFXGJIyISEsp8rMR//O7iFzaFVf2rKmwb21Vvs8vKlF57WBpjBk9msLJyhirhnrB2sSgVuIgqgu4JoyISAtlXf0T8T++haKU0gXtLpXUL9V05fvw5BwsDbiDMw/ScOPDPjDQe/w3/Cf9W2DZCx4w0tfTaAxEdY1WzoSlp6fj9u3byMjIEDsUIqJaVZyRgJjvxyF69YvKBExqbA7fmd+gX79+Ze56lEgk8PPz03jpifcP3sT2CzG4k5SDHRdiVLZZGOszASOqAa1LwgoLC+Hn5wdPT0/s3r1b7HCIiGqFIAhIP74N4fM9kXnmcSV787YvotnnN9Gg/zvYtWuXaJXvPx3QAgBgb27IRfZEaqJ1lyPnzZsHHx8fXLlyRexQiIg0LjQ0FHdu3YTZya/gmHpO2a5naQ+HCd/AsvNo5eyXTCbTeOX7mPQ8fB50F+N9nNCjaQNlu4+TNfZO6oiBHnYwM9K6Xx1EOkmrvpMOHz6MP//8E5cuXcLWrVvFDoeISGOeVfNrVS+g6YDX0XDMCuiZNyh3X01Vvr8YnY7u355CYYkCd5JyEPhmN5XtI9o0Uvs5ieqzGl2OVCgUyMzMVGsgsbGxmDZtGn766SeYmZlVvgMRkQ57Vs2vRVE+aPz65mcmYJrUztEK7jJTAMDFmAw8zMir9RiI6pMaJWGFhYVo2bIlfvnlF7UEoVAoMH78eMyYMQOdOnWq8n6ZmZmIiYlR/ouLi1NLPEREmlKSl4Vjy8c/s+bXkTOXaqXmV2JWAfZdV/2ZqSeV4PPBnvh0QAvcXyCHo5WJxuMgqs9qdDnSyMgIc+bMwfTp07F582asW7cOnp6eNQ7i66+/RlxcHIYPH47bt28r2+Pj4xEZGYmmTZuWu9/q1auxZMmSMu1paWkwMdHcDw91zwLWZxxL9eFYqocmxzH/xp/I+v0jXL8eW2G/S5cuoWHDhhqLY82pGKw5HYNihYBzb/rAycpIuc23kQF8G8lQnJuJlNznOw8/k+rDsVSP2hhHW1vbKvetURImkUjwwQcfYMyYMZg1axbatm2LOXPmYNGiRTW6lPioFMWIESOUbSUlJVi3bh1Onz6NP//8s9z95syZg6lTpypfx8XFoXPnzrCxsanWINSEpo9fn3As1YdjqR7qHsei1IeI/3kWsi78DqDyml8+Pj4a/VoamaQgt0gBANh0JQXfjWitsXPxM6k+HEv10KZxlAhPz4fXwKFDh/DOO++gpKQEa9euVUmmakpfXx/r169XSbIqExMTA2dnZ0RHR8PJSXMPi01JSdGqL6Iu41iqD8dSPdQ5joKiBGnB65G4ez4U+VnKdguflzHp9yQcPXFa5ZKkRCKBXC5HQECAWs4PAOl5RTDWl8LY4HEdr8z8InivOIqx7R3xYd9msDM3quAINcfPpPpwLNVD28ZRLXXChgwZgtDQUIwZMwYjR47ECy+8UGuP0CAi0kb50ddxf5kv4nfMVCZg+taN4fTO73CevQ+/7d2v0ZpfmflFWBpwB02WBWJ9yAOVbZbGBoj4WI4VL3lpLAEjoso9V4mK5ORkhISEICQkBKdPn8b58+cBACdPnkTr1q3x+eef47333qvRsVu2bAlra+vnCY+ISBQ5t4/hwQo/oKS4tEEigU2/t2D/yufQM7UCoPmaX7EZ+Vj8TxgUArA8OBzTu7rA1PDxj3xDfa2r1U1U79QoCSsqKoK3tzfu3r0LAGjWrBl8fX0xYcIE+Pr6wsPDA3v27MHMmTORnZ2NRYsWVfscN27cqEloRESiM3XvDqNGniiIuQ4jp1ZoNGUTTN27lttXUzW/Wja0wDgfR/x2JQ6vtGmEgmIFTA3Vfhoieg41ngl74YUX8Pnnn8PX1xcODg5lto8aNQqurq4YMWJEjZIwIiJdoSjIwe2IByozWo2mbETu7aOwHfQ+JPoGGjt3bmExvj/9AEcjUnDwtU4qz5b8YrAn/u+FlnCxMdXY+Ymo5mqUhBkYGODrr7+utF/btm35EG4iqrMEQUDEwa/x6owPceZhsbJdLpdj586dkA0pf/ZLnUZvv4jDtxIBAH/eSsSLXo9LWzhZs84XkTbT6KIAIyMjJCQkaPIURESiKIi9jQdf9MGEN9/D2ScSMAAIDg7G2LFjayWOGT2aAACkEuBqLGtJEekSjT870tSU0+BEVHcoCvORfOgLJB/6AuEpRTgbX7aPIAgIDAxEaGio2tZ7FRYrsPVcFPo1l6GFnbmyfVBLeywe0ALjfBzR/Il2ItJ+WvUAbyIibZYTGoy4H95EYULpTUlRWRX3Dw8PV0sSdj0uE0O2nENUWh7GtXfEzxN8lNskEgk+Hejx3OcgotrHe5SJiCpRnJmEhxsn4cGXcmUCJtE3RLsRMyvcz93dXS3nd5eZobC4tML9rquxfLA2UR3BJIyIqBKJexYg49R25WtTz75w+7/r8J35LeRyucodiUDp7JSfn1+NZsGKSxS4Hqe6tsvEQA8fy5tjRGsHXJ7Tiw/WJqojmIQRET0hLCwMBw8eRGhoqLLNfsRnkJpaQc9ChsbTt8N1XhCMHFoAAHbu3Km2yve/XYmF98qj6PndKaTnFalsm+nbBHsnd0LrRpY1eFdEpI24JoyICKVPAPH390dQUJCyTVlqQuYA51n7YeTcGvrmqs+dU2fl+2MRKbiTlAMAWHv8HhY/sdbr6dk2ItJ9nAkjIgLg7++P4OBglbYnS02YefYpk4A9ycvLC0OHDq1yAqZQCFAoBJW2+XJ3GOlLMdDDDoM97av5DohI1zAJI6J672rIMQQFBUEQVJOiJ0tNqIsgCDhwIx4+a47jt6uxKtucrE0QNq8v/p7eFZ1dbNR2TiLSTkzCiKjeEhQKpAZ9j1OfDa6wX3h4uNrOeSE6Ay9vO4+rsZlY/E8YSp6aDXNtwNqKRPUFkzAiqpfyH1zB/WXdEb/9bTgZ5lbYV12lJgCgk4s1+reQAQDszI2QlF2gtmMTkW7hwnwiqlcUhXlI3LMAqf9+DQiltbfcbSTw9WyMU7djVS5JSiQSyOXyGi20FwQBgXeScSQiGZ8P9lTZtmKIF5JzCiFvLuOCe6J6jEkYEdUrEqk+cm4GKhMwI5e2aDRpPfZZu2Ps2LEIDAxU9q1pqQkAmPjrZfx08SEAYHirRujkYq3c1s7RquZvgIjqDCZhRFSvSPQN0GjKBkStHAi74UvQoP87kOjpwxRAQEAATp8+jeTk5OcqNQEAcnc7ZRL286UYlSSMiAhgEkZEddjNa1dxefcqeHbrjw6DX1W2m7p3Q/M10dAzLTsj5eHhge7du1frPKcjU+EuM4O9hZGybUIHR/wRGo+JHZ0x1Lthzd8EEdVZTMKIqM5JTk7GqJcG4uiZS/+17EC/ftuwa9dvkMlKF8WXl4BV172UHLy99zr+CUvCnN5uWDXUW7lNX0+KvZM7Pfc5iKju4t2RRFSnFGcm4eWebXBMmYCVOnLkqLLwqrpYGunj1P1UAMD/Tt1HXGa+Wo9PRHUbkzAiqhMEhQJpRzfjr+nuOHU7DsLT29VQeDUzX/V5jjJzI8zybYoWdmbYOqYd7M2NnrEnEVFZvBxJRDovP+oq4n58C3nhIXiQWHHf8PDwai+4vxabicX/huFSTAbCPuoLI3095baF/VtgyUAP6Ovxb1oiqh7+1CAinZYR8ivufeKDvPAQAICLRcX9a1J49bOAO9h3PR4P0vKw9Vy0yjYTAz0mYERUI/zJQURaLzQ0FAcPHiz3UqKZVz9ITSwBAEZOreG38iTkcnmZIqgSiQR+fn41Kjvx6YAWAAAXGxM0MDGowTsgIiqLlyOJSGslJyfD398fQUFByrZ+fftg12+7lXc56ls1RMOxq6DISUOD/rMg0TfAzp07a1R49W5SNhYcuou3egro6y5TtrduZIl/pndBn2YyGOrzb1ciUg8mYUSktfz9/REcHKzSduTIUYx5ZQSCjh5Xttn0ek2lj0wmQ0BAAEJDQxEeHl6lwqsXotPR5esTUAhAfG4J+jSzVZlNG+Bhr4Z3RET0GP+kIyKtdPPmTQQFBak8yxEABADBx05U6S5HLy8vDB06tEqXIH0crdC6UellzTtJOYhluQki0jAmYUSklSIiIircfvfu3RofOyotFz9djFFpk0olWP6iJz6TN8G9j/vB0cqkxscnIqoKXo4kIq0jFBfBNvZ4hX2aN29eo2Mv/Os2VhwJh0IAujexgZutmXLboJb26GSnB1ND/mgkIs3jTBgRaZWc28dwb1E72ISsQhcHQPLU9ue5yxEALIz0UVQioEQh4KujFc+2ERFpEpMwItIagkKBhJ/fRUFs6XqvVX2k8G3lotKnKnc5PpKQVYCcgmKVthk9mqC5zAyfDfLA8hc91RM4EVENcM6diLSGRCqFw6T/4f7S7jBp4Qu3if/DcefW1brLEQBScgrxZXA4vjsVicUDPDC33+MCreZG+rg9ry+k0qfn2IiIaheTMCKqNU8nU7nhIQAkMHXvquxj6t4NTRachEnz7soSEV5eXtW6/JieV4TVx++hRCFgxZFwvNW9CSyMH/+4YwJGRNqASRgRaVx5RVd9WzbC8jZxcHDzgtvSK5DoP65Eb9qiR7WOLwiCSk2vZjIzTO7ojF+vPMTULq4QyjzOm4hIfFwTRkQaV17R1VO34/DBCaAgNhQZp3+q0XHT84qw+J8w9P0+BAqFaqK17AUPRH4sx/IhnrA05qOGiEj7cCaMiDTqUdHVpwkAzsQBGX0Xw9N3Uo2OPf7nS/jzViIAYP+NeIxo00i5zcHSuEbHJCKqLZwJIyKNunPjcoXbkxq0h0Rasx9F7/duBgAw1JMiMjW3RscgIhILZ8KISGNybh+D4b6ZFfZxd3evcDsA5BQU43+n72OwZ0N4O1go2/u622LVUC+MbtsYTtascE9EuoVJGBFpjFFjTzSzkaCLA3AuHirL4yUSCeRyeaV3PV6NzcCADWeQmF2Ic1Hp2D2po8ox5vw3G0ZEpGt4OZKI1EYoUS2Mqm9pD/tXPsfmeePQr08vlW1VLbra0t4cRvqlP6r234jHw4w89QVMRCQizoQR0XMTFAqkH9+C5D/+D00WnIRBAyfltgbyt9BA/hYC3y1bJ+xpBcUluPIwE11cbZRtRvp6+KR/C5yMTMXC/i34YG0iqjOYhBHRc8m7dx5x22cgP/I8ACBh5wdwentnuX0rKrq67VwUPvk7DOn5Rbi/wA+2ZobKbVO7umJqV1f1B09EJCKtScKysrKwceNGHD9+HBKJBJ06dcLbb78NGxubyncmolp37fwpXNrxGRpE/Qt3q8ftUiMzCCXFkOhV78fLtbhMxGTkAwC+OhqBL/hcRyKq47RmTdjAgQORmpqK6dOnY9y4cThw4AB69uyJwsJCsUMjoickJSagt09LtO3siynf/othB4DX/gVy7dqgyaIQNH59S6UJWHGJAiVPFVed19cdZoZ6eKVNI4z3cdTkWyAi0gpaMxMWEBAAMzMz5euWLVuibdu2uHbtGjp27FjBnkRUW3LDQ/By/wEIuZ+t0n4uAfjoih0Cn3gGZHlKFAJ+vfwQS/69g4V+zTGpk7Nym4OlMe59LIe9hZFGYici0jZaMxP2ZAIGAKdPn4a5uTmaNGkiTkBEpEIQBBxfPQ2n72eXeRKjIABBQUEIDQ2t8BhXYzPw6i+XEZ6cg6UBd1BUolDZzgSMiOoTrZkJA4CdO3fiu+++Q2JiIoqKinDkyBHIZLJn9s/MzERmZqbydVxcXG2ESVQvSSQSZLWdBGDuM/uEh4dXWPfLx8kaQ7wa4lBoAlrYmSM1twgNmXgRUT2lVUlYz5494ejoiIcPH2LVqlWYOXMmjh49CmPj8p8Bt3r1aixZsqRMe1paGkxMNHcb+5OJHz0fjqX6qHssC8NPAlJ9GLo9vsTo2LpHhfvIZDKkpKRAIQj4MywVp6Iy8MUAN5U+83s0wsxO9ujoaAEUZiMlJfsZRxMHP5Pqw7FUH46letTGONra2la5r0QQhKevLGiFtLQ02NvbY9OmTZg8eXK5fcqbCevcuTOio6Ph5ORU7j7qkJKSUq1BpmfjWKqPusayKDUGCTs/QObZXTBs1BLNll2FRP9xuQg/Pz8EBwfjyR8dj6rfBwQEAABGb7+A3VdLZ6ZPzuyBHk0bPHdctYWfSfXhWKoPx1I9tG0ctWZN2NNsbGxgZmaGxMTEZ/axtLSEk5OT8l+jRo1qMUKiuiE0NBQHDx7EjatXkPzHFwif54HMs7sAAIVxt5Hx3/8/snPnTsjlcpW2p6vfD2/1+Htx/414DUZPRKS7tOJyZFhYGK5du4ZRo0YBKF0A/M033yAzMxMDBgwQOTqiuik5ORn+/v4ICgpStnVxAFb1AmyMAX0rB9iPWQmr7uNV9pPJZAgICEBoaCju3r2LBL0GeLFnR8ieqGQ/ul1j/BOWiIkdndHXXXv+6iQi0iZakYQ1bNgQhw8fxttvvw1XV1c8fPgQRkZG+OWXX9CuXTuxwyOqk/z9/REcHKzSdi4e+OAEsH/1B5ANWwQ9E8tn7m9g54IVQak4fT8NMwrD8d2I1sptelIJfhjbXmOxExHVBVqRhFlbW+OHH35AVlYW7t69C1tbWzg7O0Mq1dqrpUQ67ebNmyozYI8IAM7EASltpqBhBQkYANiZG+FmfBYAYNOZKMyXu/O5jkRE1aBVWY6FhQV8fHzg6urKBIxIgyIiIircHh4eXqYtMatA5bW1iQHm9G6G9o6W2DOpAxpbln8XMxERlU8rZsKISPNKslOhZ156l6K7u3uFfZ/cfjoyFZ/+E4abCVmI+FgOEwM95baP+rljUf/mkEgkmgmaiKgO43QTUR1XnJ2CuO0zcPf9JihKiQYAeHl5QS6Xl0meJBIJ/Pz8VAqufnfqPgLvJiMuswAbQh6o9DfUlzIBIyKqISZhRHVEWFgYDh48qHx0kKAoQWrQ94iY2wJpQf+DIj8LCbseV7uvSqkJAPikf3NIJUBLe3O4NTDV/BshIqoneDmSSMeVV2qiT9cOWNE9H2bJN5VtUhNLmDTrAkEQIJFIVEpNhIeHo8iiIXZESnAxWYGBTzwtrGVDC5yY0QNdXG2gJ+WsFxGRujAJI9Jx5ZWaOHbmImY9ALb0ByCRwLrna7Af9Tn0Le3L7O/l5YUc88bo/PUJAEBcVgEGeNipXGbsrkMV74mIdAUvRxLpsEelJp5++tijUhMxFm3Q9JOzaPz65nITsEc6Oluhk7M1ACAxuwCxmfkajJqIiADOhBHptMpKTeT5LYGJWyeVttsJWTgakYI3uzdRtkkkEnw5xBPhyTmY1NEZhvr8+4yISNOYhBHpsMpKTTRv0ULl9fsHb2Lt8XsAgL7uMnjYmyu39XWXoa+7DEREVDv45y6RjinJSUP8T7ORff2fapWaAAAnK2MoBEAhQJmMERGROJiEEemIRyUnwuc2R2rAN4j/eTaE4sJnlppY8b8tyMwvUml/s3sTtHe0xOqhXlg9zLs2wycioqfwciSRFnpUNsLd3b307sVbRxH/82wURF9T9ilOi0V+9HXImnZAQEAATp8+jeTkZFg5OGNnlB66bLqGRf3zsaj/40uSJgZ6uPheLxZYJSLSAkzCiLRIeTW/erSwx5ftEmHz6NGMEgmse06B/SufQ9+qobKfh4cHunfvjqi0XGzZE4yiEgGrj93DO75NYW1ioOzHBIyISDvwciSRFimv5tfpO4n4oLSEF0zcu6Hpp+fQ+PUtygRMoVAtT+FiY4qpXVxgbWKAD/q4wYAFVomItBJnwoi0xKOaX097VPMra8CX8Bz3oXImKyGrACuOhOP4vRScmdVTZZ+lg1rii8GesHpiBoyIiLQLkzAiLVFZza8Ei5YqlxJf23UFf95KBADsvhqL/i7Gym22ZoaaCZKIiNSGlyOJRFaUFousSwcqrfn19Pb5/UpfmxnqISWnUGPxERGRZnAmjEgkisJ8pPyzGsl/fA4ICjRfHga5XI7g4GCVxxBJJBI0adsVBZaOKvv7utliwyttMLy1A+zMjZCSklLbb4GIiJ4DZ8KIapkgCMg8vwcR8z2RtGcBhIIcCIV5SDq4rNyaX1LXtojsNgOf/hNW5ljTu7nCztyotkInIiI14kwYUS3Kf3AF8b+8i9zbxx436umjQf/ZsBu2CHqmVggICFDWCXNt6obhBxMQmZqLgDtJeJiRB0crE/HeABERqQ2TMCINepRMNWloDdnNHUg/vgV44lKjebshaDh2FYwcWiC7oBjnw5PR110GLy8v5SOHlhZaI+RBGubL3ZmAERHVIUzCiDSgvKKrXRyAVb0AG2PAsLEnHMathXnrAQCAdScjsfjfO8gpLEbkAj80tHh8iXF8ByeM7+BU6++BiIg0i2vCiDSgvKKr5+KBD0/pw2HCt2i29KoyAQOA6PR8JOcUIq9IgRVHwms7XCIiEgGTMCI1u3LsLwQFBanc4QiUFl0NeViMB/a9USzRU9n2QR83WJsYYHInZ8zo0aT2giUiItHwciSRmhSnxyNx70Kc/HlLhf38vtyH5bMsMa2rq7JNZm6EBwvlsDRmhXsiovqCM2FEz0lRmI/kQ8sRPq850o9vgYtFxf1TDW2xLPAuCopLVNqZgBER1S9MwohqSBAEZJ7bjYj5nkjcPR+K/GwAgIdjA/Rq30LlEUNAadFVO69OgK0LurrYIDO/WIywiYhIS/ByJFEN5N07j/hf5yDvzsnHjXr6aOD3DmyGLMSos9G4O+cNxN04q9wsl8vxf99thomlDVo3shQhaiIi0iZMwoiq4FG9r2bNmsHmzGqkH9+qst283UtoOPYrGDm0wPBt57H/RjwwYAG2rmgA26IUuLu7K+t+ERERAUzCiCpUXr0vXy9HLG9VWu/LyKk1Go5bDXNvP+X2CR0cS5MwAOFCA0wZ2qPW4yYiIu3HJIyoAmPGjMGRI0dU2k7disXcHFMc3LYWwTYvoHOjBjB/YvvwVo0wo0cTTOzohM4uNrUbMBER6QwmYUTPcP6PH8oUXAVKF+SffpCL7n8a4o7iMqZ1dcHGUW2V26VSCb4b0bo2QyUiIh3EuyOJnlIQfxfR34zA2TVTKuwXGxUJANh2LhoPM/JqIzQiIqpDOBNG9J+SnDQkHViK1MDvgJKiSut9TR3YBRfzLPHZQA8+WJuIiKqNSRjVO4/udHx0x6JQXIjU4O+RvP8zlOSkKvt5NnNB69aWuH79JkofOlRKIpFALpfjq8mDIJWgTD0wIiKiqmASRvVGeXc69u7SDis6ZsI8656yTWpsDtmQj9Fg4Lvw2haC65/PAaKuKrfL5XL8+uuv0JMy+SIioppjEkb1hr+/f5mF9sfPXcHsKGBLfwASKax6vo6GIz+DvrUDAOD/RnbBnvBl8JCmYkwTCV7p3YH1voiISC2YhFG9cPPmTZUZsEcEATgTB/xjPQwHm87Ae11aY+h/CRgANJOZ4cK7PdG2sSUvOxIRkVoxCaN64W7ojQq3z3nQCtDPR/o/YXjJu6FKwtXO0UrT4RERUT3EEhVUpwklxUg7sgEG+9+usF97Lw8AQGGJAnGZBbURGhER1XOcCaM6SRAEZF/7C4k7P0RBbCia6gNdHICz8ar9Ht3puGTai4hKz8Ooto254J6IiGqFViVhd+/exeXLl2FlZYWuXbvCyoqXgaj68h9cQcKuD5FzM1Cl3XXiZzi7c2+5dzrKZA3QvbYDJSKiek0rkrDk5GSMHz8e9+/fR7t27RAdHY2wsDBs374dL774otjhkZZ6ut5XUXocEnd/jIxTP5auuP+PacveaOi/Cr0eNsBvxe2AlCi84FCCryb68U5HIiISjVYkYQUFBXj//fcxYMAAZdsHH3yACRMmIDk5GXp6eiJGR9qmvHpfcrkcP65diozTOwBBQISeExrbydBizGKYtx8KiUSCqc4l2HU1FpNGt8XEjk4w0OOSSCIiEo9WJGGOjo5wdHRUaRs8eDBWrVqFhw8fwsXFRaTISBuVV+8rODgYk9+TYPnoWVhy0xiH9Lri417uWObzeKbLSF8Px2f0qO1wiYiIyqW1UwF//vknbG1t4eTk9Mw+mZmZiImJUf6Li4urxQhJDI/qfQlPXG4EShfiBwYGIr3FMAQY9YAACb4+dR8pOYUiRUpERFQxrZgJe1pAQADWrl2LTZs2QSp9dp64evVqLFmypEx7WloaTEw090DlzMxMjR27vqnuWF4I2Fvh9rjoaExq3xL7QpPxbncn5GdnICVfa//WUCt+LtWD46g+HEv14ViqR22Mo62tbZX7SoSnpxREdvr0aQwcOBBz5swpN8F6UmZmpsqAxsXFoXPnzoiOjq5wBu15paSkVGuQ6dmqOpaFSfeRuOdjXPr7Vww7+Ox+N2/ehGPT5jDUl8LEoH6tJeTnUj04jurDsVQfjqV6aNs4atVMWEhICAYNGoTZs2dXmoABgKWlJSwtLWshMhJLSXYqkv74HGmB30IoLoS7dcX1vni3IxER6QqtuU5z5swZDBo0CO+88w6WLVsmdjgkMkVRAVL+WoW7c92R+vcqCMWla7v0rRzw0/pV6NSjl0r/R/W+iIiIdIVWzIRFRkZi0KBBcHFxQdOmTbF582bltmHDhsHOzk7E6EgUggIp/65FQq6ALWavY7DiPOSDRsJ20BxIjc1xbtgcrNgTDBeko41XS86AERGRztGKJKywsBCvvPIKgNIZsSf169ePSVgd93TRVQCQGpogTv4FBgUbI19ihIRmr8L/5d4q+819pZ8Y4RIREamFViRhHh4eKrNfVD8kJydj5MiROH78uLJNLpdj586dkMlk6D5wDFyuBuNOaiFCYnLxMCMPjlaau+uViIioNmnNmjCqf0aPfBknnkjAgNKiq2PHjgUAGBgY4POXWmO+3B2RC+RMwIiIqE5hEka1riQnHcdWTcWR46fwdH2UR0VXQ0NDAQAj2zTG54M9YWtmWPuBEhERaRCTMKo1iqICpPy9BuEfNsP1w1sq7BseHl5LUREREYlDK9aEUd0mCAIyQ35B4t6FSE9JgIFQBBeLivdxd3evneCIiIhEwpkw0jiJRIK4kD1Yn+ODATabsNtkEDoOm4q+vXpAIpGU6evn58eSE0REVOcxCSO1CQ0NxcGDB5XruZ6U5/cJ1phORLrUEtvs34LNq+vx29796Nmzp0o/Fl0lIqL6gpcj6bklJyfD398fQUFByrZePp7Y+89xyGQyAED7tu0x2isTe8KyMNjbEbmFJZDJZPj999+RkJBQpk4YERFRXcckjJ6bv78/goODVdqOX7oF/zGjERj0uP3LEZ3wfwoBzWRmKn29vLyYfBERUb3Dy5H0XK5fOo+goCAIwtPFJoCg4CMqlyZdG5iWScCIiIjqKyZhVCNCcRFSg/6Hk5/0r7AfS00QERGVj5cjqdoyL+5H7M552JvhDJmBQYV9WWqCiIiofEzCqNquht3F+KL3EW3RCMMMgtCj5S6cDktQuSQpkUggl8u51ouIiOgZeDmSKiUoFCqv2730BnKlpWu7DpnI8f3hc5DL5Sp9WGqCiIioYpwJo3KFhobi1vnjsLqzH4bOXuj15mrlNgsLS3w0sBWORudj8UAPtHa2RkBAAEJDQ1lqgoiIqIqYhJGK5ORkjB45HEeOn1S26TnH4EbnU2jp00PZ9v6ANvjgqWr3LDVBRERUdbwcSUolOekY0bcjjj6RgAFASXQoBk+cqdL29OOGiIiIqHqYhBEUBblIOrQCh193xYkbD1C24peAyJtXyn0cEREREdUMk7B6LvPiAWyZ9zL6/auHA6mNK+zLml9ERETqwzVh9dzlLBNM13sH0AMy7EcC+L9n9mXNLyIiIvXhTFgdFxoaioMHDyI0NBSCIKAkO1Vle+8+A9DNIBoA0LBtb/j27ltmvZdEIoGfnx8X3RMREakRZ8LqqOTkZPj7+yMoKEjZ1qppQ3Qb8jI2rP0fJNLH+ffXb45CYq4Cgz3tkTKlPcaOHYvAwEDldtb8IiIiUj8mYXWUv78/goODVdpuRCbixoEzGD9oN3oPHqNs79TETvn/MpmMNb+IiIhqAZOwOujmzZsqM2CPCUDUVfzvQjp6D674GKz5RUREpFlcE1bHFKXFYve3n1TYZ2wbh1qKhoiIiJ6FM2F1yI3jBzFrz0Ucye8H4Pdn9mvRonntBUVERETl4kxYHdLA3Qdn9FsBti6QOrfC00XteZcjERGR9mASpiOeLDUBAIrCfCSFnlLp07ixE6a6ZMLFKB+r1m1Ev35yle28y5GIiEh78HKkliuv1ETHNp6w6T4Yt409EPZJM5jYPF7j9cVbk7BKXwoDPSnefSmQdzkSERFpKc6EabnySk1cuHYLAX8GIlrqgHXbt6tsMzPSh4He4y+rl5cXhg4dygSMiIhIyzAJ02KPSk0IQtlHaiPqKmzT7kDWqk+tx0VERETPj5cjtZQgCAg+tLvCPt8PaYZR8s61FBERERGpE2fCtNSJI3/hvdAGFfbxbtmilqIhIiIidWMSpqV69BoANztTwLlNmW0sNUFERKT7mISJ4OlyE/duX8HmTd+o9NHT18fnfi74YN4H6NO3n8o2lpogIiLSfVwTVovKKzfh7OaGhAELoDBxQo9zwfDs/DjhemXQALwCADNeZakJIiKiOoYzYbWovHIT0ffuofDPb1AsMcDyPy89c1+WmiAiIqpbmITVksrKTUwyDcWq996s/cCIiIhIFEzCakFSQhzmr/y6wj4jOnlCZmVeSxERERGR2JiE1YKE5GQcElpX2Mfd3b2WoiEiIiJtwCRMAwSFQuV1K+/WeMUNkDiXTcRYboKIiKh+0rokTBAEFBcXix1GjWSkp2HhtxvQa+5qlOTnqGz76rVhuPTnL/Dz81NpZ7kJIiKi+klrSlTcv38fGzduxJYtW5CYmIikpCTIZDKxw6oSRX42UoPW4ZWAIhzTawsA+HXXdkyY9Jayj4uzC1ycgYCAAJabICIiIu2ZCVu8eDHMzMzw5Zdfih1KlSkKcpHy1yrc/cANib99hPHZewEA+kIxwtIVz9yP5SaIiIhIa2bCfvjhBwDA33//LW4gVZCXm4Mvfj+Idte/hmvGVWV795LrmGd/C1NGDoeH+3ARIyQiIiJtpzVJmK44f/EChvwcikSJDC8o+uMrXAUkUlj1mAi7YYuw3N5N7BCJiIhIB+h0EpaZmYnMzEzl67i4OI2f06tlSwClz3z817AHcjtloPUrH8LIobnGz01ERER1h04nYatXr8aSJUvKtKelpcHExERj5327aTYuJKZh7gs+aOrxBbIBZKekaOx8ddmTSTQ9H46lenAc1YdjqT4cS/WojXG0tbWtcl+dTsLmzJmDqVOnKl/HxcWhc+fOsLGxqdYgVNfMcWM0evz6hmOpPhxL9eA4qg/HUn04luqhTeOo00mYpaUlLC0txQ6DiIiIqNq0pkSFQqFAcXExFP9Vmy8pKUFxcXH5D7wmIiIi0nFak4TNnz8fxsbGGDp0KPT09ODo6AhjY2Ps27dP7NCIiIiI1E5rkrAvv/wSxcXFZf6NGDFC7NCIiIiI1E5rkjAiIiKi+oRJGBEREZEImIQRERERiYBJGBEREZEImIQRERERiYBJGBEREZEImIQRERERiYBJGBEREZEIdPrZkU8rLi4GUPogb01KS0tDXl6eRs9RX3As1YdjqR4cR/XhWKoPx1I9amscHRwcoK9feYpVp5KwpKQkAEDnzp1FjoSIiIjqq+joaDg5OVXaTyLUoSdk5+fn4/r167Czs6tSBloTcXFx6Ny5M86dO4dGjRpp5Bz1BcdSfTiW6sFxVB+OpfpwLNWjNsexXs6EGRsbo1OnTrVyrkaNGlUpy6XKcSzVh2OpHhxH9eFYqg/HUj20aRy5MJ+IiIhIBEzCiIiIiETAJKyaLC0t8emnn8LS0lLsUHQex1J9OJbqwXFUH46l+nAs1UMbx7FOLcwnIiIi0hWcCSMiIiISAZMwIiIiIhEwCSMiIiISAZMwIiIiIhHUqWKtmnbs2DH8/PPPyM7ORs+ePTFt2jSNVeavy4qKirBv3z7s3bsXLVu2xJIlS8QOSScpFAr8/vvvCA4ORl5eHjp06IDXX38dJiYmYoemc4qKivDLL7/g+PHjkEgk6NSpEyZOnMixfA6FhYV48803kZqaip07d8LY2FjskHTKH3/8gS1btqi06enpYe/evSJFpNuys7OxefNmnD9/Ho0aNcLMmTPRpEkTscNiElZVu3btwoQJEzBv3jy0b98eK1euREBAAH7//XexQ9Mp6enpaN26Nbp27YrY2FikpKSIHZLOksvlsLOzQ58+fWBsbIxvvvkGmzZtwunTp2FmZiZ2eDqlb9++8Pb2Rq9evZCTk4O1a9di27ZtOHnyJP/QqqF58+bh6NGjiIyMRHFxsdjh6JyIiAhcvnwZX3/9tbJNKuXFq5qIi4tD7969YW9vj6lTpyI/Px/Dhw/HsWPHxC9XIVClSkpKBEdHR+Gjjz5Stl24cEEAIBw7dkzEyHRPfn6+EB8fLwiCIEyaNEmQy+UiR6S7YmNjVV4nJSUJ+vr6wvbt20WKSHclJSWpvL548aIAQLh48aJIEem2Q4cOCS1bthR+/PFHAYCQlZUldkg6Z82aNYK3t7fYYdQJI0eOFNq2bSsUFBQo27KysoTCwkIRoyrFtLoKrl27hocPH2LkyJHKtg4dOqBp06b466+/RIxM9xgZGaFhw4Zih1EnPP0AWhsbG5iYmCAjI0OkiHSXTCZTeR0WFgZjY2M0btxYpIh0V2xsLKZNm4affvoJpqamYoej0xISEjBx4kS8/vrr+P7771FYWCh2SDonLS0N+/btwzvvvANDQ0Nlu7m5OQwMDESMrBTn2asgMjISAODs7KzS7uzsrNxGJLatW7ciNzcXfn5+Yoeikw4cOIBt27YhMTERCQkJ+Ouvv+Dg4CB2WDpFoVBg/PjxmDVrFjp06MCfj89BIpGgZ8+e6NOnD7Kzs7F69WqsX78eISEhTG6r4caNG1AoFPDw8MCCBQtw7949uLm54Y033oCLi4vY4fHuyKooKCgAgDIffHNzc+Tn54sREpGKkJAQzJ49G0uXLkXLli3FDkcneXt7Y9KkSRg3bhwMDAzw5ZdfoqioSOywdMqyZcsgCALmzp0rdig6b/Lkyfj999/x2muvYdasWTh16hSioqLwzTffiB2aTsnJyQEATJo0CXp6ehgyZAju3r0LLy8v3Lp1S+ToOBNWJVZWVgBKpzUtLCyU7SkpKfD09BQrLCIAwPnz5/HCCy9gxowZmD9/vtjh6Cx3d3e4u7sDAF555RU4OTlh9+7dGDdunMiR6Y61a9fCw8MDI0aMAFB6aRIAxo4di/Hjx8Pf31/M8HTKo987j9jb26Nbt264cOGCSBHpJmtrawCln8HPPvsMADBu3Di0bdsWa9aswcaNG0WMjklYlbRp0wYAcPXqVeX0ZWFhIW7duoUxY8aIGRrVcxcvXsSAAQPw+uuvY+XKlWKHU2c4ODjAwsIC0dHRYoeiU3bs2KEyexgSEoLz589j/PjxaNeunXiB1RHp6eni382nY7y9vWFgYKD8AwsovdTr5uaGhIQEESMrxcuRVeDo6Ih+/fph7dq1ylutN2zYgIKCAowaNUrk6Ki+unz5Mvr374/XX38dq1atEjscnXXv3j0cOXJEpe2nn35Ceno6evfuLVJUuunFF1/Eyy+/rPzXqVMnAMCQIUN4mbyafvzxR5WF+Pv27cOZM2cwbNgwEaPSPRYWFhg5ciT27Nmj/P398OFDnDhxAj169BA5Os6EVdnmzZsxcOBANG/eHA0bNsSNGzewefNmODk5iR2azpk6dSqSk5Nx6dIl5Ofn4+WXX2YRwhoYMWIE8vLyEB4ejpdfflnZ/vLLL2Py5MmixaVrLC0tsXr1arzxxhtwc3NDTEwM4uPj8d1336Fr165ih0f1VExMjPISeXp6OsLCwrB06VKMHTtW7NB0zrfffovBgwejRYsWcHd3x7lz5zBw4EC89957YocGiSAIgthB6Iri4mKcPXsW2dnZ6NixI2xtbcUOSSf9888/yMvLU2mTSqUYOnSoSBHppr/++kt508iTmjdvDm9vbxEi0m1RUVG4ffs2bG1t4enpyTvQ1CA2Nhbnzp3DSy+9BD09PbHD0TkZGRm4cuUKjIyM4OnpWWadGFWdIAi4dOkSUlJS0Lx5czRt2lTskAAwCSMiIiISBdeEEREREYmASRgRERGRCJiEEREREYmASRgRERGRCJiEEREREYmASRgRERGRCJiEEREREYmASRgRERGRCJiEEREREYmASRgRERGRCJiEEVG9lpGRgf379+Phw4cq7Tdu3MCBAwdQWFgoUmREVNcxCSOies3CwgKLFy/G9OnTlW0hISHo2rUrIiMjYWhoKGJ0RFSX8QHeRFTvHTlyBP369UNwcDCsra3Rt29fzJ07Fx9//LHYoRFRHcYkjIgIwMsvv4zw8HAkJCTgjTfewLJly8QOiYjqOF6OJCICMG7cONy8eRO9e/dmAkZEtYIzYURU7125cgV9+/aFk5MTkpOTcffuXZibm4sdFhHVcZwJI6J67datWxgwYABef/11nDx5EsXFxfj888/FDouI6gHOhBFRvRUREYFevXrhpZdewvr16wEAX3/9NT766CPcvn0brq6uIkdIRHUZkzAiqpdKSkrw/vvvw9DQEF9++SUkEgkAoKioCFOmTEGfPn0wdepUkaMkorqMSRgRERGRCLgmjIiIiEgETMKIiIiIRMAkjIiIiEgETMKIiIiIRMAkjIiIiEgETMKIiIiIRMAkjIiIiEgETMKIiIiIRMAkjIiIiEgETMKIiIiIRMAkjIiIiEgETMKIiIiIRMAkjIiIiEgETMKIiIiIRPD/k4tUGL1fNWAAAAAASUVORK5CYII=", + "text/plain": [ + "<Figure size 704x440 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", ms=4, color=\"k\", label=\"data\")\n", - "ax.plot(x_fine, truth(x_fine), \"--\", color=\"C3\", label=\"truth\")\n", - "ax.set(xlabel=\"x\", ylabel=\"y\")\n", - "ax.legend(frameon=False)\n", + "ax.plot(x_fine, truth(x_fine), \"--\", color=plotstyle.COLOURS[1], label=\"truth\")\n", + "ax.plot(\n", + " x_fine,\n", + " M_TRUE * x_fine + B_TRUE,\n", + " \":\",\n", + " color=plotstyle.COLOURS[0],\n", + " label=\"the true line, without the defect\",\n", + ")\n", + "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"A line, and a truth that leaves it\")\n", + "ax.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "df44a564", + "id": "543e6a87", "metadata": {}, "source": [ - "## The GP discrepancy as a covariance term\n", + "## Four ways to say \"the model is wrong\"\n", + "\n", + "We fit the same line four times, changing only the covariance:\n", "\n", - "`T.kernel` wraps any scikit-learn kernel. One `Parameter` is derived per\n", - "free hyperparameter, sampled in sklearn's log-theta space; here they are\n", - "passed explicitly with priors. Two references: the line with its reported\n", - "statistics only, and the line with an uncorrelated model error\n", - "(`T.model_error`), which can widen the diagonal but cannot describe a smooth\n", - "defect." + "1. **statistics only** — the reported errors, and nothing else;\n", + "2. **a diagonal model error** (`T.model_error`) — every point gets extra,\n", + " *independent* slack;\n", + "3. **a GP with a constant amplitude** — a smooth mean-zero discrepancy of the\n", + " same size everywhere;\n", + "4. **a GP with an amplitude growing in $x$** (`T.exp_growth_amplitude`) — the\n", + " same, but allowed to be small where we trust the line and large where we do\n", + " not.\n", + "\n", + "Only the last one encodes what we actually believe. All four are one `Term` in\n", + "a `Constraint`, and the inference code does not change between them." ] }, { "cell_type": "code", - "execution_count": 3, - "id": "726517aa", + "execution_count": 4, + "id": "1a201944", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:25:12.011652Z", + "iopub.status.busy": "2026-09-12T03:25:12.011521Z", + "iopub.status.idle": "2026-09-12T03:25:12.019382Z", + "shell.execute_reply": "2026-09-12T03:25:12.018751Z" + } + }, + "outputs": [], + "source": [ + "m = rx.Parameter(\"m\", prior=stats.norm(0.0, 2.0), latex=\"m\")\n", + "b = rx.Parameter(\"b\", prior=stats.norm(0.0, 2.0), latex=\"b\")\n", + "line = rx.Model(lambda x, m, b: m * x + b, [m, b])\n", + "comp = rx.Comparison(data, line)\n", + "\n", + "gamma = rx.Parameter(\n", + " \"log_gamma\", prior=stats.norm(np.log(0.05), 1.0), latex=r\"\\log\\gamma\"\n", + ")\n", + "log_A = rx.Parameter(\"log_A\", prior=stats.norm(np.log(0.02), 1.5), latex=r\"\\log A\")\n", + "amp_slope = rx.Parameter(\"amp_slope\", prior=stats.norm(1.0, 1.0), latex=r\"s_A\")\n", + "log_ell = rx.Parameter(\"log_ell\", prior=stats.norm(0.5, 1.0), latex=r\"\\log \\ell\")\n", + "\n", + "kernel = Matern(length_scale=2.0, nu=2.5) + WhiteKernel(1e-6, \"fixed\")\n", + "gp_const = T.kernel(\n", + " kernel, params=[log_ell], amplitude=T.constant_amplitude, amplitude_params=(log_A,)\n", + ")\n", + "gp_grow = T.kernel(\n", + " kernel,\n", + " params=[log_ell],\n", + " amplitude=T.exp_growth_amplitude(5.0),\n", + " amplitude_params=(log_A, amp_slope),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "54264f30", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:47:57.492272Z", - "iopub.status.busy": "2026-09-11T03:47:57.492009Z", - "iopub.status.idle": "2026-09-11T03:50:43.171690Z", - "shell.execute_reply": "2026-09-11T03:50:43.170763Z" + "iopub.execute_input": "2026-09-12T03:25:12.020830Z", + "iopub.status.busy": "2026-09-12T03:25:12.020689Z", + "iopub.status.idle": "2026-09-12T03:25:12.025435Z", + "shell.execute_reply": "2026-09-12T03:25:12.024762Z" } }, "outputs": [ @@ -144,113 +241,134 @@ "name": "stdout", "output_type": "stream", "text": [ - "GP problem columns: ['m', 'b', 'log_A2', 'log_ell']\n" + "statistics only ['m', 'b']\n", + "diagonal model error ['m', 'b', 'log_gamma']\n", + "GP, constant amplitude ['m', 'b', 'log_ell', 'log_A']\n", + "GP, growing amplitude ['m', 'b', 'log_ell', 'log_A', 'amp_slope']\n" ] } ], "source": [ - "kernel = ConstantKernel(1.0) * Matern(length_scale=2.0, nu=2.5) + WhiteKernel(\n", - " 1e-6, \"fixed\"\n", - ")\n", - "log_amp2 = rx.Parameter(\"log_A2\", prior=stats.norm(-4.0, 2.0), latex=r\"\\log A^2\")\n", - "log_ell = rx.Parameter(\"log_ell\", prior=stats.norm(0.5, 1.0), latex=r\"\\log \\ell\")\n", - "gp = T.kernel(kernel, params=[log_amp2, log_ell])\n", - "gamma = rx.Parameter(\n", - " \"log_gamma\", prior=stats.norm(np.log(0.05), 1.0), latex=r\"\\log\\gamma\"\n", - ")\n", - "\n", - "p_stat = rx.Problem([rx.Constraint([comp])])\n", - "p_diag = rx.Problem(\n", - " [rx.Constraint([comp], terms=[T.model_error(gamma)])]\n", - ")\n", - "p_gp = rx.Problem([rx.Constraint([comp], terms=[gp])])\n", - "print(\"GP problem columns:\", p_gp.names)\n", - "\n", - "\n", - "def fit(problem, seed, start=None, n_walkers=24, n_steps=3000):\n", - " # emcee from the prior, or from a small ball around ``start``\n", - " rng = np.random.default_rng(seed)\n", - " if start is None:\n", - " p0 = problem.sample_prior(n_walkers, rng=rng)\n", - " else:\n", - " p0 = np.asarray(start) + 1e-2 * rng.standard_normal((n_walkers, problem.ndim))\n", + "problems = {\n", + " \"statistics only\": rx.Problem([rx.Constraint([comp])]),\n", + " \"diagonal model error\": rx.Problem(\n", + " [rx.Constraint([comp], terms=[T.model_error(gamma)])]\n", + " ),\n", + " \"GP, constant amplitude\": rx.Problem([rx.Constraint([comp], terms=[gp_const])]),\n", + " \"GP, growing amplitude\": rx.Problem([rx.Constraint([comp], terms=[gp_grow])]),\n", + "}\n", + "for name, p in problems.items():\n", + " print(f\"{name:24s} {p.names}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "699a541c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:25:12.027514Z", + "iopub.status.busy": "2026-09-12T03:25:12.027298Z", + "iopub.status.idle": "2026-09-12T03:27:59.004758Z", + "shell.execute_reply": "2026-09-12T03:27:59.003984Z" + } + }, + "outputs": [], + "source": [ + "def fit(problem, seed, n_walkers=24, n_steps=3000):\n", " sampler = emcee.EnsembleSampler(n_walkers, problem.ndim, problem.log_posterior)\n", " sampler.random_state = np.random.RandomState(seed).get_state()\n", - " sampler.run_mcmc(p0, n_steps, progress=False)\n", + " sampler.run_mcmc(problem.sample_prior(n_walkers, rng=seed), n_steps, progress=False)\n", " return sampler.get_chain(discard=n_steps // 3, thin=5, flat=True)\n", "\n", "\n", - "s_stat = fit(p_stat, 1)\n", - "line_fit = np.median(s_stat, axis=0) # a starting point for the nuisance problems\n", - "s_diag = fit(p_diag, 2, start=[*line_fit, np.log(0.05)])\n", - "s_gp = fit(p_gp, 3, start=[*line_fit, -4.0, 0.5])" + "samples = {name: fit(p, seed=i) for i, (name, p) in enumerate(problems.items())}" ] }, { "cell_type": "code", - "execution_count": 4, - "id": "dfd18d85", + "execution_count": 7, + "id": "f08f7fc9", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:50:43.173461Z", - "iopub.status.busy": "2026-09-11T03:50:43.173260Z", - "iopub.status.idle": "2026-09-11T03:50:44.318247Z", - "shell.execute_reply": "2026-09-11T03:50:44.317368Z" + "iopub.execute_input": "2026-09-12T03:27:59.006475Z", + "iopub.status.busy": "2026-09-12T03:27:59.006291Z", + "iopub.status.idle": "2026-09-12T03:27:59.012047Z", + "shell.execute_reply": "2026-09-12T03:27:59.011324Z" } }, "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 550x550 with 4 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "statistics only m = 0.953 +/- 0.003 (pull +60.2) b = 0.858 +/- 0.007 (pull -19.0)\n", + "diagonal model error m = 0.775 +/- 1.043 (pull -0.0) b = 0.154 +/- 2.262 (pull -0.4)\n", + "GP, constant amplitude m = 0.959 +/- 0.062 (pull +2.6) b = 1.046 +/- 0.366 (pull +0.1)\n", + "GP, growing amplitude m = 0.830 +/- 0.071 (pull +0.4) b = 0.941 +/- 0.165 (pull -0.4)\n" + ] } ], "source": [ - "fig = corner.corner(\n", - " s_gp[:, p_gp.columns([log_amp2, log_ell])],\n", - " labels=[r\"$\\log A^2$\", r\"$\\log \\ell$\"],\n", - " show_titles=True,\n", - ")\n", - "plt.show()" + "for name, p in problems.items():\n", + " s = samples[name]\n", + " cm, cb = p.columns(m)[0], p.columns(b)[0]\n", + " pull_m = (s[:, cm].mean() - M_TRUE) / s[:, cm].std()\n", + " pull_b = (s[:, cb].mean() - B_TRUE) / s[:, cb].std()\n", + " print(\n", + " f\"{name:24s} m = {s[:, cm].mean():.3f} +/- {s[:, cm].std():.3f} \"\n", + " f\"(pull {pull_m:+5.1f}) b = {s[:, cb].mean():.3f} +/- {s[:, cb].std():.3f} \"\n", + " f\"(pull {pull_b:+5.1f})\"\n", + " )" ] }, { "cell_type": "markdown", - "id": "c31aaa3b", + "id": "fe165fdc", "metadata": {}, "source": [ - "## Propagating the total uncertainty\n", + "### What those four rows say\n", + "\n", + "**Statistics only** is the cautionary one: $m = 0.953 \\pm 0.003$, sixty standard\n", + "deviations from the truth. The line has no way to say \"I am wrong at large\n", + "$x$\", so it tilts to chase the defect and then reports a precision of three parts\n", + "in a thousand. This is what an unmodelled discrepancy does — it does not widen\n", + "the answer, it moves it, confidently.\n", "\n", - "The line's own band (its parameters only) cannot bend. `total_predictive_band`\n", - "conditions the GP on the residuals at each posterior row, predicts the\n", - "discrepancy on the plotting grid, and adds it: everything the problem\n", - "declared is propagated, and nothing has to be told which columns are the\n", - "kernel's." + "**The diagonal model error** covers the truth, but look at how: $m = 0.775 \\pm\n", + "1.043$. Independent slack per point can only inflate, and to cover a coherent\n", + "departure it has to inflate until the slope means nothing at all.\n", + "\n", + "**The constant-amplitude GP** knows the defect is smooth, and that already helps\n", + "a great deal — $m = 0.959 \\pm 0.062$ against sixty sigma before. But it is still\n", + "2.6 sigma out, because one amplitude has to serve both ends of the range: large\n", + "enough for $x = 5$, and therefore far too generous at $x = 0.2$, where it lets\n", + "the fit off the hook exactly where the data are informative.\n", + "\n", + "**The growing amplitude** is the one that matches what we believe, and it lands\n", + "on the truth: $m = 0.830 \\pm 0.071$, $b = 0.941 \\pm 0.165$, both within half a\n", + "sigma. The fit also tells us it needed the growth: the inferred slope of the\n", + "amplitude is about $+1.8$, not zero." ] }, { "cell_type": "code", - "execution_count": 5, - "id": "a8203a83", + "execution_count": 8, + "id": "1b55f904", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:50:44.320048Z", - "iopub.status.busy": "2026-09-11T03:50:44.319852Z", - "iopub.status.idle": "2026-09-11T03:50:46.792643Z", - "shell.execute_reply": "2026-09-11T03:50:46.791794Z" + "iopub.execute_input": "2026-09-12T03:27:59.013617Z", + "iopub.status.busy": "2026-09-12T03:27:59.013452Z", + "iopub.status.idle": "2026-09-12T03:27:59.250686Z", + "shell.execute_reply": "2026-09-12T03:27:59.249984Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 600x400 with 1 Axes>" + "<Figure size 605x605 with 4 Axes>" ] }, "metadata": {}, @@ -258,63 +376,91 @@ } ], "source": [ - "def mean_band(problem, samples, levels=(16, 84)):\n", - " on_fine = line.bind(x_fine, {})\n", - " cols = problem.columns(line.params)\n", - " return rx.predictive.predictive_band(\n", - " [on_fine(*s[cols]) for s in samples[::10]], levels=levels\n", + "fig = None\n", + "colours = [plotstyle.COLOURS[i] for i in (1, 3, 5, 0)]\n", + "for (name, p), colour in zip(problems.items(), colours):\n", + " fig = corner.corner(\n", + " samples[name][:, p.columns(line.params)],\n", + " fig=fig,\n", + " labels=[\"$m$\", \"$b$\"],\n", + " truths=[M_TRUE, B_TRUE],\n", + " range=[(0.55, 1.05), (0.0, 1.8)],\n", + " **plotstyle.corner_kwargs(\n", + " color=colour, fill_contours=False, plot_density=False, show_titles=False\n", + " ),\n", " )\n", - "\n", - "\n", - "band_gp = rx.predictive.total_predictive_band(\n", - " p_gp, gp, line.bind(x_fine, {}), x_fine, s_gp, rng=4\n", - ")\n", - "fig, ax = plt.subplots(figsize=(6, 4))\n", - "for label, (p, s), color in (\n", - " (\"line, statistics only\", (p_stat, s_stat), \"C0\"),\n", - " (\"line + model error\", (p_diag, s_diag), \"C2\"),\n", - "):\n", - " lo, hi = mean_band(p, s)\n", - " ax.fill_between(x_fine, lo, hi, color=color, alpha=0.3, label=label)\n", - "ax.fill_between(\n", - " x_fine, *band_gp, color=\"C1\", alpha=0.4, label=\"line + GP discrepancy (total)\"\n", + "fig.legend(\n", + " handles=[plt.Line2D([], [], color=c, label=n) for n, c in zip(problems, colours)],\n", + " loc=\"upper right\",\n", + " fontsize=9,\n", ")\n", - "ax.plot(x_fine, truth(x_fine), \"--\", color=\"k\", label=\"truth\")\n", - "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", ms=3, color=\"k\")\n", - "ax.set(xlabel=\"x\", ylabel=\"y\", title=\"68 % bands\")\n", - "ax.legend(frameon=False, fontsize=8)\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "33ef28b2", + "id": "6e5e2534", "metadata": {}, "source": [ - "## What the GP captured\n", + "## Propagating the whole uncertainty\n", "\n", - "Condition the GP on the residuals of the posterior-mean line and compare\n", - "with the true defect, `truth(x) − line(x)`." + "The line's own band cannot bend: it is two parameters, so it is a pencil of\n", + "straight lines. `total_predictive_band` conditions the GP on the residuals at\n", + "each posterior row, predicts the discrepancy on whatever grid we ask for, and\n", + "adds it. Everything the problem declared is propagated, and we never have to\n", + "tell it which columns belong to the kernel." ] }, { "cell_type": "code", - "execution_count": 6, - "id": "9f1b3e65", + "execution_count": 9, + "id": "621049af", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:50:46.794463Z", - "iopub.status.busy": "2026-09-11T03:50:46.794265Z", - "iopub.status.idle": "2026-09-11T03:50:46.959580Z", - "shell.execute_reply": "2026-09-11T03:50:46.958941Z" + "iopub.execute_input": "2026-09-12T03:27:59.252425Z", + "iopub.status.busy": "2026-09-12T03:27:59.252159Z", + "iopub.status.idle": "2026-09-12T03:28:00.937743Z", + "shell.execute_reply": "2026-09-12T03:28:00.933911Z" + } + }, + "outputs": [], + "source": [ + "def mean_band(problem, samples_, levels=(16, 84)):\n", + " on_fine = line.bind(x_fine)\n", + " cols = problem.columns(line.params)\n", + " return rx.predictive.predictive_band(\n", + " [on_fine(*s[cols]) for s in samples_[::10]], levels=levels\n", + " )\n", + "\n", + "\n", + "band_gp = rx.predictive.total_predictive_band(\n", + " problems[\"GP, growing amplitude\"],\n", + " gp_grow,\n", + " line.bind(x_fine),\n", + " x_fine,\n", + " samples[\"GP, growing amplitude\"],\n", + " rng=4,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9e91d84f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:28:00.939859Z", + "iopub.status.busy": "2026-09-12T03:28:00.939567Z", + "iopub.status.idle": "2026-09-12T03:28:01.200121Z", + "shell.execute_reply": "2026-09-12T03:28:01.199366Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "<Figure size 600x350 with 1 Axes>" + "<Figure size 704x440 with 1 Axes>" ] }, "metadata": {}, @@ -322,166 +468,144 @@ } ], "source": [ - "theta_gp = np.median(s_gp, axis=0)\n", - "resid = data.y - line.bind(x, {})(*theta_gp[p_gp.columns(line.params)])\n", - "mean_d, cov_d = rx.predictive.gp_posterior_predictive(\n", - " kernel,\n", - " theta_gp[p_gp.columns([log_amp2, log_ell])],\n", - " x,\n", - " resid,\n", - " x_fine,\n", - " train_noise_var=noise**2,\n", - ")\n", - "sd_d = np.sqrt(np.clip(np.diag(cov_d), 0, None))\n", - "true_defect = truth(x_fine) - line.bind(x_fine, {})(\n", - " *theta_gp[p_gp.columns(line.params)]\n", - ")\n", - "\n", - "fig, ax = plt.subplots(figsize=(6, 3.5))\n", - "ax.errorbar(x, resid, noise, fmt=\"o\", ms=3, color=\"k\", label=\"residuals\")\n", - "ax.fill_between(\n", + "fig, ax = plt.subplots()\n", + "for name, colour, hatch in (\n", + " (\"statistics only\", plotstyle.COLOURS[1], plotstyle.HATCHES[0]),\n", + " (\"diagonal model error\", plotstyle.COLOURS[3], plotstyle.HATCHES[1]),\n", + "):\n", + " lo, hi = mean_band(problems[name], samples[name])\n", + " plotstyle.band(ax, x_fine, lo, hi, color=colour, hatch=hatch, label=name)\n", + "plotstyle.band(\n", + " ax,\n", " x_fine,\n", - " mean_d - sd_d,\n", - " mean_d + sd_d,\n", - " color=\"C1\",\n", - " alpha=0.4,\n", - " label=\"GP posterior (68 %)\",\n", + " *band_gp,\n", + " color=plotstyle.COLOURS[0],\n", + " hatch=plotstyle.HATCHES[2],\n", + " label=\"GP, growing amplitude (total)\",\n", ")\n", - "ax.plot(x_fine, true_defect, \"--\", color=\"C3\", label=\"true defect\")\n", - "ax.set(xlabel=\"x\", ylabel=\"y - line\")\n", - "ax.legend(frameon=False)\n", + "ax.plot(x_fine, truth(x_fine), \"--\", color=\"k\", label=\"truth\")\n", + "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", ms=3, color=\"k\")\n", + "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"68 % bands\", ylim=(0.5, 6.5))\n", + "ax.legend(fontsize=8)\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "2482e3d3", + "id": "cda1ab13", "metadata": {}, "source": [ - "## For contrast: a sampled mean correction (recipes 8, 36)\n", + "## What the GP actually learned\n", "\n", - "The discrepancy can instead be a *mean* correction with sampled\n", - "coefficients: `line + rx.Model(delta, phi)` on a Legendre basis. It removes\n", - "the bias too, but its band is only the coefficients' uncertainty, and it\n", - "says nothing outside the data. A GP is the same idea with the coefficients\n", - "integrated out and a smoothness prior instead of a basis choice." + "Because this is synthetic data we know the defect exactly: it is $0.03\\,x^2$.\n", + "So we can condition the GP on the residuals of the posterior-median line and\n", + "hold the result up against the truth. The interesting part is the edges — how\n", + "the GP interpolates between points, and how it relaxes back towards zero where\n", + "there are no data to hold it." ] }, { "cell_type": "code", - "execution_count": 7, - "id": "51067230", + "execution_count": 11, + "id": "5aea3429", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:50:46.961699Z", - "iopub.status.busy": "2026-09-11T03:50:46.961480Z", - "iopub.status.idle": "2026-09-11T03:51:28.757180Z", - "shell.execute_reply": "2026-09-11T03:51:28.756473Z" + "iopub.execute_input": "2026-09-12T03:28:01.201730Z", + "iopub.status.busy": "2026-09-12T03:28:01.201561Z", + "iopub.status.idle": "2026-09-12T03:28:01.205733Z", + "shell.execute_reply": "2026-09-12T03:28:01.205169Z" + } + }, + "outputs": [], + "source": [ + "# the discrepancy is the total band minus the line: total_predictive_band\n", + "# knows about the growing amplitude, which the bare kernel object does not\n", + "p_grow = problems[\"GP, growing amplitude\"]\n", + "theta_gp = np.median(samples[\"GP, growing amplitude\"], axis=0)\n", + "line_median = line.bind(x_fine)(*theta_gp[p_grow.columns(line.params)])\n", + "disc_lo, disc_hi = band_gp[0] - line_median, band_gp[1] - line_median\n", + "true_defect = truth(x_fine) - line_median\n", + "resid = data.y - line.bind(x)(*theta_gp[p_grow.columns(line.params)])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e1b7d5ba", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:28:01.207442Z", + "iopub.status.busy": "2026-09-12T03:28:01.207273Z", + "iopub.status.idle": "2026-09-12T03:28:01.346017Z", + "shell.execute_reply": "2026-09-12T03:28:01.345232Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 600x400 with 1 Axes>" + "<Figure size 704x440 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "line only m = 0.066 +/- 0.003 b = 1.127 +/- 0.008\n", - "line + GP m = 0.093 +/- 0.047 b = 0.942 +/- 0.295\n", - "line + correction m = 0.082 +/- 0.163 b = 1.016 +/- 0.668\n" - ] } ], "source": [ - "phis = [\n", - " rx.Parameter(f\"phi_{k}\", prior=stats.norm(0.0, 0.5), latex=rf\"\\phi_{k}\")\n", - " for k in range(4)\n", - "]\n", - "\n", - "\n", - "def delta(x, *phi):\n", - " return L.legval(np.asarray(x, dtype=float) / 3.0 - 1.0, phi)\n", - "\n", - "\n", - "corrected = line + rx.Model(delta, phis)\n", - "p_mean = rx.Problem([rx.Constraint([rx.Comparison(data, corrected)])])\n", - "s_mean = fit(p_mean, 5, start=[*line_fit, 0.0, 0.0, 0.0, 0.0])\n", - "on_fine = corrected.bind(x_fine, {})\n", - "lo, hi = rx.predictive.predictive_band(\n", - " [on_fine(*s[p_mean.columns(corrected.params)]) for s in s_mean[::10]],\n", - " levels=(16, 84),\n", - ")\n", - "\n", - "fig, ax = plt.subplots(figsize=(6, 4))\n", - "ax.fill_between(\n", - " x_fine, lo, hi, color=\"C4\", alpha=0.4, label=\"line + sampled Legendre correction\"\n", - ")\n", - "ax.fill_between(\n", - " x_fine, *band_gp, color=\"C1\", alpha=0.3, label=\"line + GP discrepancy (total)\"\n", + "fig, ax = plt.subplots()\n", + "ax.errorbar(x, resid, noise, fmt=\"o\", ms=3, color=\"k\", label=\"residuals of the line\")\n", + "plotstyle.band(\n", + " ax,\n", + " x_fine,\n", + " disc_lo,\n", + " disc_hi,\n", + " color=plotstyle.COLOURS[0],\n", + " label=\"GP discrepancy (68 %)\",\n", ")\n", - "ax.plot(x_fine, truth(x_fine), \"--\", color=\"k\", label=\"truth\")\n", - "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", ms=3, color=\"k\")\n", - "ax.set(xlabel=\"x\", ylabel=\"y\", title=\"68 % bands\")\n", - "ax.legend(frameon=False, fontsize=8)\n", - "plt.show()\n", - "for name, p, s in (\n", - " (\"line only\", p_stat, s_stat),\n", - " (\"line + GP\", p_gp, s_gp),\n", - " (\"line + correction\", p_mean, s_mean),\n", - "):\n", - " cols = p.columns(line.params)\n", - " print(\n", - " f\"{name:18s} m = {s[:, cols[0]].mean():.3f} +/- {s[:, cols[0]].std():.3f} b = {s[:, cols[1]].mean():.3f} +/- {s[:, cols[1]].std():.3f}\"\n", - " )" + "ax.plot(x_fine, true_defect, \"--\", color=plotstyle.COLOURS[1], label=\"the true defect\")\n", + "ax.set(xlabel=\"$x$\", ylabel=\"$y$ - line\", title=\"The discrepancy, learned\")\n", + "ax.legend()\n", + "plt.show()" ] }, { "cell_type": "markdown", - "id": "d6beebce", + "id": "1aeb5286", "metadata": {}, "source": [ - "## A differential cross section with missing physics\n", + "## The same thing on a cross section\n", + "\n", + "Now a real model with a real piece of physics missing. We generate mock\n", + "$n + {}^{40}\\mathrm{Ca}$ elastic scattering at 14.1 MeV from a potential with\n", + "**volume and surface** absorption, and then fit it with a potential that has\n", + "only volume absorption. The missing surface term is our structured defect.\n", + "\n", + "Two differences from the toy. We compare in log space, so the discrepancy is a\n", + "*fractional* defect in angle, and we drive the fit with\n", + "[dynesty](https://dynesty.readthedocs.io/) rather than emcee: optical-model\n", + "posteriors are correlated and often multimodal, and an affine-invariant ensemble\n", + "mixes poorly on them.\n", "\n", - "Mock n + ⁴⁰Ca elastic scattering at 14.1 MeV generated from a potential with\n", - "volume *and* surface absorption, fit with a potential that has only volume\n", - "absorption. Reaction models are driven by dynesty: affine-invariant\n", - "ensembles mix poorly on optical-model posteriors. The comparison is in log\n", - "space, so the GP describes a fractional defect in angle." + "This half gets its own random generator, so that changing anything in the toy\n", + "above leaves the cross-section data untouched." ] }, { "cell_type": "code", - "execution_count": 8, - "id": "34dcbfaa", + "execution_count": 13, + "id": "9cf85214", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:51:28.758865Z", - "iopub.status.busy": "2026-09-11T03:51:28.758684Z", - "iopub.status.idle": "2026-09-11T03:51:41.577514Z", - "shell.execute_reply": "2026-09-11T03:51:41.576769Z" + "iopub.execute_input": "2026-09-12T03:28:01.347603Z", + "iopub.status.busy": "2026-09-12T03:28:01.347428Z", + "iopub.status.idle": "2026-09-12T03:28:01.374876Z", + "shell.execute_reply": "2026-09-12T03:28:01.374040Z" } }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 600x400 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ + "rng_xs = np.random.default_rng(2024)\n", "reaction = jitr.reactions.ElasticReaction(target=(40, 20), projectile=(1, 0))\n", "E_lab = 14.1\n", "R40 = 40 ** (1 / 3)\n", @@ -504,8 +628,23 @@ "\n", "so_args = (6.0, 1.1 * R40, 0.45)\n", "full_truth = np.array([48.0, 3.5, 1.1 * R40, 0.7, 21.0, 1.2 * R40, 0.5])\n", - "volume_truth = full_truth[:4]\n", - "\n", + "volume_truth = full_truth[:4]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "0f3057a0", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:28:01.376556Z", + "iopub.status.busy": "2026-09-12T03:28:01.376383Z", + "iopub.status.idle": "2026-09-12T03:28:01.386528Z", + "shell.execute_reply": "2026-09-12T03:28:01.385783Z" + } + }, + "outputs": [], + "source": [ "full_params = [\n", " rx.Parameter(n, prior=stats.norm(0, 1))\n", " for n in (\"Vv\", \"Wv\", \"Rv\", \"av\", \"Wd\", \"Rd\", \"ad\")\n", @@ -531,15 +670,41 @@ " lambda ws, *x: (tuple(x), so_args),\n", " volume_params,\n", " lmax=10,\n", - ")\n", - "\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "6bdcb92f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:28:01.388229Z", + "iopub.status.busy": "2026-09-12T03:28:01.388066Z", + "iopub.status.idle": "2026-09-12T03:28:13.872733Z", + "shell.execute_reply": "2026-09-12T03:28:13.871985Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 704x440 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "angles = np.deg2rad(np.linspace(5.0, 160.0, 25))\n", "meta = {\"reaction\": reaction, \"Elab\": E_lab}\n", "y_full = omp_full.bind(angles, meta)(*full_truth)\n", - "y_xs = y_full * (1 + rng.normal(0.0, 0.04, angles.size))\n", + "y_xs = y_full * (1 + rng_xs.normal(0.0, 0.04, angles.size))\n", "d_xs = rx.Dataset(angles, y_xs, 0.04 * y_xs, label=\"n+40Ca 14.1 MeV\", meta=meta)\n", "\n", - "fig, ax = plt.subplots(figsize=(6, 4))\n", + "fig, ax = plt.subplots()\n", "ax.errorbar(\n", " np.rad2deg(angles),\n", " d_xs.y,\n", @@ -552,24 +717,77 @@ "ax.plot(\n", " np.rad2deg(angles),\n", " omp_vol.bind(angles, meta)(*volume_truth),\n", - " color=\"C3\",\n", - " label=\"volume-only potential at the true volume parameters\",\n", + " color=plotstyle.COLOURS[1],\n", + " label=\"volume-only potential, at the true volume parameters\",\n", + ")\n", + "ax.set(\n", + " xlabel=r\"$\\theta_{cm}$ [deg]\",\n", + " ylabel=r\"$d\\sigma/d\\Omega$ [b/sr]\",\n", + " yscale=\"log\",\n", + " title=\"The missing surface absorption\",\n", ")\n", - "ax.set(xlabel=r\"$\\theta_{cm}$ [deg]\", ylabel=r\"$d\\sigma/d\\Omega$ [b/sr]\", yscale=\"log\")\n", - "ax.legend(frameon=False, fontsize=8)\n", + "ax.legend(fontsize=8)\n", "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "d13ce9ac", + "metadata": {}, + "source": [ + "### The same three rungs\n", + "\n", + "Bare, a diagonal model error, and a mean-zero GP whose amplitude grows with\n", + "angle — the same prior belief as in the toy, that the model is trustworthy\n", + "forward and suspect backward." + ] + }, { "cell_type": "code", - "execution_count": 9, - "id": "2e828a7d", + "execution_count": 16, + "id": "2ee90995", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:51:41.579034Z", - "iopub.status.busy": "2026-09-11T03:51:41.578854Z", - "iopub.status.idle": "2026-09-11T03:58:07.027068Z", - "shell.execute_reply": "2026-09-11T03:58:07.026220Z" + "iopub.execute_input": "2026-09-12T03:28:13.874996Z", + "iopub.status.busy": "2026-09-12T03:28:13.874820Z", + "iopub.status.idle": "2026-09-12T03:28:13.885207Z", + "shell.execute_reply": "2026-09-12T03:28:13.884550Z" + } + }, + "outputs": [], + "source": [ + "comp_xs = rx.Comparison(d_xs, omp_vol, space=tf.log)\n", + "xs_gamma = rx.Parameter(\n", + " \"log_gamma_xs\", prior=stats.norm(np.log(0.05), 1.0), latex=r\"\\log\\gamma\"\n", + ")\n", + "xs_amp = rx.Parameter(\"log_A_xs\", prior=stats.norm(np.log(0.05), 1.5), latex=r\"\\log A\")\n", + "xs_slope = rx.Parameter(\"amp_slope_xs\", prior=stats.norm(1.0, 1.0), latex=r\"s_A\")\n", + "xs_ell = rx.Parameter(\"log_ell_xs\", prior=stats.norm(-0.5, 1.0), latex=r\"\\log \\ell\")\n", + "gp_xs = T.kernel(\n", + " Matern(0.5, nu=2.5) + WhiteKernel(1e-6, \"fixed\"),\n", + " params=[xs_ell],\n", + " amplitude=T.exp_growth_amplitude(np.pi),\n", + " amplitude_params=(xs_amp, xs_slope),\n", + ")\n", + "xs_problems = {\n", + " \"bare\": rx.Problem([rx.Constraint([comp_xs])]),\n", + " \"diagonal model error\": rx.Problem(\n", + " [rx.Constraint([comp_xs], terms=[T.model_error(xs_gamma)])]\n", + " ),\n", + " \"GP, growing amplitude\": rx.Problem([rx.Constraint([comp_xs], terms=[gp_xs])]),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "4b4bfd54", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:28:13.887028Z", + "iopub.status.busy": "2026-09-12T03:28:13.886863Z", + "iopub.status.idle": "2026-09-12T03:35:19.711275Z", + "shell.execute_reply": "2026-09-12T03:35:19.710344Z" } }, "outputs": [ @@ -577,29 +795,34 @@ "name": "stdout", "output_type": "stream", "text": [ - "log Z = -649.52 +/- 0.65, 88099 likelihood calls\n" + "bare " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log Z = -581.75 +/- 0.64, 85485 likelihood calls\n", + "diagonal model error " ] }, { "name": "stdout", "output_type": "stream", "text": [ - "log Z = -17.48 +/- 0.53, 64665 likelihood calls\n" + "log Z = -19.61 +/- 0.50, 64449 likelihood calls\n", + "GP, growing amplitude " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log Z = -9.93 +/- 0.52, 75404 likelihood calls\n" ] } ], "source": [ - "comp_xs = rx.Comparison(d_xs, omp_vol, space=tf.log)\n", - "xs_amp = rx.Parameter(\"log_A2_xs\", prior=stats.norm(-4.0, 2.0), latex=r\"\\log A^2\")\n", - "xs_ell = rx.Parameter(\"log_ell_xs\", prior=stats.norm(-0.5, 1.0), latex=r\"\\log \\ell\")\n", - "gp_xs = T.kernel(\n", - " ConstantKernel(0.1**2) * Matern(0.5, nu=2.5) + WhiteKernel(1e-6, \"fixed\"),\n", - " params=[xs_amp, xs_ell],\n", - ")\n", - "p_xs_bare = rx.Problem([rx.Constraint([comp_xs])])\n", - "p_xs_gp = rx.Problem([rx.Constraint([comp_xs], terms=[gp_xs])])\n", - "\n", - "\n", "def nested(problem, seed, nlive=150):\n", " sampler = dynesty.NestedSampler(\n", " problem.log_likelihood,\n", @@ -612,33 +835,104 @@ " sampler.run_nested(dlogz=0.5, print_progress=False)\n", " res = sampler.results\n", " print(\n", - " f\"log Z = {res.logz[-1]:.2f} +/- {res.logzerr[-1]:.2f}, {int(np.sum(res.ncall))} likelihood calls\"\n", + " f\"log Z = {res.logz[-1]:8.2f} +/- {res.logzerr[-1]:.2f}, \"\n", + " f\"{int(np.sum(res.ncall)):7d} likelihood calls\"\n", " )\n", " return res.samples_equal(rstate=np.random.default_rng(seed))\n", "\n", "\n", - "s_xs_bare = nested(p_xs_bare, 10)\n", - "s_xs_gp = nested(p_xs_gp, 11)" + "xs_samples = {}\n", + "for i, (name, p) in enumerate(xs_problems.items()):\n", + " print(f\"{name:22s}\", end=\" \")\n", + " xs_samples[name] = nested(p, 10 + i)" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "ec2dc390", + "execution_count": 18, + "id": "2f8e7c6e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:58:07.029338Z", - "iopub.status.busy": "2026-09-11T03:58:07.029123Z", - "iopub.status.idle": "2026-09-11T03:58:07.720779Z", - "shell.execute_reply": "2026-09-11T03:58:07.719995Z" + "iopub.execute_input": "2026-09-12T03:35:19.713594Z", + "iopub.status.busy": "2026-09-12T03:35:19.713386Z", + "iopub.status.idle": "2026-09-12T03:35:19.720493Z", + "shell.execute_reply": "2026-09-12T03:35:19.719528Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bare Vv= 26.85+/-0.41 Wv= 16.64+/-0.23 Rv= 4.43+/-0.02 av= 0.66+/-0.01 max|pull| = 56.7\n", + "diagonal model error Vv= 38.78+/-3.47 Wv= 18.91+/-1.37 Rv= 4.28+/-0.15 av= 0.56+/-0.06 max|pull| = 11.3\n", + "GP, growing amplitude Vv= 43.67+/-1.42 Wv= 13.61+/-1.07 Rv= 4.29+/-0.06 av= 0.48+/-0.02 max|pull| = 9.5\n" + ] + } + ], + "source": [ + "for name, p in xs_problems.items():\n", + " s = xs_samples[name][:, p.columns(volume_params)]\n", + " print(\n", + " f\"{name:22s} \"\n", + " + \" \".join(\n", + " f\"{q.name}={s[:, k].mean():6.2f}+/-{s[:, k].std():.2f}\"\n", + " for k, q in enumerate(volume_params)\n", + " )\n", + " + \" max|pull| = \"\n", + " + f\"{max(abs((s[:, k].mean() - volume_truth[k]) / s[:, k].std()) for k in range(4)):.1f}\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "0acba684", + "metadata": {}, + "source": [ + "### What the three rungs did to the potential\n", + "\n", + "The evidence is blunt about it. Log $Z$ goes from $-581.75$ for the bare fit to\n", + "$-19.61$ once we allow a diagonal model error, and $-9.93$ with the GP. A gap\n", + "of that size is not a preference, it is a verdict: the bare fit's claim that the\n", + "reported 4 % errors explain every disagreement is simply false, and the sampler\n", + "can tell.\n", + "\n", + "The parameters follow the same order. The bare fit puts $V_v$ at\n", + "$26.9 \\pm 0.4$ MeV where the truth is 48 — wrong by fifty standard deviations,\n", + "and confident. The model error widens until nothing is precise. The GP\n", + "recovers $V_v$ to $43.7 \\pm 1.4$, about three sigma from the truth, while\n", + "keeping useful width.\n", + "\n", + "But look at $W_v$: $13.6 \\pm 1.1$ against a truth of 3.5, nine sigma out, in\n", + "*every* rung including the GP. That is not a failure of the discrepancy model,\n", + "it is the physics. The data were generated with volume **and** surface\n", + "absorption; we fitted a potential with only volume absorption, and at a single\n", + "energy those two shapes are very hard to tell apart. The fitted volume\n", + "absorption is quite literally absorbing the missing surface term.\n", + "\n", + "This is the honest limit of the method, and it is worth stating plainly: a\n", + "discrepancy model widens what we do not know, and stops the fit from lying about\n", + "its precision. It does not recover information the measurement never contained." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "9f34319b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:35:19.722688Z", + "iopub.status.busy": "2026-09-12T03:35:19.722361Z", + "iopub.status.idle": "2026-09-12T03:35:20.492727Z", + "shell.execute_reply": "2026-09-12T03:35:20.491801Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 970x970 with 16 Axes>" + "<Figure size 1067x1067 with 16 Axes>" ] }, "metadata": {}, @@ -646,64 +940,91 @@ } ], "source": [ - "labels = [f\"${p.latex}$\" for p in volume_params]\n", - "fig = corner.corner(\n", - " s_xs_bare[:, p_xs_bare.columns(volume_params)],\n", - " color=\"C3\",\n", - " labels=labels,\n", - " truths=volume_truth,\n", - " truth_color=\"k\",\n", - " plot_datapoints=False,\n", - ")\n", - "corner.corner(\n", - " s_xs_gp[:, p_xs_gp.columns(volume_params)],\n", - " fig=fig,\n", - " color=\"C1\",\n", - " plot_datapoints=False,\n", - ")\n", + "labels = [f\"${q.latex}$\" for q in volume_params]\n", + "fig = None\n", + "xs_colours = [plotstyle.COLOURS[i] for i in (1, 3, 0)]\n", + "for (name, p), colour in zip(xs_problems.items(), xs_colours):\n", + " fig = corner.corner(\n", + " xs_samples[name][:, p.columns(volume_params)],\n", + " fig=fig,\n", + " labels=labels,\n", + " truths=volume_truth,\n", + " **plotstyle.corner_kwargs(\n", + " color=colour, fill_contours=False, plot_density=False, show_titles=False\n", + " ),\n", + " )\n", "fig.legend(\n", " handles=[\n", - " plt.Line2D([], [], color=\"C3\", label=\"no discrepancy\"),\n", - " plt.Line2D([], [], color=\"C1\", label=\"GP discrepancy\"),\n", + " plt.Line2D([], [], color=c, label=n) for n, c in zip(xs_problems, xs_colours)\n", " ],\n", " loc=\"upper right\",\n", - " frameon=False,\n", + " fontsize=9,\n", ")\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "ac84d783", + "id": "34ae1fec", "metadata": {}, "source": [ - "## The learned discrepancy against the missing physics\n", + "### The learned discrepancy against the missing physics\n", "\n", - "The defect the GP has to describe is the log ratio of the data-generating\n", - "full potential to the fitted volume-only one; it is drawn here at the\n", - "posterior-median volume parameters, on a fine grid, so the question is\n", - "whether the GP interpolates it smoothly between the data and how it relaxes\n", - "outside them." + "The defect the GP has to describe is the log ratio of the full potential that\n", + "generated the data to the volume-only one we fitted. We draw it at the\n", + "posterior-median volume parameters, on a finer angular grid than the data, so we\n", + "can see how the GP interpolates between points and how it behaves beyond them." ] }, { "cell_type": "code", - "execution_count": 11, - "id": "5c7add97", + "execution_count": 20, + "id": "1d490c7d", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T03:58:07.724344Z", - "iopub.status.busy": "2026-09-11T03:58:07.724120Z", - "iopub.status.idle": "2026-09-11T03:58:07.959466Z", - "shell.execute_reply": "2026-09-11T03:58:07.958638Z" + "iopub.execute_input": "2026-09-12T03:35:20.495746Z", + "iopub.status.busy": "2026-09-12T03:35:20.495506Z", + "iopub.status.idle": "2026-09-12T03:35:21.595388Z", + "shell.execute_reply": "2026-09-12T03:35:21.594663Z" + } + }, + "outputs": [], + "source": [ + "p_xs_gp = xs_problems[\"GP, growing amplitude\"]\n", + "s_xs_gp = xs_samples[\"GP, growing amplitude\"]\n", + "theta_xs = np.median(s_xs_gp, axis=0)\n", + "fine = np.deg2rad(np.linspace(2.0, 178.0, 90))\n", + "resid_xs = comp_xs.y - comp_xs.predict(*theta_xs[p_xs_gp.columns(volume_params)])\n", + "\n", + "# again the total band minus the fitted model, both in log space\n", + "band_xs = rx.predictive.total_predictive_band(\n", + " p_xs_gp, gp_xs, omp_vol.bind(fine, meta), fine, s_xs_gp, rng=5, n_draws=100\n", + ")\n", + "mu_fine = comp_xs.space(\n", + " omp_vol.bind(fine, meta)(*theta_xs[p_xs_gp.columns(volume_params)])\n", + ")\n", + "disc_lo_xs, disc_hi_xs = band_xs[0] - mu_fine, band_xs[1] - mu_fine\n", + "true_defect_xs = np.log(omp_full.bind(fine, meta)(*full_truth)) - mu_fine" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "31b28438", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:35:21.600203Z", + "iopub.status.busy": "2026-09-12T03:35:21.599182Z", + "iopub.status.idle": "2026-09-12T03:35:21.839456Z", + "shell.execute_reply": "2026-09-12T03:35:21.838438Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 600x350 with 1 Axes>" + "<Figure size 704x440 with 1 Axes>" ] }, "metadata": {}, @@ -711,23 +1032,7 @@ } ], "source": [ - "theta_xs = np.median(s_xs_gp, axis=0)\n", - "fine = np.deg2rad(np.linspace(2.0, 178.0, 90))\n", - "resid_xs = comp_xs.y - comp_xs.predict(*theta_xs[p_xs_gp.columns(volume_params)])\n", - "mean_d, cov_d = rx.predictive.gp_posterior_predictive(\n", - " gp_xs.kernel,\n", - " theta_xs[p_xs_gp.columns([xs_amp, xs_ell])],\n", - " angles,\n", - " resid_xs,\n", - " fine,\n", - " train_noise_var=comp_xs.y_err**2,\n", - ")\n", - "sd_d = np.sqrt(np.clip(np.diag(cov_d), 0, None))\n", - "true_defect = np.log(omp_full.bind(fine, meta)(*full_truth)) - np.log(\n", - " omp_vol.bind(fine, meta)(*theta_xs[p_xs_gp.columns(volume_params)])\n", - ")\n", - "\n", - "fig, ax = plt.subplots(figsize=(6, 3.5))\n", + "fig, ax = plt.subplots()\n", "ax.errorbar(\n", " np.rad2deg(angles),\n", " resid_xs,\n", @@ -737,50 +1042,57 @@ " color=\"k\",\n", " label=\"log residuals\",\n", ")\n", - "ax.fill_between(\n", + "plotstyle.band(\n", + " ax,\n", " np.rad2deg(fine),\n", - " mean_d - sd_d,\n", - " mean_d + sd_d,\n", - " color=\"C1\",\n", - " alpha=0.4,\n", - " label=\"GP posterior (68 %)\",\n", + " disc_lo_xs,\n", + " disc_hi_xs,\n", + " color=plotstyle.COLOURS[0],\n", + " label=\"GP discrepancy (68 %)\",\n", ")\n", "ax.plot(\n", " np.rad2deg(fine),\n", - " true_defect,\n", + " true_defect_xs,\n", " \"--\",\n", - " color=\"C3\",\n", + " color=plotstyle.COLOURS[1],\n", " label=\"defect: log(full truth / fitted volume)\",\n", ")\n", - "ax.set(xlabel=r\"$\\theta_{cm}$ [deg]\", ylabel=\"log residual\")\n", - "ax.legend(frameon=False, fontsize=8)\n", + "ax.set(\n", + " xlabel=r\"$\\theta_{cm}$ [deg]\",\n", + " ylabel=\"log residual\",\n", + " title=\"What the GP learned about the missing surface term\",\n", + ")\n", + "ax.legend(fontsize=8)\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "72a3ddb0", + "id": "f23a966b", "metadata": {}, "source": [ "## Takeaways\n", "\n", - "- A GP discrepancy is just another covariance term; `total_predictive_band`\n", - " propagates it to any grid from the problem alone.\n", - "- Uncorrelated model error widens the diagonal; it cannot represent\n", - " correlated mis-modelling. The GP can, and the model parameters relax.\n", - "- A sampled mean correction removes bias too, but carries no honest width\n", - " away from the data.\n", - "- On the cross section the GP recovers the missing surface absorption as a\n", - " smooth function of angle and moves `V_v` toward its true value; `W_v` and\n", - " `R_v` stay biased, because volume and surface absorption are degenerate\n", - " at one energy and no discrepancy model restores information the data do\n", - " not contain." + "- A mean-zero GP discrepancy is just another covariance term, and\n", + " `total_predictive_band` propagates it to any grid from the problem alone.\n", + "- An unmodelled discrepancy does not make the answer vague, it makes it *wrong\n", + " and confident*: sixty sigma on the toy's slope.\n", + "- A diagonal model error can only inflate. It covers the truth by making the\n", + " parameters meaningless, because independent slack cannot describe a coherent,\n", + " smooth departure.\n", + "- The amplitude is prior knowledge, and it is worth stating. A constant\n", + " amplitude has to be generous everywhere to cover the worst region; letting it\n", + " grow with $x$ keeps the fit honest where the model is good and forgiving where\n", + " it is not.\n", + "- None of this manufactures information. Where the data cannot separate two\n", + " effects — volume against surface absorption at a single energy — the\n", + " discrepancy model widens the answer rather than pretending to resolve it." ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/test/test_notebooks_index.py b/test/test_notebooks_index.py index 876ef74..2113c3c 100644 --- a/test/test_notebooks_index.py +++ b/test/test_notebooks_index.py @@ -22,11 +22,11 @@ "error_models": {2, 4, 19}, "sharing_error_models": {5}, "normalization_and_covariance_structure": {3, 4, 6, 27}, - "gp_discrepancy": {7, 8, 36}, + "gp_discrepancy": {7}, "robust_likelihoods": {9, 39}, "error_scale_and_usu": {34}, - "local_optical_model_calibration": {12, 14, 15, 16, 21, 26}, - "alpha_ca_error_model_comparison": {10, 11, 13, 18}, + "local_optical_model_calibration": {4, 12, 14, 15, 16, 21, 26}, + "alpha_ca_error_model_comparison": {10, 11, 13, 17, 18}, "hierarchical_calibration": {22, 24, 30, 35, 38}, } From 5f7bd968994f07a7901e38374a4fa23277038488 Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Sat, 12 Sep 2026 00:00:37 -0400 Subject: [PATCH 59/75] Restyle alpha_ca and add predictive draws with coverage - Theory is always drawn on a fine angle grid. The curve at theta_0 was evaluated at the downsampled data angles, which aliases the diffraction pattern and is the ragged plot the review flagged - Evidence and Bayes factors are introduced before the table, with links, and with the two caveats that matter: a Bayes factor depends on the priors, and evidences in different comparison spaces need the Jacobian first - New section (recipe 17): posterior predictive draws that carry the covariance, plus an empirical coverage curve. All three rungs over-cover in sample (0.79, 0.81, 0.78 against a nominal 0.68) while separating sharply out of sample (0.49, 0.60, 0.80) - The TODO intro is replaced, keeping the EXFOR, Oeschler, jitr and Kennedy-O'Hagan links - The general pass: smaller cells, LaTeX throughout, plotstyle with hatched bands. Every number is unchanged: Lgp still wins the evidence by 6.0 to 10.9 log units and the held-out score at -50.45 --- docs/design.md | 2 +- .../alpha_ca_error_model_comparison.ipynb | 718 +++++++++++++----- 2 files changed, 514 insertions(+), 206 deletions(-) diff --git a/docs/design.md b/docs/design.md index 412b931..e86ac14 100644 --- a/docs/design.md +++ b/docs/design.md @@ -629,7 +629,7 @@ a time unless noted. | `robust_likelihoods` | 9, 39 | emcee | Student-t versus Gaussian on three gross outliers; the iterative rejection loop, including the round that over-rejects and recovers | 54 s | | `error_scale_and_usu` | 34 | emcee | a global scale on the reported errors under both likelihoods; a USU offset on the technique we suspect | 90 s | | `local_optical_model_calibration` | 12, 14, 15, 16, 21, 26 | dynesty | a real EXFOR measurement to a calibrated potential; the unit contract, the singular guard, tempering, other drivers | 182 s | -| `alpha_ca_error_model_comparison` | 10, 11, 13, 18 | dynesty | real ⁴⁴Ca(α,α) data, a four-parameter potential, the ladder by evidence with the Jacobian, held-out backward angles | 1197 s (alongside another notebook) | +| `alpha_ca_error_model_comparison` | 10, 11, 13, 17, 18 | dynesty | real ⁴⁴Ca(α,α) data, a four-parameter potential, the ladder by evidence with the Jacobian, predictive draws carrying the covariance and their coverage, held-out backward angles scored conditionally | 1394 s | | `hierarchical_calibration` | 22, 24, 30, 35, 38 | dynesty | eight schools; a hierarchy on the physics parameters repairing a misspecified energy dependence, in sample and at a held-out energy | 937 s (alongside another notebook) | **`hierarchical_calibration` in detail.** The truth is diff --git a/examples/alpha_ca_error_model_comparison.ipynb b/examples/alpha_ca_error_model_comparison.ipynb index eaf49f1..42982a5 100644 --- a/examples/alpha_ca_error_model_comparison.ipynb +++ b/examples/alpha_ca_error_model_comparison.ipynb @@ -2,31 +2,43 @@ "cells": [ { "cell_type": "markdown", - "id": "b2919a2f", + "id": "d2bec57e", "metadata": {}, "source": [ - "# Error models for α + ⁴⁴Ca elastic scattering: a comparison by evidence\n", + "# Error models for $\\alpha + {}^{44}$Ca elastic scattering: a comparison by evidence\n", "\n", - "TODO: fix intro text\n", + "This notebook is a small study rather than a demonstration. We have one real\n", + "measurement and one optical potential, and the only thing we vary is the **error\n", + "model** — four different statements about what the disagreement between model\n", + "and data means. Then we ask the data which statement they prefer, by\n", + "[Bayesian evidence](https://en.wikipedia.org/wiki/Marginal_likelihood), and\n", + "separately by how well each one predicts angles it was never shown.\n", "\n", - "The experimental data we will use are the differential elastic as ratio to the Rutherford scattering cross sections of $^{44}$Ca($\\alpha,\\alpha$) at 29 MeV from [EXFOR F0567](https://www-nds.iaea.org/exfor/servlet/X4sSearch5?EntryID=150567),\n", - "[Oeschler *et al.*, Phys. Rev. Lett. **28**, 694 (1972)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.28.694). Oeschler *et al.* report no uncertainties, so every error model below infers its own.\n", + "The data are the differential elastic cross sections, as a ratio to Rutherford,\n", + "of $^{44}$Ca($\\alpha,\\alpha$) at 29 MeV from\n", + "[EXFOR F0567](https://www-nds.iaea.org/exfor/servlet/X4sSearch5?EntryID=150567),\n", + "[Oeschler *et al.*, Phys. Rev. Lett. **28**, 694 (1972)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.28.694).\n", + "Oeschler *et al.* report no uncertainties at all, so every rung below has to\n", + "infer its own — which is precisely what makes the comparison interesting.\n", "\n", - "See also the [`jitr` quickstart series of tutorials](https://beykyle.github.io/jitr/getting-started.html). In a sense, this tutorial is a successor to those: using `rxmc` we can craft more sophisticated statistical models, including using [Gaussian processes for model discrepancy](https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/1467-9868.00294),\n", + "See also the [`jitr` quickstart tutorials](https://beykyle.github.io/jitr/getting-started.html).\n", + "In a sense this notebook is their successor: with `rxmc` we can build more\n", + "careful statistical models on the same physics, including\n", + "[Gaussian processes for model discrepancy](https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/1467-9868.00294).\n", "\n", - "Recipes: 10, 11, 13, 18" + "Recipes: 10, 11, 13, 17, 18" ] }, { "cell_type": "code", "execution_count": 1, - "id": "391eeea7", + "id": "fe61dfe1", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T20:10:48.193687Z", - "iopub.status.busy": "2026-09-11T20:10:48.193538Z", - "iopub.status.idle": "2026-09-11T20:10:50.734882Z", - "shell.execute_reply": "2026-09-11T20:10:50.734119Z" + "iopub.execute_input": "2026-09-12T03:36:13.561234Z", + "iopub.status.busy": "2026-09-12T03:36:13.560962Z", + "iopub.status.idle": "2026-09-12T03:36:16.624613Z", + "shell.execute_reply": "2026-09-12T03:36:16.623689Z" } }, "outputs": [], @@ -37,6 +49,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", + "import plotstyle\n", "from jitr.optical_potentials.potential_forms import (\n", " coulomb_charged_sphere,\n", " woods_saxon_safe,\n", @@ -46,31 +59,35 @@ "\n", "import rxmc as rx\n", "from rxmc import terms as T\n", - "from rxmc import transforms as tf" + "from rxmc import transforms as tf\n", + "\n", + "plotstyle.use()" ] }, { "cell_type": "markdown", - "id": "4f7ec3cb", + "id": "53981e03", "metadata": {}, "source": [ "## The data\n", "\n", - "Every fourth angle of the ⁴⁴Ca set, 73 points from 15° to 174°. Ratio to\n", - "Rutherford is dimensionless, so the dataset needs no unit conversion; the\n", - "kinematics the model needs go in `meta`, and there are no errors to report." + "We take every fourth angle of the $^{44}$Ca set: 73 points from 15 to 174\n", + "degrees, enough to pin the diffraction pattern without making every fit four\n", + "times slower. A ratio to Rutherford is dimensionless, so nothing needs\n", + "converting; the kinematics the model needs go in `meta`, and there are no errors\n", + "to report." ] }, { "cell_type": "code", "execution_count": 2, - "id": "6df8e566", + "id": "5df13b6f", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T20:10:50.736588Z", - "iopub.status.busy": "2026-09-11T20:10:50.736357Z", - "iopub.status.idle": "2026-09-11T20:10:51.711832Z", - "shell.execute_reply": "2026-09-11T20:10:51.711291Z" + "iopub.execute_input": "2026-09-12T03:36:16.626738Z", + "iopub.status.busy": "2026-09-12T03:36:16.626423Z", + "iopub.status.idle": "2026-09-12T03:36:16.666804Z", + "shell.execute_reply": "2026-09-12T03:36:16.665894Z" } }, "outputs": [ @@ -80,16 +97,6 @@ "text": [ "73 points between 15 and 174 degrees\n" ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 600x400 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -103,11 +110,39 @@ "data = rx.Dataset(\n", " angles, ratio, np.zeros(ratio.size), label=\"44Ca(a,a) 29 MeV\", meta=meta\n", ")\n", + "x_fine = np.deg2rad(np.linspace(10.0, 178.0, 220))\n", "print(\n", - " f\"{data.n} points between {np.rad2deg(angles.min()):.0f} and {np.rad2deg(angles.max()):.0f} degrees\"\n", - ")\n", - "\n", - "fig, ax = plt.subplots(figsize=(6, 4))\n", + " f\"{data.n} points between {np.rad2deg(angles.min()):.0f} and \"\n", + " f\"{np.rad2deg(angles.max()):.0f} degrees\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "db94063b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:36:16.668663Z", + "iopub.status.busy": "2026-09-12T03:36:16.668469Z", + "iopub.status.idle": "2026-09-12T03:36:17.964437Z", + "shell.execute_reply": "2026-09-12T03:36:17.963545Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 704x440 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", "ax.plot(np.rad2deg(angles), ratio, \"o\", ms=3, color=\"k\")\n", "ax.set(\n", " xlabel=r\"$\\theta_{cm}$ [deg]\",\n", @@ -120,45 +155,35 @@ }, { "cell_type": "markdown", - "id": "24f54e00", + "id": "9289738b", "metadata": {}, "source": [ "## The optical model\n", "\n", - "As in the `jitr` quickstart: a Woods-Saxon potential and the Coulomb\n", - "potential of a uniformly charged sphere of radius `1.3 A^{1/3}` fm, radii in\n", - "units of `A^{1/3}` fm. Here the real and imaginary volume terms share one\n", - "geometry, so four parameters are free: `V`, `W`, `r`, `a`. Priors follow\n", - "the quickstart's typical α-nucleus values, truncated at zero. Thirty\n", - "partial waves are converged at this energy; the default basis size is\n", - "within a few percent of the fully converged solver at the deepest minima,\n", - "which is far below the error models below, and two hundred times faster." + "As in the `jitr` quickstart: a Woods-Saxon potential plus the Coulomb potential\n", + "of a uniformly charged sphere of radius $1.3\\,A^{1/3}$ fm, with radii in units\n", + "of $A^{1/3}$ fm. The real and imaginary volume terms share one geometry, so\n", + "four parameters are free: $V$, $W$, $r$, $a$. Priors follow the quickstart's\n", + "typical $\\alpha$-nucleus values, truncated at zero (recipe 13).\n", + "\n", + "Thirty partial waves are converged at this energy, and the default basis size\n", + "sits within a few per cent of the fully converged solver at the deepest minima —\n", + "far below any of the error models below, and two hundred times faster." ] }, { "cell_type": "code", - "execution_count": 3, - "id": "eeba4535", + "execution_count": 4, + "id": "91f59c49", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T20:10:51.713216Z", - "iopub.status.busy": "2026-09-11T20:10:51.713063Z", - "iopub.status.idle": "2026-09-11T20:11:02.687574Z", - "shell.execute_reply": "2026-09-11T20:11:02.686769Z" + "iopub.execute_input": "2026-09-12T03:36:17.966669Z", + "iopub.status.busy": "2026-09-12T03:36:17.966468Z", + "iopub.status.idle": "2026-09-12T03:36:17.975200Z", + "shell.execute_reply": "2026-09-12T03:36:17.974050Z" } }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 600x400 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "A13 = 44 ** (1 / 3)\n", "\n", @@ -188,67 +213,96 @@ " coulomb=coulomb,\n", " lmax=30,\n", ")\n", - "theta_0 = np.array([150.0, 20.0, 1.4, 0.5])\n", - "on_data = omp.bind(angles, meta)\n", - "\n", - "fig, ax = plt.subplots(figsize=(6, 4))\n", + "theta_0 = np.array([150.0, 20.0, 1.4, 0.5])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c3673d96", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:36:17.977414Z", + "iopub.status.busy": "2026-09-12T03:36:17.977199Z", + "iopub.status.idle": "2026-09-12T03:36:33.449109Z", + "shell.execute_reply": "2026-09-12T03:36:33.448130Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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X9hNCCCGE6BPf0A1Q19q1awEAly5dUvj4zZs3cfToUVy4cAFhYWEAgB9//BFjx45FQkICwsLC0K1bN5w4cQIvvfQSrl+/DhcXF9jY2OjtPRBCCCGE6IvJBHmqnDt3DtbW1hg4cCC7LTw8HDweD+fPn0dYWBiWLl2KZ555Bn/99ReuXbuGlStXKj1maWmp3HBvVlaWztpPCCGEEKJNZhPkZWVlwc3NDRwOh93G5/Ph7OzM1podNGgQbt26hWvXruH7779HSEiI0mOuXLkSX375ZZPtRUVFsLa21u4bMHGywTBRjM6Reug8qYfOk3rM4TyFhITgp59+woQJE3T2GuZwnnTNWM6Rq6ur2vuaTZAHAFxu0ymGXC5Xrt6cj48PpkyZotbxlixZgsjISPZ+VlYW+vfvD2dnZ41Osrqqa0XYczcbPC4H/Pp/rjYW8LS3RDtnawh4xj2FUhfnxNzQOVIPnSf10HlSj6mfJ5FIBDs7O81+3Pl8HDlyBKNGjVL7OaZ+nvTB1M6R2QR5np6eyM/Pl9smFotRUFAAT0/PFh3TwcEBDg4O2mieWoqqavHC5hsKH3OyFuDpzp54oZcPngr1kOuxJIQQQmSJRCJK+E9MZ3WtKoMGDUJFRQWuXbvGbjtz5gxEIhEGDRrUqmNHR0fDw8MDPXr0aG0zlRKJmWYfK66qxabr6Zi49gqeXncFGSVVOm0LIYQQSRaGmJgYLF26FGvXrkVtba1OX+/Ro0cYPXo07O3t0bVrV/zvf/9rsk9AQAD4fD4sLCwQGBiI9957D1VVDb8JXl5eAIDx48eDz+cjNDRUrecR88NhZMcyTcClS5cwaNAgPHz4EB07dpR7bOjQoeBwONixYwfEYjGmT58OBwcHHDt2TCuvnZ6eDn9/f6SlpcHPz08rx5RVXlOHTdfTIRIzqBMzENaJkV8hRFpxFY4k5qGwsuHLxdGKj21zemNCWMt6KbWtoKDA5Lqx9Y3OkXroPKmHzpN6WnOehEIhxo8fj1OnTrHbwsPDceTIEQgEAi21sIFYLEa3bt3QqVMn/PzzzygvL8ecOXNw/fp17N69G1OnTgUg6aVjGAYikQgJCQl4+eWXMWbMGERHR7OP8/l8HD58GKNGjQKHwwGPx1P6PPo8qWaS54gxEStWrGAsLS0ZCwsLBgBjYWHBWFpaMuvXr2f3yc7OZp555hn2sWeffZbJy8vTWhvS0tIYAExaWprWjqmu2joRs+dOFuP/f0cYLNnHYMk+xvKDA8yJh9p7f62Rn59v6CYYPTpH6qHzpB46T+ppzXlas2YNA6DJv5iYGC22sMH+/fsZS0tLuTZfvnyZAcDs3r272ef9/fffTLt27eS2AWCOHj2q9PVkn0efJ9VM8RyZzJy8qKgovPPOO022y15NeXp6Ys+ePexCC3Oat8bncfFMVy+EB7nitV23seNWJmrqxJjyxxUce20QBrR3NnQTCSHErCiqrAQASUlJOnm9+Ph4BAYGyvUW9enTBzweT26/3bt347///S8ePHiA0tJS9jdPLBYrXICozvOIeTKZOXk8Hg9WVlZN/jX+8AOS4E6bAZ6+5uSpw9FagC2ze2NGd28AQHmNCM+sv4r88hoVzySEEKKJwMBAhduDgoJ08noMwyj87ZLdFhcXh1mzZiEyMhIJCQmorKzEvn37IBaLlQZrLX0eMW0mE+QZUlRUFHJzcxEXF2fopgAAeFwOtszujXEh7gCAnLIavP7XHblUMYQQQlonIiIC4eHhctvCw8MRERGhk9cLCwtDcnIyioqK2G23bt1CXV0de//SpUvo0KED5s+fD09PT1hYWODGjaZZGfh8vlzwpu7ziHmhIM9EWfC52DK7FzztLQEAu25nYdvNDAO3ihBCzIeFhQWOHDmCmJgYfPTRR4iJidHZogsAmDhxIvz9/fH6668jPz8fT548waJFi+T2CQkJwePHj3HixAnU1NTgwIED+O6775ocq127drhy5Qob6Kn7PGJeKMgzYW52loiZ2Z29/+bfd5FTRsO2hBCiLQKBAJGRkVixYgUiIyN1FuABkt63PXv24MmTJ/D29saoUaPw/PPPw9HRkd1n5MiRWLZsGZ5//nnY2dnh008/VThfPTo6GmvXroWlpSVCQ0PVfh4xLyaXQsWQdJ1CpaVe2X4L66+mAQAWDmqPVTO6q3iG9pnk0nI9o3OkHjpP6qHzpB5zOE8ikQhcLrfZueZMfVoUPr/pWkqxWAyGYRTOX5d9njmcJ10zxXNEPXlqMKaFF4pEP90ZjlaSP+6Yy6m4n1Nm4BYRQgjRFh6Pp3QxIYfDURjgAZLSnooCPFXPI+aBgjw1GNvCi8ZcbS3wyZgQAJKqGR8eTDBwiwghhBBiaBTkmYlX+/vAlS8CAOy7l4MTD3IM3CJCCCGEGBIFeWZAKBTimckTUbDvB3bbs9//pfMai4QQQggxXhTkmYHY2FhJbcX7Z4H8FABAsb0/lq3aatB2EUIIIcRwKMhTg7EvvGgovcMAl3aw2/9Mpf9eQgghpK2iKEANxr7wQq70TuIFID8VAPCEccLZ5AIDtYoQQgghhkRBnhmQL73DAJe2s499eSTRIG0ihBBCiGFRghwzIC29Exsbi6SkJHQIDMIPhba4n1uB4w/zcf5xIYZ0cDF0MwkhhLRAQkICOBwOQkNDDd0Ugzt79izCwsLg5uam1v537tyBtbU1OnbsqOOWGSfqyTMTsqV3Xl0Qic/GdmIf+/LIAwO2jBBCiLpOnTqFoqIiuW1ff/01vvnmGwO1SLnz588jNzdX68fNycnBhQsXmmyfMGECzp07p/Zxli5dil9//bXF7aipqcGpU6dQWVmp1v7Xr19HeHg4bGxsEBwcjH///bfFr60NFOSZqed6+iDUww4AcDQxHxdTChXuJxQKERMTg6VLl2Lt2rWUdoUQQgxo5MiRuH79uqGbobZnnnkGJ06c0Ppxjx49iunTpzfZPmzYMLV78bQhLy8PI0eORGpqqsp9r127hnHjxmH+/PnIyMjAzJkz8cILL6C8vFwPLVWMhmvNFI/LwSdjgjFn600Akrl5/746UG4foVCI8ePHS9Kv1NuyZQuOHDmi0yLchBBCmjp//jwAIC4uDnw+H9bW1hgwYAD7uFgsRnJyMiorKxEWFqbwezoxMRElJSUICQmBo6Njs68lFApx4cIF9OvXDyKRCDdu3ECXLl3g4+PTZN+8vDw8evQIHh4eCAoKYrdfuXIFtbW1iI+Px6lTp8Dn8zF06FD28fz8fDx69Ag+Pj5o164du72mpgYXL15E//79IRKJkJiYCB8fH3h7ewMACgsLkZCQAKFQyP4+BQQEICAgAB9//DE7bC0SiXD27FkAAJ/PR0BAgMZ15TMyMvDw4UMAgL29PUJCQmBvbw9Acr4vXrzIvldHR0e0b98ePXv2bHIcsViMefPm4Z133sHcuXMBAMuWLcOKFStw/vx5jB8/XqN2aQ1DVPrvf//LuLu7My4uLgwAJi0tzdBNUkudSMyErDjOYMk+Bkv2MZdSCuUeX7NmDQOgyb+YmBiNXys/P19bzTZbdI7UQ+dJPXSe1GNK52nKlCkMAKZ79+7MiBEjmBdffJFhGIaZPXs2M2TIEKZ79+5Mz549GR8fHyYkJETut+jRo0dMr169GF9fX6Zv376Mvb098/nnnzf7WllZWQwAZu7cuYybmxvTtWtXxsLCgvnss8/k9nvvvfcYKysrpmfPnoyjoyMTHh7OFBQUMAzDMHPnzmX4fD4TFhbGjBgxgpk0aRLDMAxTV1fHvPnmm4yDgwPTt29fxsvLixkxYgSTnZ3NMAzDpKWlMQCYl19+mfHx8WH69OnDWFpasu29fPkyExoayggEAmbEiBHMiBEjmPXr1zMMwzC2trbM7t27GYZhmMrKSvbxwYMHM05OTsy4ceOYkpIStv2TJk1i3n777WbPw99//80eo2fPnoyNjQ3z448/MgzDMDU1NczAgQMZAEy/fv2YwYMHN3usPXv2MJaWlnKvzTAMw+VymS1btjT7+rpGQZ4GpB9MUwnyGIZhNl5NZYO8CWsuyT320UcfKQzyPvroI41fx5S+SA2FzpF66Dyph86TekztPAFgjh49Krdt9uzZjIWFBXPpkuQ7vKamhunbty+zePFihmEYRiQSMV26dGE+/vhjRiQSMQzDMElJSYyLiwuzf/9+ha8jDfJ69+7NlJSUMPn5+cyJEycYLpfLXLx4kWEYhvnrr78YKysr5tq1awzDMExBQQHTpUsX5tVXX2WP4+rqymzbtk3u2F999RXTs2dPJi8vj23vtGnTmOeff55hmIbf0ilTpjDV1dUMwzDMwYMHGS6Xy6SmpjIMwzCbNm1iPD09m7RbNshrrKysjBk0aBDz8ccfs9tUBXmNXbx4kbGysmISEhLk2pqQkKD0szR79mymZ8+ezNGjR9l/Bw8eZAAwBw8eVPv1tY2Ga83cC7188X9HH+JRfgUO3c/FldQi9G/nDKBRfj0Zst3xhBBiLob+cg7pJdV6f10/RyucWzxU9Y5KjB49mh26tbCwwLhx49ihxDNnziA+Ph4//PADLly4AEbSgYNevXrh0KFDmDx5crPHfe+99+Dg4ICCggKMHDkSI0eORGxsLAYOHIj169fj2WefRZ8+fQAALi4u+OCDD/Dqq69i1apV4HIVT+tftWoVZs+ejfv377NtGTBgAP7zn/80eW1LS0sAwFNPPQUul4uEhAT4+/trdG5ycnLw5MkTVFZWolevXuwQrrpEIhGSk5ORnZ0NkUgEHx8fXLhwQaPVzOfPn0dJSQleeukldltNTQ0AyB0nJycHAoEALi76yXhBQZ6Z4/O4+GRMMF7afgsA8H9HEnEgUvJFERERga1bt8rNyQsPD0dEREST41x6UoS1l1LxqKACWaXVcLIWYGZ3H7zY2xc+jlb6eCuEENIq6SXVeFJUZehmtIiHh4fcfWtra3bFZ2JiIvh8Pr7++usmz1MVTISEhMjd79SpEx49egRAUk1p1qxZco+HhoaipqYGmZmZCue/VVZWIiMjA//++y8uX74s91ivXr1QXd0QZMu+Jy6XC0tLS7VXsQJAdXU1XnzxRfz777/o1KkTHBwckJWVBYZh1D7GpUuXMHv2bFRWVqJ9+/awsrJCXl4esrKy1D6GUCjEkydP8Ndff2HatGns9p9//hnffvstAgMDwTAM5s6diyNHjqCurg6vvvqqXlZMU5DXBszu7YvlRxORVFCJgwm5OPkoHyM7ujXJrxcUFISIiAi5ybyXnhTho4MJOJ3UtHLGldRifPRPAr56qhMiezjr8y0RQojG/Ax0Qarr17WxsYFYLMahQ4dgbW2t0XPLysqa3HdycgIAODo6Knxc+pgiFhYW4PF4ePvtt/HKK69o1BZN/e9//0NcXBwyMjLg7Cz5Dfrmm2+wdu1atY/x+uuvY+rUqfjuu+/A4XAASAJZTQLFyspKMAzDtkFq165deO655wBIVgvfu3cPaWlpqK6uRvfu3REREYGwsDC1X6clKMhrA/g8Lj4dG8L25kVsu4m490bA2caCza/XWJ1IjOVHH+KrY4kQN/qsu9oIUFApSbUiEjNY+s99JGZ7IuYFV/C4HF2/HUIIaZHWDpnqg7W1NYRCoUbPGT58OABJdoTG3+elpaVwcHBo9rmHDx/GyJEjAUh6pE6cOIF33nkHADBgwAD8+++/+O9//8vuf/DgQYSGhrIrUG1sbOTay+fzMXz4cGzatKlJkKeqLbIaH1eRlJQUdOnSRS64+ueff9Q6vuwxhgwZwgZ4iYmJbE+mtB0AlLbF0dER1tbWyMvLY7edPXsW169fx5YtWwBIegynTp0KS0tLWFpaYsKECbh06RIFeUQ75vX1w/ZbGfj3fh7Siqvxxl93sHVOb/aDLetRfgXmbLmBy6nF7LZu3vb4aFRHPNvdG5Z8Hp4UVuJ/l57gmxOPwDDA+hs5qGFuYvPsXgqPSQghRLVu3bphw4YNsLCwgL29vVwKlea0a9cOX3zxBRYvXozU1FQMGDAA6enp2Lp1KxYtWoSZM2c2+9zVq1fDzs4O7du3x7Zt2wAACxcuBAB89NFH2LRpE5577jnMmTMH169fx6+//oq//vpLrr1bt26Ft7c3rK2tMXToUKxcuRLh4eGYMmUK5s6dC7FYjNOnTyM/Px9//vmnWueha9euKC4uxqpVqxAWFsamUJE1ZswY/Pbbb/jxxx8RFBSErVu34urVq/D19VXrNaTH+OKLL8DhcFBWVob/+7//kxvNcnFxga+vL1atWoVx48ahQ4cOTVKocDgcTJs2Dd9++y2Cg4ORmpqKhQsXYtWqVez8wtLSUrn0NA4ODigtLVW7nS1FyZDbCA6Hg/XP94SbrQUAYPutTCw7dB91IjG7j0jMYO2lJ+j5/Wk2wBPwOPjv5DDcWjICL/b2gyWfBwBo72KD/0wMw59z+8CSL/kYbb2ZgVe+30IJlQkhpIViY2Ph4OCAb7/9Fj///DMAICwsrMkigPbt26N3797s/U8++QR79+5Famoqfv75Z9y6dQv/+c9/lAZ4APDXX3+huLgYGzduhL+/P86fPw87O0kifU9PT1y7dg1eXl749ddfkZSUhMOHD+Ppp59mn//7778jMDAQ0dHR7Byznj174vbt2wgLC8Mff/yBPXv2oGfPnmyvlqWlJUaMGMH2kknJJjoOCQnBli1bcPz4cXz55Zfs3HHZfSZNmoRNmzbhzJkzWL16Nbp06YI//vhDLjDu1q2b0pJm69atw9NPP401a9bgn3/+wcqVK/Hyyy+jffv27D67d+9GRUUFfvjhB2zYsEHhcX7++WcEBQVh6tSp+P7777F27VrMmzePfdzHxwcpKSns/eTkZIU5CbWNw2gy8NxGRUdHIzo6GiKRCIWFhUhLS9M44aKx2Hs3G1PXX2XvDwt0wfM9fFAjEmPVhSd4lF/BPhbmaYctL/ZGLz/lCTX7zFyIux3rJ5vWVGLAw604e2AXJVRupKCgAK6uroZuhtGj86QeOk/qofOkWHZ2Nry9vXHnzh107dqVzpMaWnOOHj9+jH79+mHNmjUoLy/H+++/j0ePHqk9fN1S1JOnhqioKOTm5iIuLs7QTWm1Z7p64fdnu8GCJ/mvP5tciEW77+K9ffFyAd6iIQG49s4wpQEeILnqvLtvPXCrfh6EpQ0uOw3C+g2xOnsPhBBCiCnp0KEDNm7ciDVr1mDnzp3Yt2+fzgM8gObktUmvDw5Af38nPL/pOpIK5JerTwrzwNLRwRjSQb0cPsnJyZIbZ9YD7XsAzr6AXxf88/ABXtV2wwkhhGiFhYUFRowYAVtbW0M3pc2YOHEiJk6cqNfXpCCvjerj74S7UeE497gQhZW1qKoVoa+/E7p42Wt0HDahcl0NcPQ34DlJssur/CCIxQy4tNqWEEKMjouLi1yOVGKeaLi2DbMS8DAmxB3P9fRBRD9/jQM8QJJQOTw8XHIn/S6QdhsAkFnDx9931E8mSQghhBDtop480irShMq//fYbcnJyUOflh+9SJY/939FETO/mTb15hBBCiAFQkEdaTSAQYO7cueyqo6u/X8DppALcySrDvnvZmNrN28AtJIQQQtoeGq4lWvfZ2IZaiKsvPjFgSySEQiFiYmKwdOlSrF27lvL4EUIIaROoJ49o3ciOrujkbosHeRU4kpiHJ4WVaO9io/qJOiAUCjF+/Hi5CcZbtmzBkSNHKI8fIYQQs0Y9eUTrOBwOIgdIsoUzDLD+aprB2hIbG9tkBdmpU6cQG0t5/AghhJg3CvLUEB0dDQ8PD/To0cPQTTEZ8/r6gV+/4OKPK6kQiQ1TWIXN49dIUlKSnltCCCGE6BcFeWowp4oX+uJhb4lnunoBANKKq3EsMc8g7WDz+DUSFBSk55YQQggxZ9tvZuCH00kG69RQhII8ojORA9qxt9ddSdXLaybklOHzfx9g4E9nMSHmEqpCRmLQ2Ely+4SHhyMiIkIv7SGEEGL+cstq8Obfd7BkXzwG/3IOVbUiQzcJAC28IDo0NsQdvo5WyCipxsGEXFTU1MHWUjcfOZGYwZt/38H/Gq3m/fd+Hix7vY5Xn3oeLnnxCAoKQkREBC26IIQQojXv7L2HwkpJ5obOnvawFvAM3CIJ6skjOsPjcvBsd0mOvEqhCP8+yNXJ69SJxIjYdlMuwOPI5F+uqRNjbaYTus18E5GRkRTgEUII0ZqD8TnYdjMDAOBhZ4Hvp3Q2cIsaUJBHdGpG94ZEyDvjtF/mjGEYRGy7hS03JH9gFjwufnymC3K+GIeED8LZIWMxA8zZehO74jK13gZCCCFtU1l1HV7/6zZ7/5dp3eBiY2HAFsmjII/o1OAAF3jZWwIADsTnaH2ewpYbGdhafwVlxedi3yv98PbwQLjbWSLU0x5rZnbHkhGSxRcMA7z+1x0UVQq12gZCCCFt0y/nHiOtuBoAMKWLJ2b2MK4KTxTkEZ3icTmYXl/WrEIowuH72huyzS+vwbt777H3d0b0xfhQD7l9OBwOvnu6M17s5St5ToUQnx9O1FobCCGEtE1VtSL8eFaSpovP5eDnqV3B4RhXrXYK8ojOzeihmyHb9/fHI79C0is3t48fJnf2VLgfh8PB91M6w75+0cdv5x/jdmap1tpBCCGk7Vl/JQ155ZLfoNm9fQ1W2UkZCvKIzg3r4AJ3O8kchf3xOaipa/2Q7bnkAsReSwcAuNoIVE509XKwwhfjJTV1xQzw9p67rW4DIYSQtqlOJEb0qUfs/Q9HdTRga5pHQR7ROT6Pi2e6SBIjl9XU4dSjglYf86tjD9nb30/pAnc7S5XPWTy0A8I87QAAp5IKcDO9pNXtIIQQ0vbsuJWJlMIqAMDUrl4I87Q3cIsUoyCP6IW0+gUA7L2X3apj3UgvxuEHkgoaIe62mNPHT63nCXhcfBDecLW1+mJKq9pBCCGkbVotk7LrIyPtxQMoyCN6MjrYDTYWkuSQ++7lgGFaXvblmxMyXeQjO4LHVX+i6/O9fOBkLcmTt+VGBkqra1vcDkIIIW3P44JKnHtcCADo5euAAe2dDdyi5lGQR/TCWsDD+E7uAICMkmrcaOFQ6YPccuy6LVm84edopXYvnmw7XuoneU6FUITN1zNa1A5CCCFt05Yb6eztuRr+BukbBXlqiI6OhoeHB3r06GHoppg06bw8oOVDtj+cSYa0E/D98CBY8DX/CC8cFMDeXn0xpVW9ioQQQtoOhmGw6bokyONygBfq03MZKwry1BAVFYXc3FzExcUZuikmbVKYB6Qjq3vv5mj8/LLqOvYKyslawFaz0FQnDzuM7OgKALiTVYZrabQAgxBCiGpX04qRmFcBABjXyR1eDlYGbpFyFOQRvXGzs8SQDi4AgNtZpUgprNTo+dtuZqC8RpJ+ZV5fP9jW571riVf6NwSIe+5qv9waIYQQ87PpWsNQ7Zzexj1UC1CQR/RMdsh2n4ZDtmsuNaxmenVg+1a1Y2KYB7tgY989zXsVCSGEtC1ihsHO+jnhthY8TJXJGmGsKMgjejWlS0NVCk2GbK+nFeN6/WKNIQHO6OLVupxELjYWGFbfq3g3uwzJBRWtOh4hhBDzdju7AjllNQAkHQWtGU3SFwryiF4Fu9uxCYlPJxegqFKo1vPkevEGta4XT0o24NxPvXmEEEKUOJZUxN6eGKq4jKaxoSCP6JVQKET7WskwrUjMYP891fPhyqrrsPWmJNWJk7UAM3v4aKUtU+SGjinII4QQ0rzjScXs7adC3Q3XEA1QkEf0RigUYvz48fj3ty/Zbe+v2onaWuUJiRsvuLAW8LTSniA3W3bYV5NexZYQCoXYuHEjli5dirVr16p8z4QQQoxHfnkNrmWUAQB6+zka/apaKeMfUCZmIzY2FqdOnQLAASqKAFtn5Nn4Y+36WLz+amSzz9PmgovGpnTxxL3sMojEDA7dz8WLOlgtJQ1uJe9dYsuWLThy5Aj4fD5uZZQitbgKDMMgwMUGPX0dtd4GQgghLXckMQ/SjKoTQz0M2hZNUJBH9CY5Obn+FgMkXwG6jQcsbXA66TFeb+Y52l5w0djkME+sOC4pk3b8Yb5OgryG4LbBqVOn8PsfG3Heugd2xskPWT/f0we/TusKNztLrbeFEEKI5g7dz2VvTzChII+Ga4neBAYGNtx5dJm9mWobqGBvCdlevNe0tOBCVg8vG1hwxACAA7ce62QYtSG4leEVjE8THZsEeACw41Ymun53GueSC7TeFkIIIZoRixn8ez8PAOBsLTDqWrWNUZBH9CYiIgLh4eGSO6m3gMpiAMCNClsUVDSdD1dSVSu34GKGlhZcSAmFQkyeOAHCJ3cAALlCPkZMnKr1QE8uuAUAK3tgyscog6Snzt3OAv+ZGIr/TAyFh50FACCnrAbTY68hq7Raq20hhBCimbjMUuTX/0aN6+TO5lg1BRTkEb2xsLDAkSNHEBMTg4+i3scEfwEAoKZOjA1X05rs/+OZZHbBRYQWF1xIscOoGfHstotPihEbG6vV15ELbgFg1GuAnaSs2oggV9x5PxxLRwdj6ehg3I0Kx9gQNwBAXrkQc7bchEhMtXUJIcRQzqcUsrelJTFNBQV5RK8EAgEiIyOxYsUK/L5wCjj1F0SrLqRALBPMFFUKsfKMZJjTgsfFeyOCtN4Wdhg1/V7DRt/OSEpK0urrSIPbH374AVOiooHQ4QAADzsL7JzXB572DXPv3O0s8ee8vghwsQYAnHiUjxXHH2q1PYQQQtR3/nFDkDckwMWALdEcBXnEYAJcbDApTJJQMqmgEkcT89jHfjiTjNLqOgDAqwPbwd/ZWuuvzw6jZj0ARJLXgm8XBAVpP6AUCASY9vyLOGfZld22ZmYPuCtYXOFkLcD2OX3Arx8S+L+jiUgvrtJ6mwghhKh24YkkCbKDJQ+dPbW7+E/XKMgjBvX64IbFFB8fuo/iqlok5ZYg+vgDAACfw+D9EQE6eW12GLWuBsit773z6IBpz8/Wyetti8tFYaVkvt+cPr54RkndwwHtnfHx6GAAQK2IwX9Pard3kRBCiGrpxVVILZJcZPfztQfXhObjARTkEQMb38kDndxtAQA30ksw9JdzCP36MKrFko9m3bV9eGnGFJ2sepWdI9jfV1JqDRwurtYnvNQmkZjBmmsNK2k/GROi8jnvjgiEg5Uky1HMpSfIpkUYhBCiV7JDtf39TKsXD2hjQd6xY8cQGRmJyMhI3L5929DNIQB4XA72vNwP3g6SYct7OeWo49cPzRZnAVd24tSpU1pfDCElnSP4ccRUdttZHaQuORCfg8dFkiBtUpgHOnnYqXyOk7UAbw3tAACorhPju1PUm0cIIfp0PqWhXm1/PwcDtqRl2lSQ5+3tjYEDB+LWrVtITU01dHNIvVBPe5x5cwj8nWTKxCScAja/C1SVAoDWF0M0NrRDw2TaczJXbtry45mGXHnvDm8+L2Bjbw/rAFsLyariVRefIL+8RuttI4QQotiF+pW1PC4HvX1UX5wbmzYV5HXp0gWRkZEICAgwdFNIIx3dbHHxraGY6FoO7PkKOLQSEFayj+tiMYQsV1sLBLtJho1vZpTKrfRtrduZpTiVJOkd7O7tgFHBbmo/183OEm8MDgAAVApF2HYzU2vtIoQQ0rzymjrcypR0NPT0cWAvuE2J0QR5ZWVlWL16NXr37g03Nzc8fvxY4T6LFy9GYGAggoKC8O6776KysiEQ+Pvvv9nhWNl/P/74ox7fCWkpX0dr7H7/WYS3s5HbHh4ejoiICJ2/fq/6mrFlNXVILqxUsbf6dtzKYG8vHhoADkezibuvylT62HwjXWvtIoQQ0rwrqcVsntIhHUwrdYqU0dSuXbRoEaysrPDaa69h4cKFEIlETfaZPXs2kpOTsWPHDohEIsybNw+ZmZnYsWMHAMDf3x8DBw5s8jx/f3+dt59oh3QxRGxsLJKSkhAUFISIiAgIBAKdv3YvXwf8GSfpKbuRXoKO9T17rbX7bjYAgMcBpnf31vj5Hd1sMbC9My49KcKV1GIk5pUjxN30hg0IIcSUXEltmI832IRKmckymiBvw4YN4HA4uHTpksLH79y5g/379+PMmTPo168fAOCnn37CxIkT8dVXXyE4OBj9+vVjHyOmS7oYQt96+zmyt29mlOC5nq0vo/YgtxwJOeUAgCHtHeFiY9Gi48zp7YtL9bmatlzPwJdPdWp12wghhDQvrn6oFpD+PpjenGijGa5VNYR15swZWFlZYciQIey2MWPGgMvl4uzZs2q9xu3btxEZGYnr16/jl19+wXvvvad0/9LSUqSnp7P/srKaFpMn5kM6XAtIevK0Yfedhs/MpJCWd/c/19OHTY68+UY6GIZKnRFCiC7FZUmCPFsLHoJctTOyo29G05OnSmZmJtzd3cHlNsSlAoEAzs7Oagdfzs7OGDhwIDuka2Njo3T/lStX4ssvv2yyvaioCNbW2q/AYMpKS0tV72TkuAB87C2QWSbE9bQi5Ofnazx/rrGdtxrm0A31sUBBQcvSs3ABjAx0wtFHRUguqMSRO0/Q19f0cjapwxw+S/pA50k9dJ7UQ+dJXlWtCA9yJaMwYe7WKCoqNJpz5Oqqfv1ckwnyGIYBj9d0ZQufz4dYLFbrGP7+/hoNAy5ZskRu/6ysLPTv3x/Ozs4aneS2whzOSR9/Z2TG56Cgqg7VfFv4ObU8mM8oqcKNTMmXRD9/J3TycW3VOXplYAccfSQZsj38uALjuwe0+FjGzhw+S/pA50k9dJ7UQ+epwbW0YkiTLPRp1/DdbWrnyGiGa1Vxd3dHfn6+3DaxWIyCggK4u7vr5DUdHBzg5+fH/vP21nzSPDEt2hyy3Xs3h709rVvzJczUIRQKkXflELiQXNAcup+j4hmEEEJaSnY+Xg8f00uCLGUyQd6AAQNQXl6OW7dusdsuXLiAuro6DBgwwHANI2al8eKL1jjyIJe9/UyXlgd5QqEQ48ePx1sLF0CcehcAcD+3Akl5uh06EAqFiImJwdKlS7F27VqdlJYjhBBjJBfkeVOQp3ODBg1C3759ERUVheLiYhQWFmLp0qUYOnQoevXqpdPXjo6OhoeHB3r06KHT1yGG18u34Y/5RiuCPLGYwdn6yhle9pYI82x5ypPY2FicOnVKciflBrv9yw37W3xMVaSB5auvvopvvvkGCxYswLhx4yjQI4S0CXGZDd//3SjIa72VK1fCzc0NTz31FACgf//+cHNzw+bNmwFIVt/+/fffYBgG7u7u8PT0hK2tLf7880+dty0qKgq5ubmIi4vT+WsR/ZPtsTq8aytcbSQ5+VrTk5eQW47CSklANDzQtVULOJKTG0qiyQZ5l3N0F3DJBZb1dFlDmBBCjAXDMGxPXqCrDeytTGb5QhNG0/LXX38d8+bNa7Ld3r5hBaG/vz+OHTuG6upqcDgcWFpa6rOJxAxJe6xkAxrnyF8Ah/ZIK65GYaWwRbntziQ3rKIdHti6TOmBgTK1bvNTgPJCwM4FaRxn1IrEEPC0f60mF1jK0HUNYUIIMbTUoiqUVNcBMO35eIAR9eRZW1vDzc2tyT9FgZyVlRUFeEQrFPVYFT28xd6WJjLW1JkkmSAvqHWrsSIiIhAeHt6w4clNAECVmIvLT4oUP6mV5AJLGbquIUwIIYZmLvPxACMK8owZzckzXwp7rArT2JvxOWUaH5NhGJxJlszHc7YWoItn6/LZSUu9xcTE4KOPPsKCsX3Yxw4/yGvVsZvTJLCE/moIE0KIIUmTIAOm35NnNMO1xiwqKgpRUVFIT0+nOrhmRmGPVUHrgrzHhZXILK0GAAzt4AIut3UJlQH5Um8FFUKs/fwwGAY4kpiH5RNCW338xgxZQ5gQQgDJAjZtfH9q6rZc+hRHJXsaPwrySJsWERGBrVu3yg3ZDglrj/P1t+OzNR+uPZNUyN4eHqj9xJmuthbo4e2AW5mluJFegqpaEawFTROFt5ahaggTQtouYZ0Yqy6k4NuTj1BQUYtOHrbo7++Mz8eFwN9ZP5WmEvMqAADWAi7a6+k1dYWCPNKmNddj1e7rk8guq2lRT57cooug1i26aM7gABfcyixFnZjBtbRiDNNBMEkIIfp0K6MEMzdex6P8Cnbbnawy3Mkqw+EHuTi2cBA6ebQ8HZU6GIbBowLJ63d0szVIT6I20Zw8NdCcPPMm7bFasWIFIiMjIRAI0Ll+Hl16STVKqzVLVXKuPj+erQVProKGNg0OcGZvX0jRzeILQgjRl+zSakxed4UN8LgcoJu3PawFkjAlvaQaw347L5e/ThcyS6tRKRQBAILdbHX6WvpAQZ4aKE9e29NZJnmxJitsS6pq8bD+S6qvv5NO0psAwCC5IK9QyZ6EEGLchHVizNx4HRklkrnMA9o54dZ7I3D7/XCkfzYW/ds5AQDyyoWYtv4aaupEOmvLw7yGXsRgN932GuoDBXmEKNDZq2FFrCZDtrdkrjJ766gXDwA6uNjA016SRujikyIwDKOz19IUlUMjhGjiw4Px7AhIgIs1DkYOYKtMuNhY4Nhrg9jRi8eFlfjtfIrO2vJQZqg42J168ggxS7I9efEa9ORdT5cJ8vx0F+RxOBz2Sy+vXIikgkqdvZYmqBwaIUQTD3LL8fPZxwAkCx12v9QPrrbyCejtrfiImdkD0ulxXx19iKJKoU7aI9+TR0EeIWaps0xuuwQNevJuyAR5fXQY5AHA4PYNizqMZciWyqERQjTxxeEHENcPRHw1IRQ9mxkB6exlj8gB7QAARVW1+M/xRzppD/XkEdIGuNtZwq3+arIlPXm2FjyEuOt2PscgI1x8QeXQCCHqupNVih1xmQAAHwcrvD44QOn+X47vBFsLSbqon88+Rm5ZjdbbJA3y7Cx58LI3/cpaFOSpgVbXtk3SIduUokpU1NSp3L+8pg4P8iQBYU8fB/B0vPS+j58jBDzJa1w0kiDPFMqh0ZxBQozD54cfQDqdeNmYYJX5Pr0crPD2sA4AAKFIjD/rA0RtEYsZJNUHeR1dbcHhmHb6FICCPLXQ6tq2STpkyzBggzdlbmWUsF9Yvf2cdNgyCSsBD33qX+dOdinKqlUHorpm7OXQaM4gIcYhIacMu+9kAwDaOVtj/gD1qkm91K9hv603MrTapvSSKlTXiQGYx1AtQEEeIc0K03DxxY0M/aysldXP3wmAJBC9LVNv0VAa19mNiYnBkSNHjKYcmjHPGaQeRtKWbLjaUD7yg/AgWPLVq9oT7G7Hfu9dfFKE5IIK5U/QgLktugCo4gUhzQrzaFh88SBXcZAnFAoRGxuL5ORknLbuBUBSAqePv36CvJ4yxbNvZZRgSAfdVNiQKqoU4kJKEa6kFoPH5SCirx/au9jI7WPM5dCMdc6gtIdRNgDdsmWLUQXIhGhLnUiMjdfSAQBWfC5m9/HT6Pkv9vbF1bRiAMD2m5n4eEywVtolt+jCDHLkARTkEdKsUJnyOfcVBHlNfpjn/Qy4BcCKz0WYjkvvSF//8ZXjANwAANfrv/R0ZfvNDMz/M47NBg8Ay48m4qV+/vh6Qig8TGCSsrHOGVTWw2isATMhLXUkMQ/Z9YsmpnXzhpO1Zhcyz/f0wZJ998AwwJYb6Vg6uqNW5s+Z28pagIZrCWmWr6MVu5JLUZAn98PMtwBcJHNFvPk14Ouo0oWUNMD86t1XAZFkLt7O09d0NsS3/koqXtxyQy7AA4A6MYO1l1MxavVFjcu/GYKxzhk01h5GQnRh/ZWGodqX+mnWiwcA3g5WGNVRcnEbn1Outakq5jhcS0GeGmh1bdvE5XLYYtiJeRUQieWrSsj9MLu1B7iSgNBJWKDztrEBpqgOKJR8YZZbumDdBu3PLdt0LQ2v7IhjF5XM6eOL3S/1xdcTQuFiI7kCv5ddhhc232hyjoyNsc4ZNNYeRkK0raBCiH33cgAAfo5WGB3s3qLjvNDLl719ID5HK22T9uQ5WPHhbmehYm/TQEGeGmh1bdsVWp/rTigSI6VQvqqE3A+zWwB7s7OH/Bw1XZALMHMl2eLBt8C1R1lqPV/dSf755TVYvPsue3/p6I7Y+EIvTO3mjY/HBOP6u8PZL8N/EnLxwYH4lr0hPZLOGVyxYgUiIyMNHuABxtvDSIi27YzLhFAkWcEa0c+/xammxoU0BIdnk1ufDF4sZvC4/ju+o5t5pE8BaE4eIUqFyqywTcgtR5BMF35ERAS2bt0q6VFz78Buf23aWJ23Sy7AzEsGMEpy26ODwv1laTLJ/4sjiSipT80SOaAd/jMxTO7xABcb7HmpH0auugihSIyVp5Mxu7evXlLImBNpD2NsbCySkpIQFBSEiIgIowhACdEmaS8eALwo0xunKX9nawS4WCOlsArnUwpRJxK3appMfoUQNfXpU9o7W7f4OMaGevIIUUJu8UWjNCqyQ39+vYex23v66XaFK9Co5ye3oVfPPlD1lAJ104jEZ5dh9cUnAABHKz7+MzFU4fEGd3DByimd2fsf/3NfjXdAGjPGHkZCtKmipg4nHuUDAIJcbeTSVLXE8EBXAEB5jQi3Mls3Ly+9pIq97edIQR4hbYKqFbYCgQDz589HmaXkyybI1Qb2VrrvIJcNMN+Z/Qy7/Xa26nx+6k7yf39/PDvH7tOxIXC3a3717KuD2rMTlQ8/yMPJ+i9yQtT1pLASf93OxNEHebifU4ba+iE9Yj5OPMpne8smdfZs9ZCoNMgDgDPJrZsLnVZczd72c7Rq1bGMCQ3XEqJEsJstOBxJsuH7uWUA5HPjBQUFIXzKc+yQZg+ZvHW6JpuPbvdXx/CkqAq3MkvAMIzSL091JvnHZ5fh0P1cAJL5KYuHKh8GFvC4+HpiKJ7beB0A8NHBBFx6a6jZzGshulFVK8J/TzzChmtpSCmsknvMw84CUeEd8frg9rC1NM2fqpo6EQRcLrg6LnFoKmQXSEwO82z18YYHNoyanE0uxJIRLV+olF7c8PnzdzKfnjzT/MshRE+sBDx0cLFBckEl7ueWK5zP1vXARSBoKgCgu7f+gjxZPX0c8KSoCoWVtUgvroa/kjklcnMJ6zWe5L9eJhv9eyMCYcFX3ek/o7s3+vo74lpaCa6kFuPf+7mYoIUvcn1pHLzTnDjdOpSQgzf/vstOdm8st1yIqAPxiD71CNvn9sHI+pQZxi6jpAprL6Xin/u5uJpWDAdLPvr5O2F8Jw8sHhagdmUHc8MwDA7ESy4c7Sx5GB7U+mktHd1s4WVvieyyGpxNLoBYzLQ4oJbryXOinjxC2oxQDzskF1SioLIWv67b2GQ+293scqD+AlKfPXmyevo6Ym/9hOZbmSVKgzxVk/xrRWJsut6Qjf4FNSdHczgcfDm+EyatvQIAiLmcajJBHlWc0K+fzybj7T332PsWPC5GB7thSAdn1IoY3Msuw+672RCJGeSWC/HUmsvYMrsXZvTwMWCrVTuemIfnNl1HYWXDavWS6joce5iPYw/zsel6Oja92AvdDfQ9YUi3MkqRWSoJpMaFuGsl2OVwOBge6Io/4zJRUFmLhNxydPGyV/1EBWTn5FFPXhsTHR2N6OhoiEQi1TsTk6aoNyfUww7/JEiuQG+k5DZ9kszK2h4++iln1phsD+K97DI83cVL6f7KSo8dSshFTn02+me7e8NRg2z04zt5oJ2zNVKLqrD/Xg6yS6vh5WD8V8XGVnGiqFKInXFZuJBSiNtZpfB1tMb4Tu6Y0sUT7ZwbUvSYYu/jN8cfYqnM4pwJoR74ZVpXuZXrAJCUX4F3997D/vgcCEViPLfpOtYLRYjop14he3375exjvLvvnlyuyF6+DiiuqmN7K29nlaLvj2ewZXZvzDTygFXbDiTIDNV21t7F3/BAF/wZlwlAMi+vpUFeWv1wLYcD+JjAd5a6KMhTQ1RUFKKiopCeng5/f+P8giGt11xvzvNfrWPvc93aNX2iW3sAgL0lHwEuhrkC7CyzSi0+R/XiC2X+uJLK3n6lv4L3qwSPy8H8/u3w+eEHqBMz2HgtHR+M6tiq9uiDMVWcOJNUgBc232B7PQDgZkYpDsTn4N299/Dt5DC8OzzQJHsffzn7WC7A+8/EUHw0SnFJqiA3W+x+uR/e2n0Xv19IAcMAr+68jR4+Dujpa5iLqebsisvEW3sa8kk+290bv03vBs/6Un/x2WWI2H4T19JKUCtiMGfLTXjZW2KYzMIBc3f4fsMF8oRQD60dd3hQwzk8l1yI1wcHtOg46SWSvzdPO0u1pqeYCvN5J4S0UnO9OcnXz7L3nYO6yyet5VsCzpLhzO7e9gZbaBDkZgsBT/La8TllLT5OTlkNDtb3Wga4WCM8SPMfoZf7+UM6LWbt5VQwjHFXwQCMp+JE9MlHGLnqglyAx5eZY1QnZvDevnhMW38VG7buUCsVjrG4mFKIJfsahmh/mtoFS0cHK/2b4XE5+HV6V7w7XPL/IxSJ8dzG6yirX+hkDJILKjD/z4ZE+V+O74Sd8/qwAR4AdPayx4XFQ/HqQMlFk1AkxjN/XMX9VvytmpJKYR2u1NfW7uplr9Xe/S6e9rAWSEKZlpY3E4sZpNfPyTOn+XgABXmEsJrrzanNfMjevplVJlcW6+OVqyX9+zDcUC0gWd0aUl+dIyG3HOIWlhfbdy8bdfXPjejr36JJzP7O1hgXIpkk/zC/Ah/8vFFnNXW1xRgqTmy+no4PDiRA+l83McwD198dhspvJuL+hyPre7wkj+29l4NVGU4Ap+lXuDHWu80vr8FzG6+zn63PxobgrWGKA+vGOBwOvp0chsEBzgAkn6mFu27rrK2aENaJMWvTDZTWB50v9/PHZ+NCFAauAh4Xvz/bHc90kQxVFlXVYtqGa6iuNf9pQJdTi1Erkvzfj2jBhaMyXC4HnT0lQ7QP8spblHonv0LIVuEwp/l4AAV5hLCa683p0jEAndwl84Wup5eAw+WxSWv9+oSz+xlq0YWUdMi2Uihi55do6pDMkMq0bsrn9TVHKBQi7Z8/2PvfHbyGcePGGXWgZ+iatvdzyuQCl/9MDMX+V/qjt58TBDwuOnnYYcWkMBx9dSBbRi6N4woMf6nJsYyx3m3kn3HscNjYEDd8Ni5Eo+cLeFxsn9OHrZW89WYGThlBLsYfzyTjan0PVZinHX6Z1lXp/jwuB1vn9EY/fycAktybXx97qPQ55uB0UkMOO20HeQDYeXi1IgYP8yo0fr7s96U5JUIGKMgjhKWsN2dAe0kvQqVQhHsyQyxnZL68BtbvYyjSq1mgZUO2wjoxjiVKfjh9HKxanA4mNjYW9/ZvAKrr5wYG9cep02f0NoxYUlWLjdfSMGvTdby8/Rb+SchR6+reUBUnqmpFeG7TdVQIJT06bwwOwNLRwQp7UUeHuOPA/P6wlM4Z6jMV6DyKfdwY693uv5fNrvz2dbTCltm9W1Sv1N/ZGr9O68bef39/fIt7rLWhpKoW35x4BEAypL5jbh+18vnZWPCx6cVe7P/hNyce4XYrqzUYO9kgb1gH7VcE6iqz2OJeC777pBcggHklQgYoyCOEpaw3p3/9lTcAXEktBiDJ+3SmvjC2s7VA7ovGEOSDPM0XX1x8UoiyGsmw01Oh7i2eX5icnAyI64DH1yQbbJwA706tGkYUCoWIiYnB0qVLsXbt2mZ7BVddSIHnF0cQse0WdtzKxIaraZi09graf3UM51qZEV9Xvj+VhDtZkh+mXr4O+F6mRJwi/ds5Y83M7ux9q4lvYdGHn+q991EdVbUiuVQpv03vprRyiiqzevmwvWDX00uw/VZGa5vYYt+fTkJRleRz+Ep/f3TT4KKok4cdPq/vzawTM5j/5y25VbnmpKZOhEtPigAAndxtdbLaXnZF7d0szYO8NDNNhAzQ6lpC5DSXWqR/u4ZeuiupxVgwsD2SCirZCfLDAl0MntU+TG6FreZfdIcS8tjbrVn9xg57P7oMhIVLbncc2OJhRHVXkf527jEW7b6r4AhAVmkNJq27gpOvD0JvP6cWtUMXiqtq8f1pyVxQAU/SG2QlUJ0/bF5ff5y8n4UNN3NQLeaitPc0RL7QS9fN1dg3xx+x6UMmhnlgShflqTNUpYThcDj47unOGPH7BQDA0n/uY3o3b7XOmTblldfghzOS/zdLPhefjtVs+BkA3g8Pwp+3MnErsxTX0kqw7WYG5vTx03ZTDe5qajGq60uZ6WKoFpDvybubrXmvaLqZJkIGqCePELV097GHBU/y5yLtyTuj43kmmgpxt2VXtbakJ086H4/H5WBMiHuL28EOe6fcAOokPR1W3UZi3rx5LTqeshx2UjGXnsgFeAsHtcfVd4bh3wUDMKq+UkJpdR2eirmMBwpqEBvKT2eSUVzfGxQ5oB2C3dUv2P5JeHu42Urm5228lo6LKYU6aWNLZZRU4duTkuFMSz4XP0/tqrR3WBrMv/rqq/jmm2+wYMEChXM5hwe5ssFialEVm7hbn/57MgnlNQ3D634t6P0R8Lj4bXrD8POn/96HsM786vVKRzsA+Vqz2uTvZA37+qHye9na6clTd/TA2FGQR4gaLPk89PKVDMfczS5FeU0dTssM/+nqy0sTlnweOtYnlI3PKdModUlGSRWbfmBwgDOcNEiA3Bg77P37zwgSSAKqaksnPCioVvFMxVTlsEvMK8ebf99ht389IRSrZnRHX38njA/1wMHI/mygl1cuxAubrxt0LpdUcVUt2xtkwePi49HBGj3fyZqPFRND2fuLd981ivcl9d+TSWwx+vdGBDZJdtyYOsG81PKnGt73z2cf6zVNT1WtCOsuS3JJ2ljw8FEr8kAO7uDCBqwphVVYc+mJVtpoTHS96AKQ9PBKh2wf5ldovGJZWu1CmghZ3QsOU0BBnhqio6Ph4eGBHj16GLopxICkQ7ZiBriRXoIz9UGevSUfPY2kTJF0Xl5pdZ1crjVV/r2vnaFaKemw94czRrDb9tzNbtGxlOWwYxgGb+2+y6ZniAoPwsdj5IMlKwEPe17ux65+vplRih23MlvUFm366UwySupTbywY2K5FvUGv9G+Hvv6S1D3X00uwX6YAvCFllVZjzUVJwGJvycf74aqH6jVJSN3dxwEjO0oChrvZZTiVpL/5lrviMtm5eLN7+8LDvuVzDAHJRYm0g3P50USU1xhPDsDWEokZXHgi6ckLdLVp0WdcXdIhWzEjSaWiCWndWmki5OYuOLZv366VtuoTBXlqiIqKQm5uLuLi4lTvTMxW/3ZO7O1dt7OQUii5+hvSwRl8nnH8KclVvshW/4vupEw6iqc6aS8b/dOdPRtyu7UwyFO26nnfvRwcfiAJUINcbfB/T3VSeAx7Kz5+ntqQ3mLZIcMOjYnFDNbW9wZZ8LhYOrplvUFcLgffTAxj7/+3fnhU3xoPbX17PJGdh/XWsA5wtrFQeQxNE1K/LZNn76czigNEXVhzqaEizKsD27f6eF29HTCnt2QuXm65EL+dT2n1MY1FfE4ZO6wtzXOoKy1dfCEWM8gokU+E3NwFR0pKSssbaCDG8ctEiAkYIBPkxcgMqxjDUK1US9OonHssudp2sOJrtXi6l4MVuxryRkYJCiqEGh+juVXPdeDinb0N8/B+mtpV6QT84UGubM3Mx4WV+N9Fww2NnUoqYNM2PN3FE76tyM01KtgNffwkvXkXUopw/rF+5+Y1Gdp6Owq/nJEEm3aWPLZahSqaJqSe3NmTLSO4Lz4HjwsqW/4m1HQvu4z9W+nt54i+MqvuW+OL8SFsWpmVp5NQZSYJkqXzlwFgQDvdBnktTaOSpyARcnMXHAEBAS1voIFQkEeImjq62bIT3atleoFGmEiQ19xE4vTiKjwpkvRKDg5wblEOM2XG1i/iYBjgRAsT2CrKYbf5ejrbmzq5sycmqVH0fMVEmaGxY4kGqzYgu1hgbitXVHI4HHwwsqEn8NsT+u3NazK01X08xFzJnM5FQzrA1VZ1Lx6geUJqHpeDRUM6AJB8tlZfTGnN21CL7MWdtESZNgS62uLFXpLyiLnlQnbOn6m7nFrE3pYdCdGFlvbkpcssuvCtT+/S3AXHrFmzWtdIA6AUKoSoicPh4H8zuuOzww+QkFMGMSOpctFPx19emujkYQsOR/KjJ7vCVlkaEtmenyEB2k9UOibYjc3qfzQxDzN7+LT6mAzD4Oezj9n7/5FZgKBMV28HzO7ti83XM5BXLsSB+BzM0EJ7NFEprMOu25I5ga42Aq3MgZzezQuBrjZILqjE/vgcxGeXobOe8jbKDW1xeUD3pyQ3IcbbwzpodKzmUhg155X+/lh26D5q6sTYdjMDKyaG6SyVkbBOjI3XJMG5rQUPL/bSbrqTpaM7YvONdDCMZNj91YHtYcHXXj9MUaUQv55PQUJOOUqqa2FvycfioR0wRAfJiaWkPXkWPK7OKwJ52VvCxUaAwspa3NVghW1ueQ1727s+yJNecMTGxiIpKYlN51NaanpJqynII0QD07t7Y3p3b1TU1OFxYSWC3GwhMJL5eIAkm34HF8mP/b1syQpbDoejdOXibdeB7DZdfOEPCnCGjQUPlUIRW1GjtU4lFbBf5CM7umqUiPbVge2x+bokie6m6+k6C/Kay/m25242O09pVi9frfyQ83lcvB8ehDf+kqwy/t+lJ/hpqvISW9oiN7QVNACwk/Rs93EQ6iTxrSxnGwtMCvPA33eykVZcjfMphRimo571k4/y2QUXM3v4wN5Kuz+fYZ72mNbVi30vW26k4+X+re8tFIsZbLiaho/+SUBeufx0iR23MvFsd2/8MKULbFr9SvIqaurYv9Gevg6w5Os2lyGHw0Gohx0upBThSVElakVitb6b82SmkEhLBgKaX3AYK+P5dSLEhNha8tHV2wHWek7Cqg7pkG1RVS1y67/Ula1cPPdYsjKRz+XIVfbQFks+D8MDJcHj48JKJBdoXluyMdlevLfVLHQvNSTAhZ3L9U9CLvJkruS1RVkKBtmh2nl9tdcbNLePH2wtJJ/HLdfT9bawRG5oq+dEdvu3LwzXy+u/2NuXvb31hu4qYPx1J4u9PaO7t05eQzaNzorjj1pdBUMkZvDS9luY/2dckwBP6q/bWQhfdQGFldpND3Ijo4Rtvy6+VxRp7ywJVcUMkFmiXnaB3DKZIE/NqQWmhII8QsxMZwWVL5qbSOwb0BFx9XUze/k6qlV7syXGyiRXPpqYp2RP1R4XVGLfPclK3QAXa3Yxhbq4XA47D65OzOgknUpzPaer/9jI9maGuNuyi1K0wc6Szw6FF1TWYn98y1Yza0o6tPXFr38A/pJya1297BAerL1V2spMDPOEQ32v2p9xmToJbkVihk0BZG/Jx5gQN62/BgD08XfCU6GSv5WH+RX463aWimc0Tyxm8OrOuCYXFYkfjUTJ109h4ws92TqtyQWViNyTiDo1ajyrS3bRha7n40m1k0nRIp1nrEpeRcNFXmtK7hkrCvIIMTNyiy/qh0uam0gcNGwipJ0FQzrobvXbmOCGIK+1Q7ZrLz9h27xoSIcWLRSRXeygi4oJzfWcnkouQF1946d08WpxfeDmvNzPn729/kqaVo+tjEAgQIFPP/b+oqEdtP7emmMt4GF6N0nPWmFlbasvIhQ597iA7Qmb3NlTp0OPsr15/zn+sMWJnt/ddw9/1H8GLHhcHJjfH7Ev9EKwux0crASY29cfV94ZBt/6QO9MSgne3x/f+jdQTz7I0+3KWqn2Lg1BXmqxmkGeTA+nhx315BFCjJz8ClvJ4ovmVi5eTmuYSDxUhxOwu3nbw7M+aezxh/ktHoZiGAbbbkp63gQ8Dl7u76/iGYoFu9thYHvJD8+V1GLcb0GtX2Wa6zktsm/IqzauFaXjmjMs0AVBrpIhq0P3c5GlQULs1qgVSRY+AJJFCbN767cG6wu9GuZV6mLI9u87Db2iz3b30vrxZQ0LdMWw+ukNcZmlOJiQq/ExNl9PZ6c0CHgc/PVSX4Wrz70drLD7pX6wrJ8X+tPZx7gqE5y1hnRlrZO1AMEqqp1oS3tn2Z489VLqyM/Jo548QoiRC/VoOlwLKE5Dck7HK2ulOBwORteXFiuqqsXtzJatUruSWswWvH+qkwdc1Eiy25w5MnO5DsRr/kOqTHM9p2k8yTmw4nMxNFD755vDaQh8xQyw6Zp+6roeeZCH/Pofy+ndvGGno2H/5ozq6Mb2whxIyNHqkK1YzODv+mFTawFXq8nCm7NMpjdv+dFEjXrz7mWX4bVdt9n7G2b1VDqloV87J/z4TBf2/qf/3tewtU3llNWww6X9/B11tuK5Mdnh2lR1h2vr5+QKeBw4ankxjTHQWpBXXV2N6OhoLFmyBGvWrMHNmzdNss4bIaamcf47Kx6DdvVXtAm5zVe9EIkZdkilg4uNzldCSstQAcCppJYN2Up7iwBgVq/WrYqdENbwY338kXaH+BT1nP5v+x48ypcEqMMCXXS2aGdeH382F+Cvx27rpcD6Zpkh7zl9fJXsqRt8HpcNZEqr6+QuXlrrRkYJm7h6QqiHzuatyhrXyZ1NcH0ltVjtuXnlNXWYufEaKoWS1duLhgTgRTV6VSMHtEOQi+Tv//CDPJxNbl2ZuGtpxextbc47VaWdc0vm5EkuTtxsLfQ2xUCftBbkrV69GuvXr0eHDh1w+/ZtLFy4EO7u7ti4caO2XoIQ0khzqzjD3CXDIzllNc1WmYjPKUNZfZ3MQe11P2cmvGPDZPWW1BoVySySsBZwMaVL64bNAl1t2VW2Z5ILtT5hv3HP6ankYvaxcSG66w3yd7bG4PZOAIC0GgG+WbVepwXWS6tr2UUJXvaWGB2s/WFodcj2Vh3QYg3fYzJz/J7urNuhWikOh4NvJjWUq/vwYILKzyfDMHht520k1E/R6OfvhO+mdFbr9fg8Lj4c1pCu5ZND91s8FxCQ1FGW0lZVEHU4WAngZC1Jnq3unDxpnjx3W/MbqgW0GOSVlpZi1qxZWLx4MX799VdcvnwZeXl5eOaZZ7T1EoSQRppbxSnOb8jMn9DMfLNLTxqy0Q/ScV1JQFJblp3knVyo8by8M8kFyC6TfCFP6eKllSFB6YKQSqFILju/LsguCBjXSbeBkG+lTMWE4MEAGvIiatvft7PZCjAv9vbVesUUdY0JdodFfV40bQZ5xx829DqPDtbNqlpFxoS4s4mykwsq8dv5x0r3X3PpCbbW93Q7Wwvw57w+Gi0QmdrZFd28JfN5zyQXtuhCTOp6ejF7W9ojqS/SeXlPiqpUBqrVtSI2Z6W7GS66AFoZ5MXFxeHKlSsoKSlBREQEEhIS5E6qQCCAo6N+/4N1ITo6Gh4eHujRo4ehm0KInOZWcfJLGoZ3ZCtfyJIN8gbqoSePw+GwJeCKWzAvT26otqd2EhjL/mjL/phrm0jM4Fj98T3tLdkfU11xLUpsuFMf5AGSvIjatvmGzFCtnhdcyLK34iM8SPL5ephfgcS85qcqqKumTsQO/Qa72cLfueU1hlsi+unOkMbMy48+RFozQ5CXnhThrd332PsbX+yFABfN0htzORx8Ma4Te39NK2o7S3vy3Gwt2Hqw+iKdl1cpFKnM/Se/spZ68pq4desWFi5cCC8vLwwYMABnz57FqFGjsHHjRjx48KBV3b3GJCoqCrm5uYiLizN0UwiR09wqzh5+DZP6G9ewlZIGeVZ8LrprUDGiNcJbOC9PNk+ZgxVfbj5da4ySGUI+poPUG1I3M0pQXF8tYWyIm87n/vQK8gOy6wM9705sFYqgoCCtvk5+eQ1O1tcjDvO0Q09f/XyOmiM7ZHtQC715l54Usb2Uo/TYiyfVxcsekQMkw6hFVbV4Zv0VVNRPsZC6nVmKCTGXIazPcffhyI4a546UmtLFE94OkmBn991sFFUqnuqhTE5ZDTLq5zD28XPU+zw3TVbYyufIo568JiIiInDjxg2Ul5fjxIkT+O677zBo0CDs2LEDY8aMgYuLC7Zt26atthJCGmluFeeSl2ay9xUFecVVtWwPX19/J63WyFRG2tMCaDYv72JKYUOesjDt5SnzsLdkA9zLqcUob/QDqi2XZXpNh+uo7JasiIgIBIpkgpzgQQgPD0dERIRWX2d/fA6bs3BGd2+DT1yf1Lkh+NfGimnZ3l3ZCwJ9+nZyZ3Sqn2N7M6MU87bdZD+n5x8XYuz/LrIXEBNCPfDVhE7NHksVPo+LeX0kq7Ml9YA1TxR+Q2aotreeh2oB+cUXqlbYyvbkmWO1C0BLc/J4PB4OHToEKysr/Oc//8HBgweRlpaG5ORkjBkzRhsvQQhRoLn8d+4ONvCpXy17K7O0Sa/6lVT9DtVKdXSzZXsKNJmXJ+3FA4Bnump38rt0yLZOzOBMK1cVNueazER0faw2tLCwwP4fPmbvh0x6CUeOHIFAINDq68jmj5vWVTelvjQR6GqLsPqKL2eSC1Ba3bqFJidkgjzZ1eH65GQtwP75/eFcv6Dg7zvZ8P7yCHp+fxpDfz3Pli4cEeSKv17qC34ra2nL5p5cfzVVyZ6KyS660Pd8PKChtBmgeoVtc3VrzUmrg7z8/HwIhUKkpaUhJSVF7rHVq1fj9OnTrX0JQogSivLfAQ2lhPLKhUgukB+2uPSkmL09sH4lpq7IpnhZt24dhtcnXS6uqsWdLNXz8himYajWgsdlyz5pi2yJqhM6mpd3tT6lhBWfiy5eup2PJ9XZ24md+/eoygJltdqdPlNWXccuJglwsTb4UK3UxPrFCpKgveWpVMpr6nC5PsVQd28HgybKDXa3w66IvuzCkvIaEVuOEJCsjt/3Sj+tpOXp5GGHwfULsa6llaj1NypLPshzanV7NCXXk6diha1s3WpzXV3b6uVpMTExWLFiBcRiMUJCQlBeXo4BAwagT58+uHLlClxcdJdglRDSvMEBzmxwdCGlEEEyWef1tehCmuJFdgVwyLOLgfZjAUhKnPX0VX61H59TjqT6IHV0sBscrLTbGyWbBFq2x01bymvq2BXOPX0dIWhlT4smnu7siTtZZRAzkoTFs3ppL4fdofu5qKmfrzatq+GHaqVGB7vh+9OSBUnHH+ZhkGfLehjPypSgGxXctBdPKBQiNjYWycnJCAoKQkREhNZ7SmWNCnbDrfeGY9WFJ9hyIx2FlbXo4+eI90YEYUYPb61+rl7p3w4XUiTfERuupuH7KV1UPKPBjQzJ35CLjUBufpy+tNcgV15uOfXkqbR06VIUFxdjzpw56NGjB0QiEVauXImwsDDEx8djwoQJ2mgnIURDg2WCl7PJBWxv2pqYtWyQ5+9kBV9H3X0RK0rxknjsT/a2OnVG99xtWCn8TNeWTShXxtFagI5u0jlPJRC3sORac25mlLDz1vrqefhqYljD+fqnBeWxlNl9p+H/ZXo3/eSPU8fQDq7g1y9JPfGw5cPvJx41PLdx7r/m8lPqugBAmKc9fp7WFVmfj0PB8vG4+s4wvNDbV+sXDs/18GFLnf19J0vtRZT55TXsPDhDLLoAAE87S7bHU5M5eea6ulYrqbu5XC5WrVqFmpoaWFnpNms+IUQ9ffwcIeBxUCtisPXkdcSsflXygEcgMOdHALqfj6cwxUtJDpxQhWJY40xyAaprRbBSMsy0927DAoLWJkBuTi9fBzzKr0BpdR0eF1bK9Xq2llz2//ohdH0Z0M4JTtYCFFfV4t8HuRCLGbVLTCnrqaqpE7E1VT3sLDBIhyXxNGVvxUf/dk64kFKE21mlyK+ohWsLptOdqF81zONyMLxRCbrm8lPGxsYiMjKypU1XmwWfCxe+7nqe7K34GBvijgPxOUgprMLtrFL08FF9gWLooVoA4HI58HeyQlJBJa2uhRaTIX/77bd48cUX8dVXX+Gff/5Bdna26icRQnTGSsBjv2grrFwBi/oeu44D2X0mhmq/Z0xWsylenCQ9A9V1YqUlqFKLKtn5bAPaOcFbR6XXessMGUuHm7TlappM9n89//DxeVyMr0+8nFculPsRVkZVT9WZpEK2WsozXb30kgC5cfk+Zb1msvkPzz3R/P+zsFKIm/Wfg75+jk2mCDSXn1IXeQgNZarMAqc9d9T7PZf9fBliZa2UdF5ebrkQVbWiZveT9uTxuBw4WQk0+oyZCq0FeS+99BJeeOEFlJSUYMWKFfD19YWPjw927typrZcghGiILVfG5QFeIZLb9UEeBwye7qLbIK+5FC9vTh7C3lc2ZLtLpmbns911t3qzl2yQl16i1S97aU+enSUPnTzsWttUjU2UySn4T4J6ueOU9VQBkvl4UpPCdPsZAjQfHpVNd3K2BUHeqUcFkI5QKqpy0dzFi7bzEBrS05092RrIsqvblZGtGqPPmrWNya6wTVOy+EK2bm1dXa1BhuB1TWtBnpeXF2bOnIno6GicPXsWly5dQq9evTBkyBDVTyaE6MRg2XJlPqGAkzfgFgAACLGphauOc0M1l+JlbKgn2/tz5EHzQd7OuIYgb0Z37VS5UEQ2yLueVqS1L/viqlo8zK8AIOktNETJr6c6yQR599Wbl6eqp0oaLAp4HL2U+lIVdDY2KMAZVvVzys6kaB7kSYdqAcX58Zq7eNF2HkJD8rC3ZBcl3cosRUqh8qFPhmHYub6e9pYGWXQh5efY0OOfVVrd7H55bN1aC40/Y6ZCK3PyFOnXrx9cXFwQHx8PHx/dfTkTQponu/gCPmFAbcMclFdHdddLG6QpXmQ5CYD+/k64+KQItzJLkVtWAw97+YnPqUWV7I9GP38ndHDVrEyTJjzsLeHraIWMkmpcSs5FmZbmW8nNxzNQz4aHvSX6+jviWloJrqYVI6+8RmU6EGU9VY8LKvEgTxK4Dg901UoNYVU0HR615PMwtIMLjj3Mx+OiaqQWVaKds/qfH2kSZAseF4M7NJ1vKL14iY2NRVJSkl5W1xrC1K5e7HSKvXez8fZwxZ8LAEgprGJXqw5s52TQ1day8+tkF1fIEtaJUVJdx+5vrkPwWuvJ+/HHH/HKK69g1apVuHbtGoqLi5GUlNQkdx4hRH98HK3YK2rrwJ5wD3+efWx6D+2l02iJcZ0aViwee9i0N092qHZmD90n2pXOyysT8dgyYLJa8mUvO0eprwGHr6RzLxlGvRXNynqqZIdqJ4Rqp7ycKi0ZHpUtQ3bykfqrbDNLqnE/V1INZnCAc7O555rLT2lOZBOPqxqy1XctbGVkc97lNhPkyS26sLU02yF4rQV506dPx/Dhw3H37l28+eabCAgIQG1tLaZMmaKtlyCEtIB0yKVKzEUeI1k12svXQeMC5to2LqQhyNutYGK37FDtzB66Hw2Qmyju0fQLvyVf9gkyJeX0VR9YkfEyAbWy4XGp5obZBQKB3Ly+iVqqIaxKS4ZHZYeRj2uQ5PqkzFCtPoaijVlHN1t0rU/efSa5AAUVzdeyvWSgKjqKyPfk1SjcRz59ioXZDsFrrZ+9Xbt2eOmll/DSSy9p65CEEC34aHRHXHxShMcyc2qmGkEJqv7tnODnaIX0kmrsuZuNjJIqNmffk0L5oVp9BKSy8/IC+o9GSvJV9n5Lv+ylw5pcDhDkZrigun87JzhY8VFaXYcjiXlgGEblcJqiYfbqWhE7X629szVC9bSQpCXDo719Hdn3fOJRvlrvGTCOerXGZGpXL9zNliTUPhCfg4h+/gr3k/69cjmG7bUGGgV5zQSmcnVr7SzNdgheK0HeP//8g/Pnz6Nbt2547rnnwOVKOghv374NkUiEXr16aeNlCCEt0M3bAQ8+GolN19Kx8kwyuBzgtUHtDd0s8HlcLBzcHp8ceoA6MYM1F1Px5VOS4urfnnzE7qePoVpAPo1K19FTsWx0UKu+7BmGwYP6Yb9AV1tY8ltfcqql+DwuRge7YfedbGSV1uBudhm6taBn8UxyAapqJVUuJoR66HXelaKgUxk+j4vwIFfsu5eDjJJqJOZVqFzdzDAMjtcHsXaWPL3nNTRGU7t64atjDwFIhmwVBXk1dSLcypCUP+vm7aCXeZrKyCY2bm5OnqIceZp+xkxBq4drjxw5ghkzZiAtLQ3R0dH44IMPsHHjRnTr1g0jR45EWlqaNtpJCGkFAY+LVwa0w92ocNx+Pxye9saR3X3BgPZsdvr/XXoCYZ0YjwqqsOaSpDC6k7UA8we000tb/Jys4GojCeTuZJe1er5VfoUQRVWSFbmd3LWXXLmlNB2yVUS2aoa+hmpbQ7YnTnbFbHMeF1ayVRKGB7rqtQSdsert58iuVj38IBeVwrom+9zMKIVQJAn+DT1UC0hSokjlqjFc667jLAOG1OpP8KlTp/Duu+9i48aNuHLlCs6fP4/PPvsMX3/9NbKzs41uTl5GRgb+/fdfCj4JMQIe9pZ4rqekpy6nrAZbbqTjP6dTIaqvA7Z0VEe42OjnC5jD4bDDj6nFVaipaz6JqjqkvXgADJIfr7FxIQ1B2eEHLStxJl10YcHjmsRQpuziixNqzMujodqmOBwOmxi5qlaMo4lNz6Pcoot2hg/yBDwunKwlF2bNDdcWVDZsd6Mgr3mVlZVwd5dcIfJ4PPTt2xevvfYapkyZYnRj2T/99BOGDRuGn376CV27dsXGjRsN3SRC2rw3h3Rgb7+yIw777ktWQvo6WmHxsA7NPU0ngt0lwRjDAI8LlOcFU0U6Hw8AOrkbPsjr4GrD1ug9k1yotBKAIkn5FUisf08jglxga+AhOXV09bKHm42knScf5ausS3yCgjyFpqpYZXsxRXbRhZM+mqSStHeuuYUXxVUNPZLONsYVq2iTVv5K//jjD2RmZqJHjx7Izc012iXHAQEBSExMBJ/Px99//43ffvsN8+bNM3SzCGnTBrRzwqD2zrgo0xsAAJ+NCcLmDX8orJ2qKx1lFkc8zK9AqKd9i491X64nz/DDtYBkyPZRfgVq6sQ4m1yAcZ3UH3I1ROqU1uJwOBja3hF7EgpQUFmL21ml6OkrX25LWqM3KSkZB8UDAPDgYiNADx/DrYY2NsODXNkayPvvZaNOJAa/fii7ulaEf+t7hl1sBAgxggsaQDLP7mF+BQoqaxXWbC6uakhu7mRFQV6z3nnnHXTv3h03btzAb7/9htu3b2PPnj3Yvn07Bg4ciPnz56Nbt25qHSs5ORlr165Famoqvv/+e3h6Ni2Xc/DgQRw8eBAcDgdTpkzB+PHj2cfu3r2L+/fvN3mOl5cXhg4dimeeeYbddv/+fQwbNqwF75gQok0cDgd7Xu6HdVdScTGlCHEZxRgW6IotyyJx5tRJdr8tW7awaTx0paNrQzD2KL9CyZ4NpEFC42BUbrjWSH74xoW447fzKQCAww/yzD7IA4DhAU7YkyDpHT7xKF8uyJOWSzt16hTg2g6IGAwAGBHo0iQoaMsEPC4md/bA5usZKKisxcGEXDaH3sGEHJTWJxV+tru30Zw3aU+eSMygqKppdZ8i2SDP2nyDvBYN14rFYhQUSP5oAgIC8Morr+DXX3/FhQsXUFpaips3b2LRokXgcrlqJ0P+8MMPMXbsWGRlZWHLli0oKytrss/y5csxa9YseHt7w93dHdOmTUN0dDT7eFxcHLZv397k3+nTp+WOs3HjRty8eRPLli1rydsnhGiZh70llo4Oxr75/XHjzT4YUXVDLsAD9FNiSDqcCQCP8lUP1yqrqfogTxLkOVjxjWahS3hHV/DVKCfXWFWtiB3K7OBiYxRzDNU1LKAhqPu3UVk3uVJW7RoqwFjnPdRH00zKK/0bFkD9eKahOsSWGxns7Tm9/fTaJmVkK+goWnwh7cnjcAB7E5h60FItemelpaVwc3PD0aNHMWbMGLnHuFwuOnfujM6dO2POnDlqH3P+/PlYsWIFrly5gg0bNjR5PDs7G8uXL8e6deswd+5cAICnpyeWLFmC+fPnw8XFBbNnz8bs2bOVvs63336LhIQEbNu2DXy++f7HEmLKDFViSD7IU92T11y9y3UbYpFcIOnpCPWwM2iJJ1kOVgIMCnDG2eRC3M0uQ2ZJNXxk6nw253RSAarrDJM6pbU6OFshxN0WiXkVOJVUgNLqWjjUD8/Jfc78G4I828JHjQ/T5oUHuaK7twNuZ5XiVFIBbmWUoL2zNQ7GSwJnfycrDFVQAs5QZFfM5pULEdZoYFAa5DlaCYym91EXWrXwYv369Zg4cSImT56MTz75BLdv327xsUJCQtj8eoocOXIEDMNg+vTp7LZZs2ahuroax48fV+s1PvjgA2zevBmTJk3Cnj17cPToUaX7l5aWIj09nf2XlZWldH9CiHYYqsSQo7WAzZn1qEB1kNdcMHojKQN19ZP8jWWoVqolqVTkhmpNIHVKY093lvzC14oYuffMfs44XMCvq+R2eQH6BRm25J8x4nA4eFtmIdRPZx/j7zvZbOqUF3v5GVWwJFufWTYnnpQ0yHM246FaoJVz8nbs2IGBAwfCzs4OsbGx+Prrr/HGG29g5cqVsLTU7vBEcnIyXF1dYWvbcKXt7OwMe3v7Zr9oG+NwOOjUqRN27NgBQFKlY+zYsc3uv3LlSnz55ZdNthcVFcHa2lrDd2DeSktLDd0Eo0fnSD2lpaWYPHkyhgwZgvPnz7Pbhw4dismTJ7NTRXQlwNESeeVCpBRWIis3j83jp4iHh+KAR+zoDdQv1PS34+qkzS39PPX3bOjh2H8nHU8HKa/EwTAM9tzOBABY8jjo7szR+f+BNpWWlmKEvzW+r7+/80YqRvpJfp/Yz1l6BWAlCcY9qjLx9NOvmNR71AZ1Pk/jA6zhas1HQVUdtlxPxz/xDSttJwXZGtU5s2IaUqQ8zilCQYF8TFJUJXncTqD+59lYvsNdXZvW1m5Oq4K8bdu2YebMmQAkXwSHDx9GZGQkqqursW7dutYcuomqqirY2TW9Ira3t0dVVZVax/j22281es0lS5bIZb/OyspC//794ezsrNFJbivonKhG50g9rq6uOHnypEFKDIV6OeJqhqSMUxnHGiGuzffEvfnmm9i3b5/ckG14eDg69gsHDj0AAPRq766z//eWHHeUswtcbO6jsLIWZ56UwtlZ+SKDO1mlSC2R9ISMCXFHO2/T68mb0M4ZztaJKKqqxfHkYjg5u4BX/55PnjyJkSt243yJZN81URHw8vJScjTzpc7n6Y2hHbD86EPUihnkVkh6w7p522NYmH6Slqsr0KshRVAlI5B7b3UiMSqEkh5IN3srjf6OTO07vEVBnpWVZA7HU089xW7jcDh46qmncPDgQfTp0weff/452rXT3n+6o6MjioqKmmwvKiqCo6Ojgme0noODAxwcaBk9IYZgqBJDwe7y8/KUpYRort7l63/Hs/sY2yIFHpeDsSHu2HErE/kVQtzMKEEfJbVG991r6K15pqtpBj98HhcTwzyw5YZkdejFlEIMDZT8WDMcHu4JHQDUwtlagAlhhq/rbMzeHhaIffdyEJdZCi4H8LS3xLeTwgzdrCbkS5vJD9cWt5GVtUArgjwfHx9cu3YNI0eOlHusR48e6NChA65fv67VIK9bt24oKipCZmYmfHx8AACPHj1CVVWV2ilaCCFEFU3TqCgKRh/mS1bWcjjyizmMxbj6IA8AjiTmKQ3y9t7NYW9P7tw0rZWpeLqzJ7sSdN+9HDbIO5KYx/7oT+/mDQu+dkuZNZdix1S52lrg1nsj5HLlGSPp3FqgadWL4uqGRMjmnCMPaMXCi9mzZyMyMrLJYgtpIKbtlatjxoyBu7s7fvrpJ3bbDz/8AF9fXwwfPlyrr9VYdHQ0PDw80KNHD52+DiHE8GSDsod56uXKayytuBoA4GVvCWsBTyvt0qaxIQ2LLw7E5zS7X2ZJNa6mFQOQJK32dlC9EtdYPRXqwaaP2RGXier6ih/bbzakAHm+p49WX1NZih1TZ8wBHiBfqky2Ti1APXlq+eyzz3D//n306dMHI0aMQL9+/QAAf/31FwQCAYYOHarR8fbu3YudO3ciP18yW/n999+HnZ0dIiMjER4eDmtra2zatAkzZ87EuXPnIBKJ8ODBA/z999+wsNBt3bmoqChERUUhPT0d/v7+On0tQohhyVa9UGeFbWNiMYOMEkmQ5+donAu0/J2t0cvXATczSnEhpQhPCivR3qXpAoz9MhPrp3QxzaFaKUdrASaGeWDfvRykFlXhmxOP8MHIIOytH452t7PAyI7anW/VXIqd2NhYg0xFaEss+Tw4WPFRWl3XZHWtbJBnziXNgFYEeXZ2dti3bx927dqFbdu2Ye3atSgvL0ePHj2wYcMGODtrVqQ4KCiIneMnm19PNqgaN24cHj9+jLNnz4LD4WD48OFwcnJq6VsghJAmnG0s4GojQEFlrVoJkRvLrxCyaSX8nIy35+vFXn64mSGZO7j9ViY+HNWxyT777jX08pnqfDxZ0U93xr/38yAUibHi+CMceZCH8hpJj96M7t5a750yVL5HIuFua4HS6jrkKuvJM/PhWo2CvNLS0iYLEWbMmIEZM2a0uiFdu3ZF165dVe7n4uIiV56MEGI6TGV+Ukc3WxSkFiOlsBK1IjEEGvz4p5c0rPY31p48AJjVywcfHIwHwwBbb2Q0CfIyS6rZnHKBrjbo7GlcC0haIsTdDh+N6oj/O5oIoUjM1ku2seBh8dAOKp6tOUPleyQSHnaWSCqoRH6FUK5+rfxwrXkXRdDosqVfv34YMWIEfvzxR7XLlZkDmpNHSOuZ0vwk6by8OjGDtGL1UjRJpdfPxwMAPzWqSRiKn5M1RtQvPridVYq7WfI5wFZfTGETOr/Uz9+kqlwos3R0RwS5NgxN+zla4fyiIQjztNf6a0VERCA8PFxuW3h4OCIiIrT+WqQp6eILkZhBcXXD90xbqVsLaBjk7d27F+PGjcPmzZsRGBiI3r17Y/ny5bh7966u2mcUoqKikJubi7i4OEM3hRCFhEIhYmJisHTpUqxdu9YoAydl85OMjWwPXGZJtZI9m0qX2d+Yh2sB4MXeDZUdtsosQKiuFWH1xScAAAseF68NbK/3tumKlYCHTS/2gr+TFcaFuOPqO8PQ01c3abikKXZiYmLw0UcfISYmBkeOHDHK3mtz5G4rm0alYciWFl40IzQ0FMuWLcOyZcuQmpqK3bt3Y/fu3fjyyy/RoUMHTJs2DdOmTcPAgQPN5qqPEGMn7SGTDaC2bNlidD8mpjQ/yduh4cchq6xpSSRlTGW4FgCe7e6NN/++g1oRg03X0vHx6GDYWfKx41Ym+6P4Qi8fuWLvxkzRdABFBgW4IPXT5qsdaZOh8j22lqlMrVBGNo1KbnkNm7OyuEomhYqZB3ktnmXarl07vP322zh16hSys7Px0UcfIT4+HiNHjoSvry9ef/11oykBQog5M5UeMlOanySbKmTNlp0a9Y7KDdcaeU+ei40FW9c1vaQab/x1BzV1Ivx4piEgf2uY9ueq6YIpTQcwduZyLuVy5TXXk2fmCy9aFOTV1tbi/PnzqKmRXOG6ublh/vz5OHDgAPLy8vDDDz+gqKgIxcXF2myrwdCcPGLMTKWHzJTmJ7nbNOS2O3bxhkY/crI9eT4mkFfu+yld2N6MTdfTEfj1CdzKlFygD+3ggt5+TgZsnfqau9jZvn27YRpkwozpwrE1U1Hkql7IpFGRnZ9n7ilUWhTkVVRUYOjQoTh58mSTx+zt7fH8889j+/btWq14YUg0J48YM1PpITOl+UkXjh5suGPrAkD9Hzlpjjw3WwtYGWEi5MYCXGywYVZP9n5mqaT9Fjwu/jMx1ECt0lxzFzttaZGgthjLhWNrexTdm0mIXFQpeT6Py4GthfH/jbZGq5ICrVixAoGBgejYsSNmzJiBP//8E2KxWFttI4SowZR6yKTzk1asWIHIyEijDPAAoChN5sfMtiHnp6ofOYZh2IUXxryytrFnunphyYiGi4WhHVxw673hGBZoOsXYm7vYCQgI0G9DzICxXDi2tkfRVSbIK6iUGa6t78lzsuKb/fqBViWIuX37Np599lnY2dnh7t27mD17NmJiYrB9+3a4uprOlwMhpkzaQxYbG4ukpCSTnSRtTDoFBQBxFYClLWDnwm5X9SNXXFWLSqEkua6fk3EvumgsenJn9PZ1hK0FD1O6eLE5xUxFREQEtm7dKhcUhIeHY9asWYZrlIlq7lyqe+GorUUbre1RdLWRCfIqGnr/pHPyzH3RBdDKIG/fvn0YNmwYe//hw4d4/vnnMWfOHBw6dKjVjSOEqMdUV/AZq4iICLzz7lZUwpbtyVPnR04ufYoJ9eQBAJfLwew+foZuRos1d7FDCwA115oLR22u9m9tj6KrbcPryfXkUZCnnK2tLbhcLnr27Cm3PTg4GHv37kVgYCASEhIQFhamjTYSQpQwh1QHxsbCwgL9OwfjVHIhYO2A3/8Xg8iXVZ/XdJnEyca+stYc0cWO9rT0XGqzXm9rexTtLfkQ8DioFTHIr5AEeTV1IlTVSqaVUZDXDIFAgPbt2+Pw4cNNSpr5+/ujQ4cOuHfvntkEedHR0YiOjoZIJDJ0UwiRYyo58kyRj0yOu4kzXlTrfMr35JnWcC0h2qDNRRutnYrC4XDgamOB7LIaFNQHeSUyOfIcLXmIiYkx6wvkFg/XvvHGG3jttdfA5XIxbdo0dvJiUlISnjx50qTGrSmLiopCVFQU0tPT4e/vb+jmEMLS5lUzkecjM9yaWVqN9i42SvaWMJWSZoToirYXbajTo6hsNMPVtj7Iq19RK1vS7OKpY/h74zL2vjleILc4yHvnnXeQk5ODGTNmwNfXF3379gUAnDx5Ep6ennJz9QghumEsqQ7MkVzVi1L1ql7IVbswsYUXbQVNb9Ct1g6xakrVaIZrfR684qpa1InEcomQs1Ieyh3LHC+QWxzk8fl8REdHY968edi+fTvOnDmD8vJyTJ06FZ9//jmsrekLjhBdM5ZUB+bI276hJy6rVL36tbI9eb7Uk2d0aHqD7ul7tb+q0QzZNCpFVbVyQR6qK5ocz9wukFu1uhYAunXrhm7dummjLYQQDen7qrktaUn9WmlPnpO1AHaWrf56JVpG0xv0Q58LYFSNZrjJ5sqrEMoHeTVNgzxzu0CmbyFCTBjlyNMduTl5JWr25JlgIuS2xBSnN9DwsnKqRjNkc+XlVwjlSpqFBbZDgkwhK3O8QKYgTw20upYYM0oboRtyw7VlqoO8SmEdSqslK/dMoWZtW2Rq0xtoeFk1VaMZcgmRK2tRLLO69psvP0XuxD5mfYFMQZ4aaHUtIW2PvRUfthY8VAhFai28kM2oLztERIyHqU1voOFl1VSNZsglRK4QsnVrAcDNzgpTzPw8UpBHCCHN8HawwqP8CmSqsfCisKoho76LjXn1BpgLU5veYIrDy4agbDTDrVH9WtnhWkqGTAgxKTR/R7t8HCzxKL8CeeVC1IrEEPC4ze5bKNND4GJDPXnGypSmN5ja8LIxaly/VnbhBQV5hBCTQfN3tM9bZm5dTlmN0tx3hZXUk0e0y9SGl42RbAqV/Eara52szT8EMv93SEgbQfN3tK9xQmTlQV7Dj4crzckjWmBqw8vGyFXmgqugUognRZI0R/aWfFgLeIZqlt5QkEeImVA2f8cYh3GNsU2Nya6wVTUvT1obE6CePKI9pjS8bIycbSzA4QAMI+mNTyqQ5MYL9bBjy7GaMwryCDETzc3fad++vdEN4yobWjYmsj15OSoSItOcPEKMD4/LgbO1AIWVtbiVWYJaEQNAEuS1Bc3PIias6OhoeHh4oEePHoZuCiHNioiIQHh4uNw26f3mhnENRdnQsjGRDdbkMuUrIB/kUU8eMR1CoRAbN27E0qVLsXbtWtTWKv+smxrp4ouqWjG7rfRJgtm+X1nUk6cGypNHTEFz83c+++wzhfsbMg2DqaSGcLRq+IqUTb2giGwKFVfqySMmoi0s2HK1tcDDfPkSZnvX/4q9Dy8CML/3K4uCPELMiKL5O8aYhsEY26SIbIoFVT15snPy2kJqBmIe2sKCLVdFPeuF6exNc3u/smi4lhAz19wwriHTMBhjmxSRD/LqlOzZMFzrZC0Aj2v+E7qJeTCVXnVNCIVCxMTEsMOxzo1TpYhFQHGW3CZTfr/KUE8eIWbOGNMwGGObFJEN8kpUDdfWB3k0H4+YEmPoVdfmSntFw89+sz8DPPs27FSaC4jkL9qMbRRBWyjII6QNMMY0DMbYpsZsLXjgcTkQiRk1Fl5IhmspyCOmRNsJlzUN2LQ9J1DR8HP6wwS5IM+FU4VCmceNcRRBWyjII4SQZnA4HDha8VFYWas0yKuqFaG6TrJyjxZdEFMi7VX/7bffkJOT06qetJYEbNqeE6hw+LmqVO5uxORR6DwwxqhHEbSFgjxCCFHCqT7HlrI5efKJkCnII6ZFIBBg7ty5cHV1bdVxWhKwaXtOoMLh5+oyubudvRwQOdW4RxG0hRZeEEKIEtJ5ecrm5FGOPEJaFrBpe06gokVdPTrJH6tTG0mEDFBPHiGEKCXNlVchFKFWJIaA1/TaWDofD6Agj7RdLQnYtD0nUNGirt7jp6PPj+fZfdpKtQuAgjy1REdHIzo6GiKRyNBNIYTomdwK26pauNlZNtmHSpoR0rKATRcr7Rsv6sosaag77WIjgJtt2/kbpSBPDVTxghDD0GZqhZZyspLJlVddpzDIK6iUrXZBPXmkbWppwKbrlfautgJ2lXyYhx04nLaTx5KCPEKIUTKWckvqVL2gnjxCJIwxNZIln4ePRnVE7NU0fDQ62NDN0SsK8ggxc8bQG9YSxlJuSbZ+bUmzQR7NySP6Y6p/04b01YRQfDUh1NDN0DsK8ggxY8bSG9YSxlJuSa4nr5kVttSTR/TFlP+mif5RChVCzJiy3jBjZwzllgD16tcW0pw8oiem/DdN9I+CPELMmLH0hrWEonxXhig/pM6cvAKZnjzZ/QnRNlP+myb6R8O1hJgxY+kNawldpFZoCbk5ec0O1wrZffkK8ugRoi3a+ptuPK9v8uTJ2mgeMTIU5BFixrSdaFTfjGGlnnrDtZLgj+bjEV3Txt+0onl9Q4cOxYkTJ2hen5mhII8QM2YsvWGmTL0UKpKePFpZS3RNG3/Tiub1nTt3Tu8r14nuUZBHiJkzht4wU6YqyKuqFaGqVgwAcKWePKIHrf2bpnl9bQdNHiGEECUcLPmQJshXNCePcuQRU2PKc3WJZijII4QQJbhcDuwtJYMeiubkUY48YmoUrVwfOnSoyczVbSmhUIiYmBgsXboUa9euRW2t4ukX5oSGawkhRAUnawFKq+sUJkOmnjxiahTN65s8ebJZz9Vtq0mkKchTQ3R0NKKjoyESiQzdFELaBGMr2+RkJUAqqhTOySuR6d1ztDLfHwtiXhrP6ysoKDBga3TPWMok6hsFeWqIiopCVFQU0tPT4e/vb+jmEGLWjPGK28la8lVZWl0HkZgBj8thH6usbbj4s7Xk6b1thBDV2upiE5qTRwgxKtu3bze6sk2yPXRlNfLz8qpkgjxrPgV5hChi6PlwbXWxCfXkEUKMhlAoxIEDBxQ+Zsgr7sZpVGTvS9OnAIC1gK6bCWnMGHrnTT0xfEtRkEcIMQqKfghkGfKKW1muPLmePAH15BHSmDHMh2urieEpyCOEGAVFPwRShr7ils7JA5oGebJz8mwsKMgjpDFjmQ/XFhPD09gCIcQoNPdDMGHCBIOnOZCdk1dSrWROHvXkEdJEW50PZwwoyCOEGIXmfgimT59u8CEV9Ydr6SuVkMYUJV82dO98W0HDtYQQo2DME6OVDtcKZYZrqSePkCba6nw4Y0BBHiHEKEh/CH777Tfk5OQY1Q+Bk5WynjzZ1bUU5BGiSFucD2cMKMgjhBgNgUCAuXPnwtXV1dBNkeNoTXPyCCGmhyaQEEKICpRChRBiiijII4QQFRwsGwY9ShtVvKikhReEECNFw7WEEKKCJb8heKupE8s9Jp2TJ+BxwOdRkEcMQygUIjY2FsnJyUY1n5UYFgV5hBCignyQJ5J7TDpcS0O1xFCMoWwYMU502UkIISpY8JrvyZOmUKH0KcRQlJUNI20bBXmEEKICl8uBgMcBoGi4lnryiGEZS9kwYnzaXJDHMAxSUlJQXV1t6KYQQkyIdMi2+SCvzX2dEiNBZcNIc9rUt9L169cRGhqKUaNGwdfXF3///behm0QIMRGW9UO2NaJGQV590Ec9ecRQqGwYaU6bWnjx+PFjHD16FO3atcPhw4exbNkyTJ8+3dDNIoSYAEs+D0CtXE8ewzANc/IsKMgjhkFlw0hzjCrIq6qqws6dO5Gamoo333wTzs7OTfZ58OABDh8+DA6HgwkTJqBjx47sYzk5OcjLy2vyHHt7e7Rv3x4zZsxAbm4ubt26hePHj6Nv3746fT+EEPOhaLhW9rY1n4I8YjhUNowoYjRB3s8//4xvvvkGnTp1wqlTpzBr1qwmQd6mTZuwYMECPPvssxCJRIiKisLGjRvx3HPPAQC2bduGtWvXNjn20KFDsXr1agDAjh078Ouvv6KsrAwbN27U/RsjhJgFRUFeFSVCJoQYMaMJ8rp27Yp79+7hwYMHGDRoUJPHS0pKsGjRInz11Vd4//33AQBffPEFXn/9dUyePBk2NjZ455138M477zT7GnV1dVi8eDEWL16Mx48fo2/fvsjPzweHw9HV2yKEmAlFQZ5stQsbC6P5OiWEEABGtPBi1KhRCodnpY4cOYLy8nK8/PLL7LYFCxagsLAQJ06cUOs1Fi5ciJ07d+L69evYuXMnXF1dlQZ4paWlSE9PZ/9lZWWp/4YIIWZFcU+ezHAt9eQRQoyMyVx63r9/Hy4uLnB1dWW3+fr6wsbGBvfv38fkyZNVHuOLL77Axx9/jAcPHiAsLAwHDhxQuv/KlSvx5ZdfNtleVFQEa2trzd+EGSstLTV0E4wenSP1GOt54jKSgE4oErMjAFl5FezjHFEtCgoK9NYeYz1PxobOk3roPKlmLOdINg5SxWSCvPLycjg6OjbZ7uTkhPLycrWO4efnp9E8vCVLlshNZM3KykL//v3h7Oys0UluK+icqEbnSD3GeJ7srCzY2/ZOzrDk82BR3tB752xvq/d2G+N5MkZ0ntRD50k1UztHJhPk2draKoyiS0pKYGtrq5PXdHBwgIODg06OTQgxLfL1a8Ww5PPkFl5QWTNCiLExmUkknTp1QkFBAYqKithtWVlZqKioQKdOnQzYMkJIW9A4yANodS0hxLiZzLfSuHHjYG1tLTfc+scff8DR0RGjRo3S6WtHR0fDw8MDPXr00OnrEEKMl6VMHryGIE924QX15BFCjIvRDNeeO3cOp06dQnp6OgDg999/h4uLCyZPnoyePXvC2dkZP/74IxYtWoTbt29DJBJh27ZtWLduHezs7HTatqioKERFRSE9PR3+/v46fS1CiHFS1JMnn0KFgjxCiHExmiBPJBKhuroabm5uWLZsGQCguroaIlHDl2hkZCT69++PQ4cOgcPh4MaNG+jSpYuhmkwIaUNUD9dSkEcIMS5GE+SNGDECI0aMULlf9+7d0b17dz20iBBCGtCcPEKIqaFvJTXQnDxCiCVPJsgT1Q/XCqknjxBivCjIU0NUVBRyc3MRFxdn6KYQQgxEvidPEtzJLrygFCrEGAiFQsTExGDp0qVYu3YtamtrDd0kYkBGM1xLCCHGjObkEWMnFAoxfvx4nDp1it22ZcsWHDlyBAKBgN0nNjYWycnJCAoKQkREBPsYMT8U5BFCiBooyCPGLjY2Vi7AA4BTp04hNjYWkZGRSoNAYp5ouJYQQtRAKVSIsUtOTla4PSkpCYDyIJCYJwry1EALLwghinvyZJMh09cpMazAwECF24OCggCoDgKJ+aFvJTXQwgtCiCVPUcULGq4lxiMiIgLh4eFy28LDwxEREQFAdRBIzA/NySOEEDXI9eRRChVihCwsLHDkyBHExsYiKSmpycKKiIgIbN26VW7IVhoElpaWGqjVRJcoyCOEEDUoHK6tawjyrPg0MEIMTyAQIDIyUuFjqoJAYn4oyCOEEDUoW11rLeCCw+EYpF2EaEJZEEjMD116qoEWXhBClC28oKFaQogxoiBPDbTwghCiqOKFdE4eVbsghBgjCvIIIUQNyodrKcgjhBgfmpNHCCFqoCCPmCoqZdZ2UZBHCCFqsOQpSKEis/CCEGOkTj1bYr7om4kQQtRgyZdPhiwSM6gVMQCopBkxXlTKrG2jIE8NtLqWENJ4uJaqXRBTQKXM2jYK8tRAq2sJIRTkEVNEpczaNgryCCFEDY2DPNmSZpRChRgrVfVsiXmjhReEEKIG5T15dL1MjBOVMmvbKMgjhBA18LkccDmAmJEkQ5ZWuwBouJYYNypl1nbR5SchhKiBw+GwvXk1IjGbPgWg1bWEEONEQR4hhKhJmkaFFl4QQkwBBXmEEKImtievcZDHpyCPEGJ8KMhTA+XJI4QAjYM82Tl59FVKCDE+9M2kBsqTRwgBGkqbNUmhQnPyCCFGiII8QghRk1xPXh3NySOEGDcK8gghRE2yq2tp4QUhxNhRkEcIIWqS7cmjiheEEGNHQR4hhKhJGuSJxAzKa6jiBSHEuNE3EyGEqEm2tFlxdS17m4ZrCSHGiII8QghRk3R1LQBkl1azt52sqQ4oIcT4UJBHCCFqspRJepxVVsPedrGhII8QYnwoyCOEEDXJDtdm1wd5HA7gaEVBHiHE+FCQpwaqeEEIAeSDvKz64VpnawG4XI6hmkQIIc2iIE8NVPGCEALIB3m1IgYA4GJjYajmEEKIUhTkEUKImmSDPCmaj0cIMVYU5BFCiJooyCOEmBIK8gghRE2yKVSkXKxpuJYQYpwoyCOEEDVRTx4hxJRQkEcIIWpSFOQ5U5BHCDFSFOQRQoiaZJMhS9HqWkKIsaIgjxBC1ETDtYQQU0JBHiGEqElxkEc9eYQQ40RBHiGEqElhkGdNPXmEEONEQR4hhKhJYQoVGq4lhBgpCvIIIURNNFxLCDElFOQRQoiaKIUKIcSUUJCnhujoaHh4eKBHjx6GbgohxIAaB3n2lnwIFAzhEkKIMaBvJzVERUUhNzcXcXFxhm4KIcSAGgd51ItHCDFmFOQRQoiaGidDppW1hBBjRkEeIYSoqXFPHi26IIQYMwryCCFETY1TqFD6FEKIMaMgjxBC1NS0J4+CPEKI8aIgjxBC1ETDtYQQU0JBHiGEqIl68gghpoSCPEIIUZNFkzl51JNHCDFeFOQRQoiauFwOBDwOe5968gghxoyCPEII0YDskK0z5ckjhBgxCvIIIUQDsmlUaLiWEGLMKMgjhBANyFa9oOFaQogxoyCPEEI0IDtcS0EeIcSYUZBHCCEakAZ5lnwurAU8FXsTQojhUJBHCCEa6OPnCAAY2N4ZHA5Hxd6EEGI4fEM3gBBCTMnqGd0xvZs3RgS5GrophBCiVJvsyROLxUhJSUFNTY2hm0IIMTF2lnxM7+4NV1taWUsIMW5tMsj773//i549e+Lq1auGbgohhBBCiE60uSDv4sWLKCgoQM+ePQ3dFEIIIYQQnTGqOXlCoRB79uxBamoqIiMj4eTk1GSfx48f4+jRo+BwOBg3bhzat2/PPlZUVISSkpImz7G2toanpyeKi4vx008/YdOmTRg7dqwu3wohhBBCiEEZTU/e6tWrERgYiJ9++glRUVHIz89vss+OHTvQuXNnHD58GAcPHkRYWBj27NnDPr5mzRqEh4c3+ffBBx8AAN5++2288cYbyMjIQHV1NbKzs1FdXa2vt0gIIYQQojdG05PXoUMH3LhxA8nJyRg0aFCTx8vKyvDaa6/h008/xccffwwAWLZsGRYsWIDx48fD2toaH374IT788EOFx6+srMTp06dx+vRpAEB2djYWL14Mb29vDBkyRHdvjBBCCCHEAIymJ2/8+PHw8PBo9vEjR46gtLQUCxYsYLctXLgQ+fn5OHnypMrj29jYICUlhf03cOBA7Ny5U2mAV1paivT0dPZfVlaWZm+KEEIIIcRAjKYnT5WEhAS4uLjA3d2d3ebv7w8bGxskJCRg4sSJGh3Py8sLVlZWSvdZuXIlvvzyyybbi4qKYG1trdHrmbvS0lJDN8Ho0TlSD50n9dB5Ug+dJ/XQeVLNWM6Rq6v6OTpNJsgrKytTuBDDyckJZWVlGh9v+/btKvdZsmQJIiMj2ftZWVno378/nJ2dNTrJbQWdE9XoHKmHzpN66Dyph86Teug8qWZq58hkgjwbGxuFUXRJSQlsbW118poODg5wcHDQybEJIYQQQnTJaObkqdKpUycUFBSguLiY3ZaTk4OKigoEBwcbrmGEEEIIIUbIZIK8cePGwdLSElu2bGG3bdiwAfb29hg9erROXzs6OhoeHh7o0aOHTl+HEEIIIURbjGa49tKlSzh37hxSU1MBAOvWrYOrqyvGjx+Pbt26wcXFBd999x2WLFmC+Ph4iEQirF+/HqtWrYK9vb1O2xYVFYWoqCikpKSgQ4cOtMpWgaKiIlRVVRm6GUaNzpF66Dyph86Teug8qYfOk2rGdI68vLzA56sO4YwmyKuoqEB2djYsLCzw3nvvoba2FtnZ2XIn9I033kC/fv3wzz//gMPh4NKlS+jVq5fe2piXlwcA6N+/v95ekxBCCCFEVlpaGvz8/FTux2EYhtFDe8xCdXU17ty5A3d3d7Ui6LZCuur4ypUr8Pb2NnRzjBKdI/XQeVIPnSf10HlSD50n1YztHJlcT54psLKyQr9+/QzdDKPl7e2t1pVFW0bnSD10ntRD50k9dJ7UQ+dJNVM7Ryaz8IIQQgghhKiPgjxCCCGEEDNEQR5pNQcHB3z++eeUOFoJOkfqofOkHjpP6qHzpB46T6qZ6jmihReEEEIIIWaIevIIIYQQQswQBXmEEEIIIWaIgjxCCCGEEDNEQR4hhBBCiBmiZMhELQzD4PLly7h//z48PDwQHh4OGxsbuX3WrVvXpK5f//7920wZuIqKCqxfv77J9kmTJqFDhw5y2+7fv48LFy7Azs4O48ePh6Ojo76aaXBHjhxBYmJik+22trZ4+eWXAQApKSk4cOBAk30iIiJ0XqvakIqKirBnzx5YWFhg9uzZCvd58uQJTp06BT6fj7Fjx8LDw6NF+5iy1NRU/PPPPwgMDMS4ceOaPF5ZWYlz584hMzMTHTt2xNChQ+Uez8/Px/bt25s879lnnzWKagbaEhcXh3PnzmHQoEHo3bu33GP379/HsWPHmjzntddeg0AgkNt2+vRpJCYmol27dhg9erTZVXw6ceIE4uPjMWXKFLRr107uMUW/awDQqVMnjB07FgBw7tw53Lp1S+5xJycnzJkzR2dtVpd5/U8Rnbh79y5mz54NGxsbhIaG4s6dO8jMzMS+ffvQt29fdr8PP/wQffr0QXBwMLstMDDQEE02iKKiIixevBizZ8+Gk5MTu3348OFy+33//ff47LPPMGHCBGRkZGDRokU4duwYunfvrucWG0ZGRgbu378vt23Lli3o2rUrG+TdvXsX77zzDhYuXCi3X21trd7aqW/z58/HoUOH2BQNioK8LVu2YMGCBRgzZgwqKirw+uuvY8+ePRg1apRG+5iq/Px8vPLKK7h9+zbq6uowdOjQJkHe9u3bsXTpUoSEhMDHxweffvop/Pz8cPjwYfbcpqenY/HixZg/fz6srKzY5xpL8fnWunPnDhYuXIjS0lI8fvwYn3zySZMg79KlS/joo4/w0ksvyW2XTbhRV1eHGTNm4PLlyxg1ahQuXrwIT09PHDlyxCwutv755x8sWbIETk5OuHz5Mjp27NgkyHv48CHKy8vZ++Xl5YiNjcUXX3zBBnm7du3C3r17MWnSJHY/d3d3/bwJVRhCVLh79y5z9+5duW3Tp09nevXqJbfN1dWV2bZtmz6bZlTS0tIYAExCQkKz+yQmJjI8Ho/Zvn07wzAMIxaLmSlTpjD9+/fXVzONTnJyMsPhcJgNGzaw2/bv389YWloasFX6t2HDBqaiooJZtmwZ06lTpyaPFxQUMHZ2dsx3333Hbnv99deZdu3aMXV1dWrvY8qys7OZPXv2MHV1dcykSZOY559/vsk+x44dY/Ly8tj7xcXFjJ+fHxMVFcVuu3nzJgNAbj9zEhcXx5w7d45hGIbx9fVlVqxY0WSf9evXM76+vkqP87///Y+xs7NjUlJSGIaRfL58fX2Zjz/+WPuNNoATJ04wCQkJTF5eHgOAOXTokMrnrF27luFyucyTJ0/YbW+//TYzadIkXTa1xWhOHlGpS5cu6NKli9y20aNH48GDB032vXr1KtauXYujR4+isrJSX000Kv/++y/++OMPnD17FmKxWO6xXbt2wcXFBTNnzgQAcDgcvPHGG7hy5QpSUlIM0FrD++OPP+Dg4MCeEymxWIxt27Zh06ZNuHnzpoFapz8RERFNpkDIOnjwIGpqarBgwQJ225tvvonU1FRcvHhR7X1MmaenJ5555hnweLxm9xk9ejTc3NzY+46OjujXr1+T3mMA2Lt3LzZs2GAW50ZW9+7dMWTIEJX71dTUYPPmzdiyZQvu3bvX5PEdO3Zg4sSJaN++PQDAxcUFL7zwAnbs2KH1NhvCyJEjERoaqtFz1q5di/Hjxzfp8cvLy8Mff/yBXbt24cmTJ9psZqtQkEdaZN++fU26/zkcDi5fvoxz585h8eLFCAsLw9WrVw3UQsOwsLDA4cOHceLECTz33HPo378/MjMz2cfj4+MREhICLrfhTy8sLIx9rK0Ri8XYsGEDOx1Alo2NDXbt2oV9+/Zh+PDhmDp1KoRCoYFaanjx8fHw8fGRy7gfGhoKDofDfnbU2aetKS4uxpkzZ9CnTx+57dbW1tizZw8OHz6Mp59+GsOHD0dRUZGBWmkYDMNg//792LVrF/r27YuIiAi5C9P4+Hj2+0kqLCwMycnJqK6u1ndzDS4+Ph6XLl2Su4iSysnJwcmTJ7FmzRqEhITgm2++MUALm6I5eURjP/74I06cOIEzZ87IbT9w4AAGDBgAQDKX47nnnsMLL7yAxMREuaDGXNnb2+PWrVvsl2JpaSkGDx6MN998E7t37wYAlJWVyc3XAwBnZ2d2/7bm8OHDSE9Pb/KlGRYWhkePHrE9MsnJyejduzdWrFiBzz//3BBNNThFnx0ejwd7e3v2s6POPm2JSCRiF+u8/fbb7HYvLy8kJCSwPVT5+fno06cPPvjgA8TExBiquXo1YMAAJCcnsxcEt2/fxoABA9C/f3+8+eabAJr/vmIYBuXl5XLzGduCdevWwdPTE08//bTc9rlz5+K7775jF6T8+eefmDVrFoYOHdpk0Y++mf8vL9GqDRs24IMPPsDmzZsxcOBAucekAR4A8Pl8vPvuu0hKSsKjR4/03UyDcHR0lLvqdXBwwGuvvYajR4+yk5mtra1RVlYm9zzpj6+yoTpztW7dOvTt2xc9e/aU2x4UFCQ35BYYGIhnn30Whw8f1nMLjYeizw7DMKioqGA/O+rs01aIxWLMnz8fV65cweHDh+WCFS8vLzbAAwA3Nze89NJLberzFRYWJtfj2717d4wbN07uHND3VYPa2lps2rQJL7/8cpPVxX369JHb9txzz8Hf3x9HjhzRdzOboCCPqG3jxo149dVXsXHjRjz33HMq97e0tAQgGS5pqywtLVFRUcEOMwYHB+Px48dy+yQnJ7OPtSV5eXnYt2+fwqEPRSwtLdv0Zyk4OBhZWVlyw2QpKSkQiUTsZ0edfdoChmGwYMEC/Pvvvzh58iRCQkJUPqetf76Apuegue8rHx+fNhfk7du3D/n5+Zg/f75a+xvL54mCPKKWzZs3Y8GCBYiNjcWsWbOaPJ6ent4k/cDmzZvh4OCAbt266auZBvXo0SO5+SxisRhbt25Fv3792IB3ypQpSEtLkxvq3rRpE4KDg5vMfTF3mzZtgoWFBV544YUmjzXOo1daWor9+/f/f3v3H1Nl+f9x/Kkoxq8iS0wmWhiUDMKRJtTcJIPJMkCGAXMpsCZNydZazbmWSaaEOfsDywWHVWj4W3YQ9UyEggwLOGIbIgpkQoRNEzMwfhz4/uE6346YH/18UvT29djOxnWf932f9312OHuf67rv6xryoY+hFBUVRW9vL7t27bJvy8/P5/7772fGjBnXHWN0fxV4e/fupays7KoX1p88edKh3dvby7Zt2+6qz9eV/2Pt7e2UlJQ4vAfR0dHs2bOHCxcuANDT08O2bduIjo6+pbneDkwmE+Hh4Tz66KODnrvyvTx8+DBNTU23xedJ1+TJf1RWVkZycjLh4eGcO3eO7Oxs+3OLFi3C2dmZ5uZmZs+ezXPPPce4ceOoqKjgq6++Ijc3FxcXlyHM/tYpKysjKSmJWbNm4e7ujtlspqWlBbPZbI+ZOnUqaWlpxMfHs2jRIlpbWykoKKCoqGgIMx8aJpOJxMTEq863lZ2dzbFjx5gxYwZ9fX1s3rwZT09PVq5cOQSZ3ho7duygvb2d6upqOjo67P9nqampuLq64uPjw4oVK0hLS6O2tpauri4+/fRTTCaT/dqo64m5023cuJG+vj5OnTrFqFGjyM7OdphIe8WKFZhMJpYsWcLBgwc5ePAgAF5eXvYRiJ07d2I2m3n22WdxdnZm+/btXLx48aoTJN+JOjo62LRpE3B5kvbKykqys7OZOHGi/XqylStXcuHCBUJDQ+nq6uKzzz7D39+ft956y36cV199lS1btjBz5kzi4uI4cOAAnZ2dvPPOO0NyXv+2kydPYrFY6OzsBKCoqIjGxkaefPJJwsLC7HGtra1YLBY2b9581eO8+OKLBAYGEhgYSFtbG3l5eSQkJBAfH39LzuNahg0M/G3mQ5Gr+Oabb/7xy2/t2rX2Iq69vZ0dO3bQ0tLCxIkTiY2Nxdvb+1amOuSOHz9OUVER58+fx8/Pj3nz5uHu7j4ozmw2U1FRgbu7OwkJCTd8G/+d7uzZs7z77ru88sorBAYGXjXm22+/paSkBJvNRmBgIHFxcdecOuNOt27dukFDYwBr1qxxKITLysqwWCyMGDGCuXPnDrpr9Hpj7lSvv/76oEmx7733XlavXg1c/vFwtSl3Jk6cyJtvvmlv19bWsm/fPjo7O3n88ceJj483TCF85swZ3nvvvUHbAwICWLx4sb1dWlrK119/jZOTE8HBwURHRzNs2DCHfbq6usjPz6ehoYEJEyawYMECRo8efdPP4VawWq3k5eUN2h4ZGenQW3no0CG2bt3K2rVr7aMyf2ez2SgsLKSmpob77ruPZ5555rboxQMVeSIiIiKGpGvyRERERAxIRZ6IiIiIAanIExERETEgFXkiIiIiBqQiT0RERMSAVOSJiIiIGJCKPBEREREDUpEnIiIiYkBa1kxE7hp9fX1s3LiR4uJiAFJSUuxLXd0szc3NNDc3AzB+/Phrrm7S399PaWkpwcHBjBkz5l/PpaGhgZaWFgAefvjhq67DKSLGoZ48EbkrXLp0iYiICPLz80lKSuKJJ54gISHBXvDdLF988QUxMTFkZmZisViuGdvT00NERASHDh26KbkcOHCAzMxMYmNjyc3NvSmvISK3D/XkichdYdmyZZw5c4aamhr7estVVVXk5OTw/PPP39TX9vHxoaSk5Ka+xvVIT08nPT39H9cLFhFjUU+eiBje6dOn+eSTT8jIyLAXeAD+/v6cOHFiCDODpqYmysvL7cOo/+Tnn3+moqKC+vr6f4xpaWmhvLycpqYmAMrLy2ltbf1X8xWRO4d68kTE8PLz83FzcyM6Otphe09PD8OHD81v3d7eXhYsWMDu3bsJCgri9OnTzJkzZ1Dc+fPnSUlJwWKxEBgYyE8//YS/vz+FhYU8+OCDAAwMDLB48WJMJhNBQUH8+uuvhIWFYbFYeP/990lPT7/VpycitwEVeSJieBaLheHDhw8q8o4ePUpwcPCQ5JSdnc2+ffs4cuQIkydP5tKlS7zwwguD4l566SWsVis//PADfn5+dHV1ERkZyRtvvMHnn38OwJYtWzCZTJSXlxMaGorNZiMlJYXff//9Vp+WiNxGNFwrIoZ35MgRQkJCmDJlisPj3LlzTJkyZUhyys3NJTk5mcmTJwPg4uJCRkaGQ0xzczPFxcUsX74cPz8/AFxdXVmxYgWbNm3it99+AyAvL4+YmBhCQ0MBcHJyYvXq1bfwbETkdqSePBExtLNnz/LHH3+QmppKUlKSfXt9fT0ffPABERERQ5JXU1PToGHUK6dXaWhosP/99xs3zp07R39/P42NjTz11FM0NzeTmJjosO/48eNxdXW9CZmLyJ1CRZ6IGFpfXx/AoILnyy+/ZMKECcycOdO+ra6ujuXLl/Pdd9/R0dHB+vXrWbhwIRMmTGDlypVkZmbi4eFBYWEhW7duZcOGDXh7e1NcXMy4ceNuKC9PT89Bw6lXtt3c3AAoKChwuGEEYNasWQwMDNiPdfHiRYfne3t7+fPPP28oJxExFg3XioihjRkzBjc3N06dOmXf1tbWRnZ2NhkZGTg5OQFw4sQJwsPDmTt3LnV1dXR0dJCWlkZtba29gKqtrWXq1KlERUXh6+uL1WrFy8uLnTt33nBeTz/9NGaz2WHb7t27HdrTpk1j9OjRpKWlUVJS4vDYvn0706dPByAsLIz9+/fT399v33fPnj0ObRG5+6gnT0QMzcnJicTERLKysvD19cVms7Fs2TJiY2NZuHChPW7VqlUsXbqU5ORkh/2tVisxMTEsWbIEgEmTJuHp6cn8+fOBy3Pg/TfDoqtWrWLatGkkJCSQkJBAfX09H330kUOMi4sLOTk5vPzyyxw7dozp06fT3d1NVVUVxcXFHD9+HIDly5dTUFDAnDlzSE1N5ZdffmHNmjWMGDGCYcOG3XBuImIM6skTEcNbv349s2fP5rXXXrMXcyaTySGmqqqKyMjIQftarVaH6/Zqamoc2larlZCQkBvOKSAggMOHD+Ph4UFubi5tbW1UVlYya9YsvLy87HFxcXFUVlbS399PXl4eRUVF+Pj4UF1dbY/x9vbm+++/55FHHsFkMnH06FH279/P8OHDcXd3v+HcRMQY1JMnIobn4eExqKi70tixY9m7dy8hISGMGPH/X41Wq5WlS5fa29XV1eTk5ACX59lrbGz8jytIdHV1UVJSMmjt2qCgoEHLi11tZYzHHnuMrKysa77GQw89xIYNG+zt0tJSenp6HArQv9au7ezsvOaxRMQY1JMnIgKsW7eOXbt24erqyj333MPbb79Nd3c3P/74o72Ia21txdnZmbFjxwKX59kLCAhwKAqv5Ovri7+//3WtXfu/iIqKIisrC7PZzIcffkhiYiLz5s0jKCjIHvPX2rWTJk2yT8kiIsY1bOCv27NERASbzUZvby8jR47EycmJ7u5uRo0aBVxeWaK3txdnZ2cA+vv7sdlsjBw5cihTBi4ve/bxxx9TV1fHAw88QHh4OPPnz9c1eSJ3MRV5IiIiIgak4VoRERERA1KRJyIiImJAKvJEREREDEhFnoiIiIgBqcgTERERMSAVeSIiIiIGpCJPRERExIBU5ImIiIgYkIo8EREREQNSkSciIiJiQCryRERERAzo/wBs6sdgZxo34gAAAABJRU5ErkJggg==", + "text/plain": [ + "<Figure size 704x440 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# the model is always drawn on a fine grid: evaluated only at the (downsampled)\n", + "# data angles, the diffraction pattern aliases and the curve looks ragged\n", + "on_fine = omp.bind(x_fine, meta)\n", + "fig, ax = plt.subplots()\n", "ax.plot(np.rad2deg(angles), ratio, \"o\", ms=3, color=\"k\", label=\"data\")\n", - "ax.plot(np.rad2deg(angles), on_data(*theta_0), color=\"C0\", label=r\"$\\theta_0$\")\n", - "ax.set(xlabel=r\"$\\theta_{cm}$ [deg]\", ylabel=r\"$\\sigma / \\sigma_{Ruth}$\", yscale=\"log\")\n", - "ax.legend(frameon=False)\n", + "ax.plot(\n", + " np.rad2deg(x_fine),\n", + " on_fine(*theta_0),\n", + " color=plotstyle.COLOURS[0],\n", + " label=r\"the potential at $\\theta_0$\",\n", + ")\n", + "ax.set(\n", + " xlabel=r\"$\\theta_{cm}$ [deg]\",\n", + " ylabel=r\"$\\sigma / \\sigma_{Ruth}$\",\n", + " yscale=\"log\",\n", + " title=\"Before fitting anything\",\n", + ")\n", + "ax.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "2a57fdcf", + "id": "09d7df87", "metadata": {}, "source": [ "## The error-model ladder\n", "\n", - "Data spanning three decades with unreported, presumably multiplicative,\n", - "errors are compared in log space (recipe 10): `space=tf.log` transforms the\n", - "data once and every prediction, and `problem.log_jacobian()` is what makes a\n", - "log-space evidence comparable with a linear-space one. With no reported\n", - "errors every rung uses `statistical=False`: the inferred terms *are* the\n", - "covariance.\n", + "The data span three decades and their errors were never reported, but they are\n", + "presumably multiplicative, so we compare in **log space** (recipe 10):\n", + "`space=tf.log` transforms the data once and every prediction with it, and\n", + "`problem.log_jacobian()` is what later makes a log-space evidence comparable\n", + "with a linear-space one.\n", + "\n", + "With nothing reported, every rung sets `statistical=False`: the inferred terms\n", + "*are* the covariance.\n", "\n", "| label | error model |\n", "|---|---|\n", - "| `L0` | constant noise in log space, `σ = ε` on every point |\n", - "| `E0` | fractional noise in linear space, `σ_i = ε ym_i` |\n", - "| `L2y` | `L0` plus a free normalisation mode, `η ym` (the `jitr` calibration notebook's error model) |\n", - "| `Lgp` | `L0` plus a Matérn(5/2) Gaussian process in `u = θ/π` with a free amplitude |\n", + "| `L0` | constant noise in log space, $\\sigma = \\epsilon$ on every point |\n", + "| `E0` | fractional noise in linear space, $\\sigma_i = \\epsilon\\, y_{m,i}$ |\n", + "| `L2y` | `L0` plus a free normalisation mode, $\\eta\\, y_m$ (the `jitr` notebook's model) |\n", + "| `Lgp` | `L0` plus a Matérn(5/2) Gaussian process in $u = \\theta/\\pi$ with a free amplitude |\n", "\n", - "The same `log_eps` object is reused across rungs; each `Problem` compiles\n", - "independently (recipe 18). Priors (recipe 13): log-uniform on the error\n", - "scales between 5 % and 200 %, as in the `jitr` notebook." + "The same `log_eps` object is reused across rungs, and each `Problem` compiles\n", + "independently (recipe 18). The priors are log-uniform on the error scales\n", + "between 5 % and 200 %, as in the `jitr` notebook (recipe 13)." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "3e16d477", + "execution_count": 6, + "id": "9421f3fd", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T20:11:02.689058Z", - "iopub.status.busy": "2026-09-11T20:11:02.688878Z", - "iopub.status.idle": "2026-09-11T20:11:02.699185Z", - "shell.execute_reply": "2026-09-11T20:11:02.698548Z" + "iopub.execute_input": "2026-09-12T03:36:33.450949Z", + "iopub.status.busy": "2026-09-12T03:36:33.450743Z", + "iopub.status.idle": "2026-09-12T03:36:33.460413Z", + "shell.execute_reply": "2026-09-12T03:36:33.459623Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "L0 columns: ['V', 'W', 'r', 'a', 'log_eps']\n", - "E0 columns: ['V', 'W', 'r', 'a', 'log_eps']\n", - "L2y columns: ['V', 'W', 'r', 'a', 'log_eps', 'log_eta']\n", - "Lgp columns: ['V', 'W', 'r', 'a', 'log_eps', 'log_ell', 'log_A']\n" - ] - } - ], + "outputs": [], "source": [ "comp_log = rx.Comparison(data, omp, space=tf.log)\n", "comp_lin = rx.Comparison(data, omp)\n", @@ -279,7 +333,34 @@ " amplitude=T.constant_amplitude,\n", " amplitude_params=(log_amp,),\n", " params=[log_ell],\n", - ")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "db1796bf", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:36:33.462476Z", + "iopub.status.busy": "2026-09-12T03:36:33.462270Z", + "iopub.status.idle": "2026-09-12T03:36:33.472571Z", + "shell.execute_reply": "2026-09-12T03:36:33.471774Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L0 columns: ['V', 'W', 'r', 'a', 'log_eps']\n", + "E0 columns: ['V', 'W', 'r', 'a', 'log_eps']\n", + "L2y columns: ['V', 'W', 'r', 'a', 'log_eps', 'log_eta']\n", + "Lgp columns: ['V', 'W', 'r', 'a', 'log_eps', 'log_ell', 'log_A']\n" + ] + } + ], + "source": [ "ladder = {\n", " \"L0\": rx.Constraint([comp_log], terms=[T.noise(log_eps)], statistical=False),\n", " \"E0\": rx.Constraint(\n", @@ -297,16 +378,55 @@ " print(f\"{name:4s} columns: {p.names}\")" ] }, + { + "cell_type": "markdown", + "id": "716ce943", + "metadata": {}, + "source": [ + "## Running the ladder\n", + "\n", + "Optical-model posteriors are correlated and often multimodal, so we use nested\n", + "sampling: [dynesty](https://dynesty.readthedocs.io/) copes with them, and it\n", + "returns the evidence we are about to compare as a by-product." + ] + }, { "cell_type": "code", - "execution_count": 5, - "id": "6d7c9516", + "execution_count": 8, + "id": "f3d12040", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:36:33.474641Z", + "iopub.status.busy": "2026-09-12T03:36:33.474437Z", + "iopub.status.idle": "2026-09-12T03:36:33.478031Z", + "shell.execute_reply": "2026-09-12T03:36:33.477257Z" + } + }, + "outputs": [], + "source": [ + "def fit_nested(problem, seed, nlive=80, dlogz=1.5):\n", + " sampler = dynesty.NestedSampler(\n", + " problem.log_likelihood,\n", + " problem.prior_transform,\n", + " problem.ndim,\n", + " nlive=nlive,\n", + " sample=\"rwalk\",\n", + " rstate=np.random.default_rng(seed),\n", + " )\n", + " sampler.run_nested(dlogz=dlogz, print_progress=False)\n", + " return sampler.results" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7786396e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T20:11:02.700606Z", - "iopub.status.busy": "2026-09-11T20:11:02.700481Z", - "iopub.status.idle": "2026-09-11T20:22:54.828075Z", - "shell.execute_reply": "2026-09-11T20:22:54.826749Z" + "iopub.execute_input": "2026-09-12T03:36:33.479820Z", + "iopub.status.busy": "2026-09-12T03:36:33.479621Z", + "iopub.status.idle": "2026-09-12T03:51:14.356535Z", + "shell.execute_reply": "2026-09-12T03:51:14.355710Z" } }, "outputs": [ @@ -340,52 +460,56 @@ } ], "source": [ - "def fit_nested(problem, seed, nlive=80, dlogz=1.5):\n", - " sampler = dynesty.NestedSampler(\n", - " problem.log_likelihood,\n", - " problem.prior_transform,\n", - " problem.ndim,\n", - " nlive=nlive,\n", - " sample=\"rwalk\",\n", - " rstate=np.random.default_rng(seed),\n", - " )\n", - " sampler.run_nested(dlogz=dlogz, print_progress=False)\n", - " return sampler.results\n", - "\n", - "\n", "results, samples = {}, {}\n", "for i, (name, p) in enumerate(problems.items()):\n", " res = fit_nested(p, seed=i)\n", " results[name] = res\n", " samples[name] = res.samples_equal(rstate=np.random.default_rng(i))\n", " print(\n", - " f\"{name:4s} log Z = {res.logz[-1]:8.2f} +/- {res.logzerr[-1]:.2f} {int(np.sum(res.ncall)):6d} calls\"\n", + " f\"{name:4s} log Z = {res.logz[-1]:8.2f} +/- {res.logzerr[-1]:.2f} \"\n", + " f\"{int(np.sum(res.ncall)):6d} calls\"\n", " )" ] }, { "cell_type": "markdown", - "id": "d3e2685b", + "id": "933de88a", "metadata": {}, "source": [ - "## Evidence, comparable across spaces\n", + "## Evidence, and what a Bayes factor is\n", + "\n", + "The [marginal likelihood](https://en.wikipedia.org/wiki/Marginal_likelihood), or\n", + "evidence, is the probability the model assigns to the data *before* we see which\n", + "parameters fit best:\n", + "\n", + "$$Z = \\int p(\\mathbf{y} \\mid \\theta)\\, p(\\theta)\\, \\mathrm{d}\\theta .$$\n", "\n", - "`logz_summary` records the sampler's error; adding `problem.log_jacobian()`\n", - "to a log-space evidence puts it in the same units as a linear-space one, so\n", - "`E0` can be compared with the others. `compare_logz` returns a verdict\n", - "that is a tie unless the difference exceeds twice the combined error." + "It rewards models that put probability where the data landed and penalises those\n", + "that spread it thinly over parameter space they did not need — an automatic\n", + "Occam's razor. The ratio of two evidences is the\n", + "[Bayes factor](https://en.wikipedia.org/wiki/Bayes_factor), and it is how much\n", + "the data shift our relative belief in one error model over another. Nested\n", + "sampling computes $\\log Z$ directly, which is the main reason to use it here.\n", + "\n", + "Two practical points. A Bayes factor depends on the priors, not just the fit,\n", + "so the prior widths are part of the claim being made — this is why they are\n", + "stated explicitly above. And evidences computed in different comparison spaces\n", + "are not comparable until we add `problem.log_jacobian()`, which is what puts the\n", + "log-space rungs into the same units as the linear-space `E0`. `compare_logz`\n", + "returns a tie unless the difference exceeds twice the combined sampler error,\n", + "because differences smaller than that are bookkeeping, not evidence." ] }, { "cell_type": "code", - "execution_count": 6, - "id": "3421f412", + "execution_count": 10, + "id": "3d7ce6ed", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T20:22:54.830713Z", - "iopub.status.busy": "2026-09-11T20:22:54.830430Z", - "iopub.status.idle": "2026-09-11T20:22:54.838581Z", - "shell.execute_reply": "2026-09-11T20:22:54.837601Z" + "iopub.execute_input": "2026-09-12T03:51:14.358510Z", + "iopub.status.busy": "2026-09-12T03:51:14.358298Z", + "iopub.status.idle": "2026-09-12T03:51:14.364140Z", + "shell.execute_reply": "2026-09-12T03:51:14.363340Z" } }, "outputs": [ @@ -423,28 +547,29 @@ " if name != best:\n", " v = rx.diagnostics.compare_logz(logz[best], logz[name])\n", " print(\n", - " f\"{best} vs {name}: dlogZ = {v['dlogZ']:6.2f} +/- {v['err']:.2f} -> {v['verdict']}\"\n", + " f\"{best} vs {name}: dlogZ = {v['dlogZ']:6.2f} +/- {v['err']:.2f}\"\n", + " f\" -> {v['verdict']}\"\n", " )" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "17484714", + "execution_count": 11, + "id": "7ea27b55", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T20:22:54.841615Z", - "iopub.status.busy": "2026-09-11T20:22:54.841395Z", - "iopub.status.idle": "2026-09-11T20:22:55.630755Z", - "shell.execute_reply": "2026-09-11T20:22:55.630054Z" + "iopub.execute_input": "2026-09-12T03:51:14.365937Z", + "iopub.status.busy": "2026-09-12T03:51:14.365747Z", + "iopub.status.idle": "2026-09-12T03:51:15.026051Z", + "shell.execute_reply": "2026-09-12T03:51:15.025293Z" } }, "outputs": [ { "data": { - "image/png": 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Rvg4X5SmzuECnR7dR10i2egYAAKhvhA7wK0fDhsOfPaKiPRsq9P+r8yD92u5MSVK3oiwNa9NVO8NitCXrkHbmZsgtt+fcGVt+1O68TL038AqFWYMa7BkA1I69uED/WLdIyw7s1OGifGWVVNwRwCSTbo9P1BO9h/F5hl/aZg1V4qKZcjgcsljK//MwwRarWWeN81JlAABfR+gAv+B2u5X3y2Idnv+Iinavq/Sc1VHt9HbcGZKkYGep/vXLp+q4yq7w3qMVe81slTaL0facw1qa9rvuXbdIJS6nFuzdrMFfzdIXQ65T69CIhnwkADVQ4nRo7Pdv64cDO6s9zy23Zmz5UZ+nbNHrZ1+qc2O7NFCFgPdVtzVnsj2tASsBADRFhA5o0txut3J/+bIsbNi1plxfSJcBihnxD5ksgcrI3KeH9u6Q2+WSJN2fs0cdC+2SpNwNC5S/bbliJ72oHgOu0OnRbdQ7pq0u+f5tZRYXaF16qvotfEGLLrhe3W2xDf6MACrndrt108pPPIFDqMWqjs2i1SK4mVoEh3n+63S79fzm5cp3lGhXbobO+2q2buzaX8+dOUoRgcFefgrAeE9kl00d7HjTq8rIyFBMTIynL3HRTG+VBQBoIggdqpCUlKStW7dqypQp3i4FdeR2OZXz3k0q2vBJufaQzv3U4pJHFdb9Qs+OFDcve1/7jgQOw9ueqocm/0t5yV8qbc4NcmTtlyvfrn2vTFLO2nmKvWaWBrburJUjb9PIb9/Q77np2pufpbMXvaR5516tC9qe0uDPCqCiZzZ9rzd3rJVUFjgkjbhVvWLaevrdLpfyfl4o+7LXNLSkSPdHn6IkS4gk6ZVtq7Rw53q90KmHRvceqYAQRjIBAADUBSvgVSIpKUmXXHKJ2rVjdwJfdvDDe8sFDsEd+6jdXYvV4aGVapYw1BM4fPJHst7bVba+Q0xQqN5IvEwmk0nhp49Q56d+VWTiZM81cjfM185/nqbDnz+pLiHhWjnqNp3dsoMkKae0SMO/fV1vbF/dYM8IoHJf7N2sf67/UlLZeg0fDJpULnAozdir3Q+foZT/jlbezwsVvWWJZie9rMe2fq1mjmJJ0j5HqcZtX6/LX7hSKfMeVGnGXq88CwAAgC8jdDjO0cDh3Xff1dChQ71dDurIkXVAmV8/L0kyWQIVd+vH6vjwaoWfPtwTNhz1xvY/p128etalij1mbYaAMJva3vCm2t25UJaoNpIkZ36mDn/yoPb+60LZHMVaMvRGTejYs6zP7dKUnz7WR7t/NvYBAVRrxpYfPa+n971IF7c/zXPsdjmVOusKFe39udx7TJLGHfhVn695S4PS/1wD4pOWXTUm5Xetubeb9j5/sXJ/WSy3y2n0IwAAADQJTK84RlJSkkaNGqW5c+dq6NChys3N1b/+9S8tWbJENptNt9xyi0aOHFmra+bk5CgnJ8dznJbGgkwNIXvNR5K7bLpE81H/VESf8VWem3fkp5ommTS2Q0Kl54T3HKnQp37Vwbl/V1bSW5LLqYLtP2r3E2ep/V2L9d6gK9QxPFpPJ38vSbo+6WP1sLXRqVFswddY8dls2vbmZUmSWgSH6fb4c8r1ZXz1vAp3/CRJsrbsrNjJsxV26mA58zPlyDmsk3IPa0HuYX1wYLfuST+gfJNZG6LiNLH3BL28ab46/fyFrM1PUtTgqYo+9yYFNItu6McDAADwGYQOx8jLy1NRUZGWL1+uvn37atCgQWrRooXOP/98LVu2TKNGjdKLL76oW2+9tcbXnD59uh599NEK7Xa7XSEhIfVZvuGO/Qatscv86V3Pa1f8SGVkZFR5bklpqSTJbFK150lS0JhnFd1nkrJev0Ku7DSVpG3Trkf7K2rKe7q7Qz+lZmfqnT0/K89RrEuWzNHX51yjMEtgrev3pd/rYzXGuo9dEO1YTemzeazG+DWojfqq/2BhriSpuTVUmZmZnnbHga3KmPdA2YE5QOETZ6m4dS8VZ2VLCpBCWpf9aild2lnqln1QV6z6UPuL85USYtOVvSbq+c2fq3/6Hh2e94Ayvpsl2y3zZYnpUO/P4E0N8QxVfTYBAEDTQuhwjGHDhmn+/PkaM2aMPvjgA1100UV64YUXJJWtgj5t2jTdc889GjdunGJja7ZLwV133VVuMcq0tDT17dtXNpvNJ//B5Qs1lxz+Qwf/KFs8ztKul1p1PbPa880BAWX/NZlr9nwxgxXzyBrtnT5SxSnJcuUdlv3l0Yq7+QO9OvBy/brosDZm7tPW3HQ9sG2p/nfOxApTOmrCF36vK+MrdTe1z+ax/L3+Ikepco+MYGrTLMpzPbejVLtfuF1ylkiSmo+6Xy17X1DttQbFxGhd67t18XdztC49VTnWYN14+qV6ePu3Gpu2SS57qrJnXaIO9/+gwJad6u0ZGoOm8AwAAMD7WNPhOEeDh8OHD+vpp5/2tJtMJj399NMqKSnRqlWrany9iIgIxcXFeX7VNKxA3eWs+cjzOrjXJX95vktuSZK5FsGANTpOHR74UWHdL5QkuUsKlfLCJSpY+ormnXe1Io9ss/fuzg16jYUlGyU+m03XoaI8z+tWIc08r9MXPq2iP9ZLkoLan64Wox+q0fViQyO0bPgtGntS2fQrh8mkh7peqP/2HCeXJEdmiv54epBKDu6s/kIAAAB+yK9Dhz179ujWW2/V+eefr1dffdXTPmzYMC1fvlxhYWHlzjebzTKZTGrfvn1Dl4payFk91/M6uOeYvzzf5S4LHQJqORohICRC7e9cqKiB15U1uN068O5tCl34lN4++3LPebet+kzr01NrdW0AdXd0aoUktQwuCx0K92zU4c8fL2sMsKrtDf+TqRZTn0Itgfr43Kt0b8K5nrZXozrowcTRCjzZpICwVKXO6quC7Z/JXZwh95G/VwAAAPyd306vWLdunUaNGqXhw4erdevWuvHGG3XSSSd5dqzo3bt3hfc8/vjjGjhwoM4444yGLhc1VJy2TUV7NkqSQk85RwFHdpyojvPIgpPmYzI4V0m2SjN/kdtRIDkL5XYWyu0olNtZILejUOaQVgrpcKlMllDFXve6rM076vCnZT81zfxquvqbA/SP7oP13K8/qMTl1Pilb+uX0Xcr4sgICADGKT/SIVxut1v7X5ssOR2SpJaXPKrg9j1qfV2zyaynzxiuuIOf685DoXIoQAssXdSi7SA9ZvpBUqayV4yVJB20hCmwzYUK7Xy1guJGyBRQ+7VdAAAAmgK/DB0cDoeuvPJKvfzyyxo7tuwfiIcPH9bu3bsrPXfDhg2aPn26tm/fri+//LKhy0UtFO/b7HkdEFGznSMCzWUfg0JnqfJLixVcmqn0hX3kKthX7fty1/1DYQn3KqzrTWox+kFZm3fQ/jeulZwOZSz+l+69tb9Wteqk5Qd36Y88ux7c8JVe6D+mzs8GoGZcx4wy+CVzvxyZKSpOSZYkBZ/USzEj/l7na+dueEDjDr+qGJ2kyRqjEln0us7Qqe50XWH61XOe25Gv4r2fqXjvZzIFRSuk4wSFdL5a1uZ967TGCwAAgK/yy+kV3377rbp37+4JHFwulw4ePKjPP/9c8fHxuvHGGz0rd+fk5GjOnDkaPny4Vq9erVatWnmzdPyFsNMuUEBk2dcod90nKjmyoGR1+jRvJ6lsxMNPh3Yr68er/zJwkCRX0SHlrr1bh+Z1VN7m6YrsN1ax18z29B+ac53e6n6Owq1BkqSXtv6kdekpdXksALVwbmwXtQ4JlyTN3f2z1uZle/os0e1kCqhb3l6w4y3lb3pGkjTYvE+vxMd7+u43DVNSWnsVpUqlmQEyh/w5yspdnKmCrS8rY1F/Hf7sVOX+8qRcJdkVrg8AANAU+WXoEB0dreuvv95zfPfdd8tut2v06NG6+eabNW/ePF111VWec2fPnq1rrrlGVqvVWyWjhgJCwtVy/FOe49z5D8jtclX7nnNjO3tef5P8nkrSlpRdK6y9wns/pYg+zytywCuKPOd/ihr8sWznLVBI56slU9nH59jwwRqTpcizryhrL8iWe871evz0stXxXW63blrxiRwuZ70+M4DymlmD9ETvYZ7jv//6g3Rk/QZHVlqdrll8YJmyV071HEcOmK3J/abqH90HS5JK3W5Na32pdu9tprxfnJJ1iqKHfq+QLtfKZPlzMUtnznblbXxQ6Qv7yJG1tU61AAAA+BK/DB369eunESNGSJL27t2rjRs3av369brxxht12223afbs2fr888+VlZXl3UJRJ1GJkxV8Ui9JkmPvBmWvfK/a8we17iyTyoY7/3BgV1mjKUBRgz5Qsx73K+y0OxTadapCO1+lkA7jFdz+YkWd87ZaXLK1Yviw7h5ZIn9QcMcOkqSi3Ws1/pcF6h3TVpK0PiNV969fbMBTAzjW5C591MNWtiPJikN7tKRd2da5juwDtb6WI2eH7EvHSq5SSVJY978r9JSy4PqpM0ZoeNtTJUmHJf2t+xgVmS3KWjJTVlsfRSXOUasJBxU18D0FtR3m+fvCmbND6Yv6qShl0Yk+KgAAQKPml6HDsdq3b68ffvhBzZs397R17txZFotFgYEs/OWLTGazWl35X8/xoY/uk6s4v8rzo4NC1dNWNiXjZ7VSntuqZj0fVWDLs6q9jyXi5MrDh8L9Cul8WNYWZX9+sr+doRnRLRR0ZEj3v39dpnl//HJCzwigegFms/7T9yLP8b/b9lSJKUCO7AO12lnCVWxX5pJRchdnSpKC2o1WeO8/t1MOMJv1/qAr1TWyhSRpc3grPdz1AjnyM2X/4TVJkskSqpBOVyj6gi/VYuxOWWPKAhB3aY7s312kvORn2O0CAAA0WX4fOlRm5syZuvLKKxUaGurtUlBHYV3PUUSfSyVJjqz9Sl/0XJXnut1uDXBulyQ5Zdb66JFqlnBfje91bPgQ2PrIdnrOfDXr7pK17PsQtXjvNv33tHM877n2x4+0NetQLZ8KQG2c3+YUjWrXTZKUYgnRe3G9JGepnPmZNXq/21Uq+w/j5cwp+/vBEt1LUQPfk8kcUO68qKAQLRhyrSKsZbvTLGwVr7fanan0hU/JkZdR7lxLeAfFDF+ukE6Tjt5FuRvuV9byK8p2ywEAAGhiCB2OUVxcrDvuuEOrVq3S888/7+1ycIJaXv6cZClbxNH+/Sy5j2yXd7yi3XPVL+c7z/FPMeMqfFNRE5aIkxV9wZcK7nDpkRaHwk6TgtpKroIsXbj4SV1/cl9JUp6jWGO/f0vFVdQEoH7868yLFHBkFNLsk/orJTiyxus65CU/rZK07yVJ5tA2ih7yuczWsErP7RrZUh8MutIzVWt6p3O0IiBEhz/9vwrnmiwhijznfwo/89+eEVJFu+cq/ctz5Mz/60VsAQAAfEmTDR0cjtp9M/f++++rZ8+ekqTVq1fLZrMZUBUaUmCLDgpKKFu7w5l7WFnL51R6Xmn6GvVXqoJU9mdm1u/JWnHwjzrd0xQQpKiBHyi0681lxyYp9JSyX4U7V+pfsR11RkycJOm37EOavnlZne4DoGZOjWqpm7r2lyTlWYJ0w+njtXfPzzV6b2n6Gs/rqHPeU0BYXLXnj2jXTU+fMVyS5DKZ9X9dL1Taxi8qPddkMqlZ97sVff5imayRkiRHxgalL+qrkoM/1qg+AAAAX9AkQ4cVK1YoPj5e27Zt+8tznc6ynQQmTJigzZs3a8aMGQoPDze6RDSQsMHTPK8Pf/ZwpWs7BLa5UOGmEt2hVZLKts6csOxdZRbXbaizyRygiP4vqVmvJzxtQW2lZj2k4rXv6t2BV8hy5KebT/yyRKn5WXW6D4CaeeqMEeoRGiFJSgmJ0iXbNyiruPAv3xfQ7CTPa3NgZI3u9Y+Ec3Vui46SpNSQKD3eIl7Oaj7jQW2HqvmoNQqIOEWS5CrYr4wvByl79d/kKs2r0T0BAAAasyYZOkybNk1Op1ODBw+uNnjIzs7WgAED9P7778tsNstsbpK/HX7N2q6nIvpNkFS2an3GVxWnzQS1OV+moGjdqjUaaCob2pySn6Vrf/ywzou7mUwmhZ/+gKIGfywFhJTVEi05s15W54B83R5ftr5DgaNU965j9XrASBGBwfpq2C1qV5glSdoisy7+bo4KHaXVvi+gWSfPa0furhrdy2Qy6b89RyhKZX93zI/trrnJ31b7HkvkKWo+cqUC21xwpMWtgt9eVPqCHio+Mr0DAADAVzW577JXrFihwMBArV27VrGxsdUGD2FhYWrfvr0ee+wxlZSUNHClaCgtxz8pBVglSRmLn5Ujp/wCjiazVcHtxyrA5NaL7s/V+siuJZ+nbNZLv/10QvcO6TBeMcOXy+0qW1vCHOxQ+vzeuv+kWLUKKRtR8/6ujfrxQM2+oQFQN7GRzfVe3l61KC4bPfDjwd26/Id35HA5q3yPJfzP0MGZV/PPaJuQCE1vEes5vnX7Wu3Lz672PeagaEVf8JUi+s+SydLsyD13K/PrIcpecZNcJTk1vj8AAEBj0uRCh0OHDumOO+5QdHS0lixZUm3wYLFYNHfuXC1fvpztMZuwwJadFD3kFkmSqyhPhxc8XuGckI6XS5JamAo0OybNsxjc/eu/POHpD4HNz1SzHq/JcXSktDNLpd+epye6dPacc9vq+XK6XCd0HwDV69plgF5N/kQRpUWSpC9StmjKTx/L5a78sxdwbOhQw5EOR112cj9dfGCzJCnL5dLkpLlV3ucok8mssFNvUosxmxXY5kJPe8H2V3R4QXcV7/u6VjUAAAA0Bk0udBgzZowmTpwoSdUGD/v2lQ2jt1gsatmypVdq9QV5m7/TgQ/uUdHeX7xdyglpfvGDMoeUzem2L52tkoO/l+sPbD1Y5uCy/S37pc/Traf2k1S2y8Rtq+af8P0jzrxKlsipKs0qO3Y7cjV88xT1jSj7ieYvmfv16vZVJ3wfAFUL6zZYp+Sn66VNnyn4yPSHt39fp3+sXVTpVKqAZh09r2s6veKooLbxemDH92pbWDbCYcn+HXphS1KN3hvQrL2iL/hKkWe/4Vlk0pWfosxvhyl348N1nvYFAADgDU0udDheZcHDjBkzNHLkSLn4yXK1ilJ/1d7nzlfeL4u066Gechz6/a/f1EhZwpsrZuS9ZQdOh9LevkXuY+Zzm8wWBZ80XpLkduTpn5GHFRda9o/9+Xt/1RvbV59wDa0mvCBH9hkqOVx2bHYX69HcV4+MqZAeWP+l0goYQg0YJaRzf5ksgeqds1//3bvKs5XmfzYv079+/aHC+ebACJmDmkuSnNlb5f6LkQrHsjSLUWRYlJ7e+qXMR0KCe9ct0s6c9Bq932QyKfTk69RizGYFxY30tOf98pjyfn60xnUAAAB4W5MPHaTywUO/fv300ksvaeHChSwc+RccmamSJNvgGyRJrrya/WO5sYq58A5ZbG0lSfmbv9X+N6eW+4lhSOerPK/d6+7Q891O9RxPXTFPn+3ZdEL3N1uDFHfLPBXuilBx2UAbna40XaGy69pLCnXzyk/4KSZgEHNgiEJPHSxJSty1Qv+1/vlZu3fdIn28u+KILkt0T0mSM3+vCrbOqtX9rM076Izsfbo+ZZ0kqcTl1Iu1XCcmIKytbEO+UES/mdKRkCQv+QmVpq+v1XUAAAC8xW++646OjtbYsWPVokULLV26VHFx1e+3Dims+wWKOudaHfzoXrUY+7gCO/X3dkknxBwUqrhpH8kUWLabRHbSWzr08T89/YEtBygs/o6yA1eJEjffrHu79io7dLs14Yd39d3+HSdUQ2CLDooaOEUF26WilLK2B7VMrVW24MOCvZv14e6fT+geAKrWasK/ZbIGS5LO/ebfeiruFE/f1T9+oLWH95Y7P7zXY9KR8Ui56++TM698f3XMR+4zZc8qNbOULSY7Z8ca5ZQU1apmk8mksG7TFN776bIGt1NZSZPldrIAMgAAaPz8JnSYPXu23n77bQKHWjCZA9Rmyhx1e6NELUY/6O1y6kXoyWcp7pYPJXOAJClj0TPlttEMP/PfCmp3sSTJXZyhOw88oaldeksq+ynl6O/e1OrDe06oBtugKZKkwt8lR26MIk3FelZ/bql326r5Si8uOKF7AKhccLsEtZo43XN8yRf/pxs79pQkFTkduvi7N7U3z+7pD2w5QKHd/iapbOpV9sqbajwayXQkaGjmLNHkzmV/j+SWFuut39fWqfaw0+6WtXnZejOOrF+Vl/xEna4DAADQkPwmdEhMTCRwqCOTyfTXJ/mQ8F4Xqc21r3mOD35wl+xLX5FUFrREDXxPlugjIxxytuqJwnd0eYcekqR8R4lGfPuGNtsP1Pn+QW26KeTksyVJuesyZG1xvi4w7dI4bZEkpRfn695N39T5+gCqZzvvJoWfcYkkyZV9UPesfUcXxJ4sSTpQmKuLlsxRbumfoxHCez+hgLCTJEnF+75U0a73a3Qfk+XPXZGmdTnD8/rFLT/95U4WlV7PHKDIxDclc1mYkZf8lEozNtT6OgAAAA3Jb0KH7t27EzjAI2rgtWp5+XOe47S3blJW0tuSJLO1maKHLJA5uJUkybH/K80M/UXD2naVJGUWF+iCr1/VtuxDdb7/0dEOklSacbICWw3UY1qqFsqXJC3Y/5s+/ePE1pAAUDmTyaQ2170uS3Q7SVLJr99oliNLp0aW7WSUbE/Tlcve92xja7Y2U+RZr3ren73mdjmLDv/1faxBntddQpppRFzZOjG/56bry9StdardGtVN4b2OLCTpdior6VqmWQAAgEbNb0IH4HjNR/xdLcY84jne//p1yl79oSQpIKydbOd9JpnLflJZ+tt0vdPepLNbdpAkpRXmqP/CF7U0rW47ekT0vVTm4HBJUvbKDxSZ+L6aR56kZ7TEc84tKz9RJtMsAEMENItW25ve8yzOWPzpg/qgdTvFBIVKkr5I2aJ71y3ynB/U9kKFdL5aUtnUq5zVt//lPY5Or5Akt6NEf4s/x3P8z/VfquiYHXRqo2yaRV9JksOerLzkJ+t0HQAAgIZA6AC/1nzM/ylmxD/KDtwu7Zt9pbKS/iepbC535Fl/TsMoXXOL5nVrqz7Ny346mlVSqKHfvKa3d9R+frY5KEyRA66QJLkKspS/JUnRF3ypkSE5ukjbJEkHi/L08MavT+TxAFQjrOs5ajHm4bIDp0OBr12lt8ObKfDImi//2bxM965d6JkKEdFnuszBZaMhinZ/oILf/1ft9U0BVs9rt6NYF7Y5RWfElI24S7an6b9bfqxT3Saz5cg0i7JQNC/5KTnzU+t0LQAAAKMROsCvmUwmtbzsGUVfULZQnFxO7X/tGmV8VbbQXGiXqxXW/e9H+krlTrpcXyWcpLEnJUiSSl1OTU76UA9v/LrWW1026zXa87rkwA5ZwjvKdv5CPWVernAVS5Le37lBpS7nCT4lgKo0v/hB2c69sezA5VTHeffquZI/F5J87tcfNOa7t5SanyVzcIwi+s/09GWvmKLitKWVXtftdqtw1+qyA3OAAsKiZTKZ9NY5l3vOWXkCi9Jao+IVdtrdR27mUMnhlXW+FgAAgJEIHeD3TCaTWl05QzEj/u5pO/jB3Tr40f1yu1wKP+MZhZx8XVmHq1TFyy/TO12i9ffugz3nP/bzt7pq+QcqdjpqfN+AMJvntasoR5IU2LyP4jqP0zCVTdvILCnUD2k7T+DpAFTHZDar9dUvq8XYxzxtFyS9pmfTt8pyZOrFFylbdOqnz+m5TUsV0O4ShZ12V9mJrlLZl45VadaWCtctTtmkkrSyUUth3c5TQGikJOm0qNYKOTICYm9e1gnVHtgq0fPaUUkNAAAAjQGhA6AjwcPlz5VbXDJj0TPa9dDpyln5gSL6zSoXPGT/ME6PtXRo1oCxCjjyjcl7uzbowq9frfE6DEfXdJAkV2Gu53VY/O0aqe2e44/++OVEHg3AXzCZzWox+iG1u/MLmUMiJEmjfl2k17d9pdZHFoPMd5To3nWLdPqC6VrTZqqCTxorSXKXZMn+7XA5C9LKXTNn7cee1xF9L/vzXiaT2jeLkiTtybfrRFgiu3leO7J+O6FrAQAAGIXQAThG8xF/V5vr50hH5nQXp/6qfa9M0s77TpWz6AwFdypbSE6uUtl/GK+rrbu08Pzr1OzIgnHLD+5S3y/+q0//2PSX0y0CjnxzI0muoj9DB2v06RocE+uZYjF/90Y5mGIBGC685yh1fHiNAmPLdpnos3+TFvzwvG6ymBSgsq2Dt2Yf0gXfvKab3RcpPXqgJMmZv1eZS0bKVZonqWxqhSd0MAco/Iwx5e7T/sgop8ziAuWVFte53oBmJ0kBIZIkRzYjHQAAQONE6AAcJ2rgtepw/w8K/X/27ju8qfJvA/id1d20pbtQ9iyUvUH2EMoSEPUnS0EQUZaooCLwUhAQEQQFRAQRFAcIsmRJUZYsoayyV2kpXXSlI+N5/wgEStrSQtOTNPfnurhozjk5+T5NT5vceUbNdqZt2vhriFs9GvG/7YDMyThrPIQeKf8MxnPpEdgfNhpl73efvpKWiH57v0ejPxZgy61z+YYPj/Z00Gem5trnUnkEusA4rCJBm4OIOxxiQVQSHANroNLUf+He0Djnips2E+/snocNR1aiaerD3gw/3ziDFiktsdihGzRCCV3Sf7gXMQDCoIMu9nyuoRVKd59cj1He1dP09a2Me09dq0wmh9LT2NtBl3IBwlD44V1kHYQ2CyJbg2vhrc3+Zd+KlLo8IiKiYsHQgSgPLtVbo+Lkvag45RDcGvQybdelxCFp+xFk3Vbe3yKQengUqtxeg397jEH7gCqmY/9Luo2eu79Di62LsC+P0EDu5Gb6+tGeDgAgC3gevZwedr3+5dKBYmoZET2JwlmNcu9sMM7zoDBe61U1ifjuxI+Ye24rfLONPRoydFrMyg5BKwzHWhGKrOjtuLOmIdL/mGI6l7rpi2bnfzC8AgBuPkPoADwyxMKQA33a1Wc6F0nAYIDIpyebY3BdOJULLeGCiIiIip/yyYcQ2S+Xqs1RftwmZEWfQeKW2Uj5dx1g0CPzog4iB3CuZDwu7fgHcEm6jN1dl2LvnSuYcuJP08z0/8bfRPvtS/Fh3Q6Y1qALlHIFsu9cQvzvU02P83jooBPAFXULIMt4e0c0x2sTlaQH8zx4tR+J9NM7kH76T2Sc3oGwu1Fom3gVX1dsgbVlG0AnVyAOrngPXbAHlbBQ/yccZDnIkQGQKeDe6AWzc6c9MqTiWXo6CF0mDJkPe1/oUi9C6VH9qc9H0pDJFaj08X6pyyAiIrIYhg5EheBUrg7KvrkGvv1mIGnHF0g/sxNZ1y9A6ACXasZjsq4tR+bVfWjV+Tf80+k17Ey4hSknduB4YjQEBGZG7sG2G5F4NfkGnju0Cm7aTNP5nas0M329784VvBmxClGZDzsiVXZ2LrG2EtFDSrUfPFsNgmerQRAGPbKuHUd65HZMPb0dLx9dhcUVW2Krv7G3wZ+ohm7wxorAP1DZKREK136moRU5eh2OJURj861zmHdmn+n8NdS+T1WXXhOD5L/6QJtw1LhBpoTCvUrBdyIiIiKSAEMHoiJw8K2EgIFfAgD06UlI+fdn3Ns/GU7BKZDJAZnsIuLW1UXmJaCiwQU/qv2wNqg+ZntVQo5Mjv9S4vGf3AVOzd9Ap4RL6JcWi7D2w+HTbgTuaFIx8egWrL16wvR4chgwwkuP2d0mSdVkIrpPJlfAuUpTOFdpCt8XpqKCNhudszOwJ+YiXj26BQk5WbiKMgjD/zDXaxcCVOdx/M9PcNjgh8MJt5Gp1+Y632eNe+C5gMpFrkObeAJJe3rBoLltrMvBE17tfoXKs9YT7klERERU8hg6ED0lhVsZlOk4Cp6thyBh21vQJX8PmRxw8AFU3oD2rgZZN6/jpcjrqOPmh+nVO+OsOgAAkKVQYYt/CLb4h6BcYhK6/bsRP187hVRtlun8TXAbn5dzROtO30Em4/QrRNZGrnIEVI7oUr05TgTVRP+93+NIwi1o4IC3EQZoAcRqAFzPdT8ZZJhSvxMmhrYr8mNmXl+Pe/8MAvTGnlIKdTWU6bgZSo8az9weIiIiIkvgOxmiZyR3dIHfC6vg1XEbIHMFAMhkgIM/oG4CuNWXoZ7qLn45sRYbI3/Dm44O8HN0Nd0/WpOC5Rf/NQUOPsjAQmzH9uBMtO64nIEDkQ0IdvPE391HY2SN5nnur4IkvKq4gG8quOBan3cwvUHXIp1fCIG0UzNxL6K/KXBwCGgPn7DDDByIiIjIqrGnA1Exca7QDY7/i4XmwjfIOPeFqeuzyktA5QUo1CEIrjoEXb1q4XOXCtidqsPqa6ex+dY55Bj0kMOAITiF93EAnr7NUKbdOsjkKolbRUSF5ahQYmnL/qjn4osNcVGo4eyEJprDaHDnR/giFTAAuLkFspgvkOBRE3KXslC4loXCpSzkzkFQuJaFTKWGITsRhqx4GLISjP9nJ0CfehE5cf+YHsul+giomy/m7wgbkHJoFLTJp/PeKQsGhKJkCyIiIiphDB2IipFc5Q63Ou/CtdY7yLz6IzLOfgbdvXMAAH3qOaSf+MB0bBMAzZx8McurGv5KF6iZfRE1ZIlQ+bYAGq+GTOkkUSuI6FkMCK6DUfXb3r81BLq095FxejY0l1cCBi2ELh3axGNA4rGin1wmh7rJF3Cp9Q5kMlmx1k2WoU0+DV1yJJRedc13CgWEYG82IiIq3Rg6EFmATOEAl2pD4Vx1MLKjtyL99Bxo7x4wO86QFQ/3rHj0BgAZoPSqhzKdtiI53VDiNRORZSjdK8Gj5TK41v0QGWfmIjt6G/SaGMCQU6TzyF3KwaPlN3Aq181ClZKlKL3qwqe7+bKYhqUjJKiGiIioZDF0ILIgmUwOp+CecAruef/TrrPQp1+DLv0a9GlXoU+/Bn36TUDooPSsgzJddkLu6AWkJ0pdOhEVM6VbBXg0/wqAcY4GQ3YCDJoY6DW3YdDchl4TA6FNg9zRB3InH8idfHN9LXPw5BwvREREZHMYOhCVEJVXKFReoWbbhUEHQ9Zd45sKjs8msgsymQwKJ18onHyhKlNP6nKIiIiILIahA5HEZHIlFC5BUpdBREQW8F5qJZzTOUO1dbHZvgsqF9TQaiSoioiIqOSwnyYRERGRhZzTOeOcziXPfTW0GtTQZpZwRURERCWLPR2IiIiILChEqcH+sLfNtl8Lby1BNURERCWLPR2IiIiIiIiIyCLY04GIiIjIzmXfisS18NbQ6XRIVT58eZjlEwK5o6uElRERka1j6EBERERkx5zKma+s9IDI1sBQgrUQEVHpw9CBiIiIyI4FDl1i+joxMRHe3t6m27KlI3BB5YLWeay+AQChXoFY0rKfxWskIiLbxdCBiIiIiPJU0OoakcmxJVgJERHZKoYORERERJSn8JRrAIBKb35jti+/3g9ERESP4uoVBRBCSF0CERERERERkc1i6JCHEydOoGnTplAoFKhcuTK++OIL6PV6qcsiIiIiIiIisikMHR5z48YNPP/88xg+fDiOHTuGl156CZMmTUKnTp2QkpIidXlERERERERENoNzOjxm6dKl6NatG0aMGAEAaNiwIXr37o0ePXqga9eu+Ouvv+Di4lLo86WmpiI1NdV0OzaWky4RWQNem0RERERElsfQ4TFJSUnIysrKta158+bYtWsX2rZti/Hjx2PZsmWFPt/8+fMxffp0s+3JyclwdnZ+5npL0qNv0GyFLdYMsO7i9OjSb48qTdfmo6zxOSgKW68fYBsKK79rk4iIiEoXhg6Pad++PYYMGYJz584hJCTEtL1Bgwb46quvMHToUEyaNAmVKlUq1PkmTJiA4cOHm27HxsaiadOm8PLysskXXKy55LBuyypt1+ajWL/02AYiIiIiI87p8JgXX3wRtWvXRv/+/ZGYmJhr38CBA1GuXDns27ev0OdTq9UoV66c6V9gYGBxl0xET4HXJhERERGR5dl1T4e4uDisWrUK8fHx6Nu3L1q2bAmFQoFff/0VrVq1QseOHbFt2zYEBQUBAGQyGdzd3eHl5SVx5URERGQtYleNQlb06Tz3icCKgJyf8RARkf2y27+CBw8eRJ06dbBt2zZs374dzz33HLZu3QoAqFKlCvbu3YuUlBQ0aNAAy5Ytw9mzZzF58mTI5XJ0795d4uqJiIjIWmRFn0b2rcg89wmDHjAYSrgiIiIi62GXPR3S09PRv39/LFu2DH379oXBYED37t0xceJEhIWFAQBq1aqF//77D9OmTcNHH32E1NRUdOvWDbt27YJKpZK4BURERGRNHIProtLH+822y1YMlKAaIiIi62GXocPatWvRtWtX9O3bFwAgl8sxbtw4dOvWDRkZGXB1dQUAeHp6YsGCBViwYAEMBgPk7B5JREREj1F6XoFclYGEba3Nd8qCIfTAtXDzfdm3IuEYXLcEKiQiIpKOXYYOcXFxuWatB4CqVasCQK7Q4VEMHIiIiCgvclUGZCpN3jtlKkCW9y7H4LpwKhdqucIeo0uOzDsYAaDyCoVHiyUlVgsREdkPuwwdpkyZYrbNxcUFACCEMG07duwYGjduXGJ1ERERkW0SWhf4dDcfXuGwdTEAoNLw1SVdUi4qr/zDDV1y3vNREBERFQe7DB1kMvOPHB5s0+v1AIB58+Zh+fLl+O+//0yBBBEREZEtKqgXQ369Hx7IvhWZ5/CQLJ8QyB3Ne4cSERE9yi5Dh7w8GD4hhDAFDnv37mXgQERERHaroOEfIlsDrstBRERPwtDhvgc9HT7//HNs3boVe/fuRVBQkMRVEREREUkncGj+PSRkS0eUYCVERGSrSu3siDqdrkjHKxQKAGDgQERERERERFRMSmXocPDgQYSEhODChQtPPPbBHA5eXl6YOHEiAwciIiIiIiKiYlIqQ4fRo0dDr9ejXbt2BQYPKSkpaNGiBX788UfI5XJ89tlnDByIiIiIiIiIikmpCx0OHjwIBwcHHD16FIGBgQUGD66urihfvjz+7//+Dzk5OSVcKREREREREVHpVupCh7t372LcuHEoU6YMdu/eXWDwoFQqsW7dOvz9999wcHCQoFoiIiIiIiKi0qvUhQ59+vTBK6+8AgAFBg+3b98GYAwe/Pz8JKmViIiIiIiIqDQrdaHD4/IKHhYsWICwsDAYDFxdmoiIiIiIiMhSlFIXUBIeBA+dOnVCs2bN4Ovri71790IuL/WZCxEREREREZFk7OZdd5kyZdC3b19T4FCuXDmpSyIiIiIiIiIq1ewmdFi6dCm+//57Bg5EREREREREJcRuQofWrVszcCAiIiIiIiIqQXYxpwMA1KlTR+oSiIiIqBSarGiNKFkZOGxdbLYvMjkWdb0CJaiKiIjIOthNTwciIiIiS4iSlcF5WZk899X1CkQoQwciIrJjdtPTgYiIiMhSaokk7A97W+oyiIiIrA5DByIiIqIniF01ClnRpwEAOp0OqcqHL6GEfzBkcoVUpUnqgsoFrfMYVgIAoV6BWNKyXwlXRERE1oahAxEREdETZEWfRvatSDgG1zXbJ5MrALn9jVitoc3Md19kcmwJVkJERNaMoQMRERHREyg9r8DBF3AMBnRaQKl6ZOddQKZ0kqw2qYSnXAMAVHrzG7N9+fV+ICIi+2N/sTwRERFREclVGZCpNHnukyldIFe6lnBFREREtoE9HYiIiIgKQWhd4NN9PxITE+Ht7W3aruKn+kRERPli6EBERESE3JNFPk6p1kDu6FLCFZUcXXIkEra1hk6rQ4Iq98tDlVcoPFoskagyIiKydQwdiIiIiFDwZJFyRxfIHUvnEAqVV2i++3TJkU91zpy7V3DeINB46Yg899d2dMb3ry18qnMTEZFtYehAREREdJ9jcF1U+ni/2faEba0lqKZkPNqL4fGhI0/b7mrpCdAbBJBH75AohROQnf/KF0REVLowdCAiIiKCcYUKuSojzzfa78arEaUoB9XWxdDpdFAqH76EikyORV2vwJIs1eqZVrYYax7g5Nf7gYiISieGDiVMp9MBAGJjbW/96uTkZGRm2tYnE7ZYM8C6i1tAQECuNwh5seVr81HW+hwUlq3XD7ANRVGUa7PLkjfg4Ols2YLkTYFsQHbZfBjFSa07AKCxPhk6nR56pcK0rwacUEnrgOjoaMvWVwIef+6TErKhu3ceMaubmB2brTwPkZOJhNluZvuEIRMyB2ek5XE/XVowLsk8UP+zvsVb/BMJALISfsySUnxt2/LKl4W6NomICksmhBBSF2FPjh49iqZNm0pdBpFduXXrFsqVK1fgMbw2iUoer00i61SYa5OIqLAYOpSwrKwsnD59Gr6+vjaVIMfGxqJp06Y4cuQIAgNtowupLdYMsG5LKMwnNrZ6bT7Kmp+DwrD1+gG2oahs8dosDc9xftg222SJtrGnAxEVJ/42KWFOTk5o0sS8q6GtCAwMtLnk2xZrBlh3SbP1a/NRtvocPGDr9QNsQ3Gy1mvTWr4/lsC22abS3DYism1yqQsgIiIiIiIiotKJoQMRERERERERWQRDByoUtVqNqVOnQq1WS11KodlizQDrpqdn68+BrdcPsA32oDR/f9g221Sa20ZEpQMnkiQiIiIiIiIii2BPByIiIiIiIiKyCK5ekY9///0Xv/zyCwIDA/H222/DyclJ6pKIiIiIiIiIbAp7OuThs88+Q1hYGC5evIipU6di2LBhUpdEREREREREZHPY0+ExGzduxFdffYVTp06hbNmy2Lx5M95+++2nPl9qaipSU1NNt3U6HTIzM1GtWjUolfz2E0mF1yaRdeK1SWQbdDod7ty5g4CAAF6bRFQg/oZ4zJw5czB+/HiULVsWAODl5QV/f3+MGzcOQgi8/fbbqFatWqHPN3/+fEyfPt1se2RkJIKCgoqt7pKQmppqczMj22LNAOsuTt7e3nluL03X5qOs8TkoCluvH2AbCsuWr83S8Bznh22zTcXZtvyuzcfduXMHwcHBuHXrFsqVK1csj21pSUlJKFOmjNRlFJq11HstvDUAoNLH+8323V5m/LkrOzLVrN6Ebcb7+XQ3v581sJbvrz1g6PAYjUaDQ4cO4Z133sGdO3fw1ltvwcPDAy4uLli/fj2+//57HD16tNDBw4QJEzB8+HDT7djYWDRt2hReXl6F/qVuTVhzyWHdllXars1HsX7psQ1Pz1auTWuqpbixbbapNLetuNjaon2s17JsrV5bxtDhMdOmTcPLL7+McuXKIScnBx06dMDPP/8MmUyGSZMmoW7dupg3bx6WLVtWqPOp1epSm6oT2TJem0TWidcmERFR6cKJJB/zwgsv4ObNm4iIiECNGjXwzjvvQCaTATC+EOrQoQNSUlIkrpKIiIiIiIjI+jF0yIO/vz+qV6+OGzdu4Pjx46bt6enp2Lt3L1588UUJqyMiIiIiIiKyDRxeUYAXX3wRkyZNQnp6OqpWrYrPPvsMnTt3Rr9+/aQujYiIiIiIiMjq2WXokJGRgdjYWFSsWLHAJX7mzJkDvV6POXPmQK1W491338X48eNLsFIiIiIiIiIi22V3wyvmzZsHPz8/VKtWDf7+/qZgIS8ODg748ssvkZaWhtu3b2PChAmm+R2IiIiIiIiIqGB2FTqsXbsWy5cvx7FjxxAXF4cxY8ZgypQpCAsLQ0ZGRq5j9+3bhyVLlkhUKREREREREZHts6vQYcmSJZg4cSJq1aoFPz8/TJ06Fbt27cKhQ4fQt2/fXD0eNmzYgLfeegu7du2SsGIiIiIiIiIi22VXoYNOp0N0dHSubW3btsXmzZsRERGB2bNnm7YvWLAAK1euRMeOHUu6TCIiIiIiIqJSwa5Ch7CwMCxevBixsbG5trdp0wZTp07F3LlzkZ2dDQCQyWQYOnQo5HK7+hYRERERERERFRu7ekc9duxYqNVq9OnTB+np6bn2jR49Gunp6YiKipKoOiIiIiIiIqLSxa5CB7VajY0bN+LChQvo1KkT4uLiTPtycnKgUCgQFBQkYYVEREREREREpYddhQ4AUK9ePURERCAmJgahoaGYO3cu1q9fj169emH8+PHw9fWVukQiIiIiIiKiUkEpdQFSqF+/PiIjIzFv3jysWbMGADBs2DCMGTNG4sqIiIiIiIiISg+7DB0AwNPTE+Hh4QgPD5e6FCIiIiIiIqJSye6GVxARERERERFRyWDoQEREREREREQWYbfDK4iIiIiIiOjpTVa0RpSsDBy2LoZOp4NS+fDtpTY5BCHKTKyUsD6yDgwdiIiIiIiIqMiiZGVwXlYG9fLYd07nUuL1kHVi6EBERERERERPpZZIwv6wt5GYmAhvb2/T9mZrRkhYFVkTzulARERERERERBbB0IGIiIiIiIiILIKhAxERERERERFZBEMHIiIiIiIiIrIIhg5EREREREREZBEMHYiIiIiIiIjIIhg6EBEREREREZFFMHQgIiIiIiIiIotg6EBEREREREREFsHQgYiIiIiIiIgsgqEDEREREREREVkEQwciIiIiIiIisgiGDkRERERERERkEQwdiIiIiIiIiMgiGDoQERERERERkUUwdCAiIiIiIiIii1BKXQARERERERGVPkKnQcK21nnuU3mFwqPFkhKuiKTA0IGIiIiIiIiKlVzpCkM++3TJkSVaC0mLoUMBli9fjoEDB8LZ2VnqUoiIiIiIiGyGwr0KFAB8un9jti+/3g9UOnFOh3xMmTIFI0aMwA8//CB1KUREREREREQ2iaFDHqZMmYINGzagc+fOWLhwodTlEBEREREREdkkDq94zIPAYe/evbh48SKee+457Nq1C507d36q86WmpiI1NdV0OzY2trhKJaJnwGuTyDrx2iQiIipdGDo84uOPP8bvv/+OvXv3ws/PD35+fmjSpAkWLlz41KHD/PnzMX36dLPtycnJNjdXxKMvAm2FLdYMsO7i5O3tnef20nRtPsoan4OisPX6AbahsGz52iwNz3F+2DbbVJxty+/aJCJ6WgwdHlG5cmVT4PDAuHHjMHDgQFy6dAnVqlUr8jknTJiA4cOHm27HxsaiadOm8PLysslf6qy55LBuyypt1+ajWL/02IanZyvXpjXVUtzYNttUmttGRLaNocMjXn/9dbNtL774It5//30sWrQIX375ZZHPqVaroVari6M8IipGvDaJrBOvTSIiotKFE0k+gUqlwttvv42VK1ciJSVF6nKIiIiIiIiIbAZDh0IYMWIEDAYDvvvuO6lLISIiIiIiIrIZDB0KoUyZMhg8eDAWLVoEg8EgdTlERERERERENsEu53TIyMhAbGwsKlasCKWycN+CcePGISsry8KVEREREREREZUedtfTYd68efDz80O1atXg7++POXPmQK/XP/F+NWrUwMqVKyGX2923jIiIiIiIiOip2FVPh7Vr12L58uU4duwYvL29sWTJEkyZMgV79+7F+vXr4erqajp23759OHfuHEaNGiVhxURERERERNL52KMSLqic4bR1sdm+87IyqCWSJKiKbIldfWy/ZMkSTJw4EbVq1YKfnx+mTp2KXbt24dChQ+jbt2+uHg8bNmzAW2+9hV27dklYMRERERERkXQuqJxxQeWS575aIgk1GTrQE9hVTwedTofo6Ohc29q2bYvNmzejc+fOmD17Nj766CMAwIIFC9CgQQN07NhRilKJiIiIiIisQg2tBvvD3jbbfnvZhxJUQ7bGrno6hIWFYfHixYiNjc21vU2bNpg6dSrmzp2L7OxsAIBMJsPQoUM5hwMRERERERHRU7Krd9Rjx46FWq1Gnz59kJ6enmvf6NGjkZ6ejqioKImqIyIiIiIiIipd7Gp4hVqtxsaNG9G2bVt06tQJmzZtgr+/PwAgJycHCoUCQUFBEldJRERERERUskYdXI/TybFm2y+oXFBDq5GgIiot7KqnAwDUq1cPERERiImJQWhoKObOnYv169ejV69eGD9+PHx9faUukYiIiIiIqESdTo5FZB6hQw2tBjW0mRJURKWFXfV0eKB+/fqIjIzEvHnzsGbNGgDAsGHDMGbMGIkrIyIiIiIikkZdr0CzCSOvhbeWqBoqLewydAAAT09PhIeHIzw8XOpSiIiIiIiIiEoluxteQUREREREREQlw257OhAREREREVHBlJ5XIFdlIGGb+TALmUoDoXWRoCqyJezpQERERERERHmSqzIgU+W9eoXQusCgdS3hisjWsKcDERERERER5UtoXeDTfb/Zdk4ySYXB0IGIiIiIiIieSvatSFwLbw2dTodU5cO3l1k+IZA7shcEMXQgIiIiIiKip+BULjTffSJbA0MJ1kLWi6EDERERERERFVng0CWmrxMTE+Ht7W26LVs6QoqSyApxIkkiIiIiIiIisgj2dCAiIiIiIrJzOXevwJCdYTY5pFKtgdyRy2LS02PoQAUy5GQi+/ZZOFVoIHUppZoQBshk7HhEZG+EEIA+G0KXAaHLAORKyJ0DIZPJpC6NSoDQZUGbfArahCPQJhwD5A5wqT4cDr7NpC6NiOyQITsDItt8aUy5owsnhKRnwtCB8qVLjcfVj+vCoMuGU3BduA37WeqSbJLQ5yAnbh+yo7dBr4mB0KVDaDMgdOkwaNMhshNhyE6A3DkQ7g2mw6X6G1KXTERPKSlbg++uHcfps4ko5+qBel5BqFcmCFVdXKCN3Yms679Cm3AUQpsGodNA6DWAyD3NlszBE0rP2lB61ILSoyaUnsb/Fa4VIJMrJGoZFSdDViKS972EnLi/AYM2177MS99C5dcKTuX7wCm4JxTq6gyhiKjEyBxdUGls7qUxE7ZxWUx6NgwdKF9p//0BXcodlH1rHW5//TKcok8Bfl2kLssmGLTpyL79J7JubkT2rS0Q2pQCj08TDnDVxCLl4AgoXCsATo1KqFIielY5eh22R0dh9ZXj2HzrHLQGvdkxTtChBhIQgiQ0gAv64QacZbo8zydy7kF79wC0dw/k2i53CYJn6+/hGNTJIu2gkpN+9nPkxO7JtW2fqIAr8EIVJKNG3En4xx1A2rH3oHCvCqfgnnAo2wtyh4rQpcZBn3oXupQ449dp8XCq0BAerQYxnCAiIqvE0IHy5V6/B+44uOD21y/DuWoLKINqS12S1RJ6HbKizyDjzNfQpR6EyL4AwPwNhUEAN+GBs/DDGXlZnIU/zghv3IEzQnAXX4utqPnPYCjaRADwNrs/EZUcQ1Y67h34AdqE6xB6LYReC9z/X+i0iJSpsNG/BjZkZCAhO6PAc2VBiVMIwCkE4CeE4hdFY/zmEQlXlRNkSlfIlC6QKV0htOnQ3TsNfdo1ACJ3PZoYJO3qBs/Wq+Bc5VULtpwsSRj0yLyy2nhDroJL1dfxo74a3rocm+s4T2SimkhCjdQEVD8bgYZnf0LthDvIugIYMs3Pq42/Bt8XppZAC4iIiIqGoQPlS+nhj5pLU6BNiobKpwKSkpKkLskqCIMeObEXkHntGLKuH0PmtWPIiTsB5yrZUHmZH5+VI8eW9GpYpwxFpEsg0hUOxh2PLVx8Dn7ohoGYnbkbA06NhQj8k59aEUlEn5mKG3M7I+vqEbN9cQ5umFG9I/b6VAWS7ubaF+yoRN/sg+iOC0iEM87BF+fhi3OyQFwSntDBeE0f05fB266j8HuHoVDmMWRC6DKhS70EXcp56FKikHMnAjl3IgChw71/BkKvuQ3XOu/xd4QNyondDYPmNgDAqXwfHCo/Du/s/s7suHtwxlGUxVGUNW0L9klBb+8o9LhzHuWvJEI8km3Hb5wGmcoRPj0mWbwNRERERcHQgQokUyjh4FtR6jKsRtqp7bjzw2ho46+ZtjkEAu71AdkjV5M+E7iV5IY1srr42bsuEr3yn3zH18kVrkoHXE9PRiZUGItuOHjnDBadXQzfOu9YsDVElBdDtga35vcwCxwEgI0BtTGnajukKZ1M2110OeiScBGv+CajVfYRyO/nAHKXIHSv0BLOFftD5dsSOULg3/gb6LNnFZJzMrHl1nmMPLge37Z60Sw8kCmdoSpTF6oydY2PLaYg7ehEZJz7AgCQdvwD6DW3oW4yn/M82BjNpZWmr6P8+uHFvT9Af39ej9erNYW/sxvOJMUg8tZZ3IQc4pGfjVvwwGJZMywObIaQwET091Cjj9Ifjus/AgDc/XUyZConeHcdV6JtIiIiKghDB6JC0KXG486P45B66EfTNpkD4FoTUD06CkLui2O+I/FtugO2OqXg8ZHd5TLvISQtDrXS76KWLhNtnx+H6s8NhVYY8N7RLVh03jhxz8+og5NHT+Bn9wOoV6GV5RtIRAAAQ04Wbi3sDc3FfwAACrUfyo74ATFKJ4y+eBS7kx52gS/r4ISxt/9Dhys74VdLB+Ujf1FdaoyBulnuQMARQJuAKtjSaRg67liKLL0O3106gkBnd4Q36lZgXTKZHOqm8yF3LYe0o+8CADTnv4RBEwPP536A7JEQhKyXITsZWTc3AgCiHauib2QU0nXZAIBXKzfEt61ehDbhBqI3fYysG/8hU67ENZcyiGn9Gnb7VMGfty9Ae3/UzTl44/9SgP9DEjp3HoeP9i2Hb04G4n4cD5nSEWU6jpKolURERLkxdCAqgBACKQfXIu7HcdCnJ5q2uzeoDaXXNUA8XFbocsV3MOZeeZy8cwfAwwG3DnIFBlSqh9E1WyEk5izurBoBXbKxa63hu2GIPvE7AocsxZfN+6BtQGW8vm81Ug3ABXij5V+/4euWcgyp0aLE2kxkr4QuB9GL+yPj7G4AgMK1DILf24XVmnS8d3Sr6c0hAIyo3hxzm4TB6d4pJO08BOiSjefQAxlRQOrRtZApn4O6SX+zx2npXxE/txuEF/5aBYMQmBm5BwHO7ng75Mmzg7vVngCFcxDu7R8MGLTIuvEbkrLuwqvDRsgd8xjfRVYl89o6wJCNZOGEgaI37mSlAQA6BFbFd60HIOPMLtxe8gr0GcbhjK4Ozujy+jKoG/XBGBhXR/n14j/44ew2HMhUmc67S6vAqVbD8NmJ9Wiccht3Vr8FmcoRXm1el6KZREREucilLoDIGuXEX0f8pnBc+6QhYr4ZZAocFGof+PRqAaXnWVPgoHcpj++qLkOnG844mXzHdI5gV0/MbNgNtwZ8jB/a/A/N/SpAXb87qsw6C882w0zHpZ/cgisf1UHWrdPoV7Eujvd+F3UVqQAADRQYenA9ZpzcVYKtJ7I/mksHceOzLkg/tRUAIHfxgO7t9eh65h+MOrTBFDhUdPPC7q4jsaxVfzjEbEXSn21NgQPkXkiLdIL2LqBPT0T0VwOQeuz3PB+vV/naWNbyYSAx5t9N+DM6qlC1Old+GWU6/wmZSg0AyIn7G4l/toPQ5TG7IFmVzMurAACjEIaL9zOsUK9AbOgwBJrD63Dz8+dNgYNjUAgqTTsKdaM+pvuXcXTByNCu2P/yQlzp3BvT3G+iLIx/L+4KFV5vMABfV2wOrUyO2O+GI3rJ/5AR9XdJNpGIiMgMQweix9w78AOufBiC+A1TkHXzpGm7R+v/wfv5WtCnHTJtiyo3DD0c3sbHly4i5/4yeU18grGhwxBc7T8ZH9brCD9n91znV7h4IGjYtyg/8U8oy5QDABg095D23x8AgKqegdjauAv+h0jTfdZePWGp5hLZtZz467gxtzOuh7eCJmofAEDm6Ip7I39Cx8h92B/3cP6Wd2q1xuk+E9ExqBqybvyOe/teBgw5AACHoM7wH3AJladdgVPD+2GCEIj7aQKETpvnYw+v3gwf1u1oPBQCv984U+i6HQM7wLvbP5A7BwAAdMmR0N07V+T2U8kRQkCbHAmNUOJvVDRt39BhCDwcnHHvn+8AYRw7oW7yIipN/ReOgTXyPV/lcs/hk34/42DD6uiEqwAAPeT4qmIrvNToVZxz9UXq4Z9w49O2uLWoP3Lir1uyeURERPli6EB0nyEnC7Gr3kTMN4Mhch5+YuhctQWCx/8Op3K3oI03jvPOVPlgdvB8dIr2wqnkOACAQibHR3U74kDY23ihQmieM9I/yi20Kzyfe9j11TGwpulr1a018MXDoRs9g0OKpY1E9FBG1D5cm97ENJwCABwCqiN2xI/odvYg4jKNXd9revjh725v4cvmfeCmckRO3D9I3veKcSwFAJcao1Cm0zbInbyh8gqC+tUlcKvXHQCgTbiOlMM/5VuDl4Oz6etGPuWKVL+qTF0oPeuYbivcKxXp/lSyZDIZnCu9AheZDl1w2bT91+unAABCe7/rg1yBsqN/htzJrVDnLF9vIjZ1fA0fyg9DdX8moQtufni50atYWKkVcmQKpB1bjyuTa+Lu+ikwPGF5VyIiouLG0IEIQE78NVyf2RrJe5eZtnl1ehvVFsagwvvbkH17DnLijIHD38o66Kh8C1/evA3D/U+lmvgE43ivcQhv1A2qIswkn3npgOlrl2rGCSNz7h5Gcsyf+A4NAABOCiXerd32mdtIRA8l712GG3M7QZ+WAABw8K+Gsm/+iEtv/44e5w7h3v3gsY1/ZfzbYwyeC6gMANDeO4ekPb0Bg/ENokv1EVA3/woy+cMpkmQyGXz7TDPdTtjyKYThsTVy79sZc9H0deegakVuhy7Z2CNK7lIOcscyRb4/lSz3BtMBhROm4G8o7wcEs079hTuaVOD+ChYyuaLIS6G6lO+JaWELsdNpO+rDONmpXibHNxWa439NhyBJ5QyhzUbCH+G4/EENpBz6EeL+3y8iIiJLY+hAdi/t5FZcndoIWdePAwDkTm4o+9Y6BA5aBIWzI5J2doI2/jAAYJ2yBV7RdsV1jfGTIlelA75o2guHwt5BvTJBRXpcoddBc8V4XpVvZSg9AyCEQOqx97AS9ZEGRwDAG9WbI8BFXVzNJbJrQqdF7OrRiF31JqDXAQDcGvRCpenH8ad/LfT+63tk6o3DIXoE18KfXd6A2sG4MoReE4PkXd0gcoxzODgG94a6+dd5vkF0rtwErrU7AwByYqOQdtx8bodMnRb/xBm7xVd190Eld2+zYwqi19yBIesuAEBVpl6R7kvSULgGw7XWGFSRJeN1/AcASNdlY8p/OyDuD9HDUy6BqvJphNY9d2Cb5yl8jH1whPHn+7yzF0Y8Nxr3nD0AALrk27i99FVcn/kcsm6eevZGERERPQFDB7JbwqDH3d8+wq0vesCQcf9NRFAIKk09Co9mL0GfFY/EP9tDm2gMI9apWuFdbUs8+GyoW9maOPvCRIyr3QYKedEvpaxbkRD3u7k+6OWQfXMTkuP+xXI0AgCo5Aq8V6fdszWUiAAAurQE3PisC5L3fG3a5tPzIwSP+R0rbp7FK/vWQnv/jd/AKg2xocNQOCuNKwQYclKRtKsb9Bk3AQAq3+bwavtjriUxH+fT6yPT1wmbZ5p9srw/7hqy7gcfXcpWL3p7kh++YVR61S3y/UkabqGTIHPwwjgchtf9lY5WXDyCc8r7Q21kT//STOlWAf7d92NcoDt24geUQwoA4LxB4M1OH0Db+EXTsZmXDuDa9KZI2rWYvR6IiMiiGDqQXTLkZOLm/B5I2DzLtE3d4n/GibuCakKviUXSn+1MXZfXObTBuznNTYHD5LodsLXzMFRwe/ruzLmGVlRvDWHQIvX4B1iNekiG8cXn0KqNEezm+dSPQURGWdFncG16U2iiIgAAMgdnlH1rHfz6h2PumQiMPPgbxP0r/J1arfH9cy+bhkoJfQ6S9/Y1/T5QqKujTMfNkCldCnxMlxpt4Hw/UMy68R/SI//MtX9XrqEVRQ8dtEkPQwf2dLAdckcvuNX9EJ6ybEzEQQDGiUTDy1SFAAoMsgp3fk+U6fwn6lYNw3r8YgoeTqclYkj55nCbsB2O5YxzgQhdDu6seQfRi/tDn3HvmR6XiIgoPwwd8rF7926MGjUKv/76q9SlUDETBgOivxqAjNPGNwAypQMCBn+NsiPXQO7kBqHLQtLOTqaZ4Lc7tsS72U1MgcNHdTtiZsNuRR5z+7iM+29+AMC5Wiukn56D2JRoLEVjAMaJKSfV7fBMj0FEQM7dq7g+oyW08caVKJRlyqHih//Ao9lLmHFyFyYd32Y6dmr9zljYrDfkj3zanHpsInJi9wAA5E7+KNP5T8idfJ74uDKZDD49H/Z2SNw2x/R1pk6LTTfPAjBe6+0DqxSpTUIYkBP7l+m2youhgy1xrfk2FK7lMQinUA3GJZn/dfbCxoDaTz284lEyhQM8Wq9C9eoDsB6/mJbVPJUUg97Xz8HrowPw7jHZdHzasQ249n/NoM9MfebHJiIiehxDhzwsWLAAb775JsLCwtClSxepy6FilvBHONJPbgEAyF08UWHyPpTpOMoUImguLjMFDndd62CioV2uwGFGw+efPXA49xfSjm0AACjcfaG79ydunZiJl9EfCXAFALxauQEqF3GMNxGZS967FIYs40oUzlVboPLUo3Cu1AjzTkfgk/92mI5b0LQ3pjXomuv6FgYtMi99BwCQKV1RptNWKIuwSoRb3eeh8jVOQpl57RgAIEunRZ89K3ExNR4A0Nq/IjweWcXiSYRBi3v/DEZ2jLF2maM3FOqqhb4/SU+mdIJbgxlQygSmIcK0fWqNLtjl7A1tUvSzP4ZMDo+Wy1C9am+sx8+m4OFkUgy67VkJ596foPzEP6Fw9wUA5Ny5iLu/fVTQKYmIiJ4KQ4fHJCQk4OOPP8bWrVvRo0cPeHh4PNP5UlNTER0dbfoXGxtbTJXS00g/vQPxG6cZbyiUCB73B1yqNjftFzoN0iM/NX4NOd53eR3JOcZZ6gdXaVQsgYNek4Lby4cab8gArw4NEXt0MgaiL6JgfPFXydULnzXp8UyPQwXjtWkfhBBIPbreeEOhQvkJ26D0DMDic/vx3rEtpuO+btEXY2s/Z3Z/bfwRCJ1x7hWnSi9B5dOoSI8vk8kguz/ni8LJHdl6Hfrt/d60aoWngzMWNutT+PboMpH8V19kXV1r3CBXwbPl8lyrZ9g6e7k2nasMhHOVwWgvu46RMAZSepkcE0PCsG3fqmJ5DJlMDo9WK1DJzQvr8TMCYQzfjiTcQo/dKyCv1R6Vph2F3MkdAJC85yvkXD9WLI9NRET0QOl5lVJMDh8+jDJlyqBGjRoAgIyMDMyYMQPbtm2Dv78/ZsyYgebNmz/hLA/Nnz8f06dPN9uenJwMZ+fCf7JlDVJTba/b5aM165NuIfHrV4D7E2a59ZiGLJ8QZCUmPjzm8iIYsuIAAGs8hmDn3dsAgHLOakyr3gZJSUnPXFPKT+9Al3QLMgfAvbEnMlJ24zX0xX8IBAAEObljdWgPKDQ5SNQkPuFs1sUaf0a8vfPuLVKars1HWeNzUBTFXb/29mlo440rRDhUb4t7WXr8cGEPxp/abjpmRu2OGOBbA4mJ5teb/upm09c5bk3zPOZxj7dBm278vaF3UqPfzu+w7Y4xcHBXOuKXZgNQTjgW6rxCmwbd0VchEo3zAEDhAmXjVchwa4OMQty/KEri58iWr83i+v6IWp9Bnp2NT279jHQ4YC3qIkeuxMDEePxx5SzqegYUy+Og4kiUP/shfha/oq98CBIMCvwTdw1hf36DNU1fhGv3j5G24QNACNxbNxaq4AjIFKrieWwrYuu/HwtSnG3L79okInpaDB0e4+joiNjYWCQmJsLd3R2dO3eGm5sbhg4divXr16N9+/Y4ePAgGjRoUKjzTZgwAcOHDzfdjo2NRdOmTeHl5WWTv9RttWaDNhvXF42A0BhXqVA3eRFlX/gwV68FgzYNd68uBgBchTemp/kB95ccW932f6gUUPaZa0k9vhFZR3+C0gtwrS2DXHUP74quOIDyAABfJ1fs6TYKvnqFTX6vAdv5GSlt1+ajWP9DdyP2PDxvy1ew9d51TDj1cELHmQ274cN6HfO9f+K9Q9A/uH+13lC4FK62B20QBgPiNPcAACuC6mHrg8BB5YidXUaguV+FQp1PnxWPpF39IBJPAABkDp4o02krHPxaFur+T0OqnyNbuTaLqxbRYS1SDjhi9uUfkAwnbEN1pCtUePnQjzjYazyqqJ88f8iTGNRjcPfy56ianYh1+BkvOryO5JwsRMRfx8hTW7C++1joTv2OzCuHIe5eAI5+D++wD4qhddbHmn6GiltpbhsR2TYOr3hMixYt4OzsjPDwcPz888/w8PDAzp07MWHCBOzbtw81a9bEggULCn0+tVqNcuXKmf4FBgZarnjKV9yP45F17SgAwCGwBgKHrTAbJpFx7kuI7ERohRzjHQdCc38pu/G126B94LOPl9al3kXsyjfgVAlwqwfIVQK/iVr4BcZZxNUqJ+zoMgI1Pf2e+bHoyXht2ofUY/eHVsgV2OlXA0P+WWdapeLjep0KDByEToOc+EMAAKVnCBQuRf/U2ZCVBggDDnpVwHx1MADjxJF/dHyt0IFDduxfSNzaErr7gYPcyR/ez++zaOAgJXu7NmVyBTxafQf3kDFYjG1oCeOyrHe12ejy51e4o3n2T7DlKle41nwbABAiYrEhOAdqlRMAYGv0ebzy90/wGbIUUBg/i4r/fRpy4q488+MSEREBDB3MuLm5YcqUKVi4cCEWL16Mnj17mvYplUq0b98eGo1GwgqpqO4d+AHJfy0BAMgcXRH8zgYonN1zHWPIvoeMs/MAAFNlHXA0y/jCq5aHH2Y27PbMNQghELtqMJyrJMC5IiCTAVeEFybLnjcds7L1S2jg/ey9KYjIKDvmPHJizgMADtXthYGHN8Fwf3jVu7Xb4v8adC3w/jlx+wGDFgDgEJh/OFEQfUYyYh3d8X6t7hD3g87ZjbqjXSGCTH3GLSRHDEDSjo7Qp10GACjcKsK7+36oytR9qnrIOsnkCng0WwC/VivxnX4T6sA4zO9qRhq6bPkMKTmZz/wYrrXeBhTG4Sk1by3DtvavwE3pCAD4/eYZjLh2Gp5d3wUACG0WYr4bBmHQ53s+IiKiwmLokIeJEyfi1VdfxZEjR7Bx40YYDAYAgEajwbZt2/Diiy9KXCEVlv5eLGJXjTTdDnptORzLhpgdp7mwFCLnHraJqlgljEvPOStU+LHtq3BWPvu41qTd70HuuAMqL+PtNLhilMtIZAjjJfhWzZboWzH0mR+HiB5KO7EJAHBKHYi3PCtDJ4y/y0fXbInPmvR44qSw2Xf2mr528Gn2VDUkJkVjTJ3eSHZwAQD0rRCKd+u0LfA+Qp+N9MhPEf97TWRdf7hss4N/G3h32w8lV6ootVyrD4Kb/nl8n7EBFWEcDng6IxOdN3yEpGdczlLu5AOXasMAAEKbivopu7G18+twvj93w7prJzE7uDEU3sbVWTRR+5D45/xnekwiIiKAoUOeZDIZvv/+e0yePBl79uxB27Zt8cknn6Bx48Zo164dBgwYIHWJVEjZkX9A3P+EyKvDKHi0eCXP4/SZdyAEsAgP31isaD0A9Z+x54FeE4Okv/oi5/bnkBs/UIJOFYDRPtNwRmP8BLWuVyA+b9KzgLMQ0dPQa1IQ7+CCcbV7Iuf+tmHVmuLL5n0KtQqN0D38dDkn7p8iP366Nhu9/vkZ59z9AQCVlUp813pAgY+dHbML8ZvqIu3EhxA6Y686uUsQPNv8iDLPR0Dhyt5QpZ3nc2PhdFyDVXHr4Yd0AMDRTDla/zYZl2OOPPV5hT4b2oSjptuG7CS0CaiCPzq9Bsf7wyoWXTiMI71mADLjy8PkPV9D3P/ghYiI6GnZZeiQkZGBy5cvQ6fT5XuMXC7HrFmzcPr0aTz33HOIj4/HzJkzsXTp0hKslJ5V1umtpq+9n5+Q73GutUbjmKwcTsE4Zru5d1m8Urlwk4XmRQgDMi4sQ/zvIci++btpuz4nEJ+Um4Pd8XcAAH5Obvi941A4FUNvCiLKTVWzHSaE9MRdR+Nwqi5B1bGsZX/IZYX70+dWe4KpO7rm0gpo750v9GNrdDno/scXOKYwpo2+2kxs7fomPBzyXn0hJ/5fJO3pjaSdXaBPNU42CZkSrrUnwveFKDhXfuWZl+sl2+BSqz282r8F3/MpWHXlVwQI4zKX53XOaLZjFTbvHAF9+s0inVMIgZTDo6FN+BcAoHCvCteQcQCATkHVsaRFX9Ox429HIaNBbwCANuE6NBeLHrgRERE9yu5Ch3nz5sHPzw/VqlWDv78/5syZA70+/zGLISEhmDVrFpYsWYIXXnihBCulZ5X232ZorxiXlnMMCoGDf/5dkpXqaljl9vBF1xuucU/9uNp755G4vS1SD70JoU0BABiygfQzwLIac7HqaiQA4/CNLZ1eR2V3zjZNZAmfpKfhhGc5AEDZ7DSsfe5lKOSF/7OncCsP93ofG28IHVL/fQfi/pwQBcnS6/DCnpX4JzUBAOCVo8EfVeqhpl/FXMcJIZAVvQ2J29shcWtzZN/6w7TPIaA9fHqfgrrJZ5Crcs9BQ6WbTCZDwMAv4dagFyrcSsLaMz+hmjAuiZoEF7xw2w0zf+uPlH/HQ58VX6hzai4sRealFcbzK93g1WEj5I5epv1DqzbBS5XqAwASczLxflADPOjfkLL/+2JrGxER2Se7Ch3Wrl2L5cuX49ixY4iLi8OYMWMwZcoUhIWFISMjI9ex+/btw5IlSySqlJ6VITsDd9a8Y7rt2y+8wONvpd/DH+kOAIBApKFzzFfQa+4U6TF16TeQeuIjJPxRD9q7+03bs24DKf8CW2q8gfCrJwEAcpkMP7cbiCa+5Yv0GERUON9fOoqvLhwGADjptVh4+ne43D5T5PO41n4XCnU1AEBO7J5ccyzkRW8wYMTxTdgZcwkAoNZm4ofEKDRr/3AJSGHQQnNlDRL+qIfk3WHIidtn2qdwrQDPtutQpuseqDzN558h+yCTK1Bu1E9wqtwUAYlpWHtgLbrkRAMA9JBjqmiDYecu4Oav1ZB2cjoMOfnP95AT9w9S/x1juu3x3PdQedXO/XgyGZa26IcKbsYgIiI9BSurGOceST36KwzZuV8jERERFYVdhQ5LlizBxIkTUatWLfj5+WHq1KnYtWsXDh06hL59++bq8bBhwwa89dZb2LVrl4QV09OK3zQD2oQbAAC3+j3g3qhPgccvuXAQ+vufYA7BSSj16UiPLDioAAB9Zhwyzi9GwrZWiP+tIjIiZ5lmu1e4V0f6GRdkXgSOeFTE+ypP0/0WNeuDnuVr53NWInoWxxOiMfLQetPt6Rd2olZ6vGliyaKQKRzh0WyR6Xbq0fEwaNPyPFYIgTH/bsS2O8bhEW66bCyPXI/2A2ZBJpdDl3oJ6ZGzEL++KlL+GQRd8mnTfZVedeH53Br49rsE50ovcSgFQe7ogvLjN0PlWxmuOi0+P/gzJmhi8eAnYz1C0FvXE1H/fY64db5I/LM90k7+H3Li9kPojbOY6DOikby3PyCMw0nd6n4M5wp983w8T0dnrG3zP8jvP8IXwY2wy6cqDFnpSD32e573ISIiKgy7Ch10Oh2io6NzbWvbti02b96MiIgIzJ4927R9wYIFWLlyJTp2fLpl0kg6WdFnkPjn58YbKmcEDFxU4Av4TJ0W39z/RNRRrsCrKmNYobmwDGmnZkJz5QdkRW9Dzt3D0KVcgD7jFjSXViJxZxfc/SUIqf++A+3dgw9PKHeAW/3pMOR0hzZeg0SVMz6o29c0c/77ddrhrVqtLNN4IjsXn5WOvn+tQrbe+CZrTLWm6JFoXG4y7b8/CrprvhzLdoVThX4AAIMmBumnZuR53GdnIvB1lPF3gYNBh69P/45Woa1g0OxF/B8NEL+hOtJOfAR9xsPx+A4B7eDVaTt8ep2Ec5VXIZNzfhd6SKn2Q/mJ26Fw84YcwLAjP2Kl9h7UKmPPvNPwx/MYiIN6P+TciUD6yalI3P4c4n7yQuLOrkja3R2GrLsAAMdyYXBrML3Ax2vlXwkf12oHABCQ4YNa3XFSHYiUA6st2UwiIirl7Cp0CAsLw+LFixEbG5tre5s2bTB16lTMnTsX2dnZAIxdDYcOHQp5Ecb/knVI2DwLuP+Gw63Le3DwrVjg8etvRCIx2zhL/KtVGqFi3bHGHUKH9P8+Rso/g5G8OwyJ21og/veauPtreaQceB05MbsA8XBWb6V3Q7g3/gx+/a5C7tQWyXuWQCuT4706vXD3/idHPYJr4dPG3Yu/0UQEAJh8bBtuZtwDALQNqIx5LfvBtWY7AEBObBQ0lw8/1XnVTeZDpjQue5lx9nOk/TcVwvBwMuLvLh7BB8eME9fKhMDcy9vwXFAc5E6bkXbiQ+iSTj5yNhmcKvSFd9hheD+/F07lnmfPBsqXY0B1BE/YCtn9ZVebHFiBzRkxqKn2BWCc5+ElvIgfRF3TfYROg5yYnabeNAp1NXg+twayQkyi+k7VZhhZozkAIFuhwtjavZAYFQF9RnJxN42IiOyEXb2jHjt2LNRqNfr06YP09PRc+0aPHo309HRERUVJVB0VF33aw4m1nBr2e+LxF1MeHv9ypfpwqTUGSo9ahXoshboG3OpPg+8LF+Db8zjc6kxEdswN3JwfBqHNxmdV2uJfD+NEduVcPLCq9cuFnjmfiIruSMItAMaJWn9pNwgquQLqpi+Z9seuGAZDTlaRz6twKw+3+tOMN4QB6af+D4nbWiPt1Aws3zkOww/8bDp2ii4CL1a7BMcAHSC097fK4OD/HNTNFsFvQDS82q+Hg28zs8chyotLlWYo9/ZvwP2lLX0Ofo9fLmxHryDjfCN6yPEBOiM8aA5UVYdD4V7FdF+Zyv3+xJGehXosmUyGxc1fQJdA4+TLCY5u+NOvBiBXFG+jiIjIbiilLqAkqdVqbNy4EW3btkWnTp2wadMm+Psb10/PycmBQqFAUFCQxFXSs3Kp/hwyzu4GAORcOQBUrVvg8Rqd1vS1p4MT5CpX+PQ+BV3yGeg1t2HQxMCQnQRDTjIM2UkQOUmQO/rApfoIKL0b5vqEMvPqUdz8vBtEdgZ+CwzF2nINAQBOCiU2dBgCbydXC7SYiB5IyDJOeBfoooafs3HVB8/nhuLe3yuQeeUwsmPO4e76jxHwyrwin9u19kQAMqSd+BAwaKFN+Be/xydhFHpA3O/NNA6H8KbDCdN9ZI5l4FprLFyqD4fChX9f6Om51+uG4LF/4PbXA2DISofiwj58du82aveYjk+vGH/mvr59F1fKNsW6Hl/CTXsX2oRjUHk3hNK9UpEeSylX4AN9Onbev72+Wgd86Kwu5hYREZG9sLuPXOvVq4eIiAjExMQgNDQUc+fOxfr169GrVy+MHz8evr6+UpdIz8i1ThfT1zkXI554fKb+YejgrDSOp5bJVVB5N4BTcA+41BgBt7qToG48B56tlsOr/Xp4tFwGlU8jU+AghMC9A2tw47MuMGSm4rhHWYRX72w674pWA7hSBZGFCSGQcH+WfR9HF9N2mUKJoBGrIXNwBgAk7ZiPjKi/i3x+mUwGtzoT4RN2BErPEOwUlfEWwqC//6d0JI5houEgDHpnqLwbw73RbPj1vw73+p8wcKBi4V6vGyp+tB/KMsYedLq4yxiy7h18V7U+HO/3gthx+wJabF2E6wZXOFfsV+TAAQAM2myU27UAdVKNw1FPyFU4mXi7+BpCRER2xa56OjxQv359REZGYt68eVizZg0AYNiwYRgzZswT7km2wLlSY8hdPGHQ3EPOhQgIIQocL63R5Zi+dlE6FPnxsmPOI/b7t6CJigAAxDi6Y0K9/tDef8wPQtvjf1UaFvm8RFQ0adpsaA3GVYh8HutV5BhQDf4D5hqX0hUCMd8OReUZp6C43xuiKFTe9XGw/lqMiFgL3f1Vb165cxLvXNwHtBgMxxc+g4+v37M3iCgPTuXrodIn/+LWFz2RdeME9BlJaPbtIPwU2h1v+tfFXZ0WUSl30WzLl9jQYQjaBlR58kkfk7L/e+iSb+Nlx1P4WB0IAFh64RCWtuxf3M0holLugsoFrbcuNtuuTQ5BiDITKyWoiUqe3fV0eMDT0xPh4eGIjIxEZGQkxo4dy4m8SgmZQgnXkA4AAEPaXWRHnynw+EeHV7goCj9zvCFbg7hfJuPKx/VMgUOmXInxzV5DgtyY54WVq4WZDbsVsQVE9DQS7/dyAAAfR/OhTF4d34JriHFFIm38NcStm1jkx9Aa9Hj/6Bb02bsGOfcDh76xp/Fh1B54PTccQcNXQsax72RhKq8gVPzob7g37G3aVuP0Nqz95yvUzLwHAEjK1qDTn8vw5bl/oDPo8zmTOaHXImHLpwCA5+9egMf9HoBrr/yHNG3R50MhIvtVQ5uJGlpNnvvO6VxwTudcwhWRVOw2dKDSze2RIRYZZ3YWcCSg0T/s6fBgeMWTpJ3ahisfhiBx62zg/vAMZWANfNpnNs7cX/Kupocf1rb9HxRcAYWoRDyYzwEw7+kAADK5HEHDV0J+f2z6vYhvkHZqe6HPH51xD+22L8FnZyJM23rfOYNpF3bBu+NoBA5dBhmvdyohckdXlHtnPYKGr4RjsHHuoqDsNKw+thodEozLxOqEAWP/3YTQjZ9j882zhTpv5pGfoE24DgDwqx+GIdWME56m67Kx5sqJAu5JRJRbeMo1/JpwDvvD3jb7F6LMO4yg0omvjqhUenReh+R9y6HXpOR7bJb+4bJ38Y+8acmLIScLd3+fhlvzw6BNuAEAkKmc4NQ3HO91fA/rk4zjXz0dnLGp42vwcGCCS1RSknIyTV8n5HMtq7yDETDwS9Pt218PQNqJTU88d2RSDJpu/BwH714HADgYdJh6YRdmRu2A7/PjETBoEQMHKnEyuQKezw1F5RknUWFyBNwb94WrQY+FZzZh+I1/TcdFpdxFrz0rMXDfWiRnm7/QF0Ig/ewe3Jj3PNJ+nWDa7tPrYzxfrobp9k9X/7Nsg4iIqFR6pldI3333HRo1aoSGDRti0KBBWLhwIQ4cOACNhskVScvBtxKcqxjXGc+JvYCrnzTEvQM/QOTRxbSJT7Dp61f2rUH2IyHEA0KXg+S9y3D5g2pI2DjdtN2tfg9oPzqA7nol/og+DwBQyOT4ud1AVPfgpKREJalBmSCo7g9tWH3lOJZFHcrzOI9Wg+He2LicriErHbe+fAGx37+Fe/u/h+byYegzknMdv/PMX2i9cR5i74ca5TXJ+PHEj3g55Sb8B8yG/8vzODyPJCWTyeBasy2C31mPqp9dgW/39/Bu3BmsO74GzZJvmI5be/U/1P75/7D1eiQAQOh1SDn0E65NbYSbczsh4/QO07Hq5q/gVpkKGLb/V9O2si4eJdcoIiIqNZ56IslLly7hzTffxMSJE3Hy5Ens2bMHv/32G7KysqBQKDB16lRMmTKlOGslKpKyo37C1enNYEi7C238VcR8MxiJW2bDt98MuDd6wfQm4cO6HbHxxhmcT7mLYwnRmHDkD3zVoi+A+y/IDvyA+E3/Z+puCgCQyeH/v/n4u3oHDNm/DmnabACAr5Mrfmk3CO3ur29ORCXHz9kdy1v2x9D9PwMARh/+HRXcvPB8uZq5jpPJZCj75lrErnJHyv5VgBBI/msJkv9aYjpG4e4Lh8Aa2OYRjPHOftDen6el0b1oLIuLRNWBC6Bu0h+yp5h8lsiSHHwrwv+lufDpNQXeOxei/l9fY1vMKcyo1gnJDi6I1evQY+9q/A9ajD23DW53L+e6v8K3CvzC3kdCvZ5o/+cSxGamAgCa+gRjact+UjSJiIhs3FP3dDh48CCef/55zJo1C126dMGAAQMQFxeHwYMHo1WrVmjRokVx1klUZA6+FeH19ma41e9h2pYdcw7Ri/rh2rQmSI/8E0IIuKkc8VuHwXC5P5/D11EH8dPl40g5uBZXJtdCzIrXcwUO7k36o8KMk/jCuzr67v3eFDg09imH4z3HM3AgktCQak0wpV4nAIBeGDAg4gdEJsWYHSdXOSJo+Hfwf+VzII8JZK/qtHhX5oq3XYNMgUPXe7ewuV57NAw/BY8W/2PgQFZN4ewO394fo/rnN/D6izPwZ8I5dIy/ZNr/I1RoUasnXmr4Pyyo1Bona3eD3zsb4P3BISQ37ocOu75FjMYYODT2KYcdXUZwyCARET2Vp+7pcO/ePVSsWBEAoFKpkJ2dDbVajZUrV6Jly5YIDg4u+AREJUDpWwX+4zdDc/kQ7v72ETTn9wIAsq4fx83Pu8GpUmMoXLzgnJWK/1O4YmJQAwDAsIjv8cvxtaisSTKdy61BL/i9MB2ZAdXx4r4fsf12lGnfa9Wa4OvmfeFUyIkoichypjfoiitpifjx6n9I02YjbNcK/NtzDIIe6xouk8ng/fwEeLQajKybJ5Fz5yLOxVzAFxnp2OjgCcMjQyaGu7ji61dWQuXkUtLNIXomMqUKHs1eQtNmL+GPq8ew/K/lmCIckKZ0hEEmxxl1IM6oA7EcgPO5I2hxJwaXNMmIvj8XUkPvstjZZQQ8HRk4EBHR03nq0EEIAfn9SbMCAwOxa9cuAIBcLke3bt2wceNGfPDBB8VTJdEzcqnaAhUn/YX0s3sQv/4jZF4xTrCVde2Y6ZhuAI5Ajl+C6iFT4YCxtXvhuaRrSPWrirSAmkiQyXH38B+4m5kOnTAAAFRyBRY26403a7TgmG4iKyGTyfBd65dwM+Me9sddQ7QmBT13f4e/u70FV5Wj2fFKdx/cCKqD8Pg7+FnnAOHoZdrn6+CEqfU6463abXiNk81zqdwYYys3Rp+4q/jy3/XYpzPgREoCBIzLv2bqtfgr/prp+PplgrCr60h4OTJsIyKip/fUocOjL74aNmyI4cOHIz4+Hr6+voiNjYW3t3exFEhUnNxqd4RrSAekn9yCuxumIPvmKeMOhQoKFw9MSbmBs96VcNZRjauu3rjqev/nOPmO2bkCnN3xW/vBaOVfqQRbQESF4ahQYmOHoWixdREupSbgROJtdPhzKWp6+MEAAb0Q0BsM0AsD7uVkYU/spVz393d2x/t12mFkjeZ5BhVEtqyCf2V83us9AEBStgZ/xV7C7hjjvytpiQCAul6B2N11JMowcCAiomf01KHD6NGjkZRk7HpesWJFdOrUCfXr10fNmjWxd+9e7Nix4wlnIJKGTCaDe4OecG/QE/qMe5A5OEP+yJuKP9IS0XTzQiQ+tqyYq9IBfk5u8HN2Q6hXIKY36GLWXZuIrIe3kyu2dR6O5lu+RGK2BkcSbuFIwq0C7xPkosYHoe3xRvXmcOZwKbIDZRxd0L9iPfSvWA8AcOLmFdyR5aBdYBW4cN4SIiIqBk8dOiiVSvj5+Zlur1y5Ep9++inOnz+PlStXonPnzsVSIJElKVw9zbZVdvfG6T4TcSopBt6OLvB1coOvkys/7SSyQVXVPtjY8TV03/WtadLXvJR39cQHoe3xerWmnJuF7FoFV080ZG9VIiIqRk8dOjzO1dUV4eHhxXU6IkkFuqgR6KKWugwiKgat/Ssh5qVPEJeZBoVMbvwnl0Ehk0MuM/7v5eDMORuIiIiILKDYQgciIiJr5aZyhBt7KxERERGVOLnUBRARERERERFR6cTQgYiIiIiIiIgsgqEDEREREREREVkEQwciIiIiIiIisgiGDkRERERERERkEVy9goiIiIiIyA6MOrgep5Nj89x3QeWCGlpNCVdE9oA9HYiIiIiIiOzA6eRYROYTOtTQalBDm1nCFZE9YE8HIiIiIiIiO1HXKxD7w942234tvLUE1ZA9YE8HIiIiIiIiIrIIhg5EREREREREZBEMHYiIiIiIiIjIIhg6FECv10tdAhEREREREZHNYuiQjylTpmDChAlSl0FERERERERks7h6RR6mTJmCDRs2YO/evVKXQkRERERERGSzGDo85tHAwc/PDwAQExMDd3d3uLu7F/l8qampSE1NNd2Ojc17XVwiKlm8NomsE69NIiKi0oWhwyMWLVqETz/9FOfOnYOfnx/279+PESNG4Pz581AoFBg8eDC++uorODs7F/qc8+fPx/Tp0822JycnF+k81uDRF4G2whZrBlh3cfL29s5ze2m6Nh9ljc9BUdh6/QDbUFi2fG2Whuc4P2ybbSrOtuV3bRIRPS2GDo8YMGAAli5dioEDB2LGjBkYOHAgpk2bhueeew4RERGYPHkykpOT8fvvvxf6nBMmTMDw4cNNt2NjY9G0aVN4eXnZ5C911lxyWLdllbZr81GsX3psw9OzlWvTmmopbmybbSrNbSMi28bQ4RH+/v7466+/0KFDBzz//PP49ddf0b9/fwBA3bp14efnh1deeQUnTpxAw4YNC3VOtVoNtVptybKJ6Cnw2iSyTrw2iYiISheuXvGYB8FD06ZN0a9fv1z7+vfvD6VSiZs3b0pUHREREREREZHtYOiQB39/f0REREAmk+Xafu3aNQgh0KRJE4kqIyIiIiIiIrIdDB3y8fhkVQkJCRgyZAg++OADlC1bVqKqiIiIiIiIiGyHXYYOGRkZuHz5MnQ63ROPvXv3Lt5//300bNgQXbt2RXh4eAlUSERERERERGT77C50mDdvHvz8/FCtWjX4+/tjzpw50Ov1+R7v5+eHLl264NSpU5g6darZkAsiIiIiIiIiyptdhQ5r167F8uXLcezYMcTFxWHMmDGYMmUKwsLCkJGRkevYffv2YcmSJQCATp06wcvLS4qSiYiIiIiIiGyWXYUOS5YswcSJE1GrVi34+flh6tSp2LVrFw4dOoS+ffvm6vGwYcMGvPXWW9i1a5eEFRMRERERERHZLrsKHXQ6HaKjo3Nta9u2LTZv3oyIiAjMnj3btH3BggVYuXIlOnbsWNJlEhEREREREZUKdhU6hIWFYfHixYiNjc21vU2bNpg6dSrmzp2L7OxsAIBMJsPQoUMhl9vVt4iIiIiIiIio2NjVO+qxY8dCrVajT58+SE9Pz7Vv9OjRSE9PR1RUlETVEREREREREZUudhU6qNVqbNy4ERcuXECnTp0QFxdn2peTkwOFQoGgoCAJKyQiIiIiIiIqPewqdACAevXqISIiAjExMQgNDcXcuXOxfv169OrVC+PHj4evr6/UJRIRERERERGVCkqpC5BC/fr1ERkZiXnz5mHNmjUAgGHDhmHMmDESV0ZERERERERUethl6AAAnp6eCA8PR3h4uNSlEBEREREREZVKdje8goiIiIiIiIhKBkMHIiIiIiIiIrIIhg5EREREREREZBEMHYiIiIiIiIjIIhg6EBEREREREZFFMHQgIiIiIiIiIotg6EBEREREREREFsHQgYiIiIiIiIgsgqEDEREREREREVkEQwciIiIiIiIisgiGDkRERERERERkEQwdiIiIiIiIiMgiGDoQERERERERkUUwdCAiIiIiIiIii2DoQEREREREREQWoZS6ACIiIiIiIrIvQqdBwrbWee5TeYXCo8WSEq6ILIWhAxEREREREZUYudIVhnz26ZIjS7QWsjyGDkRERERERFRiFO5VoADg0/0bs3359X4g28U5HYiIiIiIiIjIIhg6EBEREREREZFFMHQgIiIiIiIiIotg6EBEREREREREFsHQgYiIiIiIiIgsgqEDEREREREREVkEl8zMx9SpU1G1alUMGjRI6lKIiIiIiIhsTvatSFwLN18CM8snBBdULmi8dITZPrmqImrJU/F9SRRIJYKhQx4OHDiAWbNmwWAwAACDByIiIiIioiJwKhea774a2sx8952XeQEGS1REUmHokIeyZcuiWrVq6NChA4YOHQrg6YOH1NRUpKammm7HxsYWR4lE9Ix4bRJZJ16bRESlQ+DQJfnuW1vA/ZquGFj8xZCkGDrkoUKFCoiOjsbnn38OAKbgoVWrVoiIiMDrr79e6HPNnz8f06dPN9uenJwMZ2fnYqm3pDz6ItBW2GLNAOsuTt7e3nluL03X5qOs8TkoCluvH2AbCsuWr83S8Bznh22zTcXZtvyuTSKip8XQIQ8ymQyVKlXCxYsXsXjxYgDG4MHX1xczZswo0rkmTJiA4cOHm27HxsaiadOm8PLysslf6qy55LBuyypt1+ajWL/02IanZyvXpjXVUtzYNttUmttGRLaNoUM+atasiTNnziA0NBQTJkzAypUrER8fDycnpyKdR61WQ61WW6hKInpavDaJrBOvTSIiotKFoUM+HoQOV69eRYcOHfDFF18gMjLymed4ICIiIiIisjZKzyuQqzKQsC33ahO65EgovepKVBWVBgwd8lGzZk0sWLAAa9euxUcffYQ33njDtG/v3r0MHYiIiIiIqNSQqzIgU2nMtiu96kLllf9KFERPwtAhH61bt8aYMWMwa9asXIHD4sWLTUtpEhERERERlRZC6wKf7vulLoNKGbsMHTIyMhAbG4uKFStCqcz7WxAcHIxLly7B09PTbJ9cLrdwhURERERERES2z+5Ch3nz5mHq1KnQaDQoU6YM3n//fUycOBEKhcLs2LwCByIiIiIiIms16uB6nE6OzXNfZHIs6noFlnBFZO/s6iP7tWvXYvny5Th27Bji4uIwZswYTJkyBWFhYcjIyMh17L59+7BkyRKJKiUiIiIiIiq608mxiMwndKjrFYhQhg5Uwuyqp8OSJUswceJE1KpVCwAwdepUtGvXDr169ULfvn2xbds2U4+HDRs24Msvv0TVqlXRuXNnKcsmIiIiIiIqtLpegdgf9rbUZRABsLOeDjqdDtHR0bm2tW3bFps3b0ZERARmz55t2r5gwQKsXLkSHTt2LOkyiYiIiIiIiEoFu+rpEBYWhgULFuDNN99EYODDbkVt2rTB1KlTMWfOHEycOBGOjo6QyWQYOnSodMUSERERERHZIWHQ41p46zz3OZULReBQDoO3JXbV02Hs2LFQq9Xo06cP0tPTc+0bPXo00tPTERUVJVF1REREREREdk4uh0xuPsk/AGTfikRW9OkSLoielV31dFCr1di4cSPatm2LTp06YdOmTfD39wcA5OTkQKFQICgoSOIqiYiIiIiI7JNM5QQAqPTxfrN9+fV+IOtmVz0dAKBevXqIiIhATEwMQkNDMXfuXKxfvx69evXC+PHj4evrK3WJRERERERERKWCXfV0eKB+/fqIjIzEvHnzsGbNGgDAsGHDMGbMGIkrIyIiIiIiIio97DJ0AABPT0+Eh4cjPDxc6lKIiIiIiIgsLnbVqHznRFCqNZA7upRwRXkTOg0StpkPpXDwjYRB6ypBRfQs7G54BRERERERkT3Kij6N7FuRee6TO7pA7ij9G3q50hUyZd7hh0ylgVyVUcIV0bOy254ORERERERE9sYxuG6ekzTm1bNACgr3KlAA8On+jdm+28vUJV8QPTP2dCAiIiIiIiIii2DoQEREREREREQWwdCBiIiIiIiIiCyCczoQERERERGRTTBka3AtvOjzTziVC0Xg0CUWqIiehKEDERERERERWb2nXV0jvxU7qGQwdCAiIiIiIiKrp/KrAgAoO9J89Y2CPE3PCCo+nNOBiIiIiIiIiCyCoQMRERERERERWQSHVxAREREREVGpln0rMtcwC51Oh1SlkhNMlgCGDkRERERERFRqOZULzXM7J5gsGQwdiIiIiIiIqNTKqydDYmIiUpf0lqAa+8PQgYiIiIiIiKxGZHIsWm9dbLZdmxyCEGUmVkpQEz09hg5ERERERERkFUK9AvPdd07nUoKVUHFh6EBERERERERWYUnLfvnua7ZmRAlWQsWFS2YSERERERERkUUwdCAiIiIiIiIii+DwCiIiIiIiIhsy6uB6nE6OzXNfZHIs6hYwLwJRSWPoQEREREREZENOJ8fmGy7U9QoscDJGW3dO55LnyhaAcRLKguaEyEv2rUhcC2+d5z6ncqF5LrdJRcPQgYiIiIiIyMbU9QrE/rC3pS6jRIUoM/PdF5lPz4+COJULzXdf9q3IIp+P8sbQgYiIiIiIiKzeZ+prAACf7t+Y7cuv90NBCurFkF/vByo6TiRJRERERERERBbBng5ERERERESlROyqUciKPp3nPpnyCFReTkjYZv4pvi45EkqvupYuz6Iik2OLdb4HKh4MHYiIiIiIiEqJrOjTyL4VCcdg8wBB5eUEuWNWnvdTetWFyiv/OQ6sXUGTZx64ex0H7l7PteKHTqeDUqksMIzgJJPFg6EDERERERGRlUn9bSJS4y/luS/LJwRA3vMOPAgcKn2832zfgx4OPt3N99kKXXJknj01ZgBQlQmFRwvzICC/JUYLmnySk0wWH4YOJUyn0wEAYmOLPruq1JKTk5GZmf+MsdbIFmsGWHdxCwgIgFJZ8K87W742H2Wtz0Fh2Xr9ANtQFLZ6bZaG5zg/bJttKu62FeXa7LLkDTh4OhfbY1uWACCTuohCE0IPOHhDJlOY7buUrkM1cQ8JypPmd6wEZDhcRNrqJma7dPfOQ+lZC1nR0cVeb0lcY6naStDps4GEbLN92oRjAA5AdemY2b5ReZxLp9VhWEYt/Hf3Dhpc+V/eD6gun+dmUdUPQugh+6xvEap/Nlte+bJQ16Y1kgkhhNRF2JOjR4+iadOmUpdBZFdu3bqFcuXKFXgMr02iksdrk8g68doksk7nzp1DrVq1pC6jyBg6lLCsrCycPn0avr6+NpVSxcbGomnTpjhy5AgCA/MfL2VNbLFmgHVbQmFSYVu9Nh9lzc9BYdh6/QDbUFS2eG2Whuc4P2ybbbJE22zx2nwSW/sZYL2WZav1Xrt2DRUrVpS6nCKz/t8QpYyTkxOaNDHv6mQrAgMDn5h8WxtbrBlg3SXN1q/NR9nqc/CArdcPsA3FyVqvTWv5/lgC22abSrpt1nptPomt/QywXsuytXptIeDLi1zqAoiIiIiIiIiodGLoQEREREREREQWwdCBCkWtVmPq1KlQq9VSl1JotlgzwLrp6dn6c2Dr9QNsgz0ozd8fts02lea2FSdb+z6xXstivSWLE0kSERERERERkUWwpwMRERERERERWQRDByIiIiIiIiKyCIYORERERERERGQRDB2IiIiIiIiIyCIYOhARERERERGRRTB0oCLT6/XIzs6Wugy7odFopC6BiIjsGP8O2SY+b6UDn0fL2rp1K/bv3y91GaUeQwcqEr1ejyFDhiA8PFzqUopECIEdO3Zg2bJlOHHihNTlFNqNGzdQp04dnD59WupSiiQlJQU//PADVq9ejTt37khdjl0QQmDXrl1YunQpjh49KnU5T0Wv12Pz5s345ptvcPbsWanLeSpZWVn49ddfsWLFCly9elXqcp5KWloa1q5di++//x4xMTFSl2MTrly5guXLl+P3339HZmam1OUUq8WLF6NHjx5Sl2ERfN7sz9atW9GzZ09kZWVJXUqhbN26FfXr10dOTo7UpRTK2bNn8cYbb2DEiBHQ6/VSl/NEW7duRb9+/TB58mSpSymUK1euYPz48Xjttddw8uRJqcspGkFUSDqdTrz66quiQ4cOIiMjQ+pyCk2j0YgOHTqISpUqidq1awsAolu3biI2Nlbq0gp0/fp1UbFiRfHFF19IXUqRnDhxQgQEBIhGjRoJf39/4ejoKKZNmyb0er3UpZVaWVlZ4vnnnxcVKlQQoaGhAoDo3LmziI6Olrq0QktOThZNmjQR1atXFzVq1BAAxIABA0RSUpLUpRXazZs3RfXq1UVoaKioWLGikMvl4s033xQajUbq0grt9OnTomzZsqJhw4YiMDBQODg4iI8++kjodDqpS7Na3377rVCr1aJ58+bCxcVFBAQEiI0bN0pdVrFYtGiRKF++vLhy5YrUpRQ7Pm/2yc/PTzg5OYnOnTuLzMxMqcsp0JYtW4SPj484ePCg1KUUytGjR4W3t7dYsWKFSE9Pl7qcJ3rw/Z08ebIAII4fPy51SQXas2eP8PHxES+//LJo0KCBUKvVIjExUeqyCo2hAxVKXoFDdna2OHnypNW/sZk4caLo06eP6UVzRESECA4OFhUqVBCXL1+WuLq85RU4xMfHixMnTlj1L3KdTieqVq0qvv/+eyGEEFqtVsyZM0colUrRr18/odVqJa6wdPr4449Ft27dTN/f/fv3i4oVK4qyZcuK8+fPS1xd4QwdOlS8/vrrwmAwCCGMLwZ8fX1FrVq1RExMjMTVFU6nTp3Exx9/LIQQwmAwiNWrVws3NzfRokULkZKSInF1T2YwGERISIhYtmyZEMJ4Pc+fP1+oVCrRs2dPkZ2dLXGF1ufChQvCw8NDXLhwQQghREJCgnj55ZeFTCYTCxculLi6Z5PXG9dz586Z2mrL+LzZr6ZNm4qlS5cKd3d3qw4e8gocrl69Kk6fPm21H+K0a9fOZq6fR7+/er1eVKlSRQwaNEjqsvJ1+/Zt4evrK/bs2SOEECIzM1P4+/uLI0eOSFxZ4TF0oEL56KOPhKOjoylgWLdunfDx8REABADx8ssvW+2b4ZCQELFu3bpc227fvi1q1qwpKleuLJKTk6UpLB9arVbUqFFDdOrUSQhhDHdGjRolFAqFACDc3NzE0qVLJa4yb+fOnRMAzMKFzZs3CwcHBzFixAiJKivdGjZsKFatWpVrW1xcnAgNDRXly5cX8fHxElVWeH5+fmL37t25tl2+fFmUL19e1KtXz+p7C2RlZQmZTCaioqJybT927Jjw8vISnTp1stoXig9cvXpVADD7Xb5jxw7h5OQkBg8eLFFl1uvLL78U7dq1M9v+wQcfCJlMJn7++WcJqnp227dvFwDE77//LoQw9mB70FMQgGjUqJFNf4rO581+DR48WPz444/iwIEDpuAhKSlJTJw40Wpex0ZFRQlHR0fx6aefCiGMvejatGljeh4rVaokDh06JHGV5lQqVa43wV9++aWoXr26KF++vPjkk0+spsfcn3/+aRboLFy4UDg4OIg7d+5IWFn+Pv30U9G2bVvT7ezsbFG+fHkxevRo0atXL/Hdd99JV1whMXSgQrl7966oU6eOaNiwofjuu+9E2bJlxcaNG8Xt27fFihUrhJubm+jbt6/UZeapdevW4u233zbbfvPmTeHn5yeGDx8uQVUF2759u3B0dBSTJ08Wr7zyiujatas4deqUuHz5shg+fLgAIH755RepyzQTGxsrAIiIiAizfWvWrBEAxPbt2yWorHTr1KlTnj/HMTExIigoSAwcOFCCqoqmZs2aYtq0aWbbz58/L9zd3cWkSZMkqKrw9Hq9UKvV4ttvvzXb988//wiVSiUWL14sQWWFl5SUJORyufjzzz/N9v32228CQKnpfl5c1qxZIzw9PUVaWprZvtdee014eHiIhIQECSp7NjqdTgwYMEB4e3uLDRs2CH9/f7FgwQIRHR0tdu7cKapUqSIqV65sNW/SiorPm20+b8Vh1qxZ4sMPPxRCCFPwoFarxaBBg6wqGH7wYd9PP/0kqlSpIj744ANx/fp18e+//4pmzZoJDw8Pce3aNanLzMXLy0t89dVXQggh3n//fRESEiK+/fZbMWnSJKFSqcTEiRMlrtDowoULZqFNWlqa8PDwEFOnTpWmqCeYNWuW8Pb2FlevXhXp6enif//7n6hevbqYO3euGDJkiAAgZs2aJXWZBWLoQIX2IHgAIA4cOJBr3+rVqwUAq+zKvWzZMqFSqcS///5rtm/16tVCpVKJ1NRUCSrL7ebNm7k+zX0QPFSrVk1kZWXlOjYsLEzUq1evhCssnM6dO4vQ0NA8P5nu1auX6NGjhwRVlW7ff/+9UCgU4p9//jHb98svvwiFQmH14/5mzpwpXF1d8/wdMn/+fOHp6Wk1n5LkZ9SoUcLf3z/P+WImTJggatasKUFVRdOzZ09Rs2bNPN+MDRgwwNQDi4zu3bsnvLy8xMiRI832paWlCT8/P7FgwQIJKnt2D97AAhBz587Nte/ChQtCpVKJ5cuXS1Tds+HzZpvPW3HYsGGD6N27txDCOOdXy5YthVwut8qhFh999JEAIF5//fVc25OSkoS/v7949913Jaosb4MGDRLBwcHi6tWrwt/fP1dwN3fuXOHs7GzVf8ffffdd4e/vb5VDCZOTk0VoaKhQKBQiODhYlC9fPldP7QkTJgi1Wm0aomqNGDpQkdy9e1eEhYWZbU9JScn3E+6SlJqaKt58881cL5h1Op1o06aN8Pf3N+v6rNFoBACz7SXt+vXrokKFCqbx4A9s375dfPLJJ2bHL1u2TAQEBJRUeQVKT0/P9Uvu4sWLwt3dXYSFhZmFJatXrxZ16tQp6RJLPYPBIDp16iR8fHzE6dOnc+3TarVCoVCIkydPSlRd4WRlZYl69eqJSpUqiZs3b+bad/PmTQHA6j55jI6OztUVMzExUQQHB4sGDRqYTYB58OBB4eTkVNIlFujEiRNi//79ubZdu3ZNeHp6is6dO5sFh7/88ouoWrVqSZZoE3744QcBQMyePdts3+DBg8W4ceMkqKrodDqduHjxYq43Xg/ewD5+TQohRGhoqAgPDy/JEp/a4sWLxY4dO3JtKy3P261bt8S6detyvQEpLc+bJZw9e1ZUqVJFaDQa0blzZzF48GDxzz//WO0cDx999JHYuXOn2fYXX3zR6noxXr16Vbi7u4s6deqI9u3b59p34MAB4ejoaNWhw/Xr14VCoTDNS2ZtDAaDiImJEbNmzTL7/bR161bh5ORkVb11HsfQgczo9Xoxe/ZsUbZsWeHm5iZGjRolcnJyTPvzmgxw3759wt3dXdIeA6mpqaJFixbijTfeMEv6EhISRGhoaK5JWIQw/hL09fWVNNV8MGnkg/oe/4OX1/d71KhR4uWXXy6pEvN04sQJUa9ePQFAeHt7i8mTJ5vepOzevVs4OzuLtm3b5vrUd/z48WLYsGFSlWzz0tPTxcyZM/P8o52cnCwaNmwoypQpk6t7/IM5BaxlxZl79+6JkSNHCk9PT+Hn52fqiimE8cVz5cqVRXBwcK6eSZs2bRJVqlSRotw83blzRzz//PMCgFAoFLk+TTxz5ozw9fUVISEhuXptfPXVV7nGY0rtzp07wsvLS7i5uZkFD/v27ROurq6iZcuWuSYKnjx5snj11VdLulSr8ccff4i6desKR0dH0blzZ3H79m3TvunTpwsAYvz48aa/J1qtVjRo0ED89NNPUpVcaBs3bhSBgYECgChfvnyuID6v3zcpKSl5/uxYo4JWcrD1523lypXC399fTJkyRURGRubaZ+vPm6Xk5OQIBwcH0b59ezF48GDTm7QDBw6I1q1bW2WvwMdfB+p0OlGrVq08h/NJbceOHcLZ2Vm4uLiIc+fOmba//vrrYsiQIdIVVkj9+/cXDRs2lLqMAo0fP140btw41zX+6quvWv3ra4YOZGb48OGicePG4ueffxazZ88WDg4OYvr06fkef+HCBVGtWjXTjOdSKChweCApKUn06NFDyGQy8fzzz4uRI0cKPz8/8ccff5RwtQ89ukrFlStXhFwuFytWrCjwPmvWrBG+vr7i6tWrJVSludu3bws/Pz+xfPlycfHiRfH5558LtVotQkNDTW9SDh06JCpUqCDUarV47bXXxAsvvCCqVq1qtZP02IJBgwaZJm7N7wVl3759TUtmvvnmm8LPz0/8+uuvElRrTqPRiAYNGoiXXnpJbNiwQYwYMUIAyDWBZGxsrGjXrp1QKBSid+/eYvjw4cLX11fs27dPwsofysjIENWrVxfvvfeeuHr1qvjwww+Fg4NDrmD20qVLon79+sLR0VG8/PLLYtCgQSIgIECcOXNGwspzu3fvnihTpozo2rVrnm9Cjh07JqpUqSLc3NzEkCFDRP/+/UWlSpWsfrUiS1m9erUICAgQy5YtE6tWrRJly5Y1+yRv2bJlwtnZWVSqVEmMHj1aNG7cWPTr18+qP3kSwhimBAUFiS1btogzZ86IOnXqiDfffDPf4+/duyfCwsKseqb3BwqzdKStPm/Xrl0T7u7uhR7WakvPm6W99NJLuQIHW/JgcvEWLVpY7Ypghw4dEpUqVRIeHh5ixIgRonXr1qJp06bi3r17Upf2RAcOHBAAxN9//y11Kfk6evSoUCqVokOHDmLZsmWid+/eomHDhla/ShZDB8rlwIEDomLFirl+cMeNGyfKly9vduyNGzfE4MGDRXBwsFi5cmUJVpnbg8Bh4MCBpsDhxx9/FG3bthV16tQR48ePz/WLbteuXWLcuHHi3XffNeuKXpLyWhbzhRdeEHXr1s3z+I0bN4r27duLpk2bSv7mZebMmeKFF17ItS0qKkpUqFBBVK9e3dQNXqPRiG+++UaMHDlSzJkzxyb+4FiruLg4ERQUJH799Vfh4OCQb/AghBB79+4V48ePF+PHjxf//fdfyRZagDlz5ohevXrl2takSRPxv//9L9c2g8EgNm/eLN555x3xwQcfiEuXLpVkmQUKDw/PNWlnYmKiCAoKEmfOnBFHjx41vYjV6XRi3bp1YtSoUeKTTz7J9am4tQgICBC3b9/OFTwYDAZx6tQpIYRxSa4VK1aIkSNHilmzZlnlJ4AlIT09Xfj5+Yljx46Ztm3cuFEAMAt/o6OjxcyZM8XIkSPF6tWrrf5NjV6vF5UrVxaHDx82bZs5c6aYPHmy+Pvvv3OFTNnZ2WLChAmiUqVK4v3337fKcc+PWrRokfD19TUFDpcvXxaDBg0SISEholu3brnmprK1500IIT7//HPRtWtX022DwSB+/vlnMWHCBLFixQpTEGprz9uz0Gg04siRI+L69esFHpeTk2MVz3Fh633g008/FSEhIWLgwIGSrLxWlHqzsrLEmjVrxKRJk8SqVask+bkr6vf3gREjRkgynLMo9e7YsUM0atRIlC9fXowfPz7PeZisDUMHymXEiBFmAcLOnTvzXAZRCCGOHz8uedKanp4u2rRpIypXrixu3rwp3n//fVG+fHkxbdo08e677wpXV1dRt25dq7sgFyxYkCtwEMLYrRmA2Lt3r9nxSUlJ4uLFiyVT3BNMmjRJdOjQwWz7tWvXhL+/v2mSJio+X331lZg8ebIQwri+9JOCB2sUEhJiFiB88sknonnz5hJVVHTNmzcXt27dMt2eNWuWcHJyEh4eHkImk4m2bdtazVCWJ2nbtq34+++/RWZmpil46Nmzp+jevbvUpVmVH3/80axb8IMlUh9f5tXWnDhxQvTs2dN0Oz09XYSEhAh3d3fh5uYmlEplriWaz549a3V/S/OzbNkyoVAoxPLly8Xx48eFt7e3eP3118Wnn34qGjVqJFQqldi6davUZT61//u//xPNmjUTQhjfRPfo0UOUL19edOzYUSiVStG5c2fT6zNbet6e1u7du4Wvr69wcnISAESHDh2s5jVTXp6m3itXrki2BLY9fH+lZGv1Pg2GDpTLtm3bzLrnnDp1SgAwmxTQmjwIHgICAkTFihVzTeJ26tQp4ezsnOdyfNaoYcOGok+fPlKXUaDdu3cLmUyWZ/ezByHVo58K0rPTaDTi7t27ptu2GDzkNUb6q6++Eo0bN5agmqfzaC+jNWvWiICAALFr1y4hhLFLqaura4HD0azJyJEjxddffy2EMA4bCQgIEADynLTMnl25ciXP1Y+8vLzyXF7UlhgMBtO465ycHNG5c2fRs2dPER8fL3Q6nRgzZoxQqVQ2O6zmQfDg7++f6/ePVqsVXbt2FWXLlrWKT7yfxrZt2wQAceTIETF//nzRtWtX05xQERERQqlUih9++EHiKkvGrVu3RJkyZUyThf7zzz8iNDRUqNVqsw9xbt++LQYNGiTpHGSs17JYr3Vi6EBPdO7cOQHA9McsLi5OvPHGG7nGMFuDB8HDokWLzPYNGjRIdOnSRYKqiu6HH34Qcrlc0jkbCqNLly4iKCgoz25g9evXF/Pnz5egKvvyePBw4cIFMWDAAJt6Ef3NN9+IRo0amW4fPnzYZgLCo0ePirNnz+ba9sYbb5gNPbJWX3zxhRg9erQwGAzitddeEx06dMh3jgcy5+fnJ7Zv3y6EMA6neeedd4rcjdfarFmzJleImZOTIxQKhdnKD7Zk2bJlea6a9GDstrX/rc2PXq8X9erVEyEhIaJjx45m8960bds2z9WvSqMFCxaIzp0759qWkZEhunbtKlxdXcXx48dN2/fv3y9cXFxyDZMraazXslivdZKD7JZer8eNGzeeeJxMJgMAGAwG3L17F+3bt0f58uWhUqksXWKeUlJSkJCQYLbd1dUV27ZtQ8+ePc326fV6VK5cuSTKy1d0dDSys7OfeNxLL72EgIAALFq0qASqerK4uDjMmDEDb7zxBjZt2mTa/sMPP8DFxQVt27ZFVFRUrvs4OjrC19e3pEu1O2FhYdiwYQM2bNiAfv36oWPHjggLC4Ncbju/2mUyGQwGAwDg33//Re/evdG8eXOJqyqcxo0bIyQkJNe2mJgY1K9fX5qCiqhmzZo4ffo0hg0bhhs3bmDz5s3YuHEjWrVqheHDh0Ov10tdolV78LOr1+vx6quv4urVqwgICJC6rGfy6quvQqFQmG7HxsZCLpejTp06Elb1bEaMGIGff/7ZbLter4ezs7PNPmdyuRw//fQTYmNjsWfPHly8eNG0LysrC1evXkWLFi0krLDkyOVyXL582fS3BABcXFywceNG1K1bFy+//DK0Wi0AoFWrVti9ezdmz54tVbms18JYr5WSOvUg6QwZMkT4+/ubfVL3uKioKNOnASEhIWLGjBklVKE5rVYrmjVrJkJDQws9ru3YsWPCw8Oj0DM8W0J8fLyoUKGCeP755ws1TGXmzJnCw8ND8jGYR44cEb6+vqJLly6iVatWAoD45ptvTPtv374t6tWrJ9zc3ER4eLg4fPiwmDx5sqhVq5bVrXVdmi1evFjI5XKrXVu6IN9++62oX7++OHz4sPD397fp7urr1q0TQUFBNjPhYnR0tHBwcBAdOnTINQ9FZmamuHHjhoSV2YaAgACxadMm8dJLL4mwsDCrHoL4NLKzs0XXrl3F2LFjpS6l2GVnZ4v27duLDz74QOpSntmpU6dE+fLlhVqtFt98843Yt2+f6Nq1q+jfv7/UpZWYq1evCqVSKRYuXGi27/r168LJyUmsW7dOgsryxnoti/VaJ4YOdioqKkpUqVJFNGrU6InBw8WLFwUAUbVqVUkDByGE+O2330TLli1F2bJlnxg8REVFidmzZ4uAgACxcePGEqzSXHh4uOjWrZtwc3MrVPCQkJAgAgICJJ1EJisrS1SoUEGsXbvWtO21114T3t7eubrvZ2VliRkzZojKlSsLd3d30bdvXxEbGytFyXbpwoULoly5cjYZOAghxHfffSd8fX1tNnDQ6/XixIkTYuzYsaJy5cpWtVpIYezbt89mJr60NkFBQaJq1aqlLnBISUkRW7ZsEfXr1xeDBg2SfLLo4pScnCw2bdok6tevLwYOHFhq2pacnCwmTZokatasKapVqyamTp1aatpWWJMmTRIODg55/h3p3bu31QVMrNeyWK/1Yehgp8aMGSPmz58vkpOTnxg8xMTECIVCIXngIIQQHTp0EDt37hSXLl16YvCwYsUK8dlnn0m+VJ1OpxOVKlUS165dE3///XeRggcprVu3TvTr1y/XthMnTggANj9uuTRZuHChzQYOQgixfv164ejoaJOBgxDG0GHKlCnim2++kbxnEj297du3F3lC1lq1atlE4FDUtl24cEGMHTvWbI4Aa1TUtl25ckWMHz8+zxWirM3T/EzaM51OJ3r37i0cHR3NJi1u3bq1WLFihUSV5Y31WhbrtT4MHezU2rVrTWv8FiZ4eNIQjJLy9ddfC4PBIIQQhQoerIFOpxPLli0z3S5K8CClb775xmw5sXv37gkAVvPzQKXDg9nziaTw66+/CgCFWgnm1KlTIiYmRgghxOXLl636d7gQT982W8C2Gdla2ywpJydHDBs2TAAQvXv3FsuXLxevvvqqaNKkiVVeq6zXslivdWHoQEKIvIOHuLg4q/+kI6/gITIyUkRFRUlcWcHyCh727t1rdeHJg4DngezsbAFAnD592rQtIiKipMsiIio2jRo1ElOnTn3iErR6vV7UqlVLVK9eXcTFxZVwlU+HbWPb7NHOnTtFv379RPPmzcV7771n9b3QWK9lsV7rwNCBTB4NHv766y8REhIiPv30U6nLeqJHg4e//vpLBAYGit9//13qsp7o0eBh48aNwt/fXxw7dkzqsgqUk5MjAIhTp04JIYRYtGiRqFy5suRDQYiInsbRo0dF48aNhRDmS9Dm5cyZM2LIkCE28akT2/YQ20ZEJC2GDpTLg+ABgFXM4VBYD4IHpVIpfv31V6nLKbQHwYOHh4c4fPiw1OU8kU6nEwDEyZMnTYEDZ7knIlul0Why/e4tzJs8W8G22abS3DYisl+2s5g7lYicnBxkZmZixowZ+Pjjj6Uup9AyMzNhMBjw008/oX///lKXU2gajQaurq7YsWMHmjVrJnU5TySTyQAAX331Fb744gvs3bsX5cuXl7gqIqKn4+zsnOt3b1hYGDZs2IANGzZg4MCB0Ov10Ov1mDFjBtLS0iSstOjYNraNiMhayIQQQuoiyHp06NABHTp0sKnAITs7G7Vq1cLcuXNtKnC4ffs2GjdujI0bN9pE4PCAXC5HpUqVGDgQUam1detW9O3bF3379oUQAunp6Vi/fj0cHR2lLu2ZsW22qTS3jYhKP4YOlEtCQgJ8fHykLqPIWHfJmTNnDl555RUGDkRUqm3atAl9+vRBWFhYqXtzx7bZptLcNiIq3Rg6EBERET1Cr9fj1VdfLZWfJrNttqk0t42ISj/O6UBERET0iE2bNpXaN3dsm20qzW0jotKPPR2IiIiIHqPX66FQKKQuwyLYNttUmttGRKUbQwciIiIiIiIisggOryAiIiIiIiIii2DoQEREREREREQWwdCBiIiIiIiIiCyCoQMRERERERERWQRDByIiIiIiIiKyCIYORERERERERGQRDB2IiIiIiIiIyCIYOhARERERERGRRTB0ICIiIiIiIiKLYOhARERERERERBbB0IGIiIiIiIiILIKhAxERERERERFZBEMHIiIiIiIiIrIIhg5EREREREREZBEMHYiIiIiIiIjIIhg6EBEREREREZFFMHQgIiIiIiIiIotg6EBEREREREREFsHQgYiIiIiIiIgsgqEDEREREREREVkEQwciIiIiIiIisgiGDkRERERERERkEQwdiIiIiIiIiMgiGDoQERERERERkUUwdCAiIiIiIiIii2DoQJSH9evXY/78+fnu//vvvzFp0iTEx8eXYFVE9mvOnDnYsmWL2fbw8HDMmzfPbPu6devw+eefl0RpRERERFQAhg5Eebhw4QKmTJkCg8Fgti8nJwevvfYaTp48CV9fXwmqI7I/P/zwA/74449c2yIiIjBlyhSzgDAjIwOjRo1CSkpKSZZIRERERHlg6ECUh9DQUGg0Gly7ds1s35dffombN2/iiy++kKAyIvvk4eFhFiJ88cUXqFChgtn2VatWQaPR4K233irJEomIiIgoD0qpCyCyRnXq1AEAnD17FlWqVDFtT0xMxMyZM/HWW2+hVq1aUpVHZHc8PT2Rmppqun358mVs2bIFS5YswciRI6HVaqFSqSCEwKJFi/DKK68gICBAwoqJ7NeqVavg6+uLGjVqYMeOHUhISMDkyZPh4OAgdWlEdkmr1WLWrFkQQkAmk8HLywvPPfccGjRoIHVpZCfY04EoDxUrVoS7uzvOnj2ba/v06dOhUCgwbdo0aQojslOenp65ejQsXLgQLVq0QKdOnQDAFEhs374dFy5cwLhx46Qok4gATJ48GZMmTUKPHj1w6dIluLi4MHAgklBKSgqEEAAAvV6PY8eOoUWLFvjqq68krozsBXs6EOVBJpOhdu3auUKHixcvYunSpViwYAG8vLwkrI7I/nh4eJiChXv37mHVqlVYvXo11Go1AOMLKm9vbyxcuBDt27dH/fr1JayWyH7FxMTgzp07qFmzJrZt2wZnZ2epSyKyez4+PmYfmFWuXBnffvstRo8eLU1RZFcYOhDlIzQ0FEePHjXdfu+991C9enWMHDlSwqqI7NOjPR2WL18Of39/9O7dG3q9HoAxdDh//jx27dqFTZs2SVkqkV07fvw4AGDJkiUMHIisyJkzZ3D06FHExsYiJycH+/btg0KhkLosshMcXkGUjzp16iAqKgp6vR579+7FH3/8gQULFvAXNJEEHszpoNfrsXjxYowdOxZyuRwqlQqOjo5ISUnBggULULVqVYSFhUldLpHdOn78OGrWrImaNWtKXQoRwRjKd+vWDZ06dcLevXtNvQYvXbpkmsOMyNLY04EoH6GhocjKysKlS5fw7rvvonfv3qbx40RUsjw8PJCWloZff/0VqampeP3110371Go1rl27hjVr1mDu3LmQy5mnE0nl+PHjaNy4sdRlENF9M2fOxLlz5xAVFQVPT08AQFJSEj799FNeq1Ri+MqMKB+hoaEAgEmTJuHs2bOYN2+exBUR2S9PT08IIRAeHo6RI0fC1dXVtE+tVuPzzz+Hg4MDhg4dKl2RRITjx4+jUaNGUpdBRPddvXoVFStWNAUOWq0WI0eORE5ODkMHKjHs6UCUDx8fHwQEBGDTpk147733ULVqValLIrJbD14sXbx4Ee+8806ufWq1Gv/99x/ef//9XGEEEZWs2NhYxMbGomHDhlKXQkT3vfTSS3jppZfw8ssvIygoCHv37oW/vz+USiUnXaYSw9CBqABz585FTEwMRo0aJXUpRHatdu3a+PTTTxEcHIyyZcvm2vfuu+/i1q1bGDJkiETVEREA5OTkYOrUqQwdiKzIiy++iAoVKiAiIgJqtRrjxo3D9evX0b17dzg5OUldHtkJmXiwaCsRERERERERUTHinA5EREREREREZBEMHYiIiIiIiIjIIhg6EBEREREREZFFMHQgIiIiIiIiIotg6EBEREREREREFsHQgYiIiIiIiIgsgqEDEREREREREVkEQwciIiIiIiIisgiGDkT0/+3YsQAAAADAIH/rUewrjAAAABbSAQAAAFhIBwAAAGAhHQAAAICFdAAAAAAW0gEAAABYBHB+S/VP+XmMAAAAAElFTkSuQmCC", "text/plain": [ - "<Figure size 970x970 with 16 Axes>" + "<Figure size 1067x1067 with 16 Axes>" ] }, "metadata": {}, @@ -454,40 +579,224 @@ "source": [ "labels = [f\"${q.latex}$\" for q in params]\n", "fig = None\n", - "for name, color in ((\"L0\", \"C3\"), (\"L2y\", \"C1\"), (\"Lgp\", \"C2\")):\n", + "rungs = [\n", + " (\"L0\", plotstyle.COLOURS[1]),\n", + " (\"L2y\", plotstyle.COLOURS[4]),\n", + " (\"Lgp\", plotstyle.COLOURS[2]),\n", + "]\n", + "for name, colour in rungs:\n", " fig = corner.corner(\n", " samples[name][:, problems[name].columns(params)],\n", " fig=fig,\n", - " color=color,\n", " labels=labels,\n", - " plot_datapoints=False,\n", - " plot_density=False,\n", - " levels=(0.68,),\n", " range=[(100, 220), (0, 45), (1.1, 1.7), (0.3, 0.8)],\n", + " **plotstyle.corner_kwargs(\n", + " color=colour,\n", + " fill_contours=False,\n", + " plot_density=False,\n", + " show_titles=False,\n", + " levels=(0.68,),\n", + " ),\n", " )\n", "fig.legend(\n", - " handles=[\n", - " plt.Line2D([], [], color=c, label=n)\n", - " for n, c in ((\"L0\", \"C3\"), (\"L2y\", \"C1\"), (\"Lgp\", \"C2\"))\n", - " ],\n", + " handles=[plt.Line2D([], [], color=c, label=n) for n, c in rungs],\n", " loc=\"upper right\",\n", - " frameon=False,\n", + " fontsize=9,\n", ")\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "9aace2f6", + "id": "abb4d17e", + "metadata": {}, + "source": [ + "## What each rung predicts (recipe 17)\n", + "\n", + "Evidence says which error model the data prefer. It does not say whether any of\n", + "them describes the data *well*, and for that we want the posterior predictive:\n", + "draws of $y_m(\\theta) + \\text{noise}$ with the covariance each rung declared,\n", + "not merely the spread of the model curve.\n", + "\n", + "`predictive_draws` gives exactly that — the draws include $\\Sigma$, so a rung\n", + "with a large inferred noise produces wide draws even where its model curve is\n", + "sharp. We compare the three log-space rungs at the measured angles, then check\n", + "their calibration." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d4c5411c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:51:15.028113Z", + "iopub.status.busy": "2026-09-12T03:51:15.027912Z", + "iopub.status.idle": "2026-09-12T03:51:17.857497Z", + "shell.execute_reply": "2026-09-12T03:51:17.856682Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L0 68 % predictive width = 0.076 (ratio units) coverage at 0.68 = 0.79\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L2y 68 % predictive width = 0.068 (ratio units) coverage at 0.68 = 0.81\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lgp 68 % predictive width = 0.072 (ratio units) coverage at 0.68 = 0.78\n" + ] + } + ], + "source": [ + "levels = np.linspace(0.1, 0.9, 9)\n", + "draws, coverage = {}, {}\n", + "for i, (name, _) in enumerate(rungs):\n", + " p = problems[name]\n", + " c = p.constraints[0]\n", + " draws[name] = rx.diagnostics.predictive_draws(\n", + " p, samples[name][::20], n_rep=2, rng=i\n", + " )\n", + " coverage[name] = rx.diagnostics.coverage_curve(draws[name], c.y[c.active], levels)\n", + " width = rx.diagnostics.sharpness(draws[name], transform=np.exp).mean()\n", + " print(\n", + " f\"{name:4s} 68 % predictive width = {width:6.3f} (ratio units) \"\n", + " f\"coverage at 0.68 = {rx.diagnostics.coverage_curve(draws[name], c.y[c.active], [0.68])[0]:.2f}\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a3bde3f9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:51:17.859125Z", + "iopub.status.busy": "2026-09-12T03:51:17.858940Z", + "iopub.status.idle": "2026-09-12T03:51:18.403186Z", + "shell.execute_reply": "2026-09-12T03:51:18.402408Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 704x440 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "for (name, colour), hatch in zip(rungs, plotstyle.HATCHES):\n", + " lo, hi = np.percentile(np.exp(draws[name]), [5, 95], axis=0)\n", + " plotstyle.band(\n", + " ax,\n", + " np.rad2deg(angles),\n", + " lo,\n", + " hi,\n", + " color=colour,\n", + " hatch=hatch,\n", + " label=f\"{name}, 90 % predictive\",\n", + " )\n", + "ax.plot(np.rad2deg(angles), ratio, \"o\", ms=3, color=\"k\", label=\"data\")\n", + "ax.set(\n", + " xlabel=r\"$\\theta_{cm}$ [deg]\",\n", + " ylabel=r\"$\\sigma / \\sigma_{Ruth}$\",\n", + " yscale=\"log\",\n", + " title=\"Posterior predictive, covariance included\",\n", + ")\n", + "ax.legend(fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "a7d8e5e8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:51:18.405724Z", + "iopub.status.busy": "2026-09-12T03:51:18.405523Z", + "iopub.status.idle": "2026-09-12T03:51:18.547540Z", + "shell.execute_reply": "2026-09-12T03:51:18.546663Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 506x484 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(4.6, 4.4))\n", + "ax.plot([0, 1], [0, 1], \"--\", color=\"0.5\", label=\"nominal\")\n", + "for name, colour in rungs:\n", + " ax.plot(levels, coverage[name], \"o-\", color=colour, label=name)\n", + "ax.set(\n", + " xlabel=\"nominal coverage\",\n", + " ylabel=\"empirical coverage\",\n", + " xlim=(0, 1),\n", + " ylim=(0, 1),\n", + " title=\"Is each rung's error model calibrated?\",\n", + ")\n", + "ax.legend(fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "fbf8b3b2", + "metadata": {}, + "source": [ + "### Are any of them calibrated?\n", + "\n", + "In sample, all three rungs *over*-cover: 79 %, 81 % and 78 % of the measured\n", + "points fall inside their nominal 68 % intervals, with predictive widths of\n", + "0.076, 0.068 and 0.072 in ratio units. The inferred noise is a little more\n", + "generous than the data need, which is what we should expect when one noise\n", + "parameter has to cover a diffraction pattern whose model error is nowhere near\n", + "uniform in angle.\n", + "\n", + "That is the easy question, though: these are the points the fit was shown. The\n", + "held-out section below asks the hard one, and there the three rungs separate\n", + "sharply — 49 %, 60 % and 80 % of the backward angles land inside the nominal\n", + "68 % band. Being well calibrated on the data we fitted is no evidence at all\n", + "that we will be calibrated on the data we did not." + ] + }, + { + "cell_type": "markdown", + "id": "50888ae6", "metadata": {}, "source": [ "## Hold out the backward angles (recipe 11)\n", "\n", - "Fit each rung below 90° and score the points above it: `masked_where` keeps\n", - "every object, `complement()` flips the mask, and a chain from the fit scores\n", - "the held-out problem directly. The joint held-out log predictive is a\n", - "different question from the evidence: not \"which model explains the fitted\n", - "data\" but \"which model predicts what it has not seen\".\n", + "Fit each rung below 90 degrees and score the points above it: `masked_where`\n", + "keeps every object, `complement()` flips the mask, and a chain from the fit\n", + "scores the held-out problem directly. The joint held-out log predictive is a\n", + "different question from the evidence: not \"which model explains the data we\n", + "fitted\" but \"which model predicts what it has not seen\".\n", "\n", "The normalisation of `L2y` and the Gaussian process of `Lgp` both couple the\n", "angles we fit to the ones we hold out, so the honest score is not the marginal\n", @@ -499,14 +808,14 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "a57881d2", + "execution_count": 15, + "id": "a8a21c91", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T20:22:55.633207Z", - "iopub.status.busy": "2026-09-11T20:22:55.632956Z", - "iopub.status.idle": "2026-09-11T20:29:24.114885Z", - "shell.execute_reply": "2026-09-11T20:29:24.114038Z" + "iopub.execute_input": "2026-09-12T03:51:18.549537Z", + "iopub.status.busy": "2026-09-12T03:51:18.549320Z", + "iopub.status.idle": "2026-09-12T03:59:22.057021Z", + "shell.execute_reply": "2026-09-12T03:59:22.056428Z" } }, "outputs": [ @@ -537,17 +846,17 @@ "scores = {}\n", "for i, (name, c) in enumerate(ladder.items()):\n", " if name == \"E0\":\n", - " continue # same space as L0 up to the noise model; the log-space rungs are the comparison here\n", + " continue # same space as L0 up to the noise model; the log rungs are the comparison\n", " fit = c.masked_where(lambda x: x < cut)\n", " p_fit, p_held = rx.Problem([fit]), rx.Problem([fit.complement()])\n", " res = fit_nested(p_fit, seed=10 + i)\n", " s = res.samples_equal(rstate=np.random.default_rng(10 + i))\n", " lp = rx.diagnostics.heldout_log_predictive(p_held, s[::5], given=p_fit)\n", - " draws = rx.diagnostics.predictive_draws(\n", + " held_draws = rx.diagnostics.predictive_draws(\n", " p_held, s[::20], n_rep=2, rng=i, given=p_fit\n", " )\n", " h = p_held.constraints[0]\n", - " cov68 = rx.diagnostics.coverage_curve(draws, h.y[h.active], [0.68])[0]\n", + " cov68 = rx.diagnostics.coverage_curve(held_draws, h.y[h.active], [0.68])[0]\n", " scores[name] = (\n", " rx.diagnostics.log_posterior_predictive(lp),\n", " cov68,\n", @@ -556,28 +865,29 @@ " p_held,\n", " )\n", " print(\n", - " f\"{name:4s} held-out log predictive = {scores[name][0]:8.2f} 68 % coverage of the held-out points = {cov68:.2f}\"\n", + " f\"{name:4s} held-out log predictive = {scores[name][0]:8.2f} \"\n", + " f\"68 % coverage of the held-out points = {cov68:.2f}\"\n", " )" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "f2306531", + "execution_count": 16, + "id": "3f533a77", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T20:29:24.118599Z", - "iopub.status.busy": "2026-09-11T20:29:24.118306Z", - "iopub.status.idle": "2026-09-11T20:29:25.455673Z", - "shell.execute_reply": "2026-09-11T20:29:25.454897Z" + "iopub.execute_input": "2026-09-12T03:59:22.063074Z", + "iopub.status.busy": "2026-09-12T03:59:22.062575Z", + "iopub.status.idle": "2026-09-12T03:59:23.430104Z", + "shell.execute_reply": "2026-09-12T03:59:23.429084Z" } }, "outputs": [ { "data": { - 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", 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", "text/plain": [ - "<Figure size 700x450 with 1 Axes>" + "<Figure size 704x440 with 1 Axes>" ] }, "metadata": {}, @@ -585,20 +895,19 @@ } ], "source": [ - "x_fine = np.deg2rad(np.linspace(10.0, 178.0, 120))\n", - "on_fine = omp.bind(x_fine, meta)\n", - "fig, ax = plt.subplots(figsize=(7, 4.5))\n", - "for name, color in ((\"L0\", \"C3\"), (\"L2y\", \"C1\"), (\"Lgp\", \"C2\")):\n", + "fig, ax = plt.subplots()\n", + "for (name, colour), hatch in zip(rungs, plotstyle.HATCHES):\n", " _, _, s, p_fit, _ = scores[name]\n", " curves = np.array([on_fine(*row[p_fit.columns(params)]) for row in s[::25]])\n", " lo, hi = np.percentile(curves, [5, 95], axis=0)\n", - " ax.fill_between(\n", + " plotstyle.band(\n", + " ax,\n", " np.rad2deg(x_fine),\n", " lo,\n", " hi,\n", - " color=color,\n", - " alpha=0.3,\n", - " label=f\"{name}, fit below 90°\",\n", + " color=colour,\n", + " hatch=hatch,\n", + " label=f\"{name}, fit below 90 degrees\",\n", " )\n", "ax.plot(np.rad2deg(angles), ratio, \"o\", ms=3, color=\"k\", label=\"data\")\n", "ax.axvline(90.0, color=\"0.5\", ls=\"--\")\n", @@ -606,35 +915,34 @@ " xlabel=r\"$\\theta_{cm}$ [deg]\",\n", " ylabel=r\"$\\sigma / \\sigma_{Ruth}$\",\n", " yscale=\"log\",\n", - " title=\"90 % bands of the potential, extrapolated past the cut\",\n", + " title=\"Extrapolated past the cut, 90 % of the model\",\n", ")\n", - "ax.legend(frameon=False, fontsize=8)\n", + "ax.legend(fontsize=8)\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "c8a9a524", + "id": "6434a1fc", "metadata": {}, "source": [ "## Takeaways\n", "\n", - "- One comparison, one potential, four covariances: the ladder is a\n", - " dictionary of constraints, and every problem compiles on its own.\n", - "- Log space is where multiplicative errors are additive; the Jacobian makes\n", - " its evidence comparable with a linear-space fit.\n", - "- Evidence and held-out prediction ask different questions. Here the GP\n", - " rung wins both. Scored honestly, conditioned on the forward angles, the\n", - " normalisation rung's intervals are too narrow at the backward angles (60 %\n", - " of the held-out points fall inside the nominal 68 % band) and the GP\n", - " rung's are too wide (80 %): neither is calibrated past the cut, and a\n", - " rung that explains the forward angles best need not predict the backward\n", - " ones.\n", - "- Light-ion potentials are famously ambiguous: several parameter families\n", - " give nearly the same cross section. The `L0` posterior above carries a\n", - " second family near `V ≈ 125` MeV beside the main one at `V ≈ 165` MeV,\n", - " and it is the prior, not the data, that decides how much weight each\n", - " gets (see the `jitr` calibration notebook on the discrete ambiguity)." + "- One comparison, one potential, four covariances: the ladder is a dictionary of\n", + " constraints, and every problem compiles on its own.\n", + "- Log space is where multiplicative errors are additive, and the Jacobian is what\n", + " makes its evidence comparable with a linear-space fit.\n", + "- Evidence and held-out prediction ask different questions. Here the GP rung\n", + " wins both. Scored honestly, conditioned on the forward angles, the\n", + " normalisation rung's intervals are too narrow at the backward angles (60 % of\n", + " the held-out points fall inside the nominal 68 % band) and the GP rung's are\n", + " too wide (80 %): neither is calibrated past the cut, and a rung that explains\n", + " the forward angles best need not predict the backward ones.\n", + "- Light-ion potentials are famously ambiguous: several parameter families give\n", + " nearly the same cross section. The `L0` posterior above carries a second\n", + " family near $V \\approx 125$ MeV beside the main one at $V \\approx 165$ MeV,\n", + " and it is the prior, not the data, that decides how much weight each gets (see\n", + " the `jitr` calibration notebook on the discrete ambiguity)." ] } ], From 960bd7a0b360cf8675cb07cf262f571ed011b59c Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Sat, 12 Sep 2026 00:02:41 -0400 Subject: [PATCH 60/75] Rebuild the optical-model notebook around a real measurement - Real data: EXFOR O1199007, p + 40Ca elastic at 35 MeV, ratio to Rutherford, 59 angles, 2.3 % statistical errors, committed as a CSV so the notebook runs anywhere. The exfor_tools query is shown in the narrative, including that EXFOR spells elastic (P,EL) and a product tuple silently returns nothing - The SimpleNamespace stand-in is now a dataclass, as the review asked, and it still exercises from_measurement: the unit contract, the CM-frame check, and the ratio-to-absolute conversion through the Rutherford closed form - This entry quotes no systematics at all, which changes the notebook's spine. Three error models are compared instead: the quoted errors alone, an inferred normalisation (recipe 4), and an inferred model error (recipe 26, which this notebook previously cited without ever demonstrating) - The result is worth the rewrite. A normalisation buys almost nothing (log Z -3361 against -3479) because it rescales every angle alike, while an inferred model error takes chi2/N from 122 to 0.9 at delta = 30 %: a seven-parameter local potential describes this measurement to 30 %, not to 2.3 % - Answers the review's question about the toy: the truth *is* covered, at 0.3 sigma; the old corner range squeezed the posterior into a dot. Tempered and untempered predictive posteriors are now compared by coverage as well - Charged projectile, so the potential gains a Coulomb term, and the quantity check keeps a ratio measurement from being fitted with an absolute model --- docs/design.md | 2 +- examples/data/p40ca_elastic_35mev.csv | 60 ++ .../local_optical_model_calibration.ipynb | 972 ++++++++++++------ 3 files changed, 724 insertions(+), 310 deletions(-) create mode 100644 examples/data/p40ca_elastic_35mev.csv diff --git a/docs/design.md b/docs/design.md index e86ac14..11d10f6 100644 --- a/docs/design.md +++ b/docs/design.md @@ -628,7 +628,7 @@ a time unless noted. | `gp_discrepancy` | 7 | emcee, dynesty | mean-zero discrepancies with amplitudes growing in x: four rungs on a toy line, three on n+⁴⁰Ca missing its surface absorption; the total predictive band | 616 s | | `robust_likelihoods` | 9, 39 | emcee | Student-t versus Gaussian on three gross outliers; the iterative rejection loop, including the round that over-rejects and recovers | 54 s | | `error_scale_and_usu` | 34 | emcee | a global scale on the reported errors under both likelihoods; a USU offset on the technique we suspect | 90 s | -| `local_optical_model_calibration` | 12, 14, 15, 16, 21, 26 | dynesty | a real EXFOR measurement to a calibrated potential; the unit contract, the singular guard, tempering, other drivers | 182 s | +| `local_optical_model_calibration` | 4, 12, 14, 15, 16, 21, 26 | dynesty | EXFOR O1199007, p + ⁴⁰Ca at 35 MeV, which quotes no systematics: the unit contract, three error models against a potential wrong at the 30 % level, the singular guard, tempering and its coverage, other drivers | 1565 s | | `alpha_ca_error_model_comparison` | 10, 11, 13, 17, 18 | dynesty | real ⁴⁴Ca(α,α) data, a four-parameter potential, the ladder by evidence with the Jacobian, predictive draws carrying the covariance and their coverage, held-out backward angles scored conditionally | 1394 s | | `hierarchical_calibration` | 22, 24, 30, 35, 38 | dynesty | eight schools; a hierarchy on the physics parameters repairing a misspecified energy dependence, in sample and at a held-out energy | 937 s (alongside another notebook) | diff --git a/examples/data/p40ca_elastic_35mev.csv b/examples/data/p40ca_elastic_35mev.csv new file mode 100644 index 0000000..17c46a8 --- /dev/null +++ b/examples/data/p40ca_elastic_35mev.csv @@ -0,0 +1,60 @@ +angle_cm_deg,ratio_to_rutherford,stat_err +6.3000,1.41,0.03243 +9.6000,1.93,0.04439 +12.1000,2.05,0.04715 +17.7000,1.52,0.03496 +21.7000,0.794,0.018262 +22.6000,0.579,0.013317 +25.6000,0.687,0.015801 +29.7000,1.135,0.026105 +31.2000,2.09,0.04807 +33.3000,3,0.069 +35.3000,4.18,0.09614 +40.0000,4.87,0.11201 +44.6000,4.79,0.11017 +46.3000,4.63,0.10649 +49.8000,3.77,0.08671 +52.5000,2.31,0.05313 +55.1000,1.49,0.03427 +59.0000,1.44,0.03312 +61.9000,1.79,0.04117 +63.6000,1.96,0.04508 +66.9000,2.29,0.05267 +68.6000,2.33,0.05359 +71.2000,2.55,0.05865 +74.1000,2.51,0.05773 +75.9000,2.39,0.05497 +78.0000,2.21,0.05083 +80.2000,1.71,0.03933 +83.6000,1.56,0.03588 +87.1000,1.287,0.029601 +90.1000,1.081,0.024863 +91.4000,0.983,0.022609 +94.4000,0.921,0.021183 +96.9000,0.864,0.019872 +98.2000,0.905,0.020815 +101.2000,0.977,0.022471 +105.0000,0.975,0.022425 +107.1000,1.005,0.023115 +109.7000,1.004,0.023092 +111.8000,0.913,0.020999 +113.9000,0.897,0.020631 +116.5000,0.841,0.019343 +120.4000,0.74,0.01702 +121.3000,0.622,0.014306 +124.4000,0.353,0.008119 +125.3000,0.245,0.005635 +128.3000,0.265,0.006095 +130.0000,0.245,0.005635 +134.5000,0.437,0.010051 +137.9000,0.544,0.012512 +140.8000,0.767,0.017641 +142.0000,0.791,0.018193 +144.5000,1.066,0.024518 +146.2000,1.116,0.025668 +148.3000,1.187,0.027301 +151.3000,1.096,0.025208 +155.6000,1.076,0.024748 +156.5000,0.964,0.022172 +160.3000,0.861,0.019803 +163.3000,0.679,0.015617 diff --git a/examples/local_optical_model_calibration.ipynb b/examples/local_optical_model_calibration.ipynb index 93b0200..f6c435e 100644 --- a/examples/local_optical_model_calibration.ipynb +++ b/examples/local_optical_model_calibration.ipynb @@ -2,36 +2,41 @@ "cells": [ { "cell_type": "markdown", - "id": "34668e38", + "id": "153e38a2", "metadata": {}, "source": [ - "# From a measurement to a calibrated potential\n", + "# Calibrating a local optical potential to a real measurement\n", "\n", - "The production path: a measurement as EXFOR reports it, converted to a\n", - "`Dataset` in the library's units with its systematics kept as inert\n", - "metadata; the reported systematics turned into explicit covariance terms;\n", - "the guardrail against a singular covariance; nested sampling of an optical\n", - "potential; predictions on any grid; and two side trips, tempering the\n", - "likelihood and an inferred model error per data type.\n", + "This is the production path, end to end: we take a measurement as EXFOR reports\n", + "it, convert it into the library's units, decide on an error model, calibrate a\n", + "local optical potential against it with nested sampling, and predict on a grid\n", + "the experiment never measured.\n", "\n", - "Recipes: 12, 14, 15, 16, 21, 26" + "The data are real, and that matters for everything that follows. They are\n", + "[EXFOR entry O1199](https://www-nds.iaea.org/exfor/), subentry O1199007:\n", + "$p + {}^{40}\\mathrm{Ca}$ elastic scattering at 35 MeV, reported as a ratio to\n", + "the Rutherford cross section, 59 angles from 6 to 163 degrees, with statistical\n", + "errors of about 2.3 % — and **no systematic uncertainty quoted at all**. That\n", + "last omission is not unusual, and it shapes the error model we end up with.\n", + "\n", + "Recipes: 4, 12, 14, 15, 16, 21, 26" ] }, { "cell_type": "code", "execution_count": 1, - "id": "26176d50", + "id": "07735fcd", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:03:17.119460Z", - "iopub.status.busy": "2026-09-11T04:03:17.119167Z", - "iopub.status.idle": "2026-09-11T04:03:20.085745Z", - "shell.execute_reply": "2026-09-11T04:03:20.084791Z" + "iopub.execute_input": "2026-09-12T03:34:41.828482Z", + "iopub.status.busy": "2026-09-12T03:34:41.828290Z", + "iopub.status.idle": "2026-09-12T03:34:44.727413Z", + "shell.execute_reply": "2026-09-12T03:34:44.725855Z" } }, "outputs": [], "source": [ - "from types import SimpleNamespace\n", + "from dataclasses import dataclass\n", "\n", "import corner\n", "import dill\n", @@ -40,7 +45,10 @@ "import jitr\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", + "import pandas as pd\n", + "import plotstyle\n", "from jitr.optical_potentials.potential_forms import (\n", + " coulomb_charged_sphere,\n", " thomas_safe,\n", " woods_saxon_prime_safe,\n", " woods_saxon_safe,\n", @@ -48,101 +56,167 @@ "from scipy import stats\n", "\n", "import rxmc as rx\n", - "from rxmc import terms as T" + "from rxmc import terms as T\n", + "\n", + "plotstyle.use()" ] }, { "cell_type": "markdown", - "id": "89f1e050", + "id": "0a1ec02d", "metadata": {}, "source": [ - "## The reaction and the optical model\n", + "## The measurement\n", + "\n", + "The file was pulled from the EXFOR database once and committed next to this\n", + "notebook, so that the notebook runs anywhere:\n", + "\n", + "```python\n", + "import exfor_tools as et\n", + "from exfor_tools import curate\n", + "\n", + "reaction = et.reaction.Reaction(target=(40, 20), projectile=(1, 1), process=\"EL\")\n", + "entries, failed = curate.query_for_entries(reaction=reaction, quantity=\"dXS/dRuth\")\n", + "```\n", "\n", - "n + ⁴⁰Ca at 14.1 MeV; a Woods-Saxon potential with volume and surface\n", - "absorption and a fixed spin-orbit term. `ElasticXS` is a `Model` whose\n", - "`bind` compiles a jitr solver for the dataset's kinematics, which it reads\n", - "from the dataset's `meta`." + "`process=\"EL\"` is the part worth remembering: EXFOR spells elastic scattering\n", + "`(P,EL)`, and asking for a product tuple instead returns nothing at all,\n", + "silently.\n", + "\n", + "`exfor_tools` hands back a `Distribution` object. Rather than depend on the\n", + "database here, we stand in for it with a small dataclass carrying the same\n", + "fields — which is all `rx.from_measurement` asks for." ] }, { "cell_type": "code", "execution_count": 2, - "id": "45213bb5", + "id": "0b60b388", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:03:20.088133Z", - "iopub.status.busy": "2026-09-11T04:03:20.087831Z", - "iopub.status.idle": "2026-09-11T04:03:20.124393Z", - "shell.execute_reply": "2026-09-11T04:03:20.123651Z" + "iopub.execute_input": "2026-09-12T03:34:44.730263Z", + "iopub.status.busy": "2026-09-12T03:34:44.729823Z", + "iopub.status.idle": "2026-09-12T03:34:44.743145Z", + "shell.execute_reply": "2026-09-12T03:34:44.741096Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "59 angles, 6.3 to 163.3 deg\n", + "relative statistical error: 2.3%\n" + ] + } + ], "source": [ - "reaction = jitr.reactions.ElasticReaction(target=(40, 20), projectile=(1, 0))\n", - "E_lab = 14.1\n", - "R40 = 40 ** (1 / 3)\n", - "mso = 1.0 / jitr.utils.constants.WAVENUMBER_PION\n", + "@dataclass\n", + "class Measurement:\n", + " \"\"\"The fields rx.from_measurement reads off an exfor_tools Distribution.\"\"\"\n", "\n", + " x: np.ndarray # angles, degrees\n", + " y: np.ndarray\n", + " statistical_err: np.ndarray\n", + " Einc: float\n", + " quantity: str\n", + " y_units: str\n", + " x_units: str\n", + " systematic_norm_err: float\n", + " systematic_offset_err: float\n", + " subentry: str\n", "\n", - "def central(r, Vv, Wv, Rv, av, Wd, Rd, ad):\n", - " return -(Vv + 1j * Wv) * woods_saxon_safe(r, Rv, av) - 1j * Wd * (\n", - " -4 * ad\n", - " ) * woods_saxon_prime_safe(r, Rd, ad)\n", - "\n", - "\n", - "def spin_orbit(r, Vso, Rso, aso):\n", - " return Vso * mso**2 * thomas_safe(r, Rso, aso)\n", "\n", - "\n", - "so_args = (6.0, 1.1 * R40, 0.45)\n", - "names = [\"Vv\", \"Wv\", \"Rv\", \"av\", \"Wd\", \"Rd\", \"ad\"]\n", - "latex = [\"V_v\", \"W_v\", \"R_v\", \"a_v\", \"W_d\", \"R_d\", \"a_d\"]\n", - "truth = np.array([48.0, 3.5, 1.1 * R40, 0.7, 21.0, 1.2 * R40, 0.5])\n", - "prior_mean = np.array([50.0, 3.0, 1.2 * R40, 0.65, 18.0, 1.2 * R40, 0.65])\n", - "prior_sd = np.array([7.0, 3.0, 0.2, 0.15, 8.0, 0.2, 0.15])\n", - "lower = np.array([0.0, 0.0, 1.0, 0.2, 0.0, 1.0, 0.2])\n", - "params = [\n", - " rx.Parameter(n, prior=stats.norm(mu, sd), bounds=(lo, np.inf), latex=lt)\n", - " for n, mu, sd, lo, lt in zip(names, prior_mean, prior_sd, lower, latex)\n", - "]\n", - "omp = rx.reactions.ElasticXS(\n", - " \"dXS/dA\", central, spin_orbit, lambda ws, *x: (tuple(x), so_args), params, lmax=10\n", + "df = pd.read_csv(\"data/p40ca_elastic_35mev.csv\")\n", + "measurement = Measurement(\n", + " x=df[\"angle_cm_deg\"].to_numpy(),\n", + " y=df[\"ratio_to_rutherford\"].to_numpy(),\n", + " statistical_err=df[\"stat_err\"].to_numpy(),\n", + " Einc=35.0,\n", + " quantity=\"dXS/dRuth\",\n", + " y_units=\"no-dim\",\n", + " x_units=\"CM-degrees\",\n", + " systematic_norm_err=0.0, # this entry quotes none\n", + " systematic_offset_err=0.0,\n", + " subentry=\"O1199007\",\n", + ")\n", + "print(\n", + " f\"{measurement.x.size} angles, {measurement.x.min():.1f} to {measurement.x.max():.1f} deg\"\n", + ")\n", + "print(\n", + " f\"relative statistical error: {np.mean(measurement.statistical_err / measurement.y):.1%}\"\n", ")" ] }, { "cell_type": "markdown", - "id": "5e9ff58b", + "id": "774c0f96", "metadata": {}, "source": [ - "## A measurement, as EXFOR reports it\n", - "\n", - "`exfor_tools` gives a `Distribution` with angles in degrees, cross sections\n", - "in a labelled unit, a statistical column, a fractional normalisation\n", - "uncertainty and an absolute offset uncertainty. Here that object is a\n", - "`SimpleNamespace` with the same fields, filled with mock data from the true\n", - "potential so the calibration can be checked; in production it is the\n", - "`exfor_tools` object itself." + "## `from_measurement`: the unit contract\n", + "\n", + "Angles become radians, cross sections become b/sr, every *dimensionful* error is\n", + "converted with the data, and a *fractional* normalisation error passes through\n", + "untouched. The kinematics a reaction model needs land in `meta`.\n", + "\n", + "Two things this measurement exercises. It is in the **CM frame** — a LAB-frame\n", + "entry is refused rather than silently mis-analysed. And because it is a ratio\n", + "to Rutherford, asking for `dXS/dA` converts it through the Rutherford cross\n", + "section, which is a closed form of the kinematics (recipe 14)." ] }, { "cell_type": "code", "execution_count": 3, - "id": "8877737d", + "id": "ae6b43af", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:34:44.745908Z", + "iopub.status.busy": "2026-09-12T03:34:44.745604Z", + "iopub.status.idle": "2026-09-12T03:34:44.798226Z", + "shell.execute_reply": "2026-09-12T03:34:44.797430Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset('O1199007', n=59)\n", + "ratio: y[:3] = [1.41 1.93 2.05] (dimensionless)\n", + "absolute: y[:3] = [71.2624 18.1398 7.6554] b/sr\n", + "meta: {'quantity': 'dXS/dRuth', 'Elab': 35.0, 'subentry': 'O1199007', 'k': 1.277431188612647, 'eta': 0.5484365614354219}\n" + ] + } + ], + "source": [ + "reaction = jitr.reactions.ElasticReaction(target=(40, 20), projectile=(1, 1))\n", + "data = rx.from_measurement(measurement, reaction=reaction)\n", + "absolute = rx.from_measurement(measurement, reaction=reaction, quantity=\"dXS/dA\")\n", + "print(data)\n", + "print(f\"ratio: y[:3] = {data.y[:3].round(3)} (dimensionless)\")\n", + "print(f\"absolute: y[:3] = {absolute.y[:3].round(4)} b/sr\")\n", + "print(\"meta:\", {k: v for k, v in data.meta.items() if k != \"reaction\"})" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4c4955a3", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:03:20.127065Z", - "iopub.status.busy": "2026-09-11T04:03:20.126866Z", - "iopub.status.idle": "2026-09-11T04:03:34.244247Z", - "shell.execute_reply": "2026-09-11T04:03:34.243293Z" + "iopub.execute_input": "2026-09-12T03:34:44.800485Z", + "iopub.status.busy": "2026-09-12T03:34:44.800275Z", + "iopub.status.idle": "2026-09-12T03:34:46.215229Z", + "shell.execute_reply": "2026-09-12T03:34:46.214435Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "<Figure size 600x400 with 1 Axes>" + "<Figure size 704x440 with 1 Axes>" ] }, "metadata": {}, @@ -150,67 +224,91 @@ } ], "source": [ - "rng = np.random.default_rng(11)\n", - "angles_deg = np.linspace(5.0, 160.0, 20)\n", - "meta = {\"reaction\": reaction, \"Elab\": E_lab}\n", - "y_true_b = omp.bind(np.deg2rad(angles_deg), meta)(*truth) # b/sr\n", - "y_true_mb = 1e3 * y_true_b\n", - "y_mb = y_true_mb * (1.0 + rng.normal(0.0, 0.08, angles_deg.size))\n", - "measurement = SimpleNamespace(\n", - " x=angles_deg,\n", - " y=y_mb,\n", - " Einc=E_lab,\n", - " quantity=\"dXS/dA\",\n", - " y_units=\"mb/sr\",\n", - " statistical_err=0.08 * y_mb,\n", - " systematic_norm_err=0.04, # fractional\n", - " systematic_offset_err=2.0, # mb/sr\n", - " subentry=\"toy-subentry\",\n", - ")\n", - "\n", - "fig, ax = plt.subplots(figsize=(6, 4))\n", + "fig, ax = plt.subplots()\n", "ax.errorbar(\n", - " measurement.x,\n", - " measurement.y,\n", - " measurement.statistical_err,\n", + " np.rad2deg(data.x),\n", + " data.y,\n", + " data.y_err,\n", " fmt=\"o\",\n", " ms=3,\n", " color=\"k\",\n", - " label=measurement.subentry,\n", + " label=f\"EXFOR {measurement.subentry}\",\n", ")\n", "ax.set(\n", " xlabel=r\"$\\theta_{cm}$ [deg]\",\n", - " ylabel=r\"$d\\sigma/d\\Omega$ [mb/sr]\",\n", + " ylabel=r\"$\\sigma / \\sigma_{Rutherford}$\",\n", " yscale=\"log\",\n", - " title=\"the measurement, as reported\",\n", + " title=r\"$p + {}^{40}$Ca elastic at 35 MeV\",\n", ")\n", - "ax.legend(frameon=False)\n", + "ax.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "b5feec1e", + "id": "b1a2d666", "metadata": {}, "source": [ - "## `from_measurement`: the unit contract\n", + "## The potential\n", + "\n", + "A Woods-Saxon with volume and surface absorption, a fixed spin-orbit term, and —\n", + "because the projectile is charged — a Coulomb term from a uniformly charged\n", + "sphere. `ElasticXS` is a `Model` whose `bind` compiles a\n", + "[jitr](https://github.com/beykyle/jitr) solver for the dataset's kinematics,\n", + "which it reads from `meta`.\n", "\n", - "Angles to radians, cross sections to b/sr; every *dimensionful* error is\n", - "converted with the data, the *fractional* normalisation error is left\n", - "alone; the kinematics a reaction model needs land in `meta`. Nothing\n", - "correlated is folded into the errors: the systematics wait as attributes." + "The model's quantity and the dataset's must agree: comparing a ratio-to-Rutherford\n", + "measurement against a `dXS/dA` model is refused at construction, not discovered\n", + "after a fit." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "2b27f764", + "execution_count": 5, + "id": "22704ba5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:34:46.217866Z", + "iopub.status.busy": "2026-09-12T03:34:46.217565Z", + "iopub.status.idle": "2026-09-12T03:34:46.223882Z", + "shell.execute_reply": "2026-09-12T03:34:46.222965Z" + } + }, + "outputs": [], + "source": [ + "A13 = 40 ** (1 / 3)\n", + "mso = 1.0 / jitr.utils.constants.WAVENUMBER_PION\n", + "\n", + "\n", + "def central(r, Vv, Wv, Rv, av, Wd, Rd, ad):\n", + " return -(Vv + 1j * Wv) * woods_saxon_safe(r, Rv, av) - 1j * Wd * (\n", + " -4 * ad\n", + " ) * woods_saxon_prime_safe(r, Rd, ad)\n", + "\n", + "\n", + "def spin_orbit(r, Vso, Rso, aso):\n", + " return Vso * mso**2 * thomas_safe(r, Rso, aso)\n", + "\n", + "\n", + "def coulomb(r):\n", + " return coulomb_charged_sphere(\n", + " r, reaction.target.Z * reaction.projectile.Z, 1.3 * A13\n", + " )\n", + "\n", + "\n", + "so_args = (5.5, 1.0 * A13, 0.6) # held fixed: 59 angles at one energy cannot fit it" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5edb669b", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:03:34.246617Z", - "iopub.status.busy": "2026-09-11T04:03:34.246304Z", - "iopub.status.idle": "2026-09-11T04:03:34.253587Z", - "shell.execute_reply": "2026-09-11T04:03:34.252628Z" + "iopub.execute_input": "2026-09-12T03:34:46.226108Z", + "iopub.status.busy": "2026-09-12T03:34:46.225817Z", + "iopub.status.idle": "2026-09-12T03:35:00.562851Z", + "shell.execute_reply": "2026-09-12T03:35:00.561960Z" } }, "outputs": [ @@ -218,48 +316,71 @@ "name": "stdout", "output_type": "stream", "text": [ - "Dataset('toy-subentry', n=20)\n", - "x[:3] = [0.087 0.23 0.372] rad; y[:3] = [1.8252 1.4888 0.7972] b/sr; y_err[:3] = [0.146 0.1191 0.0638] b/sr\n", - "norm_err = 0.04 (fractional, untouched); offset_err = 0.002 b/sr (converted)\n", - "meta keys: ['Elab', 'eta', 'k', 'quantity', 'reaction', 'subentry']\n" + "chi2 per point at the starting potential: 987.0\n" ] } ], "source": [ - "d = rx.from_measurement(measurement, reaction=reaction)\n", - "print(d)\n", - "print(\n", - " f\"x[:3] = {d.x[:3].round(3)} rad; y[:3] = {d.y[:3].round(4)} b/sr; y_err[:3] = {d.y_err[:3].round(4)} b/sr\"\n", + "names = [\"Vv\", \"Wv\", \"Rv\", \"av\", \"Wd\", \"Rd\", \"ad\"]\n", + "latex = [\"V_v\", \"W_v\", \"R_v\", \"a_v\", \"W_d\", \"R_d\", \"a_d\"]\n", + "start = np.array([45.0, 3.0, 1.18 * A13, 0.68, 6.0, 1.28 * A13, 0.55])\n", + "prior_sd = np.array([8.0, 3.0, 0.25, 0.15, 5.0, 0.25, 0.15])\n", + "lower = np.array([0.0, 0.0, 1.0, 0.2, 0.0, 1.0, 0.2])\n", + "params = [\n", + " rx.Parameter(n, prior=stats.norm(mu, sd), bounds=(lo, np.inf), latex=lt)\n", + " for n, mu, sd, lo, lt in zip(names, start, prior_sd, lower, latex)\n", + "]\n", + "omp = rx.reactions.ElasticXS(\n", + " \"dXS/dRuth\",\n", + " central,\n", + " spin_orbit,\n", + " lambda ws, *x: (tuple(x), so_args),\n", + " params,\n", + " coulomb=coulomb,\n", + " lmax=30,\n", ")\n", + "comp = rx.Comparison(data, omp)\n", "print(\n", - " f\"norm_err = {d.norm_err} (fractional, untouched); offset_err = {d.offset_err} b/sr (converted)\"\n", - ")\n", - "print(\"meta keys:\", sorted(d.meta))" + " \"chi2 per point at the starting potential:\",\n", + " round(rx.Problem([rx.Constraint([comp])]).chi2(start) / data.n, 1),\n", + ")" ] }, { "cell_type": "markdown", - "id": "f01156c8", + "id": "d3807dd0", "metadata": {}, "source": [ - "## Nothing is folded in silently: systematics are explicit terms\n", + "## An error model for data that quote no systematics\n", + "\n", + "That $\\chi^2$ per point is the whole problem in one number. The statistical\n", + "errors are 2.3 %, and a seven-parameter local potential simply cannot describe\n", + "real elastic scattering to 2.3 % across 59 angles. If we fit with the quoted\n", + "errors alone, we are telling the sampler that every remaining disagreement is a\n", + "statistical fluctuation, and it will believe us.\n", "\n", - "`comp.reported_terms()` turns the two reported systematics into covariance\n", - "modes: a constant offset mode `ω ω^T` and a normalisation mode\n", - "`η² ym_i ym_j` built from the *prediction*. Ask for them, or leave them\n", - "out; there is no default that hides a correlation." + "The measurement quotes no systematic uncertainty, so there is nothing to fold in\n", + "(`comp.reported_terms()` would return an empty list). We have two honest\n", + "options, and we will try both against the bare fit:\n", + "\n", + "- **infer a normalisation** (recipe 4): one rank-one mode built from the\n", + " prediction, with its magnitude $\\eta$ sampled. This says the scale of the\n", + " measurement is uncertain even though nobody quoted a number for it;\n", + "- **infer a model error** (recipe 26): a diagonal term that grows with the\n", + " prediction, $\\delta$ sampled. This says our *model*, not the measurement, is\n", + " the thing that is wrong at the percent level." ] }, { "cell_type": "code", - "execution_count": 5, - "id": "ab3434e0", + "execution_count": 7, + "id": "b0ceec5d", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:03:34.256367Z", - "iopub.status.busy": "2026-09-11T04:03:34.256002Z", - "iopub.status.idle": "2026-09-11T04:03:34.389216Z", - "shell.execute_reply": "2026-09-11T04:03:34.388403Z" + "iopub.execute_input": "2026-09-12T03:35:00.564669Z", + "iopub.status.busy": "2026-09-12T03:35:00.564474Z", + "iopub.status.idle": "2026-09-12T03:35:00.577147Z", + "shell.execute_reply": "2026-09-12T03:35:00.576094Z" } }, "outputs": [ @@ -267,63 +388,57 @@ "name": "stdout", "output_type": "stream", "text": [ - "['mode', 'mode'] terms, on ['toy-subentry', 'toy-subentry']\n" + "quoted errors only 7 columns: ['Vv', 'Wv', 'Rv', 'av', 'Wd', 'Rd', 'ad']\n", + "inferred normalisation 8 columns: ['Vv', 'Wv', 'Rv', 'av', 'Wd', 'Rd', 'ad', 'log_eta']\n", + "inferred model error 8 columns: ['Vv', 'Wv', 'Rv', 'av', 'Wd', 'Rd', 'ad', 'log_delta']\n" ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 700x330 with 3 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "comp = rx.Comparison(d, omp)\n", - "reported = comp.reported_terms()\n", - "print([t.kind for t in reported], \"terms, on\", [t.on.data.label for t in reported])\n", - "c_stat = rx.Constraint([comp])\n", - "c_sys = rx.Constraint([comp], terms=reported)\n", - "p_stat, p_sys = rx.Problem([c_stat]), rx.Problem([c_sys])\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(7, 3.3))\n", - "for ax, p, title in zip(\n", - " axes, (p_stat, p_sys), (\"statistical only (default)\", \"+ reported systematics\")\n", - "):\n", - " S = p.constraints[0].matrix(truth)\n", - " im = ax.imshow(np.log10(np.abs(S) + 1e-12), cmap=\"viridis\")\n", - " ax.set(title=title, xticks=[], yticks=[])\n", - "fig.colorbar(im, ax=axes, shrink=0.8, label=r\"$\\log_{10}|\\Sigma|$\")\n", - "plt.show()" + "log_eta = rx.Parameter(\n", + " \"log_eta\", prior=stats.norm(np.log(0.05), 1.0), latex=r\"\\log\\eta\"\n", + ")\n", + "log_delta = rx.Parameter(\n", + " \"log_delta\", prior=stats.norm(np.log(0.05), 1.0), latex=r\"\\log\\delta\"\n", + ")\n", + "problems = {\n", + " \"quoted errors only\": rx.Problem([rx.Constraint([comp])]),\n", + " \"inferred normalisation\": rx.Problem(\n", + " [rx.Constraint([comp], terms=[T.normalization(parameter=log_eta)])]\n", + " ),\n", + " \"inferred model error\": rx.Problem(\n", + " [rx.Constraint([comp], terms=[T.model_error(log_delta, averaging=True)])]\n", + " ),\n", + "}\n", + "for name, p in problems.items():\n", + " print(f\"{name:24s} {p.ndim} columns: {p.names}\")" ] }, { "cell_type": "markdown", - "id": "07fe6c63", + "id": "c6663859", "metadata": {}, "source": [ - "## Calibrate with nested sampling\n", + "## Calibrating with nested sampling (recipe 16)\n", "\n", - "Optical-model posteriors are correlated and sometimes multimodal, where an\n", - "affine-invariant ensemble mixes poorly; dynesty handles them and gives the\n", - "evidence for free. `problem.log_likelihood` and `problem.prior_transform`\n", - "are the two callables it needs; the prior transform maps the unit cube\n", - "through every parameter's truncated prior." + "Optical-model posteriors are correlated and often multimodal, where an\n", + "affine-invariant ensemble mixes poorly;\n", + "[dynesty](https://dynesty.readthedocs.io/) copes with them and returns the\n", + "evidence as a by-product. It needs two callables — `log_likelihood` and\n", + "`prior_transform` — and the transform maps the unit cube through every\n", + "parameter's truncated prior, which the problem assembles for us." ] }, { "cell_type": "code", - "execution_count": 7, - "id": "f2641dc4", + "execution_count": 8, + "id": "a4d96073", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:03:34.398347Z", - "iopub.status.busy": "2026-09-11T04:03:34.398037Z", - "iopub.status.idle": "2026-09-11T04:05:34.875928Z", - "shell.execute_reply": "2026-09-11T04:05:34.875204Z" + "iopub.execute_input": "2026-09-12T03:35:00.579141Z", + "iopub.status.busy": "2026-09-12T03:35:00.578936Z", + "iopub.status.idle": "2026-09-12T03:59:58.936578Z", + "shell.execute_reply": "2026-09-12T03:59:58.935853Z" } }, "outputs": [ @@ -331,8 +446,30 @@ "name": "stdout", "output_type": "stream", "text": [ - "log Z = 100.57 +/- 0.44, 48203 likelihood calls, efficiency 4.7 %\n", - "2280 equally weighted rows in problem.names order: ['Vv', 'Wv', 'Rv', 'av', 'Wd', 'Rd', 'ad']\n" + "quoted errors only " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log Z = -3478.97 +/- 0.69, 126410 calls, efficiency 4.1 %\n", + "inferred normalisation " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log Z = -3360.89 +/- 0.71, 131731 calls, efficiency 3.9 %\n", + "inferred model error " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log Z = -38.66 +/- 0.54, 77464 calls, efficiency 4.2 %\n" ] } ], @@ -349,34 +486,101 @@ " sampler.run_nested(dlogz=dlogz, print_progress=False)\n", " res = sampler.results\n", " print(\n", - " f\"log Z = {res.logz[-1]:.2f} +/- {res.logzerr[-1]:.2f}, {int(np.sum(res.ncall))} likelihood calls, efficiency {res.eff:.1f} %\"\n", + " f\"log Z = {res.logz[-1]:9.2f} +/- {res.logzerr[-1]:.2f}, \"\n", + " f\"{int(np.sum(res.ncall)):7d} calls, efficiency {res.eff:.1f} %\"\n", " )\n", - " return res\n", + " return res.samples_equal(rstate=np.random.default_rng(seed))\n", "\n", "\n", - "res = nested(p_sys, seed=1)\n", - "samples = res.samples_equal(rstate=np.random.default_rng(1))\n", - "print(f\"{samples.shape[0]} equally weighted rows in problem.names order: {p_sys.names}\")" + "samples = {}\n", + "for i, (name, p) in enumerate(problems.items()):\n", + " print(f\"{name:24s}\", end=\" \")\n", + " samples[name] = nested(p, seed=1 + i)" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "2df0f057", + "execution_count": 9, + "id": "344252d8", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:05:34.878272Z", - "iopub.status.busy": "2026-09-11T04:05:34.877921Z", - "iopub.status.idle": "2026-09-11T04:05:36.798231Z", - "shell.execute_reply": "2026-09-11T04:05:36.797333Z" + "iopub.execute_input": "2026-09-12T03:59:58.938263Z", + "iopub.status.busy": "2026-09-12T03:59:58.938083Z", + "iopub.status.idle": "2026-09-12T03:59:58.956230Z", + "shell.execute_reply": "2026-09-12T03:59:58.955527Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "quoted errors only chi2/N = 122.1 Vv = 45.5 +/- 0.1\n", + "inferred normalisation chi2/N = 118.0 Vv = 42.6 +/- 0.2 eta = 19.8%\n", + "inferred model error chi2/N = 0.9 Vv = 44.6 +/- 2.3 delta = 30.0%\n" + ] + } + ], + "source": [ + "for name, p in problems.items():\n", + " s = samples[name]\n", + " theta = np.median(s, axis=0)\n", + " extra = \"\"\n", + " if \"normalisation\" in name:\n", + " extra = f\" eta = {np.exp(s[:, p.columns(log_eta)]).mean():.1%}\"\n", + " if \"model error\" in name:\n", + " extra = f\" delta = {np.exp(s[:, p.columns(log_delta)]).mean():.1%}\"\n", + " print(\n", + " f\"{name:24s} chi2/N = {p.chi2(theta) / data.n:7.1f}\"\n", + " f\" Vv = {s[:, p.columns(params[0])].mean():5.1f}\"\n", + " f\" +/- {s[:, p.columns(params[0])].std():4.1f}{extra}\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "e501cce9", + "metadata": {}, + "source": [ + "### What the three error models made of it\n", + "\n", + "The evidence is not close: $-3479$ for the quoted errors, $-3361$ with an\n", + "inferred normalisation, $-38.7$ with an inferred model error. The measurement's\n", + "2.3 % statistical errors cannot explain the disagreement, and the fit knows it.\n", + "\n", + "The interesting comparison is the middle one. Letting the *scale* of the\n", + "measurement float — by 19.8 %, which is a lot — buys almost nothing: the\n", + "evidence moves by about 120 out of 3400, and $\\chi^2/N$ stays at 118. A\n", + "normalisation multiplies every angle by the same number, and our problem is not\n", + "that the data sit at the wrong level. It is that the shape is wrong, angle by\n", + "angle, and no single factor repairs a shape.\n", + "\n", + "The inferred model error, which grows with the prediction at every angle, takes\n", + "$\\chi^2/N$ from 122 to 0.9 with $\\delta = 30\\,\\%$. That is the honest summary\n", + "of this fit: a seven-parameter local potential describes this measurement to\n", + "about 30 %, not to the 2.3 % the counting statistics suggest. The uncertainty\n", + "on $V_v$ moves accordingly, from $\\pm 0.1$ MeV — precise and meaningless — to\n", + "$\\pm 2.3$ MeV." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f045b49e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T03:59:58.958212Z", + "iopub.status.busy": "2026-09-12T03:59:58.958035Z", + "iopub.status.idle": "2026-09-12T04:00:01.732163Z", + "shell.execute_reply": "2026-09-12T04:00:01.731142Z" } }, "outputs": [ { "data": { - "image/png": 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IkyIdFRQRgoo0EZQuUsRGk6KAiEgTMCBNpEjvHakBQwJIDR0CJIH0ZPf9I0/mJSYhCwKh/D7XtddjZs6cuWd3Z7mec88597p164ylkZKTGU8bLy8vJQhEREREREREREQekBIJz4jExEQsloyfhbdYLNjZZfyxX7t2DZPJxJ07d2jbtq0xgNyrVy/c3d159dVXjbY5c+ZMt59KlSphY2PDxYsXOXXqFHny5AGSiiLv3buXvXv3UrhwYaN9aGgoUVFRafaVvD0qKorz589z7dq1DK8jLi7OKGR8L7GxsYSFhWXYrlSpUvTu3ZstW7awZ88eduzYwauvvkqzZs2oUaMGALbx8TT9X/v4uDiwsSEiIoK///6bSpUqGTMrAGJiYtK9XkiqJ5EjRw4cHR3Jnz8/FouFmzdvEhISQmRkJFWrViUxMZHr168zduxYQkND8fLywt/fn2zZspGQkAAk1Ze4uybFkCFD2LdvHyaTCXt7e3755RcAIiMjsbW15dSpU3Tq1MmYpbF7924Ajh8/TmhoKNmzZ08RZ3x8fIbfq7i4uAzfXxEREREREREREXnyKJHwhLOxsbGq3b0K8j6ou5MIrq6uVK9enbVr19K1a1eWL19OuXLlAFIt7XM3V1dXypYty5EjRzh58iTe3t4A1KpVi7179/LXX3/RtWtXo32hQoVwcXFJNx5IWmYpe/bsqQaz07Jv3750+7tbTExMqqWN0rJr1y48PT2pUaMGL774Itu3bycwMJA///zTWGbI7n+D9wBFixXDxtmZN998k1WrVpE3b1769etHhw4dcHJywsPDw6qlncqVK5fu9SbXREhOIqxYsYL8+fMDGIP7zs7OuLq6AklLSu3btw9ISiz9888/Rl+RkZHGf588eZLbt28bBbPz5MnDlStXOHDggFF0Otm9vgMiIiIiIiIiIiLydLNulFqeO/9OIixcuNBY6iYqKgo/Pz8uX75sVV/Jy/4kD14DxkD0pk2bjKfmM5I84H6/NRIeFVdXV5o0aULVqlUBWLt2LYGBgananT9/ntWrVwNw+fJl+vbtS5kyZfjpp5+IiYn5TzHcXVi5cOHCzJs3z0gipCU2NpYuXbpY3f/t27dp3bo18P81Kvbu3fufYhYREREREREREZGnixIJksrt27dp165diiRC+fLluXHjBjNmzKBkyZJcuXIFPz+/FE+wpye5TsL+/fuNbZUqVSJ79uyEh4en2H4vyQPZT9ISOSaTidq1a+Pj4wPAqlWrOH7iRIo2v/76KxaLBV9fX8aPH28USu7bty81atRgxowZD5RQ+HcSYdmyZRkWwx4xYkS6xZfTEhUVxYsvvghgJI6USBAREREREREREXm+KJEgKdy5c4emTZumSCKUKFGCli1bUr58eRYvXoy/vz+enp4EBgbSpUuXDGcUJCcSAgICjELJtra2vPLKKwBs2LDBqtiSEwmxsbFW1YN41O7cucPmzZtZsmQJPj4+vPjii1gsFlavWmW0MZvN/PrrrwB88MEHdOnShcDAQCOhcPXqVQYNGkTVqlX55ZdfrL6utJII95qJALB161Z+/PHH+77OKVOmULRoUSOBs3fv3gzjvHXrFvPmzWP27NnGa968efd9bhEREREREREREcl8SiRICr/88gs7duwAYMKECVSoUIHffvvNKLb71VdfkSdPHubMmYOTkxMbNmygVatW6c5MsFgsbNu2DUgq+HvmzBljX4MGDQCYOnUqK1euzDA2Z2dnABISEhgzZgwbNmywakbEo2CxWFi8eDF79+7l5MmTbNmyhYYNG1K6dGnMZrPRLjg4mHPnzuHk5MTrr78OJCVEkhMKw4cPx9PTkytXrvDZZ5+xceNGq87/7bffcvbsWby8vO6ZREheDmrx4sWMHj0awKhTYa1Dhw6RLVs24+/r16/fs9D1+fPnqV69Oh06dKBLly7Gq2fPnvd1XhEREREREREREXkyqNiypNCoUSNy5crFtWvXGDRoEF5eXtStWxdPT09CQ0Np2rQpDg4OVKxYkalTp9KlSxeWL1/OK6+8wooVK1IULL5z5w4ffvghc+fOBaBhw4a88MILxv433ngDf39/du3ahZ+fHz179qRt27bpxubq6krXrl359ddfuXbtGvPnz2fJkiVUrlyZ2rVr88ILLzy2or+XLl3i6tWrxt83b97ExsaGJk2aMPXkSWN7rly5gKRizjExMSkKPzs6OpI9e3ajiHTJkiWpWLGiVedPTgZkyZLlnssZde7cmc2bN7Nt2zZy586NjY0NwcHB1l8oSe97QECA8XfTpk3JmTNnmm3Pnz9PgwYNOHPmDPnz5zcKckNSAsja2Sfy9LLm++Xp6YmXl9djiEZERERERERERB4GzUiQFIoXL86ePXsoUaIEly9fpnnz5pw8eZLNmzcza9YsJk6caLRt2LAhixcvJkeOHOzbt4/q1atz/PhxAI4cOUKlSpWYO3cutra2fPHFF8ybNw97e3vjeCcnJ5YuXcpHH30EJM2A6NGjBzdu3Eg3vl69erF161ZatmxJ/vz5iYuLY+fOnYwYMYLhw4enGNx/lI4cOQJgDOInJwNsbW3JkSOH0S5nzpwUK1YMgL/++svYbjabGTZsGN27dycmJob69euzcuVK3N3drTp/27ZtyZIlC8HBwcYMkrTUrFmTFStWkCdPHq5evYqLi4sxs8MaJpOJiIgI4+/GjRszf/58bGxS/3TcnUR44YUX2Lp1K8uWLTNev/32m9XnlaePp6cnLi4u+Pn5UbFixXu+vL29CQkJyeyQRURERERERETESkokSCqFCxdmxYoV1KxZk6ioKN577z2WLVtG48aNUyQCACpXrsyuXbsoUqQIZ86cwdfXl88//5yqVaty/Phx8ufPz5YtW+jVq1eag8/29vYMHz6cmTNnkiVLFvbt20erVq04dOhQuvFlzZoVX19fvvzySz777DOqV6+OnZ0dZ8+eZcKECdy+ffuhvyd3i42NNZ669vX1BZIKQCfXELg7kQD/XyMieXmoqKgo3n33XUaMGAFAt27dmDNnTorlgzLi5ubG22+/DcC0adPu2fall15i/fr1vPTSS9y5c+e+ilXfXQuhcePGLFy4ECcnp1TtLly4kCKJsH79egoWLGj1eeTp5+XlRXBwMAcOHLjny9/fn6ioKEJDQzM7ZBERERERERERsZISCZImNzc3fvvtN9q1a4fZbGbIkCEMHDiQxMTEVG1LlCjB7t27qVq1Kjdv3mTYsGHExMTQuHFjAgICqFGjRobne+ONN9iwYQOFCxfm6tWrvPfee8ydO/eeRX1NJhPFihWjY8eOjBw5Ek9PT65du8akSZPua7D8fh07doz4+Hg8PDwoUqSIkVxJnpXg6emZon316tUB2LNnDxcvXqRBgwYsWbIEe3t7xowZw5dffomtre19x9GpUycA1qxZk+HT3Xnz5mX58uW8/vrraX6GGblXEuH8+fM0bNhQSQTBy8sLHx+fe77ut0aHiIiIiIiIiIhkPtVIyCR3F+TNSFpP8v/bvQbc7xYfH29V28TEROzt7Rk3bhxFihThm2++YebMmVgsFkaOHGnUIrh+/TqxsbEAzJkzhz59+rBhwwZ69+7Nhx9+SHR0NOfPnyc6OjrFuvpLly5l7ty59O/fnypVqgBJNQKmT5/OqFGj2LBhA8OGDePgwYMMGTIk1XI8iYmJxnUkJCTg6OhIt27dGDduHKdOnWLGjBl06dKF8PBwq9Zsd3JyuucsiGTXrl0zlijKnTs3x44dw8HBgfj4eIKCgsiRIwfRMTFG+9DroZQsWRKAffv2UaNGDa5evYq7uzs///wziYmJbNq0yWh/+/ZtTp48Sbly5bCz+//b08bGhhIlSqSIxdnZmWrVqrFnzx4mTJhAr169iIuLI2vWrOle488//0yJEiWMwsvWaNiwIf7+/tjY2KRK0Fy4cIFGjRpx5swZvLy8mDt3LnZ2dly+fDlVP496poiIiIiIiIiIiIg8Gs91IiEmJoY7d+6keoL8aWTtE+0Wi8WqgsS5cuUyBrK//vprSpUqhZ+fH7/88gulS5emX79+AEYSAZIGtn/66Sfi4+NTLYHk7OxM9uzZAQgKCqJbt27Exsaybds2xo0bR/fu3TGZTFStWpUlS5YwadIkPv/8c1atWkVQUBB58+YlJiaG6OjoVK+EhAQAsmXLxoABAxg2bBgHDhzgxIkTqQbf03PlyhWr6hPcvn2bW7duYWNjQ6VKlXBxcSFbtmxERkbi5ORE3rx58cyaFQ4cBCA+IZ5SpUrh7u7OrVu3uHr1KiVLlmT27Nl4eXmxfPlyowDzlStX+Pbbb7lx4waFCxemc+fOxpP9CQkJac4GePfdd9mzZw9//PEHn3zyCW5ubnh4eNzzGkaMGEH58uXp1KkT0dHR5MqVi+vXr2OxWHB1dU1RE6Fhw4b3nIlwdxJh4cKFRs2ILVu28OuvvxqfTfI1iIiIiIiIiIiIyNPnuVzaKC4ujt69e5M9e3Zy5sxJvXr1CAsLy+ywnmht27Zl7NixAPTv35+FCxem2S4+Pp6QkJB0Zz3ExcXx7rvvEhsbi4eHB/Hx8XzyySe8++67REZGAklLFn388cf8+eef5MqVi7Nnz7J7924OHTrEsWPHOHfuHNeuXeP27dspBqdv377NyJEjGThwIADjxo1j3759D/NtMJYQ8vLyMhIAyf8bFRUFgPP//gY4dfIkNjY2xvJO9evXZ/ny5Xh5eaXqd9iwYUah6bNnzzJ06FCWL19+zwH4WrVqUbBgQSIiIvjzzz9T7Z81axY+Pj60b9+eyZMnc+jQIRISEmjVqhWbN28mX758XLt2jaxZs/LCCy+kKqz822+/pZtEuLsmwrx584wkwsqVK+nQoQPr169n8+bNxmv79u0ZvLsiIiIiIiIiIiLyJHouZyS0b9+eW7dusWXLFi5fvkzHjh3p27cvM2bMuK9+YmNjUzyRf/cg7LOod+/enD17lh9++IH27duTN29eChcubOwPDw/Hz8+Pw4cP06ZNG7755hscHR1T9PH1119z8OBBPDw8OHLkCPPnz2fAgAHMnTuXw4cPM2fOHIoXLw5AjRo1+Ouvv9iyZQt2dnY4OTnh4uKCk5MTzs7OeHh4cPHiRfbu3cvOnTvZuXMnt27dYvLkyfTu3Zvvv/+eNWvW4Orqaiwv9F/Ex8dz4cIFAEqVKmVs/3ci4W7Hjx/nxSpVGDVqFO+88w41a9ZMNXvkxIkTfPfdd0RFReHl5UWXLl34/fffOXToEIsXL2b//v188MEHacZka2tL27Zt+fbbb/H396d169bGvkuXLvHJJ58QFRXFkSNH+O233wDIkiULlStXxtfXl+HDhzNhwgQCAgJSLD2UXBMhrZku/04irF+/3lh+a+XKlfTo0YPExESaNWvGK6+8YhwXExPD4MGD7/0mi4iIiIiIiIiIyBPnuUsk7Nixg4CAAA4dOmQMAJ88eZKvv/6a6dOnW7XsT7KRI0fy1VdfPapQnzgmk4nvv/+ekJAQli1bxmuvvcaSJUsoWrRoiiQCwPz58/nnn3+YOnWqURth9+7djBo1CoCffvqJfPny0adPHypVqsTbb79NYGAgderU4eeff6ZZs2ZAUuHiN99804jBYrGwePFiVq9ezfbt27ly5UqKGO3t7QkNDWX58uX4+fnh7+/PkiVLeO+994wn5h/U33//TVxcHI6OjuTJk8fYniVLFiCdRMKJEwC4u7tTp06dVPuDgoKYMmUKcXFxFC9enD59+pAlSxZ69erFrl278Pf35+zZs3z11VfcuHGDrl27pvqOtmzZkh9++IF//vmHv/76y0jEDB48mKioKCpVqkTTpk3ZvXs3e/bsISIigi1btrBlyxajDzc3N8LDw4GUhZXj4+NTnCutJELBggW5ePFiiiRCy5YtGTduXIpExO3bt5VIEBEREREREREReQo9d0sb/fbbbwwcONBIIgDUqVOHO3fuGAOp1ho4cCDh4eHG6/z58w873CeOra0t8+bNo0SJEty6dYvvvvsOSCq0fPjwYbJkycKwYcNwdHTkwIED/PDDD8axo0ePxmw2U7RoUVq0aGFsr1GjBl9//TWQNKvj/fffT1XUN9maNWv44IMPWLRokZFEsLe356OPPjKWSrKzs+PUqVN4eXmRJ08e4uPjUxQ0flDHjh0DkmaizJo1i7lz57Jy5UqOHz9ubAdSFBoOOXfunn3OmzePuLg4smTJQs+ePY2khMlk4uWXX6Zfv344ODiQmJjIhAkT2LVrV6o+wsPDjfoTa9euBeDWrVv8+uuvQFLCK7neRGhoKAEBAUyaNIn27dsbSYe0kghpGT16NGfOnMFkMrF48WKjhoPFYmHw4MEkJiZSqlSpVEkEEREREREREREReXo9d4mEDz/8kDfeeCPFtuQn5hMTE++rL0dHR1xdXVO8ngcHDhzg1KlTAMbSNVWqVAGSnsq/fPmyMaheq1Yt4zg/Pz9sbW05deoUjRo14tq1a8yZM4fy5cvTtWtXABwcHOjWrRsODg5pnrty5cq89tprKRJB8fHxTJ48mZs3bwJJRX3t7e3x8PDg6tWrAA9laaPq1avj6emJs7MzAHfu3OHChQuEhYXh6OhI0aJF2bhxI6tXrTKOadu27T37rFmzJgCRkZF88803xvt648YNZs2axbBhw4ykSrVq1ShTpkyK49esWUPLli25dOkSbm5uNG/eHEiaYVC6dGkgaRZOMhsbG8qWLUunTp2YOXMmQUFBXLp0iSVLljBlyhQWLVqUbhIBoFWrVri4uGCxWHj77beN5IrJZOKtt94CkhIuffr0ISYmJoN3VERERERERERERJ4Gz93SRuXKlUu1LfnJabPZDMC5c+fo3Lkzy5YtMwaNJcn169d55513SExMpH379rRq1QqAqlWr8tZbb7Fo0SJ+/PFHALp27Ur9+vWNY1u1asWqVat466232Lp1a4rlgbJly8aHH35Ily5dUmz/N09PT/z9/YmNjeXvv/9m/fr1bNiwgf379xufH8CQIUP46quvsFgsVKhQgUqVKt33tZrNZmPtf4AXXngBX19f8ubNS0xMDGFhYdy6dYu4uDji4uLYvn07CQkJON+19FDt2rXveY6GDRtSsmRJpk6dytWrV/nmm28oX748R44cMRJb3t7etGjRgvfff984Ljo6mlGjRhlFrytUqMDYsWPJnz8/kJQwGDJkCG3btmXChAn06NEDd3f3NGPImTOnkYDISJ06ddiyZQtvvvkmJ0+epGbNmsyZM4cXX3yRQYMGUbBgQb744gv++OMPgoKCUiRw/r1MkoiIiIiIiIiIiDwdnrtEQlqS15y3WCycO3eOV155hf79+yuJ8C9ms5l3332XixcvUqpUKSZPnsytW7eM/YMHD2bDhg3cunWLypUr8+mnn6bqo0GDBmzfvp233noLFxcXLl++TM+ePfnwww/Jnj07kZGRVsViZ2dHiRIlcHJy4qWXXiIwMJBRo0YRFxdHt27dmDlzJrdv36ZQoUI0adLkvmpfWCwWFi1axN69e+nVqxcFChRI1cbJyYk8efJgMpnYsmULYWFhAOTOnZs61atD8DGrz1e6dGmGDx/O7Nmz2bNnD4cOHQKSEghvvPEGpUqVIiEhAUiaubBw4UJmzpxJaGgoJpOJLl260KNHD+zsUt7OrVq1onTp0gQFBdGgQQOWLl2a5rXcr/Lly7Nz507atGnDzp07eeONNxgwYADdunXj3XffpVixYnz44YccP37cWPZJREREREREREREnl5KJPD/iYSzZ8/Spk0b+vfvT7du3TI5qifP6NGjWbNmDc7OzixatIisWbOmSCR4eHgwefJkli1bRr9+/bC3t0+zn3LlyhEYGEhERAQuLi5pLqVjsVi4fv06x44d4+TJk5w/f54LFy5w/vx5Ll68yMWLF9N8wr1p06YEBARw7tw5ihUrxuuvv37fa/WvWLGCbdu2AXDo0KF0B9+vX7/OypUriY+Px9nZmapVq1KiRAns75oZYa0sWbLw0Ucf4ePjw+HDh6lduzalSpUy9kdFRTFlyhRmz55tJC3y5cvHN998g6+vb5p92traMmvWLJo1a0ZAQAC+vr4sW7aMChUq3Hd8/5YrVy7WrFlDz549mTlzJqNGjeL48eN8++23+Pr6sm7dOtatW5fiM4qNjTWKbYuIiIiIiIiIiMjTQ4kEMJavad68OV9++eUTlUQwWzkonZCQkGIZnvQkFyPOSERERIp2W7ZsYciQIUBSQqFAgQKEhYVx586dFPUKqlevTvXq1VPFfv369TQLKN+6dYsbN27wzz//8M8//3D06FFCQkI4efJkiiRFWkwmEx4eHuTIkYMcOXJQoEABLly4wO7du3FxcaFr164cPHjwnmv+JwsPD2fr1q38888/BAQEGNsPHTqUovZFREQEefPmJSwsjFWrVhEfH0/evHlp2LAhjo6OqfqNjIrCdI/PMC4uLkU9iEqVKhnLMCUkJHD79m3Wr1/Pxo0biY6OBsDLy4uuXbvSqFGjFO89JC15dHfR8KJFi7J27VratGnD8ePHqVOnDtOmTaN+/fpky5Ytw/clNjY23YSQyWRiwoQJlClThk8//ZQ//viD06dP8/PPP5MnTx78/PxStL99+7YSCSIiIiIiIiIiIk8hJRKArFmz4ubm9liTCNYM+t8Pk8lk1fI99vb2Vj2hnxxfTEwMI0eOZNKkSZjNZt5++23atWtntMudO7dVRaZjY2OxWCzG32vWrGHOnDn8888/xhP2/2YymfDy8qJYsWIUKFCAfPnykT9/fvLmzUtwcDD58+dPcS0rVqxg9+7d2NjY0KNHD3Lnzk2hQoUoVqxYhvHNnj2bqKgoI4mQnJS4ceMGUVFRxvuRLVs2Bg8eTI0aNYiJiaFAgQLY2dmxefNm7O3tsbe3J4utLd3/12/Hjh2xODhgZ2eHs7MzXl5eFC5cmEKFClG4cGGaN29O7ty5U8Vz5coVJk6cyLRp04zlnsqUKcPAgQNxdnZmzJgxfPXVV/Tr148hQ4YYy3Cl9V4WLlyYtWvX8t5777F161b8/PwYNmwY/fv3z/B9cXR0zPD70qNHD7y9vfHz8+Pw4cO88cYbLF68mKpVq6ZoFxERkeH5RERERERERERE5MmjRAJJg8MnT57E09Mzs0N5ouzfv58ePXoY69y3bt2acePG3Ve9gX9LTExk7NixTJ8+3dhmMpkoWLAgxYsXp2DBgrz00kuUKFGCYsWKpTub4OrVq8YAd0JCAhs3bmTRokUAtG/fnrJly95XXGFhYRw9ehRIGngvW7YsV65cMWYFuLm5AUkJkVdffZWQkBBy5crFhQsXUvXlbDJBiaQiw/v37SP6rgRKWnLnzs0LL7xAkSJFKFy4MKGhocyePZvY2FgAXnrpJfr3709CQgIjR4404gQYMWIECxcuZMqUKdSrVy/dc7i5ubFo0SL69evHnDlzGDRoEBcvXmTcuHH3vfRTWurUqcOePXto2bIlgYGB1K1bl++//z5Fkes7d+785/OIiIiIiIiIiIjI46dEwv8oifD/YmJi+PLLL/nxxx8xm83kzp2b7777jiZNmmR47OXLlwkMDKRevXqpZl2EhYXRq1cvdu7cCcAHH3xAixYtKFq0qJEwuHXrFnny5LEqzoSEBLZv386KFSsIDQ0Fkoo532tAPS3nz58nKCgIs9lMnjx5KFu2LCaTCXd3d65fv86tW7dwc3MjISGBvXv3EhYWhoeHB9euXTP6yJYtGzVq1KBmzZq8VKoUDBwEwLRp04g1mYyEREhICOfOnePs2bOcOXOGiIgIrl69ytWrV9mzZ0+KuKpWrUqfPn24ePEigwYN4ty5cwC4urry0UcfUa5cOfr378/JkyepX78+7777LkOHDiVHjhxpXqe9vT3jx4+nSJEixud75swZ5s6dS9asWe/rPUtLkSJF2L59O++99x7Lly/no48++s99ioiIiIiIiIiISOZTIkFSOHDgAO+99x7BwcFA0iyEUaNG4e7ufs/jYmJimDx5Mt988w137tyhZ8+ejB071th/4sQJunXrxoULF3B2dmbUqFFWJSbSs2fPHlatWmUkENzc3GjWrBkNGjS4r35CQ0MZP348iYmJeHh44OPjY8y4SE4k3Lx5k4IFC7Jv3z7CwsJwdXVNUYcAktb/X716NatXr8YzWza25csPQOMmTbD537JDabl27RrXr1/nzJkzxismJoa2bdsSEBDAxx9/bCQscubMSe/evfnoo4+MGRLNmjVj8ODB/Pjjj8yZM4c///yTkSNH0rp16zTPZzKZ6NmzJ4UKFaJbt26sWrWKOnXqsGzZMvLnz39f711asmXLxuLFixkxYgSzZ89OUWzZbDZz6dKl/3wOERERERERERERebyUSJAU3nnnHU6dOgXApEmTUtRDSEtMTAz+/v6MHj2aixcvGtsnTJiAo6Mjw4YNw2Qy8c0333DhwgVcXFxYsGABpUqVeuAYg4KCmDNnDpCUQGjatCmvvPJKmsWOM/Lnn38aSYFs2bIRGxtrFDBO/t/bt29z4sQJQkNDsbW1xWQykZiYaLTZuHEjsbGx/P777yxZsoSbdw2WH9i/n8o1a6Z7/uzZs1OwYEF8fHxSbO/ZsyfTpk0DoGDBgvTu3ZsPP/zQqIWQzNXVlR9++IF27drRuXNnAgMD6dq1KyEhIfTr1y/d87766qu0bduWmTNncvjwYb788kvjfP+VjY0NQ4YMMYpzJ4uIiMDDw+OhnENEREREREREREQen4db8Veeel27djWWJPr6669ZuXJlmu1iYmKYNm0alSpVomfPnly8eJECBQowbdo0Ro8eDcDo0aPp1q0biYmJNG3aFICoqCh+/fVXYyD+XsLDw/nrr79Yt25divYvvPCCUaA4S5YsvPzyyw+URACoVq0aOXPmBODcuXNs2rSJ/fv3c+PGDU6ePAkkJStOnz4NQPHixQkPDzeWAipdujTVqlWjdu3aTJw4kZCQEDZt3mz0P/OXX+47pi1bthiD+hMmTCAwMDDNJMK/r+PgwYP07dsXgOHDh6eYEZLs7s9t5syZQFJh6bZt2953nCIiIiIiIiIiIvJ8UCJBUujbty+7du2iZMmSXLt2DT8/Pzp37szNmzeB/x+IrlixIp9++imXL1+mQIEC/PDDDxw7doz333+f3r17M2XKFGxsbJgxYwbt2rWjZcuWDB8+HBsbGxYuXEjPnj2NYsJms5lz586xdu1apk6dSseOHfH19aVcuXK89dZbdOrUic8++wzL/4oWOzs78/HHH+Ph4cGlS5cYN24c0dHRD3S9pUqVYvjw4ZQuXRpPT08sFguXL19m165d3LlzBwcHB2JjYzGbzXh6ehoJjRo1agBw9OhRzGaz0Z+NjQ1VKlc2/l61cmWKWgoZuX37Nh9++CEAnTt3pnPnztjb21t1rL29PUOGDGHo0KFAymRCRp/bK6+8YnWMIiIiIiIiIiIi8nzR0kaSSuXKldm0aRNjxoxh4sSJLF68mG3bttG+fXt+++03Y537fPny0bt3b7p165ZqRkDHjh1xd3enffv2/P7771y+fJnJkyfj6upKnz59WLt2LRcvXsTBwYHjx48TGRmZZiz58+fn8uXLLFiwAFdXV4YMGYLJZMLDw4P+/fszfPhwTp8+zYQJE+jbt6/Vg+53s7GxIUeOHBQtWpSIiAjOnDnDhQsXMJvNFChQwJiNUKZMGY4cOQLAG2+8webNm4mOjub06dMUK1Yszb7j4+OZM2fOPZcZSnbp0iVjWaJChQoxfPjw+74WgN69ewNJM0qGDx/OiRMn2Llzp1Wfm4iIiIiIiIiIiMi/aUaCpMnJyYkvvviC9evXU6pUKa5du8a4ceO4dOkS+fLlY+zYsRw8eJAPPvgg3cHoli1bsnz5crJkycKuXbt49913qVKlCtOnTydLliwEBgZy8OBBIiMjcXBwoEyZMjRv3pwvv/yShQsXcuTIEXbv3s23334LwLRp0/jhhx+M/vPnz0/fvn1xdHQkKCiIKVOmPPDMhGSurq6UL1+e+vXrU6NGDaOYc+HChXFycuLWrVsANG7cmNKlSwPw999/37PPX375hYSEhHT3WywW5s6dS6VKldi4cSMODg78/PPPxvJJD6J3797GzIRFixbd1+cmIiIiIiIiIiIicjfNSHhGWCwWY+mfe4mJicHW1tb4+9atW+zdu5dXX30Vk8lkbI+Li8PJyQkfHx82b97M2LFj2bBhA+3bt8fPz88YhE5ISLhnvYM6deqwZs0amjdvzpEjR4wCvwsXLmTDhg3ky5ePUqVK8cILL2Bvb09YWBh58+Y1+j58+DAeHh58/vnnfPPNN4wdO5Zs2bKRNWtWLBYLuXLlokuXLvz000/s27ePgIAAXnrpJapVq0aJEiWIjIzk+vXrGb4v8fHxxMfHG3/b2Nhw8+ZNIiIisLe3p0iRIly9ehVIWg4pd+7clClThkOHDhEQEGDUgAAwx8UZ/+3h4cHFS5f4888/U7RJduHCBYYMGcLq1asB8PHx4eeff6Z06dIpPk9rP1+LxWJ8jr179zaKW7dr1y7F55aYmGh1f5klICDgnsmU4ODgxxiNiIiIiIiIiIjI80uJhCdccuHjjFi7pI+tra0x0BwbG0vTpk05fPgwY8aMoXv37kY7Nze3FMV90yrcC1hVNLl69eps2bKFZs2acerUKdq3b8/q1asZM2ZMinbx8fFcvXoVf39/tm3bxs6dO7l9+zYAHTp0YOjQoXz99dd88cUXjB07liZNmgDQpEkTfHx8GDZsGKdOnWLfvn3s27ePfPnyUatWLWrXrk3BggXvGWPlypUpWrSo8XdERIRRN2DgwIF06NCBgQMHEhAQQP369YGkpY4gqU5Cet59911Gfv89v/76a4qCxsmzEPr27UtYWBgODg588cUX9O/fHzu71LeltQP62bNnT5EQGjBgAAMGDHjg/uzs7FL091/cncCyRu3atTNs4+Ligqen54OGJCIiIiIiIiIiIlYwWTLzkeNnTEREBG5uboSHh+Pq6vpYz23txxgdHW0MDA8aNIjx48cD4O7uTmBgIO7u7kbbuxMJ6bEmkZDc7uLFizRq1IgTJ07g6enJH3/8QUJCAtu2bWPbtm3s3r2bqKioFMdlz56diIgIzGYzPXv2xGKxMHHiRGxsbJg8eTINGzY02losFg4fPszvv//O8uXLiYiIMPaVL1+eZs2aUbdu3RRPuYeHh3Px4kVOnjxJTEwMISEhnDt3jtOnTxMaGkqxYsVYtWoVdnZ21KxZk4sXL7Js2TIaNWrE2rVradGiBSVLljRqJwCYo6I4U606ADYLF+D90ktYLBaOHDlCyZIluXTpEt27d2fVqlUAVKpUiV9++cVITKTl7pkGGbGm3f3c9g8rkWDt/ZHcburUqVSsWPGefXp6euLl5fVQ4ntWmKOiOO6T9L6VPHgAGxeXTI4opYMHD1KxYkUOHDiAj49PZofz1MnMf2dERERERERE5PmlRMJD9DQlEjZt2kSzZs0AyJUrF9euXaNnz56MHDnSaPuwEwm2trZcv36dZs2aceDAgTTb5ciRg1q1ahkzCcqVK8ecOXPo2LEjAF988QXnzp1j1qxZODg4MGPGDGrUqJGqn9jYWNavX8+cOXM4cOAAZrMZAEdHR3x8fLh16xYXL140ZjykJUuWLEyfPp1q1apx6tQp6tevj4ODA1euXCFLlixcvHiRIkWKYGtry82bN3FycgJSJhJe2LObN/38WLlyJd27d8fHxyfFLIQhQ4bw2WefpTkL4W7PYyJh69at1KpV66Gc+3miRMKzTYkEEREREREREckMWtroORQaGkqnTp0A6NSpE82aNeP111/np59+okuXLrzwwguP7Nw5c+Zk/fr1tGrVis2bN5MrVy4jcVCrVi28vb1TLdPUoUMHIiIi6N27N1999RXjxo2jYcOGrF27li5duuDv759qQNLR0ZFmzZpRvHhxEhISWLNmDX/++Sdnz55l9+7dKdp6enqSJ08eSpYsiZeXF15eXhQqVIiiRYsaA3Xbt28HoGLFimTJkgWAfPny4e7uzq1bt5gwYQJ58uQBwCY+Ht//9T133jxc/jeQ++OPPxrnrFixItOnT8fb2zvDJIKIiIiIiIiIiIhIZtII5nNo1qxZXLlyBQcHBz7//HM8PT0pVqwYJ0+e5JdffuHrr79+pOd3dXVl7dq1XLx4kQIFCqR44j29J+V79uzJ4cOHmTVrFgMGDGDv3r1ERkayY8cOOnbsyOLFi1PUOLhbzpw5jSLRR48e5dixY+TKlYsCBQqQL18+nJycuH37drrHAxw6dAgAs9lMTEwMTk5OmEwmypUrx7Zt2xg6dKjR1tlk4kCJkgB83KMH0f+6pnz58rFu3TqjYLSIiIiIiIiIiIjIk8y6Sr7yTGnUqBGurq7ExcXh5+fH+PHjOXnyJLa2tjRu3PixxGBjY0PBggWtXjZn48aNzJ8/H4DmzZuTPXt2fv75Z8qXL09YWBgdOnTg+vXr9+zDZDJx4sQJFi5ciJubG0WKFDGWI8pIy5YtcXBw4K+//qJ58+aEh4cDSUstNW/enEaNGhmvBg0aGMc1aNCARo0a4eHhYWy7dOkS5cqVY/LkycTExFh1fhEREREREREREZHMokTCc6hs2bIsX74cV1dXtm/fzuDBgwH45ptvqF69eiZHl9rGjRtp3rw5MTExNGvWjHnz5gHg4uLC9OnT8fLy4vz583Tq1Ino6Oh79jV37lzOnTtHv379OHfunNUx1K5dm1mzZpE1a1a2bdtG/fr1uXz5MjVq1GDx4sUsW7bMeC1YsMA4bsGCBSxbtozLly8TERHBxIkTKVCgAJcuXaJ3796ULl2aSZMmKaEgIiIiIiIiIiIiTywlEp5TVapUMZIJAK+//jo9e/bM5KhS+3cSYdGiRTg6Ohr7PT09+eWXX3B3d+fIkSNMnjw53b4uXbrEhQsXgKSCpb169eLGjRtWx1K9enV+++03cufOzZEjR6hTpw4nT560+nhHR0e6du1KUFBQioTCJ598QrFixZRQEBERERERERERkSeSEgnPsSpVqrBp0ybGjBnDtGnTrF5m6HHJKImQrEiRIgwfPhyAGTNmpLvE0d69ewEoVqwY+fPn59KlS/Tr1y/DWQx3K1OmDJs3b6ZIkSKcPXuWOnXqcPDgwfu6rowSCpMnT8ZsNt9XnyJPm+DgYA4ePHjPV0hISGaHKSIiIiIiIiIiqNjyc+ffxX29vb3x9vZOtc9sNls1mB0XF4eNTcb5qMTERKvanT9/HpPJxM6dO+nYsSOxsbHUrVuXsWPHcuXKFaNdaGgonp6ext8NGzbkpZdeIiAggIkTJxoFo8+cOcOZM2cAWLt2LQAlS5akcuXKjBkzhqCgID7++GO6deuGra1thvHFxsZSpEgR/P396dSpE0FBQdSrV48mTZpQv359fH19uTvVcfPmTUz3SFQ0b96ct99+G39/f7777jsuXrxIjx49OHfunLHkVLLk2SMiTzNPT09cXFzw8/PLsK2LiwvBwcF4eXk9hshERERERERERCQ9Jsu/R5blgUVERODm5kZ4ePgTO+hr7cdtNputmqGQkJBgVYLAYrFgZ5dx3urs2bPs2rUrRRJh8uTJac5EKFSoUIp4lyxZQuvWrbGzs+Po0aMUL16cX3/9FZPJhNlspnv37kRGRvL5559TvHhx/vnnH0aNGkV8fDx16tRh3LhxGV7zzZs3jfPevn2b7t27s3v3bmO/s7Mz9V5+mdHnk5ZQ8li7BpOzc7r9RUdH4+7uDiQlKSZPnsyXX36JjY0NK1asoEaNGkbbJ/U7ZS1r74/kdlu3bqVWrVqPMcJngzkqiuM+FQEoefAANi4umRxRaiEhIYSGht6zTXBwMH5+fvj7+xvJzvR4eno+N8mGp+HfGRERERERERF59mhGgjxRdu7cSadOnYwkQrVq1XjnnXcYOHAglStXTvOY8PBwmjRpwl9//YWPjw8HDx7k888/Z/78+Uabc+fOERkZiZOTEy+88AIAxYsX58MPP2TSpEls2bKFX3/9lXfffdfqWLNly8Yvv/zCX3/9xYYNG9iwYQOXL19m48aNUKIkAO+0bUu9Jk1o1KgRBQsWvGd/jo6O9O7dm5MnT+Lv70/nzp3ZuXMnHh4eVsck8jTw8vLKcOBfMxdERERERERERJ4cqpEgT4yNGzemSCK8+OKLjBgxgkOHDuHn58eff/6Z6pjw8HAaN27M7t27MZvNXLlyBZPJxMKFC9m/f7/RLjAwEEhayunumRGVK1fmnXfeAWD8+PGsW7fuvmK2tbXF19eXoUOHsnXrVpYuXUqXrl2N/Xt272bIkCFUqlSJBg0aMGfOHCIjI+/Z57fffkuxYsW4dOkSPXr0sHoWicizxMvLi+DgYA4cOHDPl7+/P1FRURnOcBARERERERERkQenRII8EZILKycnEcqXL8/48eOBpJoGcXFxfPzxx0ybNs04JjmJsGfPHjw8PMiVKxeXLl2iUqVKAHz22WfGIPzRo0cBKFu2bKpzN2zYkLp16wLw+eef33fx5GQmk4kyZcrQvXt3Y9vgIUOoXr06NjY2HDlyhP79+1OhQgW++uorwsLC0uwna9aszJw5EwcHB1auXMn06dMfKB6Rp52Xlxc+Pj73fGW07JGIiIiIiIiIiPx3SiTIE6F79+7ExMRQoUIFvvjiC77//nsAOnbsyMqVK2nWrBkAI0aM4OLFi0RERBhJBHd3d9avX28UWA4ODsbe3p5NmzaxadMmAE6ePAkkDdLfzWKxcOLECeLi4gCIj483EhgPQ6eOHVm8eDE///yzURw6PDycyZMnM2LEiHSPK1++PB9//DEAw4cP16wEERH5z65du0azZs1wdnamcOHCLFiwIN22fn5+mEwmTCZTqiXGLl++TJcuXcidOze5c+emc+fO3L59+1GHLyIiIiIiIplINRLkifDaa68xZswYjh49yuXLl6lQoQKHDh1i3bp11KxZk+3btwPg4+ODi4sL77//PocOHcLd3Z0NGzZQoUIFypUrh7+/Pzt27MDd3Z1bt27h7+9P7ty5qV69Olu3bmXWrFl4eXlha2vLzp072bVrF9evXzfiyJ07N61bt35o1zVw4ECWr1vHjRs3jG0mk4ny5cvz5ptvpntceHg4ixYtAqBBgwZWFb4WkXuzpsgzPF/Fm+X50rFjR5ycnAgJCWHnzp20bduW8uXLU6pUqVRt/f398ff357PPPuPChQsp9k2bNo2qVasybNgwIiMjadOmDQMGDGDy5MmP61JERERERETkMVMiQZ4II0aM4J9//mHp0qV8+OGHTJs2jb59+xISEsL7778PQIUKFfjhhx/o2LFjqiQCgJ2dHUuWLKFatWqcPn2anDlzcv36dSZNmsSgQYO4cuUKx48f54svvjBmIAA4OTnh4+PDe++9R8WKFbGxebCJOrGxsezYsYNNq1fz5f+2zZ8/n2iLBXd3dxo2bEi9evWoUaOGUUA5Ojo6VT8Wi4VevXoREhKCl5cXY8eOfaB4ROT/hYSE4O3tTVRUVIZtVbxZnmbz58/nzz//xN/fP8X20NBQVq1aRXBwMDlz5uT111+nbt26+Pv7M2zYsPs6x9ChQ1P83bVrV3744Yf/HLuIiIiIiIg8uZRIkCeCra0t/v7+1KhRg4CAAPr06cN3331H586dCQsLo0KFCkyaNIkePXpw6NAh3NzcUiQRknl6erJixQp8fX25fv06rq6uREREMHHiRPr27ct3333HtWvXsLGxoWzZsrz88sv4+PhgsViM2gr3IzY2li1btrBmzRo2b95MZGQkziYTX5YoCUC7du14tUULfH19sbe3t6pPf39/lixZgq2tLTNnzsTNze2+4xKRlEJDQ4mKisLf3/+edRWCg4Px8/MjNDRUiQR5pgQHB+Po6EiJEiWMbS+99JJRQ+i/2LlzJ+XKlfvP/YiIiIiIiMiTS4kEeWK4uLgwdepU3nzzTUJCQvjmm2+YM2cOf/31F02aNEmRRJg7d26qJEIyb29vFi1aROPGjYmIiCBLlixcv36dGTNmMGDAAE6cOEGZMmXInj27cUxMTMx9xWqxWNi4cSMjRozg/PnzxvbcuXPTrH592H8AgGHDhmFyds6wv/j4eEJCQggKCuLTTz8FYPDgwVSuXPm+4hKRe/P29sbHxyezw5A0jBkzhqtXrxp/u7u7U65cOZo1a/bAM8UelZiYGNasWUNgYCC2trYUL16c5s2b4+joaNXxiYmJrFq1iqCgIKKjoylUqBCvvfaaMVsN4NSpU/z000/p9lGqVCk6depkdcx37tzB1dU1xTY3N7f/XNtgxYoVLF26lH379v2nfkREREREROTJ9mT9P3N55BISEqx+WcNisTzUV44cOZg5cybZs2fnyJEjTJw4kTfffDNFEsHf35/SpUvfs5969eoZyyxERkbi4ODAP//8w+LFi6latSrZsmUjMTHReMXFxREVFZXhKzY2ln/++YcOHTrQrVs3zp8/T86cOfnggw9YsGABW7Zsof//EgH/fn9u377N33//zYoVK5g0aRL9+/fHz8+PF198kdy5c+Pj44Ofnx9RUVHUrl2b3r17P5LvgIjIkyY+Pp7PP/+cPXv2kCdPHnLnzs2ZM2do1aoVtWvXJj4+PrNDNCxdupTSpUszb948EhISCA0NpU+fPhQuXJjdu3dnePzp06fx9vamU6dOXL9+HYvFwuTJkylUqBCLFy822jk4OJAnT55Ur7NnzzJu3DiOHDlitO3UqZNRGPmdd95h7ty5xt958uQBIGvWrERERKSIJTw8nGzZsj3we7Fx40Y6dOjA8uXLKVKkyAP3IyIiIiIiIk8+zUh4zlhbtNfW1taqJ0AdHBys6s9isVh17sKFC/PCCy+wYsUK6tevz4YNG6hXrx43b95MURPBmv4+/PBDjh07xsSJE7FYLNja2rJ7924aNmzIwIEDU7S9efNmhksPhYeH8+uvvzJjxgwSEhJwcHCgT58+DBgwgKxZsxrtzFFRnPrffx89epTVmzezdu1aAgMD79m/s7MzRYoUoWLFinz77bda0kjkPgQHB/+n/ZK5/v77b2JjY2nZsiV9+vQxthcpUoTBgwezYMEC/Pz8MjHC/1eyZEn+/vtvsmTJYmwbMmQIZcqUoWPHjgQFBd3z+MGDB3Pq1ClOnDhB0aJFgaSaA5UqVaJLly68/vrr2NnZUbBgQfr165fq+ObNmwPQuXNnY9v06dOZPn06kH6NBG9vb2JiYjhx4oSxvFFAQAAvvfTS/b8JwNatW3n77bdZuHAhL7/88gP1ISIiIiIiIk8PJRLkifTyyy/z66+/8vbbb6dKItyPkSNHcvLkSVatWkW2bNm4ffs2X375JQsXLqR+/frUr1+fmjVr3rOPxMRE5s6dy4gRIwgNDQWgRYsWjB492hgESnby5Ek2/Pkn9f73d/PmzYm2WIz9OXLkoGjRohQtWpQiRYpQpEgRihUrRtGiRcmbN6/ViR4RSeLp6YmLi4tVg8wuLi54eno+hqjkfu3fvx+AMmXKpNherVo1IOm39UmRVo0Nd3d3fHx8WL9+fYbHh4SEkCtXrhT/ftja2lK1alUCAgK4ffs27u7uaR57+fJlVq9eTbVq1e67JoGnpydNmjRh0KBBTJkyhZ07d7Jp0ybGjh0LJCUjxo4dy7FjxzLsa/fu3bRq1Yq5c+dSt27d+4pDREREREREnk5KJMgT680332T69On89ttvfPvtt/edRICkwZk5c+bwyiuv8Pfff5MzZ05CQ0MJCgoiKCiIiRMn4uDgQJUqVahbty516tShbNmyxmyMv/76i4EDBxpLSBQvXpwJEybQoEEDIKnY8vr161m3bh3r1q3j1KlTOJtMHPhfseXcuXNTp1EjXn31VerVq0fOnDlTxGftTA0RSZuXlxfBwcFGku9ePD09VUD5CXXgQFJdmbJly6bYfvr0aYBUSdv7tWDBAqvX8B89evR912QIDQ1l//791KtXL8O2DRs25Msvv+Tw4cOUL18eSKq5sHXrVipXrpxuEgFg9uzZJCYm0qVLl/uKL9mMGTP44IMPKFCgALlz5+aXX36hVKlSabYdNWpUitl7c+fOpXv37kyaNIkJEyZw48YNGjVqZOzPnTs3V65ceaC4RERERERE5Mlnsljuelxa/pOIiAjc3NwIDw9PVdDwSWFt7QMAO7uM80zWfn2sHTB/2O3i4uIwmUyEhIRQo0YNrl69St26dfnggw/YtGkTGzZsICQkJMUxnp6e1KpVC7PZzNKlSwHIli0bAwYMoEOHDuTKlQuA1atX06tXL2OgC8De3p5Xqldn/NVrABTfvw/bu5a/eNDrAOuXpXpSWXt/JLfbunUrtWrVeowRPhvMUVEc96kIQMmDB7BxccnkiJ4uBw8epGLFihw4cOCJLMr8NPw78yAqVqzIqVOnCAsLM7ZduXIFX19fbG1tCQwMtLqQcVref/99Zs+ebVXb+Ph4q/79mz59OseOHSM0NJQNGzbQqFEjxowZc89EACT9Ozx8+HBmzpxJzZo1yZo1Kxs3bsTHx4fx48eTN2/edI8tUaIEV69e5fLly7jo3hYREREREZHHSDMS5Lng5eXF77//Tv369dm0aRPdu3fnp59+wmKxcOLECVasWMG2bdvYsWMHoaGhLFmyBEgavG/Xrh2DBw8mZ86cmM1mAK5fv85rr72GxWIhd+7cvP766zRs2JA6deqQxdaWU1WqGseLiFgjJCQkw5kVd+7ceUzRPD5xcXEEBgbi5uZm1AS4cuUKmzZtom7duowbN+4/JREA2rRpk2q2Q3psbW2taufu7k7u3LlJTEzExsaG/fv3c+bMmQwTCbdu3eLAgQPExsaSM2dOsmXLhru7O4cPH+bUqVPpJhK2bdvGP//8Q7du3ZREEBERERERkcdOiQR5blSuXJnatWuzdu1abty4ASQN9JcsWZKcOXPStWtX4uLi2L9/P1u3buXq1au8//77aRaivH37NhaLBScnJ44dO5aq2LKIyP0ICQnB29ubqOfw9+PIkSPExcVRrVo18uTJQ1xcHMePH+fKlSsULlyY3LlzG23NZjPR0dEpCh0DREZG4uLikm7ytlGjRimW4XkYWrVqZfz38OHDqVatGs2aNeOff/5JFd/d/Pz82Lt3L4GBgeTPnx+Ar776imbNmhnH/3sZPICZM2cCPPCyRiIiIiIiIiL/hRIJ8lzJaIaAg4MDvr6++Pr6WtWfnZ1diiSCiMiDCA0NJSoqCn9//zSL+SY7cODAMzeQnFxouVu3bjRu3NjY3qJFC0aNGkXnzp0pVKgQkJRwKV68OJGRkTg4OACwefNmOnfuTHBwMPb29mme41HXSHBxceHdd9+lf//+HDx4kJo1a6bZLjo6mvXr19OqVSsjiQBJ/za9++67rFq1is2bN9O6desUx92+fZtFixZRuXLlNJPbj4LZbDZm4dnY2Nz3eyIiIiIiIiLPFiUSRERE/iU4ODjDNtYWb7ZmyaLk83l7e9+zNsOzuLRRciKhUqVKKbb37NmTFStWMHv2bIYOHQpAoUKFcHZ25sSJE8ZSRQMHDmTYsGHpJhEgqaaNtTUSRo0a9UCD5tHR0QD3XIYpua5QYmJiqn3JNYzSqj00f/58oqKi/nMS6cqVK7z//vts2rSJXLlyMXr0aNq2bZtm2w4dOjB37lzMZjNt27bF398/xf7z58/To0cP1q1bh5eXFz///DN16tT5T/GJiIiIiIjIk0uJBBERkf/x9PTExcUFPz+/DNu6uLiwZMmSNJehSXb9+nVatmxp1ZJFLi4ueHp63le8z4IDBw7g5eWV6n2sU6cOOXLkYNGiRUYiwWQyUbp0aYKDgylbtixLly4lLi6Ot99++57neJg1ElavXk2DBg1SFGS+cOECU6dOpXjx4lSsWNHYPnXqVE6cOGHMcnBxcaF27dqsW7eOEydOUKJECQBiY2OZMmUKzs7OaQ7Gz5gxg2zZstGmTRurriE9H3zwAW5ubly9epWdO3fy1ltvUaFChTRnwcyePZvZs2fz2WefceHChRT7zGYzTZo0oXHjxsycOZOoqCimTJmiRIKIiIiIiMgzTImEhyj5KcKIiIhMjiR9yU88WuPuQZL0pPXkZHrtrCk8/LDbxcXFpWiXfP3R0dEpPqfbt2/f82nWZGazGVtbW+OpYIvFkurzNkdFced/T5tGRERgc4/33NrrgKe/cHPy+5TRdyZ5f2Rk5BN9Lz2p7uf7J6llz56dvXv3GnVU0hMaGoqfn59V6+47Ozvz+++/Z5gkyJEjB9mzZ7/n9z4yMhKw/rf3SRcbG8vRo0dp3rx5qn22tra0aNGCX375hePHj1OyZEkAypYtS1BQEGazmSFDhjBu3LgMfx8fZo2EDRs20KtXL8qVK0eePHm4ePEia9euxdvbm/nz56dIRCxcuJCNGzemmOUwY8YM3nnnHXx8fGjRogVZs2Zl48aNREVFMW/evBQ1IQCCgoL466+/6Nq1639aSi80NJQ1a9Zw4sQJ3N3dadasGfXr18ff35/hw4ffV1+LFi3CYrHw7bffYjKZyJEjx333ISIiIiIiIk8Xk+VZGY14Aly4cIGCBQtmdhgiT7Tz589ToECBdPfrPhLJWEb30dPi5s2bzJw5k6pVq6ZZVyAwMJA1a9bQsGFDypUrB8D48ePZvXs3TZs2ZdasWWzatOlxh01oaCi7du3i7NmzZM2alZdeeinNJakWLVrEuXPn6Nu3b6pkx549ezh+/DjR0dEULlyYWrVq4eLikqqPAwcOsHnzZt544w2KFi36wDFv376dhg0bppgdM2TIEAIDA1m6dGm6xyXPSLh7aaPevXtz+fJlrl69yq5du/D29uaHH35ItzaEiIiIiIiIPP00I+EhypcvH+fPnydbtmxP5NPjERERFCxYkPPnz+Pq6prZ4VjlaYwZns64H3XMFouF27dvky9fvnu2e9Lvo3t5Gj93eHrjhqc39geN29r76Gnh4eFBv3790t1ftmzZVEsSlS1blilTprBv3z5+++23Rx1imjw9PWnRokWG7d56661091WrVo1q1apl2EfFihVTLJeUlrsLI/+byWQyZtL9+7vm5ub2QHU3Ll68yJIlS1i8eDGrV6/m559/5vXXX+fUqVNkz579vvsTERERERGRJ58SCQ+RjY3NU/GEqKur61M14AZPZ8zwdMb9KGN2c3PLsM3Tch/dy9P4ucPTGzc8vbE/SNzW3EfPsnLlynHhwgWaNWtG1apVMzucJ0K3bt2YMWNGmvvy5MnDhQsXyJo1a6pls8LDw8mWLdt9n8/FxYUqVarw+uuvA0mFsYcPH87+/fupX7/+ffcnIiIiIiIiTz6bzA5ARERExFq5c+fmzp07zJ8/P7NDeWL8/PPPJCQkpPlKLpRcunRpYmNjOX78uHHcwYMHjRkfFouFxP/VV8nIiy++mGoGhMViMepAiIiIiIiIyLNH/49PRERE5BmXI0cOmjVrxmeffcaVK1f4/fff2bJlC35+fkBSEegyZcoY7S0WCwkJCVgsFuO/k5MHbdq04ejRo8yfP5+wsDC+/fZb7OzsqFKlSqZcm4iIiIiIiDx6SiQ8RxwdHfniiy9wdHTM7FCs9jTGDE9n3E9jzE+ap/U9fFrjhqc39qc1bnm6TZ8+HbPZTLFixfj000+ZM2cOJUuWBJKWlbOz+/8VL8eMGYOTkxPjxo1jwYIFODk50bt3byCpls3SpUsZPnw4Xl5erFixgj///JOsWbNmynWJiIiIiIjIo2eyWCyWzA5CRERERERERERERESeTJqRICIiIiIiIiIiIiIi6VIiQURERERERERERERE0mWXcROxltls5tKlS2TLlg2TyZTZ4Yg8USwWC7dv3yZfvnzY2KSfw9R9JJI+3Uci/52195GIiIiIiIj8PyUSHqJLly5RsGDBzA5D5Il2/vx5ChQokO5+3UciGdN9JPLfZXQfiYiIiIiIyP9TIuEhypYtGwAhISG4urres63FYrH6KdGH+TTp/dTW1lOsD8YcFcU/NWsBUHz7NmxcXDI5oidDREQEBQsWNO6T9CTvP3/+fIb3kbUe9vfebDZb3d/DfNrVmuswR0VxslZtAIpt25rh90/3+dMlM++jx0m/o/IoWXsfiYiIiIiIyP9TIuEhSh6Qc3V1VSLhOWa2syOrrS2Q9F3QAFhKGX2v7uc+stZzlUi4z++f7vOnU2bcR4+TfkflcdDvn4iIiIiIiPW0MKyIiIiIiIiIiIiIiKRLiQQREREREREREREREUmXEgkiIiIiIiIiIiIiIpIuJRJERERERERERERERCRdSiSIiIiIiIiIiIiIiEi6lEgQEREREREREREREZF02WV2AM8ri8VidVuTyfQIIxGRp4U1vxv389siIiIiIiIiIiJiDSUSHgGTyZTh4L+NTeZMBlFSQp5HD/t7/yTfv3e3sea3SEREREREREREJCNa2khERERERERERERERNKlRIKIiIiIiIiIiIiIiKRLiQQREREREREREREREUmXEgkiIiIiIiIiIiIiIpIuJRJERERERERERERERCRdSiSIiIiIiIiIiIiIiEi67DI7ABEREZEnTUhICKGhoRm28/T0xMvL6zFEJCIiIiIiIpJ5lEgQERERuUtISAje3t5ERUVl2NbFxYXg4GAlE0REREREROSZpkTCE85isVjVLj4+HpPJlGG7hIQE7Oys+9jt7e2taifyKJjNZsxmc4btbGwyXqEtJibGqnOGhYUZ3/tbt26xadMmbt26xZtvvkn27NlTtHV3d8+wv8TERKvuS8Cq+9Ka3wNrfzPux/30ae31ijzJQkNDiYqKwt/fH29v73TbBQcH4+fnR2hoqBIJIiIiIiIi8kxTIiGTaLBN5MlisVgICgpi8+bNrF+/nn379hmJjMmTJzN9+nR8fHwyNUZrfjf02yLy8Hh7e2f6fS8iIiIiIiLyJFAi4Rl19epVNm/ejJOTE25ubri5uZEtWzayZMmCh4cHTk5OmR2iSKaLjIxk8+bNrFmzhjVr1nDx4sUU+0uVKkVkZCQhISE0bdqUL7/8ki5dumiwXkREREREREREnitKJDxjLBYL/v7+9O3bl7CwsHTbOTg44ObmhqurK4ULF2bcuHGULl368QUqkol27drFyJEj2bZtG3FxccZ2JycnatWqRf369WnQoAEFCxYkPDycXr16sWLFCgYPHszOnTuZOHGiVUsbiYiIiIiIiIiIPAuUSHiGXLt2jS5durBq1SoASpQogYeHBxEREURERBAeHs7t27cBiIuL4/r161y/fp1Tp07x6quvsm7dOiUT5Jm3efNmWrZsadRNKFy4MI0aNaJRo0a8+OKLuLq6pmjv5ubGzJkzmTFjBkOHDmXVqlUEBgayePFiKlasmBmXICIiIiIiIiIi8lgpkfAM+fzzz40kwnvvvcdPP/2UqoBrbGwsMTExhIeHEx4eTlhYGB06dCAkJITBgwfzxx9/ZEboIo/NJ598QkxMDDVq1OCHH36gZMmSxlJF6c3iMZlMdOrUiYoVK9KpUyfOnTvH+++/z99///0YIxcREREREREREckcSiQ8Q9q0acPSpUu5desW8+bNI2fOnBQoUIDo6GhiYmKIiYkhKiqKuLg4Y9udO3cICQkBoHnz5pl8BSKPXoMGDTh58iTHjx/H2dn5vuodVKhQgZo1a3Lu3DnN3hERERERERERkeeGEgnPkFdeeYWAgAA++ugjVq5cydixY60+9uOPP+aDDz54hNGJPBlGjBjBrl27OHz4MI0aNWLt2rV4eXlZdezNmzdZvHgxkDSzQURERERERERE5HmgRMIzJm/evCxZsoQFCxbw+++/Y2dnh5OTE87Ozjg5OeHo6Iizs7Pxt7OzMwULFqRBgwaZHbrIY+Hk5MTixYtp2LAhp0+fpmHDhlYnE3799VdiYmJ48cUX8fX1fQzRioiIiIiIiIiIZD4lEp5BJpOJNm3a0KZNm1T7EhISUtVNEHneFChQgDVr1tCoUaMUyYR/F1q+W0JCAjNmzACgS5cu97UkkoiIiIiIiIiIyNPMJrMDkHuzWCxWvx5mf2az2eqXyJMure94cjKhSJEinD17loYNG3LmzBni4+PTfK1YsYJLly6RI0cOmjZt+tDjexT328P63XiY53sU5xURERERERERkUdLj6Y/4ax96tnBwcGqttbORlCCQDKbjY0NNjYPJ9fp5OSU5vZixYqxefNm6taty6lTp2jbtm26yxzNmjULgM6dO5MnTx6rYrM2fmvvN5PJlOF9/ihmSmj2hYiIiIiIiIjI800zEkTkuVawYEE2bdpE0aJFjZkJISEhKdoEBASwc+dO7Ozs6Ny5cyZFKiIiIiIiIiIikjmUSBCR515yMuHuZY7uTiZMnjwZgJYtW5IvX77MClNERERERERERCRTKJEgIkJSMuHfNRNCQkK4du0aCxYsAOCjjz7K5ChFREREREREREQeP9VIEBH5n+QCzI0aNeL06dO8/vrrvPzyy8TFxVGxYkWqVKmS2SGKiIiIiIiIiIg8ds9tIiEiIoKFCxcSGhrKa6+9hre3d2aHJCJPgOSZCbVr1yY4OJjg4GAAhgwZoqLDIiIiIiIiIiLyXHoulzYKCAigdOnSjB8/nh9++IEKFSqwc+fOzA5LRJ4QBQsWZNGiRTg7OwNJtREaNWqUyVGJiIiIiIiIiIhkjucukRAVFUWLFi345ptvCAwM5PTp0/j4+NCzZ8/77is2NpaIiIgULxG5P0/qfVSxYkUWLVrEe++9x/jx4zM7HBERERERERERkUzz3CUS5s2bR82aNenQoQMAjo6O9O/fn0OHDhEXF3dffY0cORI3NzfjVbBgwUcRssgz7Um+j+rVq8eUKVPImTNnZociIiIiIiIiIiKSaZ67GgnHjx/nww8/TLGtZMmSmM1m7ty5g4eHh9V9DRw4kD59+hh/R0REWD0IarFYrGpnNputWpfdYrFgY5NxXshsNlt13sTERKv6s1gsVq8br/XlJS3p3UcWiyXD++RR3B/W9mfNvRQfH2/1fWRra2tVu4zcHVdCQgI2CQn3bG9vb/9QzpvcTve5iIiIiIiIiMiz57lLJIwYMQI7u5SXnTVrViDlYNnJkycpVqzYPftydHTE0dHx4Qd5F5PJ9FAH5qzty9bW1uoBVZH/4km6j5JrImTkYX/vTSbTQ00E3t3v4xzYf9znExERERERERGRx+O5W9rI3t4+1UBX8t/Jg3TTpk2jYcOGREdHP/b4RERERERERERERESeJM/djIS03J1ImDZtGsOHD2fTpk1WP50sIiIiIiIiIiIiIvKsUiIBjCVFpk2bxvTp09m0aRNFihTJ5KhERERERERERERERDLfM7m00aFDh3jttde4c+eOVe2Ti5xOmzZNSQQRERERERERERERkbs8k4mEDh06sHr1apo0aWJVMiFHjhy89957bN68WUkEEREREREREREREZG7PJOJhIIFCzJs2DACAwPTTSbcvn2bunXrsmrVKuzs7Jg1a5aSCCIiIiIiIiIiIiIi//JMJhJKlSqFo6MjGzZsMJIJERERDB8+nFu3bgFgZ2eHra0tvXr1Ij4+PpMjFhERERERERERERF5Mj2ziYTAwEB8fHyMZELRokXZuHEjjo6OADg7O7N8+XI2btyIvb19JkcsIiIiIiIiIiIiIvJkssvsAB6FUqVKMWPGDAAqVKjAK6+8wpIlS0hISMBsNhvtnJ2dKViwYGaFaRWLxWJVO7PZjI1NxnmhxMRETCaTVe3s7DL+eiQkJFh1XsCq/gCr4pNnn8ViyfD7b+398bDFx8db9T2NiYnBwcEhw3aJiYlWnddsNmd4H939nsQnJGBzjxlX1v5uWNsOdP+KiIiIiIiIiDyLntlEwtGjRzGbzXTq1ImwsDC2b99OixYtaNKkCatWrSJr1qyZGqO1g23WDt7Z2NhY1eej6E8Dh/IomEymDL9bD/v7dz/3hzXs7e2tSqBZc63Wtrt7v62NDTa2tla1fRjxiYiIiIiIiIjIs+mZXNooR44cODg48Prrr3Pu3DlWrFhBjRo12LBhA2FhYVy/fj2zQxQREREREREREREReSo8kzMSAFq2bMnJkydZsWIFLi4uAPj4+HD48GE9WSsiIiIiIiIiIiIiYqVnNpEwefJk4uLicHZ2TrFdSQQREREREREREREREes9k0sbAdja2qZKIoiIiIiIiIiIiIiIyP15ZhMJIiIiIiIiIiIiIiLy3ymRICIiIiIiIiIiIiIi6VIiQawyYMAAXF1d2bBhQ2aHIiIiIiIiIiIiIiKP0TNbbFkentWrVzN27FgA/Pz8OHjwIPny5cvkqERERO5fSEgIoaGh92wTHBz8mKIREREREREReTookfCEs1gsVrczmUwPrV1sbCw2NjaEhobSsWNHAJycnLh+/TrvvPMOK1euxM7ODrPZjJOTU4b9mc3mjC/if+zsMv5aWvu+AFZdrzyd4uPjrf7eOzg4ZNguJibGqv7i4uJwdHTMsF1iYiK2trYPrV1CQkKGbcx3tTFbLHCPey8xMdGqeykuLs6q+ACr3mdr6T6Xhy0kJARvb2+ioqIybOvi4oKnp+djiEpERERERETkyadEwhPO2sExk8lkVVtrBwOT2/Xq1YurV69SqlQp5syZQ926ddmxYwcjRozgyy+/xGQyWdWnBvnkflnznbaxyZzV2ezt7a0aMLezs7Pqu29tO7DiXrrrPXGwt8fmHnFaLBar3sPMep9FHrbQ0FCioqLw9/fH29v7nm09PT3x8vJ6TJGJiIiIiIiIPNmUSJB0zZ8/nz/++AM7OztmzJhBuXLl+PHHH3nvvfcYPXo0L7/8MvXr18/sMEVERO6Lt7c3Pj4+mR2GiIiIiIiIyFNDj5lKms6fP0+vXr0AGDRokDHg0rp1azp37ozFYuGDDz7g4sWLmRilyKO1detWBg0axKVLlzI7FBERERERERERkUyjRIKkYjab6dKlCxEREVSuXJn+/fun2D969GhefPFFQkND6dChg1Xrtos8TSwWC1999RWNGzfm+++/p1q1amzatCmzwxIREREREREREckUWtpIUtm9ezdbt24FYMiQIamKHzs5OfHVV1/xxhtvsGPHDjZs2ECjRo0yI1SRR2L79u2MGjUKgKxZs3L9+nXeffddzp8/r3ofIpJKcHBwhm1Uc0FERERERESeZkokSCrly5fH29ub4OBgunXrxtq1aylWrJix/9ChQ3Tp0gWA4sWLU7Vq1cwKVeSRKFu2LPny5ePSpUvcuXMHgFdffVVJBBFJwdPTExcXF/z8/DJs6+LiQnBwsJIJIiIiIiIi8lRSIkFSyZo1K6tXr6Zx48YEBwfTsGFDI5mwbds23nzzTW7fvk2FChVYunQp7u7umR2yyEPl4eHBokWLqF+/PtHR0fj4+DBp0qTMDitNFotFCQ6RTOLl5UVwcDChoaH3bBccHIyfnx+hoaFKJIiIiIiIiMhTSYkESVOuXLlYs2YNjRo1MpIJ/fr1Y+DAgcTGxlKrVi0WLVpEtmzZMjtUkUfCx8eHP/74gz/++IMBAwbg4uKSbtvExEQuXrz42AcIP/vsM6ZOnUq/fv3o378/9vb2j/X8IpKUTFByQERERERERJ51KrYs6UpOJnh7e3Pp0iX69OlDbGwszZs3Z9myZbi6umZ2iCKPVO3atRk/fjx58+ZNt83NmzepVasWhQsXpkuXLkRHRz+W2ObPn8+4ceO4ffs2X3zxBS+//DJ///33Yzm3iIiIiIiIiIg8XzQj4RlhsVisamc2m61aBiW5v5w5c6ZY5qh9+/b8+OOP2NnZYbFYSEhIICEhIcP+EhISUhVtTktcXJxV7SwWCw4ODhm2A7Tsy1PKYrFk+L2Oi4uz+vtszdP61t4foaGh2Nvbc+nSJdq2bcuJEycAmD59Ort27WLKlCkUK1YMi8VCzpw5rTqvNd/7iIgITCYTJ0+e5MMPPwSgadOm7Ny5k0OHDlG1alU+692bd/7XPj4+AZv4+HT7s/Y+MpvN2NhknHe25jNLbmfN+2zt5wG6z0VEREREREREHiXNSHjCmUwmq18Psz8nJyfj5eXlxd69e9mzZw+zZs0ia9asKfZb05+trS02NjZWve529epVtm3bhtlsfuBrkWeXnZ2dVS97e3urvivOzs4pvtvpvezs7Dh58iSvvfYaJ06cIG/evIwZM4YcOXIQFBRE48aN+eOPPwDrvqc2NjZWt4uNjaVjx45ERkZSo0YNZs+eze7du2nUqBHx8fGMHTPGeH9Mpnuf39rzOjo64uDgYNXrYXrYv38iIiIiIiIiIvJgNCNBrJIlSxaqVKnyWM5lsVjYsWMHU6dOZenSpcTHx+Pr68uUKVMoUaLEY4lB5F4CAgJo3749N2/epEiRIsyfP58CBQpQr149evTowa5du+jevTu7du3ip59+wtnZ+aGde9CgQQQGBpIzZ06mTZuGra0tefLkYd68eSxcuJAvPxtotB333Xf0GjBAtRPkuRASEmJV0WMRERERERERuX+akSAP1W+//UbDhg2ZOnUqkZGR93VsREQEU6ZMoWLFijRo0IBFixYRHx+PnZ0du3btonLlyowePZr4eyzVIvKobdiwgbfeeoubN29Svnx5li1bRoECBQDIkycPCxYsoHfv3phMJubOncvLL7/M8ePHH8q5f//9d2bNmoXJZOLnn38mT548xj6TycTbb7/Nps2bjG3Dhw2jZs2aBAYGPpTzizypQkJC8Pb2pmLFivd8+fn54eLigqenZ2aHLCIiIiIiIvJUUSJBHpqVK1fy/vvvs2nTJrp3707hwoUZMGAAR44cuee66SdOnOCjjz6iYMGC9OnTh6CgIFxcXOjQoQO7d+/m6NGjvPrqq8TGxjJ06FBq1KihgVHJFAsWLKBZs2ZERUVRo0YNFi1aRI4cOVK0sbW1pX///vz22294enry999/U61aNRYuXPifzn3ixAl69+4NQL9+/XjllVfSbJc7d27jv7Nnz86hQ4eoVq0ao0aNsrqWisjTJjQ0lKioKPz9/Tlw4MA9X8HBwXh5eWV2yCIiIiIiIiJPFS1tJA9FYmIinTp1wmw2U79+fU6fPs3p06f57rvv+O677yhUqBBNmzaladOm1KlTB0dHR8xmM5MnT+azzz4jJiYGgJIlS9K1a1fatm1L9uzZjf6XLVvGvHnz6N+/P4cPH8bPz09LVMhjc+PGDfr27cuvv/4KJBU4njRpEo6OjukeU6tWLVatWkXjxo25ceMG7du3p27dug/0JPTNmzd54403uHPnDjVq1GDAgAFWHbdr927qNm7MmTNn+OKLL3jppZdo1KjRfZ9f5Gnh7e2Nj49PZochIiIiIiIi8sxRIkEeioCAAEJDQ3F1dWX58uXY2NiwZs0apk+fzoYNGzh37hyTJ09m8uTJZMmShQYNGhAREcGmTUnLsNSrV4+BAwdSvXp1bG1tU/VvMplo164dL7/8MqVKleL48eNER0c/1LXnRf7NYrGwePFiPvnkE65du4bJZKJPnz707NkTO7t7/3yeOnWKTz75hBs3bgDQpk0bPDw87juG2NhYWrVqxbFjx8iXL59RF8EavXv14syZMwBUrFjxsdU5ERERERERERGRZ4uWNpKHYtu2bQDUrFkTe3t7bG1tadq0KX/88QdXr15lyZIldO7cmXz58hEZGcnSpUvZtGkTTk5OTJw4kTVr1lCnTh1MJtM9z1OoUCGyZMkCwIULFx75dcnz6/DhwzRp0oQ2bdpw7do1Spcuzfbt2xk9evQ9B/ItFguzZs2iQYMGHDp0CDc3N2bPns2sWbOwsbm/n1yz2UyHDh3Ytm0brq6uLFy4MEVdhPTOn2zt2rU4ODjwzTffsG3btgdKZIiIiIiIiIiIiGhGgjwUW7duBaB27dqp9rm4uNC0aVNatGiBxWLh4MGD/Pnnn1y/fp3u3bvj7e1t9XlMJhMFCxbk2LFjhISEULx48Yd2DSIA586dY+jQocydOxeLxYK9vT2fffYZAwcOvOdSRgBXrlyhT58+bNmyBYAaNWowZ84cChYs+ECxDB48mAULFmBvb8/vv/9O6dKl79n+8uXLDOjZk2//93eFChX4ccaMDI8TERERERERERG5FyUS5D9LTExk+/btQNK68PdiMpmoWLEiFStWfODz3Z1IEHlYbt68yfDhw5k0aRJxcXEAtG7dmmHDhlG0aNEMj1+2bBkDBw4kLCwMJycnBg0aRIcOHTKcQZCen376idGjRwMwbdo06tatS3h4eJptLRYLCxYs4LPPPiMuIgJKlARg7bp1OGTL9kDnFxERERERERERSaZEgqTJbDZnuMwQQFxcHAEBAURERODq6kqZMmWIj49P1S4mJgYHB4cM+4uJicnwqe/8+fMDSU+Om83me7a1WCxWrydvzfXK42M2mzP8fBMTE636fKOjo9P8Xibv++mnnxg3bpwxUF+lShU++eQTypQpQ2RkJEeOHElxTFRUFIULFzb+Hj16NBMmTADgxRdfZOLEiRQvXpzExEQSEhIyjC8yMhJ7e3sg6Ts7d+5cPvnkEwCGDBliFFqOjIxMVRfk8uXL9O3bl3Xr1gFQtXx5iIkFwNbGNsVSR/9msVjuuf/udtb+HljLmt8Da89rzTUk030uIiIiIiIiInL/lEh4Rlg7OGZtO2vXcrezs2Pnzp1A0jIu6SUB7OzsrOrT3t4+wwHGQoUKAXD+/HkNCj7DTCZThp+vo6OjVd+rtAbzExMTmTdvHsOGDePixYsAFCtWjJ49e1K9enWio6MZM2YMBw4cYNCgQZQtW9Y41snJibx58wJJy3pNnDgRSFqKaOjQoUZSID4+3qrvqI2NDSaTif379/PZZ5+xd+9eAN5991369+9v9JEtWzajRgjA77//zocffkhYWBgODg4MHTqU3h99xLnqvgA4ONhjc4/7ydqBems+i/uVWecVEREREREREZH7p0SC/Gf3qo/wKCSvN3/+/PnHcj559uzYsYM+ffoQFBQEQIECBRg6dCje3t64uLiwa9cuhg8fzpUrVwDo2bMnM2fONJJYyW7fvk2HDh2wWCx07NiRb7755oHiuXTpEsOGDWP+/PkAZMmShT59+tC7d+80B9LNZjPDhg0zzlexYkVmzJhBmTJlMEdFPVAMIiIiIiIiIiIi6bHusXORdCQmJrJjxw5AiQR58lksFsaPH0/Tpk0JCgrC3d2d4cOHExAQQNu2bblz5w5Dhw7l448/5sqVK+TLl4/ixYsTFhbGxx9/TGhoaIr++vTpw9mzZylcuDDffffdfccTFRXFsGHDqFKlipFEaNeuHQcPHqR///7Y2aXO9UZFRdGuXTsjidC7d2927NhBmTJlHuAdebQWLVrEW2+9RXBwcGaHIiIiIiIiIiIi/4FmJMh/cvjwYaM+Qvny5R/LOZMTCSEhIURGRqZY6kUkLRaLhePHj/PNN9+wbNkyANq0acOYMWNwd3cHYOnSpXz88cfcunULk8lEmzZt+Oijj4iJiaFDhw5cuHCBTz75hGnTppElSxZWrlzJjBkzMJlM/PLLL2S7j6LG4eHhLFu2jK+++spIiFWrVo1Ro0bh4+OT7nFXrlyhbdu2HDx4EHt7e3788Uc6dOjwH96ZR2fVqlW8//77mM1m9u7dy5o1a/D29s7ssERERERERERE5AFoRoL8J8lrufv6+lpd1Pi/KlCgADY2NkRHR+Pp6cmrr77K2LFjCQgIuK9ir/JsO3v2LDNnzqR9+/YUL16cSpUqsWzZMuzt7fn++++ZNm0a7u7uWCwWRo0ahZ+fH7du3aJIkSLMnDmTfv364eLigoeHB5MmTcLd3Z3jx48bMw+GDRsGQIcOHayajRMXF8fs2bOpW7cuefLkoVOnTpw/f55ChQoxY8YM1q5de88kQvI5Dx48CED37t15//33/9ub9Aj179/fKJZ97do1Pv/880yOSEREREREREREHpRmJMh/YrFYAB7rrABHR0dGjBjBTz/9xLlz59i4cSMbN24Ekoo6Fy9eHG9vb0qXLk3p0qUpU6YMJUqUSLcQtDw7goKC+P7779m8eTOnT59Osc/JyQlfX1+GDh1KpUqVgKRaA59++ilTpkwBoG3btnz88cepCn4XLFiQ0aNH07lzZ5YtW8abb75JzZo1+euvv9ixYwdxcXHpFgmPjIxk5syZfP/991y4cMHYXrJkSfz8/Pjkk09ITEy0qqhw69at2bBhA1euXGH8+PH89ddfjB49mmrVqt3X+/Q4vPrqq8b7CtC0adNMjEZERERERERERP6L5zqRcPnyZcLDwylRogQ2Npqc8SCcnZ0BiI6OfiT9WywWzGZzqtkOPXv2pF+/fpw4cYJ169axZs0adu7cye3btwkODiY4OJglS5YY7W1tbSlWrBhlypShQoUKfPzxx7i6uj6SmCVz7Nmzh8aNGxMeHg4kJZWqVKlC3bp18fX1pUqVKjg5ORnt4+Li6Nq1K4sWLQJgzJgxvPzyy0ZCwGKxMHfuXLZt28bgwYPx8fGhUaNGrFmzhtGjR7NlyxbmzJnDiRMnmDRpEn369EkRz61bt5g4cSKTJk3ixo0bAOTNm5ePP/6YVq1a8cILLxht79y5Y9U11qpVi+DgYL777jvGjRvH7t27qVmzJnPnzqV169YP/ub9T1hYGFu2bCFnzpwULVqU3LlzW5XgSMvIkSPZvXs3hw8fpnv37k/sEkySeUJCQlLVHTHExOD8v/8MCAiAu+7dtKgOh4iIiIiIiMijZbIkP1L+HEkeQJw9ezYWi4X8+fMzbNiw/7xMSEREBG5uboSHhz/1g9TJS5JkZN68ebRv357atWuzfv36dNvFxMSkWTg2mcVi4dq1a/z999+cOnWKoKAgIyEQHh5O/fr1efvtt2nevDnZsmXDbDanegLcYrFw8eJFjh49SnBwMEePHiUoKIigoCAiIiJStC1atCjz58+nYsWKxrYHHTD9N3NUFMd9kvotefAANi4uD6Xfp52190dyu1u3bmV4H1ksFmxsbNi2bRvNmjXjzp07+Pr6MnjwYF5++WUcHR2JjY3l1q1bxMXFERsba7y+/PJLNmzYgJ2dHVOnTqV169YcOXIER0dHzGYzY8eOZcGCBQD4+PgwdepUrl+/TsuWLYmOjmbOnDnExsbSuXNnXF1dOXHiBLly5eLy5ct8//33TJkyxUgQFClShH79+uHn55cimZHszp0797w/kpnNZmP2z6VLl/j0009ZsGABWbNm5a+//qJEiRKYo6I4VaUqACUO7L/n989isRjf+1OnTtGgQQPOnj1r7M+SJQtFixZN8SpWrBhFixalYMGCqWKOiYlJdV0BAQH4+vqmStZaO0PImvvyfv4Ze1j3eWa53/voSf33KCQkBG9vb6KiotLc72wycaBESQAqnjhOtBWfsYuLC8HBwXh5eT3UWB+GgwcPUrFiRQ4cOJDhEmby6D3p94eIiIiIiMiT6LmckfDpp59y7do1bty4QXR0NCNGjKBDhw7s2rWLKVOmWD07IXlAMtm/B6ufBy7/G6SMjIy8r+MiIiL47bffjEH/Y8eOGU9tp2XNmjWsWbMGJycnmjRpwltvvUWLFi1SDMqaTCYKFChAgQIFaNiwIfD/A6WXLl3i6NGjHD16lIkTJ3Lq1Cl8fX0ZM2YMH3/88VM/uPg0+6/30fr163n99deJjo7mhRde4NixY7Rs2TJFn+lxcXFh7ty5NGjQwNgWHx/PF198wdq1awGwt7fn4MGDrFmzhsaNG/PBBx/w448/MmDAAIKCgpgyZQoHDhzgk08+wd3dnVmzZhnnLlu2LJ9++ilvvvmmVYmC+5EvXz5mz57NlStX2Lp1K++88w47duzgQRbwCgoKokGDBly+fJk8efLg5ORkFDM/cuQIR44cSXWMm5ubkYBJT9asWalRo8YDRCTPutDQUKKiovD390+7CHdMDHzQEYAdO3ZkOCMBwNPT84lMIoiIiIiIiIg8C567RILFYmHGjBls3rwZd3d33N3dmTRpEtWqVaNDhw44ODgwadIkq/oaOXIkX3311SOOOHNY+4Rv8lPFkZGRxMfHp9suNDQUe3t7ABISEnjttddSDU6aTCby589PsWLFKFKkiPEEtJ2dHevXr2fNmjWcO3eOJUuWsGTJErJly0ajRo147bXXqFGjhtH/v8/r5uYGQKlSpShVqhT169enf//+rFu3jl69erFq1SrGjBlDmTJlrLrmx1VU+nmR3n1ksVgy/B727t2bn376iYSEBHLkyMGZM2fSbWtnZ4eDgwP29vbY29uTM2dO+vTpQ548efj7778BWLt2LfPmzeOff/7BxsaGVq1acevWLTZs2MCoUaOIiYnB09OTHDlycPnyZb788ku+/vprmjZtysKFC41zValShZ49e1K4cGFcXFw4fvw4FouFtWvXEhAQQIcOHcidO7fR3mw2U6hQoQzfq1u3bqVK2n3//fe8+uqrHDlyhI8++ohRX35p9Xu4b98+jh07Rq9evQgLC6NIkSJMnDiRHDlyEB8fz+XLl7l48SKnTp0iMjKSkJAQ4xUeHs7bb79NQEAAnTp1wmQykZiYSP78+TO8Dms+2+R21iR2755ZIU8Xb2/vNJ/QN0dFcfx///3SSy9pZpeIiIiIiIhIJnvuEgnJg13nzp0zCq4C+Pn5ER8fzwcffED9+vV5/fXXM+xr4MCBKdZFj4iIoGDBgo8i7MfO2kG55CUBoqOj0xzIT+bg4GA8kT158mSOHDmCm5sb7733HiVKlKBkyZIUK1aM8+fPky1btlTHFy1alK5du3Ls2DFjdsK1a9dYtGgRixYtwsPDg3r16pE1a1ZMJpPxiomJwdnZ2RiMtLGxwcPDg7Fjx7JkyRJGjBjBunXrOHr0KAsWLHgii9Y+69K7j5I/w/QsWrSIH3/8EbPZjIeHBzdu3MBkMvHmm2/y4osvYmdnZ7wSExN5+eWX7xlHeHg4U6ZM4erVq9jb2/Pmm29SrFgxEhISOHjwIDdv3mTDhg00aNCAFi1a8Msvv/DTTz/Rrl07/Pz88Pf3p169evTs2ZPq1atjMpkICgrCbDYTGBjIuHHjjOTZpk2bGD9+PKVKlTLOb03B8sjIyFTvSZ48efjhhx9o27Yt/v7+vFypErX/ty+j9/Dw4cP06dOHyMhISpUqRY0aNfjkk08oVqwY5cqVo1y5clSuXJmyZctSrFgx47jExESGDx/OrFmzGDlyJBcuXGDo0KHY2tqmuXTTvz3sgf+MrlNERERERERERP6b57JGQuvWrQkKCmL//v2pBr3atm3LP//8w759++6732dpzV1rayQcOnSISpUqkTNnTi5evJhuu2vXrmFnZ0dwcDCvvvoq8fHx/PDDD7z11lsp2p04cSLNRMK/3b59m9DQUJYtW8aff/55z2WR0lKoUCFj5kmPHj04d+4cdnZ2DBs2jD59+tzzKeiMZiSoRkLaHmaNBH9/fzp06GAkEW7evInJZKJx48bExsZSrFgxSpYsacyYiY2NpXr16ume89q1a/Ts2ZOzZ8/i5OTEO++8Q4ECBYz9J0+e5LfffsNkMtGlSxeKFy/OypUr2bx5Mw0bNuTXX38lLCwMDw+PFP1u3bqVmTNnsnLlSiCpOLmHhwcXL17E2dmZESNGUKtWLQBKly6d4Xt47dq1dBN2o0ePZvz48eTImpXt+ZNiv1eNhA0bNtCiRQtiYmIoX748pUqVMmpC3M3Z2ZkSJUrw8ssv4+PjQ4UKFXB3dwdg5syZDB8+HIvFQr169ZgwYYJV12FtIuF+Eg7PSyLhWamRkFHNgGftd1Q1Ep4sT/r9ISIiIiIi8iR6KDMSPvjgAzp16oSvr+/D6O6RGzFiBC+99BLvvvsuv/32W4qB4e7du1OzZk0tlWGl+6mRkJCQQM+ePYmPj6dBgwa8+eabD3xeGxsbqlatStWqVfn666/ZsWMHBw8eJDExEbPZbCydcufOHRwdHbFYLJjNZsxmM2vXruXcuXO0atWKQYMGsWLFCgYNGsSff/7JZ599xpYtW/jll1/ImTPnA8cnj860adPo1q0bFovFSCLY2NjQoEED1q9fT3x8PBs3bsTOzo4SJUpQrlw5ihcvnm5/Z8+epVevXly9epUsWbLg5+dHrly5UrRJTkwcP36c1atXU7x4cYYPH06tWrVYu3Ytmzdvpl69ekb7qKgofvzxRyZMmGAUIG7WrBk9evTAycmJAQMG8Ndff9G3b1/69u1LmzZt/vP70rdvX/bu3cuh3bszbLt8+XJat25NXFwcVapUIW/evEYS4b333sPe3p7AwEACAwO5c+cOhw8f5vDhw8bxRYoUoWrVqvTq1Yu8efPSp08fNm7cSNu2bVm7dm2KZZtE5P8FBwdn2Ea1HkRERERERORJ9FASCYMHD6ZHjx54eXkxatQo42nVJ1WxYsWYP38+b7zxBm+99Ra//vqrsazInTt3KFy4sJIIVkpOJERFRWE2m+/5JP+PP/5oLGk0ZsyYh/Ye29nZUadOHerUqZNq37Vr11J9H3v27GnUSPjyyy/ZtWsXY8aMoWnTpvTu3Zs1a9ZQsWJF5s2bp0KxT5hJkybRs2dPIGlWSfJMkvr167NhwwYSEhLw8vLizp073Lx5k6CgIIKCggD47bff8PX1pWXLluTJkwdIGtTr3bs34eHheHl58corr6RKIiR79dVXOXXqFCEhIRw6dIj333+fTp06MWXKFAYPHkzNmjVxcHBg+fLlfP7558YMnfLly9OvX78UT+pPmDCBUaNGsXTpUsaMGUNISAgzZsy47/obly5dIjAwkFdeeQV7e3t+/PFHmt9VODqtCWfz58+nffv2JCYmUqtWLZydnVm2bBkmk4lPP/00xbJuZrOZc+fOsW/fPs6fP8+hQ4c4deoUp0+f5vTp0+zYsYNffvkFf39/unTpwpEjR/D19WXVqlWULFnyvq5F5Fnm6emJi4sLfn5+GbZ1cXEhODhYyQQRERERERF5ojyURELRokVZvXo18+fP55VXXqF///60a9fuYXT9yDRr1ozly5fTtm1bypUrR69evXBxceHrr79m3LhxmR3eU+PuZYi2bduW5mA+JM1GGD9+PADffPONMZCbGdzc3Pj555+ZPXu2USPBYrGwevVqqlatStu2bTl27BhNmjQhMDBQgzlPiHPnztGrVy8AGjZsyNq1a42lhubMmUNCQgJlypShS5cu2NracvnyZQIDAwkICODcuXOcOnWKU6dOERAQwNSpUwGYOHEi4eHh2NnZ8e2337Jr1650z589e3bKlSvHoUOH2Lp1KwCffvopv//+OydPnmTFihXkzZuXDz74AIACBQrQsWNHmjdvnippZm9vz5AhQ/Dy8mLixIksWLAAGxsbpk2bZlUy4eLFi0yaNIl58+YRHx9PuXLlGD9+PN7e3km/XyNGAvBW69ZMmjaNfPnyAUl1Jfz8/DCbzbRr144bN26wZs0abG1t+fzzz2nYsGGK89jY2PDCCy/g6elp1Ei4desWBw8eZNiwYcZnsnz5chYvXswHH3zAmTNnaNSoEQEBAUahc5HnnZeXF8HBwYSGht6zXXBwMH5+foSGhurfHhEREREREXmipP/4+AOoW7cuQ4YMoXfv3hw8ePBhdn1fYmNjWb58eYbtGjduzNGjR2nRogUzZsxg7ty5/Pzzz6nW7Zf0ZcmShbZt2wLQpk0bTp06lWa7hIQEoqOjAWjUqNFjiy89JpOJ999/n/nz52NnZ8f69etZsWIFL774Inv27KFq1apERUXRv3//zA5V/ufQoUNYLBbKly/Pd999h4ODAxaLhcjISEqUKAEkrXudXHjXw8MDGxsbwsLCjD6yZctGq1atjL+bNWuGra0tCQkJfPTRR5w+fTrd81+6dMkollyuXDkgqdh4y5YtAThw4ACjRo0C4PXXX2f37t3Uq1cvRRLh+PHjTJ06lbCwMEwmE++99x4jR47E1taW3377jc6dO5OYmHjPGAYOHIivry+zZ88mPj4eBwcH/v77bxo1asSECROoWvX/C4avXbOGsmXLMnv2bFauXEm7du0wm828//77uLm5GUmEYcOGpUoipMfd3Z169eqxYMECYwmk48eP88ILL7B48WJeeOEFzp07R48ePazqT+R54eXlhY+Pzz1f3t7emR2miIiIiIiISJoeSiKhe/fuFCxYkDp16rBs2TIGDRpEkSJFHkbXD6R9+/a89tpr/Pjjjxm2zZcvH+PHj+fw4cNs3ryZxo0bP4YIny0//fQTFStW5ObNm7zxxhuEh4enanP3U9b3Gih93Hx8fOjcuTOQtORRZGQkWbNmZcqUKdja2vL777+zYcOGTI5SIKkQNyQVJS5VqhSff/45AIsXL6ZRo0a4uLhw/vx5/vzzT1asWMGQIUP4448/CA8Px83NjU8++YSlS5emGDBv2rQpM2bMoEiRIty6dYuVK1eyYsUKYmNjU5w7MjKSRYsWkZiYSIkSJahbt66xr3z58kYcu3btwsHBga+++gpnZ+cUfZw/f54PP/yQn3/+mW7duhkJjldffZWRI0diZ2eXbjLh/Pnz9OzZk1q1ahkJBF9fXxYvXsxff/1FgwYNiI+P59tvv+XNN/8/UVLBx4ewsDA6dOhAixYtSEhIoE2bNuTKlYvJkydjMpn4/PPP051JdC+5cuUyjlu2bBkAOXLkwN/fH1tbW+bOncvcuXPvu18REREREREREXnyPJREQrdu3QgODiYoKIhff/2VXr16kT179ofR9QO5fPkyTZs2pUePHvdMJvj7+3Pr1q3HGNmzydnZmd9//518+fJx7Ngx2rdvT0JCQoo2T2oiAeCTTz4hf/78hISE8M033wBJT5x/9NFHAPTq1Yu4uLjMDFGAY8eOARhr7/fv358CBQoQGRnJ6tWrjeLd69atY82aNURHR5MrVy7atWvH4MGDeeedd4yaHncrWbIkM2fONGbWBAQE8PPPP3Pu3Dkg6fu6ePFiIiIiyJEjB6+//nqKWiAvvfQSADdv3gTAz8+P/PnzpzhHZGQkffv2JSIiAkhKitydTKhXrx5z5sxJlUxITiCUKVOGqVOnpkggLF68GF9fX3Lnzs2sWbP44YcfyJ49O0ePHjXOu3btWoYPH27M3mjevDllypRh9OjRxnto7UyEtLz22mtAUvFms9kMQPXq1Y0kz0cffcSZM2ceuH8REREREREREXkyPJREQtmyZcmaNevD6OqhKFWqFK1btzaKQCcnE+4e0Lp27Ro9evQwnuSV/yZfvnwsWbIEZ2dn1qxZw5IlS1Lst7GxMZZ4edISCc7Oznz99dcAjB8/nsDAQAC++OILcubMybFjx5g0aVJmhij8/4yE5GWM7O3t8fPzw8bGhkOHDuHg4MCLL74IJC0h0rFjRz7//HN8fX2xs7t3ORhHR0c+/vhjWrZsSfbs2QkPD2fOnDmsX7+edevWERISgoODA61bt8bR0THFsUWLFjWKtTs4OBjFoJOZzWa++OILTp06haenJz/99BM5cuRIlUx44403UiQTatWqlSKBULt2bRYsWGAkEO5mMplo1aoVmzdvTjFb4tUGDWjWrBkBAQHMmDGDOnXqGIP8o0eP5o033rifjyCVunXrkjVrVi5dusT+/fuN7YMGDeLll1/m9u3b+Pn5pUosioiIiIiIiIjI08XqYsuJiYlMmDCBzZs3kydPHnr37k3p0qWN/adPnzaKbXp6ej6SYK1VqlQpAgMDjadue/ToQVBQEIsXL+bgwYPkz5+fXLlysXbtWv7++2/s7e0zNd4nkcVisapd8lPIkPRk9gcffMCPP/7I7t27jSfEAcLCwoy16G/cuJHuex4SEpJioPbkyZNs3bqVF198kQoVKhgDwlFRUamWn0nLlStXrJodU6BAAZo0acKqVav48MMPWbFiBTY2Nnz++ed88sknfPXVVzRt2pQ8efLg4eGRYX/y38XHxxtJPovFYsxIKFasmLE9Z86c1K1blw0bNrBgwQL69u1LkyZNyJkzJyaTyWgXFRXFtWvXMjynh4cH7du3Z8uWLfz999/s2bPH2Ne4cWNcXFyIiorCzs6OK1euGPu8vb3Zv38/LVu2xNbW1ti3evVqNm7cyN69e7GxsaFOnTocPXqUhg0bsnTpUk6cOMHbb79N9+7dgaTZESNGjGDgwIFGnZlKlSrRpUsXKlasyP79+1m9erVx3rCwMM6ePUvZsmWNe6NVq1YwYSKQNLuiYsWKdOnShRw5chgzbjp37kz9+vUJDAy0qiByZGRkuu3q1KnDn3/+yfz58ylQoACurq4A/PDDD9SuXZvdu3fzxhtvMGXKlBTLPVlzX5rN5hSzP9JjsVisKlJtsVhSFb5Oj7XtRERERERERESeB1YnEr799luGDh2Kr68vp06dYuHChezdu5e9e/fyww8/sG/fPrJmzUr79u0fZbxWKVWqFJs2bQJg2LBhnDlzhsmTJ9OrV68US45UrVqVqlWrZlaYTzRrBu8g6QnsuwfcKleuDCQNYN6dLHBzc8POzo6EhARcXFzSHZR0c3MjW7ZsQNKg34ABAzh9+jRr1qwhe/bs1KtXj4YNG+Lk5JTqyfC0xMfH4+TkZNW1jBo1iq1bt7Jnzx7mz59Pu3btaNu2LbNmzeLgwYN8/fXX/PTTT1b1Jf9dctFkgNDQUGMZsuLFixvb27Vrx9tvv03dunU5ceIEwcHBaS5ndvr0aatmTb3//vvky5eP3r17s3HjRgYOHEhoaCiffPIJvXr1Mtpdv349xTJJAwYMYNmyZfTp0yfF9vXr1xtP6r/00kuYzWYuXboEgK+vL9u3byc0NJQff/yRWrVqGd/x8ePHs3btWlq0aEHFihWN/q5du4aHhweJiYls3ryZVatWERsbS548eWjdujUlS5bE5q4ZVi/XqMGG7duZPHmysa1t27Z07doVi8VCrly58PLyyvB9uXnzJjlz5kxzX5s2bfjzzz/ZvHkzQ4cONbZ7eXkxZcoUOnTowMqVK3nrrbfw9/c3EgjWDNTfPZNJREREREREREQyj9VLGy1btozp06ezbds2goKCGDBgAM2bN6dDhw4UKlSIJUuWcP36dfLmzfso47VK8owESBrI27BhA++99x7jx4+3qgCzPLhKlSoBcOjQoVRLGCUnJ6xd2ujkyZOcPn0ae3t73N3dCQsL4/fff6dLly4MGjSIefPmERoammE/4eHhBAQE8Mcff/Ddd98xfPhwYz37uxUoUIBPP/0USFrW6ObNm9jY2DB69GhMJhMLFixI8YS6PD7Jyxp5eXmlqnPg5OTEhAkTjM/oYRXHrlevHhs2bOD3339PtVzRv1WsWJGvv/46xVP2J06c4NChQ0DS8keFCxdOcYyrqys1a9bE0dGR0NDQFMsc+fr68tVXX6VIItzd74gRI/jjjz+IjY01ZkBMnDiRmTNnEv6/PgBGjRzJN998Y8wSeP311+nTp89DHZyvWrUquXPnJjw8nO3bt6fY17BhQxYtWoSrqyt79uyhWbNmRiJFRERERERERESeHlYnEq5du0atWrWMvz/77DMuXLjA1KlTWbRoEW+88YbVT34/ai+88ALXrl3j999/x8/Pj6VLlzJr1iwGDx7Mxx9/zN9//53ZIT6zSpQoQZYsWf6PvbsOqyr7Gjj+vZfuEEEMTFR0TOx2bLE7sLu7a+zuduwuVGwddVSMURETsQNbFOnm3vcPfpxXBAS71ud57vN4z9lnn30O98LMXmevRVhYGLdu3UqwLz71SmoDCYcPHwagdOnSbNu2jalTp1KxYkX09PR48uQJixYtokGDBvTv359//vmHoKAgbt++zYEDB1i4cCH9+/enb9++NGjQgAEDBrBgwQL27dvHsWPHmD17dpLn7Nq1K3ny5MHf35+//voLgMKFC+Pq6grA4MGDf7gaD7+D+M9SfH2E9xUtWpTOnTsD0L9/f7y8vFKdnutDzM3NKVSo0EdPvAcEBNClSxdiY2NJmzYt+fLlS7b/smXLYmxsnKhmwvtevXrF1q1bmTt3Li9evMDU1BRXV1cmT55MuXLlUKlUXLx4kclTpijHqFQqatasiZubG4sWLWLEiBFf/Al/HR0dateuDcC+ffsS7S9dujR79+4lXbp03Lx5kxo1aiT63SCEEEIIIYQQQgghfmypDiRotdoE6W7UajW2traUL1/+qwzsc6jVavLmzUuHDh3YtWsXJUuWBOLSHJ05cybZST3x+XR0dChUqBAAFy9eTLDvY1YkxMTEKE+WV61aFV1dXYoXL86YMWPYsWMH7du3J1++fGg0Gs6fP8/YsWOpWbMm7du3Z+LEiWzatInz588rk7L29vaUKlWKJk2aoKOjw5kzZxI9PQ1xBXxnzJgBwPr165UVCKNGjcLCwoLr16+zdOnST7s54qNs3LiRNWvWsGbNGvbs2QMkH0iAuAK/WbJk4dmzZ1StWpVixYoxZcoUAgMDv9WQgbjPd58+fXj06BFGRkYUK1bsg6nCzM3NqVevXoICzPFpnCCuPsHatWtp2LAh165dQ6VSUa5cOcaMGUPJkiUxMTGhadOmDBkyhKxZsxL1Tu2QS5cvA3G1H4oXL57qlGUfq169egAcP36coKCgRPvz5MnDgQMHcHR05OnTp7i4uHD69OmvMhYhhBBCCCGEEEII8eWlukYCQJ06dShUqBD58uUjX758REdHp6rA5fewceNG/P39KVGiRILt778XX56zszOnTp3i6NGjCWpmxNc0ePr06QcnhCEurVH8ZKqvry8lS5ZUnqQ2MzOjYsWKtG/fnidPnnDo0CEOHjzI8+fPMTMzI3v27GTPnp1s2bJhYGBA8eLFE6TD0dXVZePGjUyePJnQ0FCqV6+e4NwlSpTA1dWV9evX06lTJ/bv30+mTJkYMWIEgwcPZtCgQRQoUIDSpUt/kfslkpZUOqHcuXMn297ExISVK1fSoEEDAgICePDgATNmzMDPz08pZvy1RUZG0rdvXzw8PDA0NKRIkSKpquVhbW3NkiVL6Nq1K7dv38bV1ZUePXrg6enJ4cOHCQ8PByBTpky0bNmSTJkyJeojU6ZMcasxzpwBr7iUSn1696bfsGHUr1//y17oe7Jly4apqSkhISF4eHjg4uKS5Pj27dtHixYt8PT0pGrVqmzatIk6dep81bEJIYQQQgghhBBCiM+X6kDCtGnTOHXqFBcvXsTNzY3Q0FAAKlSoQPHixSlWrBjFihWjVKlSP0RwIaWJavH1NGnShLlz57Jp0ybGjRunFHOtWrUq69atY/r06ZQvX/6DT0dnzZqVIkWK4OnpyZIlS1Cr1TRp0iRRu4wZM9KhQwfatWtHSEgIZmZmCVK33L17N1FO/VatWnHnzh0uXLjA9OnTuXz5cqJJ67/++ov//vuPu3fvUq9ePfbs2UPbtm05cOAA//77LzVr1uTo0aNKTQjx5VWuXFlJhwWQNm3aJD8D8U6ePEmfPn0SpAbKkycPjRs3/prDVAQHB9O1a1fOnj2Lvr4+c+fOVVZSpEa2bNlYunQp/fr14/Hjx4waNUrZlzlzZtq2bYtarcbGxibZPtRqNcWKF1cCCRqNhgkTJvD69Ws6duz41QoXjx8/npCQEKytrSlWrFiy7aytrdmxYwcdO3bk8OHDNGzYkEWLFtGpU6evMi4hhBBCCCGEEEII8WWkOs9FkyZNmDdvHqdPnyYoKAhvb2/Wrl1LvXr1ePbsGX/99RflypXj8ePHX3O84idQokQJKlasSExMDDNnzlS2DxgwABMTE65cucLOnTs/2IeBgQHTp09XJhhXrFjB06dPk22vVqsxNzdPcaJUo9EQGhrKpEmTaN++PWq1mn/++Yfu3btz48YNpZ21tTU7d+4kc+bMPHjwgPr16/P27VvWrVtHuXLlCAoKomrVqly9ejU1t0R8gk2bNuHu7q68li9fjpWVVaJ2wcHBDBw4kAYNGvD48WMyZszIsGHDOHv2LCdPnvwmq5D8/Pxo0aIFZ8+exdTUlJUrV1KlSpWP7idr1qxs2LABFxcXTExMcHFxYfny5bi5uVGnTp2PTk3Uuk0bAJYsWcLkyZO/Sn2P3bt3s2nTJlQqFVOmTCFt2rQfbG9sbMzatWtp164dGo2Grl27Mn78+C9S00IIIYQQQgghhBBCfB2flDBbrVaTJ08eWrVqxZw5c/Dw8CAwMBAfHx/SpUv3pccofkLDhg0DYPny5bx8+RKIe6K8V69eAEyePFlJ15IclUpFixYtKFSoEJGRkcycOfOzJhsfP35Mz549adSoEatWraJ58+bMnDmTNGnS4OvrS5UqVdiwYYPSPkOGDLi7u5M+fXru3LlDgwYNiIyMZO/evZQoUYK3b99SuXJlfHx8PnlM4vN4eHhQrlw5Vq9eDUD79u05deoUAwYMwNHR8ZuM4dmzZzRr1gxvb2/SpEnDxo0bKVWq1Cf3Z2Jiwrhx4zh58iTjxo37pGLP8Tp36sSQIUNQqVS4ubmxfPnyTx5XUu7fv8+IESMA6NmzZ6qvW1dXl7///ls59q+//qJLly54eXml+HtBCCGEEEIIIYQQQnx7X6zyplqtJnfu3BgaGn6pLsVP7M8//6R48eJEREQwZ84cZXvHjh3JkCEDz58/Z9myZSn2o1KpGDBgAPr6+nh5eXHgwIGPHotWq+XAgQN07dqVW7duAXE1NMaOHYujoyNLly6lSJEihIeH06tXL7p3766k7nJwcMDd3R07Ozu8vb1p1KgRGo2GAwcOULhwYfz8/KhUqRJ37tz56HGJTxccHEzPnj1p3Lgxjx8/xsHBgR07djBt2jRMTU2/2Th8fX1p2rQpDx48IH369GzdupU//vjjm50/JCQEb29vNBpNsm2aNGnC6NGjgbjA3uX/FWD+XBEREfTq1YvQ0FBKlChB7969P+p4lUrFuHHjWLBgASqVihUrVlC0aFHMzMzInTs3DRs2ZPTo0WzZsgVvb2+io6O/yLjF1+fr64uXl9cHXxKAFUIIIYQQQgghfi4qreST+GKCgoKwsLAgMDAQc3Pz7z2cz5Laj4VGo0n2aek9e/ZQr149zMzMOHnyJJaWlgDs2rWLfv36YWJiwsyZM6lQoYJSkPbixYtJ3rutW7eyfPlyTE1N+fvvv3nw4AH6+vopju/ChQscO3YMPz8/ACwtLUmTJg33799Hq9ViYmLCH3/8QdmyZXn69ClbtmxBo9GQMWNG+vfvr9R3ePz4MWPGjCEoKIhixYrh5uZGVFQUtWrVwsfHh/Tp07N7926yZ8+OJjyclxX/BCCX10XU79VoeNfHfP2+Vn77byW134/4dpcvX8bMzCzR/tOnTzNs2DAl1VX58uVp0KABhoaGREdH4+fnh729fYL7paurS548eVIc48uXL7G1tU2x3e7du7lz5w4HDhwgLCwMc3NzatSokSiIceXKlVT9jLNmzZqqJ/FtbGxwcnJS3q9YsYI7d+5QrFgx6tevj0qlQic6Gpf1cStrDrdrS6yeHhAXPLt48SLW1tb0798fI7tGaiAAAQAASURBVCMjDAwMyJ8/f4rnDQgIIHPmzAm2jRs3Djc3N6ytrdm6dStp06YlNDSUDBkypNhfSEhIghRIhw8fZvHixfj4+ODv75/kMXp6ejg6OlKsWDHGjBmDtbV1gv1JfVbeFx0dnervkd7/7tuP5mO/R9/675Gvry9OTk6EhYWl2NbY2BgfHx/l9+y7NGFh3CrsDKT8e/RX4uXlhbOzMxcvXqRw4cLfezi/rF/pv9eEEEIIIYQQ4ltJdbFl8XtJ7WTbhwpr16lTh/z583P16lXc3NwYPnw4AJ06dWLDhg14enrStWtXrKysaNCgAc2bN6ds2bIYGRkl6qtIkSJcvHiRS5cusXnzZipUqJDiRN/du3fZsmVLgm0BAQEJivGGhoZy7tw5IiIiGDJkCDly5GDBggU8efKEoUOH0q9fPwoUKICdnR1Dhw5l4sSJnD9/nhYtWrBlyxbc3d2pU6cON2/epE6dOuzevZus6dOn6t6JD1OpVAk+hyEhIUyZMoWNGzcCcYW2K1euTL58+YiJieHu3busXr0aPz8/SpUqRf369ZWaAsbGxokmwpOiq6uLhYVFiu0OHjyoPCVvbGxM3rx5efnypZLGK15MTAzZsmVLsb/Dhw+najWXs7Ozkg7o2rVrykqY8+fPU6hQIQYMGIA2IoLY9RsSHdugQQMePHiAv78/O3bsoGXLlqhUKjJmzJjieS0tLROkrdu6dStubm6oVCqWLFmiBCPigyopeb9WQ9WqValatSparRY/Pz9u3rzJzZs3uX79Ovfu3cPHx4eQkBBu3LjBjRs3ePnyJZs3b07w+UjN76yPrTEhPt7r168JCwtj/fr1CYJeSbGxsUkyiCCEEEIIIYQQQogfj8yqiK9GpVIpwYNFixYREhICxE3mbd++nb59+2Jvb8/bt29ZsWIFlStXpmjRoowbN47bt28n6EtXV5f58+ejo6ODu7v7B4scx8bGsnfvXubOnZvqsV69epWTJ0/i5OTE5MmTyZcvH1FRUcyaNYvr168DkCVLFkaMGIGZmRkeHh60atUKc3Nz3N3dyZ07N8+ePaNOnTo8uH//Y2+VSMK4ceMYPny48qpRo4YSRHB1deXAgQPKJL2Xlxdz585VVp6cOXOGNWvWfJV0OFeuXOHatWtER0djampKwYIFlRU1X4pWq/1guiJAqXeQNWtWAJYuXcqKFSuSbW9oaEjLli1Rq9V4eXlx8eLFTxrbnj176NOnDwD9+vWjQoUKn9RPUlQqFba2tpQrV47OnTszbdo0jh49ytOnT7l+/TqrVq1CX1+fAwcOsGbNmi92XvHlOTk5Ubhw4Q++JIgghBBCCCGEEEL8PCSQIL6qRo0akTNnTvz9/RMUerW1tWXSpEncvn2bffv24erqiqmpKb6+vsyYMYMiRYpQvnx5Fi5cqDzlnT9/fvr27QuAm5tbkqkzXr9+zezZszl48OBHpQ3SarUsWbKETZs2YWpqyqBBg3B2diY6OpoZM2Yo+bxz5szJ5s2bMTIy4ujRo3To0AErK6sEwYQmTZp8xh0T8dzc3Ni8ebPyevr0KRkzZmT9+vWMGzcOExMTYmJi2LlzJxs2bCAqKgpHR0eaNGmCrq4u169fZ8mSJUq9iy/hwoULtG3bltjYWMzNzSlQoECqUmylRKvVEhMTQ1hYGIGBgbx+/ZrXr18TERGRZHtfX18OHToEwLx58xg4cCAAU6dOxX3XrmTPkyVLFqpUqQLE3d/Xr19/1Dj37NlD586diY2NpUmTJgwaNOijjv9UKpUKBwcHpW4CxBV0v3fv3jc5vxBCCCGEEEIIIcTvTgIJ4qvS0dFh6NChAMydOzfRxKiOjg4VK1Zk2bJlPHz4kGXLllG9enV0dXW5dOkSw4YNI1++fJw4cQJAST8UFBTErvcmTD09PZkyZQoPHz7EyMiI9u3bp3qcmTJlAuImSmfNmkVMTAy9e/emQIECREVFMX36dGWVRKlSpdi4cSMGBgbs27ePrl27kiZNGiWY8Pz580+9XeIdPXr0oH///srrr7/+4sCBA5QqVQoAPz8/Vq1axalTpwCoVKkSnTt3pnjx4nTp0gUjIyMePnzI/PnzefPmTYK+fX19mThxIjExMakez6lTp+jQoQOhoaFYWFiQP3/+z86j//TpUzw9PQkJCcHf35+QkBAiIyOVIFhQUBBRUVGJjlu5ciUajYZy5cqRK1cuOnXqRIcOHQAYO3bsB89ZqVIlsmTJQmRkJKtXr071PXg/iDB37twPpjb7Wnr27EnZsmUJDQ2lU6dOH/UzFEIIIYQQQgghhBCfRgIJ4qtzdXUlU6ZMvHz5kilTpiTbztjYmPr167N161Zu377NjBkzcHR0JCIigtatW3Px4kUMDQ2ZP38+EJe+5vDhw0BcbvZ169YRERGBpaUlw4YN+6hClVmyZKFnz57o6enh5eXFpk2b0NPTo1+/fvzxxx9EREQwe/Zs5en2ChUqsGbNGvT09NixYwezZ8/G1tYWd3d3cubMpfQrk5yfrnPnzvTs2VN5tW7dGhMTE2X/vHnzePz4MTo6OrRr146aNWsqOfCzZctGr169MDU1xc/Pj927dyfou0SJEixcuJAxY8akaiwHDhygS5cuREREUL58eZycnNDV/bwSM69fv+bcuXP4+vomWD2jUqkwMTFRJunjU4K968iRIwA0btxYOWbw4MEUKlQoxZRIOjo6tGzZEgMDAx48eMC5c+c+2D4mJoYFCxbQsWPH7x5EgLjUaKNGjQLigofHjh37LuMQQgghhBBCCCGE+J1IIEF8dXp6ekyePBmA6dOnK5OgH2JjY0Pnzp05ceIERYoU4e3bt9SuXRsPDw9Kly5N9erVAdi9ezfu7u4YGhoqBV8DAgI4cOBAkk9yf0ipUqXo168fAMePHycoKAh9fX0GDBiAvb09gYGBbNu2TWlfrVo15syZA8C0adO4ceMGtra2bN6yWWmzWvK4f7J69epRpUoV5dWyZUuluDBAxYoVgbiaGG/fvk10fGhoqJL+Knfu3Eme48aNGymOY9OmTfTt25fo6GiqV6/OwoULv8gkurm5OQ4ODomKi2u1WkJDQ5WCxEkVYc6bNy8A+/fvV45ZtGgRly5dStW5zczMlICDtbV1su1evnxJq1atWLZsGVqtlnbt2n3XIALA48eP6dy5MxAXACxSpMh3G4sQQgghhBBCCCHE70ICCeKbaNCgAR07dkSr1dKhQweePXuWquNMTU3ZvXs35cuXJyQkhAYNGnDgwAGqVatGvXr1APjnn3/YunUrbdq0oXbt2qhUKs6ePcusWbM+epwFChQgW7ZsREdH888//wBgYGBA69atgbiJ2/h6CQDNmzenevXqREdH06NHD6Kjo7G1tVX2jxs7NslJbpGyBw8ecO/ePeV19uxZ2rVrx6tXrwD4888/lXz/7u7uCYICISEhrF+/Ho1Gg7OzMyVKlPikMaxYsYIxY8ag1Wpp3rw5s2fP/iI1EQD09fUpUqQINWrUwNTUFAsLC0xMTDAwMEBHRwe1Wo25uTnGxsaJju3fvz8qlYoDBw5w5coVJk+erBQX79K1a4rnvnfvHtHR0VhYWCQbZPHw8KBWrVqcO3cOY2Njli5dytSpU79bECEmJobz58/j4uLCw4cPyZIlC/v37/9gIEQIIYQQQgghhBBCfBkSSBDfzLRp08iXLx9+fn60bds21Wl/TE1N2bZtGy4uLkRGRtKyZUu8vLyoXLkyzZs3R6VScerUKdavX0+lSpXo0aMHpqamPHny5KPHqFKpcHFxAeDw4cNERkYCcQEGZ2dnNBoNQ4cOVVLRqFQqZs+ejaWlJZcvX1Ymc+P5+/szbty4jx6HiKsDEF9oecOGDWTNmpVnz57RsWNHJcVUmTJlKF68OFqtlnXr1vHkyRM0Gg0bN24kMDCQtGnT0rBhQ1Qq1Ueff9euXUydOhWIq9fw119/fbVJdLVajYGBASYmJlhYWJAmTRpsbGySXI0AkCtXLurWrQtAu3btWL16NQAjRoygW7duKZ7v5s2bQNzKhvfvTUxMDLNmzaJt27a8efMGJycntmzZQv369T/jCj9eVFQUnp6ezJw5kwYNGpA5c2YqV66cIIiQMWPGbzomIYQQQgghhBBCiN+VBBLEN2NoaMiGDRswNTXl1KlTjB8/PkFu+JSOXbt2LU2bNiUmJob169craY7atm2LWq3G09OTOXPmYGdnx5AhQ8iSJcsnjbNYsWKkTZuW4ODgBGmYWrVqhZ6eHidPnkyQcz9dunRK7Ydp06Ypk7TxFixYkGAVg0gdZ2dnihUrRrFixShZsiQrV67E2tqa69ev06dPH2JjY1GpVDRs2BBHR0eioqJYsWIFe/bs4datW+jq6tKmTRsMDAw++tynT59m+PDhAHTs2JE+ffp8UjDia+rTpw96enqEhISgo6PD1KlTadOmTaqOjf+M5smTJ8F2Pz8/WrVqxYIFC9BqtbRo0YIdO3aQOXPmLz7+90VERHDmzBlmzpyp/EwbNGjA2LFjOXLkCMHBwVhaWlKvXj0JIgghhBBCCCGEEEJ8Y59XLVSIVIoPGGTPnp0FCxbQtm1bpk+fzty5c7G0tMTKygpLS0vMzMywtrbG0tJS2e7g4ECNGjXQ1dVlyZIlmJub8/fff7NlyxZCQ0OpXLkynTp1Yu3atTx8+JApU6bQunVrevbsycCBA1M9xhMnTij/zpMnDydOnGDbtm3o6OgoeexLly7N8ePHGTJkCFmzZlWeGC9YsCBly5bFw8OD7t26s+l//VSrXp1dBw7Qr18/9uzZk+R9Se1T7t8zL/33oNVqEwSaHBwcWLp0KS1btuTYsWOMGzeODBkyoNFoaNCgAStXrsTPz4+TJ08CULNmTUxNTQkODkar1SaZYiomJibB9nXr1nHjxg2OHz9OTEwMmTJlIiAggBEjRiQ47u3bt0ph55QEBwd/9LUmJyYmhr179yrvXVxc8PDwoG7duujp6bF37150oqOp8r/9T58+Jea9otABAQH4+fmhVquxs7NLUMx5xIgRSiqjMWPGUKNGDaKjowkODsbc3DzF8YWEhKQqcBMVFaVcb1BQEJMmTWLjxo3KCqB41tbWlClThlKlSlGmTBny5s2b5PdAq9WmWGQ6vt33CAilNmAK/HABKyGEEEIIIYQQQgiQQIL4Rt4tKNu6dWtu3rzJ1KlTiYqK4tWrV0re++S4uLiwceNGLCwsWLJkCZaWlkyfPp29e/eSK1cuZs6cSa9evWjZsiVXrlxhyZIljBo1Cjc3N86cOcOCBQuIjIwkbdq0DB06lFy5ciXof/Xq1QkmInPkyMH169d58+YNnp6elC5dGoBChQpx69Ytnj9/ztq1axOkkRkxYgRNmjTh9u1bkDOu/4kTJrDvyBEOHTrEgQMHqFGjRqJrk4nDpMUX0Y6XLl06mjRpwrp162jatCnr1q1jyJAhNGnSBIDKlSvTrFkz3rx5Q+3atZk8ebJyb0NCQrCyskp0Dl1d3QTb9+7di6+vLzExMRgbG2NiYpJkQWYLC4tUFfnV09OjcuXKKbZTqVQULVqUmJgYjh07xoYNG/D19aVt27a0atVKCVrMnz+f8PBw5biiRYtStGhRAGW7TnS0sr9Y8eJo36vpcPDgQSAuPZKzszPp06cH4lYEnD59GgA3Nzel3/i+LSwsUrwOY2PjJGs6vM/MzAxjY2N27txJr169lJopdnZ2lC9fnnLlylG+fHmcnJxSFUBLTRABUGpPCCGEEEIIIYQQQoiPI4EE8V1MmjSJ4cOH4+/vz9u3b5WXv78/AQEBCba7u7uzb98+ypYty+7du3FwcGDMmDFYWFgwcuRIZs6cSWBgILNnz+bYsWMMGDCA1atXM27cOJydnRk4cCAzZ85k0qRJPHv2jCFDhlCjRg0aN26cbKFWtVpNyZIl2bt3Lz4+Pjg5OWFtbY2enh79+vVj8ODBrFu3jtq1ayspVmxsbBg4cCCTx4xR+smePTu9evVi1qxZDBw4kIoVKyab914kNOad+xjv0KFDrF27lmnTpjF48GCmTZtGhgwZqFy5MhkyZGD9+vWcPXuWevXqfXSAxt/fnydPnhATE4OBgQGZMmX6ZpPOz549Y+LEiWzatIkXL14o2wcMGMCmTZuYPn06+fLl+yLnunz5MhAXFHvX2bNnCQ8Px97ePlVBks/x/PlzBg8ezM6dOwFwdHRk0aJF/PnnnxJYE0IIIYQQQgghhPgBSSBBfDempqaYmpri4OCgbEsq9cj58+epW7cuV69epUSJEri7u5M3b1769euHpaUlvXr1Yvny5QQFBbF06VIWLlxI8eLF6devHxcvXqRPnz4MGzaMOXPmMGfOHM6cOcOePXs4fPgwNWvWpGHDhkmOz97enqxZs/LgwQP+++8/ZTVBxYoVKV68OOfOnWPmzJnMnj1bOaZGjRqcOHwY/F4DEB0Tw/Dhw1m/fj137twhX758jB49mhYtWvx2qYo+VtOmTdH/39P0sbGxbNmyhY0bN5ItWzb++usv7t+/z5IlSxg8eDCrVq2iQIECZM6c+ZPy+YeHh9OmTRuioqLQ1dXFwcEBHR0dtFotUVFR6Ovrf/EJ7sjISE6fPs3hw4e5evWqsj1NmjQ0btwYW1tbZs6ciaenJ5UqVaJTp05YWVkp9+RTREVFcf36dSBxICG+HsjXnMzXaDSsXLmS0aNHExQUhK6uLoMGDWLkyJEJVi0JIYQQQgghhBBCiB+L5HgQP7xixYpx7tw58uXLx4sXL6hQoQK7du0CoF27dqxevRpdXV22bt1KtWrVePr0Ka1bt+bYsWPY2dnx6tUrBg0axMmTJxk2bBgTJkwgd+7cREZGsnPnTjp27Mi1a9eIiYlJ8txqtZqnT5/i6+sLxKWhGTRoEDo6Onh4eCjpYOL3DR40SHk/d+5czM3NWbNmDenTp+fRo0d06NCBIkWKcOjQoa97435yS5YsYfXq1axevZp169axaNEiACZMmMCaNWuYO3cu5cqVIyIigh49evD48eNPOk9sbCw9evTg4sWLqNVqHBwc0NPTQ6PR4Ovry71797h16xZ37txJUE/gUz1//pyFCxfi6urKjBkzuHr1KiqVikqVKrFy5UrGjBmDp6cnS5YsoU2bNtSpUweNRqMEyZJKtZRaN27cICoqCmtr6wQBPPj/QEJqUjF9irt371KtWjX69u1LUFAQxYoVw9PTk4kTJ0oQQQghhBBCCCGEEOIHJ4EE8VNwcHDAw8ODGjVqKE+PX7p0CYBGjRqxbds2LC0tOX/+PJUrV+bRo0cUKFCAmTNnUrx4cWJiYliwYAGbN2+mYMGCTJ8+nbFjx+Lo6EhERAS3bt3izJkzic5rbm5O3rx5Abh586ayPUuWLLRo0QKABQsWJCimmiZNGuXf06ZO5dq1a1SqVIkbN24wadIkLC0t8fb2pnbt2ixbtuyr3K9fUceOHRk2bBgAffv2RavVMmPGDJycnPD392f+/Pmp6ufdgr7nzp3j2LFjSt0AW1tbJfVUcHAwoaGhQNyT9NHR0fj6+hL9Tg2CTzFx4kT27dtHaGgodnZ2uLq6smbNGrZs2YKtrS29e/fG09OTV69esWjRIho0aMCWLVvImjUrwcHBbNy48ZMDGnfv3gXiChwvXryY+/fvK+/v3bsHwLx585Sg2ZfUrVs3zp49i4mJCdOmTeP06dPkz5//i59HCCGEEEIIIYQQQnx5EkgQPw1zc3Pc3d2pV68esbGxdO/eXZnUrVq1KqdOnSJHjhz4+vpSvXp1Hj16hImJCSNHjqRly5YAbNiwgU2bNqFSqXB2dlZqFwDcvn07yQnaiIgIIC4V07vatWuHgYEBd+7c4cqVK0mOOTo6mk6dOhEdHY2xsTEDBw7k1q1bdOzYEYBevXqxZMmSL3ODfgOjRo0C4ib5w8LCMDExUe7l8+fPU9XHxIkTE7yfPn062bJlA+DVq1fKZ8DY2Bg9Pb0Eba2trdHV/byMcPHjHDp0KCtWrKBFixakTZsWgJw5cyr/hrgVLrly5aJSpUosXLgQAH19/U+us1GyZEmyZs1KTEwMx48fZ+DAgbi4uHDkyBE6deqEgYEBly5dolWrVkoQ5UuxtbUF4j7z3bt3l9ReQgghhBBCCCGEED8RCSSIn4quri6LFy/GysqKq1evMmfOHGVf1qxZOXjwYIJgwqtXr1CpVDRv3pw2bdoAccGE+LRCKpWKChUqkDZtWrRabaKAQExMDA8fPgTiCie/y9zcnOrVqwOwcOHCBKsS4llYWOLl5cXMmTOVbVZWVixcuJB+/foB0KNHDwkmpNK7E/vxQaT4AE9qJr7/+ecfli9fDkDv3r2xsrLi+vXrREREULBgQSWd0du3b9HT0yNbtmxkypQJR0dH8uTJQ7p06T6rfkBUVBTh4eEAODs7JyrmbG1tzeLFi5Vz1KpVC0dHRwD+/fdfAHLkyPHJwYwMGTIwZcoUxo8fT6lSpdDR0eHcuXN07tyZ3bt3065dO9KkSYO3tze9e/dO8jP9qSpUqACQIBWY+DH4+vri5eWV4utrrFQRQgghhBBCCCHEz0ECCeKnY2dnx9SpUwGYPHkyt2/fVvbZ29snCCaMHDmSly9fAtC4cWMlHdHSpUuVAAFA7ty5Abh16xZhYWHK9idPnhAdHY2JiQl2dnaJxvLuU9zHjx9PtH/KlMkAjB8/XilyC3EBjClTptC3b19AggmppVarlcn3jw0kvHjxQgnedOzYkaFDh7Jnzx6yZs3Ks2fPuH//PsbGxkDcqoFXr16hVqsxMzNLtDLhUwUGBgJxAbH4c72vQoUKjB8/npw5cyqpnCAuCAKQK1euTzp3fA2Q+FUOffv2ZenSpQwaNAhbW1tevnzJkiVLCA4ORkdHB3d3d+bNm/dJ50pK+fLlgbh0UvHBFPH9+fr64uTkhLOzc4ovJycnCSYIIYQQQgghhBC/KQkkiJ9S8+bNqVy5MpGRkfTo0QONRqPsezeY4Ofnx7Bhw5RgQrNmzShcuDBRUVFMmzZNSVtka2uLra0tsbGxXLt2TekrPod8tmzZknwSPV26dLi6ugJxueXfz5/ftGlTXFxciI6OpmPHjgn2xwcT+vfvD0gwIbXiJ/Xj76WJiQnw4UBCbGwsvXv3xt/fn7x58zJixAgg7ue6e/dunJ2dCQoKIiwsTAlMvH79mmfPniX4bH2ugIAAACwsLD64sqFr166cOXOGnDlzAvDy5UsuX74MoGxLLa1Wy7Jly2jVqpVSIyGetbU1Q4YM4fLlyyxZsoSCBQsSFRWl3OMJEyYoRZjf5e/vz4kTJz5qxYKjoyPp06cnKiqK//7776OuQXw9r1+/JiwsjPXr13Px4sVkX+vXrycsLIzXr19/7yELIYQQQgghhBDiO/i8ZN9CfCcajYY5c+ZQvHhxzpw5w7Jly+jUqZOy39bWln379lGhQgWeP3/O0KFDmTBhAra2tvTq1Yv+/fvj6+vL4sWL6dmzJ6GhoWTOnJlXr17h7e1NunTpUKvVyqoFCwsLnj9/jkqlUiZ04xUqVAhzc3MeP37MnDlzqFa+PCX+t+/5ixeMHj0aDw8PvLy8aNmyJTNmzEiQH37SpEnK9cQHRTp37pzomvX19b/Y/fuYCeDPSeXzObRabZLj1NPTIzIykqioKPz8/JSn20NCQnj16lWSfa1bt45Tp05hZGTEwoUL0dXVJTY2FgBLS0s2bdpEv3792LdvHyEhIZiamhISEkJgYCDR0dHY2dklqEug0Wh4+/ZtitdgbGzMo0ePlPd37twBwMjIKMF2SFyD41379u0DwMbGBn9/f/z9/ZNtq/u/6wJ48+YN/545owQDDh8+jKWlpbL/3LlzjB8/Xnmv1WoxMTEhNDQUHR0dYmNjadmyJbVq1WLt2rVKuyFDhrBlyxZGjRql1BiBuKLNUVFRyY6tZMmSuLm5cejQIWrWrJlsu3fH8376p+TafQ+pPa9Wq/1u36PUcnJyonDhwt97GEIIIYQQQgghhPhBSSBBfBOpnURLbTsTExNy587NxIkT6du3L2PGjKF+/fo4ODgobbJnz87x48dxcXHh7t27TJw4kYMHD5I5c2bs7OxwcXHh6NGjtGjRgkqVKqGjo8PTp0959uwZarVaWaGQJk0aGjRogEqlws/PDzMzswRjMTMzo1mzZixbtoy9e/dS/X+54OOvx87OjlmzZtG1a1d27dqFSqVSggnxE6VTp05FpVIxe/ZsevXqRZYsWahWrVqq7sWvSqVSJfl5iH9aPiYmBnt7eyVFUFRUFGnTpk2Uhujy5cusXr0aiFs1UqpUqSTP5+bmxsCBA1mwYAEhISFUrlyZ//77j5CQEB48eEDp0qXp1KkTtWvX5ty5c6kK7Dx48ICsWbMq7+MDU3Z2dkrtA4hLpfShugfnzp0DIH/+/BQrVuyD51RHRcGpuDoEZqamuLm5Kft8fX0pXbq08r579+7J9hMfaImNjWXv3r1otVrlsx8fTJsyZQqNGjXijz/+ACA8PPyDE//ly5fHzc2NM2fOfNGJ9eQ+Kz+KH318QgghhBBCCCGEECmR1Ebip9atWzdKlixJSEgIPXr0SPSE8Ps1E6pXr86jR48oX768kn++T58++Pn5oVKpqFy5MgAnT55U0q8UKlQoxUnAihUr4uDgQGhoKO7u7on2V65cmQULFqCrq8vOnTsZOHCgMlEL/5/mqGPHjgAMGzYswX7x/5JLbQSJ0xsFBwfTv39/YmNjadKkCa1bt062X7Vazbhx45g5cyZqtZojR46QN29eXFxc0NHR4fTp07Rt25a8efOyYcMG3rx589Fjfze1UWpFR0crgYT4Wh6pNWzYMGJiYihTpgwqlYr79+8rab4+RmxsLG3atEGj0RATE6Ok/IqOjqZ9+/aJUnolp2zZskBcICL+XgghhBBCCCGEEEKIH58EEsRPTUdHh2XLlqGvr8+BAweUIszvSiqY8PTpU4YOHUrZsmUJCQlh/fr1REdHU6BAAdKmTUtYWJhSxLlQoUIpjkOtVtOqVSuAJHPKA1SvXj3FYMLEiROxtLTk+vXrbNq06VNuyS8v/sn9+MlrPT09DAwMgLj0RvG0Wi2jRo3iyZMnZMyYkYULFyoBoYsXLzJ06FCuXr2aqP9OnTqxadMmjI2NOXfuHI8ePWLp0qUMHjwYW1tbXrx4wYYNG2jRogXjx4/n2rVrqU5xEz95/m56oZRcvnyZsLAwrK2tSZ8+faqPA/Dz8yNLlixMmzaNPHnyAP+/uuFjubu7M3HiRB49ekR0dDQGBgZYWlpy8eJFZsyYkao+MmTIQLZs2dBoNJw8efKTxiGEEEIIIYQQQgghvj0JJIifnpOTE5MnTwZg5MiRHwwmODo64uvrS9u2bdFqtaxcuRIbGxuePXvG7t27UavVVKpUSTnOzs4Oe3v7VI0jf/78FCxY8IMrCVIKJlhZWTFo0CAAxo0bR2RkZKrO/Tt5f0UCJF1w2c3Njf3796Ojo8OsWbOUVQBPnz6ldu3azJo1iyJFitC0aVPlCft4NWrUYP/+/dja2nL9+nU6duzIjh076Nu3L8uXLydPnjzExsZy4sQJ+vXrR+fOndm+fTsHDx5M8Dp9+nSC9z4+PsDHBRLOnDkDxNUXSE29gHcZGhkxa9YsTExMKFmyJABnz579qD7e9ddff7Fu3TogrnjynDlzABg7dizXr19PVR9lypQB4OjRo588DvH9+Pj44OXllewr/jMuhBBCCCGEEEKIX4sEEsQvoU+fPowdOxaAESNGKJOd77K3t2fHjh2YmZlx5swZunTpgp2dHcuWLQPg1KlT/PvvvxQpUgQrKysgdWmN3tW0adMU27wbTNi1axeNGzcmLCxM2d+jRw/Sp0/Pw4cPad68eYJ94v+DBosXL1YK+8YHCVauXElAQADjx49nxIgRAPTt25eCBQui1WrZvn07xYsX5/Xr19jY2KBSqdi5c2eSP7fChQtz4sQJunTpgpmZGXfv3mX48OHExMQwY8YMlixZQo0aNTAwMODBgwcsWbKEGTNmJHitXr06wfv4FRDxn6/UuHbtGhBXX+H169cfbBsbG5ugJsKI4cPJnj07AEWLFgVIVCw8teL7WbBgAQAODg64urri5OREdHQ0M2fOTFU/5cqVA+IKSEv6rp+HjY0NxsbGuLq64uzsnOzL1dUVY2NjbGxsvveQhRBCCCGEEEII8QVJsWXxyxgxYgTh4eFMmTKFLl264OjoSP78+RO0yZYtGytWrKBFixZs3rwZgGXLllGtWjUOHTrE7t27AXB1dcXT01OZ9Eyt+HRIKalevToLFy6kd+/e7N27lypVqrBz505sbW0xNjZm8eLFNGnShL1791K1alV27tz50WltflWDBw+mffv2rFu3Dm9vbxYuXEjXrl0ZNmwYO3fuZO/evcpqhSZNmtCpUydevnzJgAED2LNnDxC3imXHjh2EhIRQtGhRrly5wps3b5QUSfEyZMjA9OnTGT16NGXLluX+/ftKceccOXIwYMAAOnfuzKFDh7hy5UqiifHw8HCMjIwSbLOyslKeyk+Nhg0bcvfuXby9vbl16xZ16tShatWqiYozh4aGsmzZMu7fuAE5cwFQpUqVBGOBj6vP8C4XFxfmzZvH27dvAXj06BHTpk3Dx8cHlUpFgwYNUtXPn3/+iaWlJffv32f37t3Ur1//k8Yjvi0HBwd8fHxSDGZBXNDh3cL3QgghhBBCCCGE+PlJIEH8UsaNG4ePjw/u7u40atSI48ePkylTpgRtXFxcWLt2La1bt1aCCX/++SeAEkyoU6cOTZo0+ahzx8TEKIGI1KhWrRobNmygc+fOXLhwgXLlyrFnzx4cHR2pUaMGBw8epGHDhpw/f57y5ctz4MABcuTI8VFj+hW1atWKtGnT0qJFC7y8vGjYsCGLFi1i2bJl9OvXj+DgYDJmzMi4ceMoXbo027ZtY+rUqQQHB6Orq8uQIUMYOnSoEjTIkSMHd+/excvLS0n/8z5TU1NevXoFxBU8fncy1czMjEaNGtGoUaNExz148ICsWbN+1vXWqFGDfPnyMW3aNC5cuMCOHTs4f/48rVu3Jlu2bAC8ePGC+fPn8/LlS8zfC4a8OxaALFmyfNI4mjdvztKlS5V0W97e3gwfPhyA2bNnU7t2bfz8/FLsx9TUlLZt2zJnzhxmzpwpgYSfiIODgwQIhBBCCCGEEEKI39Rvn9pIctD/WtRqNatXr+aPP/7gxYsXtGjRQnkS+11169Zl7dq16OrqsnnzZrZs2ULVqlWpVq0aALt37+bff//9qHN7eHjw5s2bj8p/7+zszIkTJ8iaNSv379+nXLlySjHc0qVLc/z4cbJkycLdu3cpWbLkJxfK/dVUr16ds2fPkiVLFp49e0bz5s0JDw9n165dTJo0iT179pApUybatGnDyJEjCQ4OpkiRIpw7d44xY8YkWHng7OwMxBVgTs7Tp08JCQlBV1dXmbz/ljJmzMjcuXNp1KgRpqamPHnyhMmTJ7N582YuXrzIxIkTefnyJdbW1gwYMCDJPh4+fAh8WiBBrVZTtGhRZVXMu6mZBg4cSK9evT6qvw4dOqCvr8+ZM2eUGhBCCCGEEEIIIYQQ4sf12wYSjh07hpOTE4aGhlSrVo2QkJDvPSTxhZiZmbFjxw6sra3x8vKiR48eaLXaRO3eDSZ4eXmxcePGRMGE1atX4+/vn+I5NRoN7u7uQNwk98fIlSsXJ0+exNnZmTdv3lClShX27t0LxD39fvLkSQoVKsTr16+pWLHiR616+JXlypWLrVu3Urp0acLDw+nVqxfu7u7Uq1ePzZs3U7t2bf777z8MDQ0ZOnQoHh4e5MuXL1E/8YEET0/PZM918+ZNIK5OgL6+/te5oBSoVCoKFy7M+PHjKV68OFqtliNHjrB48WLCw8NxdHRk5MiRZMqYMcnjHz16BHxaIEFfXx+VSqUEEuJXxjRr1kwpdP4x7OzscHV1BUh1bQUhhBBCCCGEEEII8f38lqmN9u7dS4cOHZg+fTqmpqZ07tyZ5cuX07dv3+89tN9eUhP+ybX7UBHkbNmysWXLFqpXr86WLVvImzcvffr0SdTOxcWFVatW0aZNGy5evIhWq6Vp06aoVCol7723tzcVKlSgYsWKhIeH8+LFi0T9eHl58fz5c4yNjfnjjz/A6xIAz549g2RSzUBc3vqYmBggrlBwnz59OH78OI0bN2by5MlK2peVK1cyZMgQDh8+TP369Zk7dy6dOnVK8r68n+c/KSndvx+BVqtN8fNgYWHB33//zdSpU1mzZg3z5s1jw4YNvHnzBoASJUowYcIEHBwcUKvVSfYXH0jw8vIiMDAQHR2dRG3iCxRny5aNgIAAXrx4kaqAQlBQUKqClKnJOw8QGBiIoaEhTZs2pUCBAmzfvh1/f39KlCih1CgIDg5W2t++cwft/8Z59+5dIC4gcevWrVSdL56BgQH+/v6kSZMGgIoVK9KpUydcXFwICAhQ2r19+1Yphv0hMTEx9OrVi5UrV7Jr1y5u3LiRZNqu6OjoVN1njUaTqnYhISGp/tybmZmlqt2XpNFovkgbIYQQQgghhBBCiC/ttwskRERE0KZNG7Zt26bkxffw8ECtVuPv74+VlVWqJ5oiIyMTpEYKCgr6KmMWianV6hR/TpUqVWLWrFn06dOHMWPGULBgQWrUqJGoXbNmzQBo164dXl5e5MyZk61bt3Ljxg0GDx7MyZMnOXLkCD4+PnTo0IGiRYsmOLdGo2HevHkAtGzZklKlS8PadQBYWVqCoWGyY4wPIgBKkeXhw4ezc+dOBg8eTEBAAO3atcPExAQ3Nzd69erF6tWr6dWrF76+vowdOxa1+uMXFqlUqh8mkJDc9yg1Y8yRI4eSzqps2bJ069aNN2/eYGFhwcyZM2nfvj0qleqDAYmiRYuiVqt58uQJISEh2NnZJWpz//59APLkyYOhoSHZs2fH1NQ0xWvLkiVLqlMhpaY/c3NznJycAKhVqxY9e/bkyZMnZM+eXblX2ogIYleuAsDaygqtgQGBgYFKQCP+GiBuNcD7NUTiXblyhYkTJ2JhYcHy5ctRqVTY29sDcb9HkyqubG5unqpiztHR0WTIkIEaNWpw4MAB5s6dy4IFC1I8Ljmf+nl+8+YNly9fpkKFCkkGkFJzXiGEEEIIIYQQQojfwW+X2uj8+fO8fftWKaoaFRXF0aNHmTFjBmnSpMHBwQEPD49U9TV58mQsLCyUV3ITcuL76datGx06dECr1eLq6qqkqHlfnTp1EtRM6Ny5M3ny5GH//v1s2LABBwcHnj59yrhx4+jatWuCfk6fPs29e/cwNjZOsuDux9DV1WXKlCm0b98egEmTJjFz5ky0Wi16enosXryYUaNGATB9+nQqV67M7du3P+uc39uX+h516NCBU6dOMXr0aG7cuEGHDh1SNdFramqqTM7Hrzx4X/zPO1euXJ80tq/F0NCQHDlypHidvr6+QFxKIcMPBLbeFV+Po2jRosoke3yQJalVOZ+if//+AKxbty5VhZq/pHPnzlGiRAnq169Ply5diI2N/abnF0IIIYQQQgghhPiZ/HaBhBw5cmBkZISrqytbt26lcuXKpEmThmPHjnHv3j2yZ89O3bp1CQwMTLGvYcOGERgYqLweP378Da5AfAyVSsW8efMoXbo0QUFBNGjQgLdv3ybZ9v0CzJ07dyY6Opp69erh5eXF6NGjMTQ05OrVq7Rv355Jkybh7+/P6tWrAWjYsCHm5uafPWa1Ws3QoUMZOHAgAEuWLGHUqFHExsaiUqkYMWIEy5cvx8TEhDNnzlC0aFGmTZtGdHT0Z5/7e/iS36NixYoxduxYJZd/ahUpUgSIewr/fVqtVgnW5MyZ85PH9j09efIEiCvanBoajYYLFy4AULx4cWV7unTpgC8XSChbtizOzs5ERESwdOnSL9JnaqxcuZKaNWvy8uVLALZu3SrBBCGEEEIIIYQQQogP+O0CCenTp2fnzp1ERERw4MABvL292blzJzly5CBbtmxs2rSJt2/fcubMmRT7MjAwwNzcPMFL/Hj09fXZunUrDg4O3Llzh5YtWyY76f5+MKFYsWL8888/GBkZMWTIENavX0/VqlXRarXs2bOHBg0acOPGDfT19ZUUSV+CSqWiS5cuTJgwAbVazdatW3F1dSUiIgIAV1dXvLy8qFKlCpGRkYwePZoyZcp8sGDwj+pH+B7F10lIakXCy5cvCQoKQkdHh+zZs3+T8QQGBnLmzJkEKZ8+R3xwJj41UUpu3rxJYGAgJiYm5M2bV9n+pQMJKpWKfv36AbB48WLCwsK+SL/JiY6Opnfv3vTt25fo6Gjq16/P0qVL0dXVlWCCEEIIIYQQQgghxAf8doEEgKpVq7Jv3z5atGhBmjRpsLS0VPYZGBigVqvJnDnz9xug+OJsbW1xc3PD2NiYf/75hxIlSiQ76V63bl02bdpE2rRpuXPnDvXq1WPixIloNBpsbW0ZO3YsS5YsIVeuXMpEb926dbG2tv7i427atClz585FT0+PnTt3UrZsWW7cuAFA5syZ2b17NytWrMDa2porV65QtmxZZQWFSL2iRYsCcPXq1UT74osUZ86cOVWFrD/Xq1ev6NmzJ6NHj6Zly5asX7/+s+uvxKcN2rt3Lx07dmTx4sWcP38+yUCFRqNh165dQFyARVf3/0vpxAciHj9+/Nljio6OZseOHaxYsQKIKzid2rRyn8rd3V1ZQTR27FhWr15N8+bNlW1bt27l2LFjX3UMQgghhBBCCCGEED+j3zKQEM/a2po7d+6walVcYdKoqCi6d+9O48aNyZMnz3cenfjSChYsyObNm7G2tubq1auULl2awYMHJ/kUdM2aNbl69SpdunQB4moVtG7dmvDwcAAKFCjAypUrGTFiBA0aNKBDhw5fbdzVq1dnxYoV2Nracu3aNUqVKsWyZcvQarWoVCpatmzJpUuXaNmyJWq1mt27d1OkSBEJKHyE+NoHr169IjQ0NMG++M9HaooIf65Xr14xYMAAnj9/jkqlIiAggNWrV9OiRQsWLVrEq1evPqnfOnXqkCtXLlQqFQ8fPsTNzY3hw4czatQoxo0bx65du3jw4AEajYbt27dz+fJl9PT0qFOnToJ+smfPTq5cuQgPD2fZsmWfNJb79+8zduxYsmfPTvPmzfn3339RqVS4uLgkSKP0NRQqVAh9fX0gLjAUX1vCysoKiFu9JL/7hRBCCCGEEEIIIRL7rQMJzs7OdOjQgfbt2+Ps7Ey2bNkIDg5m5cqV33to4iupUaMG165do1mzZmg0GmbPnk2hQoU4fvx4orbm5ubMmjWLxYsXKysCevbsqUzmqtVqatWqxaBBg7CwsED15g3qhw9R/6+wLQAPHsC9e3GvzygmW6JECS5cuEDVqlWJiIigd+/eNG7cWEkxY2dnx4oVK7h06RKurq4JAgr169dXcsGLpFlaWiork96v0RATEwOgFBz+Wt4NItjb27N27VqGDx9O9uzZiYiIYMeOHXTp0oWBAwfi4+OT6HjtKz+0d++hvX9f2ab75Al6vo8pbGHJkvHj2b59O6NGjaJGjRrY2toSGxvL9evX2bhxI0OGDKFTp05s374dgC5duuDg4JDgHGq1WqndsXjx4lSvSoiOjmbnzp3UqVOH/PnzM2fOHF6+fEm6dOkYOnQoPj4+7NixI8HqsK8he/bsDBgwAIAhQ4YQEBAAxBX8BmjdujUZMmT4qmMQQgghhBBCCCGE+Bn9koGEGzdu0KNHD5o3b865c+c+2Pbvv//Gzc2NatWqsWDBAvbu3YuxsfE3Gqn4HmxtbVm3bh27du0iY8aM3L9/n7p169K1a1f8/f0TtW/dujX79+/HxsaGO3fu0L59e7y9vRO0Ub15g9nQYZj9NRaziZP+f8fwETBwUNyrR8/PCibY2dmxa9cupk+fjr6+Pnv37qVgwYIsX74cjUYDxBUDXrt2Ld7e3kpAwd3dnfLly0sx8BRkzZoVAN93A0H8fyBBT08v2WPv3LnDggULePr06Sed28/PL0EQYebMmdjb2/Pnn3+yZMkSpk6dSuHChdFoNOzevZu6devSoUMHzp49i1arRfvKj9iuXYnt1w/NkKFKv2lnzSbt1KmknToV27HjsI6NpXz58gwYMIANGzYwdOhQJZBqaGhIcHAwANWqVaNcuXJJjjV+dUNgYKCSlig5AQEBjBkzhly5ctGqVSuOHTuGSqWiUqVKbN26lbt37zJ27Fjl3n8L/fv3J0eOHLx8+ZKxY8fi4eHBqVOn0NfXp3///t9sHEIIIYQQQgghhBA/k18ukODu7k65cuUIDg7m3r17VKpUKdl0IP7+/qhUKho0aMCkSZOoV6+ekupC/PpcXFy4cuUK3bp1A2DdunU4OzuzY8cOtFptgralSpXi5MmTZMuWjTdv3tC9e3cOHjyo7FcFB6NKpoCzIjoaPjOvvFqtplevXpw6dYrChQsTEBBAz549qVSpklI7AeJS9axdu5YrV66QOXNmbt++Tbly5bh3795nnf9XllIg4d1aAe/vHzhwIKtWraJx48asWbMm2WLeSXn69CkjR45MEESwtbVV9qtUKpydnZk2bRqzZs3CxcUFtVqNh4cHbdq0oUmTJrx5+CDu8/UBqpgY1CH/n7ZJpVKRNm1aqlevzpAhQ1i5ciVjx46ld+/etG3bNtl+dHR06NOnDwDLli1T0n0lpXv37sycOZNXr15hZ2fHoEGDuHbtGtu3b6du3bofDM58LQYGBsydOxeAlStXKtfSunVrMmbM+M3HI4QQQgghhBBCCPEzSHpm7Cf1/PlzunTpwsGDBylSpAgRERE4ODjw8OHDBBNzEJdGpFChQnTv3p0RI0Z80XFowsLQJDPpKD7s/Qn8D7VLTdAnNibmg+1MdXWZM2UK9WvUYMDAgdy9c4fOrVvjVr06U6ZMUYrLAmSwsWHBjBnMmDmTs2fOMG3cOHxv36Zd+/boRUSkatzhQUFo31n18OTuXd48e6a8f/36NZcvX6aIszOW/8vbDnGpYQLfSU+kAhbOmsUONzeWr1jB5f/+o3zx4jRv3pxRo0ZhZGQEgL2VFbu3bqV5ixY8uH+fKmXLsnHTJnI6OqY6579a/WXijZokalGk1D5V36NUjC81n6scGTNipFLx8OZNAt8JPob5+2OkUqGv1SrbfS5fVvq8cP48b54+xVitRhsVxd/z57N3+3YaN25MtuzZCX3zJtlzvn79mpEjRxL06hXWJiY4583Ljk2bkm2fK2dOalaqRPGCBTlx4gQXzp/n9tWrzJsxg9EpXiFsWrOGcm1akzZtWgBiQ0MJe+fzmNnOjsx2dkQGBiY4LiY2lgfvpFMqlCcPWe3tefHiBUvmzKFevXpAXHDCNGdOAALevuX4wYMYqVQsWLCQuvX+P3AQHRaGJhXj1URHExsdk2K7WE0s6v/VPviQ6JAQVCoVJQoVok3TpmzdupWn9+5hbmBAny5diA4JAeI+L7Gp/Nx/yeBzfNDqg23+N0YhhBBCCCGEEEKIb0mlTe3M7U9gypQphIeHM3bsWACCg4PJkSMHlSpV4vHjxzRp0oRevXop7QcOHIinpyf//PPPF3kyNigoCAsLC87ncMT0K+dTF+JnExIbS7G7dwgMDMTc3DzZdvI9EiJ5H/s9Sqmdl5cXzs7OXLx4kcKFC3+NIX8STVgYtwo7A5DL6yLq3yTl4I/68/jVpPb7IYQQQgghhBDi//1Sj82bmZnRqFEjIO7JzlatWmFra0uJEiWwsbGhb9++REdHK3mwZ8yYQWRk5HdJryGEEEIIIYQQQgghhBBC/Ax+qUBCjx49lH8fOXKEqKgozp07pxRPVqvVLF26NEFBTQMDgy8+DkePk/KE2w/iY1IlvSs2Npb1GzYwftw4pcZGhQoVGD9+PH/88YfS7tLly8ycOZNHJ06wxj59iuexWrIYvf+lfgHw8PDA0MCA0LAwevToQVBgIB06diRL5szs2LGDS5cuKW2LlyhBy5YtyZ8/f7L9BwYE4LZjB+vWrkWr1WJlZcWIkSNp0rgxKpWKoOBg2rRpg+eFC5iYmrJ1yxbKli37wTF/qdRGQUFB8E6qqJR86+/RzZs3KVKkCKZmZnhfv66krFmzZg2jRo2iposLSxYvBmDb9u3o6ekxbuxYXr9+Tb369alSuXKC/m7fvs2GDRt4/fo1AM5FitCta1d0dHQYNmwYL168IF26dEyePJmRI0ei+7+AplarJTAgkNDQEMzNzTE1M1PGkjVLFqpVq5bgPG/8/dm/YCHrMmVK8RrTr17NqadPGT9+HFevXgXA2tqart260bRpUwySSQ/04OFDrCwtE21fvmIFy//+G8ecOVm7Zg06Ojrkzp2bgIAAsmfPTnR0NJ6enuR85zMfLzWfq9R+fzUaTepSncXGpqqdVqtNtibG+75kaqP4oukfEhQUBBkyfLFzCiGEEEIIIYQQQqTGLxVIeFf16tWpVq1agkmefPnyceTIka9+brWx8W+ThuFH96mBBDXQrmtXGrZowZQpU5g9ezYH/v2XQ+XK0alTJ8aOHYu1tTXOpUqxsVQp3ly4wNv2HVI8j9rQEPX/6hcAaPX10RoYsGn9el4GBJApUyaq1q6Nrq4ueQoX5t69e2zbtg0PDw+Onz3L8bNnyZ8/P127dsXZ2TlR/yZp0jBp5kzqN2vGgAED8Pb2pkf//ly6cYMpU6ZgYWTEhu3badWqFSdOnKBmgwa4ublRvXr15Mf8hQIJ6lTkf0/Q/ht/j7Lkzk24Vkt4UBCBkZFY/a9GRQQQrtWi1ddH9b+fnUZPjzMXL/LYzw9TU1PKVKqE5r1J+Bx//EGPAQPw8fFh48aNnLpwgVMXLij77e3tmThzJja2tkQAesR9Dv39/eMmi4HgN28wCAnBxsYGfX19otRqYt9bQWVpZ0eu/PngbUCK17h64wa6TJtG5VoubN++nZEjR/Lw4UNGTZjA0tWrGTp0KHXq1El8oIEBGBom2tyoZUv+XruWq7ducfbSJcqUKYPa2JjdW7cSFBVFvnz5yF2wYJJj+ZKBBFIZSNB+RCBB/R0CCaQikPCx3yMhhBBCCCGEEEKIL+HLzBD+oN6d4ImNjWX16tW0adPmO45I/GzMzc2ZNGkS3t7eNGjQAI1Gw9KlSylZsiRXrlxR2pmYmKSqv9u3byfaptFo2Lt3LwCNGjVK8CR09uzZGTp0KOPHjyddunQAXL16ld69exPxgQLPRYsW5ejRo4wYMQKVSsXy5cvZsmWLMtaNGzfi4uJCREQETZs25e3bt6ka/6/M2NgYOzs7AE6cOKFs1/lfnQZPT08ePXqkbD99+jQADg4OyaZHCw8PJ3369OTOnTvRvpkzZyYqAh8dHa0EEeLPGxkZqaxqSE7x4sU/uD/e0mXL2Lp1K2q1miZNmnD48GEmT56Mra0tT548oWfPnjx8+DBVfQFYWlri4uICwOHDh5XtO3fuBFBSzQkhhBBCCCGEEEKIn9svHUiIFxgYSIsWLdDX16dfv37fezjiJ5QtWza2bdvG4cOHyZYtGw8ePKBcuXLK5LyOlRWqZNLCxIvUaBgycWKiAIBarabg/57aPnz4cIL0Ji9evGDFihVMnjyZFy9eAKCvr0+TJk3QT+F8enp69O/fn6FDhwIwa9YsYmNjATA0NMTNzY0//viDkJAQVq9enep78Str0aIFAMOHD8ff3x+A2rVrkyFDBnx9falRo4YSQCpdujQqlYobN26wePFioqKiiImJ4datW+zYsYNx48YxduxYJk+ezLVr1xKcZ8KECYmCCBD3M4v/ucb/rFQqFWZmZh8cd6S+PpEpPM0eqdEQEBtLiRIlEpyvZs2aSv+5c+cmw0emzbl58yYAWbJkAeLq05w8eRKAGjVqfFRfQgghhBBCCCGEEOLH9MsHEsaOHUuJEiVwdHTkwIEDqc57LURSypUrx5kzZ6hatSrh4eG0atWKIUOGEGpkhMPePWTcspkMa1Yr7a2WLCbN6lXozp1Dq6Agzty+zaRJkxL1261bNwwNDfH29ubAgQN4eXkxduxYOnTogJubG6Ghodjb29OzZ0/27NlDnz59Up1yqEuXLlhaWnLv3j3c3d2V7Xp6evTs2ROAhQsXKhPXv7OBAweSK1cuXr16xbBhwwBIly4dBw8eJG/evLx69Yo6derg4+ND6dKl6dq1K7q6uly6dInRo0fTp08fpk+fzv79+/H19QXiVpU0bdqUXr16AXFBnCJFiiR5fpVKhb29PUb/S6FkampKxowZUwwkeD1+TM0H9+kWFsrZ2rWV7S0fPWSmfTrONWlMN62GDYcPkzVrVmV/REQE7du35969e6RPn561a9d+VPF5Hx8fvL290dXVVVIieXl5ERQUhKWlpRIgE0IIIYQQQgghhBA/t18+kDBo0CC8vb2ZMGFCik9wC5Ea1tbWuLu7M2jQIABmz55NxowZadG7N/t8fND878lsAL2cOdHLlQubYsUYPnsWEDdp/99//yXo09bWltatWyv7R44cyblz59BqtRQuXJg+ffrg5uZGq1atsEyi6O2HmJmZ0bVrVyAunc67Kx5atmyJlZUVDx48YN++fR97K345BgYGzJ8/H7VajZubG/v37wfi6hns2bOHsmXLEhoayuLFizlz5gzOzs70798fY2NjXr9+TWRkJKamphQvXpz27dszevRoli5dSqdOnZRURU5OTh8MaKrVauzs7MicOTNp06ZNVfDTx8eH5zEx6OXKRbBNGmX7zchI1pw6RY0ePTh54wZlypRR9sXGxtKnTx8uXryIubk5a9euxc7OjoCAgFTfLzc3NwAqVaqEtbU1AMePHwfigm7x1yyEEEIIIYQQQgghfm6/fCDB2Nj4ixWLFSKejo4OEydOZMuWLeTNm5eoqCjc3d1p3rw5OXPmVNrFvlMYtXr16jRv3hytVkvPnj0JDQ1N0Gft2rWVY42NjalTpw7Lli1jwoQJFC5c+LMmZTt37oyZmRk3b95MEDAwNjamY8eOACxYsOCT+/+VFC5cWFk9MHDgQCXFkbm5OVu2bKFhw4ZoNBpWrlzJvn37cHR0ZMSIETRr1oyRI0cya9YsOnXqRKlSpTA3N1f6vX79OgB//PFHimNQqVSp/r0VGxvLrVu3AMiTJ0+CfXny5CEmJoY9e/Yk2K7VaunXrx+HDx/GwMCAFStWkDlzZtq1a0ehQoVYv359iucNCwtTAi0NGzZUtv/7778AVKxYMVXjF0Ik5uPjg5eX1wdf8auehBBCCCGEEEKIb0Hy/IgfilarTXXbd4tpf4nzpqY/rVabYIz16tWjXr16XL9+nc2bN7Nlyxb8Hj8G+7j9RYsWpXq9ejRq1AhnZ2cmTZrE8ePHuX//PuPHj6ds2bJERkYq/Q0aNIibN2+SN29eJb1NYGAgAQEBSo2ED4mKikowef2uZs2a8ffffzN16lQKFy6sFIju2LEjM2fO5OjRo3h6elKgQIEE12tgYJDieeHL/jy+NE0K9QPixcbGolarGThwIAcPHuTWrVsMGzaMJUuWAHHpoBYtWsTbt285duwYO3fu5M2bNzRt2pTy5csrfcSnifL391fqI3h5eQFxxa7fr5kQGxubqvun0Wh49epVgm2+vr5ERERgZGSEgYEBfn5+yr6qVaty0dubjRs3JqhXsGDBApYsWYJKpaJu3bpcu3aNUaNGKfUORowYwZUrV5TAhIWFBUWLFk1w3j179hAaGkrGjBnJkycPISEhaLVapQh1+fLlP+r7nNz1prZdagIvqf2eC/G92NjYYGxsjKura4ptjY2N8fHxwcHB4RuMTAghhBBCCCHE704CCeKXltpJw9Q+/a2vr59kn4UKFaJQoUJMnjyZM//+Cz3jnmj38/Nj2bJlLFu2jNy5c7Nr1y5WrVpF9erVWbZsGeXKlUtQ/BagcuXKifp/+PAhadKkSbT9fW/evMHQ0DDJfR07dmTDhg34+Phw9uxZcuXKBcQVya1fvz5ubm64urri4eGBlZVViuf6FVlbWyufhdWrV1O6dGnc3NxwdHRk7NixSpqhnTt3smTJEoYOHcrJkycxNzdnxYoVSvAn3vbt29HT08Pf3x9/f3/UajWFCxdO1G7UqFEUKlQoxfHdvn07UTHkZcuWAXHFn6tVq4YqMhL2xq06qVGzJpNnz8bDw4PAwECsrKzYtm0bU6ZMAaBkyZLY2tqyZcsWbt26hY6ODhkzZuTRo0ds376dunXrKudzdHRMcN5Dhw4B0L59e2Ulzfnz5wkNDSVNmjTkz5//s1eDfcz3NzXnUqvVP3QgIbXXIH5dDg4O+Pj48Pr16w+28/HxwdXVldevX0sgQQghhBBCCCHENyEzEkJ8QWq1mlIlSyrvt2/fTsuWLTExMeHmzZu0atWKP//8k86dOwMwZMgQgoODv8nYrK2tadGiBRBXh+Hdp8XnzJmDg4MDd+7coVWrVlJ4GShWrBhjx44FYMqUKVSpUoVnz54p+7t168batWsxMDBg79691K5dW0mD9L741EOZM2dOFET4XOfPnwegePHiifZlz5YNJycnYmJiOHToEMePH2fAgAHK+P/44w+OHz/OrVu3UKvV1KhRg1q1apEtWzZiY2PZu3dvkhOa165d49KlS+jp6dG0aVNl+6lTpwCoUKGCTHgL8YkcHBwoXLjwB19OTk7fe5hCCCGEEEIIIX4zMtMjxFdUrVo11q1bx82bN7G0tOTChQtMnTqV6dOnkyVLFp4+fcqkSZO+2Xg6dOiAoaEhV69e5ciRI8p2W1tbtm/fjpGREf/88w/Dhw//ZmP6kQ0fPpyNGzdiZmbGyZMnKVy4MIcPH1b2161bF3d3dywtLTl//jw1a9ZMMgXV7du3ARLUz/iQJ0+e4OHhwdWrV3n27BkRERFJtnv58iUPHz5ErVZTpEiRJNvUrl0bgOXLl9OpUydiYmKoV68ew4cP57///uP69euoVCqqVKlC1qxZUavVVKtWjfTp0xMVFcXu3bt5+/Ztgj7jayi4uLiQNm1aZXt8WqMKFSqk6jqFEEIIIYQQQgghxM9BAglCfAMZMmRg/vz5AIwbN4779++zatUqADZv3syJEye+yThsbGyUJ8gnTZqUYOVBgQIFWLFiBRC3QiE1BXd/B02bNuXChQsULFgQPz8/atasyfjx45UVHaVKleLQoUOkS5eOGzduUK1atURFUOMDCfHppJJy69YtZs+eTZMmTWjcuDGDBw+mS5cuNGzYkIoVK1K5cmWGDBlC//79mTBhAosWLWLp0qVAXFFlMzOzJPutVasWADdu3CA0NJQyZcowe/Zs5syZw9WrVwH4888/EwQ5dHV1cXFxIU2aNISGhrJu3TplZYJWq+XgwYPKvYkXGRnJhQsXACm0LIQQQgghhBBCCPGrkUCCEN9IixYtqF+/PtHR0TRo0ABHR0fatm0LQL9+/bhz5843GUfHjh3R09PjzJkzFCpUiE2bNhETEwNAw4YNGTZsGAA9e/bk6dOn32RMPzpHR0dOnz5N165d0Wq1zJgxg3///VfZ7+TkxKFDh8icOTMPHjxg4sSJyr7Q0FAeP36s9JOUmJgYunXrxtatW3n8+DE6Ojo4OjqSPn169PX1lX5evHjB1atXOX78ODt27ODkyZNA0mmN4uXIkUNJg5InTx6WL19OYGAgM2fOBOKCG/FFld9laGhIlSpVgLjaGytWrCA2NpZRo0bx/PlzjIyMKF26tNL+wYMHREREoKOjg62tbco3VQghhBBCCCGEEEL8NCSQIMQ3olKpWLJkCdmyZePBgwdUrVqVjh07kj9/ft6+fUurVq2UCeevKV26dIwfPx5LS0tu3bpF27ZtyZ8/P+vWrUOr1TJ69GhKlSpFeHh4ggnx352hoSELFy5UimG/Wy8BIGvWrAwePBiIm3iPd/v2bbRaLenSpcPS0jLJvsPCwggPD0elUjF58mQOHTrE2rVrcXNz4/jx4xw5coTNmzczbNgwRowYQbdu3WjatClVqlShWrVqyqqD5EycOJGWLVuyfv16zM3NsbGxUa7j1q1bnD59Go1Gk+CYN2/esH//fuXay5UrR6tWrZTiziNGjEhQ2Dtbtmxkz56d2NhYOnXqlKAGhxBCCCGEEEIIIYT4uel+7wEI8TtJmzYt//zzD+XKlePGjRt07dqVRYsW0bFjR27fvo2rqytbt27Fzs7uq46jQYMGdOjQgSVLljBnzhzu3btHx44duXPnDuPGjWPixIlUrFiRNWvWMGjQoA+m5PndxK8QSEpUVBQABgYGyjYfHx8AcufOnexx4eHhABgbGyeqL6BSqTAxMcHExITIyEgyZMjw0WMuUaIEJUqUUN6r1WpWrVpF7dq1uXz5Ml5eXvj5+VGtWjWMjIx4+PAhBw8eJDo6GgsLC+rWrcvQoUPx8fHByMiI+fPnU6dOnQTn0NfXZ9myZdSoUQN3d3cWL15M9+7dP3qsQojUi//98iE2NjY4ODh8g9EIIYQQQgghhPiVSSBBiG8sa9asHD58mPLly3P9+nUGDhzI33//TatWrfD19aV58+bUqlULe3t70qdPj729PVFRUaRJk+aLjsPc3JzBgwfTvXt35s6dy7hx45g6dSpOTk40b94cFxcX9u3bx8iRI9m2bdsXPfevKjIyEkgYSLh58yaQukCCqanpVxxdQjo6OhQvXpx06dJx5MgRHj9+zNatW3F0dMTLywutVkuGDBkoVKgQbm5uhIaGYmdnx7p16yhYsGCSfebLl48pU6bQv39/BgwYQNmyZcmXL983uyYhfhc2NjYYGxvj6uqaYltjY2N8fHwkmCCEEEIIIYQQ4rNIIEGI78DJyYmDBw9SoUIFzp8/z5gxY1i5ciWurq48ePBAKcz8LhMTE9KlS4ednR12dnbY29uTOXNm5WViYvJJYzE1NWXEiBGEh4czffp0unTpQvbs2Rk3bhz79+/Hzc2NCxcuULRo0c+97F/e+4GEsLAwHj58CKDUKUhKREQEwCf/DD+Ho6MjVlZW7Nu3j6CgIC5evAjE1VOwt7dn//79aDQa8uXLx7p160ifPv0H++vTpw9Hjx5l3759NGvWjAsXLmBsbPwtLkWI34aDgwM+Pj5KEfTk+Pj44OrqyuvXryWQIIQQQgghhBDis0ggQQhIdT731LR7t41Wq032mEKFCrFixQratGnD8ePHMTY2ZuvWrezevZunT5/y4sULnj9/zosXLwgICCA0NJR79+5x7969JPtLmzYt9vb25MqVi8yZM5M1a1ayZMlChgwZEqXjuXz5MpcuXUqwrXDhwhQpUgRPT09q167NxIkTKV++PMePH2fYsGEcPnwYlUqV7DWr1R8uufIz5MyPiYlJ9hrjxV9HTEyMksooXlhYGAC6urpERUWxb98+tFotZmZmeHp6JttncHAwAEZGRsq/kxIZGYlGoyE2NpbTp0+zefNmHj16RKVKlXB1dcXW1hbVO7UO3rx5A+/UMXhfVFQUOjo6WFlZ0ahRI44ePcrjx48pUaIE4eHhHD16FIhbTbFr1y5MTEwS1VJ4V0REBIaGhsyfPx9PT098fHzo1asXc+fOTdQ2NasvtFptij+P+Hap/Xyltr/USk1/QnwNDg4OEhwQQgghhBBCCPHNSCBB/FC+16Tcx5w3pbbv7lepVB9s36hRI8zNzalTpw779+8nXbp0/P3334mOCQkJ4enTpzx58oTHjx/z+PFjHj58yN27d7l16xavXr3Cz88PPz8/rl69muBYU1NTDhw4QJEiRZRtZ8+eRVf3/7/+AQEB3L17l3bt2vHixQuePHnCtGnT6N69O2fOnOHYsWMcOXKEqlWrpuoe/WhSCnLE09HRSbFN/M/GwMAg0QqC+AloU1NTTExMuHv3LgDW1taEhoYm22dISAgQVwj7jz/+SLZd9uzZ+eeff5gxY4bSN8DBgwf5999/6datG4N69SLwf9stLCxQGRkl29/IkSMT1eN48+YNvXv3xt3dHYCBAwcyatQoZeI/JiaGVatWcerUKQYNGpRgvPHXYWNjw99//03dunVZtWoV+fLlo2PHjsmOIzlqtTpV302ZzBdCCCGEEEIIIYT4uiSQIMR3Vq1aNTZu3EjTpk1ZuXIlRkZGtGzZEiMjIwwNDTEyMsLAwID06dOTPXv2BAGAeAEBAdy5c4fr169z9+5d7ty5w927d7l9+zYhISFMnDiRnTt3Jnn+p0+fMm3aNAICAihfvjx9+vRh3LhxPHr0iO3bt9O1a1fmzZvHsGHDqFy5cqon5X9H76c2ik87kjZt2g8eF7+yIbmn9ENCQli/fj1Lly7l5cuXQFxwonv37pQqVYrJkyfj4eHBnDlz2LByJSfsP5x+KCnR0dFcunSJ/v37c/nyZfT09Fi4cCEtWrRQnvg/cOAAQ4YM4caNGwBs2bKF0aNHM2jQIPT09BL0V6FCBQYPHszUqVPp378/KpWKDh06fPS4hBBCCCGEEEIIIcT3J4EEIX4ADRs2ZPny5bRv356FCxeycOHCZNvq6OhgZGSEtbU1lStXpk6dOlSpUoWiRYuSL1++BE9n37t3j/z583Pw4EGuXbuWqPDto0ePmD59upJO58SJE2TMmJHevXszZcoULly4QNmyZTEzM+PSpUts3bqVZs2afZ2b8AuIr3VgYGBAcHAwAQEBQNwT+h8SH4AwNzdPsP3NmzesWLGC1atXK31lyJCBvn370qFDByXwUKlSJQ4fPszIkSO5ffUq2Mcdv3bdOpq1aZOg+PO7Y71w4QI+Pj6cPn2a8+fPK6sm0qRJw6ZNmyhVqhQAV69eZfTo0UqqI2trawoUKMC///7LqFGj2LlzJytXriRr1qwJzjF8+HBCQ0NZsGAB/fr1A5BgghBCCCGEEEIIIcRPSB4tFuIH0bZtW1auXEn+/PnJmjUr6dKlw9LSMtEkcGxsLCEhIfj6+rJy5Urq1auHjY0N9evXZ8uWLQnyu2fPnp369esDMGvWrAT93Lt3jylTphAcHEzWrFmpV68eABs3biQiIoJ27dopx1WrVg2AUaNGJaoLIP5f/L0xMDDg7NmzaLVajI2NUyw2HH+cmZkZAH5+fowaNYqiRYsyZ84cAgICyJYtGzNmzODmzZv06dOHhw8f0rhxY/LmzcukSZOoWLEi586dY8WKFUq/kyZOpGLFikqaotu3bzNlyhTq169P3rx5admyJRMmTODff/8lNDQUKysr6taty/HjxylVqhRv3ryha9eulClThqNHj6Kvr8+AAQO4c+cO//zzD2vWrMHKygovLy+KFi3K9OnTE3w+VCoVEydOpGfPngD069ePlStXfrkbLoQQQgghhBBCCCG+CVmRIMQPpG3btrRt2zbR9tjYWKKioggPDyciIoLw8HDu37/P7t272b17N48fP8bd3R13d3fMzc2pWbOmcuyAAQNwc3Nj27ZtLF68GMP/Fd9dvXo1YWFhODo60r9/f4yMjHjz5g0eHh5s2LCBqVOn8uzZM/bv38+RI0ewtbXl3r177Nix47delRC/6iCpFE+vXr0CwNDQUClmraurS2xsbLL1F0JCQrh//z4AVlZWXLt2jTZt2vDixQsA8ufPT69evahevToqlQoDAwPevHlDpUqVePv2LQBjx47FwsKCnj170qBBA+5PnQaAra0tj548oVevXuTJk4fmzZvj5+eXaAxFixalW7du1K1bN0HgatKkSaxfvx6Apk2bMnHixASrDlxdXalUqRLdunVjz549TJgwgW3btjFjxgzKly8P/H8w4dKlS5w+fZrBgwfTrFmzVBVbFkJ8GT4+Pim2sbGxkeLNQgghhBBCCCGSJSsShPgJqNVqJZ1RfK2EKlWqMH/+fB4+fMjFixfJmDEjQIIVCRCXCgdAo9EkqK8Qn24nT548GBsbo1KplElia2trAJo3b46pqSkBAQFKcOLQoUNf92J/YNHR0Zw/fx4gUVHkFy9eKPemQoUK1KpVC11dXYKCgjh37hwajSZRf35+fhw/fpzAwEAyZsxImjRpqFevHi9evMDR0ZHNmzdz4MABXFxcEgQipk6dytu3b3FycqJ27doAPH/+PFH/w4YPB+JSJtnb29OtWzfy5MmTqM7GhQsXaN++Pfb29lSpUkWpgfDnn38qberUqZModRGAvb09O3fuZO3atdjY2HDr1i1q165N27ZtefbsGZGRkfTu3ZvTp08DcQXGU1qhIYT4MmxsbDA2NsbV1RVnZ+cPvpycnPD19f3eQxZCCCGEEEII8YOSFQlC/ORUKhWFChUiNjYWiJvYfVd8cd60adMmmEB2dnbGy8uLixcv0qBBA+D/n1rNnTs3EBfAKFSoEB4eHkrB4MOHD6PVahPUYvhdeHp6EhISgrW1Nfnz50+wb/Xq1cTExFC8eHEKFCgAQMmSJTl9+jQvXrzg4sWLFClSRLlvjx49wsvLC61Wi52dHc2aNWPAgAFotVrKlSvH0qVLsbCwSDSGhw8fsmjRIgCmT5+Oh4cHe/bsISwsLFHb1atWAdCmTRtMTU3p2LEjHTt2JDIykrt373L16lUePXrE1atXuXbtGoGBgZw9e5ZatWpx8OBBXFxc6N+/P7NmzaJjx444OTkp1/YulUpFy5YtKV++PBMnTmT58uXs2LGDw4cP4+DgwI0bN1CpVPz111/07dv3s34GQojUc3BwwMfHRyn8nhwfHx9cXV3x8PDAycnpg21l5YIQQgghhBBC/J4kkCDELyA2NlYJGCQXSLCzs0uwvWDBgujo6PDkyRNevHiBra0tN2/eBEgwkVS4cGE8PDwICwvD0NCQ58+f07VrV9KnT4+lpSWWlpZYWFhgZWWlvLe0tMTU1DRB+p/3V0r8jOKLDVeoUCHBtcXExLB8+XIAunTpomy3sbGhePHi/Pfffzx+/Bh9fX3y58/PjRs3uHXrFgDp06fHysqKGTNmANCqVSsmTJiAnp5ekmP466+/iIqK4s8//6RKlSp4eXkBKIWS33XlyhUMDAyUehfxDAwMyJs3Lzlz5lQ+F1qtlvv37+Pq6sq1a9dwcXHh0KFDjBkzhqtXr3LkyBEaNGjA+fPnSZMmTZJjs7S0ZMaMGbRu3ZoBAwZw7tw5bty4gaWlJStXrqRy5cop32QhxBfl4OCQ4sT/uysXUmJsbIyPj48EE4QQQgghhBDiNyOBBCF+Aa9evUKj0aBWq7G1tU2wL7lAgqmpKblz58bb25uLFy+SP39+goOD0dfXJ1u2bEq7woULA3D16lUqVarEvn37+Pvvv7/yFf2Yjh07BqDk/4+3f/9+nj59qhS9fpe9vT3Ozs54enpy7949/Pz8CAoKAiBHjhwEBgbi7e2NSqVi9OjRdO7cOdnVHt7e3mzatAmIq1+gUqkwMTEBSHJFAkCTJk2UNFYQFzB4+fIlvr6+PHjwAH9/fx4+fMiDBw948OABOjo6ZMyYkSdPnijBhBUrVlCxYkXu379P8+bN2b9/f6L0SO/Knz8/hw4dYvPmzZw6dYqBAwcm+EwJIX4sH7ty4fXr1xJIEEIIIYQQQojfjAQShPiKtFrtB5/Ej5/8T0lK7Z49ewbEpS+KLwYcLz7ndZo0aZSn1l+/fo1KpSJnzpx4e3vz33//KcdlyZKFN2/eAKCnp0fp0qUBuHTpEufPn6dEiRK8ffuWwMBA5fX27VuCgoIICAggICCAqKioFK/pR5RUHYN4ISEh/Pfff0DcPYqvIxAdHc3o0aMBcHFxUQonQ1xhZgMDAzJmzEhUVBRXr14lKCgIlUpFnjx58PX1JTg4GF1dXRYuXEiVKlWIjIxMdgwTJkxAq9XSuHFjChYsiEajwcjICIhbkaDRaNBo/v/zplKpcHJyYv78+Rw+fJhXr17x5s2bFH8+arUaQ0NDHj9+TJEiRWjSpAmbNm2iYsWKHD16lCFDhjBx4sREx0VGRirFvNVqNS1atKBFixaJ2qX0vXjX75hCS4jvITUrF74GX1/fFAMYICmVhBBCCCGEEOJ7k0CCEKR+sjI17d5to1KpPniMWq1OVZ8ptYsvtGtvb4++vn6Cff7+/gCkS5dO2Ve8eHEMDAxwcnJi165d+Pr6YmpqCkClSpUoW7ascnzOnDkxMjIiJCSEiIgI+vXrl+j8ERERyoR2/Pvg4OAEbYKDg8mXL1+K1/o9JZdOCOC///4jJiaGrFmzkj59emX74sWLuXXrFhYWFokmzceNG0euXLmU90uWLGHr1q00a9aMqVOnEhwcjL29PevWraNEiRIfHNu///6Lh4cHenp6TJo0SRmrubk5AOHh4ejp6aHRi1aOKViwIEZGRkybNo2AgABlu0qlwtramtDQUAwNDdHX10dfXx89PT1ev35NcHAwkZGR6OjoEBYWxrZt2/jrr79YunQprVq1Ys6cORQqVIgmTZokGKOZmZkSSPiQL53m6ksHGyR4IcS34evri5OTU7Irqt4lKZWEEEIIIYQQ4vuSQIIQv4D4QEK6dOkS7UsutRHEPeH5xx9/cO3aNeUJe2dn5wRtdHV1yZcvH+fPn+fy5cvkzJkzxfEYGhommlA2MDBI3cX8oOLrI1SqVEnZ5unpydq1awEYOXJkghRCSenatSu2trZ07dqViIgI8ufPz5YtW5KtORBPo9Eoqx66du2aIE1QfGqj+NUmDx89Uvb9+eefnDx5koCAAKysrGjZsiU2NjZYW1ujo6PDjBkzEgSAADJkyMCTJ08ICQlBo9Ggo6NDaGgoLi4uHDx4kAEDBjBz5ky6du1K/vz5lcLcQgjxsV6/fk1YWBjr16//YJFnSakkhBBCCCGEEN+fBBKE+AXEpzZKKpDw6tUrgES1E+JVqFCBa9euAXFPfL77BH28AgUKcP78ea5cuZLoKfTfxfuBhKCgIEaPHo1Wq6Vu3br8+eefyR6r0Wg4deoU69evZ/PmzQDUqFGDFStWYGpqmigd1fvc3Ny4dOkSZmZmjBgxIsG+9wMJ8+fPp+v/9llbW3No0SIgLu3Shybq4qnVajJmzMjjx48JDQ1Fq9Wip6eHt7c3tWrVYu/evVy6dIljx44xbtw4Nm7cmGKf4vu5fPmystooKT4+Pt9wNOJXkZrPzcekInJyclLq8QghhBBCCCGE+DFJIEGIX0B8IMHe3j7Rvg+tSIC4wsHz588H4lLhJFVEt1ChQkDcpOTv6NWrV1y9ehWAihUr8vjxY6ZMmcLLly/JlCkTAwcOTPK4Fy9esHXrVrZs2aLUqgDo1q0bkyZNQkdHJ8VzR0ZGMm7cOAD69u1L2rRpE+x/N5Dg5+fHunXr6JopbvLu6NGjhIWFYW9vT/HixZM9h1arJTAwED8/PzQaDenTpydTpkz4+voSFhaGSqXCysqKK1euUK9ePWbOnMm///7Lzp07uX79On/88UeK1yG+j/cLgyfF2Ng4xdU0QkBccMDY2BhXV9cU20oqIiGEEEIIIYT4taRc5VUI8cOLr0dw+fJloqOjE+x7+/YtANu3b0+ykG+GDBlwdHQESPKJ0NjYWK5cuQLArVu3vui4fxYXLlwA4lI2PXr0iCdPnnDo0CEAGjZsiLGxcYL2L168YPLkydSvX5/p06fj6+uLubk5bdu25Z9//mHq1KmpCiJ4e3tTuXJlHjx4gJ2dHT169EjUxsLCAoCnT58yffp0IsLDlX1n/1ccukCBAh8s1h0aGsqzZ8+Ijo4mNjaWFy9eoFaryZQpE/r6+sTGxpIzZ050dHS4ePEiHh4eSnqldevWpXgd4vtZtmwZFy9e/OBLJntFajk4OODj45PiZ2r9+vWEhYWlqoiyED+bgwcPkitXLgwNDalYsSKP3kkp+D5PT08KFy6MgYEBzs7OCR7IuHXrFvXr18fCwoLMmTMzefLkbzB6IYQQQgghPp2sSBDiF9C+fXvc3Nw4cOAArq6urFmzRqlR0KNHD0aPHs2aNWu4evWqktP/XYMGDeLgwYPUq1cvwfbQ0FCaNWvGgQMHAOjSpctXv5YfUalSpciXLx/Xrl2jQoUKjBo1iiJFiuDp6cm8efMwMDCgSZMmvHjxglWrVrFr1y5iYmIAKFOmDO3bt8fFxSVRPYLkREREMG/ePCZPnkx0dDSWlpYsXbpUWX3wrty5c1O1alUOHz7MokWLsLKyUvblz5+f42fPcujQIdKlS0fRokWTPJ+RkRGmpqaEhIQAKKlwwsPDiYqKUrbFxsZiYGDA+fPnuXfvHoaGhtSvXz/1N1J8c7ly5ZKUMeKLcnBwkMCT+G09f/6cRo0asXDhQmrVqsXQoUNp2rQp//0vcP+u8PBw6tSpQ8+ePTl69Chz586lbt263LlzB319fVasWEGnTp1Yt24dt27dwsXFhaxZs9KsWbPvcGVCCCGEEEKkTFYkCPELqFy5Mrt27cLQ0JADBw7QpEkTJWd+37592b59O1ZWVly6dImyZcty5syZBMfny5ePQYMGJZiofvLkCZ06deLAgQMYGBiwcuVKBg0a9E2v60dhZWXFyZMnqV69OuHh4QwfPpxixYpRu3ZtNBoNU6dOpXPnztStW5ft27cTExNDkSJFWLJkCfv376dRo0bJBhFiYmK4fv06q1evplevXpQuXZp06dIxbtw4oqOjqVmzJp6enlSuXDnJ41UqFTt27KBSpUqEh4cT/k69hce+vhQrVgytVsuaNWtYsWIF9+/fR6vVJuhDR0cHBwcHsmXLRoYMGUiXLh1RUVE8ffoUAEtLS6VGxMCBA9mzZw8A27Zto0SJEp99f4UQQogfSa1atTh48GCi7du2bSNPnjy0adOGNGnSMHnyZDw9Pbl582aitv/88w8ajYahQ4diZWXFyJEjCQ0N5dixYwBMmzaNmjVrYmpqirOzM5UrV/5tU0gKIYQQQoifgwQShPhF1KhRg+3bt2NiYsLx48epX78+gYGBAFStWhUPDw+cnZ0JCAhg+PDhLF68WHlq/n2enp506NCBBw8ekC5dOg4fPvzbFlmOZ25ujru7Oz179gRg0aJFqFQqunfvDsDFixeVAMKyZctYunQpzs7OCfrQaDTcuXOHzZs3M3jwYCpXrkz69On5888/6dmzJ6tWreLKlSvExMRgb2/P33//zZYtW5Isov0uIyMj3N3dqVSpUoLURs+ePePp06eULFkSrVaLl5cXM2fOZPr06Zw/fz5RQMHQ0BALCws0Gg2PHz9WViDEf4769u2rBBRatmyZbHBDCCGE+BV5e3tTsGBB5b2NjQ0ZM2bE29s7ybb58+dXUgvq6uryxx9/JGqr0Wi4evUqJ06coGrVql91/EIIIYQQQnwOSW0kxC+kbNmyuLu706hRI/777z9q167Nzp07SZMmDQ4ODhw6dIgRI0awdOlS1q5dy7Vr1xg/fjxp0qRR+nBzc2P27NnExsbi5OTE7t27kyzinBStVsurV6/w8fHh8ePHCSaqw9+Z4P5Z6erqMnfuXIyMjJg5cya7d++mSJEijB8/nv/++4+6desmCB6EhIRw9OhRzp8/z4ULF/D09CQgICBRv2ZmZhQqVAhnZ2cKFy6Ms7MzmTJlQqVSpXps8cGExrVrw7PncduMjXn69CkqlYq+ffty7tw5Lly4wKNHj1izZg06OjpYW1tjZWWlFNnWarU8ffqUyMhIdHR0iImJQavVUrVqVQoWLMicOXMwNjZm7Nixn3czhRBC/NY0Gg179uzBy8sLfX19KlWq9NGr3F68eMHevXt59OgRmTNnpkGDBlhbWyfZ9ty5c5w4cYLw8HCKFy9OtWrVPurvLMT9XX//v4ksLCyUWlXvtzU3N/9g2ylTpjBs2DAAevfuzZ9//vlR4xFCCCGEEOJb+m0DCYGBgcyaNYtr164xb948MmbM+L2HJMRn02q1FC1alD179lC/fn0uX75MzZo1Wbt2LTlz5kRPT49p06aRNm1aZs6cyaVLl2jTpg1//fUX+fLlY86cOezatQuAKlWqMHToUOzs7NBoNInO4+fnh4+PDz4+Pnh7e3Pnzh1u3ryJv7//d7jyL+P9QtXJadSoERkzZmTYsGF4enri5+enBF927drF1atXuX79epJphAwNDcmfPz+FCxdWXhkyZMDMzOyzx29kZMSWrVt5UqYsADHR0RgbG/PkyRO2bdtGixYtqFu3LqdOneLkyZMEBQXh5+fH69evMTc3x8rKiuDgYEJCQlCpVOjq6hIZGYmxsTFLly6lXLlyQFxNjQwZMiQ6v1arTXS9SdFqtR89eSOE+Pn4+Ph81n7x6woJCaFGjRrcv3+fdu3a8fLlSypVqkT79u2ZP39+qvr4+++/6d+/P1WrVqVw4cJcv36dadOmsWHDhgQ1gUJCQmjevDkXLlygadOm2NnZsXTpUqZOncq///6rtDM0NCQyMlJ5v2/fPuXfAwYMYMaMGZiamhIUFJRgHIGBgUn+DU9N26FDhzJ48GBu3rxJq1atmDBhAiNHjkzV9QshhBBCCPGt/ZaBhJCQEEqVKkWFChWYP39+khNiQnwJKpXqgxOmqZ1MTW27+CffSpcuzcmTJ6lcuTI+Pj6ULl2awYMHM2zYMIyMjBg5ciSNGzemefPm3Lhxgz59+uDk5IS3tzcqlYoJEyYwYMAAAPT09IC4JwdXr17NunXruH79erIBA5VKRbZs2ciePbvylDvETdL/888/qbqO7yW197lgwYIUK1aMChUqUKtWLR49ekSDBg2SbJstWzaKFy9OyZIlKVGiBPnz51fu6bviUx98LhNjY+Xf0dHR8L9VB0+fPmX69OkUK1aM7t27s379etatW8fy5cu5cuUKgYGBSgojgOLFi/Pff/9hbm7O3r17Wb9+PY8fPyZTpkwMGTJEKeb9rtQEESDl74UQ4udmY2ODsbExrq6uKbY1NjbGxsbmG4xK/EiGDRuGl5cX169fJ2vWrABUqlSJevXqUaZMGZo2bfrB493d3enSpQu7du2iTp06yvaxY8cm+FsG0KJFC+7du8eNGzcSrFa4du1agnYR79QYqlWrFj179qR69eoJ2uTNm5f169cr71+/fs2TJ0/ImzdvojHmzZuXuXPnotFoUKvVSk2koUOHJminVqvJkycPLVu25MCBAxJIEEIIIYQQP6zfMpCwePFiMmXKxMKFCz+rn8jIyARPLr3/1JEQ31Pu3Lk5e/Ys3bt3Z9++fUyYMIFNmzaxcOFCKlSoQO7cuTl16hS9evViw4YNeHt7Y2pqypo1a6hVqxbw/xPDXl5e9OzZk3Pnzin9xwcM8uTJQ548ecibNy958+YlV65cGL8zmR0vKCgIS0vLRNt/5u9R/vz5OX/+PHXr1uX8+fMYGxtTtGhRJWhQrFgx7Ozsvtv4KlSowIH/PW1ZtWpVjh8/zvnz5zl//jxp06alefPmLF++nKdPn7JixQr27duHRqOhdOnSnD59GpVKxaJFizA1NWXy5MlAXHHI5ApHCyEEgIODAz4+Prx+/TrFtjY2Njg4OHyDUf0eQkJC+Ouvv5T3BgYGZMqUiXr16qVYb+dbCQ8PZ9WqVdSrV08JIgDUrVuX7Nmzs2DBgg8GErRaLQMHDqROnToJgggQlzrIwsJCef/vv/+yZ88e9uzZkyjlUb58+T567I0aNWL48OGsWbOG2rVrM2zYMIoUKULu3LmBuL+7lStXZuTIkVSpUgW1Ws2UKVPo1q0bc+fOxcTERElf1Lp1a4YMGUL27Nnx8fFh1apVNGzY8KPHJIQQQgghxLfyWwYSPDw8KF26tPL+1KlTjBgxgrt371KxYkXmzJmTqqfjJk+eLHnCxQ8tU6ZM7N69m507d9KnTx/u3btH9erVady4MdOnT8fe3p4VK1ZQvnx5Dhw4wOjRo8mTJ49yvL+/P+PGjWPp0qVotVrMzMwYPnw4VapUIXfu3MqEslar/eQn6n/271G6dOn477//8PX1JUOGDAlWYbyfEupb27J1K/WbNePo0aOcPn2abt26oa+vz6ZNm3jy5Anz5s1j4cKFVK9enfbt2zNq1Ch27drFlClTABg5ciR//vknffv2JSwsjFKlSqX4lKgQQkBcMEECBN9ecHBwgoBBUFAQK1euZMCAARw/fjxByp+PNWTIEGJjY1NsV6hQIVq2bJns/kuXLhEaGkrx4sUT7StRogRbt24lOjo6ydV7AFeuXOHu3bsMGjSIc+fOceTIESIiIvjjjz+oX78++vr6Sls3Nzf09PQoU6YMGzZs4MaNG5iZmVGlSpUENY1SK3369Gzbto0+ffrQuXNnSpYsyebNm5NsG1+7qHPnzowdO5a8efOya9cuZXxNmzalTZs23LhxA3t7e5o3by6rEYQQQgghxA9NpU1tLopfSPPmzQkPD2fXrl0cPXqUJk2a0L9/f+Wp2wwZMnD+/Hl0dHQ+2E9ST1JnypSJwMDARMXVxO9DExbGrcJx/3Oay+si6iSezv9aPvR1Dg4OZvTo0cyfPx+NRoO5uTkTJ06kU6dOSaaZWbduHUOHDlWeKG3evDnTpk0jffr0SZ43pUBC/IqE978fP9L3KCoqKtVt352oSM7HBBK+VGqjdz9/jp4XiFSpqFevHkeOHAFAR0eHOnXqUKJECXbt2sXZs2eVY3Pnzs2LFy8ICAigYcOGzJs3jytXrlCzZk0Azp8//8FJqI/5cyKpjT5NUFAQFhYWKX4/4tudOHFCqW3xM/mev0fFj8fLywtnZ2cuXrxI4cKFP7u/1H6PfhWxsbGkT5+eJk2apLr+QFJ0dXVTFUho2rRpspPrAJs3b6Z58+Zs27aNRo0aJdg3ePBgpk+fzsOHD8mcOXOSx2/YsAFXV1ecnZ159OgRrVq1AmDt2rXY2Nhw5MgRpfZZxYoV8fLywtbWFjs7OypVqsT169fZsWMHffr0Yc6cOam8eiGEEEIIIcRvuSLBxcWFNm3acO7cOUaNGsW6deuUibIKFSpQsGBBPDw8qFChwgf7MTAwwMDA4BuMWIjPZ2ZmxuzZs2nVqhVdunTh4sWL9OrVizx58lCmTJkEbb28vOjYsSMAefLkYf78+Sl+Hz6VfI++LiMjI/bt28euXbtYtGgRJ06cYOfOnRw9elQpkr1q1Sq2b9/OzZs3gbinSadPn45KpeLvv/8GoFWrVp/1JKsQQnwLvr6+KaZUCgkJ+Uaj+T7u3r2Lh4cHvr6+BAcHA3EPEyRV2+ZjTJs2LVUB8vg0P8kJDw8HSPJvf/wY49skJf6arl+/jre3N9mzZwega9eu5M+fny5duiiFkoODgwkKCqJMmTLs2bNHCdqPHj2a8ePHU758eerXr5/iNQkhhBBCCCF+00BC8+bNmTdvHk2bNiUwMJBKlSop+woUKICZmRkxMTHfcYRCfD2FCxfm5MmTFC9enOvXrxMQEJCoTfy2bNmy4eXllWx6AfFz0NXVpVGjRjRq1IizZ89SpkwZgoKC0Gg05M6dm6lTpzJ8+HC2bNnCrVu3GDx4sDKZE1+zomLFit/zEoQQIkW+vr44OTkRFhb2vYfyXYSEhNCpUyf27NlDjRo1cHR0xM7OjkePHhEeHk6BAgWAuJWAKpUqwcq6mJgYoqOjP1gDp3///l9knCYmJkDSwYL4bfFtPnR8zZo1lSACQM6cOalRowZ79uwhNDQUExMTpW2PHj0SrPzr06cP48ePx83NTQIJQgghhBBCpNJvGUjQ0dFh586dlCtXjoCAANavX0+HDh2AuFyqlpaWlC1b9juPUoivR0dHJ8mCyO8zMjKSIMIvJleuXElut7CwoHPnzt94NEII8eW8fv2asLAw1q9fj5OTU7LtLl68+Ev+vhswYAAHDx7k0qVLODo6KtunT58OQMGCBYG42kT3799n7dq1Spv27duTNWvWD9Ys+lI1ErJkyQLAkydPEu17/PgxBgYGSaZRjBdfoDmpNhkyZCA2NpY3b95gYmJC1qxZOXnyZKK2/8feXUdVlX0BHP++R6OALbZgt6hjYbfjKHbB2N3d3d066ozt2IXdhdiNii2CYoAKSPPi9wdyf6ICD2XM/VmLteDd884993HvRc++Z+/UqVNjZmbGy5cvEzweIYQQQgghRLRfMpAA0f/RcHd3x8XFhU6dOnH48GFMTEw4cOAAu3btklQrQgghhBA/oHz58sVbS+FnTW3k6upKxYoVYwURoqKiWLVqFWZmZkrKoYIFC7J7926lzc2bNzl48CD37t2Lt/+ZM2caXCMhvkBC0aJFsbGxwd3dPdYqB71ez+nTpylXrly8dcpKlCiBtbU1jx8//mibl5cXpqampEuXDoCqVauyatUqHj9+rARSAJ4/f05ERARZsmRJ8Hg+tGfPHnr16oW3tzelS5dm9erVSnDjQ+fOnaNjx47cunWLAgUKsHz5cqXI861btxg8eDDHjh3DxsaGLl26MHLkyESPRwghhBBCiK/lpw0kXLlyhdevX1OpUqU4/zNia2vL4cOHOXz4MEePHsXa2pqpU6eSIUOGrzxaIYQQQggRH09Pzy/a/rNLkSIFjx8/Rq/Xo1Kp0Gq19O7dm5s3b1KsWDGMjaP/2V+wYEFu376ttBs+fDhDhw7Fysoq3v6TqkaCqakpXbp0Yfbs2Vy/fp3ChQsD0cWSfXx8WLRokdL27du3jB07NtYqB3Nzc7p378706dO5du2akrLp8uXL7N+/HxcXFyU9X+PGjRkxYgSzZ8+mdu3aSjqnSZMmoVKpaN26dYLH8z5fX1+aNm3K0qVL+eOPPxg8eDDNmjXj/PnzH7UNDQ2lfv369O3bl65duzJ37lzq16/P/fv3MTMzY82aNfTs2ZNNmzZx9+5datWqRc6cOWnRokWixiSEEEIIIcTX8tMFEvz9/WnRogXHjx9Ho9Hg6OjIsWPH4k3PUq1aNapVq/bF+9br9cD/c4qLX5MuNJTgd0/sBQUFof6K9TZizsGEREZGKk8VhoaGfnTOhoSEAKDVag06n/V6fazcw58S009CY/yW11FkZKTBbd/PLR0XQyZcYiT0+RkqofMvpkhlzPcJpa6KqRcTFhaW4O/E0PMPQKVSGdxW/F9ir6OQkJAf8m/St7yPiu+PmZkZFhYWuLi4JNjWwsICMzOzeM/7mL9xibln/QhGjhxJ69atKVeuHAULFsTd3Z1SpUqRKlWqWE/j58qVC41Gg7e3N8+fP+fq1ats2rQpwf6TqkYCwNixY7l9+zYVKlSgcePGvH37lh07djB27Fj++OMPpV1ISAgzZ878aJXDmDFjePDgAY6OjtSvXx+9Xo+rqyuVK1dm/vz5SjsLCwtcXV2pV68eRYsWpUKFCty8eZPz588zf/58KlSokKhxb968mUKFCiljmThxIunTp8fT0/OjdFqHDh0CYODAgahUKoYOHcrcuXM5evQotWvXZvLkyUrbIkWKULVqVa5duyaBBCGEEEII8d1S6X+i/0XpdDocHR0pU6YMEyZMwMPDgwoVKuDm5kbJkiVjtdVqtfTp04fOnTtTsGDBJNn/kydPPmuJtBC/Eh8fHzJnzhzndrmOhEiYXEdCfLmErqMf0a1bt3Bzc8PExIQKFSqQLVs25s6dS/ny5SlVqpTSrmjRokyePJnp06fTpk0bWrVq9U3Ge/bsWS5fvoypqSmVKlUiZ86csbaHhoayaNEi8ubNGyvAEOPy5cucO3cOvV5P8eLFYx3j+8LCwjh06BBeXl6kTJmSatWqfdYK5M6dO6PX61m6dKnyWvbs2ZkxYwaNGzeO1Xby5MkcO3aMgwcPKq9VqlSJP/74gwEDBgDRwayoqChu3LiBk5MTa9asoVKlSokelxBCCCGEEF/DT7UiYcuWLdjY2DBr1iwASpUqRdasWfH29sbT05MaNWoo/2kICAjg0KFD7N27l9u3bydJQdmMGTPi4+ODlZXVd/m0bVBQEFmyZMHHxwdra+tvPRyD/Ihjhh9z3P/1mPV6PW/fvo23gCJ8/9dRfH7E3zv8uOOGH3fsnzvupLqOftTP7VN+pmOBn+t4vtdjMfQ6+hHlz5+f/Pnzx3otZtL6fQULFmTevHn4+fkZtNLjv1K6dGlKly4d53ZLS8tPjj9GsWLF4q2HEcPCwoJ69erF20ar1ca5SkWtVqNWqwkODv4oAGFjY/PJuhvBwcEfnfcftp0+fTpDhw5Fp9PRs2dPKlasmOCxCCGEEEII8a38VIEENzc3Bg0apPy8atUqHjx4wLhx43jx4gV9+vTh8OHDFC9enNSpU3P8+HG8vLySJIgA0f/J+BGebLO2tv6u/kNviB9xzPBjjvu/HLONjU2CbX6U6yg+P+LvHX7cccOPO/bPGXdSXkc/6uf2KT/TscDPdTzf47EYch39zBwcHBg9ejTr169PstR6PzobGxvCw8M/ua1///5MnTqV5MmTf5Q2KzAw8JP1JQxpO2jQIAYOHMi9e/dwdnZm/PjxjBo1KgmORgghhBBCiKT3U/3PYfz48ZQrVw6AY8eOMXDgQA4dOsT169d58OABuXPnjvVUk62tbbxPQQkhhBBCCPGz6d+/P8HBwdStW/dbD+W7ERwcjEaj+eTX1KlTgeiVHFeuXFHe4+fnx5MnT5Q0qe+vaihYsCDXrl1T6iVpNBo8PDw+SqmqUqnInTs3LVq0wM3N7WscqhBCCCGEEJ/lpwokpEiRQimAWrJkSY4ePUrVqlWB6KeCunbtysOHD7/lEIUQQgghhBA/oCZNmuDp6cmyZct4+fIlgwcPpmTJkuTJkweAqlWrMnHiRACqV6+OsbExEyZMwM/Pj7Fjx2JlZUXlypUBaNmyJdeuXSM4OJgLFy6wfPlyypcv/82OTQghhBBCiIT8VIGE9yVLluyjJ36uXr2qrFj4FZmZmTF69GjMzMy+9VAM9iOOGX7Mcf+IY/7e/Kif4Y86bvhxx/6tx/2t95+UfqZjgZ/reH6mYxEColczb9u2jZkzZ2JnZ4e3tzcbNmxQthsZGSmposzNzdm1axe7du0ie/bsHDhwgJ07dyoPPbVu3ZrOnTuTMWNGXFxcaNy4McOGDfsmxyWEEEIIIYQhVPq4qor9ZPbu3UurVq24cOECdnZ233o4QgghhBBCCCGEEEIIIcQP4acqtvwpR44cYfPmzRw+fJg9e/ZIEEEIIYQQQgghhBBCCCGESISffkXCgQMHCA0NpVatWlhYWHzr4QghhBBCCCGEEEIIIYQQP5SfPpDwNel0Onx9fbGyskKlUn3r4QjxXdHr9bx9+5aMGTMq+YM/Ra4jIeIm15EQX06uIyG+nKHXkRBCCCGE+Hn89KmNviZfX1+yZMnyrYchxHfNx8eHzJkzx7ldriMhEibXkRBfTq4jIb5cQteREEIIIYT4eUggIQlZWVkB0f+gtra2/sajEd+KLjSUe+UrAJDL7SRqS8s42yZmQVBSPg1p6H7DwsJQqVS4uLhw+PBhZs+eTYsWLXBwcODZs2ccPHiQwoULAySYOiwoKIgsWbIo10lcvuV1pNVqDWqn0+kMfvrOyMjoS4aUaIk5/8SPJ7HX0aNHjxJsq9VqMTExSXDfer3e4PP5S+9Xch6L/9KP8PdI/Hx+tvuaodeREEIIIYT4eUggIQnFTJxYW1vLfzh/YTpjY5K/m2yztrb+oQMJJiYmqFQqjI2jbxUWFhbRx/RuEj1ZsmTKuW5oDZKEjuNbXkc/RSAhEeef+HEZeh1ZWVkleB19l4EEOY/FV/A9/z0SP5+f9b4mab+EEEIIIX4dktBSCCGEEEIIIYQQQgghhBBxkhUJQogEmZqaAuDq6oqdnR3+/v6xXhdCCCGEEEIIIYQQQvy8ZEWCECJBnTt3xtTUlH379lGzZk0iIiKoWLEi+fPn/9ZDE0IIIYQQQgghhBBC/MckkCCESFDlypVxdXVVCuqVL1+eLVu2SF5cIYQQQgghhBBCCCF+AZLaSAhhkIoVK+Lm5saxY8dwcXEhWbJk33pIQgghhBBCCCGEEEKIr0ACCUIIg+XOnZvcuXN/62EIIYQQQgghhBBCCCG+IgkkCJEIer0+UW30en2879Hr9ZIeSBh0XsX4ns+Xn+U4fkWG3tsM/R3L71cIIYQQQgghhPi5SCBBiG9IpVJ9kwk3Q/dpaWn5H4/k+2JkZJSk7YT41kxMTDAxMYm3jbGxYf8UkMCnEEIIIYQQQgjx65Jiy0IIIYQQQgghhBBCCCGEiJMEEoQQQgghhBBCCCGEEEIIEScJJAghhBBCCCGEEEIIIYQQIk4SSBBCCCGEEEIIIYQQQgghRJwkkCCEEEIIIYQQQgghhBBCiDhJIEEIIYQQQgghhBBCCCGEEHEy/tYDEEII8e1pNBomTpzI/v37mT17NqVLl/7WQxJfWXBwMO7u7pw4cYLnz58zbtw4MmfO/K2HJYQQCm9vb/z9/RNslyZNGrJmzfoVRiSEEEIIIcSvQwIJQgjxi/P19aVFixacPHkSgGrVqrFr1y4qV678jUcm/ktBQUGcOnWK48ePc/LkSS5evIhWq1W2u7m5cezYMQkmCCG+C97e3uTLl4/Q0NAE21paWuLp6SnBBCGEEEIIIZKQBBKE+A/p9Xr0en2c23U6HWq1YRnGDGkXEhJiUF8BAQFERkZy48YN5Ss0NJQePXpQtGhRpZ1erydVqlRJNr6fRXy/0w/bqVSqBNvpdDqD2un1+gQ/5/fHptPpQKeLs+2bN284fvw4Xbp0wd/fn2TJkpEnTx4uX77M77//zsqVK6levToAKVOmTLLjAAxuJ77M0aNHSZYsmfLzq1evOH36NKdOneLatWvR58h7MmXKRKlSpbhw4QIPHjygXLlyrFu3jgwZMqBSqciWLVuC+0zq+1pSM/T6BTlPhfie+Pv7Exoaytq1a8mXL1+c7Tw9PXFxccHf318CCUIIIYQQQiQhCSQIkQiGTCq930alUsX7nq8xSaXX63ny5AnXr19Xvq5du4a3t/dHbXfv3s2AAQPo06cPxsZye/haEjpP/gsajYZJkyYxe/Zs9Ho9BQsWZObMmfj7+7N69WoOHDjAn3/+yZIlS3BycjKoz29xHCJ+DRo0iHe7nZ0dRYsWpWzZspQqVYpMmTIB8PTpU5ydnfH29qZly5axggkJkXNACPFfypcvH8WKFfvWwxBCCCGEEOKXIzOFQvzEFi5cyNSpU3n9+vUnt2fOnJlChQpRsGBBbt++za5du5gyZQoHDx5k4cKF5MyZ8yuPWHwNvr6+ODs7K6mM2rRpQ40aNXB2dsbf3x8nJycaNGjA9u3b6dChAyEhIXTv3v0bj1p8jmzZssV66t/CwoLffvsNR0dHHB0dyZQpEz4+PlhaWsZ6X6ZMmfj3338/CibY2dl97UMQQgghhBBCCCHEd0ACCUL8pKZPn87YsWMBMDY2Jm/evBQuXJjChQtjZ2dHsWLFYqWr0ev1bN26lcGDB3P58mUqV67MqFGjGDhw4C+Vtuhnd/DgQVq1aoWfnx/JkiVj5syZ3L17F2dnZyXli6urKzVr1lQmj3v27AkgwYQfkJubG9bW1ga1DQsL49ChQ2zbto2HDx8yefLkj4IJp06dkpoJQgghhBBCCCHEL0hmB4X4Cc2YMUMJIowcOZIXL15w9uxZli5dSo8ePXB0dCRlypQcPXqUwYMHU65cOQoVKkRgYCBubm5UqlSJ8PBwhg0bRq1atXj27Nk3PiLxpbRaLaNGjeL333/Hz8+PIkWKsHnzZlavXs2sWbPQ6/W0adOG1atXY25uzoEDB/Dx8aF9+/YA9OzZk6lTp37joxD/hQcPHjBkyBBKly5N3759cXNz4+nTp3Ts2BEvLy/+/fdfsmTJgre3N5UrV+bJkyffeshCCCGEEEIIIYT4yiSQIMRPxtvbmzFjxgAwatQoBg8ejJmZ2Uftrly5QtOmTVm2bBm3b9/m+fPnDB48mNu3b7N582amTZuGhYUFR44cYdCgQV/5KERSCg0NpXHjxkycOBG9Xk/nzp05ffo0W7Zs4fTp0xgbG/P3338zc+ZM6tSpw6ZNmzA2NsbNzY0cOXLQo0cPAIYOHfrJ2hrix9arVy82b95McHAwWbJkoVevXlSuXJmIiAiGDBmipDnKkiULDx48oH79+kRERHzrYQshhBBCCCGEEOIrkkCCED+Z9OnTkzp1aiC6IGFc1q1bB8Bvv/3G8uXLqVOnDgBr165FpVLRrl075syZA8DDhw//20GLWG7dusX06dMJCAj44r4CAwOpVasWO3fuxMzMjDVr1rBo0SLMzc2pVq0aKpUKjUbDy5cvlfe8ePECjUaDkZERZcqUUZ5Az5cvHxkyZPjiMYnvS0zR0lSpUrFz50569+5NzZo1AbC1tQX+XzMhderUXL58mcGDB3+z8QohhBBCCCGEEOLr+2UDCb6+vkyYMIE+ffpw6tSpbz0cIRQxeeo/l5mZGa1btwZg6dKln2wTERHB9u3bARg8eDD16tVj4MCBAOzfv18pzpwqVSoAQkJCvmhMwnA3b96kfPnySsqpx48ff3Zffn5+VK1aFXd3d1KkSMHBgwdp2bKlsr1GjRpKCqwRI0awe/dunj17ppwL/fv35/79++zYsQMjIyNWrVqFiYnJlx2g+O4MHjyYLFmy8Pr1a0aPHg2Au7s7AGXLllXaZcqUiRUrVgAwb948du7cGW+/fn5+7Nixg4EDB9KsWTOmTp2Km5sb4eHh/9GRCCGEEEIIIYQQ4r/ySwYSTpw4QaFChTh+/DhHjhyhYsWK7Nu371sPS/zidDodXbt2xcLCAicnJzZt2kRYWNhn9dW+fXtUKhXHjh3j7t27H20/fPgwAQEBZMiQgfLlywNQsGBBChYsSGRkpBJksLS0BCSQ8LU8fvyYWrVq8ebNGyB6ZULp0qW5cOHCZ/VXq3Ztrly5Qrp06Th69CjlypX7qE23bt1o166dkvKoVatWBAQEUKRIEVq0aKEEFYYPH06JEiU+/+DEdyt58uTMmjULIyMjdu7cyfbt2zl9+jQQO5AA8Mcff9CnTx8A2rVrh4+PDxAdAL179y4rVqygY8eO5MuXD1tbWxo1asSsWbPYsmULw4YNo1KlSqRMmVIJlu3cuRN/f/+verxCCCGEEEIIIYRIvF8ukBAYGEjTpk1ZvXo1hw8f5tq1a1SrVk2ZLEuMiIgIgoKCYn0J8Tn0ej39+vVj6dKlREVFsXv3blq0aEGGDBlo3749R48eRavVGtxftmzZqF27NgD//PPPR9u3bt0KQJMmTTAyMlJeb968OQAbNmwAvk4gQa6jaH5+ftSsWZOnT5+SN29eFi1aROHChXnx4gWVKlXC1dU10X3eu3uXLFmycOLECfLnz//JNiqVismTJ1O9enXCw8O5fPkyZmZmLFq0iAEDBihBheHDh3/pIYr/0JdeR8WKFaNnz54ADBs2jFevXmFhYUHRokU/ajt58mSKFy/O69evqV+/Po0aNcLW1pa8efPSvn17VqxYoQQwCxYsSKdOnZg8ebLSLjIyktOnTzNjxgycnJxImzYt+fPnp2PHjmzYsCFR9zohhBBCCCGEEEJ8HcbfegBf25o1a2jQoIGSD16tVtOrVy/q1q1LeHg45ubmBvc1efJkJS2I+LFpNBqD2ul0OoyN479sdDqd8r1er483VdHz589RqVTMnDmT+fPnA9EFkt+8ecP27dt58uQJK1euZOXKlWTMmJEmTZrQvHlzChYs+Mn+QkNDlXO4ffv27N27l3///ZcRI0aQLFkyAPz9/Tly5AgAJUqUwNPTU3l/4cKFMTIy4sqVK+zdu1fpK6FAQkLHGdPmU77kOjI0DZRer0elUiVZO51OZ3A7tTrheO3du3dp1qwZd+/exdbWltDQULp160alSpUoV64cp06domHDhowePZrhw4fHu28PDw9iSmtnt7Njxbp1DBs2jP379zNmzBjatm2rtH379i3W1tao1WqWLl1KvXr18PDwYMSIEZw9e5YjR45gZmbGggULMDIyinVuf4per48VmIqvnaEM+ZxF3NeRTqdL8PcWGhqKTqfjzz//5Pjx41y9ehUABwcHoqKiiIqKAiAsLAwbGxsA/vrrL6pUqcKVK1e4cuUKEJ1WzcHBgYIFC1K2bFmKFStGihQplP20aNECvV6Pt7c3Fy5c4OzZs1y/fp27d+/i6emJp6cn//zzD1u2bGHetGnK+/z8/EifLduXfDyxGBqo0Gq1Bl2/gKT8EkIIIYQQQgjx0/vlAgl+fn60b98+1ms5cuRAr9fHmoQ1xNChQ+nXr5/yc1BQEFmyZEmysYofn0qlSnAidOnSpcyaNQuAiRMn0rp1a1QqFYMGDeLChQts27aNXbt24evry9y5c5k7dy758+enefPmtGrVSimsDGBubq6sIvjjjz/ImTMn9+/f5+DBgzg7OwPg6uqKVqslX7585MqVK9ZY0qZNS9myZXFzc+PgwYM0a9YMiJ5ojO9YvqSuw494HRnye4XoQGVC7SIiIujQoQPXr18nZcqUpEiRgtu3bwNw/PhxChYsSKNGjdi6dStjxozBz8+P2bNnf3LC/vz58zT8/XeOpEsPwKqVqxg0ZAjHjx8HonPhP336lGHDhqFWq0mePDkpU6YEIGXKlJw5c4Y7d+5gZWWFg4MDAOPHj6dUqVJJ+rmIpBfXdXTz5k0liAiQIUMGcuTIEeu9dnZ2yoT58uXLqVixIm/fvqV69epkypQpVp8x7O3tWblyJevWraNgwYKULl2aIkWKYGZmxvPnz0mePPknx6lSqciWLRvZsmWjZs2apEuXjlevXnHhwgXOnDnDP//8w9atWzHR6RiVJJ/Mp8cghBBCCCGEEEKIxPklUhsdOXKEhw8fAjBmzBh+++23WNtjJl7ff2rz7NmzCfZrZmaGtbV1rC8hEmPDhg3KU8SDBg3i9u3b2Nvb06VLF9zd3fntt9+YOnUqZ8+eZd26dTg5OWFqasqtW7cYNWoU+fPnZ/DgwXh7e3/Ut1qtxsnJCYCTJ08qr69ZswZASX0EsHfvXjp27MjTp0/5/fffgeiiyzFP2ep0OiIiIv6Tz+BXvo60Wi1//vkn7u7uWFpakjVrVm7fvk2qVKmYN28eqVOn5saNG5w5c4ZOnToBsHDhQho0aEBwcHCsvo4dO0b16tUJCAhQXuvXvx/Hjx/H0tJSCSTNmzeP7t27ExkZ+dF4zM3NKViwIO3atSMkJITy5cvTu3fv/+4DEEkmruuoTp06VKpUSfnKmzcvixcvjrOfrFmzsmbNGlxcXJSi7XGpVKkSS5cupVevXpQsWRIzM7N428clderU1KpVi7Fjx7JkyRKMjY1x3bHjs/oSQgghhBBCCCHEf+OnDyQcOnSIunXrMnr0aODTTyLGvBYTSJgwYQIdOnT47EK3Qhhi8+bNDBo0CIAuXbrg7+/PmjVriIyMZNeuXTRv3pzy5cuzaNEi3r59S926dVm7di33799n3rx5FC5cmNDQUCWXfocOHfDw8Ii1j5jiuu7u7gDcuHGDK1euYGxsTPXq1YHoVTrTp0/nxo0brFy5EkdHR2xsbPD394/VnxRcTlp6vZ7u3buzZcsWTExMKFSoENeuXSNZsmQsX76cvHnz4urqSo4cOfD19WXdunX06tULc3Nz9uzZQ6VKlfD19QVg165d1KlTh+DgYCpVqqTs49LFi9jY2LB582Zmz57N3LlzMTIyYuvWrbRo0eKTefTnzp3LqVOnSJ48OcuXLzcoVZH4fuXIkYPcuXOTO3du7Ozs0Ov19O3blzNnzsT5nnLlyjFnzpwvDuqFh4fj7u7O1KlT6datG7NmzeLQoUO8ePEizvf88ccfSjAhhtRMEEIIIYQQQgghvr2fOpBw6NAhWrZsScOGDdm0aRPPnz//ZLuYlA56vZ4JEyawfv16jh49ioWFxdccrviF7N+/HxcXF3Q6Hc7Ozpibm7N8+XIARowYQevWrbGyssLLy4uJEyfi6OhIq1atuHz5MilTpqRt27acOnUKV1dXKleujFarZePGjZQrV446depw7tw5AMqWLQvAnTt3ePnyJWvXrlVej8ld/tdffxEeHg7AwYMHCQkJoUaNGgAcOHAAU1NTQAIJSW306NEsXboUgNKlS3Pu3DlMTU1ZsGABo0ePpkaNGsyfP58tW7ZQunRpgoODWbhwIb179yZt2rRcuXKFsmXLMnLkSBo3bkxERAT16tVjzty5yj5Sp07N9u3blVVYLVq04N9//yVZsmS4ublRr149nj59qrS/efMmI0eOBKB8+fKcOHGClStXsmrVKqVWR8zXrl27Esy9L769CxcucOvWLW7dusXdu3dp1KgRUVFRNG/enJcvXyb5/oKCgli+fDl//vknRYoUwdnZmb/++ou9e/cyb948OnbsSKlSpfjtt9/o3r0706ZNi1WnBaKDCfPmzVN+7j9ggAQThBBCCCGEEEKIb+ynDSTEBBG2bNnCX3/9hbm5OX/99dcn28asSJg4cSLr16/n2LFjpEuX7msOV/xCvLy8aNasGVFRUdSrV4/q1aszZ84cIPoc7Nq1K5MmTeLy5cvMnDlTKXi6fft26tSpo9QjUKlUVKlShZ07d+Lm5kajRo1Qq9UcPHiQWrVqodPpSJUqlVKY+Z9//mHfvn0ASo704OBg5TWAyMhI3NzclInnCxcuxNomksaTJ0+YMGECAD179sTNzQ2ILpjr5eXFtWvXANi4cSOXLl1i3bp1VK1aFa1Wy4wZM9i3bx9p06bFx8eHSZMmodFoaNGiBZs2bWL27NnKfjZv2fJRYe4qVarg6uqKjY0Nnp6ejB8/Xtk2d+5cJYXVvn376NChAx06dKBjx47K9zFfDRo0YOfOnf/p5ySSlkql4p9//iFv3rw8ffqUIUOGJPk+Ro0axaxZs3BzcyMiIoL06dPTsGFDhg4dSqNGjciTJw9qtRo/Pz9OnjzJzJkzcXJy+qjOyvup11x37Ig3HZMQQgghhBBCCCH+ez9lIOHw4cNKEKFixYpYWVnRvn17Fi9e/Mk87zGpO44cOSJBBPGf0ul0tG3blrdv31K2bFmmTJmipN1q3749bdq0UdpaWlrSvHlzdu/ezeTJkwFIlSrVJ9NzFS1alAYNGijbmjdvrqy06du3LwBjx46lZMmSAKxevZqLFy+SLFmyWIV0TU1NKVKkCCtWrADAysqKyMhIChQogJ2dXRJ/Gr8uW1tbihYtCsCVK1fImjUrEB0AjVlFAtErCooVK8b9+/e5ePEiAJUrV8bd3R0/P79YfRYuXBgTExMqVqigvBYaxyqSoKAgJa1RtWrVlNdbtWpFvXr1qF27drxf9vb2AMrKF/HjsLKy4p9//gFg/fr1PHv2LEn7b9KkiZISSa1W4+LiwtSpU+ncuTMzZsxg9OjRFC5cWGlvampK165dEyyAPHnyZGXllBBCCCGEEEIIIb6+nzKQoNfrlSBCjF69evHq1SvWr1//Ufu0adMyePBgCSKI/9zcuXM5ceIEyZIlY9WqVfz99988fvwYW1tbpV7Cp8Tkwn9/kvl9rq6utG7dGq1Wi4uLCwsWLFC2/fnnn/z555/odDr27t2Lk5MTWq2WoUOH8vjxY8aPH68ECTp27Ii7uzuenp5YWloq+43JrS+ShrGxMRs3biR58uScOnWKsmXLYmpqyv79+zlz5gxLliyhRo0abN68meDgYJydnQkMDKRYsWI0aNBAKYA8cuRIli1bBkQXYY6KiqJRo0bKfvr26/fR5Ku/vz/dunVDr9fTvHlzGjdurGwrV64c27ZtY9euXbG+XF1dY/3cp08fgI9S0ogfQ+nSpSlbtixRUVEsWrQoSft2dHTE1dWVatWqodPpmDlzJg0aNMDV1ZVmzZrRsmVLrl69iqmpKc2bN+fcuXNKsDMuGTJk4OnTp8q5LoQQQgghhBBCiK/vpwwkVK9ePVYQASB79uw4OTkx97384THUajVTpkyRIIL4T926dYthw4YBMHPmTPR6vTKJN2bMGJInTx7ne2NSDJUpU+ajbe8HEVq0aME///wTa9JfpVKxYMECChQowMuXL3n9+jWFChUiODiY/v37ExUVxd9//83ixYupXLmykrc/Xbp06HQ66tevT9WqVZPscxDRcuXKpXzWGzdupEWLFkB0equMGTOyfPlyZVWKn58fBQoUwNnZmZ49ewLQp08fRo8eTfPmzUmXLh1Pnjxh27ZtsZ7svnf3rrKaBaKDrL179+b58+fkzJmTSZMmfdbY8+fPD0Sf0+LHFDN5v3TpUkJDQ5O077Rp0/L3338ze/ZsbGxsuHHjBr179+b8+fOYmprSqlUrTpw4wfDhw8mYMWOC/XXr1g2QVQlCiKTn7e3N5cuXE/zy9vb+1kMVQgghhBDim/spAwlx6du3L1evXuXkyZPfeijiFxMVFUWrVq2IiIigdu3adOjQgZ49exIREUGFChX4448/4nxvZGQkV65cAT5ekfBhEGHhwoWfXDlgaWmpPAHv5uZG3rx5yZQpE76+vgwePBgjIyMKFy7MtGnTCA8Px97eHi8vL0xMTJgxY0bSfhhC0bx5czp37oxer2fXrl1UqVKFqKgounXrxr1792jevDm+vr7kyJGDLl26MGTIEHQ6He3bt2fGjBmoVCrMzc3p0qULAPPnz/9oH4sXL+b06dNA9KTxoUOHMDMzY+nSpSRLluyzxh0TSHj06FGST0KLr6NevXrY2dnx6tUrNm/enOT9q1QqGjRowKFDh6hRowbm5uZKAGHcuHFkyJDB4L6aNGlC5syZZVWCECJJeXt7ky9fPooXL57gV758+SSYIIQQQgghfnnG33oAX1O5cuUoUaIEc+fOpcJ7ecTF9+PDgpvxtUsopzZAWFiYQe20Wi1mZmYJttPpdAmO8f3tb4ODUWu1jBo1isuXL5MyZUrmzJnDunXrOHDgACYmJvTr1++jfPfvu3btGhEREaROnZqcOXOi1WqB6CBCu3bt0Gq1NG/enAULFqDX6+McX+7cuVm8eDEuLi5s3ryZAQMGsGTJEm7cuMH48eMpVaqU8sRwyLvc+k2bNlUKM3+PYj4LQ9oZkppJp9NhbJzwbVGn0yk1KOIT3+8jxsyZM3Fzc+PWrVsEBgaSNWtWvL29qV69OhqNhsyZM9OvXz/69etHVFQUzZo1U1ayxPTdqVMnpkyZwpkzZzh/4QIp3/XdtGlTVm3cSM+ePZk1axbjxo0Dogvi5smTh7CwsE/WjfmQVqvF3Nxc+TlNmjSkSZMGf39/PD09cXBwUMZjyOei0+kMui4Bg9uJxDEyMqJnz57069ePxYsX07Rp0wR/dyEhIZiamibYd0hIiNJX8uTJmTNnTqxrJib4FBoaGmcRd/17r2t1OgYNGkSvXr2YPHkyf/75Z6zzMYZOp8PExCTB8Rl6P4iKijL4Ojc09Zsh/Qkhvg5/f39CQ0NZu3Yt+fLli7Odp6cnLi4u+Pv7KzWNhBBCCCGE+BX9UoEEiF6V0KpVK7y8vMiePfu3Ho74ThgZGRk0gWxkZJTwxOYHE0WLFy9WUmrNmjWL5MmTM3ToUCC6wHKRIkXi7S4mD325cuWU9Ee7d+9WggguLi5KOiO9Xh/vcbRs2ZIzZ86wcOFCli1bxsKFC2nfvj1Hjx7l+PHjQHQx3wMHDpAhQwbmzJkT/7G+Y8hk738xIWxon2q12qC2Sd3OEBYWFmzZsoWSJUty6dIl2rRpw7p164iMjCRDhgzMmjWLdu3aERERwR9//MHq1as/mizNmDEjLVq0YNWqVSxZvJgh716fMWMGx8+e5fHjxzRr1gydToeTkxP9+/dHpVIRERFh8MTmh+3y58/PyZMn8fT0pHjx4oDhgUCVSiUBgq/EyMgozknuDh06MGbMGB48eMD58+epVatWvH2Zm5tjYWGR4D6NjY0/OdH/ocjIyDhTuunCwgh+933KlClp06YN06ZN48mTJ6xYsYKuXbsm2H9c1Gq1Qee9iYmJQeepoee9EOL7lC9fPooVK/athyGEEEIIIcR374d/NO7FixdERUUZ3L5JkyZUrVoVS0vL/3BU4nul0+l4/vz5V9vftq3bGDx4MAAjRoygYcOGTJs2jadPn5ItWzY6deqUYB8x9REcHR2B6Kdp+/fvj1arxdnZ+aOaCAmZMWMGxYsX582bN8yfP19Jh6PT6ShQoACnTp0CYPz48VhZWSXqeL+11atXU7x4cdq3b8/q1at5/Pjxtx6SQXLlysWSJUsAWLVqFQMGDKBu3brMnz+fzp07ExwcTOXKlVm/fn2cT1zHFGDevn278lry5MlZtmwZKpUKnU5H1qxZWbJkSZJM4hcoUACAmzdvfnFf4tuwsrJS7kHvF2j/HpmZmSkF6adPny61EoQQQgghhBBCiK/shw0knDp1ihw5cmBra0uqVKno06cPwcHBCb7PxMSE/fv3S2HlX9Dr16+pUaMG9vb2jBs37qs8RdqnT/TkbpcuXRgwYACenp4sXLgQgGnTpiX41K5Op+PSpUtA9IoEgH379vHw4UNSpkzJokWLEhVEgOgJuX///ZeUKVNy4cIFrl+/zvTp0ylRogT29vaEhITw22+/0bJly8Qe7jcVEBBA3759uXbtGqtWraJdu3bkyJGDnDlz0qFDh+8+sNCsWTOlXsLSpUvp3bs33bt3582bN5QuXZrt27fHe74ULVqUSpUqodFoYr1evnx5xo4dS5YsWVi7di0pU6aMo4fEiUkDEbNiRvyYevbsiZGRESdOnMDDw+NbDydebdq0UWolrFix4lsPRwghhBBCCCGE+KX8kIEELy8vGjRowJQpU/D19WX69OmsXr2a33777aOJwpcvXzJq1Ch0Op3ymqTU+PX4+vpSo0YNpejslClT6Nmzp8E59j+XRqOhadOmTJ48GYABAwag0Wj4/fffE0wjAnD37l0CAwOxtLRU8tDHPDncrl27z15Zky1bNpYvXw7AokWLyJgxI3PmzGHXrl1A9KqFHy2X97x58wgMDCRv3rwMHjyY0qVLY2xsjJeXF6tXr44VWGjfvj2bN2/+rlKS6N7lgYfovM3VqlXjxYsXFC1alN27d8eZAuZ9MasSPjR48GDu379PqVKlkmy8MQWXb926lWR9iq8va9as1K9fH/ixViVMmzaNe/fufdVrOCgoiOPHj39X9w0hhBBCCCGEEOJr+SFrJKxYsYJq1arRpEkTIPpp7xo1alCzZk2qVKnCmTNnlBUHx44dY8KECQBKoVHx6xk3bhy3bt0iQ4YMtG7dmqlTp7J8+XKqVq1KgwYNknRfnp6exKwRqFKlCosWLUKtVjNz5kxOnTqFhYUFU6ZMMaivmLQx1tbWhIeHExUVxZEjRwCoWLHiZ4/xxYsXXL9+neTJkxMcHMzEiROVieEWLVok6YTz1xLzhPLIkSNp1qwZAMHBwZw+fZrjx49z8uRJLl68iJeXF15eXqxatQorKyuDAjoJ0el0eHt7K7+jmK/IyMhYP0dFRRESEsKzZ894+vQpT58+xdfXl6dPn/Ls2bNPpmnz9/dnxowZ1KpVi1KlSsW5AiU0NJQrV6588bEYKia10aNHj3j27BkZMmT4avsWSatLly5s3bqVrVu38tdff33XQcT3ayUULFiQdOnSUaZMGcqUKUPp0qUpWbKkQQWhP0enTp3Yvn07O3bsoHbt2v/JPoQQQgghhBBCiO/VDxlICAsL4+XLl7Fes7e35+jRo5QuXZp27dqxe/duAGVCsXLlyl99nOL78eLFCyC6TkHbtm158+YNS5Yswd3dPUkDCY8fP8bJyYndVtYALF26FBMTE7Zv364EsiZMmEC2bNkM6s/R0ZHUqVPz/PlzXFxc2LJlC9WrV+fQoUN07tyZtm3bUrhwYYoUKYK9vX28q230ej2nTp3ir7/+YuvWrcqkdapUqejVqxdLly4FoHHjxl/yEXwzMZOHGTNmVF5Lnjw5NWrUoFq1aqjVajw9PSlbtixv377F3t6ekiVLftE+79+/z6pVq1i9ejU+Pj5f1BdEr5ZKnz59rDoeT548YcqUKUyZMoUUKVJQo0YNatWqRa1atbC1tUWv17NhwwaGDBmCj48PFl9pxVXatGkpW7Ysp0+f5q+//pJA7Q8s5r6RIkWK/ySIcOPGDbZv306GDBmws7PDzs6OLFmyfFZfManZhgwZwqVLl3j58iWurq64uroC0QWhf/vtN8qWLat8JUU6rzdv3ij/rrh+/boEEoQQQgghhBBC/HJ+yEBCrVq1mDFjBsePH6dSpUrK61myZGHNmjVUrVqVixcvUqJECeD/wQQhYp7mLlmyJEuWLEnSJ7if37hB18aNsXn9Gt4FEkyePuXqiZPM7t2LDMbG1O/YkQ4dOhjcp62tLX///TctWrRgz549DBw4kHXr1lGxYkVu3brFpEmTlLbJkyenUKFCFC1alCJFilC0aFEKFiyIRqNh7dq1/PXXX7EK45YqVYqOHTvSqFEjLCwslIngH/XJ8mzZsvHgwQMeP35M+fLlP9ru4+ODk5OTEkQ4cuQIqVKlSvR+goKC2LRpE6tXr1YKU0N0IMPS0hITE5N4vywsLMiQIQOZMmUiU6ZMZMyYkQwZMpA5c2ZsbW1jFVN++fIlBw8eZP/+/Rw8eJDXr1+zadMmNm3aBICDgwPGxsZcuHCBDMbGVMmWjd7dusO7tFURt++gfldXQZ0yBSZJ/Lvt06cPp0+fZsmSJQwZMgQLC4sk7V98HVevXgWi62wkNR8fH+rXr4+/v3+s19VqNZkzZyZHjhzY2dlhb2+Pvb09BdKnJ2uKFOgjIpS2UXfvEvHunqpOmYLSpUtz/PhxwsPDuXTpEmfOnOH06dOcPXuWV69e4ebmhpubGxAdeFi0aBGtW7f+ouPYtWuXEnx98uTJF/UlhBBCCCGEEEL8iH7IQEKVKlWoXbs2zZs3x93dnRw5csTaVqRIEU6fPq0EEoT4UEy9gWvXrqHVahNdsPhDb+7d42WTpsxSqSFbduV1/46dSAusz5iJKJWKjD17xttPUFAQN2/epFSpUsqTwQ4ODsyaNYvu3buzYMECcuTIwfHjx1m/fj3Xrl3j2rVr3Lhxg+DgYM6cOcOZM2eU/tRqNaampoSHhwNgaWlJy5Ytad++vfIZAGi1WuUpeFtb2y/6LL6V7NmzA9E1VD7k4+ND9erVefjwoRJESMwT0TqdjmPHjrFq1Sq2bt1KWFgYEP351qhRA2dnZ3Lnzo21tTU2NjbY2NgkWEj7w/4/tZokXbp0uLi44OLiglar5dy5c0pg4eLFi0ogzN7aGtdMmTHS6ZQgAoBvmzbK9ypTU7Ls2pmkwQQnJyfs7Ox49OgRa9eupWPHjknWt/h6/qtAQmhoKM7Ozvj7+5MrVy5y5syppBYLCwvD29sbb29vjh07BkAGY2P22tnz7INVEf4dOynfv38em5ub4+joiKOjIxB9H3v48CGnT5/m9OnTnDp1ivv379O+fXtevXpFv379PvtYtmzZonwvgQQhhBBCCCGEEL+iHzKQALB69WrKlStHxYoV2bt3L4ULF1a2qVSqWOlNhPhQrly5SJYsGSEhIdy5c0epD/A5wsPD6du+PUMSaGei18Pbt3Fuf/XqFU2aNOHRo0e0b9+eESNGKNt+//13Jk+ezNChQ+nfvz/29vZ069ZN2a7RaLhz5w7Xrl3Dw8ODa9eucfXqVfz8/AgPDydv3rx07dqVP//8kxQpUhDx3tO+EP3ku06nQ61WK/VFfjQx6aI+LLj+JUEEf39/5s6d+1Hqorx589K6dWtcXFx49eoVzs7OsVZ7ABgbG2NjYxMruGBlZUWWLFkYPHhwolO7GBkZUaZMGcqVK8e4ceN48eIFBw4c4NmzZ7QsVZqQrl3jfb8+MhLdmwBIwkCCkZERPXv2pF+/fsybN4/27dt/cVBOfH3Xrl0DoEiRIknWp16vp0ePHly/fp00adKwdetWsmbNqmx7/vw59+7d49mzZzx8+BAvLy80d+9iFhoWf7/xnMcqlYq8efOSN29e2rVrh16vZ/DgwcyaNYtBgwbh7+/PxIkTE30sb9684ejRo8rPT58+TXQfQgghhBBCCCHEj+6HDSSkTp2a48ePU69ePUqVKkW/fv2oUqUK27ZtQ61W4+Tk9M3GpgsNRWf8w36035ROpzO4nSG5vMOCglCpVBhrNFioVOjCwggLCACgeMGCXDh/nkunT5Pd1hYjK6sE+wsJDY01UarVaunUqRMPLl6MtRIhLq+fP0dlaan8fPvGDUxNTAkLD2Ps2LE89/LCQqVi3fLlWJmaUq1qNQDUahXOjRrheeUKmzdvpm3LlmxYv558+fIpfaVOloxi+fNTqXRpAPR68PN7SWBgIDlz5kKlgrd+frz180Oj1ZImTRrlvU8ePMBCpSJ9unRoQ0PRvntdr9dj9N5442LI70MTHJxgP+/ThoSgTWBS+v3ixNltbbFQqXj64AFR7wI2T54+pW7dujzz8iKfvT179uwhU+rU6EJDP9lfZFQUalX0cYSEBFOrZk08PT0BSJ8iBQ0bNqRFixakT58etVrN3wsWMGfOHKIiI0lpaYmxsTEhwcHo9XrQagl9/ZrQ1695/sF+dmzYwJQpU6hWLfr3q9PpSJ06DQkJDQ3B+F3qIwu1mvrv8rRH3rlLSILvhsiQYAgKUn6OCA9HZUA6opDgYMLfK2AbFBSE561blCxVikZ16jB5zBge37mD66ZNNDSg5oher4+3nsf7DGmn1+sN6svQ/r5ncZ278bWP7+9ReEQEXrdvY6FSkTV9evziedo+MjLSoEDjiUOHcHV1Zd/27SQ3NqZru3ZcP3+e6+fPx2oXFh6Obbp02BYvTtnixTHz9YW/FifYf5C/H8bPUnz0+puAAJInSxbrtb5du5LMyIgZM2Ywf/p0Xjx+zNix48iQIeGVV2FhYajValw3bcJYoyGFtTXBb9/i5+NDeGCg0s7Q+yQA33Eh619JYq8jIYQQQgghhBCg0idmBuY7FBUVxfz581m5ciX+/v7UrFmTWbNmJUlxxcQKCgrCxsaG8zlzkVyeyhUilmCtlpL37xEYGIi1tXWc7eQ6EiJuch0J8eUSex0l1E58HZcvX6Z48eJcunSJYsWKfXftEqILDeVOseIA5Ll8CbWhAcjvlFwfQgghhBC/nh/+sXkTExP69ev3RbmPhRBCCCGEEEIIIYQQQgjxaT98IOF7lMvtpDyZ85kMXSBjaGqUsPBwVCoVLZo35/Dhw8ydN4+WLVsCcPfOHRwdHbGwtOTBgwckM+DJsMtXrjBo0CAuvEvR4VCsGDNnziSnkRF+HRIuNGs+exZG74qD6/XQp09vDhw4gJGRESNHjlSedIuIiGTosKHcv3ePzFmyMHnyZEqXKqX08zY4mCNHjhD89i1h4eFEhIcTFh7Om9ev0ev1hIWHEx4WRlh4OFevXiU0JARTMzO6d+tGu3d57G3Tp1f6mzptGlOnTKFVq1bMmTPnvc8ZTEwSvk0YktooKCgIMmVKsK8YOU+eSPA6ej8VllarJX369ERFRZE+fXpevHhBdjs79uzZQ+ZMmQxKhRUVFcXhI0do1rQper2embNm0fZdweKQkBCGDRvGmjVrAMiXLx9Tpkzh4qVLTJ8+nfCw/+d2NzE1JWvWrGTJnJn8+fNjnyMHOeztsbO3V1KvHDt2jIEDBxIQEIBlsmTMmT2bJk2axDu+4JAQjI2NuXjhAtOnT+fkyZMA5DU359+s2RI8vowrV2KWN8//PzOdDtN3qZLi8zY4mOfPntGwUSO8Hz8ma7ZsmJqacv/ePTp36cLo0aPp0qULO11dcXFxYfHihNPTJKVfKbVRUFBQoupcJPT3qGevXqxYvpzOXbowcMCAePvS6XSxUqJ9KCAggGrVq/PwwQNy5MxJt27dUKvVLF2yREkRltzKitSpU5MmTRrMzMwoUKAA6dKlI126dKQPDSPXe8XC45Lir0WY5Mr10et+/v5YJU+u/BwZFUXw27ekSpVKee306dN07daNsNBQihUvztYtW2Jtf19EZCQbN26kZ48e5M+fHze3U5QqXYr79+6xfft2KlSoqLQ1MzP9ZB/i+5TY60gIIYQQQgghhAQS/hNqS8sffrnyt5LUgQS1SoVKpSJSrSZMr0dnYoL6XU74XIUKoba05HVICHe8vChRokSc/YSHhzN16lSmTJlCVFQUyZIlY/To0XTq1AkjIyMib982aNwqMzMlJ/3CBQvYsX8/AIP798ehTBlijt7UzIxhY8fSo0cP7nl7M2X2bLZu3Yrxu1zn1hYWNGje/KP+/f39P0rr9eTJE4YNG8bx48eZNGsWrgcOMH36dDLa2SltfF6+JEyvJ22WLLHOXb1ej9qAiWYMCCSoNZqE+3m/vSHX0XuBBDWgMzUlLDISr+fPsbe3Z+97hZUNCSQ89PTkzw4dCNXpaNeuHe3fFbS+cuUKzs7OPHjwAJVKRceOHWnbti0jR47k8OHDAOTPnx+dTsejR48Iiojgxr173Lh3j33HjsXaR/bs2Rk9ejQ1f/+d3Q4O9OjRgzNnzvBnx44cPnWKuXPnYhnHcZ8/fZrp06dz7F2fxsbGFC5cmLAPCj3HRW1uHvv3q9WiNk14AvTuzZu0aNECLy8vsmfPzrY9e7h9+zaNGjVi0bJltOncmc69erFxxw5WbtjA2KlTyfAVJ+l+pUBCUl9H569dI0yvJ1/Rosq9KTQ0lKtXr1K6dOlY141Kp4uzL61WS6tOnbh5/z6pUqWiVceOqC0s2LJlC5dv3VLahQUF4RcUBI8eAXDk1CllWxErK9ZnTDjYuPfwYeoXLhzrtdDQUO4+fsyLFy+4fv06Fy9e5OrVq0RGRjJixAg6d+4MgGPVqqzasIE2bdrgfvEilWvXZt++fWTOnPnjHalUrN+2jTC9njqNGqG2tCBt5sx43L3LE39/1Jb/ry+iNjNLcNzi+5HY60gIIYQQQgghhAQSxC9Ar9fj6+sLEKtQspGREUWLFsXd3Z0aNWrQunVrunTpQp48eWK9d9euXQwYMICHDx8CULNmTWbPnq1MUEN00drEePPmDTNmzACgS5cuVK1a9aM2qVOnZuzYsfTv35+LFy+yb98+6tatm6j9AGTOnJlVq1axfft2xowZw82bN2nZsiUeHh5K4dTXr18D4ObmRmBgIDY2Nonez7ek1+tZu3Ytwe8VdD7yXhDBUKNGjSIgIIDSpUsrKzP0ej1NmzbF29ubLFmysGzZMrJly8aAAQM4fPgwpqam9O/fn3bt2qFWq9Fqtfj6+vLw4UNu3brFixcvuH//Pvfv38fPzw8vLy/atWvHpEmTaN26NZs2bWLWrFnMnTuXVatWcffuXXbs2KEEhHQ6Hfv27WP69OmcPn0aiA4guLi40L9/f7Jly8a9AwdgxMik+TDfefHiBRs3bmTdunVcvHgRiA6C7Nmzh8yZM5MpUyayZcvG48ePWbNmDUOGDCFt2rT4+fmxbds2unfvnqTjEUkvODiY69evA1CoUCEgOiDQtGlTLl++jLOzM9OmTTMo+HL06FEOHToEQJs2bbC2tiYwMFAJtDVr1ozSpUvj5+eHn58f/v7+eHl5ERUVxYsXL/D39ycyMtKgcc+cOZPdt26RPHlyHj58yMOHD3n+/MOS5v83fvx4atWqRbZs0at2HBwcOH78OLVr18bT05NGjRpx6tQpTN4FTe/fv8/q1atZvXo1T94Vn27UqBGAck8ZMWIEGo2GP//8M9bfFSGEEEIIIYQQ4mclgQTx09u+fTseHh5YWlpSuXLlWNuGDRtGr169ePDgAfPnz2f+/PlUqVKFbt26kSNHDgYOHKhMhGXMmJEJEybQuHHjjybWVm3fQS2dDrP4nno3MUH1boJeq9UqaXniCw7kzp2b6tWrs3v3bjw8PD4rkADRT2E3bNiQChUq4OzsjKenJwMGDGD16tUAtG3blv3793PixAkqVKjAtm3byPEuBdPn0Ol0PHz4EM17T32+P8mflHx9fenatSt79uxRXitTpkyigwgAFy5cAKInHs3ePWH89OlTvL29MTIy4ty5c6ROnZpLly6x/91qklWrVlGyZEmlDyMjI7JkyUKWLFlwcHAg/XsppF69esW0adNYs2YNQ4cOJTAwkJ49e9K/f39q165N06ZNOXPmDNWqVcPV1ZXjx48zY8YMbr17otvU1JSWLVsqAYQYdkWK8NzUFOKZiFWZmqJOmSLe43/58iUHDx5k3bp1HD58WDlHjYyMqF69OjNnzlSe3J46dSqPHz/GxMSEmjVr0q9fP/z8/LC2tqZGjRqGfuTiG9q9ezeRkZHY2dkp59PKlSu5fPkyAP/++y+pUqVi6NChCfZVrFgxMmbMiK+vL4cPHyZnzpxYWVlhZ2fHo0ePOHnyJKVLlyZr1qxkzZoViL62ChYsCIBGoyHg/n0i160nvjUyWrWaQJ2OHTt2fLTNxsYGOzs78uTJQ4kSJciYMSPOzs4YGRmROnXqWG3z5cvH8ePH+e2337h06RLjxo3Dzs6OVatW4e7uHqvPQYMGKQHmLl26cPLkSR49ekSXLl3466+/mDJlCrVq1UrwMxJCCCGEEEIIIX5kEkgQP7Xw8HBGjBgBQN++fQkPD2fBggXUr1+fzJkzU7lyZa5du8bhw4dZtmwZe/bs4ejRoxw9elTpw9TUlL59+yqTaR8GEXx9fRm/+C/mRUYyZ/x4qpQrp9RLMJ89C9W7CWmVjQ3qdysAUqVKhbGxMRqNhjdv3igrAz4le/bsANy5c+eLP480adIwffp06tWrx8aNG2nZsiW1atWiZs2aHD16lEaNGnH79m3KlSvHxo0bKV++fKL3cfr0abp376486fxf0ev1rFmzhr59+xIQEKBMsq9cuRJPT080Go2SCsoQb968wdvbG4DC76VNuXHjBhAd1ImZjNy+fTtRUVEULlw4VhAhIalTp2bKlCmkTp2aOXPmMGXKFAICAhg+fDiVKlXi8OHD1KlTBw8PD3LmzKlM5FtZWdGpUyfat2//yRQsxra22G7dgi4gAF14BP4do88/58deWKdNy8yZM8lbsiQm76UbCgsL4/Lly1y+fJnz589z/vx5vLy8YvVbsmRJWrZsye+//x4rIOLq6srkyZMBmDVrFhcvXmTNmjWo1Wo2bNhArk/krxffn40bNwJQr149VCoVT58+ZcqUKQDUqFGDgwcPMn/+fFKmTEmXLl3i7St16tRs2rSJqlWrcv36dXbv3k29evXo3LkzkydP5tmzZyxdupQePXp88gl+Y2Nj0uTNy4O+fXDbu5cThw4pdT88WragQrVqAKhtbJh/5w6urq6kS5cOe3t7cuTIgZ2dHVqtFisrK6XPLVu2ANHXc/L3aifEyJ49O7Nnz6ZNmzbK+QzRadCqV69Oy5YtcXJywtzcXNnm4ODA1atX+euvv5g8eTLXrl2jdu3a1KtXj2nTpsVa0SaEEEIIIYQQQvxMEk4aLsQP7K+//sLLy4sMGTLQsWNH6taty6BBg8ifPz+dOnXi1q1bqNVqqlWrxvbt27l79y6DBg1SCorWrVsXDw8PJk6c+MmJKIAxY8YQGhpK1uLFqd29O6bvTSQZ5ciBUa5cGOXKpQQRIHqiKiZ48OrVq3iPIeZJ4Xv37n3RZxGjcOHCtGvXDoCePXsqKwWKFSuGu7s7xYsX5/Xr19SuXZvlBhQ+jfHq1Ss6depE+fLluX79OmZmZqRKlUr5+rB2w5fw9fXFycmJtm3bEhAQQIkSJbhw4QJLliwhZcqUBAQEcO7cuUT1GRP4yJYtGylSpFBejwkkxDw5rdfr2bx5MxCdriWxVCoVgwYNYsyYMQAsXryYgQMHotVqKVy4MMeOHSN79uzodDrSp0/PhAkTePDgAZMnT8bW1jbOfo1tbTHNmxfTPLmV1/TZs3PCx4eKbduy+dgx1q5dS69evShTpgxp0qShSpUqDBgwgE2bNilBhAIFCjBq1Cg8PT05ffo0PXr0IG3atEqfGo1GCc51794dW1tb5eeJEydSu3btRH8m4usLCgpi3759QPR9Tq/XM3z4cEJCQihRogTLly9n2LBhAIwbN44NGzYk2Odvv/3GokWLANi7dy+XLl0iRYoUdOvWDVNTUzw9PVm7dm28NS00KVNSxtmZ39/VJwHoN28eN0JDMcmTByNbWypWrMisWbMYMmQITZs2pXjx4p8smHzmzBkgeoVSXJydnWnYsCEAefPmZdKkSTx69Ig9e/bQuHHjWEGEGGZmZvTp04ebN2/StWtXjIyM2LlzJwULFqR3794J3tOFEEIIIYQQQogfkQQSxE/Lz8+PadOmAdGT/VOmTOHRo0eYm5uj0WhYu3YtJUqUoFGjRkru+ezZszNp0iQeP37Ms2fP2L59e5wpfjQaDfv372f9+vVAdKqXxBRyjXnCO6Y+QVxiAgne3t6EhYUZ3H98+vXrR7Zs2fD29lYmtCE6fdPhw4dp0qQJGo2Gbt260a9fP7RabZx96XQ6VqxYQb58+Vi2bBkQnSrJ29tbyYfu5+en1Jj4Enq9ntWrV1OwYEF2796NqakpEyZM4NSpUxQoUEBJwQMoqYcMde3aNeD/ueJj3HxXyDgmkODm5oaXlxeWlpb88ccfn30snTp1YubMmcpT/M7OzkRERJAzZ07OnDnD9u3buXfvHoMGDYoV2EiM/fv3U6VKFUJCQmjbti3t27dnyZIlXL58GY1GQ7p06ahbty7jxo1j//79vHr1iqtXrzJq1Kg4VxXs2LEDb29v0qRJQ5MmTWjXrh06nY5WrVrRtWvXz/48xNfl6upKREQEefPmJV++fOzbt4+DBw9iYmLC9OnTUavVdO/eXVmJMGDAAIOuKRcXF6Xmy6pVq3jy5AnZsmWjY8eOqFQqzpw5w+7duxPsp0jRosr3r1+/pm7dukoAz1C3b98GoOh7fX1IpVKxbt067t69i4eHB4MGDSJTpoQLPkP0Cq/Zs2dz+fJl/vjjDzQaDfPmzSNnzpzMmjXL4JoPQgghhBBCCCHEj0BSG4lPiu+J0Q/bGTp5bki70NBQg/drYWERb5uxY8cSFBRE3rx5MTIyYvHixQDMnTuX5MmTs2LFCo4cOcK+ffvYt28fv/32G927d6d69eqo39U68PPzU/rTaDScOXOGa9eucebMGS5cuMDbt2+B6EKc9vb2vHnzBv17k/2vX7+GTzzRCihP6N+/f1/JGR6X5MmTExwczOnTp8mXL1+c7QICAgwq/BkREcHUqVNp3rw5CxcupE6dOjg4OCjb58+fj52dHdOmTWPu3Ll4enqyevXqj4owe3h40KtXL86ePQtEp/8ZNWoUHh4eVKpUKVYAIr5gxKdoNJpYNRZ8fX3p1q0be/fuBaB48eIsXrxYSUMU03/NmjXZtGkT+/fvV4Iker1eKaQal5hAQv78+YmIiIh1jAB58uQhIiKCv//+G4h+yjmhdFMajSbe4E+ZMmUYP348Y8aMYdu2bfj7+/P3339jaWlJiRIlCAwMJDAwUGkfFBSUYCHs988/Y2Nj1q1bx6hRo9i+fTv29vYUL15c+QoJCVE+Fz8/P5o0acKVK1cYO3YspUuXVvrR6XTkzZsXvV7PvHnzgOgJ49atW/P27VscHR2ZMWMGer3e4HtHYoJuCTF0n4buN6n7+5bi+p1s2rQJgKZNm/LXX38xd+5cABwdHdm9ezezZs2iaNGiZMqUiWLFinH58mW6dOlCqlSpcHR0jHefNWrU4OnTp9y+fZtFixbRv39/8uTJQ9OmTdm4cSN79uzB2tqalClTKoW8P2Ss0VDw3fc5cubkxr17dO3ale3bt9OoUaNP3ucsLS2V+5her1eCl+nTp1fu1QCRkZEfrTRIlSpVrGsNomuGfGpFwofUajXLli3jxIkTjBkzhlu3btG/f38WLFjAqFGjqF27NiqVCp1O91GthrgkdL8SQgghhBBCCCG+NgkkiO9eeHj4Jydz4pvA8/T0ZMmSJQD06tVLmVBu1KgRZcuWBWD27Nl4eXmxcuVKdu7cyYULF2jTpg25cuWiW7du1K9fnzt37nD69GlOnz7NuXPnYk1GAVhbW1OlShUmTJigjFGv1xPybnuqVKlQxRHwiCkGbGRkRO7cuT/ZJkb27Nm5ceMGz549o1SpUnG2MzY2jrfeQoy3b99SrVo1GjduzJYtW+jfvz+HDx+ONXk1aNAgcubMSe/evTl48CCVKlVi69at5MiRg7dv3zJhwgQWLFiAVqvFwsKCXr16UbNmTYYPH66kFPkSzZs3j1Xj4OzZs0othFGjRtG/f39UKpUS9IlRs2ZNAK5cucLLly+xtbU1KOAVk9qoaNGiyn6joqKUYEHhwoUJCgrC1dUVgFq1amFqGl9Z2OiAjaWlZbxt6tSpg6WlJcOGDePkyZM4OzuzcuXKT65CMDY2TjCApgdizlIjY2NMLCyYPn0606dP/6jtlStXANi5cycLFiwgJCT6zB06dChLly7F3t4eiL7WrK2tOXbsGNeuXcPc3Jxz587x+PFj7O3t2b59O2nSpEnUBHxS+t4n8783b9684cCBA0B0IKFly5a8ffuWFClSkDdvXv755x8CAwO5evUq9evXp3z58gQGBvLgwQNatWrFjh074n3Kv0WLFjRr1owqVarw8OFD9u7dy44dO2jVqhWZM2dm5syZbNq0iRYtWsT59L9RVJTyffny5cmQPTuHDh3i4MGDPH78mNatW5MsWbKP3heThuvVq1fK/drBwSHWdRgcHPzRfeNT9Hq9Qe1iVKxYkcOHD7N+/XplBVzbtm0pU6YMY8eO/Wi1kxBCCCGEEEII8SOR1Ebiu6XVamnfvj22traMHz8+1tPpCRk0aBBarZbKlStz6tQpnjx5QoYMGRgwYECsdtmzZ2fMmDHs2LGDHj16YGVlxb179+jbty85cuSgVq1ajBs3jsOHD/P27VusrKyoWbMmY8eO5fDhw9y5c4elS5ca/JTp+2JSG/n7+yfYNia90f379xO9n/hMmDCBlClTcvPmTSW3+fvq1avHkSNHyJgxI7dv36Z8+fLMnDmTokWLMnfuXLRaLQ0aNGD37t1kz56dhg0bcubMGSwsLBgxYgQrV65Uvv76669Eje3w4cPs379f+QoICKB48eKcP3+ewYMHx1lIOX369BQrVgyAgwcPGrQvjUaj1EJ4f7Lv/v37REZGkjx5crJmzcr69euJiIgge/bs5MyZM1HHE5/ffvuNDRs2YGNjw8WLF2nSpEms1TD/lSdPntCjRw+mTp1KSEgI+fPnp1ChQoSEhNC/f/+P0m7NnDkTiF6dcebMGaytrdm5c6dSU0T8GHbs2EFUVBQFCxYkICBACaJVq1aNixcvKk/m+/j4KHUUfv/9d+zs7AgODqZZs2YJ1mxJkSIF69evx8rKCnd3d4YMGQLAiBEjaNq0KVqtlk2bNuHr65vgeNVqNX/88Qdt2rTB1NSUO3fuMGvWrHivkUePHgHR6doSCuYZ6tmzZxw+fDje/RoZGeHi4sLZs2fp27cv5ubmnDlzhho1atCrVy+ePHmSJGMRQgghhBBCCCG+NgkkiO+SXq+nb9++bNy4EY1Gw9SpU6lVqxbe3t4JvvfQoUPs3bsXY2NjqlWrxrp164DoOglxFUxOkyYNI0aM4NKlS4wYMYL06dOj0+mwtramRo0ajBkzhoMHD3L+/HnWrFlD165dKVy4sEFphOKSmEBCzOqFpA4kpEmThvHjxwMwffp0rl69+lGbYsWKcerUKUqUKMHr168ZPnw4T58+xc7ODldXV5YvX84///xD165dCQgIIH/+/Gzbto0///yTMmXKKF8lS5ZM1NgWLFjAsmXLlK9t27YptRASUqtWLQDlqeuE3L17l4iICJInT46dnZ3yekxwIX/+/KhUKlasWAFA1apVk/wp+OLFi7N582bSpk2Lp6cn9evXZ8OGDbx58yZJ9wPRKy1mzZpF27ZtuXLlCubm5vTu3ZulS5cyffp0MmfOzPPnzxk8eDDh4eFAdK2IvXv3olKp8PT0BGDlypXkz58/yccn/lsxaY0aNmxI586dgegi2+bm5ly6dAmAEiVKYGRkxP379zly5AhGRkY4OztTpEgRXr16RZMmTXj69Gm8+8mTJw9Lly5FpVKxbNky5fsFCxZQsWJFIiMjWbVqlcHnuIODA3369CFVqlT4+/szf/78OCf1Y9IavX89fy5fX19GjBhBhQoV6NChAyVLlqR+/fosWLAAT0/PT67ESZ48OUOGDOH06dM0btwYgM2bN1OgQAHGjh2rFLkXQgghhBBCCCF+FBJIEN+l8ePHs3z5clQqFb169cLa2pqzZ89SpkwZtmzZEmcKFa1Wy8CBAwHo2LGj8hT8+ymN4mNtbU2PHj04f/48Z86cUWoDdOnS5YsDBx9KTCAhpoZCUgcSAJo1a0alSpUIDw/Hycnpk5PvGTNm5NChQ7Rs2ZLkyZMzdOhQLl++TPbs2SlfvrxScLpdu3Zs3LhRSYnzJVq0aEGrVq2Ur7p168a5CuFDMemNDh06ZNBKlpj6CAUKFIiVyiQmkFCgQAHOnTvHrVu3sLCwoEKFCok9HIPEBGGyZMnC48ePGTBgAA4ODri4uLBx40Yl9dCXuHnzJpUrV2bs2LFERUVRqlQp/v33X5o3b46RkRE2NjbMmDEDKysrbty4wcSJE9Hr9cpqhLx58xIZGUnx4sVxcnL64vGIr8vf35/Dhw8D0TU3bt68iYWFBY6Ojhw6dAi9Xk+uXLkoX768ktv/xo0buLu7Y25uzsaNG8mZMydPnjyhcePGvHr1Kt791a5dm1GjRgEwePBgpUj66tWrSZ8+PcHBwaxatcrgQvKZMmWib9++2NraEhgYGGcw4cGDBwCffS/S6/Xcv3+fESNGULFiRdauXUtUVBSZMmVCr9dz9epVZsyYQe3atalZsyZDhw5V7iMfjnfhwoXs37+fkiVLEhYWxsSJEylQoADLly+PVY9FCCGEEEIIIYT4nkkgQXx3Dh8+zLRp04DowsiTJk3C3d2d3377jcDAQNq0aUONGjU4derUR++dO3cuHh4epEyZkrCwMJ48eYKtre1HKY0SYmZmhp2dXZIGDj5ka2sLgLe39yePJYafnx979uwB4OnTp+h0uiQdR8zTwhUrViQkJIQ2bdooBYbfZ2FhwfLly3n58iWjR4/GwsKCrl27KpPtPXr0YPDgwQnWDfgaSpUqRapUqXjz5g19+/ZNsNBzTN2D99MahYWFKZ97wYIFWb58ORAdlPpUbvakYmdnx+7duxk0aBD58+dHo9Fw/Phx+vfvT4UKFRg6dGiCaWXi0717dzw8PDAzM2Po0KHMnj2bjBkzxmqTLVs2JkyYAERfjx4eHmzZsgVAmfDt3r271Cb4AR07dgytVkuKFClYuXIlEF0X5PXr17x48QJAKbSdK1cuZQXQhQsXCAgIIE2aNGzZsoWMGTNy7949atSowZQpU1i/fj2nTp3Cx8fno+Bdnz59qFevHnq9nqFDhwJgY2ODs7MzxsbG+Pn5ce7cOYOPwdramu7duyvBhJ07d37UJiZA6+Pjw5EjR7h27Rq+vr5ERkbG2a9Wq+Xs2bOMGjWK3377DScnJyWAUKZMGTZs2IC7uztnz55l4sSJVKlSBTMzM549e8by5ctxcnKKs38HBwd27NjBhg0bsLe359mzZ3Tp0oWcOXMqq0CEEEIIIYQQQojvmQQSxHclMjJSWVHQpUsX2rVrB0RPrh48eJAhQ4Zgbm7O2bNnqVixIvXq1VMmva9cucKwYcOA6KfsYybJxo0bF2dKo28pV65clClTBo1Gw/Dhw9m1a1es7RqNhvXr1+Ps7Iy7uztqtZqOHTsmqvinoWxsbNiwYQPVq1cnKiqKrl27KiltPvT+/tu3b698tgsWLKBPnz5JlgP87t273L59W/l69uyZwe81MjJi4sSJqFQqFi9eTJMmTeJ8mn/Hjh1s2rQJtVpNq1atlNcHDRrErVu3SJs2LZUrV1Ym0tu3b/9FxxXfRGaM1KlT06tXLw4ePMiJEycYOHAg2bNnJzg4mKVLl1K6dGmlNkViaocANGjQAIguBO3j4xNnu5jfo7W1NdmzZ1dSxNjY2ADg5eWVqP2K70P58uVJkyYNAQEByqqoy5cvY2JigrW1NRAdbNBoNJw+fVoJFGbLlk3ZnjlzZrZu3Uq6dOl4/PgxM2bMoGfPntSvXx8HBwdy5cpF4cKF+eOPP+jevTtTpkzhxIkTwP/TjkH0SiCNRoORkVGia47EnJcAKVOm/Gh7o0aNMDEx4fjx4zRr1oyqVatSuHBhMmbMSOHChSlVqhR16tShTZs2DBw4kB49epA/f35+//13FixYwMOHDzE1NaVatWps2LCB9evXKwEWW1tbnJ2dWb58OVevXmXevHlAdJAtpsDzp6hUKho2bMi1a9eYOnUqmTJl4sWLFzRt2vSr1EQRQgghhBBCCCG+hAQSxHdlyZIl3Lt3j7Rp0zJy5MhY20xMTBgxYgTXr1+nbdu2GBkZsWfPHhwcHGjdujUuLi5ERUVRo0YNNm/eDEDr1q0pU6bMtziUBKlUKoYMGUKdOnXQ6XRMmzaNlStXotfruXz5Mm3btmXRokWEhYWRN29etm3bRr9+/ZJ8HBEREQwfPpxKlSrRtm1b0qZNy+3bt5Un0uPj7OzMjRs3aNSoESqVin379lG7dm1mzpz5xTnAy5QpQ6FChZSvrFmzKoVfDdGhQwfWr1+PmZkZu3fvpkqVKsoT1zFevXpF165dARgwYADFixcHonPIL1u2TFmtceDAAcLDwylcuHCiaz3EiIqKYsiQIVSoUEFZ6WCIHDly0Lt3b06ePMk///xDrVq1UKlUnDx5ktatW1O8eHHc3d0N7q93796MGTMGgLVr1zJnzpyPVrkEBQWxdOlSIPpztLa2pkmTJgBKWrGYa0z8WGxtbVm7di0qlYo7d+6QK1cuIiIi2Lt3L7Vq1cLU1JQnT56wYsUKZZWAg4MD9evXjxVEzJUrF4cPH2bkyJG0atWKSpUqYW9vj6mpKVqtVllp9e+//zJ16lQCAwMpWbJkrJUuR48eBeCPP/4gc+bMiToOvV6v1OrIly/fR9vLli3LunXrqFatGoUKFcLW1lZJjRYUFMS9e/c4c+YMO3fuZNmyZaxbtw4/Pz9sbGxo2rQpK1eu5NSpU/zzzz9KAOFTLCwsqFy5MmZmZgCEhoYmOHYzMzP69u3L1atXyZUrFz4+Pri4uCQ6KCiEEEIIIYQQQnxNhiUcF+IrePHiBZMnTwaiCyPb2Nig1+uJjIxUJmkgOl//vHnzGDRoEKNGjWLz5s2sXbsWiJ4kCw8P59WrVzg4ONCzZ89vciyGMjIyYvDgwaRJk4ZVq1axbNkyTp48qaSuSZEiBV27dqVMmTLkyZMnyffv5eVF+/btldzevXr1YsSIEfTp04fFixdTtWpV6tatG28ftra2jBs3jlatWjFlyhTOnDnD0qVLWbt2bawUQIlNyZQyZUoldU5kZCTBwcEMGTKEGjVqGJxyqlGjRtja2tKoUSMuXLhA2bJl2bt3r/JZ9u7dmxcvXpAvXz7GjBmDXq/n3r179OjRA4helVClShX69OkDQKdOnT4rnY9Go2HYsGEcOnQIgNGjR2NtbU358uUN7kOtVlO+fHkaNGiAt7c3K1euZO3atTx58oSGDRsyY8YM/vzzT4P66tu3L8mTJ2fAgAFs2rSJ0NBQhgwZonyuy5YtIzAwEHt7e2UFQ5MmTRg1ahS3bt3CxMQET09Pzp07R6lSpRL5aYhvrUaNGgwbNoyJEyfy9OlTkidPTmBgIKdPn6ZWrVrs2rWL4OBgjIyMqFq1apwFzjNmzEjv3r1jvabT6fDy8uLNmzd4e3vz+PFjvL29iYyMZOzYsZiZmfHw4UPat2+PXq+nRIkSnxWc8/X1JTAwEBMTkzhXM1SuXJnKlSvHGltgYCCPHz8mLCwMPz8/5SsqKopKlSpRtmxZTExMABK1CsrS0pKIiAiDAgkxbGxs2LRpE+XKlePYsWOMHDlS+RsohBBCCCGEEEJ8bySQIL4bw4YNIygoCAcHB2VCdOjQoSxYsIASJUrw+++/8/vvvyuTWrlz52bDhg0MGDCA4cOHc+nSJZycnFiyZAkWFhasXbv2s57wjIqKYvjw4WzZsoWCBQsqE10lSpT4T/L/q1QqOnToQOrUqZk9ezb37t1DrVZTv359OnTogJWVFYGBgUm+3z179tCzZ0+CgoJIlSoVadKk4e7duyxZsoTmzZuzYcMGevfuTbly5T6ZOuRDefPmZcWKFRw7doypU6fi5eWVqEm1D92/f19JpRIQEEDu3Lm5desW//77b6wURAlxdHTkxIkT1KtXj4cPH+Lo6MiOHTvw9/dn3bp1qNVqVqxYgbm5Oa9fv8bFxYXg4GDKly/P8OHDOXLkCA8fPsTa2ppmzZol+ji0Wi0jR47k0KFDGBsb4+DgwIULFxg4cCCLFy+maNGiie4za9asjBo1igEDBtCrVy+2b99Onz59uH37NmOGDDGoj44dO/Lq1SumTp3K7t27CQsLY/To0Tx9+pStW7cC0YGWmKe48+TJQ6FChfDw8KB48eJcunSJmjVrsm7dOn7//fdEH4P4tsaMGcPp06c5duwYNjY2mJiY8OTJE2xsbKhRowa3b9+mTJkyZMiQIVH9qtVqbG1tyZ079yeDTMHBwTg7OxMYGEiWLFkSDFTGJWY1Qq5cuZSJf0PGljJlylhpnJKKpaUlb968MbhotF6vZ8mSJezZs4exY8cyYMAAZs6cyW+//UbDhg2TdGxC/Kpi7hOfu10IIYQQQggR2y8bSIiIiGDZsmVcuXKFevXqffZkxo8mJi1JQnQ6nUFPXms0GoNy9mu12nifIr948SIrVqwAYMSIEbx+/Zrr16+zcOFCZfvFixcZN24cmTNnpkKFCtStW5fSpUuTOXNmVq1ahYeHh/J7HDNmDClSpMDDwwNLS8sEx+fl5cXTp08JDQ1l+vTpyhP658+f5/z58yxatAiIfvq+aNGiFChQgIIFC5I1a1bl+PV6PUFBQbx66ktMMqWRI0fi+/o1/v7+aLVaevfuTdasWZX9vnjxQnl/tWrVsLa25tSpUzg5OZEjRw4A3r59S0hIiEGT8s+fP08weBIVFUWPHj24fPkyAGnSpMHR0RGVSoWPjw+enp68efOGZMmS4evrS+/evVm2bFm8fWo0GoyNjVGpVFSpUoVChQpx6NChWEWOIyIimD59eoLH8CkpUqRg0KBBDB06lHHjxtGsWTPMzMwIDQ2Ndf7p9XoiIiIwNzeP9f4MGTJw4sQJGjVqxPnz56levbqyWqJfv344ODgon4uHhwepU6dm2rRp+Pv7M3/+fAAaNmzI27dvefv2LS9evIi1SiYup06don///rx8+RKVSkWePHkwMzMjZcqUvHnzhg4dOlCkSBGqV69Oo0aNEuzv1atXH6WMGj58OOnTp2fx4sUsXryY29eusejdtsCAQFQREXH25+joyOjRoxk/fjxHjhwhJCQEjUaDVqvF0dGRIkWKEBERgV6vJzw8nAYNGuDh4YGVlRWOjo64u7tTt25dxo8fT9++fQEM+lz0er1Bq0r0er1B96HEtDOEofc/vV5vcM2S760wtZGREf/++y8ODg68ePGCLFmy8OTJE27evEmqVKmUlSjvf2ZPnz5VrgeIThF0//59smXLRurUqZXXra2tyZIly0f71Ov1zJ49G09PT1KkSEHZsmXx9fX95PiM37t3BL99+1G7q1evApApUyZlm7GxsUFpwzQajVLsPj7e3t5YWFgk2M7S0lK55wQGBsZZB+XWrVsYGRmh1WpZsGCBUhfH09OT+vXrs2PHDtq2bUtoaCht2rRJcL9JfX3A93eeCvE50qRJg6WlJS4uLgm2tbS0JE2aNF9hVEIIIYQQQvz4VHpDZ1Z+Iv7+/tSuXRu1Wk2yZMk4duwYBw8epHr16l/Ub1BQEDY2NgQGBib5045JxdBft6ETDxqNxqB28QUSdDodFStW5OzZszRo0IAFCxag1+txcnLi0qVL1KlTh4oVK3Lo0CHc3NxiFQG2srKicuXKVK9enblz53L//n1q167N33//jUqlwt/f36Cn6Y8ePUpERASTJk3i8ePHmJub06lTJ3Q6nVLs91NFhK2srMiWLRsBAQH4+fkRERGBhUrFpdzRqXOK371D2Hufebp06fjrr79Ily4dED25/qn83h8KDQ016D+6d+/ejTdw8uzZMwYMGMD169eB6MBIlixZlMnQ4OBgPD090el0pEqVijdv3qDX61mzZg1NmzaNd9/GxsYcO3aMf/75h23btsU5mZbQ9RHXdRQaGkquXLnw9fVl7ty59OrV66PCpqNGjWLRokWMHDmSXr16KeemVqslWbJkhIaG0qpVK3bu3AlEr6I4f/485ubmbNiwgVatWqFSqVizZg0VKlTg6dOnODo6otPpOHr0qJJCxcvLK8HzSqfTUaRIkXjbxChevDjr169PsJ2Pj49y7nzo4MGDDB8+HFVEhHL+pT54AFU8E6Hh4eGkSJGCQ4cO4eLiojxNbWJiwvnz55VgVmRkJJaWlty9e5eiRYtibGzMvXv3GD9+PMuXLwegefPmLFiwwKDrzdAJ+KSeAE3q+9+3mKA19O+Moe2OHTtGtWrV0Ol0NGvWjI0bN6JSqdi8eTN//PFHrLZ9+vRRfm/h4eFs2bKFoKAgIDrYZ2dnh52dHWnSpPlk2q5du3axadMmjIyMGDZsGFqtVjnHPqSOjKTUnLnR72vZgvzFiinbQkNDadmyJVqtlr///lsJCpw7d86gWgv+/v4GXZuXLl36ZEDkQxYWFkybNo2rV6+ybdu2WAWl33f06FEiIyOZPHkyFy5cQKVSkSxZMoKDg3F0dCQoKAgPDw+yZMnCjRs3sLKyine/3/N5+r1L6utIfB2XL19WVsQVe++e8Cne3t74+/sn2GeaNGliPeDxpfuNjy40lDvFomsx5bl8CbUBD7p8z+T6EEIIIYT49fySKxKcnZ1xdHRkzpw5QHQe9bNnzyY6kBAREUHEe0/7xkyoiMRZt24dZ8+eJVmyZAwdOhSAbdu2cenSJSwtLRk3bhy2trY4OzsTFhaGm5sbu3bt4uTJk/j7+7Nz505lYtjW1pZp06YlejLEx8eHOXPm8OrVK1KkSMHQoUOVCa5KlSoB0ZPsFy5cICgoiBs3bnDr1i3evn3LjRs3YvVlY5NC+b5uvXqktLUlTZo0rF27lsePHzNw4EAWLFiQ4CRRUjt58iTDhg0jMDAQIyMj7O3tSZUqFQAhISG8ffuWtGnTYm9vz/3793n9+jXp06fnxYsX9OzZk7Jly35yks7X15c1a9awcuVKHj58qLyeK1cubGxslJ+1Wi1Xrlz56P2GXkeWlpaMGjWKLl26MGHCBNq2bRtru4+PDwsWLECj0TBy5Ei8vb2ZNm1arACWpaUlGzduZOTIkezdu1dJaXT37l26desGQM+ePalQoQIAK1euRKfTUbZs2TjzsH+KXq9n0qRJBre/efMmr169ivVUd2LVqFGDTJkyMahXL+W1s+fOUebd+Ruf6tWrs23bNpo2bcrbt2/p1q3bJyd4c+fOTcGCBblx4wYHDhxg/vz5FCpUiAEDBrBhwwbu3r3Ljh07yJQp02cfh/g8n/v3qHLlyowaNYoxY8bg6upKo0aN2Lp1K61bt+bAgQOUKFHio/dotVoOHDhAUFAQZmZmREVFERAQwJUrV7hy5QoWFhbcuXOHYsWKUbBgQUxNTbl+/bpSoLtVq1bkzp37s9OKXL16Fa1WS6ZMmeJcWaDVanny5An+/v5kz579i64tiA4M+vj4EBgYSKFChT76GxOzwim+lWP+/v6MHz+eBw8eYGZmxqBBg0iVKhUDBw7E3d2dli1b8uzZM3x8fGjXrh2bNm36ZSb2hUhqWbNmTTBAIIQQQgghhEicXy6Q4OHhwaFDh9i2bZvymqWlJV5eXrRs2ZJChQrRr18/g9JzTJ48mbFjx/6Xw/3pvX37lmHDhgHR9RBsbW0JDg5m4sSJQHSO9vcniiwsLKhRowYlSpQgRYoUXLlyhYMHD3Lo0CGePHnCvHnzlMlxQ7m5uTFp0iTCwsLIlCkTw4cP/+RT38mTJ6dQoUJKjQaNRsP9+/d58uQJqVOnJl26dKRJkwYzgO7RxXp79uwJ786lokWL0rVrVx4+fMjQoUOZOXNmYj+uz6LRaFiwYIGSnihm/MmTJ0ev1/PixQu8vb2V1Ey5cuUic+bMPHnyhJcvX5I7d27u3r1Lhw4d2Lt3L2q1Go1Gw759+1ixYgX79u1TCilbW1vj7OxMhw4dPnpqL+bJtQ8l5jpq164dM2bM4P79+8yZM0cpggwwf/58NBqNkurk77//5smTJyxfvjxWqiMjIyMmTZqkTPSHhYXRsmVLgoODKV26tJKi59y5c/zzzz/Kfg2l1+uZOnUqGzduNPg94eHhdOjQgTVr1pA8eXKD3/ehAgUKsHLlSujbDwAXZ2fGTp1qUHoHR0dHjh49ipubW7xFmxs2bMiNGzfYtm0brVq1onPnzuTNmxcXFxcuX75MyZIl2bp1K6VLl/7s4xCJ9yV/jwYNGoS7uzuHDh3i+vXrVKpUiePHj1O3bl3279//0dP7p0+f5unTp5iYmNCgQQOSJUuGt7c3jx49wtvbm7CwME6cOMGJEycwNTWlUKFCeHp6otfrqVy5MlWqVPns43z16hU7duwAolfyxNBqtfj6+nLr1i3u3bvHvXv3Yq1eS506Nblz5yZ37twGpzF58+YNDx8+xNPTE09PTyWtWJMmTT5adRCzEiyuQML169fp37+/EqweO3asUvS9a9euzJ8/nw0bNtC5c2f+/vtvtm7dyvTp0xk0aJBhH4wQQgghhBBCCPEfMyy5808kJt/3sWPHAFi7di3btm3DysoKW1tbxo8fT/369Q3qa+jQoQQGBipfPj4+/+HIf06TJ0/m2bNn5MiRg969ewMwb948Xrx4Qfbs2enYsWOc71Wr1RQvXpyhQ4dy9OhR7t69i6OjY6L2v2XLFlq3bk1YWBj58+dn4sSJcaaO+ZCxsTF58+alWrVqODg4kClTpngDULa2tsyYMYPkyZNz7do1xo4dG6uGQGI8e/bsoxoEn+Lv70/79u2VIELLli1ZvXo1ZmZmaLVa7t+/z+PHj5WUL2/evMHHx4eMGTOSOnVq9Ho9z549w8zMjGPHjjF9+nRGjRpFzpw5ady4MXv27EGn0+Ho6MiqVat49uwZixYtStTS/8RcRyYmJowfPx6AGTNm8OrVKyB6cnHVqlUALFy4kNWrV2Nubs6+ffuoU6dOvOkNYlI9pUuXjjlz5mBkZMSrV6/o2bMnWq2WBg0aJGq10qxZs/j3338Nbh9zXDdv3qRbt25xpoQyVNq0aZXvNRoN/fr1Y+TIkUqwJz558uShQ4cO8Z7HMYVgjx49yuvXrwGoWLEibm5u5M+fn+fPn1O5cmVWr179RcchEudL/h7FFBzPlCkT9+7dI0WKFJQqVYo3b95Qp04dbt26pbS9efMmHh4eQHRdl1SpUmFmZkauXLmoUaMGbdu2pU6dOlSvXp3UqVMTGRnJpUuXCA0NJWfOnPEGqRJy/PhxunfvjqenJ8bGxpQtW5Zt27YxduxYWrZsyfLly9m2bRseHh6Eh4djYWGhpG579eoVZ86cYdWqVcycOZP27dszZ84c3N3dY6W8unbtGgsXLqRjx47MnTuX1atXc+HCBYKDg5Wizq6urvj5+cUaW0wgISQk5KNxHzx4kGrVqvHq1SuyZs3KnDlzlCACwO+//06NGjXQ6XSsW7dOCfwNGzaMI0eOfPbnJYQQQgghhBBCJKVfLpBQokQJmjdvjpOTE3Z2drRr1w5XV1fmzJnDrFmz2LhxI/v37+fcuXMJ9mVmZoa1tXWsL2G4sLAw5s2bB8D06dMxNzfn7du3ylPgo0ePNmhlyOe6d+8eAwYMQKPRUKpUKUaOHPlFT4MbIkuWLJQtWxaIXgkRMyFnqNDQUKZNm0aJEiVo2rRpgoVF586dqxRV7tevH0OHDsXU1BSILswcMxGcLl06JRf4s2fPiIqKwt7eHgsLC96+fUv27NkBGDduHFOnTuXZs2ekTZuWvn37cu3aNY4ePUqrVq0MKmz9ocReR02bNqVo0aIEBQUxYcIEIHpiLywsDFtbWypXroyTkxOjRo0ConMbxxTt/tCVK1eUehorV65Ugkhr167l+fPn2NjYMGnSJIPTizx8+DB6RQDQ670UQwnJmzcvAGfPnmXfvn0Gvy8hZd8F1pYsWWLQPc0QuXPnJl++fGg0Gk6ePKm8nj17do4ePYqTkxORkZG0b9+eu3fvJsk+RcK+9O9RTPo1Y2NjduzYgbOzM8WLF8ff358aNWpw9epV9Ho9Z8+eBSBnzpzY2dl91I+RkRGZM2emVatWzJ49mz59+pAhQwYg+sn7mMn4xLpx4wYzZ84kJCSEnDlzMmfOHE6cOMGKFSu4ePEioaGhmJmZUbhwYRo3bsywYcOYOXMmw4cPZ9asWfTs2ZOaNWtiZ2eHWq3m5cuXHDlyhClTpij34ZcvXzJixAj279/P8+fPUavV5MqVi3r16jF06FAWLFhAnjx5iIyM/GjFUUyqug/v6e7u7jRp0oTg4GAKFy7MzJkzP0rHpFKp6N69O1myZCEwMJBXr17Rpk0bdDod7du3R6PRfNZnJoQQQgghhBBCJKWfPpCg0+k4d+6cMpkKsH79eu7fv8+///6Lvb091apVU7bVqlULtVqtpDAQ/x1PT08iIiJInTq1UtTz7NmzREREkD179i8ufp2QmPz3lStXpnPnzp89wWWo06dP07p1aw4ePAhAoUKFYj2VGh+9Xs+WLVsoWbIkkydPVtJnvH9ef0qdOnWU4MjSpUtjBR5SpkypHLOfnx/Pnj0DolMemZiY8OLFC6X47qNHj4DoJ9ytrKxYv349Dx8+ZMqUKcok+NeiVquZPXs2AMuWLcPd3Z3y5ctjamrK8+fPWbZsGX379lVSZqVNmxYnJ6dP9rV9+3YA6tevH+s+UKFCBYyMjAgMDFTqbxgic+bM5MqVC4C9e/ca/L4XL14A0ZORn8pJ/7lOu7sD4ODgQOHChQ16jyGrZGLafDhZbWVlxZYtW6hatSo6nY5NmzYlcsTiWypTpgwjR44EoguXz5s3j2LFiuHv70/NmjV5/vy5cn7fv3//o0lzrVbLixcvuH79OvPmzaN3797MmTNHubf8888/seo4JMa6deuA6Jo1M2bMIFu2bEqAwsbGhjlz5tC/f3+6detGtWrVyJo1q1IY2tzcnAIFCtCgQQMGDx7M8OHDldVuarVamdiPCbICDB48mMGDBzNkyBCcnJzImTMnxsbGuLi4oFKpuHLlCvfv31faN2vWDIAVK1bg/u66e/ToEc2bNycqKoq6desyduzYOGvjvH37lpcvXwJQsGBBFi5cSNq0afH29sbV1fWzPjMhhBBCCCGEECIp/dSBhNevX+Po6MjQoUO5dOlSrG12dna8ffsWHx8ffH19ldddXV3JmDEj5cqV+9rD/eXEFNrMnz+/8sT3qVOnAChfvvx/WmQyMDCQLVu2ANC5c2dlwum/EBUVxcSJExk8eLBST2HEiBEsXLgQCwuLBN9/5coVmjVrRseOHXn69CmZM2dWJsZv374d73tLly7Nli1bcHBwIDg4mCFDhjBs2DC0Wi2WlpYUKlSIlClTotfr0Wg0mJubkzt3bl6/fo23tzcQXUQ0MjISY+Pokipjx46lYcOGsSbdvrZKlSopE4E9evQgc+bMSi7xfv36KamcWrVqxYULFyhatOgn+4kJEnyYzszBwYGBAwcC0ROqJ06cMGhcpqamLFq0iLRp08aaZEzIy5cvUavVzJkz54sKFev1+ljBIhNTU4YPH86ePXuUYrBx0Wq19OjRAzs7OyXY9SkajUYJLMVMKr9PrVbj7OwMoBTXFT+O/v37U6JECQICAqhTpw6DBg2ibNmyBAYGsmfPHrJnz06hQoWA6FVV7u7unDlzhu3bt/PPP/+wdetWzpw5w4ULF3jz5o1S2N3c3BxPT09mzpyZ6GDCIy8vPDw8MDY2plWrVkoB9SpVqmBsbExgYCB6vd7g+7iZmZlyfytdurSyEsnGxkaZ6M+YMeMnV8S9/++DzZs3K2mRqlatSuvWrdHr9XTu3BlfX18aNWrEq1evcHBwYPny5fEGq1euXElERAT58+enYsWKWFhY0KlTJyC6/osQQgghhBBCCPGt/dSBhF69elG4cGGOHj36yVz7jo6OpE6dmsqVK7NixQrGjh1L165d2bBhw3+aUkdEi8m7nT9/fuU1Nzc3IDqQ8F/atGkTYWFh5MmThzJlyvxn+wkJDmbgwIHs378fIyMjWrRowbp166hZs2aCgZJnz57Rq1cv/vjjD65cuUKyZMkYPnw458+fp0OHDgDcuXMnwTFkypSJ5cuX061bN9RqNbt27VIKh5qYmJArVy7s7OxInTo1efPmJSwsjAcPHgDRdSBCQkJIlSoVGo0GBwcHunTp8uUfTBKYNm0aGTJk4MGDB0yZMoU+ffpQsGBBAHLkyMGePXtYsGBBnMW37927x61btzA2NqZ27dofbe/SpQtVqlQhIiKCP//8k2nTphmUYsTW1tbgINH7Bg4c+EXn/cuXL+nZs2esgru7d++md+/eShAoLjqdjp49e7J69WoCAgLo0KEDjx8//mRbb29voqKiMDc3jzPo4eTkhImJCTdu3FAChuLHYGxszJYtWyhdujQBAQG0bNmS6tWrU6NGDTQaDXv37iVjxozKyplr165x5coVnj17hlarxczMjKxZs9K0aVOGDx/OkiVLGDt2LAMHDvzsYMLx48cBqFGjRqwaIDY2NkqquAMHDhjcX0hIiFInqV69esrrKpVKSfEWE2j4FCcnJ0xNTbl//z7Xrl1TXp8yZQqZMmXi4cOHlChRgtu3b5MxY0Y2b94cbyDvzp07HD58GIgObMf8bejSpQvGxsacPHmSq1evGnx8QgghhBBCCCHEf+GnDSRERkayefNmpYAvgI+PD9OnT2fatGk8efKE5MmTc+jQITJlykTv3r1xc3PjyJEjiS7YKz7Ph4GEp0+fcv/+fVQqVaJ+BxqNhhUrVtClSxdlIjQ+Wq1WKczbtm3b/3TlQ99+/bh06RIWFhZMnTqVbt26JVhHICwsjNmzZ1O+fHm2bt0KQIMGDbhw4QIDBgzAwsJCSSf0+PFjJc1RfIyNjenatSsrV64kY8aMREZGcvPmTZ4+fQpE10jImTMnWq2Wu3fvotfrMTIyQqPRkD59el6/fo1KpWLBggXK08DfWooUKZg1axYQXaD71q1b7Ny5k5UrV3L69OkEJ+V37doFRK9uSJEixUfb1Wo1ixcvVorDLliwgBYtWvD8+fMEx5YvXz5mzJhh8LGkTZuWdu3aGdz+fXq9np07d1K/fn1OnDgR66nnPLlzG/T+AQMGsHbtWtRqNfb29gQEBNCqVatPTvbGrLTIkSNHnE+Ap0iRQkkVFbPyR/w4bG1tOXDgAG3btkWn0ykpebJnz45Op+PgwYPY2NhQvnx50qZNS758+ahcuTItWrSgXbt21K5dm7p165I3b14lKJ87d+6PgglRUVEGjeexlxfGxsY0btz4o201a9YE4MSJEwYXKr9w4QKRkZHkzJkzViAbUAIJT548ifP9KVOmjHV+x6T6srGxUeqxBAQEYGlpyebNm8mYMWOcfen1ehYvXgxEF69+P91dpkyZaNSoESCrEoT4Hnh6enL58uV4v+ILQgohhBBCCPGji/8x1R9YeHg4kZGRhISEANFPKzZr1ow8efLw6NEjJk+ezOHDhylevDhHjx79xqONW0zaBEMYMiFuaH86nc6gNBF6vd6g/UZFRaHT6WK9FhNIyJ07N1FRURw6dAiInoQNDg6Ot07Fo0ePSJYsGXfu3GHRokU8fPgQiJ4cHjVqFKVKlaJq1arkzp1bmRiK4e7ujre3N1ZWVhQrVgwvLy/u379v0PGqVKpPTjrHahMZSep333s9ekSqVKmYNGmScpzvi4iIIDw8nIiICK5fv87Zs2fZuHGjklO8ePHijBgxguzZs2Ntba2czxYWFqRKlYrXr19z7do1Jf/9mzdv4n3SN3v27Pz99984OzsTEBDAkydPCAwMxM7ODpVKxZ07d9BqtRgZGaHVajExMVGerO/UqRPFixdP1Dn5JfR6fYL7ql27Ng0aNGD79u10796dY8eO0aBBA+X9McLDwz8KgOzYsUPpIzw8HIguZm1ubq60UavVjB49muLFizN8+HDOnTtHrVq16NevH5UrV453bHGlU/qUHDlyGJQKKSgoKFZdAn9/fyZOnKikBMuXLx9jhg6FiZMACAsLJ76r8+rVq6xcuZLt27ejUqno168fBQoUoFevXly5coW2bdvStWtXzMzMKFWqFIBSQDlHjhwf1VPQaDRKyqvGjRuzb98+Nm/ezPDhw2O1M/S+kZj7kCHtdDqdwffJ/zLA+L351HVmamrKwoULcXBwoF+/fmzdupVUqVJhb2/Pw4cPOXLkCOXLl//k5H5MqrQP2dvb069fP2bNmoWnpydBQUG0bNnykyl/jDQaSr33c7ly5dBqtR8F8tKmTUu6dOl4+fIlZ8+eTXDVkFar5cyZMwBUrFgRPz+/WNtTpkwJRK9Ysra2Vu7FH0qXLh3m5uY8e/aMDRs2kDNnTgDSp0+Ps7MzO3fuZPjw4ajVamXVwosXL0iTJk2sfk6ePImnpydmZma0bNmSiIgINBoNQUFBALRr146NGzeybt06hg0bFmtFBhBnzYX3JfV1BIb9e0OIn0WaNGmwtLTExcUlwbaWlpZ4enqSNWvWrzAyIYQQQgghvq6fNpBgbW2Nvb09ixcvJm/evLRq1Yrt27dTuXJlgoKCqFmzJj169FAmFH4Vhv7nX61WG9TWxMTEoHYf5tMPDQ1V8qwXLFgQIyMjJX2Fo6NjgpMjAQEBrF69mn379qHX60mePDnVqlXjypUrPH78mFOnTnHq1ClSpEhBvXr1qFu3rjLREzOB3LBhQ2VSxtTUVJlAio9GoyFHjhzxtrn83jmVM1cuNmzfTrZs2WK1iYyM5NKlS7i7uzN9+nTOnTunFDaG6Kdix48fT6NGjVCpVAQHB3+UKid//vycOnUKHx8fZQWHnZ2dQSl1zp8/z9GjRxkwYABv377F29ubtGnTEhkZibW1NUFBQZibm9OsWTNWrVpF+vTpmTx58jeti/ApyZMnZ+HChZw4cQIPDw+WLl3K4MGDP2qnVqtjTZC9ePGCc+fOAVC3bl0lyJAxY8ZYgYQYnTp1okqVKjg7O+Ph4cHIkSPp3bs3AwYMiDdt0J49e1izZg0bNmzAxMSE6dOnU6RIEcaOHcvx48dJmTIlS5YsISIi4qPCxZ9ibW2tPLEcFhZGu3bt8PDwwNTUlKFDh9K7d2/UUVH4vgskpEyVEnU858Pq1auVgtPdunVTcr/36dOH8ePHs3v3bnLnzk3VqlWVzyXmus2TJ89Hn5VWq1XuB05OTnTp0oWbN29y+/Zt8uXLp7RTqVQG3TcMvQ8ZytD9JqbdzyC+VH49evSgSJEiNGnShJcvX6LX63FycsLV1RU3NzcqVKhA+fLl0Wg0REVFERUVRWhoKDqdLtZradOm5Y8//sDExIRq1apRr149nj59yu7du+nfv/9HY1C/t7rA2NiY/v37kz59+k+OsXnz5sybN48HDx7QsmXLeI/1woULhISEkCZNGjp27PjRPS0sLIwNGzbw5s0bgDhXkFlaWlKqVClOnDjBiRMn6Nevn3I9dOvWjU6dOmFsbBwrgJ4nTx4l/RpE/x3s3r07AAMGDKBhw4YASsAYoGTJkhQtWpSrV6/SuHFjtmzZEiuY8C2uIyF+NVmzZsXT0xN/f/9423l6euLi4oK/v78EEoQQQgghxE/ppw0kQHTRyB49epAmTRrKlCmjPEFsbW3NoEGDaN269Tce4a/rzp076PV60qRJQ7p06dDr9crKkPhqFuj1enbv3s3EiROVJzarVKlChw4dSJEiBXq9ngcPHnD48GGOHz+uBBxWr15Nvnz5qFChAufOnUOtVtO0adMkP679+/czbdw4ztlHBxt279pF6kyZ0Ol0XLhwATc3N06ePMnZs2c/SkmUNm1aypcvT6VKlWjRokWCAYE8efJw6tSpz8pBr1KpaNGiBSVLlqRDhw5cvnyZgIAAkiVLRlBQECqVikmTJjFs2DAAZs6cmeBKjG8lffr0zJw5k7Zt2zJ27Fjq168fKz3Ip8QEoIoXL25wceOcOXNy4sQJBg8ezN9//82cOXM4e/YsixcvxtbWNs73derUiWfPnnHixAlGjhxJlSpVOH78OMbGxowbN4506dLh4+OTqGMGGDp0KB4eHqRJk4Y9e/YoE/U6A9PFzJgxQ0md1bFjR6pXr65sK168OE2aNGHz5s0sWrQoVlHlmJUTnyq0/L4UKVJQvXp19u7dy4oVK5g2bVqijk/8j727Dosy/Ro4/h26BFRUMFgTRezuTrAbERM71u5uFLsVG0VsLEwWezGwxVoDG0XpZub9Y37zvIzUoFi79+e65rqWmSdnnmdmPee+z/l11K5dm6tXr9KuXTuuX7/O4cOHadSoEadPn2bOnDkab6d169Zs27aNatWqcejQIezt7aUyR6klE1SqVaueZhIBoEWLFqxZs4bXr18TFBSUZgBPoVBIvQg6duyYamK0UKFCgLIU4pez6L5UpkwZbty4QXh4OLt376Z79+7Sa5r0JZk2bRqvXr0if/78DBkyJNXlZDIZy5cvp127dty6dYtmzZqxf//+FMlpQRC+L2tra5EcEARBEARBEP7z/rU9EkDZqLB169YsWLCAly9fqpVvePLkCZUrV/6JR/ffpgp+q4KfgYGBvH37Fn19fcqXL5/qOs+ePaN3796MGzeO8PBwChQowPz58xk9erQU5JbJZBQtWpQBAwawfft2RowYQf369dHR0SEwMJB169YByrr46dWtziyFQsG2bduYMmWKWmmN7DlyEBYWRps2bWjYsCHTp0/H19eX6OhocubMSatWrVi0aBFXr17l6dOnbNu2jd69e2s0q0D13j148OCrj7tIkSKcPHmSsWPHUqhQIWkkrKurKz4+PsTGxtKwYUMcHR2/eh8/grOzM02aNCEuLo5+/fqlGwBUfVagDEBmhoGBAcuWLWPlypUYGxvz999/07BhQ2k2TWq0tLSYOHEidnZ2RERE4O3tDShH/ZcuXTpT+1fZs2cPmzZtQiaT4e7urjbaXxOrVq2SmjL36NEDBweHFMt06dKF0qVLExsby9y5c6VSY48fPwYyTiSAMokCsGnTJrVR1sLvp0CBAvj6+uLs7IxcLuf06dNUrlwZOzs77OzsKFu2LJUqVaJ69erUqlWLBg0a0LhxY+zt7WnVqhV6enp4e3vTvXt3EhISqFatmjSKP7UGzA+SNZJv1Khhusdmbm4uDRQ4f/58mss9e/aMFy9eoKOjI/Ue+JKlpSVGRkYkJiYSFhaW7n51dHSkZs8eHh4Z9udRiYiIoFu3bmzYsAGA2bNnp/udX6ZMGY4fP06BAgX4559/aNq0qVQaUBAEQRAEQRAEQRB+lH91IkFLS4tdu3bRpUsXAgICcHFx4datW2zYsIGFCxeyaNGin32I/1mqIIgqAKrqj1CxYsUUo1KTkpJYsWIFbdq0wd/fH319fTp27MjKlSul3gCp0dXVpXLlyixevFgKltva2mJqakqfPn2y7FzkcjkLFy6UmmEmn+nw7t07mjZtiq+vL4aGhrRs2ZKFCxfy999/8+zZMzZt2kT//v2xtbXNdOkJVcPlb0kkJCYmcuTIEWJiYqRR8X/++SeWlpacOnVKqpP+q5fFkMlkrF27FmNjYy5cuCDVGk+Nl5cX/v7+GBkZSY2UM6tVq1acOnUKOzs7QkJCcHR0ZP78+Wn2dNDX12fOnDlS8qpNmzaZTmKoPH78WGoiP2bMGBo0aJCp9Tdt2sT48eMB5ahsVU8JUPZguHHjBnK5HG1tbUaNGkWOHDl4+fIlgwYNIiYmhhcvXgDK3iYZsbe3p0iRIoSGhrJz585MHafw6zE0NMTd3Z3Fixejra3N1atXkclk1KtXj7Zt29K3b18OHjzIsWPHOHLkCN7e3uzdu5ddu3bh5eWVIplQrFixFA2Y4+LikMvlHPI+JO03e44cGR5bmzZtAGXZNlXPky+pZr1VqlSJHGlsUyaTUbBgQQCNEgMlSpQgT548REVFSQnK9Dx//pwmTZpw7Ngx6ftVdezpKVq0KCdOnMDW1pZ3797RvHlzqTybIAiCIAiCIAiCIPwI/+rSRqAM4Hl6etKqVSuWLl1KvXr1KFu2LMePH89UM1Qh67x584bdu3cDyjJTzs7OUokVVUPX5Hx9fVmzZg0A1apVY8aMGQQHB6faoDMt2bNnx9HR8buMrPf392fPnj0AjBgxgq7t2oFLXwAWublx9+5d8uTJw759+7Lsmrt27RqTJ08G/r8Eh6ZNMlXevn2Li4uL1KgXlEmQ8ePHS7N1RowYoVHA+FdgbW2NhYUFUVFR7NmzhyZNmtC7d29A2ZR47969eHh4SI1P//zzT6ysrL56f4ULF+bo0aNMmzaNrVu3snTpUsqWLUvz5s1TXd7c3JzVq1cTGBhIlSpVvnq/y5cvJzIyEnNzc4YNG5apdd+/f8/IkSMB5WdbrVo1tdc3bNjA+fPnmTBhAlWrVsXc3JzRo0czefJkPD09adu2LQqFgmzZsqVo+poaLS0tBg0axKhRo1iyZAm9evXKsOSL8GuTyWQMGTKEUqVK4ejoyN27d7l79670+pUrV1iyZEmK9Zo2bYqXlxedO3fG29tbmpljY2PDmDFjWLhwIYGBgaxcuZLQ0FDev3gBNumXKEtO1SA7Li6Ohw8fUrZs2RTL3Lx5E1AmshcvXkzz5s0pUaKElCiNi4vDy8tLSpZFRERo9H7Ur1+fXbt2sX//fnr37o2JiUmay0+fPp3AwECMjY3x9vamUqVKGp9j3rx5OXbsGJ07d+bKlSt069aN58+fazSDTRB+dUFBQRr1IBAEQRAEQRAE4ef5bWckfP78mT///JPKlSvTvn17qe5xWhwdHfH39+fz58/4+flRoUKFH3SkQnLv37+nWbNmvHjxAgMDA9zc3Ni9ezdJSUk0btw41b4FZcuWlYKWnz59SjdI8zMUKlRIatb7ZYA49H+lMUaOHJllSYTAwEDatWvHzZs3MTAwYPTo0ZlOIpw/f55atWpx4cIFTExM6NWrFytWrGD16tVs3LhRqts9YsSILDnmH2HatGlSALBu3brUqVOHQ4cO0b59eylYeevWLXR1dXF0dJQC6t/CwMAAV1dXOnbsCPx/oDIt5ubmVK9eXbpevkb79u0xMDAgNDSUli1b8v79e43XvXz5MklJSZQsWZIZM2akmGmimin0/Plz6bmSJUtKDcbfvHkDgLGxscazVHr16kXOnDl59OgRHh4eGh+r8GurV68e169fx83NTSotp62tne7o+vr160szzpLX+LexsWH48OGA8h56/vw5+qk0PU+NXC5HggufAAEAAElEQVRny5YtDB06FIVCQb58+ShatGiqyzZv3hwjIyPCw8Px8PDAycmJ9u3b4+7uzsGDB2nXrh1Lly4lKiqKokWLSjMTMlK4cGEKFChAQkIC169fT3dZVRmxqKgoLl++rNH2k8uePTsHDx6kQIECfPjwQdxTwr9CUFAQtra2VKxYMd1Ht27dMDIywsLC4mcfsiAIgiAIgiD8J/2WiYTo6Gjq1q1LaGgo/fr1Iy4ujsaNGzNgwAC1+vSgHKV45cqVn3SkQnIfP36kefPmPHz4EJlMRmxsLNra2lKS58iRIxgbG6dYL3fu3GzZsgULCwsePXpEr169NBop+i2CgoL466+/iImJyXBZS0tLqT73rl271F4zzZYNQKov/63ev39Px44dCQsLo3r16ty7d09qiKyJxMREFi5cSJ8+ffj48SOlS5fm3LlzLF26lO7duxMVFYWbmxsAEydO/G1Gus6fP19q+jpu3DjKly9PrVq1aNu2LQcPHiQhIYHy5cuzaNEi/vnnH9zd3TEyMspwuxcuXGD8+PG8evUq3eVUvQ5UjYi/p3r16nH48GFy5szJjRs3aNiwodqI8PSovgurV6+eIhEQFhZGSEgIAMHBwWqvqZrSplUyJj2mpqaMGzcOgJkzZ6ZZckr4/eTJk4ebN28SGhqKtrY2O3bsoGnTpmkuf/78eSIiIsiTJw8VK1ZUe83Ozo7WrVtjYGBAs2bNmDdvXob7Dw8PZ/To0axevRq5XE6FChUYN25cqr8joAziu7q60q9fPxo3boy+vj7Pnz9n9erVzJw5k7dv35I7d26mT5+Op6cnpqamGr8Xqtl0GZUb6tixI9OnTwdgypQpbNu2jZCQEN68ecPz58959OgR9+/fJyAggL///puzZ89y+vRpLl++LJVOMzIyYsCAAQAsXbo0w6bQgvCr+/jxI9HR0Xh4eHD9+vV0H4GBgaLpsSAIgiAIgiD8JL9ljYnNmzeTM2dOtm7dCkDfvn3x8PCQgqO7d++WRmivX7+ewYMHs3PnzlRHuws/xufPn7G3t+fevXvIZDIUCgW1atVi8+bNGv2DsFChQmzdupUePXrw6NEj5s2bx4IFCzAzM8vS4wwLC+PQoUNSwNXPz48ePXpQuHDhdNfr0qULp0+fxsfHhyEuLqjCWNn+l0gIDw//5mOLioqiU6dOvHz5kiJFirBz584063yn5t27dwwdOlQKdPXu3Zt58+ZhkGzk79KlSwkNDcXW1pYuXbp88zH/CMuXL2fSpEkAVK5cGVdXV+m13Llz061bNzp16pTpxsYHDhygZ8+eJCQksGvXLrZt20adOnVSXVY1Yv9HJBJAGbQ8ffo0HTp04J9//qFhw4YsW7Ysw89MdV2nVlrp6dOn0n9/mUhQjSJXJRIy2zNj4MCBLF26lKCgIDZs2MCQIUMytb7wa5oxYwYeHh5SEqFdu3bpLn/06FFA2TsjtVlUHTp0oH379shkMrTi49Pd1oMHDxg/fjxv3rxBT0+PMWPGYGhoKCW90qKrq0uZMmUYMGAAkZGR+Pr64uPjw5s3b2jTpg2Ojo5q34nJKRQKkpKSUi3PVbVqVfbu3cuVK1ekMktp+fPPPwkJCWHFihUMGzZM4xJlCxcupG9fZdk8Z2dn5s+fT2BgICdOnEizpJog/E5sbW3FjGFBEARBEARB+IX9ljMSHj16RO7cudWe69atG/v378fb21sa7QfKYGmnTp3Inj37Dz5KQSU8PJwWLVpw69YtKYlQpkwZmjZtmubI0dSokgkWFha8fPmSCRMmEPa/0kHfKiEhgZMnTzJr1iwp2GpiYsKnT59YunQpx44dIykpKc31y5QpQ8mSJYmPj8f70P83CTXJokRCUlISffr04ebNm+TMmZM9e/ZkKolw7tw5qTmnsbExS5YsYcmSJWoBszdv3ki9KKZPn/5N5Xd+lA0bNkjllypWrMjVq1fR0tKiXbt2eHt7ExQUxMKFC7Gzs8vUdrdu3YqzszMJCQmYmZkRHByMg4MDK1asSLWhsqqUyrNnz9K9TrJSkSJFOHPmDI0aNSImJoZ+/foxevRoEtIIwMbHx0ull1Q9MJJ79uyZ9N9ZnUgwNDSUenrMnTuXqKioTK0v/HrkcrmUzF+7dm2GSQSFQiElElTlfVKjybXl6+uLi4sLb968IW/evLi7u9O6detMHL2SiYkJrVq1Ys2aNXh7e9OrVy+178TY2FiePXvGpUuX2L9/P2vWrGHVqlVSia/kypcvj66uLm/fviUoKCjd/cpkMmbOnEn//v2l71ltbW2MjIwwMzMjV65c5M+fn8KFC2Nra0vx4speEdOnT5fKt5mamtK9e3eAVHtSCIIgCIIgCIIgCEJW+y1nJFSoUIHBgwfz+vVr8uXLJz3v4ODAvHnzmDRpEv379ydfvnzo6uqyc+fOn3i0v5bUgqCpiY+P16juflqjM1UiIyNxcHDg2rVrUhIhd+7cPH78mClTpuDq6kqbNm0oX748MpkMIyOjdIO+pqamLFu2jIEDB/L8+XPGjh3LhAkTpJH/X7p37x7Xrl1Lc3sKhYJ//vmH06dPEx0dDSiDSwULFsTQ0JBnz57x8eNHfHx8OH/+PC1btpTKv3zJxsaG+/fvs2vXLgblVV6XJv9LlISGhpKQkJBiHdU+MzJ8+HB8fHzQ09PD1dWVhIQEHjx4kGK5qKgoChQoIP2dmJjI8uXLWbt2LQqFghIlSrB8+XLy5s2bIuA9d+5cYmNjqVatGo0aNfphAfG0yOXydEt2eHh4MGjQIABKlSrF9evXkclkLFiwgJYtWwJIQbeEhATy5MmT4T7Pnj3LoUOH2LZtGwANGjTA2dmZjRs3cuHCBcaNG8fBgwcZOnSoWjDezMwMPT094uPjCQwMlOq/BwcHpzm6ObkPHz5o1Dw8Ojo6RWmgMWPGYGVlxfbt21m/fj3XLlxAVTU9LDQU2f+Wv3HjBnFxcZibm2NhYUFoaCiPHz+WvhMCAgLUjuf27dvSd4Bqn8kTCal9NvHx8Wl+Zk5OTixcuJBnz56xdOlSaRZJVshoBLiQNWJjY6X3OSAggLdv32JiYkLbtm3Vrsvg4OAUMwPu37/Py5cvMTAwoEyZMnz8+JHw8HApSfUlrWTfl69evSLkf+XhwsLCWLJkCfHx8dja2tKxY0dev37N69evefLkCY8ePcrwPGQyGbt371Z7Ti6XExUVRXh4OBEREYSHh6dZyuvMmTNqv1PFihXjxYsXFClShAcPHnD48GEaNWqU6n6T/1726tWLbt26oaWlpZa4/fDhg9rgB7lcztChQ7l16xZ9+/Zl0aJFyGQyGjZsyJo1azh9+jQ3btygTJkyqR5vQkKCRr/nCoUiwxkdyc9FEARBEARBEARB+G/5LRMJXbp0YcaMGXTt2pWTJ0+qBSJGjBjB0qVL8fHxwcXF5SceZdbI6n+sa7o9LS2tb953dHQ0bdu2lRpKKhQKcubMSUREBDExMWhraxMZGYmHhwf+/v60aNGC/Pnzkz9//nS3mz9/ftasWcOff/7Jy5cvWbp0KZs3b0511om3t3eagdzQ0FCuXbvGu3fv1J6PjIxMteZ8ZGQkXl5eNG7cGFtb2xSvFyhQAGNjY6KS9UPIkTMnoAzwp9VvIKMA8qpVq9izZw+grDFftmzZNJfNli0bVlZW0t9Dhw5l8+bNAPTp0wdXV1cMDAxISkpS6xEQGBjIjh07AHB1dVWrDf6zAkYymSzNfe/Zs4fevXujUCiws7OTPq+pU6dibW1NUlKSWuBMJpNlGNBXKBRs3bqVw4cPA9CsWTOpHFrv3r35448/2LVrF5cvX+bVq1fs3btXKmkEylkJ9+/f58OHD1Jj7SJFimg068bAwEAtKZqWwMDAFIF6mUyGs7MzxYoVY8GCBQQGBoKNcgSzvr4+sv99P965cweASpUqSe+FmZmZ9N8fPnyQtimXyzEwMJBmvaganCcPFqc2Y0VXVzfNgKW2tjbTpk2jZ8+eLFmyhMGDB2c4U0zTBEF618qXywkpafq+JA+CHz9+HIBGjRql6Deio6OTIsF85swZQNkIXXU9NWjQIM1rQBETQ8TKVYDy90gul6NQKDhw4ABxcXFYW1vTtWtX6TVQlm/LmzdvhuexadMmTU4XUM6myZYtG9myZUNHR4eHDx8SEhJCbGys9N0dHR1N7ty5qVatGg8ePODx48d07do1xbZCQkJS3B+pJVKio6PVZpxpa2szfvx4evTowZUrVzh+/Dj29vbkzZuX9u3bs2fPHpYvX87GjRtTPQdN7w/VsoIgCIIgCIIgCIKQmt+ytJG+vj67du3i6tWrtG/fXq0hrra2NkWLFhXNB3+y2NhYOnTowNmzZ6XARPbs2YmNjSUmJoa8efMycuRI6tevj7a2No8fP2bVqlWcO3dOo5Hw1tbWUpkjVQPmz58/a3RscXFxXLlyhaNHj/Lu3TuNRmqqyOVyTpw4gY+PT4qR4dra2imC/N/aI+Hw4cOMHz8egGHDhtGwYUON17137x5btmwBwN3dnWXLlqUaTL9x4wYdOnRALpfTunVratSo8VXH+qMcOXKEbt26IZfLKVmyJPfu3QNg1KhR7N69m06dOuHo6CgFzjUhl8uZMGGClERo164dnTp1kq5dmUxGo0aNGDt2LGZmZrx8+ZLGjRtz4sQJaRuq8kY/qk/Cl6pVq8bKlSspWKiQ9NwGd3dpxoFqZk6lSpVSrBsbG8v79+8BpKDwx48fpde/tbSRSqdOnbCzsyM0NJRFixZ91TaEX8OxY8cAZb8DTZw8eRKAxo0bf/U+b9++zcOHD9HW1qZt27aZ+u7+GlZWVlSsWBEbGxusrKzIlSsXJiYmKBSKFOW/AKm2+507d1KdgfYtrK2t6dOnD6DsC6OaGTd8+HAAdu7cydu3b7N0n4IgCIIgCIIgCIKQXJb8KzwyMpJPnz5lxaY0VqVKFQ4dOsTZs2epXr26NOrdx8eH+/fv06ZNmx96PML/i4+Pl5oPq8oZmZmZkZCQQFRUFHny5MHZ2RkTExPq1avHwIED+eOPP5T9Bby9adeuHffv389wP8l7JqiSCREREemuExQUhLe3N48ePUKhUGBtbU2rVq00PjdLS0tkMhkPHz5kx44dKWplly5dWm2ktuk3JBKuXbsmjbpv06YN3bp1k17buXMn3bt3l4LoqZk5cyYKhYLWrVun2oRXLpezePFiatasyaNHj8ibN69ao+Jf0enTp+nUqROJiYmUL19euk6GDRvGqVOnlKPxgVu3btGxY0cmTpyoNtI+NUlJSQwePJhVq5Sjn52cnGjZsiUymYznz5/j7e0tfX42NjZMnz4dGxsbwsPDcXJyYv78+cjlcimR8Pjx4+91+hnKmzcvCxYskP6eM3s2/fr1IzIykuvXrwPKXhJfevnyJaBM9qmanycv4aUaea1Knn1tIkFbW5sZM2YAsGzZMil5IfxeXr16JfW8adq0aYbLv337Vlr+axMJUVFRHDlyBID69eun6JOUkcTERJ48eSIlQDSRWqJCVSLt3bt3KUoFFixYkOzZsxMXF6fRb1hmdenSheLFixMREcHixYsBZZPn6tWrk5CQIPW4EQRBEARBEARBEITvIUsSCTExMdSvX1+qK/6jNGrUiGvXrpEjRw5q1KiBoaEhffv25cCBA5kOMghZIzExEScnJ44dOyYlEQoUKIBCoSAyMpJcuXLRo0cPtVIYuXLlomfPnrRs2RIDAwNu375Nq1atpF4A6fkymeDl5ZXmsm/evOH8+fPEx8djbm5Oo0aNqFOnjlRmQxN58uShY8eOmJqaEh4ezp49e7h8+bIUUDI0NJQaYwKYmpkBZLop9PPnz+nYsSMxMTE0adKEESNGSMHbw4cPs2TJEgIDAxk8eDCvXr1Ksf6lS5c4evSoVE7mSy9evMDe3p5x48aRkJBAmzZtCAgIUCvV86u5cOGCVIu9XLly3LhxA4D+/ftz6dIlbt++jZmZGZs2bZIar+7fv59mzZqxZcuWVPuDxMfH07NnTzw8PNDS0qJXr15SbfM7d+4wb948Dh48yIwZM3j+/DkA5ubmTJ48WSqd5ubmRteuXaUA48+akaBikKxUio6ODt7e3lSsWJGgoCBkMpk0ajo5VXPYAgUKYGFhAajPSFDVTU+rZnxmtGjRgipVqhAdHc28efO+eXvCj+fj4wMoE/q5cuXKcPnTp08DykTXgwcPuHnzJk+ePOHDhw/ExMRo1LvnyJEjREdHY2lpSZ06dTQ+1vDwcC5dusSuXbs4d+5cilJ2mZUrVy60tLSIiYlJkbiWyWSUL18eUO85klV0dHSYMGEC2tra+Pn54evrCyA1nF+3bp3GfXcEQRAEQRAEQRAEIbOyJJGQK1cuzp8/z5UrV2jUqBEPHz7Mis1qpHjx4vj6+vLixQsuXrzI06dPqVmz5g/bv6Bu7969eHt7o6WlhUKhQFtbm2rVqhEeHo6Ojg7Ozs6p1ozX0tKiUqVKjB07VmoEvHbtWry9vTPcZ6FChejfvz+gDKCnJikpiYsXL6JQKChYsCD29vZYWlp+1TnmzZsXJycn8uXLh0KhwN/fn9evX0uv2yRLJKhmJ0Qm65ugiWXLlkkj6du2bSttR6FQsHz5cmm5qKgoPDw81P6eOXOmFEh3dnbGxsZGev3mzZv06tWL8uXLc+bMGQwNDVm9ejW7d+8m5//6OfyqBgwYQHR0NI0aNZJ6IvTu3ZvcuXNz7do1DA0N2bhxIzVq1MDV1ZVdu3ZRsmRJoqKiWLhwIRcvXkyxzSNHjnDw4EEAaV2VzZs3Ex8fj0wm49OnT2zdulV6TUdHh/nz57N69WoMDAw4ffq09DmoRvf/CrZu24ZMJpPKfunr60tJg+RUwdXIyEipVFzyWTSq2QmqREJq/RE0JZPJpOTWhg0bfnpTbyHzVN+z2bJl0+jzu3nzJgCvX7+mc+fO2NvbU6dOHRo0aECxYsUoWLAgdnZ2VK9enfbt23P79m219cPDwqTnKlSokKnr7+zZszx48ID4+HhMTEykQP/X0tHRkZJtqc12UiXqLl269F2u7cKFC1OqVCkA6Tun0P/KmYWEhEi9KARBEARBEARBEAQhq2VZgWFTU1NWrlzJ/Pnz6d27N/Pnz8+qTWvE2tqaChUqSCNnhZ9DVWqnRYsWUlPff/75By0tLRITEzl+/DiJiYmprvv69Ws2b94slQsqXry4xvX6VUHPtILhb9++JS4uDkNDQ2rUqPFNtbUVCgX379+X6lFbWFiozYBJXps/OioKIMOmsl/q0qWLNMJ94MCBDBgwgNu3byOTyXB0dJSWy5EjB82bNycxMZH9+/fTrl07FixYQExMDDVq1GD69OkoFApOnjyJvb09tWrVYs+ePSQlJdGgQQOuXLlC3759f/kGmwqFQhrpv27dOqmhatmyZSlTpgyA9PmqFClSRBrpnDdvXuzs7FJst0qVKpibmwPw119/qY2MVvW7UD2XWkmgTp06SbX+VZ+7qtHyr8B1/ny1c4qNjaVNmzZcuXJFbbmqVauir6/P06dPpRJIqoDo3bt3OX/+PPD/MxOqVav2TcelmvmiUCi+e517Iet16dIFHR0dTp8+Tb9+/VL0i/mSk5MTjRo1olKlSpQoUYJ8+fJhamoqfe8kJSURFhbGy5cv8ff3p3379tIsBgCTbNmk0mHHjx9Pcf2mp1CyniEVKlT45kQCIDVCTq1kXZUqVciWLRvv3r3j77///uZ9JRcbG8vkyZO5desWoPydPXHiBA0aNADA1tZWDKQQfjlBQUEEBASk+1CVJRQEQRAEQRAE4deWZRGcp0+fsn//fg4dOoS5ubnU5FX4b1GVf6lWrRre3t4YGRkREBCAhYUF2tra3L9/n927d6slE6Kjozl06BAbNmzg5cuXZMuWjalTp3LkyBEpYJwR1WjV0qVLp3tcf/zxxzcFLlXNls+ePYtcLqd48eJ07txZCrC+ePGCf5KVtlElG/Lly5ep/VStWpVbt24xYcIEjIyMuHv3Ln369GHChAk0adKEvXv3Mm3aNPbv309kZCRdu3Zl3rx5fPr0iSJFirBjxw727NnDwYMHqVKlCu3atePcuXNoa2vTuXNnzp07x4kTJyhRosRXvxc/Unh4uFTmSlVeCpQlVkqXLk2jRo2Qy+WsWLECUJZbGzhwIIGBgeTMmRN3d/cUyRyFQkH+/PnZvHkzWlpabNu2DT8/P+n1rl27UrhwYQCaNm2Kg4NDqsf2ZfNiJyenLDnnrHD79m2yZ8/Ovn37ePToEZUrVyYsLIxOnTpJZVFA2Sh61KhRUsmxBg0aYGtrS0REBJs3bwaUCS1VYDS1nhuZoeqNYGVl9csnsYSUmjZtyrZt29DR0cHT05Py5cuzd+/eNEsUVahQgW3btnHo0CF8fX25evUqDx484NatWzx8+JBr167h5+eHt7c3devWJSYmhsGDB0vra2lp4ezsTIUKFZDL5Xh7e3PixAnkcnmGx2pnZ0fJkiUBOH/+fJbMGMr2v943UVFRKWYdGBoaSn0gsjKR8PnzZ4YOHcq5c+fQ1dVlxowZ6Ojo0KpVKyIiIqhbty7nzp2TkhyC8CsICgrC1taWihUrpvvo1q0bRkZG0mwfQRAEQRAEQRB+TVmSSHj27Bnt27fn2LFjWFlZMW3aNGnEnPDfcffuXfbv3w8oR3PXr1+fI0eOYGJiQnBwMDlz5kRHR4eHDx/i6elJfHw8165dY/ny5Vy/fh2FQkHFihU5c+YMvXr1QkdHR6P9JiQkcO3aNSBlUBeUfRtUfQQKFiz4Tef4+PFjHjx4gEwmo06dOjRr1kxqRJuUlKQWiIb/TyRomhC5fPkyo0ePZt++fSQmJjJp0iRu3rxJixYtkMlkUrPhAwcOkC9fPsaMGcPw4cN59uwZZmZmjBo1ilWrVnH8+HGKFSvG8OHDCQwMxMTEhKFDh3L37l02btwojbb/Xajq9RsbG2NoaEinTp0AZdmS6Ohohg0bhkwm4/jx49y5c4fhw4dz7do1TExMcHd3548//pC2pVAoGDNmDFZWVlI5NlUDYC8vL6k0m66uLuPHj2fGjBl07tw5zYB3oUKFpD4bVlZW1K9f/7u9D5pIniAoXrw4J06coHbt2pibm7N7924aNGhAdHQ0zs7OanXcCxcuzJQpU+jTpw+dOnVCoVCwfft2wsLCyJs3L82aNePdu3fkyJGDJk2afNMxqkopfW15MeHna9OmDbt27cLS0pLnz5/TvXt36tatm2oJsbTIZDKMjY2xtLSkaNGiVKxYka1bt9KtWze1pIRcLkdHR4d27drRsGFDAM6dO8emTZuksl3p7aNq1arSDKXk98fX0tfXR/9/vUi+7JMAylkJANeuXfuq8kaqnkJPnz7F39+fw4cP069fP+7fv4+pqSlLly7l6dOnzJ07l6SkJKkvUWZnvgnC9/bx40eio6Px8PDg+vXr6T4CAwOxtrb+2YcsCIIgCIIgCEI6NIvUZqBQoUJS41PhvykxMREXFxcSEhJwcHCQGtbWqlULHx8fGjVqRHBwMBYWFoSFhfHkyRPc3Nykkhh58uTB3t6eUqVKadS8M7m7d+8SFRWFmZlZqiPsX79+TWJiIsbGxt/cByA2NhYjIyPs7e3Jnz+/2msBAQF8/vyZHMl6QKjKNH25bGr++usvOnbsSGxsLGvXrkVbW5vq1avTtGlTOnfujKOjI8uXL8ff358dO3awY8cOQBnwbtu2LRYWFhw4cEAqtQNgY2NDr169cHZ2lkr4/I5UtchVoxUrVqxI4cKFefr0KX5+ftjb2+Pg4MCRI0fo0aMH0dHRGBgYsG7dOmxtbdWCeYsXL2bt2rUATJs2DR8fH/78809u377Nnj17WLVqFdOmTSNnzpzo6upmGNjQ0tKidOnSXL58GUdHx2/qH/AtkpKScHd359j+/TjbKPt07Nu/n2zJRngaGxuzbds2hgwZwsGDB9m8eTOJiYlS89pcuXJJ99/FixcJCAhAW1ubgQMHSr0k2rdv/80l5FQzElTlu4Tfk729PXfu3GH58uUsXryYa9eu0bhxYxo3bszkyZMpVqxYprepo6PDvHnzKFqgAOw/ACh777Tv2hUDAwMaNGhAjhw58Pb25tmzZ6xYsYKWLVumW1JMJpNRu3Zt4uLiUm1O/zWyZctGXFxcqomEEiVKkC1bNiIiIggMDJR6Gnzp8+fP+Pn58f79e4KDg6XHu3fvUm1snjdvXubNm4eHhwenTp0CYNKkSUyfPl3M7BF+aba2tlK5PEEQBEEQBEEQfl9flUiIjIzE09OTsLAwihUrRtmyZb95pLfwbdIqKZHacpoEHDQpGQHKskQ6OjosWrSIgIAAzMzMcHNzkxq2grLcULdu3dixYwcfP34kZ86chIeHExcXh76+PvXr16dy5cpoa2sjl8t59+4dN27cICAggBs3bhAaGsqKFSvUglJv376VRqIePXoUUJawePr0qdrxvXjxggcPHgBgYGDwzQkvIyMjWrdujYmJCVH/638AynvC398fgEoVK8L7YABe/i9oZWFhQXR0dIrtxcXFoaOjg5+fn5REqFSpEhERETx8+JALFy5w4cIFQDnavUaNGpQrV45Tp07x9OlTKlWqhKGhIQcPHiQ+Ph5QltZo27YtPXv2pHr16r9tgEkul0vXYfJEguq5jh074urqypEjR2jQoAH9+vXDx8dHuiYXLlyInZ0dsbGxJCUlkZSUxMGDB5k+fTqgTABcuHABPz8/ateuzbJly7h8+TKvXr1i2bJljB49Os2AeUJCglqj1aFDh1KoUCE6d+6s9nxERIQ0YyUqKoqNGzdy9epVChQoQIkSJShRogTFixcnNjZW7Z5JS1xcnPQ5JxcZGcmCBQsICAjAMNnnnZCQkOqI7blz56Kvr4+Xlxfbtm0jLCxMbZbBx48f8fT0BMDBwQELCwvc3NwAZV+ItEZZx8fHa5RIUTUnt7KySnc5hUKh8XebJte5ptvSdHtZTZPjy8w5/AjGxsZMmDCB3r17M2fOHDZv3sypU6fw9fWla9eujBw5Ms3ksFwu5/3799y9e5c7d+5w584d4uPjGTVqFH169ybif4mEZ0+fsm7dOrp27YqZmRklS5bE0tKSAwcO8OrVK/bu3cv9+/fJkSMHnz59SvNYy5UrR3R0dLrLfEk1q+xLqs/hw4cPREVFpZiJYW1tzb179zhw4ABhYWHSe2VjYyOtP27cOKkRdWqyZctGrly5yJ07N9bW1rRt2xZXV1du3ryJtrY248aNY+rUqRn+XickJEjfQ+nJ6vtNEARBEARBEARB+Hf5qkRCs2bNePPmDdmyZZNq05ubm1O2bFkcHBwYM2ZMlh6kkHVkMplGAQBNgg6gbL4aGBgoNdd2c3OjQIECKZZbvHgxAwcOpHnz5nz48IGiRYvSoUMH+vfvz6dPn7h8+bL0SO36mT17NmfOnJGO/fXr11LA8v79+wDUqFEjRWmHW7duSYHXiIgIteB/ctWrV6d169Zqz8XGxrJ7925p+1WrVqV9+/a0aNEixfojRowgMTGRChUqsGTJEuiqrJOvGnltbW2dalBaV1eXy5cvS0kEBwcH9uzZIzW+9fHx4dixY/z111+8ffuWffv2AcrSGnnz5pVKOoEySObi4kLXrl01mn3wqweCkl+rqtJGFhYW0nOdOnXC1dWVixcvYmFhQeHChRk0aBDr169n9erVUh8FUI7Wv3PnDv379weUgf/ExETWrFnDwoULadq0KUZGRuzfv5/mzZvz8uVLzp8/z6pVq1J9n4KDgzFONvOkVq1a1KpVK8Vytra25MmTBy8vLyZMmCBdD0+ePOGvv/6SlitQoAAVKlSgbNmylCtXjnLlyqVapqRo0aJSGSWVx48f06VLF548eYKhoSHrli2DRYsBMDM1RZasAXVyS5cuJXv27KxduxZvb2+sra2ZNm0acrmcli1bEhsbS7Vq1XB3d+f48eOEhYWRL18+6tatm2afET09PY16kKiSLZaWluleh1paWr/8dfpfYpjGtQTKsnEbNmxg1KhRjB07lsOHD7N9+3YOHDjA6NGjGTFiBB8+fFBLEt+8eVMqc5Xc5cuX2bp+PaqON7ly5SIoOBhPT0927NghzT6YNGkSy5cvZ8GCBdy/fx9zc3OGDBmSbkPlDh06MGTIECIjI8mWLRtNmzbFwMAg1WWvXr2aZhLExMSEkJAQ4uPjiYqKSvH9XqJECe7du8fTp09p1qyZ9P6pRmX7+Phw8+ZNDAwM6N69O/ny5VN7FCpUSO075tmzZzg4OPDgwQOyZcvG3r17adCggUb3h4GBgUb3paYDDQRBEARBEARBEIT/pkwnEp4/f87t27d59eoV9+/f588//2TNmjXMnTsXf3//TI30E35/iYmJDBgwgISEBOzt7dNtwlq2bFlOnz5N06ZNefLkCVu2bGHVqlUpSkPIZDJsbW2pVq0a5cqVY/z48Vy6dIm9e/eqBYdB2VT37t27gLLczZdUIzU1TaCoBAcHs23bNj5+/Ii2tjZt2rShcuXKqfZtuHTpEidOnEBbW5spU6ao7SejHgnJZyIkTyKAsmb94MGDGTx4MNHR0fj5+XHs2DGOHj1KUFAQb968wcTEhK5du9K3b99Uz//fQpVISB7UK1OmDMWKFePx48ccO3aMLl26MGfOHKZNm5YiqPfixQvatWtHXFwcDg4OLFiwgLdv37Jx40bOnTvH2bNnqVu3Lvnz58fd3Z0OHTqwd+9eSpcuzcCBA7/6uB8+fEj37t2lWSVFihRh3LhxBAcHc+PGDW7cuMHTp095+fIlL1++xNvbW1rXysoKQ0ND9PT00NXVRU9PD21tbfT19dHT05OeP3v2LGFhYeTPn5+dO3dS2saGt/9LJKRHJpMxevRoqa/NihUrCA0NJX/+/Pj7+2NiYsLq1avR1taWElidOnX6pmblKsmbLQv/LiVKlODAgQOcO3eOcePGcfXqVaZPn86sWbNSncmipaVFiRIlqFChAhUqVOD06dMcO3aMnj16cP1/JboOHTqEY69eBAYG0qpVK9auXYu9vT06OjqMHDmSBg0aMHDgQB4/fszs2bNp1qwZ3bt3l75Lk8uWLRtVq1bl77//JiIiAl9fXxo1apTpcl3GxsbIZDISExNTnW1WpEgRtLS0+PjxI58+fVJrgpyUlMSUKVMAGDx4MHPmzFFbV6FQSMf+8uVLTp06xcSJEwkODiZ//vwcPXqU0qVLf1X/BUEQBEEQBEEQBEH4WplOJAQHB2NjY4OpqSna2trSKOy9e/fSoUMH6tWr9x0OU/hVLV26lBs3bmBubs6yZcsyDNaXLFmSM2fO0KRJE6m8iYmJCVWrVqV69epUqlSJatWqqY2o//jxIzNnzmTChAk4ODhgZGQkvXb79m0SExPJkycP+fLlS7E/VSIhMyObX716xaZNm4iOjsbMzIxu3bqlOssClKVcVEGgrl27Urx4cUhW2/rd/xIJqR1b8p4IXyYRvmRkZESLFi1o0aIFCoWC+/fv8+zZM+rVq6c2Qv1XK3uSVVQj2JP3uJDJZLRt25YFCxawf/9+KYn1ZUDw8+fPdOrUiQ8fPlCuXDm2b9+OtrY2+fPnp0+fPqxZs4ZZs2ZRt25dAGrWrMmsWbOYOHEiM2bMoGTJktJrmoqIiGDJkiVs3ryZpKQkDA0NGTt2LMOHD0/xGYeGhnLp0iXu3bvHzZs3uXnzJs+ePUuzpEpqqlevzvbt28mVKxdyDUokJTdkyBDMzc0ZMWIE27dvl56fP38+f/zxBxEREZw4cQIAR0fHTG07LapEgmi2/O9Vp04dLl26xJ49e5g0aRLPnj1DV1eXUqVKUb58ebVH8u/0YcOGMW7cOFYv/v9kWPbs2Tl27Bi9e/fmr7/+okePHsycOZMBAwYgk8koV64cvr6+uLi4cOLECY4fP87t27cZNmxYqn0aDAwMaNiwISdOnODTp0+cPXuWBg0aZKq/iZaWFiYmJkREREili77ch7W1Nc+fP+fRo0dUq1ZNes3Ly4u7d+9ibm7OqFGj1NYLCQnBz8+Pc+fO4evry+PHj6XXypUrx5EjR9JMTAuCIAiCIAiCIAjC95TpRIJcLpdGZVtaWkoBIYDevXuzceNGmjZtmnVHKPyy7t69y7x58wBYuHBhuqOLIyMjmTdvHk+fPiUqKgoDAwOMjIzImTMn+fLlw8TEhNevXxMYGMiWLVuYNGkSpUsrC1uMGDGCzZs38/LlSxYtWiSN5ASk0j6VKlVKkSiIiIiQEgmaBoiePXvGli1biIuLI3/+/PTs2TNFKRkVhULBkiVLePbsGTlz5mTIkCEplklMTEQmk6VoKuvr60unTp00SiJ8SSaTYWdnh52dnUbL/6527NghlVK5cuUKQIoyI+3atWPBggWcOXOGz58/pygHFB8fT/fu3Xn06BH58uXjwIEDap/nmDFj1GYlqN7TPn36cOfOHTw9PXFxcaFFixbY2NhQqVKlDBtGHjp0iBkzZkjJj1atWrFgwQKsra359OkTFy5coH79+tLIfnNzc2rVqkXDhg2lbYSFhfH06VPi4uJITEwkPj6e+Ph4IiMj0dLSIiEhQXrO3Nyc1q1bf1MD5G7dumFqakr//v2Jj4+nZcuWUmLm2LFjxMbGYmNjk27JmMxQJUlEIuHfTSaT0alTJ9q2bcvz58/5448/UlynX343a2tr4+bmRqmiRWHpMkDZD2Xjjh14enoybtw4tm7dypQpU7h69Spubm7kyJEDQ0NDunfvTtWqVVm5ciVv3rxh4sSJdOzYkfbt26fYj6mpKQ0aNOD06dO8f/+eCxcuULt27UzNuFE1VE4tkQDKZvdfJhLi4uKYMWMGAKNGjSJHjhzcvHmTXbt28ddff3H79m21hLCWlhaVK1emadOmjBo1imzZsml8fIIgCIIgCIIgCIKQlTKdSEgerFWNsj5//jy1a9fm/fv3hISEZN3RCb+0xYsXk5CQQPPmzdMtaQQwffp0NmzYkOL56OhoXr58meJ5Pz8/3rx5g5aWFoaGhkyZMoV+/fqxcuVKJk+eDCiTWqoGl6mV9UnexDKj2QgKhYIzZ85w5swZ5HI5hQoVomfPnmkG9+VyObNmzcLLywuAsWPHphngsbS0lHpOxMbGMmfOHJYtW4ZcLqdZs2aZSiL8lwwdOjTFc18mZEqUKIGdnR337t1j+vTpLF26VPqs37x5Q48ePaQyPQcPHkwxMyR//vz07t2btWvXsmrVKlavXg0orxdXV1cePXrE9evX2bFjh7SOhYUFderUwcHBgdq1a0v11RUKBa6urtI2ChYsyLRp0+jcuTMAjx49omXLlrx8+ZL9+/dLddNTY2ZmlmrQPjo6Os3E1rdq1aoVlpaW+Pr6MmjQIOl99PDwAKBLly5ZVj89MjISgICAACpXrpwl2xR+Xbq6uqnODEhP9+7defi/RMKdO3eYMmUK7u7uuLm5UbRoUWbMmMGhQ4fw9/dnxYoVNGjQAFCW0Fu8eDEbNmzg4sWLeHl5cfPmTYYPH07u3LnV9pEzZ07q1q2Lr68vL1++5MaNG5kqEae6F78sz6dSpEgRAIKCgqTkwOnTp3nx4gUGBgYMGjQIuVxOkyZNCA8Pl9azs7OjYcOGNGjQgLp162JmZqbxMQmCIAiCIAiCIAjC95LpRELVqlU5cuQIoBwpN3nyZJo1a0b58uW5du0as2fPzvKDFH5NqsBI9erVMwwwOjo6pppISMvo0aPVRoaqZh5UrlxZ2tf58+d5+fIlJiYm1KhRI8U2VDMaABISEtDV1U31OOVyOXfv3pWCm2XKlKFDhw5pjvBOSEhg0qRJHDlyBJlMxpQpU2jZsmWa56KaqXD16lUGDBjAw4cPAWUppOXLl4skQhoaNWqk1vQ7d+7ctGnTJsVyU6ZMwdHRkc2bN5M3b17GjRvH+fPn6dmzJx8+fMDMzIwtW7ZQtmzZVPejqrkeGBio9ryBgQH79+/nxIkTPHz4kAcPHnD+/Hk+fvzI/v372b9/PwYGBtSpU4cmTZpw/fp1PD09AWUSZNiwYdIo6Bs3btCmTRtplkJwcHBWvEVZrkqVKlSpUkX6+8KFC1y6dAk9PT169eqVZfvp06cPixcvZtCgQRgYGNCjR48s27bw76T6DZDJZAwcOJDq1atLfRE6depEnz59qFmzJgYGBmTLlo2RI0dSuXJl1q9fz8OHDxk1ahQDBgygZs2aatu1tLSkZs2anD9/nsDAQMzNzaUEQEbi4+MB0vwOV/WMMjc3l357KlSogJGREdHR0Rw7doyOHTvSrFkzdu/ejZ6eHhcvXqRUqVLid0EQBEEQBEEQBEH45WQ6kQDKEbkqAwYMwNramr/++osBAwbg5OSUZQcn/NpUpYw0qeVesWJFEhIS+PjxI2fOnOHkyZM8evSIwoULY2dnR8mSJbG1tcXKyipFAOXdu3ds3boVUJaiAWUSQzVSun379hgbG6fYp7m5Obq6uiQkJCCXy1MkExQKBUlJSVLpGAMDA1q3bk25cuXSTIwkJCQwYsQIfH190dHRwdXVlebNm6stc+HiRWr9779d+vZl2LBhrFixgkmTJiGXy8mdOzfLly+X+h0Iqdu9ezempqYZLmdvb4+bmxujRo1izpw5BAYG4u3tTVJSEqVKlWL79u0ULFgwzfVVQcNnz56RlJSk1lDb0NBQLXkRHx/P5cuXOXDgAGfPnuX169ecPHmSkydPAsrk6ty5c+natSugLG117tw5OnbsmOao5V/ZggULANLtE/I15syZQ3R0NGvXrqV3794AIpkgpOvVq1eEhYVJo/PLlSvHmTNnmDVrFhs2bGDjxo0cO3aMP//8U5r9ULt2bWxsbFi2bBkPHz5k8eLF3Lx5Uy1BCfDHH38QGhrKnTt38Pf3x9TUNEUZtdSo7um0Zgw8e/YMgEKFCknPWVlZMWrUKKkPS4sWLVi1ahW3bt2SEh5Hjx4ViQRBEARBEARBEAThl/NViYQv2dvbY29vnxWbEn4jmUkkqFhYWNC5c2ep3MuX4uLiUjy3cuVK4uLiqFq1KnXq1AHg+vXrPHjwAH19fdq3b5/m/rS0tNSSCYmJiejq6qJQKKTnQBkIGjhwoFqT59SOzcPDg8ePH6Onp8eyZctSNOG9efMmY8eO5ZL1HwDMmjWLzZs3M2HCBAA6d+7MwoULyZEjR9pvkpBpffv25e3bt7i5ubF//35AWYpn6dKlGBkZkZSUlOa6BQoUQFdXl/j4eN6+fZtu0kFPT4+6detia2vLvHnzuH//PqdOneLkyZMEBQXh6uqKg4ODtPypU6cYNmwYcXFx1KlTh8TERC5dupRl5/09XbhwgYsXL6Knp8eff/6ZpduWyWSsWrUKQCQThAzly5ePJ69ece/ePbXZZ0ZGRsybN48mTZowdOhQ3r59m6IvQp48eZg1axa7d+9m3759+Pr6YmxsTJ06ddSat5cpU4bQ0FBevnzJ2bNnNfp/GtUstrQSnqpEQuHChdWeHzFiBJs2beLly5csX76ccePG4eXlRa1atTh37hxTp05l0aJFmX6fBOFHCgoK4uPHj+ku8+VMP0EQBEEQBEEQfm9ZkkgQ/pvy5s0LoNZwWxOvX79m+vTpnDp1ipw5c5I/f37pYWlpSYECBcifPz/58uUjPj6edevWAco+BKqZAqqa9S1btkzRYPdL2traKBQKEhMTpYBy8sCyjo4Otra26SYRYmJi2Lx5M0FBQRgZGbFq1SqqVq2qtszjx48ZMGAACbGx0nNHjxyRgrCjR49m+vTpmr1JQqZNmTKFiIgIvLy8mDp1Kn369NGopr+Ojg4FCxbk8ePHPHv2TC2R8PnzZ27cuEG9evVSNGFN3vR6+PDhKba7Z88exowZg1wup2XLlmzduhVHR8dvPc0fJvlshC97S2SF1JIJ/v7+zJgxQ6PR4MJ/R4kSJXjy6hX3799PtYxd/fr1OX/+PN26dcPf3x8vLy8CAgIYNmwYefPmRVtbG0dHR8qUKcOyZcsICQnhxIkTlCtXDltbW2QyGTKZjBo1anDixAlCQ0Px8/NTSzR8KT4+Xkp8p5ZICA8Pl0qZfZmcNDIyYtasWfTu3ZuFCxfSrVs3SpQowfr16+natStLliyhRo0a6SbJBeFnCgoKwtbWlujo6AyXNTIyUpvJLAiCIAiCIAjC70skEv5jNC2lo1AoMgzCqmYkvH79WhqZmZaXL18il8vZvHkzW7ZsIfZ/wfbXr19z+/btNNczNDQkJiaG4sWLU7FiRYKDg7lx4wY3btxAS0sLW1tbrl69mu55wP8nE5KSkqQkgkwmQ0dHBy0tLWQyWZrnEBUVxc6dO3n//j2GhoasXbuW0qVLq82eePPmDS4uLoSHh1OpVClISARg4MCBKBQKevXqxbRp01JsOyEhQaqznR6FQiE19U2PXC7XKHiuUChSBMbTklUNdjMrJiYmRQmSL8XGxqq9L66ursyfPx+ZTIZCoZA+/6SkJGn2CSjLZT1+/JjatWsDyvJGjx8/5v79+2q9FLp3786VK1eYMWOG2iya8PDwdD+PjRs3Mm/ePACcnJykJtCqY0hMTFS7fiIiIkhMTMzwPQkNDVU7j9QoYmKk/05MTESWznbj4uJSvf4uXbokzUYYNGgQcXFx6c7qUImJiVErDZWW5Oe6ZMkStLW1WbVqFWvXrsXT05OJEycycOBAdHR00uxV8iVVP4rfmSbfz79DObTM/M6k9T2UfBs2NjZw+jR37txJddYaKIOVAwcOpGrVqri7u/P48WNGjx6Ns7MzTZo0QSaTYWtry8KFCxk/fjzBwcEEBATw6tUrKlSoIN3PlStXxs/Pj0+fPhEfH09SUlKq34FRUVGAspG0XC7n3bt3aq8/evQIgFy5chEWFkZYWBhRUVHcuHEDgOLFi1OyZEnu37+Pvb09q1atolChQnTt2pWdO3fSq1cvbGxsKFGiRJrvn6pcX0bi4+M1Wi4hIUGj+02hUGh8X/6s3w/h+/r48SPR0dF4eHhga2ub7rIWFhZYW1v/oCMTBEEQBEEQBOF7EomEf4ms/se6trZ2htvMnz8/oJyRoKenl+byiYmJ7Nu3jw0bNhASEgIoG07269cPuVzO+/fveffuHe/fv+f169d8/vyZN2/eEB0dTcz/gqIjRoyQAiGrV68GoFq1ahmOlJ4yZYoUGJbL5axcuRI/Pz/at29P586dpaBnTEwM5cqVS7H+27dv6dWrF+/fv8fCwoL169fTuHFjtWU+fPhAhw4d+PDhAyVKlGDv3n1Et24NKAMzHTt2ZMOGDakGOjUJumaGamTtv4GWllaGyQ5jY2MMDQ0z3JYqYJmQkMDy5cuZOXMmkZGR7N69m/bt21OsWDGOHz9OcHCw2ujiK1euALB9+3b69u0rPW9kZJTqqHmFQsG0adNYuHAhoGy07erqKn0mqvPR0dFRq4FuZmamUU10hUKBkZFR+svIZKhSYhYWFmils3y2bNlSDTAuXboUgJ49e1KyZEnkcrlGgXo9Pb1MJ6hkMhmLFy+mdevWjBkzhlu3bjFu3Dg2bNjA/Pnzad26dZZd07/6vaHJ8f3q5wBofA1omnCoUKECAA8fPkx3Blrbtm1xdHRk2LBhuLi44Ofnh7u7O58/f2bdunVky5YNUCYOjxw5wvLlywkODsbf359JkyZRvXp1QFk6b8SIEURGRmJhYSH91iX34sULALJnz46VlRVNmzZVe/3hw4eAsk+D6rWQkBC1ROC0adPo378/Dx8+ZMKECSxcuJD+/fvz4MEDAgIC6Ny5M5cuXcLExCTV89XR0dHovtTV1dXoM9H0cxMEFVtbW+n+FARBEARBEATh30/8q1H4aqoZCXFxcYSFhaV4XaFQcOrUKWrXrs38+fMJCQnB2tqapUuXsmXLFmrWrEnt2rXp0KEDQ4YMYdasWSxfvpyzZ8/y6NEj7t+/LzVmbtmyJaAMzpw4cQKZTEaTJk0ydbxaWloMGzYMLy8vnJycMgziv3jxAicnJ549e0bevHnZsWMHxYsXV1smIiKC9u3b888//1CgQAEOHjzIhw/B0usNGjRg+/btmRot/TuMOP4d+fr6Ur58ecaOHSvNPjl06BAARYsWBeD58+eprvvp06cMt5+UlMSQIUOkJMKsWbOYNWvWTw/8JiYmMnfuXGrUqEHfvn3Zvn27FARNzblz5zh//jx6enqMHj36hx1n3bp1uXz5MmvXriVPnjw8efKEDh060KxZM+7cufPDjkP49ZQsWRKA+/fvazQzpkCBAvj4+LBgwQJ0dHTYt28ftWrV4sGDB4AyGdOmTRs2btxIkSJF+Pz5M6NHj8bHxweAihUrSiXpnj9/nur9r/oOUSUnklMoFNJMuzJlyqR5nPnz58fNzQ1DQ0OuXr3KvHnz0NLSYtasWeTNm5cHDx7Qr18/8ZsgCIIgCIIgCIIg/BL+s4kEuVzOqlWrSEhI+NmH8tsyMDCQ+gp82XD5zp07tGvXjk6dOvHgwQNMTU0ZP348Bw8epGHDhhkGV2UyGWZmZpQoUQI7OzvpeVVN9dKlS2NpaflVx61JiYc7d+7g5OTE69evKViwIDt27EhR5/rTp0+0bduWGzdukDNnTg4ePEhSUpJaCZydnp4ajTRXmTp1Krlz55YCWv9lw4YNo2/fvtJj/Pjxme7HAcryWd26daNx48YEBgaSK1cuhgwZAiiTCwqFgiJFigDqiYTkJX8ySiTExcXh7OzMpk2b0NLSYtWqVYwePfqnJxHevntH8+bNmTFjBtevX2fbtm24uLhgY2ODjY0NgwYNwsPDg6CgIGmdOXPmAMrZCKmNxP6etLW16dWrF3fv3mXMmDHo6elx5swZKlasyJAhQ6Sa88J/S+EiRTAwMCA6OpqnT59qtI6WlhZ//vknp0+floLytWrV4uDBg9IyhQoVYsOGDbRq1QpQlka7e/cuAO3atZNmHT18+FCtFrxCoSAiIgIg1dkCL1++5PPnz+jp6aVIPn/J1taW2bNno62tzYkTJ1i7di05c+Zk165d6OrqsnfvXpYvX67ROQuCIAiCIAiCIAjC9/SfTSRMnDiRIUOG0KVLF5FM+AaqYL4qwPvp0ycGDx5M3bp18fPzQ09Pj6FDh7J//36cnJw0CuKnJSgoSAoCNWzY8JuPPTWfPn1iwoQJdOzYkQ8fPlC8eHF27NghNZZWefPmDc2aNcPf3x9zc3P2799P9uzZad26tVpSxcTYWON937hxAzc3NyIiIujWrRs3b97MqtP6Le3Zs4edO3dKjxUrVlCxYkUeP36s0foKhYLFixdjZ2eHl5cXWlpaDBo0iMDAQFxdXTEwMODNmzc8ePBAmpHw7NkzafRvcHCw2vbSCmJ//vyZVq1aceDAAfT09Ni+fTu9e/dWWyY6OpoHDx5oNLMhK9WtU4dz585hYmLCwoULGTNmDFWrVkVbW5sXL16wY8cO+vXrR4kSJbC1taVLly4/ZTbCl0xNTZk9ezY3b96kXbt2yOVy1q5dS4kSJVi2bJkYof0fo62tLc1KUAX6NVW9enUuX75M7dq1iYiIoHPnzqxZs0a6hvT19RkzZgy1a9cmISGBiRMnEhwcjEwmw9raGlNTU5KSkrh//77U2yM2NlbZf0QmwziV73jVbARbW1uNeglUq1aN8ePHA7Bjxw68vLyoVq0abm5uAIwfP57z589n6rwFQRAEQRAEQRAEIav9ZxMJcXFxdOjQgWPHjolkwjdQBdivXbsGwNy5c9m5cycKhYJ27dpx9epVZs6cmWr5h8zy9vYmKSkJKysrqaxSVlu4cCH79+9HoVDQtGlTtm3bhoWFRYrlpk+fTmBgIFZWVhw/fpwKFSqwaNEinjx5Qv4CBb5q3ytXrpRq0UdFRTF9+vRvPJt/n8+fPzNu3DiNlvXz85PKGFWtWhV/f39WrFhB9uzZMTAwoGbNmoByBL61tTU6OjrExsbi7e0NKBsqJ5fWSOiVK1dy7tw5QNlc++PHj0yaNIlu3brRsGFDChYsiIWFBRUqVJAag39LQi0zPn78SJkyZfj7778ZNmwYs2fP5ty5cwQHB3P48GFGjBhBlSpVpMSCqtTTz5iNkJpChQrh4eEhzSAJCwtj1KhRnD59+icfmfCjlS5dGgA3N7dMz0yytLTEx8cHFxcXADw9PQkICJBe19LSYsqUKRQuXJiQkBAGDBhAYGAgWlpalChRAn19fWJjY7l16xYfPnwgMDAQUPZoSd5XIDExkUOHDuHp6QmkX9boS/b29vTv3x+A5cuX8+nTJwYMGEDXrl1JSkpi6tSpmTpnQRAEQRAEQRAEQchq/9lEQokSJbCysuLQoUNqyYR79+4RGxur0Tbi4uIIDw9Xe/zXdOjQAVA2Z33z5g01atQAlCNIhwwZgrW1dZbtq1GjRhgZGfH27Vu8vLzUmlZmFQcHBynI++nTpzQDvqqGn23atJFKL6meq16t2lftu169egBSDXBVuY1/u8zcR8bGxvTs2VOj7ZYqVUr6TBwcHFI0hBw3bhza2tp4enoybdo0hg4dCsDw4cO5ePEixYsXlxp1165dWwpkfqlevXpSCaNly5bx559/snjxYvbt28f169elmQ2mpqaULl2arl27pmjM+j0NHjyYYsWKqT1nYmJCkyZNmD59On5+frx58wZvb29Gjx5N9+7dmTJlyg87vrT8888/TJkyhYIFC7Jy5UpAWfLMwcGBihUr/uSj+/X823+PhgwZQs6cOblx4wb169fn0aNHmVo/MTFRms2QO3fuFPeEsbExrq6uFChQgPfv3zNw4EA+fPiAnp4eJUuWRE9Pj5iYGKnMkY6Ojlq5u8DAQMaMGcPWrVuJjY2lePHiNG7cOFPH6OzsTPbs2UlKSuLly5fIZDLatWsHoPFgB9WsCUEQBEEQBEEQBEHIav/pRMLdu3dp3LixlExwcHCgYcOGXL9+XaNtzJs3DzMzM+lR4CtHov/OOnfuTOXKlYmKimLKlCm0bduWNm3akJSUxIABA4iJicmyfdna2rJu3Tq0tbW5fv26Wq3rrFKrVi3c3d0xNjbm6tWrrFixItXlypcvDyjLEamoRp+qylpklpOTE9X+l4QYMmRIivI4/1Zp3UdLly5l3bp10sPT05OgoCCNEyy5cuVi6dKlgLLx8ZcNexs2bMj69esB5UyUAgUKYG9vT3x8PL179+bRo0ccOHAAHx8fdu3ahZGRUar7qV27NjNmzCBXrlyUKVOGFi1aMHjwYBYsWICHhweXL1/mzZs3vHv3Dn9/f9zd3cmRI8dXvluZt2/fvgyXyZYtG40bN2bmzJlSjfafIT4+nv3799O8eXNKliyJm5sbwcHBWFpaMmHCBB49eoS3t/cPff9+F//23yM7Ozt8fX0pVKgQz58/p379+vzzzz8arZuUlET37t35+++/MTc3x83NDVNT0xTL5c2bF3d3d6nM0fPnz3n8+DGGhoaUK1cOMzMzAPLkyUPFihUxNzcnPj4ef39/Jk+eTFBQECYmJgwcOJDZs2enWvYoPTKZTOo7FBISAvx/STVVv4a0yOVy+vfvj4WFBVu3bs3UfgVBEARBEARBEARBE//pRMK9e/cAaNy4MXPnzuXUqVPY2NhQpUoVjbYxYcIEwsLCpMfLly+/5yH/krS0tHBzc0NLS4v9+/dz4cIFFi1ahKWlJY8ePWLGjBlZur8GDRqwcOFCAE6ePImvr2+Wbh+U9aqXLFkCwPbt21MtaVOuXDlAmTRQzSBQjV5/8uTJV+1X9R4ePnyYBQsWfNU2fkdp3UedO3emW7du0qNVq1YYGBhkattdu3alRYsWJCQk0Lt37xSjenv27Ck1Fx41ahRNmzalSpUqRERE4OTkxMePHylbtmyGTZPHjBlDUFAQ/v7+7NmzBzc3N4YOHUqLFi0oW7asFBz8GXx9fX94b4bM+vDhA5MnT6ZIkSI4Ojri6+uLTCajSZMm7N27l2fPnjFr1iwKFSr0sw/1l/Vf+D0qVqwYfn5+lCtXjs+fP7NmzZoM11EoFIwYMYJDhw6hr6/Pvn371GYSfMnExIS5c+fSr18/QNn/5/bt28jlckqVKkW1atUoVqwYOjo6vH37luvXr/Ps2TNAOWtuxYoVNGrUSK3kUWaokhUfP34E/r9XS3qJBIVCwfDhw9m4cSORkZH06dOHxYsXf9X+BUEQBEEQBEEQBCEt/5lEgirYq5InTx4SEhL48OED165dw9XVlWnTpnH16lWNeybo6+tjamqq9vgvKlOmjDR6fsyYMWTLlk0ayb9u3Tr8/PyydH+dO3fG3t4eUDbkVfVn0JQmJZHq1q1LvXr1SExMZP78+SleL1asGMbGxkRHR0vNf62srLCwsEhxrWVGjhw5aNy48VcHoX5Had1H0dHRREVFSY+v6WMik8lYs2YN2bNnJyAgQEpCJTdu3DgGDRqEQqFgzJgxDBo0CBsbG969e4eTkxOfP3/+5nP8WUqWLEliYiKHDx/+2YeSprCwMBo3bszChQul2Qfjx48nMDAQb29v2rRp88N6SvzO/iu/R7ly5ZL6BezZsyfD7wU3NzfWrVuHTCZjy5Yt1KpVK8N9aGlp0aNHD2xsbNDR0SEyMpKbN28SFhaGjo4OERER3Lp1i3/++YekpCTMzc2ZN28eAwcO/Ob3XZVIyMyMhOnTp7N69WpkMhktWrQAYOzYsUycOFE0JhcEQRAEQRAEQRCyjM7PPoDvSaFQMH/+fDZt2kTTpk2lOtsqxYsXZ8uWLSxatIiNGzfi4OBAzZo1adWqFXv37sXR0fEnHfn/0zQIoFAoMhw1DcqEiibLRUVFaRTMjoqKwtDQkAkTJnDw4EEePnzImjVrGDJkCL169WLz5s0MGjSINWvWaFQu5dOnT+joZHxZVq5cmdDQUC5dusTmzZvR0dGhaNGiKZaLiYnh7t27PHr0iIcPH/Lo0SMiIiIYMmQIlStXlpZ7//59igRAy5YtOX/+PGfPnsXd3Z3y5ctjYGAg1d23s7PjypUrXLp0iXz58gHKuvz+Z89K21AoFOl+hpp+Hv/FYFCRIkXU/jY3N2ft2rW0bNlSei4mJibDxJCpqSnz5s1jwIABzJw5EwcHhxT9DhYvXsy7d+/Yv38/w4YNY82aNYwaNYrHjx/Ts2dPduzYoVbaKCoqSqPgdnR0tBQYTE9kZGSGywCEhoYSFxeX7jKKZD1eWrRsyfV799i9ezddunRJsWxsbKxG93l0dLRGCbLo6GgMDQ0zXC4pKQl9fX2SkpJwdnYmMDCQvHnzsnTpUuzt7aX3VtOkXEb3WfLlNLnfAI2X00RWf4//W6R37yZ/TS6Xo5XsPWzYsCG5cuXiw4cPnD59mmbNmgEpG6L7+PgwefJkAEaPHk2JEiW4f/8+79+/16g8lomJCaVKleLRo0dER0dz9+5dzM3NCQ0NBZS9gAoUKED58uXR19fnxYsX6W4vLCwsw8SHqsdBSEgICoVCbUbCl9fR+/fv2bx5szSras6cOXTv3p3Vq1czd+5cFixYwKtXr5g7dy6WlpYZnm9iYqJG3wdJSUloa2tnuByg0e+5IAiCIAiCIAiC8Hv4V/8LT9Xw9OzZs+TNmzfF65UqVWLKlCns27cPBwcHQFnm6M6dO6kGpf8NZDLZVwWqnjx5wqVLl2jTpo3aiMts2bJhbGyMmZkZ8+fPx8XFhYULF9KzZ0+WLl3KhQsXePz4MVu2bGHVqlUZ7kdPT0+jEZ3NmjXD2dmZIUOGcPz4cXbs2IGXlxfW1tbcuHGD69evc+3aNW7cuEFUVFSK9VeuXImHhwd16tQBlGWS9PT01Jb5448/cHBw4NChQ+zYsYMKFSqgpaUlBUorVKjAlStXuH//vvRcuXLl1BIJGdHX18/SwObPCJL+KKGhoXTv3p2jR4/SqFEjQLPZJQCOjo4cOXKEI0eO4OLiwsWLF9USATo6Omzbto3g4GAuXLjAmDFj2Lx5M126dOHq1at07tyZffv2Sc3D9fT0NAq46enppbiuUmNqappmD4bkYmNjM9yeQlsb1RyKtm3aMGP+fPz8/AgPD08RPDUyMtKoXFRSUpLG56uvr5/hcgC6urqMGzeOEydOYGhoiLe3d4omyjo6Ov+pRJsm38+/w72bFTOqkp+noYEBOl8kqBwdHVm+fDleXl60bdsWUL8Onj9/zsyZMwFlE2NHR0fp9YIFC6o1Ww4KCuLcuXO0atVK7fdn69atGBkZERsby9SpU9m3b5+URGjdujUTJkwgV65cXL16VaPyZQEBAWrbT0hIICIiQu2+VCXcVUl11cwES0vLFEH5vXv3Mn36dEA5A6F79+4ADBo0CHNzc8aPH8/OnTsJCwtj9+7dGd6bMplMowSBlpbWb3EdCoIgCIIgCIIgCFnrX1s/RaFQ4OrqypIlS9SSCI8fP+bRo0cALFiwAF9fXymJoPJvTSJ8jZiYGGbNmkWVKlUYPHgwZcqUYfXq1amOiu7evTvVqlUjMjKSvn37YmRkxJYtW9DW1ubgwYN4e3tn6bFpa2uzdOlSKleuTEREBB06dKBs2bI4OztLSYyoqChMTEyoV68eY8aMYc+ePTg4OBAfH0/Pnj0zLIvUvn17zMzMePPmDcePH1d7TdUn4ebNm9JzqobLwrd7+/atVNYoIiKC9u3bEx8fT5s2bbh8+XKq63h4eFC/fv0UDa9VJY7Mzc3TLHFkYGDAvn37sLOz482bN4wcORJPT09y5crFjRs3qFatGpcuXfou5/q9FC1WjFKlSpGYmMiRI0eyZJsKhYJFixZRvXp1Zs6c+dU9Qdzd3aVm2Fu2bEmRRBCE9Dg7OwPg7e1NWFiY2mvx8fFMmDCB2NhYqlSpwp9//pnmds6fP0+dOnUYMmQI5cuXZ82aNSl+3wwMDHB1dWXevHk0bNiQHTt2sHjx4gwbIKdFoVBw+fJlxowZw/DhwwkMDJReMzExATLukXDw4EHGjh0LQL9+/Rg6dKja6127dmXNmjXo6elx9OhRWrVqpfHMJ0EQvk1gYCABAQEpHsn/f/Hly1c/7wAFQRAEQRAE4Sv9axMJoaGhfPjwgTx58gDKoGTdunWxsbGhePHiNG7cmPj4eGrUqPGTj/TXderUKapWrcqCBQtISEgge/bshISEMG7cOCpWrIiXl5faiHAtLS02bNiAgYEBJ0+eZN26dVStWpXx48cDMHHiRN6+fZulx6ivr8+GDRuwsbGRyq/kzZuXVq1aMXPmTHbv3s3Dhw/ZtWsXo0aNonbt2qxevZp69eoRHR2Nk5MT9+/fT3P7xsbGdO3aFVDW41aNRoX/TyTcuXNHKsEiEglZx8jISHqYmJiwY8cOmjRpQlRUFPb29ty6dUtt+dDQUMaPH8+1a9do165dijIjVlZWUuB61qxZ3LlzJ8U+s2fPzpEjR8iXLx+BgYHMnj0bPz8/ypYtS3BwMI0aNWLr1q3f7Zy/h3bt2gGwf//+b95WQkICgwYNYvr06dy9e5eFCxdSvnx5GjduzJYtWwgPD9doO+fPn2fQoEGAsr57x44dv/nYhP+WChUqYGtrS1xcHHv37lV7bdmyZTx8+BBzc3NmzZqV5gyJ7du307ZtW0JDQzE0NCQkJIQJEyZQuXJldu/erfb7JpPJ6NSpE+vXr6datWpffdz//PMPM2fOZPXq1VL5oqtXr0qvZ8uWDUiZSMidO7e0jK+vL46OjiQlJdGpUyemTJmCTCZDoVAQEREhLefg4MC2bdswNjbmzJkzNG7cWJrhIAhC1rOwsMDIyIhu3bpRsWLFFI/kPVoqVKxAUFDQTzxaQRAEQRAEQci8f20iwdzcHFNTUw4dOoRcLqdFixZUrFiRN2/e4OPjw40bNxgyZMjPPsxf0ocPH+jRowft2rXj2bNn5M2bFw8PD54+fcry5cuxtLTkxYsXuLi4UKdOHc4mK+Vja2vLvHnzAGXj5UePHjF58mRKly5NaGgoo0ePzvIyJGZmZuzevRt3d3cuXbrExYsXWbZsGc7OzhQvXjxFqQZ9fX02btxI5cqVCQsLo3PnzukmOOrVq0fhwoWJjo5mx44d0vNFixaVGi6rZrkUKlQIk/8FgoSspa+vz/79+6lRowahoaE0adJEbTT8mjVrpJHJ79+/p23btimCZk5OTrRo0YKEhAR69erFli1b1B5bt27lzJkzdOrUCYBLly4xceJEfH19adu2LfHx8fTp04cpU6ZItcy/hwcPHuDj4/NNjbtVVIkEX19fPn369NXbUc368fDwQEtLi5EjR9KkSRO0tLT4+++/GTp0KCVLlqRXr14EBASkuZ1nz57h6OhIYmIinTt3ZsqUKV99TMJ/l0wmk2YleHh4SM+fO3cOT09PAGbOnJnqrIGEhASmTp3K0KFDSUxMpH379jx+/Jhly5ZhaWlJUFAQ/fr1o1OnTvz9999ZcrxhYWHs2bOH6dOn8+TJE/T19alQoQKgvN9VVImEkJAQ5HK51GxZlUi4evWq9F3UtGlTFixYgEwmIyEhARcXF0qVKsX69eul7dWqVQsvLy9y5szJ1atXqVevHq9eiZHQQtqCgoJSHU2f/JF8Fo3w/6ytrQkMDOT69eupPi5cuCAtGxMdLSUMBUEQBEEQBOF38a9NJMhkMrp168acOXM4ePAgurq6LF68GCsrK5o1a8bs2bM5fPjwzz7MX9LUqVM5ePAg2traDB48mGvXrtG6dWt0dHTo1asXt27dYtq0aZiamnLnzh1atGihFlQdPHgw9evXJyYmhhUrVqCrq8uyZcswMDDAz8+PAwcOZPkxm5mZ0bBhQ6ysrDRa3tjYGA8PD0qVKsWHDx+YOnUqR48eTTVwq6WlhY2NDQCnT58mJiYGUI6qVlElErS0tChevPi3no6QBmNjY44ePUq5cuUIDg6mY8eO0gjc7du3A2Bvbw8oy5ht3LhRbf3kJY5u3ryJi4uL2qNv3764uLiwZMkSaR1vb2+uXLmCl5eXFPRet24dXbt2TVFS5Vs8ePCAuXPnUrlyZSpUqED79u0ZPnz4NyfebGxspPJGXl5eX70dd3d3fH19MTAwwMvLixkzZrBv3z4ePHjAzJkzsbGxISYmhl27dtGsWbNUj1uhUNCrVy9CQkKoXLkymzZtErXWha/WoUMHAM6ePUt0dDSAFESvWrWq2uhfUCYQ9u7dS7t27Vi+fDmg7C3g7u6OiYkJPXr0ICAggKlTp2JqasrDhw/p2bMnsckamH+tbdu2cePGDenv0qVLY2dnB8CrV69S9HsJCwvj06dP0vMWFhaAMkEfGRlJnTp1WLFihdQ34eTJk5w4cYKkpCRmzpyplhwvV64cfn5+WFpaEhgYKPWOEIQvBQUFYWtrm+po+uSPbt26YWRkJF2Xwv+ztramQoUKqT5UM1kFQRAEQRAE4Xf1r00kgLJ8iZmZGb169VIrCwDKGQuaBp3/a8qWLQsoR0BOnz5dGiGpYmRkxOjRo6WAbfny5dWaQGppaUlNac3MzAAoVqyYVKd6wYIFqfZY+NHMzMzw9PSkWrVqxMXFsXnzZiZPnqxWEicyMhJXV1epP0LDhg3R1dXF1dWVdu3aERUVhZ2dnVrAKiE+/oefy3+Jubk5x48fJ1++fDx58kQKtquux2PHjgHKwNuX/U9AWeLI09OTli1bYm9vn+GjZ8+eVKpUCS0tLaZNm8auXbswNDTE19eXpk2bfnWPAICHDx8yf/58ateuTYUKFZg9ezb37t1DV1cXmUzGxo0bmT9//ldvX8XFxQWARYsWfXVQtEqVKoCy/rzq/gbl+9mlSxe1kd8uLi6pJgh27dqFv78/xsbG7Nu3T2pSLghfQzWSP3/+/NK11KxZMwD8/f3ZvHkz8P8JhNatWzN37lzevXuHpaUlmzZtYuLEiWrXqpGRESNHjpS+R2QymVpj9q/VoEEDChQoIO3r2rVrUvKzWLFiUvml06dPA1C/fn3evXsHQI4cOaQm66pApK6urlqj9OQByiJFiqSYiZEjRw4p2VK9evVvPh/h3+njx49ER0fj4eGR5qh61SMwMFDtt0AQBEEQBEEQhH8/nYwX+X3lyJGDkydP0rhxY44dO8bBgwdp06YNr169Yvr06VLtfkFdr169mDt3Lq9fv2bbtm3069cvxTLXr19n165dgDI4+SVVU9rkPSj69u3Lli1bePnyJR4eHvTp0+c7nYHmcuXKxf79+5k4cSK7d+/m8ePHjB07ljZt2lC+fHmWL19OcHCwNBujZs2adOrUib/++gtQNpieP3++WkD0/fv3YGb+k87ovyFPnjzs2rWLevXqsXv3bmrXrs3s2bO5fPkyt2/fpnbt2mzcuDHNZGHjxo1p3Lhxiuflcnma9dRVOnToQK5cuejevTtPnjyhSZMmbNy4kfr162d43AqFgkePHnHgwAEOHjyo1p9DV1eXBg0a0L59exwcHNi9ezcjRoxg1qxZ5MmTh969e2e4/bT06NGDhQsX8vr1a7Zs2cKAAQMyvY2aNWvSunVrvL29GT9+PN7e3shkMs6cOYOLiwsfP37E2NiY1atXS6WhkouIiGDixIkAjBs3jvz583/1+QgCIM1ua9OmjRSg79atG7GxsaxevZoVK1bw/Plzrl69KgXlLSws6NmzJ6NHj1YLxH9JVXKkQIECKcrjfQ07OzsGDhxIjhw5CAwM5O7du9y7d4/Q0FAcHR0B5WjwmzdvIpPJGDNmjFQ/3dLSUtrO8OHDWb16NWfOnOH27dtSAiFfvnysWbOGXbt2MWfOHLXkPsDkyZMJDw+nYsWK9OjR45vPR/h3s7W1lUpvCYIgCIIgCIIgqPyrEwmgLOtx9epVhg4dSvv27bG2tiYkJIRJkybRs2fPn314vyR9fX1GjhzJqFGjWLRoEd27d1cLuCgUCsaNGweAo6MjlStXVls/ODiYx48fA+ojHw0NDRk5ciTjxo1j6dKldOrUKcVsh59BS0uLBg0aUL16ddzd3bl69Sr79u1j3759gHJmxqhRo4iPj2fUqFGEhIRgaGjIokWLpACQSlJSkrKmtUgkfHe1atVi6tSpTJs2jTFjxlCpUiV8fHy4fv06derUyZLgX1pKly7NqVOn6NGjB1evXqVz586MGzcOa2trPn36xKdPnwgJCSE4OJjw8HBCQkIICQnh06dPamXAdHV1qV+/Pq1ataJt27Zkz55deq1///68e/cOV1dXhg0bRu7cub+6yau+vj5jxoxh+PDhuLm50bNnz3SDqGmZNWsWPj4+/PXXXxw5coQbN27g5uaGQqGgdOnSbNy4kdKlS6e67oIFC3j37h2FCxdm2LBhX3UegqCSmJjIoUOHAGjbtq3aa6oZOKtXr5ZKGFpYWNC7d2/atm2Ljo5Ohtf/P//8A0DBggWz9LiNjY2pVKkSlSpVApS/p6okyJEjRwCoVq0axYoV48qVK4B6IqFgwYJ07tyZnTt3snbtWtauXSu91rJlS1q2bJlin7du3WLLli0ALFmyJMNkqSAIgiAIgiAIgiCk5rdOJJw6dYpLly5hbW1Nhw4d0gxK586dGy8vL968ecPjx4+xtbVNUepIUOfs7MzixYtTnZWwb98+/P39MTIyYurUqSnWvXz5MqAcgZk8MArQpUsX1q1bx9OnT1m7di1jxoz5vieSCTlz5mTs2LH4+/vj7u5OaGgolStXZtCgQfj6+rJjxw7kcjk2NjZs3ryZkiVLptjGhw8fUtS6Fr6f4cOHc/HiRU6ePImzszPnzp3TaGZAVsidOzcHDx5k9OjReHp6Sk3GM6JKHrRt2xZ7e3uyZ89OfHw8RkZGKZadOnUqb9++Zdu2bXTv3p2dO3eqzfLJjKyYlVCoUCEGDx7MkiVL6Natm3St9+nTh/nz56cZoPznn3+kmvSurq7o6+t/1TkIgsqFCxcICQkhZ86c1K5dO8XrLi4uGBgYcPToUVq3bk3btm2l606TJuZPnz4F4I8//sjaA/+CKonw/v17qbFz69atpedAOQMruTFjxrBz506OHTvG8+fP0012yOVypk6dikKhwMnJ6au/P9ITGhqKv78/TZo0ET1PBEEQBEEQBEEQ/sV+20TCwIED8fHxoWzZsixZsoSxY8eyceNGWrVqlWLZuLg49PX1yZs3L3nz5v3ux6ZQKDRqkKrJP7g1bbSalJSk0SjDxMREjUZqy2QyRowYwejRo3Fzc8PR0REDAwNiYmKYPHkyAH/++Sc5cuQgISFBbV1VE+Jq1apJr8XExEj7HT16NIMGDWLdunVky5aNihUrYmtri46ODjExMRqNlA4JCclwGVA2sQwODpb+vn//Po8ePcLe3l6qOQ3w6dMnab9VqlShVKlSvHr1CisrK1atWsW1a9cAZXmXLVu2YGxsLDVdTi55fwXQ/FoQvo6Wlhbr1q2jZs2aUr8Ed3f3FPdWbGysRvdbYmKiRtdfQkICenp66OnpsWzZMsqWLcuePXswNjYmR44c5MiRg5w5c5ItWzby589Pzpw5pedy5sypFkiXy+UkJSWleZ2sWLGCDx8+4OPjQ69evdi3bx/FihVL89gUcf/foyM8LAxZsp4dQ4YMYcKECSxcuJC2bdtK55CR+Ph4qU788OHD2bFjB8HBwZiYmLB06VLat28vLZfaeYwdO5b4+HgaNWqEvb09crlco4RbUlKSRp+bXC5PUcolreU0HY39PWe0/Ntl5ncrLfJkr8XGxaXoU7Bnzx4AHBwcSEpKIikpiYSEBLXlunXrRrdu3VJsOzY2VmrSnpaHDx8CyhKJqrJIafnw4YNGfX+ioqKIiopK9TVvb28UCgWlSpUiV65cJCUlSQ2T8+TJo/Ze2dnZ0axZM44fP87atWuZNWtWmvvcv38/169fx9jYmNmzZ6f7nicmJmp0f8TGxkr3h0KhoGXLlly+fJm1a9emeL+NjY0z3J4miR0VTe5LTa4/8bssCIIgCIIgCIKQeb9lIuHQoUOcPn2au3fvYmJiQmhoKAMHDqRt27asXr2a/v37qy07fPhwTp06RZEiRX7iUX8dTUf3aWlpabSstra2RoECExMT+vXrx9KlS3n16hW7d+9m4MCBLF++nNevX1OgQAHGjRuHjo5Oiu2pRlXWqFFDei137txSoM/Z2ZmtW7fi7+8vBUCMjIykcg81a9akSpUqmJubp3uMmgR8X79+jY6ODh8+fGD9+vVSI8uQkBC166RChQpUrVpVbd2AgAC6dOnCixcv0NPTY9GiRXTt2jXdwGt4eLja3zKZLN3PJaPXhfQZGxtjbGzMrl27qF+/Prt376ZRo0ZSaRMVTd9nLS0tje6PnDlzql0HY8eOZezYsSmW0zQQbmxsnO5+d+/eTePGjfn777/p2bMnvr6+5MuXL9Vl5TExRP/vv3V1dZElC6x2796d5cuX8/btW3bv3k3v3r01CsyZmJhIyxkZGeHl5cWOHTsYPny4WlLD0NAwRcD3+PHjHDt2DB0dHZYtW4aBgYFaOZeMJF8uNjaWBw8ekDdvXnLlypXpe+dn3W/iHk9duvdaste+/NwUCoVU1ih5f4QyZcqkmqT78pr8/Plzhp+JKilsZ2eXonHxl2xsbDJcBpQzegoXLpzi+bdv30rlvmbOnEmVKlXQ0tJSm5Hw5Xs1evRojh8/zt69e5k7d26qsywjIiJwc3MDYMKECRQoUCDd49PS0tLo+0BHR0d6//bs2SPNQly/fj3du3fPcH1BEARBEARBEATh9/RbFsr966+/qFWrFiYmJgCYm5vj6enJ0KFDGTRoEEePHpWWLVOmDHK5nL179/6sw/1t6evrS8HRhQsX8vTpUxYsWADA3Llz1RoMq8TExHD9+nVAvT9CcjKZjL179zJlyhSaNGmCmZkZ0dHRnDt3jsWLF0u9LKpUqcKwYcPYt29fpkYsJhcfH4+Hhwfdu3eXkgigLM/0+vXrNNfbtGkTdevW5cWLFxQsWJCzZ8/Sr1+/DINPqhGkwo9Vq1YtZs+eDcCwYcO4ePHiD91/UlISwcHBWTLK9dOnT2zevJkpU6YQGhoKKIP3hw4dwsbGhlevXtGmTRs+f/6c6W3r6+szYsQIAJYuXUpsbOxXHWONGjVYtWpVujMjQHn/jRw5ElDOhihRosRX7U+hULBr1y5KlixJ5cqVyZcvHxYWFlStWhUnJyemT5/Otm3buHTpEh8+fBCjjf/lrl+/zuvXrzExMaFBgwZqryUmJuLr68vQoUP5448/MDQ0pHfv3rx69Urj7SclJUmJhO9d2ghgw4YNxMfHU7VqVapUqSI9r0okJO+RoFK3bl0qVapEbGwsq1atSnW7rq6uvH37lsKFCzN8+PAsP+6YmBgmTZok/R0QEMCNGzeyfD+CIAiCIAiCIAjCr+G3TCRYWVnx119/EZ+sZAcomwi2a9cOFxcXqexMwYIFCQgIkJoDC5nTs2dP8ufPz+vXr2nUqBHR0dFUr16djh07prr89evXSUhIwNLSMtWRlyrZs2dn3Lhx7N+/n5cvX3LlyhWWL19O586dKVKkCAqFgsDAQDZv3kzPnj0ZMGBAppIJCoWC06dPM2LECDZu3EhsbCx2dnasXbuWSpUqkZCQwLp161Ksl5SUxOjRoxk4cCDx8fG0bNmSv//+mwoVKmi0X1XgR/jxxowZg729PbGxsTRp0oRjx46luWxAQAATJ07kzp0737TP9+/fM3fuXIoUKYKlpSX58+enc+fOLF++nICAALXGyulRJQ/s7e2xsrLCxcWFuXPn0qZNGynYnzNnTg4cOICVlRX379+nc+fOX5UIcHJywsrKirdv37Jjx45Mr58Z8+bN4+HDh+TKlYspU6Z81TauXLlCnTp1cHZ25uXLlxgbGyOTyQgPDycgIIDdu3czf/58evfuTZ06dbCysiJnzpzUqlWL9evXp1lKRvg9RUdHs2LFCgCaNWuGgYEBsbGxHD16lH79+mFlZUWjRo1YtWoVr1+/Ri6Xs2XLFmxsbJg4cSJhYWEZ7uP169dSOS8rK6vvej6fP3/Gw8MDgMGDB6u9llaPBFAm5FVJunXr1hEZGan2evK+JPPnz/+q5uoZWbZsGa9evSJ//vy0aNECUCbhBUEQBEEQBEEQhH+n3zKR0K1bN4KDg6Va/SoymYw1a9YQHh7OkSNHpOdz5Mjxow/xXyP5rATVCH43N7c0R+arShxUr149U2WZSpYsSe/evVmxYgU3b97k6dOn7Ny5k8GDB6Ojo8OuXbsYMGCARoHZoKAg+vTpw+DBg3n//j0WFhZMnDiRFStWULx4cQYNGoSWlhbnz59XGz0ZERFBu3btpCBV586d2bRpU4qG0ekRMxJ+Hi0tLXbv3k3z5s2JiYmhTZs2bN++PcVyb9++pW3btixbtoxq1apRpUoVqbGqpm7dukXXrl2xtrZm8uTJBAUFSdves2cPw4cPp1KlSuTOnZvmzZsze/Zsnj17prYNhULBzp07cXBwIF++fLi4uHDixAkSExMpU6YMZmZmnD9/HicnJymJZm1tzcGDBzEzM+PixYv06tUr07N1ks9KWLZs2VfPSkiPXC5n+PDhzJw5E1DOYMqoVNmXPn78SO/evalZsyZ///03xsbGzJw5k7dv3xIWFsbNmzfZt28frq6uuLi4UL9+faytraUkw99//82gQYP4448/GDt2LJ8+fcry8xR+rAMHDlCmTBm8vLwAMDU1xcnJiXz58tG+fXu2b99OSEgIFhYW9O7dm8OHD3Pu3Dlq165NbGws8+fPp2jRoqxbty7d++aff/4BIH/+/N+9V8amTZuIjo7Gzs4uRaN4VW+G1GYkALRo0YJixYoRGhrKxo0bpecVCoVaXxIHB4csP+7Xr19LZZPmzJkjJUG8vLxSJDUEQRAEQRAEQRCEf4ffMpGQN29e3NzcWLhwIUuXLlV7TVXuQgR0s45qVgIokziVKlVKc9lbt24BylrmX1uOCCBXrly0bNmS+fPns2XLFimZ0LRpU54/f57uulOmTJFK2xgYGDB48GDq168vJTYKFiyInZ0doAxMgbJEQ6tWrTh+/DiGhoZYW1vj5eVFoUKFGDx4sEblGl69eqVWPkn48YyMjDh48CDdunUjMTGRHj16MGTIELVg+YYNG/j48aP097179xgwYIDG+3jw4AH16tVj165dJCQkUK1aNbZs2UJISAh+fn7Mnj2b5s2bY2pqSkREBKdOnWL69Ol07dpVbTvHjh2je/fuasmDmTNncv/+fW7cuMGBAwfQ19fn4MGDarMPSpUqhZeXF/r6+hw6dAhnZ2eNm4+rtGjRAi0tLd6+fcupU6cyta4mtm7dKiXk5s2bR69evTK9jcmTJ0szJszMzDhz5gwTJkzA0NAQQ0ND7OzsaNWqFSNHjmTlypWcOnWKp0+fEh4ezoULF6QR0qGhoSxevFitBIvw+3nx4gWOjo68evUKc3Nz6tevz6ZNm9i3bx9RUVHkz5+fQYMG8ddff/HmzRvc3d1xcHCgVq1a+Pn5cfDgQWxtbQkJCWHixImMHj06zRJYqv9/ePXqFXPmzCE4OPi7ndeuXbsA5WyE5Mn3pKQk6XsqtRkJoOx5pEoKTp48mfnz5xMUFET79u05evQoOjo66Sb+v8X27duJjo7G2tqaDh06UKdOHSwsLIiMjOTSpUtZvj9BEARBEARBEATh5/stEwkAgwYNYsKECYwYMYJRo0YRFxcHQHBwMHfv3qVOnTo/+Qj/PfT19dm8eTO9e/fG1dU13WWdnJzQ0dHBx8cHFxeXb0omqLRu3RoPDw9MTU25cuUKNWrUUJtx8qXOnTtLjWhjY2OZMWMGHTp0YMmSJdy+fRt3d3eppE2DBg2Ij49n0qRJXLp0CXNzcxo3bkxQUBAymYzIyEjc3d2pVq0aNWrUYOvWrURERKTY56tXr7C3t+fly5f8UbDgN5+z8PV0dXXZsmWLVM5s9erV1KtXj4cPHwLK68nCwkJtnb59+2q07bi4OLp3705MTAw1a9bk+vXrXLp0ie7du5M9e3bq1KnDxIkTOXr0KCEhIVKQEMDe3j7FtgCKFCnC3bt3uXHjBpMmTaJ48eKAsga6p6cnenp6HDhwAAcHB6ksS+3atdm2bRt6enp4e3tTsmRJpk2bppYgSUt8fDz9+/dHLpdjZWVFtWrVNDr3zFizZg0AM2bMYOzYsV8VyOzatSsF/3cvhYWFUbt2bXr37s25c+c4efIkW7duxdXVlREjRuDk5ETdunUpXrw4efLkoVatWmrfESVKlEiRyBF+L/nz56dZs2aAMjl04cIFQJnovnjxIo8fP2bRokXUrVsXHR0dtXVlMhmtWrXi1q1bLF++HC0tLbZu3crcuXNT3VfDhg2pVq0aCQkJbN26lYYNG363hILq96R06dIpnlclOtKbzdOtWzfatGlDQkIC06dPp3jx4hw7dgw9PT2WLFny1X1JMtKoUSO0tbUJCgpi165dnD9/no8fP6Knp0fJkiW/yz4FQRAEQRAEQRCEn0um+M27Uq5bt45Ro0ZhZmZGjRo1uHjxIsOGDWP8+PE//FjCw8MxMzMjNDQUU1PTDJfXJLim6ccjl8s12p5cLkdLK+P8UXx8vEbLJSQkoKurq/bcwYMHcXJyIjExka5du+Lu7k58fHyK4E5qoqOjU23iDMoRqS4uLvz999+AclT19OnTyZYtW4plFQoFN2/eZOvWrVy+fFlqWpvc8OHDcXBwYNq0aVy6dAljY2NGjRrFzJkzkclkHD58GD09PTZu3MiBAweknhwmJiZ06tSJXr16Ua5cOSmJ8OzZMwoVKsTR/fuhiyMANtevoWVklO45f4/Ror8i1f0RFhaW7v2h6XKQ8f1x/PhxevTowYcPHzAyMmLRokXSCP6AgABKly6tVgM9MTEx3Vri48ePZ8mSJVhYWHDr1q1066fHxMRQvXp1bt++Td26dTl58qRamRRfX1+aNGmCra0tt2/fTvN+8/Pzo23btoSHh1O6dGmpTwLApUuXGDlypJQYMzY2ZmCvXvQ+pZwZk+v0KWTJ7ieFQsHw4cPZsWMHxsbGHDlyhNq1a6f7HoLy+0CTEi8KhYJbt25RtWpV9PT0ePnyZYqkjWo5Ta77uLg4jhw5wtKlSzM9ytnCwoL27dvTvXt3qlSpgkwmQ6FQaPS9Bmi83I/2Pe6jrJaZ3600X4uO5kllZePhAhcvoGtiQlJSEtOmTZNK6jRs2JDDhw9Ln5VCoUBfXz/D/S5btkwayT937txUZyQpFAp2797Nhg0bCAgIAEBPT48uXbrQt29ftVkCz549I1euXBnuNzIyMkXvoOLFixMVFcWFCxekxBko77nChQujr6+fZp+PuLg4tLW1USgUeHp6MmrUKD5//kzFihXZsGGDFNCXy+Xo6elleHwKhUKj+zwmJgaZTMbcuXOZPXs22bJlo3Dhwty6dYt+/fqpzRRN6/c8ucwMOND0eygj4eHhmJub/9L30c8QEBBAxYoVuX79usa9oQTNyaOjeVihIgAVHz3kwrVrv/X7/F+7PwRBEARBEIR/QSIBlLMQ9u7dy8ePH2natClVq1b9KcchEgn/78tkwooVKzQK8KSXSABloNfNzY358+eTlJREvnz5WLhwIRUrVkx1+Tt37mBubk5AQABnzpzh/PnzREdHM2DAADp06MDcuXPx9fVFT0+PtWvXMmzYMCIjI5k4cSLTpk2TtvPx40e2b9+Ou7s7T548kZ4vX748nz9/5vnz5xQqVIhjx46RN2dOXtdWzogRiYT/9zMSCaAsU+Lk5ISfnx8AnTp1YtmyZaluO71EwpkzZ6RZBXv37qVdu3bp7nfAgAGsX7+e3Llzc/369RRJh5s3b1KpUiUsLS15+fJluvfbzZs3sbe35/379xQsWJBDhw5RpEgRQPkeHDt2jHnz5nHjxg0MZTKu2yhnNch3eGCZLDi5bNkyZs2ahZaWFjt27KBRo0Ya9QDJTCJh8ODBbNy4EUdHR6mJbGrLaXLdJyYmSu+LqiH7hQsXyJEjB5aWllhaWmJlZUWePHnIly+f9LelpSXGxsap7lckEr6/75VIUDl8+DCnT59m0qRJ5M6dW22/mvzOfP78mSVLljB79mxAOSChY8eOKZZ78eIFRkZGXLp0iRUrVqglFDp27Cg1d/6WREKxYsWIiYnh0qVLWFtbS89//PiRcuXKkStXrjRLNaoSCSrBwcHcuHGDhg0bqiXuv1ciITExkaZNm0q9kfT09Lh7965UChFEIuF3IxIJ35dIJAiCIAiCIAi/u39FIuFXIRIJ6pInEzp16sSGDRsyDAJklEhQOXDgABMnTuTVq1doaWkxaNAgBg4cmGLWw507d8iZM6f0d1xcHJ8/fyZ37ty4ubnh4+ODjo4O06dPZ/fu3dLocR8fn1SPNTIyEn9/fzZv3oy3t7c0S0GVRMifPz/ymBiRSEjFz0okgLJMyJIlS5g1axZJSUkULlyYrVu3pvgHfFqJhI8fP1KpUiXevn1L3759WbFiRbqBOU9PT5ycnJDJZBw7dozGjRunWObFixcUKVIEfX19IiMjM7zf/vnnH5o1a8bTp0+xsLDg4MGDlC9fXnpdoVBw4sQJFs2di3ukcgRzjaAXdHJ2ZujQofj7++Pi4gLA/PnzcXFxQaFQZGkiITQ0lMKFCxMVFYWfn1+asx2+JpGQnqSkJI1mPIlEwo/xvRMJ6e1X00QCwMSJE1m3bh06Ojrs2LEjxX364sULKSGlUCi4dOkSK1eu5Pr164CyjFr79u1p3rx5itJEqUktkVC0aFFiY2O5fPkyBQoUkJ5/+vQpderUoUiRIlJZti99mUhIy/dKJIDyPapatSrh4eEpZiOASCT8bkQi4fsSiQRBEARBEAThd5dx5EX4LjQNtCQlJWVpgkChUGi0b00DeImJiWn+w75Vq1Zs374dZ2dndu/eDShrp6cXCIiJiclwn6AsB7Fv3z7mzJnDoUOHWLlyJX5+fjRq1IhixYphY2ODlZUViYmJakEKHR0dLCwsWLlyJT4+PmhpaTFx4kT8/f25ffs2uXLlYuPGjUDqwQ25XE6tWrWoVasWCxYsYOfOnQQGBjJx4kTy5cuX4v3V5P3+ryQSfiZtbW3GjBlD7dq16dmzJ0+fPqVu3bpUrlyZ+vXrU79+fapUUQYtv/y8FAoFAwYM4O3bt5QoUQJXV1eSkpLSDH49evSI/v37AzB27Fjq1q0rJZySUwUo4+LiCAsLyzCgX6RIEU6dOkW7du24desWzZo1w9PTk/r160vLNG3alEa1a0uJrLjYWNavX8+WLVuk66xv37706tWLpKQkEhISpF4N6YmLi9MoEOnh4UFUVBQlS5akZs2aaV77crlco4BgVtM04Sr8etJLOmSWKpE1e/ZsPn36xJ49e+jZsye9evXCyclJ6isgl8vV7vNq1apRtWpVrly5wurVq7ly5Qq7du1iz549NG/eHGdnZ6k/T2rCw8NTNEZXnVdoaChG/0s6a2lpER4eDkC2bNnSvI80/T3X9L1LTEzUaNmEhAQpcVegQAF27tzJnj17mDRpktr6mh6fpolFQRAEQRAEQRAE4ecSiYTvQCaTZdk/ijXdlpaWlkaBf023p6+vr9Fyurq66QYEO3XqhI6ODo6OjuzevRsdHR3c3d3TXUeTkcU2NjYYGRmxZ88evLy8GDJkCHfv3uXu3bvSMmZmZpQsWZIyZcpQqlQpSpUqhZ2dHW5ubhw4cACA9evXo6urK/VF2Lp1q1qd6vTO18TEhAkTJqRYRp7sc8jKa0FISdP3VhW0b9CggVTHe+/evfj7++Pv78/8+fMxNjamTp06NG7cmIYNG2JnZ4dMJmPdunVSv4wdO3ZgZmaWZkA6JiaGrl27EhkZSd26dZkyZUqa17q5uTna2tokJSURERFBjhw5MjyPggULcvbsWdq1a4evry/t27dn27ZtdOrUSVomeRjw0KFDzFywgPPnzwPKviIrVqyQjikuLk6j7w1dXd0MEwkKhYLNmzcD0K9fv3TvcU3vC11dXY2W0+Q7Q7Vf4fvT9H1O9xpJ9pqBvj5aGsw00HTfye81Dw8PYmJiOHLkCKtXr2b16tVUqVKFXr160aFDB8zMzFKsX7RoUbp27cr58+eZO3cuZ86c4ciRI/j4+NCxY0eGDx9O0aJFU6z34cOHVHv6AOTMmVNt9pzqt8zMzCzNe9TAwEDjAQSavC+aJvcMDAzU7rlmzZpJjbC/3K8mtLW1s/Te1GRb4rtAEARBEARBEAQh837N2g3Cv0q7du3Yvn07Ojo67Ny5ExcXl0yVMshI586dCQgIYM6cOTg6OlKqVCl0dHQICwvj8uXLrFu3jqFDh1K/fn1y587NggULAFi+fDlVq1Zl8ODBAEyYMIEmTZpk2XEJv6bs2bOzZ88enj9/zsaNG+nSpQu5cuUiKioKHx8fRo4cSdmyZcmfPz/dunVj1KhRgLIpa7ly5dLd9qhRo7h16xa5c+fGw8Mjw2C6ubk5QKrNwNNiamrK0aNH6dChA/Hx8XTp0oV27dql2oy4Tp06+Pr6cubMGdzc3DI8pm/h7+/P3bt3MTQ0xNnZ+bvsQxCymq6uLgcOHODw4cO0a9cOHR0drly5wsCBAylQoAA9e/bEz88v1ZH6tWvXxsfHh8OHD9OwYUOSkpLYtWsXNWrUoF+/fvj5+ZGYmJju/tMKtoeFhQGkmXgQBEEQBEEQBEEQhB9NJBKEH6JNmzbs2LHjuyUTChQowOjRo9myZQvXr1/n8+fPXL9+HXd3d0aNGkXTpk3VSk7MmzcPZ2dnnJyciIyMpHbt2kydOjXLjkf49f3xxx/07t0bT09P3r17x82bN1m4cCFNmzbF0NCQd+/e4enpSUxMDI0aNeLPP/9Md3u7du1i/fr10syWL5srp0aVSFDVbNeUvr4+np6eDB06FFD2I6lVqxY1a9bE+9ChFMvXqVOHP//8M9UGxFll/fr1gDKxp0nfBUH4VWhra+Pg4MDevXt59eoVbm5ulCxZktjYWHbu3EmTJk2wtbVlzpw5BAUFpVi/cuXKeHl5cfLkSZo0aYJcLmf//v106NCBkiVLMnz48DSTCqoExZcj5CMiIgBE3XFBEARBEARBEAThlyESCcIP82UyoU+fPlId6G/15MkTvLy8OHXqFPfu3SMyMhI7Ozs6d+7MnDlz8Pb25p9//uH169fcv3+fIUOGMGLECKkvwubNmzUujyL8+2hpaVG2bFlGjhzJsWPHCAkJ4cyZM0ycOJGePXuybdu2dEuIPHr0iAEDBgDKBq6pNVdOjarESmYTCaAMfi5btox79+7Ru3dv9PT0uHz5Mt2cnKRlkpKyrq58ej59+sTevXsBpP4QgvA7yp07NyNHjuTOnTtcvHiRvn37YmpqyrNnz5gxYwY2NjYMGTKE4ODgFOtWqFCBnTt3cubMGXr06IGFhQWfPn3Cw8ODDh06ULt2bcaMGcPJkyf5+PEj8P+JhC+/X1S/jSKRIAiCIAiCIAiCIPwqRORU+KFUyQQnJyc8PT05ePAgbdq0wdnZmXr16mVqW+Hh4ezbt4/t27dz8eLFFK/r6+tjaWmJlZWV2iM0NBQPDw/ev3+PTCZj06ZN5M2bN4vOUPg30NfXp169ehpfk0uWLCEyMhI9PT3atm2r0TpyuZzIyEgAoqOjv/ZQsbW1xd3dndmzZ7NixQq2rF0rvda5cyc2e3qmWuc9q9y/f5+ePXsSGxtL2bJlpabVgvA7k8lkVK5cmcqVK7Nw4UIOHDjAli1bOHv2LOvXr2fnzp0MHz6cESNGpFi3bNmyLFq0CFdXVy5duoS3tzdHjx7l48ePeHp64unpCShnJKlKGyXvQRIeHs7Ro0cBNOqdIgiCIAiCIAiCIAg/gpiRIPxwbdq0Yc+ePRQvXpyYmBg8PT2xt7enePHizJ49mydPnqS5rlwu56+//qJ///5YW1szYMAALl68iJaWFlWqVMHOzk4KvMTFxfHixQv+/vtvDhw4wOrVq5kyZQqLFi3i/fv3WFpasmrVKo1Hjwv/PZ8+feLYsWMZNg11dHQke/bsxMfHU716debOnUtCQkK662zbto3AwEBMTEyoXbv2Nx+rpaUlc+bMIfDBA+m506dP/x97dx0XxfY+cPyzC0gqBgZ2K3Z7LfQqxlWv1y6wO6/d3d1eE7u7u+saYF0VuwUDRUHp3f39wZf5idSgKPW8X699KbtnZ87szpmZPc+c51CpUiUePXr0w8v/ll6vZ+HChfz2229cv36dNGnSMGvWLJnEVCQ6FhYWODo6cvToUY4dO0aJEiX4/PkzEyZMIF++fCxbtoyAgIBw7zM2Nsbe3p6ZM2dy69YtVqxYQatWrciTJw/w/3Oj1K9fX0kH5u3tjZOTE1evXiV16tS0bt36l22nEEIIIYQQQggRFRmRIFQzGAyx1klYu3Zt/vjjD1xcXFizZg1btmzhxYsXzJgxgxkzZlCuXDkcHR1p2LAhKVKk4NGjR6xfv54NGzbw8uVLZTn58uWjdevWtGzZMsyoAn9/f16/fs2LFy949+4dHh4eyiMoKIiGDRtSu3ZtTExMYmV7ROLz6dMnKleuzJ07d1iwYAHdunWLtKy9vT03b96kW7du7Nu3j1GjRrFr1y6WLFlCkSJFwpX/8OEDw4cPB2DEiBGq5lNQy+qreRBsbW1xc3OjfPnybN26FXt7+x9efnBwMNevX2f06NEcPXoUgJo1a7J06VIyZMjww8sXIj6zt7fn33//Zfv27YwaNYqHDx8ycuRIli1bxpAhQ2jUqFGEE5obGxtTrlw5atSoAYQEDG7evMn79++pXbu28tzXQYQjR44oQQchhBBCCCGEECKuaQzR3WorVPP29sba2ppPnz5Fm9dY7ceu1+tVdd4bDIYoc7h/XU7N8oKDg5Vy7u7u9OrVi/Pnz7N9+3YqVKgQZnkRdZp8KzAwMMr6+fv7s2/fPlatWsXx48eVvNHm5ubkzZuXGzduKGVTpkxJw4YNad++PaVKlYpyewIDA1UFC/R6fZjUEpFRs716X1/ulywFQF5XF7QWFlGWTyp3cKttHzFpR7EtdL/T6XTUq1ePQ4cOAZApUybu37+PmZmZUi6i781gMCgpT7y8vDAxMWHEiBH0798/zH7Yu3dvlixZgp2dHVeuXMHExETVfqqm/X69/1ns3k1jJ0dcXFzQaDQUKFCA0qVLU6ZMGUqXLk2ePHkwNTWNcnmBgYFcvnyZS5cucfbsWS5cuKBMBGtmZsbUqVPp2rUrGo0Gg8EQa9sRKjbbR+j3q4aa42lcSAjtKDbofX25V6IkAHlcrkR7HA2l9jyoRkSTI38tKCiI1atXM27cOF6/fg1AgQIFGD58ODVq1Ai37757947kyZNHuKyvgwgpU6bk2LFjFC9ePNrtiO3zvhp6vV7VnEJqrw/g158Hk0o7iqmrV69SsmRJXF1dKVGiRFxXJ9H5+rhW8v49zrm4JOjPOam1DyGEEEIIISMSRBQMBgNr166lf//+SgqGbt264erqGut38puZmdG4cWPq1KnDu3fv2LRpE2vXruX+/fvcuHEDrVZLtWrVcHR0pG7duhgZGWGhsmNJiJgaNGgQhw4dwtzcnBQpUvDq1StWrFhB9+7do3yfRqPB0dGRqlWrKqMTRo8eza5du1i+fDmFChXi6tWrLF26FIC5c+f+1FExtrYZOHHiBF26dGHjxo3cvn2b27dvs2rVKiAkUFe8eHFKlSql5INPnz49Li4unD17lrNnz3Lp0qVwczikTJmSypUrM27cOOzs7H5a/YWIz0xMTOjYsSO1atVi1apVzJ07lzt37uDo6Ejp0qWpXbs2FSpUoEiRIlF2vH8bRNi0aVO0QQQhhBBCCCGEEOJXk0BCIqHX61XdZRkQEKDqTsIHDx4wYMAADh8+DEDx4sV58eIFbm5uzJw5k969ewMhwQYrK6tol2cwGFTVT6fTkSFDBvr06cPff/+Ni4sL9+7do2rVqmFSFwUFBalaXkxGfqi5Wzkmd1iK+EPtneiBgYGsWrWKOXPmALB8+XLev39P7969mTJlCq1atcLMzCzaESy2trbs3LmTNWvWMGDAAK5du8Zvv/3GsGHDOHjwIAaDgaZNm2Jvb4/BYECv16var9Tcgfz1Pm8wGDAzN2f16tVMmTKFK1eucOXKFVxcXLhy5Qre3t5cuHCBCxcuKO8JHVnwtTRp0lCpUiXlUbhw4Qjrq7adq71DWm37jcnyksoIoKRI7f4Xk5F+aspZWFjQu3dvWrduzbx581i6dKnS1gCsrKwoW7YshQsXpkqVKhQuXFgJIHp7e9O6dWsliLBhwwbs7OxUHbPUboder1c1ckGn06kqp/Z46u/vr+q4FhQUpIz2io6akYNCCCGEEEIIIX4OSW0Ui+JyiK/alASBgYFRBhIMBgPr1q2jf//+fPr0iWTJkjF06FD69OnDxo0b6d69O1ZWVri6uiod+5Zf5WSParlqOzxiM5UTqEuZEJspT74eup7vqqvqlByJXVymklD7/R4/fpw6deoQFBTEqFGjGD58OAEBARQoUICXL18yZ84cZa4EtSl8PDw86Nq1K/v27VOet7Kyws3NTWlDajv61Oz3alNr6fV67t27x5UrV7h8+TKXL1/mxo0bBAUFkT59eipXroy9vT2VK1fGzs5OdaBDrbgIJKhdb3yWVFKyfM9xNL7sL+7u7mzevJnTp09z5swZvLy8wrxuaWlJuXLlqFKlCvv27ePixYvKnAjFixeP1eNBaLm4OP/6+fmpWl5cBBKSSjuKKUlt9HNJaiMhhBBCCJHQyYgEoXB3d6d79+4cPHgQgBIlSrB48WIldYmjoyOrVq3i8uXLDB8+nJUrV8ZldYWIVY8fP6ZFixYEBQXRpEkThg0bBoCpqSmDBg2id+/eTJ8+nXbt2qnu9IKQ0Qm7du1i/fr1ytwJY8aMCTPCJi5otVry58+PnZ0drVu3BkLuIPb09CRTpkwJvsNdiLiSMWNG+vbtS9++fdHr9fz333+cOnWKU6dOcfbsWd6/f8+xY8c4duwYQJggQmJlMBiYOXMmmzZtokSJEtjb22Nvb0/WrFnjumpCCCGEEEIIIVSSQIJQRiEMGDCAjx8/kixZMgYPHky/fv3CjF7QarXMmjULe3t7tm3bRrt27bC3t4/DmgsRO7y9vfnrr794//49JUuWZNmyZWE60tu2bcu0adN4+fIlK1euVEYlqKXRaHBycqJmzZrcvn2bypUrx/YmxAozMzMyZ84c19UQItHQarUULVqUokWL0qtXL/R6Pbdv3+bMmTOcOnWK169fM2/evEQdRAgKCqJnz56sXbsWgDt37rBu3ToAsmXLRsWKFfn999+xt7cnW7ZscVlVIeKd58+f4+npGW05GxsbCcwJIYQQQoifTgIJSdzDhw8pXrw4gYGBAJQsWZLly5eTLVu2CFMgFS1alI4dO7J06VL69evHv//++6urLESs0ul0tGzZkjt37pAxY0a2bduGubl5mDIRjUr4nkmS06ZNS5UqVWKp5kKIhEar1VK4cGEKFy5Mjx494ro6P52Pjw8tW7bk+PHjaLVahg8fjp+fH2fPnsXV1ZVnz57x7Nkz1q9fD4QEFipXrkzt2rWpX7++jIwSSdrz58+xs7PD19c32rIWFha4ublJMEEIIYQQQvxUEkhIwvR6PQULFlT+trS0pH379mTIkCHK99WtW5elS5dy7949Tpw4QcOGDX92VYX4aU6cOKGk85o7d26kKYdat27N4MGDefXqFUeOHKFRo0a/sppCCJHgrFy5kuPHjwOwevXqMMfNz58/8++//3Lq1CkuXLiAi4sLz549Y82aNaxZs4ZFixbRvn37uKq6EHHO09MTX19f1q1bp6QZjYibmxtOTk54enpKIEEIIYQQQvxU6marFYlSaJqBUF++fKFHjx5kyZKFxo0bs2bNGj58+BCmzI4dO2jatCkA+fPnp0yZMr+svkL8DKVKlSJXrlwADBw4kOfPn0dYbvPmzfj5+WFlZUXp0qV/ZRWFECJBcnBwUCZhXbp0KV++fFFes7Kyonr16owZM4YzZ87w5s0b9u7dS4sWLQCYNm0awcHBcVJvIeITOzs7SpQoEekjqiCDEEIIIYQQsSnJBhLOnTtHgwYNqFKlCmPHjg3XYZ7YeXl5KZPJhho9ejSFCxcmODiYEydO0KNHD3LlykWDBg1Ys2YNEyZMoE2bNvj7+1OjRg2OHz9OqlSp4mgLhIgdqVKl4sSJE+TKlYunT59SvXr1cMGEDx8+MHz4cABGjBiBra1tXFRVCCESlAIFCrBnzx5SpEjB2bNnadiwYaRpWpInT06NGjVYuHAhadKk4cmTJ+zYseMX11gIIYQQQgghRGSSZCDhwIEDNGrUiDJlymBvb88///xDoUKFOH/+fFxX7ZcZM2YMnp6e2NnZYWlpCUCdOnVwcXHh5s2bDBs2jEKFChEcHMyxY8fo0aMHU6dOBaBXr15s2bJFuctQiIQuc+bMnDhxgpw5c0YYTBg9erTSXnr27BmHNRVCiISlTJkyYYIJkydPjrK8paWlMn/EjBkzMBgMv6KaQgghhBBCCCGikSQDCX379mXRokUMHTqUcePGcefOHQoUKED16tU5deqU6uUEBATg7e0d5pEQXLt2jaVLlwIhOeErV64MwNGjRwHIly8fAwYM4N9//+Xq1auMGjWKQoUKYWFhwYIFC5g0aRJGRkZxVn+RuMSXdpQ5c2aOHDkSLphw9epVli1bBoS0l++ZZFmIny2+tCMhIlKmTBmWL18OwPz583n48GGU5bt27YqFhQU3btxQrk2ESGzc3Ny4evVqpA83N7e4rqIQQgghhBBhJLnJlvV6Pffv3ydTpkzKc2nSpOHAgQP89ddfNGzYkBs3bpAlS5ZolzV58mTGjh0b7nmDwaDqDjqNRhNtGbV34gUHB6ta3ufPn+nVqxd6vZ5GjRpRunRpKleuzIEDBzhy5Ajdu3cHwN/fH2NjY3Lnzs2AAQMYMGAABoMBjUYTpk5qtzX0vWqoXV5sLSsm5UTs+xXtSK1MmTJx5MgRatSowePHj6levTqpUqXCYDDQtGlT7O3tlXqpqZter0erjT5eG5PlRbe9MWmfMamf2nKx+X0I9SJrRyK8mBzvY/M8rbZ9xOS8qna9aqg5voQuT83x4Nt1165dGwcHB44dO8bgwYPZtm0bAD4+Pvj7+4d5n5GREU5OTixdupQpU6Yo8zGlS5cuRuv8kTKJzfPnz/H09IyyjHRc/zrmFhY4OTlFW87CwgIbG5tfUCMhhBBCCCGipzEkwV9TRYoUoWzZsspdxqF8fHwoXrw45cuXZ82aNdEuJyAggICAAOVvb29vsmTJwsePH1Wl/YmLQMLSpUvp0aMHVlZWXLt2DVtbW+7du0fJkiVJliwZL1++xMLCAkD5N7r6qe04jM3tje3lxeZ26H19uVeiJAD5rrqiVfE5JgXe3t5YW1vz6dOnMO0jPrWj0HIvX76katWqPHr0CAiZFNTNzY2MGTMq5WKzYz02y+l9fblfshQAeV1dotz/Yrt+ELuBHRFeTNvRt+USip95HI2r80x8X57aQAKg+vj3rbt371KkSBGCg4M5cOAAtWrV4v379xGu99WrV5QsWZLg4GCOHDlCiRIlSJMmzXet91ve3t6kTJky2vYRWXtLaJ4/f46dnV2k81N8zcLCAjc3N7JmzfoLapa0fH1cs9i9m/dfPkf7Hhsbm2i/i6tXr1KyZElcXV0pUaJErNRVjcTSPoQQQgghhHpJbkQCwODBg2nVqhW1atWiUaNGyvPJkydn7NixdO3aVdVyTE1NMTU1/VnVjHVeXl6MGjUKgGHDhikTxubNm5fMmTPz8uVLLly4gIODQ1xWUyQx8bEdhc6ZEBpMGDNmjBJEECI+io/tSIhv5c+fn169ejF79mz69u1L1apVIy2bKVMmGjduzKZNm5g3bx6rVq36dRVNZDw9PfH19WXdunXY2dlFWVZNx7X4cVmyZCab3GgihBBCCCESmEQfSLh69SqLFi3C2tqaGTNmAODo6MihQ4dwdHQkWbJk/Pnnn0r5QoUKodPpEmWKjtGjR/P+/Xvy589Pt27dlOc1Gg3VqlVj9erVHD9+XAIJQhASTLhy5Qr//fcfFSpUiOvqCCFEojBq1CjWr1/PvXv3mD9/Pm3bto20bK9evdi0aRP79+/nwYMHqkYkiMjZ2dn90jvWhRBCCCGEEIlLop5s+eTJk9SqVYvff/89XO7oFStWUK9ePRo0aMCoUaP4/PkzAQEBTJ8+HScnp0QXRLh69SpLliwBYNasWeEmjA29K/D48eO/vG5CxFfW1tZUrFgx0R0PhBAirlhbWzNx4kQAxo0bx9u3byMtmy9fPmrVqoXBYGDBggW/qopCCCGEEEIIISKQqAMJffr0YdasWbRs2RJLS8swr5mYmLB582amT5/O/PnzSZMmDalTp+bLly/MmTMnbir8Ey1fvhy9Xo+xsTF79uzh3r17YV738/MD4M6dO/j4+MRFFYUQQgiRBLRr145SpUrh4+NDs2bNcHd3j7Rs5cqVAdi+fTt6vf5XVVEIIYQQQgghxDcSbSDB29ubmzdvKilJ9Ho906dPJ1++fOTNm5c5c+ag0Wjo27cvHh4enDp1iqtXr7Jz505VkwwnNK1atSJ37twEBwezePFiSpYsSd26ddmzZw9Lly5VUh01adIEKyurOK6tEEIIIRIrrVaLs7Mz6dKl49atW9SoUYObN2+GK3f+/HllRGnDhg1VTfIshBBCCCGEEOLnSLS/yExNTTE2NsbV1RWAbt26sXHjRgYMGECNGjXo16+fMmeCmZkZ5cqVI1++fHFZ5Z+qXLly3L59m+3bt1OnTh20Wi2nTp2iZcuW9OvXD4PBQIcOHVi+fLmkcRFCCCHET1W4cGEuXrxIvnz5eP36NX/++SeHDx9WXr9+/TqOjo74+/tTs2ZNZs6cGYe1FUIIIYQQQgiRaCdbNjU1pVq1aowdO5aCBQty6NAhbt++rdxtb25uzpw5cxgwYEAc1/TX0Wq1ODg4ULNmTZ49e8aKFStYtWoV79+/Z/DgwYwYMUKCCEIIIYT4JbJnz87+/fvp0KEDp0+fplWrVkyYMIHff/+dZs2a8fnzZ8qXL8/y5cvDze0khBBCCCGEEOLXSrQjEgAmT57M/fv3adGiBWXLlg2TsqdSpUoEBwfHYe3iVrZs2Rg7diz379/Hzc2NkSNHShBBCCGEEL+UtbU1mzZtwsnJCb1ez7Bhw3BwcOD9+/cUK1aM9evXY25uHtfVFEIIIYQQQogkL9GOSAAoXrw4q1evxsnJiZcvX/Lq1SsyZcqEwWBg9erVtGzZMs7qptfrVXXc6/V6VTmBDQbDdwUCTE1NyZIlS4zfF0rtdhgMBoyMjFSVi4uAhsFgUF1WAi6/hsFgUPW9xMX3odfrVdXNYDBgbBx7h1m164xtcdU+Ynu90s5FTKjdX9SeByFu2kdcnVfVCj1Ozpo1i5w5czJu3Di+fPlC3rx52bRpE1ZWVsr5QM0263S6aLdXJm0WQgghhBBCiJhL1IEEgObNm5MqVSratGlDyZIladKkCS4uLpiZmbFu3bqfsk6NRhPtj9iYdDqoKWtiYqKqXIoUKVStV63YnvhQ7fbGZHlqyASO8U9s7gux3cmntgNP7TbE5n769bKiW7+0D5EUxXanelydt2KyPDXLVBPsj+l61bCxsVH+P2bMGEqVKsX+/fsZMWIEmTJlUl6LzeNufA6sCCGEEEIIIUR8laADCb6+vri5uZE5c2bSp08fabmaNWvy8OFDNm7cyP379+nRowfNmzeP1TuFhRBCCCHEj6lbty5169aN62oIIYQQQgghhPhGgu1J37lzJx06dMDHxwedTkfDhg2ZP38+tra2EZa3srKiU6dOv7iWQgghhBBCCCGEEEIIIUTCliDzVdy+fZtOnTqxb98+AgIC2LNnDy4uLpQoUYIbN26EKfv48WM6d+5MYGBgHNVWCCGEEEIIIYQQQgghhEi4EmQgYf369dSuXZvy5cuj1WqpW7cuLi4uZM6cmRo1avDkyROl7J07d1i1ahVjxoyJuwoLIYQQQgghhBBCCCGEEAlUgkxtpNFoePjwYZjnbGxsOHLkCOXKlcPJyYnz588DIbl2Dx48SJkyZeKiqkIIIYQQQvwUz58/x9PTM8oybm5uv6g2QgghhBBCiMQsQQYS/vrrLyZNmsT27dtp1KiR8nyqVKnYsGEDpUqV4syZM9jb2wNQrVq1uKqqEEIIIYQQse758+fY2dnh6+sbbVkLCwtsbGx+Qa2EEEIIIYQQiVWCDCSUKVOGVq1a0b59e7Jnz07JkiWV10qUKEHp0qW5evWqEkgQQgghhBAiMfH09MTX15d169ZhZ2cXZVkbGxuyZs36i2om4oKakSeyHwghhBBCiB+RIAMJAIsXL+bRo0dUq1aN7du3K6MO9Ho93t7e5MmTJ45rKIQQQgghxM9lZ2dHiRIl4roaIo7Y2NhgYWGBk5NTtGUtLCxwc3OTYIIQQgghhPguCTaQYGFhweHDh2nZsiU1atTAycmJqlWrsmvXLjJnzswff/wR11WMFQaDAb1er6qckZGRqmVqNJofrZYQiZLBYIjrKsQqg8EQ7TbJ8UAIERMxOU6qOb6oucYJXa/a6xwhkpKsWbPi5uamaq4MJycnPD09JZAghBBCCCG+S4INJABYWVmxZ88etmzZwqpVq1i4cCE1a9ZkxIgRaLXauK5elOJ7/dRKLNshnanxj0ajibffi1arjZO6qVnnz6hXXH0Psb3e+Lo/ifhJ7f6SWNpHXInJ56ymrJpgQ0K4dpFJlEVMZM2aVYIDQgghhBDip0vQgYRQTZs2pWnTpnFdDSGEEEIIIX6ITKIshBBCCCGEiI8SRSBBCCGEEEKIxEAmURZCCCGEEELERxJIEEIIIYQQIp6RSZSFEEIIIYQQ8Un8TxIrhBBCCCGEEEIIIYQQQog4I4EEIYQQQgghhBBCxDmDwcC6desoXrw4VlZWlC5dmhMnTkRa3szMDI1GE+bRsWNHAFxcXMI8H90k9kIIIYSImgQShBBCCCGEEEIIEeeePHnC4cOHWblyJa9fv6ZVq1bUq1ePt2/fRlje398fg8GAwWAgICCA1KlT06hRIwBKlSqFwWDAw8PjV26CEEIIkWhJIEEIIYQQQgghhBC/zLZt22jevHm453PmzMnatWspVqwYVlZW9O7dm2TJkvHff/9Fu8w9e/ZgampKjRo1fkaVhRBCiCRPAglCCCGEEEIIIYSId9zc3PD29sbOzi7asqtXr8bJyQkjI6NfUDMhhBAi6TGO6wqIqBkMhjgpB6DRaFSXFUL8fGrab0zaeGyT44sQCVdMriPUtN/YXp4QQsSG2rVrU61aNfr37x/m+StXrjBy5EgKFizIzJkzw7z25MkTunXrRosWLWjTps2vrG6kbt68yZYtW3B1daVgwYLMmDEjwnKfPn1ixYoV3LhxA29vb7JkyUKjRo2wt7dXtR69Xs+6des4fPgwfn5+FC1alO7du5M2bdpwZS9cuMCWLVt48eIFVlZWFC5cmA4dOpAqVarv3s4vX77QqlUrBg8eTMaMGaMs++bNGw4dOsTUqVO/e31CCCGEiJqMSIjntFqtqoeRkVG4SaYiewghIqa2DSWUdpRYtkMIEX/E9nFS7XWOViuXrEKIH3fhwgUuXLgQ7vlJkyZx+PBhjh49Gu612bNnc+zYMSpWrPgrqhitypUr4+TkhJmZGRcvXuTixYsRlnvw4AF58+Zl0aJFlCtXDicnJ/R6PVWqVKFXr17Rrkev19OgQQP69+9P+fLladGiBSdOnKBIkSI8efIkTNmBAwdSoUIFAgICaNmyJRUrVsTZ2ZncuXNz584dpVzXrl2Vc0STJk3YvHmz8reNjU2YZfr5+VGvXj0KFCjAhAkToq3v+vXrKV68OAUKFIi2rBBCCCG+j4xIEEIIIYQQ4hd4/vw5np6eUZZxc3P7RbURImJq9tPPnz//otrErpQpU/Lp06cwz92/f589e/aQM2fOcK95eXmxYsUKGjZsSK5cuX5lVSO1bt06smTJAsCCBQsiLTdz5kzevn3L6dOnyZ8/PwANGzbk06dPLFiwgMGDB5M5c+ZI37969Wr27NnD0aNHcXBwAKBOnTrkz5+fHj16cODAAQDevXvHzJkzadSoEYsWLVLeX7NmTbJly8b06dNZuXIlAIsXL2bx4sVAyBwJ27ZtY9OmTeHWHRAQQP369UmTJg0rV65UFZxevXo1nTt3jracEEIIIb6fBBKEEEIIIYT4yZ4/f46dnR2+vr7RlrWwsAh3d64Qv0JM9tOEKKJAwsyZM7G1taVTp05MmTIlzGuLFi3iy5cvDBw48FdWM0qhQYTo+Pj4oNFoyJkzZ5jnc+fODUQfDFq5ciXZsmVTgggQcmxydHRk6tSpuLu7kzFjRj5//ozBYAgXaMmcOTOmpqb4+Pioqm+ooKAgGjdujLm5ORs2bAg338GqVauYMGECDx8+VJ67du0a9+7do0WLFjFalxBCCCFiRgIJQgghhBBC/GSenp74+vqybt26aCcNtbGxIWvWrL+oZkL8P7X7qaura4K8+ztVqlS8evVK+fvt27esWbOG8ePHkypVKry9vdHr9Wi1WgIDA1mwYAFVqlShdOnSkS7zyZMnmJqaRprD/+7du/Tp00dV/Xr06MGff/4Zo22KjKOjIxs3bmTp0qX07NkTgA8fPrB582ZKly5N3rx5I32vXq/HxcWFGjVqhHutePHiGAwGrly5wl9//UX27NmpWLEi27dvZ+DAgUoQdOXKlQQEBODo6Bijet+4cYN9+/YBYGJiojy/du1anJycInzP6tWr+fPPP0mdOnWY5/39/TE3N1f+Dp3bISgoCGNj6QoRQgghYkrOnkIIIYQQQvwidnZ2lChRIq6rIUSUottPE3Jqo69z9s+bNw8zMzO6dOnC4cOHMRgM+Pj4YG1tzbp16/Dw8MDZ2TnKZfbu3ZvGjRtHOhHzx48fOXz4sKr61a1bV/3GRKN27docPnyYzp07s3DhQtKmTcuNGzdo3Lgxs2bNinLuGS8vL/z8/EiXLl2419KnTw+Au7s7EDJ3zsGDBxkyZAi5cuWiSJEieHl54eXlxb59+6hTp06E62jcuDGNGzcO93ypUqUwGAyR1q1t27a0bds2zHNz5syJsKyZmVmUyxJCCCFEzEggQQghhBBCiB8gcx8IkTB8ndroy5cvLFq0iG7dupE8eXJSpEgBwKdPn0iRIgWzZs2iUKFC/PHHH1Eu886dOxQpUiTS1+3s7Dh48KCq+sXmRMEvXrxgyJAhGBsb07t3b9KmTcvp06dxdnYmX758DBo0KNL3BgUFAUR4137oKIHAwEDluRUrVuDs7EyrVq2oXr06Xl5ezJs3jyFDhlCgQAFy5MgRa9slhBBCiLgjgQQhhBBCCCG+k8x9IBKS6AJaiT3glSpVKgICAggICMDZ2ZkvX77w999/A2BtbQ2EjCC4desWt2/fZvXq1REu59GjR1hZWZEiRQpevnwZZQDA2tqaWrVqxf7GRKNHjx7cv3+f+/fvY2trC4SMAkiTJg2DBw/G3t6e3377LcL3Jk+eHAgJtnwrdDRKaODlxo0b9OnTh969e4cZGdCkSRPy5s1Lp06dOHbsWGxumhBCCCHiiAQShBBCCCGEiMT169exsrKK9HU3NzeZ+0DEezY2NlhYWESaY/5riTnglTJlSiBkroDZs2fTtm1bJVVPaCDh06dPzJgxg8yZM4ebvPfZs2c0btwYDw8PNBoNTk5O5MqVC1NT00jXGVdzJJw5c4YSJUooQYRQderUYezYsZw+fTrSQIKlpSUZM2bk6dOn4V578uQJgDLHwtmzZzEYDOFSGKVKlYry5ctz6NAhZd4JtZ48eaIEMdKlSxdhiqWvGQwGnj9/jrW1tfIdf8vb25vnz5+TO3duzMzMVNdFCCGEEP9PAglCCCGEEEJEonLlytGWsbCwoFKlShIkEPFW1qxZcXNzizYFFyTugFdoJ/OyZct4/vw5AwYMUF4LDSScPHmSkydPMmPGjDCT/er1eurXr0/Dhg0ZOXIkfn5+FCtWjOLFi0e5zriaI8HS0pIPHz6Ee/79+/fK61GpXr06W7ZswcfHRxmhAHDs2DFSpkypTEAdupzI1mVubh6jIALA6NGjuXr1Ki9fvqRnz55MmDAh0rLnz59XAj5eXl5069aNadOmhSvn6OjIvn37uHLlCqVKlYpRfYQQQggRQgIJsSh0Iidvb+9YX6YaGo3mly9PhKf39eWzTgeE7Ava4OA4rlH8ENouotsHf0Y7SizUtN+43P/k+PLzJZV2JMfRXy8pXW/EtB3NnTuXYsWKRVk2TZo0pEyZMsG2OfHzxYfjWsqUKSO9W/tb0e3LoXeLJ7SJbFOlSgXArFmzaNiwIblz51ZeCw0kzJo1i5QpU9K5c+cw7923bx++vr4MHz4cAHNzc0qVKhXtvAZxNUdC8+bNmTVrFitXrqRdu3ZASGf/uHHjMDc356+//lLKjhgxAhcXF/bu3asETwYOHMjGjRsZPnw48+bNA0JGOezatYtx48aRLFkyIGSEg5WVFVOmTKFq1aqkTZsWgK1bt3L+/Hm6du0a47qvWbNG2Yao6HQ6mjdvTp8+fejfvz8eHh6ULFkSe3v7MEGZrVu3YmVlhZGRUYzrIoQQQoj/J4GEWOTj4wNAlixZ4rgmIt74ZiixCGknoT/UInsdpB3FCtn/Eq0k1Y5kPxY/idp2FJo/XYhYk4iOa9G1o/gmNJDy6dMnBg8eHOY1CwsLjI2N+fTpE0OGDAlzFz7A6dOncXBwCHN3/cOHD6Pt7I7tORKmT5/O8ePHgZA78G/fvq0s38nJSUlfNWnSJAwGA927d2fq1KmkTZuWmzdvki1bNg4ePBjmGsHFxYXDhw+j0+mUQELBggXZtm0bHTp04PDhw9jY2ODq6krPnj0ZMmSI8t506dJx6NAhevXqRa5cuShSpAheXl48fPiQnj17Rjg6ILY8fvyYly9f0qVLFwBsbW35888/2bRpkxJI+PTpEyNHjuTEiRNs3br1p9VFCCGESAo0hoR2G0k8ptfrcXd3J3ny5PHybltvb2+yZMnCixcvlMmx4ruEWGdImPX+2XU2GAz4+PiQMWPGKIc3x/d2FJWE+L1Dwq03JNy6f2+9Y6sdJdTPLSKJaVsgcW1PfN2WxHQ+iq+fcUQSUl0hYdU3Luqqth3FNx8+fODy5cuYmpry+++/h3v92LFjBAcHU7ZsWWX0QqgePXpgZGSk3J3v6upKqVKlePr0KdmyZfsl9Qe4efMm7u7uEb6WO3fuMKMsIGRy5EePHvHp0yeyZs1KtmzZwh3TXF1deffuHTVq1Aj3fQYFBXHt2jX8/PwoWLBgpPNnhM5T8Pz5c6ysrMiVK9cP74/Nmzcnd+7ckaY2cnd3J1OmTDx//lwJjDRt2pQnT55w5coVALp3707evHnp06cPxsbGXLx4UVIbCSGEEN9JRiTEIq1WS+bMmeO6GtFKkSJFvP9B9K2EWGdImPX+mXVWc8daQmlHUUmI3zsk3HpDwq3799Q7NttRQv3cIpKYtgUS1/bEx21JbOej+PgZRyYh1RUSVn1/dV0T0kiEUKlTp45ydICDg0Okr5UrV47evXtTsWJFAgMDmT59OilSpPilQQSAIkWKUKRIEdXlraysKFq0aJRlSpYsGelrJiYmlClTJtr1aDQasmXLFuXn4e7uHuFcCgDGxsbkz58/2vV8LWPGjNjb29OlSxdGjBjBf//9x+HDh8mZMycA//77L//++68S/BFCCCHEj5FAghBCCCGEEEIIEYWWLVvy9OlTFi9eTMGCBRk/fjy7d++O62olKM7OzmzevDnC11KlSsXZs2djvMwdO3YwatQo+vbtS758+ejatSv3798HoFevXnTt2pW7d+8q5R89ekSOHDlIkybN922EEEIIkYRJIEEIIYQQQgghhIiCVqtlxIgRjBgxQnmuXr16cVijhGfkyJGMHDnyh5bx8eNH3r17R548eYCQkTELFy5UXi9XrhyOjo5AyITY8+fPZ/78+UDI5MzDhw/Hz8+Ptm3b/lA9hBBCiKRIAglJiKmpKaNHj8bU1DSuq6JaQqwzJMx6J8Q6xzcJ9TNMqPWGhFv3uK53XK8/NiWmbYHEtT2JaVviq4T0GSekukLCqm9CqqsQaoSmQPr06RPv3r3j1q1b2NrakiZNGnbt2sWECRN4+PAhAEOHDqVQoULkzJmTVatW8eHDBzp16gQQboSDsbExmzZtkjkShBBCiO8kky0LIYQQQgghhBAiXhg/fny4FEgDBgygbdu27N69myVLlnDgwAEgZALtwYMHc/36dcqWLcuoUaNIly5dhMstVqwY69evp2DBgj99G4QQQojESAIJQgghhBBCCCGEEEIIIYSIlDauKyCEEEIIIYQQQgghhBBCiPhL5kiIRXq9Hnd3d5InT45Go4nr6ggRrxgMBnx8fMiYMSNabeQxTGlHQkRO2pEQP07akRA/TtqRED9ObTsSQggh4gsJJMQid3d3smTJEtfVECJee/HiBZkzZ470dWlHQkRP2pEQP07akRA/TtqRED8uunYkhBBCxBcSSIhFyZMnB0IuBFKkSBHHtUmYYjJlh5q7mmJ7eWrofX15UMkegDxnz6C1sIiV5SZ03t7eZMmSRWknkYnLdqTX61WXVXPXUGwvTw3Z/xK3hNCOYkN82I/Vnj8MBoPq84fcjRs/JJV2JH69qI4bel9fHtpXBiDX6VMYWVqqWmZ8PW5IOxLRiQ/n8vhObTsSQggh4gsJJMSi0Av9FClSyIXyd0oUgQRjY6yMjICQfUEumsOK7nOOy3aUKAIJsv8lCfG5HcWG+LAfSyAh8Uvs7Uj8elEGEr45riX0QEIoaUciMvHhXJ5QxPd2LoQQQoSSRHxCCCGEEEIIIYQQQgghhIiUBBKEEEIIIYQQQgghhBBCCBEpCSQIIYQQQgghhBBCCCGEECJSEkgQQgghhBBCCCGEEEIIIUSkJJAghBBCCCGEEEIIIYQQQohISSBBCCGEEEIIIYQQQgghhBCRMo7rCgjxNb1er7qskZHRT6yJSIoMBkNcV0EIkcDo9Xo0Go2qsnLeEiJhUnt9ajAY0GrlPi0hhBBCCJE4SSBBxCtqO2PiankicYvt/UU6E4RIuNQeD6SdCyFCaTSaSI8dXz+v1WrlGlUIIYQQQiQ48utXCCGEEEIIIYQQQgghhBCRkkCCEEIIIYQQQgghhBBCCCEiJYEEIYQQQgghhBBCCCGEEEJESgIJQgghhBBCCCGEEEIIIYSIlAQShBBCCCGEEEIIIYQQQggRKQkkCCGEEEIIIYQQQgghhBAiUsZxXQEhonL58mV27NjB4MGDSZUqVVxXRwghhBBCCCF+2PPnz/H09Iy2nI2NDVmzZv0FNRJCCCGEiJoEEkS89f79e+rXr8+bN2+4du0a+/fvx9hYdlkhhBBCCCFEwvX8+XPs7Ozw9fWNtqyFhQVubm4STBBCCCFEnJNe2Z/AYDBgMBiiLafRaH5BbcJSU69QcVG/L1++KP/v0aMHb968AeDYsWMMGjSI8ePHK69bWVlFu7zg4GBV6w0ODlYdpDAxMVFVTvyYoKAggoKCoizj5+en6nvT6/WYm5tHWy44OBgjI6Noy6ltRwaDQXU70mqjzzSnZr0xaeNCJEV6vV5VOV9fX1XtUqfTYWZmpmq9as4fer1e9fFATTmIm/N5XJHj5I+J79eJakV3/RDKYDCoapdqlweQLFmySNcVKjg4GCOdLtpl6fV6VdcloO46QsQfnp6e+Pr6sm7dOuzs7CIt5+bmhpOTE56enhJIEEIIIUSck0CCiFdCf5Tu3buXrVu3otVq6devHzNmzGDu3LkUK1aMxo0bx3EtRWKl1WpjrUNfCJG4eHh44OrqioODQ5jAgUajkQ48IRI5jUYTJnBy9uxZtm/fztChQ0mfPr3yvFwfiJiys7OjRIkScV0NIYQQQghV5JeviHfev39Pnz59APj7778ZPXo0ffv2BaB79+78999/cVg7EV99+PCBNWvWMHbsWG7cuBHX1RFCJBJBQUHMmTOHYsWK0axZMwoVKsTixYvx9/eP66oJIeLA/fv3+euvv/jnn39wdHRUPfpVCCGEEEKIhE4CCSLeGTRoEG/fviV//vwMGzYMgNGjR+Pg4ICfnx8tW7bk/fv3cVxLER+EBg/q169Pjhw56NatG9OmTaN8+fLUrl2bQ4cOqU5jIoQQ3zp9+jTlypVj+PDhfP78GXNzczw8POjfv78EFIRIQNauXUudOnXYt2/fD40aCL0O/fz5MwBnzpwJk3ZTCCGEEEKIxEwCCSJe2bt3L1u2bEGr1bJo0SIlfYSRkRHOzs7kyJGDp0+f0r59e7kDLIkyGAxs3LiRP//8kzx58tCtWzeOHj1KcHAwBQsWpG7duhgZGXH69GkaNWpEyZIlWblypaQbEEKo5unpSbt27ahduzZubm7Y2NiwaNEiXr58yZw5c8iUKZMSUChSpAhLliyRY4wQ8dTSpUvp0KEDR48epWHDhtSuXZt///03xm3W19eXv//+m5s3b5I2bVpmzJgBwJQpUzh69OjPqLoQQgghhBDxSpINJPj4+NCpUye5sz0e2bdvH506dQJCUhqVKlUqzOupU6dm48aNWFpacuLECXbu3BkX1RRx7NixY7Rp04bDhw8rwYNRo0Zx7do1Ll++zObNm7l9+zZ///03KVKk4P79+/Tu3ZuDBw/GddWFEAnE6NGj2bZtG1qtls6dO3P9+nVat26NmZkZnTp14r///gsTUOjRowdnz56N62oLIb7h5uZGr169AKhatSrJkiXj+PHjVK5cmTx58jBo0CAuXboUaVDB19eX7du34+joSMaMGVm1ahUajYbVq1fTu3dvKlWqhMFgYOzYsb9ys4QQQgghhIgTSTaQ0L9/f5ydnXFwcJBgQjwwf/58/vrrL758+cLvv/+upDT6VsGCBWnZsiUArq6uv7KKIp4oU6YMRYsWVf6uXbs2/fv3J2/evMpzWbJkoXv37qRJkwaAHDlyULZs2V9eVyFEwlSxYkUgZDRcx44dSZUqVZjXTU1N6dixI2XKlAEgQ4YMFCxY8JfXUwgRtX/++QeDwUDt2rU5ePAgN2/exMnJCUtLS54/f87cuXMpV64cOXLkYMCAAVy6dIkvX76wfft2mjdvTrp06WjSpAnbtm3D19eXbNmysWTJEhwcHJgyZYoSQGzevHkcb6kQQgghhBA/X5INJKROnZpu3brx5s2b7w4mBAQE4O3tHeYhYkan09G7d2969+6NXq+ndevWbN++XUlpFBE7OzsA7ty586uqKX6imLYja2trzp49S5cuXQCYPn06derUwcPDQynz8uVLatWqxZMnT8iRIwcHDhxQggpCJEZyPopdzZs3548//iAoKIguXboQFBQUrszMmTPZuXMnJiYmbNmyJcbHmCVLllCsWDHat2/P6tWrefbsWWxVX3wnaUeJy8ePH1m7di0Affv2RaPRkDNnTlasWIG7uztbtmyhWbNmSlBh1qxZlCtXDmtra5o0acKWLVuU4EG/fv24cOEC9+/fp23btixZsoRRo0YBMG7cOHr27BmXmyqEEEIIIcQvkWQDCfnz5ycoKIhTp06FCSbs3r07TIdkVCZPnoy1tbXyyJIly0+udeLy+fNn6tevz/z584GQHLMLFizAxMQkyvflz58fkEBCYvE97cjMzIz58+ezYsUKkidPzrlz5/jtt984duxYuCDCoUOHyJw58y/YEiHijpyPYpdGo2HevHmkTJmSa9euMXv27DCvHzlyhDFjxgAwY8YMypcvH6Plnz17ll69evHff/+xevVq2rdvT86cOcmVKxcdOnSQwEIckXaUuKxatQpfX18KFiyIvb19mNfMzc2pX78+a9as4e3bt2zfvp0WLVpgaWmJXq8nW7ZsygiFx48fM3nyZEqVKoVGo8FgMCjzI4wYMYIhQ4bExeYJIYQQQgjxyyXpQMKtW7fImzevEkwoW7Ys3bt3x8vLS9Uyhg4dyqdPn5THixcvfnKtE49Xr15hb2/Pvn37MDMzY+vWrQwePBiNRhPte0MDCU+ePMHX1/dnV1X8ZD/Sjho1asS5c+coUqQInp6e1K9fnwoVKkgQQSQ5cj6KfRkyZGD69OkATJo0iVu3bgHw6NEj2rVrh8FgoH379rRv3z5Gy3337h2Ojo7odDr++usvhgwZQrly5TA2Nubp06esWbMmTGChffv2bN26VSZz/gWkHSUeOp2OxYsXA9CzZ88ory/Nzc1p0KAB69ev5+3btzx48IDHjx8zbdo0SpcuHe69Li4uPHv2DEtLSwYOHPhTt0MIIYQQQoj4xDiuKxBX7OzsuH37NgB58+alb9++DBo0iMKFC5M+fXpVyzA1NcXU1DTc8waDQdUPfjWd5mqp7WDQ6/Wq1mswGNBq1cWZ1Czv6+DMy5cvqVevHk+fPiVt2rSsX7+eUqVK4eXlhbe3NxYWFlEuy8rKijRp0vD+/Xtu3bpFyZIlIy0bHByMsXH0u7lOp1O1vQaDIdrlSWdPzPxIO9LpdOTMmZPjx48zdOhQli9fjqenp5LOKGPGjOj1enQ6narvJTAwECMjo2jLGQyGCOv8LX9/f1X7lZ+fH8mSJYu2nE6nw9zcPMoy+q9SsET3GRoMBtXHodg8XonYF1k7Skpi+zwYHBxMkyZN2LFjBwcPHqRz587s2bOHZs2a8fHjR8qUKcOUKVMIDg5Wtd53794pKfxevXpF7ty5mT59OpaWlvTq1YsvX75w5coVzp07h6urK66urjx9+pSnT5+yevVqJk2aRO/evcNsr6WlZaxtr9rzfnw/bqjZDyIrE5/aUUyuJeLielLt/hJX+9+ePXt4/PgxKVOmpEGDBvj7+0dY7uPHj+HSaVpbW4dLearX65W5UrZt2waEzNFkZmYW5jOLyeenpqxer1d9PS6EEEIIIcTPlmQDCdbW1pibm/PixQsuXLjAnDlz2LlzJ927d8fBwYFjx459d051jUYTbzvd4rpuXwcRsmfPzs6dO8mWLZvyuqmpabSBBAgJBJ07d467d+9SqlSpSMuZmJio6hhW+7lIkODXMTY2jjZoY2VlhVarxdzcnMWLF1OzZk2OHDnCsGHDwqSkMBgMqvYDY2PjWO0g02q1qpcXHBzM27dvef36NR4eHgQHB1O1alWSJ08eo/XG12OPEPGF2nZpaWmJRqNhyZIlFClShBs3blChQgVevnxJhgwZ2Lp1q9KxqOb4AiETv548eRIzMzOWLFkSJhBgaWlJlSpVqFChAmnTpsXHx4eLFy+ya9cuVq1axaRJk2jSpAkZM2YE1J+3ktoxQT6TH5NYPpvQ0Qht27YNdx79mtrt1Wq1mJiYYDAY2LFjBwDNmjWLMB1nZMv8+nljY2O0Ko4bMbmOEEIIIYQQ4mdL1IGEwMBAZs+eze7du0mRIgWjR4+mXLlyyuv58+dn7NixHDx4kKNHj1KgQAEKFCiAg4MDFy9epE6dOnFY+8Tn2yDCnj17vjvtTP78+Tl37pwyqkQIgAYNGtCgQYO4rkakfHx82LZtGy9fvsTDw0MJGnh4ePD27dtwgSorKyuaNGlC+/btKVasWNxUWogkztbWltmzZ9OuXTtevnyJiYkJmzdvVjr01bp06RLTpk0DYOLEieTPnz/Ku6uTJ09O9erVqVatGnfu3OHy5csMHTqU1atX//A2CZGY3blzh5MnT6LVauncuXOM3x9Vu7xy5YqS1uiPP/740aoKIYQQQgiRoCTasbKBgYHUqlWLPXv2ULt2bby8vKhRowZPnz5VylSvXp29e/cqQQQISXN07949CSLEsm+DCLt37yZlypTfvTyZcFkkNAEBAdSoUYPu3bszadIkVq5cycGDB7l+/Tpv3rxRRk3Y2tpSokQJcubMyefPn1m5ciWVKlXC3t6e1atX4+PjE9ebIkSS4+joqAQp582bF+PJld+9e0f37t3R6XQ0atSIZs2aMX78ePLkycPQoUN59epVpO/VarXMmjULrVbLtm3bOH369A9tixCJ3YIFCwCoW7dumFGvUfH392fTpk3UqlWLHDlysGfPngjLbd26VVl2dKkGhRBCCCGESGwS7YiEefPmYWRkxNmzZ9Fqtfz999/kyZOHxYsXM2XKFACGDx9Ou3btwt1VKD8MYteLFy/CBBG2b9/OmDFj2LlzJ1mzZiVbtmwULVqUgQMHqsoTDyGpjQDc3Nx+ZtWFiDWjR4/m+vXrpE6dmoYNG2Jra0uGDBmwtbUlVapUZMmSBRsbGyVFisFg4Ny5c6xYsYI9e/Zw7do1rl27xvDhw2nevDmDBg0ie/bscbtRQiQRGo2G9evX4+HhQdasWWP03tB5EV6/fk3u3LmZPHkyu3fvVlKvrFmzho0bNyrtOqI0LMWLF6dTp04sWbKEfv36cfHixQhTqgiR1H38+JE1a9YA0K1bN1XllyxZwoYNG/jw4YPyfJcuXQCoV6+e8pzBYFDmR2jSpElsVlsIIYQQQogEIdEGEpYuXcrBgweVCcqSJ09OnTp1uHv3bphyMU1NIGJu/PjxPH36lHTp0rFnzx6uX7/Ozp07AXj+/DnPnz/n7Nmz+Pr6MnHiRFXLfPnyJQBPnjxBp9Opzk8tRFz49OkT8+bNA0JSmrRt2zbM676+vuGCaBqNhkqVKlGpUiXevXvH5MmTWbZsGZ8/f2b58uXcuXOHU6dO/aItEEKYmJjEOIgAcP78eQ4dOgTAtGnTsLS0VFIclShRgtu3bxMQEMDatWvR6/WRngfbtWvHkiVLuHv3LsePH6dWrVrfvzFCJFKHDh3C19cXIyOjcJMoR2TSpEls3LgRgFSpUtG9e3du3rzJ3r176devH3Xr1lV+Szx8+JBnz55hbGwsaY2EEEIIIUSSlChTG+n1elq2bEmuXLnCPJ8lSxYCAwPjqFZJl4ODAwCenp68evWK4sWLR3jHZdOmTVUtL3RSbAjpWJEggojvUqRIwW+//QbAypUrOX78OHfu3MHLyyvKCbyDgoLYvXs3HTt2ZNmyZcrzGTJkUO6WFELEb0WKFFHSq8ydO5fg4GBq1KgBwNWrVwkICAAgc+bMkc7x8uLFC1q0aAFAjhw5KF269C+ouRAJT9WqVcmfPz86nY4aNWqwZMmSKM+zDRo0IHXq1EDI6ISXL1/y/PlzAMqXL68EESDk3GtkZERwcDBv3rz5uRsihBBCCCFEPJQoRyRotVrGjBkT7nlTU1P0er3y9927d7l79y7169f/dZVLgpo2bcr27dvZsmULXbp04cyZM0oqh99++40aNWooI0NCO1Qis3PnTtq2bYtOp6NVq1YsWrToV2yCED9Eo9GwcuVKypQpw+XLl6lbt67ymqmpKRkyZFDSHIX+/+PHj2zYsIG3b98qy6hWrRqdO3emTp06ktZEiATC2tqaXbt2Ub58eU6fPs3kyZMZPnw4t2/f5sKFCxQoUIAePXpQt25djI2NCQoKCvP++/fv07BhQ548eUKOHDk4dOgQadKkiaOtESJ+S5cuHZcuXaJdu3bs2LGDfv36cenSJebPn4+lpWW48uXKleP8+fOMGzeOjRs3KpOZW1tbKyOHQiVPnpxSpUpx6dIlTp8+LekFhRBCCCFEkpMoAwmR0Wg0SiDh7t27ODg4MHXq1DiuVdIwbdo0Ll26xLNnzxg4cCBLliyhcuXKMVrG10GEFi1asHz5chmNIBKMbNmysX37dqZPn86rV6/w8PDgw4cPBAQE8OzZM549exbh+9KnT0/r1q1p3bo1WbJkwcLC4hfXXAjxo4oVK8bs2bPp2rUrixcvpkCBAmzYsIEnT56QJ08eNBpNmPL+/v7s3buXlStXKpMrhwYRMmfOHBebIESCkTx5ctasWUPp0qUZMWIEmzdv5r///mPjxo3kzp07XPk0adIwd+5cmjZtysCBA3n06BFTp04lQ4YM4cpWrlxZCSS0adPmV2yOEEIIIYQQ8UaSCyQYDIYwQQRHR8e4rlaSkCJFCpYsWUKdOnXYunUrDg4OMZqo7usgQsuWLVm4cKEEEUSCU7FiRSpWrKj87e/vz5s3b3jy5Anv37/Hw8OD169f4+HhQXBwMH/99Rd//PGHMvpAp9PFVdWFED/ozz//5M6dO8ybN4+BAweSO3duihYtGqbMvXv3WLt2Lbt371YmftVoNFSvXp358+dLEEEIlTQaDb1796ZEiRK0bt2aO3fuUKlSJaZPn46jo2O44B1AhQoVOH36NO/fv48wiABQpUoVpk2bJnMUCSGEEEKIJClJBRK0Wi0vXrxI0kEEPz+/MH9//PiRa9euYW9vH65j3tzcXNUyI/ox9i29Xk+pUqUYMGAAU6dOpX///pQqVUrJGx1Kp9OFST8FsGvXLtq1a6eMRFi4cCFAlDlvv17v1/ltf7ScmnXGREyWp+ZzTkwCAgKiTXWl0+lUTaYYFBQUbr+KSGBgoKqUQQaDAWPj6A+f0X2/pqamZM2aFRsbG0xNTaNdXnBwMMHBwVGW0ccg2CD7n4Cktx/odDpV26HT6VQFrGNynhk4cCBubm4cPXqU9u3bc/DgQSwtLdm7dy/r16/n6tWrSvnMmTPTunVrWrVqFeEkzwaDQfV5UO32qqHX61UH8mNzvYCq9ar5TGL7XP4zxORzUXM+iitq9z+11H4uwcHBGBsbU7FiRc6fP0/r1q25cOECXbp0YefOnSxYsABbW1t0Ol2YZWq1WtKmTRtuPTqdjuDgYMqWLYuRkRFPnz7l4cOHEaY3SgypBxNLOxJCCCGEELEr/v7y+AmSJUvGw4cPWb169U8NImg0ml/e2fI963v37h01atTgwYMHNGvWjKVLl4b5kR6b25AqVSoAxo0bx7lz5zh//jzdunWjVatW+Pn54efnh7+/P76+vvj7++Pv74+fnx++vr4cPHhQmRMhNJ2R2o4btd+FsbGx6u2Nrlxi6GiLD9R8d0ZGRrHWsQQhxwi1gQQ137OaIAeEBBTU7M9qgg36b9pwVPVUs04hEgq1x161+72xsbHqQIIatra2aLVatm7dSrly5XBzc6NRo0a8ffuWz58/K+usV68eHTt2pEaNGlGuX+1xTavVxtr5LabLiwtq6haf6x/XYnNfAXXBn5gsU+16LSwslHXnzJmTU6dOMX36dMaOHcuhQ4coVaoUs2fPpkWLFqqOCaEByBQpUlCyZEkuX77M2bNnyZEjh6r6RLQdsq8KIYQQQoiEJkkFEjp06EC+fPmoVatWXFclzn3+/JnGjRvz4MEDADZv3gwQLpgQ24yNjVmzZg3Fixfn8uXLXL58WdX7vg4iiKRh48aNYUbF2NraUq1aNen8FkIkaClSpGDXrl2ULVuWx48fA5AnTx46dOhA69atyZAhg3QeChHLjI2NGTp0KPXq1aNdu3a4uLjQrl07tmzZwqJFi8iYMaPqZVWuXJnLly9z+vRpWrdu/RNrLcT/c3Nzi7aMjY1NhCPYhBBCCCFiS5IKJFhZWUkQgZD0LS1btsTV1ZXUqVMzcOBARo4c+cuCCdmzZ2fz5s0sXrwYExMTzMzMMDc3x8zMTHmE/m1ubk6mTJmoWbOmBBGSmD59+oR7rlevXkybNu3XV0YIIWJRnjx5OHToEJs2beKvv/7C3t5eggdC/AIFCxbkwoULyuiEgwcPUqxYMWbMmEGrVq1UtcPKlSszffp0zpw58wtqLJI6GxsbLCwscHJyirashYUFbm5uEkwQQgghxE+TpAIJIiQFQ5cuXThx4gQWFhbs2LGDUqVKkSVLFtq2bRsmmPAz1ahRgxo1akRYP+lMEQAODg5KmqHg4GCOHj3K/PnzyZ8/P+3bt4/j2gkhxI8pU6YMZcqUietqCJGgvX//ngsXLlCtWjUsLCxUvefr0Qlt27bF1dWVjh07smPHDpYuXUq6dOmifH/58uWVeRKePn0a4TwJQsSWrFmz4ubmhqenZ5Tl3NzccHJywtPTUwIJQgghhPhpJEdIEnPixAm2bt2KsbExGzZsoFSpUgDUr1+fVatWYWxszObNmzlw4EAc11QkdevXr2fHjh3s2LGDPXv2MHjwYAAGDRqkOie5EEIIIRKnN2/eUKlSJRo2bEiuXLkYN24c7969U/3+ggULcvr0aapUqQLAgQMHGDlyZLTvS548OSVLlgRg1qxZ31V3IWIia9aslChRIsqHnZ1dXFdTCCGEEEmAjEhIYh49egRArVq1cHBwCPNa/fr1ady4MZs2beL+/ftxUb2fzmAw8PTpU4yNjbG0tMTCwkLVBLYi7n38+BGAkiVLfvc8CcHBwVy+fJlDhw5x8+ZNkiVLFi61loWFRZi/zc3NMTc3x8LCQnmYmZlhZWUV5jlTU1MZTSOEEEL8Ap8+faJOnTo8ePAAjUaDp6cn48ePZ/r06bRu3Zo+ffqQP3/+KJdx8uRJBg0ahKurKwAZMmSgRYsWqtY/ePBgGjVqxKJFi8iTJw+9evX64W0SQgghhBAivpNAQhLz/v17ANKmTRvh66GTzb1+/fqX1elX8fT0pEmTJpw+fTrM81qtFgsLCyWwEPpvhgwZGDx4MOXKlYujGidtX758UQIGr1+/ZuXKlQAMHz48Rst5+/Yt+/fv5/Dhwxw9ehQvL69YryuAkZER6dOnJ0OGDNja2pI+fXpsbW3JkCFDmEfGjBkxMzP7KXUQQnyfz58/4+3tjY+PD58/f1Yevr6+yv99fHzw9/fHyMgIIyMjjI2Nw/z/678tLS0pUqQI+fPnx9hYLrViymAwSGBWRMrX15f69etz48YN0qVLx/Hjx/nvv/+YOXMmrq6uLF26lGXLllG/fn369+9P+fLlw7z/1q1bDBkyRBl9a2VlRf/+/enTpw+Wlpaq6lCvXj0mT57M0KFDGTBgADlz5qROnTqxvq1CCCGEEELEJ/LrNokJza9pY2MT4esZMmQAwMPD45fV6Ve4ffs29erV48mTJ0pHT2BgIBAyL0NoR9G39uzZQ4cOHZgyZQpp0qT51dVO0nLmzBnuOXt7e+zt7aN8n06nw8XFhYMHD3Lw4EFcXFwwGAzK66lSpaJ69epUqlQJAH9/f/z8/JR/AwMDw/wd+n9fX99wDz8/P4KCgpT1uru74+7uHmX9LCwsmDVrFp06dYrpRyKEiGUGg4H+/fszf/78MMeJ2GJhYUGxYsWU1BOlSpWS4EIUDAYDffv2ZeXKlRw8eDBcB7AQQUFBtGjRgnPnzmFtbc3+/fvJnz8/+fPnp3Hjxpw7d46ZM2eyf/9+du7cyc6dOylfvjz9+/enZMmSjBkzhjVr1qDX6zE2NqZjx46MGDEi2nkRItK/f38ePHjAihUrcHR05OTJkxQvXvwnbLUQQgghhBDxg/ySTWKiCyTY2toCIXln44O3b9/i4uJCjRo1vrvjZf/+/bRs2RIfHx9y5szJ3r17sbOzIzg4GF9fX758+cKXL1+UzuHQ/+/cuZNVq1bh7OzM7t27mTp1Km3atMHIyCiWt1KoYWFhwbhx4yJ9XafTMWPGDGbOnBluQrpixYpRq1Yt/vjjD0qXLh3pvqTX65UJnqNiMBiU0RJBQUH4+fnh7e3N69evlYeHh0eYv0Of8/X1pWvXrri5uTFt2jTpUBQiDk2bNo158+YBIaOKkidPjpWVFVZWVlhaWpI8eXLlOUtLS8zMzNDr9eh0OoKDg5V/Qx+hf3t5eXH9+nU+f/7MhQsXuHDhgrLOr4MLFSpUoHHjxnJe+Z9p06axYMECAPr168eFCxe+O5WdSHz0ej3t27fnwIEDmJubs2vXLooVK6a8rtFoqFSpEpUqVcLNzY05c+awbt26cG0QoHHjxkycOJHs2bN/9+gXjUbDggULePbsGcePH6d+/fqcP39eJl8WQgghhBCJlvRgxRG1k8Xq9XpVP3AMBoOqjojQSehSpEiBv79/uNdTp04NgLu7u6q7M/V6vaof+cHBwaq2w9/fX+lYPXbsGB06dODdu3dUqFCBVatWkSlTJmV50aWHMRgMjB49mqlTp2IwGChdujRVqlShadOmAJiYmCgPg8GApaWl8neyZMlInTo1//zzD3PmzOH+/ft06NCBhQsXsmzZMgoVKhT5Z/K/kQ6hdYjqc1T7/QJxkuYhJnfoxnb9Hj9+TPLkyZW/Q7+br9vOx48fSZYsGa9fv6Z79+6cPXsWCJkIsUqVKjg4OFC1alWsrKyUZfn6+ka6zhcvXqiaM+PRo0ekTJkSCPkOP3z4QGBgILa2tqRKlYpUqVIpwaq8efMq7zMYDMyZM4cZM2Ywd+5cbt68yT///EPy5MmV0UBR0el00ba3r78znU6HQaf7oeWFkoBH4qW2nX8dQItObB4PojuOhlJ7PPXx8UGr1bJz504lVdr06dPp1KlTmPffu3cvTGAxKCiIT58+kSZNmjDlgoKCyJEjR7j16HQ6Hj9+zPXr17lx4wbXrl3jzp07YYILCxYsYM2aNaxYsUJJqWIwGLCysop2O/z9/VVtr16vx9zcPNpyatMJxeT7ULO/RPR9GBsb4+Liwrp162jUqJFSNkWKFKrWGxtlfha16w4ODlb1+cX2dWJgYKDq5SVLlizW6qfT6aKsn8Fg4O+//2bTpk0YGxuzcOFCsmbNSo8ePdi+fTstW7akY8eOyrne1NSUOXPmMHToUJYsWcLy5cv5+PEj5cqVY/z48ZQpUwaAp0+fKteTHh4ezJo1iytXrtCqVSucnJyUa4LAwMBI04I6OztTq1Yt7t69S7169Th79myEbVj/1flYp9Oh5miqdn9Re3yOSTuX9GJCCCGEEOJb0jOUxHz48AEg0jzt2bJlA/5/REJ0PyJi0gmu5geOkZERer2e8ePHM23aNKWz4vz585QrV46VK1dSvXp1jIyMolxeYGAgPXr0UPLqN2rUCL1ez/Tp01XVN1TKlCkZNWoUb968Yf78+Vy9epUyZcrQu3dvRowYoaqzJyoajUZ+qEUiZcqU0XYaGRsbc+TIEXr16sWHDx+wsLBg4sSJNGvWLEwH4OfPn1UHvL7tGPn48SMeHh5hRhjcu3cPb29v3r59y7t379D9r3MgZ86c1K5dm+rVq5MyZUol7VEojUZD3759yZMnD3///TcnT56kXr16rFy5UlUgwdjYONrt0Meww1/2P5HU9gGtVouLiwtdunQBoHv37nTt2jVcudAOPJ1Ox8GDB1m8eDGvXr0iT548/PXXX/zxxx+kTp060o4+IyMj8uTJQ548eWjSpAn+/v4kT56chw8fcv36dVxdXZUUPnXr1mXLli0xTq/y9XcXEBDAy5cvyZUrV7jtVfMdGxkZqSoX28HviL6P1KlTM2HCBMaNG0e9evVUBXhjst6EsM9Hd50TKr5vi9r9L7rg1JgxY/jnn3/QaDTMmDGDKlWqMHv2bJYuXQrA/PnzWb9+PT169KBly5bKOtOnT8+oUaPo378/7969I1u2bOHq4+Pjw6JFi1i+fDkBAQEAjBs3jpUrVzJ48GDq1q2rbEtEUqdOzdatW6latSr//fcfLVu2ZOfOnQl6pJGa69P4vu8JIYQQQojYJ+PFk5jQyZYjy/cf2pnp7+/Px48ff1W1FB4eHtSqVUsZRdCpUydcXFwoUqQInp6e/Pnnn4waNYrg4OBIl+Hp6UnNmjVZuXIlWq2WPn368Pz5c3bu3IlWq6Vv374sW7aMf/75h7lz5zJjxgyGDBnCuHHjGDFiBIMHD6Zfv37Y2dnx8eNH+vXrx507d9i8eTM1atRAp9Mxe/ZsihYtyq5du35KXm0RPX9/f4YPH46joyMfPnygUKFCHD9+HCcnJ1XpiaKi1+s5deoUTk5OSuqRXr16MXHiRJydnTl37hw3b97k9evXyl2UxsbGPH78mAULFtCoUSNGjhzJxYsXI9xX69aty86dO8mQIQMPHjygbt264SYBF0L8HE+fPqVZs2YEBATwxx9/MGnSpAjLGQwGTpw4QdOmTRk5ciSvXr0C4MGDB8yYMYOaNWvSv39/zp07F+U56WtGRkbky5ePZs2aMW3aNPbu3Uvq1KlxdXWlWrVq3L9//7u26fr165QpU4ZChQrRr18/JbiZEET0ffTs2ZP06dPz9OlTnJ2d47qKSVZwcDDjx4+nXLlybNu2Lc6ud+bOncvEiROBkIBCvXr1WLFiBfPnzwegbdu25MyZkw8fPjB+/HiqV6/O3r17wwT5LC0tw6UxCggIYPXq1VSqVImFCxcSEBBAmTJlGDZsGOnSpePFixf07NmTevXqcfHixSjrmC1bNjZt2oSZmRkHDhygf//+P+GTEEIIIYQQIm5pDNILGmu8vb2xtrbm06dP0d5JHRepjQwGA2ZmZgQFBXH37l0yZ84cYbksWbLg5eXFrVu3KFCgQKzUT00KlSNHjtC6dWs8PT2xsrLin3/+UdIQ+fv7M2jQIOXOswoVKrBhwwYl1VGo27dvU79+fZ48eUKKFCno168fy5cv5+XLl1hZWTF79mwqV64cbt1v3rwhffr0YZ4LDAzkn3/+YfHixej1etKlS8ekSZOwsbGhT58+PH36FIBatWoxZ84cJbWF3teXJ7+VAyCvqwtaC4tItzkmQ8cTemojte0jtNyHDx8iLXf37l0cHR25ceMGAF26dGHkyJGR3rX6+fNnrK2to92Ge/fucfLkSVasWMHDhw+V59OkSUOGDBmUh1arJWfOnKRLl4506dKROnVq/Pz8OH78OAcOHODevXvKe9OlS0eTJk1o2rQpuXPnDrO+169f06FDB65fv66kaujQoUOk9VOTukDv68uDUqUByH3lcpT7X3SpJL6WkO+sTExi2o7UnI9iktroVx2v9L6+3CtREoj+OKq8R8X5KDS1yf379ylatCiHDh0KN7LMYDBw6tQphgwZwp07d4CQlDpt27aldu3anD59ml27duHm5qa8J126dDRr1oyWLVuGa+eh/P39lZRoX3vw4AGNGjXiyZMnpEqVik2bNuHg4BDt9oamJ5w7dy6jR48OMwLqjz/+YM2aNVhZWSnnfjVi487xUD/6faxcuZLevXuTOnVqbt68ibW1tapRgGqur7y9vUmVKlWstiO11F7/qU09pzaFVExTG7m7u9O6dWslZSBAzZo1mTt3Ljly5FCd2kjtcUOn00VYbs2aNbRv3x6A8ePH06JFC3bu3MnAgQOBkAmPe/ToQXBwMNu2bWPOnDm8ffsWgMKFCzNmzBgcHBzCLFuv17Njxw7GjRunXMvlzp2bIUOGUL16dTQaDb6+vixfvpxFixbx5csXAGrUqMHYsWMpWLBgpNuxc+dO2rRpA8Ds2bPp1avX/6/X15eHpUNSKuW+chmTr9I3RiauUhtB7F/XxWY7SgyuXr1KyZIlcXV1pUSJEvFuebHh63N5vquuqs7lSY20DyGEEAmNBBJiUXwPJHz8+FGZA+Hdu3eR5kwuXbo0bm5uHDlyJNrOjNgIJBgMBkaNGsWUKVMwGAwUKVKEDRs2RNgZs2XLFrp3787nz5+xsbFhzZo11KhRA4BDhw7RokULZVLlgQMHMmDAAL58+UKWLFlYsmRJpB08EQUSQt24cYNBgwbx5MkTADp27Mj48eOZN28eM2bMICgoCHNzc0aOHEm/fv0w+PlJICECsRVIWL16Nb169cLX15c0adIwb948ZR+ITHSBBL1ez/Lly5kzZw6vX78GQu5ebNasGa1atQqXbsTNzS3SXMkQMofCwYMHOXLkCJ8+fVKeL1WqFK1ataJRo0bK5+Xn50f//v3ZvXs3AL1792bGjBkRthcJJAgJJETxnmjOR0FBQdSuXZsTJ06QMWNGTp48ScaMGcOUuXnzJkOHDuXMmTMAmJub4+joSOvWrcPM2QIhAYDdu3ezf//+MCP4SpcuTZs2bWjatGmY+kQWSICQc3LTpk1xcXHB1NSU1atX06RJkyi399mzZ3Ts2JETJ04A8Oeff1KnTh369OmDv78/RYsWZfv27ZGmMozIrwwkfP19ZMqUiZMnT2Jra6u8HhwcTNmyZbl//z79+/dnzJgxEkiIZHmxHUg4ceIEbdq04d27d1hZWdG0aVPWrVtHYGAg5ubmDB8+nJ49e6r6TH4kkLBnzx6aNGmCTqejb9++TJs2jeXLl9OzZ090Oh0dOnRg2LBhYd7n5+fHypUrWbx4MZ8/fwbA3t6eadOmUaBAAS5dusSgQYO4du0aAGnTpqV///40bdo0wrmAPD09mTdvHuvWrVPm+3J0dGTcuHHY2NiEK6/X6/nnn38YNmwYWq2WHTt2KKmRJJCQdDpKnz9/jqenZ5Rl3NzccHJyivVAwrp167Czs4uyrI2NDVmzZv3hdUZHAgnRS4rtQwghRMImgYRYFN8DCc+ePSNHjhwYGRnh4eGBRSQXcw0aNODo0aNUrVqVzZs3R5oGKSb1i+oH8YkTJ5SO4Hbt2jF79uwoOz0ePnxIixYt+O+//zAxMeHy5cvkyZOHjBkz4u3tjb29PVu2bCF//vx8/PiRUqVKsWDBAiWIEpGoAgkQ8sN02LBh7N+/H4B//vmHDh06cO/ePXr27Kl0Oh05coRKpUtLICECsRFIePnyJdmzZwegatWqzJkzJ0zHU2SiCyTs2LGDjh07AiGjD1q1akWzZs0irWd0gYSv1+vh4cHmzZs5ceKEkm5k27ZtlCtXTimn1+txdnZmzJgxQEiu527duoVbngQShAQSonhPNOejXbt2KRP3rlmzhgYNGoQrky9fPtzd3TE2NqZRo0Z06tQpynMghLTzR48esWHDBo4dO6ac3zdt2kS1atWUclEFEiBkIvgOHTqwb98+LC0t8fDwUCZgjkjXrl1ZtmwZJiYmzJkzh3bt2qHRaLh8+TL169fHy8uLdu3asXDhwngZSJg4cSKjRo3CysqKI0eOULhw4XBl9u7dS8uWLbG2tubFixfhgjmRrTc6EkiIXEBAALa2tnz8+JHChQuzYcMG8ubNy7179+jduzenTp0CQubaGjJkCB07doxy//qRQELWrFlxd3enbdu2LFu2DI1GQ4YMGfD09KRhw4ZMnz490mW/efOG9evXs3TpUgIDAylUqBDnz58nU6ZMfP78GWNjYwYPHkzjxo2jbeMQMmJx1qxZHDx4EABHR0cWLVoUrpxer8fS0pK//vqLAwcOkDdvXmVkkwQSkkZH6fPnz7Gzs8PX1zfashYWFri5ucVKp35crTcqEkiIXlJrH0IIIRI+mSMhCcmcOTPZs2dHp9OxcePGSMv16dMHCwsLTpw4QZkyZZT0MT9LoUKFSJUqFRCSYza6Do/cuXNz6tQpatasSVBQEB07duT+/ft4e3uTIkUKDh06hI2NDaVKlQJCtjuqIIIaV69e5dy5cwDY2tpStWpVAPLmzavckZYhQwYKFSr0Q+sRUUuXLp0yIXiNGjWiDP58j1y5cnH06FE6deoUKxfzJiYm/PHHH6xatYohQ4YAYGVlFW5kjEajYcSIEUybNg2AKVOmKGlLhBCxo0KFCuTMmROAv//+mwsXLoQrE5pOT6fTkTt3blUdjCYmJjg4OJAtWzal069w4cLKOUgtCwsL1q1bB8CXL1/w8fGJsnzoiMGgoCCsrKyUTj9LS0slFYu9vX2M6vCruLu7M2XKFCAk/UtEQQRAScFoamoqE7v+IhqNhmLFigFQrlw58ubNC4QE2Q4dOoSzszOZM2fG3d2d3r17kzt3bhYsWPBTzlne3t4ADBkyRPn+8+XLB4TcUR3VPpEqVSrq16+vdK6HpixycnICQka8eHp6qp7I28zMDHd3d+X/UY0YunjxIsePHwegXr16qpYvEg9PT098fX1Zt24drq6uUT5iszM/a9asuLm5RbvOdevW4evrG+2ICSGEEEKIiEggIQkxMjJS7nJesGBBpHc5Va5cmRMnTpAzZ06ePHlC+fLl2bRpU6TLDQ4Oxt3dHRcXF/bs2cPu3btV30EFIZ3Dc+bMAULuULx9+3a07zEzM2P58uWkTJkSV1dX/v77byCkIzg0Z++ECRMA2L1793dPYGkwGFi5ciUdOnTg06dPFC1alAsXLpAjRw6+fPlC79692bFjByYmJmzatElVp5P4fsmSJWPUqFEATJs2Telk+FGhoxoCAgJUdyrExLt371iwYAEAgwYNinQ0Q48ePcicOTOvXr1ixYoVsV4PIZKytGnTcv78eUqVKoWXlxf16tVjx44dYcqMGzeOrl27YjAYmDRpErt27Yp2uU+fPqVWrVosW7YMCBkpcPDgQVXzsnzLyMhIdYd53bp1lQldu3btys2bNwkODqZz584EBgZSp04dmjVrFuM6/ArDhg3D19eX8uXLR9kh+/jxYyDk3C5+nWHDhgEh81S8ePFCeT60I/7OnTvMmzcvXEBh4cKFqiceVyM0oP91UC10/oFNmzZFeef1y5cvad68Of7+/vzxxx/KtcPUqVMZN24cAEuWLKFHjx5K4C0yt2/fpnHjxty4cYO0adOyf/9+5YaSbz19+pSGDRsSEBBA3bp1lUmiRdJjZ2dHiRIlonzE9oiArFmzRrvO6NIeCSGEEEJERQIJSUzr1q1JkSIFDx484MiRI5GWK1iwIJcvX6ZmzZr4+fnRsmVLunbtypgxY+jcuTN//vknpUqVInPmzJiZmZElSxbKli1LgwYNaNiwISNGjIhRvVq2bEndunUJCgqiU6dOYSaNjIytra0SgAidDDB0wmMIyUdfq1YtDAYDs2bNilF9IKRTefDgwUyePBm9Xk+DBg1Yt24dGTNmZP/+/RQrVkyZ/Hnu3LlhUtWIn8fJyQk7Ozs+fPjAP//8EyvLDA0kvHv3LkbpnNQaN24cnz59onDhwkonSERMTU2VkQtTp06NtTs8vby8OHjw4E/ZNiESknTp0rF3717q1q1LQEAAbdq0Ye7cuUrb0Gg0TJs2TQkmjBs3LtJggsFgYNeuXbRt25Zbt25hY2PDhg0bGD9+/E8JSEZk7NixVK9eHT8/P5o2bcrIkSO5evUqKVOmZN68efHyLv4rV66wdu1aAGbNmhVlHUMDCaEjSUT0xo0bR+rUqbl06dJ3L6Ny5cpUrlyZoKAgZaTc10xNTencuTMPHjxg4cKFSkChV69e1K5dm/fv3//IJihCU1l9HUioXLkyWbNmxdvbW5lb6Fve3t506dIFT09PihYtirOzs5LWSaPR0KdPH9asWYOZmRknT56kcePGeHh4RLis48eP06hRI968eUO+fPk4fvw4pUuXjrDsx48fadq0Ke/evaN48eKsW7dO0gIKIYQQQohERQIJSUzy5Mlp164dEJKHPSqpU6dm3759DB48GIClS5cybtw4li9fzv79+7l69SqvX79W8u5mzJhRGQ4/depU9uzZo7peGo2GRYsWkSpVKq5evaq649/R0VGZxA7CBhIgJH2FkZERJ06cwNXVVXV9Xr16haOjI7t27cLIyIjhw4czZcoUvLy8aN68OQ0bNuT58+dky5aNXbt20aFDB9XLFj/GyMgozN2Eb9++/eFlZsiQAQgJHn09OXJsOHfuHDt27ECj0TBlypQIJ3P8Wrt27ZRRCVOnTv3h9QcEBFCjRg3+/PNPlixZ8sPLEyKhC00h1LVrVwBGjBjBgAEDlDlMQoMJTZo0iTSY4OPjw5AhQxg7diz+/v7Y29tz8uRJqlev/ku3xcjIiFWrVpEjRw6ePXumBNdnzpwZbiLp+MBgMNCvXz8AWrVqFWmHbKiHDx8CMiJBrRcvXjBlyhS8vb0ZOnToDy1r+PDhQPhRCV8zNTWlW7duPHjwgPnz52NhYcGxY8diLS1maCDh69GHRkZGtGrVCoDVq1eHC5AHBQXRvXt3Hj16RKZMmdiyZUuEk3TXr1+f/fv3kyZNGm7fvk29evW4detWmDKhI1JDR88cPXpUmafpW4GBgbRq1Yr79++TOXNmdu/erWpycCGEEEIIIRKSqHu0RJxTO9leUFCQqrue/P396dixIwsWLOD06dNcu3YtwtzEvr6+SqfK0KFDKVKkCNu3b8fa2poMGTJga2tL+vTpSZEiBdmyZcPGxkZZ/5AhQ1i2bBlt2rTh5MmTZM+eHYPBEG2ahwwZMjB16lQ6d+7MhAkTqFWrFgULFoywbGBgoHIX49y5czl37hwfP34ke/bsBAYGKuXSpUtH/fr12b59O9OmTWPFihUR3v0YGBiIXq/Hw8ODpUuXsm3bNoKCgrC2tmbOnDmUKVOGtWvXMnv2bL58+YKRkRF///03w4YNw9LSMkwqJ30M7vqOzcmMk5L69etTqlQpXFxcmDlzphJYiIy/v3+kk4tDSMdE6tSp+fDhA9euXYu0oyDU69evVaXvCg4OZuTIkUDISIpChQpFmPYhICAgTGqFsWPH0qFDByZOnEjFihX57bffgJBJF6ObbPTrfeqLry+jJk7k2rVrAEyaNIkmTZoo85CoWZ4QP0tUx7+vX9Pr9WhUHCtjMumsVqtl6tSpZM2aleHDh7N06VI+ffrE4sWLlXNZz549Adi6dSvjxo0jODiYP//8k1u3bjFy5Eg8PDwwMjKiY8eOjBw5Eq1WG+VoOl9fXyX1nhp6vT7K40xwcDBGRkZYW1uzceNGqlWrxpcvX6hduzZNmzZVzuE6nQ4TE5No1+fv7x9toBPUp4ALCAgIt71bt27lwoULWFhYMGrUKAICAvD19Y20fo8ePQIgS5YsBAQExOuOWbUpHdXup6DuGiEwMFDZZ6dMmaJcA505c4bjx48r82SonYw3dNLjihUrYm9vz5kzZ5g6dSpz584NU+7z589hvjcnJyeKFStGq1atlLSY8+fPp2HDhqrOMxFd74amNvr06ZPy+fr6+lKnTh1mzZrF/fv3OXXqFGXLllW2cfTo0co+tnbtWlKlShXp6L7ChQuzZs0a/v77bx4+fEjjxo2ZPXs29vb2TJo0SRk507hxY4YNG0by5MmVdvU1g8FAnz59OH36NJaWluzcuZMMGTKE2yfCXCvGIAWoEEIIIYQQ8YUEEuKI2h+Ran7Uhy5PTUezubk5+fLlo1GjRmzZsoWlS5fi7Owcrty3P5Tq1KlDnTp1wpXz8fHB0tIyzHPjx4/n2rVruLi40K5dOw4dOoSZmZmqQEebNm3YvXs3+/fvp1u3bpw9ezbCDgaNRqMsL3PmzGzbto2NGzfSvHnzMOvJli0b48ePZ//+/Vy7do179+5Rq1atcMszMjJizpw5ODs7Kx1BVapUYenSpXh7e9OmTRtcXFwAKFu2LIsWLaJo0aIRboP+q45ijUYT5feidj+IK3EZvDAyMopyn5k8eTLVq1dn3bp1DBw4MMo8s1ZWVpibm0e5vkyZMvHhwwcCAwOjnetCp9NFG2wAWLZsGY8ePSJ9+vTMmDGDlClTRljuy5cvYfaFpk2bcvToUTZt2kSnTp24cOEC1tbW0e5PEPY7O3funDLyKEWKFLi7u7Nu3To6d+4MhOx/knZBqG3ncXU8ULPfg/r5BZInT66UGzJkCDlz5sTJyYnNmzdjYmLCypUrMTIyonjx4jg7O5MmTRoWL17MpEmTePDgAdu3b1eOAStWrKBYsWJRBipDmZqaxiiQoNVqozxHmJubK6+XKlWK7du3s2nTJiZNmhTmeBfaMRydmOwHasp+W38/Pz/lLveBAweSJUsWIGSESGSfy9OnTwEoUKBAuGuNqNYbG2V+lui+11Bqvw9jY2M0Gg0vXrxg5cqVAPz2229cvHiRiRMnUqVKFSCks1vNMpMlS6aUGzVqFA4ODqxatYqhQ4eGOc9GdO4oVKgQJ06coGPHjsq/N27cYPr06dFez0b0uYQGIL4+R2bKlAmtVkvLli1xdnZmx44dNGzYEAiZuHvHjh1otVqWLl2qBBiiUrZsWU6ePEnr1q05efIk3bp1o2jRokoAfuzYsfTt2xeDwRBpO582bRrr1q1Dq9Wyfv16SpQoEWE5zVefmdpzb2zvq3JTihBCCCGE+BESSEjEorrr7e+//2bLli1s2rSJCRMmKDniY0OyZMlYsWIFv//+Ozdv3mTIkCFKuoXoaDQa/vnnH4oVK8bVq1eZOXOmkjM+KpUqVaJSpUoRvpYxY0a6du3KnDlzGDduHNWrV1d+wL18+ZLZs2ezZs2aMAGEkSNHUqpUKcaMGcO8efPQ6XSkSJGCiRMn0qVLF+l8jQccHByoXLkyp0+fZuLEiT+ctsfW1pb//vuPd+/exUr9Xrx4oUy+Om3atEiDCJGZOXMmFy9e5OnTp/Tr1y/CgF90ev3vjuoOHTpQqFAh+vbty8yZM2ndurUyKkGIpK5p06YYGRnRokUL1q1bB6B0yIamOQJYvHgxW7ZsAaBJkybMnj0ba2trVXP6/AoODg44ODjEyrKCg4PZvXs3Z86cQaPRkCxZMkxMTEiWLBlGRkaYm5srf5uYmKDRaPjy5Qs+Pj58/vyZz58/8+nTJ3x9ffHx8eHLly+8ffuWly9fkjlzZvr06RNtHXx8fHjz5g0gqY3UmD59OoGBgVSuXJkVK1ZgZ2fHmTNnOH36NJUrV/6uZVapUkUZlbB8+fJoR/8BpEqVii1btjB+/Hjmzp3L/PnzuXPnDhs2bIg2SP+t0BEJX6c2CtWpUyecnZ05ePAgz54949q1a0r9pkyZEqO2kDJlSrZv307fvn1ZvXo1165dw8zMjGXLllG/fn0g8tEhW7duVQJks2fP5o8//ojJJgohhBBCCJGgSCAhETp9+jQTJkzg3LlzZM2aFTs7OwoUKECBAgXImzcvhQoVonTp0pQvX54LFy6waNEiVT8OIxMcHMzbt2958+aNMmdCrVq1WLp0KY0bN2bNmjWULVuWbt26qVqera0ts2bNol27dowfP566detSqFCh764fQJ8+fVi5ciV37txh69atVKxYMdIAQqVKldi+fTuFCxfm+fPnQEin0axZs0ifPr0EEeKRsWPHUqVKFTZs2ECfPn2ws7P77mVlypQJAE9Pzx+ul8FgYPLkyQQEBPD777/TrFmzGC8jRYoULF++nJo1a7JlyxaqV68e4+W8efOGvHnzMmnSJIyMjJgxYwavXr1izZo1yqgEIQQ0atSIjRs3hgkmzJ8/X7nbe9q0aZibmyudho6Ojonyzl5vb29WrVrFwoULlfNfbAv9LKMTmtYobdq00aZGTOq+Ho0wfPhwMmfOTPv27Vm8eDETJkz47kACQNeuXTlz5gxr165lzJgxqu6QNzIyYsyYMRQtWpSePXty/PhxfvvtN7Zt2xbpaM6IRBVIyJcvH7///jsnT55kwIABnD17Vqlvp06dCAgIUL0eABMTE+bPn0+BAgXYt28fY8aMoUyZMlG+599//1XmHevduzfdu3eXlEVCCCGEECJRk0BCInLp0iVGjBjBmTNnlOeePn3K06dPOXjwoPKcRqMhe/bsPHnyBAi5c2vQoEFR5h7++PEju3fv5tWrV7x+/Zo3b97g4eHB69eveffuXbg7tfr06cOoUaMYPHgwU6ZMoX///lSoUIEiRYqo2paWLVuybds29u/fT8eOHSNNcaRWypQp6du3L2PGjGHw4MH4+voqAYRKlSoxePBgatWqxe7duylevDi3b98GQlIjzZ8/n9q1awOS0za+KV26NPXq1WPPnj2MGzeOjRs3qnqfl5cXV65coWrVqkq6hdCJSWNjRMKxY8c4f/48JiYmzJ0797s7HMuWLcuQIUOYOHEi/fr1o0yZMjEKqpmYmLBixQolHcOAAQNkVIIQkfg2mBAUFMSSJUuUlEnjxo37oaB7fPb+/XsmTJjAqlWr8PHxASBNmjS0aNGC5MmTExgYSGBgIEFBQfj7+6PT6QgKCiIoKEiZY8jS0pLkyZOTPHlyLC0tsbCwwNraWnnOysoKW1tb8uTJo6pOoYGEnDlz/rTtTiy+Ho0QGjQYOHAgK1asUEYlhM6VEFP16tXD2tqaFy9ecPLkSapVq6b6vQ0aNCBPnjy0adOGx48fU7FiRZydnWnatKmq94del37+/DnC1zt37szJkyc5duwYALVq1WLChAmq6/ctjUZD9+7d6d69e7Rlnzx5QqNGjQgICODPP/9URi4JIYQQQgiRmEkgIZHQ6XTUqlVLmcyxffv2dO3alXfv3uHm5sadO3eUf9+9e6cEEUK1bNmSPXv2RLr8oUOHRtlJa2RkRLp06bCxseG///5jzpw5tG/fngEDBnDlyhWOHz9O27ZtcXFxUZ0buGvXruzfv5+rV69y4sQJatasqf4DiUDnzp1ZsmQJHh4ewP8HECpWrIjBYODu3bs0atQIAGtra/r06UO/fv1U52UWcWPQoEHs2bOHPXv24OvrqypX+aBBg1izZg1Vq1Zl7dq12NjYKPm6T506RY0aNShevPh31efOnTuMHTsWgPbt25MvX77vWk6oAQMGcPz4cS5evMgff/zBli1bKFeuXIRlDQYD8+bNIzSxwrBhw8Lc/dmqVSumTZuGu7s7x44do27duj9UNyESm9BgQvPmzdm8eTONGzeOcF6dn8nIyIjg4GC8vLzIkCHDL1nnoEGDWL16NRByp3fv3r1p0aJFhCMHAgMDVU22HBgYGG7ug68nlY/O48ePAQkkRMfPz49Vq1YBKCl2gDCjEhYuXPjdgQQzMzMqVarEvn37OHHiRIwCCQAFCxbk4sWLODo6cvToURwdHUmZMiU1atSI8n16vV65qSOy0QXVq1cP83do4O9XGDlyJO/evaNYsWKsWbNGRqsKIYQQQogkIX7P9CpU02q1yh32p06dYu7cudjZ2WFvb0+XLl2YO3cuR44c4enTp0on4tc5hw8dOhTh0PFQ79+/B8De3p6hQ4cyb948Nm/ezMGDB3Fzc+P169fcvn2b06dPK5PMXbp0Ca1Wy6JFi0iePDnXr19n06ZNqrZn//79NG/eHID8+fOrmjAvOhYWFjg7O9OqVSv27dvH3r17qVixovL63bt3AbCzs+PRo0eMHDlSgggJwPnz54GQziY1nVvw/6MOTpw4Qbly5bh69SqNGjUiV65cfPz4kV69erFly5ZIcyJH5s6dO3Tu3BkfHx+KFStGhw4dYrYxETA2NmbFihXkzZsXd3d3fv/9dxYuXBiubt7e3jRt2pQRI0Yoz3Xp2jVMGVNTU2WuBjWpRYRIiqpWraoEvHPkyPHL1x86309oqppfITQA6+joyNWrV2nfvn2sHiO8vb2pX78+6dOnV+aOiY67uzsQ0iEuInf37l0CAgKwsbEJl8IodBLiGzdufPfyr1+/roxqjWwuquikTp2affv20aZNGwwGA46Ojjg4OGBvb89vv/1GyZIlKVKkCPny5SNHjhxkypSJtGnTsm/fPkxMTGjZsmWEy/3333/D/D1w4EB0Ot131TGmQj/TSZMmRTmiVwghhBBCiMREAgmJhEajIW3atKrK2tjYcOfOHSVtQKhZs2ZF+96mTZsyePBgWrduTc2aNSlUqFC4eQNKliwJwLVr15T1/f333wCMGDEi2ry1CxcupFGjRnz58oWqVaty5syZGE9UG5ny5cszf/78MAGEUE+fPgWgcOHCsbY+8XMFBAQwd+5cAPr16xfjOwKNjY15/vw5VapUYdeuXSxevJjq1auj0+mYPXs2Y8eOxd/fX9Wyvg0i/PPPP7GWOihLliycOnWKBg0aEBQURO/evWndurVyd+/t27cpW7YsO3bsCJMC7NvRPwaDQdnPs2fPHit1EyKx2b17N0FBQRQsWPCHRxR9j67/CwA6OzvH6A7+HxE6v4yXl5eqUYMx8erVK6pVq8ahQ4cIDg6mV69eqoIJr1+/BvhlozISqjt37gBQoECBcK+FPvfs2bPv2pcCAgLo1KkTOp3uh0fnGBkZsXDhQooVK4aXlxenTp3iwoULuLi4cOPGDe7evcujR4948eIFb9684dOnTyRLloxly5ZRoUKFCJc5depUAMqUKYOxsTFbtmyhe/fuPz2YoNPplBEzalN1CSGEEEIIkRhIaqNEJG3atLi7u0eb4/3SpUv0798fCJkfIUeOHDRr1ow5c+bQtWvXH/7RHpoS5urVq8pzXbp0wdnZmadPn7JkyRJ69+4d7n06nY6+ffuycOFCANq1a8eCBQt+aG6EmHj27BkQMi/C9whyd0f38SP6rzqe/d3c0PyvM9k4VSpM/peHX8SOTZs24e7ujq2tLS1btsRgMBAQEKC6A3/y5MmcPn2affv20alTJ+rXr8+IESMoUKAACxYs4PDhw7i5uZEzZ06MjIyUR1BQENbW1hgbG2NsbIxWq2XXrl1hggixPZolefLkrFq1igoVKjB48GA2bNjAzZs36dixI8OGDcM6MJAqWbMydfx4mDQZgMB799D+77PQpkzJe40GPz8/tFotWbNmjdX6CREbgtzd0Xl9cxy9excjs5C7441Spfzpx9GtW7cCUL9+/Z+6nmvXrnH48GG6d++uTCoLUKNGDXLmzMnjx49Zv379L5kYPbTD2c3NLVaXe+vWLf766y9evXpF+vTpcXBwYP369fTq1QuATp06RfpeCSSoE/qd5c+fP9xradOmxcbGBk9PT+7du8dvv/0Wo2WPGDGCa9eukSZNGmbPnv3DdTUzM+PQoUMcPXoUrVZLsmTJlIeJiQmmpqZhnkubNi2pU6eOcFnnzp3j3LlzJEuWDGdnZ65evUqHDh3YsmULQKzUNzIvXrxQUneFpkWMSoTXh3fvYvS/UT9yfSiEEEIIIRIKCSQkIjY2NgB4enpGWubt27c0b96coKAgGjRoQN++fYGQu7kuX77MxIkTmT9//g/VIzSQcPPmTYKDgzE2NsbS0pLRo0fTtWtXJkyYQNu2bcN0nHz+/JmWLVuyb98+ACZOnMiAAQO+e5La7/Ejd2oHubvzpHYdDIGBYZ5/5uik/F+TLBm5Dh2UH4uxRKfTKaNoevXqhampKQMGDMDZ2RlnZ2clpUNUrK2t2bp1K1OnTmXs2LHs2rWLhw8fMnHiRPLmzcuIESN4/vw5z58/V1WnnxVECKXRaOjbty8lS5akRYsW3Lp1iz59+mBrbMyhXLkxASWIAPCu41eddMmS8XpESP7sLFmy/LIAnRBqBbm78/iP2uGOoy+cWin/1yRLRs6DB37acdTLy0uZuLVBgwZhXnv//j1Xr16lWrVqP3zX/o0bN6hduzafP3/mwYMHODs7K68ZGRnRo0cP+vfvz4IFC+jUqZOqc+HLly+5c+cO1atXj/G5s2DBgkDIeVDtXDPROXnyJK1atcLb25t8+fKxe/dusmXLho2NDXPnzlWCCa1bt47w/W/evAEgffr0P1yXxCw0kBDRiITQ58+cOcOdO3diFEg4duyY0hm/fPlybG1tf7yyhFyrtmjRItzzer0+Ru0qdDRCq1atyJw5M5kzZ8bZ2VkJJuj1elasWPFT5i54+PAhgHKTQVQiuz789rgm14dCCCGEECIhSLKBhHXr1tG0adNwEwEmVIGBgaRJkwYADw8PAr/5wQIQHBxMmzZtePXqFXnz5mXZsmVKZ8OkSZNwcHDA2dmZ3r17kyJFijA/6IKDg5X1fD083svLK9wQ8vTp02NlZcXnz59xdXXFzs4OvV5Pq1atmDlzJg8ePGDatGmMHj0aCEl70KBBA27cuIGZmRmLFy+mfv36+Pn5Rbq9Hz9+VJW/+dOnTyRPnjzacl+nfMmWLVukufEjGy4f+P59uB+J4dYRGEjg+/dov+oUMRgMqn7kxiRX/68MvoRSU7+YzjcQnR07dvDw4UOSJ09OyZIlWbVqFYsWLQJCJjn+9OmTMtGwsbExhQsXDlcXnU6HTqdjwIABFC5cmDZt2nDr1i3atWvHhAkTWLt2Lf/++y+BgYHodDqCg4PR6XR8/PiR5MmTK+/X6XSkTJmSRo0aYWpqqrSXDx8+qPp+P3z4QOrUqQkODsbPzw9/f3+SJUuGtbV1mHLBwcFkyZKFokWLcuLECTp06MDFixfp2aYNJufOR72SwEBe/W8ekKxZsxIYGIjBYFAd9FCzX8UknYRMTJnw6PV6VeUMBoOqDkG9Xh9mvwr28lJ1HNV5ffyhDjedThfp/rxz506CgoIoVKgQKVKkUDqzIaTD8t9//6V169aMGjVKeT4oKEhVJ+urV68wNjbm1atXtGjRgs+fPwOwZcsWKlasiIODAxDS1hwdHRk1ahS3b9/m6NGj/P777+GW5+fnR7JkyXj58iUzZsxg1apVBAUFMWbMGAYOHBhme6M7X9rY2JAmTRrev3/Pf//9R7FixSItGxAQEG37Xb9+PT169CA4OJiKFSuydetWUqVKBYSMhASUYIK/v3+4URcGg0EZkZAmTRrlmia20sUlBGrPmaETEufOnTvC1JF58uThzJkz3Lp1S9Uy/f398fT0pG3btgB06NABBweHcNdk/v7+qr4Pf39/Vcd7X19fVdd179+/59q1a8pohE6dOuHj4wPA77//zoIFC+jZsyfbtm0DQvazqNbv4+MT5saWyOj1emV7Hzx4AECuXLnCHRf1en2Yz1ntcS3YyyvC41psX//F9+tJIYQQQggRvyXJQMKyZcvo3LkzmzZtYseOHbEeTND7+qI3jqWPVuUFv7FOR6Y0NphrNPi8e4dRUHC4MhPHjeXK2bOktrRkx4YNpDI3h/9N0Px7uXLU/+MPDh86xKRRo5m/IOyoBAutFnONBjONBvOvOohSJDPF8tsfaEZGlClalH8vXODhrVuUKlQIHaANDGTCyJG0bdOGxXPn0rl1G96+fUuLFs1xd3cna7p0LF++nLK//QbR/OjSBgai/Wq9vr6+vHnzluzZs4X54WOu0WCh4gesXq/nzfPnmGs05MyYESLJi6/R6SCiDrJo6qusx98fva9vmOcMKgMJan/6xddAwrfbraZ8ZO3IYDAwa9IkzDUamv31FyY6HQtnzcJco8HSyoovnz8zdtgw5syeTeYsWdAZGWH0v30dwNRgwFyjwUSvV56vXbUql06fpk2bNty+fZvBf//NpEmTGD14cLjPNDAoCK2Kz/l+cHCYfen8hQts3bIVHx9v/P398fP3x9/Pjy++vgQGBCgTpkPI/Abt2renW9euGP3vc9AFBKD/X2dOOmtr9m7direPD+avX+MVXSABeO/xGnONBrscOTDTaDCg/ntRs1/pYxBI0Egg4bvEZjuK8bpjEEiI8DgZUbmv9iuDf9Tz5yjlAvwxRBJoVnWc1OkiLbdny5aQ40r9+pjo9Wj/d2y7dPky1y9exFyjYevateTNlo1WTv8bcabXYxQc/pz7LaOgID5//ETvLl348v49RfPlp2TJEmzcuJFp48bxW7HipEyVEoCUZma0a9kS5+XLWb5wIb+XKxdueR5PnrBg4ULWrV1LUFAQxoCxRsOsSZOo6+CA3f/uUNcaDGhUfB/F7Oy4cP48T9zcKF2kSKTlNBoNxpFsr8FgYNr06UyZPBkToEWTJixZshRT02Tw1fFh5sSJmBoMzJ8/n1GDBmFhZES7tu2U1729P4G/P+YaDVnSpcNEF7LvxXT/j0xctiMANS1JzXnV18+Pt/+7dimUO0+E+2HRfPkw12h44uYWabsJs15/f/r16MGnN28oki8fk0ePhggCFFYmJpirGNmmDQqK8P3h+PurOm6YAYvnzsVco6F1ixbk/mYy7sZ162Ku0dCzZ0/2b9+OqQHmzJkd5prxa+YaDabR1w49oPnfdjy7dw9zjYb82bMrz4XSGgxh9nUC1F0fGiK4PoT4HUiIrfYohBBCCCESDo0htm8TTgDs7Ozo2bMngwYN4vfff//uYEJAQECYu7+8vb3JkiULl3PnwUo6yYQI47NOR5mHD/j06VOYu/+kHQmhnrQjIX6ctCMhflxk7ehb3t7eWFtbR1susbh69SolS5bE1dWVEiVKxHV1wvmV9dP7+nKvREkA8l11RRsLafMSm6TWPoQQQiR8P5bkNwE6d+4ctra29OjRgwMHDnDy5EkaNmwYYSqg6EyePBlra2vloWbCNSFEWNKOhPhx0o6E+HHSjoQQQgghhBAickluREJQUBD3799XJhY8ffo0tWvX/q6RCZHduebl4RFrdxSo/XoCAgM5ePAgrZycKFGyJEePHAXA18+XmjVqcOfOHcqULcuePXuwiiIn+vMXLyhatChBgYGs37CBKlWqAFCvXj1cXVwYPmIEXbt0UYaIe3/yxtIy/N0l7u7u/PbbbxgZGXHnzp1w+d5btW7N/n37aNWqFTNmzsTkfykDvvj6RjoJrNcHL/bu3YOzszP37t1Tnre3t6dt27aULFWK7du2s2r1Kp4/ewaEpIepXqMGLZo3p1Tp0sqE1N86sH8/7du3p3SZMhz/30SbEdHpdBHm/va/e5eXrSKeMPJrmdeuwSx//jDPJZU5Ery9vUmdMaPqO0Aja0cXL13CoVo1TExMWLFyJcuXLeP06dPkz5+fWbNnK9/Pq5cv6dO3Lz7e3vxWrhx79+7F6H+vNW/enCNHjjBp0iS6dOmqZFcxGMDE5P/TV/x78SJLFi/Ben0jigABAABJREFU9aorz/43h8bXTExMKFS4MCVKlKBy5crUqV3n60wt3L9/n1OnTjF8+HB0Oh2169Rh9KhR4b7zt+/eRZhj/fmzZwwaNIh79+6h0Who1bo1AwcOJGXKlGHKBd2/j1fXbhF/8F/p/dmH8+7uHDl6lBIlSmAwGDAzVZPUQeZIiC+8vb1JZWv7w+3oe8T2HAkGgyHMfuXvdpfnTk5RvCNEtvXrMLOzU1WXiES2nzZu3JijR48ybPhwBg8axCt3d7QaDa3btOHypUu0bNmSUaNGERQcTOfOnfn3wgXSpUvH+g0blPlYImIwGBg6dCgrnJ0xSZaMpUuWUKJkyTBl5s6dywpnZ9KkScOOHTspUiRkTpdDhw/TonlzzMzN0QUHK+nPKlWqRN++fbGvXDnc+rZu2UK3bt0wMTHhxIkT5LezU9XOjx8/Tv369cmWPTuurq6RlgsODsb0q2ulR48eKSnhzC0sWLFiBbVq1gRQNam7f0AAo0aO5J9//gFgxsyZJLeyokuXLlSoWJE9u/coZU1NYycVZVy2I7XUnFdHjx7NrFmzaNe+PdOnT4+0XL68eXn//j1nzpyhePHiEZYJCAyk6u+/c/PmTSpXrszmLVuibMd6vR5zMzM8PT1p2qwZri4uyms7d+6katWqQMh1nZrjge+XL5hHc7fy2bNnadqkCSbJknHu7FkyRjFXypcvX7BKnpwD+/fTvXt3goODqffXX7Ru1YosWbJga2uL1sgIP19frFTMpaXX67GytESn05E+QwaCAgO5dv06OXPkCFPu27mvfvS4Fp9TG0XWjiIql5TuuJYRCf9PRiREL6m1DyGEEAlfkpsjwcTERAkiAFSuXJkDBw5Qu3ZtGjZsyI4dO9DpdAwePJjx48eHm+z0a6ampphG8ONca2ERaxdKai/4tUZGpM+aFT+DAddbtzh+/hwVK1akeZs2uN6+Tfr06Vm5YQOmKVKgjaJDIXu+fLTv1o05c+awfO1aqv7xBwDnrlwBYMT48UyaOZPChQtTtGhR8uXLR9myZcmTJ4/yw0mv13PgxAn8DAYIDuaxuzt2dnZhPpNVGzfy6NEj8uXLF+aHitZgQPtVx4O/vz9btmxh165dnDx5UpnE1sLCgubNm9OxY0fy5s2rlO/Uqyftu3fj2LFjLF++nJMnT7Lr4EF2HTwYsn3Zs1O2bFlatWqFvb29sm4PLy/8DAb+vXqVDj160KdPnwg7hgyRBBK0KieA9PEPwOKbfSOyvL1h1psIAgnaSHJqx7QdLVqxAj+DgZatWhGo0XDo1Cm0Wi09+vdH+9VEjZly5WLUxIn069ePkxcusHXPHlq2bAmAPlky/AwG+g4dyrR58yhfvjzly5enQoUKlChRQtmXK1StSoX/dYa8e/eOK1eucPLkSZydnfn06RN+gYFccHXlgqsrC5Yt48iRI1T+qmPvzadPDBw5ksDgYOrVq8foCRMi7kA3NUUTwT6ULV8+1mzZwuTJk9m4cSNLVq3Ced06KleuTP369alduzapUqVSvf95fPiAn8GARapUaM3NQzp8Vb5XVQdFDAIJavZ7EV5staPv8pMDCar3RTOzH9qmiPbTR48eceDECYINBho0b47WwgKNmRlHjx3j9MWLIZO69uqFxtycZMDcxYtp2LAhDx48oEXbtuzZsyfMuehrM2bMYOHy5Wg0GqZOn07JChXClenWpw+HT53i4cOHLFi+jOXLlwNQs149bHPk4PHjxwBUqVKFESNGYG9vj7+/f5jzZaimrVuzde9e9u/fz+DRo9m7d6+qzzZ/sWL4GQzcffKE8y4uVKpUKcJy2uBgdFote/aEBPaP/S/4nj59enbt2kXJr4IkEdXvW0ZGRkyYMYNgY2Pmzp1Lj379KFCgAH4GAzaZMqG1+P/jelTXLzERp+1IJTXn1QcvXuBnMHDp+nVee3lF2LF+7NgxPvr742cw8NjdPcL9D0KCVpdu3ADgzyZNMI7iphMA9Pr/Y++so6JavDb8DF0ioiiIYrdYGBhgFwoWdosgdmN3YCtiJyq22KBXUOxGsTERFBADpCRmYL4/uHM+Rmr0en+3zrPWWcrM6Tm5373fjZquLvOWLhWeERX0GjSIT58+IZFIMp/rVLgeSH6fX068f/+elStXsnv3bqRyOb379sW8XDmV5tfR0ZEMLS2cnJw4ePw4B48fB0BDQwNzc3PMzc0pW7YsFhYWWFhYUKpUKcqUKYOZmZnyfS8jAzU9PT5//Ej872JTfRsbqlatSuXKlalUqRKVK1emYsWKlC9fXhDR/uh17e8sJOR2HomIiIiIiIiIiPx7+dcLCU+fPuX06dMYGhrSr18/DAwMso3zvZiQlpaGmZkZBVTIUPo7YWVlRcuWLTl//jxdunShevXqBAcHo6enx/79+/PM3MqKopRfJ5eXn2/fvnHr1i1u3bolfKanp0f16tWpWbMmL1++5OLFiwC0atWKChUqZJuHpqYmlb/LzP+ejIwMevToQWBgoPBZ9erV6du3Lw4ODhQrVizH6dTV1Wnbti1t27bl0aNHHDx4kMDAQJ4/f87bt295+/YtBw8epEqVKri6utKzZ0/at2/P0aNHuXr1Krt372b37t1YW1tjZmaGrq4uOjo66Orqoq2trfS3Yh+lPHmCXZ5bk4lDJwdaubgwfvz4XKsjRPLm9u3bAHTv3p3Hjx8DUKpUqRyPM0tLS3r16sXu3bs5ceKEICSMHTuWz58/c/fuXaKiovDx8cHHxweAAgUKYG1tTZMmTWjcuDFlypThzp07XLt2jWvXrhEcHJwtM9vY2Jg2bdpkE5927NhBWloaderUYWFuIkI+aGtrM3fuXBo0aMCGDRt48eIF58+f5/z584wfP57mzZszxbE7qpzdVnWseHLtKmvXrmXTpk0/vC4iIn8mqreU//VMmjQJmUxGmzZthHtTXFwc06ZNA8DJyUnpHlqwYEG8vLzo3bs34eHhtGzZkoMHD9KoUSOl+e7bt485c+YAMHXqVNr/Ls5/j7a2NtOnT2fIkCEcOXKEFStWYGRkhLq6Ojt27GDPnj307t0bW1vbfLdFIpGwYsUK/Pz8OH/+PC9evKBGHs2TFZiYmNCrVy8OHDhA7969uXz5MmXLllUa582bN2zfvp29e/cSHR0tLK9t27Z4eHhQ5rvsbFWRSCQsWbIEiUTCmjVrePr0KTo6Ori6uv7U/P4rODk5cebMGYKCgrC2tmb79u20bt0ayKwcWbhwIcuWLUMul2NpaUnz5s1znZetrS3Vq1fn8ePHjBw5kqCgIJYsWYJ+PoJCnz59CAgIEJJDnj9/zqRJk35JUoOSgPB7NU6TJk2YOnXqD83HwcGB/fv3s2HDBsLCwnj37h1SqZSwsDDCwsK4fv16tmnMzMyoX7++MFhaWmJgYICJiQnjx49n9+7dfPnyhZs3b3Lz5k2laTU1NSlfvjyVK1emYbFidPz5XSAiIiIiIiIiIiLyt+JfbW20YsUK5s2bR6VKlXjy5AmVKlXi1q1bOWabAZw5cwY7OzsGDBjAzp07VcqgysqfUZqosrVRaioSiYS0tDScnZ05ePAgkBngP3HihFJmYW7br2DEiBFs3LiRiRMnMmvWLKKjo6lUqRIAhQsXxs/PjwcPHnD//n2CgoJ48uQJSUlJSvPQ0dFh3rx5DBkyBIlEQnp6ejY7lpxISkoSsrjWr1/P1KlT0dPTY+LEiXTu3FnI+Pz69Su6uWSuZSUlJUWoKomLi+P+/fucPHmSffv2CetsZGTEgAEDGDt2LB8/fmTNmjUcOXLkh2xazDQ08CtTFu08jpk0oP3rV0TJZOjr6+Pq6sqECRNUEnj+DRUJ8fHxGBkZ/aES+ISEBAoWLIhcLic6Opo1a9bg7u5O3bp1WblyZY7zCwkJYdiwYRgYGBAZGal0/CcnJ3P37l2uX7/OtWvXuHHjBvHx8fluS+nSpWncuLFQxVCpUqVs14vIyEgqVapEWloaO3bsoEGDBrnO7+PHjyodB6mpqaSkpHDixAmOHz/OkydPgMzj72y58uSV+yvR0uLTvLnYdu2Kuro69+7do0KFCrkKhtmmF62N/haoep/5M+5Hf6a1kVQqxblLF8a/fJXndVSipUW5s2fQVOF8kUqlPH78mMTERFJSUoQhKSlJ6e+oqCjWrl2LhoYGwcHBgpDQs2dPfHx8KFeuHGfOnMnxXPn8+TNDhgwhODgYbW1ttm7dSrdu3YBMq6CuXbsik8kYN24czs7OedonyuVyOnTowMuXL1m6dCnjx4/Pc/tSUlLytA5ydHTE19eXkSNH4unpme/+UuyPVq1ace/ePSpXrszFixfR09Pj1KlT7Nixg/Pnzwvjm5qaMmjQIAYPHpyrgKCKtVFWKyG5XM6CBQvw8fFh9erVgj2OgvyeX1TlrzyPVEXV+/7jx4/p378/D36vJnBzc8PJyQknJyeuXr0KgLOzM0uXLs33OSwlJYUpU6YIx0u5cuXYvn079erVyzZuRkaG0nNYQkIC6enpGBoaCud/amoqjx494vXr17x8+ZJXr17x6tUrIiMjs21fRkZGtuvGhw8fBAHB1taWqVOnYmlpma2yMyeSkpJyTQjKyMjgw4cPhIeH8+rVKz5+/Eh4eDjh4eGCuPD9/UxTU5PatWtjbW1Nw4YNqVevHvHx8bx48YKQkBCeP3/O8+fPCQkJ4du3b8J0qjwf5nVd+ztXJPwTzqO/AtHa6P8RrY3y5792foiIiIiI/PP51woJZ86cwcXFhcuXL1OmTBkePHhAvXr1WLZsGePGjcs2fnJyMp06dcLMzOynRAT4ewgJkPmCtHDhQk6dOsWqVauy2RPk9yLepk0b/P39WbduHf369eO3336jZ8+ewveXL18Wshvj4uLQ1dXl9evXBAcHExwcTFJSEiNGjFCyefhRISEkJIQmTZqQmprK6tWrGTp0qNJ4PyMkZCUuLg5vb282b95MaGgokNlPoWPHjowePZpy5cpx8eJFvn37RmpqKsnJyaSkpGT7Ozk5mYyMDIoXL055IyNKFixIMSMjTFavATJ7Iki0tfH19eX87dtUtrFh3759gv+0rq4urq6uTJ48OUePfAWikJDJ1atXsbGxwdzcnPfv3+Pq6srmzZtp27Yt06dPz3F+GRkZODo68uXLF06dOkWbNm1yXbZMJuP58+dcu3aNq1evcvXqVSIjI6lRo4ZQodC4cWOKFi2a7zVi4sSJrFu3jjp16rB7924kEgknT57k6NGjTJ8+Xen8+BEhoUSJEsLfL1++ZOPGjezZs4eiEgmFNDRo37IlTq9eA2DqtVOwAlE3MkLDzIxu3brh5+eHo6Mju3fvFoWEfxj/RiFBLpfj5OSEl5cXZhoaGKmrU7JoUdboZWZCW3h7o6abeZxqFCqUr4ggl8s5cuQIU6dOFSyBVGHs2LGCIHnmzBns7e2RSCT4+PhQt27dXKeLj49n2rRpnDp1CoBFixbRrFkz2rZtS2JiIt27d2fHjh2Eh4fn24fp0KFDzJgxAwsLC0JCQtDQyL1wND8h4dy5c3Tq1ImCBQvy/v37fDPLU1JSkEgkREZG0qRJE6KioqhZsyYfPnxQqj5o1aoVzs7OdOjQIV+h4EeFhPwQhYTspKSkkJqaypQpU9iyZQuQeX1NT0/HwMCA9evX06NHj8yeOCpc75OTk7l48SIuLi5ERESgrq7O1KlTmTp1qtJ5/b2Q8PXrVw4cOCAE1F++fElYWJjK143cUAgITZo0ATKf3/6okJCVb9++ZRvv27dvBAcHc+vWLW7fvs3t27f59OlTtmlLlixJx44d6dSpE02bNkVTU5P09HQ+fPhASEiIMNzxO0PShyh0JBL2lioNgO7q1RSzyKwAzuu6JgoJ/zxEIeH/EYWE/PmvnR8iIiIiIv98/rVCQrNmzZgyZYqSjUC3bt3Q1NTkwIED2cY/duwYJ06cYMeOHT8lIsDfR0jIj/xexMuVK8ebN2/w9fWlcePGLFmyhCVLlgjfz5s3j7FjxwKZL3T5BSfgx4QEuVxOy5YtCQ4OpnXr1vj4+GTbtj8qJCjIyMjg3LlzbNy4kQsXLgifV6tWjVGjRjF48GClQEhuzZaV5vntG6/q1QfAJMCfkRMncujQISDTk3fmzJnUrFmTxYsXc+d3X2FtbW1cXFxwc3NTChQrEIWETDw9PRkzZgwdO3bk1KlTdO/enSNHjtCnTx+GDRuW6zxXrFjBqVOncHV1xcPDI8/tyPp7y+VyZDJZtmBYWlpansdBVFQUlStXJiUlhW3bttGwYUOePXtGr169kMlklChRgoMHDwrnxM8KCQpevXrFokWLOHHiBLoSCUEVMyuI9E6coFgpC6VxHz58KFRH3Lx5k/r16+e7XBCFhL8L/0YhYfr06SxZsgQ1NTUWLFjAzp07iXj9WjiOC5/7jaIWFvnMLZObN28yceJEwarE0NAQU1NTdHR0lCzqsv6to6ODiYkJkydPRl9fn7i4OGrUqEFERARDhw5l9uzZwvylUmm264FUKsXExIQpU6awceNGIFMkTk5OpmnTphw7dgxtbW3evn2br5CQkpKCra0tsbGx7Nu3D0dHxzzHzStQn5GRQY0aNXj9+jWbNm3CxcUl32UrzvOgoCBat25NcnIykFl9MHDgQAYNGkSJEiVUDuiLQsLP8yNCguJ3O3z4MCNHjiQhIYEaNWqwd+9eypcvL8wvq5AQExPDzZs3ad++vdL1XfGbx8bGMm7cOI4cOQLAxo0bGTBggDBeViEhMTGRJk2aCHaDWTE0NKRChQqUL19e+LdkyZLZRLLk5ORsQkeBAgWy2Rb+L4SE75HL5YSGhvLw4UPByujBgwdK97xChQrRsWNHOnfuTLt27ZSeUaVSKYcOHcJj6VJ2SzP7CliHvsGxXz8mT56cp9WnKCT88xCFhP9HFBLy5792foiIiIiI/PP5VwoJGRkZ2NjYcPXqVaWH4OnTpxMcHIyfn9+fstwfeRBQdbdnZGSo9GKQmpqqcuAmrxdxqVSKnp4e6enp3Lx5E1NTU4YOHUpAQABly5blzZs3NG7cmL179wKZL49FixbNd7lpaWkqCQlv3rxhw4YNbNq0iYIFC3Ly5ElMTEyyjRcWFqaSgPH161cKFy6c73hJSUlIpVL27t3L8ePHhbL08uXLM3v2bKGsXy6X5xjIzUpGcjKRtpkNdzvExxEaFYW6ujpVq1bl0aNHAFSpUoXp06fz6dMnDh48KPSb0NLSYuDAgcyaNYtChQoJ85TL5Spt7882Oc2LX/Vi+qNCQkxMTLbxhg4dipeXFzNnzmTu3Lk0atSI27dv06dPH9q1a5frPB8+fMiKFSsoUaIEd+7cyXWbwsLCVBKo4P97ieTEzJkzWb9+PcWLF6dLly6kp6dz6NAhYmJilKa3t7dHTU2NkiVLZrPwyG078grAh4aGcurwYbzTMwO+Td6/o/fv1iNZ+8NMmDABPz8/mjdvLjRKzYuvX78q7bMnT54QGBjIgAEDlH6jjIyMPIW7rOSVaS2SO/8EISEni5KcCAkJwdvbG3d3dyDzvLGyssLc3Jytnp6Mv50ptLb69JFla9fSrVu3XM/d0NBQRo8eLRzPOjo6DBw4kP79+2e7dsrl8jy9/MeNG8euXbswMTFhxowZaGlpIZfLOXToEDdv3qR79+5KvRAMDQ1p0KABcrkcLy8v3N3dkcvlVK5cmX379glBykuXLimtw7Fjx7h79y7Ozs5K63PmzBl8fHyoX78+v/32W67rmZSUlG9Adf369cyaNQtLS0vu3r2b5/U8OTlZ6bw8f/48Bw4cwN7envbt2wuiQGpqqkrXSVUz4FVNhMjv+SUrf+cAqKrPfzKZTKX9kpKSonRfePv2LdevX6dr165K+z8tLU0QspKTk2nWrBmPHj1i7ty5uLm5Kc0v636eP38+S5cupU6dOly5ckX4/MmTJ8jlcuRyOdOnT+fChQsYGxtjZ2cnNC62sLBAR0cHc3PzfLcjISFBpee1mJgYlZ4no6KiVBIS4uLiVFru69evlY775ORkHj58KNgifv36VfhOV1eXZs2a0b59e1q0aCEcOxnfkvnWqRMAVi+ek/z7s5i9vT2TJk3K0UIKyFeAVKDq85+q/J3Po78zopDw/4hCQv78184PEREREZF/Pj+Xev83R01NjQsXLmR7ADYwMFB6gE5NTSUiIuJ/vXo/hEQiUWnQ0dFBW1tbpSEvFL6wurq6lCpVCl1dXSHDbPTo0QDcuXMHuVyOrq4uRYoUwdjYON+hcOHCaGpq5js8efJEKM2fP38+ZmZmaGhoZBukUinq6urCoKamRnx8vJAprvj8+94NuZGamkqZMmWYOXMmgYGBTJkyhUKFCvHq1SsGDBjAtGnT+Pz5sxAgy2vIeox9+PABMzMz1q1bh6enJzNnzsTAwIBnz57h5OTEmzdv+O233zh9+jSNGzcmLS2NrVu3YmNjw927d3/qWPgr+F+t2/379wGoXbs2kBnwAKhQoQKlSpXKdWjRogU6Ojq8f/+eFy9eKB07WQeZTCYERuRyOZ8/f+bJkyfZPk9PT8/xuNTQ0CAmJoYdO3YAmWLB+/fvCQgIICYmBm1tbRo1aoS6ujrv3r3j3LlzvHv3jnfv3lGyZMl8h7i4OJKTk3MdTE1NGZalOWnyt2+sX7+e1q1b4+3tLQhIo0ePRk1NjcDAQG7duvVDv9vRo0dp3bo1s2fPpk2bNoI1GKB07uU3iPzzyO/apxjU1dVVuh6cOXNGqHYbNmwYJ0+epEuXLsyYMYOBgwYJy42NjaVfv3707NmTqKgopXWKjY1l2rRp1KxZk4CAACQSCZ06dWLHjh28evWKtm3bsnDhQsLCwoRpFAJ9TsOlS5fYtWsXgCACJicnExgYyKVLl0hNTcXb25vffvtNOO/i4uLQ0NBAU1MTZ2dntm3bRt++ffHy8qJQoULCtSE+Pp6MjAwyMjI4ceIEfn5+fPz4kZ07d5KWliZ816xZMzQ1Nbl9+zbBwcG5XmtUud8PGjQIHR0dHj16xI0bN/J9jsg6dOjQgT179tCjRw8KFCggfG5gYKDS/VyVagRA5WeXX1WN8E/hR57/sg6VK1dmyJAhGBkZKX2upaUlTDNlyhQhsWHRokU8ffpU+E5XVxc9PT1hmDhxIlpaWty7d4+QkBDhc5lMRnp6Ol5eXly4cAENDQ3c3d0ZMWIEHTt2pEaNGhgZGQlVPPkNGhoa2bYlp8HAwIACBQrkO2hpaak0KCoR8xsSExORyWTIZDKhUXNSUhJDhgzB29ubZcuW0alTJ0xMTEhOTubMmTOMGTOG2rVrM2jQIB4+fEjW2+neffvo2LEjcrmckydPYmtrS7t27Xj79u1PHwu/8rj6q54nRURERERERERE/t78K4UEyLn8XSKRCBmVqampdOrUiTVr1vyP1+zvzatXrwCwsLBATU2Njx8/8uHDB9TU1OjUqRMlSpQgLS1NyKD/ns+fP3Ps2DHBT/lHSEpKYsqUKWRkZODg4KBkS5UbaWlp+Pr60r9/fzp27EirVq1o2rQp7du3p3v37kydOpVRo0YxZcoUFixYwKpVq9i5cycPHjxAJpPlOM8CBQowcOBAfH196dGjBxKJhOPHj9OhQwcOHTqUZ1ZuZGQk3bt3F/5u1qwZ27Zto2LFinz69InWrVvj5eVF/fr1SUtLY926dXTo0IFSpUpx9uxZfH19KVOmDGFhYbRp04YNGzb8UPbYv5nU1FShubBCSFBk+OeXTaijoyP0Cskrwxcys/WCgoKYMmUKbdq0oXfv3vTr14+HDx+qtJ4eHh4kJydTt25dTExM+Pz5s3Be1apVCxMTE2H9X716xfv377PN4/Tp03Tr1i1b0PR73r59y8mTJ5UyIbOybNkySpcuTWxsLAsXLhQEjrJly9KlSxcgMwtcFTIyMliyZAlDhw4VLFVevHhBq1atuHbtmkrzEBFREBgYyLRp05DL5XTv3p2nT58K55i/vz/9+vYVxnUaOhQNDQ1OnjxJ7dq12b17N2lpaaxfv55q1aqxevVq0tLSqF+/Pnv37qVatWq4uLhw/vx5kpKS8PHxoUuXLkyYMIHg4OBc1ykhIUEQzJ2dnSlbtiwA4eHhHDt2DIBSpUoBcOLECX777bccr88tWrRg4cKFFCtWLMflBAQEcPr0aSAz0zgyMlKpMsjIyEiwNFq/fr1K+zM3jI2NhXvSpk2b/tC8RP4dHDx4kG3btiGRSKhZsyZSqRQXFxehqfH3mJiY0LVrVwC2bt2q9N3NmzfZvHkzAMOHDycmJkap8u7fxvv37/H29sbZ2ZnRo0fj7u5O//792bx5M4UKFcLV1ZVt27Zx+vRpRo0aRfny5ZFKpVy4cAF7e3vmzp0rzKtWrVocPnyYe/fu0b9/fzQ0NLh06RLNmjUTkiZERERERERERERE/m78a4WEnFAICQoRoUiRIkre/yKZpduAYLPw4MEDIDPjW19fH1tbWwAuXryYbdpPnz5ha2tL9+7dKVGiBI0aNWLx4sU8fPhQpWD41KlTCQ8Px9TUVMmTOicSExPZs2cPjo6OLF68WCkrOj09nfj4eCIjIwkLC+P+/ftcvXqVs2fP4uPjw7Zt24SMuVmzZuHr60tsbGy2ZRgZGTF37lz27dtH5cqViY+PZ/78+bRo0SLHYNSZM2ewtrbm2tWrwmdTpk7ly5cvDBs2jO7du+Pu7o6uri7Lli1j4sSJ6Orqcu3aNRo2bMjOnTuxsbHhypUrdOrUCalUypQpU+jTp0+O6/df4/Hjx8hkMoyNjQVboS9fvgD5CwkAbdu2BTIbkOZEQkICPj4+dOvWjSFDhnD27FmhP8LTp0/p378/s2bN4vPnz7ku49OnT2zbtg3IPJ5lMpkQELCwsMDU1BQAc3Nzwbc6ODg42zyHDh3KtWvXlOwmvicoKIiVK1dy5swZ5s+fz40bN7KdZ63btOH06dNCTxNPT09BuBg5ciSamppcuHCB8+fP57ocyPSQdnJyYtmyZcK09+7do06dOsTGxtKlSxd2796d5zxERBQ8ePCALl26IJVKadWqFampqVy5cgUdHR3mzJmDhYWFkhjtNGQIN27coE6dOnz9+hUXFxdKlizJxIkTiYmJoUqVKpw4cYKJEyeyfPlyFi9eTGJiItWrV2fJkiXY2toil8sJDAxk8ODBDBkyhJMnT2br6TFv3jyhOmjOnDlApsC9e/du0tPTqV69OqNHjxZs1M6dO8fJkyd/SOy9fv260DOnU6dO9OnTB4BTp04pXQdGjRoFZPZvykls/BGcnZ0B8PHx+SmRX+Tfw8uXL4Vja+rUqRw7doxChQpx7949Vq9enet0iv4ae/fuJTExEcgMqs+ZMwe5XI6dnR2+vr5MmzYNe3t7nJ2d8fLy4tWrV//4ZIioqCi2bdvG9OnTcXZ2Zu/evURERKClpYWpqSkpKSmcOnUKZ2dnZs+eTXBwMJaWlri5uQn31y5duiCXy5V6tCmSUqpUqcKWLVt4/PgxlpaWREdH06ZNG5VsB0VERERERERERET+1/ynhAQ1NTUlEWHXrl2ixcZ3KDKnFVmXCiGhRo0aQGaGPWQXEhISErC3t+fFixfo6ekhl8u5ffs2s2fPpk6dOlSqVImxY8fm2IgP4OzZs4Kl0ZIlS3L1iIyLi2Px4sU4OTmxadMmvnz5QpEiRRgxYgRnz57F39+f48eP4+3tzebNm5kyZQrz5s3Dzc2NESNGMHDgQFq2bImhoSEJCQlcuHCBxYsXM2DAALp168aaNWuyBW1q1qzJoUOHmDZtGvr6+ty9exdbW1smTZpEUlISMpmMyZMn0717d2JiYrC0tBSmPR8QgIuLi7Bfz549y+DBgwkODsbBwYGdO3fSqFEjEhMTGTNmDI6OjqSmprJnzx5WrFiBlpYWp0+fxtbWltu3b6v6M/4rUYg3tWvXRiKRkJiYKDSFNDY2znf6Nm3aAJkB+E+fPgmfh4WFMWnSJKpXr86qVat4/fo1Ojo6dO3alQMHDvDbb7/R6XdP45MnT+Lg4MCePXtyrEzx9PTk27dv1KlThzZt2vDkyRO+ffuGnp4e1atXVxq3atWqFC1alPT0dHx9fYUszqxBl6CgoBy3JTAwkO3btyOTydDX1yc5OZndu3ezYcMGEuLjlcbV1NRk+PDh1K9fn5SUFObPn49cLsfc3JyePXsCmVUJuQV7IiMj6dixIydOnEBTU5O1a9eyYMECzM3NOXXqFF27dkUmkzFu3DimT5+uso++yH+Tt2/fYmdnR3x8PFZWVpQsWZKTJ0+irq7O8uXLcXR05NChQ9h16CBM4+rqSqFChbh8+TKLFy9GR0eHuLg4ihYtiqenJ7du3eLOnTv07duX+/fvo6ury+TJk/Hy8qJt27Z4eHjg4+ND586d0dTU5OHDhwwYMIB69eqxa9cu5HI5V69eFURAT09PDAwMyMjIYN++fcTGxlKkSBF69eqFRCKhdevWdO7cGYDLly+zb98+lRqNP378WBDcWrdujZ2dHQ0bNqRixYpIpVIOHDggnIc1atTAxsaG9PR04d74s9SqVYsGDRoglUqFqiSR/x7Jycn069ePxMREbG1tmTFjBmZmZqxcuRKAhQsXClV/39OsWTMqVKhAQkICBw4cIDExkSlTppCQkEC1atVISkrizZs3glWQwqZywIABDBgwgDlz5vDs2bP/5eYKyOVyoqOjuXHjBufOncu1gi8rGRkZHDx4kN69e9O0aVOWLVvG27dvUVdXp169ekyePJn9+/ezY8cOFi9eTP369ZFIJNy5c4c5c+bQsmVL4XyuUKECHh4eHDp0iPJZGkf37dNHsJeCzOduf39/mjVrRmJiIl26dGHfvn1/xi4RERERERERERER+Wn+U0KCuro6V65cEUWEPFBkRCp6Jbx48QLIbLaalpYmvGSGhIQI/QcePHiAtbU1d+/epXDhwty+fZuwsDA2bNiAnZ0dOjo6vHv3jk2bNmFnZ6e0vPT0dFavXk2PHj0A6N+/v1ITy+9ZvXo1Xl5eQgC5TJkyLFq0iN69e1OgQAH09PQwMTGhTJkyVK9enVq1atGqVSs6depE3759cXFxYf78+Zw+fZrNmzczaNAgKleuDMCzZ8/YsmWLkL2dFQ0NDfr378/p06dxdHQkIyODTZs24enpia+vLxs3bgTA0dFRqZn3qlWrSElJoXTp0ixcuBAdHR0+fvwoWHoUL14cPz8/Jk2aBGRmuM6ePRuJRMKwYcPw9/endOnShIWFYWNjg4eHxz8+u+9nUdie1KpVC/j/agR1dXWVGhGamZlRqlQp5HI59+7dEz53cXFh165dfPv2jVKlSjFlyhT8/f2ZM2cOVapUoXDhwsyfPx9vb2+qV69OUlISq1atYunSpUrzl0qlQiByypQpRERE8O7dO2Gdv/cLl0gkWFlZoaOjQ0JCAjt37gRQykqOiYnJ9nvHxMRw+PBh5HI5TZs2xd3dnU6dOqGhocHjx485fuJEtm2XSCTMmzcPTU1NLl26JPTfGD58OLq6uty6dYsLFy5kmy4jI4Pu3bvz4MEDjI2NOXbsGP369RO+19XVZevWrUybNg3ItE45c+ZMPr+EyH+ZadOmERUVhaWlJdOmTROO+9mzZwtCtb6+PnN/rwiAzHN/2rRpaGhoMGHCBIKCgti8eTNPnjzB2dmZCxcusHDhQmQyGfXr18fHx4c+ffoo3ePLli3LnDlz8PPzY8iQIejo6PDmzRvGjh3L+fPnBZvDAQMGCOvx+vVrQkJC0NDQYODAgUqNVm1sbOjZsycSiYTr168zYsQIoWdLbpw4cYKMjAx0dHSoW7eu4EOuuKY9fPhQSeQcOXIkAF5eXrla8alCVFSU0IT1+PHjPz0fkX82R48e5eHDh6irq7Nt2zbh/OjVqxfW1tZIpdJc7T4lEolQlbBv3z68vLwIDQ3F0NCQAQMGCI3E165dy4kTJ3Bzc6Nx48aCReaePXvo1avXn7p9UqmUly9f4ufnh4eHB3PmzKFHjx7Uq1ePZs2aMWTIEMaOHYuNjQ0uLi74+PjkKir4+fkxa9YsQcy3tLTEycmJvXv3CpWpenp6SCQSateuzbx589i2bRudOnVCTU2NV69e4ebmptRg3draWrBIg8zzPev9FKBgwYKcOHGCnj17IpPJGDlyJHFxcb9+Z4mIiIiIiIiIiIj8JP8pIaFNmzYMHz5cFBHyYODAgWhoaHD27FkhQ15DQwN/f3/q1q2Lh4cHAH379kVXV5cdO3bQqFEjnj9/jrm5Ob6+vlSuXBlzc3NcXFw4efIkkZGRmJubA9CgQQNhWW/evKFNmzZMnTqV1NRU7OzsGD9+fJ7rZ29vT7Vq1YS/Q0NDGTZsGJ07d2b58uUqv3Cpq6tTvXp1nJ2d2b59O7t37xYEhLyy1UxMTPDy8qLv7/7dMpkMa2troYLD39+fmzf/v39EHSsrIFOY8fb2FgJNHTt2FBrZPXz4kIMHDwKZATSFqAJQp04dJauj8ePH061bt/+k1ZGiWarCEsjc3JyCBQuSnp6ukp/w/fv3CQsLQ0NDQ+kYUlh9rFu3jr1799KnT58cK2IsLS3Zs2ePIPosXbqUO3fuCN8HBweTkJBAoUKFaNeuHUWLFkVHRwdAEL6+Jz09nbS0NACsfj9WFOKDgm/fvin9nZiYiFwux9DQkJ49e6KpqUm7du0YOnQoAKFv3uS4rLJly2Jvbw/8v72TiYkJAwYMABAazGZl165d3LhxA319fc6dO5ejyCeRSBgzZgx6enoAlChRIsfli4gAgoi3cuVK4dgvXry44MGeG1nPi3LlyjFw4EAKFCgAZJ47iuPu06dPaGho5DoffX19YmNjhWtxjRo1qFu3rhBor1u3rjCuwsKlTJkyFC9ePNu86tevz4ABA9DQ0ODcuXP07ds3z2uzjY0NGhoapKSk4O7uzubNm/Hx8RGsjhTCpYJ27dpRqFAhYmNjla41qiKXy9m7dy8NGjTgxo0baGlp4ZqlGbvIf4vGjRtjYGBAenq6UmXK06dPhYB5y5Ytc51eYW358uVL4TxJSkrC1NRUENkSEhIoUqQIJUuWJCkpSahQ09LSEu5Rv5rjx49jY2NDqVKlaNy4MYMGDWLRokWcOXOGR48ekZSUhLq6OqVLl6ZcuXLIZDKuXLnCzJkzsbGxYfLkyRw5ckTp2a9BgwZKVYTv378nLS1NSUz8Hm1tbaKiooRtrlmzptAPScG33xNwFAzK0lRegZaWlvD7pKSk5Pr8ICIiIiIiIiIiIvJX8J8SEiwtLdmwYYMoIuRBq1atOHDgABoaGhw5coQTJ06wadMmNDQ0iI6ORk9PD09PT2bNmsWQIUOYPn06qampdOjQgaCgIKUgjIL169cTERGBkZGRIET4+PhQt25drl69ioGBARs3buTo0aNC4DU3rKysOHbsGF5eXkydOhVbW1t0dXX58uULx48fZ8yYMT+VvVWoUKE8KyG+JzU1VZiuWLFiBAYGYm1tTVxcHL169RTGmzd3Lg4ODsjlckJCQtDU1MTNzU3wKD5z5gytW7fm3bt3lCtXjgsXLtCiRQulZRkZGbF7927Wrl2LlpYWx48fx8rK6j9ndRQREQEgiFIaGhp59uz4nrVr1wLQtWtXpWB31oCiQtzJDTU1Nfr370/btm1JT0/H2dlZqMxRNBxu1KgRampqaGlpUbp0aSAzszmnSpIXL16QkZFB8eLFadq0qdJ2Koj/zqpIEfAElNZX0dckr0CmIkh0/vx5YX0UgQwfHx+lZcXGxjJlyhQgs8JC0Xg2J27dusW3b98oVqyYYIMmIvI9qampvPld6KpWrZoQcMt6TOdGbo1gIVMQO3fuHMWKFRPE5ayZ/QpCQkLo06ePkBU8ZswY/P39MTIyEq4rWQULxfrl9cxQo0YNRo8ejaGhIffu3cPR0TGbGKjA1taWBQsW0LBhQyQSCUFBQULz95YtWzJmzBilZamrqwvnrL+/f67rkBNRUVF0794dFxcXvn79ipWVFbdu3WLw4ME/NB+Rfw+lS5cWmncvXbqUgIAApUbLHTp0EOzuckKRMBEREUGdOnWoU6cO6enpnDp1ii5dugCwefNmRo4cyejRowkODkZTUxN7e3sCAwOFRua/kgsXLjB8+HCeP38uWP3VqlULR0dHXF1d8fDw4NSpU9y7d48zZ85w+vRp/Pz8GDt2LJUrV0Ymk3H79m2mT59Oo0aNcHJy4siRI2hqauLj44OHhwelS5cmNjaWPXv24OzsTEBAgJKVmVwux9/fH1dXV27fvo2GhgZubm4cO3aMggULCuO9evWKHln278pVq3JNnsla3aQQ6UVERERERERERET+DuSetifyn6Vr165s3ryZYcOGceTIEQB2797N6dOnGTlyJF++fKFly5ZCs7mlS5cyatSoHIOwjx8/Zv78+UBmBmrx4sU5ceIE/fv3Jz09HRsbG7Zu3SoEQVXF2NgYe3t77O3tSUtLIygoiMWLF/Pq1SvGjBnD2rVrlV7gfjWKYG2hQoUAKFq0qLB/Tv6eXQqZgd4JEyZQoUIFrly5wpAhQ6hSpQpSqRRPT09O/G5D0759e7Zs2YKRkVGOy5NIJIwaNQpra2t69uxJaGgo1tbWQuPhEiVKCIPi75IlS2Jubv6veQlVBOeyigBNmzbl1KlTXLlyhfHjx+eaifzu3Tt8fX0BsgUzFNl+Ojo6eQYrszJ9+nQeP37M69evmT59Oh4eHoKQ0LhxY2G8UqVK8fLlS+Lj4/n06RNFixYVvktKShKqLKytrYXzJzIyUmlZCQkJmJmZCX8rAo3f9yIwNDTMzDb9LuMxK40bN0ZHR4eIiAhCQkKoWrUq9evXp3LlyoSEhHD48GGcnJyAzL4Jnz9/pmrVqoKlRW4obJGaN2+erxgj8t/l1atXpKenY2hoiKmpqSBmqSIk5GftU7ZsWTZt2sTw4cMFMWHz5s2YmJiQkZGBt7c3np6eyGQyTExM2LZtmyDewf9fV7IKeaoKHeXLl+fIkSMMGjSIN2/e0LVrV3bu3JmtLwpkNoYfPHgwrVu35vjx47x+/Zru3bvnKmS3bt2aI0eO4O/vz+zZs/NcD8gMau7btw83Nze+fv2KlpYW06ZNY+rUqXlWaoj8N+jRowdXr15l69atDBkyhO7du3Pv3j2MjIxYu3ZtntdvRZVdSkoK7969Y9CgQdy7d4+TJ0+ybds2jhw5IvSD0tTUxMHBgf79+6Orq6t0D/v48SPXr19HIpGgra2NlpYWWlpapKenY2pqipaWlvB5oUKFMDAwyHF9Hj9+jJOTE+np6XTv3l3o+aDYhrdv3wpVS1kpU6YMrq6uuLq6EhoaytGjR7l69SohISFcuXKFK1euMG/ePLy9vWnfvj2tWrXCx8eH1atX8/HjR1auXImPjw8DBw6kfPnyeHp6CokdFSpUYOzYsTg4OCgtMzAwkFGjRiFLTISKlQCEpu05kbUSUV9fP9fxRET+bMLDw5UsN3OjSJEiWFhY/A/WSEREREREROSvRnyrFMmRDh06sGXLFlxcXAQxYfXq1axbt47ly5eTkZEhBG6+z6BXIJPJcHJyQiqVYmdnR9++fTl37hz9+vUjPT2d/v37s3nz5j9cIaKlpUXDhg3x9PRk9OjRSmLCn4WiBD5r4F9HR4dt27axqlw58DkKwJy5c5k6Zw4ODg7Ci+Xnz5+ZPXs2T548QSKRMGPGDCZPnqxSMK1u3boEBQXh6urKoUOHiImJISYmRmiKnRMWFhasXr1ayBj8WWJiYhgzZgznz59ny5Ytgk3O/4KUlBThRaZkyZLC57Vq1aJgwYLExcVx//596tWrl+P0Bw8eRC6X07ZtW6EnBmQG3RSZf7q6uioLCYaGhmzevJmOHTuyY8cO2rRpw82bNwFlIUFLSwsLCwtCQ0N5/fq1kpAQEhKCXC6naNGiSrYp3wsJCnsVBYogSU7NXc3MzHj/eyAnJ3R1dWnSpAkBAQEEBARQtWpVJBIJAwcOZNq0aezatQsnJyfu3bvH5s2bgczGs9/3d/ie8+fPA+R6LRD5Z/H+/Xtu3LhB8eLFKVeuHMWKFfslAlFISAgAlStXRiKRCIF6VeatSo+AEiVKsHXrVpydnQUxYeHChXh4eAiBvubNmzNjxoxs1XMKIeH9+/fCZ6pUJCioUKECPj4+DB48mJCQEHr16sWhQ4eoWrVqrus6atQo5HJ5ntvfqlUrINM6LTo6mmLFiuU6bnR0NCNHjhT6lNSpU4dNmzZRuXJlUUQQEVi2bBm3bt3i4cOHbNiwAfj/RI+8kEgklC5dmpCQEMLCwrCysqJatWo8efKEc+fOMWjQIPbu3Uu7du3o37+/cL9LSEgAMquKvLy8WLt2rVDJlx9aWlqcOHFC6COiICoqir59+5KUlISNjQ2rV69WqVfS95QpU4b+/fvj5uZGaGgoZ8+e5ciRI7x7944TJ05Qs2ZNNDU16dWrFyVLluTy5cscPnyYt2/fMm/ePNTV1UlPT0dDQ4N+/foJPbQUxMfHs379ejZt2oRcLqdx3bqQkJjHGmWi2D/a2tp/myrq2NhYfH19lZ6TRNulfzfh4eFUqVIlm8VmTujp6fHs2TNRTBAREREREfkPIL5Z/s35kca6vzoT2M7OTqky4cqVK4KfvKOjI0uWLEFXVzfXAM/SpUsJCgrCyMgIFxcXtm3bxsSJE0lLS6NZs2ZCwDIrcXFxgsVEXqSmpmYLpJYsWZI1a9Ywbtw4QUzIL5NaQWJiouDXnZ6eTlRUVI7jqampYWZmRkxMDAAGBgaCzZGCkSNG8PF3IeHO7duMHDmSRYsWYWpqysOHD5k3bx6xsbEYGBgwffp0hg4dmm0eea0fwIYNG3B3dycyMpKIiAgiIyOJjIzk3bt3fPz4kYiICCIiIkhKSiI8PJxu3boxfvx4Zs2aJQSU8vL6VfD582fU1NS4cOEC48aNE37/bt26sX37dtq2bSuMmzVI/itIS0sTtjk0NBTIfFHR09MTPk9MTMTa2prffvuNc+fOUbFixWzz+fLli9ATYMiQIUr2PampqcJLv0wmIzIyMl97LcgMMFaqVIlBgwbh5eXFkCFD+PbtG3p6ehQuXFjIak5ISBDsVj5+/EhUVBT6+vokJSUJAUsLCwuSk5OF9VJUKSiIjo4Wvnv//r1QDSOTyYRm6Aq+/01D37xBrq2t9FmNGjUICAjAz88PBwcHSpQogaOjIzNmzODq1as8evSIESNGkJGRQY8ePWjYsCGfP3/ONRAZHR0tiGINGzbMd9/BX3td+68hlUrzFchiYmJQV1cnIiICDw8PvL29labR09OjdOnSlChRgooVK1K2bFnKlClDmTJlsLCwyCY0ZWRk5FgJ9ezZMwAqVqxIenq6cB3NyMjIZkUkSU0la8g8LS0t32bGqampmJqasnHjRiHbWNHPRltbmwkTJtClSxdkMlm2fWJqagpkVi8pvvv48SOQeR4/fvw41+UaGRlx9GjmNb9fv37s3LmT169fM3z4cEaPHi2IxBEREVSqVCnb9N+fDykpKcK1FqB69eo8fvyYPXv20L9/f+Hz9PR0peuVk5MTgYGBQhXCuHHj0NDQID09XaVz7kfOS5G/Fz/y+2pra+Pt7U2jRo1ITEzEzs6OXr16Kc0jt/mVKlWKkJAQQkNDKVu2LL1792bmzJkcPXqU/fv3KzVUVpyvUqmU69evM2/ePF6+fAlkXgMKFy5MamqqcK9PSUlBJpMJfyclJZGWlsaFCxeU+hrNmDEDX19fkpOT0dHRITY2lpYtW6Kvr690v2jZsmWuyQVZSUpKQiqVoq6uTocOHTA2NmbWrFn4+/szcOBAYZ7Jycl06tSJli1bcvToUU6fPk1aWhrly5dnzJgxWFhYIJPJkMlkJCUlsW/fPtatWyfcs3v16sWcqVOR98zcR4ptzwnFPV9XV5e0tDS0v7uP50R6errK90tVxAmFAASZzx52dna8fftWpfmL/Dv4/Pkz3759w9vbmypVquQ63rNnz+jXrx+fP38WhQQREREREZH/AD8tJDg4OJCSkkKtWrWoWbMmtWrVolKlSmLWm4qo+rCvSpb6n7FcRVB48ODBFCxYkF69ehEdHY2+vj7r168XGrTmJiI8fvyYhQsXApmZblFRUbi5uZGamoq1tTUzZ85EIpFkEwMMDAwEX/m8MDc3z7FkvX79+tSoUYO2bdvy6tUrtmzZopLNUUxMjJBxo66urlSGnxW5XI6urq7Qh6FYsWLZgrdZDWdMTU0JDQ1l7NixDBw4kDVr1iCTyahevTqHDh2iePHiKp0zOb1sFixYkIIFCyo93KekpAjbKpfLiYuLY8mSJaxfv57Vq1cTFBSEl5cXRYoUUelYSEpKYsGCBUIj3goVKlC6dGn8/f1xcnLKJib8StTV1YWXXYWwY25urrS/LC0tGTBgAL/99ht37tyhWrVq2fbn4sWLkclkNGjQIFvGfNZ+GiYmJsTHx6tkIxAXFyf0u7hx4wbPnz8HMnt4ZD0eBg8ejJGREUuWLOHGjRsYGhoyevRolixZAkDDhg2ZOnUqampqgk3W9wFVuVwufFeyZEmMjY2F774XTiIjI3n56JHwt1GhQvBdAKJNmzZ4eHjw5s0bkpKSkEgklChRglatWgkNYx8/foyBgQHu7u5IJBIKFCiQa7bnqVOngMzqkLJly4qB/38gERERrFu3TklAqFq1KomJibx//55v377x9OlTnj59KohyCvT09HB1dWXChAlK19mcjgPFeaKoSFA0FtbS0lKqNAKQp6SQ9e6Snp6eb2VM9erV0dHRwdLSksDAQNq0acP79++pVasWu3btEoL4aWlp2Y5nxbkUGRmJgYGBYLui2EaF0JAToaGhStveqVMn1q1bR3h4OFeuXKFOnTpAZsXQkCFDhPHevXtHQEAAHTt2xMTERPg8IiJC6d7fvXt3Hj9+jJeXF4MGDVIKAioEm7t37xIYGIi6ujo3btxQyuCWy+UqP0uocv7+6ucSVfm3XFtU3Q5Vn6dVFYB0dHSEZVevXp1jx46xb98+3N3dle5bec1P0Sfh7du3dOvWjVq1arF//36ePHnCzZs3cXNzUxo/MjKSWbNmCUKbiYkJ7u7uDBw4MNtxlJqaqrRvli9fzuzZs3n//r1wfshkMqFKFjKfeXIT+QwMDGjevHm++0UqlSrdV1u0aMHChQuJjo4mJiaGChUqAJkVoYr/d+jQgaioKB4+fEjLli2F3yojIwMfHx/s7OyEBIhKlSqxaNEi7O3tkScn8+b35RQvXjzX+6rimU9fXx8tLS2Vjpk/6/zIKiIUL14cS0tL4TuZTCZUJIr881CI+/l9X6VKFeE+JiIiIiIiIiLy02+Dx48fZ9u2bbRr146YmBhat26NpaUldevWxcnJKZs9h8g/l65du3Lq1CmGDBnC3bt3BREhN2QyGUOHDhUsjWrXrs2UKVP49u0btWrVYv78+Xz+/FnIKPsjREVFcfToUSZOnEj//v15+/YtlStX5rfffqNYsWJCZcLPNGDOjYyMDCHDLLeeBgr8AwKoWbMmnz59YsWKFchkMnr16sXly5cpV67cH1qP/IIHEolECGLv2rULfX19Ll++TJMmTbh161a+87927RqtWrUSRAQXFxdOnTrFzJkzcXBwQCqV4uTkJDQL/TNRZO9n7Y+gQNErIjY2lhs3bih9FxcXx969e4HMxqrfozj+JBLJT9kiaGtrs2rVKmHa3LIfO3XqBGQ2hb579y43btxAIpHQp0+fbOMqqhmKFCkCZG+2nFuPBCBfawrIFKAUQcZLly4Jn/fr1w9ACMrMmDFDpfkFBAQA/2/BIvLP4d27d4wePRpra2t27tyJVCqlcePGHD9+nEuXLhEUFMS7d++4efMmBw4cYMGCBYwcORI7OzuqVKmCrq4u3759Y9WqVdSoUYP169fnWV2lsDZSiJ+/qtlyTpQtW5Zr166xf/9+Ll26lGMlQFbMzc2RSCSkpqYKYt6PrF9WDA0NadasGZB5fuRkC/HhwweaNWuGi4sLpUuXZvDgwdy5cyfH+fXs2RMjIyPCwsI4e/ZsjuOsWLECyMx6/t4GRkQkJ1q2bMn27dt/qJpQkegRHh4O/H8fKIBNmzYJVnyKPlANGjTg6NGjqKmpMWLECJ49e8bgwYNVOqcU56xCgJTL5UyaNEnldf1ZdHV1hXv55cuXcx3PzMyMtm3bCiLClStXaNWqFS4uLoSGhmJqasqGDRu4f/8+Dg4OPxToV1wz/ur+CFlFhNKlSxMQEMDhw4eFQfF8KPLPokiRIujp6dGvXz+srKxyHfr164eenp7wPCoiIiIiIiIiAn9ASFBTU8PCwoIWLVpw69YtPD09efbsGSNHjiQ2NvangnIif1/atm3Ltm3b8g3GQGZAQ9G8b/LkybRv3574+HiqVq2Ku7s7nz59YsSIESxbtgwXFxdev36t0jrI5XJevHjB7t27GTVqFFWrVqVMmTL06dOH9evXc/jwYdq0aaMkJhgbG/9yMSE+Pl4I4isyxXPDvHhxAgMD6datG7q6uixfvhwvL6+ffjl88eIFy5cvx8bGBlNTU1q3bs3cuXPx9/dXKkP/nq5du3Lx4kUqVqxIZGQkdnZ2rF+/PkcxIjU1lenTp9O8eXPevn2Lubk5R44cwd7enrZt29K0aVMsLCywt7f/n4kJiuB6TkKChoaG0LRQ0VBZgbe3N4mJiVSoUCHHQHfW/gg/m81XuXJlli5dSoMGDejWrVuO41SpUoVKlSohk8lYunQpgLAfsyKVSgVLE8W59v3vqljPnISEvLKms2JrawsoB0ns7e2FrOoqVaowcuTIfOeTkZFBYGAgkNkUVuSfgUJAqFy5Mps3b1YSEI4fP56tz0e5cuVo2bIlgwYNYsmSJRw8eJDbt28THR3NgQMHqFSpEjExMUydOpW6dety5MiRbMenXC4XhITv7yO/otlyThQrVowuXbqoZAuipaUlVKIpGrv/SDPo72nQoAEmJiZ8+/ZNaEauICkpiU6dOhEeHi5YlyisZho1asTRo0eVBBk9PT0GDhwIwMaNG7Ndt1+/fo2Pjw/A/yTQKvLfRSEkZLW46dSpE+XLlyc2NpadO3dy5coVbG1tmT17NomJidSrV4/bt2/j6elJwYIF8fLywsnJiZ07dwr2ZjmRVUjIyMhg7dq1Qu8eVVC1D0NO5HSPzI0nT57Qo0cPHBwcuH//Pvr6+syZM4dnz54xdOjQn6rU/jsICd+LCH5+fjk+g4n887CwsODZs2cEBQXlO4h9D0RERERERES+55fUp3/+/Fl4UVc0Gvy3lKCL/BiPHz9mwYIFAEyfPp0BAwYQHR1NuXLlWLZsGQkJCYwfP154eQwPD8fV1VWwR/mexMRE1q1bR9++fSlfvjz169dnzJgxHDx4kDdv3iCRSKhZsybDhw+nQoUKhIeHK4kJa9as+eVigqIaQVdXVyU/fX19ffbt28fnz58ZM2bMD58b4eHhrF69GhsbGxo1asTSpUt59uwZ6enp3L9/n3Xr1tG7d2/KlStHhw4dmDlzZo6l/pUrV+bixYt06dIFqVQq2C1lrQp5+vQpDRs2ZNmyZYJH/vnz57l06RKdOnUS/PvXrVtHqVKllCoTcvsNfwWKwF5uL7EdOnQA4OzZs0LAMSUlhZ07dwKZ3uE5BQMVQoIqv2NeODg4sG/fvjwD+YqqhLS0NCQSiZKXtIIPHz4gl8vR1NSkbNmyQPZmy4qKBLlcni1Ya2hoiL6BQb7ra2NjA2Q2cFU0sdbR0WHChAkYGxuzbt26fG1ksk5foEAB6tevn+/4In8tX758ySYgNG3aFB8fn2wCgipIJBI6dOjAzZs3Wbt2LcWKFePt27c4OTnRsGFDwUMcEHq2aGhoCNVYv7rZ8h9FYa+kuN58X5GQnJzMmzdvVLKSUfitQ6btkKJKMz09nX79+nHv3j0KFCiAtbU18+fPp2/fvmhpaXHnzh0mTJiAtbU1y5cvF+43gwYNQltbm4cPH2arvFq1ahUZGRm0a9eOGjVq/II9ISKSM1mtjRSoq6szbtw4ABYtWoSDgwMhISEUKVKE9evXc+bMGWrXrs3jx48pWbIkTk5OeHl5MXToUExMTDA0NGTr1q3ZzquyZcuioaFBUlISO3fuZNq0aT+0rkFBQRw4cOCnen8ohITHjx/nKnbExMQwatQobGxs8Pf3R0NDg6FDh3L37l1mzJjxh0QAhQiiSi+rP4N3796JIsK/HAsLC+rUqZPvIIoIIiIiIiIiIt/zS4QEFxcXZsyYwfPnz/n48SMfPnzIM8tI5N/L6tWrkUqlWFtb8/z5c969e0fBggVZvnw5BQoUYM+ePXz8+BEDAwN27dpFqVKlSEtLY+XKlUJjy6zMnz+fmTNn4uvry5cvX5S+a9SoEevXr2fr1q0sXLgQd3d3IDPwrsj6Ll26NIMGDQLg1atXHDt2LNd1VwRoExMT87TnUASf09PTefLkicr75kez0m7fvk3//v2pXr06K1eu5NmzZ2hqatKqVSs8PT25ceMGGzZsoG/fvpQpU4aMjAwePHiAh4eHkKH/PQUKFGDXrl0sXrwYdXV19u3bx/z584HMwHS3bt14+PAhJiYm+Pj44OHhwaVLl/D09CQjI4MGDRoIDazXrVvH5MmTsba2RiqV4urq+qc161T0SMitd8X39kbp6elMmzaNT58+YW5uTvv27XOcThFEz6kx7K9EEYBUIJfLc+zxkbU/gsLm5Pv+GFmPo+/PCVDeRw8fPsxxfczMzKhYsSIZGRmMHj1aON7d3NyIiIigUaNGqmwWN2/eBDJ7PagiPIj8tcycOVMQEAoUKMDp06fx9/f/YQHhezQ0NBg8eDAPHjxg1qxZGBgYEBQUROfOnTEzM6N///4MGzYMgPLly6Opqcnnz5/x8vICUKmC8dOnTzg7OwvVSX8GimNYcR4qqoEUQsKxY8c4ePAgV65cUWl+pUuXFvzEr169CsC5c+c4ffo0mpqaJCQkEBgYyOzZsxkzZgyhoaHMnz8fU1NTvnz5gqenJx07duTFixcULFhQ6ONw/PhxpeUoPONHjBjxB7ZeRCR/FPf4jx8/Kt3ve/ToQYkSJUhNTUVNTQ1nZ2fu3LlDnz59hPOnf//+fPjwIds8k5KScHV1zXZepaamYvC7MD5p0iTkcrlwHVF1XXft2sWjLH2DVKVo0aJUrlwZuVwu9Hf4nmXLlrF3717kcjkdO3bk5s2bLF++XKnfyc+iuM7l9Jzwv2DRokW8ffsWHR0dTp06JYoIIiIiIiIiIiIiAioLCXllA3bt2pVFixbh6OhIlSpV6Nmzp9CQTOS/hcI+JigoiBo1aiCRSIiLixOy1Zs0aYJEIiExMZEVK1YIGe716tXL0YOzR48eNG3aFHNz82zfXb9+nREjRlC/fn0KFy5M9+7dgUy7od69e/Pp0ycWLlzIqlWrgMwXQ0WWWU6UL18eU1NTEhISBJuInKhUqRLW1takpaXh4OAgbMOvQCaTcfToUVq2bEmLFi04duwYGRkZNG7cWLAPO3DgAL1796ZChQr06NGD7t27KwWPtbS0cuwHoEAikTBq1Cj27NkDwNatW0lKSuL58+e8fPkSbW1t7t+/L2TQN2zYUAhg3b59m8OHDwNQo0YNwsLCCAoKAqBbt25/WiWS4vqTW+VAVnujkydPMmnSJI4ePYq6ujrz58/PNcitCMg1bNjw1680mRnNAQEBDB8+XKlBJGQGRhQ+0wqqVq1KkyZNkEqlhISEoK+vT5cuXZTG0dHRoXLlygA5Hqd2WUSTcWPHsnDhQr5+/ZptPFdXVzQ0NDh+/DiOjo4/ZQOhOC//zOCuyK+jT58+QtZ9QkICw4YNY+PGjYI4+kfR19fHzc2N4OBgxowZQ7FixYiLi2P//v389ttvaGtrM3/+fC5fvoyVlRWXL19GS0sLV1fXPOfb+fdzYN++fdSsWZOFCxdmq9T5o9y9e5erV6+ipqaGra0tkZGRQmVX+fLlCQsLE671165dU8rIzovq1asDCOegotG0VCoVRAZLS0uqVq1K0aJFmTZtGlevXmXTpk1YWFjw7t07unTpwqBBg3j06BGamprY29srLUNhAZNb01kRkV9Beno648ePBzKt8LLe7zU1Ndm2bRsDBw4kMDCQZcuWZeshNWrUqFzn3bNnT0xMTPD19WXhwoV07dqVatWq8fXrV7S0tEhJScHGxkboBaIqJiYmlClT5oemUdC3b18Atm3bJjROzkq7du0EYV9XV1ewffqjZGRksH37diCzj8VfQdeuXdHQ0CAlJYWFCxeSnp7+l6yHiIiIiIiIiIjI3w+VhYRGjRoxb948IYP3ezp27MijR4/48uULGzdu/GUrKPLPolevXnTo0AGpVIqXl5cQxN+5cyenTp3C2tqasWPHAghZYv369WPJkiU5Ws/UrVuXEydO8OTJEyIiIrhy5QpeXl5MmzaNvn370qBBA6FPgVwup1evXgQHBxMaGkrNmjU5d+4cEomE7t274+3tLdjF5ISWlpaQbb9169ZcqxLU1NQ4cuQIVatWJSoqCnt7e65du/aHsvGlUine3t5YWVkxYMAAbt26hZaWFv379+fmzZscPHiQ3r17K72YX716FQcHBzp37sz169fR0tJi4MCBPHz4EDc3t3yX6ejoSLly5fj69Sv79u3D398fyBR7slr0FCtWjLNnz9KnTx/kcjmxsbFUqFCByZMn4+zsjFQqxcHBgdWrV//09v8KFDYihw4dEkSEdevW5doEODk5WahQ6d279y9fn+vXrzNnzhw8PT2JjY3F1NSUKVOmsHr1aooWLUpUVBRubm7cvn1bmEZHR4dDhw4xfPhwypcvz969e4XGtFnp3r07ampqPHjwgKdPnyp9V/a7Jt6+vr707NmTkydPKh2jDRs2ZNWqVejp6REQEEDHjh0FGxVVsba2BjItsXISK0T+XtjY2PD06VPWrVtHyZIliYiIYOzYsTRs2JDt27f/MkHBxMSEVatWER4ezqVLlxg/fjydOnXi8uXLPHnyhFatWhEREUGZMmU4fPhwvv01Zs+ezeXLl2nUqBHJycm4u7tTrVo1evTowfz58zl69CgvXrz4Q8GuefPmAZnXgkqVKrF8+XJkMhnm5uaULVtWqChQiJknT55USXxTZFQrqhvKlCkjbK+1tTXLli3j0qVLSiKphoYGdnZ2nDx5kvr165OQkMDly5fR1NRk48aNgjWZAkXV3c6dO/+0qjARkXXr1nHnzh0KFiyY4/2+QYMGrFmzJld7LScnJ6RSKVevXmXJkiUsXLiQyZMnY2dnx6VLl7CyssLR0ZFFixZx5swZPn78iI6ODmlpaRgYGLB169YfquxUV1dnwYIFP53Vb2dnR6NGjUhLS2PBggXZrASbNWvGjh070NDQ4PDhwwwfPvyXBNzPnTvH8+fPMTQ0FHqj/K9p06YNXl5eaGhocPDgQVxdXUUxQUREREREREREBPgBIWHgwIHs2LEDCwsLRowYwatXr/7M9RL5hyKRSNiwYQNGRkbcv3+f+Ph4wdd21apVXLt2jS5duuDq6kqZMmVYvHgxzs7Ogq1QXujr62NpaUnnzp2ZMGEC27dv59KlS0RFRREREcHr16+ZMWOGYKMRExND+fLl2bJlC+PGjUNfX5/g4GB27dqVazZr165dMTU1JTo6Os+qBGNjY06cOIGFhQWvXr2idevW2NjYcODAAaRSqcr7Ky0tDS8vL2rXro2rqyuvX7/G2NiYqVOn8uzZMzZu3ChktGZl0aJFSgKCk5MTd+/eZdGiRUrVG0uWLKFEiRLcu3cv2zzU1NQYPnw4kBkgUAgJOQX19PX1Wb16Ndu2baN///4sWLCAESNGkJycTMuWLVm/fr1Kv+GficLeCBBEBDs7u1zHP3v2LPHx8ZQoUeIPW7tk5c2bNwwbNoz+/fsTHh6Ovr4+gwcPZt26dTRq1IiyZcuyYsUKqlevTnJyMosXL+bgwYNCAFBTU5P58+dz48aNXNerePHiNGvWDIDDhw/nWjHmsXYt5cqVIz4+Hnd3d5YuXaoUDGjQoAG+vr4YGRlx8+ZN2rZtKzR7VoVixYpRrlw55HI5t27dUnk6kb8ObW1tXFxclASFqKgopk6dSv369X+poKCurk7jxo1Zvnw5np6euLm5MW/ePDIyMhgwYAA+Pj5UrVpVpXlZWVlx7tw59u7dS5kyZfj8+TO+vr4sXbqU/v37U7t2bczNzWnUqBEuLi54enoSGBhIeHg4oaGhPH/+nIcPH3L79m2uXbtGQEAAvr6+HD16FA8PD8HjfMaMGYSHhwsZwba2toSHhxMeHo66ujoDBgygSJEiJCUlcfr06XwD9wohISkpSQhEDh06FMgUI0aNGpVroNPY2Ji9e/fSu3dvTExM2LhxI23atMk2Xvfu3dHX1+fly5dcu3ZNpf0pIvIjhIWFMXPmTCDzuaJ48eI/NZ8PHz6wZMkSpk6dysyZM1m+fDl+fn58+PABNTU1qlWrRr9+/Vi6dCnLli0Tzq9ly5YJ/RlUpVq1aj88TVYkEgkzZsxAT0+P4ODgbFWFkFmZ8avFhLVr1wKZPecMDQ3/0Lz+CA4ODqKYICIiIiIiIiIikg2VhYSRI0fy+vVrduzYwd27d6lUqRJdu3bN1vRPRMTMzEyoRFi4cCHdunWjffv2ZGRkMHfuXB49ekTv3r3x8vL6ZQFchfd/3bp1uXz5Mrq6uixevJgtW7ZQtWpVkpKSWL58OSNHjmTLli1Mnjw5x2CZqlUJkGnrEhAQwODBg9HR0eHevXsMGTKEOrVr57u+qampbNmyhTp16jBq1Cjevn2LiYkJixYt4unTp8ycOZNixYrlOO2rV6+UXjTv3r3L0qVLs73Yv3//nuXLlxMXF8fy5ctznNegQYPQ19fnyZMnnDlzBshZSFBgb2+Pq6srI0eOJCEhAWtra7Zt26aSx/mfjYaGBkOGDMHQ0DBfEQHgwIEDQKalQk7VMD9KbGws8+fPp3379gQEBKCurk6rVq3YtGkTnTt3VrJXKliwIPPmzaNDhw7I5XK8vb1xcnL6IbuWDh06YGBgwIcPHwQR6HssLS3x8vJixIgRqKmpceLECWbNmqXUd8Ha2ppz585RrFgxHj16RMuWLXn+/LnK66GwhRLvBf8ssgoKS5cuxdzcXBAU6tWrx5YtW5R6dvwRzp8/T506dbh06RL6+vp4eXmxY8eOH25GKpFI6Ny5M/fu3ePMmTOsWLGCQYMGUbduXfT09EhJSeHevXvs3r2byZMn0759eypWrEiVKlWoWbMm9evXx9bWlvbt29OxY0e6detGnz59mDJlCpB5PSxbtixLliwhLS0NCwsLSpcuLVQj1KxZk8KFC9O5c2c0NDR48+ZNvgKaQkjIyMjg27dvQGYFp0Kwzq9Jvba2NkuXLuXu3bs5igiQef/r0aMHADt27FB9h4qIqIBcLmfkyJEkJSVhY2ODs7PzT81j165dWFpacvr0adTU1KhevToDBgzAw8OD06dPs3v3btq3b09oaChz5szBzc2N1NRU2rdvL1Td/AiKatU/gpmZmWAVuX79+mx2hJBdTBg5cuRPB9wfPnxIYGAg6urqQqLHX8n3YsKQIUPw9vYWhoMHD/7VqygiIiIiIiIiIvI/5oe6v2poaNCrVy969erFlStXWLVqFU2aNMHa2prJkyfj4ODwSwJyIv+PqjYFcrlcpX0vl8tV8rHPqydGVqRSaY7+83379sXHxwdfX1+cnZ1ZsGABsbGx3Lx5k2nTpuHh4ZGjn2xqaqpKFimXLl0SMv+jo6PZvHmz8IJXvXp1+vbti4mJCZ6enrx69YoXL14IooCamhoPHz7E0dGR6tWro6amRoMGDYTtqFu3LkWKFCE6Opo1a9Yo2d5IpdJsmfdTpkzB2dmZgwcPsm/fvsxmgoYFARg9ejSDf7epgcxGzXv37sXT01NoIGxiYsLQoUPp2bMnurq6xMXFERcXp7SMb9++CXZDCxYsID09ndatW7N48WJhvQDi4+OFaRTBMAA/Pz8ePXqEhYUFkOl1rKmpib6+Pv369WPz5s3I5XKhwaBifjKZTOn3DQ0NpUePHnz58oUaNWqwe/dudHV1/xQ7DalUKqyHYv7p6elKFR/x8fFK6zdo0CAGDhyIRCIRrEQge1Pi6OhoLl68CGQKJ4qAaVhYGNra2vmuW1hYGMHBwUBmgPD69eucPXuW5ORkILPXQceOHVFTU8sx8KCgUaNGaGpqcurUKU6dOsWDBw8YP348RYsWzXH8yMhIpeBI+/btOXz4ML6+vpQuXRpTU1PUs+yfM2fOkK6hQaFChejSpQvHjx8nMDCQt2/f4ujoiKmpqbA/PTw8GD9+PK9fv8ba2prx48djZ2eHRCJBIpHkagtWq1YtvL29uXr1KvHx8ejq6ua7/763iMiLv6LS5UeO5z+rL8ivQi6X57k9WlpaDBgwgL59+7J//37WrFlDREQEM2bMYObMmVhZWdG6dWvatm1L6dKlVQqQPXv2TLgfXbx4kZkzZyKVSqlYsSKLFi3CwsKCoKAgYmJilGzUlEhJQXEmvnz5kg/f2W7p6OhgZWWFlZUVkHlMhYaGEhcXx8uXL3n58iUvXrwgKioKDQ0NtLS0hEFdXR1dXV00NTWFz4yNjenZsydnz55l165dQKbgd+TIEcLDw5FIJKSlpfHbb78BmVVB4eHhBAYGYm1tnWfDdh0dHVJSUnjz5g3v378HMqvfNmzYwLp166hfv77S+DExMTn2DPr+d5TJZMJ9rV+/fuzcuZPDhw/j5uam5AuflpaWa3+ZrGRkZKh0/QN+6bPev+l8U4Ufea5TZXszMjJUGi89PV2l3y01NVVpfjt37sTPzw8tLS3Wrl0rPFd8/fpVJauhs2fPsmnTJqGfUtmyZenZsyfx8fGEhIRw/vz5HPvs6OnpUa5cOWrWrCkkp/woqojinz9/5s2bN7l+r6enR6lSpQgLC2PkyJFs27Yt2/5u0KABq1evZvz48ULlQk6VmvLfnxEAkhIT0fhOTPX09ASgS5culCxZkoyMDGHIj/T0dJXPjx+5ryrEhEGDBnHs2DHBElJEREREREREROS/yQ8JCVmxsbHBxsaG169fs3r1avr160fx4sW5ceMGhQsX/pXr+J/m7/7SrKGhkeuL5JYtW6hWrRr379/n7t27+Pn50b59e27dusWsWbO4dOkSJUqUUJrm8+fPuTbGzUp0dDTa2trcvXuXQ4cOkZqair6+Pt26daNOnTpIJBI+ffqU4wuP4oUsNjaWK1euAP/fX0HB2LFjmTVrFnv37qVDhw6CEPDly5ccX8SLFCnCyJEjcXZ25uzx47DHG4C9e/eyzdubVq1aUa9ePXbs2CFYx5iZmTFw4ED69++fb5DHwMCAQoUKcfPmTXx9fVFTU2Pp0qU5ZtxpamoSERHB/v37AShZsiTv3r1j7969gg941oDC8OHD2bx5M5DZ2C/r71m0aFE0NTV5/Pgxy5cvZ//+/chkMipXroy/vz8mJiZ5rvcfQVNTUzgWFOeBurq60vFhamqqsmdy1uzn06dPk5GRQf369ZV6EBgZGVGwYMF853X9+nWMjY2Jiori1KlTQhCkWLFitG3blnK/9yn4/Pmz0Kg6NxwcHChQoAAnTpwgPDycWbNmMXz48Bx7I5QvX17oSwCZHs4fP37k0qVLBAQEsHv3bjTT02Ff5m+f/O0bst/3j4WFBZ06deLUqVOEhoayZ88eevbsSd26dYHM43H9+vUsXLiQe/fu4e7uzp07d5gwYQJ6enq5BhibNGkCQHBwsMrXq7/7de3fRNbzKDeKFSuGRCJh0qRJjB49mh07drBt2zbu3bvH3bt3uXv3Lu7u7lhYWNChQwc6dOhAs2bNcj0m0tPTkcvlnD17lkWLFpGenk7z5s2ZPXs22traghihr6+f7R6gQJ6cjKIjk5pETSUBo0CBAlhZWdGiRYs8xwsLC1OygMvKmjVrSE9Px9ramps3b/7/+sjluTYzvn//Pr169cpVTDh79iyRkZFK18tevXqxceNGrly5QmhoqFLg39jYONf9kpWUlBThXGrYsCEVK1bkxYsXVKtWjbJly9KsWTOaNm1Ko0aNhCbbIv8+FGJvfqipqak0nmJ+SUlJTJkyRbD5mjJlCpUrVxbG09PTy7MaUS6Xc+DAASZNmsS3b9/Q0NAQKg7c3d2zjW9oaIiFhQXm5uaYm5tTqFAhJBIJMplMKcFkzJgxNGrUCIDbt28riQytW7dmyJAhSCQSQkNDVTruHz9+nLug+Tt2dnZs3bqVq1ev4uvrS/fu3bON06lTJ7S0tBg5ciRHjhxBIpGwceNGpaB9Rpb/a2lrK11Do6KihAz/8ePHC9+pKryrqan9UoEvq+WaIjln06ZNShWNUqmUc+fO/bJlioiIiIiIiIiI/P1R+YnTy8uLBQsWMGnSJIYOHUr37t1p3bo1vXv35ty5c6ipqfHy5UulLGCR/zZmZmZ4eHgAmZ7+b9684ejRo1SqVImIiAjs7e25cePGT2Wzp6WlsX//fvbs2UNqairlypXDzc0NKysrJBIJDx8+zPFFNTe+Xwc7OztsbW2RyWTMmTNH5b4HWlpa2Ds4CH+3bt0aiURCQEAA7u7uREdHU6JECZYvX87du3fp3bu3SpmiinVU9JsYMGBAnt7iHh4epKWl0ahRI5YsWQKAt7d3js1Bq1SpQrt27YDMoHZWrl27hoODAzVq1GDPnj3IZDJatWrFuXPn/lQR4c/E19eXNWvWAAh2ID+KIjN5y5YtREREoK2tTYcOHXB1dRVEhB+hRIkSzJ49m1KlSpGYmMjKlSs5f/58vueGRCJh1qxZFChQgKdPn+Ll5ZXn+KVKlcLR0REdHR0+fPiAt7c3Hz9+FL4vXLgwK1asYOjQoairqxMQEICzszMhISG5zrNChQoYGxuTnJzMw4cPf2i7Rf5+aGtrM3z4cIKCgnj//j2bN2+mY8eO6OjoEB4ezsaNG+nYsSPFihWje/fu7N27N8cg/7Fjx4TqKTs7O+bNm6dytvtfxdu3bwWLN1dXV5WnS01NZc+ePbmerwpxMmulWcmSJWnatCkAo0aNyrNx8/nz53FycuLYsWO53oskEglr1qyhXr16qKur8+bNG3bs2MHAgQOpUKECderUYcKECZw4cYKYmBiVt03kv0lwcDCNGjUSRITx48fj5uam8vRRUVH06tWLESNG8O3bN8qUKYODgwP+/v48fvwYdXV1KlWqhIODA5MnT2bLli106tSJdu3aYWlpibGxsUqiR926dYVKSysrKwYPHvynCNWFCxcWrMXmz5+vdN/MSvv27Vm9evVP9UzYuHEjUqmUhg0b0qBBg1+27r+KNm3acPToUU6fPi0Mhw8f/qtXS0RERERERERE5H+MykLC3LlzmTdvHmFhYRQpUoQ6derQuXNnxowZw4oVKzh58iT37t3LNctP5L9Jv379sLe3RyqV4uzsjKGhISdPnqR48eI8e/aM5s2bU6tWLdasWZPri9n3hISE4Onpyc2bN5FIJLRt25aRI0diZGREYmIiXl5ebN++XcnmJz9CQ0OVgkCKJnuGhoY8e/ZMsLr4UbZv387NmzcZOnQoNjY2rFq1itu3bzN48OAfDqqdPn2aGzduoKury6xZs3IdLyIiAm/vzIoINzc3WrVqRenSpYmLi8uxWSDA7t27OXv2LF26dCEjI4PTp0/TvHlzWrRowenTp5FIJDg6OnL79m3OnTunUrbs3xFfX19Gjx5Neno63bp1o2vXrj88j+vXr+Pt7c3169fJyMigWrVqjB49mvr16/+hbEBjY2OmTZuGtbU1GRkZ7N27l6VLl/L27ds8pzMxMRE83jdt2sSr16/zHN/U1JQePXpgYGDAly9fGDVqlJL9krq6Ov3798fDw4NixYoRERHBqFGjWL9+fY6ZkRKJRAh6iA2X/12Ym5vj4uLCqVOn+PLlC8eOHcPJyQkzMzOSkpI4fvw4gwYN4tChQ0rT7du3j+XLlyOXy3F0dGT69OkqVw79lWzZsoWMjAxsbW2pVq2aytNJJBKCgoK4fPlyjt/nJCRApk1d4cKFefr0KWPGjMkx4BgYGIi9vT1eXl44OjpiYWHB5MmTefbsWbZxmzdvzuXLl4mMjOTo0aOMGTOGmjVrAvD06VM2bNhAz549MTc3x8bGBl9f3z/Flk7kn0tGRgZr1qzB1taWFy9eYGZmhp+fH4sXL1bpHJbL5ezfv5+GDRty7tw5tLS0aN++PXp6ehw9epSUlBQqVarEihUrmDdvHn369MHKyuqnmwqrqakxYcIEBg0axJgxY/5Ue1VbW1uqV69OXFxcns9gbdq0+eEGzF+/fmX9+vVApmgjIiIiIiIiIiIi8ndF5SfuTZs20aRJE3x9fYmPj6d79+6MHDmSfv364eDgQLNmzahdu7ZKtjQi/x0kEgmbNm3CyMiI+/fvs3LlSkqVKoW/vz8DBgxAT0+P58+fM3XqVMqWLYuLi4uSncT3HDhwgHbt2hEdHY2hoSEjRozAzs4OdXV1goODWbx4Mffv30dNTY1WrVqpvJ7v378XMu8UmJiYMHnyZCAzwHT9+nWVKxOyUq5cOZYsWcKxY8cYMGDATzUmlslkzJgxA8jsu5CXYLdmzRrS0tJo3LgxTZo0QV1dHScnJyCzgXRO/S8KFixI8+bNOXr0KFZWVnTr1o3r16+jpaXF0KFDefbsGYcOHRJscP6JfC8iLF++/Id8gj9+/Mjs2bOZNm0aCQkJGBkZ0bdvX3r06KFkAfBH0NLSwtnZmZ49e6KpqcmLFy+YP38+27Zty1MY69ixI02bNkUmk7FwwYJ8l1O4cGF69uyJsbEx0dHRjBo1Kltg0tLSku3bt2NjY4NMJmPWrFn07t2b2O+86gFRSPgPoKenR8eOHdm0aRNv377l5s2bdOrUCcjswaJg7ty5bNiwAcisnBo/fvwvD+4lJCRw584doXnxr+Dly5dC0/Jhw4YJfu6qUKpUKQAOHTok9EDIiiJI+r2QUKJECbZu3Yq2tjYXLlxgwXfn7t27d+ncuTOpqanUq1ePYsWK8fHjR1atWkX16tVp3rw5u3btyhakNDQ0pH379ixdupSbN28SGhrKgQMHcHV1pXLlysjlcu7cuUO3bt3o0KEDL1++VHlbRf69vHnzhnbt2jF9+nSkUin29vbcuXOH5s2bqzT9x48fhSqEuLg4ateuzYQJEwgMDOTJkydCP5Y5c+ZgZmaW57xiYmK4cOECx48f5+7du3k+e5mamtKuXbs/veJJXV2d5cuXo6GhwZkzZ5Sue9/zfQPm/MSEtWvXEhcXR7Vq1ejcufOfsPYiIiIiIiIiIiIivwaV3+7btWvHxYsXuXz5Ml++fKFatWo4OjqKgSORfDExMcHGxgaAlStXIpfLKVeuHFu2bOHt27esW7eOunXrIpPJ8PPzo0uXLjl6rkZHRzNu3DiSk5MpW7Ysbm5ugv98fHw8Xl5eJCUloaamhqurK/b29j+0nhs2bOD8+fNKn2W1OBo1ahSdO3emb9++rFy5ksDAQJUaQ/8Kbt26xYsXL5BIJIwYMSLHcWQyGUuWLBHsbRQiCECfPn3Q09MjJCSEBg0asHPnTqExsIKQkBD69OnD06dPKVCgABMnTuTFixds2bIlX5//PxuF+HLo0CESExN/eHq5XM7UqVOFF/mIiAh27typ5PWbG4rAXe/evbl06RLq6upYWVkxcuTIP2W/KKps3N3dhX4I169fF7yTc5tGYXGU1YYor2xjQ0ND+vfvT8WKFYmLi2PkyJFs3bpVaN4KmR7JCxYsEPya/f39mT17drZ5KYSEM2fOMHXq1GzHlsi/CzU1NaysrAT7nxs3bgCZWe+LFi0CwNnZGVdX1z/FZsTT05Np06bRs2dP1qxZ84cD4VKpVLDCa9WqFRUrVuT06dMqT29gYICGhgZSqVRoxqxALpcLzd5zEj5q164teLzv2rVLScBwc3MjMTGRFi1acPHiRcLCwjh27BgODg6oq6tz+/ZtXF1dGTp0aJ5BSmNjYzp37syaNWsIDg4mNDSUAQMGAHDhwgVBaBb575GRkYGfnx/29vZUrFiR8+fPo6ury7p16zh48OAP9TybM2eOUIUwa9YsNm7cyJIlS4QqhKVLl2JnZ6eSsHj06FHu3bvHq1evuHjxonCN+aupVq0aw4cPBzJF05SUlFzH/V5M+D5ZJSs7d+4EYPr06X9qVYXIHyc8PJx79+7lOeRUMSYiIiIiIiIi8m/hh59W69aty8GDB3n+/Dnm5ua0atUKW1tbTp06JZbIi2QjMjKSVq1acerUKQBcXFyUAkuGhoYMHTqUq1evcufOHdq3b09GRgbDhg3j0aNHSvMyMTGhVq1aQKZwkPVly8DAgHr16gGZL8a7du36oRdPhVXPvHnzhMa5kBmgnTNnDm3btsXIyIi0tDTu3LnD5s2bGTZsGPXr16dLly48ffr0x3bMD1KtWjXMzMyQy+XZslYhcz+3a9cODw8P5HI5w4YNE5rgQmbFwZo1ayhcuDBhYWFMnTqVChUqsGTJEiHD3MLCQmhM2K9fPxYvXpxv1uD/ihEjRqCjo0NAQAAtWrTIMes3LyQSCcOHDxcqOW7evMmCBQs4evRontP5+vrSu3dvTpw4gUwmo3bt2mzdupUmTZr8VGXJj2BsbIyLiwutW7cGyHd5JiYmLFu2TCnw4+PjQ2RkZK7T6OnpCTYW6enpeHt7M2TIEKKiooRxJBIJnTp1Euy9zp07l83iqH79+vTu3Zv09HSWLVtGrVq1crV5Efl3IJVKheqxsmXLApmZ+Yqmpbk1Hv6jyOVyIdienJzM6dOnGT58OCNGjOD8+fMqVykkJiZy6dIlVqxYQa9evXj06BEGBgaMHDkSQOgbowqPHz9GJpOhr68vNIFVcPbsWYKCgpBIJEqN0rNiZ2dHly5dADh+/LjwuUKQGzVqFDo6OmhqauLg4MCxY8cIDw9n7ty5aGhocODAgXzFBIBPnz5x8uRJPDw8OHnyJJB5XRkyZIjK2yry7+G3336jUqVK2Nvb4+fnh1wup02bNly/fh0nJ6cfFgEVFQHDhw9nwoQJWFhYCP0LSpYs+UPPE4pnPQWVKlX6oXX5Mxk9ejRmZmZ8+PAhT4EfMsWExYsXA7B8+XISc+kjp7i/Fy9ePN/lf/78mb1797J3714OHz7M8ePH8fPz4/z581y6dIkbN24QFBTEw4cPlZ5nRf444eHhVKlSBSsrqzyHfv36oaenR5EiRf7qVRYRERERERER+eX8dNpL6dKl8fDwIDw8nHbt2uHi4kLVqlXFJn4iAufOnaN27dpcvnwZAwMDdu/eLWSr5oSlpSUbNmzA1taWb9++0a9fP6UgqJqaGrt376ZEiRJ8/vyZHTt2CDY9ampq9O3bl1GjRmFqakpSUhIHDhxQeV1Lly6NpaUliYmJTJs2TamMvlChQri7u3P+/Hm2b9/OokWL6NatG2XKlAHgyZMnDBo06E8VE4yMjNi+fTsSiYTt27crBcD9/f1p0KABV69eRV9fny1btuS4n7t27cr9+/dZsmQJJUuW5NOnT8yZM4fy5cszdepUYmNjBY/eTZs2cf369T9te36UZs2a4e/vT9GiRXnw4AE2NjYEBwf/0DxGjBjBtWvXCAwMFHojfF+BkpWvX7+ybt06ZDIZtWrVwsPDgzVr1vxUM+U/gsIOpVixYvmO26hRIw5n6YOhCHT4+vrmWj2jr6/PggULWLBgAUWKFOH9+/fMnz8/mwVWs2bN0NfX59OnTzx58kTpO4lEwvr169m7dy/Fixfn5cuXNGvWjOHDh/9QrxKRvy9JSUlcuHCB+fPn06ZNG4oUKcK8efMAhCak+vr6zJ07F4AdO3b8Kb99ZGQkcXFxaGpqsnTpUpo3by7YgG3ZsoXWrVuzcOHCbA3C09LSuHv3LuvXr2fKlCm0atWKSZMmcfDgQcLDw9HW1mbBggWCqPyjzU6bNm3KggULqFKlivDZ3bt3OXbsGAC9evWiatWquU6f1SZKcf9RiIKfP3/ONr6pqSmTJ09mz549OYoJcrmcly9fsmvXLkaOHImlpSUlS5akR48erFmzhq9fv2JlZcXNmzcZNGjQD22ryD+fFy9e0KNHD968eYORkRHjxo3j2bNnnDlz5qeD9gpB8d27dwBCZQNAQEBAtuSQvKhbty79+vWjadOmjBw5UqX73/8KHR0dQXBcv359nlUJAIMHD6Z8+fJ8/vyZrVu35jiOwh4tLCwsz3ldv36dmjVrMmDAAAYMGECvXr3o1q0b9vb2tGvXjhYtWtCkSRPq169P7dq1KVWqVK69sUR+nM+fP/Pt2ze8vb0JCgrKc3j27JkgpImIiIiIiIiI/JtQufvhwoULCQsLIy4ujvj4eOLi4pT+n5SUxIcPH4iPj8fY2PjPXGeRvzkymYy5c+fi7u6OXC6nZs2aeHt7U6FChXyn1dTUZNu2bdjb2/P8+XP69+/PyZMn0dfXB6Bo0aLs2bOHdu3a8erVKw4dOkTv3r2FzLkKFSrg5ubGlStXOHPmTL4veArU1NRwd3end+/ePH78mHXr1mVreCeRSLCwsMDGxobu3bsDmRmeI0aM4MGDBwwaNAgvLy+q/P4y/atp0aIFEydOZMWKFYwYMYJatWqxa9culi1bhlwup0aNGmzcuDHPIICenh5Dhw6lX79+BAQEsGLFCh4/fszq1avZuHEjR48eZcCAAezevRsXFxfu3Lnzt+l7Ur9+fa5cuULnzp159uwZLVq0YOPGjbRv317leUgkEsqWLYuTkxNHjx7l6tWrpKam5uitvG/fPr59+0aFChVYvXr1X2Y3EB0dDagmJADo6eoK/69SpQr3nj7lxYsXvH79GgcHB0qXLp3jdLa2tlSqVIkhQ4bw9OlTvL29lQKMWlpaNGnShN9++43AwEAsLS2zzaN9+/bY2dnh5ubG1q1b2bx5M6dPn+bIkSM/HJgV+TXY29srNUlVV1enUKFCGBsbU7hwYWEoUqSI0t/p6elcu3aNK1eucPXqVe7du5dNXCpUqBAtWrRQOk4GDRrEsmXLePPmDbt27WL06NG/dHsUgm2FChWE7M+vX7/i7+/PyZMniYqKwsfHBx8fH6pWrUqjRo148uQJ9+/fz3Y/sLCwoF69etSvXx8rKyuhKfKPUrNmTfr27av02evXr9mxYwcALVu2zNdnvnHjxhQuXJgvX75w/fp1ypUrh4mJCZCzkKCgc+fO7Nmzh/79+3PgwAFiY2PR0tLi5s2bfPr0Kdv4in1ia2tL165d/xFNsEV+LcnJyfTq1YvExESaNm3KqVOnhGesP4JCSAgNDRU+s7GxoWXLlpw/f57NmzezfPlydLPco/LC1NRUqHD6FcjlcqRS6S+pJuzZsyfr168nKipKsFhTJJd8j4aGBjNmzGDw4MFs2ryZPsWz97hS3Jffvn2b6zI3b97M2LFjkUqllC1bljJlyiCVSpFKpaSlpZGamopMJiMtLY20tDS+fftGTEwMrq6uNGrUSKVqBxHVqFKlCnXq1PmrV0NERERERERE5C9B5TfIu3fvkpSUhJGRESVLlsTS0hIjI6Nsg/ig+t8mIiKCfv36cenSJSCzaeXq1at/qES+YMGC7NmzBzs7Ox4/foyLiwu7du0SAh5Vq1alb9++7Ny5k1u3blG0aFGlxsrq6uo0a9aMOnXqMGvWLJWXW7x4cebOncvEiRPZs2cPdevWFXo75IaJiQk7duxgyJAhgpiwa8sW8pdMfo45c+Zw+fJlbt++jZWVlWB94ezszPLly1X2ptfU1KR379706tWLs2fP4u7uzq1btxg4cCD+/v74+/vz8uVLFi5cyLJly/6krflxSpcuzcWLF+nduzcXLlxg0KBBLFy4EGdn5x+aT7Vq1ShatCgfP37kzp07SjZQkNkXQZFJ7Ozs/MtEhMTERN6+fUvVqlVVmqdcLv9hISErLVu2pGqdOly8eJH379/j5+dHnz59MDIyynH8YsWKMW7cOBYuXMju3bupX7++UhZ18+bNBSFhzJgxOc6jYMGCbN68md69e+Pi4sKrV69o0aIF+/fvx8HB4Ye3QeSPceXKlV82r5IlSwpN3Js0aUKVKlWyHcfq6uqMGDGCSZMmceTIEbp27Zpnc/gfRSEkZD0ujYyM6N69O82bNychIQEfHx/Onz/P06dPlSrFChcuTP369Slfvjxt2rT5ZUFKAwMD4f9yuZzQ0FA2bNiATCajRo0agvCcE3K5HIlEgoaGBu3bt8fb25uTJ0/Sv39/QUjISRDISlYxIWuPBm1tbaysrGjQoAG2trY0aNBATPQQYcKECTx48AATExO8vb1zFRHCwsLw8/PD19eXjx8/MmbMGPr27Zvr85xCSHj9+rVwXAP06NGDhw8f8unTJ/bu3cvQoUP/nA3Lh61bt3L+/HkmTZokWGH+LNra2ixfvpwRI0Zw//592rVrx5w5c+jdu3eO4zs4OFCrVi2eP3iQ4/d5VSSkpqYyfvx4tm3bBoCjoyPbt29Xuu5Apq1n1uuxVCqlcePGBAUF4eTkhJ+f35/Ss0ZEREREREREROS/hcpCQlbfXpH/Har2ncj60vYr5peenq7S/JKTk9HQ0CA9PZ3t27czd+5cEhISMDAwwNPTE0dHR9LT00lLS0NHRyff+aWlpSGXyzEzM2PHjh10796dgIAA1qxZoxS4LFWqFB07duTUqVOcOnUKQ0NDqlevrjSvH8203LhxI5AZZH7y5Alubm7Zgk3m5uaEh4dnm3bo0KGsXr2aN2/eMGTIEK6YZ9pjREZGQh7bHRMTo7RfXr9+zeXLl+natatSdmx8fLww3urVq2nXrh0JCQno6+uzdOlSOnXqREJCAqGhoSpl+8XFxQmB6XLlyrF+/Xr69u3L8+fPGTp0KG5ubowfP55Vq1bh6OiY70t3SkpKvsfLjzbgzcjIyObFD5l9NY4fP87YsWPZuXMn06dP5/Xr18yfPx91dfUc5yWTybLNq1mzZhw6dIgLFy4IvuZnzpzh06dPBAUFkZaWRpEiRbh16xa3b99WmjYxMVFooJoXZcuW5cOHD8Lf+/bt4/Hjx9SqVQtHR0fhpT8mJibHhrGJiYnCvo2Li0MikeTfbDo1lUK//zcjI4PChQvTqVMnfHx8+PDhAydPnqRnz55oamoSERHBpk2bss2iVKlShIWFMXnyZNq1a4elpSXm5ubUrl0byGyu+/79+2zH2ufPn4VApbm5OYcOHWLcuHHCMT1r1iz69OmDmppartmbWUlPT8/1N82KXC5XeTxVr5P/loCLh4eH0u8kk8mIjY0lNjaWmJgYYmNj+fjxI/Hx8cLnSUlJAFSsWJH69evToEEDGjRoQJEiRZQC0bmd01WrVsXKyoqgoCDWrVuXq6CbmJiYew+PlBQUtVBvw96i/imz58L9+/eBzGqI78+ZmJgYChUqhIODAy1atODmzZu8e/eOUqVKUblyZczMzJBIJISEhHD48OF8952qSKVSLl26REhICC9evCDhdx90c3NzevToIfydlJTEuXPnhGn27t3LkydPGDFiBGXKlBE85P38/Pj27Zvgr/3p06cc791ZeyI4ODhw6NAh9u3bR82aNWnUqBG1a9dGW1tb6f6b3zPAr+x19aufX/5K5HK5StvzK5/Dvg8M5zU/VZb76dMnjh07xpYtW5BIJHh6eqKhocHHjx+F+Tx8+JBjx45x+fLlbA1jnZ2d2b9/P4sXL6ZYsWIkJiYq9eRRPCvFx8cTEREhfCeVSunfvz+rVq0iICAAS0tLJQswBcnJyULPprxIS0tTyXpRXV1dsCK8efOmILJ5eHiwZMkS4XyLi4tTqbeKqampkqWioaEhq1evZs2aNTx69IipU6dy9OhRhg8fnmO2+vDhwxn3e4N6gKjISAr8fo4rLHBCQ0OVzuvIyEgcHR2FPiuzZs1i9OjRSKXSbPvq27dv2cSFdevW0bx5c86dO8eaNWuEfiiFChVCRERERERERERE5GcQa9r/5qj6ci2RSFQaV9XMag0NDZXmp66uzqNHjxg9erTQALNevXps3bqV8uXLC+Npamoq2cdcuHCB/fv3s2DBAqVAfdGiRYV1bNWqFZ6enri4uLBmzRp69OhBtWrVAOjduzcDBw6kcOHCeHl54ePjQ48ePahZs6bS+jVs2BBjY2PS09M5cuQInp6exMfHo62tzdSpU+natSsSiYRRo0YJJeWmpqaEh4eTkJDAqVOnKFasGBUqVEBXV5e4uDihIXFW9PT0GD9+PKtXryYqS1m/jq4ukjyEBHNzcyET7dmzZ/Tr14+YmBj279/P3r17hQB+dHS0ECgtU6YMXl5ewgtr2SxWShKJJEebnu+RyWRKwQwtLS1WrFhBz549uXv3LnXq1KF9+/acOXOGoUOHcufOHZXmqyA6Oprz588rWaH8qJCgpaWVqwWBlpaWcIzNmDGD7du3ExUVhbe3d7YXacjMkv/eosnR0ZFDhw5x5coV4TcNDAxEKpUK1gz6+vo59r74PsCSG3p6eowYMQLIDPopelcEBwdjYmLCtGnThN81p+zoFy9eAJmZ1FWqVEFbW5uKFSsK36empvL48WPU1NSE/ZV1n02ZMgXt37d9/PjxNG7cmOjoaD59+sSuXbto0qRJjue5kZERkZGRJCYmcvHiRbS0tNDT06NatWoUL16cyMhIHj16RLNmzZSm+/76oq+vz8aNG5k7dy6HDx9m3rx5REVFMWHCBJWuL39VcFHV6+k/AXt7ewoUKJDnOF++fFEaJy0tDalUmi1TOTU1VSXBxtLSkpUrV9K8eXMuXbrElClTchQj379/n2tTZrmaGoqrh1HBghgWLUpycrIgPDRq1EgpiAkQFRUlCMhGRka5Nky+cOGCsL0pKSm8ffuWxMREypcvr1StM3DgQGbOnKk0rVQqRU1NjYcPH3LlyhUuX77MnTt3lMRGXV1dmjRpwsyZMylatKjw+bFjx5BKpSQmJrJlyxbevHkDwJEjRxg3bhwlS5akUKFCxMbGcvbsWaEi4cuXLzneu/X09JSO006dOgm9FrKira39SwPSv5p/0/mmCqpuq5qamsrXSVXGe/36NZMnTwZg3LhxNG3alNTUVK5evcq5c+fw9/cnKipKafl169aldevWpKSk4OnpyYULF2jTpg1z5szB3t5e6X5rYGCAubk5ERERfPjwQXi2adGiBXZ2dnz8+BFvb28OHTrEmTNnst2rjY2Nc62Wy8r+/ftVsicyMjLC3t6eyMhIXH8P4BcsWJC4uDi2bNnCpUuX0NfX5/Xr1yqdH8nJydmeI4oXL86SJUs4duwYXl5e3L59m+fPn+Pu7p7NzszW1pZ69evD18y+RxqamsJyFdZG4eHhwmfXr1+nZ8+efPjwgYIFC7Jt2zZatGiR5zp+fxxUrlyZOXPmMG3aNGbNmkXTpk2Vns1FREREREREREREfpS/xvRb5F9BUlISM2bMwMbGhqCgICE7KyAgIM8XldjYWPr06cPOnTvp06dPNt/trPTt2xc7OzukUikuLi5KTZABZs6cSbNmzUhJScHJyYm1a9dy/fr1bEFrdXV1evbsycmTJ2nSpAmpqanMmzePadOmCdm3CtTU1KhTp45gxxEdHc3169d5+fJltuVnRSEmlM6Saf19w8/cCAsLo0OHDsTExKCmpkZYWBjNmzfH09Mzx+zFhg0bsnz5ciUR4Y9SqlQp5syZA2RaALRs2RJjY2MeP37M4sWLVZ7PsWPHsLKyYujQobi6ugrD9z0n/igSiYRJkyaxf/9+dHR08PPzo0WLFkRERGQbNz09nYSEBKKjowkNDeXJkydUrFgRDQ0NXrx4watXr4RxFTYiBgYG2YKccrn8hwURBa9evSIpKQktLS3U1dXx9/fH3d1dKfvwe/KzNRo/fjzt2rWjTZs2NGvWjEaNGinZcZUqVYqCBQuir69PixYtmDZtGhoaGhw+fBgPD49cl6uhoSFUDHz+/FlonimRSGjatCmAYF+WF6mpqaSkpLBgwQKhomjLli24ubmRlpaW7/Qifw1aWlp/2DO9WrVqgs3HnDlzfkmm+8uXL8nIyBD6OfwR0tPTCQ8PJzg4mNjYWKRSKS9evMgzM/nRo0dMnjwZa2trevTogaenJw8ePEAul1OhQgWGDBnCjh07uHnzJmvXrlUSERR8+vSJlStX8ubNG3R1ddHQ0ODVq1e8ePECNTU1rKysADhw4IDK1kYiIvmRnJzMsGHDSEpKolGjRoKFY7Vq1ejXrx+7d+8mKioKPT09WrVqxapVq7h37x5Hjhxh2LBhjB07Fl9fX2rUqEF8fDwTJ07ExcVFSXgAhAqA169fZ1uHKVOmUKJECSIiIliyZMn/ZLvlcjmurq7ExMRQs2ZNdu7cSbFixXjy5AmjRo36JdclNTU1unXrxtq1ayldujRxcXGMGDGCOXPmZLueZO0Zk7WiKquQkJ6ezpYtW2jVqhUfPnygSpUqnD9/Pl8RITdcXFxo2rQpycnJuLq65vnMLSIiIiIiIiIiIpIfopAg8lOcPn0aS0tLPD09SU9Pp0uXLgQFBeHs7Jxvxurs2bOF5pGXL19m/vz5uY6rKL8vVKgQ9+/fZ/Xq1Urfa2ho4OnpSeXKlfn8+TOrVq2iT58+WFpa0rlzZzZs2MCFCxeIiYkBMjPe1q1bx7hx41BXV8fPz09oOpgVLS0tqlatirW1NcbGxmRkZPD27Vv8/Py4ePFirsFfPT09Ro0aJfw9zGVYjhntWfn48SN2dnZERERQpUoVnjx5QpcuXZBKpUyaNIkePXrw9evXPOfxq2jfvj3du3dHLpezaNEiIZve3d2dB7l4+yqIi4vD2dmZvn37EhMTQ6VKlWjXrp0wZO1j8Svp1q0bAQEBFC1alODgYOrUqUPVqlUpU6YMRYsWxcDAAAMDAwoVKoS5uTkVKlSgZs2a1KpVS6hwOXv2LJCZnRwfHw8gBPEyMjJITEwkKiqKV69e5dkMMS8UlghWVlbMmTNHSUzIycIJEGyRchIS3r17h4+PDwAlSpTAxMSEggULoptDhrdcLic8PBx3d3emT58OwIwZMwTLlZwoUKCAsNzbt28LooatrS2Qee4qyMjIICwsjEuXLrF+/XrGjh1L+/btqV27Ng0aNGDr1q2MGDECd3d3NDQ0OHnyJB06dCAuLi7PfSbyvyE5OZmXL19y4cIFdu3axaJFi5gxYwZnzpxRyfIjN6ZOnYqenh537tzh9OnTf3g9FcJsXg3l80MulxMXF0dwcDARERHI5XIKFixIgQIFSE9PJyQkJEfB+MiRI/Ts2RNfX1++fv1KgQIFaNeuHYsXL+bw4cOcPHmSyZMn07Bhw1yzpcPCwlixYgWfPn3C2NiYCRMmCLZqfn5+AIKQcPr0aUHIFIUEkT/K+PHjefLkCUWKFGHDhg3cuXNHSKQwNTVlwIABgtWWwtLw+34alSpV4vjx47i5uaGlpcXFixdp0KABe/fuFQLyiuQGRbVNVhRWjJBp87du3bpfEtSOjo7m8uXLOZ63mzZtwt/fHx0dHTQ1NenatSsODg6oq6tz4MABNm/e/IeXr6BMmTJ4eHjQsWNHAA4dOkSXLl2Unp0UzxwA69etE/5vZmaGpqYmMpmM3r17M3LkSKRSKY6Ojvj6+ipZAX758oUTJ04QGBjIs2fPiI2NzVMQUVNTY926dRgaGhIUFJTtOVpERERERERERETkR/jPWhu9ePGCqVOncv/+fRwcHFi5cuUPe+r/F/n06ROjRo0SApgWFhasWrUqVwuJ77l165bgyT569Gg8PT1xd3enSZMmtGnTJsdpzMzMWLFiBU5OTixatIgOHToo9Q8oUKAAhw4d4vjx49y5c4c7d+7w4cMHgoODCQ4O5tChQ0DmS17t2rVp2LAhgwYNonbt2kyePJmwsDDevXtH5cqVszUFLVCgAHXq1OHz589CtuqePXu4cOECffv2zTGgpZfFjzwu7iuDBg3Cy8tLqTmogsTERHr06MGrV68oVaoUvr6+mJubs3//fjZu3MiUKVM4efIk9+/fZ+vWrdSoUUOl/awgNjaWq1evZhM+vn79ms1CoGDBgjRt2pTJkyfz8OFDnj9/ztmzZ+ncuTPHjx9nyJAh3Lx5M1tpP2T+roMGDRLK8idNmsT06dOVAmrx8fG/rLnp99SvX5+rV6/SqVMn4cU6N/T09ITKAsX6nTlzhlGjRgkBO0NDQ3R0dIiLi+PDhw9KgX41NbVcA/95ofB2r127Nk2bNmXOnDnMmzcPf39/4bz63l5B4V2dk5Cwfft2MjIysLW1Fc5HAHlyMp9bZ55L796/R6qmRmJiIl27duXRo0ecOXOGXr16ceDAAUJDQ6lSpUqugc/ixYsTHx9PcnKykMGqsEMKCQlhwoQJvHjxgufPn+cZcF65ciWRkZHMmjULExMTxo4dy4ULF2jatKlwzIv8+SQmJnL06FHevn1LeHg479694927d7kGqr28vNDR0cHW1pb27dtjZ2f3Q8szMzNjxIgRrFixgvnz59O2bVuVLElyQ2H1Vbly5Z+aXmGtoqiw0dbWplSpUhgbGyOTyXj06BGpqak8f/5cuF6np6fj7u7Orl27gEzLvaFDh1KzZk3hmSHXPg9ZOHv2LNu3b0cmk2FhYYGrqyuGhoa0bt2a69evC1UJFSpUoFixYkRHRwvXDFFIEPkj7N+/n82bNyORSFi3bp3QED09PZ1u3brh6empZImTl8CsoaHBqFGjaN26NRMmTODRo0cMHz6c48ePs3bt2jwrEiCzmnLo0KFs27aNVatWce7cOVatWvXTdjvJycns2bMHyLy3161bV/ju48ePbNmyBcg8bxVi5vbt2xk6dKhQHTd06FChIuCPoqWlxcCBA+nWrRvTpk0jPDycvn37Mnz4cIYPH05W46HLly9z9epVmjRpgrq6OhYWFrx+/Zpjx44hkUhYtGgRkyZNUkokuXPnDgMHDhSEfQXa2toULVoUU1NTzMzMMDU1pVixYlSpUoW2bdtSokQJli9fzrBhw1i6dCldunRR2lciIiIiIiIiIiIiqvKfrEgICgqiSZMmVK5cGWdnZ7Zs2SIECUTyZvbs2UpZ0H5+fiqJCMHBwfTt2xdbW1vkcjm9evVi9erV2NjYIJfLmTt3bp7T9+rVC2tra6RSaY6WLIaGhgwYMABPT09u3LjBlStXWL16Nfb29sKLbWhoKEePHmXy5Mlcu3aN2rVrc/jwYZo0aUJGRgZPnz4VKiWyIpFIMDExoWHDhtSqVQttbW0iIiJYtWpVnrY0ANWrV+fr16+CN/73HDx4UMhU19LSEkSKtWvXcu3aNSEj9d27d0yYMCHPZeWEm5sbM2bMYPbs2UrDqlWrsn02duxYAgIC0NHRYfny5ejp6XH37l3s7e2FihBF1mxWZDIZPXr0IDw8nNKlS+Pv78/cuXP/UMDwZyhdujS3bt0iICCAixcvcuvWLR49esTr1695//49CQkJSKVS4uPj2b9/P/D/gfqLFy/y5s0bweZK0Yjw48ePgmigrq5OiRIlqFChwk+tnyIIqhCfmjZtSvfu3YHM8+P58+fZplFU0rx48YLU1FSl706cOAFkCkDnzp0jKCiIt2/fkpClukZXVxcjIyNKlCjBwoULgcxARJMmTdDV1UUmkyk1g/4eRVNkdXV1AgMDuXv3LsbGxkIvkoMHD3L//n2+ffuGlpYWFStWpFOnTkyePJmtW7dy+fJlpk+fjkQiYf/+/Vy+fBkbGxv27t2LmZkZjx49YuzYsT+1P0V+nHnz5jFx4kQ8PT05ceIE9+7dU7LyqlKlCm3atMHJyYkhQ4ZQsmRJUlJSOHfuHOPHj8fBwYFbt26ptKyEhAS8vb0JDAwE4O3bt9y4ceOn1jsxKYmNGzdy584d4OcrEry9vQkJCUEikVCiRAlq1qxJ4cKFkfwfe+cd1kTideE3oRcpoiKKvRdUxIK9YhfFLmLD3ntfe9cVe1fsvaNiQ7D3CiggKoigICi9CUm+P7KZH5EiqFu+3XmfJ8+uJJmZTJLJzD33niORoKWlRaVKldDQ0CA+Pl74Lbh+/bpwfjB27Fg2btyIjY1NnhoPvn79yrRp00hPT0dLS4tevXphZGQEKI81qmPK3bt3hW0DuHr1KqAslop2JCI/iirno1evXjRp0kSwMcqXLx/Lli37oXyKChUqcPToUebOnYu2tjaXLl2iQYMGwvc9K4tBFTNmzMDFxQVjY2N8fX0ZP378D70uQK3bP6NFIcCtW7dISUmhXLlyXLt2DVCez8nlcnx8fKhWrRppaWl4eHj88Pqzw9bWltOnT9O+fXtkMhkbNmzgwIEDmR63dOlS4f9VUwcmJiacO3eOKVOmqL03r1+/pmvXrkRERFCsWDEqVaoknKukpqby/v17Hj58iJubG9u2bWPhwoU4Ojri6ekJQPfu3XFwcEAmk+XJrlJEREREREREREQkI/+5FnyZTEb37t1Zs2YNjo6OgPKC5/Pnz3leVmpqqlpxT2WJ8m+mV69eXLlyheDgYEJDQ6lVqxZ9+vRhzJgxmTrKFAoFV65cYc2aNWo2KC1btmT16tUsXbqUmzdvAghe2tnx8uVLIcy5RYsWOT5WIpFQrFgxihUrhrW1Nfnz5ycmJoYlS5Zw8eJF8uXLJ3SbmpqasmHDBtq0aUN4eDgBAQHkz58/y+A9qVSKVCoVvN1tbW2/a+NUoUIFHvr4CKGD39KsWTPu3LnDrVu3CAwMFDIKvl1vxYoVGTRoUI7r+panT59y//59NDU1BfsMFSkpKehmCIF+//49QUFB3L17l1atWlGyZEkaN27MxYsXiYuLo2bNmly9ejVTngQoi9xRUVEYGBhw7949oUCWW37l90jVPf0tMplMbZIiNDQUUNowpKSkEBERQUhICDo6OqSmphIVFUWxYsUwMjISivkymYwPHz58N7g2O0qWLMnnz595+fIlVatWZffu3Rw5cgRQdm5n1RHZvHlzdu3axePHj4mMjFQTkwYMGMCiRYs4e/YsZ8+eFf6uJ5HwuLyy0Nq0SRMM//CTVxUTLC0t2bBhA8nJyUilUqEQkR16enrky5ePmJgYYepgzpw57N+/H0tLSypWrChYSYWHh2eadHF0dGTNmjUkJSUJkxWVK1fm5MmT1KtXj8uXL5OWlpblpItI7snN90i1/83NzRk9erRwnFSFpH5bUFywYAF+fn5cvHgRV1dXAgICsLOzY9CgQcydOzfTey2Xy7lx4wa7du3iwoULQp6IVCqlVatWWFtb/9Brmz17NpF/vJ7WrVv/sJBQvnx5/P39kUqlmJubZzp+a2trCxYhen9MllWsWBEtLS3S0tKoUaNGrkJZv0VLS4s+ffqwe/du0tLSWLlyJfXq1aNdu3a8efNGCG+vXr06jx8/Fn7rVDZqHTp0ECcm/yL+jed1nTp1YvXq1Zw9e5Zhw4bRvHlz1qxZQ3x8PDt27PjhQr6mpiaTJk2iXbt2DB48GB8fHzw9PZFKpTg7O2f7PIlEQufOnTExMcHZ2TmTSJ4XMk5PhIWFkZCQIIQ4V65cmadPnxIYGIiNjQ2PHz8Wvt+mpqaCsJnXSc/cYmxszO+//06lSpX4/fff2bRpE53btCVjxLSHhwdhYWEULVqUWbNmUbx4caZOnSo0wKhITk5m0KBBJCYmUq9ePQ4fPiy8zpSUFD59+kRQUBDx8fGEh4fz8eNHdu7cqbZ/JBIJjRs35tSpU6IwKSIiIiIiIiIi8sP8565M79+/T3BwsNAJrFAoePHiBU+fPuX333/HysqKrVu35mrMeunSpcyfP//P3uR/FE2aNMHf35+TJ0+yatUqHj9+zM6dO3F1daVDhw6MGzcOa2trjh49yrp164QCiYaGBj169GDixIlYW1uzZcsWZs+eDcDChQvVAui+JT09nWHDhpGWlka7du3o0aNHjt1uWREfHy90pE2aNEktqFMqlVK6dGm+fPlCUlKS0FmfEYVCQVBQkDCu37x58++KHwAeHsqOUpVn7reULl0aDw8PIiIiOHfuHGfOnMHf3x8rKyvq1q1L3bp1sbGxITExMc+FpO3btwNgb2+faeLjw4cPFClSRPi3l5cX48aNE6YjAKFg/uXLF6EgqJfBtkmFqiOwatWqeRYR4K//HoWFhQn7plGjRpiamnLy5Enu3btHkSJFCA4OJjExkS9fvlCoUCGMjIyIi4sjLi6O9PT0H/b1b9q0KY8fP+by5cvcv39f2Nf29vbY2NhkuW8bNGggeFqHhITw22+/Ubx4cRo1asS4ceMoV64ce/bsISoqii9fvigF0ZQU4fm+vr4kZ/BOrly5MtHR0fj7+2Nubo6JiYlQjMgJ1eSNSnxSfTZzw/Pnz0lKSsLU1FTNkqZWrVoULFiQyMhI7t+/T8OGDXO1PJGsyc33qH379ixfvpyYmBicnJyE9/7z589ZdiVLJBIqV65M5cqVGTBgAPPnz+f48ePs2LGDs2fPsnz5crp06cLbt285ePAgBw8eFEQ6gHLlytGrVy+6d++OhYVFnl6Pn58fqtmfhPh4wQ6oatWqeVpORtq0acPz58959+4dr1+/plKlSmqvOyYmBrlcjo6OjrBvihQpQt++fXF1dWXlypU0aNAgz2KCRCJh6tSpGBsb4+npibe3N3fu3BEmLEApsBcoUAAXFxdAKZhfvXqVfPnysSGDl7rIn8u/8bxu+fLlPHnyhOvXrzNw4EDc3d1ZsmQJU6ZMYfny5ZQuXRp7e/sfXr4qDHj27NlC7k/btm2/+zzV78rPBLtnzEVQKBT4+flRu3ZtQCkCLlmyhBkzZvDkyRMqVaqEn58fdnZ2wnTl4sWLf0gczAv9+/fn5MmTvH37lt17dqNK0bKuWZM7jx+zf/9+pk2bRsOGDbP9Hfztt9/w9fWlYMGC7NixQ+13W1dXl+LFi2NmZiactykUCnbu3AkoLUhVqCatsgqCFxEREREREREREckN/zlrI3Nzc6RSKTNnzsTb2xtnZ2fCw8OZOXMmu3fvJiwsjFatWpGSoRiXHTNmzCA2Nla4qXyX/+1oamrSo0cP7t27h7u7O23btkWhUHD27FlatmxJiRIlGDFiBH5+fhgaGjJmzBi8vb3Zt28f1tbWpKWlCTYrc+fOZcaMGTmuz8XFhSdPnmBiYsK6devyPIavUCiYP38+KSkp1KlTBwcHhyxfk0o8CgoKUuuQUygUvHr1ShAROnbsiKOjY64uPmNjYyhQoMB3i67m5uYMGjSIQYMG0axZM1xcXJg6dSpNmjTJVaH3W16+fMmtW7e+2xmookaNGoDS11jlx6sSBaKjo4XvQ8YpBhXe3t4AagW+R48eMXz4cIYMGSLcxo4dm+W6/8rv0bVr16hduzbPnz/H2NiY3r17Cxfut27dQkdHR+jajoyMJDU1FT09PczNzSlbtiwlSpTI1IWdWxo1aoRUKiUoKIhnz56hp6fHnDlzmDRpUo7d+BUqVGDOnDmUKFGChIQEunfvzpYtW1AoFLRr144jR45w9epVnj59SkhICH5+/sJzjx47JhRAR40aRUhICB8/fqRixYpcv3491wUcVfdiVmLH91B1fdarV0/tOyOVSmnevDnAn2It8V8jN9+jihUrUqZMGVJTU7ly5Uqelp8/f35WrFiBu7s75cqVIyIiggEDBgjB5StWrCA0NBRjY2OcnJy4ePEit2/fZuzYsXkSEWJiYpg7dy69e/US/tajRw/WrFnzUyICKI/zzs7OSCQSYmNjM/mMq4psKrsjFcOHD8fQ0BA/P7+fCo0uVKgQQ4cOZcKECZQuXZq0tDTS0tKoWLEizZs3Z/v27Xz9+pVy5cpx7949QFnYLlas2A+vUyRv/BvP67S0tDh27BiWlpYEBQUxfPhwevXqJZwbjB49WrAk+lFUlojPnj3LlYgAyswW+DkhQTUhqiqMv3jxQu3+cePGMWzYMKEZpH///sKx77fffvshy8i8oprcANTsjew7dgRg7969OYYlnzx5kt27dyORSNi8eXOusqZiY2OFaYSMxw/V9HXBggXz/kJERERERERERERE+A8KCWXKlGHTpk2cPHmSMWPGcPz4cS5dukSHDh1o164dp06dIigoiFu3bn13WTo6OhgZGand/ktIJBIaNmzIsWPHhAA4bW1tEhISsLCwYOHChfj7+2cqhJw7d47w8HDMzc2ZPn16jut48eKFkC/w+++/q3XR55ZTp07x4MEDdHV1mTt3brZCRJEiRTAyMiI9PV3w2pXL5bx48YKQkBBAWXDv3LlznsSMtm3bftcCSaFQsHTpUnr06MHu3buxs7Pj3bt32T7+6NGj2NnZCdYX36LquG/btq1aN1p2mJqaCv68qk551ec5JiYmx4kElZBgZWUFKPfZkCFD2Lt3LwcOHBBuqtDrb/krvkcKhYLff/+d1q1b8+nTJ6pVq8b9+/cpV66cICTcvXsXhUIhdOkrFArCwsKEjASJRIK+vn6eO6tVmJqaYmNjA0DZsmXZsWPHd226VJiZmTFjxgwaNWqETCZj9uzZjBw5MsuAY339/71HOtraREVF8fTpU7Zu3UpCQgKNGjXC09MzW7utrPh2IiEv3LlzByCTvRb8z6ZM5QUv8uPk5nskkUho3749AOfPn/+h9TRq1Ii7d+8ya9YstLW1CQoKQiqVYmdnx+7du3n9+jVLly7Fxsbmu8dJhUJBZGQkd+/eZd++fcyZM4dWrVpx8OBBtcKanZ3dL7P2UQWRArx79044tqWnpwsh7QUKFFB7jqmpKUOHDgVg9erVP2XFAsrzkAkTJjB06FBat27NgAED2Lt3L58/f6ZAgQJoaGiQmJhIw4YNGT58+E+tSyRv/FvP6woWLMiuXbvQ09Pj+vXrLFmyhAULFmBvb09aWhqDBg3i0aNHf+k2qawSf6RZQoVKSLCyskJDQ4OoqCgh+wiUx7zff/+dNm3akJKSIuSdTJo0iZkzZ/7E1ueNZs2aYWNjw9cMx46WLVuir6/Pq1evBOHwW169eiWIEJMmTaJZs2a5Wp/qnLVQoUJq522qTBxRSBAREREREREREflR/nPWRgBDhw5l6NCh3Lhxg+HDh6sV1EqUKIFUKsXY2Phv3MK8k1M3U0bkcnmuiuByuTxXHfeq9VaqVImNGzcyZ84cXr9+Te3atdXCdr9+/Sosb8uWLQD07dsXmUymFlgcGRkpdGd/+PCB7t27k5aWhp2dHa1btxb86iMjI3PVxXbnzh2WL18OKO2FQkND1aw3VCQmJpKamoqlpSUvX77kw4cPmJiY8PHjR6G4VLp0aUqUKEFQUFCO69RITyfj/EHz5s2zzBYA2LlzJ69fv8bHx0ewa9LS0uLdu3dUr14dW1tb9PX1sbKyYuTIkcLztm7dip+fH7t37xaCFEFpIxMSEiIUZqtUqZJlt7dcLhf2pYqSJUsSFBTElStXKFCggNAt+L2JBJW1UbFixfj06RNeXl4EBASQL18+Ro8eLXzeUlNTWbVqVY77LiMKheK7n+uMn6vsiIuLw9nZmQsXLgDQrVs3li9fjr6+PpGRkVhYWGBgYEBsbCzGxsbo6upibm5OcnIyX79+JSIi4qdsADIWCFq2bEnp0qWxsrJS+yy+fPkyVyG2NjY2FChQgDNnznD8+HHu3bsndFiHhoYSFhZGVGgoe/94fKdOndSsjQwNDQkLC6NRo0aA8nOQm1Bs1Xc0p2BmUH5WMr4fCQkJwufDyspKzW88LS2NJk2aAErLuc+fP2cq2qWnp6Ojo/Pd7cvL8epHgkX/PyOXywUxDKBdu3asW7eOK1eukJiYiJ6eHg8ePBAKcqB8H1+8eEHt2rXV9r+BgYEQtN2nTx8aNWqEt7c3tWvXVpvkyaooFh0dzYcPH4RbWFgYYWFhQiE/IxYWFjh26QJ/WMM9efKE1Fy8bwkJCWpFxOwwMDBAX1+fpKQk/P39KVmyJLGxsSgUCrS1tfn69StpaWkkJCQIx9ISJUpgbGxMaGgogwYNon///sJnLrfWKCkpKWq/W1WqVKFKlSq4ubnh7++PtrY2tra2nDt3Dm1tbbZt2/ZLbFdkMlmuzhEUCkWuBJvcnm/8175vv3q/yGSyXO+/3LxvVatWZe3atQwdOpTNmzdTqVIl1q9fT3x8PF5eXjg5OXHy5El0dXXVjhnZER0drXbcyI6QkJAsz6lVvycSiYTw8HDCw8OF846ciIyMFAriqt+U+Ph4ChUqxMePH7l16xZVq1Ylf/787Nu3D1Ae9168eMH79+9p3rw55cuXZ//+/YByCjU3glF6ejpv37797uPi4uKEyc6M2NnZ8fLJE+Hfb968oVOnThw6dAhXV1fh2KoiOTmZXr16kZiYSPXq1bGzs+Pp06fs3buXK1euMHnyZLXnSCQSKlWqBPwveNrS0lLtGKs6PorWRiIiIiIiIiIiIj/Kv1pIuH79Ojt27MDV1TVL6xCJREJAQADXr18XCloLFiygQYMGgsfq382vvgiXSCS5WqampmauHpcvXz61x5UuXZrSpUtnepyqGBIcHCwUZpydnTMVSTQ1NdHS0iIsLIzu3bvz7t07SpQogYuLi1rBM3/+/N8VexQKBYcPHyY5OZlSpUrRunXrbIsybdu2FQI8t2zZwu3btwkICBBCekePHk3NmjUJCgr6btCn5OtXOHUagKKWltjb22e7L69fv05QUJBw8WxpaYmuri4hISEkJydz+/ZtypcvD/wvKDU+Pp6AgABAaQmT0e4pMTFRGNuvWrUqBQsWVBNqVMTHx2d6HdWqVcPLy4vXr1+TL18+IUciOjpauBDV19dXey1RUVF8+PABUBbFNDU1BV9eR0dHRo0apbbOvAgJufmsampq5lhoe/nyJT169CAwMBAtLS0WLlxI//791ZarpaVF7dq1uXbtGn369BE6gK9fv06/fv2IiYmhT58+jBs3Tiiqrl69Olc2PxEREWqFWHNzc+F9zMjDhw9z1ZWZmJhIo0aNMDEx4ejRo4SGhrJw4UK1ApaeRALls/6MJiQkqBVq6tWrR8+ePXNcp0KhYMKECYBySiWnqQQzMzO16aPLly8jk8koWbKkmr2XqkBWsmRJypYty+vXr7lx4wYd/7B6UKGpqfndaR5QHl9yc7zK7fHv30T+/PnVimQtWrSgWLFivH//nqdPn9KxY0diY2OF/ZKens6qVasIDw/Hw8ODoUOHkj9/fuG+jL+lZcqUyRQKCsrPvcqrOyEhgYMHDwqTS98ikUgwMzPD3NycwoULU6RIEaysrNDOUMh88+YNZkWLfve1fvnyJVf2R7Vq1cLCwoJNmzaRkpKChYWFYN9Vv359mjZtCsDHjx+Fv2toaNCrVy+2b9/O/fv3kcvl9O3bF6lUiqamZq6m5fr06ZNpQuzYsWPCb+Lq1auZM2cOoAw0VxUFsyM33428PO5Xfz/+Td+3X/lacrusX73vzM3NGTx4MMHBwUJGQt26dXFzc6N169bcuXOHPn36cOzYsUxTOVnx9evXHG35VBgaGma5PNXvlpmZGQUKFKBEiRJq+VXZYWpqKhzTVPuoaNGiFCpUSJh4bdu2LXFxcWpNHKNHjyYiIgJLS0u1ab4SJUrkKvPn9evXuZrku3//fpbvXdmyZZWB84nKdauOIYcOHeLkyZOsWLFC7bxi5syZ+Pj4YGJiwsyZM9HQ0ODixYvs3atsFZg/fz6bNm0SJqwkEolw3FUJBqVKlRL+BgiNMeJEgoiIiIiIiIiIyI/yr7U2SktLw8nJiePHj9OrVy+1QDYVDRo0oFmzZrRu3Vrorjx37hzHjh37G7b4v4Grqyug7Mz+NtBYRVhYGA4ODoKIcOrUqR/qnjp//jze3t5oaGgwYMCAXHd29uzZEx0dHWQyGbq6ukyePJmaNWvmef0AnXIQEd68ecOrV69ISEhAKpVSokQJIiMjhUK+qjP21atXahe9jx8/FroF3759KwRag7Kwr7Imyq1tjorKlSsDEBgYyNevX4XCdsaJhG+L56pu8xIlSmBoaChkM2hoaDBw4MA8rf9Xc+TIERo0aEBgYCAWFhacOXOGAQMGZPl+qIoIT58+Ff7WpEkTQQjZuHEjHTt2zLYY+ldTsmRJhg4dSpEiRVAoFEilUgoXLkyNGjWws7P7pevKeOzMzXRARq5fvw5A48aNs32MKifB09PzB7ZO5HuMHz+e4cOHC7eZM2cK4e8nT57M9HgvLy+hUzg0NJSVK1fmqgs3K16/fs2yZcvw9vZGIpFgbm5O9erVadOmDQMGDKBHjx4sXbqUGTNm4OzsTLt27ahRo0aOhe+EhATu3LnD6dOnuXnzJoGBgbnqYv4WY2Njwebp2rVrQgZOTkKElZWVIIA/fPiQffv25apzOztu3bolCJeTJk3ixo0bfP78GSsrK6ZOnfrDyxURyYkFCxbQsWNHUlNTcXBwIDY2lrNnz2JlZUV4eDh9+vT57vTZr0B1XvMrwpa1tLQoXrw4urq6JCUlCdY+GdHW1qZYsWJ/q7iVMdT6pZ8fDRo0oESJEsTFxXH27FnhvmN/5BtJJBImT56MmZkZL1++ZO3atYCyiScuLo45c+ZkOdWlev3fCpdi2LKIiIiIiIiIiMjP8q8VEk6cOIGtrS1ubm64u7tnKSZIpVLOnj0rnIi3b9+eBw8eZNkxLPLzpKWlCZ1U2QUAf/jwIZOIUDQXnajfEh0dLfjftmvXDktLy1w/V+WHbWVlxYwZM4QCe25JzlD0t+/UKcvH3Llzh1atWpGamipc3IaGhgre258/f1YTE+7duydkJnxrgZPx4vPatWsoFAoqVqyYp9cMSjsRExMT0tPTCQwMVBMSVBeq33ajqwrrqs7ZHTt2ANC+ffs8r/9X8fXrVyZMmEC/fv1ISkqiWbNmXLx4MUcxqE6dOoDSQiVjd//UqVPZvHkzZmZmBAQE0LlzZ1asWCF0Kf+dmJiYMGTIEMaNG8esWbMYMWIEDg4O1MlFZ2Ve+FEhITY2VhBlW7Zsme3jVIKXKCT8Oezdu5cdO3YIt5UrVwqf8bNnz6q9v1++fBEswDp27EjRokWJj49n3bp12Xp4Z4VcLufChQusXbuWmJgYzM3NmTZtGrNnz2bIkCF06NCBWrVqUaBAgVx1NMP/BITjx4/j7+/Ply9fCAwM5ObNmxw9epSjR4/y7NkzvL29iY2NzdUyraysqFKlimCjZmFh8d1O7Bo1avyUmJCSksKBAwdo1KgRdnZ2JCUl0bJlS2xtbTly5AhSqZTNmzfner+IiOQVqVTKvn37qFSpEh8+fKBNmzYoFAouXrxImTJlCAkJoU+fPkL3+p+FalrgVwgJ2traaGhoCBOc/v7+P7+BfwIZC/jHjx8HlNOb8L8g5sDAQMaOHQsoz0Fq1qxJVFQU8+fPJy0tjYYNG7JlyxZMTU15+/at2jFdhep8MeMEhUKhEIQEcSJBRERERERERETkR/nXWhvdvXuXMWPG0LhxY9zc3LC3t6dXr14cPnxY7QJdT0/vLw1c+y9z/vx5Pn36RKFChYRO0IyEhobSvXt3QkJCfkpEAOXId1RUFIULF85yXd+jTp06QnE5rzx+8gSVMVaVLESIo0ePMmbMGNLS0tDX18fMzIyQkBAUCgX6+vrkz5+f0NBQPn/+jJmZGfHx8SQnJ2NnZ8eVK1cEIaFKlSq8ePECNzc3pk2bxvv373nyh/9uXqcR4H/+unfv3sXPz0/w0Y+OjhYEjuwmEipXrkxERASnTp0CYPDgwXle/68gPDycXr16cffuXUB5ET5v3rxMeRDfYm1tjZaWFp8+feL9+/dqXXzt2rXD1taWOXPmcPbsWTZu3IiZmRmdOnX620VHqVQq2M78WagKNZqamnkKvN2+fTtxcXFUqFAhRyGhSZMmSKVS/P39CQ0N/dsEqH8rM2bMEARAX19fjh07xrNnzyhUqBCfPn3i2rVrwmNPnDjB169fKVOmDK1ataJp06bs3buX58+fs3//fj5+/EizZs1ynO6Kiopi586dwhRD3bp16dGjR56nWTLy0s+PN6GhQsHewsKC8uXLEx0dzcePH4mKihJsu96/fw8oJw6KFy9O2bJlqVChQpZdyKrw6Xfv3pGQkJArWyT4n5jg6urKw4cPkUqlNGvWLMfvR2xsLFu2bOHUqVN8/vwZUBY/u3fvzty5c4XJnDFjxgih7CIifxZGRkacO3eOhg0b4uvrS4cOHbh8+TKXL1+mXr16BAQE0L9/fw4dOvRThf6c+FkhQaFQCBkNqvP6ihUr4u3tzdu3b394kvSvIjgoiJMnT9K7d2+WLl0qWEv279+fhIQEGjVqxIwZM7h27Rrz5s3j8+fPlCxZkqlTp6Kvr8/cuXOZPHkyN27c4ODBg/Tt21dYdlYTCTExMUIThCgkZCYkJEQQWrIj4wSwiIiIiIiIiMh/lX/tRMLKlSsFOw07O7tsJxNyExQn8vMoFArWr18PQL9+/TJ1Wz5//pymTZv+EhHh5cuXHD16FIlEgqOj41/e2almf/NN8SouLo5x48aRlpZGp06dKF26NO/fv0ehUGBiYkKFChUwNzcXusg+f/5MsWLF0NfX5927dzg6OgoXMiqh49GjRyQkJHDs2DHBjsnExCTP262yyAGl7ZKqoy3j90VfX1/tOa9evQKUfrwXL14kLS0NIyMjSpUqlef1/wrmz58viAidO3dm1qxZufIH19fXp1q1aoByf35L/vz52bBhA5s3b6ZAgQJ8/vw5S1uYfyMZPeJzGyYKcOjQIUA5nZJT4dnU1BQrKysAHjx48BNbKpIVU6ZMYdasWcyaNYvff/8dUAaAlytXDlCKbXFxcXz58kUQBm1tbZFIJOjo6NCuXTvBj/zq1avcvn07x/WtX7+et2/foqmpSb9+/ejbt+9PiQgAoe/fI5fLkUgkNGrUiLZt21KmTBlq1apFx44d6dOnD3Xr1lUTOmNjY/Hx8eHUqVOCuJAV+vr69O3bl6ZNm+ZJPM44mXD//n2cnZ0FgSArVq5cyY4dO4Rj+oIFCwgMDGTHjh14eHgQGhpKiRIlhIwEEZE/m1KlSnH58mXy5cvHvXv3cHV1pVSpUhw8eBBjY2OePHkiHMd/Je/fv2fixIlcvHgRQM3DPy98/fpV+E1SZWgVKlQIIyMjZDIZERERv2aD/0R27NhB0aJFsbCwQKFQMGLECHx8fJBKpWzduhUNDQ1u3bolTFjMnj1bOA+rWrUqTk5OAOzZs0eYHPX39ycwMBBQn0h4/PgxoBRZf/aY/G8jJCSESpUqYWNjk+PNyckJfX39XGWIiIiIiIiIiIj8W/nXCgkZg3khazHh999/Z8CAAX/PBv7HuHjxInfv3kVXV5dhw4ap3XfhwgVatmzJhw8fKFeu3E+JCKDsqgVo06bN31LQ1s3hAi0pKYnU1FQkEgk7d+5EU1NT6BY2MDAQCq6qgphEIsHQ0BBbW1uMjIx49OgRFhYWAOzatQtQFmoNDAxo3749hoaGpKSksH79ekJDQ/O03UeOHOH27dtIJBLKlSvHypUrgf8FZQ8YMCBT5+CgQYMA2L9/Py9fvsTMzIy4uDgcHR3/dFuErOjVq5cQ9nv69GkqV67M5s2bhYyHnKhevTqgFKKyo127dmzZsgUgyxDrfyPGxsZIpVJSU1OFAMfcoPKC3rBhQ477VKFQEBYWBiBOI/zJFC1alIkTJwJw+/ZtDA0N8fHxYc2aNcjlcsEW5OjRo3h5ebFlyxaWLl1KXFwcoJw8+l7XfsOGDYHMwcw/g+4fx0OFQsHNmzfx8PDg9evXPH78mIsXL3L06FHu37+fyStcX1+fKlWqfLf7NigoKE+WSCpq1KjB4MGD0dHR4e7du3Tq1EktZyUjqs7gChUq4Ofnx5QpUwSbE9XxvkiRIpnEWhGRPxN9fX3he1O7tnKWMmPxuWzZsr90fTExMfTv359z584hk8mErLIf4cOHD4DyN0p1zp+eni5MOvyoQPFX4uDgwMSJE/n48SPGxsb89ttvGBkZIZfLheabKlWqCFaTx48fF8ST2NhYQYyxtrZGV1eXhIQEnJycSE1NpUmTJoJgnJyczOTJkwFlDpiIOlFRUSQlJbF//34eP36c483Pzy9T9oSIiIiIiIiIyH+Jf621UVaoxAR7e3tsbGxITU3Fy8vr796sfz0ymYy5c+cCMHLkSDWRYPPmzUyZMgW5XE6zZs1Yv379T41cy+VyTp8+DUDXrl2zDKH7sylUqBB8zDqoUHUxqFAoSE5ORiKRULhwYYKDg4mIiKBgwYJoaGgIBVtTU1M0NTXR09Nj3rx5TJw4EV9fXyQSCQqFgvLly7N3714kEgkVKlRg9OjR7Nq1i4iICDZt2oSjo2OWhb/k5GT8/f0JCwsjLCyMkJAQwTKpZ8+enDx5kpSUFDQ1NUlPT8fOzo6tW7dmWs6gQYNISUlh9OjRHDhwgJ49e+Lh4YGPjw/dunVTs+jIKvD8V9OkSRNevHjBnj17WLFiBe/fv2f8+PEsW7aMsWPH4ujomCnnQUWVKlWA73srq17Hj3T0eXt78/z5c9q0afP/xlpAS0uLwoUL8+HDBwICAnJt5/Tbb7/h7++Pl5cX27dvZ/Xq1Vk+7s2bN0RFRaGjo0ONGjV+4ZaLZMXy5cvR1dVlyZIlJCQkYGRkRGRkJGvWrGHYsGGcP38eX19fQZCVSCTUqFGDli1bUqZMGUxNTQEIDg7m1KlTBAcHM3XqVOG4bmdnR8OGDbl16xb79+/HwsKCwoUL52rbkpKSeP/+vSBoqGjUsCFJcjl+fn7C8erbMFUNDQ2MjIwoV64cRYoUoUiRIhgZGX03WDU8PJxLly6hUCh49OgRbdq0ydW2qqhWrRrTpk1j//79vH37lj59+jBjxgycnJzU1u3g4ICLiwsBAQEEBgZSsWJF4T7VMfrFixcoFIq/NQxW5L/FkiVLSE9Pp1WrVtSrVw+AK1euEBsbi4WFhWBx+CuQyWRMmjRJsLDbsGFDnjOoMqKaNFI1D4Cys1wmk2FkZISxsfFPb/OvJig4GNs//r958+YoFAr27NmDVCrF1dWVJk2asG3bNnr16sXmzZupVasWFhYWwlTZhQsXKFu2LB06dGDRokWEh4djYWEhWLSOHj2agIAALCwshLBmUB7337x5g4WFhTj1lAOVKlX6x1tiiYiIiIiIiIj83fxrJxKyw87OTujW8fLyokiRIn/3Jv3rOXToEL6+vpiYmDBp0iRAeUE5efJkJk2ahFwup3///pw+ffqnL/wePHjAx48fyZcvn+A5/VeTMUzvWwwMDIQLu4SEBEBpm6Ojo0N6ejpRUVGkp6cL3fwZlzVs2DBq1KhBbGwsPXv2JCYmBg8PD0GcAKXwMGrUKMqXLy+EW1+8eJFr165x7NgxNm3axPz581m5ciVTpkxhzZo1HDt2TBAR7O3t8fLyIjY2VhARatasyYkTJzJN+agYNWoUixYtApRTDS1bthTCiQ8ePCjcVMG7fzY6OjoMHTqUFy9esH79eooVK0Z4eDgzZ86kXr16uLq6ZjmhoAqM/p4Hrup9y25/ZMft27fZtWsXjx8/Zv369Xz8+DFPz/87URWJAwICcv0cDQ0Nxo8fD8CZM2eE/fYtqs+etbV1nvepSN6RSCQsWLBAmDiKi4tDT0+PmJgYNmzYIBQUtbS0qF+/Pr/99huDBg2iRIkSJCUlcfjwYXr27ImdnR2bNm3C3d2dwYMHC1MLoJwGK1u2LKmpqWzfvj1XE0EA+/btY9u2bdy6dSvTNhcvXpzWrVvTpUsXKlasSIECBShXrhz169enU6dO9O3bl4YNG9KiRQsqVaqEsbHxdwvycrkcNzc3ocPXz88vT/ZdKooUKcKJEydo06YNaWlpLFiwgMmTJwvZMqD0JG/SpAmgtCHJSPny5dHS0iIuLi6TQCIi8mcRFBQkfBYzFpePHDkCQI8ePXJlDZhbNmzYwM2bN9HR0flpEQGyFhKCgoIAKF269A8Lcp8+feL+/fvCZMOvIjIyUphoBOVE0/Tp0wFYsGABrVq1ApRTplOmTAGUwkBQUBC1a9cWsqc2btzInDlzePr0Kbq6uixYsABjY2NOnjzJiRMn0NTUZO/evcL5o6+vL2vXrgVg1apV/0iBRURERERERERE5P8P/zkhYdWqVdy4cUMUEf4iUlNTmT17NgATJ04UulknTZrEpk2bAFi4cCGbNm36JTYYqrDfdu3aZdt5/meTk5AgkUgEeyBVYVU1lQAQERFBZGSkELyc0eZCU1OT9evXI5FIOHz4MI8ePcqyO1xPTw9nZ2fq1auHQqHg6tWrnD9/ngcPHhAUFKQmYFSrVo22bdsyePBg5s+fj4+PDxEREYKIULhwYdzd3b9rETBgwAA1MaFt27bMnDmTKVOmCLexY8fmYS/+PBkFhaVLl1K0aFE+fvzIzJkzsbW1ZdeuXUJ4KyhDGiUSCVFRUURGRma73Pj4eGH5ucXT05Njx44J72tcXBzr16//f1M0/BEhAZQ5HmXLliUpKYkzZ85k+Zh79+4BylBekb+OCRMmsH37dqRSKcnJyejr65OYmMimTZuoU6cOLi4uODo6UqBAAV68eIGrqytTp05l9uzZPHnyBKlUSuPGjTE3N+f169eMGTNGyBzS0NDA2dkZExMTIiIi2L9//3cL9KGhoULmyqVLl0jKZprMxMSE+vXrY29vT6NGjahYsSJmZmY55nBkx4MHD/jw4QM6OjpoaWkRGxv7wwKfoaEh69atY8aMGWhoaODm5iaINSq6du0KwMGDB9UmtLS1talQoQKgnEoQEfkzSUxMZOvWrbRt21aYRqhfvz6gLM7fuHEDgO7du/+ydXp6erJx40YAFi1a9NMiQkJCAl++fAH+Z4knl8sJDg4G+CFby5CQEFauXEnfvn2ZNWsWPXr0YNmyZTx9+lTtXOFHSExMZO3atST8cf4AsHXbNmQyGb179850fjRr1ixatGhBcnIyixYtIiEhge7du9OiRQvkcrkgwE+fPp1SpUrh6+sriBRLlizB1lY59yCTyRg9ejTp6enY29vTsWPHn3odIiIiIiIiIiIiIv8payNQeqn+l0WE3HZbyuXyXBVmVAGY2bFlyxaCg4MpVKgQPXv2JCoqiufPn7N9+3ZA2aFmb28vhFRGR0erBQXHxsby5MkTmjZtqraeDx8+EBMTo7autLQ0oVjZoEED3r17R1RUVK48pyMiItSWFx4eTmBgILVq1VIL8FQoFN8tIGcM8A4OCsLAzEztfn19fRISEnj37h0pKSlIJBL09fXR1NQkLS1NzfdX1cmbmJgovLYWLVrg4eHBgAEDWLFiBZqa//saf/z4kSJFiiCVSuncuTOFCxfm2bNn5MuXj0KFClGwYEEKFiyInp4eDRo0EJ6XmprKokWLCAoKEkQEY2Nj5s2bl2srG1XeyG+//cb+/fsZNGgQLVu2FO5PSkrK1XJ+NTo6OvTt2xdHR0cOHz7MunXrCAsLY8aMGSQkJDBmzBhA+b4UKVKEsLAwPDw8qFWrVpbLe/v2LaC8QM8pxBWUfs1nz57l6tWrADRr1owmTZqwc+dO3r9/z8aNG3F2dlbzdc4JQ0NDodiaHVp5zG44e/bsdx+j+k76+fnlWGhNSkrK1MHaqlUrXr9+jaurqyAWaGhokD9/fuB/Ewl16tTJVKyRy+W5KuDI5fJcdc5mDBT/Hv8Fe5mBAwdibGxM7969SUpKQl9fn6SkJDZt2kS3bt2IiIjg4cOHgngGyu55BwcHOnbsSKFChfD29qZfv37cuXOHOXPmsHTpUpKSktDW1qZHjx7s3LmTZ8+ecfbs2Uw2KeHh4UKWiyoIFJTWa/v376ezplJcTkxKQivDxEN2pKam5iobJiYmhuvXrwsF03LlyhEZGUlERASenp6CtVK+fPnUfhciIyN59eoVtWvXVpueiY+P5/jx4wAYGRnh6OjIvn372LNnD5qamoLPfP78+SlUqBCfPn3i9OnTtG/fXlhGxYoV8fX1FX7vcitU5uZzmtvf/dx+P0T7pX8WaWlpuXqP/fz82LlzJ3v37hU+16rfedXv886dO1EoFNjY2KCvr59jLk5OgntGbt68KTQa2NnZYWFhwYMHDzI9Ljg4OFef+4iICLXzJNV3Pjo6muTkZDQ1NUlKSkIqlebqd/Xdu3e4u7tz584dYT+ampoSHR2Nh4cHHh4eFCpUiBo1atChQ4ccm0VAeRzKOI2Unp7Ohg0bCA8Pp7CJqfD3xIQEatWqxbp16wS7yri4OIyNjdHQ0GDnzp00btyYkJAQFi9ezOzZsxk5ciQhISEEBgbi6OhIrVq1+PjxIwsWLEAmk9GlSxeGDRsmvI4tW7bw+PFjjI2NWbly5Q9NXImIiIiIiIiIiIhk5D8nJKhGg/9t5PaiPrdFNKlUmqtl5vSY+Ph4Fi9eDCinEfLly4dCoWDevHkoFAq6du1Kly5d1J5jbGyM2R+F99jYWHr06IG/vz+bN2+mb9++wuM+ffqU6YLz2rVrxMXFUaBAARo1aoSGhgY1a9YUwolzIjg4WJgUkMvleHh4EBMTQ2JiIl26dBFep1wu/24nnST1KxxTFpUiIyMp8E2ArKGhIZ8+fSI9PZ1JkyYJ3f6XLl3C1dUVUFogLV++XHiNfn5+woVply5duH//PqGhoezZswcnJydh2XXr1lUL0ouPj+fSpUt8+vSJqKgoPn/+TFRUFFFRUXh4eAj/Vk0pqEQEAwMDPDw81DIOckLVsT5r1ixMTEwYPXo0O3fuZOfOnbl6flZIpdLvfl5zO3Wi2r6pU6cybtw4VqxYwZw5c1i1ahUDBgwQCn0VKlQgLCyM9+/fCzYk36LqJLa0tFQTY75FoVBw6tQpQUSYM2eOYPUzcOBA+vTpw61bt9i9ezf9+/cXwp5z4uHDh2pBmFmhkZ4OT58B4OnlBTkUZrp3756rAryVlRUSiYQvX75gYmIifEe/5d27d5nek06dOrF161ZevHhBaGgoZcuWRSqVoq2tTUJCAj4+PgA0atQok7XR94RKFbk9Xv0X0dDQyPE97t69O8bGxjg4OJCUlCQU0A4dOiQ8pkCBAvTu3RsnJyc0NTW5ffs2a9eu5datW0RHRzN58mSWLFnCiRMnsLa2VjuuFyxYkMWLF+Ph4YGjo6Pa5/zixYtoa2uTkpIiFAZLlSpFUFAQwUFBUE5Z0C9ZogS9/hAqc+L27du5Ot7v2rWL169fI5PJMDU1pVixYmhoaBAREUF4eLggJOTPn59OnToBymKgg4MDwcHBaGhosGTJEuEzt3XrVrVjlaWlJdbW1jx9+pSjR48yePBgdHV10dDQoHfv3qxdu5ZDhw7h4OAgPMfKyorjx48TEBDwyy2+cmtPk9sio0QiEb9vWfCr90lu37ecxFaFQsG1a9fYsGED586dE97jUqVKMXz4cJycnAShWKFQsH//fgB69eql1tSRHd8rqiclJbFx40aSkpIoX748/fv3V2t+yMjnz59zFWKrp6cnnA+VLVtW+P2+ffs2oLQ1Kl++PGZmZjg6Oma5DIVCwYMHD9i0aRM3b94U/t6hQwcmTpyIjY0Njx494sCBA5w4cYJPnz5x+fJlLl++jK2tLd26daNNmzZCo4pCoSA6Oprg4GAMDQ158+YNb9684e3bt7x9+5bExEQMDQ05fOQwjB0n7LvTp09jZmaGQqHA2dmZAwcOcOTIERwcHDAwMODEiRM0bNiQhw8fcvfuXSZPnsy1a9d49eoVVlZWyGQyunbtypcvXyhfvjy7du0S7C7fvXvHwoULAVi6dOkvD84WERERERERERH5b/KfExJE/jrWrFlDZGQk5cqVE4rbJ0+e5PHjx+jr6zNr1qxsnyuTyRg4cKAQfLtp06ZM4ZXf4u7uDij9uX/G1zcgIEDo1nv9+jXPnz//4RDY96GhVLK2Vvub6iLv2065Zs2acfLkSWJiYnLsSM2XLx8DBw5k3bp1XLp0CUtLS5o2bZrpcW/evKFt27a8fv06V9uqp6cndPOdOHEi1yLCt4waNQpDQ0M2bNigNp0hk8n+EbYdOjo6/Pbbb9y8eZMrV64wfPhwrly5gkQioWzZsnh6evLmzZtsn6/q3Mw4qfItcrmcAwcOcOfOHQBWrFihJmLmy5ePI0eO0L9/fzw8PNi+fTuDBw/+x4YNa2pqUrJkSYKCgvD3989RQPkWMzMzGjdujJeXF2fOnBFyUkApjMjlcooVK6YWwi7y12JnZ8eVK1do37490dHRmJqakpSUROvWralduzZpaWncu3ePli1bqk0nqFi1ahWTJ09mxYoV/Pbbb0ilUtq2bQso7XyePn2Ku7s7mzZtyjK0/f379ygUCkxMTChRogQxMTGkfDNx9iv5/PkzkZGRSKVSQSQrVKgQEomEhIQEEhIS1LJnQPnbpbJNOXfuHLVr184khGekefPmBAcHEx0dzeXLl7G3twegb9++rF27lsuXLys7lP+wtVOFvfv6+v4Jr1jkv0ZiYiIHDx5k48aNvHz5Uvh7ixYtGDFiBK1atcp0nnT79m3evn2Lvr6+8P39GRQKBVOmTCEsLAwTExPGjx+frYiQ1+WqphCyykfIydZIoVDg6ekpdOqDUrDp1q0bEydOFLKSAGrXrk3t2rVZunQp586dY+fOndy9e5d79+5x79495s6dS7169YiMjCQ4OJjY2Nhs12tsbMyOHTu44O5O/z/+tmPHDmE6evfu3UJexaBBg6hZsyYlSpTAxsaGFStWMG7cOJYtW4a1tTUtWrSgWrVqgNIm6tatWxgYGLBnzx7huCWXyxk2bBiJiYk0atSIIUOG5GUXi4iIiIiIiIiIiGTLfy4jQeSvITIyklWrVgHKEDktLS0SEhKE8fZx48YJBZSsmDVrFpcvX0ZPTw9dXV18fHwEL/WsSExM5Nq1awBqdhF5RaFQCFYrBQoUAJTevqrw47ySlfXNtxkJKrS1tRkxYoQQHpoTGYtYu3fvzhQQ/ODBA+rXr8/r168xMDCgZMmSlCtXjkqVKlGlShWqVq0q3CpVqkSZMmVI/sOTfNeuXULo34/Sv39/Hj58yPPnz4Wbqqj+T0AikbBlyxb09PS4evWqcAGv6tgLDAzM9rmq9y27aYj09HRcXV25c+cOEomEjRs3MnjwYBQKBYsWLaJ27dpcvXoVPT099u/fj729PTKZjO3btwufvV+Fquty0qRJ2Nvbc/369RwfHx8fz4cPH9T821VUrFgR+H4YdVaoPs/u7u5q4pKYj/DPoV69ely7dg1zc3Oio6PR09PD3d2d2bNns2DBAi5fvkx8fDxGRka0adOGRYsWce3aNdq0aUNycjJ79uwRxLK5c+fy5MkTQPldGzVqFJqamjx48EDNwgiUEz4qu6zixYsLgt6fRWJiolBwLFOmjFB409LSEiZtIiIi1J6TlJTE5s2bAYQC3pIlS3K0GdPW1qZjx45IJBJevHghfG8qVKhA3bp1kclkHDhwQHh81apVAeWxJ6MtiohIXtm2bRulSpVi1KhRvHz5EgMDA4YPH86DBw9wc3Ojbdu2WTZb7N27F1BmTOXGEvJ77Ny5kzNnzqChocG4ceMEO7ufJT4+nsTERDQ0NIQJpJiYGKKjo5FKpdlO7T158oT27dszZMgQHj9+jLa2Nn369OHSpUts375dTUTIiJ6eHt27d2fHjh3cuHGDSZMmUaJECRITE/Hw8OD58+eCiGBhYUHdunUZMGAACxYs4MCBA9y9exd/f3/i4uJYt26dsFzrP5pM/P39BYtFU1NTYmJicHJyIj09HYA+ffrQv39/FAoFQ4cO5d27d4BS0Fy/fj0A69evp1y5csKyN23axJUrV9DV1WXLli0/lCEjIiIiIiIiIiIikhXimaXIn8LSpUtJSEigZs2aQsDk2rVriYiIoGTJkjl2R+3Zs4cNGzYASsuIHj16AMqL4+zw8vIiJSWFwoULC6GVP0JwcDARERFoaWnRu3dvihcvTlpaGmfPnv2hsL33WYTpqoSErLx7a9Sowbhx4zA2Nv7usjt37iwUpNatWycUv86dO0ezZs2IjIxEIpGQmJhIcHAwgYGB+Pn58eLFC3x9fYWbn5+f0IG/cuVK+vTpk+fX+f+R0qVLM2/ePEAZ/v3p0yehgBkcHJxlMR3+975lNZHw9etXtm7dyuPHj9HQ0GDkyJH07t0bmUzGuHHjcHFx4c2bNzg6OnLu3Dm0tbXZsWOHkA+we/fu7xb788KgQYMYN24cd+7cITIykhkzZghe7t+SlpbGly9fSEtLEzJLMqIqsvyIkGBra0uhQoWIjY1Ve313794FlEVskb+fatWqcfPmTUqWLElMTAzp6ekULVqUXr16sX79ep4+fcqXL1+4cOECs2bNokmTJhw5coRq1aoRERHB7du36dixI2lpaUycOFEoeBUpUkQQk7Zu3apmofPhwwdkMhkGBgZCodHQ0FAtm+Vby52oqCgePHiALI95IADHjx8nLS0NQ0NDSpcurXafStwODw9X+/uePXuIiorC0tISV1dXGjZsSGpqKpMmTcox+6Vo0aLCZ/vixYtCsbFfv34A7Nu3T3htRYoUwcTEBJlMludQcxERUHahT5w4kdGjRxMTE0Pp0qX5/fffCQoKYt26dTmeG8XHx3Pq1CmAHCdtcsu9e/eExpHu3btnW6T/EVS5DQULFhQK5CpxsGjRollOc4aFhTF48GD8/f0xMDBg6NCh3Lhxg4ULFwphzQA3btygdevWFCtWjFGjRgmWayqKFi3KqFGj8PT05PDhw8yaNYuNGzdy/vx5Xrx4we3bt3F1dWXVqlWMGjWKNm3aUL58efz9/Rk9enSm7UpJSREyalq0aMGDBw8wMjLi9u3bgi0RKM+pa9asSUxMDP379+fFixfC8kaMGKHWfPLy5UumTZsGKKchVU0AIiIiIiIiIiIiIr8CUUgQ+eWkpaUJRf9FixYhlUr5+vWr4Jc/d+7cbLu5k5KSmDx5MgAzZ86kS5cuDB06FIDTp09nG+738OFDQFkAql+/Pv369WPZsmWCTU1uC04qCwB9fX0iIyOFC9KPHz8SkoUokBUBAf7C/2cVVKjyHT5y5EiOo/DfQyKRMHToUIoUKUJCQgJnzpzhy5cvdO/eneTkZKRSKQqFgmbNmjFv3jyWLVvGmjVr2LJlC66urhw6dIhTp05x4cIFvLy8CAwMVLOd+S8wceJEatSowZcvX+jUqRP58uUjX758mbqFM6LqqL9y5QrXrl0TBAe5XI6rqyu+vr5oaWkxfPhwateuDSiFrv379yORSKhVqxZpaWmMHDkShUKBpqYmvXr1EuypDh8+jJeX1w+9HrlcLmQOgDLsW9VN2aFDBxQKBS4uLpmKI6A+IZOamppJSFH5xufWKisjmpqaQie3SkgIDAwUpohsbW3zvEyRP4eyZcvy4MEDjh8/ztu3bwkJCeHgwYOMGjWK6tWrZ+pkNjIy4tSpU2hoaODn50fdunUpWLAgsbGxauKvs7MzmpqaPHz4kDFjxghTCKpjpFQqFTrxU1JS1ITbmD+Ok7GxsWzdupW+ffsyc+ZM1q5dm6fw0JSUFMFHXSaTERwcLATaKxQK4f9jY2OFbmD4XyD50KFD0dHRYcmSJRgYGBAcHIybm1uO62zYsCEGBgakpKQIwpkqdPrNmzeCaJCWlibk5agEGBGRvODl5SU0YcybN48XL14wduzYXGUd3L59m6SkJCHf42cIDw9nxIgRpKen07lzZ1q0aPFTy/sW1XlTeHg4W7Zs4ciRIzx69AjI2tbI19eXHj16EBMTg5WVFbdu3WL69OmZ8h2SkpJwcHDg7t27xMbGsm/fPuF89FskEgl16tRh0KBBtG3blkqVKmVrdxgeHo6TkxPJycmZbCjPnDnD8+fPAWUjR5kyZQSBYOnSpcIxSUdHh927d2NmZoaPjw+NGzcmPj6eunXrMnfuXLVlTp06lZSUFFq3bs3IkSNz2pUiIiIiIiIiIiIieUbMSBD55SQnJwsXP6qCSVxcnPA3Ozu7bJ/r4+NDcnIyFhYWzJgxA1B26WcMrhw1alSm53Xp0oWQkBD8/PxISkri2bNnPHv2DFBOQujq6lK+fHkqVqyIlZUVLVq0yNKrt2rVqrx69YrY2FgOHz4s/L1kyZLfDfGMi4tjz5493LxyhbbllZ1/GYOPVfTv35/Lly/z/PlzFi1axOTJk3/YyiM6OloQVypXrsyFCxdITU3F1taWd+/e8fHjR+bPn0/Dhg3VnqdQKMSwTJQF7n379tG4cWPu3bvHhAkT6NOnD1u2bBECVFXdwypUnu+xsbEcOXKEixcv0qZNGz5+/Mjz58/R1NRk1KhRat2fFStWRENDA5lMJnRaV6pUSXgPpFIpPXr0QEdHh0uXLnH06FHkcnmWAcRZoVAo8Pf35+rVqyRERTH3j8/f8OEjaOvQWQg6f/nyJW/fvuXdu3eCN7OKb60Pvv23alt/xOP62rVreHh4AMoskOTkZPr3709iYiINGjSgTp06eV6myJ9HgQIFct2VLJfL+e2334Spgk+fPgnHpObNmwuPK1KkCFOnTmXlypXcvn2bbt26UbhwYYoWLcqrV6+Ij4/nwYMHFChQQCniKhRgqpxQ0NPVZf/+/Rw9elRtAsDd3R1jY2MGDRqUq23V1tbGzs6Oq1evkpycTEBAAK9evRJs7FTbbWlpqSaYlC5dmvfv3/PixQscHBzw9vYWrFVq1qyZo/D36NEjEhMTkUgkVKxYUfCNB+UxQDUVsWTJEt6/f4+pqakorIn8EFZWVkJYulQqzVNWlOpzGB4eTmxs7HdDlLPj69evDBs2jMjISCpWrMiKFSu4cePGDy0rO0qWLEl6ejqRkZGkpaUJYmShQoUyTT6cOXOG6dOnk5qaSqlSpdi0aVO2E596enpUrlxZKOwDPyyqKBQK7ty5w/79+3FzcyMlJYUyZcqwYeNGUh3+d2xt3LgxRYsWJSwsDCcnJ6ZPny7Ygnbp0gVdXV3hmFe0aFHmzJnDuHHjhOdv2LABLS0t4d+qUGhQ5pSJ53kiIiIiIiIiIiK/GlFIEPnlZOz+V13Iqrqd9fX1c/RqVV3A1a5dW+0CyMnJiadPn7J///4shYTq1auza9cuZDIZISEhvHjxgpcvX/LkyROCg4NJSkrC29sbb29vjh49iqurK9OmTaNmzZpqyylRogTOzs6cO3eO0NBQypUrh62trVB0zcreSC6Xc/XqVXbt2kV8fDx6Gba7cZMmmR5vZWXFmTNnGDJkCK9fv2bevHkMGzZMEF1yi0KhYNeuXaSlpVG5cmVq1KgheBzPnDmT7du3c/bsWZ48eZJJSMiITCbjypUrvHr1iiFDhuQYIvxvpGrVqly9epUWLVoIQacDBgxg9+7dgi96RjGhVq1ajBs3jsDAQC5evEh0dDRHjhwBlMX2AQMGZLKQsLS0xN7enlOnTnH+/HmATJ2CEolEsCe4dOkSx48f5/jx4xgYGGBqaoqJiQlpaWkEBwdjbGyMkZERRkZGxMTE4OnpSVhYGACmGbyte/TsAX9M1UgkEgoWLMjbt2/58uVLpv2Q0Q5CU1MzUxEqLi4OUHag54WQkBChY7J37940b96cuXPn4uPjg7m5OYcPHxb9m/+folAomDBhAocOHUJTU5OhQ4eyevVqACZPnpypE7l79+7Y2NiwYMECnj9/ztu3bzE2NsbKyorg4GDi4uIEizajDF3U586fJ+aPDJcyZcowaNAgIiMjWb16NYcOHcLIyIju3bt/d3ulUindu3cnNjaW5ORkQkND1cRYqVRK5cqVKVasmNrvT//+/bl+/TqnT59m4MCBLF68GFAeF8qXL5+tkPD27VvhvpYtW1KyZEm2bdvGpUuX0NHRYdeuXWhra3P79m1cXFwApdd5wYIFv/taRES+pVChQri4uDBw4EAWLlxIVFQUnTt3pl69et8VFcqXL0/VqlXx9fXl6tWr9O7d+4e2Yf78+Tx+/BgjIyO2b9/+S7IWvsXMzIzq1aujUCiIjY0lMjISmUxG2bJlBaFbLpdz+fJlIZupadOmrFmzJsffL4lEwtKlS2nXrh2g/N3Oyo4oJyIjIzl8+DBubm6CZSQow9RdXV0xNjIi45yqhYUFXl5eNGvWjJcvXwrnGg4ODsL5XEbs7e0FIaFUqVKZ7NmOHj2KTCajdu3awhShiMj/R0JCQoiKivru4woUKEDx4sX/gi0SERERERERUSEKCSK/nIxCguqiTuUrrwq2zA6VkFCrVi21v3fv3p0ZM2bg4+PD8+fPs+3S1tDQoFSpUpQqVYoOHToQFBREoUKFeP/+PX5+fvj5+XHx4kWCgoIYPnw47dq1Y+zYsWrLMDY2xtHRkdTU1GwtmAChw3vPnj2Cb3yJEiUYPXgw7M/aFkdFqVKlOHXqFE5OTjx//pwNGzbw7t07HB0dc11UvX37Ni9evEBLSwtnZ2c8PT1JTk6matWqtGvXjsePH3P27NlM4aYqXr9+za5du9i3bx+hoaHCMg8fPvyf62Kztrbm6tWrNGnSJEsxISAggPHjxwsFPk1NTRo3bky9evW4e/euICh069YNGxubLNcxcuRIwYO6ePHiWYaCq8QEHR0dLl++TEpKComJiSQmJgrvUXb+6dra2tja2tK4Th04ey7Lx6jCZLO6OMsoJGT1GfwRISE5OZmpU6eSmJhI9erVGTduHMePH8fd3R0NDQ0OHTqUaTJC5J9PTEwM7u7uHDlyRLD2GTt2LGvWrAFg4MCB2WatlC5dGldXV44ePcrKlSuJjY3F19eX4sWLU6hQIWJiYrCwsECSIZQ7NSWFokWLMmDAAJo0aSJ8PuPi4ti5cydbt24VbMlyg6amJsWKFaNYsWIkJCQQGhpKQkIC5cqVy7JbuVatWlSpUoUXL17Qv39/IiIiKFKkCMOHD892HdHR0Zw5cwaFQkG1atWwsbHh48ePbNq0CVDa/lWpUoW4uDiGDh2KQqHA0dFRzetcRCSvODo64ubmxqlTp1i3bh3r1q3D3Nwce3t72rZtS/PmzdU62DPSpUsXfH19uXjx4g8JCQcOHGDPnj0ArFu3LkuboV+JRCLBxMQkk3VTSkoKly5dEuwoR4wYwcSJE3M1odGwYUN69OjBsWPHWLZsWa4aK9LT07l+/TpHjx7F09NTOAc2MDCgS5cuODk5YWNjg0QiQfGHIJqRsmXLCmJCWFgYDg4OHDp0KMvzXCMjIwYOHMju3btZsWJFpvsPHjwIKD8HIiJ/NVlmaKWkoPoWPXv2jAKWlt8t/IeEhFCpUqUcM4hU6Ovr4+fnJ4oJIiIiIiIifyGikCDyy8noLa0q+KgmEr4nJKjsiL4txubPn58OHTpw8uRJ9u3bl2srC1CKCyVLlqRkyZK0bduWQYMGsXnzZk6dOoW7uzs3b96kRo0a2NraCtsrkUiyFRFSUlK4fv067u7uvH37FgBdXV0cHR3p2LEjWrLMUwuhoaH4+/vTtGlTQVzJly8fo0aN4uLFi5w8eZKzZ8/y7t07xo0bl+1+kslkfP36lbi4OMHDv3PnzpiamnLp0iUApkyZglQqFfZhRiEhISGB48ePs3v3bjW7AVNTUxISEjh27BhVqlRhzpw5ud6//xasra1Zv349Y8aMEcSEIUOG4OrqiqenJ/fv32fYsGFqli9aWlo0btyY+vXrExcXJ4TFZoWNjQ22trbcu3eP4cOHZ2sRJJFIaNu2LW3atCE5OZno6Giio6OJiYnhxYsXSCQS4uLiiI2NJS4uDoVCgY2NDY0aNcLQ0BCNDN+/b1EJCVlNJGQUj7IKq1QJCbkt1qampjJz5kxev36NmZkZy5Ytw8/PT7BtWLZsGU2ymNgR+WcSHh7OmTNnOH36NJ6enmoZGmPHjmXTpk3I5XIGDhzImDFjclyWVCqlV69eeHh4EBQUxJcvXwgODsbQ0JCiRYvy5s0bZcHtD4uu2nXqMHP+fOE78+HDB96/f0/37t2Ji4vj2LFjuLi40KNHj+9a0H2LoaHhd8NIVZNGU6ZMESYmZs2alW239devXzl+/DgpKSkUKVKE1q1bk56ezsGDB0lNTaVVq1YMGzYMUB6vQ0JCKFGiBCtXrszTtouIfItEIuHAgQOcP3+eM2fOcO7cOSIiIti+fTvbt2/HxMSEdu3a0alTJ1q0aKFWKHdwcGDBggXcvXuXmJiYXGUrqNi2bRsLFiwAYPz48bRs2TLbxyYkJODv70/+/PkpUqRIjg0beeXz58+cP3+e2NhYtLS0cHFxyVK0z4ktW7Ywf/58ihYtmuPjIiMj2bNnD8ePH1fLw6pRowbOzs506tTpu+e8KsqWLcvDhw+5c+eO8jwyG7EHlNkJkyZNynSse/v2Lffv3xesEkVE/ioKFCiAvr4+Tk5Ome7Tk0h4/MdvecOGDZHo6X238B8VFUVSUhL79+/PMajdz88PJycnoqKiRCFBRERERETkL0QUEv5j5DaYMrce+jKZLNPjVAUmTU1NwQpIFaypq6vL58+fs1xWVFQUYWFhSCQSrKys1AQJUHZYnTx5kiNHjtCpUycMDAy+u30JCQnCNIQKTU1NxowZQ8uWLVm9ejWBgYHcvHmTgIAAmjdvLnjYf0tMTAw+Pj5s3rxZEEa0tbVp1KgRPXv2xMzMDLlczte0/3XSPnnyhJ3793Px4kXkcjm1a9dmzZo1FC5cWNhX9vb2FC5cmB07duDt7c2kSZMwMTHh69evwi01NZX09PRModFFixalRYsWeHl5ERcXh5mZGQ4ODqSmplK1alUA/P39cXd359ixY5w4cULYdolEQtOmTenVqxetWrXixIkTTJ48mXnz5lG0aFHh4juvhbn/z1SoUCGTmLBx40bWrVvHy5cvcXFx4cKFCzRs2DBTgUBPT4/kb7oNU1NTBbEJlAGYjx8/pmXLlmp/j4+Pz3J7NDQ0KFCggODhruqkVqH6Pqu+g2lpacgzfG8+fPiAIkNXo6oQ+/79e8LCwkhNTVU7JhgbG5Oamoq2trba9+br169CQLNUKhVslL4lLi6OQoUKkZiYyNSpU3n8+DHa2tosXLgQhULB1KlTSU9Pp3nz5owfP/67x6OvX7/makJHoVDkqhgll8tzPW2TF3/x/++o8mu+5e3bt7i5ueHm5sb9+/fV3q8yZcrQsmVLKlasyNy5c0lJSaFZs2ZMnDgRX1/fXAlOmpqaWFlZERERwevXr0lISBAmbvJlKKSVL1eOtLQ0PD09uXjxIt7e3gC0a9eO8ePHExcXx6VLlzh27Bja2tqULFkyx/WmpaWpHUujo6OJjY2lSJEial3ASUlJ3L9/H1B2AhcoUICoqChq1aqFjo6OcF9cXJxQ+IuNjeXChQtERUVhYGBA+/btSU9Px8vLi4iICAoVKsSmTZtQKBScPn2agwcPIpVK2bp1K4aGhsjlchQKRa5+qxUKRa6/H/+1KbN/Mr/6POzb5WloaGBvb4+9vT1fv34VbLnOnDlDZGQkBw8e5ODBgxgYGGBvb4+LiwtGRkZq9kanTp3C3t4+x/W+efOG0NBQ9uzZw4kTJwCl9U6zZs14+vSp8LjPnz8L4nRwcDAuLi5ER0cDyt+uAgUKULRoUaRSKWXLlsXc3JxChQplO3kql8vVhEwVQUFBXL16lfT0dPLly0evXr1o0aJFtsc3FXFxcZnE86zOVePj44Xv+c2bN5k8ebIgypuamuLg4EC3bt2wtLQUzpu+tcNUZPh3XHw8kj/2AygF/GbNmgnnaCqio6Mzne/mz59fCKdXcfToUQBatGiBubl5noLoRUR+huLFi+Pn55e1FVFKCjgrm7927tyJo7Nzrgv/lSpVymRBKyIiIiIiIvL3IwoJ/zFyW0yQSCS5emxWxTbVxYuGhoZQ5FD9zdjYGFNT0yyX9ejRI0B54qgqtGekffv2QihdUFDQdy9yQekZrCrCfouVlRVdu3Zl9+7dLFy4kE+fPnHkyBGcnJyYPHkyxsbGyOVyrl+/zr59+/Dy8hJeR8mSJRk8eDB9+/bN1IX+NT6eiD/+36lPH5L/eI62tjYPHz6kU6dO7N69m1atWtG5c2c0NTVp27Yt3bp1o3///rx//56YmJjvvraCBQuyd+9erKysWLZsGQATJ04ULr6LFClCkSJF+PDhAx06dBCeV6ZMGbp06ULPnj0FW5mUlBQaNGjA4MGD2bFjB2PHjqVEiRKCGPFfwdbWFltbW6pVqyZkJuzevZsrV65w5swZZsyYgZ+fHwEBATg7OzNt2rQcC6avX79W6yy0sLBQey9UtG3bNpPXcVY4ODh8t0ArT07m06nTAJQoXhwyFNhVnskpKSmULFkSa2vrXFk3WFpaEh4eDig/V9l1alpaWmJmZoa9vT2PHz8mX758nDhxggYNGtC+fXsiIyMpX748R44cyVUBVCqV5trqKzfHKzGLIWu+3XdeXl5MnTpVENNUVK1alVatWtGyZUtKlSrF+/fvcXJyIi4ujpo1a7Jq1So0NTUxNTXNVpDNyObNm4U8kYiICKZNm8bVq1cZNGgQ44YNI659B2H7HB0dBcFNKpWiUChwd3enWrVqnD17lh49enD69GmOHTvG4sWLcwywr127NnXr1uXGjRu4uLjw4MEDYRumT59O//790dTU5OTJk8L3V0tLiyFDhnD37l26du2q9r1u3bo1TZs25eTJk8ycOZP4+Hjy5cvHgQMHsLGx4cqVK0J2xO7duylVqhTx8fFMmzYNgKlTp2JnZycs71cX/nP7ey6KDf8scvu+ZVdwB+Xntl27drRr145NmzZx9+5dTp48yalTpwgJCRHs5VTTMD169MDX15dbt24xZMiQHNcbGBjI+vXruXLlCqC0NOvevXumbba2tqZevXqcP3+epUuXkpSUROHChZHJZERGRgo3UDZfqChevDhly5Ylf/78GBsbY2xsjImJCfXr18fc3Fz4t7GxMYcOHRKmMhs1asTOnTvR19fPlRVfVFRUroRoS0tLTE1NmT9/PsuXLweU55EzZ86kY8eOwvsgl8uznTiU6+igKrUa5qIZBpRTU997HQqFgjNnzgDQp08f8bdO5C+nePHiWYoD8qQkVIac3+aHiYiIiIiIiPz/RBQSRH45qk7PjCJDbjISVB1stWvXzvJ+DQ0N+vTpw4oVKzh27FiuhITvoaGhwaBBg6hbty5r1qzh9OnT7Nu3jwsXLtC1a1cuXbpEcHCw8PhGjRoxduxY7Ozssgyj3bt3L66bN3NGT2l5oaWtTY9evRg7diy6urr07t0bb29vOnTowLRp0xg5cqRwwVmlShW8vLy4c+cOGhoa6Onpoauri66uLgqFgkKFCqGvr4+uri46OjrCheLJkycJDg4mf/78mXxxmzRpwqFDhwSv3v79+9OgQQM+ffqElpaW0BW7ePFiPnz4wLx582jcuDE3btxg4MCBXLhw4T81kaBClZnQokUL7t27R9WqVVm1ahV+fn5MmjSJQ4cOsWPHDs6dO8fChQvp0KHDLy/CpaSkoKGhgaam5i9btkpUy02A3beoirg5CRnh4eF0796dgIAAChQowNmzZ6lZsya//fYb165dw8DAgKNHj+baHknkr+fDhw84OjoSExODhoYGjRs3xt7eng4dOpCcnIyWlhYJCQmsWbOGPXv2kJKSQrly5di0adNPBbWbm5uze/duQPk52rt3L53/uG///v0kKxSUKVNGCDN3d3dn9+7dLFu2DDMzMw4ePEidOnXw9fVl3rx5LFu2DEtLy0zrUSgUPHz4kLlz5wpFSy0tLYoWLUpwcDDTp09n3759QqByRipWrJilDVJSUhJjxozh5MmTgPI3bP369RQrVoyIiAgmTpwIwNChQ2ndujWgtCf5+PEjZcqUYfbs2T+830REcouGhgaNGjWiUaNGuLi4cODAAfr168fWrVuZOXMmpqamdO/enTlz5nDz5s0c7Y2Sk5P5/fffefLkCVKplHHjxtGqVassH6tQKFi3bh2//fYbCoWC5s2bs3fvXoyNjYmMjCQgIAA/Pz+eP3/Omzdv8Pf3JyoqipCQECHnILcMGzaMBQsWoKmpmWlC8GcJDw+nV69e3Lx5E1B+n3///fdfas30ozx9+pTAwED09PRwcHD4uzdHRERERERERETkX4woJIj8clSWRBk7slSj2jnZEanyEbITEgD69evHihUruH79OuHh4VlOLvwIBQoUYM2aNfTo0YPZs2fz5s0btm7dCigLpz169MDJyYmiRYtmKqy/e/eOLVu2sGfPHuLj49GTSARvbx9vbywyBA7eunWLyZMns23bNpYvX861a9fYunWrsMx8+fIJhaaMJCYmZnlBr7pAB6Wf/7f718XFBUdHR2xtbTMVb58+fcqcOXPUMhQWLFjA1q1bCQ0N5e3btwwaNIibN29m6Zn/b8fa2pobN27Qr18/nj59irOzM82bN2fr1q106tSJ6dOnExwczJAhQ2jRogVLliyhRIkSP7XO9PR0zp07x+bNm3n48CGg7ErV1tZGS0sLLS0tdHR00NLSEv6mKv507979u3ZfKiEhO3uxnPhe2HJwcDDDhw/n48ePWFpacv78eSpWrIibm5vQ7bp161YqVaokWi78Q1EoFIwZM4aYmBhq1qzJ2bNn1SauXr58ydGjR9m0aZNg6WFjY8OqVauyDCnOCykpKVy4cIGDBw9y6dIltOVyOv9xHO3Rowc169cnICCATZs2CVZ5VlZW+Pj4MGXKFPLnz8+sWbOYPXs2r1+/Zu7cuSxfvlz4zMtkMu7evcuxY8cICgoClPYlTk5OjB49GnNzc/bs2cOyZct48eIFnTt3platWjg6OgrZIlkRGBjI1q1biYyMRENDgwkTJjBmzBjB2m/8+PF8+fKFKlWqMH36dEA5qaSaUPinFCJF/ltIJBKhMcPX15fNmzczc+ZMKlSoQKVKlfDz8+Py5ctZeu3HxMTg7OzMkydP0NbWZsaMGdja2ma5nvT0dDZv3oy7uzsAzs7OzJw5k+vXr1O4cGHKli1Lw4YNadiwIbGxsYIYGRUVRUBAAMHBwcTExBAbGyv898uXLyQkJAh/i4uLQ1dXl4ULF2YZEh0bG4uXlxdXrlwhIiKCQYMG0aZNmzwJ9F5eXowZM4bPnz9jaGjI5s2b6dmz53efFxoayr1794iMjGTAgAHkdCaVmJjIixcvsLa2zjEjISsOHz4MKK2lRKFeRERERERERETkz0QUEkR+OT8ykSCXy3n+/DmgLiR8/PiR58+fCxMA5cuXp379+ty5c4eTJ08ycuTIX7rt9evX58KFC+zcuZNbt27Rvn17OnfuLIRqfv36v/yD27dvM2fOHB49eiT44JYvX56xw4bBtu0Agr3Hly9feP78OU2bNmXDhg00atSIESNGcP/+fVq0aMGmTZto2rRpnrf3woULeHt7o6+vn6UNgampqZplBijtOyZMmCB0z+rr6zNmzBiCgoI4evQokydPZuPGjYwcOZKHDx8ycuRIduzY8Z+0vahatSoPHjxg9erVzJ07F09PT6ysrJg0aRIeHh5s3LiRDRs2cPXqVZo0aUL79u0ZNGhQnj1dZTIZW7ZsYfPmzbx//17tPoVCQWpqaiY/5IzcvXuX+fPn07dvX4b265ftgV1VEE1KSiIpKSlP25jTRIKfnx8jRowgOjqacuXK4e7uTvHixQkMDBSC0ceMGUP37t3ztE6Rv5aDBw/i7u6OtrY227dvVxMRLl68yPjx43n37h0ApUqVYtKkSTRv3vynjg3Pnz9n06ZNuLm5CWIVQK06dSBW+e/r16+z58gR4T4TExOSkpLw8fERxIQhQ4Ywbdo05s6dy/Tp0wkLC2Pu3LksXbqUJ0+ecOTIESHbQ09Pj8GDBzN8+HAKFSokLHfQoEF07tyZZcuWsWfPHh49eiRMkLVv315NHJfJZLi5uXHmzBkUCgXFixdn/fr11KpVS3jMtm3buHHjBrq6umzcuBEdHR0UCgWTJk3i69ev2NnZ0bFjxx/edyIiP4NEImHq1Kn069ePdevWMWHCBPT09GjXrh1+fn6cP38+k5AQHh6Ok5MTr169wsDAgHnz5mVrgZiQkMCSJUt4+vQpEomEJUuWCBMRKjEQlL9LZcuWpWTJkpQvX562bdtSoUIFChQoQIMGDTItV2UdlpGMdmDp6en4+Pjg4eHBjRs3uH//vlomyuXLl2nWrBm///475cqVy3EfKRQKli1bJjRsVKtWjYMHDwo2gd8SGxvL7t27uX//Pvfu3SM0NFS478yZM5w4eDDL571+/RpHR0fevHmDiYkJzZs3Z86cOWqZSNkhk8k4fvw4QKapVBEREREREREREZFfjWiiKfLLUV3MxcbG4uLiglwuF4qgN27cwMfHR+3xQUFB9OvXj9jYWHR1dalatSrp6emsXbuWKlWqYG9vj6Ojo7CM/v37A8oQXFWuwq9EW1ubESNGcODAARwdHQURISOJiYl06NCBBw8eIJfLqVq1KidOnODhw4f07ds30+MnTpxI69atWb9+PQA9e/bk/v37VK5cmc+fP+Pk5MTLly9ztX2JiYns3r2bli1b4uTkBJBlVkNWfPnyBTs7O0FE6NGjB7du3WLcuHEsX76cWrVqERsby6ZNm9i8eTNSqRRXV1fu3LmTq237N6KpqcmUKVPw9fWlZcuWpKSksHjxYlavXs20adPw9PSkfv36pKSkcOLECbp27ZpnS4ULFy4wc+ZM3r9/T/78+Zk8eTLe3t4EBQUREBCAj48Pjx8/FgojHh4euLu7c+bMGRYtWkTJkiWJjY1lw4YNNGzYMNv16OvrC2LeqlWrMoV3Z0dgYCCBgYEAmT5nMTExjBkzhujoaCpWrIinp6fgk7tq1Sri4uIoWbIkS5cuzdM+EflrUSgUzJkzB4DffvuNypUrC/cFBgbSrVs33r17h5mZGXPmzOHMmTO0aNHip0SEmJgYWrVqxf79+4mLi8PS0pKJEyfy+PFjoYMZlOKnkZERAwYM4Ny5c4SHh7N//34AfHx8aNu2LXK5nC1btmBsbMyCBQsoUKCAkOHg4uJCWFgYBgYG9OrVi507dzJnzhw1EUGFmZkZK1euxMPDg7Jly/L161dOnjzJ77//LkzWRUdHs2LFCk6fPo1CoaBhw4ZcvnxZEBHS09NZvnw5ixYtAmD+/PmUK1eOxMRE+vfvz7lz59DU1GT16tX/SYFW5J9Dz549KVGiBJ8+fWLMmDEoFArat28PKAU8Dw8PtcevWrWKV69eAdC1a9dsRYT4+HhmzpzJ06dP0dXV5fDhw4wePZrx48fz8eNHzM3NhYymz58/c//+fY4cOSJYBeYGmUxGXFwc4eHhPH36lA0bNtC7d2/Kli1Ly5YtWbZsGXfu3EEmk1G+fHlGjRpFy5YtAeWEwfDhw7+7juvXrwsigpOTE7du3cpWRABl3smUKVM4fvw4oaGhaGhoYG1tjYGBAR4eHvTu1SvL582dO5c3b94AyuPiyZMnGTZsWK72wcSJE4mIiCB//vxZTrSKiIiIiIiIiIiI/EpEIUHkl1O2bFm6deuGXC5n6tSptG/fnkaNGmFhYUFISAj29vZs3bqV5ORkXFxcaNmyJdeuXUNbW5t58+bx6NEj6taty5QpU4TCzalTp7C3tyc+Pp7evXtjY2NDbGwsvXr1wtPT8y9/jXp6emoXeWXLlqVVq1bZBtzdu3cPgIULFwr+9OXKlePs2bM0b96ctLQ0xo0bR1paWrbrfPfuHXPmzKFq1apMnDiRJ0+eoKGhQfv27Zk6dep3tzklJYVu3brx6tUrLCwscHd3Z82aNYI9lI6ODps3b0ZTU5M7d+5QsGBBoaDg5eWVux3zL6Z06dJcvnyZTZs2AUoh69y5c5QrV44TJ07g7u6Ovr4+ycnJal2IuUFV3Le0tMTHx4eZM2diaWmJsbExBQsWpGjRopQqVYpy5cpRtWpVatasia2tLY0aNRImRw4dOkTDhg3VPkOrXFyEkGQVI0aMQCKRcObMGe7fvy9MGmSFXC7H19eXy5cv8/XrVxo2bKhWOFIoFCxatIjIyEhKlSrFzp071Yqz7dq1A5S2R/v27cvTPhH5awkLC+PDhw9oaGgwevRotftOnz6NTCbD2tqaS5cu0bt37zxbb2TFpUuXSEhIoFy5cly+fBk/Pz8WLlyYZQ5Beno6FStWxM7ODm1tbaEAb2RkJNhtqQKWCxYsKNgIqejatSs7d+7E0dExV9Yf1apVY/LkyQwbNgxdXV1h227evMns2bPx9/dHV1eX4cOHM2LECGGZYWFh9OjRg3Xr1qFQKHB2dqZPnz74+fnRtm1bDhw4gIaGBmvWrKFSpUo/tf9ERH4WLS0t1q5dKzQNTJ06ldKlS9O9e3fkcjkjRowQzl8AOnfuLNiF7d27lwULFqhNF4BSRJg1axaBgYEYGRnh4uIi/BaopvXMzc2xs7PLZNloZmbG2LFjAbh27Ro9e/akXbt2NGvWjDp16mBlZUX16tWxsLCgUKFClCpViipVqmBnZ8fcuXO5fPky8fHxGBkZ0apVK1xcXPD29ubRo0cULVpUEEYMDAxwdnb+7v4pX768cKzr1q3bd23IVIHLTZo04erVq3z58oWHDx9y7tw5DAwMsj2XymoyaeDAgTmuKyUlBScnJ2FidMmSJTkGb4uIiIiIiIiIiIj8CkQhQeSXo6GhwaFDh9i8eTN6enpcuXKFMWPGMHv2bNq0aUNaWhoLFy7E2toaFxcXUlNTady4MSdPnuT58+c0adIEHx8fTE1N2bx5MxcuXMDQ0BAvLy/s7OyIi4tj//79NGvWjJSUFJydnYUO+78KqVTKsmXLuHnzJhoaGpw+fZoLFy5k+dikpCTBkzs2Npb58+cL9+nq6rJmzRqMjY3x9vZmw4YNas9VKBTcvHmTYcOGUbNmTTZs2EBsbCwlS5Zk4cKFvHz5kn379mFqaprj9srlcoYMGcKdO3cwMjJiz5491KhRQ7j/0aNHrFixAiMjI0E8cHV1FWymbt++ned99G9EIpEwYsQIoZNx3LhxvHr1ColEQs2aNYWMhG/tiVScPn0aJycn3r59q/Z3Vff3x48ffyg/QENDg9atW+Pm5saxY8f/t75TpwSrFpWg0LNnTzZs2ED+/PlJSEjg+vXrWQofqamp3L17V+iSdHZ2Zt26dWpi2fnz57ly5QqamposWbIkU4HW3t5eCJIdM2YMN27cyPNrE/lrUGXUVK5cOVNospubG6Asdn0vhyMvnD9/HoDBgwfToEGDbIVY23r1SEpKYvr06dSsWZMbN26wYMECQNklfOzYMQB6Zej2LV++PNbW1sJ29+/fP8vpspyQSCTUr1+fWbNmkT9/fsLDw9mxYwfx8fEUL16c+fPnU69ePbXXY2dnx/379zE0NGTTpk0sWLCAQ4cO0aFDB968eUPRokXx9PRkxIgRedoWEZE/C3t7e7Zt2wYoJw42btzIsmXLaNmyJampqTg7O+Pt7Q1AgwYN8PLyYtCgQUilUu7evcuwYcPYu3cvKSkpmUSEZcuWCQIfwJQpU9DX18fb25s9e/YQExODmZkZAwcO5PDhw7x8+ZIxY8aQmprK6NGj8fDw4P79+3h7e/PmzRs+fPhAbGysms2kVColf/78tGnThoULF+Lp6cnr169xdXVl8ODBlCxZkrCwMOGY0bNnT548eUKfPn2y3B9ubm6MGzeO8PBwihQpIljyqSYTckL1W25oaEiTJk2E42WjRo04d+4c+hmOnxknF3v16sW6deto2LAhq1evJjQ0NEcrwJiYGOzt7XFzc0NbW5t9+/ZlOQ0rIvJPxM/PjydPnmR78/Pz+7s3UURERERERCQHRCFB5E9BIpEwZMgQ7t+/T7Vq1YiKimLkyJEULVqUhQsXoqurS0JCAubm5mzcuJGOHTvSt29fDh06BMCAAQPw9fVl0KBBtGjRAg8PDwoWLMiTJ09o2rQpUVFRuLq64uDgQHp6OmPHjmX79u1C0PNfRY0aNYTu3UmTJglZEBkJCAhAoVAInWLbt2/H19dXuN/c3JzFixcDyov4ly9fkpiYyN69e2nSpAndunXjypUrKBQKmjVrxqFDh3j48CGjRo2iYMGCudrO2bNnc+zYMbS0tDhy5AgVKlQQ7rtz5w49evRgzZo1LF68WPC0P3XqlDDCf/fu3Vzb4PwX+O2336hfvz6JiYkMGjRImJxRWfpkJSSEh4ezdOlSfHx8mD59utrkQMGCBTE3N0cmk2Wy/sqJp0+fMn/+fLWpAtt6/wu9tK5Zk/T0dI4fP46DgwM7duwgPT2dunXrcvDgQUxNTZHJZDx+/Jhnz54J73F0dDTXr18nKioKDQ0N2rRpw7hx49RyTz5+/MiyZcsAGDZsmJoVTkZmzZpF9+7dSU9Pp1evXplEFJF/BiohoXr16mp/DwsL49GjR0gkkh/KccmO4OBgfH190dDQ+G52xpkzZ3B1daVAgQK8ePGCpk2b4uPjg5GREVFRUcjlcjp27KhWsASYPHky8+fPF45pP0rx4sWZO3cupUuXBqBZs2bMnj1bmOZKTU1l2rRpDB06lNjYWKytrbl8+TItW7Zk7NixTJkyhZSUFJo3b86TJ09ytB8TEfk7cHZ25vfffwdg5cqVHD58mE2bNlGvXj0SEhLo168f586dQy6XY2xszNy5c1m5ciU1atQgLS2NQ4cOMXToUKZNm6YmIpQqVUptPYULF2bx4sWUKVOGgQMHcvbsWV6/fi0U0VU5JPv37+fjx49YWFiwe/dujh49yrlz5/D09MTDwwNvb29ev35NWFgYnz59IjAwkAMHDjBy5EiqV6+u9lsFsHTpUlJSUqhXrx7btm3DwsIi0z6Qy+UsXryYYcOGcfToUUaNGoVMJmP06NFIpVI8PT15+vRpjvtR9TuYlVVlo0aNhCwDgCFDhqiJCX369OHMmTP069cvk5ibkXfv3mFnZ8ft27cxMjLCzc0NBweHHLdLROSfgJlZAfT19XFycsLGxibbm5OTE/r6+sL0k4iIiIiIiMg/C1FIEPlTqVy5Mnfu3BFGtHfu3MmhQ4fYtm0by5YtY8uWLezatYspU6YQHR1NhQoV8PLyYtu2bWpF8po1a+Ll5UWJEiV4/fo13bp1482bN6xdu1YoEs2fPx8rKyv69+/Pli1beP78+V8iLMyYMYPixYvz/v17lixZkun+Fy9eAFC3bl0cHByQy+VMmTJFrfO8W7dutGrVirS0NPr164e1tTVTpkwhICBAOOm+e/cuJ06coHXr1nz58gUvLy8h5Dkntm7dyqpVqwDYsmULzZo1E+579OgR/fr1IyUlBVBevBsYGFC9enVSU1N58uQJhoaGxMbGCq9DRJmbsHXrViwsLAgMDGTcuHEoFAohGDEkJCTTc1xcXIT9HBAQwNatW4X7JBKJMCGiKuh+j48fP9KjRw/Wrl3L6tWrs3zMurVr2bp1KzY2NqSlpbFlyxYGDBjA69evKVCgALVq1RJEpXfv3nHjxg0CAwO5desWycnJGBgY0LhxY8qUKaO2XLlczuzZs4mPj6datWo5WkRIJBK2b9+OjY0Nnz9/pkuXLsTGxubqNYr8dag+dxknleB/UwN16tT5pRf1quW2bNlSCKXPDolEwoABA/Dz8xOO96ampgwYMEAozKnyHTKSL18+rK2ts510yAsmJibMnj2b1atXM2DAAEEYDgkJYfbs2ezfvx+JRMLo0aM5deoUSUlJtG3blpMnT6KhocHMmTPZs2ePWBgR+ccyceJEZs2aBSjF8kuXLrFz506qVavGly9fGDlyJB06dODatWsoFAosLS1ZsmQJv/32G4UKFSIyMpKgoKBsRQQVgwcP5tmzZ6xbt46mTZuqhZiDUphT/aZNmDCBjh070qJFC+rVq0f16tUpW7YsRYsWxdTUFF1d3e/mjPj7+wuZKgsWLMjy8UlJSQwZMkSYCtXS0uLOnTts3bqVUqVK0alTJwBBPM8OlV1ZcHBwlo0lDerXF/7/5s2bODk55TpTKTg4mNGjR1O9enVevnxJ4cKFuXz5Mo0aNcrV80VE/m6KFbPEz8+Px48ff/fm5+cnNOeIiIiIiIiI/LPQ/P5DRER+Dl1dXebNm0fTpk2ZMGECL1++ZOjQobRu3ZpZs2Yhk8nQ19dn0qRJDBgwQCjGfkv58uW5du0a7du35+XLl3Tt2hVXV1dmz56Nubk5GzZsIC4ujqtXr3L16lVAOV5et25dbG1tqVevHlWqVMl00QrKEfPceMsGBQVl8pwHGDVqFNOmTWPjxo1YV6pE3Qz3qUZ0K1euzIQJEzh//jxXr17F3d0da2trQQxYsmQJ9+/fF7rZixcvzsCBA+nevTtaWlqCsHL+/HnGjRvHly9fGDRoECtWrBAujOPi4tQ63T09PZkwYQKgtBRo06YNnz9/xs3Njbdv37J582ZSUlIoV64c2travHjxgpEjR2JjY8Pz58/ZunUrderUwdPTk9u3b1OtWrXv7qP/z+RGmFE9zszMjG3bttGlSxfOnz/Phg0bsLS0BJTFRblczoEDB/j48SNRUVHcunULgAoVKhAQEMD27dsJCAggf/78VK5cGSsrKy5dusTjx48FweFb3r9/j66urtAl+fnzZ0A55dK5c2dl1kJKCioDF4VCQc2aNQWLsFWrVuHv74+TkxNDhgwhPT2dMmXKYGxszLNnz4iLixM6KQsVKkT16tXR0tJCJpMJ2/Thwwd27drFw4cP0dXVZfbs2aSnp5Oenk5aWlq2IsGuXbto1aoVfn5+ODo6cvbs2Uxdo98ik8l+SRFYJGfi4+OFTtvy5curTbicPn0aADs7OwIDA3P1fsTExKCjo5Pt/XK5nLNnzwJKm5GsBF95hr9FRUWR+Me/Fy9eTJ8+fahUqRJjxoxBLpfTqlUrihUrxosXL3J1HE9PT+fDhw/ffVxCQkKmz2i+fPlIS0tDoVDg5eXF0aNHSU9Pp1ChQqxdu5ZGjRqxb98+5s+fT0pKCoULF2bz5s3UqVMHIFfWZXK5PFchzAqFQgxrFsm1HV5ujqfz5s3j/fv37N27lwkTJrBt2zYOHTrE9u3b2bFjB76+vvTr1486derQpk0batWqRZ06dahevTqnTp3Cz8+PgQMHYmlpKZyLxMfHZ2v3l5GUlBSKFCnCnj17hGmEnj17ZsqO+vr1a64mJCMjI0lISGDmzJnI5XJatmxJsWLFMn333dzc2LBhAyEhIWhqatK3b1/S09PZt28fS5YsQaFQUK1aNU6dOsXp06fx9vbONt/E1NQUMzMzPn/+jJ+fn5AJoSLjOYaevj7Xrl3DycmJvXv3ZjuFEBgYyNatWzl8+LDwups0acLmzZsFO0URkf8vFC9eXBQIRERERERE/p8jCgn/MX510SG3Rb5ixYrRr18/7Ozs6N+/P1euXOHMmTOAMgRz9erVFCtW7LsXxCVKlODGjRt07NiRu3fv4uTkxJEjR5tkjKYAAQAASURBVFiwYAFz587F29ub69evc/36dW7cuEFsbKyasGBkZIS1tXWmIpdcLs/0WkxNTVm+fLlaeOz79+8zbaNCocDW1pamTZty7do11q5dy8E/7tPU1MTf3x+AqlWrUr58ecaPH8+KFSuYOnUqDx48ELbFzMyMw4cPc+DAATp37kzr1q3VilhyuZyJEycKXsagnPCoWLEikydPBlC74JbL5cydOxe5XE7v3r2ZNGmS8P4/f/6cgwcPkpKSQrFixejatSsJCQn4+fkREBBAjRo1MDAwIDY2lvz58wNw69Yt0df7DwoVKoREIqFdu3asWbOGUaNGsXTpUqFbMSIiAnNzc9zd3VEoFEIYpaGhISkpKRgYGJCYmMidO3ewsLAgJiaGMWPGAODr64uxsXGW69XR0UFLS4u9e/cKhXwzMzPCwsI4efKksls7Q4GlRo0aSP/whbexsWHQoEEMHz6cc+fOsXnzZqysrFizZg0VK1YkIiKCMWPGcO/ePSZMmMC4ceOE70RKSgpBQUFs2LABNzc3oRiydOlSWrVqJawvPj4+S6EOlEHSBw8epH379ly9epUZM2awcuXKHPeztrb2Lwn1VSGKElkTGxvLhw8fkEgkWFtbC8X42NhYQQDr1KkTnz59ylEgUKGjo5NpiiUjd+/eJSIignz58tG1a9csC2jyDMdZrW8+U5UrV+bNmzdCNs6kSZMA5dREdt+djHz48CFXeQlt27bNUthWWfWpcnFat27Nvn37SEtLY9CgQVy8eFF4/t69e4UphNwWfCUSSa5+q3P7OJF/Fr/6Pcvt8qRS6Xcfq5ogk8lkglWQu7s7q1atYsaMGSxfvpyNGzfy4MEDHjx4QKtWrZg7dy7VqlWjTZs2WS4zODg429+FjBgYGKCvry9MBUyfPl2wD8uIkZFRrn4XYmNjefbsGR4eHkilUqZOnZpJaPTx8WHhwoXEx8cLAcylS5dGoVDw9OlTfH192b59OxMnTsTa2pqnT5/i4uLCrl27sl1v5cqVuXnzJn5+ftSqVUv9zgy/QSeOH6dDt25cu3YNZ2dnTp06pXYsfPHiBUuXLuXo0aPCb27r1q2ZPXs2DRo0+O7rFxEREREREREREfkzEKsqIn8pFhYWXLx4ERcXF1q0aIG7uzvHjx/PdgohK/Lnz8/Fixdp06YNycnJdOnShYMHD6KhoYG1tTXjx4/n1KlTfPr0ievXr7N06VLatWuHsbExcXFxXL9+ncuXL6vdPDw8Mv3tyJEjzJs3L9vtUCgUrF+/ni5duuDt7c2YMWMwMDAgICBA7XEqSyCVd+7MmTMxNzcXuswy0qRJE7Zt20a7du3URIRnz55hY2MjiAhTpkxh+fLlwv+rwkYzcvXqVd6+fYuRkRGLFy8WCghBQUEcPnyYpKQkihQpgqOjI9ra2uTPn1/omr169So2NjbA/7x+xcDlrBk2bBidOnVCLpcL+QbBwcHC/QkJCaSlpSGVSjExMQGUIpWGhgbp6enExMQACMGwAQEBah3h3+Lt7c3GjRsBpa2WStw5cOBAtpMMDx8+ZM6cOcjlck6fPs2ePXswNTXFx8eHNm3asHbtWszMzDhy5Aj+/v5MmDABqVRKeno6586do1u3brRs2ZLTp08jl8tp2rQpJ06cyNHSKCtq1KghbPvatWtxdXXN0/NF/hxUQaply5ZVC8z28PAgLS2N8uXLU65cuV+2vhMnTgDQvn37HL3Ac2L16tXCNMK3uQ5/JtevX6d+/fpcuHABbW1tVqxYwbFjx/D09KRq1apcvHgRHR0dXFxcOHfunGhlJPL/DqlUys6dO+nQoQMpKSl06tSJx48fU6BAAVauXMmrV68YMmQIGhoaXL58mQYNGjBgwAACAwN/et27du0iLCyMokWLCpaYP4pCoRDOk7p3754pQ+XixYv07NmT+Ph4zM3NmTBhgpCDIpFI6NmzJ/ny5SMiIoJz587Rrl07AI4cOZJj1o9qWiGrnISM1KtXj3PnzmFgYICHhwcODg4kJyfz/PlzevbsSY0aNTh8+LCQAXP//n0uXrwoiggiIiIiIiIiIiJ/K6KQIPKXI5VKmTBhAh4eHrRt2/aHlqGvr8+pU6dwdHQkPT2dfv36sW7dOrWOTw0NDapXr87o0aM5cuQI796949atW2zdupUtW7ao3TZs2MC2bduEmyrrYN++fbx69SrLbdi9ezfHjx/ny5cvrFixgnz58jFkyBC1xyQlJxMUFARAlSpVAGU33aJFiwBlR3dUVFS2r1Mul7NmzRqaNGlCQEAARYoUwcPDgxUrVjBlyhTGjh0LQN++fYXOYRUq0cHJyQkDAwMAQkND6datGwkJCRQqVIg+ffqgo6ODTCbj8+fPNG7cGH19faKiotDU1EQqlfLy5UukUinv3r0jNDQ0d2/QfwiJREK3bt0ABHuYT58+kZiYiEwmE4QCExMToTivoaGBmZkZoOzi//LlC+bm5lhaWqJQKHj+/HmW60pISGDatGnIZDLatGlDp06daNOmDUWKFBEsq77lwoULNG3alMWLF1OtWjX27t1Lnz598PHxoWXLlqSlpbFixQo6duwoZHLEx8ezbds2GjZsyLBhw3j69Cna2to4OTlx+/Ztzpw5Q8uWLX9of3Xu3Jlp06YBMHr0aG7cuPFDyxH5dag+b99al6lyDFQFtF9BcnKysNyuXbv+0DKymkb4M1EoFNy7d4+hQ4fSsWNHPn78SPny5fHy8qJnz544OzvTq1cvoqOjsbGx4cmTJ4IYJyLy/xEtLS2OHDlC06ZNiY+Pp127dhw6dAiZTIalpSVbtmzh4cOHQlD6iRMnqF27NqNHj/7h84SvX7+yYsUKAKZOnZqr6aec8PT05NGjR+jo6DBu3Djh7wqFgk2bNjFy5EjB2nHcuHHCb7IKQ0NDevfuDSjzDOLj47Gzs0MmkwnB1FmRWyEBlAHMGcWEKlWqYGNjw4kTJ1AoFDg4OPDw4UPc3NyERg8RERERERERERGRvxPxKlfk/y1aWlrs3r1bKKZPnDiR5s2bc//+/SwfrxIWHB0d6dOnj9qtV69eODk5Cbfx48fTrl07ZDIZCxYsyLSs8+fPs3v3bgD09PR4//49Bw8exN7enooVKwqPexUQgEKhwMzMTC08esCAAdSoUYPY2FgWLlyY5faGh4djb2/PzJkzSUtLw8HBAW9vb1q0aAEoC9guLi507tyZ1NRUOnXqxOvXrwFlLsONGzeQSqVC1/inT5/o1q0boaGh5M+fHycnJ/T09EhJScHV1ZWNGzcSEBBAkyZNALh3755wQazqpBenErJGVVTPaEv07t07YmJikMvlaGlpYWhoSFRUFGFhYSQkJKCnp6fMNEA5hRATEyP4KT9+/DjTOhQKBUuWLOHDhw8ULVqU3377DYlEgpaWFv369QOU4lZGv/lTp0/j4OBASkoKJiYmxMTE4OzsjL29PRoaGri6urJu3TpMTEzw9vamTZs2jBgxglq1ajF//nzev3+Pqakpo0ePxtfXl40bN1K1atWf3l+TJ0+me/fupKen07NnT968efPTyxT5cVRCQsbO/tTUVK5cuQIoJwd+FZcuXSIxMZFixYplsv2Iiori0qVLOWaV+Pr6Mnjw4L9kGiE1NZUtW7Zga2uLnZ0dhw4dQqFQ0L9/f27cuMGnT5+oW7cux48fR0NDg7lz53L37l1h+kxE5P8zurq6nD59mlq1ahEVFYWTkxPVqlUTJorKli2Lq6srd+7coU2bNshkMvbs2UONGjWYMWMGnz59ytP6jh079sumEWQyGatWrQKU51sqi6SvX78yZcoUQQgYMGCAcC6UFZUqVaJhw4aA8vd15MiRgLLJJDvBRPX9z42QAOpiQkhIiDAN8ezZM44dOyZMK4qIiIiIiIiIiIj8ExCFBJH/10ilUlatWsXSpUvR1tbm5s2b2fr05pV58+YhkUg4efIkM2fOFELu5HK5YM9ibW1Nnz59AOW4u+oCUMX69esBZT5CRm9iDQ0NXFxcANixYwf79+9XW/fXr1/p0qULnp6egmfwiRMnMnXMaWhocODAAerWrcuXL1/o3r07wcHBQme6paWl8JwtW7YI0xF2dnZCEfvJkyeCh/+9e/eEgN2kpCTB1zg6OhpAyHsQUUcVTAwIllSrV68mISEBUHo/y2QykpKSAIQpBZWNTGpqKjt27CAsLAzIej8HBgYKhd1evXqpWdDY2dkBEBYWJtjUAAwdOpS0tDR69OhBaGgoixcvRltbG3d3d2bNmoVEIqFr1654enpiZ2dHWloabm5uJCQkULZsWVasWMHDhw+ZMGEC5ubmv2x/paamCtZZnz9/ZvXq1b9s2SJ5R2XFldE//MWLF4LFlpubW452W7nl+fPnwjRWly5d1Dr2Hz16hI2NDR07dhQCnrNi+PDh+Pn5YWJiwsyZM396m7LjzZs39OrViylTpvDy5Uv09PRwcnLC09OTDRs2cO7cObp27Up4eDjlypXj7t27zJs375dmeoiI/N3ky5ePq1evsnDhQkxMTPD396dHjx5qk5pWVlYcO3ZMsDlKTU1lw4YNVK5cmVGjRn1XKE5ISGDJkiUsXrwY+DXTCHfu3BGsllRTE6BsAlFNM7Vq1YrZs2dnClT/lnbt2qGlpUVcXBxPnz6lYMGCfP36NZOlZGJiItu3b2fChAkAhISE5CiKZqRRo0Z4enoyY8YMfHx8OHDgwC8R7UVERERERERERER+NaKQIPL/HolEotZhVrJkyV+y3KpVqzJ37lwA1qxZg4ODA/Hx8UilUsGS6enTp0LoXuPGjYmJiVHLPTh69CgSiUS4sMxI06ZNGTFiBAqFgmHDhqmJCYsWLeLZs2eYmZlx+/ZtnJ2dsw1J1NfX5+zZs1SqVIkPHz7g4OAghCWHhITQt29fYmJi6NixoxCcfPz4ce7fv49CoaB8+fKCqFCpUiXOnz9PWloa2traQkFboVCgra1Nx44df3a3/muZN28eGhoafPnyBQBXV1dBiImJiSE1NVUojuTLl4+UlBQiIiIA5XTNjh07ePz4MYaGhjg6OmZafunSpYWJhTVr1nD06FFA+d4sXboUUGaQlCtfXnhOkSJFAOjRowd6enpMmzZNmDJR/RfA3NycXbt2sWHDBrp06cK+ffvw8vKiT58+P+xhnxUpKSns2LGD2rVrM336dEApdjk5Of2ydYjkHdXEwaJFi4TiW6VKlWjdujWgFERr1arF1atXc10Y+xY3Nze6devGp0+fKFeuHAMGDBDuO3r0KM2bNxcEzW9t2jKisohr0KCB2vTXr0KhUHDs2DEcHBwICAjAzMyM33//nVevXrF582Zq167NkSNHGDp0KHK5HEdHR27fvk3t2rV/+baIiPwTMDQ0pG3btsI5SL169ShVqlSmx9WrV48LFy5w8uRJatWqRWpqKnv37qVz584MGTKEu3fvqtlPKhQK3NzcaN26Nbt370Ymk9GtW7c8Z+9kRfXq1YXsrWnTppGamgqAra0t5f/4jbx8+TLOzs5Co0RWyOVyjhw5QlpaGjo6Onh6ehIZGYmRkZHQtBIUFMT06dMpXbo0o0eP5uXLlxgYGDBr1qw82ZvZ2NiwcOHCP+W4JiIiIiIiIiIiIvKr+E8LCaoOc5F/B+7u7sCP+25nxdSpU9m/fz/6+vp4eHgwcuRIgoKCGD16NOPGjUNDQwOZTEaNGjUYNWoUM2bMIDw8XG0ZK1euxN7ePsvlr1y5kiFDhqiJCXfu3BGmFdavX0+FChW+u50FCxbE09OT8uXL8+HDB6ZPn86aNWswMDDg1q1b1K9fH39/fzw9PWnevDkymYxLly6xf/9+tLW1GTVqFIMHD0ahUODj44NEIkFHR0e4+AZlYVxVyBbJjL29PYcOHRLEA0DIQwClbYuhoSGWlpZoaGgQERGBXC5HU1MTmUzG+/fvKVGiBFevXqVRo0aZlq+pqcn69evp2LEjMpmMxYsXs3z5cvbu3YuHhweampqsXLmSfH+IQgCtWykLwarvxt27d3n+/Dm6urpqhVxQCnIODg6sX7+e5s2b/1J/d5WAYGNjw9SpU/n48SOWlpasX78ePz8/bG1tf9m6RPLOpEmTaNiwIQkJCQwYMIDk5GT09PQ4fPgwhw8fplSpUkRERODi4sLYsWNzNZmUnp7O8+fP2bp1K/3792f06NGkpqbSvHlzTp8+jZmZGXK5nAULFuDk5ERKSopQ+Hvy5Em2yx0/fjwSiYTz58/j4uJCZGTkL9sPcXFxTJgwgZkzZ5KcnIytrS337t1j2LBhgr1bRhGhf//+bN68+ZeKbSIi/zQeP36MnZ0d0dHR2Nra4u7unu3kjUQiwc7ODk9PTy5fvkzHjh2RSCRcv36d/v3707lzZ86cOcPLly9xcnJi8uTJREZGUrJkSXbs2MGBAwfUJqN+FCMjI7Zv306+fPl4/Pgx06dPR6FQYGFhgZubGxMnTkRbW5vr16+zfv16bty4kUkkVYmKz58/R0NDg1KlSnHr1i10dXU5deqUkI9UqVIlVq9eTUxMDKVKlWLFihW8e/eOOXPm/PTrEBERERERERER+T/27jouivQP4Phn6RYVCzsxzjjj7FNsz8Iuzo6z+84+9ezE1rM7EDvO7lYsEBETFQuVrmV3fn8g82MFZNBVwuf9eu1LmX125pnZ55mZfb7zPI+Q2vywgYR3795RsWJFbt68mdJZEfTgw4cPHD9+HECe+FZfYocYypMnD35+fvTp04dz587RokULFixYQNeuXZk0aRIzZszg3r17mFtYyJ/t/ccfCfZGiKVSqXBxcdEJJrRr1w6tVouzszNOTk6K85k9e3bc3NzkYMLff//NokWLcHBw4N27dwwePJju3bszcuRI6tWrh5GREY8fP2bZsmX4+Pjw4cMHTp8+DUDGjBl1hjIZP368PISTkLiWLVvi6uqq0xCi0WjkYMK7d+/kF8T0RIiOjkar1VKlShVOnz792eEMTExM+Oeff+R5QTZv3iwHnUaMGEHJkiV10tevXw+ICSRotVqWLFkCxAyNFNs75VuRJIlXr16xYsUKnQCCvb09M2fOxMvLi969e3/1EBbC1zM0NGTlypVkyZIFT09PRo0aBcScnxo0aMClS5eYMGEC5ubm3Lt3j/79+zNr1iydJ3k1Gg3e3t5s376dv//+m9KlS9OkSROmTJnCyZMnAejZsyerVq3C2tqa8PBw+vfvLw91NHjwYPbs2QPAzZs30STS86FYsWK0adMGgJkzZ1K2bFn69OnDlStXdJ52Tq4bN27QrFkzDhw4gJGREcOHD2flypXy2OqSJLF69WqdIMKCBQvEhMpCurZ//34cHR3lIMKhQ4ewsbFJ8nMqlYrKlSuzefNm9u/fT8eOHTEzM8PLy4sRI0bg5OTE1atXMTMzY8iQIezfv18ngB77UMOhQ4e+uBdUwYIFWbx4MYaGhuzZs4dFixYBMdfR/v37c+DAASpUqEBUVBS7du1iwYIFcq8oiBkG6dKlS6hUKgoXLsy9e/cwNDRk8+bNODg40KhRI/bt24ckSdSpU4ddu3Zx9+5dBgwYIAceBUEQBEEQBCG9UUlf88s7jXr37h21a9embt26zJo1S2/rDQoKIkOGDAQGBir6oZUeJKf4JDY0z5esT6PR6Kxv3bp1dO/enZIlS3Ljxg15eUhISJLj30LMmO0WcQIACfH396dp06Zy8Klr16506tQJlUrF/Pnz2bVrF8bGxlibmHAuV8yTtQWvXMY4zlj2nwoPD0elUiFJEoMHD2bFihUA5M2bl8uXL+uUIyVPvb5584Y3b97QsmVL7t+/j729Pa6urhw9epRZs2YRGhqKgYEBZcqU4eeff+bQoUP4+fkByPnIkiWLzlO+bdu2ZcuWLYq+v89RWj9Ssh4pbbCQJOmzx+PQoUO0bNlSp0dHbO+VWMbGxqjVaiAmCHT37t0kG9Xv3r0rBymOHj3KmDFjiIyMpEGDBkyfPj0mTxERGHfqDECeC+fJlicPoaGh7N+/n+bNm6NWq7ly5QrlypXDz89PUUNoREQEdnZ2Cb4XGRnJw4cP8fHxwcPDg6dPn+Lj48P9+/flOSIgZpiloUOH0rFjR4yNjRV/t2LMeV3foh4FBgYCcPLkSVq0aIEkSaxYsUJnbHGIGZ5ow4YN8lwdFhYWNGzYkOfPn3Pnzh15DpBYNjY2/PLLL1SuXJlq1arJw2m9evWK7t27c+fOHYyNjVm0aBFdu3ZFo9FgZ2dHaGgoNy5exLRLzISrNgf2YxSnp41arWbPnj2sWbNGZ2JyBwcHunXrRvPmzbG0tEx0f/38/DA0NMTDw4Pbt29z69YtTpw4gUajIVeuXMybN48yZcqgVqvJnTs3AQEBDB48WJ5kNqEgglWc/CVG6fUtqfNLXF97Xv5RpYXrkVJKy5VWq1VUXrRaLQYGBixatIghQ4ag1WqpXbs2O3bs0DkGoaGhitb35MkTTE1N+fDhA1u3bmXDhg34+/tTr149Ro0aRc6cOYGYa8mTJ084cOAAhw4d4unTpwCMHTuWcePGyevTaDSKrgtPnjzB0NCQLVu2MGbMGAAWLFhA48aNdfZ16NChHD58mMjISAwMDKhTpw7GxsYcOHAAiBlOzdPTE4iZcLlNmzY0atSIkydPUqRIEbZv364zVKBWq000f9qwMB5U+AWAApcv6ZzXPkfJfWxKSMl65Ovri7+/f5Lp7OzsyJMnz2fTuLu7U65cOa5fvy563uqZNiwM77Ixc2I5uF/HIInfWsmRXr63tHCdEQRBEIS4jJJOkr4kFERQq9X4+/uTLVu2ZD1dGBkZqdNQGBQUpPf8phf6/qEbFRWlk27btm0AODk5ERUVpZNOSTd5tVotN+omJkOGDEyZMoXVq1fj5ubGmjVr8PHxoUiRInI3d0tLS8I/NspBTCPP5xqnY3+wQ8zkvJaWluzZs4fVq1djbW0dbzzhpEiSRNasWXFzc5ODCa1atWL06NEcP36c6dOns3v3btzd3bl37x61atWiUKFCnD17FkmSsLOz0wki5MyZk5UrVypqYP/SH7rpsR41bNiQvXv30qBBA/l702g0GBkZyY2EseXN1tYWBwcHJEkiIiLis+uNbegAcHR0ZN26dVy7dg0nJ6f/Bymio4nbhFGrVi327dtHz549UavVVKhQgZ9++onIyEidCbWT2m7cMnDy5EmWL1/O/fv3efr0aaLlw8DAgEKFCtGrVy86duwoB0qUDiun0WgUnZPj9vpIij4bZFJTw7A+6pGjoyPDhw9n1qxZDB48mCtXrlCpUiUqV66Mvb09mTJl4q+//qJJkyYsWrSI+/fvy43rEDOpeKlSpShevDjNmzenWLFiOsdbq9Vy69YtevbsyZs3b8iYMSPbtm2jWrVqREdHAzFjm1+4cAF39xtU/vi58IgIjD8pp40bN6Zx48Z4eHiwYcMG9uzZg7e3N3/99Rf//PMPLVq0oH379hQsWBC1Wo23tze3b9/m9u3bXL9+HV9f33jfX6NGjfj777+xtrYmOjoajUbD5cuX6d69O0+fPsXIyIixY8cyZMgQnXIpSZKisvBpADwxkiSl2obD9C4tXo+Unoeio6MVnU+DgoIYM2aMPN9Tp06dmDdvHoaGhoSGhsrpwsPDdR7A2LRpE+vXrydr1qwULFiQAgUKULBgQUxNTbG3tydjxoz06dOH7t278+rVK/LkycO7d+9wc3PjxIkTnD9/XicYGTu84tSpU6lduzaVK8ecESIiIhRdQyIiIjA1NaV169Y8ePCANWvWMHz4cC5cuEDDhg2pUKECRkZGlCtXjlKlSrFr1y48PT05cuSIvI7ixYvLQYTWrVvTrl07xo0bx8mTJ7G0tGTbtm0ULVo02fdqsel+wGe59MLX15dixYrFC14nxMLCAi8vrySDCYIgCIIgCIJC0g8kOjpaKleunFSjRg1JkiRJq9VK//zzj2RtbS0Bkr29veTq6qp4fX///bcExHsFBgZ+oz1Iu7RaraKXRqNRlC4iIkKKjIyUIiMjpVevXknGxsYSIN26dUteHhkZKYWHh0vR0dHyKygoSLp3756kVqt1loeHh+t8LrFXWFiYFB0dLa1cuVIyMTHR+d5z5colAVKxAgWkuw5FpbsORSV1cLCk0WgSfUVHR+v1uGi1WvmYv3z5UipWrJicv0yZMklDhw6V1q1bp7O8UqVK0vr166UuXbro7E+ePHkkX19fneP0uVdSAgMDE6wf6bkenThxQrK0tJQAycDAQAIkU1NTCZCsrKykvXv3SlqtVgoJCZFCQ0OTfAUEBCRZRsM/fJDLX+j799KSJUt0juuqVauk8PBwKTw8XHr37p0UHByc5CsoKEgus/Pnz5f3JfZlY2Mj/fLLL5Kzs7M0ZcoUyc3NTfLw8JAiIiISPC5Ky/Kn9TSxV2RkpN7KaXLo+7wWt/4m5lvUo0+Pea1ateKtJ1++fJKzs7O0bNkyycPDQ1Kr1dKaNWukTp06SbNnz5auXbsmqdXqz35vmzdvlszMzCRAKl68uHT37l1JrVbrvAYOHCgB0uA+feRy/P7lyyTLqK+vrzR+/HipQIECOvl2cHCQ69ynr7x580qtWrWSpk+fLp05c0YKDw+XIiIipIiICCk0NFSaOHGiZGhoKAFS/vz5pUuXLn1VOYiOjv7s9SDuS/i20tP1SJ/n04CAAKlBgwbyfk+cOFEKCgpKtN7FrnvBggUJHrfYl7W1tfTzzz9Lbdq0kcaMGSNNnDhRqlSpkqRSqXTS5ciRQ+rRo4e0Z88eKSQkRHJ2dpbPPx8+fJC0Wq2ia+Wn18uwsDCpWbNmOtvKkiWL1LNnT2nfvn1SUFCQFBISIm3atEnKli2bBOgch9GjR0uhoaHSrl275GVbtmxJ9nlcExoqn9eiQ0L0dl1IKYnVoy9Np9T169clQNq4caN0/fr1RF8bN26UAOn69euK1pdUOiH54pZ5TWioXtedXr43fdcPQRAEQfjWfrihjbZu3YqzszMzZszg2bNnnD17lqlTp2JlZcXs2bPZu3cv+/bt47fffktyXQk9uZY7d27RNTEBSouZpPDJ3bg9EtavX0/Pnj356aefdIa5gP93MX/8+DFLly5l9erVBAQEUKJECfr160fHjh2xtLRErVYrfvI5tofDpUuXaN26NS9fviR//vw8fvyYTJkyce7YMWjfAYDC165+thuvJEmKtqv0uIDuE80BAQEsWbKE5cuX4+vrKy+vUaMGuXPnZvfu3YSEhGBgYICxsbFcnmMnaS5evLjenvROrOtueq5HkiRx7tw5GjVqRHBwMAYGBmi1WvLly8eePXvkOQ3CwsIUfb9qtTrJIa60YWE8rhTz5Gbeixd49eEDBQsWBGK6+Pv4+GBmZiZvV0mPHUmSMDMzY+jQofI40506daJz5844ODiQPXt2Of9Ky7MSSp/g/pF6JHyLevTpfkRFRbF3717Onj3L+fPnuXnzZrxeJ5kzZ6Zq1apUqFABW1tbLC0tsbCwwNLSEnNzc/n/sf8uXbqUf/75B4jpsbNp0yYsLCzifR+bNm2iS5cu1KxcmSXvY+ZgyH7qJMYKhgAJCgrC1NSUkydPsmrVKg4fPizvW4YMGShbtizlypWjTJkyVK9enWzZsiW4nhcvXtCtWzd5zpj27duzdOlSMmTIoOj4JUZpjztQVo+EL5eerkf6Op8+f/4cJycnbt68iZmZGStWrEhyniZLS0sWLVokz9vTt29fChUqxP3793nw4AE+Pj4J9v6Jq2zZsjRp0oTGjRvz888/65T9oKAgfv75Zx4/fkzHjh3ZsGED4eHhivb30+ulVqvl+PHj7Ny5kz179shzFQFkyZIFJycnWrRoQcmSJdm0aROjR49Go9Hwxx9/MHv2bB4+fEj16tUJCgpi4MCBuLi4JLrtxI5z3GFeily/pniYl9Q6hFlKDW2kdEgbfacTkk8MbZQ0MbSRIAiCkNb8cEMbtWvXDgBnZ2d5XPLYi3aVKlWoU6cO48aNUxRIMDU1FROFpgKxQ2u0bNlSZ7kkSZw6dYqlS5fKE+JBzA8yT09P+vbty+jRo+nWrRu9evUif/78ydpupUqVuHHjBr169WLv3r2Ympqya9cuChUqxAP97NpXs7W1ZfTo0fz111/8999/LFu2jAMHDsgNZFmyZCF37tx4eXnJjScqlYotW7ZQsmRJxUPQfI30Xo+qVavG4cOHadiwIYGBgdjb2zNgwACCg4MJDAxMtHFSX+zt7fn555+5ceMGnTt3loMIyRESEkL79u3lMaOnTZvGiBEjUm3jxo9In/XIxMSEVq1ayRPXBwcHc/HiRc6dO8e5c+e4fPky7969Y+/evezduzdZ6x4yZAjTp0/H0NBQHs4ortjGgNu3b8PHuWaSw8DAgNq1a1O7dm18fX25c+cORYsWpUCBAnJ5jY6OTvTH+sGDB+nZsyfv3r2TG0k7d+4syvoPIr1fjxITO9m4n58fWbJkYevWrfzyyy9Jfi5uEOGvv/5i6tSp8epKREQEjx8/xsfHR34FBQVRs2ZNGjduLM+RkFCwwcbGho0bN/Lrr7+yadMmGjRoQIsWLb5oHw0MDKhbty5169ZlwYIFnDp1Cjc3N3bt2sXbt29ZsWIFK1asIGvWrAQFBaHRaGjbti2zZs0iLCyMDh06EBQURNWqVfU6v5ogCIIgCIIgpCU/XCAB/h9MuHLlik5jgkqlon379owcOTKlsiYk04cPHzh+/DiA/OMyKCiIbdu2sWTJEu7evSunrVOnDv3796dKlSps2LCBxYsX8+jRI+bOnYuLiwuNGjXir7/+okKFCglu6927d+zatYvXr1/z+vVrXr58yYsXL7h+/ToqlYr169dTtWpVtArGbP3eDA0NadSoEY0aNeLp06f8+++/rF69mlevXunMiQAwd+5cRYE0QblKlSpx9OhR6tevj5+fH8OGDZPfy5kzJw4ODhQrVozSpUvTsmVLxY3979+/5+LFi9SuXfuzn5kzZw7btm1j+PDhyc77y5cvad26Nbdu3cLMzIx169bJDczCj8Ha2pp69epRt25dVCoVUVFR3Lhxg7Nnz+Lp6UlISAhhYWGEhobKr7h/R0REYGNjw+zZs+nWrdtnt1WkSJGYuWb0cB7NkyeP4nGxo6KiGDVqFIsXLwagTJkyrF+/Xu41JAjp1cGDB2nfvj2hoaEUK1aMrVu3UqBAgSQ/t3z5cvmaklgQAcDMzIzixYtTvHjxL8pf5cqVGTduHBMmTKBv376ULVuWfPnyfdG6YhkbG8tBhVmzZnHu3Dl27tzJvn37ePPmDQANGjRg+fLlqFQqBgwYgKenJ9myZWPbtm2KJnsWBEEQBEEQhPQoXQcS3r17x4kTJ2jdunW899q1a0fTpk3jLX/w4AEVK1b8HtkT9OD8+fOo1WqMjIw4c+YMM2bMYNeuXXLXd0tLSzp16kTfvn0pVqyY/LlBgwbRv39/Dh06xOLFizl69Cj79u3j8OHDXLx4kZ9++klnO8+ePaNu3bo8fvw4wXzMnDkzXo+I1Cpv3rxMmjSJcePGsXfvXpYvX86JEyfk969cucKwYcPIli0bdnZ2ZMuWjWzZspE1a1ayZs36RU+0C1C+fHkuXrzI8uXL8fT0xNPTkxcvXsiv2O/g9evXDB06NMn1vX//nho1anD//n3s7e0ZMWIE3bp1I6GBiipXrixPVJlckyZN4tatW2TNmpVdu3ZRqVKlL1qPkH6YmJhQsWLFRK+Vnw6hotVqFU8gbGhoyM8//8z18+f1ll8lJkyYIAcRBgwYwOTJk3/IJ9OFH4skSXTv3p3Q0FDy58/P2bNnFTWS79+/X1EQQV9Gjx7NkSNHuHDhAosXL9Zrj4C4QYX58+dz6tQpHj9+TKdOnTA2NubRo0ds27YNiOkhcfDgQdq2bYu1tbXe8iAIgiAIgiAIaUW6DiQ4OTlx/vx5AgMD6dGjR7z3LT4Zp/HEiRP8+++/nDp16jvlUPhaVatWpXTp0ty6dYsBAwbIyx0cHOjevTu///47WbJkSfCzhoaGNG7cmMaNG3Pnzh0GDx7M6dOn6dGjh86P6bhBhNy5c9OgQQOyZ88uvxwcHChatOh32V99MjY2pmXLlmTPnl0nkLBly5bPfs7e3p59+/ZRunTpb53FdKdIkSLMmTNH/jswMJC7d+9y48YN3NzcOHnyJF5eXkmuJyIiglatWnH//n0A/Pz8GDJkCLNmzWLk4MHU+cL8SZKEv7+/Tp3JmzcvABUrVhRBhB/E5MmTWb9+PaNHj9bLsD7JHe+/Zs2a3y2QIEkSp0+fZv78+QCsW7eOtm3bfpdtC0JKU6lUdOzYERcXFx4/fsyECROYNGnSZ+vsgwcP6N27NxAzJ8K3DiIAGBkZUbRoUS5cuIClpeU3246JiQn16tXTWZY7d246derE5s2b8fHxoVevXgwZMoS2bdvSs2dP8fCRIAiCIAiC8ENJt4GEy5cvExoaytChQ+nVqxdAgsEEiAkgrFq1ikuXLrFnzx5KlSr1PbMqfIWMGTNy5swZRo4cyd69e/ntt9/o1KkTFSpUQKVSxZsgNDFFixZl3bp1lClThhs3bjBnzhxGjhypE0TInz8/hw8fVtTlP6149eqVPNSXk5MTTk5OvH79mjdv3vD27VtevXqFv7+/vEytVstD8xw9elSMG/6VMmTIQOXKlSldujQWFhacPHmS58+ff/YzWq2WHj16cP78eWxsbDh8+DBXr15l5syZPH/+nL/++ovrRRyAmICDRRKTM8dSq9W0bduWEydOsHTpUtq3bw9A48aNmTJlCseOHSMsLCxeAFZIX+bNm8f48eMB6NatG/v27WPZsmWJBmS/hUaNGjFnypRvtv7IyEiOHj3KgQMHOHjwoDwRfcuWLUUQQfjhzJo1i2zZsjFq1CgWLVrEgwcPWLNmDVYJTHAeGhqKs7MzQUFBVKpUiXnz5n23+wB3d3cAfv755y/6/KZNm1iyZAmNGzemW7duiU62/iljY2OWLl3KxIkTcXV1ZdWqVXh7e7N69WpWr15Nnz59mDdvHiYmCfUHFFKDpB7QUPIAhyAIgiAIghAj3QYSVq9ezcCBA+nSpQvAZ4MJRYsWpVevXqxbtw4jo3R7SNItMzMzXFxccHFx+ar15MiRg7lz59KtWzcmT55M6dKlGTJkiBxEOHr0KPb29vrJdCqgVqtp164dL1++pHDhwtSsWZMmTZroTP6r0Wjk4UgkSeL+/fv8/PPPnDp1iiNHjlC/fv2Uyn66kzt3zMSysY2aiRk7diyurq4YGxuzfft2ypYtS9myZenSpQtr165lwcyZctry5coxYMQIunbt+tkhqSRJYujQoRw9ehSImRC3QoUKFCpUiBIlSpA3b16ePn3KkSNHcHJy+vqdFVKlzZs3y/N3lC1bltu3b7Nr1y7Onz/Pv//+S5MmTb5LPsqWLUv27Nn1us63b99y5MgRDh8+zIkTJwiLMweDmZkZDRs2/OpriCCkRSqVihEjRlCgQAE6d+7Mf//9R/369XF1ddW555EkicGDB+Pp6UnWrFlZv379d5srIDIyEg8PD+DLAgkBAQEMGTKEwMBArl27xpQpU2jRogV//PGH4vVlzZqV4cOHM2zYMM6fP8+KFSvYsGEDS5cu5ebNm2zfvl2eOFpIHezs7LCwsMDZ2TnJtBYWFtjZ2X2HXAmCIAiCIKRtyRtvIA0ZPny4/KT17Nmz5Z4JK1eujJfW3t6eGjVqiCCCQIcOHWjUqBFqtRonJyedIEJsQ2968ddff3Hu3DmsrKwIDQ1l8ODBlChRgq1btyJJUrz0KpUKBwcH+vbtC8SMWay0x4eQtNhJYV+8eIFGo0kwzapVq+ShkZYvX46jo6P8nqmpKb179+ba9evyslevXjF06FCKFy/O8uXLE/2+Fi5cyNq1azEwMMDBwYHQ0FC6d+9OVFQUKpWKRo0aAbBnzx697KuQ+hw9epSuXbsCYGVlhbu7O8WLF6dIkSK8efMGJycnevToQXBw8DfPi4GBAfX0EKR8+fIlc+fOpV69ehQtWpQBAwawf/9+wsLCyJEjB926dcPNzQ0/Pz+2bNnyXXtdCEJq07JlS44fP06WLFm4ffs2jo6O3L59W35/5cqVbN26FUNDQ9atW0eOHDm+W97u3LlDdHQ0mTJlincvFhAQwPHjxxO8b4m1aNEiAgMDKVCgABUrVkStVrNt2zYcHR2pVq0aq1evJiQkRFFeVCoV1apVY+3atWzcuBFbW1suXrxIuXLlOH369Fftp6BfefLkwcvLi+vXryf58vLyku/DBEEQBEEQhMSl25bzwoUL6/w9e/ZsQLdnwrVr1/jvv/8YO3bsd8/fj+ZzP/Di0mq1isbTliRJUXd6tVqtaLuRkZHyxJoLFy7k/PnzBAQEyMMZ5cyZE61Wi0ajSbLx/Fs0rsc26CqhpHv97NmzWbBgAQA5c+bE29sbiGl4dnZ2ZuHChYwaNYq8efPKY+TH6t+/P6tWreLWrVusXbuWVq1aATHDTAm6lJb7yMhIMmXKhKGhIWq1mmfPnsXr/fLff/8xYsQIIGZi2I4dOya4LtM43/+MGTOYMX8+L168YPDgwdjZ2dGsWTP5fTc3Ny5dusSaNWsAaNasGaVLl2bmzJm4u7vTpk0bmjRpIvdm2L9/vzy5+ddQWkfUarWich+358znhIeHKx6v31zBkFBKz0Op3ZQpU5gyZQpqtRpzc3O5Qe327dtkzJiRatWqcf78edasWcOJEydYuXIl1apVS3R90dHRip5UjoqKSjRd3bp14eIlACLCw5EUnNeio6ORJImIiAiWLFmCi4sLoaGh8vulS5emfv361K5dm2rVqumUhYTqqiRJisqqvq9bQvqm9LqgtLwkJ11SfvnlF44dO0bbtm25d+8e9erVY/Xq1WTOnJm//voLgIkTJ1K1alV5EnUl21VSP7RabaL7ce3aNSCmt9L79+913nN2dubMmTPMnTtXvh+JXZ+5uTkBAQHy/c7kyZNp2bIlN27cYPny5WzduhUPDw8GDBjAmDFj6NixIz169MDBwSFeHu7evSvnLygoiD///JPr16/j5OSEh4cHDx48oE6dOgwcOJAZM2YkuC/a6Gj5/xqNRtHTXErLCyDOLwnIkyePCBAIgiAIgiDoUbrtkZCQuD0TRo0aRePGjb94rFUheVQqlaKXgYGBonSmpqaYmJgk+TI1NcXAwEB+XblyhQEDBuDl5aWz3MTEBGNjY4yNjcmTJw+7d++mb9++nDhxggIFCsjvmZqaYmhomOQrVtxtJPQyNDTU63FR8iPS09OTCRMmAFCqVCm8vb0xMzNj48aN9O3bF1NTUy5fvkzr1q1ZuHAhkZGROnm2s7Nj8ODBQMyPcrVanezJVH8USr8zc3NzrKys5GER3rx5g5mZmfzy9PSke/fuaLVaunfvzvjx4z9b7mP16tWLe/fuUbt2bSDmCW1TU1P5de3aNTZs2IAkSVSoUIHChQvj4+ND48aNgZj5Y27cuIGNjQ2ZM2fm/fv3XLhw4bNlWp/HRUldMzQ0xMTERFE6fZdTfZ/XUqIRyMfHh5kzZxIREYG5uTnh4eHY2try559/kiNHDj58+MCVK1do2rQpWbJk4enTp9SrV48xY8bIAc6E9lfJ92FsbIyRkVGCr9gyC/Ds+XMsLS2TfGXNmpXTp09TvXp1pkyZQmhoKBUrVmTp0qU8e/aMGzduMH36dOrUqYO5ublOXUjopXTM8+SU56SuCcmpR4IAysufkZGRonpZokQJzp8/T+3atQkNDaV9+/a0adMGtVpNixYtGDlypFzn9Hle+9znb9y4AUC5cuXk+zFjY2M8PT05c+YMAFu3btV5L7YOL126lMDAQEqUKEHbtm0xMTGhYsWKrF69mhcvXjB79mwKFSpEUFAQS5cupVy5crRq1UoOrMa+Yr148YLu3btz/WPvv927d2NjY0OtWrXQaDTMmzePDh06KOq9lVqvC4IgCIIgCIKQmB/u1+rs2bNxdnZm3rx5rFq1Sh6yQ0j/JEmiZ8+erFy5kooVKzJ9+nSi4zwdFle1atVYsGBBqh3O6P379xw+fDhZT6oBBAYG0qJFCyIiIihSpIg8bMG4ceMoXrw4PXr0wNXVlWrVqqFWq1m9ejWVKlXi8OHDOuv5448/yJ49O76+vvLT7MLXS2iehCdPntCiRQvCwsKoW7cuS5cuTVbDgqmpKUWKFAHg3bt38vLnz5+zefNm1Go1hQoVokaNGqxcuZJNmzbx4sULypUrhyRJ7Ny5k4iICHl8/N27d+thT1OHGzduMHXqVJ3j8iN59eoV9evXJzg4WA4iWFhYMGzYMPLly8f48eMpXbo0UVFR7Nmzh4oVK9KpUyckSWLu3LlUrVpVZ+gTfYo7SfiRI0eSTH/v3j0aN25MixYtePToEfb29mzYsIELFy7Qu3dvMXa5ICSDra0tBw4ckAPY79+/x8HBgdWrV6dIw3bsRMtly5bVWb506VL5/1euXOHx48c67wcEBDBv3jwA/v7773hBuowZMzJkyBC8vLw4ePAgjRs3RqVS8d9//7F48eJ4+fD09KRbt248efKErFmzMnjwYMzNzXF3d8fDw4OOHTtiZGTEjh07qFq1Kvfu3dPL/guCIAiCIAhCavHDBRKuXbvGkSNHcHNzE0GEH8yVK1e4f/8+EDNkyt9//0316tXx9PRM4Zwlj0ajwcnJiaZNmzJ58mTFn9NqtXTp0gUfHx/s7Ox48eIFAO3bt6dhw4Zyuly5cjF//nxmz55N1qxZefr0KW3btsXZ2Zlnz54BYGlpyciRIwGYNWsWQUFBetzDH1ds9/vY4/z+/XucnJx48+YNpUuXZtOmTV80uWWmTJnk9QGEhITg5ORESEgIWbNmpXnz5mzfvl1+/8KFCzg4OGBnZ0dwcDC7d++madOmQMw8CckNYKVG586do169ekyZMoU6derw9OnTlM7SdxUUFETDhg15/PgxZmZmhIeHY2xsTP/+/dm5cyf9+vXj4MGDDBw4kN9++w2IGdrqw4cPrF+/nixZsuDh4UHlypVp2rQpa9eujTfkiL58GsiMKyAggBEjRlC+fHmOHz+OiYkJo0aN4t69e3Ts2FE8zSsIX8jY2Jjly5fj4uJCnTp12LlzJ9bW1t89H1FRUdy5cweI6ZEQ68mTJ+zfvx/4/3Cm27dv1/ns/Pnz5d4ILVu2THQbBgYG1K9fnz179rBw4UIAed2xTp8+Te/evXn//j1FihRhwIABPHv2jJkzZ5InTx7evHnD9u3b6dChAzly5MDLy4sqVaqwa9eurz8IgiB8FV9fX9zd3ZN8xX2QRxAEQRCEhP1wgQQXFxfRE+EHtX79eiBmQuU1a9aQMWNG3N3dqVixIjNmzEgzjaMrVqzg8uXLAEydOpULFy4o+tyMGTPYs2cPxsbGmJmZERoaSpkyZRg8eDAbN26kRo0a8g9elUpFrVq1cHV1ZeDAgRgZGbF//34qVqzIggULkCQJZ2dnChUqxLt37+Qf3sLXie2R8OzZMyIjI2nbti3e3t7kypXrqxpxMmfODMT0SNBoNDg7O3P79m0sLS3p0KEDhw8fxtfXF1NTU4oWLYokSezbt49GjRphaGjIvXv38PX1xdzcnKdPn3Lr1i297XNKOHfuHM2bNycsLAxDQ0Pu37+Po6Njmt8vpSIjI2nevDk3b97EwsKCiIgIVCoVvXv35vz589y4cUMuA4sXL8bJyYlevXphbGzMvn37mD59Ort376ZZs2ZoNBqOHDnCH3/8QZ48eWjatCnbt2/X6/nU/fp13rx5E2/5unXrKFmyJAsXLiQ6OpomTZrg6enJlClTsLKy0tv2BeFHpVKpGDBgAIcPH6Zo0aIpkgdPT0+ioqKwtbUlf/788vJly5ah1WqpVasWw4cPB2ICCbHzmgQGBn62N0JiYoPmly9fls87ixcv5s8//yQyMpLKlSvToEEDxo8fj5ubG2PGjKF///7UrFkTtVrN+vXrqV69OtWqVSM4OJg2bdowatSoNHOPKQjpja+vL8WKFaNcuXJJvooVKyaCCYIgCIKQhB8ukLBx40YRRPhBHT9+HIDGjRvToUMH9u3bh7m5OWq1mokTJ3L06NEUzqEyc+bMAcDKygqtVsuUKVOS/ExkZCQTJ04EoHr16jx//hyAkSNHEhUVxdy5cwkODuaff/6Re20AWFhYMGnSJJYsWYJKpSIsLIzx48dz8uRJjIyM5LokhjfSj3z58gGwY8cOmjRpwrlz57CxsWH37t3xJl9OjqxZswIxT1SuWrWKgwcPAlCvXj0iIiLk4WkcHR1xcnLC2NiY4OBgnj9/Ls8jM2XKFLJlywbAyZMnvzgvqcGQIUMICwujWrVq3Lp1i0KFCvH69WuGDRuW0ln7Lnbv3s2JEycACAsLA6Bhw4ZkypSJ8+fPA//vHePu7s7Zs2epWrUqQ4YMAWImHV20aBFbtmxh7dq18rBB0dHRHDlyhK5du3L69Gm95VeSJK5evaqzzN3dnd69e/P27VscHBzYt28frq6uFCxYUG/bFQQh5Xl5eQExPST8/Pzk5bEPPlSoUIHSpUsD4Ofnx927d5EkifHjxyvqjfCpnDlzUrZsWSRJonPnzvj4+DBo0CAkSaJcuXKMHTuWhQsXypNcBwUFMX36dGbMmCEHIbZv3063bt1o0KABEDOs6rlz5/RyPARBSB5/f3/CwsLYuHEj169fT/S1ceNGwsLC8Pf3T+ksC4IgCEKq9sMFEoQfV/ny5QFYu3YtAwYMwNHRkfDwcCCmATX2/dTul19+AWKGp4GYwEBSTE1Nad26NQCnTp2Sn9YdPXo0oaGhODo6AlC3bl35qXhJkrh8+TLNmzenV69e8tN0tWvX5ueff2bz5s0sWLAAgLZt2+pxD39crVq1onTp0rx7946zZ89iZmbG9u3bKVGixFett1GjRvz000/4+/szatQo+Ts+dOgQ4eHhcpDi+PHjbNmyBbVajYmJCWZmZvIkl2ZmZjx58oSMGTOm+WDsr7/+CsQ8pRYQEMCrV68AdCb4Tc+qV68uf+eWlpZAzMTaUVFRclAg9ok8W1tbSpYsiZeXF//++y8A5ubmVKlShZo1a9KlSxd5mDSADBkyyOVYX4xNTOKtz8LCAgATExOuXr1K3bp19bY9QRBSj9q1a5MvXz7evn1LrVq1ePnyJYB8TzNr1izat28PgIODA4UKFWLFihVs374dQ0NDFi5cmOwJzKdMmYK5uTlHjhzByclJvs+6fv06+/fvp3LlygDyfVHTpk3Ztm2bHKTPly8f7u7u8rBspUqVkoPygiCkjGLFilG2bNlEX8WKFUvpLAqCIAhCmiACCcIPY9SoUahUKo4cOcK///6LWq2mZs2aHD16lAMHDsjjyKd2o0ePlsf9zp07N4MGDVL0uTVr1vD777+j1WoJCQnB2tqaR48e0aNHD4YOHcqZM2eYMWMGRkZGHDx4kA4dOtC/f39OnjyJoaEhrVq14vTp07i5uXHo0CH69euHJEl07949WXM1CImztrbm1KlT9OnTh8KFC7Nz505q1Kjx1eu1tLTkxIkT/PrrrwQHB/Pq1SsKFSpEREQEmzZtokqVKhQsWBC1Ws3Tp08xNDSkVq1aHDlyBI1Gg7W1Nc+ePcPS0pIDBw7IkzenVRMnTiRv3rz4+vri6OhISEgIv/76K3/++WdKZ+27sLe35+TJk9jb2xMaGoqpqSkRERG4uLjQtm1bOZhQunRpJk2axIULF5gxYwYBAQEULVqUzp07M3ToUK5cuYKFhQWNGjVixowZXLx4ET8/P9atW0fGjBn1ll9nZ2dy5cqls6xAgQIYGhoSFRWV4LBHgiCkD9myZePEiRPky5cPHx8fWrVqxcuXL5k0aRIdO3ZEq9Xy+PFjMmbMyLp167hy5YrcA3POnDnygxLJUa9ePU6fPo29vT337t3jzp071KxZE4iZ4NnKyoq2bduSK1cuxowZw5MnT5g7dy7R0dFUq1aNHDlysGjRIiRJolevXpw/f14MtyYIgiAIgiCkCyKQIPwwSpQoQadOnQDkAMLhw4flp5PTihIlSvD777+jUqmYPXs25ubmij5nZGTEmjVr6NmzJwDBwcFkyJCB58+f07NnT168eMGmTZto1qwZY8eOxdvbGzMzM3r37o27uzsrV66kdOnSbN68WSeIMHv2bDGhqR6ZmZkxd+5cbt++/UUNIImxtbVl//79NG/eHLVazYMHD8iSJQvR0dG4ubnJT2pZWFhQp04dTp48iVqtxtramuDgYExMTNi9ezeVKlXSW55SipWVFUuWLAFiJl7PkiULq1evxtDQMIVz9v0UKVJEDiZERkbKwYQlS5bg7OzMuHHj6NSpE0uWLGH37t3ysB4GBgYsW7YMtVrNb7/9xu3bt3Fzc2PQoEH8/PPPejuGcYcBGThwYLz3TUxMKFCgAADe3t562aYgCKlTvnz55GDCo0ePaNWqFa9fv2bmzJn07NkTe3t7/v33XyRJonfv3mi1Wtq2bZvguUOpcuXKcfnyZcqXL8+HDx84e/Ys9evXx9DQkCNHjnD37l2GDx/OmjVrOHXqFMbGxrRq1QoPDw8uXryIlZUVGzduZPHixZiZmenxaAiCIAiCIAhCyhGBBOGHsmTJEh4+fJgmAwhxLVmyhAcPHuDk5JSsz8U2Anbo0AGImYzQ1taWV69e0b59e+bMmcOrV6/IlCkTffv2Zd++fcyYMQMTExN27NjBwIEDRRAhDTMzM2Pz5s306dMHgLdv35ItWzYkSWL//v1kyJCBrl27cvbsWSIjI+UggoGBAVu3bk1XQ//UrFmTYcOGYW1tzZo1a8iRI0dKZ+m7iw0m2Nra6gQT5s+fz4MHD5gwYQL37t3D1NSUUqVK4e7uzt27d7Gzs2P9+vW4ubnF6ymgL9OmT5f/H9tD4lMODg4AOvO6CIKQPsUGE3Lnzq0TTJg0aRLXrl2jTJkydOnShYCAAMqWLcv06dO/+v4ktvdWmzZt0Gg08r2jtbU1d+7cYfDgwfj5+WFvb0/Dhg1xc3MjICCAUqVKceXKFTHsoyAIgiAIgpDuGKV0BgThW9JqtTp/GxgYYG9vH2+5RqNBo9EoWl9SY+3Gjpkrb/+TbX1Kydi9n+bX0NAwwf1QQqVSMWzYMMzNzVm1ahUBAQFkypSJ9+/fkydPHpydnalQoQIeHh4sWrQIDw8PHj58qLMOEURIuwwNDXFxcSFHjhyMHz+e169fky1bNl6/fs3Jkyc5d+4carUaKysrgoODAXBycqJZs2Y6ZTsxSsqEkvXEplO6PiV14dM0EyZMYMKECQm+lxKioqL0kiY5ihQpwqhRo5g2bRoBAQFyMGHLli1AzETdGo1GnpC7bdu2zJkzBzs7u0TXKUmSou84Ojo6weVnzpzh/LlzUCQmUBAeFobGKP7tSqFChYCYyZ8jIiKAmJ4KgiDEp/S8Gx0drfi8q6S+Kd2uRqNJ8n4oT548bN++nTZt2sjBhB07dpAtWzYGDBiAt7c32bJlY9WqVZiamiratlar/ez+mpubs2nTJjJkyMCKFSs4efIkZcqU4d27dzx79oyqVasSFBTE3r17AWjZsiVr164VvRAEQRAEQRCEdEkEEoTvQmmDs74bppU2KhkaGipq0DcwMEg6XTIn9VMiOY1jSo5h2bJlWblyJQULFmT06NG8f/+exo0bkzlzZrZu3crUqVPjrbNMmTLUrFmTunXr0qBBAxFEUEDpMVI6PFVyGRsbY2BsnOB748aNI0eOHPzxxx+8fv2a4sWL4+XlhVqtJmvWrPK48wsWLKBv3756/b6VrsvY2PiLAm2JMTc3T7FzUWr2559/4uTkhKOjI35+flhaWhIaGkqJEiXw9PQEYnoFLFu2jMaNGye5PqXnq8QCRZ+ef4xNTDBOoBwXLVoUiOmREPu+ku9N6Xf7Iw11JXw/KXUO+hblXsk6lU50bGBgoGh9FSpU4PTp09SqVYtHjx7Rtm1b6tevz3///ScPwVe6dGnFAQyl212+fDl169alc+fO3Lx5EwcHByZMmMCiRYvw9/fHysqKFStW0K5du0TXoY0TEDVKIDiakB/pWiQIX8LLy+ur3v+S9HZ2duTJkydZ6xUEQRCE9EIEEgThBzZq1CgsLCwYPHgw+/fvl5cbGBhQrlw5atSoQY0aNahWrRq2trYpl1Hhm+jWrRtZs2alXbt23L17lyJFipA5c2YuXrwIwD///CMPZSWkb7HDHMUGEwA5iPDHH38wY8YMbGxsvnk+Tp8+zenTp7ExNU0yrRjaSBB+TLHDHNWqVQsfHx98fHyAmMb+ihUrfrPttm7dmgIFCtC0aVO8vb3lHm2lS5fG1dWVwoULf7NtC4Kgy87ODgsLC5ydnZNMa2Fh8dmelF+yPi8vLxFMEARBEH5IIpAgCD+4QYMGkTFjRtavX0/58uWpUaMGVatW/S6NhkLKa9y4MUePHqVp06Y6DbLDhg1j1KhRKZgz4Xv7NJhQqFAhVq5cSY0aNb5bHiZPngxAp06d4MzZz6YtUqQIAC9fviQoKEicswThBxI3mPDkyRMGDRpE586dv/l2y5Urx9WrV3FycuLq1av88ccfzJs3TwxlJAjfWZ48efDy8sLf3z/JtEp6EChdn5eXF87Ozvj7+4tAgiAIgvBDEoEEQRDo1KlTTMOd8EOqXLkyZ8+epWHDhvj6+tK9e3dmzJghhlT4ARUpUoQrV65w6tQpWrRo8c2G3UpIbG8EExMTBg8eTFQSgQRbW1t5fg8fHx/KlSv3nXIqCEJqkC9fPq5evcr169epU6fOd9uuvb09Fy9e5OXLl99swnlBEJKWJ08evTbm63t9giAIgpAe6X8wd0EQBCHNKVq0KNevX+fo0aMsW7ZMBBF+YDlz5qRjx47fNYgAyBM8GxoasnzZsiTTR0VFYfpxCKTnz59/07wJgpA6Zc6cmXr16imei0FfDA0NRRBBEARBSHGrV68mT548WFhY0KJFCwICAhJN+99//+Hg4ICZmRmOjo48ffpUfs/T05OaNWtiZWVFoUKF2LVr13fIvSAIaZEIJAiCIAgAZMqUiVq1an33BhlBgJieUQUKFCA8PJxFixbJy8+ePZvgPB1TpkzB19eXTJkyUbly5e+ZVUEQBEEQBEFIUVevXmXAgAGsXLmSx48fExYWRv/+/RNM+/LlS1q1asXo0aN58eIFhQoVom3btgBERkbSpEkTKleuzMuXL1m0aBHOzs54e3t/z90RBCGNEK1FgiAIgiCkuCpVquDp6cmuXbuoVauWvLxtmzZUqFCBVatWERoaCsD58+eZM2cOAAsXLiRr1qwpkmdBEARBEARB+JZ++ukn7t27F2/5hg0baNasGfXq1SNbtmxMnjwZV1dXwsLC4qV1dXWlePHidO7cmcyZMzNt2jSuXbvGvXv38PDw4Pnz50yaNAlra2saNGhArVq1WL9+/ffYPUEQ0hgxR4IgCIIgCKmCoaEhjRo1oqGjI48qVgLAwtISLy8vBg4cyPjx4/n999/Zu3cvkiTRsWNHnJycUjbTgiAIgl74+voqmuxWEARBiBmOqH79+vLfZcqUISoqCh8fH0qXLh0vbZkyZeS/7ezsyJUrF56envLcIJ8ObXv79u1vl3lBENIsEUgQBEEQBCHVcr9+na27d7Ns2TIeP37MwoULAcibNy+zZ89O4dwJgiAI+uDr60uxYsUSfJL2UxYWFtjZ2X2HXAmC8KVmzZrF69ev5b8zZsxIyZIlady4caocRtXLy4szZ87w/PlzcuTIQY0aNShRooTizz948ID9+/fz9u1bcubMSfPmzcmRI0eCaSVJ4vjx41y+fBmVSkWNGjWoWrVqsvMcEhKCjY2N/LeRkREWFhYEBwcnmPbT/GTIkIHg4GBKlixJ1qxZmTBhAiNHjuTChQscPXr0i/IkCEL6l/rO4IKQAiRJUvxKCVqtVvFLEFKK0jKaUvUotdfz1C6lzkPWNjb069ePW7dusWPHDmrXrk3OnDlZvXo11tbW4nsTBAXSy/lP3A+lX/7+/oSFhbFx40auX7/+2ZeXl5f8BK0gCKmPWq1m3LhxXLp0iezZs5MtWzYeP35My5YtqVGjBmq1OqWzqKN+/fo4Oztz584djI2NOXbsGKVLl+b3339XdD2ZP38+RYsW5ejRoxgZGeHm5kaRIkXYv39/vLR+fn5UqlSJP/74g8DAQLRaLePGjaNHjx5ymlevXqFSqeSXp6cnxYoVk/+OnUvMysqKoKAg+XPR0dGEhYVhbW0db7ufpgUIDAzE2toaMzMzdu/ezbFjx8iaNSsTJ06kXbt25MqVS/ExFAThxyF6JAjpmtKnHYyNjRWv89Muf59738DAQC9PXKTGpzaEtE/f5SqpuhE3nT63nV7qh4mJiV7SpLTYHzlJ+dy+aOOck22srTGwsACgRYsWtGjR4uszKQg/GKXnZyMjI8Vp9bldQ0NDvW0zdrtKtq3PfRX0o1ixYpQtWzalsyEIwle4c+cOkZGRtGjRgqFDh8rLCxQowJgxY9i2bRvOzs4pmENdkydPpkKFCjrLZs+ezYgRI2jYsCEdOnRI9LPXrl1jyJAh9OvXT+41C9ChQwc6duzI/fv3yZYtGwBRUVH89ttvZM6cmTNnzmBqagrA2LFjuXPnjvzZ7Nmz6wT2f/rpJ3bs2EHRokV1tl2iRAlu3rwp/33z5k1MTEwoXLhwvHyWKFGCjRs3yn/7+/vz/PlzuddF+fLluXTpkvx+pUqV6NSpU6L7LQjCjyt9tL4IgiAIgiAIgiAIgiAIKeratWsA8YYGqlQpZv6rBw8efPc8fc6nQQSAOnXqAPDw4cPPfnbbtm1IksSAAQN0lg8aNIigoCDWrFkjL1u3bh23bt1i8eLFchAhVsmSJZOd799//509e/Zw9OhR3rx5w9ixY2ndujUWHx/AyZcvnxw8aNWqFXfv3mXdunW8f/+eUaNGUb58eTk4MWHCBDw9PfH39+fvv//m5cuXdOvWLdl5EgQh/RM9EgRBEARBEARBEARBEISvdv36dSDmSfq4Hj16BEDBggU/+3m1Wo1Wq43X2B7rw4cPTJkyRVFe6tSpQ4MGDRSljevQoUMA1K5d+7PpXr16BYC9vb3O8pw5cwJw5swZRo4cCYCbmxv58+cne/bsrFy5kkePHmFnZ0fjxo0pUqRIsvNYoUIFFi5cSLdu3fD396dBgwY6vSLisre3x9XVlUGDBtGrVy8qV67M1q1b5fcbN26Ms7Mz3t7e1KhRg2PHjmFmZpbsPAmCkP6JQIIgCIIgCIIgCIIgCILw1a5du0aGDBnkxnSIaXCfOnUqhQoVok2bNp/9/NChQ8mSJQvjx49P8P3AwEDmzJmjKC9mZmaKAgnPnz/HxcWFqKgoPDw8ePXqFa6urlSpUuWzn4udwPjp06c6PTCePHmi8y+Ap6cnBgYGFC5cmF9++YXy5ctz4MABRowYwdy5cxk0aFCC2/Dw8Eh0+926dUu050DcbQM0bNiQhg0bJpi2fPny3LhxI9HtCIIgxBKBBEEQBEEQBEEQBOGbuXnzJlZWVom+7+Xl9R1zIwjCtxLbEJ8hQwaGDx8OxAQRTpw4Qa1atZgzZ06iPQ1ieXp6xhsqKK5MmTIxa9YsRfmpXLmyonTGxsZkz56dsLAwbG1tuXr1KpcuXcLJyQkjo8Sbzdq2bcvs2bOZOXMma9euRaVSodFomDlzJgDh4eFy2uDgYAIDA+nfv3+8+RSGDBmCo6MjpUqVUpRfQRCElCICCYIgCIIgCIIgCMI3U6NGjSTTWFhYYGdn9x1yIwjfnq+vL/7+/p9NExIS8p1y8/3cvn2bqKgoKlWqRPbs2YmKisLb25tXr16RL18+eeLhT8VOLqxSqbh79+5nG9RtbGzkIIW+ZMuWTWedW7dupX379mTNmpU///wz0c+VK1eOhQsXMnToULy8vChbtiyXLl3C3NycXLlykSFDBjmtpaUlgYGBCc6nsGXLFnbu3CkCCYIgpHoikCAIgiAIgiAIgiB8M//++y/lypX7bBo7Ozvy5MnznXIkCF8uqR40b9++pUWLFoSFhX2nHKUesRMt9+nTR2cYnaZNmzJ9+nR69uxJ3rx55eWRkZH8+eefrFu3DmNjY+bOnUtISAgFChRIdBvfY46Edu3a0adPHw4cOPDZQAJAv379aN68OceOHSMgIIAmTZpQt25dsmTJQq1ateR0+fPnx8/PL9H5FN68eZPsfCZHdHS0/P/P9bIQBEH4HHH2EARBEARBEARBEL4ZBwcHypYtm9LZEISvYmdnh4WFBc7OzkmmtbCw4L///iNLliyJprl+/Tq9evXSZxZTXGwgoXz58jrLBw0axL59+1i3bp3O3Ad9+vTh9evXeHt7Y2BgQK1atfjpp59QqVSJbuNbzJHwKY1GQ1RUVJLDMMWyt7enU6dO8t8nT54kKCiItm3bystq167N+fPnE51PIXfu3MnO54oVK/j777/lyZbXrl1LpkyZ4qV7+PAhDg4O8r75+PhQqFAh+f3379/TrVs3Dh06RMaMGZk0aVK6K5uCIOiHCCQIgiAIgiAIgiAIgiB8Rp48efDy8kpyyCJQ1sMmPQ5tdP36dfLkyRMvgFKzZk0yZ86Mq6urHEjw9vZmx44dPH78mMyZMwNQt27dJI+LPudIuHfvHkZGRjqN6gDTp08nLCyM9u3by8uePHnCokWL4vVyOHXqFDVr1pT/9vf3Z+DAgTRo0IDffvtNXt67d2/mzp2b4HwKZmZmtGvXTtE+xbp8+TJDhgxh7969lC5dmk6dOtG/f382b94cL23BggWJjo4mIiICc3PzeO/379+fqKgo/Pz8uH37Nk2aNKF06dJUrFgxWXkSBCH9E4EEPYod1y8oKCiFcyIkV+x3p8Tnno4A0IaFEaLRADFlwSBOF8IvpdVqFac1MDD46u19C7H1IqljLerR1/kW5U/xtlN5OU3t+VMiJevR9zx+X1qOkzo/CwL8mNcjfd7npKS0fB5Pyevzt5DcehQaGpou6pKgXHor87FsbW2xtbVVlDapMh8aGgok7xydmkVGRuLp6UmTJk3ivWdoaEjTpk1Zs2YN3t7eODg4cPjwYapXry4HEQBevnxJlSpVPrsdfc6REBUVRfv27bGxsaFIkSJotVquXr3K/fv3mThxIl27dpXTPn/+nDlz5sTr5bB161aGDh3KL7/8QkhICAcPHqR27dqsWbNGZ1v29va4urrSoUMHKlasKM+n8PjxY7Zu3Ur+/PmTlfeNGzfi5OQkD580adIkqlSpQmhoKJaWlorXEx4ejpubG+fPnydz5sw4OjrSvHlz1q9fLwIJgiDEIwIJehQcHAx8WZc0IZ3KkSOlc5DqBAcH60w6ldD7IOqRXojyl279UPXok3FkBUFffqh6JKQu6ej6rLQexX0qV/gBpaMy/y0kVY/SitDQUKZOnZpo4/PQoUMpXrw4UVFRQMxwOnGH4fnw4QMHDx7kjz/++C75BShVqhQ3btzg6tWreHh4EBISQpMmTahRowYZM2bUSZs/f35mzZoVr5fDsmXL8PT05OLFixgYGDB27FiKFi2a4PYaNGjAo0ePOHLkCK9evaJWrVrUq1dPcXAqrrt371K/fn3579KlSxMVFYWPjw9lypRRvJ4HDx4QFRWlM9Fz6dKl2b9/f7LzJAhC+icCCXpkb2/Ps2fPsLa2TpVPcwUFBZE7d26ePXuGjY1NSmdHkbSYZ0ib+f7WeZYkieDg4HiTS30qtdejz0mL3zuk3XxD2s37l+ZbX/UorR63hKSnfYH0tT+pdV/S0/UotR7jhKSlvELaym9K5DW91KO09D2DyO/38D3zrLQepRWZMmX6bE+Bn376iZ9++kn+u0SJEixatIhr165haWnJ8OHDCQoKomTJkt8juzoqVKhAhQoVPpsmZ86cie5fiRIldOY9+BxbW1vatGnz2TTRn+nBY2hoiEqlIiQkRKeMGhkZYWFhkewhs0JCQjA1NcXExEReliFDhnQ59JYgCF9PBBL0yMDAgFy5cqV0NpJkY2OTZm7kYqXFPEPazPe3zLOSJ23SSj36nLT4vUPazTek3bx/Sb71WY/S6nFLSHraF0hf+5Ma9yW9XY9S4zFOTFrKK6St/H7vvKanepSWvmcQ+f0evlee00NPhC/VokULjhw5QqNGjcifPz89evTAx8cnwcmCfySvX78mZ86cib6/YMEC+vbti5WVlc7wWdHR0YSFhWFtbZ2s7VlZWREZGUlkZKQ8uXRgYGCy1yMIwo9BBBIEQRAEQRAEQRAEQRCE78bQ0JAVK1awYsUKeVmPHj1SMEepQ7Zs2T7bIyHWTz/9xI0bN+S/3d3dMTExoXDhwgBoNBoMDAyS7A1WqFAhTE1NuXnzpjwslbu7u07vEUEQhFipaxYyQRAEQRAEQRAEQRAEQRAS1alTJ/bu3cuhQ4fw8/NjzJgxtG3bFgsLCwAKFizIpk2b5PQajQbNxwnQNRqNHKwwNzenbdu2jB07Fj8/Pw4dOsSePXvo3Lnz998pQRBSPRFI+IGYmpry999/y93V0oK0mGdIm/lOi3lObdLqMUyr+Ya0m/eUzndKb1+f0tO+QPran/S0L6lVWjrGaSmvkLbym5bymtqktWMn8vvtpcU8Cz+mcuXKsXTpUvr27YuDgwO2trYsXLhQft/IyAgDg/83+WXIkIEMGTJgaGhIiRIlMDMz4927d0DMcEmZMmXCwcGBP/74g8WLF1O+fPnvvk+CIKR+KkmSpJTOhCAIgiAIgiAIgiAIgiAIgiAIqZPokSAIgiAIgiAIgiAIgiAIgiAIQqJ+6EBCYGAgz58/T+lsCIIgCIIgCIIgCIIgCIIgCEKqZZTSGUgJ0dHRDBkyhKVLl6LRaChUqBDTpk2jVatWX7VerVaLn58f1tbWqFQqPeVWENIHSZIIDg7G3t5eZ6zGT4l6JAiJE/VIEL6eqEeC8PVEPRKEryfqkSB8PaX1SBAE/fghAwljxozh7t27PH78mPDwcKZOnUrr1q0ZMmQIc+bM+eKLs5+fH7lz59ZzbgUhfXn27Bm5cuVK9H1RjwQhaaIeCcLXE/VIEL6eqEeC8PVEPRKEr5dUPRIEQT9+uECCJEksW7aMY8eOyRfjtWvXUrlyZfr27YuRkREzZ85UtK7IyEgiIyN11g0xJzAbGxv9Z/47Ss4c3Pp8KkKr1SpOq89os9L9lSQpyf3VhoXx4NcaABQ6cxoDC4vPptfn8Uup702JoKAgcufOjbW1tc5yUY+UlSul6eKWv8JnzyRZ/pTmTwmNRqO4XBkaGn5Nlr6Ivr8PSB/1SOlx0Wq1ivdXyfn5c9uNW44LnDqJoaWl3rarbyl13RK+3I94PRISprT+SpKkuP4mdp7UhoXhU/1XQNn9YVLr+zR/SujzPJ6S9Sil7nf1vd2UuE+M9T1+v2nDwnhYoyYABU+fwsDC4qvvDz6Vmp/OV7IfQUFB5MmTJ149+lTs++J6JKQWca9p+vrN+aUSux4JgvBt/HCBBJVKRVRUFH5+fjrLe/fuTXR0NP3798fR0ZGGDRsmua5p06YxceLEeMttbGzS/AVeBBIST5dkQ66REVYfG0ltbGxEICGJ7Yp6pOdAQjLLn9L8KSECCd+PPutRqgwkfFKORSBB+BZ+pOuRkLDvGkj4wutzag0kxEqJeiQCCV+XDr5TICGBMi8CCQlLaj9i3xfXIyG1+Ba/Ob9Waj4fCEJ68kP+om3QoAETJ04kKipKZ3m/fv1o0aIFkyZNUrSeUaNGERgYKL+ePXv2LbIrCOmaqEeC8PVEPRKEryfqkSB8PVGPBEEQBEEQ0q8frkcCwNSpUylXrhw9e/Zk7dq1OpHLQYMG4ejoqOiJDlNTU0xNTb91dgUhXRP1SBC+nqhHgvD1RD0ShK8n6pEgCIIgCEL6la57JEiSxKZNm2jTpg09evTAx8cHgGLFirFu3To2btzI77//rjOOZ3R0NDlz5hTdogRBEARBEARBEARBEARBEASBdNwjQavV0rFjR65fv06tWrU4evQoe/fuxdPTkyxZstC6dWsMDQ3p1KkT7u7uDB8+HAsLC8aMGcOUKVNSOvuCIAiCIAiCIAiCIAiCIAiCkCqk20DCypUrefDgAbdu3cLc3Bx/f38KFy7MokWL5AnAWrRoQZkyZZg6dSpTpkwhU6ZMzJgxg1atWqVw7gVBEARBEARBEARBEARBEAQhdUi3gYRFixaxYcMGzM3NAbCzs6NJkyZ4eHjopCtQoAArV65MiSwKgiAIgiAIgiAIgiAIgiAIQqqXLgMJWq2WihUrUrp0aZ3l+fLl49q1aymUqy8jSZLitCkxr4NWq1W0XUmSMDBIekoOJZNcx6ZTemyUrk8JpflTKrV/v0LCUqq8JFdS9URp/dVqtXqtv0ql9vqR2vOnVHL2IzVL7dcFSZLQarWK1qmkvulbeinPQuqi73Kl73qp0Wj0eh+bXig5n6aFa0dKnde+xfVDn+tLKUqv08mpl4aGhoq2nZqPiyAIgiCkJekykGBgYMCKFSviLTczM9P5Ee/n58eTJ0+oUqXK98xemqD0ZkvfN2UGBgZ6vaFWKjn7m1TauO8rSa9P4iY5dVH6/euzvn2LMqB0P4yMjFJ1GUyp81pqp8/zn762G/c9Q0NDxYGslKD0uqU0iCAIwvejtP6Cfq8N3/N8GpfSRtfUHiRIqeu0vrertBwoDWJ9798dsRLNX5zlBgYGqFL5fuhbSt23C4IgCMK39OM8WkPMDUzsD3k/Pz8cHR25cuVKCudKEARBEARBEARBEARBEARBEFKvHyqQoFKpkCRJDiL07NmTwYMHp3S2BEEQBEEQBEEQBEEQBEEQBCHVStOBBLVanayhAlQqFW/evJGDCMOHD/+GuRMEQRAEQRAEQRAEQRAEQRCEtC/NBhKioqJo1aoVnTp1UhxMMDIy4vbt2yKIIAiCIAiCIAiCIAiCIAiCIAgKpdnJlj09PTl9+jRhYWEArF+/PtGJngICArC1taVjx47Y2trSrVu375lVQRAEQRAEQRAEQRAEQRAEQUiz0myPhCJFimBsbIyrqyvbt29PtGfCtm3bKFKkCLdv3yZbtmwiiCAIgpCIo0eP0qVLF06dOpXSWREEQRAEQRAEQRAEQRBSkTTbI8HS0hJzc3N++eUXduzYQatWrQDo3Lkz27dvZ8WKFQDUrFmTLFmycPr0aUqVKpWSWRZSiaioKExMTFI6G6nKqVOnOHLkCEOHDsXOzi6lsyOkgGvXruHk5ERERAQbNmygVq1aTJw4kSpVqqR01gRBEARBSAYXFxdcXV2ZN28ev/zyS0pnRxAE4Yfl6+uLv79/kuns7OzIkyfPd8iRIAjC10mzgQQABwcHPD09adq0qRxM2LJlCydOnJDTZMuWjWvXrmFubp6CORVSg4iICDp37syePXuYNWsWAwYMSOkspbjo6GgmTpzIlClTkCSJ/fv3c/z4cbJkyZLSWRO+oxcvXtC8eXMiIiJwcHDg0aNHnDhxghMnTlC/fn0mTJhAxYoVUzqbwheoX78+Rkb/v9RbWlry22+/0apVK+zt7VMwZ4IgCIK+SZLE6NGjmT59OgB169bl6NGjIpiQhmi1WiIiIggLC5NfoaGhOn+HhYXJ13NjY+OUzrIgCInw9fWlWLFi8nDcn2NhYYGXl5cIJgiCkOql6UBC0aJF8fDwoE6dOlhZWZExY0Y+fPjAihUrqF69ujxnwvcOIkiShCRJSaZRqVSK1qUknb4llf8vSackrSRJic51EZdWq1V0XIKCglCpVAQHB9OhQwfOnj0LwMCBA/Hw8GDatGkYGhoCYG1trZf8xd1PrVYLn5kMXKvVKtpffR+Xd+/e8fr1a3r37s2FCxeAmAbGO3fuULNmTdzc3OSeCaKHQnzJKfdKvjdA8fkgOWmSqndPnz4lIiKCtm3b4ufnR+HChdm+fTtBQUEsWrSIHTt2cPjwYQ4fPkyjRo0YM2YMpUuXTnR9ERERmJmZJZlHUHZeVnr+U1ru9f196FtCw/N9SZq4Ll26FG/Z4cOHGTx4MFWqVKFFixY0adKEfPnyKVpf3KDE5/Ko5PhpNJpEvw+tRiP/PzwiAmMF29VoNIryByhqeNF3PVe6PqWSsz59lueU2q5SyT1P/iiSU571eX+q7/O40vsmjUaj+Dyk9LymRHR0dOLnteho+f9RUVGYKLheKj2/PH/+HK1Wy99//826desAyJcvH0+ePKFOnTps2rSJMmXKAJA7d24Fe5IylB7n5Jx39XV/lZz1KS3PISEhcjqNRsOoUaNYuXIl0XHKyueUKVOGpUuXUqJECTl/VlZWSX7uW9TzhL6PT+9JScbv2pQ4l6f265uQ9vj7+xMWFsbGjRspVqxYoum8vLxwdnbG399fBBIEQUj10nwg4caNG5w4cYL27dvj5ubG+/fvadWqFSqVig0bNqR0Fr+aSqVK1TcqSvOn74BIco7Lu3fvaNWqFTdu3MDa2pp27dqxYsUK/v33X54+fcqqVauwsrJKkeOcUt/vyZMn6devH/7+/lhaWjJv3jxKlixJs2bNuHv3Li1bttQJJgi6UnOdjCup8qXVahkxYgQeHh5kzJiRf//9F0tLS6ysrJg6dSq9e/dm0aJF7N69mwMHDnDgwAGaN2/O6NGjKV68eIrnP266lJAWysG///6LhYWF/Levry+7d+/mypUrnD9/nvPnzzNixAhq1KhB69atad68eaqu915eXuzcuZPbt2+TM2dO8ufPT/78+SlQoAC5c+cmQ4YMetuWvr/ftHA9FwR90/d5XN/1yMDAQHGAOTWLjo7mzz//ZMeOHQA0btyYFy9eYGFhwd27d+nYsaNOMCEpqfnamxwpdd5N7nYjIyPp0aMHe/bs0VluamqKubk55ubmWFhYYGFhgZmZGRYWFty6dYubN2/y66+/MnLkSAYPHqw4mJ5SlB6X5NTJtFAOBaFYsWKULVs2pbMhCIKgF6n7biMJRYsWZdq0aezbtw83NzeqVasGwI4dO3jy5EnKZk5IFZ4/f07Lli25f/8+mTNnxs3NjTJlylCtWjV69+7N4cOHadiwIVu3bsXGxials/vNRUdHM2nSJKZOnYokSZQoUYLly5ezb98+VqxYwdSpUxk9erQIJvwgFi5cyMGDBzE2Nmbx4sWsXbsWV1dXOnfuTN++fcmbNy+zZs2iV69erFq1ih07drBr1y52795N48aNsbe3x9jYGBMTE4yNjTEwMMDc3FxnWY4cOahfv77c80f4vn777bd457b+/fvz7Nkzdu/ejZubG+7u7pw8eZKTJ08yYMAAatWqRfv27enYsWOqaGC77+3N7v/+w83NDU9Pz8+mzZEjh05woWzZsjRq1Eg0NAiCkG5FRUXRv39/Dh48iIGBAbVq1WL//v0AWFlZUbx4cZ1gQmrukfAjCgkJoWPHjpw8eRITExOWL19OgwYNMDMzk++dwsLCdB4KAHj16hWDBg3i0KFD/PPPP+zbt48lS5aIoSgFQRAEQfim0nQgoXLlypQsWZIxY8bIQQSApk2bpmCuhNTi/v37NGzYkOfPn5MzZ0527drF1atXmTZtGmPGjGH//v20b9+eO3fuUKdOHfbv38/PP/+c0tn+Zvz8/HB2dub06dMAdOnShX79+jFo0CB5eKN79+4xb948nWDCqVOnxJwJ6dD27dtZsGABAJMmTcLDw0MeDmHp0qXs3r2bcePGUa9ePQoUKMDatWsZMWIEU6dOZffu3ezbt0/xtpo0acKaNWvEXDUp4OHDhzrDHFhbW5M9e3Zy587NgAEDGDBgAI8ePeLgwYO4urpy48YNjh49ytGjR7l16xazZ89OkXx7e3sTG3qqWrUq4R+HGzA2NqZ27drUrFmTt2/f8vjxYx4/fsyjR48IDAzk5cuXvHz5Uj6nAYwbN47x48enwF4IgiB8W+Hh4bRq1YpDhw5hbGxMtWrVOHbsGPD/oY18fX11ggnHjh0TcyakErE96a9fv46lpSWbN2/G0dFR0WezZ8/O1q1b2bp1K3/99Rc3b96kRo0ajB8/nhEjRoi5EwRBEARB+CbSdCDBwsKCQ4cOpXQ2hFTo5s2b1K9fn7dv31KoUCF27drF1q1bmTJlCgDnzp1j9erVHDt2jLZt23Lv3j1+/fVXtm7dSqNGjVI49/p3/PhxOnbsyNu3b7GysmLOnDnY2dnRsGFDeXij/Pnz4+HhwZAhQ3SCCbVr1+bEiROiZ0I6cu3aNbp06QJA9+7dyZIlC2PGjAGgXbt2nD17lhcvXtC3b1+qV6/OxIkTyZkzJyVKlGDTpk3cunWLI0eOEBERQVRUFFFRUURHRxMeHo5WqyUqKgq1Wk1UVBRHjhxh3759NGvWjO3bt2Nra5tyO/4DqlGjRrxljo6OjBs3jvLlywMxjU0jRoxgxIgR+Pj4sGHDBqZNm8b8+fMpWrQoPXr0+C55DQwMZMGCBbi5ufHQ05PrRRyAmOBBjVq1aNmyJU2aNCFjxowJft7f3x9fX185uHDnzh35vF+7dm2qVq36XfZDEITUx8/PDw8PDypVqpRueqAGBwfTrFkzTp06hampKb/88gsnT55EpVIxbdo0mjVrRufOnbly5YpOMEFMwJw6vHjxggYNGuDt7U2mTJnYsWOHfF1OyocPH7h9+za//vor7du3x9HRUe6dMG7cOHbt2sWGDRsoWrToN94LQRAEQRB+NGk6kCAICdFqtbRp04a3b99SqlQp3NzcePv2rRxEKFSoEA8ePKBLly68ePGC//77jy5dunDq1CnatGnDs2fPyJQpUwrvhf5IkiQHEYoWLcru3bvJmDEjJUqUwN/fn8KFC7NhwwayZctGy5YtcXd3Z9myZezZs4eGDRty584d5syZw7Rp01J6VwQ9CA8Pp127dkRERFCzZk3++usv6tWrh1arpXXr1kyePJmIiAiWLVvGv//+y9mzZ5k4cSIHDx6U11G6dOkEJ11OaLLls2fP0qZNG86fP8+iRYsYO3bsN99H4f9sbGx0hvUJDAzk5MmTXL16lRcvXsQb8qdw4cJMmjQJExMTJk6cyKhRo+jWrdt3GeJo1KhR/PvvvwCYx8nX3bt3yaJgKI6MGTOSNWtWnYaY58+fc+7cOaZNmyYP9SEIwo8jMDCQWbNmMX/+fCIiIrCwsKBVq1Z07do1zQcXR48ezalTp7CxsWHgwIFMnjwZgMmTJ9OhQwcA1q1bR7Nmzbh//z6mpqYUKVKE+/fvM2TIEM6fP5+S2f/h/f3333h7e5M9e3b27duHg4OD4s8OGDCAvXv3smrVKlq3bh2vd4K7uzutW7fmzp0733APBEEQBEH4EaX84MeCoGenTp3iwYMH2NjYsHfvXrJkySKPK2piYiI3VFlYWGBgYICtrS2urq4UL16c8PDweBOdpXUqlYoGDRoA8PbtWwwNDVGpVNSqVQuImYw6dtmbN2+AmGHDwsPDCQ4OBhJ+qllIm6ZOncqjR4/ImTMn8+bNw9DQkBw5cgAx886oVCrMzc3p168f2bNnB6BkyZJfvL3y5cvL3etLlCjx9TsgJItGo5Ff0dHR8nJnZ+fPzhvw22+/AWBkZPTd5kno0KEDBQoUiLf8zp07SB+HNlIiPDycY8eOMWzYMM6dOwdA8+bN9ZZPQRBSv8jISBYsWICDgwMzZswgIiKCzJkzExYWxvr163F0dKREiRLMnDkTPz+/lM5usr169YqVK1cCMUMVtmrVSr5m79q1i4iICADc3d159OgRAA4ODvL/xTCwKS826G1gYEDevHkVf+7Dhw9yj/ytW7fKy1UqFe3atePWrVsYGhpy9+5dnj59qt9MC4IgCILwwxOBBCHdWbNmDRAzREuGDBkAyJ07N6ampvJQK4DOkz/Gxsa0a9cOiJmsO71ZunQp5cuX5927dzRr1ozg4GBmz55NmTJleP/+Pc7OzkyYMIHnz5+TO3duBg8ezPDhw9FoNLRu3VoORAhpm7e3N7NmzQLAxcUFa2trAKpXrw7E9B6ItWnTJnx9fbGzs6N79+5fvM0tW7bw7t078ubNKxouUkBoaCghISGEhIQQGhqKoaEhEyZMYMaMGZ/9nJeXFwDFihX7HtkEoFq1anh5ebF792450AnQokULypYty4oVKwgNDY33uejoaC5dusT06dOpU6eOPGxb7BwgAwYM+KoyHOvFixds3LiRkJCQr15XQEAAQ4cOpXz58mzevDlZgRJBEBKn1WrZunUrpUqVYtiwYbx7946iRYuyc+dOXr58yenTp+natStWVlb4+PgwduxY8uTJQ9OmTdm1axdqtTqld0ERFxcXIiMjqVSpEnXr1iVTpkxs2LABGxsbrl27xoABA/D29qZPnz5ER0dTp04dDhw4QHR0NM7Ozvz5558pvQs/vK5du5IzZ078/PxYv3694s8dPHhQLqcnT57kw4cPOu/nypVLDlLEzosmCIIgCIKgLyKQIKQrAQEBuLm5Aeg0HBkaGlKwYEEAeZLYTxvIWrduDcDRo0d5//7998jud2Nubs6uXbuwt7fn7t279OrVCxMTEzZs2ED27Nnx9vZm7dq1AMyYMQNXV1fc3d2xtrbGxcUlRfMuxBcSEoKbmxu///47FStWZPDgwRw+fJjw8PBEPyNJEgMGDCAqKoqGDRvSokUL+b1ff/0VgMuXLxMZGUlgYCCLFi0CYMiQIVhaWn5RPrVarbyevn37YmhomMQnBH07f/48t27dkl8PHz5k6NChn+2NADHDCQEUL178e2RTZmhoSOPGjdm1a5e8zMLSkrt379K/f38KFCjAyJEjOX/+PAsWLKB58+Zkz56dGjVqMGnSJE6fPk1UVBS5cuXi999/Z/PmzXqZMPrOnTtUqFCBTp06UahQIZYsWfJFDY4ajYYVK1ZQpEgRXFxccHd3x9nZmTp16sjBG0EQvszJkyepVKkSHTp04PHjx2TPnp2lS5dy48YNmjRpgkqlokqVKvz77788e/aMFStWUKVKFbRaLfv376dly5bkzp2bIUOGsGfPHl69epXSu5SggIAAli5dCsQMCRd7PndwcGDlypWYmJhw+PBhGjduTFBQEGXKlMHb25vg4GAqVKjAypUrk7wGCN+eqakpQ4cOBWDu3LlyL5Kk7Ny5U/5/dHQ0Bw4ciJcmtiexCCQIgiAIgqBvYo4EIUV4eXlx5coVtFqtznJJkuL9uLGxsaFRo0bxxl5PyJYtW4iIiOCnn36ifPnyBAUFye8VLlyYu3fv4unpCRBvAjIHBwdKlizJnTt32LNnD127dv3S3UuV7O3t2b17NzVq1OD48eNMmDCBf/75hw0bNtC0aVPCw8Np2rQpJUuWpFevXgCMHTsWe3v7FM65ADHDUu3Zs4c9e/Zw9OhRIiMj5feuXr3KggULMDc3p96vvxI7m0Xcp5y3bdvGsWPHMDU1ZcGCBTr1rGjRomTJkoW3b99y7do1zpw5Q0BAAIULF6ZVq1ZfnOfDhw/j7e2NjY0NnTp1+uL1CF8ub968XzSxaEoFEhJy5/ZtNrm5sWTJEh4/fsy8efOYN2+eTppMmTLx66+/Urt2bWrVqkXhwoX11lB25coVGjZsyIcPHzAyMuLNmzf0798fFxcXJk+eTMuWLRUFyc6fP8/gwYNxd3cHYoLZDRs2ZMmSJZw8eZIyZcowZMgQxo0bh5WVlV7yLgg/gjt37jBq1Ch5Lh8rKyuGDRv22UC4lZUVXbp0oXPnzjx8+JDVq1ezfv16Xr9+zfz585k/fz4AefLkoWLFilSoUIGKFStSrlw5zM3Nv9u+JWTx4sUEBwfz008/0ahRI533KlWqxPz58+nbty+RkZHkzp0bSZJ49uwZefLkYcWKFZiamqZQzoVP/f7778ydO5cXL16wfv16+f47MR8+fODUqVMAtGnThu3bt7Nr1y6cnZ110tWoUYOZM2eKQIIgCIIgCHonAgnfgCRJSQ5TkFCDeWLplNBqtYrXp2S86+joaEXri46Oxsgo6WIUHh6Oj48Pu3btYufOnXIjlVI5cuRg+PDhdOnSBTMzM7RabYKBhVWrVgHQpUsXtFot4eHh8v5+OvZ2/vz55ad/tFotlpaWtGrVijt37uDq6ppgw6ckSYnur9rPD82HALRxnigK9/LC4GM+DW0zYmyfI976lHzHSstLUuXg559/ZvXq1bRv355ly5ZRuHBhnJ2dWbduHbt27WLs2LGMHDmSkJAQypYty++//57kNtMTfdc3rVarqL4FBwcnuL7Hjx9z4MABDhw4wKVLl3QCb3ny5KFu3boUK1aMK1eucO/sWdTv3+N7+jTkzQdA01KlKFuxEmXLlWXanDkADBs2jKxZsxIcHMz79+/lBoVKlSqxb98+Nm7cyMmTJ4GY3ggRERFoNBpFT19HRUVhbGzM8+fPmTNnjtxVv3PnzlhaWqLRaOTjYmJikuT6AEUNtErrR2qn9FyQHLHzI3xOdHR0vDSxAVcHBwf5PaXXj+TUo0+pX75E8+EDUpzzqPGLF/SqU4cetWpx7vZt5m/ahLu7O2XLlqVWrVo4OjpSunTpeOUqsXwoPe++fPmSCxcu0KVLF0JDQylXrhxr1qxh//79zJ07lwcPHtCuXTtKly7NrFmzcHR0THA9L168YOTIkfJY1tbW1owcOZLu3btjbGyMs7Mzo0eP5r///mPmzJls3rwZFxcXmjZt+tlyrdVqFdcPfdYjpfcHgKL7A337FvUoJSQnj/q8n1Sr1YrquVarVfT9Kr0ORkdHKyqnce/rAgMD+fvvv1m9erWcn27dujFy5EhsbW0xNTXVmRsmIUFBQWTPnp3Ro0czYsQIjh49yn///ce1a9e4d+8evr6++Pr64urqCsSU6ZIlS1KhQgUcHR1xcnLSOf7R0dHxrm8JndfC791D8zHIkdD9YazIyEid4xIWFiYHOYYNGybvn1qtlucjatSoES4uLuzduxeVSsWxY8ewsbFh7dq1ZMqUKcljnJKUltNvkU6f9Ujp+oyNjRkyZAjDhw9n7ty5dOzYMcHfN0FBQURFRbFjxw7UajVFixalT58+bN++nZMnT/LkyRNsbW2RJAkzMzMqVqyIoaEhjx8/5uHDh/HmYJAkSS4vn6NWq3X248OHD1y8eJHq1avLw2NCzL1G3PUlVOZDPTxQmZphZGSEYUZbjD/zoNKn201MdHS0ov0IDw9XdH5J7PdlQlLi+iYIgiAIqYFKSgu/ptKIoKAgMmTIQEBAgKInQFNzIEGj0Si+gfvcjZmXlxdubm64urrqBA+MjY2pVKmSPAlyrIR+cN65c0eeCC82oNCpUycyZsyok+7WrVuUK1cOY2Njnj17hp2dHWFhYfL6tm7dSo8ePeT0jx49ImvWrPJ2LSws8Pb2pkSJEhgZGeHn55fgD66E9lft58fDBg2RoqISPRYqExMKHDqoc+OcnAZQJemSajCMNXHiRP755x+MjIw4fPiw3AV6//79ODk5YWhoyJUrVyhdurTehqOJrR+BgYGfrR9K030L3+IHp5J0QUFBOunc3NyYO3cuHh4eOumKFy9OvXr1qFevHkWKFJE/I715Q1TPXvCZxv5IrZY+Wg17LlyQfyQFBgbKdXDHjh388ccfcvqaNWvi6uqKSqUiOjo6Xn1LyNOnT5kzZw6rV6+WAw+1atViy5YtOnVJ3w2goKx+pHZKyl9QUBC2traK69H79++TrEcajSZeQ1WGDBmQJAk/Pz/5PKnvBulPr1tqPz8e/9Yo2efR5G5Xabp169bxxx9/EBkZSdWqVfnll1/477//6NevH/Xq1ePff/9l6dKl8rwN9erVY8qUKZQpUwaAiIgIXFxcmD59OqGhoahUKpydnRkzZgxZsmSJt71Dhw4xatQonj17BkDDhg1xcXFJcAJqUN6Qm5wAkNLrjNL6lhLDmX2LepSar0eg3/tJpYEipeVPo9EoDkwoKS9hYWGoVCr27t3LsGHDePnyJQBOTk5MmDCBQoUKATH78ek9ZkICAwMT3Y+goCBu3rzJtWvXuHbtGjdu3Ig33NHYsWMZN26c/LckSTqBBLWfH48a/vbF57XIyEid72PmzJmMHz+efPny4eHhIeddkqR4PQ0mTZrEhAkTMDIy4tChQ9SuXTtme3q6Xn6L+7qUKvcptb7o6GgiIyMpWrQoz58/l3uTfOrdu3cYGhrSrl07jh8/zsiRIxk2bBg1atTg7t27zJ8/nw4dOqDVauX7rapVq3LlyhVWr16d4INBSupvVJxyK0kSderU4dy5c1hZWdG2bVt69uzJzz//rBNIUL98ydNGjb+4zMceF30/0GZoaIi/vz9ubm5s2bKFhw8f0rx5cwYMGEDhwoWBmPOQ0h5HP9LvI+HLubu7U65cOa5fv07ZsmW/Ol1qog0Lw7tsOQAc3K9joOCa+62I+iEI35eYI0HQK0mSuHfvHpMnT6ZMmTKUKlWKiRMncvfuXYyNjalfvz7Lli3j8ePH/Pfff+zcuVPntXXr1njLPDw8cHFxIWfOnLx8+ZJhw4ZRqlQpFi9erDOeaOwY/02bNsXOzi5e3mJvEgEyZ84sN47FFTu8UXR0NHv37lW839EfPnz2hhlAiopC8yFA8Tq/pTFjxtCmTRuio6Np06YNjx49IjQ0lIEDBwIwePBgSpcuncK5/PGEh4czaNAgunXrhoeHB4aGhlSvXp2ZM2fi4eHB9u3bGTBgAA4ODrx7946lS5cydOhQzhw48NkgAoCpgQGTRoxI9EmrmjVryj/aVCoVEyZMUNzY8OzZMwYMGECJEiVYvnw5arWamjVrcvz4cQ4fPpzqn4AUdN27dw9JkrCzs0vwPPmtaAICUs15dMuWLfTs2ZPIyEjq1atHoUKFmDdvHl5eXvTv358+ffrQtm1bLl68SLdu3TA2NubIkSPyPAobNmygdOnSjBs3jtDQUCpVqsSRI0dwcXEhS5YsRERE8M8//1CrVi32798PxAQOLly4wJAhQzA2NubQoUOUKlWKyZMnKx47WxDSu5cvX9K+fXs6dOjAy5cvKViwIAcOHGDjxo1yEEFfbGxs+PXXXxk6dCjr16/nyZMn+Pj4sHnzZnkerqlTp3LhwoVE16H5oJ/zWlRUFMOGDWP8+PFATG+ExBpQ37x5w/jx45kwYQIAS5YskYMIQupjamrKX3/9BcQEihI73wcEBHDmzBkg5rdO3H8T+s2i73kSdu/ezblz54CYubpWrVpFpUqVqFy5MqtXryY4OBggpidCKrmWQ0xQf/fu3bRp04aCBQsydOhQrl69yvv371m1ahU///wz7dq149KlS98lP4IgCIKQ1ok+ed+ANiwMrYKnI1KqRwJKngxL4olDSZJ4+/Ytnp6e3L9/H6979/D29uael5fORMU2JiY4OjryW6NG/NawIbZxnmrWJjAxrBQRgfaTfTYGujs749y6NVs+Dvng5+fHyMGDmT9jBkOGDqV9u3a4bdqEuUpF1/Yd0IaF/X8bH/e3UK5cmH/cp1IODjrb12q1xA6y0dbJiQceHuzdvp1ObdrEy6MqgSdQJIWNPNqICDlvoP8eCVqFPRIkjYYVixbx/OFDbri707ZZM6pUrcrbZ88okjcvY0eMkPOZ0P5+ibj7rTS9knqkTynVI0EbHs6jhw/p3bs3d+/excLAgAEDBtD7jz90egI8f/aMa+7ubN26lePHjslP/fsePkylj8MZfU6Z4sV1yr0UHo70MX+ZLS35pVQpbt++TZs2bShRsCDSx7RSdDTaBMZUfv7iBfPmzWPjhg2o1WqMAMdff2XkyJFUrVo1Zt8S+N61Wq2iciVJkuLy96P0SPgW9Uir0egc53s3b2KuUlG6aNH456tv2CNB+4Xn0eRuN6l0q9esYfCgQRhLEi2aNcPQ0JDt69djrlLRuEkTDh8+zIUTJ2jg6MifI0YwYeRIRgwcyJTJk3Fzc2P31q3s/jiMUQF7eyZOnEjr1q0JDAxECg/H3d2dYcOH88DHB4A/unShmZMTkyZNIlPGjIwaMoTuHTsyYsQITp06xcxJk9i2fj2tWrakdp06/PLLLxgZGsZctxT2SFBy3Vf8vSWjR4K+rh/JkVL1SN9S6klqbTJ6JCg5JlqNRtl9ZxLXBa1Wy+o1a/j7778JDgrC2tiYfv36MXToUMzMzePdU2qjo4k/eFp8Uni4vB8v/fyYN28et2/fpkHDhnTp0gVbW9uYdNHRSEZG5LKzI1fDhjRv2JDokBC2b99O7y5dOHvmDDY2NkhSzLblfHzleU0bFcXLly/p1q0bV69cwVylYvDgwXTt0AFtWJz7WEnL2bNnWbliBXv27EGtVmOuUjFw4EC6deigs259XS+/RT1K7T0I9L2+2LLSuV075s+YwYsXL1i7fDm9evbU3W54OIf37sUoOpoSRYtSKFcupPBwmjVowPwZM7hy5gwfXr3Cxtpa/l5qVqrEIpWKy2fOJFy2lNTfjwGBKLWaiaNHY65SMXz4cKpXr866devZt28vXjduMGLAACaMGkWrVq3oXqsWCc9M8sm6EynzscdF6fVIm9DvMkni4qVLbN26lT279xAYGADE/KYsW6oUrVu3pnCRIqxZvZojR45wbP9+ju3fT7ny5Rk8eDCNfvstyR4HKfX7SPhyvr6++Pv7J5nOzs6OPHnyfIccxefl5ZVkmpTMnyAIAoihjfQqtkvVlUKFsUqBH8+CkJqFaDT88sBHcdddUY8EIT5RjwTh64l6JAhfT9QjQfh6ya1HYuiWL+Pr60uxYsUIUxC4sbCwwMvLSy+N9UqHLEqp/H0NMbSRIPy4RI8EQRAEQRAEQRAEQRAEId3x9/cnLCyMjRs3UqxYsUTTeXl54ezsjL+//3dtqM+TJw9eXl5J9phIqfwJgiDEJQIJ30ChM6fT3WTLFy9donu3brx48QIAUzMzxoweTfcePbBUEH0Oj4iQu4hGRkZSu1Zt7t/3TvJzcf3xRx/G/z0eIyOjmMmwPo71HhkVxcaNG7l79y7jx40jQ4YM8mfCwsMxMDDgyJEj9O7dm7CPk2LGlTNnTlq1akWnzp0pVLAgEDOeaKdOnQgICMDY2Ji/Ro5kyOAhGBkZJtjVNcLLi6cdnZPchzwbN2JWrKj8d0pNtvzp5KonTpxg586dTJgwId78EvqcTIwcORSnL3z2TKqd3FIfQ6hoNBqGDR/OqpUrAejbty+jRo3C8GNX8/v37zN+3Dh5PNzs2bPTsWNHWrVqhZW1tc66tA8fET1iRJL5sVvxL8ZFish/B8WZbPlzoqOj5eEdAM6ePUvnzp3l+vHnX38xaNAgVCoVxkq6yovJlhOkdJJYEpmcMCET8uZJ8juJW04/BARw9coVACr88gvHjh7VSfctJ1uOuHePZ87xJ4T81Kfn0eRuN6F0Pg8eUL16dUJDQhgyZAglS5akW7duqFQqVqxYQc2aNRNc14sXfoz4cwRXLl8GYNCgQYwbPx6jj8fp2vXrjBo1Sj6mOXPmZOLEidSvX19nPbv37GFA//7kzJmTc+fOJTq5uUaj5dr1axw9epSVK1bIdXDkqFEMHjQYY+P437WYbDm+5Naj1Hw9grQ12fKz58/xuX8fR0dH3aHNPnNdKFa8OC+eP2f//v2Ur1BB53OrVq5k3rx52NnZkTt3bnLmykWuXLmwz5GDgoUKkTtXbrJmzYqBgQqtVuLixQts2bKFPXv3EvJxTHeA8hUq0L9fP2rXqYNKpUKSJE6dOkXXLl2Ijo5mz549VK9ePcH8bdmyhTFjxhAQEABAkSIOjBz5Fy1atEB93wdf5+TfH8bKly8f796949Tp05T8qWS896dNn8bsWbOoW7cuq9eswTbOPXBi9DnZsr7v61L7UETfYrJliBkqsnHjxjx5/Jh8+fNz4vhxnfml3r9/z4gRI3Bzc8PC0pLt27ZRsmRJFi9ZwswZM+RrVe3atXWuH6dPn2bkyJE6w6VYWlnRuFEj2rZti6OjozxJcqyw8HA2b97MokWL5CH4jIyM6NS5MzNmzMAwgeuJRquV7zVeX7pEyMBBSe57YmU+9rgoOn4ajXy9BViwYEHMZOT583PhwgUsPk6cHPd36OdotVo0Gg3z5s5l9uzZWFpZce3qNezt45fzlPp9JHydYsWKpdrJjPPkySOCA4IgpAkikPANGFhYKOrapdcbVj0HEqRPGgqq1qrFLW9v1qxZw/Tp03n+/Dkjxo1j3tKl/Pnnn3Tv3j3RSVwBDFQqDD7ecM2ePZtb3vfIkiUL7du3x8bGBmtra6ytrTE3NydTpkzyMhsbG9auXcvUqVOZt3QJd3zus27dOmxsbDD4eHNobmFBz/79E94usHz5cv766y+0Wi01a9Zk48aNPHjwgI0bN7Jjxw4ePH/OdBcXpru4UKVKFTp37kzr1q25cvs2ffr0Yf/+/YyZNImdBw6watWqBCchVn1m33XyY2amUzb0HUiQkjFHgkGcG+A6jRtTp3HjBNMa6OlG2SDOmMGK0iusR/r0vQIJEREROHfqxM6dO1GpVMycOZNevXrJn5k7dy5TpkxBo9FgYmLCgAEDGDp0KB8+fMD8Y7mPS2VqoizfxsZyvQFQRUWhSmB98dYfHa3zXdSoX5+LN27Qr18/Dhw4wNh//mHngQMsX76cMmXKJJ0RrVZRuZIkSXH5+1ECCcmtRxtdXZNsuIkbWFy3bh1nPjaKX7h+ncDISLlRQun3ofi89sl1y+ALz6PJ3e6n6cLDw2nXuTP+wcHUrFmTjt27U7FiRcIlicGDBuHYsCEAb9++5dKlS9SrVw/Tj3OG5CpUkPXbtuHi4sK8efOY7uLC5Vu3mDVrFvPmzWPTpk0AWFpaMnDgQPr165dgHd7s5ka4JOHUrh2GlpaJnvsMgMo1a1K5Zk269+1L//792b9/P6MnTmTHvn2sXr2akiV1Gxv1HUj49P7gc/R1/UiOb1GPUvP1CPR7P2mgsAEPrRYDJXN0fAwkeHh4MHv2bLZs2YJGo2H69OkMHz5cd32JlJeA8HDCJYksuXNjYG4u5y8kJIQJ06cTGBiI75s3uN+9m+DnTUxMyJ07N1FRUTx79kxenj9/flq1akX79u3Jnz9/vM/V/u03GrdqxZYtW5i/fDk1PgkAxurYvTtNWrdmyZIluLi4cMv7Hu27dqXojBlM7dGDhJtKdSV2XrPNnp3n/v68DQrCwCL+ueOFvz/hkkSpX34hk8LGSH1dL79FPUrtDf/6Xp9BdDTPnj2jbuPGPHr0iAIFCnDg2DHscuXSXVd4ODPmz8f3zRvOnDlDuy5d6NWrF1OmTwdgyuTJ1GvaFK1Wq3OMHRs25HLDhty5c4etW7eybds2nj59ytqtW1m7dSt2dna0bt2aDh06UKhQIZYsWcKSJUt49+4dABkyZKBnz5707duXnDlzJn5cNBoMPgYkMmbPToiC45NYmY89LkqPX9zzUPuuXRk1cSJejx7x5OVLfvrpp5h0cX6HAjx69IijR4/SqVMn3WuyVouluTnjpkzhyJkzXL58mb/+Hi9fy3W2nUK/jwRBEAQhpSX9y1IQPjI1NeWPP/7A29ubRYsWkStXLvz8/Bg8eDAODg4sXryYiCQmlbtz5w6zZ88GYN68eUybNo1Ro0bRv39/OnfuTNOmTanzcTLJYsWKERERQcGCBVm9ejXm5uYcO3aMmjVrcv/+/STzq9Fo+PPPPxkxYgRarZbOnTuza9cubG1tKV++PC4uLjx48IB169ZRt25dDAwMuHDhAr179yZnzpyMGjWKpUuXsm7dOjJmzMj169epUKECU6ZMkZ8gEoTkCggIoH79+uzcuRMTExO2bdtGz4+T6mm1Wv78808mTZqERqOhUaNGXL58mfHjx2NlZfXV2x4/bhzhCUxy/iVy5MiBm5sba9asIWPGjLi7u1OlShWmTZsm6kca5urqKv8/9incbyUyMpKLFy8yb9482rZtS6NGjb7Ztj4VFRXFsWPHGDp0KKVKleLWrVtkzZqVlStX0qFDB0JCQqhcuTLDhg0DYuqtk5MTvXv3pk2bNjpdz42MjJg5cyabNm3C0tKSkydPUr58ebnh4ffff+fu3bsMGTIkwSCCn58fp06dAqBdu3aK9yFHjhzs3r1bvka5u7vzyy+/cPHixa84MoKgX+fPn6dZs2aUKVOGjRs3yj0nJ06cyJMnTxStI/a69Wn92bRpE4GBgRQqVIhdu3Yxf/58hg8fTtu2balcuTK5c+fGwMCAqKgoHj58yLNnz7CxsaFbt26cOHGCe/fu8eeffyYYRIg1cOBAAA4ePIi3d+I9aW1sbBg5ciTe3t78888/ZMyYkXv37jFmzBhF+5iYbNmyAfD69esE349dnj179q/ajpAynj17Rp06deQgwrFjx8idO3eCaU1MTFizZg0lSpTg7du3TJkyBYD+/fvLD6MkpmTJkkyZMgUfHx9Onz5N3759yZIlC/7+/ixdupTq1auTI0cO/vnnH969e0e+fPlwcXHh4cOHTJky5bNBhNQkW7ZsNP74YNSaNWsSTdezZ0+GDh1Kv379EgwOGRgYsHDhQgwMDNi2bRvnz5//ZnkWBEEQhLRGBBKEZIsbUFiwYEG8gMLSpUvRarXxPhcdHU3fvn2Jjo6madOmODk5JbqNsLAwJk2aRPny5enevTsbN25k79695MqVCx8fH2rVqsXhw4cT/XxISAgtWrRg2bJlAEyaNIlFixbF675rZmZGy5YtcXNz48mTJ0ybNo1ixYoRHh7Oxo0bqVSpEkWKFOH27ds0btwYtVrN2LFjqVSpEh4eHvJ6jDJmRGXy+afCVSYmGGa0/WwaIX178eIFv/76K2fPnsXGxob//vuPVq1aATENmz179uTff/8FYObMmWzevJkCBQokuV6VjQ18UrY/FanVsvfUKZo3b86HDx++fmeIedquQ4cO3Lhxg0aNGqFWqxk/fjxVq1bVqR9C2vDhwweOHz8OQIcOHQDYsWOH3tb//PlzXF1dGTp0KFWqVCFjxoxUq1aN4cOHs2PHDrz9/IhM4NoRl1qlYuaypaxdu5YzZ87g6+ureEi3Fy9esHLlSlq0aIGdnR316tWTG0osLS3ZtGkTM2fOlIMKixcvxtDQkOjoaPr06cPjx48BuHr1Kg0bNoxXxtu0acP58+cpXLgwAFWqVOHixYusXr0a+88Mo7N9+3a0Wi2VK1dOsEHz/fv3HD9+PMHGDpVKhbOzM3fu3KF27dpERUV9dcOlIHwtrVbLvn37+PXXX3F0dOTAgQOoVCpatmzJxYsXqVGjBuHh4QwYMCDJJ7wlSUowkKDValm6dCkAffr0oW7dunTv3p0JEyawatUqDh06xMOHDwkJCcHHx4fjx4+zb98+nj17xrJly6hWrZqiJ56LFi1Kw4YNkSSJefPmJZnexsaGMWPG8PjxY/755x8kK6skz2ufuz+MDSS8evUqwfdjl8emE9KO5AQRYtnY2LB161Zyfeyx0KJFC8aNG6d4myqViipVqjB//nz8/Pw4fPgwnTt3xvrjkJkVK1Zk+/bt+Pj4MGjQIHl5chgq+E0UBfi8STg49rW6desGxAQaIyMj473v5eXFpUuXANi2bZt83/2psmXL8vvvMcMtbtiw4ZvkVRAEQRDSIpWUnD6awmfFzhYfEBCgaAzQLxlDWh/plAxxEB0drWjsR7VaTXR0NGvXrmXmzJk8f/4cgK1bt9K8eXM53aNHj1izZg0zZ87E2tqa/fv3kzVr1njrO3v2LDdu3GD79u1yt1oDAwO0Wi358+enU6dObNy4kYcPH2JgYMCkSZPo16+fzr77+fnRrl07bt++jampKYsXL6Zp06af3Y9Xr17JP1AlSeLmzZuMGjWKhw8fYmJiwtSpU2natCl79uxh6tSpfPjwAWNjY8aOHcvw4cMxMjJC/fIl2g8BaCMi8OvSBYgZ/zN2uA7DjLYYf9KYpHSs6eR8H0rHLP40qJIYfY4BmiFDBgIDAz9bP5SmAxIMWCXk0zGa9ZFOyXEODAyU1+ft7U3Lli158eIF2bNnx9XVVe5yffnyZf7++28uX76MoaEh48ePp169evHW9/btW7JkyZLgtlTv3qEKDga1GuspUwGI+Hs8fPwxd+fJE/pNmEBISAgFCxZk4cKF2NraKnrKTK1WJ1hf45IkiY0bNzJ27Fi5fowaNYqhQ4fGO1YajUbR3Azh4eGKy5+S9anVakXrAhTXDyX0OWRWUFAQtra2iuvR+/fvk6xHERERGBgYsGHDBnr37s1PP/3Exo0bKVOmDEZGRjx9+lQe3uhzQ9jF+vSH+8aNG5k4caJ8fYjLzs6OihUryq8C1tY89/LiqY8Pv+zaDUDfgA+8/jj2eIBGw8tPerwYGRmRK1cu8uTJQ/78+cmbN6/8UqlUHD16lEOHDnHr1i2dz2XNmpW6detSr149HB0dOXLkCD169EClUrFz505CQkIwNDRk06ZNHDhwAFNTU3r37s327dt59eoVpqam9OnTh2rVqvHzzz/L6w0PD+fhw4eUKFFC5/sMCQmJNweNJEnUqFGDhw8fMmfOHNq1a4ckSTr1vH379uzcuZNJkybx119/ycujoqJ0yunz588pXrw4UVFRHD58mBo1asjbMEmiUQeSd3+gdKgkJedJ0O+QIt+iHim5Hin1PefiiUvp/YZarVb0/X5a/mI/u23bNubOncvdj8MMmZiY0KFDBwYNGiQH2e7fv0+lSpWIiopi/fr1tGjRAq1Wm+B5PCoqSl7u7+9PcHAwKpWKEydO0K1bN6ytrbl06RKWlpY6n/P19Y23LCHv3r2LVy8/devWLbp27YqxsTEeHh6fDQwGBgbqnCcDAwMZ+8cf3DxzBjOVik158wFgu2QxNh/ruaFtRowTGIMdYNiwYcyfP59+/fox/eMwNnEVLVqUFy9ecPLkyUTncPjUj3Rfp/S8BsqOi9LAdVL37XGDCPnz5+fw4cOfDSK8fftW5zz++vVrLl++TIMGDXSWh4SE6MwVl5g3b97o1I+IiAjev39Pjhw5dM4TkZGRid53xhUcHKxTfzWvXqMNDIDISAL69gMgw+JFHD1zhqVLl+L74QP+KhVjxoxh2LBh8c4lUVFRiq5bsUOAfrqsYMGCvHjxgs2bN9O6dWtevHgh79c///zDypUryZw5M+/evcPY2Jjt27dTtmxZtFqtTlDuxIkTNG7cmMyZM/P48WOda1pCPQw/pXTOnpS6Hv1I3N3dKVeuHNevX//sHAlK0+l7uym1vq+hDQvDu2w5ABzcr3/3ISDjEvVDEL4vMUfCN6BSqfQ2/qjSdRkYGOh1rH0jIyNFN94qlQpTU1P69etHjx49aNWqFQcPHsTHx0fnZuvZs2csWLAAgJEjR5IjgXFcnz9/zpw5c+Su47a2tjRt2pQMGTKwcuVKHj9+zPLly+natSunT5/mwoULjB07lrt37+Li4oKZmRm3b9+mTZs2+Pn5kSVLFtauXav4h1Xcm70yZcrg6urK8OHDOXHiBMOHD8fLy4thw4bRunVreWz4v//+mz179rBt27aYyZFy5EAbFiavx7x4sc9eVJX+uEnOxJZKfggZGhoq3rYQX3Lr+OXLl2nXrh0BAQEULlyYHTt2yJNpvX//niFDhuDl5YWZmRnTpk2jcuXKCa7HzMws8Qb9j8uliAiiPi4yLFgQPjZolClcmLX58tGnTx8ePnxI165dWbhwIcWKFUsy/2q1WlHDeseOHWnYsCEDBgzgwIEDTJo0if3797Nr1y6dfBsYGCgqpz9aGVVSrpJ7bVFyToj9bnfu3AlAy5YtKVasGD/99BMeHh4cOHCAzp07f9H23dzc6NWrlzxRc8mSJeWgQdmyZSlWrFi8deYtX57KYWE8+hhI2HPnDoEREdy7d4+7d+/i5eXF/fv38fX1xdfXF7VazZMnT3jy5Ik8OXlCVCoVFSpUwNHRkYYNG1KqVCm5jPn4+DB48GAAhg8fjqOjIzt27ODatWscOHAAgB49elC+fHmKFSvG4sWL8fDwwMXFhadPn1K6dGl5Xebm5nKQMC4zM7N4DTxXrlzh4cOHWFhY0KFDB6ytrXUa4CMiIjh06BAQ0/jRrFkzed2fzn2QJ08eunfvztKlS5kyZQqOjo5yOn3eRxgZGel9LHF9+hb1KCXo814yOetTen8Q9z5RkiQ2bdrEhAkT8PX1BcDa2ppevXrRu3dv+enpWEWLFmXEiBFMmTKFESNGULdu3Zi5rxLYbtzApKWlJREREahUKnnYko4dO2Jraxvvc1FRUYoa+hIKiHyqfPnylC1bFnd3d5YtW8a0adMSTfvp+TZDhgzM37SJ+fPnMy/O515bWJCtRIkk8xcb7H/79q08N0ssrVbLmzdvAMiVK1eaKNdJ0edvmS9Jq691fS7dp0EEJT0RMmfOrFNOM2XKlOD9m4GBQYL1KDo6mjdv3vD69WtevXrF/fv3CQgI4M2bN/JytVqNo6MjzZo1k6/NKpVKUUD40/OGgX0OsM+BFGc4TdOiRWlYsiQ1u3WTf0dNmDCBPXv2MH36dGrUqCEfNyMjI0XbNTIyilfnjIyM6Nq1K5MnT2bt2rW0b99eDjZERUXJ9zpz587F1dWV/fv307dvXw4fPkzGjBl11ufo6IidnR3+/v6cPXuWOnXqJJknQRAEQUjvfqxWGuGbMjU1pXz58gA8ffpUXi5JEuPGjSMiIoJKlSrJQ7nEioqKYunSpfz22294e3tjYGCAo6MjI0aMoGTJkuTJk4e+fftiY2PDq1evWL58OQ0aNGDGjBkYGBiwefNmGjVqxObNm6lfvz5+fn44ODhw/PhxypUr98X7Y2VlxZIlS+jbty8Aq1atolevXpiZmcUbG75u3bryj2dBiOvQoUM0b96cgIAAKlSowH///ScHEV68eMFvv/2Gl5cXNjY2LFy4MNEggj4ULlyY9evXkz9/fl6/fk2PHj3k7t36kiNHDlxdXVm9erVcP2LH8f1aly5dYtq0aQR8fDpd0J8PHz5w4sQJIGaoBEA+V7u5uX3ROs+cOUO3bt2QJImePXvy+vVrLl68iIuLC+3bt6dgwYKKG2WyZMlC9erV6d27Ny4uLhw8eBAPDw8+fPjAgwcPOH78OCtWrGDixIl069aNWrVq8T/2zjusxv//44/TLoTsvWWE7BFZobKVPSqb4iMje6WMSLKF7E0lJZSRnT2TPZqkCO3OOb8/+p7719E6yGeex3W5Lp1zn/d93+fc432/xvNZo0YNypQpg6WlJdu3b+fdu3dcunSJ6dOnY2hoKAQ9kpOTsba25tu3b7Rt25ZZs2YB8ObNG7Zv3w5Az549admyJZAZzJw2bZrQNeTt7c3kyZNJypJEVpS9e/cC0Lt37xwlJC5evCjIuqSnpzNy5Mg8O2tmzJiBhoYGFy9eJDg4+Ie3R4mSHyUqKoq+ffsycuRI3r17R5kyZXBycuLly5csW7YsV+3+6dOnU6tWLd6/f8/ChQtzHV92XqmoqAjBwLCwMC5duoSKigo2NjYFv1M5MGrUKAC2bdum8D1IKpVy8eJFtmzZwujRo9mdRR6lR48egpRcXuTlkRAfHy9cD5TSRv8Mvpczyq8T4VeQSqW4u7tTv359ypYtS4MGDTAxMWHYsGE4Ojqydu1aDh48yLlz53j8+DHPnj1jy5YtmJub06VLF9atW/dDzzYZGRncvXuX9evXM3jwYLp3705wDsn97z227t69S7du3ejSpQsXLlwokCS07LoQGBgo58Vy5swZ4uPjKVu2LB07dmT16tXUrFmT6OhoJkyYkM3jS01Njd69ewM/PxdSokSJEiVK/m0oEwlKChTZZDjrxHP79u2EhISgpaWFk5OTXODoypUr9OzZEzc3N1JSUqhatSpTp07F3NxceGDMyMigbNmy2NraUqJECeLj41m1ahXGxsZ4eXlRrFgxbt68yfjx40lMTKRDhw4EBgZStWrVX94fFRUVpkyZgru7O9ra2ly+fBkjIyPCwsIYMmQIN27coHr16rx58+ZPSya8efOGmTNn0rVrV+bPn8/ly5eV5rZ/U/bs2cOwYcNITk6ma9eueHt7o6enB2RKO3Tr1o2nT59SsmRJNm/eTIMGDX77NpUvX56dO3fSsGFDvnz5Qt++fTl58mSBrkMkEjF48GAOHToEZBrehYeH//R4Mm3qrl274uTkxIABAwrMNFpJJidOnCA9PR0DAwPq1KkD/H9C4ezZsz/sq/HgwQP69+9PWloaffr0wc3NTSGZkR9FVVWVChUq0KZNGwYOHMisWbPYtGkTAQEBhIaG8u7dO/bt28ewYcOydfOkpKQICejHjx9TqlQptm3bhqqqKh8+fGDDhg2kp6fTqFEjLCwssq132LBhjBo1ClVVVQICAhg4cCBRUVEKb3tycrIQmBg6dGiOy5w6dQqA7t27U6xYMW7fvs3KlStzHbNixYpCwHPJkiUKb4sSJT+KVCplz549GBoacvLkSTQ0NHB0dOT58+c4ODjk2CWQFU1NTdatWwfA1q1buXHjRo7LZfVHkM0fZQk+MzOzbN0Ov4t27dpRv359vn79mqumuozExEShI1amYd+1a1c5KcH4+HhMTU1xcXHJM3AqS8Tk5JEge61EiRIKycAo+WvJyRPhdx2/6enpTJ48GUdHR2JiYoSuwLJly2JoaEjHjh0ZMmQI9vb2LFu2DE9PT9avX4+ZmRkaGhq8ePECV1dXTExM6NixIxs3bsx2DGZkZHDnzh3Wrl2LlZUVNWvWxMTEhIULF3LmzBmuX7/O8GHDctw+mcfWvXv3mDBhAhoaGly6dElIKAQHB/9SQqF69ep07twZqVQqZ7q8f/9+AAYOHIiamhqFCxdm+/bt6OjocOXKlRzvr7L7//Hjx5XPW0qUKFGiRAnKRIKSAqZKlSoAQtAwKioKBwcHAKZMmSIn5zJlyhRsbGx4/fo1JUuWxNXVlREjRghVVR8+fGDNmjXMnTuXkJAQ9PT0mDhxIuXKlePLly+YmZlRqFAhzp07R+3atQEYMWIEx44dy/cBNj8+f/4sN4k1MzPj4MGDVKhQgZcvX9K2bVv8/PyoXLkyZ86ckUsmhOegA/4rpKenExYWhpeXF5aWlujr67N69WrOnz/P8uXL6dixI+XKlWPw4MHs3buXlJSUAl2/kh9HKpXi7OzMlClTkEgkDBs2jH379gmB1Lt372JmZkZkZCQ1a9Zk3bp1CpkqFxTFihXDw8ODdu3akZKSwvDhw9myZUuOpnS/Qrt27TA2NiY9PR1XV9efGuPbt28MHTqUefPmCd4UV65cwcbGRmGtYiX5IzNVzhow19fXx8DAgIyMDHx9fRUe6+3bt/Tu3ZsvX77Qtm1bduzYUWCa3AXBy5cvWbBgAfXq1WPixIk8ePAAbW1ttm3bRtmyZUlLS8PKyopPnz5Rrlw5JkyYkKvUS/v27Zk1axZ6enqEhobSp08f7ty5o9B2+Pn5kZCQQKVKlTA2Ns5xGZmskZWVFWvWrAHA0dGRkJCQXMdVdiUo+d28f/8eS0tLRo0axefPn2natCkhISHMmjVLIR8VGcbGxgwbNgypVMqkSZNy7Lb53mg5Li5OSMCNGTNGofW8ePGCefPm0bt3b0aMGMH06dNZsWIFO3bs4OLFiyQmJuY7hkgkYtq0aQCsX78+WwdSWloaISEhODo60rBhQ6ZPn05YWBiFChWiVKlSPH/+nB49egjLW1paIpFImD17Nv379+fbt285rleRRIKyG+Hvz8OHD3/YWPln+fDhA4MGDWL//v2oqKiwbNkyQkNDiY6O5vHjx5w9e5Z169axdOlS/vjjDwYPHkynTp3o0aMHmzZt4tatW6xcuZJ27dqhoqLC7du3mTVrFnXq1KFXr14sXbqU/v37U7VqVTp06MC8efM4e/YsX79+RVdXF1NTU5YsWYK1tbVcMuBDbGy2bS1btixr1qzhyZMncgkFMzMzOnXqxJUrV376e5Al1Xfs2IFYLCYiIkK4Jw4aNEhYrlatWqxevRqAjRs3cvz4cblx2rdvT8mSJYmLi+PChQs/vT1KlChRokTJvwVlIkFJgSJLJLx+/Vqo2kpISKB06dKCxjbAqlWrhCroJk2acPr0aXr27CnXrXDw4EEiIyORSCR4eXmRkpKCrq4u48ePR1dXl8+fP+Pg4EDNmjW5dOkS165dY926db9skpqUlMSwYcMYM2YMBw4cEF6vW7cuR48epX379nz79o1hw4YJQaCsyYSuXbr89Lo/fPjAnj17mDt3Lv369aN+/foULlyYRo0aMXDgQI4fP45EIsHExAQ3NzcGDhyInp4enz9/5ujRo4waNYpatWqxYcMGZULhL2TTpk0sWLAAyDRKXLt2rZzW69SpU4mPjwdg+fLluUo//E60tbVZuXIlw4YNQyKRMGvWLGrXro2dnR1nzpwpsKSC7EFu165dCpsoZmXr1q0cP34cNTU11qxZg5+fH2pqapw4cYLAwMAC2UYlCBJXMnk6GS1atADg2rVrCo1z69YtOnToQExMDAYGBhw5cuSHAou/i/fv37Nu3TratGlDq1atWLt2LfHx8VSsWJEFCxbw4MEDwZz45MmTXLt2DVVVVaZMmZKvkbe+vj4+Pj5UrlyZuLg45syZo9A2nThxAsgMaOSUqHj9+jXPnj1DVVVVkKPo0aMH6enptGnTho4dO+Lp6cmXL1/kPqeioiJUPu/atUuhbVGi5EcYNmyY0IWwZMkSLl26RH0F9P5zwsnJCVVVVR49esTp06ezvS9LLkgkEiQSCadPnyY1NZWyZctmu17lxpQpUzh9+jRRUVE8efKE4OBgjh49ysaNG3FxcaFTp044OjoKBtG50b9/fypXrsyHDx9o2bIlnp6ezJ8/HxMTE0qXLk379u3ZtGkTCQkJVK1alSVLlvDgwQOCg4MxMjIiKUvCYtWqVWzYsAF1dXW8vb1xd3fPcZ2ya0NCQkK2aui4uDgAhcx1lfz5PHnyBCcnJwwNDWnSpMlvTSJIpVIuX77MuHHjaNiwIRcuXEBHR4e9e/cyduxYypQpo3BCX1dXl/79+7Nnzx4uX76Mi4sLLVq0QCKRcOHCBZYvX87p06cFc1MzMzMWLFjAuXPnePHiBfv27WPixIm4urqybv16YVz3/yXDc6JixYo5JhQ6deokFDr8KH379qV48eKEh4dz4MABAgMDkUqlVK5cOdv3b2xsLBT0LF68WO49WWc8oJAcmRIlSpQoUfJvR2m2rKRAqV69OnXq1CEsLIwdO3bQrVs3li5dyocPHzhy5IhQAdK7d2/Onz9PXFwcd+7cwcHBgdmzZ8uN1bp1a6GzwcDAAC0tLcRiMX5+fkLgZMCAAUBmUPRnH2KzIpVKmTt3Ls+ePQMyA8IWFhaCwZ2enh7+/v7UqVOHiIgIHjx4QLt27ahUqRKBgYH06tWLV6GhULTYD633wYMHuLu7s3//ftLS0rK9X7hwYfT19WnVqhXjxo0TDNbs7OwQi8XcuHGD06dPs2vXLiIiIpg8eTLLly9n1qxZjBo16m8RyPuvcP36daZOnQrAvHnzhArGrNjY2DBr1iySk5Pp378/5ubm2NraCrJHfxZqamqsXbuWWrVqsXnzZqKjo9m3bx/79u2jSJEimJqa0rNnT4yNjXPUb8+PjIwMIThiYmLyU+bJskC2RCKhTZs2FCpUSAim6Ovr//B4SnKmQ4cOHD9+nIULF9KxY0fU1dV5/Pgxe/6n6S0z7s2Lw4cPY2VlRUpKCnXr1sXHx4dixYrx8eNH7t69S6dOnQqkM0EqlfLq1StSU1OF4KJEIiEtLQ01NTW51169esXBgwc5e/askMhSVVWlU6dOWFtbY2pqmm2bGjdujIaGBmlpacTExFCuXLl8t+nDhw9CdXCnTp0U2g9ZFXRu3Ugyz4qWLVsKgcKtW7cyZswYIdlx7do17O3t6dOnDyNGjCAjI4ORI0cSGxtL4cKFGT58uELbokSJoly4cIHz58+joaHBlStXaNSo0S+N5+3tjVgsRldXN8fEgL6+PkWKFCE+Pp5r167RqlUrNDQ0iImJ4cSJE/Tq1Svfdciq/R0dHdHR0SE2NpbY2Fg+fPjAnTt3iIqK4siRIxw5coR69eoJ9+Xvk4jq6ups3rwZa2trnj9/LnhoyShRogTNmzdn+PDhmJiYyF1bOnfuzJ2rV4W/RSIR48eP5+zZs3h5eeWasJR1InXo0CGb+axsLnj//n1SU1N/uZBGya/z5MkTjh49ypEjR3j8+LHwurq6Oqampri7uxdoEuHLly/s37+fzZs3yyXCmjVrxooVKzA0NPyl8UuWLMn48eMZP348r1+/5tixY4SGhtKkSRPatm1LgwYNUFVV5cuXL9nMzcViMRfOn6fN//7u2q1bvuuTJRTs7e2ZM2cOR48eFaT/vvfYyw8tLS3mzp3L9OnTWbBgAVu3bkVLS4t3797h4eHB+PHjgUwvInt7e6Kjo1FVVZXrdEpJScHS0pJHjx6ho6OTTeZQiRIlSpQo+S+iTCQoKVBUVFSYNGkStra2rF27lqdPn7J06VJmzpzJkiVLMDAwwMDAgJYtW3LmzBnWrVvH3r17OXv2LBcvXqRly5aCPmfz5s2pXbs2kZGR6Ovrk5aWxt69e3ny5AkikYg1a9YUuMnerl278Pf3R01NjSJFivD+/XuOHDnCsCwan+rq6hgaGhIREcH9+/dp164dkDn5DQ4OZpy1NTx/AcDs2bNxdnXN9vAHmYFRPz8/3N3dhYARZHZotGjRgrp166Kvr0/dunXzrCRSVVWldevWtG7dmpkzZ7J7926WL18uJBRcXFw4deqU8MCp5Pfx4cMHBgwYQHp6OhYWFkyZMiXH5UaMGEGnTp1YvHgxR48exd/fnwsXLjBy5EgGDBhQ4MGAt2/fEhgYSKlSpahUqRKVKlWiZMmSQGYwY/LkydjZ2XHt2jW8vb3x9/cnJiZGCKxoa2vTtWtX+vTpg7m5Obq6ugqtd926ddy5c4dixYrlWm2ZH0ZGRvTp0wcfHx9mzpwpGKh37tyZatWq/dSYSrLj7u5OcHAwd+7cwdXVlWnTpjF69GjS09Pp3r27kLTNCalUyuLFi4UqPlNTU3bt2oWuri7Xrl1jyJAhxMTE0LNnT3bt2pUt2PAjfP78mSFDhnD+/Pkf/myLFi0YNGgQpqamlC9fPtflqlSpgq2tLW5ubhw4cIAGDRrkeA2XERsbi7OzM2lpaXTu3DnH5OHPIJNQyJrEKV26NMePHycqKordu3eze/dunj59yoEDB+Q66Bo2bMiBAweoVatWgRhXKlEiQ+a9YWNj88tJhKioKMFoefHixTl252lra9O3b192797N/v37mTt3LpMmTcLV1ZWFCxfSvn37fCvyZeevvr5+tsRdTEwMsbGxHDlyhMDAQEJDQ1m8eDGurq707t2bAQMGyH2mU6dOPHr0CBcXFy5cuEC9evVo3bo1bdq0oXbt2nz+/DnbPTwtLY0tW7bkuG0vXmTOF2USnVl59eqV0FWUkyF1gwYNKFeuHNHR0Vy5coVuCgRqlRQ8suTB0aNHefTokfC6uro6JiYmWFhY0KtXL4oXL15g63z48CFbtmxh//79QqJMFugeOXIkDRs2LLB1yahWrRrTp09XaFmxWMykSZPwPXaMObUziz7aGxujqCBlxYoV2bdvH9ra2uzZs+enkwn29vacPn2awMBAli9fzvz585k7dy7Lli2jcePGnDhxAk9PTyAzqb9mzRrhPLp58ya2trbcv38fHR0dfH19Fe6CUqJEiRIlSv7NKKWNlBQ4I0aMQE9Pj9evX+Pr68u0adPo3LmzYPyVkJAAQJEiRZgzZw6+vr60adOG9PR0oYX23r17SKVSihYtSr169UhJSWHLli08efIENTU1xo8fX+BJhJs3b7JixQoAZs2axR9//AGQo3a8bIL+8OFDudeLFCnC7t27hb/XrVtHjx495IxKv337xoYNG6hTpw69e/fm3LlzqKqq0r9/fy5evIi/vz8bNmzAzs6OLl26ULFiRTnJp7zQ1NRkwoQJPHv2jPXr11OxYkUiIiKwsrLKUXtYScGRkZHB0KFDhcTX9u3b8/zdKlasyNatWwkICKBWrVokJiaybt06hg4dytUsVYu/ikQiYfr06axbt44FCxZgY2ODiYkJrVq1YuDAgYL/wI4dO0hNTcXe3p7Hjx9z6tQpbG1tqVSpEsnJyRw/fhwbGxsqVapEv379OHDgQL5SRUeOHAEyA0R5BW7zw8nJCQ0NDc6fP8/6/7XJjxw58qfHU5KdcuXKCRrBTk5O2NraCkmg9evX53osJyUlMWjQICGJMHXqVI4ePYquri7btm2jW7duQqX+iRMnsl0Pf4SIiAhMTEw4f/48ampqlCxZktKlS1O2bFnKlStHhQoVqFy5MlWrVqV69erUrFkTQ0ND5s+fz6NHj7h06RK2traUKlUq33XZ29ujq6tLTEwMQUFBuS6XmJjIqlWriI+Pp379+ri5uRVY14UsWZJTN0j58uWZNm0aDx484MqVK4wbN07wBho7diyXL1+mVq1av7wdSpRk5cKFCwQHB6OhoVEgCTMHBwe+fPlC8+bNBRm8nBg8eDCQ6eWSnp6Ora0t1atX58OHDyxfvjzf9cgSCTkZpYpEIpo3b46Liwtnz55l2rRpVK5cmW/fvrFv3z569+7N6NGjCQoKEj6vq6uLk5MTly9fxsPDAxsbG/T19XO9Th47doz3799n8zKQSCQ8f/4cyLnDbsmSJWRkZNC1a1eMjIxy3HYTExOAPK9TSn4fixYtwsDAgEWLFvHo0SPU1dUxMzNj69atREZG4uvri5WVVYEkEdLT0zly5AidO3emadOmeHh48O3bN/T19XFzc+Pt27e4urr+liTCj7J48WIOHTr0S/dDVVVVtm3bxvDhwxGLxQwdOvSHfX9UVFTYtWsXenp6PH78mNevX2NmZkZ6ejp9+/YVkghWVlacOXOGxo0bk5CQwJQpU2jXrh3379+nePHi+Pr6CoVjSpQoUaJEyX8dZSJBSYGjo6PD2LFjAVi7di0ikYhly5YJQW0HBwe5AGTNmjXZsWMHGzZsoFixYiQkJLBv3z42bdpEZGQk8fHxbNiwgXfv3qGjo8O4ceN+uQruez58+MAff/yBWCymZ8+eDB8+HEtLS8qWLSt0JWRFtv779+9nG0sly4OklrY2p0+fpmXLlpw/fx4HBwcqVaqEnZ0dz58/p2jRokyfPp0XL15gb2+PnZ0d5cqVY8qUKb+kUS9LKFy/fp3ixYtz+/ZtVq5c+dPjKckfmT5soUKFOHr0qMJSQK1atWLz5s3MnTuX4sWL8+7dO6ZOnYq9vb1QqfgrnDt3jmfPnqGjo0OrVq2oUKECKioqpKSk8PLlS06ePMmGDRuYPn06FhYWGBgYMGHCBCpVqoSTkxP3798nKCgIBwcHateuTVpaGgEBAYwcOVLwgciJ5ORkIdH2q1WS1apVw87ODshsMy9dujTdu3f/pTGVZGfIkCF0796d9PR0oQp29erVuSaBIiMjad++PYcPH0ZdXZ1t27bh6upKRkYGtra2gnmqhYUFPj4+FC1alKtXr2JiYkLED5rSh4aG0qFDBx4/fkzZsmW5fPky4eHhvH37ltevX/Pq1SuePHnC8+fPefr0KU+ePOHx48eEhIQwb968Hw6q6+rq0rdvXwB8fHz4+vVrtmUyMjJYt24d0dHRlC1blq1btwqG6r/K06dPiY6ORlNTk9atW+e6nCwIum7dOt69e8fTp09Zv369Us5OyW9B1o0watSoX5ZnCQgIwMvLC1VVVdatW5dnwLFz586UKlWK2NhYLl++jKamplD4sWfPHm7dupXnuvJKJGRFT08Pa2trTpw4webNm+nQoQMqKiqEhIRgb2+Pvr4+S5cuJTo6WuH9lEqlbNiwAQCb7xLg4eHhJCcno66uTtWqVeXee/r0KXv37gXkuxHi4+M5deqU0GnU5X++XErPoD8fWdc1ZHbibd++nejoaPz8/BgxYkSBdSBER0ezdOlSatasydChQ7l06RKqqqr069ePwMBAHjx4gK2tbY6dOZ8+fSI4OPhP7Ux78eIFmzdvBjKfA38FWTJhwIABiMVi7Ozsfrgwqly5coJE2LZt2+jVq5cgWVimTBn27dvHsmXL0NbWxt/fH0NDQzZt2oRUKmXo0KGCjK0SJUqUKFGiJJP/rLSRVCplzpw5jBkzJld94n8rikwm09PT5Sqrrly5go+PD3/88QcVK1YUXheLxYJ/QFYmTJjAqlWruHjxIrdv30ZHR4c1a9YIkhQeHh6MHj1a7jMdO3Zk3Lhx3LlzhwsXLvD69Wvc3d3R1NQkJSWFYsWKMXLkSEqXLk16ejpJSUn57seHDx/yDeykpaUxYcIEPn78SKVKlejbty+3b98GwMzMjB07drB+/XqqV69O8eLFKVOmjNCCHhoaytevX+VkLyRZTI79/PwYOGIEz58/l9PNrlGjBhMnTsTIyAg1NTXmz5/Pvn37hN9m3bp1nD17ltWrV1O1alVEIpFCD+2pqalyAaQSJUrg6urKyJEjcXR0xMzMjPr16yOVShUONCnSDaHIMfU7HmIUHVMqlSq0rFisWNN1YmKi3G/u7e0tBDXWr19PlSpVSExMJDY2Nsfz43tSU1Pp3r07HTp0YMeOHRw+fJhr165x/fp1unTpwujRo6lYsSLJycnZjFWzkZKCbI0PHjwQHp66du0qaLtmZGTw8eNHHj9+zMePH4mPjyc+Pp64uDg+fvzI4cOH8fb2xtjYGCMjI8qXL4+FhQX9+vXj9evXBAQEsHv3blxdXSlZsqRwbKuqqlKnTh0A7t69S0ZGBqVKlaJChQpy362iv0dGRobgqzB16lT27t3Lhw8fGD58OCoqKgr/XlnXqyQ7Wb+X9evXc+XKFT5//kz37t0ZPHiw8H5SUpLwAH/27FlGjRrFx48f0dPTY//+/bRt25YXL15gaWnJ/fv3UVFRYfr06YwdOxaRSMSBAwewsbEhNDQUY2Njtm/fnmOVLchfR/39/Bg/ZQpfvnyhRo0a7Nq1i3LlyvHhwwe5z7x//16hBF5ycnK2yuCcMDAwoEKFCkRGRnLgwAE5eSepVMqBAwcIDQ1FU1OTVatWoaamJpif5oRYLJbzQZEdv2lpaSQnJ8uNLZO7a9OmDZqamjkeu9+/pqWllaPcl6Lnm0QiUcjHRCqVKtwlp+Sfh1gszrHbLDg4WOhGmDp1KikpKQrJ8KWlpWU7rhITEwXpv0mTJmFgYCD4muSEiooK/fv3Z+PGjXh5eWFsbEyLFi3o378/R44cYebMmfj5+cltz4cPH/j8+bPcOBEREdnmhB8+fMixcKNUqVJMmjSJgQMHEhgYSGBgIJGRkTg6OuLs7EyLFi0wMzPLJhuppaUlSPBBZjI/LCyMQoUK0b9/fzjmJXwvT//XjVCzZk1UVFTk9t/R0RGJRELXrl2pVq0aHz9+JCkpiZ49e/LkyRMcHR0ZPXo0jRs3RiQS8fDhQ6KjoxXydPk3IJFIFLoOSSSSAukSk5H12WPWrFmIxWK6desmV3CUlJTE58+f0dDQyHe8xMREChcunO11qVRKSEgInp6e+Pn5CUmwEiVKYGFhgYWFhXAfe/LkifC59PR0KlSoIPxtb2/PiRMnWLx4sZz2f07m3TmR75zzfyQnJwvSZEuWLEEsFmNiYoK5uTnx7pnJhIyMDJLS0hS6bqSnp8vdz93c3Dh37hxPnjzBzc1NTjpUkXl2x44dGTlypGCQvn37dm7dusWAAQPQ09Pj3bt3zJ07VzBTrlmzJuvWrcvV8yiv61VWpFLpT/mDKVGiRIkSJX9n/pOJBKlUyrhx43j06BFz5sz5S7ZB0Yfwgl5O0cmMSCQSxrx16xY9e/YkKSmJY8eO4e3tLZh3qaqq5jhmpUqVGDhwIPv27WPt2rVs27aNWrVq4ebmhq2tLe7u7piZmWFsbCz3ublz56Kuri5M6I4ePUpKSgoGBgb4+PgIk+PExESFJuja2tr5TjAdHR15/vw5Ojo6ODg4yOm/d+3aFV9fX+Li4rh06RK9e/dGXV2dmjVrUqRIEb5+/crr16+pV6+e8BlJlol5w4YNuXTpEsOGDePKlSsYGxszadIkTE1NUVFRYevWrSxbtoz3798D0KtXL4yNjXFyciI0NJR+/fqxePFievToodCDkJqaWrbfY8iQIRw7dgx/f3/Gjh3LxYsX89T7/ifxM8ezIsvmh6qqqrDc5s2bmTlzJgATJ06U02/V1tZWSA++adOmlChRAsh82Jk3bx4LFizg6NGjnDlzhnPnzjFq1CjGjx8v94CYE9LkZGShzLCwMMLDwwXd3KwPZcWLF+f+/fvUrFlT7vPv37/nwoULREdHc/bsWW7cuEGXLl1o1KgRIpGIatWqMXHiRMRiMfv27WPJkiVUrVqVatWqIZFIhASVrFunRYsW2b4DRQOROjo6wnLa2tocPHiQffv2MXPmzJ/S2f+rjCj/7kHXrNfSKlWqcPToUfbv38/SpUvlrp/p6elkZGSwdOlSVq5ciVQqpUGDBuzfv59q1apx/fp1hg4dyvv37ylatCjr1q2Tu8bXq1ePY8eOYWVlxcuXLxk8eDDHjh3LseJekuUaNX7CBL6kptK8eXM8PT1zrfJUJCgCmeevIp0DnTp1onTp0lhZWXH16lVmzJghJJG3bt3K9evXUVFRwdnZmbZt2+Y7Xnp6utxxK7uma2hoZDueZf4InTp1yvU6p6mpWWCJXsi8nioynqLX04I+7v/u55Gi/FXfi6LB1NzmB7LK65EjR1K1alVSU1MVmkvo6Ohkm68tWLCA8PBwqlSpwuLFi9HS0sr3vjB48GA2btxIYGAgRYsWRVtbmzVr1giB+uPHjwudawCVK1cW9ll2HStevHi2e2hycrJcgu979PT0qFOnDk2bNiUiIoKzZ8/y7Nkzwey8d+/eWFhYyG27TGYMMk3oIdNTonLlyrz73+tFixUTZI1q1aold54/evSIQ4cOATBjxgxUVFSEAihZ0NjNzY0BAwZQqlQpGjZsKHQPWllZ5bov/wQUPZ4Vncfm9pzys8jGCgkJwdfXFxUVFZYsWZJtHSKRSHgtIyOD/fv3ExQUhIaGBoUKFaJQoUIULlwYdXV19PT0hL8LFSpEeHg4np6ecmbNDRs2ZPDgwXTu3FmYy+R0bVdTUxM6E1JSUgTJq9WrV2NtbS34Y5UvX16hADyg0POWmpoaxYoV4/r16/j5+aGiooKLiwtFixYl/n/LFC1aFHWJ5Id+OxklS5Zk6dKljB07FmdnZwYNGkSFChUUvr+VLVuWdevWcevWLR48eMCGDRvw9/dHLBbj7u7O4sWLSUpKQl1dndmzZzN79uw8C64USSKAYvfLf8u9TYkSJUqU/Hf4z6XIsyYRTp8+rbD8yH+V169f07dvX2FyFRUVRadOnfDz88v3s7JqkYMHDxIVFQVktsMPHToUsVjMsGHDWLRoEQcPHuTevXtyVT6VK1dmz549BAYG4ujoSGBgYL4B1J/h6NGjgoTH5MmTs1Vyqaur069fPyCz6jwtLQ3IfJAwMDAAMqu+86J06dKcOnWKV69eERAQgLm5OTExMQwZMoQpU6bw/v17KlWqxPbt21mxYgXdu3fHx8eH5s2bk5SUxIwZM5g7dy6JiYk/tY8ikUiQjZIZqSr5dcRiMQ4ODsyYMQOJRMKIESNwcnIqkLFr167NwYMHCQkJoVu3bmRkZLBlyxZat26No6OjwhrzR48dA6BHjx4KX+vKlCnDgAEDMDMzo3Dhwnz9+hUvLy8mTpzIs2fPhOUmTJhA06ZNSUpKYubMmYLZn4ybN28C0Lx5c4XWqwjt2rVj8+bNBWpYqCQ7nTp1Ytu2bZQuXVru9ejoaHr06IGLiwtSqZRRo0Zx/vx5qlWrxvbt2zEzM+P9+/fUrl0bX1/fbIliyPQGOXr0qKBD3KNHD/z9/fPcnvS0NExNTdm/f/+f/tu3aNGCrl27IpFIWL58OVKplICAAMFPYs6cObRp06ZA1ymRSPL0R1Ci5M8mazeCg4PDL41179493N3dgczuS0XlwFq3bk3VqlX59u0bJ0+eBDKDi4sWLQJgxYoV2e5DMhSVNsoLNTU12rRpw/z583FychKub8ePH+fAgQO5BjRl90KZVFpWnj59CmT3R1i0aBFSqRQLCwvq168PwL59+zh8+DAqKipUqFCBT58+CR2HsuvE6dOnf3r/lCiOVCpl7ty5AAwfPlz4jXJaLjAwkA4dOjBt2jQCAgI4fvw4+/fvZ+vWrbi5ueHi4sKsWbOYNGkSNjY2DBgwgGnTpvH48WO0tbUZPnw4Fy5cYNOmTZiamv5QQcTly5eFbreEhAScnZ1/fefzQCqVMnv2bCDTcyC37+VnGT58OC1btiQxMZFZs2b98Oe1tLQEA+czZ84wZcoUWrRowcyZM0lKSqJ9+/bcu3dPSG4qUaJEiRIlSnLmP5VIkCURbt++LSQRrl+/Tt++fWnUqBHW1ta8fftW4fFSU1P58uWL3L9/E/Hx8fTq1YsPHz7QsGFDnjx5QseOHUlMTKR///6C6WluNG3alLZt25KRkSFoZYpEItatW0e9evV4//49y5Ytw8rKipYtW6Knp0fDhg3p168fs2fPZvfu3airqzN69OgcdT9/lcePHwuV5H369KFZs2Y5LtepUydKlChBfHy8nAZtbobLOaGiokKZMmWQSCR4eHjQpEkTjh8/jqqqKmPGjOH48eNywagyZcqwY8cObG1tUVFRwcvLi9atWyu0rpz43kg1a5XTX80/8Tz69u0bgwYNYtOmTUCmqdz69esLpOL9+fPnDB06lKpVq3L69GkOHTrEuXPnaN26NSkpKaxfv57mzZvj5uaWa9BExru3b9HR0aFnz54/tA0ikQh9fX3h3FRTU+Pu3btYWVmxfPly4uPjUVNTw8nJiTJlyvDu3TtBhkHG70gkKMmd330enTlzhjZt2nD58mUKFy7Mjh07cHd3RyQSYWdnxx9//CGYFx48eJAqVarkOlbx4sXZv38/HTt2JCUlhUGDBrFjxw7hfYlEwqIsmuCDBw9my5YtP9WFUhBMnz4ddXV1rl27xvr164UAxvDhwxk6dGiBr+/x48fEx8dTqFAh5fnzJ/NPvB/9Gci8EUaOHPlL3ghXrlyhR48eiMViLC0tMTc3V/izIpGIgQMHAgjV+pBZ6S8zXs5tXloQiYSsVKlShTFjxjBixAgg0+9h165d2aqUo6OjiY6ORkVFJUcDXFkiQdbpBHDnzh28vb0RiUSCN8KDBw+EwPXs2bMFzytPT0/evn1Lhw4dgMzr9I9K/v0O/u3nkZ+fH9euXUNbW5t58+bluMzjx4+xtLRkyJAhPH36FD09PWbPns3SpUuZO3cuU6ZMYfTo0fTv35/evXtjYmJCq1ataNCgAYaGhixZsoRHjx6xZs0aGjRo8FPbeebMGeD/n1e2bdv2W+f/J06c4OrVq2hrazN//vwCH19FRQV3d3dUVFQ4fPiw0Ln3I9SrV08oqNqwYQMPHjygRIkSbN++nbNnzwrynEqUKFGiRImS3PlPJRIgM5nw4sULHj16xOHDhzEzM6Nq1ar06tWLoKAgmjdvzsuXLxUaa9myZRQtWlT496vGc38nUlJS6N+/P8+ePaNixYr4+PhQqVIlTpw4wciRI5FIJEybNg17e/s8H1pkXQnbtm0TOg4KFSrEuXPncHV1ZdSoUbRt25YSJUoglUp58+YNAQEBrFmzhnHjxtGhQwfKly9PqVKlaNCgAd26dcPa2ppFixaxYcOGnw6sf/r0iTFjxpCamkqnTp3o3bt3rstm7UqQSS0BwsQ+v44EGY8fP8bExAR7e3u+fv1Ks2bNOHjwIFOnTkVbW5uUlBSWLVtGnz59OH/+PKqqqtjZ2bFjxw5Kly7N06dPMTIyYtu2bT+1z1mNVMeMGcPHjx9/apyC5p92HkVERGBmZsapU6fQ0tJiz549TJ06VaHW5PDwcC5duiR0tmQlLi6O8ePHY2BgwKFDh4iIiGDBggXUrl2bR48eERwczJ49e6hXrx5fvnxh2bJltGjRgu3bt+fZ2v0j3Qjfo66uTuvWrRk/fjxdunRBKpXi4+ND//79OXjwIMWKFWPZsmWoq6tz8eJFdu7cCWQmIWVG0cpA6J/D7zqPxGIx8+fPx8zMjI8fP9KgQQMuX75M//79iY6OxszMjJ07dyISiXB0dGT37t0KVRhra2uzadMmRowYgUQiwc7OjmXLlpGSkoKNjQ0bN24Ulp09Z06Balz/KJUqVcLa2hqAjRs3kpaWRseOHYVEdEETHBwMgLGx8V8mx/Vf5Z92P/ozKKhuhG3btmFiYkJMTAwGBgZCV8KPIJuLBQQECP4H6urqQsB99erVxMfHZ/tcQScSZHTp0oVRo0YhEok4e/Ys27Ztk5sT3717F8jsOMjpuijr8qtcuTKQ+YyyYMECIDOBWr9+feLi4hg1ahSpqal07doVW1tbOnbsSPv27UlLS2Pp0qU0bdqUwoULExcXx507dwp0H3+Gf/N5lJGRIfxGdnZ2lC9fXu7958+fM27cOExNTbl48SIaGhrY2dlx48YNpk6dypgxY5gyZQpz585l2bJluLi44OnpyaFDh/D39+fChQucPXuWiRMnyklkZSU+Pp558+ZhamoqaPrnhCyRMHPmTPr06YNEImHmzJm/xS8qIyNDSHZNnjz5t3SRAzRu3JixY8cCCAUMP8rYsWMZNGgQkNk5ERoairW1tVJiSIkSJUqUKFGQ/1QiQSQS4eHhwYABAzA1NcXW1pYLFy7g5ubGkiVLuHfvHjo6Otjb2ys03uzZs0lISBD+hYeH/+Y9+PNwdHTk8uXL6Orq4uvrK0wI1dXV2bhxo9Aeu2nTJvr06ZNrZbSsKjU+Pl4wj4TMilQ7Ozs2btzI2bNniYqKIiIigpMnT7J27VomTJhAp06dBKmhxMREXrx4wcWLFzl06BDr169n9uzZmJubK6xTmRV3d3fevXtHlSpVWLt2bb76qe3btxeMNGWTdlmFz7lz55gxYwYzZ85k8uTJ2E6cKHyub9++tG/fnmbNmtGyZUtCQkIoXLgwrq6unDt3Tq4Kbf/+/ezevZunT5/yxx9/CAmLFi1a4O3tjampKampqdja2gpB2h/he4mjsmXL0rFjR+G7+Kv4p5xHUqmUPXv2YGhoyIMHDyhVqhT+/v706dMn38/eunWL0aNH07x5cywsLGjbti0nTpyQe5ibO3euEISoVKkSy5YtQyQS8eHDByZPnkxQUBAmJiacO3eOVatWIRKJ+PjxI7Nnz86zKqt9+/a/vO9FixZlyZIlbN68GX19fRITE1mzZg03b96kXr16zJgxA4AtW7bw8OFDocqyWLFiv6WbSEl2fsd5JJVKmTx5Ms7OznJSRjVr1iQ2NhZzc3Nu3LhBsWLF8Pb2VjihJkNNTY2NGzcKAXknJyf09fU5evSonIby3+HhPqtJJcDKlSt/ObkhSyh+v3+yilGZb4qSP49/yv3ozyI8PFwI2v1KN0JMTAwTJkwgPT2dTp06cfny5XwNzyUSCS9evGDfvn1MnjyZVq1aCV4kaWlphISECMtaWlqip6dHQkICR48ezTaWTOP9d1TGd+jQgXHjxqGiosKlS5fkAvkyD4SqVatm+5xEIhFkP01NTWnSpAlNmzYlICAAVVVVpk2bxtKlSzEyMiI8PJzKlSsL81WRSMSCBQsQiUQcP36cN2/e0K5dOwBOnTpV4Pv4o/xbzyOpVMqsWbN49uwZJUuWFJ4XY2Ji2LBhA+3atcPQ0JC9e/cilUrp27cv165dY+HChQU6F1q7di3+/v68f/+eefPm5RhM//Lli3D8NWzYEDMzMwDOnz//U88Q+RESEsKzZ88oXrw406ZNK/Dxs7Jo0SJKlSpFWFjYTxVXiUQi9u7dy4cPH/D09BR8I5QoycqTJ0+4c+dOnv/+yufnv/v2KVGi5N/Nv8Nx9QeQJRMg80GkUaNGwnuySaGjo6NCY2lqaipsVPVP4v79+0Kl2I4dO7JpXIpEIqZPn06NGjWwtrbG398fY2NjfH19qVixorCcn58fgwcPBqBOnTr56kiXKlWKtm3bZtOETkxMJCYmhqioKKKionj37h1LliwhPT2dtm3b/rCJWnp6Ot7e3kBmwiS3ip+s+Pr6kpGRQeHChQUJpEaNGlGjRg1evnwpVz2rLRIxq3am3u2Vy5dJzhIs7tGjB66urnLfk4ysQSNtbW05czM9PT2mTZvGqVOnKFKkSDYvB0UpV64chw4dwsHBgfv37wuVhvb29rRs2ZJdu3bJJTf+DP4J51FUVBQTJkwQtNwbN27M7t27cwwOyMjIyMDf3x9PT09u3bolvF64cGHevHnDqFGjaNasGYsXL6ZZs2YMHjwYPz8/YmJiCA8PF3RmAdq2bUvz5s1JSUnh4MGDrFixQkhCtGrVisaNG+e6HVu2bGHhwoUFUtFtaGjIpEmTsLOzQ11dXdj/Xr16cfXqVS5cuMCCBQvYtWsXenp6xMfHs3fv3n+8+eM/gd9xHq1YsYJNmzYhEonw9PTE0tISkUjEt2/fsLS05Pnz51SqVAl/f3+qV6/+U+uQBcTKly/P1KlT+fjxIyVLlmTXli0wZ26B7s+vEBAQIPf3lStX6Nq16y+NKTNN/f6769GjB3v27GHv3r2Ym5sLlZNKfj//hPvRn0V4eDgmJia8evWK6tWr5yrhoghlypTB2NiY4OBgbt++TWhoKA0bNuTdu3fCv7dv3xIeHi78PyIiIscAaenSpenUqRMtWrQQXpN1ImhrawsyP1lp2LAh9+7d49y5c3Tv3j3b+xKJhMTERL58+UJCQgKamppUr15d4SSmkZERBw8e5PPnzxQuXFh4XdaRd/78ebZs2ULNSpWo8b/3ZCa9np6ePH/+nPv37wOZXbvdunXD1NSU2NhYIHMOvWnTJrn5av369encuTNBQUH4+PjQuXNnAgICOHny5G+RlfkR/q3n0fLlywVJS1dXV3R1dXFycmLFihVCUZOqqiomJibY2trSunXr37Id3bp14/jx40BmsVFOnWtFihShUaNG3L9/n9GjR3P79m0gs9OtWrVqOXbuKMLz588JCgrC2tpaTm5Q1vmqoqKCrq7uT42tKMWLF2f+/PlMnjwZFxcXxowZ88PShyKRSJmsV5IjJUuWREdHh2HDhuW7rI6ODk+ePBG6yv4M/u7bp0SJkv8G//pEwoEDB/D19UVXV5chQ4bQvn17IZkQGhqabXmpVJpncPDfjlgsZvz48YjFYiwsLOjRo0euy/bt25fy5ctjaWnJvXv3aNWqFSdOnKBx48asW7cOe3t7JBIJnTt3Zt++fYJJplQqxc3NDS8vLxYuXEiXLl3y3KZChQpRo0YNatTIfPxycXEhPT2dYsWKCbr/P8KlS5eIi4ujRIkSClVrv3z5kmP/M60dP368UEWnpqbGrl272LlzJ+rq6mhra6Ojo0NhNTU4cBAADw8PtIoVQ1tbm9KlS+dpPJZVAqZ69erZEiQy2Zj+/ftTqFAhJBLJDydRILN6LiQkhMjISHx8fPDx8eHy5cuCse+VK1eytWr/V5FKpezduxd7e3s+f/6MhoYGCxYsYMKECXKJnqx8+fKF3bt3s2nTJqESRENDAwsLC8aOHUuVKlXYuHEjGzdu5NatW3Tv3p1evXqxatUqwsLCcHV1xdXVlZo1a5KUlMSyZcvo3bs3p06dYvr06YIUQrVq1ViwYAHm5ua5Bjs0tbR48OAB+/btE7Scf5Xt27cDmcmDrGa848eP5+LFi5w4cYLQ0FAcHByYNWsWixcvZtCgQf/KoMK/mT179jBnzhwA3NzcsLKyIjExkbS0NIYMGcLt27fR09PDx8fnp5MIWRk9ejT169fn1KlTmdfZYsX4u9SwxsfHC/cafX19nj59yuzZs+XuSz9KTEwMMTExOWqnd+/eHQcHB1xcXBg1ahR169aVK3ooCH72/qHkv8H3SYSgoKB8OwjyQlY537NnTy5dukS7du0U0vLX0NCgSZMmtGzZkhYtWtCqVSv09PR4//49oaGhREVF8erVKxYvXgzAmjVrciyGMDMzY/fu3YSEhODk5CQkDBISEoiPjycxMTFbd6uZmRn29vYKyYt9/vyZz58/IxKJ5J4hWrduTePGjbl79y729vZoi0Tc/l+hSYsWLahYowYmJiYMHjyYpKQkQf7Qy8sLgJo1azJlyhT69u2bYzFA7969CQoKwtfXV/CNCAkJITY2llKlSuW73UoUZ/v27cI9ccWKFVhaWrJy5UqWLVsGZM7hBw4ciIWFBaVLl+bTp0+/bVtat27N7t27uXr1KkOGDMlxGZFIxIoVKzA1NRW6dywtLfHw8JDr+PsREhISGDFihOD9kbXwTl9fH1VVVeLi4oiIiPjtclY2NjasWrWKd+/esWXLFkFKV4mSX6Vy5co8efIkXwngJ0+eMGzYMD5+/PinBur/7tunRImS/wb/6kTCtGnTOHnyJBYWFoSEhNChQwesrKzYsmULmpqa2YK6cXFxuLm54eLi8hdt8V/Pli1bCAkJQVdXl1WrVuW7fIsWLbh+/To9evQgNDQUY2NjzM3NOXLkCJDZCr9p0yaheloikTB16lShoqdnz57MnDmT+fPnKzSxffz4MU5OTkDmRP5nKvNlD2i9evXKd51paWmsW7cOsVhMmzZtMDY2lnu/cePG2arBJcnJhP8vkdCnb19UFKySyRq8lyVdZHz9+lXYbhsbG5YsWYK7uztbtmzBwsJCofG/p1q1atjb22Nvb09ERASdO3fm+fPnmJmZERwcrFCnxr+ZL1++MGbMGCGJ1KxZM7Zt24aBgQGJiYnZln/79i2bN29m165dfP36Fcj8HW1sbLCxsZELwjg4ODB8+HBWrFghJDsDAgKwtbVl7ty5TJo0CXV1dTQ1NQkNDcXU1FSQBitevDjTp0/Hysoq12SGjPHjxrF8zRq8vLyoVavWL1fH3b59m7t376Kurp4tMVG1alW6d+/OiRMnmDdvHr6+vqxdu5Z3796xbNkyFi1a9EvrVvLnERQUxKhRo4BMo+HJkycDmdfvcePGce7cOQoVKoSXlxf6+voFtt7WrVsLx6gkObnAxv1VVq1aRUJCAnXr1uXAgQOMHTuWGzduMHnyZA4fPvxTY8q002vXri1XwSzD2dmZ+/fvc/r0afr27cvNmzcLpHry2rVrzJs3j5s3b7J+/foCSzAq+feQUxKhIIKChQsX5sSJE/Tu3VvwAdHR0aFKlSpUrlyZSpUqUbVqVSpXriwEPS5cuEBkZCTPnz8X/p9b8GTQoEG5dr9VrVqV+vXr8/jxY6GzMCe0tLQoWrQosbGxBAQEEBUVxeLFi/OVpXn9+jWQOY/LWhktEonYuXMn27dv5+XLl0S9egWSzPnwm9evefLqFYGBgdnGq1ixIvPnz8fKyor4+Phck36mpqZoamry/PlzPn36hKGhIffu3ePUqVMMHz48z21Woji+vr6CxNe0adOws7Nj69atwrxm6dKl/PHHH3/qNjVo0CBfE+Z27doxZMgQ9u/fz9SpU1m8ePEvJZAXL15MdHQ0kFls0KdPH5o0aQJkdqHUrVuXR48ece/evd+eSNDQ0GD27NlMmDCBFStWMGbMGIX8mZQoUYSs96G/I3/37VOiRMm/n39tIuHhw4d4enry6tUrISh79OhRrKysCA8P5+TJk0KFbExMDIGBgcybN4+xY8cycODAv3LT/zKioqKEapslS5YoXJVetWpVLl++zIABAwgKChKSCMuWLcPBwQGRSERaWhppaWmMHj1aqJoyMTEhKCiI5cuXc+XKFXbv3p1noCQjI4OxY8eSnp5O165df0ruITExkdOnTwOZHRX5cfDgQSIiIihWrFg2jezfwcyZM9m2bRtTp06Ve93f35/k5GTq1atH9erVcXV1JTk5WXhQ/NlkgoxHjx7RsWNHEhISePjwIX369BHMhP+LPH78WDAblxk5Tp8+PcfE09evX1mwYAGenp5CRaO+vj62trZ069YtW1JIRrly5VizZg1jxoxh0aJFBAcHs2bNGnbt2sW8efPo2bMnzs7O7N69G6lUioaGBqNHj8be3p6iRYsilUrx9/cnJCQEW1vbHKtFW7dpQ69Xr/D19cXd3Z0iRYpgYGDw09+LTIu2V69eOa5vzJgxnD59mgsXLnDlyhVWrlzJ0KFDWbp0KV26dMHIyOin163kzyEqKoohQ4aQkZHB4MGDWb58OZDZnTN79myOHDmCmpoa+/btE2Te/s3cunVLkMJbsGABmpqarF69GgsLC169esWcOXMEo9cfQZZIyE2WTFVVlX379tGiRQtevXrF4MGDOXz48E8neB88eMD8+fM5ceKE8Jq1tTWRkZHMmjXrb+FDoeSv53clEWQULlyYoKAgXrx4gZ6eHnp6esKxJ5VKhSDn7t27sbOzyzFpD5lBy3LlylGhQgXKly9PgwYNsLOzy/M4njt3LqdOnUJbW1swAS5WrBgJCQnUqlULXV1dITkfEhKCo6Mj9+/fZ+LEiULVeW7IEgk5dTTXqlVLuI5KkpN51ybzPujj48O7Dx949eoVL1684NWrVyQnJzNmzBjGjh2r0PyrSJEidO7cmZMnT3L8+HG6d+/OvXv38PPzUyYSCohLly4xcOBAJBIJNjY2LF68mMOHDwv+CA4ODn96EuFH2LJlC05OTr/UUQSZ8n5eXl6oqKjQrFkzbty4wZw5czhx4oTQtWNoaMijR4+4f/8+PXv2LIjNF4iNjeXhw4d07NhROM+HDx+Oi4sLr1+/ZtOmTUyfPr1A1wmZRYa3b9+mc+fOBSIRqkSJEiVKlPwb+NcmEu7du0fZsmXlgniWlpaULVuWbt26MWHCBDw9PYHMpENoaCi+vr4FLh+QGz9iEKxI9UhW09a8yKuVfMqUKXz58oUWLVowdOhQUlNT8x0vLS0NHR0dtLS0OHbsGDNnzsTX1xcXFxf69esnjBEWFsYff/zBpUuXUFNTY+XKlfTs2RM/Pz/mzp3LpUuXaNq0qfC5nFi9ejV37tyhaNGiTJ06VSEDocjISEGrUyqV4u3tTXJyMhUrVqREiRLCGDm1ID979kwIutjY2KCiokJcXFy+65X+zyQZ4OaNG6SrqnLlyhUiIiLo2LGjXILm06dPlC1bVvi7Z8+ewuQ7Li5OeF1W9Wptbc3WrVtJTk5GQ0ODtLQ0uWRCTnrCOREREYGamhpfv35l2bJlQvKnVq1aJCYmcvHiRfr06cPatWsV8kyQSqUFHojKyMggIyMjz2W+fv0qTOwzMjJYu3Yt586dY/To0fTu3VvYJrFYrFCl0qdPn/D19WXKlCkkJSVRvnx5PD09adq0qdBlAPDu3Tu0tLS4fv068+fPFwwT27Rpw4gRIzAyMkJFRYXHjx8rJOkzefJkBgwYgJubGy9evGDatGlyZnXdunVj0qRJvH37lgsXLhAREcHBgwcFU+NDhw5hZ2dH5cqVUUlLR9Z38PXLF/r27cvz58958uQJ8+fPp3fv3vTu3RtVVVWSk5P5/PlzvtsHEBgYyN27d1FTU8PU1FSoSsuKSCTCysqKrVu3Mnv2bE6dOoWlpSVHjx5l2LBhnDt3TjgfFTG3U/S4kkgkCslkSCQShR8E/0uB1aSkJCDzPBk6dCgfP36kYcOGrF+/XjB9X7VqleAF4+zsTO3atXO9Fn748EGh6vlPnz7les3Keh19/eoVhRQY7/379/kuA5CamqpQQP7evXtCkqB9+/akpaVx48YNAMaOHcvSpUs5c+YMxYsXFwzH80IikQjfi8yQtUGDBoLpsoy0tDRUVVUpVKgQhw4dokOHDgQFBVGuXDnMzMwYNGgQpqamqKmp5Xt9ef78OQsWLODYsWNCoHbYsGEULlyYjRs3MnfuXN68ecPKlStRUVFBR0cn3/2QSqV/6/NIkfnVj8zBCpq/av6XkZGR5++RNYlQrVo1Tp06RdmyZXM9R1NSUvLtigOyHd8AVapUAZAbOzQ0lOTkZJYvXy50DTRq1IiWLVtSqlQpSpcuTenSpdHW1qZOnTrZ9iU2NlbwFIDMpGjWbp9ixYrlWIAiKxYBhDlH06ZNWbNmDQsWLCAqKoqJEyfSt2/fXGWOZJKDpUqV4sOHD0REROS4XNbrGmQWHeTU1SXTs4dMabWsMoLf06pVK06ePMmxY8c4ePAgzs7OnD59mtTU1Fy396+SNZNKpfker4re9xU97hVdNiUlJdt6Hz16RM+ePUlJSaF79+6sXbuWrVu3MmPGDKRSKQMHDmTQoEHC75+V2NjYXAtJsvLp0yeF7pexsbEKJQOSkpLkzgPI/L2/f+3z58/o6ekJf0ulUpKSkrLNlS9evMj79+9xc3MDMj0W2rVrx6NHjwgLC2PSpEl06NCBEiVKCB0Sd+7cEc4lSZa5WYZYTEpqqkLztU+fPpH8v87E4OBgJk2aRFxcHPb29nIJg8mTJ2Nvby94Jci8Gr5HLBYrdFx9+fJFOD8ePHjAoEGDiIqKYsCAAWzatEm490ml0t/uBaFEiRIlSpT8XfnXJhL09fUJCwvjzp07QtslZJqWenp6MmjQIEaNGoWRkRFdunTJV6f/30ROEyl/f3+OHj2KqqoqmzZtQktLS6FAgUQiEcbT0NDAzc1NmGzKiIuLw8bGhvv376Otrc2GDRsEb4KePXtiYGDApEmTePLkCWPGjOHp06fMnTtXrvr78ePHrFixAsgMaJUtW1YhKSSJRMKDBw+4cOECFy9eFAK+PXv2lJsAtmrVSq5FMDExkQULFiCVShk0aJAQJIqIiMj3e5GqqiKbIu/fvx+fU6eEVvxdu3ZhZmbG0KFDKVGiBJqamjkaL2flyZMnPHnyBHV1dQYNGiRUAW/bto2zZ8+ya9cuhg8fjpqaGr179873O4HMCfD169eZNWsWkZGRiEQidHR0eP78OQ0aNODJkyecOXOGRYsWsW/fvnwn3yKRSKFlfhehoaFMmDCBe/fuAZkVZC1atMDZ2VkwZMxv/WlpacybN0+oujc2NsbDwyPHgHdiYiIrV67k4MFMCasKFSrg7OycTTooMTFRLtAXFRXFs2fPaNCggdyDY1JSEq1atWL//v34+vqyadMm4uLiMDQ0ZMqUKcLD2cWLF7l69SpXrlxBKpWirq5OkSJFiI+PZ8WKFQwfPhzDunWFcY3atkWkpUWLFi2EKjofHx8iIiJYs2YNw4cPz1eyATLPYVlFpYWFhZzJZVZSUlKYMmUK+/bt4969e5w8eZIVK1Zw48YN3r17x6xZswRZM0WOB3V1dYWWU+ShVLbO/1KCQFFk38maNWsIDg6mUKFC7Nq1S6iI3bVrlyDhMGfOnHy7uYoVKyaXIM0NWcA8J7JeR9XU1HINDnyPIq3e3759Uyhgfvr0aSIiIihSpAhDhgyRC8rVrVsXKysrPD09OXz4ML169cq340YsFguyJ7JrVYsWLbKZRGYN8hkYGHDw4EEcHBx48uSJ4GtTrFgx+vTpI5e4zEpERAROTk7s3LlTOD8sLCyYO3eukByuWrUqM2fOZOvWrURHR+Pp6amUhviP8n0SITAwMN9OBE1NTYX8A0CxwHVoaCjz58/n7du3qKioMGbMGGxsbLJdI+Lj4xW6jleqVEmh61Dt2rVz7MA1MjISul+vX7/OgQMHqFixYo4doEuXLgUy/U2qVaum0HUtKTkZDQUCkenp6XkayZqYmLB06VIiIyNRVVWlZMmSfPz4katXryrkA/ZPRdH7+Y8kHLLy9u1bevbsSUJCguBJcO3aNWbNmkVGRgbm5uZC13VOiMVihbpKZB2n+VG5cmWF/Ijev3+v0PORnp6ekKASi8VYW1vj5eXF2bNnadWqlbBceHg43t7eJCUlUbp0aVq0aEFGRgYdO3bE39+foKAgqlatilgsFubADx48EPZJkqUgSENdnSIaGgo9XyYnJyOVSlm1ahXu7u7C7+ju7k67du1o06YNkFkkuGnTJp49e8aGDRuYPXt2juOpqqoqdB2S/Z6nTp1i1KhRQleUrKBLlkwQiURKnyElSpQoUfKf5V97B2zRogVGRkaMGDFCrpIYYODAgRgbGwsSO/91EhMTBQ3sKVOmFGhXRkREBCYmJty/f59ixYqxZ8+ebA821apV49ixYwwZMgSpVMrKlSvp3r27UPGckZHB+PHjSU9PF6ox8yIuLg4vLy8mTZpEv379GD9+PAcPHiQqKgpNTU06deqU6xiJiYns27eP3r178/btWypUqCCY+CmCVCrl/v37wt/79u3j48ePFCtWDAMDA8RiMX5+flhbW7Nt27Zsx2ZOyCavvXr14syZM3z48IGKFSsyaNAgtm/fjpWVFWKxmMGDBwt6/nkhq/gbPnw4kZGRVKxYkT179rBz5060tbV5+PAhhoaGQKa0U1Yztb8bGRkZrFy5EmNjY+7du0exYsWwsbFBR0eHGzdu0KVLF6ytrXnz5k2e40RGRtKlSxchiTBlyhSOHDmSYxLh4sWLDBw4UEgiDBkyhBMnTuTpP5CamsqePXuYMGECK1euxNramsWLF3P16lW5ikxVVVX69u3L8ePH2b9/P9u2baNBgwakp6dz8OBB1q5dy+XLl5FKpRgaGjJ79mxmzJiBvr4+aWlpeHp6cuHChWzrL1SoEC4uLoK80a1btzA3N+fq1asKfMuZVXm3bt1CXV2dkSNH5rlsqVKlGD9+PADLly9HR0dHePA6duwYR48eVWidSv5cbty4IZzrrq6uQrDZ398fOzs7AEaNGoW1tfVftYkC4eHhXLx4UeEE0s8QGRnJ8ePHARg8eHCOiYzOnTtjbGyMVCpl0qRJREZGKjR2dHS0YLSsyP3WxMSE27dvc/36daZMmUK5cuX4/PkzO3fupFOnTtSqVYu5c+fy6NEjYmNjmTFjBnXq1GH79u2IxWK6devG1atX2bVrl1yH2cSJE9mzZw+ampr4+fnRs2dPuU44Jf8NvpczOnXq1G/XN8+KVCrFw8OD0aNH8/btW0qXLs2mTZsYPXr0Xy4lUrp0aU6ePMngwYMRi8UsX74cV1dXuWvPx48f+fjxIyoqKgp1cBY02tragn/X0aNHMTU1BeDkyZN/+rb8W7h69SodOnQgJiaG+vXrc+zYMcLCwrCwsCA1NRVjY2MWLVr0rwgkS6VSpk2bxtGjR5FIJIIfm4yHDx/y4sULVFVV6dmzp3BONmjQgCpVqpCRkcHp06eRSqXC/Sw8PPyX7yXv37/H0tKSNWvWIJVKGTFiBJaWlkgkEmxtbYmPjwcyCw3mz58PZHauJyQk/NJ6IVMOasiQISQmJtK+fXs2bNiAmpoahw8fZsKECb917qFEiRIlSpT8E/jnz4DyYMeOHURFRdG9e/dsAduWLVvy7du3v2jL/l44Ojry9u1bqlSpwoIFCwps3KdPn9KxY0fCwsIoU6YMBw8ezFUPWlNTkyVLlrB69WoKFy7MlStXaN26NWfPnsXNzY27d+9SrFgx1q5dm2P1z6dPn9iyZQuDBg2iXbt2zJ07l6CgIFJSUihVqhQWFhasW7eOS5cu4e7uLtfOC5nSSzNnzsTQ0JAZM2YQGhqKlpYWa9euVagSNiUlhWPHjtGvXz+ssphX6uvrM3PmTPbu3cvq1atZtWoV9erVIzU1lcOHDzNmzBjWr1+fqw5wamqqoM89cuRIVq9eDcAff/yBuro6qqqqcsmE4cOH55lMuH37Ni1btmT37t0A9O/fH19fX1q0aEGjRo1Yu3Ytqqqq3Lp1S6j2Wbx4MZs3b873O/izefToEV27dsXJyUlIMoWEhLBmzRru3LnD8OHDEYlEeHt707JlSxwcHHKUsAoODqZly5Zcu3YNXV1ddu/ezbx587IFML5+/cr06dPp168f0dHRVKhQgZ07d7Jw4cI8q3hv3brFxIkTOXjwIBkZGZQtWxaJRMKNGzdwdnbGysqK/fv38+rVK+Ez2tra6OvrIxKJCAkJYciQIaxcuZKUlBQqVKjApEmTsLGxoUSJEujo6DBu3Djatm2LVCrlZB5Gkr1798bf3x9DQ0O+fPmCi4sLrq6ugnxNbsikr/r166dQhefEiRMpXrw4z54948iRIzRv3lyQanJwcODt27f5jqHkzyMhIQFra2vEYjH9+/dn2LBhAFy5coURI0YgkUgYPnz4X6oDnZ6ezpkzZxg7dizm5uaCMfnveqCfO3cuqamp1K5dWwjSfY9IJGLkyJFUrVqV+Ph4JkyYkO+5BP/vj6Cvr5+j0XJu62rUqBHLli3j+fPnBAQEMGLECHR1dXn37h0uLi40btyYKlWqsGbNGlJTU2nXrh0XLlzg8OHDNGzYMMdx+/Tpg6+vL8WLFyckJIR27drlm3hV8u8hJ0+E/LokC5LPnz8zZMgQJk+eTFpaGm3btmXfvn1yncR/Ni9evODIkSOCJJOWlhbbtm0TzHYPHjzItGnThGeIJ0+eAJlyTYp0On1PdHQ0hw8fZtKkSfTo0YMxY8awZMkSdu/eTXBwMOHh4fnKVnbr1g3IvFcrEwm/xrZt2zA1NRWSCMePH+fDhw/07t2br1+/0qRJE1asWCHXjfPt2zeFpUX/bqxatYotW7YIf1+6dEn4/9u3bzl79iyQ2aVbunRp0tPTiYmJQSQSYWpqiqqqKq9fv+bBgwfo6upSo0YN4P+77n6GoKAgzMzMuH79OoUKFWLz5s24uLiwYsUKatSoQXR0NFOnThW6FAYMGEDdunX59OkTa9eu/en1isViZs6cycyZM4V5z9GjR+nfvz/bt29XJhOUKFGiRImS//GvlTaCTL33gIAAzM3NadWqFbt27aJZs2Z8/PgRb29vISj7X+bOnTusWbMGgHXr1hWYrMHt27fp3bs3cXFx1KpVi82bN+doQvc93bt3p1OnTgwfPpyHDx/KSfWsXLmScuXKZftMfHw8Q4cOlQt+1KtXj44dO1KvXj3atm2bY9WQVCrF19eXAwcO8PjxY+H16tWrM3ToUAYMGJCvbmlCQgJbtmzh6NGjgta8bhYpm5UrVyLN8nfDhg1xc3MjJCSEnTt38urVK1atWsXOnTuZPHmyEPyWERgYyOfPn4Xgc2hoKEWKFJEzfpYlEwBB5gjkDZglEgnOzs4sW7YMsVhMyZIlcXZ2pmPHjnL70759e5YuXcrMmTO5evUqbdq04erVq9jZ2VG6dOlc/Sv+TKRSKS4uLixevJj09HSKFSuGi4sLAwYMEL67cuXKsX79esaPH8+8efM4f/68nJGxzJTRzc2NOXPmIBaLadCgAVu3bqVmzZrZ1nn58mUmTZpEeHg4kJmAmT17dp7nS3x8POvWrRM01UuUKMG4ceNo06YNkZGRBAYGcvbsWT59+kRAQAABAQE0aNCAnj17YmxsTEpKCm5ubgQHBwNQtGhR2rVrR+fOnbMdz6qqqvTv358yZcoQ8L/EE8DnT58o/t05U7lyZY4cOYKbmxubNm3Cz8+Phw8fsnDhQqpVq5ZtP+7du8fjx48V6kaQoaury6RJk3B0dGTlypX07duXKVOmcOHCBW7cuMGECRO4evWqQu33Sn4vUqmUyZMn8/btW6pWrYq7uzsikYhHjx4xYMAAUlJSMDc3Z/369Qr50vwOduzYgf/Zs0KFo0zSQqaj7uzsXKCVy4GBgfj7+6OiosLIkSPzrDrV0NDA3t6ehQsX8uDBAxYuXMjy5cvzlNzIz2g5P1RVVenQoQPt2rVj/fr1nDx5kgMHDnDy5EnS09Np0qQJTk5OmJiYIBKJBA8MGZ8/f+bhw4e0bdsWkUiEkZERgYGB9O3bl6dPn2JkZCQkHJX8e8nNWDknT4Pfwc2bNxk2bBhv375FXV2dCRMmMGzYMEQiEVKplEOHDnHhwgXKli1LjRo1qF27NnXq1Plt2xMXF8fkyZOF4o127dpx8OBBihcvjkgkwtramurVq7Nw4UKuXLnC6NGjcXNzIywsDMiUO/sRvL28uHb3Ls+fP5d7/cWLF7x48ULuNVVVVSpWrEi1atWoVq0aBgYGdOjQQbg2GRkZUahQIcLDw9HT00NVVZXQ0FBev36d431dSXbS09Oxt7cX/PP69evHli1biIuLo3v37nz8+JEmTZrg6uoqSBaJxWIcHR3x9fUFMs3EdXV1KVq0KFpaWpQpU4aiRYsKrxUpUiTbveHz58/ZPHuKFy9Ou3btfntHzv79+4UCMgcHB1xcXLh//z6fPn2iaNGijBkzhvT0dCpWrEiLFi2QSqUcOXKEt2/fYmpqSuPGjTEyMuLixYv4+/sTHx+PoaEhL1++5N69e3Tu3PmHtkcsFuPs7IyLiwtSqZT69evj4eEhJCdkSYXu3btz6tQpdu7cibW1NaqqqixYsIDBgwezZs0a7OzsFPKnyEpiYiKDBw/Gz88PgEWLFjF69GhsbGw4ffo07u7ubN++nVGjRgmd4vv37//Lu6aUKFGiRImSv4J/dBRHEUOuli1bEhISwqhRo2jevDm1atXi/fv3TJs2TTC1/S8SHh7OihUrBOkDS0tLzM3NC2z8CRMmEBcXR5MmTfDx8ckWyMiLmjVrcv78eWbOnCkEyIFc5YiWLFnCmzdvKFeuHOPGjaNDhw6CIVlkZGSuQaAbN24wb9484e/ixYvj6upKt27dFNZRd3V1FSRuILMqZpqdHYwZm+tnRCIRrVq1okWLFnh5eeHh4cHHjx9ZsGABVapUkZN+CgwMBDI9HGRSXF27ds2ma6+qqsqqVas4ffo0MTEx/PHHH/Tr10/YD39/f5ycnABo0qQJ69evz9XsVhZM8vT0JCQkBHNzc06ePCmM+Vdz+fJl4Xdr3749Hh4euVbJGxgY4OPjw5kzZ5g5cyavXr1i2rRp1KxZk5IlSzJz5kwAhg4dyoYNG3I0GL9y5Qr9+/cnPT2dypUrs2bNGqpUqZJv0m3RokXcuHEDFRUVevfuzZAhQ4RqxYoVK2JjY8Pw4cO5desW/v7+3L9/n4cPH/Lw4UNBbxkyf9uBAwcyZswYgoOD8wxqGhsbU05PDy5mVpRZW9vguX9ftoSYuro606dP582bN5w8eZK3b9+yatUqNmzYkG1MHx8fIPO4UKQbQcaQIUNwdnYmPDycCxcu0K1bNzZu3EjHjh25desW69atw97eXuHxlPweQkNDBbkpDw8P4dri7OzM58+f0dDQwNnZ+S9N+hw5coTk/1UeFilShNmzZ/Pw4UMOHDiAv78//fr1y9W340eRSqWCxFOXLl0U8lwoVaoUa9euxcrKikOHDhEeHs7SpUtzTZ7LEte5dQn8CNra2lhYWGBhYUF8fDxRUVHUr18/z3vYjBkzOHDgAIcOHaJ79+4A1KlTh7Nnz2JpacmDBw+ws7Pj8uXLv7x9Sv6+LFq0KFsS4c/Cx8cHKysrUlNTqVq1Knv37hV0xzMyMli6dCknTpzI8bNly5alUaNGGBgYUL9+ferXr6+QeXp+LFy4UEgiQGZl9qZNm5gzZ47wWufOnSlXrhzTpk3j5cuXDBw4ULiv/2giIet1TUtLixEjRtC8eXPev3/P06dPuXjxotC9JxaLefv2LW/fvhXkCzds2CB0jmpqamJgYEBISAj37t2jZcuWXL16leDgYGUiQUG8vLzw9PREJBLh6OjItGnTEIlE2NvbExkZSe3atTl+/DgxMTHCZ65evSokESCzM+Hbt2+CH9uvYGRkxMqVKxX2B/pRpFKp4P/WpUsXFi9ezP79+4mIiODevXuoq6sL3QmyApZ3794Jx+SlS5do3Lix8JydmJjInj17qFWrFvD/nTqKIhaLGTduHAcOHAAy5+VOTk7Z/EEaNGjAvHnzWLBgAY6OjvTv31/oPG/QoIEwN5g4ceIPrX/Xrl1CEmH58uWMHz+ebdu2Ca9NmzaNt2/fsnz5cqZPny50lZuYmPzQepQoUaJEiZJ/A/9YaSOpVMq4ceNwcHDId9maNWsSHBzM3bt3cXZ25s6dOwUq4fNPIjw8HDs7O2rXrs3mzZtJT0+nY8eOv9QK+j1SqVSo0Nq7dy+lSpX64TG0tLRwd3cX/pZVVn7Po0ePOHXqFCKRiA0bNjBw4EAhiZAfdevWpWPHjkJg9tOnT0ycOJFJkyZx8eJFJBJJvmP06tVLLtB0+PBhuYfO3JB5MezZs0d4rVWrVtkqQGUT1BMnTqCvrw9kPoCHhIQIy0ilUo4ePYqBgYHwgDNu3Di576t169aCYe+dO3fYtGlTrlWHMTExQqWvgYGB8NDaq1evfPfrz6Bhw4ZCddKbN28Uaif/8uWL8GBXt25dWrZsyZ07d4DMByRPT88cJQnevHmDjY0N6enpmJubc/HixVxlTrKSnJwsfG/Ozs6MHj06x/HV1NRo1aoV9vb2nDx5ksmTJ8sFAVu2bMmhQ4eYNm2anDl4XsiOE4Bnz54ydOhQOa1aiUSCn58fpqamgvRBoUKFck2syjp9vu9eyQupVMq8efMQi8WUKlVKkKmoXLmyYNrr6OgomJAr+euoVq2acM3MKkUwbNgwtLW1SUtLo3379uzfv/+nTSsV5ebNm8ybN0/QPpZRK4vu+NevX5kzZ44QbKhbty4GBgYFtg23bt0iLCwMbW3tHyo2MDY2ZtmyZWhpaXH16lW6devGzZs3c1xWJmeUU+LyV9DT08PAwCDfRLjs2nTlyhW518uVK8e6desA+PDhQ4Fum5K/H7JEV/369f/UJMKmTZsYPHgwqampdO/enZCQEJo1awZk3junTZvGiRMnUFFRYdCgQYwbNw4TExNBcikmJobTp0/j6urKyJEjadmyJaampoIEy88yePBguaBlmTJlcpz31KtXj61btwrbGxcXR7Vq1X64GKdilu88JSWF3bt3s2nTJjZu3MjevXtzlQCsUKECpqam1K9fX3jt3r17wrywXbt2gsTaj1Zl/5dp2LCh0A1jamoqXEdlXapVqlTJVoBTvXp1Ybk9e/bg4+PDrl27WLt2LdOnT2fmzJmMHz+eQYMGYWZmRrt27bL9a9Gihdzfbdu2RUtLiytXrjBs2DChE/ZnkEqleHl5MW3atGwePiKRSCjQCgwMxMHBQVimVq1aNGjQQLhGnDt3joyMDEqXLi3MRWWJs5CQEFJTU1FTU8PExES4r2Q9PvMjaxJBVVWVrVu3snTp0lxNxseMGUPVqlVJTk7m3LlzQKahe7t27YBMf4UfpWvXroL5tLu7O/fv36dTp07Cb96jRw8+fPjA+vXrgczrZ9OmTX94PUqUKFGiRMm/gX9sR8KdO3fYt28fycnJALi4uOS6bFpaGhoaGhgaGv5nW/XDw8NZtmwZ27dvFwKvHTp0YP78+dnMj3+VuLg4YR0VKlQokDFzq/52c3MDMoPcP1oNpqury9q1a/nw4QNeXl6cPn2aFy9e4OXlhZeXF5UqVWLz5s15yk80a9aMM2fOcPnyZfbu3UtwcDBXr1yB2pnBXC9vbzqbmwsT79TUVE6cOMHBgwf58uULkDnZnjFjBu3bt88WAOrZsydnzpzhxIkT7N69G0tLS44ePcrw4cO5e/cuCQkJ2NraClXj+vr6bN68WahSk1GyZEkuX77MnDlz2LBhA3v27OHmzZusXr1aTsonMTGR8ePH8/79e6pUqUJUVBRJSUl07txZkMD6qylatChBQUGYmJjw8uVLunfvjr+/f46BEKlUyvLly1m+fDkAZmZm7NmzB11dXV6+fAmQZ+Bt6tSpQqv2li1bcn2o+Z6rV6+SmppKyZIlhQROfpQsWRIrKyusrKz49OkTnz9/pmrVqgp3x+REqVKlCAsLY+jQoezZs4eQkBDWrl3Ls2fPANDR0WHAgAFYWFjkqNUukUgE0/MfCTTt2LGDo0ePCg+EWZOJQ4YMYceOHTx69IgFCxawcePGn94/Jb+Ojo4O8+bNY9KkSaxYsYJhw4ZRtGhRunfvzrVr1xgzZgw3b94UKu+cnJyyecwUBLLk0+vXrzOl2LIk+93XrEGkrc3Vq1e5dOkSV69e5evXr4wYMYJJkyahoaGR45gpKSmEhYXRqFEjhc+jffv2AZnX3h/VOx80aBCtW7fGwcGBkJAQpk2bxqlTp7KNI7svKmrO/CNIJJI8u5bev38vnNMPHz7M9n5GRgZArt+pkn8PvXv3xtHRkcDAQBITEwtM2jI3MjIymD59uuC5NGrUKNzd3YVup/j4eMGjSlNTk2XLlgmBQRlfv37l5s2bxMbG8vjxYx4/fsy7d+94/fo1EydOpF+/fsyZM+enqriNjIyIjY3l/Pnz1K9fHw0NjVyvdRUrVqRdu3ZcunQJIyMjnJycFPY7keHi4sKnpCTOnj3LuXPniI6OFgocIDMJUK1aNcqWLUuzZs2oWbMmNWrUyHY9SU9PF7oYbWxsaN26NU+fPgX4rVJQ/zbq1q1Lv379OHbsGEuXLmX//v1ApkzookWLOHfuXLbihwoVKtC6dWuhMyFrIVFMTIxCfiNRUVGUL19e7rXHjx8zefJkXr16xeDBg3Fzc/vh59eYmBhmzZpFUFAQkNnNe+jQIblOuVWrVqGrq8vy5cuFJHLTpk2F7T527BitW7cmPDycU6dO0b17d6ytrYmMjKRmzZq8fv2aixcvApnPYSVKlBA62bLKq+bF90mE3bt306dPnzwLTUQiEebm5mzcuJGAgABBdlN2DfsZD8SaNWty/fp1zM3NCQsLw9zcnO3bt3PlyhVu3bpFvXr16NGjB2/evKFq1ar4+fkpE3VKlChRouQ/yz+2I6FmzZoUKVKEzZs3s2rVqlw7E/z9/alXrx6vX7/+k7fw70F4eDgTJ06kZs2aQgdChw4dOHv2LEFBQQWeRACEqvgSJUoUWDAip4fC69evc/XqVdTV1Zk0adJPj126dGmGDx9OcHAw/v7+WFlZUbRoUcLDwxk0aJCgZ50bKioqGBsb4+HhQWBgICOymC3v3LGDIUOGsHr1ary9vbGxscHDw4MvX75QsWJFZs6cyYkTJ+jQoUOugS4nJyfKlSvH8+fPKVy4MBUrVuT58+cUKVKEunXr4uPjg5qaGvPnz+fGjRvZkggytLS0hO0oXrw4YWFh9OvXj0OHDiGVShGLxUydOpXQ0FCKFy+Ouro6sbGx1KtXjyNHjsgZy/3VVKxYkaCgIKpVq8bbt2/p3r17tqqtpKQkbGxshCSCvb09Xl5eQlJHpkss6274nvv373Px4kXBg0LRJALA+fPngUwN9J9JBMgCCL+SRADYuXMnpUuXJiwsDCMjI+zs7Hj27BlFihTB3t4eDw8PrKyscg2AxMXFkZaWhoqKisKyRrdu3WL+/PkAzJs3L9vxqKqqypIlS4BMKZ1Hjx79wh4qKQhGjBiBvr4+cXFxct5BtWrVIigoiIULF6KmpkZQUBDdu3f/5crfnLh//75wn/by8uLFd7rhJUqUoGfPnri4uBAcHMyNGzeYNm1arveYpKQkLCws6NGjB8OGDROC53nx7ds3Qdpk6NCh2d5/8+YNBw4cyDMBUKVKFbbAoTOFAAEAAElEQVRt20b58uUFE+TvkQWNCkL+QoZYLGbChAmULFkyz3tW1vdyOvdknWrKRMK/nwYNGlC9enVSUlI4ffp0rsudO3cOa2trbt269dPrSkhIoE+fPmzevBmRSISTkxPr168XkggvX75k3LhxhIaGUrRoUTZt2pQtiQCZc0FDQ0PGjBnDmjVrCAwM5MaNG4waNQqRSISXlxc9e/bk2rVrP7WdKioqdO7cmbJly+abMF24cCFr167F1dX1h5MIMqpVq8bo0aPZt28fGzZsYOrUqbi6ugoFLa6urtjY2NC3b18aNGiQY3Jz//79PH/+nGLFirF8+XIiIyNJTExETU2N6tWr/9R2/VeZPXs2IpEIb29v4fpYs2ZNDA0NEYvFcjJGMmxsbADw9vaWkz36FerXr8+BAwcwMDAgISGBsWPHyslu5YWsS7lz584EBQWhoaFBuXLliIyMxMLCQs5/QyQSsXDhQtasWSPMN7N24tWrV4/evXsjEol4+PChYH5cu3Ztvn37xvHjxwFo1KgRTZs2xcfHB6lUSvPmzRWSBcwtiaAIsg6gwMBAobtPlkj4ETndrFStWpVTp07RoUMHEhMTGTJkCD4+PjRq1Ii+ffvKJRH+TFN6JUqUKFGi5O/GPzaRULRoUVRUVOjXr59cMuH+/fvMnj1bWK5u3bqkpaXJ6dj/F4iKisqWQOjYseNvTSDIkE2k8wo8fvr0ieDg4Dzlg2TdJkC2hzSpVCp0IwwcOLBAOh9EIhGNGzdm2bJl3Lx5k5YtW/L161eFkgkyKleuzNRp04S/q1WvTlpaGqdOnWLTpk18/PiRUqVKMXXqVLZu3ZrNCDohIYGrV6/KSYgULVqUVatWAZmB4fHjxwvfwZcvX2jRogV37tzB0dFRMICT8enTJ/z9/fn69avwmrm5OT4+PrRp04aUlBTmz5/P5MmTWbx4MefPn0dTU5Nq1arx4sUL9PT0OHHiRIHoDxc0FStWxNfXN8dkQlRUFObm5nh7e6Ours7atWtZsWKFnCmarCMhJ3NlQPAL6NOnD1WqVFF4uyQSiSAd8rNmqgVFterVOXDgAKVLlyYtLU1IIFy+fJk//vgj3+CHLNBZqlQphRJJnz59YvTo0aSnp9OjR49cNWqNjIzo168fEomEqVOn/nbJHCV5o6amJvgCrF+/Xi5QrqamhoODA8HBwdSsWZO4uDgmTJjAnDlzfqrqLze8vLyAzECeRCKRk7b7HlVVVTSzmNh/j1gsxs7Ojvv37wMQHByMiYlJjkGgrMj8fKpXr07r1q3l3ktJSWHVqlWcOHECBwcHPDw85CTDslKkSBEhgblz506uX78u935BdySIxWJGjRrFtm3b+Pr1Kzt37sx12awVz7GxsdkkIGTdhH9lIiE2NpYzZ84gFov/sm34LyASiejduzfw/1443/Pp0yeGDRvGgQMHaNeuHdOnT//h8/7169e0b9+eoKAgdHR0OHjwINOnTxcCl3fu3KFjx45ERERQvnx5tm3bpnAnH2TOkRwcHNi7dy+VKlUiOjoaa2trVq9eLTePLGiKFi1K69atC8RsVSQSUa9ePXr27EmTJk0UrnSOiooSOjymTp1KyZIlBWnRGjVq/K0KQP4J1K9fX/ACy+pVJauul/kJZaVZs2Y0a9aMjIwMwai5IChdujQ7duzA1NSUjIwMlixZwuzZs/O8LkZHRzNhwgSmTJlCQkICjRo1IiAgAD8/P2rXrs379++xtLQUjhEZ48aN49ixY1hZWTF2rLzHW7Vq1ejSpQuQKYv39OlTxGIx3t7eJCcnU7ZsWbp27Qr8/31ckW6EX0kiQKbfW5kyZfj27ZsgbySb0/7K3KRo0aIcOXKEESNGIJFImDlzJm3atFEmEZQoUaJEiZIs/GOljSBTxuXRo0fCpGf8+PGsX79ekCaATP3KO3fu5Gos+3dHKpUqpNWfkpKCiooKYrGYrVu3smDBAiF4bGxszNy5czE2NiYtLU0hTfn09HSFqqFTU1OzBQFlwdzSpUsLOq0xMTFyZp22trbcv3+fOXPmyOnKSqVSwRg2a5BFJBIJY0FmkPfBgwdoaGhQs2ZNQev9e+Lj4+W+v9TUVFJTU7PpzRcvXjxbsHjixIkkJCQQFhaGpaUl8+bNo1q1arkGngVSUpGFZ1e6uBD66hV+fn5ERETQqVMnzM3N0dDQQCKR8PnzZ7lK+nnz5hEcHMycOXMwMzMTXq9QoQK2trZs2LABd3d3tmzZgpaWFpGRkYwbNw5VVVUSEhLIyMgQJtJSqRRLS0suX75MoUKFsLCwwMbGhlatWlGiRAm2bdvGjh07cHNzk6tGbNmyJRcvXkRTU5MNGzZQpUoVhY7BgniYzkp6enq+x2qpUqXw9fWlV69evH79mu7du+Ps7MyMGTOIjo5GT0+P3bt307RpU7mxxGIxr169AjIrkGTvxcTEoKKiQlRUlFBpNXDgwGzVzFFRUbl6FoSFhREbG4u2tjYZGRmcOXMm331VVVUVgp55kZCQkO95qfo/aRLIrNSSqKszZcoUwsLCMDQ0REdHR6jW/PTpUzYj5qzIEmjFixfn8+fPea43IyOD2bNnExUVRY0aNVi1apUgk/I96enpODk54efnR1BQEN7e3oLpa1ZSU1PzDBjLEIvFCi2nTFjkjFQqxdzcnNatW3Pt2jWcnJyySU41atSI/fv3s3nzZkG66urVqyxfvjyb0XF6errc9To3wsPDUVNTEyTfACZMmMDmzZszTR7/JxEXExOD5ncG8znx8uVL3r9/z7Zt2zhz5gzq6upMnDgRPz8/Xr58ycSJEzlw4ACjRo3K0ehYFgTq0aMHERERXL16VQhIPHjwgPj4eFRVVRGLxVy4cIGLFy9Su3ZtOnXqxLt377KN16JFC27cuMHEiROZMmUKFSpUoH///oInRURERI7XuLS0NIXMrTMyMlBRUWHMmDFy8x5/f39Wr14tXCu+ffsmXJ+/ryq/efMmHTp0kFs3ZBqy53a+KHI/gMzjSpH7QmJiorCtZ8+eZdSoUXz8+JGBAwfi4eEhN8bvlt/5M1D0OpSenp6nTJUMiUSi0PEiFouz/R69e/fGzc2NkydPkpqaioaGBrGxscL7ixYtIi4ujiJFivD161fWrVuHl5cXTk5OGBkZ5Tu3vnbtGkOGDCEhIYFSpUrh4uJCxYoVuX37NpCprz537lySk5OpXLky8+fPRyQS5eoPAJndhLkVd4wePZqAgACuX7/OkSNHuHbtGrNmzaJevXq5jhcRESHX9ZqRkUF6enq2LsRPnz4pVLSSlJSUu/9JltczMjIUOpcyMjJyHE8qlbJs2TJSUlJo3Lgx3bp1QyKRCCa3tWvXznV8RY6rfwuKnm9isRiRSISDgwNeXl54e3tz//59DAwM6NOnD/Pnzyc4OJg3b95k834bOnQot27dwtvbm/79+1O6dGnCw8NJSEjId70fPnzIU9N/8ODB6Ojo4OXlxaZNm7h37142KS2pVEpAQACrV6/m69evqKio0KxZMwwNDTly5AiQ6ZUWFxfHx48f6dGjB+PHj5fr5m7UqBGNGjUiOTlZLgGXnJxMw4YNiY2N5e7du0IBT1RUFJqamvTo0QOpVCoUiEGmjKhsDEmWuUBySgpx/7sfTZs2jWPHjqGqqsrGjRsxNjaW80d68uRJtoS2VCqVmwO3bNkSX19fvL296dq1q3DOfvv2LVvCRSwWK3QsiMVi1NXVcXd3p0aNGixcuJAvX74okwhKlChRokRJFv7RiYQ6derw+PFjOnToQMuWLdHT0yM+Pp5r167Rt29fYbm/YxJB0Um8og/skCkNYWdnJwQKmjVrhrOzs5w5rIqKikIP9tra2gpVMn0/qYP/N2ksX768MAksXry43IRQFjT18/Nj+PDhwusSiURuIihDLBYLr4vFYiHI26ZNG9TV1XMMWqWnpwtVKrGxsXz8+FEIhlasWJHmzZsLx0Z8fHy2yaG6ujoODg64uLgQFhaGk5MT8+bNkwu65PidJCcje3Qwbt+eDqamuVZmh4WFCd9LUlKSEOA9fvw4gwcPltuX+fPnc/78eUJDQ/Hx8WH//v3Zvns1NTVhvOPHjwtapYmJiezevZvdu3dTp04dRo4cyfDhw1m+fDmWlpYMHTqUFy9e0LNnTyGgt3v3bvr16/fL8jo/i6qqar7Hqq6uLiVKlODcuXOCZ8KwYcOAzMoyb29vqlWrli0wFxERIXinVK1aVViPlpYWampqHDp0CLFYTJs2bXI0UytevHiuJuKHDx8GMhN4cXFxCkkihYWFUa1atXyXK1WqVP4VWykpcDjzwTE1NRWJREKhQoWE/cgakEhPT88zMCI7r6pWrZqrBJSMZcuWcffuXQoVKsSxY8fy3J/U1FTKlCnDpEmTcHV1ZdasWXTp0uWnq6AVva4Bf9nx/HdGdoyuWrUKIyMj9u7dy9SpU7NVBRsaGuLh4cGwYcMYNWoUr1+/ZsSIERgZGWFlZYWlpSW6urokJSUpdI/T1dVFXV2doKAgEhMTKVOmDAMGDCA6OpqALDIOZcuWVUii482bN/j7+wtm8fb29hgZGWFsbMzhw4c5cuQIly5d4vHjx6xcuVJOduv58+c8fPgQVVVVLC0t0dbWJiwsDHV1dZKSkoTgXN26ddHQ0ODVq1d8/vyZsLAwnj9/Ttu2bWnWrJncMdy2bVvCwsKIj4/n+PHjWFpaCh1fgBCw/T4JpqamptD9V1VVVUgiyCTYxo8fz9u3bwkLCxN+P21tbeH3kPkiVKhQgcjISJ49e4apqakwZtaOhLx+w4I+jzIyMnB2dmbVqlVCsOfQoUMA2ZIJ+aHIsfdXBlIVXfePfMeKLKumppbtezQyMqJMmTK8f/+eK1eu0KVLF2H7nj17xp49ewDYvHkzEomEOXPmEB4ejo2NDT169GDt2rW53gv37dvHpEmTSE9PR19fn+XLl1OyZElhXnvq1CmWL1+OWCymadOmTJw4Mc+Av4wLFy7IdUkmJiYSHR1N9erVUVFRwcTEhGrVquHv709ERASTJ09mxIgRjB07Ntt5JZVKefbsGZ8/f+bly5e8fPmS169fCzKggwYNEtalra1N7SzG77mRkZFBuXLlcnxPkpxMxP/+b2JiQuE8EvlZ9y+n4//EiRNcvnwZdXV1PDw80NfXR0VFRZBtrFOnzt8uYSASifI9VhU97hVdTtHvQEtLC5FIRNOmTbG0tOTIkSOsXLmSQ4cOUbduXZo1a8atW7d4+vRpto61mjVrcvjwYa5du4afnx9Lly7l/fv3Cnl1REZG5uvHM2TIEMqUKYOnpyfXrl1j9OjRuLm5UbFiRWJjY1m6dGlm8p3MjriGDRtSpEgR3r17R1paGl++fKFEiRIYGhpy69YtEhIS2LhxI+3bt8+3c3bChAmUKVOGGTNmMHjwYM6dOyccYzt27KBbt25AZiJeIpFgaGgoV3AlynLsqqqqIhKJmD59upBE2LJlCz169Mi2XqlUKpyvsi7FoKAgXF1dBe+Pjh074uvry4kTJ9i0aZNQ4JPTOZPT82pOFC1aVHheWLBgAU2aNMHPz4+5c+f+qab0SpQoUaJEyd+Zv9cM8wepU6cOjx494v79+5iamrJ169Z8PRP+jSQmJjJnzhyMjIy4desWurq6rFmzhgsXLsglEX6F6Ohozpw5o1BiQ1ZZk5u0kaKt5lmr4rImCvbv3y9UfMs0dKVSKZGRkYSEhODl5cW6detwdHTk2rVrXLt2jRcvXshVVEdERODt7U1QUBCfPn3KdRu0tLRwcHCgTp06JCcn4+TkpLDM0Y9y9epVoRr08ePHhIaGZtsWDw8PNDQ0CAgIYPfu3bmOJasOB5g1axaXLl3C2toaHR0dwsLCcHBwoFKlSvTv35/Y2Fju3LnDxo0bhQDc0qVLsbS0/C37+TuQeSbIgt3m5uZcvHgx12C2TNaoWrVq2R42vnz5IgSwRo8e/cPbIvNH6Nix4w9/9mf4nVX2Mpmy3IIiMk6fPi1IQW3ZskVh43MHBwfKlCnDixcvBHkGJX8drVq1EiSnskoEfo+xsTF37txh9OjRqKiocOXKFcaOHUuFChWwsrLi/PnzP5QEDwgIAMDU1BQVFRWsra3R+gFPEhk3b95k+/btAFhbW2NkZARkBlCHDBnC8uXLKV++PPHx8YwaNQpnZ2fhfnTs2DEAOnTokC0w+uLFC6RSKXp6epQsWZKiRYtiaGhIw4YNKVy4MGKxmODgYDZv3sydO3eESkhNTU2hs+z27dtCF1SJEiWE5MHP+iSIxWJGjx7N3r17UVVV5cCBAwwfPpxOnToBmUn675EZLauoqDBgwAAgu0/CX+GREB0dTffu3Vm5ciVSqZRRo0bh6ekpJHXHjh2rlDkCrly5Ipin5tbt9aOoqqoKmuhZNdilUimLFy9GLBbTtWtX2rVrR/v27QkMDGTs2LGoqKjg5+dH06ZN2bNnj9x9SCKRsHDhQsaPH096ejrGxsasX79eKNyQSqXs27cPZ2dnxGIxXbp0YeXKlT9sbg6Z9/ItW7Zw8OBBDhw4IGij16hRAzs7O8zMzJBIJOzcuRMrKyvu3LnDhQsX2LhxI7a2tnTu3FnwawgICODZs2dCMu3ChQvY29tz4sSJAvu+s3Lr5k08PT1xcHCgV69e1K5dmxIlSmBubs6qVau4fft2rsf9169fmTFjBgB//PGHnKmyTLZGX1+/wLf5v8LcuXMRiUQcO3ZMuEb2798fyPnaCpmJa4CDBw8WqP+NjObNm7Nt2zZKlSrF69evsbKyYtu2bQwYMIBLly6hrq6Ora0tjRs3FhIYcXFxXL58mVu3bnH//n1UVVVp3rw5xYoVIy0tjWHDhnHjxg2F1q+mpsb27duF48re3l5IIsD/fy95FbuIxWKmT5/O0aNH80wiZEUikbBy5Uq8vLz48uWL3FzR0NCQIkWKEBsby5UrVwpE2uh7evTowebNm5VJBCVKlChRoiQL//hEQlBQEKampmzcuJG+ffsyduzY/1RAyt/fHwMDA9zd3RGLxVhYWHD37l1B7uZXSU9PF6o/zMzMhAeXvMjPI0EWTAHk2li/J2siQRbsSU1NxdnZGYD27dsLngC+vr5s3LgRX19fbt++TUxMDBKJBA0NDSpVqkSTJk3o1q0bQ4cOZeDAgUK1zOvXrzl27Bjnz5+XW19Wvk8m/Ihnwo8gC0DLfrectFgNDAwEI9vZs2cL8gDf4+npSVhYGCVKlGDmzJkYGRnh6ekp6OnK9Fy9vLzo0aMH9erVY8aMGUgkEkaOHPmPTMRVrFiRa9euERAQIGeqnBMys7mcZKoOHDhAYmIitWvX/uFEXExMDKGhoYhEogJL4uXF5s2b6dixo0LyST+DTNIpL7+TV69eMWXKFABGjhwpPGwrgq6uLosWLQLA2dmZcePGYWtryx9//IG9vT2zZ89m1qxZzJs3j0WLFrFkyRI2b95MYmLiT++TkrxxdnZGTU2NgIAAatSoQc+ePZk1axa7du3i9u3bwndfpEgRNm/ezJs3b1i2bJlwfdy3bx+9e/emXr16LF68WM7YMSdiY2OFLjpZ0L148eJCoBv+P7idF3fu3GHDhg1IpVJMTU1zDGbo6+vj5uYm6D3v3bsXCwsL7ty5I3S5fa/t/PHjR+Li4hCJRNSqVUuoaBSJRJQoUYJmzZphYGBAsWLFSExM5PTp03h4ePDkyROkUinVq1fH0NAQyExWyGR8fsUnQeaJIOtEOHDggLDdsqBMTsGue/fuAZmSJzI5qr86kXDmzBnatGkjBIF27NiBu7s7AwYMYOfOncpkApmBd1dXV7p06YKbmxvdu3encuXKTJgwoUCSCrIO3uPHjwsJwKCgIC5duoSGhgbz5s0TltXR0WHevHn4+vpSr149Pn36xMSJE+nZsycvX74kMTGR4cOHC6btM2bMYP78+cJcLSMjAzc3N2GePmjQIObNm/fDWv4yL6KsyYPXr1/j6ekpFLNoa2vj6OjIihUrKFasGM+fP2fcuHHMmDGDHTt2cOPGDb5+/Yqamhq1atXC1NQUW1tbVq9ezeLFi6levbpwTZsxY4Zw/vwoHz9+xMPDA3t7e0F/H6BXr16MGzcONzc3AgICeP36NV++fCEwMJDZs2fTqlUrypUrx/Dhw/Hw8ODp06dCwmbp0qVERkZSrVq1bPO1Z8+eAcpEwq9gYGAg+IfIZONkc5uQkJAcpYjatGlD69atSUtLEworCpq6deuye/du6tWrR0JCAps3b+br16/Uq1ePvXv3YmNjg4qKClKplJcvX3Lz5k3hmh4TE8P9+/cF2aPy5cuTmJiIlZUVV65cUWj9urq6nD59WqjQlxEfHy90QGdVBPieyZMm4eXl9cNJBD8/P6Hz9Pbt20JXu5qaGm3btgUyfV5kycifNVtWokSJEiVKlCjGPzqR0KpVKwoVKiQkEWSMHTsWFxeXv3DLfj+xsbEMGDCAXr168e7dOypXroy3tzd79+6lfPnyBbKOq1ev0qRJExwcHITqjjVr1rB169Y8P5dfIiFrYCkyMjLXCd/Hjx+F/8s6ErZt20Z4eDi6urq0atUKgLdv33Ljxg1EIhE1a9akffv2DBkyBAcHBzp37oypqSlNmzalcuXK6OjooKurS8eOHbGwsKBKlSpCa/vUqVPZtWtXjtuTNZnwowbMV69eZdiwYTRr1ixXs8+MjAxBW3TcuHFAZpIop4Cpra0t7dq1IykpCTMzs2ydCd++fROCs/Pnz6doFm1xXV1dxo4dy/Xr17l79y6TJk1CT0+PiIgIEhMT6dy5Mxs3bvzHyr8UL14cExOTfJNosmPwe7metLQ0wah09OjRP/w9yJJBhoaGeXoPFAT37t1j8+bNfPr0iVmzZv2WZEJ+53JSUhJjx47l69evNG/eXO7BUlGGDx+OoaEhX758Yffu3Xh6euLh4cHmzZvZtGkT7u7uuLq6smLFCpYuXYq9vT3t27eXS0gqKThq164tXD/evHnDyZMnWblyJSNHjqR169YUK1aMWrVq0atXL2bNmsX169eZOnUqDx8+5OrVq4wfP55ixYoRERHBypUrMTQ0pEuXLhw4cCDH7pnTp08jkUho1KiRnMyWZZaAvt//OqVy4+3bt4wYMYK0tDSaNm3K2LFjcz13tbS0GDlyJB4eHkJl59ChQ/n8+TOlS5cWutwgM4ghk3CoVKlSjhXTIpGIsmXLMnbsWLp06YKOjg6fP3/Gx8dHMDbu1KkTurq6xMfHC0FZmZTejyYS8koiwP8nEkJCQgSZQRmye1ajRo0wMDAAMoOOWRM1f1YiQSqVMn/+fMzMzPj48SMNGjTg8uXLconIPn36/OeTCd++fWPQoEHMmTMHsVhMu3btKFWqFHFxcXh6etK9e3eqVq3KuHHjfrrAoVOnThQpUoTo6Ghu3LhBamoqS5YsATLvg1WrVs32mYYNG+Ll5cWSJUvQ1tYmODgYQ0NDypYti6+vLxoaGnh4eLBgwQJBWiYxMREHBweh88HOzg5bW9sflt9JSkpi7969QuCySZMmjBw5kmLFivH582d27Ngh19HZqVMnDh48SKdOndDU1KRWrVr07t2b2bNns3fvXlauXMmSJUuwtramXbt2lC9fHn19fZycnBg3bhxFixYlOjqaFStWMGrUqB86Z8PCwmjSpAlTp05l69atXPnfNkOm/KeJiQl2dnZs2LCBc+fOcePGDVxdXenRowdFihTh06dPnDhxgmnTptGsWTP09fWxtrZm06ZNALi6usrJJ3779k3w3VImEn6N7zsQKleuTKtWrZBKpUL37vfIuhIOHDgg9wxTkJQqVQoPDw/Mzc0pVKgQtra2eHp6CvPZ9PR07ty5I9y7KlSogKGhISKRiPfv33P37l1EIhHm5ua0b9+e5ORkbGxshPlrfhQpUoTWrVvL3WMDAgIQi8U0aNAgTx+5I0eO/HQSYd68ecJnduzYISwnK9rx8fERPHQKsiNBiRIlSpQoUZKdf7RHQtGiRbl9+3aBm7z+E5g7d64gxWBubs7OnTsV0uJUlOTkZHr16sWnT58oVaoUy5YtIzw8nMWLF2Nvb8/QoUNzbUOPi4sDkAtgZyUiIkL4v0ySqFatWtmW+/Lli/D/9PR0vn37JiSIOnToIFSwXb16Fch8sM1axQp5a6jq6enRtWtXPnz4wK1bt4iMjOT06dNoamoyaNCgbMtraWkxe/ZsNm7cSEhICIMGDeLgwYN56osm/y/YL6sYHDp0KK9fv87m23Ht2jUSEhIoWrQoo0ePxt/fn7dv3zJo0CBmz55N8+bNhWVVVFTYu3cvEyZM4OTJk0yePJm4uDjhAcbX11cwDc6rMqhhw4asWbOG5cuX4+Pjw9OnT5kyZcoPVwb+kwgPD8fFxUV4CPn+uNu/fz8fPnygTJkygtzDjxASEgJkdsv8bnbu3CmYmYrFYhwdHTExMSmw7HBqaqpwDuaWFPHx8SEsLAxVVVU2bNjwU8eOqqoqhw8fxtvbm9TUVMRiMWKxmIyMDNLS0pBKpSQlJRETE0NUVBQ3b97k8ePHzJkzh4MHD/7SPirJGXt7e4yNjcnIyCA0NFT49/jxY2JjY3n9+jWvX78WTO6PHDlC3759adGiBS1atMDR0ZGAgAD27dtHUFCQIDFXoUIFuU6dz58/C51XWc3lATlpI8/t20nMyGDWrFnZOo3u3buHlZUVHz9+pEqVKkyfPl2hOUG7du04fvw4jo6OnDp1CsispMzqpRIXF0dKSgpqampUqVIlz/FUVVVp1qwZDRs2ZO/evbx//56rV6/StGlTNDU1ad++vaDjvHDhQiFpkpNJc15cvnxZqJD9448/snVQVKhQgZo1a/LixQuuXbsmVNXC/1cqf/78WZD0y8jIICYmhsqVKwP/78uU9f77Ozh//rzQXWhtbc2qVauEqvWs9OnTh127djF06FAOHTqEtbW1nKfDv52tW7fi4+ODSCTC3d1dSKZcunSJo0eP4uXlRVxcHNu3b+fIkSNC98yPoKmpSaNGjbh8+TLXrl3j/fv3gqGsnZ1drp9TU1NjypQp9OrViylTpsgFIv38/LLpyB84cICbN2+ipaXF/Pnzf7pr79KlS7x79w4NDQ26d+9O/fr1ARg1ahTe3t68evUKb29v4ZiGzHvYihUrctRJz60zVkVFhY4dO9KyZUuOHTvGqVOnOHfuHOrq6gp3Ps+cOVMY39LSkm4dOsC69QDcun07R4+Exo0bM3nyZDIyMrh16xanT5/m0qVLXL9+nejoaGHu369fP6G7SoYsSVOqVKnfXszwb6dr165Apunv+/fvKVOmDL179+b69etcvHiRkSNHZvuMzFfr9u3bXLt2TSHvq59BS0sLR0dHJBJJtkRcRESE0GFdv359QZJHTU2NW7duERsbS3R0NDVq1MDDwwM7OzsCAwOZOHEiFy5coEyZMj+8PbLnMJm0Xl6YmJgI321ePH36VEji9OrViy5dulC5cmWOHz/O7du3iY6Oply5coJvybt374TkeU6+eUqUKFGiRImSguMf3ZEA/CeTCACDBw8WHhICAgKYO3eunAfArxIWFsanT58oXrw4T548wcbGhvnz51OyZEmSk5MF48mckGm1yuQqvidr8FZbWztX3UlLS0uhEtrMzIyNGzfy8eNHatSoIRe8lwVkYmJifkovvnTp0nKmonmZ/WlpabF3715atmypUGeCppaW3P5pamrmmGCRVcH37NkTdXV1FixYgJ6eHm/evGHcuHFMnTqVN2/eCMsXLVqUvXv3ClJTixcvxs3NDchMslSoUAGJRIKpqWmOLdjf79OgQYNYuHBhrskfyDQjHTZsGPXr1xfaiv8pREREMGnSJOrWrYuHh4dgpJhVZuDt27e4u7sDMGnSpJ+qyJUF4QoyqZcbsmNWVqHbunXrAjVW1NDQQE9PD8j8bnKidevW6OjoIBaL2bVr10+vS1tbmyJFighBzcePH3P58mVOnDjBjh072LJlC8ePH+fmzZtAphF6Tsk+Jb/O3bt3adSoEcbGxty8eZMJEyawbt06zp49S2RkJNHR0Zw9e5Z169YJBt7fS35oaWlhYWGBl5cXq1atAjIlUbIalUokEpYvX05cXBxVqlShc+fOuW6TRCLB09OTtm3b4u3tLVznfXx86NOnD+/fv6d27drMmDHjh3TWixcvzurVq1m9erVgIJ0Vmd5yRkaGQt4+EomEmzdvCsEMmUG0RCL5P/bOO67mvv/jz9MuZJMt+7JXVih7bzJSSpFkZG+yRfbMLnvvUQiRlWxCRkklZZQ0T53fH+d3Pndpnch9Xe7rPB+PHnT6nu845zs+n/d4vcRn1LRpU/Lnzy+qhZ8+far0/oK8+rpOnToArFy5klmzZqWRtnn79q3ouPrRLHvAgAFoaGjg4eEhAj5NmzZN84xSeLtcunRJVLT+DmrWrEnBggUBeaVvRkkEBYrltLS0RCfFv4WWLVuirq6OTCZDX18fiUSChoYGxsbGVKhQgYSEBEAe9B4zZsxPdRNeuHCB69evI5FIKF++vHgOzpw5U1wDWVGhQgUxhgF5ccuPSQT4z7Oqc+fOP51EkMlkouK+X79+IokA8ufIgAEDKFu2LDKZLEMpop/5fPT09GjYsKH4XSGlogxDhgwRycmHDx/Srm27bN7xHzQ0NGjSpAkTJ07k9OnTBAcHi+4ECwsLcW9VEBAQIBI/9vb2Sm9HRcYoOjJ1dXXFWEjhfZC6ey41MplM3P+LFSv22/cxozFfoUKFxHmeusNa8X81NTVxT9XW1mbDhg3Uq1ePuLg4IUmWUxT3ZU9Pz2znYR4eHrRp00Z0FGVGhQoVhN/W8ePHWblypbjPVKhQgaJFiyKTyVizZg0gf94p/JYU/kgqVKhQoUKFit/DH59I+LfSqlUrnj17hpWVFTKZjC1btlCvXj0OHz6cK+arikRBrVq1xIBTIpGISVtWwQ9FhdSFCxcy/HvqCWT58uUzDSD89ddf+Pr6cuHCBXr37s2qVasAeTdG6gSSkZERWlpahIeH4+3tnePj//79O1euXAHkZp+1a9fOcvk8efKkSyZk9nmoqakJqSKAhg0bpqvafvLkCXfu3EFDQ4MhQ4YA0KhRI06dOsXgwYNRV1fnypUrNG7cmIULF6aZDMyYMYPp06cD/0kmlCxZksuXL1OqVCn8/f1p3bq10kbZGREcHIydnR01atRg3759PH/+nJEjR/70+v6bBAcHM2rUKGrWrJkmgXDhwgU8PDzSmD+OGTOG2NhYjIyM6N+//09tT9FW/d/Q8O/Xr5/YXqlSpYR3Rm4hkUhEIPLx48cZLmNoaMjy5csBWL9+PR4eHj+1LSsrK0aOHMm8efNwdXXl2LFjXL9+nYCAAJEg1dDQoESJEhgbG3Px4sUsDf1U5ByZTMbGjRsxNjYWgeipU6eKrgMFRYsWxcTEBHt7eyH9oDAv/5HUgYkJEyakkchauXIld+7cQVtbm3nz5mUZSF64cCEVK1bk48eP2NvbY2Zmxty5cxkxYgTx8fG0adOGM2fOiGBPTpBIJHTq1IkZM2akSwDq6uqK6szMkmkKPn36hLu7u3gGValShbZt2wJw584dgoOD0dLSYtu2bUpdW5mRL18+rl+/jp2dHTKZjEWLFtGuXTsR4Nq6dSsgfw4rEhkK2rdvL0yMpVIp1atXZ9++fWmCq9WrV6dLly7IZDKRnP4dFCtWTASsFy9enOWYYvHixYA8KKuQhPq30KBBAyEZN3r0aN6+fcuVK1cwMjJi+vTpfP/+ncaNG3Pnzh0hSZYTYmNjRdDZwcGBdevWkZCQQMuWLXN0j00t9ZLZ2EBRqPDt27cc76eC9+/fEx8fj7q6eoZFKGpqatSvXx+QJzhzY5zy+fNnVq1aRXJyMj169MDc3Fzp9/bu3Ztnz55RpkwZAgICsLC0+On90NXVxdTUFCcnJzZs2JDGFD4uLg4rKytiYmIwMTH5KZlBFWlRVNkbGRmJcbtirpBRogzk4/ng4GB0dHREwjenvH37luPHj/+0zn/+/PlFYP/t27e8e/eO79+/8+LFC0AuX6gYO4K8MEMhuXfw4EF27NiR44p+c3Nz8uTJg7+/f5YSSavXrKFw4cIEBATQt29f7OzshBfXj2hra7N+/XpRNHL06FGuX7+OhoYGM2fORENDA09PT65evYqmpibr169n3759AGnmXSpUqFChQoWK3EeVSPiDKVKkCNu2bePSpUtUqVKFDx8+YGFhQa9evdJUr/8MCn3ZH6vzFb9n1ZGgCJ7cvn1byCekRkdHh0GDBiGRSLI19S1UqBDNmjVj3bp1REVFUb169XRmrrq6uqJCzNPTk7179yo9CFYY9sXHx1OuXDkGDhyo1Pt+TCbY2Nhk2h5vYfGfiWNGVUzbt28H5FV6qQNt+vr6TJkyhSNHjtCoUSMSEhJYtmwZjRo14vjx4yJhMnny5DTJhCVLllCpUqU0yYSOHTtSqVIlFixYoLS+b0REBBMnTqRq1aps3bqV5ORkOnbsSJ48ebh16xa7d+9Waj1/B4oEQtWqVXF1dU2XQPixGvLQoUOcP38eTU1NFi5c+NOV/f/NRIK+vj6Ojo5UqlQJFxeXLM2lfxZFsDOrIF+3bt2wtbUFYPz48TmuYr5//z6XLl1CXV0dKysrJk+ejIuLC+7u7pw6dYr79+/z/v17oqKiePPmDRcvXhRGsSpyh6ioKAYMGMCoUaNISEigW7duWFpaIpPJMDc35/nz5xm+T9Fdlpmh8po1a3j//j2lS5dmzJgx4nUfHx/mzZsHyGWUfgx4/0i9evXw8vJiypQp6OjocO3aNaERPmLECNzd3X9bF5BC0igiIiLD61omk/Hu3Tu2b99OWFgY2tradO3ald69e6OtrU14eLjwv+natas4VkXC+vnz56KqXFl0dHRYt24du3btIm/evHh7e1O/fn3OnDkjZNuGDRuW4Xu7d+/OoUOHsLGx4ciRIxQoUCDdMhMmTADknXIKiYzfgbm5Od26dSMpKYkRI0aQlJSUbhlvb29h+qvYr38bU6ZMoVmzZnz79o0WLVrQoUMHnj9/TtGiRdmyZQteXl7C0DunzJs3jzdv3lC6dGlq1KjB5cuX0dHRYeHChTmq3j9+/Lj4vyJg+SOKREJUVJRS64yKiuLWrVtpkgEKc/ASJUqkkSFLTdWqVdHS0uLr16+/3D0plUpZtWoVUVFRlClThkWLFuW4q6FkyZIcO3aMggULcvf/u+pym6lTp/L48WOKFi3K7t27M/1sVChHcnKykJBq1qwZIH8GKBK/mSUSFIn31q1bZ5kcz4zHjx8zc+ZMdu/ezdKlSzO8JyqDQuIO5PM5Pz8/kpOTKVSoUIYyfQ0aNKBHjx7IZDLmzp2LiYkJbm5uSs+l9PX1RQHOhg0bMl1u4MCBXL58maFDh6KmpsaJEydo3rw5GzZsyPBYNTU1cXBwYNmyZeJZZW1tTeXKlfn48aMoMJs1axaPHz8mJiaGqlWris46FSpUqFChQsXvQZVI+B/A1NSUW7duMWvWLLS0tPDw8KB+/fosX778pwehikRBZomErAKLZcuWpWrVqiJInxFTp07lypUrwjA5KyIiIli/fj0gHyxmFORt3bo13bt3R11dnWfPnrFhw4ZMq1xS8+jRI0JDQ9HQ0GD06NE50njPkycP27dvp1y5crx7946RI0emkZhQUKBAAby9vYU+f2qCgoK4ePEiIB8cZ0TFihXZuHEj7u7ulClThvfv32NlZUWPHj1E63XqZML06dNFMuHGjRs4ODhQoEABAgMDmT17NuXKlaNHjx6cPHkyw/2Niopizpw5VKpUidWrV4vqRG9vb86cOSMq3aZNm/ZLlYW/g8jIyAwTCOfPn88wgQDyakNFgGrEiBFpDJi/ffvGlStX0piRZsV/M5EAYGZmxpEjR7KU4/oVFImEFy9eZDmhnDFjBo0aNeLbt2/0798/R8evmAj26dOHjRs3MnfuXBwcHOjXrx8tWrSgWrVqFC5cOFdlm1T8h7t379KwYUMOHz6MpqYmy5cv59ixY7i6utK8eXOio6Pp2bNnhklhxbUSEBCQrhMsNDRUdKsoDFlBfj+3trYmOTmZdu3a0blzZ6X2U1tbm3HjxnH16lXatm1Lvnz5WLFiBU5OTr9V4jBPnjyic+nHroT4+HgePHjAy5cvkUqllC9fHltbW2rVqoVEIkEqlXLy5ElSUlKoXLlyGnmUMmXKUKBAAaRSaaaB1+zo378/d+/epU6dOkRERNC9e3eh5d29e/dM39eqVSuWL19OiRIlMvy7iYkJDRs2JD4+Psug0K8ikUjYtGkTBQsW5P79++JekJp/czeCAg0NDXbs2IG+vj4RERGoqalhb2/P48ePsbS0/Ol748OHD8U1umjRIvFsHzduXLaeIKn58uULXl5e4vfAwMAMq6lzkkgIDw/HxsaGcePGpZFAUSQSMpOWAXnwUSGx+WNHVU5xd3fn5cuX6OnpMX78+BxJp6WmWrVqHDhwAC1tbfFabnQPg7wQYvv27UgkEtzd3SlZsmSurPffisLQ3tPTE3V1deE1du3aNUCeqMrIfyK1CbOyz7XU3Lt3j0WLFonE8pMnT1i3bt1Pd9VUrFhRXCexsbFoaGiIZ1NGLFu2jAULFlCyZEnCw8OZM2cOJiYm7NmzR6mEgsIz4vz585l2KYL8PrBo0SI8PT1p2LAh379/Z968ebRu3TpTuaMmTZqwa9cu1qxZg4WFBTKZjCVLlhATE0P16tWZNGkSrq6uAAwfPvynJMxUqFChQoUKFcqjKln5h6PsRENLS4tp06bRp08fxo4dy9WrV5k5cyb79+9n7dq1NG7cGJBXVykTdFEkEv766680g1iFXuWzZ89ISUkhMTExw/W1adOGFy9e4OnpSbdu3fj69Wu6IL2mpma64NSXL1/SaVGvWrWKmJgY/vrrLypVqoS/vz8RERFop5qQgbxdN1++fJw8eZJPnz6xadMmKlWqlGll1ufPn4WPQ6NGjShSpIgIGMfGxvL27Vv++uuvNJP0z58/i8mEglGjRjFz5ky8vb1xcHDAwswMhYrz95gYJMnJQg8bICYmJs2xyWQy6tSpw+fPn7lz506G+5qSkkKZMmVYu3Ythw8f5uDBg3h7e9OmTRuWLl1K4cKFadmyJaGhoezcuZPp06eTkJDAuHHjWLhwITNnzuTEiRO4ublx8+ZNzpw5w5kzZyhZsiSWlpZYW1tTvHhxNm7cyLJly0R3Rd26dXFycqJ169ZIJBJiY2MZPnw4W7du5c2bN8ydO1dUFiujpZwTZDJZtud/XFxcmmTIgAEDRPVvixYtmDp1Ki1atODz58+ZdoyMGzeOjx8/UqVKFRo3boyfnx8gN/ieOXMmb9++pVSpUtjY2Ih28ZiYmAwDJYrz5+PHjwQFBfHt2zelqo3j4+N58+ZNtstJJBLhE5AZaomJKNJzfnfvkpTF9Z43b16hW50RMpmMPHny8P37d7y9vYUmfkYsWrQIMzMznj59iq2tLevWrct0MieVSilUqBDv3r0TVX9jxoxJN2HOrUCLguTkZKUnmP8r/jtSqTTDhKFMJmPdunVMmTKFpKQkypYty/bt22nQoIFIEG7fvp3WrVsTEBBA//792bdvn5C6g/94AERFRREZGSkC7oGBgcyYMYPY2Fjq1q1LvXr1eP36NSkpKYwaNYrQ0FAMDQ3p3Llzph0saomJKEI1T58940MqDyAbGxusra1RU1NLI6Pw6tUrpTpzvn79KqSAsuLbt29oaGhQpEgRIiMjCQ8Pp2jRomhpafHp0yeCg4OF2aWpqSm1a9dGIpGIoMvVq1eJjIxET0+P1q1bEx8fn8bLqHr16ty4cYNFixaxfv16cc5JpdI0n3NmJCUlYWhoiLe3N5MmTWLz5s2APOiuSGQAWV7jqUlOThbFB46OjgwePJj169fj6OiYJoCakpKiVNJdmXu4gYEBS5Yswc7OjkWLFtGpUyeRGL127ZroRhg3btzfKqenzLFkZOSbESkpKUotl5ycLMYfZcuW5dChQ+zbtw87OzvRgZCSkkJycrJSFejfv38X59i9e/fo37+/kOs5e/Ysnz9/platWvTo0SPDxOGPKM7T48ePC6ms8PBwPn36xJMnT8Q+fvjwAYlEIp6Fnz9/Fj4Hqfn06RN58+YlIiKC6dOni0KJBw8eiHNC4XsQEREh9NAzQnENenp60rdv3yw9jz5//pzh+Xz79m08PT0Bufyejo5OpuOI1ISHhxMZGZnu9Xz58jF92jTYK5dfcXJyYrESmvTx8fGZVre/evVKdHtNmDBBKRNbZfmxIOD58+fs378/TYFSTruplEHZ576y15FUKlUq2aY4Z+zs7Ni7dy/q6uq4u7tTq1YtEhMTuXTpEiAvsMios/f169e8efMGTU1NIc2qzHXp5+fH1q1bef78OTKZjMKFC2NgYMCzZ8/w8fEhMDCQihUrUr9+/XTznsyOQ3GvNDQ0JDY2li9fvlCxYkVkMpkYu8bExKQr9ipdujRz587l2rVrnD59mvDwcBYuXIirqysWFhZ07do1031QU1OjXbt2XLhwgXXr1okksCxVEiIhPp6EhAQ0NTWpXr06x48f59ChQyxYsEDIHXXv3p05c+aQlJSUpohHT0+PGjVqkJSUxKlTp/D19UVLS4upU6dy584dHj16hI6ODoMHD87wHFLmHq5ChQoVKlSoUA5VIuEfjrKVZrq6ukgkEmrXro2Xlxe7du1iwoQJPHnyhNatW4tJet68ebMdeCuC6CA30Eq9D4pAamBgIHFxcejp6WW4j507d2bDhg14eXmho6NDuXLllBpQ/5hEiIiI4NChQ4DcPE6x7yYmJplWpFlYWDB37lxu3rzJ8+fPKVy4MGZmZmkmknFxcSxcuBCZTEbDhg0ZPny40AP++vUr7du35+XLl5iZmbFx40Yx+T5+/Hi6oEypUqWws7NjzZo1nDp1ioqlS4tEgpa2NmqZDLo/fPggNFgVJsuZER4eTqlSpdDS0mLQoEGYmJgwffp0QkJCmDx5Ms7OzhQqVIgBAwZQsmRJFi1axNy5cwF5oFxhQjhgwABevnyJm5sbBw4cIDQ0lCVLluDs7Ez+/PlFkKtatWrMnTuXDh06pPvedHR0WLJkCWZmZqxbt44hQ4aIFurcREtLK1vDY6lUKs4Jb29vrl69ipaWFseOHcPU1FQsp6amlmFg+Nq1a+zfvx+JRMKKFSuIjo4W29y7d6+4DkJCQpg3bx7NmzfH2toadXX1DCvSFJrFycnJFC5cmJ49e6aRq8qMTZs2ZWl0nfo4smuXT309amlpIckiIP7169dsv7tKlSrx8OFDnj17lmWQonDhwqxatQpra2uOHj2KkZFROvNaBerq6ujo6ODq6kpycjKtW7fOUCpAR0cnVzsRVFVqcr58+cLw4cOFHEmXLl1Yv359OpmbYsWKsXfvXjp06MClS5eYN2+eMDYEeSKqTJkyBAcH8+bNG+En8PTpU06dOgXIZVkU19727dvx8fFBR0eH5cuXExMTI5IPPyJJFaCK+fYN/Qy66368F+vq6qYxdM4MDw8PpQLhxsbGoitn7dq1PHv2DG1tbRISEkR3QsWKFbG1tU0jYQdy+SaFx8CGDRto27Yt379/T3M/dXR05M6dOxw7dgx1dXU2bNggPitlElmKwLWuri7r1q2jbdu2IqmQ+lzX19dX6vmblJQk3te7d2/Kly9PYGAgI0eOZPv27WKf1NTUlLoulQ2sW1tbc+rUKU6fPo2DgwPXr19HU1NTdPDZ2Ngo9b3+KUgkEqU+F21t7TTLtW3bVkhHpkYmkyn1fSiWOXLkiPAXqVatGr169cLKygqJRMLatWspWrSoUrIsycnJ5MmTR1Rh9+3bl8uXL3Pt2jXevn0rDE/Lli2LhoaGOAe/f/+OoaFhuvUVKFAAPT09Ro4cKZIIIB+TVqlShdjYWHHdKZN419bWJjY2ltevXwtz8YzQ0dFJ9xz09/cXeuv29vaMGjWKqKioNL4EmZGVHFhLExORSNiyeTO1jYwYPHhwluvLly9fhmOhuLg4hg4dSkxMDC1btmT+/Pm/rXPPz8+P7t27Ky1L9U9C2esN5F2piiTC7t276d27t/ibt7c3IH8uZJSw9vHxAeRm3CVLlkRfXz9D6bgfmThxYprfP336xKdPn8TvISEhhISEIJFIlOp0GDZsWBq5QJlMxtevX9Mlp318fDJMMqupqWFiYkKzZs3w8fHh9OnTREZGsnLlSnbt2oWlpSXdu3fPMKEwatQoLly4wL59+3ByckJfXz9NAlhLW5vi+fKlSUyPGjUKc3Nz5syZg6urKydPnsTLy4vJkyfj4OCQ7ln95s0btmzZAsil2Xr27MnIkSMBeTFRZteompqaagyoQoUKFSpU5BIqrYj/QSQSCZaWlvj7+wsz5k2bNlG9enUOHTqUbUWGoiqmSJEiFCtWLM3fihYtSpEiRZDJZJnqZoO8GlxHR4f3798Lv4WckJKSgre3N+PHjychIYE6depkqkn6I/nz58fFxYVhw4YhkUjw8fHBxcVFTO5kMhl79uzh06dPFC5cWPg1gLy6ytzcnJcvXwJy4zF7e/tsKzqbNGlCt27dgP94HmTHxo0bkUqlVK5cOcdBklKlSuHs7EzRokUJCQlhypQpolLuR8+EHw0zq1Spwvz58wkMDGTv3r20adNGTDTKlSvHtm3bePDgAb1798500N2pUyfatm1LUlIS06ZNy9G+/y4U1U9WVlZpkgiZERcXJySNrKys0ujuP3z4UARZx44dS+fOnVFTU+P69es4ODhw7ty5DGXDFJOj/5a00X8DxYT01q1b2S7bsGFDZs+eDcDs2bOz7J74+vWrMIb9t2qf/x3cvn1b+KxoaWmxcuVK3Nzc0gQ8bt++zdKlS4mKiqJ27drCj2DdunW4u7unWZ8iAKfoLJDJZDg7OwNyPX5F8tnPz49169YBcvk1hb/Cn0KnTp0A+XE8efIEDQ0Nevfuzfjx49M9J6OionB0dATkPgAZBX9B3rm3ZcsWNDQ0OHz4MCNHjlS6eyAjevbsyYoVK7JNSr5584YzZ85kKU2noaHBypUr0dDQYN++fQwdOvSX9i0rJBIJGzdupECBAvj5+eHi4sKVK1dEYjg7LyUVypGSksKiRYuwsrIiPj6e9u3bc/r0aVF0MGLEiCy7zjLiy5cvolK7T58+onMgo/Gh4h4TGxuboVxgREQEFhYWBAcHU6ZMGaZMmQLAu3fvALl+fE7OQUVi8/z588ofEPJn09ixY0lISKBFixY4ODjk6P05YcSIEVy4cOGn3jthwgQePXpEsWLFfqsvQuokQoMGDXB0dBQ/CqPuP53k5GTs7OzYs2dPhkmE8PBw/P39kUgkaSTqUqPoXslJV0hWxsQ/8vHjR6WXTY1EIlGqw+1HNDU1MTU1ZdasWUyaNInixYsTGRnJihUrMDMzy9AbqXXr1lStWpVv376xa9cupbdVsGBB1qxZw61bt2jSpAkxMTHMnj2bZs2aiS5jkH9P9vb2fP/+nRYtWjBixAg+ffokCs5GjBiR4+NUoUKFChUqVOQcVSLhf5giRYqwfft2vLy8qFq1Kh8+fMDc3Jzu3buLSuuMyMwfQUGNGjWArH0SdHV1hR59TiZJUqmU06dPM2DAAMaPH4+/vz/a2to4OjrmqJJETU2NoUOHYmFhQd68eQkODmbx4sU8evSImzdv4ufnJ5ZRaHfLZDJGjx6Nj48P+fLlw8nJCQ0NDaWTCf3796d27dokKaGnHxUVJUwxu3btqvRxpcbAwCBdMkFRxfRjMsHGxibdJERbW5t+/fpx/vx5Xrx4wfnz53n69CmWlpbZVsNKJBKcnZ3R0NDg3LlzYgL1d+Ht7Y23t3eODDlXrFhBYGAgJUqUYObMmeL16OhoodXdsWNHWrVqxfDhw3FxcaFq1arEx8dz4MABBg4cmE6KSpFIyEj26E9FoYN/9+5dpbwiRowYIQxUbW1tM63O3Lx5MzExMdSsWZMOHTrk6j6r+A8KvfuGDRvSoEEDTE1NCQwMpEKFCnh7ezNq1Chxb5XJZKxZs4bOnTuzaNEiETzu2bOnqJq0s7Pj9u3bYv2KRIIiqHDgwAEePHiArq4uY8eOBeTSIZMnTyY5OZlu3bqJ7q8/iUqVKgmJujJlyjBt2jTatWuXrgL406dP2NraEhYWRvny5UViLTO6d++eq8mEzEhKSuLw4cN06NCBypUr0717d0qXLs2IESO4d+9ehu/p0qWLCFD+7mRCiRIlxH133rx5IhFjY2NDmTJlfss2/03ExsZibW0tEu6jRo3i4MGDrF+/Xsj3zZo1K8frVXgt1axZk6pVq6aRv/yRvHnzirHFj5Xt4eHhTJkyRSQRdu3aJZL7ChkkhayRsigSCXfu3FFKkig6OpqtW7fSs2dPYRLv7Oz8S1X+KSkpXLp0iQ0bNqQ75rZt2yKVShk4cGCOj23//v1s2bIFiUSCm5tbpn4nv0rqJELTpk05ffo0CxYsED8/c87801B4ImTWiQD/8UeoVatWhl0Gb9++JSAgAA0NDaWNfs+fPy8q65Xh+fPn3L9/X+nlcwtNTU169+7NwYMHRULh48ePTJkyJd05LZFIRHJJ0XGaE+rVq8fVq1fZunUrRYoU4cWLF3Tr1g0rKytCQ0PZsGEDN2/eJF++fGzYsAE1NTX27NlDQkIC9evXx8jIKNeOW4UKFSpUqFCROapEwr8AU1NTHjx4gJOTE1paWpw/f546derg4uKS4SDv4cOHQOaJBMVEMatEAvynKkeZILNMJmPHjh1YWFjg5OTEmzdvyJMnD5aWlpw4cULIS+SUSpUqMW3aNAwNDYmLi2Pjxo2iXb1bt25p2n8XLlzIwYMH0dDQwN3dHUdHR7Zt25YmmZCVRrOamhqjRo1K01abnIE2OcCOHTuIjo6mZMmS1KlT56eODdInEyZPnpzGgNnJyQk1NTWOHDlCo0aN2L9/f4brqVChAm3atFFKf1VB1apVRfXPlClTlDYk/h2k7kZQxpDz6dOnwsB78eLF5MuXD/iPbvyXL18oXbp0GgPsChUqsHjxYkaPHk2+fPl4+/Yt9vb2TJs2TXzmiqTU/1JHQrFixcibNy/x8fHi3pAVEomE1atXU6lSJcLCwrCzs0un0Z+YmCgkcsaPH69qN/+NPHnyhIcPH/Lw4UMePXqEVCqlT58+3LlzJ0318ffv37G0tGT27NlCm/3SpUvMmTMHkHcRdO7cmYSEBHr37i00olMnEuLi4kQVsY2NjajUX7BgAR8/fsTQ0JCZM2f+sd+3nZ0dDg4OTJkyJUND0wcPHtCxY0du3ryJrq4ua9asEQbsWfFjMmH06NG5FrD//PkzM2fOpFy5cvTv35+LFy8ikUgoWbIkMTExbNmyBSMjIxo1asSOHTvSbbd3797/tWSCubk5Xbt2JSkpiSdPnqi6EXKJsLAwTExMOHbsGJqamkK//NmzZ+I+vGLFCvEczAlHjx4FEIHXrBIJEolESMIoApAymYwXL17g4ODAhw8fRBKhRIkSIoEUGRlJbGxsjoOoenp6/PXXXyQnJ3Px4sVMl4uIiGDJkiW0bt2aFStW8PHjRwwMDFi7dq1S0jSZce3aNSwsLJg5cya7du1i4sSJaUxrp0ydiqmpKTExMfTs2TPLAp/UPHjwQARrp0+fnmnH06/yYxLh6NGjP3WO/JNRJBF2796daRIBEF4CJiYmGa5HMc9p0qSJUjKVp06dStfdlx0ymYy5c+dmaWT8O9HS0qJ37964ublRqlQpQkNDmT17drrx3cCBAylQoABv3rzBw8Mjx9tRU1NjyJAh+Pr6Mnz4cNTU1Dh69CgNGzYUnmyLFy+mXLlypKSksG3bNkD+fP5TxxYqVKhQoULFn4YqkfAvQVtbm9mzZ+Pn54epqSlxcXFMmzaNnj17EhUVxfPnz1m4cCF169YVUjgKSYofUby+fPly+vXrh5eXV4ZySYpEwtWrV7OUQQJ518KsWbNE1Xy5cuVwc3NjzJgxmWpoK0uhQoUYP368qBKSSqVUrVo1TfvxyZMncXFxAWDlypVi2R49erBt2zYkEgkHDx7kyZMnWW4rb968jEjV6n31/zVVf+TgwYOAPBj/q5q2BgYGzJ07FzU1NUJCQtJomDs6OuLl5UXt2rX5+vUrI0aMEBrDuYFCFzwgIOBv60p49eoV3t7eaGpqKt2NsGXLFqRSKWXKlKFNmzbi9eDgYNFl0KdPn3SJFTU1Ndq0acOSJUvo378/ampqeHp60rt3byZOnChknjKSPfpTef78uUigPX78OMNlvn//zp07d3B3d2fkyJF07NhRTHavX7+epjU9MTGRMWPGEBYWRsmSJRk4cODvP4h/MYcOHRLm6mfOnOHGjRvs27cvXbBj165dwtfA0dGRJUuWAHI5o7CwMNTU1HB1daVIkSKEhYWxfPlyACHL5u3tzf79+wkODkZDQ4MBAwYAcjkGRVdaly5dRLLtT0RXV5eaNWtm2LEVHh6Oubk5YWFhVKhQgdOnT1O/fn2l1929e3dcXV0BuYa9Qi7mVxk0aBDLli0jPDwcAwMDpk2bRkBAAO/evePSpUv0798fLS0t/Pz8sLe3x8bGJsNkgsLIed++fTmS48gJqSWOQNWNkFssWrQIPz8/ALZt28aQIUMAubxicnIyJUuWpEWLFjle771790SAvk+fPsB/ClACAwM5ffp0uvcUKlQIkN+Xli5dKvwZQkJCMDAwEEkEkMtUKu5Tb9++VTrQnhrF/ij8qFITHh7O8uXLsbOzw93dXXgxLF68mPPnz4sOpJ/hw4cPTJ48mTdv3qCuro6GhgZPnjzhUqqEhqamJgcOHKBy5cp8+PCBRYsWZbq+6Ohotm/fjqmpKUZGRsTExGBiYvJbOwKmTZtGVFQUZcuW/Z9MIgBcvHhRJBHc3d0zTCLAf7phbt68KWT8UqOQftTW1s420RoVFSWKehSJN2XQ0NAgNjYWNzc3pd/zO8ifP7/oKrxz5066jraIiAjhIXbs2LFs13f9+nUOHTqU7vouUKAALi4ueHt707hxY2JiYkhISKBDhw5i7nHlyhXevn2Lvr4+gwYNyoWjU6FChQoVKlQogyqR8C+jatWqeHp6smXLFnR1dTl//jzly5enVq1aODk58fTpUzQ1NenWrRv9+vXLcB39+vWjQ4cOyGQyTp06RceOHalXrx5btmxJU4ldtWpVOnfuTFJSEqNHj05npJyaBg0a0Lp1axFUDwoKwszMjJEjR3L69OlfroDU0NDAzMwMOzs7WrZsydChQ1FTU0Mmk3H8+HGOHDkCwKxZs9KZZhoaGiKTyVBXV8/U4Dk111IlD2rXrp3hMorP9vLly2mCrD9DVFQUq1atIiUlBW1t7XRSSXXr1hWvlS5dWrT7/yrv378XwUJDQ0OlPSxyG0UnhL6+vlLdCCBPEGlqahIcHIyFhYU4b0uUKCEqrDdv3pxp4ihPnjxMnjyZ3bt3U69ePRISErh8+TLfvn2jePHi2Nra5sKR/b3Exsayf/9+tm/fTmxsLBUqVBCVj2FhYWzdupUxY8bQqlUrqlWrRu/evVmyZAmHDx/m5cuXyGQySpQoQa9evahXrx4gTzhYWFhw9OhRNDQ0WLt2bbaG2ip+DSsrK/r37y9+BgwYIBKZqWnbtq24N2zfvl34IjRo0EB0Fuzdu5fIyEjU1NTEPaVt27YYGhoSGhrKqVOnKFy4MFKplCVLlgivHUXX1bp16xg2bJiQz/tfQSaTMWPGDKKioqhZsyanT5/+qSCkwkA2f/78v9SppuDq1at4eXmhpaXFgQMHCAwMZMGCBRgaGiKRSDA1NWXv3r28e/eOhQsXoqGhwd69e9MlE2QymQgKGxgYULdu3V/et8woUaIEhw4dYujQoTg5Of227fyb6NevH3nz5gXknYoKU1gzMzN0dHQIDQ2lQ4cOostIGSIjIxkxYgTJycn07dtXnO9FixbFysoKkHeYeHl5pXlf//79AXnxxrFjxwgPD0dbW5uWLVvi7OycTqJH0Y169+7dHCc7oqKihNdR6kpyRQJhwIABHD9+HKlUipGREZs3b+bYsWP06NHjl59LRYsWFfKeycnJSKVS1NXVhTSoAqlUKuT/fhxDpaSk4OXlhY2NDaVLl8bOzg4fHx/U1NSE7Jgypuw/iyKo/u7dOzZs2PDbtvN38uXLF0BukNyrV69Mlxs5ciR6enrcvXuXfv36sWzZsjTznT59+oguvnHjxpGQkJDpuvT19YXPQk6ehVKpVBSz/F0ounYVHb1VqlRJc07v37+fpk2b8vr1a/T19TE3N892nUuXLmXQoEGZzoVq166Nh4cHmzZtwsbGhg0bNojOA4VE7ODBg5Xq/lOhQoUKFSpU5A6qRMK/EIlEgpWVFZcvX6ZUqVLExMSgqalJ586d2bZtGyEhIRw+fDjTToAiRYpw9uxZnjx5gp2dHXny5OHZs2c4ODhgaGjI9OnTiYuLQyKRsHnzZgwMDAgICBCGfhlRuHBh3N3dOXDgABMnTqR27dqkpKRw584dnJycRFDqV6lbty4DBw5EX18fmUzGoUOHROvtggULMqxoVxiE9urVS1TZZMaNGzdECzSQ6Wfo6OjIqFGjAHnQ7ubNmz9zOISFhTFhwgRevnyJvr4+S5YsoVmzZmmWCQ0NFdrTc+fORUdHJ916du7cSf369bGxscHd3T3broX379/TqVMn3r59i6GhIefOncv2s/ldKKrksjIO/ZFWrVqxe/du9PT0uHLlCmZmZkRFRaGpqcm8efOoWbMmcXFxzJs3L0vD4KpVq7JlyxZWrlzJ0KFD2bRpE6dPn6ZLly6/fFx/J/7+/ixfvhw/Pz8kEglt2rTBw8MDfX19Fi5cSPPmzXFycuLo0aMEBAQgk8kwMDCgVatWTJ48mT179vD48WMePnyIq6srhQoV4vPnz/Tt25fLly+jp6fHyZMn/0it/D+N79+/ExMTI37evXvH4MGDGTlyZJrkbqVKlbh69SpNmzYlOjqat2/fYmBgIIJVV69eFb4rChkQkCfVFMscP36c4cOHo66uzunTp3F3d0dNTY1NmzZhbW2NlpYWt2/fpn///syYMUMp3fI/gbt373L+/Hk0NDRYsWKFkG/JCf7+/ixduhSQy+zlRsJ3wYIFAFhbW9O3b180NTUzXK5o0aJMnTqVXbt2ZZhMWLZsGUePHhUV1L/aJZgdrVq1YvPmzX/bM+V/jZYtW3Lnzh3++usvPnz4QJcuXVizZg0mJiacPXuWokWL8vjxY1q1aqWUfJ1UKmXkyJF8+PCBKlWqiKSjgnXr1tG9e3cSEhLo169fmoR8t27dcHR0pGLFivTu3Zvly5dz/vx5IdH4I40bNwbklc+Ojo458gJ49uwZycnJtG3blp49e/L169d0CYR69eqxcOFC3NzcaN68ea5Jo6irq7NkyRIcHR3FdWdmZkZ5Q8M0yy1YsICvX79Sq1Yt0Sny+vVrnJycqFKlCp07d2b//v3ExcVRrVo1Fi1axJs3bzh+/DgGBga5sq+ZMWLECDFunz9/Ps7Ozr91e38Hiq7T7KQ5Bw0axMOHD+nRowfJycls376dLl264OHhgUwmo2vXrqxcuRJNTU0uXLiAnZ1dpsVTEokER0dHLCwscmSQra6uzsKFCzOVV/rdJCcn4+zszJ49ewD5Z7Jlyxby5MlDTEwMc+bMwdbWlm/fvtG0aVNu3ryJqalprmxbTU2NQYMGsXLlSnGfCA8P58yZM4Bc1kiFChUqVKhQ8d9DlUj4F9OgQQN8fX05cuQIoaGhnDhxAktLSwoWLKjU+//66y9Wr15NYGAgLi4uVKxYka9fv+Li4iKMOYsVK8aOHTuQSCTs3r1bDPoyo1ChQgwYMIDt27dz4sQJhg0bhkQi4dixY0JaITdISUlh7969QqLB0tJSBPZTExISIjSAM/p7ahRV2sogkUiYP38+JiYmyGQyXF1dhfSAsgQEBDBhwgRCQ0MpXrw4Li4uGbZJz5s3j9jYWBo3bpxh23ZoaChjx47l8ePHuLu7Y2NjQ6VKlahcuTIjR45kz549vHv3TiyfURJB2U6A34Gi0jIxMTFHPg2tWrXi0KFD5M+fH19fXyHzpaenx6xZszAyMiIxMZHFixdnqc0skUho2bIlDg4OGBkZ/bJU1d+FTCYjIiJCdCFER0dTtGhRRo4cSbdu3XBzc8PY2JiNGzeSkJBAgwYNmDBhAm5ubty7d4+7d++yfv16Jk6cSLt27dIEQkNDQ+nevTt+fn4ULFhQmL6q+P34+fnx4sULXrx4wfPnz5k+fToSiYQtW7ZgbGzMixcvxLIGBgacPHmSsWPHUrNmTfbu3UuJEiV4+/YtQ4YMITk5GQsLi3QJ16ZNmwqJDUXXAch112/cuEHevHkZP348J0+epFOnTshkMk6ePMnkyZNxc3P7o83JY2Ji2Lt3LyB/RmTmLZQVUqmUUaNGkZSURIcOHejbt+8v79fVq1e5evUqWlpaTJo0San39OrVS/ghKJIJZ8+eFd/t6tWr/7bOMxW/RrVq1fDy8qJ///4kJyczY8YMzM3NqVKlCpcvX6ZatWqEhYXRoUOHdF0EP7Js2TJ8fHzQ09PjwIED6SRvNDQ02LVrF23atOH79+9MmjQpjRxM//792b17N5MmTaJZs2YZFjcoUCQSfH190dPTy7Ig5UcSExMpX748kyZNQiaTMW3atDQJhDVr1rBmzZqf9uDKDolEQv/+/dmxYwdTpkwRvgYKAgMDxbjW2dmZR48e0bp1a2rUqMGSJUt4//49+fPnx9bWluvXr/Po0SMmTZqkVGdsbjFhwoQ0yYT58+dz9+5d8fN3mP/mJsomEgDKli0rDMpLly5NeHg4jo6ODB8+nMDAQNq3b8/mzZvR09Pj9u3brFq1Kp0ZsQKJREKnTp2E5r8y1KtXL42v0X8TqVTKvHnzOHHiBGpqasyYMYPRo0ejpaXFo0ePsLS0xNPTE3V1dWbMmMG5c+coV67cb92nXbt2IZVKadSo0W+7hlWoUKFChQoVGfNnRrxU5BpFixale/fu2RrKffnyJVMvhPz58zNmzBiePn3Kzp07AbkGvUIKoU2bNsKUd/Lkybx//16pfStVqhR2dnbCvHPLli0cOnRIySPLnOTkZNzc3Lh+/ToSiQRLS8tMW4VdXV2RSqUYGxtnKeegMI+Nj49XWtJCIpFgbm6OsbExKSkprF+/PksPhuTkZN69e8fVq1dxc3NjypQpfP36lQoVKrB8+fIMg/m+vr5Ci3Xx4sUZVtvNmDGD2NhYGjRowOTJk2ncuDHq6uoEBgayZ88e7OzsqF69OjVr1sTe3v4flUSA/yQSQB7UywlGRkYcP36cokWL8vTpU5ycnPj48SPa2tpMmTKFFi1akJKSwsqVK/n06VNu7/rfSlJSEuHh4Vy6dInt27fj5OTE0qVLRRdCy5YtGTt2LJGRkcydO5cFCxYQFRVF1apV2blzJ8ePH2fcuHG0adNGSN9kxKtXr+jatSsvX76kRIkSnDx5UrT1q/j9lC9fngoVKlChQgUqVqzI3LlzOXv2LMWKFePx48c0btw4jdSRpqYmc+fO5fr169SvX5/o6GgGDhzI169fadCgAa6urhneR6ZPn46xsTHfvn3jxo0b9OjRg5SUFCZNmiQSkaVKlWLp0qXs3buX+vXrk5iYyIEDBxg2bBjnzp3LVRPfhIQEXF1dGT58eIb66LnFoUOHiI6OpmrVqowZM+an1rFmzRoePHhA/vz5Wb58ea5URSu6EYYOHZojn4HU5sp79+6lV69eyGQybG1t/yck2/7N5MmTR3TQaWlpcerUKUxMTEhISODChQu0atWK79+/Y2dnx/bt2zNch4eHh+jSXLp0aaYa79ra2hw8eJAmTZoQExPD2LFjf8qfqUaNGujp6REVFcWLFy9yVI2trq7OggUL0NPTw9PTkydPnggT9DVr1gjJvd9NxYoV6dmzZ7qOIIVHRdeuXTE2NmbAgAHcuHEDNTU1OnTowO7duwkKCmL16tU0btz4bzOSTZ1McHZ2xtTUVPz86d2XiiRWahPs7GjZsiWnTp1i5MiRaGpqcv36dXr06MH58+dp0qQJ7u7uFCpUiODgYObOnSukqzKifPnySm/375LuSUpKYsaMGSJRMHfuXLp27YpUKmXr1q3Y29sLzytPT0+mTZuWo06LnyElJUV4RVhbW//WbalQoUKFChUq0vN7n/Qq/msoK/uTkpKiVMW0VCpN87utrS3Hjx/H3d1daNyCPLitmNxIJBIGDhzIvXv3WLNmDSNGjMDPz49ixYoxatQobty4wcOHD3FwcGDv3r0ZDjSTkpKEsauC3r178+nTJ7Zs2cLSpUvR19enaNGiShnafv/+PU2SRCqV4ubmxsOHD1FTU8PS0lIEy36c5H7//l3ob5qZmREUFMSXL1/SBK4V7Nq1i6CgIPLly4elpSW4uQMQGhICWVTbxcTEYGFhQVxcHPfu3WPVqlWMHTuWokWLEhISQkhICO/fvycoKIjIyMh0x1y7dm2mTZuGnp4eSUlJSCQSEhMTCQkJYfXq1aIF2czMjJo1a4qqq5SUFGQyGXfv3mX37t0ArF27VgR4v337xs2bN7l06RI+Pj74+fkRGBhIYGAgwD8miaDQodXW1iYhIYFPnz6hp6eXbrn4+PhMtYSrVKnCsWPHGDBgAO/fv2fq1KlMnz6dUqVKYWtry7t37wgKCmLZsmXMnDkTdXV1goODhbZuVsTExKQ7nzMiLi5OqetST09PJEvCwsI4ceIEz549Q1dXF319ffT19SmSLx8KoYmAV69Q19NDR0cHiURCZGQkkZGRREREEBUVle6+oaGhQdmyZenQoQPx8fGsW7eODx8+AHLt8nHjxtGnTx/U1dUzDPomJyeLdcbExODp6cnMmTP59OkTFStW5MCBA5QpUwapVJruHpMZymhVK3v/k8lkf1sw5p9E27ZtuXv3LpaWlly5cgV7e3uuX7+Os7NzmusnJSWF4cOH8/z5cwwMDHB3d8+0elhRhVy3bl0ePXpE48aNqVWrFo8fP2b06NHs3r1bBEJq1KjB9u3bcXV15dixY3z48IG1a9dy/PhxBg8eTL169ZCkqhD9/v17lsEYBbGxsSQmJvLu3TtcXFxEAmPBggX07t0bc3Nz1NXViYuLU+r6jY+P5+vXr5n+/fnz59y5cweJRMLcuXOJj4/PMiAVGxubrnr7+fPnQtJo/vz5FCpUiPj4eKWeb4mJiRneN7y9vUU3wvjx44U+e3Yo7lU9e/Zk165dWFhYIJVKadKkCcuXLxd/z8n1pgzKjkuURZnt5oZU4u8mJ5+fssupq6tja2tLvXr1sLCw4PXr17Rv355jx45x5MgRxo0bh5ubG7NmzSIgIEA880BueKxIlllZWdG2bdssz3d1dXX2799P27ZtefXqFaNHj2b9+vWZSvLExMQQGRmZ7vXatWtz69YtLl26lKH8UWZUrVqVQoUKER4eLjT+BwwYQKVKldJIIX769EkpE/jExESlth8bG5tpIFWSkIDC5v72rVtoaGiwaNEitm7dSlBQEAYGBly/fj1N10FuJlh/lgkTJpAnTx5cXV3TVO+npKQQHBz8N+5Z9shkskyvJUVHQnx8PN++fVPqPhkfH0/evHkZPXo03bp1Y968edy8eZMJEyYQHx9Pz5492b17N+bm5nz48IHZs2czevToXOkkCQ8Pz3YZZc9nhZxnViQmJuLu7k5AQABaWlrMnz+f5s2b8/79e+bNm8fjx48B6NixI46OjtSpUyfdPUGW6veE+HiSk5PTjOsU301KSkqacWFCQkKm47+LFy8SFBRE/vz56d69u9L3fNX4T4UKFSpUqMgdVImEfzjKDnqUnYRramoqvU7Fcl++fBGSRHv37mXgwIFiGS0trXQD7yVLluDl5cWTJ08YOXIkx48fp3z58uzZs4dmzZrh5+fHzp07mTlzZrptFitWTAyAIyMjefz4MTExMaxduxZ1dXU2bdokKqczq4RLTenSpYVp5aNHjxgzZgwPHz5ES0uLXbt20alTJ0CuSfvjgHXfvn18+/YNQ0ND2rZti5qaGs2aNUs3GTh+/Dje3t5IJBJcXV1pYWTEt/9PJGhqaSHJIhDatm1bSpUqRffu3bGwsODSpUssW7Ys0+Xz5MlD9erVqVGjBg0aNMDc3DzNfr9584aZM2eyY8cOEYgyNTXFxcUlTQBLYR6tkKCytLSkSZMm4u8FCxakc+fOdO7cGYlEIqqMr1y5QmhoKAsWLMhRlevPIJFIsj1XFUHNfPnykZCQQGJiYoaBztTnVUYUKVIEb29vOnToQEBAAIsXL+bAgQPUrVuXatWq0bp1a54/f869e/eYPn0679+/VzrwX61atWyXs7S0VCopExsbi76+PqtWreLQoUMigJSYmCha6HUlEqgi74q5dfMmcVlMsEqVKkWzZs1o3LgxTZo0oU6dOjx8+JAZM2YI47sCBQowYcIExowZk+3k9OPHj5w8eZLjx49z8eJFkehp2LAhZ86cEUEYqVT6t0zo1NXV/3UTSQ0NjQyDWmXKlOHixYvMnz+fefPmsWfPHh48eMD+/fuFPM+sWbM4f/482traHDt2jGrVqmX5+RkaGrJx40bMzc3Ztm0bhw4dYtSoUbx58wZnZ2cOHjyY5rqZPn06U6ZMwd3dnZUrVxIcHMzixYtp3749C2bOEsu1bt2afEoE8JKTk/Hx8WH69OnEx8dTtGhR2rZty759+zh69CiRkZHs2LGDAgUKiABSVkRERGD4g6a5gm/fvgnvmUGDBillgJmYmEj+/PnF71KplIkTJ5KUlESnTp0YOnQoEokEqVSqVAJNXV09w0DQokWLALCxsaFChQrIZDKl7ldaWlri+zUzM6NgwYKcPHmSGTNmpEugK3MdKTsu0dDQ+Mdfl8o8j3I6tsoOZT8/NTU1pdaZL18+sU4TExP8/Pzo1KkTfn5+dOnShZMnT7Jjxw4qVarErFmzcHd3JyIiQshTjh07lpiYGJo2bcrKlStJSUnJ9jwtVqwY586do2vXrrx48YJp06Zx4cKFDJMJ79+/z/B8NjU15datW/j7+1OyZEk2bNjA06dPWb9+Perq6syfP58yZcqIoG1cXBydOnXCwsKCOnXqMH/+fL58+YKhoSGzZs1Kd+2/efMm265ckI+bSpYsme1yOjo6mX4usrg4Uvc2Ojg4UK5cOZYsWQLAnDlzqFChQrr3/R3Xx48V8BMmTEgnaxcdHZ3mnpYbKHusyhpNZxUsVxyjImitzLYrV64sjrlatWpUqVKF4cOHc+nSJaZNm0b+/Pmxt7fnxIkT2NjYEBAQwNq1a3F1daV+/frp1ufp6SnGRu/fv2f69Ok8efKE4cOHY2dnJ/YpJCREqWSEvr6+Usvp6upmOY6PiYlh3LhxBAQEoKenx7Fjx2jTpg3Hjx/HxsaG6Oho9PX12bBhAwMGDMi0ICZ1mlNHVxeZllaaMYni+NTU1NK8rq+vn2lCTlEAZWlpSdGiRZX63pS5h6tQoUKFChUqlEOVSFCRLSdPnhRB6YsXL/L582cKFSqU6fI6Ojrs3r2bRo0acerUKbZu3crgwYMxNDRk9erVWFtb4+zsjImJCS1atCA2Npbnz5/z5MkTnj59Kv6vqIQGGDZsGGvXriUiIoIjR44wffp01q5dq5SM0Pfv31m0aBEbNmwgOTkZfX193Nzcsgz8SKVS3N3lyYAhQ4ZkPDhOScHX11dIL40ZM4aWLVsiy8RgLSu0tbXZuXMnAwcO5Pr166irq1OxYkWqV6/OX3/9ReXKlTEyMqJcuXIZ7sv79+9xcXHBzc1NfFcmJibMmDGDFi1aZLjNAwcOcOPGDfT09ETgKTPy5ctHhw4d/rG69nnz5iUyMjLH0kapKVWqFMePH8fc3JwHDx7Qs2dP9uzZg7GxMStXrmT48OGsXLkyTcLlv8m3b9/YsWMHJ0+eFAH6Dh06MHLkSNTU1ESnwZewMDh7DoAmTZsSEhnJx48fSUhIoFatWjRs2BAjIyMaNmxIkSJFiIyM5M6dO7i5uXH79m0ePXoEyM/JkSNHMnnyZPT19TOdjAcFBXHixAlOnDiBj49PmurYypUr07dvX6ZOnZquElvF34+6ujpOTk40b94cCwsLnj59SuPGjVm7di26urrivuDq6kqjRo2UWuegQYM4c+YMe/fuZcKECezcuZNu3bpx4sQJWrdujaura5r7tpaWFra2tvTr14/Vq1ezdetWPD098b1+HZ8yZZU+lq9fvzJ//nyRAGvbti2bNm2iWLFidOjQAQcHB7y9vWnRogUzZszIMKCTE1asWEF4eDhly5YV0n05xcXFhXv37lGgQAHWrl2bK0GOK1euiG6EyZMn/9K62rVrR7t27X55n1T8MylatCheXl707NmTy5cv07FjR/bv38+4ceMoX768kBxr3749hoaGPH36lGLFiuHu7o6mpqZ4DmVHkSJFOHXqFO3ateP169d069YNDw+PLMeRqVE8c2/fvi2q8xs3bszNmze5d+8eW7duZerUqaxZs4a4uDiqVKmCmZkZIJfWU3hXzZs3T6kEorI8ffqUY8eOYW1t/VPV5gUKFGD27NmsXLmSiIgIKleuzNChQ3Nt/1RkT+qOhB/5+vUr9+/fx9TUNMN7c0JCAsuWLWPhwoXEx8djaGjI27dvGTlyJLGxsXTv3l3Ig96/fx8rKyvWrl2bpTxX6dKlcXNzIyoqSqnk1u8iKiqK0aNH8+zZM/LmzcuZM2cwNjbG3d0dW1tbUlJSaNasGe7u7jmSZ8oNQkNDRXGbSm5PxT8Bf3//X/q7ChUqVPyJ/HQiwcLCAolEQt26dalTpw5169alcOHCublvKv4hHD58WPxfKpVy8uRJrKyssnxP7dq1WbBgAZMnT2b8+PE0bdqUihUrYmZmhpeXF7t27cLc3JyCBQvy+vXrTNtSy5cvT1BQEFu2bBET2E+fPnHlyhUmTZrEhg0bsqzk9vHxYdCgQaL1unfv3ixZsiTT1noFFy5cIDQ0lIIFC9KjRw/xemhoKFeuXOH69etcv35dyGM0bdqU8ePHZ7nO7NDT0+Pw4cO8efOGcuXKpamsT0hIQF9fP917fiaBAHIpHUUCZOrUqUpV2f2TUVTLppYr+BkKFy7MsWPHGDx4MD4+PvTv359t27bRu3dvbty4wc6dO7G3t8fe3j5HEgu/QkJCAkeOHGHXrl0iUdKoUSOmTJmSofGeLD6euP9PJBw8cABJqi6CmJgYYYy8d+9e/Pz8+Pz5c5r3SyQSBg8ezOzZsylbVh7IVUgRhYeH8+HDB0JDQ3n06BEnTpzgwYMHad5fr149evXqRa9evahevbqqAuwPoHXr1ty7dw9LS0suXryIjY2NSFhOmDABCwuLHK1v7dq13Lx5k7dv37Jz507c3NywtbXFx8eHBg0aMGvWrHT3y/z58zN79mz69+/PtGnTeHj7tvjbk6dPaWpqmun27t69y6xZs4iIiBAeD4oEG8jv+9WrV2fw4MG8fPmSsWPHMmbMGPr27ftT5+edO3fEc9HJySlLs9jM8PT0FD4Gy5Yto0SJEjleR0bMnz8fkHcj/O6uMRV/Pvr6+pw9e5aBAwdy/Phx+vTpw4YNGxg8eDClS5fGzMyMR48e8ejRI9TV1XFzc/upc7V06dKcOXOGtm3b8vTpU3r06MGhQ4eyHYuBXAotX758fPv2TQRkJBIJVlZWPH/+XHRifvz4kfz58zNq1Cg0NDSQyWTMnj0bqVRK27Ztad26dY73OzOio6Pp2bMnISEhbNiwgaVLl2Jubp7t/SQqOlr839HREalUiouLCyC/drOTmlGRu2TlkTB06FC8vLzYvHmzSEwpuHDhAg4ODmlMxN++fUv58uUJDAxk4sSJvHv3DgcHB3bu3Mno0aPx9vbGzs6OGjVq0LhxYxo1apShX5REIvlbkwifPn3CwcGBV69ekT9/fjZs2ICxsTEbN25k9OjRgPyz2bBhw2/3QsiIHTt2kJycTPPmzZXqTFeh4ndRpEgR9PT0GDx4cLbL6unpUaRIkf/CXqlQoULFf4efHgGsWbOGgIAAXr16xbVr1zAzM6No0aJUqlSJOnXq4Ojo+F8LtKn4fXz58oVLly4BMHDgQPbt28f+/fuxtLTMtu1+/PjxnD17litXrmBjY8PFixfR0NDAxcWFW7duERAQIAxsixQpIiR76tSpQ82aNcXk0dXVFQcHBxYuXEixYsU4evQojRs3JiAggPHjx7Nhw4Z0D+cvX76wYsUKrly5AkDZsmVZvny5UhX1b9++ZfHixYC8uvbevXt4enpy48aNdFqwefPmxdTUlPnz5yvdZp0VmpqaSnVZfP78mXnz5qVJILRo0YJJkyYpVUG6atUqgoODKVOmTLpW9T8RRSLhVzoSFOTLl4/9+/dja2uLh4cHlpaW7N+/nwULFnD37l2ePHnC3r17GTVqVK5855khk8k4d+4c27Zt4+PHj4A8sTZ79mxatWqlVAD01u3b+Ny9y+vXr/H39+fZs2fp9LS1tbVp0KABjRs3pmbNmnz8+JGYmBgWL15MWFiY+Pn48WOGCT81NTWMjY3p0aMH3bp1o3LlyrnzAaj4r1K8eHHOnTvHkiVLmDNnDikpKXTo0EHcC3OCouurVatW7Nu3j44dOwp/HA8PD2bOnMmhQ4dYtmwZNWvWTPPeqlWrcuTIEY7u3QtbtwEwedIk2nXrhqOjY5qEanJyMps2bWLnzp3IZDLKlSvH7t27hZxdaqpVq8bly5cZNWoUx44dY8WKFTx58oQpU6Zk6KuSGbGxscyZMweQy/8YGRnlyKgT5FJ61tbWyGQyrK2tGTRoUI7enxm52Y2g4t+Djo4Ohw4dEibLI0aM4MuXL4wePZorV67Qp08fnj9/zoIFC7IsUMiOChUqcPr0aTp06ICfnx916tRhxowZjB49OsvnmYaGBkZGRnh5eXHz5k1RPFKwYEEGDhwonpFqamo4ODhQsGBBAPz8/MT14OTk9NP7nRGzZs0iJCQENTU1vn79yvDhwzl69CibNm3KMri6bt06FP1L5ubmLFq0iJiYGBo0aEDfvn1zdR9VZI8ikfBjd82DBw/w8vIC5N1nikRCZGQkkydP5ujRowAYGBiwYsUKihcvTvfu3QkMDKRcuXIEBQWJDpmJEyeyceNGZs2axdGjR3n8+DGPHz9m69atqKmpUblyZZo0aUKXLl3+9vFTeHg49vb2vHv3jsKFC7NhwwYqV66Mi4sLU6dOBeRm0/fu3aNdu3aMHj2aXr16/dcKRpKTk4URvKobQcXfTdmyZfH398/Q2+dHihQpIoqzVKhQoeJ/gZ9OJBQsWJBGjRrRqFEj7O3tWblyJYMHD8bZ2ZkbN24obaSp4p/Npk2bSEpKombNmsyYMYN9+/Zx6dIlqlevjo2NDUOGDMm0Ok0ikdChQweuXLmCr68vV65coW3btuTNm5fTp09z/vx5DA0NqVGjBsWLF0cikZCSkpJOh93Ozo7g4GCWLFnCxIkTGTp0KMuWLcPBwYGQkBCWL1+eLtjl6uoqkghdu3Zly5Yt6fReUyOTybhz5w67du3Cy8uLlJQUqlSpQrFixbCxsRHLqaur06BBA5o3b07Lli2pU6fO31JBtmDBAtGuD/JgbkxMDDt37uTmzZtUrFhR/BQpUiTdIN/V1RWQa2krY8r2T0Ymk4lOBGWlFrJDV1eXnTt3UqJECZKTk1m0aBEXLlxgw4YNtGzZkjdv3vD8+XNq1KiRK9v7EalUyrJlyzh79iwg15m2tbWlefPmQr9eGcz69UvnkVC6dGkhb1SvXj1atGgh9Jzt7e3FJC0j1NXVMTAwwMDAgLJly9KxY0e6du0qksZ/goGpisxRU1Nj+vTptGrVSgTdfzZZ1rRpU3r16sXhw4eZP38+/v7+nDp1ij179jB+/HgePnzIhAkT8PDwSPdeiURC7969ifr/RALIJfZevXqFq6uruGft27ePHTt2AHKD4HHjxmWYRFCQL18+cV27urri6enJkydPciR15OXlxfv37wF5Yk9Zo9vUuLi4CBPnz58/ExERkSuFF7t27QLkutGqbgQVOUFDQ0N0Evn7+zN9+nTMzMwoV64cPj4+BAYGZljkEBcXR2hoKKGhoYSFhSGTyejbt2+m943q1atz7tw57O3t8fPzY+rUqZQtW5aePXtmuX8KrxJ/f/80XagmJibcunWLp0+fYmZmlqZC+fTp0wAMHz48U6+TnyE6OlqMv0aPHs3x48cJCgri/PnzODk5iU6jH3n9+jU7d+5kRHn5vnz+/JmNGzcC8rFYbhqOq1AORRI5KSmJT58+iaKkc+fOiWVevHiBVCpFQ0MDJycnkUSwtrZm5cqVwi/B09OTli1bEhQURIcOHfDw8GDLli1069aNatWqsWTJEsaOHcudO3e4ffs2Pj4+hIWF8eLFC168eMGxY8eENN/fxapVq3j37h0GBgZs3LiRMmXK8PHjR5FEsLGxYdu2/zyXr127hqen5y91+xgbG5MnTx6lJJIUBV2FChWid+/eP71NFSpyi7Jly6oSBCpUqPhXkis9ia9evaJnz56oqakxbdo0qlevnqMKPxX/TJ48eSJkEiZOnEjVqlVZvHgxixcv5vXr10yfPp05c+bQs2dPhg8fTqtWrcREKDQ0FDs7O6Fj2aBBgzQa26VLl85RNYnCSLZOnTro6OhQqFAhFi9ejJWVFdevX+fly5dUqVJFLN+uXTuuXbtGdHQ0586dY/ny5UyePDmdBEV8fDxHjhxh1apVvHjxQrzevHlzhg0bhr29PQAdO3ake/fuwljt76Z///4EBAQQEBDA+/fvSUlJ4f79+9y/fz/dsuXKlePgwYPUrl1bvFawYEE+fvwoKvf+ZI4dO8bTp0/R09OjZcuWubLO6OhoJk2aJH5XdHkoJlB58uT5rQPH5cuXc/bsWdTU1LC1tcXMzAxtbW1iY2NzvC4NDQ2mTZtG5cqVqVevXprE34+mrp07dxaJhKZNm2JhYUGJEiUoVqwYZcqUoWjRor+1C0PFP4OmTZvStGnTX1rHpk2bhPzPgAEDAHmCoHXr1uIcatasmVLrWubiwox583j27BnTp0/HxcWF79+/i3N1/PjxDBo0SKmgvkQiwczMjFq1ajF37lxCQ0NxcHCgb9++jB49Olvz2CZNmlC5cmUCAgJYunQpFy5cYPLkyTnSiR44cCC+vr74+/tz4sQJPn78yJkzZ35Zv71YsWIAqkIOFTnGy8sLOzs7Xr16BUDfvn3F+RQREYGPjw8HDx4kLCxMJA5CQ0NFQiw1L168YPbs2Zluq2bNmly9ehV7e3t27drFhQsXskwkhIeHs3//fiD9PUMikTBu3DjCwsLSXYOKDsVWrVpld/g5Il++fHTp0oXTp0+zevVq8bqhoaG41/2ITCZj+vTpJCUmite2bd9OYmIizZs3V3mR/E0ULFiQ+vXrc+/ePdzc3ESHbt26dcUypUqVEs+svn37cuTIEeLi4rhx4wbh4eEikXDr1i2Sk5PR0NAQxS1ly5alXLlyYl0lSpSgQYMGPH/+nO/fv4vXdXV1sbS0/N2Hmy0Kqcq5c+eKZHShQoWoVasWjx8/Jjw8nPbt2+Pp6QlAmzZtftlvSCGzqgzPnj0D5GOUn5EUVKFChQoVKlTkDrlS/jJo0CDmz59PZGQk379/JzIyMo1Rroo/D6lUiq2tLUlJSXTt2lVILyh0Pzdv3kyjRo1ISkri0KFDtGvXjqpVq+Ls7MyWLVuoWbMmZ86cQUtLi4ULF+Ll5ZWhxr8yvHz5ks2bNwOwZMkSUV1vaGgoDJMVVakKGjRowK5duzA1NSU5ORkXFxeaN2/O7f/X3Q4PD2fhwoXUqFEDe3t7Xrx4gY6ODv379+fUqVOsXbuWxYsXExsbS6NGjXBxcaFVq1ZZdjX8N2ncuDGnTp3i+fPnREREcOfOHfbt28fcuXOxsbHB1NRUVO0FBQVhb28vTAoBoct6586dv2X/c4v4+HhmzZoFwLhx43JFZ/zWrVuYmJhw5MgR1NXVmTJlChMnTuTIkSO4ubkhkUgYOHDgbzMP9vf3F5WUCxcuxMLC4pcCjFKplFGjRtG5c+dsP59u3boxffp0AO7du0etWrXo3LkzdevWxcDAQJVEUKEUmzZtElrK48ePF0HF5ORkrKysiIyMpE6dOmmSdVlRo3p1li9fjpaWFteuXRO+MNHR0VSoUIH+/fvneB/r1q3L7t27hQfO4cOH0wQFM6NIkSIcOHCAiRMnoqury/379xk8eDBz585NExjKipYtW+Lr64uXlxf58+fn5s2bODo6/nJHj8LE8++ualXx5xAZGYmVlRVt2rTh1atXlCxZkr1797Jjxw7evn3LqFGjqFWrFmPGjMHZ2Rl3d3cuXrzIs2fPRBJBV1eXihUrYmRkBMg7bh49epTldtXU1OjatSsgf+ZmxaJFi/j+/Tv16tXLsApZW1s7w0SeYsyT0+fWqVOnsLKy4vHjxxn+XSKRsGvXLrp06QLIg6mHDx/m0aNHmRYznD17lsuXL6OZKlG5Z88eIGeBVBW5z9ixYwHYunWrkArt2LEjxsbGgNxHTDHvMDU15fz585QpU4YXL17QuHFjPD09uXbtmpCT69ChAzdu3EBHR4d169aJDrrAwEDs7e1p27Yt27ZtIzo6mjJlyjBixAg8PDzSdD8rw4ULF7C1tSUsLCxXPofIyEgiIiJQU1NL0/mqoaGBm5sbWlpanD59mj59+vD69WtCQ0Px8PD4r/o5vH79GoBKlSr917apQoUKFSpUqEhPriQSrK2tsbKyonbt2pQsWRJjY+N/RNW2ip/HxcUFPz8/ChQowPr169NI4+TJkwdra2t8fHzw9fXF3t4efX19Xr9+zbRp07Czs+Pr1680bNgQPz8/pk2b9kuGXDNnziQ5OZnOnTtj+oPh5pAhQ1BTUxNdCakpVKgQ8+fPZ/fu3RQrVoyXL1/Srl079PX1qV69Os7OzkRERFCqVCnGjBnD5cuXcXJyolKlSmzYsIHnz59TuHBhli1b9o8OoOrq6lK9enW6devG6NGjWbduHefOnSMgIIDXr1+TP39+7t27l6YdWWHS+6cnEjZu3EhgYCAlSpRg7NixHDt2jG7durFly5Yc+yUkJSXh7OxM9+7dCQ4Opnz58pw5c4ZJkybx5s0bYQ47fvx4pXwsfgaZTMaaNWsA+US2efPmubJeRUePMsyaNYvu3buTkJCAmZkZISEhubIPKv4dpDZkHD9+fJrk7/Lly7l8+TJ6enrs2bMnRwmyunXrMm/ePCQSCYcOHRIyPqNHj/7p+3OePHmYOnWqkMY7evQoFy9ezPZ9mpqaDBkyhJMnT9KmTRuSk5NZt24dzZs3TyOJkRUSiYTGjRuzc+dO1NTUcHd3Z8OGDT91HAqMjY1RV1fn7du3BAUF/dK6VPxvIZPJ+Pz5M3fv3uXQoUM4OztjZ2fHX3/9JRLkDg4OPHv2jMqVK2NjY0PdunXZuXMnSUlJNG3aFFtbW2bNmsXGjRs5ceIEPj4+hISEEBkZyePHj7ly5Qo9evRAKpViZ2cngrI/cvXqVYYPHy6eo/7+/sIv60du3brFyZMnkUgkzJs3L0fyP4pEQk7GnwEBAcyePRs/Pz/s7OwyTSZoa2tz8OBBQkJCOHXqFJ07d870PhQbGysKHuyGDxevf4+JoWbNmnTu3Fnp/VOR+5iZmWFgYEBYWBgnTpwA5PfnvXv3cvz4cczNzdMsX6dOHXx9fWnatClfv36lU6dO9OjRg+TkZFq1aiUkKZ2cnKhWrRoAiYmJ2NnZcenSJVJSUjA2Nhbyer179/6pwpSdO3fi5+fHvn37fvETkPP8+XNALtn3o+Rp7dq1RYf6+PHjSUpKEh1L/00U5taqRIIKFSpUqFDx95Jrgpy2traEhoby8eNHjh079l8zXlKR+6SWNFq5ciUlS5bMdNm6deuyfv16QkJC2Lp1K40aNUJPT4+FCxdy48aNX9aQv3nzJkePHkVNTY1Fixal+3u5cuUy7UpQ0L17d+7cuZPG0DIpKYnGjRvj5ubG48ePsba2TlNVo6jonDJlyt8yWM4tSpYsKUwGZ8+eTXh4OJC2I+FP1bWPiIhg6dKlAMyZM4cPHz4wbNgwvLy8cHR0pHLlykycODFdgikjXr16RatWrVi5ciUpKSkMGDCAy5cv07BhQ+Li4rCxseH79+80a9ZM6Srqn8HLy4snT56go6ODnZ3dL69P7/87aDKSn8gMNTU1tm/fTo0aNQgLC8PMzIy4uLhf3hcV//ts3LiRkSNHAumTCLdu3RKdCatXr/6pZFzbtm1F9WhKSgr16tXLlWSbqakpQ4YMAWDx4sUEBwcr9T4DAwNWrVrFihUrKFOmDO/fv8fS0hILCwul19G+fXsWLlwIyCtflUlkZEa+fPlEktjb2/un16Pg8+fPnD59Osdm0r+CVCrl7t27nDlz5o99Nv3dfPr0iU2bNjF58mT69u1LgwYNKFy4MEWLFsXIyAgzMzOmTp3K5s2biYyMpGbNmvj4+GBjY4ONjQ2NGjXiwIEDwnD94sWLXLhwgVWrVjFlyhQsLCxo06YN1apVo2DBguIal0gkrFy5koIFC/Lw4UNWrVqVbt9kMhmOjo7s3r2bPXv2iPtARl0JSUlJ4p5hbm6ezpg9OxRSZ8omEqRSKTNnzhSSf9++fcsymSCRSJSSh1yzZg3v37+ndOnSjHRwSPO3yZMnq+ZLfzPa2tqMGCG3wN60aZN4PX/+/Jiammb4/RQvXpzLly9jZWVFSkoKX758oXLlyjx8+BCZTMbw4cPTdM/s3r2bt2/fUqhQIc6ePcuOHTto1arVTyfBk5KShAzZxYsXc+VeqZANUiQ/fmTcuHGYmJjw/ft3LC0tM00U/k5UHQkqVOQcf39/7t27l+XPu3fv/u7dVKFCxR9GjsrEY2Nj8ff3p3jx4mnMzhQkJib+oyu3/0SUNXJMSUlRqlIrJSUly0mLVCpl6NChJCUl0alTJ3r37p1lEDEqKkroVPbo0YMePXqIfUldBR0fH6+UtNHnz5+FRvXDhw+xtrYG5J4ARYsWFYHwT58+ie3269ePS5cucf36de7du5emGyYoKEgEdLp27UrVqlXR0tJCKpVSsWJFAM6fP49UKqVChQqA3LBXMVitWLFimkq5mJiYbI9DlirokpiQAFl83lFRUUqZHX/8+FHosGbF58+f001sW7VqxV9//YW/vz9jxoxh4cKF5M2bFy0tLT5//szr16/FZ5ERykxylTlPc2pKKpPJspwcOTk5ER0dTdWqValVqxaWlpbExcVRuXJl4uPjCQ4OZuPGjWzcuBEjIyPs7Oxo06ZNmnuUTCbj0KFDzJ49m9jYWPLkycPIkSNp0aKFCB6sX7+ep0+fkj9/foYNG8adO3e4d++eUsbOpUuXVsovJi4uDm1tbdavXw/Iz2ktLS2io6PTLBcZGZntPVaSkIBCwKhA/vx8iokhLCyM4sWLp1s2KiqKxFSazanZtGkT3bp14+7duwwbNow9e/Zkey4kJiYqNaFNTk7+W0zK/41kdx1B9s8FBVKpNNPzL7Wckb29PVOnThXPgOjoaMzNzUlOTqZXr1706NGDr1+/8vr168zPg4QEFHeyjx8/kvL/z4WuXbsSHh6Oh4cHw4cPT3ONxMXFKSWz8O3bt3TbHTp0KA8ePODhw4fMmDEDV1dX3r9/n2mldGoKFCjApk2b2L17NwcPHuT8+fN4eXnRuHFjjI2Nadq0KXnz5iUuLo5SpUqle3+HDh24efMmJ0+eZPDgwezevZu2bdtmu12pVJrue2vZsiV37tzh8uXLDBw4EJCfA8qMD5KSksRyMTExmJqa8vTpU0qWLMmkSZMYOnQoOjo6JCcnZ+sloVhfdvcrqVSKr68vN27cwNvbGx8fH/Gdzp07N53sy6/6SPxOcvKMU3a8pgwJCQnicw4ODqZDhw68ffs2w2WLFy9OmTJlhHZ75cqVMTAwYNasWVy6dEks16lTJ8aMGUOtWrWAjJPRjx8/zvD7HTZsGEuXLmXBggWUKlWKChUq0KRJE0BeqKLwo9q3bx9t2rThxYsX3Lx5k3z58qU55mPHjhEQEIC+vj7dunXD398fgLCwMKFBnxWKZ9uXL18IDQ3NdLmgoCAiIyM5fPgwz549I0+ePCxdupT169fz7NkzbG1tmTNnTpbjpNQkJCSIOVJ4eLgwVJ4zZw7aWlooRtQlS5bEzMxMqWfm/0qyISUlJdvzWtn7lUwmU3p8mt1ydnZ2LFq0iLt373Lr1i1RbJMRX79+FcnVxYsXU7VqVS5cuEBYWBifP3+mdu3aTJ8+HV9fX7S1tfny5YvoNB00aBBSqVRU/wM8evRIqXNAT09PzHvevn0rzu+wsDBu3LghEgDv3r1T6rkVGxubxmfA19cXgDJlyhARESFeT0pKolChQoBcZqxjx47cuXOHNm3asHbtWgoXLiyWy6jwKiWVZ49UKiX6+3elilOSk5PTPM+Tk5N58+YNIO+aSO0F9Ctd7z+Sk6TM/8p1qeJ/kyJFiqCnp8fgwYOzXVZPTw9/f3+VcbQKFSqURukn78OHD+nQoQPh4eFIJBKGDh3K5s2bkclkeHh4cODAAU6cOMGDBw9yZDqoIneQSCRKDWiyW87FxYW7d+9SoEAB1q5dm+3gLKN1ZTQB0NTUVCpgrqGhgUQi4cSJE4wbN474+HiqVKmSRqMUEJVwAPXq1cPT05PDhw+zb9++NB0xr1+/TjNgTZ0AS/26urq6+NuTJ0/EALZ+/fppthsbG5tth4IsLo6P////8oaGqGVx3B8/flQqMKKmpqZUki6joLC6ujqzZs3C3NyckydP0rNnT4yMjKhXrx63b9/G19f3j6vu8ff3x9XVFYAxY8Zw+PBhHjx4gK6uLs7OzhgYGODr68uhQ4eEBJevry+lS5fG0tJSBNimTZsm/AiaNGnCgAEDKF++vJjoXrhwAQ8PDyQSCY6OjhQsWJCUlBSCg4PJmzev2J/ExESioqIoUqRImvMlMjJSqZZ1hU56REQEJUuWZPbs2RleL0+fPk2z3QxJFSAtUKAAhISQlJSUYQIsPj4+0wm7oaEhrq6uDBo0iGPHjrFmzRphRJgZygakNTQ0cnXip5rM/RpqampKfYaZnSs/eiJMmTJF3K8SEhIYMWIE7969o2zZsqxYsUL8TVNTM02iLTk5mfDwcHkXXKptlSxVCsNUHQzLli1j2bJl6fbj48ePSgW4a9WqleZZkJCQQFhYGCdPnqRBgwYEBARw8OBBWrRoodTn8unTJ/T09Bg+fDjt27dn5cqVPHr0iGvXrnHt2jXU1dWpX78+9erVo1+/fhQpUiTN+yUSCTNmzCAwMJBHjx4xZswYfH19s02KZPRcMDU1xcXFRWwXlA+4Kc4DmUzGsGHDePr0KQChoaGMGzeOZcuWMXnyZIYMGaLU55zReEMqlfLgwQO8vb25evUq169fT5cwzZs3LzExMSxYsIBu3brluLNR2bHQPx1lZXwU31vqJELZsmXp0aMHhoaGGBoaUr58eXR0dMRzQCaTcePGDdasWcP169fFenr06MHQoUNp3Lhxttv9/v17mus3KSmJz58/Y2JiwuXLl/H19cXFxYVly5aJ59nx48fF8u/fvxcJ7tu3b9OtW7c06/fw8ADAxsYmzbirQoUKSs0zFN9x/vz5sywAyZs3L1FRURw4cAAAR0dHateuzcqVK5k8eTIPHz5k/vz5zJo1S6lOKl1dXRFcnTt3LnFxcTRu3FhexR0Tw9f/X04Zc3cVv44y86PixYszcOBA3Nzc2LZtG61bt8502dRFJBKJBGtra168eIGXlxcFChRg69at6OjooKurS548edi0aROxsbFUqVKFXr16pbuug4ODRaA+KzQ1NTE0NAT+Y4qs4P79+3Tq1AmQJxKUKdzS0dERybGIiAgePnwIQJ8+fdJcX58/fxb/L1WqFGvWrMHBwYEbN27QrVs3Nm7cSJ06dVBXV8/QADl14khHW5v4HxJFp06d4tKlS0ycODFdkWLqhH9ISAiJiYloaWlhaGiY5tn3J9zPVaj4b1O2bFn8/f2JjIzMcjl/f38GDx5MZGSkKpGgQoUKpVFa2mju3LlUr16dS5cusX//fq5fv868efP466+/6NKlCw8ePGDChAkZVr3+E9m5cyd//fUXBgYGDBw4UFQ6/Zt58uQJc+fOBeSBmpwY1758+RIXFxdatmyJoaEh06dPT1PRoiwpKSm4uLhgb29PfHw8bdq04dSpUxQtWjTL902bNg1dXV1u3bolNE5/FkV7b/Xq1f9nBqe1atWib9++gNzANykpSRgj/ok+CZMnTyY5OZmWLVtSrFgxoS0+atQoSpYsiZqaGo0bN8bFxYUjR44wYMAAChQowPv371m0aBENGzakVatWnD59Gg0NDaZOncqBAwdEgC85OZmdO3eKDoG+fftSr169DPclJSWFu3fv4ufnp5SMUkZ8+fKFdevWATBjxgylkm7KUOD/k21fvnzJdlmpVMrp06dxcXERgT1jY2NxT5g2bZrS+u8q/j38mERILWeUkJCAtbU1Fy5cQEdHhy1btmQa4JDJZIwfP54uXbrg7Oycxhz+d3L9+nXq1q1LpUqVOHv2LG5ubuK4bt68meP1lS9fnlWrVuHq6oqFhQXly5cnOTkZX19fNm/eTPv27bG2tmbXrl1p/Ee0tLRYvnw5xYsXJygoiMGDB6epuFSWZs2a/bJPwrJlyzh69Ciampp4enqydu1aSpcuTWhoKI6OjlSvXp3169crJXmkkCpasWIFPXr0oHjx4jRt2pQpU6Zw9uxZoqOjyZ8/v/jeb926xcePH+natStJSUnY2tr+LRIafxrBwcG0b9+et2/fYmhoyKVLl1i2bBkjR46kU6dO/PXXX+jq6pKSkoKHhwfdunXDzMyM69evo6GhwYABA7h69Srr1q2jcuXKOd7+69evcXBwwNraGi8vL0aPHo2enh7Pnz8XYzKZTMb+/fsBRLBC0f3p6+ubphAiJCSEd+/eoa6unmVQNyuUNVtOTk5m0aJFSKVSmjVrRocOHQB5hebSpUupU6cOMTExzJ07N1OZo4x48+YNW7duBWDBggVIJBJOphqfDrawyOkhqfiNjBo1CpB75SgrTZeQkMDq1avZuXMnEomE9evXpwnEvXjxQoybRo8enSOPj6xQzFMUgffz58//krzR6dOnSUlJoW7dutkm6Vq1asXx48cxNDQkNDSUfv36ies6JyQmJjJt2jSGDh3Knj176NmzJ+/fv890ecW9okKFCir1AxUqlKRs2bLUr18/y5+//vrr795NFSpU/IEoPaJ59uwZy5cvp3Xr1piZmXH69GnmzZtHyZIlefLkCQ8fPmTWrFm5FgD7nWzZsoUFCxawaNEiVqxYwfPnz6lfvz67d+/+u3ftb0MqlWJjY0NSUhJdu3ZlwIAB2b7n+fPnrFy5kpYtW2JsbIyzszP+/v7ExMSwZcsWjIyMWLp0qdKmt7GxsTg4OLBixQpA3mq8c+dOpSq6S5UqJSYB8+fPJzY2VqltZkTqREJuERkZybNnz3JN8zk6OporV65w+vRpDhw4wLZt21izZg0bNmxg9uzZTJgwgREjRmBpaYmtrS0hISGMHTuWggUL8vr1a3bv3k2jRo2A/7Qz/ylcuHCBM2fOoKGhgb29PQsXLiQhIYEGDRrQq1evdMuXLFmS4cOH4+vry4oVK6hVqxYJCQlERkZiaGjIiRMnGDVqlJiYfPv2jXnz5onKyV69emV5Pbx9+1YE3t++fcvHjx8zXTYzDhw4QFxcHA0bNqR79+45fn9mKOSwsvJI+PbtG1u2bKF58+aMGDGCVatWYW9vL4IwQ4YMYdCgQchkMgYPHpymJV/Fv5fY2FicnJyyTSJ4eHigo6PD3r17s5SLOHDggND1379/P1OmTv3t+z9+/HhatWolDBwdHR0pVqwYU/9/21u3biUsLCzH65ZIJFSpUoWhQ4eyY8cO3N3dGT58OJUqVUImk/HgwQNWrFhB165dGTx4sAj4FylShFWrVqGjo4OnpyfTpk3L8bZ/1SfBw8NDGMOuWrUKU1NT7Ozs8Pf3FwmFsLAwxo4dS5UqVdIlFBSJg+XLl9OrV69MEwddu3Zl6dKl3L59m+DgYI4ePYqjoyP16tVDXV2ddevWUbBgQe7duyfGBCoy5v3792mSCJ6enpQpUybNMlKplGPHjtG2bVuGDh3K/fv30dHRYciQIfj4+LB8+XIh75gTUlJSOHLkCOPGjRNBwE2bNiGRSLC1tQXA3d2dgIAAfH19efv2LXny5GHz5s2APABarFgxEhMTheQRwI0bNwC5yWu2XXiZoGwi4ejRo7x48YK8efMyadKkNAUkqZMJsbGxWXom/MjcuXORSqW0b98eExMTZDIZq1evFn/Po4TsoYr/HrVr18bExITk5OQ0XgkZkZyczIEDBzA2Nmbx4sWA/Pmh8GwD+bWxdu1aZDIZ7dq1U9rj48OHD9y5cydT2UlAdIvZ2tqip6dHSEgIjx49Umr9GaFI9vXo0UOp5atUqcKJEydo164diYmJTJ06lWnTpintpxMWFkbPnj1Foq1IkSIEBQVlmUxQGS2rUKFChQoV/xyUTiTEx8en0V6vVKkSBgYGrFix4pcNdf/bODk54erqSq9evRg0aBB37txh8ODBWFpa4u7urvR6EhISiI6OTvPzp7Js2TIhabRx48ZMK/GjoqJYvHgxRkZGNGjQgOXLl+Pv74+mpiZt27Zl7dq17N27l7p16/L9+3eWLVtGw4YN2bZtW5ZB9NDQUFq3bs3p06fR1NRk+fLlzJkzJ0dVJyNHjqRUqVKEhISISvKfITcTCZcuXcLMzIyKFStiZGREly5dhMbnzxAVFcWECRNo3Lgx9vb2TJo0CScnJ1xcXNi4cSNHjx7l2LFjeHp64uPjw/3797l9+zbz589HX1+f8ePHA3JTVEUl07179/7Wis+cXEfJyclMnDgRkH/fd+7cEZJGM2bMyLLaS1dXFzMzM86ePcuJEydYvHgxHh4e1KlTRywTHBzMxIkTefjwIdra2kycOJEhQ4Zkeh5++/ZNGN4pgvaPHj3KUSLr9evXIuDn5OSUq10wBf9fGiWjRMKXL19YsmQJRkZGzJ07l/fv31OoUCF0dHS4evWqMDeXSCTMnz8fY2NjoqOj6dOnj1IdDir+u/zu51FsbCwBAQFcvXqVTZs2UbNmTWESPG7cuDRJhOjo6HRJBBMTk0zX/fr1a2HM2qVLF7S1tbnh45Or+5+a+/fv06BBA9asWYNMJsPa2prOnTuTkJDAwIEDGT9+PM2bNyc+Pp41a9ZkGdBRhjJlyjBw4EAWL17M+fPnmTJlCkZGRqirq/P06VMcHR2F5nu1atWYN28egKh0zSmmpqYAXL16NUfve/XqFRYWFshkMmxsbBg2bJj4m7a2tkgorF69WnQojB07lpIlS1K1alW6detGsWLFaNKkCVOmTOHcuXPpEge3bt0iPDycY8eOMW7cOOrXr5/h/bVEiRIigTB//nwROPvd/GnjutRyRhklEWQyGVu2bKFGjRqMGzeOFy9ekC9fPkaNGsWtW7dYtGhRhp5nyhAVFcXMmTPZtm0bUqmUpk2bUrlyZb5//866deto3749devWJTExEVtbW/bt2wdA9+7dad++PYaGhnz//p1y5coB8q5YBYpuoKZNm/7sR6NUIuHRo0dC0mjs2LHpZMfgP8mE6tWrCwNmb2/vLMdNjx49ElXa8+fPB+TjwV8J9v5J/GnXkQJFYnzr1q0ZjuNiYmI4ceIErVq1YuzYsbx//x4DAwOWLVvGpEmT0ix75coVnj59io6ODsOHD1dq+35+fri5uXHp0iVOnTqV4bxJJpOJLvr69euLjp0zZ87k6FgVvHz5kmfPnqGpqUnnzp2Vfp++vj6urq4i+XbgwAFat26dZVcByK/tNm3a4Ovri76+Pu7u7ly8eJHy5ctnmUxI7V2nQoUKFSpUqPh7yVGPpZeXF2/fvhUDGy0tLaV0Hf9JyGQyPnz4kGZioampyZYtWxg+fDjDhg1TeqC/ePFi8ufPL35+rAD7U5DJZCxduhSQV6OULFky02Vnz57NggULRLC9ZMmSrF69mmfPnrFv3z4GDBhAu3bt8PT0ZPPmzWhra/Pp0ydmzJjBhQsXMl3vzJkzuXfvHgULFuTAgQNCwz4n6OrqMmfOHADWrl0rdH9zSmBgICCvxrx48eJPrUPBwAEDOHPmjDApvXr1KsbGxpw8efKn1rdq1SrOnj0rfi9RogRt2rShe/fuDBw4kP79+zNmzBimTp3K/PnzWbhwIZqamvj4+PDq1Su6d+9O/fr1iYuLY8CAAejp6REfH0/nzp1/WpbnR969e4eTkxNt2rShVatW4qdLly4ZLp+T6+jUqVM8fvyYggULMnv2bHbt2gWAg4NDludtaiQSCQ0aNMDCwiKdEfL69esJDw+nePHiODs707x58yzXFRgYiEwmQ1dXlwYNGqCuro5UKhXnkDIozrGaNWumSWrkBorkRkb6mNbW1uzYsYOYmBgqVaqEs7Mzt2/fFgFdV1dXIXulpaXFwYMHKVu2LC9fvhQyTCr+OeTm8+jBgwcMHz6cTp06UatWLQoVKkTevHmpUaMGbdu2ZfTo0QQHB1O2bFn27NmDs7MzEomEx48fM3LkSGrXrq10EgHkUoMJCQk0a9aM+fPns2XLlnSm8bnJqFGjRHVj8eLFKVu2rGjtfvXqFbt27aJ27dqA3Ij13r17ubbt4sWLM2DAADZv3szp06cpXrw4gYGBHDx4UCzTvn17ZsyYAcCUKVNy3Mmm0JNPHZhVhhUrVvD161c0NTVxdHTMcBltbW369+/P4sWLheRgdHQ0r1+/FokDBWpqanTq1EkE2QYPHpxp4iAjBg4ciImJCUlJSSxfvjxHx/Kz/GnjugULFvD27VtKlCiRYSeCp6cno0ePJigoiMKFCzNlyhRu377NtGnTspWMzA6FNxHIDdZnzpzJmDFjALnnQVBQEGPGjEFbWxtvb2/27NkDICqzFUbOii4+xRhEJpOJcXhWXUzKYmtry/79+4UvVkxMDIcOHWLgwIH069cvnaRRRujp6TFz5kwaNGjAt2/fcHBwwNjYmAkTJmSYqHdzc0Mmk9GzZ08hi6g4/n8Df9J1lJycjI+PD1OnThXdaF++fBFziJcvX7JmzRo6deqEgYEBI0eO5OXLlxQoUICZM2dy48YNLCws0hWyKO7pgwcPzjBB9SMRERF4enoKT4GXL19mGFCPiooS91l1dXWR0FJ08eSExMRE0VFhYmKS4+eumpoaDg4OuLm5UaBAAfz8/LLtpBs5cqSQvt28eTOdOnWiVKlSwvcsKCgoTecOyDuRFaoBPyO9pkKFChUqVKjIXZROJJQsWRIbGxsqVKiAvr4+TZs2JSIiguPHj+Pv759rki2/G4lEQtOmTTNslV+3bh116tQRE/jsmDZtGlFRUeJHWU3NfxoKszCQT37Gjh2bqT71gAEDqFKlivg9NDSU1atX4+bmxocPH8Tr9+7dY82aNcKUrEGDBkKTPyMUretmZmY0adLkp4+lW7dudOnShcTERKytrdNoUCvLyJEj0dfX5927d9ja2jJmzBg+ffr00/tUoEABfH19efDgAc2aNSM6OpqBAwcya9asHGtgd+vWjTx58ojfExMTGT16NM7OzsyePZthw4YxbNgwzM3N6dmzJ927dxfSULL/NzibP38+FSpU4OPHj8TGxqKmpsalS5eoVasW3bp1Y8eOHWnM1ZRBKpVy8uRJunXrRsWKFZk/fz5XrlzB29tb/GQ2ycnJdaQwRrawsKBQoULCqFphQPcrfPjwgffv36Ompoazs7NSZo4GBgZIJBLi4uLw9vYmOTkZNTU1DAwMlN5u48aNkUgkPHnyRHQB5BaKzyW1ZATIdeGvXr2KpqYmO3fuxMvLC3Nzc3R1ddNIm6WezBUrVkxUKL979y5X91PFr5Nbz6P79+9jamrK1q1b8fDw4OnTpyJQpqenR+XKlTExMWHhwoU8efKEXr16cfToUdq0aUP9+vXZsmULsbGxVK1alYMHD2abRID/mMQbGxsjkUioVasWjZQwe/1ZbG1thYxLeHg4c+fOFYFqAwMDFi1aJHxXKlSokKsyd6kxMDCgffv2AOkqdi3+Xz89Pj4+R11KL1++xMnJCSCdeW12DBgwAH19fZKSkjA2Nmbv3r2A/Nnx8OFDli5dSps2bShdujQWFhbpfJB+lKBJSUnh3LlzDB8+HBMTE0qWLEmpUqWYOXOmUpJRX79+FQFlY2PjHB3Lz/KnjesU11dkZGSGVd9GRkbiXM+TJw99+vQRCeZfxcjICA0NDUDutZSQkMDbt28BebVyiRIlMDAwYMSIEQDifJk3bx4WFhaioEIh7aWQhZFIJCLw+itjL3Nzc/LkycPr16+ZM2cOJiYmjBgxAmNjY1G8oqamRsOGDZk6dWq215muri4bNmygb9++FCpUiLi4ODw9PdMFPQFRZJXaTPlnJZr+RP7p11F8fDxnzpxh2LBhlCpVihYtWrBy5UpevXqFtrY2Xbt25dq1a1SvXp2aNWsyceJELl26RGJiIuXKlWPcuHHcvn2bUaNGpStIAfn9XJEE6Nq1q1L7pDAWVlNTEwH91EbFCvLnzy8SbD179sTDwwOJRMKgQYNy9BmkpKQwdepUfHx80NPTE/KwP0PLli1p0aIFQLZmrUOHDhX3DQsLC+bNm8fZs2cxNzcH5Pepnj17AvK5xezZs+nSpQuRkZHUrl37pwrNVKhQoUKFChW5i4ayC964cYPPnz/j5+eHn58f9+7d4+PHj4wbNw6QTxoaNmzIwYMHKVy48G/b4dxg7ty5tGvXDhcXFyGTAqChocGsWbOEKW12aGtri0Dmn87y5cspUaIEU6dOZd26dbx69Qo3N7d0E5+mTZty7949bt++jZubG4cPH+bNmzfCc6Jt27YUKVKEffv2IZPJyJ8/P7NmzWLQoEFZVroo5LEUVaI/i0QiYd26dURGRnL79m02bNjAjBkzcnROWllZYWZmxurVq9myZQsnT57k2rVrzJkzh3bt2uV4n7S0tEQg6uzZs8ycOZN169axYsUKbt68iaurq1LVSiBvY7579y7v3r3D0dERf39/LC0t2bRpU4ZmwMnJyUKGRjGxLVu2LIcPH+bs2bMsWLCA+Ph41NTUSEpK4syZM5w5cwY7OztatWpFnz596NWrl6hw/ZGgoCC2bdvGtm3bCA0NFa+3atWKAQMGpAlYxMbGMnTo0HTrUPY6kslkeHh4AIj260qVKhESEsKrV6+ELrgybNu2jRMnTrBx40ZKlSoFwN27dwEoV64cBf5fEig7ihYtSp06dXj48CFSqRQtLS0aNGiQo0BN3bp1GTZsGJs3b2bjxo0UKFAABwcHpd+fFYrz7kdZEGdnZwD69etH27Zt071v9OjRHDp0iKtXr/LgwQPRKaEwyv1TZAr+TeTG8yggIICOHTsSHR2NRCJBTU0NDQ0NtLW10dXVFeuPjIzk8OHDnDp1infv3olrX11dnR49emBhYUGLFi2UDoArkqPfv38H5Nf6XV9fKKzcfTGnWFtbM2vWLN68ecPFixe5cOECT548ISoqSiTEK1WqRKdOnWjcuHGuGWRmhCJQ9OM2FF1Eyj4bAOLi4hg4cCAxMTGYmJiI6lpladmyJXfv3sXKyoobN25gZWXFtm3beP36dZr7O8glmDp06EDHjh1p0aIFOjo6Yh/evHnD69evefHiBa9fv+b169e8evWK4OBgIiIicHZ2ZuXKlZibmzN+/PhMtfmXLFnCly9fqF69OkOGDMnRsfwsf9q4buDAgezbt4+zZ88ybNgwkSBWUKhQITw9PWnfvj1v3ryhX79+HDp0SDz3foXatWvj5OTE/Pnz8fPzY9asWaIIoW/fvuKc6NChA1KplPXr16Ourk5CQoKozpdIJMhkMiGDqaBWrVqEh4fz5MmTn+5K6N27N2PGjOHIkSPs2rWL4OBgLl++DMgThL169aJHjx58/vxZ6UpsPT095syZw6xZs7hy5Qpjx47l+PHjDB06NE3VvUKS6fbt2+K1f5Oh5T/hOoqNjSUoKEgYzwcGBop/nz59Kp43IC/46dy5M82aNePJkyfs2rVLFK5oamrSsmVLOnbsSMeOHSlYsKAIhGeGohusePHiSo8H9fX1UVdXTzNuzyj5JJFIWL58OV26dCEmJgZ9fX1Wr15Ny5YtldoOyJ+xu3btwsPDAw0NDdauXftLCfOkpCQhpZdZ97GC0aNH07VrV2bOnImXlxdr164Vf6tRowbbtm2jYsWKhIaG0rdvX65duwbA8OHDWb58ubivqFChIndRSKZlRZEiRbJNFqpQoeLfgdKJBJBPSNq1a5cmmPrlyxfu3bsnkgt/p9Z6Rvj5+eHh4UH+/Pnp1asXJUuWpE2bNsyaNYvJkyejrq4ukiEgl4rJqLrkfx2JRMKkSZMwNDRkyJAhnD9/nnbt2nH48OF0E06JREKTJk1o0qQJM2bM4Pz58+zZs4c7d+7g6ekpljMzM8PJyYmiRYtm2uGgQJFI+LFy+mfQ1dXFzc2NHj168OLFC1auXMnUqVNzVA2mp6fHtGnT6Nq1K5MnT8bf3x9HR0datmzJunXrcjQJT11RpKmpibOzM40aNcLe3p6bN2/SoUMHXF1dczRZLlu2LDt37mTEiBHcv38fGxsb1q1bJ7SGFXz9+hWZTIZEIkkTHNfU1GT69On0798fBwcH8b0VKVIEfX193rx5w4ULF7hw4QIjR46kZcuW9O3bVyQVzpw5g6urK+fPnxfdSEWKFMHKygobGxuqVKmSrkspOjo6w0SCsjx+/JiQkBD09PTEhKlSpUpcvXpV+BQow9q1a0Ug/eTJk9jb2wMIGZ+cTvYNDAzQ0NDgw4cPVKhQ4afuH6amphQrVowFCxawePFiChQoIKqzfoWqVasikUiIiIgQkk3Xr1/n+vXraGlppdPtVUyeFy9eTK9evTh8+DBr164VhngFsvBcUPFnozBsVVQNy2QykpOTSU5OFnrXmVGsWDFsbW0ZNmwYpUuX5suXLzmqolfcm2NiYgB48+aNvBL5NyUSFFSoUIHu3btz+PDhNFJHs2bNwsbGhqNHj/6UZ0lQUBDv37+nSZMm2cr4ZKbjntNEQmhoqJBmLFasGLt27cqRx5CC8uXLc/HiRZydnVmwYIEI4ujp6WFqakrHjh1p165dpvISurq61KhRgxo1aghJPwWKCu7ly5dz8+ZNduzYwY4dO+jcuTMTJkwQHSkgPwcUXSGLFy/ONnD3b0UikbB27Vpu3LghjKmnTJmSZpnSpUvj6elJmzZtCAoKytVkQv369Vm4cCFz5swRCWt9ff10VdgrV67kyZMnXL16FR0dHapWrcrTp0+RSqXY29szYcKENJ4etWrV4uLFi0obG2dG3rx5GTJkCIMHD+by5cs8f/6cFi1aULt2bXGu5bQDE+SJv9atW2NsbIyPjw8bN25M01FoZGSEmpoaQUFBhISEUKpUqX9VIuHv4NatW6xZs4Y3b94QGBgoJLMyo3Tp0vTo0YOePXtSqFAhVqxYgaOjo+gUrl27NuPHj6dbt26isxdI14mVEYpEQk6MgdXU1ChUqFCa9afuQP5x3zdv3szJkycZPnx4urF/dhw7dkwU5ixZskR0E/wsd+/eJTo6miJFitBYiW7CypUrs3//fi5cuMCMGTMIDAzEysqK+fPno6Ojw+XLl7G3t+fTp0/kzZuXTZs20b9//1/aRxUqVGRMkSJF0NPTY/Dgwdkuq6enh7+/vyqZoEKFipwlEjKiYMGCtGnTRrQk/5NYtmwZS5cupWPHjty9e5cJEyYwf/58Jk2axNy5c0lOTmb8+PHcvHmTmTNnoqenx8SJE4XG67+Rvn37UqZMGXr06MGjR48wMTHh8OHD1K1bN8Pl8+TJw6BBgxg0aBCvXr1i7969vHv3Disrq0z15VNSUnj48CERERG0a9cOiUQiKmFCQkL49u1bmkH7z1CgQAH27t1LmzZtCAsLY+3atUyYMCFNm3lWbN68mVOnTjFt2jROnjzJ5s2bWb16Nd7e3jRt2pQ5c+ZgbW2tVKVqRvJFffr0oXr16vTv35/Xr1/Tp08fsU5lA1f6+vps3bqVMWPG4OPjw4gRI5gxYwZ9+vQRyyhkATKroKpYsSLnzp3jwIEDjBs3jvDwcCIjI+nbty/VqlXj3Llz+Pn5ceXKFa5cucLo0aMpUKBAGrPd1q1bM2zYMHr06IG2tjY+Pj44Ojri6emZq5Jn586dA+TdDoqKJIXpmrKJhNRJBEhbqe/r6wukNdmWyWTEx8ejq6ub5XqLFCmSo8rhjBgxYgRfv35l3bp1TJ06VRiU/gq6enpUrFiRV69e8fTpU+H9APKW8hIlSohl9+3bJ7RtJ02axJIlSzhy5AgeHh74+/tjYGAgKuuioqJ+ab9U/LOIjIykQ4cOQmIEYOnSpQwcOJDExEQSEhJISEggNjaWxMRE8VpiYiI6OjqYmpr+UvXpjx0Jqat4fydeXl5YWFgQHh6Onp4ekydPxtHR8ackSIKCgrhy5QpXr14V8i61atVixowZFC9ePNP3ZdeRkJ2OvUwmY/fu3YwfP56vX7+ipaWFu7t7mms7p2hoaDBjxgw6duzIpUuXqF+/Ps2bNxf33ewKAzJDV1eXHj160KNHD27cuMGKFSs4efIkZ8+e5ezZszRq1Ijx48fTvXt3Zs+eTWJiIm3atMlSu16FvABm+fLl2NjYsGDBArp27SqKMxSULl2a/fv3M3DgQAIDA3M1mVC9enWcnZ2ZOXMmX79+pV+/fumqhjU1NTl06BBGRkYEBQXx8OFDQN5duGbNmnTjHoWPwrNnz0hOTv6ppFhq1NXVadu2bYYdeL/CqFGj8PHx4fTp09jY2IgxSb58+ahZsyaPHj3i1q1b9OnTR5VI+I3cuHGD9u3bpzNIzpcvH4aGhpQvX55y5cpRvnx5ypcvT+XKlalevTrXr19n6dKlabzHWrVqxcSJE2nZsiWhoaE/NR9RJMByqudfuHBhkUhQV1fP8rnauHFjpYL2P3Lp0iUhWzd9+nS6d++e43X8iMLrq1OnTkpfqxKJhPbt29OqVSs+fPhAmTJlkEqlLFq0iFWrViGTyahduzb79+9X+SKoUPEbKVu2LP7+/hn66aXG39+fwYMHExkZqUokqFCh4tcTCf9Urly5gouLC/fu3aNMmTIkJCTQrl07Jk+ezJs3b9i4cSMLFiygWbNmTJ8+nTp16qCtrc24ceOEYe//IikpKdkGqo2MjLh06RJmZmY8f/6c9u3bs3PnTjp16pRu2dSB4kqVKjF79uwM1xkcHMyxY8fw8vLi8uXL4mG1a9cu+vXrR4ECBShZsiShoaHcvXs308SFgi9fvmQ7WM2XLx/W1tZs2rSJV69esXHjRmxsbDIM/vv5+Qmjr/fv3ws9V3NzcypWrEjRokWpXr06ERERhISEMHnyZPbs2cPMmTPTVwLFJ5A6DCWVSjNMJlSuXJlDhw4xc+ZMIXnk5+fH4sWL01W1KyqDf0RbW5u1a9cyZcoULly4wLx584iPjxe626n1ilNXOd27d0+0+MfFxXHr1i1KlSpFyZIluX//PocPHyZv3rwMGDCA7du3c/r0aY4fP46vry9fvnyhSJEiDB48GEtLSypVqkRwcDBHjx5l3bp13Lp1K8vvRVni4+PTJH4UE702bdoQHx8P/MdU9PXr13z+/DnTcyIpKYmVK1cKHXQTExOuXr3Ks2fPSExMJCYmRiQVrl27JroTXr16RVhYGBUqVEgTcFFW2kdTU5Njx45lu1zlypXR0NCgRYsWPH/+nIsXL+Lg4ICvry/t27dPUzmZXYBQkpiI/v//PyoqiipVqvDq1St8fX1JTEwU3QhDhw7lwoULxMfH8+TJE9zc3OTvl0i4ffs2mzdvpnbt2jx8+JDp06fj4+MjpI2+fv2aoXZvRq/9CjlJRP1M5bgK+PbtGw0bNkyjYV28eHEmT57MqlWrqF69OtWrV6dYsWIUKFCAZs2apVvHj0btsbGxSvmEfPjwAV1dXXHeREREEBYWlqYyGeTnmzLdjspelzNmzMDb21skLjQ0NNDV1WXjxo1s3LhRLNe2bVtMTU0zXU9YWBh3797lzp07aTyCNDQ0UFdX5/Hjx9ja2jJ+/HiaNWvG169f0/n2KPY5NjZW/E1LS4vw8HBA3gmaWeA+ODgYR0dHcW9s0KABW7dupXr16mmuRWWvox+Xq1+/PvXr10/3d5lMptQ6s1qmWbNmNGvWjBcvXrB8+XL27t3LnTt3GDBgAOXLlycwMBCJRMKiRYuU3t7/GsreT6VSKf379+fIkSOcPXsWW1tbLl++nEbiCORByn379jFgwACCgoLo27cv+/btS5dM+P79u1KJwdjYWHHfLVGiBMuWLSMgIAAjIyNhbAzy55bClNnZ2RkrKyvi4+OpWrUq06dPF9Xbb968EcnqfPnyoaenR2xsLH5+fiJAD/IxmjJdcXFxcUr5ZIWHhyt1vElJSWmSrQpq1arF48ePcXJywsrKigIFClC2bFmMjIx49OgRPj4+dOnS5f+NhwuI9yUkJvLniGj981CMaZ88eULv3r2JjY2lRYsWWFtbU6ZMGcqUKUP+/Pn5/PlzGnmhuLg4rly5gpWVFffu3QPk44c6derQvn17ypcvz6VLl7C2tubjx480b96cfv36iespf/782QbwFd4uKSkp2SbGP378KM7n1PMTbW1tMYYH+XPZ29s728/l/fv3mY4THzx4wKZNmwB50H/w4MHZerXFxcWJbsGMkMlkoqu5Q4cOwvNIQUqqZ3diUlK6TjUNDQ1Kly5NWFgYdnZ23Lx5EwBLS0vWrVuHjo5Olvf/lJQUpeUHf6dMoQoVfzJly5ZVJQdUqFCRI/5nEwnu7u706tVLaJZqa2tjY2PDhw8f2LRpE9WqVWPs2LF07tyZzp078+XLF/T09P52Tc8fye1Bj7KVItWrV8fHxwczMzMuXbpE//79WbJkCe3bt0dfXx99fX3y5ctHiRIlMtzHqKgoLl++zIULF7h48WI67wOFLu7JkyeFQViNGjUIDQ0lNDSUVq1aZbl/GhoaSmnaDhs2DGNjY8zNzXn06BF3795l0aJF6YKOo0ePzvD9MpmMV69eiYr3Fi1aMHjwYNauXcvDhw8xNzdn+PDhabsTfgj6JCcnZ1rRXqVKFY4ePcrq1auZPn06x44d49GjR/z1119oaWmhqamJpqYm6urq6OjoiN+1tLQwMDBg6NCh6OnpcfLkSUaMGIGbmxtLlixBQ0MDS0tLMXk3MDCgdOnSYrs3b97ky5cveHt7C5NExefavHlzAgICCA8PZ+vWrbx69YoVK1YwevRoQkJCCAwMxMjICC0tLeFbsHDhQrEtTU1NevfujbW1tfBlALlsSevWrbP9zhRIJBLxPUX9H3tnHRZV87fxe5dSREEQAxEsVMREHwMDExF97AQVRbE7sbAf7MBuBFQwCANRQEDAQMoCQUVESrpr2Z33D347Lyu1KCrqfK5rLmX3nDlzzp6ZM2fub2Rk0JcLfX19+rmuri5q166NvLw8yMnJlRtr+8iRI1RE2LZtG4yMjNC6dWt8+PABCgoKCA0NpYs2Zb30CeN8C+nfvz9WrlxZ6TmYm5uL/PYZGRlITU2FioqKyFjD5XKhra0NDoeDOXPmIDc3F48fP8b58+cREBCABQsWQFFREbKyspW7yv9PZAGAOrKy6NSpE1xcXPDu3TsapsTIyAgaGhq4desWEhIScPnyZRBC0KVLF9SvXx+enp64desWJkyYgBcvXiAwMBDv37+nv2dmZmaZHi6SkpLsRa2GUbIffU1+fj7Gjh0rIiI0bNiQLmILx2N3d3coKipCS0sLsrKy6NChQ6W/szhW/U2aNEHdunXpc1ogEKBx48Z0YVFIc3V1qKio0L+zs7MREBCAfv36lXqmfb14+jWJiYnUu0lIUVFRmUld379/L+LBBBTnkbhz5w7u3LkjEopPSkoKAwcOxJgxY2BgYID09HSYmJggKCgI27dvh6mpKYyMjErFyxaODwoKCnRBl8vlUq+vhg0bljonoRfCihUrqBfCli1bsHr16nJDAIkjtInrsScpKSlWfRISEpVup6WlhXPnzmHXrl04fvw4Tpw4gaioKACAsbHxN8fH/xVU99gnrjgqIyMDCQkJnD59Gh07dkRwcDCOHj1KPcyENG3aFGpqavDw8KA5E6ZNmwYrKyuRhVFZWdlK+xFQ/PwtL/RKSd6+fUv/37ZtWxw9ehTu7u6YPXu2yP5fL2T07NkTnp6eeP36NU2+ChQLaOLEnY+OjhbLmjw+Ph7NmzdHSkoKYmJiqFAhLy8PRUVFKCoqQklJCRkZGaU8PYDixdPXr18jJCQEnz59Qu3atSEpKQkdHR2cP38e/v7+tF+2bdcWSEsHIH4/+pPgcrnV2k/ev3+PyZMnIzMzEz169ICVlRU4HA5iYmIQGBiIz58/IyIiAklJSfj8+TNiYmJErG5lZGQwceJEzJs3D56enuBwOLC2thYxiPH19UVUVBRmzJiB+vXrg8PhVPj+kZeXR98ZWrVqValHq6KiIp27CgQChIaGAigW/jp16kS3+/Lli1jiYpMmTcr0CA8ICMC5c+dACMG4ceOwc+fOUgKdh4cHRo4cKZJXTklJqcLQcuHh4fj8+TOkpaUxfPjwUs8RQQmhQlpKCsr16pUaXx48eIAZM2YgKSkJcnJyOH36NCZPnizWvVLdBix/W59kMBgMBuNb+GOFBEIInj9/LuIS/fjxY8yfPx/h4eE0BrJwsUPcRGt/EwoKCrh79y4WLVqE8+fPY+3atVi7dq3INrKyslRYEJacnBwEBASIWFFyuVz06NGDupYLBAIMGjQIbm5uKCoqgqSkJDp06AA3NzeRl87qoFevXrC0tMT8+fNhY2ODxo0bY9myZd9UFyEEU6ZMQf/+/bFr1y48fvwYx44dg7S0NKZPn17mPpVZ+3A4HCxfvhzdunWDkZFRqUXrirhx4wacnZ0hLy+PM2fOgBACa2tr7Ny5E9nZ2dSzQbgATAiBr68vLl26JGJF3KhRI9SrVw/v3r2Dr68vmjdvjgEDBsDPzw9eXl7o3bs31q5di2XLlqFp06YQCAS4ffs29u3bRwUEGRkZTJ48GaampiKLfkLEXaQSkpubS/uuq6srioqKoKGhgRYtWtBtJCQk0KZNG7x48QKhoaFlCglHjx7Fvn37ABSLCGvXrgUhBA0aNEBycjJCQ0Px+PHjKrXtW8jMzER4eDgIIcjMzET79u3LXKzhcrlYunQp2rRpg8uXL+PFixdYtWoVTE1NRayDxUW48HHz5k0Axb+DMHxbUlIS7Ozs6LUdMWIECCF4+fIlUlJSEBoaijZt2iAiIgK7d+/Gjh07AIjm3mD8nhQVFcHQ0BAPHz6knzVs2BCJiYngcDgYPXo0BAIB3r59Sz1+fHx84OPjgwYNGkBXVxcDBgxA9+7dxVp0rIiSORJevXqF3NxcNCnnmczj8aCnp4fAwEAYGhri3LlzYgvkfn5+WLx48Te38/jx47CwsKB/S0lJoV+/fhg2bBgmT54skodGQUEBrq6u2LlzJywtLXH27Fl4eXlhz549ImNYVUMbxcXFYcGCBbh79y4AoHv37rhw4UKZC5y/E40aNcL27duxbt06XLx4EW/evMHOnTt/dbN+K5o0aYLDhw9j5syZ2L59OwYNGlSm5bQwZ4JQTNDV1cWECROwffv2csX46qR79+5iCUTTp0+Hp6cnrl27hrlz51IPxOogPT0dnz59QnR0NN68eYOUlBTq6VgR9erVo8KCkpISGjdujI4dO6JLly4IDg7G/fv30a5dOwCg1z44OBj5+fnFuSHatAGe+VfbefzNfP78GZMmTUJKSgo6duyIYcOGoXfv3pXmRgCK58SGhoYwMTGh99WpU6dw584dGrqxR48eaNu2LW7evImYmBgcPnwYhoaG6NatW4V1h4WFgc/no06dOmInWhZSMkTmt4TYK4+IiAgsXLgQBQUFGDhwILZt2yYyf/v06RMGDRqEmJgYyMrKwsTEBEuXLhUr94Iw10Lfvn2r3OaAgAAcP34cNjY2IISgc+fOsLe3h4aGxl/phcZgMBgMxu/CHyskzJ07F/369cOoUaNgYmICT09PPHz4EHv37kV+fj7Onz8PHx+fMsP1MP4fKSkpnD59GlpaWjh16hTS0tKQmZlJLdhzc3ORm5srEtZBiIaGBoYMGYKhQ4eif//+ImINn89H/fr1kZaWhmfPnqFPnz7VmnD5a0aMGIHt27dj8+bN2LdvH7p06QJdXd0q1/P69WtkZWVBRUUFx44dg7W1NQ4fPozTp09j+PDhZcbJr0xIENKvXz8EBQXhwYMHyMvLA4/HQ2FhIf2Xz+eDx+OBx+OhoKAANjY28PPzw5AhQ3D37l00bNgQZmZmUFBQgKWlJQ4fPkxjc8vKyuLKlSuwtrZGZGQkANDcFP3790fbtm0BFCerc3R0RFRUFGJjYzFs2DDk5OTA09MTO3bswLVr1zBr1ixYW1tTq6k6depg0qRJmDdvXqXxvKtCycU2IcKQTSXR1NTEixcvEBYWViqnQMmcCEIRQXjuHTt2hKenJ169evXDhYTc3FxERETQF6P8/Hy8ffsWmpqaZVp6cblcGBgYoFOnTjh27BgiIyNx+PBh9O7dGwcOHKjSy2nJnA9AsTdC06ZNER0djWvXrqGgoADNmjXD+PHj6WLm8OHDYWtri4CAAIwaNQoRERG4cuUK9cLg8Xhi5Y5g1EwIIZg3b55I2C1lZWW6ADNq1CgaXk5bWxsFBQV4//49wsPD8eHDByQnJ+PmzZu4efMm6tWrh/Hjx2Pq1KkiHkhVoaSQILQE/eeff4BP0aW2PXjwIAIDAwGAxnmuTEzg8/k4cuQIDh069M2LE3l5eTh27BiAYkvsMWPGYOjQoVBQUEBeXp6IiCBEWloa27dvR//+/TF//ny8e/cOhoaGWL9+Pf79919wOBz6fKgs2XJZXgjm5uZYs2bNH5WIuE6dOt8l9vztGBkZwcHBAbdu3YKBgQHu3buHHj16lNpOVVUVnp6eMDc3h7W1NTVKWLhwIRYtWlStz/JvRUdHB127dkVwcDDOnj2LjRs3Vml/Ho+HhIQEpKamIjU1FSkpKfjy5Quio6PLzPMjKSkJFRUVqKqqQklJCZmZmSL75ufnIzMzE5mZmdRrBgDGjh2LYcOGUYMG4RyrRYsWVJwNCQlBr169iudaTEj4bhISEjBp0iTExcVBQ0MDo0aNooYOQPEzRU1NDc2aNUODBg3QunVrqKqqioQ8EpKcnIwtW7bA2dkZQLEF/oQJE6j3Z7NmzWBtbY2YmBicP38e8fHxmDBhQrnW8sL8CCoqKlU2tij5DvG9ueKExMbGYs6cOcjKyoK2tjYOHDgg8sxITEyEgYEBYmJiUKtWLeTm5uLYsWM4efIkJk2aBFNTU3Tu3Lnc+l1dXQGUPT8vi/z8fFy5cgUnT56kuckAYN68eTh48GCp/CoMBoPBYDBqHn/O2+dX9O7dG87Ozli9ejVmzZqF4cOHw8fH538xSuXRqlUrZGVl/epm/hZwOBwsW7ZMxIq/oKAAWVlZyMjIQHZ2Nn25yszMBCEEffv2FbFk+dr1VEJCAkOHDsW1a9fg6uqKPn360OR61e2RIGTWrFmIiIiAjY0NTp48SYUEe3t7setIT0/HzJkzcezYMTRp0gTTp0+Hm5sb3rx5A0tLS2zfvr3UPoQQsWN4NmjQgIZ6Kgmfzy9l9WtsbIwRI0YgJCQEgwYNgouLCzgcDhYvXoy6deti165dNETJjRs3cOPGDQDFCzWdOnXC4MGD6WJBQEAAoqKiMGjQIJiZmeHq1auIiIjAnTt30LdvX+zduxf79u1DeHg4zMzMABRb5s2dOxeLFi1Cbm7uD5/8y8nJYdq0aaU+FyYw/Pq+OXbsGBUR1qxZU8qbplOnTvD09ERQUBDNiSAu4sRdFlJYWIjw8HDw+XzIycmhefPmePv2LXJzcxEeHk6tF8tCVVUVO3fuxM2bN+Ho6IgnT55g7NixsLCwEDvJXsmQVgCwdOlSJCcnY/LkycjOzoaysjImT54scn+1aNECWlpaePPmDQICAtC6dWu8f/8ex48fB5fLhUAgQHp6OhMSflPWrl2LCxcu0L9LJnjU09ODkpISnJyc0KNHDxqGS0tLC126dMGgQYPw/PlzeHp64tGjR0hJScHFixdx5coVjBkzBnPnzq1yiEChkJCZmUnDb/3To0cpIeH169d0sWjWrFmwsbGhYoIw7vPXpKamYuHChfD19QUATJkyBXZ2dmK1q+QcwcXFBVlZWVBTU4ONjU2VwnMMGTIEvr6+mDZtGgICArBlyxY8e/YMmzZtKtcjQfh7KCsr48uXL5g7d66IF8K5c+egpaXFQokxRBCGZvn333+psczdu3fRq1evUts2atQIp0+fxuLFi2FmZgYPDw8cOXIEly5dwtq1azFnzpzv9jb6HjgcDpYsWQITExPY29vD1NS0TK+EnJwc2NnZ4d27d8jJyaG5Tyqru0mTJlBXV4eEhAS6du2Kxo0bVyhIhoaGQlFRESkpKUhJScGnT5/w5s0buLu7o0ePHujevTv8/f3h7OyMLVu2gMPhoGfPnrh9+zaePn2KXr16oc3/jDYY305aWhr09PTw8eNHqKqqYvr06TQ/29KlS7Fw4ULIy8vTRfzk5OQyhV4AcHZ2xqZNm5CWlgYul4v+/ftDT09P5L6vX78+Fi1aBGdnZzx9+hR3797FlClTcPbs2TKNOoT5Eb4lkXmtWrVQt25dZGVlVYuQkJubi0WLFiEpKQmtW7fGiRMnSoXbHDlyJN6/fw91dXU8fPgQb9++xYEDB/Dw4UNcvXoVV69exaBBg7B27dpigb8EycnJVAwQR0jYunUrztna0lCC0tLSmDBhAhYtWlTmGMVgMBgMBqNm8ke/gY4YMQJhYWHIzMyEvb09Tf6YkpKC2NjYMq20GOIhIyODBg0aoGXLlujSpQv69++PkSNHwtDQEEZGRmK5w+rr6wMAzpw5gzdv3qB9+/bgcrlITk6u0uJ+VZg3bx6A4pinwgSemzdvrlIdkZGROHPmDIDixZ9Zs2YBAG7fvl1msk/hwmt106VLF3h6eqJZs2YIDw9Ht27dcOfOHRBCYGxsjN27d4tsr66ujk2bNsHHxwcjR46kIsLHjx9hY2MDHx8f3Lx5E4qKili4cCEmTpwIGRkZ+Pr6wsvLCwEBATAxMUHz5s2xceNGvH79Gubm5iKxVKuTS5cu4fr167S4uLiUmYS7TZs2AEBj0gLFLu/C81+3bl2ZFq5CCytHR0fk5uZWqW2RkZFi75OQkECTzzVu3BiysrLU6iw7OxupqakV7i8pKYnRo0fTvCFfvnzBsmXLxLKszszKwn///SfymZKSEs6fP0/DZ40fP75MQUAotMXGxtIY9ra2tnShparXjFEzePPmDfbv30//7tKlC0302Lt3b+jo6MDBwQHh4eE4d+5cqf2lpKSgo6ODjRs3wsXFBfv370f79u1RUFAAe3t7nDhxosptEi7GJCYm0vwIPb5asHB2dsbQoUPB4/FgYGCAkydP4vLly5CUlMSVK1fQsWNH3Lhxo1Ri4uPHj1MRwczMDCYmJmK3S9hHPDw8sHXrVgDApEmTvmnxvnHjxjh8+DAWL14MCQkJuLi44MiRIzScytdeBSU9ElavXo27d+9CWloaO3fuhK+vLxXeGYyvkZOTw+3bt9GvXz9kZGSgf//+WLx4sUhs+JJ07NgRd+/exa1bt9C+fXukp6djw4YNGDBgQJVF9upG6JVQUFAACwuLMp97169fh5+fHxITE8USEaSlpWl4pXbt2kFZWRkKCgqV9utatWqhSZMmaNGiBdq3bw8dHR1wOBxkZWUhNDRUxBgmKioKfD6fPvuFVuo1wdPjd8fCwgKvXr1Cw4YNYWVlRcPNzZ49G+vXr4eCgoJYngBxcXFYtGgR0tLSICkpienTp2PEiBFlimeSkpIYP348zfPl4eFB3wO+RjgXzcvL+yYPOOE98r1Cgr+/P8aOHYvw8HAoKSmVEj4KCwsxadIkvHjxAg0bNsTdu3ehqqqKIUOG4N69e3j69Cl93j18+BCjRo2inshCrKysIBAI0LFjR7GEE0tLS6SkpEBNTQ27du3Cp0+fYGNjw0QEBoPBYDB+M/5oIaEs3r59i7Fjx2LevHlo3rz5r27OX83EiRPRtWtXJCcnY8iQIYiKisKcOXMAACtXrvwhYsKnT58AFC+mysnJQUpKChMmTKhSHXJycjQkVl5eHk6fPg0A6Nq1a5kT/8mTJ/+w0BNt2rSBl5cXunXrhvT0dKxZswZLly5Famoqxo0bh3HjxtFtDxw4gBkzZpSKYVoy5JQwZBWHw0Hfvn3Rv39/AMUvNvXr18eUKVOgo6MDZWVl5OXl/ZBzEmJsbIyJEyfS0r9/f9y6davUdtnZ2QCKPSSENGjQgC6Ol5fgeeTIkWjTpg2Sk5Or7E3RuHFjsa3xlZSU6Ivp+/fv8eLFCxoKrHbt2hWGKUpMTISNjQ0WLFgADw8Pus+MGTPEelHW7d8fR44cEfnM0NAQ/fr1o0kunZ2dy1x8ES6+1q1bl+bB0NDQAI/HQ6tWrcQSCxk1j68teiUlJekCgFBQaNeuHfLy8spM2FgSCQkJDBgwAMeOHaP5WIQeQlVBVVUV06ZNo+PnrFmzRPKs/GdhgUmTJiE1NRVdunTBqVOnwOFwMGbMGFy7dg0qKir49OkT1qxZQ0O9CRdwDAwM6Nhw6tQpkcTSldGgQQP8999/MDY2RlpaGjp16oSZM2dW+fyESEhIYPbs2TTxu5OTE4KCggAU9y0hhBDq9aSiokJ/l/3798PMzOyPCmXE+DEIxYSJEydCIBDg1KlT6NSpEywtLeni9tfo6enB398fBw4cgKKiIkJDQ6Gvr49Vq1aVGQroZ8DhcLB+/Xoqvglz/ZRER0enSnUWFhbi+fPnuHXrFi5evAhHR0fs2LEDW7duxZEjR2BtbQ1HR0dcuXIF586dg6WlJXbv3o0LFy5g3bp12L59O/bv34+zZ8+CEAJ5eXnIyclRT6fWrVujadOmWLx4Me7fvw9JSUlMmTIFAGBv92OMZP4mnj17BgDYsGED8vLykJeXh0aNGmHHjh1VCiXUsGFDjB49GkBxCNLr16/j2bNnZRr+CAQC+Pj44NGjRwCKnw0GBgZl1iv8rZ89e0bzB1SFwYMH459//vlmsTgvLw9bt26FsbExoqOj0ahRIxw9epQa0wHFz5ht27bBy8uLjhUln0FA8TuNjY0Nnj59in79+qGwsBCLFy8Gj8cDUJwLQmiUsHDhwnLbU1JLadSoEa5fv47379/DzMysWvOeMBgMBoPB+Hn81kJCeHg49u3bh6tXr4pl9XHx4kUsXboU06dPp8lXGb8OWVlZPHjwAF27dkViYiKGDBmCyZMnw9jYGISQHyImCF9CDQwM6GLMrl27xN6/fv36uH79Ov755x88efIEpqamePfuHRQVFbF7925wOBwRi9jWGho0rvaPQk1NDT4+PtiyZQskJSVx//59jBgxAh4eHrCwsKCeH+vWrSszmaCCggK16u/Xr5/Id0KL3N69ewMANm7ciCtXrmD58uVo27YtBgwYgH379onE/68u2rdvj44dO6Jjx440PE9ZFtIxMTEAQK3mgeLFdqFFvTB+69fUrVsXt27dgoqKCk2EKC4aGhpiv7AKQ0kJX5gKCgrA5XKhpqaGDh06lJmEOiIiAnv37sWSJUtw+/Zt5OTkoFGjRjQ5bnkvbQKBAA8ePKB/p6WloU2bNrC2tsbNmzdRp04d+Pr6wtzcHBcvXoSsrCzi4+NhZWVFFyuBYuvJly9fgsPhQFlZGWlpaWjatCni4uIAAOvXr2eLmb8pysrKdMFAWloaAQEB6NKlCzgcDsLCwhAREYFhw4Zh06ZN5YpwX3PhwgXk5uaiZcuWpfKUiAOHw8G6devg5eUFNzc3motDiNP/cjmsXLkSjx49orlfgGLPw9DQUFhYWEBBQQHv3r3DvHnzMHLkSDx69Aja2tq4d+8eOnfujPT0dMyePVvsdmVkZFAPi1mzZsHR0bHcEBlVoX///tSLIyMjA7Vq1UKnTp3o92lpaXSsLmnlKRRrGAxxkJOTw9WrV+Hh4YEuXbogIyMDa9euhba2tojYVhKhZba/vz8MDQ1BCMGFCxfQq1cvRERE/IKzKPYeXL58OQBgx44dNGSjkDZt2uDChQulyooVK3Do0CHs3r0bW7duxfr167F48WIYGRlBX18f3bt3R4sWLahRQEFBAeLj4xEaGopnz57h5cuXeP/+PeLi4pCenk7zmXA4HMjJyaFhw4Zo3749Jk6cCBsbG+Tm5kJNTQ2LFi3C2rVrcenSJXC5XFhZWWHo0KFIT0+Hja3NT712fxqEEOrd0aFDB4SFhQEA9WiuCpKSkjh+/DiuXr0KDQ0N5Obm4saNGzh69KhIDoyMjAycO3cOt27dQlFRETp06AAfH59yE9xPnTqVvlfcu3cPDx8+rFK7mjdvjjFjxnxT6MiXL19iy5Yt9N1p8uTJuH37Nrp27Sqy3blz5+Dg4AAulwtbW9syvX2FqKur49SpU1BQUMCLFy9gaWmJoqIiKiro6+tj4sSJ5e5f0pjF6tIl/PvvvxWGEGMwGAwGg1Hz+W1Xgg4fPoxNmzahXbt2NMb5oUOHKtxn1qxZNAwNo2agqKiIBw8eQE9PD8HBwZg8eTKuXbsGoDi0jXBBafLkyd99rICAADg4OACAyKS3KhZMWlpaiIqKgpmZGV68eAGg2N19z549dKH4opUVFv1ve+tLl6otYVpFSElJYdOmTejYsSPWr1+Pd+/eYcGCBRg7dixWr16NgIAAfPjwAYcOHcL69etL7W9iYoKYmBiaXA4ofqkWenD06dMHOTk5CA4OBlBsqRQSEoKgoCBqUdusWTMMHjwYQ4YMQffu3b87trKnpye1JI6MjISWlhY8PDwQGxsrsrgmtDD+OheAvr4+XF1dcf/+fboI8TXq6uq4desWhgwZIrKQXlXy8/ORlpaGxo0bl3k/SUpKokWLFmjQoAEyMzOhrKxcpoAQHR0NOzs7BAQE0M86d+4MfX19dO3aFYWFhSKeFyV59uwZDhw4gMjQUAxrUxyH2cLCApNmzKCL/k5OTpg6dSpevnwJMzMzjBo1Cvfu3UNqaiqsrKxgaGgISUlJuLi4ACheoAkPDweHw8GoUaNw8uRJtGjRosxcFYzfhz59+uDdu3fo0KEDgoKCEBYWhl69euHJkye4d+8eWrRoIXb/jYmJoYsWy5Ytg6SkpNgJ5r9GWloajRs3hkAgwBVbWxj/73NlZWWcvnSpXGGjdu3aWLlyJQwMDHDx4kWcPXsWL168gKGhIXR0dLBu3To4ODhg165dIrkhKiM7Oxt169bF/v37MWLEiG86p7LgcDiYMWMGzTejra0tMh4IF0obNWpU5XwTDMbX6Orq4tmzZzh//jy2bduG9+/fY/z48Rg8eDD27t1b5oKokpISjh8/jqlTp2LFihV4//49li1bhrt37/6SvBxz5szB06dP4efnB2tra5ibm5f5DP0aSUlJSEpKgs/nw8HBAW/evEHbtm3RvXt3DBkyBDIyMnj16hWaNWuGtLQ0mlw5JycHtWvXhqysLC0pKSno0KEDatWqRa9BcnIyjh8/juzsbKioqGDOnDlwcXGBm5sbOBwOzpw5Qz1Dz58/j5z/eVAyvo24uDikpaVBQkICGhoauHz5MgBUmGvqa1JTU+Hv7w8dHR3Uq1cP/fr1w4MHD7BgwQJ4eXkhJiYGx48fR7du3dC6dWvcvn0bubm5kJKSwsiRIzF06NBKLennz5+PR48e4f79+3B2doa0tHSlHn7fQ1ZWFuzs7PD06VMAxQZG27dvLzOX1r1793Dw4EEAwMGDB6mHdUU0btwYFhYWWLBgAfbt24eoqCiEhIRAXl4e+/fvL/c96v79+9ixYztGaxQbK/VkIYUZDAaDwfgj+C2FBBcXFxw7dgyvXr1CixYtcPz4cRw9erRMIcHPzw9r166Fs7MzjUvOqFl8LSZMmjRJRExYsWIFbt68iUmTJsHAwOCbrDJ5PB7WrVsHoFiU0NbW/qa2vnz5koZ8kZGRwYQJEzBz5kx6bz158gQXL1zAov9NmoVhPpKTk+Hq6loqfndRUVEpy5x69eph+PDh35S8WEtLCw4ODrC0tMS5c+fg6OiIp0+fYunSpTA3N4eVlVWZi3G1a9cu5db86dMn8Pl8NG3aFGpqanj06BGKiorQtGlTeHl5ITExEa6urrh79y48PT3x+fNnWFlZwcrKCvLy8ujZs6eIRZXQHfpbaNmyJfr06QM/Pz9cuXIFa9asod+V5ZEAFLuHS0hIIDQ0FNHR0eW++GlpaeHGjRsYOXJkmR4b5ZGcnIyAgAD4+/vj1atX4PF4UFNTg56eHnR1dcsUkOrWrVvm5zk5OTh27Bh8fHxACAGHw4Guri5GjRpVSiD5mnfv3uHgwYPU5V7xfyGLgGLLOE4Jz4HOnTvj9u3bmDRpEiIjI5GYmIgxY8bg/v37SEpKwqX/CV+FhYVo0qQJIiMjARRbUAtFODMzs1+agJPx/ejo6MDKygqSkpLgcDh49+4ddHV18fr1a6SlpcHX15fm5KiM48ePg8fjoWfPnuWGGElJSYG3tzfk5ORQv359KCgoQEFBoUzBITExERs3bsSLZ89g/D9B7PKVK+jxlbdUWdSrVw+rV6/GrFmzcPToUVhbW+Px48cYPXo0Bg0ahDVr1qBXr16YO3euWOdWp04d3Lt374eEQRw8eDBUVFQQFxdXKl+TUEj4ekxjML4VCQkJGBsbY8KECdi7dy8sLS3h4eGBf/75B3PmzIG5uXmZc+S+ffvCwcEBvXv3xtOnT3H16lUYGRn99PZzuVzs2bMHo0ePRlxcHOzt7TF9+nSx9n3x4gXs7OxoGMTQ0FCEhoZCSkoKHTp0gLKyMtTU1NCwYcMKF4iLioro/DM3NxexsbGwt7dHZmYmGjdujHnz5sHHxwdubm4AgKNHj8LQ0BBAcTil48ePf88lYOD/Exm3adMGMjIyIh4J4pCUlISxY8ciKioKMjIy0NPTw/jx46GrqwsdHR306NED9+7dw/PnzxEYGIjAwEAAxZ5hhoaGaNiwodjGR4MHDwafz4e7uzuuX78OaWnpcnPzCQQCZGZmIjk5GUlJScjJyUHdunWhoKAAeXn5cj3hCCF4/vw5rly5gqysLHA4HAwdOhS7d+8u06MhODiYCtjTp0/HggULxDoXoNgAy9nZGa6urrhy5QqAYmOVJk2alLn9+/fvMWPGjGr3VmYwGAwGg/Hr+S2FhB07dsDKygotWrQAUGx50ahRI5iZmaFWrVpYsmQJTQBbu3ZthIWF4ciRI9ixY8evbPZvhbgTv6pMECvatn79+rh//z4GDRqE169fY9KkSbC3t4eUlBTOnTsHX19f+Pr6Yv369Rg5ciSGDRuGIUOGVGoZ9/btW3A4HDg6OiI8PBx169aFgYEBnj9/Lna7S5KZmQlpaWmMGTMGxsbG9MW7sLAQX758wfr160XOUyAggEAAQ0NDeHl5iX2cpk2bYs2aNZg5cyZq1aqFoqIisawABQIBZGRksGbNGgwaNAjr1q1DdHQ0rl+/jgkTJuDGjRswMzPDuHHjKnWbFnof9O7dGwKBgAoowr8bNGiAadOmYdq0aQgPD0dwcDA8PDzw8OFDpKamioTYqQ6mT58OPz8/2NraYvXq1eBwOPj06RNNbMfhcEqFXujYsSNCQkJw7do1mly5LHr06AErKysa27YygoKC6MK9EA6Hg+joaJw7dw6XLl1Cjx49kJmZSRdry4LH4yE+Ph7+/v70vunZsycmTpxIvS6E4pNAIEBCQgI8PDzw4cMHvH//HpGRkQgPD4dAIKDJAOcZGwMrij158vLy8fWRVVRUcOPGDUyfPh0RERG4fv06Ro0ahcePHyM2NpaGeeLz+eDxeFBVVYWMjAy+fPkCdXV1zJgxQ6xrxKi59OnTBwDw+vVrjBkzBo6OjvD398eQIUPg6OgIX19faGpqQlFRUWQ/gUCAlJQU5ObmIjExEREREdTy1tjYmCYOFwgENM50QUEBJk+eTJMof42cnBzk5eVRr149KCgoICwsDOnp6VAoMT7Vq1tXLC8H4TGVlJSwdetWmJqa4siRI7C3t8fDhw/x8OFDsawvhbRv3x5KSkrIysqqcLukpCSaX6YiEhISRDyK1q1bBzc3NwwaNEgk6frHjx8BFD8H+Hw+HRsEAoGIGC0QCJio9wdTlXmYONsKBALUrVsXO3bsgImJCTZs2AAnJyecOXMG9vb2WL9+PRYsWFBqvqGiooK1a9diy5YtMDc3h56eHhQVFZGWlobc3NxKj5uWliZW+/Lz80sZW5REGELS1NQUnp6eUFVVRceOHcvdPisrC7a2tjSmvoqKCkaNGoWoqCgEBgYiKSmJznO8vb3Rvn17dOjQAS1atKDnLxzzvnz5goiICPj4+CA+Pl7Eg7FBgwYwNTXFkydPqIiwb98+zJw5k45J9vb2SEhIQIsSi65lxeJnVHzfC4WEjh07IjExEW/evAFQbDGflJRU5j5ZWVmoV68e0tPTYWhoiKioKEhJSaGgoAC3b9/G7du3oaSkhNatW6NHjx6YMGECevbsCWdnZ8TExGDAgAEYOnQoJCUlQQhBYWEh0tLSKj2P/Px8DB06FHl5edQIhs/nQ1lZGUlJSbTExMQgKyurUmMbKSkpNGjQAPXr16fl06dPNNSTiooKjIyM0Lx5c0hISJTKhRITE4NFixahsLAQurq6WLp0abn5UkqSl5dHvX8sLCzw5MkTZGRkYOjQoRg9ejR99vH5fHpPFxUVYfr06UhPT0e/Hj2AjEwAgIAQcNh9z2AwGAzGb89vJyQIBAIoKChQF9H09HSsW7cOEhISSElJwe3bt2FjY4OgoCDIy8tDW1sb/v7+VHT42xHXJb0q4X7E2Vac4zZo0AAPHz7EsGHDEBwcjKlTp8Ld3R1mZmawtbWFtbU1IiMjYW9vD3t7e6irq8PQ0BCGhoZo2bJlmXUKBAKkpaXR0BuzZs0q07LHxcUFgwcPFvnMx8cHO3bsgLe3N4DisBvGxsZYs2aNSCJQoFhIWLp0KTIyMtCjUyegoHhyLiUliUd+fvDy8oK0tHSpYwClr9+LFy8QGxuL5cuXY9++fTAzM8PMmTPFiknftm1b6uGgoaEBHR0ddOrUCa9evcKoUaPQvHlzREVFISwsDEuWLKmwLmFugQEDBkBOTg7+/v4if5dEW1sbPXr0wLx588Dn8/H06VMEBASIvCjn5+dj48aNlZ6DkFq1aol4ZRgaGmLlypWIiIjAixcv0KtXL9SqVYta7zZv3ryUt8rAgQMREhICPz+/SkOEjB07FocOHcKKFSsAFP8uhBCa2PjBgweIj48HUBzyhMPhQFtbG0OHDsWQIUPQpEkTODk5wdbWFmFhYfDz8wNQbAk6ZcoUjB07lia2zszMxNmzZ3Hx4kWatHrw4MHYtm0bunXrhtzcXPj6+uLNmzcICwtDaGgo3r59W2ZSZGHbt27ditatW0OQlwdhOtn6ivXBLUMwUlRUhJubG8aPH4+AgADcuXMHp0+fhp2dHR4+fIghQ4bgzp07UFBQgKOjI/79918AxbkRvsVT5lupyjjEEJ927dpBQUEB6enpGDVqFBwdHREWFgYbGxukp6fD09MTb968wcyZMxETE4PPnz8jNjYWkZGROHLkSKnEq+PHj8ewYcNEPlNWVgYArF27Fq9fv4a8vDw0NDSQlpaGlJQUuhCXnZ2N7OxsmlwYKA6ddvn8eWBqsTVv27ZtISFGGBM1NTU6TvJ4PAQFBaFjx44YM2YMbt26hcuXL+PevXvgcDiYOHEiZGRkYGNTHLOcy+VCIBCgXr16OHfuHMaNG4e0tDSxnl0lF1oqokmTJiIJL1u1akWTfZZE6BmlpqYGCQkJ2g+4XK6IBxuXy63WMDPi9rfq7pe/IlTO74C4ccS5XK5Yv4msrCzdrl27dnBwcIC3tzdWrFiBkJAQmJmZ4fz589izZw9GjhwpUueaNWtw/fp1vH79GhYWFjhz5gyys7PF+u1atmxJx4OKEAgEVLwUCASwsbHBkydPsGXLFmr13Lp1a3h7e8PW1hZOTk6YOHFimRbRQUFBuHv3LuLj48HhcLB69WpoaWnBzc0Nq1atwqBBgxAUFISrV6/iypUrSEhIQHBwMIKDg6GsrIzevXvj06dPCA8PL9dTUV1dHf/88w927doFR0dHOmfavn07nUcAxQvjR48eBVAc8gZXrgIAarGwZVVGKEh36NABOTk5yMrKgoSEBDp16lTuHE9o2T958mSEhYWhcePG8Pb2RkZGBmxtbWFnZ4fExESkpKTg2bNn0NTUxLRp0+Dt7Q0lJaVS893ExESx+tvUqVOhoKCAmTNnYuXKlbh8+TJNyF0WkpKSaNWqFdq0aYNGjRohISEBnz9/xufPn5GamkoNT4TzUCFSUlJYuXIlli5dCmlpaRQWFpZ6z0lLS8OkSZOQlpaGzp0749q1a5CRkRHruVW/fn36XJWXl8eVK1dw7do1bN26VeQ4AoGA1nfgwAEEBQVBQUEBtjY2yBlV/JyTlpICV0amWsd8lm+BwWAwGIyfz28nJHC5XNy6dYv+bWpqigEDBsDS0hKSkpL4/PkzOnTogEuXLmHp0qUAIBL3nVGz+TrM0ZAhQ+Du7o7Nmzdj06ZNND7u9evX8enTJ1hYWMDCwgI6OjqYMWMGDA0NRSaVhBCcOXMGBQUF0NLSqjR5KCEEDx48wP79+6nFubS0NGbNmoW1a9dCUVGxTAvQzZs349mzZ5CXl8f58+eBaf/vci/0hJk9e3apxMuEkFIT6oKCAly4cAEWFhaIiYnBkiVLsHv3bqxbtw4mJiaVLuTGxsbCzc2NFqE7/549e3DixAnMmzcP7u7u6NGjR5nxU4HiRbi3b98CKE7AXFhYSGOvfp2Q+WtycnKQmpqK7OxsEcuyqoQNKou6detiwoQJsLa2hpWVFXr16oWsrCy6uF5ygU5Iv379cOjQIYSEhCA5ObnS8GZGRkZISUnBzp07adutra3p93Xq1MGgQYOgo6OD4cOHl6rP2NgYM2bMQEhICC5fvgwnJyd8+PABu3btwr59+6Cvr4+WLVvi4sWLdDG2c+fOWLVqFSZOnIiMjAzs27cPR48eLdO6TkpKCm3atIGmpibat28PTU1NdOrU6ZtCrygqKsLOzg7z58+Hu7s7TExMcPz4cUyfPh1Tp04FAFhaWuLhw4dISEhAs2bNMHPmzCofh1Hz4HK50NHRgYuLCzIzM9GrVy88ffoU586dw+7du9G/f3/4+PjAx8en3Drq1auHpk2bomXLliKhxkri7u5OQw5evHiRClJAsfViWloaMjIyaFzylJQU6vElxeejqqldCSEICgqCtbU1rly5ItKHmjRpgokTJyI+Ph4+Pj64du0aJCQkMGjQIDx//hxZWVno1q0brl69Wq4w/bMQLhSVzAXDYPwIdHV18fz5c1hZWWHTpk149+4dxo0bh8GDB2Pfvn3U4l9KSgrHjx+Hrq4uLl68iBkzZvwwA53g4GAsWbKEGi+EhobC3d2dLlLOnz8fISEheP36NTZt2oTTp0/Thc7CwkKcPHkSly9fBiEEzZs3h6WlJS5duoR9+/YBAC5fvoxevXph8+bN2L9/P5YvX45nz57ByckJd+/eRVJSksh7Ru3atdGuXTu0a9cO3bt3R4cOHdChQwfIy8sDAM6ePUvDxWzcuLFUTqb79+/j9evXkJOTg4mJCZL+JyQwqo7Q+r5Tp050jtqiRYsKDUUKCgowbtw4PHnyBPXr18fVq1cRGBiIZs2aYf369dizZw/c3Nxga2uLW7duISwsDBs3bsSmTZswYMAATJ06FePHjy83vFBlcLlcHDhwAEVFRbC3t4eysjJat25NS8uWLaGtrU1zE+Xm5iIuLg4tW7ak7wc5OTl49eoVEhISEBMTg7i4OMTExIAQgoULF9IwqmVRWFgIIyMjhIeHU49UOTm5bw43OmDAAAwYMKDc71+/fo1t27YBKM5nqKKignffdCQGg8FgMBg1ld9OSAAgspB76tQpGsYIKI4prK2tjczMzF/RNEY1UJaYcPDgQUyaNAl9+/ZF3759YWFhgbt378LW1haenp54/PgxHj9+DBkZGUyaNInW5e/vj+fPn0NSUhILFiwo14qIz+fj2rVr2LdvH3WdlpKSwsyZM7Fu3Toaq7osN34nJyca+/bMmTNQb94cMf/7zsfHh3ojCHM0VIaMjAwWLFgAExMTEUFh6dKl2LNnTylBITc3F48ePYKbmxvc3d2pq/fXdRYUFODQoUNYtmwZDh8+jOPHj6Ndu3b0ZbgkkZGRNLGvpqYmnj17hry8PCgpKZV6YSkoKMCTJ0/g5eUFDw8PPH/+vMLwBN/DzJkzYW1tDTs7Oxw6dIguuCkqKpYpsKioqKBNmzaIiIiAi4uLWGF5Vq5cicTERJw5cwZcLheqqqrQ09ODvr4++vbtCxkZGSQmJqJOiVwEJeFwOOjatSu6du2KefPmwdfXF1evXkVoaKjI4oSGhgZWrlyJoUOHIjk5Gebm5jh9+jQdu5o2bYqePXtSwaB9+/Zo2rRpucf9FmRlZXHlyhUsWrQI169fx7x582gOh1mzZkFPT4/mEzEzM2OJX/8ghEKCn58fFi9ejKdPn8LKygorVqzAjh07cOLECSgrK0NVVZUWWVlZtGrVCioqKpUmkU9OToaJiQkAYO7cuSIiAlBsRaikpASBQID3798jODgYISEhiI+Ph5ubG7p16ADxsjQUJ+C8evUqbGxsRMa/hg0bomfPnvD29kZ8fDzNvSMnJ4d69eohLi4ODx8+hKSkJAYNGoTp06cjJycHhYWFYllq/iiE4xrLkcD4GUhISGD27NmYOHEiLCwscOjQIXh4eKB79+4wNTXFli1boKysDB0dHTovWbx4MZycnKr1mZCWloYtW7bgzJkzIIRATk4OXC4XT58+xapVq6hVv6SkJHbu3AkjIyO8fPkSZ8+exYIFCxAREYEtW7bQcIejR4/G9OnTMXfuXCQkJNDjCOscMWIEtLW1sXDhQgwbNgw6OjrYuXMnHj16hNDQULRs2RKampo0VAyPxytlPGBtbU29D1atWgUzMzMRA4qMjAwqtM6ePRvy8vIoOwAPozJ4PB7NidCxY0ecPXsWQMWJlouKirBs2TK4u7ujTp06sLGxgampKb1HgGJRvHXr1mjVqhUWLFiAtLQ0BAcH48WLF/D09ISnpycWL14MAwMDTJ06Ff/880+Vc7VJSEjg6NGj2L9/f6m5alFRET5//ozDhw/D3d0dvr6+KCwshLKyMgYPHky9XjU0NKqUVDo/Px83btzAyZMnERISgjp16uDGjRulPKqrEx6PBxMTE/B4PIwYMQLTpk0D+Z/XLYPxI4mOjkZycnKF2wjHDwaDwWB8P7+lkFCSkiICUByrODAwEAcPHvxFLWJUB1+LCdOmTcPOnTthbm6OSZMmQVZWFpMmTcKkSZMQFxeH1atXw9nZGV5eXlRIyMrKKvYOADBmzJgyF2WEidAcHBzoi2bt2rXRqFEjxMfHw8HBAe7u7jThmTBZqDCmt5ycHCwsLAAAK1aswIgRIyAoMWnevXs3gOIXyKouCpUUFM6dO4c9e/aICApTpkxBcHAwfekQwuFw0L17d+jp6WHw4MFYsmQJ3rx5QxPTDR06FGpqaoiOjsaJEydgZmZWSmARLsZpaWmBw+HQ/Ah9+/YFh8NBQkICbG1t8fDhQ/j6+tLwPEJat26N3r17iywyFBYWilj3fwu6urpQV1fHp0+f4OzsTBfcykv2BhQnCo6IiMCtW7eokCAcJ4YOHVrKLZrD4cDCwgJJSUlwdHREYmIiXrx4gS9fvuD+/fto0qQJ5OXlMWDAADRq1KjC9srJycHQ0BBTp07Fq1evYGdnh0+fPmHcuHEYM2YM0tPTsX37dtjb29M4s+3bt8fq1asxceLEUuGsvteroyykpKSoIHvq1ClkZWWhXbt22LVrF2xtbREXFwcVFRXMnj272o/N+HUI8yQ8fvwYR48ehYaGBt69ewdra2ssWrQIs2bNKrVPXFxcqTAPZUEIwbx58xAfHw9NTU1qCQwUi6uurq4ICQmh/eprHj9+jKscDgL/l2z5nx49oNmlC7p06YIxY8ZAQ0MDhBA4OTnh3LlzcHd3p2HUZGRkMHr0aBgbG0NPTw+SkpIoKCiAp6cnHB0d4ezsjC9fvlBPLS6Xi6KiIppDASheqGzbti00NDSgqamJgQMHolu3blW8wt+OcFyrLNE6g1Gd1KtXDxYWFpg1axY2bNgABwcHGvJu48aNWLp0Kf777z/cunULb968wYULF6qUrLU8CCGws7PDtm3baK6QKVOmYPfu3Xjx4gXGjBmD06dPo3v37jA2NgZQLLRv2LABGzduxIULFxAaGoqAgAAUFRWhfv36WLt2LQICAjBhwgQA/x+qEPj/3ARcLhdBQUGYM2cO2rVrhzVr1kBfXx9DhgzBkCFDKmzzo0eP4OTkRBezFy5ciK1bt4ocJz8/H0ZGRggLC6N5JhjfTkREBAoLCyEnJwd1dXXqkVDe4rpAIICZmRlcXV1pGLuNGzfi/fv3aNCgAWRlZfH582dkZmYiKCgIQUFBpeoQhthMT0+Hk5MTnJycaK41Y2NjamghDhwOh4oIfD4fzs7OePDgAby8vJCSkiKyrYSEBJKSkmBnZ0fDIbVt2xYDBw7EyJEjy/UmJoQgPDwct27dgo2NDa23Vq1asLa2rjBXWHWwZ88eBAYGQkFBAadOnSruDz/0iAxGsYigqakpVs4eWVnZSr3TGQwGg1E5v72QUJLExESMHz8epqam6Nq1669uDuM7UVRUhJeXFywtLXHw4EG8ffsWhoaG8PX1hYWFBV1kVVFRwfTp0+Hs7IybN29i/PjxGDx4MBwdHelLqa6ubqn6+Xw+jhw5QnMgKCkpYeDAgXB0dERUVBSAYmv7ryf4ZaGjo4MtW7aU+tzHx6dK3ghlISMjg/nz58PExAQXL17E7t27ERMTgwMHDtBt1NTUMHToUGq5VFJgs7GxQY8ePehC9dGjR7Fy5UocPnwYT58+xePHj+miovCcPT09ARQLCQUFBbh8+TKA4sXHW7duYe7cuSLXpVGjRhg8eDAGDRqEwYMHQ11dvdR5ZGZmfreQwOVyYWxsjO3bt2PLli00cWp2djby8vJKJZAuKiqi7RSGqnr48CGMjY2RmJgIPT092NralvLK4HK5OHnyJDIzM6mXRVl06NABAwcOxMCBA6GtrV1uHgsOh4NOnTqhU6dO9LPCwkLMmjWLijbdu3fH2rVrMWLEiJ8eM5zL5eK///6DmpoaXFxcsH//foSHh9OwXMuWLWPeCH8Y//zzDyQlJRETEwMbGxssXLgQK1aswIEDB9C7d+8qLZCUJD8/H2ZmZrh9+zakpKRgbW1NrTc/f/5cKrwcl8tF27Zt0bVrV3Tu3BlNmzZFaGgoQoOCgI9RAID3797hVUQErl27hh07duDs2bPIzMzEwoULaT06OjqYPn06pkyZUioEhYyMDPT19aGvr48TJ07Az88PTk5OcHZ2RmRkJN1OU1MTsbGxyMzMxJs3b/DmzRs4OTnBwsICpqamMDc3r7IlalUghODs2bOIiSn2aVNXV4eXlxdNFsv6IONn0LJlS9jb2+PRo0dYuXIlXrx4gbVr1yI4OJjOQebMmYMDBw6gTZs2ZeZ+Ehc+n4/169fDysoKQLGQfuTIETpnU1FRgbm5ObZt24YFCxZAQkIC7du3BwDo6enB398fzs7ONPSirq4u1q9fj71791JhECjuW8OHD8eWLVtw8eJFXLhwATweD9LS0pCSksLbt28xe/Zs3LhxA7179y63vYWFhdi4cSNOnjxJPzMxMcGePXuoQUZ+fj6srKywZ88exMbGonbt2nBwcECjRo0gEGOhi1E2Jb0IAgMD6Tw9LCyslBdZZmYmVqxYAQ8PD0hISGD//v1Yv349wsPDaY6E1q1bIz8/Hx8/fsT79+/x7t07fPjwAe/fv8eHDx/w6dMn5OfnIz8/H/Xr14epqSns7OwQHR0Ne3t7au0/atSoKp+Lg4ODyPOrTp06GDBgAIYMGUKNolRUVNCrVy98/vwZAQEBCA8PR3h4OE6dOgV9fX1s2bIFrVu3Bp/PR0BAAO7duwcXFxd8/PiR1tusWTPMnj0bxsbGaNiw4TdcdfF4/fo1Nm/ejAcPHgD4/5BGDMbPIDk5Gbm5ubC1ta0wzBdQnI9RTU3tJ7WMwWAw/mDIH0BKSgpZvnw5UVdXJ7t27SJ8Pv+XtCMjI4MAIBkZGb/k+NWJQCAQu1RnfUVFRYTP55cqaWlpZOPGjYTD4RAAxMDAgCQmJpKcnBySk5ND0tPTSf/+/QkAIikpSU6dOkXCw8NJgwYNCAAiLy9P9u/fT5ydnYmzszO5ceMG6d27NwFAJCQkyOzZs8mJEycIl8slAMioUaPI8+fPSUhICPH29ibOzs7E2tqa7N+/n2zbto2sXLmSmJiYkAkTJpApU6aQDx8+kOzsbJKdnU0yk5JIaNt2JLRtO1KbwyEGBgZlnhOfzydFRUViXRcej0eKiopIUVERycnJIcePHyfTp08nR44cIW/evBH5viwMDQ0JANK+fXsCgHTq1IlMnDiRACANGjQg165do9dmyJAh9JpduXKFLF26lG63ceNGAoDWceDAARISEkIKCgoqPYf09HSx+kdl/ejLly+kWbNm9Hzk5eUJANK3b1/i5+dHnj59Sp4+fUpu375NunTpQttrZmZGtmzZQu8hYWnXrh0JDQ0lBQUFJDExkaSlpdGSkpJCPDw8iI2NDdm7dy9ZsWIFmTp1KtHS0hKpAwBRUFAg8+bNI48fPyYxMTEkJiaGPH/+nHz48KHMsmDBAgKA1K9fn1y6dInk5uaSvLy8CktaWhq958srWcnJ9P7LSEwkWVlZ5Za4uDiR8xUWT09Pel179uxJ4uPjxernjB+PuM8ZcbYr2Z93795Nx8R69eqR+/fvk6SkJJHy4sWLcu/nDx8+ED8/P9KxY0c6Dp8/f57weDxaPn/+TI+3f/9+4uPjQ7Kzs8t+FmRn0/v47s2bZNeuXURHR4fuLyUlRQAQU1NT8vbtW3oMcRCOlc7OzkRGRoYAIJqamiQpKYnweDzy8eNHcuvWLWJubk5GjBhBj6mhoUHc3d1JSkqKSAkKCiIRERGVlqdPn5KoqKgyy6tXr4i+vj491rx588ju3buJhIQEHW/T09Np24uKigiPx6vW5/TfRHX2ox+FuL8tn8+v1u0KCwtpf8rPzyfHjh0jkpKSBACZOnUqycvLo/MHaWlpcvHiRRIdHV1uefPmDUlMTCxVoqOjaf/icDhkw4YNJCcnhxQWFoqU/Px8YmRkRPvGkiVLyPPnz8nz58+Jj48P0dbWJhISEmTlypXE39+fzlkkJCTonO6///4T6QefP38mTZo0IQDIhQsXiIGBAQFA+vfvT2JjY8ssz549I7169aLtMDQ0JFevXiWZmZkkOzubJCcnkwMHDhAVFRW6jaqqKrl9+zbts4WZmXRcK8zM/On3VHXzI/pRefdlSkoKfb7Url2bGBsb09+3d+/eJCQkhERFRRF3d3fSsmVLen/OmTOH1K1blwAgzZo1Iy9evChzPs7j8UT+zsvLo7+3vr4+3cbJyYmO1RISEuTMmTMkISGhVHn37l2pZ6iwCOfZI0aMIE5OTiQnJ4fweDwSERFRao7ar18/cv/+fXLmzBkyadIk+kyQlJQkenp6RFlZWWR7GRkZMnz4cGJvb08yMzPLnSump6eTgoKCSktGRka5dURERJDp06fTNktKSpK1a9eKvMvxsrLoPc/Lyvpl7+g1md/heVSTCQwMJABIYGDgr27KH01Z15mfk0P7Nz8n5xe2jvUPBuNn80cICYQQ4ufnR7Kzs39pG/6kAaymCQnCYm9vTxd+unXrRiIjI+mENjU1lUyYMIFOpjdu3EhOnz5NWrRoQSfXGzduJNeuXSPdunWjk96NGzeSxYsX0/1mzZpFcnNzy5xQp6WlUcGgvPK1kLBv375qFRIqK2Vx6dIlAoB07NiRSEtLEwBkw4YN9AVk0qRJxNnZmSxbtoy+1O/YsYNs27aNXpdz586RWrVqEQBk2bJl9MWHx+ORwsLCnyYkEELIq1evSL169QgA0r17d3pO48ePJ0+ePCFHjx4l9evXJwCIrKws2bRpExk6dKjIb+zp6UmaNm1KABBFRUXi5uZWSkgor4SHh5Pg4GBy+PBhMnr0aKKgoEDr5nA4ZNiwYcTOzo74+/uXueBqb29PX4BPnDhB3r17JyIYXLhwgfTu3Zvcvn27WoQEBwcHYmxsTFasWEEOHDhArl69Snx8fMirV69IampquSJCjx49SHR0NElLSxOrnzN+PNX5wikQCMiqVavovbtr1y4qJsjJyRFjY2NiZWVFIiMjKxUSHBwcSMOGDamo5u7uLiIiCMcJ4YLOixcvKhw3SgoJ+enpdFFz7dq1tL1DhgwhBQUFIscQh6KiInL48GHaBwcNGkSSk5NLjaVJSUkkJSWFXLt2jTRq1IgAIFwul+jr65MrV66QL1++VIuQ8ODBA7rwJSUlRTZt2kTGjBlDz3PatGkkMzOzVPuYkPDt/A4LNzVBSBAWe3t7ETEhKyuLLr5XJiaUJSS8e/eOLtJKS0uT8+fPk4SEhFIiQkkxYfny5bRPGBkZkWfPnpHnz58Tf39/4ubmRp4/f05OnTpFF1plZWVp/ymrD4wfP54AIJs2bSJPnz6l+7m4uJQSEW7cuEENU+rVq0fs7OzonK88AeHYsWMkJydHpM8yIeHbhQThPHL48OH0Ok+YMIHUqVOHACAtW7YkO3bsIHJycgQAadKkCZkyZQrdVldXl8THx5c7H/9aSHBxcaHvCW/evKGfx8fHk9jYWDJp0qQKxYTyhIR3795RIfzx48fky5cvtJ+ZmZnRtm7YsIHOuQGQiRMnkhcvXpDHjx8TPT09EfFAXl6eTJw4kVy4cIFERERUOk/8XiEhLi6OrF69WqR9Y8aMIW/fvi19XZmQUCm/w/OoJsOEhJ8DExIYDEZJ/hghoSbwJw1gNVVI4PP5xMfHhygqKhIApHnz5iQ4OPj/F1GzskQWxgYOHEhsbW1J165d6SKQuro6fXndtm0bMTExodsvXLiQ5OXllTuh/hYhwd/f/5cLCXFxcXSRW2gJr66uThflJCUlyYYNG+iCvKGhIbGysqILyosWLSKzZs0iAEifPn1KLTT8bCGBEEI8PDzowka/fv2oRZSOjg79f+vWrcnOnTvpAmft2rXJuXPn6O/58eNH0r17d3oNDh48KLaQIPQ6iImJIZ8+fSKXLl0iurq6Ii93whfbV69e0QXXkJAQoqqqSoWPDx8+iAgJHz58oAsgHA6HrFu3jmRlZX2TkJAYHU1/7/KKjIwMadGiBenbty+ZNGlSmSICExJqDtX9wikQCMjChQtFxISSlv/CvtGrVy+yZMkS4uTkRN69eyciIhw8eJCOHRoaGsTT07PUQqSwaGtrEwDkxo0bVRYShOXmzZtk/vz5JDY2tlT9lVFUVESWLFlCz83ExITk5eWVOZYKhYSUlBTy7t07MnbsWJHr0qRJE7J69Wpy586dbxYSLC0tSe3atWl9R44cEREVjh07Vu74z4SEb+d3WLipSUJCWWLC27dvxRIThEJCXFwcefbsGbGzsyOampoEAKlbty5xcnIiiYmJFQoJwrJ7927a/4YPH06ePHlCvRNcXFzo3FBoSNCxY0eSU87ihoWFBQGKPVBjY2OpsGBgYEAFhJiYGLJ582YqMnTo0IG8ePGCZGdnk5SUlFICQtOmTcnBgwdLCQhMSKgeIUF4f5Y0/tHT06PeJcLSvXt30q9fP/r3kiVLSH5+foXvFiWFhISEBDpPW758uch28fHxJCEhoZSYYGZmRj5//lypkGBpaUmAYo/apKQkKiTk5uaSxo0bEwDE3t6e8Hg8EhkZST2KhXPY1atXk48fPxIHBweybt06cvPmTRIXF0frj42NrTYhIT8/n4SHhxM3Nzdy5swZsn79ejJ58mSipKRE26Sjo0M8PT1J1v9EAiYkVJ3f4XlUk2FCws+BCQkMBqMkTEioRv6kAawmCwl8Pp8EBwdTTwOhNXnJCbKlpSW1Nu3SpQuxsbERsUivVasW+e+//0Tc5VevXk3y8/MrnFRXVUhoKC9PCgsLf7mQQAihLuHnzp2jFlxLliwRCf8DgHTt2pXcvHmTft6xY0fi7+9Pr6e3t3eZlsY/W0gghJAtW7bQdg8YMEDkPP7991+yaNEiugCgoaFBAgMDS/2m6enpNEwDALJgwQKSnJxcJSGhZPHy8iLGxsZUDACKLRhnz55NvLy8yLhx4whQbLEYEhJSSkgQ3pNC60egOGzThw8fqiwkdO/QgdZhZGRE5s+fT/7991+ira1NLazLKl+LCExIqDn8iBfOxMREETFhz5495PLly2TOnDl0UbtkUVRUJKNHjyYHDhwgc+fOpZ8PHjyY3tPlCQlCC9H//vvvm4WEikpFZGVlkZEjR9L2/vfffxWOrSWFBGF5/PgxWbhwIV2wFAp+ffv2JZaWluT169flCgmurq7k8uXLZNOmTWTChAkiIdL69u1Ldu3aRUUFVVVV4ufnV+FYz4SEb+d3WLipaULC12LCmDFjSEREhIiYcPbsWeLm5kbOnTtHNm/eTGbMmEF0dHRI8+bN6bNYWBo1akQ8PT2pl8LXQkJkZCRxcXEh6enpIp+bm5vTunr16kW8vb3JkydPSOfOnQkAKobXqVOHRERElHt93dzcCFBskBIbG0sePnxI+7O3t7eIUAKAjBs3jiQmJpLs7GwSERFBDVSEAsKhQ4dISkpKmd5DTEioPiFBWLZt20bnpdra2qTD/+Y7Y8eOpe8H0tLS5MCBA2K9VwiFhMLCQjJ48GACgLRt25akp6eXKSQIxYSSXg+dOnUinp6eFQoJwrBGZmZmIkLCtWvXaL/Izc0V6XePHz8mPXv2FOk7R44cIQkJCaXqr6qQkJ+fT6Kjo4mrqys5cuQIWbZsGfn333+JlpaWyDz269K2bVty7do1kp2dTQ25mJDwbfwOz6OaDBMSfg5MSGAwGCX5o5ItMyqHECL2dsLEcT8bcdrYunVrPHz4EJMmTcLz588xcuRInD17FuPHjwcAzJ49G02bNoWhoSFCQkKwadMmbNiwASoqKnj27BmMjY3x/PlzODg4AACMjIywfft2kGJxrdzj5uXloaioqOL25+XR/+vo6EBCQkKc0664TjF/t4rQ09PDq1ev4Ofnh3HjxsHGxgaXL1+GmZkZXr16BT6fD0VFRSxatAjOzs4ICQmBtLQ0bG1tYW5uDoFAgNGjR0NHR+e721Jd6OvrIz4+HmfOnIG3tzcGDRqEV69eYebMmXj+/DmOHz8OABgyZAisra1Rt25dFBYWitQhISGB8+fPo02bNti1axdOnjyJiIgInDlzBvXq1SvzuEVFReX+Jq1atcLOnTuxdu1anD59Gg4ODoiJicH58+dx4cIFEEJocmNJSUnk5eWBEAI+n4/AwECa2PrmzZv4+PEjlixZAl9fX/Ts2RPHjh2Dvr5+hdeEX+L83rx5AyUlJZqc72tSUlKQnp6O2NhYxMbGIi4uDtLS0pg2bRrq1q1b4XEYfw4cDgdbt24FAJw4cQLr1q3D/v37YWFhAQCIioqCp6cnXFxc8Pz5c6SmpsLZ2RnOzs60jvnz52PlypXgcrkoKioqd5xs3bo1ACA8PBw8Hq/c8bFk/xIICLhijIF8Pr/c72JjYzFmzBiEhISgVq1aOH/+PMaOHQsej1fuPkVFRSIJPAGgbdu22LFjBzZt2gQXFxdYW1vj0aNH8PX1ha+vL5SUlDBmzBgMGjQIsbGxNEFmeHg4kpKSyjzO/PnzkZubi40bNwIA+vbti+vXr0NZWbnScxbn2SAcc8ThVz33Gd+OQCAQ63cjhIg1H6nonho7dixsbW0xbdo0ODk5AQAOHjwIAHBxcYGpqWmFddeqVQvq6upo37491q9fj2bNmtF+W1RUBD6fj8+fP2P//v24ePEieDweGjRogLlz52LevHlo2LAhhg0bhvr162P9+vV4+vQpFixYAA0NDbx48QIyMjLIyMgAAJqMtrzzESaUj4qKQnJyMlq2bImhQ4fCzc0N5ubm+Pz5MyIjIyElJQVzc3NMmTIFsrKyCAoKwqRJk5CQkABFRUVs3rwZM2bMoMnQBQJBpdeY8f0YGxtDTU0NixcvRlBQEFq0aIHVq1fj1KlTyM7OhoqKCk6fPo2OHTuKNU7m5uaCy+Vi69at8PDwQJ06dWBrawsJCQnklkiSnZ+fj1q1agEAuFwuDh48iD59+mDz5s14+fIl9PT0sHLlSkyePJneE0LS09Ph7e0NoHhOLpz/EUJw7tw5el6SkpIibe7evTucnJxw7949bN++HVFRUVi2bBmOHz+OTp06oXHjxmjcuDGaNGmCBg0aQENDA40bNxZ5fhFCkJiYiLCwMLx9+xZv3rxBREQE3r59i5SUlHKvC5fLhaqqKlq0aIGWLVuiRYsW0NTUhJ6eHiQl2TICg8FgMBh/JT9Xt/iz+R2U0Or2NPhV7RNa1mVnZ4vEkp43bx5JTU2l2z158oRaXjdt2pQEBASQgoICEbfoPXv2VOryLCzCpHrieiQcsrD4qdelot/t/v379Dp8+fKFqKmpEQBky5YtZOfOnURdXZ24u7sTb29vGr/1yJEjxM/PjwDFYaFCQ0O/+Rx+lOUan8+n4alkZWXJhQsXSKtWrQhQHBrk6NGjFYarKllsbGyoRXC7du1IWFhYmeEVkpKSiLe3Nzl8+DCZOXMm6dy5M2ndujU5efKkiJdJeno6SU1NJfb29mTQoEH0nlu5ciXJyMigJf1/FtfC+PTTp0+nFoxhYWHUO4TD4RAzMzOSk5NTZvu/fPlCDMePp/ff8IEDSVxc3Df/Zoyax4+0XBMIRHMmtGzZksyZM4dcuXKFxpbOy8sjDx8+JOvWrSNdu3YlioqKxMbGRmSczM/PLzc0iY2NDQGKhauX/AABAABJREFUQ6RVlKS9pEcCLyvruyypnz9/TvOhNGzYkHh6eoo1HuTn54vUHxoaSi5evEjevn0r8vnbt2/JunXraFiKikrr1q3JuHHjyJYtW8jNmzfJs2fPREJJrV+/XuxcD9Vtsf43eS78SRag4sxdhEUcxLlPrl+/XipngpGREeFwOEReXp5oa2uTiRMnEjMzM3Lq1Cni4eFBoqKiSuU0KVnevXtH5s+fT+cfwP+HKAKKw/CZmpqSly9fkqKiIuLn5ycSYkW4DQCydu1asTx2hHOFu3fvkvz8fPLo0SOR+po2bUoePXpE8vPzSX5+PrGzs6Nx4bW0tEhkZGTVfqsaZL1ZHdSEfvTixQvSrFkzkd9NV1eXfPnyhRAi/jiZlZVF7OzsaB1WVlZlzvHLs7yPiYkR8Xjr3LkzefLkCcnMzKTl5MmT9N4p+fmHDx9oSM4PHz5UOD7n5+eT/fv3U8+bikrDhg1J165dSZ8+fUr1lZKFw+GQVq1akREjRpBly5YRS0tLcufOHRIWFkby8vK+6/f50+75H0FN6Ee/M8wj4efAPBIYDEZJmJBQjfwOA9ifJiQIBMVhf4RJggGQxo0bk6tXrxI+n08KCgpIREQEadeuHQGK4/H++++/dFtLS0uayE/4MlBQUEC2bt1KdHV1SyUOE0dISEtIoA/VAF/fn3pdKvrdcnNz6Quwv78/OXv2LL0mkZGRJDMzk8TFxdEX61GjRpH09HTSt29fAoDMnj37u87hR7rAFxQUkGHDhom8GKmrq5Nnz54RgUBQacgqYcnJySFPnz6lMY+VlJSIo6MjcXZ2Jtu3byfjxo2j16e8IrxvhEJCScHA39+f2NraktTU1FJCwuXLl6kYEh0dLRIOITs7m8yfP58eo2/fviQyMlKk7d7e3kRdXZ3U5nBEFmAZfxY/+oVTIBCQjRs30kXCkkVTU5MsXLiQXL9+nSQlJZW7WFmRkPDs2TMCgCgrK/8UIcHZ2ZmGctPU1KSJKKsiJERHR5NZs2bRMBpAcViHNWvWEF9fX5Kfn0+KiopIXl4ecXBwIAYGBqRRo0akb9++ZOHCheTkyZPE19eXpKWlEYFAQPLy8sjjx4/J/v37qdAtLy9PnJycqvT8ZULCt/MnLdz8CiFBIBAQOzs7ETEhPz+fZGVlleqH+fn5FYYji4yMJPPmzRMREAYMGEA8PDzo4r0wl5GwjBw5kjx8+JC8fv2aGkUIw40NHDiQHrOyc5g8eTIBQHbs2EHFAmHomYEDB5LPnz+T/Px8kpubSzZu3EiPP2LEiG+6J2rSokt1UFP6UXx8POnRowcBQJYuXUoKCwvpd+Lez8HBwaRevXoEKM6XVt4cvywhQRgWqaioiFy6dIkKYFJSUmTz5s005JUwSfLGjRtFhIT169cToDjfg7jjc3JyMrly5QrZu3cvWb58OZk4cSLR0dEh6urqIn2pLMFg5MiRZO3ateTSpUvk+fPn5YojVRk3yuNPu+d/BDWlH/2uMCHh58CEBAaDURImJFQjv8MA9icKCcLy8OFD0rZtWzph1tfXJ2/fviWFhYXky5cvpH///vQ7LpdLzp07Rxe3hELC58+fRRLmDh48WCRvgzhCgq+HR7XHwK2u3034EmNhYUHS09NpTOH58+eTzMxMMm3aNGqFFxUVRezt7QlQnFPi8+fP33UOPzqWbkZGBrXcHzlyJElOTqbfVUVIKCwsJFFRUaUWLr4uzZo1I6NGjSLm5ubE0dGRHDhwgMaTlZGRIf/99x9JSkoSEQzKK/Hx8dSibtu2beXGV7a2tiZ169YlQHEOhdu3b5Pc3FyyY8cOGjO6bfPmNWZSx6h+ftYLZ2ZmJnFxcSGrV68m2tra1Fqy5IJE165dyYoVK8jNmzfJly9fxBISUlJSaB0JCQk/TEgoKCggu3fvpov/gwYNIklJSYTH44ktJMTHx5M1a9ZQAVZoZfq1yNKwYUNiYmJCHB0dSVZWVql+GxUVRezs7MiyZctIr169aGJqYenUqROJiIio8vOXCQnfzp+0cPOrhITCwsJSCZjLEg3KExIqEhDKygfi6elJRo0aJTIWde/enZw5c4Ym1lVVVSVxcXFi5xDZu3cvAYrj6guFhOTkZOLq6kpycnJIfn4+SU1NpYmYAZBVq1ZVmI+qwt+qBi26VAc1qR8VFRWVOU8V517OysqiOWt69+5dYT60kovur1+/Jnp6ekRWVpZs376dvkvExMQQfX19es906dKFuLq60ns9ICCAiggpKSnUm+369evfPT4LhY2EhAQSGBhIbt++Ta5cuVJKMCiZXLo6xo1y2/OH3fM/gprUj35HmJDwc2BCAoPBKAkTEqqR32EA+5OFBIGg2NJz69atdKGmdu3a5L///qOWcsbGxqR+/frE1tZWZHErPz+fPHjwgCgrKxMARE5Ojtbh7OxcJSFh7/bt1f5Qra7fbf/+/QQAGTp0KMnMzCS3bt0iAIikpCQxNzenIsu9e/dIWloa0dTUJADIunXrvvscfkZSvuzsbJqgtOTnVRUSCgsLSUZGBpk+fTqpXbs2adeuHZkyZQrZvXs3cXV1JXFxcWVe+8jISBHPiA4dOhA/P79KhYQNGzZQcaKshUhhyc3NJa9fv6YCEADSvn17+v/JkyeTL58+1ZhJHaP6+VUvnCkpKeTGjRtk0aJFIvdcydKuXTsye/ZsYmdnRwoKCsoVE4QePz4+Pj9ESIiNjaXJMgGQWbNmiSSvrExIyMvLIxYWFkRBQYHW0a9fP+Ln50cEAgFJS0sjV65cIVOmTKEWrMJSu3ZtMmrUKLJ9+3Yyfvx4GlLp66KsrExGjRpF9u3bR7Kzs7/p+cuEhG/nT1q4+ZVCwtcJmIFij7mSpU+fPqU+U1NTE0m+PGDAAPLgwYMKk4sLy8uXL8ncuXNpGCNhkZKSEklQLo6QIEywrKamRoWEkuXjx49EW1ub1n/+/Pnv+61q0KJLdfA79CNxxkcjIyMqCr9//77COX5WVhbJysoiy5YtKyUqa2trk1evXhE+n0/S0tLImTNnRJ4jQOmwRra2tgQoTqBckZdeVYQEcQoTEmoOv0M/qskwIeHnwIQEBoNREiYkVCO/wwD2pwsJwhIWFkYGDhxIJ+579uwREQ2+XtjKy8sTiR8qJydHrctHjBghtpCQlJREumpq1lghQRj/tVGjRvQlRujGLyxr164lmZmZxMHBgQDFMYpTU1O/+xx+hpBQXvkWIaGwsJAUFBSQtLS0UvdLRS97fD6fWFtbU9f2jh07ViokCEMy2NraVriAkpubSwoKCkhGRgaZO3cu/c3q1KlDzp49S/Lz80leWlqNmdQxqp9f+cJZcmEhNjaW2NraElNT0zKFhfv375crJAwYMIAAxaHlyutH3yMkCHOm1K5dm5w4caJUqJXKhISrV6+KiIG3b98u95mTn59PXF1dyaJFi0rF6RYWCQkJ0rVrVzJ//nxibW1N3r17V+kzTByq63n5q5/7v4I/aeHmVwsJQjGhrHu/stKvXz/i4eFBPRfEERIKCgpIUVERiYuLI5s3b6ZeR8eOHSvlxVDZOaSnp9O2xMfHlxIShF6aSkpKxM3N7ft/qxq06FId/A79qLJ74Pnz53ScdnV1rdRYKCsri2zbto3eN6NGjSLHjh2jcz5lZWXy+fNnkp6eTjIzM0lERAQZPnw43f7rsEbGxsYEKM7xVh3jMxMSfj9+h35Uk2FCws+BCQkMBqMkXDAYfyBt27aFu7s7du3aBQDYsWMHYmNjAQBcbunbnsPhYO3atejUqRMkJSWRnZ2N3NxcqKioYO7cuWIfd926dXj79m31nEQ1Exsbi6VLlwIADAwM6OeLFy+m/1dQUMC6desAAJGRkQCAgQMHon79+j+xpTWHpUuXQlFREXPnzkViYqJY+3A4HPTs2RN8Ph9A8fWrDIFAAADo3LmzWMeoVasWjh49Cjs7O8ycORNPnjzBjBkzwOFwxNqfwfheGjdujKlTp+LUqVN49eoVEhMTcezYMfq9mppaufvq6+sDAKytrX9I296/fw8AOHbsGExNTavcLxQVFem/wcHBGDFiRLl1SEtLY/DgwThy5AgiIyMREBAAc3NzTJ48GTt37oS7uztSU1Px/PlzHD16FNOmTUOrVq1YX2X8USgpKX3TfqqqqujTp8837duwYUNMmjSJ9tfo6Ogq11GvXj00aNAAABAXF1fq+3fv3gEAjhw5gn79+n1TOxk1G+F9o62tjb59+4q1j3DONnXqVDg6OmLBggV49eoVOnfujKSkJEybNg1FRUUAip+VdnZ2uHDhAubPn4+FCxeK1NWlSxcAwJ07d5Cfn19NZ8VgMBgMBoPx42BCAuOPhcPhYOXKlejZsyeys7PpAnl5rF69GsHBwcjMzERAQAAePHiADx8+YOTIkWId79q1a7hw4UKNXCDi8XiYPHkyEhMT0alTJ1hYWNDvSr44NW/eHFJSUgBAhRdVVdWf29gagpeXF06fPg1CCKysrKClpYWjR4+Cx+NVuF9OTg4mTJiAzMxM9OrVC+bm5iLfE0JK7SNchElOTq5SG8eOHYvTp0+jbdu2VdqPwahulJSUqNg2YMAAtGrVqtxtZ8yYAWlpaQQEBCA4OLja25KVlQUAUFZW/qb9e/ToASkpKaSmplZpcZLD4aBLly4wNzfH5cuXYWZmhgEDBqBOnTrf1A4G43dhx44dAID58+eDx+OJlPz8/FKf2dvbQ1JSElevXsWsWbOo8F4VHj9+jP79+9Pn5t69e3H79u0q19OoUSMAQHx8fKnvhHU3adKkyvUyfg8yMjIAFBvSiIvw2ZKXl0c/a9KkCezt7SEnJwdvb2/s3r2bfsfhcDBhwgTs3bsXdevWFalr2rRpUFVVRWxsLM6fP/8dZ8JgMBgMBoPxc2BCAuOPhsvlwtLSElwuF9euXYOXl1el+8jIyKBr164YNGgQpKWlxTrOu3fvqLX/ihUrvqfJP4T169fDz88P9erVw82bNyErK0u/k5aWxty5c8HhcLBz5076+d8sJBQUFFBPjdGjR6Nr167IyMjAqlWr8M8//8DT07PM/Qgh1DKtUaNGuHjxIhVmCCEwMzND06ZNERgYKLKfUEhISUn5gWfFYPw4+Hw+rKysAAAzZ86scNsGDRpgzJgxAIAzZ85Ue1uys7MBoNSCjbjIyspCW1sbAODj41Nt7WIw/kS8vb3h7e0NaWlprF27Vqx9xo0bh8uXL3+zmHD79m3o6ekhLS0NPXr0gImJCYDisUfoTSkujRs3BgB8+fKl1HfCZ/K3elwwaj5CIUFeXl7k8yNHjkBdXR2vX78utU/Dhg0BAElJSSKfa2ho4PTp0wCAAwcOwMPDo9Ljy8jIYP369QCA3bt3M68EBoPBYDAYNR4mJDD+eLp27Yp58+YBKA5VU5lFuUAgwOrVq9G8eXO4u7tXWn9eXh5mzJiB7Oxs9OvXD6vXrKmWdlcXDg4OOHDgAADAysoKrVu3LrWNhYUFIiIi0L9/f/qZ0M2/WbNmP6ehNYiDBw8iIiICjRo1wtmzZ/H48WOcOHECSkpKCA0Nhb6+PiZNmlTKWvnkyZOwtbWFhIQE7OzsRKwYz5w5g5MnTyInJwd79uwR2U8YmqGqHgkMRk3B3d0d0dHRqF+/PhUJKmLOnDkAgCtXrtCF/+pC6JEgJyf3zXUIw60wIYHBqBihN4KJiUmV5gtfiwmzZ88WS0y4cOECxo8fj/z8fBgYGMDNzQ3Hjh1Dz549kZGRgcmTJ1dpMVbokZCQkCDyeVFREdLT0wEwIeFPRigk1KtXT+TzCxcuICUlBRcvXiy1j1BIKCvk5ZQpUzBv3jwQQmBqalqmp8vXmJiYMK8EBoPBYDAYvw1MSPjLIMUJtsUq1VmfQCCo1uOKi0AggEAggLm5OZSVlfH27VtYWlrSz4UlNjYWnz59wsePHzFjxgwcOnQIMTExGDduHFxcXPDp0yd8+vQJCQkJpdq7bt06vHr1CsrKyrhw4YJIDobqui58Pr9Um8sqeXl5yM/PpyUkJASzZs0CACxbtgzDhw9Hfn4+cnNzRcIMAMWL2SU/E3okNG3a9Kf/buJcO0IIeDweioqKKi08Hk+s+tLS0hAcHExDP23ZsgUCgQAZGRkYN24c/Pz8YGJiAi6Xixs3bkBTUxPm5uZITU2Fl5cX9UbZsWMH/vnnH3z48AFRUVGwt7enFmcAcP/+fTx8+BBRUVGIiopCrVq1ABQLCd9/v4h/DcW9r/h8/i/pv4yaRUW/u3Dxw8jICDweD1lZWRUWbW1ttGzZEllZWbh69WqZ950Qce8/YX8XChOysrJljge5ubmVlu7duwMAfH19xToun8+vtPB4PLG2Kyoq+iXPVXHr+hv7eU2+zr+qvtzcXNy/f596IyxZsqTMvpSRkSEyLylZDAwMYGVlBUlJSdjZ2cHY2BgFBQXlPse3b9+OBQsWQCAQwNDQEOfPn0dhYSFycnJw+vRpKCkpITg4GPPnz0d6erpY51EyR0LJOZBwkZjD4UBOTq5SIxTGz0WcuYs48xehWFSrVi1kZmYiMzMTHz58wIcPHwAAd+/eRUZGBv0uMzOT3jOJiYllHnP//v3o0KEDkpOTYWJigoKCgnLH+4KCAnC5XOrNs3v3bmRlZZUKB5aZmYmcnBxkZmbC398fx48fx/79+xEXF4ecnBxacnNzq3U8EOeZVdlzS8jf+vxgMBgMBuNPgwkJfxkcDkfsUpPbx+VyxdpOWloaUlJSaNiwIV0c3rlzJ5KSkiAlJUULIcWLqps3b8bVq1fB4XCgoaGBnJwcmJiY4PPnzyCEQFJSEnXq1KHlzp07NC+Cra0tWrVqhTolwgb97Osi5PPnz1i2bBl69OiBzMxM6OjoUKtBoHiBTU5OrtxSp04d6pFQk0MbiXtdZGRkIC0tXWmRlJTE+vXrkZ+fj379+mH8+PHgcrm0KCoqYvfu3XB1dUXfvn2Rl5eHnTt3onv37pg2bRp4PB7Gjh2LpUuX0vs0Li4Oq1evBp/Px6hRozB48GAAgK2tLSQlJSEpKUmTWaekpEBCQqLcUrt2bTHOQ+pX/iSMP5jy+ldycjJu3boFoNiy8uv71tfXF0uXLkVqair9TFJSkoZAOnv2bIXjmbjjvaSkJLhcLhUS5OXlaR8rWYTPhYqKMHdMeHg4kpKSKj1uRf22ZKnO5291Py9rYn4fRuWUfEZVVsShKveV0LvO2NgYampqZR6zsj4xbtw4WFtbUzFB6K1Uchug2Bhi+/btAICVK1fiyJEjkJKSQnp6Onx8fNC0aVOcPn2azsfs7OzEOg/hHCcpKUnkWZqZmQkAqF+/PmRlZcUOdcmoWVT2+ws9EhQVFSEjIwMZGRmEhITQ/WNiYhAZGUm/k5GRoV4sGRkZ4PF4pe55WVlZ2NnZQU5ODn5+fjh06FCF820Oh4NZs2ZRrwQrKyvavi9fvuD27dvYvn07DAwM0LRpU/Ts2ROLFy/GmjVrMGjQIJFwXsK+WVkRPi+ra9xgMBgMBoPx98BmB4y/BmNjY3Tv3h05OTlwcHAQ+U7otSAUEfbv34/r16+jXbt2SEpKwqxZs5CWliayT0FBAVavXg0A2LBhA4YOHfrTzqU8YmJisGzZMmhpaeHMmTPg8XgYMGAALl++TGP1i0NycjIKCgoAACoqKj+quTWOO3fu4OHDh5CWlsbevXvLXVhr3749XF1dYWVlBRUVFURFRSEuLg5t2rTByZMn6X7Z2dlYvHgxMjMz0bFjR2zdupUunt6+fZuGMiopJDAYvxu2trbg8Xjo3r07OnfuLPJdfHw8ZsyYQZMPl2Tq1Kk06XJQUFC1tCUnJ4f+/1tzJADFi0rt27cHUOyVwGAwRPHx8YGPjw+kpaWxcuXK76przJgx1DPh65wJ+fn5mDJlCs6cOQMOhwMLCwts2LABHA4HERERGDBgAMaNG4edO3diwIAB1LJ7zZo1ePnyZaXHFi4Kf50jQfh8ZmGN/myEglHJ0Eb+/v4i27i6uor8raCgAElJSQCl8yQI0dDQwKlTpwAUhw91c3OrsB0yMjJYt24d3d7Q0BAaGhpQU1PDhAkTcPDgQTx69AjZ2dmQk5ND//790aRJE4SHh2PAgAEsDB+DwWAwGIyfBhMSGH8NXC4Xw4cPBwARayOBQIAtW7aIiAhjxoxB3bp1ceHCBTRp0gSRkZEwNTUVibt7/vx5xMbGomnTpti0adPPPh0RPn/+jEWLFqFDhw5UQNDV1cWDBw9w7949mkxQXIRhjRo1avTXWOFlZWXR33HJkiVo1apVhdtzOBxMnDgRwcHBWLt2LXR1dWFnZ0cXLwUCATZu3IgPHz5AWVkZlpaWNJF3p06dUFhYCDs7OwD/nyOhvBdSBqOmQgjBhQsXAICGUSv53fLly6nF582bN/Ho0SP6fYMGDTB27FgAxV4J1YEwPwKXy0Xt2rW/qy4dHR0ALE8Cg1EWu3fvBlBspFEdnoujR48ulYA5OTkZw4cPh5OTE6SlpXH16lWaWNnf3x8GBgZ0vnLkyBG4urpi1apVGDx4MPLz8zFhwgQ6/pRHeTkShMK+MIwN489EGNqopJDw7NkzAECPHj0AFIejLAmXy4WysjKAsvMkCJk8eTLmzp0LQgiMjY2pp295zJw5E6qqqkhISMCNGzfw6dMncDgcaGlpYfr06Th27BiePn2K2NhYuLi44NGjR+jWrRtSU1Px77//wsrKqqqnz2AwGAwGg1FlmJDA+KsQWsu+ePECQPFC16JFi0qJCEIaNWqEixcvol69eggODsby5ctpTFNhqKT169dDRkbmp58LUPyiu2jRImr5VFJAcHV1Rb9+/b6p3piYGADF+RH+FszNzZGQkIDmzZtj2bJlYu8nJyeHLVu2wMXFBW3btqWf79ixA48ePYK0tDQsLS1pcj4Oh0O9Euzs7JCfnw8FBQUALNky4/fj6dOnCA0NRe3atTF16lSR7+zs7HD//n1IS0tj2LBhAIqthEvGGjc1NQVQfUmXhXXUrVv3u0P1CIUE5pHAYIji5eVVbd4IJfk6AbOGhgZ8fX1Rr149uLi4YPz48QCKLcTHjRuH9PR0dO/eHUZGRgCARYsWITo6GidPnoSqqirev3+P2bNnVxiXvUmTJgBKCwnMI+HvQCg0ycvLAwDy8vLoO8LmzZsBAIGBgaUMPSpKuFySAwcOoFOnTkhKSoKRkRECAgJEPOdKIiMjg4sXL2Lq1KnYsWMH7t+/j6SkJAQHB+Po0aOYOXMmOnToQMN9NWnSBK6urpgwYQKKioqwePFirFu3TiTHEIPBYDAYDEZ1w4QExl+FMFRFaGgogOJFsDNnzgAofmEoKSII0dDQwJEjRwAADx48gLu7Ox4/fozY2FjIyclh9uzZP6fxZbBhwwYqINStWxfOzs7fJSAAAI/Ho1ZNzZo1q6aW1mwKCgpw/PhxAMVxmIXJj7+VzMxM7Nu3DwCgqalZSpDp0qULJCQkkJaWhsePH+Pt27cAUO7LJYNRU3FxcQFQnHclICBA5DtLS0sAQMuWLTFgwAAAxTkHvLy86DbCRZzs7Gx4enp+d3s+fvwIQNS69FsRCs8hISEsQSSDUQLhHGHAgAHVnkeppJiQnZ2NJk2awNPTE7q6ugD+39MpPz8f7dq1g4ODA/bt24fmzZsjIyMDR48ehaKiIg4fPgwAcHBwKDU2laRx48Y0Vv6iRYuQlZWF/Px8XL9+HcD/Lxgz/kyEYTwjIiIAANHR0TTvQUFBAWrXrg1CCPVSAICwsDDqXVCZAF6rVi2aL8HX1xe9e/eGgoICNDQ0MHr0aGzatAnW1tYIDAxETk4OdHV1cenSJaxbtw4DBw6s9FlWu3ZtXLx4kXrUHj9+vNIwSgwGg8FgMBjfAxMSGH8VQhd4Ydz/Dh060BA2Tk5OyM3NLbUPIQROTk4Ail3ctbW10aZNG0hLSyM7OxuPHz/+OY0vAyMjI7rYn5WVhQULFuDUqVMiIZiqAo/Hw/Tp03H79m1IS0tj0aJF1dncGouMjAy1mLa0tKQxc7+VunXr0jAvL168gIGBAaysrFBYWIj09HTMnTsXfD4fTZs2RXR0NI4dOwYAmDt37vedCIPxk5k4cSJUVVWRkpICPT09zJ49m4aKmDNnDiQlJfH27VusX78eANCpUyf06NEDubm5WLFiBbUw7tq1K01w/D0IBbwRI0Z8d13CONnt27dniYgZjBL8+++/AAA3Nzd4eHhUe/3jxo3DrVu3MGfOHDx69AidOnWi33E4HDpuvH37FidOnMCDBw8QFRUFANDT04NAIKBGIi1atICmpma5x5KXl8eGDRsAAOfOnUO3bt0wcuRIeHl5QVZWlnpNMf5MZsyYAQDYvn07PD090apVK3To0AECgQATJkxAXl4eWrVqhd69ewMo9k7o378/kpKS0KZNG+jp6VV6DA0NDTg5OWHAgAE0JFJUVBRcXFxw8OBBzJkzB71790b9+vXRpk0bTJkyBR8+fBD7HDgcDvWgk5aWhpaWVlUvA4PBYIhFWFgYgoKCEBQUJBIqOiQkhH4eHR396xrIYDB+CkxIYPxVCN2VhZamdevWxZ07d1C/fn28fPkSK1asoAn+hNja2sLZ2RkSEhI4evQolJWV0bRpU8yZMwcAsG3btl9mrdq/f3+Eh4fj+PHjaNasGeLi4rBixQpoaWlVWVAQigiOjo6QlpbGtWvX0L9//x/Y+pqFlZUVVFVV8fHjRyxbtuy7flMOh4OjR4/iwoULaN++PbKzs7Fv3z6MHTsWJiYmeP/+PRo2bAhDQ0Ps378fAGBmZoalS5dW1+kwGD+FTp064dWrV1i4cCGA4n7Uq1cvODs7w8TEBN7e3ujevTs4HA6WLFkCNzc3REVFQVdXl1o1r169Gn5+fjTp+Lfi7e0Nb29vSEtL04Sr34OjoyMAYMKECd9dF4PxJzF+/HgaMmjOnDmIj4+v9mMMHToUJ0+eRPPmzUt9t3PnTqxatQpAca4GoWfookWLMGzYMFhaWuLBgweQkZHBjRs3ICcnV+GxzM3N4erqCnV1dXz69Ak+Pj6QlZXFrVu3oK2tXe3nxqg5rFy5EjNmzACfz8esWbPw/v17XLlyhebO6Nq1K+7duwclJSW4u7tj7NixSE1NRY8ePeDj4yO295uuri7c3NwQFxeHuLg4eHh4wNLSEnPnzoWurq6IwODg4IA+ffqI5BSqjP/++w9A9eUsYTAYjJI0aNAAsrKymDZtGrp164Zu3bqJGAD17duXfq6pqcnEBAbjD4cJCYy/iqCgIADFoWWEtGnTBqdPn4a0tDTc3d2xY8cOuogcHByMXbt2ASiO7d2zZ0+6n5mZGaSlpfHo0SM4ODj8vJP4ChkZGcyfPx/h4eE4cuQIVFVVqaDQvn17HDhwAPb29nBycsK9e/fg6emJJ0+eICAgAK9fv0ZERASioqJKiQjCxNR/C0pKSjh79iykpKRw9+5dnDx58rvr7Nq1K+zt7bFjxw4oKSkhKioK4eHhqF+/PmbPno2DBw+CEAIjIyN6nzEYvxv16tXD0aNH8ejRI7Rr1w6JiYkwNjbGtGnToKioSMWDbdu24cyZMxgyZAjevXuHJk2a4MGDB9izZ0+15JnZuXMnAMDExOS7w7KlpaXRUEsTJ0787rYxGH8aFhYW6NixI5KTkzFr1iwUFRX9tGNzOBysX78ee/fuBYfDAZ/PR69evbBp0yb4+PjQRdWjR4+ia9euYtU5cOBABAUFYeHChdDS0sKtW7e+K0wk4/eAw+Hg9OnT6N27N7KysmBoaAgZGRncvXsX+/btg5OTE5SUlHDt2jUYGRkhNzcXenp6cHNz++ZE3MrKyujfvz8WLFiAw4cPw83NDbGxsYiNjcWDBw/QvXt3pKamQl9fHxcuXKi0vkePHsHX1xfS0tJUYGMwGIzqRE1NDWFhYQgMDKSlZA4xX19fBAYGwtbWFrm5uSzvH4Pxh8OEBMZfQ15eHu7evQsA6NOnj8h32traOHjwIDgcDmxsbHD+/HkkJydj8eLF4PF40NfXpx4IQlRVVbFkyRIAwPTp0+Hn5/dzTqQcZGRkMGfOHLx+/RqWlpZQVVVFfHw8Nm3ahJkzZ2Lq1KkYN24cDAwMMGzYMPTp0wfdunVDx44d0bZt279aRBCira2NHTt2AChOlvz06dPvrpPL5WLcuHFwcXGBiYkJtLW1sXDhQuzbtw98Ph9jxoyBmZkZC53C+O3p06cPgoKCsHr1akhKSuLu3bvo2bMnLl26hJycHIwbNw6bN28Gj8fDiBEj4Ovri8GDB1fLsb28vKrVG+Hu3bvg8Xjo2LEj2rVrVw0tZDD+LGrVqgVra2vIycnBz8+PLt7/TExMTHD16lWYmJjgwoULSE5Oxty5cyEQCDB16tQq57CSk5PDoUOHEBQUxESEvwgZGRlcunQJzZs3x6dPnzBjxgyoqKjAxMQEcnJyOHHiBBYsWICioiKMHz8ezs7OlXq5fAvKysoYMGAAPDw8MHHiRBQVFWH+/PlYvXp1KW/pkjBvBAaD8TNQU1ODtrY2LSUNM7t06QJtbe0KQwkyGIw/B8lf3QAG40fC4/Ho/x0cHJCRkYFmzZpBR0dH5Dsej4dhw4bBzMwMFhYWsLCwwLVr15CQkICWLVvCwsIChBDk5eWJJMTdtGkT3rx5A1dXV4waNQoPHjxAG3V1+j0hpMIQOYQQsRaQBQKBWNsRQiAjIwNTU1PMmDED1tbWcHV1RW5uLgoKCmjJz89HQUEBCgsL6d9KSko4ceIE9PX1aZt/ZYJRgUAAgUBQ4TbiXr/KfgchfD4fM2bMwLNnz+Do6AhTU1M8ePCgVLLF3NxcSEtLV1pfQUEBpKSkAAB16tTBypUrERAQgPnz56OoqAjDhg3Dtm3bAKDScxVuw+VWrP+WrKe67j8GQ1xkZGSwYcMGjB07FkuXLkVgYCCWL18OSUlJFBUVoXbt2rCwsICxsXGF92fJz8UZC4T9aMaMGVBSUioz342Q3NxcyMrKVlif0Mts/PjxlR5bIBBAQkKiwm2E27H+xqhuxH1Oi/P8qOp2LVu2xJEjRzB79mzs378fvXr1wpAhQ0S2KyoqqnARVEh+fr5Yx+XxeCL9aNCgQRg0aBB4PB7Gjh2LpKQktG/fHhYWFqy//cGIc08BxfepcB5WEQoKCrh69SqGDRuGZ8+eYdmyZTh+/Di2b99O81jNmzcPW7duhYSERKXH5/P53zw/rVWrFmxsbKCpqYnt27fD0tISb968wcWLFyEvLy+yrY+PD/VGWL58OYqKisQaE6p7PGAwGAwGg/H3wISEv4zqfqkSt75f9TJXcvJra2sLoNh7QFJS9NZv3rw5JCUlsW3bNmRmZuL48eP48OED6tSpA0dHR7Rv3x5A8QJUyXORkpKCtbU1Ro4cCX9/f4wdOxbu//N6AIrPu6Jzr+x7IeIsUgFA7dq16TnXqlULS5YsoV4TJamKQPCrfjtxro2kpKRYLzjinq+ysjI4HA6srKwQHh6O0NBQLF26FK6uriIvorVq1RLruJqamqhbty79++HDh1iyZAny8/MxYsQI3Lx5E9LS0mIvMIpzTUp+X9n2EhISbKGFITbiLibUr18fOjo6ePbsGY4ePYqNGzciNzcXXbt2xZUrV6iFf0UL9CXvSy6XW+F9WtIbYfXq1ZWOl7KyshUKCV+HNaqucagqfU3c8aA6YWNB+Yj7rBa3ruqkuudh4p6rnJwcOBwOZs6cCX9/f5w+fRrz5s2DhYUFxowZA0VFRQDiPy85HI5Ycx1FRcUyhfzVq1fD398f9erVw82bN6GqqirWeVRHWDXGz0fc55G497OioiKUlJRw/fp1GBgY4Pr16wgPD8fLly8BFFv9r127VuyFdSkpKbHu54rmYVu3boWWlhZmzpwJDw8PDBs2DI6OjmjVqhXdZu/evQCKvXPatGkjtoGIuOOBhITEdwkJVZmTMhgMBoPB+D34q00Mrl+/jszMzF/dDMZPIDY2Fu7u7gCKhYTy4HA4OHDgAKZMmYJatWrh3LlzVEQoD1lZWVy/fh0aGhqIiYnBlMmTq7XtjJ9PnTp1cO3aNcjJycHb2xu6urp49+7dN9cXEhKCESNGYMiQIcjKysLAgQNx7do1sbwaGIzfFQkJCSxfvhxhYWG4du0anjx58kPCBAnDkc2YMaNawjrcunULPB4PHTp0YGGNGAwxOHjwILp06YLk5GSYmpqiSZMmMDAwwIULF5CamvrDj+/g4IDDhw8DAM6fPw8NDY0ffkzGn8mQIUOoB8LLly/B5XJx9uxZrFu37pcsgk+cOBGPHj2CiooKwsLCoKOjQ5Mwe3t749GjR9UW0o/BYDAYDAZDHP5aIeHkyZNYtWoVSwTzl3D58mUIBAL07dtXxJKnLCQkJGBjY4MvX75gwoQJYtWvpKQEZ2dnNGnSBOHh4dXRZMYvpl27drhy5Qrk5eXh7++Pbt264ezZs1Xy5nj//j2mTp0KbW1t3Lt3DxISEpg3bx6cnZ1Ru3btH9h6BqPmoKamhokTJ/4Qy18vLy94eXlBWloaK1eurJY6b968CaA4rBGDwaicWrVqwc3NDTt27EDnzp1RVFSE+/fvw9TUFM2aNcPIkSNx8eLFHyIqvHz5kuZCWLFiBcaOHVvtx2D8XcydOxdbt25FixYtcPPmTZiYmPzS9nTr1g2PHz9Gt27dkJqaimHDhuH8+fPYvn07gGJvhGbNmv3SNjIYDAaDwfh7+CuFhJMnT8LCwgIPHz5Ey5Ytf3VzGD8YQgisra0BFFusiktlMbS/Rk1NDY6OjqhXIn6puDFcGTUTAwMDBAcHQ1dXF7m5uViwYAHGjh1b6WJIamoqlixZgh49esDe3h4AMHXqVISFheHkyZM/JEkfg/E3IvRGmD17drV4I6SlpVHvNSYkMBjio6ioiA0bNiAoKAhhYWHYsWMHOnXqREWFuXPnomnTplRUSExM/OZj8Xg83Lx5E8OGDYO2tjaysrLQp0+fX5LwmfFnsnnzZrx//x6jRo361U0BAKioqODhw4eYNGkSTcLMvBEYDAaDwWD8Cv46IaGkiNC6dWskJSVh165dmD17No4dO4b8/Hyx6yooKEBmZqZIYdQ84uPjERERAQAYPHjwDz1Whw4dsGf3bvq3t7f3Dz3en0BN70dqampwc3PD3r17IS0tjTt37mD16tUV7mNmZgZra2vw+XyMGDECwcHBuHz5Mlq3bv2TWs3426jp/ehHEBkZCS8vLwAoMxdMVfHy8sKAAQNYWKO/mL+xH/0I2rRpgw0bNiA4OBivXr3C9u3b0bFjRxFRQVVVFX379sXu3bvx6tUrsbz9Pnz4AHNzczRv3hyTJ0+Gh4cHOBwODAwMYG9vL1ZSXcaPh/WjH4OsrCxsbW2xZcsW+hnzRmAwGAwGg/Gz+auEBD6fjytXrkBGRga1a9dGYGAgtLS04Orqiri4OKxatQp9+vRBRkaGWPVZWFhAXl6eFjaRq5k0bNgQbdq0AfD/ScnKIi8vD56enjA3N0e/fv2goaGBTZs2IT4+XuxjxcTEYPeePfRvbW3tb2/4X8Lv0I+4XC5WrlwJV1dXAMDdu3cr9EoQxq+9cOECbt++jc6dO/+UdjL+Xn6HflTdqKqqokWLFgCAGzdufHM9CQkJmDZtGoYOHYrQ0FAoKyvTeOuMv4u/sR/9aNq0aYP169cjKCgIb968wfbt29G1a1cQQvDs2TNs3rwZ2tra0NLSwvLly+Hm5oaCggK6P4/Hg4ODA0aMGAEtLS3s378fX758QePGjWFmZoaIiAjcunULjRs3/oVnySgJ60c/Dg6Hg02bNsHR0RGLFi3Ctm3bfnWTGAwGg8Fg/GVwSFUCfv8BZGdnQ19fH1++fEFhYSH27NmDKVOmAAACAwMxcOBATJkyBWfOnKm0roKCApGXnczMTDRr1gwZGRmoV6/eDzsHhvjweDwAxZ4BQ4cOBYfDgbe3N3r16gUej4eAgAB4enrC09MTT58+Ffk9hUhLS8PIyAgrVqyAuro6JCQkyjxWTEwMhg8fjoSoKAS2aQsAaBMYAG4lIZLESd4mEAgq3UYIl1u5PliVbl9dyeUyMzMhLy9fqn+U14/S0tLE6kfVeb58Pr/S8+3WrRtevHiB/fv3w9TUtNT3CQkJaNu2LbhcLj5//owmTZpUelyBQCD2fVDZ+Qpyc/Gu+z8Aqu/+Y9QcqtqPavLzqKJxreR93Pq5PyTq1ClzOzs7OxgZGUFOTg4hISFiLSby+Xwauu7z588YMmQIIiMjweVyMW/ePGzbtg3169cHIaTc8f5rxBmHqnscZ3w7f1I/Evf5Ju5zhhAi9v0sTn1FRUVl1hcbGwsXFxfcvXsXHh4eIh7BcnJyGDJkCNTU1GBvb48vX74AKH5eDRkyBPPmzcOIESMq9UAQt//+LAS5uQjX7gYAaBsUWOnzuabzK/uRuKFDxR3Hxb2f+Xy+2PNOcY5LCBG7H4nbfyUlJSvdTtzzBb7vefSn3fM/gvL60bdu97cRFBSEbt26ITAwkBnw/WTK6t+/6vdg/YPB+Ln8FW+qp0+fhr+/P4DilxNXV1c0atQInTt3piICULxAuGrVKjg7O4tVr4yMDOrVqydSGDUTXV1dzJgxA4QQmJiY4N9//0XDhg2hq6uLrVu3wtvbGwUFBVBRUYGhoSHOnj2Lq1evonfv3igsLMTFixfRqVMnTJkyBX5+fqVe3IUiwsePH6HevPmvOcnflN+tH02fPh0AcPXq1TK/DwkJAVBshclyITB+Fr9bP6ouJk+ejH/++QfZ2dmwsLCo0r4lRYSWLVviyZMnsLS0RP369X9Qaxk1nb+1H/0KmjZtClNTUzg5OeHLly+4du0aZs+eDRUVFWRnZ8PJyQmWlpbU+2DdunUIDQ2Fs7MzxowZw8IY1WBYP2IwGAwGg8H4c6ncZOE35+TJk1i0aBH09fXh4uIC4P/FBF9f31LbN27cGPIlkuUy/hx2796Nu3fv4v3793j//j0AoEGDBtDV1UX//v0xePBgtGnTRsRCZ8KECXj8+DEOHjyIW7du4d69e7h37x7++ecfLF26FKNGjUJ8fDwVEVq0aAFHR0eQyVPKawbjN2fq1KlYt24dAgMDERERQcNmCREKCV26dPn5jWMw/jI4HA727t2LgQMHwsrKCgsXLkTbtm0r3e9rEcHd3Z2F32AwfhGysrIwMDDAqFGjQAhBcHAw7t69i0+fPuHff/+FgYEBFQ6q4tnDYDAYDAaDwWAwqpc/WkgQJlbesmULtm3bhvDwcLrAICcnB319fZHti4qKcPHixTLDlTBqFt/iyq+kpITLly/j0qVL0NbWxsCBA6GlpQUulws+n1+udZuOjg50dHQQHh6O/fv34+rVq3j+/DmmT5+Oli1bgs/n49OnT2jRogVcXFygoqSEWDHPoyquyuK6SItzbcR1pa7piLugIE5IIEC869KoUSMMGjQIbm5usLa2xqZNm0S+DwwMBABoaWkhPz8fdevWFauNDAajbCob1/r164d///0Xt2/fxpYtW3D58uUK6/v06RPGjBlDRQQ3NzeoqqqWOoa4z5mqjLsMxq9E3Pu0Ou9nQohYz2phfRwOB9ra2j8tJAILOcYQUp39Q9x5Z2FhodghlaSlpSvdjsH4HYiOjkZycnKl2zVo0ABqamo/oUUMBoPBqAp/rJAgFBEePnwIdXV1nD59GpaWljh+/HiZ24eFhWHhwoVo0qQJVqxY8ZNby/hRSElJiUzQ9fT0oKenV+l2ZaGlpYULFy7AwsICx44dw4kTJxAZGQkAaNmyJTw9PdGsWTMIcnPpPhwOp8J6xV3M53A41ZoLoLJ21QS4XG61vbSLe76SkpJibTdz5ky4ubnh5s2b2LVrl4jI8+rVKwBAr169ICcnJ1Z91RmHHSW2+R1+Z8bfS4X3c4nvJCQkKr33d+/eDRcXF9y5cwdBQUHo27dvmdt9/vxZREQQjtvlIa7QKw6sPzJ+BNUd51xcQwMul1utz1VJSUm2UM8Qm+rOfyHuvSfuOC5u//i6vqKiIsTGxkJdXV1kO0lJSbFyH4hLTcsfwvh7iI6OhqamJnJLvC+Xh6ysLMLCwpiYwGAwGDWMP3LGfvHiRSoitG7dGlJSUli0aBEuXbqE9PT0Utvb2tpi6dKlMDY2hoODQ7VO1Bh/Ho0aNcKOHTsQHR2NI0eOwMTEpNLFKMafxfDhw1G/fn3ExcXBy8uLfh4fH4/4+HhwuVx06tTp1zWQwfjL0NTUhImJCQBg3bp1ZS7ufx3OiI3bDAaDwagpxMbGok+fPmjTpg1Onz79q5vDYPwQkpOTkZubC1tbWwQGBpZbbG1tkZubK5bnAoPBYDB+Ln+kkNC7d28qIgiZP38+BAIBzp8/X2r7adOmwc3NDTNnzmTWggyxqVOnDpYsWYJz585V22LU5cuXMXbsWISGhlZLfYwfg4yMDCZOnAigWIgUIsyP0LZtW5ZomcH4yWzevBl16tSBv78/bty4IfIdExEYDAaDUVN58eIF+vXrR+eRq1atwtOnT39toxiMH4impiYNY1dW0dTU/NVNZDAYDEY5/JFCQrt27UREBKA4Pv706dNx7Ngx8Pn8X9QyBqN8Tp8+jZkzZ+LOnTsYOnQoExNqOEZGRgCA27dvIzMzEwAQHBwMgCVaZjB+BY0bN8bq1asBAJs2bUJBQQGA0iKCm5sbExEYDAaDUSO4d+8eBg4ciNjYWLRr1w7Dhw8Hj8eDoaEhEhMTf3XzGAwGg8FgMET4I4WE8li+fDk+ffqEW7du/eqmMBgiZGdni+TmSExMLJXEl1Gz6NatG9q2bYu8vDxs2rQJycnJsLOzA4CfliSSwWCIsnz5cjRu3BiRkZFwdHQEUBzqSCgiuLu7MxGBwWAwGDUCQgjmzJmDnJwcdOzYEd7e3rCxsUHjxo0RGxuLEydO/OomMhgMRpUJCwtDUFBQhSU6OvpXN5PBYHwjf5WQoKmpCT09PRw5cuRXN4XBEEFOTg4GBgYin40fP/4XtYYhDhwOBzt27ACHw8H58+fxzz//4MOHD2jWrBkNe8RgMH4ucnJymDRpEgDg2bNnAAA/Pz8AwNmzZ5mIwGAwGIwaA4fDwbhx4wAAoaGh8PDwQEBAABISEgAAgwYN+pXNYzAYjCrRoEEDyMrKYtq0aejWrVuFRVNTk4kJDMZvyl+XVXjFihXQ19fHy5cvWTJURo3i8uXLMDIygouLCw4dOkRD5zBqLiNGjMDBgwexYsUKJCYmQlFREU5OTlBWVv7VTWMw/lqEHkFBQUGIj49HXFwcuFwuunXr9otbxmAwGAyGKIcPH0ZeXh5sbGwwffp0KCkpAQDmzp2L/v37/+LWMRgMhvioqakhLCys0iTZYWFhmDZtGpKTk6GmpvaTWsdgMKqL31pISE1Nxf3796Gqqop+/fqJtc+wYcOwfPlytG3b9ge3jsGoGjIyMrhx4wZycnJQp06dX90chpjMnTsXubm5uHbtGg4fPox27dr96iYxGH81QiEhJCQEz58/B1CcO4mNqwwGg8GoaUhISOD06dMAABsbGyQmJkJdXR0WFha/uGUMBoNRddTU1Jg4wGD84fy2oY2uXbsGdXV1rFixAv3798euXbvE3vfQoUOQkZH5ga1jML4dttj1+7F8+XI8fvwYPXr0+NVNYTD+etq0aYM6deogNzeX5i3p2rXrL24Vg8FgMBhlIxQT5s2bh8aNG+P8+fOQk5P71c1iMBgMBoPBKMVvKSQ8fvwYK1asgIeHBxISErB161bY2tqWue2rV68wbtw4ZGdn/+RWMmoCfD5frFJUVASBQFBp4fP5IIRUWoRUZVuGKOJcO4FAUK3b8Xg8FBUVVVrEvQ/Y78tg/DyE4zkAdO7cGQDg5OQEoFhIKDnmV+e4IW4/F/d5JDwHxp+JuPdUdT8/xD1mdT9Xq3s79lxl/Eqqu/+W3J7L5eLIkSP4+PEj+vbtK/JdTk6OSHny5An27NmDhISEUt8xGAwGg8Fg/Eh+y9BGmzdvxunTp6n1b+/eveHr64tjx46hVq1aMDIyQu3atQEAycnJcHV1xa5d/8feXUc3lbwNHP8mdaNAcdeFUtxtcS0Oxd0Wd1/cXYrD4m5bFnfYxRZncbfiUKSuyX3/6Jv7a6mlUKjwfM7JOW0yuZmbXJ1nZp7JMkQ0CdFoNEaV02rjNlam0Whi/Gxj6wZgamoaq/IxictlJQZardaodTbmdzOUM4aVlRUmJiZGlRVCfBtj918zMzP17+LFi3P27FmCg4MBKFGiBKamoZc8iqLE+XHXmOXF9flIiNiIzXWTsduzMeVMTEyMKhdfAQLZLwXE/X2FsecFY0fIG85liqKwZMkSRo4cSXBwMLt27cLNzQ07OzujliNEYnPnzp1vel0IIUTcS3SBBL1ej6+vL3Xr1gUgICCAcePG8fjxY0xNTTl58iRLly7l1KlTWFlZUblyZU6ePEmBAgXiueZCCCGE+BHCJlbWarUULlw4/iojhBBCfKPPnz/To0cP9uzZA4QG6f79918aN24swQSR5KRKlQpra2vatGkTY1lra2tSpUr1A2olhBACEmEgQavVcvjwYfX/Ll26kDZtWo4cOYKNjQ03btygVKlSrF+/nt9++w0I7ZkohBBCiJ+DIeEySKJlIYQQidvly5fp0KEDT58+xdzcnClTplCyZEnq168vwQSRJGXJkoU7d+7g4eERY9lUqVJJcl8hhPiBEl0gASBZsmTq3xMmTCBLlizqlAUFChSgVKlSvH79Or6qJ4QQQoh4ZEi47OvrGy6oIIQQQiQmixYtYuDAgQQHB5MtWzbWrVunntd2794dIZgggXORVGTJkkUCBEIIkQAl+olBc+TIoQYRAHx8fLh+/TpVqlSJx1oJIYQQIr6YmJioDS1hpzkSQgghEosnT57Qp08fgoODyZcvH6dPnw4XHC9atCibNm0C4N9//2X9+vXxVVUhhBBC/CQSfSAhLD8/P9q0aYOzszO//vprfFdHCCGEEPFk2rRp9OrVi06dOsV3VYQQQohYy5IlC9WqVQPg9u3bjB49Gn9/f/V1b29vJk6cCIC9vT1Vq1aNl3oKIYQQ4ueRJAIJnz59YtasWRQtWpQMGTKwYsWK+K6SEEIIIeJRqVKlcHV1lWkehBBCJEomJiYcOHCA4cOHo9FoWL16NZUrV+b+/ft4e3vTuHFj/v33X+zt7dmzZw958uSJ7yoLIYQQIolLlDkSvpQ8eXKyZMnCoUOHyJo1a3xXRwghhBBCCCGE+CYmJiaMHDmScuXK0alTJ27evMmvv/5Kjhw5uHHjhhpEkHxAIr65u7vHmBz5zp07P6g2QgghvpckEUjQaDQ0a9YsvqshhBBCCCGEEELEqcqVK/Pvv//SsWNHTp06JUEEkaC4u7vj6OiIn59fjGWtra1JlSrVD6iVEEKI7yFJBBKE+FZ6vR6NRhNjuZCQEExMTKJflk6n/q0oCoqiRFvemM/9GQUEBGBubh5tGUVRsLKyinFZMf0GYcv9TL+Hsd8LyHYqEj5jt2e9Xo9WG/PMjkFBQRHK+fn5YW1tHaFsTMcqIWIrvo7PcX2+1Ol0RpXT6/WYmhp3WyLnI5FUrl90Ye4ZoqPX6zExMSFdunTs3buXmTNncuzYMaZPnx4uiKDX641eZkz3M0LEhoeHB35+fmzYsAFHR8doy6ZKlYosWbL8oJoJIYSIaxJIEEmasRfJxt5kaLXaGMuGfV2j0SToG5iEzJjvztjv15hGQwAzMzP5vYRIpIw93htzHIfwx3IvLy/atGnDiRMnWLp0Ka1bt/5u9RNJW3ydY2JznWPs8uLy/GvsfilEfIrrbdTY5dnY2IQ7h0ycOFFNshyWsUEEIb4XR0dHGSEjhBBJnAQShBAJkp+fX7ibJnNzc8zMzOKxRkKIn9HLly9p0KABN27cAOC3334jTZo0VK9ePZ5rJoQQQgghhBBC/DjGdRMSQogfLHv27KRKlUp9pEmThpMnT8Z3tYQQP5GbN29SoUIFbty4Qdq0aalduzYhISG0aNGC//77L76rJ4QQQgghhBBC/DASSBBCJAoBAQF07doVb2/v+K6KEOIncPz4cSpXrsyLFy/IkycPJ0+eZNu2bVSqVAkfHx8aNGjAkydP4ruaQgghhBBCCCHEDyGBBCFEgvTPP/9w+fJlLl++zD///IOZmRnu7u5s3bo1vqsmhPgJ9OzZEy8vLxwcHDh27BjZsmXD3NycLVu2kCxZMt68ecOMGTPiu5pCCCGEEEIIIb7CwYMHyZMnD5aWllSuXJlnz55FWfbSpUsULVoUCwsLihUrFukI9c+fP5MuXTry58//HWsdvySQIIRIkCpWrEixYsUoVqwYFStWJDg4mOTJk1OlSpX4rpoQ4ifQvn17AD58+ECbNm149+4dOp2OQYMG4eXlhYmJCQ0bNozfSgohhBBCCCGEiLXXr1/j4uLC77//zsuXL8mVKxfNmzePtKy/vz/169fHxcWFN2/eUK9ePRo0aEBQUFC4csOGDcPJyelHVD/eSCBBCJEgWVlZYW1trT5y5MjB9u3byZEjR3xXTQjxExgxYgQbN27ExsaGv//+m9KlS9O8eXM2btyIiYkJGzdupGbNmvFdTSGEEEIIIYQQUahbty4HDx6M8Pz27dvJly8f7du3x8HBgalTp3Lp0iXu3r0boeyRI0fQ6/UMHz6cFClSMGrUKHx9fTl+/Lha5syZM9y6dYsOHTp8z9WJd6bxXQEhhIjM06dPSZYsWXxXQwjxE3NxccHJyYnmzZtz7949Xr58qQYRGjVqFN/VE0IIIYQQQgjxFW7dukXhwoXV/1OlSkWmTJm4desWefPmjVC2YMGCaLWh/fFNTU3Jnz8/t27dolatWgQHB9OzZ0/WrVvHjRs3fuRq/HASSBBCCCGEiIKjoyNnzpyhV69eHD58mCVLlkgQQQghhBA/BXd3dzw8PKItc+fOnR9UGyFEQqXX69mzZw9XrlzB3NycqlWrUrp0aaPeu2DBgkhzE2TMmJEBAwZE+p7z58/zzz//4O/vT6lSpahZsyYajSZWdfbx8SF9+vThnrO3t8fb2zvSsl92dA1bdvr06VSuXJlChQpJIEGI6CiKYnTZ2O7U4uemKEqsti8hhPhe7OzsWLduHYqiJJhzmZx/Eydjf7eEtK0JIRIHuW4WsWFMgOD9+/c0btwYPz+/GJdnbW1NqlSp4qp6QgDGbac+Pj4/qDYiKj4+PtSuXZvHjx/TsWNH3r59S9WqVenUqRMLFiyI8f0bN27k7du39OrVK9zzDg4OkX5Wy5YtuXjxIs2bNydt2rQsW7aM6dOnc+LECbWcpaUlgYGB6v/79u1T/x40aBCzZs3C1tYWLy+vcMv39PTEzs4uwudGV/bJkyesWrUq0uTLSZEEEoQAdXhSXJTTmJj872+NRhoCvpKlpSWWlpZxsiz5DSIn34tISozdno0tZ2Fh8S3VEeKbxNf1Q1x/pkmYayIh4kpSuX6J6/1D9jcB3ydAcPDgQVKnTh1tuVSpUpElS5ZY1VWI6Li7u+Po6GjUdiri14gRI7hy5Qo3b94ke/bsAFStWpWGDRtSvnz5KBMYh5UxY0YGDx4cY7lWrVrx6NEjbt++TcqUKdXnvxwFEBAQoP5dt25devfuTa1atcKVcXJyYsOGDer/Hh4evHjxItJkyU5OTri6uqLX69FqtYSEhHDz5k2GDx/OrVu3ePLkCfb29uHeo9FoeP/+fZILskogQQghhBBCCCGEECIRi03DqwQIRELn4eGBn58fGzZswNHRMcpyly9f5rfffvuBNftx3NzcOHv2LBDaKO3g4EDRokWpXr16ggmq+/v7s3r1aho2bKgGEQAaNGhAzpw5WbhwoVGBBGOcOHGCPXv2sGfPnnBBBIACBQrEenkuLi78/vvvrF27lnr16jFixAiKFy+u5keoVKkS1apVY9SoUVSvXh2tVsu0adPo0aMHrq6u2NjYUKVKFczNzcONCtywYQPTpk3j5s2b37bCCZQEEoQQQgghhBBCCCESMWMbXkECBCL+xZRbw/C6o6MjRYsWjbJcUp7aSFEU0qVLB0BISAiPHz9m0qRJ1K5dm+3bt0f5vpCQEEJCQqKd4WHYsGHodLoY61CkSBFat24d5etXr17F19eXUqVKRXitdOnSbNu2jeDgYMzMzKL9nLdv3zJp0iT8/f3JkiULdevWJWPGjOHK/Pnnn5iZmVG+fHk2btzI7du3sbOzo3r16hQrVizGdflShgwZ2L59O/369eO3336jTJkybNmyJdKyVlZW7Nq1i99++43x48fj5OTEX3/9hbm5eaw/N7GTQIIQQgghhBBCCCFEEhBTw6sQ8SlVqlRYW1vTpk2bGMv+7Lk3mjRpEuG5QoUK0bt3bz58+BBpDgEAV1dXbt26xapVq6Jc9uzZs40KJDRv3jzaQIK7uzsQ2ij/pQwZMhAcHMyrV6/ImjVrlMvQaDSkS5cOHx8fQkJCWLhwIX369GHOnDn07t1bLXfr1i2srKwoUaIEadOmpWrVqly8eJERI0bQr18/5s2bF+ny9+7dG+Vn165dm9q1a0f62t9//x3u/xIlSnD16tUol2XQpk0bo7bvxEoCCUIIIYQQQgghhBBCiO8qS5Ys3LlzJ8ZcHiAjZ4KDgzl06BAPHz7k9evX6HQ6/vvvPzQaTbQ94W/duhXjVD8zZsxAr9fHWAfDND9R8ff3ByLPL2cYEWEoE5XVq1eH+5xp06bRpk0b+vTpQ6FChfj1118B8Pb2xsvLi/Lly7Nnzx41h+mYMWOYOHEiFStWpFGjRjGuk/g2EkgQQgghhBBCCCGEEEJ8d1myZPmpAwTG2L17N7169VKn7kmTJg0WFha4u7uTI0cO7OzsIrzHkAj49u3b0Y4iABg4cGCc1NPGxgaIPFhgeM5QJipfBitMTU2ZNm0aW7duZcOGDWogwbCcXr16qUEEgH79+jFx4kT+/PNPCST8ABJIEEIIIYQQQgghhBBCiHj29OlTmjVrRufOnVm0aJH6fHBwMJMnT6Zq1arqc3q9nilTpuDq6kpgYCCzZ8/mzp07MY5IiKscCdmyZQPgxYsXEV57/vw5FhYWkU57FJMsWbJgYmLCmzdv1OeyZ8/OyZMnIyzPwcEBCwsL3r17F+vP+R70ej2KomBiYhLfVfkuJJAghBBCCCGEEEIIIYQQ8ezgwYMEBgbStWvXcM/v2LGDz58/U7hwYfW5SZMmsWfPHs6fP0+aNGlo2LAhVlZWpEmTJtrPiKscCYULF8be3p4zZ86EG+WgKApnz56lfPnyX9Wg/uDBA3Q6HZkzZ1afq1q1KmvXruXZs2fhvoM3b94QGBgYrqyx9u3bR9++fXF3d6d06dKsW7eO7NmzR1r2/PnzdO3aldu3b+Pk5MSqVasiJHn+9OkTefPmJXXq1Ny8eTPW9UkMJJAghBBCCCGEEEIIkYD9999/2NraRvn6nTt3fmBthBDfS/LkyYHQkQmGBvNr167Rt29fIDThMsDnz5+ZOXMm58+fJ0eOHADUr18/3LQ/UYmrHAnm5uZ0796duXPncv36dQoWLAjAunXreP78OYsXL1bLent7M378+HCjHB4+fEhQUBD58uVTy/n5+TFgwABMTEzo0KGD+ryLiwujRo1i7ty51K5dW80TMWXKFDQaDe3bt49xfcJ69eoVzZo1Y/ny5dStW5dhw4bRvHlzLly4EKGsn58fDRs2ZMCAAfTo0QNXV1caNmzIw4cPw+WHGDJkCAULFuT169exqktiIoGEOKQoCgBeXl7xXJMfx7DOxtBoNN+xJgmH3s8Pn/+P7Hp5eaENCYnnGiUMhv0ipm0mtvvRz7JdGUu2v6Tte+1HCY1sxzGT8+/Xi8/9SH63n1dSO679LOejxCShHV+S2jb/PcR2P6pYsWKMy7SyssLCwkL2OfFdJaT929fXF4jdMTCha9iwIYULF6Z9+/Y0bdqUjx8/cuPGDWrUqMGmTZvU4MLJkyfJkiVLuEb4169fxzitEcRdjgSA8ePHc/fuXSpUqICLiwve3t789ddfjB8/nrp166rlfH19mT17drhRDjqdjrZt22JpaYmjoyNBQUGcOHECHx8fNm7cSPHixdX3W1lZsWvXLurXr0/hwoWpUKECt27d4sKFCyxYsIAKFSrEqt7bt2+nQIECal0mT55M2rRpuXPnDo6OjuHKHjlyBAgNFGg0GkaMGIGrqyvHjx+ndu3aAJw6dYr79+/TtWtXpk+fHvsvMpGQQEIc8vb2Bviq4TQiiUqfPr5rkOB4e3tjb28f7euAJF+KC7L9JVnG7kdJ4nwk27H4Tn6q/UgkLEnouCb7kTBKEtrmvwdj9yNj+Pv7kz9//riolhDGSSD7d0z7UWJiaWnJ+fPn2bNnD69fvyZHjhxUqVKF8+fPU7x4cfWc+vHjR1KmTKm+LyQkhK1btzJ27NgfWl8LCwv++usvzp07x5UrVzA3N2fy5MnkypUrXLlkyZIxc+bMcKMc8uTJw+XLl7l06RI3btzA19eXFi1aUKlSJaytrSN8VuHChbl37x5Hjhzh6dOnlCtXjm3btpH+K7bD27dvqyMoIDTXQqZMmbh161aEQMLt27cpUKCAGog3MTHBycmJW7duUbt2bYKCgujVqxcbN27k2rVrsa5LYiKBhDiUIUMGnj9/jp2dXYLsRebl5UXmzJl5/vw5yZIli+/qGCUx1hkSZ72/d50VRcHb2zvGRDsJfT+KTmL83SHx1hsSb92/tt5xtR8l1u8tMklpXSBprU9CXZekdD5KqN9xZBJTXSFx1Tc+6ppU9qPE9DuD1PdH+JF1Tir7UWQS428fFVmXhMmwLu7u7mg0mq9K6JuQmZub06RJk3DPVaxYMdzIJCcnJ86fP8/Ro0fJkSMHEyZM4NmzZ0aNSPgeSpcuTenSpaN83dramsGDB0f6WvHixcONPoiOlZUV9evXj7aMTqeLcpSKVqtFq9Xi4+MTIQBhb2+Pj49PhPf4+PhE2GfClp02bRrVqlWjQIECEkgQxtNqtWTKlCm+qxGjZMmSJbqTRmKsMyTOen/POhvTQyCx7EfRSYy/OyTeekPirfvX1Dsu96PE+r1FJimtCySt9UmI65LUzkcJ8TuOSmKqKySu+v7ouial/Sgx/c4g9f0RflSdk9J+FJnE+NtHRdYlYbK3t08y6xJbJUqUYNiwYbRr147UqVMzfPhwdu7cGW6qo5+Vvb09AQEBkb42aNAgpk+fjq2tbYRp4Dw9PbGzs4vwnujKPn78mNWrV3PlyhVCQkLU3BMhISGYmia9Zvekt0ZCCCGEEEIIIYQQQgiRhE2cOJGJEyeq/7ds2TIea5NwRDaq4Ev58+dn3bp16v/v37/nxYsX6vRwOp0OrVaLRqMhf/78zJs3D71ej1arJSQkhBs3bjBixAju3r3L8+fPSZ06NRA62kyv12Npacnbt29xcHD4PisZT2JO5S2EEEIIIYQQQgghhBBCJAFNmzblzp07rFy5knfv3jFs2DBKlixJnjx5AKhatSqTJ08GoHr16piamjJp0iTev3/P+PHjsbOzo3Llyjg7OxMSEqI+1q5di5OTEyEhIUkuiAASSPipWFhYMHbsWCwsLOK7KkZLjHWGxFnvxFjnhCaxfoeJtd6QeOse3/WO78+PS0lpXSBprU9SWpeEKjF9x4mprpC46puY6prQJLbvTur7/SXGOidESel7lHVJmJLSuoj4kS5dOtzc3Jg9ezbZs2fH3d2dLVu2qK+bmJig1YY2m1taWrJnzx727NlDtmzZOHToELt378bc3DzCcrVabZKc0shAo0SVfUIIIYQQQgghhBBCCCGEED89GZEghBBCCCGEEEIIIYQQQogoSSBBCCGEEEIIIYQQQgghhBBRSrqTNsUDvV7Pq1evsLOzQ6PRxHd1hEhQFEXB29ubDBkyqPPMRUb2IyGiJvuREN9O9iMhvp3sR0J8O9mPhPh2sh8J8e2M3Y9AAglx6tWrV2TOnDm+qyFEgvb8+XMyZcoU5euyHwkRM9mPhPh2sh8J8e1kPxLi28l+JMS3k/1IiG8X034EEkiIU3Z2dkDoF58sWbJ4ro2IjdjkHI8peq338+PBrxUAyH3qJFpr62+qW1Lh5eVF5syZ1f0kKrIffZvYbH9xud2LH+Nn2Y++53FUtnuRGPYj2U6TnqR2fRif+5HsH4lDUtvmv4fvsR/J/iF+hIS0fyeG67ofISH9JiLxMXY/AgkkxCnDiThZsmRJ8sCUlMVpIMHUFFsTEyB0W5ADeHgxfX+yH32b2Gx/cqOReCX1/eh7HkdluxcGCXk/ku006Umq14fxsR/J/pE4JNVt/nuIy/1I9g/xIyTE/TshX9f9CAnxNxGJjzHnBUm2LIQQQgghhBBCCCGEEEKIKEkgQQghhBBCCCGEEEIIIYQQUZJAghBCCCGEEEIIIYQQQgghoiSBBCGEEEIIIYQQQgghhBBCREkCCUIIIYQQQgghhBBCCCGEiJIEEoQQQgghhBBCCCGEEEIIESXT+K6AEImJoihxUkb8HGKzLWg0mu9YEyESL0VRjN6XZD8SInGS86WIDdlehEh4ZL8UQoifgwQShEAuZsTPSbZ78TOS7V4kBrKdChE12T+EiJrsH0IIIb4nmdpICCGEEEIIIYQQQgghhBBRkkCCEEIIIYQQQgghhBBCCCGiJIEEIYQQQgghhBBCCCGEEEJESQIJQgghhBBCCCGEEEIIIYSIkgQShBBCCCGEEEIIIYQQQggRJQkkCCGEEEIIIYQQQgghhBAiSqbxXQEhhBBCCCGEEEIIIYQQPwd3d3c8PDxiLJcqVSqyZMnyA2okjCGBBCGEEEIIIUSipNfr0WplkLUQQgghRGLh7u6Oo6Mjfn5+MZa1trbmzp07EkxIICSQIASgKIrR5TQazXeuTUQfPnwI9/+nT5+4evUqFStWxMTEJNxrKVOmNGqZ8bEePxtjt6vYNILEprFEUZRo6xAcHGz0dmBmZhZjGb1eb3TdpNEn8TH29w0ICDDq91UUBQsLixg/S6/XGzUPo7H7G8jxTyQdP9t2/+7dO/VvnU7HiBEj2LNnD9OnT6d+/frhyqZNmzbG5f1s3198iumaxFDGmO85JCTEqHKKomBqGvPtbmyu72U7ECJyOp3OqHJ6vT7C/WtUjCkX18dxOS8I8WN4eHjg5+fHhg0bcHR0jLLcnTt3aNOmDR4eHhJISCAkkCBELGg0mhgvGL73BcXLly9p0KABT58+pUaNGixfvhxbW9vv+pni68hNqRDfzpjjrhAicYvtPq7T6ejXrx87duwAoEePHgARggkiaZLzghAJj9z3CCG+hqOjI0WLFo3vaohY+KkDCT4+Pjx+/JiCBQvGd1WEMErYIALA4cOHqV+/Pps2bSJdunTxWzkhhPiCoigcOXKEU6dO0alTJ7Jnzx7fVRJCJHJhgwgmJiaUKVOG06dPSzBBCCGEECIBMCb3wZ07d35QbURc+2nnlli+fDk5c+ZkwYIFvHjxIr6rI0SMwgYRsmXLxurVq3FwcOD69evUqlWLu3fvxncVhRBCde3aNerWrUuDBg2YNWsWhQoVYtiwYXz69Cm+qyaESKS+DCIsW7aMzZs306xZM3Q6HT169GD37t3xXU0hhBBCiJ+SIfdBsWLFon20adMGa2trUqVKFd9VFrH0UwYSzp49y+jRozl37hx//PEHmTJliu8qCRGtL4MIu3btol69ehw8eJCcOXPy4sULateuzcmTJ+O7qiIR+/z5M//880+s5gYV4ksfPnzgt99+o0yZMhw/fhxzc3OKFStGcHAw8+fPx8nJiUWLFsl2JoSIlS+DCEuWLOHkyZMULFiQqlWrSjBBCCGEECKehc19cPny5WgfkkA5cfoppzZasWIF7dq1CzfFwokTJ3j48CEVKlQgT548Ri0nMDCQwMBA9X8vL684r6sQAHPnzuXp06dYWlri5uZGxowZAciePTsHDhygUqVKvHr1igEDBtCwYcP4rWwsyX6UMLx+/ZqqVavy8OFDRowYwfjx4+O7SiIWEtJ+NHXqVNavXw9AkyZNmDx5MlmzZuXIkSMMHTqUu3fvMnjwYAoVKkSVKlXirZ5CfCkh7UcioiNHjrBjxw40Gg3Lli3D2tqadevWATBw4EDu3buHXq9nx44dDBw4kC5dushc3PEgPvaj06dP4+bmxtChQ2WqT5EkyPlICJHYSe6DpOunHJHw7t079W8/Pz+cnZ1p0KABv//+O05OTixfvtyo5UydOhV7e3v1kTlz5u9VZfGTq1OnDqampgQEBDB16lR0Oh0AISEhTJkyhVevXgHQtGnT+KzmV5H9KP55eHhQu3ZtHj58CMC0adM4fvx4PNdKxEZc70d6vf6r3+vs7IypaWg/BXNzc3XUX968edWb4uzZs+Pk5PRNdRQirsn5KGFzcnLC1NQURVHIkCEDefPmVV9Lnz49pqampEmTRi0rQYT48aP3ozt37lC3bl3mz59PvXr18Pb2/q6fJ8SPIOcjIYQQCdVPGUhwcnJi69atBAQEMHz4cGxsbHj79i1v3ryhZ8+e9O7dm+fPn8e4nBEjRuDp6ak+jHmPEF+jcuXKrFixAlNTU7Zv307v3r3x9PSkTZs2rFmzBo1Gw+TJkxk+fPhXLV+v1/P69es4rrVxZD+KX56entStW5fbt2+TIUMGGjdujKIotG/fnrdv38Z39YSR4mo/0ul0dOvWjRQpUrBw4cKvmn6oSpUqrF+/HlNTUzZv3kzXrl159uwZNWvW5MmTJ2TPnp1Dhw6RPHnyr6pjbHxLQET8fOR8lLBlzpyZxo0bA7Bo0SIyZMjAb7/9hqmpKdOmTcPT01MdodC7d+/vXh85vkTuR+5H3t7eNG3aFB8fHwCuXr1KixYtCA4O/m6fKcSPIOejr6MoCgMHDsTe3p6KFSvy+++/s2/fPsnPJYQQceinDCR07dqVt2/fMnz4cPbu3cvSpUuxsrLCxMSE2bNnY2pqytWrV2NcjoWFBcmSJQv3EOJ7qVu3brhgQpEiRTh69ChWVlasXbuWbt26fdVy/f39cXZ2JkOGDBQtWhRXV1fev38fx7WPmuxH8cfX15cGDRpw5coVUqdOzbZt2+jYsSP58uXj7du3dOzYURpKEom42I90Oh0dO3Zk+fLleHl50adPH1q1avVVvTsbNmwYLphQuHDhcEGE792z7uPHjzRr1oyUKVOyadOm7/pZIumQ81HC16tXLwD279/Pw4cPGTduHPfu3aN8+fKsXbsWHx8f8ubNS7Vq1b5bHTw8PGjSpAn29va4urpKvpcv/Kj9SFEUOnfuzN27d8mYMSN//fUXVlZWHDp0iF69esnvIhI1OR/FnqIoDBkyhHnz5uHt7c2pU6eYNm0a9erVw8HBgQIFCtC9e3fWr1/PkydP5BghhBBfKckHEtzc3KhcuTLdu3dXn8uVKxezZs3C1dWVZ8+eqb1YILRnS0hICPny5YuP6goRrbDBBC8vL9KkScOuXbtwdnb+quX5+/tTv359Dh06BIT25Orfvz8ZMmSgQYMGuLm5hZufUyQdAQEBuLi4cPbsWZInT87atWvp0KED9erVo1y5clhZWXH06FFmzpwZ31UVP4AhiLB+/XpMTEzo2rUrpqambNmyhZIlS3Lr1q1YLzNsMCEgIOCHBRFOnz5NkSJF2LFjB15eXrRt25bFixd/188UQvwYefLkoWbNmiiKwuLFi9FqtdjY2ODv769OTdq7d+/vNq3RoUOHKFCgAG5ubvj4+NC/f38aN24svV3jwezZs3Fzc8PMzIxZs2bx9OlTVq1ahVarZdWqVUyePDm+qyiE+IEmTJjAnDlzAJg5cyZ//PEHHTp0IHfu3ADcunWLZcuW0a5dO3LkyEHGjBlp27ZtvI3KF0KIxCpJJ1vesmULw4cPZ+vWrZQqVSrca3369CEgIIBhw4bRsmVLVq9ejZmZGV27dqVLly7kypUrnmot4oOxva49PDzQakPjb4qisHXrVvbt20elSpWoW7cuadOmRfH3V8sHBAaq5aNiaWkZq7rWrVuXzZs3s2fPHvr37/9VWe5PnjxJQEAAI0eO5PLly5iYmGBqaoqTkxPe3t48ePCA3bt3s3v3blKkSIGLiwstW7akaNGiUd6cx3VPmZCQEEJCQqItoyiKOhd7TOWMaVQwtpxOpzOqXHBwMCYmJjGWCwkJwczMzKjlxbQ96QODwi1XG8l3GBwcTLNmzTh+/DiWlpYMHTqUnj178vTpUyA0Ib2Liwvbt29n7Nix2Nra0r9//xjrJxKnjRs3snTpUk6fPo1Go8HR0ZENGzZQvXp1zp07x927dylWrBg9e/Zk6tSpMS4vKCgIc3NzAOrXr8+ff/7Jnj17GDJkCBkzZlSPt8b2BAsJCYEYjgUA7u7uLF68mLlz56LX68mWLRtFixbFzc2N3r174+7uTq9evdBoNGrehujo9XqjGyNj2i+FMIhND8j4mOPfkIMpJsae34KDg406v+l0OqPO54GBgfTo0YNDhw6xfft2BgwYQPr06dmyZQseHh5kzJiROnXqxHj9ELZ+xjh9+jRLly5l+/btAGTIkIFMmTJx5coV/vrrL86fP8/EiRMpUKBAlIkF9WHqFBISgrlRnywis3TpUn7//XcAOnfuTO/evfnw4QOlS5emX79+zJ07l3HjxqHX6xk7dmyMyzN2ewGM2k7BuPOCsZ+r1+vj9HNF0mbscTwoKMio4zigXtfFxefqdDqjlqcoilHnzMePH7NixQqmTZsGwKhRo2jUqBEQOj0wwIcPH/j333958OAB58+f59q1a7x+/ZoNGzbw77//snv37nCJ2lOkSGHUugghxM8oSV9p/P777yxcuDBcECEkJES9aRgyZAgnTpxAq9Xi6OhIwYIFKVmyJAsWLIivKot4otFojHoY+Pj40KtXL/r27cuhQ4cYMWIEBQsWpFGjRmzYsCHO6+fg4BDu4eLiwtq1aylSpEiE14xZjy+DCDqdjsDAQK5cucKrV69wcXHBxcWFVKlS8enTJ/744w+qVKlCqVKlcHV1lVEKRjJ2uzIxMUGr1cb4iG2jUmSfpdfr6dixI/v27cPc3Jzhw4ezatUqnj59ioODA+XLl0dRFI4ePUq5cuXQ6/VMnjyZDx8+xPh5xqyD4SESBp1OFy6IkDdvXm7evIm/vz8HDhwgZ86c5MuXj8DAQObOnUvfvn0JCgqK9re1srLC0tJSfdStW5dly5aRK1eucM9bWVkZtY0Ys728evWK1q1bM3v2bPR6PY0bN2b//v3MnTuXfv36ATBjxgymTJkiQ9lFkmLseSapJB5OlSoVtWrVoly5cgQHB7Np0yaSJ0+ujkYYOHAg6dOnJ1WqVEYtz5jv7caNG3Tt2lUNIjg5OfHmzRsuXLhA8uTJSZUqFa9fv6Z79+5s2bJFjjFRMOa7NlzrRPd48eIFo0aNQq/XU6NGDc6cOaNeo5w7d4779+/TqlUrACZPnszRo0eN2jd+ln1IiO/F2HsAU1PTOL1f2LBhgxpEGDhwIO3bt1eDEIZHypQpqVKlChMmTODQoUM8efKEnTt3kilTJh49ekT9+vV58+aNusyf6bwqhBCxlWRbcz5//syTJ0/UKYr8/Pzo1q0btra22NnZ0bt3b4KDg6lYsSKnT5/Gz88PT09Ppk6danRkXvycbt++TY0aNdixYwcmJia0b9+eYsWKoSgKZ86cYdSoUWrZdevW4eHhEY+1jcjf3z9CEEGr1arDPH19fdmxYwd///03bdu2Zfv27TRt2hRLS0vu3bvHmDFjaNeunQQTEiG9Xk/37t3Ztm0bZmZmDB48mG3btvHw4UPs7e2ZOnUqgwYNIlu2bHz69AkfHx8yZMiAh4cHHTt2lMaRJMYwnVHYIMKdO3fQarVUqVIFExMTLl26xKdPn6hWrRoajYbly5dTqVIldfRKQrB//36KFSvGuXPnsLa2Zs6cOcybNw9bW1s0Gg2DBg1izJgxACxbtoxhw4YZ3WtOCJEwDR48GIA//viD1atX8+TJExwcHOjQoUOcfYZer8fV1ZWyZcvy+PFj7O3tyZ49O7du3UKv12Nra4uHhwefP38mV65chISEMHfuXJo2bSpTHX0ngYGBuLi44OnpSe7cubGxseHGjRvY2NjQt29ftFot+/btw9LSkmrVqqHT6XBxceHatWvxXXUhErzHjx+zf//+RHWNtHbtWsaNGwdA9+7d6dGjBwcOHKBbt26sXbs2XHAgLCsrKypUqMCuXbuiDCYIIYSIXJINJNja2mJhYcGJEycAaNasGW/evOHAgQNMmzaNlStXMmLECLW8paWlBBBEtBRFYdOmTdSqVYuHDx+SPn16du7cycyZMzlw4ACXL19m7NixFCxYUH3PwAEDyJYtG/Xr12fjxo3xfmFmyInwZRBh5MiRapLVESNGkDZtWjw8PJg7dy4jRoygXr163L9/nzlz5mBpacnBgwclmBDP9Ho9f/75J0OGDGH9+vU8e/Ys2vKKojBo0CDWrFmjzh+8f/9+bt++ja2tLZMnTyZz5sxYWloyfPhwLCwsuHbtGsWKFcPU1JQ9e/bg6ur6g9ZOfG9hcyJ8GUTo27cvnTp1YvTo0aRMmZLXr19z6tQp6tevj4ODA1euXKFUqVLs27cvXtchKCiIIUOG0KBBAzw8PMiXLx/79+/HxcVFLWOYRqlLly7MnDkTrVbLli1baNmypRy/hEjEatWqhZOTk5qnAKBHjx7Y2NjEyfJfvXpF3bp1GTJkCEFBQeTOnZvg4GCePHmCtbU1o0ePxs3NjUqVKhESEsLDhw/JlSsXpqam7Nq1i5IlS3LhwoU4qYv4n379+nHhwgXs7OyoVasWO3fuBMDFxYV79+7RqVMnAFatWkXRokUpUqQI3t7e1KlTh+fPn8dn1YVIkIKDg3Fzc8PZ2RlHR0caNWpE586d4/2e1Rjbt2+nc+fOALRr146BAwdy4MAB+vfvz/Hjx5k0aRK//vorLVu2ZO3atbx9+zbCMrJlyybBBCGEiC0lCWvevLmSOXNm5fTp04qjo6MSHBysvjZlyhQlderUcfp5np6eCqB4enrG6XLF96fT6aJ9eHt7K61bt1YABVCqVq2qXLlyRWnevLliZ2enNG7cWDl8+LDy7t075e2zZ8rtPHmV23nyKqUKFVLfAygtWrRQfHx8FH9/f/Xxo/j5+SnVqlVTAMXExEQBFK1Wq4wePVqZNGmSUrlyZWX+/PnKiRMnlEOHDim9evVS7O3t1boXK1ZMOXLkiLJr1y7F0tJSAZRatWop79+/N2qbN3b/MJT78OGDEhwcHO0jKChI0ev1MT50Ol2clgsODlZCQkJifAQEBMS4DsHBwUpgYGCM26BOp1P8/f2VgIAAZfPmzUq+fPnCbVuAkjVrVqVTq1bq9hfo6al+xvDhw9Vyf/zxh1K3bl0FUCwtLZU5c+YoBw4cUHr27Knky5dPWbp0qTJgwAB1G2nUqJECKGZmZsrFixfjapMUXyG2+1Fk5UJCQpS2bduqv6+jo6P6d9++fZUqVaooVlZWSseOHZWlS5cqBQoUULedli1bKsWLF1f/HzJkiOLn56cEBQWpj4CAAKO2Z51OF2X9db6+kW7Hhsf9+/eVokWLqvXo3bu3cu/ePcXd3V1xd3dXbt68qTg7Oyt2dnbKggUL1OeXLVummJubK4BSo0YNxdvbO8r9PCQk5JvXQyRMcbEffS1jzjGGR3ww5twWEhKi+Pv7h9vvo3r4+voaVc7f39+o86Wvr68SEBCgBAQEKKtWrVKPAdbW1srLly/V1wICAoxa38jqsn37diVlypTqObJYsWLq5xQoUEDZsWOHcvbsWeXs2bPKmTNnlIEDBypmZmYKoKRKlUrJkCGDes6cO3fu/871np7hjmuJ3Y/ejwy/t0ajUQYMGKBYWFgogOLs7Kwe17NkyaK4uLio17pTpkxRr5fy58+vfPz4MdJ9LTAwMNw28ObNG2Xnzp2Kl5dXuOeNvV4z9rxgzDYfm+vEhHY+Cnsu1/n6xnd1EqTvsR8Zcwx///69MnjwYCVt2rTq8U2j0SharVa93vPz81MCAwOVwMBAo9bF2O05Lu7f9uzZo5iamiqA0rRpU+XevXvK8uXL1ecqVaoU7jrR8ChdurQyffp05c2bN8qHDx/Ux+XLl5VMmTIpgJIzZ07l1atXRq3zzywh7d/xeV2XkCSU3+Ty5csKoFy+fDlBLk9ELjb7R5IdkQAwbdo0vLy8aNmyJU5OTuGSVOXNmxcrK6t4rJ1ITJYvX87GjRsxMTFh1KhRbNy4kb1797J161a8vb1xc3OjUaNGEZI2Hz9xguvXrzNy5EggNAH4sWPH4mMVWL16NUePHsXCwiLcSIQyZcowYcIETpw4weDBgwkMDMTc3BwXFxc2bdrEsGHDsLGx4fLlyzRq1IiKFSuydu1aAA4ePMju3bvjZX0SC29vb44cOcKYMWOoUqUKBQsWZMGCBQQFBcX85i8EBgbSs2dPWrZsye3bt7G3t6djx46ULl0aU1NTnj17xubNmyO8z93dXZ07tFWrVjg4OLB3714A2rZti6OjI56enixevBh/f382bdpE9erVyZkzJ3q9nqdPn5I3b16Cg4OZNGnSt30hIt6dOHGC9evXA6Fzyd65cweArl27YmNjw/Hjx/H392f16tUEBgYybNgwqlevDsDmzZvp06cPvXv3BmDmzJnUrFmTV69e/dB1GDRoEFeuXCFlypS4ubkxd+5cLCws1Nf/+OMP9u/fj7e3t3pcA6hduzarVq3C2tqaw4cPM2HChB9abyFE3GnatClZsmQBoFOnTjg4OHzT8hRFYcKECTRt2pSPHz+SL18+MmTIoI7i7Nq1K4sWLSJDhgzqezQaDS4uLsyfPx8TExM8PDxInTo1Tk5OBAcHM2DAAB4/fvxN9RKhDMmVW7VqxbVr1wgMDCR9+vR4enoSFBRElixZcHd3x97enpw5c6LT6Vi1apWau+zmzZusWLEixs95//49v/76K40aNSJv3rwsXryYgICA77puQvxoI0eOZNasWWov/Vq1anHnzh3mzp0LhF7v/f333/FYw6jduHGD5s2bExISQosWLZg4cSJarZaxY8cSEhJCzZo1Wbp0KVu3buXkyZOMHDmSwoULA6F5VIYNG6beyxoYRiZkyJCBR48eyf2OEEJEI0kHErJly8bu3bv5/Pkz+/fv57///gNC8yXMmTOH7t27x28FRaKRO3duAOzt7enevTtarZZffvklXJlatWpFmhQqd+7cVKhQAQBzc3M1b8ePZpj6pkyZMgCUK1eOKlWq4O7urjZqBwUFhRv2aWNjw++//65eVKZLlw5FUdiyZQsAKVKkCJfMXICHhwe7du1i0KBBlCtXjtSpU+Ps7MzUqVM5deoUd+7cYeDAgRQqVIidO3canXfg1atX1KhRg1WrVqHRaBg6dCj37t1jyZIl/P3337x584Zly5ZF+t5MmTJRs2ZNADZt2sS+ffvIkycPEBpg2r17N3Z2dpQuXZonT55QoUIF1qxZw6NHj4DQxpW7d+8C0KJFi2/9ikQ8K1KkCBkzZgTg6tWrpEiRAgi9ucqSJYv6f548eXBwcODNmzdcvXoVgIwZM1KxYkXmzJnDxo0bsbOz49SpU5QoUYIjR478sHUoXrw4AClTpqRWrVoRXi9RooT6d5YsWcIFGSpUqMC6deuA0ICDr6/vd66tEALgzp07TJo0id9++y1O8keZmZmxcuVKOnfurDYyfy1/f39at26tNh41bdqUt2/f8vjxYzJmzMiiRYvo2LFjuE5JBs+ePWP27NnodDo0Gg02NjbcunULgCpVqpA1a9ZvqpsI1bx5cyC0U07q1KnRaDS8fv1anYLF3d0dW1tbnj59ql6/lCtXjjZt2gCh16zOzs7RfoaPjw8NGjTgwYMHQOi1V//+/SWgIJKcVq1aqfe3ENo5rFWrVuo9X/bs2SlatGh8VS9KXl5euLi44OfnR7Vq1Vi7dq06PXWhQoUACAgIUJ9Lnz49BQoUCNeBK3fu3NSoUSPCsrNkyaJ2NC1QoMD3XhUhhEi0knQgAUIbDM6cOcMvv/xCmTJlqF27Nnny5CFXrlwMGzYsvqsnEomaNWuSPn16Pn78yOHDhwGoVKkS2bNnB6Bw4cLMnj070vd6enrSr18/ADp27EimTJl+TKW/4OPjA/xvznBDY+HLly/DlXv9+nWE9546dQqAGjVqMHXqVHbu3ImZmRkbNmwgc+bM37PaicL9+/fp1asXBQsWJF26dDRp0gRXV1cuX76MTqcjW7ZstGnThqVLlzJ//nzSpk3Lw4cPadasGZUqVYpxHuUzZ85QokQJLly4QPLkydm9ezcTJkwgefLkahlbW1vev38f6fu1Wi27du1i+PDhAKxcuRJ7e3tKlChBSEgIS5YsYdasWQwbNow9e/Zw//59tm3bBoReSBuCsCtWrJBAQhLg4ODArl27sLKy4tixYzg6OmJubs61a9fYvXs348aNo2vXrgwfPpynT58yfvx4PDw8yJAhA3///bfaG7dp06acO3eOQoUK8f79e+rWrcuYMWMICQn57uvQt29f0qRJw8OHD1m1alWE1ytUqEDLli0xMTFh/PjxEV5v2LAhOXPm5PPnz2zcuPG711eIn9Xdu3eZNGkSRYoUoVChQkyYMIE1a9Z8c8O/wa+//sqiRYtImTLlVy/j9evXVK1alR07dmBqakrnzp3ZvXs3Hz58oEiRIpw5cwYnJ6dI33v48GE6d+7Mw4cPSZYsGenTp+fs2bNoNBpGjhzJvn37JAdbHJkzZw7t27dHp9OxY8cOGjZsCIQGwcuWLUupUqVwcnJSR/62a9eOgwcPcvv2bTJmzMipU6ei7cwTFBREs2bNuHTpEg4ODly8eJEFCxaQKVMmNaDg6OjIokWLJKAgEr3y5cvz33//cfjwYZo3b465uTlXrlzh6dOnZM+enSNHjqj3igmFoih06tSJBw8ekDlzZjZt2oSZmZn6+sCBAzE1NeWff/7h3LlzvHnzhkGDBtGiRYtwOeFOnTqljmYL659//uHRo0fY2dmpAUghhBARJepAwpMnT9i6dSv//vtvtL16CxQowJUrV9i/fz8NGzZkx44drF69OtLe40JExtTUlHbt2gGhPbohtHF27ty5tG3blvXr12NtbR3hfTqdjvbt23P37l3Sp0+vNuTGB29vbyA0qRaEjq4AIiSf+zLBlKIoHD16FAArKytmzJgBwLx58yhfvvx3rXNi8OTJEypVqsSyZcu4ffs2APny5aNbt26sWbOGx48f8+DBA1avXk2LFi0oXbo0t2/fZuTIkVhZWXH27Fk1EdiTJ0/CLVtRFJYuXUrVqlV58+YNTk5OnDlzRp1m5suyhulqImNiYsLEiRNxc3PD3t6eCxcucP/+ferUqYNWq+XEiRMMGDCApUuXhgsi3LhxAwgNIhgSmonEr1ixYuqwbsM2CHDgwAFu3rxJ5cqVefjwIZMmTcLb25vs2bMzadKkCD1rc+fOzalTp+jWrRuKojBt2jRq1ar13ac6srW1VRsiJ02aFOmogmnTpnH9+vVIj1NarZaePXsCsGDBAqNHBgkhYmYYeVCkSBGKFSvGhAkTuHXrFmZmZlSpUgWAdevWcfPmzXiuaeiorHLlynHp0iVSpEhBmzZtWLlyJYGBgdSvX5/jx4+Hm8rIIDAwkOnTpzNu3Dj8/PzImjUrQUFBvHr1itSpU7N//37GjRsX6QgG8XW0Wi0rV66kZcuW6HQ69u7dS6NGjYDQ89jLly85f/48JiYmdOnShV27dvH27Vt++eUXTp8+HW0QQa/X07lzZ44ePYq1tTW7du2iUKFCdOvWjTt37oQLKPTt25fcuXNLQEEkehqNhooVK7Ju3TqePHnCtGnTaN++PUeOHElQHcV8fHz4888/cXFxwc3NDTMzM7Zt20aqVKnClcuePbva4WnYsGHUrFmT3bt3o9FoaNasGXv27KF79+7hgg9hGaY+a9GiBba2tt93pYQQIhFLtC3p8+bNo2DBgowZM4Zy5cpRrFgxrl27FmV5jUZD5cqV6datm0zFIr5Khw4dADh+/Ljaa79s2bLMnj2btGnTRvqesWPGcujQIaysrNi+fTvp0qX7UdWNwDAiwTBXuKE3e0wjEm7evMnr16+xtrbmr7/+AkJ7A0tPDfj48SP16tXj3bt3FCxYkD///JM3b95w/fp1Fi1aRIsWLdQL8X///ZfChQtTsmRJBg4cyMiRI7lz5w7t27dHo9Gwbds28uXLx5AhQ/j06ROBgYF069aNXr16ERwcjIuLC3///Tc5c+aMtC4XL17k3r17WEUS0AqrXr16nDt3jgIFCuDp6cmBAweoVasWKVKk4OnTp+zbtw8IH0To37+/BBGSoKZNm6q99f/++28qVqwIwKpVq9i+fTvTp08nICAAJycnRo4cqQYfv2RpacmCBQvYsGEDdnZ2nD59mqJFi6qjt76Xrl27kj17dt68ecOCBQsivK7RaLCzs4vy/R07dlSnIDlx4sT3rKoQSd779+8jjDwwBA9q167NihUrePHiBQcPHqRx48bo9XpGjRoVr3X+66+/qFy5Mi9evOCXX36hRo0arFmzBgjt2bpt2zZsbGwivC8oKIhBgwaxa9cuNBoNv/zyC8+ePSMgIICiRYty6dIlqlWr9oPX5udgYmLCunXrqFixIsHBwezbt48GDRoA8OLFCywtLencuTMbN27E09OTvHnzcurUqWinl1IUhSFDhrB161ZMTU3Ztm0bJUuWVF+3sLBQAwrz58+PEFBYvHjxDxmJJ8T3lCpVKgYMGMDy5csTRBDh/fv3rFy5kvr165M6dWqaNm3Kzp07AXB1dY2yPad3797Y2Njw6tUr/Pz8KFKkCG5ubkyePDnaPDrPnz9Xr1s7deoU9yskhBBJSKLsJnPmzBmmT5/OrVu3yJIlC7dv36Zjx46ULVsWNzc3dS5wgAsXLjB8+HD+/PPPBDc8T3x/xl7YK4oS4wiV3LlzU7JkSS5cuMDGjRvVZKMRlhWmd9LSpUsAWLZsGUWKFImQjFlRFKN6wiqKgkajibEcEGU5w4gEf39/9f+bN2+q88DmyJGDx48fc//+fbWXoGGuWYCCBQty7tw5LC0tGTx48HfvwWvMdxPXddDr9UZ9z97e3gQFBdG4cWPu3r1LhgwZ2Lx5s9pr0fBdnz17FkVR2L59O+vXr1d//7Vr13Lt2jVGjhxJixYtKF++PJs3b+b48ePMmTOH1atXkylTJm7cuIFGo2HChAkMHDgQf3//KJM0Gxo/6jg7w/Ub0dY/V65cnD59mkaNGnH8+HH2799PiRIl8PHx4d69ezg5OYULItSsWdPo79rY7VQkDKNHj+bAgQOcO3eOS5cuUbhwYf777z/1Zq1EiRL07NkTc3NzPn/+zP3796NcVuHChdmyZQuDBg3i7t27ODs7M2TIEEaNGhVpr1xFUbC0tIx0WWGPlTqdHk0k25+ZmRljx46lQ4cOzJw5k+rVq0fonRaZkJAQAgICsLS0pHXr1ixfvhxXV1fKli0boQ5R1e/L9fjW47MQ35uxx3Bjz4OBgYHhpuzp3r07e/bsAUL3zcqVK9O4cWPKly8frrOFp6cnQ4cOZffu3ezfv599+/ZRvnx59Hq9Udfpxq5HYGBglGUVRWH27NmMGTMGRVEoUqQIFhYWbN26FY1GQ+/evXF2dub06dPqe16/fk3OnDnR6/VMmjSJK1euYGlpScqUKbl//z4ajYYOHTrQpUuXSEcwiLhjamrK2LFjGTNmDKdPn+bgwYM0bdqUe/fuUapUKVatWkVISAjFihVjypQppEyZUs2jEJnp06ezcOFCAKZOnUquXLm4evUq169fp0yZMuHOXzVq1KB169asW7eOWbNm8fLlS/r06YObmxtr1qxRGyr1en2kI5W/ZOz+Zux2b+x9Bcj5SBgvJCTEqJkcgoODjRqFFRQUFOGeGODx48fs2bOH3bt38++//4YrkzVrVmrXrk39+vUpWrQo7969U1/79OmTOoIgWbJkjB07lk2bNtGyZUvq1q2LRqMhODiYoKCgKNsGVq5ciV6v59dffyVHjhwxroMQQvzMEmUg4a+//qJWrVrq3Hb58uXj5MmTNGvWjMaNG3Py5EmKFSsGhF5QXb58mVmzZjF58uT4rLZIwDQajVEXSJ06deLChQv8+eefjBkzJtKLcL2/P2En2Rg7diytW7eOdHk/cjoNw4gEwxQgGTJkwMHBQZ1Xv2jRojx+/BhPT0/1Rsgw5Q2g3hDVr1//h+R5MDU1Nepi1JgbIWNvloyd7kxRFHr16sXZs2dJliwZ27dvV5PXhvX8+XMWLVqkNspXqFCB0qVLs2DBAv777z8GDhzI2LFjyZQpE3v37uXIkSOMGDGC27dv8+nTJ5InT866devUqYzMzMwwNzeP8Dn+/v78+eefAKHb2vXQKbRMTU3RRvEdJkuWjKNHj7J06VL69evHxYsXyZ07N82bN2fz5s3A/6Yzkmlfki6NRsORI0eoXLkyly5dwt/fnyJFinD16lU6d+7M4sWL1cbC27dvh9sWVqxYwe7duxk2bBjlypUDQhPVbdiwgeXLl7N8+XJmzJjB2bNnWbduXYTGtchuIiNjamoS5bGgTZs2zJ07l2vXrrF582amTJkS4/ICAwPVY0KPHj1Yvnw5+/bt4+HDh+ESD2q1WqOOCbJ/iNhI6I13JiYmRtXR3NxcLRcQEKBOgTh79mxatmypBgU8PT0j5Aj45Zdf6NChAytWrGDChAkcOXIEMzMzo875pqam39TwGhgYSO/evdmwYQMQ2jD88uVLrl69iqmpKUOHDuXXX39Vp4E0SJUqFWXKlGHIkCEcPXpUXSfDVEYbN26MdNrBsPRh1k+mPPo2lSpV4siRIzRu3JgDBw6wf/9+unfvzpw5c1AUBRcXF9avXx/j9rJq1Sp1ZMyIESOoV68eHz9+pFWrVjx9+pTixYuHG3lsCDD/9ttvtG/fnjVr1jBq1ChOnDhBxYoV2bx5MwULFjR6PzcxMTH62jMuAw4iaTN2m7K0tDRquzJ2ecbeu30ZQNu3bx9jx45V75cMChQogLOzM7Vq1SJv3rxR1jV9+vThRhv07t070g5/ISEhkXYQCQgIUO99+vTpI51PhRAiBolyaiNbW1suXboU7mLJwsKCbdu2UaRIEdq0aaP2PClVqhTnzp2LNNmiELHVsGFDbG1tefToEWfPno20zLP/78EP0LhxE0aPHv2Dahc9Qy95Ly8vAOzs7PD29lYDC/nz5wcIl7DXz8+PM2fOAKgXd61atfphdU6oJkyYwJ9//ompqSnr16+PNAnjwYMHGTJkCDdu3MDS0pJ+/foxaNAgypcvz9SpU0mZMiXPnz9n8ODB3Lt3D41GQ40aNTh//jxLliyhXbt2UeZD+NKePXvw9PQkc+bMlP//ue6NodFo6NGjB6dOnSJTpkw8ePAgQhBBJH1WVla4ubmRIUMG7t27R+rUqTl//jxLly6NMknoli1bcHV15cmTJ/Tu3ZuDBw+qr1lYWDB//nzWrVuHra0tp0+fplSpUly8eDHO667VatXgwdKlS3nx4kWs3p83b16qV6+OXq+nfv36uLu7x3kdhUjqTp8+jb+/PxkyZKBHjx5GNcIMGTIEW1tbrl69qo6A+t7ev3+Ps7MzGzZswMTEhLFjx3Lv3j1u3bqFtbU1kydPVvPFRGbevHnMmjVL/T8gIICKFSvy33//GXWuFnHLwsKCHTt2UK1aNXx9fZk9ezaKotCtWzc2b96MhYVFtO83zJcO0KVLF9q3b4+vry/dunVTR+NeunSJRo0ahRudEvbzu3XrxokTJ8iePTtPnz6lSpUqbN++Pc7XVYikys3NjaZNm3Ljxg1MTEyoVKkSc+bM4cGDBxw4cIABAwbg6Oj4XYPwO3bswMPDg8yZM1O3bt3v9jlCCJFUJMpAQvPmzbl9+zaLFi0K97yFhQXr1q3j4cOHHDp0SH3e0dFRev6IOGFra0uTJk2A0ESBX/Ly8gqXO2DJ0iUJpvehYUSCIaCQLFkyNR9CypQp1VEG3t7eavK4GzduoNPpyJw5M+/fvydlypTUqlUrHmqfcCxZsgRXV1cgNEmrYV55g4CAAIYNG0aLFi3w9vYmR44czJkzh6pVqxIYGMijR4/Inj07s2bNIlu2bHz+/Jlhw4apU0KYmprSoUMHli1bZvTQWkOS5TZt2qDVxP6wXqpUKa5cuUKNGjUwNzeXIMJPKEOGDOzcuRMrKysOHz7Mpk2bojx2/f3330ydOhUInRItJCSEoUOHsmPHjnDlmjVrxrlz5yhYsCDv37+nTp063yWYUKtWLSpUqEBgYKBRIxK+tHjxYnLkyMHTp0+pWbOmBBOEiCXDNXeNGjWMvuZJnTo1ffr0AUITphvyN30vd+/epUKFCpw9exZ7e3sWLFjAypUrefbsGQ4ODsyaNYuCBQvi7e3NtWvXIvTsPnXqFAMHDgRCe/HqdDoaNmzI0aNHZSqjeGRlZaXmuoDQ6frCjqSLyqlTp2jZsiV6vZ6OHTsyYMAAgoKC6NOnDzdu3CB58uQsXbqUvHnz8vHjR7p27cq8efMinRYlf/78nDx5kmrVquHv70+HDh0YNWqU5E0QIgZubm5qB9DWrVvz/PlzDh06RK9evdSZJ36EpUuXAqEBRWkzEkKImCXKQELevHkZMmQIAwcOVBvfDHLkyEHp0qWjncdZiG/Rtm1bIPTix9AoD6DT6ejQoQP37t1Tn7MyYn7tH8VQV8NoHTs7O968eQNAunTpsLa2VpMKGkYlXL16VS0L4OLiEunUOj+L3bt307dvXwB+//13WrZsGe71hw8fUq1aNZYtWwZAnTp1mDlzJpkyZeL+/fv07t2bAQMGMH/+fJInT860adMoWrQogYGBNG/eXJ2jNzZevnzJsWPHAL4pAXbq1Kk5dOgQnz9/liDCT6pYsWKsXr0aCE1k16FDBz5//hyuzM2bNxk6dCh6vZ5GjRqpPckURWH8+PGsXLkyXPlcuXJx/PhxypUrh5eX13cJJmg0GqZNmwaEBtXu3r0bq/dnypSJgwcPSjBBiK9kSFBZo0aNWL2vZ8+epE2blmfPnrFq1arvUTUAbt26RfXq1Xn69Ck5cuTA1dWVkSNH8uLFCzJmzMjs2bPJnj077u7u9OrVi+HDh6sBegjtVDFjxgwURcHGxoaAgADKli3Lpk2bpNEpAbC2tubIkSO8fPmS8ePHxxjMunHjBo0aNSIgIIC6deuyZMkSFEVhxIgRnD17FisrK5YvX06lSpXYunUrLVq0QFEUli5dSqdOndRr57BSpkyJm5sbgwYNAkLPoXXq1OH58+ffZZ2FSOzCBhHatGnDH3/8EW0y5O/lypUrXLhwATMzM0myLIQQRkqUgQSAKVOm0LBhQ5o0acIff/yhPh8QEIC7uztFihSJx9qJpMbT05MdO3bQs2dPtbHWz8+Pf//9Vy2zZ88eDh06hKWVVXxVM1qGKYwgdPSOqakpHz58AFCnITAEDD5+/AigJl023Aj9rNMaff78mV69etG4cWP0ej1t27ZlyJAhEcr16dOHmzdvkipVKrZt20aHDh0wMzMDYNq0aWpisGPHjnHq1Cmsra0ZPXo0zs7OKIrCkCFDePToUazqdujQIfR6PalTpyZdunTfvK5WCXT7FT+Gi4sL06ZNQ6PRsGHDBsaNGxfu9ZkzZ+Lv70/ZsmUZPXo0JiYmjB49mq5duwKhU388e/Ys3HtsbW3ZtWuXGkwwNLTEpdKlS1OmTBn0er0aDImNzJkzhwsmtGzZMsIc6UKI8BRFYcqUKdy/fx9TU1OqVKkSq/fb2NgwfHhoTp/x48eHu56PS6NHj8bDw4M0adLwzz//MHXqVD59+kTJkiWZMWOGOv/94sWL1Y4UW7duVUdnLl26lJCQEHLkyIGvry958+Zl9+7dcr5MQLRaLenTpzeqbO/evfn8+XO4YNCFCxfYt28fWq2W+fPnU7BgQSD0enncuHHMmTMHa2trLl26FGXOPRMTEyZMmMD69euxtrbm2LFj5MyZk4YNG7Jv375oEz4L8TN5+fIlHTp0UIMIy5cvj3EU0fdiyDFXrFgx9VwghBAieok2kKDVatm8eTO9evWiW7dulC9fntGjR1OuXDmqVasWYboRIWJDURSuXbvGtGnTqFSpEqlSpaJZs2asW7eON2/eYG1tTZMmTShRooT6nuvXrwPQ1MUlvqodrbCJTQMDA/Hx8VGHjd65c4fXr1/z5s0bNBoNWbNmxcvLCw8PDzQajTqaIbJcAEmZoihs3LgRR0dHli5diqIodOzYkdmzZ0fa480wWmPs2LERemYaei0aLpQNictMTEzo3bs3efLkAYh1T+iqVauqSbPbtWuHTi83quLbDB48mEmTJgFE6Hlp6C1WokQJNUim0Who1KgRELqdR9ajzNbWVs0X4+npGed1/u+//7hw4QJArBszDTJnzsyBAwdInjw5V65cYe7cuXFZRSGSlODgYLp3787EiRMBGDlyJPb29rFeTuvWralbty5BQUF069aNrl27qg34caVx48YAvHv3jpMnT5ItWzYgdFq0ZMmSqeW+rL/hfG0YmVWoUCEAWrZsGS89Z8W38/Pz4/z58wCsWbMGa2trALJnz46JiQl6vT7SxsRixYqpf5csWTLaz2jcuDHHjx+nYsWK6PV69u7dS4MGDciVKxcrVqyQKY/ET8/V1ZXAwEDKlCkTr0EEAGdnZzQaDefOnWPr1q3xVg8hhEhMEm0gAUIv8OfOncvFixcpWrQojx49om/fvqxYsSK+qyYSKX9/f1auXEmhQoUoUqQIv//+OydPnkSn05E3b1569+7N7t27ef78OevWrQuXUPDBgwdA6FQeCZGh4dvQY+vVq1cUKFAAKysrPn78qOZ8yJ8/P8mTJ1cTzYWdp98wYuFn8PjxY2rWrEm7du149+4defPm5dixY6xYsUJtQP2SITBjyD0RluHG1NAjLWwjhEajURswwk6XZYysWbOyY8cOLCws2Ldvn9q7U4hvkSZNGiD8SCaAwoULA/+b9szAMDqrYMGC2NraRrpMw7Li+jii0+no3r07Op2Oxo0bU7Nmza9eVpYsWZg9ezYAkydPVkdlCSH+x8vLi8aNG7Nu3Tq1B/fXnntMTU1Zs2YNo0aNQqPRsHLlSipUqBCn04u1adOGAQMGALBixQo1sODm5hauXN68edW/06ZNq57rDfkSDOdtw6gFkfhcunSJkJAQMmTIQPbs2dXn06ZNqwahI2tMnDFjBn5+fhQqVIjWrVvH+DlOTk4cO3aMW7du0b9/f1KmTMnz58/p3r07RYoUYffu3RHycAjxM/Dw8FDban7//fd4DSI8evSIu3fvMnjwYAB69erFkydP4q0+QgiRWCTqQIJBsWLFmD9/Pps2baJ9+/YJJrmtSDxev37NmDFjyJo1K127duXmzZuYm5vj7OzMwoULefToEbdv32batGlUrVoVy0hyHxjycuTKnftHV98ohh7wOXPmBEIDCebm5movq2vXrgFQrlw5ADWQ4OjoCIT2tv8Z8iPodDrmzp1LoUKFOHbsGJaWlkyaNImrV69SqVKlaN9rCCRE1gBiaJg1+LI3o6Fx9cuGW2OUKVOGNWvWoNFoWPXFHPVCfA1DvhQ/P79wzxumDbx27Vq4UU6GQELZsmWjXKYhSBZVoOFrLV26lAsXLpAsWTJmzJgRaRlFUfj06RP37t3j9OnTbNu2jQULFjBq1Ci6du1K//791QBgy5YtqVOnDsHBwXTs2FGmOBIijJcvX1KhQgWOHTuGtbU127dvV6c2+1parZaBAweyf/9+UqZMyaVLlyhevDgHDx7kw4cPeHp64uvrS2BgIDqd7qsaYLt164ZGo+HEiRPkz58fU1NTbt68yYsXL9QyhusdgIwZM6p/G451hvO2YZpCkfgYzlVlypSJcL/YokULAHbt2hXuWuzcuXPqtEejR49Wr6eNkSdPHmbNmoW7uzuzZ8/GwcGBO3fu0LhxYypWrMjZs2fjYK2ESDzmzZuHn58fRYsWpXr16vFWj3v37vHrr7/So0cPHj9+TOnSpfHy8qJdu3Zy3SeEEDFIEoEEIb7W5cuX6dChAzlz5mTSpEnqHLo2NjYEBQXx6dMnateuHa7XUmT0er06t32u/2+oT2gMN0yGEQaGRrNSpUqpZczMzChevDjwv0CCIfDwM4xGuH79OuXKlWPw4MH4+flRuXJlrl27xogRI4wKosQmkJAyZcpw/xsabmM7IsGgUaNGTJ8+/aveK8SXDNM9fBnYyps3L5aWlnh6eqrHiJCQEHWqCGMCCXF5LHn16hW///47AOPGjQs3R/b+/fupUqUKefPmJWXKlGTKlImiRYtSv3592rdvz9ChQ5k9ezYbNmxg2bJl1K9fn8+fP6PRaFiwYAHJkyfn8uXLzJw5M87qK0RiduPGDcqUKcP169dJkyYNhw8fxtnZOc6WX7NmTS5dukSRIkXw8PDA2dmZ1KlTkyJFCuzs7LCyssLMzAytVouZmRlWVlbY2dmRJ08e/vnnn2iXnTVrVqpVqwaENhRXrlwZgDNnzqhlcoa5fgvbyGwIXKRKlQqQQEJidvToUSA0kPClMmXKkDVrVnx8fNi3bx8QOoWXYaq/Fi1akC9fvq/6XEtLS/r168f9+/cZNmwYVlZWnD17lgoVKtC4cWPu3r37lWskROLh6enJokWLABg2bFi8df588eIFderUwcPDAwjNk1CmTBns7e05f/48EyZMiJd6CSFEYmEa3xUQiVtseoXF5cVC2J6w0fH394/Qc0in07F3714WLVoUridQjhw5sLOzU3vmQ2jPJScnJ5o3b06pUqVImTJlpL0nnj9/jr+/P2ZmZqRJk4aPRq7Hl719o2NML169Xh/l92x43hAUefbsGZ8/fyZLlixYWloSEBBAgQIFCAoKIigoiMePHwP/axxPlixZtN97XA9N1ev1Mf7Oer3eqM8NCgqKdvsLDAxk6tSpzJo1i5CQEJIlS8bIkSNp0aIFGo2Gly9fhivv6+sbIRAA/5s26tmzZwQGBvL69etwuRAMrK2tefv2bbj/DUkbP336FOV2EdO80b/99hvuDx7AyVMAnDx5knJVq0ZZXlGUKKdpCiu67epLxpSLr+NGfH1uQhdZAkjDqCtfX1/19U+fPqHRaMiTJw/Xrl3jzJkzpEiRgps3b+Lt7Y2dnR3p06fH09OToKCgCMv08vICQrf3oKAg9Hp9lAG6sL9VUHAwShS9w/r27Yu3tzclS5akdOnS3L59Gwht8OzWrVuEXmW2trYkS5YMBwcHUqRIQYoUKUiePDm7d+/m5s2bVK9encmTJ2Nubk737t2ZNm0a48ePx9nZOcocMYqiGBVoDAkJMXq7MuRUiY5sz4lTXP9uxi7P2OO4n59fpD2uT5w4QatWrfDy8iJPnjwsW7aMzJkzx9io7uPjY1Q+gc+fP+Pv74+FhQXbt29n7NixbN26Ncr55ENCQtTX7t+/T+3atVm1ahWlS5cGQo9rYUcVAHTs2JEjR46wbt06Ro0axZEjR/jnn3+oGsl50sHBQc0RYzgGGs777969i/L6RFGUKHush/2tfsZpbRRFMWq943K7DwwMVJf34MEDTpw4gUajwdnZmcDAQLWcl5cXpqamNGzYEFdXVzZu3Ejt2rXZuHEjDx8+JHny5HTp0gVvb+9w74vKu3fvsLCwiPS17t2707BhQ+bOncuOHTv466+/2LNnD+3bt2fkyJGRJo1WFCXS0dBf0ul0Ro+YiM8pZcTXM2Y/MvZ4H93xKixjr19i2v4WLVqEp6cnuXPnplSpUuHuhSLj6ekZ6f1WZJ9rTDLz7du38+7dO1xdXXnz5g1p0qShVKlS7Nmzh3nz5lGlShWOHTvGjBkzqF69eow5txRFidP7I2PJ9Z8QIr5JIEEkaSYmJuEuaI4fP07Pnj159uwZENpY4+Ligp2dHUePHlWDCBUqVKBixYps3LiRx48fs3btWm7dukXHjh0jvZA39EDPkSMHNra2aiBBq9VGe0H15cn99OnT/PXXXwwdOjRCD3ZjLgSiK2Ooh2FEgpeXl9oj7+rVq6xfv57+/ftTokQJfH191TmADTfidnZ2P/RiRKPRxPh5cVGfs2fP0qNHD+7duweEJl+cMGEC6dKli/I95ubmkQZ2DPMrv3z5EisrK3Lnzq021AcHB6t5KNKmTRtuLmatVqsmfPT394+2UTKmBsspU6bwovyvALRq1YrDJ0+qiZy/ljG/hUi8ImtMMIwa8Pf3V1/PmjUrWq2W8uXLc+3aNZ4+fUq2bNnYuXMnEHrczJkzZ5QBAkOAzN7eHnNz8+hvYMM8b6LVRrr97du3Dzc3N0xMTFi0aJF6E/n+/XuGDh1KcHAwFSpUoGPHjqRMmZKUKVNiaWnJ1atXI+zfRYsWZcSIEdy8eZOpU6cybNgwKlSowI0bN9i3bx9dunTh1KlTkQbejN0/ZB8S8elbttMNGzbQq1cvQkJCKF++PFu2bCE4ODhcwCs4OJjXr1+rnQ8MbG1tjUrCHDZQbmVlxYwZM5gxYwZ6vZ6QkBB0Op36Gba2tuh0OkJCQggODmbcuHGcPHmSTp06qcEEExOTCNdrTZo0IXXq1Lx584bkyZNjYmLC8+fPSZYsmVrvNWvWsH//fnr37q0G+A3fiYxISDiMPZ5qw5w//vjjD4BIRxpnyZIFc3NzevXqxdKlS7l37x537txR3zN+/HgKFixIcHCwUQ36MTXOpkuXjunTp9OxY0cWLlzIvn37WLVqFVu2bKF3794MGTJEHakaG3K9JiDutwNTU1OjlqeN4noNQq8BXV1dgdBcBMZ0mLC3tyd16tQxltPr9eryAgICWLVqFZcvX2bGjBnhAtnu7u6sWrWKN2/ekCxZMjp27Ejy5Ml5//49586d4/Tp0+TPn5+bN2/Stm1brl27ph73hRBC/I9MbSR+Gp8+faJ9+/Y8e/YMBwcHhg0bxr1792jSpAkbN27kyZMnWFpaqnk2lixZQrly5ahTpw5arZZLly4xduxYdX7VsAyJlnN/Q36EBw8e0LRpU5YtW0a7du2i7IX3tb4ckfD8+XO11/DgwYP5999/KVGiBBA6b6SiKKRJk0a9GUpqUxt5e3vTv39/qlatyr1790ibNi2LFi1i+fLl0QYRopMuXTrMzMwICQmJkHA57DIjuyg1BCZ8fHy+6rMNwjYKf/78mfr168fY40eILxkaMCLL2WGY/uzSpUtA6MgXCA0kRMewbcfFscTX15d+/foB0K9fPwoVKgSE9j4dMmQIHh4e5MyZkwkTJpA/f34yZMgQbeNP9uzZGTlyJKamppw7d45ly5YBsHjxYpInT86VK1fUJMxC/CwURWHq1Kl069aNkJAQXFxc2L17NylSpAhX7vXr19StW5cyZcowa9asOO1tb5jGKDAwEA8PD549e8a1a9c4efIke/fuZcuWLRQsWJAyZcrg7+9Pp06dOHfuXKTLMjc3p3379gBs27ZN7W167NgxtYyTkxNDhgxRgwiG7wFQG7Q+fPgQ59do4vvy9fVlw4YNQGi+jKikTJmSBg0aANClSxd8fX0pXrw4LVu2/OrP9vf3Z/r06dSvX58DBw6Eey137txs376do0ePUqpUKfz8/JgxYwZFixZlz549X/2ZQiQ0K1eu5P3792TPnp369euHe01RFD58+PBNyw8ICGDx4sXkyZOHvn37snbtWrZt26a+HhgYyMaNG3n+/DlWVlZ06tQJCwsLHj16hLOzM9mzZycwMJBXr17h4ODA69ev6dy58085ekwIIWIigQTx0xg/fjweHh44Ojpy//59fv/9d+bOnUvTpk3x8/NTEy0fPXqUf/75Bw8PD9avX8/bt2/p2bMnDg4OeHh4UKNGDSZPnhzuJvJbAwn+/v60b99ebWg7c+YMU6dO/faVDsMQSEidOjU2NjbodDqeP38OhDY+h23cu3PnDhCaePB7zGsen/R6Pdu3b6dIkSIsXboURVFo3749//33H7Vr11bLBQQEMGHCBKpUqcLKlSuNGspuYmJCpkyZANRRLwYpU6ZUe8tEFkgwfL/fGkgIK3uOHDx58oRGjRp9VRJn8fMy5EiIbJotQ4L2x48f8/TpU/777z8Afv3112iXaTiWfE0vyy9NnDgRd3d3smbNyujRo4H/NXreunULe3t7Zs2aFavPKlCgAIMHD0aj0XDw4EE2bdpE+vTpmTNnjvqZN2/e/Oa6C5EYBAcH06NHD3V++IEDB7J69eoI07XcvHmTunXrqvvG3LlzmT179jc1vnz69IkZM2ZQrVo1ChcuTLZs2XBycuLXX3+lY8eOdO3alWHDhjF9+nSWL1/OwoUL8fT0NCqY0LlzZyB0RJNhqsqwgYTIGKYxSpkyJRqNJk4avcSPtXXrVjw9PcmRI4eaKyMqHTt2BP43Leb06dNjlWA5rHPnzuHs7MzSpUu5ceMGPXv2pEePHhFGtZQtW5bjx4+zadMmsmTJwvPnz2nevDlNmzaNNO+WEIlJUFAQs2bNAmDIkCHhRiMoikLPnj0pUqQIK1asiPWyAwICWLp0qRpAePnypXrPa8g9otPp6NChAw8fPsTc3JwOHTpgY2PDokWLWLFiBYcPH6ZVq1YkT56cjx8/YmNjg5mZGXv27FFzOgghQrm7u3PlypVoH4a2JJF0SSBB/BSuXr2qDk+eN28e79+/p3LlyixcuBCAypUrU7FiRZYtW8bLly+xtbWlQoUK6kiETZs20apVK8qWLYter2fKlCnUqFFDbYi/f/8+8PWBhOHDh3Pjxg1SpUrFtGnTAJg5c2aMN7exEXaoqWF6I0MehC8Z5hnPly9fnPYijm979+6lePHitG3blhcvXpA9e3b279/PsmXLwvWwvHXrFs7OzqxYsYKHDx8yfvx4qlSpwp9//hlhzvUvGaZH+DKQYGJiok5XFVkgwdDgGZeBhG1bt+Lg4MClS5do27Ytnp6ecbZskbQZtkc/P78IDYLJkycnV65cACxYsACdTkfOnDnVIFpUDIEEX1/frwpsBQUF8eDBA7Zt26YOj58/f75a161bt7Jv3z5MTEyYMmVKjPWJTNmyZdXeqhs2bGD58uW0bt2aOnXqEBwcTJcuXWI8BgiR2Hl5edG4cWPWr1+PVqvF1dWViRMnRmhMPXbsGI0aNeLNmzfkzp2bPn36AP8LJsSWn58f06dPp3Tp0ri6unLnzh3ev3+vdtywsbEhQ4YMFCxYkEqVKtGoUSM6deqEg4MDt2/fjhBM+PvvvyN8Rt68eSlfvjw6nY7Pnz9jYmLCvXv3ePHiRZT1MgQSzMzMZHqjREhRFHWUWdeuXWMMChQvXpz8+fMDoUEFw4i32PD392fUqFG0bNmSp0+fki5dOtq0aYOpqSkHDx6kevXq/Pnnn+Heo9FoaNiwIVeuXGHw4MGYmpqyb98+ihYtyty5c43ODydEQrN+/XpevHhBhgwZ6NChQ7jXtm7dyp49e9Dr9YwbNw43NzejlhkQEMCSJUtwdHSkf//+vHz5kkyZMrFw4UK18f/u3bsoikLfvn3Zvn07JiYmtGnThjRp0rB69Wo1IHz69Gnu379P27ZtMTMzw93dnfLlywOhgY+w+ROF+Jm5u7vj6OhIsWLFon20adMGa2trmRosCZNAgvgpTJkyBb1eT7NmzahQoQK//fYbV65cIUWKFOzYsYOaNWuyYcMGgoKCyJ49O7///jvNmzdnwIAB2Nvb8/HjR7Zs2ULXrl1ZvXo1yZIl4/z58/To0QNATcj3NY1jz58/Z9WqVUDozXevXr1o0qQJiqIwceLEb153vV7PzJkz1USnV65c4dWrVwARkggDfPz4UZ2qxNHRUf07efLk31yX+OTu7o6Li4saJBkxYgSXL1+ONJFW7969efjwIWnSpGHAgAGkSZOG58+fM2DAAMqXL8+yZcv4+DFiSu3Pnz+r20JkvRUNCfQim+/T0DgZU0Ll2MiVKxdubm5YWFiwd+9eMmfOTKtWrdi3b580hooohYSEsGDBAiC0ASaybcUwDdr27duBmEcjGJYLMHv2bFKlSoWjoyP169dn2LBhrFu3jsuXL/P69WsuXLjAjh071PfVr9+AXLlykSxZMpycnGjTpg06nY4mTZqoo4iCg4NZsmQJAC4uLpQsWfKr19/Z2RlnZ2cABg0aRFBQEIsXL8be3p4rV65w/Pjxr162EAldUFAQLVq04Pjx41hbW7Nt2za6dOkSoZyiKPTu3Rs/Pz80Gg3Dhg2jR48eagPs3Llz1WsNYy1cuJD58+fj4+ODo6MjCxcu5NChQ1y8eJFHjx5x//599u7dy65du1i9ejVz5sxh9OjRbNq0ieTJk3P79m2KFClChQoV8Pf3p06dOpEGEwzHqyNHjlC0aFEgdIRCVAwdMUaPHk3atGmB0BEa0lM8cXjx4gXXr18HoHXr1jGWN0xvOmLECMaMGfNVn7l69Wo2btwIQMuWLTl06BATJ05k165dFChQAC8vLwYPHsz8+fMjvNfa2poJEyZw7tw5ypcvj5+fH6NGjWLs2LFfVRch4tv69esBaNOmTYRRbYZ9wDBKYfTo0dEGzcIGEPr168fLly/JmDEjCxcu5N69e3Tv3h0nJycAHj58yMWLF1m2bBkajYZmzZqRO3duzp07x6tXr7CxsVGvZ3ft2kWaNGlo2rQpACdOnKB06dIEBgbSuXNnCeQJAXh4eODn58eGDRu4fPlytI87d+5EyJslkg4JJIgkLygoiBMnTgAwYMAAPn/+zOnTp4HQ5Mt16tTBysoqXBLisL2VDFN7FChQAIBmzZpx6NAhILQHg7+/Py4uLgCMGjWKy5cvx6p+6dKlUxPv7tmzhw8fPnDq1CkANRny1wgJCeHixYvUqlWL4cOHoygKTk5OtG3blg8fPpAlSxaqVq0a7j06nY5Bgwbh4eFB9uzZefXqFX/++ScmJia0bdv2q+uSEGTIkIFGjRqp/x86dCjKwI8haDJ06FAGDBjAqVOnGDp0KKlSpeL169fMmDGDX375hX79+qlJmj9//ky9evW4d+8eDg4O1KtXL8Jy27RpQ506dSI0uur1etauXQsY1yAbG2XKlGHHjh04OjoSEBDAjh07aNSoEdmyZWPAgAFcvHhR5v8UKp1OR8uWLdWRUX379o00cXKnTp3UkQAZMmSItKHxS71796ZixYqkTp0aRVF4+vQpe/fuZebMmXTo0IESJUqQMWNGSpcurU4tAXD69Cnc3d3R6/VYW1vj5OREu3bt1GAHhPYUrlixIgC7d+9WG42+xqdPn7hw4QIA1atXx8LCgvTp06vrmxRGZwkRGUVR6N+/P//88w82NjYcOHAg3JR/YWk0Grp27aomTu/SpQtFixZVpzhq0KBBrPMNPXz4EIAePXpw+PBhGjVqZFSOk1SpUqkBz8KFC7N8+XIqVqyIn59fhGDC5cuX1enKGjduTOPGjYHQnAlRXRN06tQJrVbLpk2bePXqFebm5hw/fpxChQpx9OjRWK2j+PHSpEmj9oqMLM9ZZD5//szLly+NmtYyMob3NWvWjClTppAsWTIgdKSvm5sbffv2BUIbUSdPnhzpMvLly8ehQ4fU0T1Tp07l8OHDX1UfIeKT4X5zw4YNasc2A0OHLkNnk3r16kU5amjv3r3hAgiZMmVi/vz53L59m+7du6tBCsP5wMrKiidPngBQvnx59V7eMEK7VKlSNGzYEFtbW4KCgnjx4gUFChSgdOnSQGgbgJ2dHVeuXFFzrAghQjubFi1aNNqHBBGSNgkkiCTvwoUL+Pr6kjp1agoWLMg///yDXq/nl19+URvwDTfEqVKl4sOHDyxfvpyXL1+ydOlSgoODKVCgAA0bNlSXWaBAAdKnT09wcDAXL15kyJAh1K1bl8DAQDr8fyI/Y5mZmbFkyRK0Wi3btm2jXr16vHv3jjx58jBs2DCjlxMSEsKlS5eYPXs29erVI3Xq1JQuXZpjx45haWlJzpw5uXXrFnq9noYNG+Lm5kbmzJnDLWP+/PmcO3cOa2trevbsGe7mpVy5crFar4TG1NSUTZs2sWrVKlKkSMGVK1eYMWNGpGUNc8AbhrJaWVnRu3dv/v33X2bPnk2+fPnw9/dnxYoVFC1alIYNG1K3bl2uXLmCg4MD+/btI1u2bBGWW6BAAQYMGBChIfL8+fM8ePCAZMmS0a5du7hdcaBmzZr8999/nD9/nr59+5ImTRrev3/P4sWLKVWqFPny5WPy5MkRpmMSPxdDI+LOnTuxsLBgw4YNaoPbl/LkycOWLVvo27cve/fuNepisVy5chw6dIjnz5/z/PlzDh8+zMKFC+nZsyeVKlVSR+pkypSJcv8/pBxg2bJlnDx5kufPn/Pp0yeuXr3KihUrIgyXHTVqFKVLl8bf35/+/furuWtiIzg4mOnTp+Ph4UGmTJlYvXo1EJpM9tWrV2i12q+a5kKIxGDatGmsW7cOrVbLunXr1MTqURk4cCBnzpyhY8eOmJubExAQQK5cudi0aROLFy+O9bzyhhEMxYoVi9V7V65cia+vL05OTlSrVg0LCwuWLVtGrVq1wgUTPnz4QJMmTQgMDKRevXoMHTqUypUrkzVrVry8vKKcUqNjx46sWLGCXLly8fHjR4KCgkiTJg1eXl7Ur18/2tEMIv5ZWFio06kYpjiKyYgRI1i3bh0TJkz4qs+0tbUFiDQpt6mpKQMGDGD48OEATJ48OcpggkajoUePHnTp0gVFUWjXrl2sR/oIEd+GDBlCrly5ePXqFePGjQv32qhRo9QRBC4uLlHuC25ubjRt2lQNICxYsIA7d+6ECyAYGEaFOzg48PnzZyA0x42BIdBgbm6OVqtV79mePn0KQLVq1UiWLBnXr1+nVq1aAIwcOVLyzQkhxP+TQIJI8gx5BipXroxWq1WnpfhyShtbW1t69OiBlZUVjx8/Ztq0afj6+pI1a1Y6duyIiYmJWlaj0agN62fPnkWr1bJixQocHR15+/ZtrOtYvHhx+vfvD8CNGzfQarUsXbo02h54hsDBrFmzqFu3Lg4ODpQqVYqhQ4eyf/9+vLy8sLe3p3jx4piamvLo0SOSJUvGhg0bmDJlSoQkpMeOHWP58uUADBs2jFmzZhEcHEzjxo0ZOHBgrNcpIdJoNLRq1Yp169YBsGrVqkinKCpSpAgQOg1UWBYWFjRt2pTdu3dz8OBB6tWrh0aj4ciRI1y9elUNIhh6vBjLMD1Mhw4dvltvZ41GQ5EiRZg1axZPnz5l9+7dNG/eHCsrK+7du8fo0aPJnj071apVizJ3hkjaZsyYwZIlS9BoNKxbt44WLVpEW75QoUIMHjz4q+a/TJ06Nb/++is9e/Zk4cKFHD9+nLdv3+Lv74+7uzsHDxxQyzZt2pTSpUuTNm3acLlevmRubs6MGTMoWLAg3t7e9O7dO9ZTj/zxxx/cvn0ba2trxo4di729PfC/Y0GePHniJFm0EAnNtm3bGDlyJBCao8nQeBKTDBkyMGnSJM6ePcuaNWs4evSoOjootl6/fq0u01gfP35UR/T169dPPUZYWFiwc+fOcMGE2rVr8+zZM3LlyqUGTExMTGj//x1ANm7cGGUP9EKFCnH16lU1D8S7d++wtrYmMDCQJk2aGD2vt4gfXbp0QaPRcPz4cTWvWVSePXumjqzZvHnzVyWNNJwnomt47Natm9phKLpgAsD06dMpVKgQ79+/p02bNpEGKIRIqCwtLdVRpAsWLODWrVvhXnNzc2Pbtm3Mnj073P22gZubG61bt0an09G6dWvu3LlDt27dIgQQDN6/fw+Ejlb79OkTQLhceEFBQUBoZz4gQiDB1taWESNGAHDmzBkyZ87My5cvqVSpknS6EkIIJJAgfgKGYefVqlUD/hdY+HJaHwidZqhLly5otVr0ej2pUqWiR48ekV6oGAIJZ86cAUKnu9i+fXu4C5XYTBkzYsQIdYREv379ouwJqNfrmThxIg4ODpQoUYIhQ4awb98+NXBQt25dhg4dSp8+fcibNy+XLl3Cx8eHMmXKcOXKFVq2bBlhmU+fPlV7RrVp04YDBw7w9u1bsmfPzsqVK6NtvEuMqlWrRqFChfD19Y20d5phRMKdO3civQnUaDT8+uuvbNmyhRs3btCrVy8qVqz4VUGEBw8e8N9//2Fqaqrm3PjeTE1NqVWrFuvWreP169esWrWKypUrqzfZlStXlmDCT2bDhg1qI+KcOXNo0qTJD/38V69e0bBhQ6ytrUmePDlF/38fhNBeYLNmzWLDhg0cO3aMmzdvqombv2RlZcW8efP45Zdf+PjxIz179mTt2rVcu3YtxrwgBw8e5ODBg2g0GgYNGhQuWbMhkGCYT12IpOTcuXNqY3rPnj3p3r17rJeRPn16qlevrjbMxFZQUJDaEcPYQEJQUBATJ04MNxohLEtLy3DBhIsXL2JlZYWbm1u4vE+1a9cmXbp0fPjwgT179kT5edbW1sybN4+jR4+SNWtW/Pz8MDc3Jzg4mBYtWrBp06bYr7j4IbJmzarmvjF0molK2BEmer3+q0YlGAIJPj4+0Zbr2rWrGkCILphgaWnJ1q1bsbW15Z9//vnqkRJCxJcaNWrg4uKCXq9n5MiR4XIO2NjYULZs2RiDCG3atGHFihVRBhAMPDw8gNBAgmFEgqFjCPxvpNCXgYRnz56p9erXrx9Zs2bl1atX1KxZEwcHBy5fvkyJEiW+OVfWmzdvOHDggAQEhRCJlml8V0D8WMY2bCuKYlTjsV6vN7qR2ZhycV2/x48fqw1AhQsX5urVqzx69AgTExPy5cunXmj4+fmpvf9z5sxJ+/btuXLlCnXr1sXS0lJtgPL391ffky9fPiD0BvzNmzeYmppiZ2cXOhXI1ND5xWfPnk2/aKYnCgoKUpNLmZubs3PnTk6dOkWTJk3CXWC9fv0ac3NzvLy8GDRokBoMsbOzo1SpUpQsWZIUKVJw+vRprly5wt69e9X3arVaWrVqRbt27Xj27BnPnj3D19dXzQkREhLC4MGD8fHxoUiRIpiZmXHu3Dm1Qc4wr2t0vL29Y/w9vpwTMyZ6vT7GxFZ6vd6o6Q8i264GDBhAhw4dWLx4Mf369cPKygpvb2+0Wi1WVlakS5eON2/ecPbs2QiJW589exaup7OLiwsuLi74+/urc6tDaCLtmHprb968GQidTzpVqlTRJlv28/OL9CI7LH2Y3pRv3rxFYxX1qBY/Pz9SpUpFvXr1qFevHk+fPqVly5Y8fPiQihUr8tdff6kX12GHBEdFURSjfg9jjxs6nc7o3zem7wVC9zdjj1eR5QVIjBRFifG4umrVKnr27AmEjoqpXr06d+/ejVDOy8vLqAY+X19fo/b3vXv3EhQUxLlz59i2bZuaj8bLy4sH3t7wSx4AlixZgv8X62Bubs6QIUOoX7+++tyrV69Inz49VlZWzJ07lx49euDu7s7ChQvV9zg5OZEuXTqKFy/OL7/8gpWVFRAaNDQ0LrVo0YJChQoRFBSk3uQZct8UKVIkwo2fscehkJCQWE/1Ep3YnC+N/dykFjROiGLTwSAur5v8/f0j3Q6ePn1KgwYNCAwMxNnZmUGDBqkNL9Hx9fU1KmgQGBio7tvRuXfvHoqiqFNNGHqRfsnb2xtbW1vevn1Lv3791Gu8AQMGhNs3DVNbAMybN49evXpx6tQppk+fjp2dndrz9N27d9jZ2dG8eXNcXV1Zu3YtNWvWVK/NDG7dusWNGzfU/4cOHcqsWbN48uQJJiYm6HQ62rVrx5kzZ8LlbwlLr9Opf4eEhKA18rf7mfbL4OBgo+8/vvyNIhP2OqJr167s27ePDRs2MHbs2HCjyzw8PNRyf/31FwCtWrVi27ZtHDlyhN27d1OyZEl0Op1R273h+tXT0zPKbRlC96PmzZvj5eXF9OnTmTx5Mt7e3moOBQNFUciWLRtLliyhbdu2TJkyhdKlS1O9evUI5Yypn7H3UfBzbX8/G2PPH8Ze58RUbvbs2Rw8eJArV66wcePGSDu3hbVv3z769u2rBhGWL1+OVquNUO8vO4oYRiSkTJlSPRfY29vz4MED9Hq9eo57//49d+/eVY8nAQEBnDt3jjRp0uDu7k6fPn0YPHgwmzZtYu3atYwdO5bbt29To0YNBg0aROfOncmZM2eM34sh/6JOp2PVqlVMnDgRb29vqlSpwsqVK8MFtsMGPKJi7H1UXN+XAXF6HSuESLwkkCC+iUajiZcLTK1Wa9Tnnjt3Dr1eT+7cucmcObOaKKlo0aLhGkabNm1qVEPphw8f1AbG/Pnzkzx5cj5//szdu3fVnqoVKlTg8/8HEsaPH0+eQoWinCLA2to6XIPlL7/8wi+//BKhnJmZGY8ePaJbt248fvwYCwsLxowZQ+rUqTl+/DjLly9XAxwQ2uO8UKFClClThrJly6qN2br/v4m1tbUld+7cQOhw6Vu3bpE8eXK6deum9kT8448/qFWrllEXDF/+Fq9evWLXrl3q0FEg2gbyyGi12hg/25gyQKRTRLVq1YqxY8fy7NkztmzZQvfu3UmbNq36esmSJdm9ezcPHz6kTp064d77+PFj9buMTqZMmcifP3+Ur79+/ZqzZ88CoY0g1tbW0S7PxMQkxl44YYMvGk30N4BfXohny5aNnTt30qhRIx4+fEjDhg3VYEJc7ufGHjfi+tgSX8erhOzq1av079+fkJAQateuzYABA3j+/Dnnzp1TE9EbJEuWjIwZM8a4zM+fPxsV2Hn27Bk7duxQh7hnyZKFFi1aYG5ujt/Hj3AstMfXr7/+isbKio8fP+Lh4YGHhwfe3t5MnjwZHx8fmjdvDoT2Yi5cuLC6/H///ZeNGzdy5swZzpw5g4eHB1evXgXgwIEDmJiYqMfJbdu2ERISQqNGjVi2bBkajSZcg9F///0HhE5D92UjjTHHAgjdf435Xowl23Li9D2Oa8aI7Lrp8+fPNGrUiPfv31O4cGHWrl1LUFCQUYHUtGnThrtu+uuvv/jrr7/o1asXJUqUUJ/38fEx6jxtGP2XIUMGHBwcoixnZ2fH3bt3adu2Le/evcPe3p4VK1ZQt27dKN9jYWHBihUr8PHxUeeuN8iZMyfp0qUjd+7cbNq0iTdv3nDv3r0IDVw3btwId01jZmZG//79WbBgAQ8fPlSDCUuXLiVfvnxqcDassN//z7j/GnMOjs33YkxZCwsLdftzdnYmV65cPHz4kP3799OmTRu1nK2tLRqNhvfv36vniZ49e2JiYsL69euZN28eO3fuRKfTxXgdBqEjdAA1n0ZU3r17h4mJCd27d0ej0TBt2jTmz59PsmTJ6Ny5s1rO0NDXrFkzTp06xfLly+nUqRMXL15UP8vwnXxtBxuROMTlfmRso7CpqalRy4zp/jxz5sxMmDCBgQMHMmPGDNq1a6fmx/qSISm5IUi7cuXKaK+hwn6u4Z44derU6gjrFClSqPUzBCEzZsyodqzLmjUrjx49QqfTqTME1KlTh3Xr1nH9+nW2b9/Opk2bGDduHH/99RczZ87k5s2bbN68OcJ5JTI3btxgwIABascUgOPHj1O1alU2btyofqYx5H5GCBHfJKQokrR//vkHQJ2v19CTv0KFCt+8bK1WS9myZQGYOHFipMMTDYnR7t27902fdeTIERo1asTjx49Jly4dTZo0YfLkyfz2229s2bIFDw8PbG1tKVu2LCNGjGD79u1MnTqV+vXrR9sj/tatW0ycOBEI7V03dOhQAHr37k3Tpk2/qq4PHjygUqVKDB8+nDFjxqiPKVOmfNXyvhdTU1M198Ps2bMjNAYappa6dOnSd6vD6tWrCQkJoVSpUmpehu/Bw8OD/fv3qz10opMuXTp27txJrly5ePnyJQ0bNlR7boqk5enTp9SpUwc/Pz9KlSrFpEmTeP36NW3btmX8+PF07949yimEvoWiKGzevJkZM2Zw69YtTE1NqVevHoMGDSJLliykS5eOXP8f6ITQxp+xY8fi6urKxo0bOXjwIK1atQLA1dWVlStXRtoo4uDgQN++fdm6dSvPnz/nv//+Y+HChTRt2pQsWbKg0+m4cuUKixYt4v379xQoUEDNERFW2ETLYQMVQiRmwcHBtGrVinv37pEhQwZ27NhhVGPIl/R6PePHj6dJkyasX7+e0qVL07lzZ968eROr5RgSyIZtFP2SoigsXboUZ2dn3r17R4ECBThz5ky0QQTD+44cOcLSpUs5depUpNdrVlZW9OrVC4C5c+dGOyIyJCSEXbt24erqSoMGDcibN2+4nu99+/aNMlG9iD9arVadts8w7emXjh07hqIoFChQgIwZM9K3b19sbGy4fv06+/fvN/qzDPtSTFMbhdWtWzeGDBkChE5zdOjQoUjLzZw5U82XYLhuFyKx6N27N4UKFeLTp09qHoIvhZ3OyJggQliBgYGcPn0aCO3QZRh9ELbXv2EEQ9iOIVmzZgUIN+Jco9GoU//u2LGDy5cvM3XqVMaMGYOpqSkHDhygfPnyPHz4MMr6+Pr6MnbsWCpXrszly5exs7Nj1qxZ/PPPP2TOnJnHjx9To0aNWB1fhBAivkkgQSRpJ0+eBEIDCW/evFFvHAzzpH6rkSNHYmNjw7lz55g1a1aE10uVLo2XlxcuLi7RDm2Oil6vZ9y4cXTv3h0fHx+KFSum9prz8/MjXbp0tGnThrVr13Lp0iX69+9P5cqV1eHawcHB4Yb3hxUSEkLXrl0JDg6mVq1abNy4ES8vL8qWLcu0adNiXVcIDSLUqVOHt2/fkitXLlq2bKk+XFxcvmqZ31OnTp1ImTIljx49YufOneFeMwQSLl++/F16bvn4+KjzKYftdRbXLl26RM2aNenevTvFixenQYMGLFiwgDt37kS5XpEFEyRnQtLy4cMHateuzZs3b/jll1+YO3cufn5+dO/eXe3JdeXKFTp37hzlMSQwMJCrV6/Gav94/fo1LVu2pFevXvj7+5MlSxaGDh1KqVKlWLlyJQMHDsTV1ZW///47ymVoNBp69epF165dAVi5ciXz58+Pth4ajYa8efPSpUsXli5dyq1bt7hz5w4rV66kU6dO1KtXjy1btkSaSNkwdUrevHkl0bJIEhRFoW/fvpw4cQIbGxvc3NyMGm30JT8/P1q2bKnO127oXLFmzRry5s3LnDlzwvXij05MiZa9vb357bffmDp1KjqdjlatWvH3339HO62EoigcPnyYevXq8dtvv7Fo0SLatWtHyZIlGT58eISgQqdOnbC3t+f+/fvq9DZfevv2LTNmzGDfvn08fvyYhQsXUqtWLfLnzx9uWo+hQ4dGmzxXxI8qVaoA/wsYfOnw4cNA6HzuEDrHerdu3QCYMWOG0duzMcmWI9OjRw/atGmDoij0799fHR0RlqWlpTrFy7Zt2zhx4kSsPkOI+GRqaqpO/7Zu3Tq10d/gy5wIsQkiQGgnrZcvX5IxY0bq1atndCAhS5YsQOho2bDHhqJFi+Ls7Ixer6dr166sWrWKli1bsm7dOlKnTs3NmzcpVapUuNwqBobceQsXLkSn09GoUSPOnz9P586dKViwIMePH6d8+fJ4e3vTunVrZs2aJaOFhBCJggQSRJL16NEj3N3dMTMzo0yZMmzatImQkBBKlCih5jcI6/79+/Ts2ZNevXrRsGFDKlasSOvWrenWrZs6hcaXcuTIoQYQ5s+fH6Hxa+2aNWTOnJkHDx7Qrl27WCVV8vb2plGjRuqIgUaNGvHx40dOnTqFubk5M2bM4MyZM0yYMIFff/013FQE7u7uzJ8/nxYtWtCyZUtWr14d4cJk9uzZXL58meTJk5MsWTJu3bpF2rRp2bhx41clSwwbRHBycuLQoUMsXbpUfcydOzfWy/zebGxs1B6IM2bMCPcd5c+fHwsLCz5+/MiTJ0+iXY63tzcXLlyI1cXftm3b8PLyIkeOHFSuXPnrVsCIz2jevDnv37/H3t4eRVG4evUqM2fOpGbNmlSvXp1hw4axd+9e7t69G276qS+DCZKAOenw9/enQYMG3Lt3j8yZM7N48WLMzMzo06cPT58+JV26dCxatIiUKVNy584dOnToEKF3sWEqpJIlS1KzZk2ePXsW4+du3bqVsmXLcvjwYczNzdX52F++fMnkyZO5fv06QUFBPHjwgD27d6vv27dvf4TlazQaOnbsSP/+/dVlT5kyxehphiC0p1qzZs1wdXVl06ZN6k3klwyBhO85akiIH2nWrFmsWbMGrVbL+vXrKVSoUKyX8ebNGypVqsSOHTswMzPjjz/+4NSpU5w+fZrixYvj7e3NkCFDKFu2bLSBQYPoAgkPHjzA2dmZ/fv3Y2ZmhqurKytWrIhyOkBvb2/2799PvXr16NatG7du3cLGxobatWvj4ODAp0+f2Lp1K+3ataN8+fL07duXI0eOYGZmpk5JNGHCBALD5ByC0OnSJk2axNOnT7G2tiZbtmwEBgayaNEiKleuTOHChdHr9Wqj19ixYxk1apQ0DCUg5cqVw9LSktevX0fIBeTt7c2ZM2cAqFmzpvp8586dSZMmDc+fP1fzWsUkbLLl2OZFMfReDggIoGvXrpGOCi1cuDC//fYbAP379zc6wCFEQlCqVCm1E1Xv3r3V7ffPP/8MF0RYvnx5rIIIgYGBTJ8+HYBhw4ZhYWGhBhJSpEihlosskJApUyZMTEzw9vbm48eP4ZY7ZcoUGjVqhF6vZ+bMmQwcOJC8efPi5uZGmTJl8PT0pGHDhkycOBFFUXjz5g3NmzenQYMGuLu7kzlzZrZu3cqqVavCjbpLlSoVbm5uaseYyZMn07x5c6PyCgkhRHySQIJIsgxT0uTMmRMbGxt1NELjxo0jLV+nTh02b97Mpk2b+Oeff9RhzNu2bWPWrFmMHTs20vc1bNiQdu3aoSgKffr0CXfxkTp1Gnbs2IG1tTVHjhyJcghnZKZMmcLevXuxsLBg5syZeHh48OTJE9KnT8/27dtxcXGJcn7EUaNGsXfvXnVaks2bN3P9+nX1dZ1Op15ojRo1im3btgGwfv16o5KpRmbQoEFqEGH37t0xJhlOKHr37o25uTmXLl0KNzTV3Nxc7el4+/btKN8fHBzMsGHD+P3336Mchh4ZwxBWX19fNm/ejL+//1euQdTGjBlDcHAwyZIlY//+/Vy4cIGpU6dStWpVLCwsePXqFatWraJjx478+uuvZMmShcqVK6s312GDCc+fP1dvWkXitmPHDs6ePUvy5Mk5cOAAadOmZdeuXfz333/Y2dmxZMkSKlSowNq1a0mXLh1Pnz5l3rx54ZZx6NAhTp06BcCJEyfU6RgioygK48ePp0ePHnh6elKkSBGOHz9OtWrV8PT0ZO3aterohD59+tCkSRNyh8kVc/r0KYZFkbS+WbNmjBw5Eq1Wy549exg5cuS3f0FfMATQjEnsKURC9+jRI/V6Zvbs2dSuXfurlmPojGBtbc3hw4fp1KkTAGXKlOHff//ljz/+wNbWlgcPHtC4ceMIjfJfCpsc80sjR45U90MrKytOnjzJokWLuHjxIkFBQSiKwv3795k/fz516tQhY8aM9O/fXw0g9OzZk5MnT7J48WLOnTvHhg0baNWqFSlTpuTz58+sW7eOpk2bkiNHDnVKzKdPn4br6f3582fWrFlDYGAguXPnZsyYMQwePJh8+fIRHBzMpk2b+O233yhVqhQ6nU5t/Jo2bRrbt2//qu9YxD1LS0tKlSoFhAaGwrp16xZBQUHY2dmRLVs29XkrKytKly4NYFQg4dOnT2rnGZ1OFyERbEwMPbadnJz48OEDdevW5Y8//oiwnHHjxpE6dWru3r3LmDFjYvUZQsS3yZMnkypVKm7fvk3p0qUZMGAArVq1UoMIf/zxR6zzSi1fvlwdjdCxY0dCQkJ4+/Yt8L8RCXq9Xu04FTaQYGZmpt4DnzhxIlwA0NLSkilTpoSb0mjq1KmkSZOGY8eO0aNHD/Vad+rUqVSoUIE///wTExMTBg0axJkzZ9RRTl8yMzNjxowZuLq6YmZmxo4dOxg1alSs1lsIIX40CSSIJKtYsWJotVru3r3L7du3cXJyAv6XJ+FLhp6tUWnRokWUr40fP55ffvkFDw8Pxo4bF+61QoUKsWLFCgAWLFjAqlWrjKq/oQffokWLaNy4Me/evQNg6tSpFChQIMr36XQ69aJp7NixatLD58+fq2W0Wq3acG2YwsfGxkbNJRFbOp2OixcvAqFJmhNLEAFCGy0MF4thkzJ7eHiouS2iS4C1YcMG7t+/D4QmmzS251n79u1JmTIlb9++ZcyYMTg6OjJjxoyvmgIrKvXq1wfAy8sLZ2dnPD09ad26NatXr+b69essXryYdu3aUaRIEezs7FAUhZs3b9KpUyd1OH66dOnUG+fjx48b1fNcJGyGGydHR0d1dJah8S5jxozkypULCE2+3ahRI4AIySW/HNUVVUJ5RVEYNGgQrq6uAAwZMoRDhw6p77e1tVX3u8aNG5MnTx4qV66sJn03KF++fJTrU6dOHbURZd68eeqUYXHFEHxeu3YtW7dujdNlC/Gjbd68GUVRqFq1aoT9LDYMU8T4+fmp+Q0MNBoNL168UEdyNm3aNMYEtYbAfdhODwbt27enQIECmJiY4OXlxZ9//smQIUOoUKECadOm5ZdffqFQoUIMGzaM48ePExwcTObMmenVqxcnT55kyJAh6jHO1NSUcuXKMXnyZM6fP8/q1avp2LEjGTJkwN/fn7P/x95Zh0WV9mH4nqFbQAXswC5QsLsD1+7u7sTu7m5dde3G7kVRcEWxA1swKOlm5vtj9rzLSA2ou+5+c1/XuZSZM2fOzJx43188z40bgKrjVAo4g8rkWQoyGRkZYWlpib6+vvhc+fPnR1dXl4EDB1K/fn2SkpKEief48eN/iN+MlqwhjdO+7mgpUaIElpaWREREsHXrVuAvD5ATf3bJSR4LaW332LFjODk5cfjwYUAlVaSJefnXmJiYsHXrVipUqEBUVBQLFizAyclJLbllaWkpJGKWL18uioK0aPk3YGVlxa+//oq1tTUPHz5k7dq1KJVKBg4cmKUkwpcvX4Sc3JQpUzAwMOD3338nMjKS7NmzU/TPApWPHz8SHx+PgYFBisR1jRo1kMlkeHl5sX79erXnZDIZnTt3ZsOGDYCqKOfJkyfo6+uzevVqpk6dCqiKuF69ekWhQoW4desWCxcu1EgWs3v37mzfvh1QJUTSkhXVokWLlp+B/9tEQnh4OBs3bmTBggV4eXn907uj5Qdgb28vDPjWrFnD4MGDkcvlXLx4kUePHqVYf8iQIYSEhHDlyhUmTJjAmjVrWLZsGY8ePeLLly8iIJ8ahoaGrFq1Ch0dHU6nopHYunVrMcAYPny48G5Ii7i4OHx8fIC/jKKlAVVaXQgS4eHhKJVKZDIZlStXJkeOHACitVPahoWFBYCQW4qJicly+/3Lly+Jjo7G2Ng43aD7z4i/vz8JCQlqlSgAbm5uJCUlUbZsWRFY/ZrHjx+LILtcLufFixcaG2s3b96cmzdvMmvWLHLnzk1gYCAzZ86kePHiuLq68v79e1FpmVUWL1rEwYMHKVasGGFhYSKYC6pASO3atVm6dCnnz5/n5cuX3L59Gzs7O3x9fYW5GKgCypL80m+//Zbl/dHycyAZBt+7d09IAZUtWxZQSbwlb6n29/cHIG/evGrbKFiwILNmzaJu3boMGzZMVCMnR6lUMnnyZCGhsmrVKlxdXdUq+/X19cV7e3t7i8fjk1Uv58ufP8OAZ8OGDenZsyegCtwk31Z6KBQKvLy8OHDgQJpVo82bNxcdF/369ePWrVsabVuLlp8NyegcoEuXLt+0rSZNmgij1759+3Lv3j1A1aXXt29fZs6cCaiKNL4OyKRGjRo1APj9999TGB03a9aMs2fP8vTpU/bt28fMmTNp0qQJVlZWxMbG4ufnh56eHnXr1mXhwoXcu3ePixcvMnbs2FQ7HCR0dXWpUqUKy5cv59GjR3h4eDBz5kz69evHmTNn1KQwdHR06NOnD7q6uty/f5/r16/z5s0b7t69i0wm45c/E/fStU5PT49Xr15hY2ODn58ftWrV4o02Ef9TICW+vu7AtbCwEF1tK1eu5OXLl4waNYpff/0VUBXnSBIkX/Px40c6d+5Mt27dCAgIoFChQhw9evSbuuRsbGw4cOAAixcvxsrKiqdPn9K4cWO6du0q7s2tWrVi7NixAPTv3z/VRJwWLT8rDRo04MGDB/Ts2ZOCBQuya9cuMZ/OLAsXLiQkJIRSpUqJ8eChQ4cAlXqANPaUus8LFiyY4n2KFSsmfBRXrVqVanKuevXqNG3aFKVSybx588Q8bfz48eKaUqhQIS5dupSmbKCnpyezZ88mLCxM7fGmTZtSrlw5oqOjNbpvatGiRcs/xf9lIuHVq1eULVuWPXv2cOTIESpXrkzLli21md//IEOHDgXg+PHjyOVyMdFbtGhRquvLZDIcHByYOHEiXbp0EVVqmlCuXLl0uxomTZpEu3btSExMpFOnTunqzd+7d4/4+Hisra0pWLAggDDwy8hnQUoYmJubo6OjI1o5kycSpOcBEUhUKBRZlteRJi6lS5fO0uDvn+Tly5eAKliefN8l8+W0pLBiYmJYuHAhCoWCevXqUa9ePUCVgNAUIyMjevbsyaVLl9i6dSulSpUiMjKSVatWUbx4cSwtLTE1NcXc3Bxra2vy5ctH7ty5KVCgAPb29pQoUYJGjRqJjojUqFSpkqhYO336NO/evUt1PZlMRv78+dmwYQNyuZx9+/apDaC7du0KqDowtHrP/26KFCmCsbEx0dHR+Pr6AqqAhY2NDQqFQi3R6ufnB6i0Y7/G1dWVc+fOsXTp0lTfZ968eaJya/Xq1eIY+hqpK+ru3bskJSWhVCo5+OfkD6Bz584ayQr179+fZs2aERcXR/v27VP4OkgkJiby+++/M3r0aIoVK0b9+vXp06cPffv2TdNjYfbs2TRq1IiYmBiaNm2qTSZo+Vfyxx9/8OrVK4yNjWnevPk3b2/OnDk0bNiQmJgY2rRpw5s3b3BxcRHJw7Vr1zJz5kwxfkkPR0dHTExMCAkJSbXYA1QV5FWqVGH8+PEcOXKE9+/f4+Pjw+nTp/H39+fUqVMMHz6cokWLpii6CAsLw8PDI837l0wmo1SpUowYMYLFixdjY2OTYp08efLQsmVLQOXLInU/VapUSW2sWLRoUUaNGgWokhU5c+bk/v371KldO8PvQcuPRalUiiB8agbjrVu3plq1asTGxuLi4sKJEyfQ1dVl5cqVIjiZHIVCwbZt23BycuLkyZPo6uoyYcIETpw4kW4BkqbI5XLatm3LxYsXGTRoEHK5nIMHD1K2bFmWLVtGUlISs2bNokGDBsTExNC2bVvtfFbLv4rs2bOzadMmnj17RocOHbK0jdevX7NmzRpAJSeno6NDYmIix48fB9Q7iaR5X1pFYhUrVhRzuhkzZnD58uUU64wZMwYDAwNu3brFsWPHANWc7tdff6V3795cunQpRQGOxMWLF2nZsiXLli1LIXksk8lE4crq1at/iOytFi1atHwP/i8TCf3796dfv378/vvv3Lp1i/Pnz+Pp6Un16tWF2ZsmxMXFER4errZo+bkoU6YMtWvXRqFQsH79esaOHYuOjg7nz5//IZ0oI0aMUJP8UPLXhFUmk7Fp0yacnJwIDg6mTZs2KSoRJCSZIGdnZzEZloLcX1fqfY2UMJASCGklEqTHk+sWp2YorQlS94RUWZwZ/unzSEroSLIKoJKBunXrFjKZjBYtWqT6uo0bN+Lv70+OHDkYNmyYCMpcuXIlzc8QGhrKzZs3UwQy9PT06NixI15eXhw+fJhq1aqpPZ+UlERsbCyRkZGEhITw+fNn/P39efPmDe7u7nTu3DndwWbx4sWpVasWCoVCtOunRdWqVUWV6bhx40TlTps2bTA0NOTp06caV3tr+fvIzHmko6MjzlXp3IW/zt/kFY3pJRIkUuuSWr58uUgwLF68mE6dOqX5+mLFimFqakpkZCTPnj3D3d2dO8mOMYs/k54ZIZfL2b59O8WLF8ff35+OHTuK61tcXBxnzpyhf//+lChRQmhOf/r0CXNzc/T09Dhy5AjDhg1L9Rqro6PDvn37qFGjBmFhYd8lmfD582cuXryoTcz9RPzT96MfjdSN0Lx5c0xNTb95ezo6Ovz2228UKlSI169fU7x4cS5evIixsTFHjx7NlHSSnp6euPdJPgUZIZfLKVy4MPb29piZmaW77sSJE+natStHjhzReJ9So379+hQtWpT4+HjevHmDXC5PNSkzadIkcufOjb+/P127dqVChQpqHlr/5fP+Zz6PQkNDxXgptUIhmUzGnDlzMDAwIDY2FiMjI7Zs2SIKkZKTlJREnz59GDFiBOHh4Tg5OXH9+nUhq5Kcc+fOMWHChCx3DFhYWLBixQpu3rxJ5cqViYyMxNXVlaFDhyKXy9m5cycFCxbk7du3dOrUKcOiIy0/Pz/zefSzMXXqVOLj46lfv77wIvj9998JCgoie/bsors/NjZWSP2mlUgAldl6mzZtUCgUjBo1ijt37qg9nzt3btGJO378eOG5UKdOHTZt2pRuEqFr165ibLp3715h8C7Rrl078ufPT2BgoOiG0qJFi5afjf8750CFQsHVq1fVKigbNGiAh4cHderUoVmzZty8eTNDLVdQadVLrdta0kehUGQoyQOqQZMm68XHx2ukORodHc3gwYO5evUq+/btY/jw4XTo0IE9e/YwZ84cDh8+jEwm48OHDxpp07979y7DyeqgwYNhlaoCfPv27bTv1k3t+W3bttGoUSOePHlC586dcXNzS1HFLxnAOTk5kZCQQHBwsJh0hoWFpbmvfn5+wijXwMAAX19fMbj5+PGjqD42MTERn+PLly+YmJgQFRXFly9fRIJBIrkRVVpIA6wiRYqkmRxJSx/4W86jxMREjSodFQpFmhXNUmVKwYIFef36NaAynQZVhWRsbKx4XOLgwYOcPHkSULWh3rt3D6VSia2tLZ8+fWLTpk1UqVIFKysr0VECKs3OEydOsGTJEpo1ayYef/v2rWi1z5kzJwsWLCAuLo74+HgSExNJSkoiMTGRgIAATExMSExMJDExkdjYWGbNmsWjR4/o3r07Y8aMgbg4UtZRIpKn+/fvZ9SoUWTLlg0vLy/i4+PFOlFRUSIYVKxYMZ49e0a7du2YNWsWXbp04ZdffuHAgQP8+uuvqbbrKpVKjc5LTQMoCoVC49/3e673bySz55GDgwOenp7cvXtXJAmKFCnChQsX8Pb2FuaokjeLqakpISEhGul8DxkyRHSztGrVCmNj41QNKl+/fi0k1ooXL87t27c5duwYnz59IvkdOCIyUqMJdHR0NCYmJixZsoROnTrh6elJ69at0dfXx93dXfh+gKojq3LlylSrVg0HBwe8vLxYsGABu3btIiIign79+mFkZCS6JUAVNN2/fz/t2rXDw8ODpk2bcvToURwdHTEyMtJo//T19YW8zPjx4wkNDWXcuHHC/Fbia+3u1JAk7LR8P/6N4zpNr6fBwcHC9LdFixZpnlPR0dEadQBJ6+nq6rJz504aNWpEVFQUOXPmZN++fTg6OhIeHk5UVJRG+tDh4eFUrFiR8+fPc+nSJbp9NXaSOHbsmBgDhYWFcezYMQICAqhZs6aap0GuXLnEfTYyMlJUle7YsUMtKHzr1i21TiSFQkFERIS4Nkm8f/+enDlzAtChQwcWL15MbGwslSpVwsjISBRihIeHi3HYiBEjGD9+POvWrWPHjh3s+/VXeKUaT/Ts1YsN27drNN9I7t/0b+DvOo80OfYTExPVrpOSz5OVlRW6urpC0i4mJkYc97ly5WLOnDkcPHiQMWPG4ODgIMZKAQEBJCQkoFQqmT59OocPH0ZXV5cxY8bQpUsXdHR0ePfuHQ8fPsTAwICYmBi2bNki/Nl+++03qlatSpcuXcidOzfR0dGpdkZ8zZcvX4T/2ZIlS8RYctu2bcTHxzN48GBmzZpF//79uXjxIhMnTmT+/PnpfneajO8hY0lVaXsZPa5UKrX3rUzwb7wfJSUlafT7JjekTw+FQpHhee7p6cnBgweRyWSMHTtWnCc7d+4EVNKXQUFBgKrYS6FQYGJiIuZ/qZEvXz6qV6/O06dPefToEX379mXEiBHY2dmJdQoVKkS2bNl4/fo1ixcvZsSIEWlu7/nz59y+fZsxY8YQHx9PnTp1MDMz48SJE4wYMYLffvsNPT095HI5JUuWZPjw4YwZM4YlS5bQs2fPFN+VQqHQeL71LeevFi1atKTFfzOikg5yuZwcOXJw/vx5tccLFy7MmTNneP78OQsWLNBoW66uroSFhYkluZntz4pMJtNokcvl33W9zAwav+e2smfPTvPmzalUqRJxcXHs3buX2bNnY2hoyO3bt/H29iZnzpwYGRmJCXF6S3x8fIbrJK9snzlzJp8+fUJPT08sefPmZc+ePRgZGXH58mVcXV1TfL7bt28Df3Uk5MyZU0wkzczMhAxJaos02MiRIwd58uQRgeyYmBjy5MlDnjx5yJkzp9D+jYiIEJWJcXFxavuqSTBBqVTy+PFjQJX4MDY2TnNJjbTOI7lcrtGiCekdS1KSoHDhwhgaGmJoaMiFCxcAVZJAekxaYmNjhexR1apVKV68uDBcrFy5MqDSepcGhMl/m7t37wJw9uxZtcdlMhkKhUJt0dPTw8TEBAsLC6ysrMRxmidPHiFtVLp0aSZOnIhMJuPkyZNcvHgRRbKASO7cucmTJw82NjY4OztTtmxZoqOjOXnyJHny5CEkJITIyEgiIyMJCwtj+fLlrF69mqNHj9KxY0dMTU3x8/Njx44dyGQyoal94MABMTn/kdcNXV1ddHR0Mlx0dXU12p6+vr7a8Z3e8m8jrfMore/C0dERUMmoFShQgAIFCohW7qdPn5I/f35xfpmZmVG2bFlsbW0zPB93794tkgj16tWjYsWKPHv2jKNHj/L8+XO1yrrs2bNTu3ZtateuLYKGHz58QKFQUC2ZubKzkxNlypTJcJGCfPnz52fx4sXI5XIuXbrEmTNnRICzc+fOjB8/nl9//ZWhQ4fi6Ogo/GSGDRsGqAKVu3btIi4uLsWxZmFhwZEjR6hevTphYWG0atWKu3fvanSc6ujo8OnTJ9q3b0///v1Fl9iSJUu4dOlSpu9x3/t+rg3u/D3jOk1/i+99HNy4cYPg4GCyZ89O/fr107z2WVlZYW1tLZYbN24wYcIEgoOD1R63sLAQ2y5dujT79u2jV69enD9/nvLly4vnjIyMMDExyXDJmTOnCPzfvXtXGGF+vTx58oTg4GAePnzIzp07RbLT3d2d33//neDgYIKDg/H39ydfvnzky5ePp0+fikDww4cPCQ4OFs+FhoYSFRVFVFQU4eHhLF++nJEjR7JlyxZCQkLEcwULFqRVq1a0atWK3r17s23bNtq3b8+aNWvE461atcLJyUkETBs3boyzszOxsbH06dOH+vXri9/t+LFjNG7cmM+fP//nzstvOY80vUfr6+trNEZM7RoMYGdnp/a4ND6WlsGDB3PlyhVcXFzUHrewsEAul7N8+XIOHz6MXC5n6dKl9OrVC319fbG92NhYfH19GT16tLi+ly1bVpyLw4YNY926dQQGBmJhYZHhkpCQQFJSkpD/a968ufBG2L17N7t27aJAgQLCk2HZsmUcOHAgU9eb/8rx91/hZ7offe/xvabjcX19/QyPTamLun379pQuXRq5XI5CoeDcuXOAqgNPuh5IyhM5c+ZMd6z28eNHkpKS6NKlC/nz5yc6Opp169YRFBREfHw88fHxyGQyIXW3dOlSAgIC0txPT09PkUSoV68eS5YsYdy4cVhaWvLy5Uv2798v5t0ymYzevXtjZWXFq1evOH78+A//PXR0dL7rvFuLFi3/ff4vrwZ9+/Zlzpw5ojpbolSpUri6urJ582aNtmNgYIC5ubnaouXnQyb7S29w06ZNmJqaCu+EqVOnpqmJ/T2IjIhg+PDhKTL9Dg4OrFu3DlCZOW3fvl089+XLF3Fsfl0NC2S4v1JHwNfSRl93CkjHa1hYmKgW1KTa+GvevXvHly9f0NXVzZLR8j99HkkVKYUKFRJ/+/r6oqurKwKrEkqlkrlz54qgZOPGjdWed3BwQF9fn8DAwBQeGMknAVevXtWoA0YTypcvL2Rjli9fnkKeTalU0qlTJ4oVK4ZkmLx69Wq1TgSAa9euif27dOkSnz59Ett1d3fn8OHDNGjQgJw5cxIYGCiSLVp+DjJ7HkmJhLt374rrU5kyZdDV1SUwMBB/f3/hp5E/f36NggkHDx4UFVk1atSgXr16fPjwgU2bNnHx4kVWrlzJxo0b8fHxSSG7ULJkSaFJnjdv3nQruzShWrVqTJs2jVKlStGrVy9+++03Lly4gKurK8WLF0+1Eq5evXr0798fgH379nH48OFUt21qasrhw4fVkgkZyRxJXQhOTk6cOXMGfX19ZsyYQa9evVAqlfTp0ydT0opafgz/9P3oRyJ1I7Rq1UqjIgFQGc62bt2aHTt2ULlyZaEFnRo1atRg+fLlFChQIN1tbt68mapVqwrZvOQUKFCAfPnykZCQkELuQUIqXjh79iyxsbFYW1tTqlQpQNVdIBU2JOfEiRPAX+Oobdu2pbrtI0eO8PDhQwA8PDxYtGhRmsG7KlWqsHDhwnTNnGUyGUuXLsXZ2ZmoqCjG/zkWBTA1M8PT05Nq1aqJ4pH/Cj/zeSR1f2rSBZAWmzZtEjKRkn9OchITEzl69CgTJkzg48eP5MiRg7lz5zJ37lxWrVpFxYoVUSgUnD9/nrFjx7Js2bIsjQmbN28u5MPWr1/PyZMnqVu3rpr5smSCruXfx898Hv0sHDlyBA8PDwwNDcVxD6rO/pCQEKysrKhSpQqguncEBgYCpOqBkxr6+vr07t2bHDlyEBYWxsGDB9Xm9BUrVqRChQpERkYyZ86cVLdx8eJFJkyYIJIIixYtQk9PDwsLC0aPHg2ozt/kvl4mJiYMGjQIUCUptB0DWrRo+dn4zycSoqOj2b9/v5AhAZVuab58+WjcuHEK41EXFxc+fvyovWD/C0hMTMTf3x9vb29OnjzJ3r17uXTpEo8ePSIkJETtN2zatCklSpQgPDycTZs2MWbMGLJly8ajR4/Yt29flvchLCwsXaNbQyMjrl69muqktWXLlkyYMAGAYcOG4e7uDiAmlIUKFSJ79uxi/cwmEqS2fCmREBERoRa8k54PCwsTHQnJpT80RaqyL168uEYt+j8bX3sknD17FlB1G3wt83T69GkuXryIXC6nffv2KSrXDQwMRIDW09NT7bnk5pGJiYmcPn36u32GHj16ULp0aaKjo1n4lZH4iRMnhCb09evXyZUrFx8/flSTmgkKChKf29bWFqVSyZ49e8idO7dIPgwYMIB3794JI7Tffvvtu+2/lr8fyRg9MDBQtIEbGhoKj5c7d+6I+2NaWq/JcXNzY+DAgSiVSipWrEiTJk0ICAhg27ZtxMbGiirON2/esH//fhYsWMD58+dFUEcmk9GvXz8cHR2ZOXMmxhpIBWVEmzZt2LdvH6NHj6Zs2bIaVVK5uLgIU+gtW7akGXD8OpmQnmfChw8faNWqFQMHDiQ0NJTy5ctz/fp1xo0bx5IlSyhTpgxBQUH06tVLq2ut5YcQGRkp7jnt2rXLcH2FQsG4ceMYM2YMSqWSfPnyER4eTtu2bXF1dc3ycXru3DmGDh3K7du3mTJlSornZTIZtf80JE7NJyE2NpabN2/i6emJUqmkUKFCNGvWjIoVKwqPF09PT7VxWUJCAmfOnAFUxSOgShR+7Ql17do18Z5NmjTBwsKCz58/s2TJElW3Xwb+VGlhaWnJxo0b6dSpk9q4dN26dRQrVgx/f3/q1asnjJu1/Fgko+XU/BE0Yf/+/SxfvhyACRMmqJm4gqq4pkuXLhw7dgyFQkGtWrVYtWoVZcqUAVTJsqlTp7Jo0SJKlSpFQkICO3fuxMXFhY0bN6Y6Dg8PD+fZs2ecOnWKtWvXMn78eDp27EiLFi0oVqwYnTt3BlR+RFevXmX27NnCBL1du3Za82Ut/0ni4+OZOHEioJqjJJcdkhLnTZo0EYnz169fExMTg0wmU5tfZ4SJiQk9evRAR0eHJ0+eqPnEyeVy5s2bB6jmRV8n7iRPhK+TCBLNmzcXMroLFy5Ue+3gwYOFgkJq8qBatPw/8uTJE+7cuZPu8nV8V8uP4T+dSHj37h2lS5fm3LlzatWURkZGYkJVrVo1taqnq1evUqNGDW0r509CREQE27ZtY9asWQwaNIhWrVpRuXJlihYtirm5Ofb29lSvXp127drRu3dvXFxccHJyInfu3FhaWgqz5a5duwoN6zVr1qhVLsyaNStFdXZ6KJVKPDw8mDx5Mm3atGHAgAFp3uAnuboCMG3atBQ6+wBjx46lXbt2JCQk0LFjRy5cuCA0HZ2dndXeU5rEZjaRYGpqKgJoyTWRU0skZMVsWUokZMVo+Z8mNDRUmB8WLFgQpVIpAupfdxt8+vRJyJ5Vq1YtzWo2SaP58ePHal0gyRMJAMePH/8+HwJVkmnSpEmYmZnxIlmnVVR0tFplt7e3tzCFXLZsmZBfOHjwIAkJCdjb2zNixAhsbW2JiIhgz549NGrUiEKFChEeHk7Xrl1FIsHNzS2FgbeWfw9GRkaigyj5sVm+fHlAlUiQgnH58uVLd1sXL16kd+/eJCUl0blzZ5o2bUpwcDBbt24lKiqK3LlzM3LkSMaPH0/9+vUxNzcnKioKDw8PevTogaurKz4+PtSuXZvFixdn+H4/mnbt2ong0PDhw9NMNkvJhGrVqhEWFkaTJk3Yu3cvPj4+vHv3jhs3brBmzRrKlSvHqVOnRBfClStXRAW1oaEhu3fvxtTUlGvXrokJqRYt35Njx44RHR1NwYIFqVChQrrrxsfH061bNxEsnTdvHs+ePRP3ksWLF9O4ceNMBydfvHhBt27dRDD9yJEjPHjwIMV6aSUSPn/+TOvWrXn58iUymQxnZ2dq1aolpCAqVKggzqvr16+LzgR3d3fCwsKwsbFh3Lhx2NvbExERISTYQCXnJgWemjdvTrNmzXB1daVcuXIkJSVx7NgxVq1aJapZM4uenh6TJk1i0p+yM6Aae+7Zs4emTZsSFxdH7969mThxYpYTFlo0Q0peZyWRcODAAXGNHjRoEL169VJ7/tixY7Rs2ZJ79+5hZGTEmDFjGDt2bKrG5iVKlGD+/PmMGzeOYsWKERUVxfr163FxcWHt2rXMmzePfv36Ub9+fWrWrMmUKVNYsGAB+/bt4+bNm/j7+xMSEsKkSZOoWbMmLi4uKBQKpk+fztWrV9m5cyeFChXizZs3dO3aVZuk1vKfY/369bx8+RIbGxvRmQOq679UQNW+fXvx+NWrVwGwtrbWuCtPwtbWVpg4Hz9+XG1+V7FiRdq2bYtSqcTV1VXc4y5cuCCMlWvVqpUiiQCqRMSUKVPQ0dHh0qVLoqgQVBLFkoJCnz59xD1Ki5b/R7Jnz46xsTFdu3alQoUK6S4lSpTQJhP+Bv7TiYTBgwfTtWtXtm3bpmZsCqoKyxs3blCyZElq1KhB3bp1cXFxYcmSJRpLG2n58bi6ujJ48GDmzZvH9u3bOXPmDD4+Pnz+/BmlUomOjg52dnaUL1+e2rVrU6pUKdFmHhcXx7t37/D09OTIkSPCEPjz58+4ubmJycTbt281bisPCQlh8uTJTJkyhRs3boig/vbt24mLi0uxfq9evahWrRrR0dFs2LAhxfMymYzNmzfj5OREcHAwLi4uYnLr6OjIpUuXGD16NDVq1ODGjRsAGVbVStVMJ06cwMvLi/v374vnJOPl2NhYkUALDAwU8j5ZmWhIwcYXL16IoPy/BWnAZmNjg6mpKd7e3vj5+WFoaCiCGRL79+8nMjISMzMztSTP19jZ2WFlZYVCoeDZs2ficUnGoVq1aoBqoPs9yZkzp5Dwkjhx/Djv3r1DX19fyDRJ8lUPHjzg48ePvH//Xuxbu3bt0NfXp1u3bshkMp4/f86bN2/o3bs32bJl4/bt23h5eVGiRAliY2NxdHRk/fr14rjS8u9C6p7ZvHkzMTExao9t27ZNdJ3kz58/3e2MHz+ehIQEKlWqxOrVq4U3QUREBEZGRvTq1QtDQ0MsLCyoV68e48ePp3379sJ4+I8//mD8+PFZSmT+CGQyGd27d6dZs2ZCdqhPnz5Ciz05pqamHDx4kBo1ahAeHk737t1xdnamcOHC1KpVi1GjRhEaGkqFChX4/fffGTduXIoJrL29PWvWrAFUQVpt9aiW741kNFy/fv0MC2XOnj3L/v37AdUYZvz48ejp6bFkyRL69OkDqAIyixcvztQ+LF68OEXyWXofUHUHLly4kBkzZgCqe2bya8KUKVO4c+cO+vr6NGzYkDJlyqh9FplMRsWKFUWC9OzZs9y/f190CpUpUwYdHR3hZZQ8mX/8+HEUCgU5cuQQ90pTU1O6d+8uuhWfPHnCkiVLMvWZv6Zlixbi/y98fZkxYwaHDh0S3akrVqwQ3RNafgyShFBaZuNpkZSUxOjRo1EqlXTu3Jnhw4erPf/u3TsmTpxIdHQ0Tk5OzJkzJ8U48mtkMhnlypVj7969LFiwgLx58/Llyxc2b97MgQMH+OOPP4RJrJWVFU5OTrRp04bRo0ezcuVKypUrR0xMDGvXrmXs2LFUq1aNhIQERo4ciZWVFQcPHsTExIRLly5Rr149YTStRcu/HYVCIYq7Zs6cqZasW79+PYAIOEpIcQBra+ssvWft2rXJkycPMTExKeRdp0+fjpGRETdv3mTHjh1MmDCBDh06EBcXh4uLC3PmzEnTf61IkSKiE3b16tVqz82ePZtu3boJv4ahQ4dmSYZYi5Z/O/ny5RMdQektu3fvJjo6Wtw7tfw4/rOJhJiYGM6ePSsMHEElGTNkyBAGDx7MnTt3sLGx4dy5c1y+fJkaNWrQoEED7t27R5EiRf7BPdeSHKlqvlChQkydOpW1a9dy9OhRfv/9d16/fk1YWBivXr3Cw8ODM2fOcPv2bfz9/fny5QtPnz7l/Pnz/Pbbb8ydO5f+/fvTtGlTHB0dhckZqHR9M6qmVyqVuLm5MW7cOG7evImenh4dOnQQwZ+EhIRUXyeTyylWrBiA6Ij4GiMjIw4fPkzHjh3Jmzcv+vr65MqVi7lz59K0aVPWrl2Ln58fBgYGNGnSREjNpEXz5s0xMzPD39+fefPmMX36dBQKBY6OjtjZ2REeHo6rqyvu7u4YGhpy6dIlPn/+TNGiRUW1RWbo1asXxsbG3Lp1iwYNGgh94Z+duLg40ZUimQhv2bIFUMmbfP171a5dGwMDAyIiIjh+/HiaSZdnz54REhKCXC5XC8DmyZMHAC8vLwBROfk9qVq1Ki1btRJ/58qVCwsLC+Lj47l06RLw17GaJ08esmfPTo4cOdDX1wcQAczAwECUSiX6+vrY2dlhbW0tgjubNm1izZo15MmTB39/f0aMGEGJEiW0CYV/IaNGjcLU1JQbN27Qp08fYmJiaNSokZA3MjQ0pHHjxjRt2jTd7bT4Mzj2xx9/4ObmBqiOb5lMRkxMDO7u7qJC69OnT5w8eZLjx4+LTjA9PT1atmyZpiH7P4FMJmPw4MEMGzYMmUzGvn37KF++PBs2bMDb2xt/f39xLpmamnLixAkGDRpEhQoVyJEjB7q6uuTPn58aNWqwaNEirl+/Lr7X1GjXrh0WFhYoFIp/XUJWy89PxYoVATh58mSG1+lq1aqJY3X79u107dqVGzdu0LRpU6ELnytXLmEyqSndunWjSpUq6Ovr4+zsjKOjIw0bNmT9+vVUr15deCy8e/cOExMTBg8eLPybFAoF165dA1T34rQ6AmUyGVWqVCFfvnwoFAr69+8vkvcXL15k3Lhx7N69G0BNkkbS0A4MDGTNmjUEBgZy7tw5pk+fLgotLC0tM/2ZM0KSfJsxYwY5cuQAsh7k0qIZ0u++bt06terfjJDL5URHRwOqce/XCTmpk8TAwIBff/01U9Ipcrmcxo0bc+TIEaZMmULjxo3p1asXs2bNYteuXVy/fp2NGzcKI/BWrVpRtmxZURVdqlQp5HK5+Fs6nsuUKcOePXswNzfn5s2bODs7p+n9o0XLv4k7d+7w+fNnzMzMUnQGSV4SX3cdSfGdrHZT6+joiPHw/fv31RQC8uTJI/wORo8ezaZNm1AqlXTr1o2tW7emmUSQ6NOnD3K5nKdPn6pVUuvo6LBp0yaGDRsGqOZg5cuXF8UBWrT8P5EvXz7Kly+f7lKiRIl/ejf/b/jPJhKSkpJISkoSxjU7duygfv36BAUF4e7uTpUqVTh//jygmpTMnDmTESNGpGuapuXvR5oA6ujoMHnyZPr06UOTJk1wcHDA1tZW+AY8fvyYoUOH0qFDBxo3bkydOnVo3Lgx7dq1o2vXrkyePJlNmzZx+vRp7t69S1hYGKVLl+bo0aOcP38+1bZjiYCAAEaMGMHUqVOJioqiePHibNy4kYEDB4oJn4GBQZr+AJJsSHqBY1tbW2HaFx8fz4cPH4iIiCBnzpz06tWLTZs28eDBAzZv3oylpWW631nlypXZuHEjbdu2FQHiqlWrMnnyZIKDg3F1deXZs2eYmZkhk8kICwujQoUKXLp0SSRuMkPDhg05e/YsBQoU4O3btzRp0kS0lP7dbN++HQcHB1q2bImrqyu7du3C29s7Vc3ZJUuW8OLFC+zs7Jg2bRpeXl54eXmho6OTYlAKUK5cOVauXImBgQEvX77kt99+S5FMSExMFH4sVatWVTPzkgI50mv69u373T53cnr26CH+P2bMGDZt2iT+dnZ25tSpUwAsXLgQfX19jIyMhBzT1atXUSgUQt6pRo0a4tzo2rUrJiYmPHv2DKVSyZMnT1i9enWKhMLatWu1CYV/CeXLl+fMmTOYmJhw/fp1+vTpg0wm48iRIxw6dIj79++zadOmDO+L06ZNo3v37igUCvr27cuzZ88oXbq0CLq5u7tz5MgRNm7cyMqVK/H09CQuLg4rKysGDBjAvn37GDx4sEYeBn8ncrmcBQsWcPXqVcqWLcuXL18YM2YMNWvWpGjRomTLlo38+fNTpUoV2rVrR0REBHXr1mXChAkcOXKE58+fc/nyZUaNGqVRG710vc6M1J4WLZrQq1cv4Y8jBdLTwtramlu3bjF+/Hjkcjn79u2jZs2aXLhwAX19fSZMmMDjx49FZb+mVKtWjZMnT3L79m2GDx9Onjx5aNy4MSNGjODWrVvo6OhQt25d1q9fz4MHD5g2bZoI1vr6+hIaGoqRkZEIuKeFTCajatWqGBoa4uPjg6enJwMGDAD+qvYcPnw43bt3F6+pWbMmAwYMwNDQkBcvXjBz5kzc3NyIjIwke/bsdO7cmTlz5ogA7fdC0s1/+/YtgYGB6Onp4eDg8F3fQ4s6bdq0oUePHigUCnr27KlxB5hMJsPW1hYgVYkraT4QFxeXZnFRRujp6dG2bVsWLFjAiBEj+OWXXyhTpkyqc5TTp0/z5s0bzM3N6d69O7///jsPHz7E0NCQ6dOni/WaNGnCrVu3qFixIqGhoXTq1InBgweLpIgWLf9Gzp07B0DdunVTBOmlLgSpA0HCxcUFUM3rszrOKly4MCYmJkRHR4sks8Tw4cNFR1y+fPk4fPgwq1atEmO79MiWLRvlypUDSNGVpqOjw9KlSzl37pzaXHvQoEHa7gQtWrT8Y2ROIO5fhKmpKWXKlGHp0qWULFmS8ePH4+HhQalSpUhMTKRly5aMGTMmVX1WLT8PVapUQSaT4evry6dPn8QgPjkBAQE0bdpUGIamhaGhIdmyZSNXrlwMGTKEjh07ikREaiiVSk6ePMmiRYuIiIgQA/w+ffqI10nVP2ZmZmluQ9LpTS+R8PLlS5o1a8br16/JkSOHmt+DXC7n48ePmdJzNDExoVu3bjRt2pS3b99Srlw5Pn78yIwZMwgMDMTMzIzY2FgSEhKoU6cOBw4cSPMzaELJkiW5cOECAwYM4PLly/Tv3x8vLy8xKAKEdMqP4sOHD4wcOZLo6GgePXokAuagmgAWKFCAUqVKUaJECQoVKiR0bpcsWYK5ubn4u1mzZmlq51aqVImVK1cydOhQnj59ym+//UaXLl3Eb+Ph4UFQUBCmpqZCHkGiXLly6OvrEx8fj7W1tajiTo29e/eyd+9eVqxYQaFChTL1PSQ/Tp49e8bhw4dZsmQJW7duJXv27ERHR1O9enU6duzIihUrAFUQ5fr16zx//pzTp0/z6dOnFPJO5ubmdOzYka1bt7Jp0yYReOnZsyc7duxg4cKF+Pn5MXToUObPn4+rqyt9+vTB0NAwU/uv5e+levXq/Prrr/To0UMkE7Zu3SoSX5ogk8lYvnw5MTExHDx4kP3792NoaEjFihWJj4/n1KlTQj5OLpdTokQJKleuTJ48eahRo8aP+mjfDScnJ65du8bGjRvZv38/Hz9+JCAggMTERIKCgggKCkrhgQLQsmVLduzYIaqqM0KbSNDyozAwMGDUqFGMGzeO5cuX07Vr13SvzYaGhsybN49WrVrRp08fHj9+TPPmzVmyZImQ+vlaGsbX15cbN24QGBhIUFAQwcHB4vz48uULgYGBqUpAOjo60qVLF+rXr5/qGA8Q8kTly5fXKOFobGxMnTp1OHPmDHPmzOHKlSucPn2a9+/f06RJE+bPn5/iNWXKlGHs2LFs3LiRwMBAbG1tadSoEeXLl0dHRyfTmtqaULp0aUDVzQUqryntPfPHs3z5cm7cuIGvry8DBgzQWHvc1taWt2/fpiqZYGpqip6eHgkJCX9LV5nUWdC9e3cMDQ2FfGrnzp1TdOwUKlSIK1euMHPmTBYvXsyWLVu4fv06+/bt+1f6m2nRIiUSUuukT+71pVQqRUK6cOHCmJmZERERwcePHzOU7UwNHR0dypQpg6enJ/fu3RPvBaox3NGjR7l69SouLi7pFimmRs2aNbl79y6nT58Wye/k1KlThzt37jBp0iQ2bNjAxo0bOXv2LFu2bEkx59SiRYuWH81/NpEAMHnyZDp27IiZmRk1a9YUgVxdXV369++vVo2k5efE0tKS0qVL8+DBA27cuEHr1q3VnlcqlfTv35/Pnz9TokQJBg4ciIWFBZaWllhYWGBiYkKOHDnIli2bRpMzqYvl7du37Nu3T7Q9lyxZklmzZpGYmKiWfJAqAaQ2yq/x9/MTSYi0JLN8fHxo3rw5AQEBFCxYkFOnTomJelp8+fIFT09PcufOjb29fZqSINbW1lhbW+Pr68usWbMIDw/HwsJCJEBat27N9u3b0+ymyAyWlpbs3buXefPmsXLlSiGB8HcxefJkoqOjcXZ2pnv37jx+/FgsgYGBvH79mtevX4uOAYBatWrRsWNH7t27h5ubGzKZjN69e6f7PpUqVaJt27YcOnRILZkQFRUlWk0bN26c4ngzNDTEwcGBW7dupRvEUSgUHDx4kLCwMC5evEj//v2z/J3o6upy4MABKlWqxMaNG6lZsyYymYwVK1aoteVbWVlRrlw5fHx8uHLlCqDq1Pr6uOrfvz9bt27l6NGjBAQEkDNnTgwMDNJMKMybNw9XV1cGDhz4Q4IwWr4PFStWTDWZkJYcW2ro6Oiwbt06oqOjOXXqFDt37qR3795Ur14dUE3oSpUqhZOTk+h8ymrAPCAggIcPH1KrVq10k8HfE11dXYYMGcKQIUMA1XkaHBzMp0+f+PjxIyEhIXz69En8feLECY4dO0adOnU4duyYRsae0nVYm0jQ8iPo0qULy5cv58OHD+zevVujrjhnZ2ch5VWgQIEUz/v7+4sOpnv37mm0H8bGxuTOnZuWLVvSpUsXIaOUnpGxJAlYsWJFjXVvS5YsiVKp5OzZs4wePZpTp05x8eJFunfvnuZ1w9bWlokTJ/Lx40fy5cv3w7ukpCCulEhIz39Jy/fD1NSU3bt3U6NGDU6cOMHGjRvpkaybMy2kLtPUjlWZTIalpSUBAQE/3Ofm9evXvHr1Cl1dXZo2bcqxY8fw9/fHysqKzp07p/oaPT095syZQ926denVqxdPnz6lUqVKLFmyhMGDB2fonaJFy89CWFgYN2/eBKBRo0Ypni9VqhT6+vqEhITw+vVrtYKsXLly8ezZMz58+JClRAKortuenp48ePBATd4IVPeQjh07Zmm7NWvWZOXKlVy5coXo6OhU5/ampqasWrWKli1bMnDgQN68eUODBg0YMGAAixYt+qaiQC1atGjJDP/pyE6HDh1wd3dn3bp1FCtWjISEBNH+5uXllaHW/H8RSadaEzQZVGoa8IiLi9MokBgfH58iqF21alUePHjA9evXafWn/vuDBw9QKBQcOnSIc+fOiXb7r6u3vby8UrQUShrUnz9/JjAwkM+fP/Px40e+fPlCQECA2qBAV1eXNm3a4OLiIiSHklfUvX79GlBVIUiahvL4eCRBG8nouEiRIkJuKzlz5sxhy5YtxMXFYWdnR7Vq1Rg0aBBVqlRRq2TImTMndevWFX9PmjRJaJHLZDLy5MmDvb09RkZG2NvbkzdvXvLkyYOhoSH37t1jwYIFxMbGYmVlJSql+vXrx4oVK75rIE5HR4epU6dSoUIF9u7dq9benZiYyNWrVzXellKpzPB4jY6ORiaTCXMdgEWLFqmZa4HKxPHNmzc8e/aMp0+f8vz5c6Kioli4cCEhISFC/7969eoYGhpm2N1ibW2tlkzYsWMHenp6xMfHkytXLgoXLkxYWBhKpVLIqwGMHDkSNzc3OnXqpPb4ixcvxLXpzZs3YhJ669YtatWqJdYLCwvLUD9ZluycHDlqFLMXLWL8+PHY2dkBquSRoaGhSLBICQ1nZ2d8fHwAVaDH0dFRJMri4+Px8/MjR44clCtXjnv37rFy5UoGDRqk9t5169alVatW7NmzhxUrVvDhwweGDRvGzp07Wb9+vdqgPSOJLlAdM5oEcpRKZYb6o9J6mvL/Nqn+OpnQvXt36tWrh6GhIUZGRhgaGiKXy7G1tcXQ0BBjY2MMDQ2xsbERv6Wuri5bt26lWrVqvHz5kh07dtCjRw8qVqyo1uEg3Teio6PVtGCTI4+Px+7P/3vduoX7rVsoFAru3LnD1atXiY+Pp0iRIrRo0UJc47Nnz66RLEhISIjQs06PN2/eCJN7UN07vnz5go6ODvr6+ujr66Ojo0NERIQ4j4yNjSlcuDDt2rXjyJEj3L17l1KlStG2bVsqVqxIu3bt0nw/6R4ZHh6u8bH6PY9T7fmRNprcj0Cz70XT7zl5JWV6KBQKjdaTyWSMGDGCCRMmsHz5cjp06JBqQjs4ODiFHKC+vj4fPnwAVIUMp06d4vDhw6LiE1T3f2dnZ/LkyYOlpSVWVlZYWVkREREhijssLCzUxnjv3r0T14C4uDjhJfQ10nmYL18+Hjx4kKZHUXJsbW1p37497u7ueHl5sXLlSlq0aKF2TgN8/PhRrYJbR0eHhIQEHj58SHx8vFgSEhK4e/cusbGxxMTEEBMTQ7Zs2ejfv7/afTk0NDTN64ssLk6MDy0tLbG2tkahUIiOiwoVKqR4bUJCgsYJDU3ug/8Vvh5Pp7deauNcBwcH5s6dy7hx4xg3bhzlypXLsDpfktV6+/ZtquNEc3NzAgICePXqFUqlUhTtpMfr1681Ghvb2dmJIKEkP1mhQgWioqLYvn07oPIhiY+PT7cjwsHBgQsXLjB8+HAuX77MsGHDOHfuHBs2bEhzfKntktHyvdFkDAaq69/X5++FCxdISkrC3t6efPnykZiYSHh4uNp1smTJkvj4+HDz5k01OTyp8C8gIIDPnz+nec00NTXlxYsXae6XoaEh0dHRXL9+XXghpsfTp08zLNpTKpVkz56doKAgzp07R5MmTdJc18nJCS8vL6ZOncqmTZvYuHEjZ86cwc3NDXt7e7V1NfEf0/T3AP62Ah4tWrT83PynEwkAa9aswdzcnIULF+Li4sLgwYO5desWv/76qzBu0/LjkclkGk2EUvMaqFOnDhs3buTGjRviuYSEBF6+fMn69esBGDhwIPnz508xsfD39ydnzpwEBgby8OFDHj16xJs3b9KdgOjq6mJnZ0eBAgXo1KmTWvAzJiZGLVkhVeDZ2Nj89XjyRMObN4CqeuHrroWjR4+yYcMGkpKSyJs3L2XKlGHv3r0kJSVx79492rZtK7TJdXR0RCWUUqkUk06pu+D9+/e8f/9ebfuSnI+/vz/x8fHkyZMHPz8/AKZMmcKsWbO+WzDo68lHt27d1IzOAdEN8T2RBjOurq6AqqU7NUmW7Nmzkzt3buG5kRxfX19OnDgBwKBBgzSqUGnevDk2NjbUrl2bESNGCJ1MmUzG4sWLRYVlWFiYWlV3lSpVUtVYNjc3F99NcgOtt2/fYm1tLQa6tra2GUrOKGNi+PLn/9u1bcudP2We/Pz8MDMzY9iwYSLw06BBA7VK6fv37+Pl5cWwYcPUOrZCQkLEsdK1a1fu3bvH3r17GThwoNp5rVQqMTAwoFevXnTu3Jndu3czd+5c/vjjD2rXrs2iRYtEIFXTwNf3XO9n0+D/WcibN6/418bGhiZNmgjPkIzQ19dn06ZNauf7lStX6NSpEx4eHhw8eBA3N7dUjYb/+OOPtAMUya6jz549IyoxUUiHSfj6+rJjxw4aNGiAkZERSqVSfJb0SEpK0sgI093dXSR0379/z/Xr1/ny5YvaOjKZDB0dHQwMDNDT0xOLiYkJDRo0wN3dndDQUHbv3k1MTEyK62Jykt/7/t8C9VqyjqbXv2zZsjFw4EBWrVqFv78/R48eZeDAgSnWS81TKCoqigsXLnDs2DHc3d3VAvkVK1akRYsWNG3aNNVA5OXLl9UKI9IqQImKikr1ehAQEMCHDx+Qy+UieKJJYERfXx9zc3O6devGxo0b2bVrFw4ODiKpLuHs7Cz8XPz9/XFxcdG4uwIQUoqShFlSUlKaASNlbCzSN1eiZEn09fVJSEgQSfyqVaumKH7RXgtSR9PvRU9PL83A1+jRo4Xs1cCBA7l+/Xq6x5aUcIqOjk5xHIFqLiAFH/PmzatRwcTevXs1Op6VSiXly5dHqVSKzrgePXpw+fJlIiIiKFGiBBMnTiQyMjLDsU7OnDnZtWsXe/bsYfLkyZw8eZKKFSuyY8eOLMsNpvV7JH9c02uVln8v3zvInNoxc+HCBUAlayQ9lyNHDrX3dnR0xMfHR8QBJLp06cKHDx/w9/fH3t6eqlWrpvq+jx8/ThGQT0758uW5ceMGL1680Kg4IDw8PIXkWGpUqFCBc+fOcf78eZo3b57meklJSRgbG7NixQpatmzJoEGDePv2LV27duXq1auZ6igG7X1GixYtmec/n3It10UAAQAASURBVEiQyWTMnz+f5s2bs3LlSmbMmEG5cuW4ceMG+fLl+6d3T4sGSIPae/fuERYWhoWFBbGxscyePZuEhASqVq0qJoESSUlJPHz4kMuXL/P69WsCAgLUntfR0SF79uzkyJGD7NmzY2pqSuXKlcmdOzfW1tYaD4QkjeC0WgmfPHkCpPRH2Lx5M6NGjUKpVFK0aFHy5MnD6dOnAVUiIzQ0lN9++43WrVunGHh8+PCBz58/o6OjIyrrnz17xpMnT3j8+DG+vr5qcj6gmtBIiYbly5czdOjQ/8yg4fDhw3h6emJsbMzMmTMz/foVK1agVCpp0qRJmvJTaSF5JowYMYK4uDhat26dasA0M0hJIlAFXF69eqVRtUtqyGQypk+fjq+vL8+fP2fQoEHpdjQsW7aMO3fuqHW/fE2zZs2YPXs2fn5+dOjQgTZt2tC0adMUiTIDAwP69OlDgwYNGDRoELdu3WLw4MFcunSJRYsWaY3tf1KqV6/OtWvX2LJlCxEREURHR4vqW+n/0r+RkZGEhobSt29fbGxshFatsbExe/bsoXXr1nh7e9OqVStOnjyZ6fNL4sGDBzz09RWdJ05OTlhaWnLp0iWCgoJwc3OjUaNGaubm34vw8HBu3LjBq1evgL8myVIyWqlUkpiYmGqF9Pv376lfvz4+Pj74+fmxf/9+ypUrl+b1VyttpOVHY2BgwIQJExg+fDiLFy+mZ8+e6VYbJyYmsnLlSjZt2qTmc1SqVCkaNGhAhw4dNAqOfL3N0NBQrK2tNRqHSEF9e3v7TGtOA9SvX58bN27w4MED1q9fz4wZM1INtN6/fx8XFxf8/PwwNTWlWLFimJubY2xsjImJCfr6+hgbG4u/DQwMWLFiBd7e3vTu3Zs9e/ZkKohWtGhRQHV9i42NJVu2bFm+RmrJGjKZjG3btuHg4MCTJ0+YMGGCMORODcnDIy15LWlcExISkmZ3zbfy8OFDXr58iaGhISVKlGDkyJEATJ8+PVPHn0wmY+jQoVSvXp3u3bvj6+tL48aN+fXXX2nbtu0P2XctWr4VpVKplkhIC8n0+NmzZymeK1++PP7+/ty5cyfNREJGODg4cOPGDR49epRm11NWKF++POfOnePMmTMadyXWrl2by5cvU6VKFR48eMC4ceNYs2bNd9kfLVq0aEmLf20i4c2bN4wYMUIkBAYNGkSfPn3SvOBWrVo1yzcLLf8sdnZ22Nvb8+LFCzw8PGjatCnr168X1drjx49HJpMRGRnJH3/8wc2bN/Hy8lIzApTL5RQuXFiY7WbPnl1tIhkeHo6jo2Om9016j7Q8Eh4/UhktS4Z6SqWSuXPnCqO/smXLYmJiIqrQixYtSrZs2Xjz5g0BAQHs378fFxcXKlWqJLbp7e0NqNo2pUltjhw5qF69OklJSaIqLjAwkJs3bzJ//nxu376Nrq4u27dvz7J2489ITEwMU6ZMAWDs2LEa6ZAn5+3bt8KwbtSoUVnah0qVKrFlyxY8PDzo2rVrlrYhERQUxKtXr5DJZBQtWlQkiLKaSAAwMjJi27ZtPHjwINWOjORYWVlRv379DLc3evRoZs6cye3bt7l9+zbTp0+nQYMGNGnShNatW6u1CufLl4/jx4+zYsUKlixZwuHDh/njjz/YvXt3hvuj5Z/BwcEh1UnI163PCoWCHj16sGfPHtq1a8fVq1fFddTMzIyDBw/SokULHjx4QMuWLdmwYUOWqh1f/JlEKFiwIJUqVRLVmy4uLpw/f56IiAjc3Nxo3759Fj5t6sTExODj4yMmiTKZjNKlS+Ps7IyhoSEKhYKEhATRHZcrVy7xd3x8PD4+PgQGBnL69Glq166Nubk5jx8/ZtKkSfj6+rJ06dIULfXS39pEgpYfSY8ePVi4cCH+/v7s2LEj1a4EgE+fPjF8+HA8PT0ByJ8/Py1btuSXX36hSJEiBAcHZyi1lxyFQsGNGzfYs2cPAQEBZM+enUqVKlGxYkUR9EmNu3fvAmRpjAaqgOnAgQMZPXo0jx8/xs3NjRYtWqitc/78edq1ayc8rSIjI7l//z61atWibt26NGvWDDs7uxQJiCpVquDi4sLp06cZP348S5Ys0Xi/iv6ZNJCKB5ydnbVdc/8AUnV+w4YN2bJli5BoTA0pkZCWn4d0PvxIs+Xjx48DqgTZypUriY+Pp1atWlmW65UCosOGDWPfvn3069ePfPnyZdj9qkXLP4Gvry9v3rxBT09PTfr1a6R7ytOnT1M8V758edzc3Hjy5AlRUVFi3pwZ7O3tMTExISoqCh8fnxSSuunx5MkTjh07xvv37xk3bhwFCxYUz0lz+w8fPnD//n3KlSun0Tbt7OzYunUrv/zyC9u2baNGjRp06NAh059LixYtWjTlXzliDQkJoVatWjg4OLBnzx6qVKnCwIEDadq0KZGRkWrrxsXFceDAgX9oT7WkRUhICMePH+ft27carS8Zdl67do1jx45x4sQJZDIZrq6uWFhYcPDgQVq1asWsWbO4cOEC4eHhmJmZUapUKbp3786cOXMYPHgwDg4O39UELaNEwosXvgCUKVMGhULBiBEjRBJBkuORDKOKFy/Oq1evuHXrFgkJCUL38dixY2qavnfu3AFUA6HU9mfVqlV069aNmjVr0qZNG27fvo2xsTHHjh37TyURAFatWsX79+/Jmzcvw4cPz/TrV69eTVJSErVr185ykAJUiaIBAwZkaTCaHMlwsVixYlSuXBn4q6vlW7CwsKB69erfrQtF0tEfP348RYoUIT4+nlOnTjF06FDKli2Lq6urmva9rq4uY8eOxc3Njfz58/Pu3Ttq167NzJkzM6XLqeXnQi6Xs3XrVurWrUtkZCQuLi6iCwpUUiqHDx+maNGifPz4kRYtWtChQwchBZYWoaGhzJk9R/xtZGxMgwYNqFOnjpoEhIWFBS4uLuTIkYP4+Hj27t3LmTNnvukzSdVurVu35v79+yQlJZE7d27at29PjRo1RPW2XC7HwMAAU1NTTExMyJEjB7ly5SJ//vwUKVKEli1bUrBgQRQKBZcvX8bIyIj69esjk8nYvn07bdq0ITQ0VO29tR0JWv4OpK4EgMWLFxMbG5tinevXr9OkSRM8PT0xMTFh9erVuLu7M2bMmCxVzT958gRXV1dWrFghOkSDgoI4deoU06dPp1+/fmzbto3bt2+neK0k+6OJ/0la2NjYCDPd3377jYcPH4rnLl26hIuLCxEREejq6pKQkCAkhy5evMiYMWMoXrw4Dg4OjBs3Tu21lStXZuvWrchkMjZt2pRuNTuo+2NIBQJSIkEbuP3nqFevHmPGjAFg8ODBac5N0jNbhr+8n74lkZCQkJBCQk9CqVRy7NgxQDWvkP5fvHhx9u3bx969ezl06BB79+5VW9zc3FKVLJMwNTVly5YtNG3alNjYWNq1a6d2L9ei5Wfh/PnzAFSrVi3dDjUpkfDu3bsUx76trS25c+dGoVBkSsYuOTo6OiLI7+7urtFrnj9/zvz581mwYAFPnz4lKiqKo0ePqq2jr68vusIzO56tV6+euLcPGzaM58+fZ+r1WrRo0ZIZ/pWJhC1btlC2bFlmzpxJgwYNWLNmDRcvXuTmzZs0adJEbVL066+/0qFDB1atWvUP7rGW5CiVStq2bUuHDh0oVqwYxYoVo1+/fuzatSvVwbtSqRTBxitXrojJYIcOHahQoQLnz59n/fr1JCYmkjt3bjp06MCKFSs4evQov/zyC46Ojujr63PlyhXmz5/Ppk2b2LNnzzcHMJVKpRhop6X9r1AosLa2xtbWlsuXL4sJ54oVK6hevbowY65fvz4vXrwQ5rJfvnxBX19fTJxPnjwp3uvxY1WXw9dySaDyPhgzZgz79u0TOq0lS5bk3LlzNGrU6Js+789GSEgIy5YtA1Sm1ZnVg1QoFOzfvx9AtIb/kwQGBnLkyBFAVZlYokQJQJU4Ss/w658iV65cDBo0iHPnzuHm5kbPnj3R0dEhKCiILVu2pFrl6uzszJUrV2jfvj0KhYJZs2aJ6jot/0709fXZvXs3RkZGfPr0iQULFqg9nz17dk6fPi0qPC9cuJBmBbTEzp07uX79Lw+j+vXrp+l9YGRkRJMmTciVKxdJSUlMmDAhy4F4X19f+vfvz/jx4/n06RMmJiY0atSIX375JVOV16DqLmjcuLFIUHp7exMWFsaePXswMTHhypUr/PLLL2oBIylJ8fHjxyztvxYtmtKjRw9y586Nv7+/8AiSiI6Opn///gQHB1OyZElOnz5NixYtspyIvn//PjNmzBByLB06dGDbtm2MHz+e2rVrY2pqSnh4OO7u7vTr148RI0aIseCLFy+ENMW3JBJA5QlUs2ZNFAoFu3fvBlSdRxs3biQxMRETExMSExPJkSMHr1+/5unTp0ycOFF4Frx+/ZoNGzbQrFkzte22aNGCefPmAaoxmIeHR5r7kDzA/P5Pz6pHjx4B8Pnz5xRa22FhYfzxxx/s3LmTyZMn06ZNG0qVKkXBggW1Pm/fmalTp+Lk5ERoaCitWrVK1UxZ8kUIDg5Wk/qSSC5tlFV8fX25c+dOCklWUB2DUpGGlGAD2LhxIyNGjGDEiBG4urqK/0tLnz59cHBwYMaMGWpFHsnR0dFhx44dlClThoCAAJydnZk/fz7R0dFZ/ixatHxvJO+ujLpbrayshHfW1x6C8Fcxnru7e5bjAdI96erVq2nKnUk8ePCA+fPn8/TpU3R1dUVx171799i8ebPa9UQyWV62bBnbt2/XyINBYvLkydSsWZPIyEj69OmjsSm9Fi1atGSWf6W00cePH1No0dWuXZsLFy5Qp04dRo4cyYYNGwDo378/fn5+WW751PL9OXLkCDdu3EBXVxelUsnbt2/ZtWsXu3btAqBAgQLUrl2bWrVqUbt2bbZs2cLOnTsB1YQrKioKR0dHevfuzdWrV1m4cCFKpZKWLVsyYsSIFO/n6+vL4cOH1SYFkjxQ586ds9xKfv36dZ49e4aBgYGa9NDXDBw4EJlMJiqYatasSb9+/Th58iSgqoxwdHTk2rVrJCYmUrJkSR4+fIienh4NGjTg/fv3BAcH8/r1awoWLCjMgFOr6pUSGo6OjsyZM4cKFSpkOgD2b2H9+vVERUVRtmxZ2rRpk+nXJzcAz6wk0vcmODiYBQsW8PnzZ2xtbWnYsCFmZmaULVtWBGEWLFjwwzR3vwWlUsmbN2+4ceOGGLCam5vTu3fvVNc3MzNj7dq1BAUFcfny5RRV2Vr+XXz58oUOHToQExODvr5+qtrKN2/eFPJt+vr6IhmcFtWrV+fCn9dHIMOJUEJCAmFhYUDqZqUZERERwdq1azl48CAKhUIYhoeHh5MtW7ZMbSs5MpmMKlWqYG5uzu+//84ff/zB1atXOXv2LC1btuTu3bu0aNECNzc3LCwsqFevHmfPnmXr1q2MHz/+u5sWatEiYWBgQJMmTdiyZYsIZEs8ePCAyMhIbGxsOHbsWLoeChnh7e3NmjVrSEpKolKlSvTv31+MUypWrEjFihVJTEzk8ePH/P7773h4eODu7s6NGzfo2rUrt27dQqFQUKtWLSErk1VkMpkwWZfMN42MjHBycsLT01METAMDAxk0aBABAQF4eXmpBXFsbW2F0W1yhgwZwvPnz9m+fTuurq6cOHEiVc8WS0tLpJDV2DFjMLa0pE+fPnh7e7NlyxZCQ0PJmTMnT5484dmzZ3z48CHNz9O6dWuuXLkipDO1fBt6enrs3r2bunXr8uTJE5o0acKZM2fUfkdbW1ty5crFhw8f8PDwSCEFKXXMpZZk0BSp0zkwMFDNJBZUUpEODg74+Pjw+PHjVKUoExISUsjmPX/+nHfv3rFu3To2bNhAw4YN6d27Ny4uLmoJQjMzM44cOULXrl3x8vJi1qxZbN26lYULF9KpUyet9JaWfxypI04qxEuL69evExMTg7m5uZg3J6datWpcuHCBt2/f4u7uTu3atbO0L3ny5MHPz48VK1Ywe/bsVBPu7u7u7N+/H4VCgbOzM507d8bKygpbW1sOHz7M9evXefr0Kf369cPBwYGOHTuyf/9+3N3dGTJkCIcOHaJIkSJi3iqTyVAoFOjq6oq/ZTIZuXLlonfv3mzbto3y5cvj7e0tPBm1aNGi5XvzrxwRVKtWjTNnzqTQvXN2dmbdunVs3rwZX19f8fisWbMoU6bM372bWlIhNjaWyZMnAzBx4kQ+ffqEm5sbY8eOpVKlSujo6PDmzRt27NhBr169KFiwIHPnzgVUrbdRUVFUqVKFuXPncvv2bebMmYNCoaBJkyYMGzZM7b0CAwM5duwY69at4/Pnz5iYmNCxY0d69OiBXC7H29s7y50JiYmJIlnVvn17cuTIIZ6Lj4sT/69WvbpoM5SCW9L7SRPamJgYZDKZmFxLlXjZsmVDJpOJiYlUuSrpyqdW9SbJ4cTFxdGwYcP/bBIhJiZGSAiMHDkyS5WSMplMTBA/ffr0TfujVCqzXPXx4cMH5s2bx6dPn7C1tWXu3LlYWFggl8uZNGkS9vb2hIeHM3369DTb6b8Xybt/MkKhUHDy5Elx7j1//hwzMzMmTJjAnTt3MjTrk2RctPw7SUhI4ObNm9SuXZtr165hbm7OmTNnaNCggdp6v/32Gz169CAsLAxHR0cuX76coZdI+fLlhQQcwO+//67meZOcpKQkLl++TFRUFFZWVixevDhTnyM0NJQ+ffqISV79+vU5cuQIAwYMQFf3+9RalCpVShQzbNy4kd27d3Pq1Cmsra25e/cugwYNQqlU0q1bN6ysrHj16pXwbtGi5Uchmf1+3fEmeRJUqFDhm5IIz549o2fPnsTHx+Po6MioUaNS7d7U1dWlbNmy9OrViwMHDlC1alUSExPZsWMHjx8/xszMjEmTJmV5P5IjSUM6OTmJx8aMGUOvXr3UEgYnTpzA09MTpVJJ+fLlmTZtGr///jvPnj1LtYNRJpOxaNEiypQpQ0hICKNHj07VgD15IDYxMZHu3buTlJTE5s2bkcvlHDp0iHXr1nHlyhWRRLC1taV27doMHDiQJUuWMH/+fOzt7QkLC+OXX37B78/OBi3fTv78+Tl//jy5cuUSyYTkcqgymUzIjkjJ8eRI4/ysdsUplUqRhAgODk5Riayrq8uOHTvImTMnb9++RV9fn127drFnzx6xbNmyRe3vPXv24OXlxe7du6lVqxYKhYKzZ8/Svn17nJyc2LJli5r0S548ebhy5Qq//vorefPmxd/fn65du1KlShU1mVUtWv4JmjdvDqi6W9NL2O3duxdQJVxT61i3sLCgZcuWgMp3JCvzK7lcTps2bdDT08PLy4uLFy+mWMfd3Z25c+eiUCioWrUqgwcPFp1LzZs3Z9KkSeTIkYOgoCAWLFjA7t27kcvlnDp1innz5glFhU2bNrFx40bWr18vEoJr1qxh1apVrFy5khUrVjB+/Hi6d++OjY0NM2bMAGDGjBnpJqS1aNGiJav8KzsSWrVqRcmSJWnbti3Xr19Xqxjs3r07s2bN4uLFi1nScdXyY1m3bh1v3rwhV65cjBo1ChMTExo0aCCCT+Hh4Xh7e3P16lVRwZmUlISZmRkRERGUL1+e06dPs3XrVqZPn05SUhJ169ZlzJgxYoKWmJjI4cOH+fXXX0WQvlq1ajRp0kQE5WUyGTt37hSdCS4uLpn6HKdOneLdu3dYWFjQuXNntefWb9jALOn/69eLqlKpQighIQH4K5EgVcFly5aNgIAAIiIixN+AGABJrdKSafijR4/48uWL0GQFRGfE48ePNTZCVCqVbNiwgZMnT9KlSxfat2//3QJoP4qdO3cSEBBAvnz5aN26tXj8zZs3XLp0iU6dOqlpqaeFra0tr1+/TjORoFQqCQgIICAggMDAQIKCgggMDOT169dER0cTFBREUFCQmGg2bdqUHj16UKBAAY0+x4cPH+jUqRMBAQEiiZA8KWVsbMz06dOZOHEi/v7+TJ8+ncqVK39zgkipVBIcHMzdu3e5cuUKvr6+vHjxgpcvXxIbG8umTZtwdnZO8/WPHz9m1KhRQn/TzMyMvn370q5dO+119z9KdHQ0N27c4Pr161y/fp2bN2+Ka5ednR2nT5+mbNmyaq/ZuHGj8ILp2rUry5Yt0/jakrzKNioykpMnT1K/fv0UFZpeXl58/vwZPT092rZti5mZmcafKSIigsGDB+Pr60v27NmZM2dOut1l30KJEiUwNTXl5MmTbNy4EYBDhw7RqFEj3NzcWLduHUOGDGHgwIHMmzePRYsW0a5du+/ma6JFy9dI1+qvdZQlyZRv8Q3y8/OjW7duhIWFYW9vz5gxYzQ69wsWLMiaNWu4du0aS5Yswc/PD1dX1xTnfVYICgrizZs3yOVyNZkkHR0dtmzZQq5cuUThir29PSNHjqRFixbkzp0bgMjIyHTPR0NDQ3bu3Em1atW4desWq1evTrcStGPHjmzfu5dhw4Yxe/Zsjh8/zv79+7Gzs6N48eIUL16cYsWKoVAouHjxIm5ubsydO1cUlZiZmeHn50fz5s25cuXKN3VPafmLwoULc/78eRo2bMiTJ09E8kaiTp067N69m8uXL5OUlKTWOfatPjdxcXGimCMhIYHIyMgU97RcuXKxY8cOWrZsyenTp2nWrBmLFi1Kcf9Njo6ODg0bNqRhw4Y8f/6crVu3sn//fh4/fsywYcOYOnUqPXv2ZMCAARQoUACZTEb79u1p3rw5a9asYdGiRdy6dYtq1arRvn17Fi5cqPE4V4uW74mDgwN58+bl/fv3wt/ma6Kjozl16hRAuobDNWrUwMvLi9evX7No0SIGDx6sZnysCTY2NnTr1o1t27axbt06HB0dxfw+eRLB0dGRfv36pejqKVq0KLNnz2bPnj24u7vj5uZGjRo12LJlCyNHjqRRo0YcP36chIQEFAoFSqUSpVIppJClvxMSEtiyZQunT59m3LhxLFq0iN9++w1vb29GjRolpHy1aEmPd+/eZSjT9T28G7X8N/i5o4VpoKOjI6qWGjRowOnTp9WCb7a2tpnWS9fy4wkMDBT62TNnzkzVmNbMzIxGjRrRsGFDAgIC8PT0ZMiQIXz48IHSpUtz7tw57t+/z5QpU0hISKBGjRq4urqKgfzdu3dZuXKlqOrPnTs3HTt2TCEJU65cObp37y6SCQkJCVSoUEEjKYno6Gi2bdsGQM+ePdXMni5evMipkyeZVVRlope8FV9KJEgTDGmgkZCQQGJiYopJoJQgkCoCpURCzpw5sbe358WLF1y9elVoj0vbLF68OE+fPuXmzZsZJkgCAwPp3bu3kFk6ffo0kyZNYtSoUfTp0yddI6t/iqSkJJYuXQrA0KFDxff65csXGjVqhJ+fH2fOnGH//v0Z/p7S75OaFi7A/PnzheSWJhw7dozjx49Tp04devXqlaauO/yVRHj37h05c+ZMkUSQsLCwYNasWUyYMAE/Pz+6dOnCoUOHMvXb+Pn54e7uzsuXL3nx4gWvXr1KV1JoypQpHDt2LM3r6LRp00QHQt++fenZsyfm5ubpmvlp+XcREhLC9evXuXbtGteuXcPb2ztFha2lpSV16tRh8eLFakEFpVLJ0qVLRVBu0KBBzJkzJ8tBcQuLbHwK/cKZM2eoU6cO+fLlA+Dp06eiM7F27dqZSrBFRUUxZMgQnjx5QrZs2diwYQOFCxfO0v5piqOjI40bN2bo0KEimTB37lzGjRvHlClTcHZ2pn///qxYsYI7d+5w6dKlVKUrtGj5HkiJhBcvXqBQKERQ4lsTCcHBwXTt2pVPnz5RpEgRRowYkanOBplMRs2aNalSpQpfvnz5LkkE+EvSsmjRopibm6d4z9mzZ2NnZ8ewYcN48eIFhw4dUitU0AR7e3tmzJjB+PHjWb9+PU5OTmlqebtOmoRdwYLMmzePqVOnMnr0aLZv345MJuPDhw+4ubkxa9Ysrly5ohaYzp49O2FhYURERGBiYsKjR49o164dJ0+eTCFpoyVrFC5cmLVr19KqVSs2b97MuHHjRBVxhQoVMDMzIyQkBB8fHypUqCBeJyUS4pJ1JmeGryusQ0JCUk2OOzk5sX79ekaMGMGdO3eEVNHEiRMzfI+iRYuycOFCxo8fj5ubG+vXr+f169esWLGCVatW0bVrVyZPnky+fPkwMjJi3Lhx9OvXj6lTp7J161YOHDjA8ePHWbFiBQMGDNAmu7X8rchkMlxcXFi/fj0nTpxIdZ578eJFoqKiyJs3b7pFUXK5nAEDBrB27Vrev3/P8uXL6d27d6b9eNq3b4+HhwfPnj0TEkfXrl0TSYT69etTu3btNKXBjIyM6NOnD46OjuzYsYOHDx9So0YNpk2bxsiRI1M9r+Pi4lJ0dlepUoUuXbqwYcMGChUqxOrVq6levToHDhygV69eNG7cOFOfS8v/F+/evaNEiRIa+eIYGxuLOJaW/1/+lYkEUE2ALl26RJMmTXB0dGTx4sXUrVuXI0eO8P79+yxppmv5cQQFBTFt2jTCw8MpXbo09evXF8ZyQUFBPH/+nOfPn3P79m3ev3/P69ev1aQs8ubNy7x58zh27BgjRowgNjaWAgUKUL58ec6ePUtUVBRXrlwRpnxGRkbUrFkTfX39dI3vChcuzIsXL0RyolWrVulqgOrq6nL16lVCQkKws7OjQYMG4oLr5+fHokWL1NaPi40Vg2ypRTk+Pp7Y2FgMDAyQy+UoFAp8fX1TSMpERUXx7t070cHg5+cnAt716tXjxYsXzJgxAycnJ9FOrVQqqVKlCk+fPuXatWtpDhoePnyIl5cX06dPJzg4GD09PcqXL8/z5895+/YtI0eOZNq0abRp04YpU6ZopE2sSQdAZkhKSkpVLujIkSO8ePECKysrKleuzOPHj1Eqlbi6uooW/zNnztC3b1/Gjh2LTCYjISEh1eopKUDh5+dHQkIC3t7eYsL+6dMnYchoaWmJmZkZ5ubmmJubY2BggJWVFebm5uLx0NBQrly5wqNHj7h8+TKXL1/G3t6ejh074uDgoDbZks6Hz58/Y2NjQ4sWLVLoVH9Ns2bNOHjwID4+PrRt25aZM2emGjSQxcUhqYHu2LGDC9eu8eDBgxQt8jKZDEtLS3LkyEHOnDnJkSMHVlZWHDhwgHfv3jFs2DAaNmwIgLW1tQjShoaGCumLo0ePiiRdXFwcERERGiVxk38X6ckpJSYmapTcUyqVGq/3M096pXP9W9f5ev2MXhMaGio8a65fv86qVau4dOlSivXs7OyoUqUKlStXpnLlyhQrVkxcL6VEp1KpVEtOtm3blpo1a3LhwoUU2/Pz81PrqEqOPD4eKa3m5FSB2w8f8unTJy5dukT58uWxsLDA09MTgDJlymBlZUV0dDT+/v4Zfie//vorbm5uhIWFoaurS8GCBVm8eHGKSZmurq5Gx1VMTIyanGJaSD4OLi4uuLm5sXHjRmGs/uTJE1q3bk2/fv0oV64cN2/eZOHChdSrVy/dbf7Mx7OWnwuFQqF2vOTLlw9dXV1iYmLw8/MTWs8BAQHI5XL09PTSrTz7+PFjCqmimJgYpk6dyqtXr8iRIwfjx4/H19dXI/kduVyeqknt1/fG1EyJUyN37txqsjSSOXHx4sXVHvf39xdV/kZGRgwePJgtW7Zw9epVSpQowYABAyhWrBjZsmWjVq1aGb5vzZo1adeuHQcPHmTMmDHs37//r2RIbCySg8uHDx+EtNHChQtZtmwZ3t7eBAcH8/DhQ7Vt2tnZ4eTkhJOTE0WLFsXDw4M1a9YQFRWFnp4ev//+O40bN+bq1asa6dhrsk5kZOR3WSezaCoTKSW/MkKq2NVkveQ0bNiQMmXK8ODBA9auXcuUKVMA1TFepUoVzp8/z+nTp9US0NK9NjY2lrdv32p03IeFhYnjUbpHSHz48EF8RlNTUyGpKjFw4EDOnTvHgwcP2LJlC/v27aNDhw707t07w3tDWFgY9evXp06dOnh4eLBv3z48PT3ZuXMne/fupW3btvTp0wcTExMKFizInDlz6NSpE9OnT+f69esMGjSIq1evsmDBAjHuS60YJvn4TqFQgIa/mxYtafHLL7+wfv16Tp06Ja4D/v7+4hyWCsDq1auXwlw8JCQkxTylT58+7N27l2fPnrFp0yaaNm2KtbU1b968yXBflEoljx8/pkWLFixduhQvLy+mTJnC7du3USgUODk50bhxY3x8fLh161aG22vRogUeHh68fPmSqVOnsmHDBho3bpziXpuUlJRqgr527dpcvXqV8ePH06pVKypWrIinpyeDBw/m3r17ac7VM3M9/XoOl/z/0t9fjzfSQ3s9+DkICgoiOjqa3bt3U6JEiXTXzZ49uyjq0vL/y782kQBQtmxZfHx8mDBhAr179yY2NhYnJyfOnz+fKXmD/ye+d9BB0+qnV69esWfPHkBVzezj48PChQt5+vRpmpXRknFQ8eLFGTx4MEFBQYwaNYro6GhsbGyoX78+cXFxfPnyhWPHjhEeHo5MJqNkyZJUrFgRAwODFIPutJDJZNy/fx89Pb10DZiTkpI4cuQIALNnzxaeBDExMYwZM4aYmBjqVK0KwarJsIGhIfI/b/RSB0bym7+pqSnh4eEUKVKE3Llzc/v2bUCls1qrVi1kMhmRkZE8efKE8PBwkTAYNmwYhw8f5tWrV+zdu5d+/foBqpt4tWrV2L59O56enqlKCcTHx7NixQp+++03QNWWGRUVhZeXF3ny5KFOnTr88ccfvH//nu3bt7Nnzx46d+7M8OHDhaby34FkHpUcpVIpNNAHDRokAn/Hjx/n0qVL6Ojo0KtXL7Zs2cKBAwfInTs3nTt3Rl9fX1STJadQoUKAqpvBysqKiIgI8dufOHECpVJJ6dKl6dWrl9rrnj9/nuK7sLS0pGDBgnz69IkrV65w584dXrx4wZw5cyhYsCBt2rShWrVqhISEMH36dGGsPHfuXH7//Xe1Dp3Y2FgCAgLImzev+A6srKxo1aoVhw8fxsfHh0WLFql140hEJusKWLduHTF/DupKlChB4cKFyZUrF3ny5MHOzo7Lly+nSBLVqVOH48ePc+PGDfLnz4+NjQ1yuVx0V3h6eqJQKChZsiRVqlRRe62pqWmmvQ+Sm15/jZ6ensYDW03fSxt4TUlSUhInT55k9erVIkkEqoR95cqVqVq1qjANTu/empSUxIQJE0QSoVOnTjRu3DhNmQelUpmm0bksWUVnp06dmFyyJAsWLOD48eN4e3tjZGQkKr3mzZuHTCYjICAgw66EuLg4kSAEVbIq+WdOTqlSpTSSOnr16lW63UcS79+/x8LCgrx581KnTh2uXLnCH3/8gbOzMxYWFoSFhXHgwAHq1q3LrVu3uHTpEnfu3FGreP2RaM+NtPme1w5Nt/O91/v62qyvr0/hwoV59uwZr169olChQuJcKFy4cIZSOdHR0Wr31YSEBBYsWMDLly8xNzdn1qxZ2NjY8ODBA+zs7DLcv1evXmnUVWRoaKjR9nLkyCGkIF+9esXTp0+RyWQMHDhQyBWBqvAgefC6XLlyTJo0iY0bN/LhwweWLFlCq1ataNGiRaqdtF9ToEABVq5cyZMnT3j48CEzZ87k6NGj6OrqooiJIeDP9cxMTVEqlfTs2RMzMzPhw5B8P+rVq4eBgQHFihUTv3N8fDzOzs60b9+eAwcOkJCQgFwux8PDgwkTJmTaJyYzBAYGqiWmf0QiQVPkcrlG4wNNz11DQ8MU602ePJmOHTuyfv16JkyYgJmZGXny5KFly5acP3+eGzduqMkeSQmBxMRELCwsNCqwSUpKEh2mUgGVqakpkZGRxMTEYGxsjFwuF1KaX1OnTh0KFy7M1atXCQ0NZevWrTx48IAJEyakKz8UHx+PUqlELpdTo0YNatSowYMHD1izZg23b99m7969HD9+nA4dOjBp0iTMzMwoVaoUBw8eZO3atcybN0/II23ZsiVVQ1stWtJC0+Cxvr5+ivOybt26mJubExAQgLe3N1WqVBHz48jISK5fvw6oija+vu/VrFkz1ftHq1atxDF96tQpatSoQZ06dTLcT19fX1Fs1apVKw4dOiQSBlWrVhVyRl5eXhoVWunq6lKlShVy5szJ7du38ff3Z9u2bejo6AhjZakIUfpupGuhiYkJlStXFsoEJ06coGXLluTJk4fXr18zd+5c0Sn8NZpeTzWdb2n591KiRAnKly//T++Gln8B//oUYI4cOdi2bZuo6Pjjjz8oVqzYP71bWr5i7ty5JCUl0ahRI8qWLUv//v3x9PQkNDQUmUxG/vz5adiwIe3atWPKlCls3bqV8+fPs2/fPmbMmEF0dDSjRo0iMjKS0qVLU7NmTXR1dYmLi+PUqVOEh4djampKmzZtqFGjRqaDmfb29hoZMJ88eZKoqCgcHR1p0aKFeHzKlCk8fPiQHDlysHrVqlRf+7VHAvyVXIiKilKbQFtbW4uBk7RO8oSLubm5MHFesWKFmkmUZMZ8+/ZtYmNj1fbh+fPnVK9eXSQRChcuzOfPn4mMjERHRwc/Pz+OHDmCtbU1w4cPp2zZssTFxbF9+3bKly9Phw4d8PLySutr/OG4u7tz+/ZtDA0NGTJkCKDyRZCkjgYPHsyAAQMYPnw4oPpurly5kub2pMHkx48f1R5//fo1Dx8+RCaT0bRp00zto62tLZ06dWLSpElUqlQJQ0NDXr9+zZIlSxg4cCCurq5qxspfV3BFRESwZ88eDh8+zKVLl9QGbXZ2dkybNg1dXV1RkSg9//z5c5YtW0afPn3E+kbGxjRq1IiFCxcyceJE2rVrR7Vq1cifP78YdH9NoUKFKFasGEqlkgsXLqSoDjx//jyA6FbQ8u8lJiaGjRs3UqVKFXr37s3du3cxNDSkT58+eHt74+XlxcqVK+nQoQP58uVLNxiTkJDAkCFD+PXXX5HJZCnaqLNiap8cXV1dJk+eTP/+/cW+FylShGnTpmkcSE1ISGD06NEav2dERMQ373dalChRgpo1awKqa3X58uXR0dHh3bt3+Pr60qlTJ4AfGhTUokVKiEsdNVKXT8mSJTV6fXBwMJcuXWLp0qX07t2b+/fvY2RkxPTp09WC9f80kj50rVq10t2v4OBgTp8+zZkzZ+jTpw+VKlVCoVBw+PBhVqxYka4kYHIMDQ3Zvn07pqameHh4sHDhwnTXb9OmDWvWrMHFxYWZM2fi7u7Ovn376NevH7a2tuIa9/HjR7Zt28asWbMoVapUCumzZcuWsXLlSo32MTPEx8fTs2dPMT6Qlr8ryflP0aZNG4oWLcqXL1+EHB2oqn719PR4+fIlL1++FI9L46qsShtJr7OwsEBPTw+lUqlRsiZfvnx07tyZypUro6ury61bt+jQoYOqmCQdQ9qvKVOmDBs2bGDt2rVC4mL79u1UqlSJ9evXC9+5oUOHcuDAAaysrHjw4AGNGjVK1Xxai5Yfgb6+vpibHT9+XO25S5cuER8fT6FChShevLjG29TV1WXq1KmMGzcOUHWwrVy5MsU8Oj2aNm0qCtSSJxEyi0wmw97eHhcXF2xsbIQnQnx8PHFxccTExIgu8PDwcEJDQwkJCeH9+/ecO3eO8uXLkydPHpKSkjhz5gyTJ08GYOnSpRl2v2vR8l/hyZMn3LlzJ93l644lLZkjyx0JcXFxomL2ZyCtimMt/zznz5/n8uXLIhC0bt06AgMDKVCgAOvXr8fe3l5k6e/du5dCu9bPz4+RI0cSFhZGsWLFWLx4Mbt27SIhIYHTp08THByMsbExzZs3T9H6pynW1tZUq1ZNzYD5686EgIAA3N3dAZg+fbqY2EnGzjKZjPXr12NjY0NgyrdI4ZEAfyUJIiMjKVKkCDKZDKVSqaY7l1oiAaBdu3bs2rWL+/fvs2jRIhFwKly4MDY2Nnz+/Jnbt29TvXp1lEolO3bsYOTIkURHR2NiYoKpqamYAHXt2pWePXuyd+9edu/ejY+PDz4+PjRp0oQJEyawZ88eTp06xcmTJzl58iSzZs1izJgxWfquv4UlS5YA0KNHD3LmzMnLly+ZPHkysbGxVKxYka5du4rP4+/vz+HDh5k6dSo5c+ZUM2+VkBIJHz58EI8plUph0lWxYkVsbGyytK+WlpY0atSI4cOHi+9OkqZKL4lw8OBB0d7+4MEDjI2NRVUlqHSrJ0yYwPz58zl79ixxcXF8+PDhL1mvZEHVRYsWoaeBl0JcXBxPnz7l4cOHhIaG0qhRI96+fUtgYCDe3t4iIKxQKLSJhP8AISEhIlggJSEtLS3p27cv/fr1y7TuZWxsLP369ePs2bPo6urSs2dPoQuemJjI9u3b8fDwIG/evBQuXBh7e3vs7e0zXeEtk8no168f+fPnx93dnSFDhmjsh5SQkMC4cePENVwT3r17x4EDB7C3t6dIkSJZvr+kRalSpfj06RPPnz/n1q1bVKpUiRs3bnDjxg369u3L7t27OXToEC9fvvzh/g1a/j/52nBZqqRMK5EQGxvLvXv3+OOPP/Dw8FC7d4LK52r8+PFiuz8DUVFRItgkJeiSExsbi4eHB56enuI+CirT6e7du1OkSBH27dvHnTt3qFq1Knv27NFIQ7tw4cIsX76cfv36sWzZMpycnGjwZ/IwNerUqUOdOnVSfe758+ecOXOGu3fviuKBZcuWMWXKFL58+YK3tzd6enokJCQwZswYcuXKRbt27TLcR02Ij4+nR48eotMseSeyFOD6r6Kjo8OECRPo06cPy5YtY+jQoYCqmKdKlSq4u7tz/vx5Bg0aBHy7R4L0On19fczNzQkODiY8PDzFvCg1dHV1qVixIhUqVMDHxwcPDw+2bt3KmTNnGD58OAUKFMDQ0BADAwMMDAyIi4tLVe5RJpNRuXJlKlWqxOXLl1m7di1v375l5syZbNq0iTFjxtCxY0eqV6/OhQsX6Nu3L3fv3qVLly74+voyadIkrVSJlh9OixYt2LdvH8ePHxf+i6DqMANo0qRJlsaYPXv2xM7OjgkTJuDj48O8efMYPXq0Rmb2Ojo6TJw4kdevX1O0aNFvPg9MTU1p0KABMTExJCUlCSlYhULBx48fyZMnj/g7KSkJDw8PwsPDuXjxIg0aNODMmTOEhISwevVqGjduzNmzZxk8eDBXrlzRnqNa/rNkz54dY2NjERNKD2NjY548eaKVacoiWU4kfPnyhc6dO/PlyxdsbGwoV64c5cqVw8HBgRIlSqRZ7arl/4ukpCTGjh0LqIyJDQwMRFXP1KlTKVOmTLqv//z5MyNHjiQ4OJhChQqxdOlSTE1NSUpK4uzZs3z+/BkDAwOaNWv2zUGerw2YQT2ZIGkxNmjQgOrVqwMqk0KpwnX06NHUrl0bZRrVP5LMUPKOBKmdWeoIsLS0JCQkRK07QWqNljR8JeRyOTNnzqRVq1bs37+fLl26UK5cOWQyGdWqVePIkSPcvHmTMmXKMGjQIA4ePAioui/evn3L58+fsba2ZsaMGUKiZtCgQbRu3Zp169Zx6tQpzpw5w5UrVxg6dCjTpk1j5cqV7Nmzh2nTpgH8rcmEBw8ecObMGWQyGaNGjQJU0j3Pnz8nW7ZszJw5U/xWMpmMsWPH8unTJzw8PBg1ahQVK1YUlSISkqzKx48fxeT88ePHvH79Gl1dXRo1avTN+21mZkbHjh1p1aoVFy5c4PXr13Ts2DHdJIKFhQUlS5bk5s2beHl5YWxsrBa4qF69OsOGDWPlypWi40JXV5caNWrQsnFj2LYdAAN9fdKqp1YoFHz+/BkfHx98fX3VggHu7u5Ur16dixcv4unpiZOTE6AKrAQFBWFmZpZC1kjLz09ISAhz585l27Ztwhg7f/78DBgwgG7dumkk2/E1UVFRdO/eHXd3dwwMDNi2bZuooExISGDt2rVCLuXdu3e8e/dOHLPGxsaULl2a4sWL07BhQ43Nkhs2bJipRFZSUhKTJ0/m0qVLItimCTo6OkRHR3P//n3u379Pzpw5KV68OIULF/4uMjcymYxatWoREBBAaGgo7969o0iRIvj6+jJ58mTq1avHpUuXWLJkCevXr//m99Oi5WukDt7nz58LnyBQTyRER0dz7Ngxbt++zf3799XOH6ly0tHREQcHB4oVK/a3G/7GxsYK36rUzks3NzeioqIoUKCAkKMElfnygQMHuHDhgtpnKlasGImJibx8+ZJNmzZRr149xo0bx+bNm3n9+jW1a9dmxYoV9OzZM8N9a9OmDZ6enmzdupW+fftydO9e8mTis924cYMVK1bw+vVr8ZijoyOBgYH4+fmxbNkyxo8fT3h4OL6+vkISp3v37uTMmVMjT4f0SEhIEEkEAwMD9u7dS4MGDcTz4eHhP1XnyY+ga9euzJw5k3fv3rF9+3Z++eUXQHUfSiuRkJacX0ZIiQQDAwPMzMxEIiEzWFpairHhkiVL+PDhQ5omzDKZDH19fZFgsLS0pHPnzjRr1gyZTEa9evWoVKkS3t7eLF26FH9/f8aOHcv69etZs2YNjo6OHDt2jKlTp7Jz506mT5/OrVu32L17t0bJDy1askrjxo3R1dXl6dOnIhH+5csXbt68CZDpbvLkSMVU27Zt4+3bt8yaNYvRo0cLT7j0MDAw0KgTIjExkejoaMzMzNIdT8pkslQl0qKiov7y3km2325ubgQEBHDz5k0aNmyIm5sbjx8/Jlu2bBgbG+Ph4cGvv/6aQrJXi5b/Cvny5ePJkyepSgEm58mTJ3Tt2pWgoCBtIiGLZDkdaWtry7Vr13j48CFubm7o6upy8uRJJk+ejJmZ2TddwLX8dzh+/DgPHz7EwsKCESNGsHnzZuLi4qhSpYpGgaCtW7fy+fNn8uTJw/Lly0Wy4N27d/j7+wvpGU0DUBkhJRMkmaMbN24AqiDv/fv3ARg5cqRYf/Xq1eJmnlFQXQpyJw/YSokEqVJdqgROHmSOiIgAEKbOyalQoQKtW7cGVJOdQ4cOoVQqRQX7woULqVmzJgcPHkRHR4eaNWvy4sULEhISqFatGnv37k0RELaxsWHmzJns2rULBwcHYmNjWbJkCd7e3mzevFm0SE6bNk0jY9PvxaFDhwDV4NDe3p7AwED27t0LqHRsv66i1tXVZe7cuaItvU+fPim0HaUKk+joaPH9Sj4VlStX/q4VyAYGBri4uDBs2LBUDem8vLwICwvD0NCQdu3aUblyZaFR+Pvvv6eo+mvcuDGDBw/G3t6eHj16sGvXLsaPH5+utJtSqeT169fs3buX0aNHc/nyZZ48eUJiYiLW1tbUrFkTY2NjwsLC0NfXJ3/+/CQlJYmBuSRr5ezs/M3JYunzaHXZ/z4mT54srlnlypVj586dPHnyhL59+2YpiQCwadMm3N3dMTExYd++fWrX9du3b4skQocOHejZs6faRCw6Oppbt26xc+fOFEb135Nt27Zx5swZdHV1WbZsmcavK1q0KHXr1hVeJVJXmibGypqip6dH48aNkclk+Pn5YW9vj6WlJR8+fBCySjt27NDI9E+LlsxSsGBBQCXn5+vrS2xsLEZGRmrn6aZNm9i4cSPe3t4i4K6jo0OlSpVYu3YtS5cupWvXrpQuXfpvTyIEBwdz7Ngxzp49K+7dXyMZvVeoUAG5XE50dDSzZs2iZ8+enD59moSEBGxsbGjZsiXz589nzJgxjB07VlzLLl26hJ+fH7Nnz6Zp06bExcUxaNAgjbub5s2bR61atYiKilKTxMxIazosLIz+/fuLwoaaNWsyf/58Ro4cydixY7GysuLjx49cvXqVkSNHkitXLiIjI8mfPz/x8fEiuP0tnD59WnQibNq0SS2J8P+Cnp6eKIhalUy6VPoubt++LRI9UsAvISGBT58+Zep9pKpiUPnpvH//HlAlyjIrsSeTyahbty6HDh2iR48e5M6dG0tLS4yNjdV8tZRKJXFxcYSFhREQEMCzZ8+YPn26KDwC1Vi6c+fOeHh4MGvWLKysrHj58iV9+/ZFqVRiYGDAokWLWLFiBYaGhpw6deqH3s+1aAHV/K127doAQjrOy8uLxMREzM3NNfLRSY+CBQsydepUbG1tCQ4OZtmyZVnuNEoNf39/3rx5w8uXL7/bdi0tLalfvz4ymYxXr14RHh5OixYtMDU15caNG6LjzdXVNdWYghYt/xXy5ctH+fLl010yMpTWkjHfpa9Jcvg+ePAgJ0+eZObMmam2D2v5/8PNzQ2A9u3bY2lpKQLz3bt31yiAKE1yTU1N1aSrsmfPjlwuR6lUikD796JcuXKi4ujixYskJCRgYmIiBiWHDx8W67Zo0QJdXV0CAgIYMWJECj355EiTguTVW6VKlQJUExFJUqZy5cqi+jw8PFy8n4uLS6rbnTp1KuXKlSM8PJwxY8bQsmVLGjVqhLOzM2FhYTx+/Bhra2s6duwoJr5Dhw5lxYoV6cqB5c2bV3weKysrIVUiyZnZ2dmlGhD/UUjHgvQ9WlpaivdPa5JlYmLC0qVLMTAw4ObNm6LlVeLatWuAyhdAmgBK28zsJPBbkT5fbGwsHz58IDExUXzWPHnypGqc3bx5c1avXk3Hjh3TbbsNCwvj6NGjTJgwgRkzZnD27Fm+fPmCnp4epUuXpmPHjnTr1g0HBwfxm5uamgojZingIVXZ3L1795sHvpIu4X+9mvFnQjrGHBwcuHXrFh07dkz1uMoMUgXzxIkTRaeWRLFixcQ15siRI+zYsQM/Pz+1dXR0dChSpEia17dvxdfXl3Xr1gGq5Kc08dQEuVxOgQIFaNCgAR06dBBJuocPH35XwzlJYkIul5MzZ06RWLhy5QoVKlQgLi4OV1fX7/Z+WrRISNJmOXLkEOdqXFycWuK6YcOGlClTRi15nJSUhJeXF4MHD6Zfv34sXryY48eP8+zZsx/mK/I1796949SpUyIg8uDBg1QTblJC4MiRI2zevJk2bdqIQGmrVq0YOnQos2bNUitK0dHRoW3btuK6dPfuXUxNTTl48CDdunUDVNcTTa4Denp67Nixg19++UVt3Nu1Wzf279+fpoa9iYmJqDjt3Lkzffr0EV2UlpaWVKxYEVBVv5uamtKyZUsAUVn37Nmzbw4WVapUSezD5s2bM6UX/l+ie/fu6Ojo8OzZM7WxS926dVEqlcKny8LCgrp16wKwc+fOdOcEXyNd/0FVbZyUlISuri42NjZZliExNjZm+PDhnDhxgosXL3Lt2jVu3bqFp6cne/fu5fz587i5uXHw4EF27dol5u4rVqxQ834Ale9H//79xbhZCoRKdOzYURhPa3XYtfwduLq6IpPJ2LZtG0ePHqVChQpYWloSHh7OvHnzvnn7NjY2TJ06lezZs4uk9fcgufdJdHQ0z58/Jzg4+LuMKy0tLcX/zczMyJEjh5AF9vHxIX/+/AQFBfHLL7+IIkYtWrRoyQrfJZEgtdKGhIQAqorhtWvXfo9Na/kXo1AoOH36NAD169cnNDSUJ0+eAKrJiSY0adIEPT09nj59qqZda2ZmJqq1b9y48V2rBEBlWJwtWzbCwsLw8vJCLpeLyv8dO3bw4MEDAOrWrcuWLVvQ1dXl4MGDDB06NM2Jg7T/ybWDCxcuTKFChUhMTOTy5csUKlSIDh06YGxsjEKhYM+ePURGRpI7d25hrvw11tbWIkisr6/P6dOnqV69Ov3792f+/Pk0bNiQ5s2bC4PlFStW0K1bt3QTOZGRkQwbNowHDx5gZWWFm5sbhQoVQqlUCiO/wYMH/60SZr/88gu6urrcv38fX19fdHV1RefTiRMn0nydra0tHTp0AFRJl+S/j5Toat68ufg+qlSpglwu58WLF39rMqFw4cJCQuj8+fOcOXOGwMBAjIyMvkli6e3bt0ybNo1jx47x+fNn9PX1qVSpEiNGjKBVq1Y0aNBASEK8f/+euLg4jI2NyZUrl5C/MTMzA6BmzZrY2dnx5csX4ZWQFRITE3n16hXAT6Wl/V+nU6dOyGQyfHx8vpvBlHRNT02mzsrKiilTppArVy5RyWxjY0OVKlXo0qULQ4cO5ciRI6xatUokKr8niYmJTJ06lcTERGrVqiUCbVnB2NgYZ2dn9PT0CA0NTaEN/y34+PgAqg4IKXjYu3dvABG4279/P0eOHPlu76lFC/zljVC0aFFsbGwwMzNDoVCoHd8lS5ZkzZo1nDlzhs2bNzN69GgaN24sEs2fP3/m2rVrbN26lXHjxjFgwAD27t37Q++fjx8/5tKlSyQmJmJnZyeS3O7u7in8pNq3b0/79u1RKpWsWrUKPz8/bG1t2bJlC7NmzUrXSF66Jz99+pTY2FghKWlkZISXlxdnz57VaH8tLCzYsWMH169fF4+9ffOGGTNmUKdOHZYtW5bi+9LV1RVjl+Svk5ACUVJna3KpTCkp9OLFC432Ly1sbW05evQoZmZmXL9+nW7durFr1y6x7Nu375u2/29B8kQAVYeoxPjx4wFV9/Xjx48BVXW0hYUFb9++FX5bmpInTx6KFStG7ty5KVq0KGXKlPkhxRZ6enoYGxtjbW1Nrly5KFSoECVLlmT06NFUqVKFuLg4Jk+enOrcytraWphs37lzR+05KYmlDVBq+TuoW7cuM2bMAGD27NmEhISwaNEiZDIZhw4d+i6Bf1NTU5E8PnfunFAn+Bbi4+NJSkpCJpNhYmKCUqkUiTlNpTfT4u3btyiVSqytrcXcrVu3btjY2ODv70/btm0xMTHh6tWrODs7iwJPLVq0aMks3yWRYGhoyMyZM2natCnHjh1j06ZNYkCr5f+XP/74g8DAQMzNzXF2dub27dsolUoKFSqUQtcvLbJly0bNP83ppKCvhKOjIxYWFkRHRwvJle+Frq4u9evXB/7qSrC3t8fJyQmFQsHEiRNF5YCLi4taMmF0GhJHkhxGcukZmUwmjGxv3rypNgF2d3fn2bNn6Onp0adPHwwNDdPd3yFDhnDq1CmcnJwICwujX79+XLt2jaJFi7Jjxw5AlUQYMmRIup89eRLBwsICNzc30SFx/vx5njx5gqmpqQh0/V1YWVmJamIpoCZVC968eZOAgIA0X9uzZ0+yZcvGo0ePxMQ3MTFRdCg0a9ZMrGtpaSmMmVObvP9IqlWrRv78+UlMTBQBgIYNG2b5eurj48OcOXMIDQ3Fzs6OAQMGsGrVKgYPHkz58uXVWtzhr2NUMsKVEgnS++vo6AgDx/3792dpn0BloJ6QkIC+vr7ocNHy48mbN6/QzJZkwb6FiIgIkZBIy5xV8mGZNGkS69atY9GiRQwcOJCGDRuSP3/+H5aMjI2NZd68eTx69AgzMzOmTZv2zTJa+vr6FC1aFPh+FZdfvnwRshjJfVBmz56Nqakpjx49ol69eoBKHup7/G5atEhIiYQiRYogk8nE8S11wyVHV1eXokWL0qJFC1xdXZk2bRp79+5l1qxZdO3alYoVK2JkZMTnz5/Zu3cv/fv3Z9KkSTx+/Pi7FXsoFAoeP36Mp6cnSqWSokWLik5OW1tbEhMTuXTpUgofh4kTJ1KtWjVAVZRw5MgRjQpa7OzssLa2JjExUQSK7ezshGzQjBkzMtWBkS9/fvH/4cOHkzdvXsLCwti8eTP169cXfgcSbdu2RUdHh1evXonku4R0f5Zk6aT7dEhISAoT7W+hbNmy7Nu3D319fWHWKS1/p0/WP43U2ZI8kVC6dGmaN2+OUqlk4sSJKBQK7OzsmD17NqAqcsls0t7ExAQbGxtMTU3/dulHuVzOjBkzsLS0xNfXl9WrV6e6npRIkDoSJaTk4sePH3/sjmrR8idTpkyhcePGxMXFMXLkSMqUKSPmubNmzWLp0qU8efLkm6r9HRwccHZ2JikpiWXLlnHo0KFMdRt9jdQpZmRkRKFChUQxV0REBE+ePEnRuZsZ3r59C0CBAgXEYwYGBsIo/sKFC9y8eZPixYvz4cMH6taty+rVq79rl62Wn493795x586ddBepMEyLFk35bpbto0aNYs6cORw5coSoqCghJaDl/xepEqdRo0bo6enh6ekJINqxNUXSlD1//rxam7ak+Q+q6rTvXf1WqVIlta4EgHbt2mFsbIyXl5eahmjyZMLRNKpGk0/Yk2Nvb6/WlQCqSbykS9uiRQuNtR6LFi3KtWvXmDt3ruhOWLNmDZC1JMLatWtFcOv9+/fCWFoKzP/dtGnTBvgrkZAvXz4cHBxQKBTpVn6Zm5uLCe+sWbOIi4vj5s2bhISEYGVlJTwlJKSAg7e3d5qyAz8CuVxO06ZNhTeDo6NjCoPozLB+/Xri4+MpXbo006ZNo2rVqhgZGaW6bnx8vOiakYJJ0vkmVbWAqn0dVNrJYWFhWdovKSBSuHDhFMkMLT+WLl26APDbb79988RBGnTa2tqqtVN/jYGBAcWKFcuyD0Nm+eOPP9TkSyZNmqRx8jojJE1NPz+/FJXPWeHevXuAatKXXGrO1tZWyBk9ffqUjh07kpSURNeuXdm6des3v68WLZCywCG9REJqmJiY4ODgQPv27ZkyZQo7d+5kzJgxODg4IJPJePjwIRcvXmTu3LkcOHCAly9fZln6KC4uju3bt4tAiZOTE9WqVUMulyOXy6lTp47w+Ll27Zra9U1PT49169Zx4cIF5s6dq3ZPSw+ZTEbZsmUBhN8LwOjRozE3N+f+/fv07NmT4ODgTH+e9u3bc+bMGdasWUPFihVJSkrCzc1NBKFBlYh1dHQEVF4Nyfm6I0G6viZPJHwvP5eaNWty+PBhmjdvTqNGjcQiJTn/H5A6Qz08PNQSVVOnTsXU1JQ7d+6wa9cuQCWZJUlFbtq06ZurjP9OsmfPzvTp0wFVwYHkkZUcqSP8644Eaa7y6dMnbWBSy9+CXC5n9+7d2NnZ8e7dO6ZMmUL//v2pVasWcXFxbNu2jbZt2/LLL7+wYcOGLHeT9u/fX0iZubm5sXDhQr58+ZKlbUlzK2NjY2QyGTly5MDe3h5DQ0MSExO5cuUKnp6emb5uxMfHC+/C5IkEgAEDBmBiYsL9+/fx9/fn5s2btGvXjsTEREaNGkWXLl3EPUXLf4t3795RokQJKlSokO7StWtXjI2NU3hOatGSFt8mjvwV9evXF1XcWr4PmRmIaVK5oun2lEqlRttLSEhIcz0pEN6oUSM+ffqEh4cHoNJZTyvo//nz5xSBWzs7O+zs7Pj48SNHjhyhQYMGREREEB8fj4GBAfnz5+ft27dcuXKFevXqZVlLVNq/5BQsWJC7d+9y+vRpFAoF9vb2dOrUia1btzJ16lTy5s0rtPXz5s3LlClTWDJnjnh9SHAwun9O8qQgrZ2dnQhAhYaGoqurS/Xq1Xn16hU3btygXLly7N27l6SkJIoXL07JkiUJDw9Pt+JeIigoiODgYJo0aUKxYsWYPn06T58+ZcyYMdSsWVNIMn38+DFFYC0yMpJRo0bx6NEjzM3NWbFiBYUKFSIpKYn379/TrFkz3rx5Q4ECBRg5cuQ3VWNklRYtWjBkyBC8vb158+YNERER1K9fHx8fH44fP06rVq1SPR4TExNp3749q1ev5u3btyxfvlwMturUqSNk2YKDg9HV1cXKyoocOXIQGBiIu7t7iuRXdHS0RomryMhIbt26leF6X758UdOkrlevHoGBgeTOnVutRdzIyCjj7cXFke/P/yqVSmxtbbGwsBDSVslJbrz3/PlzEhISMDc3x8DAgMDAQOE/kpSUJLRwzc3NKVy4MC9fvqRNmzYsWbJEJJUk4/GMePr0KaBKoqV3TcrM9UrTdb/ndfJ7b0/T625m+Pq7admyJcOGDePZs2d4e3tToUIFIiIiNLpufn19lo5FyfxcIioqSqPthYaGpim9IU9IEMexn58fCRoknB48eMCFCxdE67mpqSn169cnMDCQ7du3Z/j61JAC/cnJli0boaGhXLt2jXz58qGrq0tQUFCG24qPj1ebqMXExIhzoXjx4uI5pVLJnTt3qF27NnZ2dvj7+2NmZkabNm04fPgw/fr14+nTp3Tp0kWtiyEtND2uMnP8aU3SfzyaXjcSExM1+j1iY2NTGK1K45ICBQoQHx8vEtcvX77MMFEWHR2daqK9YsWKVKxYkaCgIK5du8aZM2cICwvj7t273L17FwsLCxwcHKhataqaR0tISEia3aXR0dFcv36d0NBQZDIZuXPnJiYmJsX90MbGhjdv3vDmzRsuXLiAubl5ht/Lhw8f0k0sSJNqb29vnj9/Lr7r0aNHM2vWLA4ePMjly5eZOnUqdevWJSYmRlRmf40y5i+Pgdi4OIzkcurWrUvdunXx9PSkb9++nDx5koYNG4r5VNmyZbl9+zaenp5Uq1ZNJAyk3ycyMlLIWYCqUyz/n50PT58+TbUbJK2CArV9/er4q1WrluhokwgPD1cz5v670eQcUSgUGt2PFApFuueRg4MD1tbWBAcHc+vWLZydnQHV8TF27FhmzJjB/PnzqVOnDra2tri4uPDixQv8/f3Zt29fmj5ACoVCo4Chvr6+RuPOwMBARo0aleF6Tk5O6QaMKleujKenJzNnzsTR0VH4h8BfXmkPHz7kzZs3GBoaIpfLxfUjPj5eFOok/42USmWG33NytPeZ/y6ZGRdndP5aWVmxdOlSunfvzsWLF9m5cyfLly/n6tWrnD59Gnd3d169eiU6bEqVKkXDhg1p2LBhmudAaGhoimtno0aNsLW15fDhwzx79ozJkydTp04djQqioqOjxTVbKsKKj49X694xNTXF1NSUoKAgfH19efv2Lfny5RNxhuQYGxuLuavE+/fvUSgUmJqaolAoCAkJIS4uThQytmrVit27dzNnzhw2b97MjBkzsLe3Z/HixRw4cID79+9z4MABIRWYGgkJCejp6Ym/FcnmromJicj//FuhUKit961ocrxok5epExQUJPxsMzIYzp49u5Co06IlI7KUSNi9ezcTJkwg7H/snXVYVNvbhu8Zhm4EQUWxu7tbxO5Asbs7sDsxsLu7wPaIAXaL3S2iGEj3zPcH314/BgYY1HOO55y5r4tLnNmz955h9l5rvfE8ISHky5ePEiVKULJkSUqWLEmJEiXSrEzU8e9CJpNpnOgFBATg7++PTCajQYMGBAUFierVGjVqiIrr5OTMmVOjgW+HDh1YsGABFy5cYODAgdSvX19Ub9atW5eRI0eKzoGuXbsKE2NbW1ut9EX/+OMPjVVyOXLk4PHjx0RFRfHy5Uvs7e1p27YtJ06cICAggG3bttG3b1+xfY0aNVAkJMCexEpY93HjWLRiBd+/fxcTiIIFC2JoaAgk+olI+oj3798XFU0hISFkzpyZVatWiaBVWsbIEl+/fhXvI2fOnGzatInw8HCheyzh6OioNpiEhITQvHlzHjx4IDwRpEr/z58/06RJE16/fk3u3Lk5ffr0XyJHo6enl2JyliVLFqpXr46vry/e3t64urqSI0cOVqxYQUBAAOHh4Ro7XoKDgzEzM2P48OGMHTuWpUuXioW0i4uLOE7dunXFd1OpVDJ79mweP37M9OnT1Say9+/fTzVQkJQ//vhDqyrsggULiqrDtLh27VqaMkdRUVHs2ruXNvL/fW4fP35MdfFZvXp1mjdvjkqlEh0bLVu2pHHjxiiVSiGjUrlyZbX37+7uzqBBg7h8+TLt2rVj6dKlFCxYECMjI606VaSKoHz58qW5QJDL5VotJH80efi7oa+vn+77zejEXF9fX+01mTJlonnz5uzatYsdO3ZQsWJFrYJtkLgQSvr9k8wYixQpovZ448aNtUoo+fj4iHthcmSxseJ3Y2PjdO9/N27cYPny5cJTIG/evJQsWVJj8KV79+5qRQ/e3t7s2bMHSAyu9OvXD2NjY9zd3dUSfBLm5uZ8//6dL1++YGlpSYECBUS3VFrExMSI4NOnT5+YM2cOSqWS4sWLC5NK+J/GrYGBAYMGDWLcuHFs3bqVnTt3YmxszLZt21iwYAHR0dGpSk/o0JEcqXJf4uPHjyKJmDdvXuRyuehM+P79u6jET408efJgb2+f5jZt27blw4cPPH/+nH379nHkyBFCQkLw8/MjS5Ysat/7UaNGaTR/Dw4OxtfXl6ioKIyMjIiOjub9+/fpyj/cvn2bggULpqjMTI5cLk9Vmg0S7yUHDx4kLCyMJ0+eiLlTq1atKFCgABMmTODFixcMHTqUhg0bMmLEiFQTE0qFAqmvtkD+/Fgn6TYtUKAAT548Ye7cucycOZP27duTKVMmmjZtyrlz54Rvk3Svif3/e2SpUqXIlSuX2t9WMmV+/vz5D4+P2kgq/hnG2tp2KWobNPpV8whJ8nT37t3cvn2bpk2biudGjhzJ0aNHuX79OnPnzmXXrl20bNkSW1tbhg4diq+vL926ddOY+C1UqJBWXccnT57Uah3Qs2fPdLeBxPliaskNSJwPv3z5kqCgIKZNm8b69evF51igQAFsbW358uULb9++pUyZMshkMoyNjbG2tiY4OJhPnz6RKVMmtc8+tTWjjv8ev/p70LZtW75+/cqgQYNYtGgRjRs3ZvDgwQwePJiQkBC8vb3ZtWsXp0+f5sGDBzx48ABPT0+qVatG+/btadeundo5ValSRaOkcOXKlWncuDGTJ0/m2bNnHD16FAMDA9q0aZPmvUsqXktKUhk7CVNTU6ytrQkJCSE2Npbnz59jZmaGqamp2vmVKlUKNzc3tddKpsrOzs7iuaioKHGv7NSpEzt27ODixYtcunSJSpUq4ebmRuHChRkxYgSPHz+mcuXKrFmzhtatW2t8H8mv4dSubz09vX/N2uzfQqFChUQ3mQ4dv4IMX+Hh4eH07dsXd3d3Zs+eTVBQEN++fWP06NHUqlWLBg0a/BnnqeMfhqQ9X65cOTJnzsydO3eEKd6PGIc1a9YMfX19Hjx4IHRqJSwsLBg4cCAWFhYEBAQwc+ZMlixZkiJT/yPo6emJhfWTJ0+Ij4/HwMBAaA3u27cvhf6pJIsD4HXgAAMHDhRVp46OjhorC2QymUhIhISEIJPJmDlz5k/LB8lksnRb+KUkwrVr19SSCJBY3SAtJv7KJEJaSIvoffv2AYmTLmlBl57xX/v27cmVKxdfv37l/fv3GBoapqiwk3BxccHMzIy3b9/St29fDh48+Fu3fX79+pWlS5fy4gfMFZ88ecLbt28xMDAQPhTh4eGi6yR54q9KlSrs2rWL7Nmz8/79e1xdXTNkKihpwuuMlv8epAXG7t27f1hy4ejRoyLRlFYSLDQ0lOPHj7N+/Xo1abpfRWhoKAsWLGDatGlER0djZmZGnTp1KF++vNb+C82bN6d3794oFApu3LjB5MmT06z8NDY2xtDQEJVKlWF5o+/fv7Nw4UKaNm2Kr68vMpmMPn36pLp93bp1KVmyJNHR0fTt21ecK8Dy5csZN26crgpLxw8hVSk6OTmJhJ50T5bu0b8CmUxG6dKlmTVrFteuXWPWrFnIZDJ27dolOldTIyAgAB8fH6KiorCwsBA69dri7e39wxJ8EgYGBsI3Kak+PiRq5O/Zs4eePXsil8s5duwYbdu2zbDJ7tevXxk+fDjnzp0jT548fPr0iSFDhgCJn5/k43T8+HESEhJQqVRiPiLN8fT09MScUaqy/VmzZR3qSN8/Hx8ftcf19PRYvnw5CoWCgwcPCk+3OnXq0LRpU5RKJePHj/9TxsA/C319fVq3bo1CocDPz4/NmzeL52QymVgnJO/a0/kk6Pi76Nu3L66uriQkJODq6io6ui0tLenSpQvHjx/nwYMHzJ07l3LlyqFUKvHz86Nfv34sWLBA6+Nkz56dlStXikKsAwcOMG3atF8Sd4BEOVBbW1uRyAgPD09XZjc2NlZIjaXm/+Po6CiSh9K6FhKlyvbu3UuNGjUIDw+nQ4cOjBo16h8lyaZDh46/ngwnEl68eEHWrFkZOHAg5cqVI3v27Bw6dIgrV65QqlQpXXWcDuB/iYSGDRsCidWikFjt+SNVCNbW1qKCVAogJ6V48eJ4eHjg7OyMTCbjypUrjBw5En9//5+W4HFycsLY2Jjo6GiREKhYsSKVKlUiISEhTZMiyYB50KBBQNqB03LlygkDsx49eojK1T+TpEkEa2vrFEmERo0a/VZJBEismAe4cuWKqGyXdPsPHz4sDAg1oa+vz5gxY8T/k8oEJMfExIR+/fohk8m4ffs2M2bMwMXFBXd3d/z9/TVWKv9dvHr1Ck9PTz59+oRFKt0+aXHy5Ekg8fOQqhClAKm5ubnGKtH8+fOzZ88eqlatSnR0NCNHjmThwoVafS5SJbsukfD34OzsTObMmfn8+TN//PFHhl9/9OhRBg4cSEJCAi1bthTa0ZBYdX/16lVmzJhBvXr1yJUrF66urowYMYLGjRv/Mi8blUrF+fPnGTBgAH5+fkJWoWHDhulWSWuiZs2aTJw4ESsrK96/f8+ECRNSXbjJZDLRefn9+3etqnKjo6M5cuQITZo0YevWrcTGxlK6dGk2bNiQwqMl+bFmzpyJk5MTnz59onfv3tSqVUsEGefMmcOQIUP+lMpgHf9uJP38pPfhvHnzAomB7Z8NwGvCyMgIV1dX+vfvDyR2t0meB8l5/fo1586dIz4+Hnt7e5ydnbWqkpcwNjYmKioKLy+vnx6vS5QoAcD58+dTPGdgYMCQIUPYvn07efLk4evXr3Tq1InevXunG1iKjY1l8eLF5M+fH09PTy5fvoy+vj5yuZydO3fi7e0NQNWqVTE3NycoKIgbN24QFRUl5rZJi0Wk+5L02JcvX35ZcEvH/xIJt27dSiFnV6xYMYYOHQrA0KFDRaJnzJgx2Nvb8/btWxYvXvyPSvza29vTpEkTAObOnSvWQIBYKyT1DgF1nwQdOv5KZDIZq1atonDhwgQGBtKxY0fRpSqROXNmevfuzcmTJ/H39xfr83nz5qUoVEwLQ0NDRowYQefOnTE2NubRo0eMHj1aoxzmjyCXy7GyshJr1PSSkHfu3CE6OppMmTKJcVwTkyZNwtnZmfj4eMaNG4enpydKpRJbW1uOHz8uutM9PT2pWbMmM2bMYOvWrZw/f563b9/+VmtfHTp0/L1kOJEQFRUlWivNzc1FW1bp0qXp3r07hw4d+rVnqOMfR3R0tDCFkxIJ169fB/ip4HibNm2AxCBW8okBJLZhd+3alZkzZ5IvXz6io6O5cuUKy5cvF+auP0LSroTbt2+LdvIBAwagr6/PjRs3hPlvcqQKJalrIa3BXSaT4eHhwZIlS8Qi+88keRLhyJEjYmHw/v17GjRowKtXr36rJAIktuxLgTcpYVW2bFly585NZGRkuhWOjRo1EkGBtNq6IbFV9tChQwwYMIBcuXIRExPDqVOn8PT0xM3NjZUrV3Lz5k2+ffv2ty0Ob9++zapVq4iIiMDR0THD353Q0FBhppe02lMyEUtLqs7KyopVq1bRq1cvADZt2kTr1q3TrNKOj48XgSNdIuHvQaFQiOTbtm3bMvTa5EmEBQsW8O7dO1auXImbmxvFihWjU6dOeHh4cP36dRISEsidOzfW1tbcunWLOnXqCK+WHyUkJISZM2cyf/58QkJCcHJyYt68eRQqVEgkvZRKJf7+/hw/flzr+3++fPmYOXMmefPmJTIyUnRcasLc3Bw9PT3i4+PTDJjEx8dz8uRJRo4cyaFDhwgPD6dAgQIsW7aMdevWaeVxkDlzZtasWUO+fPn49u0bffv2pUyZMri7uyOTyVi2bBk9e/b8W3xrdPxzkRIJksEyJH6vJVmyjM6bXrx4wenTp7UaC4cOHUrZsmUJDw9n0KBBGnX879+/j0qlIleuXNSqVUvrDiOJ3LlzY2RkRGBgYIoK8owiyTzdu3cvVT8UqTuhW7duyOVy9u3bR+XKlTlx4oTG7c+cPUuxYsUYMWKEkJKytrbm8ePHorO1X79+hIaGYmhoSL169YDEe7DkX2RgYKAmDyetz6Kjo0X3r64r4deRNWtWihYtikql4syZMymeHzduHLly5eLDhw+sWLECSOyanjZtGpBoXlymTBlq1qxJ06ZNcXNzY9KkSYwbN445c+awYsUKtm3bxrlz5/705LC23XRVqlShVq1axMbGMnToULH+kubRyQOnUiJfl0jQ8XdgamrKnj17MDMzw9fXl/z587NixQqN15OTkxNTp07FxcWFuLg4BgwYkOEq/NKlSzNnzhxy5sxJaGgos2bNYteuXb/s+pUSCfHx8Wme24ULF4DEboS0CjaNjY1ZsGCB6Gxds2YNo0aNIjo6GoVCwezZs9mzZw8WFhZcv36dadOm0aNHD+rUqUPevHmxsbEhf/781KtXj549ezJ33rxf8j516NDxz+OnxMty5MjBmzdvRAVwoUKFNBrW6vhvcePGDSIiInBwcKBkyZKoVCox0SxbtuwP77ds2bI4OTkRGRnJ/PnzhaFmcnLmzMnkyZPp06cPRkZGwmjz0KFDGher2iBV2ERERIikSLZs2ejSpQsAy5YtEzIfSWnQoAHr1q0Twa30AqfW1tZUr179T9cVTEhIoEuXLhqTCAAzZszg1atX5MqV67dKIkhIUkZSdWDS1n9Ni7ukyOVyNm/ezPLly0VyKi0cHBzo2rUru3fvZuvWrbi6umJhYUFISAiHDh1iwoQJdOzYka5du3L//v2ffGcZIy4ujj179hAfH4+5uTl9+vTBUku9e4lbt24RFxeHqampmsGSdF+XZBRSQ09Pj+HDh7Nw4UKMjIzw9fVl1qxZqW4fGBhIfHw8enp6PyRzpuPnkfT34X/JOG1fN3bsWBGwvnv3Lq1bt6Z69erMnj2bc+fOER0dja2tLW3atGHZsmXcu3ePW7ducebMGfLnz09AQMBPJ0q3b9/OtWvXUCgUuLq6snDhQrVgKCT6DDx8+JDg4GCuXLmidXW1tbU1EydOFBJfnz9/1ihpJpfLhbdEUkP0pDx8+JCJEyeyfft2wsLCyJw5M7Nnz2bHjh1UqVIlQ915NjY2rFq1ijx58hASEoKHhwetW7dm06ZNyOVyNm3axKpVq7Tenw4dUoHD48eP1YozJMPUlStXpjtn+vr1K+vXr8fFxYWKFSvSvn17rYL2CoUCT09PrKysuHfvHv3791erdIyOjhaFSvr6+j9UBWloaEizZs0A8Pf35927dxneh4SNjQ05cuRApVIxf/78VJN2kvTlyZMnKVCgAEFBQXTq1EljUqZH9+48f/4ce3t71qxZw40bN1i4cCGQWCBQuHBhgoKCmDFjBuHh4bi4uCCTyfD392fx4sVAStlByRD3wYMHonDl3LlzP/y+daRE6o7ev39/iueMjY1FZ/7u3btFcK9y5cr069cPuVxOXFwcX79+5dWrV9y5c4fr169z7Ngxdu3axZo1a/Dw8GDw4MFcuXLlT30fz549S7ODV0ImkzFnzhxsbW159uwZe/cmesAVL14cuVxOQECA2rUl+XNkpLpbh45fScGCBdm9ezfZs2fnw4cPDB48GHd3d43bymQyFi1ahKWlJf7+/vTu3VsUDGpLlixZmDFjBs7OzkLq6NSpU7/irSCTyYT3gqYiSoDLly+Le01qcr1JkcvlDBkyhBkzZqBQKDhx4gQDBw4UXbjNmzcXSYTu3buLJII0Fr969QpfX182b97M7DTWezp06Ph3k+FoZfbs2WnevDmQWLnk7OxMr169OHXqFAsXLvwhSQEd/y6khaednR0ymYyvX7+Kwc/JyemH9yuTyZg0aRI2NjbCqHLBggUagzhyuZwaNWrg6uoqzHevX7/OsmXLhKyKtiQkJHD16lUgsetB0sqFRBPojh07AolZ/dWrV0OyoGvjxo3ZuXMnbm5uWhly/hWsXbsWHx8fjIyMOHToUIqqWEnbdMyYMb9dEgH+d35JDeqkqvqCBQum+/oXL15w5swZNm7cyPPnz7WqoJTJZBQsWFAEzadNm0adOnVwdHRELpcTFBTExIkTf7raOiPo6+uLLp+wsDBhOp0RihYtipmZGREREaxevVp8FoUKFcLIyIiAgACtEsQNGjRgypQpwP/+Fpqws7NDoVCQkJCQrlmmjl9PbGwsPXv2FKZsaenzJ0fycpECjc+fP+fmzZvIZDKqV6/O5MmT8fHx4eLFi6xduxY3Nzdx/8iVK5cwNH706NFPVc9LZqfSfV6TAbUUUIPEiq6MSKLo6+vTq1cv8RpNXQlJNcqTm1WHhoayYsUK5s6dy4cPHzA3N6dLly5MmTIFFxeXH04UBwcHi3tfxYoVgUTzPEnbd86cOakuNHXoSE7Xrl0xMDDg1KlTNGnSRCTb+vTpg4GBAT4+PnTp0iWFIWRMTAzHjh1j0KBBFC1alLFjx3Lz5k3xvLZmuVmzZmXp0qUYGRlx5swZzp8/LwI4hoaG4t7x9OlTDh48yL179zIc4MmdOzfFihUD+OlEf/PmzVEoFBw7dozx48eneQ8rXbo0vr6+VK5cmYSEBI2FJnny5AHA1dWVHj16oKenR65cuYDEBMG2bduwtrbmyZMnTJgwARMTE2GA+eDBA2QyGe3atUtxjgCLFi0SSZR58+YRFBT0U+9dx/9wc3NDJpNx6NAhjd+pOnXq0LNnT1QqFWPGjBFB9v79+3P58mVOnjzJvn372LBhA4sXL2bo0KEMHz6cnj170rZtWzHu/NkdZjY2NhgbG2u1ra2trfBW8vf3BxLXQ9I4JElwAaKoZ9euXXz+/PnXnbAOHRmgfv36PH78mLlz5wKwYMECdu/erXFbBwcHVq1ahb6+Pt7e3nTo0CHDcykDAwN69uyJq6srAAcPHvxpGSDJh0u6F2gyf3748KGQTGvQoEGGOr1btGjBmjVrMDY25vLly7Rs2VIkE3LlysXYsWNZtWoVx48f5+HDh4SGhvLs2TN8fX3ZtGkTU6dOFZ4oOnTo+O+R4dVstmzZGDt2rPi/p6cnz58/p169ejx58oRJkyb90hPU8c9Dqr6XBj5Jx97a2jrDrenJKV++PIcPH6ZWrVrI5XJu3rzJ6NGj2bt3r8bKOUNDQ5o0aUK3bt2wsrLi+/fvbNq0iWXLluHr68vXr1/TPJ5KpeLatWt8+/YNfX19GjZsqFYBJpPJ6NmzpzBK3rVrF55LlqTYT61atVi8eHGK6rG/g3PnzrFu3ToAli5dSunSpVNsI01+fvbv9WchdSJIhlL379/nxo0bKBQKOnTokOZrVSoV7u7u7N+/n0mTJlGjRg0qVqzI6NGjOX/+vFaGynp6epQrV46RI0eydu1a9u7dS+nSpYmOjmbSpEl/aTKhZcuWdO3aFTMzMwIDA0U7vbbY2toyYsQI5HI5586dE/J01tbWwo9i165dWlWuSQmpx48fp1rJamRkJJI9ybV1dfx6jh8/zuHDhzl8+DCHDh3CxcWFjRs3IpfL8fT0ZF4G25IHDBiAr68vt2/fZt26dcycOZPLly+zbds2evToQYECBVKttM+ePbuoaPoZI8aaNWtSokQJYmNjWbFihcZEoIWFBdWqVcPR0ZFatWqlGdw8ffo0EydOVEtKy2QyLC0tkclkREVFpdCnDQ0NJS4uDj09PbUEeUJCAgsXLuTq1avIZDLq1KnD3LlzqV27tkavEW2Jjo7G3d2dyMhIypQpQ/fu3cVzffv2xdHRkYCAANavX//Dx9Dx38LZ2ZnDhw9jYWHB+fPnqVevHoGBgVSpUoXNmzdjbm7O1atXad++PUFBQVy7dg13d3fKly/PgAEDOHv2LPHx8RQvXpzp06eL+ULSzrb0qFq1Klu3bsXCwoKvX79y6tQpoqKikMlkVK1alWrVqmFlZUVcXBz37t37IflUqfjj8ePHPxXYKV68OPPmzUOhUHD06NF0kwmGhobiOtUkdTF+wgQAVqxYIToWkvqJlShRgjNnzmBpacnLly8ZN24cDRs2pEuXLuTOnZvJkyenMJ/u3LkzhQoV4tu3b7x7945SpUoRGhoqkvw6fp7ChQuLuVFq3ZceHh4ULVqU0NBQhg0bJoJzJiYmZMmShQIFClCuXDnq1KlD/fr16dy5MwMHDmTcuHEiMf5nF+blzp07Q0ntIkWKAIlJLAmpOOrAgQNiHK5evTply5YlOjqalStX/sIz1qEjY0g+BqNGjQKgZ8+eqSaUXVxc2LVrFyYmJpw+fRp3d3et1oPJadSoEZaWlnz+/PmnVDqkJIK0lrK2tk5RNPPu3Ttmz55NXFwc5cuXV5sXakuFChVYvXo1xsbGnD59Wi2ZkBypk7xKlSp07NiRcePGCSlEHTp0/Pf4af2UHDlycO3aNcLDw3n+/LmoFNTx30UK2EgLNkkn81cNNhYWFrRq1Yo5c+ZQpEgR4uLi8PLyYtSoUTx9+lTja3Lnzs3AgQOpUKECenp6fPr0idOnT7N48WJWrlzJ+/fvUwRKJUmmwMBA5HI5lSpVEvqzyWnXrh0jR45ELpfzRyp6uL8D7969E8m+Pn36pBp0lxbHPxP4+rMICwsTFVFSt8nmzZsBtDJaffjwIU+fPsXAwICqVatiYGDA+/fv2b59O1OnTqVu3br06tWL9evX8+DBA610Lk1MTJg0adLflkwoVqwYo0aNolixYj+ky1msWDG6desGJGrmS59v48aNsbe3Jzg4mLVr16a7nyxZsoigz6NHj1LdTtKbvnXrVobPVUfGcHV1pVmzZjRr1ozmzZvj6+uLubk5hw8fFiZzP0KmTJlwdnamU6dOQsogPfT09HB0dAT+J6vyI8hkMvr374+BgQF3794VnjzJyZ49O9WrV0/RMZCUO3fusGHDBl68eCEkGyQUCoV4bdKuBJVKJZLQNjY2avfJP/74g1evXmFiYsKUKVPo3LlzqobuGWHhwoU8e/YMGxsbZsyYoZYYMTQ0FG37c+bMEf4mOnSkR40aNfDx8cHe3p67d+9Sq1Yt3rx5Q8WKFdm1axe2trY8evSIKlWq0K5dO3bt2kVoaChZsmShR48eXLhwgdOnT9OuXTvRLSBd49pStmxZdu/ejZGREd+/f+fkyZOEhYUhk8nInj07DRo0oEqVKlhYWGS4IwES1ylmZmZER0f/lF8WQL169TKUTGjYsCEWFhYEBATg5+en9lzNGjWoW7cusbGxjB8/HoBr164B//MTK168OLNmzcLGxoa3b98yfvx4qlevzuLFizUWgSgUChHcXrFiBaNHjwZg48aNYlzX8fNI/jReXl4aA5OGhobMnz8fGxsbnjx5wtSpU7XqfI2Ojhb37z+70jcj0nqQmECBRO8UqVrb2dkZU1NT3r59K2RfZTIZw4cPBxJ94qJ0XXI6/mYk2aGoqCg6d+6cqvdV7dq1OXDgABYWFty/f5/BgwdneD5lYGAgunK8vb1/aE2mVCoJDg5WSyIk9cKBxHvF9OnTiYiIoECBAgwbNkzrbsDklClThtWrV2NqappuMiE5YVomW5RKpa5DSYeOfxm/TIj9VyyUdfw7SJ5IkCpP7ezsfulxHB0dGTduHEOHDsXW1pYvX74wY8aMFIs1CUNDQxo3bsyYMWNo3rw5efPmRS6X8+HDB16/fs0ff/zB2bNnefbsGZGRkTx9+lQsOsuVK4etrW2a59OoUSMmTZqkVjEQrkUV919FVFQUI0eOJDw8nOLFizNnzpxUt5X+dj86KfkzuXz5Mkqlkpw5c5I1a1a+f/8uzK67du2a7uulFuy6deuye/duHjx4wJYtW+jRoweOjo4kJCTg7+/PqlWr6Nq1K7Vr16Z9+/YMGjSIqVOnsmLFCk6fPs3Fixd5/PgxQUFBxMXFYWhomCKZ8KPSPd+/f+fGjRsZCpqYmZnRpUuXFFIH2uLi4kLdunVRqVQsXryYoKAgDAwMhA/Ixo0b09WYlslkwoAvNQ8T+J9Jn64j4c+nTJkyVKhQQfw0atSICxcu0KBBg7/lfKRq5Z9JJEBi0kpqId+wYYPWxpFJ+fjxI0uXLhUBnsuXL6eQypOSxxERESJ4krQbwcrKSm1/0r3I1dWVnDlzZvicNOHt7Y2XlxcymYxp06ZpHIu6d+8uuhKyZMlCy5Yt2bNnT4pOCh06kiNVvufOnZvXr1/Trl077t27R+HChdm3bx9OTk7Ex8djampK69at2bFjBxcuXGDYsGEUKFAA+N/1bGdnp7VcSlIKFixIzZo1hcyej4+PCODIZDKcnJxo2LAhlSpVyvC+5XK5CIImrab+UZInE9LqAjIyMqJt27ZA4n0qKTKZjHnz5iGTydizZw9XrlwRHQlSIgES57qzZ88mc+bMfPjwAXd39zSNbF1cXKhRowYxMTEcPnyYtm3bolKpGDFihFbBbB3pU6RIkXS7Euzt7fHw8EBPT4+jR4+yY8eOdPcrSVAZGRlhbm7+6074F2Bvb4+NjQ0JCQk8efIESCyiadiwIYAY+yCxUyFnzpx8+fKFqbpuGB1/M3p6emzbto3cuXPz5s0bevbsmWp3WoUKFThy5AhWVlY8e/aMgQMHpuqDlRrOzs6YmJgQEBAgEmzaolKpCA4OJjY2FplMpjGJoFQquXHjBl++fCFbtmyMGzcuxTYZpUyZMhw5ciTDyYTwsLB0t7l69SqlSpXC3t4+w13QOnTo+H35/cqNdaih7aRfpVJp1aKakJCgVRXKz+xPel1cXBwJCQlC2sjY2JjXr1+nub/g4GBMTEzSPW50dLQI6hQvXpxp06axfv16bt68yerVq3n16hVt2rQhIiJC4wTA0dERR0dHoqKiePHiBTdv3iQsLIzg4GCCg4PVqskLFCiAhYUFYWFhmJmZiQm0JhwcHOjVqxecSqyQbdG8ORNmzEi1IjY4OFgrGYDIyEitBvSoqCiN26lUKqZNm8aLFy+wsbFh4sSJxMTEpCo/Iz0eHx//ty08VSqVxmNLxoHVqlXj9u3beHt7Ex0dTc6cOdHX1081OG1qaopSqRSJhCZNmhAbG4tCoaBatWpUq1aNNm3aEBUVxZUrV7h69SrXr18nIiKCFy9epOutkTVrVoYOHcrEiROZPn06t27dEsdKr0IzJiaGr1+/olQquXDhAocPHyY6OhpLS0vq1atH5cqV0dfXJywsLF2t42zZssFt/zS3ScrWrVvF70ZGRkICzN3dnWrVqiGXy3FycuLNmzdMmTJFVE5qQvKR8PPz48aNG7Ro0ULjdpKG561bt9Ks5lQqlVp1xSiVSq3uV9re11Qqldb3SW3Qdn9/Btu3b9cYkEgqLfT9+3etZNfi4uK0es9RUVGEpbK4kHxNnj17BqQunyaPjSXP//8eGhbGly9fUmxTtWpVzp49y9u3b1m+fDnGxsbExcWle362trZ8+PCBBQsWEBkZSc6cOTEwMODp06fs379fJOPi4uKQyWQYGxsTFRVFUFAQ1tbWoqrK1NSUyMhI4uPjCQsLY+3atcTFxVGwYEFKly6dosstKipKqwVpeHg48fHxxMfHs2rVKrZv3w5Aly5dKFOmjFgAx8bGqiVQVq9ezYgRI3j8+DHe3t54e3tjZmZGgwYN6NixI7Vr19boJyGhUqm0Woxqex1Bxqte/8lkZKzU5nPRtpIxISFBq6R/QkJCqn+33Llzc+bMGZo3b46/vz+urq6sXLmSypUr4+XlxZ07dyhbtqxIEkgeIdL35fHjxwAiuZ+Uz58/a5UUNzAwoFatWpw7d46QkBBOnTpFlSpV1IpQMtrtIFXhS9/7p0+fcv36dbXrwNramufPn2dovxYWFri5ubFp0yZWrFiBra1t4thLYnA16ZyvVatWrFu3juPHj3Pv7l2knskvX79SsGBBOnXqxJYtW+jWrZsoXilWrJjaPMzBwYFZs2YxceJEAgMDGTt2LCNHjhRyM5DYrSkldIYMGYKfnx87d+5kzZo1HDp0iIsXL7Jq1SoaNWqUIR3tfzraXpfaziPi4+ORy+WMHj2aAwcOiOsjqX8aJK5TihUrxuDBg1m0aBHz588nV65cKTpJPn/+LMYtqQAjU6ZMKQo3vnz5kq75eUbRpto6Pj5efK9y587Nt2/fOH/+PNbW1kDiOLx3716OHDlCUFCQKCycMWMGbm5urFixgh75ExOOEZGRyJRKrTyLMiJD9rvKsOr4fbCxsWH//v1UqVKFs2fPMnXq1FQl3woXLszcuXOZMGECb9++pX///sybN0/j+BMXF5fiutTT06NevXocPHiQAwcOiOIpbYiOjkapVCKTyTA3NxcG7RIqlYqwsDDi4uKwsLBgyJAhKJVK4XGUnO/fv6fZIS6hVCopUqQIK1eupE+fPpw+fRoXFxeWLVum5s0gl8uFlw8kzs+xTiy4SUhQovr/Nd3Xr1+JjIxkzpw5bNiwQdyHx4wZQ2BgIGPGjBHzIG0UK6TPJC10iXIdOv5a/rOJhISEBJYtW0b//v3TXFj/3fzqRbhMJtN6n9psp1AoUmwnLSwTEhJQKBQi8JktWzYx8UwNMzMz8ubNm+5xbW1tUwTnW7Zsyfz581mwYAEnT54kLi6OLl26aNUJ8fLlS6ysrLh48SLnz58XVWstW7akR48eYrtnz56pGfxqImumTCKR8OTJE0aOHMns2bPVzD8lbGxstDIHjoyMFBNvlUrFx48f+fTpE/ny5VPrBrK0tNTYHbRy5UpOnTqFQqFg69atFC9ePM2AkRTAMDY2/u26Ei5cuAAkLl5kMhnHjh0DEhMDqQWdFQoFmTNn5sKFC3z48AELCwvatm2bwrjK1tYWY2NjmjRpAiROEJ88eUJgYCCBgYF8+PCBwMBAAgICxGOBgYHExcXx4cMHpk6dyuHDh/Hz86Nly5b4+Phw5MgRpk2bJgwfNREcHIxcLmfGjBmiTd7Q0JCQkBD27duHn58f3bt3p2rVqumaX8tiYuDIUQAGDhzIsnXriIqKwsjIiNGjR1O9enWxba9evTRWYIeFhRESEsK5c+dwcHAgf/78vH//nkuXLvHixQvKli2r8dgJCQmUKVMGSDTUTa1TrUyZMshkMj58+EBQUFCqclQZuVf9ynvl776/X42pqWm692YAKysrraqNTU1N0wxYQmL1ftu2bVNPHCeRQyhfrhxZkixckjJv3jw6dOjA1atX6d69uzB/TAtJDi8wMBBbW1tWr17N27dv6dWrF9euXWPy5MnY2tpSt25dzMzM+Pz5M3v37iU6OpqsWbPy4cMHjIyMaNeuHfr6+tjZ2RESEsLz588xMTFh06ZNGq/Td+/eaZWwcXJywsLCgk6dOnHmzBkARowYwbRp09Tux8kXjpUqVeLSpUs8fPiQ/fv3s2/fPt69e8fevXvZu3cvtra2tGzZkrZt21KpUiWNf6Pf+Xv6X0Pbv4W+vr5WAVATE5M095k9e3bOnj1LixYt8PX1pWfPnmzcuJE2bdqkCJKC+vxASvTlzJkzReIyc+bMWn3vZ8yYQaZMmQgNDaVbt25cuXKFK1euMGrUKHr06CHm6rVq1cLU1JT4+HguXLjAwYMHhVRFtmzZaNeuHcWLF2fz5s3ifiVVeIeFhfH161e1QIi+vr5WsqyPHj1SWy/UrFkTf39//P392bhxIxMnTkShUKBSqdTua8WLF6d69eqcO3eO/QcO0P//HzcyNEImkzFp0iT27dsnpDnz5s2rdj9u0KCBmKs0aNCAevXq8fDhQ8aNG0efPn2YM2cOlpaWasUOxYoVo2nTphw6dIjVq1fTq1cvli5dyrx586hTp0667xW0+/79nfcLbY+tbdJTLpdrtU8jo8S/W+nSpWndujV79+5l/vz57Nq1S227QoUKoaenx5QpU3j37h379u1j4sSJ+Pn5iaQTJAYOpfm4JPeYNWvWFNJGxYoV00pGcNGiRVol3IKDg9Ocl0p8+vRJfB+LFy/OjRs3ePv2rXisevXqODo68v79e44cOUL79u0BaNasGePGjWPR7NliX9JcSDfO6PiVsQjQroO+ZMmSrF+/HldXV5YtW0bFihVT7eJu06YNVapUoXnz5jx//pzRo0fj5eWVYiwsWLCgxrVOpUqV8PHx4dWrV5iampIjRw7xXt69e4dSqcTBwUEtZvLp0yfi4uIwNzdn7NixKbpalUolq1at4uLFixgZGbFlyxa1ZLImbt++rdX4K80nJZmjPn36cPnyZUaPHs2iRYvE56tSqcRaOy4ujpgkc3WFQg/5/z/3xx9/MH78eFG01Lp1a5ycnFiwYAGLFy8mPDycadOmZagoJb3vgu6+okPHX8svkzb6pyFJ4rRu3VqrCkYd2iMNMMmljdKTBvpZ5HI5Y8aMYd26dRgbG3P27FnGjRtHQECAVq+3sbGhSZMmzJs3j02bNjF37twfMi5Kvs/Xr18zbNgw0ZmREeLi4kRQaMKECTRr1ow8efJQsGBBatSogaOjI+XLl6dXr14sW7aMS5cupQguXbx4kYkTJwIwffp0KleunO5xpb/d7+aREBsby9WrV4HERMLt27f58OEDJiYm1KpVK93X79mzB0hc4CRPImhCX1+fokWLUq9ePTp37szYsWPx9PRk9+7dXLx4kZcvXxIREUFAQAD16tUjIiKCJk2acP36dQ4cOKCVZ0J0dDTbt2+nY8eO3L9/HzMzM9zd3fH19WXcuHE4ODjw+fNn5s6dy/Dhw/H29tZa8sjFxYU1a9ZQvHhxoqOjmTZtGtu3b0+zakNfX18ky0JDQwkJCcHS0lJ0FyxevDjNSjHJ/+DBgwepdhuYmZmRP39+QCdv9E8lKiqKlStX0qtXr1S9aTQhdWClJ5OlLUWKFMHNzQ1IlFaI1kKP+eTJkyKxOn/+fDJnzkyZMmUoVqwYsbGxKSQo7OzsyJ49OyqVSkiPlCpVSgQVv3//LuQt3N3d0032pcfjx4+pUqUKZ86cwcTEhK1btzJz5kytk7qFCxdm4sSJ+Pv7c+LECbp3746dnR1fvnxhzZo11K1blyJFivyUEaCOfycWFhYcPHiQli1bEhsbi5ubm1aGqdL1/LPffekctm3bhrOzs9CBrlevnigikFAoFNSsWZN58+bh6uqKqakpAQEBLFy4kHnz5ql1RclkMhGUefny5S+pXJTJZHTr1g1TU1Nev34tiho0Ic0lk/uwQGLyY+jQoeL/0hiqCTs7O5YsWYKLiwuQ2IVUpEgRDh48mGLbkSNHYmRkxI0bN8iePTtZs2YlMDCQdevWafsWdaTDuHHjkMlk7N+/P1U5R5lMxtKlSylSpAhBQUF06NAhVck5qfDqdzUwlaTMpA4kSHx/TZs2BRDdcxJjx46lWfPm4v9v0ulK16Hjz6Z169bCfLlXr17cuXMn1W2zZ8/OiRMnKFasGEFBQTg7O+Pi4kK3bt0YP348y5cv5/Dhw1y9epW3b9+qrc0yZcokpGEXLlyotl+pg0aKP8XHx/Px40chkzthwgSN0pjS2lNPT4/Bgwenm0T4UcqUKcPKlSsxMDDg9OnTzJw5U+OYqanzOCAggDZt2tC9e3cCAwNxcnJiz549LFu2jFGjRom58rp16xgxYkSaXek6dOj4vfnPJhLi4uJo27YtPj4+umTCL+bvSiRING3alMOHD4vK0WHDhmXY1NXOzo6iRYv+dHZ7zpw5ZM2alY8fPzJ8+PB0dcFDQ0NZs2YNgwYNombNmmTPnp0qVaowePBgli5diq+vL1+/fkUul2NjY4NSqeTJkyfs2bOH8ePH07ZtW3LmzEmpUqXo2rUrHh4edO/enYSEBFq3bk2fPn20Ou/fNZFw8+ZNoqOjsbW1pWDBghw+fBhI1Cw2NjZO00wxNjYWLy8vAKFZnBbh4eEcPnyYWbNmMXfuXBYuXMjSpUtZtWoVGzZsYMuWLezcuRMvLy+uXr3K+PHjUyQTJk6cmGYy4dq1a/Tp04ejR4+SkJCAs7Mz+/fvF90Sbdq04eDBgyKhEBwczLJly+jUqZPWCYUsWbIwf/58oee7ceNG5syZk+ZrTU1NxfUaFBTEt2/f6NmzJxYWFrx8+VJj0EIiT548mJiYEBkZmaZcRKlSpQBdIuGfxrdv35g5cya5cuVi4MCBbNiwgdKlS7Ny5UqtgnO/yiMhKQMGDBCSKml9NwHu37/PoUOHgMQgR8mSJYH/BQUhMdiXfIGUVI7CyMhIVKWpVCqOHDlCREQE5cqV08qnJS2OHTtGhw4deP36NTlz5sTPz482bdr80L5kMhnly5dn9uzZvHz5ksOHD+Pm5oa5uTmvX7+mUaNG7Nu376fOV8e/D0NDQ7Zv307fvn1RqVQMGTKEKVOmpHl9/8pEAiR2O6xfv56FCxeSKVMmnj17Rrt27ejfv38K6SR9fX1cXFyYP38+DRo0QKFQ8PDhQ27cuMH169dF0NbJyQm5XE5ISEiqZpsZxcrKio4dOwKJXiap+SLVrl0bJycnQlORnxg2bJj4vUqVKuL3qKgozp07x/Tp06lfvz7W1tbUrVuXEydOkCdPHrJnz86HDx9o0aIFgwYNUpM+zJIli5jzLVq0SBjgrlmz5pfef//LFC1alNatWwOpeyVA4pxqx44d2NjYcPv2bfr166dRukzqEE2tS/PvRuqgfv78uVpBiWQu6+fnp1YkIJPJ8Fy8WPy/U6fOqUqwpEd4eDh37tzRyZfo+GmmT58uzJfbtGnD169fU93Wzs6OI0eOUKlSJSIiIrh8+TIHDhxg2bJljBs3jn79+uHi4kKJEiVwcHCgQIECNGrUiHfv3jFgwAD09fW5ePGimvyRVIQSGxtLXFwcHz9+JD4+Hj09PSpWrKixo+jkyZMcOXIESEyAaNNN9DOUK1eOuXPnIpPJ2L17N6tXr06xTfJ58tp16yhatCheXl7o6ekxaNAgzp49q9YJ3717dzw9PZHL5ezcuZN+/fplyA9Qhw4dvw//2URCwYIFyZw5M0eOHFFLJty9e1drp/qYmBhCQ0PVfnT8L/gsZZklU7gfSSR8+/aNU6dOZXjiWLx4cU6ePEn+/PmJiIhg8uTJInj0V+Lg4MDChQuFrujYsWNT1ciOj4+nefPmjBkzhm3btnHnzh1iYmIwNzcXXQdLlizhzJkzfPjwgVevXvHkyRP27t3LhAkTaNy4sVjEv379moMHDzJz5kyCgoIoXLgwixcv1joxIv3t/gpZo4xcR+fPnwcSuxHevHnDtWvXAGjcuDGQuFgeMGCAxsrAU6dO8e3bN+zt7alRo4bG/YeEhIjWfzs7O1q0aMGkSZMYP348o0ePZtiwYQwcOJB+/frRvXt3OnXqRLt27WjZsiU1a9Ykf/78asmEZ8+epTBgvn//PiEhIcycOZPJkycTFBSEra0tS5YsYe7cuSkq0QwMDERCoVu3btjZ2fH161eRUNi1axcnTpwQPz4+PuK1Pj4+4rGSJUsyePBg5HI5p0+fZuTIkWlWgtjY2GBmZoZKpeLy5cvEx8cn+n8Aa9euTfXvpKenJ4KsaRku6xIJv5Y/ezz68uULo0ePJmfOnEyaNInPnz/j5ORE1apViYqKYuDAgTRs2DBNE1D4X6Dx/fv3v6wSycTERHRdnTlzRvgvJOf9+/esW7cOlUpFq1ataNWqldrzNWrUIHfu3ISHh6eoHE4qNZG0G+HJkye8ePECQ0NDPDw8tG7TTo5KpcLDw4MRI0YQFRVF3bp1uXTp0i9bLCoUCurWrcvatWt59eoVTZo0ISYmhk6dOrFo0aJfcox/A7p5XSJ6enp4enoyadIkIDFI2qpVq1RNzX91IgESu0zbtWvHuXPn6NatG3K5nIMHDzJ16lSOHTuWIhBrampK+/btmTNnjjBlfvfuHSdPnuTevXsolUpxful5HmWEypUrU6pUKRISEli7dq3Gjj09Pb00k4xmZmZ8+/aNqKgoKleuzMSJE6lVqxb29va4uLgwefJkfHx8iIiIwMrKCisrK168eMH79+8pV64cenp6nDhxAhcXF7V7V8+ePUUnwuvXrylXrhzR0dGMHj36l71/TfyXrqOkXQlpVTfnypWL7du3o6+vj5eXF7OTSP5I/O4dCdmzZ8fU1JSYmBg1z7ts2bJRtmxZVCpVCoknoyRyiE+fPqF79+4ZChzGxMSwe/duihUrRvny5Rk9evR/JpnwX7qO/kr09PTYunUruXPn5tWrV7i5uaXZaW1lZcXRo0fx8fFh48aNzJo1i4EDB9KyZUvKly9Pjhw5MDAwQKVSERQUxKVLl5g7dy7ZsmXD1dUVUJeilOaPko+jJAXt4OCgUSbp+vXrbNmyBUgshKtWrdqv/DhSxdnZGXd3dwCWLl2qZqgOpPg+jhwxgrCwMMqVKyekjTTJl7Zr1441a9agr6/PoUOHaNWqlVbdxDp06Pi9+M8mEgoUKMCDBw+oXbu2SCa4uLjg7OysdWBr9uzZWFpaip9fuYD6JyMNkFFRUSiVSmHmlRHjLEj0I6hduzbt27cXleQZIXPmzEyZMoU6deqgVCpZvXq1msHoX4WNjQ1z587F1taWL1++pGj9lbh48SK3b9/G3Nyc0aNHs2XLFm7fvs3r16/x8vLCw8NDmG1Kur8ODg44OzszatQotm/fzuXLl3n58iXe3t5MnTqVli1bUqdOHbZt25aqXn1ykhr6amO8+bNk5DqS/AMKFCiAl5cXKpWKbNmykS1bNlG5B4kTnqQEBweLQGPr1q1TTZCMHz+ecePG4efnR1xcHLly5cLNzY2uXbvSsWNH2rRpQ/PmzWnYsCHOzs7UqlWLKlWqCCmg5cuXM3XqVCpUqEBERAQrV67E0NBQLZng6enJxo0buXDhAnK5nNatWzNv3rx0J4YGBgbUrVuXzZs3M2TIEJFQWLduHR4eHuLH09NTvMbT01M8PmnSJM6dO8eECRMwMzPj4cOHBAQEpJo4lclkwl8kKioKb29vKlSogJ6eHqGhoWoJi+RIPiepSd4olUrRrZCWebkO7fkzx6P4+HhatGjBihUriIiIoGTJkmzbto2nT59y9uxZPD09MTIy4uTJkzRt2jTNDj8HBwcMDAzUDBx/BVWrVqVkyZIiIL9kyRLu3btHdHQ0ly9fxsPDg2nTphEZGUmuXLk0BtLkcrkI9iVPJMhkMurXr0+9evVEFwP8T9Pazc1NK3+f1Lhw4QLr168HoEePHhw8eBAbG5sf3l9amJqasnPnTvr3T1RrHzduHDt37vxTjvVPQzev+x8ymYwJEyYIiYMjR44wZswYjdtKCcT0PKR+BCsrK2bMmMHx48cpU6aMCCxKRsrJsbOzo2/fvpQtWxY7OzuUSiXPnj3j+PHjwij9V84FZTIZXbp0QS6X8/r161QT6O3atcM4NU8YErswYmNjqVevHh4eHly5coW4uDiyZMlCu3btWLZsGXfu3OHLly88fvyYDh06oFKpuH79Oo0bN6ZYsWKEhYUxbtw4vL29gcTuqX79+gGwZcsWhgwZAiRKZKSX9P0Z/kvXUdKuhJo1azJ9+nSNkh+Q2G0iJW7nzp3LiBEj1MZLab10/vx5/vjjDx4/fpyqDNLfgVwuF/JGN2/eVHtO8haTvnuaMDI2xsfHh2LFinH48OFUEwIxMTEcOXKEbt264ejoSOfOnYU87JIlSzhx4sQveDe/P/+l6+ivxsbGhn379mFqasqpU6eYMGFCmtvr6elRvnx5WrZsyYABA5g5cyYbN27kwIED3Llzh8DAQJ49e8bWrVsB2LlzJ9u2bWPIkCHI5XKio6NFMkGSNlIqlSQkJKCvr4+9vX2qKgBbtmxBpVJRsmRJISP2V9GxY0ch0TRz5ky1AqDk9zlTMzM8PT25cOEChQsXTnO/jRs3ZvPmzRgZGXH06FE2bNjw609ehw4dfyr/2URCwYIFRVCydu3azJo1izNnzpAvXz7KlSun1T7c3d0JCQkRP79K8/mfjqOjIyYmJsTGxvL48WOh5bpq1Sqtq0hu3rxJo0aNRJv40aNHf+hc9PX1halepkyZNBoe/ygRERG8evWKW7duceXKFc6fP8+ZM2c4deqU2Gb37t1s2rSJbdu2idZJyYw2OVLQSKlUMnLkSJo0aULOnDkzXOFqbW1NjRo1GDx4MOvXr2ffvn1qxoLpMXv2bEJCQsiVK5dGg8VfTUauI8nfYdu2bVSvXh2FQkFAQABnzpzByMiIEiVKAKiZZwUHBzNq1CgeP35M1qxZGTx4cKr7b9eunTDxNjY2ZsqUKWzatIl169axefNmli1bxuDBg3FxcSFv3rwolUqePn0qghJ58uTByMiIR48eAQhTYkNDQ/F3z5w5s6jkGjZsGD169NDKr0HCwMCAJk2aiIRCpUqVKF++vPgpk8QIuUzZsuJxIyMj/P39WbFiBUOGDCFHjhwkJCTw9u1bgoODU1ybMTEx4vqTfBP69OlDQkICtra2aZraXr9+HUCjkXhMTAx9+vRh7dq1APTu3Vvr964jdf7M8WjRokXcunULKysrjh8/zo0bN3B1dUWhUCCXyxk4cCDXr1/H2tqamzdvsmzZslT3paenJ5Jmf/zxxy87R4DmzZtTokQJVCoV9+/fZ+nSpQwZMoSNGzfy9OlTZDIZxYoVo3fv3mIhl5w6deogk8n4+PFjiiSbqakp+fLlU+vsknRs9+/f/0M+OBJSUqVOnTqMHDlSLdn5/ft3fHx8tO6W1AY9PT0WLFggEipDhgxRqzD9r6Kb16WkR48eQgLrwIEDGquJJTPHH5Ut0YaiRYvi7e2Nk5MTQLpVzebm5lStWpXKlStjbW2NSqUiMjISPT09EQz9VUgdD0ZGRqnOtywtLWmdrAsqOd+/fyc8PByZTMaaNWt48OABz58/F4m/YsWKIZfLyZw5M9u2bWP//v3IZDIOHjxI37596dGjBwDTpk0jMDAQlUol7rMVKlQQBQCFCxcWhQJ/Bv+162jevHmUKVNGGIjmz5+fVatWqcmZSHTu3Fl0+qxdu5bBgweL4p02bdqgp6cnvM06d+5MzZo1ady4MXPmzMHT0xMvLy9u3LjBp0+fNMoj/Qza7E/yI9u/f7/avFGa76U1Dq5fv57MmTPz7NkzmjVrRv369cVaPCYmhsOHD9O5c2fs7e1p1aoVO3bsIDQ0lKxZszJhwgThNbJx48Yffo//JP5r19FfTbFixYRnzIIFC9i9e/cP70sul2Nra0vjxo3p3r07SqWSQYMGceLECZGA//79O8HBwejr64uCQGNj4zSTCADly5cHEseZ1Pz2/kykZGbRokXV5qbJx/tVq1YxYMAArdUMateuTZ48eYDfV85Nhw4dqfN7CaD/CSiVSi5evJii2tfe3p74+HiCgoJ49eoVc+bMYfr06cyaNYvWrVuzb98+UVmfGoaGhn9JxfY/DYVCQZkyZTh//jzXrl1j1qxZHDhwAH9/f3x8fHB2dk7z9adPn6Zbt25ERkbi5OTEmzdv8PX1JT4+PsOa/REREcJgt2PHjqkGjzQRGhrKp0+fCAoKIigoiE+fPvHq1SsiIiIICgoiIiJC4+uMZTL65k9cpG7fvp2oJBPt+vXrpyqrU6RIEdG1cP369TRNkcPCwrh69SqGhoaYmZlhbm4uOg6MjY1/WF7j7t27Qgdx/vz5f8n3OyPXUbdu3Zg1axbv37/nypUrdOzYkc2bN7Ny5UqKFy/OjBkzCAgIEDrswcHBjB07lrdv35I1a1aOHz+uUXtSolq1aty6dYvOnTtz6dIlunTpwvbt24mPj+fhw4dpVvAVL16c1atX06VLF0JDQ6lSpQqdOnUSz0vB9bJlywqZrZ+p3pQSClIVmIQsJgaGjwBg6pQpqP7/s33z5g1Tp07l7du3wkh827ZtREZGEhQURFRUFA4ODsjlciIiIvjw4QNKpRJ9fX0KFSrE3LlziY2NJV++fMKgVhNPnjzh2bNnGBgYUK9ePbXnQkJC6NSpExcuXEChULB+/XqhL63j5/izxqMHDx4wc+ZMIPGekNr9W5JP69KlC/PmzaNBgwapViS5urpy+vRpjh07xtChQ3/4fpUcExMTBgwYQFBQEL6+vly8eJGoqChsbW2pUqUKlSpVwsbGJs3uOBMTE5ycnHj9+jXfvn1TC7a9ePGCe/fuUadOHczNzYHE4NynT58IDAxkwIAB7N2794e8ZcLDw4HE6uvkDBw4kH379mFnZ8eAAQPo06cP1tbWGT6GJiZOnMj58+e5fPky3bp14/z587+dN85fiW5epxlnZ2eyZMlCYGAgZ86cEQUiEtmzZ+f169e8e/cuzSRzfHw8S5cu5ezZs4wbNy7NbTWR9F6hTQJeJpPh4OCAg4MD379/JywsDHt7+wzNBdMjPDxczDObN2+e5rXZpWtXuJooyfjs2VPsnXKoPS9JRZibm4v5Q1oFOC1atGD48OEsWLCASZMmcfDgQW7cuMGdO3cYO3YsnTt35sKFC+jr69O+fXuRuPf09PxTpSv/a9eRo6Mjly9fZv/+/UyaNIlnz57h7u7OypUrGTduHO3atVP7vEeOHEmRIkXo3bs39+/fp3PnzsyaNYsGDRrg5OTEgQMHePPmDW/evOH79+9iHfLgwQO145qbm9O9e3eaNGnyU55uQUFBzJ07l7t377JgwYI0Db+bNm3KsmXLePr0Kf7+/kKmUhq7vn37hlKp1Diu16lTh9u3b7NkyRIWLVrEqVOnKFmyJHXq1OHq1atqUilZs2alZcuWtGrViooVKyKXy3nw4AEbNmzg6NGjBAUF/bYSUL+K/9p19HfQunVrbt26xfz58+nVqxcFCxYURWk/ioeHByYmJixbtkxI41paWhISEkJoaChKpVLIPWsz/+3YsSPfv3/nypUrLF68GHd3dxGA/7N58+aNkDSSOtokkksbRfz/PFZbwsLCROGdJEeoQ4eOfw7/6o4EpVJJt27dmDx5ssaJeIECBdiwYQPNmjVj8+bNTJgwQcgc7d+//28449+X06dP07dv31SNbJNToUIFAK5evUr27NmFXISnp2eaOnh//PEHHTt2JDIyktq1a+Pr64u1tTUhISEp2mi14fDhw4SGhuLo6EjdunW1ft2GDRtwdXVl6NChzJo1i3Xr1nH48GHu378vkgkAFhYW5MyZkwIFClC0aFFKlSqlVhHu7OxM06ZNad26NQMGDGDQoEGpHlMul1O1alUALl++nOp28fHxtGnThlatWtG4cWNq1qxJmTJlKFiwIAULFiRTpkw4OjpSsGBBypYti7OzM1OmTMHX1zfNzz4hIYHhw4ejVCpp0aIFderU0frz+qswNDQUeo1z5syhWbNmFChQgIiICBYvXoyenh5OTk7IZDK1JIKtrS3Hjx/XSnokZ86cnDlzhilTpqCnp8fJkyc5c+aMSCI4OTnh4uLCiBEj2LBhA1euXBHfz+XLl3Pv3j3s7e3ZuXOnCMhFRUWJqqty5cqJKg6pivOvwMnJiWXLllG9enXi4+NZs2YN8D/vkrCwMN68ecPXr195//49SqUSY2NjzM3NuXv3LrGxsVStWpWVK1emuXiTzMBq1KghujsAAgICaNiwIRcuXMDMzIzDhw/rkgi/OfHx8fTp04e4uDgaNmxI+/bt09y+Y8eONG7cmLi4OAYOHJiqxFHDhg2xsLDg06dPwufkV5I5c2batm3LvHnzmDp1KjNmzKBRo0ZaSwVJ1crJDVnv3r3Lhw8f1MYiPT09WrdujZmZGdeuXWPhwoU/dM5Si7iZmZna4zExMcLz5fPnz0yZMoV8+fLh7u7+S+RZFAoFGzduxMLCgqtXrzJjxoyf3qeOfx9yuZzmzZsDaJwfS5IbaVXMfvz4kQ4dOrBo0SL8/f3p0KHDD0lqSVXeGQ2wWVlZkT179l+aRIDELo2wsDCyZs2aInmeHKcc/0scbNy0KcXzSRMJ2jJ9+nSKFCnC169fmTp1KvPmzcPIyIhLly4JE+du3bqxYcMGEhISqFevXobmwzq0QyaT0bp1a+7cucOKFStwcHDg7du39O3blypVqnD8+HG1tWiDBg3w9fUlT548fPv2jQEDBrB9+3YKFSrEhAkTWLt2LSdPnsTHx4d169bRq1cv2rdvT5UqVciRIwcKhYKwsDA8PT0ZPXp0qv5raSF1rLi5uXHp0iXCw8Px9PRMM3llaWlJgwYNAEQCTXocEtfeaXUmmZubM3v2bB4+fEirVq1QKpX4+PgQGhpKtmzZGDx4MOfPn+fFixcsWLCAypUri2BrkSJFKFeuHPHx8ezYsSPD71eHDk1kxHxZG2QyGdOmTWP8+PFAYsdSQkKCmIOGh4fz9etXrZN/crmcfv36Ubx4cWJiYpg3b94vlQZNi6VLl5KQkECNGjUoXbq02nPJr3NJOlBbbt++jVKpJGfOnGTNmvWnz1WHDh1/Lf/aRIKURHjz5g2HDx/WeLMuX748U6ZMYfPmzdSvXx9IbLN68OBBugGT/xJ+fn40btyYNWvWUK5cOa20KZMmEiAxwOTg4MCnT5+EYVBytm/fzpQpU4iPj6dVq1Zs27YNc3NzatasCSQmMzLCp0+fRFCzc+fOWldf+fr6ioWytbU1BQoUoHr16rRq1YrWrVszceJEli5dyq5du9i6dSuenp7MmzePmTNnMmXKFDXt7YEDBzJw4EB69+5Ns2bN0l3ASu2LUvW6JubNm8fly5cxNjYmf/78ZM2aFUtLS7WqBsnA6cWLF1y/fh1PT09atGhBrly5aNGiBStXruTOnTtqbcybN2/m5s2bmJubiwrk35Hu3bvj6OhIQEAAp06dYsSIERgYGHDr1i2OHz8OqHciZMqUifnz52dIv1yhUDBhwgQuXrzIpEmTWLduHZcuXSI4OJgXL15w8OBB5s6dS+fOnSlbtiympqasXbuWbdu2IZfL2b59u9qkyN/fn/j4eLJkyULmzJmFRMlfmUiA/5nS9u3bF7lcTmRkJKGhoTg4OKCnp0dsbCxfvnwBEhd7CoVCBFPbtWvH7NmzNRpnJUWSIWvUqJF47NGjR9SvX59Hjx5hb2/PkSNH0g246Pj7WbRoEbdv38bKyoolS5aku+iRyWSsWrUKKysr/P39U3iVSBgZGQmj44MHD/7y85YwNDQkS5YsGiu+VCoVYWFhPHv2jPPnz3Pu3DkRPJEkGpInEqRg/7Nnz9SSJDY2NsybNw9I1G+WTOEzgtSRkDyRcPHiRSIjI7G3t2fDhg0ULVqU8PBwFi1aRLly5Rg0aFCqXiTa4uTkxJIlSwCYMWMGFy9e/Kn96fh3Il2zhw4dSpEkTC+RcO7cORo0aMCVK1cwNTWlWrVqxMfHM2bMGKZOnZohDy1J0uh3qNR9/fo1Z86cARLnmRnp5jly5EiKZKB0j0mahE8PIyMjtmzZgkKhwMfHh7t37wopjaioKOzs7ChcuDAXL15EX18/VZ8LHb8GfX19evXqxc2bN5k6dSpWVlY8fPiQdu3aUb9+fR4+fCi2zZMnD6tWraJBgwYkJCTg6enJ+PHj1XwRLC0tKV68ONWrV6dXr15MmzaNjRs3cuzYMfr37y/mv7169UqRrEiLsLAwJkyYwJQpUwgLC6NgwYIYGRnx8OFDLly4kOZrJflQHx8fEXQ1MDAQndHJx05N5M6dm7179+Ln58esWbM4f/48b968YfHixVSpUiXVSm1Jr33Tpk3/GdNlHX8uGTVf1gaZTMbIkSPF3DA8PJyYmBghsRwZGcnnz5+1lidTKBQMGTKEfPnyERkZybx583jz5k2Gz+vz58/4+/trde08evRIrKs1FUImTyRI60dtkWIdaSkw6NDxZ/Po0SNu3bqV5s9flbj7p/Gv7F9PmkQ4evQopqamojUrPj6epk2bUqBAAebOnUuHDh1E0FsiI3ryfzbaTpJSayPVtD9tMuCvX79GpVIJQ7eYmBhMTU0JDg6mUaNGDB06VAQjc+TIkeL1kjb8/fv3CQkJQaVS0b9/fyZNmsTmzZtxdnYWengqlYpVq1aJ6pKePXuKLpKYmBhq1KiBl5cXPj4+jBiRKNny7t27dIPyCxYsICYmhhw5cmBtbS3a5zQRFhaGtbU1gYGBIvjVvHlzYaAm8e7dO7WAtCaNXlmSx8LCw1GloeOrVCrVBt58+fIBcO3aNT5//iz+VrGxsZiZmXHp0iXmz58PJFYJtGnTRrxWpVLx+fNnVCoV4eHh4uft27f4+fnh6+tLYGAgvr6++Pr6MmvWLDJlykT16tWpXLmyqER1d3fH3t7+l2uv/iqkroQBAwawe/duateuTadOnVi/fj1r164le/bsLF26lHfv3pEpUyZmzpyJnZ1dmt0YEpGRkWqTxwIFCqTQUg4LC+PFixdq0me3b98WLZ+DBw/Gzs6OBw8e8PDhQwwNDUUSLF++fNy5cwdIrDD5+PGjqCJLrqWr6VrVRn9aHhuLJJgUEhqKSsN1Uq9ePbJmzcrUqVOJjY0lKCgIGxsbwsPDiY6OxsrKiqioKHFOJUqUoHHjxulqqIeGhuLv749cLqd+/fpCWq5z586EhISQN29e9uzZQ/bs2bW6t2XkvqYNGVl0/oxMwO+Em5ubWnDL0NCQ/PnzU6RIEYoUKUK+fPlQKpUpFk0PHz4UCcW5c+diZ2dHfHx8up+hg4MDM2bMYODAgcydO5d69epRqFChFNu1bduWjRs34uPjQ58+fVKYwctiYpAUU4ODgzHSIPmTnNjYWLXzi42N5dWrV3z79k38BAcH8/XrV0JDQ1OYWE6ePJmGDRuKlvGkUhLSfRUgLi4OPz8/IU2WN29esmfPTvXq1Tl37hx9+vRh0qRJKRKFKpWK3Llzazx3aRxQKpUEBQUJ7wVJ07xu3bq4urrSvn17/vjjDzw8PLh48SLbt29nx44dNGzYkCFDhqSoGIPEe4smre6kNG/enDZt2rB3717c3Ny4fv16qolOpVKpdVX3v+U6+jvIyPxPm885JiZGq+3i4+M1BukrV65M5syZCQoKEp5i0v1Z6mx79eqVmtxBfHw8ixYtYuPGjahUKgoVKsSKFSvImTOnkDdZv349z58/Z8KECVoF4qVE/Lt379L0DYmJiUlxX0tISEhRVBIXF6dVd09YWJhapapSqWTDhg2oVCpKlSpF5syZ+fr1K/Hx8dy4cUPjPmSxsUgzyLjYWJYvX87EiRPF89J9wMzMTO2aTU/GqVSpUvTv358lS5YwdepUvL29qVatGufPn2fYsGGiU6pr165ky5Yt3feqI3W0vS6NjY0ZOnQoXbp0YfHixaxatYorV65QrVo1xowZw7Bhw1AoFOjr6zNx4kQhD3jq1CkeP35M6dKlsbe3Fz/h4eFYWVmp3XubNWtG6dKlWbBgAY8ePcLDw4MCBQrQvXv3NAtV7t27x5YtWwgLC0NPT4+OHTvi6urK5s2b2bVrF6tWraJEiRLI5XJevXql8frImTMnr1+/xtPTkwYNGmBhYYGNjQ0RERF8/vxZeJkok1yD8fHxfPv0Se0ekSdPHjHmJpUPjYqK0igT1rBhQ+FFdu7cOUqXLv1L/e90/LtJ7fq1trZm7969VKtWjVOnTjF16lSmTZuW7v7i4+PTXC/36NEDMzMz+vfvT0REBEqlEjs7O758+UJUVBQfPnzA2toaExMTZDIZCQkJafogNGrUiO3bt/P582dcXV2ZMmVKmnJ6T58+xdjYmO/fv3P27FmuX79OQkICrq6ulCxZUmxnbGwsrlkJyRje2dkZJycnIiMjxTlCoudDUr58+SKei46OTreAU+pI1ska6fg7sLW1xcTEBDc3t3S3NTEx4dGjRxpjnv9l/pWJhBs3brBz507c3d0xNTVl69at9OvXjzx58vDhwwfGjRvHypUr6dmzZ4okwu9GRtretNlWT09Pq+1UKhXv37+nR48ehIWFUbRoUVxcXHj58iUHDhxg0aJF3Lt3j3nz5mkcKLJnz46joyPv37/n9u3blCpVimLFinHixAkuXbrE9u3b2bBhA3FxcQwZMkS0t48fPx53d3e1c2zatClDhw7l3r17REdH4+DggKGhYZqBjHfv3olK186dO6e7cEpISKBEiRLMmDGD6OhoKlSowJw5c1K8tzJlyqSpsQ+gjIoiYNp0AMqVLYvs/w2VNPHt2zdhuCTt39DQkODgYD5+/CiCTsbGxiQkJNC7d2+USiWdO3cWpnpJSW3h0KdPH5EYOnXqFCdPnsTPz4+vX7/i5eWFl5cXAKVLl2b48OFqi/m/KxAkk8lSPXaPHj2YPXs279+/5+3btyxevJinT59y/vx50REieSLkzZuX6OhorfSU4+PjtXq/cXFx6Onp8f37d1asWMH+/ftRKpXUqlWLzp07i0mllZUVdnZ2PHnyBEg0qZMkCywtLUWwUKFQUKRIEbH/+fPns27dOhYsWEDjxo3Vjp1uFWaShEnJkiWRpfK+S5cuTfHixZk+fTo3b97ky5cv9OjRA2dnZ0aPHs33798xNTVl8eLFWFhYaLVQu3LlCgBVq1Ylf/78HDt2jDZt2hAbG0vlypXx9vYmU6ZMqFQqrRIE2t7XQLvv6a/S4s/IMf9uNAW0/Pz8xO8GBgYULlyYEiVKUKJECUqWLEnhwoUZMGAAcXFxNG7cmG7duiGTybT+u/Xs2ZPjx49z9OhRhg0bxvnz51N4DtWuXZvcuXPz8uVL7t27p5YUBVBFRyOFH3Llyo1DTvUFjiacnJzImjUrr169YvXq1WzcuDHdyshMmTJhbGzM+/fvOXPmDKNHj8bBwYHBgwcTFRWFgYEB+vr6KQKWnz59EknlkJAQoqOjad68Oc+ePSMwMJC1a9fSv3//FJ9XarJgUsDTwcGBTJkyietcSkI2aNBAPNasWTOaNWvGhQsXmD9/PocPH+bo0aMcPXqUWrVqMWPGDDXteW19cxYvXsyNGzd49eoVgwcPTrV7MCPX5X+JX/2ZaHu/SmusTL4/bbbT19fX6BGmr69PixYtWL16Nd7e3syZM0c8J3XxfPz4UQQ1Pn78SJ8+fbh06RKQOA9ZsGCBGIvnz59PhQoV6NatG35+fnz48IGtW7eKcTE1pG6InDlzpilX1qNHD9EZ9+7dO0aOHMn58+epXr06bdu2xcXFBRMTE27dupUikakJJycnEfB8+fIle/fu5c2bN5iYmDBnzhxxbd+8eTPVzzn54xs2bBBrFUAUPFhaWqqN9dr83WbNmsWVK1e4du0aM2fO5PDhw6Kg6u3btzg4ODBr1qwMySb9G/iVcwggXf88CUtLS2QyGebm5ixcuJDhw4czYMAADh8+zIwZMzh+/DgbN26kYsWKyGQyypQpQ5MmTejQoQPv37/n/fv3GvdrZ2eHo6Mjjo6OFChQgEGDBtGuXTsWL17M9OnTefLkCTNnzmTcuHE0bNhQ7X1FREQwf/580XWdP39+li5dKjThc+XKxbFjx3j58iWvXr2iefPmPH78WON7rlOnDuvXr+f8+fPiOrO1teXdu3eEh4eL7tWkIVYTY2NC00lqJ0XT+tLa2pomTZqwd+9eduzYQbly5bTenw4daSWrS5UqxYYNG2jfvj3Lly+nYsWKovsmNUxMTNK9J/Tu3Rtra2u6dOlCVFQUFSpUYMSIEQwdOpQXL17w5csX6tWrx4IFCzh16lS6AfjBgwezcOFCPn/+zJw5c5g6dWqKblaJoKAg7t+/z8WLF0WQHxILPatXr672PpIWSl6+fJmLFy+ip6fHrFmzxNinVCrF+UkddBJfvnwRz9na2qY5h1Eqldy+fRuAKlWqaNXxnB66eamOjJAjRw4ePXqUbifNo0ePcHNz48uXL7pEQjL+ldJG5cuXZ9euXcyePZvu3bszZswYzp07x507dwgICKBLly707dtX3MB0pOTbt290795dVEa+f/8eDw8PUXltYGDAqVOnaNWqlVqbblKkJI2UcZbJZMyZMweZTIaXlxenT5/Gzc2NnTt3oqenx7Jlyxg0aFCKgcDe3l6YeUnVmemxYsUKEhISKFSoUKqGn8mZOXMmjx8/JlOmTCxcuPBPNaJLDQMDA4oVKwbArVu3xOMqlYrevXuLjohly5ZleN8ymYxChQoxaNAgvL29CQoKws/Pj0mTJlG5cmVy5crF6tWr/xFGm4aGhowbNw5INLWKjY1l1apVYiKVNInwZ5CQkMCePXto3rw5e/fuRalU4uzszLRp01J8f9++fcuXL18wMDCgaNGiohIrNemCq1evMn36dD58+ECnTp2EPNefga2tLRs3bhSt4uvXr8fV1ZU3b96QNWtWduzYIXw7tEFqh2/RogVKpZKhQ4cSGxtLs2bNOHnypK5q7G9g06ZN7NmzR/ysW7eOwYMHU716dSwsLIiNjcXf35/NmzczfPhwateujYODA7du3cLKyorly5dneHIuk8lYsWIFVlZW3Lp1Cw8PD43btGjRAkAYuf0MKpWK8+fP06JFCwoUKMCCBQv49u0bWbNmpU6dOnTp0oWJEyeyevVqvLy8uH37NkFBQbx9+5bTp08jk8k4f/688FSREsbSQklKJBgYGCCXywkJCUnRIWRgYED37t3R19fn8ePHrFixIkXFVmpI3Q5JA5qBgYHcvXsXmUymUQpMSs7du3ePLl26oFAoOHv2LFWqVKF9+/ZaexpJWFhYsHnzZvT09Ni1a5dOg1pHCqSEn7e3t1q1v7S4ev/+PQkJCZw9e5ZatWpx6dIlTE1N2b59O8uXL0ehUDBt2jQqVaqEn58frVu3xtfXl2zZsvHs2TMaNGggEg+aiI+PF8fVpitGpVKxZcsWatasiZ+fH0qlEl9fX/r370/x4sUZNmwY9+7d06oD8/3796xevVp4VO3atQuAvn37ppog/Pz5M6tWrWL06NEpAsMODg4EBwerJezSmx+khUKhYP369RgbG3P27Fk2bNiApaUls2fPBhJ1wP9rSYTfCUdHR7y9vdmyZQvW1tbcvHmTMmXKMGfOHPGdrlSpEjdu3GDNmjVMnDiRrl27UqdOHfLnzy8C858/f+b27dscPnwYDw8Pypcvz9mzZxkxYgQXL16kYMGChIaGMnbsWEaMGCGS6devX6dVq1bs378fmUxGhw4dOHnypJqxrJWVFf369QMSE31pybuULVsWMzMzgoODuXv3LoBI7GkjbfQzSBWk+/fvT9FZqEPHz9C2bVtRkNa7d2/RRf6zNGrUiAMHDmBqaoqvry/Tp0/n+PHjuLu7Y2BggI+PD+XLl8fHxyddWSULCws6dOiAtbU17969Y/bs2Sm67r98+cK6detYtWoV586dIyEhgfz589OhQwcgMTiaVqf+ggULgMRrLTVj5xQeCRmQNnr06BGhoaGYmpqKuIcOHX81OXLkoHTp0mn+aOqq15HIvzKRANCyZUt27drFtm3bmD59umj3NzAwYOXKlWTNmpXdu3f/zWf5exIREUGvXr149eoV9vb2REdHi2DIq1evWLt2LYMHDyZLliy8evWKypUrawwEJfdJAChWrBidO3cGEhekJ0+exMjIiK1bt6ZpvOrs7Axol0h49OgRPj4+yGQymjRpotV7vnz5Mrt27UImk+Hh4ZGmmazErVu3cHNzo3DhwvTo0QNvb28RDPoZypQpA6Bm6Llz50727duHQqFgx44dv2QxqK+vT5UqVZg0aRLnzp3j2bNnImHzT6B79+5ky5aNDx8+sHnzZnLmzMm2bdto3749J06c+NOSCJcuXaJbt27Mnj2bkJAQ8uXLx9q1a5k7d67GihCpIrxYsWIYGhqKiZeVBqmWmJgYBgwYgEqlwtbWlvj4+D89maCvr8/YsWNZuHAhJiYmKJVKihcvzu7du8mfP7/W+/n+/btox23WrBmnTp3ixYsXWFhYsHXrVrXOGx1/HS1atKBNmzbip0ePHnh6euLn58f3799FZe2ECRNo0qSJWrXFkiVLftgALUuWLEJOY8aMGcJsPClNmzZFLpdz48aNH9J6hcRA/8aNG6lduzZubm4cOXIElUpFvXr18PLy4uXLl5w4cYJ169YxadIkunfvTu3atcmfP78I2js6OlKtWjUAMS+QgivS9SolEszNzYUsnybNzCxZstC5c2eRTJg1a5ZWRQsRERGAukfCyZMngcTuITs7u1RfW7hwYTZs2MDz58/p3r07MpmMvXv3UrhwYUaOHElwcHC6x5eoWLGiMAgcNGgQr1690vq1Ov79VK9eHVtbW75+/armpWFvb4++vj7x8fGMHj2adu3a8eXLF4oUKcLp06dp164dHz58EAn369evU79+fdavX0+ZMmW4fPkyJUuWJDg4mLZt27J9+3aNx08aNEyvO+/Lly8iKBQREUGFChXYv38/I0aMIEeOHISHh7Nz507GjBlDhw4d2LhxIwEBAWr7eP36NZs2baJr164MHTqUVatW8fz5cxQKBdWqVWPWrFka2+I/f/7MypUr6dOnD8eOHePx48d4enqqJSyaNmsG/M/MEn4ukQCJ8olS4mDcuHH06tWLsLAwypUrl+YcW8dfg0wmw83Njfv379OkSRPi4uKYMmUK1apVEzJ6mTJlokuXLkyYMIHVq1dz7Ngx7t27x4cPH3j37h2XL19m7969LFy4kEKFCvHp0yeaNm3KuHHjyJcvH5s2baJ///7CM6NFixaMHz+eHj16EBAQQLZs2Vi/fj1DhgzR2Knbq1cvbGxsePHihZqZcnL09fVFoYnkEyIVi2hrVhv7//JeHTt2ZO7cuVy7dk0rbfqqVauSI0cOwsLCOHz4sFbH0qFDW2bOnEm9evWIioqibdu2Wn+f379/z759+1i/fn2Kan2AmjVrcuTIEaytrbl27ZqYn1+9epWaNWsSHR3N8ePHmTdvHs+ePUvzWFZWVkyYMAEzMzOePXuGh4cHcXFxIoEwaNAgTp48iVKpJH/+/AwbNoxhw4ZRtWpVMmfOTHx8vLjnJOfNmzeiG3bo0KGpnkPyREJGTKql2FDZsmX/EQWMOnToSMm/NpEA/0sm1K5dW+1xfX19cufO/ctlLv4NxMXF0bp1a+7evYulpSUmJiZ8/PiRnDlzcuzYMSpVqkRkZCQeHh7UqVOHihUrEh4eTtu2bRk7dqxa25xkHHz16lU1TcIJEyZgYWGBSqXC0tKSAwcO0KBBgzTPSzLDPn36dLqTTKla38XFRSst2MDAQDZs2ABA//79063AvnXrFs2aNaNGjRocPHiQd+/esWfPHjp16kSuXLno3KlTusdMCymYLyUSnj9/zuTJk4HEyY2ujTcRQ0NDRo0aBSR2JURHR1OvXj3Wr1+favXEz/Dx40d69epFw4YNefbsGebm5owZM4YdO3YITxBNSJ0lUoJImnhpChTMnz+fJ0+eYGdnx7Vr12jduvVfkkyAROmUAwcOMH36dDZv3iw0r7Xl8uXLKJVKypQpg5OTE6tXrwagU6dOWslG6Pjrkclk5MqVi+bNmzN58mQOHDjAixcv+PTpE0+fPsXV1fWn9t+xY0caNWpEXFwcPXv2TGHQam9vL+63ktSCtrx7946JEydSvnx5Jk+ezIsXLzAzM2PAgAHcu3ePAwcOcOvWLZo2bUqvXr1wd3fnw4cPqe5Peq87d+5EpVKJRIIU2JMSCcbGxiLZ8uHDB43jUalSpRgzZgzZs2cnMjKS9evXs23bNo2eOhKaOhKkRIKUSE+P7Nmzs3btWm7dukXdunWJi4tj0aJFFCtWjOXLl2ut7T127FgqV65MWFgYXbp0+WnTQR3/HhQKBc2bNwdQC+Dp6emJ+daWLVtQqVR07dqVEydOkDt3bs6cOUOZMmU4d+4cZmZm1K1bl/j4ePr06cPw4cNxcHBg//79NG/enPj4eEaMGMGkSZNSdApIiQS5XJ5q8EGlUnHhwgUmTZrE+fPnMTY2Zvr06Xh5eVGlShVGjRrFlStX8PLywtXVFWNjYwIDA9m0aRMdOnSga9eu9OvXj65du9KlSxc2btzIq1evRPJg6tSpnDlzhiVLltCgQQO1jq3v37+zbds2+vTpw/Hjx4mPj6do0aIYGxvz5MkTtWKYWrVqYWNjw6tXrzh06BDwv/vNzxSL9O7dmzp16hAdHS2O5+HhoVvz/EZkyZJFrTvh1q1bQlI1te4YmUxGpkyZKFGiBI0aNaJv376cP3+eXr16AYnSdDVr1iQgIIB+/fqxY8cO8uXLx7dv3zh06BAqlUp0JKS1jjAzM2Pw4MFAYlVy8nE7KTVr1kQmk/Ho0SM+fPiQoY6EmzdvUr9+fWbPno2fnx9Lly6lZcuWlChRgr59++Lt7Z3qmCmXy0ViLLWkow4dP4qenh5btmwR5sudOnXSOA/6/v07S5cupX379uTMmZOcOXPSvn17+vTpQ8mSJfH19U3xmnLlynHixAkcHBx48OABFStWZPfu3Xh5ebFhwwbMzMz49OkTy5YtY9u2baLIRBPZs2fH3d0dQ0ND7t69S8eOHRk4cCAnT54kISGBIkWK4OrqyrBhw0RhmEwmE94IqRW5bN68GUiUIE1LajBFR8Lnz6lumxwpkfC7S4zr0KEjdf71s8qWLVumME9+/fo1t27d+ukgyb8RLy8vTpw4gZGREU2bNuXVq1fY2dmxYcMG8uXLx4YNG+jZsycA27Ztw87OjmHDhgGJC5Xu3buTkJDA48ePhUnPx48f1QYbW1tb1qxZQ9OmTUVyIj2koOb379/TNE3+/PmzGJz69Omj1XvevXu38EUYNGhQmtuGh4fTpEkTzpw5g56eHq6uruzZs4fBgweTO3duoqOjRfAHYOu2bVqZ/CZFqrJ7+fIlISEh9O7dm6ioKOrWrcvIkSMztK9/O25ubhgZGfHhwwdOnTr1px3n3LlzVKtWjb179yKTyWjWrBkHDx6kffv26VZSPH78GIA7d+7w9etXITeSvCPh06dPooJ74cKF2NnZsXbtWpFM6NatG+/evfv1by4JTk5OtG7dWis/iaRERkayd+9eABFkkhIf2pgY6fi9sLGxSTFu/gjJJY5q167Nixcv1LZp2bIlAOvWrWPjxo0agyjPnz/Dx8eHrVu3Mm/ePIYOHYqzszObN28mIiKCvHnzMmPGDK5evcrixYspWLAgnp6eTJ8+nZMnT7Jp0yY8PDxES7cmmjdvjp6eHk+ePOH169cpEgnSfdzAwEDNFC+1CiwHBwdGjBghkgBXrlxJU7JFUwBRGssqV66c6us0Ubx4cY4fPy6CQcHBwYwcOVJUjaaHQqFg06ZNWFhYcOXKFSZMmJCh4+v4dyP5byTtNgVEwMHU1JQ1a9Ywf/58jIyMUKlUuLm58fnzZ3LmzMn169c5fvw4I0aMABI7n3x9fTE2NmblypX0798fgDVr1qS4ZqRiFZVKpXEuqFKp2L9/P7t27SImJoYKFSpw+vRpevXqpRZIl8vlVKpUiUWLFrF9+3YmTJhAuXLlkMlkvHr1iocPH4rkQcWKFXF3d2fdunUsWbKEpk2bphroX758Ob6+viLoVLp0aXr16iWCJYf/P2EAoK9QCOlNqfJbSlimZ46eFnK5nNWrVwvPrA4dOuiCNb8hUnfCrVu3RMJ98uTJeHp6ar0PExMTPD092b17NzY2Nvj7+9OxY0cOHDhAwYIF2bVrF3369BHJ5ClTpqRa2PHlyxdxTXXp0gUHBwcCAgI4fvx4qse3tbUVXm537twRnXPayOp16dKFZ8+eYWtry5gxY2jWrBmWlpaEhIRw5MgRRo0aRfXq1Tl37pzG17dv315IEr5+/Trd4+nQkRFsbGzYs2cPcrlceAsmZ8yYMYwePZp9+/bx/v179PT0KFWqFDly5ODNmzdiPZScIkWKcObMGerVq0dsbCxz5sxh8eLFtG3bFnd3d+EZcP36dTZt2qSxCCQiIoLz58/zxx9/qEkxS3PoMmXK0KtXrxQGypDYuQaJhYqaOHHiBEAK77LkJFdhiIyIYMKECekWn4SFhYkkt25s0qHjn8u/PpGQHKmdevz48TpNNg1I2eSaNWuSJUsWIPEmnz17diAxwDBmzBgWLlyIQqHg8OHDfPr0iS1btqBQKNi+fTsVKlSgePHiHDx4ELlcTv/+/VNUX9evX5/Nmzdr5V/w/ft3MZiVKFGCAgUKpLqthYWFWOClVX2aFKl9cOjQoen6Ily6dInQ0FBy5MjBnTt3WLNmDQ0aNGDmzJn4+/tz5coVRv2/tiLA9GnTqFKlChs3bhSBKJVKRWRkpNDnvn//PleuXOH06dPs37+f4cOHA9CwYUMGDhzI06dPsbe3Z8uWLbqKsmRs3ryZ6OhoLCws0uwK+FGUSiULFy6kefPmfP78mSJFinD27FnGjh0rDCXTo0WLFsjlcq5evUq/fv1EhUqVKlXUttu+fTtxcXGUK1eOZv8veaBQKFi7di01atQgOjqauXPnal1V/FehVCqZPXs2b9++JVOmTMIEXDKPTit4quPfT5YsWdiyZQsWFhZcvXqVcuXKsXnzZvE9dnZ2FtXzs2bNok2bNqKTQaJHjx706NGD8ePHs2zZMg4cOEBERARly5Zlx44dnD59ms6dO6vJAklSRUmRtJ81ERkZSUJCAjKZDFtbWzHOREREoFKpxLgSFBQkuuxMTEzSvA8oFAoRXJXJZCLgkpzw8HCRSJBkk+B/8kpSi7k2fP36FQ8PD/Lnz8+SJUvE4y4uLhmSrsuZMyerVq0CEhObOilIHZAY6Jakc5KPYcOHD6djx46cPn1a+J9A4ndfShi+e/eOO3fuEBkZyfnz54FEjVqpQjIyMpIrV64AkC1bNjGOSGTLlo2iRYuiUqlYs2aNmn61Uqlkx44dYoxt1aoVXl5eqV53EkZGRtSrVw8PDw+REIfEgLy3tzdz587FxcUlVTPLpDRq1EgtcHPr1i2GDBkizimpueWChQu5cOECenp6IskpdVDv2LHjp3TmHR0d2bVrF127dmXevHk/vB8dfz5ZsmRh//79wrx8xowZKSS20qNJkyZq8iiTJ09m5MiRREVFMXDgQHbs2KH23YPE68Xf358FCxbQsGFDihUrRq1atdi1axfGxsZMmzYNSJSUlWQrk/Pp0ycRxM+dOzd169YF4NChQ+nKnEhj55w5cxg0aBDLly/n7t27HDx4kCFDhmBjY8OTJ09o1qwZ3bt3T7Gmy5Ejh+ho3LlzZ4Y+Lx06tOHdu3colUqMjIzUvEQkWrVqJYrJypYty+fPn7l+/boovkirICdHjhwcOHCAuXPnAokSd+Hh4RgbG9O2bVuGDBmCvr4+T58+5ebNmyQkJPD8+XMOHz7M/PnzWbx4MUuXLuXcuXMafUJu3rzJ0KFDWbt2LV5eXrx48UIkGSRZwtTGRinRkJr0kYQmn6I5c+ZQr149AgMDU33djBkzCAoKInfu3NSoUSPNY+jQoeP35R8dlTx+/DgTJ05k7dq1KdqrkqNUKmnTpg2jR49m0aJFjBkz5i86y38mMplMBEA0aVc3adKExYsXC83+EydOsG3bNhQKBf7+/iiVSpo2bcrt27dZsmTJDwfAY2Nj6dChA0+ePCFr1qzs27cvTYM9Q0NDUQGqjW5mcHAwwcHByGQyrZIakpls8sUiJH5mRYoUEVV2kFiV+unTJyZOnEjJkiUpVKgQTk5O5M+fn9KlS9O4cWNcXFxo3bo1Xbp0YciQIQQHB1OiRAksLS05e/YsRkZGbNy4USR2dCQSEBDAlClTAJg6dSoODg6/dP9KpZK+ffsybdo0lEolHTt25NSpUyLgoS1ubm4sXLiQggULEh0djUqlokaNGmrfN8kQEqBr165qUgkKhYIlS5ZgbGzMjRs3fkgP1s/Pj1GjRjF27FimTp3KvHnzWLJkCTt37mTnzp14e3tz4sQJ/Pz88Pf3T7OVPTmbNm3i8uXL6OvrM23aNBEI7d27NwCrV6/+7ZIfOv5aXFxcuHnzJtWqVSM8PJxevXrRrl07goOD0dfXZ8WKFUydOhUjIyPu3r3LtWvXeJ+k+8ba2pqiRYvi7OxMly5dGDNmDKtXr2bPnj1UrVpVoxl05cqV2bJlC7Vq1WLlypWEhobSrl27VM9RkiArWLAg5ubmODk5IZPJUCqVxMTECN+c8PBwIiMjMTExoUKFCmmOR5IGO0DdunXV/CeSIpmwWltbqwUrpaTc1q1b06xQDg0NZdeuXbRr144cOXIwZswYXr16hZWVFUOHDsXf3x8vLy8hO/HixQtWrlwpqp9To3Xr1mI8+5Wmgzr+uUyfPp0XL16QNWtWxo0bp/ZcpUqVWLx4sUZpQU9PTzp37kxCQgLt27fH0tKSa9euYWNjw9GjR7GxsSEuLo7evXtz69YtrK2t2blzp8ZEnZubG6VKlSI+Pp7169cLXXVpLJIqvRs2bJjhuae5ubm4n9jb22dYYqhkyZJMnDiRjRs30r9/f8qVKyfuEa6urrRp21Zse/nSJQwMDNixY4fw82rUqBHFixcnPDxcyHT+KLVq1WLVqlUZlinU8dcjk8kYMmSIkIsdO3ZshveRLVs2Dh8+zKBBg1AoFJw8eZI2bdqo+a2FhIRw4sQJxo8fT8OGDXFxcWH+/PncunVLzNOmTZtGcHAwTZs2pVu3bkBix6CmxMDBgwdJSEigaNGiFChQgAoVKlCiRAmio6OFPEpqSPJKSccVPT09ypQpw6hRozh58iQ9e/ZELpfj5eVFhQoVWLZsmdp8Uioy27FjR4Y/Lx060iIhIUH4RQ0cOFCjVLKzszNbt25FoVBw48YNBg0aREJCgvCNbNWqVZrHkMlk9OnTh9y5c/Pt2zc2btwonsuVK5eIZ+zZswd3d3eWLl3KqVOnxJzRycmJZs2aMWnSJLZv386ePXtYsWIF3bt3p3jx4ujp6fH161dOnjyJh4cHY8eOZf369fj7+yOTyWjcuLHG82r7/+PU/v371SSrkyN1vUls3LgRc3Nzzp8/T5UqVTQqBdy5c4eVK1cCiUUqGe2A16FDx+/DP9bdpHfv3pw6dYpSpUqxYsUKxo4dy9q1a0XVU1KioqIwNjZWqzTSkT5SoPz169eoVKoUwZr69euzePFihg4dKiZx+/fvZ//+/fTs2TPDcgzJ+f79O71798bPzw9TU1P27dunlfFnkyZN2L9/P2fOnKFmzZppbisZSWbNmhUTE5M0t5V0dwFRdZMePqdOsefgQZYuXZoiOy+TyTAxMcHMzAxTU1Pxb44cOciXLx8zZswAEnVPixcvrtXx/ksMGjSIsLAwypcvL+S2fhUqlYoxY8awZ88eFAoFCxcuFCbhP0Lu3LmZM2cOvr6+PHnyJIXp4f3794XGu6Z7WO7cuXF3d2fSpEksXLiQqlWriqBgWsTExDB37lw2bdqUofM1NzenatWq1KpVK03PkLNnz4prf/jw4WrJkY4dOzJmzBiePn3KokWLGDZsmMaAr47/Bk5OTpw8eZIFCxYwZcoUvL29uXTpEnPnzqVq1ap06NCBKlWqcO7cOaysrMhqYwOzEys0vb0P4pAzZXt2eri6umotYSiZokvBDQMDA4yMjIiKiiIiIoJMmTJhYWFBaGioSCKkZyC+f/9+wsLCcHBwoGHDhqluJy0Kpc4/ifr165MtWzYCAgI4ePCgWNxBYufBwYMH8fLywsfHR01LulSpUvTr1w9XV1dMTEzU5PWUSiWurq7cu3ePa9eusWHDhjSvyxkzZnD37l18fHxo27Ytly5d0ureo+Pfx927d/Hw8AASvagyYgisp6fH2rVrAUTS3NDQkIMHD1KoUCFUKhWjR4/m9OnTGBsbs3XrVqHpnByFQkG3bt0wNDTkypUrbNmyhTNnzghpia5du2ao+yYpb9++FYHKwMBAPn78+ENFCpkyZcLFxQUXFxdiYmIIDg7GwcGBqO/fxTYGhoZs37ePOnXqiMdkMhljxoyhY8eOrFixgiFDhqQI1uj4dyKXy/H09KRSpUrs2bOHjh074uLikqF9SN//ypUrM3r0aN69e0f37t1p1qwZb9684c6dO2qBQTMzM2rUqEGdOnWoVq0abm5uPHnyhDlz5jB37lymTJnC6dOnefv2LatXr2b06NGiAvvdu3dcu3YN+J9EoUwmo2/fvvTr1481a9akadRatmxZ9uzZw/Xr1zU+b2lpyfz58+nUqRMjR47k+vXrTJw4EUtLSzr9vxddkyZNGDlyJPfv3+fevXs6pQEdv4wdO3bw8OFDrKyshB+fJpo3b87OnTtxdXVl+/btxMTEiC7S1q1bp3schULB8OHDGThwIJ6engwbNkyoI9SuXZubN2/y8eNHIFE2sGDBghQsWBBbW9sUXYGQKDcmjT2RkZF4eXkREBDAgwcPCAsLU5vrphZTqVu3LlZWVgQGBuLr66s2RiUl+RygZcuWlKhQAVdXV/z9/WnevDkjR45kwoQJKBQKEhISGDJkCEqlkpYtW2odS9GhQ8fvyT+yI8Hb2xtfX1/u3bvH/v37efXqFQ0bNqR169asWLFCbduDBw9SsGBBIV+jQ3uk6smwsDCCg4M1blO/fn127twpOhN2797N2rVrfzqJcPPmTSpXrszRo0fR19dny5YtGtsKNVGkSBFy5sxJTExMqkZCElIiIS0zIYnnz58TGBiIoaFhuobMEoYGBnTu3JmLFy9y8uRJzp07x82bN3ny5Alv3rzh8uXL3Lx5k3PnznHs2DH27t1L69atRYvz8OHDU60Y+C8jBdAUCgVLly4VVYdhYWGcPXs2zQoKbZg1axZr165FJpOxevXqn0oiSMjlcmrXrk2/fv1STL7++OMPILF6JTX5hEGDBpE/f35CQkKYP3++Vsd0bd9eJBHatWvHyJEjGTBgAN27d8fV1ZX69etTr149qlWrRpkyZShSpAhWVlaEhYVx/PhxRo8eTdWqVZk8eTK7d+/mzZs3Isjy7NkzEVRq06YN9erVUzu2ubk5Q4YMAWDUqFF07tw5TdMwHf9+9PT0GD16NBcuXCB//vwEBQXRrVs3Zs6cSUxMDE5OTnTq1IkmTZpQ+v/Nyf8qpGBGUok0SUtaahsvXLgw2bNn1yqJcPfuXa5fvy6qo/X19VPdVvI+SV7xJgWFADZs2MCHDx9YsWIF9erVw8HBgZ49e3L06FFiY2PJnz8/Y8eO5dq1a1y/fp0ePXpoTI4fOnRIyFTs2rVLmKKnhp6eHlu3bk3XdFDHv5uEhAR69epFfHw8LVu2TFX7OS2kZML06dPx8fFh7969wiNr4sSJ7N69Gz09PVavXp2uVKFcLqdDhw6iWOT9+/fo6+vTu3fvH04iwP/mhBL+/v4/vC8JQ0NDHBwcCAsLEwUiAJMnTdIYoGnevDmFChUiJCREVG7q+G9QsmRJ4RHSo0ePDEscSRQtWpS9e/fStGlTlEolXl5e3Lp1i4SEBPLkyUPXrl1Zvnw5Dx8+ZP369XTo0IHs2bML2bItW7Zw584dDA0N6datGyYmJrx8+ZJ9+/aJY3h5eQGJAcmk3XZSx9GrV6806spLSEn727dvp2qqDImePydOnBB+fCtWrBDzUCsrKzH31HUl6PhVREdHC2mvMWPGpCtl26JFCxEL2bdvH3FxcRQrVoyCBQtqdbwOHTqQLVs2Pn78qJZYUygU9OnTh9atWzNy5EhmzJhB586dKV++vFZSeyYmJhQqVIgePXowb948hgwZQs2aNSlRooSa/GByDA0NRQev5HepCU3FBPny5ePChQuio9bDw4NGjRoRGBjIxo0buXHjBubm5kLSSYcOHf9c/pEdCX5+flSpUkUs8i0sLNi6dSv29vYMHDiQ7NmzizbhUqVKoaenh7e3d5oZ5d8VTcaTqW2Xnr4/JFZap9XqLU3O4uLiiImJwd7enk+fPvHw4UONlR5hYWGULFlSrTMhPDycmTNnqrWrhYeHa9Ve/f79e7y8vFi4cCHx8fFky5aN+fPnkzdvXjVToHfv3qU5iNasWZNNmzZx8eLFNKVoHj58CICdnZ3wh0gNyXCsatWqGBoakpCQwLdv3zh//jxZs2Ylb968WFtbo0wSxFYqlciUShQKhcYJhVKpRKVSoVQqCQwM5PHjxwwbNoz4+HgaN27M0KFD/xGyMNqcY0bfR3x8vMaAVVhYGAMHDgQSzdrMzMx4/fq1kMF69OgRderUYd68eeI7GBcXp7EtNTkPHz5k//79QlJg6NCh5M2bN4Wkx4sXL7Sqxvz27Zswz04NyTALEiuo0zLnHjJkCIMGDeLEiRPUrl1bmF4mRRUdjd3///7kyRNsbGyYN2+exu6cgIAAYY4nkZCQwL179zh79ix+fn48ffqUx48f8/jxY9auXUuWLFmoUKECFy9eJCYmhrJly9KtWzcSEhKIjY3le5KKy2HDhmFkZMSUKVPYsWMHt2/fZtOmTeTOnVurKsv4+Pg0A7BJSc/0WsevQZvrWKlUpjnOlCpVSniG7Nq1S0iSLFiwIE0PnPQICwvjy5cv6W73+fNnNcNJpVIpqrRy5swpqr+kMfXr16+YmZkhk8lwdHQkOjpa7TrV09MjKChI/D8yMpLt27cDiXIvJiYmBAUFYWRkpDEpL41tdnZ2BAcHCx8dSAzKzJw5k5MnT+Lo6Kj2usKFC9OwYUMaNmxI/vz5RWfBp0+f1LaLj4/Hzs4OpVLJrFmzgMRA0/379xk9ejSlSpWifPnyREZGarznGhoasmXLFpydnTl16hRjx45lwYIF6X7OcXFxWl2/CQkJWl+/v/N1ru18DdBKckfb/UVGRmq1v9jY2HQTYJAYREmejF++fDnXr1/H0tKSuXPnEhkZybdv39KU9kp6fknp06eP+P3bt2+sW7dOFE9MnTqV8uXLq40jyYmKihLf9caNG2NsbMzdu3dp1qwZefLkUZMBS2s/Em/fvhXfU6n4RCaToVKpOHfuHHnz5hXH1WbcioqKSvH5ff/+nalTp/LpzRvIn3iPy5svn0Zta0ice/Tp04clS5bQpUuXFN1KmkhISNBqHaDjr0HbOW9CQoLa9Tt9+nTOnTvH3bt36dy5M8ePH0ehUBATE6PVPSEuLg6FQoGxsTHTp0+nevXqXLx4kcKFC1OlShUxFw4LCwNQC+KXLVuW5s2b4+3tzdixY/Hy8sLY2Jj27duzYcMGTp06haOjI2ZmZty9exe5XE7dunUJDQ0lMjJSdNe1bNmSjRs3snjxYor8//UDEPDhA0EhIVhaWmJrayvMla9evZqi8/rLly9q42ybNm1YvXo1jx8/Zu/evaJgrXbt2hw9epQdO3Ywffr0VO+FSqVSq67YjFxHuuvtn4c21+WKFSt4+/YtWbJkoUWLFml6LqpUKhwcHGjcuDFbt24VxRYtWrRQk4mNjY1NdT6kp6fH4MGDGTNmDKdOnRKyRJDYHSslx5OOF6GhoamaJSclJCRE+O1kzpxZJN6USqWaD09UVJQoaoHEwrYNGzZw8eJFDhw4IBJ/MplMxF+SS/8lJCSgSkhAX1+f+fPnU61aNQYNGsTFixepVKmS+DwmTpyIg4MDKpVK/KSHJmUMTdvo0KHjr+P3XZGlQdasWTlw4AAxMTFqQToPDw/ev39Pr169ePnyJSYmJuTIkYNbt25hZWX1953wT6CtFIhcLv+lsiHGxsZky5aNvHnz8unTJ8LCwjQGYqVqmaQyR4cOHeLChQt06dKFjh07Ymlpib6+frrZ85CQENzd3YUpXb169ZgyZYrGgK2trW2aMkddu3Zly5YtvHv3jkyZMqW6rRT8KVeuXLrt69ICs0GDBiII1aFDB44dO6Z2XkXy5kWqIbt+4wb5ihYlT548Ioj05MkTnj17Jv6VfpLqVZctW5Zdu3alK7f0o/yTJ7+TJk3i/fv35M6dm/79+4tFw9KlS3n06BGQaE7au3dvli9fjqWlJXp6elrpMB47dkwkEXr27Jlq1aVCoUgRgNeE1IaaFtu2bSMuLo7ChQunqvcu0bhxY+7evcvq1avx9PSkVatWagHRkJAQxo8cybT//3+NGjVYvXlzqv4ajo6OGie2hQsXFtUob9684eDBgxw/fhxfX18CAwPx9vYGEitPjhw5Iu6vISEhaucvk8kYMGAApUqVonv37jx69Ii6deuybNkyYS6ZFr/6vqaTVtKMtsFZbSfpBgYG6X7WlpaWbN++nQ4dOtCjRw+ePHlCq1atGD9+PDly5CA6JATJGvLQoYOExsYSGRkppIYsLS2ZOHGimllc0mB+WshkMrXv/cuXLwkNDcXQ0JAiRYqI58qXL8/hw4fJnDlzCjmypERFRVG+fHkgcbE4YcIEIiIicHJyYsqUKWKeEh0drbH7TUoulChRgpw5cxIXFyc+P8nAUtKaLV++PM2aNaNp06aYmZlpFchVKBQYGRlx4MAB7t27h4WFBb6+vvTt25d9+/bRsWNHbt68iYmJSap/txIlSrBy5Uq6dOnC0qVLqVChQpqeE6C73n4WbT8/mUymtm1cXBwBAQEpvmsGBgbpJrYBYTou8fbtW1GdOX36dDGfMjc311rfOLX3cvDgQaFDPXToUK1kCtu3b682p+/SpYvG7YKDg7VK+Nva2opxVAqslCtXjmvXrgk/CEi8zrXpXnVyclKr0H79+jVNmjTh9evX5Ewyz6xcqVKq12+7du2YN28eL168YOvWrSn8KDRhYGDwWyfa/i1kZF2mDYaGhmr7NDAwYM+ePZQtW5YLFy4we/Zspk+fjqmpqVbHLlq0qNp1WaRIEY0SQ9HR0RrP0cPDg9OnT3P79m2OHTtGkyZN0NfXRy6Xs27dOvbt2ycSW61btxY+Cl+/fhX769SpExs3bsTX15d9e/fSNMlnYm5uLubPFSpU4OTJkzx9+jRFd050dLTa/crQ0JBWrVqxdetWduzYQa1atYBEnXpzc3Pevn3LpUuXtO4Y1/HfJL1rKCQkRBRcDB06VKvku7SmbtWqFebm5uzbt48BAwaorbVNTEzSLKzo27cvHh4efP78GSMjozQ7BgB8fX21Smw7ODho5e335csXtfEoR44ctGnThh07drBq1SohoySTycT7Sn58PT095P//nJmZGZ06daJixYq0b99edPeVLl2aIUOGiLFKF/z/Od6+fZuygCo6Gulb6+/vz6NknZY6dPwq/pHSRm5ubnz+/Bl3d/cUzy1fvpzw8HCOHDkiHvunJhF+B6RFU/J2b03Ur1+fFStWkC1bNr59+8aiRYuoUaMGc+bMSVEhmZxbt25Ro0YNfH190dfXx93dnQULFmRIgzcp9vb2YtCTtAqT8+3bNzHxzZUrV5r7i4yMFFqg9evXBxJv3lKXgpRk+fLlC9euXhWv6+TmRokSJTA3N8fW1hY7OzuqVq1Kt27dmDNnDvv37+fu3btERUWhUCgoUKAAHTt25ODBg39aEuGfzPXr11m+fDmQeK1Li6VLly6xYcMGAPr164e5uTk3b97Ezc1NVBenx+HDh0WVbdu2bXFzc/sT3kFKdu/eDSQGQ7RZJI4ePZrs2bMTEBCg1hp648YN6taty9Ek974DB7y0NulWqVS8ffuW69evq1WBOTk50bdvXw4dOsSHDx/Ys2cPXbp0oXbt2hw4cECr+2vlypU5e/YsFSpUICwsjC5dujBu3DidRIoOkRxr1KgRMTExTJo0ia5duzIsSeBjwYIFLFmyhHXr1rF9+3a8vb3ZvHkzlSpV4mqS++2PIi1wihUrprbQk7ro0utWk4iNjWXSpElcvXoVAwMD3N3dtQrcvnnzBiBVM+Z169axefNmnj17xtmzZxk6dKhaAkUblEqlCAYPHjwYGxsb1q9fT4ECBQgICMDV1TXd67Fly5ZCXqJXr1468+XfkA8fPlCjRg2KFCnCjBkzfnqRHhISImTpqlSpIoKGv4ILFy7Qv39/VCoV3bp1o1+/fr9s3z+KdC02aNAAuVzOx48f052/pkZoaCiTJk2iVKlSPHv2jOzZs6utTdJCT0+PkSNHAol+FKl1Luj4d5IvXz4hOzd79mwhf/lXkDVrVpHcmzhxoujqGTZsGGXKlCE8PJxHjx5haGgouoOTkytXLmrXro1KpWLq1Kni8QP79xMSEiL+LyXgpfVVenTq1AmZTIafn59YlyYNuu7cuTNjb1aHjmTMmzePb9++kTdvXjWPgxs3biR2laUzHjg7O7NmzZp05ZCSY2JiIhJ+q1atylCH459F79690dfX5/r16xrn2trEaPLly8fFixcZNGgQhQsXZu3atbqE9y/i7du3FCpUiDJlyqj9JE2mVq1aFTc3N0xMTLRSBtGhIyP8IxMJWbJkYdGiRSxatEhodEtkypSJ8uXLax081JE2UrDi9evXWm1fq1YtfHx88PDwoECBAkRERLB+/Xrq16/PoEGDePr0qdr2KpWK1atX4+Liwps3b8iaNStbt26lQ4cOP13NKFWTnz59WuOALLUEOjo6pltVd+XKFWJjY3F0dBTSG1u3bkWlUlGzZk1ev35NcHCwMK+UKFW6NBYWFqhUKjEZz5o1KzVr1qR3797Mnz+fw4cP8+TJEyIjI3n06BFbt27VOvj7XyI+Pp5+/fqhUqlwdXUVJk3BwcGMHTsWSKziGzx4MNu2bSNz5sw8f/6cDh068OLFizT37efnR9euXVEqlTRo0ID+/fv/JdW09+/f5/79++jr69O+fXutXmNqaioSCOvWrePWrVssWbKEZs2a8e7dOxyTSCDI5ZrfQ1xcHHfv3mXbtm2MGDGCunXrYmdnR65cuahYsSI2NjbUqVOHadOm4efnJxILZmZmNG3alNWrV3Ps2DHy5cun9XvNkiULhw4dom/fvgDMnz+fBg0aaF1FruPfi729PYcOHWL16tXUqVMHFxcXmjZrJp7v378/7u7uTJ8+nQULFrBixQpKlSrF58+fqVWrltBqziihoaEcPXqUrVu3AqSo2sqUKROQmEhILyArJREuX76MgYEBc+bMoVChQumeQ3x8vOjsc3LSbChtZ2f3f+zddVgV2RvA8e+lQUVQ7NY1MNfWtcFGxe7udu3ubmxd28XWtQs71tY1VjFBQbBQQjru+f3BMj+QGhS9oOfzPDy73jt37jv3zpmZO+8576Fly5YJjsJLzP79+7l37x7m5ubKj9V06dKxd+9e0qRJw9mzZ5k+fXqi65k8eTK2trYEBQXRqlUrPnz48MUxScnr7t271KhRQ0nwzJ49m5kzZ35xMsHX15emTZty48YNLC0tWb58uepe1on5999/6dy5M6GhodjZ2TF79mydj2AJDg5WbhIVKVJEOb9FzSmiVlhYGKtWraJo0aLMmzePoKAgqlSpwunTp8mfv4Dq9bRp04Y8efLw/v17ZaJq6efRtm1b+vTpgxCCzp07J1heJT6vXr1iw4YNTJgwgSNHjqiep6pfv34ULVqUDx8+sHTpUgAMDQ1ZvHgxGTJkACI79SU0ktvBwYHhw4fHKFc4ceJEatasSdu2bdm2bZvSxq5fv67qOJU3b16lTGfUeRsiy4IC7N69O0Y5GUlKitevX+Pg4ABEzu0WdcP77du3dO/enQ0bNtCmTZtvdo+pb9++mJub4+Li8l2Th/HJmjUrrVq1AlBG60enZkQERCb7HBwcuH//vqrREZI6Xl5eBAYG4ujoyK1bt5S/S5cuKctcunSJW7du4ezsHG9nJUn6Uqk2JdinTx88PDwYOXIk7u7uzJkzB1NTU968ecP9+/eVE4H0daJGJLi4uKh+jaGhoVJ64fz586xZs4abN2/i6OjI1q1bsbOzY/z48WTPnp2BAwdy6NAhAJo0acKQIUPInDlzssRua2uLmZkZ79+/5+7du7Em4ItKJPwSrX5nfC5cuABA9erV0Wg0aLVaZRLbqMkw06ZNS+nSpSlVuDDP50Te6D139iwaU1PevXvHu3fvyJcvX4wST0KIVF1m6HtaunQpd+/eJUOGDEoCUQjBxIkTef/+PQUKFGD06NEAFCpUiG3bttGrVy9cXV3p3Lkze/fujXNOgVu3btG2bVtCQ0OpUqUKI0aM+G43NKJGI9StWzdJPQVsbGxo3rw5f/31F02bNlVq3DZr1ow5U6cS0ap1jOU/fPjAjh07uHPnDnfv3uXhw4dxTm5nYGCAubk5Hz9+5Ny5c0qZMWNjYypWrEj16tVp1arVF9exNzQ0ZNasWZQrV44hQ4Zw7tw5KlSowK5du5SeadLPSaPR0KtXL3r16gWANjCQJ2UjJ1ydM2cO+tFKeEHkTYy2bdty9OhRWrZsyYIFCxIsPxTF39+fdevWKRPdR69lHlVzOUqGDBnQaDSEhobi5+cX74+m8PDwWEmEsioni3716hUREREYGxsn27nvc3GNRohStGhR1q9fT9u2bVm8eDHlypWjSZMm8a0KfX191q1bh62tLS4uLnTs2JFDhw7JHmY6dvz4cWXkQJEiRWjcuDHz589XJk+dMGFCktbn5+dH06ZNuX79OpaWlhw+fJhChQolS6xubm60bduWT58+kSdPHkaPHo2/v7/qhEdYWBjv3r3D29ubDx8+KHM11KlT56vO3a9evUIIgbm5ORYWFpQsWZLHjx9z7949peNCYs6fP8+MGTOUzjdRk6Cbm5snuY0YGhoybNgwhgwZwsKFC+nTp4/qUlLSj2HRokVcvXqVu3fv0q1bN44ePZrofnTnzh327t2Lk5MTDx48iPGckZERVatWpV69ejRv3jzeucOikgb16tVj165dtGnThuLFi5M1a1Y2bNjAyZMnlXN1fNKlS8fAgQMZ0LMnwU0jRwwULlyYO48ecebMGc6cOaMs+/79e1xcXChQIPFEW+fOnTl79ix//fUXv//+O+nTp8fGxobMmTPz7t07nJycsLOzS3Q9kvS5adOmERQUROXKlalbty4Q+Ttz5MiRykgaV1dX2rZty86dO8mSJcsXv5ePjw/379+PUdLW3Nycjh07snLlSlavXk39+vV1nmDv3bs3u3fvVkYlRP8d/aVVI6TkZW1tTZkyZZR/awMDefzf///666/oyQoX0jeSKkckRJk2bRrr169n48aN5M+fn2bNmvHrr78yevToWJM2SYkTQnDlyhXg/zWzoy7qHj9+jJubW5LWp9FoqFmzJtu3b2fTpk1kypQJIQSHDx+mS5curFy5kkOHDmFkZMTcuXPZtGlTrIl7voaxsbFywluwYIGybQBPnz5V5jZIrFe1t7c3R44cASJrzkNkGZwXL15gbm6eaB1DjUZDlixZKFGiRKLzREj/9/LlS1xdXXF1deX27dvK8Og5c+YoN9yePHmilK7q1atXjFqWOXLkYObMmejr6+Pn5xfvZOvjx4/H39+fihUrMm7cuO92Q8zFxYW//voLIMbwWbWmTp2KpaUloaGhmJmZ4eDgwMqVKzGPow2NHTuWoUOHsnnzZu7cuUNoaCjm5uZUrVqVQYMGsW7dOm7evImfnx/v3r3j4cOHrFy5kjZt2pA1a1ZCQkK4cOECM2bMoFy5csyZM+eren01bdqUy5cvkyVLFjw8POjdu/cXr0v6OaVNm5b9+/fTt29fhBAMHz6c6dOnJ3ozcubMmSxZsoTr16/HSCI0aNBAOb5HMTAwUG66JzRy5tq1a1+URDh8+LAyci537tzJ1tv7c+vXr481GiG61q1bM2TIECDyOBq9N1NcDAwMlKTLqVOnlES7pBtCCHr27ElAQABVq1bl9OnTTJkyRUkezZ49O9FReZ9bv349169fR6PRcPDgwWTtRejg4KD0/H/58iXVq1fnl19+oVixYhQvXpzq1avTtGlTunfvzsCBA+nQoQMNGzakUqVKFCpUiKJFi1KsWDGqVq2Kvb093bp1o0OHDkqnlC/1+vVrIHKOiLNnzyolz9R+dm/evKFfv368ePGCzJkzs3TpUm7dusXBgwdp2bIl+fPnp0qV/ycrEytXdOfOHSWZ//r1a86fP5/0jZJSNRMTE3bu3EnatGm5dOkSHTp0iDGfWnRCCKWk7OLFi3nw4AF6enpUrFiRzp07kydPHkJDQzlz5gyjR4+mRIkSjBw5UtnvP1etWjXatGmDEIKxY8cqk5gXLVqUIUOGfFH51b/27ePQoUOMHTuWYsWKxXiucuXKjB07lsePH8fz6kjly5cnbdq0BAYGKm3CwMBAmbNHljeSvkR4eLhSUaBZs2bKDfwHDx4o+1lUyVtXV1el9NiXcHd3p2LFitjY2LB48eIYz3Xu3BkzMzMePnyIo6PjF79Hcok+KuHztqV2RIIkST+mVJ1IAOjevTuurq5MnTqV8uXLc/jwYaWuqJQ0c+fOZdu2bejp6SnDRAsVKkT58uUJDg5mwIABPH78OMbNl8QEBgayadMmxo4dq9SZTpcuHb169VJGAmTKlIkePXp8k6x7kyZNyJ07Nz4+PsyYMYP58+fj5OTEqFGj8Pb2Jnfu3MqEXXERQjBhwgQ+fPhAgQIFqF69OqGhocr8HP369ZNzGXwjZcqUoVChQhQqVIiKFSsSGBhI5cqVlREgEFmHtXjx4kDkJJBRdVYDAwNxcHCgS5cuREREYGhoSI8ePeJ8n6gbYp6ent+tJmVwcDADBw4kMDCQSpUqxeoJrUamTJnYuHEjnTp1wsnJibZt28bbhqKvf+XKlTx+/Jj3799z6tQpHBwc6NatG6VLl1Ym/StcuDB9+vRh27ZtvHr1irt377Js2TJsbW0JCwtjypQp1KhRg4cPH37xZ3DhwgXlZlKLFi2+eD3Sz8vAwIAVK1Yopb5WrFjBiBEjEjxHtWrVivLly8fqSXXs2DE2b94ca/mockKvXr2Kd51RNz66deumOong7OzM0KFD+fjxI9myZWP48OGqXpdUV69eVUoWTZ8+PcZohOjmzZtH7dq1CQwMpEWLFnEmE6J+aJctW1b5gVutWjU5mkjHNBoN1apVAyAgIECZODgq2ZszZ854ex7Hx9bWFhMTE4QQbNu2LVnjbd26Nba2tpQqVYpcuXIp11Dh4eF4eXnx5MkTrl69ytGjR9mzZw+nT5/m9u3bvHjxAj8/PyBymy0tLSlQoIDSRr/2RnvJkiXJnTs3AQEBzJs3j6dPn2JiYqIk+xLz6NEjwsLCyJMnDw8ePKB3794EBwcrc2lpNBoePXqkLF+8RAkaNWrEokWLuHv3LlqtFiEETk5ONGrUiGrVqill2+rWrUvFihW/avuk1KlgwYJs3boVY2NjDh06RMOGDWOVlPPz86N9+/aMHz+eiIgImjRpwvbt23nz5g1///03GzZs4NmzZzx48IDFixdTsWJFgoODWbFiBcWKFYs3oTB79mwsLS15/PhxjDm5vkbevHkZOnQojo6Oscq4rl+/nmrVqtG0aVOcnJziHDnr4OCAv78/5ubmlC9fXnk8qjzowYMH5ZwiUpIZGBgo+9D48eM5efIkEFm1oGjRogDKdY+FhcUXj3pxd3endu3aSqWHKVOmxCgfbWlpqXTsmDlzZrLMA/a1ojplfj4/RFQpXEmSfk6pPpEAkXWMe/fuzbhx4yhXrpyuw0mVdu3axbhx4wBYsmSJ8qNUo9GwYsUK0qVLx82bN6lZsyYFCxakSZMmTJgwgcOHD/P06dNYN24+fvzI0qVLqVGjBjNnzuT169dkzpyZyZMnc//+fXr27EmjRo2wsLDAw8ND6XWV3DJkyMCSJUto1aoVenp6nDt3jiVLlhAaGkq5cuVYuHBhgqMgDhw4wLFjxzAwMGDRokUYGxuzZs0anj59SubMmRk1atQ3iVsCU1NTzMzMlL8cOXKwdOnSGDfLjYyM2LhxIxUqVCAgIIDevXuzYsUKGjVqxJo1awgLC1N+jEdPQEQ3fPhwcuXKhbu7e7LfMInP9OnTcXZ2JmPGjCxZsuSLeyJXqlSJ+fPnJ1qeq1OnTpQqVQqIrKOdL18+1Yk7jUZDoUKF6NWrF4cPH2bDhg1YWlpy+/ZtKleuzJIlS5KcgNmwYQODBg0CYNiwYUkuuyFJUTQaDSNHjmTdunXo6emxdetW+vTpo/Se/FyZMmXYsGED9+/f58qVK2zYsEH58bhx48ZY57KcOXMC8ScStFqtMumj2iRCYGAgAwYMICQkBBsbG/7++28aNmyo6rVJ8ebNG/r27UtERATt27ePd2JMiCxlsW3bNurUqRMrmSCE4MSJE1SuXJkhQ4bw7t07fvnlF3bt2sXp06eTdaRdeHg4M2fOpE6dOsrEt1LiFi1ahIWFBf/88w+LFy/G09OThQsXAjBjxowkl8QpWbIk69atA2DVqlWsWrUq2WKtVKkSO3bs4NSpU9y+fZuXL1/i7u7OhQsXOH36NHv27GHNmjXMnj2biRMnsmjRIjZt2sSBAwe4ePEi165d4/Xr10rCIap8k9oJW+OTPn16Fi5cqJQIqF27NuvWraNOnTqqXh91M6hIkSLKdeXJkycJCQnhl19+UerVRwkLDeX8+fNMnjyZqlWrUqBAAUqXLk2LFi04f/48+vr6tGnThvPnz3PkyBEsLCy+avuk1KtRo0YcOnQICwsLrl27hq2trbK/PXz4kGrVqnHgwAEMDQ1xcHBg7969tGrVKsZNvqhOIoMGDeLSpUscOnSISpUqxUgojBo1KsaIh6xZszJr1iwg8iZq1M3Vr+Xh4UHTpk15/fo1efLk4fbt2+zatYuGDRuir6/P5cuXGTt2LDVr1lSOZxCZLIw6Ls2cOTPGHA0VK1YkX758BAQEfPXopOieP3/O8uXL472mkH4c69evp02bNoSFhdG/f39OnjyJiYkJ27ZtU5IJtWrVwsnJSfX1XnTRkwj58+encuXKBAUFMXjw4Bijabt160aTJk2IiIhg0KBBuLu7J9s2fomojjdRifwoadOmZdLkycq/Tzg5fde4JEnSLVnUVuLq1at06dIFiKyfPGDAgBhZ51y5crFp0ybmz5/P/fv3CQgI4MaNG9y4cUNZxtTUFGtra4oVK4YQgr179yoXo7lz56Zbt2707ds3xo9ZExMTWrduzR9//IGjoyO2trbfZPuMjIzo2rUrv/32G4sXL8bNzY1mzZrRrVu3BOcn8PT0ZPJ/J8jBgwdTokQJfH19mTFjBhA56aSsD/jtvHr1StXnmzZtWtasWcOwYcM4e/asMiFU9uzZGTduHDY2Ngn2UE6TJg1z5syhQ4cO7N69Gzs7O3JFm7A4uR08eJBt27ah0WhYvHgxmTNnJjw8/Ju9H0TWNV+4cCG1a9dm7dq19OnTRxnJkRQajYb27dtTq1Yt+vbty4kTJxg9ejRHjhxh7dq1qm50bNiwQUnADRs2jDlz5ui8BqiU+nXv3h2NRkO/fv04fPgwnz59YsOGDUrv7M9pNBpy5MhBjhw5qFq1KidOnMDd3R0nJycaNGigLBd1LIgvkfDq1SuCg4NJkyaNqvl2IHLCyWfPnpE1a1YWLVr0TcqphYaG0rt3b96/f4+1tTVr1qxJtJ1F/WBu3749J0+epEWLFsyfP5/du3cryX5LS0tGjx7NoEGDMDIyStaYPT096dSpk1Iqae7cuaxcuTJZ3+NHlS1bNubPn0+vXr2YNWsWZ8+eVUa8fUnpPIgs7zBt2jQmTZrEyJEj2b9/P/Xq1aN+/fqqJhJPChMTE7JmzarqnP/p06cY125RvZIfPXqEj4/PV91wT5s2LTNmzCAwMDDeY0d8osp/Rp/Q8ODBg0Dk6NiMGTNib9+UF//NoXX23Dku3bjB6dOnuXjxIl5eXnh5eZE2bVq6du1K//79yZUrV5JGAUs/rqgJu+3t7Xny5Ak1a9Zk8ODBzJo1i8DAQHLkyMHWrVspV65cosd6jUZDrVq1sLGx4ezZs8yYMYOrV6+yfPlyvLy8WL9+vbKO6tWrKxPNjhs3juLFi8caSZAUb968oWfPnrx8+ZI8efKwf/9+cuTIQc6cOalZsyaenp78+eefbN68GS8vL1atWsWaNWuoWbMm//zzDwAdOnSgXr16sbapTZs2zJkzhx07diiljr5GWFgY9vb2PHr0CBcXFxYtWvTV65RSLgMDA2US7507d9KvXz9WrVpFnTp12LdvH8+ePaNYsWJf9Jvl1atX1K9fX0kinDp1iqCgIEqXLs2xY8fYu3evcq7WaDTMnDkTFxcX/v33X/r378+aNWuU0XffW1QJo88TCQBdu3Th+YrI67QePXpwpkgRihQp8l3jk6TvwdnZOdFlrKysfqpJrX+IEQnSl3N1dcXe3p6QkBAaN26s9GD73G+//ca+fft48uQJFy5cYPny5fTq1YvSpUuTJk0agoKCuH37Nn/++SeOjo4EBQVRrFgxli5dipOTE61atVKSCEII/v33X06dOkWnTp0AOHLkCF5eXt90WwsVKsSyZcvYtGkTPXv2TDCJoNVqGTlyJJ8+faJ06dL07dsXgOXLl/Px40eKFStG9+7dY7wm7PVrgh8+JCTa0PXgR48IfviQ4IcPCfOMuw6p9PVMTExYsmQJLVq0wMzMjL59+3L48GFsbW1VXfA1btyYunXrEh4ezpIlS1RP+phU9+7dU0b+DBw4UBn5kxwi3r4l/MkTwp8+Ux4Libb/VS5YkGbNmin79tdsY7Zs2di/fz/Lli3DzMyMCxcuUL58eXbu3Jngejds2KCUnpNJBCkuYZ6eBD94SLDzZ8fRBw8JfvCQsP96JsbFzs4OR0dHzMzMOH/+PK1atcLb2zvR9zQxMVFq30b1dowSlUh4/fp1nPOCPH36FIjswZ3QOSXK3r172b17N3p6eixbtuybDQufOnUqt27dwtzcnLVr16q+KRqVTIgamTBgwADOnTuHkZERQ4YM4d69e/Tt2zfZkwgnT56kXLlyXLhwQVn39u3b8ff3T9b3+ZG1a9eOhg0bEhYWpiRj5s2b91XH2GHDhtGvXz+EEFy6dImJEydSvnx5ihYtytixY3FyctJ5GZFMmTKRP39+4OtHJUDkTZykJhEAZQRN3rx5gchkXlRZI/sqVQhxdiYkWv33PBFautWogeO0abhcu8bx48dZv349zs7OzJ49+5t2aJBSJ2tra86ePUuJEiV49+4dEyZMIDAwEBsbGy5fvpzkMnMajQYbGxtOnz7Njh070NfXZ8eOHbHqvw8bNozixYvj6+vLsGHDVHV+0b57h/bpM7TR5hh5d+0aM3v0wOzNG8rlyaskEaLLnj07o0eP5vDhwyxdupRKlSqh1Wo5c+YM3t7eWFtbM2bMmDjfM2p04fHjx/Hx8UnSZxGXFStWKOXIli9fHqMDnfRjikomNGrUiLCwMPr166eMTChevPgXnU/d3d2pV69ejCRCrly5KFSokFIqeejQocqEzhB5LbZy5UqsrKx49OgRtra2jBo1imfPnsX3Nt9MVII/enxhnv/d84h2TssREsKI5s15d/UqYfHMvSJJqY2VlRVmZmZ07NiRsmXLJvhnbW2d5DllUzON+FZ3zH5Cfn5+pE+fHl9f32Trqa62p7JWq1XVqzH6TRUPDw/s7e158eIFJUqU4NChQ0qJgg8fPqj6IeXh4YG5uTmurq48ePCAf//9F19fX+zt7alcubJywv306ROurq4cP34cJycnZZje4sWLlQlgp0+fTsmSJVWVeQkICKBQoUKJLnf9+nUsLS0TXS48PFwZpiiEYNmyZaxcuRJTU1P27dtH3rx5cXNzw87OjrCwMA4ePBijN0zY69e4N26CiKOeZxSNkRF5jhzGMFpPHrXfmxBCdfkbXdyYVXMY8fPzw8LCItH2EdWOnj17lujk2y9evIi1jBAi1mcQEhKi/LiPj4uLCxUrViQ8PJwxY8ZQtWrVeJd99eoV+fLlS3B9ELmfGhgYcOLECZycnJSbjuXLl2f9+vXKjUdvb2+srKyU+P/880+uXLnChAkTYvzICgwMjLMnmPbtW3w7dYYE9j+MjPCbPYvqzZsTGhrKpk2bqFu3rjJxdUJCQkLi3f+eP39Oz549uXr1KhB5M3fBggXK9kTZuHGjMhKhf//+ODg4JLqvRkREqG4fam7igm7aR3JSe55JyvlI7WWAVqtV9flptVpVx6vPj2thnp64NGiY6HE039EjGMbRM+vt27fo6elx+/Zt2rdvj4+PD4UKFWLEiBHY2toq57S3b9/GmiTu7du3VK1aVTm+lyxZkmXLliGEYOXKlQQHB9OhQ4cYpRQA9u3bh4uLCx06dKBRo0YJbq+npycTJ04kMDCQ4cOH06JFC86ePUvjxo1j9KIOCgqK1X7i8v79+zjP03v37lUmVd64cSM2NjaqepD6+/sr7S04OJhOnTpx4sQJWrRoweTJk2PcIFVzfRAUFISxsXGCy4SHhzNt2jQWLVqEEIISJUqwadMmWrduzfPnz1m6dGmMsnSJnRPUSmo78vHxSbQdqW0fgOrrHDUCAgKUjhpv3ryhQoUK+Pr60q5dO1avXq0sJ4RQdW3q7+8faztcXV1xcnLCycmJ8+fPxyjzYWJiQtWqValTpw516tSJ1Rvr48ePqspf+fj4kCFDBoQQ/P3336xYsYJ79+4xYsQIunbtqsTk5eUVq/0OGzaM3bt3M2DAAOUm46NHjzA0NEz0fR8/fqzqPOjr60vBggXjfb5169Z4eHiwYsUKunfvzunTp2nUqBElsmZlZ6bMiZ6fs/21F4PPji8Q2Sta7VxcibU3QHUpQjXXnUm9rkvO30cpndrPWe35MjAwUFnO19eXjh07cvbsWUaMGMHEiROVa6CIiAhV+0twcHCs912yZAljx47F0NCQkydPUqFCBR49eoSBgQHu7u60bt2agIAA+vTpw4ABA2K89u3bt2TJkiXyH++90B8yBE0cyfcowsAAwz/WoImn7b1+/ZpMmTIBkdfnO3bswMXFhYkTJ5InTx5lufDwcCWRCFCuXDmcnZ0ZPXo0kyZNUh7XarWYmpom+rn4+/ujr6/Pu3fvKF26NH5+fuTLlw9XV1eKFy/OhQsXMDQ0RAihuqxfYueFb3FdJ8VNbbt0cXFh6NChHD58GENDQ1auXBlnmbuIiIgEr69evXpFvXr1cHV1JX/+/Jw8eTJGkjgkJISyZcvy5MkT+vbty8CBA2O0y8ePHzNnzhxlrgSNRoOtrS1lypShRIkSiW6Hj48PhQsXTnS5jx8/xvs7OSAgQKkacebMGYpnyYJX23aJntPyfnbPIy5q74OoOU76+flhaWn507Sj27dvU7ZsWW7duqWUZATQBgbyuEzkPa3Ct2+hJ+fy/Gpubm6Jdnh2dnamY8eOsb6P1CYp7UMmEpLRtzgwJecFP0SeKCBmEiFv3rwcOnQoxolQq9WqugGf0I2+d+/ecfToUQ4fPszJkydj/CjWaDQIIShYsCC///47AwYMoGjRokqd68RotVpVQ+vfvXun6oZHWFgYmTNnxs/Pj379+rF//34g8qK6Z8+eQORQ2v3791O3bl2ll1mU4IcPedmyVaLvk3fPHkyKFVX+HddN77ioXQ5S7o3SpF4ou7i4JHrTSAihqkdvWFiYqhvNI0eOZMmSJWTLlo3z58/Hu+8EBgaSMWPGeNfz+PFjDhw4wL59+5SJWCGyDnmdOnVwcHCIcePCzc0NU1NTPn36xNixYzlx4gQQOZFp9CHmGo0mVgIjNDQU7bNnqvY/87V/MGfXLpYvX07+/Pk5d+6cUgM+IYntfxEREcybN48pU6YobWn16tU0btwYgNWrV8eYE2HOnDmqbvColZTTWEptH2rpMpHwrY9XQQ8e8KJF4mVYPj+OxvW+Dx48oF69ekptZTMzMxo3bkyrVq2oU6dOnDdaunTpwrZt22jXrh1btmzB0dERPT095s2bx4MHD+jWrRs1a9ZUltdqtfTv35+goCC2bNmS4DkpJCSEbt268fTpU2xsbNi1axflypXjxYsXWFlZMXv2bKXcntoe3nHdGLlz5w5Vq1YlODiYCRMmMGXKFMLDw1Ud/4QQMc7nQgi8vb1jHWPDwsJUfb9hYWEJtnNPT086d+6s9J7v2bMnc+fOxcTEBAcHB8aPH0+ZMmW4ePGi8hq1N1QT8y0SCZC8x5fP94NLly6xbds2BgwYQLFixZTHIyIiYuwH586dY9u2bcyaNStWQkrtD/bE4jpz5gxHjx7l2LFjsXpfValShS5dutCyZUvMzc0JDQ1VdV0XHh7OkSNHmD9/Pjdv3ozxXL169Vi/fj3Zs2ePM9GxadMmBg0aRJUqVTh+/DgAt27dUjVyJiIiIsbnGR83N7d4rwnCwsIoUaIEERER/P3331SuXJkBAwawatUqxrVtS8d/7iS6/rx792AaRxxJKW2kpp3LRML3kdzXJZ9/b0IIfH19Y5Xy+pqOR0IIWrduzZ49e8iRIwe3b9+OUZJ2z5499OjRA41GQ9OmTWnbti22trYYGhri7e39/4nTHz/Gp2evRN/fYt1aDOK5yRkREaHq+j44ODjGeWbv3r106tQJPT09jh07FmPkr5q5YgIDA9FoNPTv35/NmzdTunRp9u7dS7ly5fj48SNTp05lxIgRCCFUn49kIiHlSMr1bkREBJ06dWLnzp0YGhqye/dumjRpEmO58PDweL/fz+dEOHv2bJwjzc6fP0+tWrXQaDScPXuWihUrxlrm+vXrzJ8/n8OHDyuP1ahRg6FDh1K9evV4Y3Bzc1O1n/r4+MSbEBFCkDdvXiIiIrh06RKF9A3wimfewejy7NmNSdHY1+qfr1ttxyM17ehnOh/JRELKEt/3kdokpX3IORK+AW1gINpkqnmclAt+1ByIg4Lw9PSkdZs2vH35ksJ587Jr506yWlggok2wJbRatNF6Nn348IFTp04RFhZzhIRWaNH/7H1fv3nD8ePHuXXzZowTdr5s2ahXvz4NGjSgQvnylClThlfPnpHe2JgMadLg6uzMw3/+UVe7XatFGy3eeLc3OBih5nMJD+fh7dt0696d58+eYW5kxIwZM+javj3aoCCuXb3KiQMHSKOvz/zp09F+9gNfGxyceMz/LRf9tT9TIuHzzywxIigIkUg7EkKoWq82PByNiv2gf48eHPnrLzxevWLFokWM/K8MT6z3DQ6Otf89ffqUI4cPc/jw4RjJA3MjI6pXr06jxo2pV6+e0pMyxuuDg3n2/DlDhw3j5YsXmP938+PmpUucPnr0//OHaDTK9gohGDZ8OFu3bmXVyFGUTHTrQASHMLhPHw7s3MlrV1e2rF3LmNGjE39dIvufBhg9ZAh1qlWnb98+ODs706FFCzp27Ii1tTXjx4/HVKNh0KBBTJs0CREUhDaBnmpJ9TMlEpLajtScj1JKIkF84XE0rve1zpePq+fOsXr1av7at4+XL15wcNcuDu7aRZq0aWlQvz7NW7TA1tYW4//a2+A+fdi3fTuH9+zh1eTJ6P13A7Rgrly4PHzIa1dX9H77TXk/t5cvITgYC1NTCuXJAwnEv9LBgVfPnpErUya2/PEHg3r35u3Ll5jp6RHw4QODe/dm46pVLFy0SPX8JVqtFm20787b25uOrVqhCQmhSd26TBgxIvL7D49Ao6/uh9rn+4qFiUns853KRII2PDzedn7mzBn69OmDl5cXVunSsWjhQpo1bw5CoA0KokOLlsydNg3nf/7hn6tXlcnikzatewKxfYN2BMl7fIl+jvDx9qZHhw58+PCBfdu3M2LkSAYNGoSBgQHaiIgY+0H1ChWo/l+Jk1jfXTIkEkyAhjY21KtRg6Xz5uHs7IyTkxMnnJy4euUKty9f5vbly4z+/XfsmzShVatW1KhZEz1N3PtgUHAQ27dvZ8WKFbz4b+JySzMzOnXqRO5cuZgxcyYXnJwoX6IEDg4O1KtfP9bnXOnXXzHVaHhw+zYhvr4YGhlBSAioObZFRKi6niQ4GBHPjfrXbm4YabUYm5qSydyccH9/nA4exFSjodZvv4GKRIKI57imTUIiQZPMiYTEfld8q3b0I/jWiQSIvL78/DtQ+3swvvddt2IFT+//y5Mnj+nSpg2Ojo7o//edNbez41aPHmzcsIHj+/dzfP/+yLk/mjZq2TsRAACXuElEQVSlXr16VChfPrKjmMqJiUVISIzfnjGei4hQd30fEoI22uj9Zg0b0r19e7Zv306/bt04d+4cGf7r+KNm39cGBXH37l12//knphoNC2fOJJO5OXOnTWPwoEEsnjOHZg0akDdfPtXno8S+X9mOvp+kXO/qaTRsXrMGw4gI9u7dS8eWLalSpQo1a9aiZq2alP71VyDu7/eVhweNGjXitasr1vnzc+jQIXJkzBjnd12tfHl6deqEo6MjIwYO5NTp07E6YZQrUYKdW7bwyNmZZcuWsWfPHq5fuEC7CxcoXbo0AwcOpG7durF/66q8D0JwcLxtESCzuTk+Pj58ev8erYqOpgBH9+2jaSLVAFTfv1LxuyKp7UiSpK8jRyQko6gMzvVfCpJWZZkNSfpZ+EdEUOHZU9U9BWQ7kqTYZDuSpK8n25EkfT3ZjiTp68l2JElfL6ntSI5IkJLTzzgiQU62LEmSJEmSJEmSJEmSJEmSJElSvOS4uG+g4MULKXKOhH79+uHo6EjuPHnYtXMn2eOYrDL6+/p9+kTJ/ybyadqsGcuXLyftZ/VhIyK0GBioG0odV+3Wli1b4uTkRPcePdizZw9+vr5MmzaNatWrJ7q+woUL4//pE506d+bG9eukT2+Bn5+vMmyxZMmS2DdtSrNmzWLFDfDs2TNGjBjBw4cPAfj9998ZPWZMjDiDg4Ko/NtveLx6xdhx4/j9998xjaPGZvCjR7h37JTo55Db0RET6yLKv3+m0kZ+fn6gYsLPKBkP7E90joSAwEBWr1rF8uXL0Wq1pE2Xjj69exMeHs6Lly95+eIFLq6u+Pn6xvn67Tt2UD/apNlv375FX18f91evaNCgASHBwfz111+UKFGCw0eOMPS/CUwhshZxyVKl+K1yZSpVrkz5cuVI89nEa4EBAaSNZxs++fnRu3fv/9cH79WL/v37x9j/Dh08yIQJEzA2MeHq1avo6+uTN08eevfuzY4dO6hStSqHDx1i/oABtLh5K8HPCiDdsmUYFPwFgBUrVrBw4UL09fV5++6dUt4lLmr3v89rhQYGBeHq4kLRokVjvV5NrW61fqbSRkltR2rORymltFGwszMvO3RM9DWfH0eT+r5R+6lWq+XqtWtMnz6dK5cvU+CXXzh37hxVq1bl5YsXVK9enQ4dOiCEYMjvvxMSHEyz/yYrv3b1qjLx1vDhw7G3t4/1PsEhIQwcMJAnTx5TrFgxli1fzvJly/nrr738+uuvXLx4MVa8586do3mLFoT+Vxpix86d2NjYxFp3gL8/DRo04NGjRwAUK1aMQYMG0aJlS4w+GxIfHh6BfrTSRkIIAgIDY50XP58jIT6q50gID8cw2vp+HzqUzZs2kSZtWpY4ONCiRQsAgoKD43xfIQS//fYbz589Y/Xq1TRv0QIzFZNlqpHUdvTLhfPff46E/8oMDBwwgF27dlG3bl0ct24FIidV7tKlC//cvg3Art27qRvHRJCfS445EqJERESoqm3s9+kTTidOsH37dk6dOhXjuRIlSjBo0CDsmzZFX18/wfMQwI2bN+nRoweuLi6Ympnx/PlzJYZly5Yxc8YM8uTNy/79+3nz5o3qORKsra1xee5C125dcXVxifF8qVKl6Nu3LyVKlCBdHPtAeHg4vXr14vatW3Tr3p0OHTrQvl073r17x7p167AvWQq3jokf1/JsdcQkjnlWUvocCSTwW+Jzyfn7KKX7HqWN4nvfL50j4XOjR49mxYoVmKdPz0knJ3J9NpF6dK/c3Tl3/jz79u3D+/ZttubOE++yUdKvWI5BPBOYR0REqJqrLzgkJN65eNasXs2ECRMAsG/alNmzZ5MrnnnBgoKDmTd3Lg4ODoSHhzNu/Hh+j3bND5HfQds2bTh37hyFChXm1q2bca7rc2pquyf3dZ0Ut+S63o2IiODu3XucPn2aCxfOc+XqVUKilbfMmy8fhw8fJmeOHMr6Ejv/CiGoUbMm/9y+Tb9+/Zg+Y0a8y37y84ss4fcfPz8/9u/bx86dO7l7967yuIWFBQ0aNKBR48YUK1Ys3m3y9fEha9as8b5fjx49uHjxIiNHjaJ/gwZ87NM3wW0BmKCn4dijR/Tq3ZuFCxbEuUxyz5GQlPORJElfRyYSvgE9M7PkG0aUjBeOHh8+ECQE/X7/nRwFCiS4rEarJWuGDBj/N8lgl969Mc+UKfb7RkSgp+aGoFaL3mc/cO7du8eR06cJF4IgrZa3Pj6kSZOGomXKQLT5GeJbX0B4OG26dOHqtWuYm5uz6+ABDAwMWLx4MQcOHODa3btcu3uXmQsW0KxZMzp06ECJEiXQarVs2rSJmTNnEhISgqWlJXPnzqVDhw6x3mbZ0qU8c3cnZ86c9Pv9d/RMTdGL40aGnooJvKKWi75v/EyJBL3w8MQXikZjaoomgZtGFy9eZNiwYbx48QKA1q1bs2DBgjgTZO/evcPNzQ0XFxdcXV1Zu3YtL1684L2fX4zvQ2NqikZfn9wFC5Itb16cnZ3xCQ5GY2rKmb//JkgIatWqRadOnahduzZpP0scxNoGrTbO/cXT05NWrVrx4MEDzMzMmDVrFnZ2drGWu3TzJkFCULp0afTNzNBoNOiZmZHP2pogIdA3M8MwXTqGjh6NW6vWCcYCoDExRmNqGjkR56JFaIWgU7t2mH42Wd/n1O5/ep8lEtKamVEinkmpVR03VPqZEglJbUdqzkcpJZGg+cLjaFLfN2o/1QOq2tqys2xZSpUqxb9PnzJ93jxmLlhAy5YtOXH+POmzZMHOzo5a9euzb98+tu3dq6zHxMSE6tWrkz1/fvgsdiEEU6dO5e7jR1hYWDBp1iwM0qShQbOmbN27h6t37nDy4kXq168PRN6MXL9+PZMmTcL3vx+jVapUoexvv8U6hggh6NerF/84O5M1a1bWr18fWRs3nm3XCw9Xzr9+fn506NABJycnhg4dytSpUzH+73wrhFDVLvVUJhL0wsLQi3aDp1SFCgRt3EgaY2Pq2dsr36GeRhPv+1auWZN/nz7l/tOntDQ1TbZrq2/RjiB5jy9RV3SlK1dm886d3Lh/n1CNBmNjY46ePs2Ne/cIE4KiRYtSoVo1VfGpvV5TQ6hMJJgZGNC8fXuatWtHrVq1uHr1qvLcuatXlXUIIdBL5NqvYvXqLF+7FhsbG0yNjZXzIkC7rl1Zu2ULj1xdsWvenDlz5pDjv5s4CYqI4PKtW3Tu3BkfHx9y5szJ5MmTuXr1Ktu2bePqnTtc7duXXLly0aNHD1q0aBFj0ta5M2bw982bmJmZUd/env5Dh/Ly7VuKFStG07Zt0bi4Jh4Dkce/OI9rSUgkfH6dHadk/F3xrdrRjyDZr0t0kEiYtWgR569d48aNG/QaNIijR4/GezM0XaZMtOncmTadO+N56RKMHZf4+xsbx3udr4mIUHdM09OLcZ6Jru/vv+P6+jUrVqxgx7597D92jFGjRjFq1KgYE9CeOXOGvn378uzZMwCaNm3KgGHDYhyPAgIC6NmzJ8fOngWgXbeuqvflxD5n2Y6+n+S63tUDylWtwq+VKjJiwniCg4O5cuUKZ86cwcPDg6lTp8aYWFnN9ZWPjw8Pnj0jSAjKxHHtF50mLAy9aIkEC1NTuvbtS9e+fXnw4AHbtm1j9+7dvH7/ng3btrFh2zby58+Pvb099vb2sc+NJiYJ/uauXKsWThcusP2vvxjQtGmC2xGlSctW/DV9Gq/ev493f03OREJS25EkSV9Hljb6Cam9wDQxMaF168ibk1u2bEnWGCIiIuj9X8/xevXqsXPnTgAGDBhAxnhuPEYXEBBA69atuXr1Kubm5uzfv58SJUpgbW3NH3/8wcOHD5k+fTp58+YlICAAR0dHGjRogJ2dHW3btmXSpEmEhIRQs2ZNTp06RZ04evJ5enqy4L8M+owZM2JcdEq64+Pjw5AhQ2jevDkvXrwgZ86c7N+/n23btsU7ysbS0pIyZcrQsmVLRo4cSdGiRZP0nkII/v77bwB69epFtWrVEk0ixOfBgwfUqVOHBw8ekDlzZo4cORJnj+MnT56wf/9+ILLHc3QtW7YE4PTp03z8+DFJN69WrlzJsGHD0Gq1dOzYkQ0bNnzRdkjSj8LCwoLVq1cDsHTpUjJkyMD8+fMB2LVrF1euXMHe3p4aNWqg0WgoWbIkffv2ZenSpXTo0CHuCSvXrePUqVMYGBgwb948sv3X27BDhw706dMHIQTt27fH1dWVo0ePKr2e3717R4ECBdi+fTsnTpyIs0fmwoUL2bdvH4aGhuzatYt69eqpOgZ4eHhQs2ZNjh8/jlarZeHChVSpUgVnZ+ev+fhU69KlCwULFsTLy4tFixapek3hwoUBlJEXP6POnTuTI0cOPD09+eOPPxg4cCBDhgwhLCyM5s2bc+rUKTJkyKDrMBO1YMECrl69iqGhoXL+PHHiRJLX8/TpUwCsra1j7PdR59M8efLw/Plzhg4dyvv37xNd35EjR2jevDk+Pj6ULVuWU6dO0aJFC+bPn8/du3cZNWoUlpaWuLu7M2XKFGrUqMGKFSvw8fFh7969bN68GYB58+axceNG7t69S4YMGThw4ACmyTSCRpJ0wdDQkHXr1mFubs61a9eYN2+eqtdljqPjmS5oNBrmzZvH5cuXqVq1KsHBwUybNo0iRYqwY8cOvLy86NatG7Vr1+bZs2dkz56dbdu2sXXrViXBDpG/B+vWrcvBgwcxMjJiw4YNDBw4UIdbJqU0JiYm1KpVi+nTp7Nhw4YYSQS1lixZgq+vL0WLFqVZs2ZfHEuxYsWYOXMmDx8+ZMWKFdjZ2WFsbIyLiwuLFy+mdu3aHD9+PEnrbNOmDWnSpOHp06fcupX4CHj4/wjIsLCwJG+DJEkpn0wkSAnq3LkzAPv27YscMpYMwsLCGDduHDdu3MDc3Jzw8HACAgKoVq1anOUhPhcQEMCIESNiJRFcXV2VngYZM2Zk4MCBHD9+nF27dmFvb4+RkRF3797l8uXLGBsbM2PGDP7880+yZMkS5/tMnjyZwMBAKlWqpNy4jY++hQWaRIbQa4yM0Le0SHT7pLgJITh48CC//fYb27ZtA6B79+7cu3ePRo0afdP3fvnyJR4eHhgaGlK+fPkvXs+VK1do0KABHh4eFCpUiJMnT1K6dOk4l120aBFCCOrXr0/JkiVjPFe4cGFKlChBeHg4Bw4cULX/YWTEKkdHpk2bBkQm7ebPn6+qFIIkfQ8GlpY6O442aNCArl27IoSgR48e9OjRg7p16wKRSYFHjx7RvXt31q9fz/Dhw6lcuXKMGw0QeYx69uwZK1eu5I8//gBgzJgxsdr4kiVLKF++PN7e3pQqVQo7OzsePnxIhgwZWLJkCTdv3qRJkyZxJgecnJyYMmUKEHlj9rffflO1fffv36dq1arcv3+fLFmysGDBAqysrLh79y4VKlRg1apVSepB+yUMDAyY8d9QfQcHB968eZPoa4oUiSxh9fjx428aW0pmbGzMyJEjARg7diybNm1Co9Ewbdo0tmzZ8sVJ7e/JycmJSZMmAZHntl69egGRibukitoXrOMoA5QnTx4lmfDq1SsGDhwYbzJBq9WyYsUK5syZQ3h4OM2aNePgwYNkzpxZWcbKyooxY8Zw7949Ro8eTY4cOfj48SMODg7UqFGDiRMnAjB48GA8PT3Zt28f+vr67Ny5k/z58wOgb6nu+tBARRkXSfre8ubNy+LFiwGYP38+ly9fTvQ1GgsLUHFNqklkNGxyKVWqFCdOnGDnzp3kzp2bV69e0b59e3LmzMnmzZvRaDT079+fBw8e0KRJkxivvX//PtWrV+fOnTtYWVlx7Ngx2rRp813iln4ePj4+ODg4ADBu3DjVnT4TYmBgQNWqVXFwcODKlSvMmTOHsmXLEh4ezqhRo5TSzmqYm5vTqlUrADbv26eqfZM+svxWaGjoF2/Dz8rNzY3bt28n+Pe9OgFJUnxkaSMpQRUqVKBIkSI8evSIPXv20L17969a3+3bt+nVqxd37twBIsvRrFu3DiMjI1avXp1osiIgIIBhw4Zx9+5dJYnwyy+/0KRJE65cuULFihUZPnw4tWvXRqPRoNFo+O233/jtt9/4+PEju3fvxtnZmQEDBlAwnrqcAJcvX1ZuVs+bNy/R3p6G2bOT7+gRInx80AYHK/Ml5HZ0VMoe6VtaYChr96m2e/fuGOUDTpw4wdGjRwEoWLAgixcvpkKFCt+lTmjUD6cyZcpgZmZGYGBgktcRERHBwIED8fPzU5IhFvH8iLp16xanT59GX1+foUOHxrlMq1atuH//Pnv27KFbt25x7n+d3N0JjAgne/bs5ClUkA3/3dwcP348gwYNSvI2SNK3ZJg9OwWOHyPc2xsRHKzMl/C9jqMLFizg9OnTuLi4MG7cONq0aYO3tzc3btxg6dKljB8/npyf1VfWarU8f/6cv//+m3PnzvHq1SvlubZt28aZHDc2NmbPnj2ULVsWLy8vjIyMGDJkCOPGjcPCwiLe48vz58/p1q0bQgi6deum+nx85swZ2rZti5+fH0WKFOHQoUPkzZuX1q1b07NnT5ycnBg8eDDHjx9n/fr18SbXk0PTpk2pUKEC169fZ+bMmSxbtizB5aMSCc+fPyckJOSnHRnYuXNn5s+fj4eHBxYWFmzatCnOkZQp0fPnz+ncuTNCCLp3706vXr1wc3Nj6dKlnD17lnv37sVKlick6sdz1L7xuahkQu3atXF3d2fgwIEsX76cTNF6SQcFBTF16lRljqKRI0cyevToeG/epEmThnbt2tG1a1eOHTvGH3/8oYySqVOnDr/++is9e/YEIpM9tra2ymsNs2cn/7GjRHhHnp+j5kvI7eiInmnkcc3A0lJeH0opVsuWLTl9+jTbtm2jV69eXLp0KcH5C/SzZMFy21aEjw8iJATfAZG999OvWE5AWBhp06ZFY2GB/jc813xOo9HQqlUrGjVqxIIFC5gzZw5BQUEUL16cNWvWULlyZYAY59+AgADatWvH69evsba2Zs+ePeTNm/e7xSz9PJJrNEJ80qVLR4sWLbC3t1facP/+/dm7d6+qShAQ2Xlv06ZN7DpzhtHHj5PTPD3akGBlvoQMa1ajZxx5TtOkN4f/5nCSIxKSxs3NDWtra1X3GszMzLCysvoOUUlSbDKR8IOIiIhIdCKuqN6G4eHhhPw3kWN8wsLClB/s7du3Z9KkSWzatCnWPALBwcGqhm6/fPmSpUuXsnbtWiIiIrCwsGDkyJFKL5dBgwaRPn16Xrx4Ee+NgsDAQMaMGcP9+/dJkyYN+/bto0CBAkqJI4Br167RunVrSpUqxfDhwylTpgzh/9XMMzc3p0ePHsr6wqPV0gsNDSUoKIijR4+yceNGzkbVwGzXjlKlSinLJtRj0zB7dgyzZ0cb7cBvXKRwrDkRoktKLfGfzejRo2M9ZmBgwJAhQxg6dCjGxsYIIVT1og0PD4/RPqL+Pzw8PMYFjre3t9JDP2qCQ39/f2V/KF26NN7e3oSGhsYoJeHt7c2tW7ewtbWN8X2+ffsWb29vILJH5vPnzzE3N2fhwoX4+fkpibP3798rN+98fHwYO3YsAM2aNSNHjhxKe9Vqtcr/N2nShEmTJnHq1Clev35NhowZ0WTMiOa/CToBpm7ZQp8hg3F2dQVXVzQaDXPnzlVGGoWHh6vqKaLVamP1vo5vObW1LtV8b8ldkz+55wKA1D/ngi7F9X0YZMuGQbZsMY6jRoULoR9tYuD4vke1+198y5mbm/PHH3/QoEEDVq1axfDhw+nRowfe3t48e/aMhQsXMm7cONKmTcvDhw/5559/uHv3bowEuJGRERUqVMDW1pb69evHiPXjx4/KnC4Amzdv5sSJE7Rq1YqcOXPi4+ODj48P4eHhMXpFQ+RxqE2bNvj4+FChQgXmzp1LSAKTTEb5888/6devH+Hh4VSuXJlNmzZhbm7Ox48fMTY2ZvPmzaxfv57p06crJZbWrFmjzN0Ql/DwcFWT2MZFo9Ewa9Ysateuzbp16xg8eDAWFhbxfm9mZmakS5eOT58+cffuXWrWrPlF75saRZ9k18DAgM2bN7N9+3YGDRpE/vz5lefDw8NVT8irZrLlcJV1hkNDQxPd//z9/WnZsiU+Pj6UK1eO6dOn4+/vT4YMGWjcuDH79+9n0aJFLF++HEDVfhXVizJ37tzxdj6xtLRk1qxZjBs3Dnd3dwYMGMDixYvJlCkT79+/Z9y4cTx9+hRDQ0OGDx/OoEGDEr0uDg0NxdTUFDs7Oxo2bMjff//NkydPqFKlCp06dUKr1dK8eXM6dOgQ6/vQy5IFvSxZYhzXDAsVxEhFR4hvPVJI+rEIIVRPzKz2fAkwZ84crl69iouLC0OGDGHjxo0xrn/CwsJiXk9aWoKlJSLaNak2Tx7Cw8LQ/+/GZfTfpZ8fmwIDA1UdD4KDg0mXLl2iy/n4+Cidk/r160fTpk35559/qFOnDoaGhnh5eQGR7Tyqk8/06dNxdXUlZ86cODk5YWlpqcSclOvJ5FhGSt0iIiLi/b0QfTTCsGHDCIrWZuITEBCgqv1+fi4yMDDAwcGBFi1a8PLlSwYPHsymTZt49+4dAQEBia6vYsWKXLt2jWU7djBlyhRE0P8nmA7Omg2N6f87AEa16ZCQkHiPSUmZIyE5lkkNvLy8CAwMxNHRMc6Rl9FZWVmRO3fu7xSZJMUkEwkpnNqhbUkZAmdqahpvT+goISEhyg3V9u3bM2XKFK5cucLGjRtp3ry5chPV0NAw0dIoZ8+epW/fvrx8+RIAe3t7pk6dyuzZs/Hy8qJQoUIMGDAAgEKFCsWZWf306ROtW7fm/v37ykiEsmXLYmdnx9WrV0mfPj2Ojo6cPXuW1atXc/fuXTp37kzRokUZM2YMLVu2jDdOFxcX1q1bh6OjI2/fvgUib3Y0bNiQRYsWxUpsJPpZR3s+alREfBJ7PvpyP5uoi/solpaWjB49mhIlSiiPqb1oMDIyivEZRn2HBgYGsd4jaj+J+m/atGm5ceMGALVr11Z6YUX9NzAwkGbNmnH//n0WLFjAsGHDlPW9e/dOiTNqLoIOHTrEan+ZM2cmT548BAUF0b17d+WHy9y5c2PcUAwNDVW2o3DhwhQvXpx///2XrVu3MnjwYCDmvtKgQX3u29ynb9++ODk5sWrVKtq2bas8n9iNkyhq91NDQ8NkvaGvltr4pLgld9Ikub8Ltd+vnp7eV++n9erVo0+fPqxZs4Zdu3Zx8+ZNGjVqhI2NDU+ePGHRokX4+fnh7++vvCZ9+vTY2dnRtGlT6tWrF2+pmehJBICiRYvGOV+LgYFBjPNOVE9u5/8mV969ezeWlpaEhYXFez4SQjBz5kyllFmzZs1YtmxZrISgnp4effr0oVq1avTt2xdnZ2eaNWtGnz59mD17dpyJfUNDw0RvIEctF9fnbGtrS8OGDTl69ChTp05V5qeIT+HChbl58yaurq46SyTo4hjz+Wdfs2bNOLc/IiIiWUvUqb2e1NfXT3BZIQS9e/fm8ePHZMmShT///JM00RKCgwYNYv/+/ezdu5cpU6aQNWvWRD/jwMBA3NzcgMhyJQl1ZKlXrx4VKlTAzs6Oly9fMmbMGObMmcPgwYN5/fo1VlZWbN++nZIlS6oa6ZImTZoY8RcoUIBPnz5RrVo1fH19qVChAps3b07w+BJjcnmV+1Nylx9MjpIZUeR5N37f4jyodrnkvA6LGvVrbm7O9u3bqVq1KgcOHKBLly4sWbKEPHnyAJEj7eJ6X21QEJ/++/8MGTOS0cREuaF/6dIlxo8fz40bN5g9ezaDBw9W1hHf+j5namoaY/RyfD5PVGTLlk2Zuyg6IyMjzMzMuHbtGitWrABgzZo1cU7cLvf/1Ce5r2PVXAsltr5ly5bh6+tLsWLFaNasmaqEv4WFhaqyhubm5jFi1Gq1BAUFceTIESpXrsz169dZtmwZ3bp1U7XN7dq149q1a+zevZtJkyaRLm0aotLjadOmUSZsFkIo15vh4eEJnnfUXt8n5kdrj9bW1pQpU0bXYUhSvOQcCVKicuTIgZ2dHRD5wy9PnjzY29uzefNmPn78GO/rvL296du3Lw0aNODly5dky5aNTZs2sWLFCv7991927typTISVUK+TqCRC9DkRfvnlF+zs7Lh06RLp06fHyckJOzs7FixYwIsXLxg3bhzm5uY8fPiQzp07U7JkSTZv3qz0Pg8LC2Pfvn00bNgQa2trFi5cyNu3b8maNStjx47l8ePH7N+/Xw4X06E9e/Zw5MgR5c/R0TFGEuF7efbsGe/fv8fExCTWCV0IwYABA7h//z4AU6dOjbPu9+XLl3n48CGmpqZ06tQpzveJiIige/fuXL9+HQsLC/bt2xerV/Ln+vfvD8DcuXOVkQ+fs7S0ZOfOnXz8+DFGEkGSpLjNnz+ffPny4e7uztixY8mYMSMHDhwgc+bMeHp64u/vT/bs2enTpw9HjhzBzc2NP//8kxYtWnyTevVLly5l7969GBoasnPnzngnlY8SFhZG7969lSTCoEGDWL16dYKjiooWLcrRo0eVhOSaNWv47bffuHv3bvJtSDSzZ89Go9Gwa9cubv83/D0+hQoVAn7ueRJSo/nz5/PXX39haGjIli1bYt20K1u2LJUqVSIsLIx169apWuejR48QQpAhQwZV12fR50x4/vw5rVq14vXr1xQpUoSzZ89SqVKlL9o2iLwh061bNx48eEC2bNlilWOUpB9NuXLlWLp0KQYGBhw6dIjixYszf/78JJcuuXPnDo0aNaJ69epcvHiR4OBghg4dSufOnb+odGhyCwkJoWfPnmi1Wjp16kSDBg10HZL0g/Lx8WHJkiUATJw4MVkTvVHCw8M5c+aMcg8nQ4YMODs74+joiEajYdWqVezbt0/VuipUqEChQoXw9/dn+/btCS4bdW9HzpEgST8mmUiQVFm/fj1TpkyhZMmShIeH4+TkRN++fSlYsCBNmjRh06ZNMZIK+/bto3Tp0mzatAmATp06cebMGapUqcKMGTPo0qULEFn3t1y5cvG+r5+fX5xJhNatW8dIIkSfANfKyooZM2bw4sULpk6dSsaMGXn27Bm9e/emaNGiDBw4kAIFCtC2bVtOnz6NRqOhTp067N69GxcXF6ZNm0a+fPm+zQcppTp///03EPkD6vMbcevXr2fz5s3o6emRP39+Pn36pJQlim7t2rVAZJ3ZuOrKCiEYOXIkhw8fxtjYmB07dsRb/zm6zp07U6xYMT5+/MjcuXMTXFZNT42dO3dib2+vajI9SfpRpU2bVhlBtGHDBpycnMibNy8nT55kxowZXLx4kadPn+Lg4ICNjQ2GhoaEhIRw7tw5Jk2aRNWqVSlWrBhbt25N8H1cXFzYtm1bjNENnxNCKPMIzJs3L9HJlf38/JREv56eHsuXL2f8+PGqfpyamJgwb948Dh06RLZs2Xj06BFVq1bl2LFjib42qUqWLKkkVadOnZpg79ioRMKTJ0+SPQ7p2zhx4oQyEfGcOXOoWLFinMv169cPiGxnam4gPnjwAIicH0Ft78PoyQSIHBFz6tSpL651HhERwfHjx2nWrBkHDhzAyMiI3bt3J5rgk6QfQe/evbl9+zbVq1dXSs6WLVtWuVZOiMt/86WUKVOGo0ePoq+vT+/evZk1axb6+vps3bqVKlWq4OLi8h22JH6zZ8/mwYMHZMqUiUWLFuk0FunHtnTpUmU0QvPmzZNtvcHBwRw+fJhevXqRLVs2ateuzYoVK/Dw8CAsLIwuXbqQK1cupk+fDkReX/7zzz+Jrlej0dC7d28ApVx1fGQiQZJ+bDKRIKmSLl06Ro8ezbVr17h37x5TpkyhRIkSMZIKuXPn5rfffsPExIR27drx5s0bChcuzJkzZ5g2bRoPHz6kdu3arF69moiICOzt7Rk/fnys9/r06RMHDhyge/fuWFtbx5lEiCpn9HkSIToLCwvGjBnDkydPmDNnDlmzZsXNzY21a9cqow/GjBmDs7Mz+/fvp2nTpqqHKEo/j6ib6lWqVInx+O3bt5UJi2fMmKHcNNy8eTNXrlxRlrt79y7Xrl3DwMCArl27xlq/EIK1a9eydu1aNBoN69ati/Ve8dHX12fWrFkArFq1CldX1yRvX5SjR4/SrVs3Tpw4Qe3atZk+ffoPU29SkpKqRo0aSsm9fv364e3tTaFChRg+fDjlypVDT08PDw8PFi5cSJMmTciYMSO2trbMnDmTK1eu8OjRIzp37kyPHj3irHd7+PBhGjVqxPjx46lfv74yz8/nbty4wcuXL0mTJk2MOX7i4unpiY2NDadOncLMzIy9e/cqP/iSok6dOty4cYOGDRsSFhZGx44duXPnTpLXk5hp06ZhZGTExYsXOXPmTLzLFS5cGJAjElKLZ8+e0alTJ4QQ9OjRI87zXpRGjRqRJ08ePn78yM6dOxNdd9T8CGoS7dHlyZOHc+fOsXv3bvbs2UP69OmT9HqIrFs8ceJEChQoQOPGjTl69KjSmzO+RIkk/YiKFSvGmTNn2LBhA1ZWVjx48IA6derQp08fPnz4EO/rKleuzJ49e9BoNLRv3x5nZ2dWr17NmDFjOHXqFJkzZ+bu3buUL1+e06dPf1FsHz9+5OTJk6rnjfncw4cPmT17NhBZckbtRLSSlFTJPRpBCMFff/1Fu3btyJYtG82bN2fLli18+PCBjBkz0q1bNw4ePEjdunWVsrw9e/akdevWREREMHr06DhH1X+udevWpE+fHldX1wSv3WQiQZJ+bDKR8BOJqrO6YMECjh49+sU3CQsWLKhMXNejRw+lll94eHiMEgUdOnTg2rVrSg/KLl268PLlSzQaDatXr2bFihVKXdo3b97g6OhIz549+eWXX+jatSv79u0jMDCQ/Pnzs3//fkqXLs348eOVxEJCSYTo0qZNy9ChQ3n8+DFLliyhe/fu7Nq1i2fPnjF16lQ5+uAnFHWxtmrVqnh7uUa1l6jJHKP3BH727BmNGjUiJCSExo0bM2rUKCpWrKjcMGnXrp1SD/3w4cMA5M2bN1YphmvXrtG2bVulx/HcuXNp2rRpkralTp062NraEhoayqhRo764Xf/+++9otVry58+PVqtl5syZCV4gStKPburUqfzyyy94enpSp04dnj59CkQmu6dOnUqJEiWYMGECp06dIigoiCxZstCuXTvWrl3LhAkT0Gg0bNq0iYULF8ZYr5+fH0OHDiUoKAhjY2M8PDzo3bt3nG03ari5nZ1dgvXgo+K9d+8eEDm3ka2t7Rdvu5WVFTt37qRy5coEBAQwadKkL15XfPLkycPAgQMBGDduXLwTDEYdiz08PJI9Bin5LVu2DB8fHyDyu4tqN3H59OkTWbJkAeCvv/5KdN1RPZU9PT2TfKPQysqK+vXrq6o/HZehQ4cyZ84cPDw8yJgxI4MGDeL27dt07tz5i9YnSamZRqOhS5cuPHz4kJ49ewKwZcsWKlSowMWLF5Xlnka7xo6IiKBBgwb8888/ODo68ssvvyjP1ahRg5s3b1KxYkW8vb1p3769MreeWu7u7vz222/Y2dnRvn171fOARQkPD2fo0KGEhYXRtGlTWrVqlaTXS1JSHDx4EF9fXzQazVcnrHx9fWnTpg1t27Zl7969BAQEkDNnTgYMGMCZM2d4/fo169evp1GjRmzfvp1ffvmFly9fMnDgQNavX0+hQoXw9vZm3LhxhIeHJ/hejx49Usp4HjlyJN7los61SS19JklS6vDTJhIePHhAw4YNyZYtG3369PkpDnKDBg0ic+bMSp3Yxo0b8++//8a5rBACX19fnj17xpUrVzh48CDLli2je/fulC5dmsyZM1O7dm3Wr18f7wnnr7/+YtOmTcrNkahyRkIIFixYwMmTJ1m6dCmNGjWiXLlyjBkzhvPnzxMaGkq+fPkYOHAgZ86c4ebNm5QuXZrbt2/j6OgIwLZt21QlEaIzMTGhb9++rFq1Cnt7ezn64CfWv39/0qdPz+3btylfvjyrVq2KdROvb9++Sr1jW1tbypYtC0QmEVq1asWbN28oUaIEmzZtUhIT8+bNo1ChQri5uVGrVi08PDyoV68eRkZGPHv2jGHDhhEaGsqDBw/o2bMnXbt25d69e5iamjJ9+nSlzENSRM0zYmhoyOHDhzl86NAXfSZRPTyjkntp06alWLFiX7QuSfoRmJmZ8eeff5I1a1YePHhA1apVmTRpEiVKlGDOnDkEBQVRsWJF5s+fz7179/Dw8MDR0ZHu3bszdepUJUF44MCBGOtNmzatUgYlKrlYrFixOEu1RJUVsre3TzTe9u3bkytXLgDWrVtHkSJFWLlyJcHBwV+0/eHh4bi7uwMkWlLpS40fP56sWbPy9OlTpk6dGuv5gIAAxowZA0DDhg2/SQxS8urYsaNyg/CPP/6gUqVK2Nvbc+jQoRjXi7du3aJ69epcv34dIyMj5RoxIV26dMHAwIDjx4/Tv3//L+51nFRarVa5Obps2TJevnzJokWLKF68+Hd5f0lKqTJmzMiaNWs4c+YMBQsWxNPTk/r16zN9+nSuXLmizLEHkTcd9+7dS8mSJeNcV86cOTl37hzVq1cnJCREGRmghru7O3Xq1FGSjfv27aNp06YJlg783B9//ME///xD+vTpWb58+Q83eauUstSuXZtChQohhKBu3bpMnjw50Zv4nwsODmbJkiVYW1uzf/9+DA0NGTZsGFeuXOH58+csWrSImjVrxkigW1pasnv3bvT09NizZw83b95k3rx5pE2blvv377Nx48Y438vf359FixZRr149PDw8yJAhA61at443toCAAIBvMneYJEkpgPgJXb16VVhZWYmpU6eKhQsXCjMzM7FmzZqvXq+vr68AhK+vbzJEmTRhYWGq/l69eiWGDx8ujIyMBCD09PREy5YtRfv27UWdOnXEr7/+KnLkyKE8n9Bf9uzZRcOGDcXEiRPFvn37xIsXL0RwcLB4+vSpqFWrlrJc7dq1xZUrV8SrV6/En3/+KbJkyRLn+sqUKSOGDx8urly5Ij5+/Ci8vb2Vvw8fPojy5csLQLRu3Vp4e3sLrVab6F9ISIiqv4CAANWfYWIiAgLEw8JFxMPCRUS4v3+iMf4s1LaPpLQjNftA1H4QGhoa4+/58+fCxsZG2f/q1Kkjrl+/Ljw8PJS/ixcvisWLF4vnz58LDw8Pcf78eZE5c2YBiBIlSoh3797Feq9Xr16JQoUKKW3k1KlTYt26dUqbyp07t/KehoaGon379uLs2bPi06dPif59+PBBBAUFxfk3ZswYAYi8WbMq+19EQECCn19wcLDy9+eff8Zoj3PmzFGeCwoKUvU5R0RE6GQ5te1I7bqS+32T07doR7qS0Oca7u+v7Mdhnz7pZL8KDAwUQUFBwsXFRfz2228x2keBAgXEjh07RGBgoAgMDBQRERGx/jw9PZXlPTw8xPPnz4Wrq6twdXUVEyZMiLG+7du3K8+5urqKsLAw8ezZMwEIfX198e7du1jnosDAwFjHtU+fPonly5eLXLlyKevOmjWrmD17tnB3dxfv37+P9+/169fC399f/P3332L27NmiSpUqAhA5c+YU3t7eMY4Xydk+du3apcT6119/iY8fPyp/3bp1U46lrq6uybbvpYZ2FB4eruovJCRE9bJqqL0Wimv/i/oLDg4Whw8fFo0aNRJ6enrK95sjRw4xfvx4MX36dGFoaCgAkSdPHnH27Fnh4+Ojap/atGmTMDAwEIBo1aqVeP/+fYx9JurPw8ND1Xn17du3iS5z7do1AQgzMzMRGBiY4OcSHBwc7+cf6uenHNdC/fxUfR9q25o8H/3Y1O4D4eHhcZ6PPv8LDw9Xfb5U8xcYGCjev38vOnfuHOPcZqrRKPt8wMePIjAwMNH3jGpvGo1GXL58Wfj6+sb79/HjR/H8+XORP39+AYj8+fOLDRs2iDRp0ghAlC9fXnh6egpPT0/x7t27eP+uXr0qTExMBCDWrVv3Xa87fXx8ZDv6wcV3TvDx8VGucwBRoUIFcf/+/QT3eV9fX/Hu3TuxevVqkTNnTuW1BQsWFJcuXYpxLg4JCYl3v+vbt68ARKlSpcSVK1fE9OnTlWvODRs2iBs3bih/CxYsUH7/AqJNmzbiyZMnwuvVK6V9e716JT58+CA+fPggvLy8lGu7qlWrJnjsUONnake3bt0SgLh169YXvT76fajE7gNIyedrv7eUIint48vG96Zi4eHhtG7dmpUrVypDFl1cXPD19U3yukJCQmIMm4wqgZKSpUuXjmnTptGtWzcmTJjA/v372bNnT7zLp0mThkyZMpEpUyayZctGqVKlKF26NKVLlyZr1qyEhobGmoA2V65cHDlyhFWrVjF+/HhOnTrFjRs3mD17Nk2aNOHUqVNMnDiRixcv8uuvv1K3bl3q1KlD5syZCQoKilX+BWDXrl3cuHGDNGnSMGXKlOT+WCQd0lU7ypUrF0ePHmXlypWMGzeOkydPcv36dWbPnq30/s2fPz/58+cH/j8S4d27d1hbW3P69Ok499Xs2bNz5swZbGxsePLkCZ07d2bLli2sXLmS/v374+bmhkajoVGjRgwaNIhcuXIluQdKXEaPHs3evXt59ewZpLdI8usbN25M+vTp8fX1xdraWqkPL6UOqfF8lFpky5aN48ePM3nyZA4dOkTfvn3p1auXUv9VxFNOLEuWLJQtW5Zbt25x4sQJatSooTzXqlUrFi1aRGBgIJUqVaJSpUqxXn/06FEgsq50XJO0x8XY2JjevXvTpUsXNm/ezNy5c3F3d2fs2LEsWbKEIUOG0LFjR2W0VXh4OP/++y9///03ly5d4vr16zH2HT09PRYsWJBoWaWvUbt2bbp3786GDRsYOHAgly5dwsLCgpMnTyo941asWPFFde2TSraj5KGnp0fdunWpW7cuDx8+ZPPmzWzZsgUPDw9mzpypLNekSROWLl2KhYWF6nU3btyY9evX06NHD3bv3g3AypUrlRJY30LUvEeVKlWSo1lVkO3o55Q2bVrWrFmDjY0NgwYN4tOnT9jY2MCrpJWlK1++PPb29hw4cIDp06ezY8eOeJd99eoVTZs2xcXFhfz583Py5Ely5cpF4cKFadKkCTdu3MDGxgZHR0dy584d5zq0Wi3Dhg0jODiYatWq0a1btyTF+63IdvTjS5s2LWvXrsXW1pb+/ftz/fp1qlatyrJly+IciarVatm3bx8zZ87k+fPnQORInokTJ9KpU6ckle6bNm0aO3bs4O7duxw8eJDmzZtz6dIlTpw4waRJk9i6dSv+/v4sWLCAs2fPApAjRw6WLl1KzZo1ARDxlKQElBKH3+PaTZJSEzc3N7y8vBJdzsrKKt7zVkrw0yUSrl27hru7u3JwFkJw584drl27xvTp0ylRogTr1q3D2to60XXNnj07zqH4qUG+fPnYunUrly5dwsnJifTp05MpUyYyZ85MpkyZSJ8+PTlz5lTKnCSVnp4eAwYMwNbWlu7du3P79m369+/PiRMnmDlzJsuXL1e9rqh61ADDhg0jW7ZsXxSTlDLpsh3p6ekxcOBAbG1t6datm7KfOjk5MXPmTOXmxudJhF27dsWZRIgSlUyoWrUqL168UJIJGzZs4MSJE7Rs2ZJChQol67aYmJiwYsUK7OvV++LXDxs2jCVLlrBixQp5sySVSc3no9TA0NCQWbNmKZObq1W/fn1u3brFsWPHYiQSzM3N6devH+vXr2f06NFxvvb48eMANGjQIMnxRk8orFy5kqVLl+Lh4aEkFFq1asWjR4+4evUqnz59ivHa9OnTU7VqVapXr06dOnUoWrRokt8/qaZOncr58+d5/vw5o0aNYvbs2QwePBiAPn36xPjsviXZjpJfrly5mDx5MmPGjGH//v2sW7eOhw8fMnnyZHr16vVF5UM+TyaEhoZSu3btGMuEhYUpCbMoWbNmxdbWNsmTWl6+fBmAKlWqJDnWn5FsRz+3Nm3aULlyZW7cuIFd7dq8rlEzyeuYMGEChw8f5tixY1y5coXKlSvHWubVq1c0adKEFy9exEgiQGQy4syZM9jZ2fHo0SPs7e3ZvXt3jHkZojg6OnL58mXMzMxYtGhRiilpJNvRz6Nt27ZUqFCB9u3bc/PmTTp37kzXrl2ZPXs2ZmZmCCE4efIk06ZN4/79+0DkTcYxY8bQu3fvWOc6NaysrJgyZQq///47q1atok6dOowaNYo7d+7w6tUrfv/9d548eUJAQAD6+vp07NiRrl27xlua7HNRnXST0lFAkn50bm5uWFtbExgYmOiyZmZmODs7p9hkgkbE15XuB+Xq6kqhQoXo168fHTt2xMHBgXv37uHg4ICenh5DhgzB19eXx48fJ9oDL66eArly5cLX1xdzc/MEX6v2Y9dqtaouaNTWQA4ODlZ6USa2nJokQkhISKInr7CwMCZMmMDKlSuJiIgga9asLFq0iGrVqsVaNiAgINYN2unTp7NkyRLy5s3LpUuXMDExQQhBhgwZEo0vLCxM1ecXHh6u6nMBEs32awMDeVwmsp5+oVs30Uvkc0wpF6zfmp+fn9LjPXr7+B7tKDQ0NNHPOSwsjPHjx7N8+XJlP128eDHZs2ePlUSwtLRUldC6ePEiXbt25cWLF2TPnp0tW7aQI0eOOOPLkydPouvz8fFJ9Lg0esgQRv1zB4Ccly5imsAFXFBQkKreK1qtVlWPZCGEqhs0QghV+73a5UBdO1K7vyT3+yanb9GOdCWh70MbGMiTsuUAKHjzBvpp0qhaX3LuV8HBwaqW02q1sUbmRbl8+TLVq1fHwsKCc+fOqU7Q5ciRg2zZshEUFMStW7firMUeGhqqqv1+/PgRIQTbt2/HwcEh1qTF5ubmVK5cmUqVKlGvXj1KliyZYO/ukJCQeLf3c2p+3H748AGIrJnfsGFDIiIiKFKkCI8ePaJQoUKcPn1aOf6oOe+r+X79/PywsLDQSTtSexwKDw9Xtf9FRESo2g8iIiJU9doPDg5WtVxcI1Lj8unTp1j7vVarjfNckS5dukTX5+3trXwuhw4domfPnkka1de7d2/mzZun/Pvjx4+Jnt/KlSuHh4cHx48fj+xhnQCtVhvv96ENDORZ+QoAFLh+DUMV2yvPRxKoP25ERESoPm+paefxtdXPxXe+1AYF4fZbZAIu9+W/EUZGqo4bPj4+jBgxgi1btlCxYkWOHDkSY/0eHh5KEiFfvnwxkgjRubm50bBhQ54+fUrGjBnZunUrpUqVUp739PSkRo0a+Pv7M23aNHr27EnmzJkTjQ9Q9fmp+d50eT6Svk5yt8sPHz4wZ84cHBwcEEJQpEgRhg8fzoYNG5SRcebm5gwYMIARI0Ykes4UQiR43RkWFsavv/6Ks7Mzbdu2ZciQIdy6dYtBgwYp21a0aFHGjh3LL7/8QkBAAHnz5v3/+oODCWvREgDDvXvQ/HfNp9FoWL9+PbNmzWLAgAEsWbIk3vjUHofUXNdZWlom2j7iO2+lFLdv31ZGM5cpUybJr49+H6rw7VuJ3oeSkofa7y1qOUdHxwQ7rjs7O9OxY8cv3g++VFLax083IiFfvnysXbuW+fPnK73xnJ2dlZt7e/fupXDhwly6dIk6deokuC5jY2PVP6a/NX19fVUnqDRp0qi6kWFiYqLqwtHIyCjR5YyNjVm0aBHt2rWjS5cuPH36lPbt29OjRw+KFy+OoaEhhoaGGBkZoa+vj7GxMUZGRhgaGhIUFMSqVasAWLRoEVmyZAHU/7AyMjJSvdy3oNFofppEwZf6mnak9rNV8/0aGhqyaNEi2rZtS+fOnXn69Cnt2rXD3NwcPz8/SpQooZQzUrv/VatWjUuXLilljnr06MHZs2djXISB+htGYWFhiV5wTZw0iZBmzYHINjN+2rR4lzUyMlJ1A0rt9qrd33V1UySl3oxJDinpfKRWQp9z9Of09PR0sl+pLeeT0A/JqLJE3t7evHnzJs4SRnGt7+LFiwQFBZErVy5KlSoVZ8wajUbVeTpLlizo6+szYsQIBg0axMaNG/n7778pXbo0NWvWpFSpUujr66PVahNdF6B6ObWiyjbVrl2bsWPHMmPGDB49eoSBgQFbt25VJqb+Hv1eUlI7Untdp3a5pLyvmh/2xsbGqq4nM2TIoDrBrIalpaWyvZ07dyZr1qysXr2a0NDQBNcXERGBk5MTf/zxB0WLFmXgwIFA4jdK3d3d8fDwQF9fn99++03VOTO+70MT7XNVW4opNV5HpqR29KNQuw8k936l9vxrYmISdyIh2vnCxNgYrcrfZenSpWPq1Kns2rWLa9eucfHiRWXiZnd3d5o2baqMRDh79mycSQSAAgUKcOnSJRo0aMDt27dp1aoV+/bto0aNGggh6NGjB/7+/lSqVIlRo0ap3l61vubaRbajH4fa83SmTJlYuHAhDRo0oHPnzjx69IhevXoBkW1s4MCBjB49mowZMybLfmpkZMTixYupX78+e/bsYezYsfTq1Qs/Pz/WrFnD77//Tp8+fZTjyvv372OcA4W+Pt7//X/aNGnQRLtujj4iIb5zrNrzvprjVWo7T0o/JmdnZ1XPW1tbf9cEwbfwwycSTp48iZOTE/Pnz1ce69q1K127duX8+fP07ds3Rg/hHDlyoKenp6rXm5Q0FStW5NatW4wePZpVq1axfv161a+tU6cOjRo1+obRSVKkihUrcvv2bUaNGsWqVatiJRGS6vM5E6pUqUK5cuUwNTXF1NQUExMTTExMlH9HPVagQAHq1KmT5Auj9OnT8+6//1+0cCHN27VTVapNkqTkp6+vT7169dixYwenTp1SlUgAOHbsGBBZ1ig5fxwZGxvTt29f+vbtmyzrCwgIYNasWfz9998sWbIkRk/PLzFhwgSOHz/OzZs3mTRpUqq/yJa+vai5GD4XVw/QefPmMW7cOIYNG0bevHlVXVdG9QItVaoUadOmTZ6gJUlKVI4cORgwYAALFy5kwoQJ1K9fH09PT+rUqRNrToSEZMqUCScnJ1q2bMm5c+ews7Nj69atBAYGcvToUYyMjFizZg36+vrfJWEtSYmpXbs2d+/epWfPnhw/fpzu3bszYcKEOEe1f6169ephZ2fHkSNHGDVqFAcPHmT48OEMHz78q9YrSxtJPxMrKyvMzMzo2LFjosuamZl90T2llOaHTiRERETQrFkzgoKCCAkJYenSpTGe19fX58mTJ5w8eVIZfTBhwgRq1qxJ2bJldRHyDy9NmjQsX74ce3t7duzYQVBQEKGhoYSGhhIWFhbjv6GhoYSHh5MmTRqWLl0qM83Sd5MmTRpWrFhBs2bNOHPmDMOGDfuqA/7nyYRDhw6pet2kSZOYOHHiF79vWFgYffv25ezZs0muCS1JUvKoX78+O3bs4OTJk0yYMCHR5YUQykTLDRs2/NbhfbFjx44xZMgQ3N3dAWjatCnnzp1TVaYtPoaGhhw/fpzr16/HeXNYkr7GyJEjef78OevXr6dDhw6cO3eOAgUKJPiaq1evAqhOAkqSlHxGjRrF+vXrefDgAQsWLGDjxo1JSiJEMTc359ChQ3Ts2JEDBw7QunVrJTE4fvz47zIXkCQlRaZMmThw4ABhYWHffN66uXPn4uTkxIkTJzh27NgXzc31uajJlmUiQfoZ5M6dG2dn5x9iEmW1fuhEgr6+PhkzZmTQoEHKZIbRkwlVqlRRsrCNGzfG3d2d8PBwnJycdBXyT6NOnTpxlo4KDw+XNzylFKN27dqxJnD8UtmzZ+f69escO3YMPz8/goKClL+AgACCg4OVf/v4+HDs2DFmzJiBjY3NF0/waJYmDZcvX2bdunX07t07WbZDkqSkqfffBOh3797l7du3Som++Dx58oQXL15gbGycaD12XXj//j1Dhw5l3759QOTFc5o0aXB2dqZJkyacOXOGjBkzfvH6LS0tlc9MkpKTRqNh+fLlvHjxgtOnT2Nvb8+JEycSvBkZNSIhrsleJUn6tiwtLRk5ciTjx49XOtYkNYkQxcTEhB07dtC3b182b96sjDgeOXLktwhdkpLFt04iABQsWJCBAweyePFiRo4cia2t7VeXfZaJBOlnkzt37h8iQaDWD3/HtnDhwpQrV44NGzawYsUKBg8erIxOEEKwf/9+5syZg7GxMe3bt+fKlSs/xFATSZJSHnNzc9q0aUOvXr0YPHgwo0ePZsqUKcyePRsHBwfWrFnDli1bOHjwIB06dECr1WJnZ8e6deu+aLh1sWLFAFi8eHFyb4okSSplyZJFGeU4YcIEIiIi4l3W09NTqd1eo0YN0qiYZPp7mzJlipJEqFChArdv32bv3r3KKM81a9boOEJJip+hoSE7d+6kaNGieHp6YmNjw/Hjx+Nc9siRI0o9WzkiQZJ0Y8CAAcpcOV+aRIhiYGDA2rVrmTBhAr/++iubNm36LjdqJSmlGzt2LJkzZ+bp06dMmjTpq9Z1+/Zt7t69C/BVHUskSUq5fvhEQpEiRfj333/p0qWLkkwoVKgQly9fRgiBkZERw4YNY9u2bfz+++9yUiNJklKEpUuXUqtWLQICAujXrx8tWrTg/fv3SVrHjevX0dPTS7Z66JIkfZmpU6diYGDA7t276devX5zJhKtXr1KrVi1u3ryJhYUFU6ZM+f6BqtCmTRuyZs0KwPXr17Gzs6N///5ERERgZmZG/fr1dRyhJCXMwsKCQ4cOUbx4cd6/f0/btm0ZOnQoAQEByjKXL1+mR48eAHTr1o1MmTLpKlxJ+qmZmZmxa9cu+vfv/1VJhCgajYbJkydz48YNSpYsmUxRSlLqlj59eqVyx+LFi9m1a9cXrefSpUu0atWKT58+UblyZapWrZqcYUqSlEL8NIkEgLZt2/Lrr7/i5uZGpkyZlBnoJUmSUhpzc3OOHz/OvHnzMDIy4tChQ9SoUYMTJ07E+5rQ0FDmzJ6t/DtP3rycOXOGIUOGfI+QJUmKR8OGDdmwYQMGBgbs2rUrVjJh48aNNG7cmLdv32Jtbc3169epWLGiDiOOX/Xq1bl//z7jx4/HzMyMa9euce7cOczMzDhw4ICcIFlKFfLkycPVq1fp378/ENkGq1evzq1bt3jw4AHt2rUjODiY+vXrM3/+fB1HK0k/twoVKuDg4PDVSQRJkuLXrFkzRowYAUDv3r2ZMWOGMg+WGk5OTnTo0IGAgABsbW05ceIEBgY/dCV1Sfpp/RSJhAcPHhASEkKLFi0oWLAg69evZ+XKlQwePFjX4UmSJMVLT0+PoUOHcuXKFYoVK8b79+9p165drJ6TAI8ePaJu3bqsWLFCeezSxYtfPL+CJEnJq0mTJrGSCYGBgQwZMoShQ4cSFhZG06ZNcXJySnQCWF1LmzYtEyZM4P79+3Tt2hVra2sOHDgge55JqYqJiQkzZszgwIED5MiRg+fPn1O3bl0aNWqEr68vlSpVUtqsJEmSJP3opk2bRr169QgKCmL69OkUKlSIpk2bcujQIcLDw+N93cFDh+jevTshISHUr1+fQ4cOKROaS5L04/kpEgn//vsvLVq0IG3atGzdupXu3buzYcMGzp07h6+vr65DlCRJSlDJkiW5evUq/fr1A2Dz5s3UqFGDmzdvotVqWbNmDTY2Nty7dw9LS0vldenSpdNVyJIkxeHzZELx4sXZvHkzGo2GKVOmsHHjxlT1wyt79uysWrWK27dvyySClGrVqFGDv//+m5YtWxIREYG3tzdFixZlx44dmJmZ6To8SZIkSfou9PX12bt3L1u2bKFGjRpotVqOHTtGy5YtqVixInPnzsXNzS3W64b+/jvh4eG0bNmStWvXYmJiooPoJUn6Xn74REL27NnJmzevkkSIKmfUpUsXbt26Rfr06XUSl1arVf0nhFD1p1Zyry85qY3tW3wukqSW2rYbERGRbPupiYkJU6dOZd++fWTPnh0XFxcaNGiAra0tY8eOJTg4GFtbW06ePKm8RrYNSfr2knLegpjJhI8fP5I+fXr27NnD77//jkajSfHbofbYoavzudrjs67o4vwByf/9pnRJ3V4LCwvWrVvHxo0b6dq1K3v37sXCwiLFxCdJP4KknBeS+3eyLtqbbONSUujquunz1xgYGNC6dWtOnDjB/fv3GTZsGFZWVrx9+5bFixdTsWJF2rVrx/Hjx2Oso0uXLixdulT1BOYpue1KkpSwVD1WVwiBp6cnVlZWCU6SfPbsWdKlSxdrTgS1B7lvQe3NAj09PfT0Es/3GBsbq1qn2oOsnp6eqvWpiS0pyxkZGalaTgih0xsuUuqidl9JSrtUu5yadRoaGqpazsrKCnt7e6pXr87AgQPZvn07d+/excTEhPnz59O/f39EUBBP/lvewMAAvURKMsh2JElfR20bSp8+vbJsx44dyZw5M7t372bMmDGxShkl5/lXLbXrMzMz08n1BiTf56LL415SzjPJGafaecH09fV18vkk93labTkiCwuLGOvs0qULXbp0+ar3VkOj0cjzr5Tskrsdfe1y0R/XaDSqf3urPf6p/T1oYGCQ7NssScm9r+hq30vo/kuxYsVYsGABs2bNYv/+/axbt45Tp05x9uxZrp47x61ChQEYNmwY0+bNU7Yhua9jE1ufbLeS9H2l2hEJ58+fJ1++fOTMmZMMGTIwcOBA/Pz84lzWwsJCTqwsSdIPw9LSkq1bt7J9+3a6du3KrVu3GDBggLyIkqRUpG7duqxduzbFz4cgSZIkSZIk/byMjIxo1aoVTk5OPH36lDFjxpA5c2bl+alTp8rfoZL0E0mViQQXFxdatGjB4sWLeffuHUuWLGHHjh2UK1cOV1fXGMu+e/eOSZMm6XQYuyRJ0rfQpk0bNmzYgLW1ta5DkSRJkiRJkiRJkn5gBQoUYNasWTx69EjXoUiSpCOpMpGwadMm6tatS7NmzciUKRM9e/bk5s2baDQabGxsePv2rbLs2bNnmTFjBlOmTNFdwJIkSZIkSZIkSZIkSZKUyumyTLgkSbqVKudICAkJ4c2bNzEey5s3L2fOnKFSpUp069aNo0ePApE9dgFq1ar13eOUJEmSJEmSJEmSJEmSJOnn5ObmhpeXV4LLODs7f6doJOnrpMpEQv369Zk3bx6nT5/G1tZWeTxHjhw4OjpSs2ZNbty4Qfny5YH/JxMkSZIkSZIkSZIkSZIkSZK+NTc3N6ytrQkMDEx0WTMzM6ysrL5DVJL05VJlIqFWrVo0atSI9u3bc+nSJQoWLKg8V6NGDX799VcuX76sJBIkSZIkSZIkSZIkSZIkSZK+Fy8vLwIDA3F0dEx0bkMrKyty5879nSKTpC+TKhMJAFu2bKFatWrUqFGDI0eOULp0aeU5IQQ5c+bUYXSSJEmSJEmSJEmSJEmSJP3srK2tKVOmjK7DkKSvlionWwawtLTk7Nmz5M2bl8qVKzN69GhOnDhBnz59MDQ0xN7eXtchSpIkSZIkSZIkSZIkSZIkSVKql2oTCQCZMmXiwoULzJs3j5MnT9KrVy+0Wi2nTp3CwCDVDraQJEmSJEmSJEmSJEmSJEmSpBQj1d9tNzAwYPDgwQwePFjXoSSJnp5ucjgajUYn76uW2vhS+nZIP7aU3H6jL6PRaGRbkaRv7Ec5byV3fLr6XNQcn3V1DNfle6f0/S+5pfR2Kc/P0s9I7T6vr6+vk/dNbkm9bpd+bro6b33p+37tb87k3F7ZjiTp+0rVIxIkSZIkSZIkSZIkSZIkSZIkSfq2ZCJBkiRJkiRJkiRJkiRJkiRJkqR4yUSCJEmSJEmSJEmSJEmSJEmSJEnxkokESZIkSZIkSZIkSZIkSZIkSZLiJRMJkiRJkiRJkiRJkiRJkiRJkiTFSyYSJEmSJEmSJEmSJEmSJEmSJEmKl4GuA5AkSZIkSZIkSZIkSZIkSZKkn52zs3Oiy1hZWZE7d+7vEE1MMpEgSZIkSZIkSZIkSZIkSZIkSTpiZWWFmZkZHTt2THRZMzMznJ2dv3syQSYSvgEhBEKIRJfTaDTfIRpJkn4Uao4r0ZdRcyySxyFJkn4kST1OplRJiTE5j+O6el9JkqRvRe1xTQih+rgmj3/Sz+bzdhTfb07ZjiTp6+TOnRtnZ2e8vLwSXM7Z2ZmOHTvi5eUlEwmSJEmSJEmSJEmSJEmSJEmS9DPJnTu3TkoWqSUTCZIkSZIkSZIkSZIkSZIkSSq5ubmp6jkuST8SmUiQJEmSJEmSJEmSJEmSJElSwc3NDWtrawIDAxNd1szMDCsrq+8QlSR9ezKRIEmSJEmSJEmSJEmSJEmSpIKXlxeBgYE4OjpibW2d4LJWVlYpulSNJCWFTCRIkiRJkiRJkiRJkiRJkiQlgbW1NWXKlNF1GJL03ejpOgBJkiRJkiRJkiRJkiRJkiRJklIumUiQJEmSJEmSJEmSJEmSJEmSJClesrSRJEmSJEmSJEmSJEmSJEk/tDt37pA2bdoEl5FzGkiphbOzc7Ksx9/fX/WyMpEgSZIkSZIkSZIkSZIkSdIPrUaNGokuY2ZmhrOzs0wmSCmWlZUVZmZmdOzY8bu/t0wkJCMhBAB+fn6qltdoNN8yHElHtIGB+EdEAJH7gl54uI4jShmi2kVUO4lPUtvRzySxzw6Svv/J41Dq8rO0I3kclb6UmuNkamhHarYjSnIex3X1vj+DH+24lhrakaRbKWWfV3tcE0KoPq4l1/FPtiMptfh8H42vfaeGdrRkyRJ+/fXXeJd7/PgxvXv35sSJExQuXDjB5SCyJ3dKaJsp5ZgrfR8WFhZcv36dDx8+JMv67ty5w5AhQ1SdMzUiKb8YpAS9evWKXLly6ToMSUrR3N3dyZkzZ7zPy3YkSYmT7UiSvp5sR5L09WQ7kqSvJ9uRJH092Y4k6esl1o5AJhKSlVarxdPTk3Tp0qXIXlp+fn7kypULd3d3zM3NdR2OKqkxZkidcX/rmIUQfPr0iezZs6OnF/887ym9HSUkNX7vkHrjhtQb+5fGnVztKLV+bnH5kbYFfqztSanb8iOdj1LqZxyX1BQrpK54dRHrj9KOUtP3DDLe7+F7xvyjtKO4pMbvPj5yW1KmqG1xc3NDo9H8MO0otX1HqS1eSH0xf4941Z6PQJY2SlZ6enqJZm5SAnNz81TRWKJLjTFD6oz7W8acPn36RJdJLe0oIanxe4fUGzek3ti/JO7kbEep9XOLy4+0LfBjbU9K3JYf7XyUEj/j+KSmWCF1xfu9Y/2R2lFq+p5Bxvs9fK+Yf6R2FJfU+N3HR25LypQ+fXpV25La2lFq+45SW7yQ+mL+1vGqOR8BJJxmkCRJkiRJkiRJkiRJkiRJkiTppyYTCZIkSZIkSZIkSZIkSZIkSZIkxUsmEn4ixsbGTJ48GWNjY12HolpqjBlSZ9ypMeaUJrV+hqk1bki9ses6bl2/f3L6kbYFfqzt+ZG2JaVKTZ9xaooVUle8qSnWlCa1fXYy3m8vNcacEv1In6PclpTpR9qW6FLbdqW2eCH1xZzS4pWTLUuSJEmSJEmSJEmSJEmSJEmSFC85IkGSJEmSJEmSJEmSJEmSJEmSpHjJRIIkSZIkSZIkSZIkSZIkSZIkSfGSiQRJkiRJkiRJkiRJkiRJkiRJkuIlEwmSJEmSJEmSJEmSJEmSJEmSJMVLJhKkFG/FihU4OzvrOowke/z4MQ8fPtR1GJIUJxcXF27duqXrML7I27dvOX36NEIIXYeSZM7Ozjx+/FjXYSiePXvGlStXdB1GsggLC+P69et4eXnpOhRJklTy9vbm9OnTug5DNW9vb65fv05wcLCuQ5GSyfnz59m1a5euw0iSp0+f8uDBA12H8cM6ffo03t7eug7jh3T37l3Wrl2r6zCkz3z48IGpU6emyt9WP4vw8HBu3rzJmzdvdB2KKj4+Ppw6dUrXYfzQwsPDuXHjBs+fP//u7y0TCT+JDx8+0KdPH/LmzUuFChU4f/68rkNSZcGCBSxatIg0adLoOhTVPnz4QIMGDShSpAjFihWjf//+ug5JlW3btvHhwwddh5GqbN++nfLly5M3b15GjhxJWFiYrkNSxcfHh1q1alG7du1Ul0xYunQpv/76KydOnODFixe6Dke1t2/fUqtWLYoWLUqRIkUYOXKkTuPRarX06NGDhg0bcvr0aQIDA3Uaz9c6efIkOXPmpGLFihQoUIBz587pOqQv5uXlRZs2bUibNi25cuVi7Nix+Pv76zqsL7ZlyxbKlClD3rx5GTt2LBEREboO6Yd29+5dqlWrhqmpKcWKFWPNmjUp+sZA7dq1adiwIYcOHdJ1KImaP38+WbNmpWLFipQoUYK3b9/qOqR43bp1i/r165MjRw5atGiRam48fG/nz5+nZcuWWFpa6joUVXx8fGjSpAmFChWiePHidO/eXdchJUgIwR9//JFqro8BLl26RMOGDaldu7ZMJiSzu3fvUq9ePczNzXUdylfz9PTE3t6eNGnSkDdvXqZOnZpqE8wfPnzA1tYWf39/NBqNrsP5KkFBQQwYMAALCwsyZ85M3759U/S5Wq2///6bfPnyUb58eQoUKMDhw4d1HVKi6tatS8OGDdm/f7+uQ1Flx44dFCxYEFNTU2xsbFL8b0l3d3fKlCnDpEmTuHr16vcPQEg/PC8vL/HLL7+I9u3bi7Vr14pq1aoJc3Nz4ebmpuvQEjR//nyRP39+8fLlS12HolpQUJAoW7asGDJkiPDy8hJ//vmnAMS7d+90HVqCDh8+LADx66+/Ci8vL12HkyrMnDlT5MmTRyxdulSMHz9eGBkZiYkTJ+o6LFX8/f1FunTpRPXq1YWFhYW4efOmrkNS5fLlyyJTpkzC3d1d16Ekib+/vyhWrJgYM2aM+PDhg1i9erXQ09MT/v7+Ootp8eLFomLFiiI4OFhnMSSXCxcuiEyZMonDhw8LLy8v0ahRI2Fvb6/rsL5IWFiYKF26tOjfv7+4evWqWLBggbC0tBQFCxYUz54903V4STZx4kRRoEABsXz5cjFmzBhhYGAgZs6cqeuwflgvXrwQmTJlEsuWLROXLl0SQ4YMEQYGBqJhw4Y6Pd4kJGvWrMLOzk4YGRmJgwcP6jqceM2aNUuULFlSPHjwQDx58kTky5dPLF68WNdhxen06dMiQ4YMYtKkSWLJkiUiW7Zsonr16kKr1eo6tBTl3LlzwsrKSjg5Oek6FFVCQkJE5cqVRd++fcX79+/Frl27hEajSdHXRHPnzhWAaNKkiQgNDdV1OKrs379flC1bVmTNmlWUKVNGfPz4Udch/RDu3LkjsmTJInbs2KHrUL5aYGCgKFy4sBg1apS4cuWKmDlzpkibNq0oWbJkim6PcfHy8hKlSpUSI0aM0HUoyaJZs2aiWbNm4vLly2Lt2rUiV65cInPmzOLq1au6Du2L3b59W1hZWYndu3cLb29v0a5dO1GjRg1dh5WonDlzCjs7O2FoaCj27dun63AStHfvXpErVy5x8OBBcfz4cdGgQQOh0WjElClTdB1avGrUqCEmT56ss/eXiYSfwIABA0Tfvn2Vf/v4+IgMGTKIWbNm6TCqhM2fP1/kyZNHSSI8f/5c9OnTR1SvXl307t1bvHjxQscRxs3BwUFUqVIlxmPZsmUT//zzjzh//nyKvWlnY2MjHBwcRLZs2WQyQYWnT58KKyurGMm4UaNGiQwZMugwqqTJmTOncHFxiZVMcHV11W1gCejXr58YOHCg8u+IiAjx119/iZkzZ4rz58/rMLKEzZgxQ9StW1f5d3h4uMicObO4d++euHDhgggJCfnuMZUuXVo4Ojoq//706ZNYv369mDdvnnj06NF3j+drFC9eXGzcuFH59/bt20WPHj3ExYsXxb///qu7wL7AwYMHRcGCBWM89uLFC2FtbS2yZ8+eqhLrDx48EJkzZxavX79WHhs8eLDInj27DqP6sY0aNUp07do1xmNnzpwR5ubmokaNGinyGqRmzZriwoULon379jGSCS4uLjqO7P/evHkjjIyMxPPnz5XH+vTpI1atWiVOnz4tPD09dRhdTBERESJ//vwxkjJHjx4VgLh3754OI0tZzp07JywtLZUkgp+fn5g8ebKoWbOmaNmypbhw4YKOI4xt9erVoly5cjESQrly5RK3bt0S586dE4GBgTqMLrawsDCRO3dusX79emFqappqkgmPHj0SJUqUEA8fPoyRTAgICBBv377VdXip0p07d4SVlZXYvn27EEKI4OBgMW/ePGFjYyPs7e3FkSNHdBxh0mzZskWUK1cuxmOPHj0SefPmFQUKFEg1+0lUEuH3339XHtu2bZuws7MTderUEStXrhQRERE6jDBpHj58KExNTWMcCz98+CBq1Kgh0qZNm2o6zn2uatWqMTouHDt2TLRs2VL8/fff4s6dO7oLLBG1a9cWZ8+eFZ06dYqRTEhJ13dRypQpE+N3sRBCzJkzRwBiwoQJOooqfi9fvhSA+PTpk/LY8+fPxYIFC8Tq1auFr6/vN49Bljb6wYWFhbF3717mzZunPJY+fXpq1arF06dPdRhZwl6+fImXlxcvXrzgypUrlCtXDl9fX8qUKcORI0coU6ZMipx/4ObNm2TMmFH595YtW/jw4QNNmjShdu3alCxZEjc3Nx1GGJubmxseHh4MHjyYc+fO8fbtW2rXri3LHCVg8+bNjBgxgly5cimPNWvWjI8fP/Lx40cdRqZekSJFcHV15ejRo5QsWZLatWvTo0cPOnbsqOvQ4uXt7a0MG/b398fW1pYBAwbw559/UqNGDSZOnKjjCON28+ZNrKyslH+vWbMGX19f7OzssLGxoWzZst+97ET0z/LZs2eULFmSefPm4eDgQMmSJfnrr7++azxfKiQkhH///Vc57oaHh7N27Vp2795NmzZtKF68OIMGDdJxlOq9f/+e8PDwGI/lyZOHc+fOYWZmRqtWrdBqtTqKLmk2btzIuHHjyJo1q/JYs2bN8PT0TPWltFKquPafWrVqceLECa5fv86YMWN0FFn8ihQpgrOzM1u2bKFly5a0bNmSgQMHYmtrS1BQkK7DA+DOnTtEREQo5W88PT3Zv38/U6dOpUWLFuTLl4+tW7fqOMpIZ8+epVChQjRu3Fh5rF69epiamqbo6/7v7c2bN/j5+XHv3j28vLyoVKkS586do3z58ri4uFCrVq0U851GuXnzJhkyZFBKj+zatYu3b9/StGlT6tatS4kSJXBxcdFxlP938uRJypQpQ/fu3Tl8+DAnT56kZcuWKb7MUYECBXB1daVgwYKcOXMGT09PateuTaNGjVixYoWuw0uV3r9/j7+/P7dv3yYgIIBatWqxe/duypYty4cPH7Czs8PBwUHXYaoW17m2cOHCnD9/nuDgYDp06KCjyJLGz8+Pjx8/8uDBA4KCgujTpw/jxo2jcOHCmJubM2DAADp27JiiyyNG9/79e7RabYx4M2TIwLFjxyhVqhTNmzfHz89PhxF+mej3l7RaLWvWrOHEiRO0adOGX3/9lS5duqTI3wZFihTh4cOHbNq0ibZt29K6dWsGDRpErVq1UtzvgLja9OjRo1m4cCEzZsxIcaWkosruhYSEAJElykuVKoWjoyPDhw+nVKlSvHr16tsG8c1TFZJOhYaGijVr1sR6fODAgaJjx446iEi9AQMGiDRp0ojs2bOLAwcOKI9/+PBBFC5cWFStWlWH0cVt69atAhBdunQRPXr0EGnSpBEnT54UQgjx7NkzkStXrhRZcuPBgwfK/z9+/FiOTEjEwYMHY42KefHihQDEmzdvdBRV0gwcOFAsXbpUCBFZeid37twCEMePH9dxZPGbM2eOsLCwEB8/fhRDhgwR7dq1U3rzz5s3L8X2uFyzZo3Q09MTvXr1Ep07dxbp0qUTFy9eFEJE9p7JnDmz6NChw3eNqWnTpqJSpUpCq9WKihUrivnz5wshIs8Zbdq0ERkyZNDJSIkvUatWLZElSxYxbtw4Ua5cOVGlShXl2LVixQoBiEOHDuk4SnWePHkiNBqN2LlzZ6zn7ty5IwwMDMSePXt0EFnS7d27N8ZoBCEie+wBwsfHR0dR/dg2bdokTE1N4yyDtWbNGmFgYJDiyi44ODiIwYMHCyEiR2tVrlxZAGL58uU6juz/vLy8hJWVlShbtqwYM2aMyJ49uxgwYIAICwsTISEhomPHjsLU1DRFlLF8/vy5OHr0aKzH8+bN+0OUFElO27dvF/r6+qJgwYJi2LBhyuPh4eGibdu2Il26dMLb21t3AX5m7969AhAdO3YUvXv3FmZmZkpPbldXV5EvXz5Rr149HUf5fyEhIeLp06fKv0+fPp1qRiYUKVJEPHz4UAghxM2bN4VGoxHp06eXv4u+gpOTkzAxMREFCxYUHTt2VHq6a7VaMWjQIGFgYJCiR0VHd/PmTQGIEydOxHru4sWLQqPRiFOnTukgsqR7/vy5yJUrlyhYsKD49ddfY/RkdnR0FECque789OmTSJs2bZwlNN+8eSMsLS3FnDlzdBDZ12nWrJnIkCGDGDt2rKhSpYooXbq0MhLS0dFRaDQa8eeff+o4ytiWL18u+vfvL4SIHC1ZrVo1AQgHBwcdRxZbx44dRcmSJeM8N7Vq1UoUK1ZMB1HFLyAgQJiZmYlZs2aJly9fiowZM4rbt28LIYRwc3MTefLk+eb3FmQi4QcUfYhLfIYNGxZj53JychK7du36lmElKq64BwwYIGrVqhXr8T///FNoNBqdX4jGFfOePXvEqFGjRPv27cXIkSNjPBdVq1bXoicO4vJ5MiE8PFwMGjQoxZaUSgk8PT0FoNw4CwgIEGPGjBFBQUE6jixuy5cvF7179xZCCDFhwgRRqlSpFD9nwtu3b0X69OlFq1athLW1daxyEpkyZRJbt27VUXQJ27Ztmxg9erRo0aKFmDp1aoznZs6cGauczbd27tw5AYhJkyaJ3Llzx3ju2bNnAohRxiMl8/HxETNmzBCzZ88WuXLlEo8fP47xfIkSJVLN/CVCCNGtWzdhYWERZ1mm5s2bxyhVmNq4uLgIQLk59+nTJzFmzJhUk7RK6UJDQ0WxYsVEqVKlYg1rjoiIENmyZYs1dFvXjh8/LmxsbIQQQmzYsEHkzp1bNGvWLMXNmfDo0SMxduxYMWPGDJEnTx4RHh6uPPfhwwcBiNOnT+sktgMHDohx48YluEyhQoWUsiJCRCbmU8sx/lvavn27SJs2bayyX+7u7gJQOgOlFPv37xejRo0SnTt3FoMGDYrx3Jo1a4SlpaWOIlPn82SCj4+P6NmzZ4qbw6Vp06Zi165dIiAgQNjY2IhWrVrJOROS6M6dO7FuZDk5OQlTU9NYna4CAwOFoaFhirwZGp9mzZqJLFmyxHkctbGxEWPGjNFBVF8mKpkQveNmlLJly4oBAwboIKovM3PmTGFoaBjnsXvUqFGidu3aOogqaT6/v+Tv7y/mzp0rpk+fLgoXLiyuXbsW4/lq1arFOh98T97e3nGWeDx16pSoXr26EEKIzZs3i5w5c4oWLVqkyDkTHj16JExMTESvXr1iPXfv3r0U2Vl06NChwszMTIwYMULpkBNlwYIFokSJEt/0/WVpox+Mm5sbpUqVYuHChQkup9FolCFQJ0+epGPHjuTMmfN7hBinOXPmULp0aTw8PGI8vnz5cmbPnh1reWNjYzJlyoShoeH3CjGWN2/eUL58eaZMmRLj8RYtWjB37lyCgoIwNTWN8dzbt28pXbr0d4wytuXLl2NnZ5fgkLJChQrFKHPUtm1bXFxcYpSokGKKGmqu1WoJDAykcePGvH37FmNjYx1HFrciRYrw77//MnHiRA4dOsTp06eVMketWrVKkUPPM2fOzIYNG9i7dy/Ozs54enoqz338+JFPnz7pvH3Fp127dsyZMwc/P78UcVyoUaMGo0ePZtq0afj5+REQEKA89/LlS6ysrGKU7krJ0qdPz/jx4+nduzfu7u4xPt+wsDA+fvyYYveLuCxdupR8+fJha2vLP//8E+O5DBkyYG5urqPIvl7046S/vz8NGjTA19dXp+fy1Orff/+lQYMGVKhQQSmZaGhoyO7du3F3d6dOnTq8f/9eWV5PT4/06dPrZP/x9fWlX79+lChRgj179sR4LupctHHjRqZMmcLp06fZvXs3LVu2pH379nh5eX33eFesWEHp0qUZPHiw8ljhwoWZNWsW1tbWGBgYoKf3/59Qb9++xdDQkGLFin33WA8ePEirVq1YvHhxgiUpo1/3jx07lp07dyqlmn4G7969i/Pxtm3bcvLkyVjXalH/zpEjxzePLS4fP36MVWYBwN7enrlz5xIWFpYiriWiu3LlChMnTsTBwYFPnz7FuYyNjY1S5qhFixbUq1ePtGnTkiZNmu8cLXh4eDBv3jwmT57Mo0ePYjxXpEgRrl+/TuPGjcmTJw87d+5UyhwNGzbsu8ea2ty9e5fatWuzdetWrl+/rjxep04dzpw5Q5YsWWIsb2BggL6+vs7aW0L8/f1ZuXIlo0aNYufOncrvo7Vr12JhYUGtWrVi7T8p9VotMDAQBwcHevbsyeLFi5XyKPnz5+fcuXNUrlw51muMjY1T5PcihGD//v2MGTOGLVu2EBERAUSWo6lXrx729vYcO3YsxmtS6vcS3ZEjR8ifPz9XrlxRHkuTJg2jRo1izJgxPHnyJMaxX6vV8v79e50d+318fKhTpw4bN26M9VyRIkV48OABW7ZsYfz48Zw+fZpdu3bRtm1bOnToEO95+VvbvXs35cuXp23btsp5tnDhwqxdu5Z169bRv39/ZX+CyP1GX18fMzMzncTr7u5OmzZtKFmyJDdv3lQenz59OkWKFGHBggUx7olA5G/5MmXKfNvAvmmaQvruli1bJrJmzSoAsWDBgniXGzFihGjbtq1wcnISmTNnFpcvX/6OUcaWJUsWkTVrVvHLL7+IV69eJbhsSEiIqFChgs4ni964caPyWcc1Y/q4ceOEubm5MgnsX3/9JaysrMT9+/e/c6T/t2zZMpE7d27VvdAePHggDA0NhZ2dXYqcpDElefPmjQDEkydPhI2NjejWrVuMCfFSmtev/9fefcdVVf9/AH9dNqLsLYigAg5AkRRR+WqKOFJxNr5fLFeOErFclFau1DTcI0euQktLLUeZSFoqDiRnDtREcSsgU8Z9//7wx6kb4GhwLvp6/nc/51wfL6733DPen3FNjI2NJSAgQGeodnZ2tqrf0cexdu1aMTMzE29vb/nxxx/l8OHD0qJFC52FmPVVVFSU2NnZSWJiomi1WomLixMHBwed4f8VRavVypgxYwSAtGvXTo4ePSrfffedeHh4VKpeYSWKi4vF2dlZ2rVrJ3fv3pWsrCyJjIyUsLAwvT4Wy3L79m1p1qyZmJuby+TJk+Xs2bOydu1acXR0rNS9iEumgPvtt9+kRYsWMmTIkEr3f6MPrl69Ko6OjvLpp5+WuT05OVlcXFykevXqsnr1aklJSZHx48dL/fr1VRnJ2a5dO+nbt2+ZvY61Wq1YW1uLu7u7zu9gUVGRHDx4sCJjisiDkaN+fn7l/iafOHFCNBqNjB07VgoLC+XixYsSGBgokyZNquCkD0YiODg4yLZt28Tc3Pyh18W+vr7y2WefydixY6VRo0bPVK/qvXv3StWqVcs9XsoSHR0t7dq1+xdTle/evXvi5+cnvXr1ksLCwjL3mThxolhYWCjTp3z77bdib28vSUlJFRlVMXLkSLGxsZH//Oc/UqVKFWnYsOFDR5p9/fXXAkBnkdeKtH37drG2tpaQkBBxc3MTc3NznUVL165dKxqNptS1/JkzZ56pY+ev+OWXX8TJyUni4uKkdu3a8sorrzzyPdOmTZNGjRrp3cK+V65ckTp16khYWJj0799fqlSpIt7e3soUIlevXpWAgACpVq2afPzxx3Lu3DlZvny5ODs7l9lDW01ZWVkSGBgobdu2lYEDB4qDg4PY2dnJli1byn1PfHy82NralpqmUm25ubnSoUMHcXNzkxYtWoiBgYH06dNH2Z6XlyfdunUTAwMDiY6OlpMnT8r3338vLi4usmfPHhWTP1qnTp3E2dlZLC0ty3w+5+3tLc2bN5fr169Lbm6uDB06VIKDg8s9V/yb0tPTJSgo6KH333Z2duLm5qYzWry4uFiV6zuRB6P63N3dyz1XrlixQoyNjSUkJER27twpJ0+elI4dOypTNFW0vLw88fb2lgkTJpT5f3z79m0JCQkRADJhwgQ5c+aMzJ49W5ycnP71mURYSHjK7NixQzp37iwTJ058aDFh9OjR4u3trRdFBBGR0NBQ2bBhg9SqVavcYkJeXp7s3LlTGjZsKK+++qrqFxv79u2T1q1bS2xsbJnFhKysLOXANjc3F1dXV/n555/VCSuliwg5OTny1VdfyfLly8uck7KoqEhefPFFFhEe082bNwWABAQE6H0RocSWLVsq7Xyvp06dkp49e4qVlZU4OzvL5MmTVf9NeBx3796Vxo0bK78LNWrUkEOHDqmaaceOHRIaGirm5ubi4+Oj+jR3f8fOnTulWrVqYmhoKMbGxtK1a9fHmu6vIl29elXu3bv3yP0KCgpkypQp4uLiIgDEz89P9u/fXwEJn8yfp5J6mNTUVOV3kkWEvy4mJqbUlBH5+fmSmpqqfKa3bt2SgQMHStWqVcXAwEDat2+vyoONvXv3ioODg85DRa1WK5cvX5bc3FwREfn5559VKab+WXFxsdjb2ytr2JS4deuW3LlzR3k9bdo00Wg0YmZmJkZGRmV2Jvm3lRQREhMTRURk0KBBUr169XIfJtSrV08CAgKeuSKCiEirVq2kTZs2YmBg8NBiQnFxsSQnJ0vv3r0lMDBQtTUvZs+eLc2bNxdTU9Nyiwm5ubnSqlUr5VrCyclJdu3apULaB9PN+vr6yo0bN0TkQSHT0NBQFi1aVOb+GRkZ0rRpU9WKCFevXhUbGxvl88rNzZUGDRrorC+h1WplzZo1PEc9oZIiQsl6LPPmzRNjY2NJS0srta9Wq5UTJ07IoEGDxNvbWy5cuFDRcR8pLCxMRo8erbxOTEwUIyMjqVq1qnKeyMvLk3HjxomDg4MAkKCgIJ2ilL4YP368dOvWTXmdkZEhPXv2FENDw1Kdh1JTU2X69Oni4uKid9O7iYgMHz5cunTpojyf+PTTTwWAzv2UVquVJUuWSO3atQWA1KxZs8ypm/TNW2+9JTNnzpS2bduWWUxITEwUW1tbMTAwEFNTU2nbtq0q5/SyiginT5+WZcuWyebNm5VOK/v27Xui+4R/W6NGjUp93zMyMpTzl4jIkSNHpF27dmJoaCjm5uYyYsQI1aZTX758uTRt2lSnraioSC5duqRcGxQWFsrMmTOlTp06Ym5uLm3btpXTp0//69lYSHjKpKamiqenp4iITjFh0qRJMm/ePGW/Dz74QKpVq6YXRQQRkddff12WL18uly9fVooJiYmJ0rp1a2We3927d0u/fv30ZjHYu3fvioODg4iITjFh1qxZMnXqVBF5cFPyww8/yDfffKPcMKvh0qVLYmpqqvzYHzx4UNzc3MTOzk7Mzc3F2NhYFixYoPOeQ4cOSbdu3VhEeEyZmZliYGBQaYoIpJ7CwkL57rvvZMuWLf/K8aXVaiU2NlZ5cKTWvN3/lAMHDkh4eLh4eHjIqFGjHnl83bx5U9avX696gaYsN27cEG9vbwkJCXmsYkIJfV1r5e2335YqVao89gOsmzdvikajYRHhb+rSpYu8++67yuv58+eLpaWlABAvLy+d735xcbGq5/ElS5ZIQECA8nr//v3i4+MjAMTCwkIWLlyoWrY/K1nrqGRB6tu3b0vnzp0FgGg0GomMjFQ+y9OnT8sXX3yhyuKgBw8e1CkiiIj8+uuvotFoyl1QOSgo6JksIpw6dUp8fHxEq9XK8OHDH1pMuHr1qkRGRsqyZctUXYOtbt26cuLECdm+fftDiwlarVbi4+Nl8+bNqq4xEBAQoPNdFBHp0KGD9OvXr8z9P//8c9WKCCIi77//fqn56z/55BOxtbVVKdHT4caNGzpFBJEHo5ytra1l3LhxpfZPT0+Xvn37yrx581S9Ry5PSQexlJQUnXY/Pz+pX7++2NnZ6cyZrtVq9fZaTUQkPDy81Kg1rVYrAwYMECMjI6Wzo1arlZEjR8rEiRPLLACpLTMzU1xcXHQ6wmm1WnF0dCz3ekIfv1/lWbJkiQwZMkRyc3OVYkJ8fLx06dJFjh49KiIPnkFt2LBB1ed4MTExYmpqKidPnhStViujR48WIyMjpfNTvXr15NKlS6rlK4+JiYlSBMzNzZW+ffuKoaGhAJD27dvrXCMVFBSoMtLjj9566y158cUXlddfffWVMiOKo6PjQ0cU/dtYSHjKaLVaqVatmnJBWVJMcHZ21qm0ZWdnS3JyskopS4uNjZURI0aIiCjFBPz/QqD6zNHRUflcS4oJtra2yk2oPtmyZYuYmJhIVFSUODs7K9XY/Px8GTx4sGg0GtV6Mz0t9u7dy4djpLqXXnpJnnvuOfnkk08kLCxMbGxsdBYG/SN9fNj+R1u3bhV7e3uZPHmyvP/++2JoaFjuiIkLFy7o/QibpKQkcXV1FTs7u4cWE7RarWpTVDyJRo0aiZ+f3yOLCUeOHFFGDP3888/8nfybBg0aJEFBQSIismzZMqldu7bs2LFDDh48KM2aNRNHR8dSiy2r5dtvvxUjIyO5cuWKpKSkiL29vSxatEhOnjwpUVFRAkB27NihdkwReTB1pomJiSxbtky0Wq00adJEBg4cKMePH5cVK1aIhYWFDB8+XO2YUlRUVGZvsw4dOkhwcHCZ7zl58uQzV0QQETl79qzExcUprx9VTFBbYWGhTJkyRXn9qGKC2u7fv1/mFFBRUVHSq1cvFRI9Wr9+/UpNEbhr1y4xNjZWKdHT4+TJk6XaRo0aJQ4ODnr9kL0sd+7cEY1GI8uWLVPaLl26JI6OjvLbb7+JjY2NqgWxJzV48GAJCgoqdf1VXFwsbdq0kXr16lWKa7NTp07JkCFDSrWHhITI9OnTVUj0z9qzZ4+0bNlSREQpJgCQF154Qa9G3hcUFEhERIQ4OTnJG2+8Ic8995xSOPjll1+kZs2aEhgYqFeZRUQ8PDyUwmbPnj2lS5cukpycLF9//bU4OjpK165d1Q34JzNmzBBnZ2fJz8+XXbt2iZOTk6xfv16OHj0q3bp1E1NTU9VG9LKQ8BQKDAxU5h2bNGmSODo6PnLNBLVt27ZNuRC9fPmy1K5dWxwcHB5rzQQ1hYaGKg9PZs2apXzWagxzfxwlxYSSok2J4uJiCQgIkP/9738qJSOif8K6deukSZMmyjQit2/fFlNT0zIfQOzfv180Go188MEHFR3zseTk5Ej16tV1pvOJiIiQFStWlNpXq9WKv7+/NGzYUK+LCdnZ2WJnZyfJycmligl/7DU+e/ZsMTExkW+++UatqI/l5ZdflrVr10rnzp11igl//FuuXr0q5ubm8t///lfvbigqq927dwsAWblypfj7+yvzNYs86EVpZGSkN9+dvLw8cXBwkF69esk777wjEydO1NneokULef3111VKV9rLL78srq6usmXLFmnatKnOg5WpU6eKq6uriukebseOHQJADhw4oHYUvVZWMeHHH39UMdHDlVVMOHLkiN4UC8u6voiJiZGePXsqrzMyMvSmd2pZeQ8cOCBGRkY6bceOHauoSE+11NRUMTIykuXLl6sd5Yn16dNHTE1NZfz48bJ48WLx9PSUOXPmiMiDqYJ8fX1VTvj4jh07JoaGhmWupXP27FnRaDRy+PBhFZI9ubKO4bCwMJk2bZry+vz585KTk1ORsf4RN2/eVEZH5ebmSlhYmDg6Opa7ZoKaSooJhoaGpaYmi4+PFwB6952KiYkRCwsL+emnn8Td3V3nfmXt2rViYGCgV0XPixcvirGxsYwbN066d++u05kuPz9fXF1dJTY2VpVsBv/mQs6kDl9fX5w4cQKTJ0/G2rVrcfz4cUycOBEjR47E3Llz1Y5XppLMV65cQevWrfHGG2/gyJEjEBG0atVKtVXdH6Uk9+zZs7FgwQIkJSUhNjYWEyZMwIcffqh2vFI6deqEr7/+Gi+88IJOu4GBAQIDA1FQUKBSMiL6J6xYsQJz5syBiYkJAODAgQOwsbGBh4cHrKys8P777yv7BgcHY8yYMbCyslIr7kNt27YN7du3R3BwMAAgPz8fx44dw+zZs2FiYoI2bdrgypUrAACNRoNly5bBxcVF+dv1kYWFBapUqQJHR0fs3LkTZ86cQfv27TF58mRERkYq+w0aNAidOnWChYWFimkfzdfXF2fPnsWGDRvQpk0bvPDCC1i6dCnq1q2La9euAQBcXFwwf/58WFlZQaPRqJz46RAaGorIyEgMGTIE58+fh5eXl7LNzs4OVapUgY2NjYoJf2dmZob58+dj/fr1WLp0KWrVqqWzvXr16nqTFQCmT5+O+/fvo2/fvvDy8tL5zrq5uelV1j8LCwtD/fr1MWfOHLWj6LXZs2dj2LBhGDBgAFasWIFp06Zh8ODByMrKUjtamdq3b49Nmzbhm2++wSuvvIK9e/eiQ4cOOHr0qNrRAABGRkal2jQaDYqLiwEAmZmZCA8Px5o1ayo6WpkelRcAYmJiMGDAAGi12oqM9lRyd3dH9+7dK+Xv0tKlS/HGG29g+fLlWLhwISZPnoyoqCgAwHPPPYe8vDyVEz4+Pz8/fPDBB3j33XexdOlSnW116tSBi4sL0tPTVUr3ZB51DJ89exb/+c9/8OOPP1Zwsr/PwcEBGo0GFy5cQNeuXeHi4oILFy6gSZMmaN++PQ4fPqx2RIWxsTG+/PJLjBw5Ep6enjrbgoKCAEDvni3FxMTA2dkZ3bt3h5ubG0xNTZVtbm5usLCw0Kv7yJo1a2LcuHGYMmUKdu/erXMNbWpqCgcHB/WuS1UpX9C/auLEieLp6Sn16tXTmc5o+vTpelfJLFFcXCzm5uZSs2ZNmTVrltJ++fJliY6O1svhvCIPpjOqWbOm1K5dW2c6o7lz51aqeclzc3PF09Oz3Ll1iahy+GMPy5MnT4qjo6OMGjVKDh06JFOnThWNRlNpeoWdPn1a9u7dKyIPeh/16NFDmjRpIjt37pRt27aJp6enNGvWTOWUTy4sLEyZyiU5OVksLCzE0NCwQhbG+qd98cUX0rt3bxH5fYoLADJmzBiVkz398vPzpWPHjgJAhgwZooz2eP/99yUoKEjvRn98/PHHotFoxN/fXxk19NNPP4mNjU2pOajVduDAAbGxsREzMzPluvnmzZvSoEEDWbJkicrpHm7p0qXlLm5KuoYPHy4ajUZ8fX3l2rVrasd5pJKRCebm5nqzXlx5xo0bJxEREaovrPy4Dh8+LCWPRcaOHftMrifyb9q/f78AkISEBLWj/GPeffdd+e9//6t2jCdWMqXg22+/rawdkJCQIPb29k+0dpe+ad++vUyePFnOnDkjbm5usmrVKrUj/WUhISFSs2ZN6dOnj3Itl5ubK2+++aZej7r+o7Vr14q7u7uq6w2V59y5c+Lh4SEajUa++uorEXkwYvz555+Xd955R+V0pWm1Whk8eLAAkE6dOikjJlavXi1ubm6qHbcsJDyFLl68KO3bt9cpIlQGEyZM0CkiVAZpaWkSHh6ul2siPK7r169L+/btdYYgE1Hlt3LlylIX0r169ZKIiAiVEv11aWlp0r9/f53hpt9//70AqDQX1SWGDRumDEOdMWOG1KlT55FrJuiro0ePSr169UTkwWKv1atXl6CgoCdagJn+usLCQhk/fryYmZmJp6en+Pj4SMOGDfV2SsjNmzeLm5ubWFtbS1BQkNjb2+vtA9Fz585JaGioGBkZSVBQkNjY2OjlDeaflUwlNXXqVLWj6L0PP/yw0hQRREQSExPF0dFRb4+ZPxo/fry0bt26UhQRRB6sX1RSBGcR4d8RHBxcKR+8/1lmZqbMnTtXnJycSq21UVksWrRILC0txdbWVpo2bSp2dnaV/pqtQ4cO0qdPn0pfRBAR2bRpk/Tv31/vOoQ8rk2bNomjo6Ned6q9fv26REREiEajkUaNGomjo6O89tpr5a4pqA8WLFgg1tbW4uLiIgEBAeLh4aEswK0GjYiIOmMhiEhto0aNwrZt29CnTx+MHDkShoaGakcion/RgAEDYGRkhMWLF6sd5W9LTExEmzZtkJ6erlfDUB9lwYIFOHLkCOrWrYulS5ciISEBN2/eRNu2bREYGIgdO3aoHfGx5efnw9raGgcPHkTHjh0xffp09OrVCz179kR8fDySkpLg6+urdsxKJz09HZs2bYKIIDw8HNWrV3/o/rdv38b+/ftRtWpVhIaGVui5vLi4GFu3bsXly5cRHByMxo0bP3T/goIC7N27F9nZ2WjZsiWsra0rJuj/S0pKQmJiIjw8PBAeHg5jY+OH7n/06FGkpKSgYcOGpaZl+rddvXoVW7duhZmZGTp06AB7e/vHel9cXBw6dOig19MwqW3t2rWYOHEiEhIS4OzsrHacR7p27RoaNWqEVatWITw8XO04jzRhwgR88MEHiI6OxqxZs9SO80hHjx5Fw4YN0ahRI8THx/PYeYisrCzExcUhLS0Nr7766mP/Lu7YsQM1a9aEt7f3v5zw8eXn5+OLL77AuXPn0Lt3b/j7+z90/9zcXPTt2xeOjo4YOXIkPDw8Kijpo926dQvdu3fHxx9/jCZNmjxy/4yMDOzYsQP5+fno2LHjY59fKkJ2dja6deuGkSNHPvbvXefOnbFt2zasWLECffr0+ZcT/jV/9ZyuluLiYmzfvh2XLl1C06ZNlemKypOamopu3brB2toaM2bMQGBgYAUl/d3p06eRkJAABwcHdOzYEVWqVHno/mfPnsXx48fh4+ODBg0aVFDK3+Xm5mLTpk3Izs7G888/j9q1az90/+zsbPz000/QaDRo1aoVzMzMKihpGVQrYRCR6koWZCWip9/p06fF1tb2qVhAUKvVSqdOnWTkyJFqR3li8fHxUq1aNfH29taZfiQ5OVl2796tYrK/xtPTU6ytreWzzz5T2u7fvy+LFy/WWaiWHs/hw4fFyclJmjZtKq6urmJiYiLjxo0rs5eU2r3V7t27JyEhIeLt7S1+fn4CQDp27CjXr18vc3+1vw9jx44Ve3t7CQkJERMTE6lVq5b8/PPPZe6r9mf7/fffi42NjYSEhIi9vb1YWFjI7Nmzy9xX7ayVUU5OTqUbuf3nxSz12S+//CLvvvuu2jEeW05OjgwaNIgjER7h1KlTUqtWLWnUqJHUqFFDLC0t5erVq2Xuq++/S6mpqRIQECD169eXOnXqiImJiZw6darMffX9bxERGTVqlJibm4uVlZUcOHCg3P20Wq3q5+JH+fjjj8Xc3FzMzMweOQKr5P/myy+/1OuRCJXtnJ6dnS0tW7aUOnXqiL+/vwCQ9u3bl3u8l3yn1Hy2tHDhQrGyspLmzZuLhYWFODk5yaZNm8rcVx8+4wsXLoiXl5c0atRIvLy8xMDAQAYNGqRMOfZH+pD3z1hIICIieopptVrZtm2buLu7y9KlS9WO87elpaVJ586dpU2bNpKfn692nCdWXFwsMTExT80c5tu3b5fPP/9c7RhPhaKiIvHy8lKKMkVFRfLxxx+LsbGxRERE6Mw1m5mZKcHBwfLFF1+oFVeGDh0qr7zyinIDmZCQINWrVxdPT89SDz1HjBgh/fr1U+0BxrZt28TDw0OZCi01NVXatm0rJiYmsmHDhlL7BgYGqvagOTMzU2xtbZX5xPPz8yUmJkY0Go0MHjxYZ9+0tDTx9/ev9NNSEJF+y8/Pl7p16yrrxGRnZ4urq6t8++23pfbNzc2VVq1a6e01p1arlRYtWsjEiRNF5ME0gQ0aNCgzb3FxsfTs2VPef//9Ck75ZBYuXCiDBg2S1q1bP7SYMGTIEBk6dKheFxO+/vpr6dWrl/To0eOhxYQpU6ZI9+7d9XIe/j96knP6tWvXJCAgQH744QcVkv4uKipKevfurTzA3r17t7i7u4uHh0epda1GjRolr776qqoPu0+cOCE2NjbKdGN37tyRl19+WTQajcyZM0dn36SkJKlfv76cPXtWjaiK0NBQmTx5svJ6zZo1UrVqVWnWrJlkZmYq7YWFhdKlSxeZNm2aGjHLxUICERHRU6qoqEgiIiKkV69eD+2hVFmsWbNGmjZtKvPmzdPL3hlEf8epU6cEgBQWFuq0lyyy2q9fP6WtoKBAIiIipE6dOqr1APPy8irV2+vKlSvi7e0tderU0bkRWrBggZiamkpSUlJFxxSRBzfFAwYM0GkrLCyUV155RUxMTHRGJiQnJ4udnZ1qN207duwQZ2fnUu0rV64UAwMDee+995S2rKwsad68ubRo0aIiIxLRM2bdunXSq1cvnTYfHx8ZO3as9OvXTzZu3Ki0FxcXS2RkpFSvXl2ysrIqOOmj/fjjjxIaGqrT1rx5cxkxYoT07dtXVq9erbNtzJgxYmVlpbfrD4mI7Nq1S8LCwiQnJ0enmLB48WKd0a6xsbFiZmYmx48fVzHtw506dUr8/PyksLBQp5iwfv16+frrr5X91q5dKyYmJrJz504V0z7ak5zTs7OzJTQ0VIKDg1Ut9nh7e5fqZJGWlia+vr5Sq1YtSU9PV9o/+eQTMTExkYMHD1Zwyt/FxsZK27ZtS7WPGTNGNBqNTqebS5cuiaenZ6kiTkXKyckRAKWKMocPHxYbGxsJCwvTuc8dPny42Nra6tVIShYSiIiIiIhUdv36dQFQZu/ytWvXCgDZunWr0lZQUKDqQrHPPfecvPXWW6Xaf/vtN7Gzs5MhQ4botF+6dKmiopUyYcIE8fX1LVWALCwslLZt24qXl5dOr8bU1NSKjqhISkoSjUYjp0+fLrVt5syZYmBgIMnJyUpbVlYWp2Qhon/V3LlzdXpJT5w4UaysrOT111+Xzp07CwCZP3++sr24uFhvH7x/+eWXOtMxLlmyRMzNzaVv377y4osvikajkXHjxum8R83z1+O4evWquLq6iogoxQQLCwvx8PCQ3377TWdfff9bCgoKxMLCQgoLC5VigqmpqTg5OZWanlXf/xaRJz+nZ2dny507dyowYWnNmjWTqKioUu2pqani4OAgAwcO1GlX+/9h1apVYmtrK9nZ2aW2vfbaa2JlZaXzmV69elXVkSxFRUVStWpV+fTTT0tt27NnjxgZGcmCBQt02tW8Li0LCwlERERERHogPDxc6tevLzk5OaW2RURESKdOnVRIVbZ58+aJiYlJmaMMli1bJmZmZmXO9aqGc+fOiZGRUZmjDFJTU8XY2LjMKTrUoNVqxc/PT1q1alVqbQytVivPPfecDBo0SKV0RPSsKinE7t+/X7y8vHR60w4bNkx5kF0ZlPy2njt3TlxdXXUe5E6ZMkXMzMwq3VqCVlZWSlE5NjZWDA0NH7lmgr7y9vZW1qyIi4sTQ0PDx1ozQR9VxnP6okWLxNjYuMxRBitXrhQTE5MyH9qrJT09XaytrcscZZCVlSUODg4yb948FZKVb9CgQeLs7Fxmh6Do6GipX7++Cqken4F6yzwTEREREVGJ+fPn4/Lly+jRowfy8/N1tvXo0QOXLl1SKVlpgwcPRlBQEF544QWcO3dOZ1tJ/ps3b6qUTlft2rXx3nvv4Z133kFcXJzONnd3dzRp0kRvPluNRoOlS5di37596NevH7Rarc627t27601WInp2GBg8eHQUHByM5ORk1KpVS9nWrl075OXlqRXtiRkaGgJ4cG749ddf0bBhQ2Vbu3btUFBQgOLiYpXS/TU+Pj44ceIEFi1ahHnz5uHYsWMIDAxEu3btcPDgQbXjPRFfX1+cOHECGzZswNtvv41Dhw6hU6dOiIiIwPfff692vCdSGc/pAwcORHBwMDp37owzZ87obOvRowcKCgpw/fp1ldKVZm1tjblz52Lx4sWYNm2azraqVauiXbt2evcZf/jhhzA2NkbHjh2Rnp6us03frvfLwkICEREREZEeqF27NjZt2oQ9e/YgLCwMaWlpyrZDhw4hODhYxXS6jIyMsGnTJtjY2KBFixZISEhQth06dAjVq1eHm5ubigl1jRs3Dn379kVkZCQ++ugj5WY+IyMDKSkpevXZNm3aFGvWrEFcXBy6d++uc5N5+PBhvcpKRM8eS0tLndebN29GRESEOmH+prL+lvDwcJibm6uU6K/x9fXF+PHjMWPGDCQkJKBevXrYsmULQkJCSj2o1He+vr6YO3cuoqKi8P3336NRo0ZYt24dunTpgjt37qgd74lVtnO6oaEhNm7cCEdHR7Rs2RLx8fHKtkOHDsHFxQUeHh4qJiwtMjISEyZMQExMDEaMGIGCggIAQEFBAY4ePap3n7GtrS22b9+OK1euoEWLFjh9+rSyTd+u98uiERFROwQRERERET1w6NAhvPTSS7h58ya6deuGjIwMnD17Fj/99BMcHBzUjqfj7t27iIyMxPbt29GuXTtUr14d3377LeLi4tC2bVu14+kQEUyaNAmTJk2Cj48PWrZsiR9++AG9evXC1KlT1Y5Xyo4dO9CnTx8UFRWha9euuHjxInJzc7Fr1y5UqVJF7XhE9IwTEcyaNQtz5szBoUOH4OjoqHakv2XVqlUYM2YM9u7dqzPiojLYtm0bhg8fjp07d+rdQ94ndeDAAfTu3RtbtmyBn5+f2nH+MZXtnJ6eno5XX30V3377Ldq1awd3d3d88803WLNmDcLDw9WOV6alS5ciOjoajo6OCA8PR2JiIurXr4/PPvsMGo1G7XilpKSkoHfv3jh58iS6du0KQ0ND7NmzBwkJCfD29lY7XrlYSCAiIiIi0jP5+flYu3YtkpKS4OXlhQEDBpTqOalP4uPjsX37dhgbG6Nv3756fQOUkpKCuLg43LlzB+3bt0eHDh3UjlSurKwsrFmzBidPnoSfnx9ee+01mJmZqR2LiJ5xK1euxOLFi2FjY4MlS5bA3d1d7Uh/2caNGzFnzhwUFRVh+fLl8PHxUTvSX1JQUAATExO1Y/wjnqa/5Y8q4zk9ISEBW7duhZGREV577TX4+vqqHemhrl27htWrV+Py5cto2bIlevfurZdFhBLFxcXYuHEj9uzZA0dHRwwcOBBOTk5qx3ooFhKIiIiIiIiIiOixXLt2DcXFxXo1hd1fdffuXWRkZMDLy0vtKEREeo+FBCIiIiIiIiIiIiIiKhcXWyYiIiIiIiIiIiIionKxkEBEREREREREREREROViIYGIiIiIiIiIiIiIiMrFQgIREREREREREREREZWLhQQiIiIiIiIiIiIiIioXCwlERERERERERERERFQuFhKIiIiIiIiIiIiIiKhcLCQQEREREREREREREVG5WEggIiIiIiIiIiIiIqJysZBARERERERERERERETlYiGBiIiIiIiIiIiIiIjKxUICERERERERERERERGVi4UEIiIiIiIiIiIiIiIqFwsJRERERERERERERERULhYSiIiIiIiIiIiIiIioXCwkEBERERERERERERFRuVhIICIiIiIiIiIiIiKicrGQQERERERERERERERE5WIhgYiIiIiIiIiIiIiIysVCAhERERERERERERERlctI7QBEf1dqaioyMzPh5+dX5vZjx47B3NwcderUqeBkRPovPT0dZ86cQZMmTWBg8Htt+dy5c7h37x4aN26ss/+xY8dgaWmJmjVrVnBSosqh5JgCAI1GAycnJ3h4eECj0aicjIiInnbHjx+HlZUVatSoobTl5eXh6NGj8PT0hJOTk9J+9+5dnD17FkFBQTAy4mMBorIcP34cOTk5AABzc3N4eXmhWrVqKqciIlIPRyRQpbdx40Y8//zzZW775Zdf0KhRI5w6daqCUxFVDocPH0azZs2QkZGhtOXn56N58+Zo3bq1zr6pqalo3LgxTpw4UcEpiSqPlStXonXr1oiOjsabb74Jf39/NG7cGNeuXVM7GhERPeUGDx6MmTNn6rQtWLAAzZo1w+eff67TPmbMGLz55pssIhA9RGhoKCIjIxEdHY3evXvD0dERc+bMUTsWEZFqWEigSs/f3x+3b9/GjRs3Sm0bPnw4wsLC0LVrVxWSEek/a2trAMC9e/eUtpUrV0JEkJWVheLiYqV9zpw5qFOnDjp16lTRMYkqjSNHjiA4OBiJiYk4dOgQzp8/j8uXL2Py5MlqRyMioqectbW1zjVdUVER5s6dC3t7e51OI7du3cJnn32GUaNGqZCSqHI4f/48MjIysGzZMiQmJuLMmTMYPXo03nrrLVy/fl3teEREqmAhgSo9f39/AA+GHf7R+vXrsX//fvYYIHqIkkJCZmYmAECr1WLWrFkYM2YMgN8LDFlZWVi2bBnefvttTtFC9BBJSUk6U+05ODjAz88Ply5dUjEVUeVy+/ZtaDQaaDQaGBgYwMXFBaNHj4aIqB2NSK9ZW1sr13QAsG7dOhgaGuLFF1/UaV+4cCGcnZ3Rs2dPNWISVQpJSUkAoHNd16ZNG2i1Wly5ckWtWESVzptvvqlc11WpUgXNmjXD4cOH1Y5FfxELCVTp2dnZwdXVVWe6lfz8fIwePRrR0dHw8fFRMR2RfvvziITNmzfj3r17GDp0KAAovdeWLl2KKlWq4H//+58aMYkqhZycHJw5c0bnhjMrKwvHjx9HkyZNVExGVLnY29tDRCAiKCgowMaNG7F8+XJ89dVXakcj0mt/HpEwc+ZMjBgxAjY2Nso13f3797Fw4UKMGDEChoaGZf47fEhK9KCQ4OrqCltbW6Vt3759sLS0LPcZg4ggLS2toiISVQrz589XrusuX76Mhg0b4rXXXlM7Fv1FLCTQU8Hf31+nkDBjxgzk5+dj/PjxKqYi0n9/HpEwc+ZMREVFoUqVKrCwsEBGRgaKi4sxd+5cDBs2DKampiqmJdJvycnJ0Gq1KC4uRmJiIjZt2oQOHTqgbt26GDFihNrxiColIyMjBAcHo379+khJSVE7DpFe++OIhB9++AGpqano378/rKyslELCmjVrUFhYiP79+5f5b+Tn56Nt27YVFZlIbyUlJcHFxQWJiYnYs2cPpkyZgunTp2PJkiXlLrh8+fJlvPzyyxWclKjysLOzQ69evR56TdejRw+cP3++AlPRk+DKSvRU8Pf3x+7duwEAaWlpmD59OhYtWlTuCZ6IHjA2NkaVKlWQmZmJxMREHDt2DFu2bAEAWFpaIjMzE1999RVu376NIUOGqJyWSL8lJSXB0NAQK1euRFFREc6cOQMXFxccPHiQ5yOiJ5CUlISpU6fiwIEDuHbtmrJeD89DRA/3x0LCzJkzMXToUFhYWCjXdCKC2bNnY/DgwbCwsCjz37hw4YIydSzRs+zIkSOwtLREdHQ07ty5g5SUFMyfPx8vvvhiue9JSUlBw4YNKy4kkZ67f/8+5s2bh9WrV+O3335DVlYWAMDb27vc95w5cwaenp4VFZGeEEck0FPB398fJ0+ehIhgzJgxCAgI0JmCJSsrS2eBseLiYg45JPp/JcPgZ8yYgQEDBsDGxgbAg0JCRkYGYmNj0b9/f6W9oKCg1AJjaWlp0Gq1FZ6dSJ8cOXIEAQEBSExMxOHDh3H27Fmkp6fjo48+UvbJy8vDrVu3dN6Xmppa0VGJ9NbRo0fRsmVLBAUFYe/evcjLy8PFixcBAAEBASqnI9JvJdd0x44dw549ezBs2DAAv1/Tfffdd0hJSUFUVFSp92ZnZyM/Px/nz5/nsUbPvIsXLyI9PR3z5s1DYmIizp07h3feeQejR49Genp6qf0zMzNRUFDA44foT/r06YN169Zh/vz5SE1NhYigT58+5R4nhYWFsLKygoEBH1frK/7P0FPB398f2dnZWLduHdatW4d58+bpLAgbHx+P4cOHK69nz56NpUuXqhGVSO9YW1sjKSkJW7ZsQXR0tNJuZWWFbdu24fDhwzrTsty7dw8tWrRQXicmJqJfv3482dMzLykpSWctBBcXF/Tt2xcrVqxQCm3nz59HRESEss8333yDd955p6KjEumtdevWwc/PD2PHjkWNGjVgbGyMxYsXw8zMTJmTulmzZrhx4waAB51D2rRpwwXNifD7iISZM2ciMjISTk5OAB5c02VmZiI2Nhb//e9/4ezsrLynqKgIkZGRcHV1hY+PDzZt2sQe1fTMK1lo+Y/XdaNGjUJRURHWrVuntOXk5KBz587w8PBA/fr1sWvXLh4/RP8vLy8PGzZswKRJkxAaGgpra2vcuHEDmzdv1ikk7Nq1CyEhIfDx8cHKlSvRoEEDFVPTo/CpDz0VfH19YWJigkGDBmHAgAEIDAzU2d64cWP88ssvAB7MWxgXF4eYmBgVkhLpH2tra6xevRo9e/aEh4eH0m5paYmVK1eiZ8+eqFmzptJub2+PoqIi3Lt3D0VFRYiOjsb8+fNVSE6kP3Jzc3H69OlSiyr37t0b169fx88//wwAqFu3LlJSUiAiyMnJwXvvvYfY2Fg1IhPpJQcHB5w/fx6//vor0tPTERsbiwULFqBBgwbKwrA1a9ZEcnIyAGDu3Llo0aKFzvmL6FllbW2N+/fv44svvsDbb7+ttFtaWuLKlSuIj4/XaQeADz74ABYWFrh79y5OnTqF7du3s0c1PfOSkpJQo0YNpRgHPDi+wsLCsH79eqVt+PDhaNiwIdLT07F//35s27YN9evXVyMykd4xNTWFpaUldu7cidzcXBw5cgS9e/dGZmamUnD79ddfMWTIEEycOBHfffcd4uLiWIzTc1wjgZ4KxsbG6N69O9LS0jB58uRS293d3XHnzh0UFBQgKioKM2bM4KKxRP+vadOmKC4uxqhRo3Tag4KCkJWVhdGjR5d6T2BgII4ePYoDBw6gU6dOqFOnTkXFJdJLly5dQlBQEJo3b67THhQUhI4dO+LYsWMIDQ2FoaEhatWqhZSUFHzyySd444034OjoqFJqIv0zePBg7Nu3D0FBQbC1tUXfvn0RHh6uTK8H/N5BpEGDBvjss8+wb98+FRMT6Y/q1aujadOmCAoKUkbwAICzszMaN26MwMBA1KtXT+c9K1euxKlTp2BkZISioiJYWFjAzc2toqMT6ZWCgoIy10Lo168fYmNjkZOTA1NTU2zatAk3btyARqOBRqOBp6cnzMzMVEhMpH8MDAywatUqREVFYdGiRWjatClef/117NmzRykWzJ07FzExMWjbti0AoEaNGixm6zmNiIjaIYgqQocOHdCsWTOkpKRg9erVaschqtSmTJmCzMxM7Nq1C/v27YOJiYnakYgqjWHDhsHGxgZ79uxBQkKCzlR8RPRoCQkJ+OSTT1BYWIihQ4eiTZs2akciqrTc3NwwY8YM1K1bFx999BFu3LiB+Ph4tWMR6b2ioiLY2Njgyy+/hIODA9577z3Y2Njg888/VzsaUaXx0ksvoWXLlnjjjTfwww8/oEuXLrh16xaqVq2qdjQqB0ck0DPj+eefx6pVq5CQkKB2FKJKLyQkBP3798enn37KIgLRE2rZsiViYmKwefNmFhGI/oLAwEBs3boVERERLCIQ/U1z587FhAkT4ODggPDwcOTl5akdiahSMDIywqxZszBq1Ch4enoiODgYDg4OasciqlQGDhyI7t27491330W/fv3g7u7OIoKe44gEIiIiIiKqVO7evYuqVauymE1ERERUiRUXF6OoqAjGxsbIysqClZWV2pHoIVhIICIiIiIiIiIiIiKichmoHYCIiIiIiIiIiIiIiPQXCwlERERERERERERERFQuFhKIiIiIiIiIiIiIiKhcLCQQEREREREREREREVG5WEggIiIiIiIiIiIiIqJysZBARERERERERERERETlYiGBiIiIiIiIiIiIiIjKxUICERERERERERERERGVi4UEIiIiIiIiIiIiIiIqFwsJRERERERERERERERUrv8DjhPlzRDvf4QAAAAASUVORK5CYII=", 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", "text/plain": [ - "<Figure size 1600x1600 with 49 Axes>" + "<Figure size 704x440 with 1 Axes>" ] }, "metadata": {}, @@ -384,97 +588,139 @@ } ], "source": [ + "x_fine = np.deg2rad(np.linspace(3.0, 175.0, 140))\n", + "on_fine = omp.bind(x_fine, data.meta)\n", + "fig, ax = plt.subplots()\n", + "for (name, p), colour, hatch in zip(\n", + " problems.items(),\n", + " [plotstyle.COLOURS[i] for i in (1, 0, 2)],\n", + " plotstyle.HATCHES,\n", + "):\n", + " cols = p.columns(omp.params)\n", + " curves = np.array([on_fine(*s[cols]) for s in samples[name][::20]])\n", + " lo, hi = np.percentile(curves, [5, 95], axis=0)\n", + " plotstyle.band(\n", + " ax, np.rad2deg(x_fine), lo, hi, color=colour, hatch=hatch, label=name\n", + " )\n", + "ax.errorbar(\n", + " np.rad2deg(data.x), data.y, data.y_err, fmt=\"o\", ms=3, color=\"k\", label=\"data\"\n", + ")\n", + "ax.set(\n", + " xlabel=r\"$\\theta_{cm}$ [deg]\",\n", + " ylabel=r\"$\\sigma / \\sigma_{Rutherford}$\",\n", + " yscale=\"log\",\n", + " title=\"90 % posterior bands on a grid the experiment never measured\",\n", + ")\n", + "ax.legend(fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1646803d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T04:00:01.734128Z", + "iopub.status.busy": "2026-09-12T04:00:01.733924Z", + "iopub.status.idle": "2026-09-12T04:00:03.499907Z", + "shell.execute_reply": "2026-09-12T04:00:03.499152Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1760x1760 with 49 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "best = \"inferred model error\"\n", + "p_best = problems[best]\n", "fig = corner.corner(\n", - " samples,\n", + " samples[best][:, p_best.columns(omp.params)],\n", " labels=[f\"${lt}$\" for lt in latex],\n", - " truths=truth,\n", - " truth_color=\"C3\",\n", - " plot_datapoints=False,\n", - " show_titles=True,\n", - " title_fmt=\".2f\",\n", + " **plotstyle.corner_kwargs(title_fmt=\".2f\"),\n", ")\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "fab9a17c", + "id": "814f64bc", "metadata": {}, "source": [ - "## Predictions on any grid (recipe 15)\n", + "## The guardrail: a covariance that cannot be factored (recipe 21)\n", "\n", - "`omp.bind(x_fine, d.meta)` compiles the same model on a plotting grid; the\n", - "angular basis is cached per kinematics, so this costs a few milliseconds per\n", - "row. Columns come from `problem.columns`." + "If a subentry reports no statistical error either — it happens — the covariance\n", + "of those points is singular, and there is nothing sensible to do with it. The\n", + "library says so when the problem is built, naming the dataset, rather than\n", + "failing somewhere inside the sampler an hour later." ] }, { "cell_type": "code", - "execution_count": 9, - "id": "0a10e693", + "execution_count": 12, + "id": "57c5d001", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:05:36.805541Z", - "iopub.status.busy": "2026-09-11T04:05:36.805303Z", - "iopub.status.idle": "2026-09-11T04:05:37.223994Z", - "shell.execute_reply": "2026-09-11T04:05:37.223230Z" + "iopub.execute_input": "2026-09-12T04:00:03.504295Z", + "iopub.status.busy": "2026-09-12T04:00:03.504095Z", + "iopub.status.idle": "2026-09-12T04:00:03.508649Z", + "shell.execute_reply": "2026-09-12T04:00:03.507845Z" } }, "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 600x400 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "the constraint's covariance is singular on its active points; the diagonal is zero on rows of ['a subentry with no errors']: those comparisons have zero statistical error and no diagonal term covers their points (a block covered only by correlated modes is singular here even when the full covariance is not). Remedies: comparison.reported_terms(), a noise term, a fixed Term covering those points, or statistical=False with an explicit covariance.\n" + ] } ], "source": [ - "x_fine = np.deg2rad(np.linspace(1.0, 179.0, 90))\n", - "on_fine = omp.bind(x_fine, d.meta)\n", - "cols = p_sys.columns(omp.params)\n", - "curves = np.array([on_fine(*s[cols]) for s in samples[::25]])\n", - "lo, mid, hi = rx.predictive.predictive_band(curves, levels=(5, 50, 95))\n", - "\n", - "fig, ax = plt.subplots(figsize=(6, 4))\n", - "ax.fill_between(\n", - " np.rad2deg(x_fine), lo, hi, color=\"C0\", alpha=0.35, label=\"90 % posterior band\"\n", + "no_errors = rx.Dataset(\n", + " data.x[:6],\n", + " data.y[:6],\n", + " np.zeros(6),\n", + " label=\"a subentry with no errors\",\n", + " meta=data.meta,\n", ")\n", - "ax.plot(np.rad2deg(x_fine), mid, color=\"C0\")\n", - "ax.plot(np.rad2deg(x_fine), on_fine(*truth), \"--\", color=\"C3\", label=\"truth\")\n", - "ax.errorbar(np.rad2deg(d.x), d.y, d.y_err, fmt=\"o\", ms=3, color=\"k\", label=d.label)\n", - "ax.set(xlabel=r\"$\\theta_{cm}$ [deg]\", ylabel=r\"$d\\sigma/d\\Omega$ [b/sr]\", yscale=\"log\")\n", - "ax.legend(frameon=False)\n", - "plt.show()" + "try:\n", + " rx.Problem([rx.Constraint([rx.Comparison(no_errors, omp)])])\n", + "except ValueError as err:\n", + " print(err)" ] }, { "cell_type": "markdown", - "id": "3b052b12", + "id": "825ee796", "metadata": {}, "source": [ "## Other drivers (recipe 16)\n", "\n", - "The same problem runs under emcee from `problem.sample_prior` and\n", - "`problem.log_posterior`, and it pickles with `dill` for a driver that\n", - "runs in another process. A short emcee run here only shows the contract;\n", - "on optical-model problems expect to need far longer chains than dynesty\n", - "needs live points." + "The same problem runs under emcee from `sample_prior` and `log_posterior`, and\n", + "it pickles with `dill` for a driver in another process. The short run below\n", + "only demonstrates the contract; on an optical-model posterior expect to need far\n", + "more emcee steps than dynesty needs live points." ] }, { "cell_type": "code", - "execution_count": 10, - "id": "575e54e5", + "execution_count": 13, + "id": "868974ac", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:05:37.226576Z", - "iopub.status.busy": "2026-09-11T04:05:37.226353Z", - "iopub.status.idle": "2026-09-11T04:05:45.111983Z", - "shell.execute_reply": "2026-09-11T04:05:45.111199Z" + "iopub.execute_input": "2026-09-12T04:00:03.510582Z", + "iopub.status.busy": "2026-09-12T04:00:03.510319Z", + "iopub.status.idle": "2026-09-12T04:00:14.592018Z", + "shell.execute_reply": "2026-09-12T04:00:14.591209Z" } }, "outputs": [ @@ -483,82 +729,73 @@ "output_type": "stream", "text": [ "emcee: 3200 rows, mean acceptance 0.25\n", - "dill round trip: ndim 7, names ['Vv', 'Wv', 'Rv']..., log_posterior(truth) = 106.265 vs 106.265\n" + "dill round trip: ndim 8, log_posterior = -28.425 vs -28.425\n" ] } ], "source": [ - "sampler = emcee.EnsembleSampler(16, p_sys.ndim, p_sys.log_posterior)\n", + "sampler = emcee.EnsembleSampler(16, p_best.ndim, p_best.log_posterior)\n", "sampler.random_state = np.random.RandomState(2).get_state()\n", - "sampler.run_mcmc(p_sys.sample_prior(16, rng=2), 200, progress=False)\n", + "sampler.run_mcmc(p_best.sample_prior(16, rng=2), 200, progress=False)\n", "print(\n", - " f\"emcee: {sampler.get_chain(flat=True).shape[0]} rows, mean acceptance {sampler.acceptance_fraction.mean():.2f}\"\n", + " f\"emcee: {sampler.get_chain(flat=True).shape[0]} rows, \"\n", + " f\"mean acceptance {sampler.acceptance_fraction.mean():.2f}\"\n", ")\n", - "\n", - "restored = dill.loads(dill.dumps(p_sys))\n", + "restored = dill.loads(dill.dumps(p_best))\n", + "theta_best = np.median(samples[best], axis=0)\n", "print(\n", - " f\"dill round trip: ndim {restored.ndim}, names {restored.names[:3]}..., log_posterior(truth) = {restored.log_posterior(truth):.3f} vs {p_sys.log_posterior(truth):.3f}\"\n", + " f\"dill round trip: ndim {restored.ndim}, \"\n", + " f\"log_posterior = {restored.log_posterior(theta_best):.3f} \"\n", + " f\"vs {p_best.log_posterior(theta_best):.3f}\"\n", ")" ] }, { "cell_type": "markdown", - "id": "0abf09e9", + "id": "46a375ab", "metadata": {}, "source": [ "## Tempering the likelihood (recipe 12)\n", "\n", "With many points a posterior can collapse tighter than the model deserves.\n", - "`Constraint(weight=w)` multiplies that constraint's log-likelihood by `w`\n", - "and nothing else; the democratic `k/N` scaling of KDUQ is the case\n", - "`w = n_parameters / n_points`. A 200-point line shows the effect on the\n", - "posterior, and the posterior-predictive coverage shows that the *untempered*\n", - "fit is not overconfident about the data: the tight posterior is the model's\n", - "uncertainty, and the predictive of the data still carries the noise." + "`Constraint(weight=w)` multiplies that constraint's log-likelihood by $w$ and\n", + "changes nothing else; KDUQ's \"democratic\" scaling is the case\n", + "$w = n_\\mathrm{parameters} / n_\\mathrm{points}$.\n", + "\n", + "We show it on a 200-point line, where the truth is known exactly, so we can ask\n", + "the question that matters: does tempering improve anything we care about?" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "d808c5be", + "execution_count": 14, + "id": "e0136a13", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:05:45.114217Z", - "iopub.status.busy": "2026-09-11T04:05:45.113996Z", - "iopub.status.idle": "2026-09-11T04:06:16.271398Z", - "shell.execute_reply": "2026-09-11T04:06:16.270475Z" + "iopub.execute_input": "2026-09-12T04:00:14.593786Z", + "iopub.status.busy": "2026-09-12T04:00:14.593594Z", + "iopub.status.idle": "2026-09-12T04:00:41.760789Z", + "shell.execute_reply": "2026-09-12T04:00:41.760057Z" } }, "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 550x550 with 4 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 400x400 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "untempered m = 1.9792 +/- 0.0601 (pull -0.3) b = 3.9927 +/- 0.0305 (pull -0.2)\n", + "tempered m = 1.4720 +/- 0.3661 (pull -1.4) b = 4.1842 +/- 0.2158 (pull +0.9)\n" + ] } ], "source": [ + "rng = np.random.default_rng(11)\n", "x200 = np.linspace(0.0, 1.0, 200)\n", - "y200_true = 2.0 * x200 + 4.0\n", - "y200 = y200_true * (1.0 + rng.normal(0.0, 0.05, x200.size))\n", + "m_true, b_true = 2.0, 4.0\n", + "y200 = (m_true * x200 + b_true) * (1.0 + rng.normal(0.0, 0.05, x200.size))\n", "d200 = rx.Dataset(x200, y200, 0.05 * y200, label=\"N = 200\")\n", - "m_, b_ = rx.Parameter(\"m\", prior=stats.norm(1.0, 0.5), latex=\"m\"), rx.Parameter(\n", - " \"b\", prior=stats.norm(4.0, 0.5), latex=\"b\"\n", - ")\n", + "m_ = rx.Parameter(\"m\", prior=stats.norm(1.0, 0.5), latex=\"m\")\n", + "b_ = rx.Parameter(\"b\", prior=stats.norm(4.0, 0.5), latex=\"b\")\n", "line = rx.Model(lambda x, m, b: m * x + b, [m_, b_])\n", "comp200 = rx.Comparison(d200, line)\n", "p_full = rx.Problem([rx.Constraint([comp200])])\n", @@ -573,51 +810,168 @@ "\n", "\n", "s_full, s_temp = fit(p_full, 3), fit(p_temp, 4)\n", - "import logging\n", + "for name, s in ((\"untempered\", s_full), (\"tempered\", s_temp)):\n", + " pull_m = (s[:, 0].mean() - m_true) / s[:, 0].std()\n", + " pull_b = (s[:, 1].mean() - b_true) / s[:, 1].std()\n", + " print(\n", + " f\"{name:11s} m = {s[:, 0].mean():.4f} +/- {s[:, 0].std():.4f} (pull {pull_m:+.1f})\"\n", + " f\" b = {s[:, 1].mean():.4f} +/- {s[:, 1].std():.4f} (pull {pull_b:+.1f})\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "8ba42e69", + "metadata": {}, + "source": [ + "### Does tempering help here?\n", + "\n", + "The untempered fit covers the truth comfortably: $m = 1.979 \\pm 0.060$ against\n", + "2.0, and $b = 3.993 \\pm 0.031$ against 4.0, both within a third of a standard\n", + "deviation. That is worth saying plainly, because a posterior this tight *looks*\n", + "alarming beside a wide prior, and it is easy to read the corner plot as a\n", + "failure to cover. It is not: 200 points at 5 % each really do pin a straight\n", + "line that well.\n", "\n", - "logging.getLogger().setLevel(\n", - " logging.ERROR\n", - ") # corner warns about sparse contour bins for the prior\n", - "span = [(0.5, 3.5), (3.0, 5.0)]\n", + "Tempering by $k/N$ widens the posterior roughly sixfold, and here it makes the\n", + "answer slightly *worse* rather than better ($m = 1.47 \\pm 0.37$, 1.4 sigma low).\n", + "The coverage curve below is the check that actually matters, and the untempered\n", + "predictive already follows the diagonal: the prediction of the *data* carries\n", + "the noise even when the parameter posterior is narrow. Temper when the\n", + "predictions are overconfident, not when the parameters merely look precise." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "7d6d7fc3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T04:00:41.762360Z", + "iopub.status.busy": "2026-09-12T04:00:41.762189Z", + "iopub.status.idle": "2026-09-12T04:00:41.977163Z", + "shell.execute_reply": "2026-09-12T04:00:41.976394Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 605x605 with 4 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# the posterior is tiny compared with the prior, so the corner is drawn on the\n", + "# posterior's own scale; on the prior's scale both fits are a single dot\n", + "span = [(1.90, 2.20), (3.90, 4.05)]\n", "fig = corner.corner(\n", - " p_full.sample_prior(4000, rng=5),\n", - " color=\"0.6\",\n", + " s_temp,\n", " labels=[\"$m$\", \"$b$\"],\n", - " truths=[2.0, 4.0],\n", - " truth_color=\"k\",\n", - " plot_datapoints=False,\n", - " plot_contours=False,\n", + " truths=[m_true, b_true],\n", " range=span,\n", + " **plotstyle.corner_kwargs(\n", + " color=plotstyle.COLOURS[2], fill_contours=False, show_titles=False\n", + " ),\n", ")\n", - "corner.corner(s_temp, fig=fig, color=\"C2\", plot_datapoints=False, range=span)\n", "corner.corner(\n", - " s_full, fig=fig, color=\"C0\", plot_datapoints=False, plot_contours=False, range=span\n", + " s_full,\n", + " fig=fig,\n", + " range=span,\n", + " **plotstyle.corner_kwargs(\n", + " color=plotstyle.COLOURS[0], fill_contours=False, show_titles=False\n", + " ),\n", ")\n", "fig.legend(\n", " handles=[\n", - " plt.Line2D([], [], color=\"0.6\", label=\"prior\"),\n", - " plt.Line2D([], [], color=\"C2\", label=\"tempered, w = k/N\"),\n", - " plt.Line2D([], [], color=\"C0\", label=\"untempered\"),\n", + " plt.Line2D([], [], color=plotstyle.COLOURS[2], label=\"tempered, $w = k/N$\"),\n", + " plt.Line2D([], [], color=plotstyle.COLOURS[0], label=\"untempered\"),\n", " ],\n", " loc=\"upper right\",\n", - " frameon=False,\n", ")\n", - "plt.show()\n", - "\n", - "draws = rx.diagnostics.predictive_draws(p_full, s_full[::20], n_rep=2, rng=6)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9e421b2a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T04:00:41.978889Z", + "iopub.status.busy": "2026-09-12T04:00:41.978713Z", + "iopub.status.idle": "2026-09-12T04:00:42.384776Z", + "shell.execute_reply": "2026-09-12T04:00:42.383883Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 506x484 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "levels = np.linspace(0.1, 0.9, 9)\n", "c200 = p_full.constraints[0]\n", - "cov = rx.diagnostics.coverage_curve(draws, c200.y[c200.active], levels)\n", + "fig, ax = plt.subplots(figsize=(4.6, 4.4))\n", + "ax.plot([0, 1], [0, 1], \"--\", color=\"0.5\", label=\"nominal\")\n", + "for (name, p, s), colour in zip(\n", + " ((\"untempered\", p_full, s_full), (\"tempered\", p_temp, s_temp)),\n", + " (plotstyle.COLOURS[0], plotstyle.COLOURS[2]),\n", + "):\n", + " draws = rx.diagnostics.predictive_draws(p, s[::20], n_rep=2, rng=6)\n", + " cov = rx.diagnostics.coverage_curve(draws, c200.y[c200.active], levels)\n", + " width = rx.diagnostics.sharpness(draws).mean()\n", + " ax.plot(levels, cov, \"o-\", color=colour, label=f\"{name} (width {width:.3f})\")\n", "model_only = rx.diagnostics.predictive_draws(p_full, s_full[::20], model_only=True)\n", - "cov_model = rx.diagnostics.coverage_curve(model_only, c200.y[c200.active], levels)\n", - "fig, ax = plt.subplots(figsize=(4, 4))\n", - "ax.plot(levels, cov, \"o-\", label=\"posterior predictive of the data\")\n", - "ax.plot(levels, cov_model, \"s-\", color=\"C3\", label=\"model band only\")\n", - "ax.plot([0, 1], [0, 1], \"--\", color=\"0.5\")\n", - "ax.set(xlabel=\"nominal coverage\", ylabel=\"empirical coverage\", xlim=(0, 1), ylim=(0, 1))\n", - "ax.legend(frameon=False, fontsize=8)\n", + "ax.plot(\n", + " levels,\n", + " rx.diagnostics.coverage_curve(model_only, c200.y[c200.active], levels),\n", + " \"s--\",\n", + " color=plotstyle.COLOURS[1],\n", + " label=\"model band only (expected to miss)\",\n", + ")\n", + "ax.set(\n", + " xlabel=\"nominal coverage\",\n", + " ylabel=\"empirical coverage\",\n", + " xlim=(0, 1),\n", + " ylim=(0, 1),\n", + " title=\"Predictive coverage, tempered and not\",\n", + ")\n", + "ax.legend(fontsize=8)\n", "plt.show()" ] + }, + { + "cell_type": "markdown", + "id": "43052103", + "metadata": {}, + "source": [ + "## Takeaways\n", + "\n", + "- Real data arrive with whatever the experimenters chose to report, and often\n", + " that is statistics only. The error model is then *our* declaration, not\n", + " theirs, and the choice between \"the measurement's scale is uncertain\" and \"our\n", + " model is wrong\" is a physics judgement we have to make explicitly.\n", + "- `from_measurement` is the unit contract: dimensionful errors convert with the\n", + " data, fractional ones pass through, the frame is checked, and a ratio converts\n", + " to an absolute cross section through the Rutherford closed form.\n", + "- The model's quantity and the dataset's are checked when the comparison is\n", + " built, so a ratio can never be quietly fitted with an absolute model.\n", + "- `Constraint(weight=w)` is tempering, and nothing else changes. Look at the\n", + " predictive coverage before reaching for it: a tight posterior is not the same\n", + " thing as an overconfident prediction." + ] } ], "metadata": { From 5bf4969cee3f82ecb4ea2bd05408f47671f916cd Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Sat, 12 Sep 2026 00:18:06 -0400 Subject: [PATCH 61/75] Format the LaTeX-escape guard with black The control-character map was written as a one-line dict that exceeded the line length; black splits it. No behaviour change. --- test/test_notebooks_index.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/test/test_notebooks_index.py b/test/test_notebooks_index.py index 2113c3c..5235303 100644 --- a/test/test_notebooks_index.py +++ b/test/test_notebooks_index.py @@ -71,7 +71,14 @@ def test_no_latex_escape_lands_in_a_plain_string(name): escapes ``\r \t \a \b \f \v`` silently eat the backslash and the label. An ``ast`` walk sees f-strings too, which a ``tokenize`` pass does not. """ - control = {"\r": r"\r", "\t": r"\t", "\a": r"\a", "\b": r"\b", "\f": r"\f", "\v": r"\v"} + control = { + "\r": r"\r", + "\t": r"\t", + "\a": r"\a", + "\b": r"\b", + "\f": r"\f", + "\v": r"\v", + } nb = json.loads((EXAMPLES / f"{name}.ipynb").read_text()) bad = [] for i, cell in enumerate(nb["cells"]): From 39b34c1f688434401d8dd01488b86e4c1fb9e15e Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Sat, 12 Sep 2026 00:31:07 -0400 Subject: [PATCH 62/75] Rework hierarchical_calibration: BDA3, smaller bumps, new plots - Eight schools now stands on its own: BDA3 cited and linked, the data explained, and no pooling / complete pooling / partial pooling laid out before the non-centred spelling, so a reader need not have read chapter 5 - The bumps are 65 % of their old size, chosen by an A/B at the notebook's real sampler settings rather than by eye. The misspecified fit still fails clearly (coverage error 0.52 in sample, 0.45 held out) while the hierarchy repairs both to 0.18 and wins the held-out score, 20.3 against 7.7 - New plot: y_true(x; E) and y_data(x; E) at several energies, colour-coded by energy, offset in y for legibility and labelled with plt.text - New plot: predictive draws after calibration in that same layout, with the three cases told apart by hatching rather than colour - The hierarchy ties the correct mapping on evidence (142.28 against 143.07) while both sit 230 log units above the misspecified fit, and pays for its held-out prediction in width: 0.202 against 0.062 - Fixes the narrative that called an absolute error of 0.03 "honest 3 % errors" - The general pass: the 81-line case builder is now three cells, LaTeX throughout, plotstyle, links --- docs/design.md | 2 +- examples/hierarchical_calibration.ipynb | 916 ++++++++++++++++-------- 2 files changed, 606 insertions(+), 312 deletions(-) diff --git a/docs/design.md b/docs/design.md index 11d10f6..9d8b3bd 100644 --- a/docs/design.md +++ b/docs/design.md @@ -630,7 +630,7 @@ a time unless noted. | `error_scale_and_usu` | 34 | emcee | a global scale on the reported errors under both likelihoods; a USU offset on the technique we suspect | 90 s | | `local_optical_model_calibration` | 4, 12, 14, 15, 16, 21, 26 | dynesty | EXFOR O1199007, p + ⁴⁰Ca at 35 MeV, which quotes no systematics: the unit contract, three error models against a potential wrong at the 30 % level, the singular guard, tempering and its coverage, other drivers | 1565 s | | `alpha_ca_error_model_comparison` | 10, 11, 13, 17, 18 | dynesty | real ⁴⁴Ca(α,α) data, a four-parameter potential, the ladder by evidence with the Jacobian, predictive draws carrying the covariance and their coverage, held-out backward angles scored conditionally | 1394 s | -| `hierarchical_calibration` | 22, 24, 30, 35, 38 | dynesty | eight schools; a hierarchy on the physics parameters repairing a misspecified energy dependence, in sample and at a held-out energy | 937 s (alongside another notebook) | +| `hierarchical_calibration` | 22, 24, 30, 35, 38 | dynesty | eight schools, with the shrinkage explained rather than assumed; a hierarchy on the physics parameters recovering the evidence a misspecified energy dependence threw away, scored in sample and at a held-out energy | 748 s | **`hierarchical_calibration` in detail.** The truth is `y = a0(E) + a1(E) x + a2(E) x²`, measured by seven synthetic datasets at diff --git a/examples/hierarchical_calibration.ipynb b/examples/hierarchical_calibration.ipynb index 0b91f28..fd4cdb0 100644 --- a/examples/hierarchical_calibration.ipynb +++ b/examples/hierarchical_calibration.ipynb @@ -2,18 +2,22 @@ "cells": [ { "cell_type": "markdown", - "id": "69602826", + "id": "9ca1c7a6", "metadata": {}, "source": [ "# Hierarchical calibration: a hierarchy on the physics parameters\n", "\n", - "Several experiments at different energies constrain a model whose\n", - "parameters run with energy. If the assumed energy dependence is wrong, a\n", - "global fit under-covers everywhere. A hierarchy on the *physics\n", - "parameters*, one deviation vector per dataset with a learned spread, repairs\n", - "the coverage, in sample and at an energy that was never fit, and it costs\n", - "nothing beyond declaring the parameters. The eight-schools model opens the\n", - "notebook as the textbook version of the same idea.\n", + "Several experiments at different energies constrain a model whose parameters run\n", + "with energy. We have to assume *something* about how they run — and if that\n", + "assumption is wrong, a global fit is confidently wrong everywhere, including at\n", + "energies nobody measured.\n", + "\n", + "The repair is to stop insisting that one smooth curve describes every dataset,\n", + "and instead let each dataset have its own small deviation, with the *size* of\n", + "those deviations learned from the data. That is a hierarchical model, and the\n", + "textbook version of it opens this notebook: the eight-schools problem from\n", + "[Gelman, Carlin, Stern, Dunson, Vehtari & Rubin, *Bayesian Data Analysis*, 3rd\n", + "edition](http://www.stat.columbia.edu/~gelman/book/) (BDA3), chapter 5.\n", "\n", "Recipes: 22, 24, 30, 35, 38" ] @@ -21,13 +25,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "44d9c8ef", + "id": "b449277b", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:18:47.474201Z", - "iopub.status.busy": "2026-09-11T04:18:47.473952Z", - "iopub.status.idle": "2026-09-11T04:18:50.026496Z", - "shell.execute_reply": "2026-09-11T04:18:50.025571Z" + "iopub.execute_input": "2026-09-12T04:17:05.381735Z", + "iopub.status.busy": "2026-09-12T04:17:05.381605Z", + "iopub.status.idle": "2026-09-12T04:17:07.507015Z", + "shell.execute_reply": "2026-09-12T04:17:07.506340Z" } }, "outputs": [], @@ -36,35 +40,56 @@ "import dynesty\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", + "import plotstyle\n", "from scipy import stats\n", "\n", - "import rxmc as rx" + "import rxmc as rx\n", + "\n", + "plotstyle.use()" ] }, { "cell_type": "markdown", - "id": "a5930efa", + "id": "3416ec59", "metadata": {}, "source": [ - "## Prologue: eight schools, non-centred\n", + "## Prologue: eight schools\n", + "\n", + "Eight schools each ran a coaching programme, and each reported an estimated\n", + "effect $y_j$ on test scores together with the standard error $\\sigma_j$ of that\n", + "estimate. The estimates range from $+28$ to $-3$ points, but the standard\n", + "errors are 9 to 18 points, so the spread between schools is roughly what pure\n", + "noise would produce anyway.\n", + "\n", + "Two extreme readings are available. *No pooling* takes each school's estimate\n", + "at face value, which over-fits eight noisy numbers. *Complete pooling* says the\n", + "programmes are identical and averages them, which throws away any real\n", + "difference. The hierarchical model sits between: the true effects $\\theta_j$\n", + "are drawn from a common distribution,\n", + "\n", + "$$\\theta_j \\sim \\mathcal{N}(\\mu, \\tau^2),$$\n", "\n", - "Eight estimates `y_j` with known standard errors, believed to scatter around\n", - "a common mean `μ` with spread `τ`. In the non-centred spelling\n", - "`θ_j = μ + τ η_j` with `η_j ~ N(0, 1)`, every parameter has a marginal\n", - "prior, so nested sampling needs no joint block. `τ` piles up near zero, as\n", - "in the book, and the `θ_j` shrink toward `μ`." + "and we infer $\\mu$, the spread $\\tau$, and every $\\theta_j$ at once. How much a\n", + "school is pulled towards the common mean — *shrinkage* — follows from how big\n", + "$\\tau$ turns out to be relative to that school's own error.\n", + "\n", + "We write it non-centred, $\\theta_j = \\mu + \\tau \\eta_j$ with\n", + "$\\eta_j \\sim \\mathcal{N}(0, 1)$, because sampling $\\theta_j$ directly leaves a\n", + "funnel-shaped posterior that samplers struggle with. In this spelling every\n", + "parameter has its own marginal prior, so nested sampling needs no joint\n", + "block." ] }, { "cell_type": "code", "execution_count": 2, - "id": "bd088aab", + "id": "b9a36078", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:18:50.028780Z", - "iopub.status.busy": "2026-09-11T04:18:50.028350Z", - "iopub.status.idle": "2026-09-11T04:19:03.163703Z", - "shell.execute_reply": "2026-09-11T04:19:03.162862Z" + "iopub.execute_input": "2026-09-12T04:17:07.508648Z", + "iopub.status.busy": "2026-09-12T04:17:07.508412Z", + "iopub.status.idle": "2026-09-12T04:17:07.518349Z", + "shell.execute_reply": "2026-09-12T04:17:07.517669Z" } }, "outputs": [ @@ -72,24 +97,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "log Z = -32.11 +/- 0.19, 14976 likelihood calls\n" + "columns: ['mu', 'tau', 'eta_0', 'eta_1', 'eta_2', 'eta_3', 'eta_4', 'eta_5', 'eta_6', 'eta_7']\n" ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 800x320 with 2 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ "Y = np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0])\n", "S = np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0])\n", "schools = rx.Dataset(np.arange(8), Y, S, label=\"schools\")\n", + "\n", "mu = rx.Parameter(\"mu\", prior=stats.norm(0.0, 25.0), latex=r\"\\mu\")\n", "tau = rx.Parameter(\n", " \"tau\", prior=stats.halfnorm(scale=10.0), bounds=(0.0, np.inf), latex=r\"\\tau\"\n", @@ -100,8 +116,31 @@ "]\n", "model = rx.Model(lambda x, mu, tau, *eta: mu + tau * np.asarray(eta), [mu, tau, *etas])\n", "p_schools = rx.Problem([rx.Constraint([rx.Comparison(schools, model)])])\n", - "\n", - "\n", + "print(\"columns:\", p_schools.names)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b4c01254", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T04:17:07.520037Z", + "iopub.status.busy": "2026-09-12T04:17:07.519872Z", + "iopub.status.idle": "2026-09-12T04:17:18.092134Z", + "shell.execute_reply": "2026-09-12T04:17:18.091344Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log Z = -32.11 +/- 0.19, 14976 likelihood calls\n" + ] + } + ], + "source": [ "def nested(problem, seed, nlive=200, dlogz=0.5):\n", " sampler = dynesty.NestedSampler(\n", " problem.log_likelihood,\n", @@ -114,63 +153,74 @@ " sampler.run_nested(dlogz=dlogz, print_progress=False)\n", " res = sampler.results\n", " print(\n", - " f\"log Z = {res.logz[-1]:.2f} +/- {res.logzerr[-1]:.2f}, {int(np.sum(res.ncall))} likelihood calls\"\n", + " f\"log Z = {res.logz[-1]:8.2f} +/- {res.logzerr[-1]:.2f}, \"\n", + " f\"{int(np.sum(res.ncall)):7d} likelihood calls\"\n", " )\n", " return res.samples_equal(rstate=np.random.default_rng(seed))\n", "\n", "\n", "s_schools = nested(p_schools, 0)\n", - "thetas = s_schools[:, [0]] + s_schools[:, [1]] * s_schools[:, 2:]\n", - "fig, axes = plt.subplots(1, 2, figsize=(8, 3.2))\n", - "axes[0].hist(s_schools[:, p_schools.columns(tau)], bins=40, color=\"C0\", alpha=0.7)\n", - "axes[0].set(xlabel=r\"$\\tau$\", ylabel=\"draws\")\n", - "axes[1].errorbar(np.arange(8) - 0.15, Y, S, fmt=\"o\", color=\"k\", label=\"reported\")\n", - "axes[1].errorbar(\n", - " np.arange(8) + 0.15,\n", - " thetas.mean(0),\n", - " thetas.std(0),\n", - " fmt=\"s\",\n", - " color=\"C1\",\n", - " label=r\"$\\theta_j$ shrunk\",\n", - ")\n", - "axes[1].axhline(s_schools[:, 0].mean(), color=\"C1\", ls=\"--\", lw=0.8)\n", - "axes[1].set(xlabel=\"school\", ylabel=\"effect\")\n", - "axes[1].legend(frameon=False)\n", - "plt.show()" + "thetas = s_schools[:, [0]] + s_schools[:, [1]] * s_schools[:, 2:]" ] }, { - "cell_type": "markdown", - "id": "df381b68", - "metadata": {}, + "cell_type": "code", + "execution_count": 4, + "id": "f019a0e5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T04:17:18.093682Z", + "iopub.status.busy": "2026-09-12T04:17:18.093518Z", + "iopub.status.idle": "2026-09-12T04:17:19.086304Z", + "shell.execute_reply": "2026-09-12T04:17:19.085566Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 572x352 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "## The study: a quadratic whose coefficients run with energy\n", - "\n", - "The truth is `y = a_0(E) + a_1(E) x + a_2(E) x²`, and each coefficient's\n", - "energy dependence is a smooth trend plus a non-monotonic bump. Seven\n", - "synthetic datasets at known energies constrain it; an eighth at a new\n", - "energy between two of them is held out to test prediction. Every dataset reports honest 3 %\n", - "errors, so the only thing that can go wrong is the model." + "fig, ax = plt.subplots(figsize=(5.2, 3.2))\n", + "ax.hist(\n", + " s_schools[:, p_schools.columns(tau)],\n", + " bins=40,\n", + " color=plotstyle.COLOURS[0],\n", + " alpha=0.85,\n", + ")\n", + "ax.set(\n", + " xlabel=r\"$\\tau$\",\n", + " ylabel=\"posterior draws\",\n", + " title=r\"How different are the schools, really?\",\n", + ")\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 3, - "id": "63a9da2d", + "execution_count": 5, + "id": "ec0f35b1", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:19:03.165311Z", - "iopub.status.busy": "2026-09-11T04:19:03.165138Z", - "iopub.status.idle": "2026-09-11T04:19:03.558066Z", - "shell.execute_reply": "2026-09-11T04:19:03.557322Z" + "iopub.execute_input": "2026-09-12T04:17:19.087793Z", + "iopub.status.busy": "2026-09-12T04:17:19.087640Z", + "iopub.status.idle": "2026-09-12T04:17:19.297574Z", + "shell.execute_reply": "2026-09-12T04:17:19.296794Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 900x280 with 3 Axes>" + "<Figure size 704x440 with 1 Axes>" ] }, "metadata": {}, @@ -178,12 +228,84 @@ } ], "source": [ + "fig, ax = plt.subplots()\n", + "ax.errorbar(np.arange(8) - 0.15, Y, S, fmt=\"o\", color=\"k\", label=\"reported estimate\")\n", + "ax.errorbar(\n", + " np.arange(8) + 0.15,\n", + " thetas.mean(0),\n", + " thetas.std(0),\n", + " fmt=\"s\",\n", + " color=plotstyle.COLOURS[1],\n", + " label=r\"$\\theta_j$, after pooling\",\n", + ")\n", + "ax.axhline(\n", + " s_schools[:, 0].mean(),\n", + " color=plotstyle.COLOURS[1],\n", + " ls=\"--\",\n", + " lw=1.0,\n", + " label=r\"common mean $\\mu$\",\n", + ")\n", + "ax.set(xlabel=\"school\", ylabel=\"effect on test scores\", title=\"Shrinkage\")\n", + "ax.legend(fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "183a4d1b", + "metadata": {}, + "source": [ + "The posterior of $\\tau$ piles up against zero, exactly as BDA3 reports. The\n", + "data are consistent with all eight programmes having much the same effect, so\n", + "every school is pulled hard towards the common mean: school 1's $+28$ becomes\n", + "about $+10$, and the school reporting $-3$ moves up rather than down. Nothing\n", + "was discarded — the shrinkage is what the model concludes, not a choice we\n", + "imposed.\n", + "\n", + "Now the same idea, on physics parameters." + ] + }, + { + "cell_type": "markdown", + "id": "89f604ab", + "metadata": {}, + "source": [ + "## The study: a quadratic whose coefficients run with energy\n", + "\n", + "The truth is $y = a_0(E) + a_1(E)\\, x + a_2(E)\\, x^2$, and each coefficient's\n", + "energy dependence is a smooth trend plus a small non-monotonic bump — the kind of\n", + "structure a global parameterisation usually misses. Seven synthetic datasets at\n", + "known energies constrain it, and an eighth at an energy between two of them is\n", + "held out to test prediction.\n", + "\n", + "Every dataset reports an absolute error of 0.03 on each point, and those errors\n", + "are honest: the noise really is drawn from them. So the only thing that can go\n", + "wrong here is the *model*, which is the point." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "77630c43", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T04:17:19.299414Z", + "iopub.status.busy": "2026-09-12T04:17:19.299173Z", + "iopub.status.idle": "2026-09-12T04:17:19.306607Z", + "shell.execute_reply": "2026-09-12T04:17:19.305825Z" + } + }, + "outputs": [], + "source": [ + "BUMPS = (0.16, -0.13, 0.1) # amplitudes of the departures from each smooth trend\n", + "\n", + "\n", "def a_true(E):\n", " return np.array(\n", " [\n", - " 1.0 + 0.010 * E + 0.25 * np.exp(-(((E - 35.0) / 8.0) ** 2)),\n", - " 0.6 - 0.008 * E - 0.20 * np.exp(-(((E - 25.0) / 6.0) ** 2)),\n", - " -0.20 + 0.004 * E + 0.15 * np.exp(-(((E - 45.0) / 7.0) ** 2)),\n", + " 1.0 + 0.010 * E + BUMPS[0] * np.exp(-(((E - 35.0) / 8.0) ** 2)),\n", + " 0.6 - 0.008 * E + BUMPS[1] * np.exp(-(((E - 25.0) / 6.0) ** 2)),\n", + " -0.20 + 0.004 * E + BUMPS[2] * np.exp(-(((E - 45.0) / 7.0) ** 2)),\n", " ]\n", " )\n", "\n", @@ -211,85 +333,158 @@ "\n", "\n", "datasets = [dataset(E, f\"E = {E:.0f}\") for E in energies]\n", - "held_out = dataset(E_new, f\"E = {E_new:.0f} (held out)\")\n", + "held_out = dataset(E_new, f\"E = {E_new:.0f} (held out)\")" + ] + }, + { + "cell_type": "markdown", + "id": "1d0d39ab", + "metadata": {}, + "source": [ + "### The data, energy by energy\n", "\n", - "E_grid = np.linspace(5.0, 65.0, 100)\n", - "A_grid = np.array([a_true(E) for E in E_grid])\n", - "fig, axes = plt.subplots(1, 3, figsize=(9, 2.8))\n", - "for k, ax in enumerate(axes):\n", - " ax.plot(E_grid, A_grid[:, k], \"k--\", label=\"truth\")\n", - " ax.plot(\n", - " energies,\n", - " [a_true(E)[k] for E in energies],\n", - " \"o\",\n", - " color=\"C0\",\n", - " label=\"fitted energies\",\n", + "Seven datasets on the same $x$ grid would overlap into an unreadable smear, so we\n", + "offset each one vertically and label it. The dashed lines are the truth at that\n", + "energy; the points are what the experiment saw. The held-out energy is drawn in\n", + "grey." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "86c7868f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T04:17:19.308160Z", + "iopub.status.busy": "2026-09-12T04:17:19.308013Z", + "iopub.status.idle": "2026-09-12T04:17:19.446710Z", + "shell.execute_reply": "2026-09-12T04:17:19.446045Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 704x572 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_fine = np.linspace(-1.0, 1.0, 80)\n", + "offset_step = 0.6\n", + "shown = list(zip(datasets, energies)) + [(held_out, E_new)]\n", + "colours = plt.cm.viridis(np.linspace(0.05, 0.9, len(energies)))\n", + "\n", + "fig, ax = plt.subplots(figsize=(6.4, 5.2))\n", + "for k, (d, E) in enumerate(shown):\n", + " held = E == E_new\n", + " colour = \"0.55\" if held else colours[k]\n", + " shift = k * offset_step\n", + " ax.plot(x_fine, quadratic(x_fine, *a_true(E)) + shift, \"--\", color=colour, lw=1.2)\n", + " ax.errorbar(d.x, d.y + shift, d.y_err, fmt=\"o\", ms=3, color=colour)\n", + " plotstyle.label_at(\n", + " ax,\n", + " 1.03,\n", + " quadratic(1.0, *a_true(E)) + shift,\n", + " f\"E = {E:.0f}\" + (\" (held out)\" if held else \"\"),\n", + " color=colour,\n", " )\n", - " ax.plot([E_new], [a_true(E_new)[k]], \"s\", color=\"C3\", label=\"held out\")\n", - " ax.set(xlabel=\"E\", title=f\"$a_{k}(E)$\")\n", - "axes[0].legend(frameon=False, fontsize=8)\n", - "plt.tight_layout()\n", + "ax.set(\n", + " xlabel=\"$x$\",\n", + " ylabel=f\"$y$ (offset by {offset_step} per dataset)\",\n", + " title=\"Seven datasets, one held out\",\n", + " xlim=(-1.1, 1.45),\n", + ")\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "c733c0fe", + "id": "bfafc8d9", "metadata": {}, "source": [ "## Three fits of the same data\n", "\n", - "One `Model` per comparison, closing over that dataset's energy (recipe 35);\n", - "the coefficient objects are shared, so the problem has one column per\n", - "global parameter. The held-out comparison is part of every constraint but\n", - "fully masked: it contributes nothing to the likelihood, and any parameter\n", - "that only it uses is sampled from its prior.\n", + "One `Model` per comparison, closing over that dataset's energy (recipe 35); the\n", + "coefficient objects are shared, so the problem has one column per global\n", + "parameter. The held-out comparison is part of every constraint but fully\n", + "masked: it contributes nothing to the likelihood, and any parameter only it uses\n", + "is sampled from its prior (recipe 30).\n", "\n", - "1. **The correct mapping**, `a_k(E; φ) = φ_k0 + φ_k1 E + φ_k2 exp(−((E − c_k)/w_k)²)`\n", - " with the bump centres and widths known: global `φ`.\n", - "2. **A misspecified smooth mapping**, `a_k(E; φ) = φ_k0 + φ_k1 E`: global\n", - " `φ`, no bumps.\n", + "1. **The correct mapping**,\n", + " $a_k(E; \\varphi) = \\varphi_{k0} + \\varphi_{k1} E + \\varphi_{k2}\n", + " \\exp(-((E - c_k)/w_k)^2)$, with the bump centres and widths known: global\n", + " $\\varphi$.\n", + "2. **A misspecified smooth mapping**, $a_k(E; \\varphi) = \\varphi_{k0} +\n", + " \\varphi_{k1} E$: global $\\varphi$, no bumps.\n", "3. **The misspecified mapping plus a hierarchy**: per-dataset deviations\n", - " `δ_j = τ ⊙ η_j` in parameter space, non-centred, `η_jk ~ N(0, 1)` and\n", - " `τ_k ~ HalfNormal`, so the spread of the deviations is learned (recipes\n", - " 22, 24). The held-out dataset gets its own `η_new`, driven by `τ`\n", - " alone." + " $\\delta_j = \\tau \\odot \\eta_j$ in parameter space, non-centred, with\n", + " $\\eta_{jk} \\sim \\mathcal{N}(0, 1)$ and $\\tau_k \\sim \\mathrm{HalfNormal}$, so\n", + " the spread of the deviations is learned (recipes 22, 24). The held-out\n", + " dataset gets its own $\\eta_\\mathrm{new}$, driven by $\\tau$ alone." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "2db35ad4", + "execution_count": 8, + "id": "943e52d1", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:19:03.559765Z", - "iopub.status.busy": "2026-09-11T04:19:03.559583Z", - "iopub.status.idle": "2026-09-11T04:19:03.602951Z", - "shell.execute_reply": "2026-09-11T04:19:03.602049Z" + "iopub.execute_input": "2026-09-12T04:17:19.449166Z", + "iopub.status.busy": "2026-09-12T04:17:19.448988Z", + "iopub.status.idle": "2026-09-12T04:17:19.453663Z", + "shell.execute_reply": "2026-09-12T04:17:19.453067Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1. correct mapping ndim = 9 active points = 84 of 96\n", - "2. misspecified ndim = 6 active points = 84 of 96\n", - "3. misspecified + hierarchy ndim = 33 active points = 84 of 96\n" - ] - } - ], + "outputs": [], "source": [ "centres, widths = (35.0, 25.0, 45.0), (8.0, 6.0, 7.0)\n", "all_data = datasets + [held_out]\n", "masks = [np.ones(x.size, dtype=bool)] * len(datasets) + [np.zeros(x.size, dtype=bool)]\n", "\n", "\n", + "def correct_mapping(E, phi):\n", + " phi = np.reshape(phi, (3, 3))\n", + " return np.array(\n", + " [\n", + " phi[k, 0]\n", + " + phi[k, 1] * E / 50.0\n", + " + phi[k, 2] * np.exp(-(((E - centres[k]) / widths[k]) ** 2))\n", + " for k in range(3)\n", + " ]\n", + " )\n", + "\n", + "\n", + "def linear_mapping(E, phi):\n", + " phi = np.reshape(phi, (3, 2))\n", + " return np.array([phi[k, 0] + phi[k, 1] * E / 50.0 for k in range(3)])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "dbdb2f5e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T04:17:19.455871Z", + "iopub.status.busy": "2026-09-12T04:17:19.455692Z", + "iopub.status.idle": "2026-09-12T04:17:19.461695Z", + "shell.execute_reply": "2026-09-12T04:17:19.461073Z" + } + }, + "outputs": [], + "source": [ "def global_params(prefix, n_per):\n", " return [\n", " [\n", " rx.Parameter(\n", - " f\"{prefix}{k}{i}\", prior=stats.norm(0.0, 1.0), latex=rf\"\\phi_{{{k}{i}}}\"\n", + " f\"{prefix}{k}{i}\",\n", + " prior=stats.norm(0.0, 1.0),\n", + " latex=rf\"\\varphi_{{{k}{i}}}\",\n", " )\n", " for i in range(n_per)\n", " ]\n", @@ -312,44 +507,51 @@ " for j, d in enumerate(all_data):\n", " E = d.meta[\"Elab\"]\n", " if hierarchy:\n", - " etas = [\n", + " etas_j = [\n", " rx.Parameter(f\"eta_{j}_{k}\", prior=stats.norm(0.0, 1.0))\n", " for k in range(3)\n", " ]\n", "\n", " def fn(x, *v, E=E, n=len(flat)):\n", - " phi, tau, eta = v[:n], np.asarray(v[n : n + 3]), np.asarray(v[n + 3 :])\n", - " return quadratic(x, *(mapping(E, phi) + tau * eta))\n", + " phi, t, e = v[:n], np.asarray(v[n : n + 3]), np.asarray(v[n + 3 :])\n", + " return quadratic(x, *(mapping(E, phi) + t * e))\n", "\n", - " model = rx.Model(fn, [*flat, *taus, *etas])\n", + " model_j = rx.Model(fn, [*flat, *taus, *etas_j])\n", " else:\n", "\n", " def fn(x, *phi, E=E):\n", " return quadratic(x, *mapping(E, phi))\n", "\n", - " model = rx.Model(fn, flat)\n", - " comps.append(rx.Comparison(d, model))\n", + " model_j = rx.Model(fn, flat)\n", + " comps.append(rx.Comparison(d, model_j))\n", " c = rx.Constraint(comps, masks=masks)\n", - " return rx.Problem([c]), c\n", - "\n", - "\n", - "def correct_mapping(E, phi):\n", - " phi = np.reshape(phi, (3, 3))\n", - " return np.array(\n", - " [\n", - " phi[k, 0]\n", - " + phi[k, 1] * E / 50.0\n", - " + phi[k, 2] * np.exp(-(((E - centres[k]) / widths[k]) ** 2))\n", - " for k in range(3)\n", - " ]\n", - " )\n", - "\n", - "\n", - "def linear_mapping(E, phi):\n", - " phi = np.reshape(phi, (3, 2))\n", - " return np.array([phi[k, 0] + phi[k, 1] * E / 50.0 for k in range(3)])\n", - "\n", - "\n", + " return rx.Problem([c]), c" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c94dbbec", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T04:17:19.463655Z", + "iopub.status.busy": "2026-09-12T04:17:19.463485Z", + "iopub.status.idle": "2026-09-12T04:17:19.499134Z", + "shell.execute_reply": "2026-09-12T04:17:19.498386Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1. correct mapping ndim = 9 active points = 84 of 96\n", + "2. misspecified ndim = 6 active points = 84 of 96\n", + "3. misspecified + hierarchy ndim = 33 active points = 84 of 96\n" + ] + } + ], + "source": [ "cases = {\n", " \"1. correct mapping\": build(correct_mapping, global_params(\"phi\", 3)),\n", " \"2. misspecified\": build(linear_mapping, global_params(\"psi\", 2)),\n", @@ -365,14 +567,14 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "27624215", + "execution_count": 11, + "id": "ce3ad648", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:19:03.604458Z", - "iopub.status.busy": "2026-09-11T04:19:03.604292Z", - "iopub.status.idle": "2026-09-11T04:34:18.108586Z", - "shell.execute_reply": "2026-09-11T04:34:18.107799Z" + "iopub.execute_input": "2026-09-12T04:17:19.501463Z", + "iopub.status.busy": "2026-09-12T04:17:19.501268Z", + "iopub.status.idle": "2026-09-12T04:29:28.300514Z", + "shell.execute_reply": "2026-09-12T04:29:28.299761Z" } }, "outputs": [ @@ -380,21 +582,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "log Z = 143.30 +/- 1.13, 109383 likelihood calls\n" + "log Z = 143.07 +/- 1.16, 107467 likelihood calls\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "log Z = -408.02 +/- 0.99, 66925 likelihood calls\n" + "log Z = -87.00 +/- 0.98, 69239 likelihood calls\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "log Z = 137.68 +/- 1.31, 284517 likelihood calls\n" + "log Z = 142.28 +/- 1.24, 257058 likelihood calls\n" ] } ], @@ -407,35 +609,68 @@ }, { "cell_type": "markdown", - "id": "f90e4df1", + "id": "cc95901b", "metadata": {}, "source": [ "## The coefficient mappings against the truth\n", "\n", - "For each case, the posterior band of `a_k(E)` over the energy range. The\n", - "hierarchical case has two things to show: the global smooth mapping, and\n", - "the per-dataset values `a_k(E_j) + δ_jk` it actually used, which are what\n", + "For each case, the posterior band of $a_k(E)$ across the energy range. The\n", + "hierarchical case has two things to show: the global smooth mapping, and the\n", + "per-dataset values $a_k(E_j) + \\delta_{jk}$ it actually used, which are what\n", "track the bumps." ] }, { "cell_type": "code", - "execution_count": 6, - "id": "ed7e3d3b", + "execution_count": 12, + "id": "cdceaa49", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:34:18.110289Z", - "iopub.status.busy": "2026-09-11T04:34:18.110100Z", - "iopub.status.idle": "2026-09-11T04:34:19.156736Z", - "shell.execute_reply": "2026-09-11T04:34:19.156079Z" + "iopub.execute_input": "2026-09-12T04:29:28.301808Z", + "iopub.status.busy": "2026-09-12T04:29:28.301660Z", + "iopub.status.idle": "2026-09-12T04:29:28.305494Z", + "shell.execute_reply": "2026-09-12T04:29:28.304637Z" + } + }, + "outputs": [], + "source": [ + "mappings = {\n", + " \"1. correct mapping\": correct_mapping,\n", + " \"2. misspecified\": linear_mapping,\n", + " \"3. misspecified + hierarchy\": linear_mapping,\n", + "}\n", + "style = {\n", + " \"1. correct mapping\": (plotstyle.COLOURS[0], plotstyle.HATCHES[0]),\n", + " \"2. misspecified\": (plotstyle.COLOURS[1], plotstyle.HATCHES[1]),\n", + " \"3. misspecified + hierarchy\": (plotstyle.COLOURS[2], plotstyle.HATCHES[2]),\n", + "}\n", + "E_grid = np.linspace(5.0, 65.0, 100)\n", + "A_grid = np.array([a_true(E) for E in E_grid])\n", + "n_phi = {\n", + " \"1. correct mapping\": 9,\n", + " \"2. misspecified\": 6,\n", + " \"3. misspecified + hierarchy\": 6,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c6c8c542", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T04:29:28.306822Z", + "iopub.status.busy": "2026-09-12T04:29:28.306689Z", + "iopub.status.idle": "2026-09-12T04:29:29.194981Z", + "shell.execute_reply": "2026-09-12T04:29:29.194311Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 1000x300 with 3 Axes>" + "<Figure size 1155x352 with 3 Axes>" ] }, "metadata": {}, @@ -443,89 +678,90 @@ } ], "source": [ - "mappings = {\n", - " \"1. correct mapping\": correct_mapping,\n", - " \"2. misspecified\": linear_mapping,\n", - " \"3. misspecified + hierarchy\": linear_mapping,\n", - "}\n", - "colors = {\n", - " \"1. correct mapping\": \"C0\",\n", - " \"2. misspecified\": \"C3\",\n", - " \"3. misspecified + hierarchy\": \"C2\",\n", - "}\n", - "fig, axes = plt.subplots(1, 3, figsize=(10, 3))\n", + "fig, axes = plt.subplots(1, 3, figsize=(10.5, 3.2))\n", "for name, (p, c) in cases.items():\n", " s = samples[name][::10]\n", - " n_phi = 9 if name.startswith(\"1\") else 6\n", - " curves = np.array([[mappings[name](E, row[:n_phi]) for E in E_grid] for row in s])\n", + " colour, hatch = style[name]\n", + " curves = np.array(\n", + " [[mappings[name](E, row[: n_phi[name]]) for E in E_grid] for row in s]\n", + " )\n", " lo, hi = np.percentile(curves, [16, 84], axis=0)\n", " for k, ax in enumerate(axes):\n", - " ax.fill_between(\n", - " E_grid, lo[:, k], hi[:, k], color=colors[name], alpha=0.3, label=name\n", - " )\n", - " if name.startswith(\"3\"):\n", - " tau_cols, eta_cols = (\n", - " p.columns([q for q in p.params if q.name.startswith(\"tau\")]),\n", - " None,\n", + " plotstyle.band(\n", + " ax, E_grid, lo[:, k], hi[:, k], color=colour, hatch=hatch, label=name\n", " )\n", - " for j, E in enumerate(list(energies) + [E_new]):\n", - " cols_eta = p.columns(\n", - " [q for q in p.params if q.name.startswith(f\"eta_{j}_\")]\n", - " )\n", - " local = np.array(\n", - " [\n", - " linear_mapping(E, row[:6]) + row[tau_cols] * row[cols_eta]\n", - " for row in s\n", - " ]\n", - " )\n", - " for k, ax in enumerate(axes):\n", - " ax.errorbar(\n", - " [E],\n", - " [local[:, k].mean()],\n", - " [local[:, k].std()],\n", - " fmt=\"s\",\n", - " ms=4,\n", - " color=\"C2\",\n", - " mfc=\"w\" if j == len(energies) else \"C2\",\n", - " )\n", "for k, ax in enumerate(axes):\n", - " ax.plot(E_grid, A_grid[:, k], \"k--\")\n", + " ax.plot(E_grid, A_grid[:, k], \"--\", color=\"k\")\n", " ax.axvline(E_new, color=\"0.6\", lw=0.8)\n", - " ax.set(xlabel=\"E\", title=f\"$a_{k}(E)$\")\n", - "axes[0].legend(frameon=False, fontsize=7)\n", + " ax.set(xlabel=\"$E$\", title=f\"$a_{k}(E)$\")\n", + "axes[0].legend(fontsize=7)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "5f9c8edd", + "id": "c473b0aa", "metadata": {}, "source": [ - "## Residuals of the misspecified fit\n", + "### What each case predicts, dataset by dataset\n", "\n", - "Case 2 leaves structured, dataset-by-dataset residuals: the smooth mapping\n", - "is right on average and wrong at every energy." + "The same layout as the data figure — one offset row per energy — but now showing\n", + "what each calibration predicts. The three cases are told apart by **hatching**\n", + "rather than colour, so the comparison survives a greyscale print and does not\n", + "fight the energy colour-coding." ] }, { "cell_type": "code", - "execution_count": 7, - "id": "db767189", + "execution_count": 14, + "id": "e2b80ed8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T04:29:29.196610Z", + "iopub.status.busy": "2026-09-12T04:29:29.196467Z", + "iopub.status.idle": "2026-09-12T04:29:29.200326Z", + "shell.execute_reply": "2026-09-12T04:29:29.199661Z" + } + }, + "outputs": [], + "source": [ + "def case_curves(name, E, rows):\n", + " \"\"\"y(x; E) over posterior rows, including this dataset's deviation.\"\"\"\n", + " p, _ = cases[name]\n", + " n = n_phi[name]\n", + " out = []\n", + " for row in rows:\n", + " coeffs = mappings[name](E, row[:n])\n", + " if name.startswith(\"3\"):\n", + " j = list(energies).index(E) if E in energies else len(energies)\n", + " tau_cols = p.columns([q for q in p.params if q.name.startswith(\"tau\")])\n", + " eta_cols = p.columns(\n", + " [q for q in p.params if q.name.startswith(f\"eta_{j}_\")]\n", + " )\n", + " coeffs = coeffs + row[tau_cols] * row[eta_cols]\n", + " out.append(quadratic(x_fine, *coeffs))\n", + " return np.percentile(np.array(out), [16, 84], axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b9f2f5e2", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:34:19.158326Z", - "iopub.status.busy": "2026-09-11T04:34:19.158144Z", - "iopub.status.idle": "2026-09-11T04:34:19.643392Z", - "shell.execute_reply": "2026-09-11T04:34:19.642483Z" + "iopub.execute_input": "2026-09-12T04:29:29.201694Z", + "iopub.status.busy": "2026-09-12T04:29:29.201536Z", + "iopub.status.idle": "2026-09-12T04:29:29.427861Z", + "shell.execute_reply": "2026-09-12T04:29:29.427418Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 1100x450 with 8 Axes>" + "<Figure size 726x616 with 1 Axes>" ] }, "metadata": {}, @@ -533,94 +769,93 @@ } ], "source": [ - "p2, c2 = cases[\"2. misspecified\"]\n", - "ym2 = p2.predict(np.median(samples[\"2. misspecified\"], axis=0))[0]\n", - "fig, axes = plt.subplots(2, 4, figsize=(11, 4.5), sharex=True, sharey=True)\n", - "axes.ravel()[-1].axis(\"off\")\n", - "for ax, d, ym in zip(axes.ravel(), datasets, ym2):\n", - " ax.errorbar(d.x, (d.y - ym) / d.y_err, 1.0, fmt=\"o\", ms=3, color=\"C3\")\n", - " ax.axhline(0, color=\"k\", lw=0.8)\n", - " ax.set(title=d.label)\n", - "for ax in axes[-1]:\n", - " ax.set(xlabel=\"x\")\n", - "for ax in axes[:, 0]:\n", - " ax.set(ylabel=\"pull\")\n", - "plt.tight_layout()\n", + "shown_energies = [20.0, 35.0, E_new, 50.0]\n", + "fig, ax = plt.subplots(figsize=(6.6, 5.6))\n", + "for k, E in enumerate(shown_energies):\n", + " shift = k * offset_step\n", + " d = held_out if E == E_new else datasets[list(energies).index(E)]\n", + " for name in cases:\n", + " colour, hatch = style[name]\n", + " lo, hi = case_curves(name, E, samples[name][::20])\n", + " plotstyle.band(\n", + " ax,\n", + " x_fine,\n", + " lo + shift,\n", + " hi + shift,\n", + " color=colour,\n", + " hatch=hatch,\n", + " label=name if k == 0 else None,\n", + " )\n", + " ax.plot(x_fine, quadratic(x_fine, *a_true(E)) + shift, \"--\", color=\"k\", lw=1.0)\n", + " ax.errorbar(d.x, d.y + shift, d.y_err, fmt=\"o\", ms=3, color=\"k\")\n", + " plotstyle.label_at(\n", + " ax,\n", + " 1.03,\n", + " quadratic(1.0, *a_true(E)) + shift,\n", + " f\"E = {E:.0f}\" + (\" (held out)\" if E == E_new else \"\"),\n", + " color=\"0.3\",\n", + " )\n", + "ax.set(\n", + " xlabel=\"$x$\",\n", + " ylabel=f\"$y$ (offset by {offset_step} per dataset)\",\n", + " title=\"Predictive draws after calibration\",\n", + " xlim=(-1.1, 1.5),\n", + ")\n", + "ax.legend(fontsize=8, loc=\"upper left\")\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "424520bd", + "id": "0f394744", "metadata": {}, "source": [ "## Coverage, in sample and at the held-out energy\n", "\n", "`predictive_draws` on the fitted problem gives the in-sample posterior\n", - "predictive; on `Problem([c.complement()])`, the same parameters and terms\n", - "with the held-out points active, it gives the prediction at the new energy,\n", - "with no extra code. For the hierarchical case that prediction includes the\n", - "prior-sampled `η_new` scaled by the learned `τ`: a scale mixture, wider and\n", - "longer-tailed than in sample." + "predictive; on `Problem([c.complement()])` — the same parameters and terms, with\n", + "the held-out points active — it gives the prediction at the new energy, with no\n", + "extra code (recipe 30). For the hierarchical case that prediction includes the\n", + "prior-sampled $\\eta_\\mathrm{new}$ scaled by the learned $\\tau$: a scale\n", + "mixture, wider and longer-tailed than in sample." ] }, { "cell_type": "code", - "execution_count": 8, - "id": "204cd5ae", + "execution_count": 16, + "id": "8022b6e5", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:34:19.645103Z", - "iopub.status.busy": "2026-09-11T04:34:19.644906Z", - "iopub.status.idle": "2026-09-11T04:34:21.440546Z", - "shell.execute_reply": "2026-09-11T04:34:21.439723Z" + "iopub.execute_input": "2026-09-12T04:29:29.429622Z", + "iopub.status.busy": "2026-09-12T04:29:29.429486Z", + "iopub.status.idle": "2026-09-12T04:29:30.714005Z", + "shell.execute_reply": "2026-09-12T04:29:30.713185Z" } }, "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 800x360 with 2 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "name": "stdout", "output_type": "stream", "text": [ "case held-out log predictive 68 % predictive width\n", - "1. correct mapping 24.8 0.062\n", - "2. misspecified -21.9 0.060\n", - "3. misspecified + hierarchy 20.4 0.286\n" + "1. correct mapping 24.7 0.062\n", + "2. misspecified 7.7 0.061\n", + "3. misspecified + hierarchy 20.3 0.202\n" ] } ], "source": [ "levels = np.linspace(0.1, 0.9, 9)\n", - "fig, axes = plt.subplots(1, 2, figsize=(8, 3.6))\n", - "scores = {}\n", + "scores, curves_cov = {}, {}\n", "for name, (p, c) in cases.items():\n", " s = samples[name]\n", " held = rx.Problem([c.complement()])\n", " draws_in = rx.diagnostics.predictive_draws(p, s[::10], n_rep=2, rng=1)\n", " draws_out = rx.diagnostics.predictive_draws(held, s[::10], n_rep=2, rng=2)\n", " ci, ch = p.constraints[0], held.constraints[0]\n", - " axes[0].plot(\n", - " levels,\n", + " curves_cov[name] = (\n", " rx.diagnostics.coverage_curve(draws_in, ci.y[ci.active], levels),\n", - " \"o-\",\n", - " color=colors[name],\n", - " label=name,\n", - " )\n", - " axes[1].plot(\n", - " levels,\n", " rx.diagnostics.coverage_curve(draws_out, ch.y[ch.active], levels),\n", - " \"o-\",\n", - " color=colors[name],\n", - " label=name,\n", " )\n", " scores[name] = (\n", " rx.diagnostics.log_posterior_predictive(\n", @@ -628,8 +863,69 @@ " ),\n", " rx.diagnostics.sharpness(draws_out).mean(),\n", " )\n", - "for ax, title in zip(axes, (\"in sample\", f\"held out, E = {E_new:.0f}\")):\n", + "print(f\"{'case':28s} held-out log predictive 68 % predictive width\")\n", + "for name, (lp, width) in scores.items():\n", + " print(f\"{name:28s} {lp:10.1f} {width:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "407ccfb5", + "metadata": {}, + "source": [ + "### What the three fits concluded\n", + "\n", + "The evidence is the cleanest statement: $143.07$ for the correct mapping,\n", + "$-87.00$ for the misspecified one, and $142.28$ for the misspecified mapping\n", + "plus a hierarchy. The first and third are a tie within their errors, and both\n", + "sit 230 log units above the second. Giving each dataset a small deviation with\n", + "a learned spread recovers essentially all of the evidence that the wrong energy\n", + "dependence threw away — without our ever having to discover what the right\n", + "energy dependence was.\n", + "\n", + "The held-out scores tell the same story with a caveat: $24.7$, $7.7$ and $20.3$.\n", + "The hierarchy recovers most, but not all, of the correct mapping's predictive\n", + "power at an energy it never saw, and it pays for that in width. Its 68 %\n", + "predictive interval at the new energy is $0.202$ against $0.062$ — three times\n", + "wider. That is the honest trade, not a defect: the hierarchy does not know\n", + "where the bump at $E = 30$ sits, so it predicts with the spread it learned,\n", + "which is wide enough to cover and no wider than it needs to be.\n", + "\n", + "The coverage curves show it without the summary statistics. The misspecified\n", + "fit sags well below the diagonal both in sample and at the held-out energy,\n", + "while the hierarchical fit tracks it." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "86d573ec", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-12T04:29:30.715465Z", + "iopub.status.busy": "2026-09-12T04:29:30.715292Z", + "iopub.status.idle": "2026-09-12T04:29:30.914371Z", + "shell.execute_reply": "2026-09-12T04:29:30.913754Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 924x418 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(8.4, 3.8))\n", + "for ax, which, title in zip(axes, (0, 1), (\"in sample\", f\"held out, E = {E_new:.0f}\")):\n", " ax.plot([0, 1], [0, 1], \"--\", color=\"0.5\")\n", + " for name in cases:\n", + " ax.plot(levels, curves_cov[name][which], \"o-\", color=style[name][0], label=name)\n", " ax.set(\n", " xlabel=\"nominal coverage\",\n", " ylabel=\"empirical coverage\",\n", @@ -637,44 +933,42 @@ " xlim=(0, 1),\n", " ylim=(0, 1),\n", " )\n", - "axes[0].legend(frameon=False, fontsize=8)\n", + "axes[0].legend(fontsize=8)\n", "plt.tight_layout()\n", - "plt.show()\n", - "print(f\"{'case':28s} held-out log predictive 68 % predictive width\")\n", - "for name, (lp, width) in scores.items():\n", - " print(f\"{name:28s} {lp:10.1f} {width:.3f}\")" + "plt.show()" ] }, { "cell_type": "markdown", - "id": "dfbb2f74", + "id": "fd066d32", "metadata": {}, "source": [ "## The learned spread\n", "\n", - "The posterior of `τ` is away from zero in the hierarchical case: the data\n", - "asked for per-dataset deviations of a definite size, which is the\n", - "misspecification made visible." + "The posterior of $\\tau$ is what makes the misspecification visible: if the\n", + "smooth mapping were adequate, the data would have no reason to ask for\n", + "per-dataset deviations, and $\\tau$ would collapse towards zero the way it did\n", + "for the eight schools." ] }, { "cell_type": "code", - "execution_count": 9, - "id": "7d4ae0a9", + "execution_count": 18, + "id": "b93c1029", "metadata": { "execution": { - "iopub.execute_input": "2026-09-11T04:34:21.442127Z", - "iopub.status.busy": "2026-09-11T04:34:21.441924Z", - "iopub.status.idle": "2026-09-11T04:34:21.908672Z", - "shell.execute_reply": "2026-09-11T04:34:21.908033Z" + "iopub.execute_input": "2026-09-12T04:29:30.915932Z", + "iopub.status.busy": "2026-09-12T04:29:30.915792Z", + "iopub.status.idle": "2026-09-12T04:29:31.255261Z", + "shell.execute_reply": "2026-09-12T04:29:31.254575Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 760x760 with 9 Axes>" + "<Figure size 836x836 with 9 Axes>" ] }, "metadata": {}, @@ -688,37 +982,37 @@ "fig = corner.corner(\n", " s3[:, p3.columns(tau_params)],\n", " labels=[f\"${q.latex}$\" for q in tau_params],\n", - " show_titles=True,\n", - " title_fmt=\".3f\",\n", + " **plotstyle.corner_kwargs(title_fmt=\".3f\"),\n", ")\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "12cc623b", + "id": "f41c814d", "metadata": {}, "source": [ "## Takeaways\n", "\n", "- The correct mapping covers in sample and out of sample. The misspecified\n", - " smooth mapping under-covers both and leaves structured residuals at every\n", + " smooth mapping under-covers both, and leaves structured residuals at every\n", " energy.\n", - "- The hierarchy on the physics parameters recovers the coverage with wider,\n", - " longer-tailed bands at the new energy, a `τ` posterior away from zero,\n", - " and the better held-out score of the two misspecified fits.\n", - "- A parameter attached only to a fully masked comparison is sampled from\n", - " its prior. That is the whole mechanism for predicting a new dataset:\n", - " `complement()`, `predictive_draws` and `heldout_log_predictive` score\n", - " the new energy with no extra code.\n", - "- With few datasets the global `φ` and the deviations `δ_j` trade off, and\n", - " the hyperprior on `τ` is what resolves it." + "- A hierarchy on the physics parameters recovers the coverage with wider,\n", + " longer-tailed bands at the new energy, and a $\\tau$ posterior away from zero\n", + " is the misspecification made visible.\n", + "- A parameter attached only to a fully masked comparison is sampled from its\n", + " prior. That is the whole mechanism for predicting a new dataset:\n", + " `complement()`, `predictive_draws` and `heldout_log_predictive` score the new\n", + " energy with no extra code.\n", + "- With few datasets the global $\\varphi$ and the deviations $\\delta_j$ trade\n", + " off against each other, and the hyperprior on $\\tau$ is what resolves it.\n", + " This is the same shrinkage the eight schools showed, now in parameter space." ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, From 2dd803e5f4fd68613679026b03739b77ff33a7b7 Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Mon, 14 Sep 2026 18:33:55 -0400 Subject: [PATCH 63/75] Add grid_draws and rebuild the GP and alpha-Ca notebooks on it - rx.predictive.grid_draws draws the posterior predictive on any grid by re-evaluating each covariance term from its own definition at the new points: inferred noise, fractional noise, model error, normalisation and systematic modes, parametric matrix terms and GP kernels all carry over. Point-by-point terms (reported statistical errors, fixed arrays, per-point magnitudes) raise, naming the term and the ways out. At the measured points it matches predictive_draws, and it handles Student-t, physical=True and constraints spanning several comparisons - gp_predictive_draws replaces total_predictive_band: unconditioned grid_draws on the kernel's constraint, plus conditioned=True for GP regression on the residuals; n_draws becomes n_rep - predictive_draws gains terms= and statistical= to choose which parts of the error model a draw carries; combining them with given= raises - Breaking: all three draw functions return a percentile band by default and the draws with return_draws=True; every caller feeding coverage or sharpness now passes it - StructuredCovariance.matrix(entries=) and CompiledConstraint.entries_for build a covariance from a subset of terms - New TestGridDraws suite and recipe 40, "Predict on a new grid, error model included"; recipes 7, 15 and 17, design.md, groundup_design.md and api.rst updated - gp_discrepancy: rebuilt around mean-zero discrepancies with the three predictive objects written out, the band next to the data with its coverage, and the envelope of departures the data allow - alpha_ca: a fifth rung, Lgpn, whose GP amplitude grows with angle; it wins on evidence (dlogZ 3.5 +/- 1.3 over Lgp) and held-out log predictive (-47.8 against -50.5), and a new section shows where the potential fails - Both notebooks rewritten in the walkthrough voice with links (Gaussian process, Matern kernel, Bayes factor, nested sampling, Kennedy & O'Hagan, Skilling, Speagle, Vehtari et al.), calculation and printing split into separate cells, and the evidence verdict printed as words - The other notebooks draw model-only bands with grid_draws(model_only=True) - design.md runtimes re-measured solo: gp_discrepancy 483 s, alpha_ca 1280 s --- README.md | 2 +- docs/api.rst | 3 +- docs/design.md | 71 +- docs/groundup_design.md | 22 +- docs/recipes.md | 117 ++- .../alpha_ca_error_model_comparison.ipynb | 853 +++++++++++------- examples/error_models.ipynb | 130 +-- examples/error_scale_and_usu.ipynb | 85 +- examples/gp_discrepancy.ipynb | 836 ++++++++++------- examples/hierarchical_calibration.ipynb | 163 ++-- .../local_optical_model_calibration.ipynb | 180 ++-- ...rmalization_and_covariance_structure.ipynb | 196 ++-- examples/robust_likelihoods.ipynb | 131 +-- examples/sharing_error_models.ipynb | 112 +-- src/rxmc/covariance.py | 15 +- src/rxmc/diagnostics.py | 45 +- src/rxmc/model.py | 2 +- src/rxmc/predictive.py | 496 ++++++++-- src/rxmc/problem.py | 46 +- src/rxmc/terms.py | 7 +- test/recipes/test_recipe_07_gp_discrepancy.py | 56 +- .../test_recipe_17_posterior_predictive.py | 14 +- ...test_recipe_30_leave_one_experiment_out.py | 10 +- test/recipes/test_recipe_31_sbc.py | 4 +- .../test_recipe_40_predict_on_a_new_grid.py | 83 ++ test/test_diagnostics.py | 58 +- test/test_notebooks_index.py | 4 +- test/test_predictive.py | 421 ++++++++- 28 files changed, 2841 insertions(+), 1321 deletions(-) create mode 100644 test/recipes/test_recipe_40_predict_on_a_new_grid.py diff --git a/README.md b/README.md index c51418b..81b3e4d 100644 --- a/README.md +++ b/README.md @@ -44,7 +44,7 @@ samples = sampler.get_chain(discard=300, flat=True) print(samples[:, problem.columns(m)].mean(), samples[:, problem.columns(b)].mean()) # the posterior predictive on the data points, with the error model -draws = rx.diagnostics.predictive_draws(problem, samples[::20], n_rep=2) +draws = rx.diagnostics.predictive_draws(problem, samples[::20], n_rep=2, return_draws=True) print(rx.diagnostics.coverage_curve(draws, data.y, [0.68])) ``` diff --git a/docs/api.rst b/docs/api.rst index 226606f..ea2b092 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -108,9 +108,10 @@ Predictive :toctree: generated/ :nosignatures: + rxmc.predictive.grid_draws + rxmc.predictive.gp_predictive_draws rxmc.predictive.gp_posterior_predictive rxmc.predictive.predictive_band - rxmc.predictive.total_predictive_band Reactions --------- diff --git a/docs/design.md b/docs/design.md index 9d8b3bd..96a11b4 100644 --- a/docs/design.md +++ b/docs/design.md @@ -425,7 +425,9 @@ Everything takes `(problem, samples)` with `samples` of shape give. ```python -predictive_draws(problem, samples, constraint=0, *, n_rep=1, rng=None, model_only=False, given=None) +predictive_draws(problem, samples, constraint=0, *, terms=None, statistical=True, + n_rep=1, rng=None, model_only=False, given=None, + levels=(16, 50, 84), return_draws=False) coverage_curve(draws, y, levels=None); coverage_error(draws, y, levels=None) sharpness(draws, percentiles=(16, 84), transform=None) heldout_log_predictive(heldout_problem, samples, *, given=None) @@ -434,8 +436,12 @@ logz_summary(logz, logzerr); compare_logz(a, b, sigma=2.0) gp_posterior_predictive(kernel, theta, X_train, residuals, X_pred, *, train_noise_var=None, jitter=1e-10) predictive_band(draws, levels=(16, 50, 84)) -total_predictive_band(problem, term, predictor, x_pred, samples, *, noise_std=0.0, - train_noise_var=None, levels=(16, 84), n_draws=400, rng=None, physical=False) +grid_draws(problem, predictor, x_pred, samples, constraint=0, *, comparison=None, + terms=None, model_only=False, joint=True, physical=False, + n_rep=1, rng=None, levels=(16, 50, 84), return_draws=False) +gp_predictive_draws(problem, term, predictor, x_pred, samples, *, terms=None, + conditioned=False, joint=True, noise_std=0.0, train_noise_var=None, + physical=False, n_rep=1, rng=None, levels=(16, 50, 84), return_draws=False) ``` A held-out problem built from `complement()` has the *marginal* @@ -445,12 +451,45 @@ span it (a GP over several experiments), `given=fit_problem` makes both functions compute the Gaussian conditional `p(y_held | y_fit, θ)` under the full covariance; without a spanning term it equals the marginal. -`total_predictive_band` locates the `KernelTerm` in the problem, takes its -training rows, comparison space, kernel and amplitude columns from there, -conditions the discrepancy on the residuals with every *other* term of the -constraint as the regression noise, evaluates the amplitude at `x_pred` -through a `TermContext`, and returns percentiles in the comparison space -of the term's comparisons, or in physical units with `physical=True`. +The three draw functions share one return convention: a percentile band +`(len(levels), n_points)` by default, the draws with `return_draws=True`. +Coverage, sharpness and held-out scoring consume draws. + +`predictive_draws` works at the measured points, where every term is +defined. `grid_draws` is its counterpart on a grid that was never +measured: per row it evaluates the predictor there, re-evaluates every +selected term from its own definition in a `TermContext` whose `x` is the +grid, whose `y` and `ym` are the model's prediction and whose `meta` is the +predictor's, sums the pieces into one covariance and draws `ym + s L z` +(`s` the likelihood's `predictive_scale`). At the measured points, with +every term a function, it draws from the same distribution as +`predictive_draws`. A term that is an array — the statistical diagonals the +compiler builds, a fixed `Term(array)`, a per-point magnitude — has no value +at a new `x`, and `grid_draws` raises and names it rather than drop it: +leaving the reported errors out silently would hand back a band narrower +than the one the likelihood used. With several comparisons in the +constraint, `terms=None` needs `comparison=`, which also fixes the +comparison space. + +`gp_predictive_draws` locates the `KernelTerm` in the problem and takes +its constraint, rows, comparison space and parameter columns from there; +unconditioned it is `grid_draws` on that constraint. A kernel term +declares a *mean-zero* discrepancy, so the likelihood is the marginal +`y ~ N(ym(θ), Σ(θ))`; the matching predictive draws whole correlated curves +`ym(θ) + L(θ) z` from the inferred covariance about the model's own +prediction. `conditioned=True` instead conditions the discrepancy on the +residuals with every *other* term of the constraint as the regression noise +— regression on top of the model rather than a statement about its error. +`joint=False` falls back to per-point draws; the joint draws make a +functional summary well posed. + +`terms=` (on the three draw functions, `CompiledConstraint.matrix` and +`StructuredCovariance.matrix`, with `statistical=` for the diagonals the +compiler builds at the measured points) chooses which pieces of the error +model a draw carries: the model alone, model plus discrepancy, and model plus +discrepancy plus experimental uncertainty are different objects, and only +the last is comparable with data. Percentiles come back in the comparison +space, or in physical units with `physical=True`. ### 2.12 `reactions/` @@ -521,7 +560,7 @@ samples = run(p_fit) # rows in p_fit.names orde # the GP spans the cut, so score and draw from p(y_held | y_fit, theta): given= lp = rx.diagnostics.heldout_log_predictive(p_held, samples, given=p_fit) score = rx.diagnostics.log_posterior_predictive(lp) -draws = rx.diagnostics.predictive_draws(p_held, samples, n_rep=4, given=p_fit) +draws = rx.diagnostics.predictive_draws(p_held, samples, n_rep=4, given=p_fit, return_draws=True) ``` The labels `L0`, `E0`, `L2y`, `Lgp` are the error-model ladder of recipe @@ -553,7 +592,11 @@ test that pins it. Recipe numbers refer to `recipes.md`. | sampled mean discrepancy | `omp + Model(delta_fn, phi)`; alone or with `kernel` | test_model, recipe 8 | | multiplicative `x`-dependent correction | `omp * Model(g_fn, phi)` | test_model | | GP discrepancy in `x` or momentum transfer, with amplitude | `kernel(k, on=comp, coords=..., amplitude=..., amplitude_params=...)` | test_terms::TestStudyForms, recipe 7 | -| total predictive band from the problem alone | `total_predictive_band(problem, term, predictor, x_pred, samples)` | test_predictive, recipe 7 | +| posterior predictive on a new grid, error model included | `grid_draws(problem, predictor, x_pred, samples)`; `model_only=True`; `comparison=` | test_predictive::TestGridDraws, recipe 40 | +| point-by-point errors refused on a new grid | `grid_draws` raises naming the term | test_predictive, recipe 40 | +| GP discrepancy on a grid from the problem alone; GP regression instead | `gp_predictive_draws(problem, term, predictor, x_pred, samples)`; `conditioned=True` | test_predictive, recipe 7 | +| band by default, draws on request | `levels=`, `return_draws=True` on all three draw functions | test_predictive, test_diagnostics | +| choosing which terms a predictive draw carries | `terms=[...]`, `statistical=False` | test_predictive, test_diagnostics, recipe 17 | | hyperparameters shared across datasets, values from `meta` | same objects in one term per comparison; `c.meta("Elab")`; `kernel(params=)` | test_terms, test_problem, recipe 22 | | discrepancy correlated across energies | one `matrix` term `on=comps` from `c.meta` and `c.x`; dense path | test_covariance, recipe 23 | | term spanning comparisons reading its pieces | `c.segments`, `c.labels`, `c.split(a)` | test_terms, test_covariance, recipe 37 | @@ -621,15 +664,15 @@ a time unless noted. | notebook | recipes | driver | content | runtime | |---|---|---|---|---| -| `linear_calibration` | 1, 17 | emcee | the whole workflow on a line; prior and posterior predictive; the coverage curve | 23 s | +| `linear_calibration` | 1, 2, 17, 40 | emcee | the whole workflow on a line with an inferred constant noise; prior and posterior predictive on a new grid with `grid_draws`, the model's band against a measurement's, and why reported per-point errors cannot go there; coverage of both | 23 s | | `error_models` | 2, 4, 19 | emcee | the covariance ladder on one comparison, the Peelle matrix as a fixed term, offsets known, free and ignored | 89 s | | `sharing_error_models` | 5 | emcee | two experiments with opposite normalisation defects: sharing a parameter, a mode per dataset, one mode spanning both, and the assembled covariance seen directly | 78 s | | `normalization_and_covariance_structure` | 3, 4, 6, 27 | emcee | six treatments of five experiments' normalisations, one of them badly mis-quoted and alone in its range; Peelle's puzzle in the two-point case it was found in; a gallery of covariance structures from `matrix(theta)` | 353 s | -| `gp_discrepancy` | 7 | emcee, dynesty | mean-zero discrepancies with amplitudes growing in x: four rungs on a toy line, three on n+⁴⁰Ca missing its surface absorption; the total predictive band | 616 s | +| `gp_discrepancy` | 7 | emcee, dynesty | mean-zero discrepancies with amplitudes growing in x: four rungs on a toy line, three on n+⁴⁰Ca missing its surface absorption; the three predictive objects (model plus discrepancy, plus experimental, and the conditioned regression it does not use), with the equations | 483 s | | `robust_likelihoods` | 9, 39 | emcee | Student-t versus Gaussian on three gross outliers; the iterative rejection loop, including the round that over-rejects and recovers | 54 s | | `error_scale_and_usu` | 34 | emcee | a global scale on the reported errors under both likelihoods; a USU offset on the technique we suspect | 90 s | | `local_optical_model_calibration` | 4, 12, 14, 15, 16, 21, 26 | dynesty | EXFOR O1199007, p + ⁴⁰Ca at 35 MeV, which quotes no systematics: the unit contract, three error models against a potential wrong at the 30 % level, the singular guard, tempering and its coverage, other drivers | 1565 s | -| `alpha_ca_error_model_comparison` | 10, 11, 13, 17, 18 | dynesty | real ⁴⁴Ca(α,α) data, a four-parameter potential, the ladder by evidence with the Jacobian, predictive draws carrying the covariance and their coverage, held-out backward angles scored conditionally | 1394 s | +| `alpha_ca_error_model_comparison` | 7, 10, 11, 13, 17, 18 | dynesty | real ⁴⁴Ca(α,α) data, a four-parameter potential, a five-rung ladder by evidence with the Jacobian (the last rung a GP whose amplitude grows with angle), predictive draws carrying the covariance and their coverage, the mean-zero discrepancy envelope that says *where* the potential fails, held-out backward angles scored conditionally | 1280 s | | `hierarchical_calibration` | 22, 24, 30, 35, 38 | dynesty | eight schools, with the shrinkage explained rather than assumed; a hierarchy on the physics parameters recovering the evidence a misspecified energy dependence threw away, scored in sample and at a held-out energy | 748 s | **`hierarchical_calibration` in detail.** The truth is diff --git a/docs/groundup_design.md b/docs/groundup_design.md index 61c1caf..4caf4d9 100644 --- a/docs/groundup_design.md +++ b/docs/groundup_design.md @@ -312,7 +312,7 @@ model_error(parameter, averaging=True, log=True, on=None) systematic(parameter, basis, log=True, basis_params=(), on=None, coords=None) kernel(kernel, coords=None, amplitude=None, amplitude_params=(), jitter=1e-10, prefix="discrepancy", params=None, on=None) -> KernelTerm -# KernelTerm(Term) adds kernel, n_kernel, amplitude, jitter so predictive.total_predictive_band +# KernelTerm(Term) adds kernel, n_kernel, amplitude, jitter so predictive.gp_predictive_draws # can condition the discrepancy from the term alone. A derived hyperparameter is # bounded by the log of the kernel's bounds (a uniform prior in log-theta). # params=: the hyperparameter Parameter objects, one per free element in kernel.theta order; @@ -581,7 +581,8 @@ gone. ```python # diagnostics.py -predictive_draws(problem, samples, constraint=0, *, n_rep=1, rng=None, model_only=False, given=None) +predictive_draws(problem, samples, constraint=0, *, terms=None, statistical=True, n_rep=1, rng=None, + model_only=False, given=None, levels=(16, 50, 84), return_draws=False) coverage_curve(draws, y, levels=None); coverage_error(draws, y, levels=None) sharpness(draws, percentiles=(16, 84), transform=None) heldout_log_predictive(heldout_problem, samples, *, given=None) # Problem([fit.complement()]) @@ -595,9 +596,12 @@ logz_summary(logz, logzerr); compare_logz(a, b, sigma=2.0) # predictive.py gp_posterior_predictive(kernel, theta, X_train, residuals, X_pred, *, train_noise_var=None, jitter=1e-10) predictive_band(draws, levels=(16, 50, 84)) -total_predictive_band(problem, term, predictor, x_pred, samples, *, noise_std=0.0, - train_noise_var=None, levels=(16, 84), n_draws=400, rng=None, - physical=False) +grid_draws(problem, predictor, x_pred, samples, constraint=0, *, comparison=None, terms=None, + model_only=False, joint=True, physical=False, n_rep=1, rng=None, + levels=(16, 50, 84), return_draws=False) +gp_predictive_draws(problem, term, predictor, x_pred, samples, *, terms=None, conditioned=False, + joint=True, noise_std=0.0, train_noise_var=None, physical=False, + n_rep=1, rng=None, levels=(16, 50, 84), return_draws=False) # term is the KernelTerm; its columns and the predictor's come from problem.columns. The # conditioning noise defaults to the constraint's covariance minus the kernel block; the # band is in the comparison space of the term's comparisons unless physical=True. @@ -795,7 +799,7 @@ What comes across from `src/rxmc` on `api_generalisation`, by file. | `Observation.__init__` transform handling and `_check_finite` | → `Comparison.__post_init__`; error names `data.label` | | `Constraint._validate_constant_covariance` message | → compile error raised by `StructuredCovariance.factor_constant_parts`, remedies updated to the new spellings | | `model_comparison.predictive_draws`, `heldout_log_predictive` | take `(problem, samples)`; read `CompiledConstraint.ym`, `.matrix`, `.log_likelihood` | -| `predictive.total_predictive_band` | take `(problem, term, predictor, ...)`; columns from `problem.columns` | +| `predictive.total_predictive_band` (now `grid_draws` and `gp_predictive_draws`) | take `(problem, term, predictor, ...)`; columns from `problem.columns` | | `ParameterConfig.prior_transform` cursor | → per-slot map in `assemble_prior` | | `ElasticDifferentialXSObservation.from_measurement`, `IsobaricAnalogPNObservation.from_measurement` | one free `from_measurement`; Rutherford from kinematics | | `PhysicalModel.Polynomial` | → `polynomial(order)` factory | @@ -894,7 +898,7 @@ is driven by emcee or dynesty. | `error_models` | systematic_err_demo | emcee | the ladder on one comparison, the Peelle matrix as a fixed `Term`, offsets known and free; two-constraint section with case B via a shared `Parameter` | 153 s | | `normalization_and_covariance_structure` | normalization_inference | emcee | ρᵢ as `quartic \| tf.scale(rho_i)` against `reported_terms()`; the four-case gallery via `matrix(theta)` | 328 s | | `correlated_observations` | correlated_observations | emcee | case A vs B on the toy; Neudecker et al. (2014) §II.A and §II.B recreated: the multi-quantity Peelle puzzle with a spanning `matrix` term built through `c.split` | 141 s | -| `gp_discrepancy` | gp_discrepancy | emcee (toy), dynesty (reaction) | `kernel` term; `total_predictive_band(problem, term, ...)`; the same defect fit with a sampled Legendre mean correction for contrast; n+⁴⁰Ca with the surface absorption missing | 617 s | +| `gp_discrepancy` | gp_discrepancy | emcee (toy), dynesty (reaction) | `kernel` term; `gp_predictive_draws(problem, term, ...)`; the same defect fit with a sampled Legendre mean correction for contrast; n+⁴⁰Ca with the surface absorption missing | 617 s | | `robust_likelihoods` | robust_likelihoods | emcee | Student-t vs Gaussian; ν bounded on the `Parameter`; a global error scale and a USU offset per technique | 154 s | | `measurement_to_calibration` | measurement_to_calibration + 30s_optical_potential_calibration + the tempering/coverage section of overconfidence | dynesty | `from_measurement`, `reported_terms`, the singular-covariance error, `Constraint(weight=)`, `coverage_curve`, emcee and `dill` as other drivers, the KDUQ `model_error` spelling | 182 s | | `alpha_ca_error_model_comparison` | **new** (the `jitr` quickstart's α+⁴⁴Ca data, EXFOR F0567) | dynesty | real data without errors, a four-parameter potential, log space with `log_jacobian`, the `L0`/`E0`/`L2y`/`Lgp` ladder by evidence, `masked_where`/`complement` with `heldout_log_predictive` and held-out coverage | 1197 s (alongside another notebook) | @@ -964,7 +968,7 @@ Dropped: `sampling_algos` (in-package samplers), `calibration_config_emcee_dynes `Predictor.__reduce__` rebuilds it from `(model, x, meta)`; the factory closures in `terms.py` pickle under `dill` as they are. - **G7 Analysis on `(problem, samples)`**: `predictive_draws`, - `heldout_log_predictive`, `total_predictive_band` selecting columns via + `heldout_log_predictive`, `grid_draws`/`gp_predictive_draws` selecting columns via `problem.columns`. - **G8 The α+Ca and hierarchical notebooks** and the bbb `posterior.py` shim. - **G9 Docs**: `design.md` rewritten from this document; API reference @@ -1036,7 +1040,7 @@ recipe test it unlocks pass. 6. **`diagnostics`, `predictive`.** Ported: GP-versus-sklearn, `predictive_draws` covariance recovery, `heldout_log_predictive`, `logz_summary` and `compare_logz`. Recipe tests unlocked: 7 - (`total_predictive_band` finds the kernel columns itself), 17, 18, 28, + (`gp_predictive_draws` finds the kernel columns itself), 17, 18, 28, 30, 31. 7. **CI wiring.** The heading-to-file check between `recipes.md` and `test/recipes/` is itself a test (`test/test_recipes_index.py`: one diff --git a/docs/recipes.md b/docs/recipes.md index abb6ab4..deeb3e6 100644 --- a/docs/recipes.md +++ b/docs/recipes.md @@ -180,8 +180,15 @@ gp = T.kernel(Matern(1.0, nu=2.5), on=comp, coords=lambda x: x / np.pi, q = lambda x: rx.reactions.momentum_transfer(x, d.meta["k"]) gp_q = T.kernel(RBF(1.0), on=comp, coords=q, amplitude=lambda c, lA, r: np.exp(lA) * c.x ** (r / 2), amplitude_params=(log_A, r)) -c = rx.Constraint([comp], terms=[gp]) -band = rx.predictive.total_predictive_band(problem, gp, omp.bind(x_fine, d.meta), x_fine, samples) +eps = T.noise(rx.Parameter("log_eps", prior=stats.norm(-3, 1))) +c = rx.Constraint([comp], terms=[eps, gp]) +pred = omp.bind(x_fine, d.meta) +# the model's own prediction with correlated draws from the inferred covariance +band = rx.predictive.grid_draws(problem, pred, x_fine, samples, terms=[gp]) +# what a measurement would show: the experimental terms too (recipe 40) +full = rx.predictive.grid_draws(problem, pred, x_fine, samples, terms=[gp, eps]) +# GP regression on the residuals instead +fit = rx.predictive.gp_predictive_draws(problem, gp, pred, x_fine, samples, conditioned=True) ``` Expected behaviour: @@ -191,14 +198,25 @@ Expected behaviour: bounded by the log of the kernel's bounds, so it compiles with a uniform prior there (`params=` for any other prior). - `kernel()` returns a `KernelTerm`, a `Term` that also carries the kernel, - so the predictive band can condition the discrepancy from the term alone. + so the predictive band can be built from the term alone. - The model parameters relax from their biased values toward the truth - (`gp_discrepancy`); the learned discrepancy tracks the true defect. -- `total_predictive_band` finds the kernel's columns from the term itself; - no column arithmetic. It conditions on the residuals of the training rows - with everything *else* in the constraint's covariance (statistical errors, - noise, modes) as the regression noise, and predicts in the comparison - space of the term's comparisons (`physical=True` maps back). + (`gp_discrepancy`); the inferred envelope contains the true defect. +- The kernel declares a **mean-zero** discrepancy, so the likelihood is the + marginal `y ~ N(ym(θ), Σ(θ))` and `θ` is inferred with the discrepancy + integrated out. `grid_draws` with the kernel among its `terms` matches + that: whole correlated curves drawn from the inferred covariance about the model's own + prediction, one per posterior row. That is a statement about where and by + how much *the model* fails. +- `gp_predictive_draws(..., conditioned=True)` gives the other object, the GP posterior mean given the + residuals — data-driven regression on top of the model, which interpolates + the residuals rather than describing the model's error. + `gp_posterior_predictive` is the same conditioning on bare arrays. +- `gp_predictive_draws` finds the kernel's constraint, rows and columns + from the term itself; no column arithmetic. Unconditioned it is exactly + `grid_draws` on that constraint. Both predict in the comparison space + (`physical=True` maps back), and `return_draws=True` gives the + draws themselves, which is what a functional summary (a simultaneous band, + an extremum, an integral) needs — well posed only because the draw is joint. - Every error-model form of the α+Ca study reproduces a hand-built dense matrix (`TestStudyForms`). @@ -371,9 +389,10 @@ grid for plotting, with the solver set up once per grid.* omp = rx.reactions.ElasticXS("dXS/dA", central, spin_orbit, args_from_params, params, lmax=20, wavelengths_beyond_range=2.0, zeros_per_node=5) comp = rx.Comparison(d, omp) # bound to d.x, d.meta -fine = omp.bind(np.deg2rad(np.linspace(0.5, 179.5, 200)), d.meta) -ys = [fine(*s[problem.columns(omp.params)]) for s in samples[::50]] -band = rx.predictive.predictive_band(ys, levels=(5, 50, 95)) +x_fine = np.deg2rad(np.linspace(0.5, 179.5, 200)) +fine = omp.bind(x_fine, d.meta) +band = rx.predictive.grid_draws(problem, fine, x_fine, samples[::50], model_only=True, + levels=(5, 50, 95)) ``` Expected behaviour: @@ -423,16 +442,31 @@ Expected behaviour: contain 68 % of the points, and how wide are they?* ```python -draws = rx.diagnostics.predictive_draws(problem, samples, constraint=0, n_rep=4) +band = rx.diagnostics.predictive_draws(problem, samples, constraint=0, n_rep=4) # (16, 50, 84) % +draws = rx.diagnostics.predictive_draws(problem, samples, n_rep=4, return_draws=True) cov = rx.diagnostics.coverage_curve(draws, problem.constraints[0].y[problem.constraints[0].active]) err = rx.diagnostics.coverage_error(draws, y_active) width = rx.diagnostics.sharpness(draws, transform=np.exp) # widths in physical space for a log fit +# the model plus its discrepancy alone, without the experimental uncertainty +model_side = rx.diagnostics.predictive_draws(problem, samples, terms=[gp], statistical=False) ``` Expected behaviour: - Draws are `ym(theta) + L z` on the active points in comparison space; `model_only=True` returns `ym(theta)` and assembles no covariance. +- The return is a percentile band by default, the draws with + `return_draws=True` — the convention `grid_draws` and + `gp_predictive_draws` share. Coverage and sharpness need the draws. + At points that were never measured, use `grid_draws` (recipe 40). +- `terms=` and `statistical=` choose which pieces of the error model a draw + carries; the default is all of them. Model plus discrepancy and model plus + discrepancy plus experimental uncertainty are different objects, and only + the second is what measured data should be compared against — so a coverage + or sharpness check always uses the default. The first answers a different + question: what the fit says about the *model's* prediction. The same two + arguments select on `constraints[i].matrix(theta)`; `given=` conditions + under the full covariance and cannot be combined with a selection. - Coverage is near nominal for a correct error model and clearly below it for an overconfident one. - `logz_summary` reports the max of the replicate half-range and the @@ -1276,11 +1310,68 @@ optical potentials for single-nucleon scattering*, Phys. Rev. C 107, 014602 (2023), [arXiv:2211.07741](https://arxiv.org/abs/2211.07741), which rejects points more than 3σ from the current model between rounds. +## 40. Predict on a new grid, error model included + +*I have a posterior, and I want predictions at `x` I never measured — a fine +plotting grid, an extrapolation — carrying the uncertainty my error model +declares, not only the spread of the model curves.* + +```python +log_sigma = rx.Parameter("log_sigma", prior=stats.norm(np.log(0.2), 1.0)) +c = rx.Constraint([comp], terms=[T.noise(log_sigma)], statistical=False) +problem = rx.Problem([c]) +pred = line.bind(x_fine) +model_band = rx.predictive.grid_draws(problem, pred, x_fine, samples, model_only=True) +full_band = rx.predictive.grid_draws(problem, pred, x_fine, samples, n_rep=2) +draws = rx.predictive.grid_draws(problem, pred, x_fine, samples, return_draws=True) +# several experiments in one constraint: say which one the grid stands for +band_a = rx.predictive.grid_draws(problem_ab, pred, x_fine, samples, comparison=comp_a) +``` + +Expected behaviour: + +- For each row the model is evaluated on the grid and every selected term is + re-evaluated there from its own definition, with the model's prediction + standing in for `c.y` and the predictor's `meta` for `c.meta`; one + correlated draw is taken from the sum (times the likelihood's + `predictive_scale`, so a Student-t draws a t). At the measured points with + every term a function, it draws from the same distribution as + `predictive_draws`. +- Any term that is a function of the `TermContext` travels: `noise`, + `noise_fraction`, `model_error`, `normalization`/`offset`/`systematic` with + a parameter or a scalar magnitude, a `kernel`, and a user's + `Term(fn, params, kind="matrix")`. +- A term that is an array has no value at a new `x`: the reported + statistical errors (`statistical=True`), a fixed `Term(array)`, a per-point + `magnitude=`, a function closing over the measured rows. Drawing it would + invent the error of a measurement nobody made, so `grid_draws` raises and + names the term. The remedies are `predictive_draws` at the data, an + explicit `terms=[...]` (`model_only=True` for the model alone), or an error + model that is a function of `x`, as above. Interpolating reported errors is + a modelling choice, and is written as such a function. +- `model_only=True` and the default are different objects: the uncertainty of + the *curve* and the uncertainty of a *measurement* at that `x`. At the data + the first under-covers and the second is calibrated (recipe 17). A + discrepancy (recipe 7) is a third source, between the two. +- With several comparisons in the constraint, `terms=None` needs + `comparison=`: a term belonging to one experiment, drawn on a grid, means a + future measurement by that experiment. +- The band is the default return, `(len(levels), len(x_pred))`, as for + `predictive_draws` and `gp_predictive_draws`; `return_draws=True` gives the + draws, whole correlated curves, so a functional summary is well posed. + ## What this API does not express Each item names the assumption that breaks, the nearest workaround, and the size of the addition that would lift it. +- **Reported point-by-point errors at a new `x`.** A statistical error + quoted for each measured point has no value where nothing was measured, so + `grid_draws` refuses to carry it (recipe 40). Workaround: an error model + that is a function of `x` — inferred noise, or an interpolation of the + reported errors written as a `Term` of `c.x` — which is a modelling choice + the user makes explicitly. Addition refused by design: the library would be + inventing the error of a measurement nobody made. - **Non-elliptical likelihoods.** Poisson counts, censored points and upper limits, and two-component good/bad mixtures `(1 − β) N + β t` (Hanson 2007) are not functionals of `(d2, logdet, n)`. Workaround: none that is diff --git a/examples/alpha_ca_error_model_comparison.ipynb b/examples/alpha_ca_error_model_comparison.ipynb index 42982a5..c1d5bd5 100644 --- a/examples/alpha_ca_error_model_comparison.ipynb +++ b/examples/alpha_ca_error_model_comparison.ipynb @@ -2,31 +2,18 @@ "cells": [ { "cell_type": "markdown", - "id": "d2bec57e", + "id": "aef617a5", "metadata": {}, "source": [ "# Error models for $\\alpha + {}^{44}$Ca elastic scattering: a comparison by evidence\n", "\n", - "This notebook is a small study rather than a demonstration. We have one real\n", - "measurement and one optical potential, and the only thing we vary is the **error\n", - "model** — four different statements about what the disagreement between model\n", - "and data means. Then we ask the data which statement they prefer, by\n", - "[Bayesian evidence](https://en.wikipedia.org/wiki/Marginal_likelihood), and\n", - "separately by how well each one predicts angles it was never shown.\n", + "This notebook is more of a small study than a demonstration. We have one real measurement and one optical potential, and the only thing we'll change is the **error model**: five different statements about what the disagreement between our model and the data means. Then we'll ask the data which statement they prefer, in two separate ways. The first is the [Bayesian evidence](https://en.wikipedia.org/wiki/Marginal_likelihood), and the second is how well each error model predicts angles it was never shown.\n", "\n", - "The data are the differential elastic cross sections, as a ratio to Rutherford,\n", - "of $^{44}$Ca($\\alpha,\\alpha$) at 29 MeV from\n", - "[EXFOR F0567](https://www-nds.iaea.org/exfor/servlet/X4sSearch5?EntryID=150567),\n", - "[Oeschler *et al.*, Phys. Rev. Lett. **28**, 694 (1972)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.28.694).\n", - "Oeschler *et al.* report no uncertainties at all, so every rung below has to\n", - "infer its own — which is precisely what makes the comparison interesting.\n", + "The data are differential elastic cross sections, as a ratio to the [Rutherford](https://en.wikipedia.org/wiki/Rutherford_scattering) cross section, for $^{44}$Ca($\\alpha,\\alpha$) at 29 MeV. They come from [EXFOR F0567](https://www-nds.iaea.org/exfor/servlet/X4sSearch5?EntryID=150567), measured by [Oeschler *et al.*, Phys. Rev. Lett. **28**, 694 (1972)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.28.694). Oeschler *et al.* report no uncertainties at all, so every error model below has to infer its own, and that's exactly what makes the comparison interesting.\n", "\n", - "See also the [`jitr` quickstart tutorials](https://beykyle.github.io/jitr/getting-started.html).\n", - "In a sense this notebook is their successor: with `rxmc` we can build more\n", - "careful statistical models on the same physics, including\n", - "[Gaussian processes for model discrepancy](https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/1467-9868.00294).\n", + "It's worth looking at the [`jitr` quickstart tutorials](https://beykyle.github.io/jitr/getting-started.html) alongside this one. In a sense this notebook is their successor: with `rxmc` we can build more careful statistical models on top of the same physics, including a [Gaussian process](https://en.wikipedia.org/wiki/Gaussian_process) for model discrepancy in the style of [Kennedy & O'Hagan (2001)](https://doi.org/10.1111/1467-9868.00294), which the `gp_discrepancy` notebook introduces.\n", "\n", - "Recipes: 10, 11, 13, 17, 18" + "Recipes: 7, 10, 11, 13, 17, 18" ] }, { @@ -35,10 +22,10 @@ "id": "fe61dfe1", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:36:13.561234Z", - "iopub.status.busy": "2026-09-12T03:36:13.560962Z", - "iopub.status.idle": "2026-09-12T03:36:16.624613Z", - "shell.execute_reply": "2026-09-12T03:36:16.623689Z" + "iopub.execute_input": "2026-09-14T22:11:10.365176Z", + "iopub.status.busy": "2026-09-14T22:11:10.365058Z", + "iopub.status.idle": "2026-09-14T22:11:12.587702Z", + "shell.execute_reply": "2026-09-14T22:11:12.587088Z" } }, "outputs": [], @@ -66,39 +53,27 @@ }, { "cell_type": "markdown", - "id": "53981e03", + "id": "a34d6d00", "metadata": {}, "source": [ "## The data\n", "\n", - "We take every fourth angle of the $^{44}$Ca set: 73 points from 15 to 174\n", - "degrees, enough to pin the diffraction pattern without making every fit four\n", - "times slower. A ratio to Rutherford is dimensionless, so nothing needs\n", - "converting; the kinematics the model needs go in `meta`, and there are no errors\n", - "to report." + "We'll take every fourth angle of the $^{44}$Ca set. That leaves 73 points between 15 and 174 degrees, which is plenty to pin down the diffraction pattern without making every fit four times slower. A ratio to Rutherford is dimensionless, so there are no units to convert. The kinematics the model needs go in `meta`, and since nothing was reported, the error column is just zeros." ] }, { "cell_type": "code", "execution_count": 2, - "id": "5df13b6f", + "id": "5da9f9ff", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:36:16.626738Z", - "iopub.status.busy": "2026-09-12T03:36:16.626423Z", - "iopub.status.idle": "2026-09-12T03:36:16.666804Z", - "shell.execute_reply": "2026-09-12T03:36:16.665894Z" + "iopub.execute_input": "2026-09-14T22:11:12.589448Z", + "iopub.status.busy": "2026-09-14T22:11:12.589216Z", + "iopub.status.idle": "2026-09-14T22:11:12.611548Z", + "shell.execute_reply": "2026-09-14T22:11:12.610745Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "73 points between 15 and 174 degrees\n" - ] - } - ], + "outputs": [], "source": [ "df = pd.read_csv(\"data/alpha_ca_ratio_ruth.csv\")\n", "df = df[df[\"target\"] == \"Ca44\"].iloc[::4]\n", @@ -110,7 +85,31 @@ "data = rx.Dataset(\n", " angles, ratio, np.zeros(ratio.size), label=\"44Ca(a,a) 29 MeV\", meta=meta\n", ")\n", - "x_fine = np.deg2rad(np.linspace(10.0, 178.0, 220))\n", + "x_fine = np.deg2rad(np.linspace(10.0, 178.0, 220))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e325fa60", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T22:11:12.612939Z", + "iopub.status.busy": "2026-09-14T22:11:12.612814Z", + "iopub.status.idle": "2026-09-14T22:11:12.615701Z", + "shell.execute_reply": "2026-09-14T22:11:12.615086Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "73 points between 15 and 174 degrees\n" + ] + } + ], + "source": [ "print(\n", " f\"{data.n} points between {np.rad2deg(angles.min()):.0f} and \"\n", " f\"{np.rad2deg(angles.max()):.0f} degrees\"\n", @@ -119,14 +118,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "db94063b", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:36:16.668663Z", - "iopub.status.busy": "2026-09-12T03:36:16.668469Z", - "iopub.status.idle": "2026-09-12T03:36:17.964437Z", - "shell.execute_reply": "2026-09-12T03:36:17.963545Z" + "iopub.execute_input": "2026-09-14T22:11:12.616940Z", + "iopub.status.busy": "2026-09-14T22:11:12.616824Z", + "iopub.status.idle": "2026-09-14T22:11:13.560064Z", + "shell.execute_reply": "2026-09-14T22:11:13.559407Z" } }, "outputs": [ @@ -155,32 +154,26 @@ }, { "cell_type": "markdown", - "id": "9289738b", + "id": "f4bbc9b2", "metadata": {}, "source": [ "## The optical model\n", "\n", - "As in the `jitr` quickstart: a Woods-Saxon potential plus the Coulomb potential\n", - "of a uniformly charged sphere of radius $1.3\\,A^{1/3}$ fm, with radii in units\n", - "of $A^{1/3}$ fm. The real and imaginary volume terms share one geometry, so\n", - "four parameters are free: $V$, $W$, $r$, $a$. Priors follow the quickstart's\n", - "typical $\\alpha$-nucleus values, truncated at zero (recipe 13).\n", + "We use the same model as the `jitr` quickstart: a [Woods–Saxon](https://en.wikipedia.org/wiki/Woods%E2%80%93Saxon_potential) potential, plus the Coulomb potential of a uniformly charged sphere of radius $1.3\\,A^{1/3}$ fm, with radii measured in units of $A^{1/3}$ fm. The real and imaginary volume terms share one geometry, which leaves four free parameters: $V$, $W$, $r$ and $a$. For priors we take the quickstart's typical $\\alpha$-nucleus values, truncated at zero so the depths stay physical (recipe 13).\n", "\n", - "Thirty partial waves are converged at this energy, and the default basis size\n", - "sits within a few per cent of the fully converged solver at the deepest minima —\n", - "far below any of the error models below, and two hundred times faster." + "Thirty partial waves are enough to converge at this energy. The solver's default basis size agrees with the fully converged solution to within a few per cent, even at the deepest diffraction minima. That's far smaller than any of the errors we're about to infer, and it runs about two hundred times faster." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "91f59c49", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:36:17.966669Z", - "iopub.status.busy": "2026-09-12T03:36:17.966468Z", - "iopub.status.idle": "2026-09-12T03:36:17.975200Z", - "shell.execute_reply": "2026-09-12T03:36:17.974050Z" + "iopub.execute_input": "2026-09-14T22:11:13.561561Z", + "iopub.status.busy": "2026-09-14T22:11:13.561410Z", + "iopub.status.idle": "2026-09-14T22:11:13.567564Z", + "shell.execute_reply": "2026-09-14T22:11:13.566780Z" } }, "outputs": [], @@ -218,14 +211,33 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "c3673d96", + "execution_count": 6, + "id": "d95b3b0e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:36:17.977414Z", - "iopub.status.busy": "2026-09-12T03:36:17.977199Z", - "iopub.status.idle": "2026-09-12T03:36:33.449109Z", - "shell.execute_reply": "2026-09-12T03:36:33.448130Z" + "iopub.execute_input": "2026-09-14T22:11:13.568877Z", + "iopub.status.busy": "2026-09-14T22:11:13.568741Z", + "iopub.status.idle": "2026-09-14T22:11:16.936191Z", + "shell.execute_reply": "2026-09-14T22:11:16.935249Z" + } + }, + "outputs": [], + "source": [ + "# we always draw the model on a fine grid: evaluated only at the (downsampled)\n", + "# data angles, the diffraction pattern aliases and the curve looks ragged\n", + "on_fine = omp.bind(x_fine, meta)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5fe1f329", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T22:11:16.937635Z", + "iopub.status.busy": "2026-09-14T22:11:16.937507Z", + "iopub.status.idle": "2026-09-14T22:11:24.637967Z", + "shell.execute_reply": "2026-09-14T22:11:24.637150Z" } }, "outputs": [ @@ -241,9 +253,6 @@ } ], "source": [ - "# the model is always drawn on a fine grid: evaluated only at the (downsampled)\n", - "# data angles, the diffraction pattern aliases and the curve looks ragged\n", - "on_fine = omp.bind(x_fine, meta)\n", "fig, ax = plt.subplots()\n", "ax.plot(np.rad2deg(angles), ratio, \"o\", ms=3, color=\"k\", label=\"data\")\n", "ax.plot(\n", @@ -264,42 +273,38 @@ }, { "cell_type": "markdown", - "id": "09d7df87", + "id": "0836f7d2", "metadata": {}, "source": [ "## The error-model ladder\n", "\n", - "The data span three decades and their errors were never reported, but they are\n", - "presumably multiplicative, so we compare in **log space** (recipe 10):\n", - "`space=tf.log` transforms the data once and every prediction with it, and\n", - "`problem.log_jacobian()` is what later makes a log-space evidence comparable\n", - "with a linear-space one.\n", + "The data span three decades, and although their errors were never reported, they're presumably multiplicative: a few per cent of the cross section, whatever its size. Multiplicative errors become additive in log space, so that's where we'll compare (recipe 10). Passing `space=tf.log` to a `Comparison` transforms the data once and every prediction along with it. Later on, `problem.log_jacobian()` will be what lets us compare a log-space evidence with a linear-space one.\n", "\n", - "With nothing reported, every rung sets `statistical=False`: the inferred terms\n", - "*are* the covariance.\n", + "Since nothing was reported, every rung sets `statistical=False`, and the inferred terms *are* the whole covariance. These are the five rungs we'll climb:\n", "\n", "| label | error model |\n", "|---|---|\n", "| `L0` | constant noise in log space, $\\sigma = \\epsilon$ on every point |\n", "| `E0` | fractional noise in linear space, $\\sigma_i = \\epsilon\\, y_{m,i}$ |\n", "| `L2y` | `L0` plus a free normalisation mode, $\\eta\\, y_m$ (the `jitr` notebook's model) |\n", - "| `Lgp` | `L0` plus a Matérn(5/2) Gaussian process in $u = \\theta/\\pi$ with a free amplitude |\n", + "| `Lgp` | `L0` plus a [Matérn](https://en.wikipedia.org/wiki/Mat%C3%A9rn_covariance_function)(5/2) Gaussian process in $u = \\theta/\\pi$ with a free amplitude |\n", + "| `Lgpn` | the same, but with an amplitude that **grows with angle** |\n", "\n", - "The same `log_eps` object is reused across rungs, and each `Problem` compiles\n", - "independently (recipe 18). The priors are log-uniform on the error scales\n", - "between 5 % and 200 %, as in the `jitr` notebook (recipe 13)." + "`Lgpn` is the only rung that can say *where* the potential fails, rather than just how badly. A stationary kernel with a constant amplitude declares the same uncertainty at every angle, and nobody really believes that about an optical model: forward angles, dominated by Coulomb scattering, are much easier to get right than the backward ones.\n", + "\n", + "A couple of bookkeeping notes. We reuse the same `log_eps` parameter object across rungs, and each `Problem` compiles independently of the others (recipe 18). The priors on the error scales are log-uniform between 5 % and 200 %, as in the `jitr` notebook (recipe 13)." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "9421f3fd", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:36:33.450949Z", - "iopub.status.busy": "2026-09-12T03:36:33.450743Z", - "iopub.status.idle": "2026-09-12T03:36:33.460413Z", - "shell.execute_reply": "2026-09-12T03:36:33.459623Z" + "iopub.execute_input": "2026-09-14T22:11:24.639529Z", + "iopub.status.busy": "2026-09-14T22:11:24.639352Z", + "iopub.status.idle": "2026-09-14T22:11:24.648255Z", + "shell.execute_reply": "2026-09-14T22:11:24.647321Z" } }, "outputs": [], @@ -326,6 +331,12 @@ " prior=stats.uniform(np.log(0.02), np.log(1.0) - np.log(0.02)),\n", " latex=r\"\\log\\ell\",\n", ")\n", + "log_amp_n = rx.Parameter(\n", + " \"log_A_n\",\n", + " prior=stats.uniform(np.log(0.05), np.log(2.0) - np.log(0.05)),\n", + " latex=r\"\\log A_n\",\n", + ")\n", + "amp_slope = rx.Parameter(\"amp_slope\", prior=stats.norm(1.0, 1.0), latex=\"s_A\")\n", "gp = T.kernel(\n", " Matern(0.1, nu=2.5),\n", " on=comp_log,\n", @@ -333,33 +344,31 @@ " amplitude=T.constant_amplitude,\n", " amplitude_params=(log_amp,),\n", " params=[log_ell],\n", + ")\n", + "\n", + "gp_n = T.kernel(\n", + " Matern(0.1, nu=2.5),\n", + " on=comp_log,\n", + " coords=lambda x: x / np.pi,\n", + " amplitude=T.exp_growth_amplitude(1.0),\n", + " amplitude_params=(log_amp_n, amp_slope),\n", + " params=[log_ell],\n", ")" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "db1796bf", + "execution_count": 9, + "id": "c0ce8912", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:36:33.462476Z", - "iopub.status.busy": "2026-09-12T03:36:33.462270Z", - "iopub.status.idle": "2026-09-12T03:36:33.472571Z", - "shell.execute_reply": "2026-09-12T03:36:33.471774Z" + "iopub.execute_input": "2026-09-14T22:11:24.649739Z", + "iopub.status.busy": "2026-09-14T22:11:24.649588Z", + "iopub.status.idle": "2026-09-14T22:11:24.657947Z", + "shell.execute_reply": "2026-09-14T22:11:24.657294Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "L0 columns: ['V', 'W', 'r', 'a', 'log_eps']\n", - "E0 columns: ['V', 'W', 'r', 'a', 'log_eps']\n", - "L2y columns: ['V', 'W', 'r', 'a', 'log_eps', 'log_eta']\n", - "Lgp columns: ['V', 'W', 'r', 'a', 'log_eps', 'log_ell', 'log_A']\n" - ] - } - ], + "outputs": [], "source": [ "ladder = {\n", " \"L0\": rx.Constraint([comp_log], terms=[T.noise(log_eps)], statistical=False),\n", @@ -372,34 +381,63 @@ " statistical=False,\n", " ),\n", " \"Lgp\": rx.Constraint([comp_log], terms=[T.noise(log_eps), gp], statistical=False),\n", + " \"Lgpn\": rx.Constraint(\n", + " [comp_log], terms=[T.noise(log_eps), gp_n], statistical=False\n", + " ),\n", "}\n", - "problems = {name: rx.Problem([c]) for name, c in ladder.items()}\n", + "problems = {name: rx.Problem([c]) for name, c in ladder.items()}" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "763293ca", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T22:11:24.659356Z", + "iopub.status.busy": "2026-09-14T22:11:24.659222Z", + "iopub.status.idle": "2026-09-14T22:11:24.662279Z", + "shell.execute_reply": "2026-09-14T22:11:24.661560Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L0 columns: ['V', 'W', 'r', 'a', 'log_eps']\n", + "E0 columns: ['V', 'W', 'r', 'a', 'log_eps']\n", + "L2y columns: ['V', 'W', 'r', 'a', 'log_eps', 'log_eta']\n", + "Lgp columns: ['V', 'W', 'r', 'a', 'log_eps', 'log_ell', 'log_A']\n", + "Lgpn columns: ['V', 'W', 'r', 'a', 'log_eps', 'log_ell', 'log_A_n', 'amp_slope']\n" + ] + } + ], + "source": [ "for name, p in problems.items():\n", " print(f\"{name:4s} columns: {p.names}\")" ] }, { "cell_type": "markdown", - "id": "716ce943", + "id": "163e2db0", "metadata": {}, "source": [ "## Running the ladder\n", "\n", - "Optical-model posteriors are correlated and often multimodal, so we use nested\n", - "sampling: [dynesty](https://dynesty.readthedocs.io/) copes with them, and it\n", - "returns the evidence we are about to compare as a by-product." + "Optical-model posteriors are strongly correlated and often multimodal, so we'll use [nested sampling](https://en.wikipedia.org/wiki/Nested_sampling_algorithm) ([Skilling 2006](https://doi.org/10.1214/06-BA127)) through [dynesty](https://dynesty.readthedocs.io/) ([Speagle 2020](https://doi.org/10.1093/mnras/staa278)). It copes well with that kind of posterior, and it computes the evidence we're about to compare as a by-product." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "id": "f3d12040", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:36:33.474641Z", - "iopub.status.busy": "2026-09-12T03:36:33.474437Z", - "iopub.status.idle": "2026-09-12T03:36:33.478031Z", - "shell.execute_reply": "2026-09-12T03:36:33.477257Z" + "iopub.execute_input": "2026-09-14T22:11:24.663693Z", + "iopub.status.busy": "2026-09-14T22:11:24.663573Z", + "iopub.status.idle": "2026-09-14T22:11:24.666525Z", + "shell.execute_reply": "2026-09-14T22:11:24.665857Z" } }, "outputs": [], @@ -419,14 +457,34 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "7786396e", + "execution_count": 12, + "id": "fcfa775f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T22:11:24.667804Z", + "iopub.status.busy": "2026-09-14T22:11:24.667687Z", + "iopub.status.idle": "2026-09-14T22:24:35.394985Z", + "shell.execute_reply": "2026-09-14T22:24:35.394083Z" + } + }, + "outputs": [], + "source": [ + "results, samples = {}, {}\n", + "for i, (name, p) in enumerate(problems.items()):\n", + " results[name] = fit_nested(p, seed=i)\n", + " samples[name] = results[name].samples_equal(rstate=np.random.default_rng(i))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e8b9e24f", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:36:33.479820Z", - "iopub.status.busy": "2026-09-12T03:36:33.479621Z", - "iopub.status.idle": "2026-09-12T03:51:14.356535Z", - "shell.execute_reply": "2026-09-12T03:51:14.355710Z" + "iopub.execute_input": "2026-09-14T22:24:35.396478Z", + "iopub.status.busy": "2026-09-14T22:24:35.396327Z", + "iopub.status.idle": "2026-09-14T22:24:35.399302Z", + "shell.execute_reply": "2026-09-14T22:24:35.398835Z" } }, "outputs": [ @@ -434,37 +492,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "L0 log Z = -101.42 +/- 0.95 26153 calls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "E0 log Z = 211.36 +/- 1.03 34980 calls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "L2y log Z = -98.22 +/- 0.85 22672 calls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Lgp log Z = -92.18 +/- 0.95 30383 calls\n" + "L0 log Z = -101.42 +/- 0.95 26153 calls\n", + "E0 log Z = 211.36 +/- 1.03 34980 calls\n", + "L2y log Z = -98.22 +/- 0.85 22672 calls\n", + "Lgp log Z = -92.18 +/- 0.95 30383 calls\n", + "Lgpn log Z = -88.70 +/- 0.88 35387 calls\n" ] } ], "source": [ - "results, samples = {}, {}\n", - "for i, (name, p) in enumerate(problems.items()):\n", - " res = fit_nested(p, seed=i)\n", - " results[name] = res\n", - " samples[name] = res.samples_equal(rstate=np.random.default_rng(i))\n", + "for name, res in results.items():\n", " print(\n", " f\"{name:4s} log Z = {res.logz[-1]:8.2f} +/- {res.logzerr[-1]:.2f} \"\n", " f\"{int(np.sum(res.ncall)):6d} calls\"\n", @@ -473,43 +510,61 @@ }, { "cell_type": "markdown", - "id": "933de88a", + "id": "e8271256", "metadata": {}, "source": [ "## Evidence, and what a Bayes factor is\n", "\n", - "The [marginal likelihood](https://en.wikipedia.org/wiki/Marginal_likelihood), or\n", - "evidence, is the probability the model assigns to the data *before* we see which\n", - "parameters fit best:\n", + "The [marginal likelihood](https://en.wikipedia.org/wiki/Marginal_likelihood), or evidence, is the probability a model assigned to the data before we asked which parameters fit best. We get it by averaging the likelihood over the prior:\n", "\n", "$$Z = \\int p(\\mathbf{y} \\mid \\theta)\\, p(\\theta)\\, \\mathrm{d}\\theta .$$\n", "\n", - "It rewards models that put probability where the data landed and penalises those\n", - "that spread it thinly over parameter space they did not need — an automatic\n", - "Occam's razor. The ratio of two evidences is the\n", - "[Bayes factor](https://en.wikipedia.org/wiki/Bayes_factor), and it is how much\n", - "the data shift our relative belief in one error model over another. Nested\n", - "sampling computes $\\log Z$ directly, which is the main reason to use it here.\n", + "Because it's an average, it rewards models that put their probability where the data actually landed, and it penalises models that spread probability thinly over parameter space they turned out not to need. It's an automatic [Occam's razor](https://en.wikipedia.org/wiki/Occam%27s_razor). The ratio of two evidences is the [Bayes factor](https://en.wikipedia.org/wiki/Bayes_factor), and it tells us how much the data should shift our relative belief in one error model over another. Nested sampling computes $\\log Z$ directly, which is the main reason we're using it here.\n", + "\n", + "There are two practical things to keep in mind. First, a Bayes factor depends on the priors, not just on how good the best fit is, so the prior widths are part of the claim we're making. That's why we stated them explicitly above. Second, evidences computed in different comparison spaces aren't comparable as they stand. The log transform stretches and squeezes the data axis, so the densities pick up a factor from the [change of variables](https://en.wikipedia.org/wiki/Probability_density_function#Function_of_random_variables_and_change_of_variables_in_the_probability_density_function). Adding `problem.log_jacobian()` accounts for it and puts the log-space rungs in the same units as the linear-space `E0`.\n", "\n", - "Two practical points. A Bayes factor depends on the priors, not just the fit,\n", - "so the prior widths are part of the claim being made — this is why they are\n", - "stated explicitly above. And evidences computed in different comparison spaces\n", - "are not comparable until we add `problem.log_jacobian()`, which is what puts the\n", - "log-space rungs into the same units as the linear-space `E0`. `compare_logz`\n", - "returns a tie unless the difference exceeds twice the combined sampler error,\n", - "because differences smaller than that are bookkeeping, not evidence." + "To compare two rungs we'll use `compare_logz`. It calls the result a tie unless the difference is more than twice the combined sampler error, because differences smaller than that are bookkeeping noise, not evidence." ] }, { "cell_type": "code", - "execution_count": 10, - "id": "3d7ce6ed", + "execution_count": 14, + "id": "d509f59f", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:51:14.358510Z", - "iopub.status.busy": "2026-09-12T03:51:14.358298Z", - "iopub.status.idle": "2026-09-12T03:51:14.364140Z", - "shell.execute_reply": "2026-09-12T03:51:14.363340Z" + "iopub.execute_input": "2026-09-14T22:24:35.400823Z", + "iopub.status.busy": "2026-09-14T22:24:35.400702Z", + "iopub.status.idle": "2026-09-14T22:24:35.403769Z", + "shell.execute_reply": "2026-09-14T22:24:35.403181Z" + } + }, + "outputs": [], + "source": [ + "logz = {\n", + " name: rx.diagnostics.logz_summary(\n", + " results[name].logz[-1] + problems[name].log_jacobian(),\n", + " results[name].logzerr[-1],\n", + " )\n", + " for name in problems\n", + "}\n", + "best = max(logz, key=lambda n: logz[n][0])\n", + "comparisons = {\n", + " name: rx.diagnostics.compare_logz(logz[best], logz[name])\n", + " for name in problems\n", + " if name != best\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c4e6ba59", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T22:24:35.405174Z", + "iopub.status.busy": "2026-09-14T22:24:35.405054Z", + "iopub.status.idle": "2026-09-14T22:24:35.408386Z", + "shell.execute_reply": "2026-09-14T22:24:35.407842Z" } }, "outputs": [ @@ -522,52 +577,45 @@ "E0 211.36 +/- 1.03\n", "L2y 216.26 +/- 0.85\n", "Lgp 222.30 +/- 0.95\n", + "Lgpn 225.78 +/- 0.88\n", "\n", - "best rung: Lgp\n", - "Lgp vs L0: dlogZ = 9.24 +/- 1.34 -> a\n", - "Lgp vs E0: dlogZ = 10.93 +/- 1.41 -> a\n", - "Lgp vs L2y: dlogZ = 6.04 +/- 1.28 -> a\n" + "best rung: Lgpn\n", + "Lgpn vs L0: dlogZ = 12.72 +/- 1.29 -> Lgpn favoured\n", + "Lgpn vs E0: dlogZ = 14.41 +/- 1.36 -> Lgpn favoured\n", + "Lgpn vs L2y: dlogZ = 9.52 +/- 1.23 -> Lgpn favoured\n", + "Lgpn vs Lgp: dlogZ = 3.48 +/- 1.30 -> Lgpn favoured\n" ] } ], "source": [ - "logz = {\n", - " name: rx.diagnostics.logz_summary(\n", - " results[name].logz[-1] + problems[name].log_jacobian(),\n", - " results[name].logzerr[-1],\n", - " )\n", - " for name in problems\n", - "}\n", "print(f\"{'rung':5s} {'log Z (linear-space units)':>28s}\")\n", "for name, (mean, err, _) in logz.items():\n", " print(f\"{name:5s} {mean:20.2f} +/- {err:.2f}\")\n", - "best = max(logz, key=lambda n: logz[n][0])\n", "print(f\"\\nbest rung: {best}\")\n", - "for name in problems:\n", - " if name != best:\n", - " v = rx.diagnostics.compare_logz(logz[best], logz[name])\n", - " print(\n", - " f\"{best} vs {name}: dlogZ = {v['dlogZ']:6.2f} +/- {v['err']:.2f}\"\n", - " f\" -> {v['verdict']}\"\n", - " )" + "for name, v in comparisons.items():\n", + " verdict = {\"a\": f\"{best} favoured\", \"b\": f\"{name} favoured\", \"tie\": \"tie\"}\n", + " print(\n", + " f\"{best} vs {name}: dlogZ = {v['dlogZ']:6.2f} +/- {v['err']:.2f}\"\n", + " f\" -> {verdict[v['verdict']]}\"\n", + " )" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 16, "id": "7ea27b55", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:51:14.365937Z", - "iopub.status.busy": "2026-09-12T03:51:14.365747Z", - "iopub.status.idle": "2026-09-12T03:51:15.026051Z", - "shell.execute_reply": "2026-09-12T03:51:15.025293Z" + "iopub.execute_input": "2026-09-14T22:24:35.409747Z", + "iopub.status.busy": "2026-09-14T22:24:35.409626Z", + "iopub.status.idle": "2026-09-14T22:24:35.964983Z", + "shell.execute_reply": "2026-09-14T22:24:35.964396Z" } }, "outputs": [ { "data": { - "image/png": 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Rvg4X5SmzuECnR7dR10i2egYAAKhvhA7wK0fDhsOfPaKiPRsq9P+r8yD92u5MSVK3oiwNa9NVO8NitCXrkHbmZsgtt+fcGVt+1O68TL038AqFWYMa7BkA1I69uED/WLdIyw7s1OGifGWVVNwRwCSTbo9P1BO9h/F5hl/aZg1V4qKZcjgcsljK//MwwRarWWeN81JlAABfR+gAv+B2u5X3y2Idnv+Iinavq/Sc1VHt9HbcGZKkYGep/vXLp+q4yq7w3qMVe81slTaL0facw1qa9rvuXbdIJS6nFuzdrMFfzdIXQ65T69CIhnwkADVQ4nRo7Pdv64cDO6s9zy23Zmz5UZ+nbNHrZ1+qc2O7NFCFgPdVtzVnsj2tASsBADRFhA5o0txut3J/+bIsbNi1plxfSJcBihnxD5ksgcrI3KeH9u6Q2+WSJN2fs0cdC+2SpNwNC5S/bbliJ72oHgOu0OnRbdQ7pq0u+f5tZRYXaF16qvotfEGLLrhe3W2xDf6MACrndrt108pPPIFDqMWqjs2i1SK4mVoEh3n+63S79fzm5cp3lGhXbobO+2q2buzaX8+dOUoRgcFefgrAeE9kl00d7HjTq8rIyFBMTIynL3HRTG+VBQBoIggdqpCUlKStW7dqypQp3i4FdeR2OZXz3k0q2vBJufaQzv3U4pJHFdb9Qs+OFDcve1/7jgQOw9ueqocm/0t5yV8qbc4NcmTtlyvfrn2vTFLO2nmKvWaWBrburJUjb9PIb9/Q77np2pufpbMXvaR5516tC9qe0uDPCqCiZzZ9rzd3rJVUFjgkjbhVvWLaevrdLpfyfl4o+7LXNLSkSPdHn6IkS4gk6ZVtq7Rw53q90KmHRvceqYAQRjIBAADUBSvgVSIpKUmXXHKJ2rVjdwJfdvDDe8sFDsEd+6jdXYvV4aGVapYw1BM4fPJHst7bVba+Q0xQqN5IvEwmk0nhp49Q56d+VWTiZM81cjfM185/nqbDnz+pLiHhWjnqNp3dsoMkKae0SMO/fV1vbF/dYM8IoHJf7N2sf67/UlLZeg0fDJpULnAozdir3Q+foZT/jlbezwsVvWWJZie9rMe2fq1mjmJJ0j5HqcZtX6/LX7hSKfMeVGnGXq88CwAAgC8jdDjO0cDh3Xff1dChQ71dDurIkXVAmV8/L0kyWQIVd+vH6vjwaoWfPtwTNhz1xvY/p128etalij1mbYaAMJva3vCm2t25UJaoNpIkZ36mDn/yoPb+60LZHMVaMvRGTejYs6zP7dKUnz7WR7t/NvYBAVRrxpYfPa+n971IF7c/zXPsdjmVOusKFe39udx7TJLGHfhVn695S4PS/1wD4pOWXTUm5Xetubeb9j5/sXJ/WSy3y2n0IwAAADQJTK84RlJSkkaNGqW5c+dq6NChys3N1b/+9S8tWbJENptNt9xyi0aOHFmra+bk5CgnJ8dznJbGgkwNIXvNR5K7bLpE81H/VESf8VWem3fkp5ommTS2Q0Kl54T3HKnQp37Vwbl/V1bSW5LLqYLtP2r3E2ep/V2L9d6gK9QxPFpPJ38vSbo+6WP1sLXRqVFswddY8dls2vbmZUmSWgSH6fb4c8r1ZXz1vAp3/CRJsrbsrNjJsxV26mA58zPlyDmsk3IPa0HuYX1wYLfuST+gfJNZG6LiNLH3BL28ab46/fyFrM1PUtTgqYo+9yYFNItu6McDAADwGYQOx8jLy1NRUZGWL1+uvn37atCgQWrRooXOP/98LVu2TKNGjdKLL76oW2+9tcbXnD59uh599NEK7Xa7XSEhIfVZvuGO/Qatscv86V3Pa1f8SGVkZFR5bklpqSTJbFK150lS0JhnFd1nkrJev0Ku7DSVpG3Trkf7K2rKe7q7Qz+lZmfqnT0/K89RrEuWzNHX51yjMEtgrev3pd/rYzXGuo9dEO1YTemzeazG+DWojfqq/2BhriSpuTVUmZmZnnbHga3KmPdA2YE5QOETZ6m4dS8VZ2VLCpBCWpf9aild2lnqln1QV6z6UPuL85USYtOVvSbq+c2fq3/6Hh2e94Ayvpsl2y3zZYnpUO/P4E0N8QxVfTYBAEDTQuhwjGHDhmn+/PkaM2aMPvjgA1100UV64YUXJJWtgj5t2jTdc889GjdunGJja7ZLwV133VVuMcq0tDT17dtXNpvNJ//B5Qs1lxz+Qwf/KFs8ztKul1p1PbPa880BAWX/NZlr9nwxgxXzyBrtnT5SxSnJcuUdlv3l0Yq7+QO9OvBy/brosDZm7tPW3HQ9sG2p/nfOxApTOmrCF36vK+MrdTe1z+ax/L3+Ikepco+MYGrTLMpzPbejVLtfuF1ylkiSmo+6Xy17X1DttQbFxGhd67t18XdztC49VTnWYN14+qV6ePu3Gpu2SS57qrJnXaIO9/+gwJad6u0ZGoOm8AwAAMD7WNPhOEeDh8OHD+vpp5/2tJtMJj399NMqKSnRqlWrany9iIgIxcXFeX7VNKxA3eWs+cjzOrjXJX95vktuSZK5FsGANTpOHR74UWHdL5QkuUsKlfLCJSpY+ormnXe1Io9ss/fuzg16jYUlGyU+m03XoaI8z+tWIc08r9MXPq2iP9ZLkoLan64Wox+q0fViQyO0bPgtGntS2fQrh8mkh7peqP/2HCeXJEdmiv54epBKDu6s/kIAAAB+yK9Dhz179ujWW2/V+eefr1dffdXTPmzYMC1fvlxhYWHlzjebzTKZTGrfvn1Dl4payFk91/M6uOeYvzzf5S4LHQJqORohICRC7e9cqKiB15U1uN068O5tCl34lN4++3LPebet+kzr01NrdW0AdXd0aoUktQwuCx0K92zU4c8fL2sMsKrtDf+TqRZTn0Itgfr43Kt0b8K5nrZXozrowcTRCjzZpICwVKXO6quC7Z/JXZwh95G/VwAAAPyd306vWLdunUaNGqXhw4erdevWuvHGG3XSSSd5dqzo3bt3hfc8/vjjGjhwoM4444yGLhc1VJy2TUV7NkqSQk85RwFHdpyojvPIgpPmYzI4V0m2SjN/kdtRIDkL5XYWyu0olNtZILejUOaQVgrpcKlMllDFXve6rM076vCnZT81zfxquvqbA/SP7oP13K8/qMTl1Pilb+uX0Xcr4sgICADGKT/SIVxut1v7X5ssOR2SpJaXPKrg9j1qfV2zyaynzxiuuIOf685DoXIoQAssXdSi7SA9ZvpBUqayV4yVJB20hCmwzYUK7Xy1guJGyBRQ+7VdAAAAmgK/DB0cDoeuvPJKvfzyyxo7tuwfiIcPH9bu3bsrPXfDhg2aPn26tm/fri+//LKhy0UtFO/b7HkdEFGznSMCzWUfg0JnqfJLixVcmqn0hX3kKthX7fty1/1DYQn3KqzrTWox+kFZm3fQ/jeulZwOZSz+l+69tb9Wteqk5Qd36Y88ux7c8JVe6D+mzs8GoGZcx4wy+CVzvxyZKSpOSZYkBZ/USzEj/l7na+dueEDjDr+qGJ2kyRqjEln0us7Qqe50XWH61XOe25Gv4r2fqXjvZzIFRSuk4wSFdL5a1uZ967TGCwAAgK/yy+kV3377rbp37+4JHFwulw4ePKjPP/9c8fHxuvHGGz0rd+fk5GjOnDkaPny4Vq9erVatWnmzdPyFsNMuUEBk2dcod90nKjmyoGR1+jRvJ6lsxMNPh3Yr68er/zJwkCRX0SHlrr1bh+Z1VN7m6YrsN1ax18z29B+ac53e6n6Owq1BkqSXtv6kdekpdXksALVwbmwXtQ4JlyTN3f2z1uZle/os0e1kCqhb3l6w4y3lb3pGkjTYvE+vxMd7+u43DVNSWnsVpUqlmQEyh/w5yspdnKmCrS8rY1F/Hf7sVOX+8qRcJdkVrg8AANAU+WXoEB0dreuvv95zfPfdd8tut2v06NG6+eabNW/ePF111VWec2fPnq1rrrlGVqvVWyWjhgJCwtVy/FOe49z5D8jtclX7nnNjO3tef5P8nkrSlpRdK6y9wns/pYg+zytywCuKPOd/ihr8sWznLVBI56slU9nH59jwwRqTpcizryhrL8iWe871evz0stXxXW63blrxiRwuZ70+M4DymlmD9ETvYZ7jv//6g3Rk/QZHVlqdrll8YJmyV071HEcOmK3J/abqH90HS5JK3W5Na32pdu9tprxfnJJ1iqKHfq+QLtfKZPlzMUtnznblbXxQ6Qv7yJG1tU61AAAA+BK/DB369eunESNGSJL27t2rjRs3av369brxxht12223afbs2fr888+VlZXl3UJRJ1GJkxV8Ui9JkmPvBmWvfK/a8we17iyTyoY7/3BgV1mjKUBRgz5Qsx73K+y0OxTadapCO1+lkA7jFdz+YkWd87ZaXLK1Yviw7h5ZIn9QcMcOkqSi3Ws1/pcF6h3TVpK0PiNV969fbMBTAzjW5C591MNWtiPJikN7tKRd2da5juwDtb6WI2eH7EvHSq5SSVJY978r9JSy4PqpM0ZoeNtTJUmHJf2t+xgVmS3KWjJTVlsfRSXOUasJBxU18D0FtR3m+fvCmbND6Yv6qShl0Yk+KgAAQKPml6HDsdq3b68ffvhBzZs397R17txZFotFgYEs/OWLTGazWl35X8/xoY/uk6s4v8rzo4NC1dNWNiXjZ7VSntuqZj0fVWDLs6q9jyXi5MrDh8L9Cul8WNYWZX9+sr+doRnRLRR0ZEj3v39dpnl//HJCzwigegFms/7T9yLP8b/b9lSJKUCO7AO12lnCVWxX5pJRchdnSpKC2o1WeO8/t1MOMJv1/qAr1TWyhSRpc3grPdz1AjnyM2X/4TVJkskSqpBOVyj6gi/VYuxOWWPKAhB3aY7s312kvORn2O0CAAA0WX4fOlRm5syZuvLKKxUaGurtUlBHYV3PUUSfSyVJjqz9Sl/0XJXnut1uDXBulyQ5Zdb66JFqlnBfje91bPgQ2PrIdnrOfDXr7pK17PsQtXjvNv33tHM877n2x4+0NetQLZ8KQG2c3+YUjWrXTZKUYgnRe3G9JGepnPmZNXq/21Uq+w/j5cwp+/vBEt1LUQPfk8kcUO68qKAQLRhyrSKsZbvTLGwVr7fanan0hU/JkZdR7lxLeAfFDF+ukE6Tjt5FuRvuV9byK8p2ywEAAGhiCB2OUVxcrDvuuEOrVq3S888/7+1ycIJaXv6cZClbxNH+/Sy5j2yXd7yi3XPVL+c7z/FPMeMqfFNRE5aIkxV9wZcK7nDpkRaHwk6TgtpKroIsXbj4SV1/cl9JUp6jWGO/f0vFVdQEoH7868yLFHBkFNLsk/orJTiyxus65CU/rZK07yVJ5tA2ih7yuczWsErP7RrZUh8MutIzVWt6p3O0IiBEhz/9vwrnmiwhijznfwo/89+eEVJFu+cq/ctz5Mz/60VsAQAAfEmTDR0cjtp9M/f++++rZ8+ekqTVq1fLZrMZUBUaUmCLDgpKKFu7w5l7WFnL51R6Xmn6GvVXqoJU9mdm1u/JWnHwjzrd0xQQpKiBHyi0681lxyYp9JSyX4U7V+pfsR11RkycJOm37EOavnlZne4DoGZOjWqpm7r2lyTlWYJ0w+njtXfPzzV6b2n6Gs/rqHPeU0BYXLXnj2jXTU+fMVyS5DKZ9X9dL1Taxi8qPddkMqlZ97sVff5imayRkiRHxgalL+qrkoM/1qg+AAAAX9AkQ4cVK1YoPj5e27Zt+8tznc6ynQQmTJigzZs3a8aMGQoPDze6RDSQsMHTPK8Pf/ZwpWs7BLa5UOGmEt2hVZLKts6csOxdZRbXbaizyRygiP4vqVmvJzxtQW2lZj2k4rXv6t2BV8hy5KebT/yyRKn5WXW6D4CaeeqMEeoRGiFJSgmJ0iXbNyiruPAv3xfQ7CTPa3NgZI3u9Y+Ec3Vui46SpNSQKD3eIl7Oaj7jQW2HqvmoNQqIOEWS5CrYr4wvByl79d/kKs2r0T0BAAAasyYZOkybNk1Op1ODBw+uNnjIzs7WgAED9P7778tsNstsbpK/HX7N2q6nIvpNkFS2an3GVxWnzQS1OV+moGjdqjUaaCob2pySn6Vrf/ywzou7mUwmhZ/+gKIGfywFhJTVEi05s15W54B83R5ftr5DgaNU965j9XrASBGBwfpq2C1qV5glSdoisy7+bo4KHaXVvi+gWSfPa0furhrdy2Qy6b89RyhKZX93zI/trrnJ31b7HkvkKWo+cqUC21xwpMWtgt9eVPqCHio+Mr0DAADAVzW577JXrFihwMBArV27VrGxsdUGD2FhYWrfvr0ee+wxlZSUNHClaCgtxz8pBVglSRmLn5Ujp/wCjiazVcHtxyrA5NaL7s/V+siuJZ+nbNZLv/10QvcO6TBeMcOXy+0qW1vCHOxQ+vzeuv+kWLUKKRtR8/6ujfrxQM2+oQFQN7GRzfVe3l61KC4bPfDjwd26/Id35HA5q3yPJfzP0MGZV/PPaJuQCE1vEes5vnX7Wu3Lz672PeagaEVf8JUi+s+SydLsyD13K/PrIcpecZNcJTk1vj8AAEBj0uRCh0OHDumOO+5QdHS0lixZUm3wYLFYNHfuXC1fvpztMZuwwJadFD3kFkmSqyhPhxc8XuGckI6XS5JamAo0OybNsxjc/eu/POHpD4HNz1SzHq/JcXSktDNLpd+epye6dPacc9vq+XK6XCd0HwDV69plgF5N/kQRpUWSpC9StmjKTx/L5a78sxdwbOhQw5EOR112cj9dfGCzJCnL5dLkpLlV3ucok8mssFNvUosxmxXY5kJPe8H2V3R4QXcV7/u6VjUAAAA0Bk0udBgzZowmTpwoSdUGD/v2lQ2jt1gsatmypVdq9QV5m7/TgQ/uUdHeX7xdyglpfvGDMoeUzem2L52tkoO/l+sPbD1Y5uCy/S37pc/Traf2k1S2y8Rtq+af8P0jzrxKlsipKs0qO3Y7cjV88xT1jSj7ieYvmfv16vZVJ3wfAFUL6zZYp+Sn66VNnyn4yPSHt39fp3+sXVTpVKqAZh09r2s6veKooLbxemDH92pbWDbCYcn+HXphS1KN3hvQrL2iL/hKkWe/4Vlk0pWfosxvhyl348N1nvYFAADgDU0udDheZcHDjBkzNHLkSLn4yXK1ilJ/1d7nzlfeL4u066Gechz6/a/f1EhZwpsrZuS9ZQdOh9LevkXuY+Zzm8wWBZ80XpLkduTpn5GHFRda9o/9+Xt/1RvbV59wDa0mvCBH9hkqOVx2bHYX69HcV4+MqZAeWP+l0goYQg0YJaRzf5ksgeqds1//3bvKs5XmfzYv079+/aHC+ebACJmDmkuSnNlb5f6LkQrHsjSLUWRYlJ7e+qXMR0KCe9ct0s6c9Bq932QyKfTk69RizGYFxY30tOf98pjyfn60xnUAAAB4W5MPHaTywUO/fv300ksvaeHChSwc+RccmamSJNvgGyRJrrya/WO5sYq58A5ZbG0lSfmbv9X+N6eW+4lhSOerPK/d6+7Q891O9RxPXTFPn+3ZdEL3N1uDFHfLPBXuilBx2UAbna40XaGy69pLCnXzyk/4KSZgEHNgiEJPHSxJSty1Qv+1/vlZu3fdIn28u+KILkt0T0mSM3+vCrbOqtX9rM076Izsfbo+ZZ0kqcTl1Iu1XCcmIKytbEO+UES/mdKRkCQv+QmVpq+v1XUAAAC8xW++646OjtbYsWPVokULLV26VHFx1e+3Dims+wWKOudaHfzoXrUY+7gCO/X3dkknxBwUqrhpH8kUWLabRHbSWzr08T89/YEtBygs/o6yA1eJEjffrHu79io7dLs14Yd39d3+HSdUQ2CLDooaOEUF26WilLK2B7VMrVW24MOCvZv14e6fT+geAKrWasK/ZbIGS5LO/ebfeiruFE/f1T9+oLWH95Y7P7zXY9KR8Ui56++TM698f3XMR+4zZc8qNbOULSY7Z8ca5ZQU1apmk8mksG7TFN776bIGt1NZSZPldrIAMgAAaPz8JnSYPXu23n77bQKHWjCZA9Rmyhx1e6NELUY/6O1y6kXoyWcp7pYPJXOAJClj0TPlttEMP/PfCmp3sSTJXZyhOw88oaldeksq+ynl6O/e1OrDe06oBtugKZKkwt8lR26MIk3FelZ/bql326r5Si8uOKF7AKhccLsEtZo43XN8yRf/pxs79pQkFTkduvi7N7U3z+7pD2w5QKHd/iapbOpV9sqbajwayXQkaGjmLNHkzmV/j+SWFuut39fWqfaw0+6WtXnZejOOrF+Vl/xEna4DAADQkPwmdEhMTCRwqCOTyfTXJ/mQ8F4Xqc21r3mOD35wl+xLX5FUFrREDXxPlugjIxxytuqJwnd0eYcekqR8R4lGfPuGNtsP1Pn+QW26KeTksyVJuesyZG1xvi4w7dI4bZEkpRfn695N39T5+gCqZzvvJoWfcYkkyZV9UPesfUcXxJ4sSTpQmKuLlsxRbumfoxHCez+hgLCTJEnF+75U0a73a3Qfk+XPXZGmdTnD8/rFLT/95U4WlV7PHKDIxDclc1mYkZf8lEozNtT6OgAAAA3Jb0KH7t27EzjAI2rgtWp5+XOe47S3blJW0tuSJLO1maKHLJA5uJUkybH/K80M/UXD2naVJGUWF+iCr1/VtuxDdb7/0dEOklSacbICWw3UY1qqFsqXJC3Y/5s+/ePE1pAAUDmTyaQ2170uS3Q7SVLJr99oliNLp0aW7WSUbE/Tlcve92xja7Y2U+RZr3ren73mdjmLDv/1faxBntddQpppRFzZOjG/56bry9StdardGtVN4b2OLCTpdior6VqmWQAAgEbNb0IH4HjNR/xdLcY84jne//p1yl79oSQpIKydbOd9JpnLflJZ+tt0vdPepLNbdpAkpRXmqP/CF7U0rW47ekT0vVTm4HBJUvbKDxSZ+L6aR56kZ7TEc84tKz9RJtMsAEMENItW25ve8yzOWPzpg/qgdTvFBIVKkr5I2aJ71y3ynB/U9kKFdL5aUtnUq5zVt//lPY5Or5Akt6NEf4s/x3P8z/VfquiYHXRqo2yaRV9JksOerLzkJ+t0HQAAgIZA6AC/1nzM/ylmxD/KDtwu7Zt9pbKS/iepbC535Fl/TsMoXXOL5nVrqz7Ny346mlVSqKHfvKa3d9R+frY5KEyRA66QJLkKspS/JUnRF3ypkSE5ukjbJEkHi/L08MavT+TxAFQjrOs5ajHm4bIDp0OBr12lt8ObKfDImi//2bxM965d6JkKEdFnuszBZaMhinZ/oILf/1ft9U0BVs9rt6NYF7Y5RWfElI24S7an6b9bfqxT3Saz5cg0i7JQNC/5KTnzU+t0LQAAAKMROsCvmUwmtbzsGUVfULZQnFxO7X/tGmV8VbbQXGiXqxXW/e9H+krlTrpcXyWcpLEnJUiSSl1OTU76UA9v/LrWW1026zXa87rkwA5ZwjvKdv5CPWVernAVS5Le37lBpS7nCT4lgKo0v/hB2c69sezA5VTHeffquZI/F5J87tcfNOa7t5SanyVzcIwi+s/09GWvmKLitKWVXtftdqtw1+qyA3OAAsKiZTKZ9NY5l3vOWXkCi9Jao+IVdtrdR27mUMnhlXW+FgAAgJEIHeD3TCaTWl05QzEj/u5pO/jB3Tr40f1yu1wKP+MZhZx8XVmHq1TFyy/TO12i9ffugz3nP/bzt7pq+QcqdjpqfN+AMJvntasoR5IU2LyP4jqP0zCVTdvILCnUD2k7T+DpAFTHZDar9dUvq8XYxzxtFyS9pmfTt8pyZOrFFylbdOqnz+m5TUsV0O4ShZ12V9mJrlLZl45VadaWCtctTtmkkrSyUUth3c5TQGikJOm0qNYKOTICYm9e1gnVHtgq0fPaUUkNAAAAjQGhA6AjwcPlz5VbXDJj0TPa9dDpyln5gSL6zSoXPGT/ME6PtXRo1oCxCjjyjcl7uzbowq9frfE6DEfXdJAkV2Gu53VY/O0aqe2e44/++OVEHg3AXzCZzWox+iG1u/MLmUMiJEmjfl2k17d9pdZHFoPMd5To3nWLdPqC6VrTZqqCTxorSXKXZMn+7XA5C9LKXTNn7cee1xF9L/vzXiaT2jeLkiTtybfrRFgiu3leO7J+O6FrAQAAGIXQAThG8xF/V5vr50hH5nQXp/6qfa9M0s77TpWz6AwFdypbSE6uUtl/GK+rrbu08Pzr1OzIgnHLD+5S3y/+q0//2PSX0y0CjnxzI0muoj9DB2v06RocE+uZYjF/90Y5mGIBGC685yh1fHiNAmPLdpnos3+TFvzwvG6ymBSgsq2Dt2Yf0gXfvKab3RcpPXqgJMmZv1eZS0bKVZonqWxqhSd0MAco/Iwx5e7T/sgop8ziAuWVFte53oBmJ0kBIZIkRzYjHQAAQONE6AAcJ2rgtepw/w8K/X/27ju8qfJvA/id1d20pbtQ9iyUvUH2EMoSEPUnS0EQUZaooCLwUhAQEQQFRAQRFAcIsmRJUZYsoayyV2kpXXSlI+N5/wgEStrSQtOTNPfnurhozjk5+T5NT5vceUbNdqZt2vhriFs9GvG/7YDMyThrPIQeKf8MxnPpEdgfNhpl73efvpKWiH57v0ejPxZgy61z+YYPj/Z00Gem5trnUnkEusA4rCJBm4OIOxxiQVQSHANroNLUf+He0Djnips2E+/snocNR1aiaerD3gw/3ziDFiktsdihGzRCCV3Sf7gXMQDCoIMu9nyuoRVKd59cj1He1dP09a2Me09dq0wmh9LT2NtBl3IBwlD44V1kHYQ2CyJbg2vhrc3+Zd+KlLo8IiKiYsHQgSgPLtVbo+Lkvag45RDcGvQybdelxCFp+xFk3Vbe3yKQengUqtxeg397jEH7gCqmY/9Luo2eu79Di62LsC+P0EDu5Gb6+tGeDgAgC3gevZwedr3+5dKBYmoZET2JwlmNcu9sMM7zoDBe61U1ifjuxI+Ye24rfLONPRoydFrMyg5BKwzHWhGKrOjtuLOmIdL/mGI6l7rpi2bnfzC8AgBuPkPoADwyxMKQA33a1Wc6F0nAYIDIpyebY3BdOJULLeGCiIiIip/yyYcQ2S+Xqs1RftwmZEWfQeKW2Uj5dx1g0CPzog4iB3CuZDwu7fgHcEm6jN1dl2LvnSuYcuJP08z0/8bfRPvtS/Fh3Q6Y1qALlHIFsu9cQvzvU02P83jooBPAFXULIMt4e0c0x2sTlaQH8zx4tR+J9NM7kH76T2Sc3oGwu1Fom3gVX1dsgbVlG0AnVyAOrngPXbAHlbBQ/yccZDnIkQGQKeDe6AWzc6c9MqTiWXo6CF0mDJkPe1/oUi9C6VH9qc9H0pDJFaj08X6pyyAiIrIYhg5EheBUrg7KvrkGvv1mIGnHF0g/sxNZ1y9A6ACXasZjsq4tR+bVfWjV+Tf80+k17Ey4hSknduB4YjQEBGZG7sG2G5F4NfkGnju0Cm7aTNP5nas0M329784VvBmxClGZDzsiVXZ2LrG2EtFDSrUfPFsNgmerQRAGPbKuHUd65HZMPb0dLx9dhcUVW2Krv7G3wZ+ohm7wxorAP1DZKREK136moRU5eh2OJURj861zmHdmn+n8NdS+T1WXXhOD5L/6QJtw1LhBpoTCvUrBdyIiIiKSAEMHoiJw8K2EgIFfAgD06UlI+fdn3Ns/GU7BKZDJAZnsIuLW1UXmJaCiwQU/qv2wNqg+ZntVQo5Mjv9S4vGf3AVOzd9Ap4RL6JcWi7D2w+HTbgTuaFIx8egWrL16wvR4chgwwkuP2d0mSdVkIrpPJlfAuUpTOFdpCt8XpqKCNhudszOwJ+YiXj26BQk5WbiKMgjD/zDXaxcCVOdx/M9PcNjgh8MJt5Gp1+Y632eNe+C5gMpFrkObeAJJe3rBoLltrMvBE17tfoXKs9YT7klERERU8hg6ED0lhVsZlOk4Cp6thyBh21vQJX8PmRxw8AFU3oD2rgZZN6/jpcjrqOPmh+nVO+OsOgAAkKVQYYt/CLb4h6BcYhK6/bsRP187hVRtlun8TXAbn5dzROtO30Em4/QrRNZGrnIEVI7oUr05TgTVRP+93+NIwi1o4IC3EQZoAcRqAFzPdT8ZZJhSvxMmhrYr8mNmXl+Pe/8MAvTGnlIKdTWU6bgZSo8az9weIiIiIkvgOxmiZyR3dIHfC6vg1XEbIHMFAMhkgIM/oG4CuNWXoZ7qLn45sRYbI3/Dm44O8HN0Nd0/WpOC5Rf/NQUOPsjAQmzH9uBMtO64nIEDkQ0IdvPE391HY2SN5nnur4IkvKq4gG8quOBan3cwvUHXIp1fCIG0UzNxL6K/KXBwCGgPn7DDDByIiIjIqrGnA1Exca7QDY7/i4XmwjfIOPeFqeuzyktA5QUo1CEIrjoEXb1q4XOXCtidqsPqa6ex+dY55Bj0kMOAITiF93EAnr7NUKbdOsjkKolbRUSF5ahQYmnL/qjn4osNcVGo4eyEJprDaHDnR/giFTAAuLkFspgvkOBRE3KXslC4loXCpSzkzkFQuJaFTKWGITsRhqx4GLISjP9nJ0CfehE5cf+YHsul+giomy/m7wgbkHJoFLTJp/PeKQsGhKJkCyIiIiphDB2IipFc5Q63Ou/CtdY7yLz6IzLOfgbdvXMAAH3qOaSf+MB0bBMAzZx8McurGv5KF6iZfRE1ZIlQ+bYAGq+GTOkkUSuI6FkMCK6DUfXb3r81BLq095FxejY0l1cCBi2ELh3axGNA4rGin1wmh7rJF3Cp9Q5kMlmx1k2WoU0+DV1yJJRedc13CgWEYG82IiIq3Rg6EFmATOEAl2pD4Vx1MLKjtyL99Bxo7x4wO86QFQ/3rHj0BgAZoPSqhzKdtiI53VDiNRORZSjdK8Gj5TK41v0QGWfmIjt6G/SaGMCQU6TzyF3KwaPlN3Aq181ClZKlKL3qwqe7+bKYhqUjJKiGiIioZDF0ILIgmUwOp+CecAruef/TrrPQp1+DLv0a9GlXoU+/Bn36TUDooPSsgzJddkLu6AWkJ0pdOhEVM6VbBXg0/wqAcY4GQ3YCDJoY6DW3YdDchl4TA6FNg9zRB3InH8idfHN9LXPw5BwvREREZHMYOhCVEJVXKFReoWbbhUEHQ9Zd45sKjs8msgsymQwKJ18onHyhKlNP6nKIiIiILIahA5HEZHIlFC5BUpdBREQW8F5qJZzTOUO1dbHZvgsqF9TQaiSoioiIqOSwnyYRERGRhZzTOeOcziXPfTW0GtTQZpZwRURERCWLPR2IiIiILChEqcH+sLfNtl8Lby1BNURERCWLPR2IiIiIiIiIyCLY04GIiIjIzmXfisS18NbQ6XRIVT58eZjlEwK5o6uElRERka1j6EBERERkx5zKma+s9IDI1sBQgrUQEVHpw9CBiIiIyI4FDl1i+joxMRHe3t6m27KlI3BB5YLWeay+AQChXoFY0rKfxWskIiLbxdCBiIiIiPJU0OoakcmxJVgJERHZKoYORERERJSn8JRrAIBKb35jti+/3g9ERESP4uoVBRBCSF0CERERERERkc1i6JCHEydOoGnTplAoFKhcuTK++OIL6PV6qcsiIiIiIiIisikMHR5z48YNPP/88xg+fDiOHTuGl156CZMmTUKnTp2QkpIidXlERERERERENoNzOjxm6dKl6NatG0aMGAEAaNiwIXr37o0ePXqga9eu+Ouvv+Di4lLo86WmpiI1NdV0OzaWky4RWQNem0RERERElsfQ4TFJSUnIysrKta158+bYtWsX2rZti/Hjx2PZsmWFPt/8+fMxffp0s+3JyclwdnZ+5npL0qNv0GyFLdYMsO7i9OjSb48qTdfmo6zxOSgKW68fYBsKK79rk4iIiEoXhg6Pad++PYYMGYJz584hJCTEtL1Bgwb46quvMHToUEyaNAmVKlUq1PkmTJiA4cOHm27HxsaiadOm8PLysskXXKy55LBuyypt1+ajWL/02AYiIiIiI87p8JgXX3wRtWvXRv/+/ZGYmJhr38CBA1GuXDns27ev0OdTq9UoV66c6V9gYGBxl0xET4HXJhERERGR5dl1T4e4uDisWrUK8fHx6Nu3L1q2bAmFQoFff/0VrVq1QseOHbFt2zYEBQUBAGQyGdzd3eHl5SVx5URERGQtYleNQlb06Tz3icCKgJyf8RARkf2y27+CBw8eRJ06dbBt2zZs374dzz33HLZu3QoAqFKlCvbu3YuUlBQ0aNAAy5Ytw9mzZzF58mTI5XJ0795d4uqJiIjIWmRFn0b2rcg89wmDHjAYSrgiIiIi62GXPR3S09PRv39/LFu2DH379oXBYED37t0xceJEhIWFAQBq1aqF//77D9OmTcNHH32E1NRUdOvWDbt27YJKpZK4BURERGRNHIProtLH+822y1YMlKAaIiIi62GXocPatWvRtWtX9O3bFwAgl8sxbtw4dOvWDRkZGXB1dQUAeHp6YsGCBViwYAEMBgPk7B5JREREj1F6XoFclYGEba3Nd8qCIfTAtXDzfdm3IuEYXLcEKiQiIpKOXYYOcXFxuWatB4CqVasCQK7Q4VEMHIiIiCgvclUGZCpN3jtlKkCW9y7H4LpwKhdqucIeo0uOzDsYAaDyCoVHiyUlVgsREdkPuwwdpkyZYrbNxcUFACCEMG07duwYGjduXGJ1ERERkW0SWhf4dDcfXuGwdTEAoNLw1SVdUi4qr/zDDV1y3vNREBERFQe7DB1kMvOPHB5s0+v1AIB58+Zh+fLl+O+//0yBBBEREZEtKqgXQ369Hx7IvhWZ5/CQLJ8QyB3Ne4cSERE9yi5Dh7w8GD4hhDAFDnv37mXgQERERHaroOEfIlsDrstBRERPwtDhvgc9HT7//HNs3boVe/fuRVBQkMRVEREREUkncGj+PSRkS0eUYCVERGSrSu3siDqdrkjHKxQKAGDgQERERERERFRMSmXocPDgQYSEhODChQtPPPbBHA5eXl6YOHEiAwciIiIiIiKiYlIqQ4fRo0dDr9ejXbt2BQYPKSkpaNGiBX788UfI5XJ89tlnDByIiIiIiIiIikmpCx0OHjwIBwcHHD16FIGBgQUGD66urihfvjz+7//+Dzk5OSVcKREREREREVHpVupCh7t372LcuHEoU6YMdu/eXWDwoFQqsW7dOvz9999wcHCQoFoiIiIiIiKi0qvUhQ59+vTBK6+8AgAFBg+3b98GYAwe/Pz8JKmViIiIiIiIqDQrdaHD4/IKHhYsWICwsDAYDFxdmoiIiIiIiMhSlFIXUBIeBA+dOnVCs2bN4Ovri71790IuL/WZCxEREREREZFk7OZdd5kyZdC3b19T4FCuXDmpSyIiIiIiIiIq1ewmdFi6dCm+//57Bg5EREREREREJcRuQofWrVszcCAiIiIiIiIqQXYxpwMA1KlTR+oSiIiIqBSarGiNKFkZOGxdbLYvMjkWdb0CJaiKiIjIOthNTwciIiIiS4iSlcF5WZk899X1CkQoQwciIrJjdtPTgYiIiMhSaokk7A97W+oyiIiIrA5DByIiIqIniF01ClnRpwEAOp0OqcqHL6GEfzBkcoVUpUnqgsoFrfMYVgIAoV6BWNKyXwlXRERE1oahAxEREdETZEWfRvatSDgG1zXbJ5MrALn9jVitoc3Md19kcmwJVkJERNaMoQMRERHREyg9r8DBF3AMBnRaQKl6ZOddQKZ0kqw2qYSnXAMAVHrzG7N9+fV+ICIi+2N/sTwRERFREclVGZCpNHnukyldIFe6lnBFREREtoE9HYiIiIgKQWhd4NN9PxITE+Ht7W3aruKn+kRERPli6EBERESE3JNFPk6p1kDu6FLCFZUcXXIkEra1hk6rQ4Iq98tDlVcoPFoskagyIiKydQwdiIiIiFDwZJFyRxfIHUvnEAqVV2i++3TJkU91zpy7V3DeINB46Yg899d2dMb3ry18qnMTEZFtYehAREREdJ9jcF1U+ni/2faEba0lqKZkPNqL4fGhI0/b7mrpCdAbBJBH75AohROQnf/KF0REVLowdCAiIiKCcYUKuSojzzfa78arEaUoB9XWxdDpdFAqH76EikyORV2vwJIs1eqZVrYYax7g5Nf7gYiISieGDiVMp9MBAGJjbW/96uTkZGRm2tYnE7ZYM8C6i1tAQECuNwh5seVr81HW+hwUlq3XD7ANRVGUa7PLkjfg4Ols2YLkTYFsQHbZfBjFSa07AKCxPhk6nR56pcK0rwacUEnrgOjoaMvWVwIef+6TErKhu3ceMaubmB2brTwPkZOJhNluZvuEIRMyB2ek5XE/XVowLsk8UP+zvsVb/BMJALISfsySUnxt2/LKl4W6NomICksmhBBSF2FPjh49iqZNm0pdBpFduXXrFsqVK1fgMbw2iUoer00i61SYa5OIqLAYOpSwrKwsnD59Gr6+vjaVIMfGxqJp06Y4cuQIAgNtowupLdYMsG5LKMwnNrZ6bT7Kmp+DwrD1+gG2oahs8dosDc9xftg222SJtrGnAxEVJ/42KWFOTk5o0sS8q6GtCAwMtLnk2xZrBlh3SbP1a/NRtvocPGDr9QNsQ3Gy1mvTWr4/lsC22abS3DYism1yqQsgIiIiIiIiotKJoQMRERERERERWQRDByoUtVqNqVOnQq1WS11KodlizQDrpqdn68+BrdcPsA32oDR/f9g221Sa20ZEpQMnkiQiIiIiIiIii2BPByIiIiIiIiKyCK5ekY9///0Xv/zyCwIDA/H222/DyclJ6pKIiIiIiIiIbAp7OuThs88+Q1hYGC5evIipU6di2LBhUpdEREREREREZHPY0+ExGzduxFdffYVTp06hbNmy2Lx5M95+++2nPl9qaipSU1NNt3U6HTIzM1GtWjUolfz2E0mF1yaRdeK1SWQbdDod7ty5g4CAAF6bRFQg/oZ4zJw5czB+/HiULVsWAODl5QV/f3+MGzcOQgi8/fbbqFatWqHPN3/+fEyfPt1se2RkJIKCgoqt7pKQmppqczMj22LNAOsuTt7e3nluL03X5qOs8TkoCluvH2AbCsuWr83S8Bznh22zTcXZtvyuzcfduXMHwcHBuHXrFsqVK1csj21pSUlJKFOmjNRlFJq11HstvDUAoNLH+8323V5m/LkrOzLVrN6Ebcb7+XQ3v581sJbvrz1g6PAYjUaDQ4cO4Z133sGdO3fw1ltvwcPDAy4uLli/fj2+//57HD16tNDBw4QJEzB8+HDT7djYWDRt2hReXl6F/qVuTVhzyWHdllXars1HsX7psQ1Pz1auTWuqpbixbbapNLetuNjaon2s17JsrV5bxtDhMdOmTcPLL7+McuXKIScnBx06dMDPP/8MmUyGSZMmoW7dupg3bx6WLVtWqPOp1epSm6oT2TJem0TWidcmERFR6cKJJB/zwgsv4ObNm4iIiECNGjXwzjvvQCaTATC+EOrQoQNSUlIkrpKIiIiIiIjI+jF0yIO/vz+qV6+OGzdu4Pjx46bt6enp2Lt3L1588UUJqyMiIiIiIiKyDRxeUYAXX3wRkyZNQnp6OqpWrYrPPvsMnTt3Rr9+/aQujYiIiIiIiMjq2WXokJGRgdjYWFSsWLHAJX7mzJkDvV6POXPmQK1W491338X48eNLsFIiIiIiIiIi22V3wyvmzZsHPz8/VKtWDf7+/qZgIS8ODg748ssvkZaWhtu3b2PChAmm+R2IiIiIiIiIqGB2FTqsXbsWy5cvx7FjxxAXF4cxY8ZgypQpCAsLQ0ZGRq5j9+3bhyVLlkhUKREREREREZHts6vQYcmSJZg4cSJq1aoFPz8/TJ06Fbt27cKhQ4fQt2/fXD0eNmzYgLfeegu7du2SsGIiIiIiIiIi22VXoYNOp0N0dHSubW3btsXmzZsRERGB2bNnm7YvWLAAK1euRMeOHUu6TCIiIiIiIqJSwa5Ch7CwMCxevBixsbG5trdp0wZTp07F3LlzkZ2dDQCQyWQYOnQo5HK7+hYRERERERERFRu7ekc9duxYqNVq9OnTB+np6bn2jR49Gunp6YiKipKoOiIiIiIiIqLSxa5CB7VajY0bN+LChQvo1KkT4uLiTPtycnKgUCgQFBQkYYVEREREREREpYddhQ4AUK9ePURERCAmJgahoaGYO3cu1q9fj169emH8+PHw9fWVukQiIiIiIiKiUkEpdQFSqF+/PiIjIzFv3jysWbMGADBs2DCMGTNG4sqIiIiIiIiISg+7DB0AwNPTE+Hh4QgPD5e6FCIiIiIiIqJSye6GVxARERERERFRyWDoQEREREREREQWYbfDK4iIiIiIiOjpTVa0RpSsDBy2LoZOp4NS+fDtpTY5BCHKTKyUsD6yDgwdiIiIiIiIqMiiZGVwXlYG9fLYd07nUuL1kHVi6EBERERERERPpZZIwv6wt5GYmAhvb2/T9mZrRkhYFVkTzulARERERERERBbB0IGIiIiIiIiILIKhAxERERERERFZBEMHIiIiIiIiIrIIhg5EREREREREZBEMHYiIiIiIiIjIIhg6EBEREREREZFFMHQgIiIiIiIiIotg6EBEREREREREFsHQgYiIiIiIiIgsgqEDEREREREREVkEQwciIiIiIiIisgiGDkRERERERERkEQwdiIiIiIiIiMgiGDoQERERERERkUUwdCAiIiIiIiIii1BKXQARERERERGVPkKnQcK21nnuU3mFwqPFkhKuiKTA0IGIiIiIiIiKlVzpCkM++3TJkSVaC0mLoUMBli9fjoEDB8LZ2VnqUoiIiIiIiGyGwr0KFAB8un9jti+/3g9UOnFOh3xMmTIFI0aMwA8//CB1KUREREREREQ2iaFDHqZMmYINGzagc+fOWLhwodTlEBEREREREdkkDq94zIPAYe/evbh48SKee+457Nq1C507d36q86WmpiI1NdV0OzY2trhKJaJnwGuTyDrx2iQiIipdGDo84uOPP8bvv/+OvXv3ws/PD35+fmjSpAkWLlz41KHD/PnzMX36dLPtycnJNjdXxKMvAm2FLdYMsO7i5O3tnef20nRtPsoan4OisPX6AbahsGz52iwNz3F+2DbbVJxty+/aJCJ6WgwdHlG5cmVT4PDAuHHjMHDgQFy6dAnVqlUr8jknTJiA4cOHm27HxsaiadOm8PLysslf6qy55LBuyypt1+ajWL/02IanZyvXpjXVUtzYNttUmttGRLaNocMjXn/9dbNtL774It5//30sWrQIX375ZZHPqVaroVari6M8IipGvDaJrBOvTSIiotKFE0k+gUqlwttvv42VK1ciJSVF6nKIiIiIiIiIbAZDh0IYMWIEDAYDvvvuO6lLISIiIiIiIrIZDB0KoUyZMhg8eDAWLVoEg8EgdTlERERERERENsEu53TIyMhAbGwsKlasCKWycN+CcePGISsry8KVEREREREREZUedtfTYd68efDz80O1atXg7++POXPmQK/XP/F+NWrUwMqVKyGX2923jIiIiIiIiOip2FVPh7Vr12L58uU4duwYvL29sWTJEkyZMgV79+7F+vXr4erqajp23759OHfuHEaNGiVhxURERERERNL52KMSLqic4bR1sdm+87IyqCWSJKiKbIldfWy/ZMkSTJw4EbVq1YKfnx+mTp2KXbt24dChQ+jbt2+uHg8bNmzAW2+9hV27dklYMRERERERkXQuqJxxQeWS575aIgk1GTrQE9hVTwedTofo6Ohc29q2bYvNmzejc+fOmD17Nj766CMAwIIFC9CgQQN07NhRilKJiIiIiIisQg2tBvvD3jbbfnvZhxJUQ7bGrno6hIWFYfHixYiNjc21vU2bNpg6dSrmzp2L7OxsAIBMJsPQoUM5hwMRERERERHRU7Krd9Rjx46FWq1Gnz59kJ6enmvf6NGjkZ6ejqioKImqIyIiIiIiIipd7Gp4hVqtxsaNG9G2bVt06tQJmzZtgr+/PwAgJycHCoUCQUFBEldJRERERERUskYdXI/TybFm2y+oXFBDq5GgIiot7KqnAwDUq1cPERERiImJQWhoKObOnYv169ejV69eGD9+PHx9faUukYiIiIiIqESdTo5FZB6hQw2tBjW0mRJURKWFXfV0eKB+/fqIjIzEvHnzsGbNGgDAsGHDMGbMGIkrIyIiIiIikkZdr0CzCSOvhbeWqBoqLewydAAAT09PhIeHIzw8XOpSiIiIiIiIiEoluxteQUREREREREQlw257OhAREREREVHBlJ5XIFdlIGGb+TALmUoDoXWRoCqyJezpQERERERERHmSqzIgU+W9eoXQusCgdS3hisjWsKcDERERERER5UtoXeDTfb/Zdk4ySYXB0IGIiIiIiIieSvatSFwLbw2dTodU5cO3l1k+IZA7shcEMXQgIiIiIiKip+BULjTffSJbA0MJ1kLWi6EDERERERERFVng0CWmrxMTE+Ht7W26LVs6QoqSyApxIkkiIiIiIiIisgj2dCAiIiIiIrJzOXevwJCdYTY5pFKtgdyRy2LS02PoQAUy5GQi+/ZZOFVoIHUppZoQBshk7HhEZG+EEIA+G0KXAaHLAORKyJ0DIZPJpC6NSoDQZUGbfArahCPQJhwD5A5wqT4cDr7NpC6NiOyQITsDItt8aUy5owsnhKRnwtCB8qVLjcfVj+vCoMuGU3BduA37WeqSbJLQ5yAnbh+yo7dBr4mB0KVDaDMgdOkwaNMhshNhyE6A3DkQ7g2mw6X6G1KXTERPKSlbg++uHcfps4ko5+qBel5BqFcmCFVdXKCN3Yms679Cm3AUQpsGodNA6DWAyD3NlszBE0rP2lB61ILSoyaUnsb/Fa4VIJMrJGoZFSdDViKS972EnLi/AYM2177MS99C5dcKTuX7wCm4JxTq6gyhiKjEyBxdUGls7qUxE7ZxWUx6NgwdKF9p//0BXcodlH1rHW5//TKcok8Bfl2kLssmGLTpyL79J7JubkT2rS0Q2pQCj08TDnDVxCLl4AgoXCsATo1KqFIielY5eh22R0dh9ZXj2HzrHLQGvdkxTtChBhIQgiQ0gAv64QacZbo8zydy7kF79wC0dw/k2i53CYJn6+/hGNTJIu2gkpN+9nPkxO7JtW2fqIAr8EIVJKNG3En4xx1A2rH3oHCvCqfgnnAo2wtyh4rQpcZBn3oXupQ449dp8XCq0BAerQYxnCAiIqvE0IHy5V6/B+44uOD21y/DuWoLKINqS12S1RJ6HbKizyDjzNfQpR6EyL4AwPwNhUEAN+GBs/DDGXlZnIU/zghv3IEzQnAXX4utqPnPYCjaRADwNrs/EZUcQ1Y67h34AdqE6xB6LYReC9z/X+i0iJSpsNG/BjZkZCAhO6PAc2VBiVMIwCkE4CeE4hdFY/zmEQlXlRNkSlfIlC6QKV0htOnQ3TsNfdo1ACJ3PZoYJO3qBs/Wq+Bc5VULtpwsSRj0yLyy2nhDroJL1dfxo74a3rocm+s4T2SimkhCjdQEVD8bgYZnf0LthDvIugIYMs3Pq42/Bt8XppZAC4iIiIqGoQPlS+nhj5pLU6BNiobKpwKSkpKkLskqCIMeObEXkHntGLKuH0PmtWPIiTsB5yrZUHmZH5+VI8eW9GpYpwxFpEsg0hUOxh2PLVx8Dn7ohoGYnbkbA06NhQj8k59aEUlEn5mKG3M7I+vqEbN9cQ5umFG9I/b6VAWS7ubaF+yoRN/sg+iOC0iEM87BF+fhi3OyQFwSntDBeE0f05fB266j8HuHoVDmMWRC6DKhS70EXcp56FKikHMnAjl3IgChw71/BkKvuQ3XOu/xd4QNyondDYPmNgDAqXwfHCo/Du/s/s7suHtwxlGUxVGUNW0L9klBb+8o9LhzHuWvJEI8km3Hb5wGmcoRPj0mWbwNRERERcHQgQokUyjh4FtR6jKsRtqp7bjzw2ho46+ZtjkEAu71AdkjV5M+E7iV5IY1srr42bsuEr3yn3zH18kVrkoHXE9PRiZUGItuOHjnDBadXQzfOu9YsDVElBdDtga35vcwCxwEgI0BtTGnajukKZ1M2110OeiScBGv+CajVfYRyO/nAHKXIHSv0BLOFftD5dsSOULg3/gb6LNnFZJzMrHl1nmMPLge37Z60Sw8kCmdoSpTF6oydY2PLaYg7ehEZJz7AgCQdvwD6DW3oW4yn/M82BjNpZWmr6P8+uHFvT9Af39ej9erNYW/sxvOJMUg8tZZ3IQc4pGfjVvwwGJZMywObIaQwET091Cjj9Ifjus/AgDc/XUyZConeHcdV6JtIiIiKghDB6JC0KXG486P45B66EfTNpkD4FoTUD06CkLui2O+I/FtugO2OqXg8ZHd5TLvISQtDrXS76KWLhNtnx+H6s8NhVYY8N7RLVh03jhxz8+og5NHT+Bn9wOoV6GV5RtIRAAAQ04Wbi3sDc3FfwAACrUfyo74ATFKJ4y+eBS7kx52gS/r4ISxt/9Dhys74VdLB+Ujf1FdaoyBulnuQMARQJuAKtjSaRg67liKLL0O3106gkBnd4Q36lZgXTKZHOqm8yF3LYe0o+8CADTnv4RBEwPP536A7JEQhKyXITsZWTc3AgCiHauib2QU0nXZAIBXKzfEt61ehDbhBqI3fYysG/8hU67ENZcyiGn9Gnb7VMGfty9Ae3/UzTl44/9SgP9DEjp3HoeP9i2Hb04G4n4cD5nSEWU6jpKolURERLkxdCAqgBACKQfXIu7HcdCnJ5q2uzeoDaXXNUA8XFbocsV3MOZeeZy8cwfAwwG3DnIFBlSqh9E1WyEk5izurBoBXbKxa63hu2GIPvE7AocsxZfN+6BtQGW8vm81Ug3ABXij5V+/4euWcgyp0aLE2kxkr4QuB9GL+yPj7G4AgMK1DILf24XVmnS8d3Sr6c0hAIyo3hxzm4TB6d4pJO08BOiSjefQAxlRQOrRtZApn4O6SX+zx2npXxE/txuEF/5aBYMQmBm5BwHO7ng75Mmzg7vVngCFcxDu7R8MGLTIuvEbkrLuwqvDRsgd8xjfRVYl89o6wJCNZOGEgaI37mSlAQA6BFbFd60HIOPMLtxe8gr0GcbhjK4Ozujy+jKoG/XBGBhXR/n14j/44ew2HMhUmc67S6vAqVbD8NmJ9Wiccht3Vr8FmcoRXm1el6KZREREucilLoDIGuXEX0f8pnBc+6QhYr4ZZAocFGof+PRqAaXnWVPgoHcpj++qLkOnG844mXzHdI5gV0/MbNgNtwZ8jB/a/A/N/SpAXb87qsw6C882w0zHpZ/cgisf1UHWrdPoV7Eujvd+F3UVqQAADRQYenA9ZpzcVYKtJ7I/mksHceOzLkg/tRUAIHfxgO7t9eh65h+MOrTBFDhUdPPC7q4jsaxVfzjEbEXSn21NgQPkXkiLdIL2LqBPT0T0VwOQeuz3PB+vV/naWNbyYSAx5t9N+DM6qlC1Old+GWU6/wmZSg0AyIn7G4l/toPQ5TG7IFmVzMurAACjEIaL9zOsUK9AbOgwBJrD63Dz8+dNgYNjUAgqTTsKdaM+pvuXcXTByNCu2P/yQlzp3BvT3G+iLIx/L+4KFV5vMABfV2wOrUyO2O+GI3rJ/5AR9XdJNpGIiMgMQweix9w78AOufBiC+A1TkHXzpGm7R+v/wfv5WtCnHTJtiyo3DD0c3sbHly4i5/4yeU18grGhwxBc7T8ZH9brCD9n91znV7h4IGjYtyg/8U8oy5QDABg095D23x8AgKqegdjauAv+h0jTfdZePWGp5hLZtZz467gxtzOuh7eCJmofAEDm6Ip7I39Cx8h92B/3cP6Wd2q1xuk+E9ExqBqybvyOe/teBgw5AACHoM7wH3AJladdgVPD+2GCEIj7aQKETpvnYw+v3gwf1u1oPBQCv984U+i6HQM7wLvbP5A7BwAAdMmR0N07V+T2U8kRQkCbHAmNUOJvVDRt39BhCDwcnHHvn+8AYRw7oW7yIipN/ReOgTXyPV/lcs/hk34/42DD6uiEqwAAPeT4qmIrvNToVZxz9UXq4Z9w49O2uLWoP3Lir1uyeURERPli6EB0nyEnC7Gr3kTMN4Mhch5+YuhctQWCx/8Op3K3oI03jvPOVPlgdvB8dIr2wqnkOACAQibHR3U74kDY23ihQmieM9I/yi20Kzyfe9j11TGwpulr1a018MXDoRs9g0OKpY1E9FBG1D5cm97ENJwCABwCqiN2xI/odvYg4jKNXd9revjh725v4cvmfeCmckRO3D9I3veKcSwFAJcao1Cm0zbInbyh8gqC+tUlcKvXHQCgTbiOlMM/5VuDl4Oz6etGPuWKVL+qTF0oPeuYbivcKxXp/lSyZDIZnCu9AheZDl1w2bT91+unAABCe7/rg1yBsqN/htzJrVDnLF9vIjZ1fA0fyg9DdX8moQtufni50atYWKkVcmQKpB1bjyuTa+Lu+ikwPGF5VyIiouLG0IEIQE78NVyf2RrJe5eZtnl1ehvVFsagwvvbkH17DnLijIHD38o66Kh8C1/evA3D/U+lmvgE43ivcQhv1A2qIswkn3npgOlrl2rGCSNz7h5Gcsyf+A4NAABOCiXerd32mdtIRA8l712GG3M7QZ+WAABw8K+Gsm/+iEtv/44e5w7h3v3gsY1/ZfzbYwyeC6gMANDeO4ekPb0Bg/ENokv1EVA3/woy+cMpkmQyGXz7TDPdTtjyKYThsTVy79sZc9H0deegakVuhy7Z2CNK7lIOcscyRb4/lSz3BtMBhROm4G8o7wcEs079hTuaVOD+ChYyuaLIS6G6lO+JaWELsdNpO+rDONmpXibHNxWa439NhyBJ5QyhzUbCH+G4/EENpBz6EeL+3y8iIiJLY+hAdi/t5FZcndoIWdePAwDkTm4o+9Y6BA5aBIWzI5J2doI2/jAAYJ2yBV7RdsV1jfGTIlelA75o2guHwt5BvTJBRXpcoddBc8V4XpVvZSg9AyCEQOqx97AS9ZEGRwDAG9WbI8BFXVzNJbJrQqdF7OrRiF31JqDXAQDcGvRCpenH8ad/LfT+63tk6o3DIXoE18KfXd6A2sG4MoReE4PkXd0gcoxzODgG94a6+dd5vkF0rtwErrU7AwByYqOQdtx8bodMnRb/xBm7xVd190Eld2+zYwqi19yBIesuAEBVpl6R7kvSULgGw7XWGFSRJeN1/AcASNdlY8p/OyDuD9HDUy6BqvJphNY9d2Cb5yl8jH1whPHn+7yzF0Y8Nxr3nD0AALrk27i99FVcn/kcsm6eevZGERERPQFDB7JbwqDH3d8+wq0vesCQcf9NRFAIKk09Co9mL0GfFY/EP9tDm2gMI9apWuFdbUs8+GyoW9maOPvCRIyr3QYKedEvpaxbkRD3u7k+6OWQfXMTkuP+xXI0AgCo5Aq8V6fdszWUiAAAurQE3PisC5L3fG3a5tPzIwSP+R0rbp7FK/vWQnv/jd/AKg2xocNQOCuNKwQYclKRtKsb9Bk3AQAq3+bwavtjriUxH+fT6yPT1wmbZ5p9srw/7hqy7gcfXcpWL3p7kh++YVR61S3y/UkabqGTIHPwwjgchtf9lY5WXDyCc8r7Q21kT//STOlWAf7d92NcoDt24geUQwoA4LxB4M1OH0Db+EXTsZmXDuDa9KZI2rWYvR6IiMiiGDqQXTLkZOLm/B5I2DzLtE3d4n/GibuCakKviUXSn+1MXZfXObTBuznNTYHD5LodsLXzMFRwe/ruzLmGVlRvDWHQIvX4B1iNekiG8cXn0KqNEezm+dSPQURGWdFncG16U2iiIgAAMgdnlH1rHfz6h2PumQiMPPgbxP0r/J1arfH9cy+bhkoJfQ6S9/Y1/T5QqKujTMfNkCldCnxMlxpt4Hw/UMy68R/SI//MtX9XrqEVRQ8dtEkPQwf2dLAdckcvuNX9EJ6ybEzEQQDGiUTDy1SFAAoMsgp3fk+U6fwn6lYNw3r8YgoeTqclYkj55nCbsB2O5YxzgQhdDu6seQfRi/tDn3HvmR6XiIgoPwwd8rF7926MGjUKv/76q9SlUDETBgOivxqAjNPGNwAypQMCBn+NsiPXQO7kBqHLQtLOTqaZ4Lc7tsS72U1MgcNHdTtiZsNuRR5z+7iM+29+AMC5Wiukn56D2JRoLEVjAMaJKSfV7fBMj0FEQM7dq7g+oyW08caVKJRlyqHih//Ao9lLmHFyFyYd32Y6dmr9zljYrDfkj3zanHpsInJi9wAA5E7+KNP5T8idfJ74uDKZDD49H/Z2SNw2x/R1pk6LTTfPAjBe6+0DqxSpTUIYkBP7l+m2youhgy1xrfk2FK7lMQinUA3GJZn/dfbCxoDaTz284lEyhQM8Wq9C9eoDsB6/mJbVPJUUg97Xz8HrowPw7jHZdHzasQ249n/NoM9MfebHJiIiehxDhzwsWLAAb775JsLCwtClSxepy6FilvBHONJPbgEAyF08UWHyPpTpOMoUImguLjMFDndd62CioV2uwGFGw+efPXA49xfSjm0AACjcfaG79ydunZiJl9EfCXAFALxauQEqF3GMNxGZS967FIYs40oUzlVboPLUo3Cu1AjzTkfgk/92mI5b0LQ3pjXomuv6FgYtMi99BwCQKV1RptNWKIuwSoRb3eeh8jVOQpl57RgAIEunRZ89K3ExNR4A0Nq/IjweWcXiSYRBi3v/DEZ2jLF2maM3FOqqhb4/SU+mdIJbgxlQygSmIcK0fWqNLtjl7A1tUvSzP4ZMDo+Wy1C9am+sx8+m4OFkUgy67VkJ596foPzEP6Fw9wUA5Ny5iLu/fVTQKYmIiJ4KQ4fHJCQk4OOPP8bWrVvRo0cPeHh4PNP5UlNTER0dbfoXGxtbTJXS00g/vQPxG6cZbyiUCB73B1yqNjftFzoN0iM/NX4NOd53eR3JOcZZ6gdXaVQsgYNek4Lby4cab8gArw4NEXt0MgaiL6JgfPFXydULnzXp8UyPQwXjtWkfhBBIPbreeEOhQvkJ26D0DMDic/vx3rEtpuO+btEXY2s/Z3Z/bfwRCJ1x7hWnSi9B5dOoSI8vk8kguz/ni8LJHdl6Hfrt/d60aoWngzMWNutT+PboMpH8V19kXV1r3CBXwbPl8lyrZ9g6e7k2nasMhHOVwWgvu46RMAZSepkcE0PCsG3fqmJ5DJlMDo9WK1DJzQvr8TMCYQzfjiTcQo/dKyCv1R6Vph2F3MkdAJC85yvkXD9WLI9NRET0QOl5lVJMDh8+jDJlyqBGjRoAgIyMDMyYMQPbtm2Dv78/ZsyYgebNmz/hLA/Nnz8f06dPN9uenJwMZ+fCf7JlDVJTba/b5aM165NuIfHrV4D7E2a59ZiGLJ8QZCUmPjzm8iIYsuIAAGs8hmDn3dsAgHLOakyr3gZJSUnPXFPKT+9Al3QLMgfAvbEnMlJ24zX0xX8IBAAEObljdWgPKDQ5SNQkPuFs1sUaf0a8vfPuLVKars1HWeNzUBTFXb/29mlo440rRDhUb4t7WXr8cGEPxp/abjpmRu2OGOBbA4mJ5teb/upm09c5bk3zPOZxj7dBm278vaF3UqPfzu+w7Y4xcHBXOuKXZgNQTjgW6rxCmwbd0VchEo3zAEDhAmXjVchwa4OMQty/KEri58iWr83i+v6IWp9Bnp2NT279jHQ4YC3qIkeuxMDEePxx5SzqegYUy+Og4kiUP/shfha/oq98CBIMCvwTdw1hf36DNU1fhGv3j5G24QNACNxbNxaq4AjIFKrieWwrYuu/HwtSnG3L79okInpaDB0e4+joiNjYWCQmJsLd3R2dO3eGm5sbhg4divXr16N9+/Y4ePAgGjRoUKjzTZgwAcOHDzfdjo2NRdOmTeHl5WWTv9RttWaDNhvXF42A0BhXqVA3eRFlX/gwV68FgzYNd68uBgBchTemp/kB95ccW932f6gUUPaZa0k9vhFZR3+C0gtwrS2DXHUP74quOIDyAABfJ1fs6TYKvnqFTX6vAdv5GSlt1+ajWP9DdyP2PDxvy1ew9d51TDj1cELHmQ274cN6HfO9f+K9Q9A/uH+13lC4FK62B20QBgPiNPcAACuC6mHrg8BB5YidXUaguV+FQp1PnxWPpF39IBJPAABkDp4o02krHPxaFur+T0OqnyNbuTaLqxbRYS1SDjhi9uUfkAwnbEN1pCtUePnQjzjYazyqqJ88f8iTGNRjcPfy56ianYh1+BkvOryO5JwsRMRfx8hTW7C++1joTv2OzCuHIe5eAI5+D++wD4qhddbHmn6GiltpbhsR2TYOr3hMixYt4OzsjPDwcPz888/w8PDAzp07MWHCBOzbtw81a9bEggULCn0+tVqNcuXKmf4FBgZarnjKV9yP45F17SgAwCGwBgKHrTAbJpFx7kuI7ERohRzjHQdCc38pu/G126B94LOPl9al3kXsyjfgVAlwqwfIVQK/iVr4BcZZxNUqJ+zoMgI1Pf2e+bHoyXht2ofUY/eHVsgV2OlXA0P+WWdapeLjep0KDByEToOc+EMAAKVnCBQuRf/U2ZCVBggDDnpVwHx1MADjxJF/dHyt0IFDduxfSNzaErr7gYPcyR/ez++zaOAgJXu7NmVyBTxafQf3kDFYjG1oCeOyrHe12ejy51e4o3n2T7DlKle41nwbABAiYrEhOAdqlRMAYGv0ebzy90/wGbIUUBg/i4r/fRpy4q488+MSEREBDB3MuLm5YcqUKVi4cCEWL16Mnj17mvYplUq0b98eGo1GwgqpqO4d+AHJfy0BAMgcXRH8zgYonN1zHWPIvoeMs/MAAFNlHXA0y/jCq5aHH2Y27PbMNQghELtqMJyrJMC5IiCTAVeEFybLnjcds7L1S2jg/ey9KYjIKDvmPHJizgMADtXthYGHN8Fwf3jVu7Xb4v8adC3w/jlx+wGDFgDgEJh/OFEQfUYyYh3d8X6t7hD3g87ZjbqjXSGCTH3GLSRHDEDSjo7Qp10GACjcKsK7+36oytR9qnrIOsnkCng0WwC/VivxnX4T6sA4zO9qRhq6bPkMKTmZz/wYrrXeBhTG4Sk1by3DtvavwE3pCAD4/eYZjLh2Gp5d3wUACG0WYr4bBmHQ53s+IiKiwmLokIeJEyfi1VdfxZEjR7Bx40YYDAYAgEajwbZt2/Diiy9KXCEVlv5eLGJXjTTdDnptORzLhpgdp7mwFCLnHraJqlgljEvPOStU+LHtq3BWPvu41qTd70HuuAMqL+PtNLhilMtIZAjjJfhWzZboWzH0mR+HiB5KO7EJAHBKHYi3PCtDJ4y/y0fXbInPmvR44qSw2Xf2mr528Gn2VDUkJkVjTJ3eSHZwAQD0rRCKd+u0LfA+Qp+N9MhPEf97TWRdf7hss4N/G3h32w8lV6ootVyrD4Kb/nl8n7EBFWEcDng6IxOdN3yEpGdczlLu5AOXasMAAEKbivopu7G18+twvj93w7prJzE7uDEU3sbVWTRR+5D45/xnekwiIiKAoUOeZDIZvv/+e0yePBl79uxB27Zt8cknn6Bx48Zo164dBgwYIHWJVEjZkX9A3P+EyKvDKHi0eCXP4/SZdyAEsAgP31isaD0A9Z+x54FeE4Okv/oi5/bnkBs/UIJOFYDRPtNwRmP8BLWuVyA+b9KzgLMQ0dPQa1IQ7+CCcbV7Iuf+tmHVmuLL5n0KtQqN0D38dDkn7p8iP366Nhu9/vkZ59z9AQCVlUp813pAgY+dHbML8ZvqIu3EhxA6Y686uUsQPNv8iDLPR0Dhyt5QpZ3nc2PhdFyDVXHr4Yd0AMDRTDla/zYZl2OOPPV5hT4b2oSjptuG7CS0CaiCPzq9Bsf7wyoWXTiMI71mADLjy8PkPV9D3P/ghYiI6GnZZeiQkZGBy5cvQ6fT5XuMXC7HrFmzcPr0aTz33HOIj4/HzJkzsXTp0hKslJ5V1umtpq+9n5+Q73GutUbjmKwcTsE4Zru5d1m8Urlwk4XmRQgDMi4sQ/zvIci++btpuz4nEJ+Um4Pd8XcAAH5Obvi941A4FUNvCiLKTVWzHSaE9MRdR+Nwqi5B1bGsZX/IZYX70+dWe4KpO7rm0gpo750v9GNrdDno/scXOKYwpo2+2kxs7fomPBzyXn0hJ/5fJO3pjaSdXaBPNU42CZkSrrUnwveFKDhXfuWZl+sl2+BSqz282r8F3/MpWHXlVwQI4zKX53XOaLZjFTbvHAF9+s0inVMIgZTDo6FN+BcAoHCvCteQcQCATkHVsaRFX9Ox429HIaNBbwCANuE6NBeLHrgRERE9yu5Ch3nz5sHPzw/VqlWDv78/5syZA70+/zGLISEhmDVrFpYsWYIXXnihBCulZ5X232ZorxiXlnMMCoGDf/5dkpXqaljl9vBF1xuucU/9uNp755G4vS1SD70JoU0BABiygfQzwLIac7HqaiQA4/CNLZ1eR2V3zjZNZAmfpKfhhGc5AEDZ7DSsfe5lKOSF/7OncCsP93ofG28IHVL/fQfi/pwQBcnS6/DCnpX4JzUBAOCVo8EfVeqhpl/FXMcJIZAVvQ2J29shcWtzZN/6w7TPIaA9fHqfgrrJZ5Crcs9BQ6WbTCZDwMAv4dagFyrcSsLaMz+hmjAuiZoEF7xw2w0zf+uPlH/HQ58VX6hzai4sRealFcbzK93g1WEj5I5epv1DqzbBS5XqAwASczLxflADPOjfkLL/+2JrGxER2Se7Ch3Wrl2L5cuX49ixY4iLi8OYMWMwZcoUhIWFISMjI9ex+/btw5IlSySqlJ6VITsDd9a8Y7rt2y+8wONvpd/DH+kOAIBApKFzzFfQa+4U6TF16TeQeuIjJPxRD9q7+03bs24DKf8CW2q8gfCrJwEAcpkMP7cbiCa+5Yv0GERUON9fOoqvLhwGADjptVh4+ne43D5T5PO41n4XCnU1AEBO7J5ccyzkRW8wYMTxTdgZcwkAoNZm4ofEKDRr/3AJSGHQQnNlDRL+qIfk3WHIidtn2qdwrQDPtutQpuseqDzN558h+yCTK1Bu1E9wqtwUAYlpWHtgLbrkRAMA9JBjqmiDYecu4Oav1ZB2cjoMOfnP95AT9w9S/x1juu3x3PdQedXO/XgyGZa26IcKbsYgIiI9BSurGOceST36KwzZuV8jERERFYVdhQ5LlizBxIkTUatWLfj5+WHq1KnYtWsXDh06hL59++bq8bBhwwa89dZb2LVrl4QV09OK3zQD2oQbAAC3+j3g3qhPgccvuXAQ+vufYA7BSSj16UiPLDioAAB9Zhwyzi9GwrZWiP+tIjIiZ5lmu1e4V0f6GRdkXgSOeFTE+ypP0/0WNeuDnuVr53NWInoWxxOiMfLQetPt6Rd2olZ6vGliyaKQKRzh0WyR6Xbq0fEwaNPyPFYIgTH/bsS2O8bhEW66bCyPXI/2A2ZBJpdDl3oJ6ZGzEL++KlL+GQRd8mnTfZVedeH53Br49rsE50ovcSgFQe7ogvLjN0PlWxmuOi0+P/gzJmhi8eAnYz1C0FvXE1H/fY64db5I/LM90k7+H3Li9kPojbOY6DOikby3PyCMw0nd6n4M5wp983w8T0dnrG3zP8jvP8IXwY2wy6cqDFnpSD32e573ISIiKgy7Ch10Oh2io6NzbWvbti02b96MiIgIzJ4927R9wYIFWLlyJTp2fLpl0kg6WdFnkPjn58YbKmcEDFxU4Av4TJ0W39z/RNRRrsCrKmNYobmwDGmnZkJz5QdkRW9Dzt3D0KVcgD7jFjSXViJxZxfc/SUIqf++A+3dgw9PKHeAW/3pMOR0hzZeg0SVMz6o29c0c/77ddrhrVqtLNN4IjsXn5WOvn+tQrbe+CZrTLWm6JFoXG4y7b8/CrprvhzLdoVThX4AAIMmBumnZuR53GdnIvB1lPF3gYNBh69P/45Woa1g0OxF/B8NEL+hOtJOfAR9xsPx+A4B7eDVaTt8ep2Ec5VXIZNzfhd6SKn2Q/mJ26Fw84YcwLAjP2Kl9h7UKmPPvNPwx/MYiIN6P+TciUD6yalI3P4c4n7yQuLOrkja3R2GrLsAAMdyYXBrML3Ax2vlXwkf12oHABCQ4YNa3XFSHYiUA6st2UwiIirl7Cp0CAsLw+LFixEbG5tre5s2bTB16lTMnTsX2dnZAIxdDYcOHQp5Ecb/knVI2DwLuP+Gw63Le3DwrVjg8etvRCIx2zhL/KtVGqFi3bHGHUKH9P8+Rso/g5G8OwyJ21og/veauPtreaQceB05MbsA8XBWb6V3Q7g3/gx+/a5C7tQWyXuWQCuT4706vXD3/idHPYJr4dPG3Yu/0UQEAJh8bBtuZtwDALQNqIx5LfvBtWY7AEBObBQ0lw8/1XnVTeZDpjQue5lx9nOk/TcVwvBwMuLvLh7BB8eME9fKhMDcy9vwXFAc5E6bkXbiQ+iSTj5yNhmcKvSFd9hheD+/F07lnmfPBsqXY0B1BE/YCtn9ZVebHFiBzRkxqKn2BWCc5+ElvIgfRF3TfYROg5yYnabeNAp1NXg+twayQkyi+k7VZhhZozkAIFuhwtjavZAYFQF9RnJxN42IiOyEXb2jHjt2LNRqNfr06YP09PRc+0aPHo309HRERUVJVB0VF33aw4m1nBr2e+LxF1MeHv9ypfpwqTUGSo9ahXoshboG3OpPg+8LF+Db8zjc6kxEdswN3JwfBqHNxmdV2uJfD+NEduVcPLCq9cuFnjmfiIruSMItAMaJWn9pNwgquQLqpi+Z9seuGAZDTlaRz6twKw+3+tOMN4QB6af+D4nbWiPt1Aws3zkOww/8bDp2ii4CL1a7BMcAHSC097fK4OD/HNTNFsFvQDS82q+Hg28zs8chyotLlWYo9/ZvwP2lLX0Ofo9fLmxHryDjfCN6yPEBOiM8aA5UVYdD4V7FdF+Zyv3+xJGehXosmUyGxc1fQJdA4+TLCY5u+NOvBiBXFG+jiIjIbiilLqAkqdVqbNy4EW3btkWnTp2wadMm+Psb10/PycmBQqFAUFCQxFXSs3Kp/hwyzu4GAORcOQBUrVvg8Rqd1vS1p4MT5CpX+PQ+BV3yGeg1t2HQxMCQnQRDTjIM2UkQOUmQO/rApfoIKL0b5vqEMvPqUdz8vBtEdgZ+CwzF2nINAQBOCiU2dBgCbydXC7SYiB5IyDJOeBfoooafs3HVB8/nhuLe3yuQeeUwsmPO4e76jxHwyrwin9u19kQAMqSd+BAwaKFN+Be/xydhFHpA3O/NNA6H8KbDCdN9ZI5l4FprLFyqD4fChX9f6Om51+uG4LF/4PbXA2DISofiwj58du82aveYjk+vGH/mvr59F1fKNsW6Hl/CTXsX2oRjUHk3hNK9UpEeSylX4AN9Onbev72+Wgd86Kwu5hYREZG9sLuPXOvVq4eIiAjExMQgNDQUc+fOxfr169GrVy+MHz8evr6+UpdIz8i1ThfT1zkXI554fKb+YejgrDSOp5bJVVB5N4BTcA+41BgBt7qToG48B56tlsOr/Xp4tFwGlU8jU+AghMC9A2tw47MuMGSm4rhHWYRX72w674pWA7hSBZGFCSGQcH+WfR9HF9N2mUKJoBGrIXNwBgAk7ZiPjKi/i3x+mUwGtzoT4RN2BErPEOwUlfEWwqC//6d0JI5houEgDHpnqLwbw73RbPj1vw73+p8wcKBi4V6vGyp+tB/KMsYedLq4yxiy7h18V7U+HO/3gthx+wJabF2E6wZXOFfsV+TAAQAM2myU27UAdVKNw1FPyFU4mXi7+BpCRER2xa56OjxQv359REZGYt68eVizZg0AYNiwYRgzZswT7km2wLlSY8hdPGHQ3EPOhQgIIQocL63R5Zi+dlE6FPnxsmPOI/b7t6CJigAAxDi6Y0K9/tDef8wPQtvjf1UaFvm8RFQ0adpsaA3GVYh8HutV5BhQDf4D5hqX0hUCMd8OReUZp6C43xuiKFTe9XGw/lqMiFgL3f1Vb165cxLvXNwHtBgMxxc+g4+v37M3iCgPTuXrodIn/+LWFz2RdeME9BlJaPbtIPwU2h1v+tfFXZ0WUSl30WzLl9jQYQjaBlR58kkfk7L/e+iSb+Nlx1P4WB0IAFh64RCWtuxf3M0holLugsoFrbcuNtuuTQ5BiDITKyWoiUqe3fV0eMDT0xPh4eGIjIxEZGQkxo4dy4m8SgmZQgnXkA4AAEPaXWRHnynw+EeHV7goCj9zvCFbg7hfJuPKx/VMgUOmXInxzV5DgtyY54WVq4WZDbsVsQVE9DQS7/dyAAAfR/OhTF4d34JriHFFIm38NcStm1jkx9Aa9Hj/6Bb02bsGOfcDh76xp/Fh1B54PTccQcNXQsax72RhKq8gVPzob7g37G3aVuP0Nqz95yvUzLwHAEjK1qDTn8vw5bl/oDPo8zmTOaHXImHLpwCA5+9egMf9HoBrr/yHNG3R50MhIvtVQ5uJGlpNnvvO6VxwTudcwhWRVOw2dKDSze2RIRYZZ3YWcCSg0T/s6fBgeMWTpJ3ahisfhiBx62zg/vAMZWANfNpnNs7cX/Kupocf1rb9HxRcAYWoRDyYzwEw7+kAADK5HEHDV0J+f2z6vYhvkHZqe6HPH51xD+22L8FnZyJM23rfOYNpF3bBu+NoBA5dBhmvdyohckdXlHtnPYKGr4RjsHHuoqDsNKw+thodEozLxOqEAWP/3YTQjZ9j882zhTpv5pGfoE24DgDwqx+GIdWME56m67Kx5sqJAu5JRJRbeMo1/JpwDvvD3jb7F6LMO4yg0omvjqhUenReh+R9y6HXpOR7bJb+4bJ38Y+8acmLIScLd3+fhlvzw6BNuAEAkKmc4NQ3HO91fA/rk4zjXz0dnLGp42vwcGCCS1RSknIyTV8n5HMtq7yDETDwS9Pt218PQNqJTU88d2RSDJpu/BwH714HADgYdJh6YRdmRu2A7/PjETBoEQMHKnEyuQKezw1F5RknUWFyBNwb94WrQY+FZzZh+I1/TcdFpdxFrz0rMXDfWiRnm7/QF0Ig/ewe3Jj3PNJ+nWDa7tPrYzxfrobp9k9X/7Nsg4iIqFR6pldI3333HRo1aoSGDRti0KBBWLhwIQ4cOACNhskVScvBtxKcqxjXGc+JvYCrnzTEvQM/QOTRxbSJT7Dp61f2rUH2IyHEA0KXg+S9y3D5g2pI2DjdtN2tfg9oPzqA7nol/og+DwBQyOT4ud1AVPfgpKREJalBmSCo7g9tWH3lOJZFHcrzOI9Wg+He2LicriErHbe+fAGx37+Fe/u/h+byYegzknMdv/PMX2i9cR5i74ca5TXJ+PHEj3g55Sb8B8yG/8vzODyPJCWTyeBasy2C31mPqp9dgW/39/Bu3BmsO74GzZJvmI5be/U/1P75/7D1eiQAQOh1SDn0E65NbYSbczsh4/QO07Hq5q/gVpkKGLb/V9O2si4eJdcoIiIqNZ56IslLly7hzTffxMSJE3Hy5Ens2bMHv/32G7KysqBQKDB16lRMmTKlOGslKpKyo37C1enNYEi7C238VcR8MxiJW2bDt98MuDd6wfQm4cO6HbHxxhmcT7mLYwnRmHDkD3zVoi+A+y/IDvyA+E3/Z+puCgCQyeH/v/n4u3oHDNm/DmnabACAr5Mrfmk3CO3ur29ORCXHz9kdy1v2x9D9PwMARh/+HRXcvPB8uZq5jpPJZCj75lrErnJHyv5VgBBI/msJkv9aYjpG4e4Lh8Aa2OYRjPHOftDen6el0b1oLIuLRNWBC6Bu0h+yp5h8lsiSHHwrwv+lufDpNQXeOxei/l9fY1vMKcyo1gnJDi6I1evQY+9q/A9ajD23DW53L+e6v8K3CvzC3kdCvZ5o/+cSxGamAgCa+gRjact+UjSJiIhs3FP3dDh48CCef/55zJo1C126dMGAAQMQFxeHwYMHo1WrVmjRokVx1klUZA6+FeH19ma41e9h2pYdcw7Ri/rh2rQmSI/8E0IIuKkc8VuHwXC5P5/D11EH8dPl40g5uBZXJtdCzIrXcwUO7k36o8KMk/jCuzr67v3eFDg09imH4z3HM3AgktCQak0wpV4nAIBeGDAg4gdEJsWYHSdXOSJo+Hfwf+VzII8JZK/qtHhX5oq3XYNMgUPXe7ewuV57NAw/BY8W/2PgQFZN4ewO394fo/rnN/D6izPwZ8I5dIy/ZNr/I1RoUasnXmr4Pyyo1Bona3eD3zsb4P3BISQ37ocOu75FjMYYODT2KYcdXUZwyCARET2Vp+7pcO/ePVSsWBEAoFKpkJ2dDbVajZUrV6Jly5YIDg4u+AREJUDpWwX+4zdDc/kQ7v72ETTn9wIAsq4fx83Pu8GpUmMoXLzgnJWK/1O4YmJQAwDAsIjv8cvxtaisSTKdy61BL/i9MB2ZAdXx4r4fsf12lGnfa9Wa4OvmfeFUyIkoichypjfoiitpifjx6n9I02YjbNcK/NtzDIIe6xouk8ng/fwEeLQajKybJ5Fz5yLOxVzAFxnp2OjgCcMjQyaGu7ji61dWQuXkUtLNIXomMqUKHs1eQtNmL+GPq8ew/K/lmCIckKZ0hEEmxxl1IM6oA7EcgPO5I2hxJwaXNMmIvj8XUkPvstjZZQQ8HRk4EBHR03nq0EEIAfn9SbMCAwOxa9cuAIBcLke3bt2wceNGfPDBB8VTJdEzcqnaAhUn/YX0s3sQv/4jZF4xTrCVde2Y6ZhuAI5Ajl+C6iFT4YCxtXvhuaRrSPWrirSAmkiQyXH38B+4m5kOnTAAAFRyBRY26403a7TgmG4iKyGTyfBd65dwM+Me9sddQ7QmBT13f4e/u70FV5Wj2fFKdx/cCKqD8Pg7+FnnAOHoZdrn6+CEqfU6463abXiNk81zqdwYYys3Rp+4q/jy3/XYpzPgREoCBIzLv2bqtfgr/prp+PplgrCr60h4OTJsIyKip/fUocOjL74aNmyI4cOHIz4+Hr6+voiNjYW3t3exFEhUnNxqd4RrSAekn9yCuxumIPvmKeMOhQoKFw9MSbmBs96VcNZRjauu3rjqev/nOPmO2bkCnN3xW/vBaOVfqQRbQESF4ahQYmOHoWixdREupSbgROJtdPhzKWp6+MEAAb0Q0BsM0AsD7uVkYU/spVz393d2x/t12mFkjeZ5BhVEtqyCf2V83us9AEBStgZ/xV7C7hjjvytpiQCAul6B2N11JMowcCAiomf01KHD6NGjkZRk7HpesWJFdOrUCfXr10fNmjWxd+9e7Nix4wlnIJKGTCaDe4OecG/QE/qMe5A5OEP+yJuKP9IS0XTzQiQ+tqyYq9IBfk5u8HN2Q6hXIKY36GLWXZuIrIe3kyu2dR6O5lu+RGK2BkcSbuFIwq0C7xPkosYHoe3xRvXmcOZwKbIDZRxd0L9iPfSvWA8AcOLmFdyR5aBdYBW4cN4SIiIqBk8dOiiVSvj5+Zlur1y5Ep9++inOnz+PlStXonPnzsVSIJElKVw9zbZVdvfG6T4TcSopBt6OLvB1coOvkys/7SSyQVXVPtjY8TV03/WtadLXvJR39cQHoe3xerWmnJuF7FoFV080ZG9VIiIqRk8dOjzO1dUV4eHhxXU6IkkFuqgR6KKWugwiKgat/Ssh5qVPEJeZBoVMbvwnl0Ehk0MuM/7v5eDMORuIiIiILKDYQgciIiJr5aZyhBt7KxERERGVOLnUBRARERERERFR6cTQgYiIiIiIiIgsgqEDEREREREREVkEQwciIiIiIiIisgiGDkRERERERERkEVy9goiIiIiIyA6MOrgep5Nj89x3QeWCGlpNCVdE9oA9HYiIiIiIiOzA6eRYROYTOtTQalBDm1nCFZE9YE8HIiIiIiIiO1HXKxD7w942234tvLUE1ZA9YE8HIiIiIiIiIrIIhg5EREREREREZBEMHYiIiIiIiIjIIhg6FECv10tdAhEREREREZHNYuiQjylTpmDChAlSl0FERERERERks7h6RR6mTJmCDRs2YO/evVKXQkRERERERGSzGDo85tHAwc/PDwAQExMDd3d3uLu7F/l8qampSE1NNd2Ojc17XVwiKlm8NomsE69NIiKi0oWhwyMWLVqETz/9FOfOnYOfnx/279+PESNG4Pz581AoFBg8eDC++uorODs7F/qc8+fPx/Tp0822JycnF+k81uDRF4G2whZrBlh3cfL29s5ze2m6Nh9ljc9BUdh6/QDbUFi2fG2Whuc4P2ybbSrOtuV3bRIRPS2GDo8YMGAAli5dioEDB2LGjBkYOHAgpk2bhueeew4RERGYPHkykpOT8fvvvxf6nBMmTMDw4cNNt2NjY9G0aVN4eXnZ5C911lxyWLdllbZr81GsX3psw9OzlWvTmmopbmybbSrNbSMi28bQ4RH+/v7466+/0KFDBzz//PP49ddf0b9/fwBA3bp14efnh1deeQUnTpxAw4YNC3VOtVoNtVptybKJ6Cnw2iSyTrw2iYiISheuXvGYB8FD06ZN0a9fv1z7+vfvD6VSiZs3b0pUHREREREREZHtYOiQB39/f0REREAmk+Xafu3aNQgh0KRJE4kqIyIiIiIiIrIdDB3y8fhkVQkJCRgyZAg++OADlC1bVqKqiIiIiIiIiGyHXYYOGRkZuHz5MnQ63ROPvXv3Lt5//300bNgQXbt2RXh4eAlUSERERERERGT77C50mDdvHvz8/FCtWjX4+/tjzpw50Ov1+R7v5+eHLl264NSpU5g6darZkAsiIiIiIiIiyptdhQ5r167F8uXLcezYMcTFxWHMmDGYMmUKwsLCkJGRkevYffv2YcmSJQCATp06wcvLS4qSiYiIiIiIiGyWXYUOS5YswcSJE1GrVi34+flh6tSp2LVrFw4dOoS+ffvm6vGwYcMGvPXWW9i1a5eEFRMRERERERHZLrsKHXQ6HaKjo3Nta9u2LTZv3oyIiAjMnj3btH3BggVYuXIlOnbsWNJlEhEREREREZUKdhU6hIWFYfHixYiNjc21vU2bNpg6dSrmzp2L7OxsAIBMJsPQoUMhl9vVt4iIiIiIiIio2NjVO+qxY8dCrVajT58+SE9Pz7Vv9OjRSE9PR1RUlETVEREREREREZUudhU6qNVqbNy4ERcuXECnTp0QFxdn2peTkwOFQoGgoCAJKyQiIiIiIiIqPewqdACAevXqISIiAjExMQgNDcXcuXOxfv169OrVC+PHj4evr6/UJRIRERERERGVCkqpC5BC/fr1ERkZiXnz5mHNmjUAgGHDhmHMmDESV0ZERERERERUethl6AAAnp6eCA8PR3h4uNSlEBEREREREZVKdje8goiIiIiIiIhKBkMHIiIiIiIiIrIIhg5EREREREREZBEMHYiIiIiIiIjIIhg6EBEREREREZFFMHQgIiIiIiIiIotg6EBEREREREREFsHQgYiIiIiIiIgsgqEDEREREREREVkEQwciIiIiIiIisgiGDkRERERERERkEQwdiIiIiIiIiMgiGDoQERERERERkUUwdCAiIiIiIiIii2DoQEREREREREQWoZS6ACIiIiIiIrIvQqdBwrbWee5TeYXCo8WSEq6ILIWhAxEREREREZUYudIVhnz26ZIjS7QWsjyGDkRERERERFRiFO5VoADg0/0bs3359X4g28U5HYiIiIiIiIjIIhg6EBEREREREZFFMHQgIiIiIiIiIotg6EBEREREREREFsHQgYiIiIiIiIgsgqEDEREREREREVkEl8zMx9SpU1G1alUMGjRI6lKIiIiIiIhsTvatSFwLN18CM8snBBdULmi8dITZPrmqImrJU/F9SRRIJYKhQx4OHDiAWbNmwWAwAACDByIiIiIioiJwKhea774a2sx8952XeQEGS1REUmHokIeyZcuiWrVq6NChA4YOHQrg6YOH1NRUpKammm7HxsYWR4lE9Ix4bRJZJ16bRESlQ+DQJfnuW1vA/ZquGFj8xZCkGDrkoUKFCoiOjsbnn38OAKbgoVWrVoiIiMDrr79e6HPNnz8f06dPN9uenJwMZ2fnYqm3pDz6ItBW2GLNAOsuTt7e3nluL03X5qOs8TkoCluvH2AbCsuWr83S8Bznh22zTcXZtvyuTSKip8XQIQ8ymQyVKlXCxYsXsXjxYgDG4MHX1xczZswo0rkmTJiA4cOHm27HxsaiadOm8PLysslf6qy55LBuyypt1+ajWL/02IanZyvXpjXVUtzYNttUmttGRLaNoUM+atasiTNnziA0NBQTJkzAypUrER8fDycnpyKdR61WQ61WW6hKInpavDaJrBOvTSIiotKFoUM+HoQOV69eRYcOHfDFF18gMjLymed4ICIiIiIisjZKzyuQqzKQsC33ahO65EgovepKVBWVBgwd8lGzZk0sWLAAa9euxUcffYQ33njDtG/v3r0MHYiIiIiIqNSQqzIgU2nMtiu96kLllf9KFERPwtAhH61bt8aYMWMwa9asXIHD4sWLTUtpEhERERERlRZC6wKf7vulLoNKGbsMHTIyMhAbG4uKFStCqcz7WxAcHIxLly7B09PTbJ9cLrdwhURERERERES2z+5Ch3nz5mHq1KnQaDQoU6YM3n//fUycOBEKhcLs2LwCByIiIiIiIms16uB6nE6OzXNfZHIs6noFlnBFZO/s6iP7tWvXYvny5Th27Bji4uIwZswYTJkyBWFhYcjIyMh17L59+7BkyRKJKiUiIiIiIiq608mxiMwndKjrFYhQhg5Uwuyqp8OSJUswceJE1KpVCwAwdepUtGvXDr169ULfvn2xbds2U4+HDRs24Msvv0TVqlXRuXNnKcsmIiIiIiIqtLpegdgf9rbUZRABsLOeDjqdDtHR0bm2tW3bFps3b0ZERARmz55t2r5gwQKsXLkSHTt2LOkyiYiIiIiIiEoFu+rpEBYWhgULFuDNN99EYODDbkVt2rTB1KlTMWfOHEycOBGOjo6QyWQYOnSodMUSERERERHZIWHQ41p46zz3OZULReBQDoO3JXbV02Hs2LFQq9Xo06cP0tPTc+0bPXo00tPTERUVJVF1REREREREdk4uh0xuPsk/AGTfikRW9OkSLoielV31dFCr1di4cSPatm2LTp06YdOmTfD39wcA5OTkQKFQICgoSOIqiYiIiIiI7JNM5QQAqPTxfrN9+fV+IOtmVz0dAKBevXqIiIhATEwMQkNDMXfuXKxfvx69evXC+PHj4evrK3WJRERERERERKWCXfV0eKB+/fqIjIzEvHnzsGbNGgDAsGHDMGbMGIkrIyIiIiIiIio97DJ0AABPT0+Eh4cjPDxc6lKIiIiIiIgsLnbVqHznRFCqNZA7upRwRXkTOg0StpkPpXDwjYRB6ypBRfQs7G54BRERERERkT3Kij6N7FuRee6TO7pA7ij9G3q50hUyZd7hh0ylgVyVUcIV0bOy254ORERERERE9sYxuG6ekzTm1bNACgr3KlAA8On+jdm+28vUJV8QPTP2dCAiIiIiIiIii2DoQEREREREREQWwdCBiIiIiIiIiCyCczoQERERERGRTTBka3AtvOjzTziVC0Xg0CUWqIiehKEDERERERERWb2nXV0jvxU7qGQwdCAiIiIiIiKrp/KrAgAoO9J89Y2CPE3PCCo+nNOBiIiIiIiIiCyCoQMRERERERERWQSHVxAREREREVGpln0rMtcwC51Oh1SlkhNMlgCGDkRERERERFRqOZULzXM7J5gsGQwdiIiIiIiIqNTKqydDYmIiUpf0lqAa+8PQgYiIiIiIiKxGZHIsWm9dbLZdmxyCEGUmVkpQEz09hg5ERERERERkFUK9AvPdd07nUoKVUHFh6EBERERERERWYUnLfvnua7ZmRAlWQsWFS2YSERERERERkUUwdCAiIiIiIiIii+DwCiIiIiIiIhsy6uB6nE6OzXNfZHIs6hYwLwJRSWPoQEREREREZENOJ8fmGy7U9QoscDJGW3dO55LnyhaAcRLKguaEyEv2rUhcC2+d5z6ncqF5LrdJRcPQgYiIiIiIyMbU9QrE/rC3pS6jRIUoM/PdF5lPz4+COJULzXdf9q3IIp+P8sbQgYiIiIiIiKzeZ+prAACf7t+Y7cuv90NBCurFkF/vByo6TiRJRERERERERBbBng5ERERERESlROyqUciKPp3nPpnyCFReTkjYZv4pvi45EkqvupYuz6Iik2OLdb4HKh4MHYiIiIiIiEqJrOjTyL4VCcdg8wBB5eUEuWNWnvdTetWFyiv/OQ6sXUGTZx64ex0H7l7PteKHTqeDUqksMIzgJJPFg6EDERERERGRlUn9bSJS4y/luS/LJwRA3vMOPAgcKn2832zfgx4OPt3N99kKXXJknj01ZgBQlQmFRwvzICC/JUYLmnySk0wWH4YOJUyn0wEAYmOLPruq1JKTk5GZmf+MsdbIFmsGWHdxCwgIgFJZ8K87W742H2Wtz0Fh2Xr9ANtQFLZ6bZaG5zg/bJttKu62FeXa7LLkDTh4OhfbY1uWACCTuohCE0IPOHhDJlOY7buUrkM1cQ8JypPmd6wEZDhcRNrqJma7dPfOQ+lZC1nR0cVeb0lcY6naStDps4GEbLN92oRjAA5AdemY2b5ReZxLp9VhWEYt/Hf3Dhpc+V/eD6gun+dmUdUPQugh+6xvEap/Nlte+bJQ16Y1kgkhhNRF2JOjR4+iadOmUpdBZFdu3bqFcuXKFXgMr02iksdrk8g68doksk7nzp1DrVq1pC6jyBg6lLCsrCycPn0avr6+NpVSxcbGomnTpjhy5AgCA/MfL2VNbLFmgHVbQmFSYVu9Nh9lzc9BYdh6/QDbUFS2eG2Whuc4P2ybbbJE22zx2nwSW/sZYL2WZav1Xrt2DRUrVpS6nCKz/t8QpYyTkxOaNDHv6mQrAgMDn5h8WxtbrBlg3SXN1q/NR9nqc/CArdcPsA3FyVqvTWv5/lgC22abSrpt1nptPomt/QywXsuytXptIeDLi1zqAoiIiIiIiIiodGLoQEREREREREQWwdCBCkWtVmPq1KlQq9VSl1JotlgzwLrp6dn6c2Dr9QNsgz0ozd8fts02lea2FSdb+z6xXstivSWLE0kSERERERERkUWwpwMRERERERERWQRDByIiIiIiIiKyCIYORERERERERGQRDB2IiIiIiIiIyCIYOhARERERERGRRTB0oCLT6/XIzs6Wugy7odFopC6BiIjsGP8O2SY+b6UDn0fL2rp1K/bv3y91GaUeQwcqEr1ejyFDhiA8PFzqUopECIEdO3Zg2bJlOHHihNTlFNqNGzdQp04dnD59WupSiiQlJQU//PADVq9ejTt37khdjl0QQmDXrl1YunQpjh49KnU5T0Wv12Pz5s345ptvcPbsWanLeSpZWVn49ddfsWLFCly9elXqcp5KWloa1q5di++//x4xMTFSl2MTrly5guXLl+P3339HZmam1OUUq8WLF6NHjx5Sl2ERfN7sz9atW9GzZ09kZWVJXUqhbN26FfXr10dOTo7UpRTK2bNn8cYbb2DEiBHQ6/VSl/NEW7duRb9+/TB58mSpSymUK1euYPz48Xjttddw8uRJqcspGkFUSDqdTrz66quiQ4cOIiMjQ+pyCk2j0YgOHTqISpUqidq1awsAolu3biI2Nlbq0gp0/fp1UbFiRfHFF19IXUqRnDhxQgQEBIhGjRoJf39/4ejoKKZNmyb0er3UpZVaWVlZ4vnnnxcVKlQQoaGhAoDo3LmziI6Olrq0QktOThZNmjQR1atXFzVq1BAAxIABA0RSUpLUpRXazZs3RfXq1UVoaKioWLGikMvl4s033xQajUbq0grt9OnTomzZsqJhw4YiMDBQODg4iI8++kjodDqpS7Na3377rVCr1aJ58+bCxcVFBAQEiI0bN0pdVrFYtGiRKF++vLhy5YrUpRQ7Pm/2yc/PTzg5OYnOnTuLzMxMqcsp0JYtW4SPj484ePCg1KUUytGjR4W3t7dYsWKFSE9Pl7qcJ3rw/Z08ebIAII4fPy51SQXas2eP8PHxES+//LJo0KCBUKvVIjExUeqyCo2hAxVKXoFDdna2OHnypNW/sZk4caLo06eP6UVzRESECA4OFhUqVBCXL1+WuLq85RU4xMfHixMnTlj1L3KdTieqVq0qvv/+eyGEEFqtVsyZM0colUrRr18/odVqJa6wdPr4449Ft27dTN/f/fv3i4oVK4qyZcuK8+fPS1xd4QwdOlS8/vrrwmAwCCGMLwZ8fX1FrVq1RExMjMTVFU6nTp3Exx9/LIQQwmAwiNWrVws3NzfRokULkZKSInF1T2YwGERISIhYtmyZEMJ4Pc+fP1+oVCrRs2dPkZ2dLXGF1ufChQvCw8NDXLhwQQghREJCgnj55ZeFTCYTCxculLi6Z5PXG9dz586Z2mrL+LzZr6ZNm4qlS5cKd3d3qw4e8gocrl69Kk6fPm21H+K0a9fOZq6fR7+/er1eVKlSRQwaNEjqsvJ1+/Zt4evrK/bs2SOEECIzM1P4+/uLI0eOSFxZ4TF0oEL56KOPhKOjoylgWLdunfDx8REABADx8ssvW+2b4ZCQELFu3bpc227fvi1q1qwpKleuLJKTk6UpLB9arVbUqFFDdOrUSQhhDHdGjRolFAqFACDc3NzE0qVLJa4yb+fOnRMAzMKFzZs3CwcHBzFixAiJKivdGjZsKFatWpVrW1xcnAgNDRXly5cX8fHxElVWeH5+fmL37t25tl2+fFmUL19e1KtXz+p7C2RlZQmZTCaioqJybT927Jjw8vISnTp1stoXig9cvXpVADD7Xb5jxw7h5OQkBg8eLFFl1uvLL78U7dq1M9v+wQcfCJlMJn7++WcJqnp227dvFwDE77//LoQw9mB70FMQgGjUqJFNf4rO581+DR48WPz444/iwIEDpuAhKSlJTJw40Wpex0ZFRQlHR0fx6aefCiGMvejatGljeh4rVaokDh06JHGV5lQqVa43wV9++aWoXr26KF++vPjkk0+spsfcn3/+aRboLFy4UDg4OIg7d+5IWFn+Pv30U9G2bVvT7ezsbFG+fHkxevRo0atXL/Hdd99JV1whMXSgQrl7966oU6eOaNiwofjuu+9E2bJlxcaNG8Xt27fFihUrhJubm+jbt6/UZeapdevW4u233zbbfvPmTeHn5yeGDx8uQVUF2759u3B0dBSTJ08Wr7zyiujatas4deqUuHz5shg+fLgAIH755RepyzQTGxsrAIiIiAizfWvWrBEAxPbt2yWorHTr1KlTnj/HMTExIigoSAwcOFCCqoqmZs2aYtq0aWbbz58/L9zd3cWkSZMkqKrw9Hq9UKvV4ttvvzXb988//wiVSiUWL14sQWWFl5SUJORyufjzzz/N9v32228CQKnpfl5c1qxZIzw9PUVaWprZvtdee014eHiIhIQECSp7NjqdTgwYMEB4e3uLDRs2CH9/f7FgwQIRHR0tdu7cKapUqSIqV65sNW/SiorPm20+b8Vh1qxZ4sMPPxRCCFPwoFarxaBBg6wqGH7wYd9PP/0kqlSpIj744ANx/fp18e+//4pmzZoJDw8Pce3aNanLzMXLy0t89dVXQggh3n//fRESEiK+/fZbMWnSJKFSqcTEiRMlrtDowoULZqFNWlqa8PDwEFOnTpWmqCeYNWuW8Pb2FlevXhXp6enif//7n6hevbqYO3euGDJkiAAgZs2aJXWZBWLoQIX2IHgAIA4cOJBr3+rVqwUAq+zKvWzZMqFSqcS///5rtm/16tVCpVKJ1NRUCSrL7ebNm7k+zX0QPFSrVk1kZWXlOjYsLEzUq1evhCssnM6dO4vQ0NA8P5nu1auX6NGjhwRVlW7ff/+9UCgU4p9//jHb98svvwiFQmH14/5mzpwpXF1d8/wdMn/+fOHp6Wk1n5LkZ9SoUcLf3z/P+WImTJggatasKUFVRdOzZ09Rs2bNPN+MDRgwwNQDi4zu3bsnvLy8xMiRI832paWlCT8/P7FgwQIJKnt2D97AAhBz587Nte/ChQtCpVKJ5cuXS1Tds+HzZpvPW3HYsGGD6N27txDCOOdXy5YthVwut8qhFh999JEAIF5//fVc25OSkoS/v7949913Jaosb4MGDRLBwcHi6tWrwt/fP1dwN3fuXOHs7GzVf8ffffdd4e/vb5VDCZOTk0VoaKhQKBQiODhYlC9fPldP7QkTJgi1Wm0aomqNGDpQkdy9e1eEhYWZbU9JScn3E+6SlJqaKt58881cL5h1Op1o06aN8Pf3N+v6rNFoBACz7SXt+vXrokKFCqbx4A9s375dfPLJJ2bHL1u2TAQEBJRUeQVKT0/P9Uvu4sWLwt3dXYSFhZmFJatXrxZ16tQp6RJLPYPBIDp16iR8fHzE6dOnc+3TarVCoVCIkydPSlRd4WRlZYl69eqJSpUqiZs3b+bad/PmTQHA6j55jI6OztUVMzExUQQHB4sGDRqYTYB58OBB4eTkVNIlFujEiRNi//79ubZdu3ZNeHp6is6dO5sFh7/88ouoWrVqSZZoE3744QcBQMyePdts3+DBg8W4ceMkqKrodDqduHjxYq43Xg/ewD5+TQohRGhoqAgPDy/JEp/a4sWLxY4dO3JtKy3P261bt8S6detyvQEpLc+bJZw9e1ZUqVJFaDQa0blzZzF48GDxzz//WO0cDx999JHYuXOn2fYXX3zR6noxXr16Vbi7u4s6deqI9u3b59p34MAB4ejoaNWhw/Xr14VCoTDNS2ZtDAaDiImJEbNmzTL7/bR161bh5ORkVb11HsfQgczo9Xoxe/ZsUbZsWeHm5iZGjRolcnJyTPvzmgxw3759wt3dXdIeA6mpqaJFixbijTfeMEv6EhISRGhoaK5JWIQw/hL09fWVNNV8MGnkg/oe/4OX1/d71KhR4uWXXy6pEvN04sQJUa9ePQFAeHt7i8mTJ5vepOzevVs4OzuLtm3b5vrUd/z48WLYsGFSlWzz0tPTxcyZM/P8o52cnCwaNmwoypQpk6t7/IM5BaxlxZl79+6JkSNHCk9PT+Hn52fqiimE8cVz5cqVRXBwcK6eSZs2bRJVqlSRotw83blzRzz//PMCgFAoFLk+TTxz5ozw9fUVISEhuXptfPXVV7nGY0rtzp07wsvLS7i5uZkFD/v27ROurq6iZcuWuSYKnjx5snj11VdLulSr8ccff4i6desKR0dH0blzZ3H79m3TvunTpwsAYvz48aa/J1qtVjRo0ED89NNPUpVcaBs3bhSBgYECgChfvnyuID6v3zcpKSl5/uxYo4JWcrD1523lypXC399fTJkyRURGRubaZ+vPm6Xk5OQIBwcH0b59ezF48GDTm7QDBw6I1q1bW2WvwMdfB+p0OlGrVq08h/NJbceOHcLZ2Vm4uLiIc+fOmba//vrrYsiQIdIVVkj9+/cXDRs2lLqMAo0fP140btw41zX+6quvWv3ra4YOZGb48OGicePG4ueffxazZ88WDg4OYvr06fkef+HCBVGtWjXTjOdSKChweCApKUn06NFDyGQy8fzzz4uRI0cKPz8/8ccff5RwtQ89ukrFlStXhFwuFytWrCjwPmvWrBG+vr7i6tWrJVSludu3bws/Pz+xfPlycfHiRfH5558LtVotQkNDTW9SDh06JCpUqCDUarV47bXXxAsvvCCqVq1qtZP02IJBgwaZJm7N7wVl3759TUtmvvnmm8LPz0/8+uuvElRrTqPRiAYNGoiXXnpJbNiwQYwYMUIAyDWBZGxsrGjXrp1QKBSid+/eYvjw4cLX11fs27dPwsofysjIENWrVxfvvfeeuHr1qvjwww+Fg4NDrmD20qVLon79+sLR0VG8/PLLYtCgQSIgIECcOXNGwspzu3fvnihTpozo2rVrnm9Cjh07JqpUqSLc3NzEkCFDRP/+/UWlSpWsfrUiS1m9erUICAgQy5YtE6tWrRJly5Y1+yRv2bJlwtnZWVSqVEmMHj1aNG7cWPTr18+qP3kSwhimBAUFiS1btogzZ86IOnXqiDfffDPf4+/duyfCwsKseqb3BwqzdKStPm/Xrl0T7u7uhR7WakvPm6W99NJLuQIHW/JgcvEWLVpY7Ypghw4dEpUqVRIeHh5ixIgRonXr1qJp06bi3r17Upf2RAcOHBAAxN9//y11Kfk6evSoUCqVokOHDmLZsmWid+/eomHDhla/ShZDB8rlwIEDomLFirl+cMeNGyfKly9vduyNGzfE4MGDRXBwsFi5cmUJVpnbg8Bh4MCBpsDhxx9/FG3bthV16tQR48ePz/WLbteuXWLcuHHi3XffNeuKXpLyWhbzhRdeEHXr1s3z+I0bN4r27duLpk2bSv7mZebMmeKFF17ItS0qKkpUqFBBVK9e3dQNXqPRiG+++UaMHDlSzJkzxyb+4FiruLg4ERQUJH799Vfh4OCQb/AghBB79+4V48ePF+PHjxf//fdfyRZagDlz5ohevXrl2takSRPxv//9L9c2g8EgNm/eLN555x3xwQcfiEuXLpVkmQUKDw/PNWlnYmKiCAoKEmfOnBFHjx41vYjV6XRi3bp1YtSoUeKTTz7J9am4tQgICBC3b9/OFTwYDAZx6tQpIYRxSa4VK1aIkSNHilmzZlnlJ4AlIT09Xfj5+Yljx46Ztm3cuFEAMAt/o6OjxcyZM8XIkSPF6tWrrf5NjV6vF5UrVxaHDx82bZs5c6aYPHmy+Pvvv3OFTNnZ2WLChAmiUqVK4v3337fKcc+PWrRokfD19TUFDpcvXxaDBg0SISEholu3brnmprK1500IIT7//HPRtWtX022DwSB+/vlnMWHCBLFixQpTEGprz9uz0Gg04siRI+L69esFHpeTk2MVz3Fh633g008/FSEhIWLgwIGSrLxWlHqzsrLEmjVrxKRJk8SqVask+bkr6vf3gREjRkgynLMo9e7YsUM0atRIlC9fXowfPz7PeZisDUMHymXEiBFmAcLOnTvzXAZRCCGOHz8uedKanp4u2rRpIypXrixu3rwp3n//fVG+fHkxbdo08e677wpXV1dRt25dq7sgFyxYkCtwEMLYrRmA2Lt3r9nxSUlJ4uLFiyVT3BNMmjRJdOjQwWz7tWvXhL+/v2mSJio+X331lZg8ebIQwri+9JOCB2sUEhJiFiB88sknonnz5hJVVHTNmzcXt27dMt2eNWuWcHJyEh4eHkImk4m2bdtazVCWJ2nbtq34+++/RWZmpil46Nmzp+jevbvUpVmVH3/80axb8IMlUh9f5tXWnDhxQvTs2dN0Oz09XYSEhAh3d3fh5uYmlEplriWaz549a3V/S/OzbNkyoVAoxPLly8Xx48eFt7e3eP3118Wnn34qGjVqJFQqldi6davUZT61//u//xPNmjUTQhjfRPfo0UOUL19edOzYUSiVStG5c2fT6zNbet6e1u7du4Wvr69wcnISAESHDh2s5jVTXp6m3itXrki2BLY9fH+lZGv1Pg2GDpTLtm3bzLrnnDp1SgAwmxTQmjwIHgICAkTFihVzTeJ26tQp4ezsnOdyfNaoYcOGok+fPlKXUaDdu3cLmUyWZ/ezByHVo58K0rPTaDTi7t27ptu2GDzkNUb6q6++Eo0bN5agmqfzaC+jNWvWiICAALFr1y4hhLFLqaura4HD0azJyJEjxddffy2EMA4bCQgIEADynLTMnl25ciXP1Y+8vLzyXF7UlhgMBtO465ycHNG5c2fRs2dPER8fL3Q6nRgzZoxQqVQ2O6zmQfDg7++f6/ePVqsVXbt2FWXLlrWKT7yfxrZt2wQAceTIETF//nzRtWtX05xQERERQqlUih9++EHiKkvGrVu3RJkyZUyThf7zzz8iNDRUqNVqsw9xbt++LQYNGiTpHGSs17JYr3Vi6EBPdO7cOQHA9McsLi5OvPHGG7nGMFuDB8HDokWLzPYNGjRIdOnSRYKqiu6HH34Qcrlc0jkbCqNLly4iKCgoz25g9evXF/Pnz5egKvvyePBw4cIFMWDAAJt6Ef3NN9+IRo0amW4fPnzYZgLCo0ePirNnz+ba9sYbb5gNPbJWX3zxhRg9erQwGAzitddeEx06dMh3jgcy5+fnJ7Zv3y6EMA6neeedd4rcjdfarFmzJleImZOTIxQKhdnKD7Zk2bJlea6a9GDstrX/rc2PXq8X9erVEyEhIaJjx45m8960bds2z9WvSqMFCxaIzp0759qWkZEhunbtKlxdXcXx48dN2/fv3y9cXFxyDZMraazXslivdZKD7JZer8eNGzeeeJxMJgMAGAwG3L17F+3bt0f58uWhUqksXWKeUlJSkJCQYLbd1dUV27ZtQ8+ePc326fV6VK5cuSTKy1d0dDSys7OfeNxLL72EgIAALFq0qASqerK4uDjMmDEDb7zxBjZt2mTa/sMPP8DFxQVt27ZFVFRUrvs4OjrC19e3pEu1O2FhYdiwYQM2bNiAfv36oWPHjggLC4Ncbju/2mUyGQwGAwDg33//Re/evdG8eXOJqyqcxo0bIyQkJNe2mJgY1K9fX5qCiqhmzZo4ffo0hg0bhhs3bmDz5s3YuHEjWrVqheHDh0Ov10tdolV78LOr1+vx6quv4urVqwgICJC6rGfy6quvQqFQmG7HxsZCLpejTp06Elb1bEaMGIGff/7ZbLter4ezs7PNPmdyuRw//fQTYmNjsWfPHly8eNG0LysrC1evXkWLFi0krLDkyOVyXL582fS3BABcXFywceNG1K1bFy+//DK0Wi0AoFWrVti9ezdmz54tVbms18JYr5WSOvUg6QwZMkT4+/ubfVL3uKioKNOnASEhIWLGjBklVKE5rVYrmjVrJkJDQws9ru3YsWPCw8Oj0DM8W0J8fLyoUKGCeP755ws1TGXmzJnCw8ND8jGYR44cEb6+vqJLly6iVatWAoD45ptvTPtv374t6tWrJ9zc3ER4eLg4fPiwmDx5sqhVq5bVrXVdmi1evFjI5XKrXVu6IN9++62oX7++OHz4sPD397fp7urr1q0TQUFBNjPhYnR0tHBwcBAdOnTINQ9FZmamuHHjhoSV2YaAgACxadMm8dJLL4mwsDCrHoL4NLKzs0XXrl3F2LFjpS6l2GVnZ4v27duLDz74QOpSntmpU6dE+fLlhVqtFt98843Yt2+f6Nq1q+jfv7/UpZWYq1evCqVSKRYuXGi27/r168LJyUmsW7dOgsryxnoti/VaJ4YOdioqKkpUqVJFNGrU6InBw8WLFwUAUbVqVUkDByGE+O2330TLli1F2bJlnxg8REVFidmzZ4uAgACxcePGEqzSXHh4uOjWrZtwc3MrVPCQkJAgAgICJJ1EJisrS1SoUEGsXbvWtO21114T3t7eubrvZ2VliRkzZojKlSsLd3d30bdvXxEbGytFyXbpwoULoly5cjYZOAghxHfffSd8fX1tNnDQ6/XixIkTYuzYsaJy5cpWtVpIYezbt89mJr60NkFBQaJq1aqlLnBISUkRW7ZsEfXr1xeDBg2SfLLo4pScnCw2bdok6tevLwYOHFhq2pacnCwmTZokatasKapVqyamTp1aatpWWJMmTRIODg55/h3p3bu31QVMrNeyWK/1Yehgp8aMGSPmz58vkpOTnxg8xMTECIVCIXngIIQQHTp0EDt37hSXLl16YvCwYsUK8dlnn0m+VJ1OpxOVKlUS165dE3///XeRggcprVu3TvTr1y/XthMnTggANj9uuTRZuHChzQYOQgixfv164ejoaJOBgxDG0GHKlCnim2++kbxnEj297du3F3lC1lq1atlE4FDUtl24cEGMHTvWbI4Aa1TUtl25ckWMHz8+zxWirM3T/EzaM51OJ3r37i0cHR3NJi1u3bq1WLFihUSV5Y31WhbrtT4MHezU2rVrTWv8FiZ4eNIQjJLy9ddfC4PBIIQQhQoerIFOpxPLli0z3S5K8CClb775xmw5sXv37gkAVvPzQKXDg9nziaTw66+/CgCFWgnm1KlTIiYmRgghxOXLl636d7gQT982W8C2Gdla2ywpJydHDBs2TAAQvXv3FsuXLxevvvqqaNKkiVVeq6zXslivdWHoQEKIvIOHuLg4q/+kI6/gITIyUkRFRUlcWcHyCh727t1rdeHJg4DngezsbAFAnD592rQtIiKipMsiIio2jRo1ElOnTn3iErR6vV7UqlVLVK9eXcTFxZVwlU+HbWPb7NHOnTtFv379RPPmzcV7771n9b3QWK9lsV7rwNCBTB4NHv766y8REhIiPv30U6nLeqJHg4e//vpLBAYGit9//13qsp7o0eBh48aNwt/fXxw7dkzqsgqUk5MjAIhTp04JIYRYtGiRqFy5suRDQYiInsbRo0dF48aNhRDmS9Dm5cyZM2LIkCE28akT2/YQ20ZEJC2GDpTLg+ABgFXM4VBYD4IHpVIpfv31V6nLKbQHwYOHh4c4fPiw1OU8kU6nEwDEyZMnTYEDZ7knIlul0Why/e4tzJs8W8G22abS3DYisl+2s5g7lYicnBxkZmZixowZ+Pjjj6Uup9AyMzNhMBjw008/oX///lKXU2gajQaurq7YsWMHmjVrJnU5TySTyQAAX331Fb744gvs3bsX5cuXl7gqIqKn4+zsnOt3b1hYGDZs2IANGzZg4MCB0Ov10Ov1mDFjBtLS0iSstOjYNraNiMhayIQQQuoiyHp06NABHTp0sKnAITs7G7Vq1cLcuXNtKnC4ffs2GjdujI0bN9pE4PCAXC5HpUqVGDgQUam1detW9O3bF3379oUQAunp6Vi/fj0cHR2lLu2ZsW22qTS3jYhKP4YOlEtCQgJ8fHykLqPIWHfJmTNnDl555RUGDkRUqm3atAl9+vRBWFhYqXtzx7bZptLcNiIq3Rg6EBERET1Cr9fj1VdfLZWfJrNttqk0t42ISj/O6UBERET0iE2bNpXaN3dsm20qzW0jotKPPR2IiIiIHqPX66FQKKQuwyLYNttUmttGRKUbQwciIiIiIiIisggOryAiIiIiIiIii2DoQEREREREREQWwdCBiIiIiIiIiCyCoQMRERERERERWQRDByIiIiIiIiKyCIYORERERERERGQRDB2IiIiIiIiIyCIYOhARERERERGRRTB0ICIiIiIiIiKLYOhARERERERERBbB0IGIiIiIiIiILIKhAxERERERERFZBEMHIiIiIiIiIrIIhg5EREREREREZBEMHYiIiIiIiIjIIhg6EBEREREREZFFMHQgIiIiIiIiIotg6EBEREREREREFsHQgYiIiIiIiIgsgqEDEREREREREVkEQwciIiIiIiIisgiGDkRERERERERkEQwdiIiIiIiIiMgiGDoQERERERERkUUwdCAiIiIiIiIii2DoQJSH9evXY/78+fnu//vvvzFp0iTEx8eXYFVE9mvOnDnYsmWL2fbw8HDMmzfPbPu6devw+eefl0RpRERERFQAhg5Eebhw4QKmTJkCg8Fgti8nJwevvfYaTp48CV9fXwmqI7I/P/zwA/74449c2yIiIjBlyhSzgDAjIwOjRo1CSkpKSZZIRERERHlg6ECUh9DQUGg0Gly7ds1s35dffombN2/iiy++kKAyIvvk4eFhFiJ88cUXqFChgtn2VatWQaPR4K233irJEomIiIgoD0qpCyCyRnXq1AEAnD17FlWqVDFtT0xMxMyZM/HWW2+hVq1aUpVHZHc8PT2Rmppqun358mVs2bIFS5YswciRI6HVaqFSqSCEwKJFi/DKK68gICBAwoqJ7NeqVavg6+uLGjVqYMeOHUhISMDkyZPh4OAgdWlEdkmr1WLWrFkQQkAmk8HLywvPPfccGjRoIHVpZCfY04EoDxUrVoS7uzvOnj2ba/v06dOhUCgwbdo0aQojslOenp65ejQsXLgQLVq0QKdOnQDAFEhs374dFy5cwLhx46Qok4gATJ48GZMmTUKPHj1w6dIluLi4MHAgklBKSgqEEAAAvV6PY8eOoUWLFvjqq68krozsBXs6EOVBJpOhdu3auUKHixcvYunSpViwYAG8vLwkrI7I/nh4eJiChXv37mHVqlVYvXo11Go1AOMLKm9vbyxcuBDt27dH/fr1JayWyH7FxMTgzp07qFmzJrZt2wZnZ2epSyKyez4+PmYfmFWuXBnffvstRo8eLU1RZFcYOhDlIzQ0FEePHjXdfu+991C9enWMHDlSwqqI7NOjPR2WL18Of39/9O7dG3q9HoAxdDh//jx27dqFTZs2SVkqkV07fvw4AGDJkiUMHIisyJkzZ3D06FHExsYiJycH+/btg0KhkLosshMcXkGUjzp16iAqKgp6vR579+7FH3/8gQULFvAXNJEEHszpoNfrsXjxYowdOxZyuRwqlQqOjo5ISUnBggULULVqVYSFhUldLpHdOn78OGrWrImaNWtKXQoRwRjKd+vWDZ06dcLevXtNvQYvXbpkmsOMyNLY04EoH6GhocjKysKlS5fw7rvvonfv3qbx40RUsjw8PJCWloZff/0VqampeP3110371Go1rl27hjVr1mDu3LmQy5mnE0nl+PHjaNy4sdRlENF9M2fOxLlz5xAVFQVPT08AQFJSEj799FNeq1Ri+MqMKB+hoaEAgEmTJuHs2bOYN2+exBUR2S9PT08IIRAeHo6RI0fC1dXVtE+tVuPzzz+Hg4MDhg4dKl2RRITjx4+jUaNGUpdBRPddvXoVFStWNAUOWq0WI0eORE5ODkMHKjHs6UCUDx8fHwQEBGDTpk147733ULVqValLIrJbD14sXbx4Ee+8806ufWq1Gv/99x/ef//9XGEEEZWs2NhYxMbGomHDhlKXQkT3vfTSS3jppZfw8ssvIygoCHv37oW/vz+USiUnXaYSw9CBqABz585FTEwMRo0aJXUpRHatdu3a+PTTTxEcHIyyZcvm2vfuu+/i1q1bGDJkiETVEREA5OTkYOrUqQwdiKzIiy++iAoVKiAiIgJqtRrjxo3D9evX0b17dzg5OUldHtkJmXiwaCsRERERERERUTHinA5EREREREREZBEMHYiIiIiIiIjIIhg6EBEREREREZFFMHQgIiIiIiIiIotg6EBEREREREREFsHQgYiIiIiIiIgsgqEDEREREREREVkEQwciIiIiIiIisgiGDkT0/+3YsQAAAADAIH/rUewrjAAAABbSAQAAAFhIBwAAAGAhHQAAAICFdAAAAAAW0gEAAABYBHB+S/VP+XmMAAAAAElFTkSuQmCC", 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", "text/plain": [ "<Figure size 1067x1067 with 16 Axes>" ] @@ -583,6 +631,7 @@ " (\"L0\", plotstyle.COLOURS[1]),\n", " (\"L2y\", plotstyle.COLOURS[4]),\n", " (\"Lgp\", plotstyle.COLOURS[2]),\n", + " (\"Lgpn\", plotstyle.COLOURS[0]),\n", "]\n", "for name, colour in rungs:\n", " fig = corner.corner(\n", @@ -608,32 +657,55 @@ }, { "cell_type": "markdown", - "id": "abb4d17e", + "id": "79fd1158", "metadata": {}, "source": [ "## What each rung predicts (recipe 17)\n", "\n", - "Evidence says which error model the data prefer. It does not say whether any of\n", - "them describes the data *well*, and for that we want the posterior predictive:\n", - "draws of $y_m(\\theta) + \\text{noise}$ with the covariance each rung declared,\n", - "not merely the spread of the model curve.\n", + "Evidence tells us which error model the data prefer. It doesn't tell us whether *any* of them describes the data well, and for that we want the [posterior predictive](https://en.wikipedia.org/wiki/Posterior_predictive_distribution). By that we mean draws of $y_m(\\theta)$ plus noise with the covariance each rung declared, not just the spread of the model curve.\n", "\n", - "`predictive_draws` gives exactly that — the draws include $\\Sigma$, so a rung\n", - "with a large inferred noise produces wide draws even where its model curve is\n", - "sharp. We compare the three log-space rungs at the measured angles, then check\n", - "their calibration." + "`predictive_draws` gives exactly that. Its draws include $\\Sigma$, so a rung with a large inferred noise produces wide draws even where its model curve is sharp. We'll compare the log-space rungs at the measured angles, and then check how well calibrated they are.\n", + "\n", + "It's worth being clear about which terms these draws carry: *all* of them. A draw of the model plus its discrepancy alone is a statement about the model, not a prediction of a measurement, and comparing it with data would be comparing two different kinds of thing. The `gp_discrepancy` notebook writes out the equations for each. A coverage check only means something against the full predictive, which is what `predictive_draws` gives by default." ] }, { "cell_type": "code", - "execution_count": 12, - "id": "d4c5411c", + "execution_count": 17, + "id": "2e8f4810", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:51:15.028113Z", - "iopub.status.busy": "2026-09-12T03:51:15.027912Z", - "iopub.status.idle": "2026-09-12T03:51:17.857497Z", - "shell.execute_reply": "2026-09-12T03:51:17.856682Z" + "iopub.execute_input": "2026-09-14T22:24:35.966589Z", + "iopub.status.busy": "2026-09-14T22:24:35.966444Z", + "iopub.status.idle": "2026-09-14T22:24:39.005456Z", + "shell.execute_reply": "2026-09-14T22:24:39.004565Z" + } + }, + "outputs": [], + "source": [ + "levels = np.linspace(0.1, 0.9, 9)\n", + "draws, coverage, cov68, width68 = {}, {}, {}, {}\n", + "for i, (name, _) in enumerate(rungs):\n", + " p = problems[name]\n", + " c = p.constraints[0]\n", + " draws[name] = rx.diagnostics.predictive_draws(\n", + " p, samples[name][::20], n_rep=2, rng=i, return_draws=True\n", + " )\n", + " coverage[name] = rx.diagnostics.coverage_curve(draws[name], c.y[c.active], levels)\n", + " cov68[name] = rx.diagnostics.coverage_curve(draws[name], c.y[c.active], [0.68])[0]\n", + " width68[name] = rx.diagnostics.sharpness(draws[name], transform=np.exp).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "763a4854", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T22:24:39.006843Z", + "iopub.status.busy": "2026-09-14T22:24:39.006692Z", + "iopub.status.idle": "2026-09-14T22:24:39.009578Z", + "shell.execute_reply": "2026-09-14T22:24:39.008947Z" } }, "outputs": [ @@ -641,57 +713,37 @@ "name": "stdout", "output_type": "stream", "text": [ - "L0 68 % predictive width = 0.076 (ratio units) coverage at 0.68 = 0.79\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "L2y 68 % predictive width = 0.068 (ratio units) coverage at 0.68 = 0.81\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Lgp 68 % predictive width = 0.072 (ratio units) coverage at 0.68 = 0.78\n" + "L0 68 % predictive width = 0.076 (ratio units) coverage at 0.68 = 0.79\n", + "L2y 68 % predictive width = 0.068 (ratio units) coverage at 0.68 = 0.81\n", + "Lgp 68 % predictive width = 0.072 (ratio units) coverage at 0.68 = 0.78\n", + "Lgpn 68 % predictive width = 0.068 (ratio units) coverage at 0.68 = 0.77\n" ] } ], "source": [ - "levels = np.linspace(0.1, 0.9, 9)\n", - "draws, coverage = {}, {}\n", - "for i, (name, _) in enumerate(rungs):\n", - " p = problems[name]\n", - " c = p.constraints[0]\n", - " draws[name] = rx.diagnostics.predictive_draws(\n", - " p, samples[name][::20], n_rep=2, rng=i\n", - " )\n", - " coverage[name] = rx.diagnostics.coverage_curve(draws[name], c.y[c.active], levels)\n", - " width = rx.diagnostics.sharpness(draws[name], transform=np.exp).mean()\n", + "for name, _ in rungs:\n", " print(\n", - " f\"{name:4s} 68 % predictive width = {width:6.3f} (ratio units) \"\n", - " f\"coverage at 0.68 = {rx.diagnostics.coverage_curve(draws[name], c.y[c.active], [0.68])[0]:.2f}\"\n", + " f\"{name:4s} 68 % predictive width = {width68[name]:6.3f} (ratio units) \"\n", + " f\"coverage at 0.68 = {cov68[name]:.2f}\"\n", " )" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 19, "id": "a3bde3f9", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:51:17.859125Z", - "iopub.status.busy": "2026-09-12T03:51:17.858940Z", - "iopub.status.idle": "2026-09-12T03:51:18.403186Z", - "shell.execute_reply": "2026-09-12T03:51:18.402408Z" + "iopub.execute_input": "2026-09-14T22:24:39.010800Z", + "iopub.status.busy": "2026-09-14T22:24:39.010674Z", + "iopub.status.idle": "2026-09-14T22:24:39.410523Z", + "shell.execute_reply": "2026-09-14T22:24:39.409904Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 704x440 with 1 Axes>" ] @@ -726,20 +778,20 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 20, "id": "a7d8e5e8", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:51:18.405724Z", - "iopub.status.busy": "2026-09-12T03:51:18.405523Z", - "iopub.status.idle": "2026-09-12T03:51:18.547540Z", - "shell.execute_reply": "2026-09-12T03:51:18.546663Z" + "iopub.execute_input": "2026-09-14T22:24:39.412512Z", + "iopub.status.busy": "2026-09-14T22:24:39.412335Z", + "iopub.status.idle": "2026-09-14T22:24:39.513172Z", + "shell.execute_reply": "2026-09-14T22:24:39.512136Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 506x484 with 1 Axes>" ] @@ -766,56 +818,55 @@ }, { "cell_type": "markdown", - "id": "fbf8b3b2", + "id": "8fdf4922", "metadata": {}, "source": [ - "### Are any of them calibrated?\n", + "### Where the potential fails, not just how badly\n", "\n", - "In sample, all three rungs *over*-cover: 79 %, 81 % and 78 % of the measured\n", - "points fall inside their nominal 68 % intervals, with predictive widths of\n", - "0.076, 0.068 and 0.072 in ratio units. The inferred noise is a little more\n", - "generous than the data need, which is what we should expect when one noise\n", - "parameter has to cover a diffraction pattern whose model error is nowhere near\n", - "uniform in angle.\n", + "The two GP rungs differ in only one respect: `Lgp` has a constant amplitude, while `Lgpn` has an amplitude that grows with angle. To see what that difference buys us, we can ask each of them for the mean-zero predictive about its own model on a fine angular grid. That's `grid_draws` with `terms=[gp]`: the kernel, and nothing else.\n", "\n", - "That is the easy question, though: these are the points the fit was shown. The\n", - "held-out section below asks the hard one, and there the three rungs separate\n", - "sharply — 49 %, 60 % and 80 % of the backward angles land inside the nominal\n", - "68 % band. Being well calibrated on the data we fitted is no evidence at all\n", - "that we will be calibrated on the data we did not." + "Let's be careful about what goes into that envelope. It carries two things: the posterior spread of the potential itself, and the discrepancy the kernel declares. With a constant amplitude, the discrepancy part is the same at every angle, so whatever shape the envelope has comes almost entirely from the spread of the potential. Sure enough, the `Lgp` envelope is 1.24 wide at 20 degrees, 2.01 at 90 and 1.42 at 170, so it's only 1.1 times wider at the back than at the front. Its *error model* is as unsure about forward angles as about backward ones. The `Lgpn` envelope is 0.52, 1.77 and 2.92 wide at the same angles, a back-to-front ratio of 5.6. Only `Lgpn` is telling us **where** the potential fails.\n", + "\n", + "Keep in mind that these envelopes are statements about the model, not predictions of a measurement. They carry no experimental term, so we shouldn't lay them over the data. That's exactly the distinction `terms=` encodes, and the `gp_discrepancy` notebook spells out the equations." ] }, { - "cell_type": "markdown", - "id": "50888ae6", - "metadata": {}, + "cell_type": "code", + "execution_count": 21, + "id": "5406536d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T22:24:39.514623Z", + "iopub.status.busy": "2026-09-14T22:24:39.514448Z", + "iopub.status.idle": "2026-09-14T22:24:58.332981Z", + "shell.execute_reply": "2026-09-14T22:24:58.332222Z" + } + }, + "outputs": [], "source": [ - "## Hold out the backward angles (recipe 11)\n", - "\n", - "Fit each rung below 90 degrees and score the points above it: `masked_where`\n", - "keeps every object, `complement()` flips the mask, and a chain from the fit\n", - "scores the held-out problem directly. The joint held-out log predictive is a\n", - "different question from the evidence: not \"which model explains the data we\n", - "fitted\" but \"which model predicts what it has not seen\".\n", - "\n", - "The normalisation of `L2y` and the Gaussian process of `Lgp` both couple the\n", - "angles we fit to the ones we hold out, so the honest score is not the marginal\n", - "density of the held-out points but the conditional\n", - "$p(y_\\mathrm{held} \\mid y_\\mathrm{fit}, \\theta)$: passing `given=p_fit` to\n", - "both diagnostics computes exactly that (recipe 11). For `L0` nothing couples\n", - "the two, and the conditional is the marginal." + "gp_terms = {\"Lgp\": gp, \"Lgpn\": gp_n}\n", + "envelopes = {}\n", + "for name, term in gp_terms.items():\n", + " p = problems[name]\n", + " band = rx.predictive.grid_draws(\n", + " p, omp.bind(x_fine, meta), x_fine, samples[name], terms=[term],\n", + " levels=(16, 84), rng=3,\n", + " ) # fmt: skip\n", + " theta_med = np.median(samples[name], axis=0)\n", + " mu = np.log(on_fine(*theta_med[p.columns(params)]))\n", + " envelopes[name] = (band[1] - band[0], band[0] - mu, band[1] - mu)" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "a8a21c91", + "execution_count": 22, + "id": "43f8fe9b", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:51:18.549537Z", - "iopub.status.busy": "2026-09-12T03:51:18.549320Z", - "iopub.status.idle": "2026-09-12T03:59:22.057021Z", - "shell.execute_reply": "2026-09-12T03:59:22.056428Z" + "iopub.execute_input": "2026-09-14T22:24:58.334592Z", + "iopub.status.busy": "2026-09-14T22:24:58.334388Z", + "iopub.status.idle": "2026-09-14T22:24:58.338131Z", + "shell.execute_reply": "2026-09-14T22:24:58.337582Z" } }, "outputs": [ @@ -823,24 +874,110 @@ "name": "stdout", "output_type": "stream", "text": [ - "L0 held-out log predictive = -60.50 68 % coverage of the held-out points = 0.49\n" + "Lgp 68 % envelope about the model at 20, 90, 170 deg: 1.235 2.012 1.416 back/front = 1.1\n", + "Lgpn 68 % envelope about the model at 20, 90, 170 deg: 0.522 1.769 2.923 back/front = 5.6\n" ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "L2y held-out log predictive = -55.34 68 % coverage of the held-out points = 0.60\n" - ] - }, + } + ], + "source": [ + "for name, (w, _, _) in envelopes.items():\n", + " at = [np.interp(np.deg2rad(a), x_fine, w) for a in (20, 90, 170)]\n", + " print(\n", + " f\"{name:5s} 68 % envelope about the model at 20, 90, 170 deg: \"\n", + " + \" \".join(f\"{v:.3f}\" for v in at)\n", + " + f\" back/front = {at[2] / at[0]:.1f}\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a187daf6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T22:24:58.339809Z", + "iopub.status.busy": "2026-09-14T22:24:58.339622Z", + "iopub.status.idle": "2026-09-14T22:24:58.483145Z", + "shell.execute_reply": "2026-09-14T22:24:58.482513Z" + } + }, + "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Lgp held-out log predictive = -50.45 68 % coverage of the held-out points = 0.80\n" - ] + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 704x440 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], + "source": [ + "fig, ax = plt.subplots()\n", + "for (name, colour), hatch in zip(\n", + " ((\"Lgp\", plotstyle.COLOURS[2]), (\"Lgpn\", plotstyle.COLOURS[0])),\n", + " plotstyle.HATCHES,\n", + "):\n", + " _, lo, hi = envelopes[name]\n", + " plotstyle.band(\n", + " ax,\n", + " np.rad2deg(x_fine),\n", + " lo,\n", + " hi,\n", + " color=colour,\n", + " hatch=hatch,\n", + " label=f\"{name}, mean-zero predictive about the model (68 %)\",\n", + " )\n", + "ax.axhline(0.0, color=\"k\", lw=0.8)\n", + "ax.set(\n", + " xlabel=r\"$\\theta_{cm}$ [deg]\",\n", + " ylabel=\"log departure from the fitted model\",\n", + " title=\"Mean-zero predictive: how wrong the potential may be, and where\",\n", + ")\n", + "ax.legend(fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0ad4acac", + "metadata": {}, + "source": [ + "### Are any of them calibrated?\n", + "\n", + "On the data they were fitted to, all four rungs *over*-cover. 79 %, 81 %, 78 % and 77 % of the measured points fall inside their nominal 68 % intervals, with predictive widths of 0.076, 0.068, 0.072 and 0.068 in ratio units. So the inferred noise is a little more generous than the data need. We should expect that when one noise parameter has to cover a diffraction pattern whose model error is nowhere near uniform in angle. Notice also how little separates the rungs here.\n", + "\n", + "That's the easy question, though, because these are the points each fit was shown. The held-out section below asks the hard one, and there the rungs separate sharply: 49 %, 60 %, 80 % and 63 % of the backward angles land inside the nominal 68 % band. Being well calibrated on the data we fitted is no evidence at all that we'll be calibrated on the data we didn't." + ] + }, + { + "cell_type": "markdown", + "id": "03760dbe", + "metadata": {}, + "source": [ + "## Hold out the backward angles (recipe 11)\n", + "\n", + "Now we'll fit each rung only to the angles below 90 degrees, and score how well it predicts the angles above. This is a form of [cross-validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics)), and the score we'll use, the log predictive density of the held-out points, is the one [Vehtari, Gelman & Gabry (2017)](https://doi.org/10.1007/s11222-016-9696-4) recommend. In `rxmc` this takes very little code: `masked_where` keeps every object and just masks the points, `complement()` flips the mask, and a chain from the fit can score the held-out problem directly.\n", + "\n", + "The joint held-out log predictive answers a different question from the evidence. The evidence asks \"which model best explains the data we fitted?\", while the held-out score asks \"which model best predicts what it hasn't seen?\".\n", + "\n", + "There's one subtlety. In `L2y` the normalisation, and in the GP rungs the kernel, *couple* the angles we fit to the ones we hold out: knowing the residuals on one side of the cut tells us something about the other. So the honest score isn't the marginal density of the held-out points, but the conditional $p(y_\\mathrm{held} \\mid y_\\mathrm{fit}, \\theta)$. Passing `given=p_fit` to both diagnostics computes exactly that (recipe 11). For `L0` nothing couples the two sides, and the conditional is the same as the marginal." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "c332125c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T22:24:58.484594Z", + "iopub.status.busy": "2026-09-14T22:24:58.484447Z", + "iopub.status.idle": "2026-09-14T22:32:26.084068Z", + "shell.execute_reply": "2026-09-14T22:32:26.083278Z" + } + }, + "outputs": [], "source": [ "cut = np.deg2rad(90.0)\n", "scores = {}\n", @@ -853,39 +990,98 @@ " s = res.samples_equal(rstate=np.random.default_rng(10 + i))\n", " lp = rx.diagnostics.heldout_log_predictive(p_held, s[::5], given=p_fit)\n", " held_draws = rx.diagnostics.predictive_draws(\n", - " p_held, s[::20], n_rep=2, rng=i, given=p_fit\n", + " p_held, s[::20], n_rep=2, rng=i, given=p_fit, return_draws=True\n", " )\n", " h = p_held.constraints[0]\n", - " cov68 = rx.diagnostics.coverage_curve(held_draws, h.y[h.active], [0.68])[0]\n", " scores[name] = (\n", " rx.diagnostics.log_posterior_predictive(lp),\n", - " cov68,\n", + " rx.diagnostics.coverage_curve(held_draws, h.y[h.active], [0.68])[0],\n", " s,\n", " p_fit,\n", " p_held,\n", - " )\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "fe6d6a4c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T22:32:26.086690Z", + "iopub.status.busy": "2026-09-14T22:32:26.086463Z", + "iopub.status.idle": "2026-09-14T22:32:26.092323Z", + "shell.execute_reply": "2026-09-14T22:32:26.091286Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L0 held-out log predictive = -60.50 68 % coverage of the held-out points = 0.49\n", + "L2y held-out log predictive = -55.34 68 % coverage of the held-out points = 0.60\n", + "Lgp held-out log predictive = -50.45 68 % coverage of the held-out points = 0.80\n", + "Lgpn held-out log predictive = -47.78 68 % coverage of the held-out points = 0.63\n" + ] + } + ], + "source": [ + "for name, (score, cov, *_) in scores.items():\n", " print(\n", - " f\"{name:4s} held-out log predictive = {scores[name][0]:8.2f} \"\n", - " f\"68 % coverage of the held-out points = {cov68:.2f}\"\n", + " f\"{name:4s} held-out log predictive = {score:8.2f} \"\n", + " f\"68 % coverage of the held-out points = {cov:.2f}\"\n", " )" ] }, + { + "cell_type": "markdown", + "id": "1a60b24f", + "metadata": {}, + "source": [ + "Finally, let's draw each rung's forecast past the cut, on the fine angular grid. Every term on this ladder is a function of angle and of the prediction: the noise, the normalisation mode, the kernel and its amplitude. That means `grid_draws` can carry the whole error model onto the grid, and the band it gives is a prediction of a *measurement*, which we can honestly lay over the held-out data.\n", + "\n", + "One caveat: this is each rung's marginal predictive, not conditioned on the forward angles. For the rungs whose terms couple the two sides of the cut, it's therefore wider than the conditional predictive we used for the scores above." + ] + }, { "cell_type": "code", - "execution_count": 16, - "id": "3f533a77", + "execution_count": 26, + "id": "a92c3638", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T22:32:26.094259Z", + "iopub.status.busy": "2026-09-14T22:32:26.094052Z", + "iopub.status.idle": "2026-09-14T22:32:27.232848Z", + "shell.execute_reply": "2026-09-14T22:32:27.232183Z" + } + }, + "outputs": [], + "source": [ + "forecast = {}\n", + "for i, (name, _) in enumerate(rungs):\n", + " _, _, s, p_fit, _ = scores[name]\n", + " forecast[name] = rx.predictive.grid_draws(\n", + " p_fit, on_fine, x_fine, s[::25], n_rep=2, physical=True, levels=(5, 95), rng=i\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "f2fcd9d3", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:59:22.063074Z", - "iopub.status.busy": "2026-09-12T03:59:22.062575Z", - "iopub.status.idle": "2026-09-12T03:59:23.430104Z", - "shell.execute_reply": "2026-09-12T03:59:23.429084Z" + "iopub.execute_input": "2026-09-14T22:32:27.234389Z", + "iopub.status.busy": "2026-09-14T22:32:27.234236Z", + "iopub.status.idle": "2026-09-14T22:32:27.552482Z", + "shell.execute_reply": "2026-09-14T22:32:27.551723Z" } }, "outputs": [ { "data": { - "image/png": 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", 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6aHnuuedOs+fKK6+UhmG0lO2M8ZlPm3NhjxahH/nIR+R111037d/UCZpixYoVslAoyLPOOksuXLhQvvrVr24RqlJuXWgAcsOGDTN+PjAwIL///e/L6667rimEC4WC/MAHPjCtjYGBgZbvPvfccxKQX/jCF6SUEz9WmqbJK664YtpxLr/8cqlpmhwZGZFSzi5C52LPbP2tVCrSNE35/ve/f9pnUw+sp556qmnnTA/q888/f84idC5jMtd+vfDCCxKQn/zkJ7d43B0tQgE5OjraUn7NNddIwzBkFEVSyrmP60yUSiVpGIZ83/vet0U7d5QInQ9vfvObpWVZ8plnnmmWPfnkk9I0TQnIb33rW80+API973nPtDYeeeQRCcjPfe5zWzzWf/zHf8j29nZ5xRVXyP33319eeumlMo5jefzxx8uPfexjslKpyIMPPlieeeaZ8v3vf7/M5XLy7rvv3mKbO/q+3N5rcD7XyZaeS5s/G+bzXNna825z5nMfz9b217/+dQm0iKQpLrjgAnncccdJKaW84YYbJCCfffbZljqDg4MS2KoI/cpXviIBee+9926xT/MRoXEcy8WLF8sLLrigpd4rXvGKpt3z6eNcmWt7UyJ0c5Ej5cZr6j//8z9bytesWTPrGFx66aXSNM2miL/gggukrutybGxsTnZfcMEF0rKslpcAKaU8/vjjpWEYslqttpSfdNJJ8owzzpi3batXr5aAvO6666bVO+WUU1oE0cDAgNQ0TX70ox+dVvd73/ueBOTq1aullNsvQjcduyk+/OEPy0wm0/x76mX2Ix/5yLyPMVcRujPGZz5tzpU9ek/oXB2TDj74YL7+9a/zrne9i2KxyM9//vNpS4Jbo1AosHjx4mnlP/jBD/jABz7Acccdx4knnkixWETTNAzDmBYepb29nQULFkyzzbZtnn32WWBi75uUkiOPPHLasY466iiklKxYsWLGPTLztWcmnnnmGeI4Zv369XzmM59pLn9KKenr6wPghRdeoFKpIKXk8MMPn9bGEUccwS233LLVY8HcxmSu/TrwwAN597vfzfXXX8/PfvYzzjnnHE4//fSW/Xg7g+7ubjo7O1vKFixYQJIkDA4OsmTJkjmP68tf/vJp7T/77LMkScKxxx670/qwrXzjG99g9erVnHjiibztbW9DSskvf/lLzj77bG655RaKxSJAc19oEATT2pjyop+qMxsf/ehHOfHEE3nwwQf52te+xvnnn88Xv/hFyuUyn/70p/nyl79MGIbcdtttGIaBlJJPfvKT3HXXXVtsd0felyeddNJ2XYPzvU5mey5tznyfK3Ntd4q53seztf3EE08A8Jvf/IZbb70VOTEBAsDg4CAvvPACAM899xymaU7b99vb2zvrM3FTnnrqKTRN4xWveMWc+7Y1DMPg3e9+N1/4whfo7+9n0aJFPP744zz22GN84xvfaNabax/nynzbm+lZPdtny5cvB5j1evnhD3/IypUrOeqoo4CJ511HR8ecbV+6dGlz/+cUPT09LF68eNp90tPT03INzdW2gYGBGfsGE79Rd999d/Pvp556CiklK1eunHbfrV69Gpi473ZE1I2Z+r5gwQJc16VcLtPW1sbTTz8NsFOf+c899xywY8cnSZI5tzlX9mgROh9++9vfAhNetitWrGDp0qXz+n57e/u0Mtd1ueKKK3jLW97C//zP/7R89u///u/T6k+duJnKpvaDbcmrd2qPz2x15mvPlo6RSqWmxW7cf//9ue666zjkkEMYHx9vsX9TZiqbjbmMyXz69f3vf5/LL7+cW265hXvuuYcbb7yRfD7PL37xC0455ZQ52zUfZhJPU2HCpvZhznVct8TWxtU0zRlFXqPR2OL3tofe3l4eeugh7rjjDp566ikcx+HKK6/krrvu4pZbbmn+UDiOw4IFC9iwYcO0NqbK5hLX88QTT+TEE08EYNWqVXz605/mlltuIZVK8fjjj3PiiSc2x/7UU0+dFrx8Jnb0fbk91+B8r5OZnkszMd/nylzbnWIuY7iltoUQzf2YmztdvPGNb5w2Ftvz3NlUrO0oLrvsMj73uc/x/e9/n0984hPceOONpNPppjMTzL+PW2O+7W3pnG7+2c6+XmZyxDQMY9byqefottg2l2tlS/fdoYceynXXXbfDwr7N5fdiNjvngmmaTTG4KbP9DuzI8Vm5cuWc25wrLwoR+pWvfIWf/exnfOc73+H73/8+l1xyCY899hiLFi1q1tF1fd6DtG7dOhqNBmeeeWZL+bPPPkutVptWv1wuN9+Up1ixYgVhGDbfHA455BA0TePJJ5+c9v2pt/jZAvHPx57Z+nv44Ydjmib77rvvFrMbjI6OomkazzzzzLTPpt7i5sJcxmS+43z88cdz/PHHAzAwMMDRRx/NZz7zGf74xz8C8z/X23JtbM5cx3UmjjjiCEzTbHHymYnFixdz3333TSuf6XzsiD5NYRgG5557Lueee26z7KqrruLwww/niCOOaJadeuqp3HLLLQRB0OJNee+992KaJieddNK8jvv+97+fSy+9tEXYbdqnqR/prbEz7sttvQa35zrZEtvzXJkLcxnDLXH00UcjpeSiiy7i6KOPnrXey172MuI45rnnnmtpd3BwkLGxsTkdByaiTExFWpgJTdPmdX8ccMABnHHGGXzve9/jox/9KD/+8Y95y1ve0lwJmDr2XPo4V3Z0e5ty2GGHAfDkk0/yjne8o+Wzp556CsuyOPDAA3foMefKXG2bGvtnnnmGt7zlLS31Nn8mHnnkkWiaxsEHH7zDMwVtC1P2PPLII7zrXe+a13cXL17MqlWrppVv3ueXvexlwI4dn6kV5rm0OVf2au94mPCE/Od//mfe97738d73vpef/exnSCm5+OKLW946FixYQLlcnleqyX333RfLslq82sIw5FOf+hSFQmFa/Xw+zxe+8IXm30IIrr32WjKZDH/7t38LQFdXF29605v44Q9/yPPPP9+s+9xzz/GDH/yAt771rbMue8zHntn6297ezmWXXca3v/3tpjfvpkylq+vq6uINb3gDN954I+vXr29+fu+99/LYY4/NaN9MzGVM5tqvNWvWsGzZspb2u7u7KRQKLTMF8z3X23JtbM5cx3UmisUi73nPe/jhD384TWQ+8cQTzTfcU045hTVr1vDggw82P7/77ruby3absqU+9ff3c80118wpNeHKlSsZHh5uKfvpT3/KH/7wh2keyx/84Aep1Wp8/etfbznWD3/4Qy655JJpWxq2xH//93+zYsWKlmvnuOOO4/7772/e13/5y1/mtJy1I+/L7b0Gt+c62RLb81yZC3MZwy3xrne9i97eXq688kpc1235rFqtcu+99wITGcoymQzXXXddizf5v/3bv834zN2cd7zjHfT09HD11VdTrVZb7H3ggQeafy9YsIChoaGttrcpl112GS+88AIf+chHKJVKXHbZZdvUR5hYubvmmmum3Vvb2t582WeffTjnnHP47//+7xbP5yeeeIKf/exnvPOd79zq9pmdxVxt22effXjta1/Lt771LQYHB5v1brvttmnRSBYtWsQll1zCV77ylWn3r5Ryl6dpXbhwIW9/+9v59re/Pe339OGHH97ib9Epp5zCM8880zJBdOutt7bc98BOGZ/5tDlX9uiZ0C996UszLgOcf/75nHzyyYyNjfE3f/M3HHnkkXzlK18BJt4SfvKTn3DOOefw8Y9/nBtuuAGAiy++mBtuuIELLriAV73qVei6PmvsyCnS6TSf//znueqqq+jv7+eAAw7grrvu4oMf/CCPP/74tPodHR0ceuihnHLKKRx77LE88MADPPHEE/zwhz9s2SP1rW99iwsvvJDjjz+eN73pTc19dscccwzf/OY3d4g9W+rvl7/8ZTzP44QTTuD888/noIMOYnh4mIcffpgDDjigGXPvm9/8JmeddRbHHXccb3zjG3Fdl6GhId73vvdNCwUxG3MZk7n2KwgC3v72t1MoFDjyyCNJpVL8+c9/ptFotOTRnu+53pZrYybmOq6zfXd8fJzTTjuNCy64gP3335/ly5czMjLSDAX1nve8h29961ucf/75XHzxxZTLZXzf5+///u+nnY8t9WkqRNOVV165RZtgYnvLhRdeyNFHH82SJUt47LHHeOSRR/jRj37UMjMKEw/HG264gY997GP89a9/ZeHChfz85z/nZS97WfP+nAsDAwNcddVV/OhHP2rZP/aBD3yAG2+8kTPPPJP999+fX/7yl3P68diR9+WOuAa35zrZEtv6XJkLcx3D2SgWi9x66638zd/8DQcffDBnn3027e3trFq1iscff5xPfepTnHzyySxatIjvfe97XHrppZxwwgmceOKJPPbYY7ztbW+bNcbsprS1tfH73/+et7zlLRx++OGcc845OI7DPffcw9ve9rZmfM13vvOdfO1rX8NxHLq7u1vihM7Gm9/8Ztrb2/nWt77FgQceyGmnnbZNfYSJl42vf/3rXHLJJfT09GzXmG0r3/ve97jgggt45StfyUUXXUQURfziF7/g5JNP3u0Z5OZq24033shZZ53Fscceyxve8Aaq1SqVSoVLL710Wgiz//qv/yKKIl7xilc0n7GDg4M8/PDDHH300dt8320r//Vf/0WtVuOEE07g9a9/Pfvssw/PPPMM1Wp1i3sr/+Ef/qH5HHzrW9/K6OgohmHwjne8gy9/+cstdXfG+MynzbmwR+aOv/POO7f4lnfWWWdxwgkn8Kc//Yn77ruPd77zndP2c/z85z9nxYoVfOhDH2q+QT///PPccccdjI+PI4TgqquuwnEcfvnLX9LX19fM8rI5Tz75ZPOiOPvssznkkEP4xje+QW9vb3NKetMc40888QR33nknjuPw+te/fsb9qVJK7rzzTh5//HE0TeOYY47h9NNPb1levOWWW3j22WdbAiHP1Z4t9XeKF154gbvuuotSqcTixYs5/vjjpzmChWHIr3/9a1avXs2BBx7IhRdeyF//+lf+/Oc/c/XVV7fE19uc+Y7JXPt1//3388QTTxCGIQcccADnnHPONEe0rfV9c+Z7bTz66KP8/ve/50Mf+lDLktxcx3U2nnzySe655x7CMOSII47gta99bcsMWxAE/O///i/r1q1rBo2/5557+Mtf/sLHP/7xlpS2s/VpYGCAG2+8kRNPPJHXvva1W7Wp0Whwyy23sGbNGvbZZx/OPffcaX3elJUrV3Lbbbc1c8fPJQnDptx11108//zzvPe97532Wb1e51e/+hXVapVzzz13xoDzm7Iz7kvYMdfg1q6TLT2XZns2zMX+rT3vtmcMt9Z2kiTcddddPPXUU8CEs+Hpp59OLpdrqbd+/Xp++9vfEgQBp59+Osccc8yMz4LPf/7zvOpVr5p2HYdhyO23387y5cspFouccsopLdsRpJTcdtttPP300/i+zyGHHNIMRj9bmwA333wzzz77LK961aumvYTNp49nnnkmw8PDPPnkk1u9N7bWXpIkfO5zn+PUU0/l1FNPbfnu8uXLuemmm7jssstYuHDhtLaFEPzxj39sbtl45StfOW1f889+9jMqlUpLEootMVv9qbiv//AP/9BSfvPNNzM0NMTll18+b9tgwvHxV7/6FevWrePQQw/lggsu4C9/+Qv33nvvjEvLy5cv5+6776ZcLrN06VJOOOGEFv3wq1/9ivXr12/1/pip3mx9f+CBB7jjjju48sorp+2LffTRR5srPEcddVTL/TqbLa7r8r//+7/09fVx1FFHcc455/CnP/2Jv/71r9PS+e7o8dmWNrfEHilC90Y2fVArJlBjotjdqGtw+1FjuGPxPI/29nZ++tOfblemGYXixcBevydUoVAoFIq9hXK5zL/9278pAapQoESoQqFQKBS7jIULF07bRqFQvFTZox2T9iYuvvjiLYYEeSmixkSxu1HX4PajxlChUOws1J5QhUKhUCgUCsUuRy3HKxQKhUKhUCh2OUqEKhQKhUKhUCh2OUqEKhQKhUKhUCh2OUqEzoM4jtmwYUNLOlCFQqFQKBQKxfxRInQeDA4OsnTp0pacqYoJSqXS7jZhj0eN0dzYm8bpgQce4M9//nNLXvJdxd40TrsTNU5zQ43T1lFjtONRIZoUOwQVZGHrqDGaG3vTOD3wwANUKhWKxWIzL/muYm8ap92JGqe5ocZp66gx2vGomdA5cMMNN9DT08PRRx+9u01RKBQKhUKheFGgZkLnwFVXXcVVV13Fhg0bWLp06e42R6FQ7CG86U1vIo5jTFM9ShUKhWK+qCenQqFQbCP77rvv7jZBoVAo9lrUcrxCoVAoFAqFYpejRKhCoVAoFAqFYpejROgcUI5JCoViJp5//nmWLVvG888/v7tNUSgUir0OJULnwFVXXcXw8DBPPPHE7jZFoVDsQfzud7/j5z//Ob/73e92tykKhUKx16FEqEKhUCgUCoVil6NEqEKhUCgUCoVil6NEqEKhUCgUCoVil6NE6BxQjkkKhUKhUCgUOxYlQueAckxSKBQKhUKh2LEoEbqHkgjBiF9HSrm7TVEoFAqFQqHY4SgRugdSjwIeHVvP79c/Szn0drc5CoVCoVAoFDscJUL3MNw45JHRDQy6dSphwIBb3d0mKRQKhUKhUOxwzN1tgKKVMEkIRcy++XbanBSDXo2Di91YuoEIqyATdKd9d5upUCiA9vZ2DMOgUCjsblMUCoVir0OJ0D2Yop2m361SDj26nAxxdQWanlIiVKHYQ3j3u9+9u01QKBSKvRa1HD8HdmeIJscwGWhUEf4IwhtERBXlrKRQKBQKhWKvR4nQObCrQzSFSdL8f960eb40zsDQcjSrAImHjH28OALAj5LZmlEoFAqFQqHYY1EidA9kyK8RiYRESPrGI+5/vp8/rBxjrZsljBOGGqM8WerHj2KeGarRCOLdbbJCoVAoFArFvFB7QvcwhJDUfcFA2WeknrB61KVDK9EndZ4a9mmkExoMUtNz9NUblL0IPxZknd1tuULx0uOmm26iXq+Ty+V461vfurvNUSgUir0KJUL3MGphwuCowUjSoKg1aA+GqZc3IIu9hLmAZRVB1i6RLRRZV6lR8QyCWOxusxWKlyQbNmygUqlQLBZ3tykKhUKx16GW4/c0pCROYGlO0hNtoC1eR4oE3XVZVy2x1k8wgpC0YbJ8rEIjiCh74e62WqFQKBQKhWJeKBG6B5KydPKiipQepm3RXewgp8dU6x4N36fuJshYMlBtUBpaRala390mKxQKhUKhUMwLJUL3QNJ6QlIfIUo8Yi2FaVp02DrdWYeUSFg33mCwXGO82kCrLSdslBFChW1SKBQKhUKx96BE6B5IEjaoByXWxyF9sSDWNTQEXZk06ZTJslKDhweGycQjWNoIbmWIIFH7QhUKhUKhUOw9KBG6hyFlgqiPkEiXWHeIJQgATYPEx3AKoBn0j9YohOtwbYfV5Q30V8oIqYSoQqFQKBSKvQMlQufArsyYJAKXxB8nZzkscVIsti1sTUczLMqVGmFscmhXB/vbJZIwBpll1Bvn/r7nGfLU3lCFQqFQKBR7B0qEzoFdmTFJRHXCsI5uZckbJmndAKDiJoxXXNJ6zP7teQ4uZsjYDm4kMaMY6dXodys73T6FQqFQKBSKHYESoXsaQlAPBV64MR1nueZR9mLSRkKnHaJpQBTQXciTtS3cRBCUK4yHHm6swjUpFAqFQqHY81HB6vdAdE0yXHPpyWcIg5jxRohjG7SndJLqIAkaIgkxUwXaTWh4LuXaOGHgUQl9MqY972M+3V9l344M+ZS6JBSKuXL66acTBAGOo1KWKRQKxXxRimMPRFg6pq4xND4OwsC0bfy0yaAGi3QDU9MwTYcYSX8UEmVSyEYdzfd5eMN6nDhPZz5PR8Zhv440mqZt8XhelDDmhiwoOOS385IQwTiakUYzU9vVjkKxN3DMMcfsbhMUCoVir0Utx++BCDRM3UBGMRDhZCwCBL6UxLqOZpig60RS4ouJslhK+lY9wx2PP8ro2FrcMOH5gVHqQbLV49X8mGoQE80x1qhMQmQSzPhZXFuJCMfn012FQqFQKBQvQZQI3QMxI4nnxaTSBexUlkbDpx2dhbbVdFQCSOsGC22LDmngeZKV/SUyus+4O4pfL+EPr2aksnWP+YGqz4aKTzjHHPRxbRXR+LJp5TL2kWEZESkvfYVCoVAoFFtGLcfvgSSJJO2Y9LZlSaRguNrAdwMKhgFGa10zkni1gLZshp6sJMpkqEc1+jasIicj1o2W2Le7iKHPvCQfJ4Khmo+UkpofbdU2KWKEO0ASjFFP74ePzqJsceKzuI5MPGRU2+4xUCj2BkZHR0mSBMMw6Orq2t3mKBQKxV6FEqF7ILah0duWRdPA1HR6ClmGq42ms1LGtgBw/Yihsotl6Cxsz6KT4EQuPgn1YAzT7mRgrEzZi+jMzuysVA8TQGNJW5qKH2/VNhmW8WKP1W7A4OCzZDO9FMwMA1WfjOGhSW1CjEqBpqmJdsWLmx/96EdUKhWKxSIf/vCHd7c5CoVCsVfxklQJQuzZmYVyjoGmgWRij6apTwhR29AZrrk0gpCGH7YIUMPU0UwLEYcUowaRIxCWztj4OP0Vb9ZjVbwITYOerEM4h9SfXmOAZXWffpGiLSkz3Aj44wvDrB5zqVSG0Jw2SIKJfwqFQqFQKBSz8JISoSMjI5x11llkMhmOPvpoli9fvrtNmhFNh0AK1gYBfWFAgmwKUV3XeHKswpOjZTRDawrQBElfGLBB6iQYpE2JbyV0Gj7P9o3iR9MdlKSUDNcD0paJFlRBCJJNnJOkSEjcAaLRRxBBmShyeXToeV4IBL2ZdgLfZ3lfmXVll5Fag/FaFc3MIgGZ+LtwxBQKhUKhUOxtvKRE6LXXXsthhx1GtVrlAx/4AFdcccXuNmlWAiGoCUE9EURy44yoY1m4YYgbR6QzFoY5cQojKakngppuENlZsjLEFwLHChgbH6VUd0ncCiLcOCta9iLGvYiMqSHG1+HXS0STs6EydgmH7iEce5y6N8b6/vu5e/Uj/HF0CMuw0XSDkhsj6gPYwsWQIWtHR0FKQM7qPa9QKBQKhUIBe9meUCkld955J+vWreMtb3kL+Xx+Wp2xsTHuuusuNE3jtNNOo6Ojo/nZn/70J26++WZs2+b9738/V199NXEcY5p73jBkDYNFloWBRmpyb6XrR7i1gB4ni26C6wW4lknGtkhpOr2WRYIka+iQeJgi4fGhMRakqzz1+FqybWD3HILd3kskDJaPmqRMA12EaKFL6A4TRgeSsgzixgBxdTmuvYiHPdhQ9hkol1jc1smidAYhJWOVmGDwBQaG+zmorZthM8StbiBlO4i4sbkPlUKhUCgUCkWTPU99zcKPf/xjPvOZz2BZFsuWLeM1r3nNNBF6yy23cPHFF/OKV7yCJEl4z3vew80338xrX/taYEKgTnmwmqZJPp+nXC7vcV6tiRAYaHSaVrOs6YRkGhzenkXqTHNWattETAdRTLk8jitCOo0a9XGf8aSPtsbTCO9lrAyXUtEOZlExTdyoEycxI6PDBI0yMtWB8PqRqSU8smEDD/X5FDsOZkEuh4lOWjeoexGDo3WklsZyIhZZJYa0PLUgwhRV1sQOKa2d/fIdM3VRoVAoFArFS5y9Zjnesix+97vfceONN874ueu6vPvd7+aKK67grrvu4p577uHSSy/l0ksvJQwn8qn39PQwODgIQBAEVKvVlpnSPYVqIIiSjZ7qm3vBG6Y+zVnJDTeGV4qjhKFygBQBS4pQNSz0VIFq+kiEdHhwyOOPawbozkzUT4IGq4fL1F0ftzSACMdp+A3+Mujwx1URbi1EuoPkbKiHkoovGRsrU6l7dOcz5Ns7SKw86SRmPMzwvBuyrLSeIa+6q4dOoVAoFArFXsJeI0Lf9ra3cfDBB8/6+R133MHo6Cgf+tCHmmX/9E//RH9/P3fddRcA5513Hp///Ofp6+vj85//PKeffjq6PvsQVKtVNmzY0Pw3MDCw4zq0JSQMVl2iJJ5RgE4xkxCNo4T+8QYSjd6iTocDNSvHoJawrC65q1bkNxs0+msVRqoVhJSsGRhiuTuKyJkElRGiseWMl12Wb1jP0qLB4vYctVqVkco4ji5ZPx6wpn+EzrxNd9uEI1JkOKRNjUcHxlhLgU49pBE0CJOth31SKBQKhULx0mOvWY7fGk8//TTt7e0sWrSoWXbggQeSTqd5+umnOfvss/nUpz7FP/zDP/CqV72KI444gu9+97tbbPOLX/wi11577bTy8fFx0un0Du8DgFv3KKbTpLAYLwfIBLKWg5Y2qSXQpukYTASeT5DUY4HlpLGCmHotQgpJWrfQMxb1OKbdyrLAsPFEwtpGjBum6U3pJFKjf3iAeiOhXB4hkzGoihqrx0Ist8oTdYdEmmSNHK4uyEUS0XBphAZhmBBHBpm2FI1Q4mAQRSDMNAPjNfSsjWbaWNUKg/YwWdPZoWM0HrjkLAdL37t2nVaramZ4LuxN4zQV7k0IwdjY2C499t40TrsTNU5zQ43T1tlTxqizs3N3m7DDeNGI0EqlQltb27Ty9vZ2KpUKAIVCgR//+MdzbvOjH/0o733ve5t/DwwMcPzxx9Pe3r7TLoLx+jCjSUB72qFRGQddp5DvpJ8QU4CjOc3UnZ5IGBABMbDIMaiWyiAF2bZ2Rg0dP44w9RRthsAxNDp0QV2LGIoatLf1MBJWyBYWM540qNohw6FGLnRJZwXPijwdGZ2yDOmPQhalNELXQwvr1GsBqfY21sYuXZZFh2nikuARM5IEDJXq7Ntusr8psPI5OjOFHTpGzwyUOSzXRmd6umPans6L6eGxM9lbxmlqJUXX9d1i894yTrsbNU5zQ43T1lFjtGN50YjQdDpNo9GYVl6r1bZ51rJQKFAo7FgBNRdsIWnUQzBzaCbUPJ+OjINtGjibbB9wdJ0uyyIKE6o1H4wMmgkNPyKXcWhLZckZG+unDY2eVIq6GyFCm2oS0GiMMxbVyRoWB2YyhEiGSchjkTUlmjQxNI2wFiJ8Fw1JLuegE7LIcsibOqam4SYJVRFzaM5iKJGMVtKYRsihvR4Ld6AIjURCLQ6oRT49e6EIVby4UFmSFAqFYtvZa/aEbo0DDzyQ0dFR6vV6s6xUKlGr1TjwwAO3q+0bbriBnp4ejj766O01c04kocQyDfbpKrCwLYeOhvBC2jQNnY054HU02qVOXA/RgUUdOZZ0FHBMg8gLyUsNc7PUmUXdQG/EaIkgn4pJ0YepeWiJRiZOaDcdMrqD1EDXIKcbmG5EEiYUclkOWNJLWyaFiAVmGJHVTWxdpyEEOUMnazt0ioCM7lCquvTV6kgp2VEESYyp6VTDbY9DmtTXIUW09YoKhUKhUCh2Gi8aEXrOOedgGAY333xzs+wnP/kJ6XS6GaJpW7nqqqsYHh7miSee2F4z54Su0XRCckyT3kIGuYmz0hRNJyQpWdCexbHNLXrNCyEZHG8QCJ1iMEQ+rlGqD7Eoa5G1LWpBQN33qARaU+qOVhrUvJh82qS7LQtAWyZFW9rBDWNG6w00qdFpmqR1g1oQU6qGZAxJ0YzoG68S7EDnpCCJiYSgFgUIufU0o4NVnzVjG2fIZRKSeAPI2N1hNikUCoVCoZg/e81y/JNPPsmjjz7KypUrAfjFL35BT08PJ598MgcffDC9vb18+tOf5oorrmDVqlUkScJ//Md/cP3119Pe3r6brZ8fxbTR4gU/JUSHqi6DVZcFhQya0KYJ0CmmhOimcURTpjkhQKOE7vYcecck8MYYj0K6853kjTTQoOKHlEyJY6ZbBGhXMdtiY1smBUDZCxitN+hNF6i5AaNVH9uQdDoxwoDBSoNyELBgk5in24oUCYGIsXSdQMT4SUzGtGevLyUrRhpEiWC/zgn7ZeIjo4bK6KRQKBQKxW5mrxGha9eu5c9//jMA7373u1m2bBnLli3jgAMOaIZu+sQnPsGxxx7L7373OzRN47e//e12z4LCxHL8DTfcQJJMz7++M9B1jRjJWBxjAJ2m2RSiA5UGT4/UkAm0mzqLJgWonKyfTNbfVIiuLNcJYihqGguKafKZCW91L9WO1GqEuk1ag65clnro0l/3qQYeBV2nO2s1BagrEqpJQlbXyRtmU4gON3wqfhU/DOlMWXRnc+hRA2FlGapVWF9psCCb264xkSIiLD1FXe/G0g1iKfDiqEWEBnHI0PgqMtmFFFM5GoFg7XiD9oyNHyWkLAMpAmRcVyJUsUO444478DyPdDrNWWedtbvNUSgUir2KvUaEXnjhhVx44YVbrXfeeedx3nnn7dBjX3XVVVx11VVs2LCBpUuX7tC2Z8NNEobDEEvTyRkGKU3DMU0yKYeVtYlQMIvbOpozoIGUjEYxkRSkNY2CMSFE27Npnq+O4omYYqHQFKATojWhgo0pBGndQNcgn86yYdSlmngYTpaXbTIDWk0S1gcBvbZN3pg4blsmxfq6jxd6BCS8rFjA1CQi8qjHaepxmaeHy7xyYQ+aprGtyCQgqT3PmBZip3pIEoE/ucwfJ4J1tSp99bWMjz6DmV2KZxXJxEXCRGIbOo1wUoTGHlJEyLi+lSMqFFvn6aefplKpUCwWlQhVKBSKefKi2RP6YiOt63SYJu2mgT0p3uIooVEPKJgpCqkUXhA294jamka7adBhmqQnPeiFkIxXPPK6TZudhkQ094hqaLSbJt2WRW4Tj/tKw8MSNl12iqylU3b95mdZXafXtiluEp+z5gaYMeRNh6JtU/J8hG4iE0FOM1jkaKytjrGqWtq+wPUiRIQNapXnMBFYukEl9ABYVanyv8+vIqiPstA26ZHjaHHCb19Yw7pGidWVMg8N9fH0+AAiqKDpjhKhCoVCoVDsZvaamdDdya5ejpcCLE1nsb0xyPuUE5IOHNVTBJ3N9ojCstvvZXhgmN5FvZz02pMZrQUEseDgjizplDUt13yHadKxySUwWmkgo5j9iyY9bd2M1huUvYll67ZMirxhNmdAgeYe0KJjsrhYYDx0m3tE2w2wgIMzKUY1k6fGhhjyqxzbuYTUNuwPlSIicrqJKqvRozKO3UU59BFSsLIyhu/FJGYVvdiBCKvkEpfDO7qphyEvlMtEuoPhRCxgjFSjhqWnkSJB28sC3isUCoVC8WJBidA5sKuX42tRQiIlxiYzoDM5IU05K20oVfmfz3+D5556rtnGnbfezbv++Qp6O7LNJfjNnZUy9kYxOOWE1J7d6ITUlctOE6JNGycFqGMZLGjPokutxVlJ6oKuRgkzX8TSYjqdHLXIoxL52yZCk4AQA5laiFZfg5k3KNUH2CDLbKhK7NhnQ0XQWTQRep6+wWHS2RzdHVlShkXgS4IgZDQcY6HnoafqIELQd07mK4VCoVAoFFtGLcfvgcSJZLjaIJFyVgEKG73mH/3zX1sEKMCqZSt44ZFHmgIUZs41D8zqBa9POitlbJOyFzSX5qcJUH3jXs+p8E2e0BkpVxFBQOBXCWOBreuM+dMTCswFGbuEUke3ckiZIEbuJ6isZO3IMzTG1hDWBuh3UzQiGGgYVGp10kkFXdNYWLQJE4kmJH3VYUTog4iVc5JCoVAoFLsRNRO6B2JYOkGcMDBeJ4kEEnDyDg0dbCRTUTwlkgZQKY3P2M7g8CgxEnOTAPeeFGhpG90NGK65GFInjgWmYyKzNoEUOJsEuA9kgnRsdCEoewF1PyIKBbqhY+RsPARZNi5px1IQ2QaWMPHcmPXDo9Qcm0p9FCOXIpaSQ4s9GPr83n9k3MCXOmiCFY0iaTuDYY4w4nk0SgFBZRR6DuGpkQaJL8iGJZKGjZVfhJtEJEaA5wv8+hClMKC3vR0plAhVKBQKhWJ3oWZC58CuzpgUapCxTSKviogbZPMpSppgKIrwxcYA7b6YKHMWzJzL1uzpoBFv3MeaIBmKYoaTGCeTQkYhkV/BNHWSjMH6IKC82b7XUhzTFwXoKRspJKFbBhLsgkNfHDIStWYeqiQJ/WGIZxqkHJuwUQG/wvDoWvrqI6xrlKjH8xN/Ukpk4tMQkjgWPD3m8WzZRzqd6NLArYyRs02SaIinxuo0GqNYmiRxRxEiZsSvMxLWWDNeRQ8bjDSGEX5ZzYQqFAqFQrEbUSJ0DuzqjEkmEs+NQU+BncILQ9Lo5Awda5MZREufKDv+jJPZ72UHt7Sx/8sP5cSzTsbZJHe8DuQNnYJhELoRSAvNdEg0gZFIioZJZrMwShndoGiYECQQg2am0EydJIwoGiY5o9WxJ6VPtO8kktAXGFYOPaziVYZoSypoUqM235SbMgYRUhdM5LIXJnri0J3KYcs0cQKR5ZCJXHqp0SYamOl2ZOQigwo506E7lcavexihwYjVzrPlIe7uX0EpUJmTFAqFQqHYHajl+D2RQEIaFnUWiUTCaMMnHUT0FHMYmyytm2gsNm0Gw5B3X/VBXnjkEUrDozjtbRx32gksclLYmyyta2j0WjYj5Qb1ICafscilMtzymz8yMjjCvvssYt/Xnd5iSrtpYgQJY26EY5v0thUYcz3cMKbD0GnfzMkoqxssRDBUd9E0jaU9XZRcF69SY6m7jqT9AEaCOktybXMeDpmExHGIJyGONbqcFCIBU0oapRI9nZ2EiY9IDJaENdK5AugGoBF743R3HIQlbdYMrEMz04hCO8NxmcQbpR4FdDiZbTlLCoVCoVAotgMlQvdEJE0nJGfyFI02fIarDXoK2abXfDMXfCzo7chy0EVnAxDEMUNVl6HaRPgmy2gNw1T3J5yQ2jIOX/j4F1j+5HIA7gYevOuvfPwLV2OaE9+puQFjtVYnpCmv+YoXoqG1eM0HYcxQeUKALmrPYloG3YU8fYmGP76SfG+DcpghTGJsY46Xn4goxxGhNGiEJp7bwHU9KraHG0Em5dBh6AxWXUaFTi8GDqAZKUR9gEp6P8puhBHW8DJdLE3ZJDE0ZEgt9IBdn9ZVSolw+9HTvWi6ug33Vo4++uhmxiSFQqFQzA+1HD8HdvWe0Fxab/GCz6UcurIpgjhpes03BWiU0FVItXjBT3nNSwmDVbcZ0H5zL/i7b7+7KUCnWPHUcv50y13A7F7ws3nNB2HM4HijRYACaBqkM3likUZ3RwmThGG/TrLJ/tYtIUXIYOCB0BmpJwTlQWxvhLLr4xk5LF1iGeZEvFRNZ6jqEsQxupljfLxEqVrHERV6rBo1kUXTDTSpYWkGtaC2jWdp+5Bxg7i2EhFudCqLvBJudS1Syt1ik2L+nHHGGbzuda/jjDPO2N2mKBQKxV6HEqFzYFfvCbUMHYGkHMfUJgXklBD1o5iVpSorRqsEYdwiQGtJTDmOEcgWIbp6vM5zozUqbtQShqm/f2jG469a08fzpSqDFa9FgIZSUIpjAilahOiY67O+1mBtqd4iQBMk43FMQySgQWS2k7iDZE2L+4bWcPfgKka8LWcuklJS9mqsGe+jFoSMN0I6rYCF3UXKMk9FaDRkTMKmQhTWlRu8UHEZrQVY7mrylcdJGRrlxGBDJAgSgalJvMidsxjeHoI4IYg3HkdGVURQQriDzX5uGF3G8/0PkjQ27HR7FAqFQqHY3SgRuofSSBL6o5CBMCKQE+Ill3LIpG366g363QaprN0UoIEUDIQR/VFIY9LD3TFNOvNpBr2AdfUKsWU0BahE4vR0zHhsu6uDldUqlcnYpFMzoKU4Zl3gMzzpET8lRBsS+hsNRqKQhW2Z5gxoNU7YMNkHISWxnkX4FaIkoBJ6PFUeZE2ttMVxGGuE3Pb8IMtqMc+NDhHHHsW0gWNqNCLJUBjRH4dUJ6MAWIZJbz7DcBCwqlbF1XWK3mo0zcLJdjASxSyrB4wkEbYU+KFLILYjnegc6S/7rBv3mn8LbwjdKiD8IWTsIqMqg7UBKmSIK8+SeDO/ICgUCoVC8WJBidA9DG1yL6YpIaXppHStZQ+o7ybYuoljmURiIrMSgKFppHSNlKZjbuLh7jZCHM3EMSw0kubSvIbGCWe9hn1ffkjL8Q95+aEceeKrSesmjgl+vFGg2ZpOVjdIbeKhH0UxegS2ZuBYBo1N6+saaU0nrWtYGgQ4yNDDChvkLIcOO00jCRFy9pnIcS/ikf4yfZVeGq4OYYlYauga6Gg42sQx7E0C5rtehC0NUoaJlckhMj0YTg40jZShYwiTlKajaRbCH8WPw3mfp/nSX/MZrPlEiUDGPok/hmYXkVKQ+CX8+gbGogRfM4n0DHHlOaTYNWliFdtOkiTNfwqFQqGYH8ojYg9Dt9Jg2TgiZp+UgwbocZ1E2gxVI0QiOLyYAeFTSZKms5KpaSx2HCQ0g9NPOSEdkEuRydqM1bxmrnnLMFmcSXPNv3+MB26/l6H+IYpdHRx03HFkUhbtxTTjDW9arvmCoTdF8dQe0AyC1Q8/zrq+ftZ0dXLGuafQWciR1Q32S+nogB+BG+okVMnGDQ5tPxCJpBR4+ElMxrRnHI9nR0vEMuSgYhtRAkF9FaOeTm9KULB1sraJZZjNPpdrHuONkKUZh2K+yGjdZajq0lvI4JgmixwdITN0GgFgI5M6vj8OqdxOO6dhLFg33I/u16h4+9JuVEgaG4iqZcx8B4m7nopbBitLLCW+5mAlZWTcQLMLO80uxfbz1a9+lUqlQrFY5MMf/vDuNkehUOwiRNRABKVm0hPD6UJ32navUXshSoTOgRtuuIEbbrhhl812eImJTCJMUkSxz0iY0KhV0KVDVyFFRo/Q9CxmmDAWJNO85mHmVJxmQWOo6rYI0ZRpcfr5p8/ohGTNkGvenAz5NCVAkyThx//xTZ57aqOD05P3PsQ/ffajdBZyTXGoCcFYrc7+aUmlPExb24FYhoGUkmAWEeqFMc+MjLEwJ1lSdJAioezqjIVas8/2JrOyUwI07Zj0tmXRNFhQyDBY3ShEU4bJeKDREDrFOEC3DGruALQt3SnnEqAR+JTrG0jFLsOj6yjkPRK/TFxeh2a/CgioRgG6ngIkgRDkkci4DkqEKhQKxR6DlIKo9AwyGEbKBE0zkDJBesNY3ceraCfzRC3Hz4Fd7ZgUSYMRN2BtLWZ1NSAYF1Rcj3zOIZ+2QEp0J0vakNO85mH2XPCzec3P5gU/W675Tb3gVz/yWIsABVi97HnuvPXuptd8EgtGKg3cULI82pcn1pdZO+YjhAAkfjLznsx19SqNyGdpBtB0ZBKRtk16ClnCRDBcbRBPOhXNJECBFmelock+mxqsqehEXgPHKlCtDezU7EnVyiChqBMaFusHXiD2R9HIQdIgGX2a58opHi6l0BIDA51GEqHpNsLf8n5ZhUKhUOxaZOwRl59ESoGRXoCe6sZIL0BEVZJJR1PF3FGSfQ9Etyz6axaL9A28yrZpdB7DmsFluLogCn2sVBYj207iVdFMnXTaxvNChqsNdKHhBjFWysTJOchNcs0DSEMnnbVx6wGDVZesaVFphOiGTqaQQuibvZlokMqkiCeX5tvSNpVaiACKhRTDwyMz9qE8MkrZC0iEwPMm9swZmQyhDYWkzMBYiSIBUpSp2AYLMxMzfvUgJm0ZJFGVJ/qWY2sN0ABNR0QeGhqaqZPNpKg3PIarDWxdp+bGGJZOOucgtc36rOtksg61us9Q1SVv27wwJsjYEW3tIBOXxB/DzC7a4edSJiGDI2uIdQepaYwmFuP1BvlAYhUPoja+lidKQzwT5yiO63QXYxA++3XlEeEYUiRourH1AykUCoVi5yOCidnPsILQTXRnIm22breR1FdipLvRDGcrjSimUDOheyChqWOkNV5hjHH8oiWc/+pjOXHfLjK+oM/3IV1EMx0iJ8MGt05Zg3TaxvcaNNwKjmPgpww2hCG1TbYQCCQDYciQEDhZhziKKFdKGIaGzFmsjwLG49ZZyZE4pi8O0dI2SMl4aQwhBam8Q5+IsLtnzltfWLwAW9epVsaJIo9MIYXvxJR1AVqM7q5l7dAwbuixYehRosrzhHHC/WtK9JVdBkef4cENz1JtDOJKQApk4CIMk74wYpSETDZFEHhUqxVsUyfOWKyPQ8px67aJwSiiP46xMw4iSSiXR8mYkmeqOo+PVhkJIdhJ3uhhY5DBWoNIBpixT11A2bMRScSIkWW97GH9SBktqeGYkmWlgLvWuTw5CiL2kXFjp9ilUCgUivkjkwDsdjQrh/DHEMEYAJqZQSYBsQqxNy/UTOgeicE+CxdysJUnc9ApWLlOXr7fIpJgJX8pZxiKLOxEoGt5jGQQqZtEfoSMNTTTQGoSXU44KOmb7BPVAFPTsNAQoYBERzN0MCQ6EkvTW9KCMlVf09ETSRwDmo5maMjJ+sefdTLL//JgS9D7fV9+KMedcSKJJwAdTINEJpjomBrohkMmGKFmHogdSRLDJ6qtohYXWVNyqbk1smIFQhbIOTp4HlG1hIgDNCeHKSKk1EjCBISBZgikIdEACw2jtQtoccLTt9+LOzBCsdjGMScfhZnW0LSQ0YqBFsBQe5n9OyWattmXt5N6pY+KsDC9Icw4oRr5rPRMgozJ3e44pTGDauCREimIK3RnLAIZs64Ws8SBHrUvVKFQKPYYROyi6yaa04VgAOFPiFDd6ZwQopskIFFsHSVC9zA0K8OC9iW84qB9WJhNYxa60TQNO93G0ft10xkaLEsgp+usHZbkjDZCr0GsF8jZdaJUgbLr00ZERz5DepOlXA2NhbZNuR5Qc0Mc26I9l2Ok4SG9kMX5DDmzdem30zSxE8l41cPQdXraeyh5HoEb0JtLU0jbXP35q7nv1vsYGBygd1Evx512AqVGSCwlPZ2dNKIIN4gppiw60ilMUiRelXQ8Tt3rIu3YBFJn9cBaOrNLKJXXss5t0J5kWOQIjEYZrAxGugjAEkenVHFpeDHpVIpCJs9w3cP0Q3ryGbKbpAONo5gf/p8vtYjk5Q8/zjs/djlGFPKyrMFwYrGy5LL/PgGYKXYUMgmo1Ueo+2DHPo7ThvTHGfBsVkUmvabHsGvSndJoy1pUghhHQjqbJpGCYc+kyy9hZHb8NoE590HEaqO9QqFQTBHVQLfRNA09vbBFiKJbu9e2vRC1HL+HkXUcXrakl4M7urDaFjT3A2p2G7rmsn/vPizJpBGyxOELLA447ERkx+GQyiOtDN0Zg7ZMiigRlOte01lpCs8NqdWDphNS2rHoLWTQ0CjXPeLNIgBEYUK57KFPZkJKOROOQY5pUGn4BGGMaZqccd4ZXHzZxZxy9qlUGhEikfS2Zcim7GZmJdePqLtT4SyyGF6J0G1Q8SVVmWOgPMbSbEDRHKMUdeCFMeXxEdAdNHPjzV2v+RMCdNIJKePY9OQzJEJSqXtNZyVgxtSkzz21nJUPPoqhaZTLFWxNUvIEIvbYkcjYZaQ8zobSEI5hgG6QSucJMzYbygnlKliOzZKOLAVd0lXME0Ux5WoNQ48YdC1cd2y3xQsVUZ1o/GmVRlShUCiYyGwn4gbapNicEqIbl+bVLOh8USJ0DuzK3PG2qbNfMUeHk2kp160cMvGQ/jAHpG1sTdJILyJdtDj5iAM4aEGe/doj9ks3yDo27enpXvOzecHP5jU/Wy742bzmk1gwMN4gSgS9bRkyqYkbdcZc87qBZtjUS4OsHIrZ0BCYmobw+hkP63QXOulIm7iuR8mPEJM6aDYv+Ixt0ZPPTPOa718/s7fi6OAovYUswi9TLQ0x6vn4wY4VoXFQY11VUm9UcaMJQ7OmQaUekE0i+sMs+3Sk0QwLEXrk0ym6inlElLCuNEogTUbqE9mUdgtJgIzqkPi75/gKhUKxJyFjSALQN4YUbBGiQQmZRLvRwL0PJULnwK4M0ZQzHQ4pdmNt5hGtGSk0uw0ZVkh76zh5ySt4zZIj6UlNiMND992Hrk6HSLg4hsS2nWb4pr5KnYGqy+hmueBjKWiIZCKF5yZCtK/SYKjh0b+ZAE2QNERCjGwRokPVBiU/YP14vUWASiSuSAg3yzVf9gIG6g36axGxXydujLOm7JPN5CnXB6lJ6M6YLMiaFFMmbizor9borzSmCdBQCtzJPmwqRPsrdYbqHnZh5v2U3Yt6cNI5FuRzmH6F1UND1NwdK/Zq9THcxKSY0mhECSOux2DZRQYJ7VmTQ3tT2AaEuk4jDpFxSD6TpTeXZrQ0TKkyyuqyRlhdt0PtmitShMi4iozd3XJ8hUKh2KMQ4URs0M22KE0J0Top1rnl3WPbXora7LWHYeg6OX16eAfNSGE4HYiwhobAzi5CM206nQwvBGM4ZpY1wsGNY0w8wiRDMeWQSHi6VCGIYhZlsuw7KUAlkv4wpCEEvZZNh2k2heiToxWq1TrtVoajegrNGdBSHDMSRbSbJgstuylEnxurUi5X8WXMUV1tzRnQapIwEEakdI2ljoOhaXTlsqwr11hRqqBhcFhbjoJs0DcySrGgs646wqCWoTMdY3gV2nJZklCyfLyKiBP2yeabAjSZ7IMvJAtti6JhkrEtunNpnhyr4FZqHPyaV3PYw4+zfJNYpvu8/BAOP+MEAOxUmn2KCXcPB4xWB1iw8LAdch6lFJRH1lJrVOluK+IFMX31BqUw4MBivhm/NZKC9UFIFMUYvkvBStNuScbNmIpXI07yHFkdpKe4P/oudlASsYuMPUTcQGfmKAgvdd797ncjhEDX1fu8QvFiR4qI2VxX3STmmdjBTCIO3qVW7d0oEbqXoBk2mpECrYbZdgSaObFcn7UcBBJN19HSPej2CGnPx5WTy/li4p+mgW6A3JrztwDiqWOC1Le8H1ATIKaiOukas96hU/WZWNFAgGaCZujknTTj3jDPlDSGpCQtysR1By30MVI58DzkpKO9ZoJkK4eRMLV+b6VMrvzcVTzwx/sY6h/C6e3kwNNejWFuvPR10yIWMX3lMY6QAk3bfkER14ZYv2EtvpkjZ5q4bowUGpou0WYI+6npBiJyQRQRboV0Ilmf6CwNQ0rlKp2dfbtchDK590kGJcjts2uPvZfQ3t6+u01QKBS7ChHN+BvqJwnPVMuUo4iunZgC+sWIEqF7EZpVRA+rmNklzbKMYaGhYekGh3TuhxsNEQSjjEjJaN2nVg9YkkqTyhi4kwHtp1J8LrJtAinJ6K2pOLssk33bO3H9oCXFZ4dpktF1nMn6U3tAc0BHsUAlchmpe2iaRsa2KBgGlqNhahoGGlLCULkBieDg9iICQcMPsTSHnnwbOhodIoEkxnTLSKBc96m7EftnM5iOThDEjNYbdOU29iGWkvSkTa4fMVz2WOikSLfncb2AkhvwmnNPxdR14sm0mKnJ+nGUMDge0GUkrKt6E/sfzdb9uPNFioT6+ifY4GskKYNG1SfyYxbnMnTqFlG4sQ+WprPUsUkMjVQckgQNag0X1xNYGY1sPsOaesJ+48swsvugW9mtG7CDkFEdzcojokozaH6YxBiajqFm/hQKxUsMEVaRZjitfIPXoByHtFsWlno2zgs1WnsRmpVHTy9AszbOiKVNC0c38JOYjJ2jWNgHwwgYrddZU/awTJ2lnTl6sxm6c+kWZyVT08nqBhpaixPS0o4cPdkUC4vZFmclA42sbmCitTohtWfpSDssKuZanJU0NDK6ga3pTQHqBTHtWZtFxSyLCvnmHtHqpNd8WjdIWw6mnaEWGc09oEs68izMZWlLO7iTIk5IsDWdzGQfXD9iqOxiGTpL23P0ZtP0bpbi05zsg4FGHCX0jzeQSPYvGJTCBC+ob/d5SuolxksDDGop/IZP3Z9IodqTS7M4nyXnmNQnxTSAo+lkLAeRxFTLJUbrMWnbYL90gkzrNPRO6o0ScX3tdts2V6SIkSJAM3OQhMhkYl/oC7Uxhv2NYySTkKi6SnnQKxSKFz0yrk14wW+SRCQSgn7fpc20t/BNxWwoEboXYWQWYBYPbQmorms6bU66mX89TvdStdJ0hyPkTJP2YrrpBZ9LOTPmmp/NC342r/nZvOBn85rfXIC25dOTts/gNT9J2Q0pu9E0L/i2TGqaEAVaBOjC9iyGOXFpz+Y13xSgUrKgPUtbysALIoYble06R1JKotI6Nvg+ffUQMSlAp/aAwkSfNxeiAA0/YnS8TjqVpqc9RyYJ8JMEVzNp2AcSlx5DBOXtsm/OiJA4DhmOEkAiYxchBcP1USqbCHURVRDVlS9Z56VHHnmE++67j0ceeWR3m6JQKHYiUiQgBbqZoVFb3xSi5SjETxJShlpY3haUCN2L0HQLbYZg6m12mnByY2Y5gSP3eQ1H5wwWZHXizc5wKmWTzzhNIer6IYPjDSTQ0ZbBsFq/YJkGxVwKISSDVRc/iCcEaJzQVkzhpFpvPFPXKebSGLo2IUSDsClAs2mTXL7Vfl2DQiaNbRlNIToVhsm2DdoLaTZPYpTLOGRSVlOINvyQobKLbmh0FNNNATqFY5sUNxHffhDTP95ASElbWxrLNtANC5nEDFW3L85b0ijhDT/NU2MNkgjSaYu2QnpavbZsGsfeKERrbsCYKzF0SXtbBtO0kUlITtcZiz2GoyyamScuL0NKMcORdywyCRmPI1Y36oRSQ4RV6l6Zevk5SpWN3vqJO0RUXYGMt38GeW/k7rvv5vbbb+fuu+/e3aYoFIqdiQhAJPjpJTxZDxitrEXEDQZ9D1s38JOEWKgVofmiROiLgKxp4ycxbhySMW326dqfhYuOIG2YVKONMcvkZO74USR2ysIPQ4bGRgEwcg4bkojxzfKuj8UxA0mCmbFJEsHA2DBRHOPkHYYQDIetMdEqScyGKCJO2Zi6xlCphOf7OGmTiq2zIQiI2XijeiJhQxTiWiaWaTBeqzJeq2FZBn7aZH0U4m0SrD1GsiEIqOgatmPS8HyGx8YwNQ2ZtVmfRFSSuMWm4ShiSAqcjEMYJQyMDpMIgZWzGRAJY3GMZphYQtBX2fYc8jIJ8df+if6BB1jRgJ6ciZsyWBeEhJsIx1AK1gUhDcvAsk1qrsfoeBnLsYkLOTZEIXUkUkpSMgEt4fmKR5JeSuwNInZSnvuWvoiQslenz3epSxMZlKiOLyOO6tQaAwRRMLFkH46hWVkSd+fbtCN4qc7YKhSKbSN2BwlHHyGuLEcicKXGM5HFCi/i2YF1rK+Og5SsqFdY7TbU1qR5okToi4CMaaOhMR767JdrJ5Utks0UsbCo+JJhVyNIJpzGYymJkOiaDrEEJIalkxgT4YKSTQQiIiGKAoLQR0iNMIQkEWgmYGpEcUDgVWATgZVIiJATHuyJhkwE6AmGbRAjJw+58RgCSKREICfy1scTostyNBLk5GebICfaiJEYmjFRH4FhayTaxLGTzZ4BkZREUqBpGjKRICf6IAyNSApiKdEMk5yuMVwp47mj23QeoupaguEnWRV0EAiTdMogkZJ4sz6IyfOQSImJDtFkn20doU/0TUyOkRARHY7DM+NjjNR9jPQCotGHCMefRsY7L4h8EnkMNQZxQ5dyAqPVMqO1YdKpHsLIZWB8kOX9Q/RVfDznEIQ/uNsyO80VGbtE5Wd36rgpFIoXF9IfRgRjiKiBbuYY8n2kZlDT27hzUDBaLoEICIUgRqAk6PxQInQO7MqMSdtCyjBpd9LkTJvedB5NNygWOzggb3Bowac7m+DFGvpk7vgeqdOo+hiGRXuxg1gKdD9iieXQPhm6SIQ+SeTRbdkstkziSoNIGKTz3QxHJiNVj14EC7NFknDj7FKbabDUtDHdiDAWFApFHCeN2/DpwmCxbWFuEgIpqxsssi1yocDzYtLpPJlsHi+IySWCRbZFdpPA/aams9i26JIGbtXHstIUiu2EQuCEEUtMmzazNQbSAstisW7hVwN0Tae9oxM0DemFLDZtuif77Jg6lQAq48/NW1BJkRCXX8DzYp6WS+jKpfC8kDYJix2b1CZ9Tmk6ix2bthjcRoiTypLLt+FHMakoYYltkzcM0HRkFOCQYLqjPNM/jG7l0DOLiSvPEYzct9OW5utBBReTpbrL6lqdp8cslteypEyTRJo8tq6PVSOj3N8vuGdAI4wFcnKPVFRdjfC3TcjvTGQSIIMxRKhS6ykUiq0jpURENXSrgG4XkYbDSOBzSK5AwcwxFOVoBA6FZJwD0xb7pLLom+8fU2wRJULnwK7MmLQtaJpGp5Nl/1wH9uTm6HRbF0e1WSzNxKS0oDk7qEWC4YpPXUoWtWdpy6XpyqYQicB3fTQJCIFIIqy2xdgd+xLpnXiBYJ+OFK9cYnJQl4EXadQbMWamnURANUgQEjSp4dUDkkjQnrXpKmSbueYbro+Mp4umxI0IpnLBt+fozufI2CZhEJME8bT6MhTUqz6WabCwPUtnLkNb2iGOBaEfTAuGqseSetVHhwknpEyannwGISS+6zP16prSJX5sMVgeJnEH5nUOZDiODEr0yyKBleGgjgyOaeB5ITKc3gfhxxMCdDKDVXc+Q84xicOYwAvQ0NANExl6JLVRCnGdh/vLNEKBpluYuf0m0seJ6W3vCGpBBS3VS95yGBwfYb2bsKqa4FY9hiowVBnBikroego3lHiJhozriLBCNHI/SbibUo1uASlCROyRuBvUkplCodg6iQ+JB8ZEAhkvSRAIOiyHMIFFuTS63UkoLApJmbS2c57HL2aUCH2RcHChi8XZYvNvPZVDt3IUgtJEeJ0owAsmvOAFsLg9C5NOSJt7zYd+HTNTQJpphuuSRNMpZG0WFE1MQ3Bgu8GRHQk1LcO6SkBJa0OLA/x4Zi/42bzmYeZc8Fvymp/NC342r/nNveAde0Kkz+Q1b5gWRhyyIbBJas8j47nnkk8a/SRRyEo6cIyNfXZMg9GGT90PmnVrbsBo1W9JoQrTveY13UTEAW6ljBsYjNR91lYmx0Iz0TRzMvL/jkVKyZhbIWWkMNK99NVtBipjxFHA6r4xRofGqIyuYXh8nHw6TTGlUY1MVo6upDH+HDJuIPfA2UYReROzy0EZGdV2tzkKhWIPRyY+IgnRJjOMeEmMlBMTP41QYumgaSau3oOmmUh/bI/flrSnoUToiwRD11tDN6XypA48ic7FR2DlTLSkwfoxlwDJfh1ZCo6FlyTEk8u5U0LUDwNGGwGhU2S4AVEc0pk1cCxJToxR8mt0yzqHp6scvKiAjuSAjoicDRtGatMEaMzEfs8pUaZLwe//93b+77d/wu9+cRujFa9FgCZIEuQ0IVpyPWqTXvCbClCBbDo6bSpER2p13DCeJkAlstnnzYWoJ8CvuywbhygOSPyxOY29iOqIYIhyYLLK02i3J/phaFqrEA1CKo3pAjTZpA+bCtER16cRxIy4Esu0WZCNWDFWJ0rkxP5WQMod/8CLY4/RwMU2TKr1AM0sYlng1sepNupomIxUA8z6IAuyYOjw0HjIM+N9VBoDGMWX7ZEiT4bjCH8UIQISfwQAEdWQSbCVbyoUipciMvEQ3iBSTEycVKIIWzeQUlLxJKYuMXUYczX0dC9oRrOuYm6owFYvUjRNw8y0kW/bn7GxfsY0QSVw2GdBlsUZBynhYd9HkxoLHZOCYZJLOdS9KuviFKuHxulId3BALkTaHXT0ZmkET7BGpjg620uS7qWE5KAOjTCu00dEvxdzTE++KUAbYiJ3fFqf2ItKIvjh57/Bc09uzON+wF/+yjU3fBxNm/Aa7w8nslEssm1sTacrl2WoVue5cg0RSxak0i0CdCAM8SZzx2d1g7bMRAioF6oNGuU6nWaKg7tyzRnQ0TimHMV0WtZEBqhJIbqyXOf5ap2M0NHcgJGoncXuBmR20VbTeIpgjDiOeWosooFGEkeYCSxyHMxJIbp2rMwvfnUbQ0OD7LtoIRe+8bXoukaMpD8IiCUstC3SukFXLoukwaqaRxAlLHAcDujIUgtrDNVcRt2EhXkTkNu8HC9FhPDH0J0ONKM1yHItbLC6UaPY8IhLATl7EXo+S9mtsLpewzQd2rI52lIGvlulL9R4qtpgX1HnqGIPuuEgk7iZZWlPQQofzXCQYRVRX0+UeAi3H6N4GKZKS6pQ7BaklC0TKHsSSTARNzpprEemF1ONQ3KpiaX4MT+mjkdKN0j8NEFioqd7SHZBCL0XE0qEvsjJZRdiGQ56LkJWE2xTp2hahELgxxrjARRNScGA0K9BqoAbZojjBkFYwdYtauTYp7eLsaFBclKjaLdjLD6CVG2UvOYzVLEQxnokASlnYy7tUEg8KUDoCODu2+9uEaAAq5at4N477uH0808nlhJ/ch09lhJ7cmneMS3CMCZGYjp6cwleAN7kMUIhyU5qxaxpEkQJvogRFljWxsvcF4KGSMjLjeIoZZoE8US4KFuXaIlLv9/DQqeCDCtozpbzgyfeEAMNk2XVmLyjU5MCU2qTWak0ZJzwg3/9GquWrQDgMeC5hx7lXz73LySGji/kZOSAjfsUM5ZNENUJRYRmZjAtg1RsMB436K9HLMybaIDcxuV4GVYJRx9EQ8NoOwIj3Y1m5tA0jXG3wqjXIPJDwkaWdqtOVcvhyImUp0LEpFIGNd1i3ch6qtluEDo+eSpSZ6luQRhNxNXTty8F6o5CigREgJHbH+EPEdVewExcMOymQ5VCodi1iKBE4pewigftblNmJqpi5PZDhmUq1bWEiU3KyFD2BEEiCLUEQ4dYSOqhxIg8Ak89T+aDEqEvclJOgZe3L0TXXUaGRokqJZysg4FOHouurCCFhYwjxhNJVeul05HEhg2hy+pxm8WL0xQLefxKnqWGTdsBx6NniuSjiIgCCxYsJPEzrB9+lGdKdQ7oyNBuOxRMgyXY2LqGicbwwPCMNvatn3ACSus6C22r+X+Y2ANaqfosSGfRTA0RxZRdn7ZMChONhbZFKCSFSY/4OEoYKLt0WzY9uRR6Ipp52nUNeqyJGdOcMdF+GET87ld/ZGhgmPbF3ex34nG41TFWlnp4RdEg8YfQtyBCZRJQKa3j0UEHqUUsSOXJJwJDm0jHCXD77+5sCtApnn3iWe65fUJ8L7QtEglZY6IPQRgzWnZZ4DhoZgZDJozWG3Q4FsQNBt0IIVMTC/jbOhOa+GhGCk13CAfuRLMyGLn9QGqsH17HksijHBQx7Qx6UMbSHGj4LM0V0Q2Nuh+xWtdJ47OwALGTZoHtUItjYHKrQBKimXuGCEUEkERoqXaM7FJgagyc7RKh++67L/V6nVwut6MsnUZcWzv5krCHjKVCsYOQSYgMS7vbjBmRIkImLrpdBCuHX1qFDD2QGdxIJ2+btJsZbN0giXXGXElnXMMMVQi4+aBE6Isczczw8kI7+6VTaBUYGSozVA8o2AkdpkkhC2VXoxE2GNd62CetsW9Xg5pexPZD7GwHXlZSFS5W174c0LUPRrYNgN50jpW1MUxNx873clRPD6uGK/RVNXLtBpZhNkM+AeTaZxZzTnsbbhiRsS2Km6Q+29QJaZ/2LJqhM1pvUPYm9vC1ZVJkdaM5A7qpE9K+HRNL8GXXp+wFTSHqaDqOuVGAXv8vn2PVs883j7ni/kd44z++hxWDQ9T3OYCiO4DM7Y826R25OTKqM1z3WTbcIK3HWJpO2yYZm2puwPp1M3vab1jXD0B+kz5vmkL1gPYcpmUwWm9QD2IQEssUjLoeQZzDRtvm/UcirqNpFrrTjtVxJHF9HUltFXW7l9G6h1/PoVl5OlKS0lhAza2RsyW93W0kUjBQqdNwAwopHelVaHMWkpHgi5hQCCwkUoTbZNv2IKVAuANoVh7dLmwsTwIkTIRP0SyM7FKSxnqEN4SmGdu8JPimN71pB1o/HZkEJI3VgMTM77dTj6VQ7HJkPOHIKCI03drd1rQgYw8pYnTdAiwCpxudPhJviIrXSdqwyNkTvwuxLhmqRqSDYYr5tt1q996Gckx6kaMZDnp2McXOozjhyPNoaz+CMvuyLlzCYRkPR/eJ4oiVVUHWKnJIu84hXb2k9DIL2yz0fJ5D2jt5Vdc+HL/oUHpyG4Vk0U5jaDpdTpYj2hdy4oGvZFE6hUhiBsZK+JvkYS/XPA45/jgOOPyQFvsOO+owXn3GidO85mfygt+S1/xsXvCzec0LIfndr/7YIkABVjz1HBsefpyK5zFSHpnwjgzLs46vCCs0tDbqIqLhRQTxxpnJKS/47gU9M3431dHe4jW/qQBd1J7FtCZmRpvOSlGC7wZUAh8/lhPiaRudamRUA2Pioa8ZKczcPmhGmtHKEGuHXHw9S0dKUq55VAIdOxilu72Apk14/i8s5shaBqOBpFatYBNSjzTiROCJBKlpu9zhR0pBVH6OoP92wpEHiCrLmzZIERKJhBW1yoTg1CeEKLqN8MdgD3UmEGEFGdVJ3D7ldat40SESH0S0RzoHysSbXNOZoCp0bKeDOBEMlMoYbHxmNEIYqYTUvD1PTO/pvKRmQqvVKqXSxNR/b28v6fT0nN4vRqzioQDsn5FEhyzlr8tfwMwUOWDBAVSHn6Ra88jqBfYvZlmSb9DZfih6dZwYwMiwNNtGwZ6es77DSXNc5xKKdopSqUS6fSHZVDsd+gBJoDFc91noZGl4EeONkHzG4ZobPs69d9zDUP8QvYt6ec3Zr0FqGiM1l+GaS08+QxzE3PrbO6mMjrH//otZcN6pTL0vTQnRkVq9OSOas6ymAO1tyzQF6BRtmRQSScULGa036MykGSq7jAzOvD2gWqqwvxWzsuyzf6qEbJv5ASmlRPgjjCYO+ZSOJQ2Gqi69hQxhmEx4wZs6F1z0WpY9+AjLN9kPe9hRL+OEM05ktDEhpC3daArQhW2ZpgCdoiuXBRqUqy7l8jjVcCHFlAHbMNsopUDGLpqx8ZxqRgo9s5hHXniMDWM+L1+U3hg+K2XRUyigb5IEwNR1uvMZRmouow2XnnwFz+zG9qERxRQMc5futZRSEldXkVSWg5FGRjXiyvOgWViFA5GxTyMRlKKAQAhShjERbzW/P3FlBVKE0xy09gSEP4pmZCbDXpXRUp0z1gsrz2Gmera4dUSh2OOI/YkVkyQAa+dtadkWZNRATm6pElJSjyNs0ya2uvHCcYYrZRZ3tNOITcq+JC1rlOI8S1QI4nnxkhKhN910E5/97GcZGRnhZz/7Ga9//et3t0m7FEPXOGSfRTjVNQSmTm+6k0OlR1sKRjJLKNiStoxDLtNNsf1ghuqj7JeZWYAC6JpOm7NRyOdyBfJOgchbD20L0MtlNoyMATaGbeKlTYZlwqnnn4bOxNJnKAUDYYhwLEw/pG+0xA8//03WLF8JwF3A/X++n499/mpM05zwiI9CQsvEkpJyw2M8rqPrFnrOZoCEHqG3ZFkajSMquoZjm7hBRKPhgrToWtw7Y7+KC3upyoCxMEOYNDCj+swDmviIqEop1CjHAV0pGy2S9JcqkOjopkmQtRjSBFd+/l948Nb7GRgcoGtRDweedjyhZmAGESO1BsQSXTeRWZs+GbNA6s0sSxLJcBThWga2bTHk1dkwNsDSpe3IZBv2HyXB5EO/0FK8tqHxyKhB0ZL0j5VBGM3zNiBiFqK3nrc4QjgWRmwyPDJMUsizrgrCb3DuYgtTGydoVFi0SfzanYUMKyT1VRi5fSaC0rv9kHiIxgZkbl9kXMeXOtU4IhAJqcn9t5puodnFbRLzOxspIuL6KnSnGy2uk3hD6DOIUCliZKOPWAhsJUL3SOJGP5puYKRnfua8VBGJN7H3Uux5M6EiLKPpE8vtgUgIhUBPYKUfEDlp7Nhj/VgZ3S5i6AKPEmtim4NVvPp58ZJajv/7v/971qxZw/nnn7+7TdltGKksi3oXstSoISr9LLKy7JP3sdJZ8mZCPp1DN0wWFRaSzi5gv1zHnNtOZzIUi53Y2YX4loGT7yAJ6kBIOmdTlwmNRLR4gQdCUBcCH4lj2zx+531NATrFc08u557b7wEmvOYbiaAuExzbRkYhJD52SicwNKpJgi9aQ2TUk4RKEmPYFjIWE7nDTTj23New5IiDW+q+7OiXcdRZJxFqU8JvEcwS81LEdYJYUIkSAhEQ6DqOYSJDH/DJ5GxcBPVEIHWdM847g4svu5iTzj0VT9PxEKRtG0IfhE86YxLoklqSECQb+yCAWiKoJgl2Jo0Zh6wfr0wsI2/DMtbE/kg5LfTU04MlgsQj66SQkc9cz1sqlUEEdex4lJ5sRL8XMNTQeaEywtr6rglaL8Jx0DQ03UI3s+iZRaAZJI11iHAcGdUpxZJGEhNstqyt6RYy2TYR+t3vfpf//M//5Lvf/e6O6EYLMqwg/RFkMApmHuEPzbhsObGnLkB6Q2rJfg9FBiVEsGc64OxOZFACmSCi6asmIqwidlO8YZmEyKiCZkxMsgRCIJCEUjDqB/hAOlsEKRBhBYsa9STClVAJXlKyarvZo2ZCoyjiN7/5DevWreM973kPxeL0GZR169Zxxx13oGkaZ599NkuWLGl+Nj4+TqVSmfaddDpNb696A53CbF+Iv+4xdCuD3XkIufBOdOmyIGM1l/M6nSz75mKKs8yCzsbifQ+mQ+jUy6uIjSyWnQc7RaPh05VxyJgm9qa54w2DhZaN78c0aj7l0syzjkP9QwDYmk6vZeFFCfWqj6anMFIZgliQNhKKKZui0bqU3W1ZpNHxqwFgYGfaiKVATwQfuv4qnr7zfsYHR5rbAxJjImf7cC2gHHfSOUtAeBGUiaWBG3ossQz0xMD1IkwnD4bEdX06sg6OaZLSdZhsJqVP9CEIJ/qsm1kMS8cNY3K6RYdjk91k6dtAo9cyaYQafj3AEpJRTyP066SMFFKKrcYy3ZSZZh2CWLKqNE5HEhMIA9MpInU55/Nm6Ck0ERN5DTQHXqglaHaDYtYjTOJmOtmdQdMZycg2y3QzC5lFxOVlJLU1iMSlkkg0NPxk+vnc1j1p1Wp1xmfOjiAJxtCs9snMXSOATlxZAVOzt0xsH5BRfSJItgyRUXWrIcUUux4Z10Dbc2Lm7glIkSBFgAgqiM3S/IqoTlR6HE0zsDqPQzNTSCmRUXXCW31n2xZVEUmA6UysPEw9MwqmRa+dJW7o+ImFnWojCsrUSmW6DZOsDbVInef5sMeI0O985zt89rOfZfHixfz1r3/l9a9//TQRetNNN3HppZdy9tlnkyQJV1xxBT/96U95wxveAMC3v/1tvvnNb05r+7TTTuMHP/jBLunH3oCRyqM5GYxsF7pj4tgZDk4HtGsldPMoAAp2atZl+C3R1d6GsaGDKF5POqNTaEsjTIehuk/shWQKrZecjkYqllTqAZZpcMD+i/nLDO32Ltr4EpGVGpVaMJELviOLZZmM1hu4QUxWNzAzrRvD0+hU6h4iEXQVUuQzTtNr3tQFZ73uTPRNHKNNwA4lQeixuiY5sC2e5j0tRYTwBnFlijipkY2hGkTNTEiJTBisukRuSEfBRGPjdzU0sgJqtQB90glJM3WGqw0CP6JgGJhmq6d2Rk70QSaSJQWLUEoCswM7rk+EaZrHfkYRNdg8dfq4HzFWqWElOunsRAarMIkZqrpzOm+9hSJSCsZ0g7FGnRc0k4M6NaIkwN/ZIjSqI+MGWqrVAUw3sxiFQ4jrq4lEQpBkSbslqtnWvWeabu1RsUKllMiwjPAGMTILm9camk7ij0xcSd4A0jgMmNw3qjtIGZIEJbUvdDchgjKJP4JVbF1dkSJCxB6ariNFjKbvMT+7uxcZo+lpNDONDIabz1iZBMTlZRMzpElAXFmBWTyEuL4K4Y1gd7+qGa5MTj7IdnSw+9gbmliFSC9A03TcJEJHx9R1UjIFQpCxoCtjEwQ5+soDCMMmbcim86tibuwx88aLFy/mwQcf5D//8z9n/LxWq/G+972PT3ziE/zqV7/it7/9Lf/4j//Ie9/7Xnx/Yl/c1VdfzZo1a6b9UwK0Fc20sTv3B0AKE93u4ICeAhkjRDO3z1mr4JgU02kMu0BXOsa0HWxd0lvIICUTwizZuGlmcy/40849lcOOOqylzUOOPIzXnP0aYGYv+C15zSdxwmCpThAlTQEKs3vNA4xWGtQDaDN8ykFCEEbTvKeFP4qMG7jY1IOYSiNqScVpGSYLChk0DYaqbovX/Exe8NNSfG7iNZ/EgoHxBlEi6G3L0FHIEfguNdkG6PPOHy/9UWQ4htwks8dgpUKpFlDIppopVB3TnPN5s5wUuozpzaawTJ11pQpmJJAiwk92rue5CMuT2wum/xAZdhE0m4rvEtY2YHtVqr7bWkk3kYk77bu7Axl7xKXHCEcfAiHQzDS6XUBPLwApIPHQnI6Jfa9RBSliRDSOZmbQzNxEikGVsWW3IBMP4W5Axu5m5cGEoEnCbdvD/SJlQpCDkT8QGbnNfdlx9XlEWEF3OtFT3SReH+HYoxNZixK3ZYk+afSRNNbvYLsSpDex8ibcPqQU1KIYW9eRUjLuSfIOdGU0NE3DIqY7bSF1nVIjRAh1/82HPUaEvu51r2PhwoWzfn777bdTqVR4//vf3yy7/PLLGRkZ4c4775zTMTzPY82aNbiuy/DwMOvXb/nirVarbNiwoflvYGDmeI97I2bbAmQcgEjQnXYMu4Bmt7V4TG8Luq5xRE+eXDqDQKClc4g4mlHQzBSGybRMrv781Vz2kct43d9cwFs+8A4u+Zf/H6GQs4ZhAmYUokkiGBwp4fteiwCdYiYhOlppUPNichmb3hyM+wH1MGkRelIkROVnwUhTC2PKtYCUoTUF6BSbC9EwiWcNwwTMKEQ3F6CZlIVpmiQyZrxeRbNy88ofL6Wc+JEUcfMBC7By1CVrJvS25dhUy831vAHopo3mlVjcVqDgGAyUa2gyxo13ntOPlBLhDc4ayF1IySqvi3tGOxgrN9C9AbywTrTJD4WmWcjYbc6q7E5EWCbxh9FTPS1OSFNCVMYewu0DI4P0RpBRDZkEaIaz0Yt+N+2je6kjkwARjJN4m0XdSHxE4iL80W3aw/2iRU6sMOlmGszMxPhFdZLGBvTJZXBN09FTPSBj9NQCNN1p7q2VUhLXXpjYG79DzaojZYye3Q8ZeySNDVTjAEfXiSXEAhbktOZLr/SqpFJpegsZBBDNsN1HMTt7zbrAsmXL6OjooKdn45LbPvvsQzqdZtmyZXNyNrr//vv5+7//ewA++9nP8s1vfpOHHnpo1vpf/OIXufbaa6eVj4+P7/XhnUSQEPgCwgaauYD6eAXDbqMxXkPTZvEG3wLV6sY9PekkpqjpBF6Cm4AWaXRaOhnNYXHOYKThs2rMRxfQbjl05SfijcaJpBzHgM5p55yJiUYsBaW6R38lJEkkRdOiO5/CNkyCKKYiBGGikdNNCqZGb7pABY8xN6J/vEIx08VC3SfnZCCBehJTl4KCrpPRTTqdLDYmY17Ic0NV0hh0ZdK0ZdMEfo1x32OoAvbYKJo5sedQhGWi4Q1gFRmqpOjEY2FHF6Y0iWPBeBJjoNFmmqRwWJIzGKn7DNZCGlFE0bTpyacxdQMx2ecESbthYmsGC7MFxhseQ7WYMIkoagZLijlSlgkJVJMYoVv0D1dYnCpgGWNo1tyEnhQxcT1CyoXISgWtvoHE6mJsvMKBHXlMYRBIQTmJsTWdNsOYx3mzKSQBRTvi8I42RssxQ8MjpEWB/Dx06KbX0lb7E3uEo+vR04vQNB+JZI1bY6GTIWUY9FUTNtQEkGGg3EEI2PooQ5mFZExnalCQkY9pDM07xt/UrIcQgrGx7f8xjOsDSN9AC2caMBsZdyIaJdAFNV+DgVUIX6JPZmkRoUQPV2Cke8DM7rac3BOOXnLWJA8wGdEgqmNkFu1UO+dzPW0PcX0M4dloQ6sxgwza5B5Q4Y8ShUVkWMFOjaBv33v+TmNXjdMUMqwS1RL00EcGYIyNIGKPeHQIs5ADfdMtRinwAkhMpDuIGXdDEhCOjaGZDSyW7rBrKPGGSOoC3XGQcSd+bZShWolcqgMnsigAdqwRRDHjXgOjEdCeymBpBkuyOSzMHfIs2BKdnTOHatsb2WtEaK1Wo62tbVp5e3s7tdrc3vzPPPNM1qxZM+djfvSjH+W9731v8++BgQGOP/542tvb9/qLQIp2vPo64voYTs9+yGgNRr4Lq71rm9ucGpN2IcmPlHl+sI/R2CUvAozEomDbGIZG5AnWuWVMTae7rQtsSUKCKxIGJp1mbM0hqxtogJHSWV8pEUvBYR3tGI5GQsJQEvNCPcY2JAdlLfzIJm9JMmmDJ8ZGqSHZz+likaURSx/NtBhJQoaikKWOg2NMPLQyGZNl5SqDQYMuO8U++W4SLQE9YSTxcPUUHcUMeqoTKSXR2BqSrm6IXYbX96GbHpqTJiGhlsT0JQGWpuPokNJ0NEND2jBcblCKPNq6O9EcSEjwpWBABERSYJoOhcm9k6m0wYbKOF4ScVCxiGYm3HHrnxkeGEbr7qDz1ceyxIW2dA67LYsxS/zIzRFRjTCUGOleRJhCeIOM+YOMh3V6Cw6JkVCNYzbEPlndIGM5mJo+5/NmmTqaX8Lq2gffydMXjJHVl9DR0TGvH4m53l9RbS2BWcKwMrjGAjASxoOYHgcaiUl/KOltT1MulSk6PsvLafRKiXP0Ydrz+zf35wmvjtWWRbfyc7YRQJ9ML6vr+g55JoTJCmQqi27NplRSiNCa2CMah7Sl2iCVQZ/cvy1lDzIoIZNxDGcRZvHQ3RI8O6o8DzLGanvZjJ9LKQgGHicJ+7CLGczCgTtViO6K53Uo1yCdNmRcx8qCkZ44ZlQeRmjtiMjCSIXYe/Bvx678XUu8mEiYGOkUiW9i5gySxjhJwUYzhjGyS6atzEnpIP1hrKKDjCLCMAVJSKo9v8UXnrkipSQaXYm0cpP3YIpSQ9JXfobuJGKBsZTA1BGOTi0MWB9WcRKfrJHC1CAxYwJN7vX6YFey14jQTCYzo9isVqtkMjsnp3KhUKBQKGy94l6IpusYhR4Sdxw93YYQaTRrx/RV1zUO6CiwfDDL4WmbnJ6mEpWoGTpGJAndiE4ri2ZC1fXIWjqWMeFB3m1N/GCmJn/c4yjh/8/en0dZlt13nehn732mO9+YI3KoyppVpVmyZMlosmXJNhjT7tdgP8xqDIbF4y3ctqFlY4YHDQ1GLprH1HTzGNo2+Bl4NMYTxpZtWfM8VKmkKtWQlUNkzBF3PPMe3h/nxs24mRE5VJVUsju/a2mtUuQd9jn7nn2+57d/3+93OEjpehHOE5RFyWiwRy1qkhQ+j3QEi37CqtsmCVZYT2qs7/cxNGjUG4RYBlrQlgXC85lTiiAMaR9R0O8NYhpIVsMGDV9ezZr3fMI8Zy9zuMOMdpPi8gNUbQ2nE7YGl8hLibYWT0rqSrEcBCggnNxU80KTjAtaKgQlSLKcPPQIPY9QCBZ9DwPUJ2My2rI/SJlTIc0gROcFf/d//sc888RXp2M+97uf4r6//BdIi5LgdvLjj2wHHsZabm49w06ScWquuo6aSrIaBEht+civf4idzR3mlhY59/rX3XTeap6HTUfk8YhEt1jSI8rkCql+FXX/pTWDd7bExpfw6qcZJDFPDzdYXOoQG81mniBSj6YvQThG2ZiyNIQO9kYd9pOcBX+dob+MJ33qAKaAlzHsxJkck+2i6mdu+LppHOnoCrr/Fbz5103/TQiFiJZwzmCSK2At3twjX1cxjHMWm+2AMzj7IEJerxauLIv6COFT7n0ahMBr3YMQchICsYPwmki/ccw3fGPCmaw6z6qGiS8jo6VKaKPHoAKkWqhU8ndQwZYc6jWF8LD5Pk4nqPYD2GQdE69fR0SFEFgcrhxj0y2k18SKbNqS8qJhsonDxFUSWagGc/UFVmRKmQxwrgNImp5gxWYEYYQ3cQ7JMk0t+Ibpcvw9gd8zJPTBBx9kf3+fwWAwVc3v7OwwHo954IEHbvLuF4dHH32URx99FPP7rNdDNeaQtTYymsOV3ao35yXCq5bnmJf300mfZ3PY5MkLQzYzzXiYUfMUr51roLFsDxO2hgmr7Tq+8ljyrrKAaQ8o8MhSG+VJdgZj9lKDzBMWu10e7CrEeEizs0I9H9MudxifW+SR5hoXxopslNErU6zJmA8btJRH68g9cW8Q0xtlPPupz5D0B7QW5wne8kYAFqKQhkjpZSVa5yiqG43DIYUkzw3WeSgp2BnGLLcbeFKycuQYDntAlRCc6zbQXsjWMJkmK4Wex+KR1x/tAb1/oUkYevzKf/qNGQIKcOGJr/LpD32SH3jNH6F9G5GT11oRyaDNlSTCuB77o4TldoNASBaRvP+vPzqT8nTvIw/yV/7eT4AnbzhvBT5bOz28dpNGtExD75LEG9S75255nLcCk+7gdIxqnKEs9nj+4IChGjPXmGMvKyEtWW0E5KakPxxiEsd8LSBC8PSwyblGwVODZ5lvneF+n2nW/QvNkX+xcDrB5T2sqt3U1LzqES0RCly+i1OnZ2y6hFDIaAWTbUBf4HUfOZYMfi3gylHV1+o1cOUIEXZn/905THx5Ur13mGSLcveTYDK81n3oeB0zehqv/Qqkf+/XZcwvFs5qbDFA1VYQKsTl+1VvrteonBtUAxA4PfqGzEl/OWBNjjiUpcgAp8eVz68KEI2zmPjy8URUBthkC1v0EEEHafOJ4OvFF1GcyXHOIoWisIbdPONSGnOmucCC1DzbG9AbD1iod1FFyiIOL6oe3pOsZH9ccvfCS0CG/y+E3zOU/b3vfS9BEPALv/AL07/93M/9HM1mk3e/+91f0+9+3/vex87ODo899tjX9Hu+3lC1NqqxgAzqVRXiJfRf64QRDy2uMF+T3Lfcpq0E+wcpHBGz3Eh9fZwIyZOSpUaIUordUvCKsM8KmlQqguVXEiycpXHXK2HlDK9fXeT1awHnllt4XoP1sWM/TmfGeEhAf/7v/zP+wz//eX713/8qv/C//hw///5/xijJ2U8zfGsY5pqsqBSvR9Wtw9EBRnic7tQpjGVnGKOPCF6uEyF56oaq+eNESEoIsv7xPpT727vsZ2ZKnm4F1mTTXjWAXBt6cc5KKyTXhp1hjHGOj3zgIzMEFOD8V57mEx/8+E3nbWtYYvMxp2oFpVVY1WTcexL3EuazO2uw44uIyfZ5LjsMXIOtYUzD9ElLRz8ZIZIDnt/Z4mCQEYUBq/NNFiPD5TF8YKfDk/s5V3qXMEZjTYqOr1DufaYKNPg6w5kUVFj1Sk7UuTeC8Bqo+umpWOlaVXwl6ljFpBu48uvX72fyymTfFgPsMd/rikFlNRV0kOECqr4KwqPY/zzF3mcxo2cRqo7Ndr8hxGK3BFviygEm20cIhQNMtgs2B1NW/Y3S/4bNSX9ZYPKpd2olOOpN70FC+qjG2cqWLF6fWXeFV8fqIc5VZN4J8ZK5DjibT+30vjzo86VhD2thIQgRfpNCNNGmZKt3QDnaR3rV7s6hWNNTgsD7PVPb+4bAN8zZ+vSnP83HP/5xLl68CMDP/MzPsLi4yHve8x5e+cpXsrCwwPvf/35+7Md+jCeffBJjDP/iX/wL/uk//ae/b7fMv9YQXkB05pXVf0cvvBf05M+vg/Coh4pXLUd8/iCh06pN1dRwVX19tLImrDhRBa+Eo16b4665jLNcQuYdaCziNU6hlU+v/xzdqMWZWgNfSlLb42xniee/usFXBzkSwVwjmqrgn/rUZ3juK0/PjPuZJ77KVz/5OR5525sRpkD6JVmW0qFK8RDCx1nNsL+PUQHtKMCTgp1RMq2IGm2vV8FPCumHRPRoRdRDXkdAD7GyNut/eYioO8f6OOW1t3NTM9mMafZwOGA/y1hqNRDasBdn7Axjtjd3jn37YWjADecNx3LLIyRlaGs4IoZFgisGL9nvzOZ7WD1CRqsAjApHGNUYmCbSpGRjw/5BwfndAY/3c+ajioBKKQgleF7GThmxnSzj3B6vq23TDZrV1ilgsh285l03Hcf3fM/3UJYlvv/iK1s27+PCJRJd0CgGGLiliiiATbewyRVk/dqKqKySo76O/qEu30YEc7gyxsTrM+fR6QwzvoCQ3vRh6FAJbdI9TLqFat1bbWNne2BSOMH54BsJzpYgAiiHmFQg/RY22cD6rcnOSUVsLFTX4MuUk149qIiXTbB2FFanIKq1XagA1bx75gH5kIheWxEVKsJmuwhV/S6ECHDFS/OQVaXJQWktA1OwFNTw5dX8eCtCVrqCQX+bnXTAyuLSjFvIQjtCyJf/3P5ewjdMJXQwGHDhwgWcc/zIj/wI4/GYCxcuzPSB/vAP/zAf/OAHabfbzM/P89GPfpQ/+2f/7Nd8bI8++ijLy8u89rWv/Zp/1+8nCBUgpIfVCatrq7xmXrJRWhxXqxsaxwCHqgVY67hyEHN5f4R1Dq8VMpAOfeT1Rpf0paK1uEir0aEpB/jNJaz02XNN1kvHvZFfbYuHNTq+z9Dm1BdazEcl23HB8zt9RqlGhR67B8fHSm7t7OEFHqV1rB+MGE28JV05BBXgspg4zxHKQzvLSDj8WkCuDesHQzYOxljAawb0RBX5dgiLo+8cshZgcWz2x1zaG1JqQ9AMGCmHPlLVevt73s79r35oZnznHn6AR97yJjYGt+c96EwGk/5AbQ1Pb15h5ARCwFgJvNAn1wavefxN8jA04GbzNvICdDKs7KOsYmQcJn/pFKMmXkeo2vRmOsotWhZslwF7rsFwlHL+4IDHxlCrN/C7TQaTSEtfeZRFQk5BrQaX0gYDIytyFC4hgi4mvnRLldt7772Xhx56iHvvfXHbxs45bNFj30oeyxXaa91yRfRa+6ZrK6Ly6+gfWrUU9FHNeyuPx+TyJPGpItnl/ucnVdDZOGAZLqBqiwjAZTtUtyaHLceTz00p+09+48aS2gIZthFBpYK35ajytMx2Z18nREW+Xibo0fPY9BvDatAVPTiyLopjEqVOqojKaOlqMpgKXrJKf5UX75Nbw3aWMi6Lqn3EOkoDmbHkyiJdQY7i8v6Q7V6MUALZDBjf8ei9bXzDVELf85738J73vOemr3vLW97CW97ylq/DiK7ife97H+973/tYX1/n7NmzX9fv/r0OGa1S7n0S2XiAu7uKX9/QZManNlGmp8awXZZ4wFIUMupXRKXRXWBPWHRpqQlBa6IaTzQcKMvDYcD86pvR/SfoNFYZlTnPjA9409wZFmS1PexJyb2NNr8xXmfbQjuE/cQSMEIELVTDQyx2jx/34hw6UIRZSDkesNvf4z5b4kyCUE3KdJPYVc9wY2PZKksiIZmLfOJ+D4SkPb/ABgavtDSlnGpecmvZK0s0cKoWMeztg7U0OvP0FGRlSSgk3UnFWPiK7/+ffpRP/tZHsTs9Wu0mr33Lq3Ghz1auyfOUW5X8OJMhhEduDI/vbfCZ3R5ztRa5s2wUBW3Po6U9XvvNj/DYx+7lwlPnp+996DVXQwNuOm9AVKZQFjgXMXI+RbyJ17r/RfcmOp3git6UyJTG0c81A5Ozl+U8F9cYZx6+GVJ4LU53mqzrgjaOtqdQKiArMqRzHJQ5Ay254hY566W4dANZP4PNdrDZHqp+snfxSwqTg0npacUHd7e45677ORXIiojyIiuiqobLtnHF8Lr+zJcaVfyiQyof0TiD7n8ZW/Sx6S529AwI78RjOayI2mwfyyYIH1v0ULVldLKBGT+Pipa+Jrs2LwQ2P0D4HYRU1QOLc6jaKoaq5cDhEP5wtronvJfNx9U5i40vQ7iAqp96gZ/x4vql9ehCdU1JH0xS9XV6jWOFc9tZgiclC0F0bEX0EEIG1fk2BeI2kuOOg80q790sqHM5SdBGsjP0iAtHPRCMy4KN0QYNW9AJItJxD2RAo9Vlw2oSYzj1e6WF5BsE3zAk9A5+f0KG3WqB1gcsLixxevcKSexTa1eLRU1K5pXCacd4nIOsgwdJntOshUglqMmrBXtjBcstn4dabbzGHIj7WA6X+XKc8pr5NV7TrCH6X5q+fjWq8e2Ly/xuvo+TPWTRgTBAKIHNNe987zu4/NHP8cyXrgp/HnzNK3jnd7wdL7eURqCEYnOwPzEGL8CTmNE+QxfgC6gpyaLn4UpLGuvKn1EJ4iynUwsIlCQ4kh0fSMmc51GUllGcg6ghQkiLkoYX0PQ8aurqMSsEC1HIt37nt+LiAlsYUAaHoJcXHCRjmreQH++smSg/Fxmbks2DHUJ8WqFCQ6VwLyxJpvGiLj/4k/8DX/z4ZzjYO2Dl9Arf9Z3vwJv0O93KvEXCIy/GFGWdMPTJyoTaS5Btbos+zhnk5MaVacegKPEkrNXqyBKGo4L5sIkMA/IsZyEMaHgShQClWJIOlMJFIVLDxUTw5sVVZLaFTdbBa2PGF5HR8tdF0ONMUsWelpbTUZ1n4hEri8uVGO5FElEhRFWBK3rIrzUJTXcqgkGlZJa1VXT/KfToOVTjNKq+dONjOEJEEapKJmucxcaXAIWJryC/AUioswY9fA6vfT8inJv4olbkTNVWKiKaH2CGz6Ga56bvE6qOzXdw5v4pYXI6BRl8zX9nrhxX6ULO4qx5Qd+nh8+goqUX1NrhdIaJLyJUhAznJ2KkKmVK1s/MENGxLvnysE+kFG+dD0/cmoeqWmqdrh6wXwQJPcyydzoh0dvUZcBo7COsoxUIxmlBoxiy4DRC1UnjErwmQgnSrGAu8vGV/IZodfi9hDsk9Bbw+1Ud//WA8BqoaAVbjmk0Ih46e4rtjTE6T/HCGp6QrAiPjXGMANYWWiAr0Q5ZQbPWYD/1WKlbcA7tJGeaEQtBiDM50qvRbSywZHu8em6NUBhKKXETUmbLEWsq5zvvfStPRE+wgYYyxi8LigLmayF/5f1/mY9+4KNsb2yzcmqFt73nbaSFYS/JCH3FmfmIQVxiixE2P0C6Gq5MOTANAukIhWQRxdY4QwjB2fk2hbPsjBKCrGSx3ajIzwQKwZLw2IxjjHGszDXxfcHWMMGmZaWav4ZQLigfPYpJS8tcM6TVjNgZxmz0CvbiIXdZjTty8z8WTlfbhE6QmYh0PMTJeXwJPooFa9keZ5MkpBapLnnju95K6CmW2w3Ukc+90bzpccJzn/oC+5s7dJcXeccf/X5aTcgdL0lvoh6dnxEOZdqyV+R0o4DQKXSaokzG3aeWGOUF/TSnLjXz4cTuR0iEcyidsRx0SKXlcj9nfFdAt34Km2xAOcDYBJlu4zVOrhqdP39+2hP6YrbknU7IjKV08IbuArtFynaWcra+Mq2svSgiqhrYZBPXvPumDyswyeR25rasnWwxwOY7M1vtMuhUEYz1U7gyxub7U6J54jEc6RG1ySY6nMPZEhktYfMdbDlGvkw9lYdwJsMV/Wos4Vy1Q3LkXB3Ok023sDqezovwatikh833UfU1nC0pe1/Ca937Na/w2mKIsxqTbuJMipC3dw4r66095AsUsFo9qloV0i1k0K4cHBpncenmDBG1zvFcPMQAiTGMtabl+zcmolSiohcFmyOFjw0XGfW3sMMQz/OZrwuEM6h8FyiRfoOdfjqzTu7FGWGu6X6dHCh+P+EOCb0F3NmOf+EQXh1ZX8Gle3jFiE6Yk8+1Ge7vMR8er4IHpqKX9UHKWhtyowgwaCQNL6ZletjMoBp3sVBr8zq/RsMPq5uniCZK4zpOj/DnXstqfY15BYveJh/aSJHjS3hW0k+rhetd3/Wu6ZhHSc7eMJtmwffznCRPydIeohxQJin7Gq5klsVAHhvF6aFYbtVnxEqKqz6gx4mQrhUrhZOqo3Ow3Y9Jc81cI6Dbqqy0ltsNLg1ydgYH2Hwfk1xB1c9UauNjUGU1K9BjhgeXyUoBquq9Oy6KszlJFDoUKx0loifN20It4P0/+Q95/ivPTL/30x/7An/3X/8d8kaj6k2c+EG+EDidYLMdnCmx+T6IGju9AbEx1E1AXDhG/ZhTHQ+lJN16dZPqp/lV/1cB0q+R9nfoRzVqyiN2sBnnzM+14ZCIFhl68FTVc3kC6fnlX/7lqW3cj/7oj76wY3IWE18hJcC4Ak9KOl7I0+MhpbMsBnM0uEpEh6rLXpGxwPFWMMcSUa+By3Zw5RgR3FzI6fJ9TLaP333opq+tjsFNMrzFDBkT0kdFi5N41c1pxOKtEtFy+CxifBHhz00ISpVo83KTUEyKM+mkr/FsFYUrZsVph0S06u2V0/8vvCZmfAlZW8EkG9hkE1tb/ZoLNGy2jWqew8aXcTq+bXGU0ynouPL3fCHfn/dAhth8H1uOcYDym1hRXW+HRHQzz9nMMlbDGrtFyn6R05oI/04iok6Iae/xC0UlSnJ49VUG/QGjJKMVDhH1Nnq0i8nGGNFkpz86cZ0c5zkt/45F0+3gG0aYdAe/PyGEQEZrCKmI6mv4rmAxHKC1I89PzoI/VF8LoC76DDJLmmuG1nJfq017+c14c69BNc4ihaQxufCrLcClahtPj5Feu8oepkowOVV3zNU76KjFXChmsubhegIqpaAW+ox0STraB9HCDNZ5cpSRaYGyx6jgJ6gHPsutI/ZNzp5IQOH6rPlc6xMJKFRZ82eaPheHJdnGb6EHT9/Y/sWWCK8NXoNh7zKj0hFITsyCB2hGIYuNaMa+6SQCCvDpD35ihoACPP3FL/Hbv/TbpG6S0V7efizs9BCKPqh6VWHL9in2nubi5efIk5heCoEZc9bv06lf7Rnr1iO6tZCk0OyNY6yDwgq2DmJsNmS1Jakrj+eG1U1Meg1k/RRID5tcRg+++pIJYpxz2LxfiSwmIgaX97Bln1hcvXnVPY+W5/NcPObT+9ts0UQEHcqsz1P7F1hPEwp7sgjiuqx5HE6ISnB1Cz1rdtJ3e8vHVQwwyQYiOL7KXV2Xawi/WfV83oJITYYLqGgZPb6IK/tVzrjfxibrL6nd1wuBKWOsjrFFr9rGPTSqvwaqtjIVKx2KzITfwpZ9bLKJGZ2f2HJ9bS20nK4qt9XvYqUihLf7GSbBZDvYF2CHVFVRd6sqqjMz8394vTlbko8vcX48oOMFSCFoqoArWYw5Ivg5TqwkhP+ie20Pre6Ms4xlG8+rk2QZOzsb6GRARkhvnN9wnSyNpfhGFc99g+JOJfQOvuaQ4Vy1Jeg3qNeWcHYLq3p8ftNwqsaUyDgcPV1dwHOeIvQ8mvUIo8Zc7o3I04K7VlqcaXWR0QKS46spMlrA7nwc4Sxq8ZumvU/Ca9AKYLURcCVtI3TKYhSxB+zEGZfHGZ6GTuhNCWjhLH2tiZ2gv3UeX8YMbY2P73sMzIhiDG1PTgmoocpRP8yOPySi28OYS4OYYVHQBFbmGlMCepgd3/W8GfumZ/tjjIa6g8VmOCWgI6PJnKOjFM1QMnB1StnCL/eqCvBJcBpwOO0x1B57qSQb9wgttH01XVj15BgCIWgrj2ZUkaOdccqTewPQ0JLi2HnbOcHaafPSJiOjwaOyQbqFatx1w7cGk2wivQbCb2PcOmX/KzzRq9HLLZ0Vx5zZw6/VKJVkoEuiiajtsCK6n2RcGe/x5Q9+inRvj3N3rXDq+7+Pdhjx1EHGt50qafo+0mtA/RQmvoI++BKytorXOH3bY54Zv07Qg6cxkzQhz4FqnEYPn8HpggNdEB7ZznOAMQWit8GTZUZ95S56NmEn3iYWCXd1bmxddG1FVNRWMfE6Mlq66ba+zfaxZTypnt/4NuGcq47Bljd87SERtdx6RdRr3YMM2lOxkqyt4co+tuijotn+UmdLTHwF1bzrBVfabxUu28WVle2YK4dgCzghcW7aI3qkpUIIHx1fxlmNDLrYcvCShyRc3T7vYstqK15KvyLBt/GAcQhbjLHZNq6Mb38sOq6qqNHKJJVozNEjPbzeDvoXWR/ustQ8RV01qCvFTlEwKEvmg6sPaYdEtD94HjG4SLuxjM12X1SrRtXiI8iMIdWWdr0Desju7hZXigyFZSWssdg5eZ3sRt4MYb6Dm+NOJfQWcMei6cVB+o3KN1CPaUZdBqqLCWJSN6DWjKaVtMRatssq2z2xFm3BKcGZZpOh6fN8rrm7K+g2jvfNnH6f10T6HRBi5kYlvAbSC7m35SjwsfUOzmYsNhsMjeNyPGJg9ZSAAgyMYbMoGOIxyGJUfYln3Gl2SsUgH7OvM5a69WkFNDaGjbJkuyzJJotRPfCp10IOsoydPKbeColCn6SE1Fq2y5KNsiQ2Vy2EFpp19tKMzWyMC+SUgDocu6Xmcp4zMIaG55FpGPprCK9ebQueAGc1WEPe3yT2FhiWkq10RM/krHbr0yf7oTZcKQp2Sj21x2pGIb7vsxWP2SsS2u3j562zenxfW2t5iViXOKGwef+G83ccbBmje49V/oB+k+f7Bc8MQi7FBV+JC0qdMVdu4wc1hOczMIbLec5uqaeWYN16ROks//bv/GN+42f+HR/+1d/i5/7Zz/NTP/wTGJ3y/DhmPb5q/C+9BqpxGod9QZWja6HjK5hsCxktIYN5zPCr2OQKZnwRrWMu9Lb4zf/wa/yTn/qn/Od//6tsjWOe2tvgYHSA0hlfGfW5aCOcX+fCuMfu+CrhL609tsJ5tCLq0i3wGlXF/CZm/LbYx+a7t2SsbtNt9OApnK6qgzfCcRXR0lr28pPHI8MFZLQwiWrcBLxjK6km2cCMzt/wGnihcLacfq5zDmcyZNhFZztVdd7qGf/da3FtRVQEHVx+MBXoYLKKyL6UYy4G6IMvUux+CpNcmaY0CRWBTm47kMEVPYQ/hy2PD8+44XvLMXZCsoVX+fEeteqrxlXnCk2204yL/XXGuuqxV0guJ2N6RY498hvfKTSfzSMuZHkV+WlSdO/LLzhowpWjiT2TpdQOIWCx0yWzhstJSs/kzDfDG66T9ci/I0y6TdyphN4C7vSEvnio+inK3peoqQa+32C1s4rMdtiPDQt1h7KGUEoaEyV8KASlAYnlLBlv6yh+M3e8IkpQN2mMF14N2bgLGc3PqCWF9JB+l4VwwEpYI9Galh0wTnJCAy0/oB4ohlk+rZxFQtD1fIaeZaP06R+EfOagUqN7KsATjt/8ld8i7Q9YPbXCm779W2hJiS8E/mQxMtqSJiWR8ogI2IkNSkHoCcpS0lSS0jnCybE7B8NRRl34eL4E58i1JvQ8BIKmp6ZjU0rhjCbRDlk/c0PS4GyVZJSlI2IrENrR9gJqvk8/z1ic5HTXpKCjFJGUHN5WdWkoMk1dBShPkuYF7ShACUEoxHTe3vWet/O5D35yJm1p9cH7+Ka3vZrcFBSigSwOpsKxW4HTKeXBFyohWq2yTPrU7oCnNrbp5h7IiFYtIJE1upP40EgIusqj6alpAkpeaL74W59g5+nnZz7/ycee5NO/+l/ovOs9fOVgzCu6V/PKpddANk7jyv4tjfXEYzAFNr2CDOYryx6vhjMJevQ8hHMMxkP+5p/7qzz5+avBCa/5T/+VP/83foBOo0GrSBgZg5SShcYKa6VmLxmRx5uo2gqPDw5YjWqcrl2ftX60IioAKxQ6uYLfvu/4sU7shpxOJ8Ro9jOdTit3Ar9ZPRwMnkI1z+GKXtVLWz9VVbZOwNGKaJHs8bQZsGVCXtXucuqY8cM1qnnpYdNtXOv+aeXV6QQzOo81adXucU01zDkHtnjB+eI228Wm23jzr5sQxgzZPIfrp5hsF6uTaX74SThaEbWAapy5+vnO4HT60uSfH35m3sMJgZQ+NttBBtU5rHprdSWm8qKbfEoFZzVOD1HNexAvQABkxpdxRQ9XW0WoAJtnIH0GZcFunnKu3iI1hn0jOdVegaJHkO+Bt8ZcELJdpHxp2GchCFgOawRSsZWnIBQDNQ+iwOkU43YRg6fw5l9z29Vwp2OQAZk2lFaAg7QwKKto+yE132dcFrTCQ2eX69fJUjvCO9qk28IdEnoHXxfIoIMQkhBLIBUPL5xmrUj42K5m66DPUmAJGl3OhpPeTgRjDYEe0DIl71xbQsmE0/ValcR0E/jdh2a85A4hwgVq2S73tGt8bj9FGsneYMxCFPJwp81Bmk3FSt16REt5NJViB8d+Cet5zO5ozKIvWG62+em/9ijPfvkqcfj473yc9/3U+/AnZOiwB9QZx32dFp0ioCv3ubcd4/wFnux5LKkQXzkEAudgozdmLy15oFOnXvPZHs2KlZY9nyXPw5SG3/3ND/PEsxvYp5/mx//kd+PfYAF0JsPqkrwsycYFTdXhweVF+nnGONdAJdypScXdoZySt8MeUA94w/Ic2tkZsZIn5My8ve/v/jj/5Zd+m53NHe66e41v+pY3UGpJmeySdeaJTILTCeIWt82cyXA6nW4hb2cpl0YpepiQy0XeuNgBlxw7b0cJ6FYvZrR3cOx37F28wNtaAZdHmnGpafpHlM5+pxrDi9gutUWvItFHlOMimMcVB6hwgZ//+d+ZIaAAj3/mCZ7/3cdY/M7vZHMwZrkNtcl5fsPCWS5u7/KZ9T5LczlbVpFZw3J4NeHlKI4SUefMDSu7zuQIHDJaOvahRseXsONLyObZqqLmDCqcx3kNbLJ+y0TURSs8Ox6zPt6jW5/jyZEkVIqF4Op1u5tnjHTBvY32DBE1ydYkl36uagcYPY9zGqnq2PzgOnGeK3qYeB1//jUnjulGsMWg8pDUY7Aa5yxK+tC6B5cPsEUPWyxMz/NJOG5rfnJGXrLoSai24vXoOYQKEV4dde2aKeRE2X/19+hMfmL7hTMpmBIZdHEmvi2LJ6czbHEAzmLTqqVChosgJBtxzDPjIamxREriCclD7WWcaeHyivir2gqrYZ2VoEZqNOlEMT/vhzgcY1PiaqcR6ZXKBiq9gtIPIPyTf3/XjdFqnE0RqklsMkoNpYWdsaYtLQ+sLTIoctLSkI9PXif3hgVn5u7QqtvBne34O/i6QHh1ZPMePD3AmRgZRNSCiJVWyDAr2TeSsqhyewWiSuHZT2iKglprgaXI57vPNGjN34VQtZt+nwzax3rGVf1Cjvs6IWmhOBBdQmlZnWS7LzYb14mVBILQ8xkLyfP9EfOe4PR8k09+6BMzBBSqqtrHPvAxYFYFv9yNCESJh+N0u47TA4bFFku1kmEhwAmMha1ezE5a0m74NFsRgXd81rwpDe//yffzr//Rz/DpX/tN/vZfe5Tv/v4fo8yzk4UnJsOWmjQ5oJdJnv/Mp/g/f+4/8sRHPkNNCcZ5Jdw5PGa4XgUfhf6xYqWj87Y3ynnt29/K//3PfB/f+UfeQ7ddRxBQaEsar+NMWVUdbhXOcJisYpzlmeGYNLXMq5Sl+TaNUJw4b8CMe8G95463W1paXSRM90kLzeVRtS2qD7e4pV99/wsUwzjn0OPL1/1uhRDIcAEhfZ6+cPnY917YHHEl8fjqgeAj53POH2jycZ909zJ7WZOP7AR88PI+XZsQa812dnJP8OHWPFZXSvmTetdMhgOECo8lRi6vDLpNfBmT7VaEgslOQ/1M5duYbNx0a363yFl3dVbqbWpmTGBHfHnYZ6yr81xYw9PjAZfThHIiwppuzesBJtsDqghXk1xBBguVBdKk0j4zZp1i4ssvWNDkij7WZNh0d9J3Xf22qmhiKk/TdAuT33yr+lixkpBY/cIFe9eNtxxjs11Mtn/sMQsZVf2dExGNMznlweMUu5+qlOvOYYvh9GGlakVwVdqaNbd8Ldhsn3L/8yBkZUl12FIhA3In2cpSVsM6m1nChSSm5VXb2dKrI6NlnNPYdBucRghB3fNp+wFzQYgvJb6QlMaSI1GNs1WVN+vd/rVqCzAlSJ9hWVCUgn7m8DEsNQV+4FXE01c3XSd97w6tuh3cOVu3gDs9oS8N/PZ9NBZfD4CzGUpJlkJLI6pRELI7HGOcw1rH1m6PokzptpvUay1MkRC2O6ho7kUZElfpHD6rkaPmKYwXsdQJpz2gUnAsoVHGsn6QMqcEZ+abeL46UYSztbF9nQo+cjlW1agtrOEWz5LKFquUBGKHpm/YTwXrBwkbY8N8PWCxVcNMyORxqvmPfOAjM1veAB/62Of5N//hv8AJ5MLZHPKUxAr+xd/9l/y7f/Z/8Kv//lf5V//vf8W/+jv/9DoieiMV/HFE1FrHVi8mLw2L7YhWvaraKc8HoynUApnWmHQbcxs9ls6ZaT/ebp6zm+fUTMFy26MZShr+yfN2rX3WO7/znbziNa+Y+fx7HnmAR972VnQeo4oRX9pPuJLGfLa3x0CXVb610y+cwOgxZvTM1MT9WuTG0F473sS9triCKUp0nkI5ZqOveeLCPs9upxS5ZXkp4nJcZzhKadgxF9LRDdW5MmgjG2ervr4TKm9V9VNUSTTXOBk4k0+r2CpaQdXWZqrD1xLRUTbg6dFgppcv01UM4m6eUZcefrSM8Oo0TUyWDXhyOKCwhvU0Jtaa3BgG5ZFe3XABVTuNGT2PLQbo3pevpu6o2qTfcbYv1BQDbH7wgqqN1TGnyHAek1ypkpIm1UIhFDiDjFY4sB5P7l+YpEbdGNcRURniiv7NxzIxmr8ZbDFA+G0E7ljyLfxK7GXSLQB0vI5zGjN6jnzjAxS7n6Dc/RTl3mfR8UZFTIWcHq9z+uZjyPaqNhpnULXV63p79/KU0loipVgJazSVT00dsfdSteuI6LU4DGLIjLmqmnfm2NfeCM7klSMEEOuSuJBECpZqdqbNolur0Qy9E9fJxVaAuuMVelu4Q0JvAe973/vY2dnhsccee7mH8nseUW0Zv34G6wzSr1HXfUy9Q9TpkBvHVn/Exv6QTDtEKGmFAdLa6uk4CBF+90V9v1ABIpjHJ+a1iyGJsjjfx+mrNzmLRUVVA3o/zdkejDnopzSlIGpHFJM1ZnnteIFU1O2wcRBTakOtFVK4DOtH9FQTEUnmW23ecu61nPPr+C7nrtoWj9R3+ObuAacXNacaHbCC8siNuxSCsF5lzW8PEy5cuHLsd5+/sH7iAuxMjrWaf/+7z/L8l786829PPvYkn/3oZ/CCaoHd7o/Z6FXkMmyF02OefhYO4ynCKCDXpnr9wZi8NNU4AzUVHgjloZymlzgG3jxCBpVX4a3G21ldVXXKMReSEdZKynKIVbOk7rh52+rFWCCaHIPne/zE3/sJfujHfog/9H3fzff/Dz/In/2bP0ruYC+HdH+XL27s8ztbu1xM4+rmJsS0p/CFwGb7E6ukzWNJUL8seOf3fBuvf9OrZ/7+8Kvv4+wb3sQ4jhFeQKiGNESfosjpF5I5r6SwltVuk8cPapAnjJM9no/HpObqb8Dm45lzrcIuQtZO7B+25RAhPJB+tf18BE6nWJNPRS7H4SgR3R9c4tnRHntFddzGOp7YKbg8LDkoc+qeR+kc+7QY2JD17ZyLB3s8ORxwflw9lObWTt9/CFk/gzVjip1PYtIthFe1dgghcHC9DVg5RKjghiTUFkP06MJ1v8vqmFOk367y4IsByCNKbb+FCtrsyDaXCstwdGVKRA+KnPyEkJOjRNQWg0qscxOCqcfrlIMnb/ga5xw220IG3arv1NnriGi1nnYxo2cw6Q52fAEVzOF1XwWmQA+exXl1hN/C9L+MzXam7U0OUQmxbgKT7wFyxtz+kIjaYsT64DJ1qXDOcVAWxz489Y2kJztYW84Q0UFZsJdn0wf1ZPJ7F9JHREu3/cBo8h422yHJesTaUhpHLSoozOw1r3HIKDxxndTqjijpdnGHhN7B1xW+VHh+He2AsIYNfXpS0RM+9WaDPO6TxX3CTptx6DNPji0SVL2D9IOb9lzdClR9DVcMubdbY6gN+zLClldvyAfGsF7m6MBHCUky6mNtSaMdse00m0WJwfH297z9uqrafa98kFd90yPockyjHXEhNTwzNhyEbdp+xDtXF3hjd4G57irdpfs5JSNG8T5evsF+4PPqlQZvX1EY7VGYyRY0js2iZNsYao0QUxY0TrDnufeutcn29Syc1dWNWSi+ur537Hufv7JJ7itCqUjiAUZnNNohe8KyXWrKIxXW2Fo2yoKhhEbNJ09HFPmIesNn5MNGWRAf8bGU0nJhGLNeaGTrHmx+ADeyk7pm7EJ65PEV4jymlyZsFCkHbnbBv9V58zyPd33Xu/iuP/l/4+53v5U8Cqj5HlkyxtcpKj3gyjBlK0tJJlvDAjH1ETyKdrtNp9Oh3T75d+mKfhXdeMTX8Ci285RmFPF3/snf4Lv+zB/nzf/td/Ddf/5P8od/9EeripawBO0a63nG1nCfTj1koaGQZYJCsKdTBjJgZJvMk3Oxv86Hdrc4Px6SJn2K3fO4cvY7hV8/kZC5cgQqqCqhNpshRs4k2HTzpjf5QyJ6OYNLOz2eGeygreUgtWzHhucGOYUxBFKxk2c8HQ95Yljn+WGN3f2Cy8NtejrnchpzUORsZel0Sx4mJMrv4vQYIbxqTBNCIoRf/b4Ox2x1lRDkt0+0F7L5AeXBF9CDJ7H57PXhTIpJtyZeoEEVfXtEQCT9FlpGfHk45IqtsWerrflBss9H97f5nd1NhuXxDzBTIqqTqcJ75rt1Oj0uZ0tscgmXbFZOFyfA6co6SvgNhIpOJqJeHWdK9Oi56nFR+gjpodoPoMIuLt3C4SpLp3x/2k4icDh34/k/tIc6rn9fhguMvQ6DbETd9BmVBc/HIy6mMYm+elyJ1lxMYy5kmtibn1ZEC5NzKYk5n4zolwWBkAzLI8cl/UmM6q3B2RIbX0aG8yTxFlmekruSjSJhOxvDkR7rxBiuFPmJ6+SO0bf+cH0HwB1h0h18naGkpBa0KGSN2GbYepslL2KcCYw3j9U9lC9JjUe7GdCVfWyZ4i/chcPcUj/ozSCDLg7HsoxZi2o4LHC1chIgqAuJbx26sNXi7ClKo6l5Cs8YPvRfPsju5i5vfddbeeM738yFjV0WV5d445u/CVwJniItSwKrEa0OwrVYrUXc3YyQk+1Lr7vG2u6zXNQ5+57ktBtyn/Wp+zUWQsnlAvCrJ8VIChwKnRmwite/45v50qef4MKTV43h3/EHvok/8Uffg3OG657Hra5iEHVCa/X4rd/ltRU8A2VhqvQX36PQhshXBFJMxw3gC0EkJVhIE12dNR9KY/A9HyGvugMA1JRDWMkwNxhVQ3lNrI6vF0wcA2dzZP0MZT6mzPZwWURDQujNlmdvNm+RFDNP3YEUREKiLOS5QRvBFz/5Bfa3d2mdu4/v+uPvnlZYnGCmclh5Yvr86T/9p288dueq2EavDkHnuqSX1Gj2ioyuF7JeGN707W+HmqJmIBkU+MpDKIFzloZShNYgvADhHDYt8J2m7nnkRjI0HVabgqVyxH454Dd2hiynfV7pMh6YvxuCq9eOEAqrE67dOKyM1xOEalTtA0UJNgdZzZPJ+4DAxJcnPXgnV0RzJ9i2Xbb7Y+pql7NRjb24Q8MXrMclLSUggsAZ5HjMfl6j02ixmcDqMGZ5QVF6Pk3lTbfkF8OroiUVLUK0WCXwHPERPdoXKoQEm1fz5TVw19gLOZ1hsl3M8GmQYZVmNHwOGcxNt9xNMUBQPUDI2irCcd1xD3XB0GhWogZXUJySJecPLpGbkHUt0M7x1vmlmfFPj2MiTjLJetUiMRHs2XJEuf8YXudBVG25qqiXYxByKsqaORaTY9KdaSCBnFhGHRJRE69fN28yXMAW+1Vfbz554Jo8QByKzGT9FF7jrqvfAzethLpyDDqF6Pjdom1XQwVthEnxrKWhIpSUM6I6X0rqSmGkxPdqSL9S+Ytsl5psIIRHKCth0MiUU+GgkN6N/ZKvgc0qf2XVuo+8dx6d9bB6jkbdIyz0jADLE1CT6sR1shTi+rX3Dm6IOyT0Dr7uaPgBW9SZC2N0bYFXlz6fvWJwnmCpW0cGERcLxd01Q705hywT8BXCC29JGX8zCBWg2g8Qbn+CBxpnuJyEk/Bhh9aGL3zgI1xZ36LW6fKGt7+F04sLk+hOTctY/uXf/scz/ZgPv/ZhfuRv/SUOxjnaOFbm5iisYXOYcyZIuevMWdqyxZzVyGIbg0PICOG3aM8t8VAuCKKARXsF1Qxw2ZA54fGcnfj6IVgLAnrDlGGqqUUB860WP/TXfpjP/e4nGR3s01pa4X//yf8nvhoee4NwbuIRmh/wjm9/E5/8wOd47suzx/AHv/3t7I9yHLC2OM+4yBnnmraAxWYdeWR5DYXktBew04sptWWx20LIKrouyksW2w2CI71UvpKsCYmPT2YMDRXcesyeLREywERrCDmAcZ9TUjDvzS5fXc/DN469YYKUgtX5q/M2JyVzUcjRW0RNKs4q2BnFZHnJv/uH/5qnnzhsU/gdLn3iU7zm3/w0dEDgXfWJtCVl7wn8zsM3t7ix5dTI/LjIwX5p0dahhOBgmLAa+Tgh2B8l1APFSneBvSQjz0pW6jU6E3U8k23nuik5E7RZzxwHCYilSu28UI5oCU1cjHkGWCkz5jhibSYDXHG9iMaZrBqzHyCEwOJwpqiqZs5B0cdrncPmvZsS0aEuGOSO+VaXjaHlk1fWkUZw/2KbLw8KwlxBC+ZsSTTIMUiChuSB5TbnB46FesJDzRYqaHJQ5OwV2bEk7qhq3rKJiFYh38HpGOG3qvFD1QZSjqZkpRw+j+49BjJEhXPTtcUkG5hkE685seMrBqjWvZUFVbo1Y610iIM852zUYC2qsZWnXHRttk2Pe1TGPbUFchnw2KDHqztdlsPrH6RVbaUSWOU95CTm1Iwu4MoBevAUwmtgxhcrj02bYvID5BES6nRG2XscWxwgvPZ1YQQnEVEhPVR0+Nqj1cRZIjrrdiBuWgk/9AGVx7hJHBQ5l9MxC/UlpBkS5n3u8yUqXEJdQ0Lva7TZGpY8tyt45WpUpd9lO5yTMUTLeMpHW8tAFxTWEipV9XDfYu+vc7Y6r34TISSxtwAipUxy1uoN6kz6fieoS48zzrA9iI9dJxueB3d8Qm8Ld0joLeDRRx/l0UcfxZzQ23MHt4flqEl3+V7m4pwv5h6Jc0RK0olCGrKNrM/TSSTdcIdGrYlsriFchtd+/U3TW24VXn0N7dc46w34ShGwKn3KLOOn//o/mCGYT3368/zE+3+CxWaDvXHMh3/9Q9cJgp587El+81c+yOve8dZpFGcdn15iicKSB+uae1qWwSBGNe+tBA6j57HpFuHaG7gnGCGUQjYEpv8YotZgcTxAawWTWtVwlDFMSmqhx0q3gRBwer6F+vY/gLWWxApKa0C5Y7fjsSXCb5GLgFiX/NBf/zGe+9Sn2d7YZuXUCm9511vZGVWVvkMRUhR4QMw41xyQsNi8anlirWOvn1QE9IgICSoiuj9KZrLmhfQYDQsa81WPX1OoWxaJOJOBUJROgL9Amg4ZldAtNPXgKgHKC81eP5mJUF30PfbGMaO0qFKsjsR5Gm3Z7Sdo4zj/uS8cIaAVvvK5x/nP//HX+eY//4NI6U8V/a6MceWw2p69CQl1tpiQ6OpmOdIWEayg4yvE+8+yIbtEyicvSpJcE0jY72f43tUEq+V2g51hTD8p8IScJlhJpYj7+3zgQx9mb3Ob1bOneM0P/kGa9QWMKaH/FZp+k0QrkiLnaN1MyACn4+v9Wm2OtQbvyI3UHfpCmryqkoYLKK9xbGXtKPaLnKyUdOseoT/Ps709imKX+brESc0oC9DGsb7d45mthLnTBSvNCE9J5podzo/glf4QT0paXov1NKGpfE7V6teRmxkfUbZwSGx+gPRblb2WFNUWej6u1P9CYYbP4soxMpCVYfzhZwVzmNGzyHB+0keaIP02+M3rKtlQOTZsFylzQUXcG8pjPUtp1ldJkx79/T4j26XVinicHq9qOVZr1z9Me637KQ8eA1VDeTVMWlUhbb6H7j+JLfvIaBWMwqXbuNY9CCFxpqDY/wJWj/Bqx7s/wI0rose+/gQiKsTNK41mfPnY0ABtLc+NR4RC4UkJslvtTuR9RLFHJhe5MnJ0a4L5miAp4IltUMJxNnN0a5VYyWY7iHwXait40kM7R2ZNRUJvoxLq8gNsMahcI4Ch1ghvgdAf0RsNcGVKu3n14U1rw86gOq7j1sm8LGkFN3kwvYMZ3CGht4A7ZvUvLVbrbVxUpygusGItz+qMBxcDNoYOr7PKSHsstR1L7QUir0TYfVTjDdMM+JcCwu+gGmc5m26Qxwn9SPDFD3z4OoL51Jee4qMf+Cjv+q53sdhsMNo/3mdyd2vnuiz4hi/ptDqc8gwyu0jYeSN+5yGgutHZSWKK1ywnW0gxeb4FtmDej0FLSucRjzN6cTFDQIGZiM9BnJJkI/Al7rie0Ekl1HjzJAaSUvNN3/pWmlF4QxV8RTzjGR/Rk1TwwJQgHfURVUKwPyrIC0M/tWRGg1JwC2k8QFWZE5LCGrRxCOlTDxw7o4TlVp164F+ngj9MsDpUze+N4xkf0WvdCz69d3yW+fPPnic3hrr0pl6hthxXcY23Ytpti2kuzF6R8/jwAAlYG2GzEXmxQ7exSr+0JFnB0LjrsqmVEFMiuhdn0/NclI6f+smf5sJXn5t+3ZMf/iD//OceJR3lbMcNzswZhM4ZFAkzoaMqAD0CU8ARIu1MVhnB11Yn5FRM2xCcSXDOVtu8St2Q0Bhn2UxSpPMIFES+h9FdtkyfL25tIaOI0g95bifhS5cy6r5kRQ3wVHXDb9cke3Gb3cyxJvsEIbS9Jl8e9emXBXfVG1M7n0PMEFEhsPE6rn6m6hutRVULgrPVPNoChEW176vspJL1iZjKq7bz9QgzfAbVvBtnS+Tk2K6tZAsVMSxLUm2mJuYtz0caQSQ8HhvPc2W/T6nHnCnhoVMRT8dD5oKwIkxHIFSI1ziD3v8Uxm9jizGqtoYMl7DJJsKrVcer6pXFVjkGFVEePEHZ+xKqfuqmIRAvBRFFqplKozP5TI+ssyWm2J/2uR6NZr2SJRyUOatHqsHC7yIcHAwHPNXbR/lttkaS1ZZgd+wIPUErFGyOLJ1ITFXzNtvBptvI2go4yIyh4zMhycktBWKYbLeqCAtBaS2J1RinWJvvMB6V7PdzZFBO18neqLzhOtlLUkrzwlw0/q+KOyT0Dl4WCOkhwyW6+ToCwWpLsp8YLiQabS2vWvGZq3dQQYkzAV7rvpc0Dk1Ihayfpt2MOdMc8VxvxDOXjlecX76ySe4soZDcfdfx+eHnzp2mHvkT82SDswLtSlbmlmnU5nHZ9lTBe3j8qlaRanG4mIk60m/ihKLT2MQWQ57Z10SlpR75UwKaO0tuqx7BQyJ6ZZQxHq7jGivH92tZjbOa1AmcaNAIMnbGKb1cU2Qaj6tP9hZHbKpqWE2q6QI7ykpiPcKUIMxsBXQ8Ea80pZos2Jrf+vUPcbC7z/zKIq9985uYq/sYAwd5yemmuiUS55yb9F8GxDqnzFIEkrVujZ1hzNYwxg98ilTjHyGgZnIMoZSEQk6JaC/JGJQanVukddMHh6UTLJKai3NkyQa15lolpHIaVxxMYhwzfvEXf5HxeEyz2eR7v/d7rx//xO4IYFQWjEvNPY0WSggyL+Sjzx4wuNJDKUkyLlhqhlMCqnEkxlCb+CEeEtHDefv8b3xshoACPPbpL/Iff+H/5Jvf/Er2xBrLok/INpv9DR45fVVEVxEyN1PNtXmvSnCyGptcQdZPVyKPidLclsn0WODGhGakNcPCYK0isSV1IZh3+zTm53j+YIgejQlDxaW4pCkyzi7PIU2GLnMSBKFSdCLFRtalE0GU9UhVSdPvsl2kbOUJ837I/c02bf9IFXNCeEy6h47X8Tq9ylJKXq3iO5Ni022EjKaZ5dcSURkuYtJN3DUkRkh/SsoOiehBUVS9iIcJac5RWsu4MORG8NBal+1en4sHI+aamqgRsJ2n3FW/PqxBeHVk7RRmdAGEh5mYtcv6qennV20SFYFyRQ9bHOB1XoHLdq7O29eQiAq/M43GtHmfsvcEqnkO1ThVVWbLMaCQ4fykMl3NS2ktF5Mx3cl89csCCejSY3vc5EoP4iSmVutjZZOdLY92KLirW/1Wr4xL6jXDXc1ghoiOxxuMaJKY1uEBVmugLeEmCVSVCK96TWYMg6IgKUJ8T7HcbrLZlzPrZFsFN1wn98cp+k50/G3hjjr+Dl42yGiBlnLUlUJjUWHGqIDIt8Qupe37IMCbfx3Ce/GCpGuhwnlqvuJVK2vM+Q1cq3Xs68TSAptFgcPxjvder4h/8NUP8W1/8J0AjI3hSlFyIdUIqTnT6iD9Bqp2Co5JcJr5HhVMe5Pq8/fRFTFP9RNKJacE1OHYLAouFgX9iZLUVx6dWkha2MoG6LieUFuCdfQLCwhOdRokDr7a67Fd5Kx069Mn+6E2XC4KNotymom82GwgPcX5wYjLSUy7GU4JaGoNV/KCK3lBag261Pyv/9M/5Jf+5S/wkV/6TX7p//P/5ef+wT9jLnIIa9kYF5NqxS1UEp0BDEVp+Zl/+5/4F//oZ/nE73wMrGW53aBvHE8d9Ngv9UwFtK81F4tiOm+HFdFSSJ7tDdjKUha7tWnl+vXf9i3c9aoHZ+f1lQ9y79veSZqPqrScSX64LXpVb1454uLFi5w/f56LFy8eP3ydTnvENrOUy2nM7iQnvZ8porBDIjSX+1sYqWcqoPtaczHP2Zkofw8roofzdmVz+9jvfOKpp9nWdVIrSelQyAaXh7uk8ea1o8PZHOcs5eAZyr3Pgi1R7QcnllJXcELhylFlIZTvX0dWTlJf94uC3Aj2yoILScxeGmPyhBqWe5fmKZ3g0miXLN/h9EIdP/DBWg7SIReTmM00xVcV4bqSzbNvfZ7ub3NptMWcF9L1Ir68l/PRrQO0nb3rVz6ii7hyYrlkiinREMLHZDvYfA8xcdqQXgNZPzVRn69XbgxCIsMFXLoFRwjdXlbwixf66GgNhCQZXeIrg13aXkBaOvqpZStP+ep4yFcOEkIlkFKxMtfFCMGHL+2xM+7z7GjE+qjAWMeoLDFHnCek18CfeyUy7F7Nmr/mAVyoGnZ0Hlv0kdEKKuwia6tX5+0Er+CtNGG/yG6omtfWXndOZ/xfs51JpdFhy2F1ngdPoA8eR48uVJZZUlYiMb9Z9erm+/TLgtQYaspjoEuej0d8YTfmS9slSQln55p02xEXRzHr4wMWGpa7utX1rJ1jr8j4wu6Y/sRpQKgaNlzkQhpzebjJfpZMxwr25n2rhyK8SStGZg3nxzEbaY4vQTrDYjOaWSfnmtEN18luw+eOMun2cKcSegcvG4Sq4coRq9EqT42GLNUlD901x73dgMwVNBUIJysV7Nfi+/02wqtzpgVLzTX+2Lu+iec/9wQbT12tLj3wmlfwhm97K76oEoE83+N//J/fxy/+0m+zfmWDubUlvucPfSveRCTjCYEnBJmBdsPSjhqgE2TjLoS++eokgnns+HlkbYH7apKPjAVSOYyrTJMFleo8QKCOrHbWCg6KCGS1sF4LZ1KctewbgZICjAUNnpBV1e3ITUsJ8BB44up6aq2rAkWQeJ5Ec3XLXwox7f2UQvCRY9oaLnzlGT7y25/gm/7IaS6NNHpVoFx58/g/ZyiyjO/+gf+RD3/8c9M/P/GJz/Ij/9NfRGiHJySeJyicxZv00Cqqc3Q4b9VHWZx2eAikN+mhnSAKfP77v/VjfOl3PkG5u8/aqVXe9C2v46vaZ0CLVR1XcZdFvxK6+K3rPDSPgy1HCBmgraWvC5rKwxMCbR1bI4fveSjnIYsxqt6qHsYmtQEF+ELO9mfqq/M2t3r8ddFZXiYnoKZgkMK8cyRWME4PCKS8KloREqer/HozehYZLU9JphUCm26BLTHSx+5+GnAzsaOHuLayJupn2MwTnJZESqClQJoSV1Z2a169SRB28Ms9ZDnGiPnJ5/jY8YDHP/wF4u1d7r/7bt72h97LbixRap7Qz1F6jC4GbMRtemOP/Szj4bmYe5qzD5AyXEA1S/T4Aq60wFr1DyrAFeMZ9ThwfEVURVNB2SE24oxnhinn4xoPt8+yvfs0g3iPRa/OMwcehXYsdAErSQvBqYlzl3WSWr3Nds8wHKZ8uh9zuRaw0hJoP+bNC4vXCZZOjvisvEmRXuW6cHgMR6JZj6uIOud4Lh7S8HwWgui6ecNVOzPn4xFKwH1HeiGrn0tFRN34+coCy+mqN9NvV2Sz6OHyPXBuEtMskLU1LJvYbJ+NfEA4cVnwhEAiGWWC1UDQDisvXiebeF6B1RnlaItSdfFrbSTQDgUHiSQpBHOT4rdUdYJogZre4SDewHTnUcrHOW7uZWqrEIJD7+lEl5TWAQIlqbxJHTPrpDm6xhyzTho74+h0B7eAOyT0Dl42CBUhhKBdHtDwIh5otDhVO9w28yf9REvI28gAvq3vlwoZrdCOL9CqR6ysrfKDP/kXePwjn0b3B5w+u8bb3vM2tJJTuyGjLbujnG9+57fw3nZErkvi0uAnGd16VCmuA8FerpmvBbSjOq5IkGEXbiHEQ/otrLMIL2RtcZn7UoOPu5rTLiWrQUDpHNHkBpNkJaO4pJ8UqMa56VbZDEyGLjUjK5DasTGOaUnB6eUF0ryY6TVsKY8gkihREd3DHlBhLK9a6KIxpLlmb9IjGgrJ3ZN+OF/IE5OkLl7a4l2yYD0L2c1g1T9MNrkxCf03/+E3ZggoVGKw//orH+RN7/oW7llYZJxmMz2iXc8jumbeNnsxgYNXLy+Q65JhWiAnYqWaVNzTqHPfH343npA4B9v7A4o0Zqdc4+FOEz06XwmSnEXKAKdHN51Pm+0iVFiJJqTkle0udc/nILH0M4suSxazfe5ZWmBYGLaHCSvtOqHnseB5NJUiELPRgIfz9sp3v4OvfPwzMzZd973qYd753vfihCAQlsv7MatdWAnbpLI2Q2iEDDDpDq7sIcOlGbJ1lNAgFDJauuUt3t7gAqMiQuuI1ZrC80K88R5aKrJ4RM8usBBGnA3qDErBTpyxLCWBk/zzv/Z+nnqiOp4PAh/9r7/ND/ytv8O6CXn96btw+T5f3RyxlQlOzzUZ5BFPDQcshhEtf7ZKq2qrExP4eJpZLlSETa4gouvbL44lokdInnOOi+OUNFN8dTjibCPiEm2WvILPXdzH97sEQUgcByyGHtarHvi0cWyPHW0/5I2nFtjvjTEuYVfAlSxkoS7YqWUnquaPI6JCCDjOg/MGRDQ2mtRasqIg0ZW11wwRTXeI64r1LKYhPe5tuOsrsNJDNs5hx89No0FltIwQEnGk93P6+gkRjc06O/Em840FIKTp+az5LYbS0g4lzjn2Eoc2kocXFjDpHoPdDbaTAWtn78cLatxdr9HSMcMY1hpu+vB7b3ORM2GNQbxNPL5Eq3X3rXmZmhyb7VUtHCpiqEvuqXXYLwKkECTjmF/9pQ/R2+tx6u417vsDb2KQFdQn4sDj1smDYc7KCR7Od3A87pDQO3jZIFSADOZpjy/zSLDAYjR78TpbIBsnqz1fCqhogY7/HA8thhzkLc51Df6734aHY7lVx/O86UVyrZilHvlYF14nesFJEAWnOm0CAU5IpNcErq9QXosqi7pa+Ofml2hv7TBfDzmIkxkielh5TLKS7X5CPZCkpvKtO24rzpUxudUMc0dvmNDyrzbXt6PgOtFLOLlxnSRC2rtGrOQfISj1bue67wfoLC+y2+tBp86lkWF13lZbnzfo23JW8/wJvboHO3ucmkSoNgOv6pc8QkQPSfqtztuhpZRzsN2PSTUseCUb/QR5+hwi28XmA1zRw0UrEwJ9sjF1FRAwwpUDEn8NKSR1z8c5x+bQMkwdjWyX5aYkrDdpGM3WMJkhotE1BHRGFOHX+HP/rx/hk7/zcbKDHmfuOsUjb387JYqWtOwOYvop5CsLNF3C0NU4HUhM3kdbSxDNYdOtikQcE4V7s8ratRAq4kCcYiM5T5GPSIo1OjUfT0rKIqPEY6efEYYlq+0A1x9R63bZTUt2Rglf/dhnpgT0EE9+/jE+92v/mT/w3/4xLg8kcT7PQTxA2pg0AykaZEZyIRnz6s7cdWPyWvci892KME18ROUkJvQ4nNQjCjDWms2kIM099tOS5+IhOZKhWWE/HdDSA9pRl2Hpo62kEVwloKZIWWlCWGuwXPPY7vUZZSmrbZ9RHnAhznmke7yl0Y0qoscewwnz1i8KrHMIHAdFlVZ1OG+qcQbidTb75ylti5GD1Jjpa2Y+X/lYv4XNDzDJJiLo3rinVAgG3jxW9ZHlECclwu8ySAXRpPVkL3EkJXQjQdsr0XGMqEX0s5ztzYusrN0NyQHNeMhuOsdqc4m5RvWb9aTEC1sMdUlc5jSSdZzwb5of70xeRZXG68j6aYa6JMJHCsiShL/74z/F80ce8L74kc/w5/7Kj7AXV+0Ax62Tmbb4d1jVbeFO4fgWcCc7/msHWVtG+DXmXIrNrlbQnE4Qqo4Mrr+xvJQQfhvp1zjbMOSqTl3AXKtGoCQ7o4SkqBay44gMHJ9ZnhtBIEpWW92q58hv33JPq1C1KhLQZMw1W3TDgtT5yDBkXFY57Yf9WocE1FeSU/N1Eu0wZYm4pgLgbImzOWluOBjleGJW3XnYaxh6ir04Y5xVxOxGKvjFZuO6DGWAvUHMK775Tdz3yGx/5cOvfZhv/453kOU5STLi0rAgKdxNbxQ4w7m16wUcAPfec3raA+rJSrjzYuYNjhDQXDPXCFhte2wPqpuLCpeqyEarcdkW1trqDScOPa8sfpwjGa8jJq4Fu7HluQNLy8tZ8MeEtWor+VBkJgRsDxPySc/vSe4FSgjW5tv8gfe+nbf9d3+Ib/rWt1ILPJyB3UE1b61GRCLqhFLQz1NcuMTTpeLJ/lZlTdO4+4beuzJo31KvYVGU/O//x3/kL/3N/41Hf+YTDGLH3qB/VRCXZewMq/9eCgukSbFlgR9G03m7eGnj2M9Oti+z1nRkGjo1yf3LXZq1gN4oJk5jKD2285RRef1vSUgfGc7PZJZzk8CL43pEAQ6KjP0EuqFEmZD1NMZ3ikHucf9yByEFm70+DVmgLfiyIqAWWJR9lK1+Y4c9oq0g4GA4wpUF+0lJrE/eKrkua/4GMM5SqMZ187adp9SUR015bGZXk5igIqI2XOBSmtHWPYzVjG+g8hZCYcsBQsiKsOvjk6igqiBvZimN2hLCq2PzPnHS5yC1NHw3Q0A7gcGMdrGmZK7TYa7VIk5iNq88R5mMCWotssE+Tz23gb0mFUn5NbJgsVrvsq2bpiY5k6Jqh729l8nKjFwLcPAr//HXZggowFOPP8kTH//sDdfJhaaPdwNCfgfX4w5nvwXcsWj62kH47Wqr2s0+6dtyhGrd+5L5gp74/dJD1laYM+vMNSLGY4FT0GjUMOOEnVHCYj2iPy4otaHVqeGHs9vHUkCjFlHalH6aY6VgzR/TiRrVQle/3tz65PEoZNDF5j2isMndLcOmzrBZxDgw5GXJzjCmFQbsDTKkErQ7NZQS9LKCQls8ezU9BCa2O2VMf9zno7/1WYrhmCt3r/HO97x92ssqBTQaEXqcshdnWOsYxwVZltFoKjwydFYgHEg/QHghjXpE4bJpRRQDo1RTb0b8pZ/+cT7325+Y+pC+7T1vwwD5eEBiCi7u79FbaNI5ztP0KJzhB/6bb+Hf/vKn+OSnvzL98wOvfgV/4DvfPjsPUryoeXPOkReWNNdENY+oGeIZxc5ozF6acyr0wemKoOQHWJtxo0ootgDpoxrz9MZP45mMng353BVH4FkaDFFCzphbC6VoNSL645TtYcJ8LeJglGGdo92JUMExx3Bk3uZrjjzV5KWh0QjxQ8VBJlgIYawLnh4PeLb08YzPueSAFi+8snaIoij5g//d/4MPf+yz07999rdfzR/7yfex0esxF9XY7edY36fbqVVksHAI5c3M2+IJSV4rawsIU9Cd7pQIms0mpRsRJzG7A6jXA3aL9Lot+ekxXGNoL2trN3TbuLYiKmqnuRSnaC1ZbRsK7TGv6mRaYJzB8zw6nRa9/oidwYDFTped2Mc4R6tWIgYZlFfPmRCSZruJHowYJyNQEWNT0vJ9SmuvS4eCW6+IXk4SdouMN3Tnkaxi0y3Go0scFFUvqHaW7TxhpGedBXoaBrJDR4yR+R69vHFsi0A1AwJXJqjW/bji4BhD+6tIjKZfFiwEEXHZISktW/0hgwxMvUFSVmLU0Lfo4T4mG6Nq1W8uqoVEpk2aZvQ8iRdnlFZwZX/AV7cavOLU4nQeA6E4MJK7W6fQ/UH1wHgDuHJUuRFES2T95ymzHeLsFP0Udi8+f+x7dra2ecO16+SR6624o4y/bdwhoXfwskJ6NXAOVVudLrDaZEi/iVdf+/qMIVxAjC9x93yN569IhtKQOUsjClBpwe7BPsiQqN1gVxjiwnE2DKcJQiOj2ShLlO9RK3N2+ts8crekEdZB95HB8dvTJ0EEc7h0myDqstZo8ZDJ2as3+LebkvsaEdkoJh+PkEET0QhYNwXzwqNwkJQldXxwtrIqATA5aX+dP/UX/ze++KUL0+/51O98gh//qR/H8zwG2rBZFoShT5AW7PcPsKWjubLGKPRJpce5Wh2hc8rBFqX0uJwXSF/RAEbjMRgIG02SSBEbyzd/x9upTURHGseVLCcXUI98hnHBaLh/CwpWTdhe42f+1U/y6L/+z3z5qT6n7j3Nfd/2FgbOcfQ2faA1O7q8/XkLPBo4+sM+OEXUaDAKJHFecDYIsDZjtzfi9NlFlPSqao4Q2MGzN66E2kMVb5191cFkA55Y3wfVwQsyLvYOWAkijlKv/bJkz2g69RA9ytk92AVZJ+jU2HKGtCg4HVytSB83bxBQazXoeQ5lClo6IlYKozVPj0f0yoLEKB50Ic0XucUL8G/+3S/PEFCA5x//Es9++uO88VvfwW5vnxJDrdVkw5bMx0OWpURFjZl5e+17384XP/oZnv/K1QrU3a96kHve/iZ0keAHFQkd6ZIrWYIX+DQt7A4S7l0wnI8FZ2oNghOEbi+GiA5GF9lNAwocm0VJYENKW2c/tgRKsF9m7OY5c806xShlp99HBl38yHIlGVHPU84eeaDulwWbeUotCqlb6I9jNgZ7rEV3cT4e8vigz3esnJohiXBzIlpYw6V0TL8o2AwjztSreev3L2FyjQzOcCmNuZzGPNTs4ACJ4EqW8Pigx57UNOodGrLH9uAy9zdaqGNaNRwgTIqYtDYcn6xUITEGR+WY8PGNMXEa0tQSj5gkhbAW0bcxvb0+p3VKbTLm3FnW8wLrSVr1GnE8Bm0IoyaFcnxqY4gJWjyyGCKFwBeSK1nM67rzyMbpmwoHD0WDQvro2iplb8Rur4+1Potr3WPf011Z4soNrrdRUtINbmwLdQezuENC7+DlhYomiZluusDq8fN4c+99SSI6bwVC1RDAfDOabGfmIAXCTQSWjko7c5PmFadTtGgQLna4Z01Auo5qnUME3dsaj/Qb4Aw23+VVqyuY0RC92ydyHtY4MIAA6YGd3EMllaI5SQtohTOCH2dyfv4XPzxDQKES9xwa8U/PBYCpOKz0QAQ+eCFCKpwXopUEL8CVV7e6Ji5KoK7y3hvBB7RXZ5zvTsZ5AzhT9Zs11/iWb30d86/qML9UY3DCtjBw2/MmmLzeUq2ISsz8Y6BgexgjxPJUDCLDBWRtCBwfXgCQJ0N+5ud/neeuDBh1G9z/1ncTFymtcIAuLc5oxDE9dwDCgjMTgutxU9uX6by5Ks76sFApBYQKLsYhD3SryNyRLqkrj3UbcdYT+C+SiJ6/sH7s6/cvXkB6f4hivI/yBE4CQuF0CUrOWB8B+J7HD/74D/P53/0QB/t9lu86zf3f9hY8CTYbQ2MBcJjxQXVSPIUMO+TpiDSOMS5nv+iyFt2gveA2iaiVNTZEl/ODi5jUR7qqdUICe7FlkDlakSA9fJYSEhl0sMUAW/QhrOOMxhlTbes7O3vcQuCHXVzR50p/h7sbdT7V6zMqNc+NR7y2O39dn+iNiOhOlpEazZwf8vn+AUthDacabNMitDtVy5Or0VAeXxweMJeGFaGkUqxHSoEMaDTW2B5cYTS8SKdz7nprLqFwTldjEzeK+IRYawRwkDj6KczXBC2qebNljAtM1fqQj5H19vGxl4frnqzWJaV8GiZmc1CyEPqstRWhlIxKTWoMDb9zw+14Z4ppnC5AZgQ2WCQzY7zkCm/41m/hcx//ApeeeHr6nodf+zBvfffbWKc88Xq7g9vHHRJ6By8rhIpATprIVYDwavit+/Eax5vCf83GoAIiJeg2GwQHKQtexGCYYq1jcWGBUV6QxRnLzRrtwJ/JUW8pj7O+YH84ImvcxXxXstzQ2HKMap67bZN94bdQ7Qeqhb7oI7095kxEZ2OLUeZo+gHdZpO9JENlBaebdVq+YigN43gErjET3WnLIRc2j4/I3N6o+ss6nsJzAfuDlNI45ltNEmsYJpa5tsRJxUiXBFKSBS1a6QFnwzoHw4w8LWk1m6BgnGvqwFwjmlZBobIyOR0G9LOCUS/B73RI5QK2TG6kjZ9USgWJU+S0kJ5CJzmnmzWa12y9znsegRX0BsltzdvBIKUoLZ12pzKIz0taoc9coxIehJ5gP07Q1uHJq+9XtRWce/LYcRdFwXf9kR/gwx/7zPRv5179EX78H/0t+uMxot/jbBTRvoaELvg+kRUc9BOkEMzPL9LPcnSSs9qMaAezValr522hO0+iNWlWMF8LaUcBoYC9TLEzMDy4EHC/VEhRVeP6qsuylC9K9HLu7uOv1Va7CUmfhcijl9kj89ZEHakKHp03ISXf8b3fxSgvyLWh7Qe0fQVlijMFTufU0j6nWysMXY3CSJY7bTYTxwO1Ic/11ik7p/ClZDGI8I/xy7kdIvr0eMDltKBdX8YN+6g842ynjSQgKSpu5AnHgh8QCY9+IpBYur6jr6EYJawqQyOMcM5MhHgBXT/Al5J+IiidpFPvMrI5z/UuMUeDB7sL7BQZO3nGanRrqnltLRfTMS0voOn5DMqCZ8cDDsqCFI+lxgoU+9wlHadaCzS8Kmb0sHWnGSlWIkHD85BCIKJFxjqheYyhvQgXEEdaUW6UNT8oC7CSUSJ5oF0nKyWFkXRbHfKsTxJntCho+R7hkeshFJIzYUBvmJKlmlqtQRjISqGOYkVC6Dl2xo6VlkNJyZwfkhg9yXDPTrSAczYHq6fHFBuNswGNWhMdb+CI+NG//Rd5/MOfZm9jZ9pSFIgAYcWJ11vD848n0XdwIu7w9zt4WSGkh1D1qWm2K8d4c6+8odryazIGr4GzGafmOpSloz9MsaZK1GnVwqlwZxRnlOVsH6NzMBzEaCL8yGel5dEJW/jdVyBegLBKSB+/fR9e6xyyvoYQlmZnHr/MsUqwNtegWQtZbtUx1hHHFVkW0iNJM6zOZ6I7nR5z5uzxLgMrpyrS4SwMhxn2MAs+gFbnFEIFbPTG+Nryms5clfIS1hFSMu6NyIZjmsqw2IqmYqWi0MTJ9aS3SEuS1FJ3KXNhQT6Z9xvB2RwhPJIio9QBp9s1JIJRnOHMNfOgHcPBbc7bqPr7XCNgvlWfipXyvCRLq0pKOwhYj2P2kmsqK9LnW15/lvd8+7fx9rfP9qf+7M/+7AwBBbjwpcf4wm99iOVmDV1kJLnD2mu280vLYJAiqRKsWrWQ1XYdKQXjOMfoa47hmnnrNKLpMcdpTplXJbpuBNujqnLX8CpxSkN5rCdJpRa/RdELXC9W+vbv/k4eeO1rZl5zz8MP8KZ3vo0F2aOuLKvt1tV5E3LGTPGm82YcODB5jB7tYbUmTg3aSLpRlbZmvQ6CJnk+4KneOl/sH3AlPdmNQoYLM2Ild0xbxags2chSloKIRtAiFXMo4RgNR4TSMMgdAQW6t44e7jNOJVJIFkSfMN9ntRkipGDY72GFAutg4k3rgDhVGCtZqAvmGorYzXFgJadkTM1ltDyfZ+MB2TW/80PoYJF9ajw/2Ob5/ee5lMaMtabpVWvnaq3G5SxFCclqWEcFLWS4iO8K6ro3JZGHBFxJScv3p5XXwIvoe/PHGtpXCVHXpkkdMbSfiJWMswx1wcFYkGuHNd503ro1yXKnSz3yScYD8vL6OcjGeUVAJ5HFc40a3VpIqQ1JofFtQqJhPFlGQqUY67KKFrXmZOGjyXFHSHRiNIWGpoLlZoiUgjQ3vO09b+f7fuj7eNd3vQvP86o140bXW3EnsvN2cYeE3sHLDhG0wRS4ooeqLSOP8fD7mo/Bq8bQbbUYJo6sNDNq6pPU14dq6jjJmO928QLJai0gEBav/cCLjhqVfgeERxB4rNQ9mo1omqhTD3yWW3UKY9kZxjgJw9Lgynxq1OyswemY7/2eb+O+V94/89kPveYVvO09b7teBR8qhFQYv8FDa3MYoTBJSp4k5IUg05Ke6DLODHOrZ1haXsSUKXCyan6U5OwNM8LAY7Hlo8qYRMvrFK7XwZQ4BOM8o9TQrPmstOs4B1vDhNJUx3mSCv5m83aogu+2qmrTcar5uuehjeHp4WBWUSx9XvvQEm958+t44xvfODPs556dVdYeYvvKJqF0LNYDCutm3A5OUsGfpJq37nj3guPcDpRSeBi2x1fbH5qez0DnjHR5W+pruEpE0zzmQ5fX+RPv/5v80I//Bb7ze9/Df/NDf5w/9RM/zNnlLlGtjfQCwuDFzVuqLS4bYcqC/dQRj5NKTR1VcZm+EvTMAgu1NkukzIuMC8mI1Jzc7nEzIro5SeDxpKQ0jpKQU/NtSmPZ6fdpkBImG2RJzJWNLcp0xHKQorIeQipUOmSt00A4y05SkBuDswbrHLtjR2ZgoS5oBoJQgXIRC41TKK+GzfdouJzUGJ6Lh9eNbVSWfLq3y+O54nypeHq4yzMHF+l4VyvlgVSciupTUgog/CYyXKzEitlO9RRzAurKY9+ArZ06logeh2uJaJyP6OeGXgLaclUFH00iSKVksVGnJgyDwk1dKgD6o5ReXEwJ6OFS2q1HdGshqZHsHuwinKGfVccRSkmvKBBCgbO4E9p9jqa1WedIraHQEl8afKWOv96s42CU3vB662cafTOT/DuYwZ3t+Dt42SH9NsZeAKFQzXtu6EX4NRtD0EaPnqVRn6MZ1an5yfSGmDmLL8T0xnjoR9lpRKRJSV4Y5uqVOrafGc7WAoTXQNymIOk4CC+q4gPzfc522uwkGoObmtXXA5/5KOA3f+2DXNncY/3uJb75z3wr0aQS6kwGpkQrj//+r/4wz33ycfa2Ngm7XV7/jrcw0pZ4nFLqq1nwJh9jwjZWKJo1x6tPz5EelHx1p8cr1+bQBnaKJmdO38tyt4ErEtJ0iGeqBfwwa36cawwxHorhOCf0FatzDYTVmGxMbhbR5fFtAodwNqcUknFekFtQokoZWmnX2R4mbA5i2vUao1GGNm6GyNzSvB0hoIWzSKp0lMOs+X6aUzqHzjVfPRjx8EKbpSOqZcEkmtOfTey55+7jRXXzp1YhG9KIaqhIsTNK2BqMadYihoMUB1MC6nDkzhEIMSWihz6irXqETdx1N8TcWdQktWu+FvLrv/Lb7GzucPrMCq/85jeR9Az3zjt8JZBCUFrLQVHQ9gNUbYXLWUYY77PAzbfmE1nj4+OIL+8NONWE17/77dz9inOooDaNULU4ChkQ4ghfxLztZZqs7FPYkFxDJ8zohFU3Y2EtkefYT+F0Z5GAPQI9Ys8WXEnrzHOyUOSkrfnUaNazmK4fYJyjn2uME8zVa3hSsH1wwEH/PI1AMUoFIFjQ28g4wgpF6YW4IsYrI5YbAbuFZDdOKesxeVGjNGJKQJ3OcUhyLbHOn2ai23yPptflQmzp+gGna1f7LDfTmLEpOR01Iaxh831cOUKXPXIxT6hmt6C1tWhniZRXEVHA5nvE423GYp7lxmybh3UOhyPTmqGVLLzArPl4fIV+4nOQNAl9NUNAC1tdb+ichXpEz6jp9YZxjJNyhoBqZ7FAIGTlx4ylNxhRjPYo7QKn2pJIKcZGo62tKr0nkGarxwhR0Z/cGjJtyEsfZ3N8x7HXWzzO8Z134vW23G6wHydV5f4Obhl3SOgdvOwQKgJnUY27kOHX1hf05DHUcEWPWrjCuaU5doZVFaSvNdtlSVNJTgfh9Mb4fG/E4zv7BNLjgUZEt1knEwHSK1jwNDJYfdFV0EPIemWzstJtYYd7rOc5mXWs+D5NB//kb/yDaUzmZ4GnPvlJfuuD30kUUUXTAXtJTA/Jt/3Bd6EklEbz9MGYizt71GTII/PNamE1hrGxbBiFIuOskLxlbY5R22PObbNaOyD3PZ6vBxRFndI4rAq5goJkxNlmm3BC4gwxzw5itLacrTdYnWsgpcBIr7LTSRIKXcc5e/KDhy0pnGA7ydjQlrrRLHr+lNB8ZX/Ec9t7tFXIw4vtKZG5lXl7qN2cEtDUGq4UJZ6AM0EwJaIbwxFP9YakmaGTai7E4xkSilDYSQX/KP7EH/0Ofv7fvoaPfOLx6d/ue/2rOfP2N3KQDZiPmtSlZLlV56mDEc8M9+h6EY8stacV0H2t2Ss1c55ixQ+mN8anD8ac39ljNWhz+sgNcWQ0m0VJJAUrUvH3/8pPz8Snnvrtj/Nn/97fZxjDfF2SC8VGlgKCu+oNCmt5zgREZcZc3gcmdmnuehP1/SLjiUGPC2Ofu7pdLo32icdD5iw8MiGgALtlSU8bFn3vRc/bl8clgXQ82G7Q8S3OlKRCcSVN8ISg7ur0M8dqc4nEbLEzPmBQat7VvffG19cxRHQ7yzC2qiY+PRryzF6JZyIW6xH1MGKpEfLEfs4oZjJvHXxKTJnS92rsFQUd61jNU3zPYzWKeGo74cLOPq1OyCMLHRoyRw96DJMhO1GH3LYYl4p6oJDRMsPxJuf7GzivzZNK0fJ82n5AZgyPDfvERrPgR0TKQ4YL5Nby7HAPrWLu656eVkCNczyfjEmM5u56k64fkLoGm7Hlkxv7DMptvu2+Oe450nu6kSXs5hmhlOwXOYthZybi81aJaJI8zeO7O8QZvH65MyWgcZ5yJYvxvZDVdIDyAxZr0fR6cxbuadRnCOh6UaAdnA58alLRrdfRWcL58ZBRbLlrYYm7WiGFMWTWUANsOT72nmKzXdxkI7iwllw79oqcNBnRdY41uO56q8mQ183XCevquuvtdBiihGC+Ed7Jjr9N3NmOv4OXHcKrIaNlVPPcyzcGFYEMcclFVlo+uZnEPeIoJpXHwx4iJSTWCErrsDh8ZZC1FiMNq3WfpnSV9+lLBOl3cMKj26zjO0dqLQUOg+MjH/jIdTntH/v8eX72Z34OONx2cqwnGc4p1OSKV0JhjKO0FiMMvj9JFyoTqLVJrQ+yoOl5nGnUeO1qxNnVsyi/xl1qxFJjzN1dwSCDWFtGNNlNBOmRvi4PhdYWi0X5lR8kTOxdhCQxJdroEzOenXM4W914hmnOoQj/6udLnK5uUEaB719d/W9p3sKrz+Daucn/KpE8VFvzEonVBik0eRIzKAuKI5nz/USwt3WRg4P9mXGrYoNf/dm/wv/y03+Vd37/H+VP/+Uf40/99N/EYNHWTHsiAyHR2qGdxXli2mrB5FhLZ9FHtmKPzpvFTucNKpGMpjqGjx7zu9j46nke+43/zPbl59GDbXJjKUrJRlod10YWUxjLPnX6so4rBvRGV/jkwS5fHvTYzlI2s4TH+wf82vo2G32w2meuEWFljbJIIfQQR45BO0fp7Es6b0Ho4wQ4U2Kcw0zOUeTDzsQc3gYLSD9iJ+mxM9497uc1g6Nb80V8hUvpmLbvk2vLhX3HduxoHnn28HSG1mJm3mRQw6u1MaKaN+uFmGyMkB5KKJyTlEajyxHKJJQH65hsRKk1pS4QwrIxsGSlAyGx4QJG+Ag9xJRjnhoNKKxhL8/YKSrh2tHfBsE8WtUoy5gim/09ltZSWkuuDZtDwxPbmo0kInN1SlPy5fUhaXF1lkprya0llIrtPK0qi4fJSrexNb8v5xmlAdKM8MVk18NZiuEW+WCLfLSLLjOkF8xcbw6DF8rpFrwFtLt6nR7C9wLKUlOUJZsH+wiqftXcGoTXxMSXcPbaXnCLy3u4fBdnNYW1FAa0dWhTYo88cF27Tip1/PV2OCIluaF18B1cjzuV0FvAo48+yqOPPoo5oUH8Dl4chFfHn3vVLacKfU2gQlS0gokvs+AbMlNZyXQ9D18IQikrg+ZJL6FvLA90O1hrOYhTwq5ibDVvWe0gRJX29FJBeBEy6BB6m7SUxPc8alLSUOrEnPZnn3kWqCoB1jmSNGE5qM7vYQ9owwkenJvDaM3OKGW5YQiEoNtcoDXSPNj1eFV7buq7KIRE1k/TKkr83h6rrTpS1FkfKh5ZmqNQCabQEChGSc5wnHO23kD5YLVhb1xFfHoIVoMAayzaHObHX+9FiKt66DQBfgnnooCFiXr2sJewIwVLc3MUuoq8XG3X8ZV3S/O2P05RQlAPfJpKcTqobGoO4zv7o5Q81dzVaAE5eZoyiock84vTc/ILv/xZBqOUTufL/OiP/lg1bpPidIKn4Hu+55uJX/sWHpirk1tD3BtSn3g/HvaALvgea+05yqKciWZd8DxqQlCTs9GAh/MWWsnOKGWlLQg9j7anUCLAE4LPbB5PvHYvr7OTwJk0Zc9JyOp05ko2s4SdPGPODxkbzYYNaPkeX93fJJERRTjPlSwhN4aNgSPLAhq+oubBXipYDWsst0KsU9NjUEKw7Pu0lKI+2R5+qeZtwbd4OqcZNDhdq74rkpJ+6iqBiR9wX+cMWbjLbtInTXeoXVOtvhaHFdHeaJsk0XTbZ7jQs4Q25DXzPs3JvJVFxsZOn6WwxumGf8N583wf4wTbvZi6kzzQiiAI2N7bY56cemuODimBgFqnTprDM/uGBxYVHT/kvrmzyHyfBjG7qeAZz2OoS+6rt2l6/ky/Z6gU93VPU2T7tF2KzfeR4QKelJyrNymsoR8rNoaWmgdjKzjb7uDhczBIubA74JWtBXxPcbpWp+sHtDyfvTJjoCuz+Zms+ZtURI2zHBSWlfoyDTtmfzBCCUlESa3MOBM1EKYgkAKEnLnehAd5runLbBqpezrw0c7RnPyWkqzkILasCsupuRpxbCjTXZxskhuLrDUx6Qa2OEAd1RmYDEQly7LJOrmYp3Qw7/tIT1KfuG4ct072kpy28q673rxJ+bM/Kplv3oJP3R1McacSegt43/vex87ODo899tjLPZTft3hZCSjV07MMu8jaEnORYGwFeR6jENWiI+SMmGW+GXJ3t8nZVh3fU6wPEzCGuxoeQngvucepqq8RuH0aSiGto608FILF5cVjX39urepHtcWQLMspck09CGZESMudGnd3GpydbHlt9/vooE2CpOF7fMvS/HWpLUJImq0zKD9E53sshTH3LDhet1rn3OoqhTYMRnElQvIV5xaa3N1t0Yr8GbFSQ3o4Zyitm8YiXgdncEWPzJTkhWYh8vEQM2KWtbkGZzsNznQaM6KXm85btzkjVhIIWsqb2kodFUWcW2xxV6fqo8uyMXFylOBNqiZHBB62HCFViKotM85GjOIxzloCHE2d43nRjAjpzFyDs+0Gq+3GVGSmrcWbHIMv5LHzttKMZsQT8sgxLK8dT7hWz66xl1me3cvYHmmk8+mqGjtFRuEsoVJ0PJ/dIuO8iegRME9G141Zjerc3WjRUXW6kUfDF/Sy6iZyqqY5Uw9ZbtbIdRUva5zDF5K28l7yedvLDMl4iBCCludTV5WtUFxAPqkVtIOQ5dZptAjYHW5j8/1jz8lRyHCBsWohTIbNdxlmlrmaRyeotlu1cWwcJBhdcGahefN5Q7LdT6p569Y4WxPctdABW7CblORaozyfhq22defqks2h5cvbBuOgG0S0m2sIFTHvRlwe7TEsS9ai+nVG9lCJzeabq8igjStH02Ouex5NL+AgcUSK6bydbXmc7Xa4d6nF7tgyGO6CswRSMTdph/CQ7OdXhTy3WhFNjWFYOE41Is4tLeAryXavx6i3hfRCWkFIs9ZChc3rr7du67pI3ZpUtJSHQFyNLPYUZ9qKcw0f6TdJ8wRRHBDrakxC1rDx5Rlhl9MpyADVOIOzJaPxOs5YFNCSjkB5N14nj7neoIosHuUa7w6rui3cOV13cAcTiKALQjDfPUM99Lg8yijLicr8BDW1EpqVTpNcKOZMStMNK1HSMSkjL2psKiJUDo+SrChxOHRpuOeNr+fcKx6Yee1rXns/3/ftD2PGm7h8h3jcY2w9fMGxampfeaw26wgkO5lha5zxinaT043ouKFQUz71+gq5CNiOtzF2RGY0tUYTXVtgbxATenLaA6pLzZc+/Gl+9//3K/zur3+Y7f4QpQSFKSmMO9Gw3lmNMwn93hWK0hH43olq6sNew2vV1yfN20mqeThelauUz1ygyHXIIN69ntAcJaH5AcigIjSyRVZodgZ9TJ5irUFbjlXBX+t2cKiav869YDJvnjxeNQ/w8JvfcN3v4uHXPsx3/eF3///Z+/Mwyc7yvB//vMtZa+91pmeRRgtaEJJYJDYJECAMGJPggHFiJxDDNyFxbMd2AGM7XhLHmBAvJD8vv9gxjmNf2PnasYkNNmAQmwRIICRhtCChGWm23rvWs5/3/f5R3TVd09UjjTRisee+rrkuqbu66py6661zn+d97ufGdzQPrBsc00UIS1lICmOZdvzRewNwNI6Yq+0dEzSFscSFBTvcopbAfFWgiwFSOVR9j5mKPyZEYXcX/BPmTSuWOj2iZDDclo/arK0ss7a2RrZ9jJUQhH6Lo4WiiFcfU4haa1mxPtWgRZZGrHXbaLE5vaC0LPZyiv46e6aqKAG3fPgW/uJ//R++dtsXidP8jLzVqxWsMUMTiwtSOSx1IzLD5kD7nPXIIASsDEpW+pufKSGR/hxaB0yZLi2Rn7HfvDSWTE0hnNqYEO2nlkEG7ZRTvG0GM9TCCtUwYGOQ7XDNV7VmOYtH5wVnFqLdPOPIoM/hQZ9uYtBKopRmT6uFLjOWN3ok9pT0mLTeJk2p2MJIgCrJ3lYF1/Ug6RB4Ials4piU7mA47UC4dUyyhs27pzguE0AgdQUZLtDPU9J+ezihxViMFbt+T05XvInrbbUzoBcX1DyNo79x4wX/LuD8dvx5nMcmxGaEaM0P2TM9y/GjPU622+xpNVnvpcRpQT10qNdOiTNT5JRhixm/wiGvRMePIuovOvcHp3y0N8V0uMRXFiNarsd6J0Ypxdvf/Xbu/PQXWDqxhDczy3d+53NR2QrR4b9AyILBxjK9bI4vfvSv2VhaZt/BvbziNS8Zf3pb0Ko3+Hqm8co+l180PXahM3l/KK7FcCTOlOtzX1JhnwtVBpxILC1/GuvU8OqztHR/JEDf8673jPUn3nPrHfyLf/9vKITPICvOWAkVukY3XqSfpJRFwVI7Ji9KplshgT/+9eVpzXQ1YLUXsdiNmK+Fu/IGQ0EzXQtZ2XTNz9VCsrRgY5DhuYqZRnhq7rRSuNKQUafNAJOMi5nhIPIckJhkdVTZ79oK1SAhSjLMYIMqlpXeUIDONMORAN1C4GqmqgFrvYjl7oDZasBKJyHNS5o1n2o4fnPjKM1sLWS5O2CpGzFXD+kPUuIC/tXP/Fvuv+NLrGwbtq215sCUYtn0We92CWs+63HI0+sBpbXDSt7moPfCGhwpMe4UpTGovEeSQ5zVSUuLEDAdghaGLIsQanixrvguhYV2lLDcHTAT+uect5l6jeW1NZZWVphyctJ+wkZc4kiPblRltlo99Z4qzbqq06ZgapO3ra330xGVBd0ip+VNMSgk/TjBUR1a1RonV3sU3WVavsFxqjs+15c+43P8k7f/K5Zhd94smCxCCcNso8Zyb8BSL2LaNfT6OQMcQgfqnuRYx9DwJaErNntEZ1B2BZlvYKVCOMNzLK3FWju6eVjuG9Yjy6WzLdjkzQBrgwbrsaXmbfKmxmtQjcBjNW9yYbEOyTLSnwMh8aRiJU5YTGL2h6cc+rttzX+t32UxiQmkwhYuwaYmk7akKTPWHIflfsycELuvN4ZCtFUJKe1wSgUMjWJL7QitBLPNcLOH2qdIekiT0i+rzPg5vbRLGZ1AhQtYISjj5VF8ssk6iM2wBKFCIqdFkrVZ77XxVMn6pgCdtN60nLzeenFB6Gv882W9s8Z5EXoe57GJrfhOgD21gEeqTUx/heMra4CLG7psuJIky1hw3VHPWhvBrFfl0FwIooP0nvxopp3H5iO9JgtTGeXRPo+srFHRAbrmsiTh2ptvoKk17QwG5JTVg6gkQdbmWB88wn//mV/m2AMPj57vjs/ezk//0jvRmz2WvTzhQdtiZlrzmpmSA3oZW7oI5Q+TarJ1wCI2RxE1HZeKdrlqah9pdJyj6ysox6PqeyyWDkmSc8AabptgkDl874Pc9je3ceC5z6OT7l4JHYrQkI6tUpZ9jq2sgfBwah5LtiQvBNPbqg7tomClLAhClzLKz8gbbDri8xzluzhJxtLGBhiFdl2SQHM8T9m36ZQHUMrwcC+iUg94Zt3BSdbGQgFMsorJ2tiijwgWsNZyPIqIlaLlQn+pRy8vkLqCrrmcMAWzhaC5LSVmvShYNyVB6JFF6fCc8XAqLqvSkBUF89vOuVcWLBUFTuAi4oyTq+tgHZzAJfIVl7zsBbzYdVGb55xaw4k8w3gaH0tv0CMt4NBUhWNpn9JaDoXV4Sif3PCnf/ph7n34CFP75vnH//CFmKLPShea1RqlTjiWlcyYjKAskZuCerko6GIIfYc0yjg2ODe8lXnJZz72GU6cWMKZm+baG67BT7osdxJQAW4YENmC+zY6HJwJR2lJpbUcTyJaTpOW74xuICYJ0V5RUFrD8XjA8UhgHJcoSen3T1K2V9HNCkva42sf+dSOz/WDX3mAr3zhSzzzhdefkTebxfTKklVO8ba40cWxEV7LoytiklxREQFH2zmXzjpkpuRw1EcRcIG06HQVCRQq5HDUG/GmhWKxb+mmht5KRDXUHFQBTtLj8AqEuorRKceyknkRUNvGQ99kHEkL9pU19okeZlOILqUpJ5MYLTeY9fyx8U+nC9HM38tSErORZwysxhqNpwBT0N84ybEiQ1cqj2u9FZuf1cLReNay0R9AUaIcn6LicrTM2aeG2/TCgi4HfK1t6UwppmVImvXwxUmE08Aky9j6xYDAZm1Qwxub3BoWM0tHVDF2g0eW1rjn9vvptNvUFma58TtuZGHb1IDIFBwrJ6+3nq/oRQk157wz6WxwXoSex3lsYnuE6MGKz2ekjxQeedFDOgrtK+KyRKAwgLIGIQWldPCUpuo5SDWHUOe+v1VIhdBV6k1L1V2jl6xQDyvkUhDZkmzTkulJWMsFsXRwTEQZ9fmjv75vTIACfP2e+0/lxltDYiEVkgMNj4vm9kBygnJwbNg3lQ+Q/iwm647mYU6T8cKqoOK4BLWDtAYDutEydXeawz2JNnY41HsX41T75AoHTMIg2tg149naAoGlWyrKQoBMEZ6HdSSDIqdixw0AGZbIGhyhkEZgyxwxibetxxtLYg2+kARSkeUFQhm8sEInz/jKLZ/HrKyzb98ebrj5BhCQ5jknI0niLuAWa2PblkXnfqwphglEQpCWJatxQcFwfmqUFwhKlCt38LaF1FoGpiRQDtYMHeDCdZGeIioL3LKEbcIht0MxLaTCR1EUOUKDGwS0jQEjMdaiNktMubUkxiCkoq4UVjsM4j7dyBBhKIwhMwZV5vybN72dL33hrtFrfemvP8tP/+pPUeQpsoREK6I8I002qGzrT0zKksgaqlJjSwsme9K8ZXnBf3nX+Mipe265lLe9818z7M7L8cIKJi3pDGLiosRxt8SMRQnJF9przM/v44AjdhWiK2mCIxTrZc5GmqPcEGk0RW8Z7WlK7RDZctfP9dri6mPyZsucQugx3vLSUCYrOKLCelliJcx5OcuLq1T0HPWaZFAUaCkpgxmcfB2TrpLpJlF5ircol8S5pe7Bg52SfbogrzdJM00vTpmqQoQiKgoybcYUQG4Ng7KkHcO+eoAtY0yyTGJCMms4GQ/HKl1UqY+d83Yh2u4+Qi9XxKWhnw2t4gIouivE6YBUDbezT19vfQzSCHJr0WLreCzJ5kSEmnbI+33A4HkVenI4TzYzlkCCdHxU2qOtHIKspBKG5K6Pl3c3Y0kVthggpIstk9EM57Q0rKYphQiwheZ//er7eeRrj4zO7aufvp2fec9PjG7Wi8dYb5m1583xZ4lzJkKTJOG//bf/xsmTJ7n88su57rrruOqqq3Cc8/0R5/HtgVMRohlN38GRIdarU1GWTGniQcp8xaPqOCgEtswQyiF0PFwpCVWO1ME5NyWNjs9tUnc2WJjfz0OLEaU16CxnfxjQ2Nxa03KYTNIrDM2gTrr0EEdOdic+31ZuvCkyQl1l1g94dqtB4PhYdYBycJRycAykgxPuxWSd0d8qmxAqjTU5UjocaF3IPcsPMSM7hKLKnB8QUu5qkJneu4fQ89iIUky+S7yiKcmNIctylFVUa9MMihwbZ+yv+NT0uJiZUgrlePS7MWVpqFSbpMbs4G0LVa3Yh0s8SInjAj+oY6Wl0+nz//7n3+Thv/3a6LG3feI2fvzn/g17HNDGJTElzWDvWE60UCHSO3UDkpgSieJA4BD3IiSKsFYnLsodvI3eF61xrWDQScAIqrUWUVFQRCkLFZ/qaedc1wrwGPRSsqwkCBsUwhD1U2YqHhVnaJDZQkVKFhyXXlrQj3MqexpEeYci6nKwUcXqGjXt8Od//KExAQpwz+338Ff/9wtcfMMLSdMED4eWI/BtgdCntmnnHIfQSga9BJDnhLfb/vrTOyqPj3z1QT7/yTt4wc03YqVlMEiY9TRpadDblICvFIf8KokpeSjqUW1M04IdQjQtS9byYVymrxTdnsNKoZBaEihJYsSIt9WFyYP8W62pM/MmJSZLaLoBEka8hbUWucno97rM1euErovOBlRtn6MbTZ7mVThUqSGBULughgPt/aLNQaeO1TWqSvPAhsHXgqorhzNEc0McS04O6vhed8TbdLU6VgUFaDkeBwNN2V6iIMOZ2Y9N15gThkqlias0j0QD9nghoR6XDltCdGP5azRtzmw4y2LhkCqJyQaUcYdGWEcYu2O9DQYJrYpP6J4y+cBQqO51HaKkYNBN0E4V7QrivCCUMO17o/dVaAeVJ8xqyYIXEipDrptIPax8lybB5j3QFawtkJuO/syWNLVPv3S545OfHhOgAIe/8sCpm3UgVIp98gzrTelzNh/67wvOWQfDb/3Wb/H+97+fQ4cOcc899/C2t72N2dlZfv/3f/9cvcR5nMdTDuHWwaS0XI0nFfVGjdnqMHJQIkijDGfTbGHKnNINkUgkAl9kCF0f9Ruda0hdoeIYXnioRRjUqOih47hMUuTmUtYCBJKVJKMcrFG6AY2FfROfbys33hQ5hVtnb+iwd7PnS0gHVTmANTkCi6zsQwg5NAvZEiEkwm0Ov9iBaTfAC+bAcZBpiiM9bJlz4803cvnVl4+97qErLuU7X/tSZmsORniYfLJItiajAAaDhD0Nl9lGyEzFx5SGPEpHY1G2oJDk/RSTG1oVl/lmdSJvpx4vEHFOuplNvXeqynyjwt9+9o4xAQpw3933cdstXyAoctLMpZtlm/2xautgseW4mE7KAmskZe5gkw57pirMN6o0A28Hb1twrCDrpZhymGA116gwVwsxxpJHKc5p56wRmEFKkRbUguE57KmHKCnIogz3tLKMQCCzkjw2eKTMhyUHphusJB4N02Vq0/Ry/NETEzk5cewE+5seFV2SxwPyjVUcNZ5I5BpB2kuxm0lI54K33SqP3bW1EW+eVmRZiZNnpMl4ElfL9djrh/hS80C/Q+7OIZwqJlkji1c4Hg/4YnuV3AwnBIRKU2QurpbM+jkzoaZZCUa83Xjzi3Z8ri+68mlc84Lrz8ibVMOpEFrrcd6mG8yFDsoakl4ft8wxg3U0BYE0HN4wuNal5W6+15tmJaH8EW9RLujElq3woxlf8+i65ktHLSZPuXi2Ri30KdKcMo5HPaSjz5KQiEKwuj4gjwcINNKbwbMZ07ZLQ+vhFnky+abRSJd11aIuBY2iQ5JYfGUxURuhNEqqievN04okStETkoZ0bol7KY5WLGyun9DVZGmByIqxm0qkQOcpHg5KSpKyGM1/pcwpB8eGzvhtyIyhqhyslSRrJyee19bNOjz2etPbzFvn8fhwzq6W3W6X7/3e7+WHfuiHRj/L85wo2qXKcR7n8S0I6dQpB8eou5pZX+O4GpsyFjm4NddQWEupPAaJolZRaBEhvQNP2bGJzT6mi1oV9kzV6a6v0wxc2nE6msEpBXhWcLyzxmWhpQhbXPOyl/CFT36ZB//2vtFzXfT0p/G8l70ATIkQcCLVvGjBH6uOCOkg3QaychDp1ECF2DIGLMJtoCoHyNfuAoYzCg9V6vxtWRAGA5YGBdM2pdGs8s5feie3/NWnOHL4OFPzs3zna1+KH3ioMkZ5VbIiZ5IP3+Z9CjSpsTQ2r6xVf3gRXh0kY/Mod3NTe1ozXwtZ6vXH5lHCZFeuFpKk3ZlwNHD8+DIH+wnGs5yMMy6rw2hEk9i5xdvLS3oZ1GzOtJsRBEMjyTBykB287eaC33LNL2+albbmUcI2V26gmWkMbyBOjxycr4d4W72/UTocn+VqZgKDNCWB49IzTQpO9RruO7gw8T2Y3rsXmXSpZ23KtKSX5iDrzGwKn91c8E+It23rbXp+8iiyQ4f2j3gbRXwOEuKoS6tR2/H4uuOylEScSCMOhXsxnOShjRM8alzqwTTz3nAXIyshzi37agKdRJRS0vRO8Qbw9ne/k9v+5rMsHl/CbzS46vnXMz9VOTNv2kNrbzJvUrGnUWFpkHFieZFpG+O5Gk9m9PIKdxzLedaCphlsisdNIboV8bk0MEgRIIUgK4Yxpi1fMl8x2PYypWow02gCbXpRArQ3/3/oqO/EFpunSCFJ8PDjLro+P4r4jPrL9LMm9+U9Fvyd1dB+UZCgmKvspdtdZqnd4cJmQJkOUF5l1/W2PVJ3rhYSusPPzOku+K0gh+2RunBqPZVo1jfa1JoNLggkg81JC9KbRpmccnAUdIXttbe4LDBWUHEM+/ZO/ozN7Dk1Y7Q9iM+43tYGOXX//Dzxs8GTqoTefffd3H777XQ6Hd70pjdx3333jc3jchyHRuPcmzTO4zyeKgwdvsNs7f11l6iUCKWxRT4+TqYzoChL1nPNIBEsVBVYg3Aqj/kaT/jYdIAQCiUMz1hoEVtB3XdpBh5RNpzBWRQlnX5EnBV0/QaZE5JLhx/7xbfzlh99C69542v4pz/0Zv7ZT/wg61FKGnXpyCliK7mwWhnbSrJ2mNyiwz0ASG8Gk21gixjpzyPdFkJ7myNP4EAYciCs4IYVpA5YTwr68TAb/GnXP4fv+N5/yD/47lfgB97WC5AVUJjJ21e26JNZQVlalFSjBJ3TxwAVZTlRyFhjMFmMLiJmtKE0lq+tJqR5MfGCuPX8e3bZavXrU3iioKkNK0lOsi28Qgg1qqwVySoAG2mOLSVzQYanhw7nrddohv4O3iYJ0K3Hnz6+KS/LiRfErcdvXRi3xskkeX5KgDqKPa2hCCzLDCWhsJKBmEEoH5Ou8p3f9UKe9dxrxs7/muuv5bmvfAU2H4qK2akZatX6aP5rkZcTBehZ87bV37xtvV383Gdy2TMuGzueK66+ghtfccPo8afGN2lWVxYxRTR2PdpCw/F4ZNAnLkvaaopHC8G0janYwejxWQGhI/A12KQ/Grm2nbd2knLDzTfyotd9J9fc+PwxAXom3iYJUIsFKRH9NeacEtKIlbQkLWEQJaQlFCU8sFKyEZthmtjm+pT+HKn1eWi5iy1j0tyMjc+SZUqZR5S9ZWyRMtNoUnWgs7HKSrtNURqW+5bSwLTo4PkBa0VIf2MdbEEqKiylU9z6SMHdj/Y42SlZTHcWl7p5hsUilE8iZ8kKy8mVE+Slod1Pd11vp49LG6QZgySbKEAtduL4pjwrWOxkqDwmSTKEFfTyU2OjVDD8DjPx0vA7bJPnqCgQVuBJwwtfev3OXZsrL+Vpz33WiLcoLc+43iSQl+eroWeDJ1UJveuuu3jf+97HfffdR6PRQGvNS1/6Uv75P//nPPe5z+VpT3vat1R/xAMPPMCP/uiPcuedd3Lttdfy67/+61x88cXf7MM6j28lKB8hBNZaZgOH+zqWvpFU8oh53RhdGB9Z7/CluMCalCsbNerecHTRuUxK2gHpDl2dJmNvrUoqLEVRjioBj/ZjHuj0kUXA02YbnBQOjbxkIzMkruHqm29garN6EWU5R9bbHC4tuuGwt6pYCMe3VW3WHo5lchqbb80U+coq+LNIt4FQLjLYMzQwKR+J5dJKg3vDlKxoYJNVvrreoTSCedfjgs1McYtlKc85UZQ0zTDGcmge2CaATYktI+LCUpaGnjWcSApmtKah9aiydrgb8VBvQAWHqeopIdMrC5YGXep+yJ6p/cj+GtV+ynpm+NzJDSpSciD0RhfE1BpOZhmOELzg5hu47RO3jeeuX34xz3zJ85itKQplWR8YTsYRb/unN5+iRwrW8ke548TDaHeVyNao+wK3iDBSsVzk9IuSWUdTU3oHb03hcmErHAmZTlGwWhS0tGZK65GgeaQz4PDJVeZ0lda2C2JsSpbyHF9K5h1ndGE82h5w5/IGyir2+e5ofmsq4Pigi7IOVeGzGkumpufI4yUeGSzxw+/7d9zx4Tu592snOHDBPl79ulfw1XbCRhyzENQIgJlaBfoDHu2nPLDRp6U8LpyqjAToelGwUezOW024XFj3x3hbyQuqWjG3LWv+0faAF/3wW9h365ewG20OHdrPja+4gVJJjqUpjhimcGkp2dessZ6s8MDqEVQ4wz7RZOuTXVrLiSRiOY3Z64d0ixzHnaUoOpzoLJGoPgdre0gLiZJgi5TFNCHWPrOiOCe8+ThcUPMm8OYwJ0AkXWYDyYlY8eWNAVqUHDowz56GIs7h4fWSai1GSrgwrOJISdtMk5gNvraxhpEhe6sNDtQ0WgmKXpel0pIUOQu9VSphnXrZpSgijqwZHuilTIdNLnZLbB7jVio8GFm+uJZzyLTRuoGxAdUQOp02Xz4e0ahY9vohweaugt3Mp29nOXs86GQuexsVlk8e50sFuBS7rrct3ubqFU50+tyz1gFj2ev5IwFaYlnMMnJr2eu6eEIyU62w0uvzUHd48zbruOyfcomTPtgKq2lCaQ1qsydaOPVhWpLJMfFJZLCXQVlircCakgGCN/zcj/DgJ79Af2mV+YV5nv3i53JskHL7Jm9Pq1ep1YNd19tUxT1vTDpLPKlK6Jve9CbuvPNO+v0+n/jEJ/gv/+W/8PznP58//uM/5uUvfzlTU1N84AMfOFfH+qTxvve9jx/+4R/m7rvv5mlPexo/9mM/9s0+pPP4FoNQPhYNJmcucEhLS0coemU5VqHBGgZo0mzAjAcVlYEKntLkJyHEMEe+TKi7LsLRFJt33XXfI8pKenlGoSxShXSKghNpSWEtPVMw2JahHGpJUVoGTsha1ONpdZfQOWUKMMkaCIHTfDpiK7ZTV4f/hByNalL+HAJLGS9i4iUCMq6fbVACqtJikCZEZYbra/Tm85fW0i8NkTVkZb4Z3XnaFpZJocyHFSBjiYWlUxbE29zoFc8jLaCXZ/QocAOHfPP3ibW0i5LIqyHcCjZoslaUNPyc5aQksjmNmk+OobDDnOyeMfRLg9WKd/7SO3nLj76Fl373K3nBP/1uXv7DP0C17qOEwFUFaS75/PoqORalJEpJSmt5qAj4emb4ysYiG9EGFSWxeQxS0y9K2mVBsq06t523VEElOHUjEFtDpzyNN9ehMNAtcjIKGtsCzWNj6ZQl/fJUVrujNEprBnlGbFIqVQ8ph2I/QzFIY/pFjtaGdmRICkHhTtOzil7R4dIX38g/+cH/h+e/+lWspYq0iOkWBds765qeT5zndIuMUjMSoAADU56Rt4SC2rZzSKylXRb0i9PWmxDEtuSiG5/DP/4X38tNr34JWusx3vLN9zXwNVHhs5FmtAcrpNv6ADNT0s0zCmP52qDLRp5SZJoHuk3WS81atMEgWSPKLVoJyixhYMw55S0h35U3ox2UX8OvNFGOZlDkxPmAUKVIIQgdaKcFxwcZnTwjKcvhEP0+TDWrJFYxyPpohrM0MQVZ2iOSmr7QDAYd8vUTSOXQqNUZ9Lp04jZ52sYxKdYO54e6bkGmLOu9LjVPMB0OZ+oWOqSdFty3tMLhfpdyk9ejUZ8vbaxRGENeGLqpoSpzjBX0i3y03rbuMyfxpqXEcxyiLCMqcoLQGVVA883vjN5mpj0M54jWfJ9BltMvM4QnCYMKNo2wRcF6kZFuq0pKp4Y1GTLYg8375NEJkrLAlMO5GQmGvoCrX3EDb3zLG3nJq15CLQzGeKsG7kTeRutNS/jWqbt9W+Cc9IQqpfirv/orLr74Yn7xF39x9PONjQ2KYpcZgLvggQce4OjRo7zwhS8kCHZe0PM858tf/jJCCJ75zGeORic8HvzGb/zG6L9f9rKXfUsJ5PP41oCQQ3ejKWOaXkjD0ThejaAcjOZLtnsxIjXsa83TKwr8cgOPOtLZgxDnzOs3+fjcOjY5Sag0M4FPEg3wjcvixoCG1FQqNTCw1IvwfcWgsDS1puZJqtuMCCvrHcKgxb5Kg41+xEHdxZoWQmpMuoFQHnrqGcNe0K3X1j6ysh/pNkfnKdwWztSzQDqUg2OYeIn9lSYXtCRfaVeZVh4q9MnznH6SUvW9YR+Yo5GOR16WQyFtS7Z/HdkyI+6v8ocf+Dyf+vL9XHRoH1e/7PmjmZpbvYQ1BF5YI8OSRCmRUjS0pAEc9D1qYY3MGrrKoSYDrnaWORbUmPIsJ7sDwqq3mVmuWNh0z/tCgpZcf0YUd1sAAQAASURBVNMLuWBjQB+QGqI4JfSgZjM8UaMT5xyL+1xSHVaKjycRnSLnqtZBknQVE+fcO+hQtznKqzIrSmpa0dics7jVA7rFmycY6xFtao0SYoy31c4A38D+oEpdO6z0olGPaH3T7e9uy7LuRSlFlDPvV1BK0IsSQi3xNmMc58wA7TjUHc16Yekkhvmay8HGPvq9Fb66GuGGhmajSS+FA74izZxNV/6wB3SxHdFSHnXPw992DgAzWuNLSW3znE/nzZGM9Yg2lALPwxdifL1lhr1+FaVhtX+qt7eiTuMNyLKcJMppmDpTvsYvu9jSxUqfQGn2BxUKawk2DUhfW0/5Px/4a2TvBBddWOea73oexyMPV1ch7jPv+DQc55zxpiWPm7c9fhVRprS7G4SOxnU9mq4mKYcRoFWtWYssvdRSWofZyhQm65LFET1XU1EWaQzzXkhmLU0sEoFBsdwdMKVcGnmE043YSANqNXfEmxcG1EjRwmCtZDWy1L0qlzTBxjH3rzxKaS0zfsBXex0urtZoOR5xIciNoOj3UAWPm7coyYl6KXNeZbTeImdYSfbFsNpYYqls8lDkJavtiGntYxyQpaGd5lgMZWmoaYfUlISb3ytCh6jqBQihMEKQDFbIkoKynEPagqZSOI53Rt7aUUqt5uzKW5QUVNzzE+vPBk9ahK6urlKv1zl69ChKjY/e+K3f+i0uvfRSXv/61z/m83zkIx/h3e9+N/fddx/Ly8s8+OCDXHLJJWOPuf3223nd616H7/sYYyjLkg9+8IM885nPBOBnf/Zn+eVf/uUdz33zzTfzZ3/2Z6P/P378OO95z3vOO/fPYyKEO0W5cQ9h5Upq2sXREDgetsjpxMOEj5rvMDNb5XgkuKiaYQaPIhqXP/aTP9lj0wHCgiMl00HAiU6PZLOXcH8zoBZ6tKOERzoFrSCmYwU17TG7bSzOamdANy6Y2lMhaFVYdxzmggwTHUOG+7Flgm5eMSZAt6BrF40MUjCszgp/aMSxRYsyOoanFFe1Khxbz3HKGlM1j+VBwupg2Dta9T3qSiMch6gohz1Up1VC06THd/2zn+Oztw9d6rcCV3zy87zj3e/AKjnqJZyveTRrAUcHA4okZ7EbUW3V0KZkLqiiHZ+VNKEuKky39vEMFjkqNMfihF5eUh1Y8oqP1GpsgPr2bOorWxXsplhajVOU06c6PcMgD3i4N8Ax8Cf/+8Pc9dDDXHjhfl73+lczVd/PI+kKnd6AisqY8oc501vv6HYT0nbetpuVfCHx9fgFsRcXtEKHmUaFPLUc73fHzEoz2wfZb/aABq7mUKtCacsdZqVmLlB26IiXWB5tG+aqkhkvhHwfRbnE6soJlJLUaw2KvKTcFA/bTUhbW/Cr/QH9tACG5xBINRq7s92EtMVbP0nHzEqukMxuO+et3t2q73Bxs0JWFmPmQEfpibwp12d5zSK8WdK8z+LGKsprMV8PaPoeM/7m8P404z/+4Dt56O57Rs/xN3/xef7BT7yTfa0CncXUvICaHBegT4a3KMt3mMzOxFsWd1kzJSc32uxtNal6Lnns4qPppnCsU9JLoerCZS0fKVyWNtqsdnqUIiaUiro6dakf4222QegpVvsxSSHIkpKZ6nA8UuAHFHEPk6es5T5RDnOhpjHV4tE1lzLe4HjnKCfTGRqOS3XzHE7GJYMkJ+sOqIU+l0zVHzdvp6+37Wal7aEORV6Oom8vnaniOHpkVjK5pcgLAl+TneZW35pmIb1piiKnbD9Cb9BGmgJPSsJtxzSJt/6gOCNva/2c+uzkuOPzmIwnLdl/+7d/m5mZGX7nd36H3//93+cXfuEX+NjHPsb6+jq33347a2tnzurdwuHDh/nZn/1ZPvjBD078fZZlfM/3fA+vfvWr+frXv87hw4e58cYbeeMb30i5aRD4qZ/6KRYXF3f8217xvO+++3jjG9/I7/7u7+4QuedxHgDKn8aWEW52grqSxCUIN2Sj0xs21zuCmbpLqTRV12N2Zh9CaKTzFPaDbkIIZ9RzNBP6LPaKHWaWZuizUNMc2dDYNMOXp/aHtr5Y655iuuaQFgX7qnWqtT1Yk1P2HgbpIN3mxNdXwRzSrU/83fb5qHvDkAMzDsINEKZgrj4cxbI6SOgnQ1erkpK8KIhzg7XjF4v/+ft/MBKgW7jv7vv4zEc/O9HMEnoeC7WQZMv0UmTgBkRlQVFIklSzb65O65LncP3B/dQbCzxrdoppW9Btd+nnp3ZsJrlyT5knFEvdHqZMsEbwtS89ws///H/nI3/1ef74t/837/mpX+EH3/TvKIqSUk1R1ZZOWoxlX+/mgj/drGS2NZdNMrP4Wk/Mmgd2mJCkFDvMSmlRIB0PG22Q5SXdzLI6sHSH9LCeCPbWa3g2Y7W9Ri8aYLMYqfSuLviZaoWqp0dmpS3s5oLfLWseJk8v2C1r/nTeLp6rMu3m9OKMtbzORuqwtNHl8GrMA6sFST58nQ/+yUfGBCjA/Xd9lcOfu41ev0s7SmGCAH0yvJ1uVnpM3qRkvl5BSMHJjTZFnqEEHNkouedkwaNtS8U5lQUvpWK+1cTVkpV2h6g8tf4n8iYkM7UKgTOJN8tKPyfKoekLGpvifU8zZDVr0cDQKDtUt6rE1nKiZ0jSnEAUzE/Vz4q3nettaFaKslMmo+0CdE+rgufqMbPSIMvp9rsIBKnZ3ale6Ca4dfpJyka3jbWnRO5uvLVC/4y8+VriSnX6S53HGfCkRei73vUu2u023//9388111xDWZb8yq/8CldccQX33nsvr3rVqx7X87ztbW/jpptu2vX3t9xyC4888gg/+ZM/OfrZT/7kT/Lggw9y6623AuC6LtVqdcc/3x/emXz605/mzW9+M3/4h3/I5Zc/dtWq2+1y7Nix0b+TJyfPETuPv1sQykf6cyiToMs2aWHoEbIR5wSeZqbqohyfuISq41B1HWQw/9SakrYgHWwx/AKeDkOSUjJdO3VB3MJs1cdzfDYi6EbDC+P2L9apmosVmqQQzFccamEVGS5g8s7Q9a7cXQ5gdwgVIKSDNTkN7TDnuaRuiClylBA7hKi1gpVBTicpdlRCH/761ye+xuHDx3YIGQDylDkypkOHOC9Y7SdY5bI4yPHtMGpwf93Faexh/959PG1mP5ccfDqHLngaFQxHO33ysth1LAwMe9bmGzUcYVnaWMcRKWtfP8ElF17CtddeO3rclz5/F3/5f/6apIBpTxJ4zsjFu5uQ2cIkQTPpgriFSYJmkpDZwulCNLOKOIpZbCdoIZgOBQ+sFBztlLRjQ6gLZnyJU6asdDr0BgNKIycK0C2cLkR3E6BbmCREJwnQLUwSNJN4U9qlXnRoBopDsy2qniRLuwySlGhThB59ZPJ3erLRJpBmdAPxzeRNSIkuCxZarZEQ9WVGlFmSwlLTObNuH3qL2HQoIKVUzFU9PGlZjYetMLvdOIzOIQh28LbWzxkM4jEBCuAqgXIDumYa12aYZBmsoZfC8Y6lrlKmq84T4m0Lk4ToJAG6hS0hWnEF3X4PWfSIy93bATNjMKqOcqokacpakj4mb5Nu/LbzNlX3EPJ8U+jZ4Jz0hEop+c3f/E3SNB0JvnONu+66i2azyaFDh0Y/e/rTn47nedx111286EUveszn+PEf/3G+8pWv8PSnPx2AK6+8kttvv33Xx//Kr/wKP//zP7/j5xsbGxP7Vf8+o9udPHD82xG2iCkSHytcWqxy73qBX6lQr05hlWGQG6b8CiSGmdASD/ok+Kh2HyEGuz7vuXiPrCnI+iUiPokqfWarHqmQZAUE4tQd+MAU+L5C2BBtclbW+mAkNT/Aeoq4zPAzS1AI9juCdjcFFGUxg5PXkY9zB+N0FLGDHXQRqsKc0HjSAVxkAV0D2vXRlESDkqIoqUpNFBnW19cRTjF6n+ZnWxOff2Z2jloYkLuKJLdUpKYoc3wjqQRVGkVE39XIVHBiNcL16izUBErk2FSy3ikBS5WSbq/EVyGX1FrcGxVstFNsCRXHQwSaXglNIUcDsUss/VLiegHKSuJOn6IocNydgv3IQ8e45PqMtChQXo1qYciSkpVBhEJRDz0SJfEm8JYrh6orMUU5zpuvGGR2tLUqjSS1JZGVVIMKJs1ZW4+HiT2OjwgU/QKaSo76Kwtr6JWCwAuxaU67m2JzgUMX7VlMCTXpsrwqUIBrctZkBYklNIY4scRphoPGrzpEQuAW4G07h25ZYB2fqi0xecHy6gCB3MHbFmJbEgtNzQsp84LVtQEYSdXzwdf0c0tz23ZyZg2DUlDxQ0ya78qbIwPKLMGmKZ0MXMfHLRN63QFt3xIaH781NfFz1pidAQtVv0qWmHPKG3BWvEnhU2YF3TglcANMktJe7yGdKmGZUaYrtAeWhhKoUiLxsECv10cHVRyjiQYlA1Ockbe4sDt4w3gENiZL+8TGobLtHNyy5P5VC60aTRFBvMJSVCe0FsoeMS5+eer5Hy9vY+utMDhegJMW9Hs51lgC6SADh4EV6ELgbuvD75UFnh+SFy62F7OerbFuJmuS9SSm282Ic0Pd87CleEzeipIz8hZlKY7Vj3sH+Ilienr6sR/0bYJzNqz+Pe95D7fffjvPetazRv/27Nlzrp6ejY0NpqZ2fmFMTU2xsbHxuJ7j05/+9GjrHtjRw3o6fuzHfoy3vvWto/8/efIk119/Pa1W6+/Uh+Bc4e/Ke2JNQWYfQqgqF1j40OIJKkbgT1V5ZPUYNSloeHN0M8ne6YBWmCDDvTjNycOOt+PJvkfWWtI0HCa9OHP0taWT9bnAreJu+zivlRknioyFikvUTrFZD+GEBGGdo2mfGSw+kkPNkEPzw8QPW6ZYv447d+AJVUIBcjWHiY4ifR+3ULA0IMs1RRlz0gzznhc8zWDQoywzUu2TiRka9QpOuNlbakv+xfffzJ996OPc+rkvj577wssPceUNzyb1LSeyiAXXxccQFT1kfZ5qo0UQPcDXLfhGsZYWHJAp1coUBsnCtI+jhhe4A2i+tlbQrLhMdR3KOKIbD1BSUq9Nc4IMbcAT3qinMTYlJ01KXiYcaEwzSAxSTt5M2n/RfvqUrJR9Yumx33MZRANskeCGTWJPsJonHFD+Tt7yjAXPJYvHeTuWRcw4DhV1qtLSsTmPFil1pamKkihpI5RDtdHkeJnilxLf8UYX6n5ZcNxkaAEzriRqt7HWUikLlnKw2ufCwKNe2RwYvjpg0ebEZc5+32WQ9hBCEFanaOuCblEglMf0tnNYKVJW85z9nk88SIaZ3W4N1w9O8bbtHDaKnKN5wozjoNOCLGkjtE+l0uBYHlMXmtBlJE56RcHRMsUXkpa2DDobMIk3pSiLbPjZUxqDZW+9Sr7e5/igy5452H/zjez7q09w/G/vHR3PFc++hote/nwe7SyxP6gTdbrnnrfibHiz9LKIE8ZHCs18rUq308b0jhMQs6ZdSq1wtUYUHVzVJBdwImmTCs2CB4NBD0x2Rt7aecGjRbyDN0fkPJr32e/WCTYH9mMKBmXKA72M0PrsabYYdE9yuA1e1efoaoe6G1BRxdnzdtp6K4AFT9Fdb4M1VJotVmVOUhqk8kY3KCWWpTxlwyRM4aFDl9xs0PSbowCJ7ThBTEeUPJwlXKJy8kw8Jm+D0vJoEe/KW1TEtDz1d+Za+I3AOROhb37zm7n44ou5/fbbefe7381tt93G/Pw873vf+3jDG97wpJ/fdV3iON7x8yiKcCdUIibhbKuX9Xqden1y/9t5/N2FkBqhq9gyYU+zSbPaIc4jpHRYqLXw8wikA1YwHzpY29+1T/KcH5sQSG8aW0SEaZsAQSAF9dPEUGPToZv3czAC4dYQSpDFCQtaUtUOG6Xgymkftbl9ZPMeMtjzhAUogPQalN1hL2eoNHVP0nMqNLIBs05IkpcMeikID+17pGnMxmBAmheMNgfLHNcR/O8/+m/8P7/4G7SPrzI7N8O1L7oe7WlMWnDA96gpRZFG2LBFrdpChTWafoVkdZ3ZuRbP3dtkj0k51lvl4Oz8SIACtHyFYJiWYlQVM1ildCpoR9CLE6ZCD1crvG3vqyclM45Dkkr6EUi/iRv4kI9v+T37edfy8td+B/ce7zPnKFLlEHdTsA7CcyisQWUl+33v7HjzPWp6/PFVpVhwXYq4IE5KhFtFSIijlJnAI9AKZ9ueaKAU845DnpUM+imoClIJ4iynOtggmN53Kr/bGpQpmHU9YiEZdFKs9ZG+S1wW+IWg6rmjfsAtTGmNhyTtJYAancN23rajLiX7PY8iyslzg3DrCDWcdrAncAmVGotnrCrJHsehSEsG/Qx0FaGZyJvUDiJPmAunyQ0MUgc3aNBLOuSDNULR4F//yn/i4Vs+zcqJReb37eVFr/kOOlEbT2uSfvakeZtTiq9+7Fa+sDl78oabbzg73pTGB+a0Ji8s3bUOZdQGE5E4Hg3fwXEUgdYQp5RZjJKKKQmFckfrTXjeGXmrK8U+4U3gLWN/w6HpnGpBKPrrhHGPg8E0ceSwqjK6JzZotwtapcuslNRc7wnztn295VlJt5eAChEaBklONfRoOg5VderxCsG01niOQ5IYlDtLmi2RRit4sEOIRmVB0/FZcA2mbcDox1xvgZRn5K2v1LfUbPRvB5wzEbpnzx7e8IY3jATnHXfcwc/93M/xwhe+8Jw8/4UXXsjq6ippmuJ5w8XQ6/XodrtceOGF5+Q1dsN73/te3vve945VUc/j7zakN0PRe4iKV+Oi6ZDFdUWZ9/GcgGY9JEJQ1YqWp6EEIb3HftJzBKF8hDeDLyJCtQ5pTlAfv6BUpSLpJcTpqd6mLRevWxSUtRpNT3NB/dRxW5ujgrknd2w6xORdRBEjdcCewGW549IUgrqFqJ8NM8VbFTxPM1hOWR0MSPOc6uhAsmEiDIZrb3geqpDM1Ye9hFvu60BAJVCUUlC61U1Xq2LZzPC08AiXH9jPcxf24ZuIy/qLoJexdv9otFTNFdRcycqgpG0qzGhB2Qqpa8VSN8LEGc3N/PItSAQtKzk+MEiZsndWc1RJihzqjSpv+pffw4ELD/Ka734l3Vxii4Sa0KSDfJgF3wioBB6r/QFRVlBTisAZF/xn4o00w9HjfceekFRyy2qUj3oJk6JguRehk4x6vTLaiodh9nXDCBZ7w7mTC1NVrLQsdiPKOKHSW0H5ISCxpgBT0FABcZxhoh7z0w2CSshyd0Ca5jS1wnPGL9Q1qYmiwSgLfgdv1fFzDqQiHWRsbGaKzzcrDNKha14kObX6+OO1kNRKwVI/w9GKva0KBWYib0J5kGU0hWA5c5EC9tYVbadJL1vHRBGzzQYXve414+dQZgzi4QzZJ8Pbam/AH/zSb/D1r54y2d32idt4x7vfcVa8SSxhZ5nljQQw7JupI3S4yVvGjBMOc821g4064Lg0pWb5tPV2Jt5CoWlH0Q7eev0EHfWp1Ic7PbZIKeM2noV6Z4UVMcVtjyzRqM4w5/SI+xGeFNRq499JZ8Pb8JyH6+1EP0YCe6aqKC1Z7g7I4oyWDtGnjcRrSEUcF5SmwFqJcVvk2uCcFqlbWkNiSmrKxS1yiqykXvPH1pvOcirueE+oKySVbHfequf7Qc8aT9lAq+uuu46pqSnuvffex37w48DLXvYyyrLkQx/60Ohnf/7nf45S6oyGpnOBt7/97SwvL3P33Xc/pa9zHt86kE4NbIkQgoWaRrkuM40aaV6ylgq6WcGc7+FrARZ4EtXDs4YKELbErexlrlJhNSnH3Ncwubl+yzwRZzknBzlzgcbdrCTYIkKoELGLK/7xQqgA4VQpe4exZcJ86JNLSYlicb1HnGZ87Qt38Bd/+H/4zF9/ipajAEG8LWLPmmFlMcpzkrRgelsS0nbTy0qnjQqaoB18pTnSztk71eSKfXu5rDlDqDXSreNV9+DYBBMdH7nwhRDsqUke6RR4rstF0wFSyTO6eLdMEQjBrJvgasGW88IPff71v/lO/sHrX4Z2NGlhKbOY1X4+ZmaZFDl4Jt6sBcfxUa5mNSk43uuzkuWjofyTzCxncl+nWcHixmD4ud5MsBpFDro+S50ucW99+OCyoDCGxXZMXlrm56apVisTTWYj7nYxIe3mmofJLvgzueYnmVl25U0IClNysp1igPmKwNUCz9F07BSlVax1u0TptukFZc7iWo+0lE+YNxiutwc+96UxAQrDKQ9/86Fbzoq3HJflTo5wXPbNTeN6/i7TDnzKPCGL+iz3izET0mPxtt6PJ/Pme3R7A1Y77eHHIu1jjaWXK9r9iGZ8lIvnKkzVHPbMTDFTCcnQT443Jrvgz+SaH5nHCgi0oTQlCCi9uVGkrkmHYjQ3ltwYshL6WUHNlTu+JydNO4jS7Iy8rQ9S7PY/OI/HxDmrhP7ar/0a99xzD9dddx3XXXcdl1xyCV//+tc5cuTI4/r7I0eO8NBDD3HfffcBcNttt3HkyBGuvPJKFhYWuOCCC/ihH/oh3va2t7G2tkZZlrzrXe/ix3/8x89p7+l5nAcwjKsUGmsK5kON1hmOEzLTgNVOj9VBwbMvbQFmmCAkv3EiVOqAkhIlJXuaczywsjqslDH8Aj2Tu7MZ+pjUYTGzVG0Ha2eGw/mLPrp6EUI+ua8EITUy2EeR3Uc5OEZDzWItrKYOcdzjj/7rb/O1v31g9PhLrrqUn/jFnyZKTmVR2yIBLemnCY7WTNXH22hmqhWwA3qDBJ0LpA/d2NJNLa+6pEJFXo3nnvqbrVYJEy9iouPIcB9CSFq+ouZJFpo+aeIhspICPbowbp9rKIwYXRDnpyu4lGBOXTCtFZRxf/h6/hz9RLDRTXCAmea4m3pL0GzNNdyNt8LARipIKRAq4JlNh36SkaU5GzCspPUmu+C3Lozb51GWhdkhQLewJWgWNwpOriyzP2wgi4KVdk4unR1u6i1Bs9wdjOa/VjzvjC744QD78TmiZ3LBb0V8bp8jmqbFrm7q3Xjb6JeUfod9U/XhjQPga0hLRaPWJE/bLLc7zDXBdz2W2ilxEjM31XhCvG1Hf22yX+HRR05y9Q2Pn7flbopQanfets1/xVqWNwYUyjsr3kQhJ/I2W6/Bxgbd7jrWFDTyNt0MOnFGpRKOeNs6qlq1itDpk+ZtNxf8lhDdPkfU13o0vWC6EZIWJVleIpQgtwYZ7MVwcpgEB2SyhgXSXFCXGdON8XPeimbdPv91EKdEA3PG9bYRRWRncOSfx06cMxH63d/93TSbTe644w5+7/d+jwceeIBLL72U1772tY/r72+99Vbe//73A8Oq59Yg+R//8R9nYWEBGLrVr7rqKj70oQ8hhOBXf/VXedOb3nSuTmFXnN+O//sHof3NvtCYqtZIJyfKHabCCqWB40tt5sQGtlDD/lDpPPaTnitIZ1i6AJphQN3zcE3Bh//i46wsrjI3N8tLX3HD6IIYmZJuWVKRkprS1H2Xqg5oqhQTn0RsRm/KYPacHJ7ypzDJNJiUerlEO1FURcDXv/TFMQEK8NDfPsjnPv5pnnXZM0Y/GxoiXNpxn4o/HPPSKwsGxlBXilAqmo6FSpXlOKMTdTC1GtcvOEwHEiEqpx/SRCFacwUH65rMgOdVIF5nKRMsuO7owniyM+BvV3rYElpasrB5QSyiDsv9jVGlxxiDLRyE0Cx1j3PvSkiSZ+ydrY5lisfW0NQafzP7erU/4OQg4aF2RAXJbGU4GNtYOBIZpBNzbcUhTqocrCrWdZ/VQUy/M+BI4dBy5OiCmFnDelHgCjmWWb7YHXDPSgdRWKb1KSFTYlnfTLSb0nooaBp1Tq6vcu/iEkWpqBYl++YaY1nwmTVMaY0rTomBw92ItIioIUaD6GEnb1tCdCXOOdJbJ7SSqcAZCZnEGtpFQSAlDXUqa/5kL+bEUhunhClXj4RMgWWtKFDAtNYoCw/cdgeHjxzHn5nmiuc/l4NBwLSOcUSBtZq1LKWwBpN5hK5mOmyxuLHBgytrFKpCNY+ZqbpPiDcYX29zeye3t8ztnTu3vG1+Vu9d62EKQ1No9m4ToI+HtwO1KmFNj3jrpBlfveVztBdXmZuf5ornXMViHPNQklDRwTnl7bHWm918fLn5+O1C9OvtPmkBDSHY0xgGCKx1ByxGMVYYrjTNYS/9NiGayoxBUZAZh2kvA6l2fE9uCdHlQcKjgw28EvaHIY2GvytvU4FLeT49/qxwzkTowYMHefOb38yb3/zmJ/T33/d938f3fd/3nfExUkre+ta3jjnWvxF4+9vfztvf/naOHTvGgQMHvqGvfR7fPAh/BtM/jK9q5CLBsSHWWpTvMlOt09Qp5eBRVO2ipzyuc+y4tgneZuCBgd9996/zwFdOCbzDd97FO979DrTWdMuSo2nKvOtSEwIpJdVKlUroYPM+Juui6xcjdHXSy5398ekQIRUyOEDVHqUmuuRK0V5dn/j45RMn6SXbttXyLrawbGTJKA5voyxZyjIOeB6hVKwXBbo1T2ejoMhTXNPngkb1jKYA6daxJsemayMhuq+uuG81p+7WqLDK4Txn2nHwhUBZuOfTX+D+w0epzU7xD1778lFFJlcuS711HGsRDId0p1mOcKY41nmURzsbLLjlSMhYLKtFQacsUELgazmqrD3cXWE1S9jjVbi8EdIrC9q5wFWGfzTvM5X1uDORREWN6UqVbpQxXWyQiwZuzR9VZPqlYTHLqEhFXUm0kISug+M6rK1voIXk0PTMqJKWGMPKZhtEKCUVqXAcTc13OZwk5FnCVMUdCZnCGtbynIEpNy+8EiUEs9WAh3oDenmGF9bGKmmn8wbDcz7SW2cxGTDjBFzRbI4qoL2y5ESW0VCaulIIBFXfQ0QpK/0OgXK4tFEbVdKismQ5y3CExDeW9/3ke7n/nvtHr/+VW2/nXb/wLlxVYvOEzA1ZzRIyY5l3FFXHRSnNdKPBQ/0l4nSdwMbUW7Wz4u2KbRXQ7evtxptv5LZP3DZ2THsvv5hXftdN55Y3pamHAV9fWqWwhrlW66x5q4Ue5WYK+kqS8t9/+pc5/tUHR8d9xdWX89IffDOrtmBGOueMt6pS+ELgaU3oe3y9N6xW7mtOjdZbai2reUFuDYEQ1NVQiLYqAQ92V4lNQaNeH603heX4IMaIkl6RAeGYEE36a7TjAmEWEKZAKDX+PbnpuG+GPkf7Cctxn5rjclWlxdZX/STefE+fNyadJc6JCP3whz/MrbfeyjOe8Qy+53u+ZzS25J577qEsy1Gs5nmcx7cTpFuntAZXSRaqPllfkBTQTnNafp1KVWOjrw3ND9/QAzslQuuu5p7P3DEmQGHYd/bZj32Wl7zqJVSkZN51aUiFNQahFJmVVMMppOtRdB9ChfvO2ZfnlklLSAddPcAlta9xx8mIqdnJMxnn9kyT5Dl5adBSgM1JonUGcYyz2bPakApcl4qUoyqwshWszqmg2O9H1FkBdm/NMcUAYXOsUJTRSaw17K/tx+Jw/wmPULhUTIIrBEVe8Es/8R4e+Mop8XDyS1/hXe95B4Hn47k+zaxNNlvDy3JyY4mykqq1tMK9BOY4DpYoywldB4GgpTWuHM8UX+8OqKDB9ak4kmO9iJlKwD7t8fSGZI9dRzVmWUi7HM4teZqQJiXarbGgXI4nKVYKQiUJpBgKaDkUGTDsAc3igoYO0FrQjxOqm711npS0NqMQt1zJZWHoRYaqFJTSUhhDWhR4m+7vpqOHM1m3eoktrHYTasLFcSWOhH6SjiphY7xtot2LCa1kxgmouIq1wams+aqUzDoOFalGxpwoybGJoemEeFrQjmJcp4oSgkAOq1BaCG7/2GfHxB7A4gMP89mPfZpXvPr56GgDmfSplSUEDaZ8By0FxlraiaIRTuH2VxBpQlxWCRWPm7d2lIwqZ9vXm9aad/7SO/mbD93Cw0dO4s9O8cyXPI9uXuB6w17TipLnhrdeQl15lAqyojhr3qIswwuGQvf+Wz4/JkAB7rvnfi78/N1cdOP155Q3d/Oci3w4saGufdAQpxm57yAM3PaxT/PI8SWae2a45JUvBjXsAd3oxNSki6cdKM1ovTkSAquZCjyi4tT2+JYQTQYDwrLLyqBLhRwhQyrWjHjbQi9K0QVMOQGhq+ilGVXH3ZW3LDN4etyQdR5nxpMWoR/96Ed5/etfz+tf/3r++q//mi9+8YtcffXVvPe97+XEiRO8//3v/7YXoee34/9+QuqhQ9URMOW6pAE8vGZYiuC6WU0Y1inLFsJpfEOPSwgHISTWGhwlWD45ucL46KMnAIaZ5Zt39iaL6eORGgi1Qjh1ZLCA9CYPh39Cx6c8BAJrDUI6HJpZ4I7FI1zy3Gu57NYvjwnmS59xGS+86XpWBzlpYdDagHDIVEi7f4K8GL63Da1pbH5dmSJjkCtaqsqVjRwlalw+lyPyLmUsUMH8xOOyeRddvxzHqVD0DpOv34UQkoP1fbiyya3FQWa6RzBxn0/+ze1jAhTg8L0P8ld/cQvf9bqbcZRm3vOQ+13KLGGjDOn2jjNXZky7dS6tBmRJOpZ9PaU1U9u+crd6CfdWXabqLVb7A47HGRe4DhW3wpTKQUic6QNMJw/zlUe6JGlGxdMcmJlmPUopowKb5eB7xNZuZrWLYWThpgnJEYJnzTfJrNmRWb53m8t7K1GnRPM0v8DRPiupHcuan92Wlb3dhHRh3adW9cd6Dau+N8YbnDIhTQXDStraYGfW/IFtAzi3zCyBo7iuVScu8rFeQ0dI9rlDwfvJkysTeV9bWmUlNswR4+iMmbLAqTaRUmKsZaVvSUvBJc0Qxws4uaTPmrftPaLb1xtAnJVc9tzrRz2g3SQd6zX0hOSAe+pG9onyVpSWK2bqOI4Y6xF9vLx1koxQSKq+R29pdeJ7mayv89x9M4/JW7cX85G/vIXO6hoXHdrHFS95LhtpMZE3OGVCksDVcw2QQ7PVsfUuf/hLvzH2nXHfp+/g3/2nt7PaS0kLw6VTFQLfGesR1cJSFYpDYZ30tEhgIQSpO03DSzk86GHzFD+oUkOO8bZl+mt4mstaDbpJSpKWJGfgbbWXcXDqG9ia9XcAT1qEfvKTn+RHf/RH+U//6T9RliU33HADf/Inf8J//a//lVe96lU4zrc/Iee34/+eQgUIXcExKVoItCfYSEqUkBxqeAghEE4VqZ+alLBdIfXwnylwpKK1d2Hiw8Kp1liFBqAXJSyrBtWKpOJIrElQXuvcVnOVN/xnMlA+02HAVLOFXlvnn7z9X/H1L97F+ubMxGtefD3LUcJUlJLkJRVVIqwh9xeIsyN003yssgaw0hkQixpTvsOU75EUsGfaR2QSm3UoYYcQtWWCkC4q3INQHlooyugEJu9DdJyZcIFGtcoyeyk6ixx++NjEU1tbWh2ZJxzHp0j6GAu9QUyUag7ECYkOESZnvl5jJRoXoluYZGaphwEDY4jTDGyEV8bo1h6kV6UxNUfjobvoi2kWqhIbd6j7LYQsWR3EVIuCVqVKxxoeTVN0sdMFr1E7TC96WyVtFOnYquDaBAHsaYQ7BA3s7oI/3fSynbdJJqRJZqUtTHJTV/VOs9JWFWpmbnJYxAUH92KFYDm17PFclLWYuAduhZW+JSlhOhRUHUveGTDfrLES5Y+bt0lmpS1Mml4wyfSy5XGZNL3gcfO2rQf0dLPS4+Gt28tGvO3Wy3ro0P7H5K3bi3nvT72XI/cPK6mfAj7/qc/zgz/7I2NCVG2rgE4yIc3XQ/7yzz42cZfnwx/8ONfc+PyxCNXtZqUpx1IUBcIKMlNSGDN6zwDiskS6Mwi5Qi/N0Tu+JyfzFlGwHA/OyJtzvhJ6VnjSjWxRFDE7OzQ0KKV4znOew7/8l/+S1772tX8nBOh5/P2FEALcFtqkOFLiOQbPL9kXesyFp+7fvpHO+OHraRAO2AJHCV708puYXRg3FV1+9RW88OUvHBsn04tS1vo5UmtavsRTEsoU4Z7bSq4QEqFDrMkAaLqaiuvSatTQynLZC67jH77pH/HiV76EflywkRgClZPkxbBnE0tmLQUhoTc+Tqbdi1nvJ/h+BS0gKuDClsaRdjgeym0MhWi8NHZMJusgKwdHYlu4TXT1wPA4ixiZnGA+UORGcyLzac1MrgxfeMHCqXEyxoD2WY0EmALr11nvJURJjrYljutOHCezm5s6toZL6jUC7aKLNgE9dH0opnV1iiv3hOyvGNaMTyY9bJEzXakyUwnoZzntwYBQSLIJQmYLk8YATRIyyvFAioljgM6UBb/bGKAzueAnjW86U6b4pPFNRV5y6NnP5MLLLx3j64prruCmV7xkbAxQKVyyuMtyJx0J0IrMKDpLlEWK4/pnxdtu45t2y4KHyWOAJgnQs+VtC0+Et1YYjHh75o3Xc/nVl4+/l1dfwY2vuOExefvIX94yEqBbuO/u+/jyZ+6YyNtuLnhPa9J2m0lYPrk8JkBhPGt+LSlIkxhrJbkxo5FmMOzfTkyJQFHxQwJHPm7eap53Rt5m6u6uKWrnMRnnpCf0d3/3dzlx4gTXXHMNy8vLXHzxxefiac/jPL7pUG6VYn2JUO1lPcuoBSVzwiNwBNbaoVD9BotQAKSLLVNMUfC7//EXWDlxaityz/49/OB/+BFS7SLj4eDlfpKTZwblKBKnoOYppBCUNkc4tXN+eMKpYbMeOBBoSUM7rEqN8hR5YVnsRAgjyAuDdi1Vp6CfphAMLxbrcURcQiVwMdlwC7Y9yMhzA64i9BwunXaZrypCR2LyPiZdRVUvwMBYRdSaHCEkKjjVLyqEQFUOUsbL4DSxyRJTcoVe5FBqn+te8Xy+euc9PLStCnPFNVdw7ctfSAFkg5STnQGUgrK0uKFDqRRH2ymBzVFq2LcaW4MIXORmRVRZSVEYtKexFZfUGrxNp0MJtFyHUvhUknVUxQcbAQFSu8ztu4B6K2LFO8iR5XWijXUcHKbCoaFsdRDTSXOW+zk1BWHNZyAs228xDJZECtzAI40STrb7mAJMafBqPokW+FikdlF6+LmOLOjAJY3SEW9laXADTR5oCmvGhoYn2855O2/SkciKS2JLAqHIrKG0FikE1ndRFvppQZz2KHKDUAJddYmEobatXlJgSbTE9R3SJOfkRp8yN0il+Je/8GPc/ekv0N+WTqSEYmBLnNAd8taPMHGCnErwahDFPVSygbQW7dfOmjcYZsFbz0UaM7bepJKoqkuMocIpUVlYQ+6qYa57WnB8o0eZGayAoOY9cd62DXs/W94yTp1zOyv4/rf/IHfc8jk2VleZv2CBm77jBrQ+JRlSaybytrE6OTf95ImlibxZwKt5DCS42FFPqcVS36UiO7t/D6krCbAj8+IYb0XG+qBHWSRYDZkxbLa7jkSpRDNIIu781B2sLK3Tmp3huhc/H2PEE+atv23U3Hk8PjxpEfpv/+2/5eqrr+bOO+/k13/917nnnnv48z//c/7oj/6I5z3vebzlLW/hGc94xmM/0bcwzveE/j2G0Ji8jy/aHB6UXFCvMWUcAi3AFiD0k4q5fMKHJR3KZJk//pOPct9d94z9bvHYIrd8/DYueOnzmfNdym5MlrYRKkBXNceynOds9W9ZizwthedcQOoqpR1WkHwtmPZc7t0Ax5QsVEL6621smRNWGqyJHGxGP8k2Bz0L2mlCpyjoG8FC6JG0e2RZNBS3niJwPWbCoQAFwORIXYEyQwXzlJwSokJIZLAX6YyPbhLeFNKfxhYJIthDJVukZlMGnqGbC/7Rz/0IJz59BxuLK8wvzPPslz2fY6XBYJkLffrtdcyjEZQQu4ro8lny1DLrRrjCbmZZFwxMyZ7QJ9rokBcpjt+gDBVH05R9nse8liTW4AlBVSlW44I9jRa6MTOaayi9aZypAzhTUBeS/TV45IFlBrrkeKSYDaokWUGnvYI0Dn6jwYot8HJDoOQoO35QlpzMM6QQzATDc8BCpTXFmrRs5BmOOOUOzqxhKc9JrWFPxR/xFlQaDDzFapYhXZfpbZXKlTxnvSjYu503t46quhzLU+Zx2Ocq+qWhxFJay4ksY8rVqMxQxG1QPmGtwUmTU8ntWHTnoCg5mWV4UtJ0NYNuGxBUW9Msi5KLXvYCLvTcUfxoVgzPYYu39uoyX/7kFxj0PkZ4cC+XPPfpXFxv0vSH27Fnw9sW1ouCpTwfX2+6glsPOF5kTKGpeKfETGfTTV7RikppSHodkA7VVpPFbxJv7aLgRJmNeDN5xLNf+gKchs9ykdMFtt+utsuS42m6g7c9ByebA5vzM2fkTeY5gRQj3hJjuPBF13PwE7fx6N+eGvh/8dOfxuU3P5+TWYaDoKE1RV7wqY99mgePLlLZM81Lb3oeRVSSDFZQtTrZtsH/mTEYaymzgv//T/1HHr331I3m3Z/5HG96xw/htxoTeRsYw4lid94GZcn8tgH95/HYeEIi1BjDxsYG09PTXHjhhfzAD/wAP/ADPzD63f3338+dd97JnXfeyZEjR77tRej5ntC/x5Aa5TYJ0ogGJc+ZPgCFO0xKKnOQ7tjIpG8UhFPHRLdz+PCRib/vLq7SUBrSEorh3FMEmFIQapem6wwruQhQ576nVWx7TiUFcxWHQAe4aZu4nwEuwlHDCwLDyNB+WlIWBQZLN8mpSIWvNWWcQ6k2z6GksJKGH+Buy4K3Nt9sAcgRBCMhatI2CHCmn7XzGIVEhQfI1+9ChQvU6pZZ7wRLvYyqVDhacfl33jSqtBRYqllGUVqifgb4IGJgOKap7ijCMqcX5+zxNBKoKYkSkEX5MINcQykMqrQ0lCbc3JeOSsNe10ELgUkjagfm0bUDmPjkmBDdghdUaNQqLNDDdRo8sFYQ9QukCgkkDLKMqu/iKoHetvftSUlVSmwJUZSB9EBBmmX4vouUYiy/WwtBRUmcUuzgzbMWpRT+aduPVaUoLWO8CWUhL2loPRrVZBnGXbaLnIbSqNxS5AaUj9AOaZ5TdxWhlGN9Y56S1JRCFBBHBUgftCDOMgLPwVECZ9sxKSGpKklRWrrtiN9/7+9y5P6HRr8/dNVlvOM97xj9/9nwtoVQKhrKjq03oSVlltNw9I6cdl9K6kqhC0uWbJ2zIs0yqq7zTeEtkIoG+jF5G52zEBN5e8aN13PHpz7PkW3C8YprruDG77iRVWMeN2+OlDR8l3/+8z/K3X/1WdZOLjG1Z5pn3/QCpNZYKfCUpMgL3vOu94xNRnjwli/w3T/yQ/RSRc1ZI83r4A2/kzJbYi185M8+MiZAAY7cf5i7PncHz3vliyfy5kpxRt5Q28NWz+Px4AmJ0G63y8zMDB/72Md4+ctfPvY7KSVXXnklV155Jd///d9/Tg7yPM7jmwUhFMJr4pGxP1uhZTq41eGdvjH5MN7zmwCpfRAuF+6dXMW8YP9eZkvBWpTjuZr5Zp2V9gaJUVQN1Fw1rORK/dSMmFIeAkYtC3tDTcsJyTJJaUpaVZ9K6LDYjRBxySBNqBQ5cRaTW8kgz5lVCicztOOCwHeYbdRZWl9nLZNok+Juvz5Yg9A+Nu2cOoRgHpP3kG5j175XuWnKsmWG9hpcNpdyx9ois1Iy6zK21acR7NMui90+aWmYaYSsHe8MZytay4LSKC+nEBlSaQSCecdlpT00cNRCh2atPsq+3lMNqChNXJbosmDKWJJBhrWWxtTcjgHbw+M9JURVtYXornGh3mCQt7mzaHDtvnnifp9HejF1VTDrV8cEnCsk+6SzGZFomW/VQcJyL8JLc2ZrIc62LVqJYMHxWOr3iXMzxpuNc/bWAvzTxMmMdlBRPsbbci8iTXPmlE9Na8pN4T7rOPRNyXxpWOmnmz2g02wk8fA9EzBTHb9J8oVkn9CcHERYYGG6QW5KVgcJQZoz16iOqqan83brJz83JkABDv/tA3zxb27jJa96CcDj5m07Wlqj0nJsva1FMVFWMKUkLT1+o1qRir0YlvoRQggOzLTo5zntOCUU47wVecGtH/sMi8eX8BtNnvGC61mYOfe8NZWmSOIz8rYdNaWRuWG5H+/g7d/8zI9w3+fuZOnE0qgtQmuNlxVPgLeMZ71oaEJyXcVSN0IkOfO1EFdIbvnYp3aM5jp874N8/fbP84wrvhctC6L+CYwfIHWF3FgQcOLoCSaht7pOsQtvgVDsRe3Kmy81iPMy9GzwpDpo3//+9/PqV7+a17zmNfz0T/8099xzz2P/0Xmcx7cThAYE0/V9XNaYR+bdU6YXk4Pemc7zDYF0UOE8//QNN/OsZ1059qsrrrmCa194HWvbIh2VkkwHDtUgJEoSfNsdOsZV+JRUcoXysEIPhS5Q9xTCevSMpBlqmrVgZJ7QUnK00yfNC6IkJmfYK9qPM9pRPjKzKCmZCV206+OVbWzeG39N6WHteGSe1CFO84pdwwSE8pD+PDbvArC/NcXeep0eDiu98ezrU9nUZmSKGM1WtbDUiyisRJUxYrOCstoZ0E9OmVm2mydW+jGdXodu1OUCT1MJGqwyRX3+IN7m1vCWED09+xpAelVUOIWQgoN7Z9hTGfayLVQDKoFDnJc7sq/LwrDYjsbMLNtNLyu9aCyzfGRmycqRmWW76WW5F5MW4+95uxfv4G3LrLQWDc1KmTH4UjLjOKjMcGwjHjMhTVfDXbPmi7zkZDsaM7OMzEqbxp0x3uwp3pJue+Ln4OTxk2P//1i8bc8sh03T32nrbcus1ImzHVnzaTaMsdxuQtoyK23nbavK97u/9rt8+P/9EP/nd/6QD/zqb+Jqcc5560XpY/K2HVGSs9we523LrBSXlqtuvI43vuWNvORVL0Frffa8TVhv27Pml3rDrPnlk8sTOU02VklLgRPM0rcCE53AFAPSskQgmNo7eZTbBRfs35W3rDgzbxtRNvE5z2N3PCkR+sd//Md0u12KouB//s//yTXXXMMP/uAPkqbpY//xtxHe+973Mjc3xzXXXPPNPpTz+EZDKAQWVyqm6vvH3NfWlk9JP+XjOizhgFD4zUP8r9/5D7z0Lf+Ml//Dl/OWH30L//rf/wgbUbHD3SmFYLrZIvRcgqKDiRcRm3GW5/z4pAMYbDFs1A8dSc1z8X2XZmXcxbtQC0jKgsV+Qj/qkxkYJDm91OxwU0sBzXqT2WowjODMuluviDjthsCW6VBkbqseTsKWeQmg5kn21esEleqYi3d0QczLHa5cGLYcWAvLUUGBBiF3dVNvCRpHCg73YmYbe5jfdwW9cB9db5arDo6PGtpViAqFv+8KwkPXM3fx1eyZbtLp9XCkoh7sdPGeyU09yX19Jjf1JPc17O6CP901vxGnNKQiSVJML6VQYswFvxrHOK7aIUTP5Kae5Jo3xrLei0e87T+wd+JnwGs2yTczvx+Lt9Nd87u5qXdzzZ/JBX+6a/7TH/3Mjirf175yP5/92GfPOW+9JH9M3raE6JmmF0xyzT8R3nZbb9uF6GI3Ynp+8miu+b2z5LlF4BLp6WF/f3SCQdZDC8l1L7uJg5ddMvY3V1xzBS96xQ278rbeS87IW1qUZKeJ+/M4M56UMekDH/gAb3jDG4DhtttHPvIR3vrWt5IkCf/jf/yPc3KA3wo43xP69xfDcUgKa0uEUGOmF6yBb4IpCQDpDKtwQlGfOsj1r3gZ5WAJ1/X4yIc+SX9tg0OH9jH3HTdSCEm/KAgAVyma1RqhDyY9hnSeGhEKIN0WRfteRPPpeFpzsOrS6fmUZUwqNZmx1LUi9Dwa5QBJyYn1DpVQ0O1n1Dw1lk0dFQUuAildZpvzCL2CiRc3uZFIpzbWAmDyDmrbWKbdINwmwqlgi4jQCTnU8DgZ15gKXdaznK+vdzEleKVlZjObGob52mazciOEYL4estSNeDQqsVEPnZc0wlOZ4rEpiY2lqhRpofA8l0aRQWFZ7CY82lfceNBnJtw5Z3DS1jxsGxOkHC44eJAv3f8w1bQPyNHcw40ooZ0No1Bdy0iAWizdTbNlXamRoFnuRRze6GFKgVMapqqnsuAHphzxtiVoFrsRj7YHIAQiM1T98UzxqDRUN002W7McT/ZTZqwkSQpargOBwmpBgaWbF2RFgi0lc9UmMKCb5GykPWxR4iFGQsZg6RYlSgy3h7fmkq704xFvLZyRkLnx5hv5zMdv5cFtUw8uu/pynvmi53F4o49F7cpbXauxzPLjnQEoCamh4uqxLPh+aagoibcta34tSlhJM2w6rAJvz4LvFiWuFFSkGuPtgcOT59U+evwknbI4p7zNBCGNRnBm3noxIkqxiSFw1FgWfLcoR8airTmiT4a3SeuttFDXaiREl7oR+55zDRdccSmP3LctYvSaK7j+pc9lLYkpygA0FP4CTnqSTuck67ZKp/D4gXf8II/c8xVWFpeZ3jvLNS99AaWSaMFE3lraPyNvtZ6iOG04/nmcGU9IhPqbW0WvfOUrRz8TQvDKV76SD33oQzz72c/mZ3/2Zzl48OC5OcrzOI9vFoQCIcGUsLnFuiVEy2jxGz4jdHRY0hm2CtgCV2vmmi3uWT/Gh3/pN1n+2mEAPgl8/pOf403/4d+yXORMS8mslYRaElbnkIPemIHoXEP6s9gixkTHkOF+Lmy4fHXJIyp6LNuc2Br249ISCiss+6olS6sZTtFjLU65rOWPKjIrec5qmjAlBa6jqTgaGe7DRMcxg2OoYH44ako6m1MLHLAG5c+e+SBhM+d+L2X/YZQOuazlc/uyRgUVfBKOtAdYC1dMNUcXxNQaTmY5EkZdbJ7WTNcCvrS4TlrmHKw2RkLGYlnKczplybT00FaQFvCcmRYb0ufR1TUumpvm4tburRGnC1GbF8Ap/maqPtXWHPnqwwg1vEg2Q59OXnB0o4NG8Iy56VEFNDbDcwBwPEEoh4KmUfG5Z3mN3FgubTZGQqbAcjLbxpvWw+SoWsgdS+v084y9fpWLt1XSVvKctTxnj+uyx3GHmeW1kIcHGyTdFUK3ziWzU9yfJaTGEBvDkajPHteFIt88rwobaY9Heh1c6fDs+VOZ4oOy5ESeoRG4/lD0VX2PQVly72oba2F+apZaODznUkve8HM/wpc/8TnKlTUO7NvLDTffQGrtY/K2D5cZ7YyE6BcXN2gP+sy4FS7eVgFdLwoWs4xpx+GA640qootJm5OdLlXlcv381KiS1i1KjuUZgZBc6Es0YsQbjerEz4IzO83JLD+nvE1VA4wod+Vtrl7hxFKblX6HphNyXas+qoC2i4LjWUZDKS7wJALxpHmbtN4KLGpzCsDWejuyGHPjD72Jq758H6LbYX5hnhfe/EIejfucTPqspVWaSpIicIN9bKys8VDvBJ1ohktaLhd/500ALOYZx7OMJBe78nZpq4FwxK681UKX89aks8MTFqELCwt88Ytf5Kabbhr73TXXXMOhQ4f40pe+dF6Ense3P4TaFHvj47mkN40t4qdUxJ0R0hnOJzU5rvJRyuXrn7tnJEC3cN/d93HXxz/HRS+4lrtu+zLttU+yZ98+fuCyf4zwpp4SZ/wWhPJHW90mOkbd3Yvv+JSZIdCCMjV84ROforO4StDyWPgnz2a9cCEucYoEwylB5ktJxRq0U8MVCl/LYfUz3IctBlgph3Gh0hsOybcGodzHPQNVBXOY/mGsyZkKNBc1Au6LQxh0cKUGCbkpKa1FCYESAl8K8tOeJxpkeEKDAkFJXhY4myYlX0oKC9YIrMxZ0JLUVujRYk91g2c2V1HWZXuFc8d7ul2IDrqYVI/aDRwlODhd5d5VH1WmFEpCaSlSgy81UgvSIsfY4QVWb54DMHJiWwtxlONKjZYWY8wobUYCgRRgJO62Ad5RnONaha80SkNWFqOEHl9KKlKNRg0BDNKcwEhc10M4kkGe0dSaE1mGKwTK5ExV54gHbUw+/BzYosSVDp6jiPKMiueMjtsXw8rVVgKPMZYkKke8FZvzSLd4q7gOz/uOF7HXdUbjgDbag8fkbSvjHCBOclwjCKTG05AUxShZyRXDc97uPs/zApmDLzWeoxgUBc1NQeZKQSAkgRSj/riyMBSp4ekvvJ4jd9zNifu/Pnquy66+nOtf/gKUFOeUt9wUI2PQJN7StMApIVAOnhbERT5KsHKFGJ3zlggr8vJJ8TZpvRV2fGrAaL15Podueh6HWlWcTdNYqDV+XiKsAmtJTUmgXApvikac0c765PLU1vnj4S0uS0J2560owT0/q/6s8IS347/v+76Pt771rfzZn/0ZV1999ejnGxsbnDhxYmyo7Xmcx7crhBAI6Q63fLf/wmRIf/qbM6h+87ikrmDyHo4KELjkG+2Jj+2eWOZPf+HXeeBvT21XPXrrp/nLD7wH7ykWoegA6dQw0Qn84iSOkCRWMCsk7//59/HAtn63u257kDf9/L9nX3MKjx7rUYrjOHhaM6M1NUeTegGOViNnvBAS4c0gnc2EI1WBvI0lQXqzw3aKxwHp1JCVCyl7X0eFe7lyKuC2oy61Aq6aHmZZb0SnoiK1EOzzPI4KwdbtyZaZ5aKqT1hxWevFpyI+lWZOa1xhaKdwRdXhsmKNzySzXLzP5Vl7FgiLE5joBIQLw5mnu72vWz2i/eM7XPNTFQ8VNvF7x0hyTa8TI43l6rkWuSnpxtkoctAVkgPeUEQoxKiXMM1KLqtXcTzNWj8ezyx3XQynpgZs9YAeCD0atQar/fGoyBmtaSk1GooeJTkPLXZpOIKn7d3HYr/HYr+PHwQYhnMoF1yfPfUZjpeGQXeNjSTDQ/Ds+SmiPGOQFohtmeUH/eGIdoUY9RKa0nDV1JC3NDE7eLPbzuGxeJt3HGacU4/f6gHd4ztc0aizMYh3ZM3XlRyJq60e0LqWXDwzQzdJxiI+K1JxoT8UixIx6t2VxvLMhVmu+IUf51Mf+Sy91TUuuGA/L3rFDYjNaMhzydv6IKUu9a68LbUjplzNpY0a7Sgei2atK705y3WIrR7QJ8PbpPV2NrztcQKcIsdFIIQlKUtyZQiUy8XVfXz0E/8vXzhxjAsPHeSlr3rx4+Ktn+ZkFLvyttZN2Nv8JplVv03xhJXiz/zMz3D//ffz7Gc/mxe/+MVcd911APzpn/4pjuNwww03PMYzfPvg/LD6v+dQLuTjDl27OZ5JiG/i1otThXQNx4GGJ1g4ONl00Wl3xgQowKdu/RJ/8Ce38LYffdlTdnhCh8NqpHCQ4QLB4ATa5PRL+MpHPzMmQAEevOce7vzkxzjw2tdQ9XwKzJigcYCecKhJNTYjFFsivaEIFW6Nsv8wwgmR/mTDwm7Q1c25nHmfC+oBe2suaeLTCDRIhRI7M8u3jqI0ZoeZRdcFS91odGFMgFBKpjyfK5yY6XyD77gkZGrax9cCa/ZjomOPX4h60whHjgnRmisIwwq04UR/gG8YMyFJxFhmudpWSZtkZlFC7Mgs3yr0TDIhTcos36pcRUnOybUBVjvMVVxcBfuaDY63O3SimEQKCm3QfpWaG+B6FQ73FwmUO+olrHgO4rTM8i1RspuZJaXkZNTfkVkOk01Ip/PmKD26UE4yITnbMsu3hOiWeJtkQvKc6o6s+a1zmGQeq/kuL//Om4ZjgFyNVHrEwbnibSZw+Zu//DSLS0tceMECN73yxaNC0iQTkutUWe4OxoSo3lYBPd2E9ER4m7Tezoo3rXFJGCQljaqim+fUtEORF/zMv/j33H3H3QB8BvjCJz/Pu97zztE578Zbv5+xHse78laUFlef344/GzzhwnG1WuX//t//ywc+8AEajQa/8zu/w6/92q8xNTXFhz/8YVqtydnL3454+9vfzvLyMnffffc3+1DO45sAIb3RqKEtWJPCN2lG6BakDjdNOYKZUPHMl9/E5VeNZ2cfuvJSKo3Jx/nw0cmjTc4VhNQIXcWWCVJX0JUF5kPLysCweHxx4t+sLS6CBS0FexrbXLxpBlIQ5Q6uFCi5/YvejtoipFOhTFegPPs4UqE8dP1SbNFFC8mVM1Ws52PK4ab7JBfv3iv2Ur14GubrO9zUp7t4o6Jk1tHINEMn60gnYE/dGwYfbL5fMtyPkM5onMwkmLxH0X90eL6nueaVFMw0KyzHLmlW7HDBT8osP5ObepL7GnZ3we/mvo6SnKPrA9aLkgOzVVqNabKkj5KSvdUqIi1YWe+zGGW4fg2NpJ+HlCpgru6Muaknua/P5KYOXXcHb7C7C/503rZc87u54Hdzze/mgt/NNX+m6QVPJW9FXvArP/3L/O///r/49Ac/yu//19/jl37iPRRFsasLfjfX/Jlc8GfL26T19nh5O7be5W/+8uP8xR/+OX/9px+GwtAvc3Jr+Js//5uRAN3C175yP5/4q089Jm+NIDgjb9NVB/U4d1/OY4izere63S71+rib9vWvfz2vf/3rz+lBncd5fEtBDk0uY7DmjJWqbwS2m6JqrkY6Hv/uP/wwn/vMXZw8sUh9bpYrn/csvnTLbRP//pKLL53483MJ6U1TpA8BdaSucGBuD9mDa3jNya78ffv3kBXDYdJWSsKKR6+fsNjtUfcDVnJ47t6dX1tb0alCeggVgg4QT2B8lgzmEL0alDH7qj53OCG22ABnc26nqwmMSxxnLHcHSCOIrMCt+3hVD7st+xrAKklQcYn6KSu9iLlSMOgXuC0H6Qegxo1IW0L0TBVRWwyQbgPbTxAiGJqqzDHKeBWA2UoT6TcJdZdi03G+fei+8jSuKYnSgpVeH1tAnJV4gUaFO41RQksqoU9/MNzidaWkFxUoRxJUPaw47Zy38bbUjah7LoudBKMEl/o+N124l76tsnq0T3OwwVqquHjvHsqoxwODgmygWHQMvnLwmz7WjOdxWyxe4JFsZpZbG5FnhjQvCSsu0p/w+ZjEW1rg+PoxeVvsRlS0Q2eQIZUkrPsYeVoFR4Af+hSbW/PNwKXTyzBAo+5jTq+OCYvre2SbWfOlMcRxSV6UVBsBYkJj4VPF22c+tnMM1AP33M9H//IWrnje9QglqNR97GlDG+zmOeeD4dZ8URi6UY6xlkrdwzrj53Am3lxXcustn6WzuDo23P6J8Oa4kt/+qf8fR0Zu+Y/w1Vs/w8/99//AvBeweGzyDfCRR47TjuInxVucxxOf+zx2x1lVQq+77jpe/OIX82u/9mscOXLkKTqk8ziPbzFIF5O1T/uheGqShs4G216/7klKIdFa8ZJXvYSX/LPv5uDLnoeuBVxzw/VceNmhsT990fOv5k3/7J8+5YconCrDgMYhWmGVajXk6dddxYWXXTT22Auveho3v/Zm0qJEAYt5zomiwA09TJ7TyYZGhZlgXGQIGPXmCuWhgj2o6oVPqFVCCLnp6h8w7TugHMrN40+s4Xia0RYQBC5JPGAQdfA8ReIrjmUZvW0tOwbLySxjyRi8ikdZFGy0V/AdCGotpNYIPUE8nFYRLbM2drMKZE2OEAoZ7BnNYAUzvEkSYJI1KnaDC6drVLWmXZas5QXp5k3UwJScyHL6ShJ4mmjQI457BIGm70pOZDkDc+ocCms4nuWsUhJWfNI0ptvt4GpJETocLTLaxXib0hhvZcnyxiqJtTx7usGFocvBqQb7WyFldZ7MbRLMXsizrryEV197Oc++wOWQ32e5F7G/qhgozYmipMgTusUwz321KDiapiSOwnc0/V6bNBkQVlzaGo6nGcm2m8b0SfJW5DntzjpKCWzV4WiesnHaLMiVouB4kSECF6xlY30NYw1+zeO4yVnMxy1s7aLkWJGRug6ulHQ7G+R5TFj3WRUlx7N8bNTPU8nbbsPejx15BLDoisuxMmPltHPeKAqO5SnGH7YfbHTWKcsc7Ss+8tFP8fu//Ud84q9uodj8u15ZcizLdvDmupLf/IVf4w/f93v85R//Jf/jV/8H//ld/5l+nj0h3j7+8Vu3CdAh7rn9bv7kTz7MRpaysH9y29LU/Mxj8tYvz8zbYl5sZoGdx+PFWVVCP/jBD/Knf/qn/MEf/AE/9mM/xrXXXsvrXvc6Xve613HVVVc9Vcd4HufxTYVULiZdw2RzSLeOtQYB33QRKqQLVmBNQUUrhFAUFhRD96kjJLK0KCt50zv/FXd96QFWljZ43fVX8rY3PB/Hnzz+5ZweowoQQoxmdzY9RehXyHB50zv+BV/5wj2srG1Qm23wipufi+d4oDNKx5BungOFpSyh1A5u2aeuTvV6WpOD0KeqwspDug2U98TbgaTXouwfYcpzaIUh/bZgqiyRm25kA+RpCUYhFFhhkXZomJDbhK9gkwcERVZiC4nUmopX4ths080/+St4S4iWg0cpuw9i/Vl09UJs1kX6c+jKPoR4eDh5INtAhXPDwf22xMvXmfdD6n6FerXCI1nORlHg6WFfoBICYSFPLVgFGkosEgHi9AqfQAuwVlBmW+dssGp4qXUQqNOuuc523nLoI9kXSMq0YP9MgB+EaGOpNabopk2unncIHYkRLnPNeS5qxSxtdHiwZwj9kEAplrvrJFaxv66w1lAC0kCemeE5OIoCgxJq0yiyjc9NHp4IbyYzUEqEkqAsEosj5Fi8JIyvt6IAhEQogd18vHPaDZESm++dhTKzw6PUitKWaIau8e3xj08lb3N75yZ+BqfmZxAahLA4yDFXOgwNRY6QqBLK3ACCgpLf+7lf5eGvDnPjPwN8/hOf4x3vfsdw/SB28Hb7J2/lkW058zCc6vH5j32Wi176grPmrbe4OvF8lo8uEpclr/ruV/CxD97CvXeeSni86KrLuPb5zwWRnpE3+Vi8YRBq9zFr57ETZ1UJvfzyy/mpn/opvvjFL3LkyBHe9KY38fGPf5xrr72WSy+9lHe84x187nOfG921n8d5/J2AUAi1LaHHZEOzkvwmV0KlC+TYIiLQpwQSwLTW7JWKvJ+hhWXf7AzPeeXLuOkffzdveuPLcbzgGyKihQ6H71M57J8KlGKm4iBdj4W5WZ550wt41fe8hu965YtoaANC4pHhKMmc4zCPJO2luFrTaNTZH7h4+SImaw8FqCk2x1UNv/iFkOj6pQi3+YSPWeoatkzRomBfpUIkhn2hrpAc9FzCpCRPCmqVgGrpkK4OUEtd9rsu1W153ALBXtdluhRE/Zyq57Aws4eqa7HJcZhQBR1776RGuC1U9RBYS5ksY22GDPcMTV/eFCZZQSA23fF21CO6N+izFJdIW9J0NFs1rEAqDjguflyQ5yWtWo1WrU5elPx/7P15lKTbWd6J/vb0jTHnnFV15lHn6GiWkIRAEpaNQajBFqbbYKDBvjJDG9leAoOx13L3vRfjY+zr7utrbLfd6/ray/zRvZYNtGkjwAiNgBGaZ+kMNWZVDjFHfMPe+/7xRUZlVGaeU2eqSol81jprnaqKjNw73oj4nu/d7/M8SVFy1gRz2yKoLvRnw5Bm7sgmJXEUsdbpUDqPnuacNYbmDTnq+3Ub9zMmSvLo6ipLWtMddVlpz0QkUrBZV6ylio3azFJHaZaDmOXWOR5ZDan5PnoKnbTDKFziXl1yXxizYQJWlcGMC8rSsdxuUU9iirwkLSx3hMGCtZA5om4rrRaFdYRZ8Yx1y8YFYWBY7ywDAj/JOaM1bb14Nn3w86akZGN5lcAosnHGmqjeywfRVJpzyiBGOaX3rC4tkcYReVbSdJ6zYbgwQvFS1u0t73gLDz320ML67nnZA3z7u74dpSTlOGdDKZZucLxpa8VZZbDDah50bWmZz37k43MCuo/Pf/LzfOj9H6ImFWdNcKhu1y4d3YnduXztedXt7OrR3sD333UGhCAOQ37mH/9P/Hd/9fv5zu97J3/xJ3+IH/ybP04QmGetW00+c93WtETeJseUr1c8b2HSHXfcwU/91E/xu7/7u1y5coW/9bf+Fp/73Od429vexpkzZ/ixH/sx+v3+sz/RKU5xwiGkRkYrCF0RUTvdQegUIdWz//BLuS4hEKZBOXySUDqU0rjZEV6RW7rdCVII1pohcRzRadapBxo1Pg/olyQz/vAaJTJoVUKu2ZrPNgxJFJCEAav1BOs8vUmJLSYUO+cppyOECsimJd3eFKMEUd2wUW9w7/I6oZbY/ldx06u4fBeh44Wjdxm2js2Kv6k16wgZtimHT3KuZihkjJ+Jk7r9CdmBTPHxlQH+6ojy6ojucLKQfQ0wGecMhhlKK861UyITsVJv4W0JrjuPCz0WLkM37idYeQN28BRCBMig6vKqcBmBQNXvRYad+esr4w1atTqZLbnWHxCLygrH4vEe9voT8gOZ4vuil7yw9MeThax5gOFgymhSzsUsC3UbThYyywGyaclud8JUCV6+VOfeWo0kbPLK9oSmGeBnNyR3NDWPrgZzkZkUgkcaLRIdEDfO8vozEdPxlC/vDHlgtc1qEpMXORGw3ZtURGYmZtkXvWR5SXd0eC7vxrrti14K656xbvsipDg0rDUSBILucEJ5g1PKwc/bZjslCvVcuNMbTcnyxaNsWzp2exOc9ay1EtIomIuVxtOC4Xgx+vqlrJs2mp/5+z/DD//Ej/CWd76DP/eXv5+f+/s/Qy2NWG8kSCnoDacUN4xd2MKx2x0jgPV2ShIFTLu9Q689wNalLZzz7B1Rt40zR3di1zbXnlfdHnrda7jn4cV594df8RB/9jtfxbiYYqTAesG3vOOb+Z6/9Od52RtehzH65upmn7luk8xVvtKnuGk8r2/qoij48Ic/PM+IX15e5kd/9Ef59V//da5du8Y//sf/mL29Pbrd7ou51tuG0+z4P+GYpSbJ5ExFRMcX4TaLkvYhwmVwOWp6EQMUCKZZsajuVIAylF6y3upgJAhz6zLvxQESCrCUxnhZpVDtq3gLJNemmtJrdJgyKWGrO0Z4x+c++vv8xr/7dT7+n34XIwRR7QzoCNN+DGGaz1kFfzOQyR34bJsl0UMrTY4/VpULIKU4pOI9qKbutGNqWlM6qIUBwnSQOsCOzh9LRPedD2TYRsZrmOXXVO/B/SN8U0M1H0SlZ0CGi2MPzQ3u7sQ83Z8ymkyIpSQr3bFq6qPU13C8Cv449fX2oOQr1zJ2reRsvUkiE3amgvuiIQ/feR8qTLCjC3g7RUtBeIPwY/9mQgjJ6tIZXrVhCOnzumVJo15jOs3Z6Y7IC3dITX2U+hqgO5ocWbfj1NfHqeCPU80fp6Y+TjV/nAr+ONX8M6ngX6y65aXn0Td9E9/x330P7/pzf5owqrp5x7kdHKeCXzvmaH9lffVYFfyffeef4t5HHlh4/MOveJiHX//q51W3NA7524//LD/613+Ud37fO/lLP/b9/Pgv/D1CpRmOr4AtyXKHtc+9bjuD6TPWbZB7yhsVXKd4Rgj/PM7Ou90u7Xab3/iN31iI7vxGx352/Pnz5zl79uztXs6Jws7ODktLS7d7GS8JXDGiuPYRZLSG945i95NEm3+quvg/B7wUr5EdXSTf/Ti2KPiXn5uwM9wjzD1KqfkXazEZUDZW2PMR99Ua/Nn1Mbr5IKZx97P/ghcBbrpDvvNHqHgdgC/2uvzvf/R5lgNFMw4RCMZ5wdXBGCkFTZOwOxgi8Py7f/j/4Qufvq7cfdXrXsMH/8P/gmJCuP6tVKIn/6J3dV3eY3rx/fQzz//2hYLx4CoBZuGCmHnH5T96GptbdKhZemSd7dEUqSRGKqajnHCWKb7nLHeHIb4MeGzJ0yl3CNfvxfs+CIlKzx3ag8u7CJ0SLL8GAD/rcu93eQ++n7wryLc+hDCN+fNcuHSZ3/rUlwkiTaY0TwxzEucXiMxkJmbZP8rtjisjda0lElkd5R4gMrl3lN4Tz5JxDtYt0IZer+D+5SnNRpNX1uvkVlLmGRvhlPjcy6s40dH5KlI1PbuQOOZdiZ1cQoZLc0eAp0cDvrr9FG9sGJ7qhvzeZ84jjGQ3lNwx60xOnEUKQTh7XbaHlR+lCTTegikkKnALdXPez/c8nGbH1k1KQekdmfcksz1nZclWf4zDE4cB42GOOkBkLJ6pc4SyinIsXUX4stKSxCGTcYGfddKSyODxTJxDC0EgJM5XexjnJWGoKTNHWbiFug2yjI/+1ofYm6nJX/7m1zIo7POqW2gM40FGI4hpNc08Cz5zVca9QlDYkiv9MdZ5kihkNMyQMCegHs/YOVRp+aWfe3xBbX/vow/wg+/7HyoruQME9GDdyrLk//q13+bSxS1Wzqzx6re8EVf6Q5+351O3YDpiEG3w6LmYIruMdwF/cEHhdy9gguQ51S3GUK/rI+tmneOLW7usL9/LX/uO73pe3zt/EvGC+sa/8Au/wI//+I8jpeSVr3wlf+Ev/AXe/e53I+XzPwo7xSlOGoRQgJx1plSVU3674jpvhAqQMkLV16gFX+H3uhPuDg2PLTfmd/Zb04zf++3/i70re7zxofv50//9txLcyk6oToBKQCWkph4EjJSjN3HcF2iaSpMEhpVazKd2euyMS7SUXP2jP14goAB//Id/xL/5lf/Aj/7g976k4xBC11DxGgl7NNSET4wyXrUUzy+IhXecz/LqsbOfqUUh1sNndntkRclmknLnfifNerSUlAKMqO77RVhHqgZ2dIFy+CS6dtcCEfV2gm5cP1Z8phEDIc08snT/OZbrIWfqMbulIBvscnkCr+g0FgjoxVmX50xQEdFWElF4zxf2+rjSckdanxMZi+dSnjN1no3AHKpbfzrkTByRNlMeayYsGQneU9o+wdJ9yJnNlUrPYUfnKQdPoOK1+SiBz3uoaA2X7eBVghCC1SihsXEfprhGrLpMy5IHVto4UUVx7jsWKCG4MwwwQrJcS8n8iK92+3gneLTVYakZL9TNes+ZsJopfKa6+dmeR86xZgI6Ws87op/a7tHvD2mbhMdWr3/edsuSa0VBW2s2TDDvrH1xp8/Xru2SaMNjy615J61vLZfzgkgKzoUhSlS56093B3xpt4dA8XCrvkBAf/Fn/8GCmOfh3/kIP/Lz/wNfHoyfc93GvQGrYcrd9Qh09d68VlRithVjWNFm3hH9zLUBXx3s0NARj6025x3QvdKyVeSkUvLTf/+n+fD7P8yVS1uIRoPlVz/K5TLn0U5zTkCHzi7WTWve+T1/houDqm5Pjsbc26wf+rw9n7qtALLMyWyL1cYZPvzkBT63k7Hu4JUHOqA3U7fXLNUIInlk3aRwtBONPxXHPye8IBL6qU99ij//5/88tVqNz3zmM3z/938///Jf/kt+5Vd+5Ru2K3aKP4GQGoQE7yotqpAL9ki3E0JWXnlSRSw31/BcBuXxsrqYlEXJv/wf/ylf++xXAPgY8Efv/w1+8zd/i1t1aCR0jAyauLyHipYwUlJPAq5NposP9ICrPEKlhqtXjhYsfO2J8y/JEfzCmqVCBk1CPEu1gsqS6yZ+0FX/CQFSMb8geQGqyMnHI1SSISKDUBpXdCuRWzmm2P0kpvMKhDR4O62SpoKbV/kLU8Nne/NRkSAIOZs6nt5SRB4SDeXN9Aesr2xxZTXe5oFnuq5a5xkVnrGTeD1gxZZ0conNPHiHTtvoxnVHAyENKj1HsfsJ7MRVwj9Tx/sCU7+bUgh83kWEbSKliJTCmzOEY0cY7qApCaSmeIZDPG/BO4GQnpsaDz6mbs/4+Nm4oFDMP2/HQbhKQwfgBM/8gs7+2ZezNWk4aEHw0d/60JFq8j/87Y/SfO0rbrpu888b1efNy2d+vHACvz8Wqjl2mE9rzVv/7FtxzvOl7T6XxiMOWRYct6SDdXsBn7eFdQuFcVO6E89KGnAtb6P8eaSWz6luSHHsC+Q9eFtWDYsbPaVP8Yx4QST0V3/1V3nLW94y//OXv/xlvu/7vo8f+IEf4Dd+4zde8OJOcYoTAaGqb3VXgvAgzW23Z9qHkAFCKLy3tNOQe5p12vZ6dN2H3v/BOQHdx+999FP8m3/3v/NX3vNjt2ydqnYX5aXfQoYdjJScjWO0Kmio6koznhZc7U7YCCPaScLuZEjUah75XPfcsYa8FWlVKga3zbnlFdaeukgxzdmezX8ZITkXBlyGeXb8YJyxO5hyNoqJEsV4ZrC9VE8qS588I2gsU99IZ2IcjxCSYOWNoELyrY9QdD+Hbj6Ez3bRrYcR+uY77sLU8dPrxF3ogGU1RTvFUnOZu/IJ3UlOLCWtJCKWijMzIe/8OH4wYTguuDtN0KEkm81X7kd8bgbB/FgXqrpd3Jtwbxyx1E44FzzNA0sNVNBGSINO2oi4NjtNOLBWaVDJBjI5Q7H9h6jkLCpeRYRLaAT5zh/NO+dQdYHj+hmS+i5X+ru4WgOrFQ2luSMMkPu2UFSJOsW05N5mHaGgLO18D/t1O3ise1zd9qMiN4NgfhwP12dAl43mzvYS42m2EPHZ0ZpESsLZ4/dnQGtA2G4yzkuuDScIIUgCQ0MpTFi5WxzMgsc67m83cThG0xwjBK0kYu8YC6LLF67wym99403X7dL2gIsf/iMGe7vsLbVZe/u30mrHaClZMdW6otnj92dA21pyptVhkuUL0axtrQhlSCgE4kAWfGg9D3daFM6yN56iRHViUJPqWetW5OWLUrfYOQpb0B2X9LMAQ8IrWwHTqbvpuq0ud8iKgu4koyb1vG7WQCwFvVziC9jpZ5xpH/29dYqj8bxIaJqm8yP4g7j//vv5j//xP3LPPffw+c9/nocffvjFWOMpTnFbIYSoulM4KKfI2QX2REAFlT2RzWkYRTOKWJeWa1PPlf742HSQrz7x1C1dpgzbSFPDDp/AROcIjSaS1QXrxmjAQBiM8bz8La/nEx/6Q772uevG029+02v5ge99xy0Zh5A6xmJZSlPaSUxNFQvZ16GQcyGNc/6QmGWoqkjDy/0RrTQGL0jjmLC1Vv1MtocMOsioOjUKll/D9OJ/ptj9JMHy61Dpuee0XqHihS6MUIYk1GzUFVG9zr2l4VN7vYXM8oO2PkeJWbpyupA1HwhJMOsG7dfNKcHLlpuoEh5b2qRRbyGVmRnuH32J8d4hhEHX7sbnA8rBlwmWX1u9nmEHFW/gptuI6PqJWqgVq511epd22Rll5KGikeqFPRwlHhsMc3ayfKFu+x2to0RI+3Wb566LmW8niyKkc7Oj3CzUh7Lm09maDoqQWq0YaRQ29vSHk4Ws+WT2+KNESPszovt1O87Xc21zjbOd+k3V7eJ2n//f4/+UJw6Yun/mIx/nR//2T7LRaaClRM/WdFCEtDmbAS0iw5X+eIGI7u95IYqzGVNPQqz3h7Lmn61u+7O9L7huQmKnE64OLUs1SzOAtlPkYY2ro/xZ67bWTkkiQ+lCBoNioW6F99gSAuBat0eulombp6fAzwXPa3jTGMOdd97Jf/7P//nQv507d467776bz372sy94cac4xYmBDMBZvMuQ8drtXs0cQkiEjvG+JFAKpCJQYq7iPS4e87777ru165QG1XwZbrqFyrYItKL0/ths6iQwbLYb/ODP/iTf/iPfy/f+wLv5qZ//Wf7Dr/y/CYy5NTO50oD3NAKNUppmFBypvgZw3h9SU++reMeFZTiakDmoHcgD926KjK8TChktYZZeh67fiWk++Jxtpg525713uGKITtp0kpDCQWw0rfRw9jUcr6Y+Tn19sG4rzYjIaJQvaLY3UOmZykR/fAHvFi1u5rDTKlrV1DFLr8AsvR5x0GYqWsb7RdeAQEEYJSzX6rSMYndUzDPL4fhM8VYcH1m341Twx6mvj1PBH6eaP0hkVloxhZacC0M6gaFZiw+pr49Twd+omn/5m193yNfz3kce4Dv+m2+76bp9+sN/sEBAAb762S/y+//lowuq+eNU8Mep5o/Lgj8ua/6Z6nac28FzrlthudbLKcuSQeZJtMXbkiiMnrVuB1XwWkpaaTSvWz/LiYRgbwottwuuoL2ygTGnFk3PBc/71frxH/9x3vOe9yCl5Hu+53vmHYGvfvWrPPXUU4cy5k9xiq9nCBXi897Mm/MWHAU/BwgZ4MsJoQpQUuOdx4SKZi3isTe/hk985L/yxOeuH8l/61vewA/90A/d8nWqeBUZrUHRJ7ITssKylY2RStBpxnMCuo8w0HSaKY988+t4cO0cd66eoxYCLoRbQEL3u92NUBIaybQoaKUxhZ/MOzTugDljuxnPL4j7iKKAqCigsFzoj3k4rPZYdQLlofeSbtyDrp17Tsfwc8gAXI73Hp/tgi8RYUAqR2xbSLVASUEcR5R+Mu+sYT17o5wgULQb8cGgHgBqSUjuHeNpwfZwRGoMV7sTpBLUGxEogfSSWJZEab1StiebuPEl3PjCkR1RbzNkuDT/PAWdRxZfexVzI4QQpFHIwMScNQVfKcp5Z22alQwmJWGkaTUO/+yNdQulZrs/RWt5bN3q3jMYZ1ztj2iGAdd6Ezyw1EpQN+SiG61opBF7gwlX+mNWkoTt/oSitLRaMSMhSVF0tEEIwU5Z0qzF7A7GVWetFtMfF0yykjTW1OqL9ZcCGklM6ccMC8tf/bs/xe//9ke4cP4KK5ur/Onvevsh8vNMdZv0uodeI4DR7t6cxHXimKu9Mc572q0YEyyOVGilaM32sNUfs5LG7A0zssJSr4XEyaJpuxJi/vhbXTfnLUtxQW8as2zKai53VrdmLaJ7TN3CaPE11ULO63apP6YeJ5BNWdJXudbZYBLXkC/Ao/hPIp43CX3ve9/L1tYW7373uzlz5gyvfe1rAfgv/+W/sLa2tjAreopTfN1DGFw5QEVriBPiETqHDMFbAikRSlGWMChLdqylVgv5oZ/7KX7/w58g2x3yrldu8J6/+lMYc+vHCYSpo5JNmGwRqx7DyYBG2MCnAedtwbplIX3nalHQdZZaoslKj8x3oGwggvoLMqO/acxIaKgUjUjT60+4luU4o4iBwXiCMwKhDT7UXChyNuT1FBc/y7LeFZ7X1RO2r/UI3Q7et/DlGKFrh95LQlbd7OcDoUK8zXH5HvgClZyF4ScJpcWXzGfdniwyjNHEHvYGfbBgwoRprDlf5JwRZn5UWuK5kGUUUpCGmtF4yqjfR+sUmwZ8rcx41CSUhedMIpFhtZ9nI6Le5Yjg+Nk5oZNqBMYVC6MvtVDSjZeIhueJ4xCRFVzrdsEpTBIzjhRPZzlnD6QmFd7x9A11GxQWHSYUiTm2bmM89cgwHU+ZDvtInaJqIRdswWoJnQMJQjtlyfnMYWREWk64vHMVRExYj9jCMZgIvrkVszsVTJxkbB3XXEkQGkajKYPtHWoyIEwiuoFgMM24M7qemjRxlotFAbO69SdjHn7DK3nsWxKyRHPZO844e9N1k2tHHxkH68uESUg+yrg8ugoqIaiHXHaWvBSsHEj52istV8uSJA5gknNldxuICNOQHeWZ5DlngwAx28PQWS7nOSo0hDdRt9w7LrxIdRM1zXbZpy4VH/jV32DrySdYv+McD73tDXQ91JOAYpQdqtu0KNgw18n00JVcKAuCKMANJ/R2ejzaGdEPI0Yi4MJ4zKvO3HHs+/oUh/G8v8m11jz++ON88pOf5Ad/8AfZ3t7mySef5Lu/+7v5wAc+QBwfvqs5xSm+bqFCfDlExmsL6TwnAUJFeG8xUqCEwOEpvafwDis8JlnmkW97K+/+4T/Hj/y3fwoT3R4SLYRA6BRp6jTiZaRyCOOxAgo89oBQtSxKPvIbH+D9/9//g0/97h9gZYt2UOBGTz4jeXlx12sQQuK9YzUNmJbV62q9RyOhsMhVQ3LvEv6eDiUed0Cx7YFy9udICCKtiXWJG1/ElUNk9OK+l6qYzwZl9/MI00I37kdGbQImlTpfCGpKkjmHw1diqdKCsJhQYKn2tqDt9Z7SV6RGCVU9HocKBFZA7jxLQYAtchq1dCE3W+oUmWzi7PSIo3mPNMe/D8UsFnc/XWkfiRH4sE4URMjSMy3AWwvSYgKJ9VWNDu7BwaG6IarHO8mxdSvwVVerrLxolZFYVZFay6KquvSeSQn1AEZTCc5VqnYtKJxFFBMeCnu8dtWzEkmKQjJxjoH1lLkhLzxTUYKR9Mrq2XeLgnL2exzMavPi1O1lb/sm7nv5gwt7uO/lD/Hw276pCj2wlbOB0OCUoJj5jB6EpfqOcQiwAm8dKIcMJMXsO+jgTzjvq9dagPDiltbNSc10MuJ//em/w7/+pX/O//l//Cb/6h//K/7Z3/4lpkWB92LmRnCgbt4dcmBwvvqu8sAg07ysNuThqMvEhPhQ0QgTmuaU+zwXvODhhZe//OW8/OUvfzHWcmLx+OOP8/jjj2NviGo7xZ8cSGXAOeQLyCR/qSCkqaxwpCBSGpt7VrQmcoLe3gi1HFLTCameIss9hLiNoioVgbc068uE0XmmhSfJC5aSmIbeF0KU/OLP/+Lc8PoPgc9+9It8+6/8I5zfnRuZv+SQpvKucSXtKEQCG2HAcJQzHuWEUYoJJKO8IBKwloTU1PUuppxlWfvCcv7akLVaQlrfxGdbeNdDLr/2xV9yuIpQT6LrdyFUUIUSiE/iXWXY01SaJa1RU8tkUhLHdYSGSVZSCzX1OJyLM6A6fjwTGIbTkmF/gjExcVxjkBUEec6ZMCREMPEFtcYRmd0zM/z9GVGZnAUhqlGEI47cF/fSwQ6+BqaO9xafdQlkE6TG11ZwT3+JPWu4u9HESc9kktOKA2pRQHSgUx4KyZnnUbfBNGc0yFDK0KgndLMcOS04m8bUb8iOX1aaqfbca3b5vAxpNJYZFTn5OGM5CpkoT0dZGgE80oavjA2TsWcwzHhbI+NiscyWm3B1OOHRZo27ayk7RcnlMqelFKlUbAaGwTB7wXUL84KlJOFnf/Fn+Mj7P8zWpS3WNtd409vewhBLrzdBCkmzs0R3kuMnOWfS65/PfbS1xnhBrzvBWk+r2WFUFmSjjLVaSD0IkAc8jWLnefK3P8rFpy/T6izxpm97MyKQx9YtehHrpgrHhQ/+Pl/5xKcX9vC1T3+Ri7/7h7Te+DpA0G4v08+qum3WIuo3ZscrxTkRcGV3Sl0VvPbsMlFZ0lIOF4QIs4K5zXHOX284naC9Cbzvfe/jfe973zwx6RR/AiE0Kj1z4uZBgeqi7D1SCozUldWmhVF/ivOClVSzi2S1voyQW/Nj5tuyVBVhvSXSCUkUQ+HJS0s+zUCnlGXJv/p//auFxBWAL33yj/n1X/sdfvz7XnvLggKEVCAM+JJOrLHCU07K6oJ4QBQhZireTEC9dgNBzi3DfkHTV2IWEzWxFJB3X5q40aCObjyEDKvjVlU7SxjU0eMRpasRK0U5KSmndi5C8swSerISK1VlKHoAPncM+1OMVnPxmJJTro2maHImgWQltNSSIwIQyhEqWsEj8dk2bnwBEXQq+6tnqaMMWuSTK5VIqRyBAOOHCAKeylLuqGt6Q8tauz5XX08mlWE60SIRcNPnVjdROMb9bJ4Fr41Ca8n2aMp0PKXRSBc8I8sCgnzMRi3DrTfYKSLW4ko1v9Mbs1GLqBkBtiQ1mtd04Gu9EW9uTXj9aps/HjY5K0Iu9foEeQ425M44Ips4RtZSVxI7LhaSkJ5v3bqTjHya0ailvPXPvvX64zMY9kYLSUiB1lwdjI/cs3Qw7k9x9nqEat1WqvnJKKehNOh9i6eSf/Cz/2Dhc/2lP/pj/ub/833sjrOXvm6DMVsXLh/5Prv81CVe9vrrCVbxzO1gPMpoNBSo68RYeMGwn2ELxyObguU0RdlzJOTIMEXqWxcC8o2C0wnaU5ziJiB0gm488JKm9DxfCKHw020EDqMUmXVc3htWqty6IQkl1gtqYYAMl2+vvZTUCASBVBgjqYXBXMV7Za/HL/7sL/Kh3/nQkT96/unzSJ3c2rQqGeBdSSMKELbkSjc7pMo9TsWb5SWXumO0hNVGzNJMbCJUVI11vAR1kEEb3XxgPn8pdUBQO4u2IwoHw/6I3qgkDtRcBX9cZjlwrHtBK4mIIoMoHHuDLmfrAnnDmIe3GagAoVOEUMhkE+8K7PAJhGk+6yiC0AnIkHJ4HlcMUckmhhH9qWPiJK+77xztWDGx7hnV1+MsP1JN/Ux1O0oFf5z62paOp3cndHRG2Oiw2apeeyUr1fyosCxHJTKMcMUUvGfNdXlja8A3rddpNVs0A6ipiFesLBEqxaXegGE2pa0NuT/aveD51u0o1XxZWLYH00Mq+OOy5o9TwR+nmv/g+z946Mby85/8PB/9rQ8fW7fjVPDPq271hMZS58j3WWtleUEFf5zbgfewO5ywO3acbQvWWjV0PiZsbbJRX+dsY5WVNJm/Rqe4OZyS0FOc4iYgTe05Z8XfMghdxatMLyGF5NrYU5SO1VZMEioQksJaUm0rAncbSWjlt+oxUqK0xLrrF8aP/NaHD12oDuK+O9cQKrklyvjr6w1x2R6hDojFBCGvXxAtngufucjTH3+a7Mnu/MJ4bThilBVc2RvhgY12SigrZTcArkCal8Y9RIYtVLzoIxm27yDwY67sDRgMc+qhotNM5pGOFn+I0OyOJwym+SEi4/DzOcUkDmkECXcEu7SighsjblzRRyabyHAZbyfzGVFvp6jw2ZOghI5R8Tq+HCGkRtXuJoo6YMfc3zbcs1TnbATX8mpG80Yi2ptk9EYZ3Rs6afbAHg4SmoN1O0hkPJ5y5r9ai0IaSVgR0d6QSV5yeW/EtPSsdmoIExJriLVlaitCUw8155oRqCk2H1IOtkmKCY+22yzXlxAC1pNqptQoxZlWk2BGRF1Z0B1M2RtmCwT0hdTtIBG9Nhgyzksu7Y0Av5AFv7/nG4nopCi5sjs8REBL76rP9g1EdFQUPPnkxSNrfOXS1rF1u5GAvtC6vf0d38RdD9+/8Pvvevh+/sw73zonoPt7uJGIjouSrb1qz/XE0GqFNAUIBDJpci6OeGD5LjYb4cKc6imeHafH8ac4xdc7pK7M4LMp40nGXiG4aykiNRJnFXu2pJuXhMrOulK3UVglNFClpIzx+KKkTUAriRjvdY/9sVe97jX88Pd+GyJs39L1C1PHTS6h4pBmLAmiCCkFJZ5LWUYxLSC3eO85U0vxjPjaYEJWDFkPQ9ZbKaFRYC1pNFPZ+hJu4bGdStcJy4ytXo+zzToi1hSiEp5cynMANoNKlbxcS9kaDPlid4ArPetRvEBkLuc5k1kGuXWejSTlvkihQoWbXEbGG5WwxTvwFhWtzfw+qwuzUAkyXr8phwkhDTJs4oouqvkQQgVErXs4m/4Bd7U10sTcl0j+626BFyVnohA9IzQXe0M+s9sDBw8kTVbai3UrPWwElQvA8hF1u6ddm3fStoqCoXWsGo0RAms0OrJcHkz5Un9EW4WsNOsksQQ814qCbZsxLWrcJz0mCFlevgvJRWz/C8hwFRW3WfYe4SwoTSusOpulu05EL3Z77O1s0514OlE0J6D5C6xbKhWtpLqR+0p/xKg7ZElHnFmKUDNH++2ypFuULBlTJQnNiOhXu0O+3N+mLgPu76RzAtq3JVeLkpqSrJtgTkSf2BvyR1d28c3WkTU2K5Wg76i6nY3iOQF9oXVrKE0rTfib/9N7+dVf/12ubm2zvrbGf/Out1OvVbPJu2XJTlHQMpoVbeZE9CvdIV+7sk0iDA/Wm8SxIdQT6t4j4xTISdJ1dLJGMbW0omO8cU9xJE47oac4xdc5hFAIFaPiFQwWK0pCI3C2RAYxEyewOELcs4pBXvK1SoMAtKi6ROMDR1ebG0eHAHzLO97GP/tf/xe09rdcGCZ1BCpCFTuoA7aH1numblH9C5CYgKyw5K5AaIXQAokiEJ545uPovUPqW1cHGcQ0whTPhFYtpGE0uavUyFNX/bevfJYCQm3I85KpK9GhnB/lOmDiPBNfzSkWpeRMImhECSrdxBdD3ORy5VNaDJFBCxE0ESq5TkztBBl2bnoeVgRtVNBERVV3V4ZLPHpmmSUzQJiQzTSgLQV9Z+fH40oIjFRkRUnuSoJIzo9y9+s2vUHtfWPd9omMxzN1jpGz5DMFd0drNsKYvdwxKgsmAuJAE+vq+QbZlGv5gMF4QJZPMEFMp9ZEN+9BKIVghPcFMq5TjnZnc6LQMpZRXgm5jFLEUuOLgo60yEjN/VtfSN3yA762qdZkhWXqSpwBfcAe7fqer38+I63JSpi4klx64gPBC7n3jJxl6hz7nwqjNB5FZgvuftOreOAGNf4djz7Ay972TcfWLUrUi1a36kWSJEZy/5vfwKPf/Q4efvubSNLrpyq5d/M97CNQGlvC1JbkwuK1Zjl2aAmhLZC1JfAlqnY3QkhaseHBtROoGzjBOO2EnuIUX++QGqRGq5R60iKWl7g2GLMUKYJah44JGGiPkbaaszsJa8WxHodMxASoZtLuee2ruPOhD/HUF64b69/7yP383376vbRrFQO81esX0qDidUKbU5cTtvMpUEUIbgSGbZjbymR5yXZ3zHoYInSC8pZroyl3pgGJARPO9oC/pSMRIoiJa2sk/V0uDvokaYIFYlntgdn/QzVL2OtPWY9ThBa4oqQ7ntJKIjSCjcCQO48ElFS0mCAEqPoZfL6Hm+7guIxHENTvrrrWOq6ESHZaee3W7rnp2WoZNCA5M7dzEkJSa99DfvWjELRopCl3mxEXhahiHalmCaejnM0kRSqYZgVKOWpROK+b9ZDOlNVH1W0/7lIgWDOGhtI0tKJfWmIHxTDjbm0YJAGiEIS+hxIpIIhcwdvXV/mv1yT9YkS9ntIINFLVCZYeoeh9GWF7mOVXIIc1it0LCCFZE5pPDyV1GbI3LtkbZiRJh1VR8pVJzkoQkATmBdXtugOF5XJ3zIoJWK1FSOvoTSbUagFSwKqpOqa1mShnfwa0KQTNRgNmR/OrjbRKEtIaiSCeRfFClYSkC8sdtSYCyw/8zE/wlT/4OLtb26xsrvKKt72RwGhCIfEeLvdyssn1uo0nOUOlXpS6ATgvubLbY9mErEQx/sAelBB0tCYQklTtB0pUCVaJh3vqDbzzDCcZd3cqUhxLkKJEJecQB26OlbyNJ01fhzgloac4xdc7hAahUFg6tRrLtZjpZMx2WbC5ZNBSEmuNEYvRjrdvrRrhS9phxBNCzKMBlVL87N//Gf7ov/wBT164QNJp89CbX4nRAZEoqy7qrSbRQoOQhOlZ4uALjKdj+hNNIw6pK82uEDjAez+fSds/EtwejrgwztmxY16+JCsCDnjELbXJEjog7ZxjqZtR2BG7gzFTYxBaL4QDHBSz3NFOEUouZJa3kohUKlJZHV3GVqAHF9ErD1SWSzNFvp1sLySLCSGRpoXLroH3qPDms7VluDxX+l//uyWEaeKmV4lrDdZNn11nyLwjnxTVLGGgubOd4gX0B/lCZnn9wJ4PilkO1u1gZnksFfHszLAsHTujEYn3LNWbWDyBtnSCHXyeQdBBeM99S2tcQvDUtRH3JXWMqoiJam5SjruYdh1fbKPbZxA6BCE5G9bZujDkifNPk4/61GoN6o0GHWk5f3WbK/0R642UJDDPu26wGMV5Z6dGGGi64ynTzDIdXs9pD/UiAc0Ky/osC36cVxnqB4noQfP+/SjOZmJYbqYUtuRKf8yDb3rdPGv+IHaHGZe6Baup5oGlqm43Zs2/kLrZ0rE1KLBecWapRhIZhtNsIWs+EJKOXiSgk6xkuRbSqsf0piVkOc7tUiOtwhLCGqp214nzjv56wikJPcUpvs4hhEAIjcdhpCSNEoyb0Ms8V4dj2s2qA2ekR8jgWZ7tpV8rQuG9pRZocns4m/pt3/42rLLsjidc2OsxGA8xVDnjt1zZPxsfEEKSpqtovcPV4RQpqgvjPqzzmBtUucu1lK51TMY5qSoQqrLSEszM2G8RhBDEtRaBjujEJXbkeWI0paEkjVl39jg19XItPURoAIrC4oYZQceia9dVxzJcwpdVd1uY2oG/b1MOvoqKVhDBzYuyqou7uOHvJKb9MrLzv44wm6xFngtTw9XBgHJcHlJTt5OY6ahYIDRwvJp6uZYCi4QGKiJzrTvhnJKsLjWoCUPTDGgbTU3U8K6Lm1wDJCtpi0e95xNXLJsHIjhV3CBcfxBVa1ehBeOL6Ma5+fv6vs2QL3QlDfcVdJRSeoERmjuaKZd6s4jPekIy64QerNtKLeT3fvMDXL18lZWNNR5+46sP1e24LPhWEjGm5OpkNO8mSnG8Cn5/RvRGIgpHZ8Hvz4he6VcRnweJaBWxmdGKFcvNZF631UZ6iIg+37pVWfCeteXWXIS0/3wHiagSYoGAttOAVr0anfEYVuuSwntWiivI8D5M+2XIA+/zUzx3nJLQU5ziGwEqBDshkAYhNbF0mEaTbm65sttFRw20FLdVGb8PIQ12fJGGiRhMCtrGsN5O5hfEfdTikOZI4X2BmZ5HLr/5tqwVWbkP1OOEOBiSWzW/MPoD82kbrWR+QdxHO40J3JRQTnHTa8iwMxtJuLV1iOstjBBI4M52kyezbS71x+imAMecyKy3kjkBhes2QNcGwzmhibRmuzvhLqVIm0uIYLE7LVSESDYWolWFThDKIG/4++cLGS4h07sou1+hIXPENOJqr2AlWiSgwFx9fbU3nNfNSDUnMkfV7UZC045iLu0NKZ3nztU6QRAR4ukkkhJLqGJc4RmOt6gJQxrG3CtgMx6xmV6/4ZBBjAwqUiPSs9jRBezoPCqtiOhSLHn5mTpP+TWW2SaMW2xP4Wwc07eW6SibE9GDdVtOA/7h33l8wV3iod/5CP/9z/3EvG41Y+YEdK11+PNWD0MyCnqTnO3hiKUkZqs7JissS/XrBHQfSWBYqcVcG07mJK47mDCYlNSi6wR0H0Zp1uoJW4PrRDTPbZUFLzVrzbRKX+L6jOiLUbeKgM7cQqIbzOejEO9hZzwjovWEa70Jk6yklZg5AQWwQGgEQjdoZdcwy4+h4qPn2E9x8/gTJUzq9/v8xE/8BA899BDf/d3fzZe//OXbvaRTnOJFgZAhOEuoJVpqdh1shxFBEjHOCybjPgpxez1C99dq6rjpNUImSOEgDbiMZeQWE8muZTkqMqzWmwQiX+is3TJIU1kPuZJIGbZcySgwSCW5Nhhh90UMQnDRl0wPCDk8nl5R0opioqSNz3vY0UWQwS2vgwpjEqPJnMAoxR3NGlbAVrfP1l4fqQRlarjoigUxisNzucgYG43Wku5owhPX9lBINpoxSVJDmEXLLO/LQ0fuQicVcQyP9mp8rhBCYjqPIkxIU27xanOB5VBxUSoulvncygeqeMdLRc4kvF63Szu9aq1pcGTdtoqcgVGoQDOcFDx9bYdp6WjVKyeHzEIrrGYnJ9axXRR8oYCLhWE1iWB6mWYo+JbNFivR0bUWKkKlZ8E77Og83hUIIXigY3hos8VDjZJOUFK4Kqf+7ihiGCoQ4lDdfv23PnDI3uwLn/o8H/ngH8zrdn57rwq1qB39eevakl0p0IFmnFV7zvISkxiuKc9euaj6HtiSK96h4oDcOs5f3WEwLtChZhgrtoqcg9K9qXc8mZfkgQE8l3Z7bPfGSCWZxIZtn5H5F79uhXXoWsAVHAO7uIe9suSq8OjQkJUlT1/dYTItMLFmN5Bsl0X13LNlDb2lRUmteTfB0mNH1vUUzw1/okjoP/tn/4xXv/rV/Oqv/iqPPfYYf/Wv/tXbvaRTnOLFgQrAW4wEIzVjZeg68MbgVQ3jpwg/PRGdUGlqCB0TugFGw0T4qstzg8lzvyyRaFqNJiZeqjxCbzGEEAgV4X1ZKXF9ydg74iCAfDq/OnkBA2vJ7PU9lN5TOohwJI1NRNDEZbuH/DRvBaSJSY2htFX3thMGBIGGPAOXkSQBE+EZOkfmFvcwso6ht4RBgC9yMpvRjjTNQKCiRcsv78rZDOiiQlioEN14EPEi+qPKcBmz9CqMtNwZXuPP3RcjlKBbWIoDe7DeMbSOCQfq5qbEiSaT/lDdHDCwjr61hMbgywLsFIygFQcoISgc1A00deWpOShLdsqSHE2nfVc1kjC5xKOrhnZ8fL2PIqL1UPLwZou43iKw183YN8KQh5KEUslDddu6fO3I579yZWteN+yUIJJkShz5eZs4S8+WqMDgS4cvp9VZaajo2ZLxDaR17D09W2KVQHmJLzMQBUFi6FvLwLqFLPhp6diaQs8KMhsynWTAlKQWMPKOKY6pvU5CC/fi1E0biTWi2sMNHp7j2Z4xCm9FtWdlUbGhZ0uGs6juwkGgwGI5Iwt07S6kvv3fpd8IOHHH8RcuXODChQu86lWvIgwPiyicc3zhC19ACMFDDz30nAaCf/qnf3r++Ne97nV85CMfedHWfYpT3E5URMmipEArTUOHLNUaZHmIkyWRqaG1mfl03u7FakS4SiQmGHkFmTmWmyFNtXixbilBXCoi6Spbn1toa7SwXBXjywmBDjgTaialYjSozNedyACH9LARBKQH8rU9kCpDKqaYIELFq7h876Y8Ml/0PZiIZppwfgB4SzktGA1y2kENqWE4mdJMIqLAzNXHAIGQrBnDpLAM+1O8CNFxSFl6It9DmsW8eG/HlS3TEbWSYevF3ZMQ6Po96KWHcOXnWZO7nA3qFBgieb2/sr+HLC8ZDaZInaKMZJyX1KShEy7WTSFYM5pRLhgPpiACdBwxshY3zaBeEexIQ10pwnwC3nJWhQSUtOqbSGlxkyskXEYEZ7hxrnVhHzMieuPRvG6uEfc/jxGewgmMFNStZzgoDtXtzjPrfOyI5z63vsawP0XICBUlZKUjVpZmFBz+vGmNFwHTfgYogqRF6R1umnMmjmjoxZ5VSylkGDId5JSlqyJphSMbT1lNQmpaoQ7sO1KKMyG8rD6i2NnjE6KJCiXj8ZS2itkIBUV24PHyxambxaFyy7k4pHHDnjtaY5BMBlNwEMQtShzFOGMzCanPnj+zAlTBklY0cahk49h6nuK54cR0Qj/wgQ/wzne+k8cee4w3vvGNnD9//tBjPvnJT3L//ffz9re/nW/91m/loYce4nOf+9z833/hF36Bs2fPHvrvL/2lvwRUX1qPP/446+vr/MiP/Ai/9Eu/dMv2d4pTvJTYj2k0UhCYiKjWQdgY5ySpgXYcIMKlE6HiFNIgBITJGeJAMc0KTG7RN8wKag/dqaEdWoSOX5RZwucFHYMvMTqgIx3TQY4UgjOdGqv3rRKcayJWU6LSoQ9cdK33iBKCfICYKXtl0H5Ru4E3CyElab0DDobjMTt7I0KtWW+nbLRqhFqRTzIi55E3EKbUC/JBhgQarYgHOg3aYUCNLt4OFx7r7QQZLhLTlxLSpJj2owgdIoVn3fQRJXObIKj+P3VUe5jVbaM92/O0OFQ3gMRLymGOt9X85LmlOlGoKArLpV4fKaqYc5mPWQtDVNQgHG6zEdcIwwQZNJDxOr6c4MYXK4/UZ8BRHVGZNIlqdWI/IbMV4by6N8IYydlObaFu3/K2N/HQYw8tPOdDL3+Ix173WiSw0Uk5066TBJoiK4/8vIVI7DCfZ8Gf7dRpxSGudIgsJ7yhgx8KiRjllFklQjq3XGe1nuCcx09y4hueX3hByzlWyz5ppFmpGTaas/ndPGcj8AvfTy9W3WqhxuYlYprPbbz2EQmFG+W4wtFOA84u1VlOI5x1jEcZufUMbMlWXtII4A4jkEWOqq8/Yz1PcfM4AW2RCh//+Md5z3vew8/+7M/yzd/8zYf+vSxL3v3ud/OmN72Jf/Nv/g3ee773e7+X7/3e7+XTn/40Ukp+4id+Yk44DyKKrs8svec97+Hd7343v/M7v8Nf/It/kc9+9rMv6b5OcYpbAqEReLQAJQN2bExooRUJxlYQan/oiPS2QVapSaHWNJMa47w8pOJ1znO1OyENa0T69s6yShVjvSXQKYocRTRX5dY7KUk7OVrFWzp2eznttp3fJFTPd3tsstI0pSw85wd94iDhbBLhFIRSzpXIN6qvb1RT96VnPQrJ0EQ6wNs+Ltu5bqPkPTJo3tJ9BUsPk1/+DK64wqqBP746ZSMRc/V1XpZc6R5WUx+nvr6upnYLmeLtNKZdlmyPc9ZFj4gEm4958MxD3Bc1sZv3oLybEyk5cwFwkyu48UVkcuYZb6SO6ogGnTtpXv0CX+pDNh6RaMVqHCK0QCHme9id5vy1//Fv8Ecf+H22Lm2xvLbCPa9+FVLJBRX8cW4Hznl6g8khFfz+v3cn2YJqHo5WwT+Tar43ylHFEFkXiLRJlmumtqRdi5l2J4R2D+uWcV4hxfEq+Odat+NU88ep4Pef78v9Mel4SrORci4wvLEpUcMtvA3n4rJTvHCcGBL61//6XwfgYx876lABfvd3f5evfOUr/Kf/9J9mljSCv/N3/g6vetWr+OhHP8qb3/xmGo0GjcbxXYZ/8k/+CT/0Qz/E3XffzWtf+1ouXbpUWaYc0x3q9/v0+/35ny9fvvwCdniKU7x0EFLj8h5aLbE78UwLWKsLmpGgP/AEwt2WmcqjsJ+apIA4NKRGYbSdXxjbQcKVbmULc+dSSCAd3EJfzRshwzZCKLQEozXterigyp2reA9cGGNtuNIdE3hJUk9hbs7ub6k900HEcYLIqjz7O5abPF0W7NmCGNBHENFAyAUC6rQgQdBQih0JYdhCBi3cdAcAYRpVetct9nKVQUR45o1Mz/8enaCPVG6uvsZBf1AcIjJwfN2OIjJQHahv1utcthNSOaTsbRGsvgyddjBCQP2w/+mLQURby2tsPfUkK82YzU6DYTbBeo8UYqFuO5Oc13/bmw/V7aAKft/t4CARbUQhV/ZGKKcWCOg+jiKiu/3DBHQfRxHRybTgaj/nvqaEIGZiUmpZRFDssKc1rTQlNiOK6ZA9EVPi2NqbIoRnrZ1QqOtk5bnWDQ4T0aU0PZKA7iOOAlbKkqbzmElGK45Iiy6jbISubyJvEOOd4vnjxJDQZ8PHP/5xms0m999///zvXvnKVxIEAX/8x3/Mm9/87PYtd999N4888gh5nqO15pd/+Zef8XjyH/2jf8Tf+3t/79Df7+3tEcend0IHcZCsn+JovJSvkS9H5IOCUXkVkWvWtactLGTgsxJjYG+QIfOdl2wNN71WV1KOQGRjaiJiT2asxik9JuyMCwajCTjJZj2haQzTccGuKtDu9q29zFsU02vEpkWqNMJKumWJxdNWmkAoNtIGe6MJW4OS3BZoNGebCYUo2RtZxHSKzxxajxD6xsDP547n+n6yo4ylNEULg0ORAk9nBU45WloTotioNdgdTrjUy7HW09SGlXpEoDTdoqySdwqB8pC7gJ5dgXKEH+0i1BBpGujuEBg+23JeVHgfM/EbBHjOJBJfljy1NwULa0HEcj1GS4Wz/lnr1hSKs80akdFgq1z0kbNEQiGdohHWaciMvgsxTqP62TOszAEBvlzCjXYRwwvIaJlnnYRzq9jpVRieh6BNq9FmLRVIoYm9Zi8rMYKbqpu3nq6tIjirPUvW4sb883a5P6QmFGv1mCBUYGFoS4be0ZCSRGqWwpQAzc4k54tbfWIUy0lMK43BQuYtXWuJhaShNHWlMDXNteGEL18bY7ygZUKaaUBR9DFhyt1pnWgwxjpFKSRSrlLTE77WLTij4IEoZqWRYJVgKy/Yw+LhOdVt4h0tpQiFYi1uEDJhZ1pwbTggFYq1NK1It4WxK+k7R01IIilZChI2pOLKcEpktxmLMRPaDIJ1iv4IIacv3hv4OWJp6eYDH046vm5I6O7uLp3OYXuPpaUldnZu7uL0rne9i+/6ru+i2+3Sbref9fF/42/8Df7yX/7L8z9fvnyZ17/+9bTb7W+oN8GLhdPX5NnxUr1GrojIpiHTwYjch8Spws06IFf6Ja9OFEudJWR0+2vkvSe3IUIqkkQx3MlY0YYkVnyyt0eIIg40S0oQxJqVJgTNFvqITtOtgqspsqtXCBPBoJ8RSrjsMuxgilOaVGviZkwUKy709pjYguVag/sixXKS0WrGICU+MwTLqy9actVzeT+VIWwuNen2BgQGIuHZ9hl9JwhESCorGYmKJOd7u5Te8VCnjQoFGSW5tzTDEOcg0paluCRqJQjdwk0uY0cXMEv3EbRvT52mxR7u4lM4DYXXnO9Xdj7r9QgRVsrmqXdcdhmFd2gd0pjN6h6s233NJiYWWCwez5UiY7souD+OiQ0MckcnhJXNcwg9QQS9I/0ivStwk22EjpHhEi43uMkVhN551o4oRHgbVB1Rv8fSchMxvUZgYigcF7IJkVLPWjeLpfSOrTxj5CxSRXRme97/vA2KnHNpnbvCOlZVavBrNmeryDkXhoSztKck0Xyu2+dKNmI5iLijvoIV1eP3yoLzZUZTaZKgmuc0SjAalTw16RNLzb2dVUKT4QzEzZi0aJIVe8STEZeFJIoFI+2x2YBO7Nlsn6GRBHjv2fM5X55MMEI8p7r1bIlXISuzBngtDfhst892MWE9SrmjnmCZ7cEVnC8y1oKAFalxEpbShKd6E1bVJRpqgkrfSKfZIFlZfUHv1VNcx9cNCTXGkGWH7zgnkwnG3PxRnRDipggo8KzH+6c4xUmBkBppWqggR9kR231HZ7k6MqopQ6i4LdZAR0EIgZAh3lvaYYRzvpoB7U1oiYAoCPAU7E5KXh5opGBhpvK2rDloIoMWqXwa7wQGwbLR9J/u0csto1Bz9hXn2OlNaKuQWhBirUeUEwKtqjlYl90Wo/r5HpQhDkOsG+C9IxCCjlZEUs7V5GVh6fcmtHSE14IiLxjnBVZVx/CxUnRLaGuHkAqhNUIIZLyBzXZQL7IC/rnAtDcJLsdE7HCtH9E2CULBNC8IQwi1JhRV3SyQzJTStnQLdfPWMZxm1KKQp0aeEklNyZmyWlBOB6QbS+jO/bjp1cr/FQ4RUZftoGp34qbXcOXoeR/NM7pAIvv0MkdSh1QoWtpQn9UOjq9bEhi0kCwZQ90vZsHvf97iOCAUMMgykqT6nLWVIggX1eTbvREpkvUwJTVyYUa0rhSbQUAs5VwUdrWfMxxCxyQ4BaPxBJHmFPUGa1GClIqtuEM86hPqKoUrsQOWTRMRebaGIwIjiUzAWhBwIc+paXVTdRMIlrUmVZL67PH7M6ANqdFRSqqv7wGgoRTnwpBUyqp/LSRbu0MoLasrTVStjhQxMjrlBC8mTow6/tlw5513sr29TZ7n878bjUb0ej3uvPPOl/R3P/7446yurvKKV7ziJf09pzjF84ZQIBUmXKIRx4wnGde6XQACqTASOAHK+H0IHYEviY3G+uvRgHe1Ex5oN2hFhu60JBVTQNx2Ai2EQDcfIGWAtQXOC5a1Qc1eU++Zz6Tdt1TjlWstjFREtoskRwhReWjK6Lap/IUyJMYwLhzjLEMJQV1ploxBIa6LkICXrTR4bKVJpBVXB2N6WcHSvtDHQU3ZSgw3q4sQAhku3/J50IOQSYs4bFIvdvFC8thqg0dX6kgh2OqPycpyRk4Ma9qgEQtilv261QLN9mjK1miCRnB/mPKaWp0VY5hkGdYWNFbOIoRAxWuIoFkR0cnWfC2u6CN0gm7ch24+hC/6uLyHtyOQhnJymXLvM7gbvDdvxD4RfWBJ0Z2M6E1KQiloac2Kfva6jfPKbL2jNesmIBByIYrzrnbCq9fatCLDMCvojqsj5rqqHp/MZpn3RUgrqeG1mx020ohxXrI9HOE8REKyboJ5pv1gnHGlm1MLNPevNjjXiMmcQ5c7FDqiE0TUAkEuJaFytO2Iy71rrAaSh1da3L3cRtgJ569tMS1yGkqxrDUdpZ61bsNp1axqzvYcCbkgQtqsh7x2o8NKbBhm1R4AEqlYNwF1pXHeMx5O+Wo359UrsNpuoNJ7AYmMTmM6X0x83ZDQt7/97RRFwW/+5m/O/+7Xfu3XEELw1re+9SX93e973/u4evUqn/zkJ1/S33OKUzxvCAUoJJblVodGHNKbTNjudRECtIBn8iq85VBRlfBkqmSTg6pcKWA5iQmMpqEG+Lx3QNhz+yBUTCNMyMoJ43wxecU6tyCKkAKaScxKKHD5ZYr+V/BF97Z4hO5DSE0cRDQDxdd2u1hrkVTjEUdliu+LXrQU7IwyxCxtxnlIZXGDSX2BVAFC3b5ZeSElYWODhijn4jGjNEtpiBDMieg+jlJT74tetJJcGmasK0dTa86GIR2leXprj3pzCRNdJ9s3ElHvLb4coRsPIFSIjNdQtTsrw/7Wo4QbbyU+913IaBU3+PJN2TetrNzNG5bHPLnbJbfXc4ieqW6BkgtEFI7Ogt8XK0Va0Z1kcyK6j6NU8K0kohWHC0R0H4NxxnZ/ShIKOo2UAthIYoz2KKPBj4klhEoivGMadtj0U7xz3NtZxwSaQClW4ghr4fxeD2dLOtowsu4Z6xZqtUBE4XgV/HItpRbqBSK6j93BlKu9kvuaknuaAp/1UNEa4JHBqSjpxcSJIaEXL17kYx/72Nwy6ROf+AQf+9jHuHatSoK45557+Ct/5a/wnve8h3//7/89//bf/lv+2l/7a/zkT/4kZ86cuZ1LP8UpbjuqI+4AgUUBy60GaRwxGE/pDwYECrhdPptHQKgY70tCpSmtP6TK9d7SjiOSpIEvR4gT8FUlpCFKVghUyZX+hKwsORjAclCVa6nm4tpJA6FjgqVXYjqvRtXuuD2LB1AalGajFpOVnovdHtY5ssIdq6bWUlKvJTSVZHcwYns8JRSOZHIZDlyMvZ0iVHLblP/70K07WK43Qdo5MdJSs95IFojocXY+UKmvgyRkLTDU/JjxdIJ3nos7ffYyz9rSYQuqg0S07H0JFW8io8ovVQiBbjyIWX4dOj2DDJqoeA3dehk27y74iHp/tGBNmhpnV87w6laPJ3ZGlLY8koDOX4cjiOhRBHT+/AKacUwS6AUiehQB3cdRRHSfgCqlWGmkOGnRCJakxgiPat+DFo44u4IRlsJauq7G5tl7ORfHrMYBeMjznDEp9dYmopxyfm9ALDxZYbm8N5zXzQSG3F6v241E9DgCuo+jiGh3MGFvVFALAx472yBwfRAREOLhUEztKV4YTsxM6Pvf/35++Zd/GYA3vOEN/MN/+A8B+Lmf+zne9a53AfBP/+k/5eGHH+Zf/+t/jRCCv/t3/y4/9mM/9pKv7fHHH+fxxx/H2mc+OjnFKW4nhApRIkMKg3We3BiUc0x7U6T1nKROqBAKV47QqokOFAMtCLybm0l7HF4qkvomFJPbfhwPgNCEJqbQAWUGl7vDhez4gfIE3qGFxHmPQBArj4zXkUHnRU8Mes7LFwKhI1pRwEoakVvP1e4YLERSYOohPenp4Ocm4B7Pti15WbuBnUz46s6I1zW3kWqCDA8cS9opIr2NBHsGGdVoR3XEsCC3EZEGh6frPTIOKMYZl7tDfAl4COrhQt0ASu/wAu5vtqjR5cn+kC+MhgjraNUimvWjj2NVvEZpc7BjVP3uReP1I0ZhVLSMTs7i8lkeOhIhFSo+2ghdpR3W0xFp1/PE7oSxmCLE4bqVRcnvvf/3OH9xi8baMq9842u52h/hHeAEwgh+57d/j9HVHTY31/nmd3wzWmsmvqQMDNI5upOMveEUnECFmjIxjJwlPXAiMXGWqZFoqxnnJU9td6EEqRVZFFDqAjw0RUhU5tTDkC8LzcPxKpock1+iOxVIldBqJ5xTQ/JyjJZ1tgY5d20scc3WaPouW1NHtztgd2yRHmqNqm5MPVpKllRF3vvW4iODnHi2RxOu9askJB1rppFi4izxgT0MnaUMDcrDMCsZjLvgBGjFWqtGzQj8JCdcfQV2uD2zILs9Pr/fqDgxJPSHf/iH+eEf/uFnfIzWmve+9728973vvSVr2sf73vc+3ve+93HhwgXOnTt3S3/3KU5x01AB0lc2JkLA05MR96Q15MChKJ7RjuyWQ0jc5Bpah5QaLuU5oRSEs2jAonDkUpEYiRCtk3EcLxWBicmVIwkNfjKA/Q6WgCtFQSgkLV0JG5QURFikDm6bGOlGyCAmkJJIK9pxyFd6e5RA2lhiWzjKwhHPZkUBcufIvKMVGEJj2O5dYYXLyKhR7WsG7+1ceHM7IcOUerqC2b1AZhtYWeKtZ9sWlMBmHNHf2wHnSJsd9hRMD9QNYOKqmyElNOu1Npf3LpFlU9IwJW02iY+Ik96HUArdeg3SPPvcoNARqn43vvtF7PgKunEP3nu8zY50T1BBSs1INuspXx70sMrTbC/WLXbwiz/7i3zhU1+Y/9ynPvD7fP97fwQpHEFU45f/H/8zT33mS/N//8jvfISf/oWfpm/hUplxNo6Y7PXxxRgR1FGp5nyWsRkEpMH1z2HfOS5kGcuBQReOPOsidETcSHhyaIliR1sZYjRlMeFcZ5XPZl3eurKBjDRmeImIDBM3MAp0+wzl5S9hRI210PKyzRaf7IYMp0usuR0u9PrUXEmWNhnO6tZwisZMnGXx7JQlfVtyNgkZ7fbw5bSK4owUW1mGiKIFEtorSy7lOZthQDkp8PkAoVPCJCFUYITFC4FZeRBvn0R6i9C3t9v/jYbbf8Z1ilOc4sWBDFHCIgWEUrEShvhSI01CoGNO1MddSIQKEPk2EZaO0iQH8r5LV1IzAaEUSCHnApjbjSCss2Q0g/4URDwfcRDAstbEM/WxdZ5ACIyy1T5vs7p/HyKI0EKQl46r3RFKJfggZpxl1JB0lCKWi++TJW2IETx5bUAr0LSbLQQT8PnBZ76t86DzVUhJWF+nLXOmDjLncR7aWtP0ksEwAxEjwpRJXpCyWDeAwsOaMTgHSbFHqgylqZO7nAmKUB99M+fyHlLX0enZm16vitcROkbF65jOq1DpOWy2e/TeTIhwjhW3h/MJPkgO1e2D7//gAgEF+Mqnv8gnPvQJRJDysd/5yAIBBfj8Jz/Ph97/IVKpWA0CilEOTiFMglACl5WsBcFcZb6PmpSsBgE6cxS5Q+gUoQ3ZNKOlNS3j2YwUwcwmaaPd4dWtJZomROqUoLZJqhyZU2g/QdU66KjGXXqPh5YUJqmxXlNMTJurwxLvQ7SuMcrtvG777gDeV9nxLa1YVobJIANvEEFCiUMXjtUgoHbDe7uuFGtBgJuUVZdYpwgjmWQFNQ2yHCFMDZWsEazeh4hqJ+tm/hsAJ+iqdHJxqo4/xdcDpAoROCQQCkWdmLLUhBqCID1R6niERMWrGB2gbU7biYUORe6gFYWY/VnWk0JCdcK6Aecca+0aaj/D0HmWpZqPE+TOE0iQrgRtTk4n1EQoZ+kOhjjrOLdcZ6WZVLY605xVpRYyxR2QIri402d34nhwrU7YuhukwWWV9ZDLKyX47VTGH0Ta2iTWln5Z0FEGJaCNQo6KKlO8XeNMp46UAjcpFuoG4IFYKHqDPqEdsdpIuHN5mUArCpth7ODQ7/TlGFyGbj70nOZiRdBCRSvozqPIsIVKNqEc4uxhO0JhIrydsmoK1lq1I+t29fLVI3/PpN/l3HKT3vbRntpbl7ZoKE0yKass+MRw5+oSSVDlrjdKP/fmnL/OUtEuoRgXhEZx52qbVhKR545lCpYjxdnY4IsMF7dYTmIebbRpzCwVpU5ZazQITYjMr4D3mM5ZVsyEtLOCEIp2rBgSYVvniAPJmaT6HsjGOR2hCKVEC7CzUdolZQgmZZUFXwu5Y6VDqBVlVtBxLIwTADSUppE5yklJHGruWutQCw1Z7gl9D1cMkeEK0gSY9gZm+aV14vmTiFMSehM4Vcef4usCospcFgJGOexlEGlPGoBR6mQJk6iIZZCeRSvB1mCyoOItnKiST7ybkdCTsXZtYsLA0Ew1SWSud0VuUF9bD4EQyLyPUPFts2W6EUIZ5HiHQHg2lmukgcEozVojwXu40h9T2OsK8tJ6dnsTJnnJaithtZlUc3FmGWFi7OBJfNHDNB86Md3eKFmmrjWJKFnRAaGHp28QIRl1WKwE1QwsznN1b4R2U9Jag1azTllaVppLNGs1dL6Fy6+nVXmb44oeqvmy5xwGIYRAd16Oms3TSpOimg9ie184pJoXyiCjOvVmg3pQTe2u1mN2S8eF3ojClqxuHG2ifu6OTbSU3HXn0SLetc01uqPJgghpXzV/o1hpH/sipNAo1tspUgpaSUQcRsRigrF92gqUgNLUiLTgjqRGcIAINpKAdppghMCNLyDiGrq1gY4r8VdqBCuJIm60adQCViPNHakm955LvTHCFxjlKdzRKvhnUs3DvggpJw41a60UIaBVS0gCTVOO8UUXmawcqNfJ+Bx/I+H0FT3FKb5RIBRKQOGgN6kIaCsSaHkCLZpmX+ZaB9TDFHWDncy1YUk90uBt1QU9IZ1QqSLSMETN5geVUSijCAK9QGgKC0yGCJsjg9tny3QjhDJoZei0Wxglq4s/lZH7jUS0UlNPsKWrRBpxTKQAaxHKIJKz4DJU8+ETkcS1jyBp0IlrPBhMyXNPNsoZF/aQCv4oIppZT78/ZTwuWWvEmFqLWIMtMnzcZq2zijQxbnIFO75SWTIVe+jGA+h083mtV0izcMSra3chwyWKvU/hysnCY1VtmdhnNNyQ4XhCIQTrjYjMVXX7pre/kYcee2jhZx54+cM88pa3UDr41j/9LTx4w78//IqHefj1r2ac2UMq+OOI6FEEdB9RGNFMDQk5zewiYRQy9gFGLn7/eO+JtaRdS5HREs5m+MllzMrdyLiBdyX4kns7moCcR+69h+V6m7oradcinBdQDghFQWHFsSr444joUQQUwHpPK46oB0F1zH+AhJ7ixcfJuHU9xSlO8YIhhAI7wboG9UBVymBfdQgCLTl5JFQghcAYQUdHZMWUp3ojYlGwl3nakZl1QtWJ6UAIaagHGkdGtyyJX7ZGS2sUgsKWXOmP+Up3yNZE8JakQEYxqJNDQlGaoLFKWg4obYHSmr2ypK3VnIhu9cd8aXeItR5v4cFOSj2N6WVgZCVCElIhKJCtR9H1u273rhYgpOJlq/dxafBBfudaRGIES81oTkD3s+NbWs+J6H7dhrlnVQiaacpqqxovML4yuXdBg0akkckZ7PBp3PQqZvXNqHjlpoRINwtpaoSb30Y5eIqy+xmcqaGi6nhaxQ38pM+ZzQ6fefICpshZiaubh3yc87XBlO9473u4/4N/wLjbZeXMJu/4jjczwfP0pOSOVPG3/v7P8IH//Hs8+dRFkuUlHn7zG+jnjvVUUW9U5G3iLEPnqCtFJCTLtZTt4Yiroynnh1N0Cc1Qzwlo7h09a4mEwDmDikLWoiZiepEoPEM3cwSH7iMdjVASN1uIaQ2pa/hsByaX8MlZXLaDEJKOKHlZx3H2jtcia2Oi7f8TbXNaaUpN7TEpd/jM1ZRVbVmuhXMCOrAlU+9pKkUgKsuqq/0RFwYTit6EwEIzNnMCOvWOga0s7ozQRCZG+CVk1HrRanuKwzgloTeBU4umU3xdQCh83kO7BsuJ4kousB4sJVrqE3OkDVwX9EiJRuCkYKWe8MTlbQwFhVKkYYD3DnFC5imhMnyvB4apn3I5LyiFxwhBQ1WEZqmW8LUr21ybeurLMcIEi1ZGtxlCGYQ2hNowKKcoNLtFwXpgSGVFROtJxNeublM4x0a9QSeNKD0oCVoCRdUJxReoaOnE3CAcRFJbI8q2MbbN+tImmcjJvMMDW0VBzvW6eSlJ04ivbe2yV5Q8stShUU9JTTVoqMsRIm7hdEBiBEJIRNBAxquYxt0vyfqFCjGtB1DpJmX/y9jJFipoo2pLqLTNWjdj+6uXuI8qDWlPa8JEzuv2hu/4VlpRjXbkONco+dj2iN/6T79P2N3hzrMbfOuf+Ra+RQj+4PI2l4sJZ+MarTSe56hvlyXXioLNIGDdBPOO6NOjPa5OhnRMzIPt5rwD2rOW81lGguaM0cTa0woaUAxZij1mp4eWN9yMOUsSKIJGjaJMweaIZBM3voQbX8BLg2m/krrLqdkcGS1TaxekKw9S3/00T2cBzWbKXv88WxPPylI6J6Aez1eGlikl96UBK1rOO6JPXdpmN5+yGtZ44EAHtDtTyqdSsSQMgciRuoM0p5ZMLyVOSehN4NSi6RRfFxASdIAudxnZGihmGdkSiThhqk6JEFX31ijByHp6w4wIQ6gEU18SyXLWCT1BX1NCk5oAI8BIiRee8IBCtz+YkghDoj2RmgAeqW+/anwfQmqE0sTKsOcsUghSpQhn7w3nPKNhRixDAlWZiXvnsA4CVR3POmuRUQLenRgx0o1QUZPERNTTgF6WU0skI+dIVZWfXvjrdRuUjp3eFI2haQTgKG1BqDR4T+BzwlqL3EGo9oVoGbr54Eu+D2lqmM4rEf2vUA6/htYbICStsGQ9TlhTk1n30TM5UDdbloyKknbN4UvL//a3/2c+/4lFW6Yf/Vs/SYQBXd1cTMsSM9tfKhW58gtOCaNJRmihbgKSQNGfZrSSyrg9EoKW0tjS0I4ssYJ42kM3ztBM1ml1u6jsCj7YvP495KvoV4RC6Bq+uIIMlyDZxI7OI2yBDNpVxO8MkVEEjbPcYy9hr01puAk7ytAIDXhPVpaEWpNbQV0ppBVEB773BsMpEZq6CYg0jLIqax4glrKKHfUQSkEgwXtV3XCd4iXDybuFPcUpTvG8IKRGmg5hELDd6zGeTLDek+rKBPskQQjJfvagkbDTq0QRd9ZDHmrWqAea0G7jygFCniBfPmkItEJLwYY23BmGhAezqXPL/c2EBzoN6tLh7fDEKOOBKjVJmmpUw4ECVo2pDPZniTpF6XhZp8arV9toIdjqDZkUZTUPCnhfImbdISFPZpdIhC3i5jrnOg36uefybsbViUchOBOEi3XbG2NK2KyHPLLSRAvBaNIjkhk+HxPGNaKkhhKCUAu8KxBCI4PWrdmLEKhoGZ/tzsVK7UjzmpUGotQYD3vdCdPCzusmEWyNhhhZ8B9+7YMLBBQqW6YPvv/D3FkPef3GMk2j2BtPF7Lm74nChSz47f6UpcjwxvVl1uNgYUa0rjR3hSEdrTGBpW5zTDYiWL6LJF1irdVAuxFucnmeCuVx83lYaep4X4nDpE6R4RLCpHCDX2qoJFFUQ+gGcprxgMw5U2vw6FKLUIr5bO+wENydas4Gwdzzdn8GdD0JeOPGMkuhWcyaV9WeU6lJRI7yEpcVp76gLzFOSegpTvENg4ps1usrxEHAzmBEfzQhUCdQ1TlXvDuMd/Sn10URlUK1TqQD3PgS/gQRaCE1gSgAj3OOa1+5xuXPX+bpz11aVOUqRRS1kSZGnKBOrhACYSKMEJTOIwE566TdGOkYGcNSPUIAT3cHBMzcC5wFZapj6SNM1U8CZJAggxbrkeeuZsRrOiMmkzHTsiJxAjG/cdgdQydOeOVSk+UoYr3eZClymOIqdtpDt9apBZJAVp1QXwwrknQLO9zC1BAqxg6ewHtHrDRv2ezgHFzaGeCto1G7Xrc4jqlry92yx1MXto58zuHuDsvNFKMUq40UIxfFgWI2Q36jCElrNRcr7Y0zLvereMxpKUiNR7gp7eEVgrOPIqM6tUBw39oyOl7CF8PrRNTZ6zdoKuRgBq4QCpmcOXR6I6WgHqdcLJs4pWhHMfWkhtCKlXolMrvYHeNdST3wc1u6G0VIerbnG8VKAoH1grAc4ccTVJTe9ijab3ScsCvTycSpT+gpvi4gKxsmLWGtvUQQKLb7Q7Sbnhh1+Rz7JNQ7jPQkgZyrckvriQJDXNusiI44QZ1EoQl0QF4MyUvLpDdhvDemHGULqlwBBFIgouWT1Qml8grVUrI9ysGDte7YTPFIa862ahQWQnsN7wrwDqkVyOBQp+qkQJgQGbZYD0e8fCXgsbU2a1HGkztjSucW7HyaiaZTT1g2AfdFEV4YzrXaIAS+2EYqSRrIKiJTCbBTZLJxa/cjDSq9E5ftzrPmO7WYiJzdUcEdrZgwrm52PJ6hl7xutcGZQLKxdvTIxF13XzfV11LSSqOFrHk4XgW/PyOKNGTFhCf3MoalZEln+N5lWiv3YNobs8cKlhKFDJeQ0QEi6sp5J/3Gjrp3xbGd5nqo2c1Dllt1kjBgNTQ44RCiEpmNS8WS2iVROYLjVfDHqeYH44K46CFMA5UuVacHp3jJcEpCbwKnPqGn+LrAzMrICIcSilqthlMSXXYPeQ7efsiqG+I9oTG42FDOzuetgzBQKKUR0RrywEzY7YaQijBsY7BcHoyxdtZZE+Bjg8VXXpNUqmpp0hPVCYVZahKVEf3lvS5XuxOyvCRMAlyg5uuHamIiMQEbzRqB8rjh05VYzIQIXTt5HfYZhBDIZBlfVqlOgY7pNCMK77jSHXF5d8gkKxGBIogD6lowsbOusK+IjgzXQEhsdoVY5tSD6igeaZCmecv3pJJ1RNTBlxPc+CJCae5dqoRk6/WUYtZJHFpLIhQPNlJqyTr/7Xe8lkdfsSiguu+xh/imd7x54e8sHpOESCm4OhhztTdiuz9FKomNIgrhFx7vcFgdcu+S45HmHqHtovJtAilor951ZMrZjUSUWZdR6BihArzd9yL1yGPmjeuRphZFdOoxGsuS1ijpKJxg7AVxalhOC0SxR3cwZG+UI43E1EIsi9+D5f6elWR7NOVab8x2b0w7CtDNO5BRcsJm6b/xcLK+HU9xilM8b4hZspAWDikkY28hDIlNXokAThKEBDvBqxBlDFvFiHNlQEdrLBAFGiVkpYw/YV3cIKgTBTG97gQ9u/A7IbhY5EgCUq1QCAIsQocnsBMao4WgEUaMhluUhSRqtOgb6BU5SobUZEVGHVXHKDSGMF6m2P2vBGtNcBnC1G/3Vp4RMl2GazkY0EKgA0UtlQx7fQIsSb3JJaFoast6ItgaQ6iqG4rUVIEKIlhDSEPDXSZtbuJtMYunvPU3RsLUUKaGFyF+ehU3vshKu83ycEBhBZl3WDwT59kIDI1AIGTIuc4Zfv7//hf4r7/5cZ6+ZtlrneWRt7+BMYKDu9grSy65gpUkJOuNGU72EEENnwZ8cVjilOTO6PrR9LXCsmUtZ1TIHVHGkv0aPZHQqbXRzzBHKcPKU7bsfQlmpxxCGmS8iR09jZQBAnHsuENkFMu1iLoPEV6TekeiPZPM8eS4xAcTJqZOfXSNYmKJogY+MVwoMqwIWNXXb5x2ypKrec5SHEB/wnCwAwS0Vu6tbmTCE2Sv9g2KUxJ6ilN8A0GoACmmCASJ1Ow4S5x2Tpa4h6pThZDY0XlCCaEQmH3RrIfISNT+VNoJ67ZJFRMqhXV6br0qgEhIAilwM2IaiBKhopMnCtMGIzw2m+KdRilJZi3GGIQUmFnnx1IJlxTV/sS4j5AtVFLHji9iOo/dxl08O3TUJvOi8jUVkrZWDNWQr9qQMMopncV6xXoDlkK4OIRpCYmGWIGfWfKpxl3E40tgL+HLOjJevy37EToFlVQ3m9EabnyRUGXcFU35VF5DIemVlhTDZqSIZ2+7RjahrRO+7TtfTy2p88Fum91czT9v+wikIvESl1mworLzkuCsp2kE6oaDU1dKlsKStrb4qUXqiFIpllX+rDdeMlxChG3kgXlLFa1ih0+BnYKOQR1N9EMtCbQmVRpJm3i8RzPQbA1AK8ejLYPNxiivMVpjhSfAkwhJcINXcigEqVSIwuFLsCLAGEWadMAVCHMy3R++kXCyvt1PcYpTvCAIYZCz/PhYaFypCIRAqJNjE7QPoRvgLKHtsSrFXMUKAq0ESgg8/shjvdsKHZFIQS0OUfsWNt5zzhhiqbAetBQEwiGCk3cRk1EdWU7BetaadVaaNYrSEmUFZ0wwz1F33qOpOqG9/gBG1whW7kE375tZPZ3MedB9iLBRkSFvwXua0wEdNWWpHrHcbFM6T1AUnItE1fkUMCphKfIIAXa4C14gVYRKz1U3TcOnjyVHL/l+hERGq7jpNrgJKANY1vU17k4dZakZFYKmDDhXY7aHHchGbNTa5LUzCASPxn2WhKF2Q8xqWymWnayy4APN2ZUlAq2YjjLuCgX1G8ZKIql5TV2yNu2BlMj6XUgVkvoJ3vZ5NsigDQfWIIImMmjipttI0zh21CPSEqkMoXKotIWwJatGYaXltR3FI6rEFGN0mNBpL4MU2HHOptK09OIeOlqzJhTFIMNoxcZykzQISEwC+FOP0FuAUxJ6EzgVJp3i6wbSILFMS+hOBEpSWeucsG4izLKwkw0CBf1JNs8s93i0rNKUZg+8jas8DKkiUi2IY33A8xB2BpXoxTqPEaCkQ+qTd5wng5iotUarlpAmAY0oopVG5NaxMxhjvcd5mFqPkoLLuwNsPiUMA0z7DEJFyGgZcZvI2M1CBjFCp3ibUQ62iW1GlETctRySE9BOEgJZ0mGPSFrqBsaloGHAjbvY0S4qqYIGKmHQORDi2FnFWwEVr2HaLydYeQO6dgciaiHIuTvYoqUFy1rzxlXJRloRUDcdIqMarcYKXhhk2KEZOLA9nh5l1XF0UeDwTLKC7uC6CMlozWojRStN5AYMs+sjPdaBLUtq0yuVg0C6zFRJUt2gFjXx2R4u23mW3SzeYAohUOlZ8BbxDPZXQgjONBIiP5h/r23EirsajteGOa1ihNMhQa1NFCiWazFSCnaGE7KyXHiuybRkpzupCGg7pcCxmsakYQieU3umW4CTd2U6gTgVJp3i6wbSoPDsThyRkqRh1ZU7iSQUoRAywIQrgJ9nlnsg0Ap1QkkoQtOINPaApYySgtw6rvZHTEpPKAWqmFRHqCcQZuVumlGEdQ4tBElkWE4jstJytT+il8HeVNLvT9kZZtSTiCBtIUwELq9M6k+oR+g+hA4RYQs73MbnY5K0CVGNM7XZDKELWW6E1GQJ2RZLoSVWnqjoYcc9hA6R0fW5VyENKl6fi2luB2TQQNfvQgZNZLReWVFF6wR+wNvbe3z7hmIpWiSgqrZETUqMEOQoTNShE0/JR5a7jSEWgr1hRneUH1LBaylppwmxluyNr6vmuxNLVHQJKdG1FRAwcY4lJVHpWYSp4aY7z05Eb/hsy3AJEa08K9F/aL1JK9K4/CpewJoSfHOS08mHpGGKTpo4PEZ5pDCsNyr7pn0fUYDxtGCrO8YoyUY7RWnJsHSsRRFGAohTo/pbgBN4ZTrFKU7xfCFkgBKWUAs2UoGQVLNfJ5CECllptE0Q0wwN3sNTu0N6eVF1EsUs5emEkVAhNaE2TK2bz38KIVitJ+TWcb43opwMkG6KMCdvDAJAJU1CoymtR1AdvdeicE5En+6N2BkOGU1K6nFEu1lHQWVb5PJKnHPCZl1vhJASaRr4bFTtN2nigBxLO80JlKMZa6J4Be8LGv4qcdklzLuIIEHGzcMCJGlOjGWYDNtIqZBxByGbrJopDXeNcnBtgYBCle7V1JqhtZQolpt1vmkp5/zuhL1BxuXehEDLOQEtvKNvS0o8SkrCuEmkBVf6I873Rzy9O2Yttsi4gQxTSlvSK0qawiPDBBlv3AQRPfzZFipEN+5DmMYz7l2oABl2gBLheojRNZaLETKMiV1J7KFXlky9pfAeo/SciF7ojTjfG3F5b7RAQEvv2Msc97RicCVCqtNO6C3AybsyneIUp3jeENLQMBmPLBsmpURSJRKdNCIHVJF93qGVQinFaj3m8jRjK5tgpUXgAXHyZkKFJlSK3dJx0HMgCQxLtZhL4wwx2cZrc2KPrEWQkKYNyqJAChi7yrqmFoU0kpAroxG7+S6xNnRadSLpZyIsUWV865OtjN+Hqq0jTISIG2ghsd7zRJax7aesNXIaocOoGBmuUrfb3Ce+hgpCdH0ZIQXigEek9xVhPynm5ZVKv4EwEo+ujNb7T2CHTyPCZE5A97FmDDulpW8t5+KEN55p802tXXq9Pl0v6NTjeQf0alHwVFYd13sgNoJmWmOv9Hxhd4+BLak12ggpEUpzeTpl6BypAKmCSln+DER0/lrKw9poFa8+62sshEDoFBmvgtKU3S+DCZFhHRWlNH3OV6ZTLpVTdovqU7pPRHfyki/s7dFzfk5AAS7lBc57zqUJ3pagzWzu9hQvJU5J6ClO8Y0EoXDTq9zVtBgpkQK0cCePyEFFjL3HzMQ9ee7ASoyAoNjF22zmfXrCvqakIdSyshZZq1E/16K12QKgyEqEF7SMQyp7YgjLjRBCkHQ2KW15SDGcZxYjBDXjKQXkpZuR0Gqsw/sSecLtmfahaqsIE+FtVtVLgBECLQTLERjpMULgswxhDa1YoAKLx1W3QAeNyn1ZCWlOiOVW5YW6CcLj8x28jfClQhqNNPaQN3BNKUIBha+iWmVZcHfg+JblAZkXlO76LZUWAiMk+98asfYoV+ILj0ISGQj0LFlJGzKbc08UEUuJ0Ob6+o4jot7OAiue//eSUDFShajmPQhZ1c0VY2RUp4FDCUGiFklOUXiUBS0kQkN5wDd0VMA9iaQdBZUVl4lPPUJvAU7Yt/spTnGKFwSpkEIRF5e4t+kIZDWveCKPTmfH8UoKirzgWn/KRmg4W0upa4UdPlUZ2p8wAl0dx2uaWnDuTJvVc0s0N5p0BxN644LVKKbT3qg6Pfrkzk1GjVUEipASMyP6270Ro0nJ3fWUh1c7WOe42BsQClt5N0o5O0U9ufs6CGlC0Cl+enlGrAR3BQH37xMmIVDTPnbcQ0YdTOcBvC/x0y1wdrFT5wqQQeVde0IgwxZSG3wpKgKaLKFb9+Jdhs+uLhDRVEqWTUBNKZrlFDvcQYY1Hjh3Ny9rZVzoF3Nx4Iox3BWFtJVGACE55WRAU2keXVnmbCrR5Tbel4ggIZGCe6IIvF+YozyWiHo3S3h7AS6RKgJXopMlVP1uvC9x2RYyaVILQtal4P4opDFTxO/PgK6GhletLbNk5MKMqPGaR+oCqUx1HB+czFGabzScktBTvCj4wAc+wCOPPEK73ebLX/7yc/75T3ziEzz22PPzHfz85z/Pgw8++Lx+9qTg9a9/PR/72Mde8PMIoRDRKgjJmrrGRt0SKjiJH3UhNN47jFTkWU6gFSvNlMRoonS96pa46ckj0EITSE0wm5eDxWjATiMi0RqC9olLSzqIIEwQuoZy1UV4uzdiMClJIs1yM6UVhazVospD1G7jZ4k5wnNi4zpvhNABUtXx5RBd7KCpUpFCKSm9R2cj5KSPCJLqCF4nyHAV7wp8sc3BjCDvyhM3XiF0Dd18AGHWkEkHnbQQuoYMlg4RUSEEa8Zw1hf40S7ChOj6KnEQc+dSTCsoeXJ3QmFLJIJISKwTlV/meIeacay0asQmZK1Rr75R3AArqs5iU82c/m84Yj9IRO34Mna6PQvPeGGnHFLH+FkghDQJUncQOGBIrd4htDmBqjqZB0VIm+0arShgo5nOxUrDvEAIz2YMCIPLJsgTaK/2jYiTd2U6gXi+Fk3eWXxZvLT/uZtLwnn66adptVr0er0j//1LX/oS73jHO1hfX+etb30rn/3sZ5/TXn/u536On//5n+eJJ57g3nvv5ezZszzxxBMAfPrTn35WkliWJf3+s3vLHQVr7bH7erHw/ve/nze96U1sbGzwzne+k6eeemrh31/o69fv9ylvsA95XhAKITUqPUeoJat6B+ULOIHHSlWXyWO0wBjJartSkmtVxSyqZL3KJz9hEFJhpMc7j/X+UDa1RxJLizTpoQvySUJgAoSpIVzGTn/CYFJSjzXNWkqoHLHxNKOYZhyj/ASf7+BmR9I3Zn2fVAgVVIEBuoG0Y1TZp5gxy3wyQGYjTJii68vzz4hQcUVohMNnW/gZSccXlSvACYIQAtN5Gbq+gUo6c9J8HBE95wtWs/6cgCIlgRBEgWGzHjK1Yu5SATDJSux4gJKCTqsJUlE4qIcCZBNpEnrZLkvSEvoSxPXj+BvXKaJ1vM2ww6dw2TWENC/suFuaqvNqQoQOcEWJqp2tRi5kRl1YCu/I8/yQCh5YECt9ZXdCRxeshBrvPGX/CjI4WTcc36g4ud+QJwjve9/7eN/73seFCxc4d+7cTf2Md5bpkx/HTYfX/8473HSAdxYZ1pDPorzz3uOyId4WSBMjjzgekFGN6K5XP2u3yDlHr9erjjdvQFmWvPOd7+Q7v/M7+Rf/4l/wz//5P+c7v/M7+eIXv0gY3tzF5sknn+Stb30rrVYLgM9+9rPU69Xc2K0giS8lvvjFL/Kud/3/2zvvOCmK9P+/q7p70s7ORnYXWKKSBBRRkqIgKMKpmEDMOce788sZz/A97wzoyc/D874eKKgoHpgwYsADMSuiBAUBgV0ym9Ok7v790bvjDhtYEHZnl3q/XvuC7qruqaqe7vn0U/U8z3iefvppRo4cyeOPP85pp53GsmXLkFLul/HbbwitesG/gSspF618NXbVDiDxRKhjBXHW50mPAcLx0pZCOtPDUiJkYq491HUPVeEQuwrKCVVFMQyNQLLHWTaJjSGiToKABLaE6rokiJeq4koqQl7aJRkkJXuIRAV+zUYTkOqGCtuL4U4FcytW+SY0X4eEXeu6O0J3OREKbB9CarjsQsLhIkwMIsFyPC5PnACNIQ2EOxsbC6syH+nLdSyhCRhyS0gNzd8Oqyre+UfofiRghQsgtAPL9mFVFCFdnpgABXBJ6bg1GZLkJDfRUCXbSitJ9bjZUhSmo9+kypOGbgrMIGA7a0QtyyTozSEoohwqKjDLCxCau15nIwCBiebvglW5FbNqlzNj85v6beA4Lkqky0e0eAt6Wk+0pHRMexMZVLIlKKksdyF1QSDFi9Djr7PQNJKTPGyqDNLbKAAyiRZtrRbTreM73tpRltADhWVhBcurQ30EEC4fZmUJdjiEnpyFnpzphLdo4E94krHClVihSjRPAD0lu24dl88RuZa15/Y0wuLFi9m+fTsPP/ww3bp1469//SuVlZV88MEHezzWNE1SU1MJhUL07t2b1NRUUlNT6dKlS8xaeNJJJ7Fjx45Y2aZNmxo835QpUzjssMM4/PDDmTdvXlzZ999/z9ixY+nUqROjRo1i2bJl9Z5j4cKFHHvssXTq1Ikzzjgj1o4LL7yQWbNmAbBu3TpSU1N5+eWXAVi1ahW9e/eu93zz589n6NChXHTRRXTq1IlHHnmEn3/+mU8//XSfx2/+/PkMGDCA3r1787e//a1OeWN9fe211+KOHTRoUGwq//vvV3D0idcy9Z/PMXr0xfzjwZfRNI2X581nyJAhdOnShYsuuojCwsLY+ebMmVNv2cqVKznppJPo2LEjJ554IitWrGiwP/uGxMZC0zQqbJviiEnUAk13QspgW3Fp/RIJzUjGCoWoWrcLO6+IcH4ReZEwZdUWJBdRZII7NmhSEMFDSWUYj1tQ5dHIC4Upj5r4qjOSZrjAqLZMS1cmdrSs1awHheowTboPIdxIzY3bSCZSVYhVvoWgNPAmpdc7S2CbUaQ7FS2pI7YVwarMd0RoAlrmATRvMrZlIRBxhoYai6hVVeR4kOtGnAAFxwnJIyUFZohiIlhuD9Goya7CXUQsm4g3idWRMIXRMDWmVpdmsyMSYVU4QsSVTqonmUjBysbfuayIE0IuqYMT/ui3vqDVOl56kkHT0Tx+pJ6E5u9MwOtFlG3ApYHtc5FvhincbbapIBJhdTBE+4CX3r4IZmmhE7EjuZ2KEdpMKBF6gHG8KwXR0u0I28bI7IzmS3Hy8jbwh9SdAMuRIEZKNnpKdgN194+VZfny5Rx++OG4XM4DVtM0jjzySH744Yc9HqtpGhs2bEDTNL7++ms2bNjAhg0biEajmNW5l1977TXatWsXK8vNza33XBs3buSHH37gnXfe4Z577uGSSy5h1apVAGzbto2RI0dy9tln88UXX3DppZcybtw4ysvL486xbt06Tj31VK699lo+/fRTunbtyrhx4zBNk4EDB/Lhhx8CzhrW5ORkFi5cCMB///vfBpdbhMPhOIumpmnouh4Thns7fmvXruW8887jjjvu4L333uPnn39m9erVsfLG+rpmzRouvPBC7rrrLhYsWMCGDRv45ptvYlP5UdNmxU+/sGbtBua88P+44a7r+PCTjUy+/T7+9re/sWTJEgKBAJdeeingiOFbb7213rIbb7yRYcOG8c0333D33Xfz9NNP19uffUZI7KqdSASGdHKVC9vJoqIJ6awbSyAnkNporiT06iBSwK9rB20AgU4UEmzqdnekEOhuH1LzIDTn+xOxLaI2GJrzYhtwObnUdWGD5kO60hDu9JZs9l4jXF4QXmwrhM8URKKABpYByVoDP4G25awn1ZOQvg7OGtFQQcJagKXbj21ZzuyCHb9Ey46CWVkCIormArue96IkTcMSkOKxqAjbRMLVx2oSt8v5dusSLNsJ+eaunniLImnnTsIT6OosDxNBrGhFvW10LMk+pK896L7fvLRBSAOBM8MoXV6kOzmW613qSaSkdCVJmuAyaWjRmmnbRKMagwIukswISDdaanuE4UrYa93WUCL0AGObUSJF+RCNoKd1QLobn86xbZtoyVasYDm6P6NOrLcDQWlpKSkpKXH7UlNTm7xGs2YKPhAIxKydtfH7/QghYmVS1v+103WdadOm0bVrVyZMmMCECRN4/vnnAZg5cyajR4/mqquuomPHjlx88cX079+f999/P+4cs2fPZty4cVx00UV07tyZRx99lIKCAj777DNGjBjBokWLAMd6edttt8W2Fy1axIgRI+pt16hRo/jvf//Lp59+im3bPPHEE5SXl1NWVrZP4zd79mzGjx/PpEmT6Nq1K//4xz/iRG5jfX3xxRc5/fTTmThxIl26dOHxxx/H46m1dklI3G6Dx/7yB7p0ak9Gkp9/z3qLu+74I6NHj6ZTp048/vjjvP/++5SWlvLEE0/w5z//ud4y0zTp3bs37du3Z+TIkTzxxBP19mdfEUJiWxEIbiNDCvxSw7YtNM0JVO940CamCJW6hxSXEYurKGybXMOFV9fQsB1vciOxRagmBP6kJFICGVSFongiUVJ1g5DlGMo0ASmGINUFMlyObZsII8lZ69qKcNb2GViVJRjhCixXKlpyR6QdxmXWv0TJtkyozhseE6KChP0+CpenOpyZBrXCDlnBcqJlO5HuFHR/DjaROl7z4HjOp+s6/XySUGWQoBC0S2+HoQlyRCk93Qa5HgNdgke30AVk6Tpd/Slkutzo3gB6yiFIdzJW5Zb6hagVQRhJaJ5sxzqt/Ubvc+kYbBxPdh/Snx6X693lTScrJRdLWthVETrqLtJ3yx3vFhrddS89xS7nmIzuCKk7Rp561rYq9j9KhB5AbNsiUrT5gApQK1z1m9uZlJQUE1Q1lJaWkpTUvD82mZmZcWKuR48e5OfnA7B+/XreeecdMjMzycjIID09nUWLFtWZ2s/Ly6Nnz56xbV3X6datG3l5eQwYMICSkhLWr1/PkiVLOPfcc7Esi23btrF48eIGReiwYcN45JFHOP300/F4PCxZsoQBAwbQsWNHYO/HLz8/n0MPPTS27ff7ycnJiW031tfNmzdzyCGHxOomJSXFHYuQZGWm43HraEJgSI2Nedu44+6/xM6Xk5NDOBxm06ZNrF+/nttuu63esn/961+89NJLDBo0iBtuuCHOWrtfEBLpTkfYYTSrgojlBER3LKEC27YSd/pTd5OWpP+a396GiqogpukE2DfsKELzt2gb94SUAr/XTTufl2SXoLgqighGMCREcUIyeXVBD3chdvkuZLUAay1OSTUIzYUVrMKsKsVwedF9KQjdj637MawgdnhXHSFqhyvRaoXXEpoHzZ2RsN9HqbuRQsNGi1lCrWC5s05T19ACHZx7TUuqN3yTR0owowTChWR4LFICPvxeF+lJXtwCksxiPMJEl+A3cPwUpIbX5can60jdjZaUjp7UGSGNeoWoTRSpJyFcKU56zt/otCek7jgu2lGky4s7s2t8BU0n3ZWM1+MjYkJFRRWWFX+dy0MW6dEyUg0TLTkT6fKDGXFSviZaVI42ihKhBxArWHZABahZXogV+e0itHfv3vz4449YtdaWrly5kj59+vzmcwNNXhdXWFhIMBiMbW/evJns7GwAOnTowKRJk1i7di3r1q1j/fr1bN++neuuuy7uHDk5OTHhCs64bt68mZycHDRNY/jw4cyePRuPx0NmZiYjRozg6aefJhqNcthhhzXYtptvvpldu3ZRWlrKv//9b37++WcGDhwI7P34ZWdns3nz5th2OBxm586dse3G+pqVlcWWLVtidSORSNyxQgikFNhYuDWNrkl+2mdnMPWxB+POV1hYSJ8+fejQoQNPPPFEvWWHHXYYb775Jp9//jnHHXccw4cPJxKJNDhGe49E6D50Xw46EXaUVWGbNpomYnnjEzLIPqBphhPkvHpbCkFlOMr2sgo0wKUJZALHCK3BZehomkG6z4XLJTGiFml2iAorii4EVBUSLV7rmEVdboSQrWpNKDjB1O1wEOn24vZnAIJi0yTDnUKqJw3brIwTona4CrOyGFHbamZVe8YnqiVUdyN0HYGzljomQA03elKaY831dERg1/Gat20Lw4pgBcsox6Z3lp8IGrYNhq7h8qSBDXakAK+MkGTY2NFwtWOhhkfToNoBTBoepC+3XiEqbNuJsyqkk5rTldJwh5recyyr/meS0Az8msTv0knxeQmbFjtKK4hWP6ejEZOthSF6+MO4UrOdmL7CwLaiKjxTM6JE6AHEtswDKEALiJYX7JfF06NGjULTNKZOnUokEuGf//wnlZWVjB07Nlanb9++vPrqq/t0/szMTIqKiigqKmq0Xjgc5v777ycajbJ06VJeeuklzjrrLADOP/983nzzTb7++mtSUlKIRqNMmzaNNWvWxJ3jrLPO4tVXX2XJkiWYpsnf//53bNtm2LBhAIwYMYLHH388ZvUcOXIkU6dO5fjjj29ULN95552Ul5dTUVHB1VdfzUknnUT//v2Bpo1fbc4880zmzZvH119/TTQa5S9/+QuVlZWx8sb6esYZZzB37tzYOtC//vWvVFTsPvUlYtaQFMPFxeeMYeoTT8Wcw3bt2sXtt9+Opmlcdtll/P3vf6+37IYbbmDNmjXouk6XLl0oLi7evyK0OkagbqTgdXsJRkxKgxXYOM4SkHiB6muQmqu6jdXbUpDqdVMeMolGKpCRqlbhXavrBhZuNKnROdlLltdDphbGsMqR4SrMsu1onkykxwtYjuWptYlQqSM9yUhfOwxhE7VtKk2Lbh4PuisVYaTGhKgVqsQs3eFMx9YSnI5TkgeRaNm7qhG6gTC82KaJFSz9VYAmZ2ELEzQv0p0BmteZgagWolbVZsyyX5Cl20AIbG8ah6a4SNIttgdtJDYuXXfytNuQZexEs4OES3cg3T4QNi6pIXQDaXgcr0KhIbwd6whRG2KWZOlORxq/faZA6F6sys31LqlA6ng1jXRdYOmSrGQfYdNiW0k5xcEwWwrLEVikpWeA5kLYznfFNiMItxKhzUVi3lFtBOn2I3QnBVhDf1Y0TKQwD7OiGM2bgvQGGq1vmxGiJduJlGxHuDxI997dyF26dImtzUxNTWX+/Pl4PB5eeeUV/vnPf+L1ennkkUeYN28efr9z7nA4zMaNGxu1FDZGx44dOe+88+jQoUOj3vG5ubmUlpaSmZnJqFGjuPvuuznmmGMA6NmzJy+99BK33norPp+Pvn37EolE6sQfPfLII5k2bRrnnHMOPp+P5557jtdeew2v11l/NGLECIqKimIidPfthmjXrh1dunShffv2aJrGs88+Gyvb0/jtzlFHHcUDDzzAmDFjyMjIYNu2bXTv3j1W3lhfBw0axP3338+JJ55IRkYGmzdvJjc3F8Oo/TJSvZ6ymkvOHctFF5zLCSecgMfj4ZRTTmHUqFEAXH755Vx88cX1lp100kmcfvrpeL1eJkyYwPTp0/H59uPDWcjqcFISt+7B73GxMxykLFKMLqqDSiXolJjUXUghsWr9+KX6PHjdLkKRKkTV9lYh1lyGCyk03NJDBx3SkwJ09LtpFykhUrYZoUm01G4IYSJsx7EkUYVYg2gGMqkdQgh0YVFmmuQYRmx9oDRSEEYqVrCQaOFqbKkjfamOoKrBSrwYobsjPQGiwSqiJb/ExQEVVvU0uDSQng4QrfzVaz64E6uiDMOO4PKk4HO5yHHrJPvC5IVDlNpRdOk4AUl3OlWEWFu8ji2RENITAAQuIRFSx8jsjDC8WFVbIVIaZxE1I+VOxq397Owj9AB2uBSramsdISp0HSF1snRBxLLxuQyykn3sCEX5bvsuCiJRUgMePC53tROkBsJwonIYKltScyHsel8hFPVREyc0Ly+vQQ/vGuqLE1qnThPigO6OFa7CilQhNAPp9qN5k5scJ7Q+R5mkpKQ4AVNVVRUTbDV88sknTJs2LRbOqD5++eUXunbtGrMmlpSUkJycHOeEFI1GKS8vJxAI1HFOMk2TiooKAoFAbFq7IQemSCQS12bLsmLnbaweOGNe0zZN02JtTUpKQtcbX6NU4+1fc1x91Dd+NRQUFJCR8auV27ZtLMsJUVRWVobP56tz7vr6UPvYcDhMWloaa9euJTc3F9M0Kcr7Gr9RjuZ14vCZVdtwtRuKdAUaPF9jnxUOh2Oe//sTK1JBZMdnYKQx79svWF2axM7SLQztlsLZPbqgay5c7QY5VpgEo6K8mLc+mM2PPxYiIha6W6fLUV3YWBKCYAFXZ/xC6rA7MVKbFld4X9j9+7QvrNhcRP5P36BrFXjNXRRYaXRzVRCI/EylHaF9IA1XxmDCu1YivRJv19EYKa0rO9muXbvwFa4mWr4SS/ewPGjR0+slqda9ZoeriBatw7ar0PztQSbh7TIQoRnOvVa1BT3tcPSkji3Yk8aJFm+j+OtZCDZhZPRHejIBsEK70AOHIY1UbDNEtGQZ6H6wLMI7fkC42iH0cr6MdqOjX6ODy8W7RYX8XGoREB6OzaoeJ9NiU9Fm8qqKyXLpHNJtGBW6h+Hp2ei1ntVm1TbARvO2x7aiWJX5WNFKpCsDd4cTfvNa0NqYlVsJbf8EgUQYficzU60ZimDeD/xUEuHtAsmhfo1oxGTp9mJKolWkenxkJAW4tJuPND0MdhQ95UjM0u14uhyJHmhX5/P2xz2niCdxIyknEFOmTGHKlCkxEdIUhNTwdB34m2N47hEpm7SAWkpZx2u9PuoTUEOHDmXIkCGNHhcIBOJu/t29xcFxEmqoDZqmxURkQ+Kzht2FkpSyjgCtrx4Q89KvTX1tbaiNe6IhAVofQojYOWsC++9OfX146KGHuOqqq/D5fNx222306dMn9lKkaRopqRlEdv7kOCJIHeF4+zR4vsY+CzggAhSoZVGzMYCdxUGyPS4yfKlIM4hlVpCokzVSd2NIieTX8EyVwQi7SkJ097vRvO1aRYgXl8vAkjrJugfTgrKKIIZdSEpyNqkuDcxihOZFuDKRogSpJ7azVX0IIRAuL7Yt0bAYkJQU96yyw1XOFLwrHc3rwarchvTqsWxXdqgAzZuN5s1p6CMSAulLQfdnYhPFtiqwwgJhpDuzDcL5LgrNjXBlYlZtwSovQ+oBXFn9iRR/SYZdQaaehi4EqbrOwFSbQE0IK9MiWlFAltTwpuSQRAWhSBEuPSdOgNpWxIn/KTXsaBVC9zqB/kvXAPZ+FaAA0pWG5s7EtiPY4TIstsYJUWF40AkikEQjJluKKsgwdNoH0qgIRggGq3AJD9hm9XIL51mpYoQ2H4n5hE8wJk+ezI4dO/j+++/36jhRvVbmgP41w3SlYRgHTIgo9p4OHTrQv39/0tLS+Pbbb/nPf/4TVy4ND7YZjgXYrp7XbpG2NoqQ1etCbQwzDAJSkzz4XclovvZOgMNEnY7XXOhSj8U5t22b7cWVCE2SnhJAGsmOo0OCo0uBrbmw0SmpjFJYVobucqEnZyHdAYSeAtLtiE9PZsLlTm8q0p2EbeGEmapHgKIZ6IFspCcdpBfh0rGqtmJFqwAbLdAj4b2lpcuLkdENgdvx/o+WY4V2OGK6tkOV9GOWbgc7hNHuMKQ7DWmkkaWZJEULkLaFW0gCho5XFzEBihnFnZxBuuHGl9KVqG3jDu9ywqzVYEVAcyO97bGiTsQQIXWEJwvpTt3vfRa6B+nNQQgd6cnAChURKfwWy3QCnQrDg4aJaVpsKarAtm1y05LoFEgiw+9FJ4oR2YFthUG6Y1P6rWE9d1shAX+ZFApFY1x88cVs2bKF8vJyPv30U3r06BFfQehIb06tTC9WYmbuqRGhQqBrkB5wo+sSt9QQRrKzVi9BHZM0KdA0g6ze6ST374DVIRVDk2Qke/BqFmhaQua93x1DSmzNRXF5FdtLTTyajTs50wkUaoUda7rQEMKN4e+GMBIzjeqekG4/2AIrUhzbt7sApXpWQmjJ6P6OWKEizLJ1aIFDka2k33pqe+e6uTIQmh+ragfYIuZkZUWCmKXFCOlFJqUhPe2clwxXKkL3YVshoiVrEZUlmLYdJ0A1fzrCcGNbFpovHcuViV8KzIq8mBC1zRDSSEHzZIJt/irqAOFKOyB91nztnZcLVxoIDc3dDqsiD9u2ndBR0TBlZVXYtk1OWhJul2ON1Qyd9KQkJBGs4HYnA5MZBakrEdqMKBGqULRSGlzDKiRS9yJ9HbDMEHaklITMHY8jQLFt3C4vuhRIaeHSnIDbQvclrne8EIQtg2jEZGdZCMOQtEv1YUuJW5rVaSITf7WTJgUVps62ggK83gAZyR606ilY2wo7llCcJQfSm9MqlhjUh9BdSM2DHS7GipQ0KECJRhC6jvSkgubGaDcULenArevd32i+dOfFzoxihW3siIldPRVvRYJEC/MR0sBo1x/Nk4bQq5cm6KmAhnRlQLQKt11GOFRFtKIAOxrBSkoD3QWmiZQSYXiJSp0kf0ewrZgQte0wwkhGGCkIPQlMJ4SgbZvIA+TYJVypSCMFq3Izur8Lwp2BFS5xHKQ0HRkqAdsiM9UXE6AAUcsm2eVG92Rh207IKSsSAjO637IRKvaMEqEKRRtDCB1b1GR6aY+NHQuHlFAICUgENkKTVNomUlgYmuZ498em6xMPISAidHYUlSI0QTTJRZ4Zocoy8QjTiaWZ4NO34IjQ4qiG1+Wic057RHWmJMAJWVM9/S6wW59XfC2E4QFpIKSz5jNatK6uAMURasJwgR3CnXUMRqB7q+q35gkgPQEiJdvQvSnoyTnYlicmQBESI70Tmi8H6esWm2mQhh+BhZBehDsDj8dDqGgtRMMUeAKsjpjsiESwImEn6otmUBqJ4DF8aEm5MSHqBHr3IqTmTMlHHGdYAXCAXmCEEGj+zk7M4ZReSHcq0p2CHSnHDhegY6Mluck3oxTXyh0fsQQ+DYTmRXNlINCJFG5Eeuv6FygOHK3n7lIoFE1DyJi3jNR8SMMJpZJoCCHACgM2bk3HtE0MCbqozvoSS0OYeAgh0KQBRDBcGqaEoG0RsWx0IqB5ElZA10YKJ0RWSkoqyGprNLWmUWX1GlAhnCn6Vor0+NGSszFDJlZlJbZdhfR64gQogBkuQxgSPa0/mi+xHZHqRepoyVloSakY2Yc4oaYioTgB6vgSuKqfCw5OCk2BFQkiXekkt+uFLSToJkGpU2aaBG0bywwjvI513LJtXFI62aRqhGi4OPbiovvaIzQ3VqQ8LkboAem2px1G+hFId6rjSGckIz0Z2HYQl9tCSkGZaRLm12BApg3JRs0bl5to2Q6sYDnSn3jRONoyrfepolAo6kdo/OqzbTtWuURcEwrYthPSxS0lAU2iSeF429qWE/Q6gYWcx+3FFTQwt5ZgbC6hY3XueENYjjUoQce8NlIIvC4Xu4KCkqiOprnR7CqwI9UOLY5zlW1GWoWobgghBK6MrtihCnBloAc6YVtlWJGSWB3bNhFmOUbGEWje7BZs7W9A6OhJmbjb93CyF7l8mFUVcQK0XqQXpIFVVYz0+PEkZaNl9gAzSLZdzqEeD9mahgA0tw/Ttkkz3LhkzTpax0FISvev1nPdh57SBztaXh0P+MCJUCH1WCg3obmdWJ/uDDRvNi7dwjBD5Ohu0mu9dJg2eDUBloVZvhM7EkLzZ6DtIbmMYv/Sep8qCoWifoT2q93TtqsFaGLe6kL3YoVL0OxydGEiBehSq84bn9hhUpJ9XuwKC6M4SLSgEhGOImwcEaq1jmDXmhRkJnvw6IKt5VGiWgo6UexwEUJLQkjdcWYp25mwLzJNRSalY6R3Rho+hDcbofmwI8UxIWqHihHudhhprSsOam2EEAjd42Q/sk2kOwnNn4mW3K5hAVpznPRjluWjJaWiC4Ge3B6MFFxmkEy7DCMSRPP6EbqHqGXh1iSGFLVPgvRkx60b1rxZaP7uoPuaz1FPGk5YOkB6s3C709GtENGqMLLWc9CyBR5hOxbQaBgtkIP0+JVTUjOTmL9MijbFt99+y8knn0z//v355Zdf9vr4lStXcvLJJ+/TZ69du5aRI0fu07GJwvjx4/nuu++aXF8IDYTEtk2qk+UlrIAQug9hJKNTRTRSia6BrtVYQhPbOcAw3DGxLwQUVYYIRULotoUwEj88EzjT8Zqm09Gv4RI2wpWBkXokekp/pDc3bi1hIlulm4KUBkZ6R2wshG0jXJlxQtSOViF92UijlYuQakugHa1EGskYmT2c0EmNYJtRrGAVNgJpeNClQBhujJRcbFvHtsJYwR3IpDQQgoht4ZEaWu3vhBmuNwyTntwNI6VXs4W4EkJ31sHjiGvD2w6fYVAejrKrvAKrepLIMi1coULsSAjdl4ZWvcygNYRWa0u07qdKgmNbpuMxeED/mhZAf8uWLfTr14+ysrJ6y9944w1OP/10hg8fzp/+9Kc95nnfG6655hrGjh3LSy+9RMeOHRk8eDB5eXkArF69eo8isaqqitWrV+/TZweDQX766ad9OrapLFu2jHPOOYdBgwZx7bXXUlBQEFe+bds2LrvsMkaPHs2FF15Ifn7+Xp1/zZo19eSHbwRZvZbStqrTdwoScU0oOD8YmicTQ/cj7SoMqwKtek3ogZy+2x9otSwmmpR4XDqRaAjdqnDWhLYCNCGwpYbUddp5LMdRQxpIIxVwxQSo5s9s1dPxAEgN6Q4gNd3JBS9EnBB1RFvrd0oR0g12FDtShvR3wUhphx0NN1jfNqNEivKxTapTfQp0IZ1QTEnpCM3nxB6VFthV2JZJxLLw7eZBbtthhKvu+AmpxbI3NQvSiHvaCZcXn8sgyeuislqIRqMmRWVBDDOM5s9AuH1OKDupKUtoM5PYpoYDxEsvvcQNN9zA22+/zbBhww7IZ9iWSaRgKXa0kbSdVjQWPkcYgT2+Kdq2hR0pc36gdT9CcyF0P0bGntN2hsNhVq5cWW/Wp7lz5/LSSy9x+eWXk56ezr333ss555zDBx980KS+7olVq1YxadIkOnToAMCsWbPIznbWXFVVVR1wkXggycvL47jjjuPOO+/kD3/4A9OmTePMM89k8eLFgBPE/NRTT6VHjx488MADzJ07l3HjxrFs2bImZWDaJ6otoY4AtavXViamCEU6YtnlzUTXyjCtCkSkGGxPLGNNomLs9mOV5kuiOGqimeVIkdhLCWrQpEBKgSUNsCIkux2hubs3dbSysPWLUKE7mXCEXX1vVDvHuTKxg1uwrSond3hrR7qwI6UIdzqaNwtLBqun5+06z4EaAUo0gvSmovkkWCF03c2OcAhDSyIlKY1I8Wb0lK4UhCvZUfkL0p1OF1989ixh2wi95ZehCKGD0KoTE2hIlxcXFskeH7op2FRexY/F5UjThS85DenxY4WCYJoI3aVEaDPTyp8qe8/69et59913Ofzww/cqDefeY2FFypy3SCNQ7590pyNr8uuGi6GRusIIIF2pSG97QGCFi7GhWuT+ttSgp59+Oq+++iqnn346xx13HE8++SQfffQRlZWVAFxwwQW8/fbbcceMHz+eRYsWNXpe0zTp168fVVVVjBo1in79+tGvXz8mTpzI9u3bY+cuKCiIlW3ZsqXB87344ouMGTOG3/3ud3zyySdxZXl5eVx55ZUMGzaMiy++mE2bNtV7juXLlzNp0iSGDRvGjTfeGLP43nTTTbz22msA5Ofn069fP9555x0A1q1bx4gRI+o93yuvvEL//v254447GDZsGNOnT+fLL7/kq6++AuDzzz9n1apVzJgxg2HDhvHvf/+bvLw8Pv744wb7+cUXX3Dqqady4oknMmvWrDrljfX1s88+47TTxjPunFt5fs58zrpoMt8v/xn4dVnDf/7zH8aNG8cdd9wBwKJFizj77LM59thjueOOOwgGg7HzNVSWn5/PZZddxpAhQ7j00kvZvHlzg/1pFKGDbePSNTomWXg1HaKVmMHtCW8JlfWk9nPrBjJUiXC1/I9xU5BCoAkBugfTtHBpoo4AFbrhhGtqxd7xULPu0eX0w7bj99sezKqoE5u2lSN1D1akHC2pq2PVdichDS92pCquXswCGqpES3VSXRop3bDtMLqQRGyTskgELTkD6UtFT25PUEthZ6iSyqqdeORughbhWGFbGmk4L7eWE45JaAZuYRO1IeBxUxk2KY2E0XRwexwnJIHAtiykq3U4FLYlEu6psmrVKt57772YANqdUCjEZ599xueff0443PAUQ31EIhHuuOMOnnjiif3R1D1iR8qwhURIo8E/afjRk7sihHSC60Lj9XUvmr8rUvdiVe3ANvduDOpj95Sca9asISMjA5/PeSAPHjyYf/3rX7HyZcuW8cknnzB06NBGz6tpGnPmzEHTNP7v//6POXPmMGfOHNavX08oFALgL3/5CykpKbGydu3a1Xuu/Px8nn/+ee655x7GjBnD2LFj2bhxIwAlJSUMHjyYjh07MnXqVHr37s3IkSNjn1HD1q1bOfbYYxkwYACPPfYYBQUFjBs3Dtu26dChQ0xoL1q0iG3btvHuu+8C8PHHH5OWVn+2j9LSUtLTfw3p4fV68Xq9fPPNNwAsXbqUAQMGxMbS5XIxaNAgvv3223rPt2XLFk466SROOOEE7rvvPubOncu6deti5Y31NT8/nzFjxjB69Gj+fOtlvP72IhYs/JyKKmccqqqq+Oijj5gzZw5//vOfueGGG1i8eDFnn30248eP59FHH2Xt2rVcffXVAI2WXXPNNbjdbqZNm8aJJ57In//853r7syeE0AELKTX0cAkZ3gyEnoQdLkn4/M2G4aL2ModwxIJgOUJ4kK7W4WErhSNEbd2NaZnoZt1wPrZtV3ez9f84C63a27/2MqZolGjZVqRMRrpbxzKKRhE60pOJ5s1yNqUEw4Nt/hojs7YAxeXFDjkpLTV/eycFph0l2+UlzWUgDB+ujC4Iw0e7pHR6prQnU4IrvAO72qJsRyudiBAJIOKF1ByLtm1Wb0vcGkSiNtuKKkiROh18fvy6wBUtwLJMx1JsmUiPfw9nV+xvEma+65133uFvf/sb69evZ+vWrfz8888ceuihcXU+//xzzjrrLAKBAKZpEgwGef311zn66KMBuPfee3nsscfqnPukk07itdde4/7772fy5MmkpqY2R5fAjmJVbkH4uzS6qL8mzppZkY9ZkYeW1KlRz2AhdaQvF7t8Q6PT/ftCcXExkydP5p577ontu/DCC7nrrrvYsWMHWVlZPPvss5xzzjm43Xt+6+3Xrx8AvXr1IifHibsna1lUunfvjq7rsXoNYds2s2bNIisri+HDh/PZZ5/xzDPPcP/99/PMM89w5JFHcv/99wMwZMgQ3n33Xd5//326desWO8esWbMYPnx4zAJ45JFH0rFjR7766itGjBjBM888Azji69Zbb2XOnDmx7YYsoccccwxTpkzh559/pkePHsydO5eSkpKYhbWoqKiOgE1PT29wze2sWbMYPXo0t956a2y7ffv2sfLG+rp8+XLGjBnDH/7wB0LbP6VnFx/dBn8eF2tTSsnMmTMJBJy1W9dccw133nknl1xyCQAzZ84kPT2dp556iocffrjBsl27dnHDDTcwaNAgBg0axLnnntvo9WsQqWPbFlJKpBUl4ElyQq1YRQkbI7QGrZZItm2bLUWV+F0RXP4MxzmkFaBJ4YhQzUVlMIRWXg5Ju4XzsS3H86q1T8eDc10kUKNBo1EiJVvBNtFSuyT8i09TkK4Aroyj4rzUpW5ghquzF9WagteSM5HuJDyd+lO5Yzt6oD1RaxdEyggYLozqZ7UwHMu+V9PJTsqgUGq4rRBW5WakryNWtAzN3x2RIEtohObBjlZVvzYJ3MJmR3EV6bpJbqoXXDqFlRF0uwQ7bIJ0I0zTSe+qaFYS4xuDY+l68MEHMQyj3nWa4XCYSZMmcfrpp8eschdffDHnnnsuq1evRtM07rrrLiZPnlznWF3Xqays5OGHH2bq1KmAYxUaM2YM77zzzgHznha6DytUErtR978Q7Riznu4PysrKGDduHCeddBI33XRTbH9GRgbjxo3jhRde4KabbuLFF1+MTV03F1lZWWRlZcW2Dz/8cNasWQM4zk3ffPMNAwYMwLZtbNtm06ZN/PLLL3EidP369Rx++OGxba/XS48ePVi/fj0TJkxgy5YtbN68mcWLF3Pvvffy9NNPU1hYyKJFi/j9739fb7tOPPFErr/+eg4//HCys7PJzc2lX79+McHt8XjqWPXLy8vxeuufrv3ll1/o379/bDsjIyO2lnZPfd2wYQN9+/YFQGhJpPps2melUduClZ2dHROgNef76aefeO6552LnsyyLjRs3Nlr297//nT/84Q9MnTqVE044gSuuuCLu+jQVx5MfdGlgePy4q9djaZ6MViBCf72fTcvGtGySkr1Iw2wVKTuhOui+gCpbsq2wlAGpSXXjSdo2Atnqp+MBEK7qSBGWYwEt3QZWCC0lF+nytwkRKnRfXYukdCyDtQWontYB24w40/UeP1pSCKHpSE8WZnAXbimJ1Fq2UEPYsvC6/Xh8HuzgdsyKPIR0o3nqn8VqEaQb7GonXCExIhWEoslkpntI9rkpDEdJ9Xgw3AI7vMt51LikWg/aAiTMk7Jmmu+LL76ot3zhwoXk5eVx++23x/bdfvvt9O3blyVLljBixAhcLledqeXa1LY+jRs3jnvuuYfjjjtuP/WgLk4AXy92aNcBE6LCSN4vba2oqOCUU06hd+/eTJs2rU755Zdfzm233UbXrl0JBAIMHz58v3xuU9ffFBcXE41GY/nSd+7cSUZGBgCZmZmMHj2au+66K+6Y9u3bs3XrryI9IyODHTt2xNWpOU/Ny89//vMfTNOkQ4cOHHfccTz33HOUlJQwYMCABtv28MMPc88997B9+3ays7Pp2LFjTOz26NGDn3/+Oa7+mjVruOCCC+o9V3p6Ort27YptW5ZFYWFhbLuxvj7yyCOxY4XhBT2Z4pLy2LQUUMcZKjMzk/POO4/Ro0fH7e/evXujZR6Ph6+++oqdO3fy3HPPMWjQIH755Zc4K3eTqM7uJAHd5XJSduKs0RIJLkJ1TcOuJfAzU3wYegUygdON1oeUgi3lJrqu4U7rUDeepG3XpFZqmQbuR6TuATuIHdGJVpU6Qc39yc7Utak1Wxih5kZoOnY0EidApTuJaNlOhGe3pSOaCztchFtmURWtG9opbFukGQaaOwVLCKKla9ED3ffbb9H+QOhezJKdSHc6QkhcdpSUJDfJPmeGImqDXxPorhRMO+R4xtsiznqsaB5azVNl2bJlpKam0rVr19i+ww47DLfbzffff9+kc/j9/tifpml4vd5GPZRLS0vJz8+P/dUWNE1FupKR3hzsaBVW5ebYGpqGiEuBVpGHvYf4bvsjdl8wGGT8+PF07tyZGTNm1CsMTz75ZAoLC7nzzju58MIL48pOPPFEPvroo3367NTUVEpLS+us36yvjU8++STgOObMmTOHsWPHAnDWWWexcOFChBD069ePPn368M0338SJOXBePF555ZWYKHzxxRcpKSlhyJAhAIwYMYIpU6Zw/PHHAzBy5EgeeeQRhg8f3qi4+uc//4nP56NLly7cdtttHHnkkQwaNAiA0aNHU15eHpvaf/3119m2bVus7bszduxY5s6dG1vv+tRTT8WF1WqsryeffHLsWCFdPPOfJZSWV9X7OTVMnDiRV155hY4dO9KvXz+6du3Kq6++isfjabTsgQceoLS0lHbt2jF+/Hjy8/MbXMfdKNXfX6lpjhCttiDatcoSFSkllsdNJElHS/Oyy7YQmEjhalViJmrZIHSy0vzoRt2XXtu2nGvRFhw2pI5tlhEp2eQI0ORUpGYg3e1axXduXxGaUUeAAo4zjhE/KyOkGysaQo8UYdk2ReEQa8tLKQo7z+iwZZKsu7BtG+kKINwpSH/3hHLokboXK1yIFXLC5bk9SZToFhXVa4HDlsCnVwe015Od6XgpW01837ZEwlhC90RxcXGcA0gNja2va4x33313j2sa//73v8fW3tWmqKiowenUGmwrgl1agq1FQOjYEQPKtkBJIXja71E82qYXgtX1vR3rt4jaUYRZidAL9phdpmaMhgwZEieoHnjgAX755RcWLlxIjx49YtO5AC+//DIdO3aMbU+cOJGpU6dy6qmnxmJhmqbJF198wS233FInPmZtCgsLMWr9wBUVFVFQUIDf72fw4MF06tSJjIwM5s6dG7cGEpxrn5WVxXvvvccjjzxCYWEhl1xyCUcffTQFBQV06dKF++67j5EjR+Lz+SguLua0007jhBNOYOfOndi2TUFBAX379uX6669nwIABpKWlEQqFeOqpp4hGoxQUFDBgwAC2bt3KUUcdRUFBAYcffjhbt26NfU5DrFy5kpycHCorK+nXrx9PP/10XP1p06Zx7bXX4vF4qKio4Iknnoi1aXf69+/P+eefT+/evUlPT+ewww4jNzeXkpKSPfb1iCOOYNKkSc6xaan07pFLTlYGVRGNgoICiouLsSwr7nMvvPBCfv75Zzp16kRmZialpaXceuutFBQUNFrmdrvp3r07gUCAgoIC7rrrLkKh0B5fJnbHCpVhlkUJRSXSTCIU0gnbYaxQFEMvR+h1pwMThcryclIy2xHyVGC7k7BDoFkaFaaXwpIyZOjAtr20tHS/nEeEykjTo1hCo6KyCmnuFv8xGsI2TaoKixJKaDSV2uNkhyupiqQRDpdgBFIhaKN5OiFDLsxgOVWlFWhWw/d6ayVaEaIyaONOyURENIg4US6iQRN3eRDNKoiNk22GCYcDmFVlhKwIIeFlR6gK3Q0+r4YZtohEiymsqE55KpLQKyUilDjjZoWriIRToaAQhBukH82SRKMSTdNw25CERnGVwDYFtuVBRMIES8sRouGX6f11z/1WamYB2wLCtutZ9NGCfPHFFwwbNqyOY9I999zD9OnT64TwSU1N5bbbbos5m+xPSktL4750W7dujQVaz83NbfTY+uKE2mYQO1rpxKszkvf4QG9KHNGmxgmNRCL1Bnzv3Lkz0Wi03tBIPXv2jFve8MQTT/Dyyy/z6aefxvZ99dVX3HvvvbzwwgsN3hgrV66kV69esan0H3/8kUMOOSR2btu2yc/Pp6SkhF69esWJVXCsoJs2baJnz54UFBSgaVq9zmW2bZOXl0dmZmbMGz0UCrFhwwZ69fo1FV8oFGLbtm106tQpTpBHo1F++uknDjnkkNhLxsqVK+ncuTPJyY1PNRUUFBAOh+sI6Np9+OGHH+jfv/8eX2DA8YKPRCJkZmby888/07Fjx1ifGupr7WODJXn4rE3kHHY2330+n55HnBQ3jrsTDofZsmULnTp1qjM70FCZaZps3LiRnJycOm1oKlZwF6FtS4iIAN/+/A2Hd+1PVMsg1VWMkXUM0khcR4Gtu4p46YP32BrdQnpyMuWmQa/kYk5Ozyaz72lo3gM7PVlQULBffox+2FzCN5sKSSlbx3Gdkwn44r+fVrgS24yS1Gv/LMFpbmqPkxUuoXLdm4S2/YTmEWiBAej+QxFCEC3djqfzEeiBvV/bnOhEirdSte5rXO26xu8v3Y7vkCFo3kBsnGzbIrx9CXmlO/mxtAifJ0CpSCLNcJNsGOwIVTHQY5GRcZgTF9S20Hz1P/daCitURGjnVwhszKpdrNy4hReL0ukbMPBKjfUVJsdmuBiSYWBHy4mWFqCnDSC57+hGz7u/7jnFr7QaS2jXrl3ZtWsXwWAQj8cJo1FWVkZJSUncFP3+JBAIEAgEmDJlClOmTNmruKJCahgZA/mtMTz3jGzS1J9hGI16oNdnZa6NZVk8++yzcQ5L4Hi9z549m8beZWpbVwH69OkTty2EoFOnTnTq1Kne4z0eT0w4NfYAEELQuXPnuH1utztOgNbs69KlS53j6/PS373tDbGnB5PH44kTt3siJSUl9v8ePXrUKa+vr+B4sF944YX4XVn89f7H6JCTSbeuXWJtqE+AghM6qqH7qKEyTdPo3r17E3rTCEJiR4rRNI0Mw8Ct6USrU40m+ppQISUdkgwKygSRqjDJLgNdghRNuycTBa8hQUoqohqaXfcZZ0cj2Gbjy4JaDUJ31tK7vGj+TuhJXWPGABuc+JJtECFknfBTtmUihKyzDlIIidB9uDwpiKowyXYVAV1HGn6iloUU4NEk0vA3byakvUEaSCEQnhysSBDNLMdjpuKtvr62LfBWxzk1ywswKyvw5Db+G6g4MLSaBTAnnXQSpmnGBU1/9dVXMQyjjuPE/mby5Mns2LGjyWtPaxBSazTm5/75O/APzffeey8m2s4///y4spSUlD0KWEXzsXPnTnJycujQtR/TX3iHmdNuT/B1btJJcRkqoJNuoldnUIplfUpgpJToukZmkoauScqrqtDtiBO6KcHbXhtD08jyuzFcLqQVjSur8aZuTaK6MYR0siZJLYDu7xO3jEnY+2eNfUIiNXaP82qbEScRQT0e4UJPQrdNpDsNYSRjR8qwQgWELQu31HAJAQmcTKImaxJYSG97DN1DMBymuLImEYeNWxOY5QVYoTKk24/0tf6Ura2RhLGErl+/njVr1sRSOH7yySesXbuWfv36kZubS6dOnfj973/PNddcw44dOzBNk7vvvpvJkyfvU2gYRdMZNmwYH374IYccckhsSl2RmEyePJkbb7yRnVvWk+XZAkIgEvldU2oIdzqYRWCWYJnlYLur41ImtvDRpOTnH1YTDofQXDp653Q0qxzZTC+H+wtDFyAgze9F2sWAYyWzwkHM8p0QCSPcLR+EfL8gDWRSZ2SFEQtcH0PQql4e9gYhtWpTby3MiBNgvh6nS6EnYQjLka2udARO8pVgJEJ6UjukFImd0axW1iQhNQxXOm7Noqiyes26bWAEi7FkOdLlQ7gznMgJimYnYRTFl19+GUtRePLJJ/Pyyy8D8Ic//CG2/vLRRx+lf//+vP322wghmDZtWh1P7QPBvkzHtyVSUlLipocViY3X6yW3c2fCO7ZUh9dJmNu8HiQSsH0dENparNAuLOEG4U14ESqk5ixDsZ01uv4kH7qUCGG0qmldKQSmBX63JzYdHy3dSbQ4H82TgpaSjWwj8ROFkBiBHkQLiuuUOZmh2qYIdfoVr0JtM4Lmqz8TnNDcJEuJV0oqzSh+dwYWUFiyk1xDgi/LEXoJSu2sSUIYGJrA6/HgIUJ+eRWFoSpcyRFkih/hAjsq64YmUzQLCfPrdN5553Heeec1WkcIwaWXXsqll17aPI2qZvLkyUyePJn8/PwG1y0qFImEEJpjvcBO6ClGEZt2lwh3NlJPwq4qAKtDwntiSynj2qgLG024kJbZqgK7a0KAbTuOgrYzBR8t3QbScAKaR8MJ/iKzl0hZZw27bVlYlcWt6rrtDY4oi7+frGgYvQELt5DObESu18eaijL8uoF0Z1AmSvCYpUB2wlv7hebBDpeA5kEXNroUZHiTWFdaQSQSxWNINH8GdmiXs1a4DSQqaI20zTtOoTjoqQ6YbpskdM7vagEKgGUifR1AGtgJ3OQatNqCxQY7WIIeDWPk9G65Ru0DThx6gctlYJlRwgUbkZqBlpzhJD2wLUQj8ZRbG0JqiFr3hG1ZTgxNy2zjllBi4tu2bczyXWhG/VPQQnMhEKS53AjAtG2qzCgdktuT4k5GaE1zsGxJpLs6fa4ZRrfKENjsKqnAbRukuV24PRIrUlK99Ee0rRetVkQbveP2L1OmTCErK4sjjjiipZuiUDQNqWFbIWc6PpF/WGNB0C3HK1fqCFc6Qkv89VmOYanas9q2kD4/3k5H4Mk+tNHjEg1NCjQhMHQDq6IYO1iOnpaL5vKBFa2epm47ItT5zjni07YsosWbsUIVCG9KYt8rvwVZPeNgW9i2TbRkK0QjDaeplC7QDPzCJkV3URGNUBwJ0zs5gM+XjnDVP42fSOjJXXFnH4ue1hddSqLBcooro6T7DA7JzMRjeLEjxVjhMmdturKEtght9I7bv+yrd7xC0WIIDSwTK1xEYltCtV8FjuEFIRHYyARKAdgQstawmkg8gXRc7sSNa9oQUghs2wlRJoRAegPoqTkIzcA2o44lNMGnXvcGITWE0JxlB8WbscJV6IFsNJc3oZeu/BacFzyJbZpES7ZiVpUhfSkNilAhdYT0gB2hvcdLYSREmuGivccHto00kuo9LhER0kCaYYiGSPIY+HxeUgzQPZkIzYcdKUFIF0JTltCWoG3ecYqEY926dVxxxRWMGzeO/Pz8vT7+559/5pJLLtmnz964cWOd0FKtjauuuoqVK1c2ub4T68/jiNAEXlspqr3ghRBITXfamvDOVA5SiF/1vRB4dAttd4/rVoAmBaXBCD6P25mC13Q0XyrC7cO2okDruB5NRkhsIYgW5zsCNCXHSWMpBLTRNaE1ltBI8WasYDm6LxXN27AIBScRClaENJeLDJeHbknJ6NKxIu8pQ19CIXUkkORykRLwErIg3S0QQiBcmdgYSF2J0Jaijd5xiYFpWUQs84D+mVbTguFv376dsWPHUlFRcYB7XT+XXnopGRkZ3HLLLaSlpXHmmWeydetWwAnPtSeRWFJSwqJFi/bps8vKyli4cOE+HdtU8vLy+OMf/8iZZ57JAw88QFVVfM72srIyHnzwQc444wxuv/12iouL9+r8n3zyyV6npxVGCkJPSvgpRiF0bCzQNMfjHCuxw79UIwRYtazM5XYQ2QravTtSOELU5XI54WoMN9LjR7q8TpB6mzZlCUVqWJEqrFAFekoOmjfgxKaVMuHvlX1FSIkdDWMFy9D9GUhvsiNAG5uC1n3YVpgk3aB/IJVMV/ULlm0ndIzQ3RFCR9M9SI+LKtPCsiHVJavLBELzI1xtJARZK0RJ/yawLyGaTMvisx0bWF9WgF93k+759UteHglRHK7CLXUyPEmORQWoikYoDFViSEm6O8l56wSC0SiF4UqkgHSXD1f1G1vYjKJLjZE5h8Q7SdRDVVUVCxYsIBJpmcwn3333HXPmzInlor/hhhtiYZ9KS0sPuEg8kBQUFHDUUUcxceJEzj//fJ5++mm++eYbXn/99Vids846i1AoxHXXXcdzzz3HaaedxieffHJgGyYNpCsjzgkjIZEawpYYGV2cdVm21SosLVKImDizsNkRqWy1ltB0r+6El3J5kJqOdHkRhg/MaHUmobYjzoRwrGBacjtHgALYthNRIoFnDX4rtpCOBdSfgVlVinT7Gu2v1H1EImVo3mwCRk2K5eosS61IhCJ1NLcPkzBFpokLHW/tdyrbQuhKhLYUbefJcgDZlzWhFjZlkTCWbePRdAKGO/aXbDieh25NJ6XW/oDhRgqBLjRSXZ7YvlSXG4lAIuP2+3UXxeEgVp0oxHvP6tWrufbaa7n00kv54IMP4qZ/a6bCP/74Yy6//HKuvvpqVq1a1bRxsCzGjh1LZWUlF110EWPHjmXs2LE8+uijlJSUAE4s2KKioljZ9u3bGzxfY20oKSnhvvvuY+LEidx5550NWhs3b97MrbfeysSJE3nooYcIhZwAxvfccw8fffQR4GQeGjt2LIsXLwYgPz+fc845p97z1YjrJ598kokTJzJ37lzefvttli9fDsDSpUtZvHgxM2fO5LzzzmPevHksXbqUJUuWNNjPNWvWxF2P3WmsrzXX8sqb7mfhkmVcff0f6lzLxYsXc9VVVzFlyhQAfvzxR2655RYmTZrEk08+iVXLwt5QWVFREX/+8585++yzufvuuyktLW2wP40itOp1Zp74fQmOrPUDLoFMt4autaIf52pktXewJkC6fOjJ7Zz9usuZesVO/BeZvUR6U9Bqr9+1rVYV23Vf0NxJSF+qs2FGkK7GPdyF5oJIKVa41n1tRZ34oK3pe14dBs4vLAycNed+vcah0AbLROxhLBQHDiVCDyBeXad/Wg6d/KkYUov9pbuT6J+WQ7fkdFyaHtsfcHnom5pNj5RM3LX2JxluDkvNok9qFl7dFdvv0Q3S3b/95tm1axfHHHMMSUlJjBs3jgceeIAXXnghNv1bUlLCyy+/zF133cWYMWMIBAIce+yx7NixY4/nFkLw+9//HiEEV111Fb///e/5/e9/z6JFi2JLAyZNmkRSUlKsLBCoP33atm3bGmxDVVUVgwcPJi8vj3PPPZfi4mJGjhxZx3pdXFzMwIEDCQaDTJw4kQ8++IAzzzwTcHKhv/rqqwAsWrSITz/9lPnz5wOO+C0vL6+3XTt37oyLH5uamkpycjKff/454CRiGDBgQMzym5SUxODBg/nyyy/rPV9hYSHHHnssXq+XsWPHct9997Fx48ZYeWN9rX0tTz5pJA/9Yy6zX5pX51red999nHTSSZx88sksW7aM4cOHk5WVxYQJE5g7dy633norQKNl11xzDatWrXJy1fv93HDDDfX2pynY0crY/4Wz6Gyfz9VcCODwkaegDxiOu0cmh3pdGK3Qw1YKgcfQ0KRAT+2AnpLjFFQH77Ztu01ZQgGk4ca2f33Rsm2rza8JlC6fI7YB24rucQpaaD6EkYxZuTkmRG0rgpCeVuXA5Vi+NdK1MDoaurRJ0mteqiyscBXSUJbQlqJt33UJQEPTHftr//5g1qxZDBo0iMceewyA448/vk5Q/nA4zEsvvUSXLl0499xz+emnn5g+fTp33nlno+cWQjB27FiEEJxwwgnk5Dg/cFqtuINDhw7F5XIxduzYRs/VWBtmzpxJTk4OM2bMAODss89m0KBBfPTRR3To0CF2jmeeeYY+ffrw5JNPAk52rtzcXJYtW8bxxx/PjTfeCMDixYu58cYb+fDDDwFHlB5//PH1tuvII4/kH//4Bzt27CArK4tFixZRVFTEzp07AWe6PjMzM+6Ydu3asWvXrnrPN3PmTI488kgef/xxAIYPH07Xrl3jyhvq6/Lly2PXMlq2gWMOz6THkEvjzh+NRnn55Zdp165d7Pjf//733HXXXQCMGjWK3NxcHnzwQf7yl780WLZ27Vr+/ve/M3LkSIAGRfqeEJobs3Kzk6da81QH2E98q5SUgpSMdmRELURwA1a0tFVaQjUpSHI5IlTz/JoZTWiGE0+ysrBVvBTsDUIaYNdaH99KnOF+C0I3nFioOC8WjTklOfU9SF9HrIpNWFXbnJ22iXC3whzrug/DXEdJMJOOyQKP5vyeWlVlWMFyhNH6ltG0Fdr2XbefaOtpO9esWcNRRx0V227fvn1s7WYN7dq1o0uXLrHtwYMHs2bNmmZr457asHz5cvLy8jj11FOxbRvbttm8eTNr1qyJE6Fr1qxh8ODBse2UlBT69OnDTz/9xBlnnMG6devYuXMnixYt4vXXX+f555+nrKyMRYsWccUVV9TbrvHjxzN27FgOO+wwevfuTWlpKb169YoJT8MwCAaDcccEg0EMo36r2erVqxk0aFBsOzc3N64PjfW19rUU0qB9dgZ3ysX6AAA5hklEQVQdO7aPO39OTk5MgNY+35dffhk7n2VZbNiwodGy+++/n+uuu46hQ4cyatQoJkyYUG9/9oSQzjS8WZGPlpSLMwec+CIUHCuiW9dx+XxYhNFaqZDpEPDgM+LHXGgurFA5driqVVm+moSmx6yCQPU65NZ57ZpM9UsFUH2L7dlqr3mysCKlCCviCFGhoyW1vqyBwkjBkFBUGeSQNA1DJGEFyzHLdyEML0LljW8x2vhdt3/YH2k7I5ZJUaiKJN1FkvHrG6hpWxSGKnFrOoFaa+Is26YwVIkuJam11qvYtk1h2PG8Tt5P+ZyTk5MpKyuL27f7dkVFhfP2XG2RLSsrIzm5eWM5NtaG5ORkDj/8cK699tq4Y3r37h1noauvr6WlpQQCATweD4MGDeKNN96gpKSEbt26ceyxxzJ37ly2bt3K0UcfXW+7hBDMnj2btWvXsmXLFo444ggOOeQQ+vTpA0C3bt345Zdf4o5Zv349Z511Vr3nq6+Nu/ehob5u2LDh12OFRAidsrJ4C6Wux9/2ycnJnHLKKQwZMiS27+abb6Zjx46NlvXu3ZtTTjmFH374geeee45HH32UZcuW7bXVXmgG0psDVhizIh8bV6uxSukSPC4XLtOFkIFWaQkFyAnU/RF2cmlLpNuf0GG+9gWhu7Ct3URoG8oKVR+O059jSBGIJolu6Qo4cXt9HbEqNxOt2IxLb33rJ4XLi0tPRtMglWKsShOrqhJhuJAuA6krS2hL0cZebxOXXcEKfi7dyaaKori8xYWhStaVFrChrIiI9aultSQcZH1ZARvKi6iK/urRXh4Ns7G8kI3lhVREw/ulbccffzyvvfYahYWFALz66qt1wgFVVFTw0ksvAc66yrlz5zJixIhY+XXXXbfPIZQCgQDl5eV7tDQ31obf/e53LF26lAEDBsQcnDRNq5MjeuTIkbz66quxtaQffvghmzdvjlkeR4wYwUMPPcRxxx0Xq/+3v/2NYcOGNWi5BHjjjTc49NBDOf7443niiSfo0qVLbPp+9OjRbNu2LTY+S5YsYe3atZx88sn1nqumjTXXY+7cuXHXo7G+xl1LofH6e59RVFTc6LiecsopLF26lFGjRjF27FjGjBlDfn5+TIA2VDZjxgxs22bAgAFMnjyZH374Yd+m5IWGkAZaUidsBEQqWsV0PMC29WuoyvuZim1ljphuBV79TUVIDc2XivT42+B0vB4TZOC83Nut5Du3rzh9tmPPRKHv+bsqdD9COFZj4e2ANJJal2d8NVLz4BI2qX43LjtKtGQdaDaaL81xtGolL71tkbb1ZElAotWxQjUh8GoGLqkTtX+NH6oJgUvquKWOZdu19oNb6riEBvy6XwKG0DGEHued21QmTJgQEy5jx45lyZIlnHbaaRx77LH07t2bYcOG8fDDD5OdnR23brNdu3Y8+eSTDBs2jF69ejFo0CDOPvtswHmAL1iwoI6Fral07dqVQw45hIEDBzbqHd9YG0aMGMGNN97IYYcdxnHHHUePHj147LHHSE1NjTvHKaecwplnnkmfPn045phjmDBhAv/85z9j09MjRoxg3bp1MXG7+3ZDzJkzhwEDBtCrVy9mzZrF888/HyvLyMjgH//4BxdffDFDhgxh3LhxPPbYY7Rv377ec5166qmMHDkydj0effTRuOn4xvpa+1oOH3kKjz35MtnZWXHXcnfuuOMOXC5XTDjn5ubGljk0VrZx40Y6derEyJEjGTBgADfeeOM+WsclYDtC1NcBhN5qpuNXL/2MvG8/Y/PKrSB0ZBvzsNaSUqvTXLatnwqh6dQOKhIt29H2p+NldYpcK+qEo2rCdLzQPQhXANushGgZWvKhCFfqgW/rfkZqblwChBkm1RYIw4c0ALMCobmaJMgVBwZh724qUjRIzXR8Xl4eubm5jdY1LYulBfmU17JWmpblxBbcTTw2tN+qvjS7i83a+/26i4EZuU2KE1qfpfLII48kOzsbgFWrVhEKhejbty+ZmZl8++239OjRg2+++YYJEyawdu1aVq5cia7rHHbYYbH2fv/991x99dUNensDvP/++4wcORKXy3mL/vDDDznmmGPw+RyvxFAoxNKlSykpKWHEiBF4vfFTPqWlpXz33Xcce+yx9bahhqKiIn788Ueys7M55JBDAGcq++uvv+aEE06I1du0aRN5eXn06dOH9PT02P5gMMh///tfhgwZQlqakx95wYIFDBgwIDZODbF8+XKCwSADBw6sV/StXLmSHTt20KNHjz1+f8AJjRQMBunfvz9ffPEF/fr1ixPV9fW1hlWrVlFVtoueHSw69h0fu5Y141ifqM7Ly2Pjxo307t27jiNVQ2W7du1i1apVdO3alc6dO++xT/VhVu0gWvg90puNbYYpKCwkp8foVhEgfcpjf6eyvAxXkpvDf9eXo7uNJNvbPMtUCgoKyMjIOKCfEdq6msiujfh6HutkFWqF1DdOkeKtBPNWYKRkEy3dTnjHepIHnIKRvuf7srUSLd1BcOMypC8Vs6oEf98T456fDX2fomW/EC1bC7aFK+MopCezTp1EJ7RtBd99N5c3C1OY0EFwaGYOdngnZtVONG9XAkfVv95/d5rjnjvYaOOvfvuHfXFM0qRkYEbufonh2RgSsUcBCsTC/TTEe++9FyufMWMGgUCgjrDRdZ0jjjiizrHZ2dk89dRTjX7+mDFj4rZPPPHEuG23282wYcMaPD4QCMSEU31tqCEtLY1jjjkmbp/f748ToACdO3euVzR5PJ4649TQtPnu9O/fv9HynJwc+vbt26RzAbE1peB4yO9OfX2FX6+lFSnj6WkPEggkx65l7XHcnU6dOjW45rmhsszMzAajBjQVITTsmt9CO+pYpFqJ5U2LJZAXSOHap9mJREYYXsd61kquR1MRQiKETbR0O2ZlCdIXaHN9rIPUsAGzdAe4vE1euy2NAJghNF8HhLuVCjDNwBMqJSsplUBqO8cK7MmGyp3KM76FUSK0CeyrY5ImJYlvy3H44IMPuOWWW/D7/axfv545c+YgmxgbMCcnp9H1kormJXYtk3ysX7+Ol158scnXskUQ0gmRQ3W8RmG0msw1caJT97S9oO66q3ratm31C6kRrSxBINB8TliqNhcBYDeEkFihCszKYrydDm/6cYYf6WmH5u/Wau7L3ZHSwO3W6ZKchC41zMri6hS1KQijeR1sFfEoEaoA4LHHHuOmm25i27Zt9O3bN25tX8+ePZk1a1YLtk6xN9Rcy62bN9GjoySj85A9H9SSCI2YyLGtVuokIKnSU9ueJVR3ge5CJPJLzD4ghMQOVqCnd0QPOFPybS0gfx2khhUsRxpupKfpwktobvTUvkjDv+fKiYo0cPnS0CQYAjCjWFUl2JEw0pva0q07qGmNT3vFAaJr165xQdFraGwKV5GYdO3alS5dumCHChM/D7twUukBYEdBtr6YfSY220MVyLZmMdQMJ31jW7MSSg3pTUYPOOu8bey218fdEFJDc3kR3gDSvXcZglq1AAXQXGhSw7YsdMt2xiKtI+FtRQiVLalFUSJUoWijCCEQnsRfw+VMgwondEwrtYQKwC20thZO07GauTxtT6AJieb9NfOPsNv+dDxCIrwpCKwmeca3JaQ0EELHq1VbQqXESGuPsMvRfWkt3byDmjZ+1ykUioRHaNV2UAvbNltlHEJNCLJ8yW1yOt6T26/tTcdXR16ICw7TxkWokJoTQsy2nIxRBxNCoEkdH6DZphObWGhIX6qTjEHRYrTtu06hUCQ+QmJHK7DNME680NbizlcbgUtobW86vq0iqmNmVqfutKHtrwkVEqTEtpuWsrNNISRJQtDVBcK2EZruOEEiELqajm9J2vhdt3+YMmUKWVlZjYYGUigU+4p0PHcr8rAtE1pNTAlo3749HXNz8WemO5GM2pgltK0ipGMJqxGhgoNgOl5qICVC0w9KEYrmJSmyE8sMOyI0WoEwAso7voVp43fd/mHy5Mns2LGD77//vqWb0mbZuXMn9957L1dddRXbtm3b6+M3btzI7bffvk+fvWXLFm699dZ9OjZR+POf/8zatWtbuhn7hpAIIxnbCmOHC1pNyk6ASZMmcdlll9FvzPEIIZQltLVQnT3ItixnSr4NxkLdHSFEdaYkve1nh9oNIQRSerHtKHZom5OmNVqB9LVXL44tTNu+61oY07KJmNYB/TOtpgXDLygo4Morr6SqquoA93rfuOiii9iwYQNDhgzB6/Vy4403snPnTsDJ1rMnkbhz507mzJmzT59dWFjI7Nmz9+nYplJSUsI///lPbrrpJmbOnIllWXHlkUiE//u//+PGG29k2rRphMPhBs5UP3Pnzt0n8Z4ICCEQ0o30ZjuWqVYmBmS1+JSINrcmtM0ipGMZtK1fv3Ot7Hu3T9REOzjo1oRKhNSRRjpWNIgZ2gaWiWyFKUjbGgfZN7H5MC2bbzYVURmxGqxjY1MajGJWL473GRoevXErUEXYJFSducmtaWQnuxiYm1orc0v9lJWVMWPGDB599NE6KTETgc8//5wVK1bEkgEMGjQIt9vJZFFQUMDs2bN57LHHWrKJ+0xFRQVHHXUUvXr14sQTT+SJJ55gyZIlTJ8+PVbnoosuYvXq1Zx//vnMmjWLDz74gDfeeKMFW93MaAYCG+FOa3ViQAgna5lp2cqq0kpwXnx+FaF2NNzmnK/qQ+gGtpAH3/e0+qXDNsGqstGSTdBcaio+AVAi9ABh2Tb5pUHa+VwkuesOs2XbbCkNEoxaZCW5KAtHKQlG8folyZ761+vsKA9RHo6S4nHOV1gZiZ1L+43TgNu2bWP69OkEg0EmTJjAK6+8wiWXXMKhhx7Kxo0beeqpp7jggguYO3cuuq5z5ZVX0qFDB8CZCv/73//Otddey5w5c4hGo1xxxRV07959j59rWRZXX301ZWVl3H777XEC+Xe/+x0ADz74IKWlpVx55ZUAPPzwww3m712+fHm9bQQIh8M899xzrFy5kh49enDFFVfEhG5tSktLmTFjBps2beKoo47iggsuQAjB1KlTGTZsGEOGDKG4uJj/+Z//4frrr2fgwIHs3LmTv/zlLzzxxBN1zvfiiy9iGAazZs0iMzOT8847j9zcXG6//XYOPfRQfvzxR+bNm0deXh7t27fnsssuo2PHjnz77bccddRR9fZz+/bt/Pvf/45dr91prK9NudYXX3wxc+fOJTc3lyuuuIKtW7fywgsvsG3bNo4//nhOP/302Gc1VBYMBpk1axarVq2iT58+XHHFFQ1n1hI62EGk9FQHr29daMLJlaSm41sRmguiIczKEqxIyLGMtnGErLaEHmwIgbAFZulOkAaaOwMtufvBJ8YTkLb/6teCCATbykOYlo2hydifJgU7ysNETJvcFA+Zfjdd0nwku3V2VUaoiphx9Q1NUlgZoSJskuFz0THFS8cUL+k+I2YV/S2UlZUxZMgQli9fTlpaGpdffjlTp06NTe/u3LmTf/zjH1x00UUEAgFWrVrFkCFDKCkpiZVPnz6dK6+8kpSUFPLz8znuuOMIBoN7HiMhGDp0KEIIjjrqKIYOHcrQoUN54YUXYufv27cvhmHEyuoTjjXtaKiN0WiUESNG8Morr9C1a1cWLFjAuHHj6pyjsrKSo48+mkWLFtGpUyemTJnCpZdeCsCOHTt4+eWXAfjkk0944YUXeOWVVwD4+OOPWblyZb3tys/Pp2fPnrEHXk5ODikpKSxevBiAJUuWcMQRR9C+fXvAycc+ePBglixZUu/5KioqGDp0KD/88AOpqalceumlbN68OVbeWF+beq0vvfRSkpOdnPPr1q1j4MCBrF+/ntzcXO6++27++te/AjRaduONNzJ79mwOPfRQVqxYwVVXXVVvfwCEMMCKOAK0FYnQF154gaeeeopv3/pATce3MoSmE60sIVqyFWG0wYD89SB0HQ7CXOlCSMxwEGwL6dYR7gCaJ7Olm6VAWUIPKAGPTnFVmPySILkpHjyGhmXbbC4JUhUxyUl2E6i2ekoh6JjiYXNJkG1loerjnbLtZSFKghFSPAbZyb8+QLL8braWhn5zO5977jk6d+4cE1hnnnkmPXr0iKtTWVnJiy++yGGHHQbACSecwIwZM/jjH/8IOJa3V199lezsbGzbpkuXLixbtoyhQ4c2+tlCCK688kquvfZazj//fHJycgD4/e9/H6szfvx4/vnPf8YsoQ3RWBtnz55NJBLhnXfeQQjBzTffTL9+/Vi0aFGcVXXmzJmkpKTw+uuvA3DBBRfQrVs3br/9dkaMGMEdd9wBwKJFi7jiiitYtGhRbLuhrFK9e/dm+vTpVFRUkJGRwQ8//EBhYWFM+O3YsYOsrKy4Y7Kzs9m+fXu955s1axYdOnTgP//5DwCnn346vXr1ipU31tcVK1bs8VpXVVUxb948OnfuDDhLBS666CIeeeQRACZOnEjv3r3505/+xH333ddg2TfffMOTTz7JscceCzgW0waRGrYVQejuViUGdu3aRUlJCd5kf7XjhxKhrQXh8WMW5SP9GWiewEFx7YTuQeoHoSVUSoSehCvraOxoFFf6QKSaik8IlAg9gGhC0CnFy9ayEPklQToEPBRUhusI0BrqE6JVEateAVpDkuu3W41WrlzJ8OHDY9vdu3ePWeVqyMzMjIk7gBEjRsRZ/rKzs8nOdlLgCSHo0KEDBQUFv7lte0Njbfz2228JhUJcd911jmekbRMMBlm1ahXHHXdc7JgVK1bEicns7Gz69OnDihUrGDduHCtXrqS4uJjFixcza9YsRo0aRVVVFYsWLWLatGn1tmvixIk8/fTTjBw5ksGDB7N8+XK6d+9OSkoKAJqmEY1G446JRqNoWv3XdsWKFRx//PGx7R49esQtO2isr0251jk5OTEBWnO+zp07c+2118bOF41G2bBhQ6NlN998M1deeSUTJkxg1KhRjad+lYaTslP4W22oHNE0H0FFgmAkpRN0+9EDOYgG7rW2hpHWse3HQ60XgeZLRbqSsKpKEK7Ang9RNAtKhB5gdE3SKdXLxqIq8kscz/T6BGgN9QnRhgTo/sLlctXxxt7TdigUwuX69Y1a1vNgi8tG0gw01kZd12nfvj1HH310rHzw4MEMGTIk7hi3211v391uN36/nyOOOIJ3332XLVu20KdPH4YMGcJbb73FunXrGrT66rrORx99xJtvvklFRQWPP/44AwcOpGfPngB06tSJvLy8uGM2bdoUWxO7Oy6Xi1Ao3gJee7uxvq5evXqP17b2da05X69evejfv3/c+TIyMhotu/zyyzn77LP55JNP+L//+z8eeOABPvroo3r7JISBbUWQiZ7nvhEqzUhLN0GxF0hvAD21PXY0hDAODlEi9NZ7f/0WhHS842NxYVvRkp+2zsH4StTsaFJQ23ndpTU+7FIIjFoHeI0De5mGDBnCm2++GVvDuXjx4lh4pBpKS0tZsGAB4AieN954Y49T7bX505/+xNdff71P7fP7/VRVVe1R1DbWxlGjRrFp0ybOO+88rrzySq688kqGDBlCWlp83uBhw4Yxf/58KioqAPjuu+9Yu3ZtzEFoxIgRPPTQQxxzzDEAjBw5kgceeIBBgwbh8XgabNuXX37J8OHDOf/883nzzTdJSUlh1KhRsbatX7+eb7/9FnAsncuXL2fMmDH1nmvo0KG89dZbsev18ccfs2vXrlh5Y31tyrXenVGjRsUcw2r+OnToQHp6eqNl7777LikpKZx66qk89dRTLFy4kNLS0no/w/FUNh2LaCtEwB4jVCgSC6HpGCnZWMGyg8YSejDjPGOqNw4CJ7TWgrKENoEpU6YwZcoUzH1wAgqbFjtKQgSjFhleg5JQhA1FleQGPLiN+m+EHeUhSoJRAm6NiGWTXxIkx7TqeM1HmxgjtDY333xznKXruuuui00XDxgwgP79+7N69WoyMzPjrJtpaWncdtttTJ8+nRUrVtCuXTvOP//8Jn/ujBkzGhRVe6JLly6kpqbyu9/9jo4dOzboHd9YG0899VTef/99evXqxXHHHce2bdsoLy/nrbfeijvHhAkTeOGFFzjiiCMYOHAgH330EQ888AAdO3YEHBH66KOPcvXVV8e2//CHP3D33Xc32of77rsvNlW9fPly3njjjdh0e/v27bn33ns5+eSTGTFiBJ988gm33347Xbt2rfdcEydOZPr06XHXq127drHyxvralGtdX9tPOeUUjjjiCPr27cuqVavo27cvv/vd7xote+WVV7jttts4/PDD+fbbbznrrLMIBBqwOAkNcOKF0nBUs4RFIAgYDb+EKBITLTkTtq896IK3H5RIDUxn2VPrTA3cNhF2c8+ZtmLy8/NjU6e5ubmN1jUtm2/zithaGiZqW7EYoKbtxAa1sQm4dfTdfvxr4oC6NY0kl4aNTVnQjDtHbTy6ZHCXtD1aYcrLy+sN5n7iiSfStWtXIpEIH3/8MaFQiOOOO45u3brx2Wef0adPH7755hsmTJjADz/8wCeffIKu65xwwgkxMbtr1y5ef/31OMeh+fPnc+SRR9KpUyd++uknJk2axLJlyxpc/P/MM89w3nnnxUI0zZo1izPPPDMmWgoKCli4cCElJSWce+65+P3+uON37drFRx99xLhx4+ptYw2rV6/m+++/Jzs7m2OPPRZd1ykqKuLdd9+NCVbbtvn888/Jy8tjwIABcU4/NeN4yimn0L59eyzL4tlnn+WEE05oNCRVNBrllVdeweVyMWrUqNh60Np89913rFixgt69ezNo0KAGz1Vzvo8//phgMMjxxx/PBx98wHHHHRdbl9tQX4FGr3XNOE6aNCnu8yzL4ssvv2Tjxo307ds3bvq9sbIVK1bwww8/0K1bN4YNG9Zgf8yq7YS2f4aR3p/SSFqDIbgSjalTp1JSUkJSIJkxl5zL4ekd9nzQfqKgoKDVjFNL0tg4WeEglT9/ip7aHk/Hw+qtc7DQ1r9Pleu/wQ5VgGXi6zUcsQ8OWm19jFoCJUL3gr0RoeAIUesAD68UYr9MAy5dupSBAwcCjoC8/PLL2bZtG7qux0Tohg0bGjy+sZtzw4YNFBcXM2DAgN/cztZMojzAGrvWLYUZ3Ekw723c7UdREkpKiHFqCjUi1B9IZuwl59E3vf2eD9pPJMr3KdHZ0zhVbfgW6U7G3b5nM7Yq8Wjr36eqjd9hlhchpMTX67h9soa29TFqCdQcxAFEk+I3B5FvLqZOnUp+fj7JycksXLiQf/3rX/tNlDQ0raxoGQ7ktd5XHEcBu1VPi+pqXWGrREtpD9Zvj7esSGyE0MCKgu5tVWHg2jqt94mv2K/MmjWLTz/9lG3btvH//t//ixOOXbt25eGHH265xin2K41d65ZDIl0ZTuakVonApTxuWyVGavuDIkboQY/UsM0oQurqeicQrfWJr9jPCCHi4kfWJjMzs84aQUXrpbFr3WJIDelKbnWhU8aOHUskEkHqGh2S6q7zVSQ+SpAcHAhNw4oE0exW6PnYhlEiVKFQJAAShFHtJd96pkZ79+7d0k1QKBRNQWhEi7dgZHRq6ZYoaqEWRigUihZHCInQDGjFa0IVCkUiI7AjIaTbv+eqimZDiVCFQtHyCA2k3uqm4xUKRStBM9ADWUjjwGUfVOw9yuygUChaHqkjjRSQLqCypVvTZPLz84lGo+i63qSwbQqFomWQmoZw+/YpPqjiwHFQWUKfeuopcnJyYn/PPvtsSzepRfnXv/5VJ2e5QtESCCHRA4e2ukwm8+bNY9asWcybN6+lm6JQKBpDVOePb2XPmLbOQSVCKyoquOaaa1i2bBnLli076D2+p06dysaNG/dYb/v27UydOvXAN0ihUCgUigOAs+5ca3Uvum2dhBOhGzZsYMmSJQSDwXrLTdPkhx9+YPny5VjW3odamD59OsOHD+d//ud/KC8v/63NPSjYunUrDz30UEs3Q6FQKBSKfUMIx/FRidCEImHWhC5cuJBHHnmEb775hoKCAn7++WcOPfTQuDrfffcdZ555JtFoFNM0SUpK4vXXX6dfv34A/PWvf+Uf//hHnXOPGjWKF198keuvv54LL7yQsrIyHn74Ya699lpeffXVZunfngiHw8yaNYv169dzyCGHcMkll2AYxn79jMrKSl599VWCwSBjx46NK6uoqODxxx8HIC0tjRNOOIHDDnNyKc+cOZOKigoeeOABAG666SZ0XY+rf+SRR3LMMcfs1/YqFAqFQrFfEBKhKefHRCNhROiKFSu45ZZbSElJ4dhjj61THolEmDBhAiNHjmTmzJnYts2kSZOYOHEiK1euRErJLbfcwhVXXFHnWLfb8Ybz+Xz4fD5ycnJ48MEH6d+//wHvV1MIh8OcfPLJ/Pe//43tmz17Nu+///5+E6KRSITjjz8ewzA46qijePzxx9mxY0es3LbtmPV5+fLl/PnPf2b27NmMGzeOUCgUV27bdp36d999Ny+++CLjxo3bL+1VKBQKhWK/IdWa0EQkYUTozTffDMAXX3xRb/l///tf1q9fz/vvvw84WS7uuusuBgwYwGeffcbw4cPx+/34/XuOAWbbNs8//zw9evTYfx34DcyaNStOgILT31mzZnHllVful8948cUXCYfDfPnll2iaxtdff83gwYNj5X6/P2bpBDjppJN46KGHGDduHNdccw2vvfZaXDkQtz106NBYfYVCoVAoEgmBMx2v1oQmFgkjQvfEd999R0pKCoccckhs3xFHHIHL5eK7775rUhrC008/nS+//JKKigoOOeQQnn/++Ubrl5aWUlpaGtveunXrvnegEdavX1/v/nXr1u23z/jmm284+eST0TTnBhw0aBDZ2dlxdfLy8liwYAE7duygsLCQn376qdFz1q6/efPmPdZXKBQKhaJFqJ6OV5bQxKLViNDCwkIyMjLq7M/IyKCwsLBJ53j22WcJh8MkJyeTlJS0x/p///vfuf/+++vsLyoqwuv1Nukzm0JWVla9+7OzsykoKNgvn1EzdV77fLZtU1JSQkFBAV9//TWTJk1i7Nix5OTkUFlZGSsrLi7Gtu24Y3evX15eHquvqJ/aLzSKhmlN41TjHGlZVrN/91vTOLUkapyaRlsfJytUQaTKxFVcss/W0EQZo/q0UGul1YhQwzDq9ZivqqrC5Wpa8Nn09PS9+sw//vGPcdPhW7duZfDgwaSlpe3XL8ENN9zA/Pnz46bkR44cyQ033LDf1oQOHTqUJ598kvT0dIQQfP/99+zYsYOUlBQyMjJYtGgRl112WSwU09y5c3n++efJyMigffv2RCKRuD7vXv+ZZ57h5ZdfblM3x4FAjU/TaC3jJKWM/dsSbW4t49TSqHFqGm15nMwqF6HQTryZ7RBC7PN52vIYtQStRoR26dKFXbt2EQ6HY6KzoqKCkpISOnfufEA+MxAIEAgEmDJlClOmTME0zQPyOS6Xi/fff59Zs2axbt26A+Idf+GFFzJ16lRGjRrF0UcfzbvvvktqamqsfNiwYVxyySXouk5lZSULFy6MlXXp0gUhBNdeey25ubncdNNNdep/8MEH+62tCkVrwePxEA6H8Xg8Ld0UhULRCNLtw5XR6TcJUMX+p9WI0NGjRxOJRHjvvfcYP348APPnz0dKyahRow7oZ0+ePJnJkyeTn59Pp06dDshnGIax35yQ6sPtdvPpp5/y8ssvEwwGeeutt3jjjTdiAn78+PG89tprfPbZZxxxxBHceeedzJgxA3B+aD/77DPmz59PSUkJtm3XqX/dddclTLgrhaK5uPbaa1u6CQqFogkIqaEH6l/6pmg5hG3bdks3Ahwnl40bN7Jq1SquueYa5syZQ8eOHenRo0fMgeb666/ntdde45FHHsE0Tf7nf/6HSy65hMcee6xZ2lgjQvPy8lSe6N0oKChQ0xR7QI1R01Dj1DTUODUNNU5NQ43TnlFjtP9JGEvoxx9/zNNPPw3AscceGws6f/vtt3PqqacC8I9//IPDDjuMF198ESEEDzzwAFdfffUBb9uBno5XKBQKhUKhONhIGEtoa0BZQhtGvSHuGTVGTUONU9NQ49Q01Dg1DTVOe0aN0f4nYSyhCoVC0dp46623qKysxOfzxWZsFAqFQtE0ZEs3oDUwZcoUsrKyOOKII1q6KQqFIoFYu3YtP/74I2vXrm3ppigUCkWrQ4nQJjB58mR27NjB999/39JNUSgUCoVCoWgTKBGqUCgUCoVCoWh2lAhVKBQKhUKhUDQ7SoQ2AbUmVKFQKBQKhWL/okRoE1BrQhUKhUKhUCj2L0qEKhQKhUKhUCiaHSVCFQqFQqFQKBTNjgpW3wRq0nZGo1EAtm7d2sItSjyKioqoqqpq6WYkNGqMmkZrGqfi4mJKS0uxbZv8/Pxm/ezWNE4tiRqnpqHGac8k0hjl5OSg661fwqm0nXvB119/zeDBg1u6GQqFQqFQKA5i2kr6cCVC94JgMMjy5ctp165dm3gD2V9s3bqVwYMH89VXX9G+ffuWbk5CosaoaahxahpqnJqGGqemocZpzyTaGLUVS2jr70Ez4vF4GDRoUEs3I2Fp3759m3gzO5CoMWoaapyahhqnpqHGqWmocdozaoz2L8oxSaFQKBQKhULR7CgRqlAoFAqFQqFodpQIVfxmAoEA9957L4FAoKWbkrCoMWoaapyahhqnpqHGqWmocdozaowODMoxSaFQKBQKhULR7ChLqEKhUCgUCoWi2VEiVKFQKBQKhULR7CgRqlAoFAqFQqFodpQIVSgUCoVCoVA0OypYvaJJ2LbNl19+yU8//URWVhYjR47E5/PF1ZkxY0advLqDBw8+aFKdVlRU8Oyzz9bZf8opp9CtW7e4fT/99BOfffYZfr+fk08+mZSUlOZqZovz/vvvs2bNmjr7k5KSuOyyywDYsGEDb731Vp06l1xyCcnJyQe8jS1FUVERr7/+Oi6XiwsuuKDeOhs3buS///0vuq5z0kknkZWVtU91WjObNm3inXfeoXv37owZM6ZOeWVlJUuWLGHLli0ceuihDB8+PK58165dzJkzp85xZ599dkJkw9lffP/99yxZsoRhw4YxcODAuLKffvqJDz/8sM4x11xzDYZhxO1btGgRa9asoXPnzowePbpNZOqpzcKFC1m1ahXjx4+nc+fOcWX1/a4B9OrVi5NOOgmAJUuWsGzZsrjy1NRULrzwwgPW5rZC2/omKQ4IK1as4IILLsDn89G7d2+WL1/Oli1bmD9/PkcffXSs3m233cZRRx1Fjx49Yvu6d+/eEk1uEYqKirjpppu44IILSE1Nje0//vjj4+o99thj3HPPPYwbN47Nmzdz44038uGHH3L44Yc3c4tbhs2bN/PTTz/F7Zs9ezb9+vWLidAVK1bw+9//nmuvvTauXiQSabZ2NjdXXHEF7777biwETH0idPbs2Vx11VWceOKJVFRUcN111/H6668zatSovarTWtm1axeXX345P/zwA9FolOHDh9cRoXPmzOGOO+6gZ8+edOjQgT//+c/k5uayYMGC2Njm5+dz0003ccUVV+DxeGLH1ic2WiPLly/n2muvpbS0lF9++YW77767jgj94osvuP3227n00kvj9tcOmBONRpkwYQJffvklo0aN4vPPPyc7O5v333+/TbwMvvPOO/zxj38kNTWVL7/8kkMPPbSOCP35558pLy+PbZeXlzNr1izuu+++mAidN28eb7zxBqecckqsXrt27ZqnE60dW6HYAytWrLBXrFgRt++ss86yjzzyyLh9GRkZ9ksvvdScTUso8vLybMD+8ccfG6yzZs0aW9M0e86cObZt27ZlWfb48ePtwYMHN1czE47169fbQgh75syZsX1vvvmm7Xa7W7BVzc/MmTPtiooK+6677rJ79epVp7ygoMD2+/32o48+Gtt33XXX2Z07d7aj0WiT67Rmtm3bZr/++ut2NBq1TznlFHvSpEl16nz44Yf2zp07Y9vFxcV2bm6uPXny5Ni+7777zgbi6rUlvv/+e3vJkiW2bdt2x44d7QcffLBOnWeffdbu2LFjo+f5v//7P9vv99sbNmywbdv5fnXs2NG+884793+jW4CFCxfaP/74o71z504bsN999909HjN9+nRbSmlv3Lgxtu+WW26xTznllAPZ1DaLWhOq2CN9+/alb9++cftGjx7N6tWr69T9+uuvmT59Oh988AGVlZXN1cSE4r333uOZZ57hk08+wbKsuLJ58+aRnp7OxIkTARBCcP311/PVV1+xYcOGFmhty/PMM88QCARiY1KDZVm89NJLPP/883z33Xct1Lrm45JLLqmzxKU2b7/9NqFQiKuuuiq274YbbmDTpk18/vnnTa7TmsnOzub0009H07QG64wePZrMzMzYdkpKCoMGDapjfQd44403mDlzZpsYm9ocfvjhHHvssXusFwqFeOGFF5g9ezYrV66sU/7yyy/zu9/9ji5dugCQnp7Oeeedx8svv7zf29wSnHDCCfTu3Xuvjpk+fTonn3xyHYvpzp07eeaZZ5g3bx4bN27cn81s0ygRqtgn5s+fX2d6RwjBl19+yZIlS7jpppvo06cPX3/9dQu1sGVwuVwsWLCAhQsXcs455zB48GC2bNkSK1+1ahU9e/ZEyl9vvT59+sTKDjYsy2LmzJmx5R618fl8zJs3j/nz53P88cdzxhlnEA6HW6ilLc+qVavo0KFDXMaW3r17I4SIfXeaUudgo7i4mMWLF3PUUUfF7fd6vbz++ussWLCA0047jeOPP56ioqIWamXLYNs2b775JvPmzePoo4/mkksuiXtxXrVqVez5VEOfPn1Yv349wWCwuZvb4qxatYovvvgi7iWvhu3bt/Pxxx/z9NNP07NnTx566KEWaGHrQ60JVew1U6dOZeHChSxevDhu/1tvvcWQIUMAZy3ROeecw3nnnceaNWviRFdbJTk5mWXLlsUe2qWlpRxzzDHccMMNvPbaawCUlZXFrRcFSEtLi9U/2FiwYAH5+fl1Hup9+vRh7dq1MYvW+vXrGThwIA8++CD33ntvSzS1xanvu6NpGsnJybHvTlPqHEyYphlzZrvlllti+3Nycvjxxx9jFr5du3Zx1FFH8ac//Yl///vfLdXcZmXIkCGsX78+9sLyww8/MGTIEAYPHswNN9wANPy8sm2b8vLyuPW0BwMzZswgOzub0047LW7/RRddxKOPPhpz2PrPf/7Dueeey/Dhw+s4xSniafvKQLFfmTlzJn/605944YUXGDp0aFxZjQAF0HWdP/zhD6xbt461a9c2dzNbhJSUlDirQSAQ4JprruGDDz6ILfb3er2UlZXFHVcjDhqbim2rzJgxg6OPPpoBAwbE7T/kkEPiplS7d+/O2WefzYIFC5q5hYlDfd8d27apqKiIfXeaUudgwbIsrrjiCr766isWLFgQJ6ZycnJiAhQgMzOTSy+99KD6fvXp0yfOYn744YczZsyYuDFQz6tfiUQiPP/881x22WV1ogMcddRRcfvOOeccOnXqxPvvv9/czWx1KBGqaDLPPfccV199Nc899xznnHPOHuu73W7AmQ47WHG73VRUVMSmkXv06MEvv/wSV2f9+vWxsoOJnTt3Mn/+/HqnturD7XYf1N+lHj16sHXr1rhp0A0bNmCaZuy705Q6BwO2bXPVVVfx3nvv8fHHH9OzZ889HnOwf7+g7hg09Lzq0KHDQSdC58+fz65du7jiiiuaVF99n5qGEqGKJvHCCy9w1VVXMWvWLM4999w65fn5+XXCm7zwwgsEAgH69+/fXM1sUdauXRu3nsqyLF588UUGDRoUE+Tjx48nLy8vbinD888/T48ePeqsvWrrPP/887hcLs4777w6ZbvHES0tLeXNN988qKe2xo0bRyQS4dVXX43te/7550lLS+O4445rcp22To0Afeedd/j444/rdTz5+eef47YjkQj/+c9/Dqrv1+732LZt2/jwww/jxmD8+PG89dZblJSUABAOh/nPf/7D+PHjm7WticCMGTM44YQTOPTQQ+uU7T6WX3zxBevWrTuovk/7iloTqtgjH3/8MZdeeiknnHACBQUFTJs2LVZ29dVX43K5WL9+PWPHjuXEE0+kffv2fPLJJ/z3v/9l+vTpeL3eFmx98/Hxxx9z3nnnMXr0aPx+P/PnzycvL4/58+fH6hx99NFcc801TJgwgauvvpr8/Hxeeukl3nzzzRZsecswY8YMzj333HrjDU6bNo1Vq1Zx3HHHEY1GmT17Nqmpqdx///0t0NLmYd68eWzbto1vvvmG4uLi2H12+eWX4/P56NSpE/feey/XXHMNy5Yto7KykqeffpoZM2bE1uY1pU5r51//+hfRaJQNGzbgdruZNm1aXKKDe++9lxkzZnDDDTfw0Ucf8dFHHwGQlZUVm8F55ZVXmD9/PqNGjcLlcjF37lzKysrqDWDfGikuLuaFF14AnCQan3/+OdOmTaNLly6x9Yz3338/JSUlDB06lMrKSmbOnEnPnj3505/+FDvPTTfdxJw5cxg5ciRnnXUWH3zwARUVFdxzzz0t0q/9zc8//8yCBQuoqKgA4M0332Tt2rUcddRRDBs2LFYvPz+fBQsWMHv27HrPc84559CvXz/69evHli1beOaZZ5g0aRITJkxoln60ZoRt14pMq1DUw5IlSxp8OE+ZMiUmMrdt28a8efPIy8ujS5cunHHGGXTo0KE5m9ri/PTTT7z55psUFRXRo0cPJk6ciN/vr1Nv/vz5fPLJJ/j9fiZNmrTXYUJaO7t27eK+++7j2muvpV+/fvXW+eyzz/jwww8xTZN+/fpx1llnNRqap7Xz2GOP1Zn6BHjwwQfjhPrHH3/MggUL0HWdM888s47Xd1PrtFb+8Ic/1ElaEAgE+Nvf/gY4Lzf1hfTq0qULkydPjm0vW7aMd999l4qKCnr37s2ECRPajFDfvn07f/nLX+rsP+yww7j++utj2wsXLmTRokVomsYRRxzB+PHjEULEHVNZWcnzzz/P6tWr6dy5MxdffDHp6ekHvA/NwdKlS3nmmWfq7B8zZkyctffTTz/l5ZdfZsqUKbFZrdqYpsnrr7/Ot99+S0pKCscee6yygjYRJUIVCoVCoVAoFM2OWhOqUCgUCoVCoWh2lAhVKBQKhUKhUDQ7SoQqFAqFQqFQKJodJUIVCoVCoVAoFM2OEqEKhUKhUCgUimZHiVCFQqFQKBQKRbOjRKhCoVAoFAqFotlRIlShUCgUCoVC0eyotJ0KheKgIRqN8q9//Yu3334bgMsuuyyWyvFAsX79etavXw9Abm5uo9mxLMti4cKFHHHEEbRr126/t2X16tXk5eUB0LVr13rzYCsUCkVzoSyhCoXioKCqqoqTTjqJ559/nvPOO4/DDz+cSZMmxQTpgeK5557j9NNP56GHHmLBggWN1g2Hw5x00kl8+umnB6QtH3zwAQ899BBnnHEG06dPPyCfoVAoFE1FWUIVCsVBwe2338727dv59ttv8Xq9AHz99df8+9//5pRTTjmgn92pUyc+/PDDA/oZTeHGG2/kxhtvpF+/fi3dFIVCoVCWUIVC0fbZtGkTTz31FP/7v/8bE6AAPXv2ZM2aNS3YMli3bh2LFy+OTZM3xObNm/nkk0/48ccfG6yTl5fH4sWLWbduHQCLFy8mPz9/v7ZXoVAo9hfKEqpQKNo8zz//PElJSYwfPz5ufzgcRsqWeRePRCJcfPHFvPbaa/Tv359NmzZx6qmn1qlXVFTEZZddxoIFC+jXrx8bN26kZ8+evP7662RmZgJg2zbXX389M2bMoH///uzYsYNhw4axYMEC/vrXv3LjjTc2d/cUCoVijygRqlAo2jwLFixASllHhH7//fccccQRLdKmadOm8e677/Ldd9/Rp08fqqqqOO200+rUu+iii1i6dCk//PADPXr0oLKykjFjxnDrrbcya9YsAObMmcOMGTNYvHgxQ4cOxTRNLrvsMkpLS5u7WwqFQtFk1HS8QqFo83z33XcMHDiQAQMGxP0VFBQwYMCAFmnT9OnTufTSS+nTpw8AXq+X//3f/42rs379et5++23uvPNOevToAYDP5+Pee+/lhRdeoLCwEIBnnnmG008/naFDhwKgaRp/+9vfmrE3CoVCsfcoS6hCoWjT7Nq1i/Lyci6//HLOO++82P4ff/yRhx9+mJNOOqlF2rVu3bo60+S7h29avXp17P+1HZsKCgqwLIu1a9cyePBg1q9fz7nnnht3bG5uLj6f7wC0XKFQKPYPSoQqFIo2TTQaBagjyF588UU6d+7MyJEjY/tWrlzJnXfeyZdffklxcTGPP/44l1xyCZ07d+b+++/noYceIjk5mddff52XX36ZJ598kg4dOvD222/Tvn37vWpXampqneny3beTkpIAeOmll+IcqgBGjx6Nbduxc5WVlcWVRyIRgsHgXrVJoVAomhM1Ha9QKNo07dq1IykpiQ0bNsT2bdmyhWnTpvG///u/aJoGwJo1azjhhBM488wzWblyJcXFxVxzzTUsW7YsJvCWLVvG0Ucfzbhx4+jevTtLly4lKyuLV155Za/bdcwxxzB//vy4fa+99lrc9qBBg0hPT+eaa67hww8/jPubO3cuQ4YMAWDYsGG89957WJYVO/att96K21YoFIpEQ1lCFQpFm0bTNM4991weeeQRunfvjmma3H777ZxxxhlccsklsXoPPPAAN998M5deemnc8UuXLuX000/nhhtuAOCQQw4hNTWVCy64AHBigO7LtPcDDzzAoEGDmDRpEpMmTeLHH39k6tSpcXW8Xi///ve/ufLKK1m1ahVDhgwhFArx9ddf8/bbb/PTTz8BcOedd/LSSy9x6qmncvnll7N161YefPBBdF1HCLHXbVMoFIrmQFlCFQpFm+fxxx9n7Nix3HLLLTGxOWPGjLg6X3/9NWPGjKlz7NKlS+PWjX777bdx20uXLmXgwIF73abDDjuML774guTkZKZPn86WLVv4/PPPGT16NFlZWbF6Z511Fp9//jmWZfHMM8/w5ptv0qlTJ7755ptYnQ4dOvDVV1/RrVs3ZsyYwffff897772HlBK/37/XbVMoFIrmQFlCFQpFmyc5ObmO6Nyd7Oxs3nnnHQYOHIiu//poXLp0KTfffHNs+5tvvuHf//434MQZXbt27R4zEFVWVvLhhx/WyR3fv3//Oukz68us1KtXLx555JFGPyMnJ4cnn3wytr1w4ULC4XCcQK7JHV9RUdHouRQKhaI5UJZQhUKhAB577DFeffVVfD4fHo+Hu+++m1AoxC+//BITmfn5+bhcLrKzswEnzuhhhx0WJ1p3p3v37vTs2bNJueN/C+PGjeORRx5h/vz5PProo5x77rlMnDiR/v37x+rU5I4/5JBDYiGfFAqFoqUQdo17pUKhUCgwTZNIJIJhGGiaRigUwu12A05mokgkgsvlAsCyLEzTxDCMlmwy4KT1/Oc//8nKlSvJyMjghBNO4IILLlBrQhUKRcKiRKhCoVAoFAqFotlR0/EKhUKhUCgUimZHiVCFQqFQKBQKRbOjRKhCoVAoFAqFotlRIlShUCgUCoVC0ewoEapQKBQKhUKhaHaUCFUoFAqFQqFQNDtKhCoUCoVCoVAomh0lQhUKhUKhUCgUzY4SoQqFQqFQKBSKZkeJUIVCoVAoFApFs6NEqEKhUCgUCoWi2fn/UiK4hPhJyYwAAAAASUVORK5CYII=", "text/plain": [ "<Figure size 704x440 with 1 Axes>" ] @@ -897,9 +1093,7 @@ "source": [ "fig, ax = plt.subplots()\n", "for (name, colour), hatch in zip(rungs, plotstyle.HATCHES):\n", - " _, _, s, p_fit, _ = scores[name]\n", - " curves = np.array([on_fine(*row[p_fit.columns(params)]) for row in s[::25]])\n", - " lo, hi = np.percentile(curves, [5, 95], axis=0)\n", + " lo, hi = forecast[name]\n", " plotstyle.band(\n", " ax,\n", " np.rad2deg(x_fine),\n", @@ -915,7 +1109,7 @@ " xlabel=r\"$\\theta_{cm}$ [deg]\",\n", " ylabel=r\"$\\sigma / \\sigma_{Ruth}$\",\n", " yscale=\"log\",\n", - " title=\"Extrapolated past the cut, 90 % of the model\",\n", + " title=\"Extrapolated past the cut: 90 % posterior predictive, error model included\",\n", ")\n", "ax.legend(fontsize=8)\n", "plt.show()" @@ -923,26 +1117,17 @@ }, { "cell_type": "markdown", - "id": "6434a1fc", + "id": "98393809", "metadata": {}, "source": [ "## Takeaways\n", "\n", - "- One comparison, one potential, four covariances: the ladder is a dictionary of\n", - " constraints, and every problem compiles on its own.\n", - "- Log space is where multiplicative errors are additive, and the Jacobian is what\n", - " makes its evidence comparable with a linear-space fit.\n", - "- Evidence and held-out prediction ask different questions. Here the GP rung\n", - " wins both. Scored honestly, conditioned on the forward angles, the\n", - " normalisation rung's intervals are too narrow at the backward angles (60 % of\n", - " the held-out points fall inside the nominal 68 % band) and the GP rung's are\n", - " too wide (80 %): neither is calibrated past the cut, and a rung that explains\n", - " the forward angles best need not predict the backward ones.\n", - "- Light-ion potentials are famously ambiguous: several parameter families give\n", - " nearly the same cross section. The `L0` posterior above carries a second\n", - " family near $V \\approx 125$ MeV beside the main one at $V \\approx 165$ MeV,\n", - " and it is the prior, not the data, that decides how much weight each gets (see\n", - " the `jitr` calibration notebook on the discrete ambiguity)." + "- We used one comparison, one potential and five covariances. The whole ladder is just a dictionary of constraints, and every problem compiles on its own.\n", + "- Log space is where multiplicative errors become additive, and the Jacobian is what makes a log-space evidence comparable with a linear-space fit.\n", + "- Evidence and held-out prediction ask different questions, and here they agree: `Lgpn`, the GP whose amplitude grows with angle, wins both. Its evidence margin over the constant-amplitude `Lgp` is real but slim, $\\Delta \\log Z = 3.5 \\pm 1.3$, only just past the two-sigma bar `compare_logz` insists on. Its held-out log predictive is the best on the ladder, $-47.8$ against $-50.5$.\n", + "- It's also the only rung that comes close to being calibrated where it counts. Past the cut, the four rungs cover 49 %, 60 %, 80 % and 63 % of the held-out points at a nominal 68 %: `L0` is far too narrow, `Lgp` too wide, and `Lgpn` nearly right. On the fitted data all four over-cover and look much alike, which is exactly why in-sample calibration isn't evidence of anything.\n", + "- *Where* a model fails is prior knowledge worth stating. A stationary kernel with a constant amplitude declares the same uncertainty at every angle. Letting the amplitude grow turns the discrepancy into a claim about which angles we can trust the potential at, and the data reward it.\n", + "- Light-ion potentials are famously ambiguous: several families of parameters give nearly the same cross section. The `L0` posterior above has a second family near $V \\approx 125$ MeV alongside the main one at $V \\approx 165$ MeV, and it's the prior, not the data, that decides how much weight each gets. The `jitr` calibration notebook discusses this discrete ambiguity in more detail." ] } ], diff --git a/examples/error_models.ipynb b/examples/error_models.ipynb index a5b6744..7d06c2a 100644 --- a/examples/error_models.ipynb +++ b/examples/error_models.ipynb @@ -25,10 +25,10 @@ "id": "6a1a51a0", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:05:15.850717Z", - "iopub.status.busy": "2026-09-12T02:05:15.850564Z", - "iopub.status.idle": "2026-09-12T02:05:17.826186Z", - "shell.execute_reply": "2026-09-12T02:05:17.825240Z" + "iopub.execute_input": "2026-09-14T19:54:01.495037Z", + "iopub.status.busy": "2026-09-14T19:54:01.494691Z", + "iopub.status.idle": "2026-09-14T19:54:04.716929Z", + "shell.execute_reply": "2026-09-14T19:54:04.715783Z" } }, "outputs": [], @@ -93,10 +93,10 @@ "id": "ba80ce49", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:05:17.827760Z", - "iopub.status.busy": "2026-09-12T02:05:17.827567Z", - "iopub.status.idle": "2026-09-12T02:05:17.833322Z", - "shell.execute_reply": "2026-09-12T02:05:17.832558Z" + "iopub.execute_input": "2026-09-14T19:54:04.720022Z", + "iopub.status.busy": "2026-09-14T19:54:04.719673Z", + "iopub.status.idle": "2026-09-14T19:54:04.728354Z", + "shell.execute_reply": "2026-09-14T19:54:04.727212Z" } }, "outputs": [ @@ -129,10 +129,10 @@ "id": "99cfee79", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:05:17.834576Z", - "iopub.status.busy": "2026-09-12T02:05:17.834330Z", - "iopub.status.idle": "2026-09-12T02:05:18.674600Z", - "shell.execute_reply": "2026-09-12T02:05:18.674025Z" + "iopub.execute_input": "2026-09-14T19:54:04.730807Z", + "iopub.status.busy": "2026-09-14T19:54:04.730571Z", + "iopub.status.idle": "2026-09-14T19:54:06.091586Z", + "shell.execute_reply": "2026-09-14T19:54:06.090369Z" } }, "outputs": [ @@ -175,10 +175,10 @@ "id": "f4522e3a", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:05:18.676189Z", - "iopub.status.busy": "2026-09-12T02:05:18.676025Z", - "iopub.status.idle": "2026-09-12T02:05:18.680266Z", - "shell.execute_reply": "2026-09-12T02:05:18.679568Z" + "iopub.execute_input": "2026-09-14T19:54:06.094255Z", + "iopub.status.busy": "2026-09-14T19:54:06.093858Z", + "iopub.status.idle": "2026-09-14T19:54:06.102493Z", + "shell.execute_reply": "2026-09-14T19:54:06.101453Z" } }, "outputs": [], @@ -196,10 +196,10 @@ "id": "573b525e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:05:18.681654Z", - "iopub.status.busy": "2026-09-12T02:05:18.681535Z", - "iopub.status.idle": "2026-09-12T02:05:18.685517Z", - "shell.execute_reply": "2026-09-12T02:05:18.684835Z" + "iopub.execute_input": "2026-09-14T19:54:06.105405Z", + "iopub.status.busy": "2026-09-14T19:54:06.105044Z", + "iopub.status.idle": "2026-09-14T19:54:06.113880Z", + "shell.execute_reply": "2026-09-14T19:54:06.112765Z" } }, "outputs": [], @@ -212,10 +212,14 @@ "\n", "\n", "def band(problem, samples, levels=(5, 95)):\n", - " on_fine = line.bind(x_fine)\n", - " cols = problem.columns(line.params)\n", - " return rx.predictive.predictive_band(\n", - " [on_fine(*s[cols]) for s in samples[::10]], levels=levels\n", + " \"\"\"The line's own band: how well each error model pins the curve down.\"\"\"\n", + " return rx.predictive.grid_draws(\n", + " problem,\n", + " line.bind(x_fine),\n", + " x_fine,\n", + " samples[::10],\n", + " model_only=True,\n", + " levels=levels,\n", " )\n", "\n", "\n", @@ -244,10 +248,10 @@ "id": "63d1e287", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:05:18.686883Z", - "iopub.status.busy": "2026-09-12T02:05:18.686766Z", - "iopub.status.idle": "2026-09-12T02:05:26.869596Z", - "shell.execute_reply": "2026-09-12T02:05:26.868964Z" + "iopub.execute_input": "2026-09-14T19:54:06.116655Z", + "iopub.status.busy": "2026-09-14T19:54:06.116292Z", + "iopub.status.idle": "2026-09-14T19:54:20.279211Z", + "shell.execute_reply": "2026-09-14T19:54:20.278082Z" } }, "outputs": [ @@ -285,10 +289,10 @@ "id": "3dee571d", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:05:26.871042Z", - "iopub.status.busy": "2026-09-12T02:05:26.870883Z", - "iopub.status.idle": "2026-09-12T02:05:40.769356Z", - "shell.execute_reply": "2026-09-12T02:05:40.768568Z" + "iopub.execute_input": "2026-09-14T19:54:20.281880Z", + "iopub.status.busy": "2026-09-14T19:54:20.281545Z", + "iopub.status.idle": "2026-09-14T19:54:49.356382Z", + "shell.execute_reply": "2026-09-14T19:54:49.355377Z" } }, "outputs": [ @@ -338,10 +342,10 @@ "id": "61371390", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:05:40.770711Z", - "iopub.status.busy": "2026-09-12T02:05:40.770585Z", - "iopub.status.idle": "2026-09-12T02:05:56.399396Z", - "shell.execute_reply": "2026-09-12T02:05:56.398574Z" + "iopub.execute_input": "2026-09-14T19:54:49.360238Z", + "iopub.status.busy": "2026-09-14T19:54:49.359852Z", + "iopub.status.idle": "2026-09-14T19:55:15.259158Z", + "shell.execute_reply": "2026-09-14T19:55:15.258095Z" } }, "outputs": [ @@ -387,10 +391,10 @@ "id": "911272d9", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:05:56.400693Z", - "iopub.status.busy": "2026-09-12T02:05:56.400574Z", - "iopub.status.idle": "2026-09-12T02:06:04.853254Z", - "shell.execute_reply": "2026-09-12T02:06:04.852563Z" + "iopub.execute_input": "2026-09-14T19:55:15.262217Z", + "iopub.status.busy": "2026-09-14T19:55:15.261864Z", + "iopub.status.idle": "2026-09-14T19:55:29.399126Z", + "shell.execute_reply": "2026-09-14T19:55:29.398014Z" } }, "outputs": [ @@ -423,10 +427,10 @@ "id": "5a4feed4", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:06:04.854644Z", - "iopub.status.busy": "2026-09-12T02:06:04.854514Z", - "iopub.status.idle": "2026-09-12T02:06:04.868017Z", - "shell.execute_reply": "2026-09-12T02:06:04.867282Z" + "iopub.execute_input": "2026-09-14T19:55:29.401902Z", + "iopub.status.busy": "2026-09-14T19:55:29.401538Z", + "iopub.status.idle": "2026-09-14T19:55:29.441045Z", + "shell.execute_reply": "2026-09-14T19:55:29.440116Z" } }, "outputs": [], @@ -451,16 +455,16 @@ "id": "c9568854", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:06:04.869442Z", - "iopub.status.busy": "2026-09-12T02:06:04.869269Z", - "iopub.status.idle": "2026-09-12T02:06:05.105438Z", - "shell.execute_reply": "2026-09-12T02:06:05.104887Z" + "iopub.execute_input": "2026-09-14T19:55:29.443670Z", + "iopub.status.busy": "2026-09-14T19:55:29.443418Z", + "iopub.status.idle": "2026-09-14T19:55:29.821757Z", + "shell.execute_reply": "2026-09-14T19:55:29.820503Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 704x440 with 1 Axes>" ] @@ -476,7 +480,7 @@ " plotstyle.band(ax, x_fine, lo, hi, color=colour, hatch=hatch, label=label)\n", "ax.plot(x, y_true, \"--\", color=\"k\", label=\"truth\")\n", "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", color=\"k\", ms=4)\n", - "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"90 % predictive bands\")\n", + "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"90 % bands of the fitted line\")\n", "ax.legend(fontsize=8)\n", "plt.show()" ] @@ -487,10 +491,10 @@ "id": "7b2a4566", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:06:05.107337Z", - "iopub.status.busy": "2026-09-12T02:06:05.107183Z", - "iopub.status.idle": "2026-09-12T02:06:05.308324Z", - "shell.execute_reply": "2026-09-12T02:06:05.307528Z" + "iopub.execute_input": "2026-09-14T19:55:29.824798Z", + "iopub.status.busy": "2026-09-14T19:55:29.824546Z", + "iopub.status.idle": "2026-09-14T19:55:30.155241Z", + "shell.execute_reply": "2026-09-14T19:55:30.154125Z" } }, "outputs": [ @@ -575,10 +579,10 @@ "id": "96e6305c", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:06:05.309910Z", - "iopub.status.busy": "2026-09-12T02:06:05.309754Z", - "iopub.status.idle": "2026-09-12T02:06:05.314928Z", - "shell.execute_reply": "2026-09-12T02:06:05.314204Z" + "iopub.execute_input": "2026-09-14T19:55:30.159078Z", + "iopub.status.busy": "2026-09-14T19:55:30.158697Z", + "iopub.status.idle": "2026-09-14T19:55:30.169951Z", + "shell.execute_reply": "2026-09-14T19:55:30.168905Z" } }, "outputs": [], @@ -604,10 +608,10 @@ "id": "57ab6fc3", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:06:05.316410Z", - "iopub.status.busy": "2026-09-12T02:06:05.316284Z", - "iopub.status.idle": "2026-09-12T02:06:41.673933Z", - "shell.execute_reply": "2026-09-12T02:06:41.673141Z" + "iopub.execute_input": "2026-09-14T19:55:30.173305Z", + "iopub.status.busy": "2026-09-14T19:55:30.172936Z", + "iopub.status.idle": "2026-09-14T19:56:30.225875Z", + "shell.execute_reply": "2026-09-14T19:56:30.224943Z" } }, "outputs": [ @@ -666,7 +670,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/examples/error_scale_and_usu.ipynb b/examples/error_scale_and_usu.ipynb index 720d04b..0657243 100644 --- a/examples/error_scale_and_usu.ipynb +++ b/examples/error_scale_and_usu.ipynb @@ -26,10 +26,10 @@ "id": "332f2403", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:12:13.972538Z", - "iopub.status.busy": "2026-09-12T02:12:13.972395Z", - "iopub.status.idle": "2026-09-12T02:12:16.067199Z", - "shell.execute_reply": "2026-09-12T02:12:16.066579Z" + "iopub.execute_input": "2026-09-14T19:51:32.295519Z", + "iopub.status.busy": "2026-09-14T19:51:32.295270Z", + "iopub.status.idle": "2026-09-14T19:51:35.508421Z", + "shell.execute_reply": "2026-09-14T19:51:35.507303Z" } }, "outputs": [], @@ -63,10 +63,10 @@ "id": "0cc00aeb", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:12:16.068995Z", - "iopub.status.busy": "2026-09-12T02:12:16.068762Z", - "iopub.status.idle": "2026-09-12T02:12:16.074344Z", - "shell.execute_reply": "2026-09-12T02:12:16.073526Z" + "iopub.execute_input": "2026-09-14T19:51:35.511079Z", + "iopub.status.busy": "2026-09-14T19:51:35.510712Z", + "iopub.status.idle": "2026-09-14T19:51:35.519306Z", + "shell.execute_reply": "2026-09-14T19:51:35.518202Z" } }, "outputs": [], @@ -93,10 +93,10 @@ "id": "759f7383", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:12:16.075605Z", - "iopub.status.busy": "2026-09-12T02:12:16.075473Z", - "iopub.status.idle": "2026-09-12T02:12:16.078503Z", - "shell.execute_reply": "2026-09-12T02:12:16.077959Z" + "iopub.execute_input": "2026-09-14T19:51:35.522011Z", + "iopub.status.busy": "2026-09-14T19:51:35.521776Z", + "iopub.status.idle": "2026-09-14T19:51:35.527102Z", + "shell.execute_reply": "2026-09-14T19:51:35.525831Z" } }, "outputs": [], @@ -148,10 +148,10 @@ "id": "0b23054e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:12:16.079826Z", - "iopub.status.busy": "2026-09-12T02:12:16.079706Z", - "iopub.status.idle": "2026-09-12T02:12:16.084743Z", - "shell.execute_reply": "2026-09-12T02:12:16.083716Z" + "iopub.execute_input": "2026-09-14T19:51:35.529857Z", + "iopub.status.busy": "2026-09-14T19:51:35.529500Z", + "iopub.status.idle": "2026-09-14T19:51:35.538661Z", + "shell.execute_reply": "2026-09-14T19:51:35.537490Z" } }, "outputs": [], @@ -174,10 +174,10 @@ "id": "139378c4", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:12:16.086015Z", - "iopub.status.busy": "2026-09-12T02:12:16.085841Z", - "iopub.status.idle": "2026-09-12T02:12:45.842767Z", - "shell.execute_reply": "2026-09-12T02:12:45.842115Z" + "iopub.execute_input": "2026-09-14T19:51:35.541123Z", + "iopub.status.busy": "2026-09-14T19:51:35.540773Z", + "iopub.status.idle": "2026-09-14T19:52:25.139784Z", + "shell.execute_reply": "2026-09-14T19:52:25.138713Z" } }, "outputs": [ @@ -235,10 +235,10 @@ "id": "44ebaa50", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:12:45.844154Z", - "iopub.status.busy": "2026-09-12T02:12:45.844029Z", - "iopub.status.idle": "2026-09-12T02:12:45.847434Z", - "shell.execute_reply": "2026-09-12T02:12:45.846964Z" + "iopub.execute_input": "2026-09-14T19:52:25.142972Z", + "iopub.status.busy": "2026-09-14T19:52:25.142598Z", + "iopub.status.idle": "2026-09-14T19:52:25.149962Z", + "shell.execute_reply": "2026-09-14T19:52:25.149093Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "29aca1f6", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:12:45.848699Z", - "iopub.status.busy": "2026-09-12T02:12:45.848584Z", - "iopub.status.idle": "2026-09-12T02:12:45.853705Z", - "shell.execute_reply": "2026-09-12T02:12:45.853202Z" + "iopub.execute_input": "2026-09-14T19:52:25.153009Z", + "iopub.status.busy": "2026-09-14T19:52:25.152779Z", + "iopub.status.idle": "2026-09-14T19:52:25.161357Z", + "shell.execute_reply": "2026-09-14T19:52:25.160402Z" } }, "outputs": [], @@ -301,10 +301,10 @@ "id": "b113437e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:12:45.854940Z", - "iopub.status.busy": "2026-09-12T02:12:45.854825Z", - "iopub.status.idle": "2026-09-12T02:13:39.792126Z", - "shell.execute_reply": "2026-09-12T02:13:39.791469Z" + "iopub.execute_input": "2026-09-14T19:52:25.164040Z", + "iopub.status.busy": "2026-09-14T19:52:25.163801Z", + "iopub.status.idle": "2026-09-14T19:53:55.563312Z", + "shell.execute_reply": "2026-09-14T19:53:55.562427Z" } }, "outputs": [ @@ -339,8 +339,9 @@ " f\"{name:16s} m = {s[:, cols[0]].mean():.3f} +/- {s[:, cols[0]].std():.3f}, \"\n", " f\"b = {s[:, cols[1]].mean():.3f} +/- {s[:, cols[1]].std():.3f}\"\n", " )\n", - " lo, hi = rx.predictive.predictive_band(\n", - " [on_fine(*r[cols]) for r in s[::10]], levels=(5, 95)\n", + " # the line's own band, compared with the truth rather than with the data\n", + " lo, hi = rx.predictive.grid_draws(\n", + " p, on_fine, x_fine, s[::10], model_only=True, levels=(5, 95)\n", " )\n", " bands[name] = (lo - y_true_fine, hi - y_true_fine)" ] @@ -351,16 +352,16 @@ "id": "675db827", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:13:39.793565Z", - "iopub.status.busy": "2026-09-12T02:13:39.793418Z", - "iopub.status.idle": "2026-09-12T02:13:40.751618Z", - "shell.execute_reply": "2026-09-12T02:13:40.751161Z" + "iopub.execute_input": "2026-09-14T19:53:55.566499Z", + "iopub.status.busy": "2026-09-14T19:53:55.566248Z", + "iopub.status.idle": "2026-09-14T19:53:57.093993Z", + "shell.execute_reply": "2026-09-14T19:53:57.093004Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 704x440 with 1 Axes>" ] @@ -391,7 +392,11 @@ " label=\"B (offset)\",\n", ")\n", "ax.axhline(0.0, ls=\"--\", color=\"k\", label=\"truth\")\n", - "ax.set(xlabel=\"$x$\", ylabel=r\"$y - y_\\mathrm{truth}$\", title=\"90 % bands\")\n", + "ax.set(\n", + " xlabel=\"$x$\",\n", + " ylabel=r\"$y - y_\\mathrm{truth}$\",\n", + " title=\"90 % bands of the fitted line\",\n", + ")\n", "ax.legend(fontsize=8, ncol=2, loc=\"upper right\")\n", "plt.show()" ] diff --git a/examples/gp_discrepancy.ipynb b/examples/gp_discrepancy.ipynb index 87ef56d..0500f44 100644 --- a/examples/gp_discrepancy.ipynb +++ b/examples/gp_discrepancy.ipynb @@ -2,29 +2,19 @@ "cells": [ { "cell_type": "markdown", - "id": "b89d79ac", + "id": "75c9df24", "metadata": {}, "source": [ "# Model discrepancy with a Gaussian process\n", "\n", - "Our model is always wrong somewhere. When it is wrong in a *structured* way —\n", - "smoothly, as a function of $x$, rather than point by point — the residuals it\n", - "leaves are correlated, and a covariance that assumes independence will read\n", - "those correlations as precision it does not have.\n", + "Every physics model we calibrate is wrong somewhere. Often it's wrong in a *structured* way: it drifts away from reality smoothly as a function of $x$, rather than missing randomly point by point. When that happens, the residuals it leaves behind are correlated with each other. If our covariance assumes the points are independent, the likelihood reads those correlated residuals as a series of independent confirmations, and the posterior ends up far more confident than it has any right to be.\n", "\n", - "The standard treatment is [Kennedy & O'Hagan\n", - "(2001)](https://doi.org/10.1111/1467-9868.00294), who add a discrepancy term to\n", - "the model. There are two quite different things people mean by that:\n", + "The standard way to handle this is from [Kennedy & O'Hagan (2001)](https://doi.org/10.1111/1467-9868.00294), who add a *discrepancy* term $\\delta(x)$ to the model, so that reality is $y_m(x;\\theta) + \\delta(x)$. Usually $\\delta$ gets a [Gaussian process](https://en.wikipedia.org/wiki/Gaussian_process) prior. People mean two rather different things when they say this, and it's worth separating them before we start:\n", "\n", - "- a **mean-zero discrepancy**, which learns only the *covariance* of the\n", - " mismatch: we say the truth departs smoothly from our model by some amount, we\n", - " say how far and how smoothly, and we integrate over every such departure;\n", - "- a **non-zero-mean discrepancy**, which gives the mismatch its own parameters\n", - " and builds a posterior for the discrepancy function itself (recipes 8 and 36).\n", + "- A **mean-zero discrepancy** only learns the *covariance* of the mismatch. We tell the likelihood that the truth departs smoothly from our model, roughly how far and how smoothly, and then integrate over every departure consistent with that. We never try to say what $\\delta(x)$ actually *is*.\n", + "- A **non-zero-mean discrepancy** goes further. It gives the mismatch parameters of its own and builds a posterior for the discrepancy function itself (recipes 8 and 36).\n", "\n", - "This notebook is entirely about the first. We will also give the discrepancy an\n", - "amplitude that **grows with $x$**, because that is genuine prior knowledge here:\n", - "our data look like a line at small $x$ and stop looking like one further out.\n", + "In this notebook we'll only do the first. We'll also let the size of the discrepancy **grow with $x$**. That's genuine prior knowledge in this problem: the data look like a straight line at small $x$ and stop looking like one further out, so we trust our model more on the left than on the right.\n", "\n", "Recipes: 7" ] @@ -35,10 +25,10 @@ "id": "506a99dd", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:25:08.887811Z", - "iopub.status.busy": "2026-09-12T03:25:08.887654Z", - "iopub.status.idle": "2026-09-12T03:25:11.158430Z", - "shell.execute_reply": "2026-09-12T03:25:11.157614Z" + "iopub.execute_input": "2026-09-14T22:03:05.646786Z", + "iopub.status.busy": "2026-09-14T22:03:05.646632Z", + "iopub.status.idle": "2026-09-14T22:03:07.761378Z", + "shell.execute_reply": "2026-09-14T22:03:07.760752Z" } }, "outputs": [], @@ -67,39 +57,27 @@ }, { "cell_type": "markdown", - "id": "beea011b", + "id": "c44ee40c", "metadata": {}, "source": [ "## A line, and a truth that leaves it\n", "\n", - "Our truth is a line plus a small quadratic departure, $0.03\\,x^2$. At the left\n", - "edge that defect is 0.001, far below the 0.02 noise and quite invisible; by\n", - "$x = 5$ it is 0.75, thirty-seven times the error bar. A straight-line model is\n", - "therefore excellent at small $x$ and hopeless at large $x$, which is exactly the\n", - "situation the growing amplitude is meant to express." + "We'll make synthetic data from a truth that is a straight line plus a small quadratic departure, $0.03\\,x^2$. At the left edge of the data that departure is 0.001, far below the measurement noise of 0.02, so we couldn't see it even if we tried. By $x = 5$ it has grown to 0.75, thirty-seven times the size of an error bar. A straight-line model is therefore an excellent description at small $x$ and a hopeless one at large $x$, which is exactly the situation a growing discrepancy amplitude is meant to describe." ] }, { "cell_type": "code", "execution_count": 2, - "id": "46904250", + "id": "b77965a8", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:25:11.160039Z", - "iopub.status.busy": "2026-09-12T03:25:11.159798Z", - "iopub.status.idle": "2026-09-12T03:25:11.164302Z", - "shell.execute_reply": "2026-09-12T03:25:11.163753Z" + "iopub.execute_input": "2026-09-14T22:03:07.762938Z", + "iopub.status.busy": "2026-09-14T22:03:07.762734Z", + "iopub.status.idle": "2026-09-14T22:03:07.766517Z", + "shell.execute_reply": "2026-09-14T22:03:07.765984Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "defect / noise at x = 0.2, 2.5, 5.0: [ 0.1 9.4 37.5]\n" - ] - } - ], + "outputs": [], "source": [ "M_TRUE, B_TRUE = 0.8, 1.0\n", "\n", @@ -118,7 +96,31 @@ "data = rx.Dataset(\n", " x, truth(x) + rng.normal(0.0, noise, x.size), np.full(x.size, noise), label=\"toy\"\n", ")\n", - "x_fine = np.linspace(0.0, 6.0, 80)\n", + "x_fine = np.linspace(0.0, 6.0, 80)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "81c09486", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T22:03:07.767754Z", + "iopub.status.busy": "2026-09-14T22:03:07.767639Z", + "iopub.status.idle": "2026-09-14T22:03:07.770330Z", + "shell.execute_reply": "2026-09-14T22:03:07.769736Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "defect / noise at x = 0.2, 2.5, 5.0: [ 0.1 9.4 37.5]\n" + ] + } + ], + "source": [ "print(\n", " \"defect / noise at x = 0.2, 2.5, 5.0:\", np.round(defect([0.2, 2.5, 5.0]) / noise, 1)\n", ")" @@ -126,14 +128,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "cf684d62", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:25:11.165619Z", - "iopub.status.busy": "2026-09-12T03:25:11.165497Z", - "iopub.status.idle": "2026-09-12T03:25:12.009917Z", - "shell.execute_reply": "2026-09-12T03:25:12.009065Z" + "iopub.execute_input": "2026-09-14T22:03:07.771629Z", + "iopub.status.busy": "2026-09-14T22:03:07.771498Z", + "iopub.status.idle": "2026-09-14T22:03:08.506900Z", + "shell.execute_reply": "2026-09-14T22:03:08.506161Z" } }, "outputs": [ @@ -166,36 +168,31 @@ }, { "cell_type": "markdown", - "id": "543e6a87", + "id": "000c2900", "metadata": {}, "source": [ "## Four ways to say \"the model is wrong\"\n", "\n", - "We fit the same line four times, changing only the covariance:\n", + "We'll fit the same line four times, and the only thing we'll change between fits is the covariance:\n", "\n", - "1. **statistics only** — the reported errors, and nothing else;\n", - "2. **a diagonal model error** (`T.model_error`) — every point gets extra,\n", - " *independent* slack;\n", - "3. **a GP with a constant amplitude** — a smooth mean-zero discrepancy of the\n", - " same size everywhere;\n", - "4. **a GP with an amplitude growing in $x$** (`T.exp_growth_amplitude`) — the\n", - " same, but allowed to be small where we trust the line and large where we do\n", - " not.\n", + "1. **Statistics only.** We use the reported errors and nothing else, which amounts to claiming the line is perfect.\n", + "2. **A diagonal model error** (`T.model_error`). Every point gets some extra slack, but each point's slack is *independent* of its neighbours'.\n", + "3. **A GP with a constant amplitude.** Now the discrepancy is smooth and mean-zero, but it's allowed to be the same size everywhere.\n", + "4. **A GP whose amplitude grows with $x$** (`T.exp_growth_amplitude`). This is the same smooth discrepancy, except that it can be small where we trust the line and large where we don't.\n", "\n", - "Only the last one encodes what we actually believe. All four are one `Term` in\n", - "a `Constraint`, and the inference code does not change between them." + "Only the last one encodes what we actually believe about this problem. The nice thing is that each of these is a single `Term` in a `Constraint`, so the inference code doesn't change between them at all. The kernel is a [Matérn](https://en.wikipedia.org/wiki/Mat%C3%A9rn_covariance_function) with $\\nu = 5/2$: smooth, but not as unrealistically smooth as a squared exponential." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "1a201944", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:25:12.011652Z", - "iopub.status.busy": "2026-09-12T03:25:12.011521Z", - "iopub.status.idle": "2026-09-12T03:25:12.019382Z", - "shell.execute_reply": "2026-09-12T03:25:12.018751Z" + "iopub.execute_input": "2026-09-14T22:03:08.508369Z", + "iopub.status.busy": "2026-09-14T22:03:08.508236Z", + "iopub.status.idle": "2026-09-14T22:03:08.515186Z", + "shell.execute_reply": "2026-09-14T22:03:08.514613Z" } }, "outputs": [], @@ -226,14 +223,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "54264f30", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:25:12.020830Z", - "iopub.status.busy": "2026-09-12T03:25:12.020689Z", - "iopub.status.idle": "2026-09-12T03:25:12.025435Z", - "shell.execute_reply": "2026-09-12T03:25:12.024762Z" + "iopub.execute_input": "2026-09-14T22:03:08.516511Z", + "iopub.status.busy": "2026-09-14T22:03:08.516350Z", + "iopub.status.idle": "2026-09-14T22:03:08.520796Z", + "shell.execute_reply": "2026-09-14T22:03:08.520369Z" } }, "outputs": [ @@ -263,14 +260,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "699a541c", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:25:12.027514Z", - "iopub.status.busy": "2026-09-12T03:25:12.027298Z", - "iopub.status.idle": "2026-09-12T03:27:59.004758Z", - "shell.execute_reply": "2026-09-12T03:27:59.003984Z" + "iopub.execute_input": "2026-09-14T22:03:08.522178Z", + "iopub.status.busy": "2026-09-14T22:03:08.522056Z", + "iopub.status.idle": "2026-09-14T22:05:31.303024Z", + "shell.execute_reply": "2026-09-14T22:05:31.302372Z" } }, "outputs": [], @@ -287,14 +284,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "f08f7fc9", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:27:59.006475Z", - "iopub.status.busy": "2026-09-12T03:27:59.006291Z", - "iopub.status.idle": "2026-09-12T03:27:59.012047Z", - "shell.execute_reply": "2026-09-12T03:27:59.011324Z" + "iopub.execute_input": "2026-09-14T22:05:31.304445Z", + "iopub.status.busy": "2026-09-14T22:05:31.304287Z", + "iopub.status.idle": "2026-09-14T22:05:31.309024Z", + "shell.execute_reply": "2026-09-14T22:05:31.308432Z" } }, "outputs": [ @@ -324,43 +321,30 @@ }, { "cell_type": "markdown", - "id": "fe165fdc", + "id": "bf2aeeb7", "metadata": {}, "source": [ - "### What those four rows say\n", - "\n", - "**Statistics only** is the cautionary one: $m = 0.953 \\pm 0.003$, sixty standard\n", - "deviations from the truth. The line has no way to say \"I am wrong at large\n", - "$x$\", so it tilts to chase the defect and then reports a precision of three parts\n", - "in a thousand. This is what an unmodelled discrepancy does — it does not widen\n", - "the answer, it moves it, confidently.\n", - "\n", - "**The diagonal model error** covers the truth, but look at how: $m = 0.775 \\pm\n", - "1.043$. Independent slack per point can only inflate, and to cover a coherent\n", - "departure it has to inflate until the slope means nothing at all.\n", - "\n", - "**The constant-amplitude GP** knows the defect is smooth, and that already helps\n", - "a great deal — $m = 0.959 \\pm 0.062$ against sixty sigma before. But it is still\n", - "2.6 sigma out, because one amplitude has to serve both ends of the range: large\n", - "enough for $x = 5$, and therefore far too generous at $x = 0.2$, where it lets\n", - "the fit off the hook exactly where the data are informative.\n", - "\n", - "**The growing amplitude** is the one that matches what we believe, and it lands\n", - "on the truth: $m = 0.830 \\pm 0.071$, $b = 0.941 \\pm 0.165$, both within half a\n", - "sigma. The fit also tells us it needed the growth: the inferred slope of the\n", - "amplitude is about $+1.8$, not zero." + "### What those four rows tell us\n", + "\n", + "**Statistics only** is the cautionary tale. It gives $m = 0.953 \\pm 0.003$, sixty standard deviations away from the truth. The line has no way to say \"I'm wrong at large $x$\", so it tilts to chase the defect and then reports a precision of three parts in a thousand. This is worth remembering: an unmodelled discrepancy doesn't make our answer vaguer, it *moves* it, and confidently.\n", + "\n", + "**The diagonal model error** does cover the truth, but look at how it manages it: $m = 0.775 \\pm 1.043$. Independent slack at each point can only inflate the errors, and to cover a coherent, smooth departure it has to inflate them until the slope means nothing at all.\n", + "\n", + "**The constant-amplitude GP** knows the defect is smooth, and that alone helps a great deal: $m = 0.959 \\pm 0.062$, compared with sixty sigma before. It's still 2.6 sigma out, though. The trouble is that one amplitude has to serve both ends of the range. It has to be large enough to cover $x = 5$, which makes it far too generous at $x = 0.2$, and so it lets the fit off the hook exactly where the data are most informative about the line.\n", + "\n", + "**The growing amplitude**, the one that matches what we believe, lands on the truth: $m = 0.830 \\pm 0.071$ and $b = 0.941 \\pm 0.165$, both within half a sigma. The fit also tells us it really did need the growth, since the inferred slope of the amplitude comes out around $+1.8$ rather than zero." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "1b55f904", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:27:59.013617Z", - "iopub.status.busy": "2026-09-12T03:27:59.013452Z", - "iopub.status.idle": "2026-09-12T03:27:59.250686Z", - "shell.execute_reply": "2026-09-12T03:27:59.249984Z" + "iopub.execute_input": "2026-09-14T22:05:31.310346Z", + "iopub.status.busy": "2026-09-14T22:05:31.310228Z", + "iopub.status.idle": "2026-09-14T22:05:31.494525Z", + "shell.execute_reply": "2026-09-14T22:05:31.493770Z" } }, "outputs": [ @@ -399,66 +383,135 @@ }, { "cell_type": "markdown", - "id": "6e5e2534", + "id": "61c8dadf", "metadata": {}, "source": [ "## Propagating the whole uncertainty\n", "\n", - "The line's own band cannot bend: it is two parameters, so it is a pencil of\n", - "straight lines. `total_predictive_band` conditions the GP on the residuals at\n", - "each posterior row, predicts the discrepancy on whatever grid we ask for, and\n", - "adds it. Everything the problem declared is propagated, and we never have to\n", - "tell it which columns belong to the kernel." + "The line's own posterior band can't bend. It has two parameters, so all it can ever produce is a fan of straight lines. The GP supplies the piece that *can* bend. But once we have a discrepancy in the mix, there are actually three different things we might draw, and they answer different questions, so let's go through them carefully.\n", + "\n", + "Because our kernel declares a **mean-zero** discrepancy, the likelihood we fit was already the marginal over $\\delta$, a [multivariate normal](https://en.wikipedia.org/wiki/Multivariate_normal_distribution) whose covariance is the sum of the experimental covariance and the kernel:\n", + "\n", + "$$\\mathbf{y} \\sim \\mathcal{N}\\big(y_m(\\mathbf{x};\\theta),\\ \\Sigma(\\theta)\\big),\n", + "\\qquad \\Sigma(\\theta) = \\Sigma_\\mathrm{exp} + K(\\theta).$$\n", + "\n", + "In other words, the fit integrated the discrepancy out rather than estimating it. The first predictive that matches this is a correlated draw from the inferred kernel, centred on the model's *own* prediction, with one draw for every posterior sample:\n", + "\n", + "$$y_* = y_m(x_*;\\theta) + \\delta, \\qquad\n", + "\\delta \\sim \\mathcal{N}\\big(0,\\ K_{**}(\\theta)\\big).$$\n", + "\n", + "We get this from `rx.predictive.grid_draws` by naming just the kernel in `terms=`. It's a statement about **the model**: where it may be wrong, and by how much. Notice that it carries no experimental error, so it isn't what we should compare our data against. For that we need the second object, which includes every term:\n", + "\n", + "$$y = y_m(x;\\theta) + \\delta + \\varepsilon, \\qquad\n", + "\\varepsilon \\sim \\mathcal{N}\\big(0,\\ \\Sigma_\\mathrm{exp}\\big).$$\n", + "\n", + "`terms=` is how we choose between them. Below we'll draw the first on the fine grid, naming only the kernel. We have to, because the reported errors are one number per *measured* point, and they simply don't exist at a new $x$. Then we'll draw the second at the measured points, where they do exist.\n", + "\n", + "The third object, which we're deliberately not going to use, conditions the discrepancy on the observed residuals $r$:\n", + "\n", + "$$\\delta \\mid r \\sim \\mathcal{N}\\big(K_{*t}(K_{tt}+N)^{-1}r,\\;\n", + "K_{**} - K_{*t}(K_{tt}+N)^{-1}K_{t*}\\big).$$\n", + "\n", + "This is ordinary GP regression, as in [Rasmussen & Williams (2006), ch. 2](http://gaussianprocess.org/gpml/), and it's available as `rx.predictive.gp_predictive_draws(..., conditioned=True)`. What it does is interpolate the residuals, stacking a data-driven regression on top of the model. Where that band is narrow, it's narrow because the data pinned it there, not because the model is any good. Our question in this notebook is where the *model* fails, so we want the first two objects.\n", + "\n", + "One more detail matters for what follows. Each draw is a whole curve, not a column of independent points. For every posterior sample, `grid_draws` re-evaluates the kernel, growing amplitude included, on the new grid, takes its [Cholesky factor](https://en.wikipedia.org/wiki/Cholesky_decomposition) once, and draws a single correlated vector from it. That's what lets us ask a question like \"how far does the truth wander from the model *anywhere* in this range?\" and get a sensible answer." ] }, { "cell_type": "code", - "execution_count": 9, - "id": "621049af", + "execution_count": 10, + "id": "152c7f05", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:27:59.252425Z", - "iopub.status.busy": "2026-09-12T03:27:59.252159Z", - "iopub.status.idle": "2026-09-12T03:28:00.937743Z", - "shell.execute_reply": "2026-09-12T03:28:00.933911Z" + "iopub.execute_input": "2026-09-14T22:05:31.495891Z", + "iopub.status.busy": "2026-09-14T22:05:31.495762Z", + "iopub.status.idle": "2026-09-14T22:05:32.317850Z", + "shell.execute_reply": "2026-09-14T22:05:32.317172Z" } }, "outputs": [], "source": [ "def mean_band(problem, samples_, levels=(16, 84)):\n", - " on_fine = line.bind(x_fine)\n", - " cols = problem.columns(line.params)\n", - " return rx.predictive.predictive_band(\n", - " [on_fine(*s[cols]) for s in samples_[::10]], levels=levels\n", + " # the fan of model curves alone, with no error model at all\n", + " return rx.predictive.grid_draws(\n", + " problem,\n", + " line.bind(x_fine),\n", + " x_fine,\n", + " samples_[::10],\n", + " model_only=True,\n", + " levels=levels,\n", " )\n", "\n", "\n", - "band_gp = rx.predictive.total_predictive_band(\n", - " problems[\"GP, growing amplitude\"],\n", - " gp_grow,\n", + "p_grow = problems[\"GP, growing amplitude\"]\n", + "s_grow = samples[\"GP, growing amplitude\"]\n", + "\n", + "# the model plus its discrepancy, on any grid we like: name the kernel alone\n", + "band_gp = rx.predictive.grid_draws(\n", + " p_grow,\n", " line.bind(x_fine),\n", " x_fine,\n", - " samples[\"GP, growing amplitude\"],\n", + " s_grow[::10],\n", + " terms=[gp_grow],\n", + " levels=(16, 84),\n", " rng=4,\n", + ")\n", + "# every term the error model declares, at the points that were measured\n", + "draws_data = rx.diagnostics.predictive_draws(\n", + " p_grow, s_grow[::10], n_rep=2, rng=4, return_draws=True\n", ")" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, + "id": "d8b317c3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T22:05:32.319190Z", + "iopub.status.busy": "2026-09-14T22:05:32.319063Z", + "iopub.status.idle": "2026-09-14T22:05:32.323530Z", + "shell.execute_reply": "2026-09-14T22:05:32.322947Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "68 % width of model + discrepancy at x = 0.2, 2.5, 5.0: [0.311 0.671 1.394]\n", + "mean 68 % width including the experimental errors: 0.749\n" + ] + } + ], + "source": [ + "w = band_gp[1] - band_gp[0]\n", + "print(\n", + " \"68 % width of model + discrepancy at x = 0.2, 2.5, 5.0:\",\n", + " np.round(np.interp([0.2, 2.5, 5.0], x_fine, w), 3),\n", + ")\n", + "print(\n", + " \"mean 68 % width including the experimental errors:\",\n", + " np.round(rx.diagnostics.sharpness(draws_data).mean(), 3),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "id": "9e91d84f", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:28:00.939859Z", - "iopub.status.busy": "2026-09-12T03:28:00.939567Z", - "iopub.status.idle": "2026-09-12T03:28:01.200121Z", - "shell.execute_reply": "2026-09-12T03:28:01.199366Z" + "iopub.execute_input": "2026-09-14T22:05:32.324868Z", + "iopub.status.busy": "2026-09-14T22:05:32.324748Z", + "iopub.status.idle": "2026-09-14T22:05:32.488960Z", + "shell.execute_reply": "2026-09-14T22:05:32.488553Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 704x440 with 1 Axes>" ] @@ -481,7 +534,7 @@ " *band_gp,\n", " color=plotstyle.COLOURS[0],\n", " hatch=plotstyle.HATCHES[2],\n", - " label=\"GP, growing amplitude (total)\",\n", + " label=\"GP, growing amplitude: model + discrepancy\",\n", ")\n", "ax.plot(x_fine, truth(x_fine), \"--\", color=\"k\", label=\"truth\")\n", "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", ms=3, color=\"k\")\n", @@ -492,36 +545,144 @@ }, { "cell_type": "markdown", - "id": "cda1ab13", + "id": "58b57dfa", "metadata": {}, "source": [ - "## What the GP actually learned\n", + "### Which of those belongs next to the data?\n", + "\n", + "The band above shows the model and its discrepancy. A real measurement also carries its own experimental error, and only the sum of the two predicts what a *measurement* would read. `predictive_draws` at the measured points includes every term by default, which is why that's the one we use for a [coverage](https://en.wikipedia.org/wiki/Coverage_probability) check, and why we don't use the band from `grid_draws` for it.\n", "\n", - "Because this is synthetic data we know the defect exactly: it is $0.03\\,x^2$.\n", - "So we can condition the GP on the residuals of the posterior-median line and\n", - "hold the result up against the truth. The interesting part is the edges — how\n", - "the GP interpolates between points, and how it relaxes back towards zero where\n", - "there are no data to hold it." + "In this toy the two turn out to be very nearly the same curve, and it's worth seeing why. The experimental error is a flat 0.02, while the discrepancy's standard deviation is already 0.16 at $x = 0.2$ and 0.70 at $x = 5$. Adding 0.02 in quadrature to numbers that size changes the width by well under one per cent, and the mean 68 % predictive width at the measured points is 0.749, almost all of it discrepancy. So the choice of terms only matters when the terms are comparable in size. When they are, it matters a lot: in the $\\alpha + {}^{44}$Ca study the inferred noise and the discrepancy are of the same order, and a coverage check run against the wrong object would tell us nothing.\n", + "\n", + "We'll also see below that 97 % of the measured points land inside a nominal 68 % interval. That isn't a bug. It's what a mean-zero predictive *is*: it never looks at the residuals, so it can't pull itself in where the data happen to agree with the model. It tells us how far the truth *could* stray from the model, not how far it actually did." ] }, { "cell_type": "code", - "execution_count": 11, - "id": "5aea3429", + "execution_count": 13, + "id": "30f35b57", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:28:01.201730Z", - "iopub.status.busy": "2026-09-12T03:28:01.201561Z", - "iopub.status.idle": "2026-09-12T03:28:01.205733Z", - "shell.execute_reply": "2026-09-12T03:28:01.205169Z" + "iopub.execute_input": "2026-09-14T22:05:32.490525Z", + "iopub.status.busy": "2026-09-14T22:05:32.490355Z", + "iopub.status.idle": "2026-09-14T22:05:32.494255Z", + "shell.execute_reply": "2026-09-14T22:05:32.493614Z" } }, "outputs": [], "source": [ - "# the discrepancy is the total band minus the line: total_predictive_band\n", - "# knows about the growing amplitude, which the bare kernel object does not\n", - "p_grow = problems[\"GP, growing amplitude\"]\n", - "theta_gp = np.median(samples[\"GP, growing amplitude\"], axis=0)\n", + "lo_d, hi_d = np.percentile(draws_data, [16, 84], axis=0)\n", + "inside_d = np.mean((data.y >= lo_d) & (data.y <= hi_d))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "21bc61b5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T22:05:32.495574Z", + "iopub.status.busy": "2026-09-14T22:05:32.495459Z", + "iopub.status.idle": "2026-09-14T22:05:32.608494Z", + "shell.execute_reply": "2026-09-14T22:05:32.607720Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 704x440 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "plotstyle.band(\n", + " ax,\n", + " data.x,\n", + " lo_d,\n", + " hi_d,\n", + " color=plotstyle.COLOURS[2],\n", + " hatch=plotstyle.HATCHES[0],\n", + " label=\"model + discrepancy + experimental\",\n", + ")\n", + "plotstyle.band(\n", + " ax,\n", + " data.x,\n", + " np.interp(data.x, x_fine, band_gp[0]),\n", + " np.interp(data.x, x_fine, band_gp[1]),\n", + " color=plotstyle.COLOURS[0],\n", + " hatch=plotstyle.HATCHES[2],\n", + " label=\"model + discrepancy\",\n", + ")\n", + "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", ms=4, color=\"k\", label=\"data\")\n", + "ax.set(\n", + " xlabel=\"$x$\",\n", + " ylabel=\"$y$\",\n", + " title=\"Only the wider band predicts a measurement (68 %)\",\n", + ")\n", + "ax.legend(fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "6b2e05c6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T22:05:32.609853Z", + "iopub.status.busy": "2026-09-14T22:05:32.609725Z", + "iopub.status.idle": "2026-09-14T22:05:32.612494Z", + "shell.execute_reply": "2026-09-14T22:05:32.611992Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fraction of the measured points inside the 68 % predictive: 0.97\n" + ] + } + ], + "source": [ + "print(f\"fraction of the measured points inside the 68 % predictive: {inside_d:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "7f6aebb1", + "metadata": {}, + "source": [ + "## The departures the data allow\n", + "\n", + "Because this is synthetic data, we know the defect exactly: it's $0.03\\,x^2$. So we can take the band, subtract the posterior-median line, and look at the discrepancy on its own. Remember that we drew it mean-zero, so what comes back isn't an *estimate* of the defect. It's the envelope of departures from the line that the data are willing to allow. Its centre sits at $-0.062$, which is zero to within about a tenth of its own width, and it contains the true defect over 72 % of the grid.\n", + "\n", + "When our question is \"where is my model wrong?\", this is the honest thing to look at. A conditioned GP (`gp_predictive_draws(..., conditioned=True)`) would thread through the residuals and hug them instead. That would look far more impressive, but it would be answering a different question, one whose answer depends mostly on where the data happen to sit." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "940dde08", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T22:05:32.613789Z", + "iopub.status.busy": "2026-09-14T22:05:32.613673Z", + "iopub.status.idle": "2026-09-14T22:05:32.617238Z", + "shell.execute_reply": "2026-09-14T22:05:32.616627Z" + } + }, + "outputs": [], + "source": [ + "# the discrepancy is the band minus the line: grid_draws evaluated the growing\n", + "# amplitude on the grid, which the bare kernel object cannot\n", + "theta_gp = np.median(s_grow, axis=0)\n", "line_median = line.bind(x_fine)(*theta_gp[p_grow.columns(line.params)])\n", "disc_lo, disc_hi = band_gp[0] - line_median, band_gp[1] - line_median\n", "true_defect = truth(x_fine) - line_median\n", @@ -530,20 +691,50 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 17, + "id": "7a96ab0c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T22:05:32.618482Z", + "iopub.status.busy": "2026-09-14T22:05:32.618322Z", + "iopub.status.idle": "2026-09-14T22:05:32.621015Z", + "shell.execute_reply": "2026-09-14T22:05:32.620605Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fraction of the grid where the true defect lies inside the 68 % envelope: 0.72\n", + "envelope is centred on -0.062\n" + ] + } + ], + "source": [ + "print(\n", + " \"fraction of the grid where the true defect lies inside the 68 % envelope:\",\n", + " f\"{np.mean((true_defect >= disc_lo) & (true_defect <= disc_hi)):.2f}\",\n", + ")\n", + "print(f\"envelope is centred on {np.mean(0.5 * (disc_lo + disc_hi)):+.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, "id": "e1b7d5ba", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:28:01.207442Z", - "iopub.status.busy": "2026-09-12T03:28:01.207273Z", - "iopub.status.idle": "2026-09-12T03:28:01.346017Z", - "shell.execute_reply": "2026-09-12T03:28:01.345232Z" + "iopub.execute_input": "2026-09-14T22:05:32.622415Z", + "iopub.status.busy": "2026-09-14T22:05:32.622276Z", + "iopub.status.idle": "2026-09-14T22:05:32.745568Z", + "shell.execute_reply": "2026-09-14T22:05:32.744966Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "<Figure size 704x440 with 1 Axes>" ] @@ -561,46 +752,38 @@ " disc_lo,\n", " disc_hi,\n", " color=plotstyle.COLOURS[0],\n", - " label=\"GP discrepancy (68 %)\",\n", + " label=\"departures allowed (68 %)\",\n", ")\n", "ax.plot(x_fine, true_defect, \"--\", color=plotstyle.COLOURS[1], label=\"the true defect\")\n", - "ax.set(xlabel=\"$x$\", ylabel=\"$y$ - line\", title=\"The discrepancy, learned\")\n", + "ax.set(xlabel=\"$x$\", ylabel=\"$y$ - line\", title=\"The departures the data allow\")\n", "ax.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "1aeb5286", + "id": "1a4be8ce", "metadata": {}, "source": [ "## The same thing on a cross section\n", "\n", - "Now a real model with a real piece of physics missing. We generate mock\n", - "$n + {}^{40}\\mathrm{Ca}$ elastic scattering at 14.1 MeV from a potential with\n", - "**volume and surface** absorption, and then fit it with a potential that has\n", - "only volume absorption. The missing surface term is our structured defect.\n", + "Now let's try a real physics model with a real piece of physics missing. We generate mock $n + {}^{40}\\mathrm{Ca}$ elastic scattering data at 14.1 MeV from an [optical potential](https://doi.org/10.1016/S0375-9474(02)01321-0) with both **volume and surface** absorption. Then we fit those data with a potential that only has volume absorption. The missing surface term is our structured defect.\n", "\n", - "Two differences from the toy. We compare in log space, so the discrepancy is a\n", - "*fractional* defect in angle, and we drive the fit with\n", - "[dynesty](https://dynesty.readthedocs.io/) rather than emcee: optical-model\n", - "posteriors are correlated and often multimodal, and an affine-invariant ensemble\n", - "mixes poorly on them.\n", + "There are two differences from the toy. First, we compare in log space, so the discrepancy is a *fractional* defect as a function of angle. Second, we drive the fit with [dynesty](https://dynesty.readthedocs.io/) ([Speagle 2020](https://doi.org/10.1093/mnras/staa278)) rather than emcee. Optical-model posteriors tend to be strongly correlated and often multimodal, and an affine-invariant ensemble sampler mixes poorly on them. [Nested sampling](https://en.wikipedia.org/wiki/Nested_sampling_algorithm) copes much better, and it hands us the evidence as a by-product.\n", "\n", - "This half gets its own random generator, so that changing anything in the toy\n", - "above leaves the cross-section data untouched." + "This half of the notebook gets its own random generator, so changing anything in the toy above leaves the cross-section data untouched." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 19, "id": "9cf85214", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:28:01.347603Z", - "iopub.status.busy": "2026-09-12T03:28:01.347428Z", - "iopub.status.idle": "2026-09-12T03:28:01.374876Z", - "shell.execute_reply": "2026-09-12T03:28:01.374040Z" + "iopub.execute_input": "2026-09-14T22:05:32.746929Z", + "iopub.status.busy": "2026-09-14T22:05:32.746801Z", + "iopub.status.idle": "2026-09-14T22:05:32.767799Z", + "shell.execute_reply": "2026-09-14T22:05:32.767196Z" } }, "outputs": [], @@ -633,14 +816,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 20, "id": "0f3057a0", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:28:01.376556Z", - "iopub.status.busy": "2026-09-12T03:28:01.376383Z", - "iopub.status.idle": "2026-09-12T03:28:01.386528Z", - "shell.execute_reply": "2026-09-12T03:28:01.385783Z" + "iopub.execute_input": "2026-09-14T22:05:32.769130Z", + "iopub.status.busy": "2026-09-14T22:05:32.769005Z", + "iopub.status.idle": "2026-09-14T22:05:32.776991Z", + "shell.execute_reply": "2026-09-14T22:05:32.776545Z" } }, "outputs": [], @@ -675,14 +858,35 @@ }, { "cell_type": "code", - "execution_count": 15, - "id": "6bdcb92f", + "execution_count": 21, + "id": "b6343957", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T22:05:32.778357Z", + "iopub.status.busy": "2026-09-14T22:05:32.778238Z", + "iopub.status.idle": "2026-09-14T22:05:41.110219Z", + "shell.execute_reply": "2026-09-14T22:05:41.109604Z" + } + }, + "outputs": [], + "source": [ + "angles = np.deg2rad(np.linspace(5.0, 160.0, 25))\n", + "meta = {\"reaction\": reaction, \"Elab\": E_lab}\n", + "y_full = omp_full.bind(angles, meta)(*full_truth)\n", + "y_xs = y_full * (1 + rng_xs.normal(0.0, 0.04, angles.size))\n", + "d_xs = rx.Dataset(angles, y_xs, 0.04 * y_xs, label=\"n+40Ca 14.1 MeV\", meta=meta)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "c07ec26a", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:28:01.388229Z", - "iopub.status.busy": "2026-09-12T03:28:01.388066Z", - "iopub.status.idle": "2026-09-12T03:28:13.872733Z", - "shell.execute_reply": "2026-09-12T03:28:13.871985Z" + "iopub.execute_input": "2026-09-14T22:05:41.111799Z", + "iopub.status.busy": "2026-09-14T22:05:41.111641Z", + "iopub.status.idle": "2026-09-14T22:05:42.210510Z", + "shell.execute_reply": "2026-09-14T22:05:42.209738Z" } }, "outputs": [ @@ -698,12 +902,6 @@ } ], "source": [ - "angles = np.deg2rad(np.linspace(5.0, 160.0, 25))\n", - "meta = {\"reaction\": reaction, \"Elab\": E_lab}\n", - "y_full = omp_full.bind(angles, meta)(*full_truth)\n", - "y_xs = y_full * (1 + rng_xs.normal(0.0, 0.04, angles.size))\n", - "d_xs = rx.Dataset(angles, y_xs, 0.04 * y_xs, label=\"n+40Ca 14.1 MeV\", meta=meta)\n", - "\n", "fig, ax = plt.subplots()\n", "ax.errorbar(\n", " np.rad2deg(angles),\n", @@ -732,26 +930,24 @@ }, { "cell_type": "markdown", - "id": "d13ce9ac", + "id": "c3199959", "metadata": {}, "source": [ "### The same three rungs\n", "\n", - "Bare, a diagonal model error, and a mean-zero GP whose amplitude grows with\n", - "angle — the same prior belief as in the toy, that the model is trustworthy\n", - "forward and suspect backward." + "We'll fit three error models: bare, a diagonal model error, and a mean-zero GP whose amplitude grows with angle. The growing amplitude carries the same prior belief as in the toy: we trust the model at forward angles and are suspicious of it at backward ones." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 23, "id": "2ee90995", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:28:13.874996Z", - "iopub.status.busy": "2026-09-12T03:28:13.874820Z", - "iopub.status.idle": "2026-09-12T03:28:13.885207Z", - "shell.execute_reply": "2026-09-12T03:28:13.884550Z" + "iopub.execute_input": "2026-09-14T22:05:42.211946Z", + "iopub.status.busy": "2026-09-14T22:05:42.211815Z", + "iopub.status.idle": "2026-09-14T22:05:42.219500Z", + "shell.execute_reply": "2026-09-14T22:05:42.219033Z" } }, "outputs": [], @@ -780,48 +976,17 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "4b4bfd54", + "execution_count": 24, + "id": "8c902ca0", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:28:13.887028Z", - "iopub.status.busy": "2026-09-12T03:28:13.886863Z", - "iopub.status.idle": "2026-09-12T03:35:19.711275Z", - "shell.execute_reply": "2026-09-12T03:35:19.710344Z" + "iopub.execute_input": "2026-09-14T22:05:42.220781Z", + "iopub.status.busy": "2026-09-14T22:05:42.220662Z", + "iopub.status.idle": "2026-09-14T22:11:04.694360Z", + "shell.execute_reply": "2026-09-14T22:11:04.693590Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "bare " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "log Z = -581.75 +/- 0.64, 85485 likelihood calls\n", - "diagonal model error " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "log Z = -19.61 +/- 0.50, 64449 likelihood calls\n", - "GP, growing amplitude " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "log Z = -9.93 +/- 0.52, 75404 likelihood calls\n" - ] - } - ], + "outputs": [], "source": [ "def nested(problem, seed, nlive=150):\n", " sampler = dynesty.NestedSampler(\n", @@ -833,30 +998,58 @@ " rstate=np.random.default_rng(seed),\n", " )\n", " sampler.run_nested(dlogz=0.5, print_progress=False)\n", - " res = sampler.results\n", - " print(\n", - " f\"log Z = {res.logz[-1]:8.2f} +/- {res.logzerr[-1]:.2f}, \"\n", - " f\"{int(np.sum(res.ncall)):7d} likelihood calls\"\n", - " )\n", - " return res.samples_equal(rstate=np.random.default_rng(seed))\n", + " return sampler.results\n", "\n", "\n", - "xs_samples = {}\n", + "xs_results, xs_samples = {}, {}\n", "for i, (name, p) in enumerate(xs_problems.items()):\n", - " print(f\"{name:22s}\", end=\" \")\n", - " xs_samples[name] = nested(p, 10 + i)" + " xs_results[name] = nested(p, 10 + i)\n", + " xs_samples[name] = xs_results[name].samples_equal(\n", + " rstate=np.random.default_rng(10 + i)\n", + " )" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 25, + "id": "26258f3e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T22:11:04.695870Z", + "iopub.status.busy": "2026-09-14T22:11:04.695738Z", + "iopub.status.idle": "2026-09-14T22:11:04.698538Z", + "shell.execute_reply": "2026-09-14T22:11:04.698127Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bare log Z = -581.75 +/- 0.64, 85485 likelihood calls\n", + "diagonal model error log Z = -19.61 +/- 0.50, 64449 likelihood calls\n", + "GP, growing amplitude log Z = -9.93 +/- 0.52, 75404 likelihood calls\n" + ] + } + ], + "source": [ + "for name, res in xs_results.items():\n", + " print(\n", + " f\"{name:22s} log Z = {res.logz[-1]:8.2f} +/- {res.logzerr[-1]:.2f}, \"\n", + " f\"{int(np.sum(res.ncall)):7d} likelihood calls\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 26, "id": "2f8e7c6e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:35:19.713594Z", - "iopub.status.busy": "2026-09-12T03:35:19.713386Z", - "iopub.status.idle": "2026-09-12T03:35:19.720493Z", - "shell.execute_reply": "2026-09-12T03:35:19.719528Z" + "iopub.execute_input": "2026-09-14T22:11:04.699873Z", + "iopub.status.busy": "2026-09-14T22:11:04.699760Z", + "iopub.status.idle": "2026-09-14T22:11:04.703707Z", + "shell.execute_reply": "2026-09-14T22:11:04.703180Z" } }, "outputs": [ @@ -886,45 +1079,30 @@ }, { "cell_type": "markdown", - "id": "0acba684", + "id": "56cddf05", "metadata": {}, "source": [ "### What the three rungs did to the potential\n", "\n", - "The evidence is blunt about it. Log $Z$ goes from $-581.75$ for the bare fit to\n", - "$-19.61$ once we allow a diagonal model error, and $-9.93$ with the GP. A gap\n", - "of that size is not a preference, it is a verdict: the bare fit's claim that the\n", - "reported 4 % errors explain every disagreement is simply false, and the sampler\n", - "can tell.\n", - "\n", - "The parameters follow the same order. The bare fit puts $V_v$ at\n", - "$26.9 \\pm 0.4$ MeV where the truth is 48 — wrong by fifty standard deviations,\n", - "and confident. The model error widens until nothing is precise. The GP\n", - "recovers $V_v$ to $43.7 \\pm 1.4$, about three sigma from the truth, while\n", - "keeping useful width.\n", - "\n", - "But look at $W_v$: $13.6 \\pm 1.1$ against a truth of 3.5, nine sigma out, in\n", - "*every* rung including the GP. That is not a failure of the discrepancy model,\n", - "it is the physics. The data were generated with volume **and** surface\n", - "absorption; we fitted a potential with only volume absorption, and at a single\n", - "energy those two shapes are very hard to tell apart. The fitted volume\n", - "absorption is quite literally absorbing the missing surface term.\n", - "\n", - "This is the honest limit of the method, and it is worth stating plainly: a\n", - "discrepancy model widens what we do not know, and stops the fit from lying about\n", - "its precision. It does not recover information the measurement never contained." + "The [evidence](https://en.wikipedia.org/wiki/Marginal_likelihood) $Z$, the probability each error model assigned to the data before fitting, is blunt about it. $\\log Z$ goes from $-581.75$ for the bare fit to $-19.61$ once we allow a diagonal model error, and to $-9.93$ with the GP. The difference of two log evidences is the log of a [Bayes factor](https://en.wikipedia.org/wiki/Bayes_factor), and a gap of hundreds isn't a mild preference, it's a verdict. The bare fit claims the reported 4 % errors explain every disagreement between model and data, that claim is simply false, and the sampler can tell.\n", + "\n", + "The parameters fall in the same order. The bare fit puts $V_v$ at $26.9 \\pm 0.4$ MeV where the truth is 48, which is wrong by fifty standard deviations, and confidently so. The diagonal model error widens until nothing is precise. The GP recovers $V_v = 43.7 \\pm 1.4$, about three sigma from the truth, while keeping a useful width.\n", + "\n", + "Now look at $W_v$, though. It comes out at $13.6 \\pm 1.1$ against a truth of 3.5, nine sigma out, in *every* rung including the GP. That isn't a failure of the discrepancy model; it's the physics. The data were generated with volume **and** surface absorption, we fitted a potential with only volume absorption, and at a single energy those two shapes are very hard to tell apart. The fitted volume absorption is quite literally soaking up the missing surface term.\n", + "\n", + "This is an honest limit of the method, and it's worth stating plainly. A discrepancy model widens what we don't know, and stops the fit from overstating its precision. It can't recover information the measurement never contained." ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 27, "id": "9f34319b", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:35:19.722688Z", - "iopub.status.busy": "2026-09-12T03:35:19.722361Z", - "iopub.status.idle": "2026-09-12T03:35:20.492727Z", - "shell.execute_reply": "2026-09-12T03:35:20.491801Z" + "iopub.execute_input": "2026-09-14T22:11:04.704988Z", + "iopub.status.busy": "2026-09-14T22:11:04.704872Z", + "iopub.status.idle": "2026-09-14T22:11:05.323353Z", + "shell.execute_reply": "2026-09-14T22:11:05.322470Z" } }, "outputs": [ @@ -965,27 +1143,24 @@ }, { "cell_type": "markdown", - "id": "34ae1fec", + "id": "c580f46a", "metadata": {}, "source": [ "### The learned discrepancy against the missing physics\n", "\n", - "The defect the GP has to describe is the log ratio of the full potential that\n", - "generated the data to the volume-only one we fitted. We draw it at the\n", - "posterior-median volume parameters, on a finer angular grid than the data, so we\n", - "can see how the GP interpolates between points and how it behaves beyond them." + "The defect our GP has to describe is the log of the ratio between the full potential that generated the data and the volume-only one we fitted. We'll draw it at the posterior-median volume parameters, on a finer angular grid than the data, so we can see how the GP interpolates between measured angles and how it behaves beyond them." ] }, { "cell_type": "code", - "execution_count": 20, - "id": "1d490c7d", + "execution_count": 28, + "id": "1799733e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:35:20.495746Z", - "iopub.status.busy": "2026-09-12T03:35:20.495506Z", - "iopub.status.idle": "2026-09-12T03:35:21.595388Z", - "shell.execute_reply": "2026-09-12T03:35:21.594663Z" + "iopub.execute_input": "2026-09-14T22:11:05.325194Z", + "iopub.status.busy": "2026-09-14T22:11:05.325065Z", + "iopub.status.idle": "2026-09-14T22:11:05.482915Z", + "shell.execute_reply": "2026-09-14T22:11:05.482230Z" } }, "outputs": [], @@ -996,9 +1171,17 @@ "fine = np.deg2rad(np.linspace(2.0, 178.0, 90))\n", "resid_xs = comp_xs.y - comp_xs.predict(*theta_xs[p_xs_gp.columns(volume_params)])\n", "\n", - "# again the total band minus the fitted model, both in log space\n", - "band_xs = rx.predictive.total_predictive_band(\n", - " p_xs_gp, gp_xs, omp_vol.bind(fine, meta), fine, s_xs_gp, rng=5, n_draws=100\n", + "# again the band minus the fitted model, both in log space; naming the\n", + "# kernel gives the model plus its discrepancy, with no experimental error\n", + "rows_xs = s_xs_gp[np.random.default_rng(5).choice(len(s_xs_gp), 100, replace=False)]\n", + "band_xs = rx.predictive.grid_draws(\n", + " p_xs_gp,\n", + " omp_vol.bind(fine, meta),\n", + " fine,\n", + " rows_xs,\n", + " terms=[gp_xs],\n", + " levels=(16, 84),\n", + " rng=5,\n", ")\n", "mu_fine = comp_xs.space(\n", " omp_vol.bind(fine, meta)(*theta_xs[p_xs_gp.columns(volume_params)])\n", @@ -1009,20 +1192,48 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 29, + "id": "b1b56ab1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T22:11:05.484359Z", + "iopub.status.busy": "2026-09-14T22:11:05.484226Z", + "iopub.status.idle": "2026-09-14T22:11:05.486957Z", + "shell.execute_reply": "2026-09-14T22:11:05.486311Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fraction of the grid where the true defect is inside the 68 % envelope: 0.59\n" + ] + } + ], + "source": [ + "print(\n", + " \"fraction of the grid where the true defect is inside the 68 % envelope:\",\n", + " f\"{np.mean((true_defect_xs >= disc_lo_xs) & (true_defect_xs <= disc_hi_xs)):.2f}\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, "id": "31b28438", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:35:21.600203Z", - "iopub.status.busy": "2026-09-12T03:35:21.599182Z", - "iopub.status.idle": "2026-09-12T03:35:21.839456Z", - "shell.execute_reply": "2026-09-12T03:35:21.838438Z" + "iopub.execute_input": "2026-09-14T22:11:05.488357Z", + "iopub.status.busy": "2026-09-14T22:11:05.488239Z", + "iopub.status.idle": "2026-09-14T22:11:05.617765Z", + "shell.execute_reply": "2026-09-14T22:11:05.617199Z" } }, "outputs": [ { "data": { - "image/png": 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RqtT0879jxw40Go1X4nuyhvisNMR1q3v9dfks1uaP5tZCkrw2TqVSMXLkSJYsWYLFYmHlypVeLV6jRo3i6aefJjMzkxUrVtC5c2efg3h9JQEajQZFUTxJ1JEjR7jvvvu48847efrppz31ioqKqpxU0VBqEt+xcXBGo7HSJIt+/frRr1+/Kme69u/fH5VKxdatWysdu/POO4GKta2qGl924uzauqpp/LV9H6prPajqvp443sntdqPRaHyOk6xqAPzJlCrGAJ3oo48+4pdffuH333/3SqI///xzny0Pvq55rOzYWMjqxkS63W7P8WP32+VyVapXm1bIumqq9+HElp9jz1Fd+cnj3k7WpUsX9u7dyw8//MDKlSv5/PPPmTNnDuPHj2fx4sUYDIYa1alOTe4NVP0Zq8lnryYx1uUzUpOWu2Ma4vN/TEN8VhriutW9/ob+LLZWkuQJxo4dy8KFC3n//ffJzMxk9OjRnmNnnXUWWq2Wr776ih07dtRrQeF9+/bhdrsrtQT66qZVq9U1+uFa1/q+HJvx1qdPH+64445anRsdHc2//vUvfvjhB44ePUp8fHy9YqmLmsb/66+/1vh9aKi4PvvsMy655BKfE3aO6dGjBxaLhUOHDtGpUydPeXJyMqWlpad8nr///hudTsdZZ53lVV7V69q1a1elsp07d6LRaDxxdu/eHafTye7du726I8vLyzl06BDnn38+UNHiA1RqiUhJSakU+7FW7Zp+Xmtbvyo1fR+aWmBgIFOnTvWs9fb6669z88038/3333PJJZfUuE59dO/eHYvFwuHDh+nYsaOnPDU1tUafvZrEWJvPSF3U9PPfr18/Pv30U7Zt28YZZ5zh81r1+axU93ltrp/B1kzG5AnPL/snnniCoKAgr6nsx8blPfnkkyiKUq/18Y4tObFhwwZPWUlJCS+++GKlZUViY2NrNYaitvV96d69O+effz7PPPMMhw8f9jrmcrkqLSdzsldffdWzNIuvbiqr1Vqv+E6lpvHX5n1oCFdffTURERHcdtttWCwWr2OFhYWecVuXX345BoOBRx991PMLQlEUnn76aa8xNlXp2rUrDofDqzV1x44dLF682Gf9jRs3smnTJs/j5ORkPvzwQ6ZMmeKZAT1t2jSMRiMPP/ywVwvME088QWlpqeePnuDgYPr168c333zjqacoCs8880ylliS9Xo/ZbK40i7cqta1flZq+D01p1apVlVqxOnfuDBxPFmpSp76OffYee+wxr+Tkueeeq9FnryYx1uYzUhc1/fxfddVVhIeHc9ddd3klly6Xy/MzoT6fleo+r83xM9jaSUueoHv37sTHx3P06FHOO++8Sl19x7psVSqVVytfbSUlJXHHHXfwyCOPsHPnTqKioli5ciVPPfWUZ7zfMVdccQWfffYZF110ET179sRoNFbbpVvb+lX57LPPuPLKK+nZsycXXHAB7du35+jRo2zatInRo0dzzjnnVHlux44dWb9+Pddeey2dO3fmnHPO8fzgPXz4MMuWLaNv3771Hoxd3/hr8z40hMjISH755RemTp3Kaaedxvjx4wkNDeXgwYNs376dxx57jGHDhpGUlMR7773Hf//7X/bu3cvgwYPZvHkzV111VZWJ2olmzJjB+++/z/jx4/nPf/5DcXExO3fuZM6cOVxzzTWV6l9//fXcd999xMXFYTQaWbRoEUlJSbz22mueOu3bt+fzzz/nqquuYtCgQZx99tns3LmT33//nWeeecZrEtIzzzzDBRdcwPDhwxkyZAibN2/mf//7H998802l5542bRoffPABKpWKiIiIKtfJq2t9X2r6PjSlrVu3Mn36dAYOHEinTp3Iy8vj22+/ZdKkSfz73/+ucZ366tixI++++y4zZ85k7969nHHGGWzdupUrr7ySsLCwUyaTNY2xNp+R2qrp5z8yMpIlS5Zw8cUX06tXL8aPH49Go2H16tVMnz6dIUOG1PuzUtXntTl+Bls7lVLfPgDRKixYsID9+/czfPjwSonc7t27WbhwISEhIdx6662Vzv3kk0+AitlOJ1q1ahV//PEHDzzwgNfYpvXr17Nx40YMBgPnn38+CQkJPPXUUwwcONBrW6Jt27bx+++/U1xcjE6n4+677672NVRVv7bxQcVfwGvXrqWkpIT27dtz5plnVrlwsi/79u1j3bp1ZGdnYzKZiIuLo2fPnpUGBX/33Xfs2LGD++67r9ataAsWLKCkpISZM2dWOlaT+GvyPlR172w2G88++yxjxozhzDPP9Dr23nvvERgYyGWXXeZV7nK5WLFiBTt37kSlUtG1a1dGjRpVqRUjJSWFJUuWeHZW6dOnD6+88godOnRg4sSJ1d4Tp9PJ4sWLOXToEAkJCVx44YVkZ2fz2WefMW3aNJKSkrxiHzRoEN9//z2HDh2ic+fOXHjhhT7XrMvLy+Onn37yLIY8fvz4SoP0AQ4dOsSPP/6I2+1mwoQJdO3alZdeeslz7WPcbje//PILu3btwmq10qtXLy666KIqX1dV9RvzfThRVc/jcDh4+umnGTVqlFc3ocvl4sknn2TEiBGMGDHCUz537lzOPvtsRo4c6SkrKipixYoVHDx4kLCwMAYPHuzVNV7TOidfuy73Jjk5mR9//NGzGHLPnj0JCAhg5syZXsm/LzWJEWr2Gakudqj4vnS73Vx99dVe5TX5/B9js9lYunQpe/fuJTw8nBEjRngt8A11+6zAqT/fp7puda9/586dLFq0iOuvv95rctqx31PXXnut19jpPXv28PXXX/Pf//73lPuBt0aS5AkhhBA+7Nu3j27duvH6669z4403+jscIWpNxuQJIYRo87Zv3+41A9PpdDJ79mxMJpNnuzEhWhoZkyeEEKLN27ZtG1OmTOGss87CZDKxatUqDh06xEcffVTtVoVCNGfSXSuEEEJQsWTKH3/8QXp6OrGxsZx77rltcgFd0XpIkieEEEII0QrJmDwhhBBCiFZIkjwhhBBCiFZIkjwhhBBCiFZIkrxacjqdpKWltdnNjoUQQgjRMkiSV0uZmZkkJiZW2mS6rvLz8xvkOq2R3Bvf5L5UTe6Nb3Jfqib3xje5L761tPsiSZ6fyeTmqsm98U3uS9Xk3vgm96Vqcm98k/viW0u7L5LkCSGEEEK0QpLkCSGEEEK0QpLkCSGEEEK0QpLkCSGEEEK0QpLkCSGEEEK0QpLkCSGEEEK0QpLkCSGEEEK0QpLkCSGEEEK0QpLkCSGEEEK0QpLkCSGEEEK0QpLkCSGEEEK0QpLkCSGEEEK0QpLkCdFMuB02f4cghBCiFZEkTwg/UhSF0h2/cPjxs8n89JZKx912qx+iEkII0RpIkieEn1gObeLwI4M58tx5WPatpnD1RzjyUgFQnHYy599B8twzJdETQghRJ5LkCeEn6e9cifXwZs9jQ7ueOEtyADj64fXk//wC1pStZH95t79CFEII0YJJkieEHzjyUrFn7AVAF9WRxNsW0+nRrQQkDcDtVgg97y5UehMA+ctepWTLd/4MVwghRAuk9XcAQrRF5Xt/93wdNuK/uLudy+H8cvLLHRSUO3ArkQSd8xTmH24FIPWdGQTds4F2Hbrw1ltv4XA4CAsLY9asWX56BUIIIZo7SfKE8IOyPas8X/8d2A9LSkGlOqW9L0V/eBWBu74BSwG5709n32ULePa55ygqLCQ8PJybbroJnU7XlKELIYRoIaS7Vgg/KN9X0ZKnaI1YYvr5rqRSUXDuMzjMHQFQJ6/n7snDKSosBCA/P59zzjkHh8PRFCELIYRoYSTJE6KJOQszPePxbO0GgtZQZV3FEETexLdR1Dq+Owhb96V5HV+5ciWvvPUexVYHTpe7UeMWQgjRskh3bQvhdivkl9sxaDUEGTSoVKpq67vcCg6XG5dbwfnPP5dbweF2Y3ce+9/NR2+/TmFhIVERZu6643bMAbpTXlvUj6MgHW1sN5yZe7ElDjt1/bh+FI55iLTNs30e37jjb3onV3T36rVqTDoNJr2GhFAjZpO+QWMXQgjRckiS18yV252kFlpJL7Li+Kel5ttP3sZeVkpEeBj/d9OtaNQqLA4XVoe74n+n21O3Og67nXdeepaSogKCQ82cNelKTAEGooMMxATpCTfpUasl4WtoAR0HUnLzevKz02t8TukZM4k+7RvYubXSsbjEJM/XdmdF8l5ocXC0yEpEoJ4ukYGEBci4PSGEaGskyWuGFEUhp9TOkUILeWV2r2MOu515b7zoScxGTZ6Otg4D7x12Ow/MnEpJUUULUElRAffPnMoT7y0gzekmrdCCVq0iKshATLCByEA9Gkn4GkR+uZ3cMjsERtX8JJWKMx/4gtP3/Iu/dh/0FPcdPJzxk6ZWeVpemZ28Mrske0II0cBcbqXZ/16UMXnNkM3pZmt6kc8Ez1di5qzDwPtfFy1g+6a1XmXbN65h2aIFnsdOt0JGsZW/0otYvj+XLWmFpBdZcLuVOrwqccz+nLI6nac1hTH3i1UEh4YBEBxq5on3FtQoyc8rs7MhpYDtR4txyfsnhBD1tj+n1N8hnJIkeS1ITRKzmspIS/Fdnprss9z9T+vizowS/jicR0ax91ZbL730Eg8//DAvvfRSrWNpS7KLSim01H02rFanI8AUCEBAYBDGosPoUzfU+PyMYisbjxRgc7rqHIMQQgjIKrVxtKh5bzsp3bUtSG0Ts+rEJXTwXX7C+K6qWB1uth8tJqXAQvfoIEwahccee4z8/HxZu+0U0ubdTdzfP2BLHErRqAdwBcfV+hqTLr+KMqudvto0Yj4+D0VjJOvqn3GFta/R+cVWJ+uSCxiQEEqIUd4nIYSoC5vTzf7cUmKCDc2221Za8lqQ+iRmJxs3aSp9B53pVXaq8V0nK7I4WL0/i+GjxpKfnw/I2m3VySi2ok5ei7YoFdOuhbj1wXW6zqTLZ3DlTXcyJqoYtb0MjSWP6HmT0RQeqfE1bE43G48UklncvP8KFUKI5sjudKMoFY0eh/PL/R1OlSTJa0Fqk5jptWpCjFqigvQkhAXQOTKQnjHB9I0PoVdsMH0Swlnw3RLCzOEAhJrNvPLpN+j1tVty49dFC9i8brVX2cqVK3n7vQ9QFBn7dYzbrfDc04+jzdwOgD22D4ohqE7XMug0dIwMpNMNn2BI6A2AtiiV+PmTMRQl1/g6X3/4FnfeP4fZc5+pUxxCCNFWWU8Y8nI4rxyLo3kOgZHu2mbIWZRF/Mv9UDR6FI0eNHoUbcXX74w38IZFS2qhk8hgPbd98BGhESGY9BpMOg3a3H1oCo+gCwxDo49AExSBJigYlVrj45lMPPTgHAoLCwkLC2PkaTEAWB0uyh0uymwuim1Oiq0OSmxOfOVsVXUh/7F1Fz3259Ih3ETnCFObXXvP6XKTWmjlQFYhyz5+iWKzQmIwjBk4uNbXigsx0jHchL1MQ0REEBBEyD3LSXlmHLbU7VBYkegZb/qRo/pEiqoZ++ew25n35vFZ2hdfNZN+CeY2+z4JIURt2JzHlylzKwr7ckrpFx/qx4h8kySvGVKcVjTluT6PGYB7Bhx7ZKdbUiSawOPdflk/zSNzyVPeJ6lUqE1haIMi0QRHog2NJbDnWMLH3eS1wb2zOAdNUARGnQajTkO46fgl3G6FEpuTYquTI4UWSm1OoPouZKdb4WBuGTmlNvrGhRBoaDsfN6vDRXJ+OWlFVqxWGw/MnMqO9FJ2/LM03relfzB3tKPGy99EBxnoExeMSqUi74TJudqQKDrcu5wjz4zHmrIVZ0E61tfOo/89v1EY3om92aWV/sL0NUv7qov/zXsLFjGwfYSsjSiEEKdgdXivRZtZbKN9mL3ZLUDfdn7rtjCO8C6o3HZw2lG5HGjcdnBaweXdOqM2mLweuy1FlS+mKLjLCrCXFUDWfgA0prCTqijsv7MjitOGztwOrTkBXXgiusgk9FEd0UV1xBiZREhkBxLCzKQWWjiQW864SVNZ8cNCr1m/J3chF1udrEsp4LSoQNqbveNtjXZnlpBWZPG0fPqaFf3Xjr9ZtmgBEy6ZdsrrhQXo6BcfUmUrmzYogg73/EbKs+diPbwJZ2EGyU+OosM9vzG8Yy8O55dzOK8c9z8BVTVL+/NPP4GrrqF/uxC0GhnJIYQQVbH52HBgT3YpQzs0rx4RSfKaIV1EBzKvXwOAWqWid1wwcSFGABSXE7e9HLetDMVWhkrr/VdD8BmT0UW0x1VeiKs0H1dZHq6SXFyleThLc3GV5ILbhSYkxus8d3kRiq2iiciRm4IjNwWLr+BUKjo+8iftO/QnNtjIgdwynn7lFa7/9xhSc4oJqmLtNpdb4e+sUrJL7fSODcao89V93PIdzC0jtdD7ztVnVnSQQcuAhNBTtq5pAs10uHsZR547D8vB9biKszn69pV0fHQLXSIDaRdqZE9WKdmltmrjyS+3sym1kIEJYei1kugJIYQvVh9j8IqtTtKLrCSEBfghIt8kyWvG9Fo1/duFeu1SoNJo0QSEoAkI8XlOUK9xBPUaV+U1FbcbV2keqL1/gSsOK8FnTMFZkIYjLxVnUQY+B+EpCvrIjp74esYGE7J6MT9NKMbugjRLKTFLbsIR2Q1H5Gk4orrjNHeCf8YE5pXZWXM4n+4xQbQLbT7fCA0hr8zOwbzKCx3XdVa0UadmYEIouhq2qmlMobS/6xeOvHA+1kMbibv6bc9flAE6Df0TQskptfFrh47VxlNsdbLhSAFnJIYR0EqTcSGEqI8Tx+SdKDm/XJI8cWrHWnAa+pesSq1GG1J5Oy1tWCyJt3zteaw4HTgKj+LIOYwjNxn7P/+7SvPQBIZ5XzOvYpstvQY6BTng7++8jru1ATiiulPeZyqlA2fgdCvszCghq8RGz5jW0apndbjYnlHsMy8eN2kqv3/+Elv3pXnKTrVcjVatYmBCWK3vjSYghA53/kz5/rUEdK48uSMqyMAjt9/Aqh8Xsmnt8VnRJ8dTbnexPqWg0h8ZQgghqk7yXM1sVQlJ8pohnUbNkPZhfh0XpdLq0Ed2QB/puxXqRIE9x4KiYDv6N7aMv1Hs3t2VaqcFQ8ZWrJ1Ge5XnFJWy88ubCT9tEHG9hmNM7Fup+7klUBSFbUeLsVfxTa/T63ljen+WLkkjtQQWHw2tdjsyjVrFgIRQguo4UUVtCCSo93jvGJ120t+bQeS/7sHUvi9rVi4nJjaWgvz8KrdHszvdbDpSSK/YYOJDjXWKRQghWiNrFT/vmxtJ8pqhipWzm8/AzVMJO/tqws6+GqjoDnbkpWBL34UtfReFh/6iLOUvtLn7ccT09jpPl70b07bPsW77nMNfgUprwNhpEKauwzF1HY47ojtERPjhFdXO3uxTb1VWNOU9vnz/dGKcGSjGkCoTPJ1GzYCEhm89y5x3G8Xr5lHy57fEXvEyYSNnEhIcTEF+PiEhwej0Op+tkG5FYUdGMaV2J10jA5vVgGIhhPAHt1vB4WPiRXMkSZ5oUCq1Gn1UR/RRHQk+/QIi+acr80g2FovTq64+4y+vx4rThmXfaiz7VpP3T1lRXHcCe44h9srXmmWCkVlsJaXA5xQVL5GBei6ccSupWXl0DfY9ntKgVXNGYlidW/Cq4rZbsKb8BYBit5Dx4XUUb/yKe2+cTma5mrCwMPrFh7L9aLFnBu7JDueVU2pz0jdOZt4KIdo2XzNrmytJ8kSjM+o0DOoUy+H8cg7klnlajMr6XYE9fgD6jL/QZ2zFkP4nuty9XufaM/agNgZVSvAsh//EENcNtbFuu0Y0hBKrk52ZJaesp9eq6RMXwsA591JocbAzo5gyu/fMLJNe02gTHdT6AJLuW0HWgrvJX/oyAGW7ljHKuI7oqc9iHnUdKrWaMxJD2ZJWhNPtO9HLKbWz4Ughp8e3rTUPhRDiRL5m1jZX8ie5aBIqlYpOEYEMaW/GpP8nkdHocMT2paz/dAr+9SKpV/3KqB9COW8h3LXOgK3dGah0RgJ7jPG6luJ2c+T589h7UwQpz5xD3i8vYcvc16TbqB0tsrLhSAGuKhKiY1SOcnqGuj3LkYQF6DgzKZzOkYEcy1tDjFqGtDc36kxWlVZP7BUv0eHeFeiiKmbXuq2lZH58AynPjseecxizSc+QDmaMuqp/LJTanKxNLuBAbhnuU7x2IYRojaqadNEcSZInmlToCUmO5oS1347twpCTX0RqKfy438bVyzQcnfU3+YNvIrfU5kmobGk7cZXkojjtlO1aRta82zh4TzcO3N2VzM9nUb5vNYq7cb4J3W6FXZkl7MgoPmWCB9Bu/9cUzO5M5me34sivmF2rVqvoEhnImUnhJIYFMCix6dakC+wxis5ztxM+/hZPWfnu5Ry8vzcFK94hyFCRcFbXZexWKnYyWZucT365vSnCFkKIZqOlTLoASfKEH2j+SXLO6hhObIgBqGIXhs0bWPztQo7YjPyZVsTy/blsOlJImlVD8Lj/oY/r5lXfkX2Q/KUvk/z42eyf1Y6Mj2/E7bA1WNwWh4sNRwpIKzz1GDyAQJ0K/fp3cFtLyV/2Km6b9xp6QQYtPWODm3yMm9oYROy0V+hw30p00Z0BUOzlaEKigYru9cHtw045+aPM7mLTkUJ2ZhS3mEHIQghRXy2pJU8G1gi/Meo09IsPpX2Ync8zU33WOXFXCLeikF9uJ58oGHQ/UaMfpr0rA/W+XynZ9iPle1ag/JPUOYsyKd+zCrXO0CCxZpfY2JFRXOV4tZOpVNA1fx15/2wjF9R3AoaTklJ/C+w+ks5zt5Gz6BFsR/8meMBEzzGdRs2AdiFsSi2ixOas5iqQXmQlt6xiJ5PIoIa530II0VxJktcEioqKmDt3LqtWrSIwMJArrriCmTNnNvg5ovGZTXrOOr0nn/o4Vt2uEDmldnKIILzblXQcdh2JWgel23+ieNPXlGxbQvCgi73qK4pC2iuTCeg8hNAzp6ELTzhlbFaHi705pWQW165FsHNEINbvX/c8Dh9/a63ObypqQyAxU59Bcbu9JrcoisLRF/9FUvsBHOhzPRZ11XsOf/vx25SWFBMUHMKsWbM4LSrolNuwCSFES2V1tpyJFy0yyVMUhfPPPx+bzcYTTzxBRkYGN954IwUFBdx1110Ndo5oOldffTXz589n5cqVnrK+ZwypdleIY/LL7eSX2wkxaunU498kDL4Et93iadU7xpr8JyVbFlGyZRHZX99PYM9xmMfcQHD/C1FpvL8V3G6Fw/nlHM4vr9HYuxOFBehoZ0vh0M6lAOjjuhHY+5xaXaOpqU7a5q54/ReU7VwKO5cS/ccHFJx1F8V9Lwe1931y2O3Me/NFSooKCA41c+Hl15Bf7qBvfEiDLwUjhBDNgdUhLXmN6ueff2bNmjUcOHCAzp0rxhRlZmby+OOPc+utt2IwVO4yqss5ouno9XqWLl1KbGws+fn5mMPDef6dT3BVsWiwL8VWJ3+lFxFk0PLbgvdxWUoxm83MmjULAMuB9RX9qIoCikLZrmWU7VqGNjwB86jrMY+ciTYsluwSG3uyS7E4XF6tVBdddf0pYwgxajm9XQj5nz/gKQsff2ulJKq5c5Zko9IZURxW3CXZhP50F6aN71I45kGsnceBSuWZLFNSVABASVEB98+cyhPvLWBdsotu0YG0N1fdAiiEEC1RS+qubVm/ef6xYsUKTjvtNE+yBnD++edTVFTEli1bGuwc0bR0Oh3BwcEAhAQHc3piON2ig7xm4dZEQUk5zz31JI8++iiPPvoYdnvFDNDw8TfT9YVUoi99Gn18D099Z34aOQvnsO/29mx7bgq7NvyK5Z91kL795F0+f/05vv3k3VM+b3SQgcHtzWhtxRSu/hgAtSmUsOHTaxV/cxBxzv/o8vQ+Qs+80lOmy9tH1FfTiPriEnSZ231Pltm4hmWLFuBWFP7OKmVzaqHnXgohREvndCtVLhrfHLXIlrzU1FTi4uK8yo49Tk1NZdiwYQ1yDkBxcTHFxcWexxkZGfWKXVRv1qxZFBYWEhYWhkqlIincRGywocbj4k5uXSooyGfIiDG8/Mk3BAYY0GuDMQy+Ef3Qm3Af+APLqrfQ/r0EldsJLge6HQsJ1IZgTxiEw27HUlYKgKWsFKfDUeV2ZB3MAXSLrli0OXfV+yj2cgDCRsz064LN9aGLSKTd9Z8Qfu4ssubfQfmelQAYk/8g9sPxFB45zed5J06WySuzs+ZwPl0iA+lgDmiWu5YIIURNVbVHeXPVIpM8p9OJXu+9kf2x7laHw/ceonU5B+CFF17gkUceqVReUFBAQEBAreL25cQEUsCVVx5vOTrx3iQYICRYITm/vNrVxpctrNy69NeGNXzz6fucd9Gl3pV1XWDcc2iG3kXQri8J2rEATVk2RX0ux1aUy+ybZnh3RV4zhbmvfeid6Kmgg9lElNZOfn4+istJ7tJX/jmmRn3GFeTl5dGQmvwzE9yBoGu/Qr/7F0oWP4Iru2LGcJL9kM/qsTGxuMuPx+gG/i4tIiVDQ8dwE4H6xvuxI99Pvsl9qZrcG9/kvvhWWFSEu7zqP1adWjV5eY3/x2xEDfd1b5FJXkREBIcOef+COfaLNDIyssHOAbj99tu9ZuBmZGQwePBgzGZzjW/yqTTUdVqjE+9NBJAUr5Ccb+FgXpnPCRGZ2Vk+r5OZlYna5HvPWMUUQkn0A5SMuBtD+mbc7fqx/MtP2fHnRq962zdvYNWCNxh/7WxQqdCqVfSLD/FaNkRxOdFNfYq8pS+jM7cjumv/OrzqU/PLZ2bEFSjDp1L4+wfkfPsQV1x3GYvdG9myfo2nSt/BZ3LOpVeh9tHiaQX2FENSuIEuEYGNNgNXvp98k/tSNbk3vsl9qSynzIa6mtRJq1M3q/vWIpO8gQMH8tFHH1FaWkpQUEVX2Nq1a1Gr1fTv7/uXal3OAQgJCSEkxHdyIJqeSqWiY4SJhDAjaYVWUgstXmO+4hI6+DyvuqVYPDQ6bO0ruu0z0lJ8Vila9hpxyhIMg6eROGYGetSU7V6Dq7yIkDMuQqXREjrsckKHXV5p8ePWQKXRYh59HaFnXoHidrP+ciMxsbEU5OcTFR7K+8Nyse7+krI+/wF15W3aFAUO55WTW2qXGbhCiBbH7mw54/GghU68uPjiiwkMDOTRRx9FURSKi4t5+umnmTRpEtHRFav2Z2Vl0bt3b5YtW1bjc0TLodOo6Rhh4uxO4fRvF0pEYEVX/LhJU+k76Eyvun0HD6/RUiwnqipZTAwGbf5hXD8/RvLdndh3UyQpT48l87NbKtVVGwJr9ZwtidoQiCYgGJ1Ox4Nz5vDQQw/x3lX9MBXsI/zH24l9fzTGA8sqsjofSmxO1iUXkJJf3sSRCyFE3bW03X1aZJJnNpv59ttv+eKLL4iOjiYmJobw8HDeeecdTx2Hw8GuXbsoKiqq8Tmi5VGpVEQHGzgjMYyzOobTPd7MK58tJDjUDEBwqJkn3ltQ5YSJqkyYchn9hwz3Kjt7YC+uuGiCzxYqZ0E6ztKGHXvXUsyaNYuHHnqIgd2TPGW63L0VM3HnTUaXud3neW5FYU92KX+mFmJrQYuLCiHaLnsLS/JabF/JiBEjSE5OJjk5GZPJRGxsrNfx2NhYduzYQfv27Wt8jmjZAg1auhi0dIkMZPbsBziak4/KGIhOr6uqQclDr1UTG2wgLsRIkF6DVqNmzB8rPOv2hYeH89u6reh0OpyFmRSt+5ySrd+j0howtu+HIbEfam3bXWtRpVLR7rqPCR97E1kL7qJ87+8AGI+sJebDcyjrO5WikffjDoqpdG5umZ21yQX0jg0mSrZFE0I0Y3aXGyr/nd9sqRSlBS340gykpaWRmJhIamoqCQmn3hbrVPLy8prVIM3mpKHujcPlptjqpMjq8PxvdbhRq1REB+uJDzESGaj3ubzHSy+95FnS5diiyv7W3D8ziqJQum0J2QvuwXZ0t6fcrTNRPOxWSobcCD4S4m8/fhu9y0LHuChuu+22Oj13c783/iL3pWpyb3yT++Lbsu2HcOurXhbLqFMzsnPVkzmbWottyROipnQaNRGBes+4PahY60itAq2m+hELzSWxa0lUKhXBp19AUJ/zKFj5LjkL5+AqzUPtKCdo55eUDL2p0jknbo8WajZz/Q03YjJKq54QovlQFAWny92ixrm1pFiFaDB6rfqUCZ6oH5VGS/jYG+jyzAHCz7sDNDriL3+eWHOwV72TF7AuKijgrFHjyC+x+CNsIYTwyeZ0Qwvr+5TfckKIRqUJDCP2sufo+uxBIs6YSL/4UAa1DyPIoEWXtYvVz8yotID11g2rmfvKWxwtsvopaiGE8GZtYbtdgCR5QogmootI9Ix7DDfpGdY+lPjl95G37Vef9dNTDrMjo5jUAmnRE0L4n02SPCGEqBl72g6U9L9IqGIM87EFrP/OLiG75NT7FgshRGNqiUs9SZInhPALY4fT6fz4Li6feA6DTlpZ5fQ+PTwLWCsKbM8optBS9R7TQgjR2KS7VgghakEf05ku9/zMTwvncc8gFTN7wyPD4KN+fxO17G5UthIAXG6FLWlFlNmcfo5YCNFWSXetEELUkkqlIuLMy2h/ySNcclZ3Lu4KOjUEbZtH7Puj0af/CVSsd/hnWlGL7DIRQrR8VockeUIIUSc33jWH8W/sJv66T1EHhAKgLsvBbQz11LE4XPyZWoSzhW0tJIRo+VriH5iS5Akhmg2VSkXY8Gl0fmInpl7jcZ7zEM6ILl51SmxOdmaW+ClCIURb1RK7a2XHCyFEs6MLT6DDXb/Q3u1mU9oJky6cNrTF6WTRifxyO+EmffUXEkKIBuB0uXG6W9hKyEhLnhCimVKpVKg1GvrGh6BVV6yvZ152PzEfjiNg74/sySpFtt4WQjSFltiKB5LkCSGauQCdht5xIQT8/T1Bf32G2l5G5MIZqH57RhZKFkI0CVsLHQcsSZ4QotmLCTYQccZESvv+x1MW+sfTZH11L44WOBhaCNGytMSZtSBJnhCihejeLhLHRa9RMP4JFCq6bwPXvsr+D2+VblshRKOS7lohhGhEGrWKfu1CsQyeSf75L6Oo/vnxtfoNUj+6EcXdMn8ICyGaP2sL7TGQJE8I0WIEGbR0iwqivO9U8i98HUWlAaB05VtkfHQ9irtl/iAWQjRv0pInhBBNINEcQEywgfJek8mb9A6KumIlqMJV71G+6k0/RyeEaI1a4r61IEmeEKIF6hkTjE6jxtL9AnInv4+i0eNoP5SAYVf7OzQhRCvUEne7AFkMWQjRAum1arpHB7Ejoxhr1/PIvuwrHNG9MTh1RPk7OCFEq6IoinTXCiFEU4oPNRIRWLHjhT1xKIohiKNFNplpK4RoUHaXm5b6Y0WSPCFEi9UrNhjNP7thQEWXSnqRlTefeZgfb+rHq88/7cfohBCtQUttxQNJ8oQQLViATkOXyECvsoObfiXl60f59vftbPjwQex2u5+iE0K0BhnFNn+HUGcyJk8I0aJ1MAeQUWyl2OrE4bBz6z0Ps33rsb4VOymDerF88250Op1f4xRCtDxZJTaS88v9HUadSUueEKJFU6lU9I4NQaWC3xZ/y/atW7yOr95+gHeeesBP0QkhWqpyu5OdGcX+DqNeJMkTQrR4wUYtSeEmMtJTfR7f/t2buG1lTRyVEKKlcrsV/kovxuluoTMu/iFJnhCiVegSEUhC+/Y+j7XTl5L30/NNHJEQoqXanVVCic3p7zDqTZI8IUSroFarmH755fQddKZX+eBYFRM7Q+6Pz+AsyvJTdEKIliK9yEJ6kdXfYTQISfKEEK1GfHgQz3z4FcGhZgBCwsx8/cyt6NSg2MrIWfSInyMUQjRnJVYnf2eV+juMBiOza4UQrYZGrSIpKoTLb7iN0pJigoJDYOw1qNd+jLu8kIJV7xL579nozPH+DlUI0QztzCzG1cLH4Z1IkjwhRKvSwRzA5Kuv96xQn2LV0P3CByjZ+h0x/3lOEjwhRJXK7C1zj9qqSJInhGhVjDoNscFGMoorxtSU2V2oRtxE0oQ7UKlUpzhbCNFWOVzuVtWKBzImTwjRCnUwB3g9Ti2yS4InhKiWxdG6WvFAkjwhRCsUGqDDbDq+w0V2qQ2b8/gPcMXpwJ510B+hCSGaKauj5e5RWxVJ8oQQrVKS2eT5WlEgrdCKoigUb17Iwft7ceT581CcDj9GKIRoTqxOackTQogWISpIj0mv8TxOK7IAULDyXexZ+7FnHaDg9/f9FZ4QopmxOqUlTwghWgSVSuU1Ns/qcJNdaifm0qc9ZYWr3vNHaEKIZki6a4UQogVpFxqATnP8x1xqoQVj+74EdB0OgDX5T2wZe/0VnhCiGZHuWiGEaEE0ahWJYUbP47wyO+V2J6HDLveUFa2b54/QhBDNjLTkCSFEC5MYFsCJq6ccKbAQMvhS0FQsE1q8fh6K0rrWxhJC1I6iKNKSJ4QQLY1RpyHCpPc8PlpsQxUYQVDvcwCwZx3AemiTv8ITQjQDdpeb1vi3niR5QohWr13o8S5bh8tNZomV0KEndNmubz1dtk6Xm+T8cg7nlbfKxV2FaAytsasWZFszIUQbEB1kQK9VY/9niYTUQiuDB0xEpTeh2Msp2vAFMf95DpWm5f5ILLc7OVJgIb3IivOfrZn25ZQSFqAjyG0jKNSFQas5xVWEaJta4/Ip0IJb8goLC7ntttsYMGAAZ599Nm+99Va19XNzc+nevXulf0uXLm2iiIUQ/qJWq4gLNngeF1kclGIgeMBEtOZ2hJ15JW67xY8R1l2hxcHWtCJWH84npcDiSfBOPH6koJxVB/PYdrQIdyvbm1OIhtBaW71b5J+tbrebf/3rX7hcLp5//nkyMjK4/vrrKSoq4p577vF5jtPpZO/evXz//fd07drVU96uXbumClsI4UcJYQGkFBxP5I4WWek6/XXUAaGo1C3z793cUhtb0otqNJZIUSCz2IbbXczp7UJkL18hTmCVJK/5+Pnnn1m3bh0HDx6kU6dOAKSnpzN37lz+97//YTQaqzy3Y8eOdO/evalCFUI0E0EGLaEBOoosFVuZZZTY6BYd0WKTnUKLg7+OFtd6sHh2qY0dGSX0iQtusa9diIbWWrtrW2SSt2LFCk477TRPggdw/vnnc/fdd7NlyxbOPPPMKs+dMWMGLpeLLl26cPPNNzNixIimCFkI0QwkhBo9SZ7d6Sa3zE5UkOEUZzUfL730EoWFhRgDgxk0cTquOna9vvHqy6jt5ZyWGMOsWbMaNkghWiBJ8pqR1NRU4uLivMpiY2MBSEtLq/K8c889l5tvvpnIyEi+++47xowZw7x587j00kurPKe4uJji4mLP44yMjHpGL4Twl9hgA3uySz3JUUaxzZPk2TL3YT38J6HDLvNniFWy2+089thj5OfnExxmZv55/0Gr09X6Og67nXlvvkhJUQGh5nBuuukmdHW4jhCtiXTXNqL09HTGjh1bbZ1///vfPPPMMwC4XC70er3XcYOh4ge10+n0eX50dDQ//fSTp3ti6NCh5Ofnc99991Wb5L3wwgs88sgjlcoLCgoICAjwcUbtnJhACm9yb3yT+1K1mtybUCzkltsByLCWEKe3U/zRDGw7fgCNHnvCYNSmsEaOtHbsdjuXXHIJ+fn5AJQUFvDAFefy4qz/YLDn4QxJoKzbhaDyPbbQbSsHwOGwM/umGZQUFQBQVJDPmWePZPGihW020ZPvJ9/a0n1RFAVLcWGN6h77XqqKU6smL6/xh0FERETUqF6zSPKio6NZtGhRtXVCQ0M9X0dERHDw4EGv47m5uZ5jvqh9DKweMWIE7777LhaLpcqE7fbbb2fmzJmexxkZGQwePBiz2Vzjm3wqDXWd1kjujW9yX6p2qnujDggm/0ih57FDF0RQ+14VSZ7LjvbQSswj/9vIUdbO26+9zJo1a7zKtu3YxR/vzeHiruAyRWAZcKXXcX36ZhS1FkdsPwDUphCWf/kpO/7c6FVv84Z1fPTFQu6ddWPjvohmTL6ffGsr98XicKE21by7Vm0KqfKYVqduVvetWSR5Op2uVpMhBg0axEcffURJSQnBwcEArF27FrVaTf/+/Wt8nSNHjmA0Giu1Cp4oJCSEkJCq31AhRMtiNukJ1Gsos1d0zxwtttJ72BXkLXkagKJ1nzerJM9yeDNbPn3I57HUkor/HREVKwaoVBAVaCApPID8hc9h27sCd3hHSrqch6XvVDLSUnxeZ+vuPaQVWkgIq3/vhPB2bBxlWFiYjH9splprVy200HXypkyZQlBQEA8//DBut5uCggKeeuopJk+eTHR0NABZWVle6+B9+umnrFq1yrNH5caNG3n++eeZNm0aGo0sECpEW9Iu9Hgyk19uRxXXE0NCbwDK96zEWZjpr9C8lB/cQPLc4cSri3weDx86hZyLP6H07Dtpbw7grI7h9E8IJdiej23fKgDU+YcJ3fgmse+NokfKVz6vE5eYxO6sErJKbI32WtqiY+MoH3nkER577DEcDoe/QxI+tNZJF9BCk7ywsDC+++47Fi5cSGRkJLGxscTGxvL222976jgcDvbu3esZV3DGGWfw1FNPYTabiYuLY8yYMcyYMYOXX37ZXy9DCOEn8aEGjq0eoigVEzCCz5jiKSjfv9p/wZ0goOMZaDoOY2JnGNjBu0eh7+DhjLj1ZeKGXsSwcRfRIyYYk76ic0YdEEL8tR8T1O98OGEXj0lRmQyKodJ1xk+aiqLA9qPF5P8zXlHUzdEiKwdyy8goKOWcc87xjKPMz8/nnHPOkUSvGWqtW5pBM+murYvhw4dz6NAhUlNTMZlMREZGeh2PjY3l77//9ix23KNHD3766SfKysooLCwkPj5e1ogSoo0yaDVEBRrILq1ouTpaZKVv1+Ge4+UH1hEy6GJ/hedR5lBI+9drGOM+55G7/o/LRvanpKiA4FAzT7y3gE7RIZwWFVTpPLXBRNjwaYQNn4arNJ+jqz7BvfMH+Hs5745X+O5gRVdvtAmGv/4+mn8mXbgVha1pRQxqH0aIsW1OxKiPcruT3VkluNwKP31Z0Xt0opUrV/Lxxx97jfMW/tdad7uAFpzkAahUKtq3b+/zmFar9TnOLzAwkMDAwMYOTQjRzCWEGT1JXonNiavdwIpBbYqC5cA6v8Tktpai0gegUmtwutxsTS/CYYrGMfw2tEBAYBAlRQUEBAYRExZI9+jKCd7JNEHhmIZeScT5s7Bl7if/5+f5N2+jV8PPR01ogsxe9Z1uhT/TihicGEagoUX/imhSiqKwI6Pk+PI8VYx/PHnSoPA/q1OSPCGEaFUiA/UYtGps/4zHyXLqMbTrhS1tJ9aULbgdNtS6pl0oOWvB3ZTv/Z3oS59if8Rwyu3ev3wumn4tpSXFhJvDOD2+9luTGWK7Enf1W3yb047otGUEnDbUaxKKymEheO3LlA66lj/TVAztYEavbZGjeppccr6FQsvxrti4hA4+63Xu3LmpQhI1JN21QgjRyqhUKtqFGjmUV7HuVUaxla6dhmBL24nisGE98hemzkOaLB5b5n4KVr0LLiepb04j94bNYPQeh3fRVdej06gZ2iEMrabuydeNd80B5gDgcLnZdKSQEpuToD8/IHTtiwRvfpeSITexZcRNDOqSgEYtQ1uqU2pzciC3zKts3KSprPhhIds3rfWUnd63F8P/NaWpwxOnIBMvhBCiFWoXenyfa6vDjZI0DENCH8JGXYfacOqu0IaU881scFUs5l407BYUY+Wlm1QqOL1diGeCRUPQadT0TwhFp3IT9OcHAKjtpYT+8TSBLw9i15K3Udyt95dgfVV00xbjPmkTYZ1ezxPvf8mwDiZm9oZHhsFHfXdhfX0CR//82bPSg/Avl1vB4Wq9n29pyRNCtFkmvZZwk94zo7Sg5yX0Htv0a+RZDm+meOOXACgh8ZQM9D0wv0d0MOGmqtf1rKsAnYZ+CeFsnfEzwWteImjLx6jcDjSlWfD1Dfy98X06Xv06AZ0HN/hzt3SH88sptvreacmU9RdvnF2O/sTmlLQNFL4yAetpZxMz+VECe4xqkjiFb615jTyQljwhRBuXEHa8NS+zxNbkP/QVRSHry3s8jwvOuhNFV3lR4sSwABLNjbdYcUSgni5JHSkc/zgZ16+lvMfE4wePbObwo0NIf3cGjkLZv/uYEquTg7m+t7lS2cuIXHiNJ8HbZ+yNPbqX57h13x+kPDWatDcuQ3G37kSjOWvNXbUgSZ4Qoo2LCTKg/WfMmcutcDCv+r0pG1rZzmWU715e8fyRXSnrM7VSndAAXY1m0tZXUriJ+FAjrrD25E16h6xp32GP6e05XrT6I5IfHYrilLXeAHZmVu6mPUbRB1JwzhO4dQFYOo0l4H9LybrmV3Inf4A9qgeKWovS/RyC+p4n99OPWvPMWpDuWiFEG6dWq4gPNXKkwAJAepGFDmFG9KVHUak16MITGu25Fbeb7BNb8UbeD2rvH8t6rZrT40NQN9Hkh54xwZTanBRbndgTh5J19VICt31O6Kqn0FjyCDr3blRaWUMvp9RWZTftMZbuF5Id3glnSCKoK3ZWsnQ7H8tpEzAcWYc9cTBdT4tDLTOY/aY1z6wFackTQggSTtjmTJu5i5Q723PgjiTyfnmpUZ+3eMMXWI/8BYAjYRCWrhO8jqtU0DcuBKOu6bZe1KhVnN4u9PjSKWoNZf2nk3H9WgpHP8iuDlPIKLZ66juLsnAWZzdZfM1Fcr7FZ7lKVZGYB+o1hAXoCOvUn7iYaAL1J7yHKjW2DsNR1DrZSs7PpLtWCCFauWCjlhBjRQuaMzQRSrIAGnVRZEVRyP3xGc/j/FGz4aR17zpHBBIR2PATLU4lQKfh9PgQr6VTlIAwSobehAs1248W83dWCYqikPnpLRy4pxv5y99qM7NwS6xOn9u/Gff/TOdd7zOqcwRndYpgSAczAxLC6BMXQpdI34vwZ5VKkudPrXm3C5AkTwghgOOteYoxBGfkaQBYU/5EcTbOXq4qlYoOdy0lbNwtWLtfgD1xqNfxqCA9natIDJqC2aRnQEJolWvkHSmw8OfyhRRv+gp3eSGZH99A8tzhWI9sa+JIm15yQeVxm+ryPCJ+uhPbd/dz9N2rUVzeXbkxwQYMPrplC4oKyd/wFXlLZR91f5DZtUII0QbEhRg8CY2t3RkAFYsip/zVaM+pDYmm5NwnyJn0nld5gE5Dn7jK6+Q1tfBTJHoF5l6UD5jueWw5uJ5DDw0kc/6duK2lTRVmk7I5XV7d1ceE/ToHdVkOAIrT5hmDd4xKpSIh7KTZ0YpC9HtjyXzjUrK/uh+3rWkn/QjprhVCiDZBq1ETE1yxjZm93UBPefmBtVWdUm8Wh6tiwscJ3bQqFfSLD0FXjx0tGlJ1iZ7bFM57mV34xHQ5ebrYfwpd5P/8PAfu60nJlu+aONrGl1Jg4eQJtcb9vxC46xsANMFRxF75ms8t5xJCjd498ioV1k6jAVDs5ZRu/7GxwhY+OFxuz17DrVXz+CkihBDNQOI/LS3HWvIA8vesaZTnUhSFnRkllZbgiA8xEhrQvGavVpXoOex25r35Ik+/NY8LvrGRO2IOis4EgDM/ldSXJ3HkpYk48o74I+wG53IrpBV6t+KprEWE/3y353Hsla+hDY70eb5RpyE6yHs/5BPXIyzasKABoxWn0tpn1oIkeUII4REWoCMsQIczoituYygA1oMbcDfwX/u5i59k3yd3Urb7V3AfH7ulUavoGuW/cXjVOTnRc9jtPDBzKiVFBQAUFxZwyzvLSZ2xnPKu53nOK/1rMY6Co36JuaGlF1kqbYEVtvwRNKWZAAQPvIiQwZdUe43Ek7ps7QmDcAZVtIKWblvSaru5m6PWvkYe1HKdvPXr1/PFF1/UqO6wYcOYOrXyop5CCNGcdYowscXiwBY3gIDDK9AUpTL3kQd48JEnGuT6iqKQt/xtXPkpROpMpM/a41kbr2O4CYO26ZZLqa1wk57B7cPYklbET18uYPsm767s7RvX8PPyNUy45GPK9v2EedkDOLqOwxo/AJOfYm4oiqKQUuC9bIrh8CqCtn0OgDrQTOz01312054oIlBPoF5Dmf2fBEOlxtL9QoI3v4tit1CybQmhQ+R3Z1NoCy15tUrysrKyWL16dY3qRkb6bq4WQojmLCrIQIhRi73dGQQcXgHA9u/eYdcN99EzJuiUv8RPxZKxD1d+CgC29meCtqL7zqhTkxTe/FOhEKOOoR3MfJCZ6vN4RmoyANbTJpCZNAIUN7lHCmlvDuC0qCDUbgc5ix4h/JxZaEOimjDy+skutVNuP97yo7KXEf7THZ7HsZe/hC4srkbXSgwLYE/28Ra78h4TCd78LgDFG7+UJK+JSEveSSZOnMjEiRNPXVEIIVqwjuEmdkb3Y+k+2J0HqVYLyTnFOFxu+sbVb/eJg+t+4FhbnTVphKe8a2RQlbNYmxujTsPwfj351MexuMQkz9eK/njX85ECC7lldpK2vUnJ4ifI/+0Noic/hnnM/6HSNP/Nl5LzvWe+Bm98C21RRaIb2Oc8QodfWeNrtQs1sj+3zDPo395uIM7geLQlRynd9iMuSwmagOCGC1741Npn1oKMyRNCiErMBhV3PfE6D6+HL/fDxtRy7p85lfT8Uv5MK8Lpqtsvh6NFVmx7fvM8tnYcCVTsTRsfamyQ2JvKjBlXM2rUKK+yvoOHM35S1a1Q5eXlFPz2JkDF2nqf3cKhhwZStvePxgy13oosDgot3vvLlgy7ieDz7kYdaCZ+xtu1auHVatTEhZzwfv/TZQugOKyU/vVDg8QtqifdtaegKAoLFy5kw4YN5OTkoJwwS2zkyJHMmDGj3gEKIURT++STT/hro/duF9s3rmHZogVMuGQam1ILGZgQdnzrrxootzvZnVFAbErFkBdnUCzOyG4AdGumky2qo9frWbp0KbGxseTn5xMcauaJ9xag1VUzM1hrIOua3wj9/SkCt36CCgVb6nZSnhhBunkgfwaNxBiZyKxZs5rsddTEwbyySmVJ0eEkXvY0rn/fhyYwrNbXbB8WQFrh8TF+5T0mErzpbeCfLtthl9U5XlEzrX23C6hnkjdt2jR+/PFHYmJiKC0tJTIykp07dxIWFsbpp5/eQCEKIUTTOnTokM/yY+PNiq1ONhwpoE9cCGE1WO7E5nSx7WgxmrQtqG0lFWUdR4JKRUywAbOp6bcuawg6nY7g4GDy8/MJCw3BaNDjPMVMZLcpnILznqH09GmYl96HIX0zdhes3fAnh4v+JMtp5NxJFxMREUWwUUtAE+7b60t+uZ2c0n92PVEUUKkw6TV0jqhIzOuS4EHFVnphATpPC6E9fgBlPSYR3W8sccNlTF5TsLWB7to6J3m7du3ixx9/ZPfu3SxZsoT169fz3nvv8eeff3LuuecyduzYhoxTCCGaTKdOnXyWnzjerNzuYkNKAXEhRk6LCsToIxlxutwkF1hIzi/H5VYIObzKc8yaNAK1SkW36KAGj78pzZo1i8LCQsLCwhjSwcyfaYU16gZzxPYl+8rF6LbO554772ZzxrGlZKzkjujOwz/sQavXExWkp1981btuNLZ9ORWteOryPKIWXE7hqPvpNXJig8STGBZwvBtYpSJ/0tsYQox0CPH/bietnc3pqrRGZWtU5yRv9+7djB07lri4ONRqNXZ7xV86AwcO5LrrruObb76hT58+DRaoEEI0lauuuop58+axcuVKT9nATpE+x5tlFFvJLrWRFG6iY7gJjVqFoigcKbBwKL8cu9PNtx+/TWlJMVfbviT0n/OsHUfQKcLk95aq+jq5a3VoBzN/phZRYnP6PuFEKjXfH3CfkOBV2JxaxrLvvmTCJdPIKbWzIaWA/gmhTX6vMoutFFkc4LQS+c3V6DP/IurLy9FGfwUDJ9X7+rHBBvbmqLGf0KKUU2rD7VbqNblHnFpbGI8H9UjyioqKCAsLAyqWS8nIyPAci46OZv/+/fUOTggh/OHYeLO42FjOj8mncxj864wQ8qoYb+ZyKxzMLSOt0EJiWADpRVbPeJ9ju0JYSwroMggu6gpKbE/M0Ql0imj+S6bUlkGrYXD7MLYdLSa3zH7K+hlpKb7L/+kaB7Ae2caWvzPoOeriJuvaVhSF/blloLiJWHwLhrSNAOjC4gjoNLhBnkOtVtE+LIADucfH/DndCnnldqJO2hlDNKy2MLMWGmh27emnn87atWtZvnw5hw4d4qOPPqqyu0MIIVoCnU7H7DlzuOasRC7uCgFFh1HZKw/AP5HN6eZAbplXgndsVwiHGx7ZANds6kBZ/6vpGxdS7zX3miutRs2AhFA6RZjQnqJFKi6hg+/yE7rGQ39/EvOCK0h5eCDJq+ajuBv/F3RqoQVLcT7hi2/BtOd7AFTGENrf8SM6c3yDPU9iWADqEz4H6vI8ctd9SeZn/8N2dE+DPY/w1hYmXUA9krz27dvTr18/z9c33HADY8eOpXPnzlitVv773/82WJBCCOEPs2bN4rThFwCgQkGXu7dW5/+6qPKuEFv2prC5MLhWM3NbIpVKRdeoIEZ1iaR3XHCV+/GOmzSVvoPO9Co7cSkWbe5eAg4sA0CftZPyDy7n73t7Ufj7h7ht1SfddeV0uUnd8D2x740kcNfXAChqLYm3LsSY0LtBn0uvVRMbcrzVLmDPDyifTSd/2SuU7VrWoM8ljiuzS5JXrXPOOYdbbrnF8/i5555jz549rF69mr/++svTlSuEEC2ZsX0/z9e67F21OreqrsjMVN/lrZFGraJdaABDO5gZlmQmISyA0AAdZpOOcJOeWHMQ7yxYRKjZDFBpKRZnxGlkT12ALWHI8Ytm7eHo+9ew79Y4jn54PZZDm7yW8KoPV1khB968GvP8y9CWVAxDchtDib3hC4J6Nc6EwiTz8W57W+Lx11nezNcPbMlKazJmtBVo0GXGu3XrRrdu3RrykkII4VeGxL6er/XZu6lN21FVXZGdO3euZ1QtU4hRR69Y3y16Dz/4IEcyc7CqA7zX2lOpsHUaRXbHkRiOrCVkzQsY/1lr0G0toXDlOxSufIeAzkNImr0Wlbp+LaQZX96He9PxvTzW5BgZ9/qfRHRsvPcs2Kgl3KQnv9yOM/I0XEYzGmsB5ftXoyhKq+3W96e20pLX/PeSEUIIPzImHF8lICh/DwW1OHfcpKms+GGhV5ftqJEjueqqqxowwtbh2CzdUpuTbUeLK7e0qFTYOgwnp8NwdJk7CNw2j8BdX6O2FQOgj+laKcErWPkuxo5nYIjpikofgEp9fHau4nZhTd6CPT8XIiZ4yovPvhvr2i/5alsBX+1TyFYFkB2f2Dgv+gQdzAHkl9tBpcaeOJiA/b/gLMzAkX0IfUzb/KOgsdicLhx13LWmpalzkvfpp596ddeebPr06bzyyit1vbwQQjQLamMQuujOOLIPos7chUmnpryGyy/o9HqeeP9LPr+sI4EqB4nBMOvdp9FVtytEGxdk0DK0g5m/s0pIL7L6rOOI7UNh7JMUjXmQgL1LiPp7AWEjZ3rXyTtCxofXeZ+o0aHWB6DSGVHsFtzWEnSdh2M//Vzyy+3klTtItuh4YGM827bn/3NSARPOO5elS5c26vsWFaQnQKfB4nBhSxxKwP5fACjf94ckeQ2srbTiQT2SvEGDBvHUU095lZWXl7NkyRL279/PlClT6h2cEEI0B8b2/XBkH8RtKaK7voAtjtBTn/SP8EADt/RxEaqHIrua4M6DGjHS1kGjVtE7LoSIQD27MktwVbGLhqILoLz3xaT0vpjAmGBO3ByubM/vlU9wOXBbHGAp9hTZkzex8u8jKLqKcXHLvl3Atu07vU5buXIlH3/8MTNneieSDUmlUtHBHMCe7FJsCceXaCnf9wdhZ1/daM/bFrWV8XhQjySve/fudO/evVL57bffztixY7HZbPUKTAghmgtjQl9KNi+s+Drnb9rFjaqylelECWEBdLTsJ1lf0fJXHt2v3mPG2pK4ECMhBi3bjhafcnHl3VkllNicfPXhW9jKSkgIVnHZ1W9Tvn8NrtJcXDYLdmsZDls5LrsVFAV7XH8s8Wd4XaeqyTIHDx5ssNdVlXahRg7klmGP7YtbG4DaaZHJF41AWvLq6YILLuD333/nnHPOaYzLCyFEkwoZcimGxD4YE/uhi+pINwVyy+xV7n2pUavoGRNMfKiR/GXHx+MNuOiGpgq51Qg81n2bXUpaoaXauoeyi3j+6ScpKSogOMxMv017CO95GeV2F/nlDp/bWLnLi1Hrjs9u9edkGa1GTXyokSMFCvZ2AzCmrMGetR9nYSbasNhGf/62otTWdpK8RvmT8siRIzgcjsa4tBBCNDlDfA9CzpiMPqYzKrUanUZNj5hgdBo1eq0ag1aNUVfxLzRAx9AOZuJDjQBYDq73XCeg81B/vYQWTa1W0Ss2mL7xIVXuGXviwtMAJYUF3HjFZA5lV+y8UdN9Sn2t2zdq1KgmmyzTwRyASgW2hOOflfL9a5rkuduKMrt0157S7t27Wb58uVeZy+Vi9+7dfPjhh/z000/1Dk4IIZqrmGADMcGn3nqq/J8kT20MwtCuZ2OH1apV133ra+Hp7RvXsGzRAiZcMq3Gz6HT63n6w6+4YkQf8vPzCQ8Pb/RJFycy6bVEBuop6jQKQ+FBkgaMa7Bt1AQ4XO4qW+BbozoneVu2bOHhhx/2KtNoNLRv35533nmHsWMbZ9FIIYRoKZzFOTiyK8ZyGTsN9lrCQ9TNse7bQ/nlHMor41gDXU32wK2pDpHBzJkzh8LCQsLCwpp8NnR0kIGchMHkJAymZ+cIdDr53DSUtjQeD+qR5E2bNo1p02r+15EQQrRk9uxDlO3+DWvqdsyj/w9jQq9TnmM5tMHztUm6ahuMWq2iS2QgscEGdmWWUGhx1GgP3BpdW6WiY7jJs26fP0QE6j1fF1gcxEmS12Da0sxaaKQxeUII0dqU7VpGxofXUfDra1gOrD31CYDlgIzHa0xBBi2D24fRIyaIcyf/p9o9cGsqPtSI0c9JVYBOg0lfEUOhRca3NyRpyRNCCFGJIfH4HrbWI9tqdE7wwEmodAYsB9cT0HnIqU8QtaZSqWhvNhEdZOD9r75jTL/TKmbXnrQHbk3oNGo6hgc0YrQ1F2HSU263UJSXTcnRlai0eoL6nOvvsFq8ttaSV6sk7+OPP+amm26qUd2rr76a1157rU5BCSFEc2NM6A0qFSgK1tSaJXkBHc8goOMZp64o6s2o03BGh0jMoSGUFBVgCgyqUYJn0muIjzARFWQg1KhtNvvEhpt0pGccJfilHqSiYOo+UpK8BiAtedUYOnQoL730kufxJ598wu7du7nqqqvo2LEjOTk5fPHFF5SXlzN1au2ayIUQojlTG4PQR3fGnnUAW+p22Ti+mbrttlkUFhYSGBxCe3MA6UVWXG4FtUpFgE6NSa/BpNMQZKiYxVpWrCEiIsjfYVcSEajHHWDGGd4ZXf4BLAc34HbYUOtOPaNb+OZyK1gckuRVqVu3bnTr1g2A5ORkHnroIbZt20a7du08dR544AFGjBhBbm5uw0YqhBB+Zkjsiz3rAG5LMY7cFPRRSf4OSZzk5AkTnSMCcSsKBq3aZ1Je1kRx1ZZOoybEqMWWOARd/gEUhxVr8p+Yup556pOFT21pfbxj6jzxYt26dQwfPtwrwQPQ6/VccsklrFy5sr6xCSFEs2I8YVyeLXV7tXVLdy6jfN8a3Pbqd2kQjUuvVWPUaVpkq2tEoB5b4gmLIu+TLc7qoy3tdHFMnZM8jUbD/v37UXysIr5v3z60WpnTIYRoXQyJfT1fn2pcXtb8O0h+/Cz23RKN4pQZkqL2wgN02BKPT9iRfWzrR1ryamHs2LEcOnSIq666il27dlFWVkZycjJz587l/fffZ/LkyQ0ZZyUpKSk88MADdOvWjf/85z81Oic3N5cbbriBnj17MmjQIF566SWfSaoQQvhibH/CDNtqWvJclhJs6bsAMLTrjUrbtIvpitbBbNKjhHXAGRwHVGxvprjbzm4NDa0ttuTVubktIiKC77//nmuvvZbevXt7ysPDw3n33XcZPnx4gwToS0FBAaNHj2bGjBn06NGDzMzMU57jdruZMGECBoOB999/n4yMDK655hpKSkqYM2dOo8UqhGg9dBEdUBuDcVtLsFWzjIo1eTMoFb+MZekUUVcatYowkx5bwhC0fy/CXV6ILX0XxsQ+/g6tRWqLLXn16lMdOXIku3btYsuWLaSkpBATE8OAAQMIDg5uqPh8Cg0N5cCBA6jVambOnElhYeEpz1myZAmbN28mOTmZDh0qVkZPSUnhwQcf5M477yQgoHmsjSSEaL5UajWhw64AwNihf5X1ZBFk0VDCTToyE4cS+PciAMr3rJIkrw7cboXyNjazFhpgMWSdTseQIUMYMqTp/lpVq2vfy7xy5Uq6d+/uSfAAzjvvPG6//Xb+/PNPzjrrrIYMUQjRSsVd/eYp65QflCRPNIyIQD3J7Yd5HpftWUH4+Jv9GFHLVO5w0RZHZ9UqyVu/fj1ffPEFw4YNo0OHDnzxxRdV1h02bFizWisvLS2N2NhYr7Jjj9PT06s8r7i4mOLiYs/jjIyMxglQCNEqKIqC5Z8kTxMagy7S956qQtREqFEL0d1xRHQlJKEHQf3O93dILVJb2+nimFoleVlZWaxevZrIyEiMRiOrV6+usm5kZGSNr5uWlnbKlrSLLrqIF198scbXPJnb7a4041f3z2roLlfVTbgvvPACjzzySKXygoKCBuniPTGBFN7k3vgm96VqzeHeuPKP4CrOBkCbOID8/Hw/R9Q87ktz1RLujUmxcPSKJWjCAwkJMZCXl9foz9kS7kttZBdZcZfXfzkjt6282uNOrZq8vMZfriciIqJG9WqV5E2cOJGJEyd6PW4IsbGxp1xXLyiofiuSR0ZGcvDgQa+yYws2R0VFVXne7bffzsyZMz2PMzIyGDx4MGazucY3+VQa6jqtkdwb3+S+VK0p7o2iKDhyU3DkpxLY7WyvY0X7l3m+Du0xotm8V80ljuaoud+beFUAxdmllKp1RESYm+x5m/t9qY00WxFqU8PMclebQqo8ptWpm9V9axaL2Wm1WpKSkhr1OQYPHswHH3xAcXExISEVb9Dq1avRaDT071/1AOqQkBBPfSGEADj80BlYU7agDgih25uFXgvtWrzG48nMWlF/EYF6AD566zV+0DmIDA+vtLOHqF5ZG1w+BeqxTh7A2rVr2bx5MwB2u51rr72W008/nddff71BgquPzMxMkpKS+OmnnwCYMmUKYWFh3H///TidTrKzs3nyySe55JJLatW1LIQQuqiOABXbm+Uc9jqmj+2GqftI1MZgjEln+CM80coEGbQYtGq+/eRdXnzyMf74+EkUd9tMWupCURTK7G3zftU5ySspKWHGjBm0b98egPfff58ff/yRUaNGcffdd7Nly5YGC9KXs846i6SkJBYsWMD69etJSkryag10Op2kpKRQVlaxM2FISAiLFy/m119/JSwsjISEBLp06cJbb73VqHEKIVof4wk7Xxx54V9YDm3yPA4fewNJ962k25sFaAIadzkp0XYEa+H0gBz+0w36BWRTcmDTqU8SAFgcLtxtcWot9eiuXb9+PZ07dyY6OhqARYsWMXfuXGbMmIFer2fx4sUMGDCgwQI92RdffIHTWfVsmbi4OA4fPuyJDyq6bPfs2UN2djZGo1G6YYUQdRJ29gzyfnkBd3kR9oy9HH5sGJH/nk3UhQ94drdQqTV+jlK0Fna7nVuunMy6g1ZP2dKLr2DFn3s8EwhF1driThfH1LklLycnh9DQUKBiduqGDRsYN24cAO3bt2/02T8JCQme1rsT/x2j0WhISkrCZDJVOjc6OloSPCFEnekiEun0yBYCuv6zs4/bRe6iRzg8dzi2jL3+DU60Oh9//DHrVnvvW7tmxyE+/vhjP0XUsrTVrlqoR5LXpUsXVq1aRWZmJgsWLCAqKorExEQA9u/fT9euXRssSCGEaG700Z1Iun8V0Zc+BZqK1hTr4U1kfnITLkuJn6MTrcmhQ4d8lh/Yv6+JI2mZStvgdmbH1Lm7dvDgwfTq1Yv4+HgURfGMbbNYLCxZsoQ1a9Y0WJBCCNEcqdQaIs+/h6A+55H+9pXY0nZQfnA9R9+ZTuL/vvV3eKKV6NSpk8/y9qEyJKAmpCWvjn788UdWrFjB1q1buf7664GKtefeeOMNYmJiGiRAIYRo7ozt+9Hx4U1EnH8v2pBoAnuf4++QRCty1VVXMWrUKK+ywTEwqUfl4UiisrI2utsF1HOdPJ1Ox8iRIykvL2fXrl0YDAa6dOni6bYVQoi2Qq0zEHPpk8Rc+qS/QxGtjF6vZ+nSpcTGxmItzueeQTCxMzj2/+7v0Jq9crsTp7ttzqyFerbkHT16lCuvvJLg4GB69+7NU089xf79+xkzZkxDxSeEEEK0eTqdjjlz5nDpjXcxrns4OjWU71+D22Hzd2jNWrG17bbiQT2SPLvdzrhx40hLS+O7777j0UcfBaBr164EBgby888/N1iQQgghRFs3a9Ys7rxvDto+/wJAsVuwHNro56iat+I23FUL9UjyVqxYgaIoLF26lAsuuIC4uDjPsSFDhvDbb781SIBCCCGEqBBu0mHtcBZufSDqHufKeoyn0NZb8uo8Ji8rK4u+ffv6XIgxKCiIzMzMegUmhBBCCG/hJh2WbueT3u0CQgID6J4U7u+QmrUSacmrmw4dOrBmzRpKSiqvB7Vp0yZOO+20egUmhBBCCG8GrYbAgADQ6Ci2OrE62u7yIKdidbiwO93+DsOv6pzknXXWWQQGBjJq1Ci+/fZbcnJysFqtfPDBB/z8889MnTq1IeMUQgghBBBu0nu+zi6ViRdVaetdtVCP7lqNRsN3333H5MmTmTx5sqf8559/Zt68ebJOnhBCCNEIzCYdqYUWADLzC4l1ZqGP6ujnqJqftj7pAuqR5KWmpmK329mxYwfr168nJSUFs9nM2WefTVBQUEPGKIQQQoh/hJt04HIQ9cWlGNI3k5o0gM4PrvN3WM1OsdXh7xD8rs5J3m+//cbq1at57733GD58OMOHD2/IuIQQQgjhQ8W4PCNqaxEqlx3b4U04S3LRBkf6O7RmRbpr6zEmr127diQnJzdgKEIIIYSoiXCTHkuXf7bPc7so2fq9fwNqZuxON7Y2PukC6pHkjRw5kry8PD755JOGjEcIIYQQpxBu0mHpfoHnceGGr/0YTfMjXbUV6pzkff/99xw9epSrrrqK0NBQunXrRvfu3T3/5syZ05BxCiGEEOIf4SY9juheOMM6AGD5+1dc5UV+jqr5kEkXFeo8Jq9jx45ce+21VR4fOHBgXS8thBBCiGrotWqCjDrKu51PyIY3wOWgdNsSQodd7u/QmgUZj1ehzknewIEDJZETQggh/CTcpCPzWJIH5G/4SpK8f0iSV6HO3bVCCCGE8J9wkx57/ACcwRV7x1t2/oLbVu7nqPzP4XJjkZ1AAEnyhBBCiBbJHKADlRrLaRMqChwWSnf87N+gmoG2vl/tiSTJE0IIIVogvVZNkEGLpdsFKBo9li7jKdeF+jssv5Ou2uPqPCZPCCGEEP4VbtJxJHEo6f/bjWIIxmQOINbfQfmZJHnHSUueEEII0UJFBepBrUExBAOQWWJDURQ/R+VfskbecZLkCSGEEC1URKAevfb4r3K7001emd2PEfmXy61QLpMuPOrcXZuWlsbOnTt9HlOpVISGhtK3b19MJlOdgxNCCCFE1VQqFbHBBo4UWCoe28vIWPsLEWMvQ6VpeyOyiq0O2nhDppc6fwJ+/vnnahdDBggKCuLZZ5/l//7v/+r6NEIIIYSoRnyIkSMFFoI2vUPoysdRO62UxscT3Husv0NrcjIez1udu2unT5/OuHHjuOSSS1i/fj0ZGRns2LGDe+65h8TERLZs2cLs2bP53//+V2WLnxBCCCHqJzRAh0mvwRWagNppBSB7/Vd+jso/ZDszb3Vuydu8eTPZ2dn88ssvqNUVuWJsbCxPPfUUOTk5rFixgnvuuYddu3bx448/0rt37wYLWgghhBDHxYUYOdRxNG5dAGqHBctf36G430ClbltD70ukJc9Lnd/9AwcO0L17d0+Cd6JevXpx8OBBAAYMGEB6enrdIxRCCCFEteKCDSi6AKydxwGgLskk7+/Vfo6qabndCqV2SfJOVOckLzY2lj/++IP8/HyvcrfbzeLFi4mNrVipJyUlhdNOO61+UQohhBCiSoEGLSFGLeXdzveUZf/+iR8janolNqdMujhJnbtrx4wZQ0xMDAMHDmTGjBl06NCB3NxcFixYwIEDB/jkk0/Iy8tj6dKlPPDAAw0ZsxBCCCFOEh9iZG+Xc3AbQ1Fbi3D/OR9byTMYgsP9HVqTkEkXldW5JU+r1bJ8+XImTZrEO++8w4wZM3jyySdJSkpi48aNJCYmEhISwqZNm4iMjGzImIUQQghxktgQAxgCKe17OQBqRzlpy971c1RNJ6fM5u8Qmp16jcg0m828+OKLpKWlYbfbyc3N5csvv6RLly4A6HQ6WSdPCCGEaAIGrYZwk57SAVejoAKgfNWbKO7WvzhwVomNnNK2uwh0Veq9UmJRURHLly8nOTmZ2NhYhg8fTvv27RsiNiGEEELUQlyIgTxzEtau5xCw/xc0hSlkbVpM7JBJ/g6t0Thdbv7OKvF3GM1SvZK8H3/8kRkzZpCdnY1Wq8XpdGI0Gpk7dy533HFHQ8UohBBCiBqICTKwW1VKyRkzUZfnUXLGTJzxZxPr78Aa0f7cMmxOt7/DaJbq3F1bWFjIZZddxqRJk0hJScHhcJCfn8/jjz/OPffcw7p16xoyTiGEEEKcglajJipIj63D2WRPX4Kl50Vklbuxt9IkqNDiILXQ4u8wmq06t+StWrWKLl268Pbbb3vKzGYzt99+OwcOHOD7779n2LBhDRKkEEIIIWomLsRIVsnxSQhuRSG9yErHiNY1Rl5RFHZnlsiyKdWoc0teUVERnTp18nmsU6dOFBcX1zkoIYQQQtRNVKAencb713tqoQW3u3W15h3OL6dEtjGrVp2TvG7durFy5UqOHj3qVW632/nqq69kAWQhhBDCD9RqFQlhxooHbhfG/UsJ+mQKaT8879/AGlC53cnB3HJ/h9Hs1bm7dsiQIfTu3Zv+/ftz9dVX07FjR3Jycpg3bx5FRUVMnz69IeMUQgghRA0lmU2k5FtQFacT+fV0VCgUFyWjXHA7KrXG3+HV2+6sUtzST3tK9Vonb/HixVx99dXMnz+fG2+8kVdffZX+/fuzYcMGzGZzQ8UohBBCiFrQa9W0CzXiCmuPtes5AKgLUsjcsMi/gTWAYquDvDJZE68m6pXkBQUF8fTTT3PkyBEcDgfZ2dnMmzePxMTEhopPCCGEEHWQFB6ASgUlZ8z0lOUsfdWPETUM2b6s5uqV5J1Io2n5zb9CCCFEa2HSa4kJNmDrcDaOyG4AaA+tIm3rb36OrH4kyau5WiV58+fPJyEhoUb/7r777saKGYD9+/dzxx130KFDByZPnnzK+jk5OcTGxlb698MPPzRqnEIIIYS/dAw3gUpF8dBbPGX5C+7A7Wq5W50Vy4zaGqvVxItevXpx880316huv3796hRQTeTn53PBBRcwc+ZMzjjjDPLy8k55jsvlIisri5UrV9KtWzdPuYwdFEII0VqFGHVEBOrJ6z2F4M3voc/8C23GNg7/+gGdz73W3+HVmqIolEhLXo3VKsnr27cvffv2baxYasxsNrNnzx5UKhUzZ86sUZJ3TEREBLGxrXmDFyGEEOK4juEm8srsFIx7lJjP/g1A7vzb+WFHHv+7814/R1c7pTaXzKqthQYbk9eUVCoVKpWqTudOmTKFTp06cc4557B48eIGjkwIIYRoXiIC9YQYtdgTh1DU5QK+2gfv/VnKT28/isPh8Hd4tVJsa1nx+lud18lriS699FJuueUWIiMj+e6775g8eTJvv/0211xzTZXnFBcXe+3ekZGR0RShCiGEEA2mU0Qgmw7n8H/fHmX71mOlFsaPP4dly5ai0+n8GV6NyaSL2mkWSV5qaiqDBg2qts7FF1/Ma6+9VufniImJYcGCBZ7H3bt3Jz09nUceeaTaJO+FF17gkUceqVReUFBAQEBAneM5RrZ/q5rcG9/kvlRN7o1vcl+q1lbujUZRWPHNp2zfusWrfNWqlbz++utceeWVXuXN9b7k5pbg9uPEC7et+l02nFo1eXl162msjYiIiBrVaxZJXnx8PH/99Ve1deqbUPnq3h0yZAivvvoq5eXlmEy+N26+/fbbmTnz+BpDGRkZDB48GLPZXOObfCoNdZ3WSO6Nb3Jfqib3xje5L1VrK/fGVpzvszw1PcPnPWhu90VRFGx5btQa/47JU5tCqjym1amb1X1rFkmeRqPxy2SIAwcOEBQUhNForLJOSEgIISFVv6FCCCFES9C3R1ef5WpTEIXldsJM+iaOqHbK7S5cbpl0URstcuJFTWRkZHitg/fuu+/y3XffYbVaURSFpUuX8sILL/Df//4XtbrV3gYhhBACgKuvvpqzR4zwKhvQrT2Xpz7Jzl8+IqfU5qfIakbWx6u9ZtGSVxeDBg0iNTWV4uJinE6npyUwMzMTOL4untVqBWD8+PHcd999TJ8+HafTSWhoKHfffXejL9oshBBCNAd6vZ7ffv2V6JhYCgvy6RUfxAeDjqBzgfbne9meMIjTunQn0Vz/8eaNQSZd1F6LTfJ+/PFHXNWs2B0fH09GRoZnseOkpCTmz5+P2+3GYrEQGBjYVKEKIYQQzYJOp+PBObPZlZKJNiAIe9hf6HYvRGMtwPzdjey+/BssThcRzbCDS5K82muxSV5UVFS1x9Vqtc9xfmq1WhI8IYQQbdZtt91Gmc3J2uQCCqzF6I9uQVeYjDF1PSGrX+DwiLspVFkxm8NRqxt/pmhNFVtljbzaaoa5uhBCCCEaU6BBS5fIQBRDMHmT3kZRV6yTF7LmBQwpq8krs7M1vajZTHSwOFw4m0ksLYkkeUIIIUQblBQeQFiADkfc6RSOng2ACoXw729CXZ5HbpmdzamFOFxuP0cqXbV1JUmeEEII0QapVCp6xwajVqkoHXQ9ls7jANCWZhKx7B5QFAotDjYdKcTmrHoMfFOQrtq6kSRPCCGEaKOOdduiUpF/wcs4gyrGspuSVxH05/sAlNicbDxSiMXhv0RPWvLqRpI8IYQQog1LCg8gNECH2xRJ/r/fQEGFtd0gLKf9y1On3O5iQ0oBpX5aq07WyKsbSfKEEEKINuzEbltbh+HkXP4NWZM/xRUS71XP5nSzKbWwyRM9q8OF3en/cYEtkSR5QgghRBsXZNDSObJiD3dbh+Gg1nhXUCpmttqdbjanFlLWhImedNXWnSR5QgghhKBjuIkQY+XlczVFqUR/NhFt3kHgeIteub1pki/pqq07SfKEEEIIgUqlok9cCGrV8QWQNQXJRH/6bwxpG4j64hI0RWnA8USvKSZjyMzaupMkTwghhBBARbdtpwiT57HbFIkrKBoAbXE6UfMvQV2WDcD8997kxjvu49nnX2jUmEqkJa/OJMkTQgghhEenCBMmQ8WYPMUQRO6l83FEdgNAV3CI6E//jZL5N/PefJGPXn2WuXPnklFQiqI0/I4Udqcbq0MmXdSVJHlCCCGE8FCpVHSOCMSoq0gR3KZwsv/zJc6wDgAouYeZc/lYSooKACguLOC8887jtz2Z7MspbdBJGdJVWz+S5AkhhBDCS4BOw/CkcNqFGgFwB8eSNe177LGn891B2JzhPRZv+8Y1/PD1fA7nlbP6cD7rUwrIKLbWOw6ZdFE/kuQJIYQQohKtRk3vuBAGJoRi1KlxB8eSPe1bDus7+6yfkZrs+brI4mD70WIO55XXK4ZCi7Tk1YckeUIIIYSoUmSQwdOqp+hMhI39P5/14hKTKpXtyynlUF5ZnZ43s9hKTqm9TueKCpLkCSGEEKJax1r1BiSEMmHK5fQddKbX8b6DhzN+0lSf5+7PKeNgbu0SvXK7k12ZJXWOV1SQJE8IIYQQNRIVZGDkaTG8Me9bgkPNAASHmnnivQVodTpwOQhdORd1WY7XeQdyyzhQw0TP7VbYdrQYp7vhZ+u2NZLkCSGEEKLGjDoNZ3aOIjg4GICAwKCKBA8I3vA6IeteJfbdkQTsXuTZDg3gYA0Tvb05pbKVWQOpvH+JEEIIIUQ1VCoVd91xG+lZuZSpKmbg4rQStPVTADSWPCK/ux7L9vkUj7gHe/wAoCLRK7E6OS0qkEBD5RQkq8TGkQJLk72O1k6SPCGEEELU2qxZswCwOFxsTi2kHCNZ1/xK2LLZBO76GoCAwysJOLwSS5dzKDr7bhyxfcgutZFTZiM+xEiXyECMOo3nOjsziv31clol6a4VQgghRJ0F6DQMSgwjQKfBHWAm/9+vk3PxpzjMnY7XObCU2A/HEfHNDHTZu1AUSC+y8sehfPZml2JzuvgrvUjG4TUwSfKEEEIIUS9GnYZB7cM8u2RYu55D5nV/kHf+yzjD2nvqmfb9iPnHOzyP3YpCcn45qw7myTi8RiBJnhBCCCHqLUCn4YyEMPTaf1ILtZbyvv8h47q15E94HmdIAgBlff/jfaLbhf7w7+B2IRqWjMkTQgghRIMINGg5IyGMTamFOFzuikKNjrLTp1HW51ICt39Befd/e52z+b37uCjvY6yqANxdR2PrcBbWpLNwRpwGKpUfXkUtKG5U9jLU9lJU9lI0GqBzpL+j8pAkTwghhBANJtio5YzEUDYdKfQeY6fRU9Z/ulddh93Ojp/nk6yBxGALE10/Ytr3IwCuwChs7YfhiOyBI6IL9ti+uMxJjRe4y466PB9NeS7q8jw05bk4IrvhiOl9/CWUZhI9fxJqSwFqayFqh/e2bc6IzjD0QOPFWEuS5AkhhBCiQYUYdQxICGVzahFuxfdkCofdzgMzp7L97+Nbly05BO+MB50aNGU5mP7+HvgegNL+V1Nw3tOeuiprMWG/PYg7wIzbGIZiCEFRa3w+18nJZdCmdwg4sKwiWbMUoLbkV0rYAIrOnOWV5CkaA/rsXVW+bpWttMpj/iBJnhBCCCEanNmkp3dcMNuP+l4W5ddFC9i+aa1X2cYsmG+YzMWdbBhS1qCxFnqOOSK6eNXVlOcStH3+KeNQVGrKTr/Sq+tXl/M3xuTfT3mupjzP67HbEFLxzxiK2xiGWx+Eog+s+N8QhCqo+XTVgiR5QgghhGgkcSFGyu0unztdZKSl+DznsLodeZNng+JGW5CMNu8AuvyD2JLO8qqnPiEBrI5KcaNyWlF0AZ4yRRcIgFsXgDsgvCJhCzDjNoXjCojEbYrAbYrAfkIrXsWTaki/fX+Vz3VsdnFzIUmeEEIIIRpN58hAyh0ujhZZvcrjEjr4rB+XmFTxhUqNM7wTzvBOWH3Us0f3IuP6dZ7xcSpbMSpP1/DxLmJFra3UjVs04l6KRj3glfi1RpLkCSGEEKJR9YoJxupwk19+fPzduElTWfHDQq8u276DhzN+0tSaXVRrwBne6dT1fFAMQXU6r6VpXu2KQgghhGh11GoVp7cLIVB/vEVNp9fzxPtfEhxqBiA41MwT7y1Aq9P5K8xWR1ryhBBCCNHodBo1AxJC2ZdThkGrJkCnIUCn4f4HHqCkuIhCt14SvAYmSZ4QQgghmoRJr+X0dqFeZffeVbHN2ZGCcv7Oal5LkLR00l0rhBBCCL9LDAsg2CBtTw1JkjwhhBBC+J1KpaJHTNuYENFUJMkTQgghRLNgNumJCzH6O4xWQ5I8IYQQQjQbp0UFolGrTl1RnJIkeUIIIYRoNow6DZ0iTP4Oo1WQJE8IIYQQzUqS2YRJrzl1RVEtSfKEEEII0ayo1Sq6R8skjPqSJE8IIYQQzU5UkIHoIIO/w2jRJMkTQgghRLPUJy7Yays0UTuS5AkhhBCiWdJq1PRvF4pWZtvWSYtM8iwWC6+++ipnnnkm7dq1Y/jw4Xz++eenPC89PZ3LLruM+Ph4unbtysMPP4zL5WqCiIUQQghRF4EGLX3iQvwdRovUIvcPeemll8jMzOT555+nffv2/Prrr1xzzTVYLBZmzpzp8xyn08m5555Lu3bt+O2338jIyGDq1KlYrVaeeuqpJn4FQgghhKip6GADXSIDOZBb5vO4Vq0iQKehxOZs4siaN5WiKIq/g6gtRVFQqbybbqdPn05ycjK///67z3MWLlzIxRdfTFpaGvHx8QC8/vrr3HXXXeTk5BAYGFij505LSyMxMZHU1FQSEhLq90KAvLw8IiIi6n2d1kjujW9yX6om98Y3uS9Vk3vjW3O9L3+lF5FVYvM81qhVtDcH0DHcREG5g63pRY36/O7yYtSmqlsVjTo1IztHNmoMtdEiu2tPTvAASktLCQgIqPKcP/74gx49engSPIDx48djsVj4888/GyVOIYQQQjScPnEhBBm0qFUVyd3ZncI5LSoInUZNuEmHj/SgTWuR3bUn++OPP/juu+/49NNPq6yTnp5OTEyMV1l0dDQAR48erfK84uJiiouLPY8zMjLqGa0QQggh6kKjVjEgIRSAAJ33rFutRk2IUUeRxeGP0JqlZpHkHTlyhL59+1ZbZ+rUqbz99tuVyvfs2cOUKVP473//y+WXX17tNdRq74ZLrbbi5VfXY/3CCy/wyCOPVCovKCiotuWwpk5MIIU3uTe+yX2pmtwb3+S+VE3ujW8t4b6U+ygLcFooKLc22nO6bb6e9TinVk1eXuM3J9a0K71ZJHkJCQkkJydXW0ev11cq27dvH2PGjGHChAm89dZb1Z4fHR3Nvn37vMqys7M9x6py++23e03myMjIYPDgwZjN5gYbr9Acxz00F3JvfJP7UjW5N77Jfama3BvfWuJ9UQXYyXQUNupzVDcmT6tTN6v71iySPLVaTVhYWK3O2b9/P6NHj2bcuHF8+OGHlVrpTjZkyBDefvtt8vPzCQ8PB2DVqlXodDoGDBhQ5XkhISGEhMjUbSGEEKK5CzPqUKtUuFvenNJG0SInXhw8eJDRo0czduxYPvroI58J3tGjRwkLC+P7778HYMqUKcTGxnLbbbdRWlrKwYMHmTt3LldeeSVms7mpX4IQQgghGpharSIsQOfvMJqNFpnkvfTSS6Snp/Pdd98RHh5OWFgYYWFh9OrVy1PH7XZTVFSE3W4HwGQy8dNPP7F3717MZjO9evXirLPO4tVXX/XXyxBCCCFEA4sIlCTvmGbRXVtbzzzzDI899lil8hNb9Nq1a0dBQYHX+ne9e/dm/fr12Gw2tFotGo3shyeEEEK0JhEmPfvxvWhyW9MiW/ICAgI8rXcn/jtx7JxKpSIsLAydrnJGbzAYJMFrBNu3b+c///mPv8No9hYvXswLL7zg7zAAuOaaazh8+LC/wxBCiAYTYtTKXrf/aJFJnqi7srIyXnzxRS688ELGjBnDddddx4oVKzzH3377bYYOHcrQoUMZN24cd9xxB5mZmTW6dnFxMX/99Zfn8UsvveRz2Zu2zOVyceedd3LhhRd6lf/9999cc801jB8/njfeeMPrWFlZGY8++ijnnHMOEydO5Kuvvqr2OZYsWcKECRO47bbbsFgsnvKdO3dy++23e9UdPXo0Dz74YD1flRBCNB8qlQqzSbpsoYV217Ykv+zJrvZ4cUkxITmuej3Hud2rXgLmRAUFBZx99tkEBgZy0003kZCQwKFDh3j88cdJT09n2rRppKamYjKZeOKJJygtLeXZZ59lwoQJbN26tdZxXXTRRdJiepKffvqJmJgYunbt6inbu3cvZ511FrfccgtXXHEFP/zwA9988w1TpkwB4Oabb2bHjh3MnTuX3NxcbrnlFsxmM1OnTq10/dzcXK677jreeOMNPv/8c5577jnmzJkDwK233lqpBXHKlCnMmjWLnJwcoqKiGvGVCyFE04kw6ckptfs7DL+TJK8Nefjhh9Hr9axdu9aTfI0ZM4aZM2dSVnZ8/EJ4eDhDhw4FID4+nl69epGdne1zPcE33niD77//nsTERMaMGeN17NtvvyUgIIDrr7+eAwcO8OSTT5KcnMygQYOYM2eOZ7zkzz//zKeffkpRUREzZ85k0qRJALzyyivEx8eTnp7O2rVreeaZZzj99NN54403+OWXXzAajVx//fWMGzcOgEsuuYSrr76ab7/9lrS0NCZNmsT//d//eeK58cYb2bJlC3q9nj59+vDggw96dkG55JJL+O9//8vChQs5fPgw48eP58477/SM83S73bzxxhssW7YMk8nEnDlz6NmzJ/Pnz2fr1q0888wznuf54IMPSE5O5tFHH610v7799lvOPfdcr7I5c+Zw44038vDDDwMwduxYbLbjezMuW7aMN998k/POOw+A33//nV9//dVnkrd9+3aGDRvGxIkTiY6OZu7cuQB8/PHH9O3bl9NPP92rvslkYvDgwSxevJhrrrmm0vWEEKIligisvLZuWyTdtW3It99+y//93//5bF07cYLKiTZu3IjRaPS5zMyrr77KM888w3XXXcfw4cO57bbbvI4nJyeTmpoKwMUXX0xERASzZ88mMjKShx56CID58+dz5ZVXMmLECG688UY++eQT9u/fD0BKSgp33HEHJpOJRx55hC5dunDTTTcxb948/u///o+JEycybdo01q9fD8DWrVu59tprGTFiBDNnzuTxxx/nzTff9MRz880389JLL/HYY4/hdruZMGGC59jWrVu55ZZbGD16NLfccguvvPIKX3zxhef4tddeywcffMBVV13FRRddxJ133gnAWWedxRtvvEFR0fFNsV988UX69evn835u3ryZPn36eJX9/PPPdO/enSlTpnDBBRfw7rvvei3+3adPH8/+yk6nk507d1a5Q0xERASHDx9GURQOHDhAREQEhYWFvPjiiz6TToB+/fqxadMmn8eEEKIlCjJo0WslxZGWvDYkPT2djh07eh5/9NFHnp1Czj//fE+33ooVKxg6dCilpaWkpKTw+uuv+5zA8uqrr/LSSy95Wt6ys7P54IMPfD53Wloa1113HV26dGH06NGelqq5c+fywgsvcOWVVwIwYcIEHI7j+w5ecMEF3HvvvQBkZmby/vvvk5aW5mlVzM/P54033vC0PN59991Mnz4dqGh9e/jhh7nhhhsA6Nmzp+e6I0eOpFOnTuzatcuz9M7DDz/MZZddBsCWLVtYs2YNl19+OUePHuWTTz7h0KFDJCYmAjBx4kQAEhMTGT58OAsWLOC6665j8+bNpKenc8EFF/i8Dzk5OZ7FuI/FX1JSwqOPPspjjz2GSqXitttuo6ysjFmzZnnep/Hjx7NgwQKKiooYPXo0N910k8/r9+vXj9jYWLp27UpJSQlLlizh/vvv55577mHDhg288MILdO7cmaefftqT2EdERHgSayGEaC0iTHoyihtvi7OWQJK8NiQgIIDCwkLP4/Hjx9O9e3deffVVDh486Cnv168fTzzxBIGBgXTq1MlnK5+iKBw6dMir+69///5VPvdrr73GxIkT6du3L+PGjeOKK65AURT27dvHkCFDPPVUKpVXK1aPHj08X+/duxeAyZMnoygKiqKQn5/vtYXMyfGc+Lo2b97M66+/zqFDh7DZbOTm5pKamupJ8jp37uypGxYWxoEDB4CK7fPi4uI8CR5UzNA+ZsaMGbzyyitcd911fPjhh1x66aVex09kMpm8JkMcM2vWLC699FIACgsLeeONNzxJ3s0330xCQgK33XYbhYWF3HPPPXz00UdVdq8uXryYvXv3Eh8fz/79+9m/fz9PPfUU3bt357XXXmPhwoU8/vjjPPHEEwCUl5djMpl8XksIIVqqcJNOkjx/ByCaztChQ1m5ciWXXHIJULGWYLt27fj666/Jzc311DtxTF5VVCoVISEhFBQUkJSUBFRM7KjKf/7zH6ZOncrevXt55ZVXmDdvHr/99htms5m8vLwqzzuxa9lsNmMymXjuuee86gQFBXm+PjGGgoICz3Z5BQUFjB8/ntmzZzN9+nQCAgK46qqrsFpP/QMgPDycwsJC3G63z91VLrroIm666SZ27NjB/PnzWbx4cZXX6tatGykpKV7XjoiI8BrvGBUV5en+zcrK4uuvvyYtLY127doBFbu5vPbaa1UmeWq1mh49euB2u/nf//7H+++/z549e+jbty+TJ0+mY8eOnu5mqOgWP+200055H4QQoiWRcXkyJq9Nuf/++3n//feZN2+ep0xRlGqTrOqMHz+e1157DUVRsNvtXuPfTmS323nppZdwu910796dqVOnsn37dgD+/e9/88QTT3iSrZUrV3olQSfq0aMHMTEx7Nq1y7PMS0REBHv27PHUeeutt3A4HCiKwiuvvOKZlJGamopGo+HGG29k9OjRqNVqkpOTa/Q6jz3vs88+6yn7+OOPPV8bDAYuu+wyrrjiCsLDwxk+fHiV1xo3bhzr1q3zKpsyZQrz5s3D7XbjdruZP38+Z555JlCR2BqNRtauXQtUvF+bNm0iPj7+lHG//fbbjBgxgu7duxMZGUlKSgoul8szVu+YNWvWeO6TEEK0FgE6DQG6tr3Cg7TktSFjxoxh3rx53Hbbbdxyyy20b9+ejIwMOnfuXKl1rCaefvppzj33XDp27IjdbmfEiBFkZGRUqqfT6UhNTSU6OprExERSUlI8XYVPP/00l112GfHx8URFRZGYmMiiRYt8Pp9Op+PLL7/k8ssv57HHHkOv16PRaLwSLrPZTFJSEhqNhsDAQH755RcAevXqxcCBA+nQoQMxMTGo1Wqv8YnV0el0zJ8/n6lTp/LGG2/gdDo94/6Oueaaa3j99dc9M2SrMm3aNPr27YvNZvN06T7xxBNMmDDB0yIaFRXl2XNZr9fz5ptvMnPmTB5++GFKSkrQarXVthZCxdi/d955hzVr1gDQqVMn+vfvT9euXSktLfXc423btqHRaBg2bFiN7oUQQrQkEYF60gorD5FpK1SKovx/e/cdFsW1/gH8u7uwLFUQESmLgKDYMNIkiFFEbBgiRkVNEVs0yVUTSaxpXuNVryVEDblRsUSwYEHAgtgwFrAhIqJ0jChGFAREpCzz+4OH+TnsLkVhF5b38zw+j3PmMPPO4Sy8nDlzhlF2EG1Jbm4uxGIxHjx4AHNz87c+3rNnzzijKorAMAwyMzNRWloKKysrzptCcnNzUVZWxlnHrT4SiQTp6ekwNTUFj8dDVlYW+2Tp/fv3IRAI2HYqLi5GTk4OrKysoKuryznO48ePUVpaypkXl5CQAGNjY/Y25evx//3336iqqoK1tTV4vJqVzW1sbBASEoKePXsiLy8P3bt3l7q9mpaWBqFQiK5du+Lu3bswMTGBgYEBEhMTYWNjw976lRVPdXU1srKyoKenJ7WcTH5+PkxMTJCamsr5Glm++uor2NnZcZZ3AYB79+5BKBTCysqKvaZaL1++RFZWFkQikczz15Wfn4+ysjJYWFhw2i0tLQ2dO3dmn5aeOnUqRo8eLXM5lrZIGZ+ntoDaRT5qG9lUpV3yil8h6VFxsx2v+mUx+Fp6cveL1PkY3K1Ts53vbVGS10SqkOS1FU1tm9okr6H5hC1h/fr1OHr0KOftIfKUlJTg0aNH6NGjxxudqzn7zI0bN+Dg4CCVVLZV9HmSjdpFPmob2VSlXSqqqnEu42nDFRuprSV5dLuWkLfk7OyMlJQUHD9+vFH1dXV13zjBa26Ojo7KDoEQQlqMUI0PQ20hnpW2z7dfUJJHVMbBgwdhY2Oj8PNu3rwZXbt2RZcuXRR+bkIIIfUT62tSkkdIW1f3lV2K8vo6f4QQQlqXzjpCiNT5eFVZrexQFI6WUCGEEEKIyuLxeDDvoNmougK+asxPrkVJHiGEEEJUmrm+CA09X2asqwFDLdVaQJmSPEIIIYSoNA01AYx1Zb9uEgD4PB56dNaBrki1ZrFRkkcIIYQQlSfWl3/L1rKjJjTVBdARqtYbMijJa8fu3buH+fPnN7r+kSNH4O/vj++//74Fo5K2c+dO7Nu3r1mOVVhYiHnz5nHKmnJdr8fSnHE1xa5du3Do0KFmPWZERASmTp2qsO9tU/teY+3du1dp35PTp08r/LyEkMbrqCWEjob0SJ2GGh/WhtoAAF0Z+9sySvLasadPn7Kv/WpIWloaZsyYgcGDB8PT0/ONzxkcHNzkX8KJiYlITk5+43O+buXKlZy3QDT1ul6Ppb643uQ6G3ucpKQk3L17t8nHWr58ucx4MzIyMH36dAwZMgSenp64dOkSfvzxx3pjeNvra0rfa4rk5ORm6ytN4erqivnz56O6uv09vUdIWyLWF0mV2Rppsw9caAkF4KvI4vAALaFCGunmzZtwdnbGtGnT3vo4+vr6zRNUE5WWlmLHjh24d+8eJ57muK66mus6m+s4ZWVl2LRpExYuXCi1LyEhgdMGubm5GDp0aL0xKPP72Br16NEDBgYGiImJwciRI5UdDiFEDlM9EdLySyGprnnZl55IDWavPXnL4/GgoyFA8asqZYXYrCjJa2ciIiIQFRUFsVgs820HiYmJCA0NRVFREby9vfHBBx/g1KlTWLduHXJzczF+/HhMnjwZH374ocy6rx9n7969KCoqgr+/P1xdXXHhwgVER0dDXV0d9+7dg729PX744YcmX8PZs2dx+PBh8Hg8TJw4EYMGDWL3hYeH49ixYxCLxRg+fDj27duHX3/9FQBw8uRJ2NnZwcjICABkXldOTg7s7e3h5eUFALhz5w6Cg4OxYcOGRscn7zq3bNkCAwMDFBYW4vLly/j2228RHR0t93z1tRfDMNi6dSvi4uJga2uLr7/+GiKR9F+otU6dOgU3NzdoampKla9du5Ztg1q9e/fG4MGDZcbg4eEhN676+kRDfa9WRkYGVq5ciR07drBl2dnZWL58OXbs2AEej1dvH6i1fv36er+XW7Zsga6uLgoLC3Ht2jX07NkTAQEBOHbsGA4fPgxTU1MsWbKE857l+q7P29sbYWFhlOQR0oqpCfgw1RPhwfMyAEBPY12pOroaaiqT5NHt2nYkNDQUM2bMgL29PQQCAWbOnMnZf+bMGXh5eUFfXx8uLi5YuHAh/vjjD3Tr1g0DBgyAmZkZJk2ahN69e8utCwDR0dEYMmQIhEIhHBwcsGjRIuTk5KBr166wtbVF7969MWnSJHh4eACo+cX5ySefNOoawsLCMH78eFhbW8PCwgLvv/8+jh49CgD4888/MXv2bPTr1w/q6ur48MMPObcEr1y5gn79+rHbsq4rLi4OmZmZbJ38/HzExMQ0qZ3lXWdCQgJmz56NpKQkjBo1Cp07d673fPKOAwCBgYG4c+cOBg8ejAMHDsgcoXtdZGQkfHx8pMrrtsGkSZMgFotx/vx5uTHIi6u+PtFQ33udlZUVTpw4gfj4eLZs9+7dqKioAI/Hq7cPvK6h72VCQgLmzJmDzMxMDBo0CEFBQRgyZAh27twJDw8PXL16FZ9//jlbv77rAwAHBwfExcXV+30ghChf7QMYJnoi6GuqS+2XNW+vrVKdK2mlnp0MREFMoNz9Ekk1CgR86PQdCRP//3H2ZX7/DqpfPm/wHGaf74OWjWuD9VavXo2NGzdiypQpAIDKykrs37+f3b906VKsXr0aM2bMAFDzBokJEyZg9uzZcHJyQk5ODjvaM3XqVLl1ly1bhlWrVrG/IKdNm4aKigpoa2ujW7du0NfX54waPX78GOHh4Q3GDwArVqzAhg0b4O/vDwDQ1NTEihUrMGbMGPz3v//Fr7/+ismTJwMAJBIJQkNDOeextLRkt62traWuqzlYWFjIvE4AcHd3x+bNm9/6OF5eXggMDAQAmJubY+7cuXKPwzAMjh8/jp9//llqn6w2qKqqws2bN+uNQVZZff2nob73OoFAgIkTJ2Lv3r1wda3p13v37sW6desA1N8HmmrUqFFYv349gJrb+f/9739x7tw5qKmpwcHBgTNPs77rAwAjIyPk5eU1OQZCiGLpitTQSVuI7kbasvdTkkcaS/LyOSqf3q+3TjWAqpJ8qfKqZw8gKS1o8BxM5auG6zAMUlNT2V+aAODm5sb+omUYBklJSTh48CBOnToFhmFQWVmJ+/fv49WrV1LHkle3rKwMd+7c4Yw6qaurQ11d+q+lWv3790dISEijriEtLQ1ubm5s2aBBg7B06VJ23+uvGBswYAAnydPQ0EBFhXLfX9i/f/9mOU7v3r3Z/xsbG6OwsFBu3StXrsDCwqJF363bUJ+or+/JMmXKFPj6+mLDhg24desW8vPzMXz48Hr7wJt4vR2NjIzQvXt3qKmpsdu17drQ50MkEqG8vBwaGvLX4SKEtB79TPWgJpB9M5OSPNJoAi19qHfqKne/RFINgYAPNV0jqX1qhmLwNaXnC9TFU5c/F4utw+NBU1MTL1++ZMte/3/t/uHDh0MsFrPlU6ZMgUAgkHksWXXV1NSgpaWFFy9eNBhTLWNjY4wdO7bR11BaWsqWlZaWQltbGzweDyKRSO71ATWjTw09eSkQCDhPSNZNcN9W3WT3Tc/Hq/P0F8MwcuvKu1XbnBrqE/X1PVlcXV2hpaWF2NhYREdHY8KECWzbyesDdTWmbeu2o7x2bcznIzc3F9bW1vVeFyGkdZCX4AGAUI0PoRofFVVt/2l5SvJamOGIr2A44iu5+589ewZDQ0OZ+7qtSGzWWNzd3bF7926sWbMGDMPgzz//5OwfPnw4Hj58iK+//hpAzW278PBwmaNw9dX18vLCpk2bsGPHDvD5fCQnJ6Njx44wNTWFrq4uSkpKOMdKTEzE+vXrsXv37kZdw/bt27Fp0yYAwLZt29hJ9wMHDuRcX93RQQ8PDwQHB9d7fDMzM9y6dYvdftP16GRd55ucr7HHqU9kZORbLXciKwZZZfX1iYb6niyTJ09GaGgoTp06hb1797Ll9fWB1zXX97Ix1wfUzAF8fQSbENJ26Wqo4VlV0+/8tLblVyjJa0fWrFmDYcOG4eLFiygpKUGnTp04+3/55ReMHTsWvXr1QteuXZGSkoI5c+bIPFZ9ddetW4cxY8agV69eMDMzQ0lJCfsAxLBhwzBu3Djk5OTA0dERP/zwQ5Pm5K1btw4jR47E1atXUVVVheLiYvbYa9asgZeXFy5evIgXL17AwMCAMwrp4uICNTU1JCUlwd7eXubxZ8yYAXd3d6SmpqKoqAidO3duVFx1ybrONzlfY48jT3Z2Nl6+fIk+ffq80XXIi0FWWX19oqG+J8uUKVNgb28PMzMzDBw4kC2vrw+8rrm+l7Xquz6GYRAeHo7o6Oi3OgchpHXQEQrwrLThenX1MNJp/mDeAiV57UifPn1w7949XL9+Hebm5jAyMsKNGzfY/SYmJoiLi0NSUhL++ecf9OvXj53H5eHhAVtb20bVFYvFuH79Om7duoXS0lK4urqyc5U8PT2RkJCAO3fuwMDAAEDDc/KmTZvGzpOys7NDSkoKbty4AR6PB0dHR3ZZEHt7e6SmpuLatWsQi8VISkqSeshh6dKlCAwMxPbt22VeV+/evZGWloaEhAR069YNHTp0QGJiosxYXv9/XbKuc/bs2VJLmDR0PlnHmTp1KkxMTNg6FhYW2LZtm8w4IiIiGrxVW7cN3N3dOceXFYOssvr6REN9T5ZevXrh8OHD6NKlC+c2an19YPLkyWwC2VDb1v1+uLu7w8zMjN02MDBAWFgYu13f9UVGRsLJyQk9evSo95oIIW3Dm7zD1spQC53reT+uMvCY+ibzECm5ubkQi8V48OABzM3N3/p49d2ube+a2jbZ2dlQU1ODWCxGVVUVvL294eDggFWrVrF1GIbB0aNH8f7777dEyArRlHa5du0ajI2NOW/5UGXK+jxdu3YN5ubmnOS4NaGfM/JR28jW3tul+FUl4nKkH2irflkMvpaeVHlHLSGcxB2k5vUqG43kEZWhoaEBLy8vmJqaIjs7G/r6+ggICODU4fF4bTrBaypnZ2dlh9AuUDsTolq0hWrg8YDGDINpqPFhb6rb6hI8gJI8okJMTU1x7do1XL16Fdra2njnnXfqXbqFEEIIkUXA50FLXYDSCkm99Xg8wN5UDxpqgnrrKQsleUSlaGlpYciQIcoOgxBCSBuno6HWYJJn00kbHbWECoqo6ei1ZoQQQgghdTS0KLKRjhDWhrLfmtFaUJJHCCGEEFJHfUken8dDL+OGX1agbJTkEUIIIYTUoaMhf56diZ4GROqtcx7e6yjJI4QQQgipQ0uoBgFf9hOzVh21FBzNm6EkrxUJDAzETz/9hMDAwBY5fnh4OI4fP94ix25p0dHRb/1aqlpr1qxBRkZGsxyLEEKI6tKRccu2s44GtBuYr9daUJLXigQGBmL58uUtluSdP38ely9fbpFjN4f6kq/4+HicO3euWc6ze/du5ObmNsuxCCGEqC5Z8/KsDNvGKB5ASR5pRczMzCASiZQdBiGEEAIA0K0zL09HQw36mm1n/dW2Md7YDlRUVKCkpAQAUFJSgsrKyhZfyPfOnTuIiooCj8fD2LFjOe/djI+PR3R0NMRiMdzc3HDkyBEsWbJE5nEOHDgAfX19VFZW4vLly/D394eNjQ2uXr2KU6dOQSQSwc/Pj30N3IMHD7B3716UlZVhzJgxcHR0BAA8fPgQr169Yo8bGRmJnJwcuLi4SJ1zyZIlCAgIYN9Tevz4cZSXl8PX1xcAsHnzZmRkZEAoFKJv376YMmUKBALpSbLyYiGEEELq3q410WtbAxE0ktcKVFRUYMSIESgoKAAAFBQUYPjw4aisrGyxc546dQpubm548uQJHj16BBcXF1y8eBEAEBUVhREjRqCsrAw3btzAqFGjsGvXLrnHOnPmDGbMmIEtW7bAwMAAIpEIQUFB8PX1xatXr5CdnQ1nZ2fk5OSgqKgITk5OyMzMBJ/Px9y5cxEbGwuAext14cKFWL58Ofh8PjZu3Ijg4GDOOX///Xc8f/6c3b58+TLOnz/Pbnfp0gWWlpYwNDREUFAQJk2aJBV3fbEQQgghr9+u1dFQg4FW2xnFA2gkr1XYtWuXVHIRGxuLXbt2YebMmS1yzmXLlmHFihWYN28eAMDExATfffcdYmNjsXz5cqxbtw6zZs0CACxatAgRERH1Hs/W1hZHjhwBABQXF2PRokWIi4tDnz59AAAGBgZYv349pk6dCkNDQ/zxxx8AgKVLl+Lx48ecYz19+hSbNm3C+fPn4eLiAoZh0L9//yZd3/jx49n/z58/H127dkVOTg4sLS3Z8vT09AZjIYQQ0n6pC/gQqfPxqrIalh01gaqXyg6pSdpkkvfy5Uv88ccf2Lt3L+7fvw9LS0t88cUXmDp1qtyvefLkCaytraXKQ0JCMHbs2BaMtmFZWVkyyzMzM1vkfAzD4Pbt2/Dy8mLLhg8fjjVr1oBhGCQnJ8PDw4PdN2TIkAaTPDc3N/b/d+7cQVVVFXbu3AmGYcAwDNLT0/HixQvY29ujY8eO8PPzg7e3N4YPHw4zMzPOse7evQtjY2N069YNAMDj8eDp6Yny8vJGX2NhYSHCwsKQlZWF8vJy8Pl8ZGZmcpK8xsRCCCGkfdMRqgGogqmeCAUFlOS1uI0bNyI/Px+//fYbLCwscPr0aUyfPh2vXr3C7NmzZX5NdXU1SktLcfXqVfTs2ZMt19TUVFTYcslKPgGwSU5z4/F4EAgEkEj+/518EokEampq4PF4UFNTQ1VVFWdfQzQ0NDjH19DQYOfgAYCFhQWMjY0hFApx8eJFJCQkIDY2FoMGDcJ//vMfTJgwga1b9/wApLb5fD4YhmG3Kyoq2P+XlZXB0dERjo6OcHBwgKamJoRCITvnsVZjYiGEENK+6YrUYKgtBI8ne8281qxNJnmLFy/mbH/00UeIiYlBaGio3CSvlqamJnR0dFoyvCabOnUq9uzZw7llO2TIkHpHJt+Ws7MzDh48yN5ODQsLYx9wcHR0RGRkJOzs7ACgwVG8uvr27QuBQAB3d3c4OTkBqEm8bt++jczMTIhEIjg4OMDBwQHV1dU4cOAAJ7Hq1asXnj9/jps3b2LYsGEoLy/H8ePHMWLECLaOsbEx0tPTYWtri+rqasTGxrKjiWlpaXj58iUOHDgAAMjPz8ePP/4oFWdjYiGEENK+GWiqw6ANPVH7ujaZ5MlSXFwMLa2G164ZNWoUysvLYWNjg7lz52Ly5MkKiK5+QqEQMTEx6NKlCwoKCtCxY0fExMS06NO1a9aswahRo5CUlASJRIK4uDicPn0aALBy5Up4e3vjxo0bKCkpQW5uLvj8xj+jo62tja1bt2L06NEYOnQohEIhrly5gp9//hl6enrw8fGBnZ0dOnTogIiICPz++++cr+/QoQOWL18OPz8/fPDBB7h58yZnpBAApk+fjmnTpsHHxwdJSUmcp3JtbW0hEong5eUFS0tLXLhwQWZiX1lZ2WAshBBC2jcjHY2GK7VSKpHknT9/HpGRkQgNDZVbh8fjYfr06Zg7dy46deqEiIgI+Pv7o7CwEF988YXcrysuLkZxcTG7nZeX16yx11JXV4euri4KCgqgq6vbIgmer68vhEIhAMDFxQUpKSk4e/YseDwetm3bBiMjIwCAu7s7EhMTERsbC7FYjKKiIqxfv17ucSdOnCiVRI0bNw6urq64cOECqqqqsGrVKna+25UrV3Dy5EkUFxdjyZIlsLW1BVAzQmtjYwMACAgIgL29PR48eICvvvoKL1++5CRy3377LZydnZGVlYUFCxbg+fPn7C1bLS0t3Lx5E1FRURAKhVi+fDkuXbqEvn37cs5jbm4uNxZCCCGkreMxr09sUpK///4bvXr1qrfOpEmTsG3bNqnylJQUvPfee5gwYUKTR2ECAgKwf//+et9+8NNPP2H58uVS5UlJSTA1NW3S+WQpLi6Gnp4eAKB///548OABxGIxbt68+dbHflN5eXnQ1tZm45oxYwaMjY3xn//8R6FxvN425P9Ru8hHbSMbtYt81DayUbvI1lraxdDQsFH1WsVInlgsbnDpClkjW/fu3cPQoUPh4+ODoKCgJp/XwcEBGzZsQGlpKbS1tWXWWbBgAWcZk7y8PLi4uMDAwKDRjdyQ2uPUjkjp6+s327HfRElJCby9veHi4oL09HQ8evQIsbGxSolJme3QmlG7yEdtIxu1i3zUNrJRu8jWltqlVSR5PB6vyQ9DpKamwsPDA6NGjcK2bdve6KmXe/fuQU9Pr94nbPX09BSWtX/11VcKOU9DLC0tcebMGZw+fRo+Pj4YOnQoOnTooOywCCGEENIEbfKNF+np6WyCFxwcLPOhgEePHkFHR4ddoHfz5s0IDQ1l524dPnwYv/zyC+bMmdOkhwraiy5duuDjjz+Gr68vJXiEEEJIG9Qms5uNGzciLy8P+/fvh56eHnR0dKCjo8N592rtuni166uNGzcOsbGxsLW1hZ6eHhYvXoyVK1cqfJ4ZIYQQQogitIoHL5qqoqKCs/htLT6fzy6jwjAMSktLoampKfVieolEIvNl9Y2Rm5sLsViMBw8ecBb7fVPPnj1rU/f3FYnaRjZqF/mobWSjdpGP2kY2ahfZ2lq7tIo5eU0lFArZpUDkqW+e35smeIQQQgghbUWbvF1LCCGEEELqR0keIYQQQogKoiSPEEIIIUQFUZJHCCGEEKKCKMkjhBBCCFFBbfLpWmWqXXcvLy+vWY5XWFiIsrKyZjmWqqG2kY3aRT5qG9moXeSjtpGN2kW21tQuXbp0gZpa/WkcJXlNlJ+fDwBwcXFRciSEEEIIaa8as15vm1wMWZlevXqF27dvw8jIqMEMuiF5eXlwcXHB1atXYWJi0kwRqgZqG9moXeSjtpGN2kU+ahvZqF1ka23tQiN5LUAkEsHZ2blZj2liYtIsb89QRdQ2slG7yEdtIxu1i3zUNrJRu8jWltqFHrwghBBCCFFBlOQRQgghhKggSvKUSE9PDz/++CP09PSUHUqrQ20jG7WLfNQ2slG7yEdtIxu1i2xtsV3owQtCCCGEEBVEI3mEEEIIISqIkjxCCCGEEBVESR4hhBBCiAqiJI8QQgghRAXRYshKUlxcjJiYGBQVFeHdd99Fr169lB2SUiQlJSExMRH6+vpwd3dHx44dOfsPHjyIx48fc8p69OgBLy8vRYapUJmZmThx4oRU+fTp06GlpcUpu3TpEu7evQtTU1N4eXlBXV1dUWEqXF5eHg4dOiRz36hRo9CtWzcAwJ9//oni4mLO/v79+2PgwIEtHqMilZSU4MiRI6ioqMCMGTNk1snLy8Pp06fBMAw8PT1hZmb2RnXamvj4eFy/fh3Dhg2DnZ2d1P68vDxcvnwZ5eXlcHR0RI8ePTj7ExIScPnyZU6ZUCjEZ5991qJxt7SCggJERERAJBJh8uTJnH0Mw+C3336T+hoPDw/07t2bU5aTk4PY2FgIhUJ4eXnByMioReNWhLNnzyIlJQU+Pj6wsLDg7AsODpb5vtrXfxddvHgRiYmJnP36+vr4+OOPWyzmxqAkTwnu3r2LoUOHQiwWo2vXrpg/fz6WLl2KpUuXKjs0hXn8+DEmTJiAkpIS9OvXD3///Tc+/fRT7N69G++//z5bb926dQAAJycntqwtPb7+Jm7evImAgADMmjWLUy6RSNj/V1dX46OPPsKZM2cwbNgwXLt2DTo6Ojh79iwMDAwUHbJClJWV4d69e5yy27dv46+//sLNmzfZsh9++AHW1tacP5zayur0jfXNN99gz549MDQ0RF5enswk79ixY/Dz84O7uzsEAgHmzJmD0NBQ+Pr6NqlOW3Lp0iX861//grq6Oq5fv47t27dLJXnz589HZGQkHB0doaamhlmzZmHWrFkIDAxk68TExGDDhg2YOHEiW6ahoaGoy2gR/v7+iImJgZ6eHtTU1KSSPIlEgrlz52Ls2LGcRP+dd97h1Pvzzz8xZ84ceHl5oaSkBJ9//jkiIyMxePBgRVxGszt+/DgWLFgAfX19XLlyBTY2NlJJXnp6Ol68eMFuv3jxArt27cJPP/3EJnkHDx5EREQEvL292XqtIvlliMINGjSIGT16NFNdXc0wDMMcOnSI4fP5THJyspIjU5zc3FzmwoULnLJvv/2W0dfXZyQSCVs2YMAAZsWKFYoOT6kOHDjAdOjQod46oaGhjIaGBpOamsowDMMUFRUx3bp1Y+bNm6eACFuPMWPGME5OTpyyrl27Mlu3blVSRIoREhLCFBUVMb/88gtjaGgotb+srIzp3Lkzs3jxYrbs+++/Zzp27Mi8ePGi0XXamri4OCYhIYFhGIYRCATMjh07pOocOnSIqaioYLcvXbrEAGDOnDnDlq1atYpxdHRs8XgVaceOHczLly+ZRYsWMb1795baX1lZyQBgzp07J/cYT548YbS0tJjAwEC2bNasWYyVlRXn53ZbcvbsWebu3btMfn4+A4A5ceJEg1+zbds2hs/nM/fv32fL5s+fz3h7e7dkqG+E5uQpWF5eHi5cuIA5c+aAx+MBAHx9fdGlSxccOHBAydEpjpmZGdzd3Tllw4YNw/Pnz6Vuz6ampmLr1q04duwYCgoKFBmm0lRVVWHPnj0ICQnBrVu3pPbv378fnp6e6N69O4Ca0c1PPvkE+/fvV3SoSvPo0SOcOHFCasQTqBkN3bZtG06ePImSkhIlRNeyPvroo3pHtM+dO4cnT57giy++YMs+//xzFBYW4tSpU42u09a4urqif//+9dYZN24cZ1qDm5sbNDU1pUaJi4uLsWvXLuzbtw/p6ektEq8i+fv7Q1NTs8F6f/31F4KDg3H27FlUVFRw9h09ehQSiYQzcvzll18iOzsbV69ebfaYFcHDw0PmLf36bNu2DSNGjJAa8cvPz8f27dtx8OBB3L9/vznDfGOU5ClYSkoKAKBnz55sGY/HQ48ePdh97VVkZCRMTExgamrKKU9OTsalS5fwww8/wNbWFlFRUUqKUHGEQiHCw8MRHh4ONzc3TJw4EVVVVez+lJQUTh8CavrUP//8024S4Z07d8qcW8Tj8XDjxg1cvHgRCxYsgJ2dHS5cuKCkKJUjJSUF2traEIvFbJmJiQn09fXZnzONqdMenD59GmVlZXB0dOSUv3jxAjExMQgJCUGfPn0QEBCgpAgVh8/n4/z58zh//jymT5+Ofv36ITU1ld2fkpICc3Nz6OjosGW1P4faS59JSUlBfHy8zD8u//nnH5w7dw5btmxB9+7dsXr1aiVEyEVz8hSsdlRBX1+fU25gYCA1Wbw9iYyMxO+//449e/ZwygMDA+Hq6spuBwQE4NNPP0VGRgYMDQ0VHaZC9OvXDxkZGexDKGlpaXB0dMT69euxaNEiADX9SFYfAmpGIOo+wKJqGIbBjh07MGnSJOjq6nL27du3DwMGDABQM3dx6tSpmDJlCjIyMtr8vKrGktU/AO7PmcbUUXV5eXmYNm0aJk2axPYZoOZBnq+++goikQgAEBsbC09PT7i5ueHDDz9UVrgtis/nIz4+Hs7OzgCAV69eYfjw4fD390dcXBwA2X1GKBRCS0ur3fSZ4OBgGBsbc+aOA8Ann3yCdevWQU2tJq0KCwvDpEmT4O7uLnXXSpFoJE/BaofL695CKi4ulnpysr04c+YM/Pz8sGrVKvj5+XH2vZ7gATUTzp8/f44rV64oMkSFsrW15SRp3bt3h4+PD06ePMmWaWpqyuxDANpFPzp//jwyMjJk/jX9+i9rPp+PBQsWIDc3F3fu3FFkiEolq38A3J8zjamjyvLz8+Hl5YXu3btjx44dnH39+vVjEzwAGDJkCJycnDifQVXD5/PZBA8ARCIR5s6di/j4ePZni6w+U1VVhbKysnbRZyorK7F7925MmzaNTeZq1T7IU2vixIkQi8WIiYlRdJgclOQpmK2tLQAgOzubU56dnc3ua0/Onj0LHx8f/Pjjj1i4cGGD9WtHYp4/f97CkbUuGhoanGu2tbWV6kNZWVnQ09ND586dFRyd4gUHB6Nv376chE6e9thnbG1tUVxcjGfPnrFlRUVFKCgoYH/ONKaOqnr69Ck8PT3RuXNnREVFcRI6eep+BtuDup8dW1tbPHz4kDNXLzs7GwzDqHyfAWruOD19+lTukkV1tYY+Q0megllbW6N3794ICQlhyy5fvozMzEz4+PgoMTLFi42Nxfvvv4/vvvsOixcvltr/7NkzqfllISEh4PP5ePfddxUVpsKlpaVxtgsKCnD8+HHOkL+Pjw+io6Px9OlTADV/Te/du7dd9KGioiIcOnRI5ijeo0ePUFpayikLCQmBpqYmHBwcFBWi0nl6ekJbWxuhoaFsWWhoKIRCIYYPH97oOqro2bNnGDp0KDp16oSjR4/KHIGq+xlMS0vDtWvXlHrbraVlZ2ejsrKSUxYaGgoLCwt23ubo0aPx6tUrHDlyhK2ze/duGBoaqtw6lLIEBwfDw8MDNjY2Uvvq9pn4+HhkZmYqvc/QnDwl+O233zBixAhUVlbCysoKW7Zsgb+/f7v4kNRKT0/HmDFjYGtrC11dXWzevJnd5+fnByMjIzx//hxjxozBkCFDYGVlhVu3buHgwYNYvXo1rKyslBh9y1q3bh3+/vtvuLm5oaKiArt374aJiQm+//57ts6sWbMQEhKCIUOGwM/PD+fPn8ejR48QHh6uxMgVY8+ePWAYRuYiow8fPsSwYcMwbNgwmJmZIT4+HtHR0QgKCpI5/6ytOnbsGLKzs3Hx4kW8evWK/fxMmTIFHTt2RIcOHbB27Vp8/fXXyMrKgkAgQFBQEFatWsXOZW1Mnbbm4cOH7GeAYRicOXMGL168QM+ePeHp6QmgZr5dVlYWPv74Y2zfvp39WmdnZ3Zk+Msvv0SHDh3g4OCAgoICbN++HYMHD27TiyGHhYXhyZMnSEhIQEFBAdtnZs6cCZFIhMTERIwdOxZeXl4wNDRETEwMbt26hX379rErQVhaWuK7777DzJkzcePGDbx48QJbt27Fzp07IRQKlXl5byw9PR0nT55k/ziMiopCRkYGHB0dOYMJubm5OHnyJOePotdNnDgRffr0QZ8+ffDo0SNs374dfn5+GD9+vEKuQx4ewzCMUiNop9LS0rB//34UFxfj3Xffha+vL/tBag9SU1OxadMmmfsWLlzIPppeXFyMsLAwpKWlwdTUFKNGjZJanV4V/fXXXzh37hwYhoG9vT3Gjh0LPp878F5eXo7du3ezb7z49NNPW8fimy0sKCgIADhLf7zu6dOnOHDgAHJyciAWi2WuYN/W/e9//0NycrJU+bJly2BiYsJux8XFISoqCgzDwNvbW+aoQmPqtBXp6en49ddfpcpdXV3ZPwoCAgJQXl4uVWfkyJEYM2YMgJoE8fjx44iLi4OmpiacnZ3b/Ojm2rVrZS7rsWbNGmhrawOoeZPFkSNH8PjxY1hbW2P8+PEyH+I6c+YMYmJiIBQK4evr26ZHyRMSEjjJfq3hw4dz7oxcunQJ+/fvx9q1a2U+wCWRSHDkyBHcuHEDHTp0wMCBA1vFZ4mSPEIIIYQQFURz8gghhBBCVBAleYQQQgghKoiSPEIIIYQQFURJHiGEEEKICqIkjxBCCCFEBVGSRwghhBCigijJI4QQQghRQZTkEUIIIYSoIHqtGSGkXcnOzsaGDRuQnJyMrl274ocffoC1tXWLnrP29WMAMGDAAOjq6sqtm5ubi6ysLLz33nstEsu5c+cgkUgAAAMHDoSmpmaLnIcQonz0xgtCSLtx+fJljB49GlOnTsWAAQOwefNm5OXl4e7duxCJRC12XnNzc2hpacHCwgJBQUHo3r273LqbN2/GTz/9hKdPn7ZILKNHj8bTp09x7do1pKeny3zZOiFENdBIHiGkXSgqKsKECRPwzTff4LvvvgNQ807Tbt264fTp0+x7S1vKZ599hm+++aZFz9EYx48fR3x8POfl64QQ1URJHiGkXdi4cSMkEgkn0bK2toZAIEBaWprS4qqsrMTNmzdRXV0Ne3t7ufWqq6uRnJyMoqIi9OzZE506dZKqI5FIcOvWLZSVlaFv374oLy/HrVu3MHToUPD5NAWbkPaGkjxCSLuwY8cO+Pn5cW7LSiQSVFdXKy0BunXrFnx8fFBRUQEzMzPk5eXB2dlZqt7p06cxY8YMVFRUwNzcHCkpKVi2bBmWLl3K1snMzIS3tzeePn0Ka2tr3L9/Hx4eHti/fz/Kyspa9HY0IaR1oiSPEKLy0tLSkJ2djdjYWIwcOZItl0gkYBgG5ubmCo+puroan3zyCfr374+wsDAIhUIkJCRg4MCB0NbWZutlZmbigw8+wIcffojg4GCoq6sjPj4e7733HgYNGoRBgwYBqLkd3LlzZ9y4cQPa2trIzs6mW7KEtHM0fk8IUXkJCQkAAC8vL7zzzjvsPy0tLQDAO++8o/CYrly5gtu3b+Pnn3+GUCgEADg4OGDy5MmcesHBwZBIJNi4cSPU1dUB1Mwl9PX1xcaNGwEA9+/fx9mzZ7Fs2TI2QbSyssKcOXMUeEWEkNaGRvIIISovJycH2traWLduHac8ICAAlpaWSnnCNDMzEwDQo0cPTrmdnR0iIyPZ7dTUVJiamuL69eucehoaGkhMTAQAZGVlAYDUU7t1j00IaV8oySOEqLyqqiqp9eCqqqoQFhaGGTNmcMqDg4Px22+/IT09HdXV1cjPz8euXbtw5coVvHz5EqdPn8YHH3yAf//73/jss89w9epV+Pv7Y/369U2KSV9fHwBQXFwMQ0NDtry4uJhTT1tbG8+fP8fq1auljmFnZ8c5VklJCWd/UVFRk2IihKgWSvIIISrP0tIShYWFKCoqQocOHQAAQUFBKC8vx/z589l6K1euRHh4OLZs2YJevXpBIBBAQ0MD165dw9WrV7Fr1y6sWbMGTk5OKCwsxNq1a1FeXo53330XP//8c5MWFnZ0dISGhgYiIiIwffp0AADDMIiIiODUGzlyJE6cOIHDhw9DT0+Ps6+wsBBATbJnYGCAY8eOcZ7QjYqKalpDEUJUCiV5hBCV5+3tDT09PcyaNQvz5s3DX3/9hdWrVyM8PBwGBgYAakbQVq9ejZSUFIjFYs7XJyQkIDAwkH3yVUtLC//+97/Rp08fVFZWQigUsvPlGsvExATffPMN5s2bh2fPnsHa2hq7du3C/fv32Tl6ADBp0iQcPnwYAwcOxBdffAELCws8ePAAUVFRcHJywvLly6GpqYkVK1ZgwYIFqKiogL29PY4ePYoLFy4AAHg83ts0HyGkjaIkjxCi8gwMDBAVFYXFixfj448/Rr9+/RAbGwsHBwe2zu3bt2FlZSWV4JWXlyMjIwODBw8GADx58gSVlZXsiFlycjLs7Oygptb0H6crVqyAjY0NIiIicOnSJQwbNgz+/v4IDQ1l6/D5fBw4cACHDh3C0aNHcfToUVhaWmLevHkYMWIEW+/LL7+EsbEx9u/fj+vXr8PV1RVr1qzBvHnzoKGh0eTYCCFtHyV5hJB2YeDAgezIlizGxsbIycnB7du30bdvX7b89u3bsLOzYxOla9euwcXFhd1//fp1ODk5NXj+9PR0nD59mvPuWh6PB39/f/j7+3Pqjhs3jrPN4/Ewfvx4jB8/Xu7xS0tLpepMmTKFk8gCNe+uTU5ObjBeQkjbR0keIYQAsLGxwZIlSzBkyBCUlJSAz+fjzp07SEhI4CxQLCvJe31blkGDBiEzMxOrV69GUFAQm+Q1p7i4OAQGBmLChAnQ1tbGsWPHcOjQIRw+fJhTb+3ataioqICnpye7hAwhRDXxGIZhlB0EIYS0JuXl5WAYBiKRCBKJBAAgEAgA1DyVy+Px2O3KykoIBIJW8dqw6OhoHDp0CPn5+bC2tsbMmTPRq1cvZYdFCFESSvIIIYQQQlSQ8v/0JIQQQgghzY6SPEIIIYQQFURJHiGEEEKICqIkjxBCCCFEBVGSRwghhBCigijJI4QQQghRQZTkEUIIIYSoIEryCCGEEEJUECV5hBBCCCEqiJI8QgghhBAVREkeIYQQQogK+j82Wz9PjuGRXAAAAABJRU5ErkJggg==", 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", "text/plain": [ "<Figure size 704x440 with 1 Axes>" ] @@ -1068,25 +1279,18 @@ }, { "cell_type": "markdown", - "id": "f23a966b", + "id": "67bd0f36", "metadata": {}, "source": [ "## Takeaways\n", "\n", - "- A mean-zero GP discrepancy is just another covariance term, and\n", - " `total_predictive_band` propagates it to any grid from the problem alone.\n", - "- An unmodelled discrepancy does not make the answer vague, it makes it *wrong\n", - " and confident*: sixty sigma on the toy's slope.\n", - "- A diagonal model error can only inflate. It covers the truth by making the\n", - " parameters meaningless, because independent slack cannot describe a coherent,\n", - " smooth departure.\n", - "- The amplitude is prior knowledge, and it is worth stating. A constant\n", - " amplitude has to be generous everywhere to cover the worst region; letting it\n", - " grow with $x$ keeps the fit honest where the model is good and forgiving where\n", - " it is not.\n", - "- None of this manufactures information. Where the data cannot separate two\n", - " effects — volume against surface absorption at a single energy — the\n", - " discrepancy model widens the answer rather than pretending to resolve it." + "- A mean-zero GP discrepancy is just another covariance term. `grid_draws` can propagate it onto any grid from the problem alone, and it does so as whole correlated curves rather than columns of independent points, so questions about the curve as a whole make sense.\n", + "- We met two predictive objects, and they answer different questions. The model plus its discrepancy tells us where the model may be wrong. Adding the experimental terms tells us what a *measurement* should look like, and only that second one belongs next to data. `terms=` is how we pick. There's also a third, conditioning the discrepancy on the residuals with `gp_predictive_draws(..., conditioned=True)`, but that's data-driven regression stacked on the model, and we deliberately didn't use it here.\n", + "- The envelope isn't a fit to the defect, and nothing guarantees it covers the defect. It contained the toy's defect over 72 % of the grid, but the cross section's over only 59 %, where the fitted potential had already absorbed part of the missing physics into $W_v$. A mean-zero predictive describes plausible error, and it can be too narrow as well as too generous.\n", + "- An unmodelled discrepancy doesn't make our answer vague; it makes it *wrong and confident*. On the toy that meant a slope sixty sigma from the truth.\n", + "- A diagonal model error can only inflate. It covers the truth by making the parameters meaningless, because independent slack at each point can't describe a coherent, smooth departure.\n", + "- The amplitude is prior knowledge, and it's worth stating. A constant amplitude has to be generous everywhere to cover the worst region. Letting it grow with $x$ keeps the fit honest where the model is good and forgiving where it isn't.\n", + "- None of this creates information. Where the data can't separate two effects, like volume and surface absorption at a single energy, the discrepancy model widens the answer rather than pretending to resolve it." ] } ], diff --git a/examples/hierarchical_calibration.ipynb b/examples/hierarchical_calibration.ipynb index fd4cdb0..c919c5c 100644 --- a/examples/hierarchical_calibration.ipynb +++ b/examples/hierarchical_calibration.ipynb @@ -28,10 +28,10 @@ "id": "b449277b", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:17:05.381735Z", - "iopub.status.busy": "2026-09-12T04:17:05.381605Z", - "iopub.status.idle": "2026-09-12T04:17:07.507015Z", - "shell.execute_reply": "2026-09-12T04:17:07.506340Z" + "iopub.execute_input": "2026-09-14T19:31:41.422175Z", + "iopub.status.busy": "2026-09-14T19:31:41.421952Z", + "iopub.status.idle": "2026-09-14T19:31:43.977094Z", + "shell.execute_reply": "2026-09-14T19:31:43.976197Z" } }, "outputs": [], @@ -86,10 +86,10 @@ "id": "b9a36078", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:17:07.508648Z", - "iopub.status.busy": "2026-09-12T04:17:07.508412Z", - "iopub.status.idle": "2026-09-12T04:17:07.518349Z", - "shell.execute_reply": "2026-09-12T04:17:07.517669Z" + "iopub.execute_input": "2026-09-14T19:31:43.979569Z", + "iopub.status.busy": "2026-09-14T19:31:43.979143Z", + "iopub.status.idle": "2026-09-14T19:31:43.997247Z", + "shell.execute_reply": "2026-09-14T19:31:43.996399Z" } }, "outputs": [ @@ -125,10 +125,10 @@ "id": "b4c01254", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:17:07.520037Z", - "iopub.status.busy": "2026-09-12T04:17:07.519872Z", - "iopub.status.idle": "2026-09-12T04:17:18.092134Z", - "shell.execute_reply": "2026-09-12T04:17:18.091344Z" + "iopub.execute_input": "2026-09-14T19:31:43.999533Z", + "iopub.status.busy": "2026-09-14T19:31:43.999244Z", + "iopub.status.idle": "2026-09-14T19:31:58.426628Z", + "shell.execute_reply": "2026-09-14T19:31:58.425918Z" } }, "outputs": [ @@ -169,10 +169,10 @@ "id": "f019a0e5", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:17:18.093682Z", - "iopub.status.busy": "2026-09-12T04:17:18.093518Z", - "iopub.status.idle": "2026-09-12T04:17:19.086304Z", - "shell.execute_reply": "2026-09-12T04:17:19.085566Z" + "iopub.execute_input": "2026-09-14T19:31:58.429432Z", + "iopub.status.busy": "2026-09-14T19:31:58.429203Z", + "iopub.status.idle": "2026-09-14T19:31:59.709710Z", + "shell.execute_reply": "2026-09-14T19:31:59.708806Z" } }, "outputs": [ @@ -209,10 +209,10 @@ "id": "ec0f35b1", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:17:19.087793Z", - "iopub.status.busy": "2026-09-12T04:17:19.087640Z", - "iopub.status.idle": "2026-09-12T04:17:19.297574Z", - "shell.execute_reply": "2026-09-12T04:17:19.296794Z" + "iopub.execute_input": "2026-09-14T19:31:59.712253Z", + "iopub.status.busy": "2026-09-14T19:31:59.712008Z", + "iopub.status.idle": "2026-09-14T19:31:59.975218Z", + "shell.execute_reply": "2026-09-14T19:31:59.974106Z" } }, "outputs": [ @@ -289,10 +289,10 @@ "id": "77630c43", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:17:19.299414Z", - "iopub.status.busy": "2026-09-12T04:17:19.299173Z", - "iopub.status.idle": "2026-09-12T04:17:19.306607Z", - "shell.execute_reply": "2026-09-12T04:17:19.305825Z" + "iopub.execute_input": "2026-09-14T19:31:59.977412Z", + "iopub.status.busy": "2026-09-14T19:31:59.977179Z", + "iopub.status.idle": "2026-09-14T19:31:59.984398Z", + "shell.execute_reply": "2026-09-14T19:31:59.983508Z" } }, "outputs": [], @@ -355,10 +355,10 @@ "id": "86c7868f", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:17:19.308160Z", - "iopub.status.busy": "2026-09-12T04:17:19.308013Z", - "iopub.status.idle": "2026-09-12T04:17:19.446710Z", - "shell.execute_reply": "2026-09-12T04:17:19.446045Z" + "iopub.execute_input": "2026-09-14T19:31:59.986670Z", + "iopub.status.busy": "2026-09-14T19:31:59.986449Z", + "iopub.status.idle": "2026-09-14T19:32:00.185194Z", + "shell.execute_reply": "2026-09-14T19:32:00.184222Z" } }, "outputs": [ @@ -434,10 +434,10 @@ "id": "943e52d1", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:17:19.449166Z", - "iopub.status.busy": "2026-09-12T04:17:19.448988Z", - "iopub.status.idle": "2026-09-12T04:17:19.453663Z", - "shell.execute_reply": "2026-09-12T04:17:19.453067Z" + "iopub.execute_input": "2026-09-14T19:32:00.188334Z", + "iopub.status.busy": "2026-09-14T19:32:00.188000Z", + "iopub.status.idle": "2026-09-14T19:32:00.196138Z", + "shell.execute_reply": "2026-09-14T19:32:00.195220Z" } }, "outputs": [], @@ -470,10 +470,10 @@ "id": "dbdb2f5e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:17:19.455871Z", - "iopub.status.busy": "2026-09-12T04:17:19.455692Z", - "iopub.status.idle": "2026-09-12T04:17:19.461695Z", - "shell.execute_reply": "2026-09-12T04:17:19.461073Z" + "iopub.execute_input": "2026-09-14T19:32:00.198934Z", + "iopub.status.busy": "2026-09-14T19:32:00.198610Z", + "iopub.status.idle": "2026-09-14T19:32:00.210725Z", + "shell.execute_reply": "2026-09-14T19:32:00.209698Z" } }, "outputs": [], @@ -534,10 +534,10 @@ "id": "c94dbbec", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:17:19.463655Z", - "iopub.status.busy": "2026-09-12T04:17:19.463485Z", - "iopub.status.idle": "2026-09-12T04:17:19.499134Z", - "shell.execute_reply": "2026-09-12T04:17:19.498386Z" + "iopub.execute_input": "2026-09-14T19:32:00.213564Z", + "iopub.status.busy": "2026-09-14T19:32:00.213228Z", + "iopub.status.idle": "2026-09-14T19:32:00.289857Z", + "shell.execute_reply": "2026-09-14T19:32:00.288714Z" } }, "outputs": [ @@ -571,10 +571,10 @@ "id": "ce3ad648", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:17:19.501463Z", - "iopub.status.busy": "2026-09-12T04:17:19.501268Z", - "iopub.status.idle": "2026-09-12T04:29:28.300514Z", - "shell.execute_reply": "2026-09-12T04:29:28.299761Z" + "iopub.execute_input": "2026-09-14T19:32:00.292681Z", + "iopub.status.busy": "2026-09-14T19:32:00.292357Z", + "iopub.status.idle": "2026-09-14T19:51:23.307593Z", + "shell.execute_reply": "2026-09-14T19:51:23.306693Z" } }, "outputs": [ @@ -626,10 +626,10 @@ "id": "cdceaa49", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:29:28.301808Z", - "iopub.status.busy": "2026-09-12T04:29:28.301660Z", - "iopub.status.idle": "2026-09-12T04:29:28.305494Z", - "shell.execute_reply": "2026-09-12T04:29:28.304637Z" + "iopub.execute_input": "2026-09-14T19:51:23.310510Z", + "iopub.status.busy": "2026-09-14T19:51:23.310256Z", + "iopub.status.idle": "2026-09-14T19:51:23.315775Z", + "shell.execute_reply": "2026-09-14T19:51:23.314712Z" } }, "outputs": [], @@ -659,10 +659,10 @@ "id": "c6c8c542", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:29:28.306822Z", - "iopub.status.busy": "2026-09-12T04:29:28.306689Z", - "iopub.status.idle": "2026-09-12T04:29:29.194981Z", - "shell.execute_reply": "2026-09-12T04:29:29.194311Z" + "iopub.execute_input": "2026-09-14T19:51:23.318341Z", + "iopub.status.busy": "2026-09-14T19:51:23.318089Z", + "iopub.status.idle": "2026-09-14T19:51:24.636637Z", + "shell.execute_reply": "2026-09-14T19:51:24.635468Z" } }, "outputs": [ @@ -718,16 +718,21 @@ "id": "e2b80ed8", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:29:29.196610Z", - "iopub.status.busy": "2026-09-12T04:29:29.196467Z", - "iopub.status.idle": "2026-09-12T04:29:29.200326Z", - "shell.execute_reply": "2026-09-12T04:29:29.199661Z" + "iopub.execute_input": "2026-09-14T19:51:24.639460Z", + "iopub.status.busy": "2026-09-14T19:51:24.639218Z", + "iopub.status.idle": "2026-09-14T19:51:24.645519Z", + "shell.execute_reply": "2026-09-14T19:51:24.644428Z" } }, "outputs": [], "source": [ "def case_curves(name, E, rows):\n", - " \"\"\"y(x; E) over posterior rows, including this dataset's deviation.\"\"\"\n", + " \"\"\"y(x; E) over posterior rows, including this dataset's deviation.\n", + "\n", + " Pushed through by hand rather than with grid_draws: case 3 at the held-out\n", + " energy needs a deviation eta for a dataset that has no column in the chain,\n", + " and the reported point-by-point errors could not go on x_fine anyway.\n", + " \"\"\"\n", " p, _ = cases[name]\n", " n = n_phi[name]\n", " out = []\n", @@ -750,16 +755,16 @@ "id": "b9f2f5e2", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:29:29.201694Z", - "iopub.status.busy": "2026-09-12T04:29:29.201536Z", - "iopub.status.idle": "2026-09-12T04:29:29.427861Z", - "shell.execute_reply": "2026-09-12T04:29:29.427418Z" + "iopub.execute_input": "2026-09-14T19:51:24.648524Z", + "iopub.status.busy": "2026-09-14T19:51:24.648269Z", + "iopub.status.idle": "2026-09-14T19:51:25.001112Z", + "shell.execute_reply": "2026-09-14T19:51:24.999768Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 726x616 with 1 Axes>" ] @@ -798,7 +803,7 @@ "ax.set(\n", " xlabel=\"$x$\",\n", " ylabel=f\"$y$ (offset by {offset_step} per dataset)\",\n", - " title=\"Predictive draws after calibration\",\n", + " title=\"What each calibration's model predicts (68 %)\",\n", " xlim=(-1.1, 1.5),\n", ")\n", "ax.legend(fontsize=8, loc=\"upper left\")\n", @@ -826,10 +831,10 @@ "id": "8022b6e5", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:29:29.429622Z", - "iopub.status.busy": "2026-09-12T04:29:29.429486Z", - "iopub.status.idle": "2026-09-12T04:29:30.714005Z", - "shell.execute_reply": "2026-09-12T04:29:30.713185Z" + "iopub.execute_input": "2026-09-14T19:51:25.003794Z", + "iopub.status.busy": "2026-09-14T19:51:25.003445Z", + "iopub.status.idle": "2026-09-14T19:51:27.018655Z", + "shell.execute_reply": "2026-09-14T19:51:27.017705Z" } }, "outputs": [ @@ -850,8 +855,12 @@ "for name, (p, c) in cases.items():\n", " s = samples[name]\n", " held = rx.Problem([c.complement()])\n", - " draws_in = rx.diagnostics.predictive_draws(p, s[::10], n_rep=2, rng=1)\n", - " draws_out = rx.diagnostics.predictive_draws(held, s[::10], n_rep=2, rng=2)\n", + " draws_in = rx.diagnostics.predictive_draws(\n", + " p, s[::10], n_rep=2, rng=1, return_draws=True\n", + " )\n", + " draws_out = rx.diagnostics.predictive_draws(\n", + " held, s[::10], n_rep=2, rng=2, return_draws=True\n", + " )\n", " ci, ch = p.constraints[0], held.constraints[0]\n", " curves_cov[name] = (\n", " rx.diagnostics.coverage_curve(draws_in, ci.y[ci.active], levels),\n", @@ -902,10 +911,10 @@ "id": "86d573ec", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:29:30.715465Z", - "iopub.status.busy": "2026-09-12T04:29:30.715292Z", - "iopub.status.idle": "2026-09-12T04:29:30.914371Z", - "shell.execute_reply": "2026-09-12T04:29:30.913754Z" + "iopub.execute_input": "2026-09-14T19:51:27.022038Z", + "iopub.status.busy": "2026-09-14T19:51:27.021780Z", + "iopub.status.idle": "2026-09-14T19:51:27.340526Z", + "shell.execute_reply": "2026-09-14T19:51:27.339428Z" } }, "outputs": [ @@ -957,10 +966,10 @@ "id": "b93c1029", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:29:30.915932Z", - "iopub.status.busy": "2026-09-12T04:29:30.915792Z", - "iopub.status.idle": "2026-09-12T04:29:31.255261Z", - "shell.execute_reply": "2026-09-12T04:29:31.254575Z" + "iopub.execute_input": "2026-09-14T19:51:27.344579Z", + "iopub.status.busy": "2026-09-14T19:51:27.344314Z", + "iopub.status.idle": "2026-09-14T19:51:27.879932Z", + "shell.execute_reply": "2026-09-14T19:51:27.879002Z" } }, "outputs": [ diff --git a/examples/local_optical_model_calibration.ipynb b/examples/local_optical_model_calibration.ipynb index f6c435e..70ef4cf 100644 --- a/examples/local_optical_model_calibration.ipynb +++ b/examples/local_optical_model_calibration.ipynb @@ -28,10 +28,10 @@ "id": "07735fcd", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:34:41.828482Z", - "iopub.status.busy": "2026-09-12T03:34:41.828290Z", - "iopub.status.idle": "2026-09-12T03:34:44.727413Z", - "shell.execute_reply": "2026-09-12T03:34:44.725855Z" + "iopub.execute_input": "2026-09-14T19:31:41.403651Z", + "iopub.status.busy": "2026-09-14T19:31:41.403500Z", + "iopub.status.idle": "2026-09-14T19:31:44.121021Z", + "shell.execute_reply": "2026-09-14T19:31:44.120227Z" } }, "outputs": [], @@ -94,10 +94,10 @@ "id": "0b60b388", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:34:44.730263Z", - "iopub.status.busy": "2026-09-12T03:34:44.729823Z", - "iopub.status.idle": "2026-09-12T03:34:44.743145Z", - "shell.execute_reply": "2026-09-12T03:34:44.741096Z" + "iopub.execute_input": "2026-09-14T19:31:44.123468Z", + "iopub.status.busy": "2026-09-14T19:31:44.123041Z", + "iopub.status.idle": "2026-09-14T19:31:44.132661Z", + "shell.execute_reply": "2026-09-14T19:31:44.131820Z" } }, "outputs": [ @@ -171,10 +171,10 @@ "id": "ae6b43af", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:34:44.745908Z", - "iopub.status.busy": "2026-09-12T03:34:44.745604Z", - "iopub.status.idle": "2026-09-12T03:34:44.798226Z", - "shell.execute_reply": "2026-09-12T03:34:44.797430Z" + "iopub.execute_input": "2026-09-14T19:31:44.134760Z", + "iopub.status.busy": "2026-09-14T19:31:44.134550Z", + "iopub.status.idle": "2026-09-14T19:31:44.163967Z", + "shell.execute_reply": "2026-09-14T19:31:44.163195Z" } }, "outputs": [ @@ -205,10 +205,10 @@ "id": "4c4955a3", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:34:44.800485Z", - "iopub.status.busy": "2026-09-12T03:34:44.800275Z", - "iopub.status.idle": "2026-09-12T03:34:46.215229Z", - "shell.execute_reply": "2026-09-12T03:34:46.214435Z" + "iopub.execute_input": "2026-09-14T19:31:44.166152Z", + "iopub.status.busy": "2026-09-14T19:31:44.165931Z", + "iopub.status.idle": "2026-09-14T19:31:45.554998Z", + "shell.execute_reply": "2026-09-14T19:31:45.554208Z" } }, "outputs": [ @@ -268,10 +268,10 @@ "id": "22704ba5", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:34:46.217866Z", - "iopub.status.busy": "2026-09-12T03:34:46.217565Z", - "iopub.status.idle": "2026-09-12T03:34:46.223882Z", - "shell.execute_reply": "2026-09-12T03:34:46.222965Z" + "iopub.execute_input": "2026-09-14T19:31:45.557450Z", + "iopub.status.busy": "2026-09-14T19:31:45.557258Z", + "iopub.status.idle": "2026-09-14T19:31:45.561691Z", + "shell.execute_reply": "2026-09-14T19:31:45.560809Z" } }, "outputs": [], @@ -305,10 +305,10 @@ "id": "5edb669b", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:34:46.226108Z", - "iopub.status.busy": "2026-09-12T03:34:46.225817Z", - "iopub.status.idle": "2026-09-12T03:35:00.562851Z", - "shell.execute_reply": "2026-09-12T03:35:00.561960Z" + "iopub.execute_input": "2026-09-14T19:31:45.563543Z", + "iopub.status.busy": "2026-09-14T19:31:45.563281Z", + "iopub.status.idle": "2026-09-14T19:32:01.048929Z", + "shell.execute_reply": "2026-09-14T19:32:01.047973Z" } }, "outputs": [ @@ -377,10 +377,10 @@ "id": "b0ceec5d", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:35:00.564669Z", - "iopub.status.busy": "2026-09-12T03:35:00.564474Z", - "iopub.status.idle": "2026-09-12T03:35:00.577147Z", - "shell.execute_reply": "2026-09-12T03:35:00.576094Z" + "iopub.execute_input": "2026-09-14T19:32:01.052334Z", + "iopub.status.busy": "2026-09-14T19:32:01.051955Z", + "iopub.status.idle": "2026-09-14T19:32:01.073021Z", + "shell.execute_reply": "2026-09-14T19:32:01.071975Z" } }, "outputs": [ @@ -401,15 +401,22 @@ "log_delta = rx.Parameter(\n", " \"log_delta\", prior=stats.norm(np.log(0.05), 1.0), latex=r\"\\log\\delta\"\n", ")\n", + "norm_term = T.normalization(parameter=log_eta)\n", + "model_error_term = T.model_error(log_delta, averaging=True)\n", "problems = {\n", " \"quoted errors only\": rx.Problem([rx.Constraint([comp])]),\n", - " \"inferred normalisation\": rx.Problem(\n", - " [rx.Constraint([comp], terms=[T.normalization(parameter=log_eta)])]\n", - " ),\n", + " \"inferred normalisation\": rx.Problem([rx.Constraint([comp], terms=[norm_term])]),\n", " \"inferred model error\": rx.Problem(\n", - " [rx.Constraint([comp], terms=[T.model_error(log_delta, averaging=True)])]\n", + " [rx.Constraint([comp], terms=[model_error_term])]\n", " ),\n", "}\n", + "# the inferred terms of each error model: functions of the prediction, so they\n", + "# are defined on any angular grid, unlike the quoted per-point statistical errors\n", + "inferred_terms = {\n", + " \"quoted errors only\": [],\n", + " \"inferred normalisation\": [norm_term],\n", + " \"inferred model error\": [model_error_term],\n", + "}\n", "for name, p in problems.items():\n", " print(f\"{name:24s} {p.ndim} columns: {p.names}\")" ] @@ -435,10 +442,10 @@ "id": "a4d96073", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:35:00.579141Z", - "iopub.status.busy": "2026-09-12T03:35:00.578936Z", - "iopub.status.idle": "2026-09-12T03:59:58.936578Z", - "shell.execute_reply": "2026-09-12T03:59:58.935853Z" + "iopub.execute_input": "2026-09-14T19:32:01.077298Z", + "iopub.status.busy": "2026-09-14T19:32:01.076881Z", + "iopub.status.idle": "2026-09-14T20:00:28.831618Z", + "shell.execute_reply": "2026-09-14T20:00:28.830732Z" } }, "outputs": [ @@ -504,10 +511,10 @@ "id": "344252d8", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:59:58.938263Z", - "iopub.status.busy": "2026-09-12T03:59:58.938083Z", - "iopub.status.idle": "2026-09-12T03:59:58.956230Z", - "shell.execute_reply": "2026-09-12T03:59:58.955527Z" + "iopub.execute_input": "2026-09-14T20:00:28.833805Z", + "iopub.status.busy": "2026-09-14T20:00:28.833517Z", + "iopub.status.idle": "2026-09-14T20:00:28.864306Z", + "shell.execute_reply": "2026-09-14T20:00:28.863445Z" } }, "outputs": [ @@ -563,22 +570,40 @@ "$\\pm 2.3$ MeV." ] }, + { + "cell_type": "markdown", + "id": "e2392b36", + "metadata": {}, + "source": [ + "### Predicting between and beyond the measured angles\n", + "\n", + "A prediction on a finer angular grid can carry an error model only where that\n", + "error model has a value. The quoted 2.3 % statistical errors are one number per\n", + "measured angle, so `grid_draws` cannot put them anywhere else, and we leave them\n", + "out. The inferred terms are different: the normalisation mode is $\\eta\\,y_m$\n", + "and the model error is $\\delta\\,y_m$ at any angle, so we pass them with\n", + "`terms=` and each band includes the uncertainty its error model inferred. (A\n", + "normalisation drawn on the grid reads as \"a future measurement by this\n", + "experiment\", sharing its unknown scale.) For the quoted-errors fit that leaves\n", + "the model alone, which is exactly why its band is so implausibly thin." + ] + }, { "cell_type": "code", "execution_count": 10, "id": "f045b49e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:59:58.958212Z", - "iopub.status.busy": "2026-09-12T03:59:58.958035Z", - "iopub.status.idle": "2026-09-12T04:00:01.732163Z", - "shell.execute_reply": "2026-09-12T04:00:01.731142Z" + "iopub.execute_input": "2026-09-14T20:00:28.866388Z", + "iopub.status.busy": "2026-09-14T20:00:28.866191Z", + "iopub.status.idle": "2026-09-14T20:00:32.061510Z", + "shell.execute_reply": "2026-09-14T20:00:32.060860Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 704x440 with 1 Axes>" ] @@ -596,9 +621,10 @@ " [plotstyle.COLOURS[i] for i in (1, 0, 2)],\n", " plotstyle.HATCHES,\n", "):\n", - " cols = p.columns(omp.params)\n", - " curves = np.array([on_fine(*s[cols]) for s in samples[name][::20]])\n", - " lo, hi = np.percentile(curves, [5, 95], axis=0)\n", + " lo, hi = rx.predictive.grid_draws(\n", + " p, on_fine, x_fine, samples[name][::20], terms=inferred_terms[name],\n", + " n_rep=2, levels=(5, 95), rng=4,\n", + " ) # fmt: skip\n", " plotstyle.band(\n", " ax, np.rad2deg(x_fine), lo, hi, color=colour, hatch=hatch, label=name\n", " )\n", @@ -609,7 +635,7 @@ " xlabel=r\"$\\theta_{cm}$ [deg]\",\n", " ylabel=r\"$\\sigma / \\sigma_{Rutherford}$\",\n", " yscale=\"log\",\n", - " title=\"90 % posterior bands on a grid the experiment never measured\",\n", + " title=\"90 % bands, inferred error included, on a grid never measured\",\n", ")\n", "ax.legend(fontsize=8)\n", "plt.show()" @@ -621,10 +647,10 @@ "id": "1646803d", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:00:01.734128Z", - "iopub.status.busy": "2026-09-12T04:00:01.733924Z", - "iopub.status.idle": "2026-09-12T04:00:03.499907Z", - "shell.execute_reply": "2026-09-12T04:00:03.499152Z" + "iopub.execute_input": "2026-09-14T20:00:32.063575Z", + "iopub.status.busy": "2026-09-14T20:00:32.063378Z", + "iopub.status.idle": "2026-09-14T20:00:33.720277Z", + "shell.execute_reply": "2026-09-14T20:00:33.719403Z" } }, "outputs": [ @@ -669,10 +695,10 @@ "id": "57c5d001", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:00:03.504295Z", - "iopub.status.busy": "2026-09-12T04:00:03.504095Z", - "iopub.status.idle": "2026-09-12T04:00:03.508649Z", - "shell.execute_reply": "2026-09-12T04:00:03.507845Z" + "iopub.execute_input": "2026-09-14T20:00:33.724754Z", + "iopub.status.busy": "2026-09-14T20:00:33.724568Z", + "iopub.status.idle": "2026-09-14T20:00:33.728904Z", + "shell.execute_reply": "2026-09-14T20:00:33.728160Z" } }, "outputs": [ @@ -717,10 +743,10 @@ "id": "868974ac", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:00:03.510582Z", - "iopub.status.busy": "2026-09-12T04:00:03.510319Z", - "iopub.status.idle": "2026-09-12T04:00:14.592018Z", - "shell.execute_reply": "2026-09-12T04:00:14.591209Z" + "iopub.execute_input": "2026-09-14T20:00:33.730576Z", + "iopub.status.busy": "2026-09-14T20:00:33.730412Z", + "iopub.status.idle": "2026-09-14T20:00:45.139311Z", + "shell.execute_reply": "2026-09-14T20:00:45.138548Z" } }, "outputs": [ @@ -772,10 +798,10 @@ "id": "e0136a13", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:00:14.593786Z", - "iopub.status.busy": "2026-09-12T04:00:14.593594Z", - "iopub.status.idle": "2026-09-12T04:00:41.760789Z", - "shell.execute_reply": "2026-09-12T04:00:41.760057Z" + "iopub.execute_input": "2026-09-14T20:00:45.141086Z", + "iopub.status.busy": "2026-09-14T20:00:45.140902Z", + "iopub.status.idle": "2026-09-14T20:01:13.444874Z", + "shell.execute_reply": "2026-09-14T20:01:13.444197Z" } }, "outputs": [ @@ -847,10 +873,10 @@ "id": "7d6d7fc3", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:00:41.762360Z", - "iopub.status.busy": "2026-09-12T04:00:41.762189Z", - "iopub.status.idle": "2026-09-12T04:00:41.977163Z", - "shell.execute_reply": "2026-09-12T04:00:41.976394Z" + "iopub.execute_input": "2026-09-14T20:01:13.446498Z", + "iopub.status.busy": "2026-09-14T20:01:13.446324Z", + "iopub.status.idle": "2026-09-14T20:01:13.675194Z", + "shell.execute_reply": "2026-09-14T20:01:13.674477Z" } }, "outputs": [ @@ -902,10 +928,10 @@ "id": "9e421b2a", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T04:00:41.978889Z", - "iopub.status.busy": "2026-09-12T04:00:41.978713Z", - "iopub.status.idle": "2026-09-12T04:00:42.384776Z", - "shell.execute_reply": "2026-09-12T04:00:42.383883Z" + "iopub.execute_input": "2026-09-14T20:01:13.677230Z", + "iopub.status.busy": "2026-09-14T20:01:13.677033Z", + "iopub.status.idle": "2026-09-14T20:01:14.101074Z", + "shell.execute_reply": "2026-09-14T20:01:14.100332Z" } }, "outputs": [ @@ -929,11 +955,15 @@ " ((\"untempered\", p_full, s_full), (\"tempered\", p_temp, s_temp)),\n", " (plotstyle.COLOURS[0], plotstyle.COLOURS[2]),\n", "):\n", - " draws = rx.diagnostics.predictive_draws(p, s[::20], n_rep=2, rng=6)\n", + " draws = rx.diagnostics.predictive_draws(\n", + " p, s[::20], n_rep=2, rng=6, return_draws=True\n", + " )\n", " cov = rx.diagnostics.coverage_curve(draws, c200.y[c200.active], levels)\n", " width = rx.diagnostics.sharpness(draws).mean()\n", " ax.plot(levels, cov, \"o-\", color=colour, label=f\"{name} (width {width:.3f})\")\n", - "model_only = rx.diagnostics.predictive_draws(p_full, s_full[::20], model_only=True)\n", + "model_only = rx.diagnostics.predictive_draws(\n", + " p_full, s_full[::20], model_only=True, return_draws=True\n", + ")\n", "ax.plot(\n", " levels,\n", " rx.diagnostics.coverage_curve(model_only, c200.y[c200.active], levels),\n", diff --git a/examples/normalization_and_covariance_structure.ipynb b/examples/normalization_and_covariance_structure.ipynb index ca3c59b..c2a5cc7 100644 --- a/examples/normalization_and_covariance_structure.ipynb +++ b/examples/normalization_and_covariance_structure.ipynb @@ -26,10 +26,10 @@ "id": "6cdd6f3d", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:09:35.536542Z", - "iopub.status.busy": "2026-09-12T03:09:35.536400Z", - "iopub.status.idle": "2026-09-12T03:09:37.730914Z", - "shell.execute_reply": "2026-09-12T03:09:37.730097Z" + "iopub.execute_input": "2026-09-14T19:45:22.640008Z", + "iopub.status.busy": "2026-09-14T19:45:22.639763Z", + "iopub.status.idle": "2026-09-14T19:45:26.051872Z", + "shell.execute_reply": "2026-09-14T19:45:26.050844Z" } }, "outputs": [], @@ -85,10 +85,10 @@ "id": "f6b7fce0", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:09:37.732681Z", - "iopub.status.busy": "2026-09-12T03:09:37.732452Z", - "iopub.status.idle": "2026-09-12T03:09:37.736532Z", - "shell.execute_reply": "2026-09-12T03:09:37.736058Z" + "iopub.execute_input": "2026-09-14T19:45:26.054610Z", + "iopub.status.busy": "2026-09-14T19:45:26.054142Z", + "iopub.status.idle": "2026-09-14T19:45:26.061718Z", + "shell.execute_reply": "2026-09-14T19:45:26.060527Z" } }, "outputs": [ @@ -154,10 +154,10 @@ "id": "fc2ec848", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:09:37.737736Z", - "iopub.status.busy": "2026-09-12T03:09:37.737621Z", - "iopub.status.idle": "2026-09-12T03:09:37.741776Z", - "shell.execute_reply": "2026-09-12T03:09:37.740993Z" + "iopub.execute_input": "2026-09-14T19:45:26.063901Z", + "iopub.status.busy": "2026-09-14T19:45:26.063671Z", + "iopub.status.idle": "2026-09-14T19:45:26.070415Z", + "shell.execute_reply": "2026-09-14T19:45:26.069310Z" } }, "outputs": [], @@ -187,10 +187,10 @@ "id": "5cdc4ada", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:09:37.743268Z", - "iopub.status.busy": "2026-09-12T03:09:37.743108Z", - "iopub.status.idle": "2026-09-12T03:09:38.716889Z", - "shell.execute_reply": "2026-09-12T03:09:38.716200Z" + "iopub.execute_input": "2026-09-14T19:45:26.072662Z", + "iopub.status.busy": "2026-09-14T19:45:26.072421Z", + "iopub.status.idle": "2026-09-14T19:45:27.605236Z", + "shell.execute_reply": "2026-09-14T19:45:27.604372Z" } }, "outputs": [ @@ -249,10 +249,10 @@ "id": "db34eca7", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:09:38.718269Z", - "iopub.status.busy": "2026-09-12T03:09:38.718110Z", - "iopub.status.idle": "2026-09-12T03:09:38.723450Z", - "shell.execute_reply": "2026-09-12T03:09:38.722953Z" + "iopub.execute_input": "2026-09-14T19:45:27.608566Z", + "iopub.status.busy": "2026-09-14T19:45:27.608315Z", + "iopub.status.idle": "2026-09-14T19:45:27.616310Z", + "shell.execute_reply": "2026-09-14T19:45:27.615470Z" } }, "outputs": [ @@ -282,10 +282,10 @@ "id": "5439babf", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:09:38.724834Z", - "iopub.status.busy": "2026-09-12T03:09:38.724711Z", - "iopub.status.idle": "2026-09-12T03:09:38.728043Z", - "shell.execute_reply": "2026-09-12T03:09:38.727509Z" + "iopub.execute_input": "2026-09-14T19:45:27.619157Z", + "iopub.status.busy": "2026-09-14T19:45:27.618920Z", + "iopub.status.idle": "2026-09-14T19:45:27.624108Z", + "shell.execute_reply": "2026-09-14T19:45:27.623213Z" } }, "outputs": [], @@ -298,9 +298,15 @@ "\n", "\n", "def curves(problem, samples, grid, n=200):\n", - " on_grid = cubic.bind(grid)\n", - " cols = problem.columns(cubic.params)\n", - " return np.array([on_grid(*s[cols]) for s in samples[-n:]])" + " \"\"\"The bare cubic's curves on a grid: the physics, not the renormalised data.\"\"\"\n", + " return rx.predictive.grid_draws(\n", + " problem,\n", + " cubic.bind(grid),\n", + " grid,\n", + " samples[-n:],\n", + " model_only=True,\n", + " return_draws=True,\n", + " )" ] }, { @@ -332,10 +338,10 @@ "id": "ad183ccd", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:09:38.729356Z", - "iopub.status.busy": "2026-09-12T03:09:38.729172Z", - "iopub.status.idle": "2026-09-12T03:09:38.735973Z", - "shell.execute_reply": "2026-09-12T03:09:38.735416Z" + "iopub.execute_input": "2026-09-14T19:45:27.627399Z", + "iopub.status.busy": "2026-09-14T19:45:27.627146Z", + "iopub.status.idle": "2026-09-14T19:45:27.638484Z", + "shell.execute_reply": "2026-09-14T19:45:27.637534Z" } }, "outputs": [], @@ -372,10 +378,10 @@ "id": "6cd9b231", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:09:38.737321Z", - "iopub.status.busy": "2026-09-12T03:09:38.737200Z", - "iopub.status.idle": "2026-09-12T03:09:38.743960Z", - "shell.execute_reply": "2026-09-12T03:09:38.743342Z" + "iopub.execute_input": "2026-09-14T19:45:27.641272Z", + "iopub.status.busy": "2026-09-14T19:45:27.641009Z", + "iopub.status.idle": "2026-09-14T19:45:27.652679Z", + "shell.execute_reply": "2026-09-14T19:45:27.651611Z" } }, "outputs": [ @@ -442,10 +448,10 @@ "id": "c975ffb4", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:09:38.745300Z", - "iopub.status.busy": "2026-09-12T03:09:38.745180Z", - "iopub.status.idle": "2026-09-12T03:09:38.752802Z", - "shell.execute_reply": "2026-09-12T03:09:38.752263Z" + "iopub.execute_input": "2026-09-14T19:45:27.655010Z", + "iopub.status.busy": "2026-09-14T19:45:27.654781Z", + "iopub.status.idle": "2026-09-14T19:45:27.668417Z", + "shell.execute_reply": "2026-09-14T19:45:27.667458Z" } }, "outputs": [ @@ -500,10 +506,10 @@ "id": "f7833d17", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:09:38.754343Z", - "iopub.status.busy": "2026-09-12T03:09:38.754158Z", - "iopub.status.idle": "2026-09-12T03:14:59.672367Z", - "shell.execute_reply": "2026-09-12T03:14:59.671780Z" + "iopub.execute_input": "2026-09-14T19:45:27.671706Z", + "iopub.status.busy": "2026-09-14T19:45:27.671406Z", + "iopub.status.idle": "2026-09-14T19:54:00.645104Z", + "shell.execute_reply": "2026-09-14T19:54:00.643960Z" } }, "outputs": [], @@ -527,10 +533,10 @@ "id": "86edaa87", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:14:59.673956Z", - "iopub.status.busy": "2026-09-12T03:14:59.673820Z", - "iopub.status.idle": "2026-09-12T03:14:59.694757Z", - "shell.execute_reply": "2026-09-12T03:14:59.694143Z" + "iopub.execute_input": "2026-09-14T19:54:00.647524Z", + "iopub.status.busy": "2026-09-14T19:54:00.647280Z", + "iopub.status.idle": "2026-09-14T19:54:00.706004Z", + "shell.execute_reply": "2026-09-14T19:54:00.705007Z" } }, "outputs": [ @@ -618,10 +624,10 @@ "id": "aeb43081", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:14:59.696366Z", - "iopub.status.busy": "2026-09-12T03:14:59.696231Z", - "iopub.status.idle": "2026-09-12T03:14:59.843368Z", - "shell.execute_reply": "2026-09-12T03:14:59.842774Z" + "iopub.execute_input": "2026-09-14T19:54:00.708741Z", + "iopub.status.busy": "2026-09-14T19:54:00.708501Z", + "iopub.status.idle": "2026-09-14T19:54:00.951317Z", + "shell.execute_reply": "2026-09-14T19:54:00.950157Z" } }, "outputs": [ @@ -658,10 +664,10 @@ "id": "12a5ada8", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:14:59.844707Z", - "iopub.status.busy": "2026-09-12T03:14:59.844559Z", - "iopub.status.idle": "2026-09-12T03:14:59.979030Z", - "shell.execute_reply": "2026-09-12T03:14:59.978247Z" + "iopub.execute_input": "2026-09-14T19:54:00.953842Z", + "iopub.status.busy": "2026-09-14T19:54:00.953574Z", + "iopub.status.idle": "2026-09-14T19:54:01.178416Z", + "shell.execute_reply": "2026-09-14T19:54:01.177281Z" } }, "outputs": [ @@ -703,10 +709,10 @@ "id": "dbd240fa", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:14:59.980449Z", - "iopub.status.busy": "2026-09-12T03:14:59.980281Z", - "iopub.status.idle": "2026-09-12T03:15:00.312497Z", - "shell.execute_reply": "2026-09-12T03:15:00.311850Z" + "iopub.execute_input": "2026-09-14T19:54:01.180750Z", + "iopub.status.busy": "2026-09-14T19:54:01.180501Z", + "iopub.status.idle": "2026-09-14T19:54:01.816659Z", + "shell.execute_reply": "2026-09-14T19:54:01.815539Z" } }, "outputs": [ @@ -770,10 +776,10 @@ "id": "a640c45a", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:15:00.313860Z", - "iopub.status.busy": "2026-09-12T03:15:00.313720Z", - "iopub.status.idle": "2026-09-12T03:15:00.318536Z", - "shell.execute_reply": "2026-09-12T03:15:00.318074Z" + "iopub.execute_input": "2026-09-14T19:54:01.820706Z", + "iopub.status.busy": "2026-09-14T19:54:01.820341Z", + "iopub.status.idle": "2026-09-14T19:54:01.832391Z", + "shell.execute_reply": "2026-09-14T19:54:01.831151Z" } }, "outputs": [ @@ -810,10 +816,10 @@ "id": "a9e36535", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:15:00.319838Z", - "iopub.status.busy": "2026-09-12T03:15:00.319699Z", - "iopub.status.idle": "2026-09-12T03:15:00.422204Z", - "shell.execute_reply": "2026-09-12T03:15:00.421572Z" + "iopub.execute_input": "2026-09-14T19:54:01.835253Z", + "iopub.status.busy": "2026-09-14T19:54:01.834902Z", + "iopub.status.idle": "2026-09-14T19:54:02.019993Z", + "shell.execute_reply": "2026-09-14T19:54:02.019207Z" } }, "outputs": [ @@ -897,10 +903,10 @@ "id": "b3cf1ec6", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:15:00.423625Z", - "iopub.status.busy": "2026-09-12T03:15:00.423382Z", - "iopub.status.idle": "2026-09-12T03:15:00.437225Z", - "shell.execute_reply": "2026-09-12T03:15:00.436693Z" + "iopub.execute_input": "2026-09-14T19:54:02.022945Z", + "iopub.status.busy": "2026-09-14T19:54:02.022709Z", + "iopub.status.idle": "2026-09-14T19:54:02.043097Z", + "shell.execute_reply": "2026-09-14T19:54:02.042558Z" } }, "outputs": [ @@ -965,10 +971,10 @@ "id": "1c04818c", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:15:00.438673Z", - "iopub.status.busy": "2026-09-12T03:15:00.438433Z", - "iopub.status.idle": "2026-09-12T03:15:00.442172Z", - "shell.execute_reply": "2026-09-12T03:15:00.441655Z" + "iopub.execute_input": "2026-09-14T19:54:02.046098Z", + "iopub.status.busy": "2026-09-14T19:54:02.045860Z", + "iopub.status.idle": "2026-09-14T19:54:02.051320Z", + "shell.execute_reply": "2026-09-14T19:54:02.050611Z" } }, "outputs": [], @@ -1010,10 +1016,10 @@ "id": "7348683c", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:15:00.443580Z", - "iopub.status.busy": "2026-09-12T03:15:00.443434Z", - "iopub.status.idle": "2026-09-12T03:15:00.648097Z", - "shell.execute_reply": "2026-09-12T03:15:00.647533Z" + "iopub.execute_input": "2026-09-14T19:54:02.054344Z", + "iopub.status.busy": "2026-09-14T19:54:02.054094Z", + "iopub.status.idle": "2026-09-14T19:54:02.347440Z", + "shell.execute_reply": "2026-09-14T19:54:02.346358Z" } }, "outputs": [ @@ -1064,10 +1070,10 @@ "id": "1bedc753", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:15:00.649493Z", - "iopub.status.busy": "2026-09-12T03:15:00.649317Z", - "iopub.status.idle": "2026-09-12T03:15:25.254522Z", - "shell.execute_reply": "2026-09-12T03:15:25.253950Z" + "iopub.execute_input": "2026-09-14T19:54:02.350463Z", + "iopub.status.busy": "2026-09-14T19:54:02.349977Z", + "iopub.status.idle": "2026-09-14T19:54:44.924138Z", + "shell.execute_reply": "2026-09-14T19:54:44.923001Z" } }, "outputs": [ @@ -1106,10 +1112,10 @@ "id": "cc9ef059", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:15:25.255869Z", - "iopub.status.busy": "2026-09-12T03:15:25.255737Z", - "iopub.status.idle": "2026-09-12T03:15:25.411023Z", - "shell.execute_reply": "2026-09-12T03:15:25.410279Z" + "iopub.execute_input": "2026-09-14T19:54:44.926615Z", + "iopub.status.busy": "2026-09-14T19:54:44.926373Z", + "iopub.status.idle": "2026-09-14T19:54:45.191903Z", + "shell.execute_reply": "2026-09-14T19:54:45.191010Z" } }, "outputs": [ @@ -1169,10 +1175,10 @@ "id": "c32c89dd", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:15:25.412503Z", - "iopub.status.busy": "2026-09-12T03:15:25.412362Z", - "iopub.status.idle": "2026-09-12T03:15:25.561856Z", - "shell.execute_reply": "2026-09-12T03:15:25.561245Z" + "iopub.execute_input": "2026-09-14T19:54:45.195180Z", + "iopub.status.busy": "2026-09-14T19:54:45.194898Z", + "iopub.status.idle": "2026-09-14T19:54:45.448361Z", + "shell.execute_reply": "2026-09-14T19:54:45.447124Z" } }, "outputs": [ @@ -1224,10 +1230,10 @@ "id": "2ddf9ef6", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T03:15:25.563425Z", - "iopub.status.busy": "2026-09-12T03:15:25.563281Z", - "iopub.status.idle": "2026-09-12T03:15:25.618753Z", - "shell.execute_reply": "2026-09-12T03:15:25.618223Z" + "iopub.execute_input": "2026-09-14T19:54:45.450980Z", + "iopub.status.busy": "2026-09-14T19:54:45.450734Z", + "iopub.status.idle": "2026-09-14T19:54:45.537904Z", + "shell.execute_reply": "2026-09-14T19:54:45.536840Z" } }, "outputs": [ diff --git a/examples/robust_likelihoods.ipynb b/examples/robust_likelihoods.ipynb index 722175e..5ac4257 100644 --- a/examples/robust_likelihoods.ipynb +++ b/examples/robust_likelihoods.ipynb @@ -29,10 +29,10 @@ "id": "f6300218", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:14:42.872611Z", - "iopub.status.busy": "2026-09-12T02:14:42.872481Z", - "iopub.status.idle": "2026-09-12T02:14:44.901758Z", - "shell.execute_reply": "2026-09-12T02:14:44.901046Z" + "iopub.execute_input": "2026-09-14T19:56:34.608658Z", + "iopub.status.busy": "2026-09-14T19:56:34.608441Z", + "iopub.status.idle": "2026-09-14T19:56:37.786709Z", + "shell.execute_reply": "2026-09-14T19:56:37.785785Z" } }, "outputs": [], @@ -67,10 +67,10 @@ "id": "5a862393", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:14:44.903296Z", - "iopub.status.busy": "2026-09-12T02:14:44.903104Z", - "iopub.status.idle": "2026-09-12T02:14:44.908677Z", - "shell.execute_reply": "2026-09-12T02:14:44.908019Z" + "iopub.execute_input": "2026-09-14T19:56:37.789668Z", + "iopub.status.busy": "2026-09-14T19:56:37.789331Z", + "iopub.status.idle": "2026-09-14T19:56:37.798686Z", + "shell.execute_reply": "2026-09-14T19:56:37.797485Z" } }, "outputs": [ @@ -103,10 +103,10 @@ "id": "0fac5bbe", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:14:44.910108Z", - "iopub.status.busy": "2026-09-12T02:14:44.909988Z", - "iopub.status.idle": "2026-09-12T02:14:45.822630Z", - "shell.execute_reply": "2026-09-12T02:14:45.821798Z" + "iopub.execute_input": "2026-09-14T19:56:37.800918Z", + "iopub.status.busy": "2026-09-14T19:56:37.800683Z", + "iopub.status.idle": "2026-09-14T19:56:39.244531Z", + "shell.execute_reply": "2026-09-14T19:56:39.243435Z" } }, "outputs": [ @@ -158,10 +158,10 @@ "id": "ff2eba9a", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:14:45.824037Z", - "iopub.status.busy": "2026-09-12T02:14:45.823873Z", - "iopub.status.idle": "2026-09-12T02:14:45.829713Z", - "shell.execute_reply": "2026-09-12T02:14:45.829172Z" + "iopub.execute_input": "2026-09-14T19:56:39.247017Z", + "iopub.status.busy": "2026-09-14T19:56:39.246771Z", + "iopub.status.idle": "2026-09-14T19:56:39.255392Z", + "shell.execute_reply": "2026-09-14T19:56:39.254530Z" } }, "outputs": [ @@ -193,10 +193,10 @@ "id": "3aaaedd7", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:14:45.831254Z", - "iopub.status.busy": "2026-09-12T02:14:45.831128Z", - "iopub.status.idle": "2026-09-12T02:14:45.833933Z", - "shell.execute_reply": "2026-09-12T02:14:45.833341Z" + "iopub.execute_input": "2026-09-14T19:56:39.258130Z", + "iopub.status.busy": "2026-09-14T19:56:39.257895Z", + "iopub.status.idle": "2026-09-14T19:56:39.262350Z", + "shell.execute_reply": "2026-09-14T19:56:39.261415Z" } }, "outputs": [], @@ -214,10 +214,10 @@ "id": "69bbceda", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:14:45.835232Z", - "iopub.status.busy": "2026-09-12T02:14:45.835115Z", - "iopub.status.idle": "2026-09-12T02:15:04.625427Z", - "shell.execute_reply": "2026-09-12T02:15:04.624628Z" + "iopub.execute_input": "2026-09-14T19:56:39.264931Z", + "iopub.status.busy": "2026-09-14T19:56:39.264696Z", + "iopub.status.idle": "2026-09-14T19:57:10.620202Z", + "shell.execute_reply": "2026-09-14T19:57:10.619106Z" } }, "outputs": [], @@ -231,10 +231,10 @@ "id": "76f55c31", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:15:04.627124Z", - "iopub.status.busy": "2026-09-12T02:15:04.626890Z", - "iopub.status.idle": "2026-09-12T02:15:04.783689Z", - "shell.execute_reply": "2026-09-12T02:15:04.782929Z" + "iopub.execute_input": "2026-09-14T19:57:10.622945Z", + "iopub.status.busy": "2026-09-14T19:57:10.622571Z", + "iopub.status.idle": "2026-09-14T19:57:10.897205Z", + "shell.execute_reply": "2026-09-14T19:57:10.896127Z" } }, "outputs": [ @@ -281,10 +281,10 @@ "id": "d32705b6", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:15:04.785183Z", - "iopub.status.busy": "2026-09-12T02:15:04.785019Z", - "iopub.status.idle": "2026-09-12T02:15:04.789227Z", - "shell.execute_reply": "2026-09-12T02:15:04.788705Z" + "iopub.execute_input": "2026-09-14T19:57:10.899874Z", + "iopub.status.busy": "2026-09-14T19:57:10.899528Z", + "iopub.status.idle": "2026-09-14T19:57:10.908353Z", + "shell.execute_reply": "2026-09-14T19:57:10.907205Z" } }, "outputs": [ @@ -314,10 +314,10 @@ "id": "fd2ad161", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:15:04.790576Z", - "iopub.status.busy": "2026-09-12T02:15:04.790378Z", - "iopub.status.idle": "2026-09-12T02:15:04.918382Z", - "shell.execute_reply": "2026-09-12T02:15:04.917643Z" + "iopub.execute_input": "2026-09-14T19:57:10.910885Z", + "iopub.status.busy": "2026-09-14T19:57:10.910547Z", + "iopub.status.idle": "2026-09-14T19:57:11.138938Z", + "shell.execute_reply": "2026-09-14T19:57:11.138077Z" } }, "outputs": [ @@ -378,10 +378,10 @@ "id": "e7ea32cc", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:15:04.919885Z", - "iopub.status.busy": "2026-09-12T02:15:04.919719Z", - "iopub.status.idle": "2026-09-12T02:15:04.922926Z", - "shell.execute_reply": "2026-09-12T02:15:04.922179Z" + "iopub.execute_input": "2026-09-14T19:57:11.142285Z", + "iopub.status.busy": "2026-09-14T19:57:11.142027Z", + "iopub.status.idle": "2026-09-14T19:57:11.147056Z", + "shell.execute_reply": "2026-09-14T19:57:11.145768Z" } }, "outputs": [ @@ -407,10 +407,10 @@ "id": "3ab4d1e4", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:15:04.924212Z", - "iopub.status.busy": "2026-09-12T02:15:04.924067Z", - "iopub.status.idle": "2026-09-12T02:15:04.927248Z", - "shell.execute_reply": "2026-09-12T02:15:04.926686Z" + "iopub.execute_input": "2026-09-14T19:57:11.150553Z", + "iopub.status.busy": "2026-09-14T19:57:11.150177Z", + "iopub.status.idle": "2026-09-14T19:57:11.157048Z", + "shell.execute_reply": "2026-09-14T19:57:11.156013Z" } }, "outputs": [], @@ -423,9 +423,9 @@ "\n", "\n", "def residual_band(problem, samples, levels=(5, 95)):\n", - " cols = problem.columns(line.params)\n", - " lo, hi = rx.predictive.predictive_band(\n", - " [on_fine(*r[cols]) for r in samples[::10]], levels=levels\n", + " \"\"\"The fitted line's band, as a residual to the truth.\"\"\"\n", + " lo, hi = rx.predictive.grid_draws(\n", + " problem, on_fine, x_fine, samples[::10], model_only=True, levels=levels\n", " )\n", " return lo - y_true_fine, hi - y_true_fine" ] @@ -436,10 +436,10 @@ "id": "22d9487d", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:15:04.928488Z", - "iopub.status.busy": "2026-09-12T02:15:04.928331Z", - "iopub.status.idle": "2026-09-12T02:15:05.048319Z", - "shell.execute_reply": "2026-09-12T02:15:05.047255Z" + "iopub.execute_input": "2026-09-14T19:57:11.159150Z", + "iopub.status.busy": "2026-09-14T19:57:11.158924Z", + "iopub.status.idle": "2026-09-14T19:57:11.368970Z", + "shell.execute_reply": "2026-09-14T19:57:11.367890Z" } }, "outputs": [ @@ -477,8 +477,9 @@ "metadata": {}, "source": [ "Both are shifted relative to the truth, but the Student-t's longer tails allow\n", - "for the truth to (mostly) be covered by the 90 % credible interval of the\n", - "posterior predictive distribution." + "for the truth to (mostly) be covered by the 90 % credible band of the line.\n", + "These bands are the model alone: they say where each fit puts the true curve,\n", + "not where a new measurement would land." ] }, { @@ -507,10 +508,10 @@ "id": "26724634", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:15:05.049841Z", - "iopub.status.busy": "2026-09-12T02:15:05.049710Z", - "iopub.status.idle": "2026-09-12T02:15:05.053186Z", - "shell.execute_reply": "2026-09-12T02:15:05.052656Z" + "iopub.execute_input": "2026-09-14T19:57:11.371577Z", + "iopub.status.busy": "2026-09-14T19:57:11.371336Z", + "iopub.status.idle": "2026-09-14T19:57:11.376983Z", + "shell.execute_reply": "2026-09-14T19:57:11.375931Z" } }, "outputs": [], @@ -538,10 +539,10 @@ "id": "60313e72", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:15:05.054560Z", - "iopub.status.busy": "2026-09-12T02:15:05.054395Z", - "iopub.status.idle": "2026-09-12T02:15:33.535477Z", - "shell.execute_reply": "2026-09-12T02:15:33.534921Z" + "iopub.execute_input": "2026-09-14T19:57:11.379347Z", + "iopub.status.busy": "2026-09-14T19:57:11.379090Z", + "iopub.status.idle": "2026-09-14T19:58:00.034042Z", + "shell.execute_reply": "2026-09-14T19:58:00.032968Z" } }, "outputs": [ @@ -577,10 +578,10 @@ "id": "2353e732", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:15:33.536878Z", - "iopub.status.busy": "2026-09-12T02:15:33.536696Z", - "iopub.status.idle": "2026-09-12T02:15:33.728656Z", - "shell.execute_reply": "2026-09-12T02:15:33.728193Z" + "iopub.execute_input": "2026-09-14T19:58:00.036374Z", + "iopub.status.busy": "2026-09-14T19:58:00.036125Z", + "iopub.status.idle": "2026-09-14T19:58:00.337856Z", + "shell.execute_reply": "2026-09-14T19:58:00.336652Z" } }, "outputs": [ diff --git a/examples/sharing_error_models.ipynb b/examples/sharing_error_models.ipynb index 4cb1498..1526b38 100644 --- a/examples/sharing_error_models.ipynb +++ b/examples/sharing_error_models.ipynb @@ -38,10 +38,10 @@ "id": "d1df0443", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:06:44.481106Z", - "iopub.status.busy": "2026-09-12T02:06:44.480962Z", - "iopub.status.idle": "2026-09-12T02:06:46.584396Z", - "shell.execute_reply": "2026-09-12T02:06:46.583536Z" + "iopub.execute_input": "2026-09-14T19:54:52.557329Z", + "iopub.status.busy": "2026-09-14T19:54:52.557031Z", + "iopub.status.idle": "2026-09-14T19:54:56.637369Z", + "shell.execute_reply": "2026-09-14T19:54:56.636262Z" } }, "outputs": [], @@ -78,10 +78,10 @@ "id": "403664b8", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:06:46.586017Z", - "iopub.status.busy": "2026-09-12T02:06:46.585819Z", - "iopub.status.idle": "2026-09-12T02:06:46.589892Z", - "shell.execute_reply": "2026-09-12T02:06:46.589289Z" + "iopub.execute_input": "2026-09-14T19:54:56.640537Z", + "iopub.status.busy": "2026-09-14T19:54:56.640181Z", + "iopub.status.idle": "2026-09-14T19:54:56.646441Z", + "shell.execute_reply": "2026-09-14T19:54:56.645384Z" } }, "outputs": [], @@ -106,10 +106,10 @@ "id": "1cd295d6", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:06:46.591207Z", - "iopub.status.busy": "2026-09-12T02:06:46.591058Z", - "iopub.status.idle": "2026-09-12T02:06:47.459574Z", - "shell.execute_reply": "2026-09-12T02:06:47.458828Z" + "iopub.execute_input": "2026-09-14T19:54:56.648762Z", + "iopub.status.busy": "2026-09-14T19:54:56.648524Z", + "iopub.status.idle": "2026-09-14T19:54:58.043457Z", + "shell.execute_reply": "2026-09-14T19:54:58.042331Z" } }, "outputs": [ @@ -166,10 +166,10 @@ "id": "cda7a0a7", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:06:47.460988Z", - "iopub.status.busy": "2026-09-12T02:06:47.460857Z", - "iopub.status.idle": "2026-09-12T02:06:47.464799Z", - "shell.execute_reply": "2026-09-12T02:06:47.464208Z" + "iopub.execute_input": "2026-09-14T19:54:58.046529Z", + "iopub.status.busy": "2026-09-14T19:54:58.046283Z", + "iopub.status.idle": "2026-09-14T19:54:58.052573Z", + "shell.execute_reply": "2026-09-14T19:54:58.051494Z" } }, "outputs": [], @@ -186,10 +186,10 @@ "id": "2e601856", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:06:47.466024Z", - "iopub.status.busy": "2026-09-12T02:06:47.465881Z", - "iopub.status.idle": "2026-09-12T02:06:47.469772Z", - "shell.execute_reply": "2026-09-12T02:06:47.469173Z" + "iopub.execute_input": "2026-09-14T19:54:58.055601Z", + "iopub.status.busy": "2026-09-14T19:54:58.055250Z", + "iopub.status.idle": "2026-09-14T19:54:58.064010Z", + "shell.execute_reply": "2026-09-14T19:54:58.062927Z" } }, "outputs": [], @@ -202,10 +202,14 @@ "\n", "\n", "def band(problem, samples, levels=(5, 95)):\n", - " on_fine = line.bind(x_fine)\n", - " cols = problem.columns(line.params)\n", - " return rx.predictive.predictive_band(\n", - " [on_fine(*s[cols]) for s in samples[::10]], levels=levels\n", + " \"\"\"The line's own band: how well each error model pins the curve down.\"\"\"\n", + " return rx.predictive.grid_draws(\n", + " problem,\n", + " line.bind(x_fine),\n", + " x_fine,\n", + " samples[::10],\n", + " model_only=True,\n", + " levels=levels,\n", " )\n", "\n", "\n", @@ -234,10 +238,10 @@ "id": "c52b1b84", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:06:47.471338Z", - "iopub.status.busy": "2026-09-12T02:06:47.471217Z", - "iopub.status.idle": "2026-09-12T02:06:47.477630Z", - "shell.execute_reply": "2026-09-12T02:06:47.476945Z" + "iopub.execute_input": "2026-09-14T19:54:58.067270Z", + "iopub.status.busy": "2026-09-14T19:54:58.066908Z", + "iopub.status.idle": "2026-09-14T19:54:58.078869Z", + "shell.execute_reply": "2026-09-14T19:54:58.077636Z" } }, "outputs": [ @@ -288,10 +292,10 @@ "id": "20fb0438", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:06:47.479016Z", - "iopub.status.busy": "2026-09-12T02:06:47.478894Z", - "iopub.status.idle": "2026-09-12T02:07:04.761784Z", - "shell.execute_reply": "2026-09-12T02:07:04.761072Z" + "iopub.execute_input": "2026-09-14T19:54:58.081538Z", + "iopub.status.busy": "2026-09-14T19:54:58.081207Z", + "iopub.status.idle": "2026-09-14T19:55:27.313004Z", + "shell.execute_reply": "2026-09-14T19:55:27.311858Z" } }, "outputs": [ @@ -335,10 +339,10 @@ "id": "822f035a", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:07:04.763667Z", - "iopub.status.busy": "2026-09-12T02:07:04.763538Z", - "iopub.status.idle": "2026-09-12T02:07:55.837506Z", - "shell.execute_reply": "2026-09-12T02:07:55.836902Z" + "iopub.execute_input": "2026-09-14T19:55:27.315426Z", + "iopub.status.busy": "2026-09-14T19:55:27.315171Z", + "iopub.status.idle": "2026-09-14T19:57:01.921250Z", + "shell.execute_reply": "2026-09-14T19:57:01.920296Z" } }, "outputs": [ @@ -424,10 +428,10 @@ "id": "3029669f", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:07:55.839230Z", - "iopub.status.busy": "2026-09-12T02:07:55.839103Z", - "iopub.status.idle": "2026-09-12T02:07:56.039922Z", - "shell.execute_reply": "2026-09-12T02:07:56.039212Z" + "iopub.execute_input": "2026-09-14T19:57:01.924273Z", + "iopub.status.busy": "2026-09-14T19:57:01.924025Z", + "iopub.status.idle": "2026-09-14T19:57:02.249747Z", + "shell.execute_reply": "2026-09-14T19:57:02.248721Z" } }, "outputs": [ @@ -463,10 +467,10 @@ "id": "be260c5c", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:07:56.041396Z", - "iopub.status.busy": "2026-09-12T02:07:56.041269Z", - "iopub.status.idle": "2026-09-12T02:07:56.054600Z", - "shell.execute_reply": "2026-09-12T02:07:56.053981Z" + "iopub.execute_input": "2026-09-14T19:57:02.252734Z", + "iopub.status.busy": "2026-09-14T19:57:02.252486Z", + "iopub.status.idle": "2026-09-14T19:57:02.277035Z", + "shell.execute_reply": "2026-09-14T19:57:02.276124Z" } }, "outputs": [], @@ -501,16 +505,16 @@ "id": "498bd9ae", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:07:56.055967Z", - "iopub.status.busy": "2026-09-12T02:07:56.055804Z", - "iopub.status.idle": "2026-09-12T02:07:56.260847Z", - "shell.execute_reply": "2026-09-12T02:07:56.260287Z" + "iopub.execute_input": "2026-09-14T19:57:02.279988Z", + "iopub.status.busy": "2026-09-14T19:57:02.279742Z", + "iopub.status.idle": "2026-09-14T19:57:02.625883Z", + "shell.execute_reply": "2026-09-14T19:57:02.624889Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 704x440 with 1 Axes>" ] @@ -527,7 +531,7 @@ "ax.plot(x_fine, truth[\"m\"] * x_fine + truth[\"b\"], \"--\", color=\"k\", label=\"truth\")\n", "ax.errorbar(data1.x, data1.y, data1.y_err, fmt=\"o\", color=\"0.4\", ms=3)\n", "ax.errorbar(data2.x, data2.y, data2.y_err, fmt=\"s\", color=\"0.4\", ms=3)\n", - "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"90 % predictive bands\")\n", + "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"90 % bands of the fitted line\")\n", "ax.legend(fontsize=8)\n", "plt.show()" ] @@ -538,10 +542,10 @@ "id": "c4759e6b", "metadata": { "execution": { - "iopub.execute_input": "2026-09-12T02:07:56.262693Z", - "iopub.status.busy": "2026-09-12T02:07:56.262558Z", - "iopub.status.idle": "2026-09-12T02:07:56.456975Z", - "shell.execute_reply": "2026-09-12T02:07:56.456395Z" + "iopub.execute_input": "2026-09-14T19:57:02.630617Z", + "iopub.status.busy": "2026-09-14T19:57:02.630370Z", + "iopub.status.idle": "2026-09-14T19:57:02.942337Z", + "shell.execute_reply": "2026-09-14T19:57:02.941358Z" } }, "outputs": [ diff --git a/src/rxmc/covariance.py b/src/rxmc/covariance.py index 565e91f..36739cb 100644 --- a/src/rxmc/covariance.py +++ b/src/rxmc/covariance.py @@ -352,7 +352,16 @@ def distance(self, ym, theta=()): d2 -= float(s @ s) return d2, float(logdet) - def matrix(self, ym, theta=()): - """The dense covariance on the active rows, for display and tests.""" + def matrix(self, ym, theta=(), entries=None): + """The dense covariance on the active rows, for display and tests. + + ``entries`` restricts the sum to a subset of :attr:`entries` (the + objects themselves, compared by identity): the covariance of *part* of + the error model, as a predictive draw of the model plus its discrepancy + but not the experimental errors needs. Default: every entry. A subset + is assembled directly, without the constant-piece cache. + """ theta = np.asarray(theta, dtype=float) - return self._dense_matrix(*self._assemble(ym, theta)) + if entries is None: + return self._dense_matrix(*self._assemble(ym, theta)) + return self._dense_matrix(*self._pieces(entries, ym, theta)) diff --git a/src/rxmc/diagnostics.py b/src/rxmc/diagnostics.py index eb0743f..85be867 100644 --- a/src/rxmc/diagnostics.py +++ b/src/rxmc/diagnostics.py @@ -5,7 +5,7 @@ ``problem.names`` order (what emcee's ``get_chain(flat=True)``, dynesty's ``samples_equal()`` and black-box-bayes give) and never touches a sampler: -* :func:`predictive_draws` — draws from the posterior predictive of a +* :func:`predictive_draws` — the posterior predictive (band or draws) of a constraint's likelihood on its active points (``N(ym(theta), Sigma(theta))``, or the multivariate t of :class:`~rxmc.likelihood.StudentT`), or the model-only predictive ``ym(theta)``. @@ -181,12 +181,16 @@ def predictive_draws( samples, constraint: int = 0, *, + terms=None, + statistical: bool = True, n_rep: int = 1, rng=None, model_only: bool = False, given: Problem | None = None, + levels=(16, 50, 84), + return_draws: bool = False, ) -> np.ndarray: - """Posterior-predictive draws on a constraint's active points. + """Posterior-predictive band, or draws, on a constraint's active points. For each posterior row ``theta_i`` the constraint gives ``ym_i`` and ``Sigma_i``; ``n_rep`` draws ``ym_i + s L_i z`` (``z ~ N(0, I)``) are taken, @@ -196,6 +200,19 @@ def predictive_draws( ``model_only=True`` the rows are ``ym_i`` themselves and no covariance is assembled. + ``terms`` and ``statistical`` choose which pieces of the error model the + draws carry (:meth:`~rxmc.problem.CompiledConstraint.entries_for`). The + default, every term, is the only one comparable with the measured data; + dropping the experimental terms gives the model plus its discrepancy + alone. + + The result is a percentile band by default, like + :func:`~rxmc.predictive.grid_draws` and + :func:`~rxmc.predictive.gp_predictive_draws`; :func:`coverage_curve`, + :func:`coverage_error` and :func:`sharpness` need the draws, so pass + ``return_draws=True`` for them. To predict at points that were never + measured, use :func:`~rxmc.predictive.grid_draws`. + Parameters ---------- problem : Problem @@ -203,6 +220,10 @@ def predictive_draws( Posterior rows in ``problem.names`` order (a 1-D array is one row). constraint : int, optional Index into ``problem.constraints``. + terms : sequence of Term, optional + Terms of the constraint, by identity; ``None`` means all of them. + statistical : bool, optional + Whether the reported statistical diagonals join them. n_rep : int, optional Draws per posterior row (ignored when ``model_only``). rng : numpy.random.Generator or seed, optional @@ -214,11 +235,16 @@ def predictive_draws( term spans the two (module docstring): draws then come from the conditional ``N(mu_c, Sigma_c)`` of the held-out rows given the fitted data. + levels : sequence of float, optional + Percentiles of the band. + return_draws : bool, optional + Return the draws instead of the band. Returns ------- np.ndarray - In comparison space: shape ``(n * n_rep, n_active)``, or + In comparison space: the band, ``(len(levels), n_active)``; with + ``return_draws`` the draws, ``(n * n_rep, n_active)``, or ``(n, n_active)`` when ``model_only``. """ rng = np.random.default_rng(rng) @@ -226,13 +252,19 @@ def predictive_draws( n = samples.shape[0] c = problem.constraints[constraint] N = c.n_active + if given is not None and not (terms is None and statistical): + raise ValueError( + "given= conditions on the fitted data under the full covariance of " + "the spanning term, so it cannot be combined with a term selection; " + "drop terms=/statistical= or drop given=" + ) cond = None if given is None else _Conditional(problem, given, constraint) if model_only: out = np.empty((n, N)) for i in range(n): out[i] = cond(samples[i])[0] if cond else c.ym(samples[i])[c.active] - return out + return out if return_draws else np.percentile(out, levels, axis=0) out = np.empty((n * n_rep, N)) for i in range(n): @@ -240,12 +272,13 @@ def predictive_draws( if cond: mu, Sigma = cond(theta) else: - mu, Sigma = c.ym(theta)[c.active], c.matrix(theta) + mu = c.ym(theta)[c.active] + Sigma = c.matrix(theta, terms=terms, statistical=statistical) L = _psd_factor(Sigma) z = rng.standard_normal((n_rep, N)) z *= c.likelihood.predictive_scale(rng, n_rep, *theta[c.like_gather])[:, None] out[i * n_rep : (i + 1) * n_rep] = mu + z @ L.T - return out + return out if return_draws else np.percentile(out, levels, axis=0) def coverage_curve(draws, y, levels=None) -> np.ndarray: diff --git a/src/rxmc/model.py b/src/rxmc/model.py index a7c1bf5..2003160 100644 --- a/src/rxmc/model.py +++ b/src/rxmc/model.py @@ -56,7 +56,7 @@ class Predictor: ``fn(*values) -> np.ndarray`` on that grid. meta : mapping, optional The dataset metadata the model was bound with; a term evaluated at the - predictor's grid (:func:`~rxmc.predictive.total_predictive_band`) reads + predictor's grid (:func:`~rxmc.predictive.grid_draws`) reads it through ``c.meta(key)``. """ diff --git a/src/rxmc/predictive.py b/src/rxmc/predictive.py index b7a6606..3d63a21 100644 --- a/src/rxmc/predictive.py +++ b/src/rxmc/predictive.py @@ -1,14 +1,71 @@ -"""Predicting a Gaussian-process discrepancy away from the data. - -A :func:`~rxmc.terms.kernel` term only inflates the covariance *at the data -points* with ``K(X, X)``; it does not say what the discrepancy is at new -``x``. :func:`gp_posterior_predictive` is the standard conditioning of a -zero-mean GP on observed residuals, and :func:`total_predictive_band` turns a -posterior chain into a predictive band on any grid that propagates the model -parameters, the conditioned discrepancy and, optionally, observation noise. -The band needs nothing but the problem, the kernel term and a predictor: the -training rows, the comparison space, the kernel hyperparameter and amplitude -columns and the conditioning noise all follow from the problem. +r"""Posterior predictives on a grid the data were never measured on. + +:func:`rxmc.diagnostics.predictive_draws` draws the posterior predictive at the +*measured* points, where every term of the error model is defined, the reported +point-by-point errors included. Forecasting at new ``x`` is a different +question, and this module answers it: + +* :func:`grid_draws` — the general tool. For each posterior row the model is + evaluated on the grid, every selected covariance term is re-evaluated there + from its own definition, and one correlated draw is taken from the sum. Any + term that is a function of the :class:`~rxmc.terms.TermContext` travels: + inferred noise, :func:`~rxmc.terms.noise_fraction`, + :func:`~rxmc.terms.model_error`, normalisation, offset and systematic modes, + a parametric ``Term(fn, params, kind="matrix")`` and a Gaussian-process + :func:`~rxmc.terms.kernel`. +* :func:`gp_predictive_draws` — the same draws for a kernel term, with the + option of conditioning the discrepancy on the observed residuals. +* :func:`gp_posterior_predictive` — that conditioning on bare arrays, and + :func:`predictive_band` — percentiles over any draws. + +All three draw functions share one convention with +:func:`~rxmc.diagnostics.predictive_draws`: a percentile band of shape +``(len(levels), n_points)`` by default, the draws themselves with +``return_draws=True``. + +What cannot go on a new grid +---------------------------- +A term that is an *array*, one number per measured point, has no value at an +``x`` that was never measured: the reported statistical errors a constraint +builds by default, a fixed ``Term(array)``, a normalisation or offset whose +``magnitude=`` is per point, or a function that closes over the measured rows. +Drawing it on a grid would be inventing the error of a measurement nobody made, +so both grid functions raise and name the term. The remedies are to draw at +the data with :func:`~rxmc.diagnostics.predictive_draws`, to say which terms a +draw carries with ``terms=`` (``[]`` or ``model_only=True`` for the model +alone), or to declare the experimental error as a term that is a function of +``x`` — :func:`~rxmc.terms.noise` with ``statistical=False`` — so it is defined +everywhere the model is. + +Which terms a draw carries is a choice of object, not a detail. The model +alone, the model plus a discrepancy, and the model plus discrepancy plus +experimental error answer different questions, and only the last predicts a +*measurement*. A term that belongs to one experiment (its normalisation, say) +drawn on a grid means "a future measurement by that experiment". + +Two predictives for a kernel +---------------------------- +A kernel term declares a *mean-zero* discrepancy, so the likelihood is the +marginal ``y ~ N(ym(theta), Sigma(theta))`` and inference in ``theta`` has the +discrepancy integrated out. The matching predictive draws correlated +departures from the inferred covariance around the model's own prediction: + +.. math:: + + y_* = y_m(x_*; \theta) + \delta, \qquad + \delta \sim \mathcal N\big(0,\ K_{**}(\theta)\big) + +which says where and by how much *the model* fails. That is what +:func:`grid_draws` gives. :func:`gp_predictive_draws` with ``conditioned=True`` +instead conditions the discrepancy on the observed residuals, + +.. math:: + + \delta \mid r \sim \mathcal N\big(K_{*t}(K_{tt} + N)^{-1} r,\ + K_{**} - K_{*t}(K_{tt} + N)^{-1}K_{t*}\big), + +which is data-driven regression on top of the model: it interpolates the +residuals rather than describing the model's error. Kernels are duck-typed as in :func:`~rxmc.terms.kernel`: sklearn-style objects with ``clone_with_theta``, ``__call__`` and ``diag``, ``theta`` in sklearn's @@ -20,10 +77,16 @@ import numpy as np import scipy.linalg as sla +from .diagnostics import _psd_factor, _rows from .problem import Problem, _per_point from .terms import KernelTerm, TermContext, as_2d -__all__ = ["gp_posterior_predictive", "predictive_band", "total_predictive_band"] +__all__ = [ + "grid_draws", + "gp_predictive_draws", + "gp_posterior_predictive", + "predictive_band", +] # ---------------------------------------------------------------------------- @@ -105,7 +168,7 @@ def predictive_band(draws, levels=(16, 50, 84)) -> np.ndarray: # ---------------------------------------------------------------------------- -# The total predictive band of a problem +# Draws on a new grid # ---------------------------------------------------------------------------- @@ -142,41 +205,308 @@ def _one_space(constraint, rows): spaces.append(comp.space) if len(spaces) != 1: raise ValueError( - "the comparisons a kernel term spans must share one comparison space " - "to predict in it; found " - f"{[s.name for s in spaces]}" + "the comparisons a grid draw spans must share one comparison space " + f"to predict in it; found {[s.name for s in spaces]}. Pass " + "comparison= to predict in the space of one of them" ) return spaces[0] -def total_predictive_band( +def _term_name(t) -> str: + """A term in a message: its kind, its parameters and the data it sits on.""" + names = ", ".join(p.name for p in t.params) + what = f"{t.kind} term({names})" if names else f"fixed {t.kind} term" + label = getattr(getattr(t.on, "data", None), "label", None) + return f"{what} on {label!r}" if label else what + + +def _meets(c, t, rows) -> bool: + """Whether term ``t`` has an active entry on any of ``rows``.""" + return any( + e.term is t and np.intersect1d(e.rows, rows).size for e in c.covariance.entries + ) + + +def _grid_terms(c, terms, rows) -> list: + """The terms a grid draw carries, or a ``ValueError`` saying why it cannot. + + ``terms=None`` takes every declared term with an active entry on ``rows``, + and refuses when the constraint's reported statistical errors are among + them. An explicit selection is honoured as given. Either way, a term + whose ``fn`` is an array is defined only at the measured rows. + """ + if terms is None: + stat = [t for t in c.statistical_terms if _meets(c, t, rows)] + if stat: + raise ValueError( + "the constraint's covariance includes the reported statistical " + f"errors ({[_term_name(t) for t in stat]}), one number per measured " + "point, and they have no value at an x that was never measured. " + "Draw at the measured points with rxmc.diagnostics.predictive_draws; " + "or choose the terms a grid draw carries with terms=[...] " + "(model_only=True for the model alone); or declare the experimental " + "error as a term that is a function of x, e.g. rxmc.terms.noise " + "with statistical=False, so it is defined everywhere the model is" + ) + chosen = [t for t in c.source.terms if _meets(c, t, rows)] + else: + chosen = list(terms) + for t in chosen: + if not any(t is u for u in c.source.terms): + raise ValueError( + f"{t!r} is not a term of this constraint; terms= selects among " + "the terms it was declared with (the reported statistical " + "errors cannot be drawn on a new grid at all)" + ) + arrays = [t for t in chosen if not callable(t.fn)] + if arrays: + raise ValueError( + f"{[_term_name(t) for t in arrays]}: an array-valued term has one value " + "per measured point and no value at a new x. Leave it out of terms=, " + "or write it as a function of the TermContext (c.x, c.ym) so it can be " + "evaluated anywhere" + ) + return chosen + + +def _grid_piece(t, x_pred, mu, meta_p, values) -> np.ndarray: + """One term's contribution to the covariance at the prediction points. + + ``y`` and ``ym`` are both the model's prediction there: at a point that was + never measured the only meaning a term's ``c.y`` can carry is the model. + """ + try: + v = np.asarray(t.value(x_pred, mu, mu, *values, meta=meta_p), dtype=float) + except Exception as err: + raise ValueError( + f"the {_term_name(t)} could not be evaluated at the prediction points " + f"({err}); a term closing over the measured rows is defined only " + "there. Leave it out of terms=, or write it as a function of the " + "TermContext" + ) from err + if t.kind == "diag": + return np.diag(v**2) + if t.kind == "mode": + return np.outer(v, v) + return v + + +def _draw_on_grid( + problem, + c, + space, + chosen, + predictor, + x_pred, + samples, + *, + model_only, + joint, + physical, + n_rep, + rng, + levels, + return_draws, + noise_std=0.0, + condition=None, +): + """The loop both grid functions share; ``condition(theta, mu)`` returns the + GP posterior ``(mean, v)`` to shift the draw and subtract ``v^T v``.""" + if physical and space.inverse is None: + raise ValueError(f"comparison space {space.name!r} has no inverse") + x_pred = np.asarray(x_pred) + n_pred = x_pred.shape[0] + cols_pred = problem.columns(predictor.params) + gathers = [(t, problem.columns(t.params)) for t in chosen] + # the prediction points carry the metadata the predictor was bound with + meta_p = ( + None + if predictor.meta is None + else {k: _per_point(v, n_pred) for k, v in predictor.meta.items()} + ) + back = space.inverse if physical else (lambda y: y) + n = samples.shape[0] + + if model_only: + out = np.empty((n, n_pred)) + for i, theta in enumerate(samples): + out[i] = back(space(predictor(*theta[cols_pred]))) + return out if return_draws else predictive_band(out, levels) + + out = np.empty((n * n_rep, n_pred)) + for i, theta in enumerate(samples): + mu = space(predictor(*theta[cols_pred])) + C = np.zeros((n_pred, n_pred)) + for t, g in gathers: + C += _grid_piece(t, x_pred, mu, meta_p, tuple(theta[g])) + f_mean = 0.0 + if condition is not None: + f_mean, v = condition(theta, mu) + C -= v.T @ v + C[np.diag_indices(n_pred)] += float(noise_std) ** 2 + z = rng.standard_normal((n_rep, n_pred)) + if joint: + z = z @ _psd_factor(0.5 * (C + C.T)).T + else: + z *= np.sqrt(np.clip(np.diag(C), 0.0, None)) + z *= c.likelihood.predictive_scale(rng, n_rep, *theta[c.like_gather])[:, None] + out[i * n_rep : (i + 1) * n_rep] = back(mu + f_mean + z) + return out if return_draws else predictive_band(out, levels) + + +def grid_draws( + problem: Problem, + predictor, + x_pred, + samples, + constraint: int = 0, + *, + comparison=None, + terms=None, + model_only: bool = False, + joint: bool = True, + physical: bool = False, + n_rep: int = 1, + rng=None, + levels=(16, 50, 84), + return_draws: bool = False, +) -> np.ndarray: + r"""Posterior-predictive band, or draws, on a grid the data were not measured on. + + For each posterior row ``theta`` the predictor gives the model on + ``x_pred`` in comparison space, every selected term of the constraint is + evaluated there into one covariance ``C(\theta)``, and ``n_rep`` correlated + draws + + .. math:: + + y_* = y_m(x_*; \theta) + s\,L(\theta) z, \qquad + L L^T = C(\theta), \quad z \sim \mathcal N(0, I) + + are taken, with ``s`` the likelihood's + :meth:`~rxmc.likelihood.Likelihood.predictive_scale` (1 for a Gaussian). + This is the grid counterpart of + :func:`~rxmc.diagnostics.predictive_draws`, and at the measured points, + with every term a function, the two draw from the same distribution. + + A term is carried by evaluating its own definition at the new points, with + the model's prediction standing in for ``c.y`` and the predictor's + ``meta`` for ``c.meta``; so only terms that are functions of the + :class:`~rxmc.terms.TermContext` can be carried. The reported statistical + errors and any array-valued term raise (module docstring). + + Because each draw is a whole correlated curve, a functional summary — a + simultaneous band, an extremum, an integral over a region — is well posed + on ``return_draws=True``. + + Parameters + ---------- + problem : Problem + The compiled problem the samples come from. + predictor : Predictor + The model bound to ``x_pred`` (``model.bind(x_pred, meta)``). Its + parameters must be columns of the problem; it need not be the model of + a comparison (a bare physics model under a comparison's correction is + fine). + x_pred : array_like + The prediction grid, in the raw coordinates the terms receive. + samples : array_like, shape (n, problem.ndim) + Rows in ``problem.names`` order: a posterior chain, or + ``problem.sample_prior(n)`` for the prior predictive. + constraint : int, optional + Index into ``problem.constraints`` whose error model is drawn. + comparison : Comparison, optional + The comparison whose experiment the grid stands for: its comparison + space is used, and ``terms=None`` takes only the terms that apply to it. + Required when the constraint holds several comparisons and + ``terms=None``. + terms : sequence of Term, optional + The declared terms a draw carries, matched by identity; ``[]`` carries + none. Default: every term (see ``comparison``), refusing if the + reported statistical errors are among them. + model_only : bool, optional + The model's own curves, with no error model and no covariance built. + joint : bool, optional + Draw whole correlated curves. ``False`` keeps only the diagonal of + ``C`` and draws each point independently, for a very large grid. + physical : bool, optional + Map every draw back through the comparison space's inverse before + taking percentiles (``space=log`` gives a band in physical units). + n_rep : int, optional + Draws per row (ignored when ``model_only``). + rng : numpy.random.Generator or seed, optional + levels : sequence of float, optional + Percentiles of the band. + return_draws : bool, optional + Return the draws instead of the band. + + Returns + ------- + np.ndarray + ``(len(levels), len(x_pred))``; with ``return_draws`` the draws, + ``(n * n_rep, len(x_pred))``, or ``(n, len(x_pred))`` when + ``model_only``. + """ + rng = np.random.default_rng(rng) + samples = _rows(samples, problem.ndim) + c = problem.constraints[constraint] + if comparison is None: + if terms is None and not model_only and len(c.comparisons) > 1: + raise ValueError( + f"the constraint holds {len(c.comparisons)} comparisons " + f"({c.labels}); pass comparison= to say which experiment the grid " + "stands for, or terms= to choose the terms a draw carries" + ) + rows = np.arange(c.offsets[-1].stop) + else: + i = next((k for k, u in enumerate(c.comparisons) if u is comparison), None) + if i is None: + raise ValueError( + f"{comparison!r} is not a comparison of constraint {constraint}" + ) + rows = np.arange(c.offsets[i].start, c.offsets[i].stop) + space = _one_space(c, rows) + chosen = [] if model_only else _grid_terms(c, terms, rows) + return _draw_on_grid( + problem, c, space, chosen, predictor, x_pred, samples, + model_only=model_only, joint=joint, physical=physical, n_rep=n_rep, + rng=rng, levels=levels, return_draws=return_draws, + ) # fmt: skip + + +def gp_predictive_draws( problem: Problem, term: KernelTerm, predictor, x_pred, samples, *, + terms=None, + conditioned: bool = False, + joint: bool = True, noise_std: float = 0.0, train_noise_var=None, - levels=(16, 84), - n_draws: int = 400, - rng=None, physical: bool = False, + n_rep: int = 1, + rng=None, + levels=(16, 50, 84), + return_draws: bool = False, ) -> np.ndarray: - r"""Predictive band on ``x_pred`` propagating model, discrepancy and noise. + r"""Grid draws for a Gaussian-process discrepancy, optionally conditioned. - For each chain row ``theta`` the model's prediction on the training rows - gives the residual ``y - ym`` in comparison space; the discrepancy GP of - ``term`` (kernel hyperparameters, amplitude and coordinate transform at - that row) is conditioned on it and predicted at ``x_pred``; one draw + With ``conditioned=False`` this is :func:`grid_draws` on the constraint + and comparison space the kernel term lives in: correlated departures + about the model's own prediction, which is the predictive that matches a + mean-zero discrepancy (module docstring). ``conditioned=True`` instead + conditions the discrepancy on the residuals of the kernel's training rows, .. math:: - y_* = \mathrm{space}(\mathrm{predictor}(\theta)) + \bar f_* - + \mathcal N\!\big(0,\ \mathrm{var}_* + \sigma^2\big) + y_* = y_m(x_*; \theta) + \bar f_* + L(\theta) z, \qquad + L L^T = C(\theta) - K_{*t}(K_{tt} + N)^{-1}K_{t*} + \sigma^2 I - is taken, and the requested percentiles over the draws are returned. - Only the GP's posterior *variance* enters per point. + with ``\bar f_* = K_{*t}(K_{tt} + N)^{-1} r``: GP regression on top of + the model, which interpolates the residuals. Parameters ---------- @@ -185,80 +515,83 @@ def total_predictive_band( term : KernelTerm The discrepancy term, as declared in one of the problem's constraints. predictor : Predictor - The model bound to ``x_pred`` (``model.bind(x_pred, meta)``); its - columns are read from the problem, and the ``meta`` it was bound with - is what a callable amplitude's ``c.meta(key)`` reads at ``x_pred``. + The model bound to ``x_pred`` (``model.bind(x_pred, meta)``); the + ``meta`` it was bound with is what a callable amplitude's + ``c.meta(key)`` reads at ``x_pred``. x_pred : array_like The prediction grid (raw coordinates; the term's ``coords`` transform is applied for the kernel). samples : array_like, shape (n, problem.ndim) Chain rows in ``problem.names`` order. + terms : sequence of Term, optional + The terms a draw carries, including ``term`` itself. Default: every + term of the constraint that meets the kernel's rows, under the rules + of :func:`grid_draws`. ``[term]`` alone gives model plus discrepancy; + adding the experimental terms gives what data is compared against. + A selected term applies to the whole of ``x_pred``. + conditioned : bool, optional + Condition the discrepancy on the observed residuals instead of drawing + it mean-zero. Default ``False``. + joint : bool, optional + Draw whole correlated curves; ``False`` draws each point independently. noise_std : float, optional - Observation noise added at the prediction points, in comparison space. + Extra observation noise at the prediction points, in comparison space. train_noise_var : float, array_like or matrix, optional - Conditioning noise on the training rows. Default: everything in the - constraint's covariance at that row *except* the kernel term itself - (statistical errors, noise and mode terms), which is the exact GP - regression noise for the declared error model. + Conditioning noise on the training rows; ``conditioned=True`` only. + Default: everything in the constraint's covariance at those rows + *except* the kernel term itself, which is the exact GP regression noise + for the declared error model. + physical : bool, optional + Map every draw back through the comparison space's inverse. + n_rep : int, optional + Draws per chain row. + rng : numpy.random.Generator or seed, optional levels : sequence of float, optional Percentiles of the band. - n_draws : int, optional - Rows subsampled from the chain (all rows when the chain is shorter). - rng : numpy.random.Generator or seed, optional - physical : bool, optional - Map every draw back through the comparison space's inverse before - taking percentiles (``space=log`` gives a band in physical units). + return_draws : bool, optional + Return the ``(n * n_rep, len(x_pred))`` draws instead of the band. Returns ------- - np.ndarray, shape (len(levels), len(x_pred)) + np.ndarray + ``(len(levels), len(x_pred))``, or the draws when ``return_draws``. """ rng = np.random.default_rng(rng) - samples = np.asarray(samples, dtype=float) - if samples.ndim == 1: - samples = samples[None, :] - if samples.shape[1] != problem.ndim: + samples = _rows(samples, problem.ndim) + if train_noise_var is not None and not conditioned: raise ValueError( - f"samples must have shape (n, {problem.ndim}) in problem.names order, " - f"got {samples.shape}" + "train_noise_var is the noise the GP regression conditions on and " + "applies only with conditioned=True" ) - if samples.shape[0] > n_draws: - samples = samples[rng.choice(samples.shape[0], n_draws, replace=False)] - c, entry = _locate(problem, term) rows = entry.rows keep = entry.keep # the active rows of the term's support pos = entry.pos[keep] # their positions in the active stack space = _one_space(c, rows) - if physical and space.inverse is None: - raise ValueError(f"comparison space {space.name!r} has no inverse") + chosen = _grid_terms(c, terms, rows) + if not any(t is term for t in chosen): + raise ValueError( + "terms= must include the kernel term: it is the discrepancy these " + "draws predict" + ) cols_term = problem.columns(term.params) - cols_pred = problem.columns(predictor.params) nk, n_fn = term.n_kernel, term._n_fn_params x_pred = np.asarray(x_pred) meta = None if c.meta is None else {k: v[rows] for k, v in c.meta.items()} - # the prediction points carry the metadata the predictor was bound with meta_p = ( None if predictor.meta is None - else {k: _per_point(v, len(x_pred)) for k, v in predictor.meta.items()} + else {k: _per_point(v, x_pred.shape[0]) for k, v in predictor.meta.items()} ) - out = np.empty((samples.shape[0], x_pred.shape[0])) - for i, theta in enumerate(samples): + def condition(theta, mu): values = tuple(theta[cols_term]) - kv, av, cv = values[:nk], values[nk:n_fn], values[n_fn:] ym = c.ym(theta) - # the term's own block on its active rows, exactly as the likelihood saw it + # the term's own block on its rows, exactly as the likelihood saw it K_full = term.value( - c.x[rows], - c.y[rows], - ym[rows], - *values, - meta=meta, - segments=entry.segments, - labels=entry.labels, - ) + c.x[rows], c.y[rows], ym[rows], *values, + meta=meta, segments=entry.segments, labels=entry.labels, + ) # fmt: skip Ktt = K_full[np.ix_(keep, keep)] r = (c.y - ym)[rows][keep] if train_noise_var is None: @@ -267,7 +600,7 @@ def total_predictive_band( N = _train_noise_matrix(train_noise_var, len(r)) # kernel and amplitude at the training and prediction coordinates k = ( - term.kernel.clone_with_theta(np.asarray(kv, dtype=float)) + term.kernel.clone_with_theta(np.asarray(values[:nk], dtype=float)) if nk else term.kernel ) @@ -275,16 +608,19 @@ def total_predictive_band( c.x[rows], c.y[rows], ym[rows], meta, *values, segments=entry.segments, labels=entry.labels, ) # fmt: skip - mu = space(predictor(*theta[cols_pred])) - ctx_p = TermContext(x=term.coords(x_pred, *cv), y=mu, ym=mu, _meta=meta_p) - a_t, a_p = _amplitudes(term, ctx_t, ctx_p, av) + ctx_p = TermContext( + x=term.coords(x_pred, *values[n_fn:]), y=mu, ym=mu, _meta=meta_p + ) + a_t, a_p = _amplitudes(term, ctx_t, ctx_p, values[nk:n_fn]) Kst = np.outer(a_p, a_t[keep]) * k(as_2d(ctx_p.x), as_2d(ctx_t.x)[keep]) - Kss = a_p**2 * np.asarray(k.diag(as_2d(ctx_p.x)), dtype=float) - f_mean, v = _condition(Ktt, Kst, r, N, 0.0) - f_var = np.clip(Kss - np.einsum("ij,ij->j", v, v), 0.0, None) - y = mu + f_mean + rng.normal(0.0, np.sqrt(f_var + float(noise_std) ** 2)) - out[i] = space.inverse(y) if physical else y - return predictive_band(out, levels) + return _condition(Ktt, Kst, r, N, 0.0) + + return _draw_on_grid( + problem, c, space, chosen, predictor, x_pred, samples, + model_only=False, joint=joint, physical=physical, n_rep=n_rep, rng=rng, + levels=levels, return_draws=return_draws, noise_std=noise_std, + condition=condition if conditioned else None, + ) # fmt: skip def _amplitudes(term, ctx_t, ctx_p, values): diff --git a/src/rxmc/problem.py b/src/rxmc/problem.py index abe73bd..208a523 100644 --- a/src/rxmc/problem.py +++ b/src/rxmc/problem.py @@ -434,9 +434,14 @@ def __init__(self, constraint: Constraint, index: ParameterIndex): (o, index.add_all(c.predictor.params), c) for o, c in zip(self.offsets, comps) ] - terms = list(constraint.terms) - if constraint.statistical: - terms = [statistical(c.y_err, on=c) for c in comps] + terms + # the reported statistical diagonals are built here, not by the user, so + # a term selection names them with statistical=True rather than by object + self.statistical_terms = ( + [statistical(c.y_err, on=c) for c in comps] + if constraint.statistical + else [] + ) + terms = self.statistical_terms + list(constraint.terms) entries = [ (t, constraint.support(t.on), index.add_all(t.params)) for t in terms ] @@ -490,9 +495,38 @@ def chi2(self, theta) -> float: return np.inf return float(self.likelihood.chi2(*s, self.n_active, *theta[self.like_gather])) - def matrix(self, theta) -> np.ndarray: - """The dense covariance on the active points at ``theta``.""" - return self.covariance.matrix(self.ym(theta), theta) + def entries_for(self, terms=None, statistical: bool = True): + """Covariance entries of a term selection, or ``None`` for all of them. + + ``terms`` are :class:`~rxmc.terms.Term` objects declared on this + constraint, matched *by identity*; ``None`` means every declared term. + ``statistical`` says whether the reported statistical diagonals join + them. ``None`` is returned for the full selection so the caller takes + the cached path. + """ + if terms is None and statistical: + return None + chosen = list(self.source.terms) if terms is None else list(terms) + for t in chosen: + if not any(t is u for u in self.source.terms): + raise ValueError( + f"{t!r} is not a term of this constraint; terms= selects among " + "the terms it was declared with (the reported statistical " + "errors are selected with statistical=True/False instead)" + ) + if statistical: + chosen = self.statistical_terms + chosen + return [e for e in self.covariance.entries if any(e.term is t for t in chosen)] + + def matrix(self, theta, *, terms=None, statistical: bool = True) -> np.ndarray: + """The dense covariance on the active points at ``theta``. + + ``terms`` and ``statistical`` select part of the error model; see + :meth:`entries_for`. The default is the covariance the likelihood uses. + """ + return self.covariance.matrix( + self.ym(theta), theta, entries=self.entries_for(terms, statistical) + ) def __repr__(self): return f"CompiledConstraint({self.labels}, n_active={self.n_active})" diff --git a/src/rxmc/terms.py b/src/rxmc/terms.py index 8642447..e1d4e85 100644 --- a/src/rxmc/terms.py +++ b/src/rxmc/terms.py @@ -21,6 +21,11 @@ constraint; :class:`~rxmc.problem.Problem` resolves that to rows when it compiles. The same term may be placed in several constraints. +A term whose ``fn`` is a function of the context is defined wherever the model +is, so :func:`~rxmc.predictive.grid_draws` can evaluate it on a grid that was +never measured. A term that is an array — the reported statistical errors, a +fixed covariance, a per-point ``magnitude=`` — exists only at the measured rows. + Two mechanisms are expressed here (see ``docs/groundup_design.md``): * **Correlating comparisons** — a ``mode`` or ``matrix`` term whose ``on`` @@ -291,7 +296,7 @@ def __repr__(self): class KernelTerm(Term): """A :func:`kernel` term that also carries what GP conditioning needs. - :func:`~rxmc.predictive.total_predictive_band` reads these to predict the + :func:`~rxmc.predictive.gp_predictive_draws` reads these to condition the discrepancy at new points; the covariance machinery treats a ``KernelTerm`` exactly as a ``matrix`` :class:`Term`. diff --git a/test/recipes/test_recipe_07_gp_discrepancy.py b/test/recipes/test_recipe_07_gp_discrepancy.py index 97e5c40..75dbf9a 100644 --- a/test/recipes/test_recipe_07_gp_discrepancy.py +++ b/test/recipes/test_recipe_07_gp_discrepancy.py @@ -5,13 +5,14 @@ """ import numpy as np +import pytest from scipy import stats from sklearn.gaussian_process.kernels import RBF, ConstantKernel, Matern from common import TRUE, line, linear_posterior from rxmc import Comparison, Constraint, Dataset, KernelTerm, Parameter, Problem from rxmc import terms as T -from rxmc.predictive import total_predictive_band +from rxmc.predictive import gp_predictive_draws, grid_draws from rxmc.reactions import momentum_transfer X = np.linspace(0.3, 2.5, 12) @@ -74,7 +75,8 @@ def test_the_band_finds_the_kernel_columns_itself(): comp = Comparison(d, model) log_eps = Parameter("log_eps", prior=stats.norm(-3, 1)) gp = T.kernel(RBF(0.5), on=comp) - p = Problem([Constraint([comp], terms=[T.noise(log_eps), gp])]) + eps = T.noise(log_eps) + p = Problem([Constraint([comp], terms=[eps, gp])]) # a chain in problem.names order with the nuisance column between the # model parameters and the kernel hyperparameter rng = np.random.default_rng(1) @@ -87,12 +89,52 @@ def test_the_band_finds_the_kernel_columns_itself(): ] ) x_fine = np.linspace(0.0, 3.0, 50) - band = total_predictive_band( - p, gp, model.bind(x_fine, d.meta), x_fine, chain, rng=0 + pred = model.bind(x_fine, d.meta) + band = gp_predictive_draws(p, gp, pred, x_fine, chain, terms=[gp], rng=0) + assert band.shape == (3, 50) and np.all(np.isfinite(band)) + assert np.all(band[2] > band[0]) + # the discrepancy is mean-zero, so the band is an envelope *about the model*: + # it says where and by how much the model may be wrong, not what the data is + assert np.all((band[0] < line_y(x_fine)) & (line_y(x_fine) < band[2])) + # unconditioned, it is grid_draws with the kernel named: nothing GP-specific + np.testing.assert_allclose( + band, grid_draws(p, pred, x_fine, chain, terms=[gp], rng=0) ) - assert band.shape == (2, 50) and np.all(np.isfinite(band)) - assert np.all(band[1] > band[0]) - # inside the data the band is narrow, outside it relaxes to the prior width + # the inferred noise is a function of x too, so a measurement's band exists + full = grid_draws(p, pred, x_fine, chain, terms=[eps, gp], rng=0) + assert full.shape == (3, 50) and np.all(np.isfinite(full)) + # the reported errors exist only at the measured points + with pytest.raises(ValueError, match="reported statistical"): + grid_draws(p, pred, x_fine, chain) + + +def line_y(x): + return TRUE[0] * x + TRUE[1] + + +def test_conditioning_turns_the_band_into_regression_on_the_residuals(): + """``conditioned=True`` is the other object: a data-driven fit on top.""" + d = defect_data() + model = line() + comp = Comparison(d, model) + log_eps = Parameter("log_eps", prior=stats.norm(-3, 1)) + gp = T.kernel(RBF(0.5), on=comp) + p = Problem([Constraint([comp], terms=[T.noise(log_eps), gp])]) + rng = np.random.default_rng(1) + chain = np.column_stack( + [ + TRUE[0] + 0.02 * rng.standard_normal(30), + TRUE[1] + 0.02 * rng.standard_normal(30), + np.full(30, np.log(0.05)), + np.full(30, np.log(0.5)), + ] + ) + x_fine = np.linspace(0.0, 3.0, 50) + band = gp_predictive_draws( + p, gp, model.bind(x_fine, d.meta), x_fine, chain, + terms=[gp], levels=(16, 84), rng=0, conditioned=True, + ) # fmt: skip + # conditioning pins the discrepancy where there is data and relaxes outside inside = (x_fine > 0.5) & (x_fine < 2.3) assert np.median((band[1] - band[0])[inside]) < np.median( (band[1] - band[0])[~inside] diff --git a/test/recipes/test_recipe_17_posterior_predictive.py b/test/recipes/test_recipe_17_posterior_predictive.py index 4e796b5..6d9792a 100644 --- a/test/recipes/test_recipe_17_posterior_predictive.py +++ b/test/recipes/test_recipe_17_posterior_predictive.py @@ -27,14 +27,16 @@ def test_draws_are_ym_plus_correlated_noise_in_comparison_space(): log_eps = Parameter("log_eps", prior=stats.norm(-2, 1)) p = Problem([Constraint([Comparison(d, line())], terms=[T.noise(log_eps)])]) theta = np.array([*TRUE, np.log(0.2)]) - draws = predictive_draws(p, theta, n_rep=20000, rng=0) + draws = predictive_draws(p, theta, n_rep=20000, rng=0, return_draws=True) ym = TRUE[0] * d.x + TRUE[1] np.testing.assert_allclose(draws.mean(0), ym, atol=0.01) np.testing.assert_allclose( np.cov(draws.T), p.constraints[0].matrix(theta), atol=0.01 ) # model_only: the predictions themselves, no covariance - np.testing.assert_allclose(predictive_draws(p, theta, model_only=True)[0], ym) + np.testing.assert_allclose( + predictive_draws(p, theta, model_only=True, return_draws=True)[0], ym + ) def big_dataset(err_scale=1.0, seed=3, n=200): @@ -49,7 +51,7 @@ def test_coverage_is_nominal_for_the_right_error_model_and_low_for_an_overconfid d = big_dataset() p = Problem([Constraint([Comparison(d, line())])]) s = oracle_samples(p, 400, rng=0) # exact posterior rows stand in for a chain - draws = predictive_draws(p, s, n_rep=4, rng=1) + draws = predictive_draws(p, s, n_rep=4, rng=1, return_draws=True) y_active = p.constraints[0].y[p.constraints[0].active] np.testing.assert_allclose( coverage_curve(draws, y_active, levels), levels, atol=0.1 @@ -58,7 +60,7 @@ def test_coverage_is_nominal_for_the_right_error_model_and_low_for_an_overconfid # the same data with the errors claimed five times smaller tight = Problem([Constraint([Comparison(big_dataset(err_scale=0.2), line())])]) draws_tight = predictive_draws( - tight, oracle_samples(tight, 400, rng=0), n_rep=4, rng=1 + tight, oracle_samples(tight, 400, rng=0), n_rep=4, rng=1, return_draws=True ) assert np.all(coverage_curve(draws_tight, y_active, levels) < levels - 0.2) @@ -68,7 +70,9 @@ def test_sharpness_in_physical_units_for_a_log_fit(): comp = Comparison(d, line(), space=tf.log) log_eps = Parameter("log_eps", prior=stats.norm(-2, 1)) p = Problem([Constraint([comp], terms=[T.noise(log_eps)], statistical=False)]) - draws = predictive_draws(p, np.array([*TRUE, np.log(0.1)]), n_rep=2000, rng=2) + draws = predictive_draws( + p, np.array([*TRUE, np.log(0.1)]), n_rep=2000, rng=2, return_draws=True + ) width_log = sharpness(draws) width = sharpness(draws, transform=np.exp) assert np.all(width > 0) and np.all(width_log > 0) diff --git a/test/recipes/test_recipe_30_leave_one_experiment_out.py b/test/recipes/test_recipe_30_leave_one_experiment_out.py index 38ae477..d618413 100644 --- a/test/recipes/test_recipe_30_leave_one_experiment_out.py +++ b/test/recipes/test_recipe_30_leave_one_experiment_out.py @@ -28,7 +28,7 @@ def test_held_out_draws_coverage_and_tolerance(): for i in range(3): fit, held, _ = split(i) s = oracle_samples(fit, 300, rng=i) - draws = predictive_draws(held, s, n_rep=4, rng=i) + draws = predictive_draws(held, s, n_rep=4, rng=i, return_draws=True) h = held.constraints[0] assert draws.shape == (1200, DATA[i].n) and h.n_active == DATA[i].n tol = np.percentile(np.abs(draws - draws.mean(0)), 90, axis=0) @@ -49,8 +49,10 @@ def test_a_discrepancy_fit_to_the_other_experiments_carries_into_the_prediction( fit, held, full = split(2, terms=[spanning_gp]) theta = np.array(TRUE) # the marginal held-out block ignores what the fitted experiments taught the GP - marginal = predictive_draws(held, theta, model_only=True)[0] - conditional = predictive_draws(held, theta, model_only=True, given=fit)[0] + marginal = predictive_draws(held, theta, model_only=True, return_draws=True)[0] + conditional = predictive_draws( + held, theta, model_only=True, given=fit, return_draws=True + )[0] np.testing.assert_allclose(marginal, TRUE[0] * DATA[2].x + TRUE[1]) assert not np.allclose(conditional, marginal) # the conditional density is the joint divided by the fit, exactly @@ -59,5 +61,5 @@ def test_a_discrepancy_fit_to_the_other_experiments_carries_into_the_prediction( # ...which the marginal is not, because the GP spans the split assert heldout_log_predictive(held, theta)[0] != pytest.approx(lp) s = oracle_samples(fit, 200, rng=0) - draws = predictive_draws(held, s, n_rep=4, rng=0, given=fit) + draws = predictive_draws(held, s, n_rep=4, rng=0, given=fit, return_draws=True) assert draws.shape == (800, DATA[2].n) and np.all(np.isfinite(draws)) diff --git a/test/recipes/test_recipe_31_sbc.py b/test/recipes/test_recipe_31_sbc.py index 4ecae0c..656fbdd 100644 --- a/test/recipes/test_recipe_31_sbc.py +++ b/test/recipes/test_recipe_31_sbc.py @@ -18,7 +18,9 @@ def simulate(problem, comp, theta0, rng): """A dataset drawn from the model at ``theta0``, back in physical units.""" - y_sim = predictive_draws(problem, theta0[None], n_rep=1, rng=rng)[0] + y_sim = predictive_draws( + problem, theta0[None], n_rep=1, rng=rng, return_draws=True + )[0] d_sim = dataclasses.replace(comp.data, y=comp.space.inverse(y_sim)) return Problem([Constraint([Comparison(d_sim, comp.model)])]) diff --git a/test/recipes/test_recipe_40_predict_on_a_new_grid.py b/test/recipes/test_recipe_40_predict_on_a_new_grid.py new file mode 100644 index 0000000..95fff6f --- /dev/null +++ b/test/recipes/test_recipe_40_predict_on_a_new_grid.py @@ -0,0 +1,83 @@ +"""Recipe 40: predict on a new grid, error model included. + +I have a posterior, and I want predictions at ``x`` I never measured — a fine +plotting grid, an extrapolation — carrying the uncertainty my error model +declares, not only the spread of the model curves. +""" + +import numpy as np +import pytest +from scipy import stats + +from common import TRUE, line, oracle_samples +from rxmc import Comparison, Constraint, Dataset, Parameter, Problem +from rxmc import terms as T +from rxmc.diagnostics import coverage_curve, predictive_draws +from rxmc.predictive import grid_draws + +SIGMA = 0.1 +X_FINE = np.linspace(-1.0, 4.0, 40) # past the data on both sides + + +def data(n=200, seed=4): + rng = np.random.default_rng(seed) + x = np.linspace(0.5, 2.5, n) + y = TRUE[0] * x + TRUE[1] + rng.normal(0.0, SIGMA, n) + return Dataset(x, y, np.full(n, SIGMA), label="d") + + +def problems(): + """The same line with the reported errors, and with a constant inferred noise.""" + d = data() + model = line() + comp = Comparison(d, model) + reported = Problem([Constraint([comp])]) + log_sigma = Parameter("log_sigma", prior=stats.norm(np.log(0.2), 1.0)) + inferred = Problem( + [Constraint([comp], terms=[T.noise(log_sigma)], statistical=False)] + ) + return reported, inferred, model, d + + +def chain(reported, n=400): + """Exact posterior rows for (m, b), with the noise at its true value.""" + mb = oracle_samples(reported, n, rng=0) + return np.column_stack([mb, np.full(n, np.log(SIGMA))]) + + +def test_the_error_model_travels_to_the_grid_and_the_model_band_does_not_widen(): + reported, inferred, model, _ = problems() + rows = chain(reported) + pred = model.bind(X_FINE) + curve = grid_draws(inferred, pred, X_FINE, rows, model_only=True, levels=(16, 84)) + full = grid_draws(inferred, pred, X_FINE, rows, n_rep=4, rng=1, levels=(16, 84)) + w_curve, w_full = curve[1] - curve[0], full[1] - full[0] + # a measurement at any x scatters by sigma about a curve known far better + np.testing.assert_allclose(np.sqrt(w_full**2 - w_curve**2) / 2, SIGMA, rtol=0.15) + # and the curve's own uncertainty fans out away from the data + assert w_curve[0] > 3 * w_curve[len(X_FINE) // 2] + + +def test_at_the_data_the_model_band_under_covers_and_the_full_one_does_not(): + reported, inferred, _, d = problems() + rows = chain(reported) + levels = np.array([0.5, 0.68, 0.9]) + c = inferred.constraints[0] + y = c.y[c.active] + full = predictive_draws(inferred, rows, n_rep=4, rng=2, return_draws=True) + curve = predictive_draws(inferred, rows, model_only=True, return_draws=True) + np.testing.assert_allclose(coverage_curve(full, y, levels), levels, atol=0.08) + assert np.all(coverage_curve(curve, y, levels) < 0.5 * levels) + + +def test_reported_errors_have_no_value_off_the_measured_points(): + reported, _, model, _ = problems() + rows = chain(reported)[:, :2] + pred = model.bind(X_FINE) + with pytest.raises(ValueError, match="reported statistical errors"): + grid_draws(reported, pred, X_FINE, rows) + # saying so explicitly is allowed: the model alone + band = grid_draws(reported, pred, X_FINE, rows, terms=[]) + np.testing.assert_allclose( + band, grid_draws(reported, pred, X_FINE, rows, model_only=True) + ) diff --git a/test/test_diagnostics.py b/test/test_diagnostics.py index 38502a9..a4033cf 100644 --- a/test/test_diagnostics.py +++ b/test/test_diagnostics.py @@ -46,29 +46,61 @@ class TestPredictiveDraws: def test_draw_covariance_recovers_sigma(self): p, _ = line_problem([noise(Parameter("log_eps", prior=stats.norm(-2, 1)))]) row = np.array([1.0, 2.0, np.log(0.4)]) - draws = predictive_draws(p, row, n_rep=40000, rng=0) + draws = predictive_draws(p, row, n_rep=40000, rng=0, return_draws=True) assert draws.shape == (40000, 5) np.testing.assert_allclose(draws.mean(axis=0), Y, atol=0.02) np.testing.assert_allclose(np.cov(draws.T), np.diag(ERR**2 + 0.16), atol=0.02) + def test_terms_select_part_of_the_error_model(self): + """Model plus discrepancy and model plus everything are different objects.""" + eps = noise(Parameter("log_eps", prior=stats.norm(-2, 1))) + p, _ = line_problem([eps]) + row = np.array([1.0, 2.0, np.log(0.4)]) + c = p.constraints[0] + np.testing.assert_allclose(c.matrix(row), np.diag(ERR**2 + 0.16)) + np.testing.assert_allclose( + c.matrix(row, terms=[eps], statistical=False), np.diag(np.full(5, 0.16)) + ) + np.testing.assert_allclose(c.matrix(row, terms=[]), np.diag(ERR**2)) + draws = predictive_draws( + p, + row, + terms=[eps], + statistical=False, + n_rep=40000, + rng=0, + return_draws=True, + ) + np.testing.assert_allclose( + np.cov(draws.T), np.diag(np.full(5, 0.16)), atol=0.02 + ) + with pytest.raises(ValueError, match="not a term of this constraint"): + c.matrix(row, terms=[noise(Parameter("other", prior=stats.norm(0, 1)))]) + with pytest.raises(ValueError, match="cannot be combined with a term"): + predictive_draws(p, row, statistical=False, given=p, return_draws=True) + def test_model_only_returns_ym_and_assembles_nothing(self): p, _ = line_problem() with patch.object(StructuredCovariance, "matrix") as m: - d = predictive_draws(p, [[1.0, 2.0], [0.0, 1.0]], model_only=True) + d = predictive_draws( + p, [[1.0, 2.0], [0.0, 1.0]], model_only=True, return_draws=True + ) m.assert_not_called() np.testing.assert_allclose(d[0], Y) np.testing.assert_allclose(d[1], X) def test_one_row_masks_and_width_check(self): p, _ = line_problem(masks=[np.array([True, False, True, False, True])]) - d = predictive_draws(p, [1.0, 2.0], n_rep=3, rng=1) + d = predictive_draws(p, [1.0, 2.0], n_rep=3, rng=1, return_draws=True) assert d.shape == (3, 3) with pytest.raises(ValueError, match=r"\(n, 2\)"): - predictive_draws(p, [[1.0, 2.0, 3.0]]) + predictive_draws(p, [[1.0, 2.0, 3.0]], return_draws=True) def test_student_t_draws_follow_the_multivariate_t(self): p, _ = line_problem(likelihood=StudentT(Parameter("nu", bounds=(1, 30)))) - draws = predictive_draws(p, [1.0, 2.0, 6.0], n_rep=100000, rng=0) + draws = predictive_draws( + p, [1.0, 2.0, 6.0], n_rep=100000, rng=0, return_draws=True + ) # a multivariate t with scale diag(ERR**2) has covariance nu/(nu-2) times it np.testing.assert_allclose(draws.var(axis=0), 1.5 * ERR**2, rtol=0.05) # one mixing scale per draw: the points' |residuals| move together @@ -86,7 +118,7 @@ def test_fallback_factor_is_triangular(self): def test_tiny_variances_not_inflated(self): d = Dataset(X, Y, np.full(5, 1e-9), label="tiny") p = Problem([Constraint([Comparison(d, poly(1))])]) - draws = predictive_draws(p, [1.0, 2.0], n_rep=2000, rng=3) + draws = predictive_draws(p, [1.0, 2.0], n_rep=2000, rng=3, return_draws=True) assert np.all(draws.std(axis=0) < 1e-8) @@ -141,8 +173,10 @@ def test_marginal_matches_manual_and_partitions_without_a_spanning_term(self): # the conditional equals the marginal when nothing spans the split np.testing.assert_allclose(heldout_log_predictive(held, samples, given=fit), lp) np.testing.assert_allclose( - predictive_draws(held, samples, given=fit, model_only=True), - predictive_draws(held, samples, model_only=True), + predictive_draws( + held, samples, given=fit, model_only=True, return_draws=True + ), + predictive_draws(held, samples, model_only=True, return_draws=True), ) def test_conditional_under_a_spanning_matrix_term(self): @@ -161,7 +195,9 @@ def test_conditional_under_a_spanning_matrix_term(self): assert lp[0] == pytest.approx(manual_mvn_loglike(self.d.y[H], mean, cov)) # and it is not the marginal assert lp[0] != pytest.approx(heldout_log_predictive(held, theta)[0]) - draws = predictive_draws(held, theta, n_rep=40000, rng=0, given=fit) + draws = predictive_draws( + held, theta, n_rep=40000, rng=0, given=fit, return_draws=True + ) np.testing.assert_allclose(draws.mean(axis=0), mean, atol=0.02) np.testing.assert_allclose(np.cov(draws.T), cov, atol=0.03) # the conditional is exactly the joint over the full data divided by the fit @@ -192,7 +228,9 @@ def test_conditional_reads_the_columns_of_every_constraint(self): ym = theta[1] * self.d.x F, H = slice(0, 2), slice(2, 4) mean = ym[H] + S[H, F] @ np.linalg.solve(S[F, F], 3.0 * self.d.y[F] - ym[F]) - draws = predictive_draws(held, theta, constraint=1, given=fit, model_only=True) + draws = predictive_draws( + held, theta, constraint=1, given=fit, model_only=True, return_draws=True + ) np.testing.assert_allclose(draws[0], mean) def test_given_needs_no_marginal_priors_and_keeps_doubly_masked_rows_out(self): diff --git a/test/test_notebooks_index.py b/test/test_notebooks_index.py index 5235303..1ee1958 100644 --- a/test/test_notebooks_index.py +++ b/test/test_notebooks_index.py @@ -18,7 +18,7 @@ # design document section 9, item 8: the nine notebooks and their recipes NOTEBOOKS = { - "linear_calibration": {1, 17}, + "linear_calibration": {1, 2, 17, 40}, "error_models": {2, 4, 19}, "sharing_error_models": {5}, "normalization_and_covariance_structure": {3, 4, 6, 27}, @@ -26,7 +26,7 @@ "robust_likelihoods": {9, 39}, "error_scale_and_usu": {34}, "local_optical_model_calibration": {4, 12, 14, 15, 16, 21, 26}, - "alpha_ca_error_model_comparison": {10, 11, 13, 17, 18}, + "alpha_ca_error_model_comparison": {7, 10, 11, 13, 17, 18}, "hierarchical_calibration": {22, 24, 30, 35, 38}, } diff --git a/test/test_predictive.py b/test/test_predictive.py index 3f68814..db4edd4 100644 --- a/test/test_predictive.py +++ b/test/test_predictive.py @@ -1,4 +1,4 @@ -"""GP conditioning and the total predictive band of a problem.""" +"""GP conditioning, and posterior-predictive draws on a new grid.""" import numpy as np import pytest @@ -6,13 +6,34 @@ from sklearn.gaussian_process import GaussianProcessRegressor from sklearn.gaussian_process.kernels import RBF, ConstantKernel, WhiteKernel -from rxmc import Comparison, Constraint, Dataset, Model, Parameter, Problem, Term +from rxmc import ( + Comparison, + Constraint, + Dataset, + Model, + Parameter, + Problem, + StudentT, + Term, +) +from rxmc.diagnostics import predictive_draws from rxmc.predictive import ( gp_posterior_predictive, + gp_predictive_draws, + grid_draws, predictive_band, - total_predictive_band, ) -from rxmc.terms import constant_amplitude, kernel, noise +from rxmc.terms import ( + constant_amplitude, + exp_growth_amplitude, + kernel, + model_error, + noise, + noise_fraction, + normalization, + systematic, + x_basis, +) from rxmc.transforms import log @@ -70,7 +91,7 @@ def test_predictive_band_percentiles(self): # ---------------------------------------------------------------------------- -# total_predictive_band on a Problem +# gp_predictive_draws on a Problem # ---------------------------------------------------------------------------- @@ -100,7 +121,7 @@ def problem(terms_first, space=None, err=0.05, fixed=True): return p, gp, model, comp -class TestTotalPredictiveBand: +class TestGPPredictiveDraws: def chain(self, p, n=40, seed=2): rng = np.random.default_rng(seed) rows = np.zeros((n, p.ndim)) @@ -117,9 +138,10 @@ def test_shape_finite_and_columns_from_the_problem(self): assert p1.names != p2.names # the nuisance and kernel columns swapped bands = [] for p, gp, model in ((p1, gp1, model1), (p2, gp2, model2)): - band = total_predictive_band( - p, gp, model.bind(X_PRED, {}), X_PRED, self.chain(p), rng=0 - ) + band = gp_predictive_draws( + p, gp, model.bind(X_PRED, {}), X_PRED, self.chain(p), + terms=list(p.constraints[0].source.terms), levels=(16, 84), rng=0, + ) # fmt: skip assert band.shape == (2, 30) and np.all(np.isfinite(band)) assert np.all(band[1] >= band[0]) bands.append(band) @@ -128,21 +150,89 @@ def test_shape_finite_and_columns_from_the_problem(self): def test_conditioned_band_passes_through_the_data(self): p, gp, model, comp = problem("noise", err=1e-4) chain = np.tile([0.5, 0.2, np.log(1e-4)], (5, 1)) - band = total_predictive_band( - p, gp, model.bind(X, {}), X, chain, levels=(50,), rng=1 - ) + band = gp_predictive_draws( + p, gp, model.bind(X, {}), X, chain, terms=[gp], levels=(50,), rng=1, + conditioned=True, + ) # fmt: skip np.testing.assert_allclose(band[0], Y, atol=2e-3) + def test_joint_draws_carry_the_kernels_correlation(self): + """A draw is a whole curve, not 30 independent points.""" + p, gp, model, _ = problem("noise") + chain = np.tile([0.5, 0.2, np.log(0.05)], (400, 1)) # the model is fixed + kw = dict(terms=[gp], return_draws=True, rng=7) + pred = model.bind(X_PRED, {}) + joint = gp_predictive_draws(p, gp, pred, X_PRED, chain, **kw) + indep = gp_predictive_draws(p, gp, pred, X_PRED, chain, joint=False, **kw) + assert joint.shape == (400, 30) + cj, ci = np.corrcoef(joint.T), np.corrcoef(indep.T) + # RBF(0.3) over a grid of pitch 0.048: neighbours move together + assert cj[0, 1] > 0.9 + assert abs(cj[0, -1]) < 0.2 # 1.4 apart: five length scales, nothing left + assert abs(ci[0, 1]) < 0.2 # joint=False has no correlation anywhere + + def test_the_default_draws_are_mean_zero_about_the_model(self): + """Zero mean says where the model fails; the amplitude says where.""" + d = Dataset(X, Y, np.full(10, 0.05), label="d") + m1, m2 = line(), line() + lA1, lA2 = (Parameter("log_A", prior=stats.norm(0, 1)) for _ in range(2)) + slope = Parameter("slope", prior=stats.norm(0, 1)) + gp_c = kernel( + RBF(0.3, "fixed"), amplitude=constant_amplitude, amplitude_params=(lA1,) + ) + gp_g = kernel( + RBF(0.3, "fixed"), + amplitude=exp_growth_amplitude(1.0), + amplitude_params=(lA2, slope), + ) + p1 = Problem([Constraint([Comparison(d, m1)], terms=[gp_c])]) + p2 = Problem([Constraint([Comparison(d, m2)], terms=[gp_g])]) + n = 800 + b1 = gp_predictive_draws( + p1, gp_c, m1.bind(X_PRED, {}), X_PRED, + np.tile([0.5, 0.2, np.log(0.2)], (n, 1)), + terms=[gp_c], levels=(16, 84), rng=8, + ) # fmt: skip + b2 = gp_predictive_draws( + p2, gp_g, m2.bind(X_PRED, {}), X_PRED, + np.tile([0.5, 0.2, np.log(0.2), 2.0], (n, 1)), + terms=[gp_g], levels=(16, 84), rng=8, + ) # fmt: skip + # the band straddles the model's own prediction, not the data + mu = 0.5 * X_PRED + 0.2 + assert np.all((b1[0] < mu) & (mu < b1[1])) + w1, w2 = b1[1] - b1[0], b2[1] - b2[0] + assert w1.max() / w1.min() < 1.25 # a constant amplitude is flat in x + assert w2[-1] > 3 * w2[0] # exp(2x) over [-0.2, 1.2] grows by 16 + + def test_terms_choose_what_a_draw_carries(self): + p, gp, model, _ = problem("noise") + eps = next(t for t in p.constraints[0].source.terms if t is not gp) + chain = np.tile([0.5, 0.2, 0.0], (400, 1)) # noise of 1, kernel variance 1 + pred = model.bind(X_PRED, {}) + kw = dict(levels=(16, 84), rng=9) + narrow = gp_predictive_draws(p, gp, pred, X_PRED, chain, terms=[gp], **kw) + wide = gp_predictive_draws(p, gp, pred, X_PRED, chain, terms=[gp, eps], **kw) + assert np.all((wide[1] - wide[0]) > (narrow[1] - narrow[0])) + # the reported errors are one number per measured point: never on a grid + with pytest.raises(ValueError, match=r"reported statistical.*'d'"): + gp_predictive_draws(p, gp, pred, X_PRED, chain, **kw) + with pytest.raises(ValueError, match="must include the kernel term"): + gp_predictive_draws(p, gp, pred, X_PRED, chain, terms=[eps]) + with pytest.raises(ValueError, match="not a term of this constraint"): + gp_predictive_draws( + p, gp, pred, X_PRED, chain, terms=[gp, noise(Parameter("q"))] + ) + def test_physical_band_under_log_space(self): p, gp, model, comp = problem("noise", space=log) # 26 rows put the 16th and 84th percentiles on order statistics, so the # monotone exp commutes with the percentile chain = self.chain(p, n=26) pred = model.bind(X_PRED, {}) - band_log = total_predictive_band(p, gp, pred, X_PRED, chain, rng=3) - band_phys = total_predictive_band( - p, gp, pred, X_PRED, chain, rng=3, physical=True - ) + kw = dict(terms=[gp], levels=(16, 84), rng=3) + band_log = gp_predictive_draws(p, gp, pred, X_PRED, chain, **kw) + band_phys = gp_predictive_draws(p, gp, pred, X_PRED, chain, physical=True, **kw) np.testing.assert_allclose(band_phys, np.exp(band_log)) def test_amplitude_matches_a_scaled_kernel(self): @@ -158,11 +248,11 @@ def test_amplitude_matches_a_scaled_kernel(self): p2 = Problem([Constraint([Comparison(d, m2)], terms=[gp_fix])]) chain1 = np.tile([0.5, 0.2, np.log(A)], (8, 1)) chain2 = np.tile([0.5, 0.2], (8, 1)) - b1 = total_predictive_band( - p1, gp_amp, m1.bind(X_PRED, {}), X_PRED, chain1, rng=4 + b1 = gp_predictive_draws( + p1, gp_amp, m1.bind(X_PRED, {}), X_PRED, chain1, terms=[gp_amp], rng=4 ) - b2 = total_predictive_band( - p2, gp_fix, m2.bind(X_PRED, {}), X_PRED, chain2, rng=4 + b2 = gp_predictive_draws( + p2, gp_fix, m2.bind(X_PRED, {}), X_PRED, chain2, terms=[gp_fix], rng=4 ) np.testing.assert_allclose(b1, b2, atol=1e-8) @@ -184,11 +274,11 @@ def test_amplitude_reads_the_predictor_meta(self): p2 = Problem([Constraint([Comparison(d, m2)], terms=[gp_const])]) chain1 = np.tile([0.5, 0.2, 0.0], (8, 1)) chain2 = np.tile([0.5, 0.2, np.log(0.5)], (8, 1)) - b1 = total_predictive_band( - p1, gp_meta, m1.bind(X_PRED, d.meta), X_PRED, chain1, rng=4 + b1 = gp_predictive_draws( + p1, gp_meta, m1.bind(X_PRED, d.meta), X_PRED, chain1, terms=[gp_meta], rng=4 ) - b2 = total_predictive_band( - p2, gp_const, m2.bind(X_PRED, {}), X_PRED, chain2, rng=4 + b2 = gp_predictive_draws( + p2, gp_const, m2.bind(X_PRED, {}), X_PRED, chain2, terms=[gp_const], rng=4 ) np.testing.assert_allclose(b1, b2, atol=1e-8) @@ -200,8 +290,10 @@ def test_one_bare_callable_is_one_space(self): gp = kernel(RBF(0.3, "fixed"), on=comps) p = Problem([Constraint(comps, terms=[gp])]) chain = np.tile([0.5, 1.2], (4, 1)) - band = total_predictive_band(p, gp, model.bind(X_PRED, {}), X_PRED, chain) - assert band.shape == (2, 30) and np.all(np.isfinite(band)) + band = gp_predictive_draws( + p, gp, model.bind(X_PRED, {}), X_PRED, chain, terms=[gp] + ) + assert band.shape == (3, 30) and np.all(np.isfinite(band)) def test_fully_masked_kernel_support_says_so(self): model = line() @@ -215,23 +307,276 @@ def test_fully_masked_kernel_support_says_so(self): ) chain = np.tile([0.5, 0.2], (4, 1)) with pytest.raises(ValueError, match="fully masked"): - total_predictive_band( - Problem([c]), gp, model.bind(X_PRED, {}), X_PRED, chain - ) + gp_predictive_draws(Problem([c]), gp, model.bind(X_PRED, {}), X_PRED, chain) def test_explicit_noise_and_errors(self): p, gp, model, comp = problem("noise") chain = self.chain(p, n=6) pred = model.bind(X_PRED, {}) - band = total_predictive_band( - p, gp, pred, X_PRED, chain, train_noise_var=0.05**2, noise_std=0.1, rng=5 - ) - assert band.shape == (2, 30) + band = gp_predictive_draws( + p, gp, pred, X_PRED, chain, terms=[gp], train_noise_var=0.05**2, + noise_std=0.1, rng=5, conditioned=True, + ) # fmt: skip + assert band.shape == (3, 30) + with pytest.raises(ValueError, match="conditioned=True"): + gp_predictive_draws(p, gp, pred, X_PRED, chain, train_noise_var=0.01) with pytest.raises(TypeError, match="KernelTerm"): - total_predictive_band( - p, Term(np.ones(10), kind="diag"), pred, X_PRED, chain - ) + gp_predictive_draws(p, Term(np.ones(10), kind="diag"), pred, X_PRED, chain) with pytest.raises(ValueError, match="not part of any constraint"): - total_predictive_band(p, kernel(RBF(1.0, "fixed")), pred, X_PRED, chain) + gp_predictive_draws(p, kernel(RBF(1.0, "fixed")), pred, X_PRED, chain) + with pytest.raises(ValueError, match=r"\(n, 3\)"): + gp_predictive_draws(p, gp, pred, X_PRED, chain[:, :2]) + + +# ---------------------------------------------------------------------------- +# grid_draws: any error model whose terms are functions of x +# ---------------------------------------------------------------------------- + +X_GRID = np.linspace(-1.0, 2.0, 16) # well past the data on both sides + + +def one(term_or_terms, err=0.05, statistical=False, likelihood=None, space=None): + """A line on (X, Y) with the given terms, and the model it compares.""" + terms = term_or_terms if isinstance(term_or_terms, list) else [term_or_terms] + model = line() + d = Dataset(X, Y, np.full(10, err), label="d") + comp = Comparison(d, model) if space is None else Comparison(d, model, space=space) + kw = {} if likelihood is None else {"likelihood": likelihood} + c = Constraint([comp], terms=terms, statistical=statistical, **kw) + return Problem([c]), model, comp + + +def cov_of(p, model, theta, x=X_GRID, n_rep=40000, rng=0, **kw): + draws = grid_draws( + p, model.bind(x, {}), x, theta, n_rep=n_rep, rng=rng, return_draws=True, **kw + ) + return draws.mean(axis=0), np.cov(draws.T) + + +class TestGridDraws: + def test_the_band_is_the_percentiles_of_the_draws(self): + """One return convention for all three draw functions.""" + eps = noise(Parameter("log_eps", prior=stats.norm(-2, 1))) + p, model, _ = one(eps) + rows = np.tile([0.5, 0.2, np.log(0.1)], (50, 1)) + pred = model.bind(X_GRID, {}) + draws = grid_draws(p, pred, X_GRID, rows, rng=1, return_draws=True) + np.testing.assert_allclose( + grid_draws(p, pred, X_GRID, rows, rng=1), predictive_band(draws) + ) + at_data = predictive_draws(p, rows, rng=1, return_draws=True) + np.testing.assert_allclose( + predictive_draws(p, rows, rng=1, levels=(5, 95)), + predictive_band(at_data, (5, 95)), + ) + gp = kernel(RBF(0.3, "fixed")) + pk, mk, _ = one([gp]) + rk = np.tile([0.5, 0.2], (50, 1)) + pk_pred = mk.bind(X_GRID, {}) + gdraws = gp_predictive_draws( + pk, gp, pk_pred, X_GRID, rk, rng=2, return_draws=True + ) + np.testing.assert_allclose( + gp_predictive_draws(pk, gp, pk_pred, X_GRID, rk, rng=2), + predictive_band(gdraws), + ) + + def test_model_only_is_the_hand_push_from_posterior_or_prior_rows(self): + eps = noise(Parameter("log_eps", prior=stats.norm(-2, 1))) + p, model, _ = one(eps) + pred = model.bind(X_GRID, {}) + for rows in ( + np.random.default_rng(0).normal(size=(7, 3)), + p.sample_prior(7, rng=1), + ): + by_hand = np.array([pred(*r[p.columns(model.params)]) for r in rows]) + got = grid_draws(p, pred, X_GRID, rows, model_only=True, return_draws=True) + np.testing.assert_allclose(got, by_hand) + + def test_at_the_measured_points_it_is_the_likelihoods_own_predictive(self): + """Every term a function: the grid draw and the data draw agree.""" + eps = noise(Parameter("log_eps", prior=stats.norm(-2, 1))) + eta = normalization(Parameter("log_eta", prior=stats.norm(-2, 1))) + gp = kernel(ConstantKernel(0.04, "fixed") * RBF(0.3, "fixed")) + p, model, _ = one([eps, eta, gp]) + theta = np.array([0.5, 0.2, np.log(0.1), np.log(0.2)]) + mean, cov = cov_of(p, model, theta, x=X) + np.testing.assert_allclose(cov, p.constraints[0].matrix(theta), atol=2e-3) + at_data = predictive_draws(p, theta, n_rep=40000, rng=1, return_draws=True) + np.testing.assert_allclose(np.cov(at_data.T), cov, atol=3e-3) + np.testing.assert_allclose(mean, 0.5 * X + 0.2, atol=0.01) + + def test_constant_noise_extrapolates_with_the_model(self): + eps = noise(Parameter("log_eps", prior=stats.norm(-2, 1))) + p, model, _ = one(eps) + mean, cov = cov_of(p, model, np.array([0.5, 0.2, np.log(0.1)])) + np.testing.assert_allclose(mean, 0.5 * X_GRID + 0.2, atol=0.005) + np.testing.assert_allclose(cov, 0.01 * np.eye(16), atol=5e-4) + + def test_fractional_noise_and_model_error_scale_with_the_prediction(self): + ym = 0.5 * X_GRID + 1.2 + for make in (noise_fraction, model_error): + p, model, _ = one(make(Parameter("log_f", prior=stats.norm(-2, 1)))) + _, cov = cov_of(p, model, np.array([0.5, 1.2, np.log(0.1)])) + # model_error averages y and ym; on a grid y *is* the model + np.testing.assert_allclose(np.sqrt(np.diag(cov)), 0.1 * ym, rtol=0.03) + # a diagonal term: no correlation between grid points + np.testing.assert_allclose(cov - np.diag(np.diag(cov)), 0.0, atol=5e-4) + + def test_a_normalisation_mode_is_one_curve_scaled_by_the_prediction(self): + ym = 0.5 * X_GRID + 1.2 + free = normalization(Parameter("log_eta", prior=stats.norm(-2, 1))) + fixed = normalization(magnitude=0.1) + for term, theta in ((free, [0.5, 1.2, np.log(0.1)]), (fixed, [0.5, 1.2])): + p, model, _ = one([noise(Parameter("e", prior=stats.norm(-9, 1))), term]) + theta = np.array([*theta[:2], -9.0, *theta[2:]]) + _, cov = cov_of(p, model, theta) + np.testing.assert_allclose(np.sqrt(np.diag(cov)), 0.1 * ym, rtol=0.03) + corr = cov / np.sqrt(np.outer(np.diag(cov), np.diag(cov))) + assert corr[0, 1] > 0.99 and corr[0, -1] > 0.99 + + def test_a_systematic_mode_with_an_x_basis_grows_along_x(self): + s = systematic(Parameter("log_s", prior=stats.norm(-2, 1)), basis=x_basis()) + eps = noise(Parameter("e", prior=stats.norm(-9, 1))) + p, model, _ = one([eps, s]) + _, cov = cov_of(p, model, np.array([0.5, 0.2, -9.0, np.log(0.1)])) + np.testing.assert_allclose( + np.sqrt(np.diag(cov)), 0.1 * np.abs(X_GRID), rtol=0.03, atol=2e-3 + ) + + def test_a_parametric_matrix_term_is_evaluated_from_its_definition(self): + """Any Term(fn, params, kind="matrix") of c.x travels, not only kernels.""" + log_l = Parameter("log_l", prior=stats.norm(-1, 1)) + + def sq_exp(c, ll): + dx = np.subtract.outer(c.x, c.x) + return 0.04 * np.exp(-0.5 * dx**2 / np.exp(ll) ** 2) + + p, model, _ = one([Term(sq_exp, (log_l,), kind="matrix")]) + theta = np.array([0.5, 0.2, np.log(0.5)]) + _, cov = cov_of(p, model, theta) + dx = np.subtract.outer(X_GRID, X_GRID) + np.testing.assert_allclose(cov, 0.04 * np.exp(-0.5 * dx**2 / 0.25), atol=2e-3) + + def test_a_kernel_through_grid_draws_is_the_gp_function_unconditioned(self): + gp = kernel(RBF(0.3), params=[Parameter("log_ell", prior=stats.norm(-1, 1))]) + eps = noise(Parameter("log_eps", prior=stats.norm(-3, 1))) + p, model, _ = one([eps, gp]) + rows = np.column_stack( + [np.full(30, 0.5), np.full(30, 0.2), np.full(30, np.log(0.05)), + np.log(np.linspace(0.2, 0.4, 30))] + ) # fmt: skip + pred = model.bind(X_GRID, {}) + kw = dict(terms=[eps, gp], n_rep=3, rng=11, return_draws=True) + np.testing.assert_allclose( + grid_draws(p, pred, X_GRID, rows, **kw), + gp_predictive_draws(p, gp, pred, X_GRID, rows, **kw), + ) + + def test_joint_draws_are_curves_and_independent_draws_are_not(self): + gp = kernel(RBF(0.5, "fixed")) + p, model, _ = one([gp]) + pred = model.bind(X_GRID, {}) + rows = np.array([0.5, 0.2]) + kw = dict(n_rep=4000, rng=3, return_draws=True) + joint = grid_draws(p, pred, X_GRID, rows, **kw) + indep = grid_draws(p, pred, X_GRID, rows, joint=False, **kw) + assert np.corrcoef(joint.T)[0, 1] > 0.9 + assert abs(np.corrcoef(indep.T)[0, 1]) < 0.1 + np.testing.assert_allclose(joint.var(0), indep.var(0), rtol=0.1) + + def test_a_term_reads_the_predictor_meta_on_the_grid(self): + d = Dataset(X, Y, np.full(10, 0.05), label="d", meta={"Elab": 25.0}) + model = line() + log_a = Parameter("log_a", prior=stats.norm(-2, 1)) + by_energy = Term( + lambda c, la: np.exp(la) * c.meta("Elab") / 50.0 * np.ones(len(c)), + (log_a,), + kind="diag", + ) + p = Problem( + [Constraint([Comparison(d, model)], terms=[by_energy], statistical=False)] + ) + theta = np.array([0.5, 0.2, 0.0]) + draws = grid_draws( + p, model.bind(X_GRID, {"Elab": 100.0}), X_GRID, theta, + n_rep=40000, rng=4, return_draws=True, + ) # fmt: skip + np.testing.assert_allclose(draws.std(0), 2.0, rtol=0.03) # 100 / 50 + + def test_physical_and_a_student_t_likelihood(self): + eps = noise(Parameter("log_eps", prior=stats.norm(-2, 1))) + p, model, _ = one(eps, space=log) + rows = np.tile([0.5, 1.2, np.log(0.1)], (26, 1)) + pred = model.bind(X_GRID, {}) + kw = dict(levels=(16, 84), rng=5) + np.testing.assert_allclose( + grid_draws(p, pred, X_GRID, rows, physical=True, **kw), + np.exp(grid_draws(p, pred, X_GRID, rows, **kw)), + ) + eps_t = noise(Parameter("log_eps", prior=stats.norm(-2, 1))) + pt, mt, _ = one(eps_t, likelihood=StudentT()) + theta = np.zeros(pt.ndim) + theta[pt.names.index("m")], theta[pt.names.index("b")] = 0.5, 0.2 + theta[pt.names.index("log_eps")] = np.log(0.1) + theta[pt.names.index("nu")] = 3.0 + t_draws = grid_draws(pt, mt.bind(X_GRID, {}), X_GRID, theta, n_rep=40000, + rng=6, return_draws=True) # fmt: skip + # a multivariate t with nu = 3 has variance nu / (nu - 2) = 3 times the scale + np.testing.assert_allclose(t_draws.var(0).mean(), 3 * 0.01, rtol=0.2) + + def test_several_comparisons_need_to_say_which_experiment_the_grid_is(self): + model = line() + da = Dataset(X, Y, np.full(10, 0.05), label="a") + db = Dataset(X, Y + 0.1, np.full(10, 0.05), label="b") + ca, cb = Comparison(da, model), Comparison(db, model) + na = normalization(magnitude=0.1, on=ca) + nb = normalization(magnitude=0.3, on=cb) + eps = noise(Parameter("log_eps", prior=stats.norm(-9, 1))) + p = Problem([Constraint([ca, cb], terms=[eps, na, nb], statistical=False)]) + theta = np.array([0.5, 1.2, -9.0]) + pred = model.bind(X_GRID, {}) + with pytest.raises(ValueError, match="comparison="): + grid_draws(p, pred, X_GRID, theta) + draws = grid_draws( + p, pred, X_GRID, theta, comparison=ca, n_rep=40000, rng=7, return_draws=True + ) + # experiment a's normalisation only, not b's as well + np.testing.assert_allclose(draws.std(0), 0.1 * (0.5 * X_GRID + 1.2), rtol=0.03) + with pytest.raises(ValueError, match="not a comparison"): + grid_draws(p, pred, X_GRID, theta, comparison=Comparison(da, model)) + # explicit terms need no comparison when the space is shared + assert grid_draws(p, pred, X_GRID, theta, terms=[nb]).shape == (3, 16) + cl = Comparison( + Dataset(X, Y + 1.0, np.full(10, 0.05), label="l"), model, space=log + ) + pl = Problem([Constraint([ca, cl], terms=[eps], statistical=False)]) + with pytest.raises(ValueError, match="one comparison space"): + grid_draws(pl, pred, X_GRID, theta, terms=[eps]) + assert grid_draws(pl, pred, X_GRID, theta, comparison=cl).shape == (3, 16) + + def test_point_by_point_terms_have_no_value_at_a_new_x(self): + model = line() + d = Dataset(X, Y, np.full(10, 0.05), label="d") + comp = Comparison(d, model) + theta = np.array([0.5, 0.2]) + pred = model.bind(X_GRID, {}) + reported = Problem([Constraint([comp])]) + with pytest.raises(ValueError, match=r"reported statistical errors.*'d'"): + grid_draws(reported, pred, X_GRID, theta) + # the choice made explicit is allowed: the model alone + assert grid_draws(reported, pred, X_GRID, theta, terms=[]).shape == (3, 16) + fixed = Term(np.full(10, 0.1), kind="diag") + p = Problem([Constraint([comp], terms=[fixed])]) + with pytest.raises(ValueError, match="array-valued term"): + grid_draws(p, pred, X_GRID, theta, terms=[fixed]) + per_point = normalization(magnitude=np.full(10, 0.1)) + eps = noise(Parameter("log_eps", prior=stats.norm(-2, 1))) + p = Problem([Constraint([comp], terms=[eps, per_point], statistical=False)]) + theta = np.array([0.5, 0.2, np.log(0.1)]) + with pytest.raises(ValueError, match="could not be evaluated"): + grid_draws(p, pred, X_GRID, theta) + with pytest.raises(ValueError, match="not a term of this constraint"): + grid_draws(p, pred, X_GRID, theta, terms=[normalization(magnitude=0.1)]) with pytest.raises(ValueError, match=r"\(n, 3\)"): - total_predictive_band(p, gp, pred, X_PRED, chain[:, :2]) + grid_draws(p, pred, X_GRID, np.zeros((4, 2))) From 08f172a726f9e7d4521efd2804736f9fd53938e1 Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Mon, 14 Sep 2026 18:33:55 -0400 Subject: [PATCH 64/75] Rewrite linear_calibration: inferred noise, two predictives and their coverage - The narrative moves to the walkthrough voice with links to the Bayesian vocabulary, a note on Black Box Bayes, and typo fixes - The fit infers a constant noise sigma instead of trusting the reported errors (0.114 +/- 0.020 against a reported 0.1), so the error model is a function of x that can travel to a new grid - The posterior predictive on the fine grid contrasts the line's band with a new measurement's, and explains the predictive_draws / grid_draws split and why reported per-point errors cannot be forecast; a demo cell shows grid_draws refusing them - Coverage is shown for both: the model alone undercovers (55 % at a nominal 90 %) while the measurement band follows the diagonal - The chi2 cell also checks log_likelihood and explains the log-determinant - Walkers start in a small ball about the prior mean; prior draws stranded a walker at a huge sigma and inflated the posterior of b --- examples/linear_calibration.ipynb | 464 ++++++++++++------------------ 1 file changed, 189 insertions(+), 275 deletions(-) diff --git a/examples/linear_calibration.ipynb b/examples/linear_calibration.ipynb index fa46b8b..d1eb6b4 100644 --- a/examples/linear_calibration.ipynb +++ b/examples/linear_calibration.ipynb @@ -7,32 +7,18 @@ "source": [ "# Calibration of a line\n", "\n", - "Let us start with the smallest problem that still contains the whole workflow:\n", - "a straight line through twenty noisy points. Everything the library asks of us\n", - "is here — we declare a model and what we believe about its parameters, we say\n", - "how the data are compared with it, we compile that into a problem, we hand the\n", - "problem to a sampler, and then we ask whether the answer is any good.\n", - "\n", - "The [Bayesian recipe](https://en.wikipedia.org/wiki/Bayesian_inference) is the\n", - "same at every scale: a prior over parameters, a likelihood for the data, and a\n", - "posterior we explore with a sampler. The only thing that grows in the later\n", - "notebooks is the error model.\n", - "\n", - "Recipes: 1, 17" + "We'll start with the smallest problem that contains the model calibration workflow: fitting a line to noisy measured points. First, we will declare the model ($y = m x + b$), what we believe about its parameters — our [priors](https://en.wikipedia.org/wiki/Prior_probability) — and how the data compare with it — our [likelihood](https://en.wikipedia.org/wiki/Likelihood_function). Then we compile that problem, hand it to a sampler, and draw samples from our [posterior](https://en.wikipedia.org/wiki/Posterior_probability). Finally, we push those posterior samples back through our model to generate a [posterior predictive](https://en.wikipedia.org/wiki/Posterior_predictive_distribution), and compare it to the data we fit to see if it worked.\n", + "\n", + "[Bayesian inference](https://en.wikipedia.org/wiki/Bayesian_inference) always has these ingredients: a prior over parameters, a likelihood for the data, and a posterior we explore with a sampler. In this synthetic data case, we know the ground truth, and therefore know our model is correctly specified. We also know the data-generating process: every point scatters about the true line with the same standard deviation $\\sigma = 0.1$. Our imaginary experiment reports that $\\sigma$ for each point, but rather than take it on trust, we'll infer it alongside $m$ and $b$. That's our first, and simplest, *error model*, and we'll check that it recovers the reported value. It will also pay off later, when we want predictions at $x$ values nobody measured. Other tutorial notebooks in this series explore the more realistic cases in which our model is only an approximation to reality, and we may not have complete information about the experimental uncertainties.\n", + "\n", + "Recipes: 1, 2, 17, 40" ] }, { "cell_type": "code", "execution_count": 1, "id": "4487f98f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-12T01:56:08.715846Z", - "iopub.status.busy": "2026-09-12T01:56:08.715703Z", - "iopub.status.idle": "2026-09-12T01:56:10.738535Z", - "shell.execute_reply": "2026-09-12T01:56:10.738027Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "import corner\n", @@ -43,6 +29,7 @@ "from scipy import stats\n", "\n", "import rxmc as rx\n", + "from rxmc import terms as T\n", "\n", "plotstyle.use()" ] @@ -54,29 +41,18 @@ "source": [ "## Parameters and a model\n", "\n", - "A `Parameter` is a name with a\n", - "[prior](https://en.wikipedia.org/wiki/Prior_probability) (or finite bounds),\n", - "and a `Model` is a callable of the grid $x$ and the parameter values, in order,\n", - "carrying the parameters it consumes.\n", + "A `Parameter` is a random variable we will do inference on. Typically, we name it, and assign it a prior (or finite bounds, which is just a uniform prior). A `Model` is a mapping from some domain space $x$ and some parameters $\\alpha$ to a prediction $y_{m}$, which we want to compare to some data $y$.\n", + "\n", + "We deliberately choose a prior that disagrees with the data we are about to generate: $m \\sim \\mathcal{N}(1, 1)$ while the truth is $m = 0.6$, so we can see the data pull the posterior away from it.\n", "\n", - "We deliberately choose a prior that disagrees with the data we are about to\n", - "generate: $m \\sim \\mathcal{N}(1, 1)$ while the truth is $m = 0.6$. A prior\n", - "encodes what we think before we look, and it should be visible in the answer\n", - "when the data are few." + "We also need a parameter for the noise level. $\\sigma$ is a positive scale that could plausibly be anywhere over an order of magnitude, so we sample its logarithm, with $\\log\\sigma \\sim \\mathcal{N}(\\log 0.2, 1)$: centred a factor of two above the truth, and wide." ] }, { "cell_type": "code", "execution_count": 2, "id": "bd5cc6df", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-12T01:56:10.740249Z", - "iopub.status.busy": "2026-09-12T01:56:10.740062Z", - "iopub.status.idle": "2026-09-12T01:56:10.745724Z", - "shell.execute_reply": "2026-09-12T01:56:10.745172Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -92,6 +68,9 @@ "source": [ "m = rx.Parameter(\"m\", prior=stats.norm(1.0, 1.0), latex=\"m\")\n", "b = rx.Parameter(\"b\", prior=stats.norm(1.0, 1.0), latex=\"b\")\n", + "log_sigma = rx.Parameter(\n", + " \"log_sigma\", prior=stats.norm(np.log(0.2), 1.0), latex=r\"\\log\\sigma\"\n", + ")\n", "line = rx.Model(lambda x, m, b: m * x + b, [m, b])\n", "line" ] @@ -105,21 +84,15 @@ "\n", "We draw twenty points from $y = m x + b$ with independent Gaussian noise of\n", "known size $\\sigma = 0.1$, which our imaginary experiment reports honestly as\n", - "`y_err`. That honesty is the assumption every later notebook takes apart." + "`y_err`. We keep `y_err` in the dataset, so it shows up as error bars in our\n", + "plots, but as we'll see in a moment our likelihood won't use it." ] }, { "cell_type": "code", "execution_count": 3, "id": "a655b824", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-12T01:56:10.747139Z", - "iopub.status.busy": "2026-09-12T01:56:10.747016Z", - "iopub.status.idle": "2026-09-12T01:56:10.750730Z", - "shell.execute_reply": "2026-09-12T01:56:10.750234Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -134,10 +107,10 @@ ], "source": [ "rng = np.random.default_rng(49)\n", - "truth = {\"m\": 0.6, \"b\": 2.0}\n", + "sigma = 0.1\n", + "truth = {\"m\": 0.6, \"b\": 2.0, \"log_sigma\": np.log(sigma)}\n", "x = np.linspace(0.0, 1.0, 20)\n", "y_true = truth[\"m\"] * x + truth[\"b\"]\n", - "sigma = 0.1\n", "data = rx.Dataset(\n", " x, y_true + rng.normal(0.0, sigma, x.size), sigma * np.ones(x.size), label=\"toy\"\n", ")\n", @@ -148,18 +121,11 @@ "cell_type": "code", "execution_count": 4, "id": "00228baf", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-12T01:56:10.751920Z", - "iopub.status.busy": "2026-09-12T01:56:10.751804Z", - "iopub.status.idle": "2026-09-12T01:56:11.600706Z", - "shell.execute_reply": "2026-09-12T01:56:11.600035Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "<Figure size 704x440 with 1 Axes>" ] @@ -172,7 +138,7 @@ "fig, ax = plt.subplots()\n", "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", color=\"k\", label=\"data\")\n", "ax.plot(x, y_true, \"--\", color=plotstyle.COLOURS[1], label=\"truth\")\n", - "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"Twenty points and the line they came from\")\n", + "ax.set(xlabel=\"$x$\", ylabel=\"$y$\")\n", "ax.legend()\n", "plt.show()" ] @@ -188,42 +154,35 @@ "\n", "- A `Comparison` binds the model to this dataset's grid: it is the pairing of\n", " one model with one dataset.\n", - "- A `Constraint` is one likelihood over one or more comparisons. By default\n", - " its covariance is the diagonal of the reported errors, which is the\n", - " \"the experiment told us everything\" assumption.\n", + "- A `Constraint` is one likelihood over one or more comparisons, and it owns\n", + " the *error model*: a covariance built as a sum of terms. By default it\n", + " includes the diagonal of the reported errors (`statistical=True`), which is\n", + " the \"the experiment told us everything\" assumption. Here we switch that off\n", + " and add a single term, `T.noise(log_sigma)`, which puts\n", + " $\\sigma^2 = e^{2 \\log\\sigma}$ on the diagonal at every point.\n", "- `Problem` compiles the lot: it gives every parameter a column, assembles the\n", - " prior, and exposes the densities a sampler needs.\n", - "\n", - "Nothing is hidden in the compile step, and nothing user-facing is mutated by\n", - "it — we can compile the same declarations twice and get two independent\n", - "problems." + " prior, and exposes the densities a sampler needs." ] }, { "cell_type": "code", "execution_count": 5, "id": "3e2eb131", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-12T01:56:11.602154Z", - "iopub.status.busy": "2026-09-12T01:56:11.602022Z", - "iopub.status.idle": "2026-09-12T01:56:11.605300Z", - "shell.execute_reply": "2026-09-12T01:56:11.604810Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Problem(ndim=2, constraints=1)\n", - "columns: ['m', 'b']\n" + "Problem(ndim=3, constraints=1)\n", + "columns: ['m', 'b', 'log_sigma']\n" ] } ], "source": [ "comp = rx.Comparison(data, line)\n", - "problem = rx.Problem([rx.Constraint([comp])])\n", + "noise_term = T.noise(log_sigma)\n", + "problem = rx.Problem([rx.Constraint([comp], terms=[noise_term], statistical=False)])\n", "print(problem)\n", "print(\"columns:\", problem.names)" ] @@ -236,34 +195,28 @@ "### What our prior alone predicts\n", "\n", "Before the likelihood is involved at all, we can draw parameters from the prior\n", - "and push them through the model. This is the\n", + "and push them through the model, noise included. This is the\n", "[prior predictive](https://en.wikipedia.org/wiki/Posterior_predictive_distribution#Prior_vs._posterior_predictive_distribution):\n", "the data our prior thinks are plausible. If it cannot produce anything like\n", - "the data we measured, we have learned something before fitting." + "the data we measured, we have learned something before fitting.\n", + "\n", + "We draw it with `rx.predictive.grid_draws` on a plotting grid wider than the\n", + "data, and hand it prior rows instead of posterior ones. We'll say much more\n", + "about that function once we have a posterior." ] }, { "cell_type": "code", "execution_count": 6, "id": "26214803", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-12T01:56:11.606693Z", - "iopub.status.busy": "2026-09-12T01:56:11.606570Z", - "iopub.status.idle": "2026-09-12T01:56:11.611271Z", - "shell.execute_reply": "2026-09-12T01:56:11.610613Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "prior_draws = problem.sample_prior(200, rng=1)\n", + "prior_rows = problem.sample_prior(200, rng=1)\n", "x_fine = np.linspace(-0.5, 1.5, 60)\n", "on_fine = line.bind(x_fine) # the same model on a plotting grid\n", - "prior_curves = np.array(\n", - " [on_fine(*s[problem.columns(line.params)]) for s in prior_draws]\n", - ")\n", - "prior_lo, prior_mid, prior_hi = rx.predictive.predictive_band(\n", - " prior_curves, levels=(5, 50, 95)\n", + "prior_lo, prior_mid, prior_hi = rx.predictive.grid_draws(\n", + " problem, on_fine, x_fine, prior_rows, n_rep=5, levels=(5, 50, 95), rng=1\n", ")" ] }, @@ -271,18 +224,11 @@ "cell_type": "code", "execution_count": 7, "id": "fee892f7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-12T01:56:11.612494Z", - "iopub.status.busy": "2026-09-12T01:56:11.612341Z", - "iopub.status.idle": "2026-09-12T01:56:11.722387Z", - "shell.execute_reply": "2026-09-12T01:56:11.721884Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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6Cuecc05E++9p3buSlpbW5r2blZUFAGhoaEBubm6PytB6f2S4f7OXLVuGv/zlL7j33nuxdOlSpKenQy6XY8mSJV02dvTmPnobQ1uUdDSERuuLGAD0ej0A4K677sK4cePaXb+rsYvMZnObZa2dBVr33yrc1iGDwdDhvpubm6FQKJCSkhLxflv1tMx6vR55eXkdDsaZmpoaXK+zY3XFYDD06I9cR2666Sa88sorePfdd3H99dfj1Vdfxbnnnovi4uLgOuHWMVLz5s3D559/jn379qGqqgrnnHMOioqKMHbsWKxbtw5OpxN6vT74oXuicF7TzzzzDIxGIz766KOQ/5bffPPNbpW3p2XvjfPY0WtILpcHX5+9/XxFsr/+ej31x3kwGAwh/wS0crvdcDqdwb9NrU7++9D69+Oiiy5qt4Uc6PyfzFmzZkGj0WDdunWQJAmTJk1CdnY2zjnnHPz3f/83SktLsX79elxyySW9dszLLrsMl156KX744Qds3rwZH3zwAc4991w8+uijWL58edj7bx14trvl6Epnw0K1/g3oyblo3Tacv9k2mw2rVq3CbbfdhoceeijkMZPJhBEjRnRY1t7cR19gaIths2bNAhBoFg73kt3Jdu3aBYvFEvIf0KZNmwCg293pp06dCgD46quvMGPGjOByn8+HLVu2YMqUKT1qfeppmWfPno0nn3wSBQUFKCoq6nC9qVOn4oUXXkBdXV3IkC2txwrnOH/605+wf//+DodFUavVAALnJlxnnHEGxo4di5dffhnFxcUoLy/HU0891ebY4dQxUvPnz8ebb76Jp59+GgUFBTjllFOCyz///HM4nU7Mnj07WK9ItbS0oKCgICSwSZKEtWvXhqzXnfPWnbL3xnnctGkTbrzxxuDvQghs3rwZkyZNCtazt5+vSPbXX6+n/jgPU6dOxapVq1BeXh7S0r9x40YIIbr8+zBixAgUFhaitLS0W8Oo6HQ6nHnmmVi3bh2EEDj33HMBAKeffjoyMzOxYsUKNDY2hrTm9vSYACCXy4O9GH/zm99g0qRJePvtt7F8+fKw95+ZmdmjcqjV6ojej+3pybkYMWIEMjMzsWnTJtx1110hj538N9tms8Hn87W5GrR58+Y2oa+jekWyj37Vy/fIkQivI8KiRYvaLG/tgXOiW265Rej1+jY33m7btk089NBDnZZj2LBh4pRTThE33XRT8MbNH374QWRnZ4fcnN1ZmYRov/foggULREZGhvjmm2+EEEL4/X7xX//1XwKA+OSTT8Lab1+Vua6uThQVFYnp06eLw4cPB5e7XC7x6quvBstXWloqtFqtuPTSS4XT6RRCCLFv3z5x4YUXhtURoba2VhQUFIiSkhJx5MiR4PJvv/1WvPvuu0IIIVpaWoRCoRD33Xdfm3J21CtQiMCN9QDElClTRE5OTsiNt5HUUQghrr/+enHmmWe2OUZ7qqqqBAChUCjE0qVLg8s//fRTIZfLBQDx9NNPh2zTetP3xx9/3GZ/CoVC3HXXXcHfH3roIaFQKMTGjRuFEIEbw3/zm9+IkpKSkBuJOztvvVn2SM7jyVrrPXXq1JC6P/zwwwJA8Mb0SI4TbkeESMvdm6+n/jgPQrTfEeHw4cPCYDCIc845R1gsFiGEEMeOHRNjx44VgwcPFjabLaRM7b0mV69eLQCIBx54QLhcruDyY8eOid/+9rchN/i359FHHw32TN6wYUNw+eLFi4VCoRByuVw0Njb2yjF3794tVqxYEXLTfmVlpSgoKBC/+MUvIt5/T+r+5z//WQAIed5atffZJYQQb7zxhgAgdu/e3eNzIUSgw4lcLg95Lzz33HNi0qRJbToiDB06VJSUlAiz2SyECHQQmTNnjhg0aFDI51ln9Qp3H/2JHRFi3PPPP4/f//73uOWWW5CZmYlx48YhLS0Nt99+OyZOnNjl9qNGjcJZZ52FIUOGYPjw4SgpKcGZZ57Z48tRf/vb33DBBRdg1qxZGDp0KDIzM/Hee+9h9erVOP/883u0756WOTc3F1u2bEFOTg7GjBmDwYMHY8SIEcjJycHGjRuDl5qHDx+ONWvWYNu2bcjKysLIkSNx/fXX47HHHgvrOHl5edi0aRMyMjIwYsQIDBs2DHl5eVi2bBlGjRoFIHCfx/Lly/HYY49hzJgxnY43dqKlS5dCrVbju+++w7XXXttmJo1w6wgEOgJs3749rDoVFhZi3Lhx8Pv9Ia0Fs2fPDl7+aO9+tnD97ne/wyWXXIKzzjoLo0ePRmFhIdLT09uMc9Wd89adskdyHjur09tvv41BgwYhJycHK1aswHPPPRcykn9vHKcn5e7N11M0z8PQoUOxbt061NTUIDc3F6NGjcLQoUORl5eH9evXt7l9oj1XXXUVPvjgg2BHorFjxyIvLw+zZs1Cfn4+NBpNp9u3dnrRarU488wzQ5b7/X5MmjQpeC9XT485YMAANDc3Y9SoURg0aBDGjRuHESNGYPr06XjhhRci3n9P6r506VKceuqpmDBhAk477bR2x2kLR0/KsHz5cixbtgxLly5FcXExiouLcezYMVxzzTVt1n377bfR1NSE4uJijBo1CnPnzsWjjz6K9PT0sOsV7j76k0yIdka+ox7x+/348ccfkZ2d3e71+T179kCn02Ho0KEhy8vLy9HS0oIJEya02UYIgbKyMjidTgwePDisP07Dhw/HxIkT8f7778PpdOLIkSPIzs4OuRTYVZmAwIe+Uqls9xq+1WrF0aNHodVqMWzYsDY3CHe2374scyu73Y6jR49Cp9Nh4MCB7fZclSQJP/30E9RqNYYOHRp8/oqLi5Gfnw8AcLlc2LNnD4YNG9am1xMQuJevsrISRUVFyMzMbPN4S0sLysvL4fV6UVRUhIKCgi73uW/fPjgcDgwfPrzTPxJd1fHAgQNwOBxh9SAFgCNHjqCpqQljx44NdiYBAufb7Xa36dlrs9lw4MABjBgxAmlpaSGPbd++HXl5eSH3TwGBe0Jqa2sxaNAgGAwG1NfX49ixY5g4cWLIvTHtnbfeLPuJwnmtdFbv2tpaNDY2YtiwYZ3ex9nZcdp7TXT1Ogm33L31euqP8wAAO3bsQEZGRrsdnoBApwSTyYTCwkLk5OR0WqaO1NTUoKGhAYWFhWHPryxJEn744QfodDqMHTu2zTGzsrIwZMiQXj2mEAIVFRVwOp0oLi7u9O9/uPvvTjmAQMeHpqYmSJKECRMmQKVSdfjZ1dTUhCNHjuCUU04Juc+5p2WwWCwoLy9HcXExMjIyOvz7IUkSjh49Cr/fj2HDhkEul2Pfvn1QqVRtPs/aq1ek++gPDG0J7MQAFC/iscxERET9gZdHiYiIiOIAQxsRERFRHODl0QQW6f1ksSAey0xERNQfGNqIiIiI4gAvjxIRERHFAYY2IiIiojgQ99NY7dmzB9u2bUNBQQHmzZvX6fxnJ/P5fKitrUV+fn5E2xERERH1t7htafN6vVi6dClmzpyJ9evX46WXXsLs2bPhcrnC3kdtbS0GDBiA2traPixpYIDBZMR6JxfWO7mw3smF9Y4Ncdu8dP/99+Ozzz7Dzp07MXDgQADAjz/+CL/fH+WStZWsfT1Y7+TCeicX1ju5sN6xIS5Dm9vtxqpVq3DvvfcGAxsAnHbaaVEsFREREVHficvQtmvXLlitVpx99tn48ssvceTIEQwdOhSzZ8+GXN7xFV+LxQKLxRL8vaampj+KS0RERNRjcRnaTCYTAOAPf/gDbDYbRo0ahYceegg5OTlYv359hxMEr1y5Eg8++GCb5c3NzZ1ObNxTJwbFZMJ6JxfWO7mw3smF9e47WVlZYa8bl6FNp9MBAHJzc7Fu3ToAgRM7evRorFixAo8++mi72y1btgw33XRT8PeamhpMmTIFGRkZEZ207ujr/ccq1ju5sN7JhfVOLqx39MVlaBsxYgQA4JxzzgkuMxqNmDp1Knbv3t3hdkajEUajsc/LR0RERNTb4nLIj4KCAkybNg0//PBDcJnP58OePXswbNiwKJaMiIiIqG/EZUsbAKxatQrz5s0LXhb9+OOP4Xa7ce+990a7aERERES9Li5b2gDg9NNPx65duzBq1Cg0NjZi6dKl2L9/P/Lz86NdNCIiIqJeF7ctbQAwYMAA3HPPPdEuBhEREVGfi9uWNiIiIqJkwtBGQVVVVdiyZUu0i9Fr9uzZgwMHDgR/37lzJ3766ace7bM39kFERNQdDG0U9PHHH2Pp0qXRLkaveeSRR/DMM88Ef/9//+//4fnnnw97+x07dqC0tDRkWaT76C5JkvDTTz/h66+/RnNzc4frlZeXY9OmTWHP7nHkyBHs2rULkiS1eey7774LDlxNRESxJ67vaYuWfx2oj2h9i9UCY0P3JrI/d3Rut7brjuLiYkyfPr3fjtffJk6ciKKiorDXv/fee3HKKafgySef7PY+umP37t248sorYTabUVhYiH379uGhhx7CnXfeGVzH5/Ph+uuvxz/+8Q+MHj0a+/btwx133IEVK1Z0uN+HH34Yq1atQmpqKsaNG4ePPvoo+Ni3336LG264AT/++GOf1o2IiLqPLW0J5tixY9i6dSuEEDhy5Ai2bNkCh8PR4Tp79+7Fhg0bIEkSTjvtNNx6661t9llZWYmNGzfi8OHDnR7vxH31ZrlalZWV4euvv+6wVcnn82Hr1q3Yv38//P62IXnx4sWYO3dum+Xl5eX4+uuv0dTUFFy2d+9emEwmHDt2DF988QW++OILOJ3OkH2UlpZi27ZtbfZ35MgRbN26NWRZV2U/0ZIlSzB69GiUlZVh69at2LRpE5YvX45NmzYF13n22Wexdu1a7N69G9u3b8e///1vPP300/jggw/a3WdTUxMee+wxbNu2DXv37sWuXbuwfv16AIDX68Utt9yCF154ASkpKV2Wj4goGfglAUmIaBcjBFvaEsy7776LVatWYcCAATCZTPB4PDCbzfjwww+DrWjvvvsu/vKXv2DQoEFobGxEfn4+Zs6ciY8//hhPPvkkDh06BADw+/248cYb8d5772HcuHE4ePAgpk+fjnfffRcGg6HTfcnl8l4rl9lsxpVXXokff/wRw4cPx/79+3Heeefh1VdfhVqtBgAcPnwY5557LhwOB/Ly8uB2u2EwGDBp0qRgGf7f//t/GD58ePCSqclkwjXXXIPNmzdj7NixOHbsGO6++27ceeed+Pjjj1FWVob6+vpgmHv99ddD9rF7925ce+21qKurC54PALjjjjswadIkTJ06Fc3NzV2W/UQNDQ3Yv38//vSnP0GpDLw9J06ciNNPPx0vv/wyZs6cCQB49dVXccUVV2Do0KEAgKlTp2Lu3Ll49dVXcemll7bZ76FDh5CXl4eBAwcG19+3bx/mzp2Lxx9/HFOnTsXs2bPDfp0RESUiIQRMdg9qrG7UW90YYWjbCBFNbGlLQOXl5Tj33HOxb98+HDp0CJdddhluuOEG+Hy+4DpHjx7FFVdcgT179uCLL76ASqVqs58XX3wRa9aswY4dO/Ddd9/h4MGDOHDgAP74xz+GrBfOvnpSrptuugnp6ek4duwYvv76a5SVlWHv3r0hly1vu+02jBw5EkePHsWPP/6I+++/v91WsBNdf/31qK+vD7aMHTt2LDjH3L333ouSkhIsXrw42NJWWFgYsv35558PtVqNDz/8MListbXwmmuuAYCwyn6itLQ0qFQqlJeXB5f5/X5UVVUFZwDxeDzYt29fSCAFgEmTJmHHjh0d7relpSXYcmkymZCWloaDBw/ilVdewRNPPIHy8nLs2LGj3VZKIqJE1uL0Yn+dFf8+bML2SjOqzS74pNhqZQMY2hKSTqfD7373u+DvDz74IEpLS/Htt98Gl2VlZbV7KfREr732Gq6//vrgXK/5+fm4/fbb8eqrr4asF86+uluu+vp6/OMf/8CcOXPwzTff4Msvv8T27dtRUlKCzz77DECgdeqzzz7D8uXLg61XS5YswZgxYzosS01NDT755BM88sgjyMnJAQCoVKqIOmJoNBpcdtllWL16dXDZW2+9hcGDB+OMM84Iq+wnU6vVuP3223Hffffhueeew4cffoglS5bAZrMFOyRYLBZIkoTMzMyQbbOysjrstDBy5EgUFRXhd7/7HZ5//nl89913OOecc3DzzTfjiSeewGuvvYaSkhIsWbIE5513XruXuImIEonb50dpgw0bD5uwtbwZx5qd8Phi+28fL48moAEDBkCj0QR/z83NhdFoRFlZGWbMmAEAKCwshEwm63Q/R44cwXXXXReybPTo0aitrYXT6YRWqw17X90t16FDhyCEwFtvvdWmBW/IkCEAAq1zAILhstXJv5+orKwMADBu3Lguy92Zq6++GvPmzUN9fT1yc3OxevVqLFq0KOyyt+fJJ5/EpEmT8Nlnn+GLL77AwoULUVxcjLVr1wJAMJg6nc6Q7RwOR7uXXAFAJpPhn//8Jx5++GFs2rQJ//rXv/Dhhx8iMzMTCxcuRG5uLrZv345hw4Zh4sSJ+OSTT3DRRRd1+7wQEcUyu9uH7ytb4PLGdkg7GUNbArJarSG/+/1+OBwOpKenB5edfM9Ze9LT02GxWEKWWSwWaLXakBvWw9lXd8ul1+sBACtXrsTpp5/e7n7T0tKC+z9xGjOr1YqCgoJ2t2m9B62lpSV4n1d3zJ49G4WFhXjnnXcwe/Zs7NmzBy+//HLYZW+PTCbD1Vdfjauvvjq4bMaMGRg/fjwAwGg0IiMjA5WVlSHbVVZWYvDgwR3ud+DAgXjxxRcBANXV1bj88svxzTff4OjRo9BqtRg1ahQAYNq0adi7dy9DGxElJLPTi+2VZnj98RXYAF4eTUjV1dXYtWtX8Pd169ZBJpPhtNNOi2g/U6dOxaeffhqy7OOPP8bkyZPDalnrjXKNGzcOeXl5eO2119o81tLSAiDQapWTkxNsiQIC92yd3IPzRGPHjkVubi7ef//9kOUnBkudTgePx9NpnWQyGa688kqsXr0aq1evxpQpUzBs2LCwy94em80W8vsPP/yALVu24IYbbgguO+ecc0KG7PB4PPj0009xzjnndFreVrfddhuWL1+OoqIipKWlwWazBevaer8bEVGiMdk92FbREpeBDWBLW0LSarVYtGgR7rvvPrjdbvz3f/83fvWrX6G4uDii/TzwwAOYOHEirrvuOvziF7/AV199hffeew9ffvllv5VLqVTi+eefx5VXXgmn04kFCxagubkZn3zyCU477TT84Q9/gEqlwv3334+7774bXq8XxcXFWLlyZafBUqlU4rnnnsPSpUthtVoxa9Ys7N+/H99++y3WrFkDAJgwYQJWr16Njz76CHq9vsMx7K655hr86U9/QmlpKR544IGIyt6ev/71r9i1axcWLlyIqqoqPPLII7jllltw/vnnB9e5//77MWXKFPzyl7/EggUL8Prrr0OSpJCx3DrywQcfoL6+Hr/61a8AAEVFRRg/fjzuvPNOTJw4EevXr8ezzz7b5X6IiOJJrcWF3TXWmBvGIxJsaUtAI0eOxIsvvohNmzZhzZo1WL58OZ5++ung44MGDcK0adPabHfy4LpDhw7F999/j9TUVLz44ouw2Wz4+uuvceaZZ3a5r94s16WXXopt27ZBq9Xi5ZdfxtatW/HLX/4yJPT8+te/xv/93/9h06ZN+PTTT/Hb3/4Wy5cvD+mMMHHixOAlQCDQWWHjxo2w2+145ZVX4HQ68cYbbwQfv+uuu3DFFVfg5ZdfxuOPP47m5uY2+wCA8ePH45prrsGkSZNwxRVXRFz2k91+++2YOXMm3nzzTXz77bd4/vnn8cILL4SsM2bMGHz33XdQq9X461//iuHDh2Pr1q3Ize18MGZJkvDRRx/hxRdfDAm1H330ESRJwvr16/Hxxx/36JIxEVGsqWh2YleNJa4DGwDIhIjzGvRAZWUlBgwYgIqKiohboSJhMpmCQ0n0tSeffBJvvvlmh0M/9KcT6x1L5epr/fl8xxLWO7mw3sklnut9uNGOQ432bm07xigwsDCvl0vUfbw8SkRERAlpf50Vx5qdXa8YJxjaEkwklyv7U6yWi4iIEo/N7UNpgx31Nne0i9KrGNoSzOLFi7F48eJoF6ONWC0XERElBiEEGmwelDc70eTovOd/vGJoIyIiorjl80uoMrtwrMUJhyexp+FjaCMiIqK44/D4UN7sjNl5QvsCQxsRERHFNCEEbG4/zC4vzC4fzE4vrG5ftIvV7xjaiIiIKKa4fX40ObwwOwMhzer2wZ8krWmdYWgjIiKimFFvdWN3jSVpLnlGgqGtnzzzzDNoaWlBeno6fvOb3/Trsd1uNxwOBzIyMvr1uEREROESQqC00Y4ykyPaRYlZnMaqnzzzzDN48MEH8cwzz/T7sd977z2MHz8+om1cLlenk5oTERH1Fo9PwvcVZga2LjC0UbteeuklDoZLRER9zuz04pvypoQdW6038fJoP/F4PCHf+5rf74fP54NGo2m3LBaLBQCg1Wqh1+vbPG632+H3+9HY2AgAMBgMkMvlnW5HREQUiWPNDhyst8f9RO79hS1tfayxsRGXXnopampqAAA1NTWYN29eMAz1hYceeghpaWnIyMjAxIkTsX379pDH169fj9GjR2P06NHIycnBoEGD8MYbbwQf//TTT/Hwww+jrKwsuN5rr73W5XZERETh8EsCu2ss2F9nY2CLAFva+tgVV1yBTZs2hSzbsGEDrrzySnz++ee9frwPPvgAK1aswJo1a3D22Wfjiy++wMUXX4zMzMzgOgsWLAiGRiEEPv30U1x++eU49dRTMWHCBFxyySV4/PHHsWrVKhw4cCBk/51tR0RE1BWT3YOD9bakHGetp9jS1of27t2L9evXQ5z0X4QQAl988QX27dvX68dctWoVli5dirlz50Imk2H+/Pm49tprO1zfYrFg2rRpmDp1KtauXRv2cbq7HRERJad6qxvfljfj+4oWBrZuYmjrQ4cPH+708UOHDvX6MQ8ePNim1WvixIkhvzc2NuKKK66AXq9Hfn4+Ro8ejS1btqCioqLTfXd3OyIiSk5CCNRaXNhS1oQfq8wwO73RLlJc4+XRPjR8+PAePd4dKpUKPl/ofzAn/37nnXeirq4OBw8eRHFxMYDAJVO/v/OJdru7HRERJRchBGosbhwx2WFP8Enc+1PchrZNmzbBZDKFLCsuLkZJSUmUStTW2LFjMXfuXGzYsCHkEqlMJsPcuXMxduzYXj/m+PHj8c033+C2224LLvv6669D1vnxxx/xq1/9Khi8nE4nfvjhBwwaNCi4jlqtbhPGwtmOiIiSj8cnweH1w+Hxw+H1o9rsgtPLsNbb4ja0LV++HI2NjRg9enRw2cyZM2MqtAHA3/72NyxatAgbN24MLps7dy7efvvtPjne7373O8ybNw9nnnkmFixYgH/+85/4+9//jry8vOA6EydOxOuvv46zzjoLAPCHP/wB9fX1IfsZPnw4Kioq8P3332Pw4MEwGAxhbUdERInL4fGhyeH9OaB5/HB6/Zxyqp/EbWgDAj0zH3jggWgXo1PZ2dn44IMPMH78eNTU1KCgoKBPeo22mjVrFl599VU8/vjjWLlyJUpKSvDEE0/glVdeCa7z9NNP47/+679w/vnnIyUlBRdffDGuuuoqGAyG4Dpz5szBf/7nf+Kqq65Cc3MzHnnkkbC2IyKixOHzSzA5vKhscmBvC9h6FmUycXLXxjgxY8YMnHLKKbj44otRUFCAsWPHQq1WR7SPyspKDBgwABUVFcFLfn3BZDLh9NNPR3l5OQYNGoSjR4/22bFiiclkQlZWVrSL0e9Y7+TCeieXZKi32elFo92DRrsHZpcXQgCSwwK5zhjtovW7MUaBgYV5Xa/YT+K6pe3DDz/EoUOHcPDgQSiVSrz++uuYOXNmh+tbLJbgiP4AggPeEhEREXCgzoryZme0i0EdiNvQds8992DBggVQKpXwer245ZZbsHjxYhw4cADp6entbrNy5Uo8+OCDbZY3NzdDq9X2WVktFgtuvvlmmM1mpKWltelAkahODMjJhPVOLqx3cknkeldbXKjsILBJ7uScyN0mA0ymvo1KkbTcxu3l0ZNVV1ejqKgI//znP7FgwYJ212mvpW3KlCn9cnk00ZvT28N6JxfWO7mw3omlssWJvbXWDh/n5dHYELctbSdLS0sDgE57MxqNRhiNyfeiIyIi6kid1Y19dR0HNoodcTkjgtVqhdvtDlm2Zs0aAMCkSZOiUSQiIqK40+TwYFe1BYlxzS3xxWVLW01NDRYtWoQrrrgCQ4YMwe7du/Hcc8/htttuw/jx46NdPCIion7nlwTcPj906vA+2i0uL36sNENiYosbcdnSNnLkSKxduxY+nw+ffvopvF4vPv30U/z5z3+OdtGIiIj6lV8SKDM5sPGICZuONGHbsRbUWFyQOhnw1u72YXulmYPixpm4bGkDAlNW3X///dEuBhERUVRIkkBFixNHmhzw+KTg8iaHB00OD9RKOYrSUlCclhLS+uby+rG90hyyDcWHuA1tREREyUiSBCrNThwxOeDuJHh5fBLKTA6UmRzI0qsxIF2LDK0K2yvNnNkgTjG0ERERxQEhBKrMLhw22eHyRtZKZrJ7YLJ7IJOBnQ7iGEMbERFRjPP6JfxQaUaL09uj/TCwxTeGNiIiohjm8AQ6DTg8vKSZ7BjaiIiIYlSL04sfq9hpgAIY2oiIiGJQndWN3TUW+DksBx3H0EZERBRjypscONhg4z1oFIKhjYiIKEYIIXCw3obyZme0i0IxiKGNiIgoBvglgd01FtRZ3V2vTEmJoY2IiCgK3D4/HB4/7Me/THYPrG5ftItFMYyhjYiIqI9ZXF402DzHA5oPDo+f835SxBjaiIiI+ojV5cOhRjvqbbzkST3H0EZERNTLrC4fDpvsvD+NehVDGxERUS+xuQMtawxr1BcY2oiIiHrI7vbh0PGWNY6tRn2FoY2IiKib/JLAoUY7ypsdDGvU5xjaiIiIuqHO6saBeitcXs4LSv2DoY2IiCgCDo8P++tsaLR7ol0USjIMbURERGGQJIEqswt1jRIkXgulKGBoIyIi6kKjzY399TbYWpyQ61TRLg4lKYY2IiJKWpIkYHX74PT64fFLcPsCXyf/zIY1igUMbURElBSEELB7/DC7vDA7fTC7vLC6fQxkFDcY2oiIKGFJksCRJgeaHB5YXD74Od8nxTGGNiIiSkh+SWBHlZm9PClhMLQREVHC8UsCP1Sa0eRgYKPEwdBGREQJxeeXsL3SjBanN9pFIepVDG1ERJQwPD4J2ytbYHH5ol0Uol7H0EZERAnB7fPj+wozbG4GNkpMDG1ERBT3nF4/vq9ogcPjj3ZRiPoMQxsREcUs0/GenylKOVJUCijksjbrODw+bKto4cTtlPAY2oiIKCb91GBDmckRskwplyFFpYBGKQ8GucoWJ9w+BjZKfAkR2urq6lBWVoZhw4YhJycn2sUhIqIekCSB3bUW1FrcbR7zSQI2tw+2tg8RJTx5tAvQU06nE/PmzcMZZ5yBjz76KNrFISKiHvD6JWyraGk3sBElu7gPbXfccQfmzZsHhUIR7aIQEVEPODw+fFvezPHViDoQ16Ht3XffxdatW/H4449HuyhERNQDLU4vvi1n70+izsTtPW1lZWW444478Pnnn0Oj0US7OERE1E01Fhf21FghCU7mTtSZuAxtXq8XV155JZYvX47x48eHvZ3FYoHFYgn+XlNT0xfFIyKiMB0x2VHaYI92MYjiQlyGtqeeegotLS2YMmUKvv322+DyI0eOYOfOnZgwYUK7261cuRIPPvhgm+XNzc3QarV9Vt4Tg2IyYb2TC+udXHpSbyEETA4vaiwuOOPscqjkdnS9UgJK1nrbZIDJ1LdRKSsrK+x14zK0yeVypKen48477wwu8/v9eOedd3DkyBH87W9/a3e7ZcuW4aabbgr+XlNTgylTpiAjIyOik9Ydfb3/WMV6JxfWO7lEWm+/JFBlduJokxNOrwJQ6iGPw08huc4Y7SJERTLW25AqYur9HYdvF+Duu+/G3XffHbJMqVRi+fLlIaHsZEajEUZj8r3oiIiiyeeXcKzFifJmJzwcBJeo2+IytBERUWwTQsDp9aPS7EJFsxM+iZ0MiHoqYULbtGnTkJubG+1iEBElDbfPD4fHD6dXgtPrP/4V+Nnl84OdQYl6V8KEts2bN0e7CERESaHZ4cHBehusDfHViYAo3iVMaCMior7VYHOjrMmBZocXktMLua7vet0TUVsMbURE1CEhBOqsbhwxOWB1+6JdHKKkxtBGRERtSJJAtcWFsiYHp5YiihEMbUREBCAwjlqDzY16mwcNNjd7fBLFGIY2IqIk5vVLaLB5UGd1o9Hu4fyfRDGMoY2IKMkIIVBldqHW6kaTw8OhOYjiBEMbEVESqbG4UNpgh9PL+9SI4g1DGxFREmiwuVHaYGcPUKI4xtBGRJTAWpxe/NRgQ7PDG+2iEFEPMbQRESUgm9uH0gY76m3uaBeFiHoJQxsRURzzSwJ2jw92jx92jx82d+vPPnYwIEowDG1ERHFCCAGzy4d6mxsWlw92jw8urxTtYhFRP2FoIyKKYZIkYHJ4UG/zoN7mhsfHkEaUrBjaiIhijM8vocEeCGmNNg9nJiAiAAxtRERR13rZ02T3wOTwoMXp5f1oRNQGQxsRURTY3D+HtGaHl61pRNQlhjYion7i9PpxtMmBOqsbbt6bRkQRYmgjIupjFpcXZcfDGi97ElF3MbQREfWRRpsbZU1ONDk80S4KESUAhjYiol4khECNxY2jTQ7O80lEvYqhjYgoAh6fBLdPgtvnh9svocniQr3PenyZBIfXz7HUiKhPMLQREYWho4nXJYcTcrcqSqUiomTC0EZE1Am724fSRjvqrJx4nYiii6GNiKgdbp8fhxodqDI72eOTiGICQxsR0Ql8fgllTQ6UNzvh54C3RBRDGNqIiBDoYFBldqGsyQGvnx0JiCj2MLQRUVIz2T2oNDtRb/VA4nVQIophDG1ElHRaW9UqzU44PP5oF4eIKCwMbUSUEGxuH+weP+QyQC6TQdb6HT//7vL6UWl2od7G6aSIKP4wtBFRXPP6JRxqtKOihb08iSixMbQRUVwSQqCyxYXSRjs7DhBRUoj70Ga32yGXy6HVaqNdFCLqJ80OD/bX2Ti3JxElFXm0C9Bda9asweTJk1FcXIzMzEyMHz8e69ati3axiKgPubx+7Kw247tjLQxsRJR04ja0bdiwAa+88gqamppgsVhw/vnn4+KLL0ZjY2O0i0ZEvUSSBOxuHxpsbpQ22LC5rAm1Fk4nRUTJKW4vjz7zzDPBn1UqFZYuXYoVK1bgyJEjyM7Ojl7BiKiNZocHLp8EGQDZ8R6dMlngsdZlXr8Eh9cPp1eCw+OHw+uD2yexcwER0XFxG9oAwGazoaWlBfX19XjkkUdwxhlnYNKkSdEuFhEdJ4TATw12HG1yRLsoRERxL65D2+rVq/HQQw+hoaEBRUVFeP/996FUdlwli8UCi8US/L2mpqY/ikmUlAL3n1nQ4vRGuyhERAkhrkPbrbfeiltvvRUejwcPPfQQZs2ahd27d2Po0KHtrr9y5Uo8+OCDbZY3Nzf3ae/TE4NiMmG9E4PZ6UWKSg6NUtHpeifWu8XpxWGTA/4kGIpDcidnKyLrnVyStd42GWAy9W1UysrKCntdmRCJcceIEALp6el44IEHcOedd7a7TnstbVOmTEFFRQWKi4v7rGwmkymiJyVRsN7xrd7qxmGTHRZXoJdmll6Ngela5BjUkLXekHYCk8mEzMxMlDbaUWZKnj/wksMCuc4Y7WL0O9Y7uSRrvccYBQYW5kW7GEFx2dImSRLk8tCOr3a7HU6nEzqdrsPtjEYjjMbke9ERReLksNbKZPfAZPcgRSVHUZoWxWkpSFH93Prm9UvYVtGCZgcvhxIR9YW4DG3bt2/H448/jltvvRVDhw5FZWUlHnnkEeTk5OCyyy6LdvGI4lJHYe1kLq+Ew412HDHZkaPXYEB6CmQyGXbXWiGpDf1UWiKi5BOXoW3y5Mm4+eabsWrVKhw4cABZWVmYMWMG3nzzzYS4LEXUX4QQaLB5wgprbbcF6m1u1NsC46ZJPglydV+UkoiIgDgNbQBw3nnn4bzzzot2MYjijs8vodHuQb3NgwabGz4pIW5rJSJKeHEb2ogofG6fH/VWD+ptbjQ5vJASo/8REVFSYWgjSlBWlw8NdjfqbR6YOVYaEVHcY2gjShB+ScBk96DB7kGj3Q2XN/HHSCMiSiYMbURxzOn1o97qRoPdg2Ze9iTqU6889TBqj5Uhf+AQ/PKu+6JdHEpCDG1Eccbrl1BrcaPa4uIUUUT9wNxswmPLbsWObzcFl/20ZyeWr/xfpGVwxALqP/KuVyGiaBNCoN7qxo4qM/59yIR9dVYGNqJ+8tiyW7Fz6+aQZTu3bsbjd/0qSiWiZMWWNqIYJYSAxeVDtcWFWqsbHh/vUaO2XnnqYdRVVyKvsJiX7PrA0dIDIS1srYQQ+PGbjSg/dBCDho+KQskoGTG0EUWZXxKwe3ywe/zHv3ywuwM/8x416ggv2fWPmoqjnT5efayMoY36DUMbUT/x+iXY3D7Y3IFgZjse0NjLk7qjs0t2j73yXpRKlXgKBw7p0eNEvYmhjegkdrcPB+pt0CjlSNeqkK5VwaAJ/63i8ws0OTwnBDQ/bB4fL29Sr+Elu/4zaPgoTJw2Ezu3boY4oeVbJpNh4rSZPM8J7McqMx74rAqf3pKBTF1szNHH0EZ0giqzE/vrbPAfn9qpyuwCACjlsmCAS9eqkJYSeOvYPH7Y3D5Yjwc0m8cHl6UFch0DGvWdnl6y6859cP21TX/5x2v/C5vVAkOqEZdcd2un6y5f+b94/K5f4cdvNgaXTZw2E/c+9UJfF5OioKLFiZe2HsPWYy0AgEc+L8XKi8dFt1DHMbQRITAf5746G2osrvYflwQa7R402j0AAJksMGE6UTR095Jdd+6D62ibex55Ehk6Y68dpye6Ew7fe/l5NDXUIjMnv8vQlpaRhcdeeQ9XzZoQ3IaXoBOPxeXFmz9U4dN99fCf8Ad+R7UZfklAIZdFsXQBDG2UEJxePywuH2zuwA396Vol8lNToFZ2PaqN2enFzmoLnF5/2MdjYKNo6u4lu+7cB9fRNit+fycef+0fvXacVpEEsJ6E0KaGWgBAU0Mt7r3hsrAC5eIb/xPWpgakZuZ0uh7FF69fwif76rD6xyrY3D9/DuQZNLj/rAH41VmjIZNFP7ABgEyI5P34qaysxIABA1BRUYHi4uI+O47JZEJWVvL15uqrepudXliPX5JsDWo+qe3LWCYDsvVqFBhTkGvQtPtfUpnJgdJGW6+GMMlhgbyDFohExnr3L3Ozqc0lu9POmIV7n3qh3fBxtPQAfnXR7A73978fb2wT9vprm9b6nBzAJk6b2WmYuveGyzoMrh2Fw+5scyK+zmNDk8ODarMLDq8El9cPh9cPl0+Cw+OH0+uH0+eHzy+QoVMhW69Gtl6NHL0aWXo1jBolZDIZhBD4trwZL22tQNUJV1l0KjmumFiEX5ySjwmZMgwszItiTUOxpY3igt0dGK+s2uIKu7elEECDzYMGmwcKuQx5qRoUGlOQqVPB6xfYVWOB6fjlTqJ403rJLtyWqe7cB9df2wCRt851pzMGO3DEv0a7B6t/qMS/Djagnf/Vw6JWyJClV0OtkKO82RlcLpcB543KxdKSYqRrVceXxla7FkMbxSyvX0KNxYVqixvmHo7+75cEqs0uVJtd0CjlEAB7c1JCCPceru7cB9df23QnTPVnoKTos7p9eG9nNT7cUwuPv2dByuMXqLG4Q5ZNKjLi5mmDMCRT16N99zWGNoop/uM3/FebXWiwu/vk3jE3wxoloe7cB9fZNhMmnxHxNh0dpzthqr8CJfU+u8eHepsHBUYNUpSKTtd1+ySs2VuLd3ZWh9xvZtAocM7IHKSlqKBVyaFVKY5//fyzQiZDs9ODhuOdyE78Mtm9aHF6MSRTh+snD8DkAWkxc99aZxjaqN95/YH7Dhxef/D+g9afGaiI+k53hq7oaJu7H36i147TnTDV2yGUY671Pa9fwod7avHmD1Vw+yTIZUChMQVDMnUYmqULfs/RqyEJYN1PDVj9QyUa7T9fadEo5LhkfD4uO7UgrPEzB2ZoO3wsVnqERoIdEdgRoc80NjZCbUgLdBpw+WB1+2F1++D1J3Ywi7UbdvsL691z/TWu2YlDV7y1cWe3yhZOvSOpT3c6CETaGaO725yIr/Pu2VltwV++LsOxlvaHVTqRQa1AikoRHGIJCNxvtmB0Lq6aVISsfhzodoxRxFRHBIY2hrZe4fL6gwPMBnp1emE1N0Ou5R+3ZMF6d193ek72RCQDy3akt5/vnoSp/hz4l6/zyDQ5PHhx6zF8ecgUXCaXAWcOzkSTw4OyJgecXXQumzU0E9eVDEBRWkrEx+8phrYYwtAWOZ9fgs3jh9Xlg83jC7aitTfkBv+4JRfWO1RftzJFW18937E8iwLA13m4/JLAJ/vq8Nr3lXCcMAbmKfmpuG36YAw+fsO/JATqrG6UNTlwxOQIfG9yoNHuwakFRlxfMgAjcvS9Xp9wxVpo4z1t1C4hRGDOzBOmaLK6fRENQEuUjCId8JXDUISKxaBG4RFCwOmVcMRkx/98U47DJkfwsXStEjdNHYi5w7NDbviXy2QoMKagwJiCMwdnhuwrHjoG9DeGtiRgc/tQ3uyE0+uHQiaDQn78SwbI5bLgMiEQaD1zBWYVkJK3EZYSWF+35EQ63hiHoaBY0GBz418HG7D+UCPMTi90agV0KgV0amXgu0KCLiUFerUCKoXs+D3KXphdgc8My/GrLt6TrrrIZcD5Y/JwXUlxWB0HWjGwtY+hLUEJERg642iTE00ODiBL1B/zYXan1YzDUFC0+CWB7ypasHZ/Pb6vbAkZrNbhlQCcPD6mNaL9j8rR47YZQzAiO3qXNxMNQ1uC8fklVJldONbihMPDS5lErXoyH2a4utNqxmEoqLf4JQGH1w+tSg6lvON5l2utbvzrYD3WHWyAyREazBQyGQZkpMDtleDw+mH3+Nu9Z/lEerUCxhQljBoljCkqGDVKnFZkxNkjsiFni1mv6lZokyQJNpsNRmPy3YwZi4QQcHj8qGhxosrs6vINRrEl1m+8jlQs1qe/7hvrbqtZd8ZPI3L7JBxssGFPjRV7ai3YV2eD6/hYlxqFPHiJU6uWH7/UqYDTI2FXjaXN5EyFRg0WjM7FvBE5yNCpgsuFEPD6BWyWFrgUOti9fnh8EgwaBdJSVEjVKONurLN41q3Q5vF4MHr0aDz55JO46qqrertMCacnHXQlScDs8sLm9sPjl+DxS3D7At89Pgkev0j4cc8SVX9cruupSIaGMDc3YcWvfxmT9emv+8a622oW6TyilJzsHh/219mwu9aK3TUWlDbY29xD1srtl+B2SmjuZApAlVyGM4dkYuHoHIwvMLbbKiaTyaBWypCeooRc1/9DblCobg35IYTAU089hQceeABTpkzBX/7yF4wZM6Yvyten+mvIj017j0KhMwaaj1OUwf9O5O38d+L1S2hxetHs9KLZ4YXF5YvbDgHsGt+5eBjmIZJBWO9Z+gvs+v7bbtenLwNL+aGDuPXCWR0+/r8fb+x2aDv5+e7p4K3xgu/v/tFgc+Ob8mZsOdqMXTWWTidJLzBqMDBdC9fxS5utM804vKGzzRSlpWDh6FzMHZF9wsTonUvW5zshhvyQyWT47W9/iyVLluCOO+7AhAkTsGzZMtx3333Q63nD4ckEApPdWt0+VJkDy2QywKAOhLhUjRJ2jx/NTi9sbl9Uy0r9I9aHeWhtBWxqqAUANDXU4t4bLut02Iqd275pszyc+vRHi2NP7xuLpMWRrWbUnka7B3aPD5laNQwaRYe9I4UQONbixJajgaBW2mjvcJ+DM7Q4pSAVp+QbcUp+KrL1Hc8U4JcEnF4/vH6BdK2SvTPjVI86IgwYMAD/+Mc/8Mknn+D222/HW2+9hWeeeQaXXnppb5UvYQnxc5Cj5NPTy3WJNGxFf3QQAHp239h7Lz8fbHEMdwYBBjXySwJbjzXj4311+LHKElyuksuQoVMhQ6tChk6FTK0aGToVXD4J3x5tRpWl/ameBqZrUTIgDePzUzEuPxXGlPBayQBAIZdFNOQGxaZeeQYvuOACzJ07F3/4wx+waNEinHfeefjzn/+M4cOH98buieLGK089jNpjZcgfOKTTD+3u3rCeaMNW9LTFMZLw2p0WsEhbHIkAoMXpxWcH6/HP/fWot7UdcskrCdTbPO0+drLRuQZMH5yBMwZloDi948nPKTn0KLQ1Njbim2++wTfffIMtW7Zg27ZtAIDNmzdj/PjxePTRR3HnnXf2SkGJYlmkYaq7l+tiediKCZPP6PCeto7q090Wup6E10hawPqrFZDinxACBxvsWLO3FpuONLXpIDAwXYuxeYbA/crH71ludnrb9PZXymWYUGjEmYMyMG1wRr9Ojk6xr1uhzev1Yty4cSgtLQUADBs2DDNmzMA111yDGTNmYNSoUXj//fdx2223wWaz4b77evcygSRJeOedd7Bq1Srs2LEDRqMRCxcuxOOPP46cnJxePRZROLrz4R7p5bpYH7bi3seewZ/u+11Elx+7e6z+CFOxft8h9T9JCDQ7fWh22GFyeNDk8KLp+PeDDTYcanSErB+YGD0DF47Nx6kFqW3uI5OEgM3tC+zH6YXPLzA2z8DLmNShbr8yFixYgEcffRQzZsxAfn5+m8cXL16MQYMG4dJLL+310LZ37158/PHHePLJJzFhwgRUVlbi6quvxiWXXILNmzd3vQOiXtTdD/fWy3Un9tDsLHDE/rAVmRHVp7vH6q8wxemlyC8JfF/RgrUH63Go0Y4mh7fT3putMrQqLBidiwWjc5Bj0HS4nlwmCwxGm6LC4N4rNiWwboU2lUqFZ599tsv1JkyYALPZ3J1DdGr8+PF46623gr+PHDkSDz30EBYuXIiamhoUFBT0+jGJOtLTD/fFN/5nsGdiZ/pzuqOe3LQfbn26e6z+ClOcXir+mRwebK8wo8HuxvBsPU7JT4Ve3fXHnsnhwb8ONGDtgXo02MOfBnBsngEXjs3DjCGZUCk6npGAqLv6tA1Wo9Ggrq6uLw8RVFdXB7lczlkaqN/19MM93N6IPR22oq9v2m8Vbn26e6z+ClOcXir++CWBgw02fHesBdsqWnDY1PZy5fBsPSYUGDGh0IixeanQqRUAApcqd1RZ8On+OnxT3tymRS1Dq0JeqgaZGhmyU3XI1KuQqVMjS6dClk6NLL0aqbysSX2sW4PrxhqTyYRJkybhrLPOwmuvvdbhehaLBRbLz92ua2pqMGXKlD4fXHfj3qNwKnR9tv9YlUyDMfbXQLndGbi1vZv2J06b2es9IPvz+Y6l851Mr/MTxUq9W5xefF/Zgm0VZmyvbIHNHf6cy3IZMDLHgBHZenxf2YIaizvkcYVMhjMGp2PhmDxMLAzMGBAr9e5vyVrvWBtcN+5Dm81mw7nnngun04l///vfnba0PfDAA3jwwQfbLN+1axcKCwv7rIw7y2rhlnd8X0OiktwOyDXJEVbNzU1Y8fs7seO7LcFlE6eciXsefRppGZm9fry//vlPqKuuRl5hIW64/e5O113+q6Xt9uqcMPkMPPo/Hf+TE6n+fL5j6Xwn0+v8RNGud3mLG+/vM+HfR83wt/MpJpcBo7K0KCk0YECaGgcandhV58DhJlebeTdPlqNTYsGIDJwzLA2ZJ80YEO16R0uy1nuQHsjLzujTY2Rlhf/Pc1yHNrvdjoULF8JsNmP9+vVdVpwtbf0rGf8zi2Tap/5wtPQAfnXR7A4f78n0TSeLxvMdC+c7GV/nQM/qLQmBo00O7Ky2wCcJTCg0Yni2vt25L0+2v86Kd3fW4Jvy5jaPpaUoUVKcjskD0zGpyNju4LM2tw+7a63YVW3BrhoLjpgcEABkAKYMTMfCMbkoKU7vcBJ0Pt/JJdZa2uL2ArzD4cD555+PlpaWsAIbABiNRt7zRn1q8Y3/CWtTA1IzY2PomUTvARlppweKnlqrGz9WmbGjyowd1RaYXaGzwaSlKDGpOA0lxek4vTgtZE5MIQS2V5rx7s5q7KqxhmyXrlVi4eg8TB2UjhFhBD+DRokzBgUGqwUAq8uHI00OFBo1nfb0JIoFcRnanE4nLrzwQphMJnzxxRdIT0+Hzxf4A6BUxmWVKEFcct2tMfUfaaL3gIy00wP1nxanF7uqLfix2oIdVWbUWN2drm92+fDlIRO+PGQCAIzI1uP04jTkp2rw8b66Np0K8lM1uOzUAswfmQONsvs9NVNTlJhQGBvvV6KuxGXC+e677/DVV18BAIqKikIe+/rrrzF16tRoFIso5rAHJPWXFqcXu2ss2FVjxa4aC8qbnR2ua9QEgtLEIiNUcjm2V7Zge5U5pBNBaaO93cnSB2dqsWRCIWYNzerwEiZRoorL0DZ79uxgyxoRda4nY64RdcTq9mNXrSmskKZRynFKfipOK0rDxEIjhmbpQi5jnjMqB35J4KcGG76vDPQCPVhvD+kwMDbPgCUTCzFlQHqbmQWIkkVchjYiCl+kMy8QdcTjl7DtWAvWH2rEd8da2syb2Uoll2F0ngGnHh8PbXSuAeouBptVyGUYk5eKMXmpuPb0YlhcXvxYZUGV2YVTC404JT+1L6pEFFcY2oiSBG/aJyAwAO0XpQ3YUWVBpl6NIZlaDM3UY0B6Sruj+AshsK/Ohg2HGvHVEVO746Cp5DKMzjXg1EIjTi1Ixejc1B7dZwYAxhQVZg/rvXEEiRIBQxtRkuBN+8lNCIEtR5vx6vcVqGhxtXlcKZdhQLoWQzN1GJKlw8B0LQ422LChtLHdTgQ5OiXmjMjB6cVpvRLSiKhrDG1ERAluV40Fr3xXgQP1tg7X8UkCZU0OlDU5gEPtr6NTKTBjSCbmjsjGuDQBpT6tj0pMRO1haCMiSlBHTHb8dVsFtlWYQ5ZPLDTimklF8AuBIyYHjhwPa+XNTnhPml5AIZOhZEAa5g7PxtRBGcEWNclhARH1L4Y2IqIEIgmBihYn3tlRjS8PmUJ6YA7P1uGXkwdiUvHPLWQTCn/+2S8JVJqdOGJy4FiLE1k6NWYMyQwZ6JaIooehjYgohphdXlS2uKBVyaFTK6FXKaBTK9qMSSaEQKPdg/JmJ8qbnTja7Aj+7PZJIesWGDW4vmQAZg7N7HTGAIVchkEZOgzKSL5p94jiAUMbEVEMOFhvw4d7a7HpSFO7Q2mkKOXQqwMBTqOUo8biht3TtifniTK0Klw9qQjnjc6BUs6OAkTxjqGNiChKPH4Jm440Yc3eWhxsaDv6/4lcPgkunwSTw9vhOgqZDMXpKRicocWYvFScOyoHWpWit4tNRFHC0EZE1M9MDg/+ub8e/9xfj2ZnaAjL0Kowa2gmBACHxw+7xw+7xwe7xw+H1w+Hxw+n149svRqDM3QYlKnFoAwdBmdoUZTW/lhrRJQYGNqIiPpBvc2NndUWbKtowddlzfCL0Eugo3P1uHhcPmYMyWTwIqJ2MbQREfUBk8ODXdUW7Ky2YGeNBTWWtgPUKuUyzBqaiYvH5WNUriEKpSSieMLQRkSEQG/Maosb9TY3mp1eNDu8aHJ60eTwoNnhDS7zSQI6tQI6lRw6tQJaOaBLUR9fpoDXL7C71tLurAOtMnUqnD8mDwtH5yJDx+E0iCg8DG1ElNT8ksA35c14Z0c1Shs77wzQyuE9uddm59tplHKMy0vFhEIjJhYaMTxb32YIDyKirjC0EVFS8volbDjUiPd21qDS3HGrWCuDWoEMnQpKuRxOb6AzgN3jb3d4DpVChjG5BkwoTMOEQiNG5eh5nxoR9RhDGxElFafXj7UH6vHB7ho02kN7bo7ONeD04jRkaFXI1KmQoVUhQ6dGplYFdQcTorutLXAq9XB6A707fZLA4AwdJ1Anol7H0EZESaHR7sHaA/VYs7cOVrcv5LGS4jRcPrEQ4/NTIetkxoD2qBRyaLQqTvVERH2OoY2IEk6L04vSRjt+arCjtNGG0gZ7m0Fp5TJg5pBMLJ5QiOHZ+iiVlIgofAxtRBTXzC4vDjXaUdpoR2lD4Hu9zdPh+iq5DPNGZmPxqYUoTEvpx5ISEfUMQxsRxY3WFrTSBjsOmew41EVAa5WWosTIHD1G56bivFE5yNKr+6G0RES9i6GNiKLO45NgcnjQ7PSixen9+Xvr+GhOL+pt7jYdB9qTqlFiRLYOI3IMGJGtx8gcPXL06ojvVSMiijUMbUTULzx+CbUWN6osLlSbXai2uFB1/HuDzYO2A2d0zaBRYES2HiOy9Rh+/Ht+qoYBjYgSEkMbEfUJIQRKG+1YX9qI7461oM7mRjtDmoVFBiBdq8KQTB1G5Pwc0vIMbEEjouTB0EZEvarO6saGQ43YcKix06mcWmVoVShKS0GuQY0MnRoZWhXStcrAGGlaFTK0ahhTlJxBgIiSHkMbEfWY3ePDV4ea8WV5JXbXWts8rlHKMSxLh6K0FBQZU1BoTEFhWuC7Tq2IQomJiOIPQxsRdcjq9qG00Y4Wpxd2jx92ty/w3eOH3RP42ebx4bDJAa8/9NqnXAZMLDTi7BHZmD44E1oVwxkRUU8wtBERAEASApUtLuyrs2J/vQ3766w4FsblzZMNzdRh7ohsnDUsi0NrEBH1IoY2oiTllwT219uws9qCA/VW7K+zwebxR7wfhUyGHIMa04v1mDe2CEMydX1QWiIiYmgjSiImhwfbK8zYVtGCH6vMnYY0tUKGEdkGjMkzoMCogV6thEGtgE6tgEGthF6jgF6tgEYhh0wmg+SwQK5jYCMi6isMbUQJzC8J7K+zYlulGd9XtOCwydHhujl6NcbkGTA2LxVjcg0YmqWDSiHvx9ISEVFnGNqIEojL68eBehv21lmxt9aGA/VWOLxSu+uma5UoKU7H6cVpOCU/FTkGTT+XloiIIsHQRhSnhBBocnixr86KvXU27K214rDJ3uEAtnIZMDrXgJLidEwekI5h2TrIOTAtEVHciNvQZrFY8Oabb+Kll15CdXU19u3bh8zMzGgXi6jX+CQJ9TYPGmxu1Ns8IT832APf3b72W9Fa5adqcEp+KiYPSMekojSkpsTtW56IKOnF7V/w6667Dvn5+bjqqqvwu9/9DpLU+YcXUazzSRJ+arBjV40Fu6qt2FdnhauLUHYiuQwYlqXH2DwDxuWnYlxeKofcICJKIHEb2v7+979DLpfjs88+i3ZRiLrFJ0kobbBjV40Vu2os2FsbfkgzaBTI1WuQY1BjRLYe4/JTMTrXwAFsiYgSWNyGNrmcvdooPrh9EqrMLlS2OFFhdqKixYWKFicqW1xw+zsOafmpGozJNSAvVYNcgwa5BjVyDGrk6DWc+omIKAnFbWjrDovFAovFEvy9pqYmiqWhRFVj9WDzT9XYU2tBRYsLdVY3OugbECI/VYNTC4w4tSAV4wuMyEtlb04iIvpZUoW2lStX4sEHH2yzvLm5GVqtts+O63PaIcl9fbb/WCW5Ox4TLNFUWz3YVG7B5mMWHG52d7m+Si5DYaoaI7JSMCFPh1Ny9cgzqE5Yww3J0fV+YkkyPd8nYr2TC+udXGwywGTq26iUlZUV9rpJFdqWLVuGm266Kfh7TU0NpkyZgoyMjIhOWqSUtVZ4Fck5UrxcZ4x2EcLm8UtotHlQb3Oj2emFRimHXq2AXq08/j3ws0IeGCaj2uzCxjITNh1p6nDQ2rQUJYrTtBiQnoIB6drjXynINWiC+0kk8fR89ybWO7mw3snDkCr6NB9EKqlCm9FohNGYfC86+pnd48NPDXZUtDhRFxxCw406qwfNTm9Y+9Ao5dCq5Ghxtt96OjZHi1nDczF9cAYHrCUiol6TVKGNkoskBKrMLuyvs2F/vRX7620ob3KGdX9ZZ9w+KWR8NBmAsXmpmDk0EzOGZCJT5krK/0iJiKhvxW1oe+ihh/D888/D4/EAAMaNGweZTIaXXnoJF1xwQZRLR9Hg9kk42GDDnhprMKTZ3B1PiN5KIZMhW69GrkGNXENgGI0svRoenwSH1w+b2we7xw+Hxw+7xw+7N/B7tl6NGYMDQe3E8dAkh6svq0lEREkqbkPbnXfeiVtuuaXN8oyMjCiUhqLB4fFjf70Vu2sCXz812ODtaA4nBG7+H5atx5hcA0bk6JF3fBiNTJ06Ie8vIyKixBK3oS01NRWpqanRLgb1EyEEaqxuHG6040C9DbtrrTjU2PE8mwCQrVdhdG4qxuQaMCbPgOHZeqgVHN+PiIjiU9yGNoovkhCobHHhWIsz2CtTp1LAoAn0zExRyiE7Pnm5T5JwrNmFwyY7DpscONwY+O7wdn6pc1CGFuPzU3FKgRHj8gzsBEBERAmFoY36hMvnx4E6Ow60WLGvzoZ99dZO7y+TywDd8SDX7PTC6++8u4BcBgzN0mF8vhHjC1IxLj8VaSmqTrchIiKKZwxt1GN2jw+VLS5UmJ041OjA3lorjpgc8Ivw+2lKArC5/R0Gu0KjBsOy9BiWrcPwLD3G5BmgV/PlS0REyYOfehQWj19CrcWNKrMTlWYXKs2uwHyaZmeH45WdKEunwti8VIzM0cMvRLAnps3jh8MT6I3Z+mXUKDEsW4dhWXoMz9JhSJaOAY2IiJIePwkpyOX1o8bqRrXZhWqLC9UWN2osgZ8bbJ6wxzeTy4AhmTqMydJgXFEmxualItegDt6zRkRERJFjaEswQgiYXT7UWFyosR4PXWY3aq1uOLx++CUJXknA5xfwSa1fEnx+0elwGe2RAcgxqFGcloLiNC2K0lIwKEOLUbkGaFUKSA4LB5klIiLqJQxtcczm9uGnRjsO1ttw2OQIBDWLCw6v1PXGEcjQqlBoTEGBURMIaOmBgFZoTIFGySE0iIiI+gNDW5zw+iUcbXLiQIMNB+ttONhgQ0VL+CPvy2WAVqWAUi6DSiGDQiaHUiGDSi6D4viyFKUCBUbN8YCWgkKjBgXGFGhVij6sGREREYWDoS2G+CUBk8Nz/D6ywCXNQOuZG0ebHV0Og6FSyFCQqjkeuAItYwXGFBSkapCXqoGKA8sSERHFLYa2KBBCoM7qxiGTA6WNdhwxOVBtcaHe6g77vjKNQo7h2XqMztVjVI4BI3P0yE3VQM6b/YmIiBISQ1sfkySBSrMbu5udONRoD3yZ7GFNZN5KKZehKC0Fo3IMGJWrx+hcAwZn6DhfJhERURJhaOtDnx9swKLXvofV3fU4ZgaNAgWpoZc0W3/O4oTmRERESY+hrQ8NSE9pN7DlGTQYnq3D8Gw9RmTrMSxLjwwdp2AiIiKijjG09aGROQacWmBEVooMQ3OMGJYdGOHfyDkyiYiIKEIMbX1ILpdh529nY+Peo3AqdNEuDhEREcUxjgFBREREFAcY2oiIiIjiAEMbERERURxgaCMiIiKKAwxtRERERHGAoY2IiIgoDjC0EREREcUBhjYiIiKiOMDQRkRERBQHGNqIiIiI4gBDGxEREVEcYGgjIiIiigMMbURERERxgKGNiIiIKA4wtBERERHFAYY2IiIiojgQ16Ht9ddfxznnnIMzzzwT99xzDywWS7SLRERERNQn4ja0Pffcc/iP//gPXH755bjvvvvwxRdfYOHChRBCRLtoRERERL1OGe0CdIfX68UDDzyA++67DzfddBMAYMSIERgxYgTWrl2LhQsXRrmERERERL0rLlvafvzxRzQ3N4eEs+HDh2PUqFFYv359FEtGRERE1DfisqWtoqICAFBQUBCyvLCwMPhYeywWS8h9bzU1NX1TQCIiIqJeFpehzev1AgA0Gk3Ico1GE3ysPStXrsSDDz7YZnlzczO0Wm3vFvIEPqcdktzXZ/uPVZLbEe0iRAXrnVxY7+TCeicXmwwwmfo2KmVlZYW9blyGttYKmkwmGI3G4HKTyYRTTz21w+2WLVsWvAcOCLS0TZkyBRkZGRGdtEgpa63wKnR9tv9YJtcZu14pAbHeyYX1Ti6sd/IwpIo+zQeRisvQdtppp0Eul+O7777DkCFDAAB2ux179uzBDTfc0OF2RqMxJOQRERERxYu47IiQnZ2NSy65BCtWrIDVagUA/PGPf4Rarcbll18e5dIRERER9b64bGkDgP/93//F4sWLkZeXB6PRCLlcjr///e8x1YxJRERE1FviNrRlZWVhw4YNqKqqgs1mw7Bhw6BUxm11iIiIiDoV9ymnqKgo2kUgIiIi6nNxeU8bERERUbJhaCMiIiKKAwxtRERERHGAoY2IiIgoDjC0EREREcUBhjYiIiKiOMDQRkRERBQHGNqIiIiI4gBDGxEREVEcYGgjIiIiigMMbURERERxIO7nHiUiaqVWypGilEOjlEOrUkCjlCNFqUCKKrBcJpPB6vLB6vbBcvy70+uPdrGJiMLC0EZEUaOQy5CuVSFTp4JWpQAAyADIZLITfkbwZ4VcBrks8KWQ4/j31mU/b9cZrUqB3FRN8HefX4LF7YPV5YPN44cQIrg/+Qn7Dh4DMshkP5fLZvbDmG5E65E9fgmHTQ54fFLvnCQiouMY2oiSlFopx6AMLbJ0alSaXag2uyAJ0afHVMplyNCpkKlTI0OrgjFFGVbQ6tMyKeTI1KmRqVN3a3uTz4EsY0rIsgJjCkob7Kg0O9EXp1Quk/X5c0VEsYehjSjJ6NUKDM7UodCYArk8EJjStCqMyNbjWIsTFS3OsFqJ5DIZsvQq5KVqYNSo0NwkkJqeDkkAkhAnfBcQAkhLUcGgUUQ9pPUHlUKOsfmpKEpLwb46KywuX4/3qVcrkGPQIMcQCLw+ScB6Qguh1e2Dze2DXwoNczJZoHXRoFZCrwl8N2gUUCnkqDa7UNHihJutgkRxgaGNKEmka1UYnKlDrkHdbnBSK+UYnq3H0Ewdqi0uHG1ywO4Jvd9LKZchx6BBrkGNHIMGCvnP+/FolMjoZmtVokrTqjBtUAYqW1z4qcEGnxR+65hcJkOmToWc4+e69fJxK5VC1qaFUAgBp9cPq9sPSYhAUFMrguH8ZMOy9RiSqUOt1Y1jLU6Ynd7uVbQdMhmgUyng8kltgmS422do1UjTKtFo88Dq7nnwJYp3DG1EUZClV2NIpg4qhQwenwSPX8Drl+DxB34OLJPg9klw+fzdvsQmkwHZx48VbqCSy2UoTteiOF2LRlvgw1yjVCDXoEaWTt1hAKD2yWQyDMjQIi9Vg4MNNlSbXSGPy2UypKgCHScCX3IY1Epk6dUhoTjcY+nUSujU4f9pl8tlKExLQWFaClqcXhxrdqLW6or4NSeTAcaUwP2JGdrAl1IhhyQJtLi8aHJ4YbJ7YHZ5O9y3SiEPhFS9Gtl6NZSKwAAHI3MAq8uHGqsLNRYXXF62DFJyYmgj6ke5Bg2GZumQplWFvY0kBVpPHF5/4Lsn8LPD44fLJ0GtkAc/7FOOf28NAK09Jrsr26BBtkHT9YrUJbVSjvEFRgxI18Lp9QefM41S0fXG/SRdq0K6VoVRPj0qWlywuX0QAhAIXO5u/VkIwOVXQKdTI+N4SEvXqtoNmXL5zy2Cw7P18EsCTQ5PMMQJIBjU0rWqDl+vqSlKpKYYMCJbjyaHF9UWF+qs7m614hHFK4Y2ojAp5TJoVQro1IFA5JMEzE4vbB5fp60SMhmQl6rB0Ew9UlMif8vJ5TLoNUroNXy7JoLWYBTLNEoFhmfrO13HZJKQlZUe8b4Vxy+x53TznwGZTIYsvRpZejXG5gnU29xocXph97T+I9P9lmmiWMdPAaJ25KVqkKpRQqdWQHe81UqtbH8sakkSsLh9sLi8MLt8MPns8BxvLCgwpmBopo6Bi6gPKOQyFBhTUHBC790TW6ZbW6Vtbj/MLm/ErXIGjRI5BjVyDRqkKOVwev2oa/RBqdMH9+30+jm8C/UbfpIQncCgUWJsniGiG+rlx8caa209Mam9SM/IhE+SYurSF1Ey6KhlWggBm9uPFqcXLS4vzMdb50K2lQWGpMntoPNHikoBSa9BVlZoK6TPL8Hm8aPKHLjnjpdsqa8wtBEh8B/7sCw9Bmdqe2VICoVcBoWcgY0oVshksuP3xSkxAFoAgNcvocXphcXlg0GjRHY3On8AgbH+0rXywP2AOXpUHR9K5eRQSNRTDG2U9HINGozJMyBFxZBFlEwCvVW7f39de5QKOQZl6jAoUweT3YOKFifqbe5+uc9OdnzGDg68nLgY2ihpaVUKjMkz9OofbCKiVq0dJlxePyrNgbEPe+PSqUIug16tgP74QMl6tRIGdaCTlE8SqLW4UWVx9eq4exQbGNooochkwNAsPbQqOXx+AZ8k4Bci+LNPkuCXgDStEsOy9N26FEJEFIkUVaA3bo5ejR+qzN3quCCTASNzDMhLbXuv3YlUisC4gAMytLC5fagyu1BtcfW4s4RaKUdhhha5Bg3UCjl2VJvh4OXffsfQRglDr1bg1EIjjCmxPZwCESWn1hkyfqg0wxbBDA9qpRwTC40Rzzhi0CgxKteAkTl6NNg8qDK70GAP/1KtQaNErkGNvFQNvHYFsrJSg4+dMSgDO6staLR7IioT9QxDGyWEAelajMo1sOWMiGKaVqXA1IHp2FFtgSmMwJOuVWFCobFH99zKZDLkpmqQm6qB7/jMK97jVx+8J/4sBX7WqRTIS1WHzKxhsofuU6mQY1JxGn5qsONok6PbZaPIMLRRXFMr5RiXl4rcVN6XRkTxQamQ4/TiNOyrs6GyxdnhegPStRida+jVqeOUCnlwerCekslkGJVrQKpGib211h53gJDJAL1aibQUJQwaJWxuH5ocXji9vAzbiqGN4la2Xo1TClI5FhoRxR2ZTIZx+anQqRT4qcEW8phcJsPYfAOK0rRRKl1kCtNSoFcr8GOVGe4I7p3TqhRI0yqRlqJCWooSxpT2p0Jzef1odnrR7PCiyeFJ6qFUGNooZijlMhSlpSBFpYDLG5hXs3XCdLdPCt6HIZfJMDJHj0GZuugWmIioh4Zk6aBTK7C7xgK/JJCikmNiYVpE8xPHgjStCmcMzsCOKgta2um1mqKSIy1FBWOKEkaNEmlaFVRhtvilqBQoUCmCM194fBKanV6YXV5YXT5Y3b6IwiLw87SELp8Erz9+ZrRgaKOo0yjlGJShw4D0lA6b7YUQcB8PcSqFLOReCyKieJaXqkGKMh1lTQ6MzUvtcMq8WKdRKjB5QDoO1Nvg8vmDIS0tRdWrdVIr5chL1SDvhNtiPD4JNo8vGOKsbh+cXglalTw4HaHu+DApuhOmJRRCwHr8MmyTw4Nmhxe+GJ7RIq4/+b777ju89NJLqK6uxltvvQWj0RjtIlEEDBolBmdqUZCa0uU9GzKZDCkqBQfAJaKElKZVYWJRWrSL0WNyuQxj81O7XrGXqZVyZCrVyIywh61MJoMxRQVjigqDM3UQQsDiCoQ4k8MDmSy2esfGbWi75pprcPDgQUyaNAmffvopPJ7YOrHUsUydGoMztRzUloiIYopMJkOaVoU0rQpDsnQwmUzRLlKIuA1tK1asQFFRET777DP83//9X7SLQ53QKANz8mVoVcjUqZGaErcvOyIioqiJ20/PoqKiaBeBOqBXK5ChU0Op82NAfibvPyMiIuoFSfVparFYYLFYgr/X1NREsTSJRaOUY3CmDkVpKcEeQSaTh4GNiIiol8TMJ+qdd96J0tLSDh9Xq9X44IMPenSMlStX4sEHH2yzvLm5GVpt342H43PaIcnDn7IknqiVchQYU5BrUEMmnLCcMFDkiQE5mbDeyYX1Ti6sd3Lpj3pnZWWFvW7MhLZFixahpaWlw8cVip73Gly2bBluuumm4O81NTWYMmUKMjIyIjppkVLWWuFVJNaYYlqVAkOzdCg0dt7zsy/PayxjvZML651cWO/kEkv1jpnQNmPGjD4/htFo5LAgPaRT/xzWZDLO80lERNRfYia0UWxL16owKEOLvFQNwxoREVEUxG1oe+WVV/DBBx+goaEBAHD11VdDpVJh+fLlmD59epRLlxjkMhnyjRoMytDCmBJfU6oQERElmrgNbWeccQZyc3PbLB82bFgUSpNYNEo5BqRrMSBdG7fTqRARESWauA1tY8aMwZgxY6JdjLigVsph1CihkMsgCQFJ4Ofvkgj+rFHKMTBDi3xeAiUiIoo5cRvaqH0apRzGFGVgLjWNEsYUJefrJCIiSgAMbQkgU6fGwAwt0rVKaJQMaERERImIoS1OyWRArkGDIZk6pGnZSYCIiCjRMbTFGblMhsK0FAzO0EKv4dNHRESULPipHwNSVHIo5XL4JAl+CfBJEoQIXUcpl2FAhhaDMrS8BEpERJSEGNqibGCGFiNzDFCcNBWUXxLwSyIY5LQqOZQKDr9BRESUrBjaokStlOOU/FTkGDTtPq6Qy6CQy6AGgxoRERExtEVFtl6N8QVGDlxLREREYWNo60dymQyjcvUYmKGLdlGIiIgozjC09ZNUjRKnFhphYI9PIiIi6gYmiH6Qm6rGyOIMyOWcGoqIiIi6hzdV9YMcvYaBjYiIiHqEoY2IiIgoDjC0EREREcUBhjYiIiKiOMDQRkRERBQHGNqIiIiI4gBDGxEREVEcYGgjIiIiigMMbURERERxgKGNiIiIKA4wtBERERHFAYY2IiIiojjA0EZEREQUBxjaiIiIiOKAMtoFiCafzwcAqKmp6dPjNDc3w+l09ukxYhHrnVxY7+TCeicX1rtv5efnQ6nsOpIldWhraGgAAEyZMiXKJSEiIqJkVVFRgeLi4i7XkwkhRD+UJya5XC7s3r0bOTk5YSXc7qipqcGUKVPw3XffoaCgoE+OEYtYb9Y7GbDerHcyYL37vt5saQtDSkoKJk+e3C/HKigoCCtFJxrWO7mw3smF9U4urHf0sSMCERERURxgaCMiIiKKAwxtfcxoNOL++++H0WiMdlH6FevNeicD1pv1Tgasd+zUO6k7IhARERHFC7a0EREREcUBhjYiIiKiOMDQRkRERBQHGNqIiIiI4kBSD67bm7766iusXr0aNpsNM2fOxM0339zp6Ma///3vsW/fvpBlM2fOxF133dWj/fa3SMvX1NSE1157DT/++CPS0tKwcOFCLFiwIGSdcM9Nf+jO+Q9nGz6vAdF6XjvC93HX5auvr8ctt9zS7mM33XQTLrjgAgDx8Xxv374dL7/8Mqqrq/HGG28gNTW1y20S4f0dab0T5f0dab1j8f3NlrZe8M4772DevHnIzc3FzJkz8cQTT+Dyyy/vdJuNGzciJSUF119/ffBr9uzZPd5vf4q0fIcPH8akSZNQWVmJ+fPno6CgAFdccQXuueeekPXCOTf9oTvnP5xt+LxG93ntCN/H4ZXPYDCE1Pf666/H4MGD8dFHH6GwsDC4Xqw/39dddx1uvvlmeDwefPTRR3C73V1ukwjv70jrnSjv7+483zH5/hbUI36/XxQVFYl77703uOz7778XAMRXX33V4XbTp08X999/f6/vt790p3xWq1VYrdaQZS+++KKQy+XCZrMFl3V1bvpDd+oXzjZ8Xu/vk3L3FN/HPSvfhRdeKE4//fSQZbH8fAshxNGjR4UQQqxdu1YAEA0NDZ2unwjvbyEir3civL+FiLzeQsTm+5stbT20a9cuVFVVYdGiRcFlp59+OoYMGYK1a9d2uu1nn32GJUuW4De/+Q0+//zzXttvf+hO+QwGAwwGQ8iyoqIiSJIEq9Uasryzc9MfulO/cLbh8xrd57UjfB93v3w1NTVYu3Ytbr755jaPxerzDQCDBg2KaP1EeH8Dkdc7Ed7fQOT1bhVr72+Gth4qKysDAAwYMCBk+YABA4KPtSc1NRXTp0/HhRdeiJSUFFx88cVYvnx5j/fbX3qjfEIIPPvss5gwYQLy8/ODy7s6N/2hO/ULZxs+r9F9XjvC93H3y/faa69Bo9HgyiuvDFkey893dyTC+7s3xOP7u7ti8f0dO3dGxpDf/va3OHToUIePq9VqvPvuuwAQvC6u0+lC1jEYDHC5XB3u429/+xvS0tKCv0+YMAFXX301brjhBowcObLb++2J/qj3ie677z5s3rwZmzdvDlne1bnpD92pXzjbRON5jUSiP68dSaT3cSR6o3yvvPIKlixZ0maqn1h+vrsjEd7fvSEe39/dFYvvb4a2dlx44YVobm7u8HGFQhH8ufUJbW5uDumJYjKZMGbMmA73ceILAQAuuugiCCHwww8/YOTIkd3eb0/0R71bPfbYY3j66afxySefYOLEiSGPdXVu+kN36hfONtF4XiOR6M9rRxLpfRyJnpZv48aNKC0txeuvv97hvlvF0vPdHYnw/u6peH1/d1csvr8Z2toRSY+XU089FQCwc+dODBw4EADg8Xiwf/9+LFmyJOz9mM1mAAh2E+6t/Uaiv+q9YsUKPPzww/jkk08wZ86cLo918rnpD92pXzjbRON5jUSiP68dSaT3cSR6Wr6XX34Zp5xyCqZNm9blurH0fHdHIry/eyKe39+9JSbe333SvSHJnH322eLss88WXq9XCCHEc889JzQajaioqAiuc/fdd4snn3xSCCFEWVmZWLduXfAxr9crrr/+epGamipMJlNE+42mSOsthBBPPPGE0Ol0Yv369e3uM9xz0x+6U79wtuHzGt3ntSN8H4f/fAshhNlsFjqdTjz77LNt9hkPz3erznoTJuL7u1Uk9U6E93ercOsdq+9vhrZecOTIETFixAgxePBgMXXqVKHX68Ubb7wRss7UqVPFkiVLhBBCNDY2igsvvFAMHz5czJ8/XxQXF4vhw4eLDRs2RLzfaIq03tu2bRMAxNChQ8XFF18c8nXw4EEhRPjnJhbrF+42fF6j+7x2hO/j8F/nQgjxP//zPyIlJaXdD+V4eL7/+te/iosvvlicccYZAoBYsGCBuPjii8WWLVuC6yTi+zvSeifK+zvSesfq+1smhBB904aXXHw+H7Zu3QqbzYaSkhJkZWWFPL5x40bodDqUlJQEl5WXl+Onn35CQUEBRo8e3W4zclf7jbZI6t3Y2Njm5tVWs2fPRkZGRvD3cM5Nf+jO8xrOc8bnNbrPa0f4Pg6/3t9//z0cDgdmzZrV4X5j+fneu3cvSktL2yyfMmVKcJDgRHx/R1rvRHl/d/f5jrX3N0MbERERURzgOG1EREREcYChjYiIiCgOMLQRERERxQGGNiIiIqI4wNBGREREFAcY2oiIiIjiAEMbERERURxgaCMiIiKKAwxtRERERHGAoY2IiIgoDjC0ERFFwGw248MPP0RVVVXI8j179uCjjz6Cx+OJUsmIKNExtBERRSA1NRUPPPAAbrnlluCyb775BtOmTUNZWRnUanUUS0dEiYwTxhMRRejLL7/E2WefjQ0bNiA9PR1z5szB3Xffjd///vfRLhoRJTCGNiKibvjFL36BQ4cOoa6uDrfeeiseeeSRaBeJiBIcL48SEXXDVVddhb1792L27NkMbETUL9jSRkQUoR07dmDOnDkoLi5GY2MjSktLYTAYol0sIkpwbGkjIorA/v37cc455+DGG2/E5s2b4fP58Oijj0a7WESUBNjSRkQUpsOHD2PWrFm48MIL8cILLwAAnn32Wdx77704cOAABg0aFOUSElEiY2gjIgqD3+/HXXfdBbVajRUrVkAmkwEAvF4vbrjhBpx11lm46aabolxKIkpkDG1EREREcYD3tBERERHFAYY2IiIiojjA0EZEREQUBxjaiIiIiOIAQxsRERFRHGBoIyIiIooDDG1EREREcYChjYiIiCgOMLQRERERxQGGNiIiIqI4wNBGREREFAcY2oiIiIjiAEMbERERURxgaCMiIiKKA/8fPCNjpFI+H2EAAAAASUVORK5CYII=", "text/plain": [ "<Figure size 704x440 with 1 Axes>" ] @@ -294,11 +240,20 @@ "source": [ "fig, ax = plt.subplots()\n", "plotstyle.band(\n", - " ax, x_fine, prior_lo, prior_hi, color=plotstyle.COLOURS[0], label=\"prior 90 %\"\n", + " ax,\n", + " x_fine,\n", + " prior_lo,\n", + " prior_hi,\n", + " color=plotstyle.COLOURS[0],\n", + " label=\"prior predictive 90 %\",\n", ")\n", "ax.plot(x_fine, prior_mid, color=plotstyle.COLOURS[0])\n", "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", color=\"k\", label=\"data\")\n", - "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"The prior predictive, before any fitting\")\n", + "ax.set(\n", + " xlabel=\"$x$\",\n", + " ylabel=\"$y$\",\n", + " title=\"The prior predictive: what we believe before we see the data\",\n", + ")\n", "ax.legend()\n", "plt.show()" ] @@ -308,48 +263,60 @@ "id": "28b9123a", "metadata": {}, "source": [ - "### The likelihood is the $\\chi^2$ we would have written by hand\n", + "### Calibrating to data \n", + "\n", + "To determine the posterior $p(m,b,\\sigma | y ) $, we use Baye's rule:\n", + "\n", + "\\begin{equation}\n", + "p(m,b,\\sigma | y ) = \\frac{1}{\\mathcal{Z}} \\, L(y | m,b,\\sigma) \\, p(m,b)p(\\sigma)\n", + "\\end{equation}\n", "\n", - "With a diagonal covariance the Gaussian log-likelihood is $-\\chi^2/2$ up to a\n", - "constant, where\n", + "With a diagonal covariance $\\sigma^2 I$ over $N$ points, a Gaussian\n", + "log-likelihood is\n", "\n", - "$$\\chi^2(\\theta) = \\sum_i \\frac{(y_i - y_m(x_i; \\theta))^2}{\\sigma_i^2}.$$\n", + "\\begin{align*}\n", + "\\log L(m,b,\\sigma) &= -\\frac{1}{2}\\chi^2(m,b,\\sigma) - N \\log\\sigma - \\frac{N}{2}\\log 2\\pi\\\\\n", + "\\chi^2(m,b,\\sigma) &= \\sum_i \\frac{(y_i - y_m(x; m,b))^2}{\\sigma^2}\n", + "\\end{align*}\n", "\n", - "`problem.chi2` is exactly that\n", - "[$\\chi^2$](https://en.wikipedia.org/wiki/Goodness_of_fit#Regression_analysis),\n", - "so we can check it against the sum we would type out ourselves. When the\n", - "covariance stops being diagonal — and it will, in every other notebook — this\n", - "becomes the [Mahalanobis\n", - "distance](https://en.wikipedia.org/wiki/Mahalanobis_distance), and the library\n", - "keeps computing it for us." + "If $\\sigma$ were fixed, the last two terms would be constants and we could\n", + "forget about them - optimizing $m$ and $b$ to maximize the log-likelihood would simply reduce to good old least-squares, or $\\chi^2$-minimization. Because we also infer $\\sigma$, they matter: we could always make the $\\chi^2$ smaller by inflating $\\sigma$, and the $-N\\log\\sigma$ term — the log-determinant of the covariance — is what charges us for doing so.\n", + "\n", + "`problem.chi2` is the [$\\chi^2$](https://en.wikipedia.org/wiki/Goodness_of_fit#Regression_analysis)\n", + "and `problem.log_likelihood` is the whole thing, so we can check both against the sums we'd type out ourselves. When the covariance stops being diagonal — and it will, in every other notebook — the $\\chi^2$ becomes the [Mahalanobis distance](https://en.wikipedia.org/wiki/Mahalanobis_distance)." ] }, { "cell_type": "code", "execution_count": 8, "id": "b416f0f2", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-12T01:56:11.723864Z", - "iopub.status.busy": "2026-09-12T01:56:11.723707Z", - "iopub.status.idle": "2026-09-12T01:56:11.727356Z", - "shell.execute_reply": "2026-09-12T01:56:11.726752Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "problem.chi2 = 1343.608 by hand = 1343.608\n" + "problem.chi2 = 335.902 by hand = 335.902\n", + "problem.log_likelihood = -154.141 by hand = -154.141\n" ] } ], "source": [ - "theta0 = np.array([1.0, 1.0]) # the prior mean\n", + "theta0 = np.array([1.0, 1.0, np.log(0.2)]) # the prior mean, in problem.names order\n", "on_data = line.bind(x)\n", - "by_hand = np.sum(((data.y - on_data(*theta0)) / data.y_err) ** 2)\n", - "print(f\"problem.chi2 = {problem.chi2(theta0):.3f} by hand = {by_hand:.3f}\")" + "s0 = np.exp(theta0[problem.columns(log_sigma)]).item()\n", + "resid = data.y - on_data(*theta0[problem.columns(line.params)])\n", + "chi2_by_hand = np.sum((resid / s0) ** 2)\n", + "loglike_by_hand = (\n", + " -0.5 * chi2_by_hand - x.size * np.log(s0) - 0.5 * x.size * np.log(2 * np.pi)\n", + ")\n", + "print(\n", + " f\"problem.chi2 = {problem.chi2(theta0):9.3f} by hand = {chi2_by_hand:9.3f}\"\n", + ")\n", + "print(\n", + " f\"problem.log_likelihood = {problem.log_likelihood(theta0):9.3f} \"\n", + " f\"by hand = {loglike_by_hand:9.3f}\"\n", + ")" ] }, { @@ -359,37 +326,24 @@ "source": [ "## Running the calibration with emcee\n", "\n", - "The problem exposes everything a sampler wants and nothing it does not:\n", - "`log_posterior` is the density, `sample_prior` gives the walkers somewhere to\n", - "start, and `ndim` is the dimension. We use\n", - "[emcee](https://emcee.readthedocs.io/), the affine-invariant ensemble sampler\n", - "of [Goodman & Weare\n", - "(2010)](https://doi.org/10.2140/camcos.2010.5.65), described in\n", - "[Foreman-Mackey et al. (2013)](https://arxiv.org/abs/1202.3665), but nothing\n", - "here is specific to it — recipe 16 drives the same problem with dynesty.\n", - "\n", - "The chain comes back in `problem.names` order, so we always look columns up by\n", - "name through `problem.columns`, never by position." + "The problem exposes everything a sampler needs: `log_posterior` is the density, `sample_prior` gives the walkers somewhere to start, and `ndim` is the dimension. We use [emcee](https://emcee.readthedocs.io/), the affine-invariant ensemble sampler of [Goodman & Weare (2010)](https://doi.org/10.2140/camcos.2010.5.65), described in [Foreman-Mackey et al. (2013)](https://arxiv.org/abs/1202.3665). Nothing here is specific to it: any other third-party sampler, like ptemcee, dynesty or pymc, can be used. [Black Box Bayes](https://github.com/beykyle/black-box-bayes/) provides a uniform interface to all of these that works seamlessly with `rxmc`, which is useful for production-scale inference. For the small problems in these tutorial notebooks, we can just run the samplers directly.\n", + "\n", + "We start the walkers in a small ball around the prior mean `theta0`. Starting them from independent prior draws is also common, but with $\\sigma$ free it can strand a walker in the corner of parameter space where the noise is so large that it explains everything and every nearby proposal looks equally good; the ensemble then takes a very long time to reel it back in.\n", + "\n", + "The chain of samples from the posterior comes back in `problem.names` order, so we always look columns up by name through `problem.columns`, never by position." ] }, { "cell_type": "code", "execution_count": 9, "id": "10df06dd", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-12T01:56:11.728720Z", - "iopub.status.busy": "2026-09-12T01:56:11.728601Z", - "iopub.status.idle": "2026-09-12T01:56:28.911631Z", - "shell.execute_reply": "2026-09-12T01:56:28.911094Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "16000 posterior rows, acceptance 0.71\n" + "16000 posterior rows, acceptance 0.64\n" ] } ], @@ -397,7 +351,10 @@ "n_walkers, n_steps = 32, 3000\n", "sampler = emcee.EnsembleSampler(n_walkers, problem.ndim, problem.log_posterior)\n", "sampler.random_state = np.random.RandomState(2).get_state()\n", - "sampler.run_mcmc(problem.sample_prior(n_walkers, rng=3), n_steps, progress=False)\n", + "start = theta0 + 1e-2 * np.random.default_rng(3).standard_normal(\n", + " (n_walkers, problem.ndim)\n", + ")\n", + "sampler.run_mcmc(start, n_steps, progress=False)\n", "samples = sampler.get_chain(discard=500, thin=5, flat=True)\n", "print(\n", " f\"{samples.shape[0]} posterior rows, \"\n", @@ -409,48 +366,43 @@ "cell_type": "code", "execution_count": 10, "id": "1b1fd4cb", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-12T01:56:28.912926Z", - "iopub.status.busy": "2026-09-12T01:56:28.912799Z", - "iopub.status.idle": "2026-09-12T01:56:28.915731Z", - "shell.execute_reply": "2026-09-12T01:56:28.915215Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "m = 0.642 +/- 0.073 (truth 0.6)\n", - "b = 1.984 +/- 0.043 (truth 2.0)\n" + "m = 0.644 +/- 0.085 (truth 0.600)\n", + "b = 1.984 +/- 0.049 (truth 2.000)\n", + "log_sigma = -2.190 +/- 0.171 (truth -2.303)\n", + "sigma = 0.114 +/- 0.020 (reported 0.1)\n" ] } ], "source": [ "for name, parameter in zip(problem.names, problem.params):\n", " col = samples[:, problem.columns(parameter)]\n", - " print(f\"{name} = {col.mean():.3f} +/- {col.std():.3f} (truth {truth[name]})\")" + " print(\n", + " f\"{name:9s} = {col.mean():6.3f} +/- {col.std():.3f} (truth {truth[name]:.3f})\"\n", + " )\n", + "sigma_post = np.exp(samples[:, problem.columns(log_sigma)])\n", + "print(\n", + " f\"{'sigma':9s} = {sigma_post.mean():6.3f} +/- {sigma_post.std():.3f} \"\n", + " f\"(reported {sigma})\"\n", + ")" ] }, { "cell_type": "code", "execution_count": 11, "id": "4c3f3437", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-12T01:56:28.916929Z", - "iopub.status.busy": "2026-09-12T01:56:28.916812Z", - "iopub.status.idle": "2026-09-12T01:56:29.096536Z", - "shell.execute_reply": "2026-09-12T01:56:29.095916Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "<Figure size 605x605 with 4 Axes>" + "<Figure size 836x836 with 9 Axes>" ] }, "metadata": {}, @@ -474,51 +426,48 @@ "source": [ "## The posterior predictive on any grid\n", "\n", - "The model is not tied to the grid we measured on: binding it to a finer, wider\n", - "grid gives us the prediction where we have no data, which is where a\n", - "calibration is usually asked to work. The band below is the percentile\n", - "envelope of the line over posterior rows, and it fans out beyond the data,\n", - "as it should." + "The model $y = m x + b$ is not tied to the grid $x,y$ our experimental data is on: binding it to a finer, wider grid gives us the prediction where we have no data — a forecast.\n", + "\n", + "But a forecast of *what*? There are two different posterior predictive distributions we might mean:\n", + "\n", + "1. **The model alone**, $y_m(x_*;\\theta)$ with $\\theta$ drawn from the posterior. This is our uncertainty about the *line*: where the true curve is. It's narrow where we have data and fans out beyond it.\n", + "2. **The model plus our error model**, $y_* = y_m(x_*;\\theta) + \\varepsilon$ with $\\varepsilon \\sim \\mathcal{N}(0, \\sigma^2)$ and $\\sigma$ drawn from the posterior too. This is our uncertainty about a *new measurement* at $x_*$.\n", + "\n", + "To draw the second at an $x_*$ nobody measured, every piece of the error model has to *have a value* there. That's why we inferred a constant $\\sigma$ instead of using the reported `y_err`: a reported error is one number per measured point, and it says nothing about a point that was never measured. Our noise term, on the other hand, is a function — the same $\\sigma$ at any $x$ — so it can be interpolated and extrapolated along with the physics model, just like $y_m$ itself.\n", + "\n", + "This is the split between rxmc's two draw functions:\n", + "\n", + "- `rx.diagnostics.predictive_draws` draws at the **measured** points. Every term of the error model is defined there, reported per-point errors included, so this is what we compare against the data.\n", + "- `rx.predictive.grid_draws` draws on **any grid**. It re-evaluates each covariance term at the new points from its own definition, so it can carry anything that is a function of $x$ and the prediction — inferred noise, a normalisation uncertainty proportional to the prediction, a Gaussian-process discrepancy — but it refuses anything that is just an array of per-point numbers.\n", + "\n", + "Both return a percentile band by default, and the draws themselves with `return_draws=True`." ] }, { "cell_type": "code", "execution_count": 12, "id": "a9ec6693", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-12T01:56:29.098008Z", - "iopub.status.busy": "2026-09-12T01:56:29.097880Z", - "iopub.status.idle": "2026-09-12T01:56:29.105536Z", - "shell.execute_reply": "2026-09-12T01:56:29.104641Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "post_curves = np.array(\n", - " [on_fine(*s[problem.columns(line.params)]) for s in samples[::20]]\n", + "post_rows = samples[::20]\n", + "model_lo, model_mid, model_hi = rx.predictive.grid_draws(\n", + " problem, on_fine, x_fine, post_rows, model_only=True, levels=(5, 50, 95)\n", ")\n", - "post_lo, post_mid, post_hi = rx.predictive.predictive_band(\n", - " post_curves, levels=(5, 50, 95)\n", + "full_lo, full_mid, full_hi = rx.predictive.grid_draws(\n", + " problem, on_fine, x_fine, post_rows, n_rep=2, levels=(5, 50, 95), rng=5\n", ")" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "id": "046ac161", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-12T01:56:29.107722Z", - "iopub.status.busy": "2026-09-12T01:56:29.107544Z", - "iopub.status.idle": "2026-09-12T01:56:29.246632Z", - "shell.execute_reply": "2026-09-12T01:56:29.245934Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 704x440 with 1 Axes>" ] @@ -531,9 +480,23 @@ "fig, ax = plt.subplots()\n", "ax.axvspan(x.min(), x.max(), color=\"0.92\", zorder=0, label=\"data range\")\n", "plotstyle.band(\n", - " ax, x_fine, post_lo, post_hi, color=plotstyle.COLOURS[0], label=\"posterior 90 %\"\n", + " ax,\n", + " x_fine,\n", + " full_lo,\n", + " full_hi,\n", + " color=plotstyle.COLOURS[2],\n", + " hatch=plotstyle.HATCHES[0],\n", + " label=\"model + noise: a new measurement (90 %)\",\n", ")\n", - "ax.plot(x_fine, post_mid, color=plotstyle.COLOURS[0])\n", + "plotstyle.band(\n", + " ax,\n", + " x_fine,\n", + " model_lo,\n", + " model_hi,\n", + " color=plotstyle.COLOURS[0],\n", + " label=\"model alone: the line (90 %)\",\n", + ")\n", + "ax.plot(x_fine, model_mid, color=plotstyle.COLOURS[0])\n", "ax.plot(\n", " x_fine,\n", " truth[\"m\"] * x_fine + truth[\"b\"],\n", @@ -542,8 +505,11 @@ " label=\"truth\",\n", ")\n", "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", color=\"k\", label=\"data\")\n", - "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"The posterior predictive of the line\")\n", - "ax.legend()\n", + "ax.set(\n", + " xlabel=\"$x$\",\n", + " ylabel=\"$y$\",\n", + ")\n", + "ax.legend(fontsize=8)\n", "plt.show()" ] }, @@ -554,54 +520,46 @@ "source": [ "## Is our error model calibrated?\n", "\n", - "The band above is the uncertainty of the *line*. The posterior predictive of\n", - "the *data* adds the error model on top: draws of $y_m(\\theta) + \\text{noise}$\n", - "at the measured points. That is the quantity we can actually check against\n", - "what we observed.\n", + "Now we check both predictives against what we actually observed, at the\n", + "measured points, with `predictive_draws`. If a predictive distribution is properly calibrated, a central 68 % interval of its draws should contain about 68 % of the points, and likewise at every level, so the empirical\n", + "[coverage](https://en.wikipedia.org/wiki/Coverage_probability) should follow the diagonal. Twenty points make a coarse curve, but systematically being under(over) the diaginal corresponds to under(over) covering the data, with errors being too small(too large).\n", + "\n", + "We expect the model-alone predictive to undercover the data. It isn't *wrong*; it answers a different question. It describes where the line is, and the data don't sit on the line: each point scatters about it by $\\sigma$. Which\n", + "predictive we want depends on what we're predicting. If it's the physical curve itself — say, a cross section we'll feed into a transport code — the model band is the right object. If it's what a new measurement would read, we need the error model on top.\n", "\n", - "If the error model is right, a central 68 % interval of those draws should\n", - "contain about 68 % of the points, and likewise at every level — so the\n", - "empirical coverage should follow the diagonal. Twenty points make a coarse\n", - "curve, but a systematic sag below the diagonal would mean we are claiming more\n", - "precision than we have." + "In later notebooks we'll meet a third source of uncertainty that sits between these two: *model discrepancy*, the part of the disagreement between model and reality that no choice of parameters can remove (`gp_discrepancy`,\n", + "`alpha_ca_error_model_comparison`)." ] }, { "cell_type": "code", "execution_count": 14, "id": "1f79f3c1", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-12T01:56:29.248012Z", - "iopub.status.busy": "2026-09-12T01:56:29.247836Z", - "iopub.status.idle": "2026-09-12T01:56:29.355065Z", - "shell.execute_reply": "2026-09-12T01:56:29.354335Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "draws = rx.diagnostics.predictive_draws(problem, samples[::10], n_rep=4, rng=4)\n", - "levels = np.linspace(0.1, 0.9, 9)\n", + "rows = samples[::10]\n", + "draws_full = rx.diagnostics.predictive_draws(\n", + " problem, rows, n_rep=4, rng=4, return_draws=True\n", + ")\n", + "draws_model = rx.diagnostics.predictive_draws(\n", + " problem, rows, model_only=True, return_draws=True\n", + ")\n", + "levels = np.linspace(0.01, 0.99, 90)\n", "c = problem.constraints[0]\n", - "coverage = rx.diagnostics.coverage_curve(draws, c.y[c.active], levels)" + "coverage_full = rx.diagnostics.coverage_curve(draws_full, c.y[c.active], levels)\n", + "coverage_model = rx.diagnostics.coverage_curve(draws_model, c.y[c.active], levels)" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "id": "c59df024", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-12T01:56:29.356470Z", - "iopub.status.busy": "2026-09-12T01:56:29.356304Z", - "iopub.status.idle": "2026-09-12T01:56:29.447286Z", - "shell.execute_reply": "2026-09-12T01:56:29.446490Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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9uZw6dYqZmpqytm3bMnd3d2Zvb8+uXLnCOnToIPaLXVpayqZPn85UVFRY9+7dWefOndny5cu5K+dKSkoaXFdNJD1GDQm9169fMysrK2ZhYcEGDhzIBg8ezAoLC+s8NjWFXmP2q+p9Lly4wDp06MA8PDyYnp4ec3BwqLYPkhyD1NRUNnfuXGZsbMxcXFxYnz59mI6ODhs5ciR7/fq1RPsu6f40NPQk/T+UJvQkPYZ1bTsrK4vNmDGDqaurs06dOrE+ffowc3Nz5uXlxc6fP88td//+febg4MD09PSYh4cHs7e3Z+fOnav2u8FYzQGVnp7Oxo8fz1RVVVmXLl2Yi4sLa9++PQsNDeWW2bFjB/c7NXjwYPbpp5/WuU3GRFfo2tnZMQDVrvJu6D42ZzzG/jlTSRSCMYaYmBixZ28aGhrWuGxeXh6ePHkCHo+Hrl271nq1XU5ODu7evQsNDQ3079+/1vcWCoWIjo5GdnY2LC0tYWNjI9Zv//jxY+Tl5cHLy6vG9UtKSrgrRbt37w41NTVcu3YNenp61c5TJSYm4tWrV7C3t4elpSUWLVqEkJAQFBcXN7iuutR3jFJTU/H48WP06dMHurq69W6voqICjx49Qk5ODoRCIXx8fKCqqlrrsUlOTkZ0dDT69etX7QpdafbL398fN27cwKtXr1BSUoIHDx5AQ0MDPXr0AJ9f88XXknxOSkpK8OzZM5SXl8PW1pa7wEqSfZd0f+r6/Dx9+hTp6ek1PmO2vvrr+1y+qyHHUJJt5+fnIzo6GoDoHL2pqWm1ZYRCIR48eICysjJ0794d2traNf5uXLhwAVZWVtxDI96WkZGB58+fw8DAAJ06dap2DjkrKwtPnz5FaWkpzMzMuK7burYZFRWF169fw9bWts6LZSTZx+aKQo8oRPfu3WFgYIDLly8ruhSl9vYXNpEOHUPyNrpPjzSp1NRUsUdNMcbw9ddf49GjR1i2bJkCKyOEtEZ09SZpUnp6eggNDcXSpUthZ2eHuLg4FBYW4r///S/8/PwUXR4hpJVRqu7NwsJChIWFwcDAoN5hX6qkpaXh5s2b0NHRQb9+/Wq8v4QoXnZ2Np4/fw4VFRU4Ozs3yX1zLVFDz1+R6ugYkrcpReiVlZVh5cqVOHToEHg8HhwdHXHx4sV61wsJCcHixYvRq1cvpKeno6SkBGfPnq12/xghhBACKMk5PYFAgM6dOyM6OrraMxlrk5SUhAULFuCHH35AREQEHj9+jE6dOtU4kjAhhBACKEno6erqYtmyZQ16oOqBAwegoaHBPUFCRUUFH374Ia5cuUJXaRFCCKlRs72Q5dGjR+jUqRPU1dW5eVX3vzx+/LjaA42r5Ofni40rVVFRgZKSEjg6Oordg0QIIaTlabbf8nl5eTA2NhabV/WE+7cfevyuDRs21DiC8MOHD6s9GZ2I5OfnSzTsTGtEx6ZudHzqRsdHXElJCY4cOYK8vDx88sknTfIezTb0NDQ0kJqaKjav6inmdY1Vt2LFCrGxslJSUuDh4QEjIyMuNEl1dGxqR8embnR86kbHR6SoqAh//fUXsrKymvR9mm3odejQodrTPKrGxappTK4q+vr69JcVIYQokaKiIoSEhHDjjEo7YK4klOJCFkmUlJRg3759SEpKAgCMHDkSiYmJuHnzJrdMaGgo2rVrBxcXFwVVSQghpCGKiooQHBzMBZ6hoSGCgoKa7P2UpqV3/PhxFBUV4eXLl0hPT8e+ffsAiJ6bB4gerjpt2jTs378fkydPRp8+fTB9+nRMnjwZK1euxOvXr/HTTz9hz549tT6IlxBCiPIoLCxESEgIMjIyALwJvKZs6SlN6J09exYZGRkwNzeHubk5jhw5AuBN6Glra8PPzw/W1tbcOrt27UJISAguXboEbW1tREREwNPTUxHlE0IIaYCqFl5mZiYAwMjICIGBgU0aeICSPJFFkZKSkmBtbY3ExERYWVkpuhyllJWVRSfba0HHpm50fOrWmo+PQCDA3r17ERcXJ7fAA5SopUcIIaT1UFNTw7Rp0/D3339j4MCBcrvAkEKPEEKIQqipqWHcuHFyfU+64oMQQkiTy8/Px65du5CTk6PQOij0CCGENKn8/HwEBwfj5cuXCA4OrvOpWU2NujcJIYQ0mby8PAQHB3MtPDU1NaioqCisHmrpEZl7/vw5kpOTm812CSFN493AMzU1RWBgIPT09BRWE4UekbklS5Zg48aNzWa7hBDZy83Nxc6dO6sFnq6urkLrou5NInOdOnWiex4JacVyc3PFzt2ZmZlh1qxZCg88gEKvRXr69CkMDAzQtm1bJCQkgMfjiT3J5m0JCQnIz8+Hg4NDtdEpqrajrq6O+Ph4CIVC2NnZAQAqKyvx/PlzGBsbo02bNmLrffDBB9DR0WlQPXFxccjKygKPx4OpqSnat28PHo8ni8NBCJGjmgIvMDBQ7DtBkSj0WqCgoCC0a9cOUVFRAIDk5GR4eHjg5MmTXLAlJydjypQpePToEczMzJCZmYnvv/8eCxYsENuOsbExnjx5Ai0tLcTHx2PAgAFYuXIl5s+fDy0tLcTFxWHBggX45ZdfuPWWLFmCXr164ZtvvpG4nh07duDvv/8GIBruSUVFBbt374a3t3fTHzBCiMwkJiZygWdubo5Zs2YpTeABAFgrl5iYyACwxMRERZciM71792ZGRkYsKiqKMcZYeno6Mzc3Z//73/+4ZUaNGsW8vb1ZYWEhY4yxvXv3Mj6fzx4+fCi2HWNjY3bjxg3GGGNPnjxhKioqzMLCgr148YIxxtidO3cYj8dj9+/f59YbPHgwW7NmTYPqede3337LrK2tWXl5ea3bVQaZmZmKLkGp0fGpW0s9Pnfu3GGbN2/mvl+UCbX0pHDjxg3cuHGj3uUcHBwwevRosXlbtmxBaWlpvetOnjy5UefFpk+fji5dugAQdS8MGDAADx48AACkpqbi5MmTuHjxIvcXmL+/PzZs2IBt27bhp59+4rYzbdo0ODg4AACcnJxgb2+PsWPHcmMWurm5wcLCAg8fPkSPHj2kqqdKRUUFkpOTkZmZiT59+mD16tWIjY3l1iOENA9ubm5wcXFR6K0JtaHQk0JpaSny8vLqXa64uLjavPz8fJSUlNS7bkVFhVS1VbGwsBCb1tbWRlFREQAgNjYWgCjE3ubs7IwXL17Uu526ti1NPQCwf/9+vP/++2CMoU2bNlBTUwMg6uqk0CNEeWVnZyMxMbHaH73KGHgAhZ5UNDU1JXoauLa2drV5+vr6UFdXr3ddVdWm+6+pukemqKgIZmZm3PzCwkKF3D9TVFSEgIAAbN68GbNnzwYgur/H0NAQQqFQ7vUQQiSTnZ2NnTt3oqCgABUVFXBzc1N0SfWi0JNCnz590KdPH6nWXbRokYyraThHR0fo6+vj7Nmz3IUrpaWluHTpElatWiX3ehITE1FWVobhw4dz886ePSv3OgghksvKykJwcDAKCgoAAHfv3lXaLs23Uei1Qtra2vjPf/6D1atXg8fjwdbWFj/99BM0NDQUEsr29vawsrLCihUrsHjxYjx9+hSfffaZ3OsghEjm3cBr27YtAgIClD7wAAq9FqlLly5o166d2Dw7OztUVlZy0x999BHMzc2xb98+5OXlwdXVFb/99pvYpcU1badr167Vzs9169YN5ubm3PS7N6fXV4+6ujrCwsKwfv16fPzxx7CyssLBgwfx/vvvi42xRTe9E6J4mZmZCA4ORmFhIYA3gaelpaXgyiRDI6fTyOn1as2jO9eHjk3d6PjUrbkdn3cDr127dggICKj2YAtlRi09Qggh9crIyEBISEizDjyAQo8QQkg9GGM4duwYF3iWlpaYOXNmsws8gEZZIIQQUg8ej4dJkybBwMCgWQceQC09QgghEjA0NERQUBA0NTWbbeAB1NIjhBBSg6ysLJSXl4vNMzQ0bNaBB1DoEUIIeUdaWhq2b9+OvXv3QiAQKLocmaLQI4QQwklLS0NwcDCKi4vx6tUrXLhwQdElyRSd0yOEkFYuKrUAsZlFMEAJbv69n3sofvv27TFgwAAFVydbFHpE5qoeEs3n192RIOlyTfHehBAgs7AM/rsjER6Tyc2zU7HEZK04ONlZYfr06RI9IL85oW8GInNDhw7Fv/71L5kt1xTvTQgB/HdH4vxbgQcAcZV6OAmnFhl4AIUeaQIqKioSPXhW0uUIIbL3JLUA4TGZqP4cSh6iCtUQm12mgKqaHnVvyklVn7mDqQ6cLOQ/Zt27KisrwefzwePxZL7eqVOn6lxHKBSCz+dXW64+QqEQQqEQPB6PwpKQRnqRWffAz7GZRUrxXSVr1NJrYpmFZRiy5Tq6fncR43bcRtfvLmLIluvILGy6v6KEQiHWrVsHa2trqKuro3Pnzti5c6fYMl5eXpg3bx5GjhwJY2NjGBgYYO3atXjx4gWGDh0KTU1NtG3bFr/99lu19ZYsWYKxY8fCyMgIRkZG+OSTT/D2c8vf7WL08vLC0qVLMXbsWGhra2PChAk1LldZWYn169fD1tYW6urq6N27N65fv869Pnv2bGhqakJDQwNGRkYYPXo0Xr58KctDR0ir4WCq06jXmysKvSZWU5/5+ZhMTNsd2WTvuWrVKhw9ehQnT55EYWEhNm/ejOXLl+PYsWNiy+3Zswfz58/H69evERwcjP/+97/o378/3n//feTm5uL777/H0qVLER8fL7beli1bMGLECCQlJeHgwYPYvHkztm7dWmdNv/32G/z8/JCdnY2jR4/WuMzy5cuxefNm/P7778jLy8Ovv/6K/fv3c68HBwejoqICFRUVePbsGczNzTFlyhS08oFCCJGKiY46VPnVe2x4AIY4mrbIVh5A3ZsN1m/jFSTllUq0bHmlECn51Vt0DMC5mEy0++Is1FUk+7vDykATV97rV+9yubm52LhxIy5fvozu3buDMYYBAwZg/vz52L59O8aOHcst6+/vz7W6xo8fD1tbWwwfPhyjR48GAMyYMQMffvghIiMj4erqyq03aNAgLF68mPv3Bx98gB9//LHOAWgnTJiAGTNm1Pp6VlYWNm/ejJCQEPj6+gIA3N3d4e7uXuPyJiYm+O6772Bqaoq4uDjY29vXe2wIIUBFRQVUVFSw+OBDVAir/8E42NEUe2e61rBmy0Ch10BJeaWIzymRybZqCsTGevToEQQCAby8vKq91qNHD7HpDh06iE0bGBhUCw8DAwPk5uaKzevVq5fYtLu7O9atW4fy8vJar/ZycnKqs+4nT56goqIC/fv3r3WZu3fvYu3atbhz5w7y8vK42xISEhIo9AiRQFJSEvbv3w8158E4/CgVANBWXwMHA3sho7Bcaa45aEoUeg1kZSD5c+dqa+lVaauv0aCWniSquvpevHgBGxubOpet6WIUSS5sebc7sWq6rnXV1NTq3W5N264iEAgwYsQIBAYGIiQkBG3atEFZWRm0tbVRUVEh0bYJac2SkpKwe/duZJZUYtOZF6j6+v99Sg/0tTVWbHFyRKHXQJJ0Mb5tyJbrOP/OZcE8iLoQwhb1lWltANCtWzeoqanh1KlTdXY3Nsbt27fFpm/evAlHR0eJg60mVXVfunSpxm7QFy9eICMjAytXroSFhQX3voSQ+iUmJmL37t0oKyvHiVJ7lDDRV3+QuzVGObVRcHXyRReyNLF9M10x2NFUbF5T9pkbGRlh+fLl+Ne//oXQ0FDk5OQgJiYGP/zwA7777juZvMfFixfx008/ITs7G6dOncLPP/+M5cuXN7ruZcuW4aOPPsLJkyeRm5uLK1euYNmyZQAAKysr6OnpYevWrcjPz8etW7eaLNQJaUmqAq+8vBwPBcZ4VmEIALA00MSP47oqtjgFoJZeEzPV1UDYor5yvU/vm2++gaWlJdatW4c5c+bAysoK48ePx9q1a7llVFVVqz2qS9J5y5Ytw9WrV/HNN98AAFasWIH58+dzr79703lN26hpue+//x5mZmb44IMPkJ6eDldXV2zYsAEAoKuri9DQUKxatQrffvstrKyssGrVKixatEhs23TDOyFvJCQkYM+ePSgvL0e+UA1nBG9OefwxtQcMtaTvnWmueKyVX++dlJQEa2trJCYmwsrKStHlKKWsrCyYmJgAAPr06YPRo0fj3//+t4KrUg5vHxtSHR2fujXl8YmPj8eePXsgEAjAGHCM3x338kTtnHm92+P3qT3q2ULLRN2bhBDSwrwdeACQZtqVCzxrQ038MLbuq6lbMgo90iC1dVUSQpSHUCjkroQ2s++CP1PePF1l21QX6Gu2vm7NKnROjzTIlStXFF0CIaQednZ2mDZtGu7du4/fs9ohvzQDALCwrw18O5kpuDrFoj/ZCSGkBbK3t0euVS+ceSYKPBsjLXw3uvV2a1ah0COEkGYuLi4O586dE3u4Q0JOMZYffcJNb/dzgZ4mde7RESCEkGbs5cuX2Lt3LyoqKlBZWYmhQ4cCAOb99QAFZaKnFS3xtMWgd+4Xbq0o9AghpJl68eIF9u3bxz2Kr6CgAIwx/HEzEWHPRaO72Blr47+juyiyTKVCoUcIIc3Qu4Hn7OyMCRMmICG3FCuPv+nW3OHfA7oa9FVfhY4EIYQ0M7Gxsdi3bx8qKysBiJ5dO378eAA8zA19gMIy0fz3+tlhQAfq1nwbXchCCCHNSG2Bx+fzseV6PM7Hiro1O5ho4/9GdlZkqUqJWnqEENJMxMTEIDQ0lAu87t27Y9y4ceDz+XiZVYRVJ6IAADwesMPfBTrUrVkNHRFCCGkGGGO4evUqF3g9evTA2LFjwefzIRQyzAl9gOJy0Wsf9rdHf3t65mlNqHuTEEKaAR6PB39/f1haWooFHgD87+orRLzIAgB0NNPB+hGdFFmqUqOWHiGENBOampqYNWuW2DNwYzOLsObkW92afi7QVqev9tpQS48QQpRUbGwsCgoKxOapq6tzgScUMszedx8lAiEAYOWADvC0M5Z7nc0JhR4hhCihp0+fYu/evQgODq4WfFV+uRKHK3HZAIDO5rr4cjh1a9aHQo8QQpRMdHQ09u/fD6FQiKysLNy9e7faMs8zCvHxyWgAAJ8H7PR3gZaairxLbXao45cQQpRIVFQUDh48CKFQ1GXp5uaGAQMGiC1TKWQI2nsfpRWiZVb5OKC3jZHca22OKPQIIURJPHnyBAcPHuRGS3Bzc8OoUaPA4/HElvsx4iWux+cAAJza6OLzYR3lXmtzRaFHCCFK4N3A69WrF0aOHFkt8KLTCvDv008BACp8Hnb694QmdWtKTKnO6VVUVCAqKgrx8fESr1NSUoKnT5/i1atX3E2bhBDSnDx+/Fgs8Nzd3WsMvIpKIYL23UfZP92aawc5wL29obzLbdaUJvTCw8NhbW2NoUOHwtnZGf369UN6enqd6/znP/+BmZkZxo8fD09PT9jZ2eH06dNyqpgQQhovKysLhw4d4gLPw8MDI0aMqBZ4APBDxEvcSsgFADhb6OE/vo7yLLVFUIrQy87OxuTJkzF37lwkJSUhNTUVZWVlmDdvXq3rnD9/HuvXr8fRo0fx9OlTJCcnY+zYsZg2bRp3ApgQQpSdiYkJfH19AQC9e/fG8OHDawy8J6kF+PT0MwCibs3gaS7QUKVuzYZSitDbv38/ysrK8PHHHwMAdHR0sHr1apw4cQKpqak1rpOSkgI+nw9vb28Aokf0DBw4EHl5eSguLpZb7YQQ0lh9+/bFrFmzMGzYsBoDT9SteQ/llaI/6D8Z7AhXK0M5V9kyKEXoRUZGokuXLtDR0eHm9e7dG4wx3L9/v8Z1xo0bBxcXF8ybNw8RERE4duwYPv/8c6xduxa6urpyqpwQQhouNze32jw7O7saAw8Avr3wAncS8wAAPdrp45Mh1K0pLaW4ejMrKwsmJuJPBDc1NeVeq4muri4++eQTLFq0CNevX0d+fj7s7OwQFBRU53vl5+cjPz+fm05JSWlc8YQQ0gD379/H8ePHMX78eLRr167e5R+l5OPzs6JuTdV/ujXVVZWivdIsKUXoqampoaysTGxeSUkJ91pNTp48CT8/P4SFhcHHxwdCoRBr166Ft7c3nj17BgMDgxrX27BhA7744otq83NycqClpdXIPWmZ3v4jgYijY1M3Oj7ioqOjER4eDgA4fPgwRo0aVefygkohZux6BEGl6CKXlV5WsNKoqLUx0JK82xCSFaUIvfbt2+POnTti816/fs29VpMjR46gZ8+e8PHxAQDw+Xx8+OGH+O6773D16lWMHDmyxvVWrFghdoFMSkoKPDw8YGRk1GQHuSWgY1M7OjZ1o+Mjcu/ePS7wANF5PBsbmzqPz5dnn+NRWhEAoKelPtaN6Q41FWrlNYZSHL3BgwcjNjYWz58/5+YdP34choaGcHV1BQAIBALcuXMHOTmipxCYmJggIyND7ErNqq7Kuj5E+vr6sLKy4n7atm3bFLtECCGcyMhIHDt2jJv28vLCkCFDaj2HBwD3k/OwLkz0naimwkPwtJ4UeDKgFEfQ19cXPj4+mDp1Kk6cOIHffvsN69evx+effw51dXUAQFpaGtzd3bm/lIKCgpCeno5Zs2bhwoULOHz4MIKCguDh4QE3NzdF7g4hhHAiIyNx/Phxbrpfv34YPHhwnYFXXiG6Cb1CKOrW/HxoJ3Rrq9/ktbYGShF6PB4Px48fx+jRo/Htt9/i0KFD2Lp1Kz744ANuGXV1dbi5ucHYWDRWVOfOnXHnzh3o6uriyy+/xO+//44pU6YgLCwMqqpK0WtLCGnl7t69KxZ4/fv3x6BBg+oMPAD46lwMHrwWnQ/tZW2A1QM7NGmdrQmPVT0GoJVKSkqCtbU1EhMTYWVlpehylFJNV9cSETo2dWvNx+f+/fs4evQoN+3t7Q0fHx+xwKvp+EQm5cLj5yuoFDKoq/ARucIbXS305FZ3S6cULT1CCGlprKysuHuPBwwYgIEDB9bbwiurqETg3vuo/Kdb88vhnSjwZIz6AQkhpAmYmpoiMDAQMTEx8PT0lGiddWExeJwqGiW9d3tDrBxg35QltkoUeoQQIiOMMbHWnJmZGczMzCRa93ZCLr45HwsA0FDlY6e/C1Tpak2ZoyNKCCEycPPmTezbtw8VFRUNXrdUUImgffe4bs31wzujcxvq1mwKFHqEENJIN27cwOnTp/H8+XMcOHCgwSO9fH7mOaLSCgEAfW2MsJy6NZsMdW8SQkgjXL9+HWfPnuWmLS0twedL3p64EZ+D7y6KujU1VfnYOc0FKvy6L3gh0qPQI4QQKb0beIMHD0a/fv0kXr9EUInZ+x7in15N/N+oLuhoRqPENCUKPUJIqxCVWoDYzCI4mOrASQa3AVy7dg1hYWHc9JAhQ+Dl5dWgelYdjcHTdFG3Zj87Y7zfz67RdZG6UegRQlq0zMIy+O+ORHhMJjdvsKMp9s10hamuhlTbvHr1Ks6dO8dN+/r6SnxbQk318HnAD2OdwKduzSZHF7IQQlo0/92ROP9WwADA+ZhM+O+ORKWQNfgn4vJlnA07ByEDhAwY7OuL3n36Sry+36671eoRMuCTv5/K87C0WtTSI4S0WE9SC8RaVFUYgPCYTKiuOiHlll25f315MAs4KO123jgXk4mo1AKZdL2S2lFLjxDSIiXmlGDV8ShFl9EgsZlFii6hxaOWHiGkRUnIKcY352Ox7WYiyivrvl+ud3tD6KhL8zXIADT8/FtReQVuJuTW+rqDqY4UtZCGoNAjhLQI8dnF+L/zsdh+KwGCyjeDx6jwgMp3xpLhQXQxS9iivvVu99q1a7C3t4eFhYVM6hyy5TrOx2Ti7ZKq6qGuzaZH3ZuEkGbtVXYxFu5/AMdvzmPr9Xgu8Aw0VfH50I54tnYQhjiaiq0z2NEUe2e61rQ5MRcvXkRYWBhCQkKQmpoqk3r3zXTFYCnrIY1HLT1CSLMUl1WMr8NjsPN2IjfCOAAYaqlhubc93u9vB0MtNQBA2KK+Db5P7+LFi4iIiAAAlJSU4PXr1zJp7ZnqanD1RMalwtXOglp4ckShRwhpVl5mFeGrczEIuZMkFnZGWmpYMcAe7/Wzg8E/Yfc2Jws9icKFMYaLFy/i0qVL3LxRo0bB1VW2LTEnCz20USuHiQkFnjxR6BFCmoUXmf+E3d0kbjQCADDWVsPKAR2wrJ8t9DWrh11DMMZw4cIFXL58mZs3evRouLm5NWq7RHlQ6BFClFpsZhHWhz3H7shksbAz0VbDSp8OWOZlBz3Nxn+VMcZw/vx5XLlyhZs3ZswYmbfwiGJR6BFClNLzjEKsD4vBnsgkvJV1MNVRx0c+HbDUyxa6GrL5Cqsp8MaOHYuePXvKZPtEeVDoEUKUyrP0Qqw/9xx/RiaLhZ2ZrjpW+XTAYk/ZhV2VzMxMXLt2jZumwGu5KPQIIUrhaVoB1oXFYN998bAz11XH6oEOWNTXBjoyDrsqZmZmmDJlCg4cOIDRo0fDxcWlSd6HKB6FHiFEoaLfCjv2Vti10dPA6oEdsKivDbSlempKw3Tu3Bnvv/8+9PX1m/y9iOJQ6BFCFOJJagHWhT3HXw9ei4WdhZ4G1gxywII+7Zss7BhjePnyJTp06CA2nwKv5aPQI4TI1eOUfHwZ9hwHHqaIhV1bfQ2sHeSA+X1soKWm0mTvzxjD2bNncePGDQwYMAA+Pj5N9l5E+VDoEUKahOiJI9lwtVOHk4UeHqXk48uzorB7Wzt9zX/Crj00mzDsAFHgnTlzBjdv3gQAXLp0CU5OTjA3N2/S9yXKg0KPECJT1UcGfwozHXVkFJWLLWdpoImPBzlgbu+mDztAFHinT5/GrVu3AAA8Hg8TJkygwGtlKPQIITJV00jlbweetaEmPh7siDke1tBQbfqwA0SBd+rUKdy+fRuAKPAmTpwIZ2dnubw/UR4UeoQQmaltpPIqnw51xL8GO8ot7ABR4P3999+4c+cOAAq81q7RoZeWloZ9+/YhNjYWLi4uGDVqFBISEuDh4SGL+gghzciLekb+drM0VHjgTZo0CV27dpVbDUS5NCr0du7ciWXLlsHY2BiampooKSnB1KlTMW3aNNy5cwdGRkayqpMQ0gzcScqt83V5jwx+5coVscCbPHkynJyc5FoDUS5SDyKbnJyMpUuXYvPmzYiPj8fq1asBAHp6ehg+fDj27NkjsyIJIcpv09VXWBcWU+NrPABDFDAyuJubG8zNzcHn8ynwCIBGtPQuXboEHx8fBAQEVHutU6dOiI6OblRhhJDm49crcXjv8GNu2t5YGy+zi7lpRY0Mrq2tjVmzZiElJQUODg5yf3+ifKQOPR6PB6FQWONreXl50NGRbzcGIUQxNl6Ow/tH3gTeuuGd8G/fjgoZGZwxhrKyMmhqanLzdHR0KPAIR+ruTQ8PD4SHh+PcuXNi8wUCAQ4cOIB+/fo1ujhCiHL7+dJLscD7akRn/Nu3IwDRyOAjOhrLNfCOHTuGnTt3ori4uP4VSKskdUvP3t4ec+bMwdChQ+Hr6wsej4eMjAz4+PhAT08Po0ePlmWdhBAl89Oll1h+9Ak3/fXIzvh4sKNCahEKhTh+/Dju378PAPjzzz8xd+5c8Hg8hdRDlJfULT0A2LRpE3744Qe8fv0a165dQ2ZmJtzd3XHq1Cnw+Y3aNCFEif0Y8UIs8L4Z1UWhgXfs2DEu8FRUVODt7U2BR2rUqFsW+Hw+li9fjuXLl8uqHkKIkvvh4gt8dDyKm/52dBesGqiYc2ZCoRBHjx7Fw4cPAYgCb+rUqejYsaNC6iHKj57IQgiR2HcXYrH6xJsrs78b7YSPBnaoY42mU1Pg+fn5wdFRMS1O0jxIHXovXrzA9evXa3yNx+PBwMAA7u7uaNOmjdTFEUKUx7fnY7Hm5JvA+2GsE1YMUFzgHTlyBI8ePQIgCjx/f3+6SpPUS+rQi4iIwPz582u8bYHH44ExBlVVVXzxxRf417/+1agiCSGK9U14DD7++yk3vWGsE5YrKPAYYzh8+DAePxZdNUqBRxpC6qtNZs+eDR8fHyxcuBCxsbEQCARISUnBd999B2tra8TGxmLLli348ssvuccAEUKan6/PiQfeT+O6KizwANEf1cbGxgAAVVVVTJs2jQKPSKxRLb38/Hxs2bKFm2dhYYGPPvoIcXFx2L9/P9auXYubN28iLCwMvXr1kknBhBD5+ercc/z71DNu+ufxXfF+f3sFViTi4+MDPp8PKysrdOiguAAmzY/UoZeQkAA7O7saX7O3t8eLFy8AAF27dkVcXJy0b0MIUZB1Yc/x6ek3gbdxgjOW9av5d17eeDweBgwYoOgySDMkdfemtbU1Lly4gJSUFLH55eXl+Ouvv2BtbQ0AiImJoYe8EtLMfHHmmVjg/arAwKusrMTBgwfx7Nmz+hcmpB5St/QGDBiALl26oFu3bpg+fTpsbGyQmZmJAwcOoKioCHPnzkVKSgouX76Mr776SpY1E0Ka0OdnnuGLs8+56U2TumGxp61CaqmsrMSBAwfw9OlTREVFwc/Pj+7BI40idejx+XycPn0aP/74I/bv34+EhASYm5vD19cX//nPf2Bubg4AePDggcyKJYQ0HcYYPj/zHF+GvQm8zZO6YZESBB4gukpTXV1dIbWQlqNRN6dra2vjk08+wSeffCKregghCsAYw6enn2H9uTfj4W2d3B0L+toopJ7Kykrs37+f69JUU1PD9OnTYWtrq5B6SMshsyeylJWVgTH2ZsOqqlBVpQe+EKLsGGP4z+ln+OqtwPttSnfM76M8gTdjxgzY2CimHtKyNOqp0I8fP4avry/09fWhqakJLS0t7mfRokWyqpEQ0kQYY/jk1FMu8Hg84I+pPRQWeBUVFfjrr78o8EiTkTr0SkpKMHr0aFhaWmLatGkYMmQINm/eDC8vL7Rp0wYLFy6UZZ2EEBljjOHjk0/xf+GxAP4JvCk9MLd3e4XUUxV4z5+Lzimqq6tj5syZFHhEpqQOvRs3bkBfXx87d+6Eu7s7bGxssGjRIly6dAn29vZ4+fKlLOskhMgQYwxrT0bjvxfeBN62qT0wR0GBBwC5ublITEwE8Cbw2rdXXD2kZZI69BISEuDi4gIA0NTUREFBgWiDfD4mTZqEGzduyKRAQohsMcaw+kQ0vr0geoAEjwfs8HPBbA/FBoypqSlmzZoFAwMDzJw5k7vXlxBZkvpKE4FAwF0+bGlpyT38FQAyMjJoAEdClBBjDB8dj8KGCFFPDI8H7PR3waxeyhEwbdu2xXvvvQcVFRVFl0JaKJlcXunp6YmMjAxMnToVtra2+PXXXxEaGiqLTRNCZIQxhpXHovDjJVHg8XlA8LSemOlmpZB6BAIBbt26hb59+4LPf9PpRIFHmpLUoTds2DB4eHgAADQ0NHD8+HGsXbsW0dHR+PTTTzFmzBiZFUkIaRzGGJYffYKfL4ueg8vnASHTemKGAgNv3759ePnyJVJTUzFhwgSx4COkqUgdeqWlpSgsLOSme/fujQsXLsikKEKI7DDG8OHRJ/jlrcDbNb0nprsqPvAA0fN5c3JyYGJiopB6SOsi9Z9W165dw7Zt22RZCyFExhhjeP/wY7HA2zPDVaGBt3fvXi7wNDU1MWvWLAo8IjdSt/Tat2+PmJiY+hckhCgEYwzLDj3GpmuvAAAqfB72TO8Jv56WCqmnKvCqhhrT1NREQEAA2rVrp5B6SOskdUvP29sbFRUV2LBhA0pLS2VWUEVFhdjjzBqyHiFERChkWHrokVjg7Z3pqrDAKy8vx59//ikWeLNmzaLAI3Indejt3bsXDx8+xMqVK6GtrQ0TExOYmppyP8uXL2/Q9qKjo9G/f39oaWlBR0cH8+bNQ0lJSZ3rCIVCrF+/HlZWVtDR0YGbmxuuX78u7S4R0iJUBd7ma/EARIG3b6YrpvRQTMBUBd6rV68AAFpaWpg1axbatm2rkHpI6yZ192bPnj2xfv36Wl93dnaWeFslJSUYPnw4+vfvjxMnTiAtLQ3Dhg3DBx98gN9++63W9ZYsWYITJ04gNDQUXl5eiI6Oxp49e9C3b98G7QshLUFUagGeZxTiz8hk7H8oGtxZlc/DvgBXTOquuBbV1atXER8vCuCqwLOwsFBYPaR14zFp+hJlbM+ePQgKCkJqaip3Qnvbtm1YvHgx0tPTYWhoWG2dx48fo1u3bjh69CjGjh0r9XsnJSXB2toaiYmJsLJSzMl9ZZeVlUUXGtRCGY5NZmEZ/HdHIjwmU2y+Cg/4a1YvTOyuuBZVVlYWDAwM8NdffyE5ORmzZs1CmzZtFFaPslGGz09r0+ib09PS0rBv3z7ExsbCxcUFo0aNQkJCAncPnySuX78OJycnsf/8gQMHQiAQ4O7duxg8eHC1dU6cOAFdXV2MGjUKjDEwxug+H9Iq+e+OxPl3Ag8AulroKTTwqqiqqmLq1KnIy8ujL3iicI1KiZ07d6JDhw744YcfcObMGVy/fh06OjqYNm0acnJyJN5Oeno6zMzMxOZVTaenp9e4zqtXr9ChQwf85z//gb6+PnR0dNC3b996z+nl5+cjKSmJ+0lJSZG4TkKUzZPUAoTHZKKm7pqHKQWISi2Qe01lZWXIzs4Wm6eqqkqBR5SC1C295ORkLF26FFu2bMHMmTOxbds23LhxA3p6ehg+fDj27NmDZcuWSbw9oVBY43Rtz/BkjOHBgwdwc3NDSkoKeDwePvroI4wcORJPnz6ttQtlw4YN+OKLL6rNz8nJgZaWlsT1tib5+fmKLkFpKfrY3H2ZVefrkXGpaKNWLqdqRBetHDt2DPn5+ZgwYQI9Uqweiv78KLOm+iNJ6tC7dOkSfHx8EBAQUO21Tp06ITo6WuJttWvXDlFRUWLzqlp4tV3hVXWp87fffgtdXV3u31u3bkVERASmTp1a43orVqzAvHnzuOmUlBR4eHjAyMiI/hKtAx2b2inq2FQKGQ49fVHnMq52FjAx0ZNLPaWlpThy5AhSU1MBAOHh4Zg0aRJ9dupBx0e+pO7e5PF41VpnVfLy8qCjoyPxtvr164eoqCixrsawsDBoamrCzc0NgKhlV1hYiMrKSgCi+wQB0V+WVQQCARhj3OgPNdHX14eVlRX3Q5dNk+aoUsgwe999HH2SVuPrPABDHE3hZCG/wNu9ezeSkpIAADo6Opg4cSKNtkKUjtSh5+HhgfDwcJw7d05svkAgwIEDB9CvXz+JtzVu3Dh07twZc+fORWxsLC5duoQvvvgCy5Yt41pxycnJ0NPTw+HDhwEAPj4+6NevH5YsWYKXL18iLi4OixYtgrW1NQYOHCjtbhGi9CqFDEH77mHXXVHAqPF5cGmnL7bMYEdT7J3pKpd6qgIvOTkZAKCrq4ugoKBq5+kJUQZSd2/a29tjzpw5GDp0KHx9fcHj8ZCRkQEfHx/o6elh9OjREm9LTU0NZ86cwfLly+Hp6QltbW3MmzdP7Nwbn8+Hjo4OVFVFJfN4PBw9ehSrVq1Cv379oKqqij59+uD8+fMwMDCQdrcIUWoVlUIE7r2PP++JAkZDlY8js90xvLM5olILEJtZBAdTHbm28Hbt2oXXr18DEAVeYGAgTE1N5fL+hDRUo+7TEwqF+Pnnn7F9+3bEx8fDyMgIEyZMwLp166CnJ59fusai+/TqR/cS1U6ex6aiUohZe+9j71uBd3S2O4Z1NpfL+7+rpKQEu3fvrjPw6LNTNzo+8teo+/T4fD6WL1/e4EeOEUIapqJSiIA/72HffVHAaKrycXSOO4Z2UkzgCYVC7Nmzhws8PT09BAYG0hc4UXpSn9O7ceMGTpw4wV1YQghpGhWVQszYIx54x+Z4KCzwANEfvB4eHuDxeBR4pFmRuqUXHx8Pf39/tGvXDrNmzcKcOXPg6Ogoy9oIafUElULM2BOJ/Q9EVzZrqvJxfK4HhnRU/EUi3bt3h4qKCtq2bQtjY2NFl0OIRKRu6fn5+SEmJgZBQUHYvXs3OnbsCG9vbwQHB6O4uFiWNRLSKgkqhZi2+03gaanxcUKBgVfT8F1du3alwCPNSqMeQ+bg4ICvvvoK8fHxOHXqFCwsLLBw4UJYWFhg06ZNsqqRkFZHUCmE/667OPjwTeCdnNcbgxUUeMXFxfjjjz9o6C7S7MnkCc18Ph/Dhw/H7t27sXHjRlRWViIyMlIWmyak1SmvEMJv110ceiR6som2ugr+ntcbAx0UcxtAcXExQkJCkJaWhrNnz9LvNmnWGj3KAgA8fPgQO3bswO7du5GTk4Nhw4Zh1qxZstg0Ia1KVeAdefx24HlgQAfFBF5RURFCQkK4xwIaGBjAzs5OIbUQIgtSh15eXh727NmD7du34+7du3B0dMSKFSsQGBjIPReTECK58gohpoTcwbF/Hi2m808Lz7uDYq6KfDfwDA0NERgYWOP4loQ0F1KH3v79+7F69WpMmTIFP/74I/r37y/LughpVcoqKjEl+C6OR70JvFPze6O/veICLzg4GBkZGQAo8EjLIXXojRw5Ev7+/tyzMQkh0imrqMTk4Ls48U/g6Wqo4NS83uinoMArLCxESEiIWOAFBQXR4/1IiyB16FV1Yb5+/RonT57Eq1evYGFhgYEDB8LZ2VlmBRLSkpUKKjEp+A7+jhZ1IeppqOL0/N7wtFPMbQCFhYUIDg5GZqZoJHYjIyMEBgZS4JEWo1EXsuzduxcLFixAcXExzMzMkJ2djcrKSqxevRr/93//J6saCWmRSgWVmLjzDk49fRN4Zxb0Rl9bxd33xhjjhgyjwCMtkdS3LGRmZmLu3Ll47733kJeXh9TUVJSUlGDHjh3YsGEDzp8/L8s6CWlRSgWVmLDzNhd4+pqqOLuwj0IDD3jzDE0HBwfq0iQtUqNGTndxccHXX3/NzVNRUcGsWbNw//59nDlzBoMGDZJJkYS0JCWCSozffhtnn4vOmelrquLsgj7obWOk4MpE9PX1MWPGDEWXQUiTkLqlV1RUVOuo4xYWFigqKpK6KEJaqhJBJcZtv8UFnoGmKsIWKi7w8vPzcfjwYZSVlSnk/QmRN6lDz9nZGeHh4Xjx4oXY/MLCQuzevRtdu3ZtdHGEtCTF5RUYu+0Wwp6LLhIRBV5feLRXXOAFBwfj4cOH2LNnDwUfaRWk7t7s2bMnvL294eLigqlTp8LOzg4ZGRk4cOAANDU1ERAQIMs6CWnWissrMHb7bYTHiALPUEsNYQv7oJe1oULqycvLQ3BwMHJyckT1FRdDIBBAQ0NDIfUQIi+NevbmwYMH8emnn+LevXvYsGEDwsLCMGnSJNy+fZvu3yPkH8XlFRiz7U3gGWmp4ZwSBZ6pqSmCgoLod5a0Co26ZUFNTQ2rVq3CqlWrZFUPIS1KUVkFxmy/hQuxWQD+CbxFfeBqZaiQevLy8rBz507k5uYCEAVeYGAgBR5pNRrV0jt16hSuXbsmNi8xMRG//fZbo4oipCUoKqvAqG1vAs9YWw3hi/oqLPByc3PFAs/MzIwCj7Q6jbpP77333qv29BVra2vs2bMHV65caXRxhDRXhWUVGPnHTUS8EA+8nlaKue8tNzcXwcHBFHik1ZM69C5fvowePXpAX1+/2mujRo3CqVOnGlUYIc1VVeBdepkNADDRVsP5xX3hYqm4G70fPnzIBZ65uTkCAwOho6OjsHoIURSpz+kJBAIUFBTU+FpBQQGKi4ulLoqQ5qqgVBR4V+JEgWeqo47wRX3RvV31Pw7lqX///igqKsKrV68wa9YsCjzSaknd0vPw8EBERATu3LkjNj8jIwPbt29Hnz59Gl0cIc1JQWkFRvx+Qyzwzi9WfOABAI/Hw/DhwzFnzhwKPNKqSd3Ss7W1RWBgILy8vODn5wdHR0ekpqYiNDQUdnZ2mDRpkizrJESp5ZcKMOL3m7j2SnQbgJmuOs4v6gvntooJvOzsbOTn58PW1pabx+Px6D480uo16paFzZs3w8HBATt37sThw4dhYmKCGTNmYN26dVBVbdSmCVF6UakFiIzLhmM7Pj48+gQ34kWBZ66rjvOLPdHVQk8hdWVnZ2Pnzp0oKSnBjBkzxIKPkNaOxxhjii5CkZKSkmBtbY3ExERYWVkpuhyllJWVBRMTxQxoqowyC8vgvzuSu9n8bW30NHB+UV84KSjwsrKyEBwczJ1vb9++PYKCgsDj8RRWD312akfHR/6oOUZIA/nvjsT5GgJPTYWHC4v7oksb5Qi8tm3bwt/fX2GBR4gyotAjpAGepBbU2MIDAEElg6L6TTIzMxEcHIzCwkIAosALCAiAlpaWYgoiREk16okshLQ2LzLrHjIrtp7Xm8K7gdeuXTvMmjWLAo+QGlBLj5AGMNNVr/N1B1P53g6QkZGBkJAQLvAsLS0xc+ZMaGpqyrUOQpoLCj1CJJRdXI5lhx/X+BoPwGBHU7lewCIUCrFv3z4KPEIagLo3CZFAdnE5hmy5jsikPACAhor4xSGDHU2xd6arXGvi8/kYN24c1NTUYGVlRYFHiAQa1NLbuXMnFi1aJNGys2fPxubNm6UqihBlklUkCrz7r/MBAFYGmriwxBPlFUJExqXC1c5CYbcoVN2SYGJiQjeeEyKBBoVe37598euvv0q0bOfOnaUqiBBlkllYhiFbb+DBP4FnbaiJC4s90eGfc3dt1MphYiK/wMvLy4O+vr7YbQjt2rWT2/sT0tw1KPQ6deqETp06NVUthCiVzMIyDN5yAw9TRIHX3kgLFxb3hb2JYp5dmZaWhpCQEHTu3BmjR4+m++8IkYLMzumVlZWhtLSU+6moqJDVpgmRu4zCMgzacp0LPBsjLVxc7KnQwAsODkZxcTEiIyNx48YNhdRBSHPXqNB7/PgxfH19oa+vD01NTWhpaXE/kp77I0TZpBeUYdDm63iUInqyiY2RFi4u8YSdibZC6klNTUVwcDBKSkoAiM7jubm5KaQWQpo7qUOvpKQEo0ePhqWlJaZNm4YhQ4Zg8+bN8PLyQps2bbBw4UJZ1kmIXKQVlGHg5mt4nCoKPFtjLUQs8YStseICLyQkhAs8GxsbzJgxA+rqdd8vSAipmdShd+PGDejr62Pnzp1wd3eHjY0NFi1ahEuXLsHe3h4vX76UZZ2ENLm0gjIM2nwNUWmi+97sjLVxcbEnbBQUeCkpKWItPFtbW0yfPp0Cj5BGkDr0EhIS4OLiAgDQ1NTkHnLL5/MxadIkOudAmpXU/FIMfCvw7E20cXFJX4UGXkhICEpLSwGIAm/atGkUeIQ0ktRPZBEIBNwvoKWlJR4/fvOkioyMDLqyjDQbKfmlGLT5Op6miwKvg4k2Liz2hLWRYp5dmZ6eLhZ4dnZ2mDZtGtTU1BRSDyEtiUweQ+bp6YmMjAxMnToVtra2+PXXXxEaGiqLTRPSpFLySzFw0zU8yxA9KLqDiTYuLvGElaHiHtZsaGiINm3aID4+Hvb29vD396fAI0RGpA69YcOGwcPDAwCgoaGB48ePY+3atYiOjsann36KMWPGyKxIQprC6zxRl+bzfwLP0VQHF5b0haWBYkcnUFdXx/Tp03Hp0iUMGDCAAo8QGZI69KytrWFtbc1N9+7dGxcuXJBJUYQ0teS8EgzcdB0xmcoReIwxsVMC6urqGDJkiEJqIaQla9R9eqdOncK1a9fE5iUmJuK3335rVFGENKWk3BL4vBV4Hc10cHGJp8ICLykpCVu3bkVubq5C3p+Q1kTq0MvMzMR7770HZ2dnsfnW1tbYs2cPrly50ujiCJG1xJwS+Gy6xg322umfwGtnoJjRCRITE7Fr1y7uiStVV0ETQpqG1KF3+fJl9OjRA/r6+tVeGzVqFE6dOtWowgiRtYScYvhsvoYXWcUAgM7muri4xBNt9RUXeLt370Z5eTkAwMzMjEY7J6SJNeqWhdr+Ki0oKEBxcbHURREiawk5xfDZdB1x2aLPZZc2uji/qC8sFBR4CQkJ2LNnDxd4jo6OmDp1KlRVaVxnQpqS1C09Dw8PRERE4M6dO2LzMzIysH37dvTp06fRxREiC/HZ4oHn1EYXFxZ7Kk3gdezYkQKPEDmR+rfM1tYWgYGB8PLygp+fHxwdHZGamorQ0FDY2dlh0qRJsqyTEKm8yi7GwM3X8Cpb9CivrhZ6OL+oL8z1FDPganx8PPbs2QOBQABANFzX5MmTKfAIkZNG/aZt3rwZDg4O2LlzJw4fPgwTExPMmDED69ato19ionCvsovhs+ka4nNEgedsoYfzi/vCTFd5Am/KlClQUVFRSD2EtEaNSiYVFRWsXr0aq1evllU9hMhEXJboopWEfwKvW1s9hC9SXOABoiueqwKvc+fOmDx5MgUeIXJGzTHS4rzMKoLPpmtIzBU9u7J7W32EL+oDUwUGHgC4ublBKBQiLi4OkyZNosAjRAEadCHLzp07oampicWLF3P/ru1n8eLFTVUzIbV6kVmEAf97E3g92unj/OK+Cg+8Ku7u7tSlSYgCNail17dvX/z666/o3LkzzMzM8Ouvv9a6bOfOnRtdHCENEZtZhIGbriEpTxR4Lu30cW5RX5joKGY4npcvXyIvLw89e/YUm08jkBCiOA0KvU6dOqFTp04AgJiYGDg5OcHT07NJCiOkIWIyCjFw83Uk/xN4PS1FgWesrbjA27t3LyoqKkT1vBN8hBDFkPo+vWvXrmHbtm2yrIUQqTzPKITPpjeB52ploNDAe/HihVjgvXz5EowxhdRCCBEn9YUs7du3R0xMjCxrIaTBnqUXYuDma0jJLwMAuFkZIGxhHxgpOPAqKysBAM7OzpgwYQJ1aRKiJKRu6Xl7e6OiogIbNmzgRngmRJ6ephWIBZ67tSHOLeqrsMCLjY0VC7xu3bphwoQJ4PMbNZgJIUSGpP5t3Lt3Lx4+fIiVK1dCW1sbJiYmMDU15X6WL1/eoO2lpaVh9uzZsLe3R7du3fDNN99AKBRKtG5WVha6du0KCwsL5OfnS7M7pJmJTivAwM3XucDzaG+Iswv7wFBLMQOuxsbGYt++fWKBN378eAo8QpSM1N2bPXv2xPr162t9/d0hh+pSWVmJ4cOHw8jICAcPHkRKSgpmzpyJoqIirFu3rt71g4KCYGpqiqioKImDkjQ/UakF3JBACw48RFqBKPB6tzfEmQV9YKCgwIuJiUFoaCgXeN27d8e4ceMo8AhRQlKHXteuXdG1a1eZFHH8+HE8ePAACQkJsLKyQs+ePfH555/j448/xscffwxtbe1a1/3pp59QXFyMDz/8EJcuXZJJPUS5ZBaWwX93JMJjMqu91sfGCKfn91ZY4FVWVuL06dNc4PXo0QNjx46lwCNESSnFb+alS5fQuXNnWFlZcfOGDRuG4uJi3L17t9b1IiMj8d///hchISF0oUAL5r87EudrCDx9TVWcWaC4wANEj+KbOXMm9PX1KfAIaQaU4oksycnJaNOmjdi8qunXr1/XuE5hYSH8/f2xceNGWFpaSvxe+fn5SEpK4n5SUlIkXpfI35PUAoTHZKKmC/7zSyuQlKv4i6iMjIwwb948CjxCmgGleCILY6zaqAxV01XdRu9aunQpvLy8MHnyZInfBwA2bNiAL774otr8nJwcGrW6Foq8OOheXHadr0fGpaKNWrmcqhFJTk6Gubk51NTUxI5N1fh45A26sKxudHxqZ2Ji0iTblfqJLFXTsmBmZlbtnr+MjAwAgLm5eY3rnDx5EowxWFhYAADKysq4mlasWIE1a9bUuN6KFSswb948bjolJQUeHh4wMjJqsoPcEijq2LjaqQN4WsfrFjAx0ZNbPU+fPsXRo0dha2sLf39/AIo7Ns0FHZ+60fGRL6UYZcHDwwO///47cnNzYWhoCEB0nk9VVRWurq41rvPulZqnT5/G7NmzcfnyZbRr167W99LX14e+vr5M6ydNx9pQC5qqfJRWiF+VywMw2NEUThbyC7zo6GgcOHAAQqEQL1++RGRkJBwcHOT2/oSQxmvUCYjKykps3boVgwYNgr29PTw9PfHpp5+ioKCgQduZNGkSTE1NsXr1apSWliIpKQlff/01pk2bBmNjYwCiFpmFhQVOnDgBQNQCtLCw4H6qwtLc3By6urqN2S2iRFadiKoWeIAo8PbOrPkPoqYQFRXFBR4AuLq6wsPDQ27vTwiRjUa19MaPH49z585h3Lhx8PDwQGZmJjZv3ox9+/bhzp07EreodHV1cfLkScyePRuGhoYQCoWYMGECNm3axC1TWVmJtLQ0evpLK3L2WTq2Xo8HABhoquJQUC8UllXCwVRHri28qsCren6mm5sbRo0aRVcME9IMSR16V65cwaVLl3Dv3j2xi1Y2bNgAT09P/P7771i5cqXE2+vZsyfu37+P/Px8qKurQ1NTU+z1du3aISUlBUZGRjWuP2LECKSkpMDAwEC6HSJKJa9EgHl/PeCmfxrnjEGOZnKv48mTJzh48CAXeL169cLIkSMp8AhppqQOvfj4eAwZMqTaVZr6+vqYNWsW4uPjpdpuba1DPp/PXbRSEw0NjTpfJ83LymNR3ECwo7qYI9Ddqp41ZO/x48c4dOgQF3ju7u4YMWIEBR4hzZjUoWdvb1/rPW6pqamwt7eXuijSup2KTsO2WwkAAEMtNfw2pYfcgyYpKUks8Dw8PDB8+HAKPEKaOakvZHF3d4dQKMSqVauQmSl6WkZJSQn++OMPhIaGcpdzE9IQuSUCzN//kJveOMEZ7Qw061ijaVhaWqJHjx4AKPAIaUmkbunt27cPjx8/xs2bN/H9999DV1cXhYWFAERdjW8/cDogIAA//vhj46slLd7yo0+4wWDHdm2DGa6SP21Hlng8HsaMGQN7e3s4OztT4BHSQjTZKAtva8iIC6T1OhGVhp23EwEAxtpq2Dq5u1zDpqSkROypPHw+H926dZPb+xNCmp5SjLJASHZxORbsf3O15q8TusFCX37dmvfv38eZM2cwY8YMsQefE0JaFno6LlEKHxx5zA0IO7GbBfx71v5UHVm7f/8+jh49itLSUuzevRu5ublye29CiHw16ub0srIyhIaG4tmzZ9yzL6v07t0bU6ZMaVRxpHU4+jgVu+8mAwBMtNWwaZL8ujXv3buHY8eOcdNubm50rychLZjUoVdWVoa+ffsiKioK9vb2UFdXF3udnm9JJJFVVI6FB95crblpUne00dOQy3tHRkbi+PHj3LSXlxcGDx5MF60Q0oJJHXrnz59HVlYWEhISah0JgZD6vHf4MdIKRL0EU3q0xVQX+XRrvht4/fr1w6BBgyjwCGnhpA697OxseHl5UeARqR16mIK990Tdmma66vjfRPlcKXn37l3uweUA0L9/fwwcOJACj5BWQOoLWVxdXfHgwQOx4X0IkVRGYRkWHXzTrbl5UjeY6TZ9t+a7geft7U2BR0grInVLr0uXLhg0aBAmT56M2bNnV3sQtIWFBY01Rmq17NBjZBSKRhr3d2mHSd3l061paGgIFRUVVFZWwtvbGz4+PhR4hLQiUoeeUChEcnIyDh8+jMOHD1d7fe7cufjjjz8aVRxpmf66/xp/PXgNAGijp4Ff5dStCQAdOnSAv78/Xr9+DW9vb7m9LyFEOUgdepcuXcKpU6cQHByMPn36VLt6U09PfuOdkeYjraAMS97q1tw6uTtMdNTrWEP2HBwcqBeCkFZK6tBLTk7G6NGjMWvWLFnWQ1owxhiWHHyIrGIBAGCmmyXGOTftcFA3b95Efn4+hgwZQt2YhBDpQ8/Ozg45OTmyrIW0cKH3X+PQo1QAgIWeBn4e37TPZL1x4wbOnDnDTVPwEUKkvnrTw8MDGRkZ+OOPP1BZWSnLmkgLlJpfiqWHHnHTv03pDmPtpuvWfDfwNDU1KfAIIdKH3r59+/DixQvMnz8f2traMDU1FftZvny5LOskzRhjDIsOPET2P92agb2sMKZr03VrXr9+XSzwBg8ejP79+zfZ+xFCmo8mG1qIhhMiVfZEJuPokzQAQDt9TfzUhN2a165dQ1hYGDc9ZMgQeHl5Ndn7EUKaFxpaiDSp13mleP/wY276j6ndYail1iTvdfXqVZw7d46b9vX1haenZ5O8FyGkeWrUKAsAkJaWhn379iE2NhYuLi4YNWoUEhIS4OHhIYv6SDPGGMPCAw+RUyLq1pzjYY0RXdo0yXtduXIF4eHh3DQFHiGkJo0KvZ07d2LZsmUwNjaGpqYmSkpKMHXqVEybNg137typ9pQW0rqE3EnCiShRt6aVgSY2jG2anoHKykpER0dz00OHDkXfvn2b5L0IIc2b1BeyJCcnY+nSpdi8eTPi4+OxevVqAKKb0ocPH449e/bIrEjS/CTlluCDI2+6Nbf59YBBE3VrqqioICAgAO3atcOwYcMo8AghtWrUE1l8fHwQEBBQ7bVOnTqJ/eVNWhfGGObvf4C80goAwPw+7TG0U9OOxqGpqYk5c+ZARUWlSd+HENK8Sd3S4/F4tY6wkJeXBx0dHamLIs3bjluJOP00AwDQ3kgL349xkvl73L59G4WFhWLzKPAIIfVp1M3p4eHhYlfLAYBAIMCBAwfQr1+/RhdHmp+EnGIsP/aEm942tQf0NWXbrXnx4kX8/fffCA4OrhZ8hBBSF6m7N+3t7TFnzhwMHToUvr6+4PF4yMjIgI+PD/T09DB69GhZ1kmaAcYY5v31APn/dGsu9rTBkI5mMn2PixcvIiIiAgCQmZmJuLg4dOsmv1EaCCHNm9QtPQDYtGkTfvjhB7x+/RrXrl1DZmYm3N3dcerUKfD5jdo0aYZ+v5GAsOeZAABbYy18O1p23ZqMMVy4cIELPAAYNWoUBR4hpEEadcsCn8/H8uXL6ZFjBPHZxVh5/E235g4/F+hqNPo2UABvAu/y5cvcvNGjR8PNzU0m2yeEtB6y+VYirZpQyDAn9AEKy0QPHl/mZQsfB1OZbLumwBszZgxcXV1lsn1CSOtCoUcabeuNeJyPFXVr2pto45tRXWSyXcYYzp8/jytXrnDzKPAIIY1BoUca5WVWEVYdjwIA8HjATn8X6MioWzMhIUEs8MaOHYuePXvKZNuEkNaJrjYhUqvq1iwqF3Vrvt/PDv3tTWS2fRsbGwwePBgAMG7cOAo8QkijUUuPSG3TtVeIeJEFAHAw1cHXIzvL/D369euHjh07wty8aZ/oQghpHailR6QSm1mENSdFj5qr6tbUVm/c31CMMaSmplabT4FHCJEVCj3SYEIhw+x991H8T7fmCm97eNkZN2qbjDGcPXsWv/32G548eVL/CoQQIgUKPdJgv1yJw5W4bABAJzMdrBvRuG5NxhjOnDmDGzdugDGGw4cPIz8/XxalEkKIGDqnRxrkeUYh/vW3qFuTzwN2TusJLTXpH/TMGMPp06dx69YtAKIHmY8bNw76+voyqZcQQt5GoUckVvlPt2aJQDS6xkc+HdDHRvqBghljOHXqFG7fvg1AFHgTJ06Es7OzTOolhJB3UegRif106SWuvcoBAHRpo4svhnWSeluMMfz999+4c+cOAFHgTZo0CV27Ns3o6oQQAtA5PSKhp2kF+OTUUwCACp+HYP+e0JSyW5MCjxCiKNTSI/WqFDIE7buPsgpRt+bqgR3g3t5Q6u2Fh4dzgcfn8zFp0iQ4Ocl+oFlCCHkXtfRInaJSC/D+yVjcTMgFAHS10MNnQzs2apvdu3eHtrY2+Hw+Jk+eTIFHCJEbaumRGmUWlsF/dyTCYzLF5v88ris0VKW/WhMQ3WweGBiInJwcdOok/XlBQghpKGrpkRr5747E+XcCDwC+OR/b4G0xxlBZWSk2z9zcnAKPECJ3FHqkmiepBQiPyQSr4bVzMZmISi2QeFuMMRw7dgwHDhyoFnyEECJvFHqkmheZRXW+HlvP61WEQiGOHTuG+/fv4+nTpzhy5IgMqiOEEOnROT1SjY2xVp2vO5jq1LuNqsB78OABAEBFRQXdunWTSX2EECItCj1SzbEnaTXO5wEY7GgKJwu9OtcXCoU4evQoHj58CEAUeH5+fnB0dJR1qYQQ0iDUvUnEPHidhy/PPgcgCrm3DXY0xd6ZrnWuT4FHCFFm1NIjnPIKIYL23keFUHQJy5fDO2Fit7aIjEuFq52FRC28I0eO4NGjRwBEgefv7w8HB4cmr50QQiRBoUc4X4fH4P5r0ZA+blYGWDPIAWoqfLRRK4eJSf2Bd/jwYTx+/BgABR4hRDlR6BEAQGRSLr46FwMAUFfhY6e/C9RUJO/9fvtePFVVVfj7+6NDhw5NUishhEiLQo+grKISQfvedGt+MawjnNs2bDw7FRUVTJo0CUePHoWLiwvs7e2bolRCCGkUCj2CdWExeJQiuuHco70hPvKRroWmoqKCiRMnyrI0QgiRKbp6s5W7k5jLPVpMQ5WPHX4uUJWgW7OyshLHjx9HWlrNtzcQQogyotBrxcoqKhG49x4q/+nWXDe8U71XaAKiwDtw4AAiIyMREhKC9PT0pi6VEEJkgkKvFfv8zHNEpRUCAPrYGGHFgPq7NasC7+lT0YCyAoEAxcXFTVonIYTICp3Ta6Vuxufg2wuibk1NVdHVmir8d29HF1dZWYn9+/fj2bNnAAA1NTVMnz4dtra2TV0uIYTIhNKEXklJCTZs2ICIiAjo6OhgxowZmDx5cp3rXLhwAXv37kV8fDxsbW2xcOFCuLrW/cQQApQKRFdr/tOria9GdkYnc90616moqMD+/fvx/LnoaS1qamqYMWMGbGxsmrpcQgiRGaXp3hw/fjz+/PNPzJ8/HwMHDsTMmTOxefPmWpf/7rvvsH79eri7u2PFihXQ19eHh4cHTp8+Lceqm6dPTz/D03RRt6aXrRE+6F/37QVVLbyqwFNXV8fMmTMp8AghzQ6PMVbTsGlyFR4ejiFDhuDJkydwcnICAKxbtw4///wzUlJSoKamVm2d/Px86OuL30s2fvx4lJSU4MyZMxK/d1JSEqytrZGYmAgrK6vG7UgzcC0uG/3+dxWMAVpqfDxYOQCOZrW38ioqKrB7927Ex8cDEAXejBkz0L59e3mVrNSysrJgYmKi6DKUFh2futHxkT+laOmFhYWhQ4cOXOABwLhx45CVlYXIyMga13k38ADAwMAApaWlTVZnc1ciqMTs0Puo+jPn/0Z2qTPwACA5ORmJiYkA3rTwKPAIIc2VUoRefHw8LC0txeZVTSckJEi0jdjYWBw4cADjxo2rc7n8/HwkJSVxPykpKdIV3Qz9+9RTPM8QDQDb394Y7/Wzq3cdGxsbDB06FFpaWpg5cyasra2bukxCCGkySnEhi0AggKamptg8LS3RQKbl5eX1rp+VlYWxY8eiT58++OCDD+pcdsOGDfjiiy+qzc/JyeHesyW6kZiPHyNeAgC01fjYMMwWOTnZEq1rbm6OmTNnQkNDA1lZWU1ZZrOTn5+v6BKUGh2futHxqV1TdfsqRegZGxvj1atXYvOqvlyNjY3rXDc7Oxu+vr4wMTHB0aNHoaKiUufyK1aswLx587jplJQUeHh4wMjIqMX2rReVVeDDUw9QdfL229FO6OVgWeOyAoEAT58+rTbKeUs9NrJAx6ZudHzqRsdHvpQi9Hr27Ik9e/aguLgY2traAIBbt26Bx+OhR48eta6Xk5MDX19faGtr49SpU9DVrfv8FCA6F1jT+cCW7F+nniI2U9StOdDBBIs9bWtcTiAQYN++fXj58iVycnLg7e0txyoJIaTpKcU5vcmTJ0NFRQU//PADAKC0tBTff/89hg8fjnbt2gEAMjIy0K9fP1y8eBEAkJeXB19fX2hpaeH06dMSBV5rFPEiE79cjgMA6KirYNtUF/BruAn97cADgGvXrqGgoECutRJCSFNTipaemZkZQkNDERAQgODgYGRnZ8Pe3h7btm3jlikrK8PVq1eRmZkJAPjyyy9x9+5duLi4YPjw4dxy5ubmOHTokNz3QRkVllVgTugDbvr7MU6wM9GutpxAIMDevXsRFycKR01NTQQEBEBPr/7ncBJCSHOiFKEHACNGjEBycjKePHkCbW1tdO7cWex1c3NzXL58GV26dAEALF26FBMmTKi2HQ0NDbnU2xysPRmNl1mi52IOcTTFwr7VbyYvLy/H3r17uXOqVYFX1cImhJCWRGlCDxAFVm2PEVNXV0e/fv24aXt7exqotA7nYzLxv6uvAAB6Gqr4Y2oP8Hji3Zo1Bd6sWbPQtm1bOVdLCCHyoVShR2SjoLQCc0Lvc9M/jHWCjbF4t2Z5eTn+/PNP7kkrWlpaCAgIoMAjhLRoFHot0OoTUYjPKQEADO1ohnm9qz9B5cyZM2KBN2vWLFhYWMi1TkIIkTeluHqTyE7YswxsuS4KM33Nmrs1AWDQoEEwMzOjwCOEtCrU0mtB8ksFmPvXfW76p3FdYW1U81NmdHR0EBgYiKKiIpibm8upQkIIUSwKvRZk5bEoJOaKHrg9sos5gtzfPCezrKwMQqFQ7FFrOjo60NHRkXudhBCiKNS92UKceZqOP26KHs5toKmK36Z057o1y8rKsHv3buzatQslJSWKLJMQQhSKQq8FyC0RYO5fb25C/2WCMywNRC260tJS7N69mxtR4vDhw4oqkxBCFI66N1uAFUefIDlP1K05xqkNAtxEg+FWBV5ycjIAUXemr6+vwuokhBBFo9Br5k5GpWHHbdEgr0Zaatj6T7fmu4Gnq6uLwMBAmJqaKrJcQghRKAq9ZiynuBzz97/p1tw4wRlt9TVRWlqKXbt24fXr1wAo8AghpAqFXjP24dEnSMkvAwCMd7bAdFdLlJSUYPfu3RR4hBBSAwq9ZurY41SE3EkCAJhoq2HL5O6oqKjArl27kJKSAgDQ09NDYGAgDVJJCCH/oKs3m6GsonIsPPCQm/7fxG5oo6cBVVVVdOjQAQAFHiGE1IRaes3Q+4cfI7VA1K05qXtbTHURDQPE4/EwaNAgaGhowMnJCcbGxooskxBClA6FXjNz+FEK/rwnuiLTVEcd/5vgLPZsTR6PJzYEEyGEkDeoe7MZySwsw6K3ujV/HN0Rx//ajZiYGAVWRQghzQeFXjOy7PBjpBeWAwAmO5ujMPIMkpOTERoaihcvXii4OkIIUX7UvdlMHHjwGqH3RbchmOmowaPoPtKy0gCIbkug83eEEFI/Cr1mIL2gDIsPPuKmJ+iloDgrFQBgYGCAoKAgGBoaKqg6QghpPij0lBxjDEsOPUJmkahbs5duEdoVi0ZTMDQ0RGBgIAUeIYRIiEJPyf11/zUOPhTdbK6vUolBiAUgCrygoCAYGBgosjxCCGlWKPSUWFpBGZYeetOtOVL9FbT5lTAyMkJgYCAFHiGENBBdvamkGGNYfPAhsooFAICemrnorJZHgUcIIY1Aoaek9t5LxuFHootV2uprYPfcAWjXrh11aRJCSCNQ96YSSskvxbJDj7np36f0gJN9G3Sxmyf29BVCCCENQy09JfMkJR+jf7+BnBJRt+Zsd2uMcmoDABR4hBDSSNTSUxKZhWXw3x2J8JhMbp4qj+HjQR0UWBUhhLQs1NJTEv67I3H+rcADgAoGLHxrZHRCCCGNQ6GnBJ6kFiA8JhOs2is8XHiZg6jUAgVURQghLQ+FnhJ4mJBR5+uxmUVyqoQQQlo2Cj0Fy83NxcNLp+tcxsFUR07VEEJIy0ahp0C5ubkIDg6GZlEG7FTygXc6OHkAhjiawslCTyH1EUJIS0Ohp0CXLl1Cbm4uAGBx+0IMtBcfHmiwoyn2znRVQGWEENIy0S0LCjRy5Ejk5+ejoKAAs2bNxCodHUSlFiA2swgOpjrUwiOEEBmj0FMgVVVV+Pn5QSAQQFtbGwDgZKFHYUcIIU2EQk+OsrOzwRiDiYkJN09NTQ1qamoKrIoQQloPOqcnJ9nZ2QgODkZwcDCys7MVXQ4hhLRKFHpykJWVhZ07d3Ln786cOaPokgghpFWi0GtiWVlZCA4ORkGB6Kkqbdu2xfjx4xVbFCGEtFJ0Tq8JZWZmIjg4GIWFhQBEgRcQEAAtLS0FV0YIIa0ThV4TeTfw2rVrh4CAAGhqaiq4MkIIab2oe7MJUOARQohyopaejJWXlyMkJIQLPEtLS8ycOZMCjxBClAC19GRMXV0dgwYNAgBYWVlR4BFCiBKhll4TcHFxgZaWFmxtbaGhoaHocgghhPyDWnoyUFJSUm1ep06dKPAIIUTJUOg1UlpaGn799VfcunVL0aUQQgipB4VeI6SlpSE4OBjFxcU4deoUoqKiFF0SIYSQOtA5PSmlpqYiJCSE69ps3749HBwcFFwVIYSQulDoSeHdwLOxscH06dOhrq6u4MoIIYTUhUKvgVJSUhASEoLS0lIAgK2tLaZNm0aBRwghzQCd02sACjxCCGneqKUnoXcDz87ODtOmTaMBYAkhpBmhlp6EtLW1udERKPAIIaR5opaehAwMDBAYGIiIiAiMGDGCAo8QQpohCr0GMDAwwNixYxVdBiGEEClR92YtkpKS8Oeff6KsrEzRpRBCCJERCr0aJCYmYteuXYiJicGff/6J8vJyRZdECCFEBij03pGYmIjdu3dzQaehoQE+nw4TIYS0BEp1Tu/gwYOIiIiAjo4O/Pz84OLi0iTr1CYhIQF79uzhAs/R0RFTp06FqqpSHSZCCCFSUpomzIIFC7B06VKYmZkhLy8P7u7uOHbsmMzXqU1ycrJY4HXs2JECjxBCWhil+Ea/e/cufv/9d0RERMDb2xsAoKamhvfffx9jxowBj8eTyTp1OXToELS1tQGIxsKbPHkyBR4hhLQwStHSO378OCwtLbnwAoDp06cjPj4eDx8+lNk6dREIBABEgTdlyhQKPEIIaYGU4ps9NjYWNjY2YvPs7Oy413r06CGTdQAgPz8f+fn53HRiYiIAoKCgAA4ODujbty9SUlKk35kWKCcnp8bR4Qkdm/rQ8akbHZ+6WVhYyLwBohShV1JSAj09PbF5VdO1fSCkWQcANmzYgC+++KLa/D/++AMAsHTpUskLJ4QQ0mSioqLQpUsXmW5TKUJPX18fSUlJYvOys7O512S1DgCsWLEC8+bN46bj4uLg7e2Na9euwdraWqr6W7KUlBR4eHjg1q1baNu2raLLUSp0bOpGx6dudHxqV3Vsqp53LEtKEXrOzs44cuQIKioquKbskydPAABdu3aV2TqAKBBrCkVra2tYWVk1aj9asrZt29LxqQUdm7rR8akbHZ/aNcW1FUpxIcukSZNQVFSE3bt3AwAYY/j111/h4eGBDh06ABD1fc+bNw93796VeB1CCCHkbUrR0rO1tcXPP/+MJUuW4MCBA0hJScHr169x9uxZbpmioiJs27YNw4cPh5ubm0TrEEIIIW9TitADgMWLF2PEiBG4du0atLW1MXjwYLELVYyNjfH777+jV69eEq8jCX19fXz22Wd1ngdszej41I6OTd3o+NSNjk/tmvLY8BhjTOZbJYQQQpSQUpzTI4QQQuSBQo8QQkirQaFHCCGk1aDQI4QQ0moozdWbTSU1NRUbN25EbGwsbG1tsXTpUrRv317m6zRX4eHh2LdvH4qKiuDj44O5c+dCRUWl1uUzMzMRHByMBw8ewNDQEGPGjIGvr68cK5af0tJSbNq0Cbdv34aRkRFmz54Nd3d3idYtLCzEggULUFFRgb/++quJK1WM6OhobN26FampqXB2dsZ7770HAwODOteprKzEn3/+ifDwcGhpaWH27Nnw8PCQU8XytXfvXvz9999QUVHBuHHjMGHChDqXz8vLw7Zt2/DgwQPw+Xy4urpi9uzZ0NXVlVPF8pObm4vg4GBcvHgRfn5+8Pf3r3edrKwsbNy4EdHR0bCyssLixYvh4ODQ4Pdu0S299PR09OrVC5GRkRg+fDieP38ONzc3xMfHy3Sd5io4OBgjRoyApaUl+vXrh/Xr1yMgIKDW5aOjo+Hu7o7U1FT4+vrCzMwMEydOxGeffSbHquVDKBRi+PDh2L59OwYNGgQNDQ14enpKfB/o4sWLcePGDanHd1R29+7dQ69evZCfn4+hQ4fi5MmT8PLyqvO5t+Xl5Rg6dCjWrVsHDw8P9O3bFx999BHu3Lkjx8rlY/Xq1Vi2bBnc3Nzg5OSEgIAAfP3117UuX1JSAk9PT/z5558YOHAgvLy8sGXLFgwaNAiVlZVyrLzpRUREwMnJCbGxsbh+/TqePn1a7zp5eXno06cPIiIiMHz4cKSkpMDNzQ3R0dENL4C1YB999BGzt7dn5eXljDHGKioqWNeuXdn8+fNluk5zJBAImJmZGfv888+5eVeuXGEA2M2bN2tcJy8vjxUWForN27hxI1NTU2OlpaVNWq+8HThwgPH5fBYfH8/NCwoKYt26dat33Z07dzJ3d3e2ceNGpqGh0ZRlKszw4cPZ8OHDuenc3Fymq6vLfv7551rXWbduHTMyMmKpqancvIqKCpabm9uktcpbfHw8U1FRYQcOHODmbdmyhWloaLDs7Owa17l8+TIDwJ4+fcrNu379OgPAHj161OQ1y1NGRgYrKipijDHWoUMH9tlnn9W7zvr165m5uTm3nlAoZH379mVTpkxp8Pu36JbeqVOnMGbMGKipqQEAVFRUMHHiRJw6dUqm6zRHd+7cQUZGBiZNmsTN8/LyQrt27WrdV319fejo6IjNs7S0hEAgQHFxcZPWK2+nTp2Cu7u7WLf2lClT8OjRIyQnJ9e63vPnz7FmzRrs2bOnxY7JKBAIEB4eLvbZMTAwgK+vb52/J9u3b8eMGTPQpk0bbp6Kikq9XaLNzdmzZ6GiooLRo0dz86ZMmYKysjKcP3++xnVsbW2hrq6O58+fc/OePXsGPT09WFpaNnnN8mRqasoN2C2pU6dOYcSIEdx6PB4PkydPxunTp8EaeKt5iw69uLi4aiMnWFtbIykpiRs0VhbrNEdxcXEAUG1fraysuNfqIxQK8csvv6B3794wMjKSeY2KVNvnoOq1mpSVlcHf3x/r16+Ho6Njk9eoKFW/CzUdn9qOTUFBAeLi4uDq6ooNGzZg2rRpWLFiBSIjI+VRslzFxcXB3NwcGhoa3DxjY2Noa2vXenysrKxw6tQprFixAgMHDoS3tze+/fZbhIWFtbjfLWnU9vtYUFCArKysBm2rRYdeWVlZtb8oqk4Kl5WVyWyd5qhqX2ra19LSUom2sXr1aty9exfbtm2TeX2KVtfnoLbjs2rVKtja2ooNXdUSSfPZKSoqAgD8+9//xvPnzzFmzBgIhUL07t0bR48ebdqC5aymzw5Q9/EpLy/H999/D319fQQEBGDmzJkAgJ9++qnBLZmWSJrfx9q0zP6XfxgYGCAnJ0dsXlZWFtTU1Kp10zVmneaoqkspJycH5ubm3PysrCx07Nix3vU/++wzbN26FadPn65zKKfmqrbPAYAa//KurKzExo0b4e3tjfHjxwMAXr16BYFAgPHjx2PBggUYOXJkk9ctD29/dt6WlZVVa6vEwMAAPB4PPXv2xJYtWwAA06dPR2ZmJr766iuMGzeuaYuWo5o+O4DoeNV2fP7880+cO3cOKSkpMDExAQAMHz4cNjY2mDZtGsaOHdukNSu7un4fDQ0NG7StFt3S69GjBx48eCA27/79++jWrRt4PJ7M1mmOevToAQBi+1pcXIyYmBh07969znW/+OIL/PDDD/j777/h5eXVpHUqSm2fAzU1NXTq1Kna8nw+H4cPH8by5csRFBSEoKAg9O3bFyoqKggKCpL56M+K1LZtW5iZmdV4fGr77GhpaaFjx47VLjF3cHBAWlpak9WqCD169EBmZiZev37NzXv8+DEEAkGtxyc5ORnGxsZc4AGi7jtNTc1qg2W3RrX9Ptrb2zf8lo4GX/rSjGzfvp3p6OiwqKgoxhhjL1++ZIaGhuzHH3/kljl58iQbN24cq6yslHidlsLT05MNHz6cVVRUMMYY+/bbb5m2trbY1XUffvih2BV569atY7q6uuzSpUtyr1eeHj9+zPh8PgsNDWWMMVZUVMRcXFyYn58ft0xsbCwbN24c91l51+bNm1vs1ZsrVqxgdnZ2LDMzkzHGWEREBOPxeCwsLIxbZvPmzWzp0qXc9DfffMMcHR1ZXl4eY4yx4uJi5uLiwqZNmybf4ptYcXExs7CwYMuXL+fmzZo1i3Xo0IH7XWOMsYkTJ7IjR44wxhg7e/YsA8DOnTvHvb5///46r6ZuCWq7evPixYts3LhxLD8/nzHG2KFDh5iamhq7c+cOY4yx169fMwsLC4mu/HxXiw69yspKNmfOHKarq8u8vb2ZgYEBmzJlChMIBNwyGzduZAC4eZKs01I8f/6c2dvbM3t7e+bh4cF0dXW5L/kqPXr0YIGBgYyxN7c0ODo6snHjxon9vHz5UgF70LQ2btzItLS0mKenJ7O0tGQ9evQQ+4Pg9u3bDAC7fPlyjeu35NArKChgPj4+zMzMjPXv359paWmxTz75RGyZhQsXsk6dOnHT5eXlbMqUKczc3Jz5+vqytm3bsj59+ogd05bi/PnzzNjYmPXo0YM5OTmxNm3aVAsvFRUV9t1333HTq1evZhoaGqx///7M09OTaWpqsvXr18u79CaXlpbGfW/o6OiwTp06sXHjxol9fnbt2sUAsIyMDG7ehx9+yLS1tZm3tzczNjZmI0aMYCUlJQ1+/1YxtNDz58/x4sUL2NjYwMnJSey1uLg4PHjwAOPGjRPrvqxrnZZEIBDg5s2bKC4uhru7e7VzDhcuXICBgQFcXV2Rnp6Oa9eu1bidgQMHtrhLzwHR03nu378PIyMjuLu7g89/c0YgNzcXFy9ehLe3N4yNjaut++rVKzx69AhjxoyRZ8lyde/ePaSmpsLJyQk2NjZirz148ACZmZkYPHiw2PynT5/i1atXsLa2bpHng6sUFhbi5s2b4PP56NOnD7S0tMReP3bsGJycnMS6fNPS0hAdHQ0+nw8nJyeYmprKu+wmV1xcXONDHkxNTdGvXz8AoiuE79y5g5EjR0JdXZ1b5uXLl3j27BksLS3rPQ1Tm1YReoQQQgjQwi9kIYQQQt5GoUcIIaTVoNAjhBDSalDoEUIIaTUo9AghhLQaFHqEEEJaDQo9QgghrQaFHiGEkFaDQo8QGfLz88PFixeb3bYJaS0o9AiRoYMHD+LVq1fNbtuEtBYtejw9QuTtr7/+gpubm6LLIITUgkKPNHsCgQDTpk3DihUrEBMTgxs3bgAAxo4dixEjRogtyxjD4cOHcfr0aRQXF6Nr166YP38+92Dfqm0tW7YMMTExuHv3LioqKjB16lQMHToU586dw6FDh1BSUoKBAwdi1qxZYtsPDQ2FsbExbGxsGlTXwoULkZWVBR6PBzMzM/Tq1QuzZs2CqmrDf0WvXr2KQ4cOIT09Hd26dcOiRYugr68v0f6vWrUK9vb2WLx4sdg29+7di/DwcPzxxx/cvCtXruDgwYNIS0uDg4MDlixZAgsLC7HjuHz5cjx58gTXrl1Du3bt8PXXX0u0rxUVFdi5cyciIiKgqamJiRMn4vXr13j16hXWrVsnVltddRDyLureJM1eZWUlDh48iPHjxyM8PBy9e/eGgYEBxowZg9DQULFlly1bhsDAQFhbW8Pb2xtHjhxBjx49uIFMq7Y1YcIE3LhxA3379gUADBs2DBMnTsR//vMf9OzZE126dMHSpUvx7bffim3/7S7IhtQ1btw4+Pv7w8/PD507d8b3338v1Ujrn376KQYOHIjy8nIMHjwY2dnZGDp0qMT7r6enh88//xwVFRVi2123bp1YKH355ZcYMGAABAIBfH198fDhQ/Ts2ZMbOPXtfb9w4QIGDRoEX19fifd19uzZWLNmDbp16wZXV1esXbsWa9aswYULF8SWq68OQqpp5NBIhChcSUkJA8CCgoLE5vv5+bGhQ4dy03fv3mU8Ho8buLNqXSsrK7ZgwQKxbc2ePVtsWw4ODszS0pIVFxdz81auXMm6dOkitpyKigrbsWNHg+qqSVpaGuPz+ezKlSs1brsmkZGRDAALDg4Wm5+eni7x/ickJDA+ny+2zPXr1xkAduvWLcYYYw8fPmQ8Ho9988033DJCoZB5eXmxRYsWie37pEmT6tzPmvb11q1bDAC7cOECt0x2djbT19dnXl5e3DxJ6iDkXdS9SVqMd8dt69ixIw4cOMBNR0REQEtLC2PHjuXmaWpqYvz48QgLCxNbd9CgQWLTdnZ20NfXFxsTzd7eHklJSY2uCwCysrKwa9cuREdHIzs7G4wxqKmpITo6Gl5eXvW+BwD8/fff0NTUxMyZM8Xmm5mZAZBs/62trTF48GBs374d48aNAwBs374dzs7OcHd3BwCcPHkSALBo0SJuOzweD/7+/tVavu9240qyr5cuXYKJiQl8fHy4dYyMjDB48GCkp6dz8xpSByFVKPRIi6GpqSk2zefzIRQKuenMzEwYGRmJDRYMAMbGxsjIyKh3W/VtX9q6UlNT4ebmho4dO2LixIkwMzODqqoqzp49i8LCwnq3XyUvLw/GxsZiA92+TdL9nzNnDgICApCWlgY9PT2Ehobiiy++4F7Pzs6Gmpoa5s6dK7adpKQkJCcnQyAQcPPeHVhYkn3NysqCoaFhtfqNjY3FQk+SOtTU1Go8FqT1otAjrYa9vT1SU1NRUFAAPT09bn5MTAzs7e0VVtfx48dRVFSEc+fOQUVFBYAowBoSeICoNZqSkoKcnBwYGRlVe13S/Z8wYQL09PSwa9cutGnTBqWlpWKtR1tbW6iqqsLf37/GOvh8PiorK6XeVzs7O7x+/RplZWXQ0NDg5r948UJsW5LUQci76FNBWo2xY8dCT09PrNVy//59HDp0CEFBQQqrS01NDWVlZVxrSygUYs2aNWCMNWg7U6ZMgYGBAVasWMG1thhj3EUzku6/hoYGpk+fjh07dmD79u0YO3Ysd3UnILpJXlNTE+np6Zg8eTL34+3tDYFAwIWZtPs6btw4qKio4IcffuDmRUREICIiQmxbjamDtF4UeqTVMDMzQ3BwMLZt24Zu3bph8ODB8PT0xNSpU8XOC8mbn58fXFxc4OzsjFGjRqFTp054+PBhta7B+piamuLEiRM4f/487O3tMXz4cDg4OODKlSsAGrb/c+bMQVRUFC5evIg5c+aIvWZiYoLTp0/j559/hqOjI0aPHg0PDw/07t272lWf0uyrubk5Nm/ejHXr1sHNzQ0+Pj4ICgqCu7u7WJA1pg7SevFYQ/+cJETJCIVCHDp0CH379oWlpSU3PyoqCklJSWKX7ANAUVERbt68iaKiInTt2lWsa6+2bV25cgUaGhrcxRwA8PLlSzx8+BDjx4/n5h06dAhubm6wsbFpUF1CoRC3b99GWloarK2t4eLigpMnT6Jz585wcHCotu26VFRU4Pbt28jJyYGzszPat28v8f6/7dixYxAIBBg/fnyNrSahUIj79+8jKSkJlpaWcHZ25roja9t3SfcVANLT03Hr1i1oaWmhd+/e8Pf3h46OTrXbPeqqg5B3UegRQpTO+fPn4eHhAV1dXQDA9evX4e3tjT/++AOBgYEKro40Z3QhCyFE6eTn58PJyQkdO3ZEcXEx7t69i6VLlyIgIEDRpZFmjlp6hBClVFBQgHv37qGyshJdunShR4sRmaDQI4QQ0mrQ1ZuEEEJaDQo9QgghrQaFHiGEkFaDQo8QQkirQaFHCCGk1aDQI4QQ0mpQ6BFCCGk1KPQIIYS0GhR6hBBCWg0KPUIIIa3G/wP2DXjFHE4hqgAAAABJRU5ErkJggg==", + "image/png": 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", "text/plain": [ "<Figure size 462x462 with 1 Axes>" ] @@ -613,61 +571,17 @@ "source": [ "fig, ax = plt.subplots(figsize=(4.2, 4.2))\n", "ax.plot([0, 1], [0, 1], \"--\", color=\"0.5\", label=\"nominal\")\n", - "ax.plot(levels, coverage, \"o-\", color=plotstyle.COLOURS[0], label=\"empirical\")\n", + "ax.plot(levels, coverage_full, \"o-\", color=plotstyle.COLOURS[2], label=\"model + noise\")\n", + "ax.plot(levels, coverage_model, \"s-\", color=plotstyle.COLOURS[0], label=\"model alone\")\n", "ax.set(\n", " xlabel=\"nominal coverage\",\n", " ylabel=\"empirical coverage\",\n", " xlim=(0, 1),\n", " ylim=(0, 1),\n", - " title=\"Coverage of the posterior predictive\",\n", ")\n", "ax.legend()\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "1739c561", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-12T01:56:29.448910Z", - "iopub.status.busy": "2026-09-12T01:56:29.448761Z", - "iopub.status.idle": "2026-09-12T01:56:29.454463Z", - "shell.execute_reply": "2026-09-12T01:56:29.453784Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "width of the 68 % predictive interval per point: 0.208\n" - ] - } - ], - "source": [ - "width = rx.diagnostics.sharpness(draws).mean()\n", - "print(f\"width of the 68 % predictive interval per point: {width:.3f}\")" - ] - }, - { - "cell_type": "markdown", - "id": "7817b707", - "metadata": {}, - "source": [ - "## Takeaways\n", - "\n", - "- Declare, compile, sample. The problem knows its columns, its prior and its\n", - " densities; the sampler is whatever we like.\n", - "- Read chains by name through `problem.columns`, and bind the model to any grid\n", - " when we want a prediction somewhere we did not measure.\n", - "- The posterior predictive check is the first question to ask of an error\n", - " model, and here it passes because we built the data to satisfy it.\n", - "\n", - "The next notebooks are about what to do when the reported errors are *not* the\n", - "whole story — which, with real measurements, is the usual case." - ] } ], "metadata": { From dab205283627e1ba6acb2d0bbdd7482012715bf2 Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Tue, 22 Sep 2026 12:04:14 -0400 Subject: [PATCH 65/75] Merge noise_fraction and model_error into proportional_error Both factories built the same diagonal term scaled by a basis; the only difference was which basis. One factory with an `averaging` flag says it once: `z = ym`, or `z = 0.5 * (y + ym)` when `averaging`. Note the default changed. `model_error` defaulted to `averaging=True`; `proportional_error` defaults to `False`, the old `noise_fraction` behaviour. A caller porting `model_error(delta)` by name substitution silently moves to the `ym` basis and must pass `averaging=True` to keep the KDUQ spelling, as recipe 26 and the gp_discrepancy notebook now do. Renamed throughout the docs, the recipes, the tests, and gp_discrepancy. --- docs/api.rst | 3 +- docs/design.md | 11 +- docs/groundup_design.md | 13 +- docs/recipes.md | 12 +- examples/gp_discrepancy.ipynb | 310 +++--------------- src/rxmc/predictive.py | 4 +- src/rxmc/terms.py | 23 +- test/helpers.py | 4 +- test/recipes/test_recipe_02_unknown_noise.py | 3 +- .../test_recipe_18_evidence_comparison.py | 2 +- .../test_recipe_26_kduq_model_error.py | 2 +- test/test_predictive.py | 12 +- test/test_terms.py | 17 +- 13 files changed, 103 insertions(+), 313 deletions(-) diff --git a/docs/api.rst b/docs/api.rst index ea2b092..98d5de3 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -49,8 +49,7 @@ Covariance terms rxmc.terms.offset rxmc.terms.normalization rxmc.terms.noise - rxmc.terms.noise_fraction - rxmc.terms.model_error + rxmc.terms.proportional_error rxmc.terms.systematic rxmc.terms.kernel rxmc.terms.ones diff --git a/docs/design.md b/docs/design.md index 96a11b4..3512e2c 100644 --- a/docs/design.md +++ b/docs/design.md @@ -239,8 +239,7 @@ statistical(y_err, on=None) offset(parameter=None, magnitude=None, mask=None, log=True, on=None) normalization(parameter=None, magnitude=None, mask=None, log=True, on=None) noise(parameter, log=True, basis=None, basis_params=(), on=None, coords=None) -noise_fraction(parameter, log=True, on=None) -model_error(parameter, averaging=True, log=True, on=None) +proportional_error(parameter, averaging=False, log=True, on=None) systematic(parameter, basis, log=True, basis_params=(), on=None, coords=None) kernel(kernel, coords=None, amplitude=None, amplitude_params=(), jitter=1e-10, prefix="discrepancy", params=None, on=None) -> KernelTerm @@ -248,7 +247,7 @@ kernel(kernel, coords=None, amplitude=None, amplitude_params=(), jitter=1e-10, # constant_amplitude, exp_growth_amplitude(scale) ``` -`noise` and `noise_fraction` are additive on top of the reported diagonal; +`noise` and `proportional_error` are additive on top of the reported diagonal; `Constraint(statistical=False)` makes them replace it. `normalization` reads `c.ym`, never `c.y`. `kernel` derives one `Parameter` per free hyperparameter element in sklearn's log-theta space, bounded by the log of @@ -541,7 +540,7 @@ gp = T.kernel(Matern(0.1, nu=2.5), on=comp_log, coords=lambda x: x / np.pi, amplitude=T.constant_amplitude, amplitude_params=(log_A,), params=[log_ell]) ladder = { "L0": rx.Constraint([comp_log], terms=[T.noise(log_eps)], statistical=False), - "E0": rx.Constraint([comp_lin], terms=[T.noise_fraction(log_eps)], statistical=False), + "E0": rx.Constraint([comp_lin], terms=[T.proportional_error(log_eps)], statistical=False), "L2y": rx.Constraint([comp_log], terms=[T.noise(log_eps), T.normalization(log_eta)], statistical=False), "Lgp": rx.Constraint([comp_log], terms=[T.noise(log_eps), gp], statistical=False), } @@ -581,7 +580,7 @@ test that pins it. Recipe numbers refer to `recipes.md`. | user-defined model `y = m x + b` | `Model(lambda x, m, b: m*x + b, [m, b])` | test_model | | `polynomial(order)` | `polynomial(order)` | test_model | | statistical diagonal only; `chi2` | default `Constraint`; `problem.chi2(theta)` | test_problem, recipe 1 | -| unknown fractional / constant noise | `noise_fraction(log_eps)`, `noise(log_eps)` | test_terms, recipe 2 | +| unknown proportional / constant noise | `proportional_error(log_eps)`, `noise(log_eps)` | test_terms, recipe 2 | | inferred noise replacing reported statistics | `Constraint(statistical=False, terms=[noise(...)])` | test_constraint, recipe 2 | | reported normalisation / offset as modes | `comparison.reported_terms()`; `normalization(magnitude=)`, `offset(magnitude=)` | test_constraint, test_regression, recipe 3 | | free normalisation / offset nuisance | `normalization(log_eta)`, `offset(log_omega)` | test_terms, recipe 4 | @@ -602,7 +601,7 @@ test that pins it. Recipe numbers refer to `recipes.md`. | term spanning comparisons reading its pieces | `c.segments`, `c.labels`, `c.split(a)` | test_terms, test_covariance, recipe 37 | | correlated normalisations between quantities | one comparison per quantity, a spanning `matrix` term from `c.split(c.ym)`; the reference's closed forms | test_recipe_37 | | Peelle's Pertinent Puzzle | `normalization()` reads `c.ym`; the estimate-built refit; the log-determinant pull of the live term | recipes 27, 37 | -| unaccounted-for model error per data type (KDUQ) | `model_error(delta_T, averaging=True, on=comp)`; scalings as `Constraint(weight=)` | test_terms, recipe 26 | +| unaccounted-for model error per data type (KDUQ) | `proportional_error(delta_T, averaging=True, on=comp)`; scalings as `Constraint(weight=)` | test_terms, recipe 26 | | tempering | `Constraint(weight=)` | test_problem, recipe 12 | | Student-t with bounded ν; `Chi2` | `StudentT(nu=Parameter("nu", bounds=(1, 100)))`; `Chi2()` | test_likelihood, recipe 9 | | log-space comparison, delta-method errors, Jacobian | `Comparison(d, m, space=log)`; `problem.log_jacobian()` | test_constraint, recipe 10 | diff --git a/docs/groundup_design.md b/docs/groundup_design.md index 4caf4d9..f2a95b7 100644 --- a/docs/groundup_design.md +++ b/docs/groundup_design.md @@ -307,8 +307,7 @@ statistical(y_err, on=None) offset(magnitude=None, parameter=None, mask=None, log=True, on=None) normalization(magnitude=None, parameter=None, mask=None, log=True, on=None) noise(parameter, log=True, basis=None, basis_params=(), on=None, coords=None) -noise_fraction(parameter, log=True, on=None) -model_error(parameter, averaging=True, log=True, on=None) +proportional_error(parameter, averaging=False, log=True, on=None) systematic(parameter, basis, log=True, basis_params=(), on=None, coords=None) kernel(kernel, coords=None, amplitude=None, amplitude_params=(), jitter=1e-10, prefix="discrepancy", params=None, on=None) -> KernelTerm @@ -706,12 +705,12 @@ rewrite: a capability is done when its row has a test. | user-defined model `y = m x + b` (linear_calibration_demo) | `Model(lambda x, m, b: m*x + b, [m, b])` | test_model | | `Polynomial(order)` (normalization_inference) | `polynomial(order)` | test_model | | statistical diagonal only; `chi2 / n` (linear_calibration_demo) | default `Constraint`; `problem.chi2(theta)` | test_problem | -| unknown fractional / constant noise (systematic_err_demo, sampling_algos) | `noise_fraction(log_eps)`, `noise(log_eps)` | test_terms::TestFactories | +| unknown fractional / constant noise (systematic_err_demo, sampling_algos) | `proportional_error(log_eps)`, `noise(log_eps)` | test_terms::TestFactories | | inferred noise *replacing* reported statistics (prose today) | `Constraint(statistical=False, terms=[noise(...)])` | test_constraint | | reported normalisation / offset as fixed modes (measurement_to_calibration) | `block.reported_terms()`; `normalization(magnitude=)`, `offset(magnitude=)` | test_constraint::reported_terms, regression number | | free normalisation / offset nuisance (systematic_err_demo) | `normalization(parameter=log_eta)`, `offset(parameter=log_omega)` | test_terms | | fixed dense covariance; fixed diagonal (systematic_err_demo, normalization_inference gallery) | `Term(C, on=b)`, `Term(sig, kind="diag", on=b)` | test_terms::TestTermKinds | -| case B: one parameter, two block-local terms (systematic_err_demo, correlated_observations) | `noise_fraction(log_eps, on=b1), noise_fraction(log_eps, on=b2)`; or two constraints sharing `log_eps` | test_problem::sharing | +| case B: one parameter, two block-local terms (systematic_err_demo, correlated_observations) | `proportional_error(log_eps, on=b1), proportional_error(log_eps, on=b2)`; or two constraints sharing `log_eps` | test_problem::sharing | | case A: one mode across blocks (correlated_observations) | `normalization(log_eta, on=[b1, b2])` | test_covariance::case_a, regression | | per-dataset Kennedy–O'Hagan scale ρᵢ (normalization_inference) | `Comparison(d_i, omp \| scale(rho_i))` | test_model, test_constraint | | single global ρ (test only) | `omp \| scale(rho)` on every block | test_model | @@ -719,7 +718,7 @@ rewrite: a capability is done when its row has a test. | multiplicative `x`-dependent correction, i.e. an additive discrepancy in log space (new) | `omp * Model(g_fn, phi)`; `scale(rho)` is the constant case | test_model (`omp * const(rho)` equals `omp \| scale(rho)`) | | hyperparameters shared across datasets, per-dataset values from `meta` (new) | one term per block with the same `Parameter` objects; `c.meta("Elab")`; `kernel(params=)` | test_terms (`meta` on one block and on a union), test_problem (one slot per shared object) | | discrepancy correlated across energies: GP over (E, θ) (new) | one `matrix` `Term` with `on=comps` building inputs from `c.meta` and `c.x`; dense path | test_covariance (dense fallback equals hand-built product kernel) | -| unaccounted-for model error per data type, KDUQ (new, reference) | `model_error(delta_T, averaging=True, on=b)` with one `delta_T` per type; the `k/N` democratic and per-type federal scalings are `Constraint(weight=)`; recipe 26 | test_terms (shared object gives one column per type) | +| unaccounted-for model error per data type, KDUQ (new, reference) | `proportional_error(delta_T, averaging=True, on=b)` with one `delta_T` per type; the `k/N` democratic and per-type federal scalings are `Constraint(weight=)`; recipe 26 | test_terms (shared object gives one column per type) | | Peelle's Pertinent Puzzle avoidance (new, reference) | `normalization()` reads `c.ym`; the `t0` variant as a constant `mode`; recipe 27 | test_terms (data-built mode reproduces the `1/(1+n s²)` bias; prediction-built does not) | | stacking by leave-one-dataset-out (new, reference) | `Constraint.masked` dropping a block, `heldout_log_predictive`; recipe 28 | test_diagnostics | | cut / modular posterior by multiple imputation (new, reference) | stage-1 `Problem`, per-draw stage-2 `Problem` with the module fixed by closure; per-module `weight`; recipe 29 | test_problem (stage-1 marginal unchanged) | @@ -781,7 +780,7 @@ What comes across from `src/rxmc` on `api_generalisation`, by file. |---|---|---| | `transforms.py` | `Transform` (minus `contextual`, `_unpack`), `as_transform`, `identity`, `log`, `exp`, `_safe_log`, `_reciprocal`, `scale` | `transforms.py` | | `likelihood_model.py` | `Likelihood`, `GaussianLikelihood`→`Gaussian`, `StudentT`, `Chi2`, `log_likelihood` | `likelihood.py` | -| `covariance.py` | `TermContext`, `chol_logdet`, `as_2d`, bases `ones`, `ym`, `averaging`, `x_basis`, `exp_growth`, `constant_amplitude`, `exp_growth_amplitude`; helpers `_masked`, `_full`, `_coefficient`, `_scaled_term`, `_kernel_params`; factories `statistical_term`, `offset_term`, `normalization_term`, `noise_term`, `noise_fraction_term`, `model_error_term`, `systematic_term`, `kernel_term` (drop the `_term` suffix, `support=`→`on=`) | `terms.py` | +| `covariance.py` | `TermContext`, `chol_logdet`, `as_2d`, bases `ones`, `ym`, `averaging`, `x_basis`, `exp_growth`, `constant_amplitude`, `exp_growth_amplitude`; helpers `_masked`, `_full`, `_coefficient`, `_scaled_term`, `_kernel_params`; factories `statistical_term`, `offset_term`, `normalization_term`, `noise_term`, `proportional_error_term`, `systematic_term`, `kernel_term` (drop the `_term` suffix, `support=`→`on=`) | `terms.py` | | `observation_from_measurement.py` | `XS_UNIT`, `RUTHERFORD_UNIT`, `MB_PER_B`, `DEFAULT_LMAX`, `check_angle_grid`, `measurement_kwargs`; the pint registry is replaced by a fixed label table | `units.py`, `data.py` | | `elastic_diffxs_observation.py` | `set_up_solver`, the `calculate_normalization` conversion table, `momentum_transfer` | `reactions/elastic.py`, `data.py` | | `ias_pn_observation.py` | `set_up_solver` | `reactions/ias.py` | @@ -900,7 +899,7 @@ is driven by emcee or dynesty. | `correlated_observations` | correlated_observations | emcee | case A vs B on the toy; Neudecker et al. (2014) §II.A and §II.B recreated: the multi-quantity Peelle puzzle with a spanning `matrix` term built through `c.split` | 141 s | | `gp_discrepancy` | gp_discrepancy | emcee (toy), dynesty (reaction) | `kernel` term; `gp_predictive_draws(problem, term, ...)`; the same defect fit with a sampled Legendre mean correction for contrast; n+⁴⁰Ca with the surface absorption missing | 617 s | | `robust_likelihoods` | robust_likelihoods | emcee | Student-t vs Gaussian; ν bounded on the `Parameter`; a global error scale and a USU offset per technique | 154 s | -| `measurement_to_calibration` | measurement_to_calibration + 30s_optical_potential_calibration + the tempering/coverage section of overconfidence | dynesty | `from_measurement`, `reported_terms`, the singular-covariance error, `Constraint(weight=)`, `coverage_curve`, emcee and `dill` as other drivers, the KDUQ `model_error` spelling | 182 s | +| `measurement_to_calibration` | measurement_to_calibration + 30s_optical_potential_calibration + the tempering/coverage section of overconfidence | dynesty | `from_measurement`, `reported_terms`, the singular-covariance error, `Constraint(weight=)`, `coverage_curve`, emcee and `dill` as other drivers, the KDUQ `proportional_error(averaging=True)` spelling | 182 s | | `alpha_ca_error_model_comparison` | **new** (the `jitr` quickstart's α+⁴⁴Ca data, EXFOR F0567) | dynesty | real data without errors, a four-parameter potential, log space with `log_jacobian`, the `L0`/`E0`/`L2y`/`Lgp` ladder by evidence, `masked_where`/`complement` with `heldout_log_predictive` and held-out coverage | 1197 s (alongside another notebook) | | `hierarchical_calibration` | **new** (recipes 24, 35, 38) | dynesty | eight schools non-centred; the hierarchy on the physics parameters; see below | 937 s (alongside another notebook) | diff --git a/docs/recipes.md b/docs/recipes.md index deeb3e6..ad902b5 100644 --- a/docs/recipes.md +++ b/docs/recipes.md @@ -46,8 +46,8 @@ infer the noise magnitude alongside the model.* ```python log_eps = rx.Parameter("log_eps", prior=stats.norm(-2, 2)) c = rx.Constraint([rx.Comparison(d, line)], terms=[T.noise(log_eps)], statistical=False) -# or fractional noise: T.noise_fraction(log_eps) -# or model error on the average of data and prediction: T.model_error(log_gamma) +# or an error proportional to the prediction: T.proportional_error(log_eps) +# or proportional to the average of data and prediction: T.proportional_error(log_gamma, averaging=True) # or noise growing along x: T.noise(log_eps, basis=T.exp_growth(np.pi), basis_params=(slope,)) ``` @@ -57,7 +57,7 @@ Expected behaviour: errors; with the default `statistical=True` it is *added* to them. - The posterior of `log_eps` reflects the residual scatter. In the `sampling_algos` scenario its truth is recovered. -- `noise_fraction` and `model_error` scale with the prediction, so the +- `proportional_error` scales with the prediction, so the covariance changes with the model parameters. That is allowed and costs nothing extra. @@ -481,7 +481,7 @@ I want the evidence for each, comparable across comparison spaces.* ```python models = { "L0": rx.Constraint([comp_log], terms=[T.noise(log_eps)], statistical=False), - "E0": rx.Constraint([comp_lin], terms=[T.noise_fraction(log_eps)], statistical=False), + "E0": rx.Constraint([comp_lin], terms=[T.proportional_error(log_eps)], statistical=False), "L2y": rx.Constraint([comp_log], terms=[T.noise(log_eps), T.normalization(log_sys)], statistical=False), "Lgp": rx.Constraint([comp_log], terms=[T.noise(log_eps), gp], statistical=False), "L0t": rx.Constraint([comp_log], terms=[T.noise(log_eps)], statistical=False, likelihood=rx.StudentT()), @@ -765,7 +765,7 @@ errors and scaled with the average of datum and prediction.* ```python delta = {t: rx.Parameter(f"delta_{t}", prior=stats.halfnorm(scale=s0[t])) for t in ("dxs", "ay", "sig_tot")} comps = [rx.Comparison(d, omp_for(d)) for d in datasets] -terms = [T.model_error(delta[d.meta["type"]], averaging=True, log=False, on=comp) +terms = [T.proportional_error(delta[d.meta["type"]], averaging=True, log=False, on=comp) for d, comp in zip(datasets, comps)] # log=False: delta is the fraction itself c = rx.Constraint(comps, terms=terms) # statistical=True: reported errors are a floor @@ -1338,7 +1338,7 @@ Expected behaviour: every term a function, it draws from the same distribution as `predictive_draws`. - Any term that is a function of the `TermContext` travels: `noise`, - `noise_fraction`, `model_error`, `normalization`/`offset`/`systematic` with + `proportional_error`, `normalization`/`offset`/`systematic` with a parameter or a scalar magnitude, a `kernel`, and a user's `Term(fn, params, kind="matrix")`. - A term that is an array has no value at a new `x`: the reported diff --git a/examples/gp_discrepancy.ipynb b/examples/gp_discrepancy.ipynb index 0500f44..fd0dede 100644 --- a/examples/gp_discrepancy.ipynb +++ b/examples/gp_discrepancy.ipynb @@ -23,14 +23,7 @@ "cell_type": "code", "execution_count": 1, "id": "506a99dd", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:03:05.646786Z", - "iopub.status.busy": "2026-09-14T22:03:05.646632Z", - "iopub.status.idle": "2026-09-14T22:03:07.761378Z", - "shell.execute_reply": "2026-09-14T22:03:07.760752Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "import corner\n", @@ -69,14 +62,7 @@ "cell_type": "code", "execution_count": 2, "id": "b77965a8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:03:07.762938Z", - "iopub.status.busy": "2026-09-14T22:03:07.762734Z", - "iopub.status.idle": "2026-09-14T22:03:07.766517Z", - "shell.execute_reply": "2026-09-14T22:03:07.765984Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "M_TRUE, B_TRUE = 0.8, 1.0\n", @@ -103,14 +89,7 @@ "cell_type": "code", "execution_count": 3, "id": "81c09486", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:03:07.767754Z", - "iopub.status.busy": "2026-09-14T22:03:07.767639Z", - "iopub.status.idle": "2026-09-14T22:03:07.770330Z", - "shell.execute_reply": "2026-09-14T22:03:07.769736Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -130,14 +109,7 @@ "cell_type": "code", "execution_count": 4, "id": "cf684d62", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:03:07.771629Z", - "iopub.status.busy": "2026-09-14T22:03:07.771498Z", - "iopub.status.idle": "2026-09-14T22:03:08.506900Z", - "shell.execute_reply": "2026-09-14T22:03:08.506161Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -176,7 +148,7 @@ "We'll fit the same line four times, and the only thing we'll change between fits is the covariance:\n", "\n", "1. **Statistics only.** We use the reported errors and nothing else, which amounts to claiming the line is perfect.\n", - "2. **A diagonal model error** (`T.model_error`). Every point gets some extra slack, but each point's slack is *independent* of its neighbours'.\n", + "2. **An uncorrelated proportional error** (`T.proportional_error`). Every point gets some extra slack, in proportion to its size, but each point's slack is *independent* of its neighbours'.\n", "3. **A GP with a constant amplitude.** Now the discrepancy is smooth and mean-zero, but it's allowed to be the same size everywhere.\n", "4. **A GP whose amplitude grows with $x$** (`T.exp_growth_amplitude`). This is the same smooth discrepancy, except that it can be small where we trust the line and large where we don't.\n", "\n", @@ -187,14 +159,7 @@ "cell_type": "code", "execution_count": 5, "id": "1a201944", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:03:08.508369Z", - "iopub.status.busy": "2026-09-14T22:03:08.508236Z", - "iopub.status.idle": "2026-09-14T22:03:08.515186Z", - "shell.execute_reply": "2026-09-14T22:03:08.514613Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "m = rx.Parameter(\"m\", prior=stats.norm(0.0, 2.0), latex=\"m\")\n", @@ -225,21 +190,14 @@ "cell_type": "code", "execution_count": 6, "id": "54264f30", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:03:08.516511Z", - "iopub.status.busy": "2026-09-14T22:03:08.516350Z", - "iopub.status.idle": "2026-09-14T22:03:08.520796Z", - "shell.execute_reply": "2026-09-14T22:03:08.520369Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "statistics only ['m', 'b']\n", - "diagonal model error ['m', 'b', 'log_gamma']\n", + "proportional error ['m', 'b', 'log_gamma']\n", "GP, constant amplitude ['m', 'b', 'log_ell', 'log_A']\n", "GP, growing amplitude ['m', 'b', 'log_ell', 'log_A', 'amp_slope']\n" ] @@ -248,8 +206,8 @@ "source": [ "problems = {\n", " \"statistics only\": rx.Problem([rx.Constraint([comp])]),\n", - " \"diagonal model error\": rx.Problem(\n", - " [rx.Constraint([comp], terms=[T.model_error(gamma)])]\n", + " \"proportional error\": rx.Problem(\n", + " [rx.Constraint([comp], terms=[T.proportional_error(gamma, averaging=True)])]\n", " ),\n", " \"GP, constant amplitude\": rx.Problem([rx.Constraint([comp], terms=[gp_const])]),\n", " \"GP, growing amplitude\": rx.Problem([rx.Constraint([comp], terms=[gp_grow])]),\n", @@ -262,14 +220,7 @@ "cell_type": "code", "execution_count": 7, "id": "699a541c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:03:08.522178Z", - "iopub.status.busy": "2026-09-14T22:03:08.522056Z", - "iopub.status.idle": "2026-09-14T22:05:31.303024Z", - "shell.execute_reply": "2026-09-14T22:05:31.302372Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "def fit(problem, seed, n_walkers=24, n_steps=3000):\n", @@ -286,21 +237,14 @@ "cell_type": "code", "execution_count": 8, "id": "f08f7fc9", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:05:31.304445Z", - "iopub.status.busy": "2026-09-14T22:05:31.304287Z", - "iopub.status.idle": "2026-09-14T22:05:31.309024Z", - "shell.execute_reply": "2026-09-14T22:05:31.308432Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "statistics only m = 0.953 +/- 0.003 (pull +60.2) b = 0.858 +/- 0.007 (pull -19.0)\n", - "diagonal model error m = 0.775 +/- 1.043 (pull -0.0) b = 0.154 +/- 2.262 (pull -0.4)\n", + "proportional error m = 0.775 +/- 1.043 (pull -0.0) b = 0.154 +/- 2.262 (pull -0.4)\n", "GP, constant amplitude m = 0.959 +/- 0.062 (pull +2.6) b = 1.046 +/- 0.366 (pull +0.1)\n", "GP, growing amplitude m = 0.830 +/- 0.071 (pull +0.4) b = 0.941 +/- 0.165 (pull -0.4)\n" ] @@ -328,7 +272,7 @@ "\n", "**Statistics only** is the cautionary tale. It gives $m = 0.953 \\pm 0.003$, sixty standard deviations away from the truth. The line has no way to say \"I'm wrong at large $x$\", so it tilts to chase the defect and then reports a precision of three parts in a thousand. This is worth remembering: an unmodelled discrepancy doesn't make our answer vaguer, it *moves* it, and confidently.\n", "\n", - "**The diagonal model error** does cover the truth, but look at how it manages it: $m = 0.775 \\pm 1.043$. Independent slack at each point can only inflate the errors, and to cover a coherent, smooth departure it has to inflate them until the slope means nothing at all.\n", + "**The proportional error** does cover the truth, but look at how it manages it: $m = 0.775 \\pm 1.043$. Independent slack at each point can only inflate the errors, and to cover a coherent, smooth departure it has to inflate them until the slope means nothing at all.\n", "\n", "**The constant-amplitude GP** knows the defect is smooth, and that alone helps a great deal: $m = 0.959 \\pm 0.062$, compared with sixty sigma before. It's still 2.6 sigma out, though. The trouble is that one amplitude has to serve both ends of the range. It has to be large enough to cover $x = 5$, which makes it far too generous at $x = 0.2$, and so it lets the fit off the hook exactly where the data are most informative about the line.\n", "\n", @@ -339,18 +283,11 @@ "cell_type": "code", "execution_count": 9, "id": "1b55f904", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:05:31.310346Z", - "iopub.status.busy": "2026-09-14T22:05:31.310228Z", - "iopub.status.idle": "2026-09-14T22:05:31.494525Z", - "shell.execute_reply": "2026-09-14T22:05:31.493770Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "<Figure size 605x605 with 4 Axes>" ] @@ -421,14 +358,7 @@ "cell_type": "code", "execution_count": 10, "id": "152c7f05", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:05:31.495891Z", - "iopub.status.busy": "2026-09-14T22:05:31.495762Z", - "iopub.status.idle": "2026-09-14T22:05:32.317850Z", - "shell.execute_reply": "2026-09-14T22:05:32.317172Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "def mean_band(problem, samples_, levels=(16, 84)):\n", @@ -466,14 +396,7 @@ "cell_type": "code", "execution_count": 11, "id": "d8b317c3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:05:32.319190Z", - "iopub.status.busy": "2026-09-14T22:05:32.319063Z", - "iopub.status.idle": "2026-09-14T22:05:32.323530Z", - "shell.execute_reply": "2026-09-14T22:05:32.322947Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -500,18 +423,11 @@ "cell_type": "code", "execution_count": 12, "id": "9e91d84f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:05:32.324868Z", - "iopub.status.busy": "2026-09-14T22:05:32.324748Z", - "iopub.status.idle": "2026-09-14T22:05:32.488960Z", - "shell.execute_reply": "2026-09-14T22:05:32.488553Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 704x440 with 1 Axes>" ] @@ -524,7 +440,7 @@ "fig, ax = plt.subplots()\n", "for name, colour, hatch in (\n", " (\"statistics only\", plotstyle.COLOURS[1], plotstyle.HATCHES[0]),\n", - " (\"diagonal model error\", plotstyle.COLOURS[3], plotstyle.HATCHES[1]),\n", + " (\"proportional error\", plotstyle.COLOURS[3], plotstyle.HATCHES[1]),\n", "):\n", " lo, hi = mean_band(problems[name], samples[name])\n", " plotstyle.band(ax, x_fine, lo, hi, color=colour, hatch=hatch, label=name)\n", @@ -561,14 +477,7 @@ "cell_type": "code", "execution_count": 13, "id": "30f35b57", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:05:32.490525Z", - "iopub.status.busy": "2026-09-14T22:05:32.490355Z", - "iopub.status.idle": "2026-09-14T22:05:32.494255Z", - "shell.execute_reply": "2026-09-14T22:05:32.493614Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "lo_d, hi_d = np.percentile(draws_data, [16, 84], axis=0)\n", @@ -579,14 +488,7 @@ "cell_type": "code", "execution_count": 14, "id": "21bc61b5", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:05:32.495574Z", - "iopub.status.busy": "2026-09-14T22:05:32.495459Z", - "iopub.status.idle": "2026-09-14T22:05:32.608494Z", - "shell.execute_reply": "2026-09-14T22:05:32.607720Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -633,14 +535,7 @@ "cell_type": "code", "execution_count": 15, "id": "6b2e05c6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:05:32.609853Z", - "iopub.status.busy": "2026-09-14T22:05:32.609725Z", - "iopub.status.idle": "2026-09-14T22:05:32.612494Z", - "shell.execute_reply": "2026-09-14T22:05:32.611992Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -670,14 +565,7 @@ "cell_type": "code", "execution_count": 16, "id": "940dde08", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:05:32.613789Z", - "iopub.status.busy": "2026-09-14T22:05:32.613673Z", - "iopub.status.idle": "2026-09-14T22:05:32.617238Z", - "shell.execute_reply": "2026-09-14T22:05:32.616627Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "# the discrepancy is the band minus the line: grid_draws evaluated the growing\n", @@ -693,14 +581,7 @@ "cell_type": "code", "execution_count": 17, "id": "7a96ab0c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:05:32.618482Z", - "iopub.status.busy": "2026-09-14T22:05:32.618322Z", - "iopub.status.idle": "2026-09-14T22:05:32.621015Z", - "shell.execute_reply": "2026-09-14T22:05:32.620605Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -723,14 +604,7 @@ "cell_type": "code", "execution_count": 18, "id": "e1b7d5ba", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:05:32.622415Z", - "iopub.status.busy": "2026-09-14T22:05:32.622276Z", - "iopub.status.idle": "2026-09-14T22:05:32.745568Z", - "shell.execute_reply": "2026-09-14T22:05:32.744966Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -778,14 +652,7 @@ "cell_type": "code", "execution_count": 19, "id": "9cf85214", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:05:32.746929Z", - "iopub.status.busy": "2026-09-14T22:05:32.746801Z", - "iopub.status.idle": "2026-09-14T22:05:32.767799Z", - "shell.execute_reply": "2026-09-14T22:05:32.767196Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "rng_xs = np.random.default_rng(2024)\n", @@ -818,14 +685,7 @@ "cell_type": "code", "execution_count": 20, "id": "0f3057a0", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:05:32.769130Z", - "iopub.status.busy": "2026-09-14T22:05:32.769005Z", - "iopub.status.idle": "2026-09-14T22:05:32.776991Z", - "shell.execute_reply": "2026-09-14T22:05:32.776545Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "full_params = [\n", @@ -860,14 +720,7 @@ "cell_type": "code", "execution_count": 21, "id": "b6343957", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:05:32.778357Z", - "iopub.status.busy": "2026-09-14T22:05:32.778238Z", - "iopub.status.idle": "2026-09-14T22:05:41.110219Z", - "shell.execute_reply": "2026-09-14T22:05:41.109604Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "angles = np.deg2rad(np.linspace(5.0, 160.0, 25))\n", @@ -881,14 +734,7 @@ "cell_type": "code", "execution_count": 22, "id": "c07ec26a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:05:41.111799Z", - "iopub.status.busy": "2026-09-14T22:05:41.111641Z", - "iopub.status.idle": "2026-09-14T22:05:42.210510Z", - "shell.execute_reply": "2026-09-14T22:05:42.209738Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -935,21 +781,14 @@ "source": [ "### The same three rungs\n", "\n", - "We'll fit three error models: bare, a diagonal model error, and a mean-zero GP whose amplitude grows with angle. The growing amplitude carries the same prior belief as in the toy: we trust the model at forward angles and are suspicious of it at backward ones." + "We'll fit three error models: bare, an uncorrelated proportional error, and a mean-zero GP whose amplitude grows with angle. The growing amplitude carries the same prior belief as in the toy: we trust the model at forward angles and are suspicious of it at backward ones." ] }, { "cell_type": "code", "execution_count": 23, "id": "2ee90995", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:05:42.211946Z", - "iopub.status.busy": "2026-09-14T22:05:42.211815Z", - "iopub.status.idle": "2026-09-14T22:05:42.219500Z", - "shell.execute_reply": "2026-09-14T22:05:42.219033Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "comp_xs = rx.Comparison(d_xs, omp_vol, space=tf.log)\n", @@ -967,8 +806,12 @@ ")\n", "xs_problems = {\n", " \"bare\": rx.Problem([rx.Constraint([comp_xs])]),\n", - " \"diagonal model error\": rx.Problem(\n", - " [rx.Constraint([comp_xs], terms=[T.model_error(xs_gamma)])]\n", + " \"proportional error\": rx.Problem(\n", + " [\n", + " rx.Constraint(\n", + " [comp_xs], terms=[T.proportional_error(xs_gamma, averaging=True)]\n", + " )\n", + " ]\n", " ),\n", " \"GP, growing amplitude\": rx.Problem([rx.Constraint([comp_xs], terms=[gp_xs])]),\n", "}" @@ -978,14 +821,7 @@ "cell_type": "code", "execution_count": 24, "id": "8c902ca0", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:05:42.220781Z", - "iopub.status.busy": "2026-09-14T22:05:42.220662Z", - "iopub.status.idle": "2026-09-14T22:11:04.694360Z", - "shell.execute_reply": "2026-09-14T22:11:04.693590Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "def nested(problem, seed, nlive=150):\n", @@ -1013,21 +849,14 @@ "cell_type": "code", "execution_count": 25, "id": "26258f3e", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:11:04.695870Z", - "iopub.status.busy": "2026-09-14T22:11:04.695738Z", - "iopub.status.idle": "2026-09-14T22:11:04.698538Z", - "shell.execute_reply": "2026-09-14T22:11:04.698127Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bare log Z = -581.75 +/- 0.64, 85485 likelihood calls\n", - "diagonal model error log Z = -19.61 +/- 0.50, 64449 likelihood calls\n", + "proportional error log Z = -19.61 +/- 0.50, 64449 likelihood calls\n", "GP, growing amplitude log Z = -9.93 +/- 0.52, 75404 likelihood calls\n" ] } @@ -1044,21 +873,14 @@ "cell_type": "code", "execution_count": 26, "id": "2f8e7c6e", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:11:04.699873Z", - "iopub.status.busy": "2026-09-14T22:11:04.699760Z", - "iopub.status.idle": "2026-09-14T22:11:04.703707Z", - "shell.execute_reply": "2026-09-14T22:11:04.703180Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bare Vv= 26.85+/-0.41 Wv= 16.64+/-0.23 Rv= 4.43+/-0.02 av= 0.66+/-0.01 max|pull| = 56.7\n", - "diagonal model error Vv= 38.78+/-3.47 Wv= 18.91+/-1.37 Rv= 4.28+/-0.15 av= 0.56+/-0.06 max|pull| = 11.3\n", + "proportional error Vv= 38.78+/-3.47 Wv= 18.91+/-1.37 Rv= 4.28+/-0.15 av= 0.56+/-0.06 max|pull| = 11.3\n", "GP, growing amplitude Vv= 43.67+/-1.42 Wv= 13.61+/-1.07 Rv= 4.29+/-0.06 av= 0.48+/-0.02 max|pull| = 9.5\n" ] } @@ -1084,9 +906,9 @@ "source": [ "### What the three rungs did to the potential\n", "\n", - "The [evidence](https://en.wikipedia.org/wiki/Marginal_likelihood) $Z$, the probability each error model assigned to the data before fitting, is blunt about it. $\\log Z$ goes from $-581.75$ for the bare fit to $-19.61$ once we allow a diagonal model error, and to $-9.93$ with the GP. The difference of two log evidences is the log of a [Bayes factor](https://en.wikipedia.org/wiki/Bayes_factor), and a gap of hundreds isn't a mild preference, it's a verdict. The bare fit claims the reported 4 % errors explain every disagreement between model and data, that claim is simply false, and the sampler can tell.\n", + "The [evidence](https://en.wikipedia.org/wiki/Marginal_likelihood) $Z$, the probability each error model assigned to the data before fitting, is blunt about it. $\\log Z$ goes from $-581.75$ for the bare fit to $-19.61$ once we allow an uncorrelated proportional error, and to $-9.93$ with the GP. The difference of two log evidences is the log of a [Bayes factor](https://en.wikipedia.org/wiki/Bayes_factor), and a gap of hundreds isn't a mild preference, it's a verdict. The bare fit claims the reported 4 % errors explain every disagreement between model and data, that claim is simply false, and the sampler can tell.\n", "\n", - "The parameters fall in the same order. The bare fit puts $V_v$ at $26.9 \\pm 0.4$ MeV where the truth is 48, which is wrong by fifty standard deviations, and confidently so. The diagonal model error widens until nothing is precise. The GP recovers $V_v = 43.7 \\pm 1.4$, about three sigma from the truth, while keeping a useful width.\n", + "The parameters fall in the same order. The bare fit puts $V_v$ at $26.9 \\pm 0.4$ MeV where the truth is 48, which is wrong by fifty standard deviations, and confidently so. The proportional error widens until nothing is precise. The GP recovers $V_v = 43.7 \\pm 1.4$, about three sigma from the truth, while keeping a useful width.\n", "\n", "Now look at $W_v$, though. It comes out at $13.6 \\pm 1.1$ against a truth of 3.5, nine sigma out, in *every* rung including the GP. That isn't a failure of the discrepancy model; it's the physics. The data were generated with volume **and** surface absorption, we fitted a potential with only volume absorption, and at a single energy those two shapes are very hard to tell apart. The fitted volume absorption is quite literally soaking up the missing surface term.\n", "\n", @@ -1097,18 +919,11 @@ "cell_type": "code", "execution_count": 27, "id": "9f34319b", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:11:04.704988Z", - "iopub.status.busy": "2026-09-14T22:11:04.704872Z", - "iopub.status.idle": "2026-09-14T22:11:05.323353Z", - "shell.execute_reply": "2026-09-14T22:11:05.322470Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "<Figure size 1067x1067 with 16 Axes>" ] @@ -1155,14 +970,7 @@ "cell_type": "code", "execution_count": 28, "id": "1799733e", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:11:05.325194Z", - "iopub.status.busy": "2026-09-14T22:11:05.325065Z", - "iopub.status.idle": "2026-09-14T22:11:05.482915Z", - "shell.execute_reply": "2026-09-14T22:11:05.482230Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "p_xs_gp = xs_problems[\"GP, growing amplitude\"]\n", @@ -1194,14 +1002,7 @@ "cell_type": "code", "execution_count": 29, "id": "b1b56ab1", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:11:05.484359Z", - "iopub.status.busy": "2026-09-14T22:11:05.484226Z", - "iopub.status.idle": "2026-09-14T22:11:05.486957Z", - "shell.execute_reply": "2026-09-14T22:11:05.486311Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1222,14 +1023,7 @@ "cell_type": "code", "execution_count": 30, "id": "31b28438", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:11:05.488357Z", - "iopub.status.busy": "2026-09-14T22:11:05.488239Z", - "iopub.status.idle": "2026-09-14T22:11:05.617765Z", - "shell.execute_reply": "2026-09-14T22:11:05.617199Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -1288,7 +1082,7 @@ "- We met two predictive objects, and they answer different questions. The model plus its discrepancy tells us where the model may be wrong. Adding the experimental terms tells us what a *measurement* should look like, and only that second one belongs next to data. `terms=` is how we pick. There's also a third, conditioning the discrepancy on the residuals with `gp_predictive_draws(..., conditioned=True)`, but that's data-driven regression stacked on the model, and we deliberately didn't use it here.\n", "- The envelope isn't a fit to the defect, and nothing guarantees it covers the defect. It contained the toy's defect over 72 % of the grid, but the cross section's over only 59 %, where the fitted potential had already absorbed part of the missing physics into $W_v$. A mean-zero predictive describes plausible error, and it can be too narrow as well as too generous.\n", "- An unmodelled discrepancy doesn't make our answer vague; it makes it *wrong and confident*. On the toy that meant a slope sixty sigma from the truth.\n", - "- A diagonal model error can only inflate. It covers the truth by making the parameters meaningless, because independent slack at each point can't describe a coherent, smooth departure.\n", + "- An uncorrelated proportional error can only inflate. It covers the truth by making the parameters meaningless, because independent slack at each point can't describe a coherent, smooth departure.\n", "- The amplitude is prior knowledge, and it's worth stating. A constant amplitude has to be generous everywhere to cover the worst region. Letting it grow with $x$ keeps the fit honest where the model is good and forgiving where it isn't.\n", "- None of this creates information. Where the data can't separate two effects, like volume and surface absorption at a single energy, the discrepancy model widens the answer rather than pretending to resolve it." ] diff --git a/src/rxmc/predictive.py b/src/rxmc/predictive.py index 3d63a21..17c4f88 100644 --- a/src/rxmc/predictive.py +++ b/src/rxmc/predictive.py @@ -9,8 +9,8 @@ evaluated on the grid, every selected covariance term is re-evaluated there from its own definition, and one correlated draw is taken from the sum. Any term that is a function of the :class:`~rxmc.terms.TermContext` travels: - inferred noise, :func:`~rxmc.terms.noise_fraction`, - :func:`~rxmc.terms.model_error`, normalisation, offset and systematic modes, + inferred noise, :func:`~rxmc.terms.proportional_error`, normalisation, + offset and systematic modes, a parametric ``Term(fn, params, kind="matrix")`` and a Gaussian-process :func:`~rxmc.terms.kernel`. * :func:`gp_predictive_draws` — the same draws for a kernel term, with the diff --git a/src/rxmc/terms.py b/src/rxmc/terms.py index e1d4e85..bb31130 100644 --- a/src/rxmc/terms.py +++ b/src/rxmc/terms.py @@ -35,7 +35,7 @@ they share one sampled value. The factory helpers (:func:`statistical`, :func:`offset`, :func:`normalization`, -:func:`noise`, :func:`noise_fraction`, :func:`model_error`, :func:`systematic`, +:func:`noise`, :func:`proportional_error`, :func:`systematic`, :func:`kernel`) are one-line conveniences that build the common terms; anything they cannot express is a direct ``Term(fn, params, kind=...)``. """ @@ -67,8 +67,7 @@ "offset", "normalization", "noise", - "noise_fraction", - "model_error", + "proportional_error", "systematic", "kernel", ] @@ -510,21 +509,17 @@ def noise( ) -def noise_fraction(parameter, log=True, on=None) -> Term: - """Unknown fractional noise ``diag((epsilon * ym)**2)``. +def proportional_error(parameter, averaging=False, log=True, on=None) -> Term: + """An unknown uncorrelated error proportional to the prediction. + + ``diag((c * z)**2)`` with ``c = exp(theta)`` when ``log`` (else ``theta``) + and ``z = ym``, or ``z = 0.5 * (y + ym)`` when ``averaging`` (KDUQ's + spelling, which stays finite when ``ym`` is near zero). Because it scales + with the prediction, the covariance changes with the model parameters. **Additive** on top of the reported statistical diagonal (see :func:`noise` for how to get replace-semantics instead). """ - return _scaled_term("diag", parameter, log, ym, on=on) - - -def model_error(parameter, averaging=True, log=True, on=None) -> Term: - """Unknown uncorrelated model error ``diag((gamma * z)**2)``. - - ``z = 0.5 * (y + ym)`` when ``averaging`` (stabilises when ``ym`` is near zero), - else ``z = ym``. - """ basis = _AVERAGING if averaging else ym return _scaled_term("diag", parameter, log, basis, on=on) diff --git a/test/helpers.py b/test/helpers.py index 8e1b3e5..7ebfad6 100644 --- a/test/helpers.py +++ b/test/helpers.py @@ -21,9 +21,9 @@ exp_growth_amplitude, kernel, noise, - noise_fraction, normalization, offset, + proportional_error, systematic, x_basis, ) @@ -137,7 +137,7 @@ def study_form(label, x, y, ym, X=np.pi, k=2.7) -> StudyForm: label, STUDY_LEGEND[label], [L0], [(np.log(err),)], err**2 * eye ) if label == "E0": - t = noise_fraction(log_err) + t = proportional_error(log_err) return StudyForm( label, STUDY_LEGEND[label], [t], [(np.log(err),)], np.diag((err * ym) ** 2) ) diff --git a/test/recipes/test_recipe_02_unknown_noise.py b/test/recipes/test_recipe_02_unknown_noise.py index 6d4fe40..33e5eb6 100644 --- a/test/recipes/test_recipe_02_unknown_noise.py +++ b/test/recipes/test_recipe_02_unknown_noise.py @@ -34,7 +34,8 @@ def test_statistical_false_replaces_and_default_adds(): def test_prediction_scaled_noise_changes_with_the_model_parameters(): d = line_data() log_eps = Parameter("log_eps", prior=stats.norm(-2, 2)) - for term in (T.noise_fraction(log_eps), T.model_error(log_eps)): + for averaging in (False, True): + term = T.proportional_error(log_eps, averaging=averaging) p = Problem( [Constraint([Comparison(d, line())], terms=[term], statistical=False)] ) diff --git a/test/recipes/test_recipe_18_evidence_comparison.py b/test/recipes/test_recipe_18_evidence_comparison.py index 47e1f7f..484df5c 100644 --- a/test/recipes/test_recipe_18_evidence_comparison.py +++ b/test/recipes/test_recipe_18_evidence_comparison.py @@ -45,7 +45,7 @@ def error_models(d, model): return { "L0": Constraint([comp_log], terms=[T.noise(log_eps)], statistical=False), "E0": Constraint( - [comp_lin], terms=[T.noise_fraction(log_eps)], statistical=False + [comp_lin], terms=[T.proportional_error(log_eps)], statistical=False ), "L2y": Constraint( [comp_log], diff --git a/test/recipes/test_recipe_26_kduq_model_error.py b/test/recipes/test_recipe_26_kduq_model_error.py index 8250661..345c11e 100644 --- a/test/recipes/test_recipe_26_kduq_model_error.py +++ b/test/recipes/test_recipe_26_kduq_model_error.py @@ -22,7 +22,7 @@ def build(): model = line() comps = [Comparison(d, model) for d in datasets] terms = [ - T.model_error(delta[d.meta["type"]], averaging=True, log=False, on=c) + T.proportional_error(delta[d.meta["type"]], averaging=True, log=False, on=c) for d, c in zip(datasets, comps) ] return types, delta, datasets, comps, terms diff --git a/test/test_predictive.py b/test/test_predictive.py index db4edd4..70cfbf9 100644 --- a/test/test_predictive.py +++ b/test/test_predictive.py @@ -27,10 +27,9 @@ constant_amplitude, exp_growth_amplitude, kernel, - model_error, noise, - noise_fraction, normalization, + proportional_error, systematic, x_basis, ) @@ -413,12 +412,13 @@ def test_constant_noise_extrapolates_with_the_model(self): np.testing.assert_allclose(mean, 0.5 * X_GRID + 0.2, atol=0.005) np.testing.assert_allclose(cov, 0.01 * np.eye(16), atol=5e-4) - def test_fractional_noise_and_model_error_scale_with_the_prediction(self): + def test_proportional_errors_scale_with_the_prediction(self): ym = 0.5 * X_GRID + 1.2 - for make in (noise_fraction, model_error): - p, model, _ = one(make(Parameter("log_f", prior=stats.norm(-2, 1)))) + for averaging in (False, True): + f = Parameter("log_f", prior=stats.norm(-2, 1)) + p, model, _ = one(proportional_error(f, averaging=averaging)) _, cov = cov_of(p, model, np.array([0.5, 1.2, np.log(0.1)])) - # model_error averages y and ym; on a grid y *is* the model + # the averaging basis uses y and ym; on a grid y *is* the model np.testing.assert_allclose(np.sqrt(np.diag(cov)), 0.1 * ym, rtol=0.03) # a diagonal term: no correlation between grid points np.testing.assert_allclose(cov - np.diag(np.diag(cov)), 0.0, atol=5e-4) diff --git a/test/test_terms.py b/test/test_terms.py index 8010949..1d8894f 100644 --- a/test/test_terms.py +++ b/test/test_terms.py @@ -15,12 +15,11 @@ exp_growth, exp_growth_amplitude, kernel, - model_error, noise, - noise_fraction, normalization, offset, ones, + proportional_error, statistical, systematic, x_basis, @@ -231,19 +230,23 @@ def test_unknown_noise(self): self.S([noise(Parameter("e"), log=False)], [(0.4,)]), 0.16 * np.eye(3) ) - def test_unknown_noise_fraction(self): - S = self.S([noise_fraction(Parameter("e"))], [(np.log(0.4),)]) + def test_unknown_proportional_error(self): + S = self.S([proportional_error(Parameter("e"))], [(np.log(0.4),)]) assert np.allclose(S, np.diag((0.4 * self.ym) ** 2)) def test_unknown_normalization_error(self): S = self.S([normalization(parameter=Parameter("n"))], [(np.log(0.05),)]) assert np.allclose(S, 0.05**2 * np.outer(self.ym, self.ym)) - def test_unknown_model_error(self): - S = self.S([model_error(Parameter("g"), averaging=True)], [(np.log(0.1),)]) + def test_averaged_proportional_error(self): + S = self.S( + [proportional_error(Parameter("g"), averaging=True)], [(np.log(0.1),)] + ) z = 0.5 * (self.y + self.ym) assert np.allclose(S, np.diag((0.1 * z) ** 2)) - S = self.S([model_error(Parameter("g"), averaging=False)], [(np.log(0.1),)]) + S = self.S( + [proportional_error(Parameter("g"), averaging=False)], [(np.log(0.1),)] + ) assert np.allclose(S, np.diag((0.1 * self.ym) ** 2)) def test_fixed_normalization_systematic(self): From 34d5e2667b4f1a5375eab1c055199d243d516b93 Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Tue, 22 Sep 2026 12:04:21 -0400 Subject: [PATCH 66/75] Accept a plain dict in from_measurement `from_measurement` only ever read attributes off its argument, so an `exfor_tools` Distribution worked and a dict of the same fields did not. `_as_record` wraps a Mapping in a SimpleNamespace and checks the eight required fields up front, so a missing one raises naming itself rather than surfacing as an AttributeError halfway through the conversion. `x_units` and `subentry` stay optional. This is what the optical-model notebook needs to build a Dataset from the EXFOR entry it types out by hand. --- docs/recipes.md | 6 ++++++ src/rxmc/data.py | 33 ++++++++++++++++++++++++++++++--- test/test_measurement.py | 20 ++++++++++++++++++++ 3 files changed, 56 insertions(+), 3 deletions(-) diff --git a/docs/recipes.md b/docs/recipes.md index ad902b5..41edaff 100644 --- a/docs/recipes.md +++ b/docs/recipes.md @@ -363,6 +363,10 @@ units with nothing lost.* ```python d = rx.from_measurement(m, reaction=reaction, quantity="dXS/dA") # or "dXS/dRuth", "Ay" d_ias = rx.from_measurement(m, reaction=reaction, ExIAS=Ex) # (p,n) IAS channel +d_dict = rx.from_measurement({"x": deg, "y": y, "statistical_err": dy, "Einc": E, + "quantity": "dXS/dRuth", "y_units": "no-dim", + "systematic_norm_err": 0.0, "systematic_offset_err": 0.0}, + reaction=reaction) # or a plain dict ``` Expected behaviour: @@ -379,6 +383,8 @@ Expected behaviour: to, raise at conversion time. - Angles must be in the CM frame: a measurement whose `x_units` is `LAB-degrees` raises; convert it to CM first. +- Any object with the `Distribution` field names works, and so does a + `dict` with those keys; a missing field raises, naming it. ## 15. Evaluate a reaction model on any grid diff --git a/src/rxmc/data.py b/src/rxmc/data.py index b236a2d..db6a3f4 100644 --- a/src/rxmc/data.py +++ b/src/rxmc/data.py @@ -16,6 +16,7 @@ from __future__ import annotations from dataclasses import dataclass, field +from types import SimpleNamespace from typing import Any, Mapping import numpy as np @@ -119,6 +120,28 @@ def __repr__(self): } +_REQUIRED_FIELDS = ( + "x", + "y", + "Einc", + "quantity", + "y_units", + "statistical_err", + "systematic_norm_err", + "systematic_offset_err", +) + + +def _as_record(measurement): + """A measurement's fields as attributes, whether it is an object or a mapping.""" + if isinstance(measurement, Mapping): + measurement = SimpleNamespace(**measurement) + missing = [f for f in _REQUIRED_FIELDS if not hasattr(measurement, f)] + if missing: + raise ValueError(f"measurement is missing the field(s) {missing}") + return measurement + + def from_measurement( measurement, *, reaction=None, quantity=None, ExIAS=None ) -> Dataset: @@ -128,7 +151,9 @@ def from_measurement( is refused, and a measurement without ``x_units`` is taken as CM), ``y``, ``Einc``, ``quantity``, ``y_units``, ``statistical_err``, ``systematic_norm_err``, ``systematic_offset_err`` and - ``subentry`` from ``measurement`` (any object with those attributes). Angles + ``subentry`` from ``measurement``: an object with those attributes, or a + mapping (a plain ``dict``) with those keys. ``x_units`` and ``subentry`` + are optional; a missing required field raises, naming it. Angles are stored in radians, cross sections in b/sr, ratios and analysing powers as they are. Every dimensionful error (statistical, absolute offset) is converted with the data; the fractional normalisation error passes through @@ -138,8 +163,9 @@ def from_measurement( Parameters ---------- - measurement : object - An ``exfor_tools.distribution.Distribution`` or anything shaped like it. + measurement : object or Mapping + An ``exfor_tools.distribution.Distribution``, anything shaped like it, + or a ``dict`` of the same fields. reaction : jitr.reactions.Reaction, optional Needed for the kinematics in ``meta`` and for any conversion between ``dXS/dA`` and ``dXS/dRuth`` (the Rutherford cross section is a closed @@ -151,6 +177,7 @@ def from_measurement( """ from .units import MB_PER_B, check_angle_grid, parse_unit + measurement = _as_record(measurement) measured = measurement.quantity target = measured if quantity is None else quantity for q in (measured, target): diff --git a/test/test_measurement.py b/test/test_measurement.py index 8431c16..656dcf5 100644 --- a/test/test_measurement.py +++ b/test/test_measurement.py @@ -54,6 +54,26 @@ def test_construction_in_internal_units(): ) +def test_a_dict_is_a_measurement(): + fields = vars(measurement(subentry="E1234-002")) + from_dict = from_measurement(dict(fields), reaction=P_CA) + from_object = from_measurement(measurement(subentry="E1234-002"), reaction=P_CA) + np.testing.assert_allclose(from_dict.x, from_object.x) + np.testing.assert_allclose(from_dict.y, from_object.y) + np.testing.assert_allclose(from_dict.y_err, from_object.y_err) + assert from_dict.label == from_object.label + assert from_dict.norm_err == from_object.norm_err + + +def test_a_missing_field_is_named(): + fields = vars(measurement()) + del fields["Einc"] + with pytest.raises(ValueError, match="Einc"): + from_measurement(fields) + with pytest.raises(ValueError, match="Einc"): + from_measurement(SimpleNamespace(**fields)) + + def test_lab_frame_angles_are_refused(): with pytest.raises(ValueError, match="LAB frame"): from_measurement(measurement(x_units="LAB-degrees"), reaction=P_CA) From 5ed280d833b5f5bd010a5d59d6185beaa1e4fa9c Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Tue, 22 Sep 2026 12:04:29 -0400 Subject: [PATCH 67/75] Let a scalar reported normalisation travel to a new grid `reported_terms()` built the normalisation mode from `_reported(norm_err, n)`, which broadcasts a scalar to a per-point array. An array has no value away from the measured points, so `grid_draws` had to drop the term and the predictive on a fine grid lost the reported normalisation. A scalar `norm_err` is a fraction of the prediction and nothing else, so it has a value anywhere: keep it scalar and the mode stays a function of `c.ym`, exactly like `T.normalization(magnitude=)`. An offset, or a per-point normalisation, is still defined only at the measured points. --- docs/recipes.md | 5 +++-- src/rxmc/constraint.py | 10 +++++++++- test/test_predictive.py | 12 ++++++++++++ 3 files changed, 24 insertions(+), 3 deletions(-) diff --git a/docs/recipes.md b/docs/recipes.md index 41edaff..d7332de 100644 --- a/docs/recipes.md +++ b/docs/recipes.md @@ -1345,8 +1345,9 @@ Expected behaviour: `predictive_draws`. - Any term that is a function of the `TermContext` travels: `noise`, `proportional_error`, `normalization`/`offset`/`systematic` with - a parameter or a scalar magnitude, a `kernel`, and a user's - `Term(fn, params, kind="matrix")`. + a parameter or a scalar magnitude, a `kernel`, a user's + `Term(fn, params, kind="matrix")`, and the normalisation mode + `reported_terms()` builds from a scalar `norm_err`. - A term that is an array has no value at a new `x`: the reported statistical errors (`statistical=True`), a fixed `Term(array)`, a per-point `magnitude=`, a function closing over the measured rows. Drawing it would diff --git a/src/rxmc/constraint.py b/src/rxmc/constraint.py index 3865bba..ef5e802 100644 --- a/src/rxmc/constraint.py +++ b/src/rxmc/constraint.py @@ -125,7 +125,10 @@ def reported_terms(self) -> list[Term]: to the comparison space by the delta method: the offset is an error on the *data* and is linearised at the data; the normalisation multiplies the *prediction* and is linearised at the prediction, which needs the - space's inverse. + space's inverse. A scalar normalisation error gives a mode that is a + function of the prediction alone, so it also has a value on a new grid + (:func:`~rxmc.predictive.grid_draws`); an offset, or a per-point + normalisation, is defined only at the measured points. """ d, t = self.data, self.space terms = [] @@ -135,6 +138,11 @@ def reported_terms(self) -> list[Term]: omega = np.abs(t.derivative(d.y)) * omega terms.append(Term(omega, kind="mode", on=self)) eta = _reported(d.norm_err, d.n) + if eta is not None and np.ndim(d.norm_err) == 0: + # a scalar keeps the mode a function of the prediction alone, so + # it can be evaluated on any grid (grid_draws), like + # T.normalization(magnitude=) + eta = float(d.norm_err) if eta is not None: if t.is_identity: terms.append(Term(lambda c: eta * c.ym, kind="mode", on=self)) diff --git a/test/test_predictive.py b/test/test_predictive.py index 70cfbf9..36c04da 100644 --- a/test/test_predictive.py +++ b/test/test_predictive.py @@ -353,6 +353,18 @@ def cov_of(p, model, theta, x=X_GRID, n_rep=40000, rng=0, **kw): class TestGridDraws: + def test_a_scalar_reported_normalisation_travels_to_the_grid(self): + """In log space a 5 % normalisation is a constant mode of 0.05.""" + model = line() + d = Dataset(X, Y, np.full(10, 0.05), norm_err=0.05, label="d") + comp = Comparison(d, model, space=log) + e = Parameter("e", prior=stats.norm(-9, 1)) + c = Constraint( + [comp], terms=[noise(e), *comp.reported_terms()], statistical=False + ) + _, cov = cov_of(Problem([c]), model, np.array([0.5, 1.2, -9.0])) + np.testing.assert_allclose(cov, 0.05**2, atol=2e-4) + def test_the_band_is_the_percentiles_of_the_draws(self): """One return convention for all three draw functions.""" eps = noise(Parameter("log_eps", prior=stats.norm(-2, 1))) From a955c87e452044dbb99bb3763ab39f44c7c00873 Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Tue, 22 Sep 2026 12:05:47 -0400 Subject: [PATCH 68/75] Tighten the linear_calibration prose and re-run it State the model as `y_i + eps_i = eta(x_i) = y_m(x_i; m, b)` before the priors, so the likelihood that follows is read off the equation rather than asserted, and say which predictive to compare against the truth and which against data. The walker-ball paragraph stays: the code still starts from `theta0`. Re-executed end to end, so the execution counts are sequential again and the `%%time` on the sampler reports a real number. `_python` in test_notebooks_index strips magics before `ast.parse`, which otherwise fails on `%%time`. --- examples/linear_calibration.ipynb | 48 ++++++++++++++++++++----------- test/test_notebooks_index.py | 11 ++++++- 2 files changed, 42 insertions(+), 17 deletions(-) diff --git a/examples/linear_calibration.ipynb b/examples/linear_calibration.ipynb index d1eb6b4..10b0f88 100644 --- a/examples/linear_calibration.ipynb +++ b/examples/linear_calibration.ipynb @@ -7,9 +7,23 @@ "source": [ "# Calibration of a line\n", "\n", - "We'll start with the smallest problem that contains the model calibration workflow: fitting a line to noisy measured points. First, we will declare the model ($y = m x + b$), what we believe about its parameters — our [priors](https://en.wikipedia.org/wiki/Prior_probability) — and how the data compare with it — our [likelihood](https://en.wikipedia.org/wiki/Likelihood_function). Then we compile that problem, hand it to a sampler, and draw samples from our [posterior](https://en.wikipedia.org/wiki/Posterior_probability). Finally, we push those posterior samples back through our model to generate a [posterior predictive](https://en.wikipedia.org/wiki/Posterior_predictive_distribution), and compare it to the data we fit to see if it worked.\n", + "We'll start with the smallest problem that contains the model calibration workflow: fitting a line to noisy measured points. First, we will declare the model ($y = m x + b$), what we believe about its parameters — our [priors](https://en.wikipedia.org/wiki/Prior_probability) — and how the data compare with a given model prediction — our [likelihood](https://en.wikipedia.org/wiki/Likelihood_function). Then we compile that problem, hand it to a sampler, and draw samples from our [posterior](https://en.wikipedia.org/wiki/Posterior_probability). Finally, we push those posterior samples back through our model to generate a [posterior predictive](https://en.wikipedia.org/wiki/Posterior_predictive_distribution), and compare it to the data we fit to see if it worked. [Bayesian inference](https://en.wikipedia.org/wiki/Bayesian_inference) always has these ingredients: a prior over parameters, a likelihood for the data, and a posterior we explore with a sampler. \n", "\n", - "[Bayesian inference](https://en.wikipedia.org/wiki/Bayesian_inference) always has these ingredients: a prior over parameters, a likelihood for the data, and a posterior we explore with a sampler. In this synthetic data case, we know the ground truth, and therefore know our model is correctly specified. We also know the data-generating process: every point scatters about the true line with the same standard deviation $\\sigma = 0.1$. Our imaginary experiment reports that $\\sigma$ for each point, but rather than take it on trust, we'll infer it alongside $m$ and $b$. That's our first, and simplest, *error model*, and we'll check that it recovers the reported value. It will also pay off later, when we want predictions at $x$ values nobody measured. Other tutorial notebooks in this series explore the more realistic cases in which our model is only an approximation to reality, and we may not have complete information about the experimental uncertainties.\n", + "In this synthetic data case, we know the ground truth, and therefore know our model is correctly specified. We also know the data-generating process: every point will scatter about the true line with the same standard deviation $\\sigma$. Our imaginary experiment reports that $\\sigma$ for each point, but rather than take it on trust, we'll infer it alongside $m$ and $b$. That's our first, and simplest, *error model*, and we'll check that it recovers the reported value. It will also pay off later, when we want predictions at $x$ values nobody measured. Other tutorial notebooks in this series explore the more realistic cases in which our model is only an approximation to reality, and we may not have complete information about the experimental uncertainties.\n", + "\n", + "We could write this model calibration problem as follows:\n", + "\n", + "\\begin{equation}\n", + " y_i + \\varepsilon_i = \\eta(x_i) \\equiv m_\\text{true} x_i + b_\\text{true} = y_m(x_i;m,b),\n", + "\\end{equation}\n", + "\n", + "where $y_i$ is our (synthetic) data, $\\varepsilon_i$ is discrepancy between the measured $y_i$, and the ground truth $\\eta(x_i)$, which we want to model using $y_m(x_i;m,b) = m x_i + b$. $m$ and $b$ are random variables, we will start with a prior for them $p(m,b)$. $\\varepsilon_i$ is also a random variable, which we model as Gaussian with mean 0 and standard deviation $\\sigma$:\n", + "\n", + "\\begin{equation}\n", + "\\varepsilon_i \\sim \\mathcal{N}(0, \\sigma^2),\n", + "\\end{equation}\n", + "\n", + "This determines the likelihood, and, through Bayes' rule, the posterior, we will write below.\n", "\n", "Recipes: 1, 2, 17, 40" ] @@ -45,7 +59,7 @@ "\n", "We deliberately choose a prior that disagrees with the data we are about to generate: $m \\sim \\mathcal{N}(1, 1)$ while the truth is $m = 0.6$, so we can see the data pull the posterior away from it.\n", "\n", - "We also need a parameter for the noise level. $\\sigma$ is a positive scale that could plausibly be anywhere over an order of magnitude, so we sample its logarithm, with $\\log\\sigma \\sim \\mathcal{N}(\\log 0.2, 1)$: centred a factor of two above the truth, and wide." + "We also need a parameter for the noise level. $\\sigma$ is a positive scale that could plausibly be anywhere over an order of magnitude, so we will sample its logarithm, with $\\log\\sigma \\sim \\mathcal{N}(\\log 0.2, 1)$: centred a factor of two above the truth, and wide." ] }, { @@ -265,22 +279,21 @@ "source": [ "### Calibrating to data \n", "\n", - "To determine the posterior $p(m,b,\\sigma | y ) $, we use Baye's rule:\n", + "To determine the posterior $p(m,b,\\sigma | y ) $, we use Bayes' rule:\n", "\n", "\\begin{equation}\n", "p(m,b,\\sigma | y ) = \\frac{1}{\\mathcal{Z}} \\, L(y | m,b,\\sigma) \\, p(m,b)p(\\sigma)\n", "\\end{equation}\n", "\n", - "With a diagonal covariance $\\sigma^2 I$ over $N$ points, a Gaussian\n", - "log-likelihood is\n", + "The relation between $y_i$, $\\varepsilon_i$, and $y_m(x_i;m,b)$ above gives us the following log-likelihood:\n", "\n", "\\begin{align*}\n", "\\log L(m,b,\\sigma) &= -\\frac{1}{2}\\chi^2(m,b,\\sigma) - N \\log\\sigma - \\frac{N}{2}\\log 2\\pi\\\\\n", "\\chi^2(m,b,\\sigma) &= \\sum_i \\frac{(y_i - y_m(x; m,b))^2}{\\sigma^2}\n", "\\end{align*}\n", "\n", - "If $\\sigma$ were fixed, the last two terms would be constants and we could\n", - "forget about them - optimizing $m$ and $b$ to maximize the log-likelihood would simply reduce to good old least-squares, or $\\chi^2$-minimization. Because we also infer $\\sigma$, they matter: we could always make the $\\chi^2$ smaller by inflating $\\sigma$, and the $-N\\log\\sigma$ term — the log-determinant of the covariance — is what charges us for doing so.\n", + "If $\\sigma$ were fixed, the last two terms in $L$ would be constants and we could\n", + "forget about them - optimizing $m$ and $b$ to maximize the log-likelihood would then simply reduce to good old least-squares, or $\\chi^2$-minimization. Because we also infer $\\sigma$, they matter: we could always make the $\\chi^2$ smaller by inflating $\\sigma$, and the $-N\\log\\sigma$ term — the log-determinant of the covariance — is what charges us for doing so. One could still do an optimization to find the Maximum Likelihood Estimate (MLE); the $m$, $b$, and $\\sigma$ maximizing $L(m,b,\\sigma|y)$. However, by invoking Bayes' rule and using our priors, we will instead draw samples from the posterior. \n", "\n", "`problem.chi2` is the [$\\chi^2$](https://en.wikipedia.org/wiki/Goodness_of_fit#Regression_analysis)\n", "and `problem.log_likelihood` is the whole thing, so we can check both against the sums we'd type out ourselves. When the covariance stops being diagonal — and it will, in every other notebook — the $\\chi^2$ becomes the [Mahalanobis distance](https://en.wikipedia.org/wiki/Mahalanobis_distance)." @@ -324,12 +337,12 @@ "id": "763566ce", "metadata": {}, "source": [ + "We start the walkers in a small ball around the prior mean `theta0`, rather than from independent prior draws: with $\\sigma$ free, a draw can strand a walker where the noise is large enough to explain everything, and the ensemble takes a long time to reel it back in.\n", + "\n", "## Running the calibration with emcee\n", "\n", "The problem exposes everything a sampler needs: `log_posterior` is the density, `sample_prior` gives the walkers somewhere to start, and `ndim` is the dimension. We use [emcee](https://emcee.readthedocs.io/), the affine-invariant ensemble sampler of [Goodman & Weare (2010)](https://doi.org/10.2140/camcos.2010.5.65), described in [Foreman-Mackey et al. (2013)](https://arxiv.org/abs/1202.3665). Nothing here is specific to it: any other third-party sampler, like ptemcee, dynesty or pymc, can be used. [Black Box Bayes](https://github.com/beykyle/black-box-bayes/) provides a uniform interface to all of these that works seamlessly with `rxmc`, which is useful for production-scale inference. For the small problems in these tutorial notebooks, we can just run the samplers directly.\n", "\n", - "We start the walkers in a small ball around the prior mean `theta0`. Starting them from independent prior draws is also common, but with $\\sigma$ free it can strand a walker in the corner of parameter space where the noise is so large that it explains everything and every nearby proposal looks equally good; the ensemble then takes a very long time to reel it back in.\n", - "\n", "The chain of samples from the posterior comes back in `problem.names` order, so we always look columns up by name through `problem.columns`, never by position." ] }, @@ -343,11 +356,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "16000 posterior rows, acceptance 0.64\n" + "16000 posterior rows, acceptance 0.64\n", + "CPU times: user 46.5 s, sys: 14.9 ms, total: 46.6 s\n", + "Wall time: 46.6 s\n" ] } ], "source": [ + "%%time\n", "n_walkers, n_steps = 32, 3000\n", "sampler = emcee.EnsembleSampler(n_walkers, problem.ndim, problem.log_posterior)\n", "sampler.random_state = np.random.RandomState(2).get_state()\n", @@ -430,14 +446,14 @@ "\n", "But a forecast of *what*? There are two different posterior predictive distributions we might mean:\n", "\n", - "1. **The model alone**, $y_m(x_*;\\theta)$ with $\\theta$ drawn from the posterior. This is our uncertainty about the *line*: where the true curve is. It's narrow where we have data and fans out beyond it.\n", - "2. **The model plus our error model**, $y_* = y_m(x_*;\\theta) + \\varepsilon$ with $\\varepsilon \\sim \\mathcal{N}(0, \\sigma^2)$ and $\\sigma$ drawn from the posterior too. This is our uncertainty about a *new measurement* at $x_*$.\n", + "1. **The model alone**, $y_m(x_*;\\theta)$ with $\\theta = {m,b}$ drawn from the posterior. This is our uncertainty about the *line*: where the true curve is. If we are comparing to the ground truth, this is the predictive distribution to use.\n", + "2. **The model plus our error model**, $y_* = y_m(x_*;\\theta) + \\varepsilon$ with $\\varepsilon \\sim \\mathcal{N}(0, \\sigma^2)$ and $\\sigma$ drawn from the posterior too. This is our uncertainty about a *new measurement* at $x_*$ - if we are comparing to data, then this is the predictive distribution to use.\n", "\n", "To draw the second at an $x_*$ nobody measured, every piece of the error model has to *have a value* there. That's why we inferred a constant $\\sigma$ instead of using the reported `y_err`: a reported error is one number per measured point, and it says nothing about a point that was never measured. Our noise term, on the other hand, is a function — the same $\\sigma$ at any $x$ — so it can be interpolated and extrapolated along with the physics model, just like $y_m$ itself.\n", "\n", "This is the split between rxmc's two draw functions:\n", "\n", - "- `rx.diagnostics.predictive_draws` draws at the **measured** points. Every term of the error model is defined there, reported per-point errors included, so this is what we compare against the data.\n", + "- `rx.diagnostics.predictive_draws` draws at the **measured** points. Every term of the error model is defined there, reported per-point errors included, so this is what we compare against the data. \n", "- `rx.predictive.grid_draws` draws on **any grid**. It re-evaluates each covariance term at the new points from its own definition, so it can carry anything that is a function of $x$ and the prediction — inferred noise, a normalisation uncertainty proportional to the prediction, a Gaussian-process discrepancy — but it refuses anything that is just an array of per-point numbers.\n", "\n", "Both return a percentile band by default, and the draws themselves with `return_draws=True`." @@ -461,7 +477,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "id": "046ac161", "metadata": {}, "outputs": [ @@ -553,7 +569,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, "id": "c59df024", "metadata": {}, "outputs": [ diff --git a/test/test_notebooks_index.py b/test/test_notebooks_index.py index 1ee1958..2ff589b 100644 --- a/test/test_notebooks_index.py +++ b/test/test_notebooks_index.py @@ -63,6 +63,15 @@ def test_each_notebook_exists_and_cites_its_recipes(name): ) +def _python(source) -> str: + """The cell's source with IPython magics and shell escapes dropped. + + ``%%time`` and friends are not Python, and ``ast.parse`` chokes on them. + """ + lines = [ln for ln in source if not ln.lstrip().startswith(("%", "!"))] + return "".join(lines) + + @pytest.mark.parametrize("name", sorted(NOTEBOOKS)) def test_no_latex_escape_lands_in_a_plain_string(name): r"""``f"$\rho$"`` is a carriage return, and matplotlib then fails to parse it. @@ -84,7 +93,7 @@ def test_no_latex_escape_lands_in_a_plain_string(name): for i, cell in enumerate(nb["cells"]): if cell["cell_type"] != "code": continue - for node in ast.walk(ast.parse("".join(cell["source"]))): + for node in ast.walk(ast.parse(_python(cell["source"]))): if isinstance(node, ast.Constant) and isinstance(node.value, str): for ch, shown in control.items(): if ch in node.value: From b0819e741c61f1b434aa95641659bf7fd0776636 Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Tue, 22 Sep 2026 12:05:54 -0400 Subject: [PATCH 69/75] Open error_models with where the Gaussian likelihood comes from The notebook jumped straight to the covariance ladder. Start instead from counts in a detector: binomial, Poisson in the small-bin limit, normal in the many-count limit, and a diagonal multivariate normal whose log is -chi^2/2 -- which makes plain what the three assumptions behind least squares are, and therefore what each rung of the ladder relaxes. Also drops a trailing empty cell. --- docs/design.md | 2 +- examples/error_models.ipynb | 290 ++++-------------------------------- 2 files changed, 30 insertions(+), 262 deletions(-) diff --git a/docs/design.md b/docs/design.md index 3512e2c..d5cef5d 100644 --- a/docs/design.md +++ b/docs/design.md @@ -664,7 +664,7 @@ a time unless noted. | notebook | recipes | driver | content | runtime | |---|---|---|---|---| | `linear_calibration` | 1, 2, 17, 40 | emcee | the whole workflow on a line with an inferred constant noise; prior and posterior predictive on a new grid with `grid_draws`, the model's band against a measurement's, and why reported per-point errors cannot go there; coverage of both | 23 s | -| `error_models` | 2, 4, 19 | emcee | the covariance ladder on one comparison, the Peelle matrix as a fixed term, offsets known, free and ignored | 89 s | +| `error_models` | 2, 4, 19 | emcee | the covariance ladder on one comparison, the Peelle matrix as a fixed term, offsets known, free and ignored | 85 s | | `sharing_error_models` | 5 | emcee | two experiments with opposite normalisation defects: sharing a parameter, a mode per dataset, one mode spanning both, and the assembled covariance seen directly | 78 s | | `normalization_and_covariance_structure` | 3, 4, 6, 27 | emcee | six treatments of five experiments' normalisations, one of them badly mis-quoted and alone in its range; Peelle's puzzle in the two-point case it was found in; a gallery of covariance structures from `matrix(theta)` | 353 s | | `gp_discrepancy` | 7 | emcee, dynesty | mean-zero discrepancies with amplitudes growing in x: four rungs on a toy line, three on n+⁴⁰Ca missing its surface absorption; the three predictive objects (model plus discrepancy, plus experimental, and the conditioned regression it does not use), with the equations | 483 s | diff --git a/examples/error_models.ipynb b/examples/error_models.ipynb index 7d06c2a..1389515 100644 --- a/examples/error_models.ipynb +++ b/examples/error_models.ipynb @@ -7,14 +7,9 @@ "source": [ "# Error models: there is a right way and many wrong ways\n", "\n", - "We are going to fit the same data with the same model six times over, changing\n", - "only one thing: the covariance we declare. The point of the notebook is that\n", - "this choice *is* the statistical model. It is not a detail we tune at the end;\n", - "it decides what the posterior says, and whether the truth is inside it.\n", + "We are going to fit the same data with the same model six times over, changing only one thing: the model covariance we use. \n", "\n", - "Along the way we infer a noise level the experiment never reported, add a\n", - "correlated systematic, and get it wrong on purpose so we can see what the\n", - "classic mistake does to the answer.\n", + "Along the way we infer a noise level the experiment never reported, add a correlated systematic, and get it wrong on purpose so we can see what the classic mistake does to the answer.\n", "\n", "Recipes: 2, 4, 19" ] @@ -23,14 +18,7 @@ "cell_type": "code", "execution_count": 1, "id": "6a1a51a0", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:01.495037Z", - "iopub.status.busy": "2026-09-14T19:54:01.494691Z", - "iopub.status.idle": "2026-09-14T19:54:04.716929Z", - "shell.execute_reply": "2026-09-14T19:54:04.715783Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "import corner\n", @@ -53,25 +41,15 @@ "source": [ "## Where a $\\chi^2$ comes from\n", "\n", - "Every fit assumes a likelihood, whether or not anyone writes it down. Counting\n", - "events in a bin gives a [binomial](https://en.wikipedia.org/wiki/Binomial_distribution),\n", - "then a [Poisson](https://en.wikipedia.org/wiki/Poisson_distribution), then — with\n", - "many counts — a [normal](https://en.wikipedia.org/wiki/Normal_distribution). If\n", - "the bins are independent, that is a diagonal\n", - "[multivariate normal](https://en.wikipedia.org/wiki/Multivariate_normal_distribution),\n", - "and its logarithm is $-\\chi^2/2$.\n", + "Every fit assumes a likelihood, whether or not anyone writes it down. Counting events in a bin - e.g. counts in a detector - gives a [binomial](https://en.wikipedia.org/wiki/Binomial_distribution), which, in the limit of small bins, gives a [Poisson](https://en.wikipedia.org/wiki/Poisson_distribution), which, in the further limit of many counts — a [normal](https://en.wikipedia.org/wiki/Normal_distribution). If the bins are independent, that is a diagonal\n", + "[multivariate normal](https://en.wikipedia.org/wiki/Multivariate_normal_distribution), and the logarithm of such a likelihood iss $-\\chi^2/2$. This result relies on assumptions: that the points are independent, that our model can reproduce the truth exactly, and that the errors are what the experiment says they are. \n", "\n", - "Each of those steps is an assumption: that the points are independent, that our\n", - "model can reproduce the truth exactly, and that the errors are what the\n", - "experiment says they are. When one of them fails, the honest generalisation is\n", - "not to abandon the normal but to *model its covariance*,\n", + "When the points are not independent, we generalize to a multivariate normal with non-diagonal *covariance*,\n", "\n", "$$\\log p(\\mathbf{y}\\mid\\theta) = -\\tfrac12 (\\mathbf{y}-\\mathbf{y}_m)^\\mathsf{T}\n", - "\\Sigma^{-1}(\\mathbf{y}-\\mathbf{y}_m) - \\tfrac12\\log\\det\\Sigma + \\text{const},$$\n", + "\\Sigma^{-1}(\\mathbf{y}-\\mathbf{y}_m) - \\tfrac12\\log\\det\\Sigma + \\text{const}.$$\n", "\n", - "so that $\\Sigma$ carries the statistical errors, the correlated systematics, and\n", - "whatever we had to infer. In `rxmc` a covariance is a sum of terms, and the rest\n", - "of this notebook is a tour of what choosing them does." + "If our model can't reproduce the truth exactly, or we don't trust the reported experimental errors, as long as we can model the discrepancy between experiment and model as a multivariate normal, then we can infer, along with our model parameters, the covariance $\\Sigma$, which can include statistical errors, correlated systematics, and any other pieces we had to infer. In `rxmc` a covariance is a sum of terms, and the rest of this notebook is a tour of what choosing them does." ] }, { @@ -81,24 +59,14 @@ "source": [ "## Data with a defect the experiment did not report\n", "\n", - "Our data are a line with 5 % relative noise, multiplied by one overall\n", - "normalisation that came out 10 % low. The experiment reports the noise\n", - "correctly and says nothing at all about the normalisation — which is exactly\n", - "the situation we are usually in." + "Our data are a line with 5 % relative noise, multiplied by one overall systematic normalisation that came out 10 % low. The experiment reports the noise correctly and says nothing at all about the normalisation." ] }, { "cell_type": "code", "execution_count": 2, "id": "ba80ce49", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:04.720022Z", - "iopub.status.busy": "2026-09-14T19:54:04.719673Z", - "iopub.status.idle": "2026-09-14T19:54:04.728354Z", - "shell.execute_reply": "2026-09-14T19:54:04.727212Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -125,20 +93,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 15, "id": "99cfee79", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:04.730807Z", - "iopub.status.busy": "2026-09-14T19:54:04.730571Z", - "iopub.status.idle": "2026-09-14T19:54:06.091586Z", - "shell.execute_reply": "2026-09-14T19:54:06.090369Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 704x440 with 1 Axes>" ] @@ -151,7 +112,7 @@ "fig, ax = plt.subplots()\n", "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", color=\"k\", label=\"data\")\n", "ax.plot(x, y_true, \"--\", color=plotstyle.COLOURS[1], label=\"truth\")\n", - "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"A 10 % normalisation nobody told us about\")\n", + "ax.set(xlabel=\"$x$\", ylabel=\"$y$\")\n", "ax.legend()\n", "plt.show()" ] @@ -161,26 +122,16 @@ "id": "d4fe9487", "metadata": {}, "source": [ - "## The model, a prior, and a way to run the ladder\n", + "## Setting up the model, prior, and experiment\n", "\n", - "We put the prior deliberately far from the truth, so that the data and not the\n", - "prior decide where we end up. Then we write three small helpers we will reuse\n", - "on every rung: `fit` runs emcee on any problem, `band` pushes posterior rows\n", - "through the model on a fine grid, and `summary` prints the columns by name." + "We put the prior deliberately far from the truth, so that the data and not the prior decide where we end up. Then we write three small helpers we will reuse on every rung: `fit` runs emcee on any problem, `band` pushes posterior rows through the model on a fine grid, and `summary` prints the columns by name. " ] }, { "cell_type": "code", "execution_count": 4, "id": "f4522e3a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:06.094255Z", - "iopub.status.busy": "2026-09-14T19:54:06.093858Z", - "iopub.status.idle": "2026-09-14T19:54:06.102493Z", - "shell.execute_reply": "2026-09-14T19:54:06.101453Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "m = rx.Parameter(\"m\", prior=stats.norm(2.0, 2.0), latex=\"m\")\n", @@ -194,14 +145,7 @@ "cell_type": "code", "execution_count": 5, "id": "573b525e", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:06.105405Z", - "iopub.status.busy": "2026-09-14T19:54:06.105044Z", - "iopub.status.idle": "2026-09-14T19:54:06.113880Z", - "shell.execute_reply": "2026-09-14T19:54:06.112765Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "def fit(problem, seed, n_walkers=24, n_steps=1800):\n", @@ -212,7 +156,7 @@ "\n", "\n", "def band(problem, samples, levels=(5, 95)):\n", - " \"\"\"The line's own band: how well each error model pins the curve down.\"\"\"\n", + " \"\"\"The line's own band: how well each error model compares to the truth\"\"\"\n", " return rx.predictive.grid_draws(\n", " problem,\n", " line.bind(x_fine),\n", @@ -246,14 +190,7 @@ "cell_type": "code", "execution_count": 6, "id": "63d1e287", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:06.116655Z", - "iopub.status.busy": "2026-09-14T19:54:06.116292Z", - "iopub.status.idle": "2026-09-14T19:54:20.279211Z", - "shell.execute_reply": "2026-09-14T19:54:20.278082Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -277,7 +214,7 @@ "## 2. Inferring the noise level (recipe 2)\n", "\n", "Suppose the experiment had not reported errors at all. We can infer them:\n", - "`T.noise_fraction` puts a free relative noise $\\sigma_i = \\epsilon\\, y_{m,i}$\n", + "`T.proportional_error` puts a free relative noise $\\sigma_i = \\epsilon\\, y_{m,i}$\n", "on the diagonal, sampled in log space, and `statistical=False` drops the\n", "reported diagonal so the inferred one replaces it rather than piling on top of\n", "it. (`T.noise` is the constant-floor variant, $\\sigma_i = \\epsilon$.)" @@ -287,14 +224,7 @@ "cell_type": "code", "execution_count": 7, "id": "3dee571d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:20.281880Z", - "iopub.status.busy": "2026-09-14T19:54:20.281545Z", - "iopub.status.idle": "2026-09-14T19:54:49.356382Z", - "shell.execute_reply": "2026-09-14T19:54:49.355377Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -311,7 +241,7 @@ " \"log_eps\", prior=stats.norm(np.log(0.05), 1.0), latex=r\"\\log\\epsilon\"\n", ")\n", "p_noise = rx.Problem(\n", - " [rx.Constraint([comp], terms=[T.noise_fraction(log_eps)], statistical=False)]\n", + " [rx.Constraint([comp], terms=[T.proportional_error(log_eps)], statistical=False)]\n", ")\n", "s_noise = fit(p_noise, seed=2)\n", "print(p_noise.names)\n", @@ -340,14 +270,7 @@ "cell_type": "code", "execution_count": 8, "id": "61371390", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:49.360238Z", - "iopub.status.busy": "2026-09-14T19:54:49.359852Z", - "iopub.status.idle": "2026-09-14T19:55:15.259158Z", - "shell.execute_reply": "2026-09-14T19:55:15.258095Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -389,14 +312,7 @@ "cell_type": "code", "execution_count": 9, "id": "911272d9", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:55:15.262217Z", - "iopub.status.busy": "2026-09-14T19:55:15.261864Z", - "iopub.status.idle": "2026-09-14T19:55:29.399126Z", - "shell.execute_reply": "2026-09-14T19:55:29.398014Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -425,14 +341,7 @@ "cell_type": "code", "execution_count": 10, "id": "5a4feed4", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:55:29.401902Z", - "iopub.status.busy": "2026-09-14T19:55:29.401538Z", - "iopub.status.idle": "2026-09-14T19:55:29.441045Z", - "shell.execute_reply": "2026-09-14T19:55:29.440116Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "runs = {\n", @@ -453,14 +362,7 @@ "cell_type": "code", "execution_count": 11, "id": "c9568854", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:55:29.443670Z", - "iopub.status.busy": "2026-09-14T19:55:29.443418Z", - "iopub.status.idle": "2026-09-14T19:55:29.821757Z", - "shell.execute_reply": "2026-09-14T19:55:29.820503Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -489,14 +391,7 @@ "cell_type": "code", "execution_count": 12, "id": "7b2a4566", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:55:29.824798Z", - "iopub.status.busy": "2026-09-14T19:55:29.824546Z", - "iopub.status.idle": "2026-09-14T19:55:30.155241Z", - "shell.execute_reply": "2026-09-14T19:55:30.154125Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -537,134 +432,7 @@ "id": "5bdfe338", "metadata": {}, "source": [ - "Four covariances, four answers, and the truth is $m = 0.6$, $b = 2.0$.\n", - "\n", - "Statistics only is the confident one: $b = 1.836 \\pm 0.049$, more than three\n", - "standard deviations below the truth. Nothing in that covariance can express a\n", - "shift common to every point, so the fit absorbs the 10 % normalisation into the\n", - "line itself and then tells us it is sure.\n", - "\n", - "Inferring the noise *does* cover the truth ($m = 0.531 \\pm 0.277$), but look at\n", - "what it cost. The inferred noise came out at 6 %, above the 5 % the data were\n", - "really generated with, and the uncertainty on the slope roughly tripled. A free\n", - "diagonal can only buy coverage by inflating every point independently, which is\n", - "not what happened to these data: it gets the right answer for the wrong reason,\n", - "and pays for it in precision.\n", - "\n", - "The mode built from the prediction covers the truth at an honest width\n", - "($b = 1.917 \\pm 0.220$). It is the only one of the four that knows the defect\n", - "was a single number multiplying everything.\n", - "\n", - "And the Peelle covariance, built from the data, is pulled further down *and* is\n", - "narrower than the honest one ($m = 0.439 \\pm 0.097$ against\n", - "$0.503 \\pm 0.104$) — confidently wrong, the worst of both." - ] - }, - { - "cell_type": "markdown", - "id": "f4f25df1", - "metadata": {}, - "source": [ - "## 5. Inferring a normalisation or an offset nobody reported (recipe 4)\n", - "\n", - "The magnitude of the mode need not be known either. `T.normalization(log_eta)`\n", - "samples it, and `T.offset(...)` does the same for an additive shift. Here we\n", - "plant an offset defect and treat it three ways: with the magnitude known, with\n", - "it free, and by ignoring it." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "96e6305c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:55:30.159078Z", - "iopub.status.busy": "2026-09-14T19:55:30.158697Z", - "iopub.status.idle": "2026-09-14T19:55:30.169951Z", - "shell.execute_reply": "2026-09-14T19:55:30.168905Z" - } - }, - "outputs": [], - "source": [ - "offset = 0.3\n", - "y_off = y_true + rng.normal(0.0, noise_fraction * y_true) + offset\n", - "data_off = rx.Dataset(x, y_off, noise_fraction * y_off, label=\"offset\")\n", - "comp_off = rx.Comparison(data_off, line)\n", - "\n", - "log_omega = rx.Parameter(\n", - " \"log_omega\", prior=stats.norm(np.log(0.3), 1.0), latex=r\"\\log\\omega\"\n", - ")\n", - "p_off_known = rx.Problem(\n", - " [rx.Constraint([comp_off], terms=[T.offset(magnitude=offset)])]\n", - ")\n", - "p_off_free = rx.Problem([rx.Constraint([comp_off], terms=[T.offset(log_omega)])])\n", - "p_off_ignored = rx.Problem([rx.Constraint([comp_off])])" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "57ab6fc3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:55:30.173305Z", - "iopub.status.busy": "2026-09-14T19:55:30.172936Z", - "iopub.status.idle": "2026-09-14T19:56:30.225875Z", - "shell.execute_reply": "2026-09-14T19:56:30.224943Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "offset known : m = 0.535 +/- 0.109 b = 2.359 +/- 0.310\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "offset free : m = 0.547 +/- 0.108 b = 2.553 +/- 0.725 log_omega = -1.140 +/- 1.013\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "offset ignored : m = 0.535 +/- 0.110 b = 2.301 +/- 0.061\n" - ] - } - ], - "source": [ - "for name, p in [\n", - " (\"known\", p_off_known),\n", - " (\"free\", p_off_free),\n", - " (\"ignored\", p_off_ignored),\n", - "]:\n", - " s = fit(p, seed=5)\n", - " print(f\"offset {name:8s}: {summary(p, s)}\")" - ] - }, - { - "cell_type": "markdown", - "id": "ecd7ab34", - "metadata": {}, - "source": [ - "## Takeaways\n", - "\n", - "- The covariance *is* the statistical model. Declaring it is the work; the\n", - " inference machinery does not change from one rung of the ladder to the next.\n", - "- A defect the experiment never reported can be inferred: a noise level, a\n", - " normalisation, an offset — each one more column in the chain.\n", - "- A correlated systematic is a mode built from the *prediction*. Built from the\n", - " data instead, it is Peelle's Pertinent Puzzle, and it will quietly pull the\n", - " answer down.\n", - "\n", - "When two experiments measure the same thing, the same question comes back one\n", - "level up: do they share a parameter, or do they share a mode? That is\n", - "`sharing_error_models`, next door." + "Statistics only is confidently wrong, inferring the noise *does* cover the data but not the true line. The systematic covariance built from the prediction covers the truth at an honest width, it is the only one of the four that knows the defect was a single number multiplying everything. And the Peelle covariance is the worst of all, biased below the data." ] } ], From f27876e03a60892f4216dcf155441a3881ff0eff Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Tue, 22 Sep 2026 12:06:01 -0400 Subject: [PATCH 70/75] Sample sharing_error_models with dynesty, one panel per error model With one normalisation mode spanning both experiments the posterior grows a long thin tail: the shared scale runs away and the line is barely constrained. emcee walkers that wander in stay in, and a few stuck ones move the means and widths the notebook reports. Nested sampling works inwards from the whole prior and does not get stuck that way, so this notebook now uses dynesty and says why. The comparison plot gets one panel per error model instead of four bands over each other. --- docs/design.md | 2 +- examples/sharing_error_models.ipynb | 252 ++++++++++------------------ 2 files changed, 91 insertions(+), 163 deletions(-) diff --git a/docs/design.md b/docs/design.md index d5cef5d..968bc6b 100644 --- a/docs/design.md +++ b/docs/design.md @@ -665,7 +665,7 @@ a time unless noted. |---|---|---|---|---| | `linear_calibration` | 1, 2, 17, 40 | emcee | the whole workflow on a line with an inferred constant noise; prior and posterior predictive on a new grid with `grid_draws`, the model's band against a measurement's, and why reported per-point errors cannot go there; coverage of both | 23 s | | `error_models` | 2, 4, 19 | emcee | the covariance ladder on one comparison, the Peelle matrix as a fixed term, offsets known, free and ignored | 85 s | -| `sharing_error_models` | 5 | emcee | two experiments with opposite normalisation defects: sharing a parameter, a mode per dataset, one mode spanning both, and the assembled covariance seen directly | 78 s | +| `sharing_error_models` | 5 | dynesty | two experiments with opposite normalisation defects: sharing a parameter, a mode per dataset, one mode spanning both, and the assembled covariance seen directly; one panel per error model | 102 s | | `normalization_and_covariance_structure` | 3, 4, 6, 27 | emcee | six treatments of five experiments' normalisations, one of them badly mis-quoted and alone in its range; Peelle's puzzle in the two-point case it was found in; a gallery of covariance structures from `matrix(theta)` | 353 s | | `gp_discrepancy` | 7 | emcee, dynesty | mean-zero discrepancies with amplitudes growing in x: four rungs on a toy line, three on n+⁴⁰Ca missing its surface absorption; the three predictive objects (model plus discrepancy, plus experimental, and the conditioned regression it does not use), with the equations | 483 s | | `robust_likelihoods` | 9, 39 | emcee | Student-t versus Gaussian on three gross outliers; the iterative rejection loop, including the round that over-rejects and recovers | 54 s | diff --git a/examples/sharing_error_models.ipynb b/examples/sharing_error_models.ipynb index 1526b38..e196d02 100644 --- a/examples/sharing_error_models.ipynb +++ b/examples/sharing_error_models.ipynb @@ -36,18 +36,11 @@ "cell_type": "code", "execution_count": 1, "id": "d1df0443", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:52.557329Z", - "iopub.status.busy": "2026-09-14T19:54:52.557031Z", - "iopub.status.idle": "2026-09-14T19:54:56.637369Z", - "shell.execute_reply": "2026-09-14T19:54:56.636262Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "import corner\n", - "import emcee\n", + "import dynesty\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import plotstyle\n", @@ -76,14 +69,7 @@ "cell_type": "code", "execution_count": 2, "id": "403664b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:56.640537Z", - "iopub.status.busy": "2026-09-14T19:54:56.640181Z", - "iopub.status.idle": "2026-09-14T19:54:56.646441Z", - "shell.execute_reply": "2026-09-14T19:54:56.645384Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "rng = np.random.default_rng(16)\n", @@ -104,14 +90,7 @@ "cell_type": "code", "execution_count": 3, "id": "1cd295d6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:56.648762Z", - "iopub.status.busy": "2026-09-14T19:54:56.648524Z", - "iopub.status.idle": "2026-09-14T19:54:58.043457Z", - "shell.execute_reply": "2026-09-14T19:54:58.042331Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -156,22 +135,16 @@ "source": [ "## The model and the way we run a fit\n", "\n", - "The same line, the same wide prior, and the same three helpers as in\n", - "`error_models`: `fit`, `band` and `summary`." + "We use the same line and the same wide prior as in `error_models`, and three small helpers: `fit`, `band` and `summary`.\n", + "\n", + "One thing is different. There we sampled with emcee, but here we'll use [nested sampling](https://en.wikipedia.org/wiki/Nested_sampling_algorithm) through [dynesty](https://dynesty.readthedocs.io/) ([Speagle 2020](https://doi.org/10.1093/mnras/staa278)). When one normalisation mode spans both experiments, the posterior develops a long, thin tail in which the shared scale grows and the line is barely determined. Ensemble walkers that wander into a tail like that tend to stay there, and a few stuck walkers are enough to distort the means and widths we report. Nested sampling works inwards from the whole prior, so it doesn't get stuck that way." ] }, { "cell_type": "code", "execution_count": 4, "id": "cda7a0a7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:58.046529Z", - "iopub.status.busy": "2026-09-14T19:54:58.046283Z", - "iopub.status.idle": "2026-09-14T19:54:58.052573Z", - "shell.execute_reply": "2026-09-14T19:54:58.051494Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "m = rx.Parameter(\"m\", prior=stats.norm(2.0, 2.0), latex=\"m\")\n", @@ -184,21 +157,20 @@ "cell_type": "code", "execution_count": 5, "id": "2e601856", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:58.055601Z", - "iopub.status.busy": "2026-09-14T19:54:58.055250Z", - "iopub.status.idle": "2026-09-14T19:54:58.064010Z", - "shell.execute_reply": "2026-09-14T19:54:58.062927Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "def fit(problem, seed, n_walkers=24, n_steps=1800):\n", - " sampler = emcee.EnsembleSampler(n_walkers, problem.ndim, problem.log_posterior)\n", - " sampler.random_state = np.random.RandomState(seed).get_state()\n", - " sampler.run_mcmc(problem.sample_prior(n_walkers, rng=seed), n_steps, progress=False)\n", - " return sampler.get_chain(discard=n_steps // 3, thin=5, flat=True)\n", + "def fit(problem, seed, nlive=250):\n", + " sampler = dynesty.NestedSampler(\n", + " problem.log_likelihood,\n", + " problem.prior_transform,\n", + " problem.ndim,\n", + " nlive=nlive,\n", + " sample=\"rwalk\",\n", + " rstate=np.random.default_rng(seed),\n", + " )\n", + " sampler.run_nested(dlogz=0.5, print_progress=False)\n", + " return sampler.results.samples_equal(rstate=np.random.default_rng(seed))\n", "\n", "\n", "def band(problem, samples, levels=(5, 95)):\n", @@ -236,14 +208,7 @@ "cell_type": "code", "execution_count": 6, "id": "c52b1b84", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:58.067270Z", - "iopub.status.busy": "2026-09-14T19:54:58.066908Z", - "iopub.status.idle": "2026-09-14T19:54:58.078869Z", - "shell.execute_reply": "2026-09-14T19:54:58.077636Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -258,7 +223,10 @@ "shared = rx.Parameter(\"log_eps\", prior=stats.norm(np.log(0.05), 1.0))\n", "c_shared = rx.Constraint(\n", " [comp1, comp2],\n", - " terms=[T.noise_fraction(shared, on=comp1), T.noise_fraction(shared, on=comp2)],\n", + " terms=[\n", + " T.proportional_error(shared, on=comp1),\n", + " T.proportional_error(shared, on=comp2),\n", + " ],\n", " statistical=False,\n", ")\n", "p_shared = rx.Problem([c_shared])\n", @@ -268,7 +236,7 @@ "eps2 = rx.Parameter(\"log_eps_2\", prior=stats.norm(np.log(0.05), 1.0))\n", "c_separate = rx.Constraint(\n", " [comp1, comp2],\n", - " terms=[T.noise_fraction(eps1, on=comp1), T.noise_fraction(eps2, on=comp2)],\n", + " terms=[T.proportional_error(eps1, on=comp1), T.proportional_error(eps2, on=comp2)],\n", " statistical=False,\n", ")\n", "print(\"two objects -> two columns: \", rx.Problem([c_separate]).names)" @@ -290,21 +258,14 @@ "cell_type": "code", "execution_count": 7, "id": "20fb0438", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:58.081538Z", - "iopub.status.busy": "2026-09-14T19:54:58.081207Z", - "iopub.status.idle": "2026-09-14T19:55:27.313004Z", - "shell.execute_reply": "2026-09-14T19:55:27.311858Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "m = 0.502 +/- 0.133 b = 2.115 +/- 0.065 log_eps = -2.338 +/- 0.114\n", - "inferred noise fraction: 0.097\n" + "m = 0.502 +/- 0.138 b = 2.115 +/- 0.068 log_eps = -2.334 +/- 0.114\n", + "inferred noise fraction: 0.098\n" ] } ], @@ -337,22 +298,15 @@ "cell_type": "code", "execution_count": 8, "id": "822f035a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:55:27.315426Z", - "iopub.status.busy": "2026-09-14T19:55:27.315171Z", - "iopub.status.idle": "2026-09-14T19:57:01.921250Z", - "shell.execute_reply": "2026-09-14T19:57:01.920296Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "statistics only : m = 0.569 +/- 0.043 b = 2.162 +/- 0.020\n", - "a mode per dataset : m = 0.648 +/- 0.064 b = 2.054 +/- 0.154\n", - "one mode across both (case A) : m = 0.484 +/- 0.573 b = 1.831 +/- 2.202\n" + "statistics only : m = 0.568 +/- 0.044 b = 2.163 +/- 0.020\n", + "a mode per dataset : m = 0.644 +/- 0.064 b = 2.048 +/- 0.153\n", + "one mode across both (case A) : m = 0.602 +/- 0.081 b = 2.280 +/- 0.255\n" ] } ], @@ -392,22 +346,13 @@ "id": "8b68bb40", "metadata": {}, "source": [ - "That is what declaring the wrong sharing costs.\n", + "Here's what each of those error models did.\n", "\n", - "Statistics only is precise and wrong, as usual: $b = 2.162 \\pm 0.020$, eight\n", - "standard deviations from the truth, because with no way to say \"these two\n", - "experiments are on different scales\" the fit splits the difference and believes\n", - "it.\n", + "**Statistics only** is precise and wrong, as usual: $b = 2.163 \\pm 0.020$, eight standard deviations from the truth. With no way to say \"these two experiments are on different scales\", the fit splits the difference between them and believes the result.\n", "\n", - "A mode per dataset is the model that matches how the defect arose, and it\n", - "behaves: $m = 0.648 \\pm 0.064$, $b = 2.054 \\pm 0.154$, both covering.\n", + "**A mode per dataset** matches how the defect actually arose, and it behaves: $m = 0.644 \\pm 0.064$ and $b = 2.048 \\pm 0.153$, both within a standard deviation of the truth.\n", "\n", - "Case A insists that one unknown moved both experiments together. It cannot\n", - "explain a *relative* disagreement, so it does the only thing left: it inflates\n", - "the shared scale until the line is barely determined at all\n", - "($m = 0.484 \\pm 0.573$, $b = 1.831 \\pm 2.202$). The fit does not fail loudly;\n", - "it just stops saying anything useful. Declaring the wrong sharing is not a\n", - "small error." + "**Case A** insists that one unknown moved both experiments together. It can't explain a *relative* disagreement between them, so it compromises. Its line comes out at $m = 0.602 \\pm 0.081$ and $b = 2.280 \\pm 0.255$. That still covers the truth, but $b$ sits more than a standard deviation high, with an error bar almost twice as wide as the per-dataset model's. The fit doesn't fail loudly; it quietly gives a less accurate and less precise answer, because it's describing a situation that didn't happen." ] }, { @@ -426,14 +371,7 @@ "cell_type": "code", "execution_count": 9, "id": "3029669f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:57:01.924273Z", - "iopub.status.busy": "2026-09-14T19:57:01.924025Z", - "iopub.status.idle": "2026-09-14T19:57:02.249747Z", - "shell.execute_reply": "2026-09-14T19:57:02.248721Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -464,59 +402,42 @@ { "cell_type": "code", "execution_count": 10, - "id": "be260c5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:57:02.252734Z", - "iopub.status.busy": "2026-09-14T19:57:02.252486Z", - "iopub.status.idle": "2026-09-14T19:57:02.277035Z", - "shell.execute_reply": "2026-09-14T19:57:02.276124Z" - } - }, + "id": "a75f0507", + "metadata": {}, "outputs": [], "source": [ + "# one colour and one line style per error model, so the corner plot below\n", + "# stays readable even where the contours overlap\n", "runs = {\n", - " \"statistics only\": (p_stat, s_stat, plotstyle.COLOURS[0], plotstyle.HATCHES[0]),\n", - " \"shared inferred noise\": (\n", - " p_shared,\n", - " s_shared,\n", - " plotstyle.COLOURS[2],\n", - " plotstyle.HATCHES[1],\n", - " ),\n", - " \"a mode per dataset\": (\n", - " p_per_set,\n", - " s_per_set,\n", - " plotstyle.COLOURS[4],\n", - " plotstyle.HATCHES[2],\n", - " ),\n", - " \"one mode across both\": (\n", - " p_case_a,\n", - " s_case_a,\n", - " plotstyle.COLOURS[1],\n", - " plotstyle.HATCHES[3],\n", - " ),\n", + " \"statistics only\": (p_stat, s_stat, plotstyle.COLOURS[0], \"-\"),\n", + " \"shared inferred noise\": (p_shared, s_shared, plotstyle.COLOURS[1], \"--\"),\n", + " \"a mode per dataset\": (p_per_set, s_per_set, plotstyle.COLOURS[2], \"-.\"),\n", + " \"one mode across both\": (p_case_a, s_case_a, plotstyle.COLOURS[3], \":\"),\n", "}\n", "bands = {label: band(p, s) for label, (p, s, _, _) in runs.items()}" ] }, + { + "cell_type": "markdown", + "id": "9ec693d2", + "metadata": {}, + "source": [ + "### The fitted line under each error model\n", + "\n", + "Four bands on one set of axes overlap too much to tell apart, so we give each error model its own panel, on the same axes, with the truth and both datasets drawn in every one." + ] + }, { "cell_type": "code", "execution_count": 11, - "id": "498bd9ae", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:57:02.279988Z", - "iopub.status.busy": "2026-09-14T19:57:02.279742Z", - "iopub.status.idle": "2026-09-14T19:57:02.625883Z", - "shell.execute_reply": "2026-09-14T19:57:02.624889Z" - } - }, + "id": "d7b36626", + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 704x440 with 1 Axes>" + "<Figure size 880x660 with 4 Axes>" ] }, "metadata": {}, @@ -524,34 +445,33 @@ } ], "source": [ - "fig, ax = plt.subplots()\n", - "for label, (lo, hi) in bands.items():\n", - " _, _, colour, hatch = runs[label]\n", - " plotstyle.band(ax, x_fine, lo, hi, color=colour, hatch=hatch, label=label)\n", - "ax.plot(x_fine, truth[\"m\"] * x_fine + truth[\"b\"], \"--\", color=\"k\", label=\"truth\")\n", - "ax.errorbar(data1.x, data1.y, data1.y_err, fmt=\"o\", color=\"0.4\", ms=3)\n", - "ax.errorbar(data2.x, data2.y, data2.y_err, fmt=\"s\", color=\"0.4\", ms=3)\n", - "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"90 % bands of the fitted line\")\n", - "ax.legend(fontsize=8)\n", + "fig, axes = plt.subplots(2, 2, figsize=(8.0, 6.0), sharex=True, sharey=True)\n", + "for ax, (label, (lo, hi)) in zip(axes.flat, bands.items()):\n", + " colour = runs[label][2]\n", + " plotstyle.band(ax, x_fine, lo, hi, color=colour, label=\"90 % band\")\n", + " ax.plot(x_fine, truth[\"m\"] * x_fine + truth[\"b\"], \"--\", color=\"k\", label=\"truth\")\n", + " ax.errorbar(data1.x, data1.y, data1.y_err, fmt=\"o\", color=\"0.45\", ms=3)\n", + " ax.errorbar(data2.x, data2.y, data2.y_err, fmt=\"s\", color=\"0.45\", ms=3)\n", + " ax.set_title(label, color=colour)\n", + "for ax in axes[1]:\n", + " ax.set_xlabel(\"$x$\")\n", + "for ax in axes[:, 0]:\n", + " ax.set_ylabel(\"$y$\")\n", + "axes[0, 0].legend(fontsize=8, loc=\"upper left\")\n", + "fig.suptitle(\"90 % bands of the fitted line\")\n", + "fig.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, - "id": "c4759e6b", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:57:02.630617Z", - "iopub.status.busy": "2026-09-14T19:57:02.630370Z", - "iopub.status.idle": "2026-09-14T19:57:02.942337Z", - "shell.execute_reply": "2026-09-14T19:57:02.941358Z" - } - }, + "id": "e82ade5a", + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 605x605 with 4 Axes>" ] @@ -562,7 +482,7 @@ ], "source": [ "fig = None\n", - "for label, (p, s, colour, _) in runs.items():\n", + "for label, (p, s, colour, style) in runs.items():\n", " fig = corner.corner(\n", " s[:, p.columns(line.params)],\n", " fig=fig,\n", @@ -570,12 +490,19 @@ " truths=[truth[\"m\"], truth[\"b\"]],\n", " range=[(0.2, 1.1), (1.6, 2.6)],\n", " **plotstyle.corner_kwargs(\n", - " color=colour, fill_contours=False, plot_density=False, show_titles=False\n", + " color=colour,\n", + " fill_contours=False,\n", + " plot_density=False,\n", + " show_titles=False,\n", + " levels=(0.68, 0.95),\n", + " contour_kwargs={\"linestyles\": style, \"linewidths\": 1.8},\n", + " hist_kwargs={\"linestyle\": style, \"linewidth\": 1.8},\n", " ),\n", " )\n", "fig.legend(\n", " handles=[\n", - " plt.Line2D([], [], color=c, label=lab) for lab, (_, _, c, _) in runs.items()\n", + " plt.Line2D([], [], color=c, ls=ls, lw=1.8, label=lab)\n", + " for lab, (_, _, c, ls) in runs.items()\n", " ],\n", " loc=\"upper right\",\n", " fontsize=8,\n", @@ -600,7 +527,8 @@ " is the model that matches how the defect arose here.\n", "- One mode across both says a single unknown moved them together. That is a\n", " stronger statement, and here it is the wrong one: our experiments drifted\n", - " apart, not together, so the fit could only widen until it said nothing. It\n", + " apart, not together, so the fit could only compromise, less accurate and\n", + " less precise than the model that matches what happened. It\n", " is the right model when the experiments really do share a calibration —\n", " recipe 37 is the version for several quantities of one experiment whose\n", " normalisations are correlated." From cd5b4b2730b8455ef0b4040fca5160a5dbddb10b Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Tue, 22 Sep 2026 12:06:20 -0400 Subject: [PATCH 71/75] Add the tempering section to robust_likelihoods (recipe 12) Student-t and outlier rejection both answer "this point does not belong to the measurement". Tempering is reached for in the same spirit but answers something else, so the notebook now ends by trying it on a correctly specified 200-point line: the prior on the corner plot, the posterior widening with the weight, and what that does to predictive coverage. Recipe 12 joins the notebook's citation line and the design's table. --- docs/design.md | 2 +- examples/robust_likelihoods.ipynb | 400 ++++++++++++++++++++---------- test/test_notebooks_index.py | 2 +- 3 files changed, 274 insertions(+), 130 deletions(-) diff --git a/docs/design.md b/docs/design.md index 968bc6b..97476d0 100644 --- a/docs/design.md +++ b/docs/design.md @@ -668,7 +668,7 @@ a time unless noted. | `sharing_error_models` | 5 | dynesty | two experiments with opposite normalisation defects: sharing a parameter, a mode per dataset, one mode spanning both, and the assembled covariance seen directly; one panel per error model | 102 s | | `normalization_and_covariance_structure` | 3, 4, 6, 27 | emcee | six treatments of five experiments' normalisations, one of them badly mis-quoted and alone in its range; Peelle's puzzle in the two-point case it was found in; a gallery of covariance structures from `matrix(theta)` | 353 s | | `gp_discrepancy` | 7 | emcee, dynesty | mean-zero discrepancies with amplitudes growing in x: four rungs on a toy line, three on n+⁴⁰Ca missing its surface absorption; the three predictive objects (model plus discrepancy, plus experimental, and the conditioned regression it does not use), with the equations | 483 s | -| `robust_likelihoods` | 9, 39 | emcee | Student-t versus Gaussian on three gross outliers; the iterative rejection loop, including the round that over-rejects and recovers | 54 s | +| `robust_likelihoods` | 9, 12, 39 | emcee | Student-t versus Gaussian on three gross outliers; the iterative rejection loop, including the round that over-rejects and recovers; tempering a correctly specified 200-point line, with the prior on the corner plot and the predictive coverage | 80 s | | `error_scale_and_usu` | 34 | emcee | a global scale on the reported errors under both likelihoods; a USU offset on the technique we suspect | 90 s | | `local_optical_model_calibration` | 4, 12, 14, 15, 16, 21, 26 | dynesty | EXFOR O1199007, p + ⁴⁰Ca at 35 MeV, which quotes no systematics: the unit contract, three error models against a potential wrong at the 30 % level, the singular guard, tempering and its coverage, other drivers | 1565 s | | `alpha_ca_error_model_comparison` | 7, 10, 11, 13, 17, 18 | dynesty | real ⁴⁴Ca(α,α) data, a four-parameter potential, a five-rung ladder by evidence with the Jacobian (the last rung a GP whose amplitude grows with angle), predictive draws carrying the covariance and their coverage, the mean-zero discrepancy envelope that says *where* the potential fails, held-out backward angles scored conditionally | 1280 s | diff --git a/examples/robust_likelihoods.ipynb b/examples/robust_likelihoods.ipynb index 5ac4257..d2c6be0 100644 --- a/examples/robust_likelihoods.ipynb +++ b/examples/robust_likelihoods.ipynb @@ -2,10 +2,10 @@ "cells": [ { "cell_type": "markdown", - "id": "ffc28c78", + "id": "5bcc02f5", "metadata": {}, "source": [ - "# Robust likelihoods: Student-t versus the multivariate normal\n", + "# Robust likelihoods\n", "\n", "A few gross outliers make a Gaussian fit confidently wrong. The good news is\n", "that the likelihood *functional* is a drop-in choice in `rxmc`: we keep the\n", @@ -15,26 +15,21 @@ "with a sampled tail parameter, and the fit widens instead of breaking.\n", "\n", "That is one of two honest answers to an outlier. The other is to decide the\n", - "point does not belong to the measurement at all and reject it, which we do at\n", - "the end, in the outer loop of recipe 39. If instead we suspect the *errors* are\n", - "larger than stated, that is a different question, and it has its own notebook\n", - "next door (`error_scale_and_usu`).\n", + "point does not belong to the measurement at all and reject it, which we do\n", + "next, in the outer loop of recipe 39. At the end we look at a third knob that\n", + "is often reached for in the same spirit: *tempering* the likelihood, so that\n", + "the data count for less. If instead we suspect the *errors* are larger than\n", + "stated, that is a different question, and it has its own notebook next door\n", + "(`error_scale_and_usu`).\n", "\n", - "Recipes: 9, 39" + "Recipes: 9, 12, 39" ] }, { "cell_type": "code", "execution_count": 1, "id": "f6300218", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:56:34.608658Z", - "iopub.status.busy": "2026-09-14T19:56:34.608441Z", - "iopub.status.idle": "2026-09-14T19:56:37.786709Z", - "shell.execute_reply": "2026-09-14T19:56:37.785785Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "import corner\n", @@ -65,14 +60,7 @@ "cell_type": "code", "execution_count": 2, "id": "5a862393", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:56:37.789668Z", - "iopub.status.busy": "2026-09-14T19:56:37.789331Z", - "iopub.status.idle": "2026-09-14T19:56:37.798686Z", - "shell.execute_reply": "2026-09-14T19:56:37.797485Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -101,14 +89,7 @@ "cell_type": "code", "execution_count": 3, "id": "0fac5bbe", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:56:37.800918Z", - "iopub.status.busy": "2026-09-14T19:56:37.800683Z", - "iopub.status.idle": "2026-09-14T19:56:39.244531Z", - "shell.execute_reply": "2026-09-14T19:56:39.243435Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -156,14 +137,7 @@ "cell_type": "code", "execution_count": 4, "id": "ff2eba9a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:56:39.247017Z", - "iopub.status.busy": "2026-09-14T19:56:39.246771Z", - "iopub.status.idle": "2026-09-14T19:56:39.255392Z", - "shell.execute_reply": "2026-09-14T19:56:39.254530Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -191,14 +165,7 @@ "cell_type": "code", "execution_count": 5, "id": "3aaaedd7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:56:39.258130Z", - "iopub.status.busy": "2026-09-14T19:56:39.257895Z", - "iopub.status.idle": "2026-09-14T19:56:39.262350Z", - "shell.execute_reply": "2026-09-14T19:56:39.261415Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "def fit(problem, seed, n_walkers=24, n_steps=2000):\n", @@ -212,14 +179,7 @@ "cell_type": "code", "execution_count": 6, "id": "69bbceda", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:56:39.264931Z", - "iopub.status.busy": "2026-09-14T19:56:39.264696Z", - "iopub.status.idle": "2026-09-14T19:57:10.620202Z", - "shell.execute_reply": "2026-09-14T19:57:10.619106Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "s_gauss, s_t = fit(p_gauss, 1), fit(p_t, 2)" @@ -229,14 +189,7 @@ "cell_type": "code", "execution_count": 7, "id": "76f55c31", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:57:10.622945Z", - "iopub.status.busy": "2026-09-14T19:57:10.622571Z", - "iopub.status.idle": "2026-09-14T19:57:10.897205Z", - "shell.execute_reply": "2026-09-14T19:57:10.896127Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -279,14 +232,7 @@ "cell_type": "code", "execution_count": 8, "id": "d32705b6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:57:10.899874Z", - "iopub.status.busy": "2026-09-14T19:57:10.899528Z", - "iopub.status.idle": "2026-09-14T19:57:10.908353Z", - "shell.execute_reply": "2026-09-14T19:57:10.907205Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -312,14 +258,7 @@ "cell_type": "code", "execution_count": 9, "id": "fd2ad161", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:57:10.910885Z", - "iopub.status.busy": "2026-09-14T19:57:10.910547Z", - "iopub.status.idle": "2026-09-14T19:57:11.138938Z", - "shell.execute_reply": "2026-09-14T19:57:11.138077Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -376,14 +315,7 @@ "cell_type": "code", "execution_count": 10, "id": "e7ea32cc", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:57:11.142285Z", - "iopub.status.busy": "2026-09-14T19:57:11.142027Z", - "iopub.status.idle": "2026-09-14T19:57:11.147056Z", - "shell.execute_reply": "2026-09-14T19:57:11.145768Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -405,14 +337,7 @@ "cell_type": "code", "execution_count": 11, "id": "3ab4d1e4", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:57:11.150553Z", - "iopub.status.busy": "2026-09-14T19:57:11.150177Z", - "iopub.status.idle": "2026-09-14T19:57:11.157048Z", - "shell.execute_reply": "2026-09-14T19:57:11.156013Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "x_fine = np.linspace(-0.5, 4.5, 60)\n", @@ -434,14 +359,7 @@ "cell_type": "code", "execution_count": 12, "id": "22d9487d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:57:11.159150Z", - "iopub.status.busy": "2026-09-14T19:57:11.158924Z", - "iopub.status.idle": "2026-09-14T19:57:11.368970Z", - "shell.execute_reply": "2026-09-14T19:57:11.367890Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -506,14 +424,7 @@ "cell_type": "code", "execution_count": 13, "id": "26724634", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:57:11.371577Z", - "iopub.status.busy": "2026-09-14T19:57:11.371336Z", - "iopub.status.idle": "2026-09-14T19:57:11.376983Z", - "shell.execute_reply": "2026-09-14T19:57:11.375931Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "def reject(constraint, data, *, k=3.0, max_rounds=6, seed=11):\n", @@ -537,14 +448,7 @@ "cell_type": "code", "execution_count": 14, "id": "60313e72", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:57:11.379347Z", - "iopub.status.busy": "2026-09-14T19:57:11.379090Z", - "iopub.status.idle": "2026-09-14T19:58:00.034042Z", - "shell.execute_reply": "2026-09-14T19:58:00.032968Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -576,14 +480,7 @@ "cell_type": "code", "execution_count": 15, "id": "2353e732", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:58:00.036374Z", - "iopub.status.busy": "2026-09-14T19:58:00.036125Z", - "iopub.status.idle": "2026-09-14T19:58:00.337856Z", - "shell.execute_reply": "2026-09-14T19:58:00.336652Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -657,7 +554,251 @@ }, { "cell_type": "markdown", - "id": "bc6c79d5", + "id": "afe6bf44", + "metadata": {}, + "source": [ + "## Tempering the likelihood (recipe 12)\n", + "\n", + "So far we've changed the *shape* of the likelihood (the Student-t) and *which points* it sees (rejection). There's a third, blunter knob: how much the likelihood counts at all. `Constraint(weight=w)` multiplies that constraint's log-likelihood by $w$ and changes nothing else, so the posterior becomes\n", + "\n", + "$$p_w(\\theta \\mid y) \\propto p(y \\mid \\theta)^{w}\\, p(\\theta),$$\n", + "\n", + "a *tempered* or *power* posterior. With $w < 1$ the data pull less hard against the prior, and the posterior widens. KDUQ uses exactly this, with a \"democratic\" weight $w = k/N$ for $k$ parameters and $N$ data points ([Pruitt, Escher & Rahman (2023)](https://arxiv.org/abs/2211.07741)). There's also a principled version for misspecified models, where the weight is learned from the data: SafeBayes, from [Grünwald & van Ommen (2017)](https://doi.org/10.1214/17-BA1085), which is recipe 25.\n", + "\n", + "Tempering is usually justified as a guard against overconfidence, so the first thing to check is what it does when nothing is wrong. We'll use a *correctly specified* problem on purpose: 200 points on a straight line, each with 5 % noise that is reported honestly. The truth is known exactly, so we can ask whether tempering improves anything we care about." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "50b30e1e", + "metadata": {}, + "outputs": [], + "source": [ + "rng_200 = np.random.default_rng(11)\n", + "x200 = np.linspace(0.0, 1.0, 200)\n", + "m200_true, b200_true = 2.0, 4.0\n", + "y200 = (m200_true * x200 + b200_true) * (1.0 + rng_200.normal(0.0, 0.05, x200.size))\n", + "d200 = rx.Dataset(x200, y200, 0.05 * y200, label=\"N = 200\")\n", + "\n", + "m200 = rx.Parameter(\"m\", prior=stats.norm(1.0, 0.5), latex=\"m\")\n", + "b200 = rx.Parameter(\"b\", prior=stats.norm(4.0, 0.5), latex=\"b\")\n", + "line200 = rx.Model(lambda x, m, b: m * x + b, [m200, b200])\n", + "comp200 = rx.Comparison(d200, line200)\n", + "p_full = rx.Problem([rx.Constraint([comp200])])\n", + "p_temp = rx.Problem([rx.Constraint([comp200], weight=2 / 200)])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "99219706", + "metadata": {}, + "outputs": [], + "source": [ + "s_full, s_temp = fit(p_full, 3), fit(p_temp, 4)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "03b8da7c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "untempered m = 1.9792 +/- 0.0601 (pull -0.3) b = 3.9927 +/- 0.0305 (pull -0.2)\n", + "tempered m = 1.4720 +/- 0.3661 (pull -1.4) b = 4.1842 +/- 0.2158 (pull +0.9)\n" + ] + } + ], + "source": [ + "for name, s in ((\"untempered\", s_full), (\"tempered\", s_temp)):\n", + " pull_m = (s[:, 0].mean() - m200_true) / s[:, 0].std()\n", + " pull_b = (s[:, 1].mean() - b200_true) / s[:, 1].std()\n", + " print(\n", + " f\"{name:11s} m = {s[:, 0].mean():.4f} +/- {s[:, 0].std():.4f} (pull {pull_m:+.1f})\"\n", + " f\" b = {s[:, 1].mean():.4f} +/- {s[:, 1].std():.4f} (pull {pull_b:+.1f})\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "551b1d2e", + "metadata": {}, + "source": [ + "### Does tempering help here?\n", + "\n", + "The untempered fit covers the truth comfortably: $m = 1.979 \\pm 0.060$ against 2.0, and $b = 3.993 \\pm 0.031$ against 4.0, both within a third of a standard deviation. Two hundred points at 5 % each really do pin a straight line down that well.\n", + "\n", + "Tempering by $k/N = 2/200$ widens the posterior roughly sixfold, and here it makes the answer slightly *worse*: $m = 1.47 \\pm 0.37$, 1.4 sigma low. That's the prior showing through. Our prior on $m$ is centred at 1, two prior widths below the truth, and with the likelihood turned down to a hundredth of its strength the prior gets a much bigger say.\n", + "\n", + "It helps to see this on the prior's own scale. Below we draw the prior, the tempered posterior and the untempered posterior on one corner plot, zoomed out far enough to show the whole prior. The untempered posterior collapses to a spike. That can look alarming next to a wide prior, and it's easy to read as a failure to cover the truth. It isn't one: in a correctly specified problem with this much data, a spike is the right answer." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "cbb33f78", + "metadata": {}, + "outputs": [], + "source": [ + "prior_draws = p_full.sample_prior(len(s_full), rng=5)\n", + "prior_span = [(0.0, 3.0), (2.8, 5.2)]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6be1289a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 605x605 with 4 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# the untempered posterior is drawn last, so the 1-D panels are scaled to its\n", + "# spike and the prior and tempered histograms show how flat they are beside it\n", + "fig = None\n", + "for s, colour in (\n", + " (prior_draws, plotstyle.COLOURS[6]),\n", + " (s_temp, plotstyle.COLOURS[2]),\n", + " (s_full, plotstyle.COLOURS[0]),\n", + "):\n", + " fig = corner.corner(\n", + " s,\n", + " fig=fig,\n", + " labels=[\"$m$\", \"$b$\"],\n", + " truths=[m200_true, b200_true],\n", + " range=prior_span,\n", + " **plotstyle.corner_kwargs(\n", + " color=colour,\n", + " bins=60,\n", + " fill_contours=False,\n", + " plot_density=False,\n", + " show_titles=False,\n", + " levels=(0.68, 0.95),\n", + " truth_color=\"k\",\n", + " hist_kwargs={\"density\": True, \"linewidth\": 1.6},\n", + " ),\n", + " )\n", + "fig.legend(\n", + " handles=[\n", + " plt.Line2D([], [], color=plotstyle.COLOURS[6], label=\"prior\"),\n", + " plt.Line2D([], [], color=plotstyle.COLOURS[2], label=\"tempered, $w = k/N$\"),\n", + " plt.Line2D([], [], color=plotstyle.COLOURS[0], label=\"untempered\"),\n", + " ],\n", + " loc=\"upper right\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7eff4d24", + "metadata": {}, + "source": [ + "### What coverage says\n", + "\n", + "A tight parameter posterior isn't the same thing as an overconfident *prediction*, and the check that actually tells them apart is the [coverage](https://en.wikipedia.org/wiki/Coverage_probability) of the posterior predictive at the measured points (recipe 17). The untempered predictive should already follow the diagonal, because a prediction of the *data* carries the 5 % noise even when the line itself is pinned down. For contrast we also draw the band of the line alone, which should miss, just as it did in `linear_calibration`." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "45b54540", + "metadata": {}, + "outputs": [], + "source": [ + "levels = np.linspace(0.1, 0.9, 9)\n", + "c200 = p_full.constraints[0]\n", + "coverage, width = {}, {}\n", + "for (name, p, s), seed in zip(\n", + " ((\"untempered\", p_full, s_full), (\"tempered\", p_temp, s_temp)), (6, 7)\n", + "):\n", + " draws = rx.diagnostics.predictive_draws(\n", + " p, s[::20], n_rep=2, rng=seed, return_draws=True\n", + " )\n", + " coverage[name] = rx.diagnostics.coverage_curve(draws, c200.y[c200.active], levels)\n", + " width[name] = rx.diagnostics.sharpness(draws).mean()\n", + "line_only = rx.diagnostics.predictive_draws(\n", + " p_full, s_full[::20], model_only=True, return_draws=True\n", + ")\n", + "coverage[\"line alone\"] = rx.diagnostics.coverage_curve(\n", + " line_only, c200.y[c200.active], levels\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "d9e93adf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 506x484 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(4.6, 4.4))\n", + "ax.plot([0, 1], [0, 1], \"--\", color=\"0.5\", label=\"nominal\")\n", + "for name, colour, marker in (\n", + " (\"untempered\", plotstyle.COLOURS[0], \"o-\"),\n", + " (\"tempered\", plotstyle.COLOURS[2], \"^-\"),\n", + "):\n", + " ax.plot(\n", + " levels,\n", + " coverage[name],\n", + " marker,\n", + " color=colour,\n", + " label=f\"{name} (68 % width {width[name]:.3f})\",\n", + " )\n", + "ax.plot(\n", + " levels,\n", + " coverage[\"line alone\"],\n", + " \"s--\",\n", + " color=plotstyle.COLOURS[1],\n", + " label=\"the line alone (expected to miss)\",\n", + ")\n", + "ax.set(\n", + " xlabel=\"nominal coverage\",\n", + " ylabel=\"empirical coverage\",\n", + " xlim=(0, 1),\n", + " ylim=(0, 1),\n", + " title=\"Predictive coverage, tempered and not\",\n", + ")\n", + "ax.legend(fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "37909987", + "metadata": {}, + "source": [ + "The untempered predictive is already calibrated, so tempering has nothing to fix here: it only moves the line towards the prior. That's the lesson to carry into real problems. Tempering is worth reaching for when the *predictions* are overconfident, which a coverage check will show you, and not merely because the parameters look precise. When a model really is misspecified, as the optical potentials in the other notebooks are, a better first move is usually to say *how* it's wrong in the error model (`gp_discrepancy`), and to learn the weight from the data (SafeBayes, recipe 25) if we temper at all." + ] + }, + { + "cell_type": "markdown", + "id": "306562f7", "metadata": {}, "source": [ "## Takeaways\n", @@ -669,6 +810,9 @@ "- Rejection is the other answer, and it is an *outer loop* of problems with the\n", " mask refit between rounds (recipe 39). It gives the tightest result and makes\n", " the strongest assumption.\n", + "- Tempering, `Constraint(weight=w)`, turns the whole likelihood down. On a\n", + " correctly specified problem it only lets the prior back in; check the\n", + " predictive coverage before reaching for it (recipe 12).\n", "- If what we actually doubt is the size of the reported errors rather than the\n", " points themselves, that is a different model: see `error_scale_and_usu`." ] diff --git a/test/test_notebooks_index.py b/test/test_notebooks_index.py index 2ff589b..0395a81 100644 --- a/test/test_notebooks_index.py +++ b/test/test_notebooks_index.py @@ -23,7 +23,7 @@ "sharing_error_models": {5}, "normalization_and_covariance_structure": {3, 4, 6, 27}, "gp_discrepancy": {7}, - "robust_likelihoods": {9, 39}, + "robust_likelihoods": {9, 12, 39}, "error_scale_and_usu": {34}, "local_optical_model_calibration": {4, 12, 14, 15, 16, 21, 26}, "alpha_ca_error_model_comparison": {7, 10, 11, 13, 17, 18}, From 63a4416f178ab4f495866a647afac03356672f9b Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Tue, 22 Sep 2026 12:06:41 -0400 Subject: [PATCH 72/75] Rebuild the two reaction notebooks on the jitr error model Both now compare in log space with the same error model the jitr quickstart uses -- reported statistics plus an inferred point-to-point and an inferred common relative error, written as custom terms -- rather than each inventing its own ladder. local_optical_model_calibration reads EXFOR O1199007 from a plain dict (no exfor_tools import), and scores the jitr model against linear residuals with the reported errors alone: by evidence with the Jacobian, by the predictive for the potential alone on a grid and the full predictive at the measured angles, and by three coverage curves each. alpha_ca_error_model_comparison keeps the Matern GP in angle, with its length-scale prior spanning one point spacing to the full range, and compares a constant amplitude against one growing as an inferred power of q. Its cut is shorter than the full study, which section 3 of the design still describes. Citation lines and the design's table follow the new contents. --- docs/design.md | 12 +- .../alpha_ca_error_model_comparison.ipynb | 1078 +++++++-------- .../local_optical_model_calibration.ipynb | 1172 ++++++++--------- test/test_notebooks_index.py | 4 +- 4 files changed, 1067 insertions(+), 1199 deletions(-) diff --git a/docs/design.md b/docs/design.md index 97476d0..4ff6e64 100644 --- a/docs/design.md +++ b/docs/design.md @@ -518,9 +518,11 @@ hook: that contribution is subtracted from `data.y` as preprocessing ## 3. Worked example: an error-model comparison -The shape of `examples/alpha_ca_error_model_comparison.ipynb`: real data -without reported errors, one potential, a ladder of covariances compared by -evidence and by held-out prediction. +The α+⁴⁴Ca study in its full form: real data without reported errors, one +potential, a ladder of covariances compared by evidence and by held-out +prediction. The notebook `examples/alpha_ca_error_model_comparison.ipynb` is +a shorter cut of it, two Gaussian-process amplitudes on top of the `jitr` +error model, with its predictives on a fine grid. ```python import numpy as np, dynesty, rxmc as rx @@ -670,8 +672,8 @@ a time unless noted. | `gp_discrepancy` | 7 | emcee, dynesty | mean-zero discrepancies with amplitudes growing in x: four rungs on a toy line, three on n+⁴⁰Ca missing its surface absorption; the three predictive objects (model plus discrepancy, plus experimental, and the conditioned regression it does not use), with the equations | 483 s | | `robust_likelihoods` | 9, 12, 39 | emcee | Student-t versus Gaussian on three gross outliers; the iterative rejection loop, including the round that over-rejects and recovers; tempering a correctly specified 200-point line, with the prior on the corner plot and the predictive coverage | 80 s | | `error_scale_and_usu` | 34 | emcee | a global scale on the reported errors under both likelihoods; a USU offset on the technique we suspect | 90 s | -| `local_optical_model_calibration` | 4, 12, 14, 15, 16, 21, 26 | dynesty | EXFOR O1199007, p + ⁴⁰Ca at 35 MeV, which quotes no systematics: the unit contract, three error models against a potential wrong at the 30 % level, the singular guard, tempering and its coverage, other drivers | 1565 s | -| `alpha_ca_error_model_comparison` | 7, 10, 11, 13, 17, 18 | dynesty | real ⁴⁴Ca(α,α) data, a four-parameter potential, a five-rung ladder by evidence with the Jacobian (the last rung a GP whose amplitude grows with angle), predictive draws carrying the covariance and their coverage, the mean-zero discrepancy envelope that says *where* the potential fails, held-out backward angles scored conditionally | 1280 s | +| `local_optical_model_calibration` | 10, 14, 15, 16, 17, 18, 19, 21, 40 | dynesty | EXFOR O1199007, p + ⁴⁰Ca at 35 MeV, which quotes no systematics, read from a plain dict: the unit contract; log-space residuals with the jitr α+Ca error model (reported statistics plus inferred point-to-point and common relative errors, as custom terms) against linear residuals with the reported errors alone, compared by evidence with the Jacobian, by predictives for the potential alone (on a grid) and with every error term (at the measured angles), and by three coverage curves each; the singular guard | 929 s | +| `alpha_ca_error_model_comparison` | 7, 10, 13, 17, 18, 19, 40 | dynesty | real ⁴⁴Ca(α,α) data without reported errors, in log space with the jitr error model (inferred point-to-point and common relative errors as custom terms); a Matérn GP in angle, its length-scale prior spanning one point spacing to the full range, with a constant amplitude against one growing as an inferred power of q, by evidence; the potential alone and the full predictive on a fine grid, their coverage, and the mean-zero envelope that says *where* the potential fails | 426 s | | `hierarchical_calibration` | 22, 24, 30, 35, 38 | dynesty | eight schools, with the shrinkage explained rather than assumed; a hierarchy on the physics parameters recovering the evidence a misspecified energy dependence threw away, scored in sample and at a held-out energy | 748 s | **`hierarchical_calibration` in detail.** The truth is diff --git a/examples/alpha_ca_error_model_comparison.ipynb b/examples/alpha_ca_error_model_comparison.ipynb index c1d5bd5..bb5195e 100644 --- a/examples/alpha_ca_error_model_comparison.ipynb +++ b/examples/alpha_ca_error_model_comparison.ipynb @@ -2,32 +2,32 @@ "cells": [ { "cell_type": "markdown", - "id": "aef617a5", + "id": "6999ba52", "metadata": {}, "source": [ - "# Error models for $\\alpha + {}^{44}$Ca elastic scattering: a comparison by evidence\n", + "# A Gaussian-process discrepancy for $\\alpha + {}^{44}$Ca elastic scattering\n", "\n", - "This notebook is more of a small study than a demonstration. We have one real measurement and one optical potential, and the only thing we'll change is the **error model**: five different statements about what the disagreement between our model and the data means. Then we'll ask the data which statement they prefer, in two separate ways. The first is the [Bayesian evidence](https://en.wikipedia.org/wiki/Marginal_likelihood), and the second is how well each error model predicts angles it was never shown.\n", + "This notebook takes one real measurement, one optical potential and one piece of prior knowledge about where that potential goes wrong, and asks whether the data agree with it.\n", "\n", - "The data are differential elastic cross sections, as a ratio to the [Rutherford](https://en.wikipedia.org/wiki/Rutherford_scattering) cross section, for $^{44}$Ca($\\alpha,\\alpha$) at 29 MeV. They come from [EXFOR F0567](https://www-nds.iaea.org/exfor/servlet/X4sSearch5?EntryID=150567), measured by [Oeschler *et al.*, Phys. Rev. Lett. **28**, 694 (1972)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.28.694). Oeschler *et al.* report no uncertainties at all, so every error model below has to infer its own, and that's exactly what makes the comparison interesting.\n", + "The data are differential elastic cross sections, as a ratio to the [Rutherford](https://en.wikipedia.org/wiki/Rutherford_scattering) cross section, for $^{44}$Ca($\\alpha,\\alpha$) at 29 MeV. They come from [EXFOR F0567](https://www-nds.iaea.org/exfor/servlet/X4sSearch5?EntryID=150567), measured by [Oeschler *et al.*, Phys. Rev. Lett. **28**, 694 (1972)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.28.694), who report no uncertainties at all. A four-parameter Woods–Saxon potential describes the forward angles well and the backward angles much less well, and that disagreement is what we want to model.\n", "\n", - "It's worth looking at the [`jitr` quickstart tutorials](https://beykyle.github.io/jitr/getting-started.html) alongside this one. In a sense this notebook is their successor: with `rxmc` we can build more careful statistical models on top of the same physics, including a [Gaussian process](https://en.wikipedia.org/wiki/Gaussian_process) for model discrepancy in the style of [Kennedy & O'Hagan (2001)](https://doi.org/10.1111/1467-9868.00294), which the `gp_discrepancy` notebook introduces.\n", + "We'll model it in two layers. The first is the error model of the `jitr` [$\\alpha$ + Ca Bayesian calibration](https://github.com/beykyle/jitr/blob/main/examples/notebooks/alpha_ca_calibration.ipynb) (the same one `local_optical_model_calibration` uses): an inferred relative error $s$ that is uncorrelated from point to point, and an inferred relative error $b$ common to all points. The second is a mean-zero [Gaussian process](https://en.wikipedia.org/wiki/Gaussian_process) discrepancy in the style of [Kennedy & O'Hagan (2001)](https://doi.org/10.1111/1467-9868.00294), which describes the *smooth*, correlated part of the misfit. We'll compare just two versions of that discrepancy:\n", "\n", - "Recipes: 7, 10, 11, 13, 17, 18" + "1. one whose size is the **same at every angle**;\n", + "2. one whose size **grows as a power of the momentum transfer** $q$, with the power inferred from the data.\n", + "\n", + "The second encodes a physical expectation: large momentum transfer is sensitive to shorter distances, where a simple Woods–Saxon shape is least constrained, so we trust the potential forward and are suspicious of it backward. We'll ask the data which version they prefer by [Bayesian evidence](https://en.wikipedia.org/wiki/Marginal_likelihood), then look at what each one predicts on a fine angular grid, and how well calibrated those predictions are.\n", + "\n", + "It's worth looking at the [`jitr` quickstart tutorials](https://beykyle.github.io/jitr/getting-started.html) alongside this one, since we reuse their potential and priors.\n", + "\n", + "Recipes: 7, 10, 13, 17, 18, 19, 40" ] }, { "cell_type": "code", "execution_count": 1, "id": "fe61dfe1", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:11:10.365176Z", - "iopub.status.busy": "2026-09-14T22:11:10.365058Z", - "iopub.status.idle": "2026-09-14T22:11:12.587702Z", - "shell.execute_reply": "2026-09-14T22:11:12.587088Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "import corner\n", @@ -53,32 +53,28 @@ }, { "cell_type": "markdown", - "id": "a34d6d00", + "id": "c0cf56ce", "metadata": {}, "source": [ "## The data\n", "\n", - "We'll take every fourth angle of the $^{44}$Ca set. That leaves 73 points between 15 and 174 degrees, which is plenty to pin down the diffraction pattern without making every fit four times slower. A ratio to Rutherford is dimensionless, so there are no units to convert. The kinematics the model needs go in `meta`, and since nothing was reported, the error column is just zeros." + "We'll take every fourth angle of the $^{44}$Ca set. That leaves 73 points between 15 and 174 degrees, which is plenty to pin down the diffraction pattern, and it keeps each fit to a few minutes. A ratio to Rutherford is dimensionless, so there are no units to convert, and the kinematics the model needs go in `meta`. There are no reported errors, so the error column is zeros, and every uncertainty below is inferred.\n", + "\n", + "We'll also need the momentum transfer $q = 2k\\sin(\\theta/2)$, so we compute the wavenumber $k$ from the reaction's kinematics now." ] }, { "cell_type": "code", "execution_count": 2, - "id": "5da9f9ff", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:11:12.589448Z", - "iopub.status.busy": "2026-09-14T22:11:12.589216Z", - "iopub.status.idle": "2026-09-14T22:11:12.611548Z", - "shell.execute_reply": "2026-09-14T22:11:12.610745Z" - } - }, + "id": "92ec14d2", + "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv(\"data/alpha_ca_ratio_ruth.csv\")\n", "df = df[df[\"target\"] == \"Ca44\"].iloc[::4]\n", "reaction = jitr.reactions.ElasticReaction(target=(44, 20), projectile=(4, 2))\n", "E_lab = 29.0\n", + "k = reaction.kinematics(E_lab).k\n", "meta = {\"reaction\": reaction, \"Elab\": E_lab, \"quantity\": \"dXS/dRuth\"}\n", "angles = np.deg2rad(df[\"angle_cm_deg\"].to_numpy())\n", "ratio = df[\"ratio_to_rutherford\"].to_numpy()\n", @@ -91,43 +87,31 @@ { "cell_type": "code", "execution_count": 3, - "id": "e325fa60", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:11:12.612939Z", - "iopub.status.busy": "2026-09-14T22:11:12.612814Z", - "iopub.status.idle": "2026-09-14T22:11:12.615701Z", - "shell.execute_reply": "2026-09-14T22:11:12.615086Z" - } - }, + "id": "5a03da60", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "73 points between 15 and 174 degrees\n" + "73 points between 15 and 174 degrees, q from 0.56 to 4.32 fm^-1\n" ] } ], "source": [ + "q_data = rx.reactions.momentum_transfer(angles, k)\n", "print(\n", " f\"{data.n} points between {np.rad2deg(angles.min()):.0f} and \"\n", - " f\"{np.rad2deg(angles.max()):.0f} degrees\"\n", + " f\"{np.rad2deg(angles.max()):.0f} degrees, \"\n", + " f\"q from {q_data.min():.2f} to {q_data.max():.2f} fm^-1\"\n", ")" ] }, { "cell_type": "code", "execution_count": 4, - "id": "db94063b", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:11:12.616940Z", - "iopub.status.busy": "2026-09-14T22:11:12.616824Z", - "iopub.status.idle": "2026-09-14T22:11:13.560064Z", - "shell.execute_reply": "2026-09-14T22:11:13.559407Z" - } - }, + "id": "b2e6b303", + "metadata": {}, "outputs": [ { "data": { @@ -168,14 +152,7 @@ "cell_type": "code", "execution_count": 5, "id": "91f59c49", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:11:13.561561Z", - "iopub.status.busy": "2026-09-14T22:11:13.561410Z", - "iopub.status.idle": "2026-09-14T22:11:13.567564Z", - "shell.execute_reply": "2026-09-14T22:11:13.566780Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "A13 = 44 ** (1 / 3)\n", @@ -213,14 +190,7 @@ "cell_type": "code", "execution_count": 6, "id": "d95b3b0e", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:11:13.568877Z", - "iopub.status.busy": "2026-09-14T22:11:13.568741Z", - "iopub.status.idle": "2026-09-14T22:11:16.936191Z", - "shell.execute_reply": "2026-09-14T22:11:16.935249Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "# we always draw the model on a fine grid: evaluated only at the (downsampled)\n", @@ -232,14 +202,7 @@ "cell_type": "code", "execution_count": 7, "id": "5fe1f329", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:11:16.937635Z", - "iopub.status.busy": "2026-09-14T22:11:16.937507Z", - "iopub.status.idle": "2026-09-14T22:11:24.637967Z", - "shell.execute_reply": "2026-09-14T22:11:24.637150Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -273,173 +236,188 @@ }, { "cell_type": "markdown", - "id": "0836f7d2", + "id": "f58bf0a9", "metadata": {}, "source": [ - "## The error-model ladder\n", + "## The error model\n", "\n", - "The data span three decades, and although their errors were never reported, they're presumably multiplicative: a few per cent of the cross section, whatever its size. Multiplicative errors become additive in log space, so that's where we'll compare (recipe 10). Passing `space=tf.log` to a `Comparison` transforms the data once and every prediction along with it. Later on, `problem.log_jacobian()` will be what lets us compare a log-space evidence with a linear-space one.\n", + "The data span three decades, and the disagreement between a local potential and real data is naturally *relative*: a fraction of the cross section, whatever its size. Relative errors become additive in log space, so that's where we'll compare (recipe 10). `space=tf.log` transforms the data once and every prediction along with it.\n", "\n", - "Since nothing was reported, every rung sets `statistical=False`, and the inferred terms *are* the whole covariance. These are the five rungs we'll climb:\n", + "### The inferred errors, $s$ and $b$\n", "\n", - "| label | error model |\n", - "|---|---|\n", - "| `L0` | constant noise in log space, $\\sigma = \\epsilon$ on every point |\n", - "| `E0` | fractional noise in linear space, $\\sigma_i = \\epsilon\\, y_{m,i}$ |\n", - "| `L2y` | `L0` plus a free normalisation mode, $\\eta\\, y_m$ (the `jitr` notebook's model) |\n", - "| `Lgp` | `L0` plus a [Matérn](https://en.wikipedia.org/wiki/Mat%C3%A9rn_covariance_function)(5/2) Gaussian process in $u = \\theta/\\pi$ with a free amplitude |\n", - "| `Lgpn` | the same, but with an amplitude that **grows with angle** |\n", + "Following `jitr`, we think of each measured point as the prediction multiplied by two independent [log-normal](https://en.wikipedia.org/wiki/Log-normal_distribution) factors with mean one, $y_i = y_{m,i}\\,A_i\\,B$. $A_i$ is a relative error uncorrelated from point to point, and $B$ is a relative error common to all points, like an unknown normalisation. Choosing $\\mathrm{Var}[B] = b^2$ and $\\mathrm{Var}[A_i] = s^2/(1+b^2)$ gives, in log space,\n", + "\n", + "$$\\Sigma^{\\log}_{ij} = \\log\\!\\left(1 + \\frac{s^2}{1+b^2}\\right)\\delta_{ij} + \\log\\!\\left(1 + b^2\\right),$$\n", + "\n", + "which we write as two one-line `rx.Term`s (recipe 19); `local_optical_model_calibration` derives it in more detail. Both $s$ and $b$ get log-uniform priors between 5 % and 200 %, as in `jitr`.\n", + "\n", + "We need $s$ for more than bookkeeping. These data were digitised from a figure with deep diffraction minima, and their log residuals scatter from one angle to the next by tens of per cent. Only an uncorrelated term can absorb that. Without it, a smooth discrepancy would be forced to become rough enough to chase every point, and it would stop describing anything about the potential.\n", "\n", - "`Lgpn` is the only rung that can say *where* the potential fails, rather than just how badly. A stationary kernel with a constant amplitude declares the same uncertainty at every angle, and nobody really believes that about an optical model: forward angles, dominated by Coulomb scattering, are much easier to get right than the backward ones.\n", + "### The discrepancy\n", "\n", - "A couple of bookkeeping notes. We reuse the same `log_eps` parameter object across rungs, and each `Problem` compiles independently of the others (recipe 18). The priors on the error scales are log-uniform between 5 % and 200 %, as in the `jitr` notebook (recipe 13)." + "On top of $s$ and $b$ we add a Matérn(5/2) [kernel](https://en.wikipedia.org/wiki/Mat%C3%A9rn_covariance_function) (recipe 7), evaluated in the scaled angle $u = \\theta/\\pi$. It's tempting to evaluate it in $q$ itself, but $q \\propto \\sin(\\theta/2)$ flattens out at backward angles: there, neighbouring measured angles are almost on top of each other in $q$, and a kernel would insist they agree exactly. In $u$ the measured angles are evenly spread.\n", + "\n", + "The correlation length $\\ell$ gets a log-uniform prior between the two natural extremes of this dataset: the spacing between neighbouring measured angles, and the full angular range they cover. That's a range of about $N$, the number of points.\n", + "\n", + "The two versions differ only in the amplitude $a$ that scales the kernel, and that's where $q$ comes in:\n", + "\n", + "| label | discrepancy amplitude |\n", + "|---|---|\n", + "| `constant` | $a = A$ at every angle |\n", + "| `power of q` | $a = A\\,(q/2k)^{p} = A \\sin^{p}(\\theta/2)$, with $p$ inferred |\n", + "\n", + "In the second, $A$ is the size of the discrepancy at 180 degrees and $p$ says how quickly it grows towards there; $p$ near zero would mean the data see no growth at all. $A$ is log-uniform between 5 % and 200 %, and $p$ is uniform between 0 and 4 (recipe 13)." ] }, { "cell_type": "code", "execution_count": 8, - "id": "9421f3fd", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:11:24.639529Z", - "iopub.status.busy": "2026-09-14T22:11:24.639352Z", - "iopub.status.idle": "2026-09-14T22:11:24.648255Z", - "shell.execute_reply": "2026-09-14T22:11:24.647321Z" - } - }, + "id": "6a54d545", + "metadata": {}, "outputs": [], "source": [ - "comp_log = rx.Comparison(data, omp, space=tf.log)\n", - "comp_lin = rx.Comparison(data, omp)\n", - "log_eps = rx.Parameter(\n", - " \"log_eps\",\n", - " prior=stats.uniform(np.log(0.05), np.log(2.0) - np.log(0.05)),\n", - " latex=r\"\\log\\epsilon\",\n", - ")\n", - "log_eta = rx.Parameter(\n", - " \"log_eta\",\n", - " prior=stats.uniform(np.log(0.05), np.log(2.0) - np.log(0.05)),\n", - " latex=r\"\\log\\eta\",\n", - ")\n", - "log_amp = rx.Parameter(\n", - " \"log_A\",\n", - " prior=stats.uniform(np.log(0.05), np.log(2.0) - np.log(0.05)),\n", - " latex=r\"\\log A\",\n", - ")\n", + "comp = rx.Comparison(data, omp, space=tf.log)\n", + "\n", + "\n", + "def log_uniform(lo, hi):\n", + " return stats.uniform(np.log(lo), np.log(hi) - np.log(lo))\n", + "\n", + "\n", + "# the jitr error model: point-to-point and common relative errors, in log space\n", + "log_s = rx.Parameter(\"log_s\", prior=log_uniform(0.05, 2.0), latex=r\"\\log s\")\n", + "log_b = rx.Parameter(\"log_b\", prior=log_uniform(0.05, 2.0), latex=r\"\\log b\")\n", + "\n", + "\n", + "def point_to_point_sd(c, log_s, log_b):\n", + " s2, b2 = np.exp(2 * log_s), np.exp(2 * log_b)\n", + " return np.full(len(c), np.sqrt(np.log1p(s2 / (1 + b2))))\n", + "\n", + "\n", + "def common_sd(c, log_b):\n", + " return np.full(len(c), np.sqrt(np.log1p(np.exp(2 * log_b))))\n", + "\n", + "\n", + "error_terms = [\n", + " rx.Term(point_to_point_sd, (log_s, log_b), kind=\"diag\"),\n", + " rx.Term(common_sd, (log_b,), kind=\"mode\"),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7cab4bee", + "metadata": {}, + "outputs": [], + "source": [ + "def by_angle(x):\n", + " return np.asarray(x, dtype=float) / np.pi\n", + "\n", + "\n", + "u_data = np.sort(by_angle(angles))\n", + "shortest, longest = np.median(np.diff(u_data)), u_data[-1] - u_data[0]\n", "log_ell = rx.Parameter(\n", - " \"log_ell\",\n", - " prior=stats.uniform(np.log(0.02), np.log(1.0) - np.log(0.02)),\n", - " latex=r\"\\log\\ell\",\n", - ")\n", - "log_amp_n = rx.Parameter(\n", - " \"log_A_n\",\n", - " prior=stats.uniform(np.log(0.05), np.log(2.0) - np.log(0.05)),\n", - " latex=r\"\\log A_n\",\n", + " \"log_ell\", prior=log_uniform(shortest, longest), latex=r\"\\log\\ell\"\n", ")\n", - "amp_slope = rx.Parameter(\"amp_slope\", prior=stats.norm(1.0, 1.0), latex=\"s_A\")\n", - "gp = T.kernel(\n", + "log_A = rx.Parameter(\"log_A\", prior=log_uniform(0.05, 2.0), latex=r\"\\log A\")\n", + "log_A_q = rx.Parameter(\"log_A_q\", prior=log_uniform(0.05, 2.0), latex=r\"\\log A\")\n", + "power = rx.Parameter(\"p\", prior=stats.uniform(0.0, 4.0), latex=\"p\")\n", + "\n", + "\n", + "def power_of_q(c, log_amplitude, p):\n", + " # c.x is u = theta / pi, so q / 2k = sin(theta / 2) = sin(pi u / 2)\n", + " return np.exp(log_amplitude) * np.sin(0.5 * np.pi * np.asarray(c.x)) ** p\n", + "\n", + "\n", + "gp_constant = T.kernel(\n", " Matern(0.1, nu=2.5),\n", - " on=comp_log,\n", - " coords=lambda x: x / np.pi,\n", + " coords=by_angle,\n", " amplitude=T.constant_amplitude,\n", - " amplitude_params=(log_amp,),\n", + " amplitude_params=(log_A,),\n", " params=[log_ell],\n", ")\n", - "\n", - "gp_n = T.kernel(\n", + "gp_power = T.kernel(\n", " Matern(0.1, nu=2.5),\n", - " on=comp_log,\n", - " coords=lambda x: x / np.pi,\n", - " amplitude=T.exp_growth_amplitude(1.0),\n", - " amplitude_params=(log_amp_n, amp_slope),\n", + " coords=by_angle,\n", + " amplitude=power_of_q,\n", + " amplitude_params=(log_A_q, power),\n", " params=[log_ell],\n", ")" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "c0ce8912", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:11:24.649739Z", - "iopub.status.busy": "2026-09-14T22:11:24.649588Z", - "iopub.status.idle": "2026-09-14T22:11:24.657947Z", - "shell.execute_reply": "2026-09-14T22:11:24.657294Z" + "execution_count": 10, + "id": "0fce2c51", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "length-scale prior on u: 0.0099 (one spacing) to 0.883 (the full range), a ratio of 89 for 73 points\n" + ] } - }, + ], + "source": [ + "print(\n", + " f\"length-scale prior on u: {shortest:.4f} (one spacing) to {longest:.3f} \"\n", + " f\"(the full range), a ratio of {longest / shortest:.0f} for {data.n} points\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f5f42a8b", + "metadata": {}, "outputs": [], "source": [ - "ladder = {\n", - " \"L0\": rx.Constraint([comp_log], terms=[T.noise(log_eps)], statistical=False),\n", - " \"E0\": rx.Constraint(\n", - " [comp_lin], terms=[T.noise_fraction(log_eps)], statistical=False\n", - " ),\n", - " \"L2y\": rx.Constraint(\n", - " [comp_log],\n", - " terms=[T.noise(log_eps), T.normalization(log_eta)],\n", - " statistical=False,\n", - " ),\n", - " \"Lgp\": rx.Constraint([comp_log], terms=[T.noise(log_eps), gp], statistical=False),\n", - " \"Lgpn\": rx.Constraint(\n", - " [comp_log], terms=[T.noise(log_eps), gp_n], statistical=False\n", - " ),\n", + "gps = {\"constant\": gp_constant, \"power of q\": gp_power}\n", + "problems = {\n", + " name: rx.Problem(\n", + " [rx.Constraint([comp], terms=[*error_terms, gp], statistical=False)]\n", + " )\n", + " for name, gp in gps.items()\n", "}\n", - "problems = {name: rx.Problem([c]) for name, c in ladder.items()}" + "colours = {\"constant\": plotstyle.COLOURS[1], \"power of q\": plotstyle.COLOURS[0]}" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "763293ca", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:11:24.659356Z", - "iopub.status.busy": "2026-09-14T22:11:24.659222Z", - "iopub.status.idle": "2026-09-14T22:11:24.662279Z", - "shell.execute_reply": "2026-09-14T22:11:24.661560Z" - } - }, + "execution_count": 12, + "id": "c707911e", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "L0 columns: ['V', 'W', 'r', 'a', 'log_eps']\n", - "E0 columns: ['V', 'W', 'r', 'a', 'log_eps']\n", - "L2y columns: ['V', 'W', 'r', 'a', 'log_eps', 'log_eta']\n", - "Lgp columns: ['V', 'W', 'r', 'a', 'log_eps', 'log_ell', 'log_A']\n", - "Lgpn columns: ['V', 'W', 'r', 'a', 'log_eps', 'log_ell', 'log_A_n', 'amp_slope']\n" + "constant columns: ['V', 'W', 'r', 'a', 'log_s', 'log_b', 'log_ell', 'log_A']\n", + "power of q columns: ['V', 'W', 'r', 'a', 'log_s', 'log_b', 'log_ell', 'log_A_q', 'p']\n" ] } ], "source": [ "for name, p in problems.items():\n", - " print(f\"{name:4s} columns: {p.names}\")" + " print(f\"{name:10s} columns: {p.names}\")" ] }, { "cell_type": "markdown", - "id": "163e2db0", + "id": "2f17a91f", "metadata": {}, "source": [ - "## Running the ladder\n", + "## Running the calibration\n", "\n", "Optical-model posteriors are strongly correlated and often multimodal, so we'll use [nested sampling](https://en.wikipedia.org/wiki/Nested_sampling_algorithm) ([Skilling 2006](https://doi.org/10.1214/06-BA127)) through [dynesty](https://dynesty.readthedocs.io/) ([Speagle 2020](https://doi.org/10.1093/mnras/staa278)). It copes well with that kind of posterior, and it computes the evidence we're about to compare as a by-product." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "id": "f3d12040", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:11:24.663693Z", - "iopub.status.busy": "2026-09-14T22:11:24.663573Z", - "iopub.status.idle": "2026-09-14T22:11:24.666525Z", - "shell.execute_reply": "2026-09-14T22:11:24.665857Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "def fit_nested(problem, seed, nlive=80, dlogz=1.5):\n", @@ -457,16 +435,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "fcfa775f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:11:24.667804Z", - "iopub.status.busy": "2026-09-14T22:11:24.667687Z", - "iopub.status.idle": "2026-09-14T22:24:35.394985Z", - "shell.execute_reply": "2026-09-14T22:24:35.394083Z" - } - }, + "execution_count": 14, + "id": "4cadad4e", + "metadata": {}, "outputs": [], "source": [ "results, samples = {}, {}\n", @@ -477,40 +448,30 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "e8b9e24f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:24:35.396478Z", - "iopub.status.busy": "2026-09-14T22:24:35.396327Z", - "iopub.status.idle": "2026-09-14T22:24:35.399302Z", - "shell.execute_reply": "2026-09-14T22:24:35.398835Z" - } - }, + "execution_count": 15, + "id": "c213d49e", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "L0 log Z = -101.42 +/- 0.95 26153 calls\n", - "E0 log Z = 211.36 +/- 1.03 34980 calls\n", - "L2y log Z = -98.22 +/- 0.85 22672 calls\n", - "Lgp log Z = -92.18 +/- 0.95 30383 calls\n", - "Lgpn log Z = -88.70 +/- 0.88 35387 calls\n" + "constant log Z = -90.25 +/- 0.88 29792 calls\n", + "power of q log Z = -84.86 +/- 0.98 35396 calls\n" ] } ], "source": [ "for name, res in results.items():\n", " print(\n", - " f\"{name:4s} log Z = {res.logz[-1]:8.2f} +/- {res.logzerr[-1]:.2f} \"\n", + " f\"{name:10s} log Z = {res.logz[-1]:8.2f} +/- {res.logzerr[-1]:.2f} \"\n", " f\"{int(np.sum(res.ncall)):6d} calls\"\n", " )" ] }, { "cell_type": "markdown", - "id": "e8271256", + "id": "b07bf0f2", "metadata": {}, "source": [ "## Evidence, and what a Bayes factor is\n", @@ -519,25 +480,16 @@ "\n", "$$Z = \\int p(\\mathbf{y} \\mid \\theta)\\, p(\\theta)\\, \\mathrm{d}\\theta .$$\n", "\n", - "Because it's an average, it rewards models that put their probability where the data actually landed, and it penalises models that spread probability thinly over parameter space they turned out not to need. It's an automatic [Occam's razor](https://en.wikipedia.org/wiki/Occam%27s_razor). The ratio of two evidences is the [Bayes factor](https://en.wikipedia.org/wiki/Bayes_factor), and it tells us how much the data should shift our relative belief in one error model over another. Nested sampling computes $\\log Z$ directly, which is the main reason we're using it here.\n", + "Because it's an average, it rewards models that put their probability where the data actually landed, and it penalises models that spread probability thinly over parameter space they turned out not to need. It's an automatic [Occam's razor](https://en.wikipedia.org/wiki/Occam%27s_razor): the power-of-$q$ amplitude has one more parameter than the constant one, and it only wins if that parameter earns its keep. The ratio of two evidences is the [Bayes factor](https://en.wikipedia.org/wiki/Bayes_factor), and it tells us how much the data should shift our relative belief in one model over the other.\n", "\n", - "There are two practical things to keep in mind. First, a Bayes factor depends on the priors, not just on how good the best fit is, so the prior widths are part of the claim we're making. That's why we stated them explicitly above. Second, evidences computed in different comparison spaces aren't comparable as they stand. The log transform stretches and squeezes the data axis, so the densities pick up a factor from the [change of variables](https://en.wikipedia.org/wiki/Probability_density_function#Function_of_random_variables_and_change_of_variables_in_the_probability_density_function). Adding `problem.log_jacobian()` accounts for it and puts the log-space rungs in the same units as the linear-space `E0`.\n", - "\n", - "To compare two rungs we'll use `compare_logz`. It calls the result a tie unless the difference is more than twice the combined sampler error, because differences smaller than that are bookkeeping noise, not evidence." + "Two practical notes. A Bayes factor depends on the priors, not just on how good the best fit is, so the prior ranges we stated above are part of the claim. And since we fitted in log space, we add `problem.log_jacobian()` so that $\\log Z$ is a density for the measured ratios themselves (recipe 18). Both models share the same Jacobian, so it cancels in their Bayes factor. `compare_logz` calls the result a tie unless the difference is more than twice the combined sampler error." ] }, { "cell_type": "code", - "execution_count": 14, - "id": "d509f59f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:24:35.400823Z", - "iopub.status.busy": "2026-09-14T22:24:35.400702Z", - "iopub.status.idle": "2026-09-14T22:24:35.403769Z", - "shell.execute_reply": "2026-09-14T22:24:35.403181Z" - } - }, + "execution_count": 16, + "id": "efffbe81", + "metadata": {}, "outputs": [], "source": [ "logz = {\n", @@ -547,75 +499,63 @@ " )\n", " for name in problems\n", "}\n", - "best = max(logz, key=lambda n: logz[n][0])\n", - "comparisons = {\n", - " name: rx.diagnostics.compare_logz(logz[best], logz[name])\n", - " for name in problems\n", - " if name != best\n", - "}" + "verdict = rx.diagnostics.compare_logz(logz[\"power of q\"], logz[\"constant\"])" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "c4e6ba59", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:24:35.405174Z", - "iopub.status.busy": "2026-09-14T22:24:35.405054Z", - "iopub.status.idle": "2026-09-14T22:24:35.408386Z", - "shell.execute_reply": "2026-09-14T22:24:35.407842Z" - } - }, + "execution_count": 17, + "id": "578ce107", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "rung log Z (linear-space units)\n", - "L0 213.05 +/- 0.95\n", - "E0 211.36 +/- 1.03\n", - "L2y 216.26 +/- 0.85\n", - "Lgp 222.30 +/- 0.95\n", - "Lgpn 225.78 +/- 0.88\n", + "constant log Z = 224.23 +/- 0.88 (density of the ratios)\n", + "power of q log Z = 229.62 +/- 0.98 (density of the ratios)\n", "\n", - "best rung: Lgpn\n", - "Lgpn vs L0: dlogZ = 12.72 +/- 1.29 -> Lgpn favoured\n", - "Lgpn vs E0: dlogZ = 14.41 +/- 1.36 -> Lgpn favoured\n", - "Lgpn vs L2y: dlogZ = 9.52 +/- 1.23 -> Lgpn favoured\n", - "Lgpn vs Lgp: dlogZ = 3.48 +/- 1.30 -> Lgpn favoured\n" + "power of q vs constant: dlogZ = 5.39 +/- 1.32 -> power of q favoured\n" ] } ], "source": [ - "print(f\"{'rung':5s} {'log Z (linear-space units)':>28s}\")\n", "for name, (mean, err, _) in logz.items():\n", - " print(f\"{name:5s} {mean:20.2f} +/- {err:.2f}\")\n", - "print(f\"\\nbest rung: {best}\")\n", - "for name, v in comparisons.items():\n", - " verdict = {\"a\": f\"{best} favoured\", \"b\": f\"{name} favoured\", \"tie\": \"tie\"}\n", - " print(\n", - " f\"{best} vs {name}: dlogZ = {v['dlogZ']:6.2f} +/- {v['err']:.2f}\"\n", - " f\" -> {verdict[v['verdict']]}\"\n", - " )" + " print(f\"{name:10s} log Z = {mean:8.2f} +/- {err:.2f} (density of the ratios)\")\n", + "words = {\"a\": \"power of q favoured\", \"b\": \"constant favoured\", \"tie\": \"a tie\"}\n", + "print(\n", + " f\"\\npower of q vs constant: dlogZ = {verdict['dlogZ']:.2f} +/- {verdict['err']:.2f}\"\n", + " f\" -> {words[verdict['verdict']]}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a6773971", + "metadata": {}, + "source": [ + "The data prefer the growing amplitude: $\\log Z$ is $229.6$ against $224.2$, a difference of $5.4 \\pm 1.3$, comfortably past the two-sigma bar `compare_logz` insists on. A log Bayes factor of about 5 means the data raise the odds of the power-of-$q$ model over the constant one by a factor of roughly $e^{5.4} \\approx 200$, even after it pays for its extra parameter." + ] + }, + { + "cell_type": "markdown", + "id": "37c0afce", + "metadata": {}, + "source": [ + "### The potential under each discrepancy\n", + "\n", + "The discrepancy model changes what the data can say about the potential, so let's overlay the two posteriors for $V$, $W$, $r$ and $a$." ] }, { "cell_type": "code", - "execution_count": 16, - "id": "7ea27b55", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:24:35.409747Z", - "iopub.status.busy": "2026-09-14T22:24:35.409626Z", - "iopub.status.idle": "2026-09-14T22:24:35.964983Z", - "shell.execute_reply": "2026-09-14T22:24:35.964396Z" - } - }, + "execution_count": 18, + "id": "360da7f8", + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 1067x1067 with 16 Axes>" ] @@ -627,29 +567,23 @@ "source": [ "labels = [f\"${q.latex}$\" for q in params]\n", "fig = None\n", - "rungs = [\n", - " (\"L0\", plotstyle.COLOURS[1]),\n", - " (\"L2y\", plotstyle.COLOURS[4]),\n", - " (\"Lgp\", plotstyle.COLOURS[2]),\n", - " (\"Lgpn\", plotstyle.COLOURS[0]),\n", - "]\n", - "for name, colour in rungs:\n", + "for name, p in problems.items():\n", " fig = corner.corner(\n", - " samples[name][:, problems[name].columns(params)],\n", + " samples[name][:, p.columns(params)],\n", " fig=fig,\n", " labels=labels,\n", - " range=[(100, 220), (0, 45), (1.1, 1.7), (0.3, 0.8)],\n", " **plotstyle.corner_kwargs(\n", - " color=colour,\n", + " color=colours[name],\n", " fill_contours=False,\n", " plot_density=False,\n", " show_titles=False,\n", - " levels=(0.68,),\n", + " levels=(0.68, 0.95),\n", " ),\n", " )\n", "fig.legend(\n", - " handles=[plt.Line2D([], [], color=c, label=n) for n, c in rungs],\n", + " handles=[plt.Line2D([], [], color=c, label=n) for n, c in colours.items()],\n", " loc=\"upper right\",\n", + " bbox_to_anchor=(0.95, 0.95),\n", " fontsize=9,\n", ")\n", "plt.show()" @@ -657,95 +591,130 @@ }, { "cell_type": "markdown", - "id": "79fd1158", + "id": "a7f0240b", + "metadata": {}, + "source": [ + "The two posteriors for the potential don't even agree on which family of potentials to use. With a constant amplitude, the posterior for $V$ splits between a shallow family near 125 MeV and a deeper one near 165 MeV. That's the well-known discrete ambiguity of $\\alpha$-nucleus potentials: depths with different numbers of interior nodes in the scattering wavefunction give nearly the same cross section. With the power-of-$q$ amplitude, nearly all the weight lands on the deeper family, and $r$ and $a$ tighten with it. A discrepancy that is allowed to be small at forward angles makes the forward data count for more, and those are what distinguish the two families." + ] + }, + { + "cell_type": "markdown", + "id": "6eb7d309", "metadata": {}, "source": [ - "## What each rung predicts (recipe 17)\n", + "### The inferred errors and the discrepancy's hyperparameters\n", "\n", - "Evidence tells us which error model the data prefer. It doesn't tell us whether *any* of them describes the data well, and for that we want the [posterior predictive](https://en.wikipedia.org/wiki/Posterior_predictive_distribution). By that we mean draws of $y_m(\\theta)$ plus noise with the covariance each rung declared, not just the spread of the model curve.\n", + "Next, the error model itself: how large $s$, $b$ and the discrepancy came out, and over what correlation length." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "c9b6b639", + "metadata": {}, + "outputs": [], + "source": [ + "hyper = {}\n", + "for name, p in problems.items():\n", + " s = samples[name]\n", + " amp = log_A if name == \"constant\" else log_A_q\n", + " hyper[name] = {\n", + " label: np.percentile(np.exp(s[:, p.columns(par)].ravel()), [16, 50, 84])\n", + " for label, par in ((\"s\", log_s), (\"b\", log_b), (\"ell\", log_ell), (\"A\", amp))\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6c91234f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "constant\n", + " s = 0.610 (68 % interval 0.524 to 0.764)\n", + " b = 0.421 (68 % interval 0.166 to 0.941)\n", + " ell = 0.019 (68 % interval 0.015 to 0.024)\n", + " A = 0.611 (68 % interval 0.528 to 0.708)\n", + "power of q\n", + " s = 0.574 (68 % interval 0.500 to 0.743)\n", + " b = 0.342 (68 % interval 0.149 to 0.981)\n", + " ell = 0.024 (68 % interval 0.017 to 0.033)\n", + " A = 1.139 (68 % interval 0.865 to 1.488)\n" + ] + } + ], + "source": [ + "for name, rows in hyper.items():\n", + " print(name)\n", + " for label, (lo, med, hi) in rows.items():\n", + " print(f\" {label:3s} = {med:.3f} (68 % interval {lo:.3f} to {hi:.3f})\")" + ] + }, + { + "cell_type": "markdown", + "id": "7ce3ca63", + "metadata": {}, + "source": [ + "Both models agree that the data scatter a lot from one angle to the next: $s$ comes out around 60 %, which is the digitisation scatter we expected. The common error $b$ is poorly constrained in both, between roughly 15 % and 95 %; a single measurement can't tell a common shift of all its points apart from the potential moving.\n", "\n", - "`predictive_draws` gives exactly that. Its draws include $\\Sigma$, so a rung with a large inferred noise produces wide draws even where its model curve is sharp. We'll compare the log-space rungs at the measured angles, and then check how well calibrated they are.\n", + "The correlation length comes out short in both, $\\ell \\approx 0.02$ in $u$, only about two point spacings, so the discrepancy describes departures that change on the scale of the diffraction pattern. The amplitudes are where the models differ: the constant one needs about 61 % everywhere, while the growing one reaches about 114 % at 180 degrees." + ] + }, + { + "cell_type": "markdown", + "id": "1426eb12", + "metadata": {}, + "source": [ + "### How fast does the discrepancy grow?\n", "\n", - "It's worth being clear about which terms these draws carry: *all* of them. A draw of the model plus its discrepancy alone is a statement about the model, not a prediction of a measurement, and comparing it with data would be comparing two different kinds of thing. The `gp_discrepancy` notebook writes out the equations for each. A coverage check only means something against the full predictive, which is what `predictive_draws` gives by default." + "The power $p$ is the one parameter that says whether the growth with $q$ is real. Its prior is flat between 0 and 4, so any shape in the posterior comes from the data." ] }, { "cell_type": "code", - "execution_count": 17, - "id": "2e8f4810", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:24:35.966589Z", - "iopub.status.busy": "2026-09-14T22:24:35.966444Z", - "iopub.status.idle": "2026-09-14T22:24:39.005456Z", - "shell.execute_reply": "2026-09-14T22:24:39.004565Z" - } - }, + "execution_count": 21, + "id": "571262cb", + "metadata": {}, "outputs": [], "source": [ - "levels = np.linspace(0.1, 0.9, 9)\n", - "draws, coverage, cov68, width68 = {}, {}, {}, {}\n", - "for i, (name, _) in enumerate(rungs):\n", - " p = problems[name]\n", - " c = p.constraints[0]\n", - " draws[name] = rx.diagnostics.predictive_draws(\n", - " p, samples[name][::20], n_rep=2, rng=i, return_draws=True\n", - " )\n", - " coverage[name] = rx.diagnostics.coverage_curve(draws[name], c.y[c.active], levels)\n", - " cov68[name] = rx.diagnostics.coverage_curve(draws[name], c.y[c.active], [0.68])[0]\n", - " width68[name] = rx.diagnostics.sharpness(draws[name], transform=np.exp).mean()" + "p_q = problems[\"power of q\"]\n", + "power_draws = samples[\"power of q\"][:, p_q.columns(power)].ravel()\n", + "power_lo, power_med, power_hi = np.percentile(power_draws, [16, 50, 84])" ] }, { "cell_type": "code", - "execution_count": 18, - "id": "763a4854", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:24:39.006843Z", - "iopub.status.busy": "2026-09-14T22:24:39.006692Z", - "iopub.status.idle": "2026-09-14T22:24:39.009578Z", - "shell.execute_reply": "2026-09-14T22:24:39.008947Z" - } - }, + "execution_count": 22, + "id": "94d95f16", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "L0 68 % predictive width = 0.076 (ratio units) coverage at 0.68 = 0.79\n", - "L2y 68 % predictive width = 0.068 (ratio units) coverage at 0.68 = 0.81\n", - "Lgp 68 % predictive width = 0.072 (ratio units) coverage at 0.68 = 0.78\n", - "Lgpn 68 % predictive width = 0.068 (ratio units) coverage at 0.68 = 0.77\n" + "p = 3.22 (68 % interval 2.39 to 3.84)\n" ] } ], "source": [ - "for name, _ in rungs:\n", - " print(\n", - " f\"{name:4s} 68 % predictive width = {width68[name]:6.3f} (ratio units) \"\n", - " f\"coverage at 0.68 = {cov68[name]:.2f}\"\n", - " )" + "print(f\"p = {power_med:.2f} (68 % interval {power_lo:.2f} to {power_hi:.2f})\")" ] }, { "cell_type": "code", - "execution_count": 19, - "id": "a3bde3f9", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:24:39.010800Z", - "iopub.status.busy": "2026-09-14T22:24:39.010674Z", - "iopub.status.idle": "2026-09-14T22:24:39.410523Z", - "shell.execute_reply": "2026-09-14T22:24:39.409904Z" - } - }, + "execution_count": 23, + "id": "828bd51e", + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 704x440 with 1 Axes>" + "<Figure size 572x352 with 1 Axes>" ] }, "metadata": {}, @@ -753,47 +722,76 @@ } ], "source": [ - "fig, ax = plt.subplots()\n", - "for (name, colour), hatch in zip(rungs, plotstyle.HATCHES):\n", - " lo, hi = np.percentile(np.exp(draws[name]), [5, 95], axis=0)\n", - " plotstyle.band(\n", - " ax,\n", - " np.rad2deg(angles),\n", - " lo,\n", - " hi,\n", - " color=colour,\n", - " hatch=hatch,\n", - " label=f\"{name}, 90 % predictive\",\n", - " )\n", - "ax.plot(np.rad2deg(angles), ratio, \"o\", ms=3, color=\"k\", label=\"data\")\n", - "ax.set(\n", - " xlabel=r\"$\\theta_{cm}$ [deg]\",\n", - " ylabel=r\"$\\sigma / \\sigma_{Ruth}$\",\n", - " yscale=\"log\",\n", - " title=\"Posterior predictive, covariance included\",\n", + "fig, ax = plt.subplots(figsize=(5.2, 3.2))\n", + "ax.hist(\n", + " power_draws,\n", + " bins=40,\n", + " range=(0, 4),\n", + " density=True,\n", + " color=colours[\"power of q\"],\n", + " alpha=0.7,\n", + " label=\"posterior\",\n", ")\n", - "ax.legend(fontsize=8)\n", + "ax.axhline(0.25, color=\"0.4\", ls=\"--\", label=\"prior\")\n", + "ax.set(xlabel=\"$p$\", ylabel=\"density\", title=\"The power of $q$ in the amplitude\")\n", + "ax.legend()\n", "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "07372261", + "metadata": {}, + "source": [ + "The data want real growth: $p = 3.2$, with a 68 % interval from 2.4 to 3.8, far from zero. With $p \\approx 3$ the discrepancy at 20 degrees is $\\sin^3(10°) \\approx 0.5\\,\\%$ of its size at 180 degrees, so in effect the data say the potential is almost exact forward and increasingly wrong backward." + ] + }, + { + "cell_type": "markdown", + "id": "6bf38e5d", + "metadata": {}, + "source": [ + "## What each model predicts (recipes 17 and 40)\n", + "\n", + "Now we'll draw the posterior predictive on a fine angular grid with `rx.predictive.grid_draws`, which re-evaluates every covariance term at the new angles from its own definition. For each model there are two different things we might draw:\n", + "\n", + "1. **The potential alone**, $y_m(\\theta;\\alpha)$ with the parameters drawn from the posterior. This is our uncertainty about the *cross section the potential predicts*.\n", + "2. **The full predictive**: the potential plus the discrepancy and the inferred errors $s$ and $b$, all drawn together as one correlated curve. This is our uncertainty about *what a measurement would read*, and it's the one to lay over the data.\n", + "\n", + "Every term here has a value at any angle. $s$ and $b$ are the same at every angle in log space, and the kernel and its amplitude are functions of the angle. That's exactly why the full predictive can go on the fine grid at all. `physical=True` maps the log-space draws back to ratios, so the bands are in the units of the data." + ] + }, { "cell_type": "code", - "execution_count": 20, - "id": "a7d8e5e8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:24:39.412512Z", - "iopub.status.busy": "2026-09-14T22:24:39.412335Z", - "iopub.status.idle": "2026-09-14T22:24:39.513172Z", - "shell.execute_reply": "2026-09-14T22:24:39.512136Z" - } - }, + "execution_count": 24, + "id": "86779a36", + "metadata": {}, + "outputs": [], + "source": [ + "bands = {}\n", + "for i, (name, p) in enumerate(problems.items()):\n", + " rows = samples[name][::10]\n", + " bands[name] = {\n", + " \"potential alone\": rx.predictive.grid_draws(\n", + " p, on_fine, x_fine, rows, model_only=True, physical=True, levels=(5, 95)\n", + " ),\n", + " \"full predictive\": rx.predictive.grid_draws(\n", + " p, on_fine, x_fine, rows, n_rep=2, physical=True, levels=(5, 95), rng=i\n", + " ),\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "58f077e7", + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 506x484 with 1 Axes>" + "<Figure size 1100x440 with 2 Axes>" ] }, "metadata": {}, @@ -801,112 +799,115 @@ } ], "source": [ - "fig, ax = plt.subplots(figsize=(4.6, 4.4))\n", - "ax.plot([0, 1], [0, 1], \"--\", color=\"0.5\", label=\"nominal\")\n", - "for name, colour in rungs:\n", - " ax.plot(levels, coverage[name], \"o-\", color=colour, label=name)\n", - "ax.set(\n", - " xlabel=\"nominal coverage\",\n", - " ylabel=\"empirical coverage\",\n", - " xlim=(0, 1),\n", - " ylim=(0, 1),\n", - " title=\"Is each rung's error model calibrated?\",\n", - ")\n", - "ax.legend(fontsize=8)\n", + "fig, axes = plt.subplots(1, 2, figsize=(10.0, 4.0), sharey=True)\n", + "for ax, (name, pair) in zip(axes, bands.items()):\n", + " lo, hi = pair[\"full predictive\"]\n", + " plotstyle.band(\n", + " ax, np.rad2deg(x_fine), lo, hi, color=colours[name], label=\"full predictive\"\n", + " )\n", + " lo, hi = pair[\"potential alone\"]\n", + " plotstyle.band(\n", + " ax,\n", + " np.rad2deg(x_fine),\n", + " lo,\n", + " hi,\n", + " color=\"k\",\n", + " hatch=plotstyle.HATCHES[0],\n", + " label=\"potential alone\",\n", + " )\n", + " ax.plot(np.rad2deg(angles), ratio, \"o\", ms=2.5, color=\"k\", label=\"data\")\n", + " ax.set(\n", + " xlabel=r\"$\\theta_{cm}$ [deg]\",\n", + " yscale=\"log\",\n", + " title=f\"{name}: 90 % bands\",\n", + " )\n", + "axes[0].set_ylabel(r\"$\\sigma / \\sigma_{Ruth}$\")\n", + "axes[0].legend(fontsize=8, loc=\"lower left\")\n", + "fig.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "8fdf4922", + "id": "ddb882ac", "metadata": {}, "source": [ - "### Where the potential fails, not just how badly\n", - "\n", - "The two GP rungs differ in only one respect: `Lgp` has a constant amplitude, while `Lgpn` has an amplitude that grows with angle. To see what that difference buys us, we can ask each of them for the mean-zero predictive about its own model on a fine angular grid. That's `grid_draws` with `terms=[gp]`: the kernel, and nothing else.\n", + "At first sight the two full predictives look alike, and that's worth understanding. The uncorrelated error $s \\approx 60\\,\\%$ is the same at every angle and dominates the width everywhere, so the discrepancy only shows through where it's comparable to $s$. That happens at backward angles, where the power-of-$q$ band opens up past 150 degrees, and in the middle of the range, where its band is a little narrower than the constant model's. In both panels the potential alone is a narrow band that misses many of the points, which is what the next check quantifies. To see the discrepancy on its own, we'll take $s$ and $b$ out at the end." + ] + }, + { + "cell_type": "markdown", + "id": "a7594448", + "metadata": {}, + "source": [ + "## Are the predictions calibrated?\n", "\n", - "Let's be careful about what goes into that envelope. It carries two things: the posterior spread of the potential itself, and the discrepancy the kernel declares. With a constant amplitude, the discrepancy part is the same at every angle, so whatever shape the envelope has comes almost entirely from the spread of the potential. Sure enough, the `Lgp` envelope is 1.24 wide at 20 degrees, 2.01 at 90 and 1.42 at 170, so it's only 1.1 times wider at the back than at the front. Its *error model* is as unsure about forward angles as about backward ones. The `Lgpn` envelope is 0.52, 1.77 and 2.92 wide at the same angles, a back-to-front ratio of 5.6. Only `Lgpn` is telling us **where** the potential fails.\n", + "A band is only as good as its [coverage](https://en.wikipedia.org/wiki/Coverage_probability): if a predictive distribution is calibrated, its central 68 % interval should contain about 68 % of the measured points, and likewise at every level. We check this at the measured angles with `rx.diagnostics.predictive_draws`, for the potential alone and for the full predictive.\n", "\n", - "Keep in mind that these envelopes are statements about the model, not predictions of a measurement. They carry no experimental term, so we shouldn't lay them over the data. That's exactly the distinction `terms=` encodes, and the `gp_discrepancy` notebook spells out the equations." + "We expect the potential alone to undercover badly. It isn't *wrong*, it answers a different question: it describes where the potential's curve is, and the data don't sit on that curve. The full predictive is the one that should follow the diagonal." ] }, { "cell_type": "code", - "execution_count": 21, - "id": "5406536d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:24:39.514623Z", - "iopub.status.busy": "2026-09-14T22:24:39.514448Z", - "iopub.status.idle": "2026-09-14T22:24:58.332981Z", - "shell.execute_reply": "2026-09-14T22:24:58.332222Z" - } - }, + "execution_count": 26, + "id": "7de4ceb1", + "metadata": {}, "outputs": [], "source": [ - "gp_terms = {\"Lgp\": gp, \"Lgpn\": gp_n}\n", - "envelopes = {}\n", - "for name, term in gp_terms.items():\n", - " p = problems[name]\n", - " band = rx.predictive.grid_draws(\n", - " p, omp.bind(x_fine, meta), x_fine, samples[name], terms=[term],\n", - " levels=(16, 84), rng=3,\n", - " ) # fmt: skip\n", - " theta_med = np.median(samples[name], axis=0)\n", - " mu = np.log(on_fine(*theta_med[p.columns(params)]))\n", - " envelopes[name] = (band[1] - band[0], band[0] - mu, band[1] - mu)" + "levels = np.linspace(0.1, 0.9, 9)\n", + "y_obs = comp.y\n", + "coverage, cov68, width68 = {}, {}, {}\n", + "for i, (name, p) in enumerate(problems.items()):\n", + " rows = samples[name][::10]\n", + " full = rx.diagnostics.predictive_draws(p, rows, n_rep=2, rng=i, return_draws=True)\n", + " alone = rx.diagnostics.predictive_draws(p, rows, model_only=True, return_draws=True)\n", + " coverage[name] = {\n", + " \"full predictive\": rx.diagnostics.coverage_curve(full, y_obs, levels),\n", + " \"potential alone\": rx.diagnostics.coverage_curve(alone, y_obs, levels),\n", + " }\n", + " cov68[name] = {\n", + " \"full predictive\": rx.diagnostics.coverage_curve(full, y_obs, [0.68])[0],\n", + " \"potential alone\": rx.diagnostics.coverage_curve(alone, y_obs, [0.68])[0],\n", + " }\n", + " width68[name] = rx.diagnostics.sharpness(full, transform=np.exp).mean()" ] }, { "cell_type": "code", - "execution_count": 22, - "id": "43f8fe9b", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:24:58.334592Z", - "iopub.status.busy": "2026-09-14T22:24:58.334388Z", - "iopub.status.idle": "2026-09-14T22:24:58.338131Z", - "shell.execute_reply": "2026-09-14T22:24:58.337582Z" - } - }, + "execution_count": 27, + "id": "d23a080a", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Lgp 68 % envelope about the model at 20, 90, 170 deg: 1.235 2.012 1.416 back/front = 1.1\n", - "Lgpn 68 % envelope about the model at 20, 90, 170 deg: 0.522 1.769 2.923 back/front = 5.6\n" + "constant coverage at 0.68: full 0.85, potential alone 0.21; mean 68 % width of the full predictive 0.082 (ratio units)\n", + "power of q coverage at 0.68: full 0.74, potential alone 0.30; mean 68 % width of the full predictive 0.060 (ratio units)\n" ] } ], "source": [ - "for name, (w, _, _) in envelopes.items():\n", - " at = [np.interp(np.deg2rad(a), x_fine, w) for a in (20, 90, 170)]\n", + "for name, c68 in cov68.items():\n", " print(\n", - " f\"{name:5s} 68 % envelope about the model at 20, 90, 170 deg: \"\n", - " + \" \".join(f\"{v:.3f}\" for v in at)\n", - " + f\" back/front = {at[2] / at[0]:.1f}\"\n", + " f\"{name:10s} coverage at 0.68: \"\n", + " f\"full {c68['full predictive']:.2f}, \"\n", + " f\"potential alone {c68['potential alone']:.2f}; \"\n", + " f\"mean 68 % width of the full predictive {width68[name]:.3f} (ratio units)\"\n", " )" ] }, { "cell_type": "code", - "execution_count": 23, - "id": "a187daf6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:24:58.339809Z", - "iopub.status.busy": "2026-09-14T22:24:58.339622Z", - "iopub.status.idle": "2026-09-14T22:24:58.483145Z", - "shell.execute_reply": "2026-09-14T22:24:58.482513Z" - } - }, + "execution_count": 28, + "id": "65192f70", + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 704x440 with 1 Axes>" + "<Figure size 528x506 with 1 Axes>" ] }, "metadata": {}, @@ -914,26 +915,29 @@ } ], "source": [ - "fig, ax = plt.subplots()\n", - "for (name, colour), hatch in zip(\n", - " ((\"Lgp\", plotstyle.COLOURS[2]), (\"Lgpn\", plotstyle.COLOURS[0])),\n", - " plotstyle.HATCHES,\n", - "):\n", - " _, lo, hi = envelopes[name]\n", - " plotstyle.band(\n", - " ax,\n", - " np.rad2deg(x_fine),\n", - " lo,\n", - " hi,\n", - " color=colour,\n", - " hatch=hatch,\n", - " label=f\"{name}, mean-zero predictive about the model (68 %)\",\n", + "fig, ax = plt.subplots(figsize=(4.8, 4.6))\n", + "ax.plot([0, 1], [0, 1], \"--\", color=\"0.5\", label=\"nominal\")\n", + "for name, curves in coverage.items():\n", + " ax.plot(\n", + " levels,\n", + " curves[\"full predictive\"],\n", + " \"o-\",\n", + " color=colours[name],\n", + " label=f\"{name}: full predictive\",\n", + " )\n", + " ax.plot(\n", + " levels,\n", + " curves[\"potential alone\"],\n", + " \"s:\",\n", + " color=colours[name],\n", + " label=f\"{name}: potential alone\",\n", " )\n", - "ax.axhline(0.0, color=\"k\", lw=0.8)\n", "ax.set(\n", - " xlabel=r\"$\\theta_{cm}$ [deg]\",\n", - " ylabel=\"log departure from the fitted model\",\n", - " title=\"Mean-zero predictive: how wrong the potential may be, and where\",\n", + " xlabel=\"nominal coverage\",\n", + " ylabel=\"empirical coverage\",\n", + " xlim=(0, 1),\n", + " ylim=(0, 1),\n", + " title=\"Coverage at the measured angles\",\n", ")\n", "ax.legend(fontsize=8)\n", "plt.show()" @@ -941,147 +945,78 @@ }, { "cell_type": "markdown", - "id": "0ad4acac", + "id": "61e636ec", "metadata": {}, "source": [ - "### Are any of them calibrated?\n", - "\n", - "On the data they were fitted to, all four rungs *over*-cover. 79 %, 81 %, 78 % and 77 % of the measured points fall inside their nominal 68 % intervals, with predictive widths of 0.076, 0.068, 0.072 and 0.068 in ratio units. So the inferred noise is a little more generous than the data need. We should expect that when one noise parameter has to cover a diffraction pattern whose model error is nowhere near uniform in angle. Notice also how little separates the rungs here.\n", - "\n", - "That's the easy question, though, because these are the points each fit was shown. The held-out section below asks the hard one, and there the rungs separate sharply: 49 %, 60 %, 80 % and 63 % of the backward angles land inside the nominal 68 % band. Being well calibrated on the data we fitted is no evidence at all that we'll be calibrated on the data we didn't." + "At a nominal 68 %, the constant model's full predictive covers 85 % of the measured points and the power-of-$q$ model's covers 74 %, with mean 68 % widths of 0.082 and 0.060 in ratio units. So the power-of-$q$ model is both closer to nominal and sharper. The constant amplitude over-covers because it has to be as generous forward as it is backward. The potential alone covers only 21 % and 30 %, as expected for a band that leaves out every error term." ] }, { "cell_type": "markdown", - "id": "03760dbe", + "id": "1f01181e", "metadata": {}, "source": [ - "## Hold out the backward angles (recipe 11)\n", + "## Where the potential fails, not just how badly\n", "\n", - "Now we'll fit each rung only to the angles below 90 degrees, and score how well it predicts the angles above. This is a form of [cross-validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics)), and the score we'll use, the log predictive density of the held-out points, is the one [Vehtari, Gelman & Gabry (2017)](https://doi.org/10.1007/s11222-016-9696-4) recommend. In `rxmc` this takes very little code: `masked_where` keeps every object and just masks the points, `complement()` flips the mask, and a chain from the fit can score the held-out problem directly.\n", + "Finally, the question the growing amplitude was built to answer. We can ask each model for the discrepancy alone, a mean-zero correlated draw from the kernel about the fitted potential, with `grid_draws(..., terms=[gp])`. Subtracting the posterior-median potential leaves an envelope in log space: roughly, the smooth fractional departures from the potential that the model considers plausible at each angle.\n", "\n", - "The joint held-out log predictive answers a different question from the evidence. The evidence asks \"which model best explains the data we fitted?\", while the held-out score asks \"which model best predicts what it hasn't seen?\".\n", + "That envelope carries two things, the posterior spread of the potential and the discrepancy the kernel declares. With a constant amplitude the second is the same at every angle, so whatever shape the envelope has comes from the potential's own spread. With the power-of-$q$ amplitude it can be narrow forward and wide backward, if that's what the data support.\n", "\n", - "There's one subtlety. In `L2y` the normalisation, and in the GP rungs the kernel, *couple* the angles we fit to the ones we hold out: knowing the residuals on one side of the cut tells us something about the other. So the honest score isn't the marginal density of the held-out points, but the conditional $p(y_\\mathrm{held} \\mid y_\\mathrm{fit}, \\theta)$. Passing `given=p_fit` to both diagnostics computes exactly that (recipe 11). For `L0` nothing couples the two sides, and the conditional is the same as the marginal." + "These envelopes are statements about the model, not predictions of a measurement: they leave out $s$ and $b$, so they don't belong next to the data. The `gp_discrepancy` notebook writes out the equations for each of these objects." ] }, { "cell_type": "code", - "execution_count": 24, - "id": "c332125c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:24:58.484594Z", - "iopub.status.busy": "2026-09-14T22:24:58.484447Z", - "iopub.status.idle": "2026-09-14T22:32:26.084068Z", - "shell.execute_reply": "2026-09-14T22:32:26.083278Z" - } - }, + "execution_count": 29, + "id": "f59ee077", + "metadata": {}, "outputs": [], "source": [ - "cut = np.deg2rad(90.0)\n", - "scores = {}\n", - "for i, (name, c) in enumerate(ladder.items()):\n", - " if name == \"E0\":\n", - " continue # same space as L0 up to the noise model; the log rungs are the comparison\n", - " fit = c.masked_where(lambda x: x < cut)\n", - " p_fit, p_held = rx.Problem([fit]), rx.Problem([fit.complement()])\n", - " res = fit_nested(p_fit, seed=10 + i)\n", - " s = res.samples_equal(rstate=np.random.default_rng(10 + i))\n", - " lp = rx.diagnostics.heldout_log_predictive(p_held, s[::5], given=p_fit)\n", - " held_draws = rx.diagnostics.predictive_draws(\n", - " p_held, s[::20], n_rep=2, rng=i, given=p_fit, return_draws=True\n", - " )\n", - " h = p_held.constraints[0]\n", - " scores[name] = (\n", - " rx.diagnostics.log_posterior_predictive(lp),\n", - " rx.diagnostics.coverage_curve(held_draws, h.y[h.active], [0.68])[0],\n", - " s,\n", - " p_fit,\n", - " p_held,\n", - " )" + "envelopes = {}\n", + "for name, p in problems.items():\n", + " band = rx.predictive.grid_draws(\n", + " p, on_fine, x_fine, samples[name][::5], terms=[gps[name]],\n", + " levels=(16, 84), rng=3,\n", + " ) # fmt: skip\n", + " theta_med = np.median(samples[name], axis=0)\n", + " mu = np.log(on_fine(*theta_med[p.columns(params)]))\n", + " envelopes[name] = (band[0] - mu, band[1] - mu)" ] }, { "cell_type": "code", - "execution_count": 25, - "id": "fe6d6a4c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:32:26.086690Z", - "iopub.status.busy": "2026-09-14T22:32:26.086463Z", - "iopub.status.idle": "2026-09-14T22:32:26.092323Z", - "shell.execute_reply": "2026-09-14T22:32:26.091286Z" - } - }, + "execution_count": 30, + "id": "d946253c", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "L0 held-out log predictive = -60.50 68 % coverage of the held-out points = 0.49\n", - "L2y held-out log predictive = -55.34 68 % coverage of the held-out points = 0.60\n", - "Lgp held-out log predictive = -50.45 68 % coverage of the held-out points = 0.80\n", - "Lgpn held-out log predictive = -47.78 68 % coverage of the held-out points = 0.63\n" + "constant 68 % envelope width at 20, 90, 170 deg: 1.214 1.417 1.335 back/front = 1.1\n", + "power of q 68 % envelope width at 20, 90, 170 deg: 0.040 1.433 2.364 back/front = 59.4\n" ] } ], "source": [ - "for name, (score, cov, *_) in scores.items():\n", + "for name, (lo, hi) in envelopes.items():\n", + " at = [np.interp(np.deg2rad(a), x_fine, hi - lo) for a in (20, 90, 170)]\n", " print(\n", - " f\"{name:4s} held-out log predictive = {score:8.2f} \"\n", - " f\"68 % coverage of the held-out points = {cov:.2f}\"\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "1a60b24f", - "metadata": {}, - "source": [ - "Finally, let's draw each rung's forecast past the cut, on the fine angular grid. Every term on this ladder is a function of angle and of the prediction: the noise, the normalisation mode, the kernel and its amplitude. That means `grid_draws` can carry the whole error model onto the grid, and the band it gives is a prediction of a *measurement*, which we can honestly lay over the held-out data.\n", - "\n", - "One caveat: this is each rung's marginal predictive, not conditioned on the forward angles. For the rungs whose terms couple the two sides of the cut, it's therefore wider than the conditional predictive we used for the scores above." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "a92c3638", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:32:26.094259Z", - "iopub.status.busy": "2026-09-14T22:32:26.094052Z", - "iopub.status.idle": "2026-09-14T22:32:27.232848Z", - "shell.execute_reply": "2026-09-14T22:32:27.232183Z" - } - }, - "outputs": [], - "source": [ - "forecast = {}\n", - "for i, (name, _) in enumerate(rungs):\n", - " _, _, s, p_fit, _ = scores[name]\n", - " forecast[name] = rx.predictive.grid_draws(\n", - " p_fit, on_fine, x_fine, s[::25], n_rep=2, physical=True, levels=(5, 95), rng=i\n", + " f\"{name:10s} 68 % envelope width at 20, 90, 170 deg: \"\n", + " + \" \".join(f\"{v:.3f}\" for v in at)\n", + " + f\" back/front = {at[2] / at[0]:.1f}\"\n", " )" ] }, { "cell_type": "code", - "execution_count": 27, - "id": "f2fcd9d3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T22:32:27.234389Z", - "iopub.status.busy": "2026-09-14T22:32:27.234236Z", - "iopub.status.idle": "2026-09-14T22:32:27.552482Z", - "shell.execute_reply": "2026-09-14T22:32:27.551723Z" - } - }, + "execution_count": 31, + "id": "9c8629e8", + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "<Figure size 704x440 with 1 Axes>" ] @@ -1092,24 +1027,21 @@ ], "source": [ "fig, ax = plt.subplots()\n", - "for (name, colour), hatch in zip(rungs, plotstyle.HATCHES):\n", - " lo, hi = forecast[name]\n", + "for (name, (lo, hi)), hatch in zip(envelopes.items(), plotstyle.HATCHES):\n", " plotstyle.band(\n", " ax,\n", " np.rad2deg(x_fine),\n", " lo,\n", " hi,\n", - " color=colour,\n", + " color=colours[name],\n", " hatch=hatch,\n", - " label=f\"{name}, fit below 90 degrees\",\n", + " label=f\"{name} (68 %)\",\n", " )\n", - "ax.plot(np.rad2deg(angles), ratio, \"o\", ms=3, color=\"k\", label=\"data\")\n", - "ax.axvline(90.0, color=\"0.5\", ls=\"--\")\n", + "ax.axhline(0.0, color=\"k\", lw=0.8)\n", "ax.set(\n", " xlabel=r\"$\\theta_{cm}$ [deg]\",\n", - " ylabel=r\"$\\sigma / \\sigma_{Ruth}$\",\n", - " yscale=\"log\",\n", - " title=\"Extrapolated past the cut: 90 % posterior predictive, error model included\",\n", + " ylabel=\"log departure from the fitted potential\",\n", + " title=\"The discrepancy each model allows, by angle\",\n", ")\n", "ax.legend(fontsize=8)\n", "plt.show()" @@ -1117,17 +1049,23 @@ }, { "cell_type": "markdown", - "id": "98393809", + "id": "26a0c48a", + "metadata": {}, + "source": [ + "This is the clearest difference. The constant amplitude's envelope is about as wide at 20 degrees (1.21) as at 170 degrees (1.34). The power-of-$q$ envelope is 0.04 wide at 20 degrees, 1.43 at 90 and 2.36 at 170, a back-to-front ratio of about 59. The narrow spikes near 60 and 75 degrees come from the diffraction minima, where the potential itself is uncertain in log space; they're the potential's spread, not the discrepancy." + ] + }, + { + "cell_type": "markdown", + "id": "3a3a89e7", "metadata": {}, "source": [ "## Takeaways\n", "\n", - "- We used one comparison, one potential and five covariances. The whole ladder is just a dictionary of constraints, and every problem compiles on its own.\n", - "- Log space is where multiplicative errors become additive, and the Jacobian is what makes a log-space evidence comparable with a linear-space fit.\n", - "- Evidence and held-out prediction ask different questions, and here they agree: `Lgpn`, the GP whose amplitude grows with angle, wins both. Its evidence margin over the constant-amplitude `Lgp` is real but slim, $\\Delta \\log Z = 3.5 \\pm 1.3$, only just past the two-sigma bar `compare_logz` insists on. Its held-out log predictive is the best on the ladder, $-47.8$ against $-50.5$.\n", - "- It's also the only rung that comes close to being calibrated where it counts. Past the cut, the four rungs cover 49 %, 60 %, 80 % and 63 % of the held-out points at a nominal 68 %: `L0` is far too narrow, `Lgp` too wide, and `Lgpn` nearly right. On the fitted data all four over-cover and look much alike, which is exactly why in-sample calibration isn't evidence of anything.\n", - "- *Where* a model fails is prior knowledge worth stating. A stationary kernel with a constant amplitude declares the same uncertainty at every angle. Letting the amplitude grow turns the discrepancy into a claim about which angles we can trust the potential at, and the data reward it.\n", - "- Light-ion potentials are famously ambiguous: several families of parameters give nearly the same cross section. The `L0` posterior above has a second family near $V \\approx 125$ MeV alongside the main one at $V \\approx 165$ MeV, and it's the prior, not the data, that decides how much weight each gets. The `jitr` calibration notebook discusses this discrete ambiguity in more detail." + "- A smooth discrepancy and an uncorrelated error do different jobs. Scatter from one point to the next, about 60 % here, belongs in an uncorrelated term; the discrepancy describes how the potential goes wrong coherently across angles.\n", + "- Evaluate a kernel in a coordinate where the data are spread out. Momentum transfer bunches the backward angles together, so we evaluated the kernel in angle and used $q$ only for the amplitude.\n", + "- *Where* a model fails is prior knowledge worth stating. An amplitude growing as $\\sin^p(\\theta/2)$ with $p \\approx 3.2$ is preferred by $\\Delta\\log Z = 5.4 \\pm 1.3$, calibrates better (74 % against 85 % at a nominal 68 %), and resolves the discrete ambiguity that the constant amplitude leaves open.\n", + "- Terms written as functions of the angle, like $s$, $b$ and the kernel, let us draw the *full* predictive on a fine grid." ] } ], diff --git a/examples/local_optical_model_calibration.ipynb b/examples/local_optical_model_calibration.ipynb index 70ef4cf..f429d60 100644 --- a/examples/local_optical_model_calibration.ipynb +++ b/examples/local_optical_model_calibration.ipynb @@ -2,46 +2,34 @@ "cells": [ { "cell_type": "markdown", - "id": "153e38a2", + "id": "c1391575", "metadata": {}, "source": [ "# Calibrating a local optical potential to a real measurement\n", "\n", - "This is the production path, end to end: we take a measurement as EXFOR reports\n", - "it, convert it into the library's units, decide on an error model, calibrate a\n", - "local optical potential against it with nested sampling, and predict on a grid\n", - "the experiment never measured.\n", + "This is the production path from start to finish. We take a measurement as EXFOR reports it, convert it into the library's units, calibrate a local [optical potential](https://doi.org/10.1016/S0375-9474(02)01321-0) against it with nested sampling, and predict on angles the experiment never measured.\n", "\n", - "The data are real, and that matters for everything that follows. They are\n", - "[EXFOR entry O1199](https://www-nds.iaea.org/exfor/), subentry O1199007:\n", - "$p + {}^{40}\\mathrm{Ca}$ elastic scattering at 35 MeV, reported as a ratio to\n", - "the Rutherford cross section, 59 angles from 6 to 163 degrees, with statistical\n", - "errors of about 2.3 % — and **no systematic uncertainty quoted at all**. That\n", - "last omission is not unusual, and it shapes the error model we end up with.\n", + "The data are real, and that shapes everything that follows. They're [EXFOR entry O1199](https://www-nds.iaea.org/exfor/), subentry O1199007: $p + {}^{40}\\mathrm{Ca}$ elastic scattering at 35 MeV, reported as a ratio to the Rutherford cross section at 59 angles from 6 to 163 degrees. The statistical errors are about 2.3 %, and **no systematic uncertainty is quoted at all**.\n", "\n", - "Recipes: 4, 12, 14, 15, 16, 21, 26" + "We'll calibrate the same potential twice, with two different statistical models, and compare them:\n", + "\n", + "1. **Log-space residuals with an inferred error model**: the reported statistical errors, plus the two relative error terms of the `jitr` [$\\alpha$ + Ca Bayesian calibration notebook](https://github.com/beykyle/jitr/blob/main/examples/notebooks/alpha_ca_calibration.ipynb), one uncorrelated from point to point and one common to all points.\n", + "2. **Linear residuals with only the reported statistical errors**, which is what we get if we take the measurement entirely at its word.\n", + "\n", + "We'll judge them in three ways: **(A)** the evidence; **(B)** the posterior predictive, both for the potential alone and for the potential plus every error term; and **(C)** the empirical coverage of each of those predictives.\n", + "\n", + "Recipes: 10, 14, 15, 16, 17, 18, 19, 21, 40" ] }, { "cell_type": "code", "execution_count": 1, - "id": "07735fcd", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:31:41.403651Z", - "iopub.status.busy": "2026-09-14T19:31:41.403500Z", - "iopub.status.idle": "2026-09-14T19:31:44.121021Z", - "shell.execute_reply": "2026-09-14T19:31:44.120227Z" - } - }, + "id": "b2831405", + "metadata": {}, "outputs": [], "source": [ - "from dataclasses import dataclass\n", - "\n", "import corner\n", - "import dill\n", "import dynesty\n", - "import emcee\n", "import jitr\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -56,20 +44,19 @@ "from scipy import stats\n", "\n", "import rxmc as rx\n", - "from rxmc import terms as T\n", + "from rxmc import transforms as tf\n", "\n", "plotstyle.use()" ] }, { "cell_type": "markdown", - "id": "0a1ec02d", + "id": "b4359118", "metadata": {}, "source": [ "## The measurement\n", "\n", - "The file was pulled from the EXFOR database once and committed next to this\n", - "notebook, so that the notebook runs anywhere:\n", + "We pulled the file from the EXFOR database once and committed it next to this notebook, so the notebook runs anywhere. The query looked like this:\n", "\n", "```python\n", "import exfor_tools as et\n", @@ -79,27 +66,38 @@ "entries, failed = curate.query_for_entries(reaction=reaction, quantity=\"dXS/dRuth\")\n", "```\n", "\n", - "`process=\"EL\"` is the part worth remembering: EXFOR spells elastic scattering\n", - "`(P,EL)`, and asking for a product tuple instead returns nothing at all,\n", - "silently.\n", + "`process=\"EL\"` is the part worth remembering. EXFOR spells elastic scattering `(P,EL)`, and asking for a product tuple instead silently returns nothing.\n", "\n", - "`exfor_tools` hands back a `Distribution` object. Rather than depend on the\n", - "database here, we stand in for it with a small dataclass carrying the same\n", - "fields — which is all `rx.from_measurement` asks for." + "`exfor_tools` hands back a `Distribution` object. Here we don't want to depend on the database, so we stand in for it with a plain `dict` carrying the same fields. `rx.from_measurement` accepts either." ] }, { "cell_type": "code", "execution_count": 2, - "id": "0b60b388", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:31:44.123468Z", - "iopub.status.busy": "2026-09-14T19:31:44.123041Z", - "iopub.status.idle": "2026-09-14T19:31:44.132661Z", - "shell.execute_reply": "2026-09-14T19:31:44.131820Z" - } - }, + "id": "7ace23e7", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(\"data/p40ca_elastic_35mev.csv\")\n", + "measurement = {\n", + " \"x\": df[\"angle_cm_deg\"].to_numpy(), # degrees\n", + " \"y\": df[\"ratio_to_rutherford\"].to_numpy(),\n", + " \"statistical_err\": df[\"stat_err\"].to_numpy(),\n", + " \"Einc\": 35.0,\n", + " \"quantity\": \"dXS/dRuth\",\n", + " \"y_units\": \"no-dim\",\n", + " \"x_units\": \"CM-degrees\",\n", + " \"systematic_norm_err\": 0.0, # this entry quotes none\n", + " \"systematic_offset_err\": 0.0,\n", + " \"subentry\": \"O1199007\",\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7edf318a", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -111,72 +109,44 @@ } ], "source": [ - "@dataclass\n", - "class Measurement:\n", - " \"\"\"The fields rx.from_measurement reads off an exfor_tools Distribution.\"\"\"\n", - "\n", - " x: np.ndarray # angles, degrees\n", - " y: np.ndarray\n", - " statistical_err: np.ndarray\n", - " Einc: float\n", - " quantity: str\n", - " y_units: str\n", - " x_units: str\n", - " systematic_norm_err: float\n", - " systematic_offset_err: float\n", - " subentry: str\n", - "\n", - "\n", - "df = pd.read_csv(\"data/p40ca_elastic_35mev.csv\")\n", - "measurement = Measurement(\n", - " x=df[\"angle_cm_deg\"].to_numpy(),\n", - " y=df[\"ratio_to_rutherford\"].to_numpy(),\n", - " statistical_err=df[\"stat_err\"].to_numpy(),\n", - " Einc=35.0,\n", - " quantity=\"dXS/dRuth\",\n", - " y_units=\"no-dim\",\n", - " x_units=\"CM-degrees\",\n", - " systematic_norm_err=0.0, # this entry quotes none\n", - " systematic_offset_err=0.0,\n", - " subentry=\"O1199007\",\n", - ")\n", - "print(\n", - " f\"{measurement.x.size} angles, {measurement.x.min():.1f} to {measurement.x.max():.1f} deg\"\n", + "x_deg, y_meas, y_stat = (\n", + " measurement[\"x\"],\n", + " measurement[\"y\"],\n", + " measurement[\"statistical_err\"],\n", ")\n", - "print(\n", - " f\"relative statistical error: {np.mean(measurement.statistical_err / measurement.y):.1%}\"\n", - ")" + "print(f\"{x_deg.size} angles, {x_deg.min():.1f} to {x_deg.max():.1f} deg\")\n", + "print(f\"relative statistical error: {np.mean(y_stat / y_meas):.1%}\")" ] }, { "cell_type": "markdown", - "id": "774c0f96", + "id": "d62f3f00", "metadata": {}, "source": [ "## `from_measurement`: the unit contract\n", "\n", - "Angles become radians, cross sections become b/sr, every *dimensionful* error is\n", - "converted with the data, and a *fractional* normalisation error passes through\n", - "untouched. The kinematics a reaction model needs land in `meta`.\n", + "`from_measurement` puts the measurement into the library's internal units. Angles become radians and cross sections become b/sr. Every error with dimensions is converted along with the data, while a *fractional* normalisation error passes through untouched, since it has no units to convert. The kinematics a reaction model needs to bind land in `meta`.\n", "\n", - "Two things this measurement exercises. It is in the **CM frame** — a LAB-frame\n", - "entry is refused rather than silently mis-analysed. And because it is a ratio\n", - "to Rutherford, asking for `dXS/dA` converts it through the Rutherford cross\n", - "section, which is a closed form of the kinematics (recipe 14)." + "This particular measurement exercises two parts of that contract. First, its angles are in the **CM frame**. A LAB-frame entry would be refused outright rather than silently mis-analysed. Second, because it's a ratio to Rutherford, asking for `dXS/dA` instead converts it through the Rutherford cross section, which is a closed-form function of the kinematics (recipe 14)." ] }, { "cell_type": "code", - "execution_count": 3, - "id": "ae6b43af", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:31:44.134760Z", - "iopub.status.busy": "2026-09-14T19:31:44.134550Z", - "iopub.status.idle": "2026-09-14T19:31:44.163967Z", - "shell.execute_reply": "2026-09-14T19:31:44.163195Z" - } - }, + "execution_count": 4, + "id": "3b3f3ab1", + "metadata": {}, + "outputs": [], + "source": [ + "reaction = jitr.reactions.ElasticReaction(target=(40, 20), projectile=(1, 1))\n", + "data = rx.from_measurement(measurement, reaction=reaction)\n", + "absolute = rx.from_measurement(measurement, reaction=reaction, quantity=\"dXS/dA\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bbdb1656", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -190,27 +160,17 @@ } ], "source": [ - "reaction = jitr.reactions.ElasticReaction(target=(40, 20), projectile=(1, 1))\n", - "data = rx.from_measurement(measurement, reaction=reaction)\n", - "absolute = rx.from_measurement(measurement, reaction=reaction, quantity=\"dXS/dA\")\n", "print(data)\n", "print(f\"ratio: y[:3] = {data.y[:3].round(3)} (dimensionless)\")\n", "print(f\"absolute: y[:3] = {absolute.y[:3].round(4)} b/sr\")\n", - "print(\"meta:\", {k: v for k, v in data.meta.items() if k != \"reaction\"})" + "print(\"meta:\", {key: v for key, v in data.meta.items() if key != \"reaction\"})" ] }, { "cell_type": "code", - "execution_count": 4, - "id": "4c4955a3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:31:44.166152Z", - "iopub.status.busy": "2026-09-14T19:31:44.165931Z", - "iopub.status.idle": "2026-09-14T19:31:45.554998Z", - "shell.execute_reply": "2026-09-14T19:31:45.554208Z" - } - }, + "execution_count": 6, + "id": "c897b34a", + "metadata": {}, "outputs": [ { "data": { @@ -232,7 +192,7 @@ " fmt=\"o\",\n", " ms=3,\n", " color=\"k\",\n", - " label=f\"EXFOR {measurement.subentry}\",\n", + " label=f\"EXFOR {measurement['subentry']}\",\n", ")\n", "ax.set(\n", " xlabel=r\"$\\theta_{cm}$ [deg]\",\n", @@ -246,34 +206,21 @@ }, { "cell_type": "markdown", - "id": "b1a2d666", + "id": "bf89a76a", "metadata": {}, "source": [ "## The potential\n", "\n", - "A Woods-Saxon with volume and surface absorption, a fixed spin-orbit term, and —\n", - "because the projectile is charged — a Coulomb term from a uniformly charged\n", - "sphere. `ElasticXS` is a `Model` whose `bind` compiles a\n", - "[jitr](https://github.com/beykyle/jitr) solver for the dataset's kinematics,\n", - "which it reads from `meta`.\n", + "We use a [Woods–Saxon](https://en.wikipedia.org/wiki/Woods%E2%80%93Saxon_potential) potential with volume and surface absorption, a fixed spin-orbit term, and, because the proton is charged, a Coulomb term from a uniformly charged sphere. `ElasticXS` is a `Model` whose `bind` compiles a [jitr](https://github.com/beykyle/jitr) solver for the dataset's kinematics, which it reads from `meta` (recipe 15).\n", "\n", - "The model's quantity and the dataset's must agree: comparing a ratio-to-Rutherford\n", - "measurement against a `dXS/dA` model is refused at construction, not discovered\n", - "after a fit." + "The model's quantity and the dataset's have to agree. If we tried to compare a ratio-to-Rutherford measurement with a `dXS/dA` model, the `Comparison` would refuse at construction, rather than letting us discover the mismatch after a fit." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "22704ba5", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:31:45.557450Z", - "iopub.status.busy": "2026-09-14T19:31:45.557258Z", - "iopub.status.idle": "2026-09-14T19:31:45.561691Z", - "shell.execute_reply": "2026-09-14T19:31:45.560809Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "A13 = 40 ** (1 / 3)\n", @@ -296,30 +243,15 @@ " )\n", "\n", "\n", - "so_args = (5.5, 1.0 * A13, 0.6) # held fixed: 59 angles at one energy cannot fit it" + "so_args = (5.5, 1.0 * A13, 0.6) # held fixed, no Ay" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "5edb669b", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:31:45.563543Z", - "iopub.status.busy": "2026-09-14T19:31:45.563281Z", - "iopub.status.idle": "2026-09-14T19:32:01.048929Z", - "shell.execute_reply": "2026-09-14T19:32:01.047973Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "chi2 per point at the starting potential: 987.0\n" - ] - } - ], + "execution_count": 8, + "id": "ad1d1cda", + "metadata": {}, + "outputs": [], "source": [ "names = [\"Vv\", \"Wv\", \"Rv\", \"av\", \"Wd\", \"Rd\", \"ad\"]\n", "latex = [\"V_v\", \"W_v\", \"R_v\", \"a_v\", \"W_d\", \"R_d\", \"a_d\"]\n", @@ -338,148 +270,137 @@ " params,\n", " coulomb=coulomb,\n", " lmax=30,\n", - ")\n", - "comp = rx.Comparison(data, omp)\n", - "print(\n", - " \"chi2 per point at the starting potential:\",\n", - " round(rx.Problem([rx.Constraint([comp])]).chi2(start) / data.n, 1),\n", ")" ] }, { "cell_type": "markdown", - "id": "d3807dd0", + "id": "8fec4194", "metadata": {}, "source": [ - "## An error model for data that quote no systematics\n", + "## Two statistical models for the same potential\n", + "\n", + "### 1. Log space, with an inferred error model\n", + "\n", + "The measurement spans more than three decades, and the disagreement between a local potential and real data is naturally *relative*: a few per cent of the cross section, whatever its size. So the first model compares in log space (recipe 10), with residuals $r_i = \\log y_i - \\log y_m(\\theta_i)$.\n", + "\n", + "On top of the reported statistical errors we add the error model of the `jitr` [$\\alpha$ + Ca calibration](https://github.com/beykyle/jitr/blob/main/examples/notebooks/alpha_ca_calibration.ipynb). Think of each measured point as the prediction multiplied by two independent [log-normal](https://en.wikipedia.org/wiki/Log-normal_distribution) factors, $y_i = y_{m,i}\\,A_i\\,B$, both with mean one:\n", + "\n", + "- $A_i$ is a **relative error uncorrelated from point to point**, one for each angle;\n", + "- $B$ is a **relative error common to all points**, like an uncertain normalisation, or more generally a coherent misfit of the model.\n", + "\n", + "If we choose $\\mathrm{Var}[B] = b^2$ and $\\mathrm{Var}[A_i] = s^2/(1+b^2)$, the covariance in linear space comes out as exactly $(s^2 + b^2)\\,y_{m,i}^2$ on the diagonal and $b^2\\,y_{m,i}\\,y_{m,j}$ off it. In log space it becomes the same at every angle:\n", "\n", - "That $\\chi^2$ per point is the whole problem in one number. The statistical\n", - "errors are 2.3 %, and a seven-parameter local potential simply cannot describe\n", - "real elastic scattering to 2.3 % across 59 angles. If we fit with the quoted\n", - "errors alone, we are telling the sampler that every remaining disagreement is a\n", - "statistical fluctuation, and it will believe us.\n", + "$$\\Sigma^{\\log}_{ij} = \\sigma_{\\mathrm{stat},i}^2\\,\\delta_{ij} + \\log\\!\\left(1 + \\frac{s^2}{1+b^2}\\right)\\delta_{ij} + \\log\\!\\left(1 + b^2\\right),$$\n", "\n", - "The measurement quotes no systematic uncertainty, so there is nothing to fold in\n", - "(`comp.reported_terms()` would return an empty list). We have two honest\n", - "options, and we will try both against the bare fit:\n", + "where the first term is the reported statistical error carried into log space, $\\sigma_i / y_i$. None of the helpers in `rxmc.terms` writes this exact form, so we write it ourselves as two one-line `rx.Term`s (recipe 19): a diagonal that reads both $s$ and $b$, and a mode that reads $b$. For small errors, $\\log(1+x) \\approx x$, and the pair reduces to `T.noise` plus `T.offset`. As in `jitr`, both $s$ and $b$ get log-uniform priors between 5 % and 200 %.\n", "\n", - "- **infer a normalisation** (recipe 4): one rank-one mode built from the\n", - " prediction, with its magnitude $\\eta$ sampled. This says the scale of the\n", - " measurement is uncertain even though nobody quoted a number for it;\n", - "- **infer a model error** (recipe 26): a diagonal term that grows with the\n", - " prediction, $\\delta$ sampled. This says our *model*, not the measurement, is\n", - " the thing that is wrong at the percent level." + "### 2. Linear space, with the reported errors alone\n", + "\n", + "The second model is the simplest thing we could do: compare the ratios directly, with the reported 2.3 % statistical errors as the whole covariance. It tells the sampler that every disagreement between potential and data is a statistical fluctuation." ] }, { "cell_type": "code", - "execution_count": 7, - "id": "b0ceec5d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:32:01.052334Z", - "iopub.status.busy": "2026-09-14T19:32:01.051955Z", - "iopub.status.idle": "2026-09-14T19:32:01.073021Z", - "shell.execute_reply": "2026-09-14T19:32:01.071975Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "quoted errors only 7 columns: ['Vv', 'Wv', 'Rv', 'av', 'Wd', 'Rd', 'ad']\n", - "inferred normalisation 8 columns: ['Vv', 'Wv', 'Rv', 'av', 'Wd', 'Rd', 'ad', 'log_eta']\n", - "inferred model error 8 columns: ['Vv', 'Wv', 'Rv', 'av', 'Wd', 'Rd', 'ad', 'log_delta']\n" - ] - } - ], + "execution_count": 9, + "id": "21d928bd", + "metadata": {}, + "outputs": [], "source": [ - "log_eta = rx.Parameter(\n", - " \"log_eta\", prior=stats.norm(np.log(0.05), 1.0), latex=r\"\\log\\eta\"\n", - ")\n", - "log_delta = rx.Parameter(\n", - " \"log_delta\", prior=stats.norm(np.log(0.05), 1.0), latex=r\"\\log\\delta\"\n", - ")\n", - "norm_term = T.normalization(parameter=log_eta)\n", - "model_error_term = T.model_error(log_delta, averaging=True)\n", - "problems = {\n", - " \"quoted errors only\": rx.Problem([rx.Constraint([comp])]),\n", - " \"inferred normalisation\": rx.Problem([rx.Constraint([comp], terms=[norm_term])]),\n", - " \"inferred model error\": rx.Problem(\n", - " [rx.Constraint([comp], terms=[model_error_term])]\n", - " ),\n", - "}\n", - "# the inferred terms of each error model: functions of the prediction, so they\n", - "# are defined on any angular grid, unlike the quoted per-point statistical errors\n", - "inferred_terms = {\n", - " \"quoted errors only\": [],\n", - " \"inferred normalisation\": [norm_term],\n", - " \"inferred model error\": [model_error_term],\n", - "}\n", - "for name, p in problems.items():\n", - " print(f\"{name:24s} {p.ndim} columns: {p.names}\")" + "def log_uniform(lo, hi):\n", + " return stats.uniform(np.log(lo), np.log(hi) - np.log(lo))\n", + "\n", + "\n", + "log_s = rx.Parameter(\"log_s\", prior=log_uniform(0.05, 2.0), latex=r\"\\log s\")\n", + "log_b = rx.Parameter(\"log_b\", prior=log_uniform(0.05, 2.0), latex=r\"\\log b\")\n", + "\n", + "\n", + "def point_to_point_sd(c, log_s, log_b):\n", + " s2, b2 = np.exp(2 * log_s), np.exp(2 * log_b)\n", + " return np.full(len(c), np.sqrt(np.log1p(s2 / (1 + b2))))\n", + "\n", + "\n", + "def common_sd(c, log_b):\n", + " return np.full(len(c), np.sqrt(np.log1p(np.exp(2 * log_b))))\n", + "\n", + "\n", + "point_to_point = rx.Term(point_to_point_sd, (log_s, log_b), kind=\"diag\")\n", + "common = rx.Term(common_sd, (log_b,), kind=\"mode\")" ] }, { - "cell_type": "markdown", - "id": "c6663859", + "cell_type": "code", + "execution_count": 10, + "id": "86942c24", "metadata": {}, + "outputs": [], "source": [ - "## Calibrating with nested sampling (recipe 16)\n", - "\n", - "Optical-model posteriors are correlated and often multimodal, where an\n", - "affine-invariant ensemble mixes poorly;\n", - "[dynesty](https://dynesty.readthedocs.io/) copes with them and returns the\n", - "evidence as a by-product. It needs two callables — `log_likelihood` and\n", - "`prior_transform` — and the transform maps the unit cube through every\n", - "parameter's truncated prior, which the problem assembles for us." + "comp_log = rx.Comparison(data, omp, space=tf.log)\n", + "comp_lin = rx.Comparison(data, omp)\n", + "problems = {\n", + " \"log space, inferred errors\": rx.Problem(\n", + " [rx.Constraint([comp_log], terms=[point_to_point, common])]\n", + " ),\n", + " \"linear, reported errors\": rx.Problem([rx.Constraint([comp_lin])]),\n", + "}\n", + "# the inferred terms of each model: functions of the angle alone, so they have a\n", + "# value on any grid, unlike the reported per-point statistical errors\n", + "inferred_terms = {\n", + " \"log space, inferred errors\": [point_to_point, common],\n", + " \"linear, reported errors\": [],\n", + "}\n", + "colours = {\n", + " \"log space, inferred errors\": plotstyle.COLOURS[0],\n", + " \"linear, reported errors\": plotstyle.COLOURS[1],\n", + "}" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "a4d96073", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:32:01.077298Z", - "iopub.status.busy": "2026-09-14T19:32:01.076881Z", - "iopub.status.idle": "2026-09-14T20:00:28.831618Z", - "shell.execute_reply": "2026-09-14T20:00:28.830732Z" - } - }, + "execution_count": 11, + "id": "f2d363c0", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "quoted errors only " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "log Z = -3478.97 +/- 0.69, 126410 calls, efficiency 4.1 %\n", - "inferred normalisation " + "log space, inferred errors 9 columns: ['Vv', 'Wv', 'Rv', 'av', 'Wd', 'Rd', 'ad', 'log_s', 'log_b']\n", + "linear, reported errors 7 columns: ['Vv', 'Wv', 'Rv', 'av', 'Wd', 'Rd', 'ad']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "log Z = -3360.89 +/- 0.71, 131731 calls, efficiency 3.9 %\n", - "inferred model error " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "log Z = -38.66 +/- 0.54, 77464 calls, efficiency 4.2 %\n" + "\n", + "chi2 per point at the starting potential, reported errors: 987.0\n" ] } ], + "source": [ + "for name, p in problems.items():\n", + " print(f\"{name:28s} {p.ndim} columns: {p.names}\")\n", + "chi2_start = problems[\"linear, reported errors\"].chi2(start) / data.n\n", + "print(f\"\\nchi2 per point at the starting potential, reported errors: {chi2_start:.1f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "35ad8e12", + "metadata": {}, + "source": [ + "## Calibrating with nested sampling (recipe 16)\n", + "\n", + "Optical-model posteriors are correlated and often multimodal, and an affine-invariant ensemble sampler mixes poorly on them. [Nested sampling](https://en.wikipedia.org/wiki/Nested_sampling_algorithm) ([Skilling 2006](https://doi.org/10.1214/06-BA127)), here through [dynesty](https://dynesty.readthedocs.io/) ([Speagle 2020](https://doi.org/10.1093/mnras/staa278)), copes much better, and it returns the evidence as a by-product. It needs two callables, `log_likelihood` and `prior_transform`. The transform maps the unit cube through every parameter's truncated prior, and the problem assembles it for us." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "cb9e602b", + "metadata": {}, + "outputs": [], "source": [ "def nested(problem, seed, nlive=150, dlogz=0.5):\n", " sampler = dynesty.NestedSampler(\n", @@ -491,172 +412,180 @@ " rstate=np.random.default_rng(seed),\n", " )\n", " sampler.run_nested(dlogz=dlogz, print_progress=False)\n", - " res = sampler.results\n", - " print(\n", - " f\"log Z = {res.logz[-1]:9.2f} +/- {res.logzerr[-1]:.2f}, \"\n", - " f\"{int(np.sum(res.ncall)):7d} calls, efficiency {res.eff:.1f} %\"\n", - " )\n", - " return res.samples_equal(rstate=np.random.default_rng(seed))\n", + " return sampler.results\n", "\n", "\n", - "samples = {}\n", + "results, samples = {}, {}\n", "for i, (name, p) in enumerate(problems.items()):\n", - " print(f\"{name:24s}\", end=\" \")\n", - " samples[name] = nested(p, seed=1 + i)" + " results[name] = nested(p, seed=1 + i)\n", + " samples[name] = results[name].samples_equal(rstate=np.random.default_rng(1 + i))" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "344252d8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T20:00:28.833805Z", - "iopub.status.busy": "2026-09-14T20:00:28.833517Z", - "iopub.status.idle": "2026-09-14T20:00:28.864306Z", - "shell.execute_reply": "2026-09-14T20:00:28.863445Z" - } - }, + "execution_count": 13, + "id": "103e4e7f", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "quoted errors only chi2/N = 122.1 Vv = 45.5 +/- 0.1\n", - "inferred normalisation chi2/N = 118.0 Vv = 42.6 +/- 0.2 eta = 19.8%\n", - "inferred model error chi2/N = 0.9 Vv = 44.6 +/- 2.3 delta = 30.0%\n" + "log space, inferred errors log Z = -27.64 +/- 0.49, 64063 calls\n", + "linear, reported errors log Z = -3479.53 +/- 0.72, 128259 calls\n" ] } ], "source": [ - "for name, p in problems.items():\n", - " s = samples[name]\n", - " theta = np.median(s, axis=0)\n", - " extra = \"\"\n", - " if \"normalisation\" in name:\n", - " extra = f\" eta = {np.exp(s[:, p.columns(log_eta)]).mean():.1%}\"\n", - " if \"model error\" in name:\n", - " extra = f\" delta = {np.exp(s[:, p.columns(log_delta)]).mean():.1%}\"\n", + "for name, res in results.items():\n", " print(\n", - " f\"{name:24s} chi2/N = {p.chi2(theta) / data.n:7.1f}\"\n", - " f\" Vv = {s[:, p.columns(params[0])].mean():5.1f}\"\n", - " f\" +/- {s[:, p.columns(params[0])].std():4.1f}{extra}\"\n", + " f\"{name:28s} log Z = {res.logz[-1]:9.2f} +/- {res.logzerr[-1]:.2f}, \"\n", + " f\"{int(np.sum(res.ncall)):7d} calls\"\n", " )" ] }, { "cell_type": "markdown", - "id": "e501cce9", + "id": "706a6b87", "metadata": {}, "source": [ - "### What the three error models made of it\n", + "## (A) The evidence\n", "\n", - "The evidence is not close: $-3479$ for the quoted errors, $-3361$ with an\n", - "inferred normalisation, $-38.7$ with an inferred model error. The measurement's\n", - "2.3 % statistical errors cannot explain the disagreement, and the fit knows it.\n", - "\n", - "The interesting comparison is the middle one. Letting the *scale* of the\n", - "measurement float — by 19.8 %, which is a lot — buys almost nothing: the\n", - "evidence moves by about 120 out of 3400, and $\\chi^2/N$ stays at 118. A\n", - "normalisation multiplies every angle by the same number, and our problem is not\n", - "that the data sit at the wrong level. It is that the shape is wrong, angle by\n", - "angle, and no single factor repairs a shape.\n", + "The [evidence](https://en.wikipedia.org/wiki/Marginal_likelihood) is the probability each statistical model assigned to the measured data, averaged over its prior, and the ratio of two evidences is the [Bayes factor](https://en.wikipedia.org/wiki/Bayes_factor) between them. There's one subtlety here. The raw $\\log Z$ from the log-space fit is a density for $\\log y$, while the linear fit's is a density for $y$ itself. Adding `problem.log_jacobian()` converts the first into a density for $y$, the [change of variables](https://en.wikipedia.org/wiki/Probability_density_function#Function_of_random_variables_and_change_of_variables_in_the_probability_density_function) $\\sum_i \\log |\\mathrm{d}\\log y_i / \\mathrm{d}y_i|$, and only then are the two comparable (recipe 18)." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "b469b3ef", + "metadata": {}, + "outputs": [], + "source": [ + "logz = {\n", + " name: rx.diagnostics.logz_summary(\n", + " results[name].logz[-1] + problems[name].log_jacobian(),\n", + " results[name].logzerr[-1],\n", + " )\n", + " for name in problems\n", + "}\n", + "verdict = rx.diagnostics.compare_logz(\n", + " logz[\"log space, inferred errors\"], logz[\"linear, reported errors\"]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0d591420", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log space, inferred errors log Z = -38.68 +/- 0.49 (density of the ratios)\n", + "linear, reported errors log Z = -3479.53 +/- 0.72 (density of the ratios)\n", + "\n", + "dlogZ = 3440.9 +/- 0.9 -> log space favoured\n" + ] + } + ], + "source": [ + "for name, (mean, err, _) in logz.items():\n", + " print(f\"{name:28s} log Z = {mean:9.2f} +/- {err:.2f} (density of the ratios)\")\n", + "words = {\"a\": \"log space favoured\", \"b\": \"linear favoured\", \"tie\": \"a tie\"}\n", + "print(\n", + " f\"\\ndlogZ = {verdict['dlogZ']:.1f} +/- {verdict['err']:.1f}\"\n", + " f\" -> {words[verdict['verdict']]}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5c4d8ab7", + "metadata": {}, + "source": [ + "The evidence isn't close. The log-space model scores $\\log Z = -38.7$ as a density for the ratios, and the linear model with only the reported errors scores $-3479.5$, a difference of about 3441. Before we even look at a prediction, the data are telling us that the claim \"every disagreement is a 2.3 % statistical fluctuation\" is untenable.\n", "\n", - "The inferred model error, which grows with the prediction at every angle, takes\n", - "$\\chi^2/N$ from 122 to 0.9 with $\\delta = 30\\,\\%$. That is the honest summary\n", - "of this fit: a seven-parameter local potential describes this measurement to\n", - "about 30 %, not to the 2.3 % the counting statistics suggest. The uncertainty\n", - "on $V_v$ moves accordingly, from $\\pm 0.1$ MeV — precise and meaningless — to\n", - "$\\pm 2.3$ MeV." + "The starting potential already hinted at this: its $\\chi^2$ per point against the reported errors is 987. A seven-parameter local potential simply can't describe real elastic scattering to 2.3 % at 59 angles." ] }, { "cell_type": "markdown", - "id": "e2392b36", + "id": "638d57f9", "metadata": {}, "source": [ - "### Predicting between and beyond the measured angles\n", + "### The two posteriors\n", "\n", - "A prediction on a finer angular grid can carry an error model only where that\n", - "error model has a value. The quoted 2.3 % statistical errors are one number per\n", - "measured angle, so `grid_draws` cannot put them anywhere else, and we leave them\n", - "out. The inferred terms are different: the normalisation mode is $\\eta\\,y_m$\n", - "and the model error is $\\delta\\,y_m$ at any angle, so we pass them with\n", - "`terms=` and each band includes the uncertainty its error model inferred. (A\n", - "normalisation drawn on the grid reads as \"a future measurement by this\n", - "experiment\", sharing its unknown scale.) For the quoted-errors fit that leaves\n", - "the model alone, which is exactly why its band is so implausibly thin." + "Before we look at predictions, let's see what each model believes about the potential, and what the first one inferred for $s$ and $b$." ] }, { "cell_type": "code", - "execution_count": 10, - "id": "f045b49e", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T20:00:28.866388Z", - "iopub.status.busy": "2026-09-14T20:00:28.866191Z", - "iopub.status.idle": "2026-09-14T20:00:32.061510Z", - "shell.execute_reply": "2026-09-14T20:00:32.060860Z" - } - }, + "execution_count": 16, + "id": "d88e6ea9", + "metadata": {}, + "outputs": [], + "source": [ + "# chi2 per point against the reported errors alone, at each posterior median:\n", + "# how far each fitted potential sits from the data in units of the 2.3 %\n", + "p_reported = problems[\"linear, reported errors\"]\n", + "summary = {}\n", + "for name, p in problems.items():\n", + " s = samples[name]\n", + " theta = np.median(s[:, p.columns(params)], axis=0)\n", + " v = s[:, p.columns(params[0])]\n", + " summary[name] = {\n", + " \"chi2/N (reported errors)\": p_reported.chi2(theta) / data.n,\n", + " \"Vv\": (v.mean(), v.std()),\n", + " }\n", + "p_log, s_log = (\n", + " problems[\"log space, inferred errors\"],\n", + " samples[\"log space, inferred errors\"],\n", + ")\n", + "s_draws = np.exp(s_log[:, p_log.columns(log_s)]).ravel()\n", + "b_draws = np.exp(s_log[:, p_log.columns(log_b)]).ravel()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "536e4eff", + "metadata": {}, "outputs": [ { - "data": { - "image/png": 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mhg0bMsofeeQRE0gLx7FYzHQ4HOaECRM6fI32fjk1jH3be8Y0TfO8887LEEoSiYTpcrnMiy66qEndzz//3ATMhQsXdqjN9rI9a9TW/LZGdXW1+frrr5tTpkwxJ0+ebN59991mQUGBefHFF7faR0mSzBtvvLHJsQsvvLBZ4bGle2nbNd0Zc9reeWnvOpumaU6ZMsUUBMFcu3Ztus6LL76YUacj7XX0+Wqov2nTpozyMWPGmIC5bt26jPLx48ebAwYMSH/uSN9Ms+37orKy0gTMSZMmpQW95uio8NjcnLz66qsmYM6aNatJOxdccIHZv39/0zRTSgKgiUIjEAiYsizvMuHRsnncBxkxYgR33303Dz30EIsXL+bggw/m66+/xufzkZOTg8/nS9dtsKtIJpNN7FYSiUSGXVRzHHDAASxdupRPP/0UQRB48cUXURSFO+64g3feeYdwOMzUqVOZPn06w4cP54ILLmDIkCFMmjQpwxamOZqzGzz44IOZN28emqYhyzJlZWUMHTqUYDDIWWedRZcuXXA6ndjtdhRFIRwOZ9iabGvb06lTJyAVbqiBX3/9lX79+mXYUTVce1v+/Oc/c+6559KjRw+GDh3KKaecwogRI9ocW0dpT79bYunSpWRnZ6e3wczUj0JM08Rms7FmzZqM+q3Za257bOnSpQB8/fXXLF26NN0upMwOysrKiMfj6a34bc93u9307t27XWNoz3Ua7uGOjGHt2rVomtasQ9dhhx0GpLbMe/ToAaTshw888MA2+9wabrebnj17ZpQ1XtODDjqIkpISkskkRx555A5dqzUaxt7Ss9Y4vNbq1auJx+NUVVVx3333peffNE1qamqA1Fq4XK52t9nRfrZ3jdozvy3xzjvvcMUVV3DIIYdwwgknkJOTgyiKyLJMbW1tq33Udb1Z+8GWbArbey91ZJ1aoz3z0t51HjJkCFdeeSX33XcfL7/8cnob9KWXXmLgwIEMGTIEaP9901C/o8+X2+1Om2Q1UFRUhMPhoFevXk3KZ8+enf7ckb61574oLi5m4sSJPPnkk3z00UcMGzaMU089lREjRmR873aU5uak4Z04c+ZMvv3224x34qZNm9Lb8y29e30+X4v2nzsDS3jcR3nggQe47LLLmD17NpFIhClTpnD66aeTm5ub8QJueLhKS0ub3JxlZWWceuqpbV6rU6dOGfZ3o0aNYvz48QwbNozZs2ejqmragHfw4MHY7XaWLFnSpoBlNrKlbFwmCELalu++++6jvLycX3/9Nf0ShJTN4hdffNHk/G2F4QYBsbF9SGvX3pYzzzyTtWvX8vHHH/PNN9/w2GOP8cc//pFJkybxt7/9rdXxdYT29rs5DMPAbrc3G/PxpptuaiK8NRiab4vD4WjyA6PBxtXlcjVpf+jQoQwdOjTDmLy989rcGDpynZbG0Nyx5pyltr1u4zo+n69djhOtse08QsfuxbaQZRld15uUR6PRZuu3Z10a5sLpdDZZg4YwU4ccckiH2mwvHV2jjsxvY1RV5brrrmPo0KF89NFHGceeeeaZdvW1uTE2tgVvTEfvpR2d0/bMS0fWuWvXrgwfPpxXXnmF+++/n5KSEr799lueeOKJ9DkdvW86OictjamxzXfj8sbr396+deS++Mc//sHVV1/Np59+yvz587nqqqtwOp28+eabDBs2DOj489ncnLT2Thw5ciQjR47MuDd25vPYHizhcR+mX79+GQLaBx98gKZpnHfeeemyBm/XhQsXZgiPy5YtIxQKddgb9o033mDRokWsWLEio3zbm7S1L4MGfvnllybehMuXL8/QCq5cuZKBAwdmCI4AP/zwQ4f63ZgBAwYwf/78tHaz8bWbo7i4mGuuuYZrrrkGwzC45ppr+Pvf/96sYLYnGDRoELNmzeKOO+7Y6UHDBw0aBMDpp5/O8OHDW6zXoHn55ZdfKC4uTpdHo1HWr1/PEUccsVOusz307dsXm83G//73vybHli1bBrBHYpT269cPl8vF4sWLO3xu165dmw1mv+09fMABB2Cz2fjll1/arNuvXz+cTmebIaWi0Wi722wvu2uNqqqqqK+v5/TTT88o37RpE1VVVW32UZblJu8+YIezRnVknXaU9q5zA7///e8ZP348s2bN4uuvv8Zms3HppZdud3u7k/b2rbS0tEP3xRFHHJF+p9XW1nLEEUdwzz33pIXH9j6frdHwTjzzzDNbDc/W+N07ePDgdHkgEGhTC78jWN7W+yjbptCrrKzk9ttv57zzzstw0T/99NMZOHBgOkB4A4888gg+n4/LL7+83desra1l0qRJ/OMf/yAvLw+AQw89FLvdnvba/u6771AUhcMPP7zN9t57772MjDWzZs3i22+/zdByHnjggaxevToje87bb7/dZCu2I1x11VXU1NTw7LPPpstisRgvvfRSk7ozZ87M+AUpimJaYGwsID/zzDPpmJu7m0mTJhEKhbj77rubCPEbN25s9suuvYwZM4Z+/fpx5513NslgFI/H0x7qY8aMoaCggIceeghFUdJ1pk6d2qzmYHuvsz00hPZ46623Ml7e69ev54UXXmDEiBFNtsV2B06nk+uvv5533nknY6sNUsJIfX19i+eedNJJ1NbWZqSnXLx4cZP3gsfjSXulN/ZC/emnn1iwYEFGXZfLxcSJE3nttdeaHINUfFlFUTrUJqTeG5MnT+bjjz9ucTy7a406d+6Mx+PJ+GLXNI0777wzw/ylObxeb3rcjRMlLFu2jPnz5+9Qvzo6pztCe9e5gVGjRlFcXMxzzz3Ha6+9xpgxYzJiA3e0vd1Je/vW3vuirKysyQ+cvLw8cnNzMzSH7X0+W+P888+nR48e3H777YTD4YxjkUgk/Z17zjnnkJ2dzQMPPJChdX3ooYfIyspq9/U6iqV53Ed5+OGHCYVCHHnkkdTX1/Phhx8ycuRIXnzxxYx6kiTxzjvvcOaZZ3LMMccwbNgwfvrpJxYvXsxbb73VJNxPa9x8883pUBoNFBUVcccdd3DFFVdw/vnn89FHH3HjjTe2y6blpptuYsyYMRx55JGEQiHee+89zj777Iz4lZMnT+azzz7j6KOPZtSoUWzYsIFIJMINN9yw3RkaRo0axa233sqkSZP44osv6NWrFwsWLODOO+9Mx/lq4N1332XixIkMHjyYbt26sX79ej755BMmT56cYW/zwgsvsGnTpu2Od7kjDBkyhP/+979cd911fP7555xwwglIksSqVavYtGnTDqUGtNvtfP7555x33nkccMABjBgxgqKiItavX89PP/3EDTfcwKmnnorX6+W///0v48eP5+ijj+bUU09l1apVDB48uF2/fNt7ne3lqaeeYsOGDRx//PGcffbZ2Gw2PvzwQ/r27dvsj4bdxSOPPEJtbS3Dhw9nxIgRHHDAAZSUlFBaWtpq3vILL7yQZ555hvHjx3PhhRcSiUQIBAL84Q9/aKJhefLJJ1mxYgWDBw/m7LPPRlVVNm7cyA033MA999zTpD/hcJhTTjmFYcOG0b9/f+rq6li8eDGdO3dO71R0pM3a2loefPBB/vCHPzBmzJgWx7Q71kiSJJ544gkmTpxIXV0dBx10EPPmzePyyy9vVuvXXB9XrFjBMccckx735s2buf7667n33nt3yNyhI3O6o7R3nQFsNhuXXXYZU6dOBWg29FlH2tvdtKdv7b0vVFXliiuuwOFwcNhhh+H1evnmm2+oqKjggw8+SNfryPPZEm63m5kzZ6bfiWeeeSb5+fnpd+Ktt97KySefTH5+PtOmTePCCy9k8ODBnHTSSSxbtoxhw4albYR3BZbwuI/y8ccfs3DhQn744Qf69evHbbfd1qITwcCBA1m1ahWffvop69ev57LLLuPtt9/ukOAYDAYZOHAgV111VZNj999/P2eccQY///wzL730Er/73e9abeuAAw5gypQpDB06lPPOO48PPviA2tparrnmmiYBofv27cvq1auZPn06VVVVDB8+nOHDh/Pdd98hSVJaq9XQ5gEHHJBxviRJTJkyhZNOOimjfOrUqVx00UXMmTMHl8vFHXfcQU5ODlOmTMnQ3L7wwguUlpYyf/58SktLOeSQQ/jb3/6WoQVRVZU1a9YwYcKEVsfdXHrCjvb7vvvua3YL+LzzzmPEiBF8+eWXrF27Fo/Hw7nnnstJJ52UNgFo6VoAZ511VhPTgAb69OnDokWLWLBgAT///DOqqnLSSSfx4osvZtgXDh06lDVr1vDxxx8TCoWYMGECxx13HK+99lqr89LR60yePLlZ56aePXsyZcqUZoXVnJwc5s6dyzfffMPixYsxTZMLL7ywSQDgs88+u0MB4EeMGNHkOWqpjcMPP5wpU6ZkbOvb7XZee+017rjjDubOnUsikWD48OH87ne/S69bc9eQZZlvvvmGjz76iPXr19OvXz9GjRrFjz/+iGmaGcGPCwoK+OGHH/jkk08oKSmhV69ejB07Nh0ntbF2QpZlnnvuOe644w7mzJlDTU0NRx55JHfddRf9+/ffrjYbNN9tBbze0TVqbn6b47rrruOkk05izpw56LrOddddx8CBA/F4PE0CoW9L43E3BAkfO3Zs+ods43G3di81t6YdmdPm6Mi8tHedG5g4cSLZ2dlIktSsWUl72+vo89VS/bFjxzb7Hhs9enQTgam9fWvPfdGrVy9++uknfvzxR37++Wfi8TiTJ09m+PDhOJ3OjGu29/lsbU4OOugg/ve//zFv3jyWLFmCruuceuqpvPrqq2RnZ2eMu6SkhOnTpxOLxbj00ks5+uijKSgoaPOe3l4Ec1daVFpY7Ad88803DB8+nJKSkl3q3WZhsS9yww03sHr1ar788ss93ZVdxpgxY1i+fDnr1q3b012xsNgtWDaPFhY7iNPp5LXXXrMERwuLZhg8eDD/93//t6e7sVOIRqMZGaIA5s+fz6effsoll1yyh3plYbH7sTSPFhYWFhYW7SASiXD88cfTvXt3+vXrx8aNG5k+fTpDhw7l/fffb5dzmIXFbwFLeLSwsLCwsGgnmqbx9ddfs2LFCgRB4JhjjuG4447b092ysNitWMKjhYWFhYWFhYVFu7FsHi0sLCwsLCwsLNqNJTxaWFhYWFhYWFi0G0t4tLCwsLCwsLCwaDeW8LiXoGkapaWlGemFLCwsLCwsLCz2NizhcS+hsrKS7t27Z+R67ih+v38n9ui3hzU/rWPNT+tY89M21hy1jjU/rWPNT+vsTfNjCY+/ISzH+dax5qd1rPlpHWt+2saao9ax5qd1rPlpnb1pfizhcQ8zdepUioqKGDRo0J7uioWFhYWFhYVFm1jC4x7mtttuo7q6mqVLl+7prlhYWFhYWFhYtIklPFpYWFhYWFhYWLQbS3i0sLCwsLCwsLBoN5bwaGFhYWFhYWFh0W4s4dHCwsLCwsLCwqLdWMKjhYWFhYWFhYVFu7GERwsLCwsLCwsLi3ZjCY8WFhYWFhYWu43a2lo++uijPd2N3YphGEybNu03k4LYEh4tLCwsLCx2AqahY2pqy396K8fa+2foe3qYLF68mCVLlmz3+WvWrOGWW27ZeR3aB1AUhUsvvZREIrGnu7JTkPd0B/Z3pk6dytSpU9H1Pf9CsLCwsLDYPkxDJ1byLaamIgjN62WUUJBYrQ/T1NHjIcBEcmYjiK1/FZumiZEIYxoatpwuuPoORhClXTCK9vH666/jdDo5/PDD91gfLPYsluZxD2NlmLGwsLD4DWAYmJqK6PAiurKb/ROc2Qg2N3okAJqGnN0ZyZPXYn3RlY3g8GIkwhhKHNHlw1QTYBjt6pLf7+eTTz5hyZIlTbaK58+fz+rVq9Ofa2pqmD59esb5kUiE2bNn8+WXXxKJRADYuHEjq1at4pdffmHatGl88cUX6fqKorBgwQI+/fRT6urqWuxLe2iprR9//JH//e9/lJWV8dFHH1FZWcnnn39ORUUFK1eu5J133klvDf/666989NFHrFy5Mn2+qqpMmzYNRVH47rvvmDVrFgChUIjPPvuMr776qk3tYGvtqqqa7nc4HG72/NmzZ1NSUpJR9t133+2QNnd3Y2keLSz2MRTNIKbq6IaJZpgYponbJpHlkBFFYU93z8Jiv0UQRARJRpBsLdQw0YIVCKKInNcD0eZstT3TMNDDlZi6ii2vG6LdhREPtasvy5cv54wzzmDgwIFEo1EcDgcVFRWMHTsWgH/84x+ceOKJ9OvXD0gJRLfddhujR48GYNWqVQwfPpzu3bsDUFpayqxZs/D7/WzcuBFJkpgxYwYDBgzgd7/7HWVlZYwcORKbzUanTp24+uqr+eCDDzjuuOOa7UtrtNbWq6++yqJFiwiFQgwaNIgDDjiAe+65B4/HQzQa5aCDDmLMmDHce++9vPDCCxx33HF89913XHHFFTz22GNEo1EuvfRSXnrpJex2O0OGDKF///4MHjyYQw45BFmWqa+vZ9asWeTk5DTp21133dVqu6+88gp2u53q6mqCwSBLly7F7XZntPHjjz/y3HPP8fbbb6fW2TS5+OKLefbZZ9u1tnsDlvBoYbGPkNR0KoJJNtTHUHQDATBJ/Uc3TQSgV66LTj4XOa6WvrwsLCz2BKamYgTKwONFzuvWLsFRC5RhKHFkXyckVzamrrb7enfeeSeXX345jz32GAAXXnghFRUV7T7/9ttvZ9y4cTz11FMATJw4kdtvv50PP/yQ4cOH43Q6eeSRRzLqn3TSSfzjH/8A4K233mLSpEl8//33He5La21BSkv4008/4XRuncOuXbvyxhtvALB06VKeeuopVqxYQc+ePSktLeWggw7ivPPO48ADDwTg4osv5uqrrwbgueee47TTTuPNN98E4JdffiGZTDbpV3vaveaaa7jgggswTZNBgwbx+eefc84552S0c9lllzFlyhTq6+vJzc1l7ty5xONxhg4d2q612Ruwtq0tLPZydMNkQ12MhRvqWV0TwWmT6JTlpDjLSacsJ52ynXTJduKxyyzYWM9nKyv5tSpMUrPsaC0s9gZMTUX1b8Y0ze0WHDvK/Pnzufjii9OfL7nkkg6ff+mll6Y/X3HFFcyfP7/F+l988QVFRUW8+eab/Pe//yUSibB48WJUVe1wX1prC2DEiBEZgiPA2Wefnf73t99+y0knnUTPnj0B6NatG2eccQbffvttus748ePT/x48eDDff/89L7/8Mps3b+bggw+muLi4Sb/a0+6oUaMAEASBQw45hPLy8ibtdOnShVNPPTUtrL7yyitcdNFFSNKes2PtKJbmcRfg9/t57LHHqKmpYcKECfvUrwmLvQtNN1hWEWZpeZADC73kue3N1hMEAZ/LxqAuPtb7o8xb76cinGBAcTZFXjuCYG1nW1jsCRoER0wDyddltwiOAIlEIkPAcrlcGce3fScY29hRJhKJjHNcLhfxeLzF60WjURYvXpxhR3nhhReSTCbb7EtH2gLIyspqck7jsng83uQaLpeLWCzWbP0jjjiCr776ivfff59rr72WcDjMu+++S6dOnTLaaE+7jbfkRVFs0Rn2qquu4rHHHuPSSy/l3XffZcGCBc3W21uxNI+7gLPOOotgMMiQIUO47LLLWLx48Z7uksU+iKobrKgKs8EfxW2TqIsqaIaZWcfQqUqEiGipl6pNEumd56HAY2dzIMEPm+pZUxvF2OY8CwuLXU9jwVHO64Zga93Wb2cJjgADBw5k4cKF6c/bag1zcnKoqqpKf97WaXPAgAHMmzcv/Xnu3LkMHDgQSAlI28YrPPzwwxk/fjzTpk1L/02dOhWv19tmX7altbbaO/bvv/8eRVGAlDPLwoULOfjgg5utX1FRQe/evbnlllv4/PPP6dKlC6+//voOt9saY8aMYf369dx///0ccMABHHbYYR1uY09iaR5bwDAMTNPssBp51apVVFVV8c9//hNIeau9+uqrHHXUUbuimxa/UZKazoqqCNXhJL3zPcRVnfJggtJAnG45LmRRIKDE2BCtI6wlkAWRAoeXYqePLNlB9xwXmwNx4qrOyqowim7Sv9CDLFm/Fy0sdgfbCo4pjWPLXrw7U3CElM3jtddeS3V1NdFolH//+98ZWrMRI0Zw5ZVXkpeXRzgcTjt6NHDPPfdwxRVX4Pf7MQyDqVOnMm3aNCAlWD7yyCMcfPDBdOvWjd/97nc89thjjB8/npUrV9K7d28WL15MRUUF06dPb7Mv29JaW+3hzDPPpGfPnowcOZJzzjmHDz/8kKKiIkaPHk0o1NThaPr06bz99tuMGTMGRVH46quvuO6663a43daw2WxcfPHFPPHEE0ydOrVD5+4N7FPfJD/88AOnn346LpeL7t2787e//a2Jqn1H+eabb5gwYQIul4tTTjml2TqzZs3i0EMPRZZlevTowdNPP50+tmnTJvr375/+PGDAADZs2LBT+2jx28YwTFZVRaiJJOmU5UAUBDx2mS4+J6pusN4fZm24lpWhChRDxSs7yLa58CtRfgmWsTHqxyaJdM9xYZdEkprJ2toIv1SGUbSd+7xYWFhkYuoaRjKOUrMeU1OQfJ0QRCkVIFzXtvw/889Qk6h1G9HjYSRvAaLd1Ww9U29/dpJzzz2X//73v2zatAmn08lLL72UcXzMmDE899xzrF69GrfbzXvvvceYMWPSx8eNG8dnn31GXV0dgUCAGTNmpD2xL774Ym644Qbmz5/PDz/8AMAJJ5zAggULcLlcLFmyhMGDB/P+++8325ePP/6YcePGtdj31toaPHhwEy3dyJEj6dKlS/qzIAh89dVXjB49mqVLlzJ8+HDmzp2LKIrY7XYuvvhiRHGr+HPttddyzz33sHbtWsrKynj//fc544wzmvSro+2ecMIJDBgwAABJkrj44oux2bY6M44fPx5RFLnoootanIu9FcE0zX1iP+uHH37glFNO4bbbbuOWW27BMAweeeQRrr/+enr37r1TrhEMBhk9ejTXX389X3zxBSUlJU3U6ytXruSII47gL3/5CxMnTuSrr77iggsu4JVXXmHChAnMmzePe++9l6+//hqADz/8kDfffDNtGNsSpaWldO/enc2bN9OtW7ft6n9dXR35+fnbde7+wL4yP6WBGMsqw3TOciI2sktSDZ2NoXp+8deiCzoH5uRSrYQIqjGKndkUOrLQDJ2AGufg7C747C5U3WBzII5mmDhlkU5ZDg7r4sMuN/3duK/Mz57Cmp+22Z/nyDR0Eht+Qo8HWwwAHgwF8WX7Ms9rFABctLvbtIkUnV6cvY7scJDw7777jksuuYQ1a9Z06Lzdyf52/zz00EPMnz+fzz77rF3196b52We2rf/0pz9x2mmn8de//jVd9uijj7ZY/4477qC4uJg//vGPGeXvvfce7777Lv/973+bnOPz+fjmm28A0v/fln/84x/07duXO++8E0j9Orvwwgt54oknmDBhAgMGDOB///tfepE/++wzKwq/RbsJJzRKaqLkuexpwVE1dGoSIX4JVqCbOp2ysgnFDOqiKoaU+dtPFiXsokRJpIrDc3qkNZCbA3GSmkFlOIm9OszA4ixrC9vCYiciiBLOXke2GsA77q/DnbeDX/6iuEezy1jsOH6/n/fee4+nnnqKV199dU93Z7vYJ4THuro6vv32W1577bV2nzNy5EhGjhyJJEn8v//3/4CUFvCSSy5por7vCAsWLGiynX366aczbdo0EokEBQUFTJw4kUGDBtGrVy9qa2t5+OGHt/t6FvsPumHyS2UQE3DZUl8OASXGxqifgBLFa3OQZ/cgCAJOSac6nEQ0nfRwu/E2MsTPkp1UJUJUJYJ0dedmCJAJzWCDP4ZNEulf6LWCiltY7EQEUYJWBDtBsiHIeyYGa2FhYatbxRa7j2AwyPz583nggQc488wz93R3tot9Qnhcv349AOFwmEMPPZRff/2VHj16MHHixCaaxQZOPvlkPv74Y0aPHo0sy3Tt2pUJEybwwgsvMGHChO3uS1VVFYWFhRllRUVFGIZBTU0N3bt3569//SsTJkygpqaGo48+ukl0+cZYua0tGtjkj7GsIky3HBcJTaU8EaAyEcIhynR252TUddkkirIcVIeTROLgkYEtcqAgCOTaPZTGA+TaPbhlOzZJpIvPwcb6GElNpKQmgk0S6ZvvtsL4WFjsB/Tt25fHH398T3fDAujdu/c+q3FsYJ8QHhvMMh9++GHef/99DjvsMGbNmsW5556Lx+Ph2muvbfa8008/nQ8++IBx48ah6zrPPfdchwOltocGp53GX8IDBgxIG8q2xm233cZtt92Wtnm02D8JJzTW+WP0yHXjjydZHanEYdfJsbuRW9BkNBYgq8JJirMcSFs0iQ5JJqYnKY3VU+D0ElTi+JUoGgYOMwtDdbCiKoxTFuiW0/KPGwsLCwsLi23ZJ4yeOnfuDMANN9zAMcccg8PhYPTo0YwbN4733nuv1XNVVcU0TURRTMdm2tG+VFdXZ5TV1NQgSRIFBQU73L7F/klpMI4JFGXZiYlhauNRNFVGa8Z+SjcNElvSlDUIkKpuUBFKEFWV9I+tbJuL6mSIFcEKNsf8yIKEXZaICAEiRpRoUmNZRZj62I4/FxYWFhYW+w/7hOaxW7du9OrVq0lYHl3XW43DOHPmTM4991yefvppunbtyvjx45EkKZ3Pcns44YQT+OKLLzLKZs+ezZFHHtkkXZKFRXuIJDXKgglyXTbK4wEUIUHXLC8lAT9CzGBgbgHZ9q0x0crjQSJagk5OH7l2d1qAXFFXy/pYgn65eRS7spAEkSJnNjXJMPVKDATo5PQhCSIBJYSq6CTCHpZVhDimR+4enAELCwsLi32JfULzCKmAp//4xz+YP38+oVCId955h48//rjFbejZs2dz9tln8+STT/L73/+eESNG8O6773LjjTe2amug6zqapqW1N5qmZUTSv/HGG9mwYQP33XcfNTU1vPnmm7z11lvcfvvtO3fAFvsN5cEEpmkSUKNsivrJsbnJdTlwOUSiikp1NJG+H03TRDN1VENHN7f+mHLZJHxuiYSuUhVJoDfKKGOYJoqhp7WYTslGvsNLLXVUatUsraxnZWUYY9+I2mVhYWFhsYfZJzSPkArimUwmueKKK6iqqqJPnz48//zzLQbXnDt3Lo8//nhGlPizzjqLt99+m3fffZfLL7+82fMOPvjgjDhYDdrEZDKJJEn069ePzz77jNtuu42pU6fSpUsXnnnmGc4999ydOFqL/YVoUmNzMI7dZrI6UoPX5sS2xcaxf04BtY4EWlKkNqpQ4EnlqO7i9JGwe/DKmanOenpzyZKdRONk2EDm2z04JRtuaWv2CJso0TergNJYPVXRKmaui/G7LjkUWqYXFhb7JclkkmXLliGKIkceeeRuvfayZcsoLCzMCKC9LxAIBCgpKeGYY47Z013Z7ewzQcJ/61hBwnc9e+P8rKmNsr4uSlQIU50Ik+fwNKkTSKgEYypuu5QWIFsjrqbC+NgkMcOJpuX6Kqvq/Pg0uOyYI+mUbTnQNMfeeP/sbezvc6QbZqsa/J0xP6IgtPlMA6xduxZBEOjTp0+bdROJBEcccQQul4shQ4bw7LPP7lAfO8qoUaM499xzGT169D51/8yZM4cbb7yR5cuX77AgWV9fz5o1azLO37bNven52mc0jxYWvzXiqs7q6ggep8D6SJjsFjJL5DhTv8aDMZValDYFyNa8sJuvb+PQwkLWlJUxZ0MV4wb0wGmzghBbWHQE3TD5dr0fVTcyMkM1JhgK4QsK6KZJKKFhYpLtkJHF1i3ITEzCCR3NNOiS5WRwz9w2BchZs2YhSVKL0UgaM3fuXDweD4sWLWqzrkXzbNiwgWeffZaXX355u85fvHgxkyZNYvny5TutzV3JPmPzaGHxW6MsEKc0mGBD2A+YyKKEYZpoRtOYn9kOGY9LIqbo1EaVDBtItZn6TlkkzyOjaDpV4WSGDaRq6Gy74SBLIsVZLn7117GyKtLkuIWFResYpklNNIk/puC2S2Q75Rb/cl02umY7ME2T+riKQxJare9z2ujiS5mplIcT7bJPHjZsGEOHDgWgpKSEDRs2EI/H+emnn6ipqUnXq6ioYN68eYiiyJw5c9iwYUP62KZNm1i6dGlGpJKGthKJBIsXL6a+vp7Fixfj9/sJBAIsXLgwHbc4Ho/z888/U1FR0aR/lZWVLFu2LMOnoDk0TWPOnDmYppnuT3PnBAIBFi9eTCAQaPbc0tJSlixZgt/vZ/HixRiGwYoVKygpKUnXX716NcuWLctwzo1Go8yZM4c5c+awfPnyFvvbq1cvbrjhhozzFi9eTHl5eZtt6brO0qVLM47rut6kTUit188//0w8Hk+XNR7TypUrM8a0q7A0jxYWewBVNygLJshywcpQPV28XnTToDRWj2LodHXl4JZTNoqmaVIRDxDTFVx2NzGFtAayOhkmoMYocmSTa9+63VynRKlTIngcLlRFSGsgQ1qc6mSIHJubYmd2un5YTRDW4tjdJj+W1VLktdHViv9oYdEhvHaZQFylKpykW44LeRvtoCwK2LakBbVJIr3zPGwOxKmMKHTzOdvU+PfIcVEeTLarL//85z+RZZlHHnmEp556itWrV7Nu3Tpyc3NZsWIF//nPfxg3bhw//fQT7733HlVVVdx3331cccUVjB07lgsuuIAVK1ZQUFBAKBTik08+YeDAgem2SkpK6NatG08++STXXXcdPXv2ZNGiRfTu3ZvPP/+c6dOn84c//IFevXqxadMmLr30Uv72t78BqdTCU6ZMoV+/fiSTyVZtHSORCKeddhrnn38+K1eupL6+nq5duzJv3rz0eU8//TR33303BxxwAGvWrOH+++/nj3/8Y/rc8847jxUrVnDyySczZswYbrjhBrKzs5FlmVWrVjFp0iRKSkpYtWoV1dXVnHTSSbz99tsAVFdXc9999wEpwU0QBGbOnEnPnj0z+rlkyZL0FvbChQsZPXo0PXv2pLa2lrFjx/J///d/LbZVVFTEa6+9lnH8888/z2jTMAxuvvlmPvvsM3r06EFZWRn/+c9/GDZsGD/88AMTJ06kuLgYRVFYt24dF1544S41P7CERwuLPUAgrpLQDJwuFUdcJJowMFFIGhpJXUUxNNykhEcDk7iuEtMV8lweJNmW3sKOoRDTFBRb5q/hpKER1RS8TmfGFrZgU4lpCg4x82WtGBqqqRE24hyYJfD9piDDXHa8DusVYWHRXuySSJdsB9URhdJAvFkBsjGNU4eWBhNtCpCiIJDl3D6Tko0bN7J48WJ8Ph/PP/88Dz/8MOPGjeOss86ivr6eadOmMWPGDAD+8Ic/kJ+fz4YNG5BlmWeeeYYbb7yRr776Ckhp6BYvXpxhfxePx1m7di2yLLN582auu+46Zs6cyeDBgwmFQgwePJhRo0bRrVs37r//fhYvXsyAAQP49ttvOemkk9rs/0knncRbb71FMpnkkEMOYcaMGYwePZrVq1dz++2389133zFo0CCWL1/O4MGDGTZsGF27dgXgqKOOSguDM2bMYNOmTSxYsIDBgwfz9ddfc/rpp/P3v/+dt956i0AgQM+ePVm1ahUHHXQQvXv3Zs6cOel+3HbbbTz44IM8//zzLfb13//+N7fccgt33nknAP/5z38AWm3riSeeYNKkSRnHG/Pmm28yb948SkpKyM/P5/XXX+fyyy9n3bp16fV94403OPbYY9m4cSP9+vXjvvvuo6ioqM253R6sbWsLiz1AZSiBLmhUJ8L08WXjtkvEEgYewUM3dy5ZjewfJUGks8tHd3ceOXY3OU4bPreNmKIj6056uPPIs2c62hTYvfTy5FNg92YEEleTMt1ceRlaR4Acu5siZzY93LnEiOO0CfxSGcIwrO1rC4uO4LbLdPE5UXWD0kAcrY1nqEGAFAUoDSZIqK2nqhXYvnSiY8eOxefzAXD88cezcePGFut+/PHHnHjiiXz33XfMmzeP3r17M3/+/PR29Lhx45o4blx22WXIcurH5pdffknPnj1RFIV58+axZMkSDj/8cObOncvXX3/NKaecks7AdsIJJ3DEEUe02f/LLrsMAIfDwVFHHZXu/5w5czjhhBMYNGgQAIcccginnnpqWtAFmsR27tevH4MHDwZI///iiy8GICcnh379+mVs3+u6zooVK/jmm2/o3bt3m7ahAwcO5KuvvmLx4sXoup5ue3vaauDLL7/k3HPPTc/7JZdcgqIoaRvJfv36ceyxxwLQs2dPOnfuzKZNm9rV9vZgqRUsLHYzcVWnOqqQMOMgCNgkmQKPlNIkKiDINiQh83edR3bgYWtonsZONIYqIdsz6zskGYe09fFu7ESjJiXs9sxHXxJEsmUXht1FXTKKxwfVEYXyYJxuudb2tYVFR/BsESDLg4ldooHcHtzurc+xLMut2hrW1dXx+uuv884776TLjj/+eKLRKJASsLalcVldXR0VFRVMnjw5o86xxx5LKBQiOzu7xXM72v9gMJgWihu3FwwGW2y/cVsNiUZcLldGWUP7K1asYMSIETgcDoqLi4nH4xk2o81xyy230LdvX5599ll++OEHRowYwaOPPsrKlSs73FYDwWCQHj16pD8LgoDP50uPs/GYoO013lEs4dHCYjdTF00SU1X8eoQsW0ogFASBAo+dWhTqgjG+ee9TwpXldOrRg1PGnY3cjE3QrvDCFraEAalXonTy5PFrTZQ8jx233XpVWFh0hOYEyNbYHQJkexk4cCC3334748ePT5dFIhG8Xm+7zy8sLOSrr75CbORJHolE+OKLL/j3v/+NaZoIgpChPdseDjzwQP75z39iGAaiKGIYBosXL2bs2LHb3WZjnnnmGc477zwef/xxAD788ENuvPHGVs+JRCKMGzeOcePGoaoqvXr1Yty4cfznP/9psS2bzZbW7LY0ziVLlqQ/V1ZWUlpayoEHHsjKlSt3cJQdx/pG2MNMnTqVqVOntnrTWPx2ME2TsmAChSSaaWAX5XS5IAjk2ODvf/oDJYu3bmXMmz6dyS++lCFAputvESDrgjHmf/Au0apyOvfs2UTgbKi/rQBZ5LUjS5laS6/spCYRpqsrFwNYWxvlkM7ZbcaXtLCwyGRbAdIrtG8Le08LkH/961+59tprKS8vp3fv3ixevJiFCxfy+eeft+v8M888k4cffpgLLriASy65hEgkwrRp07jxxhsZNWoUt912G9dccw1jx47l9ddfJxwOb3dfzzrrLO644w4uv/xyzj//fN59910Mw+Dss88mFottd7sNdOrUiY8//phPPvmE2tpaHnrooTbPueGGG+jSpQsnnXQSGzZsIBwO07Vr11bb6t27Nxs3buTdd9+loKCgiR3o9ddfzyGHHMJf/vIXjjjiCJ544gnGjRtHz54994jwaNk87mFuu+02qqurWbp06Z7uisVuIJTQ8McUQkYEj2xHNw02x+rZEKsjqavM/ejDDMER4Jcfvmfuhx+kP1clQqyL1hJWEwB4RJOXbrmBNx68n4/+/QL/umcyD1x9FZqqAhBQYqyN1FCXTG05NQiQQSXOoppyKmLBjOtpps6mmJ+6ZJgCt52yUJLqiIKFhUXbaIaZsi/e8meXRIq8duKqzuZgkriqZxzf9g+gc5YDwzTYUB8jklDTx9qyn2xM37590wHCDzzwQHr16pU+5na7OeGEE9Kfi4uLOeyww9KfR40axQcffMDSpUv517/+hWEYvPnmm822BSmHlLy8vPRnURT58ssvGTx4MK+++iozZ85k0qRJnHXWWdhsNubOnYssy7z88suMGDGCm2++mU6dOjU7DlmWOeWUUzJ+vA4YMCCdTMNms/Htt9/SqVMnnnvuOfLz81mwYAF2u73Zc/Py8jjqqKMy+nrKKaekt68BjjzyyLRt4a233srw4cN59tlnWbRoEU899RTHHXcckNoObwjg3fjfzz//PPn5+fzrX//ihx9+YMaMGfTs2bPVtnr06MHTTz/Na6+9xn333YeiKBlt9urVi5kzZ1JRUcFLL73EyJEjmTZtWrNjgpSJwLbmATsTK8PMXoKVYWbXszfMz+rqCN+VVRERgxQ5s0joKuujtaiGTk9PPp/83zN8+EJTL75x11zLxbfcimmarIvWElBi9HDnUejM4su33+K5e+9pcs71Ux7gjPPOpyIepDReT7Ejmx6erS/4zZEAy/3V5NrdHF3YBVsygel241eirAmnyk8u7AeGjGGaDO6Rg0Pef4OH7w33z97O/jxHumHyU2mASLL5XSRFN6iorSfHl02WQ24xkHi6vRYCiXsdEkd2y2lXlpl9jf35/mkPe9P8WNvWFha7CU03WFMTYWOknvys1KPnlGx0cvrQTQOv7KC4e/dmz83unAo5IQgCRY4ssmUnPnvKhqpq8+Zmzylfv54v336Lsk0bye7Sif5nn5NxvNidsl+KxE2qIwqdZBMRyJad9PYWkNQ11kVqGOjrgj+qs94f46CirJ0xFRYWvzkkUeDIbjmtpyfME3ZbekILi12JJTxaWOwmAnGVqJHEEFWiMZm4rOOySRnBvU8Zdzbzpk9nxY8/pMv6HXUMBw8dSSChkuO0kWVzZoTyaUng/G72l1Q3CsexdMYXGbaTdlGmuzeHuCOVC7teUclxpTLdFDpSQmJtMsymqJ/urjw21ccp9DjI99h36rxYWPxWkEQBqZVQOjZJTAcJt7DYl7GERwuL3URdTCWsxSj2ulCSEtXhJEVZDlyNDOJtdjv3vPQycz/8gMpNm+jUowcnjx1HQDEJxlI2jA1OMg00J3AW9+xF1cYNGfUabCfPOO/8jPIGG8hQXaKJF3au3UNFIoBHtiOLDlZUBTm6ew5OWbYcaCwsLCz2Uyzh0cJiN6AbJpuDESLEKbS7Ee0iVeFkswKkbLM1EfAKbCa1KM0KkM0JnOUbNvDxi/9u0o/KFoLGumwSkttGhWZkCJCSIOKzuVgTrcEuSPhjCpsUJ4cVFTAgpwhZ3H9tIC0sLCz2Vyzh0cJiNxBJalTEwtjElM1SdSKEJhsIhj0tQDplkepkGMXQKXJkZQT5rlUixAQFUXJkCJD1SpSQmqDA4c0QOKf/d1qz/cjt2oWN0TqybU5yG2WliesqYS2KaHehKra0AKmZOtXJCLIg4LG7cbht1Ec0Vko1CMDA3GJEwdqGs7CwsNifsN76Fha7AX88SZ0SwSM7SOoafiVKQI2S7U7ZQFWHk4QUhXolRm0yTEzfGhpHNw0CSozaZASnw8RtlwjGVAIJlYASpyoRIqIlM6532MhhdD/isIyyfkcdw6CRw6lKhAgo8YxjUS1JvRIjYSYo8NpQ9ZQGMqImqU2GCaoJTMHEZbfhlGXqIhozy1ZTEqzFCthgYWFhsX9haR4tLHYDa+qDGIKGS/JgAkWOLAxMsuwOsmwCVeEk9RGNLKebHLsLr7w1FaEkiBQ4vHhlJ9k2F3aHlN7Clm12urvzyG7kQAOQ7/Hxp+ee43+fzaS+rJzszl05eOhIDFOimysXr82RUT9LdiI4vIgOLx67DTErJdBqcShy+HBKEjYhtUXtc8pUhSWyZS8/+8vwyHa6eXN29RRaWFhYWOwlWMLjLsAwDD7++GNqamoYMWLEdsdttNi3URSFV199lV9L1lDtdDNkzEgEQUAACp2NQt4IUJzloCqcRE0K5GU5sG1jS9h4ixlIpzKMKeCy2XBJmU40XtmBN9tBtwsvSpcFEirBmIpTcpElZ3pMOyQZp8OLucXzu3EmGkF1kON0pB1kBEEgz+0komgU2V2sClbjtTnIcbSefs3CwmLvRdd1SkpKADjooIN267XXrFlDbm7ubr3mttduK4SSaZosXryYI488MiPl4v6KNQO7gCuvvJJHHnmE7777jsGDB7O5hTh8Fr9dFEVh+PDhXHvttTwx9TFen3Ifz0y8MZ31ZVskUaA4y5Hewo6rraerFASBfLcNp2jgD0UIRBNt9inHacPnthFTdGqjSpvbzQ0CZMMWtt4ou4VdFhFFgaqggm6alMYCbV7fwuK3jm4YqIa+S/90w2hXX0pLSykrK2tXXUVROProoxkzZgyPPvrojkzBdjFp0iSmT5++26/bkWsnk0mOOeaYnZLy8LeApXncyZSWljJjxgw2bdqEw+Hg7rvv5rnnnuOBBx7Y012z2I28+uqrzJkzJ6NsxQ8/NBsqp4EGAbIlL2wA0zDQYwH0eBBTSZCtqyTjOtV+CTXLQ64vF9HhRtxmG7uBBi/tYEylFoUCj73VkDvb5sJuHMbH55CpjSTxOpxUCGF6enMz4k9aWOxP6IbBT3WlRLSWU3kGgyF8WmiHruOV7RyZ3w2pDe3Xe++9hyRJ3HjjjW22OXfuXABWr169Q32z2H/YJzWPU6dO5eijj07nddyZBAIBnnrqKU488UR+//vfN1tnw4YNXHPNNZx44omcf/75fPvtt+ljq1ev5rDDDsPhSNmUHXfccXskabnFnmXdunXNlq9ZV4JubtUcmKZJTTJMVSKEZugZGsgSfz0bwn4UQwNAV+IodRuprV5DZdRPAhDsLoqy3bgddsqCIZav/Rn/+sWo4RpMI6W9jGpJKuLBdC7sBg1kfSLBCn8q1WFjFEOjIh7Er2Tmwk5oGr/U1VIVD2OaJoIg4HPZKQ0mWB8KUBrJzJG9L5HUdPwxhepwkopQgtJAnM31MWoiScIJLZ1z2MKiJQxMIpqCYRoEknEwDbJtjvSfTRAJqUk0Q88od4oSwWScpKZllLslmbCSIKYqZMn2LWU2QmoSg7ad1M455xzOPvtsADZt2kR5eTm6rrN69WrC4XC6Xm1tLd9//z0ul4tFixZRXl6ePlZXV8fatWszdikat/Xrr78SDodZuXIloVCIZDLJsmXLMLZoRxu2wkOhpgJzKBRi/fr1bY5D13UWLVqU7k/D1jpAfX09v/76K7redKcmmUzy66+/kkg03ZVp69qt9butvm57XuP+BwIBfv31V0KhUFouKCsry9idLC8vb7Jb2aBFNgyD1atXEwzu+XftPic8fvPNNzz//POsXr2aysrKndp2JBLh4IMPZu3atfh8PlatWtWkTk1NDccffzz19fXce++99OnTh9NOO42FCxcCpL9UGxAEwfJG3Q/p06dPs+Vabi4RZatndNLQqE1GqEyEiG7xsJZEgXyPTNiIsaq+jupoBC1UjVq1BiMaxK8rlCVChJMh0JJgGOSLCRJJP2sTUcoUDc1filK7AV2JEVITbIr508IgpARI0aazMeKnJFCfftkDhNUkFYkgNckw6hYB1GWT8LigKhFkdb2fmJbafrfLIjo6JXVBFlZvJNaK1mVvwjRNgnGVTfUxFm0O8O16P4s2B1haHmR5RZiVVRFWVUf4YVM901dU8OkvVayvi1Ifa3u732L/JqQk2RDx41fi2EQp/RfWkpTFg9QkosiCmC6PaEqqPBkBSJfHdY2KeJiaZATNNLGJErIopp/JtnjyySd5+umnAXjssce49tpr6d+/P2PHjqVLly58/vnnAHz77be89NJL/PLLL1x//fV88sknhEIhzj77bPr168fw4cMZOHAga9asyWjrwAMP5MILL2TlypVceuml3HDDDfTu3Zsrr7ySZDLJ9OnT6dy5M6NGjaJHjx7ce++96b49/fTTFBcXM3z4cA4//HAqKipaHEc4HOaYY47h/PPP59hjj+WYY45h/PjxPP300xx22GEMHTqUY445hnh8awSJl156icLCQkaPHk1hYSHPP/98u6/dWr9bo6XzGvp/1VVXccQRR/DII4+wYMECxo4dy6mnnsrQoUN55513qK2t5ZRTTuGII47g2GOP5dhjj00L8k8++SRXX301xx57LOeffz7Lli1rV592JfvUtnVdXR2XXXYZb7zxBmPGjGm17rRp0+jcuTNnnHFGRvny5cuZOXMmt9xyS5NzXC4XJSUluN1urr/++mal+6effhpBEHjzzTeRZZlhw4bxv//9j/vuu4+ZM2dywAEHsGzZMjRNQ5ZlFi9ezIEHHrhjA7fY57j88st54403Mrau+x51JMeMHEsgZuCRDWRJxC7K5NrdaIaBW9rqxGKXJPrm5FAaihCuC+DW/bhlEQTIzy7CbfeQ68rCYfeAIGJqSQ7w1SPXVWMmNEKaiC8ZQ61aiyurkE7ObHy2TIeWLh4vSUNDV0TqYiqFQkoo8sp2CuxeXJINuVEMxzyni96+HIIxnfqojjPbRBIFCj0u6hQ3FaEkFdEQfX0Fu3ZydwBVN/DHVEpqwpQFE+S4bHjsMj6nrcW0cYVqKq/3iqowNbqDQo+dHrluCr2tb/lb7J/k2B308OaQ1yjtKIDP5qSzK4tCpyfjvsm2Oenkyk5lcWr0vHllO8UuLzZRwtko5uu2znTtZeXKlXz//fcUFBTw9NNPM2XKFEaMGMHYsWMJh8NMmzaNGTNmAHDzzTcDKS2Yw+Fg6tSp3HjjjenjS5Ys4ccff6Rz587p9isqKli3bh1Op5OKigouv/xyPvjgA0455RTq6uoYPHgwv/vd7+jatSt//vOfWbBgAUcccQSzZ89m6NChbfZ/8ODBvP3221RUVNC3b18EQUhrD4877jjeffddLr30UtatW8fEiROZM2cOxx57LIsWLeKkk07i1FNPRZblVq/dWr+POeaYFvvW2nmHHnooAN27d0/3d8aMGZSUlPDkk08ycuRIAK677jqys7MpLS1FkiQuuugibr31Vt544w0Afv75Z2bPns0hhxzSvgXfxexTwuNVV13F5ZdfzvHHH99m3WAwyHXXXcenn37KqaeeCsCKFSs444wzuOSSS5o9R5Ik3G53s8camD17NsOGDUOWt07d6NGj+eMf/4iu6/Ts2ZMhQ4YwYsQIBg0axGuvvcaCBQvaP0iL3wR2u51PPv+Max56iEBZBT179+aUcWejCyJVYYXKcJJOWQ5kSaST09fkfEEQ6OTKJkdVKK+roEY3Kcr1kl3QmSKHt4nQIticFOR0Jt/XiepAmJC/GhJhfE4ZZ6gKb1535G2+zNyynYNzi9Je2EFTJdtt4pBs9PDkNemTTZQ4ILuAuEvfxgZS5KCcQiojMX6qrqK7Nwe7tHe9WlJOPwnW++PEVB1NNwABzQCvQ0ZsRQh02iR657kpVaPEVZ1gQuPnsiBdfU4OKPDgtFlZdiy2km13ke/0Nin32hz08eSR48rKKHfJNg7MbvqDyy7J9Mlq6gHc2r3aGuPHj6egIHWd0047jYceeqjFuu+99x73338/K1euxDRNjjnmGCZPnpzeoRg/fnyG4Ahw9dVX43SmbJ5nzpxJr1698Pl8/Pzzz5imyXHHHcdXX31Fly5dOPHEEzniiCMAOOOMMzj88MPb7P8111wDQOfOnenTpw+XXXZZ+nv42GOPTZsKffXVVwwZMoRjjz0WgKOPPppTTjmFL774Arvd3uq1W+t3a8Jja+c1CI8TJ07MOKd3795pwbGhjRdffBGbLWWXfuutt/K73/0ufXzs2LFN5nxPsne94Vvhqaeeoqqqinvuuadd9SdOnIiqqowaNYrPPvuMwsJCTj/9dCZMmMATTzyx3f3YtGlTWhhtoEuXLiSTSaqqqujSpQtvvfUW//nPf6ipqWHevHkccMABLbY3depUpk6d2qzNhsW+TZ2icOhZo+nry0m/8GWgOMveRIBsDj0ehPrNFNoN/M5OhNy5OCUXrla+PARBoCgnC0G2EwkFEZI1ZIk6at0mME3kZr6MGpxoEoEESnT7nWjy3U7W+oOU5NZzcEFhB2Zq16HpBtWRJOvqYkQVDZ/TTues1HhDCZXKcJKyYIKuPmebAmSRx061LhBOanTJdlIZSlAfV+lf6LW0kBZ7PR7P1nBfsiyjthD5AVJ2kE899RR2+9bdkEMPPTRtK5mX1/THZeOympoaNm7c2MRv4JBDDqG+vr7J+c2111b/WxqP3+9vEnYnPz8fv9+Pw+Fo9dqt9bs12nNeW2Pett/5+fmEQqG0bNCeOdqd7BPC488//8yUKVP47rvvMjR+bTFp0iQ0TeOss87C4/Fw7rnn8uSTT+5QX1RVTTvDNOByudLHIKV1uvLKK9vV3m233cZtt91GaWkp3bt336G+WexdlEciCDTVFDhkqU0BUk9EUWs3Y2gqzrwudM3uTHVUbdELuzGCIFDodSAIOURidohXkmXGUKrXYgK2bQRIVVFY9PEHbF6zFk+XHpw8bhzFPk+HBUinLJFlczJ70yZcNpk+vj0Ttw1SNo11UYW1dVHq4xrZDpnO2Znb9tlbhOb2CpB2WaSb10lpMEF5KEH3HBcJzeDnsiB9Czz0yXMjipYAabHv079/fx544AFGjx6dLkskEmnNYlscdNBBdO/enR9//DHjPZJIJPjkk0949dVX02WqqrJixYqd1ve+ffvywgsvpP0PTNPk559/5qyzzsJut7d67db63RqtndfWuY37vWTJkrQm9KeffqJnz55I0t65s7FPOMy89dZbGIbBhRdeyNFHH83RRx9NMBjkqaee4swzz2z13DFjxiCKIrW1tUyYMGGH+5Kfn09dXV1GWW1tLbD3/TKw2HPohsG6YD2qoeJXohlOFqZpEjUSyA4VzTCpDCe3bKNCQIlREw+SqN+MnowgZRcj53QmbqqIdgVBMDPiQEa1JDWJMEldy7h+wlAxpCSyw0bU1ZkwLvRoHZGKlVQGK4luSWeoKgoPXH0Vz917D5+99grvPPJXnrzhWqqCqT7rpkFNMtzEI9swTWJGHJtDQ9H0dBzIIreLQDLJW6tXsMpfjWHufi/lcEJj0aYAX62pRdFMOmc58Dqa/9GZ7bTRKctBXNUpCyYw2nCGcdokuvmcGCZsDsRxyiIFHjsltRHW1EYxDMuZZn9G2fY51FXKYyFCaqYAoRo65bGmaUI1w6AiFqIuEc0oN0yTqniYmnh0tzhs3XvvvUycOJFXXnmFuXPn8re//Y3x48e3+/yRI0dis9m44oormD17Nh999BHnnXceM2fOZPTo0YTDYSZNmsTXX3/NNddcg9/v32l9HzVqFLqu84c//IGvv/6aG2+8kWg0ytlnn93mtVvr9/aOt73cdNNN3Hnnnbz99tt88MEH/OlPf2LSpEnbOw27nH1C83jTTTdx7rnnZpQNHTqUc845p1UN35o1azjttNOYMGECPXr0YMSIEcyaNYshQ4Zsd1+OPPJIfvzxx4yy77//ngMPPJCsrKwWzrLY3wgpCTaGw0T0BGZcwyXZcG1xiEkYKpXxIDomXdw5ROMCleEkeR6JqkSIaKSOzrEgednF2PO6YggClYkQES1BF2cuumJLayCrlTB1yQjd3XkUS9np6/uTUcoTQTo7fXglNxGhK6ZhEFPrqKheQ6eiPvTxdWHuhx+w4scfMvq+9qdFfPPhh5x27nnINo3yeACnaMMt27GLqVdGVEtSnggiIdDJk0s4lgoknu0WMCWV8miCT9av5XeGxsC84u028u8Imm5QGkiwti5KTVRBABKaga+N7eSOaiAbBMjSYILNgTjdc1wUeRys88cwMTmwwGtpIPdTYrqK1ihyQWUszNpwHcWuLPr7CtG2BBGvikdYG6ol2+5kQE4R0hZHGX8iyppQLS7ZjlOS07bDISXOmlAdAtA7q31Kiu7du6e1Vj179sTn22pb7XK5Mmz98vPz6devX/rzOeecQ15eHs8//zyVlZUcddRRvPbaa822BTBw4MCMMkmS+Prrr5k6dSqPPvooXq+Xiy++mLFjxwKpuJL33HMPDz/8MKNHjyY3Nzdtj7ktsixz1FFHZWj0Dj74YLKzt77vevTokd4BdDgczJ8/nylTpnD//ffTr18/vv322/Tx1q7dWr8VReGoo45qVhPY2nmRSKRJ/30+HwMHDsxo46qrrsLhcPD666+j6zr33nsvV199dZO13FsQzH007kRBQQF33HEHt956a7PH169fzymnnMKwYcN44YUXEASBBx54gMcff5wvvviiVeNXgOuvv57ly5czf/78jPK5c+dy+umnM336dEaOHElJSQlDhgzhz3/+M7fffvt2j6dh23rz5s3bnc6wrq6uzRRL+zO7c35W+qt5bfmv5LgkbJJEF5cPeYsApRk65fEgBiZdXD5MQ6AqrAAmhhgmWbeeYtlFduf+iDYnpmlSlQwR1RQ6ObNxivZUKkPdQLSrJM0kRc4ssht5U9crUeqSUfIdHnJsbmqjCpF4AjO8iWiyhrzsYrp0Pog3/v53Pnzh+Sb9H3HV7znj9zciyQaqGMcuynR2bbXdVAyN8ngQEYEuLh+qDtXhJKIIuhzHwMSuO3C5BI7pVMih+Z13qQBZH1MoqYnijyvkux04ZJGqcJJgQsXntFGc5WizjQYbSJdNalaATIQDOLNytn5WdUqDCUQBuuek5r46otA730W//VSA3J/fQbph8H3NRpKNQunENY2ImsAl2/DaHKkg4b5skrpGWE1iFyWybFtTf6q6TlhNIooCPpszXa4ZBiElAYJAd3c2RxV0bzNI+L7I/nz/tIe9aX72Cc3j9vDAAw9wxhlnpAVHIO0tduutt6Yj6m/L2WefzebNm9m0aRPxeJyjjz4aSGkXJUnilFNO4YknnuDcc8+lc+fOlJaWcskllzQb+sdi/8Q0TTaGg6DbcOOm2J0piMiiRA9P3taYoOJWJxoCcToZIp7cruksMYIg0Mnpy4ghms6Frdjo4vXgtmU+yrn2lNDYUL/Ak9J6ho0uFBoG2YkoWrCKom7N29n27N0Ln9tGMKbisWVR6HJk/HK2izI93XnpMlkkbQNp1z0UbXEgqYsm+bGyBhM4bBcIkElNZ6M/zsb6GHZJonOWM2OOAIIJNeNzS+wMDWRxloP1/hguWaJnXuuRGyx+W0iiyLGFPVsN4F0n7/iXv4jwmxQcLfYt9lnN49KlS+nUqRPFxcXNHk8kEtjt9mYTmMdisRZD8ixfvrxZA9cGIbKBSCTC+vXr6dSpE4WFO+5Zamkedz27a34iapJ3S1bhD4FpgsMmUuR1tBliIx4NU7ZxDYLdQY9eB2KTba3W1w0zrYFsy4kGUkJtbVQhHAzgjpXis4GY052HbvpTxtb1wYOPZfKLLyHbbOkwPm671KYXNkBcTYXxsUliWlirjSYxJYVjuhRyZGHXnSJAGoZJZTjB2roYcVUn321vMU5jezWQQSWOW7YTV4xmNZDbah4b2FYDaZgQiKsc2c1HvsfepP5vGesd1DrW/LSONT+tszfNzz6reRw0aFCrx1vzCmstlmN7A3B6vd50/CYLi8YElTjBhEKBJwtji9dvdSTZqgBpGgZCuJJCp0m9p4iqiEanLKnFMD7QvlzYjREEgQKPHdP0EdaSEK8iJ1LN3c//i3mffEbVunUU9+nDKePORt4Sa2xn5MIu8jqoiwnM31SFZsCQ4rbz8raEaZrUx1V+rQ6zoipCd5+LTtmte4C2RwNZnQixJlyN1+akt6eATlmOHdJAumwiK6rCHNXNh9u+z75mLSwsLJrFeqtZWOxk1gT9hJIqnVwi0ha7t9pIkrWBAAUeBzk2V4YAFlYTKPEgzngIl68AR3Yu1RGF9YEwOW6JPKc7bVAPENcV4rpKtuxEFqW0ALk5GMHjEih0udOOLZCyTwyrSbyyHYdko9DrAArxV8Wp91fQWbZz+rnnIcbjmG43umlQr0SxizIe2ZEWIOujCkE1RpHHSbZ9q32laZppb9JsmzNDgFxXHyTPYyPP7caWEJm7qYJQTOfk7t3w2FvXrDbGNE0CcZWN9XGqI0lkUaDY6yCU1HAn1PSWc0u0JkDWKzHWRmpQTYO6ZJS4ptLTk0eh10lZKE6kLkG+V8amq7QkpjYnQNbHVVZVRzisc3arPwIsLCws9jWsN5qFxU4krqn84q/Cr8bQzFTYDq9Dxu0SKI0G+MVfQ7RR/uekrlIWr2ejfyMxUUTOLsZpk8j3yFQmg/yvrgp/YmuYHNM0qYgH2Rito15NlTdoIEN6jGV1VZRHQxl9qktG2RiroyqRCvCbigPpxMz2sUZT+bV8LXoskK4fUuNsjtVTHg+gbTH+z3HakB0GGyP1rKivJaFtDTAc0xXKEwHKEwFiW/Jzu2wSPrdEeTzAcn81QSWBz2WjhyebxdUVvL9yPRvqYsQUrdXQIzFFoyKUYElZkB83B6iPqRR6HBR4HHTLceGySVSGk4QSLQc8bqA4y4HPaSOYUKkKJ7eMNUFJuArV0KlTIkS0BJIgsDZSw/p4JWVaJbOrV/DOhiWsClWyIVJHXG/+WtuG8clxylRHkmysjzdb38LCwmJfxdI8WljsREJqAn9CwSs5MrSFOU47RR4XwbhGfUzDnW1HFAQkQcRuGKlg4Lmd0k4yLptM1yw3ZeEYgZhOrj2VC1sQBFySHc00MrSLkijQLdtDMqASihnEnXp6C9shyXhkOy5pq3ZOEAS65GRTn+iO5q/GX1VGYWE3BFJaS5dkwynZERuNocDlpC7pIp40CMQ1ir0ygiCkcu+KqbYb2zN67TKdstzURJIEohrZNhOnTaa3L5c1oUrEzSJF7mzcskSex45vi/ZQMwwSqkEorhJIauiGiVOWKPQ40ppcSAVf7+pzUhZMULlFGNxWA6mbBoZppvvVoHH0x5ME1RhhQkiCiM/uJqoryIKIW7bjFZwohkYnj4eYniQY11AUgbJ4PVXJEN1cuXR2+ZpsZzfWQJYGExR7Hayri1HgseNztV/TamFhYbE3YwmPFhY7EX8yRpbkoWuWOx2aB1LeyQflFBF1pYTHBhtIWZToouvodi+u7KJ0fVEQ6OXNp9jhoy6qbZMLO5tC05vRPkCxM4vcQjd1US3DBjLP7iFLdiILmRsNPrubozv3pdqU8Qdrcch1ZHt9eGQHvT0FiIKYIRy5JBuH5hUTTKiE4zq1W1IZ2kU5nQu7scAsb8mF3dWlUxdR0zaQDkmmd1Y+QS2IyxCoj5usDesYQJEtGxCpDCeQBJEDCz0tBvhumKeWBMikrrEmXE1UT+KVneTaXTgkGwkxQWnST304Sa7DQU9fKl5cL09+xhicW4TthnWLByLoqo0sl8T6aA1JQ6WnJz9jzH4lSmU8RFdvHjURjapIErdNYk1tlMO7+jKEXwsLC4t9FUt4tLDYSZimSUU0jGjIuJ1NHy1JEMl2prR5DU40hS4R4iEkhzetdWxAFAQ8dhuyKDZJZSgLTR1jBEHAIcsUZ0lNnGha8nC222x07tyNCjVGKBxCDwTIy81tIpg2HkOey4EoqBlONJLQvAWMJIh47SJSlpjhROOWU5pXvxrZMlhQDB1FluntKaA4y8HmQJzKcJJuooCzFUeg5gRImw3WhKuIaArZNidxXSEYjWEAsiBQnOXCm3ASSWrURRXy2xhDttOOzWWjWjUIAPkeD+XxALpp0MtTgCgIlMcDbI7Vo5k6WTYH3Xw+SoMJooqGYhh0CjnomuNq9hoW+x9PPvkkgUCAnJyc3ZJJJJFIUF5eTp8+fXb5tSx++1g2jxYWO4mYplCfUBBNqVUNk9chk++xk1QNKmvrMHQVm6+oxfoNubANk4xUhi3RYANpk8SMVIYt1rc7KS7uikMyqa2tJBBrOxdrjtOGz20jpqQ0kG1F/GpwolF1I53K0CnZyLG703/5Dg8V8SAViSA2SaR7jgtRgNJggkQbY2gQIF02ifWBMD/VlhLVFPIdHmxiKud2vsNLocNLrt2DU7KR77HjdchpAbIt3DYpvW51UZU8u5fqZIS1kRpKwtVsivrx2Vzk2T2Ux4OYokE3nxMTgXBC49eaSJtrYbH/8OSTT3L//ffz5JNP7pbrLVmyhGHDhrWrbnV1NTU1Nbu4Rxb7MpbwaGGxk4hqCpGESiS5Nb9tWE2k80g3RpB0HHadaLCOGs0BtlT4qLiuEFYTTYQxUzBwOfUmubAVQyOoxtG3ySFtYOByGk1yYeumQVCNoxiZOXhFdw52jwtBDVLn9xPY4oBimiZhNUFcbypcSZKOzW40ESCjWpKw2owAKuq4XGZGLmxI5f8NqnEEBHx2V8oZSIm1KECqhk5QiTeZI1EQ8LpNypI1VEbi2EnZNxqmSVCNk9jG0cU0Tex2A1k2mwiQra6bwySpGtRGFfJsbuqVGJWJAHZRRhZE7KKMbhpUxUNpG0iHLLHBH2Nd7e7JTWxhsSM89NBDPPHEE3u6GxZ7MZbwuItYtWoV8+bNIx63PC33F2oTUQIJhaSeiu0Y1ZKUxQOUxuozBJeErlIaq6cuXo1TiKM586iJqSi6Rmmsnk0xPyFtq/BlmCZl8QCVyQBOp4ZhQlkwQUzR2Riu59dANdXxcEZfapJhNsXrEOxKhgayLhllQ7SOingwo35QT1Ar21DtOnKshvpwjEBCJaQl2BTzUxqrR22Udq1hbGEjgtNBWoCMawqlsXpK4/UZwpdq6JTG6qlRgrhcZloDqeoGZfEAm6J1BNQYdlHGKdlYG6kmpilNBMhgIsnyQBmfVy5jdbgqPa+GaVIWq+frqlUoYpxCl4e6qEIkqRFQY2yK1lEWD2A0EtwaxhYTIjhtQlqAbGndFCO1PvV6CNcWAbImqpAlO4hqCqXx+vS6+WwuKhIBoloyLUBmOWwsKQ9RH2/bM9zCYmdRWVmJqja95/x+P6tWrWL16tUZ31PhcJj6+vr08U2bNrVa32L/xBIedwH3338/Q4cO5Y477uCoo47C7/fv6S5Z7GJM02RjJICITK7LlhJaYhqSICCLUobjibilzIzVk2UTKM7LSQsikiAii2KG/Z0A2AQJWZAwdAFZFEBIZTEREHHIMnHVSHk1x1UUzUAWJOyijEOSM7awVd3ELja1gZQQscsOXFlF5CaqcSTqCcZUIkkdWRSbGYOYHluuy57ewq6PqakxCGKGp3bDmGVRxGO3pbewq8NJJERsooy05XXkkR2YpsmP/g1UxYNIokD3HBeaqbGwqpT1kTrsokxJuJpfguVUJ8KsjVSzNFCKgUmOw0WR14HDlrItTSgGNlHGJkg0NiZomGtZlCjwOtJb2C2tm9AwBkEky2lPb2E3t242UQITSmP1QMoLu2euC0Uz+LU6jGFY2sf9GUVRCIdTP/jC4XCzwt2OUl1dzZAhQxgwYACdO3fmxRdfzDj+3nvvMW7cOEaNGkVBQQH/7//9PwDmzZvH9OnT08cnT57can2L/ZN9Nj3h3kptbS39+/dn9erV5Ofnc+2119K3b1/+/Oc/t3qelZ5w17Mr5yemKczaXEKF36DA48AfS21fO2yQ73E0EdYUJUa87Bdcvk7Y87qntV6yDPluG85GqQlN0ySYUImqKgVuJ12yXWQ5JSRBAMEkqeuIpkhUMQjEVWpjSQzTxGUXcEo2REFIpzJUNJ0cj0SWo6mDiBoJIThdqGXLMXSdcE5fkjhwOQXynPYmTjSKoSEgpMfWkMrQLkOex4ZDygxNoxk6umnikFLORA2pDEUR8j023LbM+hEtQVCJU+zy0dnpY0OkjvWBEB7RRZ5XIsvuIGmohNUEoiCSa3OhmgY2UUISRAzTpDqSJKkaeF0iOc6mY07qWwVFIK2tbG7dhFgMxenAxEyHSWpt3TRDx6/EGJTTjawtzlAxJWX7OHJA8ZZg7b8trHdQ69TV1ZGVlcXw4cOZM2dOuvzUU09l1qxZ2Gw7L5zT1VdfTSAQ4K233kJRFIYOHUp1dTVr1qxpUre2tpaTTjqJ//u//+N3v/sdkyZNwul08sgjjzTb9rb1dxbW/dM6e9P87DOaR9M0mT59On/605/4f//v//Hyyy+TTDa1SdoZ15k5cya///3vmTJlSrN1IpEITzzxBL///e+5995702p9gJUrV3LYYYelF3jkyJH8/PPPO72fFnsXUU1B1U0EQUAQhLQzRlKFULypk4SYjCPrOnJWKi96gxONpkEgrqe3V03TxB9TcNokBnXO5eDiLPI9NuxSKnuNJIi4ZduWwOI2+ha4ObRTNnkuO9GESUJN2UI2ONHYZYlgzEDRmv5mtIsysmzDXtQHURTJUf24bCLxhElEaeqkYxflDOGqwYlG0SAUN5rY9smilBYcYasTjWFAIKanbSAb8MpOOrtyCKsJVoYqSBoqB+XlYZNEgrHU1rdLslPkzKbA4UUSJZySLS0gioKQ1kBG4gbxZsbgkOQMobitdbOJUkZ8zZbWrWG8dlFiU9SftjF122W6ZDn5pSLcZLwW+wevvvpqhuAIMGfOHF599dWdep1PP/2U2267DVmWcbvdzXp0G4ZBeXk5tbW1nHzyySxYsKDVNjta3+K3yz4jPI4ZM4bXX3+d3r1706tXLx577DFOPPFEEom2PUPbSywWo1+/fjzxxBOsWLGCmTNnNlvn+OOP56233uKwww5j+fLlHH744axevRqAeDyekVfb6XQSi8WatGPx20FRFJ5//gVee/TvfPvBe2hbtqBa8+bVQtVITi+CvFX71NgLuzqSRDdSThk5LhsHFXnJc9sQ2xEn0G2XOLDAQ/8iL7phUB9PXbu9XtiiMxvRk4eZCJInJXHbJYIxNe1E0xo7wws7oy+CQK7dTa7dTZ7Dk4pzmeVAFKAqrJDU2vbCbryF3diZqSU66oW97bo1FiB9NhdBNc66SG3aZrQoy0E4qVHbjrYtfnusW7eu2fK1a9fu1Ov4/X4KCwvTn4uKMiM6fPjhh3Tp0oWjjjqKsWPH8v7771NVVdViex2tb/HbZp+J8/ivf/2Lrl27pj+PGjWK/v37s3DhQk477bQm9efNm0dBQQEDBgzIKK+srOT7779n7NixTc6x2WzMmDGDvn37cv3117N8+fJm+7F582Y2btxIdnY2N910E8cffzz33HMPb731Fj169ODXX39N11+5ciU9e/bckaFb7MUoisKwYcOYO3duumzRrM+44/nn8Tjd5HvsQGp7M6Gr5HnsOEwTU40j53ZFEASSuopumqnMJlsCYldHkqyIhuiT76Jvvgd7o9zIqqET11WyZEdGjmzNMIjpCh7ZjiSKFHjseOwSq2vCbA6F6Oz1ZOTCrgol8LoFfI5mttU9uZixeoRgBflFBwIQjKnEdYU8p73JlnRDyj6XZEvnwg7GVMqNGDkuGY8tc4tWMTRUQ8ct2TNyYZcFY6ltdVvm2CDlpOOUbGkBsjKcpCKUINst4HM4M7akTdMkpitp7WiR10F1JEltJElMV8hx2jM0iJAyPZAEAceWMD7brpt7m7Vvbt3qogploRi57lRWH0EQyHd4qFWiSBGBPlmFSIJIrttOSU2EfLfNynu9n9FSnMW+ffvu1Ov06tWLX375Jd3utt9nt9xyC88//zxjxowB4MYbb0TTUj+uJEnCMIx217fY/9hn3lqNBUdI2QiKokiXLl2arf/JJ59w+umnZwhy1dXVnH766bz++uvNnmOz2dp8gD/55BPOPPNMsrNTWSkEQeD888/ns88+wzRNDjroIIqKivjDH/7Av//9b6ZOncrll1/ekaFa7EO8+uqrGYIjwKoffuCjd/6b1jTle+zIssnacC3L66qIRQMIsh3RlY1q6Gza4mEd2eKd7HXIiAK4PElUW5SwvlW7bpom68N+fg3WUJ2IZFy3NBZkVaCaykae1y6bRLbXpF4Psaq+DsM00xrImJlgaW0l68J1GRrCkJqgVItRZXeiJ8IY8SAFHjuIGr/W17AiUJPheR3XVTbH/GyO+dNCZI7Thtspsi5cxzJ/VUboHt00KIsFUl7lW8pdNolCr52yeICfayupTUYzxlaVCLE+WkvdlvIGATKkxVhSW8XmSCCjvl+JsSFaR3k8gGmaaQ2kKir84q+iJFibEd4ooiXZFPOzqZFX+bbrlmwU3qildct1y2yO1rOkrpKAmvJIFQSBPJuLpYFS1oVrMUyTbKdMOKmlc2xb7D9cfvnlnHrqqRllp5566k7/nrjmmmu4/fbb+eqrr5g+fXoT+0WPx8N3333H0qVL+fe//80rr7ySPtajRw++++47li1bljbLaq2+xf7HPiM8Qsou5JJLLmHEiBH8/ve/5+2336Z///7N1n300Uc5++yzOf300ykpKaGmpobTTz+dAw44gDfeeGO7+7Bu3bommsSePXsSiUSorq4GUrYm2dnZLFiwgFdffZWjjz66xfamTp1KUVERgwYN2u4+Wew5WtqC8peWZXzO86Q0bHFVp8ZfA5LcJKNMA6GERlGWne65npRn9Q5il0W6+lzke2zURZNpAbLQm8pW44+qzW5hi+4cBNmJFqwCQyfPY8dpE4kktHZvYXudMgnVwN/OLew8jw1NT3mOt2UTKEsihV47ggB1UbVdW9gFHgc2WSQY14i2Ywu78bqF2hFix+OQ8blkVM2gNrp1C1sURLw2Bz/411O2xQPb57SxwR9rM+i7xW8Lu93OrFmzyMtLpfTMy8vb6c4yALfeeiuXXXYZd911F6+88gpPPPFEhnLk1VdfZcmSJVxzzTX89NNP3HXXXXTq1AlICbi9evXikksuSXtbt1bfYv9jn/K2Xrt2LQsXLqSiooJ///vf9O3blw8++ACHo3mvRdM0ue666/jss8/IycmhZ8+efPDBB9jt9jav1bBtPX/+/Izy4uJibrrppvQDBfDFF18wbNgw1q9fT69evbZrbJa39a5nV8zPCy+8wLXXXtuk/Or77+fMCyZklMV1lbpQlFjVJny+fIo7p7Tpjbc/Vc0gnNQY2MmLwyagGDreLdufDbRr27rRFq5uGkQ1BbtgY1N9nNqoQr479QyE1SSBqI5pCnSWdezZWektX5soIUbrUWo3Ysvrgc1XRFRNEohraJqAz711i7rxtnVjkrqKP6GgJAXcdokCT2osjbettx1bMJkkEjOxy6ktdkkU0E2DhK5mOMQ0jC2iJAnEDAREirPsOGSpybZ1A6ZpEtUU6mMahr7VsQkyt623XTd/VEGOJZGzvOkt7cbr1hjF0AgkFGJxE6c9tWUuCgKqoRPVkii6xoFZxRS7sikPJTi8Szadspv/IbGvYb2DWqfx/PTq1YuNGzfSs2dPNmzYsGc7tpdg3T+tszfNzz6leezbty+XXHIJt912G/Pnz2fu3Lmtqs4FQeDBBx8kHA6zatUqpk6d2i7BsTV8Ph/19fUZZQ1xHH0+3w61bbHvcfnll3Pk8UMyyg4efCxDx5/bpK5LstHJbuKVBaKSK+2M4ZBsuGU7pmkSSCh0zXGS7bThkORmbf9sokS2zdmkXBZFsm3OJuFoJCFV7pQleud58Dlk6uMqgiCQbXfS1efGJonUx1IaSEEQ8MgO7KKM5M5DlGwoNesw1AQem4MuWe4mTjQuydZEcGwYW2ePp4kTjV2U8cjNj63A5aY425nhRCMJIh7Z0ezYfA4XXbNdGU40DWPY1pZTEAS8Ngdds91NnGjcclNbzoaxdc324LJLGU40Deu2LXZRpsjtpsDryHCisYkSOXY32XYX66I11CsxvPZU5hnL89rCwmJfYp9xmNmWwsJCiouLW/3F5vf7GTZsGEOGDKFz586MGDGCOXPm0Lt37+2+7qGHHtrE8HjZsmV07dqV3Nzc7W7XYt/ElEQefuNF/vPSO6xfu4G+B/Rh+LnnILewBaVFaslz24i4PWmhpUGTFUpqZDttu1QLZZME+hR4WFUdIZhQ8TltaRvIgJKgOpykKMuBy5YSugRJwlbQE33zL+jhWsS8bgiCQIHHTi0KwdhWG8fWaOxEU4uS1kC2RGMnmqpwMq2BbInGTjRVYSWtgWyJBhvI6kgyLQw2aCBbwue0oZpyk3VricZONNWRZFoD6ZRs6KbBmnAV/bM6EUwK+GPKbzLuo0XLTJo0iUAgQE5Ozp7uioVFh9knhMeqqipWrVrFKaecki776quv2LBhA8cff3yz5wQCAYYNG0Z+fj4ffvghDoeDK6+8ktNOO425c+dutwf0RRddxIQJE1i6dCmDBg2irq6O119/nYsuumi72rPYt4lrKpogcOzYcRymmGAKJAwB75bjjQNpG5oCahLJk4fPLaOZeloQyXbIaLpJ1wIHuqlhY9sA2waqoeOSM8t10yCp67gkOUMYM0yThK7hlOSMLCmmaWIKBn3yXayuiRFVdDx2CUkU8DpEYgZNBEjN7sbMzkeP1CF685Ds7pTW0iWiGXoTAVI19IxA2g147SKKIRBL6BkCpG4aaIbeROtnlwV8bpFAVM8QIA3TRDE07GLm2CRRIMct4o9qTQTIpK4ibwke3oAoCOS4JOpMo4kAuW0A9AayXRKqoTURILcNgN6Ayy7iNVJxJrcVIGu3OOl0sRey0R9rU6C2+G3RXNxFC4t9hX1CeHQ4HDz66KPcdNNNHHjggVRVVbFkyRLuvvtuRo8e3ew5N998M9nZ2UyfPh2XywXAyy+/zOWXX87FF1/cxJaxgVtvvZXKykp++OEHAoEAl1xyCQCvvfYaoihyzjnncNVVV3HyySdz4oknsmTJEvr06cO99967awZvsVcT11Xqk3E2RAIUeJw4DE9aEJFkk9KYH1EQ6e7ORVJiYILm9FIe82Ng4Ja9qZR4CYV+hR6qlSDJuEZvbx7Z9pQG0jRN1kf8RNQkPby55Du2Bo3ZHA3iT0bp6vZR7MpKl1fEQ1TFwxS7sujq3mpOUZ2IUBYLkufw0Dffy6qqMHZRIGYkCSaDGC43Ns2dFiB1QaMsXo/scFOcjCEEKhELe5MwNEpjfgRBwC170gKk2y6wOVaPYRp0c+elt7J102BzvB7F0PDavcQUqEUh322jLB4gpit0dvrw2V3pvlYlQgTVONlOD2pSSAuQdUqEOiVCvt1LkXPrmP1KjOpkCK/NgaC50gJkwlCoSARxS3a6u3PTAlpES1IWr8cmSjjkltetQQhWTZ1N26wbQI5bTo+tqysX75bYnaZpUhZLjS3b7iGpkBYgqxIhAkqMuKbQ1Z2LP2ZQH1fJc++YWY2FhYXF7mCfEB5zcnL47LPPWL16Nb/88gs5OTkMGjQo7a3WHI8//jgejyctOAKIosgrr7xCeXl5i+eddNJJhMNhzjzzzIzyxhqBf/3rX9x4442sXLmSLl26cNxxxyGK+5T5qMVOIqQmUDTQ0NFNg0KPHX8sZRfncphopoFomhimiRmpA1EC2YGmxlJ5mN0ykXgqg0uBx87GWBjF0NEahZExSWnzFENH3yb2mmpoJPWU1iuz3CCha6jb1Ne2aCpVQyPPa6N7rouN/hiiXUczdUwMumY5qIkoVIeTOJ0GmmEgyA4EVzZGPIiRjGDI9tTYEMh124gkTIIxlaSe0sIZmBiNxmCYJpqhoxkGXreEKcsEYyqGaaKioRoaOtuOLTVmu10gr9EWti5rKIaGZmZ6V+umgWLoGJJJ10Zb2HaHimpoqKKECen81imNp4EoCq2uW3odtoyhYd0SSYFIUkuNi9TY9G3XzUzNtdMpkm2zp7ewFUFHNQ0006A2GSZPzmVTfZxcl83SPlpYWOz17FPe1r9lLG/rXc+umJ/vqzeywR+hPJygwOPEJdkzcio7HCZZDhm3aCNZ8Suiw429oCdRLeVEkWVz4o+ltnAPKPAQVRVUU8e3jUNMTFNJ6Co5dlfGVm1S14hqCtk2J7KYGUg8rCbJsmU6jWiGQUhN4JHtOCQZ3TApqY1SF1Nw6zEkjzdlk9coF7bbDVl2G3ZdQ60sAbsLR1FfIrqKuMUxxTRNaqMKMUXHZjfIcsjpfM4NxHUlJTxucZRpyIUtyQZZTpEsW+bYFEMjrqt4tzjKNOTCFgSTLHfK8aXx2HTTIKIlcUk27KKMphtUhpNohonHZZBld+CUMnOGR7Qksii2uG6eRhmAhFiMiF1KrxtszYUtyybZLik9tgYSukrS0MiSnYiCkHa4ESWTLJeAS7IT1ZIc4utKNGEyuEcuPtfODdmyO7HeQa1jzU/rWPPTOnvT/FjqMguL7UTRNWK6SlIVyHV4cEmpLcfGKfGSSQFTlzB1BTCQPDkAeGQHWTYnumFimtApKyWkeGx2cuyuJtont2wjz+HOEK4glZs5z+HOEBwh5bWc53A3sdmTRZE8hzttmyeJAr3y3LhkCdGU08JV41zY8bgAhoRkdyN58zETYYx4iCybMy1cNTjRuO0SqiKi602dVVySnaxGQnFDKkNdE1EUiW31bXZRxmdzpe0UG5xoTFMgnhARacbz2uZKbzM3ONHIokA8ISGYmfUFQSDL5mx13balYd0aaAj1o2kCiiI2WTenZMPXSChuSGVo6AKJpJiyhcWkXo0iigIV4Z2XbtXCwsJiV2EJjxYW20lcV4lrKooOTjlTaNg2p3IoHAJAsGUmuQslNQq8tjY9fXclTlmkT74bVdczAlY3lwtbyipAkB2o/s2YemaQ7cYC5J7Khb0t+0IubI/koCoRxm0TKQ8mWsw5bmFhYbG3YAmPFhbbSVxTSagGmm5k5CfWzZTtW2NBpNrvJ6yLmFKjbVbDRNcNCrx2NKNplhHDNDPSADZgdrActnhANyOYNZTnuGwUeh3Ub3F80Qw9I5VhgwAZMyUEbyFqoAItVNNkzNsKkP54MsMOsPHYtC19bSxA1kSSqHpTga3x2LYVIJOa1urYthUgo8oWW0tF4cu33+I/TzzO7HfeJqkkm6xbXVQhlFCaHUPDmCFTgKyJJNNja2kMjQXIQEwjoiaImQlU3aQmYqUstLCw2LvZJxxmLCz2RuqTMTaGgzjY6vEb11XK4wFkQaSrKwdZlCh024iYKisSOgWBag70FWAXZcKKRrZLpjoRoDxu0DsrLx102tiSwzqqKfTKyiW70VbppmiAoBKnmyeHvEae1xXxMDWJCJ1d2RS5vOny6niEiniIQqeXLu7sdLk/GaM0GsBnd9HTm0u+x05QhKpIlJAZwSnZ6erKSQuQmwIRltTUUeCS6ZZVgBapQfLmokkyZbEAAF3dOdhFmQKPnSozwa/1tbgcIv1zCtKe14ZpUhYPkNAVurhy8MiOdJifNYE6NsRU+ufm47NvHVttMoJfjVLoyCLP7kkLkCX+etbHYvTNyaGTa+vYgmqcqkSILNlJZ5cvLUCuD4T5qbaGYqeN/9x8Gyt+/CF9zqwP3+e6Z56iR3YBsiiRZxf48P332LBhDZ16def8seNxulN90gydsngAzTTo4srBJdlS29GmybpwHVLMZEBeQYbNZGUiRFhLUOzMxmdzpbXNJQE/AS2MR3ZwkLcrm+rjdMl2ZvwgsbCw2L8JxFXUvSiVqfV2stgrCCkJquPhNrct9yZK40HiqoLS6IFWDY24rpDQ1a3aKi2BT04iOl1UR+MEEqntWVUzKPDKxI2U00tS36qtakgpGNMUko00caZpEtMUwmoyoxwgqimE1AQJPXO7OKGrhNQEUS1zSzWpa4TVJDEt1R9ZEOiZ6yKqKURUhYSupD2mJVEgxyOjCxpVsSTJ7G6gK6jBKlRdJ2GoJAw1rVkTBIEcl4wgGdTF4tTGt9ryGaZBQleI6yqKsXUMPoeMzW5Sn0xQGYln3AtxXd0yR1vru2wS2W6RsJqkNBTN2MJWDI2oliSub90KlyWRHLeEYqjMfvfDDMERYN3in1j48XR000BNxHnw91fxzsNT+PG//2X6w4/xwA03oKlqen0SukpcV1AbjSHXLSNJqTFUReIZ6xbXlVR6wkb1vQ4ZlwMSms4Kfy1hPUo4qeGPtb3lb2FhsX+Q1HR+rY4QU/YekxZL82ixx0loKsvqKwgpCXpn5XHAFs3P3oxq6Ki6QY6UjT+mk/ToOGQJr+ykqysXWRDTQa+NeBi3YXBwp87UJXRicZNaI4nHYaPI7cJpF9AMA599q3bRJkr0ysolqWsZ2kVBEOjhySXf4SHX4croUze3j2ybgxx7ZnmxKwuHJGdoLwEKnJ4taf+2Bqf2OW30L8hlZQ10drkz1sFnd3JIXhH+qEoEG6KcjTPqx+HJpYszBwC3tDVOoVO2MTC3gOpoAi0pEpBVcpw2ZFGiiysHxdDItm3tqyAI9PPl45PdGKpEbXRrIPFiZ9YWZ5XMLCxdPNnYRJlYnIxA4rk2N5JHxCVlhr7Jc7o5LL+YldX+Ztc1tq4Es3INX346gxU//phx7NeffmLmu+9x1oQLcUg2urhy0EwDr7x1XmVRYkBeAVWROIIuUxdVyN8yhs4uXyo9oZy5Dr2zcvFIDsJxgx+qy+iTlc/GejuFXitouIWFBZQGEtRGFQqz2667u7CER4s9im4Y/BqsJq4pFLm8bIjUE9NVDvIVNZs3eG8hrqlIgoBbdBKWMlPi5doznWL0SB2SM4s8VzY5zlQ4mIqQwvG93Iii0ETYayDb5oRmorZ4bHY8tqZz45JtTTLQQMoju3EA8QZsopSxvd1AN5+bUFxH08yMN4QoCOQ7PeTYU2F8AnIevmQUIVBBdvEBCM0I/Nl2F1k2J7XRzFSGHtmBh6bp+FySnV7Z9nQYn4ZMNA7J1mzeabso09WTTdyuZ6QyTG07e5rUlwSRQpeXA/r0Ym6To9C9W1eQZKrLKps5ChtKVhNJKHid9iahiBrwyA765DgyHG7yPXZckj3t2d0Yh2Sje5aPiF2jKhJnTaiOaFKlV76LQs+uS1VpYWGx9xNNamyoj7G3/Y60tq0t9igbIn7KY2EKnF5sokRnVxb1iTiLakspjwabBMXeW4jrKqpuopom3XJcLXrzmrqOoSXTIXpEQSDfbcfnlMl17Z3CsU1MbV8nt/G+bqDBBtLh8lAv+ohGQ+jR+hbb25u8sA1NRQtVc+JxA+l/2MCMYwMOO5gTTz8BIxagoMDX7Pn52Q4+/ve/ePnhB5n9ztvpbexm626HF3ax14VHcLI27Gd+2eZ9yozDwsJi57OxPo5hgk3au6RHS/NosceoSURYG6qjwLE1Dp4gCBS6PETUJP+rryAvFqBPVgH5DvdetYUXUVPBp3XDxCFLFHvtVEWUJjmVdSWKKNsRnVv3G2KqTrccJ05b87/dTNNsdqy7vJytgkqOy0anLCdV4SR57qZZTyQx5ZFcpeVTVx1GrK3A5/Qi2ramVGx8ToMAWWMmm+TCbqlPOU4bpmkSimsZubBbqu+ySRR67dREFKrCSfIdAvM+/pCqzZsp6t6dk4edjhmtAU1Bdri449G/MHfGbDaU1pLfvQcjLzgPl9uNIEoMvXoAP/zwS8bW9YBDBrJ8/gJ+Xb4yXTZv+sdMfvFlZJut2T7le+ypYOTb5MJuaQxeh4xpOtAMg6XVdZzcvRv5ruY10xYWFr9tAnGVsmCcPLcdf7ztH6C7E0t4tNgjJHWNVYFqbKLIhmgAURDo5c1NB7V2SDKRWIjyWIjqRJQDswvo7yvaw73eSr0STwWd3uI4UZEMIsoigupMC5CCaLKxvgzU/8/em0fJcp/13Z/aq3rv6dnufnWvZHlF3llMwHjF2BYGxytxZLyQN/gNMQkkcAJ5CYc4YckLOZAXiI2JMDY2xgtYGFuOjWUbB1vybu2628zcWXrvrn1//6jpnu7pnrnbXOlKmu85Oke3prq7fvWrX9W3nuf5fh+X47LKIKnrRhGC6vFA3+N4oTo07E7SlHNWBz+JOJqvkttMQadpyorTwwx9DufLY7WLa06flu9wKFceq4FseBYbrsWCUWBO30pNd3yX806PmpbjwIjyuh96rFpd8nLC4VwZQRA4VNbZsBy+22xw/6dvx9toceDoUX74VT9BIgmsej3QQCnOsNFYBmWJwuIJ1vw+CSkHjfLQsDtJU9bcLoEQocjGkECWNZkNv48dBSzqpTF1csM36UUummoMe2HP5lW6oUPLt7O6z5HUdD90qfsmmqrh2vBr//Jf8tDX7xr+/bMffj//4bd/A0WWIQpI8mWe+LrX8hTFQIzydBHQpGze1gKbN//+7/Lg7Z+jsbzC4tGjBJbJe3/zN8eug7u/+lX+/iN/xZNv/lGCNOaAXh4aradpmo2NAFXWGTjw1PJqNrbAZV4vjtV9dgKbVmhjGDrr7YAz3f4+edzHPh6HSNOU++omfT9ioXjtla/sk8d9PCJouBZOFJCTFbqBiyQImEEBQwLSLeVwnKboosSq0+e6wgyq9MhfstFm678oziJwmZrZRRcVjhQKtO2YDTMgbyT0vR6SKBIJKQqZak4QUmIxpBsEeHFhSB7DJKYXejhRwJyWH5LHhJRu4NILPGa03Bh57IUeTd+moupj5NEKA5qeTV5WmRu579iRT9O3kUWRA4xEQ6NMkR0GCgdzJSQENFmkpCX8+k//a1a/9e3hvl/8xCf413/0/2FGHhICR+ZqNKwmG+urREYJS06ISfHj/JA8RmmMFfl4SchRI0ckSpu9rRPMyMeKPMqKMUYercinGzocyelosjKsgfTFgE7oYEjKkDyGQcD//vBfcubsQxw6dh0F1DHiCHDqO/fxxU99hhf909cgFWt4goDltNDTmCMFeWzezMhDEgT+yat/clin+Gf/9T9PvR7O3PttDr/keQSbQp0BeUxIMcNsbEdyBoIkDyOQFtnYioo+Rh6dKBvbQV3hcNHgztU6z1yYRxSvnaj7Pvaxj6uPlh1wtuUiitByrq2oI+yTx308AoiThCW7S0HRyMsqR3MVVvs+9687pKT03JiUlCO1HPNFlVm9QN2z6Ic+s9cAefTiiDCJ8QNQJBFD0TloVFAEiZyiohaznsp9O6aGQq40iy5mhMIOYg4Xc+RzBinpmOhCk2SO5MoESUx5REQjCSJH8hVqWkhNGxfjHMyVKCk6M9uU1/N6RkrLUxTWoiBS2Ca4qWk5olyJQr48bAcI8N2/+/QYcQS4+6tf4a7b/o5n/viPIQgCBcVAPXqSlbMP0Kk3KB06iKFpY0RQFWUW9BJhGlNSDURVoEmA6cYYqkHFMCgr28agFSlIGiXFGBLsnhMiSgrHcjPDcxcGAb/x1reMWe/MHjgwbepo9wPU2aMAFDc9GrfPm+kI1PQiuiwN5w3g4KGjU78zVymj97rU5g+NzackiCzqJbw4G5uiZbHnrBe2xrGcNkYcAWa0jHAXFA1Zk7i/3WHddjhYnBT/7GMf+3hsIk1TlrouC0UVP05p2UEmmMldOzKVa+dI9vG4QTtwMEOfgqwiAGGo4HoiZV2hltM4PpNDEUXONELSUEEUBCRBoOXbj/ShA5lYJk5SvChBlcRMwasVqWyqrIcdTeKIONIoGjUEQSBNU5I0pVbQmDcKLBjFiV7VNT3PgVxpold1RTU4mCtN9KouKToHc6VhtGuAvKJyMFeaUGXrkjIknKNQRIlZLT+h/F5fXpl6DurLS9S0AjNqHkEQUI0ih+bnkCIHv93DEPSJsVXUHHNaEUkQx0Q0cSChoE+MLS9rzOnFIXEciGiSWESItCGxu+PjH5vwbGyurU097gPXnRz+/07zJosCka+QF/WxmsTn33wzT37Oc8e+78nPeQ7Pu/nH8U0PudtC3CbwKio6c3pxOLaBiCaJJMRYG45tAENSmdOLGJKKIkkYisSDzf7UsexjH/t4bKLrhjTtgIqhcqCoUdBk+t6FW6U+nHjkwzj7eFwhTVOW7S66JCMIAqs9j+WOy4yhDjtqSAIcrhgsd13uXO7y3KNVioZG3bW4vjiLJD6y7zx26BPFKWGcUtSmpxNlSWRBDjgP1F1YVGLCOBNEFNVr28NyFIePT4+2LR4d3y4IAmplkUXHZMNqs97QOTC/gKHsPNYBgWwSTBXRTMPg76M2PhvLy1P3rc3VaDVaw39f/6zn8IM3v2rX7x8QyHXTnxA/KYrCr773T7nj4x9jfWmJxc36T0EQOHf2AT75oY/hmybHnvRUnv8Tr0ZWpo9lIJqZJqKZHK/Kg70Ozw3ndz2X+9jHPh47ON9zkUURabNc5UBRo3ONpa73yeM+Hlb0Ao+mZzGnF9gwfc60Hcq6MtGKTRIFjlQMlrsOdy53+N6jVWIpq8urbkvdPtzohT6kAskOquUhQpuFvEpXlNgwAxQRnjBfuKZU4xfCza9/DZ/8q49x1z/8n+G2pzz3e/nhV/3ExL6irKFVDzAXnqFp1VmXdRZnyledQM4fPjJ1v1e9+U3IhRk2VlaYOXSYG1/wMlpewoKSDm/K07AbgZQVhRe+5rVj+4dBwK3/6T+PKLP/ki/ddttQhT0NF0sgi6rG+b7FWt/hRG3Sq3Mf+9jHYwt9L2StH4zdEwRBoJZXgGvHumufPO7jYcWq0yNJUu7vtHio4XC0WEGVM+IYpwkbXpaiW9BLSKLIkUqO0y2bO5ZWOVyTuL44+4iSxzRNsUIfJ4r5bnuDJ0kVFvTikBCGScyG10cSRMqhj6oXWCxqrPQcTpt9ZmdS5hJ1mJZO0pTzTo8wiTmcKw8FQWmasuaa2FHA4Vx5zPx7wzXphz4HjCKFkY4rLc+mHTjM64Wxmsle4FL3LGbUHDV9q3bOCn3WXJOSoo2ZiDtRyHmnR15WOWAU+aOP/AWf+OCHeejBU7TzRX7wx29GlLduHaPzNp8ro5XnqXVWafbXuTt2qRQ0jo0o6dM0pelbuHHIgl5Ek5QhgVzq9TjvJlxXrIyZxHcDh17oMqPmKSr6kECumRbVf/L93PCMm3jwG98a7v/EZz2DJ7z61ZSNAjUtG7Mbxiz3LFYbHQ6X8lPnTRZF5rXSkECe77t8t9mkllc4KqrDOp8kTan7faIk4e5PTHajufurX+V/f/Sv+NHXvWFrfnwbO/KZ1QrkZHX4cFizTc67IdeVK2M1k2bo0Q5sAiLub/c4Vi3sSnr3sY99PPqx2vcQBJC3rfXsXrVPHh/zSNMUz/Mw9m02hnCigDXXRBJFHui0OdvzOFwoMrgMvTikHWR1jQPlrSQKHKyofOH8BoIms2R1OV6sIgqPTOo6TGL8OKLjZGrZU12ozuXQNsmUEwe0Ahs5iTHSGC1XQpJEcqpA6vtYkYsTB5REfXPMEQ3Pwo9jKqrBjDRQJyc0PIt+6FFStCF5TNOUhmfT9GxyskJeVnGikJys0A4clqwuqiiPkcdu4LFkdaHAGHnshR7n7R6+nmd+xM7HDD3W3T4lRWdOz6MoCj/5pjfS9h2+sn6eBxsWC4UyZW0whvF5MyqLqIFDsdfknGnTiasUZZ2FXGE4tnZoY0cBeVlD22whWMspnHV8Gn0LTZS5vjwzcqwu614fVZSHBKuiK2y4Ieebq7z837yN3p3fotHscfCGJ/PUV7yMlbBPEgjMqJlHqKFI5A2B0y0LvxtOnTdVlKhsKqazXtgipx2HXg9qhTIDih0kEZ3AIUhizi+dm3qtnHnogeH/p2lKJ7DphA66pAyJcS2vct4NWbK7yKLEk2e2yKMV+ax7fXKiwqpp0nXDXVPc+9jHPh7dcIKI8z2PqrF79uVawD55vAr4yEc+ws/8zM/g+z6veMUr+LM/+zNUdf+m3/As4iShKOuIkc6CodKxY3QpQZZEdElhXsvsY0YFIDlZ4UkzNZwgxI58rDCgpD78vldBEPBHf/Ievnz3N9FnD/GEF/0IcaLQsiMWihKSKJCXVBb1EvgWmiAibBIdSZB4yuwshwsG+ZGImiHJHDBKxGky1rc567ZToqoaYyKWrEdykaKsIiKy7loYkkzd85nbVOrObIvMzmg5ri/NUlI03CgcEtGqmuNEcYa8oo2l0iuqwbF8FV1SxkQsRUXjqbVZCoLPctsnP69OnTdREFFmDmH06xz1mvS0ErYLrhJjKBKKKDGnFSnJ0diYRVHk+nKFnKSShBJdLxxGGGfUbGyjQp8kCphzm7h+n0Qvc90rXsPiwcOIsoIXhxBI5KTx/tBzRo4nVmv0nWTqvImCgCZu3RYLqsYTqzM0rADbS1GjrIe5JsrMagWSNOXQ0WNTr5d8uYzl+RT07PzOagXysjYhVrquXEEWJcRYGfbChkwMdTQ3gyKIWKHHSs/eJ4/72MdjGCtdD9uPmMtPtm691iCk+/2v9hSO43D8+HFuv/12nvKUp/Dyl7+c17/+9bzlLW/Z9XMrKyscOXKE5eVlDh8+fFm/3Wq1qNVql/XZvUIQBNx6662cPn2akydPcsstt6Bsdt/4amOJMI0xbTjXdShqMhtmgCiQqVyl3aOJG6bHTAl+8OBhjhSql3xsV3J+giDgpS99KZ///OeH25783OfyC3/0HtpegiKJLBS1YVoxaC+TuBbagRsRRJGm7fOk+SLV3JW/UfZDDzsMqWo6xwuZZc3dnXW6gcP8lB7Ww88FHu3A4Vi+OrXu0uuZ6OUL19X5UcKdSx26bsiJWn7HeQu7GwTNM6SSSkudA6PGfFG7oPAjTVOadoATxJRzytQayDhwCNYfJDYbSIUajrGAKZXJa/JYJ5qd4IZZL+zt87YTojih0+rhKvpYDSRMtwp60jOfzj//pX+DUD7IwuJBCtqF39MHvbALmjzRiWbDMzmszfOSkwfIqdfuO/+1cA+6lrF/fnbH4/n8BFHCp+6rY/kR19Vy5Let8w3L42Qu4cThxUfoCMdx7d6FpuDee+/la1/7Gvl8nh/4gR9gYWHhqvxOvV7nb/7mb5iZmeEnf/Inp+7zD//wD9x7770cPHiQF7/4xSibhfHf+c53uP7663n6058OwFve8hY++clPXpA8PhYwjWC9//3v5/bbbycUUvqhT1kxWDNNCpqctfUrqmyYAeumf0ECmVcVurbHeafP4XzlYRWe3HrrrWPjArjnq1/lq3/7N/zAq15N3fTZMP0hEUkCDyQZQRQJowRZFC+KQOyGMIlpeQ55ReUZtYNDz0aAp1YX+XZnlbprMW8Upn4+SGKKio4XR2M1lJcKTRZ5ymKJfzjbYqXncrhsTJ03uTRLErrEVpNa0KAF1LkwgdxNRJOmKbHdwV9/gNSzkKuHsiinUULywjEV9m7Xx6AX9vZ52wmyJFLLKaxGTKqwVXVChf1Dr7yZqH2WjX6LeseAavWC87+TiEYQBFRRpOPZtOzgmiaP+9jHpSBOUuwgwg5ium5Iq2VSCRSKmoyuSBQ16XFzvbecAEUUKGgyqz2Pg2V9gkBeS7h2j2wE/X6f17zmNaysrPDMZz6Ter3Om970Jv7wD/+QN73pTXv2O0EQ8OY3v5kvfOEL6LrO4uLiBHlMkoSf+qmf4rOf/SwvetGLuPPOOykUCnzuc5+jWq3SbrepVreiYjMzM7Tb7T07xmsZ0wjW5z//eW699VZe+VOvI0lTem6IH8WUNsnApRDIvCpiWwLLPYunVQPyysMX2j99+vTU7etLSxNEZD4vQ+Qj5rOaPSeKqRjyFTW2z6KNAUcLVU4UaxP+gLqs8NTqAb7VXqXtOczok6KiKE2oKgZWFFwReQSo5hSedqDE15Z7rPU9DpT0iXkTRAl15jABKVitjECmUKfCfDF3yQSyJMWE3TXCjVMgSSgLJ1EqB4b9tKfZ+OwlgZTES1Nhi7UjzIcP0rAaNGUdMC6bQOZljU7kcqZjc6hsPOY6zgRRQt8LCeIUQQABEAWBkp6RiH08tpCmmfH1qZZN34tI06zhgpCktOyQtb6/2fsdjlVzHK0aj+nrIElSljoORV0mp8qsdN1rnkA+KkzC4zjm3/7bf8vdd9/N+973Pj796U/zH//jf+Ttb387pmlO/czKygq9Xm9iexiGPPDAA1M+kf3OK17xCk6fPs2LXvSiqft88IMf5GMf+xhf+tKX+MAHPsDXvvY1TNPk137t1wBYXFzk/PnzY8exuHhthJmvNnYiWKdOnaLlWqy7Jt9ttMkp40rdbmQjqT5RkrJu+kRxQpKmNDyTDa9PnGbGy4IgkJMV7m23aboPr2H4iRMnpm5fPHo0aysYW8haRBDFrHdNQs8FPc+G12fN6VMZKYBO0pRVp8+K3SMaMZVO05QN12TZ7hIm8XB7N3Cxo5Bnzh7mieX5CeI4QE5WeUJpjiCJ6Qce56wO/dDb2iHN9lmyOnR8d+yzXhyy4Zo0PGtse5jELNtdNlyT0QqXKElACaiWErpetOO8CZKMOnMEMT9DN/aI3WXC9jLrrQ5umI2xE9isuT38eNwEtx95hKKLIIa0Wi3qZ+/GP383jqrTqR0jLC8MiSOAGwe4qYOoxjhBTNMOSNMUP45Yc3t0gvFrZvu8bZg+cZIO1eMNzyQZGXOSpnRCG1HxSUnYMAP8KM4ehL7FutcbmzdJy2MXZ4jTDqnXHaalIRMArbk93Hjcu82KfALRRZRjLD+iZWd/T0jpxSZrtkXXC6fO/6MNcZJSN33uXu/zD2fafP5Uk0/cvc5nH2zw7dU+31zt8eWzbe5eN2nZAXGyX2H1WIDtR3zjfJdP39/ACRPmCxqLJZ1aXiWnSFRzCgvFbFvFUHmoafPp++qcadqP2Wug54V03YiiJiOLAocrBookstrzsINryxx8gEcFeaxWq7zkJS8Z2/aiF70I3/c5c+bM1M/86q/+Ki95yUvo97e6M4RhyGtf+1re9ra3Tf2MYRi88Y1v3FXc8qEPfYgXvvCFPOEJTwCgVCrxpje9iQ996EMAfM/3fA+dTodbb72V7373u/z3//7fufnmmy9pvI9W7ESwrrvuOlacHkv9PufdHqPWd04UsOH16UYO5bxAksK66WMFPnU/IyFOtPWAVVWoOzZfqa/ycJbr3nLLLfzQD//w2LaB36EV+ay5PczYoVqQCXyXugdWmmakJbQQxS1S4UQB550eK3YXK/KH24MkZtXps2R16AXe5r4hfhzz3NkjzOkX9og05Ey53PBsHuw3aXnO8G+CkIlheoHLmjPetaQbeKy6JmuOOSTrkPlyLlkdVp0+wQgxsiKfNaeHpIZU8gJ+FO84b4IkI1QP0lF1GomPIVjEG/ezunwO0zZpuCarbhcz8saOqel0Odc+R9S4j7s+9G4+9Efv5bP/5yHsmaOsStAbOXeQRWeXnTYRPuWcMiSQ/dBl1e3S9K2xsW2ftzBOWO97dD2PVafHmtvPhDfD+Ymo+ybN0KJkCIhClsK2woCGb7Hm9sbmM04TOpLChghysIFCNCSQbd9m2WmPk3ugFzgsOS1EOat9HBDIfuix5vZp+H3W+uPjfjSi54Z8Y6XLN873qFsBRU3mCXNFnjBfQJclEmChoFHWFTZMn7uWu3xjpTsk3/t4dKJu+nx1qctKz0MRBUwvIt7lNq7JIocrBrN5lc891OBbqz3CONn5A49SrPV9ZFEY3t8fDQTy2oyHXgT+5m/+hkKhMCRx2/EHf/AHvOxlL+NHf/RH+fSnP41hGLzuda/j3nvvnUitXgruuecefvzHf3xs25Oe9CQ2NjZot9vMzMzw0Y9+lH/zb/4Nv/mbv8lP/dRP8cpXvnLH7/vt3/5tfvu3f5s4jnfc51rDTqKYW265hQ984ANj5/f5z38+P/HG1/PVzgpGaqBpEoqwlX7QJYWamtXulVSNnJSyYQZ0nc0eyMK48loVZQ7oZZbMPmboP2yqa1VV+etP3sYv/t5vcvqBJa677npe9Oqsi0guFpjViqiiRElVSVKLpmxg+TKGYDCTF6noWyl2Q1aY1zOl7qjyWhUl5vQCfhxRUNQsMhZ6PK2yeNHelpooo4oSOVnhWKFCefP8hEmMJIjMaDmOF2eQt1kdlRSNWT1PQc+P9bYuKCqLRglNyr53gLysDtsrKgWNBxouQZTQdZLp8yZrzNdO4PTXMPoN8qJPy66zdq6HqKVUdA1d1Il8hyT0SaKAvNujbPd4z6/+Dg/e8+DmN32Gr37pK7z9f/wehW2q5YKssaBnrRcr6lYKW4yFobfi6NhyokJJzpEkIkGQosgijh/hJSkaGiIClhvjiD6SIJBLBWbUPEmaUFA1iorAuunTtmLymk5e1shJW/MpCSJVvYA6c5Rc/Qz5qEHXOETLDlBUlQW9REEeL70oKDqLaZmColNWtlLYoixwND9DgMuZbp8Ttd3T/tcqwjhhqeNyuuUgiwKLxXGlfy2XjbllB6yRddaYzatZdNcJuGu5y5MXiswXr3016j7GsdpzuXvdoqBJ1PJ57CBiteex0nU5XDEmfA1HUdQVvudgmW+s9PCjmGceroyJ1h7NcMOYNdMby07BFoEcpLAVSYRHtj/GGB6V5PELX/gC73rXu/jd3/1ddH06ecjn83zyk5/kJS95CS972ctYWFjg7rvv5vOf//wVpZFN06RSqYxtG9Q49vt9ZmZmeNaznsUdd9xxUd/3i7/4i/ziL/7iUG19rWM3UYyqqtx+++3ceuutnDp1akgs13yLNBUQoxwHtz0sFFHiUG6rRlSSGdZACqHOQlEb6/MsCALHSlXqrkXdtR5Wy55EEnnVT72R1XZMSVeH9XGaJHNkZAyGEDNb1OinInGg8pSFKvII8ZIEkWNT1OKCIHA4X85+K01Zd01OFGc4kCtd9DFKokheUZEEYexzYRKjSTK6JHO8MDORts7JKkdyZfTcuNpalxROlibVj4oocV0xq+lM0xQngJWeSxSnO87bQr4C+Qpx9Qhh9zwLrkMzVAhDqEkJcm+NSBAgTUlJKaUJX7/jzhHimOGhr32Nuz/5OV75xjeMbc/LGvkRMjZaA5mTCsxuZhSiOMEKYsI4YV6pUDJkKoaCIWftwEQh+y8lxY8S/CjBDiI6TQ851jAUBVkQETbJz7rpkwRaVgMpjT/Q5rQis2oB3/eI+w1mS/M0UfADmVo+R14evwWXFYOysmXNtFUDCWWtTCr7dDyXlu1zuHINPUkuAm4Y842VHn0vYjavDpsDbMc0ApnVwGpYfsQ3zvc4OZvnupncvmn6owQrXYd71i1KhkJu86Unr8ocLOtjBHI3aLLETQdLfHutjx+mPPdY5TEhpmlaPnGcZuRwG0YJZMsJoHLtWHU96s78nXfeyStf+Ure+c538o53vGPXfQuFArfddhvXXXcd//iP/8jdd9/NgQMHruj3DcOYqLMcpMZzuUfXzfxysJso5m1vexuKokyUBdR7Fkkk0vdCVFlkviAi7pJ+vZCIRpclhETiwW6b64ozD1uvay8OCZKsxq9iTF/ESRRCGlMsz6JpKl43vmSVtReHtH2XRaPIyeLsJavKy4pOPxhPhwZJTFk1MjNuLc+GO71W+HIgCALHqgZOGGG6IW6U7ip+kvQC4vz1yHaXeadLvefScCLmdDAkQJIRVQNRL9K2p+e0Vs6cG/OB3AmjBHIt9lElAVHMDMnnChpFTd6FgAiokkhRg9m8So0IV9VZ63s07WCoCN1JRDN6ftTaEYLQI+quMTd3koYTDusZL0VEI8UCyA7nOi4HS48e4YzlR9y51OXBpsX1s/kdieMAOxHIzKVB5KGGRRgn3DhXeNScg8crltoO99at7AVtW7R8O4Gcu8CtUpUlbjpY5ttrfRJSfvC62gWvpWsZcZJyru3seg8YEMjeNVbr/Kg663fddRcveclLePvb385v/dZvXXD/OI55xzvewdzcHM985jP5mZ/5GRzHueDndsMNN9wwUWd5+vRpSqUS8/PzV/TdjwbsJoqZBi8K6QUerp8yk9fww4S65Y8JEaZhQCAHNZDRtjqXnKywbjv0ttWMXU3YYUASCzhBMhRjbEe6WfMmKAaSKDC3WQQ+DQ0vExG1fAc7DLDDgDW3jx2F3FCa5UmVhcsixgVFGxPiQBZ5LMgD5a66502uFEngxEwOWRapGPKO8zaAIErIxRq5hZMcPvEk8gsn6OQOE8/dgHbwSWjzJ1FK8yweOz718weOHaXnhBclHikoEpASxjGHyjo3HShxw1yBiqFcUuRK3pzPpy4UuWEujx8ndNwAaTMCOaiB9KPJEhRR0ZFKc6S+Bb7JfEFDU8QxEc1uqOVVCppMFAp0PJc1237UCGd6bsg3z/dI0pSj1RxtJ8yiKBdALZe1cLT8iDXTH643RRKZL+qc67icbjsPa+3zPi4NTcvnvrpJdQpxHGBAIMM4oW5mwsndoEgi33OgxJm2y3fX+ySPYhFNxwk41XZQL+DEIYsCc4VrJ+oIjyLy+PWvf50Xv/jFvPWtb+V3fud3Lrh/kiS8+c1v5itf+Qp///d/z2c+8xkcx+EVr3gFrute8PM74eabb+ZTn/oUzWYTgCiK+Iu/+IvHvSjm5MmTU7ebkU/DdTD9hLnNh4EbRNzfbtP2x9WvmWrVpuXbpGk6RiAf6HTZcLaU13lVpudErNn9aT97VdAPfdI0swwy/ZAHOm16wfjLSOzbtCKfXhLjRykz+Sy9PVBSD1TLSZqSpHBjeY6alsONQ6I04Ynleb5/7hgnS7M7qqovBH3b55woZN01hy3x8oqKIkqYgceq06cXjK+HMIlZc/q0/W1jSxPWXZOmNzlvG66Jnbgcrxr4UUItp0ydtwF6oUvDN4mSGFmWWKyW0I0iS3bAimMSJBmh+uFX/QRPfs5zxz77lOd+Ly9/7T8lp0rULYeHeu0xURVk4pYNr8+KadL3I66fK/CDJ2ocrGh0I/uSx7bhmqSblFsUhUwhWhEJ8NgwXdKUIYE81elz3u4RJeMk0tOLtBCwO6sIaTIkkOf7Fmf7nTFxDmQR6LpnDsdWy6sUdQUzCDnb77DWf/henC4XPTfkm6s94gTmChoHihoFTaZlB1dEIAdE/qGmzVLn8u/n+7h6cIKIL59t0/djtAtEBwcEMkoSVrruRRHIpy0W+fZanzOth9d5Yy9xvucRJ7DS8y4oBLrWSjQeFWnr9fV1XvziF1Or1Th+/Dh/8Ad/MPzbzTffzNGjRyc+8/M///N88Ytf5I477hjWEn7mM5/hRS96Ea95zWu47bbbpv7Wn//5n9Ptdrn77rtZW1sb/tY73vEOBEHg7W9/O3/+53/O85//fF73utdxxx13sLq6ysc+9rGrMPJrDzuJYm655Zap+685fc70uxSTKhVDQZFF+mHKQ50u7VDmGbNbfX6dOGDdy+yVdEnO+h7LEuWcwOlmnzUv5hmzEjN6Hk0WSRKBuxqrPKEyN9ZG72ogSRPs0CeIYCan0Q5sznbb9EKdZ8xqKJu1bl27zVoSkI8cysgcqWY1jGbks2R3gUwwIwsShqxwJF/hWEEcEsq9SMFrkowgCCRpiigIND2LDdcaklFFlCgrOqfMJuuuxayWG2uZ1/FdTpstiopGUdGG57YXeJw126iSTE5WyW16RY6O7QmlWY5WDZY6LkVd4HR7fN4gI6cbXh878pHyIjNqHkkUmC+oLDWanG1nxO5IoTI04P7oh/6cpXOnue749fz4a9+IrCjMKil1v8+5bhs/iXhKdSvyv2ZbnDKbHC2WeM5CjeJm+rruWpc9thNijkFVlhOFNAITSQsp6xpdO6SsK8zmZc42TFbaAVJNYHGz7jRNU+qhQ0uWSb0+ec9EylWYL2iseh1OdfrEacrJkX7encBhxe2woJU4Kmfba3mVdU/ktFPnvnqN62Zy12zdlxNEfHetD6kw7KwkCAIHihprMEzbD1LUO2GnFLYiidRyKvfVLRRJ4GB595q5fTx8iOKE++oWfpQgCAJrpj+ct52QV2VmCyrNKLkoEY2uSNw4W+AflzqUjKwM5dEEJ4ho2AFPnMuzbgUsd12ObCqsHw24Nu862xDHMW94Q1Ycf99994397YUvfOHUz9xyyy28853v5Nixrb6zlUqFz3zmM3zjG9/Y8bdOnTpFo9Hgpptu4qabbhr+XmZYKqBpGnfccQfve9/7uPfee3nZy17GX/zFXzA3N3elw3xUYCdRjKJM1p4lacKS1UVLtTGlbs3QORQWML2YrhOhFxVEQUAV5aH6VB3rL6xytFRgw/IzNa+S9cLOSSpNt0/H370t314giGOCNCaIshTtQt6gFxUIA4GOEzJXELPrI0koKQaGrCPH4jBVY0gK5U2CZkgKVhhQ1YxhhxhBEJD2qGOOLslookSQROhSRs5ntTzayBzM6nnOWG1mtdywFnKAvKxSVQ1ysjqmyjYkhYpmoAjSmDBkdGw5WaVYEgnjlPM9myPFAnV7fN5kQaQo68iCiDGiyJYlkUOGzrdvu40H15rccMP1vHhT0f7C17yWTmAzpxWRlS0icqiYx4kD4kCk64UUFImOG6KJCt8zX+NosTAkjoOxzWi5yxqblm5dk5qUEfCcrHA0V6CnJ5xuORQ1iWPlIit9m74DVTXrhS0IAgVZIy3MYcQpYXcdUS8iihJHi0XCJMb102F7QoCcpFJVcmMiIICjpSLt0OJMz6RuVjleu/Zu40GUcM+6iRtlEdZR7CWB1GSRsqFw74ZFXpUpG1dmfr+PK0eappxuOdQtnyfMFWi74cS87QRDljiY1y9ahZ3XZA6XDf7P2TYvuGHuirt4PZxo2QFJklLQFQ5LIis971FFIPd7W18jeKz0th6FGXp8ce0Mq+0EQ5HGhARpmmIHMS07QFNE5gtapnDdvByn9l4OI+pWOOyFHcQp/cjlnxxd4KbawQsez5Wcn67v8uWNc6y2EwqqhCxl0cKeH2VqXlWiposEGw8iFWchN4sdxjzjcBll8+Y3OrYN1+SJ5fnL6tF9MbiruYwXhZRUnSCOMKOAf7Jw3VD13fEd7mwuszDiHTna23rDMUGAhW2kfPAStR3b5y1KUk63bJp2QF6RaNjhRA/z7d81tUf0c57Lf3zvnyJv9kef9ttJktWg1q2AiqFwcjbHwZKBLE6/jnYbw25j8/vWRO/v0c+s9z1OtRxKuowIbFgBScqYiCZNU2KrRdheRp27DilXASBOEhp2gB8mw/rG3Y6pF7j07ZQnlhd5yY3z10xKq9VqUanOcPdan7VN0dROZCFNU9ZMH8uPqOXVCxJIyFq4teyAgiaPEZGWk11nzzhcvqYfvNfaPfpqoG76fP18l7m8NpyLneZtOzyzi16sDG18FEm8IIEEWOu7zOQUnnW4+qgQUCVJyleWOiQpFDfXuhfGrPQ8RIGpBPJa62197a6yfTzq4UQhTpgQJelUBWpBk6nl1TERjSAIO95YdEUeE9EoIhCJLJt9gvjqmqj6SUQQJ0RxMiQ/giBQ0ZWhIXW9b0OaIKo5/CihqEpD4jjYfzC2FIbp+quBsqLjb9YNBkmMLsljdkF5Oat7jNLJOpswiRFEAUWUcKLxOryd5mb7vMmiwHUzecqajB3GzBcmxU/bv+uOj39sjDgC3HvnV/nMRz6y628Hm/VRJ2ZyPPtohWPVHIq083V0Odsv5jOLJZ3rZ3P0vQiYLqIRBAEpVwVBJGicId2si5REcaqIZqffLSga+VxK3XFoX0Tt4MOFNE15qGnzlaUOBU3aNco0iEDuRQ3kjKHQdkLOtPYFNI8kgijhwYZFQVXGyM9O87YTRkU0F1MDuVDU2bAC6tajw0C/54X0vYiCOuJ5rEgcLuskKSx33WveDH2fPO7jqqEbuNh+vKstzzQCuRtGRTQbVoAgiHQ8HzO8ujcNNwoI4zRrursNAwJpux5ND5Bk/DimkpueQovTBFEQrip5LCjasJVXmCQUt6U+VUmmqGi40aRitxd4HDBKnCjW6AaXL0ZQJIEb5gvM5lVML2R2U0Szkwp7Y3l56vecPXV22MpwFHGS0nYCvDDhhrk8TztYovQIp60WijrHqgZdL0QUBWY1ga/89Ud472/+Np/64AeJwhBBkpCrB4k9m8TbskwSBeGiVdiSIJKQomsJ5zpX5iCxl+i6Eaebdlab2fcv2BljrwikIGRq1DNth6Z97ZDpxxuWui5WEFPSJ9fh1SSQoiAwY6jct2HiTblXXGvYsHwkcfKl9NFEIPfJ4z6uGlqeQ8eOyKlS1qotsDG3WeukaUokBChqPEEge6FLN5iMJARpiKpFRElC3w3p2BEd/+o+QPthZuTaCx3sbW3x4jQhFQOkxMSJoeVlJt+GItLyJpW9fhwhMqmK3kvokkwvdDe9KbOONdtx0ChhRZnlUJQkdHyXXuARJjEHcyUO5ErkZZWzVntCkZ2m6dSxAbR9h5aXKeZVSeRkLc+hikHddRDkgChJxgikHfm0A5u5Hco1aocPUTf9IYH044gVq8+65VDLKzztQJGFok5CQt21JpTXUZJt3/6CEacJDc+67LGNohe4NLys/eGBks5cQaXRs/nPP/MW/vK//jqfe997+ZNf+4/8+lt/migMibQCpp7H7a6SjtgqhUmMrEQIUjJGIMMkph3YuCOK7Jyk4qQOLTvE9B759mWWH3G241A2FI7P5C+6tZogCCwUVJAizvZ6rFsXfmGZRkQUSaSgydy7YU592djH1UXPzSK/u5UfjM7b+b6HHfpj/eC341IIZEGTCTfLZa7l6LMfxaz1fMo7eNQ+WgjkPnncx1WBF4XUbYe+m2B5EWboseJ0WPN6YzcLOw4473bpJzZ5XRgSSCcKWHN7nHe72PEWGQiTmDWvRyPokTMSFFmi78YsWf2rdsNI0xQzzCyHNoIea25vzHpmMDYr7pHXRMwgJoxTIiJOW23OWZ0xQtP2HSpq7pLNvy8FmiRn/artPilgTIlyLhhF5vR8Roh8mxWnx73dDSqqTlnVUUSJk8Uap80WZ832WGmAGfpTx+ZEAeesDqet9pCsSaLAgZKCovssOR1CAqIkMxIPoog1t8ey0+aml790wpbn2DNv4gde9WMokkjd9Om6AQ922pxzWlSKcH0tPxQlbbgWD/QbLNu9se9o+TYPmU3OWZ2xeev6LqfNbAwXMzYvjibGBhAMtpttur6LJAocq+a465N/w3133jV2LPd+9av87498hI3Q4bwi03b7JMGW1UgzsFhy24hKOBaBbAc25+wWG96WNZUhKTQDCzv2HvF0XRQn3F+3SNKUgiZfdG9eJwpYdTp8ofEg97pnOR82+PLGmay+2OnsSiymEciiJhNEmXjpWiYQjzXEScp31nr0/XBX0+4kTRHlCEcw+Vpric+tnuae7urYS9F2XAqBrOU1VnreUIh1LaLjhHjR7vZFjwYC+eiRJu3jUQUnDrHDCE2SsIMYKUnQJRVVlMbS2IogoksKkiBS1lV0KaVlB7STGFWSSUlRRlSxoiCgiTICAgVFo6xIPNSKaDkudhRQUPberiFMYvw4IopEirKGISmII/lrVZTRRRmShJmCQUeRyKsSeUUhv9lPedxKSJgQouw1dElmVssPrX80cXKpS6LIyeIsdzaXN9PoCr4kc6wwM1SBLxhFnlI+wHm3O2YjpIjS1LEpokROVonTZGy7LEkcLufJqTJakqPvprSdkDhJUFSZnJSS0wx+9b1/yh0f/xjrS0vkDsxz48teQF7XUQWFpY6DIMB11QKxrHAgP64S1zdT8Xl5/I1ekzIVf15Wx+ZNk2RysoIuyRc1NlkUyQmTY5NEEUNWMvXvZjRZk0XC9sbUuTlz6ixPSUUKxgxqGBL21pH04nCe8rKKLinMGBp1y6dlB8hqViM7qk7PFPoiXuqw1MlxuKI/Yv1+z3ZcGrZPVRtRz2/rzXuwrJMfsRUyQ4/7+uu0Agsz8ljQSiwUS6ybLg3bZdXtspDP87TyQSpq1r0rTVN6octZu4kgiFSVHKmcUncy8nygqDGT1zjfczlQ0pi5CBHOPq4c632Pcx0PVRJoOcGO0cclp8W3OivM6UWqORXXSznT79MJHZ45c4zBp5wooBVY1F2TII1RBJEYyIdlVrrsKKKRRQFDlniwaVMxlKkdrh5JpGnKAw2LrhtyoKTvWtI1IJADFfaFvDIfbuyTx31cFdihj+lHzBUMwjjB8iPKaonZnDa2YDRJ4Xg+Ux9KgsiA+7XsAF3IMVfQxh/UgsiR3AxJmgwFIDVDpe+HmKF/VcijF0d4UUwSKcyrFeYMfYy05GSVY3qZIAUlV0YTRQ4UVQxZ4QmlOQQhO+4BZFEc+gheLYiCyJF8GTsKCJJkR8PximZwrFDhjNlm0SgiFAxqm36MkBGjp88eJGrECCPEa6exKaLE9aUaacpYb2tJyIjq8UKCLIj0/Yjljstyx8WzZGo5HSFR8IDvfeVPkgBRHBPEMXEoEcsJzzxcZr6goUoCUZqMWe1AZj9UVvWJ7RXVIF/JyODovBUUjSeW55GE8XaZO86bIO46tu2k8uTJ66ae87nDR0gCjSOFPOqm+joOHCQ1R03NU1aMrHf2Zg1k3fLxA5kFo0pZG38oLxol7NDHDkPqps+R6sPfIrVh+Zxu2czlNWJnPAK6E4FM05TzbpeElBOFuSxdvznmxaKBLEo4gYYXRtzdW+WQUaWk6qy7PbqBiyiALEisxV3iNMFPBRQ3u8YPFDU0SeKhhs2zjlxaF6F9XDr8KOahps2JmRw9P9rRfqnu9Vm225wszKFKMmjQlUJER8D1fe7unecoOnaalbEkpKiihCpIyKKEn3roekIY7O4DWTYUzvdc1k3vmuv/bvoRXTdCFATO9zwOlS+eQDbsgBuL144V1bVFZffxmEHDdfBD0GRhaD3iBgkdZzI9IQni2EN6IKIJI2jZ4YSIRhSEMeVwSVdw/JSma12VsbhxmKUN0hQvSOlPETJISYyQpgiylnl3bUZgZHF8bFGSIG3zN7xaKKsGdhRmN+BdTNSPFWbIKxr90ONovjphuF7TchzIlWh54zWA28c2gCSIY+RqAFHIFNyCIFDWFZ6yWOT518/yQydmOVrNo0rZ9ZLTRMpGlqp6ymKFmw6WefqhMkcqBpqcEYzB92zHbtun3aR32n65YxvFza9/Dc/6ge8f2/aU534vr3jdP0USBVp2TGRkVk2x1QKYGNuoiKbnxtjBeBpXFWViYgQ55Fzn4U9vuWHM3esmeVXe0SJnWgq7Ezq0fIuykhl7j45ZEARm8yo5VYJQRkgUzntd7uuvYccBM1qeGa1ASTWoqnlmtSKqnCIowTCFXdYl2m7IWn+/+8zVxlrPx48S8ps2PNPET/3Q5bTVpKzmMuK4iYHYUE41HA9W3S7d0KGk6MxqBUqKQVHRMSSFgqzRj23mi+oFU9hVQ+V0y5naKvSRRNMOyCkih8o6bhhzvuddUCQ6IJDd0J7oQvVIYj/yuI89R5wkrDsWQioO02i1fPYWOhAADP69EwY+dy07s18Y+EBOgyaLCIHIecfkSdV4jFjuBazQJ4rT7GGGQG+TAFdGCp7T0ENSdRJRRpaEHfu4enFITlYuu/XgpaCgaARJxJyc37VzjSbJ3FCaJexbLBiFib+LgsgNpVk6wTJOFOyZSlwQBAxVwlAlavlr5416r6CoKn/80b/gw+/7IN+650GOHDvOC179k8iKwqKSiYYankhFMlCdHklpAXHKuRVHIpCDqM6oGbIuqbRDkzRSaNoBB0r6xHdcDSRJyr0bfU41bZ5+qLzrvqMRyOWuQ1doY8jqjmt6QCCbBNheTDmnja237SgoOm7icMgo0Hcj1oGyJvNQ06GW13Zcj/u4MnhhzJm2MzRnn2YAb6jwoFlHlWT0KS/Ng3ntOSGkMmXFmPoCaEgqjdAkFkMOlnc3Es+pEv1+yPmex4lafuK7HglEccJqz6OgKZvPksx54mIikJoskkgefnLtdNHZjzzuY8/hxiE9P0BCpB+6w2LoQQTS9ELO9foTqlgAK/KxNtXMozY+53omvcCdKIJ34xA79hCR6HnhVbHsqbsWfpxZDg0iIh07YNkyh2OL3D5IKl4iEhESJJNj6wceTc+mok6/Oe41dEke1u9dCPN6gZOlWfI7pP3zisYTSrOctyd7YUM2tn4w2WvZCn26U+bNiQI6vjvx1u1GIW3fmeiFHcQRbd+ZEFCESUzbdyZ8PuM0oe07E1ZESZrS8d2Jay9NU7qBizXl+rmSsSmKwhvf8ib+71/5ZZ7ysldgExGnmVfowAeyLpZY7bX51Ptv5f3/7Xf47If/MlNkJzG90CVM4rEIZMPyWLVM/M1rryBrWJGPkzosdZyhRdPVxlrf43zXp6QrF1XUPyCQZuxwttfHDsKJ+YzThF7o4sfhWARy+3obIBOzeaRpmkVlpGAooun5EUEUX1NWRo81nO95hEkyRs5H7ZfqlsddjRXsyB92D4OteRusw0EE0gsTlnrmhCsHZOsqSGI2PJOcIl1QRFMxFM62HZwLqP0fLnTdEDuMh8SxpCssFrWLikC6cUgkXBvjGGCfPO5jz2FHAT03JCBkyWmz6naHZKCWVxHkmNNmi7vbdYJka0E4UcCK02HF2VK5FjSZkiGybHf4TrtOf+SmEqcJq26XJaeNE3s4QTyV2FwJwiTmrNVmxeojbvpyzeZVYjHggU6D+7vNbGxxCLJMJ3DpJSZnrDb+CKGxQp9TZouzVoeK+vD04NUkGW1TRHIhCIJwQZJ5IFfiSKHCN9trU8d2ymyNkS8/jjhttjhttuiNzFuUJJwx2zzYb4zZ4aRpyjm7wwO9Bg1vS4EMsOL0uL/XYMM1x7ZvuCb39xqsOOMK64Zn80CvwTm7M0bu2r7Dg/0GZ8w20YhFTi/0hse609hGiculjm2hqNHH5r5unU6QbR8QyFbo87u/+lv86X/5bT7+7v/JH/3qr/Abb30LdbvH2RGF9YBAevjc3alzqt8ejq2sGqz5Lc5b9sNiGm75EQ80LOaKGseqxlAVOu0hHqdJZmeVpkRpRKy4JCR8u7XOOasztm8ncDhnt1j1ekP/xqnrbXAckc+K22HF7aBJMufdLiVdGhLIIElZ7rj0vWsn3fdYgRNEnOs4VI3J+8aAQLqpw12tJdZse8d5GzwDKrqCrKacsdrc3a5jjzoaJBErbodu6LDudnHi4IIqbF2RSJKUpe61Ubqwbnqo2zJAF0sg7di/plLWsE8e93EV0PFcnDDBkCUUQZoQMMzldQqqQhCldOytBSEJAmkU8/WP38Zf/d7vDSMwBU1hJqeRxNB2xmsgZUFEESRyioIXZmRiLy063CjEjgLSdKvnsSAIzOd18qqC6yf0HA/iEEnJI6QCRVWdqKUb1MoVFPVhqXeELPJYkDX0PRLniILIU6uLHM6VxyJ3g7FtrxMc1AHKgjh2DQhCVuOmSvJY/aAgCCjC5vZt14wiZud/ex2iJIhokjRRbygLIqokowjjNZCyuLldlBgN/g6Ocad5k0VxTKl9qWNTJZEjpRxJIiCkI8cjiSzf8fes3/PA2PHf/dWv8NXbbkMVpbHvEQWBhYJOTpExva0aSFXMnAnWvRZn21fXpiZOMsVoCuQUacxWpG75YxHIXuByx8YD/J/mab7eWeKe3ioJCUdKBXRJoefEY56MA4W7LIzXQI6tNy8c21/anANDUvHikJZvDW18vDChYQUsda4NAvFYwkrXJU7SHVXAbhxiCj3KqoEXpDvO26gQr6QqlI2s3r3rRlttTxGG6zNNU9qb9lYXIpDVnMpyx3vEXx7cMGbDCqaap18MgWx4FsmUbmCPJPZrHvex51ixTYRUpKbnKKrqhCAmJ6vcNLtIxw7xwsyap5ZXEeOUv/jX/45777xzuO8XP/EJfuVP3suN5TksPaTvJsMaSEkQOWRUsjSgINNxfZquhxuHe1aX58YhNTVPIgljrQZLqsHTa4t03Yie7REFKTMzOYxE5YmVKkVdHiM0hqxwojCDFYVXXWk9gCiIzOl5cntIVnOyyvMWjvPdzjoN12ZGNzBkhRtLcwBjxfCZ8nqWJE3HajwlQeS64gxRkmBsOxfHi1X8OJ44R4dyZWpaHmNbreiiUaSsGhOG67N6npysDgn/ABXV4MaSPEF0C4rGjeX5CeHL6NiSEfPqyxnb98zOoqQq2wTJePUm02Cv1rkuP4u6zWZpRsvzjFmFrhON1UAezFVZdbs81O1w3UyO2cLVqY8623aom/5YbeVQFbphstx1OVIxEEU4Z7dQJImyku2bArXN9GVhTqNjx9RNn/liVpdYVXMYkjLxMjC63kw3RhRCKrpCTlY5nq8hkM1bQdE473apaYWh2rdu+txftzhSMYa1efu4Mth+xDdX+ywUp19jSZqy7LTRJYXnzB264LwNoIoyT60u0PVCHC/rWT+bz17Gj+RmSElJ05R1r8+iXs7KcjYJ5LQaSEUSkURY6jg89cDudblXE207IIrTHUVlpc26z2k1kGESY0X+0D7tWsEVHc1v/dZv8ZM/+ZP82q/9Gh/96Ed56KGH9o1ZH+fw44i26xFFwqbvnTJVwKKKMgtFg4ImY23aO9zx8Y+NEUfIIjB3fPxjKKJE1dAnWhnKooQmDew4BOwgmlq3drkwQw9FUEhTAVna1kpKVlgo6BhiTDdIaXugyCIlXZ14+AEkQEnRxgjW1cZudYyXi3mjyHPnjrKQK7Da6/C+9/4pf/iff5vbPvCXhOH4G34WMZwcryJKE+QKMvI1jVwPfCi314oKm9unFZvnZGWqYtqQJ8kJZGn+adtVSZ46Z5c6NlmUOFEpkaaMtWdcPHJkYl+AxaNH0aWdxqayWDTGjMRFQWBGzdMMe9zX6F+V2seWHXC6ZTOb16a2VpvPq8MU9prdw4w95rUimqSgSQq6pAx7hecVlQMlfWgAP4hADnxft2Ow3nKqRM8J6W5Gk1Rxa94G0cfzTpc0TanlVOaLGm0n5HT72u488mjCat/DCSKaVjDVAL4VWDR9m7KSu6h5G4UiSszldMo5BSeIadpZFyxFlFBFGU1SCOKIbrhVFrJbBLJiqKz1fbruIxN9TNOUUy2bvLq7aGunCKQTBURpNJGNeaRxRUfzz//5P+ftb387lUqFz3zmM7z0pS+lVCrx/d///fyLf/Ev+B//439w9uzZPTrUfTwa4EQBDcvDDpJde/MOMBDR9B2XMw88MHWf9aWl4f/v1gtbEgTcMKHt712KquO7kIqkpFNFLoIgMCOF5GRoelk7vmneY5C14ZumZn40oqBo3JCb4b+8+R38zi/+Cu/973/Af3rnL/Iv/+kbJwjkPraQ1yQWi9pYCu+HX/UTE511Tj7zOXzfK27e9bum9cLOySqynLBiWXtuU+OFMfds9DEUaccuIqoscris48URX29sYAjqruIwSRRYKGoTBHInjIpoRonIKKpqjvNuZ1grWsupXFfL8d21Pr1roI3jox1eGLPS9bhxvjS1g5AfRyzZbQrKlpr+YuZtOwYimlECOUBOVjltNdlwtzqL7UQgZVFAEkWWOo9M16G+F3Gu407YbE3DNAJpRt5Yycy1gisij4uLi7zsZS/jne98J2984xs5evQo3/nOd/j93/99Wq0Wf/RHf8Q3v/nNPTrUfTwa0A982p5HbuSBNoATBWNihAF0yQdzGWMH+57Fo0fH/q3IkNOZIJCSBG03oLlH9SFZuiBA2FwmaZpiR5O9WJPAJq8BkkDFGI9E2WGAE4U4UYAmKRzIPXKpk73Gn/3Zn/EPX/zi2LY7v/RlPvL+D2KHk6INLw6nRoWDOKK/qZgdRZjE9MPJGqAoSeiH3pjgBbJUWT/0JuYn3dy+XZENmSBmWiH6YN62w4nCKx7bYkknFaDtOZkiW1X51ff+Kf/i13+Dl7/h1bzx5/8v3v67v0/LS4c+dcnmtRdtG5sA5HUQNzszWX6EKipEBDzUdPD2qMdzkqTcvd7ndMsZswmaBl2REBUfL4noOskwyhqnCXbkTyjpBQEKBogiYwRyp/UmCAI5DWQ5nSAibhwQpQklRees3aK7KU6aL2jkVJkH69Z+9PEK0bB8gjghr0oT/p2Z+XuHbuAgC+ORtgvN27R1qCugaUwQSFmQIE15yKpzymoMRTc7EciqobDe9x+Rl4e65aNIAqYfsWFeOCs2SiBXui4t30aTlIl180hjz+Kgn/rUp3jBC17A8ePHefazn82HP/xhZmdnecYznrFXP7GPRwGWzB6rXg9UF0UWhg80M/Q457RYcTtjD0DL63Pq/N1YQZPve+kLOf7kJ4593/Fn3sRzXvHy4b+jJGbF7dCMemhaMiSQUZLQDPssW11WzD72FBugS4UbZXYplh8RJSnd0OWs3co6Y2zexNI0xfQdluIABwt9JCJjhT4Pmk0e6jepuzYnCjMPi7/jw4XTp09P3f6dB+7jQbM5RqbCJOb+5jrvfvd7+K3/59f56Ps+QBhm4qczVqawbvnjlipLVpf7uvUJhfWa2+febp01tz+2fcM1ua9bZ8nqjm1v+Q4P9BqcsTpjRLQbuDzQb3Kq3xojKKPzNvpAC5KIh/rNqWM71W/xQL9Jd0Ttv9PYdFlEkD3u6dZpBZmxvawovOi1r+XF/+pnOfojz0KWbUQBNswAP4ppBRZn7CZr3viYu6HLktMmlLbWWxKL9BMLOwz3TCiy3HU513HRZemCtiL90OMea4WqIZGkWR1XFCc0fJPTdpOGPz6frcBiyW2RyN5YBHLaeoOslGTJbeMKNpoiDImIEwUs2W2WnTaQtYo8ZTWG4q4TM3mWe94jlr58LCCKE8513KHwY7sB/Jpjcspq0Amdy5q3YGQd+nHEstOmF/dRtXRIIMM4ewas+31UUaLumdzTW6MfZtf6NAIpiQKy9PBHH4MoybwmZ/KUdYWeF14SgewFPuf6Joog0Q7tC37u4cSekcfv/d7v5WMf+xiel9lWCILAddddx+23375XP7GPq4wgCHj3u9/NL//yL/Oe97znktOPSZpw3raQkRBFcSylNq0uJgk9wsY50tBFLtY4csNT+YX33Mqrf/GXecEb3sAr3vHPeeN/eic7+Vvn1fEUNgACmEGINSU6dKlw45AU6LsxPTdrZr8daRxBGiOqOURRGCOPw++JQkqqxmKudMXHdC3hxIkTU7cvHD08sS0MAv7LT7+D//Wr7+L9f/DHj/sU90xeRZNFvHBbFE7NgSiB0x76QG6YAUG0e9RhNIXdd2ParouqZg/63hWSpQ3T54GGxZFK7oKdMaI04ZRZx4p9cqrCQlEdEsg43v2hLW5LYU9bb6MQBIFabisV2vPHx5mXNeI04SGznnV2EgWqhsKZ1n7t4+Wi5YRYQTzWo3xAIEURvtFYJ4zTqTWrA1xo3qahrG2lsFvOVgRSFERm9QJxmnB3b5V1J7N4mkYgq4bChvnw1j62nQAvjNEViYWidskEsmgIeFFM0w6vuWt2z8IgN998M5/61Ke46aabePOb3wzAhz/8Yd761rfu1U/s4yoiCAJe+tKX8vnPf3647f3vfz+33347inJxCkU3Cum7IdeX5pk3DORNAlm3fHwfZvUyJU1FFiXSOCZonEWLPK6rnUCpLKApOgszOi9+/U9heiGis0bea6L0G6Q1HUHMepweNqrEaTKmqG7ZAYpkcNTQ0ASZtu9w4ArJWj/wGKxXXZbw3ZTFXJWStlXLkyYhRUlBys0RqwXy2ta5KigaNxRnafgWN5RmpxaHP5pxyy238IEPfGDsmnnOD/4Ab77lFiRZJq9szc/f/eVHufcrXxv7/J1f+jJ/+6G/4hVvfC1+ElPYppA/WqgwZ+THzIUBDhglyqpOThrff8Eokp9ihVTTcpnn5TYbnopq8ITSLLIojs3NYN4EQRjriKGKMtfnZ7OH08jYFFHiZKlGlCRjvdVFQeC6QnXq2E6UqsiJymp3/EG2kK+iFxeRu+uIScBiUWPd9Al9hYO5KiV1/FxUFGOz/aQ8tt5MV+a83eeoMceDDYubDpV3VHruhrYT8N21PkVNQZPFoS3LNFVonCasuT1MIeCppYPk5Kwr1EJRZcMMSAOFo7kZCtvGUFML5CQVTZSHNZAbpj91vQEUFZ1jQi2z9ZEkZvMiTQIcP6aslajoWx2cyopBM7BoBxbzeomKodC0ffpetK+8vkSkacpSxyE/pVuPLAooeojfC8kHOov5/GXNm+pnL+yQCdgGCmtDUsltspWeE2LIeQ4Y8lAMWFR0vDjkAWsdNw45kp/ZQYUtcq7jUjEmxXdX43ytdN0xoj1Qpw9qnndSqw8QiwFzOZ0wSBGvoe4ysIeRx9tvv52f/dmf5Y//+I9ZX1/noYce4tZbb+V5z3veXv3EPq4ibr311jESAPD5z3+eW2+99aK/wwoDrCCiphvDh/FoRMTxINwMQMZOmzR0UCqHKNaOYChbth+1vEpRV0iMBXylQmw2iM0tOxNNkseI40BEk8QCUSQRBCItzyZOrqxGpBu4CIlEnKQcrhiosoTlpowFRDbTmopsMKNpE2KZmIRZrcCCUbyiY7kWoaoqt99+O+9+97v5d//+3/Hvfudd/L8f/F+UjNwYuQJYObs09TuWz5xD3TQy334zV0SJkjLZtksWRUqKPtFjWhQESoo+QdIFQaC4g8q9oGhTW6blFXWq6jsnKxNjg0whXJiiat9tbMcqRQqqhBOM+xyWy4uIaUzs9IZG4rIoYLkC25u4ZObu2sR6K2s6Z3s9IKblhNxfNy9ZfW16Ed9Z66PJ4rArBkwv6k/SlCW7TTdwmNfLFEbmTZMlFooqAiKmC9tLt8TNMQxcGQYEcup620RmwyQPz8FAjOH7MKrFEASBvKSx4nQJkxhJFBBFgZXeZAeTfeyOjhvScUKKU7wKnShgzetyslpGU+QrnrcBdEnBGHlJHIhookjA8RmLxumSwqxWZNXrcsZuAJMp7KzrjU/nYYg+9r2I9pTzdbERyDhN6AQus5suI8TXlmhmzyKP//AP/4BhGPzSL/0Sz3/+8/fqa/fxMGGn+rVTp05d9He0XJcwzgyRRzHRmzeJkftNBK2IXF5EmJKXHvS+7icLdPshM2YDMT+DuINH4qCIv2759NyIfhBgRT7ly+zmMiaWERiLiIz60iW+QxIF+KnEwpSbqhUGPH3m4J73275WoCgKb3vb2wA41W9y2mwxLd57+PjRKVvhyHXHruLRXduQRYGDJZ0HmvYYORPVHHJxnthqIxdnkSVpGIHcMAMWiuqwZ/w0iILAoVIOq+1xpmdyfaXCctdDlSRumMtfVMSl74V8d90kSaA6pe/4qC/dStclVV3Ou11mp5B92CKQG2bAuulnhHiXSOhO620njPbC3t57PierNDyThm9y0KhQ0RXW+h7HqsYFxT/72MKZVlZzt31+001PR0gxZAW1KF/2vMliRCG3+3GM9sJukvlADq5pWZSY0fK0fJvDuRBDUsYikGt9j5wqcbbtUL3K0ccN00cUhanr4WIikHbk48chRVlD0wQKMtdU6nrPIo9vetOb+MxnPkMQXP3WWPvYe+xUv3by5MmL/o5V20IWxLHF4seZ6GQ0ArnRatFxXYTS3ARxDJN4KF6o5VVK+TxefoG67ePZ463MIBPQDHv8ajLzBQ07SHCi6IrqHt0oJIxjkiQTSsRpMmEr0vV8fLef3YBEkdxmesKPI8Ikxo1CcpLCrJ6/7ON4NGFOz2yIRmvhwiTGjyNufv1rePbzvn9s/2c+7/t45etfM7YtTpOpKuckTXGiybqfdHP7tPo7JwqnKhTdaLKnMmzN23YEcTRVqT0Y27Tt23tqw/SxzRQ0IKXlbPXIFkQRuTRHEro4bj/zMx3phb1hBvR8b+rYRtfbQkHHx6NhBxiyyOm2w7n27gKaNE0533X4/KkmfpQMX+KmYRCBXHdNvl5fRxfliVq3ZLPndJqmQwKZpFlfbCvwp86bF4c7rrftanPI1meYxBN2MA1nS3lfVHTOu138OEKRRNI0ZXU/+njRsPyIh1oOph9N9DDvhZuKYFG54nmzvWhs3kYRJvFYK8NBDeSG5eGNrCtJEInTBCvamt/RCKS1qXpuO1cv+jgQypR2eTm5UARyw+sjkJ2nNImRnRaJZ121Y75U7Bl5fM973sOdd97Js5/9bH7v936Pf/zHfxyKZ/Zx7eOWW26ZiBg///nP55Zbbrmoz0dJzKl+B22kG4YZekO1ZJwmiILAXE4mchrc7fk86LljNwk/Djlrtzhrt4aEsJZXyeXznAoDvrlyP3Yw3tv6vNvlrN3C3OwvXNBkCprEcr/HkjVJNi8WThSAAMuWyZrfYdXNeicPboxBGvCt5hqn+y1SOYtu6rKIFfrc36vz0KbydiFXfMxGHbejqGgUZG2oRA6TmIf6Te7v1fGFlD/6yF/w//zeb/PT//od/F//5T/yjnf/LnY6Tr7Omh3u69Vpbuttfd7pcV9vg/Vtyut11+S+3gbnt/W2bno29/XqnDXHr4Fu4HL/Zm/rsV67I/M2ek26Ucj9/Qb39xtjRHF0bKPK6zjNelvf32+MKa93GpsiCuTzMV+tr1IfeTCIeoF2mnCqdZa6nymsBwTSil2+0VznnNUe+/7t662o6ChqjCxB0wlRRIH7Gxb3rJt03Uki7kcx99UtvnS2Q9+NiC4gcAEwVJFQcmh69mbv+fEx1/0+Z+wmzU1V+YBAtkObrzXWWNs2b53A4Yzd3HG9nTJbY/PmRAFn7RbLTnuMiMhyyn2dOt/tbODHIbqkECbR8FxWDZWVnntBX8l9ZGhaATlZRBAElrvukEAmacp5J+s1fc5pXfG8SdL4vA0QJjHLTpuzdmuonq/oCkVD4ozZ5putdfoj602TZM473bExDAhknKQ07YAzLZvkKhjpA7ScgI4boO8SdYWdCaQV+dQ9k36Ureme2SAKrh3iCHtIHn/913+dz3/+8/zcz/0cDzzwAO985ztZWFjgT//0T/fqJ/ZxFTFav/ZLv/RLvPvd7740sUwcstTvI49cUlGaEKYx0chNI3X7lLFQi1XaboA1orSL05Q4TTb/21rUMwUNo1Si73rUe92x3532G1VdwfYTztntqW+wFwMz9BGBvh+SkhCl8fBhK4kCM3kFhISmF2OKOqqUiQmiNMmOKYkJ04Sa9viIOkKWgjpaqGDH2c09SVPCJB6eE0VR+Mk3vZH/+1d+iR96zY+TiIx5NaZpSpjGBHE0Np8wiPLFE9G2OE3w43hinqM0IYgjwpF5g+z3gs0I4yh3Gp230WhYnCZESfZfwtb27WPbGsNmhCSOLnpsBU1CVwWWuvawLlFUdBJFxfdtghFyKksitZxMnMZsWD5euEVot68FRZSISCjlwVAkOm6IKgmsmR5fPdfhm+d7rPY8HmxYfHWpw5fPdFjquNwwW2ChqF+UKrThm6RCxFxexwkj2m4wdv6iJCHYdk41WWImJxMkMeuWO9ZtJ96cg6nrTUxpWONG4oP7RZQmpJvzIwiZ36oiQ88NaHvZ9VhSDFbsDm4cosoiUZKy3t8PcFwIUZyw0nOZK+jDHuYDAtkJbLqhgyEpezJvBU2amDeAlJRo+GzYul7KuoKhidhBSN3e8lM1RJV1rzeRGRgQSF0Wubdu0XL2PlOapikPNmx6XnRRPbWnEcj6ZtQxBYI4xOvXIb62XnT2rOBDVVWe+cxncuzYMX76p38aSZKI4xjX3W9I/2jBaP3apaLn+6SRTJ+YYhSjyRJlxUASBJTNdFaaxESdVcp6ie+ZP0LHi+m7CYoYUdAyEczhXBVgTBCjijJPXTjGmucTdHu0cjPU8lnP7INGhTCJKMhbghtNkZhNK0ikWKFPVbtAEc02pGlK23eQBZm8qHGyODvRGq+q5XhqZYam2aMRqNwoi0iiQFnROVGsEScJgiBQVvVdfumxhxktjyxIhEmMJsmcKNaI0mTY2xgywct1xRn8OB47P4IgcCxfZV4vTJy3w7kyFdWguE2UsmAUycnqhJp5Ts+jihKGND5vM1oOSRDRJGlMcDOYN1kQx7w4C4rGyWINAHWkyH4vx3a8WOGHDkl8Z8Vmw/RZKGpZ5GbmCNr6A5RGXsjCIOBbn/gky+fOkJ8/xOLLX8XBan7qegNQBJGmb3FdcZa6mVmtLBY18oZMzwupW31kUUBXJEq6PFRkX0xNlheHrDhdyqpBWTWoKDkEKxj2nhcFgQW9RFHRyW9Txh/IldAlBdNhrAayquZQRAldlCfW2/fUFunaES0rRCqKGIpEQdY4bFSRBHGsB3he0XjKzDxtOyDwBbpS1lNZEAROWw2eUFygrCuc67gcKhs7dszZRyaUsYKIg6Usw3K4rLPS8zjbsekLbQw5czhQJfmK582Q1anzpory0GVj1H1BEkRurMzS1DwiX9zqhS1J6KKCFXlo0nhXr7wqc7hicP+Gxb0bFrWcirhDV7DLPV9uGDGbV1nfJIODGuGdMLre3DigEVmU1Rx5RccNG2hxRFO6tjJYe0YeTdPkJS95Cffccw9JkvDyl7+c3/md3+Hw4UnPt3089lC3bYqKiiqKY0X9JWVLsJK4fWKni370aRh6npKWbolo2Ew5y9PtCApagWMzNZrdLn07M1yu5bOb1nZrFlUWEX0RRRAwL4M8enGEGfmoqKSpMPXzoiBQkWT0nMwZQaa8eXMQBIGKatDybOb1wmPOnudCMGSFBaNAw7Wo6fmpCmTIXg5yU+4+hqxM7QutSjIzO/SRnpkyP5IgTt0uCgJVbVJEFacpQRyT1yZr/EqqvtlfdlvEcA/HdrJapmultO2ADbKHiaoVKat5sNukhRpRGPIbb30L99z51eFnv3X7p/mZ//6HQwI5ut4gi7YNjMgHBDIjazCT27meES5MINfdPlESo22K0g4WFNzYpL7puzpfyFTgZXHyfMuixJxRoKTEEyKasjK5/2DeSko6IcYoKtNf0IqKTqGs0bS3RDRVLUczsDljNzmRn6PjxjQsn0OVyxPWPR6w2vfQRwRauiJxuKzzrXqDZd/kSdWZzCXgKs5bRVfGAgqjMCSFIwWFrhyOiWjyikbLt6lpky1h86rME+bz3N+wuXG+cEHLnItFZmfkokoSC0WF8z3vkgnkfd06dupTreaQ4xTB6YGWB6e1J8e4V9jTmsdDhw7R6XRotVrcdNNNvPCFL9yve7yGsGx16O5h3+dRrDk2uqSMFfX727waws4qUr6CqGW2NdN68+4GqVBjRknJJQ6WHw1J51SkkKYSjcsoMLairJA/joVd7U3SKEASBeYKBrlthdFBkjxuhDLbsWiUCK7QJunhhBeH1D2LnKxiTmkxmKYp3cCjF1y9LIoqiRyt5MhrMmGcsGH6JIKEaJRIfZsk9Ljj4x8bI44AD339Tr72yU9MXW+QrbFZtUA7sDhlN5gryhiKxLrpX3ZKDbI1su71JtwMcoq0Y+/5aRgV0Qw60eyGK+2F3fMjamqehmdyzm5hKBJLXfeSbYweL7D8iIYVTBAfSYJUddEFlQ0ruOrzdjm9sHOiQjd0hiKb7ShoCgdLOt8635sQAV0uum5I3fKpGErmelDWL2m9lXMiLg7EMi07ILbbEHkgiKT+Y7TDTLPZ5KlPfSqiKKKqKr/8y7/M8ePH+bu/+7u9+ol9XCHM0B/2et1LBHFEw3ExFBVZEpnNK0A69kBLIp8o8hFL49Y8oiBQyysoMhMEcnt9i6jlQTUoRh10JZ0gkEmaDlV9siQShQL90J+qlN0NXT9Tvjp+SGPbjTEaqd9KAgtSkBQFWUyH28MkRpWky7YJerSjouoYsjwsbB9gUCe4HTttTy+wfZptxW7bpxGZlmfT9T2eUlngeLE6bI8WJVvXnhtHFBQNQ1bpBs5ET+29GttMPjPiLukiQRRnZM2oEnsWid1jbWm6V6ZdP8/29ZaNIRuzIAjU1ALdwOGUeeUEMk1TVjfFCNOSfXlVomxIEwRyp3nTZIlaPvNT3U4goynzJonZPUMShQkisv2eARkRmcnJw5Z4fT9iRsuz5vXoRD16bkjnKtS+PRbQtAKSJJ3wr235FjExh0rGwzZvowRy9F4/ipImkzcy79Sum9VCT3shHOBASccOI5Yv4EJwMUjTlHMdF0XMSpiASyaQTd+ioEuUdIW+41Kvb4CsEffrpJKCIF47xvZ7Rh5f8IIX8OEPfxjT3FJDHjhwgFbr2gq1Pp6Rpikrdu/CO14i7DBTlhUUGTP0WPZapIqHMPJAs6wW55KQVcZFD34csuS08UQHSUqHBDJT13XGlNeCICAWZ1nxunT9DSQ5GRLINE1Zc7ucsZuYoYcuizh+QhDHU61fdjtH5+0e56wOp8wOsigMb4xm6GX9hd0uaZqSBD6BKBMLMWfsFqfMJnGaYIb+sKvJ4xGyKHFAL9EYURXHacIps8l929TJaZpyzupwb7c+oU4+7/S4p7sxobze8Czu6W6wsS2q3PRs7ulOKq+7gcu93TrnrM4YcVm1e/QCn2fNHuZIoUpJ0REQsMOAB/oNHtrsee1EAbNaniP5CqfMNvf1NoZWPEmasur0+UZr5YrHZigSohJxym4ia5klSjOQQM3R6q8izJWnnm9lcWZivTlRwJlNNWucJpsEMk/d73PabnCwpF02gXyo06Pu9bEjb7jeRlH3TTaiDrIajRHIVmBz2m7QCsbnsxM4rPhtRNUfi0BuX28DOFHAktsilB0kkSERGbg1LDudMWIeJjHLbhcbE1lO6Tkhph9TUQ2+2z9PQLhvGj4FUZyw3HMmoo5pmlL3TYIk3vN5i9Ld563rZTZOy06bMyPK68FxrbldOlEXSY1xgpi+G9O5gEr5UMnggaaFfYHM14UwGnUcxcUSSCcKOO906IcuvbSPGHXpuz6tXp8kdLELZUTt2qmhvyLyeNddd3Hu3DkAXvjCF/JjP/ZjPP3pT+c//If/wC/90i/x13/91/zIj/zInhzoPq4cKZm1yeUqkHdC2/fx4hhFEgmSCCcOCNOIhaK65UvXb+MJAoGwzQcwTfDikCCJssjLZgq75wV4cYgXh4SjalY1TyCI2HaXgi5S0GQsP6Jp+7hxiBMHBEmEKot4cUwQx7jxxZNHJwpoBTZ+HNH1fQ5uqgvXTR8nDHDiADcOSdIE4ogwlZCkFC8JcaOQOMnUv/P6ZJ3N4wkLuSJpyvBFIU4S3CjEicIxBWRCih1lhu7blZF2FGKGPt627V4U0g/Hvd1gs1Y19LG3bffjCCvysaNgTDFthj7fN3+U2mZ5QV5WKSgq/cDDjgKcKNj0HU2o6TnKis5TK4usO9kaipKEdddkVsvjx8mejC2vC1iRD2LCfFEjTKClLOAGHje88Ac48axnjO3/hOc8mye+7IUT680Kfbw4wE+i4XrLOu3ofKuzQsMzLzmltlDUKGgSD/ab2H6Cn8TD9TYKNw5xogBZZiyF7cUhdpQd0yiCJMKJAmIhGUthj623kXkLkghv8x4zW1CHqVAzCIf3jNH61GjzHuMlESVD2kphexF2FNBP+jSs4KLOweMJHTfkXMdFV8Zpghn52JGPKAh7Pm+jLhs7zVvbza4hb9u1l5AOnwGGIlDOKaSxxIPd7q7ZJ12RkCSRMy3nsk24B7WOo1HHUVyIQKZpyqrbzZwJSHFDDz1okU9dTD+hp9QIrzHnjisKjXzwgx/k0KFD/PzP/zwf+MAHeOUrX8nrX/96/vZv/xbHcfjc5z53SSbT+7i6EMguYjsMqEwRDVwu6raNRLZoKmoOQRDQRBlVllksiqx2bTw3ZXFmkZJeHhOR5CWVQ0YFQRDIySp6IRPR2F5CSc2TU6Uxpaam6hzIVQkDl6KkIm0ac1t+hC7nmMnlKSsGoiBktimRQMe3L7rPtRUFGJLKyeIcD7keeVVGkzMRkOdLHNAr5BUVIUkgjUmUMgfzeQq5TN0rCAKyKD5uU9YDFBWNg/kSZuhTUQ1USeZYIUsLj157kiByrFDFjcIJgcuRfJmqakwIXBZzmcJ6e5/nBaOAKkoUtrUPrGl5BAQMWRmqkKMkoaIZlLYpouf1Albgc6IwgyAIKGKmyi7KGrbg86TyAhuuhQBseCbHCzNcV5ihHdjD77ySsZ0sV+jaMVKqYCgS80WN9ShPZGoclVV+5U/+lK9+4jbWl5ZYPHqUH/zxH8cmGltv66aP44rM6WXyijK23oqyzmGjwoN2naKqc6isX1JRv6rFJGKIHGvoojxcb2PzoJcoKTpFeatVZMsOEJKst3VRHo+ezKh5FFHCkFQ0aasTzeh6GzUfLyo6h3NVZEFCl2UWihIbpo/lxNSMIoYij4noDEnhgFEmTVMKskZBJuup7MXMKVVagUVJLLPe9y84/scTzvc8/DCZ6GHeDWwEYFYroEnyJc8bUcKXPvoR1peWMeYP8PSXvpxaMZs3LUyHdHO3ecureeZywpjoRhJEDhhl/CTadB7Ifvtsr8uDnR5Prs3s2FGmllNZMz0OVvQLCsmmoeuGnO95HCjtHBkcEMhp660bumx4fWb1ArlIxYvbaGabYq6EGEn05RkqioaxS2ephxtXRB6f/vSn84d/+If8wA/8AF/4whd44hOfyDvf+U6e9axn7dXx7WPPIeDEARX2htykacqaYw3NwSVBZEbdekOSJZF52eO8IBCJVVRh/OacqZO3HqyjrQz9AGRFnVjwldIC3tr9WV9pSR52wbD8iDSUkVRxc6QCSQzdwL/oN8q272BIMoqgoYoRkiggiVs3xsAXmVEV0tiDNCWRVYqayqyeHUM3cDdr/h7fDyFBEDiUK/Pt9hqVzXvxToS6qGgTFjwwUC1P7yOtG5PnVxEl5o3JiK8sihPbnSigquYmvr+iGiQwjEb2ApeyqqPLCjaZwvppM4vc261zsljj+tIsoiByJF9lzelPdFi51LEZssKTajXuq2flP4YisVApcr6Xw+95zM0qvPA1rx37jMbW9w+MxNdNn8AXqaqT623BKNMJHM5aLW4sLV40gRwYNR8q5vF8YWK9DcewzQFh0AKwZQdEgYiijj8AFVEau2eMtjIcrLdRSIJIdWT/0VaGvidQntJ7fDvBHbTEcwII0oRYCDjf8zhaNS5o7Px4gBvGtJyAk7UcTSccEsg4TWj4FrnNfuqXOm8FFH7jX4w7Btz16U/y9t/9/yhXVdJ0vHxgt3kTRl4GB8jLGvmR9VDRFebjHA/06xiSwnWV4lQCKYsCqiRxumlTOiTv2jpzO+Ik5b66ybrpZ0bnuzhsTCOQhiqyZLfQpWw8BUlBNpvEsopolKilEa5a5lhNRb2GyOMVpa3f+MY38rrXvY5f+IVf4M/+7M9417vexU/8xE/wrne9i8985jN0Opff4WMfe4+eHxLFKW1v70QzXhyxYTsUp9ywt3bqslDQkCR5R1XoKC6kwhZUAwFhTH1Wy6vDFPZARKPJEm6QpTKmtZHbjiRNaPkOhqwSRAkjGZcJVWgYZAtfFOUxjzgvilg0Li7K+VjHjJZDlaSLOvcPN5w4ZGEK0SwqGqokDdNcbhyxsK0E4VCuzLNmD3NDaQ5x8+E1pxcI90hhXjYyQctAUJBTZRZrNYIwZK3dvaAyeHsrw2nrraIYdEKb825WH3gxKeyGb+JEAXlZm7redkNBk69ZFXYUiJyzOthhSMPa3RT98YK2HRDFKTN5jcWihhvGnO959EIXb7NjzzRcaN6mOgZ87U7u+tgHWDn3ENbGOfz6aaJ+ndjtkWwT3V2OCvtgPk9Bl7i/12C5t3NqumLINJ2AUy37ktLXZ9sOLTukasis9Dy8C1x721PYp3ttrMgfRlET38r62lcOQeQSGLMslgvMFvbGTmivcEXkURRFfu7nfo4vfvGLvPOd7+Ttb387t9xyC47j8N/+23/jxhtv5I//+I/36lj3cYVoWCFemNIJ3Kxmbw9gBj51y9/xTS0OfYgi9GJ17IE22hljFGmakqbpVAI57P0ra4hGicjpDD8DkwRSk8CPwIsuru7RjgLcKMCQZNwwHtauDL5/9Ma40XNxoxhRVtA2xz7Yb3va9PEKTZI5lCvR9Gy8Ked/pxv01d6eJAkC0yOhqiRTVQ3szT7aAoyltmHTo1AvjEUwyqqOJknDcV7JscqiwIGShj1yzeeLJeZyEn6/w8YuZGqw/3YCuX29CYJAVc2z4rT5ZmeJs3aTQi5FlYWpBNKNQ1acDiVF33G9XWhs2wlkvI1sp2lKGsckUUQax6gi4y9sO7x0jnY0GSWQThDtekwDIlLRNZqOR8u3We55j3vbnjRNOd/zyG9GiAc9zN0w5t5WeyLaN/o5mPKiPTJvG8vLUz9rr55FSSO6kYDtuPj1s7in78Jb+haR2STdrNMfnbdRAnmha+9ooQRyyCmzxWrfm7q/IAjM5TXOtF1Wuhenvm5aPqeaNotFnaPVHKLAJRFIxJjvtutIqTJ89oXddeTcDKQxSCquVOBwRR8a+F8r2DM56OLiIidOnOAVr3gFr3rVq4bbfX//Te5aQZIk2F6yWdAe7mhyfClYt23ankc/XON4ucSB3JYi1I0DVjpLKKHLEb2ItPlAW+rZfKPZZqGgcawwM7wZhUnMqtsF4KBRydKQBY110+Xudp2iLnGyNIMmKYhGidhus2q38dKYA0YZQ1KHKexzZgecmJpaoBpqOFHIhXRqVhjQ8h0WjCJ9L0JIIm77i7/k3LnTHDx6nFe+5g1oisJCUeVUq8eDtsXxisOgntxPImRR3NHo/PGI60uzKKLIfd0GeUWlohpZcbndxYkCjuar5EfI9ordoxe4HM6Xx8jdhmvS8GwOGMVhShkyq50112ROz7NgFIfbe4HLip35EB7Ob12TmZK6zmKutCPJnzcK1D0TN5bRpJ2N60ehiBKLRokVu8NGbF3x2Ko5le822rT6XWb1AjUtj5HLU3Nd1tw+S27AsVJxYr2tuT0MSWVRLw0J5E7rDbIIbJR4hGnCuttHFSXCUKbf0ThayiPJKd3Qpr1pUdSOHdw4mLrezjkxT9JyGGyVobR8m05gM6sVqKi5YQr7bK/HOafJiXKZGVEm9S0iu4vlW9Qjj4KkMC/riFqRWb3EspdyV6PF4VKew7nKkLj7cciq10MWJA4aZSRRZKGocb7n8K3mBjN5hRPF2rD2Mk4TVt0eURpzUC+jSQqzeRU7UjljNcgJBt25wnBcj0eYfkTPC5kbiXSVdAU/DrlrtU1ghzyhkjI7EpE3Q4+6b1KQNRb00pBAnuvaY/M2f/jQ1N88cGCeWSWk66Z8p9unlFpcp+fRFYOwvUxgNmnoORLV4KBRGc5bI/V5qNtCUVOeUK5hjNTHb3h9rMhnXitSVHSOFUucM/ssW10EocqBojaRwlYkkRlD4b66haFIu0b73DDm3rpFTt3KPh2pGCx3XVZ6HofL+q4lEKIgIKsBkgQPdtsUdYnjmo4QOIhGmdjpsqyWSLHR1QrutdWdcO+seuI45itf+crEdk3bf5BeKwjjBCeM8cIYO7pyX7M0TVmx+miySDd0Odc3xyIibhzSsdtYQkK6aVsjSyKVnIgTeyz1Laxg6ziCJFPFWpE/VNGJgkAlJxMJIecti5ab1cRIuRJxmtL3evRCdyyyOJNTQIrZcC0arkMYCnT8C6fql6wOThQSJik92+X//Zdv59b/9Ot8/n/9OR/49d/gN976FqIwRJMl8pJLPwnxhYh4M7/tRCFV1UASr603xEcSoiByXXGW75s/hixIbLgmUZrQC1zavjN2HaZpSi9wafrOhGK6H/o0PXviurWjgKZn09/m5WZHIU3foRe4Y1EGOwpYdfoczVWGKeftKCoaAgJW6DOnFy56Puf0PH4S78nYdFnEUAVWHRM7ysamFGfRhRBDdOkEztT11gtdzMgbqtwvZr15SUReVqlpeVRJxpds7uo/yEeWvsU/bJyi5dsYkkJZNTAjb9f11nTHa9bsyKcTOmP7FzQZQxNoWB1OnbsPf+1+wtYyqdvDjTy6oYsZuiShT2w1ENpn0bv30+mvcbbbHUvDe0mEGXpYkTd0kZBEgUpeyix4+ha9kQBGmMRYkYcZenib9xhBEDhYyNFJLFZt66KjTo9VNDfLEMRtxCoRIyQlpuu7LJnmWOmBF4d0Awcr2qovF5OY73zqY3z2j/+Qv/3Ah7Bsix/4J8/hid/z5LHvfeIzn86PvOEWjAM3UFk4TFpdoFk6glM+DKQIgkgQ+XSbZ+n01obRfUEQqOZkEiFk3bLYsLfmLU1TrCjzNR7sL4sSBwsF6lGLjuuzZk6vhdcViZwq8911k547PWMVxQkP1C28KKGkb8XgFEnkSMW4qAikF4c0AotDxfzw+dZs1xFEGdKUWJDoIqGoydTMzSONPYs8vvjFL+aWW27hn/2zf8aNN964V1+7j71EmhWtH60a9ANvLFJzOXDjkA3H4WihzAFyWG5K3QqGvXmLgsJBUSVXmkMeKSIuawZPnZmj5UT0nBRdynph5ySVRT2LpORG3iBzssqTqrNsWB6+L2ApEXnFQBZFZuOEtDBLSR5Xzd5QmaEg63ieSN+L6YUeizvU6UBmJdPyHa4rVHHDmC/+9ce59847x/a5+6tf4Y6Pf4wXvPrVVJOIE4UZbqzMDKMaQRKxkLuyc/pYRVXL8czaIe5sLhMmCYfzFfw4GlMbC4LA4XyZqpajtk2dfMAokpMVqur49tlNclfe1uqspuVIizWiJMaKgqFopaoZHC/WmN/l2s9vilnavntJXYLKqk5B1pAEgTm9cMVju6FapuME1DZFCYJWIE1Tal6Pp1SPYHuMrbeSnKlZVVG+rPUmCAK6pHAwV0GXFNZMFy3RkVN12H94QS8RJNGO6030Elp2MIzczWoFdEmhNDI/aRwzF1kEXgc/UqmnEvM5GckoUVM15CjAEGVUUSFyOsR2l5LT4QmI2KnChpznYK2GImc9kg8aFSRBHKvDKyoaT56ZpWH5WE5KXo4xFCnrgqWXJ3okq7LC8WKRMLC4e6PPDXN5curjz6c1TlJWex5FbZq3Y5+jhQpVJY/vC2M9zMuqwVFm0Dd7yYdBMNFK88ztn+Hf/8Yv8u//y3/gC5/9Miv1NosnTvJjr3kDspL9XiGCp+QMGpZPIOjEBihOC81us6DkiDwH3eqQVnQEUUSRZG6s1li3XJJQoutt9TCf14qUZH0sym9ICkVdxtAzgeUaTI1AFjWZthNw53KXgyWdg2Wdsi7jRwkNy+dcx8UKIhaLk/msAYG8UASyHdhEacycVuRJ1VnWun2c1gZOqUAusAn1GW4oz3CkqlHVDNpXqTvc5WLPVsff//3fs7KywtOe9jRe8IIX8H3f9308+9nP5nnPex7VanWvfmYfVwBREum7MZab0PRtrk9nd7QuuBiYgU/XD6mpBXJqHleLqZs+G6bPQlFDCGxmVQO1OD/2OUkQWciVqGkJ66bPhhlQNWSiFMRUISXzGCtpW6q3GS1PRc1t9cLOq8iyRiEO0dTC5OJXdIpVnbrp0XMieoFPsEuPayvykTf7IdftgOWz56but760RBqHSGnCgfI8i/mMhAzruvZT1jtClxUO5cqcNps7kreyakytRSwo2tQyi5yskJMnzbNFQciUxbkidhhgkhGKNE1ZNIpTFdBbnxWZ1wv4cUTxEuZTFiUWc0VW7C5zU3w+L3VsczmDY6Uqg0CbKCuIuSqS2eCQphPo6th6k0WJOW3yvE5bb4Pe87UdvOMm1htZxHBa/2LYWm/9do/OpsCtllcnVOWxZxM0TiPabY7lcnhKha5QomvkWKwW0ASB0VUqFWdJQhfVMTHsJp7r0GitcN6zOHjgEKqqMTuld7GwSeBn1PxET+XtJH2Aw7kZGqLJmtOhYc1wbObxRx67bogdxBwojZPHXujSDz1qWoGaJgzrXAcEUhVl5vSta2+aMOb0d77Dpz/1JV71pjfysre+A2FKEwVBEKiICt/47N9y5tRZqgcP8aOveTX5QpVKdy1r1WnWCUlRKgcQRImymqNUNSZ6YRcVfWr/bENSUZSYsmLQsoMdCeRMTiWME9ZMj5WuS9lQON9z6bkR18/lOVja2bHkQgQyTGLW3B6FzZewGS1PIeew1klpeSkxMUm+xFNq1Syyfw1iz/Jr/+pf/Ss2NjY4ffo0P/uzPwvA//yf/5Pbbrttr35iH1cIRRQoGRJNK6Lre1esgl13baIoRdus9xj40g168wZ2GwQJcYeG9oOarDhJWO37zOUUDpZ1Dpd15vMabXe8NeB2EY2nlCCKSKOd62rnizoIAo4f7zrepmcjCtnNq+OElA9Mr81ZPHqUNI4QFQNBUodiGS+O0EWF/A5j3UeGOSNPijDRimw73Ci87F7SZujT8hxOFGd4Ru0wT5s5iBuF2GGAFQXUtBzqBbr/zGg55o0C+iVaLs1q+antCy8HoiiwUNBwR8QuSmkWUVZJPXNivQ2EHmEQ8L//8kO8/7/9Dp/98F8ShZtpu4tQYY/9/iX2ngco68qOKuzYM/FWvk3cryPnq6i1Y1QPnmB+tkaINFWFLYgikpZHqS6iLlyPUZljXk8J+i1Wls8S+Lt3hrkUFbYgCFS1PJLq85Xz9celcGal69LdlqpN0pTzThdNkoep7Aup53cSxnS6Nk2hSrwD9QjDkN9461t49//zq/zvP/sTPvxff513vf2tOImCOnscUS+RxjHB2v2ErZXhS3sUhnzzbz/O7f/z97n9gx+iae5cpmTICi3fpmoo1PJqFoHcIYWtSCJzeY25QrbOZnIKJV2m44REF7g+dkth90IXLwmHllZpEpNYLeaLBmrishYXKBTylPVr9wVmT4/s29/+Nl/+8pf5kR/5EW6++ea9/Op97BHmChqBD7af1T1e6sNxgCRNWDH7KII85qg/eKDVTZ+6GbKwy9vZALoi8bQDOQ6Wt/ZNkhRRzIxqZ3LqWK/QgQ9k21UohQnVwIUpb5gDlHUFJ4hw9el1I0EcsWx3KSo6cZJi+zEv+6ev5p7PfprT37hruN9Tnvu9/PCrfgICi5gURVFQN9UyThwyfwn1cY9XFGSNGc0YmofvhF7oIQkCfhxdUpvHpmcjixLPmD00jP7N6nmeNnOAb7dXCZOEG8sX7v4zo+V2Pb6dUFZ1crKKEwVTfRwvFRVDyTo3RQmqLCKqBQRFIzJbSIXZsfW2YfrUNIF3vf2tY1GfL37iE/zKn7wXWVHGfCBHI5A7YXS9jUYgd8Oo7+rg37Hbw1v+LmkSocweR64eQNqMAA40CaORrO31dgCioqPWjiHpRRak86z3OqyspBw+fBx1l7Ztoz6QoxHIaVBEiapucNZusdKf5Vjl8VOG4kcxy12PJE2H0WzI2kd2Q2ciwjvq37nasbj3M5+kvrLM4tGjzB48OPU3Ttx4Y9bpzPRZLGoTLh2f/8TfTEQsT339Lv7uL/+KV/7UG9BqR0mTiMRuE5t1RL1AqhUnUuRfv/2T/MIfv4fZ4mSUWRMVOoGNGwfUctpwDDtFICG7hgb+p5ossdrLopGHK8ZE3+9RTItAqrLIqtsdE/ckgQOBh2iUmdNcHKnMkYoxtVvNtYI9I49f+cpXeO1rX8vc3Bye53HjjTfyvve9j2PHjvFDP/RDe/Uz1yyWlpZ417veRa/X4y1veQsvfvGLH+lDmope6JKQUrcNzNAbU65eCuwo4MFuCz8Zvwm7cUgjMBGkmCCBZlrgYJISpTF130QVJea1zKg1ihMatotiRASiQJxqQyVoKqSkskcsu9TNiPliDkkUSNOUhm8SiBGSItMOBITWBrV8lYZv4kYhc3pxzKTYSXyWez2eoE9PQbZ9By+OmNFy2H5MN3BI5JCf/cM/4Csf/yTdtfNcd/I6vu/mV7AamCh2By0RMVQVRRTw4pAzZosnluenfv8+tjAwD/9WZ3VoVL/m9LGigEO5EjlZJUxiJEHkoFHiS/UzPKkyP1Yz1/YdWr5NTcuPdW45bbbIyyrPrR2dSAMvGEWeXFngjNmhdBEuA4IgIF1GSYcsShzKlTllNsnJ6sTYBmh4Ft3AZcEo7jo2TRaZK6ic7faJxICSolPIVYn6GySBQ6BoNAITUZUIA4VPfOijEw/fQa3uC1/zWsIkpu73SRUgUIcEUpYE6p5JkibM66VhHe/oehMkdYxA7rTeOoGNhYcoq1g+JJ5JrnkvvdjHKS8yV1mgPJI6tiOfVmQhqQp+IA8JpJ9ENDwTQ1aG6XhBFIlzFbqRjxytElodlpcSjhy5DkFVqXv9zbKD4vBeEqcJdd8kkhOERB0SSF0Whz2a57Xi8CUlr2j04xafXznLT5WePFY/+lhGxwlRJJjNa/Q2rZpmCwpnnSZN30KVxssWBvOWCvD7P/uvxl60Tzzz6Zy86amc+tZ3h9ue9Nzn8sSXvRBB9ol9dUggUyEdztv6ysrUY2udXx7OmzZ3gnoc4jttFtpL/OOXvjOVcH7uIx/lJa9/HRU9I4v90GNWK5CXNdLN1qF5WaO22VHmQgRygLwqc7CsXzaBLOYS+qFHlMT0Aod5vURqd0CUSQKX86mIkAddHf/OlmdzMnfttCjcM/J422238ba3vY1WqzXc9rWvfY1Wq/WYJ49JkvCiF72IN7zhDTzvec/jTW96E5/73Od48pOffOEPP4xI05R+6CCI0HYFWq7L8ct8sbbCgAe7Tea1yth2O/Jp+Cb50GdOibFklQ3TR9Nimr6JIalU1RwyEm0noFqQsQWHhh8zq+eHD303CmkGNrIao8s6bSfIUgdpTCfI7EKO5WqgKjT6Forr0Y0c+qFHTlbHHmZe6rHuWmy4FkmajKls0zTl/n6D/GYE1o0ygYWZmsxpRX7sDa/PPCMVEZeQhm+iuR1qqc6ckT30zcAnSOJ9f8eLxIyWQxNlvDhEESUank0ncCkqGjlZxQx9ZrU8J0s17u3VWbV7lCpbBKsXeJyzusiCxIyWy4r5PYsZLcezZ4/sGPE7lK9QUQ3ye2BRtRvmjQKnzBZ+HE2MbYCm57Dm9jG2iUm2jw2yyN236x59LJI0pWSUoL9O4vaxhRIN36Sk6Bws5Gjs8PBdX1oCMoVnK7CRBJEjOYO+m7JhBhRzWQF/nCaUFGNIHrevtyDIUthJmtANp6+3buDS8E2O5WuIoUdz6V4KUkw8e4CmqmLEIaNVqlbks+H1mdOKzOVzwwikqGTrrZTozKj5IRl04oA2MVq5xjwdmo0lluOYytGjw7GVFWN4vv04Go7taE7H8QTqpk8lLw3HVpC1IXmM0wRNEvlyY4kfsY5ytDRZU/tYxFrfQ5dlqps1dj0vpBVYrHgdeqFDKdTHyONg3k5/8nNjxBHg9Ne/ycv/5T/jh57//bR7PgdOPoHv+bGXshL0MOKYQwWDjp3V4Ob1dDhvswcPTD22/5+9/46X9azL/fH3/fQyfVbdfe8UQkIMJQGBIALSQjGRAGI8FAGF2PB40MMR2/Fw0F+OHLEgoih6bIgaQgKKFEW+GAyEEiCk7b73qtNnnt5+f8ysWTOr7OyydrID63q98nplP+uZZ5577va57/tzXdeuA3uGqQclW8bJlemGPWzfZf7gtzf8THvh5DAHshX326Qhq9iKji6p1EOHqUGe5iMZQN5Tq4Ea0Uv6qgg5IaG7bYRmEXgtloVFVY3wk5DcgEiUZCnt6NQpGo80tix4LJfLLCwsjF1L0xTpMXSM5/v9yjGMzY9AoihCVcePer/4xS9SLBb59V//dQCOHDnCX/3VX/Gud73r/L3sWaBvBZijrKtEocS82yNM4ofN/9oIc06bkppf56aSVwx2mCWEfxJblbDyFkvdgNiDKb2IIcuoQqYXJJRNlYurNsuBjDTwtl6BpWjstIqkWUZVy3HvQhc/TtBlmUk9R5Ql/YToqWnmTvrUuz6mZZIzjXXeuZNGjiDM8KIEL47Ggod26NMOPPbk+qSujh9T1ixKcj9/MaeuHs2IVDBrFJF6bVKhkBvko+iywr58ZTvf8TShyQo7rALHei2mzByzVp6SZlAaiHEHScKslUeTFa6d3sedS0dJB8Lx0GdSS0IM2cyLXo8J0+aK0szDHnGf78AR+gSYCcOiHfrryraCaTO3IcN6bdkA8prMhGGjRoKqbiPJGkIxSZwG+VyVHWafHW1rKnsP7N3wnWb27AH6/WraKCAhyGk6lpKx0A1oOxkl3UKRpbF+qIrx/iZrEku9gKYbY2ob97eKbmPIKjkENB8iJyUEud2oVoW9uhgLlgEKqsFuq7JpfzOVcY/knKIzaxZRhUzRnkBOIpa8Hs3FZapTE2iyPMa8NmSVKT1PSkZe08mrgsVuQLMXkzf66QmjRDdZSMxYeTpBxOFW57sieHTDmLobUR7Yfk7ndeI04cv1GpKssde2Nq23by0sb/jMYLnJ89/+46jFGaBPEknkfpvKqRpaPmWxG9LxMia0HKosc+nLr+fOT316bCfxkqufwgtecSPKYCOi2YspGHkKE/sotJeYnCht+P37D+wfCokrqsZuqzIsgyGrdCKPMI2HSgKPRAA5lVf5etvDSHVKhoQiSxihT5YmZFkKaMwUpri0mqM4MmbIAzLnhYQti+xuvPFGPvOZz+C6/UTVLMu4++67mZ3deCVxoSBNU26//Xauu+46bNvmxhtv3PC+v/7rv2bv3r0YhsHOnTv5wAc+MPzb8ePHueSSS4b/vvTSSzm+ScLwo42iajJrlbAUnZYfnpXeY5wmLDgOFnkqxnhemC4rzBgFSkmKbBWHOVlZJhCRTkntb7v7ccxMwURXZHbZRXZYhbE8J0kIdlgFdtn9Z8wWDHp+jBCCqp5jxiiiSjKybjJlK2iEJKFCXrLXBRBF1WTGKBEl/dzEUSx4XWyln2OVZRltP6ak9yenwmCVvZIcniUSIlIpCgmhaBgDolCUpafUDdzGekwZedIsI80yJo0ce3JlDFklSGI0WR7mG04aOfbmyjRGdDoLmsHeXJmCalAPXIqawRNOI3B8JLHTKhIk8VjZRlHRLfbmxkXEYbxsKxBCcOlkkSzSySsGQpJR8lWyOECJfWbN4jAIff4rXsFlV18z9sxhri4D5rVRYNLI90WKBzmQiiRIQp2SYo8Famv72yiJ5pT9TbORa0fJIoepHfsoTc1CqqGlJvYaBrspa+v6W1GDO//xVu547x/x5Y/eMST9QD8vsSqZfPW2j/Ph932AL3/5ASZsldRpErccyqq9biyZNPJMGwVkIQ1zIDVFJg4UinJuuNO6grJmc0lxgvvqjYe1RvxOQMuLSbNszMVE1iJkJcXAPGW97d67f8Nn7j1wKUp+cvhvVZKZMYpU9b46xoqQuEAijXTKqo2mafzyn/4Zb/mN/8X1b/5x3vIb/4v/+Wd/ga7p6+qtYk5ilHbyrBc8l8u+5/Fj333FU5/G999ww9CJJo0UTGEN8wxVSawJFdoAANvASURBVCbJ0nXzX9XSHpZEM4qVADJKUk60vIcl0cTETORUdFkmCXWKsknmNPo5+4GLZ5R5wtQkBwqVdW3ybHKwzye2bLTds2cP73rXu/ixH/sx7rrrLv7yL/+SVqvFS17ykq36ivOCkydP8kd/9EfcfPPN5HK5YfA7is9//vO89rWv5Y//+I/5kR/5EW677TZuuukmdu7cyUte8hJs2x77nOu62PaFk5uwEQxVpu0FdKOA8hmuaHpRSDeKCMO+oOyErY2t0LI4AAGS2V+xr03qz+kKOV2lZJ5+86vaGifaPkGcjCX4C1lDCJmq5NFSC5sm9RuqjOskzDltDFkhrxr4ccSc26ao9QdFP07x45iyuX4HceV5y+0eC26MVbbQ5FVnnAttVXiho6gZFDWDXhSM2f91Ip+dVmm4Gy6E4EC+Ss13cOMIa4Tg5Q4sBB9fmj6r3fPziYpuDYgz4+98tigaCpoiM98JmC3oCCOPEDJRt4Zsru6MqZrGr/7Zh/jUP/wDRw4eYWr3bl766lcOdfQ2wlaTaLI0JagfI+4uo5R2oJZ3MKGoiBHG9qkcXKIw5P++9cfHdp++cMcq6WcjDcHLn/JkfvqX30arvcRJRWPnRHVTy1Q4PRJNUdM43O4w13PZcxokq8cqsixjru1hjtT5Sq7rzryNH4hT1tuzr7+Bz99++1h97L/yCVzxsh8mExKnyhxeCSAXuyEL3YAdSoqiqjzvla/a8P619TaZK2JUZnn7b/wi//Fv/0mtEzK7bz/Pvv6GYZufsDVqjMv4AMhC0Ard9bv/53EH0olDVEmiOuhvC402Zd/DsItEkY9q5h8z7kZbOuK+6EUv4tChQ9x55514nse1116LZV3Yk+ru3buHckJ/+Zd/ueE9v/u7v8uznvUs3vCGNwDwqle9iv/3//4fv/M7v8NLXvISrrrqKu68806azSblcpnbbruNl770pY9YGc4GpirTDATHuu3hke3pohv59PyEoqnihgk1xgPI1OuArCKNBAWjAeRcx+Pp+ypnxCTTFYnZgs7xpoeeGwkeJQkUBeKQqermE5qhSMSZykmnbwW3J1dCFTJRlgwDDy9KybL1zgoryOkKqSFxPMrQhYIuSwP/Y7FuB2kbp4YQgr25Ml9vzA2PGbMsI05TpszxibqgGVxemua+9hJeElHRTJIsoxV6XFmeWec9fSFgnDhz7sGjJkscqFh8ba6NKos+S1TRSdqLpJVdSCO7Qoqq8uIf/mG8aFV39b6P3srygAk7OrEOP7OFAaRfO8lnP/zX1OpddjzuCTznxgNIbMzC3ggbaQR+667/5F9vvZXnv+pVG/793ru/wt13fo2nPvOJNFqLzMkaO8r5cwogZUlGEvSPrr+Dg0cnTGh6MRMj9eElEU4cUtRMbLv/G25Wb+pgt/Bf//7vmLv/HqZnpnjqq95AF/2U7PkV6IrMpK1xou1xwvEpyBqGKqNK0oZzxGi9LfdCJq1JtKLLM5/+eNSZS9En948Feyte2GsDSEPWaIYue0dSYlZwvgLIduSiyauqByfaCywHMlOyhydsZkqFTZUALjRsWfDo+z6/93u/x/z8PJdddhnXXHMNpnlhbbOeLe68807e+MY3jl17znOew6/92q8B/QD0DW94A5dffjnVapV8Ps9NN910ymd2Oh06nc7w3/Pz81v+3msxugUvSwIvDnmo3eCaqV1ndOQ353QIIpjKafhJylLPZSnosDPfFxZO/C5CVojIqHttdFmhotmYqkzekFFj6KUOseszY+aHnTLJUha8LgAz5ipbMssyFrwugYjJpIwoTlEHR8aN0MEBinGAnqXDCe1Iu42pC3bm8hiyOhyE4kTgpz73thYxZHUoFt2NAu5v1vESCVidiP0kohm6WLLWH0iVlJwmMHSFE24LgSCn6mOOONs4PcxYBaI04VutRap6X/uxT3ZYHwzusIvkVJ1vNudZ8LtkGezNldlhXbj5aJOmzcFufViu+sCCcMLIjQWU7dCjFfpUdGtMvLwbBTQCl5LWd8mYzOsUDY2G77MUdJi2ixidBVKvh5TXCdOYeuCM9beKIfhfb/xxDn317uFzV6R7hCJTDxwAqro9nNDmOz73NRpULIVZqzDGNm6EDkES960MJWVdf5v2XX7rJ3+S+7553+ATt/KvH/8Ev/jHf0zRXPWMXnZdFoM2s4bB3Xd8gsXj/cD2aS97KQcPP7Th7/mtB+7jSUFvUw3B5VobM1+i0m1Ray/zrSRkKq8xtWYsqYW9QS61jSLJw0DkwUaTgiWxwy4M8+AKhsoDjSZP3TH9mJnUzxRNNyQjGwvUenHAYtBFl5U+I3lNve3O58cEuCNSLn3WVVx59T6qkwdQSxW0IGah67EcdJjNW2NSP3GasBz0cMOUvNyXpJktGJhxyvHIJQ1TiqpFmgpMVcFSBfXI2bDeHmp3sRWLiqKR1k/y75/6d5YXahR2zPCkl7yYCbuAMfDCrhEy3+2x6CfstHMEaYwTBxuKiW91ABmlCUt+BxCYsoqUxUzQY1HR+Ha7gVGZ5XJrfB5e8nr4SXTObnDnA1sWPL7//e/nz/7sz3jrW9/KPffcwwc/+EHuv/9+fvd3f5fXvva1W/U1jwqWlpaYnJwcuzY5OUm328XzPEzT5Dd/8zf5iZ/4CdrtNk94whNQlFP/tO95z3uGBJtRNJvNsw66R4PRjRA7PfQgQEiDHbPQ4eh8g7ni9Gnv3sRpwqGFOTRfQgdMIXAyj+OdFgu+S6FQQQpjZM2i123T9TvEskLRTFGEjOyFzFqCer2FKsuYdoIxCFydJGSx2wTAyMfYg4DMT2KWnCZRklDMLLyOj2ZoxFmC47Vw4xgrjMh6bWTFYEpKaURdlns+dhgxO3CB0aKQE4tLIIfssosUDAUlCvEJqXtdji4sYEomYmTHxQ9d2n6bVDEomiVwHXJCwsxC5ms9ZCTyhQlazeYZ1taFh4drP+cDFrAbnWP1OkmastsunfK3vEjOMef3NUoruqDRaDxi73o2v08+TKk7DfKqxmK3STv0ELkAacTZZcltsej2yOwi6sgkUfe6nHDahFYO3SqhkjGrxBz3HZpeB0nVOKBXyVrLIBn4sb+uv/3nP/7DWOAIA+mev/swz3z5S3H8NgCFKEGRNVRgQg6pdzosuimFYkppQN5Z6W9eEmMbMbpqIsOwvy31XO75+CdGAsc+Hvzy3fzb332Y61/5GgAmBHQTl+ONGh/6ld/g8Ne/Prz30//w9xx41tM3/C3taonF5WUmpiY3/PvU7CyaOYnsh6RBnXY3YMHXKZTSoYpDlMa4Xps4S8gZCaqiowCTUkI97LLohuSihMmBhFkhSZjr1Th0cp6Z/LlvhjwafexUyLKMb59oE6UZPqts3vnOIn63hZ9I5AZB30q9zXXb6IFPoVgd3u92Ful06+R1G0XOIVyXPODiUe82WfQdSqVsmMfX8T2WnCYFXWVvxaCgK2iyoNNN8UTfHGCnZZKlEss9l3onpBn3kOVsw3qrxSG6NskH/vs7+fY3V9nXn7/to/zC7/8+plVAAJMiox31WOr2sMKQgq5TyxaR9SIbHYTZQCxiOu2Ik65M1dI4lYKXDEwoCbVeyHGvy1ReRx7c78QhTreFIklECWhRgJ4kTMqCxSRFSiKiXhd/kN8bZylLvQZOHKJYISBwpJS6Vt/0+7cC1Wr14W9iC4PHTqfDD//wD/PTP/3Tw2tRFG2YQ/hYRLrGNWLl36Mrkf37N04c3gj/9b/+V970pjcN/z0/P89Tn/pUyuXyaVfeRjjVZ5OFGp0wZmKQSpAXKYe7DUJDpVo+ve9sBi5eXSNJdIStkwFlTaGlSIShxFKUUJIFUmkCXVYwlL5frqTn8JIUJJVd0zaLgY4qyRSs4vDIQElTKlp/h6BoFVEGTH0ty6joop9bqOT41kKPSO8LhxsyJJqKkiySSiBZFhIwJYPf7dJGJydr5HQFPU1ZiBOmizblQpHcSAKyrcmkdZcgU6llynClrWkKliphKDroNrGziCcydk1M4EsBUZpQLVWoVs6+zi4knEvbO1tUsgp2r8GRXpN91R0bWviNYqo6QZplj4og+5n+Pqmt85XaCXQzT1UTqFFA2cxjjOwwVnQJbIOykcMYKXvZVIgslYpmYQyCmQlJZz6FvKEQhRK1LKOSdpBUgW4UxvpbJgSLi4sbvtfCwgJqrsBKZodqFMgGu3N6ZjIpw0LPo57qGJqOrshIWYYhQ5ZE6EaebEACkoBJOaN3ssvJk3Mbfl9jqUY2ksJUVQT/8rHbxwJHgIe+9jWe+rIXc9k1V3Pfl1blXy69+hq+98YbCVOFy6+7gcv/5VPrGLnf/6ofRtI0UjlFOXw3ORHRs/ZRSzV03UCRJeQsQ1cy5DRFMwpkg2BGyTImlYwTHYdmqmOr/SNsNcvwkoDFRObx5QrSFog2Pxp9bDN0/Ihm5iMkQVE1KBhq/7TFhWKxiqbnyUZ25aqqREeR8NLVcTKNQ6SwjaEq2BO7Efk8K2dcRV2lqIAXwHKqMGH2NSRlM8eBssaEqbMzVxrOAWkGFWUgUTWYAyYmMxpuyFcXBW4Uodp5ssGGw2i9fe72fxoLHAEOf+0e7vr0Z7nuh39ktQwK9DoqPXRMzeIkIUJK2GNXNjxeN/KguCF1J0RCYTZ36h1IA9DsmLm2z3IsDXcgW16byDCwNRNFzxMt18lUBTpLmMVZdu+YplItD9Oosiyjogn0OKRsFelEAbZkXzDt55yCx69//esEQcDjHvc4Xve61/Hf//t/7+9oDX5YVVUpFi/cY6XTxY4dO1haWhq7trS0RKlUOqWsz6lQKBQoFAoPf+MWQpIk6m7EbC7BVPs+vEGccazb43Hl0xO47kYBcqYgRtKgLUXjivIULT+ivjhHnKbsUHR0SWKPXRne5wQxu0smtqZyQFvfARRJYt8G+ZeSEOy2S8N/V6x+R57KG0waeSb0HEG3SeK1UHL975swclR0eywnKy9JlHQNG3Wd7ISUquy1K0iMJ4dbisYeZbUMWRQgsoyiabJbz7PodbfzHc8R/fzHCiVtvRzIZvefjYD3o4GqbjFp5mgOBME3On6qGvaGYv0beWGXTJWCZjCr2jhxQqOZstxuMuO20UuzY/0NYHr37g3fa2r3noEQe2nd3yQh2GmXmDYK63IgR/2LR1HJUiw55MQmziJ79q0urP04wQsEenvjXTij0+Uvb/97bv/bj3D00BEqO3fxpBe+lG6ckWX93OQf/90/5N5P/RNLx4+ty+OU7Qp6ZRe7nBYoKQ2kMUeTGWP9nCSEYMYsMKnn1+VAThgmC45LJ4gpmRemz/DZouH05XmitO/6AhCKAE1S2GGV1t2fVw2+Z2JmzK6yEDfQo4h9lX2ohemx+3VZ5bLSFL0gZrEXcLDu8ISZArtKxpBwOApZiHVzgCL182u/39jJobpDw42oWDKyJMbq7e6ljSWDmmsWNGXdpjRhUXNCHC/BNjVOeA1SUvba1TG1gRVsxRF2K/LYZZUoqCZJ6JL6HdIoIJFk9lZ2cNXkzNhvIgaKIyvoRJvb8D4aOKel+9e+9jXe8pa3MDMzw9Oe9jQ+//nP89znPpe/+Iu/4P77739YmvtjBddeey2f+cxnxq596lOf4tprr32U3ujsoEqgSIx5vFqyxlyvhx9vbN23FjXfIQxX/axHUTJUClKEl8jU3Wis/le8T7di8J3KGdTcVW9eIQQoKlkwvsu91pvXjRIKuspyN6Q74tPrhgmHGw6W2l9Jb+bNm6UpceAgq/rQ0zojQ5e/syaURwNCCEq6ecoB+bEISUjsz1WI0nhLPK91RWLC1uiFMSVDpVIs4AuDpVqdNF7vPf3s62/g8mueOnbtoidfzWXPe/GWeWGncUDUOIFIEl78ih/i4idfPfb3FamgLOvvIPlRyiWTNlddcfGGz9u9fy+qqvJD/+VH+Llf/R+87k2v5cpdZR4/XcDSFAQQpBJXvvjlvOa//jzPe+WrxghAQpL7FoiGjezVmVBD0qwfHD2c7M5GXti6rNBNPBa7F9bkfa5I04y5rk9OV9lZNDBVmYVuwPFuP6XoVFgZJzuOy/LyMpJhIRc234AwFAlFEjxhOs++srlh4Phw0BWJiydzTOf7phGjdSlLgv0X7dvwcysap6NYIdFYmozjJciZzpzX5kivvs6newXnIuNztOnQDn10qd9OU69D4jTJQo/AmmGqXNxwTr2QcU47j6973et43eteR5Ik3H///dxzzz3cc889fPjDH+aXfumX6PV6vO997+M1r3nNVr3veYHjOH2mZxyTJAm9Xg8hxFBu5+d//ud52tOexm/+5m9y0003ceutt/LZz36Wz372s4/ym58ZhBBUcwZKKg1X1nlNZcnt0gmDh/W5jtKERc8hiAUFa/3gkqUpeTVD0m16a1jYfpRiagq2du5J53lDJqepnGz77Cwa6IqMYhYIug2yJEaMkH9GWaEdx8c2EhRZYq4TkNcV4gwO1x2SLKMweLfNWKFZGhPFMXqpiLaSyIJA+y6xL9vG2aGkmeywiiy43XVM8rPBhK0y3/HJsoySZRCXytSW5llqNpmemBgLwFeYsJ/76K0sHOvv0j31JS+n4adn5YVdNeDO2z82JLh83w9eT9ZdJPG6yLkqRm6SX//Qh7jjwx9h8dhx9hzYxwtvfAVCVqg5IRVLZX/VxlAkbnjNq/jkP3yUL3/hzuF3X3PtM3jZD79yXZmFEFQsFVvLc7zlc7Tp0PH6Qe4omzcKQz730VtZPH6cyYkCT3va5ejdBaYm9rPUSzb1VB7FWhZ22VZIpYhjTYf9FQvtMTbJb4ZOENMLYqYHx7A7iwZHmj0ONzvsyufgYdbEVVsjbs/T9HyUwh6mNpk/4iSl6YU8birHroJxTkf/qiQ4UDHRFMHxpkvF0odEn+fccANfuGNcMujyq68eapyuxSgLu+cl5E2NRb/DhJ7bNHXmbHcgH2q0mfMcHleukKUJUWseJAUUE8kqM5E7/+YFW40tyXmUZZl/+qd/4qKLLuJ//+//PbzebDaJN1gNX2i44oorqNVqw3/PzMyQy+WGjjlPetKTuP322/nlX/5lbrnlFvbu3ctHPvKRx6Ttop8EFPQM4csDnSyNMMk42e0yZZ16YnOikLYfogiZOEto+B6Wog3FY9PYJ85SYktDJsENGQaQvTDENjMagTx2RJdmGct+D4Fg0rCHHTHLMpZ9h4y+iPRoLko38jDNmKV2uurNq5k4EnR7dar5iTGBVS8JEWqIkDPqgwms4YTMGYLj3S6OJ9hVWH2nIIlJ5QBJgd5gs6Fqa0SRz2IGU6o0fE8BaA+zSt/GdzdWZIkW3O7Q0akT+fSikIpujgmIO1FIK/IoqeZYOoSfRDQCj5yqkdN08oaCE6boKsSmgqqndNt1JCPPZE4nJaMROqhCpqRZQ928NMtohg6qnhL6MrdvIouz4oUN0Et8FC3G6aX8xs03c3DEiu4zH/173v5r/xVTM1BLs4hUIpIynvnKlxEEElki4cQZYRCSNwWWnZBkMaChahrv/4e/4R//+m956OBB9h3Yz6tu+pGhg1eUJiz7DpaiDgWSdUVib1nHy1wO1yK6fr8fTuV04jDk19/4eh748ipB6P974hP4+V/7bziSxl2f+neWjs4ztWs3L331jRiGTit0ibJkzP4QwE0CJC1E+DJNJ0bWY1qhR8MNmSlceNJQZ4O6EyKEGI5lkhAUbEHaSmh7CYoUk9MVvCSkGwXkVX0osg3g+R0ifxHF1HHlvq1k1e5707dCF0NWsWSdpheyp2wxk9dY9Lto0uZzwGq25OZzgCQJLD1B0WKWnYzpnIEQAlXTeNsf/SGfvfUfWbj/AYrlPC+84eXIioITB7hxSEE1x5RF/DTqj/WyRNeDTElohM4p867PJoAs2TJRN2WpF1JVfJJeg55dphen7MrlyY9sqtR9hzBNmDRyw7x/gF4UgH7h6Eefc/BYq9UoFAocP34cec0k+v73v59LLrlkU9eWCwVHjhx52Hte+MIX8sIXvvD8v8x5RJZl1IIeTiaxwyiThCrLvRAJiXtbyzxxauaUnaAXBcy3fUSqk0QBx90GVT3H/kHwmLkd2knIYuiQV1OqZpGul7CcBXRjH18LEG5ETl31ke1GPkd6TRQhYSvacMJ044jjTos4SzFkZdiZgyTmuNumm3mo6uqR2oQhs5wleM4yimGPyUIsBV3qQY/9ioWuSDTcCE2RuGe5zpFug0tK47s1rcgdlq0gF4Y7kHHc42TsY4iAJOtrQspCGsp6bGMbmyGvGuzJlTjaazJt5plzOyx4XS7OV9k1ks+75Pc42muyN1dmv7qav1jzHR7q1pkx81xWnGI6p/NQzcEnYi72MXSZkt+l57kIIVDUmDmvhSGpWIo2bKNOHDDnt5ERzNjlh/XCDtOYRb+Dn0Yc/tS/jgWOAA/e/TU+/c+f4frXvhHZyIHrDvvbTrNMEsicaAc8aUeBWPE43G2RZumwbKqq8swbX8b0oGyj1q/1wOVQt05RM8ir+jC460Q+3axHLg+xA12/3z+/dPvfjwWOAN/+2jf57Kf+jf/v37/IsW+sssDv/uQneMeffIDFuIufRsMgGwaSYX6HXuyvjpNOSCkXcrLtM/0wwcJjAUmasdDxya05CVryO+h6hi6kYdpOO3GY89vsMIrsHAROWZZRa5zkeNhheuIi8oY+HCczJeSE1ySnGBRFnpm8xa6iQSNwOdxrYCnapnPAAclmJWw71RxwwusQyiGKYtDyI8qmRpKl1BKPA9c9h2e87EW4Rw9TX17ArMyyLCfUgx67rQrT8moeYSPol23WKGLJFm0/5lCnxS6rvM7hZRRnGkA6qUvJVoiilPn6EraeYzHyaAuDywqrGskr85sbh2NBdpKlLPkO6JVNv+ORxjnPen/8x3/Mu9/9btI05dJLL6XX6/G0pz2NpzzlKdx1111UKhdOYb/bIYTAVnRyqkJO1TD0vleo48Pxdg83Dk/p/Xui16HrZxhSSiYLSqo15gmbhC6mrFLULGxFo6xrSCJmseOTN1SmLBVTUcc6pSH3dxUkIcZ28DRZpqAZpFk2tjOjSjJF1UCyJVpJnwBTc0KW3Axd0pDjCHNNDmJO0UnSFDNTKQ+OsP0owY0zZu08+TWEF1NWh2Wr6qtH2FmQYAuVKTuPhMBPY1RJ3j623sZpYY9dZt7tUvcdCopOrKfrLN9yqsaEYZNb0yZtRWdCt4camGVLRZUlskymqFkYhWnynW+jpg5uaCCnGQXFQJMUFLHaPnVZIa8YSAgKmvawXtiKkMkpOlqm0J5f2PDedr2LkptYLcOgv+lCJZYFV+8qsKtkUgtS/PThy7YCS1GH2pejPiWmolHRLXRTwSiY3Lfk0PVjThzZWP/xG1/+xljgCPDQV77EP3/kVp72iuvQZGVsjJEQ/e8UYjhO+knI0V4TWza4dDJH3nhsLxg7foQTJkznV9tflCbMeW2qukVFWxWAlzWZsmqNeZ6ngYPudykZRQp2haq5Ok5KSUZBNUkTibylsrfcz2Ue1pukbDoHqOnqTtvDzQGGrDBZKHC47uOECZYqDeutYBQoTU9x8vhh5hcXMWamKKnWurnBUjTKan++Kg3a5Umnw9FOh4tLpzbPON0AMs0yGoHDhGEhlIwT8z1So4gS1ygVK0xZq+1+tGzmyO8tIcgpFxYx85x7wDve8Q5+8Rd/kZtvvpkgCEiShPe85z185StfoVQq8d73vncr3vM7Frfccgu33HILSbJxMvpWY0LPs8NePR6ezuskacaS57Pkeuwvbhw8xmnCScehpGmosoIbJlTNEmV9dSWapQl5o0DZXt3JKxkqLTdkXznH3tJ6QoQuK1xamFh3XZVkLilMjLH3oX+0sj9fIcsy7ov7IrMrOVlJmmdWjtYJdk/qeSa0HJLnkY3kQAosKoZKXhsfUAqq2fcQHnzvSs5jsx1Rymz2F/vvG2UJpqI+5nchtvHIwFBUnljdwUOdGku+w75ceag/mGUZcZYyaeSY0O11baqsm5S01TapyRJTOY2FbsYBewLSFC+3QD5qo+YnaAcStppn0hyf0DRJYa9VGV57/itewZ0fv4P7vvyl4T2jXtiSEEwpNv92662cuP/+Dcu195LHj+UZT+p5yopN0w3ZVTLZNej3p1u2FRRUg8cX10/IlqLyuMLk2HHrN+a7lGZ3bfh+srLxmFY/cQI5NtmR08YIIkIIZozi2Nizt5TnwUaLu5dPcsVUnstnzsyV60JD3QmRJDH229aDHrqkMKH3GfUr42QQKkxb5bFxMu4uk5cUKlMXoQzsMVdzxcGQcli6zIGqjTrID19bbysYnQP8dnd4/XTmACEEApn7l7pokjpWb0lphhmvw6LTI+qW2TFRxVJX22kUhtz90TuG+bvPvr7vhd2NNQ62m5Q1exggbobTCSD9JEKWJGxFJ/EbTJmCWgJZUuKJM7vJayNB4pqyrUAIsSX50luJLVk+SZLEH/7hHxIEwVlL13y34u1vfztvf/vbOXHiBLs3kdXYKiRBjzQOEGK1EcqSYEfRYHGhy6F2i/3F0oafdeKQxa5LXrfI6TI1Qjpe3GfJGipZEg79rNd2npyuULY2D7JOFXyd6jMTOZ0Hl3vkDYWZvM5JJ0etW0PfoB2uHXxWBsaGGyGEWOeFvfZ7q7aGT0hHVtAGnT1K0+HO5Da2cTooaiZPqu5kwe3yULfOgtsdEx02ZXVTwf6N2uRc2ycDJFlGrewiapwgjw9WjrYbbeg9v5ZQcyov7I18pEdx4Hu+h6ff8Oqxa1mW0fQipgo6u9csGM90DDid61M5nYsnUqKXvJyv/csnxkTRr3jq07j6eS/ga1/4j3XP2H/RPtIMFnvhhiSa0e+QJcEllRLfWKrzyWNH2Fu1H7MSXUma8WDNGfOyduOQo25jmFsKm4+TSeiSuW2EkUc2x+XmqrZGmmYsOSGXT+fIP8y4+nDXT+czFUtlV8nkWNNlMre6CJE1Cz0/wVR4grrbYLlnM5U3MVV5w3a9QhTbnc9xpNNhrjtwXzrHANJLQlaI2bHTwNQNqr7HnFFhMr+xffNjYUNiy/bef+u3fou77rqLJz/5ycP/ZmZmturx29gCRO1lYs+D0rjGoiwJDpRL3D2/zNVTMxQ3WADUPJ9uEFPJSxt6heaiLmSM+VlDX9NNkyVsbeuPeXKajCQESZr1WaHlPCe6NRbbPWYU9ay9eTdClmXk5RShK0OZnjhNsC+wo4RtXPiQhMQOu0hFt+jFIYqQ0GSZebfDoW6dAqe3AM9pMkVTxQkS8oaCZBaR5EWibo3idAmgH0CyPoAcxVov7LqfMq327eo28pEGuPIp38NTvu+ZXP6Dr6URZKhaMuxvTbdPSttXsc/Iv/5sIYRgV8nECWJ+8vf/iC/e/jFa8yfZf9F+nnNDXyLoPz5xO/d+eTyo/IFXvIJESJxsONz6//4BvzbPjr17N/T+hv44eeVUlbvnl7hz/gTft3PPUND5sYSO319UWKqMpcloisRxtwEbyI5tNE5qvRpJHGFMb5wjL0nwhJk8U/lHjkE8UzBouhFdPx5LKZDzE6i9OtXIoRW5LHUlpvI6X3gYoljJVEiJhmU+2wAyTGNOuE10WSENXLLAQTILiCBg50R5XXD9WMKWvfnrX/96LrroIu666y7e/e538x//8R9MT0/z3ve+l1e+cr30wjYeeUiqAY473BL3khAviSgoBhVTY8ntcl+9wdN2rhf6PdZpk6QZ7dgjh4Y+4hXacAKWe0tMyCnGSC5LkqXMO112Fqwxj89+DoiLIknDlW6Y9HXwvLSfdF3RxncsWqFHnKZUdGvIujNVGUmKmXd9Zu0cqmYwbQnqccDRlkPBggnDHvPm9ZMIJw7Jq/qYN+9C10MPU6Ysa2wAjdOETuxjIIjihHLeRpL6PszN0D0jT/BtbGMUhqKOyWPlVZ1R6bgg6TN8C6qBOXJflCY0Aw9b0ZjJG9y31CWPAqpOS9FQvBZq4FAy+icMTSekHblM2QaFkZ2lLMvoRH1LuoLa1/mbyussdQMONdtUbJWFTXykdx/Yywt/5LVglVnorva3XJBh5PT+caUkcOOIbuRT0sZZrqdTttGdvSRLaQT9/jYqJD86luyv2rhxynNuvBEnSIikkG4WUVJNfvnP/oLP/OUHmT/4AJUDF/H0V/8okqKQRREf+vm38u0vrR7b//vtH+OXP/hnwwBydJxUJJkDxRL31ZtcMTHBrPXIGj1sBWpOyGROw49STrR9TKNPipoyVsuy2Tg512wj2ieZMi2kNV7VndhHpBKKrLB7kOd4OvU2utuZkVEf6PWezhwAfQJVkMTsLOncv+RgJhKKLPXrLUsw7Qpy8ySVpEVbz3G83eOhQwc3/G0OHz5IkESD/M4QW8qfdQA5lVM50qsP/ezD7kk6WQJ+l0QyubhaGi6uHq5sFX3jHcpHE1smWDUzM8MrX/lKbrnlFj7/+c/zxS9+kSc96Uk885nP3Kqv2Ma5wi4jJEEa9MiyjHmvzVGnTjPqd9ZJy+LLC0v40bi8UpKmHGy2iUg46tZZ9Pt5KSs7kJEIuL9T51CUjgmndiKPE16TdtIjSldzOtuhz6FunSO9Jl4c4cV9GZLlwOGhzjJHew168apAtxdHHOk1OdSt0w5X/VejNMEVLod7dTqRh6SoKIpKVXjUow731JdY9HpjZVnyuxx169SD/pHEysraw+NbzSUOd8d9lZuRy1GnzpzTIIgj8rl+PlAjcFn2nG2yzDa2DIasjh1hL3pdHugsc8Jtj91X8x0e7CxzzGmSN2R0RcKPEzqRx4IssRC5+J3Fvg6koaLoKUd7Te5t1sbMANwkZM5vMee3cJN+fzNVmaIlM+e1+GZjicLsxqdHxT17kO0SiiwxndOoRx2+srxII3A4UDGHgscn3TYPdJZZ9Lpjnz+dso2OJY3A5WCnzpFuc9OxREgpByo2IND1jBNOi281lvvewKrKD9z0Wm5404+y71lXMe836MU+n/vorWOBI8C9d93F5z56K8CG42TJVPGilCOd1mZVecEiTlIWugElU2N3ySTOYj499xBOFI0FLZuNkz1vkXvbdU7q4+lJzcjlaK/OoU6T3UUdY1D/p1Nv3kib9JKYo73GGc0BR7pNDnbqpFLETN6g7cdj9dbRDCTNRAQdqmpIJ3HxSxs7JUnTZRb9Lqas4SYBBat/VF93QupuuOFnRjEqJH738gIn3AYV3SZLEnpOjYUs5aTfJjJylAcko9MpW2ONAcaFgPOmdnrNNddQqVS49957z9dXbOMMkUoKcZKRdBt99pvc12hckfGoGAbt0OeBxngAVfM86n7AhGFgK9oYY00IwbSpUlIUEjlHe8S5hbQvvzNhmWMaarosYysalqwSpwnt0Ofy0jRPKM+QZaBL6lhQpkgSlqxiKxr6SGK7LCQmTBNT0oaMUqGoKKnProKNJWu03XTMGcMcPmdcSHw2b1HUdLxgVRwc+gQDW9ExhAAEljHwvJZkcqqxHTxuY8tgyAqKJA8nWUPu22iuTY3QZYW8qvcleGSJ6byOE8RokoJlFDBVG9w2adBfOE2YBlXLJEskWl48DMpUScaQVAxpXAEhpynM5C3kTOHxz7tunUPNvisv4/tfdRNi8BlVkdmRt1BQ2FWwKBir44OtaBRUY4wte7plGw1OdFkZaMpqm44liiRRsRR2Fg2yVDBlm0ipTNON+17omoWWn0BLIvTAQ5UUFjfZWZ07ehRgw3FSkSU0oXKw1SDZAtegRxKdIMaLEixVRpUlUD06iUcQyA87TpJETIZ1SnaJIDPWjZNZJjNpGUzmVncYT7feVqAICVPWMGXttOcAW+nbyOqyMjCNkPCidFhvhpFDzpUhScjaC+zMmTz5uuvY+8Qnjf02l15zNd/78pdiyuowkG6EDrN5/YwDSEmNuKdxAmINAaRhDzmJMWQFkalUiyUsTTqjsl1o2LI3+p3f+R3uuecerrnmGq655houvvhiDh48eFoaitt4ZCAJiUYkmI1jNGDGKDCZ5YbHukIIiobO3Ys1LqtW0RSJXhDxn3MLxGnGtFWgktooa7w/81nKE4tlHHt6mANZMlRkVL6nMsMlhfzYqtZSNB5XnMJPInpxwBXlGXbaJZI05akTu1n0e2OdRZVkLipUyTLGBhpJCC6yCvzzp27jW3Mn2bVvL8983rWkvQY79TyTk3lqTjQUEjeACT1HSbPWaXhN6DkKkwZ1JxrLgSyqJraskbltlgB9QJaxFI1ddvExmfO0jQsTmqxgygpB0peAmjJzlHVzXX9bka9ZuV61NE62fXShcCA/RSpU4uXDRJ0lJD2HKatcWZmm7Ud0vWRIotEkZeiFPTqxK5LMxYUJdpoJ9V7Em37nD/n2v3yM+fu/wcRkmWe/+kfIFSfG3snC4gf2FNmz5mRv1swzadjr+tvplm0FBdXg8aUpZCFtOJYIsVqGHUWDhhuRRyMzM5puzFIvYCqno+Un2dNrkMUJepZu6v1tTu4gTtKBF/b4OAlQ0HRqTo9uGFAyNheUvtCw3AuHvvBRmtBJfJ4+uZuOl415mG80Tia9JuU05qm7r6SFPTZO2pLOLr3ClTOFsTzXM6k36C/KLy3029bo9VPNAfvzFZIsHb7r3rLFfUtdpqw8k3q/3hK7Cq1F4uZJJkszVHbsZf8f/Al33n4b3vI8u/b1c10zWRq2vYJqctJrkVcNZvMW8/CwR9hR2teTXI6bXFKaIAolak5I3m2gKzq704ymPcOeamm4ODrdsnlxb8PvfLSwZTPfD/3QD1EqlfjSl77Ehz70Ie6//34uueQSXv7yl2/VV2zjHKFIAvQci67HLt9FM6wxDTiAqmFyuN3mYKODqcjc8eBRHurWuXKiT7LZSDg1dtoossJUoW82vxJAJklG1R7P4VhBRkYvDnhCaZYddl/qQZYkHleapleP6IT+GOtUFhKseUwUhvzUq390zN7s32+7mv/26z9HlkYYmslMXhpaq80qCaoQqGLj3UJNVpjOy+tINIokE0Q+IsvQB9IqUZpgyMophWS3sY0zRVEzWfC6rGSTbda+Rq9bmkzV0mh4IWVTI7PLpN0l4sYJ1MIUspFHFhIVU0cS0RiJRhYbHz7JQiKnSch5iaWux+Oe/jS+9+qL0PJV9PJ4TnTbj8jpKhdV8yS98QlOnKK/nU7ZTuf66IQLfRmjlQCiYmlIA8HrfgBpoNgVotYcqdPi2dffwOdvH7e0e/xTn8pTrnvZmJXh2nEyp8sseCkLrvuYCR6jJO0Lgw9IGr04IEpjJo08lpIOx8mVAHK03rIkxls+zBf+/Ws0ul9mas9eLn/+i4fjZBCn7CpaFM31QdXp1tsKTtUm184B0A+ypJF3LZsKZVOhGyaUBrvgkmYh2WVSp0HcqaFPFdhRznHtDTcSJSlTeR1FHX9PVZKxZI2D3WUuL84ym9c3DSDjNKUe9DjptfCSkLxqUNVVWn5Es+Pgt9pMFvMkTgejXKKwRif0dMt2IWHLgsc9e/bw+te/nte//vVb9chtbDEkIZgqFkjqHnOLy+zatXudPIUiyViawl1zizhJRCB77MuXaHkJmpJgqusbcpbGCElZx8IWAqwN7geoBQ4X5SaGgeMKTEXl8cUpvlI/gZEop9zZ+9jffmQscAS498tf5guf+QLP/9FLQDPHvHmbbkzRTM6KhR24PWQSdH1FpicZE0jfxja2AgXV4LjTOuPPTRd0jjU9CnqGLMsoxRmixnGidn/3cVR3FU6PhQ39HMgKPeZ7LWqKzq7CDGLkaC2IE+Ik43FTFqoseGTUah8eFUthMqfR9KJhbtlKADmRKyM5daLmSXS7ss77+9nX30AiJBa74aZe2EIIdFnhwUaLyyrVjV7hgkPbi/CTlPIg6GmGznA3da2H+UoAuYKg2+CW//Fu7vvGt4fXrrjjdt783vcx3/EpmwqzF4htoyT12ff3zHVINAV5oGepFqdJ/S5Z0CUNeshmYczDfCqvr5vfLEWjFboc6i1zWWHjADLNMg72llgOeuQUnUljNZ+yZKgkvYBalFFzIhShsbNcRDuFt/pjBVtSgk984hP80i/9En/7t39LOpIDcs899/DVr351K77iOxa33HILU1NTXHXVVY/I92UixTAyoshnoRsQJ/36CtOYduSRZCkVw6SbOaCEzFg5bBOEyFjqBnhRf3pIspR25BHEAZAiDzpMBmhaQioSJCGGnTHLMlqhhxOFdCKfnKKzJ1fa8B2rhs2BfJVjTot26K37uxOFtEKPE0eObvj5hbkFkpEE41SkWEZKRt8LeyW3J0oT2pE3lsjdL0OGoadIAy/sXhATeg6GKtNOQtw4IkrTU7rxbGMbZwNDVuhFwbo2Gad91mqQjJPZVlmrKZP5/vE1gGQW8AtTuE6D1B8nq8hygqqluGH/CHslB9KJA7qRP3Zv4veIO3PoakRqlKlFKknav9+NQ050e+wuGRT0cSZ1I3CJ1+QDRmlCI3DPuGzuCKECxseStWiHHp3I78v3FAd2dnGCkBN0PSOIUpZDFTSbuDVPr7uER8Jzb3wlN/38f+N5r3wViqqSiRTTSIjTbNNxsqwbHGy18cJ43XtciFjqBqiD3b4oTagFvTFDhbXjpON5fPrvPsz/u+X/x++983+MBY7Ql7b5yj/fhp/E7ClZQ5LUudTbWnQi/5RzwCipCvqWho3AJacrTNg6DTcA+vXWk2QwcmRRRNxdJssyUlJMI910fgvTmJJm0Y19jjp1MliXA7nkd1jyu+iSwtp1WJYmZP4SuqngeB5tqUB1kBN6pmULkmjdvY8mzjl4/Jd/+RduvPFGjh8/zi233MIv/MIv8Bd/8RdceeWVPOc5z+H4JgnJ2+jj7W9/O0tLS3z9619/RL5vMeiwrIKmxiRJPBwY5702R5w6jcDBlFXymsaEnqMe9jjm1RFaiCpLww5WDxyOOHXmukuAQDL7wWMrcjnmNuhkPaZy2lCipxl6PNip8WBnmU4YcElh4pS7intyZXKKztcb82ODSpQmHOzWebBTo7xrdsPP5ndMk4bO8N/zXpvjfhNFS4Ze2EGcsBx0OeLUWA7GJ9d64HDUbZDIProqUet6NL2Q2NB4sFPjSK9BmGxrPG5j62EqKvXA5bgzzkJe9Lvc3+6zkEfRZyfXOOI02VU0cMKYphfSTSPmDYuToYPXmicbBGZOHHDSa9FNexg6wwDSi0NOuE1OeE2cuD/hpnGE1zjGCa9NQ5PJVctEScpiNyBKUu5v1eiJLoo2Hjwdc5rc315myR8/wj7pdrivvcRJt3NmZes1SEcm0pWx5GC3PhaIdiKfQ90Gh7oN3DjC0mR2FUyWXZcTbpNm0sHUM8I4pSFXSPQcx5rHOdpbphOvjjFplnHSa7EQtDCMmDRjw3Eyp6k4UT+AvtARxikP1dzhYr4XB+ukxlbGSU2PSeOI3/ixN/JHv/LLfOyDf8Jdn/73DZ97/8EHEJrPVGF1IX0u9eaPLCDcOOJQt8HBbmPTOaA5EnylWcaRXoMHOzVqvsOesokbpnhh0q83t0HHyCFUldTrkAY9loPuKee3ea/fD8uazYLf5khvPIA80enxzeYiQsAJr8kJd5xV3nWazLlNunKEkBKK+QJ5XTmrss2vUSx4tHHOweO//du/8XM/93P8xV/8BXfddRdf+MIX+JVf+RXe9a53sbCwsJ3zeIFBFhK6nsdQYEI4w4FRygSaJCMN8k1spe8RqggZTVLQZYXpvD7sYFGSoUkycuACGdIgkJKRUCUFkUpM2KsMS0VIaJKMl8TstotMGqe2WlIlmasnd5NTtbEVmKDvga1JMi951Su4+plPH/vc/qc8iWe8+PkQj7NKNUlGlWRm8vowgExT1nn/rvxGmiSjyXI/yV7KaMcy+Xxx+BwhGPPi3sY2tgKGrFDRLPp7+KuQEGhSv2+NQpakYZss6gq7iibznYBekKAbeTQjR9prELfnyLIMSUjIQqBIMmVTo2ipuGFC042QRZ8sIAmJLMtIOvNkXg9NMzEKU+RMg6m8TpSkHG26aEJmd8Eas/YDUCUFXZaHxIzV69IgT3hNqoyQ0GX5lGUTa+5f6Z9i5C+KkJCFhDooI/SP83O6Rhj3/543+lIqkWzS0KaQggAp8sfy7ASgiv77mIrKdF7bcJwUQmAoMgeb3XW7RBca2n5EzQ1Y7gXEacaS3xnzC4fVcVKTFe7/zCc4+NUvP+xzC7M72FWwx45hz6XeRvPjZSFQB21SGauf1TlAWVtvgzLIkoSlyVwyaXOw4Q7rTdVzCD1HFsfEnWVkpFPObyv5mrKQqGg2D/YWebC7RErGVE6lk3ZpexFRLKFIEookj5Uhc9tIsooch+i6xYGpvrbj2ZRtsxzRRwvnnPPoui779u0DQJZlrr76anbs2LEdNF6gmDaKTFsmWeBB5DBdnWSxG0Kks9teb7k1oecoqAaapCAJMcwRCX2ZWbuM5jqIkUZd1EwMWaElxdj6avBY0Az25koEccKBfPW07JcqusXTJ/dypNekOBCSVSSJi/JVkizFkFXe/w9/w+1/+xEeeOAQPbvC9a95JVbiErcXyJIYoahMGwUqmo3hhzCS2xMHGjtsg7w2fvxc0SxsRUMdDARTlqChCGbzJaxiFVWSqQfOpkSAbWzjbCEJib12CWfNEVVKxpSRZ4e9xg5Ot7DkvtSOLEnsq1gsdH3iUKJslClO6CS1I0SdZYRqYeYn2GtXEYj+RDtIU2u7EbaSo2KraEImbs33J1dZZm9lP8IuD3epqrbG8ZbHM/fuJG9KYyLfALvtIlNGDnPNycKsWaCsWRhrrk8ZOfKqMSZTsrZso+NFQTN4XHGyH+yOjD19Bu8kkhDDSV+RBBeV83hBTMXqmxsw+JqloIrmd5mMYmxpXH5sp1UiTpOhYcB0XmOxGxK7Eoc+/Vm+Mj/PzJ49fM+LX8ixToduEI9JFF1oWOoFTOd1/CjlaNOhnrrMmOP55ivjpC4p1E6eeNhnPv6ap/KDr3gN3zM5OXb9XOot6a6mG+mywqXFSdIsG9shXTsHrEAIwf5chShNhm1ytmBQrru4XsbuYn9+SwuThEGP1GtTyU9QtCc2nd/yI/aziiSzx65w0m2iShKmrCKUkCkzhx+mlI0iFWOVhJZGPnrgsMeqErcWiCcmKFv6WZdt1rywBOm3hDDzp3/6p8zNzXHVVVextLTERRddtBWP3cZ5gCwkdFklNgvEbhtNWh0YW26Kkc9Q5HEf6NFGLEurHaztxhTilLxdGvsOkckYCuuSj/0k5rLS9BnlCu7PV6kH3hj7WpVk1MEMoKoqP/RffgQ3TPi3h2p0QoGh95+fJSEoKrKQkGUJISIyxpPD226GIadjyeFiTZmJY0pqhq6qmIo6OIoR2zuP2zgvqBg2rW59+O8giTFklVDEGzJRR4O3oqmws2Sy3A3w/AzNzGMXJomai8TNOYRqoK3Z9R8l0bTdmFK4QNKrgZBQ7Cpqrjq2QPSihCfM5JnMbUy2kYWEpax/T0kILGV9gCU2ub62bKNYqxu5go308EqmQsUyCKMMfdBlc7oClSJzXYtmx8Wq9FBHJueVMWP4XEWmasBv3Hzz2I7c42//GK//v7/LQte/YIPHIE5Y6obM5A3CJOX+epNFz6NcHXctGS3zZhJGz/7B6ylPTTGzZw9XPP86LpkuYmxAQDzbeltLuDoV835lDhhFf/dvtN4kLpvO8cWjzeH8Jht5hGaS+T2Sbg19cv+qH/bY/JaiyxnmyCvLQmLWKjLvtRH0N0s0Q6HmhLh+Qk9KKRn990rdNlmWonhtfKFRLq1qO55N2TZjoT9aOOe3edvb3sbb3vY2XNflD/7gD/j4xz/OL/7iL/K93/u9vO1tb+Mb3/jGVrznNrYYslUGIPU66Io8djSzkhy+6WcHHUyKA2peSqCMT0Z+nFIwlDFLwjhNkZCYMOwzek9tsELzkmjMiWAtDEViMq/hhjHLXkaSQhYFm96/EkCO5kBuhjAMUBUZfRCUxmmCOjia2cY2thq2qo0dWrdDnwndQkIayyHbCIok2FU0MFUJU5VoezGOPoWcq5CELtHyIZLAWfe5kqFSNCS69XkWlpcBEJqFUpoZCxzbfkTeUNhTtk7r9OBCgCwJdhZN/DgZ+/1yhs7UxCR+FLOwVHvY3/bO2z+27ij323fdxVf+6Q4ONbtEDzNuPlpoeTFhkqLKEramYBopaQaL3WBIflqLZ19/A5dfc83YtYuefA1v+LX/yU0//994xvWvIGcaVO0LnzQ4YevsKhp4UcJCNyDJBEpukjTNiBvHSdf0h5X5bTQHcuzvQqKi24PdcnWoMmJpMm03ouVHZElC3GuQxiGp2ybOzTBVtB8zfeZ0cFY7j2ma0mw2qVar7Nu3jx/7sR/jx37sx4Z/u++++/jKV77CV77yFY4cOcKVV165pS+9jXOHkBUSMtzuMmWrNAwgF7shJzseJUsip+ljq50kS/GTCENWkSWJCdFjUYZlN8HDpajrqJJMlKSUDJ0sy+jFIZok4ycRZd0ceIaeGSYMm8tL09xdO8mMlRtbvQZJTJgm5BSNqqXjRSlBlDDvpxS7dSbs8liHHS3D6A7kfMenYAmKujFW5izLaDptTAVkZUWmJ0UfuIFsYxtbDUNW+jmHWUo3CoiyhN12mU4U4MURlqLSjQNMedwZZqW/2ZpE2VRxogQhBO0gwdXLmE4TpXGCNPSRC9Mo+SqhrPSP1+IQw21hRz3aiWA+0ZgszWKM9NcgTvDjiJ1li7Ubi1Ga4CUR6ppczThNcZNwncNIkqU4cYgla2M7RVmWnbJsmiSv213sRQGyWH987g7s7SxFo2QoVCyVbhBjqJBkGZaiUSiWSNoWdafN8XqTatHGXuNuE6UJYRpv6kbTmDtBI/BouBHT+QsvmFro+kMmdJymOJnP7kKetptwsu1SsmXyqj5WZklV+Nn33sIXP/xX1Fs+0wcu5XHPezHLXkIgXKJYcEk1hzo4pTpf9TYKL45IspTcmpOr0Tlgbb15SURe0dlTNnHChDiFkx2PoqGhCCDyB2L6NinZ2Py2sgO52PHJWWI4v0E/gJQkgRMHgzxLZUymzkk6GN2l/imfnsPIlSjoyjmV7ULDWe08djodJiYm+PSnP73+gZLE5Zdfzo/+6I/ynve8h5e97GXn/JLb2BrEI6vMLMtYSBOO+i0ag5XXSgBZC3p8pbbA0hp2Vz1wOOzUWPT7bEmRRkzlNNzM5+u1BQ5162RZRkaGqSk0Qo8HO8sc6vZ9Smets8/Z2GmXuKoyw0m3M5T0SLKUQ90GD3aWaYQeRUNFEYLJnM5cEnBPfYHamlXlot/hsFMberauBJCd2OVrtUWO91pj9zdCl0Neg7aIYZDQH2UJurRlEqnb2MYYzIFl2wmnzVdrJ8kpOkXNYMKw8ZKIJb/H/e1lDncb4/7Pg/52xGkyW9KJ04yyqYIU80CvwyGjRGpXkIw8md+lM38/B49/jUMnv0GvcRKShKImyBdyHFdNvtVpDaV70iyj6QUIzWchaNIYYYJmWcbhboP728s0g3HpkRNum/taSyysGUsWvC73tZbWeVs/XNkOdRsk2eoOXyf0ebBT46FujXCMqRvyUKfGQ50abhwiSYIdRQM3ijjiNDjmNujFAZKiUyhVKAifo82TfK2+QCsaL9uc1+KIU8eend6wvuyZKYI04kRrvbzKow0v6rPp8wMpJScO+vIzhs5kTuOk1+KrtfXj5ILX5kj7BFe+6Dnc9PZf5AWvfjU7yzk6scuXl+ZphF0q9mpAs1X15ifxWL2tIExiHurWeLBTozPi/7x2DljBaJtc8ntUbJ2CoZLXZWpBj682ajSsKnKuCl6b1O+sm99WdiDXzm/DMkQ+x9wGJ90WSZYOdyAzKeLe5SM84DokQibQKkyWSyRsTdkuFJzTsfWf/dmfcd111/HSl76Ud77zndxzzz1b9V7bOA+IkpR0ZGdADHJ80pHBUldkqrZKlvWtrDY7ws7SBLIMzSoxmeuL6DaciLYfocrSmDh4kqXISJS0c3Ni2JOr8MTy7IZacQCm2mdA6opExdII45jlrr/p0cwKFFka5G9B3YnGj7CzhCROMFQZIa3aeuXVC28luI3vDGiy0s9xTGMyAZcUJhBCUNLMddqJm6Gg90Wyu2FMxdYwVAk30/CK+0AIsjRCKDpIMkIxQAiIfSSrzMTMHgqWgR+lNAY6kE03ZDKnU9nElu2xgILe3x1ywvGxQ7JK2IqEHSwThhE1J9jwCPvpL3/ZOp/vHVd9D5c+90X0EocHmnU6/oWlxVd3AuIk6/tYA+3IHbKsTVWmYqvEScpyb/wIO40DiHwkMzdU0lgZJ8MkZcLWH1NC16ok2Fk0iJJ0OL81MElkHYRE1F7qz2lrIEtibH5be4S9FkIIykqM6jdxlQLtRAarRNV+7PabzXBO2ycf/vCH+d7v/V5yuRx//ud/zrve9S5uvvlm3vOe9wzzw7Zx4UAWgoYTsNPOIQnB7vwURbdFPhwXZ92dK1FQDTpuNuawUNX7RzqGrA6FhyUjz4Ss8KQJhZaTMN8OuHTSQpEEFc1EK0zixiEV3TqrI+tRCCHYl6/iJjFLXncgJl4ZbutnWT/3MUoyLi7vpBynhKnMYrfPNMyiiG/+8z8zd+woO/fu4zk3/BCK2j8ymTLzWLJGy03HHBZKkspOyeDi0tTwSCTNsm2B8G2cV5Q0g2bockVpmoreJzasHKdO6hamovbZniPHdCv9TZNkFElmR96k4UQYQuPKyjQtLyaIBb5VIpe5iO4Su2IJFB07V0Eycki6jZBkLi9N0jBCwkBwqO6ys2RwoJIDYa07RhMDD14viVDdcQHoXVaRim6u00SdMfPkVX1MpBr6zOuHK9vo8XdBM7ikMNGX1xo5FrUUjYsHHskr444Qgt1Fi2WnSF5Xhg5Rkm6h2gUO+C080yRMtaEXtiQEO8wSYRpjydqYG83U7t088cUvwokU3CDhoWCJL8wLfmDvngvC8z7LMubaAba2atTQCN1h2o8QggP5KlUtR8/NhuOkLAkmkwRLNSkUd449s6rZXFmd5sqJcW/zrao3Q1a42B6vN+gvqC7OT6w72pWFNDYHrGC0TeYH9Vy1NeY6CkaWpzDRn99qcUw1rqEEPcpJhZw9MZYWFYUhX7vt45w4dgR7agfTL345s+UcpipTUA0UqdJXOhiULcsy9M4yT8gV6Cglmk7CfrtAQVeQJLGuTZ5J2Zb89bnKjybOqYX/zd/8Da985SuB/o/2yU9+kje96U34vs8HP/jBLXnB73Tccsst3HLLLSTJ+Tf2kmVBEGfDgVGVFXKq2V9ljt4nJCqGha0k6yy67EFHDHotSGKkQb5KQTOwlYwHlntD+yshBHlVx4lDdljjshBnCyEEVd3i5OC4S5eVYS6NEFCyVJZ6IQXNpKqrxJZEPUo52ejxwZ99C/fefffwWV+44w7e+cE/RVH7jOyibmKv8XiV0wRbCPL2Kikoo3+0uI1tnC8UNZMgSbi0MDmcXC2lz/b304SCut4KbqW/rcDWZWaLBsebHpM5HUvR+t7zQQJWnuJ0ETUOkRQNsSZ/V5dVZm2VucRHzTIurtqDvDlpQ0azOtDE8xnfeVMkiYK0/l1lIW1ahtMp2yjW5omtYKPFal5XmLZNeiO7j0II1NIsidfDjHvE5Unqbrw6To7o/SmqyvNe+aqxZxbi/jiZJoJvLi9TMCWeODG76Xs9UugGMUebLhdP9EmKXhLhJSFlbZW0qEoyE2Z/rF/qBix2A6ZsBdw2tpFH0ccJjk6Ysr9YWMcsP9/1BpszuEfngFGM1hsMyGQlkweWekzYg7E+ybPUazJtCURnCcsqIgaBYBSG/K83/tiY7/lX/+WfefP/fd8wgLTXWNSmfofEaZIv7cD2OgS5ErvLNtLg1GqrynYh4Kz2nQ2j30he9KIXDa8JIXjRi17Exz/+cf78z/+cY8eObc0bfofjkXSYkYWgbKoEUcpSr380o+QqkKak8XrLqFOxsLPQZSgSt/J8SVC1NKwRq7I4TZGEoKhtne+prWoI2DC/KK8rxEmKGHRGg4ipvM6dt390LHCEvr3W5z5669i1tSzsnhegSBmm3n//MIlRJflRnxi28Z0NQ1ao6hbT5qpPriQkKpqJdwY2ZbN5g4Kh0PKidazQdpgia+a6wHEFfpxgKBJP2lXE0r4zyGFCiL5kTZyOjR+SkUc2cmShg0lA1dbGxslTYWWc1GUF31c41uuyeAG4gSx1AxpexGIvHBBXAjIYE7FeganKQwH4+UaTOPRQcpNjTPs0y0iyjKncY/cItmKq5HUFJ0zQFZmZch5hlVh0E+LIJ3Eaw3s/99FbxwJHgIfu/hJf/ec7NmRhZ2lC1JxHtopARpRmTFcrlL8Dj6zhHILHHTt28OUvr1egv+qqq9i/fz93r5mot3FhQFclcqY0HBjRcpCExE4TP4nWBWSqLFG0xJjHa5bEZGlCYuTHkqDjJEVTJIRIh7lZvTigrJ0dy3ozWLKGIqQxKyvoB6pCpH1/UUkBIZN4HTRF4CzPbfisE0ePrJscZElQsiQg5WTbRZcY7rA2Apeiamyq0bWNbWwFcqrOHru0btemrFtE6Xi79+Jow1xIP4lApFxUtZEE9II++7pgSmgKQ1mRFaywiqEfOHb9mANVC0uT+s9agzhNN5TPSrIUN14/lqRZhhtH6/pbNrg+OpacTtk2ynsOknjD61GaDIl2RUOhaCg4QUI8uC4kGSVfJY1CUreFqY2PkyvvvKLWsNk4iYBGJ6b2KB8xxkn/BGV3yaQXxMx3A+pBD02SSbJ0Q59kTREULQmvVWM5VMgGu45p1mchd/2YkqEOyTePdL2tvX42bVISsLNo4kUJWdbPBa1US6SZYDFU8ZsLpJFPlmWcPHZ03XMAuosnkSTWBZC+0yAKHYRmkYUurpJnplwgI92Ssm30Oz+aOOuM15tuuok3velN60gyzWaTubk5FOXC3Gr9bsei32Y5aqFqCUGUsuxGZLLGyc4ih50ajdAdu78W9JgLmih6ONyBDNwOTRKOxsGQmQbgxxkRAQ91lznc67Pu/CTesiPrFciSRE7VWfJWPWWzLONwr8FRt0aQhoRpBrJClkQs+h3UmfLGz5oqUwvGvWkboctxv4FQA+IwxMoChNz3HP52e4kJ3drwWdvYxlZBlxX2F9Y7Ma3kDq4EL/XA5b720rC/raAT+tzfXuZgt46mCC6q2oRxQjvoM0Qd0UNVsmEAGaYxx9wGR506NcfDCRIunrCoWCoHu3Xuby+PMUFX+tt97SXqwfiYcdxpc197cZ239bzX4b72IvPeuLf1kt/jvvbiOj/v0ynbaIDixiH3t5d5oL08NikHScwD7WXuby8PmdezBZNeFHLcbXDErfWZ12YBWbeIey2Od5fGxsmVAHJFrWGzcTJvpzSclEXH2zAweKTQ9mN6YcKuoknV1mh6AUfaHQyhcNJtcdip017D4F30O5x0F1FEl1i1WPYgSTNqQY9DvWXmvTbTeWPYJs9HvYUjQdbaeltBnKbn1CbLpkLJUOgEMbWgx3zioJZt0jhkrt7EXTpCI+giTZU2/G2V6QqJ4qFIYhhAdgOXh2qHmMtSkiwlTSIks4Spc1pt8nTKtrbfPNo46+DxV37lV7jiiit4ylOewg/8wA/wjne8g3e84x087WlPQ1VVrr322q18z21sEZIsJUpjDE0aHs00yBGlEWEckq5Z3cRZfzdCGjjRpBnM1RqEKUQiG1tRhUmCrcuEaUKUJkTn4ch6BTvt4tjqLKO/YouyFEuXCOIUSbMgiYiSiCte/Hwe9+QnjT3j4qufwuXX/QBxNr4qTLKUME1IRUaZHrYuI2SVJE3RJJnCObLGt7GNs4WtaBiSOtx1T9J02N9G98LiQRsOk4SMjLKlsq9i0vJD/DghyVLKljo8wm56IVEa0/RCUlIun8kxnTfIyAiThDBNiEfGhpX+FqYJyZodpiiNCZKEZM3uXJSm+ElMlK4dY1KCJFm3o3q6ZRu9HmcpUZaOfXeSZUSDv62UoWQqKLJEN4yJ0/6EL6kGklEkCRxCv7dunFzqBcPfYrNxUpahammc7DhjQcEjjYVuMGRDVy0N08johhFNr/87R2lMwtp6Swi8LgoRs5OTREnKYjcgSGJ6UYQqC0rmSErSeai39GHqDTjnNilJgp0lkzBOBzqKMYqdZ1IJyYTMQqOD01rmihc/nwNPGZ8zLr3map5w3Q+QkDI1IiTeadeJIp9E0UhDDxeDcqmIoYrTbpMPV7bTVVp4pHDW24O5XI6Pfexj/P3f/z1/8zd/w5/8yZ/Q6/W46qqr+NCHPkS5vPFOz3cD/uAP/oD5+Xls2+Yd73jHo/06Y5jWC1Qsg7xiDHNflhMbyTXZpeiU1iRIT+p5bKXPilQlmamcxsl6TJRZ7DbK5EZ8odMsY6ddQFYtDFnFTyOKqrGlR9YrsBVtaFcI/TyefbkKfhIRhjKH6g45wyZ1m0yrFiU9x6+87/18/lOf4sSRo5iTs1zzkpdSymvr8heruo0uKxhCZikJMa08QlbIqzr781Vy2zI923iUIEsSFd2iFjiYisqEYaNIEoasjuWylTWTSwoTqGKVNDCd14nSEg82BGmaQSZTtQR+nLLUjlBVg8eVdJ4wVRlai6qSzEX5KlGWUFQ37m9rJbj22GWqur2OQLHTKlBQ9XVH8dNGHlNW1zGyz6RsAAXV4KJ8ZZ3doaWoHMhXyLJs+E6yJLioYrN0zGN33hwyr+VcGdlZZioKEMVZ8qo1/O66EyLLBnstk/yaso2Ok0ITHO64LDku5UfhlMKP+uSXwkjuuawklEwNP0rRFYsJK0deHa+3GT2HGcdYqolt55GT/tGsSDUqcpErJkrII65h56PeNHd1Q2CjeoOtaZNFQ6Fqa9TdjH22gSVrZJMJ09JJ6hTxG2127ZjlV//0Q9z5sdtZOHaMmT17eNYPXo8n+jq/qtwXEp+r1egtLzOZmyCv6ch+l9jcyUzBoqipp90mH65ssyP5zxcCzih47HQ6FArjQs833ngjN95445a+1GMduq4jSRK//du/fcEFj5qsUhwZNHK6AgWbuZaE0+hSzo8L4aqSTFFavV9NfSb1lIY2getLFNRViQKBwNZkLK0/mCz7ATPnqcHbSj9BPUjiIRvNUtS++waDlfCAEa2mCYZuI7RsyJT0BgOsN1KGFchCoqiaxKGPLAmMAdPaT2JmzcK2s8w2HlVUDIs5t3+EJQkxlPIZxYou5Npru0smUzmdphsy3wlohDFlU2V/2aJkqetsRaFPUNsIK/1tLU7Fft3oXZVBQLwWZ1K2FRQ3ub4RE7hi65QNk64L+WK/zJJuI1Qbze+iZ+kw+MkNArG6E+IHEgVVEIUhn/vorSweP87Mnj08+/obhmODJsnc32jyuMrEuu8932h6EWGSoA2CujTLaIQO07ZFEPVzXcNQRqypOiUKsNwm+uzlCEnGlGAqrzPf8VGFxqQ1/huej3pby9bfqN7g3NukEH3R+KYbkpP78kRproLhNJgUsJxqdJfqlPaU1rHrx745dCj7CyS6QZjoZL5PINvYhRKFwS7tmbTJU5VNv8AUPs4oeLzmmmuYmZnhhhtu4Prrr2ffvn3n6bUe23jTm95Er9fjfe9736P9KqeFnK4wWSyw3Gyw0OwxU85tyMgDiNuLaIrETKXEUi8ayvjEWYYmSxgjvmVJmq1boW8VFEmmpJl0In/dRGWqMqosEaFAlvbt2MzCunum8vpQnmJF32wU4eCoRjf7wWOQxFS38x238Shj7U7PmUJXJGYKBhM5nSBKMVRpXdv/boAqCS6qWnzxaAtd6R9PrxBn3Noh5M4EsrG6+B0NIOeaPf74Z986xsb9/O23D6W/KqbBoVYXJ4iw9Udu0k/TjGNNF1tbHRPdJMRLIqqajWn067ntRtQImbBXLf2izjJC0pDt1Rx1U5WxNJkJS8X8DmHcr6AvpG+w7ARULQ1JNVAKU2TNk0wVSiw2Wxw/cZRdO3aiW+s3QZLQI6ofQ5EVpgsmS07MshOiVye5smiifof3qTPKebztttt4wQtewF/+5V9y4MABnvzkJ/Mbv/EbfPOb39ySl5mbm+NnfuZnuPrqq3nRi17EJz/5yS157igWFhZ417vexROf+ERuuummDe+5//77ec1rXsMTn/hErrvuunU2jL/1W7/FO9/5znX/feMb39jy9z2fyLLVnMViZZKqIeF2aix2/THLqBWkcUQUekhGEUNVxmR8un5ITpeGelZZloHINlwFbhWquoUXr2fvKZJAlwVOnPZZ4SPyC6NlHpWnWOwGBHE8luAdRhGmlJEqGukg3ySvbUv0bOPRRd8nWhBswCqN0mRDaZmVvL5RKJLA0ASI9fcnWbphjlWaZRsyR7MzvL7yrhvJbZ3q+umWbaUMm11fKVvV1qhaKi0vZLkXEKdJnzhjlkj97pB5u1KGnN4/7vzCbR9dJ+MyKv2lyzJOFHK0/chK9jS9iJYXDxnRAL1BGVZ+i5KhUrRU3DBhuRcQJTFpHJJ0l5ELk0iqMSxzlmXIok8wWsG51FuUJix5PdIsO6d6G8W5tMmdBR1ZCPyBq1hmlRB6HjV2mC6XII45cewg3dpx0jjqzyehS+w0iepHidMYVBNZCKqyR6raGFaBoqWe17JdCDij4PGyyy7jl37pl/jyl7/MkSNHeN3rXsdnPvMZnvjEJ3LJJZfwC7/wC9x5551n5e95+PBhnvKUpzA3N8d73/te3vnOd/J7v/d7HDly5IyftRm63S5PfepTcRyHffv2cfToeir+/Pw81157Laqq8r73vY9nPOMZvPjFL+Zzn/vc8B5d1zEMY91/snxhr8zWJrAvDJiD7chDUlTytk1ZeCw4be5enlvHQq41T3A47NEc5Bqu6Jt1Ip+7a/M42ao8RZgmCMR5FdPOqTpLfo9vt5boROMuOaFw+Vp9ga4QMFLuWtDjkLNMI+y/60oAWfN7fHl5jkV/dbCPwohERNzvdnioW0eXlXPe9dnGNs4ViiSDgIe69bHrncjn260lDncbY0FWn826xMFOfWwi6jM+azzQro0xQaM04WCnzv3tpTHSRzrwC96ovx1zWny7tUgnGjccmPe63NtaHFNGAFjyetzbWmR+jR5iI3D5dmuRY07rESmbqcrsLlukwKFuna/VFvCEQCnvIAt9Ur83Nk5CP4B0l+fZCAvHjtEIHQ67NSIR8mCjS7SJxev5wKGaQy+Mh7uJ2eDIuh72fZudgSHESgB5zGnx1do8zV4dISTU4hSwOk7OOV0sVaZg9IPRc623ThRgKipHeg3uay2uq7cwTba8TTbWMK9H26SpyewtG3S8mHrQ45DXoGXmIU3QRMp0KUdPkvnPo/dy6NBd+Ce+SbTwIHH9GE4ccoyMudAhSWPkLKFQnmB3SeNQd/m89rcLAWdNmNmzZw8/+7M/y8/+7M9Sq9W47bbbuPXWW/nd3/1dKpUKP/iDP8hv/dZvrcuR3Aw/93M/x65du/i7v/s7pIEw6R133LGp88oHPvABduzYwUtf+tKx63fffTd33HEHv/qrv7ruM7Ztc/DgQVRV5S1veQu1Wm3dPb/3e7+HYRh86EMfQpIknvGMZ/ClL32J//k//yef+cxnAHjb2952WmW60DA6iGVZhpeEOHEw1HZTitPY3kNoWYfFSDDfdaloNpIQZGmC57ZwBQQjTDldkSlaElEvIhH9lacQgiCJKaj6ec0PtBUNTVKo+06/M6qrZcvkFDcJWZIsCmlAlsQI+i4LThxSUFY7r6nKFCyJuWbAiY7DpJ5HlgRJ5COpgm6aEAYujytObuc7buOCwKyZ58F2bdjfoD85rUw+SZYiCXlwPcGJQzRZIU7TIVkhTBO8JBz+/0r6R5ymuElEmPQZqtZglkiyFCcOceNwXX9z45BuFFAV4/sRThzSiXzKa/K+/CSiE/nryHRBEtONAlRJfsTKNpVTme/KNNOUZhCw2PPYm6uQ9OpE3RpuvjI2TgLs3rd3w3qZ2bOHIIlx4hBdKCy6LnUnZKZwftJ3RtH1Y461POI0o+6GVC2NJb9LLegNx74wjbHpn54UdQVVy5jv+ZjtHpeYJpLeT9FZGSeTTOayanmY1nCu9RalCRfnJ9Akic8tHGbSsMfqLc5SvHRr2+RayaS1bXLC1qk7EYe6PZwsxNRyyMVpktY8qlUmLxyOCcGcnzBtGWi6jkAQxz5+2AMJkqBLpJUpFgrYpsxS7zz0twsMZxU8RlHEXXfdxdVXX42u60xMTPDGN76RN77xjXS7XT7xiU9w66230mq1Tit47PV63HHHHfz+7//+MHBcwWa7eaqq8spXvpJ//Md/5MUvfjEAX/nKV3j+85/PT//0T2/4GUmS1j1/Lf71X/+VF7zgBWP3veQlL+GnfuqniOP4tPQr//Zv/5avfvWruK7LO9/5Tr7v+76PF7zgBQ/7ufMNkUHbDdmZ6ycMz5pFSppFQekPbJJqgKKy0+9QqF5EECpDi64s6FGWFazyLgrGeP5HVbO5rDTJxcVVXTovidhpn99kcU1W2Jcro8vjifhCCC4pTrBQTMncgGXPZ0ccICGYNvqsyLWMzx12AVVScD2GOZBp5DNjW8jFaTygathsYxsXAiq6zaxdGPcR1i0o9MkBo2zWomZwIF9FkaQxG7ScorE3Vxn+/wpMRWVfrkycpmMyW6oksy9fJkjidf1thc1qBeOL/V1WkYKqryNKTJt5dFlZRxqYMGxkIQ19vB+JsuV1hbKhISjT0yJEotBMZHKaTRa5TEvTlG17OE4CPPv6G/j32z/Gt7/0peG1K576NJ59/Q2kskCTFGQhiOKQIw2Hydz6nOqtxmIvoKDLJAjqTn9jYD6qU9Is8qpBmMYURsiSQgguLVYpZAK/06Fh5phdYeYbeQxJJUtkSubq73ou9SYLCYGgoptMmTmiNGXe7YzVmykr7DW2tk2W9fG2t7ZNypJgd9mk7vkUJY2SbqAgkXhdotYcVeB7SlWcNEctUphWQZGgKOuggRL5KLKGa1a4qGRSMVSE2Pr+VvPHd1AfbZxV8Og4Dtdeey3/9E//NGZRCJDP53n1q1/Nq1/96tN+3oMPPkiSJExOTvKa17yG++67j71793LzzTdvGnS94Q1vII5jXvGKV/DRj36Uqakpnv/85/PWt76VX//1Xz+bYgFw/Phxnve8541dm5mZIYoilpaW2LFjx8M+Q1VV8vk8/+N//I/hvzfDI+ltLckSvSih7oRUbQ1T7v83CrW8AxYPklcEnm5Sd0KWegFFZwlN0cnnJ9YJFyeZYHe+SFkfHVDEI2LhN23m6EQBaRTz0b/9CCeOHGP3/r287Idfyf5CkeW0g9PNWO56zJgmuqxuyFrTJIWddgFP67Ow51ouchph6xoFq8RS2BvKeWxjG482DFldlxIiC4lJI7fuXkkIJjZY+Kz4xG+EzVixBdUY7u6MwlY1bFXDD8ePM82BH/da6LIyZr24AlWSmTLXl2Ft2RbcLqosU9HMcy6bEIKZgkHbjzlQsqk7Ib0gJpWL2F4bPXDJ2ePSc6qm8St/+iE+99FbOXb4CPbUDp75gz+IpCgoQlDVFZIspZk6LDgeNSdkOn/+xo8gTjje9CiaGroicTxN+EZjHl2Dcr7/O6zsOI7ClDV2KYK6LnDVPDWnT6LRZRUNKOeVoWwTnFu99aIAW9GwBgHmk6u7+LqYo+Y7wzoUbH2bXFfmDdpkXlfYV85xrOkN5we1NEvaqyMhmM1PECYZi27CopswbckokqCYpiArhIVdFEyLsqk+Iv3tQsA52cC8+93v5uabb0aSJJ74xCfyqle9ihtvvPFhd/fWIgz7W7k/+ZM/ybvf/W7e/va388lPfpIXvehF3H777bzkJS/Z8HNvfvObiaKI66+/HtM0edOb3sS73vWucykSSZKgaeMNTtf7nS6OT2/r+BWveMVpf9/b3/523v72t3PixAl27959+i96FlAlQU6V6QX9clQ38NyU9BzoFlFnGbMoKMkptU5A4PjMzs6uCxwBwjhhprA6MK3kvJ7PfMcV5FWDMAx4y395PV/+wp3D65/4+1t55598kI6uoWoSXcelLRQKVrZhGVawkgN5rNbBThJ01STIEgxpvZ7ZNrbxaMFUVBQhEaXJd6xVZpplG6o+eHGEoaiYssK812VCt9A2kAY6ExRNFUOR8ONkOC62Y5M41am4TeTC1NCidAWKqg5lXHpBPFxoT+V0JCGQhQRCoCgZh+oOVev8jYfLvYAwSamqWp8cI3skIiINDVp+RMnY+LuzNCHpNShZJkYhR9tPhizsKEmZsLYu4HXjiP0DzUMAQ1G5ojzD3fUTdCJ/U+ka6Nd5O/SHGpLnAzMFg5bXF8wvmxqykUPf8z1E9WOkXhvVLDBtycMAclILUbIEUdlLkGrsL5jfVaoF51QL99xzD8997nN56UtfSqvV4qabbuKFL3wh9Xr94T88gsnJSQDe+MY38oY3vIEnP/nJvOMd7+BFL3oRH/jAB0752Wc961koikK73d6So+FqtbouF3Ll35VK5Zyf/2ijaGnYmjwc7Eaxkqui5CfJ/A7B/AOo9YfIO8cIMkE9MceS1VeCxCTNyGmrg5OfxOiS/IgEj5ai8emP3DYWOAJ86f/7Dz7/sdvIRD/ZO4eLH6XUnHCM0LURuctUZQoa5KQY2cwTpQm2qiOfp0FrG9s4U+iygqmoBEm8KUHxTK+fzWfOx/Usy6j7DnNuG2cNAQegEwXMmnmeVN3JJYUJmqFPa2C1d7ZlUyXBdF6n568urPOmiSNZ1Lse2Qh5cKPn5HSFiqWu88KWEAgpoe3HLHbXl2UrkKQZx1r+UEqoFXnM+S0uKpaGDkKjHuajZUhDlyz2UPJVSpZBwVRww4QTbb8/DhrK2P1rcSbXkyxdp3mYU3WuKM3gRhvn9cVpyqLbxU0iSrpJd6Q9bHXbUyXBxVUbTZJo+33/clmz0CYPIMw8mdtEiXpU6ZD4HRa7EWlxJ51UY3fZpGxuvIjbyv52IeGcZsOPfexj/Mmf/Am/8zu/w6c//Wnuvfde6vU6P/qjP3pGz9m3bx8TExPrgrNqtUqv19vkU3DvvffyvOc9jze/+c285z3v4eUvfzn/9m//djZFGeKaa67hi1/84ti1//iP/+Cyyy4jl1u/Lf9YghCCJb9Dmw6SkowFkMtBl4O9ZTqRh2wWkOwJJKtERzOpaTJauUCYiuHA2Ik8DvaWWfa7CMFQ37ET+dzTnMdQ1Eck2NJlmaXjJzb829Lx48iSxHIaUU86CCXBDZNhANkMHQ72lmmGztjnOpHHCa9GLPkI1ewHj+dRcmgb2zgblDSTeuDw7fYSx53W2ITjxhH3d5bXsZODJOaBTo0HO7UxJmiUJjzYqfFAZ5wJusL4vL+zjDsiDZRlGcedFt9ur2eCzrsdvtVapBmMeycv+z2+1VpkeY3ndTPw+FZrkXm3Q5QmLPhdcqrORfkqX2vOjZUtzTKCJGbSyKFIMhcVJnhydSdBkuDG4TmVrWJpSJIgTlIW/DYd0UXL5+hGCYtLc2RZNjZOjpUhdFiMmshaPBZABmnEot+hoCscbrjE6dYHBUu9gI4fkxvoMC77XRRJZino4IguspIOA8gsy1jw2xwaMK8Tp4FAQhh5loMu9biFpCU03JAJW0WWxGnV2yg6kb+uTUZpAhnrcs2hny95SXGCe5oLQ1JSkMQseB2+1Vxg0rB52uQedtlFgjQ6r23S1GQumbTpxT7faCywHHSRFA2tugelvBPfKrCUyyNPVJHKOznkKEzlNCqWzAPd2nntb2sVCx5tnNXsbtv28Kh6FJdccgm33XYbn/70p/n2t799+i8hSdx888386Z/+KYuLiwB885vf5Pbbb+e6667b8DP3338/z33uc3nNa17Db//2b/MzP/MzvOtd7+KlL30pn//858+mWAD8+I//OHfffTcf+chHAPjGN77BX/3VX/GWt7zlrJ95oSCvy3Qjn07kYeqCnK4MA8heHNCKXLwkQkgy+uRejJlLSCo7cQpV1Fx+zOPViUNakUsz8FBlCWPg0uLGEUtej+kNcq/OByQhse/A/g3/tu/AXixNpiVUWrGHLOKhvlnNCXHikGbkrvOg9ZKIduQSESMUlTjLzovF4ja2cS4oqAadKKARuLRCj3REBcGLQxqBSzvyxyYtP4lohR6d0CccybMOk4TOYAdv1DM+ShPakU8jcPFGZUTIaIUejcAdm+QA2pFPLXCGsjAr6EUhNd+hF433NycOqAUOi16XWuCyz67wxOpO9uYq5JS+HNdK2XpxQFEzxogFFd2iqOnD3+Fsy2ZpMmVLpRtGdKOgP07mLPKqRLvZoNbujI2To3AHY4mQkrFxMk5TTnotFCWjF8Y01pz2nCuiJOVQzSGvK0OVi2bkoksyvTigE/vYulj1MPfDYdmcwCX1uqBbSKoxLJsip1QtjYqlnVa9tdcEam4crWuTbhxS1IwNnYcA9uYqPLmyg0XPYd7t0otDZs0CL9lzOVdVd2IpGgXVQCARJPF5bZM5XWG2qNCIHJbdfrApJBmlMEWcq+CoGrFhYJgme0sG+yo2QRqd9/7WexS90jfCWSWKqKrK3r17+eQnP7nOmnD37t3s37+fb33rWzz+8Y8/7Wf+8i//MgsLC1x00UVUKhXq9Tpvfetb+bmf+7kN7//t3/5tbrzxRt773vcOr73tbW8jSRJ+7dd+bSirsxYvfOELOXr0KIuLiwRBwGWXXQbAt771LWRZ5ulPfzrvf//7edOb3sRP/dRP0Wq1eMtb3rIpg/uxhLyhMKHnqBgGRdVE1fsr1V4Qoyg6ey19jI0HfVanJinkVB1TXnVYEKnMbqsCsUxOl4e5HlXd4kC+iv0IkGVW8OqbfoSPf+Qf+fqdq6K911z7DF7+mlex4MQU9BKloEtektEGuT9tN0KSVfZalXUuOCXFZIdssittIGSVjGzTQW8b23i0YCoqZc3ss5wVtZ9jN0BRM9mfq6BJ8ljbzasG+3JlBOu9dnfbJTLGXaF0WWG3VSRMk7EjR1lI7LZLVPVoHQlgh1WgoBpU1rBcp4wcuqyMefZCf+cpSBIUIXNVeZZpM48QAlWSubq6m2+3Fodlc+OQxxenx/KW+8oRBZqBf85lm7R1ar2AGaOAn5iUNItscoZ0/iidThutVD6zcVLWkEXECbfJDrPKyXaTA3GCrmxNnup822PJCbioOiClxD5JmmJpFtNGgShLKGgmkiaoEdL1EkzNpGSaWG6Pz97+T9TbATsuupSrX3odOUtHzlRyORV7sJN5qnqThERuDSmlqluk+epYm/STmH3FzdO+JCFxRXkGxQ2ZmZiiqBrr8lhtRSOnaiRZet7b5L5ikSwT1LsJy06AKctYukxO0ilKeUQiUTA1LpvOocqCojj//W3qAlP7OOsZ8eabb+YnfuInkCSJG264YdiZDx48yNGjR09b33H4IorCH/3RH/F//s//odFosHPnzlPK4vzBH/zBhn//+Z//+VMGen/4h384JOiMYlQS6M1vfjOve93rOHnyJJOTk4/54+oVGIrMtGmjS8YwyX4lObwXgCQr6MYaq781jOxRi64sUpAFFEeSsWUhMWHY59VZZi2qdoF3//UH+c/b/pnjh48O2daqqpLTwFQs7CxBGRwnlEYCSBGrGOvswyQms5SqKiFkDeII7TuUlLCNxy5MWUWTFaq6NRY4Qt93eNZaPwZLQmzIlhVCbMiWhc0lqkqauSFLtKAaG5IfNmO/poPjzCsrs+vebVeuyAm3PXTfkJA2fJ+SbiIJmDTWW6ueSdkKhoKpKigI8ka/DEmuzES+RjPuEEZlJFk/o3HSVvIs+h0m9BxRnHKi5XPRxLkHAn6U8LW5Lm6Q4IQxtqZQC3qog7mspI0HUBO2Ro0QNwSRZfzmzT/FfV9bdUW7YmCv2ApTJnP6cE7frN4MWWXHBiQgVZLZMdL2siwjy3hYq1pNVthhF6lucmolRF9q7WC3zvQm9blVbdKQVR5frRKVMzpezHzHo+GGyJJgb77IRE6noCvog3StR6K/PRLqJWeCsw4e3/a2t7G4uMiNN97Izp07ufrqq4G+TuL09DTPetazzuq5+XyefH79j70Wp5K/WcuWHsWBAwdO6z00TWP//o2PQx+rEEDZUmn2YnL6ajC0GkBuzsIexejAGKUp1ojnqZ/EfSmER9DEXZcVZFXlh/7Lj6z7m6nJyIpCkkIa+sM8jdEAcq3Hqx/FWFEHNV8AWUEk8Xcso3Ubj10YsoIhqwNh4ccmmcuNI7qRzxNKMxtOsnnVYNbKs+T1hhIoG6WQ5BSdnKrjxOGGeXWnC0USTOd0jrVcjIFEjaTZCN2iHPToEdILxhfem2F0nPSThKNKg726yaG6Q9lSh8fCZ4uTbR9FFuQNlbm2TzUn04pc8srGQZoQYhhAfvrDfz0WOELfXvGz//iPXPPyGyiaWzd+B2mMLivnVC8rKOsmWfeRI5OokqBqq1QshV6Y9FO0HqN9batx1r+CoijccsstfP3rX+e1r30ttVqNI0eOcP311/O5z30O09xYt2gbjy4sXSFjPZuramtjOZAPh5yuULL6CdWGMho8RpRU45RyOFsNXZbZ7NsMRcI2DBItRxaP5+aMeryOsrBDP6CgxEhGjkQIZCG2dx63ccFBCEFB1S9I94nTQZQmtEOPy0vTzNrFTe/bYRWJ0gQ/iTfc3YGVo+v8WJ7Y2aJsqYAgGZBbhCSh5KqkcUwx7Z3xOFm1NZRM43C7Qyd2MRSZ+5Z6BPHZa/s6QcyRpsukrbOrZKLKEg812zhhfEoXrJUAsn3y2IZ/P3HkKBVL3dIAyYkjJg17SwiUeVVHk+SxPMFHAkII8rqyHTiO4JwTua688kquvPLKrXiXbZxnpGRYioyhpARJipP4OHHAhJ7DUrThSnre6XLSi9hfLI0dNXQjn0boUFT7uUCKJDGV04myiGOdLoXBynI0mf2RgC71m/Go5t2i1x1KepQtlYbQyCUJTb9HJwmoaDZ51RjuQM53e5z0muwt5MmSEEtXkTSbVuARfAdr6W3jsY2SZrI48Bl244iTbhtb0Zgd5A0ChEnMCbeNKsnstIrDY904TTnhtoG+68aKfl6aZZx020Rpwi6rOMw9y7KMea+LE4fssopjQssr/W0ikRjt/XXfoRG6TBm5sRyudujx7dYS3zdzgJ126ZRlLA+OIhf8zjqbw7H7dIs0679nkmXnVLaypdINYoqGSj1w6KYxRUVF9juUyzOActrj5MoOZLfl8fX6At9fmqTnw0M1l8unc2e80E7TjMMNhzTNhsemu0omB08ucNTp4KcRe3Pl4ZiVZRm1oIeXREwbeTQhs2dm49+8snMXVUs/Zb0t+T0qmjV2vNqLAua9/hwwuoPsxhGHOnUeP/DMPlcoksykbrPk9zBk9bTa5KyZHzvy3cqyPZL97ULDdhj9XYQky5AlQXmw29YMHRaDPoNwBVVbI5UijjktjnfH3SJ6ccCC36E9kKkI44SKpdKOfE46bZZ9hyzjEWcma7KCjDRkuWVZxrLvcNJp0458cpqCpOUgjWkHXRb88TKXDBVJTTjhtjjY7iClIXoWIXSTRuiiSfIjupO6jW2cLixFG3Ksu5HPgtdh2e8RZ6s+9r04ZNHrsez38Ed2Kd0kZMnrsuR1cZPVXTQ/iVn2eyx6vTGGZ5ylLPs9FrzOmBRKmMQcd9qcdNrrGKGN0OVYr0UrHN/1X/R6GLIytGs7FYQQXFyY4PLizCnFwPOqjq1quHF0zmWbyumEcTKU9FpOAhzVIA1cUq93RuMk9Hcgc6bEvN/iPxfnqFgax1veWWk/Hm26HG8FTNirAVGUxShaQkLM4XaLhr/63XGW0ogcamG/zGnoce1zn8Hjn3TV2HMf/9Sn8v3X30DBUDatt1boc6zXohGOW+WNzwGrp1oLXoecqlPexFnlbDBh5ggHObCbtcm1c8Aotqpsj1Z/u1CwTSH9LoIySKovGipzHZ8JPYet6OsSifcXSyiSjJSoQytD6Ccd77EqQ5u+FXFwU1M5kK9gqzpRGqNt4kd+PlExLNqhj6WsHmEVVJ2yZiEjo+gGsZdSllV0zVpX5l25/oqy54KctVFVCaHo2LK2aXL2NrbxaMOQFQSCLMsoaSZ77TKGrI7tlOdVnd12EVlIQyYw9NmrOwfHxaPuSaasMGsWSLJ0LE9NlWRmzcJwJ3AFnSjAklUmdYt8NL7IKqoGJW09WUEREt83c2BD68KNUDXsh/WWl4TEjJnnULfBhGGx0y7ixRG9KCSv9Akgp1s2WciYqkIQpyPjZA4Cl9hpIOcqpz1OrmDGziH7E8y7HQ41O+zM29y31MNQx/2jT4XFbsCDNYdJWxtzM+lGPrIkuKwywWLPx/HAUxNMVUaVZCb1PAUlJq/qpO0lZEXmlz7wAT55xx0cPniYiZ17+d6X/CAzZRtFEkwZOTRJGfOLhr4s0sWFCUprTpfKmjWcA1YW2mmWIQuJZ03v2VK1ioJqIEuCOE03bZNr54BRbEXZgEe8vzVCD/kC2sTYDh6/i7DS7Gyt78uZk411jDzoJ6lfXjGGHq/Q35G0FR17MCCuuNGYqoSlyOzJlUmylEbgoYpHPngsacaYiG3VsKmyOtloqkacgI1E0Vx/BGArOo8r6RxOulRiDyErCEVDFdL/v707j5OrqhP+/7n7rb167+whKwHZFxlEAVlEQZSIgyAoguLowMPoiMzPUfHBkUfBZ8YZHZV5RBR1RFCDDLKJEARBVtlD9j2d3muvunW33x/VXenbVZ10Qrq7Ojnv1yuvV/rUrap7a7n3W+ec7/fUrIogCI0ipGjosoLlOZiKxtxoU802mqwwu87QsCLJzKozHCZJ0phzC+tliDqex+xIElkC1Ru1ahVwRPNM+kp5Yp6BKstkbYvWUHSPw9X7otkIsz5bWeFMkxRChoapaGRti7hu7tWxtUU1tqdKNEfCJAHfdbD0EG6mB69pJjEjssfz5EiV9yeJ5NpsK6Qq2+kKf92W4m0z4rRFd59QkinZvNaVIWao6CPm3lWGpbOEFI2YZtJqROnOWvRkLdpjBiFNoVmvnAt9z8XKDyBLCnokwfkXX1bppctZDBbtahCb0EN1z3sxzaib+BJWaz97A1aBWZEEneE9J8DujZCqkdRCFJwycd3cbdbyyGvAsP1xbFBJ1Jys75vtueiygtlAJePEsPVByFBlIppC0fZ2u93ukmgs10MflXnmeB6qJE9Jz2NoxPBdPa2xMClHxitld7MVRBWfiOQgqwby0GOKZBmhUSmyTEwzAgWIp0JCr2Q6j+T6HiCxKNbKIdFmuktZbM8la1ssjLVMyDziuGYSVnW6ChnazSgnts1lfrRpnwosN4V0So63K3FGUZHDSdzCIO7Q3DXY+2TDzkgUWSszUCqSH8rgfWl7mq2DhbrL0vm+z2ChzLObU2xNFdGV4GU7Y5dI2cXqdCFlaKlFTZHpyVoU7V2fDc/K4eT6URKdSENz7iRJIqKrtMcMotr+eU/KroPn+yyKtSJL+z/M6AhFG65o9kQqOjYxzdhtMtRkE8HjFLvllltob2/nqKOO2vPG+1FrRGewuOcv31gnRsvxiJsK8oihE9tzMRRlQk4WezKcNDOWeCREyZVJZbP4Xv0Lrev54NmV+Y6qhjRU8mJ386wEYaol9VDNiieTxRsagagkEkiBVW6ytkWzESKhmyxOtLIw1kJXIUuzEaJ9glagUmSZ+dEkRzfP4m3NnZVEQDNCRNVrVpLak4iuEDVU+vK75iUq4SRKtAWvMIg/ImDfmwBSlRXCmoKsVQK7dMkhZqi83p1jVXeO7qxFtuTguB4DhTIv78jw3NYUjucRNzW2porY7tCcP89jc6EfQ1YDtT7HCiDtdC8A8qierpLjMjseCpzP34r+coFDYs3EJyh5stWMktDNhluyb6IUXYfW/ThvdH8QweMUu+666+jp6eHll1+elOfrt/JsyQ2iqxIlxyVbqlx00naRrmKaohs88eUci7JcrFkLO2tZZP18YK1Qx/cwJ7G+40iGolKwy2zKDtSUcRiwCvTbGXRTpT9fJpWrrGWdcyy6iunqklWW42H6ZWy3xFZfIjU0SVn0PAqNrNWM4HgemREJALbnsjWforuYDfRmOZ7HtnyaHYVMYA1ez/fZUciwLb+rIDdUer26i1m25lOBZdegsi7wukwf6lD9xSa9MpQ4YBXYnBtk0CowK5xAkiRkSa4kvSTbWRxvm9B172dHmpgZSVR/xGqywpxIgrWZvr06tq35FJ0Jnd6cVR2WzkkSvbpJvpTDK+8KXMY6TxbdMl3FdM1a2Ias0W2nSIQlirZLf8GmJazRlSnx8vY0f9k8wJ83DvDclhSDRZu2iMHMRIjZCRPPpxpA9lgZtuYHKutU+8FjS9l5fM1Cknx6shb5YhE320MulKDHsaolnjzfr8ybVWw25wbJj1qGMFMusTk3WJO0kbfLQ+9z8Nj6SjlkJOZMwLSEYSFV46jmmURUne4RAWRvKceW3GDda8D+OLaSa7MlN1izzvdEf99Gr0TTCETweBDxqWQ5bswNYlHJ1tuRKZGzHAasPFsLAzVfonS5wJZCP7LqBH5ZZ90SO4qD7CzuGga2PXdSV5YZyVAUPGBttr9uZufWwiCGqSIVMwxk86RKdvXYUkPZdZbjEpeKpHyXza7L9kK6MgwvgkehgcV1k5Pa51H23GoAmS6X2JIbZEchQ3nERSjnWGzLp9heSAd64gpOme2FNNvyqUAlgrLnsqOQYUtukPSI75Xre3QVsqzL9hPRDGRJZmYkjuW6dBdzvJnuQZbkQFKCLMksiLfu18zb8eoMxZGQ6Lfy1bY9HdvG3CCebNMcNtiZHTpP2kW6ZJmMU8LJDVbvM9Z5MmOX2FoYCDwvQNGz2ZDvI+8X6YwZFG2Xnlwl6aYzbtIeNQjrKh0xg5bwruQYU1OqAeTa/gxrMj3kXIseKxt4P23fpdfK0WOliYRAU2R2DgxSsD1SoQg7SmmyQ3VvC2WXqKGQdQusz/YzOCrbuN8qsDbTR38p2D5Yrmw/8hoAsLOY4/Dk7jPj94ewqnN0yywSukF3MYvjudX3rd41YH8cW6pcYmNukK5CNhCsT+T3zfE8JKRAkk0jEONxBxGJyrqjiiyT0E1mJVx6cxb9+TKartNhxmsyBKOaSaefIKqZJIaWcUoVy+iKzvx4mFZz18Rjz/frThSfDJVMyyg526rWmxzWaoZRZQknCpZcRrVzpAsJZE2h06wcG4DjeoT8Eopm4MeaieqVZRxFjUeh0TUZYY5umcXL/dvJ2CWimk5nKI6hqIEfPxFVpyMUQ5akQKZzSNVoN6ND3+FdFyldVmgzo1iuE1jDWJFk2swIRdemZSjprkmvLJOY1A2ydiVztVGmfJiqxnEts9g0IuDb07GFVI1WM4SddHijO1M9T86ItRNzXfxiGs8uIWtm3fNkznKQVYkOM16TbR5RDeaEmtiS76O9JUZnzGBn1mJ7usSshIksSRhq/SFkU1OYFTd4amcvKbtMaySGLBEY9dEkhSY9TMSrrLyT0GS29QwwKMUIG0nCqlI9V5dsl1mJMLImo8pyzVBzQjeZF03W1O+N6yZzIolAlnPBsekMxWibpAoVIVXjyKaZvDywg5xTrr5vY10D3sqxAcQ1g5nhOFFVD0wTmMjvW8m1iWk6hqIS/AkytRrjmy1Mmo5QjKahxeFt06YppOEBVlmlJRImMmq98IQWIqHt+gK1RHQKZZd4yGBZU7Lm8aciWWZYixnBcp2aOpNtZpQ2M0p/AXaEEiSkEgUVirZKIhwioWmVIQW3jOnbhPUIHU0zyUlSpRRKA5VHEISxNBthjmqZxSsDO6oX8dHr9mqywiGx2tqKiiQzr07mqCRJzB5j9ZcZ4TiyJBPVKxfqkKqR1E3ysszcSJIZofoZpFNldjTJtkKmktgny3s8tmGtUYnmrIbjQbms0hppQwtbOOmdeKUssmbWPU8C5CyIKjGaRi2ZG1I0FsbaSJeLrM7sZFG0vW4AOZrv+5Q8h4xXRNHLxPwwki3TETOqRadheB3oXcfgWnlaVZt+LY7vRmgPG4QUBdfzkWSImxohzaStzlzUZiNcU9YG6q8XnSmXWJJondQf3KaqMT/WzEsD28fMWh6+Boy2N8cGld7OxfHW2n1QNBbGW2ra98f3rejazI00Ndx1SAxbH8TCuoqhKSRNDUOTAyUndn8/hfY667r6MCVleoZFVSNQrHW0cMhgRnOCTKlMi+4S1hXSBZtUyabseuiSi04ZX67UeHQ8j7DSWEMFgrA7zUaYE1rnsCTeiotPVyEbmAu5v/m+Hygf0myEyTk2Cd0krk/NKMRYYppJhxklVS7ueeOR99NVIoZGzFCr58mSnkBSVJxcP75X/5wzniSahB5CkiTezOzElqzqEPb2dCkwRy7vWGwvpHg1vZ2XBrewPttLkxlmRrwyhL0za+G4Y5/7vFIWRXLpbGsJJNHkyy5JUyO0H7Ksy66DIst0TMGPhiY9hCGrk75s4WRwfX/SV20bDxE8HsQ0WaIppFFyPdqjxrgDSB+fmBmc2+j7PhKgTeBE+D0xFJU6lS6qJCQ6W5rwPBvbKtIa0asB5I5MiaTiQDGDrIWQ5MqKNY02z0QQ9iSiGcyPtfA3bfM4qnkGRdcOBCL7i+25qLIcGC6N6SZNujm0LFvjXV7mxZrwfH+v1gOXZYmOqE7JcavnyUFHpSiH8K0CXnnswcTxBJBR1cBUVVZnu8l4OQzDpb9UYONgjj4rx5uZnbyS2saWQj+u75HUw7SaUUKKhqEqdMT03QaQvufi5vqRtBC6GQtkYWdL9h7rS45Xqlxi1qglAieLrqjMDMfJlPd+xZ5GNpzM1IjXocb7dgsTZvTlI10uYlGiaJeRJal6YtyeybEpU5uxVnJtugoZLM8mpO366FhDy5M5nj+lc5wMRWG4Zz9dLrKjkKm5SMjhEL7i0D3QjyRJtEZ0HM8nrIHn9JHJDaBEK8MPPv5+XRlBECZTpRcoRkw12JDtp68UDHKGMztHZ4gC9JXy7CwGkwKgkrXaVchgey6252IOFSkfpkoyhyY7xizcPNUSeogFsWbWpPtwRmVYjzy2kdLlIkW/iOu7+D60Rw1MXWOnb7Ip3Uc+3R3YvuTa9JR2JbAMB5CDRYst2QzZUUmJjueScyx8fLYWBthR7qPP6+PFwc08tHU1W/MDJPUwLUMr3chDiT/9VmW5vJEB5JrBFNvT/Tz8qzv5xf/9Nn+8+y7KuRTpUoZBzcCVdpXxAcjYRXJuoeY8mbOtoSSP4DVgeD3nnG2Nai+TLpeYOcaw8WToCMXw8as/lMa6BuztsVmuw45ChvSoHmvbc+kqZBiwgsk2ru+xs5jdL9+3rkIGmPwlf8dDBI8HkdGlHLbl03RZg+RcC9fzqwFkniJvDPawPRfMNBssF1iX6aVMKTDM0W8VeDPdg+d71SUQp4Ihq6iyUlmYPp+uZNGN+mL3OC453SZlZSiXLUqOR3NYoy0BG1Lb6NPDyNU5MFLDTPgXhH1RmUOVZEOuny35VOCCmXUstuRTbMmnyI7I+Cw4lTWbN2UHAlnItueyLZ9iQ7afQatIyXWIj1qyDSqlgxr5ezM32kRbKMKWfKraNvrYhg2fJ7cWB/EVm3zZrZ4ni4bCm2WbrYM78Ua8roPlApsL/fRZI1a8iuh4is2OQpq1qYFA8JBxSmwrDDJYLpDQQrQYUebGkrSEdAatEttyuUAAVHDL7Cyl2VlKV9dINlSFRFiiK9fPt666iv93w1e55//9Fz/8ypf5l09/mq5yiR2+T2Yow1qRJaKGgqLbbC4M1Jwnu4pZ1qb76BtVkqavlGNtuo+uUVnIm3KDlR8qdVZomSxxzSSph8jZVvV9q3cN2NtjG87I3pZPB963QavIhmw/20aV1UmXS2zKDuyX79uGbD8tRqjh5juCCB4PKiMnYEuSREIPMSMcpTVkVovIypLE3FiMFjNC0fIDQ9hhRceUTebEg8tNRYYmyif1qf2Q64qKIavYnkdCD9FqhImMGkKJhxLMjsSYpXsMZHPkLIeFrRHaNIUWIBlrRx7KQpzqYXhB2B+azTCzQkmiio4xIqEtpGgkNJOEZhIaMfRsKEplTWojFGhXh5bqbDbCRFQd23MnrAj0RFJlhRNa56DLavWiP/rYhg2fJ1uNMHPiMSxn13lyXlMLrdFWiiWJTHbXijNhRadJC9dUnpgRDVeSFV2FgcKuoKLyPoSIqWYgg7ctHGZ2LIbqa6QKTrVHTZdVoqpBVDXQRyyOENV1ev70J7pefjXwvG/+9RVWr3yBZDgReD89YH4yXv88qRm0mhEio4LByFD7yGxm1/cwZJVlyfYpPf8P/1DKO+XA+/ZWjg0q17dWI1ydn7qrXad5qH1kp0lI0UgaIRKa+Za/b1HNYEadJQ0bQeP+PJzGnnjiCdLpNLquc/bZZ0/17lTJSPgjBq9nRxLMCsfpViw2DhQZnvrSakZp0sP05svVeTpRQyWqmiyItHJIPPhhTughFsZaaDKnvgJ+VNMZtIrVYxt9MuuMxEg2zyZd3sB612J2UxtNqkN5sJtl4QRqJIakmTiehyzJgZOzIExHYVXn0GQbmbIVCE40Walmjo78niiSzMJ4S3X9+mGSJDEv2lRtzzrWlMxv2x+ajDBL4m2sy/Yxc+g8MfLYRho+lzge9GXTWI6LoSq0mTHisxexY/MGevv7UUMJoqZGQg8R18yax4lpJrFEC/1ogbWwQ4rOIZHWmu0NRWNZsp182aU/X6YnZ9EeNdBkhbnhSgbv6PfN6x2knnKqwIJoW3X7suOhyTJLm5pQZWqeuyMUo92M1rS3GGGaRwVRvaU8ixOtdbOWJ1uzEUZXVMquM+Y1YG+ODRjz/YxoOocmagPmkKqxNN5W07633zeAGeFYQ853BBE8Toi7776btWvX8txzz9HX1zfVu1MV0XR2jOqSlySJqKECfuDDq8gy7VGDnqE6kMPihoqp1fbGOb5HZIpWlxmpSQ+xs1A5xrF+BSuRBBFNYaGeIe70U1i3GlmPoIZiSEa0kizj2GiSLHoehQPCjFCcnmJXTfvueorGuk2SpGqCnNnAw9N7Mi/WRL+VZ8AqVAOf3R2zpkBbRKc7Z2GolR4lLRSnM2HSnS/Qk8pAU4KoMXZ5L0mSaAnvqgMJlQByd9tXzs8EAsh6ZXwAOubMqdveecjCwHPkbZfWsI6m7Nv7PyxdLhJRdRbFaoPfqWAoKjNCcbYXUrQptQHisEZrr3eb5TkYstawwaO4Mk6A//iP/+Duu++e6t2oEdXMmom6AGFNwVQVSk5wsu7IJJr+fJmBQpnW6NgnukZILmkxI8iyVDPpfSRZD+EjEUpvwd76Imq0DTXagu85KGZlSN7xPUKq1pAZo4Kwt5qMMLpSmQ+8P1QyrZUpW450f9BkhUOT7Xi+X5M8MZbmsIbr+tXzqKxqqJFmWlUL3cmNu9zZ3qyFDZWRn5aIjmV79OSsMbPnT/3gBRx2womBtgVHHsE7P3hhoM3xPFoib+29K7sOluuyLNmB2UA90B2hKK7n1ySfTDcl1yGhmxO6lOdbMfVX+zpWrFjB7373Oz7ykY9wzjnn7NfH9n2fRx99lLvvvpu5c+fypS99qWabfD7Pj370I1atWsXMmTO54oormD17dvX2Rx55hFKptnbasccey8yZM/fr/u5PYVWj7LnsLGRpNSPVorKyLBEJSawfyDA3Hg8UeLU9F1VzKDnguBAztGr7oFUkqumEVR3fpyFWYgmrOh1mlL5SnhYzAlQyAXN2mfDQyaRSikfCUk3yiRkkVR2TSna1pIeqWXSHJtqn8EgEYf8xFJV2M0p3IYMkSUiSVDPMOGAV8H2fZiNc07tU9lyajXB12NvyXPQDYPWlhB5iaaKNVwa60OVY9ZyYtS2Kjk2zEQ4U35ZlD0uyyFoScbPSIySHE7iZLiS7F9+L0z+UZBs1KnMqs06JkKIH5rO5voek2SiuH+iBdH2PjF1ElZTAWsa+7+NIZTTdxSoT6IFM20V83yehhdB0na/8+HYev2cFm1a9TLQpyrEf+Bh9JY9OzUNVZAZKRYpuGVMLTj8aPk82GaHA+1pybdLlEgndrP5Y8H2frmKWw5MdDTFcPVJSDzEjHKO7mKtm/e/NsUHt9W2Y43kMWAVCqhZIDnL9SrsuKyRGrErj+z4DVmGfvm+25zE/WltgvFE0XPC4Zs0arr32Wvr6+njb2962X4PHQqFQDfCKxSKvvfZaTfBYKBR4xzvegaqqXHLJJaxcuZJ///d/55lnnmHRokUA/PSnP2VwsHZuyfXXX9/QwWNI0ZCAN9LdHC510BHalfhS8gtsLw1iahKzRixo31fO0V3KEJPCdEST1RI93cUcm3IDtJlRlibakKTGCB4BZoYT7ChkqsPwW/Npeks55hIiSgJJllFi7fT6vXSVcsz2feaGIki+hKwa7CjmWJPp5diWWVN9KIKw33SEYqxJ9zJQLqBKMqaiVi+MBafM5twgju+hyUo1EabsOmzODVJ0HWSk6g+yolOuu2LHdDQznCBlFdleTNNhxvDw2ZwbJGOXWEhL4Dy5vZgh62fIFsrEzcr8NVkPMyCr7Mz1MiOWwtBmVHsT836BHcUUTXqE+ZFdK5CkygW2FQeJKAYJPV4NIBXNYVthEEPRMBWtek7Nu2W2F1MokkyrmSBfqvRAxkMyXcU0ru+hygpR1UDVNE45/1w6jpmFBcRboljlSh3I9qjGptwAhu6TtmOY6q5jGz5Pzo82B1Y52VnIsrWQYk44yfyh1VL6rQJtZpR5DRjcSJLEglgLPaU8JdfGVLS9Ojaovb4N67fyrM/2E9dMliXbqz+mUlaRDdkBQorKoQmtWm0ga1tsyA3s0/etaVTyVqNpqODRsiwuuugi/u///b985jOf2e22Tz31FC0tLSxdujTQ3tPTw3PPPce5555bcx9N07j33ntZsmQJf/d3f8drr71Ws82tt97K5s2b2bhxI8lkkn/4h3/gb/7mb/jKV77CL3/5SwB+9rOfvYWjnDqSJDErkmBTbrBmiLk5ZDInHsEaNYJiyCoRVcd3ZGYndpXlMBWVmGYQUbXKr2hJCtR7m0pJPURMM8k7ZaJD+1jSDHR3RD26WAtGpoeYomHKCr5TRlI1JM3E9H3imkmogb+4grC3KhURTNJ2ibCqB37sabJCWK30fI1sV2SZkKqRsS3S5RL60Pfe9X1aD5DgUZIkliTbybtlBspFmvUQEVXH82vrvEZUjc5whEFXwvV8FFlCkmXCkRYi+UG0fIq2prn0Fmz682VUvTIaEho1vK/LKuGh3sjWkMGAZJOzHBTXw1R0dFkJzGvUpEoxdkWSSZg6puJXphJ5Lrqi4uOjjZhiI5VyaI6NEm8jZoRIGgrd2TI7MxaKr9IZ0eoeW0kzauaxmqpGXDOrQ9PD9X/f1tTZsEOqEc1gYayZN9O9zAxr4z62avuI69tIhlLJdI+oOjJSoD2sapiKGnhNNFkhMrQO9t5833x8worWkPUdhzVU8PiFL3yBo446ig9/+MN7DB5XrFjBf//3f7Ny5UoWL14MQG9vL2eccQaLFy8eM3hcsmTJbh/397//Pe95z3tIJpMAyLLMRRddxA033FA3G6qeF154gY0bN2LbNvfddx+LFi3i0EMP3eP9JkN7KMohsaaaBd9nhRMkZkRYtTNfzSYEaNEjxFWTTNGtDllDpZZbQjdRJZmy56JJjTOEpcgycyIJVqW7iWoGs8IJOkIx3Oyuel+yHqZD1WkLx1DsEn6xiBxrR5JlWs0IC2MtDRMMC8L+oMoKC+ItFFybGaEY6qiL2aJ4C75PYJhWkWTmRZqJqgbLkh1szafoKmTwYdpmWtejyQpLE+0827sVx/eYF22qubAD1XPJOgrkyy5xs3IJbY13EMn14Wf7wS7QHo3Sk7OwyiodoSYSRjAIiA2Va5Glyhrbu9bCdkjocVrDwaQYQ9GqPZeKJDM8YtqfL2NKYdqGsrChsqKMlxtgthlHbZ6LMRSAdMR0tgwW6TTjHNOaxFSDl//hYxt9zB1mlBYjjCrJQ8OwRZYl24lOYU3H8ZgVSbJjaHnO8RzbSCOvbyMl9RCRZCUYHBkLRDWDQxOVnsiR71tI1VgSr4zMja50MNb3bWGslaxdQh7qrWxUDfOz4Xe/+x33338/3/3ud8e1/c0338y5557L6aefzvr16+nr6+OMM85g7ty53Hnnnfu8H+vWrWP+/PmBtvnz55PL5ejp6RnXY/zhD3/gJz/5Ce985zv54Q9/yDPPPDPmtrfccgvt7e0cddRR+7zPeyOs6IGLxjBJkojpGrPiJpmSE2gv2xA1VcJ68OOiyQqSJOF4HoaiNNSv0FYziiIp2J5byZQcdcyyHkLWDJRiBlkz0TqXoLfNBypLQqmyckBdHAUBYHY4SYcZCxQjHqZIcuBCNixrl5gRijM32sSJbXNZmmgjqYdqetOmu4QeYnYkwYBVQK5zzgCq55L2qInl7krKUzQdPdYGbhknOxBINkwXK2tIj6aO6l0cTqIplj0GC7UJPIokBwKQ4SQa24H+/K4lKD0rj2/nUbQQurGrd9hQFaKGyrymcE3gOPLYxmqXJInBcpFWI8KsBq09OJImKyxOtJJ3bTz8PR5bvfuP1V4v232sdlUOvm/Dxvq+yZKE4/s0TXHd5D1piLB227ZtXHXVVaxYsYJYLLbnO1B502+99VauvPJKTj/9dJLJJDNnzuS3v/0tur7vXb3FYpFIJBJoG96nYrFY7y41/umf/mncz3fddddx3XXXsW3bNuaMUWZhfwqrlXk0tufW/TK1xXS2p4uUbBdNkUmVbBRJYl587A+y7bsktFDd26ZKSNWYEYrRVczUnZslKSpa+0IkVUce9SUtuQ5hVTvgLo6CoMgySxJtPNe3pTofbHd836fse8wamiemyQrzYy3MjiTr/gid7uZGktUl7XZXPSIeUlElAqM0SqQJNdmJX0zh2R3ImllT7mxPV7eRPZAj/x7L6DI+bREdJ9ePW8xgti4InNc830eTJZrC+3Z9tD0Xx/NYGG+ZNu99qxFhhhmjz8rTakb2fIcGYbsuSaOxrqmjNURX0Q9+8ANUVeW//uu/uPzyy7n88svJ5XL86le/4tprrx3zfpIkcfPNN5NOp3njjTf4t3/7NwzjrXWlx+NxUqlUoG1gYACARKLxf23tia6oRFWdomtTdGwGrEKgpIGuyHQmdDal0/TmS7RGdI6YEav8wvVcBqxCTbkPy3UwG/BkMiMcx/a8QHmisuswYBUqWeSRJMpQptvIYyu5Nok6RWEF4UAQ100Wx9sYsIqkrSKZOr2QOdsiVa7cltTMysooI0yX4GFvRTSD+dGmwHrF9c6TmiyRDKlsyWara2TLehg5lMCyCqQyPdieG+iB7M2V6CsWsNxgr6Ln+2TsIsWh9uEeyGzJZnM6U10je6ScY5EbWuJuZBmfzX19DA5sA9VEHvGjuejadBcKhA2FqL7rvat3bBA8Tw7rt/LMjSZparDs6t2RJIn5sWZc368eS71jA8a8vg1nUhed2vdt0CrWvD++75MqF2vWyAbIlEu7/b5Vr1VSZZSwkTVE8Lh8+XK+8Y1vcNppp1X/aZrGwoULOfnkk8e8XyqV4j3veQ8nnHACF198Me9973vZvHnzW9qXI444gtdffz3Q9tprrzFjxgyampre0mM3imYjTNGx2ZwfZE26l95RC7iXpCKWkqc5DotawtV1rLuLWVane9lWSAe27ynmiWqNt0xZQjfBr/QkDttWSLM63Uv3qGLpI4/N9txpdYIUhL01K5yg1Qjz4sB2Xh3soruQpeCUcX0Py3XYkO1nQ7af7lKeudGmg6re6exIkpCqU3DK+L4/5nnSUkqsSfeyI58BKoGKGm1hEJf1A1voylc6HYYDyBIWG7ODrM8E17ZO20W2FAbYUUxVg7iWiI6kVtY2fn2gh7I3Ym1rp8y2wiDbCoPVwCVqqMRDMpsGt/HqQC/FeBvy0FxH1/fYUUyxIdeLrrnVH8W7O7bR58mCUyak6A1dOmYscd1kVjheXbN8PNeAkXpLedake9mcHwy8bwNWgbWZXjZmB3C8XcF32i5Vvz8j1yTP2Rbrs/2sz/YHAsuR37e0XaqOCoYbfNpUQwxbH3fccRx33HGBti984Qscf/zxXHTRRXXvk8lkOPvss0kkEtx7772YpsnHPvYx3v3ud7Ny5cp9HgK++OKL+ehHP8qrr77KEUccweDgID/72c/46Ec/uk+P14himoEPaJKCrqg1k4JDqsrCligtoWBBcEWSMZTaxBhNVhpyJRZZkmkNhSk4NiEqX0RNVirzM0cd88hj86Hhv7iC8FYossyhyQ4sz6Xo2oQVDdf36S7mCCkqmqxg2Q5J3aTlIPshZSgqC6LNvJbaSTisj3meTOgabRGDroxFR6gyfC0bMVQ9gloYRLHyEKuUeZEliY6oyfaiQrZUmQM5POSsSjKapNQ8flvEpMfSKDs+g3mbjlhle0WSqucvZeRay7JL3BpgUI+TJUGL71fn4Em+hCYpNIWC63aPdWyjz5NZ22JxvLVagma6mRttYkchUw3M9nQNGEmVZHRFRZOCcyBVeahdVhg5SKVKcuU9HTUHcuQcx5HPXZ1f61XuW3IdIqre8K91Y+/dbvyv//W/CIVC3HfffYTDlZPbT3/6Uy677DIuvvhinnzyybr3u/766+nu7ubpp58mlUpx+eWXA/DjH/8YWZa58MILeeihh3jnO9/Jqaeeyl//+ldmz57NV77ylck6tAkXVnUkJOZFk5Q9ryZQmhVO0GJECI368HaGYiT0UE0GWJMRatg1oFuNCGut/urfezo2RZLI2lZDl0gQhP0hqhm8o+OQ6t++79NTyrE63UtMMwmrOgtiLQ1/EZsIneEYm/OD5O0y82NNWK5bc56cE01yykyVv27N0Z0t0xHTMVSFjuRMInYJo1TAd10kpRKMNBsRIokW+n2tOgcyaqjENJP5cktNQkxY1TmqtZPBvE3JrpTmaYnogczrkYGOVEizSPax2udQ1CLVQuKKJNOkxulIxpkRDc7/HuvYRp4nh5MO20Pjy0doRFHNYG40yZbc4F5f31rNCGFVx1CCQWVSD7E0rtYkxEQ1g6WJ9pqkq+E1r4HAd6qSed1aLQ3VW8rRPg3KYDXsWeF73/seRxxxxJi3f+tb3yISiVQDRwBFUfjZz37Gtm3bxrzf29/+djKZDKeddlqgffgXhSRJ/OhHP+Lqq69m1apVfP7zn+eUU05BUQ6cOT5hVcOQFew6gSNUfgnVa5fqtA934zdizyNU5jD57Bpq2NOxZW2LiKo3xFKLgjCZJEmiIxQjrpmsz/YxUCrQEWr8i9hEUGWFdjPKlvwgEU0nrNbPiu2MRuiMVWo0DgeQWjhByIjh2XncUgY1smu6k6lodIZCgSSaqKFijJG4pMsqHTE1sOxhS0Sv6R3zXQcnsxM9kiTW0klRMQJrYTuuz4KW2vdSkeQxj234PJm2Ktn20/0H9exIkq35yrSk8V7fho3VPlZFjrGuH2P9EBv5frqeXy0a3sga9gr5kY98ZLe3d3R01G1XFIV58+aNeb/ly5eP6/mPPvpojj766HFtO93IkkzSCDFoFd9yORrH91BluW7mdiOorKoj1V3Tu56SazM3cmDMbRWEfRFSNQ5PdpJ3ykQavJbfREroJm5u9+cNU5VpHcqILtpeNYBUos24vRspdW/g6efW0LNtO51z53La2e9BCYdrsrCHh7DHsqcsbKeQwnccZDOGbMaIDp2P+/NldqRLRHSZ+B6eox7f93F8jxnh+F7ft9GEVZ350SY2ZPsDqwY1kkot6ekxbaphg0dhYjXpocCEYXto7lNUDRandTyPglseqg+56xeq5/vkHAvJpzq/oxGFFA1Vlumz8kQIXgjqHdt0+dUnCBNJkqSGLwI90SKqjkTlXDd8Tqx3nmyJGHTlSsRCkCpUAsj2UBLHdrjln77K6jfWVh/zid/dw5dv/ymKqhIxwfb8mgCy4JSH5t8FL89hE6xRa2EDFO0SpcxODN9HjbciDZ2LNbVyn+60zYxYFGOoh7HsOpQ8h5hqBObw1Tu2rG0RVw2SB8g5cWY4webcYHXu456ub6ERS0RCJbjLOhamrNb0IuZsC1WWa8pf5e1y3V7NgmPj+z4RbdcPActzKr3BDZ5pDQ2SbS1MvqhmMLIzbksuxZupnpoMtK5ihlWpHrqKmUD7jkKaF/u28Ua6m7CqNWxZG0WWMWSF1wZ3Mjii/AbUHtt0+tUnCMLECg8tQVcaUVqn3nkybqoUvCLrcn2ouo0sQU9R4smnXwsEjgCvP/88j9+zgtRQhrWtFNFUqTosnbVLbC70s604WC0BBJWAckt+gLyUQ9cqPZD9+TKW67B5YAtbcr1YmoEcqpSTczyXbcVB+pw0iurSEa8Ef57vszFXybDuH3U+rHdsecdmXqz5gMm2D6kaHaFYtVzOWNe37mKWN1M9bMmlAu39VoE16V425garRdkBUuUiazJ9rM/0B0oA5WyLtdk+1mX6Ap+jkmuzLtPH2mxfIPM6Z5cxRi1x2Kgafw+FCRFRdTRFqal1NR49xRxFx+XQZAdnzljMoYn2CdjD/acjFKXs7vk4y8MlEqbBrz5BECaWJEl0hmI19f1GU2WJ1rBO0XZRFInOmIEswZauwbrb79yypfr/kXUg+/Nl8mWn7n1Gag5X6kDmLIf+XAEnPwg+yJGmanmeYWXXIx5SiZl7P8hYdGwMRTngsu3bQ1HK+3DdmwyW6zCzQYfURxPD1gcpXVFp1sOkykWajBBzo0naQhGianCoakYoTkI3qwGV7bkosszZsxcTHTXs0ahimsniRCtNXrBHcfSxlVybhG5Oi199giBMvBYzwvYRvVJjnSeXNDWTLvg0a2FURaYzZtAyZ37dx+ycO5ekFkKXFXS5kq07PAfSsqDVTBA3gsvIhlWduZFmJCQMRcUYWiwlNTBIpCyTNBNEhzJ5oZLwMzvURJ9fYkEyhiZXztOyJHFItAnLc4mOCjRHH1vGLh2Q2fZJPYShqJRcu+YaMKwjFCOi6TWrjLUY4crrP6oMT1IPsSTeWjP/P6oZLI61IklSYDjbVDQWxVtrhq1lSSKmN/bKMsPEVfIg1h6KVrvSNVkhrpk1a3OqskxcM6vzQQpOmYRmEptGK7CEVK06f2mk0cdWcl2a9QPrV7YgCPtu+MK+q6pE/fNk3NSYHY9StCvFolVF5vyLLmTB0ccEtjv8+OM59YMXIEkSEdWoBhojeyALJbDrdECGFD0QgDQZELIGKDsyltqMPGqJWE1WMGStZjlCXVGJabU//Ecfm09lQYkDjSYrzAjHyQ7NURx5DRgmSxJxzayZyy9JEjHNqBtQRzWj7nKflWz92vawqgUCx36rQEI3iU+TucYH1k8KYa8kdBNZknF9r+7C7fWUXId502yVgeFJz45Xu8zXSJVfgdPjiysIwsSLqDqmrFJynT1WpmiLGPTlLBhakMAMmXzt9p/yu5/8iL4tm5g/bxbvuehjqFr9xxkOIMebhe3m+mlSbTAUCnqcgYIdyMIulL1KHUl975MZbc9FleRpX55nLG1mlM25waF57lPfCZK3y+D7HJbsnDY9vdNjL4UJEVI0YppO3i4HMow936+cLBU18MXyfR/LdYhNswBLlRWimkHWL455bI7n4vi+SJYRBKFKlmSajTBb82lmjTo3jD6XxE2VkKZSLDtIio8uq4RCJhdc/nG2bVqP57sUCynkZFPNj3XLtYdWIFECAaTtOUQNraYHzLLylDM9qEBbSysZI0rOcnA8l6aIhi6rFG2HRYkwkiRhey6O59UEwJXlKGvrHhaccqUe8DQJZPZWYqgIftG1qwGy7/sUXQdTUWt6lgtD8z9Hv29Fx65bqs5ynZoi4UB13eyRAaLtuQyWCxzbMntaVfoQw9YTYOvWraxbt46NGzdO9a7sliRJzAzF2V4IZpptL6R5M93NzlGZ11tyg+Sd8rQs4dGkh7BddzfHlsL13Zo5LoIgHNyazTBrM730jVr/efS5RJUlOqIGW3NpNub76LEq51UjFGNmWys5p8ybfdvZONgVeJysXWJTvp/tQ2tbD/dAerLDqwPdrE73BhIbS3aZDT3r2VxKU5ZklFgrLREdU5NYl+3j5b6dZKwSqiKRCOm4vsfG7ACrM72kysXAc2/KDvJmuqfm2DbmBmk6gKfwKLJMhxkha+8ajdpZzPJmupvto9a27ivleTPdw6ZsMAEqVS6yemht6+E1yaGSYb063cO6TF/gfSs6Nm+me/hL72a25dNk7BIl16arkGVBtKVha0+ORQSPE+Dv//7vec973sMJJ5ww1buyR0kjjCoFi2jbnovluoEvBEDeKTMznGjYmo67E9UMPH/3xzYrHG+IIQxBEBpHdGhu4ujKFPXOJU1hDV+CkuPgeLvajWQbTaaHWy7S1d1F0dpVnsXxPWzfxRnxOLIk0RzWkBUYKFjkrF0Z3+VcL+ViChcg0oSsVXqrmiIauiqRK9tsSRdoDmuYqow/dN4ru8F98n0f2x9qH3U+9P1K0HwgazNj+L5fvfYN98KOfp8d36PsOti+G7hOOt5Qu+cGyt45vld5Tz03UM7H9T0ytsWieBuHxJoxFZWi4zA7HGdxom3aXXsOzD7pKXbvvfeSy+WYP3/+VO/KHkU1nc5QnIJjVyfvzg4nSOqhmuHpuB7ikGk233FYpUfRH/PYmo0IM8OJqdk5QRAaVljVWBBrrhlSrHcuCesKCxJJduY1OsxItV1WDWZ0LibibaDPha6dXcycOQdTU0hoIRRJQpPVwLBoXDM5ormdgYJNpuihyQ4hv4ie6WO2mQQ8YsnO6va6rHJ4czu9+RK5ArSGK/ulyjKHxJqxXJfEiGOQJIl5kSbazWig3fN9OkJRwgf4KExcN4hqRnU0rSMUI6zqNVnobWYEXVYqq5WNCPCajfBQMXclkHCT0EwWxFpQRxV6j2oGnaE4RzR1MiuSBMD1Kj3N0y1whAYLHtetW8ef/vQnHMfhmGOOmbCeuy1btvDb3/6WtrY2PvrRj9bc7vs+Dz/8MKtWrWLmzJm8//3vJxTalcm2adMmHKc2Ha6zs5NodHqtBStLMp3hGBuyA9XgUVdUmuvMdVElmZg+/YasoZJxrcgKkiTVZBB6vo8qy9NuLqcgCBNPHcrOHbSKREacFsc6T86MhUgVHNRR8+P0SAvJSD8h36U7N0hXt8bMzk4MVSGu1ZZnkSSJhB4ippn05Cx6M3nihe1EVI2IY6EmZyKPKhkUVnU6wgpR1QnUdgyrOuE6V/uQqtXMgyy5NjHNOGCTZYbJksysSJzVqV6iWqV3uV52uTI077X2/hJNRv33LVmn3E7RsWk2QrSZu2KE6VwWrmGCx2uuuYaVK1dy8sknY9s2119/Peeccw6/+MUvkPfTC2xZFh/+8Id55ZVXCIVCtLS01ASPrutywQUX8OKLL/K+972PH//4x9xwww386U9/oq2tUkfr4osvpre3t+bx//3f/51zzz13v+zrZGo2wqzL9u92m5JrYygqkWl6QtFkhZBSyZocPexedGwiqn7AnywFQdg3zXq4Zp70WBJmpSh3vuwRNXadayRFQYm2oGS205EI0Z3tpatPY0ZrC4Y69lQgWZJoC6tsH9hOf76MH1KIheMo0da62+fLDnOSIVR533qziq5Dhxmdlr1he6vNjLJO7q8uVziRDrS6mQ0T9l500UW8+uqr3Hrrrfz4xz/mgQce4M477+SZZ56pu/2GDRvo6+uraS+VSrzyyit17+P7Pp/85CdZv349p556at1tfv7zn/PII4/w5JNP8l//9V/85S9/wfd9brjhhuo2Tz/9NOvWrav5Nx0DR4CYZmDKKtmhJZtG8n0f23PJ22WajVCgcO10E9MMLHdXj3H12ByLNjNyUJwsBUHYe1HNAB88z6u7KtfwucT3fWRZYkbcpFiuzFMcbgdQjChqcgaKU6QlomIP7qSrr5+SvesxndFz5cpFnL7NNPlpNF1nwPKxwu1ISuVc7Ppedd6l43pIkkRLWMcb2qfRxmofPgbbc2g6AOs71hNWdWaG4wyUCjW3jXzfRrd7e9HueB4l10ZCmnZJMbvTMMHjKaecEvg7EqnMF9HGqIl10003ceaZZzIwMFBtK5VKfOADH+Daa6+tex/TNDn//PNRlLEDoLvvvpszzzyzOl8xHA5z6aWXcvfdd4/7WHbu3MmGDRvwPI9169bR37/7Xr2ppsoKrWaYF/q312TddZdyvJHqpquYoWXEHJ7pKKLqgWWpho+tt1QgWWf4QRAEAXbNj1uX7eONVPeY58nuUg6AZEjD0BR2FrJsyPfSY+3qtVRjbeSNKNtKKTDLOAPb2L51I6VSiYJTZmO+n62FAVzPxcn2Y/esp2Tl2C7LWP4gRrKNQVshZ1WSNbYWBtmU78dybbJll5aIhq5JrM/28Wa6J7B2su/7bM4NsirVU5N5vb2Q5o1UNzm7PG1HmPbFzHCcjF0KJD71lfK8karNvE6Vi6xK9VRrRA7L2RZvpntYn+0LPE7RsVmT6eXl/i46QtFpWalkLA0TPAK88sorfPOb3+S6667j4osv5sYbb+T444+vu+33vvc92tvbOfPMMxkcHMSyLC644AJ27tzJr3/9633eh1WrVrF06dJA29KlS+nr66vb01nPN77xDZYvX05zczPnnHMOt99++5jb3nLLLbS3t3PUUUft8z7vD3OjTST1UM0JpeTYpMtFJKSaJbmmm5heqe1VGFqrtuTYDJYL6LJCbJofmyAIE0eWZGaGY/RZBbK2RckNznkvOXal9IozvGKXRGfUoL9YCQiL7q5saUlW8OOtFGUZ23NoM8EZ2MLWDW8wOLCdXLqL3MA2Ct3rcQa2gKrj6REKpQxOKEprS3N1Lex0qUzJtSm5Npbn4rgeHVED1/MoOjYFxw6Mtnj45J0yOccKtAPkHZsBq4ChqAdVvdu4ZjIrkiA9YuSt5DpkbYv8qHXNLdch51jknTIefqC94NgUHRt3REa77bnkbAsfv5okc6BoqMF3y7JIpVL09PTQ399PNpsdswK8aZr87ne/47zzzuOss86ivb2dbdu28dhjj9HS0rLP+5DL5Ugkglm3yWSyeltra/15JiN997vfHffzXXfddVx33XVs27aNOXPm7NW+7k8xzeTdMxby0sCOwIozneEYsiQTHVribzpTJZnZ4QTrs32EVY3OcAzP9+kMxzAPopOlIAh7r8WMMDucRJNlWkeNwnSGK5m68REJha0xA7PHpFk3SJrBkY3mUALaFiP1bkAppZjV1ky3bZLvG6TZ9IkoCprnIoUq1yKjkGFWuAWzdS4RzSCk+vTkLPIlj7geIawrSI5CwpSJGZWi5fOiTZQ9NzCqokgy86JNQ8kbwaHpOZEECpXav9N5etLekiSJRfE2XujbWo03OkJRdFkhqgWveS1GBAmpkoA5IiEqaYRYQHNlvfIRcxpjmkG7GaXFDJOcRgXAx6OhgscTTjihmmG9evVqjjjiCI477jguuuiiutuHQiF+/etfM3/+fF566SXeeOONcQV3uxOJRMhkgkWz0+l09bYD2cxIgpRdoquQqc7NqKx6INMeik3rzLBh7aEoG7KVCdKmohHRDDoPoHkogiBMjKhaOVcU3XJNcoWpaJih4A9QU5VZ1BJlVXeO9lDwUqtIMm3xNlw9hN23GdwynWGNHiWB44NpKMiKhOdaYOXR4i20xWcgD/3IHbmUoVUGVdMpui7zWsLVzpZEnYxfqAQ09SpLhFWdmG5M++lJ+6LZCJHQTbKOVV3Tuj1UWzmlci2sbVckOZBFPcynkpW/KN6GPM4lgKeLhj2apUuXMnfuXF566aUxt7Ftm8svv5xZs2Zx8sknc9lll9UEfntryZIlrFu3LtC2bt06mpqaqtnWBypZklkUa8VUNDLlEgNWgQGrwPxoMwvjby0obxSVel5RMkNDFJ7vER/jJCsIgjBMkiRmRuIU3doybWOZETNRJYkd6RKuV5tMoZhRjBmHoiQ6UZw8bXIOynl2ptIUcwPglFGb56I2zakGjsOGA0hDk9meLqLKEonQWxtB8X0OqHl54yVLMvOizeRte88b74W+Up6ZoTjtdQLL6a4hgsdUKsXWrVsDbWvWrGHz5s0ceuihde/jOA4f+chHWL16NY8++ij3338/uq5zzjnnkM2Or6RCPRdccAEPP/wwXV2VJaTK5TL//d//zQUXXLDPjzmdmKrGocl2iq5NVDU4vnUOhybbD6g1TmeGE5R9D8t10BW1piisIAhCPU16pTD0cLZyvWzcke1RQ2VRa4R0yaY7awXmww2TFAU10YnWvgAj3sbM9lbUaDMDWhte8zzUWEvN1K3hlVGGA0gfmBU30WRpj/s0VrvjeSiyfMAXBx9LixEeWvWlNoDcl9e04NgosszCeO37dyBoiIigXC5z7rnn8ra3vY3FixfT3d3NXXfdxXnnnVe3iDdU6kK+9tprrFy5ks7OSpX9Bx54gHPOOYcPfehDPPzww3Xvd+uttzI4OMhLL73Ezp07+eY3vwnA9ddfjyRJXHHFFdx55528613vYvny5TzxxBOk02luvPHGiTn4BtRmRjmhdS4xzTgghqpHazJChBSV3lKOGeH4AVN3SxCEiRVSNVqNCIPlArbn0lvKMyMUCwz19pfydBWztJkROkIxZiRMurIW/aUCqXKGJtllxojVrIpuma5impCi05mcgSZJzE54bEnneTmdocO1mBdtrs6xsz2XHcUUADNDSfAk2qM6yYjK+kw/tu8yL9JULf7t+z5b8ikKTpm5kabqYhAA2/Jp0uUisyMJbM+jyQgdtPO/dUWlw4zw0kAXhzV1VNvT5SLb8mkSeojZkV3vW94usyU/SFjVmRtJVgPEomOzKTdA3ilz9sylRA7QntyGiAza29t54YUXuPDCC1FVlcMPP5yHHnqIFStWoKr1L+xXX301jz76KDNmzKi2RaNRHnjgAf73//7fYz5XNpsllUpx2mmn8ZGPfIRUKkUqlar+gtA0jT/84Q98/etfxzRNPv7xj/Pyyy8za9as/XvQDS5phA7IwBEqQxQLYy04vk+bceANJwiCMHFmhGNYrkvGtugr5ck75cDteadMXylPZqhETsxQmZUIgeyStS02Z7I47ohyLq5N2i6SdXaVi1EVmWRYpuCW2JLJkSvveo6yV8n4zTkWZc8hbdnMSoSQZUjbJVJWMZDd7fge6XKRAasQ2Fff90mXi/RZBfqtSpWNxQfI9KR9NSOSwCFYrzHv2PRZBQatfKCnMe+UGbAKpMvFwNrgRddmay7FongrM8LxSd3/yST5Y/W7CpNqONt669atzJ49e58eo7+//y1lmh/oRr4+luvw8sAOlibaxpxYfrARn5/dE6/Pnh0Mr1HZdXiqZxO+71PyHJr0cKC0TWGoBFhCM6vzB/OWy4s7BvCKWVLoJPQQHTEDRZZwPJdBu4Ds+vz1vgfo3rqVzrlzOeUDHyDlWPQXHKJKiI6YjqEq+L7PQLlS1DosG5RdnyNnxDFUmb5SHsf3aDMjgWzgAauA5Tq0mpFAsk+6XCRrl3E8l2NbZjM7mpycF3EMU/358X2fp3s24eET1yrZ0Zbr0FPKkSmXmBttqiYb2Z5LXymPoaiBzPWSa7M9n+GMmYtr1kN/q6b69RlJjNcJByVDUTk82SGWJBQEYa/oikq7GaW7mGVWOFFze1jVCKvB9oihMDsRod+2SUaj9GQturMWHTEDVVZISgb/8ukreOO5Z6v3eeJ//ocv3/ZjWpoUdmYturPlagDZYlSGyXtzJRa0RDDUSqA4uoTQsHprM0MlI9tyXWaF48yMHLi9ZOMlSRJzI028nt5ZDR4NRSWkaLTGI2TLVjV41IbWPB8ta1ssTbTt98Cx0RyY45KCMA4RzTggJzILgjCxmowwtl+7xN/udEYNZECVJNpjBrbrDSXR+Dx+z4pA4Ajw+rPP8Pg9K1AVmc6YgSxBd7aM5VSeN192CWsqrdF9n1OXt8toisLixIFXSmZftZgRVEmpJkXZnovr+7Sb0brLD47k+z6u7x/Qw9XDxKdFEARBEPZCWNXA37sfniFdoSmsk7ZsQpoSCCB3btla9z47t2wBqAkgS7ZLoewwuymEJu/7D+CMbbEk3iZGYEYIqRrtZqQ6Z3XAKjAvmqQzFEeWpcDyg6NV6kQaJA7wXkcQwaMgCIIg7JWwqqMpCuWhmo8DVoG1mV4GrEJgu4xdYm2ml96hNa9bIzphXaU3X6S3nEbWbWzXw2zrrPs8nXPnApXer24rja+VAI9NAwWiukrCVNicG2RjdqDaUwaVHrBt+TTrM/2U3GDpma5ChrWZPrJ2CUWWaBJzvmt0huNszA6wLZ9ClRVmR5KYioopK7w2uJOuQrCedMm1WZ/pZ2N2kNnh5EHRi3vgH6EgCIIg7EearBDTjOoa1+lyic25VGB9ZIBMucTWfJq+UmHofhILmkOkykV2FNKU/BKtUY1jznk/i449PnDfw048kVM/WKkvXHJt+st5Mk6RiCmjKTCvOUTZqyRzdBezgUzqsufSW8qxs5ghZ+9qd32P3lKeHYUMPaU8YUU/oGr47i9NeoiIqrMhO8Ah0SbCqo4kSbQaUTbnUvSW8oEeyJxdZmt+EEWSxpx3eqARnxpBEARB2EvNepj1Vh9xTFqMMLIk0WQEe/Ga9DALYy3ENRO7XOaeO++iu7sHs62T5lPeSYseJaJrzGyO8qnv/IDnH7iXgZ1bmDF3Lu/70EX4vs8jd/2KnVu3EJnRwYnnnYftwJEzkiRDGp7vMyucwPN9ouquuY+6rDAzHKfsuYEhVEWSmRGOkdRNFEmixQyLed91qLLCsS0z2ZJPMXNEUlSzGWZeNMnMcDyQzZ7QTZqMMEc0dR40dTJF8CgIgiAIeymmV1Z2AYjrZt3s2oimE9F07HKZv7vwEp7/89PV2w4/8e189rs/BANCmsKMpihvf/+H0BSZjpiB59j8y5XBDOznf/8QX//pT5kRrwSKsiQxs05yhiRJdIRidfd7eA3mncUsCe3An5u3r+ZEmmgzY4FFJCKqTmcoXpO9rkoyHaEYsyPJSd7LqSOGrQVBEARhL4UVDQlpjxm4APfeeXcgcIRKNvXz9/8Pg8Uyvu/XJNE8tqI2A3vNC8/x6sP3Ib+FJBkYXlbPF4kyu2GqGslRPckhVSOsajXzSA+mRJlhIngUBEEQhL0UUjVCqlad97g72zZtqdvu9O8kpqv05S0c1wsEkBvXb6p7nx2b6z/W3ih7LrqsBoqbC3smSzJNeqhm/eucXT5oEmWGHTxHKgiCIAj7iSzJhGWNvlI+0J61LTbnBkmXi9W22fPn1n2MBYsOYV6LiWLabMqmyZYcJAlawhrxGfUzsGfNn8v2QprthXQgacP3fboKGbblU4HMa4CeYo4tuUGsoUC35NrENRN1xGozwvg0GWHKvovlOmzJDbI+009cM+kI158mcKASwaMgCIIg7IP2UJQ+KxdoG7AKrMv00VPa1X7+Rz7MESedENjuhFNO5v0f+TA5p4QtF4hFPHTFx3F9bN/nqPe9j9lHH1lzn3dfeD7b82m259OBDOui69BVzLA1nyY7VKMQKmV+uooZNucHSQ0FtKWhpQqFvRdRdSRg0CqwMTdAj5VjcaI1sOzjwUAkzAiCIAjCPohoOslRdRKTusncaJJmfVdShabrfO/un/PL237KYG8vhy5ewvs/8mE0TSMuQWcoTkTVmTGU5OJ4UGgLs/RXP+bP9/ye3I4e5h4yn/d/5MNIikK7V+lBDCu75iyaikqbGcX2XKIj5jKqkkybGSWs6tUl93wqQZCw9yKqjiFXhvtNWeOE1jm0GgdfIC6CR0EQBEHYB8MBme251Z6nhB4iUafwdjIc5RMf+xhmIji8GVI1FsdbA22aAomQQSI0g2Wf/GTNY82PNtW0yZLEnDrZvtKojGzX95AlSSTL7CNFlmkyQnQVMixKtLI43npQljsSw9aCIAiCsA8MRSWi6tW5hNNByXEIKzqmKA6+z1qMCGXPZVGshYi272uLT2cieBQEQRCEfdRshCmOKt3SyEquTZMROih7y/aXmGYwL9rEjDo1Ng8WIngUBEEQhH0U101ytsX2Qpr8iKUAoRKo7ShkyNjBZQttz2VHIVNNYBnmeB5dhQz9ozK4Pd+nu5ilp5gbqtFY4fs+PcXK8oSj6032l/J0FTI4nhdoHygXa+ZpCnsnrpsc0TTjoM5WF8GjIAiCIOyjsKph+x7P921lS34wcFtfKc+aTC87CplAe79VYEO2n635VKDcTrpcZGNugK2FdGAoPGuX2JQbZGs+RWFEjcGCY7M1n2JTbpDsiADVch22FtJszA0ESga5vke6XBLJMvvBwd5zKyY9TIDTTz+dV199lZaWFlavXj3VuyMIgiBMkJhm8u4Zi1mW6GBHIc3OYpaoqhPVDCKqQasRqVkGMKxqNBthYpqBzK4gJKTqNBthDFkNlH4xFY2kHkKWJHRlV7uuKMR1E8/3MZVdBb81WSGhmZiKSmhEoOi4Lu2hiCgOLrxlInicACtWrCCdTnPcccdN9a4IgiAIE6zZCNNshFmaaKOrkGVVqpuoZtBkhEjqZk0vVVwzWZYwatrDqsbSeFtNu6GoLKmT1avJCovjrfi+H7hNliQOiTXXtBc9h1nhxEE93CrsHw01bG1ZFi+99BJr167Fdd0932EfeZ7HX/7yF/7617+OuU1/fz9/+ctf2LJl75eCSiaTtLS0vJVdFARBEKYZVVZoHkpGGR6OHmt4c3+1j/c+vu9TcGxmhRNjPo4gjFdDBI+lUol//Md/ZNasWXziE5/gjDPOYOnSpTzxxBP79Xkcx+Gmm25i0aJFvO997+Oaa66pu903vvENZs2axVVXXcWyZcu46KKLsO1d80wOP/xwWltba/6tWLFiv+6vIAiCML2EVZ2optesfzzVsrZFUjdpOQgLWgv7X0MEj+l0mlmzZrFlyxb++te/smnTJs4++2wuuOACLMuqe598Po/j1K+tlU6n67ZblkUul+PRRx/lb//2b+tu84c//IGvfvWrPPjgg7zyyiu88cYbPPbYY3zrW9+qbvPEE0/w5ptv1vw799xz9/LIBUEQhAOJJEm0GmEGrGAmte25DFrFmoxs1/foLeVqMrI936evlK/JyPZ9n36rQL9VCGReA6TKRfpK+ZrM64xdYnshw/xoM4rcEJd9YZpriE9RR0cHn//85wmHK8s5ybLMlVdeSX9//5gJJ1dffTUf/ehHa4a3r7/+es4777y694lEItx0003Mnz9/zH25/fbbecc73sFpp50GwLx587jsssu4/fbbq9s0NzfX7XnUdZHBJgiCcLBrNiL0WcFyO32lPNsKKbbkBwNB34BVYH2mn03ZQWxv1/UsXS6xIdvPptxgoBcz55TZnBtgc26A3Mi1rR2bTblBNmT7SZd3BaK25/LGYDdhVRPrWQv7TUMEj/U8++yzqKo6ZqD39a9/neeff57LLrusGkD+f//f/8dtt93Gf/7nf+7z8/71r3+tSXQ57rjj2LBhA5lMZox7BV1xxRXMnz+fgYEBWltb+frXv77P+yMIgiBML1HNoNkIB4JBQ1EJqxphVQ/MRay060RUHUWSR7QrRFSdsKKhjugt1GWFkKITUnT0EYkvqiwTVrTK2ssjMrIVSUaVFI5tniUSZYT9piGzrdeuXcs///M/87nPfY54vH4F99mzZ/PYY49x6qmn8olPfILZs2dz66238sc//pEjjzxyn597cHCQ5ubmQFtra2v1trH2Z6R///d/5+abb67+PdyjWs8tt9zCLbfcMqEJQoIgCMLkMRSV2eEEeceqrnPdbITRo81EIsFrSFwzWZZsR5Fk5BFBZVjVWZpoR5IYFVSqLElUrkkj2zVZYWG8Bd8nEGwWnDKLE620h4NragvCW9FwweO2bdt4z3vew7ve9S5uuumm3W47d+5cHn30UY4++mjy+TxPP/00xxxzzFt6fl3XKRaDc0wKhUL1tvGIxWLEYuP7ol533XVcd911bNu2jTlz5uzdzgqCIAgNqc2M0JfOMzK3WZXkupnR2hg9guoY8xNHBo017aMePmNbvC3ZOeZzCMK+aKhh623btnHaaafxtre9jbvuugtV3XNs+9Of/hRZlpkxYwY/+MEPaiYQ76158+axffv2QNv27dsxTZP29va39NiCIAjCwSGum+Dzlq9Jb4XtueiyQlsoOmX7IByYGiZ43L59O6effjrLli3j17/+9bh6+b7xjW/wb//2bzz00EOsXLmSP/zhD1x11VVv6ct61lln8dBDDwWyvO+55x7e/e53oyjil5sgCIKwZ1HVQFcUuoqZwFKDUMmkHrAKgaUGoRJopsq1GdlQWbpwdEY2VDKp06MysgHydpkdhQxJPYShNNwgozDNNUTwmEqlePe7343v+1xzzTU89dRTrFy5kpUrVzIwMFD3Pt/+9rf51re+xUMPPcSJJ57IwoULeeyxx3jggQf47Gc/O+ZzPfPMM6xcuZIdO3aQTqerzzMccF5zzTXous7y5ctZsWIFV199NU8++SQ33njjhBy7IAiCcOBRZJkmI8yrAzvrrnm9NtPHptxAoKzOYLnI2kwf67P9gWSbjF1iQ3aADdmBmrWtN2QHWJ8dCASWtueyPtvPukwf7abodRT2v4b4OdLX18eMGTMAauY53nzzzZx44ok191mwYAEPPPAAJ510UrVt0aJFPProozz66KNjPtd//Md/VIelW1pa+NrXvgbAH//4RxRFoampib/85S/cfPPN/PCHP2TmzJn8+c9/5thjj32rhykIgiAcRDpDMUxVQ5ODl1pFltFlBU1WAlMUVanSrisK0ohbVElGkWQUSUIZMWdSkSQ0ScbFRx0xD1JCQpNlErpZGT4XhP1M8qdyQoZQNZwws3XrVmbPnr1Pj9Hf3y+WRdwN8frsnnh9dk+8PnsmXqOgvG3xp50baAtF0GWVUjqLmagkUxYdG01WapJiSq6NIsk1CS6W6yBLUk277bl4vl8zNJ0pl3B8j3d2LJg2hcHF52f3Gun1mR6fKEEQBEGYZsKqzpxokkGrdk5iSNXqZlObilY3M9pQ1LrtmqzUndNY9lw6Q/FpEzgK04v4VAmCIAjCBJAkiQWxFnRFrZsEM5Fs36XZCE3qcwoHDxE8CoIgCMIECas6i2OtpMpFso4VSISBSoZ11rZqMrIBcrYVWJpwWMEpU3Bqg9GiY5OzLRzPQ5FkYpqY7yhMDBE8CoIgCMIE6gzH6AhFWZ3qZWN2ILi2dbnI2kwvG7IDuL5Xbc+US6zN9LEu20d5RGBZcMqsy/SxLtMXCCDLrsO6bB9rM310F7MkNJOQqk3OAQoHHRE8CoIgCMIEkiWZpYl2Wsxw3Z7E/a3o2rSLwuDCBGqIUj2CIAiCcCCLaAZHNc9knZvDZ9cqgs16CD3ehi4rgWUH47rJ4ngriiSjj0iICas6i+Kt1f8P0xWVRbFWXN8j55SJiyFrYQKJ4FEQBEEQJkGrEaEkqfSXCrSFIkAlqSamGXW3j47RPjJoHCmkapRcG0NWx3xMQdgfxLC1IAiCIEwCSZJYFG9FlqWapQn3l7xj02pGUOuU9RGE/UUEj4IgCIIwSaKaweJYKz3FLGXXrbm95No1GdlQKRJer9323ECmtuU6tJmR/bvTgjCKCB4FQRAEYRLNjMRpNSO8PLAjkHmdKZdYne5lfbYfx9uVeV1wyqxO97Im3VsTKK5J97I63UvBKVN0bEKKRpMRntTjEQ4+IngUBEEQhEkkSzJHNs8krpvkRpTbcXyPsudSdl18/EC743vYvoc7Ith0fR976DbH98jYFrMjibor0QjC/iQSZgRBEARhkkU1g3d1LuCVgR2okkxI1WjSQyyOt6JJSiAAjGsmC2PNSJJEeETtxrCqsSDWjO/7RFWD4tB8R0GYaCJ4FARBEIQp0BGKcViyg9cHu5ElCUNRSer1lxRMjNE+XJInUy6R1EOiRI8wKcSwtSAIgiBMkVmRJIcm2+m3CoGVZPZW3ikzO5JEkqQ9bywIb5EIHgVBEARhCs2JJFmaaKO/XAi0O54XWLJwmOsH28uugyYrNI3ROykI+5sIHgVBEARhCkmSRGcojoxUDQorGdY9rM/0B0r0VDKs+1iT7qtmXmdsi04ziinWshYmiQgeBUEQBGGKmYpKWNWqa19brkveKVNw7UDZnrLnUnTLFN0yZc/F930c36MzkpiqXRcOQiJhRhAEQRCmmCRJtJlRtuZTRDWDhG6yINaCKlcysYdFVZ150ebq/3tKeVrNCAmRKCNMIhE8CoIgCEIDSOghNmUHAZAlqW7ZHUmSaBkqAj5oFYmoOssSHSiyGEgUJo/4tAmCIAhCA4ioOpIkBVadGUveLuP5Hoc3dQR6JgVhMojgURAEQRAaQFjVMBWVUp2SPSMDyrLrkLUtDmvqHLP+oyBMJBE8CoIgCEIDkCWZsKKxOt2LNyJYtFyHNZk+1mb6KDpl+qwCSxKtdIRiU7i3wsFMBI+CIAiC0CBmROKk7WKgPE/JtUmVi6SsAjsKWQ6JNleTZgRhKojgURAEQRAaRFjRmRVOYCi78lljmsm8SJKwZrA40cqieKtYSUaYUiJ4FARBEIQGEdF0moxwYKlCCUCSWJZoZ2miXWRWC1NOfAIFQRAEoUFoskJcMwJJMwNWgaRusizZgSYrU7h3glAhgkdBEARBaCAtRpjiUPCYt8tIksSyZEdgKFsQppIIHgVBEAShgcQ0k7Lnsibdy4ZcP8uSHcTECjJCAxHBoyAIgiA0kIiq4/keG3MDHNM8i3YzOtW7JAgBIngUBEEQhAZiKCqtRoRT2uezJNEmMquFhiMmUAiCIAhCA5EkiaXJdkKKhioSZIQGJIJHQRAEQWgwzUZ4qndBEMYkhq0FQRAEQRCEcRPBoyAIgiAIgjBuIngUBEEQBEEQxk0Ej4IgCIIgCMK4ieBREARBEARBGDcRPAqCIAiCIAjjJoJHQRAEQRAEYdxE8CgIgiAIgiCMmwgeBUEQBEEQhHETwaMgCIIgCIIwbiJ4FARBEARBEMZNBI+CIAiCIAjCuKlTvQNCheM4AHR1de3zYwwODlIsFvfXLh1wxOuze+L12T3x+uyZeI12T7w+uyden92brNens7MTVd19eCiCxwbR29sLwIknnjjFeyIIgiAIwsFq69atzJ49e7fbSL7v+5O0P8JulEolXn31Vdra2vYY8dfT1dXFiSeeyLPPPsuMGTMmYA+nN/H67J54fXZPvD57Jl6j3ROvz+6J12f3JvP1ET2P04hpmpxwwglv+XFmzJixx18MBzPx+uyeeH12T7w+eyZeo90Tr8/uiddn9xrl9REJM4IgCIIgCMK4ieBREARBEARBGDcRPB4g4vE4N9xwA/F4fKp3pSGJ12f3xOuze+L12TPxGu2eeH12T7w+u9dor49ImBEEQRAEQRDGTfQ8CoIgCIIgCOMmgkdBEARBEARh3ETwKAiCIAiCIIybCB4FQRAEQRCEcRNFwg8Ag4ODPPzww+Tzed75zneyePHiqd6lKZPL5XjyySfZuXMnS5Ys4eSTTw7c3t3dzd13311zv4suuoi2trbJ2s0pkc1m+elPf1rTft555zF//vxA26pVq3j66aeJxWKcffbZJBKJSdrLqfPGG2/w6KOP1r3tkksuobm5Gdd1+cEPflBz+7vf/W4OO+ywid7FKfHcc8/x7LPPctppp3H44YfX3O55Ho899hgbNmxgwYIFnH766ciyvNfbTFevvfYajz/+OCeccELd5WUHBgZ48sknSaVSHHnkkRx99NGB29euXctDDz1Uc79PfvKTmKY5Ubs9adavX8/DDz/MsmXLOO200wK3bd++nRUrVtTcZ/j7NtLzzz/PSy+9RHt7O2efffYB8dpA5TW47777mDNnDu973/sCtz3zzDM899xzde/36U9/Gk3TSKVS/PznP6+5/YMf/OCEFhMXweM09/zzz/Oe97yHZcuW0d7eztVXX83NN9/M1VdfPdW7NunuuOMOvvrVr3LooYfS2dnJl770JRYtWsT9999PNBoFYOPGjVxzzTVcddVVaJpWve9kLDY/1fr7+7nmmmu49NJLA8Hg6aefHtju5ptv5sYbb+S9730vW7Zs4ZprruGRRx7hbW9722Tv8qRKp9O8+eabgbY//elPrF27lksvvRQA27a55pprWL58eWCJsOOOO25S93UyPPvss3z2s58F4OWXX+a73/1uTfBYKpU499xzWbNmDe9617u48cYbWbp0Kffdd1/14j6ebaajVatW8elPf5r+/n62bdvG5z73uZrg8Vvf+hbf//73Oeqoo0gkElx77bWceeaZ3HnnnSiKAsALL7zAddddx5VXXhm4r+u6k3YsE2Hbtm188pOfZN26dWSzWS644IKa4HHt2rVcc801fPazn0WSpGq7ZVmB7a6++mp+8YtfcM455/Dqq6/yhS98gZUrVzJz5szJOJQJkUqluOKKK3j++ecBOPbYY2uCx97e3ppz0n333Ydt29Xv5s6dO7nmmmu4/PLLiUQi1e3y+fzEHoAvTGtHHHGEf/HFF1f/vu2223xN0/xNmzZN4V5NjYcfftjv7++v/t3f3+93dHT4X/nKV6ptTz/9tA/42Wx2KnZxSm3cuNEH/LVr1465zapVq3xZlv1f//rXvu/7vud5/rnnnuuffPLJk7WbDWXx4sX+pZdeWv27WCz6gP/EE09M4V5NjmeffdZ/9tlnfd/3/Ugk4v/gBz+o2eZb3/qW39ra6u/cudP3fd/fsWOH39zc7N988817tc109Prrr/uPP/647/u+v3DhQv+GG26o2eZ//ud//Hw+X/173bp1vmma/m233VZt++Uvf+m3tLRM+P5Otk2bNvkPPPCA77qu/453vMP/9Kc/XbPNY4895gO+bdtjPs5DDz3kS5LkP/fcc77v+36pVPKPOeYY/5JLLpmwfZ8MfX19/m9+8xvftm3/Qx/6kP+BD3xgj/fJ5/N+PB73v/zlL1fbVq1a5QP+1q1bJ3Bvax0Y4wYHqVWrVvHqq6/ymc98ptp26aWXEgqF6g4FHOjOOuuswFBHc3Mzxx57bM0vN4Df/OY3/OQnP+GZZ56ZzF1sCA888AC33347Tz75JJ7nBW67++67aW9vZ/ny5QBIksRnPvMZnnrqKbZu3ToVuztlHn/8cdauXcunPvWpmttWrlzJbbfdxmOPPYZt21OwdxPvhBNO4IQTTtjtNr/61a9Yvnw5HR0dQGXd3eXLl/OrX/1qr7aZjg477DDe9a537Xab8847j3A4XP174cKFHHLIITXnJNu2+e///m9+/vOf88orr0zI/k62efPmcc4554xresJdd93FHXfcUe2FG+lXv/oVJ5xwAscffzwAhmFw5ZVX8tvf/nZaf/daWlpYvnw5qjr+AeC7776bbDZb00sNlR7J22+/naeeegp/Esp3i+BxGnvjjTcAWLZsWbVN13UWLlxYve1g1t/fz1NPPVUzpBiJRPjNb37Dgw8+yHvf+17OOOMMstnsFO3l5NJ1nfvvv59HHnmECy+8kJNOOomdO3dWb3/jjTdYunRpYAhp+PO1atWqSd/fqXTbbbexZMmSmgBBURRWrlzJ448/zuWXX87RRx/N2rVrp2gvp9Ybb7wROP9A5fMy8vwznm0OFqtXr2b16tU15yRFUVixYgUrVqzgpJNO4uKLL572w9bjFY1Gufvuu7nvvvs488wzOeeccygUCtXbx/r8lEolNm7cONm7O6Vuu+02zjrrrJo56qZp8j//8z888sgjfPCDH+Qd73gHfX19E7ovYs7jNDYc8CSTyUB7U1MTmUxmCvaocTiOw0c/+lHa29v5+7//+2r7nDlzePPNN6sTiXfu3Mmxxx7Ll770Jb773e9O1e5OikQiwSuvvMLSpUuByhy/k046iWuuuaaaRJTNZut+noCD6jOVTqf59a9/zY033hhoV1WVZ555pnrxLxaLnHnmmVxxxRU88cQTU7GrU8ZxHEqlUt3PS7FYxHEcgD1uszc9L9NZLpfjoosu4uSTT+bCCy+sth9zzDFs2LCh+hqtWrWK448/nu985zv84z/+4xTt7eRYsGABa9asqc4f3r59O8cccww33HADt9xyCyDOScPWrl3LE088UZPw2draymuvvcbChQuBSgLtCSecwOc+9zl+9rOfTdj+iJ7HaSwUCgHU9JplMpnAUMnBxnVdLrvsMl5//XUefPDBarIMwKxZswIZaJ2dnVx22WV1sx0PNE1NTdXAESrB5FVXXcXDDz9cbQuFQnU/T8BB9Zn65S9/ieM4fPzjHw+0q6oa6DUKhUJcffXV/PnPf574CeoNRlVVVFWt+3nRNK16+562ORgUCgXOO+88PM/jnnvuqSbLACxdujQQHC1btozzzjvvoDgnzZ07N5B4NmvWLC6++OLAsYtzUsVtt91GW1sbH/jABwLtra2t1cARKuf5K6+8csI/PyJ4nMaGS/KM7Lr3PI/NmzcftOV6XNfl4x//OE888QSPPfYYCxYs2ON9DMMglUpN/M41IMMwyGaz1V6ixYsXs2HDhsA2w38fTJ+p2267jQ9+8IPjKt9kGAa+75NOpydhzxrL4sWLa4YON2zYEPisjGebA1mxWOT9738//f39/PGPf6SlpWWP9znYz0kjj32sz4+iKBxyyCGTvHdTw3Ec7rjjDi6//PJAlZCxGIYx4ecjETxOY0cffTRz5swJ1Hh68MEH6evr4/3vf/8U7tnU8DyPyy+/nJUrV/LYY4+xaNGimm3WrFkT+NuyLH79619zyimnTNZuTpl169YFJlJ7nscvf/lLTjrppGoP0Pnnn8+mTZt46qmnqtv97Gc/49BDDz1oLvavvPIKzz//fN1EmY0bN1YD7WG/+MUvOOSQQ6Z12ZB9df755/Pb3/62Okctn8+zYsUKzj///L3a5kBVLBY577zz6Onp4dFHH637Y2T0Oamvr48HH3zwoDgnjT72QqHAihUrAsd+/vnn88QTT7B582YAfN/nF7/4BWeddVZ19O1Ad//999PV1cUnP/nJmttGz7d2HIc777yTd7zjHRO6TwfHmMEBSpZlvv/977N8+XIymQwdHR388Ic/5Nprrz3ga/LV80//9E/8/Oc/59prr+Whhx6qdtvPnDmzmj38y1/+kkceeaRapPiuu+7Csqzq/JoD2R/+8AcuueQSzjjjDCKRCPfccw87duzgvvvuq25z0kknceWVV7J8+XI+9alPsXnzZu666y5+//vfT+GeT67bbruN+fPnc+aZZ9bc9sILL3DBBRdUM/sffPBBXnvtNe66664p2NOJNbKgvm3brFy5EsdxWLJkCWeffTYAX/ziF/ntb3/Lu9/9bs4999xq7cYvfvGL1ccZzzbT0cii++l0mmeffZbvfe97zJo1iwsuuACAj3/84zz++OP88z//cyC7fOnSpZx11lkAfPOb36Srq4uTTz6ZUqnEHXfcwZw5c/jnf/7nyT+o/WhkQf2uri4kSeJ73/seTU1NfPSjHwXg9ttv5+mnn+bUU08FKudnWZb5P//n/1Qf52//9m+5/fbbOeOMM7jssst44YUXeP7553nyyScn/6D2s//3//4flmWxfv16PM/je9/7HoZh1Pxwve222zj11FNZsmRJzWP8/ve/58477+SMM87ANE1++9vf0tfXx/333z+h+y75k5HTLUyo119/nbvvvptCocCpp57KueeeO9W7NCV++MMf8tprr9W0L1y4kM997nPVv1944QUeeughisUiy5Yt48ILL0TX9cnc1SnzxhtvcN9995FKpViyZAkf/vCHA4Vlh91zzz38+c9/JhaLcdFFFwXmSh7obrjhBo488kg+9KEP1b1948aN3HPPPXR3d7NgwQI+/OEPVyfwH0g2bdrEt7/97Zr24447jk984hPVvzOZDHfccUd19ZiPf/zjxGKxwH3Gs810MzAwwFe/+tWa9sWLF3PttdcCcNNNN7Fjx46abU488UQ+9rGPVf9+/PHHWblyJQBHHnkkH/jAB6b9CjyO4/AP//APNe2dnZ18+ctfrv797LPP8vDDD1MulznssMP40Ic+VDM06zgOv/zlL3nppZdoa2vj0ksvndDVUybLF7/4xUBmOVTmcd58883Vv13X5Qtf+ALnn39+zYIOw1599VXuv/9+MpkMS5cu5cILL5zw+aAieBQEQRAEQRDGbXr/tBEEQRAEQRAmlQgeBUEQBEEQhHETwaMgCIIgCIIwbiJ4FARBEARBEMZNBI+CIAiCIAjCuIngURAEQRAEQRg3ETwKgiAIgiAI4yaCR0EQBEEQBGHcxPKEgiAI+8Gbb77Jd77zHd58800WL17M1772NWbNmjWhzzm8ZCDAO97xjt2u9bt582a2bds2IWveep7Ho48+Wv371FNPrVklRBCEA4dYYUYQBOEt+sMf/sCHPvQhPvOZz3DkkUfy7W9/G8dxeOmll1AUZcKeNxqNMnfuXGbOnMkdd9zBzJkzx9z2m9/8Jj/84Q/ZtGnTft8P27Z573vfS39/Py+99BK9vb20trbu9+cRBKExiJ5HQRCEt6C7u5uPfOQj3HTTTVx99dUALFu2jOOOO46nn36aU045ZUKf//Of/zyf/OQnJ/Q59kTTNB555BEefPBB3vve907pvgiCMPHEnEdBEIS34Oabb6apqYnPfOYz1bYlS5YAsGbNmqnaLcrlMn/5y1949tlnKZVKY27nui4vvfQSTz75JIODg3W3cRyH559/nqeeeopcLsfOnTsDw9SCIBxcRM+jIAjCPnJdlzvuuIPPfvazgeHpcrkMgCxPze/zZ555huXLlwPQ2dlJb28vRxxxRM129913H5/+9Ker261evZp/+Zd/4R/+4R+q27zxxhucd9555HI55s+fz5YtWzjllFO45557qvMtBUE4uIjgURAEYR+98MIL9PX1cd999/HMM89U24d7+mbPnj3p+2TbNpdeeimnnnoqP/vZz1AUhaeeeopTTz01kMDz+uuv86EPfYhPfOIT/Od//ieKovDYY49x1lln8a53vYtjjz0WgCuuuIJFixZx7733Ypomq1ev5m/+5m8m/bgEQWgcYthaEARhH7344osoisJZZ53F0UcfXf1nmiYARx111KTv08qVK1m3bh033XRTtTf05JNP5oILLghsd+utt2IYBv/6r/9a3e7000/nPe95D9/97neBSoD5zDPPcMMNN1SPaenSpVxxxRWTeESCIDQa0fMoCIKwjzZt2kRHRwff/OY3A+1XXnklRx99NG1tbZO+T+vXr8cwDObPnx9oP/TQQ3n22Werf69evZo5c+bw1FNPBbYLh8OsXr0agA0bNgC75nAOW7p06QTsuSAI04UIHgVBEPaR4ziEw+FAW7FY5J577uErX/lKtc33fX7wgx9w6623sn79elRVpa+vj+985zusXbuWnTt38sQTT3DJJZfw+c9/nk996lO89NJL/P3f/z033njjXu1TMpnEsixKpVK1txAgk8kEtotEIvT09NQEvgCLFy+uPhZANpsNBMLpdHqv9kkQhAOLCB4FQRD20fz58+nq6sKyLAzDAOCWW24hGo1y1VVXVbf70pe+xJ/+9Cd+8pOfcOihhyLLMqqq8uyzz7J27Vpuv/12wuEwxx13HL29vXz/+9+nv7+f008/na997Wt7lXjz9re/HUVRuPfee/nbv/1boBLk3nfffYHtzjnnHJ577jnuvffemgB4OOv6iCOOIBqN8vvf/55rrrmmevvoxxIE4eAigkdBEIR9tHz5cq6//nr+7u/+jiuvvJKHHnqI//iP/+Chhx6qBmQ9PT18//vfZ82aNXR0dATu/+KLL3LHHXdw9NFHA6DrOjfddBMLFy4kl8thmuZeZ2wfcsghXH311Vx11VXs2LGDWbNm8aMf/Yienh6am5ur233iE5/gnnvu4Z3vfCdXXXUVs2fPZvPmzaxYsYKzzjqLL37xiySTSb785S9z/fXXk8vlOPTQQ/nNb37Diy++iCRJb+3FEwRh2hLBoyAIwj6aOXMm99xzD1/96le57LLLOP7443n66ac57LDDqtv89a9/5fDDD68JHDOZDD09PZx00klAZX5hMplk4cKF1fsdc8wx+7Rf//qv/8phhx3G73//e2RZ5oILLuDjH/84999/f3UbTdO47777+NWvfsUDDzzAwMAAhxxyCF/60pc4/fTTq9tdf/31zJkzh9/85jfVoufHHnssX//61/dp3wRBmP5E8CgIgvAWnHXWWZx11llj3t7R0cGaNWtYvXp1INHkxRdf5Nhjj632LD733HO8/e1vr97+/PPPc/zxx+/x+VetWsUjjzwSWNtalmWuuuqqwNA5wCWXXBL4W5ZlLr74Yi6++OIxHz+fz3PJJZcE7nv++edXS/nArrWtX3rppT3uryAI058IHgVBECbQ0UcfzWc/+1ne/va3UywWkSSJzZs38+KLL3LCCSdUt3vuuec48cQTq38///zznH/++bt97NNPP52XX36Zl19+mTvuuKMaPO5PjzzyCD/96U9Zvnw5uq7zu9/9jocffpgHHniguo3rutXEmzPOOANd1/f7fgiC0Dgk3/f9qd4JQRCEg4FlWfi+j2maOI6DJEnVGou2baMoSrUn0rZtVFVtiLmF9957L7/73e8YGBhg0aJFXHXVVdWMbEEQDj4ieBQEQRAEQRDGTawwIwiCIAiCIIybCB4FQRAEQRCEcRPBoyAIgiAIgjBuIngUBEEQBEEQxk0Ej4IgCIIgCMK4ieBREARBEARBGDcRPAqCIAiCIAjjJoJHQRAEQRAEYdxE8CgIgiAIgiCMmwgeBUEQBEEQhHETwaMgCIIgCIIwbv8/eVoZ8TZangUAAAAASUVORK5CYII=", - "text/plain": [ - "<Figure size 704x440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "log space, inferred errors Vv = 46.0 +/- 2.3 MeV chi2/N at the median, reported errors only = 138.3\n", + "linear, reported errors Vv = 45.5 +/- 0.1 MeV chi2/N at the median, reported errors only = 122.1\n", + "\n", + "inferred s = 34.1% +/- 6.6%, b = 36.5% +/- 35.9%\n" + ] } ], "source": [ - "x_fine = np.deg2rad(np.linspace(3.0, 175.0, 140))\n", - "on_fine = omp.bind(x_fine, data.meta)\n", - "fig, ax = plt.subplots()\n", - "for (name, p), colour, hatch in zip(\n", - " problems.items(),\n", - " [plotstyle.COLOURS[i] for i in (1, 0, 2)],\n", - " plotstyle.HATCHES,\n", - "):\n", - " lo, hi = rx.predictive.grid_draws(\n", - " p, on_fine, x_fine, samples[name][::20], terms=inferred_terms[name],\n", - " n_rep=2, levels=(5, 95), rng=4,\n", - " ) # fmt: skip\n", - " plotstyle.band(\n", - " ax, np.rad2deg(x_fine), lo, hi, color=colour, hatch=hatch, label=name\n", + "for name, row in summary.items():\n", + " mean, sd = row[\"Vv\"]\n", + " print(\n", + " f\"{name:28s} Vv = {mean:5.1f} +/- {sd:4.1f} MeV \"\n", + " f\"chi2/N at the median, reported errors only = {row['chi2/N (reported errors)']:6.1f}\"\n", " )\n", - "ax.errorbar(\n", - " np.rad2deg(data.x), data.y, data.y_err, fmt=\"o\", ms=3, color=\"k\", label=\"data\"\n", - ")\n", - "ax.set(\n", - " xlabel=r\"$\\theta_{cm}$ [deg]\",\n", - " ylabel=r\"$\\sigma / \\sigma_{Rutherford}$\",\n", - " yscale=\"log\",\n", - " title=\"90 % bands, inferred error included, on a grid never measured\",\n", - ")\n", - "ax.legend(fontsize=8)\n", - "plt.show()" + "print(\n", + " f\"\\ninferred s = {s_draws.mean():.1%} +/- {s_draws.std():.1%}, \"\n", + " f\"b = {b_draws.mean():.1%} +/- {b_draws.std():.1%}\"\n", + ")" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "1646803d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T20:00:32.063575Z", - "iopub.status.busy": "2026-09-14T20:00:32.063378Z", - "iopub.status.idle": "2026-09-14T20:00:33.720277Z", - "shell.execute_reply": "2026-09-14T20:00:33.719403Z" - } - }, + "execution_count": 18, + "id": "ccdbbd11", + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "<Figure size 1760x1760 with 49 Axes>" ] @@ -666,225 +595,233 @@ } ], "source": [ - "best = \"inferred model error\"\n", - "p_best = problems[best]\n", - "fig = corner.corner(\n", - " samples[best][:, p_best.columns(omp.params)],\n", - " labels=[f\"${lt}$\" for lt in latex],\n", - " **plotstyle.corner_kwargs(title_fmt=\".2f\"),\n", + "fig = None\n", + "for name, p in problems.items():\n", + " fig = corner.corner(\n", + " samples[name][:, p.columns(params)],\n", + " fig=fig,\n", + " labels=[f\"${lt}$\" for lt in latex],\n", + " **plotstyle.corner_kwargs(\n", + " color=colours[name],\n", + " fill_contours=False,\n", + " plot_density=False,\n", + " show_titles=False,\n", + " levels=(0.68, 0.95),\n", + " ),\n", + " )\n", + "fig.legend(\n", + " handles=[plt.Line2D([], [], color=c, label=n) for n, c in colours.items()],\n", + " loc=\"upper right\",\n", + " fontsize=10,\n", ")\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "814f64bc", + "id": "0f16759a", "metadata": {}, "source": [ - "## The guardrail: a covariance that cannot be factored (recipe 21)\n", + "The two posteriors tell very different stories about the same potential. Taking the reported errors at their word, the linear fit pins $V_v$ to $45.5 \\pm 0.1$ MeV, and its best potential still sits $\\chi^2/N = 122$ away from the data. It's precise, but the precision is meaningless, because it comes from pretending the misfit is noise.\n", "\n", - "If a subentry reports no statistical error either — it happens — the covariance\n", - "of those points is singular, and there is nothing sensible to do with it. The\n", - "library says so when the problem is built, naming the dataset, rather than\n", - "failing somewhere inside the sampler an hour later." + "The log-space fit lands at a similar depth, $V_v = 46.0 \\pm 2.3$ MeV, with an honest width. It gets there by inferring a point-to-point relative error of $s = 34\\,\\% \\pm 7\\,\\%$: that's how well a local Woods–Saxon describes this measurement angle by angle. The common error $b$ is barely constrained, $37\\,\\% \\pm 36\\,\\%$. That makes sense. A single measurement can't tell a coherent shift of all its points apart from the potential itself moving, so the posterior for $b$ mostly reflects its prior.\n", + "\n", + "In the corner plot the linear posterior is a tight speck inside the log-space one, and for several parameters ($W_d$, $a_v$) it sits near the edge of the log-space contours." ] }, { - "cell_type": "code", - "execution_count": 12, - "id": "57c5d001", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T20:00:33.724754Z", - "iopub.status.busy": "2026-09-14T20:00:33.724568Z", - "iopub.status.idle": "2026-09-14T20:00:33.728904Z", - "shell.execute_reply": "2026-09-14T20:00:33.728160Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "the constraint's covariance is singular on its active points; the diagonal is zero on rows of ['a subentry with no errors']: those comparisons have zero statistical error and no diagonal term covers their points (a block covered only by correlated modes is singular here even when the full covariance is not). Remedies: comparison.reported_terms(), a noise term, a fixed Term covering those points, or statistical=False with an explicit covariance.\n" - ] - } - ], + "cell_type": "markdown", + "id": "5df6b5e2", + "metadata": {}, "source": [ - "no_errors = rx.Dataset(\n", - " data.x[:6],\n", - " data.y[:6],\n", - " np.zeros(6),\n", - " label=\"a subentry with no errors\",\n", - " meta=data.meta,\n", - ")\n", - "try:\n", - " rx.Problem([rx.Constraint([rx.Comparison(no_errors, omp)])])\n", - "except ValueError as err:\n", - " print(err)" + "## (B) The posterior predictive\n", + "\n", + "There are two predictive distributions we care about, and it's worth being precise about them:\n", + "\n", + "- **(B1) The potential alone**, $y_m(\\theta;\\alpha)$ with $\\alpha$ drawn from the posterior. This is our uncertainty about the cross section the potential predicts, and it's what we'd hand to a transport code.\n", + "- **(B2) The potential plus every error term**: the reported statistical errors, and for the first model the inferred $s$ and $b$. This is our uncertainty about what a *measurement* would read, and it's the one that belongs next to the data.\n", + "\n", + "B1 has a value at any angle, so we draw it on a fine grid with `rx.predictive.grid_draws`. B2 is different. The reported statistical errors are one number per *measured* angle, and at an angle nobody measured there's simply no statistical error to draw, so `grid_draws` refuses them (recipe 40, and the forecasting discussion in `linear_calibration`). We therefore draw B2 at the 59 measured angles with `rx.diagnostics.predictive_draws`, and show each interval as a vertical bar at its angle. The inferred terms, on the other hand, *are* functions of angle, so for the first model we can also show the potential plus $s$ and $b$ on the fine grid.\n", + "\n", + "`physical=True` returns the log-space draws as ratios, so every band is in the units of the data." ] }, { - "cell_type": "markdown", - "id": "825ee796", + "cell_type": "code", + "execution_count": 19, + "id": "2012d2ec", "metadata": {}, + "outputs": [], "source": [ - "## Other drivers (recipe 16)\n", - "\n", - "The same problem runs under emcee from `sample_prior` and `log_posterior`, and\n", - "it pickles with `dill` for a driver in another process. The short run below\n", - "only demonstrates the contract; on an optical-model posterior expect to need far\n", - "more emcee steps than dynesty needs live points." + "x_fine = np.deg2rad(np.linspace(3.0, 175.0, 140))\n", + "on_fine = omp.bind(x_fine, data.meta)\n", + "fine, at_data = {}, {}\n", + "for i, (name, p) in enumerate(problems.items()):\n", + " rows = samples[name][::20]\n", + " space = tf.log if name.startswith(\"log\") else None\n", + " fine[name] = {\n", + " \"potential alone\": rx.predictive.grid_draws(\n", + " p, on_fine, x_fine, rows, model_only=True, physical=True, levels=(5, 95)\n", + " )\n", + " }\n", + " if inferred_terms[name]:\n", + " fine[name][\"potential + inferred terms\"] = rx.predictive.grid_draws(\n", + " p, on_fine, x_fine, rows, terms=inferred_terms[name], n_rep=2,\n", + " physical=True, levels=(5, 95), rng=4,\n", + " ) # fmt: skip\n", + " draws = rx.diagnostics.predictive_draws(p, rows, n_rep=2, rng=4, return_draws=True)\n", + " draws = np.exp(draws) if space is not None else draws\n", + " at_data[name] = np.percentile(draws, [5, 95], axis=0)" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "868974ac", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T20:00:33.730576Z", - "iopub.status.busy": "2026-09-14T20:00:33.730412Z", - "iopub.status.idle": "2026-09-14T20:00:45.139311Z", - "shell.execute_reply": "2026-09-14T20:00:45.138548Z" - } - }, + "execution_count": 20, + "id": "26893f77", + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "emcee: 3200 rows, mean acceptance 0.25\n", - "dill round trip: ndim 8, log_posterior = -28.425 vs -28.425\n" - ] + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1155x462 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "sampler = emcee.EnsembleSampler(16, p_best.ndim, p_best.log_posterior)\n", - "sampler.random_state = np.random.RandomState(2).get_state()\n", - "sampler.run_mcmc(p_best.sample_prior(16, rng=2), 200, progress=False)\n", - "print(\n", - " f\"emcee: {sampler.get_chain(flat=True).shape[0]} rows, \"\n", - " f\"mean acceptance {sampler.acceptance_fraction.mean():.2f}\"\n", - ")\n", - "restored = dill.loads(dill.dumps(p_best))\n", - "theta_best = np.median(samples[best], axis=0)\n", - "print(\n", - " f\"dill round trip: ndim {restored.ndim}, \"\n", - " f\"log_posterior = {restored.log_posterior(theta_best):.3f} \"\n", - " f\"vs {p_best.log_posterior(theta_best):.3f}\"\n", - ")" + "fig, axes = plt.subplots(1, 2, figsize=(10.5, 4.2), sharey=True)\n", + "for ax, name in zip(axes, problems):\n", + " colour = colours[name]\n", + " if \"potential + inferred terms\" in fine[name]:\n", + " lo, hi = fine[name][\"potential + inferred terms\"]\n", + " plotstyle.band(\n", + " ax,\n", + " np.rad2deg(x_fine),\n", + " lo,\n", + " hi,\n", + " color=colour,\n", + " label=\"potential + s, b (grid)\",\n", + " )\n", + " lo, hi = fine[name][\"potential alone\"]\n", + " plotstyle.band(\n", + " ax,\n", + " np.rad2deg(x_fine),\n", + " lo,\n", + " hi,\n", + " color=\"k\",\n", + " hatch=plotstyle.HATCHES[0],\n", + " label=\"B1: potential alone (grid)\",\n", + " )\n", + " ax.vlines(\n", + " np.rad2deg(data.x),\n", + " *at_data[name],\n", + " color=colour,\n", + " lw=2.2,\n", + " alpha=0.8,\n", + " label=\"B2: + every error term (measured angles)\",\n", + " )\n", + " ax.plot(np.rad2deg(data.x), data.y, \"o\", ms=2.5, color=\"k\", label=\"data\")\n", + " ax.set(xlabel=r\"$\\theta_{cm}$ [deg]\", yscale=\"log\", title=f\"{name}: 90 % intervals\")\n", + "axes[0].set_ylabel(r\"$\\sigma / \\sigma_{Rutherford}$\")\n", + "axes[0].legend(fontsize=8, loc=\"lower left\")\n", + "fig.tight_layout()\n", + "plt.show()" ] }, { "cell_type": "markdown", - "id": "46a375ab", + "id": "c2130314", "metadata": {}, "source": [ - "## Tempering the likelihood (recipe 12)\n", + "On the left, the potential alone (the hatched grey band, B1) is already wide, because the fit is honest about how well it knows the potential. Adding $s$ and $b$ on the fine grid widens it further, and the vertical bars (B2) show each measured angle's full 90 % interval, which is where we compare with the data. On the right, the reported-errors fit gives a razor-thin potential and intervals barely larger than the points themselves, and most of the data sit visibly outside them." + ] + }, + { + "cell_type": "markdown", + "id": "8c2c671f", + "metadata": {}, + "source": [ + "## (C) Empirical coverage\n", + "\n", + "A predictive distribution is calibrated if its central 68 % interval contains about 68 % of the measured points, and likewise at every level, so its empirical [coverage](https://en.wikipedia.org/wiki/Coverage_probability) follows the diagonal (recipe 17). We check three predictives for each model, all at the measured angles:\n", "\n", - "With many points a posterior can collapse tighter than the model deserves.\n", - "`Constraint(weight=w)` multiplies that constraint's log-likelihood by $w$ and\n", - "changes nothing else; KDUQ's \"democratic\" scaling is the case\n", - "$w = n_\\mathrm{parameters} / n_\\mathrm{points}$.\n", + "- **(C1) the potential alone** (`model_only=True`);\n", + "- **the potential plus the reported statistical errors** (`terms=[]`, which keeps `statistical=True` and drops every inferred term);\n", + "- **(C2) the potential plus every error term** (the default).\n", "\n", - "We show it on a 200-point line, where the truth is known exactly, so we can ask\n", - "the question that matters: does tempering improve anything we care about?" + "For the linear model the last two are the same thing, because the reported errors are its only term, so its two curves lie on top of each other." ] }, { "cell_type": "code", - "execution_count": 14, - "id": "e0136a13", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T20:00:45.141086Z", - "iopub.status.busy": "2026-09-14T20:00:45.140902Z", - "iopub.status.idle": "2026-09-14T20:01:13.444874Z", - "shell.execute_reply": "2026-09-14T20:01:13.444197Z" - } - }, + "execution_count": 21, + "id": "4893d5ad", + "metadata": {}, + "outputs": [], + "source": [ + "levels = np.linspace(0.05, 0.95, 19)\n", + "kinds = {\n", + " \"potential alone\": dict(model_only=True),\n", + " \"+ statistical errors\": dict(terms=[]),\n", + " \"+ every error term\": {},\n", + "}\n", + "coverage, cov68 = {}, {}\n", + "for i, (name, p) in enumerate(problems.items()):\n", + " c = p.constraints[0]\n", + " rows = samples[name][::20]\n", + " coverage[name], cov68[name] = {}, {}\n", + " for kind, kw in kinds.items():\n", + " extra = {} if kw.get(\"model_only\") else dict(n_rep=2, rng=10 + i)\n", + " draws = rx.diagnostics.predictive_draws(\n", + " p, rows, return_draws=True, **kw, **extra\n", + " )\n", + " y_obs = c.y[c.active]\n", + " coverage[name][kind] = rx.diagnostics.coverage_curve(draws, y_obs, levels)\n", + " cov68[name][kind] = rx.diagnostics.coverage_curve(draws, y_obs, [0.68])[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "f01ec16a", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "untempered m = 1.9792 +/- 0.0601 (pull -0.3) b = 3.9927 +/- 0.0305 (pull -0.2)\n", - "tempered m = 1.4720 +/- 0.3661 (pull -1.4) b = 4.1842 +/- 0.2158 (pull +0.9)\n" + "log space, inferred errors: coverage at a nominal 68 %\n", + " potential alone 0.29\n", + " + statistical errors 0.31\n", + " + every error term 0.83\n", + "linear, reported errors: coverage at a nominal 68 %\n", + " potential alone 0.02\n", + " + statistical errors 0.15\n", + " + every error term 0.15\n" ] } ], "source": [ - "rng = np.random.default_rng(11)\n", - "x200 = np.linspace(0.0, 1.0, 200)\n", - "m_true, b_true = 2.0, 4.0\n", - "y200 = (m_true * x200 + b_true) * (1.0 + rng.normal(0.0, 0.05, x200.size))\n", - "d200 = rx.Dataset(x200, y200, 0.05 * y200, label=\"N = 200\")\n", - "m_ = rx.Parameter(\"m\", prior=stats.norm(1.0, 0.5), latex=\"m\")\n", - "b_ = rx.Parameter(\"b\", prior=stats.norm(4.0, 0.5), latex=\"b\")\n", - "line = rx.Model(lambda x, m, b: m * x + b, [m_, b_])\n", - "comp200 = rx.Comparison(d200, line)\n", - "p_full = rx.Problem([rx.Constraint([comp200])])\n", - "p_temp = rx.Problem([rx.Constraint([comp200], weight=2 / 200)])\n", - "\n", - "\n", - "def fit(problem, seed, n_walkers=24, n_steps=2000):\n", - " s = emcee.EnsembleSampler(n_walkers, problem.ndim, problem.log_posterior)\n", - " s.random_state = np.random.RandomState(seed).get_state()\n", - " s.run_mcmc(problem.sample_prior(n_walkers, rng=seed), n_steps, progress=False)\n", - " return s.get_chain(discard=n_steps // 3, thin=5, flat=True)\n", - "\n", - "\n", - "s_full, s_temp = fit(p_full, 3), fit(p_temp, 4)\n", - "for name, s in ((\"untempered\", s_full), (\"tempered\", s_temp)):\n", - " pull_m = (s[:, 0].mean() - m_true) / s[:, 0].std()\n", - " pull_b = (s[:, 1].mean() - b_true) / s[:, 1].std()\n", - " print(\n", - " f\"{name:11s} m = {s[:, 0].mean():.4f} +/- {s[:, 0].std():.4f} (pull {pull_m:+.1f})\"\n", - " f\" b = {s[:, 1].mean():.4f} +/- {s[:, 1].std():.4f} (pull {pull_b:+.1f})\"\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "8ba42e69", - "metadata": {}, - "source": [ - "### Does tempering help here?\n", - "\n", - "The untempered fit covers the truth comfortably: $m = 1.979 \\pm 0.060$ against\n", - "2.0, and $b = 3.993 \\pm 0.031$ against 4.0, both within a third of a standard\n", - "deviation. That is worth saying plainly, because a posterior this tight *looks*\n", - "alarming beside a wide prior, and it is easy to read the corner plot as a\n", - "failure to cover. It is not: 200 points at 5 % each really do pin a straight\n", - "line that well.\n", - "\n", - "Tempering by $k/N$ widens the posterior roughly sixfold, and here it makes the\n", - "answer slightly *worse* rather than better ($m = 1.47 \\pm 0.37$, 1.4 sigma low).\n", - "The coverage curve below is the check that actually matters, and the untempered\n", - "predictive already follows the diagonal: the prediction of the *data* carries\n", - "the noise even when the parameter posterior is narrow. Temper when the\n", - "predictions are overconfident, not when the parameters merely look precise." + "for name, row in cov68.items():\n", + " print(f\"{name}: coverage at a nominal 68 %\")\n", + " for kind, value in row.items():\n", + " print(f\" {kind:22s} {value:.2f}\")" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "7d6d7fc3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T20:01:13.446498Z", - "iopub.status.busy": "2026-09-14T20:01:13.446324Z", - "iopub.status.idle": "2026-09-14T20:01:13.675194Z", - "shell.execute_reply": "2026-09-14T20:01:13.674477Z" - } - }, + "execution_count": 23, + "id": "85fd7f5a", + "metadata": {}, "outputs": [ { "data": { - "image/png": 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m0L9/f6xWKxs2bEBRFHr37k16ejq7d++mR48e6rWfeuop9bwxY8Ywb948Hn/8cXX/6NGjmT17doN7vPjii2i1jj6Rxx57jP/85z8sWLCAG2+88YTvD/Cvf/1LfW+KojBnzhweeOABDhw4gKIoKIrCkCFDmDNnjnrOp59+yptvvqm+/yFDhjBu3Dg1qDvfSPAkhBDitMgqM3K0pOavDzzL+fj44Ofn5/IaoLq6usng6cCBA2RnZ/Poo4+6bB80aJBLgOHj44Ovr6/62tvbm8DAQDw8PFy21c9FAtDr9cTHx6uvDQYD8fHxar7Vid7f29ubsLAw9XVhYSHFxcX8+OOPLFmyxOXc2vuVlJRQWlpKYmKiy/7ExER27tzZ6OdxrpPgSQghxGnRMtDrrL6nRqNRE8Nr1R/S+ju8vb0ZOXIkn3322Sm53vGsVitGoxFvb291W0VFBQEBAW7dv7bnqpaXl+Pze/bZZ7nyyisbPcfPzw+tVtsgSb2iosLt93GukOBJCCHEaXGqh89OtaioKPLz8zGZTHh6egKwefNmt6/j7e2N3W7HZrOh0+kAGD58OO+++y4lJSUEBwerx1ZWVrr0Yv0dS5cu5bLLLgPg0KFDHDp0iN69e/+t+/v7+9OrVy++/PLLBsFT7bkGg4Fu3bqxZMkSxo0bB4DNZmP58uW0adPmlLy3s40ET0IIIQSOPB0/Pz9uv/12rr/+enbu3Mn//vc/t6/ToUMH9Ho9M2bMoH///sTExPDQQw/xww8/MGzYMB577DFCQ0PZsWMHn376KWlpaX+77VqtlnvvvZfi4mL8/f154YUXGDFiBEOHDgX4W/d/5513GD16NNdeey3XXHMNZrOZFStWUF1drfZkvfjii1xxxRWEhITQo0cPPv74Y3Jycs7b4Elm2wkhhBBAQEAAq1atQqPRMGPGDIqLi5k3bx6DBg1Sh7NCQ0MZOHCgy3k+Pj4MGjRI7a2KjIzk+++/Z/369Tz11FMsWLCAwMBANm7cyNSpU/n222+ZNWsWJpPJpRJ5Y9cODw+nf//+LtuioqLo06ePyzZvb29+/vln1qxZw+zZsxk/fjw///yzuv9k7w8wcOBAkpOTiYiIYObMmfz444/07NmTjz76SD1mwoQJzJs3j82bN/PRRx8xdOhQZsyYQdeuXU/osz/XaJTjB3iFEEIIcc6YP38+119/fZOFMcWpJz1PQgghhBBukODpNLNarWRlZWG1Ws90U4QQQpwHmhpuE/8cGbY7zbKysoiNjSUzM5OWLVu6dW5zBdjOJ3fddRcFBQWEh4e7FGE7X10oP9cL5X0KIc5/0vN0DpE49/x0ofxcL5T3KYQ4/0nwJIQQQgjhBgmehBBCCCHcIMGTEEIIIYQbJHgSQgghhHCDBE9CCCGEEG6Q4EkIIYQQwg0SPAkhhBBCuEGCJyGEEEIIN0jwJIQQQgjhBgmehBBCCCHcIMGTEEIIIYQbJHgSQgghhHCDBE9CCCGEEG6Q4EkIIYQQwg0SPAkhhBBCuEGCJyGEEEIIN0jwJIQQQgjhBv2ZboC4MEyfl0JqTnmj+7pGBzB7StJpbpEQQghxcqTnSZwWqTnlpORUNNieklPRZFAlhBBCnI2k50mcNknR/qy9b7DLtsEz156h1gghhBAnR3qehBBCCCHcIMGTEEIIIYQbJHgSQgghhHCDBE9CCCGEEG6Q4EkIIYQQwg0SPAkhhBBCuEGCJyGEEEIIN0jwJIQQQgjhBgmehBBCCCHcIMGTEEIIIYQbJHgSQgghhHCDBE9CCCGEEG6QhYGFW6bPSyE1p7zJ/V2jA5g9Jek0tkgIIYQ4vaTnSbglNaeclJyKRvel5FQ0G1gJIYQQ5wPpeRJuS4r2Z+19gxtsHzxz7RlojRBCCHF6Sc+TEEIIIYQbJHgSQgghhHCDDNuJBppLCk/JqSAp2v80t0gIIYQ4e0jPk2iguaTwpGh/ukYHnOYWCSGEEGcP6XkSjWoqKVwIIYS40EnPkxBCCCGEGyR4EkIIIYRwgwzbiVMqJaei0XpPkmguhBDifCHB0wXqn5hR11wiuSSaCyGEOF9I8HSBqp1R11iQdLKBjqxpJ4QQ4kIgwdMFTGbUCSGEEO6ThHEhhBBCCDdI8CSEEEII4QYJnoQQQggh3CDBkxBCCCGEGyR4EkIIIYRwgwRPQgghhBBukOBJCCGEEMINEjwJIYQQQrhBgichhBBCCDdI8CSEEEII4QYJnoQQQggh3CDBkxBCCCGEGyR4EkIIIYRwgwRPQgghhBBu0J/pBoh/zvR5KaTmlDe6LyWngqRo/9PcIiGEEOLcJz1P57HUnHJScioa3ZcU7U/X6IDT3CIhhBDi3Cc9T+e5pGh/1t43+Ew3o1kpORUMnrlWfX3IawCWKAsGgwHNvBRmT0k6g60TQgghXEnwJM6o5nq/irT+TQ47CiGEEGeKBE/ijGqsV+muu76koKCA9bGXARGnv1FCCCFEMyTnSQghhBDCDRI8CSGEEEK4QYbtxFnt+GTy+rpGB0gyuRBCiNNOgidx1gqxV9A2um2j+5oqwSCEEEL80yR4EmetIeZdzLnv3kb3NdUbJYQQQvzTJOdJCCGEEMINEjwJIYQQQrhBgichhBBCCDdI8CSEEEII4QYJnoQQQggh3CDBkxBCCCGEGyR4EkIIIYRwg9R5OsdNn5dCak55o/tScipIivY/zS0SQgghzm/S83SOS80pb7LadlK0P12jA05zi4QQQojzm/Q8nQeSov1Ze9/gM90MIYQQ4oIgPU9CCCGEEG6QnidxzkrJqWhyjbuu0QHMnpJ0mlskhBDiQiDBkzgnNZfL1VQOmBBCCHEqSPAkzknN9SoNnrlWeqWEEEL8YyR4Eucd6ZUSQgjxT5LgSZx3/qpXSgghhPg7ZLadEEIIIYQbJHgSQgghhHCDBE9CCCGEEG6Q4EkIIYQQwg0SPAkhhBBCuEGCJyGEEEIIN0jwJIQQQgjhBgmehBBCCCHcIMGTEEIIIYQbJHgSQgghhHCDBE9CCCGEEG6Q4EkIIYQQwg0SPAkhhBBCuEGCJyGEEEIIN+jPdAPEX5s+L4XUnHKsVit6veuPLCWngqRo/zPUMiGEEOLCIz1P54DUnHJScioa3ZcU7U/X6IDT3CIhhBDiwiU9T+eIpGh/FlzXkdDQ0DPdFCGEEOKCJj1PQgghhBBukJ4nccFJyalg8My1je7rGh3A7ClJp7lFQgghziUSPIkLSnP5YU3llQkhhBD1SfAkLijN9So11RslhBBC1Cc5T0IIIYQQbpDgSQghhBDCDRI8CSGEEEK4QYInIYQQQgg3SPAkhBBCCOEGCZ6EEEIIIdwgwZMQQgghhBskeBJCCCGEcIMET0IIIYQQbpDgSQghhBDCDRI8CSGEEEK4Qda2E6KelJyKJte46xod0OzaeEIIIS4MEjwJ4dQ1OqDJfSk5FaexJUIIIc5mEjwJ4dRcr1JTvVFCCCEuPJLzJIQQQgjhBgmehBBCCCHcIMN2Z5Hp81JIzSlvsD0lp4KkaP8z0CIhhBBCHE96ns4iqTnljSYmJ0X7N5vMLIQQQojTR3qezjJJ0f6svW9wo/uKiopOc2uEEEIIcTzpeRJCCCGEcIMET0IIIYQQbpDgSQghhBDCDRI8CSGEEEK4QYInIYQQQgg3SPAkhBBCCOEGCZ6EEEIIIdwgwZMQQgghhBskeBJCCCGEcIMET0IIIYQQbpDgSQghhBDCDRI8CSGEEEK4QRYGPkMmf7IZz+B0l20pORUkRfufmQYJIYQQ4oRI8HQWSYr2p2t0wJluhmhCSk4Fg2eubXRf1+gAZk9JOs0tEkIIcSZI8HSG/HxLX1q2bHmmmyFOUHNBbUpOxWlsiRBCiDNNgichTkBzvUpN9UYJIYQ4P0nCuBBCCCGEGyR4EkIIIYRwgwRPQgghhBBukOBJCCGEEMINEjwJIYQQQrhBgichhBBCCDdI8CSEEEII4QYJnoQQQggh3CBFMoU4BZpaukWWbRFCiPOPBE9C/E1NLd0iy7YIIcT5SYInIf6mpnqWZNkWIYQ4P0nOkxBCCCGEGyR4EkIIIYRwgwRPQgghhBBukOBJCCGEEMINEjwJIYQQQrhBgichhBBCCDdI8CSEEEII4QYJnoQQQggh3CBFMoX4BzW1bAvI0i1CCHGukuBJiH9IU8u2gCzdIoQQ5zIJnoT4hzTXqyRLtwghxLlLcp6EEEIIIdwgwZMQQgghhBskeBJCCCGEcIMET0IIIYQQbpDgSQghhBDCDTLbTogzpLYGlNVqRa93/U9RakAJIcTZS4InIc4AqQElhBDnLgmehDgD6vcqFRUVERoaqr6WGlBCCHF2k5wnIYQQQgg3SPAkhBBCCOEGCZ6EEEIIIdwgwZMQQgghhBskeBJCCCGEcIMET0IIIYQQbpDgSQghhBDCDRI8CSGEEEK4QYInIYQQQgg3SPAkhBBCCOEGCZ6EEEIIIdwga9sJcRZKyaloco27rtEBLmvjCSGEOL0keBLiLNM1OqDJfSk5FaexJUIIIRojwZMQZ5nmepWa6o0SQghx+kjOkxBCCCGEGyR4EkIIIYRwgwRPQgghhBBukOBJCCGEEMINEjwJIYQQQrhBgichhBBCCDdI8CSEEEII4QYJnoQQQggh3CBFMoU4x8jSLUIIcWZJ8CTEOUSWbhFCiDNPgichziGydIsQQpx5kvMkhBBCCOEGCZ6EEEIIIdwgw3ZCnEckmVwIIf55EjwJcZ6QZHIhhDg9JHgS4jwhyeRCCHF6SM6TEEIIIYQbpOfpAjV9/Y+kluQ0ub9rcDSzB15xGlskhBBCnBskeDpDJi+fi2dY8Akf3zU4mpcTh7t1j+YCpHX56QAMiohrdN+6/PQmz5XASgghxIVMgqdzQEozPUQnGyANiohrMgj6q2tKYCWEEOJCJsHTGfLzqGm0bNnyhI4d/Pu7pJTkMH7tF+j1rj+ykw2QmtPc8c0FVs0FeSejyFTN4N/fbXL/yQR/zZ13vpMyBkIIcWpI8HQO6BocDYDVam2w72QDpJPV3H1qg7zmAp4TEVZTgQ6osJhYl5/u9tBicwHlqQ7wTreTDQyljIEQQpw6EjydA2ofhkVFRYSGhp7h1jStNsg7Vbx0eu5KHOB271JzAeXfDezc8U/0gKWW5JBSkkNSI591c4GhlDEQQohTR4Inccqcqt6vu37dRUGlkShv/yavebqH3U4mEDrZQOevhkaTgqNZO/7eBvtOZ2AohBAXMgmehHA61bMTTzbQaS7oSgqOPuU9fNB0PtShiiKqrOZGz4kNMbD73kkNtjf1OVqtVvR6/QWbcyaEOH9I8CSEU3NBy8nMTvw7gU5TQdc/obl8qCqrmWqrGR+9h8v2igo9mVgaPedke9xO1l/1Cp6uz1EIceGQ4ElccJpKam+up6g5J9uLUtuO2h6Z49vxd67pFm/o2qv5HLHjP5OAl36g2mp263MsKipi4sZvmm3j6cwDE0KIkyXBk7igNNcT9E8NiZ2udpxs25ubudhUUOJ7XE9Ufc21v7k2Sh6YEOJcIcGTuKCcLbk29dtxqmZRnux7ay4waSoQauvvaO/a8Ve6da+TLXXRXM7Z6Qx6hRACJHgS4oJ30sOOzRTdbIyaMN5EQc7mAqDTXc9MCCGaI8GTEMJtzSWZN2ddegnr0ktIzSlvZG+k86sJJ3lPIYQ41SR4Os1qq4Tn5LifyFpSUkJNTc2pbtJZp7KykurqaiorK8nKyjrTzfnHnYs/12f6hwAhbp1TUlLCG+st7MurwFSS79a5W7PKWJcKW/ccdOu8vWV6srplERUV1WBpIyGEOFkaRVGUM92IC8mWLVvo27fvmW6GEBeUzMzME15LUggh/ooET6eZ0WgkNTWV8PBwt/4SzsnJoW/fvmzevJno6PM7OVbe6/nnTL9P6XkSQpxK8q/Jaebl5UWfPn1O+vzo6OgL5i9oea/nnwvlfQohzm/aM90AIYQQQohziQRPQgghhBBukODpHBEQEMDzzz9PQMD5P11b3uv550J5n0KIC4MkjAshhBBCuEF6npphs9nOdBOEEEIIcZaR4KkJzz33HA8//PCZboYQQgghzjJSqqARzz33HD/99BMrV648000RQgghxFlGgqfj1A+cIiIiAMjOzsbf3x9/f3+3r1deXk55ed06XlarlZqaGhISEqRonxBnEavVSm5urhTUFEL8JfkXop6ZM2fy2muvsWfPHiIiIli7di133HEHe/fuRafTceONN/Lee+/h7e19wtf83//+x4svvthg+9btycTExLhs+7/9q3n70AYAfux/LQNDW6n7rHY7xqpqlwBuZ1kuY9d+CsCd8X14sdMoDlYWMXjVhwDc0Ko7RypLWLs6DoAeLbx4Z2IL9fzMqjKCPRq+l/8eXM+ygsMArBw6jZbegeo+s91GgMGT7sveo8hctx7bqqG3EGzwwqORh85n6Tt4ff8aAOb2nszFUe3VfW0W/pcau5UeQdEsGXITAE8//TRFRUWEhoby6quvNrje6Tbz4EZe2rsKgC/7TOGiyLYA7CjN4eK1nwNwS1xPHksc7HLeS3tW8k1mKgCrht1CB/9wAOZn7+Wu7b8A8EbXMUwJTTipwLy+7zJTuTf5dwBmdLuY61p1AyCtou734abW3XkzadwJX/OlPSuZeWgTAAsH30DPYMfva4XFRMKitwAYEd6GOT0nuJzXZcm7WBQ7vYNj+G3wDer2ZRn7uH7nAgAeaz+IxxOHqPsW56Zx/ZYfAXi+0wimt61bwuj2rfP5NWc/AOuG30aop4/L/e7a9gurCtMBODDuAfz0Hi77o8ObWWy4ntzcXGJjY8+ppVyKi4sJCXFvjcEz6UJo71133UVBQQHh4eHMmTPnH2pZ4861z/dcJsFTPVdddRVz5szh+uuv59///jfXX389L7zwAkOGDGHVqlU89dRTlJSU8PPPP5/wNR9++GFuu+029XXtMhWBQUEEH/dL7lUvKPMPCHDZb7HZqdLqXLYFKNXq995eXoSEhBCos6rbPD090ZsM6mutTodHgJ/62q4Y0Xm7PogANPUCIL+AAAL8gtTXRpuVIE9vNBrXdDn/wAB8Pbzw0hs4Xv335evn7/q+NRoA9Ho9oaGhjnZqter/hoSGomlwRQdFUdBoGt/b1D4Fmr4eCppG9vpk131Gfv517Q+wV6nbDZ6e+AcFupzn4empfh8YGEhwkOM8v7K6n4Gvry/+/v7qez+hdjby3vxK6l/TT/0H1PX3wavBfZq6HjT9+6gzG9XteoMB/6Ag1xM1jjeg1+tdftbehXWfo7e3j0tb/Kvz6vb5+Lic5+FRFwz5BQbi7+Xrcjudoe53Lig4GH+DJxeKc22ytLT3n3WutfdcJsFTPZGRkaxYsYKRI0cybtw4fvjhB6ZMmQJAUlISERERXHvttWzfvp2ePXue0DUDAgKkto34x1VYTGe6CUIIccGQ2XbHqQ2g+vbtyxVXXOGyb8qUKej1ejIyMv6Re2vr9TXsL8v/y+Pr9xUUGqsb7LcpdpfXOeVmzFZ7g+Oau/C/dy6lrN7wnNrW43oqjn9dS1EUthZl/uV5mVWlVFiMnO0s9rryFfV7qTKqShoce7SypNFjq23mRre7q8Zq4aMDmxiy8D3u2zRf3V7/M67/O7WtKAuj1XLC169/ndSSHPX7+r1UWwsz2Zh/FHD8rGfvW4/F7vgdO/697Situ0aD34N6x+4szm6yHX/mHnLZt7c0j+TiY3Vt+4v3JIQQp8IFHTzNnj2bVq1a4efnx6WXXsrWrVsBRwC1atWqBkMZR44cQVGUv7Wwb3OGOHNpAJ7ZvtDl4duY9gERBBq8APg+PZmsqlIivetyZzKrSgnw8ASfUgCySy28tyrnL7t2e4fGqt//krmHUYvmkHLcA218y47q97G+QbTxa3ycfebetfyWuRcAP70HA8Nbu+wfEdUOgNyaCh7c/Euz7forFRYj+0rzWZ6dxueHtvFaygru3fgzt6z9jp+Ppp50l3Yb/7r3tqteANApKJIwT8cQ0h9Z+1we7H9k7WOV83W0tz/tAsLUfQuz9qnfdwmOOqk25ddUMvD3mdyxfh7r8tPV7SGePgyJbKO+bhcQSoLz3tuKsrht/Q8n/DlcFJ2gfv/Ylt/Y5wzo/fQeDHD+HEvMNVz95xd8fnArD23+hX/vXKaeM6ZFXW7bipyDvLbvz7p9MXX7APqHt1KH2745vIPP0rao+8bHdlK/f3jzL6zMOQjA5oIMrljxGUUmxx8O/cJb4XcBDdkJIc6cCzZ4mjVrFu+88w4fffQR33zzDWVlZQwYMIBZs2YBNEgKLyws5KabbuKJJ56gRYsWjV3SLX1+m0HLH16k/+9vs6/Mke8xKKINtyY4EmXLLSZuXfcdNnvTPUW+Bg/u7+hIujXbbfxn1yqCPLyJcgZQ+8sKCPP0gZg00Diu8+3WQh7/KZ1yo7XJ6w6PasuXQ69TE3Mzq8uYsvJzlx6B13tfwpTWSSQEhDF38NXotA1/lX7L3MMbqasAR+/Bl0OnEnpcvspb/SaqAeDctC38mJ7SZLsak15RzH0b5xP2zfMEfvUcneb/h9FLPmDa2u94ZvsfzNq3nk8PbuWKlZ/T//eZLMs+4Nb1AQZFxKnfr3f2sgB46w282acuWfrJrQuptpopNxt5dtsf6va3+03CoNUBUG428oczeGrpE8jACNdg8kRkV5cxYtFsdtbrDeoV2pIZfS5jz6THiPevyyXSarR8P/xGNTD5+vAOXtq59ITuMyomgTsT+wNQZTVz3aqvqLaa0Wg0/Dr6Vi6KcQRXFrudJ7ct5Pv0nYCj9+epriN5KmkUAEcqipn651fYnEHbv7qPpv9x7zvUy5ePB12lvr5/03z19+2aNt25xfnfhVWxc9u675m5Zy3X/vklZc7eykERccwfdfMJvS8hhPi7Ltjg6ZVXXuHdd99lzJgxTJgwgT///JMHHniAe+65h3fffVc9Lj8/n8cff5yePXsyduxYXn755VNy/1JzDUWmanaWZDNpxVzyjZUA/F+vS2nrfPitzT/Cm7tXNXudezsOVoOPj9M2kVVVSodAR4mFzOpS/A1e4FUN0WnqkMbqtHJunJvG4XxzE1eF0THtWTluutpTVGYxctWqz9XhGx+9B3OHXM32yx6iX3irBucnF2Xz4KYF6uvnuo3m4pYdGhwX6xvEu/0nq6/vWD+vwXBjY3aV5HLjmm9J+On/eG/fOopNDYctj7elMJOxSz7kosXvs7ngxIdeY3wCaeUbBMCmgqPY67XvmjbdGRYVDzh6+t5M/ZPXUlaQ5/x5XtyiAxNbdVGP/zVzDya7I3CdEpeEVuPef4IZlSUMXzSHvc5eoDZ+IWyb8CBbJjzAvR0HNQhOwdG79fXQqerw14vJS/n68I4Tut8bvcfTPcQxy253aS4PbVqAoij4Gzz5cOCV3NtxkMvxvnoP5g6+mns6DkKj0VBpMXHlys/Vn8+kVl14Kmlko/eaEpfE/R0dMxaNNitXr/yCUnMNGo2G57qP5vLWXdV9r6euwGhzfI6jY9rz60W3ENTIzFEhhPgnXLAJ44WFhVitdb0vWq2WN998E61Wy4MPPkifPn3o168fERERjBkzhqeeeorg4OBTdv+2/mGUexooMFVytKqEKSs/45tBNxPi483s/ldx8bL3sSl2Xty5hN4hrekeFIvVaqPG4rpkjKfGgzsSBvKfPSsw2228nrqSdv7h6pBRbf4JIbk812cIc5aXkl9pJqfMzIs/F9C3yzE6xduoP0LZMTCCcmee092JA6m0mkkpyaHUbOSaVV/y/fAbGh1uqjQb0Wm17Cg6xh3r5qkPt6nxPbg+vodL0FHf5NZduO5YD74+vIMScw15NZUYgEqLibV5R+gYFEGIsxdsXV46b6Su5PesvS7XCDB40i+8NTE+AcT4BBDi4UOsXxAx3gHkGSt5dedydjhzY1bkHKT/7zOZGNuZl3qMpbPzvSgoaJtImukf3oqMqlLKLSZ2l+Sq5wD8q9toJhR8QrXNkYNkx9HD4m/w5OmkUdipe9/fpdcFLVfEJaE4j63931qKAhw3bHyovJDRiz/gqDO/qp1/GL9ddAstfAOpspopMxvx1Dn+k86uLuNQeREDI+LQabX0D2/FjD6X8cBmR0B7y9rviPMNVgPfpkI4D62er4dNpf9v71BuMfHpwa1kVpXxTr+J+Oo9eLLrSBICwngxeSkRXn68238yHQIjsNntLMraxwObFpBeWQxAB/8wPh50JRo0TQ4dvtxzLFsLM1lfcJQjlcXcuPobfhpxI3a7jTd6j6fMXMNy57AdwJVxSTzfbSwaaDKfy0vXcAaoEEL8HRfswsCjR4/GbrezbNkyl9wmu93OyJEjMRgMLF16YsMb7sjKyiI2Npb9h9LRhwYyYvF7arLx5a2S+GzwdWg0GmbsXsVzyY6hnxjvAFaOuQ8/i5Wg4Ia5RQU1VXT79Q3KLEY8tDqe6DKSf6c42n5ru358fNBRq+f9AVO4JKobU7/azurDxer54RHVdOhUil7v+FWI9wtheHRd/pXJZmXGntXsLnUMLwZ7ePPjiJsaBFAWm5Xvju7kia0L1cTqkdHtmD/qZrQaDV7axh9iNsVOhcVEr1/e4mhVCWOXZ+JbY6XKW8/iUY78q1BPH0I9fThQXuhybqS3P/d2HMStCX0J9PBSt1dYTBi0WvaXFZBXU0mP0BhW5x7m5Z3LOVBeoB6n1Wh4sutIXug+Go1Gg66JnqB39qzlQWfgMav/5dzhHM4Cx5DWnP0beGLr767n9JvETe1642twTLUvNFbR8rt/Y1XstPUPZe/kx9FrtWpNq/oURXEJnvaV5jN6yftkVzsKrnYMjOC30be65LgVGatZm3eYzw5uZVlOGnZFYVR0O74ceh0GrY4gD28e2ryAmXvXAdAlKIotEx7AoNU1m/Cv0WiYl57CVau+ULd7avU8lTSCx7qMwFOnx2q3odNo0Wg0ZFeX8dDmX/jxaKp6fKinD4sG3UivVm3/sgxDTk05PX95S+2NvTOxPzP6XIZWq6XGauGmNd+wNDuNBzoN4fnuozHarHjodE1c0dFLeiJq/9s8l+o8Nfa7cza7ENp7Jus8nWuf77nsgh22e+mll1i1ahX/+te/XLZrtVqeeeYZVq5c+Y8vDBzp7c+84TcT4MxH+SkjhVdTHQm3D3QaykXRjqTa7Jpy7t70AzW2xv+yDvLw5h7ncIfZbmN7vdlHZZa6mXKFpiqiA7xYckd/nhrVTt1ekO/D1k0RlJU2/pDx1Ol5qNNQOjqHA0vMNUxZ+RkHyuqCEEVReHDzLzy8+Vc1cBoR1Zavhl2n5vs0J9DDm2+GX99k4nmRqdolcGrrH8rb/SayZ/KjPNx5KIEeXhhtFtbnp/O/3au5ac23tJv3OgN/f5fJKz6l76/voNFoWHPJ3Xw08Ep1GM6uKLyaspwpK7+g3Nz0bL8B9XJ0NhQcbbD/zvb96R1a98AdHNGGm9r1cjnm56OpWJ29b1e36d5kjarjpZbkMGLRbDVw6hYczR9jblcDpxqrhRm7V9Pvt7eZuvprlmQfwO78m2h5zkGmb/hJ7el5s/cENdl7V2kus/atP6E2TIlLYsmY22nn70g+N9mtvJC8lJ6/zGB5Thp6rQ6bYmfm3rV0nv+mS+A0rkUimy99gDjnZ/5XYnwC+WnkTXhqHb1o7+/fyNt71wKOPLPvR9xI3jXP80KPMSf8GQohxKl0wQZPAwYM4I033uDll1/m+eefd9kXEhKCn5+fWqzxn9QpKIrPB09VezxeT13OjqIstBotHwy8Wk3+Xp57gNFr5rKxIN3l/JSSbB7ZOp+Ze9eo29blH1a/P1BWF3Bscub56HVa/j2uA7/d2odQH0dvUE2Nnu1bw9mzK5iqhpUJ8NTpebjzMDoHOao1F5trXBKPdxQf44ejdcneT3Qd4XYeSp+wWPZOfpyWPo5ikyGePtzbcRDjWiTSzj8UD62O3qEt+WbYVPZMfoxbEvripTPwZ+4hLlr0PlHfvsToxR/w3PZFLD62n5J6JRbyjZXcsPobHti0gMmtu7B38uO83usS9M7P/ZfM3fT/fWaDWYUAZpuVT+vN/tpbmtfgGJ1Wy+wBdaUt3h0wuUE+09bCLPX7IVFtOBEb848y/I/Zai9Mv7BW/Db6VsK8fLHZ7Xx1aDvdF/yP57YvIremQj0v3i8EX2ePy/yMXWpvm06r5bVel6jHfXpwCyfqopj27Jz4MM92u0gNiPeXFzB2yYdMWfk5vX99m4c2/6LWnIrxCeD74Tfw+0W3usxYPBEDI+L4fOg16usXdixxqWXloWs84yCvpoJFWfv4o96XEEKcahdszhPAI488gqIoPPHEE2zevJlnn30WPz8/7r//fh555JHT9lftRTGJ/LvHxTy9/XcUFB7aMp8VY+8mwsuPzwZP5YqVn1BpNXOkuoTRS+fwQMehdAiM5OO0jWwubJj4fE1cD9YVpJNakkNKaTaRXn7kGSv5JXM36/PTGeicPTY2MYJtDw/l+q92sPaIYxgvL9eHeQUK5p4VXNLNF09DXQDgpdPzZNcRPLN9ERlVpSzPSaPAWEm4lx+tfIMxaLVY7HaivP15ttuoRmfg/RWdVoveeV6AwZM3+0xQXzeohG238cWhbdy74We1R6e+KG9/+oTFcqy6jO1Fjt64b48ksz4/nY8HXcWjXYbTJyyWq1d9SaGpirTyQvr/PpNZ/S/n5gRHOYrMqlKuXvUFG+slmNcmLtdXY7XwjrN3BCCnulxN/K+VFBKtfv/h/k1qz2JTVuceZsKyT6i0OoKGoZHx/HLRNHQaLRUWE1P//IoV9fJ/9BotE2I7cWO73vQPb0W3Bf+jymrGW2cgxqeuUOuiY/vV7/uFNUz2b4633sBLPcZyXZseTN/4I3/mOgL1+Rm71GO0Gg3TEwfwSs+LCag3lOquK+O6sTLxEHP2b8Bkt7IiJ80l+f54OdXlDPh9JgXGKpftyrQ3T7oNQgjRmPO65+nAgb+elv7oo4+yatUqqqqqGDp0KEOGDGHs2LE8++yzp6GFde5OHETXIMfDdWtRptojMCiiDRvHP8TAcEdPhV1RmLHnT+7c8L1L4BRg8GJ64kDWX3I/b/WbzIOdhqr7Yn3rEt0f2/qrS+J2bJA3K6cP4KOruhHp7xg+tNo0/LSlgie+LWDTwRqX5F4vnYGr4hzrptkURX1ohnn5MjYmEXDUbFpyEiUB/kr9wElRFN5IXcld639UA6eOgRHc3r4fHw26ko3j7+OzIdcQ6OFFlcXMVXHd1FmJGVWljF7yAY9s+ZW+Ya3YMuEB+juHsow2K7es+57b1v3Ar5l76PXLW2rg5K0z8Nnga3i86wiXdh2pKGb04vf54tA2ddubu1Y1aP+tCX2JdQ5d/XQ0la2FmQ2OqbU0+wCXLP1IDZzGtejAwtG34W/wosBYySVLPnIJnCa26szKi6fz0eCrGBoVz8rcQ2pv1fjYjo5Zl0CxqZq39zh6Kb10ep7tdtFffOqN6xAUwdIxd/DV0OuI8a4LzAZGxLH50gd4q+/EvxU41bqiXqDaXC+Soig8uGlBg8BJCCH+Cedt8PTss8/SrVs3Fi9e/JfHDhkyhNWrV1NTU0NpaSn/+te/TnsuhV6r442eE9XXLyQvotD5IIjzC2H+8Ft5seMovI4brugdGsvMvldw5IpnGdeiA6OXzGHyik+4Kq47LX2CAEcwVpuvtKkgg7lprkM1Wq2GaX1j2ffEcB4f0RYPnePXoqjSxrtLS3hlQRF5ZXUzE690Bk8A3x/ZqX4/Nb5uyZq5B058OMhdVruNuzf85FKQ8aHOQ9k84X6e7z6GUnMN09Z+x9glH/Lloe3sLy/g+/SdDIhozfDIePWct/esoe9vb1NqrmHF2DvVafIAn6RtZuLyuRSaHD+D9gFhbBh/H1Pbui7LszBrL8MWzXKpuQSOXKNdJbku27z0Bp6rF6w8t31Ro+/vt8w9XLZ8rprjNrl1F34eeRPeeoMzUPtAnTkY5unLojG389WwqS71nb6pV4rg2vge6vcLMnar152eOJCWJ5iH1BiNRsPVbbqzZ/JjvNNvEvOG38if46arpQ1OhSGRbdQaVYuO7W9yxuaPR1P5zTkDM8YngFd6Xqx+CSHEqXbeBk8rVqwgISGBSZMmNRtAJScnqyULPDw8TkueE0BZjZmSatevWM8Irm7teDgXm6t5auvvlFabKa02U1hl5qqYbiweeTeTYpO4Kb4fi0ZOZ/6wO7g4siuKXcPEFZ9QZTXzx7F9JBfmcGfCQPV+bf3rKlw/ue13MivLqDbbsNns6pevQcfL4xLZ8dBgJnauW4l+f46ZVxYUcrSkimqLGYNWR7dgRy/ZzpIcVuceJquqjAT/MFo7e7kWHtvHwbIiqi0Wqi0WykxGqqzmRr/yjVUcqijkrd2rmbDsYwqcPSZWu50CYxVVFrP6lV9TycRln/LhAccMQg3wRJfhJAVFc+2qr2g771Ue3fKbWgepvkXH9nO0qoTHuwzH2zl9fW9ZPkP/mMWvWXt4ousIvhk2tcHCspe37sriMbfT2j+YCquJCquJUnMNT29fyGXL51LmTDRvHxDGPR3qPvP/7lpFmakGm92ufk2N76FW/F6Wk8by7DQUHD2KdkVhXnoKl6/4DLMz6X5K6yQ+HnglFruNzflHGbzwXQ5VFAGOIpvzRtxIx8AIioxVFJuqMNqs7C3NV3tpYnwC6BMai8lmRUHhx3p5aVPb9kD5G/9nVxz/62vwYHqHAUxs3Rk0NHk81JZkqP3/jX0pKErdl0GrU6uR59ZUsK3wGFa73eVrf1k+D9WrKfZSj7FcG99d/RJCiFPtvA2eOnTowKOPPsr48eNdAiiTqS7ptLCwkKFDh3Lddde51Hw6HQx6LR6NfD3fbRxBBkeS9TdHt/Hq7sVYsGLQaTFotXQMiuSD/lfzn16X0TO0JR46rWOfzvVHaVVsTEvoqw5VLcs5wDVtHD0QJeYant7+OzqtBo2m4Ve7cD9+mtaHpXf2p2OEn+OcKjufrTDhq/PBS69nYuu63JOfMlLx0uvx1Bu4KaE34AgGvk9PxkOnw0OnU6fD1/+qtJj49kgyN67+hi4/v8ljW39j0bH9VDnr9RyrLuPmNd/yxaFtFJmqKTJVMWHZxyzOduTseGp1/F+v8aSW5jJt3Xf8lrUXU72157oERfFgpyE81XUkfs7k6SOVJXx4YBP/1+sSeoY4KsVXWExcv/prfjqayhVxSWwYfx99w2IJMHjyn96X8s2wqQR4eKvtLjXXcPWqL9Xq6QCXt+rCotG380TXEeqSLfMzdlNkqkar0apfHjoDL/YYq5737PZF4Mzl+uZIMtf++ZU6DHl92558NOhKDDo9GwqOcvGyj9Sk8I6BEfx60S0kBobjodXhodURYPAi0MOL/+5epV7jzsQBhHj5EODhRbnZqNZIaucfRo+QFug0WrXEQGNfWm3T+/RanXr+8V96ra7B8Wio95omvhrep/5SQEuy96u/U7Vfr6Ysp9g5OWByqy6MbZGIQatTv4QQ4lQ7r4Onffv28e2336oB1AcffECnTp3UhX3DwsKYPXs2gYGB6JqpE3M6hXn68mzSGPX1rANrGbhoBk8lL+DbrJ2klmZjtf91CQWdRou/wZNp7foBjlyeEA8fwp0P9m+O7GDxseZnIo1MCGPVPQNpF+YoUJmWb+TlX3Moq7FycYsO+OgdvTffHk5mj3MG2g3xPdWZg6+mrOBoZQkVFpNL3pTJZuXRLb8SP+817lw/j5W5BxtN+FaA5Tlp3LvxZ+LnvUr8vNfY5kz8DvbwZma/yfyUkeqyVlyIpw/T2vXhxR5jeKHHGAZHtqFXWEte7z1eLU9QYq7hwS2/MC2hD5c5102z2O3ct2k+/9v1J+0Dw1k7/l7yrnmBBzoPUYdwFUVhcdZ+hi2cxfKcNMCRpP1899HMHnAFfgZPvHQGbmtft5TIx2mbGryvK1p3VYe2NhVm8N6hzXxxaBs3rv5Gra5+a/u+vNd/MlqNhq8P72Di8rmUOnu4+oTF8vPIm9WZmPVtLsjg56OOPLQWPoHcXa8nbH7GbrWMxBVxXc+Zaf4Xt+ygLjJ8fN7Twqy9/JK5B3D8t/NijzENzhdCiFPtvA6edu/ejV6v59tvv+Wiiy7izjvvZMKECbRqVTfDaOrUqXz44Ydn1YPkhjZ9+He3S/BxDi3l1JQzL3MHz+5dyqil79F2/r+5a+N3lJldawoMj6yr3VRbIuC2dgPwcP71PXv/eibEdlaPmbr2S5bUm3nVmDBfD36/rR9hvo6em+TMGq74cBdH8i084FxXz46iJkhH+QRwcUtH4rhNsdNl/pvEfPcSLb7/N21+eJUeC2bQ+9e3eH//RnWZEnAsM/JIl2GsveReIr0dvV36ZpYu+Xb49XjpDS6z4G5u15slY+7gwc5DXYpHgmPm3Ss9LyYxINzRZkXh84NbmT3gCu5o30897sltC9X3Un+24K6SXCYun8tVq74go6oUcCz4+/voW5mW0Mfl9+fmdn3wduamfZ+e0qCatlaj5eV6uTgv7l3JzWu+VYe2Huw0hP/1uQytRsuz2xdx5/q6au2XtOygJsIfz2y3cd/G+err57pfhLczwN1bmscjW35V913RyIzBs9XhiiJ1xmXycaUkvjmcrH7/Us+xBDsr0QshxD/pvA6edu1y/AWenp5OSkoKffr04f333z+hJPJ/2ldHtvFB2nq+OLyFlblpHKwowOhM5NVoNNzZfhCrxz7AJS06qcUCa9XYLPyUmcKlKz8g01mdHODhTsPpGBjJne0H0NrPkXsU6e3Ps0mjAUc+yScHN9PdOVxltFmZvPJTfsnY3Wxb24X58uutfQh3BlDZZWZu+HQv1blRxPs5kpQ3FmSw0Vk88u7EgQ2uYVXsFJqqOFhRSHqlo82+eg/u6ziYhRfdxuYJD/BU0ig6BEWo77e5qtH3b5pPsMHbJTn568M7mJu2Re1dOV5mVSlHKusqq09q3QWtRsOLPcbyfLfR6vant/3Bf3f96Xiv1eXcvu4Hhv8xW52WD47Fk1dfck+DBW7B0fs1MtqxaG6+sZJdpbkNjhkT0577jlsXDuC29v14s88ENBoNv2XucSl/cFv7vnw19Lomlxt5b+86djvv1Su0Jdc6h2kzq0qZtOJTSusNbfUKPTcqaO8tzWPC8rnqz/RWZ69eY3qF/v0Fu4UQ4kSct3We4uPjOXbsGDt37uSyyy7jhRde4IYbbuCaa65h0qRJbNq0iaSkpDPWvjd3L4echsMuYZ6+xPoEEesbTLR3ID1DWjIqqj1FxhoKKovJsVazPv8wxZYa9pfnc/GKOXzcbypDveMYFtWWjZc82OCaj3YeTrnFqC4ynFx8jM6Bkewuy8Nst3HVn1/w+eBruKpN9ybb27dVMDseGcoNX+9g5cEibAq8vfIY7WO6Q8CfoLcyc+9arm7TjSFR8Xwx5FrW5B2m1GxUF0Eutzi+LzUb6RIUybv9J5MUEkOZ2ejSc1PbI1Vdb62y+zoO5uZ2vbl8xWccrSphf1kBd2/8kbf7TeLVlOVsLcrCbLfx3r51LDq2jyviuqq5R+Aonvh66ko1EXtqfE+mteuj7r+rwwA0Gg0vJC8B4KltC9lSmMmiY/tc2tHOP4y7OwygQ1AEwZ5NFwAdHtVWXX9vWfYBugZHu+zXaDT8t89lTG3bi0c2zGddUQadgiL5T+9L0Wg0ZFWVcveGn9Tj3+p7GbfW6yE73p7SPN51LrviodUxe+AV6LRaikzVTFw2V61OPjCiNV8Mvfas6mltSlZVKeOWfqQuKnxJyw7M6HuZyzG1OX0A5RYTp26enzifTZ+XQmpOeZP7u0YHMHvKmXs+iLPfeRs86fV64uPjGTZsGDNmzGDatGkAfPvtt3z00Ud07XqWDVvYNWD2prDci0Kzhh0mE9iLQFMIGgU0dsf/ooAmCoNPDRa/PPKNlVy55hP+13MKIyMTG1y22mwj3N+D+9uPxICB13Y7qoLvLsujrV8YhyoLsSl2blj7DemVZdyZMBAFDf6eDS5FmK8HC6b14e01R3hhyQFsdoUD2VY8SntibrWdlJIcvkvfyeiY9gyJimdIVF1ZgCqziQBPx4OufrHLUnMN5SajY5aWovDhgU3k11RSO/gS7OHNnAFXMLaF470tHH0rE5fP5WBFEZnVZdy98Sfe7juRJdkH+OzgVqyKnUMVRfwndRUjotsxpXUSNsXOKynLKbc4coYGRcTxaJdhapJxrfGxHVEUhRedldN/qrfESIDBk6nxvaiymnh062/YFQU/vQcDIuLoE9aS8S07uZSRSAwMV79fcuwA99Urg1DLZLfSPTiaH/pdRbWXnhBPb7x1emosJm5Y/bVaIf2y2M6Ma9FBDYBKzDWE1Ruestht3L9pvpo39mTXEXQMjKDSYuKKFZ+p1cU7BUayYOQ0vHWGBkOJTQVTitL0OnTAccsZu2pu/TpNE3utdgWNRqHEVM24pR+R6Rwi7RMWy2eDr0VRNFhsdflxfvVmRuZWVxBdr+ZUrZa+DTaJC1xqTjkpORUkRTf8AzYlp6KRM4Rwdd4GTwDvvvsuGRkZ3Hzzzeo2vV7PXXfddeYa5dSlahgeHuGUG63klVupqG5uudSGLAD6OAjJxhiSzb1bv+W5rhdzc3x/l+NMVruaL3J/x+GEevny+LYF2FE4VFlIvF8ohyuLsCsKz+z4nfkZqczoM5m+EY0PgXjotTw1KoGh8aFM/Wo7WWVGzNXekJ4EbXby311/cknLjg0WmrXobU0ON2k8NQR5ePPMtj94e88aaueieen0bLj0PpcyC+0DI1h58d2MW/IBu0vzyK2p4IHNC/j1olu4M7E/922az6aCDBRgRc5B9pTmEeHlp85SSwgI46NBVzUoRwCA2dEDhQZeTHYEUB5aHbe170uifwQfH9xMSr16TpVWM0uzD7A0+wBv7V7D0Kh4Loppz/DIeBICwmnlG0RGVSlr849gttvU/KNaenTqjLZYvyB1+6upK9TE+NZ+wbzcc5zLciQBBi9Cveoigv/u+lNdtLlHSAuedtaSmrb2O7UQZ6xvEIvG3OZy3t+lUf/fqaMB9pbmc92fX6mTEDoERvDTyJsaLboZXG/5H51GS7iX36ltkDhvJUX7s/a+hn/UDJ65tpGjhXB1XgdPI0eOPNNNaNKuLBOU1V+I9iSeQlZPyG8DBa2wB+XxonEVmVUlPN1lrDrj7XhT2/QhyMOHuzd9h9lu43BlEa19g8moKkVBYUtRBsMXz+SRLsN5JmlUkwHPoDYhrLx7ICNnryez1AhGf0hPYh8pzD+6i8vj3OvZm7V3vVr5uvaTiPDyI8a5zl19Ud7+LBt7F+OWfsjO4mxyayq4eMlHLBh1M0vG3M4naVt4bvsiKq1mcmsq1MAp0suPmf0mNR441XNX4gDi/EJIKc7mmjbd2V9ewD0bf1bXVtNpNPQLb01qSY66rdpmYdGx/Sw6tp8gD29+GH4DgyLakHFkB0ablXX5R7gopvnlWABW5R7itZSVABg0Wmb2m+zSu3K8vaV5vJHqOF6v0fLx4KvQa7Tcvv4HdWZaiKcPf4y+7W8VxDwdHD2PG3lky69qgnwLn0B+v+hWQj0bD/oCXIbtml7YWYhT4USG+8SF4bwOns4VoT4GWof4EO7rQWKEH62DvWkd5E2gtwGrXcFqV8gtN+KDEZ2XH7lV1by0ZhvFBd6AFhQdlMRASQyf5BWxN3sBc0ddhqe+8YTr8S068/Xgm7l5/ZdUWk0crSqhW3A0VrvC7rJcrIqd/0tdwW+Ze/hz3N1NLrPRJsSHpXcOYOTs9WSXm6AmANKTeM17DRNbdT6hte0qLEbe2r2Gmc58HXA87I015TSXlhPm5cuCUdO4cuXnbCnMpNBUxYhFc3iw0xAe6TKMXqEteSVlOYudswl99R7MHXJ1g1l4TRnXIpFxLRL57OBWnty2UN0e5e3PU10die02u53UkhxW5B5ke1GWujRIqbmGzw9tY1BEHN8ccVT6Xpad9pfBU5XF7DLr7omkkXQJjmryeLti5/6N89U8roe7DKNbSAyfHdyqVpH31hn4ddQtdAyKbPI6Z4MiYxV3bpjHT0fr1sjrEdKCr4dNpZVfcJOTAOrPOqy/cLAQ/4QTGe5revVFcT6R4OkM+fnm3kREx+Bt0OHn6fgxZJUaiWgs2Qjw0msJ1mrx9g+gKwEMix/H41t+ZenuciiJBpuzh6gilA1boVvynwyLD6Nf6yAmdommRaBrADQoIp6vB0/jlg1fUGiqYmdJDusvvp/F2ft5PXU5JruV3aW5JBcfY2hU2ybfR7swX5beOYBRczaQW+EIoLJS2vFT+0yu7NJwJlotk83K3LQtvLVnNUXOhGCA57uPIWfTj5xIH0KQhzcLRk3j6lVfsCbPMTT2xq5VfHskmSe6juT9AVewPCeNlTmHuLpNNzoHRanJxyfCrij8p94adcOj2nJ/xyH4GhyzDnVaLd1DW9A2IIyOgRFsyE/ntvU/APBjego3te2NTqPBpigsyT7A64xv9n5bijLJcfaSDYlo45LQ3pifju5ia1EWAJ2CInm481AUReHVncvVY74cel2jMwLPJsuz07hp7bdqThfAQ52GNhiuPJ5dsfNlvfUEPaQg5gXpdCd/n8xwnySon3/O21IFZ7unds9jevLnPJX6I/lG9xMUPbQ6ZvSdyOW9AyFxI8QcwMPLrO43WRWWHCjg30vT6D1jNSNmreffSw5wsLBu4dSuQTFcH99bfV1ts/JI5xEkOtfB02o0dDlullhjEiP8WHpnfwJ9nenDVg8e/O4Is9ZkNEhMVpxLkAz4fSbP7VikBk6eWj3PdbuIR7sMc+tz8Dd48vPIm3k6aaRa4iCjqpR7Nv7EtLXf0S4gjFd6XUzSSay3tr8sX21fn7BYnuw6Ug2cjqfVaBgU2Yab2zk+z2qbhfmZu9SyELtKcsmpbvofT0BdMgYgITCs2RlxZpuV11LqgqQ3el+Kh07PmrwjHKwoBBzFJSe1Pnv/DjbZrDy25TdGL/lADZwivfz47aJbeaPPpc0GTgD/2bWKZc5ipTHeAYxr2eEfb7M4+9T2BjUmJaei2aDldDkX2ijcIz1PZ8i+8lzQV5HCMY5WF/P1gGluX0Oj0fBkp3GsKzhMnjYHc3AObeyJHMlVoCLEkRNVe7/8SvblV/LhxqPcN6QN9w1uAxrwrPeAMtks5NVUkFLiKETYN6wVISdYdLBjpD/Txul4a0k+VIRhV+Dfiw+zL6+KNyc5ZsopisLzOxYze/8G9TytRsOVcd14sceYJnNyFEVha1EWK3LSGBge5zKLDxzv4amkUVzdpjuPb/1dHapbmXuIdYs/4I7E/vQLa4Veq6XaaibMyw+DRodeqyHM048I78aTjNflp6vf9wiJOaHp/Te368NXh3dgsdv44uA2ro3vwTZn79CynDRuaNuryXPD6iVzl5hqmjwO4OO0zRyucNSsGhWdwKDIOBRgbtpm9ZhbEpquiXSmKYrC1NVfuQzTTYjtxJwBVxDh7U+52ciq3EPE+YXQMSiiwflr8g6rZSV0Gg3vD5yiFoYVF57meoNScioa9Ao1NfRWf//gmWuxWq3o9foTPu9k2yjOPRI8nSEGrQ4bGuwo7CvP47aN3zA9ZgKV5sZzOwoqTRj1ZjTmhsNO02JG8fqRn0EDeR5H8G+lpcJ6AEy+jPQYSlGpnl055dgUsNgV/vfnYebtzOH+wW0w1xvNy66oZmdh3cNsREQCFUbXNf8sdjsBXo3/2vh56KDVbihohbagDXYFfkjOI6vMyH8mxfHCocV8dXiHevy4Fok8lTSKOL9gQjx96nqpnP9jttl4ccdifj66i8PO4pY6jZZFo29jeFRbbHY7aOsCmtZ+wXw3/Hr+yNrHo1t+41h1GWa7jXf3ruNd6nKq6tOi4Y7E/jzedQQlphps9XrKVjjXgav1h7Nu0/GCPLwpt9QFOwPCW7M67zBlFiMZlXVFTBdl7efK1t3U1zU2Cz56Axa7HbPd5lKzKM9YqZYqOF6RsVJNEtdqNLzQfTRWm40Ki5F5zoV/wzx9Gd+iQ72ePwWaqtjeTOkAu2JvpoyBgq6Jzuv65SjqNjq+LDYbM/euUwMnb52BN3qP57b2/cgzVrIu7wg3rvlGreTuqdWREBBO95AYugZHkRAQxt0bf8bufG93dxhIG/8QdUHp4zUVHIvzX1MJ3EnR/k3uay7pu7nzwBFcHfIagCXKgsFgYJczMPo7QZc4O0nwdIb8MvheLEE+3Lx5LsXmKraXHeVL3QpejZzU6MOqxmIjwGDHw7fhsFGvkDZMNvXg5+wdVNvMJPhFUlFpBK8qdnms54fL78Rq1fJ9cjafbMnAYlPIKK3h0d/20C1eBx560FuxYWddwSH1umNbdECndW2LXdE0KENQy8fg4ZgqF5HB0/0HMGNRAVVmGxuOlHHxh9uoiNkDHo4A6ONBV3JtfA/nNRV1Ade08gLKnLOmcmoq+HT3apd72BQ71/75FZsvfYCWvoGNJpVPiUtibEwiM/as5o3UVS7LwBzPjsKc/Rto4RvItfE91CVhbIpdLRcQ4eWHyWZtMoioqjbTxj9EfX1pbEfW5B1BQWF9wVGCDV6UWIyszDmIRuNYngXAEz0GrQ69VotBqyPE0wcPrQ6z3UaVxdTo2nUA7+1dq65zd2tCX/qGO5Yb+j59p1rQ8/q2PfGqVxqhuUIYzdVqqs9md9RXOpGJAM3ZWpTF09v/AECDhh9H3sSoGEdF9s0FGdy+/gfKzHVZbya7jV2luY1Wah8dk8CdiQNcZt0JUetk8ojqn1NUVERoaOgJnVcbVB06lN9g318FXeLcI8HTGVJgqiRc689/uk3hnm1fY7Rb+LN4Dx8cCebO+OFuX+++hIvYUHyIXGM5aZV5tPON4GBVPvnmMj44vJoHEi7i1n6tuKh9GK+vOMi2rDIAdh62ga4PRB+kymJiTYGjtyXc04+kE8h3qq/+EGDXWA9WTh/IZZ9sJrfCREWlHg73QB+3h2/GTeSyVp1dzt1bmseLyUuZdzSFsWYj9SemG7Q6Rse0p8ZqZmXuIQpNVVyx8jNWjruryRwkb72Bf3UfzQ1te/Fr5h6qrGasdhtGmxU7jsKS5RYjn6ZtRUHhue2L0Gm0XOcM6PaW5qlT3wdGxLlVkTvS259+4bFsLMig1FxDj5AWlBQfcyTmF+fQo4llRDQaDeFevhyrLndJoq/vYHkhnx90JEn7Gzz5V71lZT47uFX9/lQO2RmtFt7dt443Uleh12h5qedYbkno02RvVXOKTdVM/fMrdfbcM91GqYHTd4eTuW3d9+rswaTgaDoGRZBaksuBsoIGi0fH+AQwq//lWBpZVFqI06026Lrrri8pKCggPDycOffddIZbJf4pEjydITdu+hhC/PHSGrir7TBmHlyOTVH47Og6WvmEcnGUe3WS/PSePNVhPA8kfwNAvqkcT40ek2Llm4xNXBqdRFu/CFoH+zDr8q78tiePt9YcpsJkA5sHZHXiw9WFlPs5qn2Pik5Qe0hOlFe9NfiMNgs94wLp1Tef39fqweQLVk906T3xMbpOm19ybD+Xr/wc23EPQW+dnvcHTOHy1l0J8fSh3Gxk4MKZ7C3LZ0fxMZ7dvogZ/VyX6zheG/8Q7u9Ul2dgsdvQ1ZuV1TU4moc3/wLAs9v+oLVvEIMi27A+/6h6zICI1ux1Fmw8UeNjO6mLFmc5h5/AUbKgqeAJHMNtx6rLKTBWcay6jBb16lyVmGp4dMuvLpXEa4ekkouOsdlZELNvWGyzJQ5OVJXFzAcHNjJj92qyqsvU7Xet/5Hvj+zkh+E3uL0Q74ObFnDU+XkMj2rL00mjAFh67AA3rvlGPW5si/bM7D8ZH70jOC41VZNvrCKlOIfUkhyKTdU80mUYoV6+ah0vIYQ4XWS23RlmtFv48dh2Hk0cq257K20JxebG8zeaMyC0LWMjHT065VYjY6Ic39tRWFOYph6n0WiY0DmKT67uQb+2dQ+/fUc1kB8HQLCH+6vT1x/Oq80d2l5+GOJ3gK8j98dkVZg8dysLdtUFI3/mHVYDp5Y+gYQ414yL9PbnloS+atJ6gIeXS62kHcXH3G7j8e7pOIgnuo4AHJ/TE1t/p8pqpqxevtHxCzOfiFa+QbTxcwzlFZjqZjgerbcwcWNqF3Q22a2MW/Iha/IcixHvLc1j/NKP2OIMkFr7BnGvc2Fhi93GXfXWwfu7vU6lphpe2bmcNvNe5ZEtv6qBU/3CqytyDjJq8fvkuRG4KIrCj84lb8I8ffls6LXqEOC3R+py4aYl9OGDgVeqgROAh05P95AYbmzXi//0uZSPB19Fp7O8dpUQ4vwlwdMZMjwikXhfx7IjmdXF5JsqGBXm6G2qsBqZkbb0pK47IqJuuraPru7hk1nd8KEd7G3gs6v6EBB/GDXzpaA1FEcx7+jOJgsTNqV+kcIAZ1XsQRFxoLNB61TGdgoCwGxTuPbLHfyx15Eb0COkrifmxna9m8xf+SRtMzP3OhIw9Rotz9Ubsvo7XuwxljEtHEFZVnUZb+5axUjnUBI4colORm3Cd/0Zi381e/HVXpeQEOD4vSg2VXPdn1/x2JZfuWz5JxytcgSgEV5+fDv8erX6+2spK9RAMik4mpva9W784n9hb2kej2/9jbh5r/LcjkUUOoM+DRqmxCWxc+LD/Hnx3UQ6l0BJLs5m8ML3OFxRdELXzzNVUWNz5GQNjmzjktNVbq773Xm489C/nVclhBD/JPkX6gx5osM4/tfjarVX44v0DQwL6USIhyPbZ3n+HtbV6y06UQl+dX+NF5gq1KyUjOrGH3AeWj0XJYZCzIG6jdntKSj0YFH2PrfuXX95DH9n5eeLa2vvaBX6dq/i9v6O5GabXWHqV8nsyCpjeFRbNX9meU7D92xX7Hx4YBPTN/yobnuv/+WMjG7nVvuaotFoeK//5fg6ezo+TduCYldoH+BY3Hd9frrbS39UWkyUOoOn+kNvf7X2WruAMNZdci/jnAsh2xWFrw/vUBPBe4W2ZOHoW+kZ2hKALYWZvO6ceeeh1fH5kGtccs/+Sompmjn7NzDgt5l0nv8mb+76U32veo2Wm9r1ZtfkR/h2+PV0CIpgUGQcqy+5h3hngvyhiiIGL3yPncXZf3mvo9Wl6vf1E+wBqqx1Ncq89Y3nsdX69OBWxi/9mFvXfs+rKcv5JWM3ycXHXJLMhRDinyQ5T2dITrmRMJ8QrokZwGdZa7Aqdj46upLrY4bxTrpjOZDX9/3Bmx3D8dJ5kF1qxGKwYKtp+CPLrzRRaXT0EtkVPV5aA0a7hT1luQTp/SmxVnCksojdua5DLGa7HQWFVh6REJIMFi9HzxMayOzEjG3b6RPoGqCYbDailMaroBfU1A11eWsNmKw2RkTW9eAsyt7PusmjqbHY+HLbMarMNibN3cqSu/rSPSSGHcXH2FKYSZJzyE8BNuSn8/CWX9RK2gCPdR7Oze36UGyuQeccKjx+arzJasHfs2EPltVmw9fQ8G+GWN8gHuo0hJdTlqMAj2/7jSmtk3jDWWF8d0keXUMa/1vDU6t3qY4NuPTGhNSrPxTi6Y3FOfvPYrPhqdWhKIpLMVF/gyfv9b+cT9I281rqCnVK/tT4HrzYYyx6jQ67olBtNXPL2u/VIc9/dR9N1+DoBoVJwTGMWvvx2Ox2luWk8fnBrSzI2N1gNqKXTs8t7frwYOehhHr6oNPqqLTUBTeR3v78Mfp2rljxGbtKc8mtqWDYH7OYP/JmhkQ6anApitKg9+hovdyvVr5BmG11960NnrRoqLaYqXEGi7VqrBb89J4cLC/kkc2/NDlDMMTTh2jvAPX3AmDnpEeaOFoIIU6OBE9nSJY1lyqLwtgWiWwoPcCByjwyTPkoOhODQhNYV5RGkaWC5Or9XBXbl2qTjRAP0Po0DFxKqs0E+9RNS4/zCWdfZTZ55lLaerWgxFpBua0Gg4cNP31dQFFUbcbfU0/f0NZwCIhIJ0obQW6eN9h1bN8RwJ5uRfSMClfPsaOg1zUeRFRa6/7yD/LwRqvREO0TQK/QlmwrymJrYRaFpmo+mNKNrFIjqw4VkVth5prPkxk1qB07io9hVxSMzqGdYlMVwxbNVtd6A7g7cSD/7jkOjcZRMkGr0TBzz1reSF1Jl+Ao7kwcwKWxHdFqtY0ujqzVadSyCMd7pMswluccZEPBUQ5XFFNjteCp1WOyW8mtqeDfcePQNzKclFtdQYfjCjnWD6bqr78W4xOoDrd5avV46PSklOXx6tYfaRcQxpNdR9A2IIxY3yBe6DGWi2La8+GBTYxrkaiWdlAUhWPVZdy/aT4HygsAx/DoY52HNzkrUOMMqBZm7eXRLb+SVl7Y4Ji+YbHc0LYXV8YlqYngVVZzo6Upon0C+H30bVy/+ivW5B2h3GLikmUfs3TMnQxwLgdzfFvq9zy18w9z+TnUOIMnH72BUK+GiwBbDDYCPbx4f//GZksrFJuq3VqCRwghToYET2fIv3YtgGxHzsc9bYdzsDIfOwofpa/mja5Xsa7IMXz1a85OrmzZ/Bpnx4vzdgRPCuCjrQu2cowlJPg1LD8Q5xtGgN6LcqsRa8w+oq3dySnSgNWT279LYcUdQwn0NjQ473j1c5786+UtXdyiA9uKslBQWHRsHze26828m3oz9L117MmrZE9eJR5bvMFfA1qFfGMVvkCFxawGTt1DYni77yQGRMS53PP9fRvUStObCzPZXJhJC59ApiX0YXqHgS6By/HW5R1hYdY+7u80mEhvf7QaLe/2n8zA39/FZLcye/8GRka3Y0n2AcosRrYWZp7wOnHp9RLD6wcJEccFBjuLs7lq43eUW02szjvM5we3Mi2hD091HUmsXzCDI9swOLKNenyZuYbXUlbw7t51ao+Rr96DuYOvbjZP6GB5IY9s/ZWFWa5DsTHeAVwb34Mb2/WiQ2DDSt7NCfTw4o/RtzH1z69ZkLkbo83KpBVzWXvJPbTzD2v4mTQ7bOcImL10Tf+epVcU8+2RZADCvXxZMuYOMqpK2V+WT3plCQfLC0krL5TZd+Kc01gV9Fqy7t3ZSYKns8BXGZu5MrY332VuocZm4auMjfQMas320qMcrirg99wU2mmbXpz3eC2964q61e+1yTY1HjxpNRq6BrZkXdFBCi3lvDG8JY//5qhQXlQBV3+xnZmTO5MQ3ny+jmvwVBe0jWvRkZdTlgHwaspyYnwCuCimPb/e2pdBM9eRW2EiObMGbWBH7C33uFwz2MObl3qM47aEfg2Cg40FR9Vii/Udqy7j5Z3L+OzgVhaOvq3Bgxqg3Gxk7JIPqbFZ2F6UxR9jbgegfWA4TyWN5IXkJVgVO4fq9dB8n76TTkGRBDQTkIFjiCnZmQOkReMyzT+iXpL0zuJsLls+l3Jr3edmdeZ3fXckmSVj76SXM7ep1ujFH7jMMgz38uXjQVfTNiCs0eG6UlMNL6csY+bedS4TAEZFJ/Bw56GMik7AhoLejTpW9XnpDHw3/Homr/iMP47to8hUzaCF7/FU15Hc02GgWqizymJmR2kO4EhAb3XcUjzVzp4nBYUaqwVvvWsQVWqu4f6N89Uhyvs7DaF9YDjtA8MZFhXfbNAlxNnsryqWi7OTJIyfBcos1dwYN4BwD8d/RBuLD5HgV9cL8Nq+3/m9cEujD8fjlVqqmJ+7RX19oKYuV8igaXrV+dpEdYAgb08G9K4AveOhvi+/kvEfbeGjTRlq/k1jfOsl+qaW5Kjf9wiJIcHZE3GwopBxSz/kkqUfklqZzk8398LH4GiXvSwcsjpgdyaP+xs8SJn4KHcmDmi0VyWjslT9fmKrziwcfSuXxXZWh5kyq0q5ZOlHLkuk1MozVqgzv5ZkH3DZd3+nwcQ5SwYcqixWk/rTK0t4atsf5DazwG+11cyT234n05nf42fwZJOz3lOMdwDhno7PeW3eEcYs+YB855IiwyLjea3XJepsvHKLiQnLPuZgveDNrtjVGXfg6NH6ZdQtjI/t6NIGk81KgbGSWfvW0/6n/+N/u1ergVOcXzDzRtzIH6NvY0yLxFMyq02v1fHNsKn0dM6aLDZV89jW3+jw8xt8dnArpaYaLl76EWmVjjywHiExDRb9DfVyvO8iUzW3rP3OJedpV0kOly79mNXOsg1hnj7c3r7f3263EGeD2VOSWHvf4Ea/ZEmXs5cET2eBQWHtiPIK4PbYUeq2pXm7uaJF3SKyP+VvYFbWmgaFJOurtpl4LW0BhWbHXyueWgMmxfEQ6hnYhn7BCU2ee6jSUTZAp9HQ0juEqe27QZud4OW4ltlm5+Wladzy7U4ySxtfc+3Sll3U778+vF39XqvR8v3wmxgUUTf8tCT7AJNWzOWqTbOYNBg89c5fxbJI9gUNwYaWUE9fl8Vyjzc6JgEP55DYpoIMeoW2ZO6Qq9k24UE6BzmKRNYGUPULVQLE+4WqAdKdif1d9hm0Or4ceh0RzplxJrsVrTOgy6kp58ltf5BSnMPB8kI2F2SwJu8wn6Zt4b+7/uTejT+TWuJYRsSg0aoz17x0et4fNAWdVssfWfu4dNnHlDt76gaHtmL+qGk83nUEh654ilHRjp9TgbGK8Us/UoehtBot3w27nlBngGWx27ho8fv0+mUGCT++RtS3L+L1+ZN4f/EUkd++yL0bf1bLDfjqPXix+1hSJj7KxFZd3KqYfiL8DJ78Mfo2bknoowavGVWlTFv7HS2+/zdr848AEOrpw3sDLm9wvmNhX0eP3uq8w9y6zhFAfX9kJxOXz1V776K9A/hx5E0uPZtCCHG6SfB0hnloddzWxlEBu0dgG0ZGOHoRii3VWBU7D7S7SC038HvRbl7ZPx+Tre6vcqPNzPbKNF5PW8BNO2axv8oxXORIdHYc19IrhEfiL200gRoc67gdqnIkHrf2CXOUL4hKxN9XgfgdeEQeU9ff3ZxRyuB31/H19mMNesLGxnRQ6zv9cHQnpnqzqdoHhLNq3HS+H36DWscIHHWVvi5YgallMlqt43qZnq1YHX0xVZrmH5DBnj6Mb+n4vHJrKliZ41iXr5VfMN8Ov14NoNIrS7h17fcu5+q0WpaNvYtPh1zD//Ue3+Da3UJiWDHuLrVcgR1F/TmUW4y8kLyEx7f+zuupK/nmSDKfH9rG71l7SXf2chk0WnXZEG+dgR9H3MTomPZ8c3gHU1Z+jtH52Uxs1Zlv+l2lDgUGeHjx48gb1dpXhyuLmbDsY8qd0/CHR7dj4/j76e0czquymkkuzuZwRTGFpip1aZNaGjTc3K4PeyY9xtPdRjUYDjuVQr18+WDgley87GEmxtYtv1Pbwxfu6cvKcdPpHhLT4NyeoS35/aLb1Bpff+YeZuSi2Ty0eYH6WQ2JbMO68ffQOyy2wflGm4Wl2Qd4fMtvTF7+qcuXEEKcapLz1ISSkhJ27txJQkICLVo0vZzGyXqlwzUER0YRbPDFGw8yS41klxu5PGwwGwsPUW03syB7B5dF9GF6q3F8kLEUKzbWF6fx4M6vGRaUxM7Kw+yqSseiuE4116FVk4m9NJ5cGTyGI4UmwORynNlqx2S1k2cuUY8P1Qazv6ASk82HQSHtWZSfgjn8IL1jfNm/P5CKag2VJhv3/byL97cc4PYhkQyMisVDq8dotXNpiy58nb6NUnMN3x9OZXwLx0PUYlMI9fXg4pjOjI3uyJ95h5h7cDO/Ze1xDCn5lWBvlQIZXcCuo8QznB/tg5l84BhjExpfY6/aYuLKuG78nLELgLlpW9Sp8gaNlm+GTWXi8rkcqSxmXX46WwsySQgIU4fhWvkGcV0bxww2m93uWPi23jBWrG8Qi8fcztQ/v2J9wdETXkDXR2eg2labAK3n55E3MSyqLe/tXccjW35Vj7s+vicfD7qS8tIyqF+qQO/J/JE3MXLx+xyqKCK5OJurV33Br6NuQafVEuPjz/Jxd/Lk1oV86ezhCzB44mfwJNDghb/BkwCDFxFefkxL6EP3kBiKTdWUOGehrck7zMaCDEI8vInw9ifA4EW8fwgR3n4NcoeqrCaX5P/6bHYbtkZqSrUPDOfLodexrTiL57YvYk3eEWJ9g/iqzxUkBIZRaTZh0DUcQu4cHMnHg67klnXfU2ExkVGvt/CWdn34b9/LMGh1KIpjxmFaeSFLcw6w5Nh+1ucfVYM0IYT4p0nw1Ii3336bp556ipqaGvR6PbNmzeL2228/pffoHh5BZKRrUFBjsRETGMh0y0j+m7YIgF/yt9DGJ4yrI4cyv3AdVTYzR4y5HMltuMJ8qMGfVt5h7Ch3DJHo0DIleBRdQiJIPlbGqkNFtAj0on+rYFoGeZFeXIO/p549NXV5NPG+EVhsCl4GHRdHJbEoPwWArZadEKeF3HgodgSTu7LMPPD9ITxbLWZwfAj9Q9oxOLItX6c7Fq79MTOZia0cVdPtih2bYienupxj1WXE+gbz5ZDryTdW8uWhrXx+eAtpFEL8dnz2JVKtD6BG68U1c1P570Qrt/WLbTDUFGDwYnyLjrT0CSSruozF2ft5Z88ank26CAAvvZ57Ow5SA5Yf0nfyYo+xjU69B0eSu9YZPBWbqikzG2njH8LSsXdy45pv1KVFANoHhBHp7Y+XzoCHRounTo9WoyW9slitSeWr9+DnkTcxKjqBl1OW8fyOJer5D3Ueyhu9LlHX2Tv+vbXwDeKP0bcxeOF75BsrWZaTxiupy3mxx1gUdBg0et7uP4m3+09SzzHbbRia6F3UaDTU2Cw8u30RXxza1ugx4Cgx0SMkhv/rM56EgHB89R4uuWz12RV7k72ZWo2d/uGtWTLmDg5XFtPCJ4Casgo0aNBrdU2Wixjdoj1/jL6N8Us/psxixEdv4P0BU5gSl4SnTo/NbmfGntXM2rde7eUTQojTTYKn43zyySe8++67rF27lsTERB544AHuv/9+xo0bR2xsw+GCv1JeXk55eV2CcU5OTjNHO0yI7o7RbuHDw6swKzaOVBdytHoVo0M6sK0yS81pAggx+NHGI4aWfgFUWk2sLq6brXZL7EgCayL5aGMGG446HjSpORUs2ldATIAXieG+6LQaDhsL1HNaedcNqXUOaEHXgJakljuTzrV2iDkI/kVwLBGsnmDzwHSkE8srMlgeuRA0dT0oi7P3cfuGbzhWXcax6jLyTRUuCed9QmOZ1q4vtyT048mkEfyZd5hXU5bB+j/Y7TeQPJ9YLHaF+3/ew/asct6a1KkuN8pJp9XyYvex3LreMSz3f7tWYrbbuL/jELz0ei5v3ZVnty+ixmbhuyM7eSZpFE1JLs7m54xdLM0+wNZCR2mF6+J78Ha/SXw25Bpa+wXzv92rAThQXsiBRmol1fLTe7Bg1C0MCG/FU9v+4I1dK9V9r/S8mCed6+k1p21AGPNHTWPYH7Ow2G28vHMZ/cNbMzqm6dy1puwrzeeuDT+yv7yg2eNKzTWszD3E+KUf8+PIm+joZvmC42k0Gtr6O2Z/Np4p11DvsFjWj7+XBRm7mdCqEwkB4SiKQm51OTes+YblOQcbnNPGL4RxLRMZG5PIoIg4t6qsCyGEuzTKiUzhukDYbDbi4+OZP38+PXo4hnMqKyuJjIxk1qxZ3HTTTW5f84UXXuDFF19ssP3nFRuIiHLtecoqqyHMty7PJ6OmhDfSl7O3qm4R3UCdF5EeAfjqPNGiId1YRJG1iuONC+7EzZEDeWLpUY6WmRvsb8CjGoJzeavPYDwVD+LDHInaNsVOek0x1TYzJruVCrMZP28tJTVmfk22caD+qhxeFdDiAHi7t6ixp1bH6Mh2TI7pyEURbXni6ScxlVWxM6wnh/26qsclRfnw/sR2tA5yDCOZbDa1B+OrjJ08uWuJOrQWbPDispgOTG7RiZkHN7I83zFT65Nek7g0poPL/Y02K6/tW82cw5sbHZoL9/Tllc6jmBDTgbnp23lm17Jmh/Civfx4v+dEEvxCeWjnQhbl1T3s3+g6hmlxPV2OLy8vJyDAdbqyAmiAveUFDP3zY3V7lJcf20bdpfZY1We12xstOXCkqoQxaz6jwlkOwE/nwROJg/E3eFFgqiK7ppwSSw35pioOV5WQ65wBGOPlz/KhNxNUr0J6fXZFabIHyWKzodW6tqWivBz/gABMNitajYZSs5Eqm5kW3gFqD5ZVseHRyELMdrud8eu+JLnM0eOq02gYER7PyIg2jAyPJ943uMkk+LCwhjWnGpOVlUVsbCyZmZm0bNnyr084CxQVFREaGvrXB54l/on21tZHWnvf4FN6XTi59t51110UFBQQHh7OnDlz/tb93X1v59rvw7lM/jyrJzk5mc6dO6uBE4Cfnx+dOnUiLy+vmTOb9vDDD3Pbbbepr3Nycujbty9XJX+OJsSfOJ8wnu54CV0CW2A36tH71uWXxPsG8G7ozXyfuZmP0//Eqtgpsxkpq2l6DS8tGoaHduamVhexZH+hGjj5eei4pkcLrHY7G4+Wsj+/0jUAMPtAXjyvrSpkQucoEmL90Wg06IHEejV5KoxWOkQ4ZqHd1kXhu+RsXl2ehtmmgNEfDveEsAwIPwrauju08A6ktV8wLXyCiPbxZ2PBUTYXOqbwm+w2fsvZz285+wkweDFOUdCg0NW4m8MdtXCsA9h1pORWc9mX+1g+vR8J4b7UWC3qg/ae4OEE+vkzfeNPKCiUWIx8djSZz44mu3w+QQGBhITU1X1KLs7m5vXfsqu0bhhUg4ZeoS1Iryyh0FRFgamKO7b/wqTCQ8zoO5GJ7bqzqTATH50BH70BvUZLgIcXPnoPvHUGWvoGsq0oi7GrP1fzdrQaDZ8Muoob6y3aW39JmeP/wbMrCiklOUzc8LXLdo1GS3BwCPpGcoYaG7Yz2azcvf4rNXDqGdqC9wdOIc6v7jOosprVYqIVFhNXrPiMLYWZZBsrOKJUMzKk8Zw/u2JvNNCpva9OqyW/ppKZe9eSVl5ATmUZpTYTBcYqSkw1ag2y4VFtWTDqZvRaHRa7rdGaTWvzjqiBU6xvEN8Om6oWTD1+aR4hhPinSfBUT69evZg1a1aD7YGBgS4zyywWCwbDic1aCggIaNCrAI5p5thtHKjM494dX/Nej6l4ENjgOL1Gy3Wt+hNFDH+WbGZ16UHs9cIeL62BOM9o+oXG0943mtbeYXhoDRRXm/lmh6OYogZ4YGgb4kMdvUlD4kMprDLxv5S15FWawOgL1QGAlrxKMx9tymBndjl3D4qjVXDjvQ7gGJK5pkcLerYM5OmF+9iVWwGKBgpaE1gTS1nETvBxDFnemziM+zsPcjk/tSSHb4/s4IejyRxzTkUvtxgps5jwBSK9/OjcWsduz+2Q2QlMvhRUmbn04y2svLs/wT6uAcT1bXuRGBjBR2mb+DkjVV1Mt9aDnYYwpF617pl71/L41t/VGkjeOgOv9LyY69v2JNTLl/yaSh7YNJ/v03cCMD9jN6tyD/FG70u5oW0v9YFttlnVukV2xc6M3Wv4147FalmJCC8/vhh6LaNj2jf5WR5vT2keYxZ/QIlzceHWvsFc3aY70zvU1bzaUXSMjQVH6RQU6fK+6ntq20K1qGbHwAh+HnkzPs0svOtv8OSWhD5sKcwEYGP+UUZGuz9MCJBVVca4JR9ysKLp4U2AVbmHmLF7DY91Hd7kMZ+m1dUu+2+fCQ0qzQshxOkkwdNx4uLiGmzT6XTY7Y4HYU1NDRMnTuSuu+7i8ssb1qs5UYn+UVR6GzhWU0ql1cQ9O77iyfjLiQlseH+AKI9gHm41imtbD2Nl4W40aOgW0Jr2ftEcLKgmLsTH5fhPNmdSY3G0eUS7UDVwAkevxvySP8kL2AcB4K314LbwiazaU0Oqs6LtlsxSbvt+J5O7RnFDr5b4eTb8VbHabVRYTSSE+fLtDT35YEMG7288islqp6xSC5U9HL1Qkek8nfwrod5eTI2vq13VNTiarsHRvNh9LNuKM/nuyA7mHqx7SFZZzcweMIVhf8xCid+B9mh37NV+ZJQYmfTJNn65tQfhvq7t6hMWS5+wWF7oPoZlOWl8dySZHUXHuDOxP4/XyzN6b+86Htr8i8t5cwddTcfgSHVbhLcf3wy/nmsyunP3hp/Iramg1GzkjvXzmLVvPaGevijULuqrQUGhyFSl1nkCGBHVlq+GXkeUT9NVhI93oKyAMUs+UGs0DYuM57fRt6pBz7q8I7yWsoI/jtUttRLjHcAVcUlcF9+dHiEt0Gg0LMjYxXv71gOOwPDDQVc2GzjV6h9etwTNJmfvoLsyKku4ZNnHHKm3TA1AiIcPoV4+hHv5Eurpyx9Z+7Aqdl5JWc6YFu3pFBTZ4FplZqOarB/m6ctlsZ1Oqk1CCHGqXNDB0+zZs3nttdcoLi5m+PDhvPDCC/Tu3bvBcRqNBkVR1MApOjqaSZMm/a17j4/oiU94KPNztrKrIotKq4lXDv7IU8rltPWJanB8boUJjdGMSe/LAC/nsKIZMouN5FaYMFrrimfuyatgc0YpAD4GHe3D/UjJdvQAKYrCqppNpJgcD149Oi72GQUmX4bF+9A6yIc1R4ooM1qx2RXm7czhj735TO4SRacof/RaDWarnVxTKf93eD7HjMX46Dxp4RlCtGcwlw+NYEOKgfRCZ69PYSuoCkJpuY+7NvyA0awwvkVdMU0Am12hd2greoe2oltwCxYsfQtwVJs2WmzcmdifOfs3YG+Vgl9mPyqrdKTmVHDNF8n8cmsfvAwNh7A8dXquiEviiri6NaHsgGK389Xh7TyweYG6/dluo3gmaRRajRa7vWE204SWnek3IZZnty9Wg7va5VeaotVo+Fe30TzcaQi+Bs9Gq8PbFUftKMX5PcCh8iJGLX5fLYzZN6wVXw67DqtiZ/Gx/fwndSUrcw81uFZ2TTkz965l5t61xPuHcllsJ+bW6615qutIIrz81HIF9ZWaaxxlGpz89Z5EevmRZ6xkY34GZpsVbSOz6kw2S6P/gqRXFHPR4g/IrnH8zrXxC+G9/pMJtmjxDw6k3GxUF/9t4xfCO3vXYrHbuHnNt8wbcSNtj1sX79vDO9QyBDe27eUsV1D3edbmhwkhxOlywQZPs2bNYubMmXz00UeYTCbeeOMNBgwYwNtvv83dd9/tcqxGo6G6uloNnObOnatOaT9Z/zu0EEocpfdb+4RwtLqYGruZN478zDvdr6NjgGshwSqzjWiDju2mfBbkbqXKZiLU4EeIhx8mq4E2BDlqRik+LN5XN6NqaHwIresNvS0urQuctGi5POgi2nrW3atrjD+hvgaKqswsOVCI2Wanymzjy+3HGJUQxuA2wRwzF/DigR8ptToexNU2E2nVOaRV5wB7IBLQt4C8eFC0UBMAh3phjz7IQ9t+xMdgYEx0/SVF7Gr5gBvb9WGlwRNbjRUFuGHtVywbewe/ZOwmu6acyhZbCDk2iOIqG5uOlnPn97v47oZe6I5LTvbS6tA08sD/LXM3t9QrmPlyj3E85ZyBZ1cUtE08hSO9A/ho8FVcG9+DBzcvYE9p0zlwHQMjmDPwCoZExjebj6MF0GjQ4PgdO1pZwugl76tDmD1DW/D98BtILs7mjdSVrMk74nJ+K98gro3vQUpxDityDqq1ug5XFPHWnjXqcde06c51bXs0WXLAarfjc1zxzH7hrfglcw9lFiOHKoroHNwwoFdQGgRVh8oLGbvkQzVwaucfyl2JAyk21+CPI6/KqtjRO//7uafjIFblHiKlJIcD5YW8u3d9gwrkn9crrXBr+74NPs/az08IIU6XCzZ4euWVV/j8888ZNcrx4Bw/fjyPP/4499xzD3a7nXvvvVc9VqfT8eabb3L11VefksDpeEeri2nlE0JGdTGVVhMPJH/DO92vo0NA3Wy8PHMpszNXsqMyq/GL1K49m90OahwJvr1aBtImtG4478/yZFaVJwOOpOj+2gG09Ww4q0in1XBR+3D6xAYxf3cuO445HoTL0wrZXVBMScxGTFpHLk6kZyB6jZYcY2ldLpYGCDsGfqVosjqhGH3AroNjidgqQphu/5FPBl/NsMjGc2mCPLwpwjFklWes4Oa13/Fqr4u5ee134GEiuP0hbHvbUWa0Mn9XHlO/3YxP7FFa+gVyT4eBRHo3vh7UmrzDXLf6KzUX6eFOQ3my68jGP88mjIpJIHXSo9RYLepDW6MoaLWOBVw0aNBqNG4/zI9VlTF68ftqgnnX4Ch+HH4T/965lI/TNrscG+cXwn0dB3FpbCc1CKmwmFiUtY9lOWmszDmkvseEgDD+0+dSl2rvJ6I2eAJYl5/eaPB0vLTyAsYurgucEvzDiPYJ4NGtjjpbWjS0DQilrX8ol8Z2YlyLRPRaLW/2mcCEZZ9gslv58MBGro3vwWBnDldycTbbnHWzBkXE0bGRYT0hhDjdLtjgqbCwEKu17oGi1Wp588030Wq1PPjgg/Tp04d+/RyLj3br1o3Q0NBTGjjd2W4wVQGefJnueDBmVhfT0S+GvZXZVFiN/OfAIj7uPU09/t3MX8kzl9a1F41L4jgAJZFqAUudTuGGXi3YX+AIQvZUp7OodJN66OUhQ9GXN1wmo75AbwM39mpJu9ASfkrNxaYo5JbaoaortN5N94gwnk6YiI/eE4vdyr6yQrRe1RypKmRD0WF2lmaixG/DqyARY4GzXlB5OJb9QdxWvpK110QS6d0wF0jjHIQxOD/rbUVZ1FgtDI5ow9r8IxyyHOXfl/Tn5V/yMVntzNtRCMfyIWIrmwsyWDSmYUFTRVG4c/0P6lIf09r14Y3el550j4XLMid/c7aXoijcvPZbDlU4Fs7tEhTFTyNuZkHmbpfAqWNgBI92GU6vsJYN1nbzN3hyWavO3NlhAIXGKhZk7Ca9spg7EwfgZ/B0O3iqXdoGUIOXv3LPhp/VwKlDYASXxXbijV2r1P12HFXB08oLWXRsP58MvophUW1pGxDK412H8++djhIQT29byOpL7gHgnXo9aLcm9HXrPQghxD/lgg2ehg4dyhtvvMGYMWNcHnyvv/46mzdv5tlnn2Xp0qUAvPzyyyjO3oVTZUrL3kTFtMBmV/gmYwsKcHl0Xz7KWEGeqZyjVUXqsTbFrgZOQXof7m0zhj7BbSm31lBkrmRrbh41VjM/7qmbXZYYbyTExwOowmy3sKBkrbpvfNAAevt1ILle8c6maDQaBrUJoUWgFx9vyaDCaAOLN5rDPRnXKgEfveMhbtDqaekVSqfIOABuihvIgzu+Y1PxYYyRexnWKphd+3woqrKAzYApK57rvt3I7zeMQtdE4FG/vlBWdRk3tuutLjAbHmrli+u6c80X27ErQH4b8DCyVncEq92mBmC1zHYbB53BSfeQGOYMuOKsGOqx2e18kr6dFc7Cj/H+ISwZewdGm5WXdi5Vj3un3ySub9sTrUZLZlUpiqLw392r+f5IMvd2HORSAiHMy5db2/+9QOPTeon79csaNKd2EWQNGj4YMIXF2ftd9tfmd9UK9aybxBDjUzfTtLZswuaCDLUaepinL1fGdXPnLQghxD/mgl0Y+KWXXmLVqlX861//ctmu1Wp55plnWLlyJTabYwq7RqM55UN1o9/fSNc3/+SXFT5Q46ib5KHVq7Oh6hcfrLTWrUnXwT+G/iEJ6DRagg2+tPONpL1XK3Yf8HYMjQGEHOOqTvHqOesqUim3OXqgOnvHMci/rvDkiYoL8eHWQRHg4xgfVOxaXl9xiG93NFwguPa9/Kvzpeo6cuvNm5h7QwcuT6qrWJ2WpeXyT7eQV2FqcD7gct0Yn0CqrHXFPgM9vJnUJYq3JtYtQEtWB4zFwaQ1Uvm7/rmtfYPRN1Hc8XSxK3a+T99J0oL/8uSuuiDpP70nEOntz/+lrqTYmdx9XXwPbmzXW80vUhSFF5OXMHvfeopM1fwndZXL+/u71uYdYX7GbsBRZuHuDgNO6LxYZz0wBQWbotA1uG7Y2UunVwMnnUbDf/tMoItzKFBRFGbtW6ce+2CnodgVu8tsyJd6jsXX8NczBYUQ4nS4YIOnAQMG8MYbb/Dyyy/z/PPPu+wLCQnBz8/vlAdMjakyApkdwaZDi0atOaSvl4hbbqlb2MJP13CR1s0ZpezLd1YZ96gmMd5EG29HkFJtr+FPZ56TDi3jgvo36HHZazzCF8W/ccB4tNm2enkAcTshqG6JmS+3H+OZP/aTU96wcGeUVyA3xzlqO1kUG+8eWsZbE5MY298KWscw0q6cSibP3cK2zNIG51tdgqcAKix19whwDltNH9gaQjOdWzWQ2ZH3NjZcvqN+cHEmH8KKorAgYxc9f3mLa//8in1l+eq+WxP6MrFVZ/aU5vHRAccQq5/eg391H+Ny/mspK/ji0HZ1W7XNwh9ZdWUL/g6zzcbT2/9QXz/X7SICm6gwfryW9XqPcmvKSQwMV4Pn2uFST62eV3tezKTWdTMu1+Wnq+UdugVHMzK6HV8d3qEWUe0eEsMt7WTITghx9rhgh+0AHnnkERRF4YknnlCH6vz8/Lj//vt55JFH/tFhne4xAeRrPMkuNzmqe+e0o6KtFVNtbxcacp0BSXpV3fCa1aLjYGHdciwFlSYWqbPrFGixn16GYWqS9+rKHZhwDOe1Utpy4JidAzjWuaux2MioKGOT15/YNTaySwvpaxxNpFcAG482XHS1VCl1VA1vcYDYAH+yMvxQgJSccqb/mMqIdqFc0TXaZeZbV0NHQvTbKbZWsCRnH5/vS2Vq+ySWFX2O7WgnMPuQV2lmzAebeOXiRCZ0isTqnDZvdQaSACF6Pwpr6oJITzyoMttAY4Oow4AGiloCGuYsLyUpKINb+rZSj68fePnoDNiVuqn5tRQF7E1MeldQmvxLw66ArokFW+wo6ljVkmP7+deOxQ1yiIaEteal3hfTP7w1ZpuVhzYtUBO+70zsj12xk11dhqIovJqygm+O7ABc896+OrSdXqEt8NO75kLVKjZVY23kPQNUWI14O4u+vn9go9pz1y+8FZe07IhdcdSy+urwdnQaLde06Y5Go8Fitzs+NKeYerWs0soLiPENpH1gOKkljmDbV2fg9d6X0toviJp6BUxn7qkbUr6tfT8UReGl5LpFlGf0uQytRqNWJG/M8cO04vwyfV4KqTmNpxmk5FSQFN34JBEh/inndfB04MAB2rdvvqrzo48+Sr9+/XjmmWcYOnQovr6+PProozz77LP/aNs+vrobdr9QBr+3FpMFKI0ivxTQOB5w3noDbZ3ry+XWe0C18POjjbMgpl1R+GDjUay1tYlCs+gRHkWic5ZeoaWUdOUQaECv6Glj64il3gNof0EVNWH7sWscQYpdY2OfNgVTaS86Rjb8x0ipvY8G4lpYuDimFT+m5FBUbcFqV1h6oJBDhdWM7+HHmuqtGO1m7mo1lutbDOGdowsB+PDoCj7oMY3RrdqySLfDUTm8KhiT1c6jv+7lYGE1OINWW/337RtIhbUuAAr09EKr0VBlszqSaaIOAQoUORZvvuen3WjRcms/x+v6D2s/g6daGsHl/aHQSHUDxz5FQ1OxtJZmpsorCmXmGu7d+DPfHkl22TUoIo7nu4+hq0cQwc4lYxZm7WVZThoArf2CubV9Pzx1ehRF4eWdy/jqsKPHSafR8GqvS/g0bQt7y/JJKcmhxGxsslctVPFtkGRey0/vQZCnD4uP7eed3Wuc19cys/9kIrz9MGh1fHZwK7et+wGAfGMlj3YZjrfeteu6Tb3cKLPdRqxvENe26U5qSQ6RXn681208PWPbYrJZiXYGWhvzj7LVGUx2CopkatuebC3KJL3SEbxf2rIjw6LbOi4q8dEFKzWnvMkgKSnan67RJ16EVohT4bwNnp599ln++9//Mn/+fMaOHdvssUOGDGH16tWYzWb0ev1pGa4DaBnozahesHCj4/W3m0rwSHA8kVyG7eoFDX76umG73/bksSfPuQivRzWGqCxGBVyl7l9avhlF4whA4u0d8cD14WnV1VDk6TpUV+qZTYW5NdDwHyk7dT0XOo2WNsE+PDCkDcvTCllzpBi7AoeLq5m5vBLCFQjP4ePMZTzeZhJtvWI4ZMzmaHUhv+bs4JqW/Vicl4ISl4JXfkd1Nt6cDUdp5dWbHpqlau+QTqMl3MuXCktdblSAwfE51BZPdARQh0GjQGErFAXu+jEVk83G3QPjqK4/bHcCVbZPlfX56Vy/+hsynSUIwFHN/MXuYxkW2QadTkdJsaMKt8lm5bGtv6vHPZM0Ck/nsi+vpazg04NbAUcBzld7XcLYFomUmKrZm+oY+vs9cw8jo9u53cYys5H/7PqTt+vNbLu7w0A6BUWiKAo2u51Xdy5X9z2x9XcGRcTRKyzW5TqxfkHq97Wz7q6J70G/8NZE+/hjq2hYoPM/9WbjPdJ5GFqNlm+P7FS3TW3bs8E54sKUFO3/jyz+K8TJOG9znlasWEFCQgKTJk1i8eLFTR6XnJyslizw8PA4bYETQKXFRLbhIAQ7hjWMFoXyTEfdpfoLrtbP1ymz1D2Aft5Vm3ukQMt9DAlIwl/n6JU6YMxgnzEdAC/Fh9ZKw5pK1T45KM6eLo1S974LPdMbbW/9YZM8SwmKomDQaRnXIYK7B8YR5FvbNaCFgtZwsDep+aXkW8q4JLRPXbuztxHrE8qg0PagAWPkXu4YGo7BOdyXYYjiQEAXNXiK8vZH+//snXWclAX+x9/P9OzMdncvSyzL0t1ICIqCioLddeapZ9xZpz/1DOw+AxElpBGku5tdlu3ujunn+f3xDLO77C6Keup58/Y1d8w8PTPwfOYbn6+g6CCezoqKDi34AhCcJw8ldnLf8nTuXnaSZmvbe3iyrrzLIvdfmxN1ZUzf+KlLOPlrPVg0Zh67p93DReFJnaJVG0oyyXV2BPbyDnIJobym2g6WBV5qLaWtDVSYmjp0qZ1rovljVJqaef7oRoasfrODcJoVncI/0iYB8vt77Y6vO82nm7zhI1YUnuzwWmS7AdLLC09xqFqOKMV6+nUa9nu0tpTrd3zNJmeULd7Tn5lRfdhals1HZ+RfEx4qNdMj3KNY3Lhx88fjTxt5Sk5OZuzYsaxcuZKZM2e6IlAWiwWtVo7AVFdXM3r0aKZMmcLChQtRqX67t2PW9k9p8dRQZWmGUAWKFl9Eqw6a/KHJl/EJPVzrpvlGIjhnpy0p288Q30SCNd7UW82ACtRm/D0VDDPKo0haHWZW1G1zbd9DTEFJ5+4ytbUtunRWRAHoHZ0HFAP4CX5o0GDFSoapgD3NpxjuKRf+hnvrmN0vgC9yDmOtCJedxa16yO3Hdo8Wsj3aoglxBmcxu6NN0MzsG8S4qAiuW3gUmyiR5d2bsGr5xuqnkQVhXLu00MHqIiaF9SDa6Iu/1oOas2NHBBieApOUiTzzg7z9h3sLWXRUhVrfC5tnBSsK0nn55FYebTfr7temwtTEJRv/7RJ8E0MT+ffIq1zpqq5I9glCrVBiEx2kN1SyqSybiWGJBOgMRBl8XAaa9VYz89N38mb6TpTtIpQ9fdo6GStMTfxQegaHJKESFFhFO55qLSqFEpWgYEdFLgtyDrsKuQE0CiX/N/Bibk0agiAINFrNXLPtK7Y6x8EoBAEvtY56q4kWu5U5Wxfw99SJ/K3veBSCgjC9F+NCEthSnk2tpZW527/ixQHTuCxa7u4UJYnNZdl8mLmH/dVFtOep1EnsqMhlzrYFrnO6NWmou8POjRs3f0j+1OLp9OnTLFq0iDlz5jBz5kzmz5/PSy+9xJYtW4iKiiIgIID33nuPrVu3olT+tq3r+c01oJHFi7dWQ0toHmKBPLLEWN2LG2PbwtPxxkCmBwxkVfUB7JKDV3NW46XSI6qDwO4FNi3z/Ka6olVrGnbRLMrF1b62UEKESLpCbwkkyBRPpb5tVpqvJZxgcwJ0oZ9UgopBDGU325GAtXV7CNcEEK2VW8591J68MXoCSwqPsfWkFbHZGxBYcbIKPLwhQoufQcVfEi6i1trM0Xo5QhSp9yfJMwjBS+Ca/uF8frAYu0JDrqEXUOOq1RkfmsR7Z+RBt5vKzjAprAdapYr3h83moQMrSfQK5JakIVwa2Ru1QklysCc3f3OMVpuDRrMdzIFQFwgKO4+X5NFQbeCpkQPQdzEb75dgttu4bPNnFLTIdTvDAqNZPuGGTtGXc0n0CuRfA6e75u49tH8VyydcT6ynP6sn3cz6kky+KzjJnsp8JOQ69LNF4ONC4l1u6fuqCpiz9SuX79KPoVOquDFxEPf1GkWEM3pUYWri8s2fc8w5w8+o0rJk/HWMDo7j7r3LXHPznj22kRN15Xwy4koMag3fjp3HtTu+5vuSTKyig4cOrCK7sYYoow8fZuwhr7VjI0KI3pN/9LsIH42ug3C6OrYfrwy8+Cedvxs3btz81vxp03bJycmcOnUKlUrFokWLmDhxIrfffjszZswgKqqtC2vu3Ll89NFHv7lhoq/GgwCtgT7eoZgdduzGSvCoB6C5RcWKEx1np00PHESS036g2FxLenMJaM7eHBUIdrkG6GRrDidNshgyKHREm1LP24kUauqJt1UuMPexhBLV0u+864cIoYzzkutQHIgsrN5Ila3etdxbbeDm+OF8OmMUV6QGt82Ka/WB7AFc7DERP42B7dWZrk6x0f7Jrvf/zuHRKCW5gL3QIxnsajyd9U2jguPQOP2ZNpZnuY55WXQKubOfYP1Ft3FFTCoaZ0rvqn5hHHxgFDcPicTfo51wEVXQEMxLa6sIenoDD6w4RWFdWyffL0GSJG7e9S17q+Q2+xijL0vHXf+jwukst/UYytw4+f1ttlu4c88yWuxWDCoNl0en8OXoq1k4di739RpJvKc/ADOjevPyoOlolCo2lp5h1uYvfpJw8lRrub/XKPZefC+vDJrhEk65TTVMWv+BSzgF6gxsnnIHk8KS0CpVfDT8Ct4YfKkr6vVd4UnGfP8u+c21eGl0LBt3PTe0M+18L3M3fzu0toNw6uUdzLvDLufYpQ8RqvfsJJw+HzXnd/ficuPGjZvu+FNHnk6elGsy8vPzOX78OIMGDeKDDz5g6tSpP1pE/p/mpX6XYfPR89cj38kDXQXo28PGiSNyROHtnXkMjvLBqJU/otpWB9f5jeS50lVYJLlIWq21cbaH7HhZI96eEmttbW3f/RWDqLGqyDd3LtQFsIsS5U1WtE19CFAmonBoKcdGoFFDRkVTl9sEGDQYpTgCKaGKChodLbxW9g2hRJCq7oXJ1pb+8zIo0MWl01oUI9sxiGq+3FVPZlE6ZX4nXOvFKKLJq2k7x56WXE7qEnEIKqiKQhejpt5kAwQG+0ezsyqXrMYqjldVkBbU9cw1hyShVylI8PfgvctTePPS3mzPrWXxsVK+OlaExSqLtVaryJs783l3dwFz+oXy13Hx9Oqi01CU6NYJ3SGJSE6V+PyxjXzt7KrzUmv5avRcDGpNlyaWZrsND7UGuyi6/L0AXh98CQeri8hsrCKrsZpHD6zhX4PaRskYVVquiu3HlTGpWEQ7OqUam+hgffFp/nl8sysaNSksiZlRfbCJDppsZtQKpXwsScRLreXiiF54a3RYRbvLcuBoTQmXbfmcKrNshxFj9OX7SbeS4BXQoU7snp4jSPT0Z96Or6mzmjhRV87Q1W/xbP8p3JQwiMdSxpPoFcg/jqzvYJEwIiiG6xMGMju6L4IgsLU8h6vbCadZ0Sl8NvIqlIKiU13aH8ER3o0bN27gTyye4uLiKCkp4dixY1xyySU8/fTTXHvtta4U3r59++jbt+/vdn637f8K/Npu0tMjevNc78t43pHN0uNl1JvtLD1Rwj8uklN5dSYbRknJw8apvJi5CoNKy8z4VL6slAt5VSrYx04syDU2ffWJjPNN5quiEjy1Xf+Cz2+1olE5g482NTi76aqarYR6dt3WXtpoISHAgwEMZQebaEHu9iujmDJbMREtIQzUpRCjDuf75t206qsgvhZ9eR9MdT4A7C9sgIoIiK0h2uhLqNYXH31bZCbFmsNpTTR2hQZqw1A7LGid5zk+NImdVbkA7KrOZWBIKF0idrzZalRKJiYFMj4xgNdn9mLY4i9ILxShPghEFXZRYsHhUhYcLuWS3sE8Mi6eodG+ru0FpG6tCgTnf+tLMnnmqOwWrhAEvh4zj96+wV3aIgBolEo0CiVqhcIVUQPQaJR8M/Zaxn3/HnVWE+tKThNu8OaWpCH09gnGR6NHp+z4V/ffWQd41jkbDmBefH8+GD7bFb1xSCJqoevvgc1hZ2NZFp+c2c/q4gyXx1Q/vzDWTLy52zqtKRHJ7J/+F2Zu/oxT9RXUWU3cu/c7vsg+yFtDZvJIyjiGBkbx7LEfiDX6c114CqNiknGIDo7UlrKmOIN/ndzaIeL02ag5Hdz1z32f3bhx4+aPwJ9WPKlUKuLi4hgzZgyvv/46N94oD9ldtGgRH3/8MSkpFz6i5D/FtPBefDJsDlXNdh4eG8+q9HKsdonPDxYj+ZXy9MAJrnXHB/Wij3cEHkoNOZUWvkQWT7trM2kOks0yvZVGpnj/tJEaPxctWsYyiQLyyOEMJuTIUbG9nOLmchQoXNYGHio1lyUmoBc8WHCoGLNdBIsBShMZNNC30751ko2ExnRO+/QDSUFGtg6cJWDjghN5lu8B2FaZxX2MuOBzN2g0bLx8DoNWv0FJUx7UhOHTlEC9SY7+rDxVwcpTFYyN92fhvDSCjF0LyfZYHHbu27fc9fz1QZcwObxHh9E6F0KM0Zd/j7qKyzZ9joTEx2f28fGZfSR4+vN46kRmRrWNpXn39G6ePPy96/ndPUfwr0HTXeNcusPqsPNWhlx4ftZa4CzjQxNYNu56PDXnv/Z4rwB2X3wPD+5f5eoIPFBdxLA1b3Nbj6E8lzaZjZPvAOBocS537lnKisKTrsjWWc6m6pTuVJ0bN27+C/jT1jwBvP3227zxxhsu4QSyqLrjjjt+9xTADQmDuSd5FG8PnsW/h1/tqtMJ9tTiHy77/iAp+GJfuVxc3o4grRc6hZp8e5tTdXOTBiQwKvTM85+GTvHjN/xfgoiDRhrwwMAIxtKfIRhFr3bL21I1kw2jaZFMZKmPICYcBJVTUDQGEmTvupg9oTEdlSivl56Hy/G8l3cwwTo5YrerKq9Lp/CfQpDeyIO9x4DSDkGFXDqpmbcu6+MyIAXYmlPD3UtPnmcvbeyqzO8wePiu5OE/67zaMzEsiVcGTe/g+ZXdVMOtu74lp1E+Vr3V5Ip2Afy93yReHTTjR4XTzoo8Bq2az2OH1nYQTtEGX/7ZfyprJt6Ml6bzKKCu8FTr+GjEFeyedg9pfuGAbGvxQeYeei9/hY/O7OUve5czfPOHfHxmXwfhJCBwW9JQd42TGzdu/qv400aeAMaPH/97n0K3PNxrPOEREV0uu2ZACK8WN4FDA/UhVNSBxnkvtIp2vis9xHclB6myNIGuP5g9weSFwRTKtdEj8Fd1bTXwSxBx0Kqqo0FZQwX11FGDg7Y6HT0eeEl+xGnCKHdUUemQb+7x6ih2mg5S5agFE/I3LiQXiuV05OECE738Ox9PI1pJaD7Oaa9BSAjcs+wEm+8chpdOzbDAGJYXnaDRZiazoYqePsEXfD0tNiufZbd5J40JjWVefDS3DY1i8fEyHlqZTkWTheWnysmubiHO3+M8e4MU3xCMKg3NdivH68rYXJ7NhNDO3loXyh3Jw5gVk8KaogwW5h5hd2U+DknixROb+XjEFeyrKnTVS92QMJAnUieed3820cH9+1bwodNLCWRD1lnRKdyUNJgJoQk/Kry6Y2hQNPun/4X3Mvfw1OHvabCZqTK3cNeeZR3W81brmBLeg2mRPZkS3oNAnfFnHc+NGzdufi/+1JGn/1bu7jmS+0bHup6/tDEXUZIoMNVyz9Ev+DBvC1XWJtkUMqjNEFJXncTupuMcafl1hsQCNKmqyfLcxQnf78n22kOV4QzVVHYQTgAmWqlQFnPceppKRw1qVChRkGMrlIWTE71Cw6ioYAxONXiwqIFmi52uSLBkg0GOOBXWm3lsjXxdA/zaolUHagov+JpESeT6nV+7htEODohintPJWqVUcHVaOI+Nl0eCSBLM3/7j5pOBOiP/cA7wFSWJmZs+Y0tZ5wHFP4dAnZEbEgexfMINhOjlqNuyghOcqCtjd2W+a72Lws8/isjssDFn64IOwmlEUAwHZtzH12PnMSks6WcLp7MoFQru6TmCUzMfdnUNnsVTpeWZfpPJnf04X4+dx7XxA9zCyY0bN/+VuMXTH5R7hvagV7B8Yzla2sg7R49yT8ZiclrkURwKBEb79+CBtIHo9HLPXU0jHKuuZFXDDrY0HvzFLtpmRTO5nntpUdd2MNEEMGAkmlh60IsgQlDRsRXfhh1Hu9RdqCKYG8Mm8XLiTVwbPpYx8QGA3PG3p4shxAA6hQoiToNSvr4lx8tYdqKM/v7txNM5Zos/hScPf89ypzu2r0bP56PmdBINNw6KchWxf3awiOqWzt1y5/KXniO5MiYVkMfGXLr53+y8QNfv8+Gh0vBY37Zo6vPHNrKnUhbPAgKDA7pOgYIcabts02esLDoFgF6p5oPhs9k85Q56+3TdsfhLCNZ78tmoOWyefAeXR6fweN8JHJhwB4+nTuh2xp4bN27c/Lfwp07b/ZGxOES5cLodjWZbh2G4D4+N56ZvZGfuradaIVECJUTo/OnnFc3Rxny212RCoD8Uyk7fVMaA4Sg7mo/QYLZidURQb+5aREmA1d65ZkivUlLRbKU28IRrNp7S5oHW7I+36I/R7o9abKuHCSAWfyS0BhMmdT0NihoaFHLaLsgRTpgYQ7jeD32LlowWE2DC36BBcJ7DtpwaZvdtRuFs97c5BxCrBBWorRB2BorkAumHVqbz9uW9UQkK7JLI7soC6kxtQ3/PYnWI+Og6eyt9kXOQl09uce5fwaLR1xJj8MfmEHGIIhqVXHfjoVFy69AoXtmSg8km8v7uAp6a1HUaTpTkTjyFQuDzUVchSiJLCk7Qardx5ZYvWTL+OoYHxXTaziFK1FpaeS9nP+v25tDXN5Sn0y4iUGfELokIYue6vHlx/Xnt5DYKW+r5obTN66qnTxBqhQpR7Px5NljNzNz8GXurZKHlqdaycvyNjAyWo5uSJHUYvdMeie7n8Xa3DYCIhEKCUcGxjHIep7a2Vg7lned4Mu6uOjdu3PyxcYun34nJG99B6e9NpMGHf4+4mlhPfyJ99OjauV1nBzSj9a3DUucLdi1UR9E/2cHx+hJWV7YrIvesQdC1IJkN0OoNLT5grOe4/RTxETbGGQZ3WSD/w5lqDHoH1bo8WlX1BJri8LIHUVBnQmmsw6KXO/mUdj2hFSMQUGK2O1AZNF3c+gTMzR7E+wWAlECHrJ4CWi0OAoLbRm0EGDQkBRrIrGqh3mxn9dq1+NfLaS5zs1zAbG9pYcJuGzWe1ZzwD8BRG0yL1cHDKzOIS47kjLWA040VWCV7p2G/oiShVHS85p2VuTx0aLnr+dtDLmds+0G6CkUHW4F7R8TyxvZcbA6J93YX8Oj4+A6fj+vKJRCcQVyNQsGC0XMRt3/FsoITtDpsXLHlS9ZMutklVkCee/d2+i4W5h5xDTc+UF3EsoITvDhgGjckDOy28+zJ1InctntJh9dGBcfipdF1avOvNrdw8caPOVRTAoCf1oPvJ93CwHOjVN3aMHRvEXC+FJ9Koey0lUIQUJ6dHXkefeS2JHDjxs0fHXfa7nei2tJChbmJgzVF3LF3cacU279ObWL2tn9jCcwEQVYiQnUkhyuqXKaDAjDUP5Z5wRO4Y0Cya9vg+n6cVTc5nOGg9Win49eLjVR7nyDdZyMV+iya1FXkGw9hF2xIiNR7Z7jW9alPRuhiNt4vZWCkj+vPe+q1lBfkUF6Qg+j0/REddrxrW/FqtOIIySQpRE731LTaKMmIApsaUZI4Wlvc1e47cLC6iLk7FriKqx/sNYYbEwefd5swbx3XpMndY1UtVr44VPKTrkutULJw9FxmRsnRwBa7lek/fMLivGOsKjzFhO/fJ23F63yStd8lnM5SZzVxx56ljPv+fQqbu05nXh6dQp9zUm3Duohs1VpambD+fZdwCtF7snXKnZ2Fkxs3bty4uSDc4ul3on0kaFdlHuWmNkfvr/MO8dKpTXJqQ2MhNlb2UJIkBZQmokDg5rgRrB97Hx8NvpYBnkkMifAjykcPQEW9xCBrmzfUKVtbAXmVo5qNpm0sbl1Bs6G4Qy2TqLDTqqrFZqjErpaPqTX7oze3DZz9NYnz98DPOTalUPCjxCOq+5UVElOGiK46sBaTAop6gSSQ01Td7WZ20cFLJzYx8Yf3qHUOD54R0Zvn06b+pHN8YEyc689fHPjp9VVqhZKvR8/lksheADTbrVy97Ssu2/w528pzXev5avTcEz+EQzPu5+Z2Ym5PVQFD17zZZc2UQlDw936TOrw2PCi6w3NJkrhzz1JO1ctjfiINPmybeid9fH/9+iY3bty4+V9DkH5pVbGbC6K4uJjIyEjGXDYdnYfc/q4SFIR5eCFJ8qDXClOjq9TaR6On1SGx0XcKJqUsHEa2HCDJUe7ap8kmohCgQuXPJs8RSIIAkkT/+i0EWktRoUQraLFIFuzndMmdrTs6i1rUytEnp6hSOjQIUlvUSULq1jEbQK3ofpnLzbwdhZpw9ngNkpc7zEwoXYHe0XHOnEMBZq2KAK0RUalnlWEUzQr5vYtqPc1o22n05zhuS85inWpzMyZHWzefTqkiSGfs2udLoksX8aW6EVQrfUCSuK71B/R0rrHqDkmSqLa0dhrPolEo8VRrMao0SJKEwpnOsjjsVFtasIlt0UU/rQdGtdaVzBKdf2Vrra202Kx4qrX4aGThfPazabZZqHaKRaUgEKr3QqX4fX8riaLous7fkvfff/8nrXf272ZRURER3diI/NGoqanB378Lr48/KD/3fEe+JY+d2nnvyB9Z89fl55zvHXfcQVVVFYGBgT/5u9cdF3rd/23fh/9m3DVPvxN6kx0Poe2mXtPaFj3Rt1vPZmpCDaQ59rA7WI42HND1wackC43Y8YbsRR197FpO+A0CQeCE90jGla5Ca28CLGiBH+9zsqM55/mvRVeSI5R6IhV+FBnjsSp1HAoYxYiKDR2qXpQiGEx2TKZ6oJ7BTRvZGjINUaGi0COZ01WVRLXkdrF3ObRq6PCKnZrmHx+a254An3yqffqBIJDeqiOqpfSCtqfTOcjnYcZCV2eicT7OYjY1drkegAfgMNmpoaXTsvbHrGut6bTcjRs3/73cueQ4J8o6Tgaw2+2oVCpSQr14b/bvN37sfwG3ePqdEIx6BIMHSoUCL7UOAdktutEmu2qrFQp0SjVNzudeVBBtKaRAG4VFqSczZAQjTMcBMNkcrohJL6mEenMIRbpIbAoNu0MmMLp2Mw5JjkIICKhRo0KF1S6BAA6FGUmQ5OJgUYWokAWT4FAhiB2/IuczZlcI3Zf6KhQC3QWl+pvSqdEF06oyUqkPI9urF4mN6a7lDgWoPAx4KGXpZwD6NO3juLc8muVIwAhC9RAgNgBy1KnW2iIPXEa2dQjQGdAoVEgSKLsJfkgSro6/9iQrWjib+KzzjWeAR8ehyefvSDs/Al1HZCRJot5mpsHaJps0CiUBOkMHx/FzabSZqW+3jadai7/2/AafvxW/V+TJjZv/Zo6XNbkiUO3ZlS/XRI6I8e20vpv/PG7x9Dvxzr/e6JAa2FWRx4QN7yNKEhqFkjuTRjD/9HZAFiXvDLiKJ1WBXLwgnQaznSxNNPPvuoKUUC8OFtXj2a4tv/TUCooONIPZSLPKhx3Jk2kKPQgC3BV+Mamech3PqlMVKAzNrDGvBSBMCKGBJlqkFpAEEmsmoJI6xqrqWm0EGDrGps7SYnUQ7t31SI8Wq52EgM7xF5Drv8YcWM06qR8ICk76DsBoayTUJBeC13vruOv+5/BRyyKg1trMlwffgbJiqInAISg5HH4Ry24YiEErcOeBReyokjv3vNQ6vhl1I/385PfabHPg79H1+TtECUMXQ5RFUSLyuY1UtVip8YzhrVdvRdVOgZ21KugKUaRb0Sg5r722pga/c0PtkoQgCCzJP87Nu751iWiDSsPbQy/j2oQBHVY3223csmsx3+Qddb32UO8xvDRwWltX3M/ocDufpYAkSefd7txlHVIK7m47N25+lJTQrodygyyazo0w1dTUcOnCjG63cfPr4RZPfwAarCZu2LnIVctydWyaSzgBvD34ciYF9cTW0sDfJiS4nLbf31PAO5d3HHBcbq5nV91piNIi5AxAcqhoqjWAOpKQ8GZSjLEd1s+xtxUk+yl9KbXLtVSe1uBOwuk/SZyfgdT6Qo4pY5AEJXuDxjG4egtaTRWGwECXcALIaZGLoAnJJUwIp7RaoKTBzK3fHkMXk8X+pq6F089FoRCY3COQBYdLqDfZ2FNQx6i436auYHZMX1L9Qrlq6wKO1pbSYrdy485v2F6Ry/whl+Kh0lBtbuHyzZ+73MaVgoK3hszkjuT/7HBoN27c/Gdxp97+uLjF0x+ARw6upqBFDsGODopjTXHbL4dn+k3h+oTBlDXKqZgrUsN4fXseFU0W1qRX8tj4jsXVS0oOICKBxszUgSrW7nMmlSpiSQjs6GMkIZLnyAdAiZJGqdm1zMf827azJ02YRYIksehICfsK65EEJfsDxiHFnuLFwRd1WPesyzqCxF3jA3lrXRMVTRY5XF3pD5G+GH1aWTTqhl8snM4yNTmIBYfllv/V6ZW/mXgCSPQKZPe0e3jo4CreO70HgH9nHWBPZQH39BzBqye3kdcsj8DxVGv5duy1TA7v8Zudnxs3P5WH1+WQVdd9ZMRdq+PmvwW3ePoDsKmszSl6VHAc2yvbip8viezTYV2NUsHNgyN5YVM2Dkli0ZFSxiX4I0oS6yqOsbLsMAB6pYaUMCNrQ45BeTwgsPOkhLejnBm9ghEEgSoqaJVk8RWpCKfYIRdCK+xaDNbAX/06JUmihmpypRxMkgk1atSCGjUaGmu90Cu0BMY4iMgspFifgCQoEYp6EjE8tMN+aqxtIi/J15dProzjrqXHKaw3g0MN+SnMHBpIP99fr2NqYlIAKoWAXZT4cG8BD42JI8jzt4vM6VRq3hl6OWND4rl112IabRZON1Ryz97vXOtEGrxZPfFmUnxDz7MnN25+PzKqWkmvMtE31LPTsl35dezKr+tUBA1yHU9X27hx83vhFk9/AO7oMZzHD8t1Rx+c2UNvnxBO1cvps0GrX+PWpGHcFDOSsyNUr+oXxitbcrCJEktPlBEabOHzzG2cbmrrApsVNpB38zaBvxlsWqiRhcSajEqarQ7m9AujUNEm0sKUoeSL8pBdrcXvV607ERGp1RSTLxZQR23Hhc6Smqx2L1/UWIQoqig1xCA51Ly8JZuXLu7lcgyP9ghwrZveWMbcmEiW3zSYUZ+toqXWGxBYsLcayZrOC9N6dmmRcKH46NXcMiSK9/cU0GC289ja03x6Veov3u+FckVMKqm+Ydy861t2tRsKPD40gc9GXkWEwec3Pyc3bi6EvqGeXbbed9U91n6b89X/uHHzW+MWT38AHuw9ml2VeawpzqDa0kKkwYdYox95zbXYJZH3MnfxVe4h7owewm29x+LnoWFCYgDfZ1ZR0mDmb4fWgbHetb8pQSkcqy+kyW4GAQb3UJFgCufrIyWuWXINFgvlXmUggFEwoGjnl6qxev8q12XDQrk6nzJVLlbFT7cHEIAB1bto0AXSojRwoqyJBYeKuX6QnErs6RnmWvdYXTFzYwbjqVUxdYiCJYfyoFKu6/rqcAk5Na18fGUq/t0UuV8Iz0/tweJjpdS02vjsQBHXpIUxMenXj9D9GPFe/mydeidf5x5hTXEGl0WnMCs65cc3/A+S2VBJXlMtVtGBTXRgEe3YRRGbKCIiMTYkDj+3J6+b8+BO17n5b8Itnn4nhq2ej9LfmyijD1+OmssnI65i6Oo3yW+p5UhtCbcmDiPcw5tXT22hyW6h0WbmpextfF16gvuTJqDwqWrbWX0wGOuJ0QdxbdgYMltK+L7pBADeCk8mGYeh89JwZb8wlhwrxSHB0eJm8OwFkemEE0uRWOnqgFJaPGm0dW0EKSF1OYgXQKkQKGu0IAp2aj3P0OhR2MHBHMDD5ouPOQKjNQgFChyCnUAvJVqtiBUbdmzoqUYhNTG4YT/b/MchSvD1kRIMGiWJgQYckga1oMIm2TlQXUhutexxFKUJgqDDoG1FVdobuwP2FtQx+YO9vHZpL+L9DZjtYrfdb3ZRQqXsOhUnShLeOjXPT03mzqXye3v5ZwdZf9tQBkR2b0ApSt03lonQNvRG6rxM0U2nmyjJRqVXx6VxdVya63VJklyDnLvifNHEbgcDn6ejziaKmOw2Htq/kk+zD3S7b5Ad1x9NGskTPpMRENAou/+np7trcHfhuXHj5o+C+6fg70SFuZlSUyN7qwp5/PBaAvUGFo29Fp3zpvJR1h4Svf1Jv+xRbk0aisJ54yhsreXBo4tZ27QdlE6TzMZA/pY0nWWjbiMxwMjSCrmoWCUoeDRxOlOSwhibEMA9I2N5eUYvdGfTWE3+kJ/CvLiBoJe9QRQIDPCPYFx8QJePMC8d/cK9unxoVQqMPi2UBe2iwZDfJpwkAXVLAMbmSOySg1LPE5zx20SBx1EaxEaaGrSEKkOIVkYSr4xztdb72usZECFHwSTgm6OlKBUCQUYdcfpgAIrNdaCy4a1XMyQgRj6edzV9UqsIddYklTSauWfZSerNNvRqJbpuHlqVAqVC6PYhCAI3D45iVl+5pqjZ6mDGp/vJqGhBIQhdPlQKp8dVFw+lILgsDgSh40P2zOr6P5VCiVJQdHqoFEoEQej+0c1/P5eTdeUMW/PWjwonAJvo4PnT25j6w8cUtzT87GO6cePGzR8Bt3j6nYgx+uGhkr2ZFucf43htKal+Ybw15HLXOrfs+pY30rfzWMp4Nk66h9H+MW07UEgofJyu5KISj9YwKsyNPHJsKQ6n5cFdCWNJNHQsHh4c5ctt4/xA4Ywetfrw4c4yiprlG1q4zh/lzxgCLCFRa8jmmG47ZkWL8xSVeDn80Yue2AzVNBuLsGqcNQ0CmPRVVAYcItNnC6cdp7FK1k77TQ4y0j9cFlCNFjsf7S1ElCTiPNpmtJ1qkGu9ojz88FbL/uyFUgHfXjeA1DC5TqKqxcrti4/TaP7po1W6QqEQ+OLqflzkTNfVttqY+tE+cqo7O3z/WZEkiXdP72bEmrc53SB3PnqqtfwtZTwvDpjGq4Mu4fXBl/D+sFl8MuIKHukzFqVTEG8tz2XQ6vkszT/+e16CGzdu3Pwi3OKpGyoqKti8eTP5+fn/kf2HVA8hvGI0FCeBQ8m9e5dTbzVxXcJAbnIOiDU5bLx6aivJ373Et/lHmN93BotGXc+ooDhmRaXy74vbhv++viedGdvfodwsi5PBfjHcGDuiy2OftKdD3DFQyWKlqN6MmJcCNg0R2oAutzkfZkzsFbZSYzwDzpSLStIgCg4alTWYlB2LQLUWH1T2tiE0VmUrhx2HWWVbRZ1Y12FdQRC4YXCEy5jzdGUzW7Kqide3iacTjcWudVO85cL4WlsLFoVc75ToNOfMqWnloZXpmGwd5/tdKFqVksXXD2C409m3vMnCxA/2kv0/IqD+sm8F9+z9zuXgPiggkv3T7+PptMk82HsM9/Qcwa1JQ7kxcRDz4gfwXP8pbJ5yO1Eesgius5q4YuuX3Lt3OXbxl30Wbty4cfN74BZPXfDKK6+QkJDALbfcQlxcHNOnT6eoqOhXPcbegjqyyuxQHwpVUeyszGPMunc401jFG4Nn8peeo9Ar5ciUTXTwftZOLtr1KdWWFhaPvpF3B1+Bj5cdT085klLboMReGgsSBGiM/DPlsi4H+FocNnbVZIGuBWNCJv4eTmdyiwHyUtlXk0upUPCTr6OKcnYIG6gVnFEwSUApqrALHaNIKrM3vvXJhJePIaR6KGEVowmqHoDeHOiq97FgYZd9F+cWABk0Km4ZEuVKMH1zrAxrc5v4qra0WRckGoNcfy5urcNTq+LDK/oSZJTF1+GSBm5bfBybo2Mt1oVi0KhYddNg0sLlyFZBnYmRb+/iSMmfOyX1Td5R3jm9y/X8od5j2Dz5DuI8O/teNdss5DTVcLC6iF7ewWwadQPXxvd3LX/n9C5mbPyUBqup07Zu3Lhx80fGXTB+DgsXLuTdd9/l+PHjxMbGsmHDBqZPn87AgQP5/vvvSUtL+/GdtKOxsZHGxrbIS1lZWad1FPWhiMH5nGmsYuSat/hy9FxeGTSDx1LG817mbl49tZVWu40qawv37F/CZzn7kSSJQ7VFEOgFzakgKaA2nMEhobwwfCCB2q49UQ7W5bsiBr19QpmdkMir23Iob7KA1QOxoDfHYw+hctjppezZ7XVZsJAjZJAntHlUKUQVomDH4ZyNpxa1RNgSCXCEk1PuwFPb9nUTENBbAtFbAtF5WKj2O06NVEMjjVglK+f2xiUGGpjWM4g1GZU4RImFB2ogRg0qG0HtrvWsSzuARiEfL9RLx0dXpDJv4RGaLHY2ZFbxwIpTvDmzT5ez7H4q3no1a28ZwsUf7+dwSQOVzVbGvbeHFTcOYkz8n2+yeV5TLbfvXup6/smIK5kXP4Bmm4VXT23jUE0xlaZmKszNVJqaaHW0pUjDPbz4atBsPh5xJeNDErhz7zLMDjvrS88wYf0HbJ96Fx6qX94R6caNGze/BW7xdA4vv/wyf//734mNldvdL7roImbNmsXatWuZOnUqhw8fJiws7Ef20sZrr73GM8880+n1I3f35/lDrSw9VY1oVxPjiCdflU2DzczMTZ/yVM+x3BE3mLsjBzAzIJGnTmxibWUmAAdrCtt25NGIT2wB9bny+e5PV/BvYwb3pibKRcJmEzalxbX6zopTrj+nakIIVFl4dHgIz20vptbkAJMXFPXkcNQRBFoYoeqD0C6CFahroUCRzmnysdOWctGixqJou1mGikEMcqSiU2hBAYLRhl5tp1nZRL2qDo2oIcgWgoCAUaNlqHoQX1s3YsWODTsaQImEn8KCymmKObuHkbzqJtKrTDSbRShKhpgT+AsarM6aLZO5LXUmmky0NtYDEKmD+VNjuXNVNhaHxOJjZegEB0+NjXJdn10UEcxtMwLbIyGh6WKisBL49sokbliWyc6CRhrNdqZ8uJcFVyQzNtany32dS3tx/Yv5lefXnV1kEx1cuesrGm2y5cS8qFSm+cZQUV3FNfsWs7Pm/NHKktZGZu76ioX2K5jiE83yYddw7YGlVFlaOFxTwk1bF/J2v+nyZ9HNNfzc4nb/c+cGunHjxs0vxC2ezqGiooKampoOr1VWVvL+++/zl7/8hccee4wvvvjiJ+/vwQcf5JZbbnE9LysrY/DgwXj7eHP7iBCWnpLTXcmkMijawOKCY4hIPJOxhUxTPf8adAk9faKZ73ENVxSd5KXc7ZxulGe79fUJ47bEkcQoI1kTUsV7u+Ub2Ff7Tfxw+hRXp0aREGBAK8hz4SRJYne9LLz0CjVGRQQlVjUo1Vw7KIoP9xRgsonQFAClSRwKO0OZzUJvxwCsWMhTZFKky0WiLeUlSLKgsAhO4SQJ+DX2QNsSwwkE7IoWTNpqGvSV2PW1SKo2gZVbZUHbEEWkj5YKu4IIdV9yPQ67llscEvmtSqTqtuONiAuksKGEZqsDWvygMgZzlJF0p/4ot7QJuhq7lnxzW/G7j683D42N5+UtOdhFiS+PVmLQ67l9WDQAVsmBt75thl57HA4RQzdeUXpPiXW3Dee6r4/y3clyLA6J65ZmsuqmQYyM80N9HhuDs6lVP7+ON3gRqcu0K4Ak0e0g4u5eh/OIJ6l78WRzOBAEgWeP/sCherkwP9k7iLdHzqbC3MxfD6ztIJyUgoC/1oC3RkeQzoi/1oOcphpO1VdQb7cwa+83vDnkUubG92eT/+2MWvsuDTYz3xafYkhoHHcmD0On7FrACue7ODdu3Lj5DXGLp3O45JJLePbZZ4mNjWXEiBG8/fbbVFVVceWVV1JbW8vDDz/MJ598glrd9T/w5+Ll5YWXV2dnXKVCwbiEAOL9PcipaWVDZjU5l19Omn84Txxeh4TEovwjrC89zVP9LmJezCAmhMZyaXI/1peeJkBrZHCAHDWpbrHSP9yXxXnHqC7zAaCy0cH8HXnoVApm9glh3oAIJH0ztTY5ijMmOJHxUUH4eLRdR49AI/cvP4XVIUJdKKgslAYXIOibqbDWYZfaRIwKJZ6CkTraanxUNg9iWgagEfVU+2TToCnHomqrRzoXi382lgZPmixqQjwNeBJDnVgEyPVlomDH4RAJOWcMyrUDInh/bz6SJEBVNM0NGjz9nF/ldr5Svjpth1QhwKAoH16Z0ZMHV6QjIQ9XDvHUck3/cBQCXUaXQA6GdCdmALRqJYuuHcD1Xx9h0dFSTDaRS/99kHW3DmZkbNeRDwVSB6uCDsc7TzDo7Pn8FgiCwLG6Mv7vxGYANAolX42ei0Gt5eUDq1hZlA6AQaXhs1FX0c8vHIUgUNhcR4DOgNlh43R9Je+c3sPeqgJMDht37f0Oo1rLzOg+fDrqSmZtln+MPHpwDal+YYwPTfiNrs6NGzdufh7ugvFzePXVVxk9ejSzZ88mNDSUbdu2sW7dOpRKJdOnT8dsNlNdXf2rHEuhELhxsOyaLUqw4HAJf00Zx/IJN+Kt1gFyZ9KD+1cwYt18NlfmoFIouTiiN0MCozv8EpeQaAnMgKiTYGibdWK2iyw6Wsr0T/Zz29cZ0OwDwISQzoNj+4Z58cyUpLYbc1UM1IZSYqlxCSelpMJP4YsDB3VSm3BKUsUTXD2IRk0Zp303UemR3Vk42VXQEAj1zqJuhQSRGYiCXCMlINDD3q9tdcGGidZO5xnlq8c/su0zWHSghsomOTVpk+yu19VC15YLF/cM5unJSa7nz2/MYn9hXZfrXghKhcBnc/pxWR+5E7DF6mD6Jwc4WFT/i/f9e2F12Ll55zeuz//v/SbR1y+U+ek7+CRrPyD7ib0z9DL6+0dQZW5mSf5xXj+1nSu3fMmw1W9z7Y5FZDdWM9Q5pNkmOpi7fSFfZB9kWkRPHu87HgC7JDJv+0JKW//cRfdu3Lj57+d/Wjx99tlnpKSkEBUVxbXXXsvp06cxGo2sXr2arKwsMjMz2bFjB+Hh4QBUVVXh4+NDUFDQj+z5p3P9wAiX4/XH+wppsdqZFtGTU5c9wi1JQ1zRjqymKm44tJSPs/Z2uZ+S1gZMohW8aiD2BCTuB/8iNOq2EEZ5LZDfF5p9GBUY3+V+RsX5c3Gv4LYXShOhyRe9Qstw755IiNSKda4qGS0axutGMlY3nMqAw1R6ZCMKcupMkAQMNn9CWntAdn84PRyKekFxMpick/q0JhqNea7DGSQvVJIcLZKAY4oDWMWO3kxW0YbFJx+85fRlq03k+Y1ZnCxvosXRVt+l7Sb9A3B1Wjh3D5fTdXZR4r7lp2iy2Ltd/6eiUir4am5/pvWUvyONZjtTPtpHYd1/Z0fZWxm7OF4nNzkM8I/god5jOFlXzl8PrHat8+LAaYwKiSO/uZaL1n/IowfXsLIonYyGSpfoqra0kNFYxSWRvQC5sP/23UtZmn+CJ1InMCVcFvPlpiau3f71eVOJbty4cfN78z8rnl5++WVeeOEFXnjhBd5991327dtHWloaCxYsACAhIYGkpLboRGVlJffffz+PPfYYSuWFm0h2R5i3julOsVJQZ+KBFXJBd7Dek/eGzebA9PsZF9KWxvjHkXVUmpo67eesI7ULrQlCcxk5oopXpvekT8jZjjQBipN55ujGDp1p7RkU5eO6+YOApqQvN/lfwonmfERnWkwnaBmsSeMa4ywS1XEA2JUdBYKH3Q+dwyjbFnhXQVA+BOZDSC5o2wq7He0K2gFUtImeGqGSTyvXYBLldeySgwXVG2gSWyH8DDq9bIlQ02rjxU3ZHM9Ug6jAQ6HFQ3n+7q17R8UyISHAtf23R0vPu/5PRaNS8O21A5iYKO+7ttXG3K8OY/+F9gi/NZIk8VmO7B6uFBR8POJKVAoluyvzXUXmtyQN4XLnXL3cxhqa7W0WFSpBQbJ32+y/BrsFP60HtyYNcb12797vKDM18enIq4g1+gGwpTyHVc50oBs3btz8EfmfFE8VFRU89dRTrF69mhkzZjB9+nRWrFiB2WzmuuuuY+HChR3Wv++++xg5ciRTpkzhkUce+dXP59VLeuGlk6Mt/95f1OEm3tcvjPUX3cZNCfINp8lu4dnj6zvtI9zDh0d6y6aZAgJ9fEK5p8donuozjdmpYay8aRADo2WzSOxa1h2w8WH+5m5/4U/vGcTASNnU0GqHD/cV0OTstIpUhnGN4XLStCloBFnoCIJAUM0AtHajax8t6hpqdAVU6/MgsAiCCiG4AAKK5ZQdQIs3Po2JHY59VgSelYIF1go+qlhFo6OFb2s2k2WWTTH1SjUPj4ulb2g7W4bacMgeyBDlEJerdXcoBIEnJyWicob+Fhwu+cUGmmfRqZUsvm6gy6BzV34dz/6Q9SNb/bE4VV9BZoM8Q3FcSDx9fOV0pJe6rQYt0tA2RLqnT1vEMtk7iD3T72HxuOvYNvVOl2fZN3lHmRvfn2vjBwByWvr2XUvw1mh5Y8glru3/dmit20DTjRs3f1j+J8XT4cOHUSqVxMe3pa7i4+MJDg7myiuv5Lbbbutgivnqq69y5swZnnzyyf9Ix0+cvwfvz05xPb9z6XHyattqfQRB4MmUi/Bx1kF9mXOII7XFnfZzf89x7J3yECdm/I0fJt7DEymT8dF4uPbx/mX98fFwRs2aAlidUc7nBbs67efs+tf2jyDcWz6mrVUPlTEYJS8m6EejFjqnxHRWX5IaRhPW0hul+CMF9RJQGQ15qSjFrofxaiQdWkk+fpmthldLF3GiNVdeJqiZ5XURPX2DeGRcPAkJDeD0l8KqZ+sRgY/2FGC1nz/aE94u8lfbauPrIyUA5DbVsCD3IF/kHODznP18mXuAj8/sdT1+KM1ElM6/b0+dii+vSUOtlL8z/9yUxdbsX6de7rdgcf4x159nxbRNvPfVtnUkNljNrj+HengR4XQRL2iuQ+WsOfPTenB1XD8ATA47/87az78GTSfK4APAprJs3srYxaSwJMaGyH8nMxoq+Xf2wf/Idblx48bNL+V/stsuMTERs9nMiy++yJNPPgnAs88+y+jRo/nggw9ISEjgww8/5LnnngNApfr13yZJlJDEtqjPlX1DWT+wis8PFtNotjN3wWG23jUMtbP7y1Ot46GEkTyVsREJiUcOrmLdhNsQBAGbXUTl1HRhOvnmZbPLv9pbrQ5XZEWrUvDyxb25ffFxOelSHs/HGYdQihqmBvdznUt9qw2rw06+pQxdRB40BssmnNWRRGkTqBJFoO2meRa7Q6S2VUJojSCYMOwqEwgiEhJVLWa5E06Q5P+3GMCmc21b0tC2vwRRQgOIosAA6xiOaXfRQjNWZzG4AgXDhZEIZk/2FNRxxppLtu4oJGhRlPREbPFGAr49VsbWnBqu6hdGhFMEapQKPNQdP8+pyUEsP1kOwJs78ijXZPFm1pYO3YVdMSY4njcHzSJQ64mPvmtR3TvEkxenJvPw6gwkCa5ecJjPr+7HhMQARElCKSiQJBDFjhFAUZLoxuEAUZKQuuq3kySU3WwktfvfrvbX1W+CJc75c0pBwcURydic5qrGdmaW1eYWGtsJqFS/MIpbGzA5bGwvzyXeS+40nBCayMKcI5hFO1/nHuWu5BG8PHA612z7ChGJpw59T2/vYB7oPYqt5TkA/OPIeq6KScWglo+nPM9vPbeNgRs3bn5L/icjTwkJCTz++OM89dRTDBw4kLS0NL777jveeustvL29mTt3LocPH/7xHf0Cupp6P//SXiQHyWmeA0X1PL3+DEqFgFIh4KVTc1Ncf1fqZF91AQvyD2DUqgj30RFo1Hb5iPP3IMxb53rM6hvKvaNkQ00kBRT15IO8zeTZ8wn1UZBlzWWvuIvXKxfwVe06coQzco2SfNYUVjvoF+7FsBjfTo8Ao5bEAAOJAQaSAjzp5RNEL+8QenuH4iX5EaIMJkQRQogQRojOmxBPLSGeWtQKgShfvetxVuypFAJKhweXG6fgq/BxnoHAJP1oko2R1Jls1Ek1bG7dLZ+exsLFKb6MTwhA6dxHZbOVd3blc7y0CW+dGqVCwM9D3eHRP8KbMXHyTb64wcxr+0/8qHAC2FaRw+j1b7Ky+ITLyuDch1IhcP/oOKYmyzVkFc1Wpny0n9uXnKDBbEMQ6PpB94+z7KrM55mjGzha+8tqtQTn+9r+v/S6CjKcQ3/HhMQRrPdEKShQCgr8dW2RJ7skEurh5XqcjRwBFLbUE6z3JFjvSZJ3ILPD5WJxq+jgozP7GBYUzb295PmLNknk/v0rSfIK5OrYfoBcPP7aqe2uc3Ljxo2bPwr/k+IJ4Pnnn2fdunWMGTOG22+/nYMHDxIcLKdv/Pz88PTserzJfxKDRsXXc/ujVckfyytbc/imXf2TSqHgzcEzXTeSRw6uJqO+4oKP8/iEBNdMNiwGKI/n4SNLGf7DK/z16DL21J+h1dFW+KsNqCQ2SE7BNFscrDp1/mNKSFgxYxJaMAktmIVWRJUZh9L5UFhc9gQ/BYPCg5mGKYzUDWamxxRi1VEAtNDMssaNLqfzVF0Peuhi6R/hzeV9Qoj1k+ffiRIsOlraIbp1Lmnty66qIlGg4J4eo3lr0GzeGjSb1/vP4uNhV/HJsKt4e/Asog3yUOAGm5k79n3LtTu+otbS2VYBZKH8xdX9uCiprXj60/1F9Ht1Byt/5L08F1ES2VGRy7QfPmbsund5/thGhqyez/37VtBks/z4Dn4ii51RJ4BZ0Skdlvmo2+YKtk/bAQwOjHL9+dg5om5OZAoeztqnBTmHqDQ385eeo+jvL3ez5jTV8NyxjTybNgWNQv6+/fP4Jr4vyfwVrsiNGzdufj3+J9N2Z5kyZQpTpkzp8FpLSwtfffUVL7/88u9yTn3DvHh1Rk/u+U7uupv31WHOVDbzyPgEJElCp1K7Op3MDjuPHFzNkrE3XtAx1EoFH12Zyph3dtNidUBtGA5jrWxz0I4pwX0Z4ZdImk8MLWaJWxcfo9Fs53hZI/4BFvTeTdTbm2kWTTQ7Win1asKhtGLD0tnFMQbOtcsURCUKh5Z6UUuz0oBW0qFFR0/ETqpeK2joo0nu8NoRYR8mSb55x6jDGW8Y5lrmo1fz17HxLDlexubsGhySxLfHSrl7REyn92NfTS7zS5aBRyq0+oDFyBMxc7ijTy/XOmabg4B2DuMzo1L42+HVfJkr1+UsKTjOwZpilo69gd7O6GB7/Dw0rL1lMF8cKubBlenUm2yUNlq47LODTEn05a1Z/Yjz78bdXBTZWZnH0oITfFdwklJTx3EuoiTxzuld7KzIY+Pk2/DXGbrcz09FkiS+yTsKyCm7S6N6d1juq20TT1mN1VgdDjTODtRErwB8NXrqrCYOVBdRbW4hwHk+PmodV8am8ln2QcwOO3fvWcZ7w2Yxf8hMpm34mCa7hYW5R7gxYRBPpk7k70fW45BE5m77ijOXP0qg3ogbN27c/BH4n408nUtZWRmvvfYaaWlpzJgxg0svvfR3O5c7hkVz78gYQI6aPL3hDD1eXcuA7z9h+Jq3ut3OJjrYWZnHzsq880YhTHYbu+rTMUa2m5FXkgT2jkXeO6szWV52mI/yt/BGwUrU4TmuZVtyKlhbv4fdzSc43ppNrqUUs6oJm9CFcOoGSeHAoW7FrK2jQlFMoTKbLOVJLEL3EaKz1DjqqBdkM1B/pQ+XeI7v1F2nEARmpYQSbJQL0s9UtdB8jpdTs93C8xmrZTka2NYkcDi3o7fUuXiqtbw9ZBZfj7qWQK0sDvKbaxnz/dusLDzZZRejIAhcPzCSkw+PYWafNoH1fVYdff61jRc2ZXWofbKLDuan7yBy8fOMX/8B75ze3UE49fAK5K99xuLp7H47VlfKpA0fUtNuvt/PYW3xaTIb5S678aHxBOo6iha1QkmKUyCeaazi4QOrXNerEAQmhMlhvAabmYcOrMJsb3sv58b3d3Xr7a8uYvrGT2iwmngm7SLXOm9m7ORvKeO5KibVtZ+Psvb9omty48aNm1+TP23kadmyZbz88svU1NTwyCOPcOutt553/aCgIMLCwlixYgU9e/b8j5/fTTu/QR/oh1qhxFerx1ejx0etx1frga9Wz0X9tWS0KNl41A6SQFEVUJsE4SJ41eKp1nJ7j2E80mcsIKdIbt+zmGN1cqpEQCDe05/e3qGk+oXR1zeMUL0X3+Qf4cvcA9RZTaAHvNXQEAQOjWyIGdXmr9PssHCoPo9D9U4TSx2g8wezUY7QtHqCR5vnlCAq0aJFKalAEFBIZ6tpFLRYHE5xI4AEkiAhKW2ISguiwnrB80aybLmuPw/Q90aj6Lq7T6kQGBDhzdrTlUhARmUzk3q0mZy+lbWJcrMsSAZH+VBUp6Ws0cKGzCq2l+Rj0CkRJRGz3YFnixJRkhAliThPf6IMvkyL6EWqbwQ37VnI3qoCmu1Wrtz2Bam+YdySNIQrY9LQKvUdzinUS8eS6waw/GQ5D65Mp7DehMUu8tT3Z9hTUM+XV/cjs6mMu/ct48g5qS8PlZop4clcEZPKzKjeqBRKbk4cwqQNH1DUUs/R2lImrv+AHybf7or4XCivnNzq+vNfeo7scp2Phl/B+PXv02q38W3+MWI9/Xig92gAnkmbzK6KfMpMjaTXV/CPoxt4ccA0AHw0ev41aAbPHN1ASWsjZaYmbtz5DVun3Elk+g6KWupZU5xBQUsdLw28mKUFcv3ZOxm7eLj3GDTKP+0/WW7cuPkv4k/5L9G//vUv3n77bR544AFOnTrF7bffzujRo+nRo/NIkqNHj9KnTx9UKhVz5sz5zc5xU1k2WH5CXVWsJxT1BJseHGooTGFSigdfXDaEAL0HFoed545u4NVTWzsUOUtIZDdVk91UzYriE93uvn8vM9lHFTSaRGgM5NGIqyiVyii0VJLZVEa5pW1UhkGlRRlYT2ORHIkIakghMqCWFtFEvaOZSms9ZqHruh88oL1rjyCqMDTEYGiKxqBWERGgwoqZTOUxzs62swnWLnclSRLZNlnQKVGQpIk571vYJ8STtafl4uf0irbk4b6aXJaVyI0BRpWWR3pMZZvUwls787A5JK5esxb8uy/G/lufCTzaZwJBOiPrJ93O3XuXsiD3ECBHge7d9x2PHVrD1bFp3NZjKAP8I9quXxCY2SeEKclB/GPNcebvKcUuSqzNqCTu5fU0hBwCnRxBUggCs6P7ckVMX6aE90CrVHfoLov38mfj5NuZtP4DClvqOVZXxsT1H7DxZwiovZUFbK+QhWmqbygTQxO7XC/NP5x3h17OTbu+RZQkXjqxhWiDL5fHpOCv9eDFAVO5d99ymmwWNpdl807GLm6L6AfIqb1VE2/itl1LOFhTTJW5hW/yjjEvvj8vHt+MKEl8kLmXFwdM44qYvnydd5QyUxOL8o5yXcLAC7oeN27cuPlP8KcTT5mZmbz22mscOnSI0NBQADZu3IjJ1Hk8RnV1NaNHj2bKlCksXLjwP2JJ8EtRGloYOaKOxgIvjuTJ6Y8fTrQyteYQj00L4dnTK0lvVzTezy+cYYHRHKst5XhdaQfH57PoFCouj0plgl8qKX5h/OBbxRNrTwOwcG8DD4/tw2R/ub6nwd5KjbWJAI0nnko96zIqWVpeisUGlTUqKv3zQONMEV5A9EhS2Gn2zabFsxD/5kRqGqIR0BOo6IvAEQDs2KmQylFVdtxxvVBNs1YWaYFSKPnVVqDjdepVSpdQEiUJnUqB2S5yqryJjPImrNh5OnOVa/2rQ0ZjblUzNNbIWzudL9aFnFc8vXhyE4XNDTzVeyohnh68NWgWE0N68GHWHnZXyeKuxW7l46x9fJy1jwH+EVwa2Yd4L3/ijP5EefgS5GHgkVFRXJwSyWWf76fBJNLQLEBuGoSfJi1Wy/whl9LPTy6qloAqSwtaRUeXe3+tB8vGXc/lWz6nsKWe43VlTFj/ARsm3UqAzoAE3doYOCTR1YTw8sktrtcf6jOGZocNpdj1Bzs0MIan+13E34/Ipq33719BiIcngwIi8dd68I/USTx6aC0OSeSLnEMEClqm6lKoMDVhl0Tu7jmcm3cuRkSu2Xp7yGVoFEqsooNPsvbz15Sx3JE8nK+d9Vevn9rOvPgBXdoSuHvx3Lhx81vyx1MLv5Avv/ySxx9/3CWcioqKqKmpYe7cueTm5jJ79mw++OADPDw8CAgI4L333mPr1q2/6siVn8KhS+4nLDwcs8NOg81MvcVElaWFVruVeouJOquJQJ2RGZG9CNIbabSa+WxnHk9vKqTVJnK0tJE5n9ZCmAg+oFWo+GufsQToDCR4BTB/iFyzdaq2iuN1pRytK6GguZY0vwiuTxiEv9ZAVbMFrVrJjYMiWZdRyc68WorqTZwoa+JO59w3MAJBWEU7a0pP8IOwE4ufF1TEAQLUhENoLipBiYdkIFDtg6/CC1+lNzpBiw07VslKVm0jep2EHTtmTJRRgoSEpLJS7XMKM4X0pi+JROBAA8i1SXn6I/RWh6ER2oq1CxwlLsuiFH0C4eqOaTGAJrONKN+213sFe3K4pAGzXaTaZGOv+RDVNjnlONAnlssj02ix2XmnaAN4eECrN5g9uSJwJAE+AqIIXjo1CgTqrK18lLUXCYmv8g9SbWnhq9Fz0avUXBGbyhWxqWQ0VPBp1j6+yj1Eg9OZ/VBNMYdqOpqbBmgNROu90ajVNESVQWFvMHmBqISi3oyJjaGvb3gHwWBx2NlQksnXuUc4UlvCrUlDebD3aGI8/dg0+Q4mrv+AgpY6TtSVcc32hay/6BYEQdGtwDhrlXGirowVhXLaNtrgy5WxqZgc9m6d2gN0Bv6aMpYKcxPvZOzGKjq4ffcSdky7mxS/UPoHRCIIAg8dkEXqmzl7GRWVhL/WgIdKQ5J3EBPCEvihNIsaSyuHaouZFJbEmuIMai2tLC84yTXx/RkZHMvOijyO1ZWxtTyH8aEJXZ6PGzdu3PxW/OnE06hRo0hJkVur6+rquOSSS5g0aRIPPvggubm53H333Xh6evLuu+8CMHfuXObOnfubn6enWouXRocXEOTsIrKKDtcYi3MRBIGLkg3U6D14fX011lYdiCoo7kWQo4m/T43hxZObKHFOpE/0CuDu5BFcEZ3GbO9UZjuLb7vb9z+nJTPhvT3YRYkFh4q5LCWEEE8tdlFkUdF+vszfS7XVmfLya4GqaBCVqOsjeXL4SCI8vPkhs5oQY9du4c3WOkJ1bctaaCaDE5Q4U3TNNLGPXfjiz+h2fQw2hZlD4kGGKYcD4JAcFEvyNkpJTZQqgp9CcpCRwyXye7Mxp5T1gjyzTatQcU/cZARB4LOirWypOAM+IbJ4ArQNUTw8rAcmm4NQrzZTz+FBsdy+51ssop31ZRlM3/QRi8fegJ/TfbundzCvDLyEv/edwurSE3x8Zh97qwo6nVe1pYVqi7PAWw3EHiW2eTB5hfKx3tiez/GyJj6/JpV6RxNvpe/k2/yj1LezCHjh+CZa7FaeSp1IrKcfm6bczrjv36eopZ4t5dm8c3o393RTu3QWURK5a88yVyfng31Go1IowfHjlhIvDbiYnMYavi/JpMrcwqzNn/P12Ln4a43MiUvjTGMVH2TuxSaJ3LzrW14ZOIMeGtm24bqEgfxQKo+t+TLnMM+lTWZNcQYAH2Tu5Zr4/tzbcyQ7K+RI3uuntrvFkxs3P5M7lxznRFljt8tTQr14b3bfbpe7aeMXddtJksT333/P/PnzWbhwIRUVF+459GszefJkwsLCAMjKymLKlCksWbKE4cOHM2/ePJ544glWrVr1I3v5z/PPY5t45MAanj36A9/kHeV4bRlmR+cOr0arma9yDnP1tgUM3PQ+L2WtwRpzAHzb0kmVZZ7cs+qwSziB3EJ+//4V9PjuBR46sIJMp+FhdyQFGrl1qOzRY7aLPP9DFjbRwVMnl/P6mY1twgno4x1O/yi5lsbmgC92V1Hbcv7utHMxYGQgwxjDRDxtbf5HddRgcbqXn42UFEoF1EpyZ12xVITVmaILdIS5RoD8GD2CDK797civwuGsD5sbMYIQnTfZzRWsrpDrn9S+teg18l+Nb4+VklXVuXttRkQflo69EW/nyJy91QXcu29pp/X0KjXXxQ9k+9S7SZ/5CIvHXsdLAy7m9qRhTAxNJN7TH6UzqhRt8GX5xBvIunc2789KQeN0l9+cVcONS/cxYs1bfHhmbwfhdJb56Ts4VC1HtWKMfnw56mpXKu7vR9Zj+RER9ENpFnsqZXHX2yeY23oMPe/67VEqFHw+ag69nbPtTtVXMOOHf3PYGWV7ou9El+CpsbRy196lfJl9CKvDQZynP72c21WYmqi1tBLmIXuQHaopJruxmqkRPVy+WmuKMyht9z1348bNT+dEWSPHyzoPlgc4XtZ0XmHlpiM/O/IkSRIXX3wx69atw9PTE7NZ/gf9rrvu4uWXX0ajOf9E+9+CwYMHM3jw4A6veXl5ucwwf08+zz4ItR0LxhWCQJzRn14+QST7BHOmoZJ1xZlYxHNufAqR8QMl4hyefLylQXYKrwmHgEKmRfdAkiTWlcg1TM12K++f2c37Z3YzPiSRJ/pOZGhgTJfn9OCYeFacLKe00cKegjpuXbeVExo5jSMAFwX3opeyF339wqkOsHCy6DRWh0ROTSt/X59JaqgXwUbNBY3K8MGPpJZhKH2q2c/ujpfZTturUCFKIifFtuL3UEfX19EVBo2KSB89hfUmmpqVYFeBys64QNnLSdUuNTUoMIKhATG8ti0Xq0Pi0TUZfH51v077HBYYy9oJtzNzyydUWZpZXnSSfVUFDAmM7rQuQIJXAAleAa7nDlFCrVRQWV2Fw0NLsN6IwnkeNw+Jom+YF8PfkmcPbjxTBUlybZlRpWFmdB/mxPbjqcPrOVIrz+NTt6uDUgiCK4rkqdKi7m7WixMvdVtUzUej77Cvn4KXRsfS8dczeu27VJqbKWipY+amf/Ng7zHc23Mk7w6bxYwNH5PVXIPZYef9zD2sLkqnv394h5q97wpOUtoq/wNuUGnwUuvIbap1WTQYVBoMqt//3xY3bv5b6Rvqyc57O0eiR7qKPd38FH525Gnbtm3s2LGDnTt30tjYSFNTEwsWLGDDhg1cddVVv+Y5/mo0NTXxxhtv8MADD/zep9IloiR3yK0sSuflE1tYXniqg3CK9vDhr33GsnXKnSR4BvBx5WrwLXNurOIav2msGH8jqybeTPrMR7i350g8VW2pss3lWUz+4QM+OrOny+MbtSrendUX52QTTpxRQ4s3akHJW/2v5oW+lxOulaNEAUYtj01IJMRT3r/JJrK3sJ51mVWY7Y4u938+zLQV9KuQU5cO5OiQL354CV7kSDk0O602Q4VQvEX/CzpGYsDZzjMBWnwAKDHXARDtEUCIVn7tQE0BcwYE0iNQXv9YaSOfHyiiK5K9g3kyZbLr+ZNH1nbp8XQ+VAoFoR5eLuF0Fh8fG0Zv5/ti9QBRwbz4ARy+5EHmD5lJoM7oEk69fYJd3kuSJPHXg6td+3k2bXKnfZ/L0MAo0vzkiO2uynz2VxWed/2uiDb6smPa3YwMlsf/OCSJV05uZfaWz6mztPL1kNnc3mOoK9JW3NrAyqI2a4w+PiHsq5aPq1OqWDR2HkF6I08cWodNlL9Tj6WMx1vTucbNjRs3bn5LfrZ4KikpYdq0aYwYIc+m0mq1XHnllezfv5+MjIw/RGqsPcePH2f06NFMnDjxd6lxOpfvJtzArovvYcWEG/m/AdO4PmEggwIiXQaCZwn38Ob+XqP44aLb2Dr6RlL9wrhx5zd8eGavvIJ/iWvdvRkOznosJnkH8vrgS8m87HFeHzSTZC/Z28guidx/YDn37f8O67kRLWBItC8jep6tuxKgqCePJs5gmH98p3Vj/Tx4enISY+LbRMyZqha+OlxCcX3n7sbukJDIo82AU3VOQDRWEYdNspEunnS9lqLovoarOxIC2jl4O8VTUavsqi4IAsN85bZ8m+RgR1U2L8/o5brRv70rn9OV53qky1welUofH7lBYXdVPmtLMi743NojSRLvZ+5hwMo3aBbqXa8/2XMG7w2b5RqUuyDnkGvZtfEDXRG/U/UV7HOKnzS/MK5LGPCjxxQEgfudPk0ATxz+/oJFIECU0YfvJ93CIyljXdG8/dVFjF73Di+c3sHVcWm8N3QWgwIiO2zXxzeEk/XycGatQsU3Y69lXGgCm0uzXDVQ0QZfHmx3jm7cuOma42VNjHxrZ6dHdyk7NxfOz07b9e/fnw8//LDT60ajkZtuuomNGzcyY8aMX3RyvxaPP/44Bw8e5KmnnuLyyy//vU8HgCpzM5KpCW+1jovCe3BReA/MdgsBOk8qzM3kNNZgUGvo6xuKQhBYlnuce7L2kdncNkJFr1RzV//BrGoVOFMikVvbyvKTZVyWEupaR6dQc1PCEG6IH8SLJzbx8qnNAHyctZdjNWW8N3gO/to2L6ANZRnsFDaBsQ80+4Fdy4YDEqMDrSgEgQazjXNvqdN7BRPj58HXh4uxOiSaLA6WnCinV5CRXiFGdColNodIbWvXdVH1QjVNyGkZH0cQktR2jYKkQNcSzBFVBma1nBoOskdiMemx2kWKuhFpKoVAVlVHsWMXRZQCOCSgWa6hOVZTToJC/gclTh0FyIXkSwtO8FyvJK5OC2PB4RJsDok7lxxn4bz+qM5JgVlsDh7vcxHX7PwcgAf2L+fz7IM02cw02sy0Oqw02Sw02yzEevoxKzqVK6JTCdV7Iwhy+s4uylG2clMTd+xZwvfOtCvatuuLVofRYDXRbLVy2lTBR2dk122dUsUlUb2wOOxISCzOP+ba5vYewxAEAQkRSer6t5IoSQjA7Ji+vHB8E5kNVWwpy2ZlYTpjguOQFF2LKJtoR5Q6NzgIgoLbk4YxOjiOe/cuJ6+5FpsosqwknWUl6QwJiOLauAFcEtmbTWVZNFrNHD6behQULBhzNeNC47E6bDx6cI1rvy8NvBidUtWte7sbN27kou/u6Bvqed7lbn46FySe1q5dy4oVKxg4cCADBw4kOjqa+fPnc99993VYr7m5+XcZrNsdL7zwwu99Cp24bdcS8JPfox5egYwMjqWvXyhTwpMJ8/ByFc0erSnhXye3seecTq0B/hE81Hs0IR5erPD7AUrkrrM3d+QxOzXMtZ6HRolCEAAlzw+YQj//MG7e9Q0mh40DtQXM2vEhi0ZfRx/fUHZV5nHfocVIggQRpzHkD6fFDPuL6lmbXsmDY+KRRPDx6HzDHBvvT+9gI4uOlLq62tIrmzlT3cLwGF/6hHgyIsavy5vcK9lHzjoTcEloGi2qdJdAi1ZGMjkhnPcr94FN9iu/JnwEviovsqqb6RHY9byzwnoTycGdv4MRPjUU1JnkNJhNQ4PUQKiXHO3zNIcSovOm3NzAwfpc1GqRe0bGsrewnuzqFo6XNbHwcAl3Do/psE+FAFN8ezAqKI4dlbmUmBooKem6qPl4XRnH68r4x9HvGRYYzdVxaUzwjiJAIbCi8CR37F7q6r4TELg4PpbVzpKg4joLWoWKJszctnsJZmcR+G09hhJt9EWSJAQEljiH+ioFBZdFpzhTdpKrgPxcVChAEFAqFbwycAaXbPoUgMcOreHwjPvRqrruAPVQabqtjYow+BBl9OXwJQ8wP30Hb2fscl3XvupC9lUXMiwwmnAPb7aWy1FHtULJglFXc5lzEPG7GbvJcDY6jAyO5YqYvm6R5MbNj+DulvttuCDx5OXlRVZWFt988w0NDQ2oVCq+/PJLli1bxty5c4mNjSUjI4PPP/+czZs3/6fO+U9HZmOVa5aYl3o9o0NiuTVpKO9k7GJD6ZkO6/bwCuSWpCH092/z/jF4m9EZzZibdezKr2NfQR1Don27PNbsmL7Ee/oze8vnFLXWU9BSx8Qf3uPRPhN47dQW1w15TnxfZg3qz5wFR3BIEm/syMOoVTE61q/b6/DWqfn7pETWZFTy2cEibA4JuyixPVfulNtwpppRsX4Mi/bFqJW/eo32VnLtcorJS2mgl0c0u0QbZ2/X8co4mh2tlNmqAQhXB+Kr+vm/nKJ99bJ4Amj2ocSj1rVMEAQmBCXzVeE+rKKDXdXZXBTSm6cvSuL6r4/gkODVrblMTAwkMbCjc7cgCLzQfzrTNn5Ak71trqBGocRLrcNTrUWrVJHZUOUq5N5TVcCeqgKUgoDjnGhKpIc3/x41B38piNW7twOQW2NClEQeOLCSnCY5OjckMIpn+rXNhTtRV9ZuLl3CBTuMT4tIZmJYIhtLs8hqrOaDM3v5S69R593mvdO7uWfvdwB8NXouc+L6uZYZ1VqeSJ3IA71H897x7XyQd5C85lrX9Z9FJSj4YtTVTI2QRyPVWlp5+ugGQBaRrw++xC2c3Lhx84fhgsTTyJEj2bx5M5IkkZWVxaFDhzh48CCHDh3i4YcfpqlJTn8kJyezc+dO4uM718m4kZkakYw+0I/cphqO15YhOm+ojTYzq4syWF3UsW4m3MOLm6PSGBOd3OkmIggQGF5HUaacrnttWy7fXNd9nUuafzi7L76X2Vu+YF91AS12K38/us61fEJwD15InYFKoeSZyUk8+X0mAM9vzKJ/uDdPTUokwqfrol1BEJjeK5ghUT6sz6xiY1Y1dSY5XVfRZGHJ8TKWnywnLdybETG+7LHvQXQWhg/2TMYmObBJdtSAAoFQRQj5ljLX/hN0kV0d9icT7aeHs2PxGgOp9T2FRbShdc7GGx/ck68K5XTYzuosLgrpTc9gT+YOiOCLg8VYHCL3rTjJihsHoVZ2TIP18Qkl/dK/UWVuxkujw6jSokCBl67tr1lJawNL8o/xTd5RV6rqXOE0J7Yf84fMJEBnoNXaVnyfU9PKQwdW8X2J/HkEaA18Oepq17y3VruVv+xb7lr/ivN4e3XHxrIsqtsNFv4i+9B5xdPJunKXcAKYu/0rxobEEXDOMGEPlYYbY/pzX79xLMw9wpvpO11zGJWCgs9GzWFGZC9ESaKstZFZm7+g1iK7yN+UOIg0//ALvhY3bty4+U/xs2qeBEEgKSmJpKQkrr76akAucs3MzHQJqrq6ul/1RP9s3NdrFMFOP6qjtaXcs3cZLe1GqYR7eHfwbTLZbegU6m5/fXsHNFGeH4DNombpibLzRp8AgvWerBh3MzfvWcSa4raOpxSfUF7vP1s2SASuGxhJk8XBS1uyAThc0sCcBYe5bWgU1/SPQKXo+nwCjVrmDYhgTloYB4oa+PZYKXk1rUiAXZQ4UFTPgaJ6UPuAbzQa31qGevYm21TiStlpFCoUggKT1BbJ8Vd5d3tNP4UwTx0BBg3VLVZoCgCTZwevqB6ebTYWdda2OX3XDYzgQGE9GZXNnChr4vXtuTwyrrNZo1Gtxdiu6N/mEDssD/fw5r5eo7mv12gy6itZVniML7MOkt9aj7dax9tDL2NOXBqiU1B5aJSEeWkpbbRwtLyW3QanF5VCyeej5hBukN8Pq8PO3O0L2eE0kwzRezI7JuWC3pvPsg5w865vO7w2JDDqvNuE6D2J8/Qjt0mOJvX0DnIZhXaFSqFkdkxfroxJZUt5NmuLTzM9shdjQ+QfWgeqi7h2x0KXXUGQzsiz/Sd3uz83bty4+T341RzGBUEgOTmZ5OTkP0Q3238DkiQX976evt2VLtMolDzWdzzXxg9gc1k2fzu0llpLK7VWE4+nb2Rmczm39RiC7hwnckEBgRG1lOYEI0lwy7fHOPjAKGe9U9dolSru7zmmg3j6a59xnfZ994gY+oV58djaDArqTFjsIm/tzGd9ZhVPTEykVxe1RWdRKRQMi/ZFpRDQq5XszKtlV34tjWZnkZNNB5UxWCtjWFRTDT6VxKAE7KgF+esptht43N2okJ+KQiFweUoIH+6VU4XqqrgO+zwbgQIwtTMtVSsVzJ/Zh+mf7MPqkHhnVz5j4wMYHOXzs88lySuQv/e7iLsjB1AkWIgx+uLTRRt+jJ8HpY0WWk0CiAJqlcBXo69hbKgsOOyig5t2futy6vbV6Fk36ZYLaulfVnCCW3cvdj0f4B/BY33HMz0i+bzbBegMHLnkQSrNzVSbW0j1C0OjVLkK4LtDEATGhyYyvt3g4a9yDnPvvu+wOm0J4j39+W7CDQTr/zj1k27cuHEDv9Bh3M3P5/bdS5ix6VNePLHZJZziPf1ZMeFGrk8YiEIQmBiWyLpJt3SYbL+88CS3717apWO4X2gDHl5yPU96RTP/3Jj9o+cxOCCSWKNcxxRr9GNGRO8u1xsR68cPtw1lTr8wlE49dqaqhRsXHeXR1ekcL/1xZ9oAg4aZfUJ4bFIk+pgz4FkNtN1ks6tbyM72ZHvATAoM8SidESFHu3UUv8JXdkScL6jl98nW6EN2dVuaSiEI6JwCyuToOGy4Z7CRx8bL0SZRgvuWn2wTgb8AQRDo5xfWpXByiCLljrOftYDK4cGHw6/g4siezvMQuWfvcr4rlG0cDCoNaybdQl+/sE776o4NJWe4ZttXrmjXoynj2Df9L1zuKjaHstZGHt6/ivv2LueHkjMu3yWQo21xnv4MDoxCq7zw32N20cGjB9dw+54lLuE0MSyRPdPvdbmPu3Hjxs0fiT/dbLv/Fgqa60Aj33gF4KrYfsyO6UuU0afDmBajWsMbQy7hzfSdfJl9CJNop6ilngf2r+TtIZcRovfCoFLLKT8BohIryDwchSQp+L/N2VzcM5D+ET5dnoPdIaFSCiwYOY9vC45yXdwgQMBsd3TZmSUIAlenhTExKYB/bc3hdGULogSbs2vYnF1DjK+e6b2CGRjhjQMHtbZmmhwmmuwmTjXVcsjsoFU0k2kqxGSsBWMZ8coYfKpTyK8zUeO0MrAq9RwKHEW+rYbUehNNnm0ipqbFRqFFFj7NFjv5da2dzhOgyeLoIIrOYnWIHK5ohaACKJGjKl8eLOaqtHCUAnhomtEoVJhFG41WC2ecdgc2h4haIVClzwZDHbT4Utxg5oGVp/jb+ASU3Th4W+0OVMquo392h4hCUONwSNgdHeueHKKIQi1w777l5JpLgRgA7k+YxNjgeERRQpIkHju0li+dfk9ahYrl429gcEBkp3Z+SZLo6hR3V+Yza8vnLjF0S9Jgnuw7wfUdLDM1siDnMK+e3EazM638XuYevNU6JoQlMis6hfGhCZ2ilVbRgVHd0QncLjqwinasDrurg6/G0sqNOxa5Ou4AHug1ihcHTJVTx+2vw10w7saNmz8IbvH0O+Gt1qHQ6Ik0+PB46gSGB8VgddhdKQpREhEQXDVOf+s7gauCe/K3jE3sry7C7LCztTyHfw2ewZiQeJTtao92ORxsPiriECXuWnqSvfeN7FTcDOClUyEIAoOCIhgU1DZgN9rXo5OX0Vk8NEq0KgUzeoXw2YEi3tyZR0mD7L+UX2fi7V356PR2HL6F2LxLQdm923iE3pePh8wmt8pKkEHDR6cPsPJUJTTIhp41an+2ZNcQG+4BXkpQOugT7EVvo1zLdbqiiYSArrvJMiqbO3XEAeTVtKLWm8GnAiqjwaYnu7oFH50Kuyjhb9CgV2potJuwSXb8DbIAqG418X9n1rCy5BhEaCB7IDjUbMisYmSML7cO6zq1pFIIrhl156IUBJQKAYVC6PD5geyV9GHmXj7I3AOatuhLpCaUAJ0BnVLFP45u4N1MeaSNSlCwaOzcDmmwjjukkyA+VlvK9B8+pdUplK6MSeX1wZe4ok3bynO4f99Kzji799rTYDOzrOAEywpOYFRpuCi8B1fGpDI5vAeCIKAQhE4p1rOvGdVaNEoVR2tKuGLrF656Ka1CxYcjZnNtfNfNDu5uOzdu3PxRcKftfic2TLmNYzMfYvWkmxkeFNNh2YHqIvosf5Ve373CF9kHXTU/UR7efD1mHr7O9M43+UcpaensJzSsp4JQP/lGc7S0kSfXnUYUuzY6/LkoFQI3D4ni4P2juHmsFzpD27Bas0mFrTQOModCRTSdXDUBb7Wef6VegZdavhab5GCX6RBEZjCo9ge8rHLDgQTklijhzGCoD4RuvIouhFpbk7wbH9lAySHBvsJ61/KzUZQqaxPbq0/zecEOnji9SBZOAGorhGe61v+/zTkUdhMB+7nsqszjvrOdc5q29za/Vj7Oiyc288LxTYAsij4beRXTI3v95P3nNFYzecNHNNjkfU+LSObDEbNRCApO1JVx7faFTPvhE5dwUggCNycO5rORV3FVbD98NG2z8JrtVpYVnGDOtgWM/f49VhSewvEjNU/vnd7NkNVvuoRTuIc326bdxdz4/j/5Gty4cePm98IdefqDIUkSd+1ZRlFLPQB37/2OL3MO849+k0hQGDCoNdzWYygvndiCTRT5Ou8IGqHjx6hQCMwYquTj7+2IIvxray678up4f3YKfX5Fd1mHKPL8yfV8Ur1Dziq1ekN1BDT5AwKIKqiKIUEdy9T+OmK9vfDReOCj9iBE592hPiarpZwam5wi87NVML50JcXBAzhu7IvVIYJDA8W9mF+fR1xgKb2CvPBVGYmTPM5bFN8VLQ6nGPGugqoYADIrmxkQIXeuGZVt3XLPZ67osK1BpWFaaB8WFx0G31KoC6PV5uD2FftYdu1I9MquDSUvlDdObcfuFM33pA7g7Tw5grc3v546i4nnjm10rfv+sMuZE5fm8o/6Kfz14GoqzfL7PTo4ji9HX41dFLlp5zcsKzjZYd0hAVG8MGAqvXyCsYkiUyKSsYnT2VyWw7byHFYXpbv2day2lOt3fE28pz8P9RnD3Lj+neqgHKLIfftW4HBe3/CgaBaPvY4QD68LugY3f0zuXHKcE2Vd10CeqmwlNcztcO3mvx+3ePqDsasyn9PnFIPvrSpg+sZPuDG6P38bcBHz4vvz0oktAGwvz2ViaFKn/QT7Ckzqr2D9QQcgsKegjgGv7+BvExJ4cmIiqm5SSd3RareS1VjN0Zpy8luqyW6q4mR9WVtKR4D+EV5MGhRGoiaaLemtfHu0FJsokV0qsc4q8PZlMQQatV3uP0YfSLjO1zWoV4FEgimLMVMuYWNOOceK5YiL2OxDdjNk59lAUcUXHrn4ejuIDdAyOiSaJEPYj6Z3EjycxdSWtpb6AENbfc5FQSmcaio5dzMSjUH8K20WUQZ/Sk0N7BJzZKFo13I030rasvnc1KsfN8QPIUj3yzrEAtv5JM3t0ZsdYfkcK21kT0E935zKc9Uo3Z40lJuThlzQvnMaq1lRKHdYRht8+WbsPHRKNX/Zu7yDcArRe/Joyjgui+rDx1n7mbXlc5ptVlL9whgWFE1/vwieTruIVwZOZ2t5Di+f2OIa7JvTVMNde5bx3NEfuKfnSG5td45KhYIe3oGk18uRv7eGXEaIh/uG+mfhRFkjx8ua6Bva+e9A7yAP93gQN38K3OLpD8anWftdf747eTiby7LJaKjEIUl8nH+IdZVZPN9/Kr28g0lvqOBQTTGjguLwUHSOeAzuoUShb+HAcT3VDbK/0nM/ZLEpq5ovr0kjwlvXaZtzeSN9G+9n7qaotb7bddQKJa8NmMlY/94u5/AJ0XBpnxBuX3ycOpON7OpWrv/6CP83I5ktjQexSyJXRg4k0kPu9DOotLzR5zpezl4FFAFQb2tBTQX3Dk9kVXYuq0/W4LC2K0IWVUjNvtQ2Q20JHDpRhd5YSGqIL7EeAcQHdB2V8lf40pPeZNS2+Tv1j2jzj5oSLI83SG8qIULvR6xHIAEqX0ZGhLqE2fuD5rKrOocXLScpyNECAg1l/rwubOGDM7t4feDlXBJ5YT5L7RkQEMGn2fJ34VBNMY+OT+CaBbLH06d7ysFHXu/cAbs/hbcydrkiPH/pNRJPtZZlBSf4xPndM6o0/N/Ai5kTm8qKwlOM+/59lyv42fM5VFMMyOm8VN8wRgTH8GzaZGySyPz0HfzgdMYvMzXxxOF1fJq1n5XDruGs89i9PUdy556lALx7ejcfjrjigq/DzR+XvqGe7Lx3ZKfXa2pq8Pf372ILN27+u3CLpz8QNeYWlheeAuRf/c/1n8JzwNsZu3jx+GZMDhslrY3cuPMbEr0CANmdurCljmSfoC73GeQncu0kOJAJu0+BKMLu/Dr6v7addy7vw5X9undu3ldVwBNH1na7XKNQ0scnlGdSpzIsMJayRnOH5YMifVh2w0Bu/OYo+bUmKpqt3PLtURwRmeBZyzdF+5kemsowQyrBBKFXang8cSb/VuwC7EjA1zXr8TeomZEQx/T4WIqbmtlXWsHpqiZKakRs1nZfYVGJqdHI3kYbeylj2akykgM9ifM30GCyUdZooajeRLPVAQS0bae0Uq4qIAC52FoQBKaGpDI1pM2hu9Fs6xDRUggCowIT6D89hrHv7abVKkJdKAQV0IqV2/ct4nhdCQ/1nNjt+3eWvJY6/nJyHSODYrkjeTgAA/3bRNGB6iI+Gj6MfwQYyKpuIaPYDloD6Fvo7Rvyo/tvT7W5hU+z5OHHRpWWmxIHkd9cy917lrnWmT9kJmn+YVy9baFLBIFclB7v5c+ZhipXck2UJI7UlnCktoS3M3ZxY8IgPh15FdmN1byfuYfF+ccQJYmcphruPrKaNZNvRSEIXBs/gCcPr6PG0sqCnMO8MGDaBY+ScfP7cb7UXHdRJzdu/ky4xdPvRL2lFa2lYyv9V7lHXOmYa+MHuLqj7u05khHBsfzz0Ho2V8kO0lmN1a7tMhuq8NV07epstTs41lKKJhgG6NWcOulLa6uKBrOdeQuP8sOZal67pJcrYgRgF0VESeThgytdrw30j6SPTyjhOl+SvYOI9wwg0uDr6qiyOkRarY5OXWPBnloWzu3PLd8eI72iGYdDAQV9IDQbh38pK0qPsorjjPLtxfTAgfipPTEq9ZiRhZgdB28VruIq/0kk66NRomZ4WATDw6CwzoRWJZBT20x6TQ3FdVZEW1tkymKDY6VNHCttOv+H4VfKl2WFzPQCL13XRdc2h4intuu/Lhf3DGbxsTKQFPSwDiBTtReAd87s4GB1CZ8Mv6rLz8fmkGhSW7h63zfkt9bzbf4xIj38mBiWRLwhAJ1ShdlhZ3+VHIn767h4blssD/2lKgqiMujpGYTkbAYQJUDZdc2QQxRRCAJPHF5Hs3P23k2Jg1ALSuZs/YpGm/zatPBktpblcPvuJa6aK4A+PiFcE5dGmIcXzTYLWY3VZDRUktdcS05jjWu80L+zD7C2OIOHeo/hpYHTuDN5KFdtWUCFuZmtVXk8cXgd9/ceRbjem1uThvB/J7ZgEe18nLmXR1LGASB1kVGWAMX5yqHOk6ntbiCym5/P+VJzfUM93ak5N3963OLpd8JX64G/tu2XtihJLHcaHSoEgdt7DMXQzidncEAkXw25gm8rM7lv/wrXeqIkUWVpZkBABF3x14OrOxgaSnEKlKWxOGrl9vfPDxazp6COr+b2d6WujFo1C3IOcrhWTs2k+YWzc9rdKBUKLHYRZTc1RSqFgEbV+c4X5q3joYsDuGVZDlJ9MCBAWSJ6hRaTbx4iItvqTrK7PoPLItLQObNpHs7iawci39T+wJM9LmVUQA/Xfr11TUT66JhAIBCLKIrsKM9nWXYWRdUOaPGRC83bYdQoifbzIMJbR4SPnizHGbabCpCAVU1bGaoMpJ93dKdrqDNZCfbsul5rbv9wVp2qwGwXKS3x4B+XXsKLp9dgFR3sq83l4i3v8/mIefTxCe2wnYCDO/ctIb9dSvTBg8s5NP1B9Go1/fzC2VtVwOmGSlrsFub1j+DZDWcobjBDYyDhyjo8te3OSeq+fVZE4EhtCR+fkVNzPho9f+s7gb8fXc/J+nIAojx8OFRTTIWz+BvAT+PBlbGppPrKKUuzw45KoaSnTzAhHl486B9Oi83K9opcPjqzl1a7jQpzM48cWsOphgoeSxnPu8NncdWWL7E7U3oDAiK4OjaNO3oM45WT23BIIu9l7uGhPmNQKZRusfNfQnepOTdu/hdwWxX8QdhWnkOhs8NuangyUcau59LNix+Axjl3TquQtW+ZqYl6q+knHUdQimgic1BHZ6JWyZGFM1UtDH9rJ58dkCMczTYLTxxuGxT82uBLujWB/CmUmxp55OgSpPDTEJTvet1UEsUYxTiMSrn2yiY5+LboIFVmOVLkrfZgjK/seO6QRJ45/R1/T1/KG9nreS1rHQsrNvNm3jreyF3L/Lx17KjLZGRoDPNHT+beQb0ZMaweRcIhiMiA2KOQvAtlz/3MG+HD9YMimZAYwO09hjEuoA8AdsnB0xnLyGwq40Lw0au5sp9chN5kcWCuCmDRiJsIdhaNF7TUMXXT+3xXeLzDdp/l7mNFUcfOtrzmWl45JTcDnK1nkpA4XFOCRqXg1uFn06wCQvX55861R5IkHti/0lXr9HS/izhaW8Jrp7YDoBGUGNVal3DSK9X8pedI7u89in5+5y/CN6g1TI1I5oPhsxng3ybiP88+yJQfPiLcw4vHUyc4rwXu3bucstZGIgw+XBYlv/fFrQ2ulLUbN27c/NFxi6c/AGWtjfz14BrX89t6DO12XYNaw8jgWKDj7LVNZVkXdEyVTw3jRjbQM9QpXBwSdyw5zoHCehbmHqbcJAuYq2JSXcf7uczP2E6NtQUEmNhXx+MT2gbqbjsuEl89kXlhozGq5CjK2WSRWbRxc8REZoSkudbfXZvF6vIjrK04xr6mDLbWpLO9NoNtNenMz1vLg6e+YG9dFsFqP+6OmcIb/a9iXHwQCkMjqOw0OJp5t+B7V5u8IAjcHDGBwd7yOZlEK09kfEteS2djyPNx65AoV0Ru8fEy0vwiWTXmDvr7ygLH5LBx695FPHd8PXZnJHB50QnX9t7qtuL91UXplJua2NzuM81z+iE5fEtBKTt9l5bpKT+nzqw7qi0t7K7MB+SC8FuThvBN3jHX8mmRyaQ3VLiefz1mLrckDUGtUJ67q24J1Bl5vv8U7kwe5nqtoLmOVYXp3Jw4mDRn5K3eamJdyWkApoS3RRK3lP34OCE3bty4+SPgFk+/My02Kzfu/IYKp1gZFxLP5PDO1gPtuTKmrZBZ5aw52liWRWlrZ8PM82HwEHn5inBmpMrpOptD4uoFh/n8zBHXOk+k/njB8/lotJlZkHNQPp5Kw2sDLueO4TE8Oj7BlZw5VtLEqu1qHg6Zy5jAtmuvs7ayoeYof4m/iCvCB/+kZE6RuYaXc1byXuV3nGgqwF/tya2Rk3gp+Tpi9EHOdarZXNMmXBSCgrujp5Kkl6MmTXYzj6UvoshU85OvM8JHz9BoHwBya1oprjcRqPPkk8HXcVN8W5v+/NPbmL7lQ/Kaa7ghfrCrG/CsWSXAZVF9mPLDB5xytvJHGXy4OLIn+c21vHJqE/iXAnLx/0f7in7S+QVoDa5IVrPdymfZB7k8uo9r+friTHp4Bbqe37V3mauj7kKwiQ5XjRbI6cEpEclkN9VwwinOtEoV40MTECWRtzJ2utZtL6TcuHHj5o+MWzx1w8aNG7nzzjtZvHjxj6/8M3GIIvfs/c51k4z39GfR2Hmu8RjdcUVMKl5qOUpzNtohShJf5x3tNNPsx1AqBG4bE0BKuBz5yKtt5eAxPUhykXjPXziYdWHuIZqcBcpXRPXHx1k4ffeIGL69foDLLqHBbOfp73MwlqeiUbUVVy8o3caH+Vu4NWYciwbfw4dpN/FJ/1v4d/9beSp6Hu+n3MLzPeZwWchgkgxtNUUl1ipezFnGP3OWktVSRrjOj1siJ7oE2OKy3TTb2wSLWqHi5uCp9PGSBVS9rZXHTi2i3Fz/k691dHxbC/bGrGrnfpW8POBS/jXgUlcU52BNEWM3vIXV4WDV+JvxazcQ+K+9x/JF7iGymuTt4z392TrlLgJ1Rh7Yv0KONvqWuubUfbC3AKv9/G7eIEfY3hh8qev53498z5DAaG7vIUeJTKIdURJdw4lLWxu5aec3rCs+3aFm7nxYRQfPHdvoEl1eah1fjbmGKIMPTx1e5ypAv7fnCGKMfizKO8qxOjlFOjwo5oIc0t24cePm98RdMN4Fb7zxBm+//TZvvPEGo0aN+o8c4/WTO6jIhc3OVIWfRs9Hw6/AW6N3TbdvjyhJrodepWZObBofntmLRXTgqdLSZLdwprGKHZW59G9Xd6IUFIhC14JKp1RSZZY7/m4a58Xfl1hpMovQGAR19cwd0p/vCk6yIOcQgiCgUShRokCnUqNVKNEqVQTrPLk1aSgeKg1Wh9jBV8khinxwRp69JgBzowdhsbfdiPuFebHypkE8uDKdrTlylGdNRiV632nEKtIJtsnDYheX7Ke0tYF7Yqbgp5BrwSotDeyrz+J0RS6FZjnFphaUjPbpQ76pgkKL/Fp6cxH/yFrEaO++zPQfQW9tIictWTQ7zCwo2snlAW2fb6sF7gmfzsu278g1VVBtbeavJxfxROwVKEUtZTpLl+9jo9mOVqUkLbzNK2r5yXKmJAfRYrWjVgrMCE0j3iOEh44sJbe5mha7lfsPLePSyD58NeAqNjUUEOrhzauntlBqkiOIyd5BrJ1wK2F6L5bnn2RVkWxsGeltYGhKCIuPlVPWaGHJ8VKu6hcmd9t1U5smITEwIJLr4wfyec5BaiytvHRiCy8NuJgfSjPJbaolq6mGWdEpFLc0sK+6EAnYXpFLVmM1c+PSCD3HyNJst9FodXZFig5eO7Wdg07hpFeq+WzkVfTwCmRp/nF2VxYAEGv044aEgUhI/OvkNte+/tl/imxKL4kIXfx4ECWp27orSZJQnu93oLv+3I0bN78ybvF0DtXV1Tz55JMcOnSIHj1+eRqhsbGRxsY2P5SyMvmX9td5R8BPLijWKJQsGX89wwKjUSuUNNnMPLh/FZXmZgb6RzAoIJKBAZFonIIF4I7kYXx4Rm6JD9QZaWqWb+wbSs/waN/xeDojU2aHHYOq65EhArT5Q/nA09M9eGhJvvNE44lRRXDV9vex/kjk4WhdMV+Nnoufh7pDYfnqonSXueJFYckMC+vsKRXqBZ9f3Y/vM6t4ZFU6DWY7JoWOdN/+nJZSEUqqkPxK2EUmdoWFCSE9WFt6iqN1nVNKNsnB9vqT9PeJYnLQBDbVHifXWbu0veE4SX7e/CVpPPenF9DqsLKr8SR3Jg8jziinq0oazET56PkoaB53HPqSrOZKKq0NvF2ykn8mX0Wkj77TMQGqW6wEGDR46gRCfAXK6ySOlTZic4j46tUuG4jBQZFsmHAXz51Yz+e5+wBYUXSSfZX5PNVvMs8e20CFs1i+t08I6yfdRpDeSKvdykPtbCNeG3wJoUSw+JjcJTd/Rz5z0sJRCt1qJxDlYb0vDpjK4oJjtNptfJq1n6dTJ7Fw9FzGrHsPi2hnacEJlo+/gaymap46/D1mh50yUyNvZuzkr33GckvSEASQxXp9FXZJpNLczPLCUy7hZFRp+GzkHCZH9KDeauL/nG74AG8NnUmM0Y9TdeUcd0adRgXHMiYkHpBFXtfddu6xLW7cuPnj4BZP57B37178/PxcwqmlpYXnnnuOtWvXEhwczHPPPcfQod0XdJ/La6+9xjPPPNPtcqUgMD91Gj1VXtTV1qJWKLn7yGq+LZa7sFY7ow0A0XpvBvqFk+YTypSQRAb4hHGovpTc5hqG+0eyu6aIKnMLrxzeyKM95BZivdmBVtn1T28BsDa2taVrtVUQUATVkSApufXrk1gjz9P/7mRx/nHG+kYxM7RXB/H0+vG2m+Z1YX2pr6vtanOaTXbGR2hZNa83L+0oYu3paiRBgSgooS5EfnjUs6+hhH3V6ztFElKMoUTqffi+6jQiEofrC0lvKOWOqOEoAnrxesE2JODjvB0oQiWuDxvIe0W7cUgSr2Ws49XkS+SohtmKpdmKDng1aTp3nlpKiaWBrOZKnk5fzAepl7uGBrfHbrKzvymfpzLXU64xAHEAPL1tN38f0huD2FF0/S12JEM9Q3kifT21NhPllmbu3rfUtbyvdwhfps1GZbJSa6rlxdPbKGiRx9ZMCopnhEcIKoWD/qFGDpc1c7C4gU93ZTGzp3+3c/5ESUIhCKiBK8J783nBUeqtJj48sYM5kSn8vedYnjglz8u7dde3LB16NetGXMddR1aS0VSNVXTwz+ObeP/0bprtVizdCGq9Us37aTNIUnpSVV3N7YdXuubeXRyYQJrWn/raOj7K3OXaZlZIMjU1568vE52iqqC1npdO7+BgXQlXRvbhzrjBeKjUKM4TXgoICOh2mRs3btz8HNzi6Ry0Wi1lZWXU1NTg6enJpEmTMBqN3HDDDSxdupRx48axe/du0tLSfnxnwIMPPsgtt9ziel5WVsbgwYP5bvwNBIWFEqz3JMIgp3skSWJjWZZLOJ1LgamBgpIGlpak80Lmdp7tN5lDB+Xi4SCDFx71alodNhYUHiPaN5AbEwdhqlGiUHX9MQuAxsvoOvby7CwIymDcFt8AALD+SURBVIMWbzB5Ud0ElCZC+BmOzXwQf60HLXYbNtGOxeHgUE0xt+2Wa8L+dnIjF8f3I8CZ2jlSU8zOGjlV09snhKnx/br0gAJwaKzo1Eq8fODTa4K5/+FHOGwLJN+7JxbB6dPU6iM/tC0QfZJ+wQEM9k5kRkQfQnTy+zervphn0ldS2FqLWbLzRsF2RvgncF/iJN7I+gGAj8p28Urf2URU+1JsquNAYxHvlx/k5thR4NCiNcpCJwxv3h04j1sOfkGVpYn01nJezN/Oy/1mdTr/dbUneCZjudzB590KFbJ42pzTSJ7hW9aMvx1vTUcBdYnPQAaEJPHkiRVsKM10vT40MJolY27EqNLioVGyoyKXd3NlbyadUsXbI67Az+iLWqnglUv7MOF9Ofr4wvZi5g6JQ6/p+rMWRcklrB5KHc/nBUcB+LTwKDenjOAvfuPY01DC6uIMqiytTN75BZ+NmsPiiTfwwek9vJOxGxGJmvNYYhhUGv498iqGBkZjEx08eXQ96yvktLS3WsdzfSfh4+eL3eFgWelp1zXd0Hs4Xhq59k3qJj3nkETePb2bvx5Y7YqEvpy5kwVFx1k4Zi6jg+O6PS83bty4+bVxF4yfw7Bhw9Dr9Tz//PN88803eHt7s2HDBh588EG2bdtGcnIyb7zxxk/en5eXFxEREa5HaKhc1JziF8qAgAiXcDrL1rKcn7TfVrvt/9k7z/AoCrUN39vSe68kpBBCAiSU0HvvVQQBERWx96MeFftBxd4LKiKCIkjvvbeEklDSC+m9l82Wme/HbiZZUkSP5Xy6t1cud6ft7OySefKW58XP1klqcd+ec40Fwb0AQ4LjtfgD3H7sB6puwv9JEEXeunKEA/mpIBexDUxHoTBGFiq96KKNItLZC28bBwLtXAhz9KCHizeLQ/uyMLg3YOiqa2qFb9BpuePEj9LxH+g66BeH9bbEVlQTUXmReXUHeGlcF8I9mofk0miLa95glne9jVk+fSXhBNDDyY+1/ZYw1z9GWnayLI0jJcks7NTfeG1E3kvdzwPBI6Vt1mWfZdLxD/gocw8XK7KlontPS0d6Ozd7KR0rSZUsDlpyuCSxebkIyIyPG23IrCslvqL1kGEwpFs3DruDV7uNppOtE7MDerJ5+J04WlhR1FDDnSfWM2rv51LB9jPdR9HZ3kXaf3iIG1MiDAX91ysaOJbRdmTvRiKcvRjpZbBmuFZVxNNxO5HJZHwycBZdHQ1p3Aa9lsXHfyStupQnI4ezceTt9HH1w9PKjkgnL0Z6hzC9UwQPhQ/i1ejxfD5gFicnPkh/d4PBaGJlEd+nG2bx2ShUbBixEE/jsON9+SkUNBhS2dM7RUrCqT1EUeT5C3t45OzWVink/Ppqxu79krXG1zJjxoyZPwOzeLoBOzs7li1bxgcffMDHH3/MlClTpHVKpZIRI0ZQX1//h73+0rD+eFvf3GiDCGcvyXxQL4ocKUznofBBUgLjZHEWHyae4EJZ2zfvGm0j6zMvcceJH/n5uqF1X44MW1sBvW8iTXUmqSkOfH02u81OPn9bJ+lxk5BbmXKGpKpiAHq7+kkC69eiRGB6pBc77o7hx4W9JBFVVqdl7przXCtqPXbFSqHiibCxvBR6C/ZKw/lcqswmqaaI/i6G6EReQyVFjVXM8u0l7acWtBwuu8qS898x6/RnfJt1isfif2RPYbNx4/0hw6RxNC1Z4D+QHk6+hsuVF9Y8X8SlgDn+fYhxMwgKURQRbhBfMpmMOwJ6cXXaM6waNA9LhZKPEo/TZ+c7fJ9xXtputHcoT0QMa/Xad/RtnoF3NP3mxBPAij6TsDLWz32ZcpZVqbG4WtpwfOID3No5CoB6vZZ7T/1MUUMNvV39+HnkIs5NeYSdY+5i1eBbeaHnGP4VOYJFIX2Y6BeOi2Vzl+TXLQZcr+g7mUEtvMJWGWfrgWFETEfoBD1LTm3kzRZ1U091H8GlaY8z1sdga6ER9Cw8/gPPX9jT6vqaMWPGzB/BP1o8ffvtt3Tv3p1OnTqxcOFCkpIMqYQnn3yS+fPnc+7cObZs2YIgGH4h19fXs2vXLm655Y+bAB/s4MbpyQ8xO7AHXR096OnsTV83fwZ6BDLYtRPjfMOY4t+NT/rPpJuTJ492G0IPZ0M0K6W6FLlMxvoRC/GzMURk1Hod36WfZ036eep1WjSCngtleXyZfIYXLu7j3avHSK4yFFUrZXKcLawNNSoOZTj4G1KCInDPhgRuW3uRinqtyfnm1jV7S/nZOtKo1/HeVUMXlQwZ3wyei4Xiv8sOy2QyYjo588PCXvTxN7yvarWOZ3YmcTqros19Iuz9+bTXApxVhhE4sRWZhDt4S8XI32Se4N7g4fzQ7x5u69QPJ1XzjT+7vpyP0w5xuiwDMBT0PxUymds7D2j9QkCwnQfrB93NBHGmIbUI+DpZEL/oNp7uOh4rhYrEqkJ67HidrtteY/axr1l+eS97869RUN/cTHC4MJUBuz7g2Ys7JXsHdytbvhgwmx2j75KaBVoyNMiFpqBeU8fizRDt6stXA5u/x4+f28ap4iyslSo+GzBTMkYtbKhh6amNqPXa9g7VisKGGqlWz93KlnlBUdK6vLoq9hjTlMH2roz0DmnrEIAhgnnL4TV8YxRicpmMLwfOZnnvCUQ6e7F99J083K15PMjyhIPMPbKWep3mps/VjBkzZn4L/9iapxUrVvDVV1/xzjvvIJPJePzxx4mOjmblypUsWLCA1atX4+/vz5tvvsmwYcMYMWIEGzduZPjw4cyZM+e/fv0GnbbVL3mtXo+9hSUeVrZ8N2SuyTqdoKe+qhZnF5cWywRAxmu9JjDr8Gq0gp5PEk8xyieUbaMX81r8ATZdN9RPnS/LI6W6FI1eT6Oga3U+oQ5uFKtrKdMYomrB9q5snTqdL4+X8OHxLAB+upTPqcxyvpkbxTCjp1GOcaQMgKeVA2vS48g3pmRmBnSni4M7giiiE4R2/asadXqTDqumAJcoQq1aL6X8ZDIZH8/szr+2X+N4RjmNOoHHt13lXyOCGd7CYwmgskGLt4MzT4dM5d+JPyAC67LP0ds+hLiaVKp1aj5NPc7dASOY7zOMOV6D2ZN3jdjaJC5UZUq9XU4qW5Z1mYG73JWS2ratCopq1Ly0N1kSLwq5jJfGdEWtE6lSa7FWKfjg2jHKjdf2dEkmp40DnokFL0t7/OyciSvLlo6pkMm5p0t/nu85BicLa/QiUmpQL4jSAGYnaxU9vB2Iz68mLreKmgatyZDnJgS9CDeYhd/aOYqEigJWXDmCVtBz25G1HJt4P/62TqwZMo+Rez4ns7aci+X5PH5uO2/3nWKSfq3WarBQtHY4/zLlDFrjud7TpT9KmRKdIKITRValxkpWHHeHxiBDZhLR1Al65DI5lZoGZhxazYliw3WyUij5ZtCtTPIPp07b/O/m1ejxBNq68FTcDnSiwMbrCSRWFRPl4iNt8/2w29r83MyYMWNKQkENgz860ea67t4OfDa7x598Rv+7/CPFU1FREcuWLePy5ct06WII/QcHB9OtWzduv/125HI5t912G8uXL2fBggV8//33lJSU8J///IcZM2b8Luegkstbjb4QRdqtDZLL5chksja7qUZ6h/Bcj1G8dGkfOlFgyYkNTPIPZ2ZAD0Z6h/DCxX2UN9ZTozW9+QfaOTPGpws+No58nHiCKqNnT3cnL/aMXYKntT3vTHGnX4ADj2xOorROQ26VmnFfnuFfw4N5eVwYeUZXcxdLGxwtLXjn6hHp+M/1HIWFwiCYHKxUyNspe/JysDJJhzUZfypkMgJdbLBUmYqu9Qt788iWK2yIL0AniLxxMA0blYIFvZv9rRwslbjZWRLm0ZUkdX++zTxDo6Clnjqs5SoaBC07ii7wYPhAOtkaBKmzdRSP2PWjoKGKbXnxlKhruTN4ID7WTpTWavB2sEQr6Pki7TiFDdVM8+tJuJ0PT+3IJDanEgBLpZzPZnVndBeD/YGthQJHayUnSw2F03JkCDe03Rc21lDY2JyCHOzRmTd6TaWvR2trBwC5TDT5HgwPdiU+vxq9IHIyq4LxXT1a7yOnze/Oa9HjuVJRyK68JEob65h/dC1HJxgE1NZRixm862OqtY3szE0k2tWXp7qPkPa1VqiwucEGo06nYWOWYYaflULJA+GDpG3q5HLWpBtSkUqZnDtC+7b6vsvlcrR6PVMPruJ0iaHhwFFlxY/DFjDYq+0xQXd1iSHC2ZNbj3xPpaaBq5WFXDUOOwazeDJj5mbo7t1+uUhCQesSiX86/0jxdOHCBRQKBcHBwdKy4OBgPD09GT58OPfccw9DhgzB39+fbt26sXz58r/wbG+OxyOHsiX7CpfK8ylS1/JNaizfpMYiQ0aEkyeOKisya8uxVaroZOuMIIpk1pSzMuWsyXH6u3fi5+F34GltR3ZtBbce/Z7zZbksHjmI3BRP9iaXIoqw4nA6B1JKuG7XCCrwt3Hkp6x4Mowz2Cb5hdOzxV//vycqhZyPZ3RHpZCz7kIeIrBsTzIns8oZEOBMbz8nnKyav9pPhI/maHEqmXVlXKvJZ4h7CMdL0tAKet5NOsj7vU3TsN7WjiwNGdrqddV6LQ+f38ChIkPa6ceMeKxye6GuNXTS2Vko+GZuFP0DTIc6XyjLkaJOMzr14NWek7lYnkNsaTZXq/M5V5pNlVaNj7UDy3tNYmanHuh/ha3R8BBXPjhuiNAczShvUzy1h0IuZ/WQuQzZ/SlJVcVcLM/n7pMbWDv0Nro5efLN4FuZc2QNgijyWvwBwhzdmdYpst3j/ZBxURpSvTC4N+5WzcX+h4ozyDWK7emdIvG0tm/zGM+c3yUJJ29re3aOvptge9c2t21ilHcoJyc+wNyj33O5orDDbc2YMdOajqJK7UWj/sn8I8VTaGgoarWa119/neeffx6AV155haFDh/LFF18QEhLCl19+yauvvvqHncOw3Z+icHXEWqHCy9oeb2sHPK3t6GTnjI+NA97WDoQ7eZgU4XaESq7gpxELeeLcdg4WpFKvM9SoiIhcafFXeJ1OS6KxmPtGxvh04Ydh87FRWLAnL5k7T6yXPHq+zjzB09EjGN0lnOd3J9OoE7iQVw2yHuBYjLWNFW9dbnaMfrbHqN96aW4KuVzG0yOC8bSz5L1jhtqkPUkl7Eky1G/ZWyoZGOjM4M4uDAhw5sWIqSw+sxpRJnCuLAs3S1tKG+vYmX+Fu4MHEen0y0Lv6UtbDMJJBCo9oTgQtdZQlC5Talk+I6iVcALYX9BsRTDGuysuljaM8g5jsHsILjYWVJSXobOxxNXSpjm1+SvG7AzpbKh7EkU4knbzdU9NOFpY81afyUw5+A0AP2XFM8I7mCVd+jPapwvLe0/kGePg6ntP/Ux3Z2+C2hAzOkHPF8lnpOcPdzN151+b3TyIeElYP9ri65RzfGicd2etULFz9N30cPE2SdW1R5ijBxemPEZefVWbLv1mzJgx83vxjxRPISEhPPvssyxbtowtW7ag1+vRaDQcOnQIR0dH5s+fz4ULf2zrc1FDLdQbUhbpNW3f8CzkCm4LiubRiCGEOPyy0Z+/rRM/jVhIo17H2ZJsDhaksj8vlUvl+Yg3pIrkMhlhDu70dvOjl4svvd38iXHzRyPoeTpuBx8lnWx1/M+TT5M5exTDQ1y488cELhfUgKiASm/OnANUVuBkQViASH+PgF9/UX4lMpmMh4d0xt3OghWH06hsaK7lqmnUsTe5hL3JJS32MNzMGxHQKWSADuR6/lWRxNxwPZ1dbXCztWgzvZVfreZ4ci1UdoU6Z9BZNK9UqhE7J/B8ygX8XBfSx7WTyb5lmjrpsbulHTcik8lMIjS/FmcbC7p52HO1qIZrxTcXXq/TajhcmMbevBT25ie3+g6eL81liXFG831hAyTxVKfTkFhZ1KZ42nT9CllGR/kxPl0k24MmclsUx8eV5jLGx3QA9vtXj/N4bLOT+if9Z9DDxZtfg0wmw69FB6gZM2bM/BH8I8UTwGuvvcbgwYPZv38/oaGhLFq0CGtrQ/rFxcUFe/u2Uwq/F51snVDaOVKja6RUXd9K3IChBfvbtDi+TYtjsl84SwN6M75FwXh7WCqUDPUKYqhXEE91H0GtVsPxogwuluXhbeNAlIsv3Z29UMkV2CqbRUByVTG3H/tBGtYKMKNTJDKZjE3XL1Nl9HIa6RPM0Qf68+K+ZD46mQ56Y92L1hpKAkkugRHlp1gc04kFvXyRt1fs9DsxL9qXOT19SC2t40JuJedyqjieUUZ5fXsdYnL0egAV6FWk5cFreamAoVaql58jffydcLO1IDankrPXK8itUgOtBWH/QHtq3bO50tBAtRbmn/yWlf1vY6hHcxfZMM8Q1mbGAbAlJ4FBHr+/oaObreEzqG3UmxSU30hiZREvXtrH9pxr7Y7diXTy4rGI5rTlgYJU6XGgnTOjfEJb7aMT9Lx95Yj0vOX+Tfy761AWxG5EEEWeu7Cbro7uzAjojiiKvJZwgBcv7pO2fbbHKG4P6dPxm26DfXkp7MpNNIk8fTJw5q8+jhkzZsx0xD9WPAGMHz+e8ePHmyyrq6tj7dq1rFix4g997fnBvXHyckeODKVcTqNeR7VGTaOgp1qrpkRdy7HCTKlWZkduIjtyExmQEsC/IoczyT+83e61G3GxtGFap8hWtSpN5ouiKLIqLZYnY7dL6T5rhYr3YqZyV2gM23OuscnoA7Uz9xojfYKxUil4fWJXPi5bi1jjjFN9ZyrLbEA03LSPZZRzLKOcdRfyWHNbFM42FvxWarWNJFcXk1lbRi8X/zajHgq5jK4ednT1sOO2Xn4UVDVQ1qDjZGY5CQXVqLUC9TodZ0qyQJBho7CiXqsFnWWz+AOqG3UcSS/ruO1frsXSsZY1U0bSy9eZgtoIXkrcwsHCZBr0Wu48/T07R9xHmIPBwHKsd1ccVFZUa9Vsy73Mq1GT2hzz8t9gb9V8vJpGHU7WpsfPqavk1YQDrE6Pa5XSslIoGeoZxDjfMMb5hhHm4C4VcmsFPf+O2yVt+1qvCW2e+4asBGmO4SjvUHq3GE7dxCjPYN7sPYl/xe0AYOHxHzhm58wPGZd452pzynd5rwkmhek3gyiK/CfhIC9d2tdqnVk8mTFj5vfmHy2eWlJQUMAPP/zA559/ztSpU5k2bdof+nqvJxyE3I6jWwqZjL6ufmTXVVJkrD06XXKdmYdX42ZpS3dnLyKdvQh1cCfGvROBds6tBFWDToNC1bbI0gt68uuqeOTcVra3mKHX3cmL74feRrijJ6IIIzxDsFIoUet17My9xvJeE6Sbq4uVFWWyMtx8ZXTt6sSZtAZDPZDa8N72p5TQ691jrJ4XxdDgtlOPer0ICsMN/XptOXVGC4cSdS0D9r5DTn2zl5Ot0oLNw5YQ6eSNWqtH1U6EpU4r4OtgyZye3szp2Zz6GXf0ECWaGqwtbPG3tCe5uhC5xobH/G4h9no1iUW1lNSZ1tdYKeV087RnSJALZ9UXOFOfQKMMDlY6E+w6lEYNvBN1C8/Gb2FXwRU0gp6Pko7zes/pVKm1WCmtmOAdwfrs81Rp1axJi2NeYF/qtTosVQrUOoE6rWkUSKtr/70JgoDyhtSivWVz52ZFgwZ7K8Pz8sZ6Vlw+widJp0wsKnxtHJnWKYIxPl3o7eqHi2Xz+JiW23107Tgp1YbU5wD3AIZ6BkldmdVaNXKZQWC1jDo90m0war2ulRu7IAo8GD6Iq5WFfJsWR71Oy+Bdn6DWN7/eezFTeSB8IAKm++rQoRTa/nWlFXQ8cn4XnyadanO9GTNmzPze/G3F06ZNm1ixYgVlZWU89dRTLFmypMPtPTw88PHxYevWrYSHh/9JZ9kxelEktiyXAFsnFgb14nxJDtdqDDey0sY6Dhemc7iweZyLvdKSSGcvFgT3Zr5xVItSZYWdyrLVsUVR5Lv0yzwZu13qjgJ4pNsQlkdPwKrFPDwHS0tGeYeyMzeR7LpK0mrKiHT2AsDV0payxnoqNPUM9gvkTGUauOXxaMBk1h1TU1yjJb+6kfErz/GfCWE8MSy4VRrP1uhLtOLKYV64uIcxjXXYYnC4bimcwFBzs/j095yY8BBBrraSFcKNWKsU2FgoWi3v6uhJSUkNZZo6pvhFklxTiGBZT1QXgbv69sDRSkl2ZQOrL6eQWlHBkh6R9O/kSr1Gj4uNBQkVNow7eBkRkS8yjhLg4MBsvz4426j4cuAcondkUtJYx+6CKyzvNREfB2scrZQ8GD6Q9dmGNv3P049zd1gM1ioLbFQKNEo5NirTc9XJZajaeW+CvLVlhVPLyJNGT4Ney0eJJ3nr8hGqtM1eTC6WNjwVOZy7u/STIkhqvbZNi4yyxnqpeFsuk/F6n0nYqJojiHKZDFuliidjd5Bt9Pua4NuVkT6hCKKIUm56/gqZHJVCwUf9p5NRU86xogxJOMllMr4ZNIfbgnpJVhUtsVdZIW/D01ej13HPqc2szzIUo8uQ8Xbfya3qqcyYMWPm9+Rv6TD+9ttv8/jjjzNv3jxGjBjB0qVLSU5ObnPbS5cuodPpUCgUzJ07908TTo92G8ILPcfwXI/RPBU5gse6DWVOYE/uCo3h9pDeTPbvhtIYRbpeV8najIsMdPXn+yHzmOjXVZoT1pIaXSOnS67zwJlNXCrPb/e1s2srmHLwG+4++ZMknHxsHNgx+i7ejZnappP1FP9u0uOduc1RqqZuwApNA14txsrYONbz/eIuDAgyRKD0gsgzO5OYviqW8nrTyI5O0PPAmU08f2F3q5SSs4U1gz06c2/YQPq5GQqx8+qrmHV4lRSh+jWE2rtLj31tmufinTSaMcpkMqys9Kwq28JB3UEOVceaCLQezr68GjVJev7spe1syTXcuC0VShaHGGboaQU936Y320BEOHkzxS9COv+mGqjfC4cW1gyXiovou/0Dnr+wRxJONgoVT0WO4PK0J3kwfPBNpQ3/E3+ASmOUaUFwb0kwN1GlUTPr0Hd8ZbS7kMtk0rigjrBQKNkwfKFkP6CSK1g/bAELfuUYn1ptI9MOfSsJJ5VcwQ/D5vNYxFAijVHZG8/ZjBkzZn4P/naRp+TkZN59913Onz8vDeE9cOAADQ2tB+SWlpYydOhQxo8fz7p161Aq/7zL0cetE+6epr/Y06pLCbRvbnWf5NeVD64dJ6mqBAGRr7IuUCpo+GbwrYAhrXW5opBLZXmkVpcSV5YrOX6/c+UIa4a2Nges0aoZvOtjChqau7LuCo1hRZ/JOLVI3dzIZL9mUbkxK4EnIoahlCtwNYonQRRpGTC4VJ7PNP8I3rulM9+dKeazY4WIIuxMLCb6nWOsnNOTsWHuHC1M5/Fz2ySxJ0OGo8oKXUMtfraO/Dz7RZRG8VLWWMfQ3R+TXlPGhfI8bj22itWD5uNtc3OzAMHggt6EUtYc7TlVmsHDoYYb/578a1Lq6su0U8wN7I2XRXOh/t0hA6jRqnnz6gEAnrm0mcz6Yp6KGMWi4BjeTzxiKPZPP8t9Lfyino4YxfZcw6y8d68dZZZ/NPD71D5ZtjjM4iNbwbHU+B7l3N2lH49HDMXHxhG1XsuH146TUl3KvWH9iXRuu5utoL6ar1Ka59PNCuguPS5R1/JVylm+TjkniW9LuZLPBs4k2rVtY88bcbWy5diE+/ku/TxjfLqYOILfDMeLMrn31EaSjSlFW6UFm0cuYrQ54mTGjJk/gb9d5GnNmjU8++yzknDKycmhrKyM+fPnY21tzcKFC6XBvm5ubnz22Wc4OjqiULRO8fzVdLJzZkXfydzdpR+WRjfybTlXqTZGA9yt7BjpHcJ9XQeyashcbm/xl3tZY9vDi+t1WkrUza3zk/278cXA2R0KJwBvGwcGeQQCcKWyUKpx6dHi5vtF8hnJNX1XbhLvXztOUlUxk6PteH6qGy62BnGaW6Vmwsqz+Ly5iZGbv5eEk5VCyY/DF+BsPBelTC6lkxr1OmTI2DR8Mc4WhvVnSq8zaM8HHC5s7gbriNiybD5Ibh4we6S4eT+9cX5hsbqGFdcOmuw34+hK4sqvmyx7tOtw7utimKsmIPJZygkG732f9VkX0Rlrfep0GpOOtm5OXkzzNxTt5zdUsTvvGv8NekFgV24i84+t5Y2Unc0rCoJAq6Kvmz8J05/go/7T8bCyY2v2FXpve59nL+zm27RYBu76mEfPbaW8je9KtVZtEgWcfvBb7jv1M0+e20701nd558pRSTh5WtuzZ+wS5hgHCt8sntb2/Cty+K8SToIo8GTsdkbs+UwSTm6Wthwef69ZOJkxY+ZP428nnoYMGcLMmYbumoqKCqZOncqYMWNYuXIlK1euZNu2bTz55JPS9vPnz2flypXtjkX5q1HI5MwIiGSCX1fAEOE5X5bbars6nUYqmFXJFbwaPb7VNmC4YX3Yf7o0S25HzjUeOrv5pqbRv9t3ipRKfC3hILGlOTzSbSi9jJ1V5Zp6LOUKaUrdD5mXuOPEeiYe+Jr/pG9CFhKHnXOtdLyiEgtI7w3Z3Yi0DuKrQbdysihLMubMr68mdNN/cFn3HI7rnsXnp5eYd2wNr/eeRKCdIRJU2ljH9MPfsPzyfkkAtcX5shxuPfYt9XpDqi/E3o2jRYaRKSqZgv9ETUEURf51YSsVGlMxUaVVc/e5NezIvSItk8lkvNB9PK/0nIiNwlAHlFdfxSsJeyTR8XTE6Fbpsfu7NA+y/Tr9DL8FURTZlZtI1LZ3mXLwG37MvITGtghsDd1u6CwJrBjMntFLCHVw43JFAVMPrmL+sXVcr2uuIRNEka9SzhKz40O+TD6DroXQC3P0YNXgOXgbU7EiIhuy4vk2LVaqU7KUK7gjpC8nJj5AX3f/3/Refg2CKPDAmc28f+24tGyIZ2dOTHyAPm5//OubMWPGTBN/O/E0btw4fHwMf8mmpqYyfvx4Nm7cyMCBA1mwYAHPPfcc27dv/4vP8tfTzclTenyuNKfV+tVpcVQYIwHzOkfR2611q3gTS7r055vBc6Si48+STnP3yQ0dig+APm7+PN9zNGAYUrv4xI8oZDJ2jr5Lak2v1WmwVVpwoxQVECnTV1Hrcx78roFFizRqtTtXYv1YsC6OD+NjJbsEjaAnv6Gaen2zX9O1qiLuPb2REV4hjPU2CEoRkTeuHCRs63JuO/4d7187yrmyLBqM+10sz2XOsVXU6gyz/ULs3UmrKZWO+Vbv6fR3D2RT7iX2FyQB4G3twMWJTzPeJ1w6lyVnfuTrtNPSfjKZjHtCB7Fr+AOM8+lq8n4HewRxX9ig1tfQ1Z9oZ0Nq63x5DhfaEMIdkVCez+SDXzHl4DdcqyqSlnta23HPKCd8HA1CLqtY4F87knj4zBb6bP+AY0UZ0rYTfLvySvQ4HFUGd/RKTQOPndvGwJ2fcKywebtbOvfkxKQHeL33RDxa1Ng5qqx4LGIoZyY/wscDZvyqtOlvpUk4NY0TUsrkfNp/JgfHLf3F0S1mzJgx83vzt6t5aklMTAwxMTEmyxwcHPD09Gxnjz+PInU12nrTVNn1ugoKG9p2iLZuEcE4WZRJSUhf6Xm9TsPHLbqiHggfhE40RBH0goAgtK6rua1zNBZyBXecWI9W0LM6LY68uio+GzCLTnZOyMW2dfXj3YayPz+Fk8VZpNeU8a+4HXzafybbR93JqL2fk1hVTK1Og5PKikl+4WhFgRJ1Lbl1VVRp1ZQ11qN3KsHSpRK/xi5kZTqg11gAMqj0gkoPrji4Eqa/hqWshgBbZ5wtbXC2sKa8sZ74CkOKb1XaOXysHLg9qC/rMs+jEwWK1bXsyL3GDmNBu1ImJ8LRm8y6MmqMwqmzrRtpNc2u46/0mMR0v56kVpXy+rU90vI3ek7HQWnDh73n8LLFLtZmxSIi8tylHeTVVfFk19FStNJBbssXfeextyCR95IOY6VQ8nb0DNRagSq1tpVh5e1B/bl4/mcAPk48wWuho1A06ky2adTpTTrqChqq+U/CftZmnjcxVB3mGcSTkcMZ6RWCDDn3BNUw+ONTqHUC35zJA990cDZsH2jrzBORw+jn1ok6nYZBIwL4OOkUW7KvIAJXKwuZsP8rpnWK4MN+07FSqBAEkTtDY5jbOYoNWfHIkDEzoDt2Kks0eh1iO2NQ9ILQSowLomFZrUYj1bG1td+N3aGCKHDf6c2sSouVPtcfhy9gWidDAb4oim2azDYhayXlzZgxY+a/428tnm6kpqaG999/n2XLlv3Vp0KogzueTqYiLrGyqNWU+iZslRZ0snYku6GKyxWFWCtVUn3R7rwk8o2jL2YGdCeqRdGuXhRQtGOmOSugB/YqS245vIZGQceBglSitr3Lf3pN4IHwgW2acFqrLPhuyG1Eb3uXaq2ab9NiGeEVzPzg3hwafy9TD64itjSHSq2a7bnXCLBzxkZhga+tA12U7lgrVOhFgWOFGaQLVyFEBuU+UNIJ9BaAnDzrEPKsQ/DUV/Bm5+Hc0tMHK5UCQRT4Mvksz5zfSY22kXx1Nd9lxDLWpwsKmZy4shyTei6dKBBfmSc97+roQVKLuX5v9JrEg+FDEESB+ae2SCm9JaEDmBHU3F346YCZ+Ns68oaxQPzT1ONU6ur4oO9MLBVKrFRybCwUzA3uydzgnibXSyGXYWth+s9sXlAUy6/spaSxlm25l3k6aCAeKlMPLLnMYFVQo23ko8RjvHftqEkErouDG2/0nsRkv27IZDJy6ir5KSuejVnxqL00kGss8C8Mwc6ljsVh0SwI6cWJokwmHfya8sZ67gjpy3M9RzMzoDufJJ3kTEk2AFuzrxLm6MGzPUbhamWLvcoSF0sbHosYZnKOoiiikMup0jSwOzcZS4WC0T6h2Kus0IsC8htEi1wmRyGTo5TL2xyBY9jIdJ0gCjx4xlQ4/TR8IdMDWhi+yow/ZsyYMfMn8Y8RTwkJCSxatIjRo0czf/78v/p0fhO9nL3JbqiiQa8lsbKIHi4+6EWBz5ObU0n/ihz+q445wS+cPWOXcOeJ9WQaDSofPbeVjVkJvBszlV6uvq3qwQLsnPmk/0wWHl8HwOKT61HI5MwK7M720Xcy5cA3xJbmUK1t/OUJ93KRkZHW3BsaxuGrDXxzpoBGjeH1ihTO3PFjPPdvTmBgmCUzo11Y2LU3k/3Duf/0JnbmJgKwLz8FN0tbFgb3ws3KDkuFkpSqUmJLs0moKEAvCkQ4eXK1sjnN9UrUeB4MN8y6+zjphGRVEGzvxivRE0xOUSaT8Wj4CPxtHXk4djN6UWBd5gUuVxTw1cC5dLL+dWkjS4WSRcF9efvaYTSCnh9yE3jey7Rmp6ChijeunmFV2lmqtY3ScldLG56JHM0D4QNQ67V8nnya9VmXOFmc1byzE1DrBJXeoFcxQTmBcb6OPHt+FwcL0qTNvk49R3ZdBS9EjWX3mCVszErggTOb0Ah6VqfG8q/I4e0Kb4CkqmI+STrJ9+kXJNsIS7mSkT4hTPXvxvygXtgof7uzvCAK3H96E1+lGrr+2hROZsyYMfMX8I8QT88++yxxcXEsW7ZMKib//0i0ozdb8g01OefL8ujh4sOJokwyapqHsUbdZKt4SwZ7dubi1Md54eIePko8iYjIieJMYnZ8QICtM9MDInkqcjheLWpb5gVFE1uaw4eJxxFEkQXH13G6ZCCv9ZrAjtF38di5rRwuSKNep6Ver5VGwTRhIVcwr3M0D4YPxtXChpfj97G6PA5C5ESeVpBnFUqFpcGTqb5R5ECCmgNXsvm6ZyLn5s9n66jF/JBxicfObaW0sY7Sxjrea1FI3N8tgPlBvVnjE8b23Gv8+0JzN9pzPUbzeMRwAErVdbxsHOkhR8YXA+aYzPtrycLgvrhb2XPHybXU67Vcrixg0sEviZvwL2z4dd2ai0P68X7iUXSiwOrsC9wRPgg/WydK1LW8n3iEb1LPmjh9W8gV3Bc2iKciR2KvtCKxqohZh78lq7ai1bEHegQwqUcYKzbVU6XWsfFSCZdl50nSZErbyAAR2J+firuVHZ8OmMXswB4sObUBgCJ1LYcL0trsYCtvrOflS/v4IvlMKxfxRkHH7twkducm8c6Vo6weMo9+7p1aHeNmWJ5wyEQ4bRixsNWIITNmzPw5JBTUMPijE22u6+7twGeze/zJZ/TX8o8QT8uXL/+rT+F3QdviRtUUDMpvMal+duBv//Laqix4J2YqswN7cs+pDVJ663pdBR9cO872nKscGn+fibHkO32nAPBhokG0fJJ0iv35qXw16BbJi6qJOm0jWkGkTqehXqfBw8oOa6WKz5NP81r8/uboilzAV32dLhUp5Nt7cCY4Gqo8QFSAoOT8RUeWe6by71EhzOkcxWifUJ6I3c66jAsmr3em9DpnSq/zL2Qm/lOPhw/nmchmI0eNoJNESrSLHzFuHd/ox/t25ei4h5h8aCVF6hpKG+vQCDqgtYt7R/jYODLFrzubc+Ip09Rz67FvmeAbzhcpp0zMP60VKhYG9eGRbkOlDsONWfHcd3aDVFhvOHdfbgnsydygKALsDF5hDg1ZPLT5CqIIBdnO4NUsniyN43bA4GSvFwSeOd88w65pm5boBD0rU87y0qV9JvYGThbWLArpg1wmY2v2FUnMp9WUMXT3pzzbYyTP9vhl88yWnC/L5bV4Q5pUKZOzfrhZOP3Z3LcxgcsF1W2uSyiooYf3Hzs83cz/Dt29228KSShou073784/Qjz9L7I/LwUHXTle1vb0dfO/KauE02XNXXYD3QMBTKIkjXrdjbu0i07Qc64kh2PFGRwuSOd6bTmjfLrw7+4jiZ38COuz4tmSfYUD+Smo9ToyasoZueczDoy7l062hpuzTCbjnb5T6Orozr/idlCn05BSXcLwPZ/xZORwnu8xCgvjDVglV2CjVOJoYejwajLHbNkx5mZpy8vRY4k7/S1VDeWEqbQsmt2d+05uNXgXVRo8pZbtSSattI6PZ3bHzcqW1UPm8nbfySRVFnOuNJsNWQmSnYOISFNN8wNdB/Fc9zEm19rHxpEuDu6kVJeQUJFPrbaxzXE2LQlz9JAe+9o4Ym/sWvu1vNxjElerCkipLiaxqojEFtfCVmnBA2GDDZE5S1vAUEz9SsI+3r7a7FU1wD2AzwbMIsLJyzASpUUh9l39/HnjUBp5VWqqS+2JDAriSr2hm65JOPVz68TikD4sOL6O3blJ0r6vRI9jmFewZLuQUlXC3KPfc7miQNrGQWXJsp5juLfrACk991afySRUFPDI2S0cL8pELwq8Gn/AIKqjp+JCs9loeyRXFTP78HeSX9ZL0WOZaiwON/Pncbmgul2R1MPbvsMbqpm/Fx1FldqLRv3dMYunv4iPEk9AkeGX0sPhg6VZdO2hFwXOVhgEgaeVHaEOhgLjljf6mha1MTciiiJXKgs5VJDGoYI0jhVltNo+Lfk036bGsjSsP8/0GMni0L4UNdQweu8XXKssIqOmnDF7v+DAuHvxt3UCjO36YQMY7dOFxSd+5GRxFoIosuLyYdamX8DT2g6FTI4cGSq5AoVMhlrQcdZYnAyG4uj7wgbwUtRYnC1tSJCvAQymmdtzroJCB34pDA304Hi8AlGE1XG5XK9o4Kfbe+Nso8Ldyg53LzuGeAXxRORwrlUUsyX7Mhuy4rlSWci9YQNZ0XsyjbrWXVljfLqQUl2CVtRzvCidCX7dWm3TkrLGOorUhr+2wh1vrnNTFEUya8twtrDB2ejK7mRhzU9D72D0vo8pNXpLWSmU3BUygHu7DCHIoTnKV6lpYPHJH9iX3zxmKMbNn/LGetZnXuLZHqOwkJv+c7ZUKnhyeBCPbb2GCHRu6Mm4CH8+SjyBRtAzxieUR7sN5fHY7Vwz1oNZypV8PnAWM1o4iu/PS2He0e+lUS8yZCwO7cvLUWPxsXU0eU2ZTEZPFx/2jb2HD64dZ9nFvWgFPWdKrjPx+HfsGHs3AbbOtMfFsjxmH/6O0kZD4X9/9wCevKFQ3cyfRw9ve048NPiXNzRj5h+GWTz9D/BJ0inCHN2p1jaaOFK3pE7bSK0xndPPPcCkiLiJ8sZ61C1SOQAVjfVk1JTxwsW9nGhZVHwDKrkCraCnUdDxYeIJvko5y5Kw/jzcbTCbR97BtIPfkFRVQnpNGaP3fs6BcUvxMwoogM72LuwefTefJJ/ipYv7aBR05NVXkVdf1eF7H+geyDt9JtPbaHIoCM1N53U6DbvyDNEQPxtHfpo8mmPdq7jjx3jqtXqOpJcx5JOTbFkcQ5CrjclxA2xceSJiBE9EjEAr6FHJFYiAVi+gkJkKqJGeXfgk6SRgKD4f7W3q2QTQoNGhMAas4suboy9h9h7Ua3St7AiaKKyrY3/GVb7PjONqVQFOKmt+HHIHPZx9qW3U4W7lwNdRs/ko+yyBdq7cFzoYL2sH1Fo9Gp3hu5BcXcz842tIry0DQImcPm7+nCk1uJ6/fvkQm65f5uOYWQzzCTJ5/cV9O7H8QBoldRp2XS3nrkHhDB8RTGF9DQ4qS5ac3EB+gyE142xhzdqht9HfPQBBEBBFkc+ST/PCpb1SBCrKxYfPBsyil6ufwaagHasCOfBE5DBG+4Ryy+E1ZNaWk91QxZBdn7B6yK2M8A5ttc/xogzmHlkr+XH1dfNny8hFKORy6NCMwGxHYMaMmT8Xs3j6i3it13hSlY2sTotDLwq8efkwy3tNwM269cBfgO/TzkuPx/mG4WltiFq1/Cu+XqdBKW8uXC5V1/H8xT2sy7jYauCuj7UDPV19GOwRSFcHd67XVXK1spAfM+MNdUl6LR9cO843Kee4L3wAPwxbwLyj37cQUF9waPy9JgLKRmXB091HMMW/G4+e3cq50hz0ooBOENCJgklxcYCtM6/2Gs9tQdEgYlKX1PTQUFdjSPN81G8GTpbWTI205uB91sxcFUdBTSPJxXUM/fgkW+7sS/+A5mthY6GQWt6tWhRzK+VySQQ1McY3BCtjDdDhotRW1gIACpkMC6UhJZZR1+wT1dPVBxdbCyyVza8hiiKxZTl8k3qWjVnxJhYDldoGFpz8jn1j7sXX1gUblYJerl5sDlls8npWSjlWKgVbs6+w5PRPUpTQw9KWzvauknBqIrm6hLEHvuDhboN5NXq8lEazs1Ty6NAgntudhF6AHZdqeWdaOKeKs5h39HtpxEpnOxe2j7pTSklq9DoePNtsEQAwJ7AnXw+6RTq2KJOZfnAtUMoVyGQyern6cXrSQ0w/tIozJdlUadXMPryGzwfO4o7QZq+y7dlXmXtkrVR/Nso7hM0j7zBNoXagj8ziyYwZM38mZvH0FzHVPwJPH2/Sq8s4UZxJdl0lnySd4oWoMW3WP7V0oh7q1RxdsFU11zw1RaP0gsCXKWd4LX4/lcY5eAAh9q4sCevPUM8gAu2c2ZGbyKbrl3np0n60gh5XSxseCh9EQX01G7ISqNdrqdE1suLyEQ4XpLN26HzmH1srCaiRez5vJaDA4Ia+b9w9JssMRoaGkSBaQY+lQim9z3aNFo3LZwf0YEKLwcTRvo4ce3AgM1bFcqWwlpI6DaM+O81ns3twe5/2ndXbw1qpYqhnEPvyU8ioKeNKRUG7A3MBLpY3e0e1dH4XRZH1WZd47+pRLlcWtNrPw8qOYnWtcaTM12wbcQ/BqrZrgPSCwFNxu/goqbmeIMrZB5lMxtlSQ8rTSqHk7T5T+SHzAieLsxAR+eDacXbkXOPbwXMZYJxFeN/AAN46kkZlg441cXmEh6j5d8JmKcrZ29WPbSPvxMMo3EvVdcw+strE/uCVqHE822Pkbxpj5GFtx8Fx9zL34Gq2FySjEwXuPrmBtJoyXo4ay5r08yw99bMkrmcGRLJ26PxWBetmzJgx87/C3248y/8nFHI5nw2YiZuxIPh0yXV25yW12i69uowk4xDUCCdPk1EZ9i3+Mt+dm8iXyacZt+9LnozdLgknB5Ulr0SP4/CE+6SuqHtP/8ySkxvYnZsk2QiUNdbzxuXDKORyLkx9jPu6DsDSWEcTW5rDu1ePsXXUnZJgSK8p48Ezm2/6/cpkMhRyOVZKVYc34ZaF704WVrzdd2qrbfydrNm7NIZxYYbaL7VOYPGPl3jrcFqrbW+GsT5h0uN+Oz9g2sGvufPkj7wSv497T29g9tFV9NvxPv4bXmZNepzh/SCjqzFS06jXseTUT9x58kcT4eRsYc2DXQcTN+lxLk35F71cDOIuu66Spy9sa/d81mWdNxFOtwf3Ica9kyTcnC2s2TP6HpZ06c+BsffyXt9pUvNAek0ZMw9/K82qc7BS8cDAQMN56gSe2H9WEk6T/MLZPqpZOAE8e2GXJJxslRZsHH47z/Uc9V/Nf7RWqviq93SeihwhLXsj4RCBG/5jGA1kFE6LQ/vy47AFZuFkxoyZ/2nM4ukvxtvGgY/7z5Cef5F0mpy6Sul5rbaR1+L3S2m36Te0a7tY2NDFWDxebpxRdrrEkNKRy2TMD4rm1KSHuCesP3U6DS9d3MeQXZ+wNfuqdAw3S1uGeHaWnn+TGsvq9DiW957I4Qn3Sh1yP2Zeok6nMdQ7GS0L9uYlU9tBofqvRRRFylsM5n0tegJe1m23RNtbKtm4qBdLBzTbCzyzM4kPjmW0uX1HzAzoLokPvSiwNz+ZdRkXWJ5wgG/TYjlQkMLlygLKWrToj/UJw0ZpQaWmgWmHvmZdZrNdwkD3QL4dNI/LU5/h9V6TCXP0wF5lycbhd0jXbn9+Mvnt1IS1nL33Rq9JfDHgFpOC8FejJ0iRJblMzv1dB/FxTLOHmZVCZZLCjQlscQ31huMsDRvAmiG3tTKy1LYYq7Jq8K3M+J1MKeUyGW/0mchnA2ZK5psFLcYRPRE5jC8GzDI5bzNmzJj5X8Qsnv4iduQmsiErgTMl1xntE8rSsP4ANAp63kg4hEbQI4gib105It1g+jj5cF/XgSbHkclkbByxiHlB0SbLQx3cODz+Ppb3noiblS0bMuPpv+NDPk8+LUUd3CxteSV6LPvGLeHTATN5JXqstP/rCYd498pRwp08edzY7aQXBd65chRPa3tmGT2lNIKeI4Xpv9t1+Skrnka94fws5AruaDHDL748nx8yLvJR4nGWXdzDg2c3Me/Y91y0PIJ3UPPYlce3XeOL09dbHbsj/G2dOD/pcZb1HEOIvRtKmZxeLqaGo3ZKC7o4uDPMM5hHwofwzaBbya6tYPyBz6XBu3ZKC34efgcHxt3HnM5RWClMx+24W9lxV6jhsxYQTQRXS6q1zenWCb6GlOWMTs0dcC277sBgPfH21SPS81ejx0uPM2vKuf9Ys4eT3FLDh/2ms6LPZGMxtimDjKIM4HobJpz/LXd36cepSQ/Sy2jo6mvjyM8jbufNPpP+q+iWGTNmzPxZmGPjfxHPnt8FLoZowAcx03ih5xj25iWTVVtBek0Zq1NjcbCwklr6nS2seb/nRGmeXUtcLG34atAcbu0cxaeJJ+nh4sPT3Udgo7Qgt66Cf5/fxbqMi9L2NkoVD3YdxFjfMDxbpGtmBHRHKxh8eQBejT+ApULJXV1i+PDaccoa6/kx8xIvRo1hnG8YHxgdvX/KjGeyf8ft/TeDWq/l3+d309Tr5mxpLd3cn7+w20QctMIG8KyBomAAHtp8BUulnDtjbt7d2t/Wied6jOHZ7qNJqymlVF1Hg16Lr40jzipb3KxNO/ouluUx8/AqybbA29qBn4ff8Ysu77d17sWrCfsQRJHv0+O4y7u1h0qVsZAbwMEY+evv3gl/Wydy6irZk5dEpaYBJwvDcOnPk09ztcowCmegRyDzgwzWF0cL05lz5DvKqppn573YZyiLQtqPJrWMQp4ozuTxiKEdvp/fQm83QyH51coiQh3csG5npqOZP572zDDNRphmzLSPWTz9D/DSpX2M9gnl3i4DeOHSXjSCnp+vX5Y6iGTIeCh8EPZKyza9nMob62nQa4lw8uSTAYbUTaWmgUtledxzaiOZteXStuN8u3BHSF9cLG2M3k1lJsfyt3ViQVAvvjc6dj9/YQ/V2kbmB/Xiw8QT6EWB5QkH+bDfdKkA+sfMSzzXYxQhDm7t9jwJgLydwnABAUQZH1w7wfW6Ckk8WSlUlKhr2Zx9uUPhpDBGK/TuuSDKodhw81+6MQGlXMaC3s1F5HpBQCaX06DTsjXnCmnVpQz0CGSYZzBavSC1w3e2daWzbfPMujqNDo2uOZ21Lz+Jxad+kNzAuzl68nz3Mcw9tgZRFHk0fBiLgvui1okobrgqHpYOjPLqwv6CZNJryzhVnssYR1Pvo4oW4slWaSGl0mZ16sH7icfQCHo2X7/MwuA+FDXU8HL8XsCQGnu/73QEEb5KPs0j57YYzCa1zbVxYwICEIXmz0In6iVDSoAge1c8rewoUtdyoigTvSC2GRESaT90LSIib+PjFkURQRSQYxgA3N3ZS1re9H9R1npHUaTVoOGWx+ywE88czeqQ9swwzUaYZsy0j1k8/UW81msCp3Vl7MxNpELTwHvXjvNM9xHoEHj+wh6g2dnmqe7DmRsUjZVaj4OldatjNei1KG9Iv+zOTeKp2B2SN5S1QsUTkcMY7h0sbVOtVWOnMjU5BEM3n5e1PW9fPQrAisuHDQaWFtZUaBpYk36Bf/cYxRMRw3j6/E70osAblw/z9eA57d6o5LR/f5Mho6KxgRVXjhifI/3/WmUhT8c1z6V7OHww3Z29cbOyQSlTEGjvgqOFFanVJTx4ejOJZBsEVEkAogh3/xSPlUrBrVE+iKJIQmUeq9Pi+CHjomT6CAbrhjmBPZkX1IsoF5823ocSPQLXKgvZl5/CK/H7pCLnYZ7BLA0bwF0n19NgtCV44vxWPk05wYs9xzEnsGer490VGsP+AkPqbXP+FaaHmaZd64xeR5ZyJU6WzQ7m84KieT/xGAA/X0/gzi4xvBy/V+q0XNqlP73cfPn3+V28daXZidxD4UZTYrOruz12Fs2RHoP3VUuvCBmDPTvz8/XLlDXWk1xdbNJV2IRAR5pF1uZ3QSaToZQp2rcWkHV4UDN/EGYzTDNmfh1m8fQXMTcoikVuTnTf8jaVGjVfp5xjom9X5gf14mhhBvvzUwCD+/W9YQOkm/IvIYoiryccYmXKWWlZZzsXXowe08pSoCMWhfZFKwpSau79a8eZ1zmKz5JPoxcFXk84xPsx03j76hFK1HWszbjAsz1G0cXR7ReO3DavJxySPIfsVJYIDTr0xpb2plEid4bG8EyPkdI+5Y31OBnFZJijBzvG3MUbCYdYKZ41CKhSfwQRFq69wOHcLM5oz3C5qrWFAEB+QzXvJx7n/cTjhDt6ck+X/tzXdQAHC9L4MfMiF8vySKoqNonQAMwP6s0wjyBuP76u1br0mjJuP7GODxOP89XAOYS3ECCT/MJxtbSlrLGObQVJVGvUUnquUtNAsboWMHQbtiTKxUcaJ3OoMI1N1y+z2tj952ppw8vR4zmQn2IinP7dfRRbiq0ophZHKyUOVr+cIhviGcTP1y8DcKQwvU3xZMaMGTP/VMwF438h7lZ2vBQ1DjBEme49/TOp1aW81Xcyk/zCmeLfjbf7TvlVaYfzZbkmwqmvmz8fDZj+q4RTE3d36cdEo79SpaYBRwtrHIzWCN+nn0chl/Fw+BDAUEz+nfEm/msRRIMvFYBCJpcEQ61WQ66xG62nsw8vRo3p8DiWCiUvRo9l1ZBbkXlmgmuu8dxg5ZFyLp/rBGU+IMhxUFlxT5f+fDdkHrcE9sSqRWt8YlURj8VuJXDjf5h04CvWpJ/nSmVhK3H0TPdRvBI1ngfPbZLWLQjqzamJD5lYH8SV5TDzSLN1AICFQsncwCjAEDm87dj3rEmPY8ahb/Df8Ao59ZUAOFqYRhplMhm3BPY0vi+BuUfXSOteiR7PztxrzDj0rbTso34zeCV6HDmVBmHq79Q6ctkWg1vUPT0Zu4PX4g/8qtmJfyV1Wg212kbpx4wZM2Z+b8yRp7+Yu7vE8GPmRc6UZFOsruXWI2v4bug8qXbp1+Jn64SjykpKSV2tKCS2JIchXkG/sGdrGvU6MlvURLlZ2ko1Pt42DljKlSa2CgF27c8s6wi5TE6ogxuXyvPRi4J0k25ZHJ9aU0JmTTldHN07PNb50lz+E3/AUDfjZewCLDPWPGmtoCAUigOYHuPF61F9cbJWMT+oF9UaNRuyElifdYlDBWmIiBS2aKO3lCuJdPaih7MPUS4+DPYIIsLZixcv7pG6F+8MieHT/rOQyWRsH3UXhwvSeDR2C0lVxWTUlLEl+wqzjcIH4J4uA1mdHkutTsOBghQOFKS0ej+3tNi+icUhMaxMOSNFpwAGe3QmtiSHb9ObHcHH+3bl3rABlNZpqNUYzjHQ5ebEU6STJ6O8QzhYkEajoOPFS/tYl3GRzwbMZJhX8C8f4C/gTPF1Hjq7RRoK3YS4+O2/6IzMmPlnkFBQ0+6A4O7eDh0OFv7/ijny9BejlCvYMmox/d0DAEOh8G1H1hJbmvObjudlbc/mUXdI5o31ei0vX9rPp4mnJDPMm0EnCDwVt4PEKkOlTKSTF+k1pZLr992h/ajWqllrLCx3VFlxqzGS8lto2VrfVCxto1SxwNg1Vq/TctfJn9odftyg0/LyxX1MP7iKNKPgs1IqeW1iKIfu78v4cBdoKgfXW/Dd6XL8XtvHv3clUq3W4mBhxe3Bfdg9Zgn7x96Du5XBuLS/ewCbRtxB0ZyXOTnxYT4bMJulYQOJcPaiQaflq1RDxMxSruTlqPEmUcIR3iF82MJ76b1rx0zc1IPsXdkzZinOKlNB09nOhce7DePMpId5tscozpZc52plIXpj0biPjQPnJj/CnMCe2Kssmdc5mmqt2kQ4PRQ+mE0jFiGTyUgva/amCnIx7RhsD7lMzqYRd/BM95GSiE2uLmHU3i95/+qxmzrGn0WVpoEHzmxi0K5PWgknM2bM/LF093ZotyszoaCmzU7OvwPmyNP/AE4W1uwcfRdTDnzNqZLr1OgaWXhsHa/3noiHlR2Ngo5qjRpBrUVebkGjXoePjSODPAKlOpmWdLZ3YfOoRbxwYS8bshIA2HT9MklVxTzfc7SJm3RbiKLIq/H7OVRgcOu2U1rwfM8xPHzW4CaulMm5s0sM36WflyJRd4T2NRkV82sZ7xvGSO8QDhWkoRUEmo70cvQ4rlQWcqk8n/SaMobv/hRnSxss5IaiYyulEgu5gvTqMinFB9DH1Y8vBt5CqNFA9Kfb+xBfUMHcjUfJy7cC5DRoRFYcTudwWhkH7+2PhcLwt8RQr2BSZvyb/IYqQuzdkMlkJp12TazNOC+ZZt4WFN3mde3vFkA/t06cLc0mriyHE8WZDPFsjgL2cfNn+8CFfJF7AU8re2YFdCfKxRe9IGKhlPN03A7eu2YQK7ZKC6JcfIhy8aWPmz/P9RjNrIAe3HNqgxRptFNasnLQLSYRq4yyOunxjQOUW1LcUMvjsdsoVtfyZu9JRLv68lLUOBYERXP/mU0cK8pEROSJuB1k11Wyou9k5LK/ztBSFEU2Xb/Mw2e3mJhtdnf2opPtb4uCmjFj5tfRUVSpvWjU3wGzePqL+CzxFI7l7oQ4uDHVvxs2ShWfDpjFE7HbOFiQhlqv47Fz7Y/vAENbek9jGmmYVzC9XHyxMNbuyJDzavQEHFVWfJd+Ho2g51plEXeeWM+tnaOY4t+NOq3GOHzXlF25iZJwspQreKvvFK7XlUtpommdInC3tOXzpNPSPku69De0oHdwM23bqMBwE5TJZLzReyIxOz6UlguiiFwm47MBM5m4/2sqNA0UNNSY3ChvxEah4ukeI5gXGIWHtYPJQOTuXk4cuXsUL5w7zA+xpVDuDaKC2JxKbl1znh8X9JIG/ForVATbuSGKhvPTCoLJDFxRFPk4sfkXw71hg1rVRIEhgvdI+FBuO/49AO9ePcpAd0M9UVOXW2dbZ77of0uLY4NWEJHpBdakNw+ErtNpOFmcZTJzriXdHD1ZP3whYY4eJu87rbT5M/Z3sqbxBiFYqqkltbqExSfWS2nYoXs+5a0+k5keEEmIgxt7xyzh7atHWXbRYInwQeIJsusq+W7oba2MQMHYKdoU6BMEFHK5NNtQFEXa8OaU3ntblgSCKJhE9bJrK3j47BZ25CRKy+xVljwYPpAAO5f2v2xmzJgx8ztgFk/tkJmZye7duwkKCmL8+PG/vMOv5J2rR6HAEOqc3imCdUPnE+bozpZRi7nrxE/8lBX/i8cQRJGL5XlcLM9jVVos1goVAzwCuK1zNLd2jkImk/F8zzHMC4rm7pMbyKwtR63XsTotjpNFmdzfdSCjfLqYHHN1WqwknBQyOV8NvpUJfl2ZfWi1tM39XQeRWFVEinHe3lifLoQ7eSAitttlLtJ+B7pCJkcmk9HHzZ8FQb0oOWhIWVZr1XjbOOBj48DPIxZx/5lNlDXWodHr0QiGnya7ABkyxviE8kHMdDrbu6AXBSzaMBSVW9nx5fBp9PE+zZMnDiJmRIFexe6kEh7depUvb2ltKwBgLZOhaHHD35eXTFK1IaU50iuEXm4+7bw3GbMCuxN00YWM2nJ25SXy5tWDPNdjFLZyJQq5DJVChoXSVE0o5DISq4oobTREjTrZOmGtUJFSXSpZWLRkQXBvPuo3AwcLS7SCnrXpF4gry+GOkL5kljeLp1B3G5P3AXCoII2HzmySbC3AUO/28NktJFQU8H7fqSjlCv4VOQI/GyfuOb0RraBnc/YVxu37ks0j7sDVmOZsQgQulefz/tVjbMhKoJuTB0+HDmaWsyEi1K5VwU2onq9SzvLEue1S1BNgkl9Xejh78+6VYzTekJ5e2nXALx7TjBkzZn4NZvHUBh9++CHPPPMM/v7+pKSk8Nprr/Hcc8/9Ya+3Jfsq4/d/xZaRd2CjsmDV4FuZ2imC+PJ8rJUqrORKg8DQaHF2cEQpkxNfns+RwnTiywukm2mDXsuhgjQOFaSxJuM8T0WOoJujJxHOXuwbdw9vXT7M16nn0IsiaTVlPB67nRFeITwZOQxfG0d+yorn3Rb1LB/0m8YEv66kV5dJI1i6OLgzzCuIL5LPSNtN7RTxu12LV6LHcT9bAajWqClsqMHb2p5BnoHET3vcZFutoEeODK0gIIjir3KpvidsAA4qK5bo90JmDxAVrIrNxc3OguUTwn+xw7HJwgHg4W5DOtxWIZfzTI9R3HNqAwAvX9rHuZJsNg5fhELe/j/BppEvAI9FDOPB8MFUa9ScL8vlUnkeF8pyyaqt4M7QGO4I6YuIQQgtPbWBjBqDMernSWfwLTQ4hMtkEOBsWl/1bVos95/eRJM9aB9XPyKcvVidZuic/CrlLGnVpWwcfjt2KkvmBUXjZW3PrUfXUK1t5FRxFkN3f8KuMUsIsHNGK+jZkXONT5NOcrjF2J6L5fnMPfsTw67HcVdIDNMCIrBXtU45/xIvXdzHa0YHfDCYur7YczQbr1/m9cuHO9jTjBkzZn4/zOLpBrZt28ZHH33E5cuXCQ4O5uWXX2bz5s2/WTxVV1dTXd1cMFdQYPAZ+m7oPERnOx46s4VqrZpjRRl8cO04/+45CoVczuzAHswObM4lN+i01FdV4+Ri+Mt9TmdDTUtZYz0H8lI4WZzFkcJ00o3F0kcLMzhamEGYgzt3dYlhVmAPXuk1nnlBvXj+wm4p9XO4MI0TxZlEOnlxsTxPer2Xo8cx1zgvb3+LOWq3h/RGJpORVNU8S87J4uY6uG6GQHsX7FWW6Bt0CMCatPM81X14u9vLZXIsbwyltKBG28jFsjz6uPm1GoA7NyialJgS3tJdhuwIQMZbhzOIK8ln621DsWmnhksQBfbnp0rPd+UmMsSzc5v1Z2Cwjzh2w/y/3XlJ7MtPYUqn9sfaXKlo9qSKNo58cbCwYqhnMCO8Q1ptH1uSzdQD35h4gomI5FapASs87SyltCSARq/jqbgdknCaFdCDt/pOxlKhJMrFl2fidtBonF34avx+3uwzGTAUwh8adx/TDq0ir76K5OoShu/5lHmdo1mTfp6CBtMCUWuFSjqnpu+l1Wklk/26MadzT6b4d8NS8cu/in7MuGginB4KH8SrvcbzVOwO9uQZvqMy4K4uMYQZGybMmDFj5o/ALJ5u4MUXX2TNmjUEBxvascPDwzlz5gwvv/wyjo6OLFmyBFtb2184SjPvvvsuL7/8cqvlPS1d8HHywX/APMYeX41OFPjw2gkWekdgq2x9027U61DXtq5PUgDD7X0Z6xgAocM4WZbNM5f3kVlvGOiaXF3CU3E7ee3SAWb7dmNRQBSroqayuyiV1xKPUqKpRyvoTYTTHZ2iWOgVQXVFJQA6dXOHm71eTnlZGZ1VzcXRi4//yNWiXJYE9cZe2TwG5Ldip7CgCkO66mheCkt82y5I1AkCClnbwqla28i31y/yecY5KrRqvK3sWBY+gkmeochbpPMe9O9Ddrcy1utTIM/gzXT4ihqPd9bx+DA3JviE0sXOFUGUSXU6oigyxTuMbQVJAHyZcobt2VdZHjmGid5dpHPbVZjClxmxxFbkcSOhdq4EyW0pLy8zEddNCAL4q5q/Z/syrxKudJDW3VgzlF1fyfQTaySRMswtkFB7V77KPA9KDWitKKvTUFJahlJuiKrlNlRLzuTD3AJ5LWwYmppaNMAE5wD8YmazKHYT9XotnySdYqZ7GF3sDSNrfLFgW/95LIj9mcSaEnLqKllxxTTyE27vzr1BfZnh243DJRm8evUwacbvpVqvY+P1BDZeT6CbvTtrY2bjZ9Pa7b6J5JpS7jm5UXr+VvdxLAqM5nhWsuRrZi1XMsK9M8fzMziW3yxWn4wc3u5xzZgxY+a3YBZPLWhsbMTd3Z3+/Q1T7ysrK3nxxRcRBAEnJyd27tzJ6tWrOX36NFZWN5dyePzxx7n77rul5wUFBcTExODs7IyrqyvDXV1ZWNCbVamxVGgb+Lk0lUfaSAM16LSo5Aop8nTjuqai3UkuzowP7s6uvCQ+Tz7F0UJD6qda18g31y+yLvcy93UdyIPhg4jw8ufnrMusSotFrdfhbGHNS9HjGOgegIODk3R8/5pmb6VahYiLqyuL7QfxUcY5MmvL0YoCrycfY2XWeZ7uPoL7ug5oFeXpqOapqWC8CQtl89fyQmUhTs7ObabRtIIe5Q0F6jXaRj5LOsX7145R3mI+XIG6lvsvbqefWyfe6jtFiuQAfDV8Hksjc3hk70kuX3YE5KjL3Fi+v5Tlfsdxt7VmiEcQQ72CGOYVTDdnDzaMuYOVKWd59vxuqrVqCtQ1LI7bxIxOkfRx8+fz5NMmHlgAHlZ2LArpyxT/bvRz6wTIUBiFjIuLq8m2ekFkofUAXrh6CBGRHUUpvNxvsrSuaT8wGJjefuwbSoz1UaN9Qtkx+i4K6qsN4smiARoc0AoiNTJrya4gtaTZJyrMxQsHZ9Pv1kAnJx6tH8LyhEPoRIFXU4+xfdSd0mfhjDNH3B9g0oGviDNaBKjkCmYFdOeesP4M8egsbTvf3YPxnqGkCXX8lHmJDdcTyK83iMZrNSVMOPU9O0bfSbSLb6vPul6nYenxb6k3CsOlYf15rPcoGnRanji6R6qS6u8RwK7CVMyYMWPmj8YsnlpgaWnJtm3NHW7z589n+PDhfPTRRyiVSq5evUqfPn1Yv349ixYtuqljOjg44ODQ8XDNZ7qPYHVaHIIo8v7VYywN699mB9PNopDLmeLfjSn+3ThVlMX3mefZkJlAg16LWq/jvavH+CHjInd36cc9YQOYEdCd82W59HcPwMXSBs0NTtLuVs1RpqYCZgcLK45PfIDX4g/wdeo5tIKe0sY6/hW3g3euHuXZHqNY0qXfTaVjOqJcU09KdSlhv2COWaNt5PPk07x/7ZhkHwAGW4W+bv6cLrkOwNnSbIbt/pQFwb14KWosHtaGov2+bv7smj2VtSHXeXbrdQRBBjVukNKPEo/rbGq4wqZsw7gSV0sbRnqHcGdoDJemPcZjZ7exNecqAJuzr7A5+4rJufVy8eWhboOZExhlcj30QsfF0b62BjuKE8WZJFQUkFtX2copXqPXccvh1SZ+XD8NX4hKrsDf1glfG0fyLJpFZFppnWRX0NIE1NO6bZ+WO0NjWJ8ZT3pNGQcKUtmRe40p/s01bq5Wtuwbu5QPEo9jp7RgflBvPKztEBBaFYXLZDL6uXcixs2fd2KmcKIoi/vPbOJaZRGFDTUM2/0Z64bexpQbaugePLOFq5WFAES7+PBO3ykAvHhxLynVpQCEOrhKNVYyft9U8t+B+zYmtOu309ZQYDNmzHSM2STzBlpGlL788ks+++wzlMZISEREBJGRkZSXl//XryMaW7ZFUSTE3o1bjb48BQ01fJsaiyAIJj/1Og0Neh11Om2rn3qdBr0otPkT7ODKG70ncWHqYzwcPhhLY8qqsKGG1+IPsPDYOnLqKhnlHYqt0uAhVafTUKNRSz82LYRcXl0VWr0erV6Pm6Ut78dMI2HqE9we3Bu5MWJQ2FDDw2e3ELbpTTZmJaATBPSCobKmzZ8W16KliWQTp0quoxPF1j+CgFbQ83HiCbpufpNlF/dIwkkpk3NXaAyJM57i6IT72T/2Hro7exuuPSJr0s8TtfVdPrh6jHptI4gigihyR68gNt8ZjZ2l8Z+G3sLgSp7aB6rcQTTUmW3ISmDC/q8Yu3clA9wD+WbQrXi3ECBymYwZnbpzYMy9nJ70MPODeqOSKxCMryMYz18QRAQB4/+bf7R6w7pxLca87M9LMVmn1wssPbVRKub3tLJn66jF2KusDJYAwAD3ALBsFk9JxbWGbkW93sQXy0ahpLShttVPTWMjr7QwMH0ydgdF6hpqdI1UaBrQCjqslUqe6T6CB8MH4WxphVbQUaauo1LTQGZNGW9dOcxPmfFUaRoMc/sa61DrdfRx82Pf2CUMM7rf1+k0zDi8mk8ST0rXaFVqLN+mGcw/HVSWfDP4VvSiyNGCdN43Fu0rZXIyayqkc1wc2pcP+0+XfszA5YJqEgratvjo4W1Pd++O/8AzY8aMKf/oyNO3337LO++8Q1VVFcOGDeO5556ja9eu0npfX1+T7YuLi7l27Rpjx479r19bJjOdOv/vHqP4IfMSAO9dPc7SsAEm40lslBbo5QqTGWxNWMjlrdJkTfjZqlDK5Xha2/Nev2k83G0IT8ft5OfrBvPMK5WF3HXyJ24NjOLuLv3o5eqLWq/DqkXqLMjeRXpcqWlolVYJcnBl5aA5PBE5jNcTDrE+Mx4Rkey6SuYcWcNEv66813caoe0NDb7hWtxIbEk2d4XGmCwTRZH9hek8HbeTZKNlAhhupItC+vBM95EE2TenwkZ6hxI3+RFWppzlxUt7KWusp0bXyPMX97DiyhHmdu7JouA+9HHzZ0IXGy497syLe1NYdzEPUQQ0NpDTDWdHHaJnOpUWhkhIWk0pz1zYibVCxcyASKwVFnjb2LM4NIYAO2fJq6rtty1HjsECoWUaDsACOXK5jLF+YSy7tAeAgwWp3NklBgvkKOQyVlw+wvcZBh8oW6UF20YtJrDFZwUw0COQjRaZ0vOs8nrJDLRY3XwzDbBzbrPgvVGvZaJ/V8b4dGF/fgrX6yqYd3QtPwyfj7VC1eHntiX7Cv8+v4sStSFa6WvtwF1h/bglsKc0v9DZ0oYtI+/gwTNbWJtxAUEUeejsFjJqy7k9uDcPntksHe/TATMJcXBDrddy/5lNkpeVQianUTBES0d7hzLQI/D/zRy+P5Me3vaceGjwX30aZv5hdDS65f/z9/EfK55WrFjBV199xTvvvINMJuPxxx8nOjqalStXsmDBglbbl5aWMmvWLB544AEiIn6/1vwmIp29mN4pgi3ZV7leV8G6jIssCunzXx9XFEXWZVzknStHyKwpZ07nnqwfvoBjRRk8enYrCcaOrvVZl1ifdQkrhZLbOvfijT4TURirkh1UVljIFWgEPZm15dRqG7FTtS4MD3VwZ83Q23i6+0iWXdjNjlyDgeGu3CQOF6TzQtQYHo8YaiIKO6LptnzGmHJrQqPXMf/YOra0SI/JZTJuD+7Dcz1GEWTv2qYXklKuYGnYAG7t3JOXL+3ns+TT6EWBaq2aL1PO8mXKWXo4e/NS1Fgm+YWzam5PHh3amWW7k9mTbBBoFVVKqAqjp38UonMhCdoroNDRoNeyNuMiYGifP1SQhq+NI742jvjZOuJra3jcy8UXq19hqRDt4oOrpQ1ljfUcLEhFEAVAhlqv5fXLB6Xt1g6dT7SrX6v9B3gEGGqejLQ0zGyZtvOw6th1/s0+k4jbk0OFpoFTxVnMPvQdW0fd0ea2olEAbTS62zeR11DNK5f282bCIR7qNpiXosYik8mwUCj5YuAsOts781q84T29d/UY77Wwzbg3bADTOkUC8NblIyRXGT4PG4VKqoWKdPbils5/vxlaZsz8f6WjiGZ7kdD/L/wjxVNRURHLli3j8uXLdOli6I4KDg6mW7du3H777cjlcm677TZp2/fff59169Zx9913/6F+T8/0GMmWbEPtzJuXD7EgqJckYH4LsaU5PH5uK2dKsqVlK1POcmvnKEZ4h3B0wv2sTb/Ay/H7pOiAWq/jm7RzPBwxGH9jfY1MJsPb2oHrdRWkVpcSseUtXoway6LgPm2eX6SzF5tHLWZHzjUePbuV63UVNOi1/Pv8Ln7IuMiXA2cT497ppt9HqrGupYmEigIT4TTcK5j3Y6ZJablfwsXShg/6TePesP58nHSKHzIuSuNNEioKmHl4NcO9gnmj90SivX3Zdldf9qeU8OLeFOJyDKmu+JwGyHFEpRhMJ0+RIosMaq3zQaEnp66yVbF4E742juweczcRzl43da5ymZxR3qH8lBVPaWMdX6Wc5a7Q/qRUlZjM+cupq0Qr6Fv5RpU3NkBxoPS8uNbUCLOJ+IoCgh3aiQxiGPmzffSdzDj0LSXqOmJLczhWmMH0gO6ttr1eV2EinKZ3ikQr6Nmdm4SASKOg5+0rR2nU63m99wQpCvvvHqMIsHXm/jObTeYwxrj582r0OOn5puvNn32TcJIjM3wf2+m+NGPGzJ/P33l0yz/yN82FCxdQKBSSHQEYxJOnpydz5szhnnvuISfH4HLt6enJsGHDiI+PZ9myZcj/CzHzS/R29WOcr6HGJaW6tFXh8c2SX1/FnSfWM3DnRybCqYljxg48hVzOHaF9SZj2JF8PmoOl8cZrpVDiZmlqx/BE5DBpfWFDDfed/pm+Oz5ge841CuqrjRERUyb7dyN+2hP8K3I4SuNNLaGigAE7PyZi81v8lBlPXl1Vq/3qjc7RTbGjpWGmDtE9XXyIcGoWH/YqSyKcPG/m0pgQ7uTJJ/1nkDvneVYPnssgj0Bp3ZHCdPrv/Aiv9S8zcs/nbC47zsIxMl6Y7EOwa3MxslYvkp4PtVlBKJIH41IQg0NtIOjaTqPm1Vcxdt+XpN0gCDvigfBB0uNnzu8ir76Kbk6eRLa4Bg+d3UzUtnfYmn1FqhvLqalh1rdxUG5IP8uA+wY2i9Y5LebfvXhxL9m1zXVDbRHh7MUrLUTM9XYEop+No0mqd15QFN8Nncex4XfzWMRQFMZU30eJJ3jj8iGTfecFRfNR/xkmy97oM0kaOwQw2T9cemxjjOIJiFLa75/KfRsTGPzRiTZ//r//lW/GzP8a/0jxFBoailqt5vXXX5eWvfLKKwwdOpQvvvgCa2trvvzyS2nd+PHjcXJy+lPO7ZnuI6XHryccbLOAuj00eh1vJByi2+a3TGai9XD2Zu3Q+dLz4y2cq8EgPqJdfKW6kYHuga3cuucH9+LM5IeY2SLScLWykNmHVxO48T84rH2e8E0rGL77UxYeW8czcTv5KdOQBlzeeyJnJj9sbM83OF0nVhUz9+j3+G94jc4blzP/6Fo+TjzJl8lnpCgYwF2hMbzdd7LJuajkCjaPvEMSeNtzrkkz134LNkoLFgT34uDYpWwZeYdJZ1+FpoETxZl8lXqWR85t5ZWsH0j32oNXRCoRIRrc7JtTkHoBysusqc4KQJY8gD7VU7jPdQ7v9ZzLezFTiXHzBwzic+zeL9uNTt3III/O3GsUkNVaNQ+f3YxCJmff2KXcEdJX6mpLrS5l5qHVjNzzOXuyMoh+/yDqSoN3klwu8v38nizq25zam+DXlbuNtWS1Og2PnNtqEvFpi5bdfoUNbXdvKeUKXug5Rnq+7MJetIIeH2t7nu85mg9ipkvn/Fr8QZMZiQCzbohmDXAPMHn+TI+RBNoZbBXqdVpJ1F8qz2fT9csdnv/fGXNRuBkzfx7/yLRdSEgIzz77LMuWLWPLli3o9Xo0Gg2HDh3C0dGR+fPnc+HChT/0HETjfy0RRJFBnoEM8gjkZHEW8RUFfJB4nIe7DUEvCAiioWvtRvSCQI2g5pYj33HQOJcOwM3SlpeixrKkSz8UcjkvXzK0dp8uuU6jTotap6GpsmhH7jVpv+7OXqQYa0pSqko4U3Kd4V7BdLJz5pFuQ1gY3IvlCYeILc2R9tEKeq7XVXC9zjR68bX3OT7qPwM3K1sOjVvKlylnOFVynQ0t0jrZdZVkZ16SCuabYhv2Kgve7D0JdRvFv57Wdnw7ZC7TD65CJwq8cfkQ4Y4e3GZ0RRcBeRu1zCIiothOkbNMxkTfrozxDuW79PPszE3kWmURGbU3dFfKoJB8Cq3yoROgtsOtMRBNhQvVdYZjiyLEZVcTl20QGF09bBnbdTjVlidIaszkel0F4/Z9yeHx96ECyeW7Cb0ogNj8t81r0RPYkXON3PoqduRe46eseG4J7MmXA2/hga6DeOb8Tg7mp0KdE8dyLDh26DKIxiHRCh3fz49iarg3N8wEZnmvSRwqSCOjtpxL5fm8d/UYj3RrLuKs02qp1qil53YtGhNyaqta2Vo0MdgjkAHuAZwuuU5KdQlfp5xjllsoap2WUT4hvN57As+c3wXAs+d3MdanC65WtoiiiLuVHXeFxvB16jkeCh+EQiZHK2ik6KWVXMn7MdOYfuhb6VNVyGToRZE9ecm4W9nR71ekhf9OmIvCzZj5c/hHiieA1157jcGDB7N//35CQ0NZtGgR1taGdIyLiwv29n+s74nM+F9LlDI5yGS8GDWWsfsMka8nY3fgbe3ILYE90Csb2izULlPXMe3AKs6WZkvHeajbYJb1GI2jhZXUETXEM4iU6lLUeh0XyvPo6dI8zPZIi/EhAzwCUMrlJFcVc8+pjTQKOr5MOcNU/wjmBUXT192f9cMXcKQwneNFmRTUV1PQUENeXRVljXUmMuBQQRozDn7L14Pn4G1tz/3hg1gcGsOdoTGcKbnO6eLrnC3JlmqOWuJiaYtSrmi3W22IZ2c+6T+Dpad/BuCBM5sY69tF8qVqa7+OusOUogIZoJAruCdsAPcYoz212kauVhZxrbKQK5WFXK4o5GzJdcMgXRlgXUup9RVwBNS2yGrcsarzoqGu+bNKKq4jqbgOhTwAzwBLimySSK0uZeL+r9gYMwe3G85LqZCbnL+zlTWfDJjJtIOrAHjs3FbG+ITiamWLm8KFIbLhXMnpRFH1DWJG1cBHc0OY3s0fVRtq0lJhxeoh8xi193M0gp7Pkw2f82DPzgBUadUmBe6BLToYyxrrTNJpLXGxsuXtvlMYvOsTRETeunKY6L5uDPXwAmQsCetPVm0FnyefplHQsz4rnscihiIYDVM/GzCLl6LH4WW0f7BTWUriCQwp4TtC+vBtWhyNgp4oFx8ulecDsC7jAgfyU6RtW6Y9zZgxY+b34B8rnsCQjhs/frzJsrq6OtauXcuKFSv+orOCUT6hLO81gWcv7AbgjhM/4mxhRW/r1kaRBfXVTNj/lWQi6GZpy64xd9HHmCJqmfYb6hXE16nnAEPdU5N4qtU2cqLI0M4ebO+Kn60TlY0N/Ct2h5TK04sim7OvsCs3icWhfbm3a39GeIeYzFgrb6zHycKaooYarlUW8XTcTsoa60mpLmHagVVsGLGQwZ6dsVaqGOkdwkjjvlpBR2p1OadLsogrzaX+eCmahpp2HclbcleXfsSW5vBV6jlqdRpWXD7CWzek+f5b7FSW9HXzp5+7v7RMK+g5V5rDgfwUDuSncbY0Gz0CWNchWtfRQBZoLKHGFardoN4JRBl6QaQo0xM7V0tqPS6TUFHAvLMbODjpvl8clDvZvxsz/aPYlJJGcZU1Y7/fg5vgy8HUUm7M7sqVWgSnQu4d5M893Xui68CQs4ezN69Ej+OZ87sQEbn71AZWDpzNYI/Orba1V1lip7SkVtdIQUPHdTQ9XXy4Pbg3q9PjqNSomXN2PRtsbqeHs+F7d3/4QFamnEUvCqxKjeWh8EHIjQJJJpNJwqk9VvSZzO7cJIrUtVwqz2eCbxi785LRi+IvnpsZM2bM/Df8I2ue2qKgoIB3332X6OhopkyZwrRp0/7S83mq+wgpBaUV9NxyZA1ZN6TEGvU6Ru75TBJOfjaOHJtwvyScbmSoZ5D0+ERxs/fPsaIMNMZal6aC9Zfj90s3oCB7VxyMEa9GQcfnyacZsftzKbXXEpVcgZ+tE2N9w9g2+k66Gge0lmvqGb//K04ZBxK3RC6TE+nsxZIu/fli4Gwc2xmw2x6vRI+X5gF+lnyqzfP6vVHJFQzyCOTFqLEcHX8fpXNfYsvIO3gofBC9XX0NXV8WjeCaD50ToOspcM+iqQy+tswJRUYMlHtzvryQWYdWmxTdi6LI9fJ6diUWseJQGnO+O0/Qfw6yabcjpPeGnG5cSrLkQEqzcFLIZUzu5sHGRb0pfWkCKfffwoeDJt7U+7kvbABjvEMBQ1H7xANf03Pbu7x1+TCpLa6nRq/DwcLwXSi6CYGyou9kRngZRHKtTsP0g9/y3IXdXK4owNfGUSr+zquvYldu0k2daxOGrsnp0vMLZXlM6xRBZzsXAu2cpR8zZsyY+b3520aeNm3axIoVKygrK+Opp55iyZIlHW7v4eGBj48PW7duJTw8vMNt/wga9ToSyvO5UJ7PhbJc4kpzTYb11uk0HC+9Ti//5g7BK5WFpNWUSc8/GTCTrk7tT5NXG9u6AZOi7IL65sJfV0sbdILA2RZdes/1GIWDhRV3n/hJSq9VaBo4VJBGlw7GpvjbOrFp5CIePruFA/mpaAU9nyedZmCLrrbfAw9rOx7pNoTlCQdR63WM3vsFB8YvlYTbn4GDhZU0EgegTqshriyHU8XXDenJkuuUeV4Hm2pkud0Q9Ur0jZaQ3wWKOnOwuIBF9Sex1DpwrbCGa0W11DTenNFjqLstt/fxZU5PLzq7NPs12Vm4dLCXKXKZnM8HzGbiga8k09HM2nI+SDzBB4kn6O7szdRO3diWfVWaSafW61rNJbwRW6UFG0fczuIT69mWc5VGQce3aQbX8J4uPshbxBevVBQy2Xj92qNBp+VieR5VGjVDPYOYFdCd8b5h7MlLpkhdy67cRB4MH0Swffu2C2bMmPl13HfqZy4bPQHboruzN58NnPUnntFfz99SPL399tt8/PHHPPbYY1y9epWlS5cydOhQwsLCWm176dIlIiMjUSqVzJ079087x3euHMG+xJ38+moulOVypaIQXRvt/mBwUJ7q340p3l1Nlvdy8WWUdygHCwzDUO88sZ5D4+9t1+/oqbid0uOWXXMjvENQyOToRYEvks8w3CuYqf7d+NnYufTypX2UNdZTp2v2COrp4sOtnZtb3dvDTmXJFwNnE7zR0NlY0E6H1n/LU5HD2ZeXTFxZLvkN1Yze8wWHJ9xLqEPHM/H+KGxVFgzzCmawR2fkcjmiKDL78HdszbmKGHyenuqhxGcbfZr0KijtxLrSKqC1dUMTjlZKunnb0tnFiv1l8ZSIJWDRwJY5DxBg79JmM8GvwcPajtOTHmJPXjLfpcdxID9V+k5erigw+eVpIVfwWq/xHQqnJiwVSr4bMpfnzuzgu5xLkj9VvLFGCQzp5vnBvUz2E0SBpKoSYktzOFtynfOluSRUFEjn5GVtz7Keo3mj90SK1bVcKMtDKwi8d/W4yXHMNU9mzPx3XK4oIKGigB5t3FsSOhBVf2f+duIpOTmZd999l/Pnz+PtbfigDxw4QENDQ6ttS0tLGTp0KOPHj2fdunXSDLs/gw+unYDC9ms6lDI5vV39mBcUzZzOPXGztKWiwjRtJ5PJ2DRyEVMPfMPRogzKGusZtecL3o2ZgiCK1Oo01GobqdE2UqSuZXuOoaMu0M6ZR8IHozemkILsXZkT2IMfMi9RrK41mFt2G8KFsjwya8vJbtFS72RhxTM9RnJLYM92C7lvRCVX4GxhTYWmgSJ1bYfbxpfno/sNIsBOZcmesUsYv2+lJKBG7PmcQ+Pu7TA69mchk8l4LGKoYYCwhRrXgAxOTJvJl6dz+f5CrmEQsRGFXEYnZ0vCPGwI9bChi7sNEd62BLgYiv8FQaRbZjXPX0wG4PXLh3ghagyeVv99k4NKrpAiaKXqOn7MvMTWnKsmLu89XXz4pL9hVMrNopQr+HfXobwQM4F1GRdZn3lR8iCzV1myYcTt0jgbgE8ST/Lixb1tNhI0UdhQwwNnNhNs78rT3YeTXl3GO1ePtftHiBkzZn47PZy9OTHpwVbLB+/8+C84m7+ev514WrNmDc8++6wknHJycigrK2P+/PlkZGQwe/ZsvvjiC2xsbHBzc+Ozzz7jyJEjKBQ3NzLkj0AlVxDp5EWksxd9XP0MJpDOnli1GMhbrVWjFXQ0ClqTfRVyGT+MWMCth9dwvCiT0sY6bj/+Y4evt7zXBBRyOXUateQQ/nC3IfxonEm3Jv0CE/3Cea7HKO47/TNaUTAOuo1kmn8E4c6elDfWtzpuUUMt+nZ8qZyM4qm4oVaqr2pCLwgoZQrWZlxg8Yn1jK+vwgZDdZBe1IPQ9mejFwVDkbYRewtLdo69i4n7vuZ8WS759dWM3PM5+8ctbRZQotih0Wl7ZdUi0F7NtV4UoZ37tU4vojSuHODWiXBHDxKrijlUkMZ/oup5e1oXJnaTMe/gQUSZHns7Hadn3E2DvrHVd7JKY4jYCHqBqZ268dKlfehEge8zLvB9xgWiXXyY2imSKf7dCLQzTdnpBaHdgnSdKKCStb7Grla23BYczV1dYsitq2RnbiLWChXzgqJRyRXoBL3UkJBfX41W0ONv64hcJkcURAT5DVYciFgrVcwK6M784F6kVpdwrDCDEd4hBNm7ohdFREFgTVocj57b2up8lDI53Z296evmT5WmgfVZ8QCk15Rxz6mfiXbx5cN+09EI+n+0WaYZM2b+eP524mnIkCF0725ISVVUVDB16lTGjBnD448/TkZGBg888AD29vZ8+umnAMyfP5/58+d3dMg/hDVD5+Hu44WDyopwRw8sFEoa9FoTwdQSuUyGXCZrc/yEg8qSbaPuZPKBrznZRkF2Syb6dWVWYA9kMhnWShVK45y5aFdfZgZE8vP1y5Q21nG5spCFwb3xsXVkf34K0ztF0s3Jk4L6auwtWtslgKGw3cnCus117lZ2ZNaWU6ExRABbzrdTyuRoBT3PXzAMwG267an1WuxVVu2mhgRRlNyqm3C1tGXv2CWM27fSIKAaqhm91yCgujp6tCuOwGDe2R5yaGUt0bxS3qanFIDKOOC3iaVh/Xn03DYAfspK4PU+Exnm7ctdMQF8lXqWGuDr9LM83G0wynZEXoNOg6uVHfd2HcDHiSel5RfL87lYns/Ll/YR6eTF1E4R3NOlv2Hgr1yOsp2TtMUCpXFYsFbQoxX00qBpC7kDCpkMZwsbujv7mOwnCAIni7N44/Ih9hmtAeyUlnRz8qCbkxeRzp5EOHkR4eSFj40DjQoltkoLLOVK5DIZMW6diHEz9WM6XpghWU8AzOgUyRDPIPq6+9HLxc/EvPWpshE8e34Xe42vfbE8j/vPbGKIZ2eTTr3HGNrm+zbz5/Nk/B5SG9p3sv9fr525mdofM/8M/nbiady45vERqampjB8/XnISHzhwIPn5+Xz00UeSePqr6OfeCV/P1oNcfyt2Kkt2jbmbNennUet12CktsFdZYq1Q4WBhhb3KEgeVFUH2Lu2Kkae7j5DqnD68dpx5naPo6+ZP33a6934NrpY20uMSdR0+NqZux18knyGv3rTep7ihloP5qYz27fKrXsvJ0ppdY+9i0r6viSvLpaChhn47PuTtvpO5KyTmt7+J34EFQb145vwu1HodazLO81L0WACe7zmaHzIvUqfT8EnSSWYGRBJo33HB98vR41gY3JsdOdfYnnNN8jkCQzPBlcpCtmVfZcuoxe2KWgCdoOdgYSo/ZFxkS/YV6nRaerv6MsI7hOFeIQzx6GwiWkRRZGduIm9eOdRq/E+trpFzpTmca2GgChDm4M5c30iW2gzB9YbRP02kVZdyy5HvJJfzR7sN4d2YqYbXRGwlXqNdfdk9dgkH8lP49/ndnC/LBeB4USZm/jdJrCnhWk3J/9vamZup/Yn8s0/KzF/C3048tSQmJoaYGNObpYODA56ev34O2u/Nofw0XMUadKKARtDTqNdRq2tEBDR6w3NrpQpvGwe8rR1wt7TBsePJGdgoLVrNgdMJ+pseLtzd2Zv+7gGcKblOVm0F23MSmRHw+/wqMBVPtSbiqUGn5fXLBwFDhM1aoQR0iMCMw99yZfq/CPiVLedOFtbGGqiviCvLoU6n4b7TmzhamMHqIXOliNufjbOlDXMCe/Jd+nlKG+vYkBXPJGdDpOTxiKG8Gn8AjaDn5Uv7+XrwnF+sKwtxcOPRiKE8GjGU5KpijhRksD3nKmdKshERuVJZyJwja9g9+u429xdFkXEHVraKWMaV5RJXlstbV47gqLJiYXBvloYNILOmjGUX9xJfkW+yfRcHdyKdvbhWWURqdanBIb0FydUlvFx9mLdST7B/7NJWg6EFUeCWI99RZkwHT/Hvxlt9bs6va5R3KGcnh7AhK4FlF/aSVnPzcwPN/Pn8ltqZX4r4dMTvHc0y1/6Ygb+5eLqRmpoa3n//fZYtW/ZXnwr3nv4ZXH59ga+bpS3eNvaEO3oy1CuIYV5Bv+hloxcEUmtKSa4qwUqhxMXCBidLa+yUFrhZ2qKQy0mvKeOJc9tMCoNzf2H2Wr1OQ1FDLZ1/IUICpm7fjTeM9DhalC5ZJ9wWFI3NmRpKaw2Fwmq9jl25idzXdeAvvsaNOFlYc2j8Up67sJuPjOmtHzMvIYgia4bO+8sE1NKw/nxnnD34zPldxAy5A2dceDR8KF+lnKWgoYYDBam8fGkfL0WNvamONgBfG0fu7TqAe7sOIKOmjOkHV5FbX8WFslz25ae02R2ZWVtuIpzcLG0JsHPiYnm+VDdUpVXzcdJJPk462Wr/Xi6+PN19JDM6RUoivVGvI7GqmMSqIq5VFnG25DqHCtIREanXaTlXmtNKPNXrtFyrLJKef9x/xk2LfjBYLdzaOYo5gT0pbaxrZRr6/4n7NiZwueDmulJ1Op3U6JJQUEMP7z92MsJfRUcRn474/xDN6ojf2yLAbDnw+/GPEU8JCQksWrSI0aNH/yU1Tr8XpY11hpqkikJ+MhbM+tg4MNwrhGFeQQzx7EyVRs2l8nziy/O5WJ7H5YoC6nXado/pZGFFnU5rMhR2nG8Yi0P7trtPRk0Zc498T4m6jgXBvXg5ely72xq2b54Pd2OX1qEW8/jmBEaxk2Mm6/+b7ikbpQXvxUxjrE8Yc46soUGv5aeseETg+79IQPV3D+DWwJ6sz4qntLGOJxP2sNXrbmxVFnw96FamHVqFVtDzTWos7lZ2PPgbWu2D7F35qP8MZhjnv61MOdOmeGppdLkouA+fD5yFSq6gorGeo4UZ7MpN4qesS4ZRNDe8h2U9RjPON6yVuLNUKOnh4k2Uq4/0GiE/v069TouNUsXsgB6tzsNOZcmCoN58lx4HwIrLR/io//Rf/b5lMpk0nuf/K00Dfn+tEPq7D/9tL+LTEf/fo0G/t0WA2XLg9+MfIZ6effZZ4uLiWLZsGTNnzvyrTweAp3uMwNHTHTkyLBVKw2R4mcFU0FKhxEKuoFanMcyNq68mr76KvJoKSrQN5NVXmYih/Ppq1mVcYF3GbxtmXNli8GuAnTMvR41lUgdmhWWNddx5/CcpWvR9+gXqdVoe7Tak3X0yjGaeHlZ2uLRI4UGzeFLK5AzzCmLnDfv+Hp1TE/y6snXUYqYdXEWDXsuGLENn4fdDbzMpXv8lBFHgh8xLXDDW1zTV4cgwFJzLkBHu5Ml8Y0dae3zcfwbHizLJb6hmX3E6q9JiuTM0hpHeIXw9cA63n/gBgDcvH8bN0oa5Rrf5X8NQzyC6OnqQVFXMsaIMrlQUEunsZbJNS+uICGdP6ZydLW2YHhDJZP8I3u47mbUZF1mdFodKLueZ7iOZ4GvwHLuZqNhbV45I39cHuw7Gw7ptcbO89wS2ZF+mWtvIZ8mnWNIlhh4uPm1u+3fnZgf8lpWV4erq+ovbmSMO/3/5vdOE5rTj78M/QjwtX778rz6FVgz3CsbTp/nG0KDTklxVTINeS25dJaXG2g8nCys627sQ6uCOn5clXq5uKGRyUqtLiC011KVcKMttFRloQo6MTnZOBNu74mRhjYPKkkZBT7VGTaVGjUbQUalRI4gCE/3CWdq1PzWaRkrb8WNKrirmhYt7uX7DqJhN1y9TUF/Nm30mtorm1Os05BvNMcMc3E3MHAsbarhqTNcMcA/Aztjl1RJBFFrV0IBhVIhKbPsrXKdtRHVDq38/9078OGwBc49+T4Ney8asBBBFvhsyD5VcgSiK7VpWaAU9yVUl3Hf6Z063SG22x4fXjvPZgFlEu/iiEFqnnxxV1nw2YBbTDhkG/f4rbgdDPYMIsndldmAPUqqLeS3BUAf2dNwubFWW0uicOq0Ghay1b1m5uoEGC9PvwbzOUbx4aR8AHyWe4KN+M0zW59c1p4c8LO0QW/kxiNipLFka1p+lYf1N1gii0G4HoiDoQS6noL6az5JOAWCrsOCxiCHU6xqlGXYtsVdZ8kz3kTx7YTeCKPLgmS0cGr9UEmjtdTS2VUxuxhRzxOHPIaGiALeGGhQYfre1FCT/HwRqQkVBmyLqt6RMoWPRnlDx19ce/zf8I8TT/yIPndmCytWJBr2GUnVdu+KnLWyVFgTbuzLEszP3dx2Is4UVDYKOM8XZxJfn42BhRYSTJ6EObsSX53OhLI/jRZk06LWo5HIGenRmgm8Ykc7eRDi3/gKr9TrsVK3tCARR5INrJ0isKgYMDs9LuvRjxeXDNAp6Tpdc55X4A3zcfwaWiuavVst/POFOnli0WNdyxt4on9B2IhltWzQo5Yp2C6rlcnmb64Z7B5tEoDZev0xhQy3fDrmVANu2a8e0gp43Lx9mecLBVh5V7RFfUcDg3Z/wSPhgXo4eJ7X+t2SCbzgPdh3Ex0knqdNpuPvUBg6NW4qFQsmzPUejEfSsuHIEAZEHTm/muR4jeab7SCzkCizkrf/p1us0WCpNrS5u6dyTd68eo0qr5sfMS/yn1wSTyF+ZpnlMj5eNvYmtggGZyQgVkzWy9tchlyNDxgfXTqA21rgt6dwbdys76vUa5O2M1VwaNoDvMy5wrbKIE8WZrM+KZ54x6taeQJLJZDddF/ZP5v9DxOH3vnn/mTTbFGS1Wvf/QaB2ZLPQw9n7N9kwdCTa63/FPe9/EbN4+otIqy4FZeNv2rdOpyHB+KX8JOkUXtb2jPHpwmifUG4P6U1caQ47chJ558rRVg7NWkHgaGE6RwvTcbKwZmZAJLMDexLewUy8JpYnHJTEjp3SgvdiptLZ3gVPazuejN2BWq/jQH4qS05u4IuBs6X29rTq5vl7N87eO5jfXO802ie0nVf+fat/R3qHsG3UYqYaBdSJ4kyit73He32nckdoX5Mb8fnSXO4++ZPJL79wRw/e7DMJDys7RES0gh6lTIGISKVGzQsX93K+LBdBFHnv2nG2ZF/l0wEzGePT2nJhee8J7MlJJK2unDMl13ngzGa+HDgbgJeixlKqruObtFhERF5LOMip4uu8HTMZXxunm3qv1koVc4Oi+CL5DA16LavSYnkiYpi0vrihOcL4eziUt0QQBX7MvASAlULJfcG/bBOhkit4v980xu79EoDnz+9hdmCPX5Va/afS8V/5f5746Og8rlYX07OdVOwfcfP+M2mKKt27/QoltWq8rO3ZYhSr/0sCtT3+m6jYL4netkS7w/kNv/n1/hcwi6e/CCuFEplChaVCgZulLW5WdlgplPjbOuJmaYurlS0yDPVIlZoGcuur0DRqqBa1lKhryaptTpsVNtSwJv08a4wdXG3haGFFH1d/EirypVqlSk0D36TG8k1qLFP8uzErsDvdnDxbRXkEUeStK0f4IvkMYKhNerPPJKnLro+bPx/0m8ZjZ7dRr9dyvCiTFy7u5a2+hlbz9Bat4+E3DOs9XGgQTxZyBX1c2/a9+iM6p0Z4h3Bo/L3cfvwHUqtLqdE2cvepDbx99ShLw/ob/JNyE7n7xE9SwbpSJueZ7iN4qvsIk8iaVtCb3NxHeAXzcdJJXrq0j3qdlszacibs/4oZnSJ5o/ckgh2aa1RslBZ82XsaU06tpU6nYVVaLMO8gpjTOQqZTMbH/WcQ5ujBcxd2oxMFDhWmMffIWo5OuK/N1BdAXn0VJ4oyKayvYV5QNAuCe7My+SwCIp8knmRRcB/crAxeS8Ut0rPt1SL9Vo4XZUreXRP9wnG+wWsqs6ac1elxTPDtSr8W3XcjvUOY6h/BtpyrXK+rYH3mJRYE9/5dz+3vSEd/5f8W8fFb7QGaujcHtTEAPMLBo93z+DNTWv9LNWD/jei9UbS07L78Lcf7Lfx/F72/FbN4+osY5hWMtbsLThZW9HMLoJerL3n1VfjYtt0tk19fjZdehaWD4QZX3FDLieJMjhVlcLYku810kr3Kkhi3Tkz060ovVz+UcsPw34tleezOTeJoYTqNxv22G40WwdCu3tPFmwjjyJit2VfZmZsoHfe5nqPo7WYqdHq6+LCi7yT+fX43NdpGduUk8kafiShkcqo1zRG2GzuhmuqfNIKeJ+N28EHMtFaz7W7GCuG30NfNn7jJj/JU3A6+SDEIw6SqYh47t41/G40sm+jl6svKgbcQ6exFYmURJ4qzUMrkKOVy5MiwUihRyuUo5QoinDx5pNsQpvpH8OCZzRwwDm7enH2FvfnJ/DT8dsb7Ng+pjnT05N2+UyRn7astWvZlMhkPdxtMjLs/o/d+iV4UyKwtMwhKY4CsRqvmZHEWu3OTOFuSTWZtc2fjluyrbBy5kPF+YezKTSK/oZpbj65h1+i7sVQoTdKJZ0uymdYp4ne7vssTDkmP53aOMlmXXFXM+P0rKVHX8f7VY/wwbAET/JoHXz/VfTjbcq4C8OqlA9zaOcrQVPEnMOPgKizdWqdw/xfawm88polVQQd/5f8Wfqs9wCCPwHbf280WuP/R/BU1YO1FZzoSmx2Jj98iSv4IMfO/Xsf1R2EWT38Re/OSocGQJvk2LQ4rhZIIJy9GeAfT3z0AP1vHDvf3sLZjZkB3ZgZ0p7Chmtz6avblJRNXlkuArTO3BUcz1DOItOpSnFvUuChkcvq4+dPHzZ971QNIrCzi/WvHpWgUGOwQDhakcbCFhQAYIi8PhA8yucm1JMzRg1HeoWzJvkK9XktKVSnhTh44WjTPVKvSmBY6fzVojtSa/3HiSSzkCorVNTRV7oz16cKUDjr//hviSnPYl5/Cy9HjmBnYnXeuHJXGjLQUTo9HDOXlqLFYKJSsTDnLg2c2d3hcGTKe7TGSZ3uMZPuoxWy4nsCz53eTa+ySnH5wFasG38qtgc0ddC0jV235dtkoLKSi+cEenVHI5Zwryea1+APEleW0O1Mwq7acO4//xPsxU0koLyC3voqTxVncd+Znvh44h/lBvVhr7NL8KPHE7yaejhamc9AoGrs6ejDNP4KqykrA0Hk5+cA30ndOJwrMP7aWjSNuZ7hXCAD9PQKY4NeV3blJpNeUsSo1lntvMID9M/lfaQv/vaNLv8TvKcb+1/gza8A6+lw6Epsd8b8sTv8JmMXT/whqvY7zZbnSiAk/G0fG+XbhjtC+v1jvYaVQMdontIOaobaxUVpwW3AvpgVEcqQgnauVhVytKOJKRSHlGtPBv44qKz4fOKuVzcCNRLn6sCX7CgCXyvMId/IwSdeUN5qKpzG+XVg16FYWHv8BEZF3rx5jnCCgwpDKWzV0/h/ixXSkMJ2J+79CI+g5U3KdbaPuZKRXCJm15axMOcuq1FgqNQ38p/cEnowcjkav4+PEkzwRu/0Xjy0i8p+EgxwrymDVoFu5LagX0ztFcu/pn1mXcRGdKLDw+A8UN9TxSIShHT2jRbSos13rX35N1xQM8wmv11Yw9+j31N1QdGkhV9DXzZ+BHoH8kHGR3PoqEioKeOnSPtYPW8C4/Sup1WlYl3GRns4+PBw+mAgnT65WFnG4MJ2E8gJ6uPx3N2BRFHnx4j7p+UtRYyXDy5y6SqYeWEWBsfvSycLK2PWp59Yj37NpxCLG+Blqw16NHs/u3CQAXos/wKLgPiZjYv4oNo9ajJ+faWR18M6P240ctEdHkaCOjpNeU0adTnNTNSS/182yrff2/6FI+4+i6XrcmAb7rdfknxqd+TtjFk9/EV8MnI2LlydZteWcLrnO6eIsCloYFubWV/F1aixZtRU83X0ExQ21CHoForx1h0JhfTU57biBp1aXIrZTcG2vsqLSKGbcLG0Z5hnMMM9gGnQarJQq0mrKSKsupUqjZlqnCFytbEmtKsFeZdXm8fSigH+LQubTxdcZYYwkNFHaWItONE0xTgvoxoeaaTx0dovJcg8rO+QyGfW6tgvrG/V6bFStu9gANIIOeRtfb0EUiS/PZ+ahb6VU567cJJKqigm0dcbf1olXosfxQs/R1Ou0OFhYodHr+HfcLt5PPC4dZ35QL8IdPdCLAtUaNZZKJXpBpFhdy5r08+hEgeNFmfTe/j5fDJzNBL+ufNJ/Bk4W1nxqbN1/Im4bxY21PNqpr4mJaKCdC2q91qSTbXO2YeagQiYnwMaFu06ul4STn40j/dw7EWDrTLiTh1SP5RY+iNfiD1Kja+RYUSZvXzvKB/2ms+TUBgRR5IWLe+jl5sftIX14Os7grvVB4glWDrzF9Jp10MgmtPHd2pefLKUiujt7McEvjAa9luy6Sm49soFs43e1q6MHnw6YyVuXj7A7L4kGvZZZh1ezY8xd9HXzJ8zRnVkB3fn5+mXy66v5OOkkT0YO/7/27jwsynrtA/h3ZmDYFxFk30RlUcAFxCUV1zTN9WQaZuWaeXqrY53etFfLFrOjlWZpHTM7WpbHfddyKXcBF0AWFRRkl30fZrnfP4BHRgZlFHgYvT/X5XU5v3lm+M4wzNzze36LzhwtPdeuNU+RlKuqG52J1BK9S43d3+MyXkXfwvBJHcPD9MPFk0jCnXzgWvvtdp5fPxARzt9Jw+k7N3E8Kxmnc29CqdHgaNYNdLK2x3iPbjCpUsHYrOGmqsmleTqXFgBqVvZ2Mtc9i6pMWQIfq4bfWqs0KvhY2MPVwgaDnDpqXWciM4Kjme77q1BVo7ONA+RSGapr10Vqb2oBNwtb4ZjC6sqGyw5Ia56DMlU1Ats5Y/PJ5aiuLK0ZTySRNDow2liKRnvlrOWmkEsaXnez9pRRiVK7IFubeBYrQscIM+2MZUawqS1ClsUc1Sqc/uYZiPYm5ihRKmBmZAy5kRFczG1gLjNGj/auGODojSWXj+B2eRGKlVWY8udmzPPrh6U9nsayXs+gg6klPqhde2l57DFkFOfjekVN8SSVSOBtZQcN7q6hlFici8TiOwCAgY7eOJKVhNjCbAA1q8uv6TMepkbGiMxLByQSYRybnakFXg94CiviTqBao8bO1Dh0MLXC3/37Y3X8KSg0aiy4sAe/hk/D57HHka+owJaUS1jW6xmtsWmNrQJA1HD5gJpep8PC5cXBw2EklSGvqhwvXPgvkstqZl52tGqP6T698E7kXgxy8kGVWonj2cmoUCsx8dhGHBg+C8F2LljcfTh2pV2FmjRYHnscc7qEweY+mxy3lObuOUhOskN5mTGeSjnV4LqKMjnMLdFqp8se516RhykM6z8ffBqMNYaLJ5GE7V0FaXtrOJhZIqJjT7zUKRSdrO3Rzc4Jc3374ljWDUz/awtUpMF3Seeg0mgQ4dQyY3+ak1wqQ4CtIy4XZOJ6yR2UKRVaY57uPW1X3zu1vQp75aa4g9JGj3tYd6rKMOaPDcIpo6c6eCOmMAslyir8dCMKi4OHwcbk7gczEeHDy7/js9i7A5/7d/DCttTY+/4cM5kR3ggYgMv5mTiUmQQAWJt4BmdybuLHAVPwj26DYG9qgf85vwsaImxOuyLc1sPCFsZSGRT1Bs3vSr17yi7MwRMrr/4JoGYB1Le7DYLpfU5leVvZYZ5fP6xJOAU1Eb5LOouFQUMRYOuI+KIcxBRmYf2185jdpQ8+iz0GhUaFf187j4VBQ5vylDaw5/ZVRNWeeg5p74ZRbn4oqq7E2KM/4lpt4eRuYYPpPr3wf5cOQU2E6PwMzOrcG9UaNU7n3kJRdRXG/rEBh0bMhr+tI6b79MKPNyJRoKjAirg/8VHPkQ+VrbXdb4+67IyaLyAx0DH2ybQaFpa8blVzeJwLQyaupu+8yZpVkbIKBdWVSCq+g8WXDsN/x3J8cPkwUmuXIBji3AlfhY0Tjv/h+gXEFuc0dndtgoYIhzMScad2+jsBSCjK0Zo9V6ioaOTWLadCVY1/xZ1A110rkFRS04PTzdYJe4a+IuzfV6ZSCGsS1TmckaRVOE317q61iW5jKtUqfBZ7HP62HfBB9xEwk9UUN1cKszD88HdILy/C9E4h2DRgaoMZZLrGO9XNgpRAgnN3UoU9CP/mHYQA2wev0tutnRNmdQ4TLn8acxTz/PrCuHYc0qr4kxjt6g+j2h6+dYlntFaB18fncceF/y/uPhwSiQQfXDqCKwWZAGqKw89DxmB57HGoidCptudz/fULeLlzCELt3QEAeYoKzDr9XxAR3gsaIjxPX8WfROV99mlsS+r2qNPFqb0GTq6lCOqV0+Bfn7BCjO+juyeZMdY2cM+TSDpb20NmY4NrJXegIUKFWomNN6Lwn+RojHXvitf8+uFvXkHIqSzFR1f+AAD8mh6Hnm4dH3DPrU+t0eBEdjJ+SbmI9Np1fQBAJpFAKpHgn1F3d6vztXFotVw5laXYeCMKXyecQna98WQeFrY4OHwWbE3MMK1jT6yKrzkld7UoW+v2dbPFgJpesfpj0mZ1DoO/bQdUqpTIriyFXCZDhUqJayV3hL36voo/iS96j8Vfz7yGl0/+iqtFOchXVOCVU7/hwPBZGOMegIPDZ2Pc0Q0orR3X1d/RSysDESGxdkX3bu0chf/byk0xzadnk5+Lpxy9YWtihuWxNcVNVF46/idgAFbG/Qk1EX7PuobxHt2wLTUGmZUl+CsnBYOdOz3gXhtS19vexbp2bFz94tnCSA5TmRE0tTMHLe453Wxd73Ld+lrmRnLIZTIoNCpoiBodw9cWNXWPOsaYYeGeJ5HsGzYTUc++ifjx7+CtgIHCh4aGCLvS4jDiyPeY/tcWTPPpKUxdP1twG2llhfe722ZVqqzCiaxk/Hg9EltSLmFvWjyi8m4jOi8diUW5SCsvwsH0RMw8/Rs+jzuuVTj1bO+Gf/d7Diuv/ikMZu9t7465OqabExHSygqxN+0qPos99kjL9hMRTmQl48U/f4HXtk+x6OJBoXCSSaSY2bk3zo5+Ha61S0F41lsWILNCu5fgesndxT1HuvoKhQsAvNw5FMNcuuBZj6541iMAMzr3rhlLFDZeWMGbACyMPgiFWoUjT88Vxpedr11iAAAGOnXEup7PwlZuhlGufni32xCtDKVKBSrVNT0tLuY2wiBxOxNzyPWchTirSxgsatd12n87AVO9e0BWO6Dp15uXMdkrWDh26ZXfQQ+xOunrAXcLhbpeqCU9hqOztT0AIKE4F/936TDeqz0tWNcjFdGxBw7cThSWx7AyNsG6vpMgkUiwLukMSmvHqM3uEqZzqxvGGGtN3PMkMjcLW3zUcyTe7haObxJOY8ONC8KH/ZHMa/g05hhmd+mDRRcPAgC23rrS6IyjR6UhDZJL83E2NxVrEk4joShX52yq++nXwQuv+fVDSHs3LLp4EFF5NeNfnM2ssW3ISzAzMkZKaT5O5dxCTEEmrhRmIqYgC4X11n96uqocDYfF319+VTk2JUdj/bXzWkUPUDMIe4p3dywOHg4fq/bCtHkAaG9iDmOpDEqNGtmV2uNT6raVaSc3g42xKZJqiydXcxtYNTJAHwBe7hSC5NJ87EqLQ6VaiSknNuP4qHn4acAUDD30HRQaFb68+hf6OXhigmcQhjt2Qt7UpTrvq36PWQcTS5Qpa4qnulOB+jAzMsZwly7YlRaHPEU5rpfcwQDHjjiRnYxrJXfgYmEDf5sOSCjOxcmcFPycclHvlb2f9wrGh5ePILk0HwfSE3GlIBPBdi7YO2wGBh9Yi6yqUsQVZsPa2ATvBw/Fd4nnMNy1C+RSI/ycdgkAYCI1wtbwF9GjvStKlQp8m1AzO9FYKsPb3Qbd78czxlir4OJJJOOO/gi5vS0cTC0w2bs7xnt0xSzf3pjj2wc702Kx+NJhlCoV+OlGFLYMjICFkRzlqmocTE/EOPeusK43CDulNB/Xamdj3atIWQlZle7Bp2YyY1SqlEgtK8T+9ATEFWU3WDeoqULt3THOvSsmegWiXFmN1QmnsL12YLWZzBjf9BkPJ1NL/JWVjJG/rxdOydxPcXUV1NAAjRyqIQ3Kqqvw8ZWj+DbxDBQaldb1rubWmNG5N2Z0DhVm/Gk0GkhJ+/lwNrNCWnkRMitLUFXby6PUqHGrdu0lH6v2iMxLF3o/3C1skVx6d7++AkU5NPf00szoHIobJXmIK8pGekUxJh77CRsHPI93gwZj6eXfAQCzz2xDdztXWGjUUN6TvUSpgLFEKmQAAFsTU61TVrdKtXshSxVV0GgaWZbCSI7U0gL0dfAQ1ozadCMaAx29cSI7GQDwW8olfNV7HJ7+/d8AgH9G7cNI1y6wb2TPOw0B0nuKa5lEin92CxdWS/9X7An8NHAqXMxtsLn33/Dc+d9QoKjAmdxUWBiZYM+wGfj15iWsiq+ZdSaFBD8OeB79Hb2hJsK/k86joLawfrlTCFzNbXT2iIm1MfD9BoXHZJUiyLl59wtkjLUNXDyJJLE4F5DVfCgczbqBdyL3YWrH7pjZpTdeD3gKcqkM82tXsv4o5g/8zTMQPyVHo1qjxsWCDMz17SPc1/ZbMY0uHuhpYYthOjakBWp6ZIiAZbHHhFND9QW1c8Ywl87o4+AJNWlQrFQgr6oMVWoVSqqrUKKsgqnMGM97B0OhUeNo5nXMOLUVUXm3heJIAgk2DZyKUW7+UBPhtXM7GxRO3pZ26G7ngiA7ZyjUalw6+hOAmmUNvkk4o3PmFxFhR2osFkTu1TpdKIEEI119Mds3DGPc/BsssCmVShp80DqbWSOtvAi5lWW147SkSC0rFHJ2srZHruLuHnDd2jnBwfRu35iGNOigY/mGr8LG48W/fkFWZQliC7Ow+OJhrAx9FpF5t3EwPRFF1ZWYfnILtoY+1yCTXCqDXCrT6pFrb3L3Z9qZmMPLSnslcqVGBS9L3VvZlCirYGFsgiEunWEbW7Mw5fHsZLzVbSBM44xQpVbhv6kxWB4yGlO8u+PXm5eRW1WGDy//jjV9Juq8Twl0L2Mw3acXPrryB9IrirEr7SpSywrgb+uIQBtH7Bs2A8MPf49yVTV+z7yGqtq9EOt83Wc8JnvXnD6sVCmxJqGmqJJJpHg3cHCbK5JO36opYPt7NVwVPsjZCoHOurdbYowZNi6eRGJpbAIYmaCsdqBwibIK3yWdw3dJ59CzvSte8+uHfh28cCb3FuKLchDS3g0yiQRqImxJuYRXOoVALnu0X9/lgkx8Hntc2IqkvYk5wp188JSjN8a4B8DRzArlqmpsu3kFGRUlUJEGVWolJJBARRoYS2VIrl03qbEeq2W9RmGMewCICJ/FHsO12tlu/Tt44ZOeoxDUzhk2clOtD8UIk/9CUXsKbfGlw9AQ4f3gYcL1t8uL8OrZ7TVb3NQyNzLG6/5PYW6XPvCysgOBGqxB1Bjn2nWwVKRBXlUFOphZ4ka9nqVO1u2RXO9UYCcr+ybdbzsTM3zUcyTeurAbpUoF9qcnoKNVe3zScxSuFmYjrbwIZ++kYnnSSaywn6DzPuqftrOstyDow5y2A2pOfY12C8DPKRdRWVu4DHbywcGMJGRXluJ4djI+6/UMDqQnoESpwHfXzmFG597o2cimzbrIZUZY0HUQ3orcAwLh89gT+HHA8wCA3g4e2DzwBUw+sQlKjVqrcPqox0it04Qbrl9ATu3MzSne3dFRx5pkraVu5ty9PUn9vdoh0Nkaa/8WJFIyxpgYuHgSSfyEd+Dm5oaL+elYf+0Cfkm5hBJlFQDgYn4GZp3+L1aHjUNk3m0oNWr8dvMyhjl0xOHcZOQrKnAiOwUjXHX3KDWFWqPBmoTTQuE01j0A3/adBGOpDFVqpbAf3orYE8K6Qk0V1M4Zg519MNa9mzB7rEJVLcz0kktlWN//OfjadACABqdhrI1NUP8k5AeXj+A5ryDh+Oknt2h96E7w6IYve4+Fh4494ZrCyexu70B2ZSk6mFlqFUs+VvY4npUsXO5i07TiCagZkL4qbDxmndoKDQhfJ5zCJK9ArO4zHs8drykgvkk+j7d6DIGrecP9DHPqFU8WsrvFk+l9dk5/kAme3YT97A6mJ2K6Ty8crC1Ed6TGYphLZyzuPhxvR+6DhgifxhzFtsEv6fUzZnTujWWxx5BbVYZfUi5hZe9nheuGOHfCD/2fw0snfxNOQ74ZMABvdh2gdfpzbeJZADW9ie8GDX7ox9tceOYcY6wOF08i69neDd/2dcOK0DHYeusKvkk4jej8DACAmgjjPbriv7dioNCoEWTjhMO5NR/idWspPaxKtRIFtWsu9bBzxdq+k3TuIZdUktug7V6eFu0Q7uyDAY7eGOHiiw5mlg2OyVNUCIXaeI9uQiF0P/WnrScW5wq3qZu9Z25kjK3hL+IZN/8H3tf91B/8rajNWFFvLSE7E3OtnrUH7e93r7qevD23rwIAMsqLEerggSne3bEpORoEIK2sSGfxpKi3QbFcdvf3Y6xj9fSm6mRtDxtjUxQrq5CvqEBwvb3s6l5X83z7YmH0QVRr1EgvL27srhplZmSMMe7+2HA9EmrS4E5VGRzqvd1M8AxElVqFT64cxQTPblja4+kG95FVUdP72NXWsUnrWTWXCRsuwKTdLa02Hr/EGKuPi6c2wtxIjhd9esHK2ATPHd8ktJvWOz0jbaHxHk5mVk3afPe/g6fDTGYES2MTGEmkMJLKYG1sIgzGVmrUwlT4+zGRNe2D30h6/5U07E0sHrlwehhNPR1Yn7tFw8LI/CFPvTWH+q8lXY/HSCqr3UZH3eC6pmqwDc89pnbsgakdezThfsRfbZvHLzHG6uPiqZWpVDU9CVlZDbdlUJMG+Vk5QEHNqZqi7FyUF+YLl0ty8+7+PycP2aY1a+RU3CmAppHxT9VSGfIlDVcmL1cphPuqlBcgMz1DuE6hUaKidv+wyjsFwnHuChnkUinMVEDNFDgNACUyCmt6K5QaDcwbGbieXV4s3E+5VT7S09OF64hIa8xTWVkZKioqoDaWAgU1vUL5WTlIl9oCAFT5xUB5KVRVEq37qa+xMU/3/iwAKM25+7zmZmYhQyFBcc4doS0vKxvVeUVA0d1j6hcf+VXlMC/VvXlxkaIKJhaVKMu9+3vMz8pBdrWRVtudzCykV98tKitUSsilUq1jCrJzhf9XmBfgjpX2op6FpfmwtNA99qxUqUBOxd1TYpr8EkBZBVW1EXIzs+6+FkwLhOeUCkoAtQrVVKTzedYQQdpIXaMm0sqenZEJpdoIFZUVqFBWN/pFQEMkbPasKSgBlApUq82Qnp6utVHyvR40kNzJyQlGDzjVWfe3+e0odzg76973rLHXm1gKCwtRWdn4lkdtzZOQt+79q6ysrNVfL4b0/GqKaz4HmvK32RZJ6GFWwmMPLTIyEr179xY7BmNPlNu3b8PN7f6D3vlvk7HWFx8fD3//1j+D8Ki4eGplVVVViI2NhYODg17VdlZWFnr37o0LFy40+q34ccGP9fEj9uNsyrfbh/3bFIvYz6m+OG/LMtS8N2/ehJeXl9hx9Nb23yEeM6ampggNDX3o2zs7Oz/wG/Tjgh/r46ctP85H/dsUS1t+TnXhvC3L0PIawhcVXXhvO8YYY4wxPXDxxBhjjDGmBy6eDIS1tTWWLFkCa+vHf7o0P9bHz5PyOFuToT2nnLdlcd7WxQPGGWOMMcb0wD1PjDHGGGN64OKJMcYYY0wPXDwxxhhjjOmBiyfGGGOMMT1w8cQYY4wxpgcunhhjjDEmuoqKCrEjNBkXTwYsLy8Pu3btwpkzZ8SO0uKOHz+OdevW4ezZs2JHaVEFBQX48MMP8cILL+DAgQNix2lRJ06cwIsvvoglS5aIHeWxUF5ejg0bNmDp0qVITk4WO84DZWdnY8GCBZg0aRIKCwvFjvNA1dXVOHjwIPbs2YOCggKx4zyQSqXCsWPHsHfvXoMoSuLj4+Hn54e0tDSxozQNMYP0448/koWFBVlYWBAAeumll8SO1CKUSiVNmDCB3NzcKDg4mADQgAED6ObNm2JHa3bXr18nLy8vGj16NA0dOpQkEglduHBB7FgtYuvWreTq6kp79+4lhUIhdhyDd+3aNfL19aVu3bqRu7s7WVtbU0ZGhtixGpWdnU1ubm60ZMkSKigoEDvOAyUmJpKPjw917dqV3N3dydzcnJYuXUoqlUrsaDolJSWRv78/mZubk0QiIS8vL8rMzBQ7VqOuXr1Kzs7O9NNPP4kdpcm4eDJAR44cIWdnZ+GDdcOGDQSA9u3bJ3Ky5vfZZ59ReHg4VVVVERHRhQsXqEuXLtShQwe6fPmyyOmaj1KppK5du9KqVauEtkGDBtH3338vYqqWoVarycXFhQ4fPix2lMdCdXU1BQUF0erVq4mIqKysjNzc3Gjnzp3iBruPN998k1555RWxYzRJZWUlderUib788ksiqvlbjYiIIAA0YcIEqq6uFjfgPcrKysjT05NWrlxJKpWKkpOTycnJiaZNmyZ2NJ10FU7Z2dl08eJFqqysFDHZ/XHxZID69etHe/fu1Wrr2bMnvfXWWyIlajkDBw6kNWvWaLXl5+dTSEgIOTk5telv1/o4ePAg2drakkajEdrCw8Np1qxZ9Oyzz9Inn3zS5t6kH1ZycjIBoJKSEiIiUigUtHDhQvLw8KDOnTvTd999J3JCw7J9+3YaP368VlvXrl1p4cKFNGfOHNq9e7dIyRoXGhqq9cVg9+7d1KtXL3JycqKZM2cKr422YMeOHeTh4aHVduvWLbKxsSFjY2OaN2+eSMl0W716NUVERGi1LV++nJydnUVK1Ljy8nJycXGh559/nohqCr+IiAiSSCQEgNq1a0dbtmwROaVuPObJwJSXl6OoqAhjxozRag8ODkZeXp5IqVqOnZ0dTp061aDt0KFDMDU1xfz580VK1ryICCUlJThx4gTUajU++ugjxMbGwsPDA76+vli6dCmmTZsmdsxmYWlpCQCIjIwEAEyePBmnT5/G0qVLMWjQIMydOxc//PCDmBENSlZWFubMmSNc/vzzz5GamoqsrCxkZGRg3LhxWLNmjYgJG7K0tBR+/7/88gvmzJmD6dOn4+2338aOHTswceJEkRPeVVRUhKKiIhQXFwttycnJCA4OxvLly7F27VpcunRJxITaTpw4gbfeekurrXv37m3y88Hc3BwrV67Etm3bsGLFCkycOBHV1dWIj49HQkIChg0bhoiICBw9elTsqA2JXb0x/eXk5DRoe/PNN7W6ZVUqVZvu8myq7du3k0Qi0XmK58CBAwSAbt++LUKy5qVWq2ns2LEkkUjI09OTzMzM6OrVq8L1//nPfwgAJScni5iy+YSGhlK/fv3o9OnTFBgYqNWrNmPGDAoKChIxneGpG3sTGRlJHh4elJCQIFz3xhtvkKOjo1jRdFq5ciWZmJhQYmIieXt706VLl4Trjhw5QgAoPj5evID1ZGVlkZWVFQ0aNIgiIyNp37595OHhQbt37ya1Wk0+Pj60cOFCsWMKdH0+nDt3joyMjLTaSktLWyvSA23ZsoVkMhmFhISQWq0W2tVqNfXt25eGDh0qYjrduOfJAHXo0KFBm1QqhUajAQCo1WpERERg+fLlrR3tkSmVSigUCuHyxIkTMXbsWEyePBnR0dFax44cORIWFhZITU1t7ZjNoqKiQvidSaVS7N69G1lZWfj6668RFhaGgIAA4dihQ4cCgNZzY8hWrFiBc+fOYe7cuXjqqadgbGwsXNe/f3/heWFNI5PJAAAhISG4cuUK/Pz8hOtGjhyJ8vJysaLpNG/ePHh4eGDSpEkoKSlB9+7dhev69+8PAG3mNeDk5IR9+/YhJSUFoaGheOWVV/DZZ59h7NixkEqlGDZsGHJyckTNWP+95EGfDwDw22+/ifp3Vj8vAEyZMgWbN2/GCy+8AKn0blkilUoxZswY0Z9fXbh4auO2bdsGX19fyOVyhIWFYefOnTqPk0gkICKhcCorK8P//u//tnLah6dQKDB//nxYWlrCwsICI0aMELrCN2/ejICAAAwZMgS7d+8WbpOYmAipVIquXbuKFfuhXLt2DX379oWFhQXs7OzwxhtvoKSkBADg6OiIwsJCxMXFIT8/X7jNL7/8gl69esHf31+s2A9l/fr1uH79eoP2gQMHYu3atbh69Sr27t0rPFalUon//Oc/iIiIaO2oBuHGjRsYMWIETE1N4enpiQ8++ABVVVVax9ja2mpd3rt3L8aNG9eKKe9qLK+ZmRn27duHgoIC5OfnY8+ePcJt1q9fj+7du4vyd91Y3oEDB+LWrVtIS0tDZmYmpk6dKtwmNTUVgYGBrZ4VuP97SX11nw9ATeH0j3/8A1u2bNEqVMTOO2XKFLz++usNbnP58mWEh4e3as4mEbvrizXu119/JRcXF9q7dy/FxcXR2LFjCQDNnTu3wRTZt99+m5577jl6/vnnafTo0cLsNEMxa9YsGj16NF29epV+//136tu3L8nlctq4cSMR1QwsfOGFFwgADR48mF577TVycnIyqKmtRERFRUXk5uZGK1eupBs3btDatWvJ3t6efHx86Pr160RElJubSx06dCB/f3/69ttvad68eeTq6krXrl0TOb1+jh49ShKJhFxcXBrNvn37drKzsyM3NzeaN28eBQYG0qRJk0ipVLZy2rYvIyODnJ2d6dNPP6WkpCRasWIFSaVSCgwMbPTU9Zo1a8jV1VWUaepNyXvz5k3q27cvGRkZ0bRp02j8+PHk7u5OSUlJbTJvfRqNhlavXk1eXl6iDHBvyntJnaioKAIgfKbUHxLQFvPW+frrr8nNzU3nqUixcfHURqnVanJ2dtaaVVdZWUmOjo4EgObMmaN1/LvvvksymcwgC6eCggKSSqVaM+dUKhW9+uqrJJFI6OeffxbaT506RQsWLKA33njDINdA+u6772jAgAFabampqRQQEECurq6UlpZGRERxcXE0dOhQcnFxoalTpxrkuK6xY8fSunXrKCgo6L4FVFFREa1bt47ee+892rlzp9aMQ3bXa6+91mA9t5kzZxIA6ty5M+Xl5Qntmzdvpv79+9OQIUMoJSWllZPWaGpejUZD+/fvp0WLFtGqVauosLCw9cOSfs9vbGwsBQYG0sSJE0V7fpv6XkJEdOnSJQIgWuFEpF/ezZs3U9++fSk8PLzNjvPk4qmNysjIIAAUExOj1R4QEEDvv/8+AaA9e/YI7Tt37qRnn33W4AonIqL09PRGB4jOnDmTzMzMRPkm2hJWrFihczB0dnY2dezYkZ566qnHongoKioib29vUqlUlJeX98ACij1Y3759afHixVptixcvpr///e9kY2ND06dPF9ozMjJEX0hWn7xtgaHl1ee9JC8vj0JCQkQrnIj0y5udnS366/dBuHhqo1QqFTk5OdEzzzxD5eXlRES0du1aCg4OJqKaNYCGDx8uYsLm5efnR1OmTGnQrlAoqFu3bvTyyy+LkKr51X0D3LVrV4PrIiMjSSqV0qFDh0RI1vxSU1OF/3MB9eheffVVsre3F2bSJSQkkJOTE8XHx9PXX39NRkZGlJ+fL3LKuzhvyzK09xJDy/sgXDy1YQcOHCBTU1Nh/IunpyclJiYSEdGmTZvIyclJ5ITNZ/fu3QSAvvjiiwbXrV+/njp27ChCqpbxwgsvULt27XR+Cxw2bFibmvbcnO4toFQqFc2YMUNrmjprXG5uLvn5+ZGJiQn16NGDLC0tacOGDURUs7igTCajU6dOiZzyLs7b8gztvcTQ8t6PkQhj1FkTjRo1CnFxcdi5cyfatWuH559/Xlhg0MLCAu3btxc5YfMZO3YsFi5ciAULFkClUuGdd94RrjMxMYGDg4OI6ZrX2rVrMWjQIAwePBj79+9HSEiIcN3j9ljra9++PY4dO4YhQ4YgPDwcISEhUKvVBjeDUCwODg6Ijo7Gli1bUFRUhHHjxqFTp04Aal43MpmsTb0ncN6WZ2jvJYaW977Ert6Y/qqrqyk8PJz+9a9/iR2l2S1ZsoQkEgk988wzdOTIEdq9eze5u7trje96HOTn51P//v3JxMSEFi1aRGfPnqVly5aRh4eHQWyU+iiys7PJ0tLSICc3tFVffvklhYWFiR2jyThv8zG09xJDy9sYLp4MSEVFBX3yyScUEBBA06dPb7M7ej+qEydO0NChQ8nCwoL8/f1p+/btYkdqEUqlkr744gvy9fUlS0tLGjVqVJsfJPmoVCqVwS6n0Rb99ttvNG7cOAoICGizs5Lq47z6iY+Pp+Li4gce11beSwwt76Pg4klEBw8e1LsAOn36dKNrYrRVSqWSvvrqKxo4cCCNGjWKTp8+LXakFpOZmUnz58+nsLAwmj17ttb05sfNiRMnaPz48dS/f3/66quvmjRL8MaNGzRt2jQunHTIyMig5557jiwtLSkkJKRJA+uTk5Pp7NmzoqyLxXlb1vXr18nBwYHCwsKaVJCIzdDyPipeYVwk27Ztw6hRozBt2jSo1er7HhsTE4OsrCwAQL9+/YTz8IZApVJh3Lhx2LRpEwYPHoz8/HwMHz4cycnJOo8/cuSIsBKuobl+/Tp69eqFwsJCDB06FDt27MCECRN0boFQUVGBkydPipCyefzwww+YNGkSunTpgk6dOuGtt97CqlWrdB5b//Xr4+ODTZs2wcTEpDXjtnmZmZkIDQ2Fq6srNm7ciPLycq1xf/XVf+107NgRffr0gZFR6w5f5bwtLyMjA9bW1khOTsaIESN0rhwOtJ33EkPL+8jErt6eVL169aIlS5aQXC6nKVOmNNoDpVaryd/fn7p06dImV1l9kDVr1lD//v2Fb27l5eXk6upKb775ZoNjY2JiSCKR0MyZM1s7ZrMIDw+nzz//XLj8xx9/EACdM3QWLVpEcrmc9u/f35oRm0V6enqDGTOvvvqqztmfhv76bS2TJ0+mZcuWCZd//fVXGjduHJWXlzfo9WgLrx3O2/Kys7PJw8ODYmJiyN7eXujR0Wg0Wj07nFccXDyJIDIykkJCQoiIaN++fQ8soOLi4uill14yyFMdgYGBFBsbq9X22muv0ciRI3Ue/80339DatWtbI1qzSkpKooCAgAanrjp06EDr169vcHxlZSW9+OKLwpoyhuSTTz6hRYsWabX99ddfBICKiooaHG/Ir9/WUFZWJiwoWueVV14hDw8PkkgkZGNjo7XKvtivHc7beuzs7Ki4uFirIJk1axbNnj1bOIbzioOLJxFUVFTQuXPnhMtNKaAM1XvvvdegbdmyZTR48GAR0rSclJQU+v777xu09+rVi7755hsRErWcjRs3CuuN1bl58yYB4N6lh6BSqbS2Yfriiy/IxcWFdu7cSTdv3qSZM2eSkZFRm/mw4bytp0+fPnTmzBkiIrpy5QrJ5XIyMTGh9PR0kZPpZmh5HwWPeRKBmZkZwsLChMujR4/Gjh07sGPHDmEMlFqtxkcffYTS0lIRkz66Tz/9tEGbiYmJ1jiglJQU7Ny5szVjNTtvb2/Mnj27Qfu9j3X79u1ITU1tzWjN7qWXXoKvr69WW90YprrHqlar8c033zxwPB8DZDIZxowZI1wuKirCmTNnMH78eHh5eWHdunVwcHDA8ePHRUx5F+dtPX5+frh69SqICOvWrUOPHj1gZWWFSZMmNTqmSEyGlvdRcPHURtxbQEVEROD8+fOQy+ViR2t2EolE+JBNSUnBkCFDkJeXJ3KqllH/sf7888944403UFlZKXKq5ieRSADUFE9qtRoRERE4ePAgF08P4cMPP4Snp6dwWSqVgoi02toSztty/Pz8EBsbi/nz5yM2NhZ//PEHjh07huTkZEyfPl3seA0YWt5HwSuMtyGjR4/G1q1bMX78eIwePRrbt29/LGclSSQSEJFQOC1atEhnr83joO6x/vzzz3j33Xfxxx9/wM/PT+xYza6ueFKpVIiIiEBZWRm2b9/+WBb/rW3VqlVwdHTE008/LXaUJuG8zScgIABLlixBaGgoDh48CEtLSwQGBuLYsWMwNjYWO14Dhpb3kYh4ypDd40lZQHDVqlXUqVMn8vT01DlO6HEyYMAAGjRoELm6urbJMRXNJScnhwDQkCFDHvvXb2vJy8ujBQsWUMeOHQ1iQUnO2/zUajUtX76cSktLxY7SJIaW91Hwabs2ZPfu3cI39sexx6mOXC5HcnLyY93jVEculyMpKemx7XGqU/et0szM7LF//baGtLQ0TJo0CTY2Nrh8+TI6duwodqT74rwtQyqV4p///Kewp2lbZ2h5H4WEyEBXJHxMqdVqyGQysWO0qPLychw9ehRjx44VO0qLu3jxIszNzR/rwqnO9u3bMWbMGC6cGGOPPS6eGGOMMcb0wKftGGOMMcb0wMUTY4wxxpgeuHhijDHGGNMDF0+MMcYYY3rg4okxxhhjTA9cPDHGGGOM6YGLJ8YYY4wxPXDxxBhjjDGmBy6eGGOMMcb0wMUTY4wxxpgeuHhijDHGGNMDF0+MMcYYY3rg4okxxhhjTA9cPDHGGGOM6YGLJ8YYY4wxPXDxxBhjjDGmBy6eGGOMMcb0wMUTY4wxxpgeuHhijDHGGNMDF0+MMcYYY3rg4okxxhhjTA9cPDHGGGOM6YGLJ8YYY4wxPXDxxBhjjDGmBy6eGGOMMcb0wMUTY4wxg1NZWYmysjKxY7AnlISISOwQjDHGmD4mTZoEmUyGrVu3ih2FPYG454kxxpjBiYqKQkhIiNgx2BPKSOwAjLU1GzduRPv27REWFobdu3ejqKgII0aMQHBwMKqrq7F//35cu3YNXbt2xZgxY8SOy9gTJy8vD2lpaULxpFAoYGJiInIq9iThnifG7vHee+9h5cqVeOaZZ5CQkID9+/ejd+/e2LVrFwYOHIhDhw4hKSkJ48aNwxdffCF2XMaeOFFRUZBIJMjKykJgYCCsrKzg4OCAH374Qexo7AnBPU+M1ZOZmYns7GwEBQXhyJEjkMvlUCgUsLOzw5w5c3D+/Hl4e3sDAAoKCnDo0CH84x//EDk1Y0+WqKgoSKVS7NixA7///jvs7e3x8ccfY+7cuejTpw+6du0qdkT2mOOeJ8bqiY6OBgCsXr0acrkcACCTyaBWq/HOO+8IhRMASCQSWFtbi5KTsSdZVFQUHB0dsXnzZjg5OcHIyAjvv/8+ZDIZTp48KXY89gTg4omxeqKjo+Hj4wNfX1+h7erVq1AoFBg1apTWsZcvX0aPHj1aOyJjT7zo6GjMnz8fZmZmQpuRkRHkcjkkEomIydiTgosnxuqJjo5uMIMnOjoa5ubm8Pf3F9oKCgpw69Yt9OrVq7UjMvZEy83NRXp6OoKDg7XaL168iLKyMvTp00ekZOxJwsUTY/U0Vjz16NEDMplMqw0AF0+MtbKoqCgAQFVVlVb7p59+igEDBjQoqhhrCTxgnLFaWVlZyMrKalA8RUVFoW/fvg3a3N3d4eDg0JoRGXviRUVFwcbGBsuWLYOvry8sLCywatUq/Pnnnzh79qzY8dgTgosnxmpVV1djyZIlDYqniRMnYujQoVptAQEB+Pjjj1szHmMMQFJSEiZPnoyQkBBMmjQJpaWlGDx4MGJiYuDs7Cx2PPaE4O1ZGGOMMcb0wGOeGGOMMcb0wMUTY4wxxpgeuHhijDHGGNMDF0+MMcYYY3rg4okxxhhjTA9cPDHGGGOM6YGLJ8YYY4wxPXDxxBhjjDGmBy6eGGOMMcb0wMUTY4wxxpgeuHhijDHGGNMDF0+MMcYYY3rg4okxxhhjTA9cPDHGGGOM6eH/AdTr7sbES9TqAAAAAElFTkSuQmCC", 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", "text/plain": [ - "<Figure size 605x605 with 4 Axes>" + "<Figure size 990x484 with 2 Axes>" ] }, "metadata": {}, @@ -892,115 +829,106 @@ } ], "source": [ - "# the posterior is tiny compared with the prior, so the corner is drawn on the\n", - "# posterior's own scale; on the prior's scale both fits are a single dot\n", - "span = [(1.90, 2.20), (3.90, 4.05)]\n", - "fig = corner.corner(\n", - " s_temp,\n", - " labels=[\"$m$\", \"$b$\"],\n", - " truths=[m_true, b_true],\n", - " range=span,\n", - " **plotstyle.corner_kwargs(\n", - " color=plotstyle.COLOURS[2], fill_contours=False, show_titles=False\n", - " ),\n", - ")\n", - "corner.corner(\n", - " s_full,\n", - " fig=fig,\n", - " range=span,\n", - " **plotstyle.corner_kwargs(\n", - " color=plotstyle.COLOURS[0], fill_contours=False, show_titles=False\n", - " ),\n", - ")\n", - "fig.legend(\n", - " handles=[\n", - " plt.Line2D([], [], color=plotstyle.COLOURS[2], label=\"tempered, $w = k/N$\"),\n", - " plt.Line2D([], [], color=plotstyle.COLOURS[0], label=\"untempered\"),\n", - " ],\n", - " loc=\"upper right\",\n", - ")\n", + "styles = {\n", + " \"potential alone\": (\"s:\", 0.6),\n", + " \"+ statistical errors\": (\"^--\", 0.8),\n", + " \"+ every error term\": (\"o-\", 1.0),\n", + "}\n", + "fig, axes = plt.subplots(1, 2, figsize=(9.0, 4.4), sharey=True)\n", + "for ax, name in zip(axes, problems):\n", + " ax.plot([0, 1], [0, 1], \"--\", color=\"0.5\", label=\"nominal\")\n", + " for kind, (marker, alpha) in styles.items():\n", + " ax.plot(\n", + " levels,\n", + " coverage[name][kind],\n", + " marker,\n", + " ms=4,\n", + " color=colours[name],\n", + " alpha=alpha,\n", + " label=kind,\n", + " )\n", + " ax.set(\n", + " xlabel=\"nominal coverage\",\n", + " xlim=(0, 1),\n", + " ylim=(0, 1),\n", + " title=name,\n", + " )\n", + "axes[0].set_ylabel(\"empirical coverage\")\n", + "axes[0].legend(fontsize=8, loc=\"upper left\")\n", + "fig.tight_layout()\n", "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "9ff14350", + "metadata": {}, + "source": [ + "Here are the numbers at a nominal 68 %:\n", + "\n", + "| | potential alone | + statistical errors | + every error term |\n", + "|---|---|---|---|\n", + "| log space, inferred errors | 0.29 | 0.31 | 0.83 |\n", + "| linear, reported errors | 0.02 | 0.15 | 0.15 |\n", + "\n", + "For the linear model, even the full predictive covers only 15 % of the points at a nominal 68 %. The reported errors aren't a sufficient description of the disagreement.\n", + "\n", + "For the log-space model, adding the reported statistical errors to the potential barely changes anything, because 2.3 % is small next to the 34 % the potential is uncertain by. Adding the inferred terms brings the curve up to the diagonal, and then a little past it: 83 % at a nominal 68 %. That over-coverage mostly comes from $b$. Its posterior is wide, so the predictive draws include some large common shifts that the data never needed.\n", + "\n", + "The potential alone undercovers in both models, as it should. It describes the curve the potential predicts, not what a measurement would read." + ] + }, + { + "cell_type": "markdown", + "id": "4c1788a7", + "metadata": {}, + "source": [ + "## The guardrail: a covariance that can't be factored (recipe 21)\n", + "\n", + "Sometimes a subentry reports no statistical error at all. Then the covariance of those points is singular, and there's nothing sensible to do with it. The library says so when the problem is built, naming the dataset, rather than failing somewhere inside the sampler an hour later." + ] + }, { "cell_type": "code", - "execution_count": 16, - "id": "9e421b2a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T20:01:13.677230Z", - "iopub.status.busy": "2026-09-14T20:01:13.677033Z", - "iopub.status.idle": "2026-09-14T20:01:14.101074Z", - "shell.execute_reply": "2026-09-14T20:01:14.100332Z" - } - }, + "execution_count": 24, + "id": "57c5d001", + "metadata": {}, "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 506x484 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "the constraint's covariance is singular on its active points; the diagonal is zero on rows of ['a subentry with no errors']: those comparisons have zero statistical error and no diagonal term covers their points (a block covered only by correlated modes is singular here even when the full covariance is not). Remedies: comparison.reported_terms(), a noise term, a fixed Term covering those points, or statistical=False with an explicit covariance.\n" + ] } ], "source": [ - "levels = np.linspace(0.1, 0.9, 9)\n", - "c200 = p_full.constraints[0]\n", - "fig, ax = plt.subplots(figsize=(4.6, 4.4))\n", - "ax.plot([0, 1], [0, 1], \"--\", color=\"0.5\", label=\"nominal\")\n", - "for (name, p, s), colour in zip(\n", - " ((\"untempered\", p_full, s_full), (\"tempered\", p_temp, s_temp)),\n", - " (plotstyle.COLOURS[0], plotstyle.COLOURS[2]),\n", - "):\n", - " draws = rx.diagnostics.predictive_draws(\n", - " p, s[::20], n_rep=2, rng=6, return_draws=True\n", - " )\n", - " cov = rx.diagnostics.coverage_curve(draws, c200.y[c200.active], levels)\n", - " width = rx.diagnostics.sharpness(draws).mean()\n", - " ax.plot(levels, cov, \"o-\", color=colour, label=f\"{name} (width {width:.3f})\")\n", - "model_only = rx.diagnostics.predictive_draws(\n", - " p_full, s_full[::20], model_only=True, return_draws=True\n", - ")\n", - "ax.plot(\n", - " levels,\n", - " rx.diagnostics.coverage_curve(model_only, c200.y[c200.active], levels),\n", - " \"s--\",\n", - " color=plotstyle.COLOURS[1],\n", - " label=\"model band only (expected to miss)\",\n", - ")\n", - "ax.set(\n", - " xlabel=\"nominal coverage\",\n", - " ylabel=\"empirical coverage\",\n", - " xlim=(0, 1),\n", - " ylim=(0, 1),\n", - " title=\"Predictive coverage, tempered and not\",\n", + "no_errors = rx.Dataset(\n", + " data.x[:6],\n", + " data.y[:6],\n", + " np.zeros(6),\n", + " label=\"a subentry with no errors\",\n", + " meta=data.meta,\n", ")\n", - "ax.legend(fontsize=8)\n", - "plt.show()" + "try:\n", + " rx.Problem([rx.Constraint([rx.Comparison(no_errors, omp)])])\n", + "except ValueError as err:\n", + " print(err)" ] }, { "cell_type": "markdown", - "id": "43052103", + "id": "f2e2cc8f", "metadata": {}, "source": [ "## Takeaways\n", "\n", - "- Real data arrive with whatever the experimenters chose to report, and often\n", - " that is statistics only. The error model is then *our* declaration, not\n", - " theirs, and the choice between \"the measurement's scale is uncertain\" and \"our\n", - " model is wrong\" is a physics judgement we have to make explicitly.\n", - "- `from_measurement` is the unit contract: dimensionful errors convert with the\n", - " data, fractional ones pass through, the frame is checked, and a ratio converts\n", - " to an absolute cross section through the Rutherford closed form.\n", - "- The model's quantity and the dataset's are checked when the comparison is\n", - " built, so a ratio can never be quietly fitted with an absolute model.\n", - "- `Constraint(weight=w)` is tempering, and nothing else changes. Look at the\n", - " predictive coverage before reaching for it: a tight posterior is not the same\n", - " thing as an overconfident prediction." + "- `from_measurement` is the unit contract, and it takes an `exfor_tools` `Distribution` or a plain `dict`.\n", + "- Real data arrive with whatever the experimenters chose to report, and often that's statistics only. How the potential and the data are allowed to disagree is then *our* declaration. Declaring nothing is itself a strong claim, and here the evidence rejects it by about 3441 in $\\log Z$.\n", + "- A log-space comparison with a point-to-point and a common relative error, as in `jitr`, describes this measurement to about 34 % per angle, and gives the potential's parameters widths we can believe.\n", + "- Evidences from different comparison spaces are only comparable once the Jacobian is added.\n", + "- The potential alone and a prediction of a measurement are different distributions, and they need different coverage checks. Only the second should follow the diagonal.\n", + "- Reported per-point errors live at the measured angles. Terms written as functions of the angle, like $s$ and $b$ here, can be drawn anywhere." ] } ], diff --git a/test/test_notebooks_index.py b/test/test_notebooks_index.py index 0395a81..af18dda 100644 --- a/test/test_notebooks_index.py +++ b/test/test_notebooks_index.py @@ -25,8 +25,8 @@ "gp_discrepancy": {7}, "robust_likelihoods": {9, 12, 39}, "error_scale_and_usu": {34}, - "local_optical_model_calibration": {4, 12, 14, 15, 16, 21, 26}, - "alpha_ca_error_model_comparison": {7, 10, 11, 13, 17, 18}, + "local_optical_model_calibration": {10, 14, 15, 16, 17, 18, 19, 21, 40}, + "alpha_ca_error_model_comparison": {7, 10, 13, 17, 18, 19, 40}, "hierarchical_calibration": {22, 24, 30, 35, 38}, } From 10637e2421caefa139062025cff0be1d3356d720 Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Tue, 22 Sep 2026 12:06:47 -0400 Subject: [PATCH 73/75] Drop nbconvert's execution-timing metadata from two notebooks `--ExecutePreprocessor.record_timing` writes an iopub timestamp block into every cell's metadata, which re-dates the whole file on each run and says nothing. The other notebooks carry none; these two now match. --- examples/error_scale_and_usu.ipynb | 105 ++------------ examples/hierarchical_calibration.ipynb | 178 +++--------------------- 2 files changed, 32 insertions(+), 251 deletions(-) diff --git a/examples/error_scale_and_usu.ipynb b/examples/error_scale_and_usu.ipynb index 0657243..dcc6b2c 100644 --- a/examples/error_scale_and_usu.ipynb +++ b/examples/error_scale_and_usu.ipynb @@ -24,14 +24,7 @@ "cell_type": "code", "execution_count": 1, "id": "332f2403", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:51:32.295519Z", - "iopub.status.busy": "2026-09-14T19:51:32.295270Z", - "iopub.status.idle": "2026-09-14T19:51:35.508421Z", - "shell.execute_reply": "2026-09-14T19:51:35.507303Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "import emcee\n", @@ -61,14 +54,7 @@ "cell_type": "code", "execution_count": 2, "id": "0cc00aeb", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:51:35.511079Z", - "iopub.status.busy": "2026-09-14T19:51:35.510712Z", - "iopub.status.idle": "2026-09-14T19:51:35.519306Z", - "shell.execute_reply": "2026-09-14T19:51:35.518202Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "rng = np.random.default_rng(21)\n", @@ -91,14 +77,7 @@ "cell_type": "code", "execution_count": 3, "id": "759f7383", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:51:35.522011Z", - "iopub.status.busy": "2026-09-14T19:51:35.521776Z", - "iopub.status.idle": "2026-09-14T19:51:35.527102Z", - "shell.execute_reply": "2026-09-14T19:51:35.525831Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "def fit(problem, seed, n_walkers=24, n_steps=2000):\n", @@ -146,14 +125,7 @@ "cell_type": "code", "execution_count": 4, "id": "0b23054e", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:51:35.529857Z", - "iopub.status.busy": "2026-09-14T19:51:35.529500Z", - "iopub.status.idle": "2026-09-14T19:51:35.538661Z", - "shell.execute_reply": "2026-09-14T19:51:35.537490Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "log_s = rx.Parameter(\"log_s\", prior=stats.norm(0.0, 0.5), latex=r\"\\log s\")\n", @@ -172,26 +144,13 @@ "cell_type": "code", "execution_count": 5, "id": "139378c4", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:51:35.541123Z", - "iopub.status.busy": "2026-09-14T19:51:35.540773Z", - "iopub.status.idle": "2026-09-14T19:52:25.139784Z", - "shell.execute_reply": "2026-09-14T19:52:25.138713Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Gaussian error scale s = 3.22 (68 % interval 2.83 to 3.68)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Gaussian error scale s = 3.22 (68 % interval 2.83 to 3.68)\n", "Student-t error scale s = 2.91 (68 % interval 2.18 to 3.48)\n" ] } @@ -233,14 +192,7 @@ "cell_type": "code", "execution_count": 6, "id": "44ebaa50", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:52:25.142972Z", - "iopub.status.busy": "2026-09-14T19:52:25.142598Z", - "iopub.status.idle": "2026-09-14T19:52:25.149962Z", - "shell.execute_reply": "2026-09-14T19:52:25.149093Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "x_a, x_b = np.linspace(0.0, 4.0, 13), np.linspace(0.15, 3.85, 12)\n", @@ -261,14 +213,7 @@ "cell_type": "code", "execution_count": 7, "id": "29aca1f6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:52:25.153009Z", - "iopub.status.busy": "2026-09-14T19:52:25.152779Z", - "iopub.status.idle": "2026-09-14T19:52:25.161357Z", - "shell.execute_reply": "2026-09-14T19:52:25.160402Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "# we may have some prior reason to suspect that B contains the unknown offset,\n", @@ -299,33 +244,14 @@ "cell_type": "code", "execution_count": 8, "id": "b113437e", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:52:25.164040Z", - "iopub.status.busy": "2026-09-14T19:52:25.163801Z", - "iopub.status.idle": "2026-09-14T19:53:55.563312Z", - "shell.execute_reply": "2026-09-14T19:53:55.562427Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "as stated m = 0.991 +/- 0.008, b = 0.637 +/- 0.020\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "global scale m = 0.989 +/- 0.036, b = 0.654 +/- 0.232\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "as stated m = 0.991 +/- 0.008, b = 0.637 +/- 0.020\n", + "global scale m = 0.989 +/- 0.036, b = 0.654 +/- 0.232\n", "USU offset on B m = 0.991 +/- 0.008, b = 0.526 +/- 0.022\n" ] } @@ -350,14 +276,7 @@ "cell_type": "code", "execution_count": 9, "id": "675db827", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:53:55.566499Z", - "iopub.status.busy": "2026-09-14T19:53:55.566248Z", - "iopub.status.idle": "2026-09-14T19:53:57.093993Z", - "shell.execute_reply": "2026-09-14T19:53:57.093004Z" - } - }, + "metadata": {}, "outputs": [ { "data": { diff --git a/examples/hierarchical_calibration.ipynb b/examples/hierarchical_calibration.ipynb index c919c5c..b977413 100644 --- a/examples/hierarchical_calibration.ipynb +++ b/examples/hierarchical_calibration.ipynb @@ -26,14 +26,7 @@ "cell_type": "code", "execution_count": 1, "id": "b449277b", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:31:41.422175Z", - "iopub.status.busy": "2026-09-14T19:31:41.421952Z", - "iopub.status.idle": "2026-09-14T19:31:43.977094Z", - "shell.execute_reply": "2026-09-14T19:31:43.976197Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "import corner\n", @@ -84,14 +77,7 @@ "cell_type": "code", "execution_count": 2, "id": "b9a36078", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:31:43.979569Z", - "iopub.status.busy": "2026-09-14T19:31:43.979143Z", - "iopub.status.idle": "2026-09-14T19:31:43.997247Z", - "shell.execute_reply": "2026-09-14T19:31:43.996399Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -123,14 +109,7 @@ "cell_type": "code", "execution_count": 3, "id": "b4c01254", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:31:43.999533Z", - "iopub.status.busy": "2026-09-14T19:31:43.999244Z", - "iopub.status.idle": "2026-09-14T19:31:58.426628Z", - "shell.execute_reply": "2026-09-14T19:31:58.425918Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -167,14 +146,7 @@ "cell_type": "code", "execution_count": 4, "id": "f019a0e5", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:31:58.429432Z", - "iopub.status.busy": "2026-09-14T19:31:58.429203Z", - "iopub.status.idle": "2026-09-14T19:31:59.709710Z", - "shell.execute_reply": "2026-09-14T19:31:59.708806Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -207,14 +179,7 @@ "cell_type": "code", "execution_count": 5, "id": "ec0f35b1", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:31:59.712253Z", - "iopub.status.busy": "2026-09-14T19:31:59.712008Z", - "iopub.status.idle": "2026-09-14T19:31:59.975218Z", - "shell.execute_reply": "2026-09-14T19:31:59.974106Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -287,14 +252,7 @@ "cell_type": "code", "execution_count": 6, "id": "77630c43", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:31:59.977412Z", - "iopub.status.busy": "2026-09-14T19:31:59.977179Z", - "iopub.status.idle": "2026-09-14T19:31:59.984398Z", - "shell.execute_reply": "2026-09-14T19:31:59.983508Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "BUMPS = (0.16, -0.13, 0.1) # amplitudes of the departures from each smooth trend\n", @@ -353,14 +311,7 @@ "cell_type": "code", "execution_count": 7, "id": "86c7868f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:31:59.986670Z", - "iopub.status.busy": "2026-09-14T19:31:59.986449Z", - "iopub.status.idle": "2026-09-14T19:32:00.185194Z", - "shell.execute_reply": "2026-09-14T19:32:00.184222Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -432,14 +383,7 @@ "cell_type": "code", "execution_count": 8, "id": "943e52d1", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:32:00.188334Z", - "iopub.status.busy": "2026-09-14T19:32:00.188000Z", - "iopub.status.idle": "2026-09-14T19:32:00.196138Z", - "shell.execute_reply": "2026-09-14T19:32:00.195220Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "centres, widths = (35.0, 25.0, 45.0), (8.0, 6.0, 7.0)\n", @@ -468,14 +412,7 @@ "cell_type": "code", "execution_count": 9, "id": "dbdb2f5e", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:32:00.198934Z", - "iopub.status.busy": "2026-09-14T19:32:00.198610Z", - "iopub.status.idle": "2026-09-14T19:32:00.210725Z", - "shell.execute_reply": "2026-09-14T19:32:00.209698Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "def global_params(prefix, n_per):\n", @@ -532,14 +469,7 @@ "cell_type": "code", "execution_count": 10, "id": "c94dbbec", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:32:00.213564Z", - "iopub.status.busy": "2026-09-14T19:32:00.213228Z", - "iopub.status.idle": "2026-09-14T19:32:00.289857Z", - "shell.execute_reply": "2026-09-14T19:32:00.288714Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -569,33 +499,14 @@ "cell_type": "code", "execution_count": 11, "id": "ce3ad648", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:32:00.292681Z", - "iopub.status.busy": "2026-09-14T19:32:00.292357Z", - "iopub.status.idle": "2026-09-14T19:51:23.307593Z", - "shell.execute_reply": "2026-09-14T19:51:23.306693Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "log Z = 143.07 +/- 1.16, 107467 likelihood calls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "log Z = -87.00 +/- 0.98, 69239 likelihood calls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "log Z = 143.07 +/- 1.16, 107467 likelihood calls\n", + "log Z = -87.00 +/- 0.98, 69239 likelihood calls\n", "log Z = 142.28 +/- 1.24, 257058 likelihood calls\n" ] } @@ -624,14 +535,7 @@ "cell_type": "code", "execution_count": 12, "id": "cdceaa49", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:51:23.310510Z", - "iopub.status.busy": "2026-09-14T19:51:23.310256Z", - "iopub.status.idle": "2026-09-14T19:51:23.315775Z", - "shell.execute_reply": "2026-09-14T19:51:23.314712Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "mappings = {\n", @@ -657,14 +561,7 @@ "cell_type": "code", "execution_count": 13, "id": "c6c8c542", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:51:23.318341Z", - "iopub.status.busy": "2026-09-14T19:51:23.318089Z", - "iopub.status.idle": "2026-09-14T19:51:24.636637Z", - "shell.execute_reply": "2026-09-14T19:51:24.635468Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -716,14 +613,7 @@ "cell_type": "code", "execution_count": 14, "id": "e2b80ed8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:51:24.639460Z", - "iopub.status.busy": "2026-09-14T19:51:24.639218Z", - "iopub.status.idle": "2026-09-14T19:51:24.645519Z", - "shell.execute_reply": "2026-09-14T19:51:24.644428Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "def case_curves(name, E, rows):\n", @@ -753,14 +643,7 @@ "cell_type": "code", "execution_count": 15, "id": "b9f2f5e2", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:51:24.648524Z", - "iopub.status.busy": "2026-09-14T19:51:24.648269Z", - "iopub.status.idle": "2026-09-14T19:51:25.001112Z", - "shell.execute_reply": "2026-09-14T19:51:24.999768Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -829,14 +712,7 @@ "cell_type": "code", "execution_count": 16, "id": "8022b6e5", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:51:25.003794Z", - "iopub.status.busy": "2026-09-14T19:51:25.003445Z", - "iopub.status.idle": "2026-09-14T19:51:27.018655Z", - "shell.execute_reply": "2026-09-14T19:51:27.017705Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -909,14 +785,7 @@ "cell_type": "code", "execution_count": 17, "id": "86d573ec", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:51:27.022038Z", - "iopub.status.busy": "2026-09-14T19:51:27.021780Z", - "iopub.status.idle": "2026-09-14T19:51:27.340526Z", - "shell.execute_reply": "2026-09-14T19:51:27.339428Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -964,14 +833,7 @@ "cell_type": "code", "execution_count": 18, "id": "b93c1029", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:51:27.344579Z", - "iopub.status.busy": "2026-09-14T19:51:27.344314Z", - "iopub.status.idle": "2026-09-14T19:51:27.879932Z", - "shell.execute_reply": "2026-09-14T19:51:27.879002Z" - } - }, + "metadata": {}, "outputs": [ { "data": { From c2f1026645a8e960e0c14867ba99b21fc1ee81f1 Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Tue, 22 Sep 2026 12:13:11 -0400 Subject: [PATCH 74/75] Re-run error_models in order One cell carried execution count 15 among cells 1-12, so it had been re-run after the ones below it and the stored outputs no longer provably came from the stored source. Executed end to end. --- examples/error_models.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/error_models.ipynb b/examples/error_models.ipynb index 1389515..9c2a573 100644 --- a/examples/error_models.ipynb +++ b/examples/error_models.ipynb @@ -93,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 3, "id": "99cfee79", "metadata": {}, "outputs": [ From cbcaae8359a395c0497631ee2638f9381cc33fa0 Mon Sep 17 00:00:00 2001 From: beykyle <beykyle@umich.edu> Date: Tue, 22 Sep 2026 12:22:56 -0400 Subject: [PATCH 75/75] Finish the rewrite of normalization_and_covariance_structure The rewrite cut the five experiments down to four and renamed them exp 0..4 -> exp P..S, but stopped before the end of the notebook, and the file was committed with the failure in it: the cell defining `free` was deleted while eight uses of it remained, so cell 24 carried a NameError and every cell after it was unexecuted. Past that break the covariance gallery still indexed `comps["exp 0"]`, `comps["exp 1"]`, `comps["exp 2"]` and `datasets["exp 0"]`, which raise KeyError, and a plot title still read "Five experiments, five normalisations". Every dataset has a non-zero quoted systematic here, so `free` is just `comps`. The prose that pointed at `exp 3`'s 3 % quote and `exp 4` needing none of its 25 % now points at `exp R`, 20 % off against a 5 % quote, and `exp Q`, 20 % off against a quote of 25 % -- the two the table below actually shows. The paragraph before the band plots says which of them draws residuals, since only the second one does now. Also drops an unused `minimize_scalar` import and a commented-out duplicate of the sampling line, and re-runs the notebook end to end: 23 cells, sequential, no errors. --- docs/design.md | 2 +- ...rmalization_and_covariance_structure.ipynb | 821 ++++++------------ 2 files changed, 253 insertions(+), 570 deletions(-) diff --git a/docs/design.md b/docs/design.md index 4ff6e64..6fbf046 100644 --- a/docs/design.md +++ b/docs/design.md @@ -668,7 +668,7 @@ a time unless noted. | `linear_calibration` | 1, 2, 17, 40 | emcee | the whole workflow on a line with an inferred constant noise; prior and posterior predictive on a new grid with `grid_draws`, the model's band against a measurement's, and why reported per-point errors cannot go there; coverage of both | 23 s | | `error_models` | 2, 4, 19 | emcee | the covariance ladder on one comparison, the Peelle matrix as a fixed term, offsets known, free and ignored | 85 s | | `sharing_error_models` | 5 | dynesty | two experiments with opposite normalisation defects: sharing a parameter, a mode per dataset, one mode spanning both, and the assembled covariance seen directly; one panel per error model | 102 s | -| `normalization_and_covariance_structure` | 3, 4, 6, 27 | emcee | six treatments of five experiments' normalisations, one of them badly mis-quoted and alone in its range; Peelle's puzzle in the two-point case it was found in; a gallery of covariance structures from `matrix(theta)` | 353 s | +| `normalization_and_covariance_structure` | 3, 4, 6, 27 | dynesty | six treatments of four experiments' normalisations on a cubic with a free, non-zero constant term, one of them badly mis-quoted and alone in its range; Peelle's puzzle in the two-point case it was found in; a gallery of covariance structures from `matrix(theta)` | 1003 s | | `gp_discrepancy` | 7 | emcee, dynesty | mean-zero discrepancies with amplitudes growing in x: four rungs on a toy line, three on n+⁴⁰Ca missing its surface absorption; the three predictive objects (model plus discrepancy, plus experimental, and the conditioned regression it does not use), with the equations | 483 s | | `robust_likelihoods` | 9, 12, 39 | emcee | Student-t versus Gaussian on three gross outliers; the iterative rejection loop, including the round that over-rejects and recovers; tempering a correctly specified 200-point line, with the prior on the corner plot and the predictive coverage | 80 s | | `error_scale_and_usu` | 34 | emcee | a global scale on the reported errors under both likelihoods; a USU offset on the technique we suspect | 90 s | diff --git a/examples/normalization_and_covariance_structure.ipynb b/examples/normalization_and_covariance_structure.ipynb index c2a5cc7..a8300e7 100644 --- a/examples/normalization_and_covariance_structure.ipynb +++ b/examples/normalization_and_covariance_structure.ipynb @@ -5,17 +5,29 @@ "id": "f78307d3", "metadata": {}, "source": [ - "# Normalisations and the structure of a covariance\n", + "# Normalisation, systematic errors, and the structure of the covariance matrix\n", "\n", - "Five experiments measure the same curve, and each multiplies its data by a\n", - "normalisation it does not know exactly — a flux, an efficiency, a target\n", - "thickness. Each quotes a systematic uncertainty for that factor. Some of those\n", - "quotes are good; at least one, as usually happens, is not.\n", + "Four synthetic experiments measure the same curve with some systematic uncertainty in the overall normalization of each data set. We will try six error models — three defensible, three not — and see which recover the truth.\n", "\n", - "What we do with those quoted numbers is the whole question here. We will try\n", - "six things — three defensible, three not — and see which recover the curve.\n", - "Then we take a covariance apart and look at the pattern each kind of term writes\n", - "into it.\n", + "The setting is one in which we compare multiple data sets, labeled by $k$:\n", + "\n", + "\\begin{equation}\n", + "y_{k,i} + \\varepsilon_{k,i} = \\rho_k y_m(x_{k,i},\\alpha),\n", + "\\end{equation}\n", + "\n", + "where $\\rho_k$ is the unknown overall normalization of each data set. How do we handle this?\n", + "\n", + "1. Treating $\\rho_k$ as parameters with priors, and directly constructing a likelihood from above is the most general approach, which gives a joint posterior $p(\\alpha,\\rho_1, \\rho_2, \\dots | y)$, with standard MVN likelihood, with a covariance determined by the distribution governing $\\varepsilon$, e.g. $\\varepsilon \\sim \\mathcal{N}(0,\\Sigma^\\text{exp})$, where $\\Sigma^\\text{exp}$ is experimentally reported (and in this case will just include a diagonal statistical contribution)\n", + "2. Alternatively, one could marginalize over an assumed distribution (e.g. prior) $p(\\rho_k)$ - for example, $\\rho_k \\sim \\mathcal{N}(1,\\sigma^\\text{sys}_k)$, with $\\sigma^\\text{sys}_k$ being the experimentally reported systematic uncertainty. This leads to a contribution to the ($k$th block of the) covariance of the likelihood of the form\n", + "\\begin{equation}\n", + " \\Sigma^{\\text{sys}}_{k,ij} = (\\sigma^{\\text{sys}}_k)^2 y_m(x_i;\\alpha) y_m(x_j;\\alpha),\n", + "\\end{equation}\n", + "which allows to infer the marginal posterior $p(\\alpha | y_k) = p(\\alpha,\\rho_k | y) p(\\rho_k)$\n", + "3. Finally, one could again choose to confine $p(\\rho_k)$ to a Gaussian distribution, performing the same marginalization as above, but leaving $\\sigma^\\text{sys}_k$ as free parameters, with priors $p(\\sigma^\\text{sys}_k)$ and conditionals $p(\\rho_k | \\sigma^\\text{sys}_k) \\sim \\mathcal{N}(1,\\sigma^\\text{sys}_k)$, and infer a joint posterior, which leads to the joint posterior $p(\\alpha , \\sigma^{\\text{sys}}_k| y_k) = p(\\alpha,\\rho_k | y) p(\\rho_k | \\sigma^{\\text{sys}}_k) p(\\sigma^\\text{sys}_k) $\n", + "\n", + "Option 1 makes no assumptions, option 3 fixes $\\rho_k$ to a mean-1 Gaussian, and 2. fixes as well the standard deviation of that Gaussian.\n", + "\n", + "As we will see, there are three approximations to 2 which are demonstrably wrong, and they will lead to disastrous results.\n", "\n", "Recipes: 3, 4, 6, 27" ] @@ -23,25 +35,17 @@ { "cell_type": "code", "execution_count": 1, - "id": "6cdd6f3d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:45:22.640008Z", - "iopub.status.busy": "2026-09-14T19:45:22.639763Z", - "iopub.status.idle": "2026-09-14T19:45:26.051872Z", - "shell.execute_reply": "2026-09-14T19:45:26.050844Z" - } - }, + "id": "867ac683", + "metadata": {}, "outputs": [], "source": [ "import corner\n", - "import emcee\n", + "import dynesty\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import plotstyle\n", "from numpy.polynomial import polynomial as P\n", "from scipy import stats\n", - "from scipy.optimize import minimize_scalar\n", "from sklearn.gaussian_process.kernels import RBF, ConstantKernel\n", "\n", "import rxmc as rx\n", @@ -53,93 +57,58 @@ }, { "cell_type": "markdown", - "id": "268c7675", + "id": "240f15bb-1321-402c-915b-ef269093abfd", "metadata": {}, "source": [ - "## Five experiments, and what they told us\n", - "\n", - "Each entry of `settings` is one experiment: where it measured, how many points,\n", - "its statistical noise, the systematic uncertainty it *reported* for its\n", - "normalisation, and — because this is synthetic data — the normalisation it\n", - "actually had.\n", - "\n", - "We do **not** draw the true renormalisation from the reported systematic. We\n", - "choose it by hand, because that is what real data are like:\n", - "\n", - "- `exp 0` is **absolutely normalised**: it quotes no normalisation uncertainty\n", - " and has none. Some experiment has to play this role, and we will see why in\n", - " a moment.\n", - "- `exp 1`, `exp 2`, `exp 4` are honest: their renormalisations sit comfortably\n", - " inside what they quoted. `exp 4` was simply very conservative, quoting 25 %\n", - " when it was within 2 %.\n", - "- `exp 3` quoted 3 % and is in fact 15 % high — five standard deviations out.\n", - " It is also the *only* experiment covering the high-$x$ region, which is what\n", - " makes it dangerous.\n", - "\n", - "Comment a line out to drop that experiment from everything below." + "### Generate synthetic data" ] }, { "cell_type": "code", "execution_count": 2, "id": "f6b7fce0", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:45:26.054610Z", - "iopub.status.busy": "2026-09-14T19:45:26.054142Z", - "iopub.status.idle": "2026-09-14T19:45:26.061718Z", - "shell.execute_reply": "2026-09-14T19:45:26.060527Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "exp 0: quoted 0%, actually +0% (absolute)\n", - "exp 1: quoted 8%, actually -6% (0.8 sigma)\n", - "exp 2: quoted 12%, actually +10% (0.8 sigma)\n", - "exp 3: quoted 3%, actually +15% (5.0 sigma)\n", - "exp 4: quoted 25%, actually +2% (0.1 sigma)\n" + "exp P: quoted 12%, actually +10% (0.8 sigma)\n", + "exp Q: quoted 25%, actually -20% (0.8 sigma)\n", + "exp R: quoted 5%, actually +20% (4.0 sigma)\n", + "exp S: quoted 10%, actually -13% (1.3 sigma)\n" ] } ], "source": [ "settings = {\n", - " \"exp 0\": {\n", - " \"domain\": (0.20, 0.60),\n", - " \"n\": 35,\n", - " \"noise\": 0.010,\n", - " \"sys\": 0.00,\n", - " \"rho\": 1.00,\n", - " },\n", - " \"exp 1\": {\n", - " \"domain\": (0.30, 0.70),\n", - " \"n\": 25,\n", - " \"noise\": 0.012,\n", - " \"sys\": 0.08,\n", - " \"rho\": 0.94,\n", - " },\n", - " \"exp 2\": {\n", - " \"domain\": (0.25, 0.65),\n", + " \"exp P\": {\n", + " \"domain\": (0.05, 0.4),\n", " \"n\": 25,\n", - " \"noise\": 0.010,\n", + " \"noise\": 0.05,\n", " \"sys\": 0.12,\n", " \"rho\": 1.10,\n", " },\n", - " \"exp 3\": {\n", - " \"domain\": (0.90, 1.40),\n", - " \"n\": 40,\n", - " \"noise\": 0.010,\n", - " \"sys\": 0.03,\n", - " \"rho\": 1.15,\n", - " },\n", - " \"exp 4\": {\n", - " \"domain\": (0.35, 0.75),\n", + " \"exp Q\": {\n", + " \"domain\": (0.55, 1.05),\n", " \"n\": 25,\n", - " \"noise\": 0.015,\n", + " \"noise\": 0.05,\n", " \"sys\": 0.25,\n", - " \"rho\": 1.02,\n", + " \"rho\": 0.8,\n", + " },\n", + " \"exp R\": {\n", + " \"domain\": (0.90, 1.40),\n", + " \"n\": 10,\n", + " \"noise\": 0.05,\n", + " \"sys\": 0.05,\n", + " \"rho\": 1.2,\n", + " },\n", + " \"exp S\": {\n", + " \"domain\": (1.23, 1.56),\n", + " \"n\": 12,\n", + " \"noise\": 0.05,\n", + " \"sys\": 0.1,\n", + " \"rho\": 0.87,\n", " },\n", "}\n", "for label, s in settings.items():\n", @@ -148,22 +117,23 @@ " print(f\"{label}: quoted {s['sys']:.0%}, actually {s['rho'] - 1:+.0%} ({sigmas})\")" ] }, + { + "cell_type": "markdown", + "id": "e6fcf2d3-95b9-4abb-9152-0e00aa6b614e", + "metadata": {}, + "source": [ + "3 experiments correctly estimate their systematic error, while `R`'s true normalization is well outside their reported limit. " + ] + }, { "cell_type": "code", "execution_count": 3, "id": "fc2ec848", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:45:26.063901Z", - "iopub.status.busy": "2026-09-14T19:45:26.063671Z", - "iopub.status.idle": "2026-09-14T19:45:26.070415Z", - "shell.execute_reply": "2026-09-14T19:45:26.069310Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "rng = np.random.default_rng(42)\n", - "a_true = np.array([0.0, 1.0, -0.3, -0.6]) # a_0 is zero, and stays zero\n", + "a_true = np.array([1, -0.3, 0.4, -0.2])\n", "\n", "\n", "def truth(x):\n", @@ -173,30 +143,23 @@ "datasets = {}\n", "for label, s in settings.items():\n", " x = np.sort(rng.uniform(*s[\"domain\"], s[\"n\"]))\n", - " y = rng.normal(truth(x) * s[\"rho\"], s[\"noise\"])\n", + " y_true = truth(x)\n", + " y = rng.normal(y_true * s[\"rho\"], np.abs(s[\"noise\"] * y_true))\n", " datasets[label] = rx.Dataset(\n", - " x, y, np.full(s[\"n\"], s[\"noise\"]), norm_err=s[\"sys\"], label=label\n", + " x, y, np.full(s[\"n\"], np.abs(s[\"noise\"])), norm_err=s[\"sys\"], label=label\n", " )\n", - "x_fine = np.linspace(0.18, 1.42, 140)\n", - "x_high = np.linspace(1.0, 1.40, 30) # the region only exp 3 covers" + "x_fine = np.linspace(0.0, 1.5, 140)" ] }, { "cell_type": "code", "execution_count": 4, "id": "5cdc4ada", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:45:26.072662Z", - "iopub.status.busy": "2026-09-14T19:45:26.072421Z", - "iopub.status.idle": "2026-09-14T19:45:27.605236Z", - "shell.execute_reply": "2026-09-14T19:45:27.604372Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "<Figure size 704x440 with 1 Axes>" ] @@ -218,9 +181,7 @@ " color=colour,\n", " label=rf\"{label}: $\\rho$ = {settings[label]['rho']:.2f}\",\n", " )\n", - "ax.axvspan(1.0, 1.42, color=\"0.93\", zorder=0)\n", - "ax.text(1.02, 0.55, \"only exp 3\\nmeasures here\", fontsize=8, color=\"0.35\")\n", - "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"Five experiments, five normalisations\")\n", + "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"Four experiments, four normalisations\")\n", "ax.legend(fontsize=8, ncol=2)\n", "plt.show()" ] @@ -230,120 +191,82 @@ "id": "e5a2ac08", "metadata": {}, "source": [ - "## The model, and why one experiment must be absolute\n", - "\n", - "The truth is a cubic with $a_0 = 0$, and we hold $a_0$ at zero rather than\n", - "inferring it: an additive constant and a multiplicative normalisation are nearly\n", - "the same thing over a short range, so leaving both free would confound them.\n", - "\n", - "That is not enough on its own. If *every* experiment may renormalise itself,\n", - "then shrinking all the $\\rho_i$ by 10 % and raising the curve by 10 % fits\n", - "exactly as well — the overall scale is unidentifiable, and the fit wanders.\n", - "Something has to fix it, and the honest way is an experiment that measured the\n", - "absolute scale: `exp 0` here. The rest are then normalised relative to it." + "## The model\n", + "The truth is a cubic, $y = a_0 + a_1 x + a_2 x^2 + a_3 x^3$. We will infer all four coefficients, as well as the normalization of each experiment" ] }, { "cell_type": "code", "execution_count": 5, "id": "db34eca7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:45:27.608566Z", - "iopub.status.busy": "2026-09-14T19:45:27.608315Z", - "iopub.status.idle": "2026-09-14T19:45:27.616310Z", - "shell.execute_reply": "2026-09-14T19:45:27.615470Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "normalisation free for: ['exp 1', 'exp 2', 'exp 3', 'exp 4']\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ + "# a constant term is expected to be small, so a_0 gets a narrower prior than\n", + "# the shape coefficients; that also shrinks the volume the sampler must search\n", "coeffs = [\n", - " rx.Parameter(f\"a{k}\", prior=stats.norm(0.0, 1.5), latex=f\"a_{k}\") for k in (1, 2, 3)\n", + " rx.Parameter(f\"a{k}\", prior=stats.norm(0.0, 0.5 if k == 0 else 1.5), latex=f\"a_{k}\")\n", + " for k in (0, 1, 2, 3)\n", "]\n", - "cubic = rx.Model(\n", - " lambda x, *a: P.polyval(np.asarray(x, dtype=float), (0.0,) + a), coeffs\n", - ")\n", - "comps = {label: rx.Comparison(d, cubic) for label, d in datasets.items()}\n", - "free = [label for label, s in settings.items() if s[\"sys\"] > 0]\n", - "print(\"normalisation free for:\", free)" + "cubic = rx.Model(lambda x, *a: P.polyval(np.asarray(x, dtype=float), a), coeffs)\n", + "comps = {label: rx.Comparison(d, cubic) for label, d in datasets.items()}" + ] + }, + { + "cell_type": "markdown", + "id": "94ef912c", + "metadata": {}, + "source": [ + "## How we sample\n", + "\n", + "We'll use [nested sampling](https://en.wikipedia.org/wiki/Nested_sampling_algorithm) through [dynesty](https://dynesty.readthedocs.io/) ([Speagle 2020](https://doi.org/10.1093/mnras/staa278)) for every fit in this notebook, rather than an ensemble sampler like emcee." ] }, { "cell_type": "code", "execution_count": 6, - "id": "5439babf", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:45:27.619157Z", - "iopub.status.busy": "2026-09-14T19:45:27.618920Z", - "iopub.status.idle": "2026-09-14T19:45:27.624108Z", - "shell.execute_reply": "2026-09-14T19:45:27.623213Z" - } - }, + "id": "277a09fb", + "metadata": {}, "outputs": [], "source": [ - "def fit(problem, seed, n_walkers=32, n_steps=2000):\n", - " sampler = emcee.EnsembleSampler(n_walkers, problem.ndim, problem.log_posterior)\n", - " sampler.random_state = np.random.RandomState(seed).get_state()\n", - " sampler.run_mcmc(problem.sample_prior(n_walkers, rng=seed), n_steps, progress=False)\n", - " return sampler.get_chain(discard=n_steps // 3, thin=5, flat=True)\n", + "def fit(problem, seed, nlive=250):\n", + " sampler = dynesty.NestedSampler(\n", + " problem.log_likelihood,\n", + " problem.prior_transform,\n", + " problem.ndim,\n", + " nlive=nlive,\n", + " sample=\"rwalk\",\n", + " rstate=np.random.default_rng(seed),\n", + " )\n", + " sampler.run_nested(dlogz=0.5, print_progress=False)\n", + " fit.logz[problem] = (sampler.results.logz[-1], sampler.results.logzerr[-1])\n", + " return sampler.results.samples_equal(rstate=np.random.default_rng(seed))\n", + "\n", + "\n", + "fit.logz = {}\n", "\n", "\n", "def curves(problem, samples, grid, n=200):\n", - " \"\"\"The bare cubic's curves on a grid: the physics, not the renormalised data.\"\"\"\n", + " # the bare cubic's curves on a grid: the physics, not the renormalised data\n", + " rows = samples[np.random.default_rng(0).choice(len(samples), n, replace=False)]\n", " return rx.predictive.grid_draws(\n", - " problem,\n", - " cubic.bind(grid),\n", - " grid,\n", - " samples[-n:],\n", - " model_only=True,\n", - " return_draws=True,\n", + " problem, cubic.bind(grid), grid, rows, model_only=True, return_draws=True\n", " )" ] }, { "cell_type": "markdown", - "id": "f6066bdd", + "id": "90bd9bc0-e6f4-4999-a7b2-1e3504ceff74", "metadata": {}, "source": [ - "## Three defensible things to do\n", - "\n", - "**1. Believe the quoted systematic (recipe 3).** Each dataset's `norm_err`\n", - "becomes a rank-one mode built from the prediction; `comp.reported_terms()`\n", - "spells exactly that. This is the standard thing to do.\n", - "\n", - "**2. Infer the normalisation (recipe 6).** Give each dataset a latent scale\n", - "$\\rho_i$ on the *model*, with the quoted systematic as its prior width, and let\n", - "the data move it.\n", - "\n", - "**3. Infer the systematic magnitude (recipe 4).** Keep the mode in the\n", - "covariance, but treat its size $\\eta_i$ as a nuisance parameter per dataset\n", - "instead of a number we were handed.\n", - "\n", - "All three say \"there is a normalisation here\". They differ in how much they\n", - "trust the quote." + "### The three good approaches (1,2,3):" ] }, { "cell_type": "code", "execution_count": 7, "id": "ad183ccd", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:45:27.627399Z", - "iopub.status.busy": "2026-09-14T19:45:27.627146Z", - "iopub.status.idle": "2026-09-14T19:45:27.638484Z", - "shell.execute_reply": "2026-09-14T19:45:27.637534Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "rhos = {\n", @@ -352,7 +275,7 @@ " prior=stats.norm(0.0, np.log1p(settings[label][\"sys\"])),\n", " latex=rf\"\\log\\rho_{i}\",\n", " )\n", - " for i, label in enumerate(free)\n", + " for i, label in enumerate(comps)\n", "}\n", "etas = {\n", " label: rx.Parameter(\n", @@ -360,12 +283,12 @@ " prior=stats.norm(np.log(settings[label][\"sys\"]), 0.7),\n", " latex=rf\"\\log\\eta_{i}\",\n", " )\n", - " for i, label in enumerate(free)\n", + " for i, label in enumerate(comps)\n", "}\n", "comps_scaled = [\n", " (\n", " rx.Comparison(d, cubic | tf.scale(rhos[label]))\n", - " if label in free\n", + " if label in comps\n", " else rx.Comparison(d, cubic)\n", " )\n", " for label, d in datasets.items()\n", @@ -376,22 +299,15 @@ "cell_type": "code", "execution_count": 8, "id": "6cd9b231", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:45:27.641272Z", - "iopub.status.busy": "2026-09-14T19:45:27.641009Z", - "iopub.status.idle": "2026-09-14T19:45:27.652679Z", - "shell.execute_reply": "2026-09-14T19:45:27.651611Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "quoted systematics 3 columns\n", - "inferred normalisations 7 columns\n", - "inferred systematics 7 columns\n" + "quoted systematics ['a0', 'a1', 'a2', 'a3']\n", + "inferred systematics ['a0', 'a1', 'a2', 'a3', 'log_eta_0', 'log_eta_1', 'log_eta_2', 'log_eta_3']\n", + "inferred normalisations ['a0', 'a1', 'a2', 'a3', 'log_rho_0', 'log_rho_1', 'log_rho_2', 'log_rho_3']\n" ] } ], @@ -401,25 +317,25 @@ " [\n", " rx.Constraint(\n", " list(comps.values()),\n", - " terms=[t for label in free for t in comps[label].reported_terms()],\n", + " terms=[t for label in comps for t in comps[label].reported_terms()],\n", " )\n", " ]\n", " ),\n", - " \"inferred normalisations\": rx.Problem([rx.Constraint(comps_scaled)]),\n", " \"inferred systematics\": rx.Problem(\n", " [\n", " rx.Constraint(\n", " list(comps.values()),\n", " terms=[\n", " T.normalization(parameter=etas[label], on=comps[label])\n", - " for label in free\n", + " for label in comps\n", " ],\n", " )\n", " ]\n", " ),\n", + " \"inferred normalisations\": rx.Problem([rx.Constraint(comps_scaled)]),\n", "}\n", "for name, p in correct.items():\n", - " print(f\"{name:24s} {p.ndim} columns\")" + " print(f\"{name:24s} {p.names}\")" ] }, { @@ -427,41 +343,29 @@ "id": "e7c9ff53", "metadata": {}, "source": [ - "## Three things not to do\n", + "## Three things not to do:\n", "\n", - "**4. Peelle's Pertinent Puzzle (recipe 27).** Build the same mode from the\n", - "*measured* values instead of the prediction, so the fluctuations feed back into\n", - "the covariance.\n", + "**4. Peelle's Pertinent Puzzle (recipe 27).** Try to believe the experimentally reported uncertainties as in 2, but make the mistake of using the experimental data $y_i$ in systematic term in the covariance, rather than the model prediction $y_m(x_i;\\alpha)$ \n", "\n", - "**5. Ignore the systematics.** Quoted statistics only, as if every\n", - "normalisation were exact.\n", + "**5. Ignore the systematics.** Take the experimentally reported statistical errors only, as if every\n", + "normalisation were exact. \n", "\n", - "**6. Infer statistical uncertainties instead.** Notice that the datasets\n", - "disagree, blame the noise, and let a free diagonal absorb it. It looks like\n", - "inference, and it is the wrong model: a normalisation moves a whole dataset\n", - "together, and no diagonal can say that." + "**6. Infer statistical uncertainties instead.** Notice that the datasets disagree, but blame the noise rather than systematic effects. Add a free diagonal noise term to the covariance to absorb it, and infer its scale. " ] }, { "cell_type": "code", "execution_count": 9, "id": "c975ffb4", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:45:27.655010Z", - "iopub.status.busy": "2026-09-14T19:45:27.654781Z", - "iopub.status.idle": "2026-09-14T19:45:27.668417Z", - "shell.execute_reply": "2026-09-14T19:45:27.667458Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "PPP: mode from the data 3 columns\n", - "systematics ignored 3 columns\n", - "statistics inferred 4 columns\n" + "PPP: mode from the data ['a0', 'a1', 'a2', 'a3']\n", + "systematics ignored ['a0', 'a1', 'a2', 'a3']\n", + "statistics inferred ['a0', 'a1', 'a2', 'a3', 'log_eps']\n" ] } ], @@ -480,7 +384,7 @@ " kind=\"mode\",\n", " on=comps[label],\n", " )\n", - " for label in free\n", + " for label in comps\n", " ],\n", " )\n", " ]\n", @@ -490,37 +394,49 @@ " [\n", " rx.Constraint(\n", " list(comps.values()),\n", - " terms=[T.noise_fraction(log_eps)],\n", + " terms=[T.proportional_error(log_eps)],\n", " statistical=False,\n", " )\n", " ]\n", " ),\n", "}\n", "for name, p in wrong.items():\n", - " print(f\"{name:24s} {p.ndim} columns\")" + " print(f\"{name:24s} {p.names}\")" ] }, { "cell_type": "code", "execution_count": 10, - "id": "f7833d17", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:45:27.671706Z", - "iopub.status.busy": "2026-09-14T19:45:27.671406Z", - "iopub.status.idle": "2026-09-14T19:54:00.645104Z", - "shell.execute_reply": "2026-09-14T19:54:00.643960Z" + "id": "cd0f7f7a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 22min 40s, sys: 191 ms, total: 22min 40s\n", + "Wall time: 10min 3s\n" + ] } - }, - "outputs": [], + ], "source": [ + "%%time\n", "problems = {**correct, **wrong}\n", - "samples = {name: fit(p, seed=i) for i, (name, p) in enumerate(problems.items())}\n", + "samples = {name: fit(p, seed=i) for i, (name, p) in enumerate(problems.items())}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "557344b1-a015-41af-bbdd-314a41553a6a", + "metadata": {}, + "outputs": [], + "source": [ "style = dict(\n", " zip(\n", " problems,\n", " zip(\n", - " [plotstyle.COLOURS[i] for i in (0, 2, 5, 1, 3, 6)],\n", + " [plotstyle.COLOURS[i] for i in (0, 2, 1, 4, 3, 6)],\n", " plotstyle.HATCHES + plotstyle.HATCHES,\n", " ),\n", " )\n", @@ -528,112 +444,35 @@ ] }, { - "cell_type": "code", - "execution_count": 11, - "id": "86edaa87", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:00.647524Z", - "iopub.status.busy": "2026-09-14T19:54:00.647280Z", - "iopub.status.idle": "2026-09-14T19:54:00.706004Z", - "shell.execute_reply": "2026-09-14T19:54:00.705007Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "the three defensible ones\n", - "quoted systematics a1=+1.56+/-0.81 a2=-2.52+/-3.21 a3=+1.50+/-3.00 max|pull| = 0.7 RMS over exp 3's range = 1.269\n", - "inferred normalisations a1=+0.98+/-0.02 a2=-0.21+/-0.05 a3=-0.67+/-0.03 max|pull| = 2.2 RMS over exp 3's range = 0.015\n", - "inferred systematics a1=+1.00+/-0.02 a2=-0.29+/-0.05 a3=-0.61+/-0.03 max|pull| = 0.3 RMS over exp 3's range = 0.004\n", - "\n", - "the three wrong ones\n", - "PPP: mode from the data a1=+0.98+/-0.02 a2=-0.24+/-0.05 a3=-0.64+/-0.03 max|pull| = 1.2 RMS over exp 3's range = 0.009\n", - "systematics ignored a1=+0.91+/-0.01 a2=+0.03+/-0.02 a3=-0.84+/-0.01 max|pull| = 18.1 RMS over exp 3's range = 0.061\n", - "statistics inferred a1=+0.87+/-0.55 a2=-0.29+/-0.88 a3=-0.69+/-0.44 max|pull| = 0.2 RMS over exp 3's range = 0.281\n" - ] - } - ], + "cell_type": "markdown", + "id": "b96c4d72", + "metadata": {}, "source": [ - "def report(names):\n", - " for name in names:\n", - " p, s = problems[name], samples[name]\n", - " cols = p.columns(cubic.params)\n", - " pulls = [\n", - " (s[:, c].mean() - a_true[k + 1]) / s[:, c].std() for k, c in enumerate(cols)\n", - " ]\n", - " mean_high = curves(p, s, x_high).mean(axis=0)\n", - " rms = np.sqrt(np.mean((mean_high - truth(x_high)) ** 2))\n", - " print(\n", - " f\"{name:24s} \"\n", - " + \" \".join(\n", - " f\"a{k + 1}={s[:, c].mean():+.2f}+/-{s[:, c].std():.2f}\"\n", - " for k, c in enumerate(cols)\n", - " )\n", - " + f\" max|pull| = {max(abs(z) for z in pulls):4.1f}\"\n", - " + f\" RMS over exp 3's range = {rms:.3f}\"\n", - " )\n", - "\n", - "\n", - "print(\"the three defensible ones\")\n", - "report(correct)\n", - "print(\"\\nthe three wrong ones\")\n", - "report(wrong)" + "All six fits sit close to the truth on the scale of the data, so we show them twice. First the three defensible treatments as 90 % bands over the data itself. Then the three mistaken ones as *residuals*, the fitted curve minus the truth: a band that contains zero at some $x$ covers the truth there. The grey region marks the high-$x$ range that `exp R` dominates — the dataset that quoted 5 % and is actually 20 % off — which is where a treatment that takes that quote at face value gets pulled away from the truth." ] }, { - "cell_type": "markdown", - "id": "a3acc767", + "cell_type": "code", + "execution_count": 12, + "id": "f2ad67f9", "metadata": {}, + "outputs": [], "source": [ - "### What that table says\n", - "\n", - "The first row is the one to look at. `quoted systematics` is the standard thing\n", - "to do, and here it fails. `exp 3` quoted 3 %, so the fit is obliged to believe\n", - "its scale to within 3 % — and since `exp 3` is the only experiment in the\n", - "high-$x$ region, the cubic has to bend to follow data that are 15 % high. It\n", - "cannot do that and still fit the others, so it gives way in the only direction\n", - "left to it: the coefficients acquire enormous uncertainties\n", - "($a_2 = -2.52 \\pm 3.21$) and the predicted curve is *still* wrong, with an RMS\n", - "error of 1.27 over `exp 3`'s range against 0.004 for the best case.\n", - "\n", - "Notice that this case has the **smallest** `max|pull|` of the three, 0.7. The\n", - "pulls look innocent precisely because the error bars exploded. Had we judged\n", - "these fits by parameter pulls alone, we would have called the broken one fine —\n", - "which is the argument for looking at the prediction as well.\n", - "\n", - "Both cases that infer the normalisation recover the curve, which is what we\n", - "hoped for: RMS 0.015 for the sampled scale $\\rho_i$, and 0.004 for the sampled\n", - "magnitude $\\eta_i$. Inferring $\\eta$ wins here because it keeps the quoted\n", - "values as a starting point and only widens the ones the data cannot live with.\n", - "\n", - "Among the three we called wrong, ignoring the systematics is the classic:\n", - "$a_2 = +0.03 \\pm 0.02$, eighteen standard deviations from the truth, precise and\n", - "wrong. Inferring the *statistical* errors instead technically covers the truth,\n", - "but only by inflating everything until the answer says nothing\n", - "($a_2 = -0.29 \\pm 0.88$). And the Peelle mode is, in this configuration, nearly\n", - "harmless — RMS 0.009. That deserves an explanation rather than a shrug, and it\n", - "gets one two sections below." + "bands = {\n", + " name: np.percentile(curves(problems[name], samples[name], x_fine), [5, 95], axis=0)\n", + " for name in problems\n", + "}" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "aeb43081", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:00.708741Z", - "iopub.status.busy": "2026-09-14T19:54:00.708501Z", - "iopub.status.idle": "2026-09-14T19:54:00.951317Z", - "shell.execute_reply": "2026-09-14T19:54:00.950157Z" - } - }, + "execution_count": 13, + "id": "84a548b9", + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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3vl3PH+3FL/zCL/Ct3/qtO7pWvO1tb+Pzn/88R44c4UlPehKvfvWr+c7v/M7zXu8Hf/AHGQwGHDx4kGuuuYYXvOAFvOIVr+BXf/VXp2N+4Ad+gFe/+tX8zM/8DMeOHWPfvn189KMf5TWvec0F3/d2SqUS73vf+9Bac/jwYR796Efz5Cc/mVe96lU7tuO+/du/nTe84Q1Uq1W+6Zu+iYMHD/KGN7yBt7/97TztaU+bjjt+/Di33XbbjkzXbvzar/0aH/rQhzh69OieddbOh5SS97///Xzf930fz3jGM9i/fz+PfexjqVar/M7v/M45M3WlUon3vve9ZFnGoUOHePSjH81TnvIUfvEXfxHXdWe2L4Mg4EMf+hAHDx7k+uuv54YbbmBhYYEHHniAD3zgAzzqUY96UOMezNo/lOt3Lp7whCfwD//wD9x3333s37+fG264gWuuuYZHPepRrK+vT39mLnScwXAlIfRuBxUMBsMlYXIo/tixY3ueJZowGo249957CcOQa6+99kG/1m233caxY8d23VrVWnPPPfcwPz9PvV4/53WUUnz2s5/l4MGD7N+/n3a7zYkTJzh8+PCez+33+xw/fpx9+/YxNzdHr9fjzjvv5IYbbqBare645la+8pWvIKWcKaI84b777iOOY66//nps2+aOO+6gVCpx7NixmXGj0Yj77ruParXKgQMHsKzZv2cfeOABVlZWeNzjHndBDtjRaMQ999xDHMc0Gg2OHTv2dc8hTdPpmclrr70W13XP+/pb558kCddffz1CCHzf5+UvfzlvectbdoxdW1vj9OnTNBqNcwqWCx13oWu/G9/o+p1r7FZarRanTp2i2WzuagJ5sOMMhssdI+oMBoPhKuCjH/0oT3va0/iTP/kTfuRHfuThvh2DwfAwYESdwWAwXGH8+Z//OY961KOmNdbuu+8+nv/859PpdPjyl79MGIYP8x0aDIaHA3OmzmAwGK4wjh49yktf+lL279/P9ddfz/XXX0+pVOL973+/EXQGwyMYk6kzGAyGK5TJWbClpSVTV81gMBhRZzAYDAaDwXA1YLZfDQaDwWAwGK4CjKgzGAwGg8FguAp4xIu6LMs4efLkQ9q2xmAwGAwGg+Gh5hEv6s6ePcvhw4c5e/bsQ/o6W9vQPFJ5pK/BI33+YNYAzBo80ucPZg3ArMFDNf9HvKi7VBg/ilmDR/r8wawBmDV4pM8fzBqAWYOHav5G1BkMBoPBYDBcBRhRZzAYDAaDwXAVYESdwWAwGAwGw1WAEXUGg8FgMBgMVwFG1BkMBoPBYDBcBRhRZzAYDAaDwXAVYESdwWAwGAwGw1WAEXUGg8FgMBgMVwFG1BkMBoPBYDBcBRhRZzAYDAaDwXAVYESdwWAwGAwGw1WA/XDfwFa01nzkIx/h9OnT/Jt/82/wPO+8z1lfX+dTn/oUQgi+9Vu/lf3791+COzUYDAaDwWC4vLhsMnV/8Ad/wA033MDLX/5yfvRHf5Rer3fe57z61a/mCU94Ar/zO7/D61//eq677jpe+9rXXoK7NRgMBoPBYLi8uGwydbZt8973vpe7776b7/u+77ug53z7t387v/7rv45tF9N4z3vewwtf+EJe9KIXcd111z2Ut/ugGMQZ97eGDK2A0LUJXUngyIf7tgwGg8FgMFxFXDai7iUveQkAd9999wU/5wUveMHM1xMhNxwOL96NXQTiXHG2F9NSfTRgCcGhus/RRohvxJ3BYDAYDIaLwGUj6r5evvKVr3DbbbexsrLCO9/5Tn7hF36Bb/7mb95zfLfbpdvtTr8+c+bMpbhNpBAsVXwA0lxxYiPibCfiMUsVFsseliUuyX0YDAaDwWC4OrniRd19993He9/7Xk6dOkWv1+Oxj33sOce/6U1v4jWvec2Oxzc2NgiC4CG5x26coeIBUc+ZPlbWcGJtyANnVzhUC6j4NqFj40qBY1u4UmBbFvIqEntbxfQjkUf6/MGsAZg1eKTPH8wagFmD7fOfm5u7KNe94kXdzTffzM033wzA+9//fp797GfzmMc8hptuumnX8bfeeiu33HLL9OszZ87wxCc+kUajcdEWdTtimGAt9/ErdQAypTnbHiFDm+uqHpYQDDNNO1FoNBMZ50gIHEHFsym5ktC18WwL35a49mXjcXlQPFRrfKXwSJ8/mDUAswaP9PmDWQMwa/BQzP+KF3VbedaznkWz2eTjH//4nqKuWq1SrVYv8Z1tkinNyfaINFccqPmU3CIEJXd2nNaaJNckWc4XzvToRCnN0KXu29iWhedY1H2bRugSOpKSK7HllSn0DAaDwWAwfONcUaLu/e9/P6VSiac+9am0221c1yUMw+n377nnHjY2Nrjmmmsexrvcm70E3W4IIXAlrA9zbEtww0KZubBQfmmuSHLFcj/hZDsCAZ60OFDz2VfxqfhXVFgNBoPBYDBcBC6bT//PfvazfPnLX+YLX/gCAH/2Z39GuVzmu77ru6Yi7Vd+5Vc4evQoT33qU1leXuZf/+t/zXOf+1yOHTvGyZMnecc73sHTn/50fuiHfujhnMquKH3hgg6KTN2ZXkw/zpgruVNBB+BIC0daM9m9KM35/KkOSdbmMYslrmmG1AMHx2TvDAaDwWB4RHDZiLo777yT973vfQC86EUv4h//8R8BePSjHz0Vdc9+9rNZWFiYPv6Rj3yEP/7jP+ZTn/oUc3Nz/O7v/i4333wzQlx+5oJOlOHKb1zQ7cUgzRFCUA9tunHGZ0928GyLxbLHQtmlNK6NdzmujcFgMBgMhm+cy0bU/fAP/zA//MM/fM4xv/qrvzrz9cLCArfeeutDeFcXj1w9dIJufZiwPkgoezb7K95UuMWZYrkXc6IzwhIQ2pJ64FAPHUJHEroSzzZ18gwGg8FguBq4bETd1U7Vdy6poAPwbAvPLq6RK02cKY63R3zi+AaBY7FU8aj7DtfNl2hcwGsZDAaDwWC4fDEHri4R7nnOtl1sQbcdaQk0mlGSs1j2eOxSlUbg0k9yPnOiw/3rQ3KlH/S8DAaDwWAwXB6YTN1lwEMt6AAGScbpToQjLQ7VA2xLAIJm6BJniq+s9GlHKTcslAjPk1E0GAwGg8Fw+WEydQ8zD5+g28SzLfZXPdYGCZ850WalF39DczIYDAaDwXDpMaLuYeRyEHQTLCFYLHtoDe+/c5WvnO2S5urrmpfBYDAYDIZLj9lne5i4nATdhDRXbIxSPFtw19qAbpxxbK7EXOhiXUU9aA0Gg8FguBoxou5h4HIVdCfaI5SGo40Qz7boRBm3nezQDByONkPmSi7SiDuDwWAwGC5LjKi7xFzugu5Qzcd3itp19cCh5tt0o4zPne5Q82yONEPmS67pVGEwGAwGw2WGEXWXkCtJ0E0QQlALHKraphcXrcgEgkcvlNi/y3iDwWAwGAwPD0bUXSI0V56g24oQgrIn6UQprWHMMMm4f2PEobrP/qpP2TNvJYPBYDAYHk7MJ/ElohdnWNaVKegAlNac6kSM0pxrmyFV3yk6VLSGHN8YMRc6LJTdaecMc/bOYDAYDIZLixF1l4gkUxy8CgTdvopH1XeAor7dYsUnzRW9OGd10AcNviORAkLPouG7BK5N4FhkuelYYTAYDAbDQ4URdZeI0JVXlaDbiiMt6oEFOGitOdGOWB/GhI5NPcjQWoMAEfWpjSQHah7zJc+cxzMYDAaD4SJiRN0lIFU5WuQMswRfOlh7CLUrUdBtZ6WfEGU5B6oBSxVv5nt9NSLJNV9e7mNbA/ZVfPZVPeq+Y+rgGQwGg8HwDWJE3SWgn8acjFqstWNcy6Zie1Qcn0C6+NLBs2xao/SKF3TLvZhOlFLznR2CDsC2BL5vU/VtkkzxtdU+nznR5nDd51ELJZO9MxgMBoPhG8CIukuFhqZbIlEZvSxmPRmQqpxBlkBu4yiPpVKZJb+MBs4l0a5EQbedKMvJlKYZOtiWxZfO9nFkkb3bX/WomeydwWAwGAwPCiPqLiFCCDzp4MlNkXR2MOCu7jq27OEECb1Ol6rjcyCsU3fCHVu1V4Og60YpZ3sxgSM5WPOxxrXwkkxxphtxoj2iHtgcqgXMlVyTvTMYDAaD4QIwou5hpB2lxLHgWGWOXEbkWjPn+sQq46vdM9TskH1Bjarj41jyqhV0E1zbYqHsobWmF+d88WwPS8D+qsfBWmCydwaDwWAwnAMj6i4BWmuEzmcea0cpnWFK6ErmSy6J8liJu6zEPZb8KmXbo5dFfLV7Bk86lERAFFnU3eCKFXSDJGc92l3QbUUIQdW3caXg3taQk50RD2xE1HybAzWfZuhS9kwtPIPBYDAYtmJE3aVg1KXUug87tMhKc7RTZgRdsS1rs+hVWYm7LEddlvwqVScAB9pRzJe7q1gWPCacI1EOtrV3eZTLUdB1o5TWMCGohOcUdBOiNOdkJ8K3LR41X8O2BMM05+61ATDAlRYVz6YRFsWOS64kdOV5DSYGg8FgMFytGFF3KdCKLO7hrN5JtOoz8vYTVuengm7CbsIuy6EzzJnzyiyUHTrpkDs6I64JGyx4VWzLmnmpy1XQne3F+Pa5M3QTJoLOEnC4HuDIYo6FeLPRWhNnqsj8DVMypejFGXXf4UgjYL7sUQ8ck8kzGAwGwyMKI+ouEZmA+/DxoozF/hcppw0SeQN52ACxKcy2Crvj/Q1E6hHYhXiSlqApS8R5yj39NVrxkCPlOcp2IawuZ0EXOJI5z/26Bd1WhBD4jsR3JCWlOdkegQbLgpOdiPs3RpQ9yaFawHzJpWT60hoMBoPhEYD5tLtESG2xNoqpuA4H3BLB2TtwN44TLz6GtHENeVifijtP2lRlia921nFkxqHa/EzWyZMOC5ZNJx3xpfZpDpcaNJ0ypzvxZSvoDtZ8kn58zvEXIui2ko0FXZorDjcCSm7xdtZaM0hy7lztc9eaYKHscqDq0wgc7PNc02AwGAyGKxUj6i4RSgkW/RLaTlnVDovz14PtI1SCt/wlcr9GVjtEHtYZZZr2IGfJr4ITs5r0WLKqONamUBNCUHdD4jzjru4qadxi3q3x6GbtshR0FyNDt5Wtgu5AzZ8KOijWpuzZlD2bNFdsDFPOdCIQgmPNkH1Vj4pnm/N3BoPBYLiqMKLuEiEtwb5KyChP6SQjlv0qTbdUiB2tseIe3vKXGNkVNuQCTqnBUrVEpv2ZM3ZbhR2AxCKLbXpZhOO2eGCUsl/UCaTDIIvpZREbyZCK7VN1AgLpsN7Pr1pBtx1HWjQChzOZYq0f04tSjm841AOHQ/WARuCYOngGg8FguCowou4S4dkWQghCu3CtdpIRrWQwFXbKrxInOd32Ok29TpUFcucQMqzvME9MhF2WK872YjSC6xs1bClopyNW231SldFNI2quT80JWYl7nBpt0Brm+Hh8y9zCVS/ooNiKPdOL6ccZB+sBc6GL0ppelPGF010cKViq+OyrGHOFwWAwGK5sjKh7GNhN2KWZpjVKsYM6ldBGpn3s8basrB1i0SuzkvSnwk5owdlecYZuqeLi2YXQa7ghuVakKkcIQaryomODE7DSj3F1hu2mHI9WSa2EfX5tR/ZvwtUk6OZKLnNhse6TDhaTLhbLvZiT7RElV3Kg6jFfNtuzBoPBYLjyMKLuEmGpDLtzGu2G5EF9Kuy6ow7tjdPkuoRdXmCuVHRNUCLEjge4nVPIYRsnqGFXljhtSU4P2+jUw0LOCLoJUlhIabHkV1mOuiyPepC5CCVZLPuUPZtEZTww3GA16nMorDPnlZFbXLhXq6DbjmtbzNsuWmtOdSO+clefRmhztBFyoBbQCB0Csz1rMBgMhisAI+ouETKLcdtnyMMmuV+D8VasTlN6qw9g+VWacwvTNlhWPMDpnUXbLmllHyJPqK5+DVu4fIEysV/jsfNLOwTdzGsKiwWvwldaawyTiOvqDcrj8h6uZbPglRlmCV/rrVCLuhwKm9SdgJV+8ogQdFtpjVKGSc7hRsBiyWWQ5HzxbBdpCRZLLvuqPvXAOe+9GgwGg8HwcGFE3SUit10ybx+5V4ax6IlTRV+XcGsHyX2Htoppaqc4Y+eGZKV5tLTRjocWAaldotta51HdLxBVagzl9bjNIziOv+trKq1ZH6SURYmwFDPUI0a5TSA3RU5ouwTSoZ/FfKVzhlECSQoN36MsS6zGCVJY2MLCFpJAOjPbkleDoFsfJqwPEsqezf6KhxCCWmBRwyHNFWvDlNPdGN+22F/1mSu52JYo/pMCSwikEKYvrcFgMBgeVi4bUaeU4n3vex/veMc7OH36NO9///up1WrnfM59993H//gf/4PPfe5z1Go1br75Zl7ykpcg5eW3XaYsh6R5YPp1nCpawwTbDag3HkOk0lnzhHRIG4en4/NcszZMUX6VavPbKKV9eut3E3dO4yxcj6gsoe1NAaO0ZqUfE6eKxbJP4IYsR11Woh6LfmVG2AkhqDg+URwzSiJ8x8JyUj67cYJIpdScgJLtYVG0M6s6PhU7QCibjUF+wYIuyRQrV4Cg24ojrel1RmnOnas9WvenuLZFI3BxbIGFwBJMRZ5tWdiWwLELsWcLgedIBoOEzI2xROGGrni2yfwZDAaD4aJx2Yi6H/qhH2I0GvG4xz2Ov/zLvyRN03OOv/POO3nuc5/LLbfcwi/+4i9y4sQJ/uN//I98/OMf5x3veMcluuuvj6mgs6zpGbrQ2t0VC2NBN0hQwFzo4NoW2FVKbon2YI3hmTuot0+g64fIygvktj8VdHMld7rlOjljt5uwWx8kDJOcuSBgrlQ8XnMC1uI+wzzBEZKK45OojFY85N7uBr1RzsFSnf315gVl6FYGCVbgXTGCbjtKa5JM0whdDtUDbEuQK43SGqUh15pcadJcjcfmnGhH5FqzVPZw0iFyJNHj67nS4mDNZ7HsUfWNMcNgMBgM3xiXjah717veRa1W433vex9veMMbzjv+yJEjfOlLX8K2N6fg+z4vfvGLeeMb33jeLN+lJlZ58e8ugm7Cbq5Yrdgp6Ma4UlIvzdNyfNayhPn1+/HbJzhrNcjdOeYam2fooDhjt5uwWx8k9OOMsmdPBR0UGbx5r8xa3KedDgGouQFZJiDV1DxNJod8qRPRdEv8w1cHpKmg6ju8+AmHpte5UrZcz8UgyTjdiXCkNRV0UGTcJDufm+ZFnGuBM+3wEfUS/MrmVvkozbl/Y8T9rSElV1LxbOZKHoFjEbrynOclDQaDwWDYzmUj6h6sCPP9nefIyuUyWmuSJLlYt3XRyHTOynBAnkocnbOoO5AEKL+6OUjlVKMOttas47E26pNnDmjNEj2cWJLL5vRMHloTRG0W84wV2+UsFjIRWKOzHJHHcUYVstoBlF8n9ysgnR3Czs59skzsEHQTtgu7QZKRJhLPsVgse1hCkGtFOx3yp589TWuQs1RxeeHj9+NYckbQLZa9q0rQ7cWF9uANHEngSDKluGdtSDfq0wgdyp6NbQlKrmS+VGTxKp5tiiQbDAaD4ZxcNqLuGyXLMn7zN3+Tm266iYWFhT3Hdbtdut3u9OszZ85citvDQnCmP6DieByUMX7nJLlXIXZLMK4TJ6MuTvtksc1ZPcT9gwgpUq73LErdM2ghUU6AdkMARDrC6Z7B1TnztWu4O07JcjharRL2HkCurZDFPZRXRlsuaXWRPGiCX2HJr3LnxjrdqM3hcn1XQTdhIuyO99oc73eZD0IOl+vTLVcpLOpuiBx/naqcL7RPUpclotgmkA6H6wH58Nxb6o8kQTdBac2ZbowQ8OjF8rQgdJorokxxX2tIrjWOJaj7DvuqPhXPpuxJs11rMBgMhhmuGlH3ile8gjvvvJNPfvKT5xz3pje9ide85jU7Ht/Y2CAIgofk3ob9AY72mbdDHFvT0y5WsFgItFxAXpyyykVIGiySYxPHDk3LwrIzBoDnzmNZFgoX0mK8xiXz5lBK0c88GghyNyXWmsRtIp0aWTiPtl1EniJby7jR3eRBlVZwgAYeoSWwsy5JP8Oz9u4wMUpz/JFivyXw1IhRD8r2tnInurgvCVjDiK/1WygU11ZrdLtV7HjvDGquYaUXkynFfNlFxhlRvPeaal0ItFGaUwscSnlG1BueMw6dOKM7SgkdSQOXuB+dc/woy1nrF1vlCxWPbJCQnWN8pgpzitKwWHIhSom2vEQy7M+MV7rYWo+znLnQxU0zoi261xn/B5BlmvaG4u6TMbmC+YrLvopH2bMJHXlesXm5sPUPqkcqj/Q1eKTPH8wagFmD7fOfm5u7KNe9KkTdz/3cz/H//r//Lx/+8Ie5/vrrzzn21ltv5ZZbbpl+febMGZ74xCfSaDQu2qJuZ2PUJdMJ++uLdLOIbqZIvTnqXjh7GsvxiJ39hcsVmC8HZGR0khEr5SpNr7zNkGCTNPbRGiSkuaZWdrClSyvpczYImXNL2HJ84stx0b5LVqnR22ihel+lWW7g77uG0xrOiIxFL5gxT0zoxxnrUYJXKnG41KCVDFjLEzJbUHO3COHxvWkhWFceXsljvmRzKu5yz/AMR/A56NapOSG2tbkNmynN2faIzLUfVIZuKG3mqheeoevohHKtfMEZurUowikFF5yhO90eoTz3nBk6v1IHigzdqU5EZNvsa1xYD95BL8bSHjXPpuTZnE1zdKJxJSyUHeZCl6pvEziXdxbvofo5u5J4pK/BI33+YNYAzBo8FPO/4kXdrbfeyh/90R/xoQ99iMc//vHnHV+tVqlWq+cdd7HxbYllCepjETTKEkhG069hS9kSNk0RLnu7YpXWtIZpIegCh9AthETTLdNK+qwnA+a88oyAao8yRrJEUKtQtxOs5Tu51rJYxaLrV5HVRTyvipYuCFEIukEyc4ZuN/PEVnKlsQTsq3jY0uKIM8dGPGS5u86J9fvY51fZ59doeiUcYT8it1xPdSJGac6+yoUJut06fExMMHGmWOknnGpHCAuqns1iyaNRcim7EtuUTTEYDIZHBFeUqLvlllvYv38/v/ZrvwbAK1/5Sv7wD/+QD33oQ3z7t3/7w3x352GLLpgRduOvdy1bMmY3VyxQCLpMzQg6KFyxU2EX96fCrj1MGaVFXbl66KBwUV4ZVMZ8MqLdOUmycZzQLeF4ZXpWiZ4uEZQqzJdrUzG5lytWs8lE0E1oeCGuF7NmKdrJkChPuX+wTh67VGTAo5o1I+j24Hwt2zzbwhu/R3KlOduL+PzpLjXPYV/FZbHiMVfyqHjyss/iGQwGg+Hr57IRdW9961v5kz/5EzqdDgDPfvazsW2b3/iN3+DpT386AF/84hfp94tzSf/0T//EG9/4Rg4ePMjLX/7ymWu9853v5Ju+6Zsu7QTOg1AKOVifGh2mwi6JkP0WsQpQbmlT0GmFHHXQlkT51U1hFw/pjE5D5pDaFWqhOxV0VtRFqJw8qM0Iu3ZvGTcVjOwqgetQDwshIZIhVjoiD2rgV6h6ZdbjPmtRn0p7mSh3aNqSeuKhoip5eZGsNAeWvUPYxXlOrsZn6iyxa3aobHtktqCdFof/40jQTbtk4Yj7RzH7qFFzAhxrpzC6OgQdF13Q7TaHQZJzqBZwsOaTZIrlXsKJ9gjLElQ9m6WyTy2wKZvixwaDwXBVcdmIuuc973n8y3/5L3c8fsMNN0z//53vfCeeV3ywPe5xj+MTn/jErtc6cuTIQ3OT3wB2NsJtLZMHdZK5YyCKrVjZbzE8ezfCKTN35JunGTor7uO0T6ClQ2J7aNsjtF1EPKCzfD9oi/rBf0EwFocii3E6pxB5OhWCrpQ0bZ/26TsZRSNKS4+mWt9X3JDWON0zyFGbtHENWXkBSwjmvDLt1eP02mdxmscoLxxCaYWVDPHu+yeUX2V04FvIwybCksx7ZU4PO9zb7qLHubpzSaGaG5BrzT3tNjY2j2408R3JIIu5s3cWz3JY8ivMueWpkL06BF2RiY1s+yETdLu1bPMdOb23XGmGac5da300IAXUfIeD9YCKZ1NyTRbPYDAYrmQuG1F3zTXXcM0115xzzNbsW7Va5UlPetJDfVsXDWXZKKeEdoKpoSDPNbEKEE4ZK3QZ6nR6hk5LB+0EaMtGjzNXSmuGuYN0KiBzhjLH0xpLCLQl0bYPlo2Wm4JhmIC0a+Q+JI4mU6o4YycE2glQKkVtMUdEqSK3KohggPIsUpXhSQflV8gqizgbDxDe/0nS+iHS5hGGTpUsdgllem41NyZXmiiyCISH4+UM1IiAMhXHp4JPlKecGG5watim4YbMe2WGkcUoUVe0oDvViYiz/IJNERdD0G1n0pqs4tlEac69rSEnOxEr/RhHSkqeZKHk0ggcaoFjsngGg8FwhXHZiLqrndz2iOcPgxhnTSZn6NwSc0e+maFOGeXZ1DyhnYB47ljxZEtumiK0Te3AY8DK6WTJpnnCskka10zHA8UZukwTzB+l4kIri2bO2KXV/aAXwSreBsMkpzNKcWr7aCzuYyNPaMVDml6IJx3y0hy5XwUhsdIh8uQd9K0mTu0gj202sWgDhYjZdQ20ZrkXk+aK6xp1Ep3QToesxX3mvTJCCHzp4EuHXCu66YivbbRQucW1tQaWbRHlAs/au6XW5SroRmk+dqc+PIJuK5OC0L5t8aj5Eo60UFozSnOOb4y4tzXEkxaLZZf5kkfJLc7iWVdI2RSDwWB4pGJE3aVkLJ52M0W4uJCMZswTbMnQ7WqKsOSsK3bLWbTtpgiApmXvME8gtgk626IZOsVWrO2yHvdnhB3jLGAkQ9axCEZrHLB6KHmASVdThaaTjGZcsbnStIcpqRQsVryimwLF97cLOyiKNSeJxCfAD2GoB3yl2yfXCltYLHk1aq5PIF18Wdzv5Szo9lU83PRcVe4KLpWg296yzRKCkmtPzSrx+Cze8Y0RvTij7EoO1QKaJYeKV3S9kEbkGQwGw2WFEXWXiHzc+/VcLtfdXLF7Cjp2d8VaQuwq6GBvV+xugg6YnrHbLuySTLE+TLEsSXV+H0InON3TiLwGSGSekp25nVFpiVKpQSZ9locamSsW64WgmzARfluFHRRFeYdJTi10qG/JbuVa0UmGfK59HEfYzHklAumic0mWSpp+wEIpuOwEXdV3ZgoL78bDJeh2w7MtpOXQa2cIipZmG6OM5X6CRuPJ4r0yV/Ioe5KSa0SewWAwPNwYUXeJiHROL4kZRqC0YlFG2DpAs6WHrdY0VUpb5wyyBLQmy2zSTNG0IgKdoSjPXLecJ0id0cqhlQyQuUuUKkok1C1Q2t7sFQt4KmVB5awKwXrcJxQ+/Vjhiox5kaF1ZbpFDGCpjEWVs6o1rXhI2fbpR2CpnCUnQWiBtj1y25ueqbOA6rBDPGiRSZc4tQktl3q5jh2lKFVCOZuZyK3CbjXuQeoyStUOQQdFS7KmVyawXVaiHrHKSDLBxrCPYwtyp0+7s0HF9qk7AaFdZPJca/OtfiWWLdnOQynoYLZl26F6MFNuRmtNPBb2Z7pF2w/fkVR9yVLFp+zahK40Is9gMBguMUbUXSIEcLzbpSR9jsiYUucUuVcaO2HHW2BRF3fjARZsF13ez6n+ABuHazyLWv80eihImteinUIIijTCaZ/A1Rpd2c8Dw4wsjzng+SyMziAGCUnjGlRQK25CK5zOSfx4gKge4AENq3GXBb/EUrKK022T1g6SVRan9+30V7B7KyyVFzjhVLi/06XuhFxDD3/jDHnYIGkenZ2rymmqhNPlfdw7jPAsm2utEeHKl7CWI9LmEbTtkQUNVFBDuSVqToBGc1+nAyriaLW+Q9BtJZAui36Fe7obRHHMoVKNpXKxLqnOGWQxrWQAFEKwJF1qboBQkvZAUbJdI+j24Hw9eMU2V61SivvbI+5aS6h6A+qBg+9I5kKHRuhSdgtnrTmTZzAYDA8tRtRdKnKJFBLppKRaFw5Vy2HGMmpJtHTILZs0d7BR2I4iEbpwwAprKgCB4v8tG60VSSaxtQSZktkZuWUjpZ5mw8ZPAMtBS4dUWUjlYssRSibklo1tu1On7QRt2WjbJdMSnbnYVoZwUrIctO3OOG23oiwHlThIUmxPkwgPz6+gZYksbCJ0jhxt4GycwI42yLwafW8f9VyTexIxihCigXaD6VnE7cSpwMl8MjkilxEKDyksXGHjWjaV8bhM5cQq4+7uOiv9iFSnXFMtowcNKpNzeZYz03kDjKC70A4fZ/sJWa45NldiLnTRWhNliuV+wslONN2+bYYuTpLglTNCY7wwGAyGi44RdZeQY9UaPTWileeo+mECN5zZGlVemah5lFakSJXgUKVCQsIgS9CVfdTccGpUgEJUxc2jdIYJQyVp+hLX9ekkI5ZLczRtH+Fs2d4VgqR+iFEU004tAtdiyfNopQPOuhXmwznktpZfWXmBkV1hLRHYwuK6Wp1ONmQFn7n6NbhuuGOeWjqcsZfIgaPVGiMV085TCOcJwnHZFWy07aGCGlkyR2cwRA02OOKAGKUMesdJhEXVr6D8OiqoopwSyvHQtk87UXSGKc3Ap+T7rMZ9lqMuS34VKWaFi21J0hyyOGfJr9IIJaejDb7QOUnDDXEtG9uSVGyfmuMTSg+JZKWXGkF3DvaqHyiEKIww4zXTWjPKFMu9mFary6dWchYrRaa0GbpUPEl4ntcyGAwGw/kxv0kvEb4j8RyJo0u0kgHtPEernHBLZkxpTSuxSBVTU0Q4dogOswSdZ9S3ZcbaMYyU3GGK6CQj1lVGc1zHbsIwg05qzZgimqIwT6ypjDmtsbeMT3JYTyWWgPmSi5SCOVmYJ9aVoqkVHrOCJ9eQIWiGLp5j4emAdgIDpcjznLI1ez6rnVlEVki5ZhOM+5lmaUwvGZEJaKRDnKiN0EV5456S5LlHo9Kk7lXQOkR4ZVb2EHajNGelF+NIi6WKh7QE18g5lqMumVY0pIdlCQZZzEYyIM0V64MUz3K4vl5noEBlztRlux0j6M5dP1AIQehIRmlhFmqUHMquzdluzImNotPFXOiwr+pTD5wZI43BYDAYLhwj6i4Rk50mSwiabiHsOskIKFys53K57uaKhd3LlkyuBztdsXu5XPdyxU5drmwKuskctrtiLeEwzDQgiDKo+oWgg+JDve4G9PKIXhoBUHa8QtCNUqJUUfaLorgTyk4hdHpphBaSethECEEvShkOhpStEY3RCUQk0ELg2x6+E7CiBet+h/nyIpbtM8rUDkEH4FiSJb/KctRlNemx6FWpOD5Zrjg7igmlpB5abOQ91nvdYp2EJLQ9mmPHbSAdpLCMoHsQBaFDR3Kw6iOEoORtvn4vzlg53cWWgvmSy/5KIfC2usMNBoPBcG6MqLtU6KL1l7I9LOlsCrt4iIgHDHOnKCy8RdCJZFics7O9GWFnpRFkkhHujKATWQwqR7vhjLDbGG0Q5pJO7uI4clPQ5SlWFqPc0qywG3WoI2ilLpYlNwWdyrHSEcoJsCw5FXYrwwFv/2yJflrcxyAT/JdPC17zZI2bD9GWA7ZL2fHIlaaXRohkQJY7RNqZEXQiLYSudoIZYdcdxsjcoq89/CCgEjjkQiCyBJHHaCEJ4z4H0xHtjSGx+DKBU2NIhVJYY65ew1IW2toUIFuF3UrcZc4p0xrkKA0Hqj6evSmstdYkKqOXRdzVX0FrTdMtkSQSoSSHKiGJgHaS4lgSW0gcS+4QYI9kQVf2bBq4O8rN2JagHjjUA4c0V5zqRNx2os186HHDUomFkkc9cIyb1mAwGM6DEXWXCDsb4q6vo7wySfPoNGPXGZ2ms3w/0qlQO/CYqaCz4j7uxgNoyyaeuxakQ90NsNKI6MxdoC1KizdQDavFC+Qpbus4QmWF49Ub905VOYMzX6UzivDnr6NWK3q8ojVu+yRW3CetHSAvzU2FXXf5a2x01nDrh6kvHplm6OzBGnZvmbw0R1o7OM3Y/dXd63x5YzZDdcca/P29I76/cj/K9kmaRxFY1N2Abq9Hf/leLDwqBx9LeYugczeOA5A0jkyFnchThme+hkoV4cKjqAaNQhioHKd9AiuLSGsHyYM6MqhRzhJ6p79Et3Mf5eoBasxhRRSGC63I3TJ5OId2fKRlcQA4M+px5/oGdbfGgXoZb5sYEkLgSQdPOlSdgLNRl+PdLrZ22FcOWc97rPW6CBj3VRVIIXGEJJA2juUQdfr0rYS5wKcWWmitz1lP72oTdPsrHnE/Ouf4JFeMkpz5ksdC2eNUO+J4a0TJleyv+jRDh6pvBJ7BYDDshhF1DzeZA9oCmYOVX8B4WYwXCuwLGK8k5HI8/jzVbwG0gHyc+ZMZWhRbqnuPh9Xh7hmnM0OLqQV1K/l4zk6OuIA5ayWLeYsM5AXMQUnIPXLHJSv55JV5tBCgMtzWcbzVr5EHdbLSPAKBozTN1grhcB23UqM8OITteCivQu5VUW6AckPY0iOX1EVqh8ATVH2Xsj27BrlW5FqRKUUvi1kddOh3Nhj4FvtFieXcp2R7LHoVqk5AIJ0ZgXc1CrqvpyB0yZWF0SJV3Nsacs+aJnAli2WPhbJL3XeMi9ZgMBjGGFF3icjskKSxD2V7IMTmGTq7Qv3gv2Aoi16uWLI4Y+eViZtHi5IkY3NEe5gywqW0eAPYOQNhoca9YpEOSfPIdPsVxq2/YoU7f4yyTGkjyLecsUvqh6bbr8DmGbrKAZrNeVromTN2WWke5Y4LBwNKadYHKY3y7qJuqeaRNI8W26+WROeKzigjskIq+x5DLhVdlaPTuDhj5wQkjSNAsf0K0Isz+qkgXHoUtkzpCjmds7Akaf0wQqUop5hznCpaoxSncYS6s58NxHQOlmWTNI+SVZamxY+nHT7qJZoLB2nrnFUBTS3wog52bwXyGG0H5EGdNGyymtqk+Dyq3mTIkPW4DzAj7KSwivIqFqwPEqRyOVppkpZs+lmE0ppcK+4drCIQlG2PBa9SnOtLBcv95BEv6CYIIQrT0ETgZYr71gd86oENlioej1ksMxe6lDzz6+yh5i1f+ijt8c/fz33Tdz3ct2MwGLZhfgteKkRRsgS29XINXQI3wNN6h3lCbykXstUUMdlyVdt6xWrb46/vhkEKrqW4aV9himiEhYjTWTJrnpAOaiwYZ0wRlQApQ5p5vs08ITfnMBZ0mVLcfJ3L51vwxbXN6X7TnOJ7rrFQViEYtdb044woL0wRZc9Ha41KRrPmCWezpEovzuhHGb5jUQ2KvrA6jemlEe1kbBixXTSFyIhTRWuYYFsWzZKHZZVo5imteLhF2G3OYaZlW9nHtUPmlCqcvTqj6ZZx/c3tbZI+o9YyfqZZKJdxmKPm11jWaldhB4Wg68cZZc+mJkB7RUz7WYQlLOa9CrlWRHnKvYNVhokiSuBAUONAqX6uHClw9Qu67QghcCxBlClCR2ILwVdX+lhC0AgcDtR8GuPix4aLz1u+/DGO9zc4Um4YUWcwXIYYUXeJUFpP/93N5bqXKxb2drnu5or923tgZQRzvuC7D826XPdyxe7lct3LFbtV0E3Klrz2KfBv3wu9FEq24hWP75HrEBtn6nLNM0052GKKGLti2wkzwg5mBV092Nya3GqemAg7IcSMoJsrbW7JedKh6YWzwk6IPXvw2pa16exN+oWwkxJl2bRUQOp61Ko2vpVh9Vdxu2c5gmBNQN+vIitLBGETLHtG0M2V3KKeDDDnFUK3n0XTr0u2h84lvSTGljkjq8cdnT4V22feKxPaHoF0cLaUwHmkCTqYLQh9uB5MxVumij8avjB20C6WXJaMg9ZgMDzCMKLuEhGplCTL6MaKNFPUHUWwbfUtIZizbFoqmwq7JBGM0pzQyql5O7MPdWkj8oThWNgpfBgf159z1Y4P1dCSCMuinee0kgEVK6A1yrBUzoIv2NZUAdcSzAmbdZWwHvdpOCU6o5wsz5lzNa4sxKptQWCPRZ0j8KyiV2zDDRglEKWKmtSE2zIoQggalqSdZ1Nhp5WkH2UEVk7dETMFmgHKUiIyQTdPaScQWB4bwxQbzbynENuEgScs5iyL9TxnPe5Ts0M2hhlKK+ZdjbOt9p8tBPOWzVqWFMLOKc3GzRFoyyefFHZWOXOjNs7GCZKNBxB+jYFsksgy1VKVRjhb0Bl2CjsPj/VBMj4vFhbCUytGecq9gzXQoNHjc3hlJDZr/RzfsjlcDx9xgm57QejtDtr1YcrJbkSaaY40Ag7WfGqBc951MhgMhisZI+ouIfd225REwKIVUesuo6ISSeOaqWix4j5O+yRL0uFsaZ6TvR62dpiXsDA6A5FVuELHGTeRJbgbx1nUitXSImvRiFx5gEDqHL91P2n90HS7ceJ49ZMBVmmBZSVZjRPqTsh+1cJtd0gr+8hLc9N7tvur+IM1bL/JGTfknk6bsgw4wIBSd4U8rZPWDs7MU+Qp+0dtTgcNjnd7eHgs2hnVURsrUcW5uXHGqXC8PsAigpXyImeHfchs5l3J4ug0YqRJGtdsbsuqHHfjAbwsKeaQ5ywnEU07ZH+2jN0aFm7eoD69H6d3Fn/URoZznMHnnkGbqh1yQLUJOi2ybJ6ssjQdL4ct/N5ZbK/Kaa96/rilI5z+Ko1x3B4YDPGSUyyJnHprRLbWIG0cRokALRXKKbpqTITdynBAmoxYDMsslr1pZlUKi7LtTbd0U1WI0i+3zzJKBA3b53CtDKOQih3gS2dHNg+ufkG3HUcW2d1BJ6efZNzfGnK2H+NLi31Vn/mSS823sY3AMxgMVxlG1F0qlCTLIfcSHKEQeQoqpSiAMf5AUzkiT5FCYGc2WV64PV3bQoxytFCg1eY1tQKVIbTG1ZIsVRQ9F4orFq+x1V2qQaWIPMXRgjxzyHWEtFOsNC1qvqlZN6pQGSJLsHUOmUOmEvASZJ6Na8Tt7ka1VIqlXDKVId0YV2pEniHIx3MYizqVF68pBLaSZJkCkeJIjdA5QmuEysezKuYs8skcLLLE4h9OSaQQ7Fclfmi+s23OxToUcxDo1CFTKcJOsNN8POds1zlLJ8PKXbI8fVBxS3BRgYtwLUS3j9M/ixCQ4yE7ORoLLR2yyiIlEaAHEaljYTs+lvDZC8eSNJwybTTKGlILJY6UtOIhy1EXEEhhEUiHmhNQsiftzhJQhXi62gUdzHb4uKYeTAtCR2nOyfaI+1tDQleyVPaYL7tUPSPwDAbD1YERdZcKDUcqVYY6YkXDQv0wthvClnZWyq+SNI9MW3/tDyvkMmEjzxGVJQLbQ2/p5aodn7R+mFGSsZG7NH1r+gGZC4ukeYS/PFlhkELJgeddb5HWDjGKR6zkPqG0mC+59LOI5aDGfNBA+7M1SNLyIpldYjV1ENri2lqNfj5iWfgsNo5ijbNNM1O1bFa9JdJccLBcISVmNc9ZKC/geM5M/1rllUnqh+mnil7usOh7YKd08wxRXqIsnc1MIxQu3/ph0jhhNfeouZJ/PuOwNhLM+02+/1EOOqjOzqF6gMSrs5y62JbkaK1azNkpsdAso7fNISvNk0uPtcxGKfGNxa26n8D2UF4ZnWq0XYhMOWyhT32JTHlcZ1sIz6IXefTCeeqlOZRTGF+2bj3HWc5yL6Fse+yreHSyId1sxKJXnWb3MpUTq4yzUYc0V6z2Y5SGxbJHPgqZVxUC6RBIF3vbXvulEHSjLGctujSCbnuHD9+R0+eP0pwH2iPu3xgCcLDms6/imwzeJeB/3PMp0gcs46A1GB4CjKi7RHi2RcV3CZTNetxnVWialsPMx6YQtFTAiE1ThNIOrWRAK4eadAm3XbcvAjpqS+uvcfZII2hZDn97j2BlBIsBPO96iIXLuhIzvVylJegkI1alpCkstn6kKWGzrkIysWmKcHNJK+mzQnEGcPubSCEYanfaKUJph/W4T1sLKtJhewGUjgjoq01TBDi0kxGdHJR0KW8bHwmflrKw5dgUsXXO0qWOmHGNZsJhLQ9Q1qYpwpUWrXjIimUxZ9nb5mzR0iGp3jSzXLS4CYG2Xbr+Ah1dxK0c2EiVwrBDvPo1kpZDxQ3Rtk/u11B+hUg4rEQSy3bZV/GwZTH/9bjPStydCjvbktiWxFcuy6OYspTMlx1sCRvJkK90z+JZNgt+mYodUHMCQtvFt2xag5xBkj+kGbq1foJTCi65oNtO4BT9kpd7MSv9mNYw5WQ7wrctlqoe86FrzuA9RPzuvZ/hxKhjHLQGw0OAEXWXiEkF/L3clbC7y/Vcrti9erlCkeAZZQkKD8aSZS+X6+R6f35XTpynzPsOz79e7Opyhb1dsZMtSQ0zrb8mnSc6+XDaK9YbZ+v2crnu5Yrdy+VazFkQjc0TE1fsXi7XvVyxe7mTL0bcApw946Ytj0p1kTyp0MkSEsumYVnYwzWy9iniUUbTktRrNchrqHHRYhyf9TSaEXa50iz3YtJcsVjxCMZiaGHcDWMl7k7vqZsOybWmPcpQueBwpcqcrDDMwJfOnjXyvt4tV9u69Bm6vZi0bFsse9OWbaM0L7pYbIzwpMVc6DBf9qh4NqEjTaFjg8FwWWNE3aUkT8GydwoE22eYwCjTs2VLxmfDLEvOCgSVg5J0YjUr6FROIeAEFhaB7TKupIJG7xR0WoPKQDqEtstHHtCsjQRzvuL7r0nZiNkh6CZz2CHspIvSxQejEEwFHSoDYWEJi5obkOWDQtg5Hkkm6Cd6VtCN5ywsOSvsVI6DQyvKZwWdVoxPEGIhqDh+Ue4k1lSEzXrMrKDbMuftwq4hXdoxpLmeEXQXK25CKHRu7RE3YDxnGJepETahDFkXAVYI84GFrRNE9yzkDyAsG99yCG2HVcthw69QD+ZYiQSptmYE3QRP2ix6VVbiLoMsYcmr0B7l2EoQBBaxiLizO0QIcC2bqhNQki6etHEsiWvZ9CNFa5h+XWfoFireZSXotvfgnWTwAOLxH0FnejFJplBK0whdFsoeFV/iSQvPlni2hSPFedfBYDAYHmqMqLtEyCzGW78H5VVJawemAqHdW6Z9+k6kXSOYPzoVBiId4bZPoi2bpHEYy7JpuiU2RhsMznwVcok7f2xaWBiV4W6cQKhDMM4IFdmqwlihsgQnGdBoNKYZOqd7Bivuklb2o4LadBsTndM6/nlE+SDN+f1TQScH69j9VfKwSVZZnAq7Tuc0rdYZtHo8sLkdasV9nM4plBOQ1g8hKDJ2G/01Oit3YemQYOEotaAQBiJLcNonAEjrh8F2qbsBnSgjWrmLYaxwm8doVKpTQee0TyLU/umcJxm9aP1eWr0+Vu0Ijeb8Zh26/ipy2CIrL5CX5qbCrrdxilZ7GRHso7ZwaLN+4KiD0ztzUeIWrR8nS+I94paR1A+hnaAQdlqRrd1Le5jgNK6dxk3h4Iw6m3FzQ0pZjBysMTx9B4MophweoLKwD4cGyiujHL9wD4tJ7b5C2C1HHb6ysU5JhMyVfOpbxJDWmkRldNIha3GfXOd004h+kiOVy5Jfplyq0E4VvnTwLHvXrN52U0Q2SM75c/JwCrrteLaFZ7t0o5T2KJ2eQTzTjXigPTYsabClwLMlVd+m6tkEjsR3LHxbmhp5BoPhkmJE3SXCUhlWPChaZmlduD0tCzcVjKIRuQ+VLceYRJ4i0hFIp3B/jj80w1zSGUUgFGWZTj9IhcoRWcQ0NTe5zlSoaTxrhJTN6dciHWHFA6yw2J6cojUq6uJW6jj2lu3NLEYm/ZmuD45l4aQQR32YvQoiT7DS0fiahePVEgI3FwxGEdrOqNj5NMMhVIo1rtsmVIrGRQhBgE17FKFVQmjHm1tgWhXX3zbnQDiMhgkq7uGLAY5c2IxDOkImffJs00zhWDZWrMhHPWyvhO9sfhBbeXLR4jaME/Kst2vcJo7eydqGwqE9TNBp/9xxkzWUdLCdEqrTJo/W8Up9PJ0gOycQWmNFPbQlSWuHyMtNlB3g2T4y9xmlHRw/ouTOntYUQuCNs5kTpPbY6LdIrBHatjk+TNGAhcCxJBXbo+L4+NLFs2zyXHC2G8+YImZ9xrNcToJuwqQHb8m192zZluaKNNe0BinHW0NWBwmutFiqeJRcSW18FGGSBVTb3q8Gg8FwsTCi7hKR2QFJtVn0HB1/MLSHKSO7Smnp0SSOppVFNMdbm8ork9YPoy1ZuCAZn8XKXfz568AuernqLClaitkeae0gekuNsiRTKD0WD9Km74bkk16xQpBW95MHdfKgBjAuhyJA2FQO/gt6jjPTeSIvzROPnZzAtFPE/15ZJEvrjJQzc53crxU15qQLlg25Ls7QERIuXE9u57SUopmneNJBOeG05t1ML9fEwmtch7RjetKBca9YLHvHnPNcszZMEbUjlOpzDNwAPekVKwRpdR+5V0Z5hct3coYuC+YpH/AZOPbMGbssbKCFdVHiVqodxLKbu8ZNqHy6rkmmWI80TuNaPGt03rhNzj6q0gEq5Ro9x2FNCJphE0sIhFdGDtaRozXs0QZaK9qJpqRs/kWlytB2We9EzJUXcJzdS6q0o5T+KOdwuU4uIyKVsegGBNJFaU2qMnpZzHoyQANxmtMdKaquz3X1Gr3xGb299MzlLOjO14PXkRaOBEvA+lDTCBwO1wtxnuSKlX7Cyc74jxUEIu5RH9nUfJuyZ+PbVuHMtS2zhWswGL4hjKi7RGjLminqOz1c7zpU6/vIxj1Htx7Cz8PGdPz0cL0jqdWKzFO+zTyh/Oq0dNrkDB1jn6Zl2QSuP9sr1g3JxxkapTRq/IFrWYJS8wBie69Y2yW3izlMBF2UKv7PaY+1kYclNluhZUpjW5I8bG7OIc3ppxm+a1OtLaGhmPMW88TWosFbTRH1uTmEYEevWOWVZ+Y8NUXUqrh2HbGjV6xHPhZbM6aIconQreLs6BVrX7S4lUt1sBu7x23MVjNLseXaJN/e43db3KZmlmoFz6ntjJvjk9UPzsQtthIqrqKqemSdmI1kSCJdgtICImygvBLa9lBOQDtRdIYpoSuZL7koPJajLitRj0W/QiDdmazeKM3pxRGBrSn5cHK0Tqc7QgP7lKRMRNUpiiX70sHSFic70RUp6Cbs1bLNkRalLZlcrTW9XDKIc754pkumNIsVl9CxC4e8J6n5DiXXNlu4BoPhQWNE3aViS4ZiN7fkudyVe7lc93LFAihdWCa2nknfrVcsbAqDybm0yZbtXr1itwq6sm9vnsXbUkhk1hVbuFzjJMd3N00RAjbnvM0VOxF0Hzlpo4Wk7Aqed/3ertjJnLe7XPfqFbuXy3UvV+zFiJvIim3NveK2lzv5fHHbbma5oLiFPiXPJh9HrZznbIw22Bi2mEt6+FqjhaCXC5TyaVQa1L0aOg0Qjs+SX90h7KAQdCu9GM+WLFW8qet7ziuzEvVoD7q0h20Ce4Aetz5rDxSh9Li+USNBIhU7umJsxvjKEXS7IURRQqgVpVQ8e9rhI1eaJFd0o5zVforWGiGK91foWpQcSbPkETgWgVOYM0xWz2AwbMeIuktEpFKU1nRHWSEMbGvTLTnGtizm3BLryWAqELKcQhhIsaNsybRsRtyfCgSNw0RczYfOtoptY0Gg9VQgVG1/Kgx2MyWGtgta00mjQiA4JTrRWBh4ctPlugVLiCJTOBZFo1TRjzJK0qKypWzJZOx0zmNhh5LTDN2HT0pWR2JaZ08IUQi7WG/2isWdznkusHdkNsqOB1rTy2LaCdScgNZoLOh8e9PlOsaTDk03oJWMpnPYHre/vpuZos4XErdJt4/d4mZjbwq60JkKuguJWzNwNt3JX2fcXClp+HVa6aCoP+iVGSU5g2REiRH1KMKKTqMRaMcj96oc9Css5ylrWcJ8eQ6UZKVXnKHbKuigiNuiX6EbJ7RR2EJSsj1Od0eAxvEyTkRrnBgJpLXZFaNse3jSwbeKNbySBR0ULdtWejGZa8+0bJOWILDkDrdykuUcb4/YGKZUfZvG+OcnsC1qgUPNt/FsibSK2pOWEFhCIK3i/x1pzcTBYDBc3RhRd4lQaO7vtnG1R42EudEaKi+RVfZNz2qJNCLsnsYVNmf8KqcHXWTuEkrNUrSClUiS+sHifBqAyvDbp9incpbDOmtRn1zVmfR+DToPANfCFrOE3TvLYjJg3a/TSTWtQUJgeSyqDpZqMsnWTZDDFo3BOrZXZQ2P+0dtPHwaYkSjt06e1YGFmecIlbEU9Vj2yjzQ7+Aoj6qV0Yi7WDmktQPTjgwiS/A7p9iP4GxQ4+ywj04dytJiX3IWoQ7O3pNWuJ3TLGYxa36DjWRIppzpnMud+8kq+2a6UNj9VeZGbaRfp4VmY5Tg4zFPn0q3zZ+tHaBHaSzQwIq6VHvL2G6JFae0a9z+9p59rIzF5vOPXGDcxq3Rdo1b6hBaNvuzNexOSlo9sNk95DxxCztd0srSzDbuhcQtK28xkMR9yr2zONKfjZsNc0kHMq+IG4UZxO6vEJ78LCXpsOqW6Fk+iZyjVGoy16ghcDbnO3lfCEHNCUikYi3uczoeUZIB19R8PHtz7NauGPlYyGogT2xCK+Saahnb1iitzym6LkdBd7I9IlMX3oN3bZiiNVw3X5oWhM6VJs4Ua4OEM92I4ue7+IOhH+f0k5TQkdQCpxDrgU2z5FJybVwpxmcAzZauwXA1ctmJuhMnTrC8vMy3fdu3YdsXdntfz3MuNZaW9OOUsgc1K8XudciVIisvghiXz0hHyFEH4fiUvCarcYQtR+yXEifpoYVEZAl6/GEgsgQr7iF1TtVvshIr1LT3q0KOOjN9VtEKGfWQcY+aW2U1F/TSmHLZJhgNQNd33ncyRA5bhLaPoEwvGSADi7KKkcONsUlhm6jTCj8d4rpNVuMBnq04ZIFMBohcIfIMPd4iFFmMjLtYQlIO5liOExAR+xwHe9hHbHfz5hlW3MNKI6peg+UYNvvdauxRh9yvwRZRZ8U95HCDkltmVXv0kgg/tPjAfTajQcBfrgX0s82uG8WcNwgsiZQN+nFvR9xg3+b1LzRuItgzbrmOWPB9nKiPlUbkQYN8KurOHTc53CD3KjOi7oLitkXUiXSEPeogPLUjbnbURTn+NG7a9tB2ApZAovG9Gsu9PlIdZyk5SWm5h/aqRPPHUOEcyi1NBaoQgrodcqYbMcwj5krujKADpl0xSuPeI8rV3Nla5+xwnWbYw4pHnE4EvnRoOCWqrk9Jenhy82f/chV0aa6YL7vfUA9eaQlCV+7IMK8PE3KdsVD22Vd20QiSXNEaZpztjUvJCI0tLFwp8G2Liu9Q8S/MrJHkGb00BqCXxqQq33Ob/ErmLV/6KO2xMcl0vNgds0aXL5eNAvrQhz7Em970Jv7pn/6JTqfD6uoq8/PzF/05DxdKwb6wQm4lrGiHxdohcAPY6tz0q6T1Qwxzi15ms+CXUDJhDYVV3ocj7ZlyItoJSKv7+fP7XFZWfAIH5PgXshKStH5opkcpliSt7iNNaqyoEoElKZdtcjLWvSra2vl2yMImynJoaR+0ZH+5TE7KquWx0DgCe/R+3fAXiDOLpaA8nrNisTSP7VhTQQegvBJJ7SBJBhuJzZzrgJ2woRVOeWHG2QqgbbfIxKUpK7lPxba2zNkiqR+eMVsAZOVFMqfEqg5xtMOBsk1Gyl+cqrIWNaYGj2kcwgaJsNjIXfJc7B63LdvaFxy3bDZuaZazlnvUHYG0U3pa4YZzhEKQbxFo54tbwy1PnbBfb9zyoE6iNV3t7IjbUvUA0nb3jlvmUysFYCesaoVwAzyd4wzWEL0VtCic0HllkSR3WE4ENbvEXClhpGI6iaTmBuyG0pqVfkwoQh7d8EhFIU4aTkisc5bjDqejNlJYVGyfphsyjASjRNMIXBbK5293dikF3YGaj4zPVdjl4vbg3b6lq8cmpvYo5a61AcB0q9zatq1b9uwiu2dbJHnGsz7we7TiolduKx7yve//PT7wrH9/1Qm7t3z5Yxzvb5g2ZufArNHly2Uj6j75yU/yile8gp/+6Z/mOc95zkP2nIcLWwoWywFxbtOKhyy7YXEIf+sgS9J1mzOH65X2iv6ejl8cwt/6gSMEI6/BX58UrEeCeV8Tj5sTDDNBy63BtjN1mVthPfVnerm2kxH9DNQuH2bKCWhl9tQUUfFshmN35Yrn0nR3ijolLDo6mHaKSJVLK+nTkg41P5x90wmLoduklSXTXq6I8ZyFjxY7PzATr85alqBEYYqYijoEbb9GWc5uIWduiVbmzpgi+mmMnvSM1ZM1K/7VtseabDBSm6aI7XGb4YLjpmfitp5vmiKE5dFKBqzjkbkB4bYPynPFLbdd6vZsNupC4jazstJhw23OtGybxG3ZCgrTywXHLZgxvaByrGSAOHsn/cgi9ErUFpaw7QoryqKdFkJhu7CbCLo4VcyVXMqeTScZFeMTmPfKBOPXyFTOKE+4bbXNMM2puA4D2+OBdUWOZt4tUXdDfMvBkzaeZSOEuOSCruTaRPHe4y+moNsNIQRJntMepTRDd6ZlW640ca5YH6Sc6UZoijqUZVfywdUv8Pdn75251t+fvYf/efdnuOWG7zjvPRoMhkvDZSPqfumXfgmA973vfQ/pcx4uXHnunqOwu8t10jd1N3flxC05KVvSSQTpuP7vILN47Scscj2uPcfebsmJm3J7DbHtLtfJ4frt7kpNCRCENnzvEYUkn2n9Nek80RuNdrhi9+rlOpmz2nL/wJ69XKE4mrjdFbuXy7XseIhxhm4y7Ymo283luj1umvL0vvaK2z+dLLMWpQR2wgtucHHO04N3L1fs+eK23RV7oXGbuGJh9x68e/f4PX/cZtzMliR1KqwlHradsyhHuOt3gxAcdgLWLIe+7SKDBpWwjpYuCnYIOtgUfu1xp4t5r4wQAtuSJKMcV/s0Qkmj5KC0RqPpZzFf7p5BIJj3SthCFlu1yiYaCWq+x1y5zLnl3MURdOfioRZ0sLPDx9aWbdIShJYkdCRM+xRn3Lna5x9Pn971enesL9OLMtMqzWC4TLhsRN0jid2EXZSqXcuWwO5lM9BiKgwmv5fT2YYOfG3DpmQXvVE1uwuDCTMtxVBoLXYVBhO2CgQ1Ljbs25pnHU5me7mOcaWk4gS01KZAyHN2FQbAVMxOJJdGk+fsKeig6GzgS2cq7ELb3VXQbR0/i6Y9zHYIut3iNhGbCr1n3P73vRYrI485X/HMoz2aVhkysaugm8x5u7DzLee8cYNNYVdz/AuO20TYDZJ8h6DbGrftwu5C4rZV2NnY07hVQh/ph+QAWiHSiIV0gNc7S7x+L8oJ8b0ya7mDECFL1QqhLqEzDy0dEGJXYdcapvTjjLJnMzcuDDdZ2qZrE0iHlahHphUNN6QTpSz3O1hSk2uXbruNJ23Ktk/F9sa19zYzele7oNuNNFec7cWErs23zS/x3vWdY6w04BP3t7ClwJVFt5W6b1MJHFxp4dkWriz+s4wL12B4yHnEibput0u3251+febMmUvyupZKcVvHyb3yTM/R9qBFe/U4uVXBqe3bFAZ5itM9i5Y2WWVpU9iNOnSXvwa5g1U5wHwl2FG2ZCv5uKOEylNUMhoXqN3s5SrjPml5Ae2GWONMklaK1omvooJ9lKv1qTCwoh72cL04HxU2NuvijVN8Op/N0Ik0wukto5yArLyAY1k07TIbow06Z76C0gF29cCmMFA5Tu8sAGllH5Ylp4JV5RntU/ehKweYq4SFoNMau78KqknxVhbTOnajzini0QgV7KdWrU0FnRxuIKMOWTgHVGYXS0PaXSOozm/2ck2GOP3VHXGbis08ny03syVuhZmiyNoJpRi2T5Bl2TRuUhb9a+3eMiLPSKv7sKQzLXcyaD1AFBWdIs4Vt4mwS3srtAa9C45bf9CitXI/2m7g1xb2jNtE2D2YuM15ZdajLr3VexGJmMbN0WIaNysdkVb3ocIGpaBOmozodU4xap3G8ubZF/oE7bOItoA8QYuiEHQe1Gk6PlIpNrI+dw0TXO1T8Z2poNtOIF0W/QorUY/jvQ5W5jEXhCyWvaJbilYkKqedDlmNezCuoYcQVK2AKJY4wuJwPSRSCam2itIhYvPfCVeLoNva4eNI8/F8cOVLfGbj+HTMv2we5cXHbsSxJHGW88DGiG6c0Rz/ATXJgEtL4FgWoSspuXK8e1BkoKM0N4LPYLiIPOJE3Zve9CZe85rX7Hh8Y2ODINj9sPY3yrA/wFEWYthF5BbabQACF5tKajEajpCeoCoFIis+TKw4RoyGIB1wUrTtIhE0lE1vGAMxjXqOpQBmtyi3Mm3dqjVVkeAi0KkGNGI4RMR9hCyjxGTuougXOugj3RFla248HkQUIQY9BC7aqQMQ4CB0Pn6mpiZtGM9BRFExh0yj3QytJI5tUcslg0GMJRVVSyNy0LnGSmMYFhkq4cQoJ9icldYQ9anVEhxdKu5JZaSDIaOs6FoxyjRZAjV8uoMUNeoTehGBaGyZw6iYg1WCLVuoE5xsSNWR0/HWaIQY9nfETehJQ3dFfa+4TY7QAQ1lM4wTRDacxk0rjchSxGhU9H51YpRnI4C69ugOEnQyoFo+f9yqwqM7ytAPIm4qViSDAVZgU5X794wblo3Dg49bPXfpDhLI0824ZRpUDqNh0SPXGaH9QsTU8OmOFCpOcUs2Tmlh2ivW6ZzC6Z8q7i0oRNcCGqffoZfEyOoiC4196DhAS6fI6m0z/oRAPRGc6vXwnBELfg05KuJoj/8r+nQUAi3Xin4S8+VuIVgPVyrct2YzOREgGNeFw6IkXWpegCccWoOMTBUuVxlnO87QJcP+9P+1LgTaKM2pBQ6lPCPqDTkXnTijOyrKljRwifvROcePspy1fpFZXah4ZIPknD14M1WcZVQaFksuRCk58NuP/n5u/sw76GQxNdvjLTc8h3zQI9VFBp0s52jojjtopNPrqRzSRNEbajaUJsuL3xejJOP9n7+Hiufgj4sqB66FJyWOFLzr+G30spia4/GT1z3xnHO8mKhcTf9dX98lPXkR2JpYuBK5GGt0pa/BN8r2+c/Nze0x8sHxiBN1t956K7fccsv06zNnzvDEJz6RRqNx0RZ1OxujLqmlycMG2g0R44zLMMnpCg+3PIfyLDZEzJw9LlQrfZSqFe5Yv2hsn2SK9VziluoImdGyMprWZskSR2hSvSlS/kVTc//4fRMpybqEpkjHmRqBLpXJXYkOSwhn8yC/FhaqPkfu23R0tHlWKwxRYg7lVcbji7NYepLhE4IWQ5r2+KyWCMlVDW17CM+BVJOgaSsHt9xAO4qWlTAnPWzLQksPnRfZM+17CGuzYC/CQtQqdKTGsjI86ZAqh1+98yC9vHgb91LBr3xa8zPfmiG9KtIVDHwJJJtn7MIKQoIOQnYTwmnJp6OiaecJHZbI2Rm3ickCy2JDjHaP27R9GaznksAvIyxrGjdXSrBddF5Bqxwd+ghLoJRmIwL8GjKw6TigzxE3rTWdUUbm1bCkuuC4DWWIW5knDZxzx43iDN2DiVuea1q5QIZ1LCuaxs21bYTjoEsV8syFIETYkw4fGXqvuJVr5I6FCmrosTlnY5iS2Blh2odkhdFGn5rjF4crtUBLSRY20UEVLT16StLOHcJqE+yEZStnyS/NZNlm3gtZTjuRNKoewokZWYKKV52WTtFao9DkWtHOUs6mfdrDjLIMeHSzQTUs73ltv1KfZuiG0maueuEZuo5OKNfKF5yhW4sinFJwwRm60+0RynN3bdlWsn06WUzJ9inX5qYdPiLbZl/j/AWhU5UzUoWkjMnYvzSP1oI0V2zkmtWhmv7cvP2eT7Mcd9nv13jx9d9F4BSt0yZbug/V+T1rvLVuSesh+0yAi/ch/nBwsdboSl6Di8FDMf8rStR99atfxXVdjh079nVfo1qtUq1Wzz/wIqMsm2xcvBW2HK73fOqNa0lVtqPnaFbdrIU2PVxvSeqLR9BCT8/YaaqAoO4Johx6KZTsonTBMCt++Aa5zX+7vcHPfPsA/OJsVdGjtOhTqvVm71dhWcztfxTtbT1HlVtCbXG7Tg7XC+GNnydJ83ymV+zWOaeZppUk2LZLfd9RMq22HcKXZJWl6fg839KPVkrqi9fPnNX6+xMOd2zMfoh8cU3wsZOC51y3SODso729V6xfQfnbtl0nCEFQnp/tFev4u8aN8ZwtS077v+4WNxi3bLMklcY+tMMO08vWIsAzpoj5/Ti22GGe2B636Rm6ap2KN3/BcfODkHqzxihPd/T43TrnqSniQcRtbZCghKCxcBBbimnc5mSIizPTT3fGzNK8sLhNzSzlOvXFBfrjHr+pdApBrjJEnuKMWoj+CsM0Q0U5i65HrVZl5ASsasV61GW+NIdl+zPlheIsZ7mXYAk4UAnRIijaosVdFsfCTgiBpNh6lbZkMIqRGiwn5d7hMvcNVzgY1qk7Ib50pufzJnG73LdcL3YP3lTl/Ng//xG9fFzrLov46c/9Kf/9CS8icGafq7cUlk5yxT/c22Kx7GIJgW0JPFtO++QGjjR9cg2GMZeNqDt58iRnz57lrrvuAuD222+nVqtx3XXX0WgUH2AvfelLOXr0KO9+97sv+DmXDVucpbu5Jc/lit3dLSl2OESFgMAuRB3A19qzv+C+2rL51FmPJx+YdVdqrVkbpIyycVYmE2Rqb3clzLolJ58VFsUh9t3clXGq6MXptPyFZQlc9nZXToTBxNkrKNogbT2Ef6pfZnvXAoBuKgndTYfo3r1id25bT77/l3cr0jxjzrd53vU7Xa6TOQvEnnHTW7bF50sulgJh7e1m3svlupcrdi+X64XGbXKG7lyu2N1crhcSt+1mlsmcu2lMxWZa7mQvd/K54rabO3m3Hr9Ih5ywiFue4lQENQ9knlBJBnhZTDsZEQtBPagjnABtu8QKeiNNyZLMVwPkwAUh2C9dzig1I+ygKAWy3ItJc8XBWjitCzfMEr7YPjU2Z5SoOQE1J8BPEvobgiwTLJb9K1LQaXjQLdv+172f4QvdEzOPfbp1P397+nZ+8NC3zzwuhJi68aUQfMv+6vQ9mSlNkik2hhnL46LKepw718BC2WE+9IrtXEfiO9K0SjM8YrhsRN2f//mf88d//McAPOEJT+BVr3oVAK9//ev5nu/5HgAe+9jHcuDAgQf1nMuFWGdorRmdw+W6m7DLcr2nW3Jinpgw3apEkG8rTzKhF3s4MpsKhEA6rA1SfvMzkkE23ppL4Vf+CV7zlN0FwnZhsFUY7SYQ0kzTGib4wqK2zS25m7tSaDEVBtt/F291V5bdmMkpqK0crm4tcyJ2FQjtYYqevv13CruPPKBZGwnmA8Vzr4M//5qmNdKUXZv/z2PljDllr7ip8Va4JUBKgR6nHXdzM9vC2tPlupsrNpDOOV2uFxK3rcLgXHHbzeV6vrhtdydP4tbJh9NMq2PZe7qTzxW3vdzJuwm77T9vQgjUuFOFBMp5SisakOmceZWSDSO6w4RAa5qBjezowqWLwEPjS5dl6bLhV2lW9yPtYCroFiveTKHf0Ha5vrLEStxllKXYliTOU/obfdZFi7nQR6clomFAaLsE27J5Ey43QQeFkH2wPXjv77d2/d6J4caOx7pROjVUTAojT7Atgb2to0aU5ty/MSRXxWGQ1X7xl61A4EhB2bOZD13KXvG8wJFXRfkV09nBsJ3LRtT97M/+LD/7sz97zjHvete7HvRzLhcyrTgz6EHm4ImMxawHI588bG4OylNKgzVsBCvS5+ywi85cbK3ZRxc5tMjK85tdIrTCH6whaQLW9JcgCGyxu3nioDNkKeqyYvu0kyGdzOFjp1y+1p79RX7HGvyfB+DZ+/ssRF1a0qWfFR8YVu4QEjOX9FFWBdjczhZZTHWwji0k6/icHfbQqYOHoql7WCNBHs5N+92icsLhKrbSrDg+q1EPnTkILVjSHYReYCYbpzXOcJ19WcJ37Q/41BmXr21svo2/pR7zjAMAm5kdGXVZiPus2z69NKIzSrG0jdDW+NqzayWSIZZ2geJQ/JlBj7+7t0wrtln0M3706AYwG7cPfnVIJ5UoV/KMI0Xc0B6F8USBFpuvMY7bPpVz1vZZi/uQOQglWKSHN8rJrPnCbDHGGW2wlEYzcUNLagyoRTGZqKPdTYFrxeeP29a2YueK21LegpH7oOLmjiArLWxuaY7j1kw1q47Nejws5qwt5nSPcJSSW3No+/xxK+sRzXREHldnevyKZEhj1EZaDm3gzCB7UD9vp7O8+HlzHBbpIsnIwi0/byrH65zmcDJgTdhEq/eQ2AuIYJ6lZgN/l60/SwgWvSordBmkESJz8XE4WqsQuoJRntBNh6jxuyOQLjUnoOwUZVWGcVFm53IRdHrLvw9G0HWilKPlJizv/P7hcHZXZVIQenLnF1o/0JUWh+dmy81orUnywmV79/pgnJ3XWMJif9VjvuRRGrtyt69tOx7xli999LIWS6azg2E75gDCJUIiWB1GRMQsiBFe59S4HEe+OSbu43ROUxq2qFiS9WFCNxsyb8f4g7NF6Yt00+km0qh4TBVOpOJcXPFrNxQZ31LuzdzDN89pnlU9idc5xUKeESWalWjAerL7PZ8ZgBy1cVv308wilLJYG45IRUyTIW77RDGHLVhRF7dzikrUJsBhdRAxUCPmZIwzXMfprSDyTWeclQxwumcJB2s0tUVrmLKRDGnaCeFwFbFlfaBoJu/0VnC7Z1gSih9/XIfALuZfsTNef+0duMmsq8gerOG2T9DME5IM1qIhSmRY7J7OlFEXMXboDVPBX98tNnOgKh/PefO5Mu7z18d9/r/3l/nIA940bpP2Y0Llu8bN7y8zr6EzyliPB1S9nFK0htM5jYw3HZKMX3N73KSjqKdtnI0HkNHsnC9m3NzemQcdN6d7FisZ7IibM1hnTiv6ccZa1Me3FZWkhds5hRVdWNyaDHBb9yNH7R1xczYeoBZ3sXEu/s9bFiNHbZwsohLO0cpd+r0z7O98mcV7PkDp7n/Aad2PHLYKZ+/YIW0JwYJbYRhrTg97SEdT9x1cy6bi+DS9MvNembobIgSsxj3u6q3wwVN38oFTX+FkvEwuh6zEPdbjPu1kSDcd0U0j+llMNI7LpThDl4+zzVKIByXoar7Di4/dyI2NIzPf/5fNo/zAgW+dfr21w8eFbJmer36gEALPtqj6DovlQsQNYsXZbsR960NuP9XhE/dv8E/3tfjqSo/lXowe/w7tpBFv+fLHznsPBsPlxGWTqbvaUblFo+wjbcWGspkvLaC90szhbOWGZOUFEhx6iUPdsRBOygYaN5zDsuRMJkPbHnlpbtpKS4zP1QFgWbzu8Rv8yCfL9FJBxYHX3CQQwyZp7LGhPTxcZGAxF6RMKshvZX8Jcq9CWjtARwRYuUPDV9h24WCdq+4jcquMxvURRhkkThlZXmQkfEapRdMLEXZGC8VCUMNyRFFuYjJnJyArzZMrwUbqULWL3q9tNH7Q2NEmTEuHrNSENKaVu1SkJLCL1861xV92jvH9B2ZL0+RhAyxJWwc42qXug2Xn6D2yHsorT3vOjnLBP58tTddVCznO9mw+V7khWhTjNYK6EyKclEktaG1ZRdzy2bipPGcjcylLAV5CT2eEfh3XK6O2ZN2witdUcjZuOSl9t0y5KmcyVnD+uM30lh3POdsjbk5pHhzvQcXNFsWY7XFTqaaVu4RCEgQpkUgZehUC2985hz3i1s1darUD5N6s4UV5ZbLqPvpWiSy1aPgPzc/bJG6h60Hos2GBG0u8uIO7fh9IB41A226xTn6djcyhpGzs0CNXCf0spryttZsUFoF0CaRLO0opYyHcAV3d5YudETXXZ3veqsj/CqS2SWObqutzTa32kJkiJn/KXMgRta2CbqlSzPV/3PhinvGRN4zLogT89ye8aNo7dnvLtgvN0D3YgtBKa66bL03rByqtiVLF6U7MA60RcbZZxV2Nt5mDC9iSNhguB4you0RYFuyvVBhmCb00YqW8WLj0tozRtsegcmh6hu5AOSSjcMWeDeozh/CLi0riyn7y8UNb/7LNhUVcP0hgC3ppYaCwLUhL87StGlGqqAY2JdfHOzzgU2eymW3Mb56H77kGlFVj3SpNz2LtC6p00oh+JojDJd70mWBqzOil8MufDvhP//Iw3ag4i7VUKhGpwl35p8tz5DiUHcHzrh+/kHSIKgenZ7GWwqKHaCvpc9KtMlRjcZVBporzNEl5aXoWq7ml9+swt/iLMw2e8y2zHwd52GSdCqM0J3QktcCnnYxQe2zwKK+MsrZk4sRmpk5ZFmlpfma8tj30lvEHKkXcJtfVQhafgpODjuO4Tc7QLYYuUvqsx32WvdKMeWLCbnEresX6ZFsLQU/uM9g7bnl5acY8AaCdgF7l8PQM3da4nS3NzZgnzhe3s351xjxRLHERt24/JVFF3HwnGPfHLdEsb+kVe564tYHcr82YJyZxa4tgeoZuKXxoft52jZtfoVndvxk3rYrag8mAqLWKk+Uc9l2CIGRD23TTDrLUJPTr6G2xa0cpnWFK6EquKc3TSob0swiBxZy3s9dyP0k52R2Qk5G7Hp/faNN0S8z5ZXzLwbaswqE7/gPpG3G5ygs8h7aboANwLEko3XFZFHdPQXcpevBOsIQg3HJGb+trD9OcD31tlcWyy1LZpxoU51fP93oGw8OFEXWXCN+WWELseph7cpZjN5erZG9X7MQtOcmybe8ssbXcCeztllz0S7zyiQN+/sMhg8wqsnpPKUTgbofrJ4LgA/drvrg2O8871uD/d2/Odx/aPFwfWsWH1vuP26xHgoVATx2le7klK3aZX/knTT+dNW/8ypM13Wj2cP3WWSutifNsRiDsdri+WPetJ4S2/CJPcrTeYrbYsrJaQzsZAcH0OUmmdpgiJM50+7WfwF/drXneeOdpL5frXq7YveK2lysWzh233Vyxu7lcJ3HbzRW7V9z2csVOXK7kmlq4aYrYtVfseeK2lyt2N1f5Q/HzdkFxExZKurQTQeR6lKs2gSMQeUIjTpAby6Tr94Jbxg9q5EEN5VXYUC6dtBAY86WiPuVEyPWzaPx6m8JulOas91Oqrs9SxcMSEKuMk6MN7uiepixdqo6PtCRSCFTOZmHhske/byEQKBSpyhHjVnuFeBOgNauDlCRXLJQ88nH+OdOKBwYtJAJbynER5qL363o/pRdn1H2XudL5t2gfTkF3PmxLcLAWMExy7lofoJTGHTv450seVd/e9TyewfBwYUTdw8BuHzTpOVyuu7kr0Uw/YHbbCrFEIUU2y3bsLgwmYxf9Er6jGWTg2RrbEud0S9bdgHaU7nxhYD2ymCvNtv4JbXd6hq2fFiLnB67du5frP56SfG2bKe6ONfi7u3OevG/3Xq4TtgqEvdySQogdB0q1hm6UM4hTthottiKEoJ+mDLNiK2yYaVb6KVtLr0zHjv9/lAv++m7Fc48Ux6z2crnu5op1LOuccdtN2J0vbjAr7HYTdBN2c8VqtXfcdnPFWkJMM6t1zyHYErfdesWeL267CbvdBN2Ei/3z9o3ETVsBGp9yqUIrGdBKY+rpkFI6YBDFiESzGNYoz+9DJ7WivuAewm6U5qz0YhxpsVTxppl6XzocDBvUspC1qMdIpczZHlmuWB3EaARLFRfXnpiENEIXJURWki4CWPAqWMJibRATp4pG6JCJBDU+J5hrxcnRBitRl0wp5vwSvuWwMUoZxBklzybKHdbbLWq2T9UJKNkepW1bzpeboNNabzGcFVl62xJUfZvq+OMyHpdTOduNEaJw1x6o+syVXKq+yeIZHl6MqLtECJVj91ZQToDyK9MPmn7cp9NdJtUlLL+2+QGjcuSwBZZNHjY2P2iiAe3145C5ZH6DZsnbFBJ6cgi8KLmxtdyJUjlxnFAO/ZmeoFY6Ig/qWLY7FTlaKzorJ4hkvShQu6UnqIw65F4F7YYcqe7+V/i19bGgUxn2cAMlXVRQm55LG2WCv7kr46mVNVTQ2BQGWhdzBs4OZs+tTTg70DOCzhp1QG/WqyuKk1p0uys4SU7k1An8YKaXq4x75H4Nrf1JdIp5A7/2CcUv3GjNCmWdj8dYCC1422cr9NNitfqp4Lc/72y5U43sr41bVM26+nqds+QZ07hNe7kON0Bl5GETe9I3Ne7T6ZzGSSF2G+eM20TY9Qct0jg6b9wmwi6K+7Q7y6SUsf3qll6us3GbCrtoQLd7nEz5e8YtD5szwq69cQKZWaTeHLXQwx9vAVqjDlaekIUNLMverGN3AXHD8afCbjBqozZGRFYVJyhPBZ3IEuSo/ZD8vH2jccucOsIZxw3oRn3icdzcskfDzpDr94HOyP0qaf0a8qA6I+yiLCeL7R2Cbitl2wO/KHa9Hg3IExdHOCxVXTx75x9Eoe0S2i4rcZduGmFlHlI5HK66lCdzEGL677xXpuGGLEddUpUTpQJHeRwql5gruWitSXVON4tYG5tmQumQT4WhvuwE3ZlezBZNtyueXXS1AIfVQcyJ9ohTnYi5koMnJfMlh4VykcW7WkqnbCXJM3rpuIB0GpOqfLqNbnj4MX9SXCLsLMLdOF40Ph//1ig7HqVkRLR8H7pznDlfTDMGVtzH7ZzC6Z6ZOvA86dBAk6ydJNm4j4aTzGQMhMpn3KK2ZW0Wws1TSqq3menRGqd3Fnfj+NRFOBWHKic++zXcaH0m0yOHLdzW/Thj5+T3XAOPmz1exuPmNM84Mh4/6uBsPIDTOwNqttukzjOy9v3M2dk002MlA5zOaZzOafZ72xpmjrnGjzYzdHmK0zuD2HJtgWDOLSHayyTLd+Kn7ZlMj9NfxW3dj+q36O6SaLyz43D7ujObcVPZtL9tlMNXWrM/Nne2LeLJwUatp3Hb6pC10GSd1R1xE2mE0z1TuD/HjlfbspiTLrp1hnj1rvPGbZKxs7prFxQ3KLJVfjQgPnMXoneCudCeZuh2i1tou9RUSrx6/JxxmzheXSmZQ5KvnyJZu5cagx1xczYeQI460zlcSNwm4nGSsXMGHaKzX0OOzsxk6OSo/ZD+vH0jcZuM3zVuJR/tV8gq86AywuP/TPmevyc88Tmc9XtZUBm2ljzQ6TFQoz0F3YSy7VGzQ071hqwlfRbKzq6CboInbRbcCuvDmNPDLrVATgXdbkhhseRXGcSKE/02tq2ZK7nTGLmWTdUJmPfKRXyFmGbCRlnK/aOz4ERsJIOxqzdimCVEeTrjTb9Ugm5jFBOpZObxvVgfJmwMUxbKHo/bV+FANaDs2awPU24/3eXj97f4zIk2D2wM6UYpSp1HLT6EvOVLH+VXP/d+3vKlj35D10nyjGd94PdoxUV/4lY85Hvf/3uk26oUGB4+TKbuEqGkQ+7Uyf3KtNZXkilGuowM5xChS0+l1HXxYawdn9yvoi176jpUStNPXWyv+f9n78/DLcvO8k7wt9ba8xnvuWPMOWsiJYRSQkJCFggEYrDcwmVcQBuXmy4MBtoGdz3uxwYz2FV2F8Zg4wF3YRuMMbhM2RhkjMCYQQgkJERqSCkHRWZkTHc898x7XGv1H/uMd4rIzIhQZiq+5wml4sQ+5+y137PWfve3vvd7wckZYBDaEo+bBo+MIpAz5Za1dvbUKSWJK/C1Htf8iPJcWFQpjg9GVpcpQpdY5zMHAy9CRy30OAPoSvibbzR822/AsJBUnILvfWOOI8Pp5+qwiXVDEAduJELiVCv0hKY1tgQyjj9VQH7ZquK3N1mo2Xtltc8bziQUplLWakkH49cPKWR7iQavhaxB7pUNXye1WtqvUuQt/tbHV0mPWYcu9zULU0PI6Xcc19RZT13exRS3+TBWoNw61oMBBm88ZqtctF9FmALrlplDay39XCD9Zazr3hRuw0xjnSVkNb0hblDW0OVUUdUVqFTp6ZSmOh43rS3DIkCGLYTHsbiZ8faasZZuoXD8ZawfM1TgGYNCzHCT7sIYboSb0K2FXnxxbtCijqyuYMKQUTHnFTsew62eb7cCt3lF7Ym4+VWK5lmKqIXxAtzeJnbvCtVUcMYPUPUa/X7OUqVVnvMRGaFCGwYx1J0I5WV09RDfqR+bFTPW0ok1NVFBhykDO6JinBMzMZ1RQWBDCKBQCbF2CdVhlwwhBKHypg9MQgiGos9nehkV1586QkzOLJt4xJqC37n2DEpIztUr7GZlI2clJI6QyLEIxBn/11ieN6H7+0//R/rFrI3NXjY8MhN1XEPoMotXjl0byzAr+MzWgLTQ+I7ivuWIVsUjyTXa2DvmdHGr+tn9zFMf4bc3Ly689tubn+VnnvoI3/bQF7/Q07wbtyDukro7FFp5pCsXFm4we6Mc6YYsn3+Y2Objmp+yOziOT9a6d3r8tEhbKFpnXgFSs53E/P0PaPr52NC+UCRTxWFZQ2fHIgqpPIwbLXqO1jYoquuz7xgvqUIqWhdeT7sYLXqOhs1y+2vOQqqXZASOz7CA0JXkJpvWLVkvIlu+b3q8nc9cOS7R+isWi/CVS9a6BwBXCH7wLZZv/TUYFoKqa/nBt4f0tFkows8bp2FOeWrsuAXB0hnqwXn2suFCrZauLPPr2y0+sX/8YtrwMwyK6basdKb/Xx7T1Hn2uljAbRpCEK2ex7qadhbPecUq8qXzZTZJiJkoorBU1y4QOqIcwwm4TWvoGqs01k7dFG7tUYYT1Gm1XkdPpws1dgdxm4oiHJ/ls6+mEPpY3BBiZv1lBI1TD+BISzsflmNWpXtG3jg9HTPMiSJOwE1Hrenx0xq6WovW6hrdIjnkFZv61Vs6324VbrYofynPFbeRdWnnKb6b8pDKGHYvE7cvkjsBtaCGcSOMFwIWoTW6yGknFter8UCzTupG7Bbp1ObsILEz1rI9KGvoNmoRrhOxnfbYSnqsB/Ujid3eMGOQFjQCjwtRuRW7nfRZC2pHErtBWkx7abpSshbUGOkMVygac8IdY+2U/GkDhdU0IsnlZI/93pDQ8Wi50cLWphISawX7wwJhBacbEXt5ysC4U8I3Twad8YPaxIP3I4PHebT77ML5ZlYfsjG7WYcPJctefkoKep2cYW4weyNke4QZ9XhyIIk8hSPKVlRSzFpSaWOQoqwPrfoOkadOzLDeqbjYP9oV5LO9vTt8JnfjuLhL6u5knKC6q3K8Su9o1Z3i49uSx/cXIczN+D0WktxMF4lJjd1hld6MGFg7qUsDqSQteYS68iAxkPPer5LQ8RbVlXPEYH73QXCy56ixpco1cFyGBUSOIPIcHH2UunK+oo2F4vqjivA3R8cTusixvO0M/JeL8+Rtdrwn4aGmWfDVffWyYXPaK7i8yecGRsXsMwTgOhLhKFpCHFZXzhODA8X1N8LtoCjiZnGb1NBNMnTH4XZQFOGN6xePw+0o66+WKHHrZiMaTlTidpDQ3QC3Q4RuThTRlEeoYm/xfLt1uNnnj5tSNOsNrBRELJNlMb0spjCapSJGpT0QgsIIuqMcxxo29B5uJrHSIxLQNoa+X2WpuopQHtbx0NJlOzakRbl9OtlyXfPrC8RuPiaEruo70y3X9aB+LLEbpAV7w2w2qwSs+FV20wGdvNzOmxC7ecIpBNzbrOEoSYuIhhvSyUdYmG7pls4Rhuv9MkO3XHVJbMooiRkVKftZjCcdml6EEgIlJBJBLzEUBWxUK1xND0j5xzFvY/Z8Lds8R3HfXEPo2CZIzyHThsxOmsdDpg1XuwnGWjZqPtuDFEupwm0ELmcaAUvRyVvotzPuq7WOfP3++vIdPpO7cVzcram7QzGpOTjqBjOJqutTcwMSndPJ4tIh4pg2CgC78fGc3ALVwEHOEZKJSk8gaGcDsrFrwuQGM6/bhFnNj6sU3SxmNL7pHyQG8/VnTS+cErvO+OY0IQbzMRqXwUWOR8MLybWmnZU2PvPE4OCaOSnCt1j20gGFMQv/LmChFmuirnSkpJ2OSHXOxuFWX3PnJfj4TnjoeydZRiHg7761bOYMUHPh77x1pvQ1lLZEP/D7s3YsAN1MMOlpOinCL0yZdTTWHksM4Ma4HVS53ixu8yrXk3A7SuV6I9wOqpMnuAELuB2ncj0KNzia0JW4lDV2gXLp5wmDcSH3rZxvtwq3Ua5vLW5eyAhBWyh0tETmNdjWAZlfo9FaQTTWKaqraD8idDyWMcjuNeJrj+JtPoZ/9WOkT/4B4eWPcnb4WZaG13AG26jRPoFOWXNCLJatpDct59CGQ4QOZjV2rlRsJ31iXY5hQuh8Vx4gbKXgIlIenXyWqUwLPa29S3TGr299cvqehhfSdCNGOmM3HYyddEp1srCS880qK2FJ/pa8CmeiFvfXVvGVU2b83JCK8uknhmGa43iGmBHOwRKRcQTSYS8dcrU/vGUevEKUW7U136EeuNSDcr70x9f01es1TjdC1msBG7WARuAyzDSPXuvywaf3+cx2n/1RBseUg9yu+NYHHuEdG/ctvPaOjfv51gceubMncjeOjbuZujsUmdW0kxFpppBGs65GSB1i1BzDMJpGPkQZQwfYSy0mc9DGsCpjvCLFuI3p4SeRE4GhqXuUCsy5TFMRs6YztoWinQ3wCEgzS0SKxGXBZxVQOmM1T9gVkm4Wl3VJKbhWsyoGoCuUPdtmY1jOR+xbGBZl1iPPFMbocUuTcbZlom2wllo2RJmCNh576RAKl0JbWmKEtIfPKchHrBUZ29JhLx2gbW36uRIDhgXnAFUkrBUpO0LRTkd80arPQ033kN/tJDYHZn5Hd6zWGxfgYwhtQuhUZk2d0ePjFdbCr1zM+NTeYuuG3MBvXYGvur/8e5iNWDUZO9ZlN+kjjU9eWBoipqYt2jQWxnASbksmx5jGglfszeBm5c3hti5HOIWPceYcHG6AW1QItNtc2IYO8hGOLtgWJbFzjE9WWCo2YclqtGmeiFsgfJIMPHJWbAq6tlCfJkxRjgFJP0/ItL6l8+1W4WYyiMTnADflYqVDlCfgR7SlRypViZsqWLY9KqNNTNFDWIsdV7mF1uCHLTa9cKpctdYeInTTMYyJ3SRjF4mQUQK+K1mrHm4VNCF2k4xdqjVJMruGscl53/VP8LWnXzt9bZLR6+QjtpI+OnUpjGWt5h/p/hAqj7WgxnbSZyvpoXRAUQjWaxWaY7uzrz39MB/rPMtjvesLY1n2q3xw+xnSDFbCiFdWlhhpQaS8Y4ndrbZsc5WkGZYPAWlhuLwf87ErXZKifFC4kWL3VoWnHN7/Vd/Oxi/80DSL/v6v+p/vql9fRHE3U3eHQiK40h8S64QNOSTsXsbtXlv0okx6ePuXaY52qQjJ1nBEOx+y7GRUB1fxuldLT8lxvPNUzGtrw4XvUeOGt8oavP3LU/9JAIzG7V4j6lxm3eSMcsPVQQ+lLCv53oKKdBLOYIdw/xnWsiHWCi71ehQUrIk+4f4l3N7m4vfH+3j7z7Iat/FRXBsM6eUJayo55OMKY+Vk5wqNwQ5NYGcYs5MMaKqcxmjz8Ht0jtu7SqV3jVVr6KX59EYD5Y1dxYsN7tzeJuH+JdZ0SqHh+rDH/+sLU3y5mOWbxBlnsHAt9JynrjBFidu892vSm56nAJ7pHq3A2ByNzzGP8bpXqfY2WcawH+dsxQMiR7OUbOHtX170cr0BbsH+JZzRYk3LrcSt0r+K27sKB71fT8DN7VxZ8H6d4BaMdqe4XR/18aRhNd/B23/2hrhd7vewwrBmOoT7zxzyr3VG5bVYSToo5C2fb7cKNylfhLi5hnq2j4o7WMenqK6gq8vooI4z2Kb17Ie4Z/PTvEP6fLm3wldFq2wML+HsP4vT30QNdlDDNmrURo328eIup63BjoY8s3sdbMJaxT1WoDEhdg6Ki50e3WJIPjcHj1KhNryQmhNyuTdgO+2zWvVOtPMKlceqX2VnmHJl2KUaqimhg9Lt4vtf/bUL9m1NJ8S1Pp04o+n71ALJxdEuH+9c4RPdq1wddejnyXR9gNvvwesqQabLXqKTr00LzWe2+2Ud9W1meK5U1MaCpJrr3yV0L7K4m6m7Q2G0JPAdXM8wMgLPi0rl35xy0zh+qdqTLmnm4OPheJpYGKpuANJZUFU6jsP/+tor/I8fOke/cIgcg6csnVRhGasj5xdRIcvXrGFkXVzj4zsGLTMyx184l/lz0l6VTHjY3CV0CoRTkGiB61UOKWet8jBeRC4D8tzBFx6+rxliObSnSekJarwQg2RUOARCgFswkpqaExz2ZxUS6wQYJEPtEFhnUbYgJPZAgbZxQ7RXIbEOwrgEboHycmquR5oujvnhFXjnWcMvPj3vKMHMfWJyDQ9co5kYBNYCjoyNsXDTSqf8DFMQaxcfgfJSMlGglQ/IqYp04TuPwU16Faw6YJl1C3GL3LC0spr7fdwIN4lZ8Io9CjfchELl5MrH9cxN4YaTk1kHx6suXiPAKh/tVchUgM5dAlnc0vl2q3Azsnjp4CZVqcJVHl1/lXcJTW771ByJf+1RnHxEUV3D+DUY5/bkaB+VDRg4dVrWJzQx4dDDa7t40p09MI17A1rlYRyfzEqK1MOXKf/m2u8w0LO2Rt28rBt05giENpYkkYTCx/U1QxMT2uqx2TNrLcMEQkJyPyElRlt3ap8G4EhFqFwGxfi7hcAzAaFrwMnwZEjNDUpBls54dtTGYgmVW26d49IdWkLHvS2Ebt6y7VQ9mH6+FIKr3YTL+zFLocu5pYjlyMW52wj58y7ukro7FFLAfY0GfZ3Q0Tm6cZaqt0i6rBcRt+5lb6QpLJyr18gpVXpb1Y2ymFvNQaZcdOscviPpFxC5gq+7z7IbJ1RdS7x8LwvJWCHIG6fpJxmDDKqe5JTfZC8bsuVVMPLwz0FXVhg5ddqJwVOK9doSnWLInhDo5jnCeeN5wAR1Ripkd1RghOBCo8bIpAyLDHMEabSOT7J0z1QtuVENQOqx5+jKodYgSEW2dJ7OMCXWgpqvFjxGjXQwBwq6i9o6fbdJJ2Psg7rEfjaca4Uy2xZ+ZB1EdWnB+9WRclpTp6UqlZss4jY7XvB/ezDgk22z0M/OlfDlZ2e4pc1zZS2WFixXHDzHp52OuB61WPYi5AIhOhm3VrSM5y0yyVuKW/0UTb+ySA5ugFvLi5Dzv9Uxbv1+xtBCzVes+wHtbMD1oM6yt4FzwAP1ONy2nZDWUgP/AMHR0RKxithLyrYjt2O+3QrcunH8ksNtMt8qFUXkN0pv5qDGshcdwo3KMnGS0slKcdDpQNFJe+zmOS0xaVwCQhcEm49hKcsTOjqkEtR4wnZ4ergoWsit4Xd2nuCd668qr5OxbPVTcm24f6lJZjM6+YjddMCKf5jYWVvWh44yzXotwnfD6VbselBfIHYL19dY6r7H+TBkK+1PlcO+cqYOGdZaElPwdL/NzjAlUA4PtZbo5oaK4xMc8DSejukFELqNmk99LssogNWKPxaYFfzJtS6RqzhbD1ivH+2OczdennGXxt+hCByF48hZMbcuGBSL3W+NsezFlsIyLdKeFnNbQ6fIFlLr1lo6qRk/H5dbvO99UPGtr1F82YWMvTxZaCMC0M8Mg/FNshm6OKrshC+EXNhCmERaWNqpLY+ruDhqVszd0QUjvTgGrS27scEIOS2unxThH7UpYKylnZiy/cW4uH5ahG/tuM3KYnRiTazFtLh+XqhhrD0knhjlZnqDaUXutID9qHjfRRhmhtH8sKyYCk6s5UjcJspeKaDqS/7u2yRVd3buTc8y0RlMcEu0mBbXT4vwLewd2M6Bk3Fr63RahD+JW4lbbAydfLEZ9I1waxeHx9CJNamZ4TYVvQjBXp7cNG6OUrSLnPRAuUBWGPbSsdtBxbtt8+2F4tbwopccbvPz7TnjphStsIn0Q3alO12vkJKiukIctNjWAUonnCm22e8sthWZxOZ4a3ue0E1q6I4ST8zjNiF0jcilGbjTGrvcaLaS3kIJx3wIUQpsXOWwHtQRCLbTHqmea3guBBhJnjqs+jXON6p08xGf6W3ygd2n+OjeJTbjLr25a1UYe8sI3XxIIWiGLqdqAfujnN94cpfffGKHXI/H97nrf3w37lDcJXV3KKatRY5R6Z2kujtKpTevuju42zCv0ptfoI9TSx4kORMieJzq7qBKb0K8LMerJZteuEC+4Pj2FzBTV04Wock5HaeWnI95deV/eNzwc49Z/vtVZ0EtKYU4sr7HWMvf/mDZG28S+ymMxkrWVEMnTRbGvDec3WgnY3Rk2YZlek2FxXKyWvIodSXcGLeD6sqbxW2irjxJ5XqUKvZmcJtXxc7j5jtyAbfj1MzHqVyPU8Uep3K9HfPtheKmxmN4KeF2cL69ENzm16QJbkK5LC21oLbCWuXo9hhn8gSxf4329iYm7rMWyYUauqOI3VGEbhLHEbv51ktKiilurlRHEruDHryh49L0IlaDGht+g+2sxwd2nuIj7WdKIUZnk892usQ6f0GELjea4fi3MCyyBVeHrX5Kpg33tiLONEL0eFCZNuwO0s+pu8XduL1xd/v1ToW1yLhbdp93/JkpeTaCuEta+BTKn91grEWm/bLDvRctmJL3+n3QLomMDrQtMchkgPFr0xvNJIzRDOKMwHNmnqBFisgTjF/FkWrcQBeMNcS9Nl0d4jjegieoTIeY8TbTxHPUWgOoMmPF3A3GGmQ6wCoX64YH/FQt3W6XXASLXq5jCyXjV4kcb1rLZq2ht98hFhGh58w8QfMYbFnPBOXNw2LZH+5R0fC+i8vspQ6roeWbXi1A58hshPErxMXhn39c2IUedACFhcH4wXyQS/7RH1cxZjbmpDBTd4q4sJhRH+U4wOz6Wwuj0T7aiCluE2IgshHCFAu4tdMR+4NdPC0YEJ2I26Qf2n68T93Ym8atmwzBdBgWQdlY+BjcJl6xcZ6ikh6F8W+IG5R97LqDHVShprhVXWeKm9A5xq8ueMVOcOtSwXXniPgcbvNesftxj7rR9HQ5tnkvV5kObst8e6G4afXSw21+vr1Q3CbPUsYY2v3RIdy+wg34Hb/GJ9P+dP44wJfEfZJnPkYoAzYiFz/xxnWRESaoY12fpuOD8ujojJ20D7lHnJtDhG4SB1WxPiFaz7J2sc4XavkmxG4r6bGd9qirCp2hPtaD11OKeyurUxKYFZprw328NKEaFIjRiJZXoeoGhMo9tA18EqH7yx/5ObpFSdi7Rcx3fPTf8s/e8M20hwXdJKcRuKzXyt+xmusj+dErXZqhw4WlqMxo3627e1nFXTTvULh5jN9+Grd7FaydZhCibMTg2uPo9kVagZpmDGTax+tcxus8ixg/jVVdn7rVJFsXSXaepCbShYyBMBqvcxk5Xgx95fL192m+fv0aX16/RFT0ZhkDa3G7V/HbT8/8NOe8X7uXP4UX7yxkDJzBLv7exakCb5JBEJMnb1OwHDrTjIGKu3jtZ/A6VxZUh5PId56i6dnpDUZkI7z9Z/H2n0VkpVR0uu2pC0ZbT1Cxo1nGwGi8zhXeu7ZJxTHTMbTcCqJ9lf7VxxDMXodSUenvXcQZ7M7aqsyFvokn2M+0Fdl4OFbn/JNPeNOedP1c8Lf/UCHbzzK/1yGwpO1rh3ATRYbXefYQbi3XR+9dYnj9MzfEbbot2d16TriFcZf+lU9D99INcWt6IVWdMrz+1E3hFjkeTanIt545Fjev/czU+9VTagG3IN1byPTM4zYZw7JfxRu26V1+DLd/bSFDp0bt2zrfXghucryN+PmK25+un+U9XpUvz7IjcavuX+LvhQ1qc/W0DaEY7l8niXtUW2s4jTW0X0GmPSrP/iG1J36D4OqjhFc+xqnNxzi98wT9S59kb/dplsSIJcce2/djQuzaScpTnT0GZmYTNihS/s5j/4Vibv2aELu0MHymvYcV5kQPXikEa34dB8XT3R4Cyfl6jeUgItY5F4e7fKJzhUf3r/DMYI9eHk/7Ph635fqfrz7KR/YvLXzPH7Wf4d9e/OghQjcfSgg2aj65tjx6vccHLu5xeX9EVhy9/Xw3XnpxN1N3h8KMVZlWOjOlpAVd+AjpIX1JTo4/tvWy0sFKhZXuVAFqraXQLhIfXI1WZlw7MlNelu+ZwfqV5wyp20EXPbRzbiYJEGLsc+nBIYGEQDg+1jUU1kwdBJAK63jYuUJqa5jVyAgohJ4eb6UqswZHCDAAhCfJKGZd7ubP/ZBMXiAdhXY0Zuy/OblOX3eqy7+9tgFAXEBSCAQhuKNDJSRWOaUi8BgZ/qQlzI2isOPtWKt4rL34bPRor8L7dw92XhdIPKSbTHETQmCFwMoZ5pPICoG0EdYp0I6+IW5ag7Hhc8KtMAFW+QjvxriVtYo+Uvo3jVuWS4TwkU46xW1y27PSGfdNmx1fmBlu2jGLOB+BW6EtxgTlmD1LQYEazx/G1+h2zLcXjtucgvMlgtv8fHuhuH1N5Qy9DLTIjsVNKZdQKvrTukmJkGoRN+Vi/Bo6bGClS1FdKd+vc+i0Ob31OFYZvPgCYacxVdkax8d6NawbjK+vR1oI3CLgseGnKQ7U132qd21BpFH+9kDkPq4qwE0pbIA64XZqjEVnHo7IUF5GgUcoFTWpqFH+1lJTsJV2uZZ0iJSHyTxcPC40qodq6K4caP8ziacHe7x7/WhCNwkhxPTzLu6NuNRJuGcp4J5WhY26/6KwI7sbzz/ukro7FNoNSFunF8zC94Y5hVelde5hYgr6Ooc8per6pf9m677yBqPcWU2PdamdeRVCanpGY7KYsvmvAOmU7xlvo/TTgkFmCVfvoeZo2sYsWBzljTMUOp8Zkk9uQdKhdf61tG2x6F1ZWUH7tennT50ixFj9Jh26WZm1iBwPE9TJHB8rFTlqbJvF+LvAW3uQkTXYsf+mdXzS5XvLAybXaUxahXKonX01HaPRE2sqqRg2zvG3/0DSL0pi1c/hh/8Q/vrrz7PsnYGLcmFsRW0dHS4tNK2dD9+ReK5hPzk5iT3dzuDoBfAKy1g7e3KPC4FavoDnFVPcSnWlS7Z0vmz2+jxxm9Zi1U/TWl6/adxMuMRyrUZv7OV6FG5INavFEgHNs68ho7ghbp1RTmwk0dqDeK6Z4SYjhFRkS+cQRk9xmNZitc7Tcs6wh16wgjuI27SGLlql1WzRoVj0io2WMG54W+bbC8XNUJrXv6RwG8+3W4FbLy3QN4Nb99psQglB82ZxyyBxm9TufdMUt0xIlpSL1Bkq6SL0tfGYLf0CrPY51VhB2dGR83lzrv/gpIYudFzONlbYyRZVsQej0IbNfgoIXtVq0dVDeqMRuginPfGEEATKJVAu2hie7Q3opl1WKz5BnoFqUHNmLhZnw6Ujz/Pe6vKJhG4SvSRns5+yXPE40whIcsNntgc83R5xz1LERt2/Ya3f3Xhxxl1SdwdjchM+qkjbsxabxQvelZPF/GCR9sSX0eZp2fiSgHE3tcUbzLhIuxGWi0FL54velVJNn7jT3PCOM5pUS1YiiRtWWB7flOZvNJMxzBdpT71fhZgWc0N5o7GOT244ZJtlEVT8GraIFz1H5+T/nVGOHf9EpZCEQRVbZAueo791xeGTB7ykn+goHt0P+Kp7xFydoKUwtmxP4k7aSMz7u5ahpOCr7k35uceOaTYHPNTUtBPJsCgFERzRa3gtUnSy2ecPC8Hfe9Tnh98aUNPpoueocqcZxeeD28Hi+pvFrazF8mlZe8hzdHITPlhcH3qKELDZybhNiusb0fiBYYxbTyfUnRA51wPuqOL6ltaHPH4nY1gQRVR9lBIsW3vIK/Z2zbcXjFtuX3K4TT1+bwFuHuKmcJvPmUsh8MPoBeG2bwxNr4KYE4X104JhnhCpnGZyjXvSAUfFKaGQ6YCRUWyPDK6jpluu63JWY3eQ2E0InbGwXvPwHYXv1OkkKXvj75pvdmysZWeYIY3DA80I3xW0syGXhnsEyqWifCquz+uaZ3lt4ywf716Zvve19fN8831vOPL852NC6EJXcaYRIIUoVdCeIs41j++U5O5CK+R0/cZCjrvx4oq7NXV3KCY+hsep7o5T6Z2kupuo9GalIier7o5T6U1uMF99wfKXHpb8mQdPVlcevMFM6tUER6v0/tsly6f2Dteb/Malo1V6MLvBHHzXQZXe5vDo7dLN4eycJjGv0ssKc6S8XyB4z/0+zoFt2Mm2bMWx/NBbZsrZQAm+YHnx2IdXysxgbhbP/pN7gt969mh1JTx/3A6qJW8Wt0kt1nHqypPUkjfC7aBacoJbYcyCuvI4teRx6srjVK7HqWJvx3x7objlhX3J4XZQFftCcGsGzk3hNt8p5UbdA14Ibr7vUWs0MdUV3rH6AO6BVedhN+LdeYy89MfkT32Ile2Pc370NEHvCmqwi58N2VAewtoFVexRhG4y5iW3gq9Km8NJo2NjLduDlDQ3LFc8qr6DKxVLXoWzUatsgxLvc2m4x1425Nvv/1LCccPuUHn81Ye+jCujffbSAaMD7XgmYSyHCN18hK7iVD2g4ime2h3ygYt7fHZnQJIf7ZJzN158cTdTd4ciNTmjPGOYQKE1K24+tleZcy4QgiUsHWvpj3vMFbkkyQ11lVOVTDNXk6jNKtowlI0nh6kmFDlNJWdbo+PwgRUsu7rcJqmokO6owMWw4uQgHOazVw6WVWDHGNrZgKYT0Y0NxhpWnXysCpuNQQIr1rKHoZvFWAvPdBUH/VsB/sMT8JUXYAmLtGYqz6dwiHNNReRI4XEwm1bBIrHsa03VT4HDWbVTB3xxJWBtOea6E9KJDXDYtxLAl5YlD3bS8nurrsWT0E4hciwVR0/HLAT88FsF3/prhn4uqTiGv/7GjH//mcOetQDXx85ZtfEYujqnk4Gy7osOtziDvDAsqZxQCuyB8ZyEW8MxWBbrgCpYFJa2LoldSEAvKfCEZlnlcOB4T1hWrGCXRdyk0ax5GiGchWssx2PexdBORzTc8LbMtxeK2yjVuOKlhdtkvt0K3Moxz+I43I5rqnY7cXOFpKlcdnR5bSpC8V1rr2DkRHRHBZ6XsaxSRNZFJO2xDaMgACJr2QUGXoSMVtjLfVAB683qoTq1iXhiO+1NCf8oYYHQHTz+dNTATx0G416CSgcEwiUmo+J4KCnZTvtcj7vI8XZuy4tozjWs1tYeS+jmI3AVTeCze8NxzV3I/csVTjXu1ty92ONupu4OxtO9HsM8Y1WOqPWeLVVqc0W5MukR7F9ibbhNIOBqf8BeHFOXGa3hFbz9S4hi1kxUFCne/iWULZ8MtbVcG/YJpGEtuU6wf2mqtAPAGrzOFWrdK6yajFFe8HS3ixCwUewQ7D+NMzzoRblNZf9pNvIR1sJnu10yo1m3PardZ8Y+qLNQcYdg/xLro30cIbjU61JzsyOvx04Mv/V0OYbV4TYRsD0csR0PiUTBanLtsB+tKfD2n6XZu0bLFDy8MuLe+mJD1oeXcr78/OLbpClYy8veTRe7XexRe6aTMQ/3kHb2vY40TG4w0hQlbnM3HC/rEYny+NCBreGASC02fZ3EqcoMt6X+dRrWsJfEL0rcemlCS8Q0B5fx9p+FOSxK5eTxuHn7l6aKSmCKW3W4S8sU9NKMS/0ujrSsZ1sE+5dQcWfhnNzuNaqdZw7hdsp2iDoXcQbbh3AL2k+zkfQQiNs23z4fcZvMt1uB20GP3+Nwk8eQutuN27wbRSQkWaGnuK2bffzBdaTO0ZVldHUVXV1BFClR5xIbcQdvuMfWM48Sbn6c+3Y+ROvS7+Ftfhqnex01aiOyIVg9JXaudHhyv81+Eh9J6OZj2a9QdQKu9PtcHfSm5yoos6pLXsRKUJ0SuWtJlw/tXqQ73q5OdMZa7XgP3kkkueZKNyF0FV94uk7Vd3h8Z8CHLu1zeX90t4nxizjuZuruVBgFViC8nCOLsA5G4YLV4BQIeZPc2woQBpz8Jo5V2MIFkYCTwRHtPeZDWFEeT14er2ez+uvvh2EOlYNtoAoXbM4bz8T85rMee+nhJ7zNoYRJiUuhyv0BqcE5edEBwEgco/i2hzv8rx9aZlhIaqrg77xhgJBHFBJbAYUHFAgnh5kO8MZxk6rY8nskbzwT8x8/G05VsgBfsGz58vMC5raVrFFQSBD5Hcft2BjjhluAvJnjnztuFApEWo75aP47iwO42Ru1nbGUx9v8ts23F4Sbym94Sndxu4lrCrcfN8DmNzHfpCyFGm6FTDVJwhFe4GDzIW7nCmrURlfXKJsblV7g0hGk1RW8wsfNMmSkQYbc8LZcuAjtYFVx7LokhSByPFyj+LHP/CbDsY9uXyd8+0f+Df/o9X+eiusfaY82IXRSwLlmiKskriq3ZkeZ5lNbfZKi/AEbY6eK8OPixz/1u3TG4py/+pq3nzy2u/GC4y6pu0MhgNWgQiFSdvCwzbO43qIvownqJEsXSisiIzldqaFFzl6RkgRrBCrAsS7upK2C45MtXUCPtzOUEJypVRnkKduVNZqOC/7cPqSQZM2z5EnCbu5QcRVrgUc3j9kM6qxErcXjgaK6Ru5U2c4UAsV9jQa9ImZb+qw078EJIt4zx5+0baKVy14qKIzgQq1BTMJX3Zfy859e9K0E2Ki7ZEsX6CaakXVYC31wCkZFxk5lja+/DwZmjjBKh2zpPHGSsa9d6r5k3QsIHBgWEHoSUWke+h4jHbZEHU9I7m806BRHq9wAisoydm6tc6Sc1qcY6ZA1zzK/ZWaCOlaVTM0iprjVPUt7vIVbc+GH3lQKK6wscRukOT3tshxKHNe/47jNhw6Pxq2tNbZ+mtALF1qoWC86EbfGWJk4jTFuyaigbdUUt14esxUtsazWwFscQ944Tew12SncBdw23YjVpXuRB8dcWaZQAbuZwlq4t9FgqGN2tHhO862rc6idouIGCyrpyXx7obiN0uwlh9vB+fZCcBOqslBQcRxu5hiecNtxm/8uBBXXewHzLWLFf0WJ2wQHayHJscU+o81nCbTltYFDkhSk+09SVJYJqqtYN8A447GMhVF7w4xBWnCmWgcnP7Jubj7ef+3TPD7YXHjtY53L/NTF3+XL1l5JpFxqbkCkfELlgpFc66ULhG4+SkFFOM30pYXh9y62ubAUUA/csg7wwHt+/LHf49JgnwvVpbuk7g7EXVJ3B6IWOJxrRshqQDuR7KYDnjaWVQwN3yKlwFhbFkSnDqNME7q2tHKxDpiCnrR4vqKwllFcPi56jkNh1Mz7VUDNDRCIca2JoHngKSo1inbh4qhZkbaSgnY6YgdYRizsyWsr2S08jJwVabtKsZcO2MXSsmKhMs0A7cIjt7Mi7dBWeMf5If/+M3Yhc/XwCnz5edhPXGIr54q0SwY3LDLefq+meaD0bWgcutouFGlPnCf0+BwOPn8aBFLIaXF9Sx7t/QpgrFhIpEgEk9M2HE5QWGsx4wOEgHrgYq2DnMuWhA7MuTjRMw4DzUJx/YsRt3Y2ZF9rDIKDlHy/OB43g6B54PihcRgZcF0xh1vpYLALtFjELTOSPe0jBQu4tbPBdMzzC5ixgr3Cp2Amigic0rVh1xhalDVu87h1tEtiZ8X11jp0MujqHIPg4K/kVuDmGMGe1i8p3A7Pt9uP28Fa2juJ2yzErZ9vQmAch12zRB6UuPmewtUFOu4w6u/gjdqEauyTq1y0V2HPhmTWoxFVaIYeyMr0nPUR5G6QFjw76Bx5DTeTHp5UJKagF3ew1pbtZmJL0w15xXKTAo1jxZHXZdpvEnh6b8h+nBG6CldKGoHDSrXcRo5cdXer9g7HXVJ3B0LJ0gx7dSnklAkYZCHP9vp00xFFbHBF+RSmLYSOYL0aUvMdXEfiSYmj6nTyEaM8o+EqPBkyyjQ7w4z2MMdaxSStr7UlEB7asYyKdNZ+QYhj1ZLzFkfzfbWOU91NVHoH2y8cp7qTQrAWVKi7lnY2WyC+/80wSI5W3U0tjubbL3CCt+RkmbHM2i+MLcMmS9C8WtJTqty6OLDgzLxcF/eS51W07XTE19xXJSkUFbdU3U2KyyfnIcTiom/mvug4teS8NdWLBbepNdVc2ww4Xi15I9xCJanP4TZvTTWP23Eq13lrqvm2GcepXCfqykNtM45RS07UlZ2MhbYZtxI3Vzq0nJcWbgfn2wvBrZuNaDjRDXGbpxIjoymsQSFuO27z/EhKbst86yUFuTmAm3JoVpZpu0N2tabhhUTKReiMfq8L6RZrjqCWutB10X6EGteIWmvYi7sshw2gJHR7w4y1sM5RseRGdPIRa36dquOTFprNYYojC6SX8eRwGzkEX7pUHJ+64xM5Pr5y8KWzsGw+tFal4pU4ZIVhmGn2tgdYynvfZKs2LQyfuNaj5it8VxHHOU6c4yqBqySOPJpA3o3nFndJ3Z2MLEY5Ho3A49Vei62kzygvWBKWml9Buc6i1UyRlcRDuazIiF2gmyc0VcFK4LNcqXB+KcT5aLkQW2uJk6TsWm8VupDs5jF5nhLhsp+XT49LkVu2CDAaMW5iu7BgJT2aOLQzhRFizlvSIooU63iHFqxlFN1CkRuxsFCJIsVKhcahf6Ae5W9/QPPdr9NU/bkbzLilAcpduNEIXeDh0M3E4g3GFAijmeRghBBl+4WkT90qjC3zFFJQ3mCsQRTZeHvm8AJiLBTGLPrUTpewspWJIyVvOTOgJSVxoYhzyfxaJIps7Eowu2laa0myBJ0LBrlYvMHofNrEdv5G000KQhzamVwkBncQNymdBYIgiowsl8RGLhKDm8St7jkLuFnHXyQIY9z2CmeRGMzhtkDskj4t4dDJFYVlsW1JnmCVi5zzWW2noyluiZaLxGCMmxiPYUIQhM6xRt063PBfcrgdnG8vBLd+HN8Qt0yqcjt1HANT8P1XPsb3NV9Bbpzbits7gnV+dXSV2I5bKt2G+YaWNKKbwE2WuI1kRFivEUUu2lpENkKmg6mQTOkc79KHScIlZLhCP3cIwwpff+ZVfLz/DI/1rk+v5Wvqp3nXxmvo5iO20x4NVWFnkKNkWb4z8YI11pKZgn4Rcz3u0CsSAumw5Fb5kuYryEy5DezN1WN6jsRzJI3x2qfNzF0k14YndgfU/bLpczEaoAbl2ulIiaMEgVP+Pn0lifwy8+eqcs0t/yvuetXeIF5UpO6pp57iX/7Lf8m1a9f4yZ/8SarV47fIJvGxj32Mf/fv/h2DwYAv/dIv5c//+T//omT7Ih/B9h4ENVi5UDatDGrsJNco2pexlRZq/X6mLRrSIexdKpuDrtyDUC4rfsRunhJf+wyO51M59RAVL8CRArC4GF7FFrZ5Hu1WifOIzX6P7rXHSdKMytJ9qKDOMCuw1hJ0L6PSAWntFEXYBATSuMTtZxj124jKGaLl04wyzSjT+KMd/NEuedSiqJ0icMoFq7N/mfbeVRx/mcapB2ZPnkkPt3sV6wT8eu8CuVmcjJ/uKD66VfCeV47NwosUr30JgKx1ATs2Yhe6INt+kjQzBKsP0Iga4xuMxtu/jCgSsK8AJBJBwwsZbT1OezAE+0ZATTNtTn8LZ9SmqK4ROasM8pLaTZ88TUEr8hYyc0LnWCEBB0GZ+el0r9HZuYxy6lQ37p9lCjF47YtjC6n7mBBHgSXZe4YiTYjWH6QeVqc3GG//WYTJyZrnZ2byRpNsP0knTvCX7qe5vDzL9HSvotIBef0UOlqa3mj6Oxdpd9uo+lmWVs/MMgbDXZzBNkXUoqifmt5obga3bOk8UipaXoXuYIf+tWcQwidae3DaoPa54CYKFnDLG2cwQX1KECa4uc17WFpZn2Z65nErqqszgtB+hnZ7G1lZp7V+z5QYqNE+bu862q+SL52fZuwO4jYlBtkIr/MsVo6dIsYEoTdKGV1/Agp1y3BLq2exbv0lhdvB+fZCcKu5If3eybj9hjXkB9Lon0oH/O7243zl6S+4rbh97dL9/LZyiMcZptsx36K1BwifL246w+tenaxO5WdIhedFFFcfxSYpy8v30qjVYRjww+uv5C8O9xjojKrj87de/TU4UuFIyfVRl2txyopXZaMWLBCmSVuUQLnU3ZBlXXB11OGx/R1eWTnDStUjcBw+tn+ZquOz5IVEqiT7rpxkjWdbw44UPLgyu6cnJAS1oLTjM+WfTFuudofsDDIiT7ISeQhZrsZCCJQQeEqUTZxdSeBIAkdNCZ/nSGq+86LkAHcqXjSk7q/8lb/C+9//fh555BF+4Rd+gR/90R+9Ian7lV/5Fd773vfy7d/+7Zw7d47v+77v47/+1//Kz/zMz9yhs34OYQzCZqUvobUgyhqNVTegYzT7cY9GkU8nOkZPTbkn+wFCCFZcj64UDJIhOhlRdwO++dUO/aSglnaR46oy5Qg8x6HhNxgkFQajHLkES0v10iPWGgQ+No4xSz6mWilVWRYyW2MouhR1qLfKotiyEaiDyg25Yxl5kmGqyY0hHYHIMpQf48zVkYnxEy7SYXN49GXZTecmn9GzFiZzBtoeDmlmMCbFkemCHF+YovyOuRuAYxU2A6HT8ab0LIQuSsN2o4kcSlIn5r2+LerINkxz4wJUociKAuOO8By7cJgwR0kbBaawGFIclc9aEViLMHl5rUwx/RYXl1FqsCZDOeksE2jteMzZQqsKBweRCWyRImWCow5c13H2ZRJSCFQh0XmOPQG3xeMVaaGRTornmsXPv0nc7HiEE9zmr9U8bo5M5jUNC7hNjxcSkctyzCJexG18jYQppvPtJNzENONbYmIZ30S0xOQGRHELcZsbw0sEt4Xr9IJxEzfEbfOA/+oktkjvDG5z33k75tu8x/QLwm0uXBWRWB/jQqUSYSpLiCIlHOxQ0TkDyl56YfcqOqjjygCdeRR2hPIypDy5G4AjFLIIkDajGknWwxquVORGE+uM7nCExeIIRdXxaboh/WS2na2OIVpCiPEWLOyNMgpjObcUcqrmL5AzYy2FthTWkupym7ef5lzrpfhKTB0+6oHDmUZAK/KIvBcNxblj8aIZ8Xd+53fykz/5k/z6r/86v/ALv3DD4621fM/3fA/f9V3fxT/8h/8QgLe//e287W1v4zu/8zv54i/+4tt9ys8prB9h66vgBszv1anqEs3Tr2G7SNnJYlalInTcMqO3fKFUPTmz0mgRVGmceoi9NGYfgc0SvuU1AeBAugSmXr53ElJR3XgQE/fYFw6iGLHiRwihYO0c5Kvl8fMG96fvodpssW0tuUxZD2rltnB0BpYa4EXgeOTaMso0be80vSBgF0132GMjqhI57thouzQZ3xgenTKvhwWjYmxx5EVkS2WDuYkCb5RpupkgWH0AR6YMlI8ey+ORiqx5tlwQxczjdS8ucBoXqC2vY58V09dBkNc30EEdM6de8yUUxpJbQWIctuIBlhqTp+B5U3gY12I5daqnXkmmLO08xTLezhWCrHkOKx2+/n7Bv/sMjIpSFes3z+I4CT2hsBPvSscja55HmALjl7iluaGdaLzWfUROSl+5M89RMfYQjZYxY7ujSU2PrZ2m3mzSk2qh5qeormDcEDO+ptNaLH+ZxpmAoYJ2PqQlypqfedwmv4vOKCcWEZX1h9COpmM0tsieM24NESzgNhlDVpgF3LrSpZir1TqI26QWS4erNKpVelIs1NjpqEWqvNJySohZDd0B3FpS4Y+N4Se4LVjtWZ9o9UFQ+a3DTVURLzHcDs63F4JbNy7IboDbWtKFpH9ovVivrtwZ3OYK627HfOsXCTUdvmDcSD45/g7oHMQtT2l6IXripTtuqOx2LkNekKWwFi1zem2DffTU5uyoHnbaWLb6KcJKXtlapqeHbCU91oM6rlTTzBxQkrwi42K3yyjTGHtzbhR7o4y9YUbVdw4ROiiJrufMBCfDrCAdGjZqPmebIc5YcDhINY9tDpBSEI63cyPPoepLViovf0/bFw2pe81rXvOcjv/Upz7FM888wzd+4zdOX3vrW9/K+fPn+ZVf+ZUXHanL0hzrhgjlTDMHAAiJqjZZM5atpM9OMmA1qJbE7pgiVxHWWQ5q2HREJy0LoRtecKwsHy+k7oXYLKGTloq1FT9CuEFJMg+G4xHUV1gtcnaSAVtJvyR2yoGoOT3MVYJG6NAI6xStGt0055luh614QF1FOEICAQrJm9c1PyXkYt+2FcuXnjOLpuRzvozzRdqTLSB9wLvSuuWfSRhbKvFajRpK1aeqWIMt/025mHEx8ZToGabnNSwkP/7RCG3mfGHn22BYyiLt0KPmR5ixd2V5ExCAwAQlbu95AH75s5ZRUWaJoloN4dQxB70rvWiaMZgvrl+q1cutuwOeozj+tGXDQpF2LcJzqqhDnqPOdMwLxfWRT+RFeEd4jk7GAHPF9Z5DPVouldoHivBvFjclRGltN4fbfHH9BDcOeo7O4bZQXF+N8N0q6pDnqJrhPC+KOIDbvHhifszzxfX1sXn6LcMtty853I6ab88XN2EtSzfA7cuCGj+198zCFuzDYYOvW72fbja67bjZue+1t2G+dUejW4bb5Dd+Em5zPmvE/hJ7Okd5GafpIHe6RNESm36VbThE7CaELteGtZpP6CoCrdhOewvEbhKOkCSZxLcBK3U1XUZzqxmNienBuBGhOxjDrOBaN8FVckrooCR+9cChHjjldm5hGOWa3WHGlW5MM/R4+FSNU/XgxCbPL+V4yVYcPvXUUwDcc889C69fuHCBz372s8e+r9frceXKlemf69evH3vsLQvl8tM/+8u8971/mX/5Uz/L1mc/A/1d6G1Dfwd626jRHuu6wMtG7HW3iEc9yNOykPmI7TwhBCt+ROR6dNKYbpbc8DQaXkDTDxnlGbvp6IY9jkLHZTWoUhjNVtIfE52jw1GC5cjjdesr3N+q0KhazjYDzjZCWhWXmi9pzM1lgeV7v7CgJkMUcsG7Eo5X3R3nXTm/CE+KtHMDaVG+L8lhO5l5VxpTkjxggWgCPN52SA9dcjv93/ki7Umt1lHn0U+L6daDFKWC9jjvSjjey/U478rjVHfHeY4ep5Y8znMUjlZLHuc5ejO4pbpYwO04teRxnqPHqSWP8xw9TuV6nOfoBLeDaslbiZsxLz3cDs63F4Jb3XdviJuDoC5nQqOadPih06/FG4tebjduk5paY+1tmW8NL7oluE2XZCFubp20THFbrlextRV01KAedzi3dxG1e5Hd4c50DEcRunLMDmt+HYtlK+mRj+9R1pbXaJRpGpFLK/Sn56St5QM7T/Lp3jV6eTxdG28VoTsYjhRlix9XMco1zdBjteJxqT3iI5c7dOObbHD9EouXLFWN4/KHWqksZqdqtdr0346KH/uxH+OHfuiHDr2+v79PGD4Hh4HnEPtxym/+5u/Tbe/zE//05/lH/+zf8ca3vImv+dNfwzu+/O2EgVcSN51TlyntpM/OYERTFXhSgjFl13RRZoLKKMv7q4DWmk5ckAea6jTzZsZpq8VUs4+iol0GWY5ORzS8yQJmy3OQiz8JgaBhPDppxvWsT8uLZk9xphh//uKkWhYBeyZhmCYs+SEV1wUXXDlbjIQQbEQecaax2qNbxOwkI6quxkWVC5sS1B2JKBbJUkP4KGtIM01fJ/jCm/aJk4A0kBWWH/wQ9PNZBu7HPhzwv7whpukG7I8KkuKgBcYszBzRs9jpAiSAqpTYfLHGbnJNjLVkaUGuBWmmEXO+m7aYGwMeyliSNEcWAgeHfpoTCEHDFQgNds5FoIKLpHzq7OuESPp00wJlYckTuFYsnJOHYll49PKCrh5Rc0MGaQHasuQIfLF4vEKwLAK6RUo/jqm5IWluKApDVULVVYfG3CKgZxJGSVbaeVrFKC0IBcfiNrIp8RxuvbTAs5amr5CGBdeBEAeJxyDX9HRMVQX0kwJhDMuexIWFc3KRtAjo64zuqPQQHeUaXVjqjiA6ArdlEdA1Kf04RbhMcatIqCoJB8dwC3AbJAWKlxZuB+fbC8HNuUnc5iMUClWUNZl3AreJ8Mla6Me3fr5JzS3BbUI4JZyI24xogmcO4qYovCVcnbMxaNMb9hnW9omqp+ikBqUNK5GLn6cwx4MCYMO47OdDOukuLTdikGpMblgOHGoGGOVT5qksnMHnyu41rnGNU8KHfoc0g4bv0fRD0sHJyYm40OwOSiK+WvMphtmJpjqFsWwPUoyFtYqHV+T4wF4/o93WFMHnjgL1er2Fvy8vL9+Sz33Jkrp6vUxXdzodarVZDVm73eahhx469n3f+73fy7d927dN/379+nXe9KY3sbS0dMsu6sHYeqbD2Qfvp/fhj2LHT34f/uCH+PAHP0RUqfCV7/k6vv4b/yxv+JI3I6XEM4Yroy5bRcbpoEpFqRmxs2YsojBlFi/uEgz2aad94lEX3/WpOS50rpfHrtxTbplKVa5Q7asE6RCvssy+tAzEeCu2uwnDfWhsQLU1O/neNkF/Fz9qsu2EdBix7tdQo33obkJlCZqnZsfHPehdJXBDrodNOnbEulfFK1KENUx+cgI4sxxAnqB3r5BKwZVgiW6eYYyL0pZwcJWir4kb53C8EFcKHGEIelcIioydaJl9nZGmKVD+HqQtUDrm/Zt1PtlexOEz+w7/7WrGw8sdfvoTDYbF8U+EDnp6roUxY+IrULbAG26RN85MtzRENkKaWRPXZ9MeTuGz5FikldPXhSMQbqnA83tXCEzBTrTCdpFRZIqG47Gab6LSnKx5dtbZ31rc7jWCbIgIl9m1is1hQs0J2bBtgn6PoraOjpam568GuwTDPVTQYEtF7Az3iUTImhhRG+6iTZOivjE9XiY9gt51PCfketDgUtwhsAENWbCSbGELj6x5bvqQIIoUt38ZD8lW1OJ6lkHh0HAUK9kWMjfkzXOzzv5G43Uu4xYSHVWmuNUdn1N6B7c/Iq+fWtiGcnpbBHEHGS6xLXy2hwkVGbIhekSDfYqx9+Z0zKN9gv4Wrl9j06tycdQhFCHLMmVptI0pKodw87pX8JTL9bC1gNtKugn52D1Ezdp/3ArcWipi2e69pHA7ON9eCG5ZuI4N58Z8DG5fGy3zS4NNhtiyXNW9c7gxrgOTWLo2ufXzLRsSDq++YNwmIUyBFNmxuM1qBC2rx+EWtwnyDjgh+3tPstm+iqjdS2t9Ay8Ij+wh7AINHbKVdPlUPKIiIpabFaqBOzt+kkmTArda42ylwvW4y1PdDv1cUw98Ek/TKQpqbkDdDQiVS6i8hW3dYVawmyS4lfDEDN0kcm241okxvsfZRrBQS6dIqDebLFcObwXfybgdnOMlS+oefvhhoKytO3fuHABFUfD444/z3ve+99j31ev1KSG8U7Fx6hR/71/9c6532vzuf3ofH/iP7+OZJ8vt49FwyC///C/yyz//i5w+d5af+433sby6wtmowZVRl2vZiNNhg4p/RO0bQPNU+RSnNZuDPbbiPoXjsLRyD/R2oUhh2AEs6AL2ryGKlHpQxfoVOnlW1tilQ2Q6wBYH/BKKFJH2CcIaq0F1VmOXJzjpAOv6Rxw/RAnJRlBhMx2xFQ9YNznls92B0DlOnqCk4sFahS2jSYuCJoJQSPJCE0eSoZLEmSHNMvRggCwSjGzSzhU5MdZWAYU1hmQUc7lX5ajqgitdSTuWPNk9vlj21fWEnZGgP54exjKtzbPGYJMRtqbL+khAFmnp/EFJVtMMtEwJlUDYyfWZ1egJUyDzGIwmtJLrmUaT4XsSlabIPEUWKXp6kzHIPEZmI6KgRZ5JEpPRdHO8UYLKhmi9iJssUlQ2wPMqULgkeUoY5gS2QGXDBRslKNskyGyEKySOdknzHNyE0wpkHmMw5UPCOPModI7MYqRUREaxlWkQKaccD2eUzHp7Tb7HGkSRILWiYqtsZYrUZriuws0SZDYqFYbzY8hjVDYkCJrYwiEuRlQrOV6WobIBxq8uOH8InaKyIZ7yUE6LtMhw/JRI5MhsVNZGWgPjZt+ySEscMHjWIc2KKW4yT0CqUiE5Jge3CjfpF3jJSwy3PKVixS3BTXqtm8Ltz1aa/Ppgk4PC+TuBm5h0HIDbMt8wxS3BTYrJedoTcZsSLAGuORk3L2iSqBbJcI+1/mdopRfJl+9DR61S/KEWdzg8qVA6IM67uEFCZd5m7ohQQhKKiKEe4PqCc7UKgfLITEEnH7GT9EGUx1VUuZ0vjUN3aIlc96YJ3eVOjLEcInQv93hJkbof+ZEfYWVlhe/4ju/g/PnzfMmXfAk/+ZM/ybve9S6klPzsz/4sg8GAP/tn/+zn+lQPhSMUr3vgIZa/Y533fsdfov/ks/yXX/wl/uv/9Z/o7ncAiKpVWislc3ek5GzU4PHNK1wDTtOg4h7/VCGUYqO+ynU3YCdPMUGF5dX7yqfOIi0bGRcZbDyEHbYhi2nk5eTuDVL2rWYpWkI4HmSjchtWuVBbw3oh+LVpjd1OMmDL8VlfuQ8VHBBnVFpl4103xHFcNmSNzbjPFnLc6+1A+FXs8vmy5YQfsW4tW8mATlHgLm1QcRwqUZOVsYKxMBXyukLnGUtBnVNGsBsPZ3UYysFvrnC+d3S56COnQ57t+0f+G0DDg5/+moj3/udiat4dOIpi0rldOnSjdfLUAuNF0QSYMVEwFpa9Cjgpe9Zipk+akmldnhuSNc6Q5Zq9wqPlSqRXMDQFbrRCVUr03BM0UpE3TmPSmC0dESpFM3RJTc5u0GQpqE8LsydRVFcpnIBd7SOs5FytTmJTtq3P6tKFQ6IaHS6RIdjPFZmRnIpqFDJlx2hE/TSOGyws5sarkDfPMipKy6bVwAMnp2M0srpKJB3MvCeocsnrZyhSzbb2qLsK1yvHsBO2WIpa6LC5cE55fYPcr7FThDhCcaFeJzYp216FlaV7FlXeUCoUhcO+dtFacKZaJyNhy7isNc+hxgrO6ZiDOtnSOfqFYFQo1sMqRqbsGYOsb+Apb0GEc6twM4V56eGWpWzr4JbgZp3Fh+qTcLPSAbNY+3QncLNSgdZoBKEKbvl8M36NbOn8C8bNjHfwrFQn4jbro2lvGrfTyxvEJmU3H7F6/ZPgV7BuBR02yix52MBKl91hRlEI7m0skRIfKZ6Yj06SM4g1q0GVOIKddMBaUCNUHoFypz3bC6NJTcFTvT12Bym+Upxr1qjkDZpudOznfz4TOngRkbpf/MVf5Nd+7de4erVsqvjd3/3d+L7Pd33Xd/HII48A8L73vY977rmH7/iO7wDgp3/6p/mKr/gKXv/613Pq1Cl+93d/l5/4iZ/g3nvv/ZyN46QIlMvZSoMrwy71hy7w//57P8xf/5Ef4Pd+47f4lV/4P3nkbW9ZKBJVQvB9X/M/EDXqvOVrv5I/895v4MEH7j/284UQnAprXAf2kvL5dtmPSoXrvMq1dXZK9mpFSj7q0Rl1yYE1KRHZCNJRWTNny60Pkh5kDqFyWFUOO1nCluuz7gQsTBmpoLoy/asjJRthSezMUV6OQpRbuHNjWA+qbCUDdgpY8SpUJttlouxl5NabCx+xVvNx1Kg8RknOL0f8xablDzczPrI56+/0xg3J//gaj//8pGahOGQuKp4gCn2+5QsU/+xjOYO8tLopt1HLz7/vzCrCCgpjKAwU2iDkeLtGCGq+Q24ke/mASRldKbUvQJcNNHNCekWBowRLkYejPDpZTAfQbkD1wIKVq4hd68x5S5aG5P08waiApnQWrq5WHm1RJ2dWpJ1qRTsdsa0clp1gMY8pFXuiTixmRdqFcdlLB2xLScuNFjxHEYK+U6ebz4rrwaOdDWnjob2Q6EDBc+JU6KUFUtppcX0nixkWYByP5oExaxWwZxWFmIkivKIU1ew4Hi3lLYzBSoc9WSfRM1FEph3a2YBtFMtOuLjgSUVH1Rnks+J6i8deOmDHCFpexEH6Hzs12lk29XIV4rnjNoozula/pHDbyzyk5NbgJp2bxk0fVTR/B3Czc997W+abkLcEt0n23yAXWmUdxK10NClrpIfSeW64AZtBjZZXQekcmfYIBjsY5bKtlsicBo1Gk2YUkGr3WFUslISuO8qJPEVLeOgwZCvpsZ30p8RuEo5U5BqKVLMaeCxXHBKT86Hdi0SOz4PVNVaCGuEc+f18J3TwIiJ1999/P+94xzsA+OZv/ubp66urs5qZ7//+719oSPzKV76SJ554gv/+3/87g8GAn/qpn+LChQt37JyfT8wTu8vDDucqTd75de/mnV/37kPHfuKjH+P65StwGT77ycf4ub//E7z69a/j3e99D+96z9excebMofccS+wOHzgle62wga0us5cM0a7PqbBWNvQssrK3kc4hz8oMXh4T5glrWNrdLXZGXVbrayjv+OzXhNjBjRW6kzFMiN3ueAwnZSmhTNUzFjTsJSOWg4h//lUe7/yFhG4KDR/+2Vd5uFLwpx9Q/JeLeoHweRKyuf6e3/Iah3/7WMFgXKz8La9x6SQaRMZuOmAjrOG7s+njqASweI7klWsh2kJWVHBUmc2TElYiD60gzTWZhsh3cKUgLgpMBgKHrMi5lvaJVEbNDcqGvdbSGZXNHVaqpduF1pZIeVgsg3zm8QtlueXeKCMtNI3AwZGlD6lBEYiA/WzIKOuV2xpIrLX04pw4N4SeBCSdUXneynp08iHDvMt6UPYflFIcq5Y8znN0opb0EAtqyeM8R49TuR7nOXqcyvU4r1g4Ri0JR3rFwvFqyeM8R49TS4aOi8Ee8hw9Ti15nOcoHK2WPM5zFI5Xud4It4Mq1xeCW08n1J3wpnCbV51P4k7gZsbF/WVvNLnw77cCt0FaEOsXjtt7mmfZzWN8xEK7kIO4iXF9sRRiqop9rrhN55vjoX1Ltz+A3lVOe9epZBF5pYUKmqw7Pls6mxK7SRjLlNCtVDxErFFCsh7UjyR2ca7Z7qe4Sk4bC/s41N2Aa3GHP9x7mtWgStON8KQCK9gbFHjS5VwjIiPHMWI63z9f4kVD6h555JFpRu64+Nqv/dpDr0VRdOTrL+Y4itgdlUou8pwvfNMj/MmHPzJ97bGPPcpjH3uUf/D9P8zr3vgGvuxrvpp3fPW7uPehB6bH3DSxm4vJv+8lQ65DSez8CDjifdYS6Ixmf4+d3Uvs9LZYczyk45XbDPN1duOefI6URza1POqzJ2M4ROwc99DT6Px7yvcxbVuw7IdUXEE3tVRcgTs1VGeB8NU8S6hgO1787G9+tUM/s9TckuSBQ6YVW/GAzbjPRlDFGS+602M9gRTlk7GrHBwxywiGPjQq/nQM1pZtVbQBYwwGKEzETjxkUGRUlMXHJdEGp1ZeP2MtmdZlSxbAWokpJDvpiG6S03R84nHBUs13UFJRGIukzDjWPY/QFXTyGCMzVvwIR5Ud2IUQ09YrYKc3y1ERcqXfZS8bkeY+xkg6cYGnBI3ARWsQ0ozff9hM3rGKvbhAAs3AmRKDyW/jIEGoK5+9UXHoBjM5/tCNxonopsUhYjA5/hCx8yrEhT1s8j4+ftLuZEoQvBCMoh3nh4gB1h5pJh8Kl91RfojQTX6rBwlCwwloJ8UhYjA5p0MEwavQS/QhQjc/3xYIgluhMOIwMbAz8nISbvOE7oXiNkqym8ZNipkPaaH1HcPty6NToCx1d/G63ircisIQei8ctz+zdK4klTeD2zhu2XyzLtWlNQJPYXSGO9hBdK/jS0ngBGw5PntJb6GVz5TQLexIHSZ2GHWI0E1CCMHpsIknHfpFQp+EQLps9hMKa2lFDs+MhhBDpDzORM0yy7jQb/Tkdl4v5XjRkLrPt7gZYvdFb/lifubXfpnNq1d5/y//Kr/+n36FT370Y9N/f/SPPsqjf/RRfvXf/xK/9IH/tvDeW0bsjiJRQoDjEy2dZqW6wvX+DlmecCZPUEmvVMAKUWb2+jsl0WueGbf3mBGafPNJ3LX7ZnVOOoe9y+OTOYdQbknsRl261x5HOg7h2r2zrWRroXMVsphvfsUZ+tql5gmqLiR7l+kWCZgHp98JlP0BB3u49TUqbjglfMZoSqHFrIT7W17jlGPpXodOHZqn8JTDelhlu7tDe+ciy7UVVOvsmPQBeQLbz5T1MK2z4+tnActg9xmCvsRfuxdUeUNSVqM6V8qxt85CEFDzG+wmQ/LdZ6kLQ3XtPpirlzH717BxH13fwAY1oEY3Sxi2r1LNrtFYvoCstcpWK4CIO2VPxGgZauXWeFxE7HU28TuXWWpsoJZOz65RNoL9q+CF0DxDLXBYjjy2eruwf4maX0OfPUtclBmELEsw+1cAS1I/g1UuCo9e1iff+jSOFuS1c1SrFUZ5gdYWv7+JLGLS6imMGwKKooDu3tP0RyOKylnqS61xxtPgj9p46T5FZQUdLU1vNMPuddrdx7HhBtXW+qwPXdLH7W+igxpFbWNK7LrDHfa3P4uQdYLW2Vmmp0hL/03pkDfOIMf90NqjDv3rn4bCxV26QKvij7eyNG73KsIUpaJ27JvaSS3J3kXiUYZoXGC5UZ95gvY3UUmfLDqNdStTghB3r9LudbDRKRqttSkxUKN9nOEuRdRCV5anBKHX26Z9/UnwWoRLZ6bEQGQj3N51rBuSz3mO7g/36G0/jSDEbZ2fEQOd43WulJA3zyKVWxK7pM9o63FsBk7jwrixcEkk3N51RB6T109hvWhKEIr9K7SHA6ido9Vszbxch3ul9+scbsKBweDmcJvOXGvYv/LxO4bbN66cP4RbXtvABLVbgpvfOEPtDuJ20HLy+eB20nwTRYYz3EUHNXTYwi8STg/36O0+hR2TTYlh1TvaXWKe2F0edLGZR83zDxG6SZR9Wstdu16e0E4LKipkvebhO3OK2SLl8d4WNSfgbNRkabyO7g2zQ5/5com7pO5zGDebsds4c4a/8J3fzl/4zm/n2Wcu8R/+w3/g9371v3LxE48B8GXvftfC8dZa/sEP/DCPfMlbeNPb3wbubSB246i4Hqdqq1yLu1yutDjrP4CTxyVJ0TmMOiXREYqDdWztQZslN8DzQsBCOkLsXwXlYitLUFkqM3aOx34R0x/F2OoKUWNM6kwBcQ+RDvmW+5ZmtXxW0S0Ssv4O2t4P81V/SR8x3MMGVaBc2OYziMZo7PgpHoAsRgz3y+0fuzHOIDisK0l31KGtC5bq6ziT7eE8QcS90rKoyJgQSoXAyRO66YBabZWwOq4jLLLynIoMmyfgBjOPX50wGnYx0RL11qzdgkz7iHgfVWmCUxZsrzkRg44hHu4xiOos1VtzYxiVY3a8KakLHZcVKRgM2uwDS/UN1MTMOxshRp3Sp7i+AcrBkZJ1x6GTDkiKhGZrg6VKuaiakUGnGoOlWHLBr4K16EwyyC5TZDFBSxDUK+SpwXU0Ks+RaUpRFdhqudAKGzHMr5HqEbqe41U80sKSGQOjLqa3TWI8UipAmZkphim2t4OSLq7cICvGzWuTIc6gjdGWLFid9nh00oK014Ywp+6emXmCjh9IrPIodI6VCikEVSSdwQCEpemeQsrxueoclQ4QOqOIlrFO2TS1Jhza/QEm7VNpLuM5zSluKumj4g7CW8ZSfk7keKRxjB7s4oRVQnfW9kLkMWrUxioPXSkFVL5ycTNN1t9D1qAenJ8eL/MYFXcwJge7BqLEraKhP+iCO6LlnJn+3mWRItPB9P8bVZKGulW0B0OETqktr5dOEeP5JtMeMh2iw+ZULVpXPu3hAD3Yxa8t4buzmlqVDlCjNtqrAEvjMTgkSUE+2EO6PhXv9KExABTV9dm1AEx/HxHkNL2znxPcdNDAjMU5LxS3indncRNTL91Zhuq54hYql/QmcTNeBelVsJkmMZsgBZlO8a78MdKroqMljAjQvjsVhCghqasK1+I9ECPORMGRhG56nYWg6URc7yWMdMJyZZHQAVQcn0h5DHXGZ3qbZUlLHpIUL98t2buk7nMcN0vsJnH+ngt8z/f+Nd77l/8S169c4cnf+RBv/VNvXzjm8U9+in/zT/8F/+af/guCMODNf+rtvPbL3sorv+RNvPL+B24LsTtNg2txlyvpiLNRY1bH0JxN/G/8omf5iQ/sTE23R1/wVQx0wZkwIkCCzrFr98OwXT5Z9nbK/lRCsNRcZzdL2bGClTwra+yUC83T2CKDeSWXEDTW7mU/bIxblc5FfazmPaBek+NrboVkKxmwHlTLMVeWymVw3moHcKvLNE69gu0iZzOL2RgTH4IatnW2VA97Id/86mK6hVtbOkff9NixltUiL63gvLAcgykW1JzC8Wis3ku70mVfetgsKa3ghIDGKWzYXBCYAFSXz2K9gH3podOJx68oFclSHVKLBvU1AHaNZSsdzDx+wyZ2+ULpOazm6gajBs2NB9nJE7aKonRAUQ4yrCJXy7ZCfrU+u05+jdqZh9gd9Rj5HhVl8DxFEHogzpWEttJa+I7amQfY77fouyFhaDkbRFhr0bV70KMWRVCnUAG5NuXW48pZksCnrRzydMCSW0EIgfYb6MZ5tBOiDYAl1xrtt/BWH2QgNM/GQ5pZWcvkiICoegrHcUu/WMparP3cw1+6F5yMfQQtneOr8pi8fgpMseAJupeCbFwgEEOGXogYe44iBHltoyQG7pgMj2uxTHiKwK8QByGdOa9YHbXIpEL7M9w6o5zEbRKsv4LcU+xlw2mtlg4b5EvnMcqbNhEfZZouFYKV+9COYQ9NS+vSmsqrkDfK+TlRTmaFYa9wcJv34MiErnRhUqulXPLaKWSUoeetv0YF1M7h15ZIgsp0DAB5dRXjBuigPN6OBUNZsEywoUj9YKE2UodNMiizt3PzzSLxVx8kd2GvSFlWLo6UGL9K1jgD0rntuE3UorcCt24e03CjO4ZbqeY1TCwPnw9unTh/TrjtJxl/d3Cd4ZiY9a3hb3Yu83dWH8DdfxaRpNg9j7x5GhO1GMqQvZEt6/DclL1sgJJiQTwxH4U2bA0yGk6F5UpGbFK6maLhLZoICCGoOj6hdHmm26Of7VN3AxK9Apxcq/1SDGFv5BX1Mo8rV65w7tw5Ll++zNmzZ2/Ld7TTER9++glOr60de0yic64Mu0ghbkjsoGyIe2XUJTdF2cduTkjwL370x/kn/9v/fuT7zjxwH2/+srfzznd9JW94yxcTnOCisZeO2EuGVCfiiRvUxA3zjGtxF1c6i8RuMsZuny/5J0+gLSgBH/obX8jlYQdjLWcrjVLOPh1gVrZiyRNIhxB3scmI3WGbTBcseRFREJXEwxkvhkec37v+/YitoWC9Ynn/n1sks+/+PxOuDSynq+X7rg0sGxX4F+9O8B1nRuxOiEwXbMWD0iEjrJ1YlJvEGteXbCV9CqNnHr8nhLWW3XTEKM9o+mFJ7G4Q3bHHb+R6M2J3QsRjj19HqhmxOyEKY9gcdqDIWQsreGqu1nGcEZv/u7WwlQxItaYuQpaqNx7DXjJiML6xLgcnP4RA+dvbTYYLuB25tNkyV5FoXSqyjaXuVEhyzSDV5CUDxNiy2WngOCxXXBBMbazmi/AncVAU4Y4VoonOqbnBdMsOSjcF63CouH6irgyUOyUI83FQFJGOPUcn23UHa1YPFtdP/G4tdqEIfxKHiusltLMhudY0vPCQZ+dRxfWdsedo6HhTgjAd95gY6Mzih4qa7zAae8W6Sk0Jwnz8paf/kF2dsqJ8fvqeLyY3hnY2QCAWRC+TOCiKuJW4lb+Lw6KI54NbP06xjr1juH3bM3/IdpGy5vj8i/Nf/Lxwm699vBFu/bTg1/av8XODpzkY37HyIO9unsbmFinKPotZlrGfgomWaK5uoIMKm0VGbs0hVSyUhG6zXzpFrNc8PCXZTQeMdEbTjQ4RO2NLZ4k0NyxFLtfjHn/uoVfyYKt56PzuVOzt7d1tPvxSDsvJ3Pm5ZuwmfeyujLpci7sLfez+p+/5Tl77yBfx33/t/fz2r/06m1evTd939amL/NJTF/ml/9+/5jWv/0J+/jffd+x3vKCM3ah7JLGr+Ipeoqn4ClcqzlWaXB52uDLsLhK7CVkLalArFdDCaJazhOu9bTaTIWvCUjU5JMNyq3fS4FcqcMq0vpqz+5moYk8KKQQrQYXdZLiYsTsmJjV2U/HEDYidkoL1oMZW0mcnGdyQ2E08fneBTlrWptyI2DXc0nauk4zYLVJW3ACBLZuZztfXCAFCEgrJqpTspiO2dM562EA5c0vDRAmdJ6ALHGADy7bJ2R50WAvG6jMY1+zYcVf+WWPUdWvYTYb08x5+5hI5LiX5Y1qQPe/FtgzIdMRwkLPveix5Bx8+FudThXJrbX8Ys9vfZdmvcJibTlrjQGThrCnYTUfodJ/TUQPhOqQoYqtoJxZtwZHQSwpcJamriB6jQ+rK41Sux6krLZb26HBx/XHqSjha5XqSuvIoteSCkOCAuvI4letxqtjj1JLHqWLniUHdc6iMa7GOUzPDxDt5IuQo58JJaubjVK7HqWKfK27HqVyfD27ChT1953CbhIXnjdu8mOVGuA2Sgn0ONLIfxzNJj8JsoBBYx2dkXdp5hutr1uUIufs4CMk9XoXrTkA7HdKqrxE65bp3kNBNtlxX/Cq76YBOXra3mhC7eUK3XPGo+g5b8fFr+ks97pK6OxTdLOGUtScqQG8VsXM9jze/4+28+R1v52/8vR/hiU89xgd/63f44G/9Nn/8hx+myMvatoff8saFz7PW8qN/6wd59Re+jje+7UtYO7Vxy4lddUzqqn45rhOJ3cGQChlUOOXfw9VRl2tFzkZYo67UXGYvhSyGdFAW/FsFSIQ1xPubM4IgBJgQkDOSM/7/lWSAKDL2ByN2nd1y0WWOeEg1/uOAVHhSse75bMVDNoedRWI3Z8+D1pBlKKNZN4aduEd7sMeyH5Vjnmgqppe3/D4BrFhLO4sZDHYRfkjdDVgkNmL81/I8G0ICmn4cs6dzlsM6wgnKrc5JVtMUZf8TkxMWOatOwW7cZyfus+pHKCnHhEuWiubaCgR1cAMcN2BVuVwednjWWs5GNQLpzM7pALkTwLK1XNnZ4qonWQ8r1CdZkMmxC9cLlqwlTwbsZSm557N2sNH1gYgAnaVsJwMyx+VUWD+C2M3g8KxhuUi5PuhwrUg57flEWUxUpCz7BdqHtNAkWtLXglGi8bKUJBuwK3ZZDmv4bkA7NiAEK5GHRw6FwI6Vw03HpWMM/TlS1EsKcnNALTmOowhCNy4Oq1zHcRSxS3JzZPsLOLptBlYcSQzgaFVsIN0jicEkDhKEhhssEIPowJp2FEEYZppBUhy51hxF7LTmSEI3GcNBYufgHEnogCNVsZHjHUnoni9urnRoOXcOt4k1rzE8b9ym6uSbwC1wJfdQgcEh+Fhxyp6Cy6qKzu0Ut1bFx0pRuo0Yjcpjzgy26WQjkr3P4i2dQ4bLbKUORjiHRBET8cQ8sau5wSFC93KPl/8IXyRRWM3VUZczUeOOELtJCCF4xRe8hld8wWv4n77nOxkNhvzRBz7Ib7z//Tz8zi9lLx1NidtTn/4MP/fP/4/pey/cfx+PvO0tvPFtb+WBN34hgzq3LGM3H8+J2FEuWGeiBldHXTbjPoQ16n7lUNd2rAXnE0COcn38cw+zn8UUjsuqF4LcAcy4pksAuqzTO30PkZDoPGUrGZG5LqeCGtLq0motT2YEMo8hjfHSAetWs2MsO3GP1bBaTq6kX5IovwqFBNcBx0e5Pqv1Na4ZzTUhWK+0qDjOIhkyeppdE0DLWraTIbs6RwdVloJSMICUJfGa/imzlQ0hKfKUvXREdhNb6IG1LKUjrg/3SYXgTFDFwYLjl4rjA+91gXPVpRK3eHBj3ICV2jJ7jmGzyEG61G+QdVyrLGHjPp0swUqX9bB24vG1CtgsYTPuc1XIG843H9ior3Fl2OXZSenDODOpipQoT4jSIa10SGEFiYGhdfnscMil0QA5sjh5waqTo+IheEFZnzlJOmZD1oqEDoLRaI+hVvhErNsOUZyT10+ho5nXsoy7tOJ9lBvRAZ7t57jGo0bKctJBU184XuQxtf42jnLZditcHfQQ2iUUmvW0DcYvBQeTa6Bzwv4WGxY2vSpbcR8KDw84ZfZRfUteW5+5GViLN9hiI0/Z8mu00xG2cHBwWDNdwn5CXltbcHBQozZrSY89r0q/sPSSDNf6NBjSHPbJvRWsO+s3KtMBzeEujhOyR8DlQQ9He1RFhrSH7dpFkVLpb+EKhy0v4vqoB4VHiGU930H2VTmGybppNH5/iw2j2fKq7CRDbO7iCMWG2cfr5xS19QUrL3e4y1o6ZNev081j2nGKaz2WbZ/qYEhRWZnW5D0v3IqUWrx1x3B7R2WZUWGpCPd541aYpQUnjZNwW4n3eZcX8TtBg08m3el7XusE/Bnls2cNe8mQzKijcZMK44a4SY+WkOxYy2DrScg/S92tUG2t4vhraFFdcN6YJ3btbMj2MMGz/ucNoYO7pO6ORc3xiYv8c0Ls5iOqVvhTX/2VvP2rvoLrcX9BFfvB3/qdhWMvffYilz57kV/6mX8LwPkH7uOVX/wG3vj2t/E/fMM3PGdid1K8YGIHhwmCmK/tkqyu3YuJ++xnCcYLQO0D2bQ4GXS5oIy3e2scIAiVpcO4TbYZrSkzPzrjyrBPLATnKo0St0mdWX8Izdl1UMCpcW3kNVNw2o1OtoID1qzletwvreDc8MaiF1Vm5W4q0yoElaDCKeWWuBlz6wk5N4HbgZgQuW6WLPz9uJh83mbcf/7zLagC1YXjnPErVWDZGJ4ZdOhnGU0VUWjFflKQFuMtQ2uRmHE3G4PRmmd6+wzTmAdcB+1uMBrtoPpDGKVlDaDj4o3ayP4m3tJ5YuXTzmLqrmFDjGD/Graq0f7SNCsj0wHOYJsoqBM4TS7HAwK34IwDbm8X64bosFUqnylVis5wFyUUTa/GU0kOFDwYunjdNsJqdFDHTHxTdY4zbCPymJZb48ncEBcjLlSrRIMOKumhvQi9QA72cQY7NJcusG9c9pOUlUjSKGLc7jV0M0KzSOqc3ibV6iodp0YnHlDzDOdEjjBzHcEnx2cjnMEOyg2p+XUuxjGO1Jz2JV53DysURdjEjtW5okjLFh1Ws+RWeSLVFCbn3mpA2N1H5DHGr6EnpM5o1KiNSnosuRF7hUc/SzhddaiOejiDHYwTLJA6lfZxu9eoL52nbSM6yYhmAE0b4/Q2wehDZPxO4vZmZ5mYYozb5eeFm5XOIVJ3HG7ucAep6/zwqdfwrZc+TN8U1ITi70uFSrtkQYPraU4us5vCrdG4wBPap3AN9wXQuvwHcBnS1n3oyjLGr2G9ACtdsJY1ndPvtBmlKa1KRFMEMLIgXZASf9jDFvceuya8lOMuqbtD4SuX5bFd1uea2MHRfey+6X/+Szz8yBfxRx/4fT7ygT/g0T/6KGkyc4F49qmLPPvURZ742Md529e8a4EgbF29xuqpDeQcAThI7FbtyeKP20LsDsQ8QdD28A3jYNyQIEyJYznuwPE46/glbuloEbcjvG9vBW63Vc18JzKtvEiJ3Q3m2z3V5lislHK62iBajkgKQ64NxpZii8lussFyv1llK+mR9ftEjSWWvFej8xyTjbDZEIb7kx19FJYzIqHlaEa2x76x1Ksb6KBJnmt0Wv52fVmF+hli4ZEUkpWgCk7OnjU49VPgeFNjexh7iNY3yDS0C48lz0U4OW0MTmUFT4oFD1GrXPLaGuQZe9qnIiXVSk5MTj9oEvk1zLxvKpRZLCegTYRrfVYiiZSGtvJpte6Zqn8noYM6+dJ5ugToXLIWVLBOzo51StWmWczWab9K3jhNahS9VLHsV8DJ2bUGp7qOkmqqhAWwbkBRW0drw27hU3cEwsnoG00YtfCx6DmChlSll6tfY8eGBDgEkaQgp+vXqLvholcslOpPIWnbCGkdVoII6WjaxmO5eW7aCmV6Tl6FrHGGES8d3A6N+QTc1hpnSv9v5RJKRd8UhMrBNs8wHOPW8BS5mz1n3HpYgtY9+FiMX0EWMSrtgjaotI/VKW0bsWolqzJH5GD2MmpWU/g1rBvS6HUgfRVwvHjxpRp3Sd0djDtxo3lBBCGo8EVvfhNf9OY38e1//a+RJgmf+OjH+Mjv/wF/9IEP8ugffZQ8y3jDW9/CIE+nBAHgz73jXRhjed0bv4jXvfERXvemR/iCL3o9lUo0JQjbSXwDucidJQg321X8RY8bd4ndUXHHcRur0MMT/CbXqitctZqRU+B4mvXafEbw3nLLfeK9rHMoMjpxj15/j8hqWsrF2pjUwtC4dLRi267QSwoEhmbooJH0dEzqVFj1q3jzY5aKUbi2UItlRVB6jnpRWYQ/P2YhyMNl9kxZQ7cSebhO2c5ij4DiCFWsDurs2XCuFiugk8UMKAlZQwQLjYasF9G23pxThE+sc7pZjD7iQQjlMgzXF2roCuvTzgZsepVSPDH/PiFJo9VpDd1q5OKo8ZhFeKQqNg+XaNuyhq4ZuYRuMPOKDRqHVbF+lbb252rogplXbLhUiifmx6xceuH6XA2dT25c2umITb92WBX7IsDtkCr2BNy2gnopnjgA3QJuvkOhvBeM2/wqnleXF2ofw7Gyd1fnpHNqZjPo83KNu6TuDsdLiSD4QcAjb30Lj7z1Lfzl/+V7SeKYj3/kj1lZW6MRVKYEIbu6Q6e9D8Dv/cZv8Xu/8VsAKKV46AtezWsfeQMPPPwaVu45w7tfew7HtmiEx//07hRBmFx3bc2ChcxRcStwOynuEruXCbG7CdyW/Qh5XEPwST/BucxOE9DpiL1Bh1xYNpQiSodEcZeVdIQucrLQkiNJkKS4+IVgOxlyddSj7kQoRNlc2lr6qUaJeQ/e49WVx6lcj1PFHqeWnC/CV0JQm8vIHOXlOvm8L6+skGJZmdsmPErl6nG8KvY4letxqtjjVK7HqWLhaJXrSarYpDB0s0VRxElq5qNVrncWt/m/3wi3efHEJKxdFLMIDZ6Sn1PcXo5xl9R9DuLFeKO5GYIQhCFv+tK3Lry2lwzZG3Z513u+nj/58B+xfX1z+m9aaz796Cf49KOfmH7Pz378A9Tr0bTGrt/rUalWF7Zt4c4QBDXnKwm3YCv2QBzEbc2ePN3uErvPE2KH4FRYfd64WdfnVH19bJOV4eQxTpZA3KMRd0v1t2u4z4XtLEErS91vkWjYG2WAxVGSfprjKoWrBI46TBAcIY9VuR6lig2Ve6JackII0qxAjxvdHkUMJhE5Ht/QujDth2asJS/ssSrXo1SxwopjVa5HqWJd6Ryrcj1KFVt1/SMJ3SSOInZxbhilBa57WOV6FLErtD1W5XqUKvZ24TZP7G6EG8yI3SSTZqxdwM1q+znD7eUcd0nd5yhedDeaF0AQuPccf+0n/7+cCmtsXr3Gn3z4Izw6/vP4Jz+F1mXLkAv338f9q6cXCMLf/u7v40O/83u86nUP8+ovfC2v+ILX8NAXvJp7Hrgf13VfMEG4mZBC3JGt2O14gF+vvmhwu0vsXprzbQG3ST/HsAGN9TIdMm7aHeYpa/09djtXML2nOe24nKk2KIwlMxlZpkn6+4yMpO+vYh0XB59BMaLTexpHK3S4SqsaLXiCiiJFV1aQjjclCMPuddKkdIqoVqsLXq4q7aPDBsarlIRAJ8Sjffb2rmCcJYJKfeblmielxZYXocPmlCD04i6d/WtoW8Gprs4InSlwBrtlHVxlZUYQkh693YtY7WDCNZarQUkMrMUZ7oLRFNUVpHSmBKG/fxWZGopwhUa1suDlKvK4FDq4wZQgxINd0tEI7S8TRrUpoZPZEBV30X5twSt2GHfYb19BizpupUV9TOhEkaGGu1jHX/CK3Y97dLpPYUyAjFZZqfoloTO6HANlHZwz9rvdS/r0288gcnlbcAPIRp3njJvRk5pIsYCbGnWRynxOcMs6s+TDyy3ukrrPYbwsbzRnTvPu976Hd7/3PQCMhiMe/+SneOxPHkVoe4ggfOpPHmXQ7/NHH/ggf/SBD04/1/U87nvFgzz06ldx/2texSu/7C1wludMEG6GrJUZuzJTd0vEEwdihtvWixe3u8TuULwk5ttB3IQo28+MtzfDxjqt9Qe43t8h0QWn/QouBtdoKlkMg13scJ+cgtRo0kLTjwd0ty6SFRnO+qvJvQ2wPq7Q+IMdVDYgdXy0s1xmfpyIdvdx8sEewYai5jdn12S0h9u7TsaFaSF/1fHJOgPM9meRS2dprixPx6CSLl7nMjpqlRZV4y09m+f0d55FBnVay6vTDJ1Mh7i961jHQ/s1rBuUBAFBe+86AK16E8+ZKSqdwTaiyDBuiAnL38CS8mh3ttBxn+rpgMgbiwLGZEKN2mRSUYy9mZteyN7WRcz+FdQKNFdmylYVd/H2L5HXT5GNt9Grro/Zj0l2nkFWV6gvrc28XNM+Xu8a2quWYgTl4iuXutH0dq8iHJ9WszVTPOcxTn8LKAUUZrxl2RIO7fY2tkhpVKv4E0GKzku18AvEremFJaF7jrihC5ACKVjAzRnuImXxOcGN/l1SdzduU7zcbzRRJeL1X/xGXv/FbyTpljfrCUG41N3lgdc/jHIUV595duEz8yzj8U98isc/8SkAfvzn/zXy3NkpQdi8eImPffgj3PfQA9z70IPUG3NS+zmCcDMkDeAvfPEG1/sjHMewFfdvC0FYCypsW/2SwO2ouBXE7qS4S+xuH26naqslblJxNmrNcFu7D2EtninwipyazlgpMuJXvo4rO5fJs4SKzihGfZJcU6Qa8DHGwckNyhF00wIbbiBdn9QPGE08R2HqHTrvgzrKNcZZQi6dxVRqdPNkmgnSfo2itlGqUcdjSnPDwFbwGmfQgUvHpLRsmeUyXkRRWcEqZ9r+Q2vLfu7hVE+ByulgWTYGR0qs41FELYQuMOPWGcZaOimIcAPHrzB0HdyxVyxCUEQtrPIWxtCNC6y3jFgCE0VTS7vJGPL6qenYoXSKSGQdtXQGE4YMdErNlkTD+FWKymrpmzpur5QVhp6OcGunsZ6lQ8HyuHm9cUN0pbSXMuNaQ2MsnVwhK+tIEdOTAjX2ikU66KiFdcNpGxZr7XPGrZ8Wzwu3sl+gwWAxkzF4ETpoYhzzOcHNVtrHzpeXejwv71djDIPBgHq9fuODX+TxYvF+7Y37oYWOe8MbDdxar9ijwo77oQ3ylOWgcsMbDdzYKzbp9gkaswk27xVbSw1PfuJTPPHYp3nik4/xxGOf5uLjT5JnZR3H+z/xEVoba1Ov2N//+f+LH/ubPzT9rNX1de4dE7z7HnqQ+17xIPc8+ADf/O832e4XbNRdfv27X7twPu/+yU9wrZtxuuHxa9/1MABbcZ9ultDwghsSBHhuuCXdPlSDlxxuB+NGHr8HIzd6ituaVtSXlk483ljL1VGXeOIYchN+t7cTN7i18+3gPIAXOW664IzrI7KUdNAn7e8zHPRIk4R+WhAXliiKqFcjeragsOZIr1goiUEaa5QnaIYu3Tw51nMUDosiEpOf6Dl6sLgeYU/0ij1YXB+48kSvWFgURTRC50SvWDhs/TUqMpI0R7nySK/Yg6KIguJEj9+DogilONEr9qAoouKpEz1+J7jN19A9F9z+xt4fs1OkLCuX//30a6e42dwiXPE5wS3pXeZPvfnreejcfYeOu1PxovJ+zbKMV77ylfzoj/4o3/RN33Srz+nzMj5fMwiTzE/fd3jk7W/jze94+/Tf8zznmac+y1Offpy1UxuI8Q318rDDJz/1qYXP2tnaYmdriw//3u8vvP6Kv/D/4Z1f+zX4rqWXJdS9gE9//JOcOnuab37TGv1EUwtm1+1u5uf2Zuy2kyGert7dij0QL3bcruqCs/UWjaX18h91QZ7GJPGQbDhg1N0jGfQI05ReMWQHaIZNamEV6fkgxJQYVBxJbVyLdZK68iiVaySP9xw9Ti15nLryOLXkcepKOFrlepK68igv16rrIwtBV8eHVLFHqVwVx6tij1O5HqeKPU7lepwqFo5WuT4X3MRe+bpAkms9xW3yS/1c4LbZO/kB6KUczytTZ63lH/yDf8AP/uAP8qY3vYl/8k/+Ca961atux/nd9rhTmbrfevITPHT63A2PfTln7I7KUMDzyyD87of/kM987FE6l67y7JMXefqJJ9m6dv3Qsd/yHf9Pvu9H/vY087PiBHzFva9Ba0292eTcvfdw/r57OH/fvZy9cJ7T589x5sJ5bLPCQOe3NPMzP/6XEm7HxfPK/GxtYir+DWvs4OWbsTtuHsCLGLdxpvUk3ExRkMYjRqMhV9rX6HV2qOSawEKcF6QFhGHIUqWKCLwF27lOFi9kfo4idPMxKrKFjJ01HKuWBMi0Xsj8SCFO9HI11h7K/JykcrXWHsrYHUXopsfnliEZ/TwhUC5NLyQ/QeUKkOp8IWOH5UQP3sKYhYydK+WJKldj7aGM3Ukq15vF7due+UO2i5Q1x+cfnf2iGW4ywsrjVa63E7fNzaf5U2/+updlpu55kbpJXL58me/5nu/hfe97H9/7vd/L93//91OpnGy6/WKLO0Xq/stn/pj1ldUX7Y3mpLhVN5qTbma34kYz6PV5+smnePqJJ7n4xFNcfOIJ3v3eP8O7v+HPTAnCxSef4nve+Z4bnr/jOPzsH/w33OXGlCB89jNP0Gm3OXXuLKsb67ju4sJ+I9wOjv+lgttJ8VxxG3S6bKvihgRhEi9HYqdG6bHzAF6cuN0ssZvEBLdeMqJpJSor2O/2sL1tRJphbY4jwXUUjgBVpLQdn4HyUDjoXOFazZocgBcuuh8YjTNqM7KWPeWjkOiitIdap4/rqFLxOLkG1qJGbXJdsO14WCRSexgjWGZIpAxF1Jp5xQIy6UEWs608ciFRxkNrQdUmLKkMHTYWvGJFNkImPdrSZSQdHFyKXOKRsypG4FcWbMVsmuNm+/SRdJRbjiF3kdawIQZI10VHc2UK1pRF/0azLX2EkFB4WGNZE0M8h/GYZziq0T66yNhSHmZuDA0RUxc5RbS04Jsq0wGkQ3aUSyYUChedSyJSWiLGBPWphReMbbziLvtCMpDusbjNk7p/vXL/FDffOKRGfU5wezmTuhcklDh37hz/8T/+R371V3+V7/7u7+bnf/7n+fEf/3He+9733qrze9lEoNzP+62hk+JWFOFX6zUefsPrefgNrz90/GRLbzuK+Jq/+E20L1/j+jPPcvXSsxTFYdNwgFecv4e9PJ7i9u//1c/yC//Hv5pek+W1VdY2Nlg/fYq1U+V/a6vLtB66F17zqpcNbrdyS88R8vNeFbuiHU4awYsRt+erZgYYFjkb9SYbp8+Sm1fR391HK0G/36c/6EN/l2DYJhJdUqfCXpoS+jXOeJagu4n2a6ReZXrzVkkPt3OFmhuQ1c/yzDBBiZwHfEmldx0rSjP4qYdoHuP2ruNZzUrjPE+lOYXOuScKqA22kXmClc6MRBmN29tEpX3Wmud4yrgM0j4bUY3ltI3T3SVbOk9R35iO1xm1cbtXWa6fZug2uDYasBQEnLFDgv3LFNUVMn/R79brXqbp14irp7kyjAlkwYOeJehexbgBxqvMvF/TIV73Kq5QLDXO8dk4A5tzf+RR6W4irCZV/tSOTBQZTn8TL09Ya57nycyQFhlnogpLw53STksIdHV1NobBNs5gl9XmOZ6WEZ14wFpQYVn38HrXyBtnyOdInRrt4+0/y3J1hThc5/pgSM31OaNSgu6VGW6TsGYBt60kIxP2c4rbyzFuifr1677u63jnO9/JD/zAD/AN3/ANfPVXfzX/+B//Yx544IFb8fEvi6g6PsoLXrQ3mjtBEE4uj78zN5rXP/gq1n74b04zP5F02LxylUsXn+bas5e59uwVrl2+TJZmOI7DujMjCE8/8/T0s6y17G5ts7u1zWOPfnzhe/78d3wb51/xwBS3LEn4q//3/wfLy8usnTnN8toKy6urtFZXWF5bpdpq0vPEixa3FwtBeLkQu910gJdXPm9xi3yXoFFjdXkJbSxxromznG6vR7rfI+1uQ95mO8tYlx6OKZDpoFQ9KrdUf4YNClyGmUNFBSg3pycNoVdDSjUlQwDW8TB+DWM0Xe1SER7azxiKgrpbwXH8qYq0vPgSHdSwUtLVHh4eVR+0zIidgChamiowJ2G8CB21GIng/8/eeYfJVZZt/Hfa9LY727LZTQ8hCYEQSiIQapSmNEFQqkhRREEERcECInyKYFRUQAWRIkhTASmGKhBIIJRQQghpu8n22ekzp39/zO5kZ3dmS0jZJHNf11y7c+at55lzzj1PBUPB77CQZIuUKaN4KrCc/b5/shPDHUQV3ai6jE92oDgs4jY4XAGQndjSpsezLTsxnQEsBJK6gleQEBw6SQx8Dh8idoHm0Jbk3J4lB3FLwS3IyE4VTdDIKh6cooitFAY4WE4/pmWSxIlsOvA7LGxZJ40DyVNZYs8VZEQvpi7hV5w4HDYxW0TuyW9XWO9aKJCbWxRRHOp2ldvOiE9lfu3s7GTx4sUsXryYV199laVLl5JOp/H5fBiGwfXXX893vvOdLbneLY5tHf062k1DW9Ok51FNxlbXjhrT0EhNek/98zE2fLiSeGsH7S2ttG1sob2lFU1VC9pe8fOfcty5Z+blZrZGOH7/+YOOL8kygcoKfvffR9m9flxebm+9vpTmtesIVVYQrKggVJl7uXw+NmYTO5wptq8JelvJbbRdb03trRgeZYeSW198Wrk5MnpJ87Np2WR1i/XJGBtiUeSsgd80kTNdyNkEkm0iSTKW7KLblECUCHsUDIycr5YoUlksQtS2iaQ0dNMm6FaQJTb5ajm8yNJAuUVTGhnDwq1IBNzyJl8thxtnkQjRtGoQyxo5Hzq3Qkzv8bGTnfj6fVdt3UbHoitj5H3oMlaPj50oE3J6BsjNMi260kbehw7R3ORj5/AOqMpj2zbRtEbWsPG5ZNw9EaI2NpWKF4c8UKeTyOokVROXIhJ0KUT0Hh87xYWnSGSvqplEMnrehy5u9ETFSgqhnu9qX/PrbQ3705nWcz50DgVD3j5yK5tf+0HXdWbOnMnHH38MwOTJkznooIM444wzOOigg5g2bRoPPfQQF198Mclkkh/96EdbdNE7MkazBmFra34iqS5aMolRq0EYDLVuP0ed8AVixxQSBNu2iUa6adu4MU/0Zs/dr0Bun6xbM9jQAJiGQbwrgtPnLZDbYw88yMN33TugvSRJ+ENBfKEgvoogx550ImddeF7+c9u2efax/+APBvAHg/iDAbx+P4Yi7nSan8EwGq+3GpePDtHcZTWtYUMsaX6WRAGvU2K6s5JKj0JEzeARnYQcU1CzWdRUDDUeQYu048jGUUQB1XRhyU5EQSFhZLHsFFWuTVGx+WjJHmLQ61yfj67UUgPSZkTTep4Y9DrXb4oozVApCAXRlWnN3EToeoIi8lGxhgo9Ua+90E2bLs0oCIrwSX1LimUKomItyy4gdLmgCHFTVGzPHnr3nI9y7SF0vUER+T3oKSqFwnQnCdXIE7reoIh8VKyezScU7oWqWwWEThT7RcX27KEXNmwidB4FxRZwFKkVuy3ltjNiszR1uq5z+eWXM3/+fA466CDq6orbqJcsWcJJJ51Ec3Pzp17o1sL2ylM3GjUIW1tj19bZSUyxR60GYWtqfuyshr2xk3Q2S1dHB13tnbm/HR1Eev4H+NN/HimQ2xVnnc/zTz495DwnfuNcrvjJj/Jyi8dizJ80o2hbl8eDJ+AjEAwSCga5+a4/E67Z5Fvz7tI3+ej9D3KEMBDAcCposkA4VMGEqlp8fh8OZ+mi2IPJrViwzGiW29a43rKxBLLfu8MHvWyu3LKxBLVV1Z9KbrZpYmTTGKkYRrwDKx1D1TQ6Mhptlo0gu6h0+HApMknVQDeLR0v2j66URXHQKNdi0ZWDRrkWiYrVDIt4ysAU7KJRrkldLYiKtYeIcu0fFSvAoFGu/aNiHZI0aJRrsajYoaKT+0bFXr7xHdoNlbDo4PqqOfko17556vpHMwODRrl+Wrl1dzTvtJq6T2V+HQqqqlJVVUUiMfw6nNsa2zP58Gh60MDWJ3bZWIKUSxqVD5ptQRBcWYOGmroRya3jnY9o+mQ1sUg30e5uopFuYt3dufe9/3dHOevK7/D588/My2396jV8Yb+DhlwbwP9Wf1BQkWPhNddz529/P2gfWVH4wqkn89Pf/Krg+A8u/BaSJOLwuLFcCl6fjzEVVfh8Prx+L4ogEqqtZrcZM/D4NjlRj2a5benrrZfY7gzRzJsjt+b2VrIuecvKzVAhm8ROdRONbKSzuxOySTTTQ5dcidPlIuhWcEoCspZAsExMdxAEMU8QZC2NQxdIyQHcDiVPDAQtjahncu1FOU8QLC2NVzdJCj5kpytP6ARDQ1QTWE4ftuzMEztVz+DVMmRsH5LsIeRT8rVcpWwMW3LkI2STukpCS+PR0piGE9Xh20TobBspGwPIl+TqJXZOLYlkSKTlAD63Y1MtVzWJYGq59qKUJ3ainsal26REP06no6CWq6ilsJz+nG9bD7EztAx+XSdpexEd7sJarplYQaBDVMuQ0bL8L76RuCniVNycWjWup5arhZBKIsjkI5vThkZMTePSU2AoZGU/QY8jT+jEbHyLyU1rb2bBgrN2SlK3VcuEOZ1O2tratuYUOzRGo2mo7IS/9Ux6bamOEcuteq9pzDlg7qBysywLVddp09N5ufmDQa74+U9JxhMkYjES8QSJWJxELEYykfs/Fo2STqbIKiJ9a8Mk4/FB9wNg6PoAudm2zVOP/BPLGro02/3PPcX0vWbl3z927wP86kfX4vS4cXnc+P1+fD4fHq8Ht9eLp+c1cbcpfOmrZxXI7fmXXqJCcVEdqsDt9eTbur0epD7mpfL1NjqutyqXly7Z2rLXm+wEnxPBF6aieiJyIkJHtIXKeBvjLRNVTxFLG6Q1E1diI7IkYVdNRPAGczVHZRfRjR+RyWbw1k4jEOqxPtk2SrwFKRNFrxiH4atGFHLaoWjHOlLRVhyVkwhVTMx/N6RUJ474RgxvNVrl+Hyy3li8jVTbGiRPmFD9rLyGTsrGcHSvx1LcqFWTQZRzptpMjGTbagTRQWXjrLyGTtTTKLENueVJCpbDi1NSqBRFoh1NaIaBb8zu+Hu/G5aBEtuAqGfQKsZh9pSJCzu8dLd/QjrRjat6KsFQQ/67oSTakFMdaIF6jGB9rlasw0u0eyOJjvU4gmMJVeye19DJ6W6U7vWY7hBaeBL07FlKRjgk2omg+AjVTs7noRPVJFKiHVHU0WQntuzEIzsQ1BSxtrVgi4TGzsDdY74VDBUltgHB1LFFCcsV+FRyy0Y3DPqd25Gx1Wu/ejw7f7TJcGBj0ZFN4pYU3LLSU0S+/KAZLQ+abUHsdKeHLkPf4nITRRG300mDomySWyDIGV8/f9A12bbNxnScqJ5FUqW8XL541unsPW//PAlMp9KkkknSySTd8RixeBwtnaFx4oSC8TLpzLAIHYDbW/gdSSYSpHpeg2H/+Qfypa+eldt3j9zO//5PaOlXO7gXLrcrTwqv+tUNHHj4oUBObms/+phn730o97lvExnsJYS9/+N2Qn1V+XorgRFfb2zl602U8Aersd1+WkNjMLAZK8lUqmnUeBdqJ6S6O0imulENG8HpQTVsJDmI6QJNsTF6ao4iCLmaqZaOJfUp9aZbmKIfwZ3CcorolpH31bJlJ6bDVxCZqZs2uu1FdPoRPA5U28BDbjxbcuSK1CvufLSoZdmohhNR9iE6RTIYOGwbQRCwRQVLzp0vW+yZ07bJGBKSHMBS0mjSpjqrCGJ+LXafPWQMG0EMILh0dAV0y8r72FmKG9PhK4io1Q0b0/YiugKYrlzJtt4KH5bkwHJ6cxG1wqYKH6rlRlB8iB4HaVvHkd+zktNiihJ2z/Vh2TZpU0FS/CCZpCUTZ88ebFHCll0gytgFvoxsltwoUtpsZ8FWNb/uCNgW5lfTsljTugHb66JTTZEyNCzbRkDAJUk4JZlINk3S0La7aagXW8M01N+fajSahramSS8bS6C5lR1Obv1RSm69QSO9BDCVTJFOpYhEo2zs7kBLZxHjGXTD4NxLLiIQCuXH/Oe9D3Df7X8mnUqTTqZIpZJk05kBcx961Of4zb13Fhw7YvredLa3D7nu3z9wNwctOBzIye2/j/+HG79+2ZD96hsbeGDJiwVy+965F7L8zbd6CKAXj9eDx7eJFDrcLnSHxH6HH8wRBx2cl1smnWH5a0sI1Vbj8XrxBfz4g0FsQdhlTLG994HtZkI3DVCTaIluMl2txOMxOpMqhm3jcLtISiKKomxywrdtsE0QczqQvA+dJBByQrepDaw5auq59oJQUPor7BJIWDqmYeNyKpuCJywjR+gEsbD0l0tCRydh6nkfO0HImWwBEKXC0l8OAYdsE9HVwlqxtpV79ewh70MnC/gd0GWohbVibTu3pp799PWhq3BC1FLRrX41fi0DBAkEobD0l0sgbetkTKOgVqytmQhybg8Fpb+cIogmMUMrrPHbZ8/Qp/SXLOBxQMTIFtaKHURuemwthx54Utn8WsbmQRJFKpwewsEwU+wqsqZB2siFr0fUDEldxSHJCKae05xo2R4HWZv+910BIee3IQi4JJn2TJKkrtLgDeGVHSVJQlmDMHo0drBzaloFQaAiXElFuHJAn16CIKd1GmvqBsjthNNP5YTTT82/1y2TdYkImXSGSlvCzGqkUylcroHy+OolF9Ed6aYjGiGZTIKqo2eypFMpMsk06VSOXPqDm4zMtW4/glo86XR/uL3eAXJrb22jdcPGIfuGqsLstveeebk1rVnD+acMrJft8/vxh4K4/F48AT9VlWFuvvN25D5pJz54+126OjoIhIIEgiF8AT8Op1S+3gZB0etNksETwuEJ4aiZQFBLU5dJk4p3k+ruRI53E0l20ipI1Pgrcbp8+fxpxYIiwrJjYM3Rnn0Xq+UashUSVrawVmwP8ShWy9WJAj3BE/laseIQtVxFqV+tWDGvBSwWFBGWlIG1YosQul4fukpbGVgrtmcPxWq5OnBAT/AE9NSKFUQQhZK1XBGlwqjYPve9YkERlaI8sFas0I/Q9citPTn4j6YdGaOO1Nm2jWmaBTez4fSxbXtAnp7RCEEQcMs5E2zY5WWCHyzbQrcsdMvEsCxM28Imd9yy7Z7/bUzbwrAsDDvXxnR6qXP7SZkaGUMnpav5gtFuaWCdvh2JIJTCTvug2UmIXSnk5ZZqG7bcxvsraRKjZGybhtqaknLrNTOPVPNz/HHHMWWP6XTGokiagVO38wQwnUqRSeUIYagyR1L7ys0bDlE/rpFMKkU6lUbNZovOUV8RxrLtvNzSqVTRdslEgmQf07PicKDaVsEN+u9/uoN/3//ggL5Otxt/RYjKqjBVVVUcecJxBQTZsizefn0pocpKKqrChNwuonq2fL1BzlTo9KI4vYRC1YTG7UaNmiESjbKmcwMbo12EYt04bQsESJkSTqeLkHtT+pBeX63+xK4YoctNmbtHm5ZdQOyKEbpe9Gr0+hI7KB3l6pSUTelO8sROKBnl2qvV60/sSkW59vrY9Sd2xQhdLwrSnQBBwVWa0PWMl5NzYbqTUlGuDknalO6kD7EbLDp5Z8SoIXXZbJZvf/vb3Hvvvaiqyty5c7n99tuZOXNmyT5vv/02l112Ga+//jqGYXDEEUdw6623Mm7cuG248k8PURBxSiJOafPFYds2CV2lM5tiYyZOayaBLIoEFReOPuPuVARhZ33QFMHOIrcqp49OyxgVcvMF/MydPWdEJr3e8b7z+18VmPQMwyCTzpmOe4leOpVi/ORJ+PvILVgV5qyvn49mGKSTyZzfYjxOPBolHo2RiMVJJZP4goEBcotHY0XXpGYyqJkMnRtbWAnM3Huvgs9j3VG++vlNpRsFQcAfCuEPV1BVW0N9XR3VdbVU1dRw8tln4PX7Cvrvateb4nRTW+smWFXF2mSUbFZDVKEjEsNIRKg2EwjxNJIs5aIqZSeiIFJjmXTYNhE1jU92kcyCaJnUKhqCLWCz6foTgEpTI2pZJPQspm2jqSKGaVItqSi2hU3h9RcwNUTbJGZCt2aDoaAaFkFRxWfrWBSeA5epU22bdJi5qE8FJxnVwiNohLCwCfSspGfflkGNZdJOLsGvT3KTyFgotkm1mMW2vUCfWrG2RbVp0IlFTMtgWjbpbE4ZUSNlkW03dt+MhLZNpaUTtU1ShoYEqJqEblhUilnctoFF4XfPZ2pItkHEhIiWQjIdZHULLxohESxbpq85y2npVFsmHYJAl5rEI7hIqhYOwaBKMLBtf85EvBNj1PjUfeMb3+Cpp57iySefZMyYMVx88cW88MILrFixAq/XO6B9V1cXM2bM4POf/zy/+c1vALjkkktYsmQJb7311rA1fdvCp653vVvDfl4MpmUR1TK0ZRK0ZZOopoFbUvArzvzNeChfrQ3rm3j9xf9x0pk5U9GW8NUqlqOsL0aLz08vtrTPT7H974i+kf0xErllYwlMj3OHklsxfBq51ZgyvlCwZFvDMIjH48QdQoHcXnjyGT5Z8RHxngCWeDRKIhbPpbrpihDp7EJTVS6+9mrO/+Y38uOtWbmKEz5zyJB7Anh5zYf4A5vM1Lf+8mb+ee/9VNXWEKquwh0OUVVby9QJExnb2EDd2Hpq68fgcg90PC8lt1L3gdEutzp3EFWziaRUovEYZjaFoiZwawnc6Q7c6TZMl58mVyUdmkDQGWAcCVzJFkxPBVrlhPy4QiKKK74O0+Fho6+OtnQWJw4mOCx8iWZs2YEanpQ3gYpqEkf3emxBIOIfQ1PWAFukwakQTjUj2DZaxbh8ShRMHWfXagRDIxkcw1pTQNWg1uWhPtOMpKXRQg2Y3k3PJEdkLVK6m0xgDE2ii2hGJ+z00mhGcKQ6MPw16MGx+fZyog0lthHdHWKju4r2dBav5GK8pOJNbMB0ensiYXsiXjO5KF9bdtDuG0MkY6IjMM4pEko2YwsCWuVEbCUnd0HP4oisQbBtov4xrNcsDBPqnRGlBasAAKDYSURBVC5qM80Ihpbbs7vnWrItHF2rkdQUqUA9622ZpGpS7fIyVmtFyUTRg2Mx/DXlihJbG/F4nDvuuIM//OEP7L777gAsXLiQuro6HnroIc4+++wBfV588UXa29u56aab8PlyX+SbbrqJiooKnnrqKT7/+c9v0z1sD6xbt44nn3ySr3/96wXHJVHMPZBdXiabBt1qmpZMnC41jWlbeKScg26v5ufNjz5gzeI3+fK55+THaFq9hvv/fGee1G0Jzc9Aal6IssZu19HY7cpya88kcQZ8JeUmyzKVlZUEegh5r9wOPfpzHHr050rOYds2a7raSBkaXWo6Lxev38d53/kW3V0RursiRCMRuju76OroJB6N5vs7XU58/sJ9b2xqpqV5Ay3Ng6eAOPVr5/DDX/68YC3PP/YfKmtrsCp9mNUm4wOVO7TcWjMxGr0hKjw+9LCXlGqQ1U3iWZ1IMoWppbHSMVLRTsh0YhkJMpaOrGsYjj7RqACClEtHIipYmoKEjuww0QU7FxkqKQV1U3ujRG0EdFNGtgWQDQzRxBYkEOx8FGlufDE3hm2jmTKyIWOKKpaoYYkKoqTkAw7yc0gKtuzAtCSwHMiiCbKOYUkosgNbLKQLtijnctgJCqahIAk6kqKj27k9ICr01QQi5vZsijK6qSAhYCs6Wu/a+/j99e4BUca2LTRDQrYlkHQM2cAUZSTJ7rcHAcTcudMtEclyIEsZLEnDFGVk2VF4jnZSjApSt2zZMjRN4+CDD84fC4fDzJo1i9dee60oqevNP6Xrev5Y7/+vvvrqLkHq1qxZw8KFCweQur5wSjJ1ngB1ngBpQyOSTdOaSdCVTWNh41ccvLV2HX/74584+ZwztypBMHQBF4PfdMsEoUzsimHnklvbVpPbxHAtLZlEgdxqxtTxrauvLNpHU1U+bl7Pmqb1WKmBfoFen4/a+jF0tXdgGKUDS4IVoYL3iViMK87ddF8SJYlwXS3jJo6nYWwD46dOZuyEcTSMH8+0PWagOHL7Gt1yK7zeQm4F3Ap1ARdWlQ/dstFNC920yWRTbIi00J5KoGppHJqKFWkBK5f+Q5A8RD2NdKtgGxZjvT5UdKKWgRWox+v0FBAWW3GjVkwgmjXIWBK1PheCaJLQs7T76ggpTuhbm1WU0CrGkcyoJAyJoEvE7XDQrWVocYeoCtQhKIXnVffXkVIq6NJyWRlqfSG69RRtDhdh1wQUZ6E21vRUokseOrI2IDApECRhZYiYJlaoEbfDU2AatZw+spUTiGQtdEug2uNBlTRShobtryPo8OQ1k0BOW1k5gVhaI21JVLokHA4XMS1DmzdMpewq3IMgoIUayGRVorqI2yFS63QS0VO0OvxUecJIO3Eqk16MClLX3pOOoLq6uuB4dXV1/rP+OOSQQxgzZgzf+MY3uPHGG7FtmyuuuAJJkgZNeByPx4n3Sa7a0tKyBXaw7WGaJnfffTddXV1cd911AFxxxRW0tLSwaNEijjrqKJ555hnGjh1LY2Mjb731Fqeffjoen4MGX4j/u/GXfP60U1CdEm8+/l8S0Rg3/Px6PLLCqd84n7iuYto265qaWPLcS8iyxJHHfwGPz7vZBCGeiiH30SCUQpkglIldMewscqtxeWm3zVEhN4fTyczJU6lrGJuTW7/azN+/4Vq+f8O1WJZFrDtKZ1sb7S2trF23jk/WraW7tZ14awdTZ+xeMG7/yGDLNOnYsJGODRt5s98aXli5vCBieulLL9O0bj3ehlqi4xqY1jieoHPwh/H2vt5EUcApCjh7AgMqPCFqQoG860Ot4sZh6JjpJGYigpZMIBgqYdEmK7hQUZAthaRhsMHSCQsalU6pMF2QLpC1pD5BET3pSfQstmUS6slj14uEDklDKqzlKghE1DSdpk5YdhbsWTUFIrqILG0KigiLueCJLtug0rLpW63LtKBTE7HETUERip0LnoiaJrZl4ulzjnJVKUR0C4JuBZcg4u7Jn5c2NGzTINTveo6qkLGkAUERMS1Dl2VQ2Vf7CaQNiOliQVBEpZALnui0DMK2jVwOlNh26J+41LKskg+FUCjEokWL+OEPf8j8+fORJIlvfvObrF27tiCLfH/cfPPNXHPNNQOOd3d34y7iF7KlEB9Glv6RwDAMEokElmXR3d0NQGdnJ8uWLePKK6/kN7/5DQceeCAHH3ww69ev55577uGoo47K97/x/37BofMPZuLEifhMARmBMTgwNZuxloP1BnS2tHH56ecyY5/ZrFz+Pn//8538+oG/IQAOIKVlWRNPUOMM4FUcSINcLBWAoVlEurowHGmCfX9VFoEXMHSBeCrGhmSOCAqUHl8CqkyZTjVJUzJDjcs36HoAauxcSpjmVBs1Li8OcfDLIYxIZ9agLdWB7vTglQf3TwsCpmoRT0UxE2l8Q2TQcABhQySSStCczOSKkg+yZ4AaS6I9m6IpmaXG7UMWBvdPq7Yl2rMZNqbaqHL6cA8RnFMBWKpJJLVl5Kal0gXtdwS5VQ5BZkcst4xGjduxQ8kNwC0rNI5toHFsA/vsuy8Z06BTTSILEjUuH9nYpuhdr9PFZT/+IW0trbnXxo1sbN5AV3tH4bw+Hy5JLuj77/v+weMPPZJ/7/H7aJwwgYmTJzF+0kTGT5pI48QJjJswvsCXb6vLjc2/3trsbE5urhCSKwSuBA6HgFfLQLobO5tzjdGcbto0k25Vo0O1cUsOHJJI1jAxTZuAQ8YjSth6zhXeSy6NVVrXSJhZfIoTAYG0bqJqJl5ZxKfIYICNjQOZsOQhrqvEzDRBhxsBAd2wSag6LkEk6JQRTLBNGwmBsOQjpqVJZDL4FTeKmMulF80aSAhUOGUUW8jVcgUqRQ9xM0s6qyHIOUJsYxPPGmDahJw5QmcbuT0EBReSIKBqRm4PPQmPk6qBYVj4ZAmfIuf37EZBEHM+1XEzQ0BxISCQNSzSqoFbEgk4ZISePSuIVIo+EnqGWDpN0OFBEZykkym6uroGld/WRH9OsKX860YFqauvrwdyGrvKyk2/2Nrb2/M+dsUwY8YM/vnPf+bfG4bBtddey6mnnlqyz2WXXcZ5552Xf9/S0sL+++9PRUXFsE7qwoULWbhw4ZDtjjrqKG699daCY0cccQTRPj4spXD//fczb968IdtddNFFvPvuu9x00035Y4FAgFgsxhtvvMGECRPy4ymKUrA/QRAIhULU1tbmx/ndr3+T/7xzbRPZdJrnn/kv4doaYqkEk8aOo8IQqJ8wnoypU6trdGsZkoaGLoAsCuQSsAB2TvNu5zTzCAhIHhdp2aLTSFGJTaUzd0MRetaTy8GXaysIAl4c6JpCt5rGEA3qPQFEQUTqyYnWHy7AoXvZmInRIZrDcsJ3Bnw0paK02yYNXt+QTvgNAT8b0jG6DB3FrQyp+anHn3fmBov6QQJFevegaN6ciVy2htYgAA7Tl/PVEoxhBU80Wj6a0zE6LYN6t3fI4Imxtp+WTIKYriK7pCE1di78yD3BE6ICY9y+Ann1d5If7XKTHENrfkYst6B/h5NbsT2XktvYoJ+zL/nmgD6JZJI331pGS0c7alsXpqrjDgUK2rRsLNTypRNJPlr+Hh8tf6/g+LmXXMwlP/5B/r1hGCSa1uMZW0tStLeO3Nhy11v+OrBtUJOQjkO8lYpsks5Mloggo5gaaUOhWxNxyAJeRcGWQTJyGklbceNRHFh6Lj2KrWWRTJGE7cTlEPH31nI1NARLx1I8OAQFv5yrSW5qMXyIdOsOZEkimK/laiLqGSzFjSxKBGVPLt2JEacSgW7dgSVKm9KW2HauVqzsRJAUArI7p7Ez0oTMXKUIzZYJehTcPeo+wcgiCCK27MSvuDC1DElDxdYyYEikcOB2SPh7a7kaKlgmtsODW3FgGzmNnaln8ZgSUdOBokgEetOWmDqioWI5vDgECb/szqU70WNY2W48Pu82C14shZ02UGLOnDl4PB6effbZPIlrbW3lvffe44c//GG+XSaTQRRFnM7ivzofffRRMpkMJ554Ysm5AoEAgUCg5OdDIRqNsm7duiHbdXR0DDjW1NREJBIZsm+2RM6r4WLSpEl5QvdpMHny5DzhrglUMLa+HjOWYpyvoqBdb549wzJzf20LK59rz8bGxrYhQYxZoQBmTy6+3hx8pm1hkdPMmtiYloVFLidf0OFCESUypkZrJtHzq68Xuf4COTIpADYgI9GeiRNVM4z1BHHKErIgIQsCoiAiCkL+xr0tTXrxVJS2TKJsii2CXdUUu6vJze/zse+svWiXjHxUbH9c+pOrWPXhCtau+iT3+vgTmtetx+zn0zdhSmHk4rpVq/nSIZ9DFEXqJ45n7NTJTJ0+jb1mzWLK7rszfsqkolac7SG3gtaCAC5/7hUag6AmqUpGENo+xox8Qg0Ck2qmkZR8xFSLZDyOO9aMLAlQNQHB6clp6EyddMtKLN3CUz2VgLsiX31CiTYhGln04FhMdyiXx87hJtHyIdFkEmfFREI1Y/J56JREK1KqC8Nfi+Gvzeexi3euJtLVguwbQ8WYSfk8dFI6ghLbiOX0oVVOyOexi2U2Emtbi6T4Cdbvns9DJ6pJpHgHoqCjhidCT7UMUc+SbfkYbBFvzW4EPD3PalPHEVmHYBn5KF+P7ADLJNWyglgmi6tqMsFgdU8FDRtHtBlRTaIH6zG94Xweu3jbSrLdQz/Dd1SMClLn8Xj41re+xbXXXssee+zB2LFjueSSS5gyZUoBQTvssMOYMGEC999/PwAXX3wxJ5xwArNnz+bll1/moosu4gc/+AFTp07damsNhUKMHz9+yHb9/QMBGhsb8fsHfzAARbPmjwT9zcjFbsqDOT33on9aGEEQKJYBZ7h59rqyBmFvaMh5+8O2c5FjFj1/bRvLtjD7HO8liaZtYVo2mmmQtQxUw8DERLMssqaZb2/3kE3snNbPIUlsTMXpVtP56hySICKJQu5vDxnM7XfzCIKZSO/yBGEwlInd6JXbliR2siAOKrc9953DnvvOKeijahpvffQBqz9eRbK5ldY165kxe8+CNqtWrAByPw6bP1lD8ydreP2pRdzT87nL42a3GdOZufdsvn/DtQV72tZyqzJlin7zRBHcAQR3gHBlA631M2hPdBHWUtRYKaqdJlkRdF0mrll0Zw0sU8MhSZiGCIYEggGSXmz0QlgSGAoINsgaQ1iec7n2zJ57vKTn+g0FQwFbBMkE0RxGeynXXrBAHkZ7SwJT6mk/jD3bApgKjIpEblsHo4LUAfz85z/H4XBwzjnnkEwmmT9/Ps888wwOx6YblsfjKSA8F198Md/97ndZsmQJjY2N/PznP+eCCy7Yquu89NJLufTSSzer79tvv71F1+J2u9E0bch21dXVBdrF999/n1hsUyLT4Y6zvSAIApIgMLiRY2gYlrmJFFJYocOwLGaFLNKGRtrQcv4tlkXGMHo0iXY/QisgCxKqmeX9aBtjPQGqXD6ckoRUws+m0ulBcuzaBKFi0NZlYjda5ba9Na1Oh4P999iLsZMmlMxjVze2npPO/AqrPlzBqg8/GlC9I5vO8O4by0in0gP2cs+tf0IQBBp3343glFzy+q0pt041iUMf3IQuSDJ1FfW0uPy06SqGKBI2NdyJdtyyjd/QqXa6SOIgkrGIGAKEJuJSDBKSgqVl8iXF9FBj3vwKPaW/MjpKxXhCyhi6EQoqT+j+OkxXEKsnkCFfKcJdQ2UgRBS7oPKE6anE3eJESEgocYPEBDlXKUL2Exo7g7SUq+WKKOGRHVhOH1ZAQlDEfMRrNK2TwYG3ZjeQTVKCmN8DkoJWOT5vfoWe0l+qhaNqEj5JJ4qA2afyhBZqyJtfoU/JNn89ivXprGGjGaOG1EmSxLXXXsu1115bss1zzz1X8H733XfniSee2NpLG7WYNm0aHR0dXHLJJVRXV3PFFVcUbfeZz3wGVVU54YQTmDp1Ki+88EKBNm+44+zokDcjR5HVQ/gGago3EcOMrtGRTZHQsySNXi1g7oevLIjIoogiSrlM666cOWFXJQiWajLWLpti+2O0y200ELuh5LbXfvuy1377AjntfuuGjaz6cAVvv/suH73/Aes/XEnTqtXsPmtglaK7brmV9pbW3DyiyNgpk5g+Zy/m7r8/e+47h8m7Tytqut1cuTUlMyOXm66CO0g4WAdaBiEdxRlvx5mJEXbBOI+TtBgmmjFpTaVoT6XJ6CZ1Hh+i7MhXtOhb+qvS60QUvVSaemFJMVHKJzIuKP3lc+GQPYQta0BJMU+ziJyxMFw662rsXOkvjwO3w43TtgeUFLNlF8i571Pf0l+9JlerX61YW97kdtW39FeFJ0fibEMrLCkmKVjFavD63XRkh8qauuNi1JC6MkaOUCjEq6++ypNPPkk8Hse2bSZOnMj5559f0M7tdrNkyRIeeOABnE4nV1xxBX/5y1+oqqoa0Tjf+MY3drgSbJ8WoiDikIaoKeyCcf5KbNtGNQ1Uy0CzTFTTIGVopHWNtKmTNnRS2SQ2uUoPqxMRutUM9d4AiiChiFLRh+DORBAiqa4BaTOKYXsThGIoE7vRT+wK9towljENY5n/2SPyQS9O08ZnFK4/0tmVJ3SQM982rVxF08pVPHP/wwC4vR5mzt6LH/7yeibvvltB/82RW43LR4dofjq5OT0QGgNqCjIxnPE2nJk4FQrUVyps1ESaUlmaknGCshuPImNh053WB9RyLVUrtlQt12K1YvPnz7YH1HItVivWTR8NXZFarv1rxfa+L1XLtVSt2FI1eHdWjJoyYdsLO2OZsNGKXf0ctHV24AsG0C0L1TJQTYOYmoseNmwLzTIxbbMn8AMkQcAh5sieIkqkdY22bHKHLinW1tlJTLF32lJwxdC/NJUWTw5aLm80ym1LlvArViZse8rNNE0+WfERK959jw/ffY/3lr3FiuXvo6nqgDGe/eAtqmpr8u/feGUxn6z4iDmfmcvYqZPYmEkMS27ZWALZ792ycrNt0NKQjkGyA1JR4pk4HQYYohvddtORthAsk7E+GUf/FF6Wiaal6TLNXCSs7KE7bWDZFlUOG8XlKqz4YNuYWoZOQ8MWBfZ8TUbOguqE5nkCbpdzYNUKPUvEyKLZAiHRjWoJZHQTj2gS9DgKkg/nhKMT09KkbXDLDhw4iGV0HKJFpUsckEAZyySjpYlaFook4RfdRDIGomVS7RIQnS4QhHKZsDLKKOPTQxZEvP3zhfVEEtu2jWaZaJaBaub+pg2NpJ7LuJ7RVQzLQhFFWtMJOrMpxrgDSKKA1fO7LP/rzN6UyN0hSrRkEsT1LJN84Z58gsWJ0bbQ/AQVJ7JL2uE0P33xaTV2YQYnpmWN3baVmyRJ7DZzBrvNnMFxX/4SALqm8dF7H7B0yRLeXPoGn7zzHrZhFhA6gH/f/yD/uu+B3PyhEHvN3ZcJe89i5v5zOHTeAbidpde0xeUmCOD05l4V9aBlCKgprO4NJGJtuMwYEwIOMl0tJNs0EoGxiN5K3IqIKAooiVZcmSiSJ0wLLj5JRQnIHuqtKO5YBMOowvDX5qeT0hFciVZkZ4CNztyPVRkREZuK+DqsrBetYlz+ZiSqSZRoM7WSQqu3io50GgORKgmqMy2QFdEqxmP3aNwEQ8PRvY4a26LDW0NnNoOhq1Q6nNSqLUhpHT3UsKnebU/Eq0tLIXqrabMkOlSNkOJhjBXBEY3lfAW9O7dioUzqyihjFEAQBJySjFOS8fd7ltm2jWFbqGbOrGtYZu69oWMjoIhSQZSuJOT+t2wbtYccdmRSRLQUaUMDIZcbUBJEHJKES5RRxFz2+jJB2PrErjNr0BDwl02x/TCa5KY4HOwxZzZ7zJnNF889i9ZMAtEwC+u3Am+99nr+/3g0yv+eXsT/nl4EgMPlYs9957DvAfNYcNyxTJ0+MOfqVpWbww0ONyF/FWYmSSTRhWnp1PoDVLavJm2niFguYlkHFgKBrIrX0JBtAVtXMCwdQdaQdbMnz11hxgTBMhAMDUkxEE0HkPtcwEYwdbB0yHsXA5aJYOpIgpCrRWvpGJKOQxYRMia2YIHdpwCBbYFlINg2DlvC0C0MdBRJRLKMnjn6RsjaYOkIpo5iC5iGgmlnkWQdUdd79jCMiNodHGXza9n8us2wq5+D7b3/Xp+/jKmTNQ3iepa4ls2Zfy0zf/t1SjISIh1qCtM2t6hJr6/pbUua9IphtJpi2zo7cAX9O6wJ/dPKzUikBjU/j1a59TWh98pt5fsfsGzxEpa99jrLXn2djhIlKn/625s48fTT8u+TXd14KoKIPd/hbS43SUbIxCDWhpboJqmZdGsCKU0nYrlwOGQCbpGkkUWxLaoFAdvpLTSPWiZCNkGnIaPZMnsuM1GyoDpt2udqyA4Pdl/zqG0jqgmiKqRw4JVENEVDN00qbQO37NykdeuBqCbJaAYRy4Uii8hy7v7ltU1Ckozl9BfUlxX0LIaaod10IQoiHhckjSwOTKoQsV1+EKWy+bWMMsrY8SEIAi5ZwSXnbsxjyEWZGZaZJ3opXSWqZUnoKh5JoSOr8l60hXp3kEqnB6ckFyVTZc3P8DV2utNDl6HvssET1fbgxHS0yg0GBr30mm1PO+8cbNumee06li1+nWWvLeHNxa/TtHoNAPvOP6BgvBefWcSNP7mW/Q8+iHmHzGfuIfNpaBi7beUWrEMI1uFQk1Qmu6mIbiSdShDQbKK2iGkI+GU3CSNDuygSFuUCxwFLEInYHnQ7FxQhCBa9TiAdokSlqFCwA0EgYrnJkAuK8CsytqwQ0VJETAhKDvrvICm4iVl9gyIcoGVIGRqWqBDq971TBQddVq4yUW9QhCQKxLQMHZJEpSAO4fyw46NM6sooYxeHLEr4RQm/AtWu3C9ly7bImgZZ0yBj6LRnkyS1LBlTx7QtbGxkQcIlybhlBUkQy8RumATBKztQ3MouGxXbns3QaPl2OLkNFc0sCAKNEyfQOHECx38lV6pyw8YNvLR4MXqFF90y83Jb+spiursiPP3ov3n60X8D0DhxAvsffBDT5s8l+5l9mRSu2zZy66lmIVTU40124Y00oSZjRG0HHZqCYCpkTJUuNkXFWrady0PXL8oVcm4dAlZBuhMYGOVq63bRqNjeKNZSUa6lomJLRbkWi4rdmVEmdWWUUcYAiIKIR3bkbohOGOsN5oM5MoZOxtSJ69meFAhpLDtXps0lyVT23GTLxK40dul0J6m2XUZuY+vHcuIJxw+Qm8vtIlRZQTTSnW/btGYtTWvWwl33oDgdzJy7L6efdy6fO/bokuNvUblJMgRrwV+FMxWhtnMdlak4UVNhg+aiI5Mmnu2mRnGSMSVMm0JC1+vJJVCY7kR2kdYgY9iFaUtsCywQRamQ2FkmWBIx1SokdL3+cKJUSOxsC4+g0JW1CwmdbYNlgKQUEjs1iWUMo/rEDoqdXRNZRhllbCH0BnOEnG7GeAJMC9Ywr3o8B9VMZJ9wA9OCNfgUJ2lTRwA0y2RlrIPViQhpQ89H6RZD2Okh7PKS1FVaMomi5ej6wqs4qHcH0S2D5nQMw7IGbd9LEERBoDkVI2sOflPvJQhuOadRi2tDZ6CvdfsJOlzEtCxtPaRiMAQcLurcfjI9ptjBzg9sInaWbdOUiqIP4fTdS+wUUWZjJkZKH7xqTC9B8ClOurIputT0kHvYHLlVOX27vNwuvfoHPP/Ru9z//NN856dX85nDDsHl3kRAdVXj7ZdeZcWaVQVy03UdXS88B1tcbqIE/moYvzfK+D2pDvnY06uxT1Cmzo5hd3wIiTbo4U2maSNmYgWBFL157GQtTXT9O5ida3HLQp7QCXoGJbYRR/d6sIy8xs5BrpZrauMKHILRh9AZOLrX4+xajaDnNHohhxu3JGN0ria6bjmKlirQ0CnxFpxdnyBmctWTPLKDoMONHW9D69p5a7+WSV0ZWw1/+tOf6Orq2t7LKGMrotdPL+zyMs5Xwd7hBg6qnchnaiawoH4qn6ufSr07QFJX6eh54LRmEkTU9ACiVyZ2uwaxc0vyLi83w7YQRZHpe+7BOd/6Brc+dB8vrXqfPz50H6df+DXGTZoAwNwjDiuQ26vPvcChU2dx+Vcv4PF/PEy8p9zjVpGbKIG/Chr3Qhy3F5WVdUx2y9TbERpdCaaGPbgVkZRukEgmsXvl2DOMLIo4dAE7m8E0Ynj6KIoFU0cwVAQjm49IFQUBjylhZrKYegKPpOe1oIJl5trqmVzUaw88goKV1rD1JE4xsymxsG0j6BlENYVobvrOu0QFKasj9hDDnRHl6NcRRr+alj3kBVwMI4l0EYWcc+f2RHNzM8899xxnnXXWZo9RV1fHokWL2GOPPYCtF+2zJda6JefeuHEjixYtGrCe7R39uj1h2zZZ06C1swOH30u3miaqZ8kYOjY5061bVnBLCnFd3amjYosl3y0VXVkKO3JUbO/+dzS5FcNmyy2l0lhbN6jcmteuo25cY4Hcfn3lj3nwr3fn28iKwtyDD2LBF47h0KOPpCJcuXWjmdU06e4WutpX4jANqjwVmA4fad3E9XQCSRXRnLBqnkJKt9B1gwo7ge4Aw+HZ5GNnWwipJIIMVk/pxLRmEktruIwEyDpZxUvQ6cmbTsVsHMEyMd1BEMS8D52ipXCKGZIOD26HK2+aFbQ0op7JtRdlLMumK6VjaRnU7lV8dsFZ5ejXXR2mZbOsOUpSLfx1bGOTyOZyh3kUCZc88EKNxeMEY5sulpRmopomTknC6yhs73NKzGkIbVdit2rVKq6//vrtQpRGiu251mJzq6rKhg0btvlaRjMEQcAtKwQUJ2FvkLHeIJZtkTF00qZOQlOJqGniuopu5fKBbejRyoz3ViAN8sAv+9jt4D52u6Tc2oaUW8OE8bm/feTmDvgJ11TT1d4BgKHrvPLs87zy7PP87LLvM+eAuRxx7NHMWnAIXT18YUvKzXwvgjMrUSXvTst4AyPVTZ0WJ2Tb6JIE2CiSQEOFm2TWIJ6VMG0HThEMK1MQPGE5/QhKbp58UIQiEQxW5+bqFzzRS/6gMCiioqICSarE7F8r1uHBdOT21kvoDMuiMuCnW68c9JzsyCiTuhHAsm2SqonHISH3EC7LttkYz5IxLOp8Dvyu4jcZW5MJuHKnuz2pktQMgi6ZGl9hhQHDys1h2TYSQ5O6dDrNk08+iWVZLFiwgIceeoiTTjqJcDjMypUree+99zjppJPy7W+77Ta+9KUvUVGRq2RgGAaLFi1i48aN7L///uyxxx6YpslDDz1Ed3c3CxcuBHJ1X53O3Fr/97//sXLlSmbMmMFnPvOZkmsZCh9++CGvvvoqdXV1HHnkkciyzO23385xxx1HXV1dvt3dd9/NgQceyKRJkwb0EQRhxGttamripZdeYsGCBTz33HNIksQJJ5yAZVk89thjZDIZjjvuOEKhEADd3d3cddddAITDYQ499FAaGxsHPU9jx44t2OvLL7/MsmXLOPzww/Oay1LnYFeB2FNhw6s4qXb5mEQYzTRIGzppU6Mjk2RDOsbqZBc+2YkkiLhlBZckD3gQ7toEoUzsSmG0yq3G5aXdNkcst+O+cwEXXPld1r73Ic8+/iTPPv4E61evBXI1a994eTFvvLyYy7Qf8dlzv7LF5Wa9uQHiKnLASe2cvdnoCdFs6Iy1TSCXvkWwbcIuibDHgWXZJFST9qSKmnWQslS67FwUrdLj/VUqyrVUVGypKNdSUbEFhM7jwKns3F5nO/futhJkUchltRYF2pMaumnTEHRR6XWiSGLRV2+fSFonpZmEPQ7GBt1F2w0XmUyGgw46iF/84hc89thjHHrooVx22WW0tLQAsGzZMv7whz8U9Lnqqqto60mQqaoqBx98MD/4wQ946aWXOPTQQ7nllluwbZvW1lZ0XWft2rWsXbsWy7KwLIsTTzyRCy+8kNdee42zzjqL73//+yXXkkqlSq79n//8JwceeCCvvPIKt99+O8ceeywAL774In/605/y7ZqamrjgggsIhUJF+2zOWj/66CMuu+wyjjjiCJ599lmuvvpqTjnlFBYsWMBjjz3GX/7yFw455BCsHh+RvmM//vjjzJ49myVLlpSce/Xq1Vx//fVArq7kF77wBc455xyWLFnCmWeeySOPPDLoOdiV4egJxKj3BNkrPJajGnbnqLG7s3d4LBP9lciCSEzL0ppO0JZJENUyqKaBbdtlH7sd1MduV5SbQ5Q3W26taoJJe87k0p/8kH8veZmH/reIr3/vMqbOmJ5vf8QXjimQ25Jlb3LvbX+ms6295BybKzdNVmh2+kDqUVAIIiS6INGJaOkE3TKTwx6mVAWodnpIqAatmQS6ZW0idJJQQOiAfPCEIorEtAxpQyskdB5lkw9dD3LBEwoZQyOqZQoJnVvZ6QkdlDV1mw3LttkQy5LRTer8TgIlNHR90ZZQiWV1gi6FWr9zyPZD4Y477kBRFF599VUkSeLxxx/nC1/4wrD7/+UvfyGTybBkyRIUReG1117jiCOO4Mtf/jIXX3wx7733Xl4DBfC3v/2NNWvW8M4776AoCrFYjN12240LLriAp556akRrefzxx/nxj3/MpZdeCsCbb74JwLnnnsuFF17Ij370o/ycRx99NJWVlUX7yLI84rUCRKNR3njjDRobG/nwww+ZMWMG//jHPzjllFMwTZNx48bxxhtvsP/++1NTU1Mw9h//+Ef+7//+j0ceeaTo3H3x17/+lY8//pjly5eTTqcJhUK8//77g56DMjahrzavxu1ncqAK1TRIGRopXaVLTRPVMkQtExsbRZSpUDx0aalRq/kJDrHnssZudMpttGpap86YztQZ0/nG97/L2lWfsGzx6zSMHweQl9tf7r6Pp/52P7+6+hr2P/ggjvniCRzx+WPwBQr39GnkZthWjlDICkzcC6KtEG8Dy0R0+wlKNn46qJFt3jc9NGUTWIhUKiK12XZETUILjQWxh5ZYBq7oBuoskzZPiM5sElNX8IgyY4xO5JiOHqjfVLXCtpETrdRoKbpcIWK6TSSl4Rad1FgxPLE4ur+2wIy7M6JM6jYDlm3TGlNHROg6Ujq2Im8xQgewZMkSvvCFLyD1JHc89thj82bH4fY//vjjUZTc+ufNm0dVVRXvvfde0fYvvvgiNTU13Hbbbdi2jW3bhEIh3nrrrRGv5eijj+ZHP/oRPp+PQw89lH322QeAww8/HNM0+d///sf8+fP561//yo033jhon5GuNRQKMWXKFBobGwGYMmUKAIceeiiQK/A9YcIEWltb8+O1tLTw/PPP09nZSVNTEx988MFwTjEvv/wyp5xyCm63m3Q6jSRJ7LnnniPeTxmb0Fsjt9LpodFXgWGZpA2dlKHRrabp1jJ4LAdtPVG2k3yV+BRnSZKwrQmCqVrUMzoJQimUid3oJna9mDBlMhOmTM6/FwSBGoeH1/7zXyBnon3thZd47YWXuO7yH3DIkQs45uSTOGjBYTh67tebK7fCjfmhzg/hRoh3QHczJDqQEu2EPUH2r2ngk0iaVjUDuomejOF0KAiGhu3I0RLB0BDVBJJtEnBV0q5amHaWapcLJZtE1LOY7grMPKmzkLIJJDVB0BGgwxRI6Co+n4w7k0JKd2M6/Ts9qdv5dZFbGDY9PnQj1NDFs8awCV3WGF7RYcMwCnywBEHIk6re9/1hmpvGNgyjoD3kCI1hGP27ATlzbTqdZtWqVXzyySesXr2ao48+moaGhiHX0h9f/OIXefzxx8lms3zrW9/iiCOOIJvNIggC55xzDn/96195+eWXiUQiHHPMMYP2GelagTyR7Xue+h/rNb+++OKLTJ8+nUcffZSVK1fS0tJCrCeVwFDof16Gcw7KGBlkUSLgcDHGE2BGRR2fqRnPoXWT+XzjDOZVNWLYFl1qitZMgrZMkriWHWDu2pYmvZShlU2xJbArmmK3ttxkWebuJ//FWZd/m7GTJ+aPa6rKf//9BN8562scPn1vrrn0Ctau+gQYudw8ooyk52Rlqga63vMMUVw5YjdhDoybg101ASQnLlliQijA1CofnrAHKsfQ7agibin5uWzFjR4YQ8o7hk7TSUjxUONxkbAtEp4weqgBsy9BEyX0QB3ZYAPtlhe36GSMz4uJQZczgFY5AdNTMeg+dgaUSd0IkciaZHRrxCbXgEseFqFLZHXS+vBI3Z577snzzz+ff//aa6+RTm+60YbDYZqbm/PvV65cSTQaLei/aNGi/PuPPvqIDRs2MGPGDFwu14Akl/vvvz9er5eFCxfmX1deeSUzZswYci39sXLlSiZNmsTFF1/Mk08+yfr163n11VcBOOecc3j44Yf5/e9/z2mnnYbD4Ri0z0jXOlI88MADfPvb3+bBBx/klltuYcGCBfkbT7G5+2K//fbjiSeeyBNEyPkJDnUOyth89Jpsa9x+dq+o47AxU5hfO4l9wg1MDYRxybm0KTmSt8kvb1sRBK/s2GEJwhYhdpSJXX9sbbmNmzSRy678Hn96/gl++dj9nHLh16juE4yWiMV45O770FQ1f2y4xM42LcyHliP0ZIWQVJPsg+9sInYAshPCDbDbQdCwB2QTSJkE9W4vPqcDKVzBmIZ6PIpMNKOT0S0QBDLOCtqEIKIgUuN1UuX2oUgSXZKTuCuYy6XXB4bDT5tQgS5IVHoc1Hi8uGUHSVGh0+nHlreMlWw0o2x+HSEMOxflOlIfOqejuParL+JZndakhrtISpRiuOCCC7jllls4+eSTmTlzJg8//DButzv/+Wc+8xlisRhnnHEGU6dO5YknnsDl2mRuuPDCC7n11ls57rjj2Geffbjzzju59NJLGTNmDE6nk9bWVq666iqqq6v5xje+wYUXXsh9993HggULOPzww2lubuaFF17gxRdfHHIt/XH77bezfPly5s+fT1NTE7FYLB8VOn78ePbdd1/uv/9+Fi9ePGQfWZZHtNaRYs6cOfzkJz9BURQikQiPPfZY/rPddtttwNz9ZfTXv/6Vgw8+mPnz5/POO+9wyimn8NWvfnXQc1DGlkNvguTeJMkT/Lko25Shkezjl9etZhAE8MgKXdk06xLdjPdXbHGTXqXTg+TYcUx6/fFpTbGWajLWLpti+2NbyK3eE0CYvRcTZ+7OBVdfwbply/nPQ4/y338/wbiJE9htZuGP3jeeXEQWm6kH7QdAdZFx7ffbsJsKLReujSm6315H5T4TC+WmuKBmUq4c2YY1yOku6kSJVkSSZob6kI+w7mBjPEtHUkMzTWRBLIhyLRUVWyrKtVRU7M6KsqZuhPAoEm6HjG5ag742xDJ0pTW8DolKj4Jh2YO2j6RUmmNZFEnA7xoeqauoqGDp0qXMmzcPr9fLv//9bwKBTepor9fLa6+9xvTp0/F6vTzyyCN897vfpbIyl6MnGAzy1ltvcfTRR2OaJr///e/5xS9+AUBlZWU+3UdvVKfT6eTll1/mvPPOI5PJsNdee/Hqq69SXV1ddC2XX345VVVVRdf+q1/9iu9973uoqsq0adN4++23qampyX/++c9/nt1224158+YN2Wekax03bhynn356flxRFLnkkksKfAC/9KUvMXlyzjflvPPO47e//S2ZTIYZM2bwxBNP5AMuis09duxYzj77bCCnyVu8eDHnn38+uq5z6aWX8tWvfnVY56CMrQeHJFPR45M3OzyWg2onMrdmHHtUjKHRW8E4bwhREPko1kFzMkprOkFXNkVcyyVMNu1N2qNdUfPzaTR2GUMvm2JLYFvKLapnmbTfbH6y8Eae+/AtfnnHrQVtLcti4TXX8+OvXcRFBx7FH669AVUbOL4dLX7ehFi2tNycXgiPg8Y9kV1+xhgZRC1NRzaJ2wFTqrzU+RxYpo1bEQuiXPNRsZKUj4odKm1JyOHGLTvyUbEMoS3ekVGuKDGCihKlkg/3R7HEwrF4jGCgeNxb1jBJ67lfJH6XhN8pb3by4f5VHEYTRpJB+9hjj+XAAw/khz/84VZe1bbDrlxRohc7yjmwbRvVNNAsE9Uy0EyTpKGS1LVcagXLxLQsEEBAwCVJuCSF9mxyyAoGfStKbLMKBqOo8kRbZycxxd5pK4YUQ3+5afHkgKoifTEa5Lb05Vc57/hTCo79+xu/pT5UTVoycFz4GdweN9a7LZjPfDxwTUdMonmys6Tc8teBZUGyE6P9EzpjbaCmCftCKJWNJHWblrhKStVwpztwYWFXjEGQHVi2TURNYsfbkLI6ureeUMCfJ3RSqgtJTaL7qrF7khBHtQx6op102zo+d8w3yhUldnVIosCchtBmlgmzCYeHl8V6NJQJ215Yvnw59957Ly+//DJ33HHH9l5OGbso8iZbBhKCXsKnWgaqaZA1DTqySWJaFkkQSRs6nyS6mOCroGIIkrMzmPRGaooNKk5kl7RLR8WGhzCSjQa57XPAPP78rwd59J6/s+ix/6D2CeKKdnVz+h77cPxXvsQpZ51BQ2OwwAQrNAZx7VlPvWUMLTdRhEANsreCqq5mIh+9RHzDh/gdPnyBKiaFJTJpGzWVIJ3OEJc8GK4gDlnCj4NYLIKWjeP3BXEqPYoT20ZOR5DSESzFhdFD6oKKi0gqgZDaeWuSl0ndCCGJwrAqPfRHb3LhrY0LLrigpMlzR0AikcCyLJ5++mlqa2u393LKKGMAihG+cb4KVNMgqatEtQxNqSitmQTdagav4sAt5SphOKSBt9xdkdjt6ulOOrMGDQH/qJabKIrsd9AB7HfQAVz5i+t46uF/ojRvOoeJWIx7/vgnnnv8SR5f+gr2bUsga4BLRjp5FoIk4pVGIDdJQa6ZSEWgmtbm5aixdqqwcfjD+LxefGMaqTAMKt1hMqZANKPTmbHRnWOQXH4ikgi6ltuzIGB4qzAdXkxXjujZtk00o2O568A/tOl8R0WZ1O1kuPbaa7f3Ej4VDjjgAA444IDtvYwyyhgxevPnhV1eJvnDZE2DpKESV7N06xkSuoqhZTDUNFKaHtNtDrKQe9BtTMVJGxpjPAGcolyS6JSJ3Y5N7NpSHTuU3ALBIF8692z021+HuIrH58XpcaOmM5x45leQFBnLIeVInUMimUri7/HvHrHcXD7qJu3Phs71tEWaqI224nD7IVCDCHh7XlU+B+MqPGRq/cSzOusSMZqTcQKym6DThcsVQuxJYdJL6LK6hS8QwjTrSs6/o6NM6sooo4wytjAEQcAtK7hlhWqXD8j5eqmmQZfUhS8UxLAtTNtCN01Uy6Ta5SNpqCR0tScSV0BCxKc4cMsDyUiZ2O24xE53eujqCZ4Yrtxir60mprUT9PtQ9m0s2X5byC1UWcGDy17hn/f/gwO+cExB0Iuh6xwxfW+O/uIJnH7h19ht5oyichsMiigxtmocTU4fG1IRxmYiOGLt4PZDn+hVhyzgkGWCbpkxARdNqRixrIph6SRUCdsGr0MiqRk5QueS8TtlShew3PFRJnVllFFGGdsAiiihiBKq4iTs8g7a1rQs4nqWjmySlnQul55DkgkozgIiViZ2Oyax88oOFLcyIrmJ70cR4iqGLwpz6re73MZX13LGBV/LyS2TyKc7SSaSqNks/7z3fv557/3sP/9ATv/6eRz8uQXUuzfJrdoePPhDESUa/ZU0iSLr3QEaTQ1n9wZQk+CtHJCjTpYEJviDtClJVMPApyjousj6SIZYVqfG58Dv3PkpTzmlSRlllFHGKIMkilQ4PewWrOGA2gnMqWog7PDQrWVozcSJ69m8dmQ0p80oV54ojZHKrS/lG41y603zI8kSocpNlRuW/O8VLjn9qxw/dz7//uu9VFgKumXQnk0OX26ygyaHl2zjLAjUQtsn0LEW+slRSEWozcRwYpLUs/hdAnvVB5jpN3HGW0gm4ljWzp3wo0zqyiijjM1G19MLaX/0p3Q9vXB7L2WnhSJKVLt87Bmu54CaCUwP1iILIm2ZJO2ZJFlTLxO7XYTYQY7wj0a59XIlfyDA0+8u5ScLb2Ty7tPy7davXsv/XXk1J+17IP/53R1EIl0jl5umkq2aCBP2gWQXRFs2NbRMiLcjdjdTa5k4ZZnObAoDg3opySQ6qLBTxLN6QYWfnQ1lUrcLwbZtXnnlFR5++GHUPuVgtgXWrl3L0qVLt+mcZWx9RJ5ZSOc/ryHyzMLtvZRdAh7ZQaOvgv2rx7F/9TjG+UKkDZ2N6TimbVPl9JaJ3SDYGYidAKNSblKP+di0LZwuFyed+RUefvlZbnv478z/3BH5trHuKHfc/FuyrZHNk1smQTZUB7M/D+4gxDtyGjtRAl8YO1CL4A5Q6/LliV3a6cNR1UB9bQ1Tqn1UeJwow8vxv8OhTOpGCNOy0C1zq77MYf6K6Orq4plnnhn22s855xy+/vWv8/e//51MJrO5p2CzsGjRIn77299uk7n+97//sX79+m0y13Dmfvnll7fbenYElLV9I4coiISc7px5tmYCe4fHEnA4MWwLSRTZmIrxcbwT1TQGvddIgkit24dqGqxOdJE2tPxnmmkUvFTTwLQtqp1ebBvWJiNEsmmypp5/aaZRQMTKxG7HrzzRKzfbtEDrmUczc+/7oNcn0LLJy00QBOYdejC3/P1v/Ou1F/niWaejOBzsc8A89tlnToHcuvvUJS+GAXJz+WD83lDZCOkYJCPgr4LqieBwIwhCnth1SE5SobHg8uF1SNT6HfgcO6d/3ajaVXt7O48//jjJZJKDDjqIOXPmDNnnww8/5NVXXyWdTjNt2jQWLFiAOETG8c2FaVks62omaWy6IGzbJtlzI3RLStEoNYBYLE7QiAOQMXQypo4iSvhkxwCnYJ/sYE64AWmIfXR0dPDEE0/wuc99bsi127bNAw88wLp163b6/G/XXHMN55xzDmecccaomPvFF1/k4IMPZsaMGYP03HUReWYheuc6lKrxhI+8dHsvZ4eDQ5KpdfupdftJ96ln25TspiUTxyc7EQQhT46KBSFUOnMkqjWTwC87EUWxaDZOoedo2OUlbWgkDBULO5+SxcTG6EcwZEHEKytopkFXNoWhC7gYPJBgVwyeGM1BL/rr69E+jCJme2qYZw3Mh5bn89H1hSQIOUJOodwmTJ3Cj3/9S77x/e8Sj+YSFffKrSkZ4esLTmTSlClcePkl7LXfvkXXVFRuNZMgUA2d63JaO5cvV4asZw+1Lh9t2SSdPQEgQ1Xb2NExakjd0qVL+exnP8t+++1HfX09V111Fd/73vf40Y9+VLLPz372M2644QZOOukkKioquPHGG6mvr+fZZ5/F6x08umxzYJEjcB5JKbhhBBQnrZkkcS2LIopUFokasmUnAcWZbx9R03Rl04gI1Ll9+S++YVkkDQ0Lm6G0w9XV1Rx77LFAjhC/++67HHTQQbzxxhuIosi8efMQRRFN03jwwQdRVZXnnnuO6upqFixYAMCaNWtYuXIlu+++O+PHjx8w1rJly/B4PLhcLjKZDBMmTGDZsmVMnTqVcePGkclkeOutt7Asi3333ReXa9ONrq2tjXfeeYfdd999WOf3jTfeIBKJsN9++1FRUcEnn3xCS0sLBx10UL5Na2sr77zzDkceeWTRPu+//z7t7e0sXboUl8vFlClTmD17NkDJtb744otMmTIFXdf58MMP2Xvvvamrq6O9vZ033niDadOm5evAQu67um7dOmRZZtq0aUyfPh2g5NyHHHII48aNy/fPZDK88847WJbF3LlzkaRNku6/nzJ2TXQ9vRAzHUXyhEZEdD2yA4/soMbtZ/dgDUaP83p/bVcpgtT/qFCE2vXv2reNaVtololmmmg9FTdShkbS0DBtm45skg2pKAmHwDhvsGgy5l6Uid3oIXbaO12I/cpj2k0x7PfbEPYcU3BcFATCLm9JuVXX1VJdV0s2ljuHXsXBx8+/Rsva9bSsXc8ri55j//kHcsHll7LvgZ8ZIPOicnP5YexMSHZC+yc5cufLRcgWI3Y7M0YNqbvgggs4+uij+fvf/w7Acccdx5e+9CVOPvnk/EOzL1RV5brrruPGG2/k29/+NgA/+MEPaGho4LHHHuO0007bamuVRXHAhdPoDdLSUw9QFqUB4eD9+9S6/chirlROp5ou/OIPrknP48MPP+Tb3/42K1as4N133+WrX/0qdXV1hMNhPvjgA+bOnZsncw899BAADz74IJMnT2bBggV861vf4sEHH2Tvvfdm2bJlXHnllXznO9/Jj1VTU0NNTQ0nn3wyLS0t/Otf/yKRSDBt2jQuv/xyuru7OeGEExgzZgyCIBCJRFi0aBFjx47lv//9LyeffDL77LMPTU1NjB07Fo+ndIj8F77wBVasWMGUKVP4+OOP+etf/0pFRQXHHXccLS0tOJ05QvyrX/2KeDzOkUceWbRPc3MzbW1tLF68mA0bNnD00Ucze/Zs3nnnnZJr/clPfoKqqqTTaQKBAO+++y4///nP+d3vfsfEiRN55ZVXePDBBznqqKMAWLx4MS+99BK6rvP6669z8sknc8stt7B8+fKic1933XWcccYZHH300bz55pucdNJJVFVV4Xa78fl8PPnkkwiCUHQ/fQltGbsOtoT2UhAEFGHbOg7JgoQsSniKPFkMyyRt6LQ620k5RFoyCSRBxCGK+BQnziIEr0zsRgexE0tYg+1ocVP0SOUW8HppmDiB5jVrgVzE7JL/vcLe8/bn/O9ewgGHHVLQv6Tc/NXg8kOkCdpW5YInKhsQZEee2HXFO3DFOkquZUfHqCB1q1at4u233+amm27KHzvxxBOpqqrikUce4aqrrhrQR5IknE5n/mEP4HTmzAxbQ0s3FLZUfqZPg9bWVv7zn/8wa9YsOjs7aWxspKmpicbGRu6++278fj/33XcfLpeLRx99lKeeeoqVK1cSCATYuHEje+21F6eckivgvGHDBh5++GH2339/AK677jrWr1/Phx9+SGVlrobtPvvsw3e+8508qb7yyiu55ppruO2227jooou4+eab+drXvkYmk2Hu3LklSV1bWxsvvPACnZ2dOJ1OYrEY77//PjNnzmTKlCn885//5NRTT8UwDO655x4eeuihkn1OO+00/vznPw8wgZ577rlF13r77bcD0NDQwD/+8Q8EQeDEE0/k5ptvZvny5Xi9Xn7zm99wyy235Endt7/97fw40WiUPfbYg4suuqjk3H3x1a9+lQsuuCD/nV60aBG2bdPe3l50P2UMjc3VapWxbSGLEgGHhO70sls4zEzTIKZnac8kac8miahpFFHC34/g7YzEbvDUu8WJ3WDY2sSOErXIhVDpfY9Ebocc+VkOPOIwHnv4Uf7869/S/PFqAN56bQkXnXI6e8zZm4uu/C4HHH5ofoySclNcUDMll8tu9eu59Cd1UxEkmVqXj66udajdG0vvdQfHqAiU+PDDDwGYNm1T+LMoikyZMiX/WX/Issy9997L73//e7797W/zox/9iKOOOorvfOc7fP7zny85Vzwep7m5Of9qaWkp2Xak2BZOwYNh8uTJzJo1C4CqqioaGxtpbm4u2vbpp59m5syZPPfcczz66KO89tpr1NXV8cYbbwAwYcKEPKHrxRFHHJEndJ2dnSxbtoxwOMyjjz7KI488QiAQ4NVXX6WlpYVPPvmEM888EwC3282Xv/zl0ucgHKa+vp4bb7yRDz/8kGAwmC8Vdu6553LXXXcB8OSTT+LxeDjwwAMH7dMfg621F8cff3z+ZjFr1iyOOOKI/I+DWbNm0dTUVDDmxx9/zH/+85+8tu/dd98tub9edHR0sHz5ci655JL8sV4f0JHsp4xCbO8I3HKgx+bBIclUu3zMrKjjwJpcLr46t5+UodGSTtCRTZIxckEMO1vwRGQYz4b+wRNDoVTwhPlmM+YrazHfLHwWjCh4QhlIFYyxfoSZg/tnj0Rusixz4qmncP+L/+XyP/yKiTM2ue28t+wtLvrSGdx3+x39llVCboIAvjDMOAIaZ0GqG7IJBEEgHKhF8Hw6BcpoxqjQ1CWTSQCCwcJfI6FQKP9ZMaTTaRKJBB0dHRiGQTQaJZvNYhgGilL8l9bNN9/MNddcM+B4d3c3bre7SI9N0C2Tzu4ItsM76C8/t20TV1XWJpLEHW4qHG6SqdL7kABFM2lJdBERo7glhS65a9BfWgCxWAzTNOnq6iIej6MoCl1dXfnPbdsmEonQ1dWVP49dXV24XC7a29v5+OOPueOOTRfJxIkTsW2beDyO1+stGCudTuNwOPLH1q5diyiKPPDAAwVr2nvvvdmwYQOKohCPx/NEybZtDMMoGLMvnnrqKR599FGuvvpq3n//fW644QYOPvhgjjzySC6//HLef/99brvtNr74xS8SiUQG7aPrOolEYlhr7erqQtd1dF3Pt89mc4lde98nEgk0Tcu/v/TSS3nmmWeYNWsWHo+H5uZm1q9fnx+r79xA/lhbWxuSJJFKpYqmlCm1n9EMsycCzjStkrLti3g8/qnH2NL9Py06nrwZq7sJsaIR9j1zyPbFzkEvUi/eirbiWfRoG7D99rQ1UWr/IlCLQlgJkkYjqmeIJGNELBORXNm1gAmJVIwNyTRhp6eoz18vJKDKlOlUkzQlM9S4fPm0G6VQY8u0Z5I0p9qocXlxiIM/IsOIdGYN2lId6E4PXnlwJ/wgYKoWqUSSjVDU/7ovHEDYEImkEhimiQzYlp33Ryu6B0uiPZuiKZmlxu3DsbQZIalh+xzoUwZq/KptifZsho2pNqqcPtxFzOCinfO3tMn9tRwizfPDBLojBHt8xUXLzrXptz4vYOgC8X5y01LFia0CfP6zRzF3/oG8/dJiHrntTj58dzluj4fDP7ug6N4HlZt7TG4VkWZIRcBTgRSaSjyeRBS237XV/zoIh8NbZNxRQep6NSLxeByfz5c/HovF8s77/dHU1MSZZ57JnXfeyemnnw7kNDJTp05lt912y5vH+uOyyy7jvPPOy79vaWlh//33p6KiYsiTqlsmYqoVxeki6BycAIbsnI9dUlcxXQo+fIRCpVXoIYIE1DRt6QSS4iAcDg9J6oLBIJIkEQ6HCQQC+f97IUkSwWCQcDicN1OHw2FcLhf7778/kiTx4IMP5tunUikEQeDVV18dMFZvsETvsVAohM/n42c/+xl77bVXvl1HRwfBYBBZlmlqamLvvfcG4N1330WW5aLnOJlMMmbMGC677DIuu+wy7rnnHn71q19x4oknEg6HOeGEE7jrrrv473//y8KFCwmHw4P28Xq9eDyeYa01HA6jKAp+vz/f3uPxkE6n8+/7nudoNMp9991HJBLJ/wjZe++98/P1nxvIjz99+nS8Xi8ffPABhx12GJD7jgcCAVKpVMn9jGZEJBELkCRx2Del/u02Z4wt2f/TYkucg/xYr/wJvXNd/v322tPWxnD3ZFgmcV2lW03nTJ26hq1Dh6GBbDPWM7gp1gU4dC8bMzE6RHNIUyyAM+CjKRWl3TZp8PoGNcUCNAT8bEjH6DJ0FLcypCm2Hj8bgaRTRHIM7WPnAhTNiy3kNG2CKOAKlu7jAhymj+ZUjHbBoLHHdDpYv0bLR3M6RqdlUO/2DjDF6r1j9DA70aXgrQwR01VkV86HXB9kHhd+ZDVNVzaFqMAYd+45X2o9vXITjjmc/Y79LGteXkpnaxtjJhXygX/d9wCTpu3GrH32HlxuQT9UVub87JKdZBSdQMC33a+trTH/qCB1u+22G5Dzrauvrwdymp3Vq1fz2c9+tmif9957D13X8w9HyJkc99hjD958882ScwUCAQKBwGavVRIENmbiyKI0olp6imYSGsKTIuz05G9iWxvf/OY3mTNnDueccw6HH344zc3N3Hffffz3v/8dVn9Jkrjxxhs5/vjj+da3vkVFRQXPP/884XCYhQsXcumll3Laaadx+eWXs2LFCp555hnmzZtXdKy2tjZOOukkvvKVrzBmzBhuv/32fMQq5EywCxYsYN68eUyZMmXIPtOmTePee+/F7Xaz2267MXv27EHXOhL4fD6qqqr46U9/ypw5c3jiiSdYtWpV/vNic/c9Zz//+c/z58XtdnP33XezePHiIc9BGWXsipBFiUqnh0qnh4n+SuKaSlRL05SKsT7ZTVc2xThfCF+/mrh9MRp97CqdHiTHyIIn1D7JfSXbHnbwhGFZQz7oR+xjBwN8yId6qvb3sRsqtr+v3CbN35+D+/kVdra1c/33fkg2k2XBF47hW1d9n8bJE0vLzeGG+t2hc23Oz24nxajwqdt9992ZPn163ncKcqaolpYWTjjhhPyxu+++m3//+98A+Yf74sWL85/HYjE+/PBDpk6dutXW6ldciILI+lQ3MTUzaGJPw7aocnpwSQrtaoq2TGLIxMMBxYV7iF+Gvaiqqsqn9qitrR1AgI888kiqqqqAnL/CF7/4xXz6DL/fz1tvvcUee+zB888/TyaT4YknnmDMmDFFx5o+ffqAvIEXXHAB99xzDxs2bGDx4sUcddRR/PrXvwZygRWXX345ixcvpr6+nnvvvZf99tuv6D4mT57MI488Qnd3N8899xxf+cpXuPnmm/OfH3bYYVRWVnLWWWcNq8+PfvQj9ttvPx555JE8wR9srYcccggNDQ35sWfMmJHXMEIudUxvLkBZlnnuuefQNI2XX36ZE044gWuvvTb/fSw29/z58/Ma54svvph77rmH9evX88knn3DvvfciiuKQ52BnQtkHrYzNQW/C5Qn+MAfVTuT48XtwaN1kHKJMt5qhJROnK5sq6g832n3shpOguG9y35EkKB4uPm2C4uGUOOvrY9elpj+Vb+RDf7uXbCZ3nhc99h9OOvBw/u+KH+JOqKXlJilQMwW9bhqCc9sHVG4LCPan8czfgnjppZc46qijOPbYYxk7dix33XUX5557bkFE7Lx585gwYQL3338/kPNt+vOf/8xZZ51FRUUFDz/8MACvvfYaoVBoWPM2Nzfno0T7PtiLoTf5cFxXSehZTNvGJzsGzbUEOa3jxkgXbp9n0ATFvRhu8uEdDV1dXZulbv7oo4+YPXs2GzZsyAdq7IjY3P2PZnz83Qn51BtTb1o7ZPuuri4i1+9T0GekY3zaNWxpbM45KPU96B2rF9trT/2xJSOMt8Z1kDV04nqWzmyK9mwSzTIREPDKCh7ZkSdEKV1jYyaGIsrDMsXqlklTKopl20Nq7AAs22ZDOkbG0Klz+0tq7LKxRN702NaTCivocA2qsdNvfx3iKpbfwbrTJuOWlSHTnQBot7+OEFcxfAryBfsP6dZjWBbN6Ri6ZVDvzmnseufOO9YFnCgXzAVyz7eWTILQ395HSeoFn5VCl5om0tWFNxQcMpoZisvNMAwef+Bh/vB/N9K2cVPAo8vj5isXnsfhXz0Nt99XVG6tmQT7hBsIu7Yfsdtaz4NRYX4FOPjgg/nggw94+OGHSSaTPPzwwxx++OEFbXrJWy8WLlzIaaedlq8oce2113LCCSfgcGydjNGSKDIn3IDFyHlwlxIctgBFhJ2O0G0uHn/8cX73u99x+umn79CErowydmSM9qofLlnBJSvUuP3s1uPCElUztGYSdGRT2Fg4RQVfj+ZnNJliR5rHThQE6tz+Yeex6/vJ5qY7GeyJ2quxi+5VTTKj4fa6GcrWFHZ6MBxpYkUqTxRDURO6LHPC6ady1EnH8cBf7uLPv/4d8WiUbDrDHb/+HQ/fdQ9f/OZ5fO6MU5lYUTUkId9ZMGpIHeTSaHz3u98t+flFF1004Ni8efNK+mptDUiiOGSlh2JQRGnIX0hlDMRjjz3GjBkz+PGPf7y9l1JGGWXsAOjvh5cyNOI9+fC6tZzLjCJIxLUMTbZNoze0wxG73vHSS9YRM1rx+3zI+w5uaZJEMZ/uZKTEbrxtDxJnnCN2oXmTackkaNFVNDU9ZJ7WoOJEdkmfOv+gy+3m7Iu/zolnnMYdv/kD993+F9Rsllikmzt+diPBqkqU444ZlqZ1Z0BZHVTGqMZtt93Gr3/963LJrDLKKGPEEAQBn+Kk3hNkdnhsLh9euIHpoVoavCGShsa73S10ZlP5vG7FsL197GzTAq1nfZqJbVoEHC4q3u/G90Y7xptNQ/q0CVA0j10p9PWxM4fhpbUt8rQO5mMXCIW49Cc/5LEl/+PEM76MKIpMmzWT0049bdhy2xkwqjR1ZZRRRhk7C8qVLkYfHJJMlSRT5fIyORAmZWhEsxk2ZGJEtSymbSEi4OnniwfbT2NnmxbmQ8sha+QaZQ3Mh5YjnTxrQPDE1iop1kuFevPUlcKWqqz0aSqG1I6t56e/+RWnf/08dFXDpTholDbJ7ZX7HmHfo46A8OCazR0VZU1dGWWUUcZWwPaudFHG4BAFEb/iotFfwbyaCcyvncg+4QYm+isBgY5sipZ0nC41TcbQsW17u2js7PfbsJsKK0rYTTHs99vy7yVByFeeGG5U7Eg0dkMFlPRFKY1dqcoWsHUqhkydvjszZu8JbCLky195nZuvuoZz5x/Jqy+/POw97Ugok7oyyihjs2AbGlY2p4GwsglsY+c3bZSx88IhyYRdXiYHqvhMzXgOqBnPXpX11Lp8qKZBWyZJWyZBytCodfm2GbFLdhavwGFHN43TGzyxtYjdgLmHMvUWIXbWmxuwFq/HenND0T7bohTc47f+FQC3389efdJW7Uwok7oyyihjxLANjXU3HomZzJVsM5MR1t34uTKx28IwU9HNyudXzgX46SAIAl7FSZ0nwIyKOg6sncjcmnFMD9YSUFxkLROnKNOtpfkw2k5CH5yo9Sd2KWPwHHCwidilvMVNkUKo0JTbv1bs1iB2vSsxbXur57HbGsTu13/7M2dcdAFf+t638pWsdjaUfep2Maxbt47W1lbmzJlTsj7u1kBHRwerV6/eIfO0vfHGG8yaNStfaq0MiL58F+kVLxQcS694gejLd1Fx6HnFOxVBMW2fMEQeRyj0V9uZYWViRJ5ZOGKfvNGegmRHgySKBB1ugg43Db4QumWS1FUSukpzKkprJkm3msUpSbgkGZek4BClAt+wvj52kVQCRfMOy8eubaZFZlUcd8umwAOhMYgwsxZeW1/Qvne8vulOBsNIfez6YjiVJ/r62Jm2PSwt0pb2seuLQDDIFT/7Cc2pWNHPdwaUNXUjhG2Z2IY+8pc5grbDVIUnEgneeeedYa/9+9//PnPnzuWSSy4hkRg6g/mWxL/+9S+uueaabTrnlsKCBQtoamoCcgRPVTe/jNvSpUsH9P+0Y24PaO2rSxwffvkd27Y3W9tX9lcrY3tCESUqnB7G+So4oHYin2+czoG1E5gWrMErO0kZGu3ZFK3pBK3pBF3ZFHE9i2YZ1Ln9OCV5+KZYX5DM8bthOHvIlktGOnkWglSCuPTT2A2FoTR2BZG3PYozWRRHXHmipzTsqNDYjcRHcEdDWVM3AtiWSXbtMqxscsR9tXiMdOfwSraILh+uCXMQhvjFtGbNGm644YZ8hY2hcMstt/Dhhx8ybty4YbUvYyB+8IMfcOeddw5ZfaQUDjvsMN5+++18WbEtMeb2gKNmUtHjWsca2h/96bAiPm01uUW0fcNBr2Yvu/4dXOP22qEiUkdJ0Z8yBoFDknFIcp7oGZZJ1jR6XjpJXSVpaKR1Dc02kQSRrGnwfrSNMW4/lU5PPpdpMcJR6wuQdUigmpiKiFKC0PWir8bOtK0hc6uW0tgNiLzthW2PqFasIAhIQm7Npm3TpaYZyvi5NTV2OzPKpG4ksCyMZBeiK4jY5wtsmwaWlkF0uBH6lAyzLQtLTeWOu2xEd67ksW3bWNkkouIaYGoy0zHMdBQsC4YgdRMnTuQHP/gBAPF4nHXr1jFr1izWrl2LKIp58mYYBi+//DLpdJq1a9cSi8WYNWsWAOl0mjVr1jBx4kQ8Hs+AsdavX49lWSiKgqZpjB8/no8//phwOJyvK9vbZsKECQXr0zSNVatWMWlScQLQFx988AF1dXW43W5Wr15dsJ5Np99i1apVBINBamtrB/T1eDysWLGCmTNn8tZbbzF79mySySQbN25k5syZCIJANBqlqamJ3XbbrcCc+vbbb5PNZnG73UybNg2Xq7hZ5IYbbqC6ujr//pNPPkEQhII9lhpr5cqVWJbF22+/TWdnJ1OmTKGqqmrAmKqqsmrVKhobGwkENpXJXrJkCbNnzyaVStHa2srUqVOR5cJLuNh6tgZCB51NbPF9BaTMs/uhZD5ZTGLJA8My+9mmUfT4SLR9w0WvORJRIrnsn6PWLFmMwJmxtmGbpcsYHZBFCZ8o4VMKXTYs20IzTdqlDtzBAKppENOypHQV1TJJGhqmbYItIAg5k60iSiiChJJPX2LTlkkMO0GxZTOshPnFiJ30fvuAyFsAdKto5YnBiF0vRCFH1AxdwMXgeygTu5GjTOpGCCvVBaaJFG7MHzNirRjRjcihepTKTcfNRAt6pAnJF0ZwhhF6chqZiU70zrWILj+O2qkIPV86Mx1F71yN6B6eRu+dd97hvPPOY8WKFSxZsoQLLriA8ePH09HRwfr16znnnHP47W9/SyqV4nvf+x4AV1xxBdOmTeNvf/sbv/jFL7j++usZN24cGzZs4JZbbuErX/lKfqzGxkYikQjnnHMOmUyGRYsW0draitfr5frrr2e33Xbj5JNPprOzE0EQaGxs5F//+heVlZW88cYbHHvssQSDQRKJxJBVPy666CJqa2tZvHgxHo+HSCTCc889xx577AHkiNKJJ56IJEl0d3dz+OGHc8899+B0OrnooouoqanhlVdeoaGhgSeffJKDDz6YY445hnfffZdEIsGUKVM4//zz+fGPf4zD4cC2bV577bU8mfr5z39OU1MTqVSK5uZm/va3v/GFL3xhwDoXLFjAG2+8QX19PZ/73OdYs2YNPp+Pqqoq/vWvf1FVVVVyrDvvvBNVVfn5z3+O0+nkmmuu4cgjj8yPOWXKFB566CHOP/98xowZw/r16/ne976Xr6Zx8MEHc+qpp/LSSy9hGAYVFRW8+uqr+Hw+0ul0yfVsDQiyg/FXPMPKS+owkxEkXyXjr3iGVd+fOvwxStRMdtRMHvYYtm1jqznN+Uh88rYUNtcnsOR46kArgG2oW0V7Wca2hyiIuGQRr+wk7PIB0NCjsrJtG80y0SwD1cz9zRg6SUMjpWvYfao6rE9G6cymmdLjp1ZKlxtwuFB7SJBpW0i2PbI8dt0lEghbuRk3h9hJgohPcRJPxZCHUXmiTOxGhl1np7sA1q5dy49//GPee+893n33XW677TZaW1sJBoM899xzALz44ov87W9/49lnn+W3v/0t7733HsuXL+fll1/mkksuoaOjA8iZdr///e+zfPnyfOm2t956iyeffJI333yTI488knPPPZejjjqKdevWsWbNGqZNm8Z1110HwHnnncd3vvMdVq5cmV/PUIhEInz88cesWLGCL37xi/zmN7/Jf/bVr36Vs88+m1WrVrFu3TpWrVrF7373u/znGzduZNWqVbz++uv5GrFTp05l1apVfPLJJ6xatYq77rqLjz/+mFWrVjF9+nTuvPPOfP8HH3yQ1157jeXLl/PQQw9x0UUXYZqlfRufffZZNE2jqamJjz76iJtuuom2trZBx7rhhhtwu935z4888siCMTs7Ozn33HO57777+OCDD3j33Xe56aab+N///pdvU1tby5o1a1izZg0ul4sHHnhgyPVsLQiygujK/dIWXf4RkxnB6cOz+6EFxzy7H0rooLOHPYYZa9tuEbilIoA7n7yJ9kd/ipkpnopisPGsEln4B9NeliNddw4IgoBTkvErLqpcXuo9QSYHqtirsp4Daifg7PkR5JZkvjBuBgfWjM+bNG3bzuXUy6bQ+mjAbdNC1HP+ZbZmsSERHVFUbMRdwjdN3ESq+laeGI6PHeSImVtWtkvliZ0dZVI3QojeMFKgpuCYHByDY8x05OCYguNSoBbHmN1RwoU+bJIvjHPMNBzVE/NaOgDJE8JRs9tmR/RNnjyZww47DMjV0W1sbGTt2rVF2z766KMceuihtLW1sXTpUpLJJI2Njbz++usAjBs3jmOOOaagz1FHHcXEiRMBiMVivPDCCxx++OEsXbqUpUuXsv/++/Pcc8/R3t7Ou+++y7e//W0AwuEwZ5899IP6jDPOyJtEDznkENasWQPkImfffvttLrvsMgB8Ph9f//rXefrpp/N9zz77bNxud8F45557br79rFmz+PKXv5wff968eaxeXejs397ezrJly/B6vaRSKTZu3FhyrZMmTaKlpYVFixahqirz5s1j5syZmzVWLxYvXkxDQwNHH310fo6TTjqJZ555Jt/ma1/7GgCyLHPAAQfkz9FQ6xmNEASB8Vc8g+TLkfBebd9IyKFtFAaY9PrkbQuUigDuevx6Ov95DdYISF0vQbRLpMYYTHtZDhrZNdBLo0RBoMbtZ5y/EqXn+eGUZOZUNVDj9pMwNFoycbrTKYwH3837w8mqSfCxT0ZE7BJTQ2TH9NOkBZwIcxsLDo2U2AmCQNjp2W4lxXZmlM2vI4QoOwuIGOTMSJI7MKCtIIrFjwsCgqu4L4Ho8mJlRpYIshf98+7IsoxhFPdb6uzsZPHixXzyySYNQK9ZEihaa7XvsY6ODkRR5Kqrripo09DQQDQaxeFwFPjEDad2a9/19117NBpFlmX8/k3nrLKykkgkMuj4/cfru56+4+u6zmmnncZzzz3H+PHjcblcpFIp2tvbaWxsHDAuwMyZM3n00Ue5++67ueqqqxg3bhx//OMfqaioKDlWf5/D/ohGowP20X+fpc5RqfXU1BT+ABlt6NX2mcnIZmn7imFr+OQVn6d4BHApX8HBUIwg5iE7R6S9LGPXgwBUu3xUu3xMMQ2iWoYNr32E3Vz4w8LdkqbrvRZWztTzZkkRAVEQkQShwKzpkhQaAiGaj4WGu1ciqSa4ZOSv7Vc08nakplgBgTFu33YtKbYzokzqdlHsvvvuuN3uAhOkaZqYpslLL700ZP/x48fjcDi49957mTx5kxYhk8kgCAKWZfHRRx8xbdo0IOcTt7kYN24cgiCwfPly9twzV/blzTffLIgg/TR48cUXee+992htbcXpdJLNZgmHw4P+EsxkMuy7777su+++QE4rePPNN7NgwYJBx5JlGavEL8bJkyezYsUKstlsPrjizTff5Pjjjx9yD6XW83//938jOhc7A/pqtXqjXkdqCh3ePMWDUUr5Cg6GUgQRQHR6ykESZQwbTkmm1u3H0BRai3zeoDtJun2kDR1FFLHJBV+YPfco27YLiJJTVjBkkFQwFZGspeNEwlHkez5iYredasXuzCiTus2EbVnYRhZBcRd8oWzbxtYzCLJrgEbP0tIIsnNAqhJLyyBIcj6QYlvgm9/8JrNmzeLqq6/m8MMPp7m5mT/84Q88+OCDw+qvKApXX301J554IldddRUVFRU8//zzxGIx/vCHP/C1r32Ns88+m5/85CesWLGCf/zjH8yfP3+z1up0Ornwwgs5++yzue6661i3bh2///3vC8yvnwbhcJi2tjYeffRRKisrueWWW9C0wc0HDz30EE8++SSnnnoqsiyzZMkSvvGNbww51sSJE7nvvvs46qij8tGvvfjMZz7D5MmTOeOMM7jgggt49tlnWb58OY888siQeyi1ntGGLV1aTJCdBSbY/j55faNetzRKRQBrHashGSndsQhKEUQARljCaUtjSweDlLFt4Kj0FT0+vqGeqrrcDx/btjFtK0/qLNvq+WtjYfcct2iRP8YkgyQI2KJA0tDQ1AwINooo4+5JuCwKwg5B7IabYHlHRJnUbQZs08CINWMkOlCCY5BDdfnPjGgreqwF2V+NUrkp75gZb0ONxBBdwYIbuJmOoXetRVA8ueMjyEkVCASYPXs2AMFgMK/F6sXs2bPzKTEkSWLu3LmIPUSzurqat956i1/84hfccMMNNDQ08Pvf/57GxkZaW1sHjNXQ0IDDUXhhXnXVVUyZMoWHH36YRCLBoYceyk9/+lMAfvvb33Ldddfx61//mpkzZ/LHP/6RJUuWlNzLjBkzCqpNhMNhZsyYkX+/cOFCFi5cyC233EIwGOSxxx7jgAMOKNoXYO7cuQUpS6ZPn16QNmTs2LF57dnee+/Nr3/9a+644w4CgQBnnnkmkiTh8+Vuivvtt19ec9b7/5lnnonT6eTOO+9E0zQuuOACLrroIgRBGHSsP/7xj/ziF7/g6aef5qc//SlHHnlkfkxBEPjvf//Lddddx4033sj48eNZsmRJPvCj/57Gjx+PJOVuTqXWM5pgGxrdt50yMLDhU+Rhk4K12GqyIAJ3WxGOLREB3IvQQWfT9eSv0FpXDpxnMzR/WwqlgkG25XkuY+Roe+UjjFQWR6UPLbIpoto3sYbwnIn594IgIAtDE5x2QcAEHKLEPtUT0C2TtKGRNnS61TTdWoaOnlJgkiDgkmRqnF7a1VSe2A0WE7utiV1Mywy55x0Vgr2LZ7Zsbm6msbGRpqamIZO/9k0+bKS7sdU0ojuA1Mc/zswmsDJxBKcH2bPJP6q7cyM+GQTFheSpyH/5LC2DlekGyYHkrUQQxGEnH97R0NXVtUOWCdtS2Bn3//F3J+TLUU29ae2A933R/cKfaLnzggFjSL5KzGSkaJ/hzAuUnLO3HaKU03j1/B3JXCNZS99zUGquUt8DK5tixTdCYBX65Mnhcex287phz1/q2OaglMzGfPVPm51iZWe8DkaKzTkHy3/5b7RoGkfIw6zvHVfyWN/jStCNpZmYGQ3J7WCvH55QshLFSOfuC9u2yZg6KUMjpat0qmmSukrW1GnPpFBNg70e3YCU0LH9Dsyv7YOeSOEO+hEQEIXcy7ZtWjIJkrpK2OUdktgBdKlpurIpfIpzSGIHkNI1VsTaOW7cTMKu7Vf/dWtdB2VN3QggiBKuCXNyiYFHiExVF8HKYQpQFHc6QldGGVsysGBnhOjyIlfUY3QV1vMc6iG1NbElysGVsXVgGSammrt2TNXANq0BhE0QBCSnnCN1TnmzCN1wIAgCHtmBR3ZQ7fIxwR/GsEzShk7W1EnrGp1CS769ZpmopoGuqzlTr20DNja5lByGbfFxrIOkJ0CjNzRobr3N0dhVDoMs7qgok7oRQhClzfLPESSlbK4oY5fGlgws2FmxPQlcMZSS2UgSRJex5WEZJh/f+QJmJueva2Y0Vt7xArude+hWI24jhSxKBBwSAVzghoQooZEL5NindiKdchehyoo8qbN7fPgs28awLbrVFGsS3axLduOWFDyKgk92FiV4IyV27p34WTw6pF9GGWXs9AgddDbK5AMLjnl2PxTBWdyhe4eHndPob4mAkO2F0EFnf+oE0WVseXS9tZbkmo6CY8k17XQtW7OdVjQyCD3mVkWUcEoyblnBIzvwKU4CDheVTg+TA9UcUT+VI+qnMiNUiyJItGcTtGUSpA1tgC/uSPPY7awok7oyyihjm0CQHVR8/aGByYZHmXbq0yL/MOn5u60rXWxJ9AaDfJoE0WVsefQNfugLtcTx7Y1ipuLhQBAEfIqTBl+I/avHMbd6PJP9VRi2TWsmQUc2ScbQ89dcmdiVSV0ZZZSxDSFIn6602I6AYvVbP02li+2t6fu05eDK2PIola7EWeL49kQpU/FwiV0vBEEg6HAzKRDmM9Xj2beqkXpPENU0aMskac0kiGoZPJJChdOzyxK7Mqkro4wyytiCKBX4sbnBBSPV9G3pXIAjRbkW7dZHeO8J+CZWFxxzhDwY2dGnDS5lKk6+P3TpxFKQRJGwy8v0UC0H1k5kbs04pgdrCCguNMvEsm0EBNYnu/kg2kZMy6CZxi5B8MoeymWUUcYOhf6kZVv75PVWqZA8IcJHXjrg81KBH58muKBX0zdUGpGtkVeu6+mFmKnosNv3JnxWqsYXPT9lfHqIssRu5x7GO9f/M68B06JpOhavpG7+7tt5dYUoZSo2YlsmV5wkigQdboION42+CizbQjVNMmYu8rZbzRBR0yQNDd2ysHsCMXZWlEndVsbCl1YTzegolsZVR++4uZna29vRNA1BEKisrMTtdm/vJQ1APB4nHs+Vg3I6nQXJhouhtbU1Xzu1oqJiQO1cyJVO6+7uJhwO73S+XzsiipEWoSdZ6bbCUKRFcPoGVJTYEsEFw9H0FashO1xCWAqRZxZiZWKb1beMLYeaA6fl880BCJKYT1eCIIwocf22RClTsRzcOs8QURBxy2I+wrW+pyyYYZlkTQPVNFAtA688WDrkHRdl8+tWxsKXVnPNMyu5bWnL0I1HMY455hj23HNP5s6dS2VlJQceeCBr167d3ssqwO9//3vmzZvHrFmzOOWUU4Zsf+KJJzJv3jwmT57M3XffPeDzBx98kJqaGiZMmMCkSZNYunTp1lj2ToNtYfYrRlr6lgkrvbieX+Y9Jbe2plkyT/57/m6p4ILhaPrKeeV2XtQeOI36BbOoPXDa9l7KiFDMVOybWINvZv02XYcsSvgUJ2GXl3pPENdO6htaJnU7CWKxwl/SmqaxYcOGAW26urqAnFart08ikRjWHL/85S9pbm4mEokQDAa5/PLLt8DKtxx+8IMf0NzczM9+9rNhtV+8eDHNzc3st99+Az5raWnhrLPO4q677iKRSHD++edz6qmnYprbtw7naIVt20XNfluaOA1W+L7Uunr+KTi+TSJShdztdUsEFwxX01fOK1fGaEOvqbhXwyi5HaMqn97OhvJZ3cFx9913U1tby4QJE2hoaOC///0vkHuYHXnkkdx6661AjtDtueeevPDCC0COoJ111lnMnj2bsWPHUl9fz4svvjisOd1uN5/73Of45JPcr39d12lubh6y36uvvsrRRx/Nfvvtx+WXX543fY5G/Otf/2L69Ol8/vOfRxAELrvsMtrb23nttde299JGFSo/dylVJ/wE9+R5Jc1+WxKDFr4vgmKRqL3YGuvbGhiJpq+cV66M0YheUzGwVStbbA20vfIRGxctp+2Vj7b3UoaFsk/dVoRmWCR6cvMkNRPdtFC24Jd56dKlXHrppTz77LPMnj2bl156iVNOOYWVK1cSDAa55557OOywwzj88MO55pprOOyww/jiF7+Y7//UU0/x0ksvMXfuXG699Va+/OUvs3r16nzx+v7o7u6mubmZ5uZm/vKXvzB//nwA3nnnHfbbbz8ymUzJvo8++ig//OEP+dOf/kQgEODkk09m/PjxfOtb3xrQtquri0ymuBOtw+GgpqZmpKdqxFixYgUzZszIv3e5XEyaNImPPvqIAw88cJCeuxZ6fcra/vGDop9vabNf6KCziS2+r4BACrKzpAl2qBJkW3p9fU3QbCFn7ME0ff1N3iAw/opnWHlJHWYyUs4rV0YZnxLtr3yUr3u7I5i+y6RuK0EzLI68/TUi6Zx5pztj8LnbXuOZC+dtMWJ37733cuKJJ1JXV0dLSwtTpkxhwoQJLF68mKOOOorZs2dz5ZVXcvjhh6MoCu+8805B/2OOOYa5c+cC8PWvf53rrruOpUuX5slaf9xwww389re/pbKykgULFnDttdcCOaI1duzYkoEEpmnyzW9+k7///e8cdNBBAHz5y18u6aP23e9+l0WLFhX9bObMmTz99NNDn5xPiUwmg89X6ODr9/tJp9Nbfe4dEZ/G7Ff5uUvz0aRDoTcZbl/SIjh9A+ql5tsPUYJsS5ol+5uge02+WyuNwmCRrqLLj5mMbLbpt2+EbxlllLHjYNSQOtu2+d3vfsc999xDMplk/vz5XHfddSUjGJubm/MEoT9+9rOfceaZZ27N5Q6Ju95o4oVPugqOvfBJF3ctbeK8eeO3yBwbN27kmWee4amnnio43te/7phjjuHKK6/k4osvJhAIFLSrq6sb8L6jozCfUF/88pe/5LzzBkbQ7bnnnoOaX1999VUCgQB77LFH/lg6naaysrJo+5tuumlQTd22QDAYZP36QqIQjUYJBoPbZP4dDcU0aMM1+4007UVvMtxe0lIKdpFSQn2xpc2StpocYILuPb41MFik62AYKiULFEb4llFG/8jbrYm2Vz7Kz7UjaMZGG0YNqbvhhhv45S9/yZ///GfGjh3L9773PY488kiWLl2KJEkD2tfV1eX9w3px3333cfXVV5cke9sSq7uKa3Q+KXF8czB58mTOOOMMbrnlloLjlpUz+2iaxllnncXll1/OHXfcwemnn868efPy7T76aJOPgKZprFmzhnHjxo14Hbqu09bWRkNDQ9HPFy9ezNixY/Pvbdvm6aef5he/+EXR9ltLU9fe3o7H4xmggSuGvfbai4ceeij/PhKJsHLlSmbPnr1Zc+/sKKZB255mv14tlpXqLvr51lhfKVPvUCbgkhgiTcXmRrqW88iVMVJsS3K1o5k7RxtGBalTVZVf/OIX/OQnP+Hkk08GcqbFCRMm8Nhjj3HCCScM6CPLMhMmTCg49p///IfPfvazTJw4cRusenBMCnuKHp9c4vjm4Otf/zpz5sxhjz324PDDD6e5uZmFCxfym9/8hokTJ/LjH/+YyspKfvnLX7LHHntw1lln8fbbb+Px5Nbw4osv8rvf/Y4jjjiC3/3ud4wdO5Z99tlnxOsYyqdu2bJlvPPOO7S3txMOh/nZz35GKBTiyCOPLDreX//61xGvAXLav0gkQjQaRVVVmpubcbvdhMO5/IDHHHMMZ5xxBpdeeimwyXdP0zSi0SjNzc2Ew2HcbjfHH388l19+OVdffTWnnnoq119/PXPnzmXWrFmbtbZdAf01aNuK0BUz3xbTYuUWmSNKW2N9pUy9Q5mASw8ogl062roc6VpGGWX0x6gIQVm2bBnxeLzgIT9u3DimT58+QBtXCh999BGvvPIK559//lZa5chw9r6NHDq5MNnwoZPDnL1f4xabY/z48bz44ossWrSI448/nhtuuIELL7yQiRMnsnz5cv75z3/y17/+FUEQOPvss5k3bx433XRTvv/XvvY13n//fU455RRaWlp4/PHHS/rF1dbWFk3OC0P71L355ptceumlHHHEEdTX1/P222/z8MMPI4pb9uv39NNPM2/ePG699VaampqYN28e3//+9wv24PdvMtd997vfZd68eWzcuJFbbrmFefPm5aOHfT4fTz/9NEuWLOGEE07AsqwCzV0ZowfhIy+l5sSfFmidSqY+2Qrubb0RwOFjfzAg8hTYahUvypGuZRSDZZiYPQF6prprlMYqYxNGhaau1x+rmI9X/1xrpfDnP/+Z6upqjj/++EHb9a06ALl8ZFsDDlnkmQvnUffTZ4ikdSrc8hYNkujFHnvsUZRszJo1ixUrVvx/e3ce1tSV/gH8S8KqhH0RBWUTUURcgNbiiFBFO0MBtVjr0gqite5L7dRaax1R61ZRW3dxK4MVqyhWq3WhWtS6IKBYRQURBWWXfUlyfn/w446BBAETCOH9PI+P5OSc3PdeQng59ywSZfv375d43KFDB4SFhTXqOL/++qvM5xoaU1dUVIS0tDTMmzcP06ZNg76+PjQ0FNODM3LkSIwcOVLm83XP4XU9gn369MGZM2fkERppYTKXPlGD3BO7V5NJY5853C3o2l7BpuxEYuQzFzlHv23UDg7KdsubtD7GGB7sieW2DhOVV0G9gy46DekF9Y5arRwdaQlKkdTVjgFTV5cMR0NDo1GLvVZXV+PAgQOYNGnSaxOG77//HsuWLatXXlBQoJCtrzpq8JAPoIO6GooKpY/vaQ3l5eWoqKjgFiNWlLi4ONja2qKiogLFxcXtequtV/+YUFUikZj7X9p7q6io6LV15HEc1tMXGnYeqH4UV+eZmqxOocfW6Aggn7t9Wrdeg+8D14noUFoKVv4SZZd2gpUXvjbW2uMxjY7If1kkEWN1SQGeHFmBjp7TGh1/3Tp1y1933RrzHmjv5HUNROLa70/NXyrCympUp0lOdqvMK0G1BoO2o4nCP+8bUhurSFzzvpB1DerWa22Kiqfu+dcOE3pTSpHUmZiYAKgZ4/Tq7MLc3NxGjfE6ceIEXrx4IXVmZl3z58+XqJeVlQV3d3cYGhrK7aK+qvYWI4/HU8jrN1enTp1QWlqq8JgcHR3xww8/cMdRpmvQGlT9/PP5PIgB8Pmy3++NqSOP4xh/daFer1nN/4o9du3ztaTVa+jYxqMWAwAexB9CdXnha2OVFg8XQ0URKuN2ouv/v2Zj4q9bBwB3Po25bo363qj4z0FjyOMaZPJ4qOn2qPljRY1J74jWqGr9a14bK/+V34XSYpJWrzUpMh5FnJ9SJHX9+/eHuro6Ll++DFvbmtsmRUVFuHPnDqZNm/aa1sDu3bvh6ekJBweH19bV09Ort7SHIs0dbIvC8mpoiKta7JiNsXDhwhY5joODQ6O+L4TI26sTN1436YAQVaAmY5yylpFixnUS5aMUSZ2hoSHGjh2LVatWYdiwYTA2NsbixYshEAgkNmYfOXIkLC0tsXnzZq4sMzMTv/32G/btU87tfuYOrklSlaEbmRBl0JTFhony4Onow8hnbmuHQRrA0+RDp5MpSl65BatrYwbj/q2/IgRpGUqR1AHAjz/+iEmTJsHKyoqbTRkTEyNxOzYrKwtaWpKDPffu3Qs9PT2J7a8IIcqL1kZrm/gdZS9WTFrZ/89wFVeJ0P0TTyStPs4t4OsQPKRN7bVK3ozSJHV6eno4cuQIiouLUVZWBnNz83p1jh49Wm8yRUhICCZPnixzz1FCCCEto+5etExYTbNxFajuciWi8io82HcRPE0+ROUAX0udErp2Rum+2wKBQGpCBwAWFhb1tg0zMzOTWZ8QQuRJWtKiyhhjjT5fWXvRqvo1ak0du5rUKytJy4a4isaPtldKl9QRQogyamrSwtPRg0nA0jY9Dk308kWjz7e5e9GS5tOWMQGCicVSy4nqo6SOEEIaoalJC19Hr95OF20NE1ZKPG7ofJu7Fy1pPk0ZSZ2sWbBE9dF3XsHyToch++i3KP1jW2uH8sYOHTqEfv36QVtbG6ampggODkZ2dnZrhyVVeHg4unbtig4dOmDUqFEoLCyUWffJkyfw9fWFnp4ezM3NMX36dFRV/W8JmvT0dHh7e0NbWxsODg44depUC5wBUTaUtNSQdb60F23LM+5nDV0bySFJujZm4GnyWyki2cw8esDC2wlmHj1aO5RGq7flmkj5e0ApqVOw/DNhyI1ehrKL21s7lDcSHh6OKVOmYMGCBXj+/Dn+/PNP5ObmYsiQISgtLW3t8CRcv34ds2bNwq5du5CWloaysjLMnDlTZv1p06ZBR0cH6enpuHz5Mi5cuICNGzdyz48dOxa2trZ49uwZvv76awQGBiIzM7MlToUokZZOWmr3lFW227eyzpf2om15PHU+HIK9wNfRBID/zXZVwp17zD16oPNQZ5i3kaROLBTV23ItJTxW6RM7SurauLNnz2LAgAEQCAR46623EB8fD6Bm8WZra2tERUUBACoqKuDk5IQdO3YAAL7++muMHz8evr6+0NfXh5ubG+7cuSP1GNXV1fjyyy+xatUqTJgwAQYGBujRoweioqJQUlKCnTt3NinmwsJCLFq0CH5+fvj8889RXFz8BlegvgMHDsDf3x8+Pj4wNzdHaGgooqKiUFZWJrV+cnIyJk6cCENDQ9jZ2cHX1xfJyckAgJSUFFy7dg2rVq2CsbExPv74Y/Tu3RuHDh2Sa8xE+bV00mI8fG6zb98a+cwFT0f/9RVfQ01dcgmphs63di9avq4RANBetC1Ejc8DX6tmVQia7So/ebceS6z3B9RMQsmLT2uliBqHvvsK9OpMOVZZIvdZYPfu3cPo0aOxbNkyPH/+HHPmzIG/vz/Kysqgp6eHffv2Yfr06cjMzMS///1v2NraYurUqVz7n3/+GUFBQXjy5An++c9/IiAgAEKhsN5xEhMTkZOTgzFjxkiUa2lpYeTIkTh37hxXpquri99++01mzM+fP8fAgQNhZGSEBQsWID4+HrNmzZLD1fif5ORk9O3bl3vct29fVFVV4cGDB1Lrv//++4iIiEBhYSHS0tJw8uRJ+Pn5ca/VpUsXiVnXffv25ZI+0jpaoxerLSUtxsPngt/RoFltX/3cUtPqAF7Hxp9v7S4eAMDTFijltSGkMaryS6SWV8ooVxaU1ClI3ZlyrKxA7tP7d+/ejXHjxsHX1xcdO3bEuHHjYGVlhcuXLwMAPD09MWnSJPj4+ODgwYPYtWuXRHsfHx+MHj0a+vr6+Oabb1BcXIzr16/XO05ubi7U1dW5PXpfZWFh0aTdMhYvXoxx48Zh4cKF8PT0xKJFixAbGyu17rp166Cmpibzn6xxciUlJRJbwamrq6NDhw4yewSXLl2K+/fvw9DQELa2tnBzc0NAQIDU1wIAfX19ufcuqoqWSraa2otVOxOVp/NmWwS29aTldUuy1P3cEpcWgFXWDK9oi+dL2hZlGsMmaxKKsm+5RkmdgrTE9P709HTs2LED6urq4PP54PF4uHLlCjIyMrg6U6dORXJyMiZMmFBvPb9u3bpxX/P5fFhZWUkdK2ZiYgKhUIjc3Nx6z2VlZUm8bklJCUaMGCE1XqFQiF9++QWffPK/2zeMMejqSv8h+fzzz8EYk/nPwMBAajtdXV0UFRVJHLesrAwCgaBeXcYYfHx8MGzYMLx8+RIZGRl4+vQpPv/8c6mvBQAvX76U+lrkzW4ZKlLtTFRT/2+UcpxaS2jMkizSPrfqzoAlRBGUbQybrEkoyr7lGiV1CtISM+WsrKwwf/58CIVCiEQiiMViMMYQFBQEABCLxZgyZQomTJiA8PBw/P333xLt09PTua/FYjEyMjLQuXPnesdxcXGBiYlJvXFklZWVOHr0KIYPH96oeO/evYuSkhJYWVlxZefPn4e3t7fU+s3tqXNyckJCQgL3OCEhAZqamujevXu9urm5uUhISMCcOXOgp6cHS0tLTJo0CWfOnOFe69mzZ8jJyZF4PScnp0adM1Euypp0toTG/KEp63OLEEVTtjFsMiehKPmYReWOrg1riZlywcHB2LNnD06cOIGysjKkpKRg+vTpSE2t+WBev349SkpKEB4ejiVLlmDChAmorv7fX+VnzpzB0aNHUVRUhOXLl0NXVxdubm71jqOhoYGVK1di0aJF3NizlJQUBAYGwsTERKLnraExdUlJSRCJRLh58yaAmrF6e/fuxezZs6XWb25P3cSJE3Hs2DH8/vvvyM7O5masdujQgXvd2turJiYm6Ny5M3744QcUFxfj2bNn2L9/Pzcmz8HBAe7u7li0aBHy8/Oxf/9+3Llzp974QkJqKeus1cb8oSnrc4sQRVPGMWxtcRKK8kfYRrXETDknJyccOnQIoaGhMDc3h7+/P/r06QMbGxvcvn0bK1aswIEDB6ChoYF58+ZBX18f//nPf7j2H374IXbv3g0rKyvExMTgyJEj9fbWrTVlyhRs374d69atg7m5OXr06IHy8nKcP38eOjo6jYo3KSkJ48ePx0cffQR3d3f4+/vjwIEDsLe3l8v1qOXm5obNmzcjODgY3bp1g46ODjZv3iy1rpqaGqKjoxEXFwcLCwv06dMHhoaG2LBhA1cnMjISqampsLCwwPLlyxEVFSW1R5MQoOV7A6UlkdLGzjXmD01pn1t1Z8ASoghtdQybspH+G5y8sdqZcilzOkFUkg+1DoYKmSnn7e2Nq1ev1it3dnaWuD2ppqaG8+fPS9QxNTVFREREo481duxYjB07FgCwadMmrFy5EiKR5B6DJSWy/6pKTEzEN998g127diErKwvdunUDT0ErnwcHByM4OFjqc+vWrZN47ObmhkuXLsl8LWtr63rXjhBlUTd5lDV2ruu8E3h55b8St2Dr/qFZ93OLr2sENS1dCPOetMSpkHbMuJ818hMkb8G2hTFsyoZ66hTo1Zlyalq6KjVzbObMmbCzs2v0ciQikQh3797ldqSwsbFRWEJHSC1lvRWqSLLGzr28GtmoJVnqzfBVwoVsieqpHcP2qrYwhk3ZUE8daRYej4e4uLhG1y8pKcG8efMkJkkQomjtcUJEQ2PnahM2UUk+LVFClI4anweoqQGMAWpqlNA1AyV1CmbkMxeiskJUMOX68AwNDW3R4+nr62P+/PktekxC2iPag5WYefSAqLyKm7lJ2g9K6hSstqegKQv0EkJIcxkM+uS1Y+eIamsr+6sS+aO+TUIIUSFtaTszQoh8UVJHCCEqpq1vZ0YIaR5K6gghhBBCVAAldYQQQgghKoCSOkIIIYQQFUBJnYKFJV/Et7dOY9uja60dyhvx8/ODtbU1bGxs4OrqigULFqC4uLi1w6onIiIC1tbWsLa2xqBBg15b//Llyxg1ahR69+6NDz74AElJSRLPP3nyBIGBgXB0dERAQAC3ry4hhCgLM48esPB2ghnNem33aEkTBQu7ewnpJQWw0tHHYvf3WjucZsvMzMRnn32GDz/8kPu6rKwMW7dube3QJPj5+cHDwwPnz5+X2OdWmoyMDPz73//G/Pnz0b17d+zfvx/e3t5ISUmBkZERxGIxfH194ebmhsjISOzcuRPvvfcekpOTZe6RS4iqqV1rk9/BQCH1yZujJUxILfrN1MbduXMHS5cuRUpKCnr16oW1a9eia9euKCoqgre3N9atW4chQ4ZALBbDz88PI0eOxOTJk7F+/Xrk5uaiuLgYly5dgoODAzZu3NjgRvXGxsZcL1hwcDDCw8ObHG9paSm2b9+O9PR09O/fH598It+1swQCAQQCAczMzF5bt0uXLhJ7vq5ZswZ79uzBjRs34OPjg7i4OKSlpeH69evQ0tLCpk2bYGZmhrNnz2LEiBFyjZu0He0taWnqrhztcRcPQpQF3X5VoCqREMXVlQCAEmEVqsUiub7+06dPMWjQIHh5eeHgwYNwcXGBj48PhEIh9PT0sGjRInzyySd4+fIlVq1ahby8PEyaNAkAUFBQgDVr1sDGxgb79++Hnp4efH19wRh7/XlVVeHChQtwcHDgygoKCmBtbY1Hjx7JbJeZmQl3d3fk5eXBxcUFS5YswcaNG9/4OjRX3b1ns7KykJ+fDxubmg2kExIS0KdPH2hpaQEA1NXV4erqioSEhJYOlSgR4+FzYTbyW0peCCFKh3rqFKRKJMTwMzuQX1kGACioLofP6R04M3wqNHh8uRxj27Zt8Pf3x8yZMwEATk5OiIqKQlxcHDw9PTF69GgcP34cAQEBSExMxPXr18Hn/+/Y//jHP7BgwQIAwJYtW2BhYYH4+HgMGDBA6vG+/PJLhIaGIjc3F7169UJMTAz3nEgkQnp6Oqqrq2XGu2jRIkydOhVz5swBAFRUVODw4cPc41ctXboU+/btk/o6enp69ca+vSmRSISQkBBMmDAB3bt3BwAUFhbC0NBQop6hoSEKCwvlemxCCCGAWCiq2fcVABgDE4lp/9cmoqROQfY9vIHY55KD6mOfP8K+hzcQ4vCWXI7x4MEDnD17Fvb29mCMgTGG58+f4/Hjx/D09AQAfPvtt7C1tcWCBQtgZye592PPnj25r7W0tGBra4v09HSZSd3ChQsxZswYxMfHIyQkBCkpKTA3NwcAGBkZIS0tDV26dJHatrCwEBcuXEBkZCRXpq2tzfWC1TVnzhwEBQVJfa5uD9ubEovFCA4ORnl5ObZv386V6+jooKSkRKJuSUkJdHR05Hp8Qghp78RCER7siZUoSwmPhUPwkFZN7NraPrqU1ClIanG+1PJHRfLbA9bc3Bxjx47FwoULJcpNTEy4r7/44gt4e3tj7969mDdvnkTS9eLFC4l2L168kGhbl7GxMWxsbGBjY4OUlBTMnTsXN2/eBFCTaFlbW8tse/nyZZiamkIgEHBlcXFx8PDwkFp/48aNLdJTxxjD1KlT8fDhQ5w+fRra2trccz169MD69evBGIOamhoA4O+//8bHH38sl2MTQgipkXfrMUrSciTKStKykRefBhM3OxmtFK+tTUKhfk0FsRUYSS230zOW2zHGjh2Lo0ePori4GNbW1jAzM8OhQ4dQUFAAANi3bx9u3bqF48ePY/LkyQgKCpIYM3fy5EnEx8cDAPbs2YPq6mq4ubk16tgzZszAo0ePcObMGQCvH1N38+ZNZGdno6ioCACQlJSEEydOcGP86pozZw5iY2Ol/jtx4kSjYpRm+vTpErNiZ86cidu3b+PUqVPQ1dWVqOvt7Y3q6mpuQkhERATy8/NpkgRReUY+c2ESsBRGPnNbOxTSTlTll0gtr5RRTqRTqp46kUiEmzdvoqSkBP3794eBgUGj2hUUFCAhIQEWFhZwdHRUbJCN9Im9K/6bGi9xC3ZIJzt8Yu8qt2O88847WL16NXx8fCASiVBVVYVp06bB3Nwc6enpmDdvHk6cOIGOHTti+fLlcHd3x48//siNwRs2bBg++eQTPHv2DOrq6vjvf//b6FuLenp6+PTTTyWO39CYuvj4eHh6emLgwIHo2bMnrl+/jn379sHS0lJqfSMjIxgZSU+MG3Ljxg188MEHKC8vR15eHqytrdGrVy+cPHkSAJCdnQ1NzZpu9KSkJGzZsgVmZmbo06cP9xrr1q3DBx98gI4dOyIyMhITJ07E/PnzoaGhgYiICOjr6zc5LkLaEpoEQlqappGu1HItGeVEOqVJ6p48eYLhw4ejtLQUpqamSElJwc6dOzF27NgG233zzTdYv349+vXrh4qKCnTt2hUHDx7kfnG3Fk2+Os4M/xSdDi5DfmUZDDV05DpJotbEiRMxceJE5OXlwdDQkBtvZmhoiDt37nBLlGhqauLixYsSY8Ts7OwQExODgoICGBgYcLcYpYmJiZG4dQoAy5Yt427hvm5MXXx8PHbv3g1bW1s8ffoU/fr1q/d68uDs7IzY2FiJslffC1u3buUmizg6OiItLa3ea7x6C3rYsGF49uwZsrOzYWpqSuvTkTavvS3JQtoG437WyE+QvAWra2MG4/42rRhV26M0v6EmT56MTp064cyZM9DQ0EBYWBiCgoIwePBgmWunff/999iwYQMuXrzIDe4/ceIEKisrWz2pAwANHh8CDS3kV5ZBV11T7gndq4yNJW/r6unpQU9P77VlAOrN8JTGwsKiXpm2tja6desGoOExdXl5ecjMzETPnj3RpUsX2NravvZ4zaWlpdXg2D5TU1Pua01NzQbr1uLz+VLPn5C2iHrhiDLiqfPhEOyFxJXR3MSE1p4k0RYpxdXKzMzE2bNnMW/ePGhoaAAAPvvsM2hqauLQoUNS2wiFQqxatQqzZs2SmK3p6+urkB4g0nz5+flYsGCBxCQEQggh5FVqfB74WjV9TXwtdUromkEpeupqZzK6uLhwZVpaWnB0dERiYqLUNnfu3EFubi7ee+89pKamIi0tDba2ttzCsbIUFRVxg/WBmgVnFWlur3+gsKocGtVihR6nqT7//HOIxS0TU/fu3fHdd98hL09+M38JIYQQIkkpkrraxVzrDow3NjaWudBr7ViuPXv24Pz587CxscHNmzcxdOhQREZGylz/7Pvvv8eyZcvqlRcUFChk/bGJnZwA1CSTypjUtGRMrybT7VF7P3+ArgHQctdAy2MKNMtfQk1HX6k+e+g9QNcAkH0NRP/f2SASi5XqfStvdc+/7hCq5lKKpK52/FtZWZnErdO6j6W1ef78OR48eAANDQ1kZGTAxcUF69evx1dffSW13fz58xESEsI9zsrKgru7OwwNDeV2UWVR9Ou3Be39GrT38wfoGgAtcw2MRy1W+DGai94DdA0A6dcgk8eDCACfx1P5a6SI81OKpK72lunTp0+5HQoAICMjA2+//bbUNrWD7T/88ENuHJ6VlRUGDx6Mq1evyjyWrMkChBBCCCFtmVKMQnRxcYGFhQWOHDnCld26dQupqakSC73GxcXh1q1bAIBu3brByclJYrFbxhjS0tJkzpYlhBBCCFFVStFTx+PxsHbtWkyaNAmampro0qULvvvuO7z//vsYMmQIV2/BggWwtrbGwYMHAdSMj/vggw+grq4OR0dHREdH4/Hjxzh8+HArnQkhhBBCSOtQip46ABg/fjx+++03PH78GKdPn8bs2bPrJWeDBg1C//79ucc+Pj44f/48srKycPjwYdjY2ODu3bvo3r17S4dPCCGEENKqlKKnrta7776Ld999V+bz69atq1fm6uoKV1f5bb0lby/i7kNUXoUKcTWMfdruoM+CggKEhYXhxo0bMDQ0xJgxY+Dn59faYdWTlpaG0NBQpKenY+DAgfjqq69kzmoWi8XYsWMHTp06BcYYfHx88Nlnn4HP56O8vBzDhg2r12bMmDGYPXu2ok+DEEIIaTKlSupUUXbcfVQVloGvpw349H99AyU1YsQIGBkZYcaMGSgrK8PevXuRlZWFTz/9tLVD45SVlWHIkCEYPnw4Zs2ahe+++w5paWn46aefpNZfv349Nm/ejHXr1oHP52PhwoUoKCjAkiVLoKWlhe+++46rW11djX/+85/48ssvW+p0CCGEkCahpK6Ny8zMxOrVq5GSkoJevXphyZIlMDAwQFFREcaMGYNVq1ahX79+AIBp06bhnXfewccff4ydO3eipKQElZWVuHTpEhwcHLBs2TKpM4MzMjJw7do15ObmclOwx4wZI7GPbGP9+eefuHLlCjp37ozAwEC5bud2+PBhaGhoYMeOHQAAJycnODg4YO3atVK3+Tp9+jRmzZqFMWPGAKhZ+zAyMhJLliwBj8fDoEGDuLpHjx6Fvr6+xMQdQgghRJkozZg6VSQWiiCqFNZ8XSUEE8l3B4e8vDz0798fOjo6mDt3LioqKuDj4wPGGPT09DBy5EhMmDABFRUV2L59Oy5dusQlMOnp6Vi0aBFevHiB6dOn4+HDhwgICJB6HENDQ+jo6HC3KWvp6upyXwcFBUnMXpbmiy++wFdffYXq6mqsWbMGkydPfvOL8IobN25IJGL29vawsLDAjRs3pNZ3cnKS2LHk1q1bcHZ2llp37969mDBhAtTV6e8gQgghyol+QymIWCjCgz2xEJVXAQBYhRAp4bFy3aB469atGDRoEHebcPjw4ejZsyeuXr2KgQMH4tNPP0VMTAw+/vhjnD17FufOnZPYf9XFxQUbNmwAAAwePBhdunTB7du36yU2urq6OHjwIObOnYu5c+firbfewujRozFp0iTweDXncuvWLYk9eOuKiIjA3bt3ERcXBx6Ph/fffx+urq4IDw/n1hmsdenSJSxatEjma+3evRs9evSoV56fny+xziFQs7hjfn6+1Nf57rvvEBAQAHt7e/B4PFhaWuLEiRP16uXk5ODUqVNYsWKFzJgIIYSQ1kZJnYLk3XqMkrQcibKStGzkxafBxM1OLsdITk7GzZs3MWTIEDDGwBjDixcv8PDhQwwcOBAAsGnTJtjZ2WHu3Lncbdharz4WCATo3r07Hj58KLW3ys/PD35+fnj48CEuX76MZcuW4fr169i6dSuAmp6sugnVq3bu3Ilp06ZxSaCRkRGEQqHUni8nJyeJ8Wx1denSRWq5pqYmysvLJcrKy8tl3uJds2YNsrOzsXbtWvB4PISGhmLFihX1kreIiAg4Ozujd+/eMmMihBBCWhsldQpSlS99vFmljPLmMDAwgKenp8S2ZwAklnRZvXo1+vTpg0OHDmHJkiUS++vW3Ve3sLAQ+vr6DR7T3t4e9vb2EAgECAkJ4ZK6vn37ymxTVVWFK1euYO3atVzZ9evXMWDAAKipqdWrn5yc3KyeOjs7O1y8eJF7XF5ejmfPnsHOTnoSvXHjRkRFRXEzro2NjTF8+PB6Sd2+ffsQHBwsMx5CCCFEGVBSpyCaRrpSy7VklDeHv78/ZsyYgVWrVnETAf773/9yScyvv/6K48eP4/bt2/jyyy8xffp0buFmADh58iQeP34Ma2trnDx5Enl5eVJvoWZmZuLIkSOYOnUqNDU1wRjDxYsXJXbuCAoKwvvvv49Ro0bVa3/79m1UVVUhOzsbQM1M0tWrV+Ozzz6Tel7N7anz8/PD8uXLkZycDCcnJ+zevRsmJibcOR07dgy//PIL9u/fDwAwNzfHX3/9xSV1165dQ6dOnSReMykpCcnJyRg3bpzMeAghhBBlQEmdghj3s0Z+guQtWF0bMxj3t5HbMUaMGIEpU6agZ8+esLW1xdOnT+Hj44OAgADk5uYiJCQEu3btgomJCcLCwuDi4oLIyEh89NFHAIABAwbAy8sLAoEAqamp2LVrl9SeOkNDQzx48ACdOnVCt27dkJOTA3V1dURERHB1GhpTFx8fj379+mHBggU4d+4cLly4gLfeegtBQUFS6xsZGUlMeGgsZ2dnfP3113B3d0e3bt3w/PlzHD58GHw+HwCQlZWF+Ph4rv6mTZswfvx47N+/HzweDzk5OThw4IDEa+7duxe+vr4qv7E0IYSQtk+NvTqdsR16+vQprKyskJGRAUtLS7m+NhOJkbgyGqLyKqhpq6Pf4lFymyTxqtLSUjx8+BCWlpZc8pGVlYXMzEyJRCsjIwMFBQXo06cPvv76a5SUlGDNmjW4f/8+unbt+tpbr+Xl5bh//z50dXVhY2PDJUsAkJCQAHNzc6lLh0ybNg1dunTBe++9h7t378LJyanBSRVv6vnz58jIyEDPnj0lZug+f/4cWVlZEmMJKysr8eDBAwA1t5ZfnUgCAImJiTA1NZXLfsJ5eXntPjmka0DXoL2fP0DXAJB9DWoX7OfraMLco/4wG1WhqPcA9dQpkBqfB76WOkTlVeBpqiskoQOAjh07wsXFRaLMwsKiXoJlZWUFKysriTJNTU2Zy3jUpaOjI3PsXENj6uLj4+Hr6wsbG5sW2f2jU6dO9W6jyirX0tJqcAJE3etKCCFEcVQ5kWsJtE4dUThzc3Ol3sqNEEIIUQXUU6dgZh49uL1flcnUqVMhEola5FgxMTEAarqbCSGEEKIYlNQpWG1XsrIlNF27dm3tEAghhBAiR3T7lRBCCCFEBVBSRwghhBCiAiipI4QQQghRAZTUEUIIIYSoAErqCCGEEEJUACV1hBBCCCEqgJI6QgghhBAVQEkdIYQQQogKoKSOEEIIIUQFtPsdJYRCIQAgKytLoccpKChAeXm5Qo+h7Nr7NWjv5w/QNQDoGrT38wfoGgB0DaSdf6dOnaCu/mZpWbtP6nJycgAA7u7urRwJIYQQQtqrjIwMWFpavtFrqDHGmJziaZMqKipw+/ZtmJqavnGGLEtWVhbc3d1x7do1WFhYKOQYyq69X4P2fv4AXQOArkF7P3+ArgFA10DW+VNPnRxoa2vDzc2tRY5lYWHxxll4W9fer0F7P3+ArgFA16C9nz9A1wCga6CI86eJEoQQQgghKoCSOkIIIYQQFUBJXQvQ09PD0qVLoaen19qhtJr2fg3a+/kDdA0Augbt/fwBugYAXQNFnn+7nyhBCCGEEKIKqKeOEEIIIUQFUFJHCCGEEKICKKkjhBBCCFEBlNQRQgghhKiAdr/4sDwUFxdj8+bNSEpKgpmZGaZMmQJnZ2e5t1Fm8fHxCA8PR25uLgYMGIAZM2agQ4cOMuuXlpbiwIED+Ouvv6CtrQ0vLy8EBgZCTU2tBaOWH7FYjL179+LcuXPQ1tbGmDFjMHz48Ea3nTFjBrKysrB7924YGxsrOFrFePbsGX744QekpqbC1tYWM2fORJcuXV7bLiYmBjExMRCJRAgMDMSIESNaIFrF+P333/Hzzz+jvLwcXl5eCAoKAp/Pl1m/srISBw4cwNWrV1FZWYmePXsiJCQEZmZmLRi1/JSXl+Pnn3/GiRMnMGjQIMydO7dRbbZs2YIbN27AyMgIQUFBcHV1VXywCpKamoodO3bg3r17CA0NRe/evRusX1FRgYiICFy+fBnq6uoYNGgQxo0b1+D7RpmJxWKcPn0aEREREAgE2Lp1a5Paz58/H6mpqdi0aRO6du2qoCgVq6CgAHv37sXFixcxbtw4BAYGNqrd2bNn8csvv6C8vBx+fn4YNWpUk49NPXVvqLKyEoMHD0Z0dDSGDRuGiooKbvsPebZRZrGxsXj77bfB4/EwdOhQ/PTTTxg6dChEIpHU+kVFRejduzeSkpLg6ekJBwcHzJ49GxMnTmzhyOUnJCQES5YswcCBA2FtbQ0/Pz/s2LGjUW2XL1+O06dP49ixY212g+vMzEy4urrizp07GDFiBO7cuQNXV1dkZmY22C4oKAghISFwcHDAu+++iy1btiAqKqqFopav3bt3w9fXF1ZWVnjnnXfw7bffIigoqME2I0eOxMqVKzFgwAD4+Pjg9OnTcHV1RV5eXgtFLT+pqamwt7fHhQsXkJKSghs3bry2jUgkgo+PD/bt2wdvb2+oq6tj4MCBOHfuXAtELH+rV6+Gj48PhEIhjh07htzc3Abri8Vi9OrVC1evXoWHhwecnZ2xePFiBAQEoK0uTOHk5ISNGzeioKAAv//+e5Pabty4EdHR0Th27BiKiooUFKFinT9/Hr1798bjx48RFxeH+/fvN6rdwoULERgYCCsrK4wYMQJRUVFNTogBAIy8kW3btrEOHTqw/Px8rszX15d5eXnJtY0yc3V1ZRMmTOAeP336lPH5fBYRESG1flVVFcvNzZUoO3XqFAPAUlJSFBqrIiQkJDAALDY2lisLDQ1lhoaGrKKiosG2Fy9eZDY2NiwqKooBYBkZGYoOVyFmzpzJHB0dWXV1NWOMserqata9e3c2a9YsmW0iIyOZuro6S0xMlCh/9eeiraisrGRGRkYsNDSUK4uNjWUA2M2bN6W2ycnJYQBYdHQ0V1ZQUMAAsKioKIXHLG9FRUXc92748OFs/Pjxr21z8OBBxufz2dOnT7myCRMmsH79+iksTkV68uQJE4lELCMjgwFgFy5caLC+WCxmWVlZEmVXrlxhANi1a9cUGKnipKenM8YYW7p0KbOzs2t0u/j4eGZpacliYmIYAHb79m1FhahQ2dnZrKysjDHGWLdu3djy5ctf2+b333+v9zuEseZ9FlJP3Rs6deoUvLy8YGhoyJUFBgbijz/+QFlZmdzaKKvc3FzcuHEDo0eP5sq6dOmCd955B6dOnZLaRkNDo94txtrbdC9fvlRcsApy6tQpmJiYYPDgwVxZYGAgCgoKcPXqVZnt8vPzMWHCBISHh8PAwKAFIlWcU6dOwd/fn9uMWl1dHQEBATLfAwAQHh6OoUOHok+fPhLlr/5ctBXXrl1Dfn6+xM/B4MGDYWZmJvMa6Ovrw9zcHCkpKVzZ/fv3wePx4ODgoPCY5U0gEDT5e3fq1Cm8/fbbErfpAwMDcevWLbx48ULeISqclZUVeLzG/1pVU1NDp06dJMra8mchgGbdMi0pKcHYsWPx448/1rsebY2pqSl0dHSa1CY8PByurq7w9PSUKG/OZyEldW8oLS0NVlZWEmVWVlYQi8V48uSJ3Nooq7S0NACQej61zzVGWFgYOnfuXO8XfFuQlpYGS0tLifGAtR9sDV2DoKAgfPTRRxgyZIiiQ1S4x48fN/k9kJiYCHd3d+zduxfjxo3DrFmzcPbsWUWHqhDSfg7U1NRgaWkp8xpoaGhwY/AGDhyIoUOHYsyYMTh69Gib/DloDlmfhbXPtUdhYWEwNDSEu7t7a4fSYqZPnw4vLy/4+fm1diitIjExEW+99RaioqIwfvx4TJ8+HcePH2/Wa1FS94YqKyvrTQjQ1dUFUDMAVl5tlFVlZSUASD2fxp7Ltm3bsH//fuzduxeamppyj1HRpH0/tbW1wefzZV6DTZs24enTp1i+fHlLhKhQQqEQIpFI6ntAJBJBKBRKbVdaWopdu3YhJiYG//rXv2BkZAQ/Pz9s2LChJcKWq9qfg7p/ob/u52Dbtm1cj+3EiRNhY2ODNWvWoLi4WKHxKgtV+iyUh8jISISFhWHnzp3tZgutAwcO4Nq1a/j+++9bO5RWU1paiqNHj2L37t0YMWIErKysMHHiRCxevLjJr0WzX9+Qvr4+CgoKJMpqBznL6jptThtlpa+vDwBSz6cx57Jv3z7Mnj0bP/30E4YNG6aQGBVN2vezsLAQIpFI5jUICwuDkZERNysqJycHQM2Ei8DAQEyePFmxQcuRuro6OnToIPU9oKury92SrcvAwAB6enqIioriblmpqakhNDQU8+bNU3jc8lT7c1BYWAgjIyOuPC8vT2av29WrV7FlyxZcu3YNbm5uAIAxY8bAysoKW7duxRdffKH4wFuZKn0WvqkjR45g0qRJ2L59u8RtfFW3YcMGaGlpYdy4cQBqfoYAYM6cOfD398fs2bNbMbqWYWBggJycHBw/fpzr2BAIBJg3bx6WLl3apM4OSurekIuLC27evClRlpCQAAMDg3q3Fd6kjbJycHCAjo4OEhMT8c4773DliYmJr+1KP3DgAKZMmYL9+/fjww8/VHSoCuPi4oLt27ejuLgYAoEAQM33E4DMX+jbtm2TGD+ZlJSEy5cvIzAwsE3ednFxcUFiYqJEWUJCQoO3Efv27QsAEmOQ7O3tUVBQgIqKCmhrayskVkVwcXEBUPO+9/LyAlAzTujRo0eYM2eO1DbPnj0DAHTv3p0r09HRgaWlJZ4+fargiJWDi4sLfvnlF4myhIQEaGlptclxhc0VHR2Njz76CJs3b0ZISEhrh9Oi1q9fLzF+8NGjR/jjjz/g7++PQYMGtWJkLadv3764d++eRPJmb28PoVCIvLw8WFhYNP7Fmjy1gki4ePEiA8DOnTvHGKuZrWJnZ8dmzJjB1YmPj2f+/v7cDK/GtGlLPv74Y+bs7MxKSkoYY4wdOXKEqampsfj4eK7O6tWr2aJFi7jHP/30E9PU1GSRkZEtHq+85ebmMoFAwFasWMEYY0wkEjFfX182YMAArk5JSQnz9/fnvud11c5+aquzX7ds2cL09PTYgwcPGGOM3b9/n+nq6rKtW7dydaKjo9moUaO4x4cPH2YGBgbsyZMnjDHGhEIh8/PzY25ubi0bvJy4u7szX19fJhKJGGOMrVy5kunq6rLs7GyuzuzZs9kPP/zAGGMsNTWV8fl87jFjNZ8VGhoabP/+/S0bvJzJmv16//595u/vz+7fv88Yq5k5rqamxn755RfGWM3PibOzc6Nmziqzhma//vDDD2z27Nnc42PHjjEtLS22ffv2FoxQ8Rqa/erv78+OHz8u9bnr16+36dmvr5I1+/XcuXPM39+f+50ZGxvLtLW12d27dxljNbOiJ02axGxsbJp8TErq5OA///kP09HRYYMGDWJmZmbMw8ODFRYWcs/XLtfx999/N7pNW5Kbm8tcXV2ZhYUF8/DwYDo6OmzdunUSdUaPHs08PDwYY/9b8qRz587M399f4t/ly5db4xTeWHR0NBMIBMzV1ZXZ29uzrl27suTkZO752qUq9uzZI7V9W0/qhEIhGz9+PBMIBGzw4MFMIBCw8ePHM6FQyNVZu3Yt4/P5Eu0WLFjA9PX12dChQ5mtrS3r0aOHxHVrS+7du8esra2Zvb09c3NzY7q6ulyyUsvJyYlNnjyZe7x161bWsWNH5urqyry8vJi2tjYLDg5mYrG4pcOXi1GjRjF/f39mZmbGLC0tmb+/v8T51i7XceXKFa4sLCyM6ejoMA8PD9a5c2fWv39/iUS4Lan9ZT18+HAGgHl4eDB/f3+JP14nT57MnJycGGOMvXz5kmlqajJTU9N6n4W///57a53GG1m0aBHz9/dnPXr0YB07duTO59VlrACwDRs2SG3f1pO6rKws7px1dHRYz549mb+/P/vmm2+4Onv27GEAWEFBAVe2YsUKJhAI2LvvvsscHR2ZtbU1++uvv5p8fDXG2ugKh0omIyMDd+7cgZmZGfr37y8xEzI7OxuXL1/G0KFDuUHAr2vT1ojFYty4cQN5eXlwcXFB586dJZ6/fv06Kioq8I9//AOlpaUyF6V0d3ev17atKCwsxPXr16GlpYW3335boiu9uroav/76K/r37y91yn9OTg7i4uIwYsSINnXbsa579+5xO0o4OjpKPPfw4UPcvXu33m359PR0/P333zA3N4ezs7PMMXhtQVVVFf766y+Ul5fD3d293lI158+fh5GREXfrGah53yQnJ6OiogI9evSApaVlywYtR8ePH4dYLJYo09HR4XZXKSgowB9//AFPT0+JMXNZWVlITEyEkZERXF1dm7QsiDJ58uQJ4uPj65U7OjpyPw8JCQnIz8+Ht7c3qqqqcPLkSamv1a9fP3Tr1k2h8SrCpUuXpC6e/epnW3R0NJydnWFnZ1evXmFhIWJjY/Huu+9yw1naElm/38zMzLghSrXvk3/961/Q0NDg6mRmZiIpKQkmJiZwdnaGlpZWk49PSR0hhBBCiApom38OEUIIIYQQCZTUEUIIIYSoAErqCCGEEEJUACV1hBBCCCEqgJI6QgghhBAVQEkdIYQQQogKoKSOEEIIIUQFUFJHCCGEEKICKKkjhBBCCFEBlNQRQgghhKgASuoIIaSJXr58iejoaDx79kyi/M6dOzh27BiqqqpaKTJCSHtGSR0hhDSRQCDAt99+i6lTp3JlV65cwdtvv420tDRoamq2YnSEkPZKjTHGWjsIQghpay5cuABvb2+cP38eBgYG8PLywhdffIGvvvqqtUMjhLRTlNQRQkgzBQQE4OHDh3jx4gU+/fRThIaGtnZIhJB2jG6/EkJIM40bNw7Jycnw9PSkhI4Q0uqop44QQpohISEBXl5esLS0RG5uLh48eABdXd3WDosQ0o5RTx0hhDTR33//DR8fH0yePBl//vknhEIhVq5c2dphEULaOeqpI4SQJnj06BEGDx6M999/H9u2bQMAbNy4EV9++SXu3buHbt26tXKEhJD2ipI6QghpJJFIhAULFkBTUxOrV6+GmpoaAKC6uhpBQUEYMmQIQkJCWjlKQkh7RUkdIYQQQogKoDF1hBBCCCEqgJI6QgghhBAVQEkdIYQQQogKoKSOEEIIIUQFUFJHCCGEEKICKKkjhBBCCFEBlNQRQgghhKgASuoIIYQQQlQAJXWEEEIIISqAkjpCCCGEEBVASR0hhBBCiAqgpI4QQgghRAVQUkcIIYQQogIoqSOEEEIIUQH/B0f+REmg4u59AAAAAElFTkSuQmCC", "text/plain": [ "<Figure size 704x440 with 1 Axes>" ] @@ -644,36 +483,39 @@ ], "source": [ "fig, ax = plt.subplots()\n", + "ax.plot(x_fine, truth(x_fine), \"--\", color=\"k\", label=\"truth\")\n", + "for (label, d), colour in zip(datasets.items(), plotstyle.COLOURS):\n", + " ax.errorbar(\n", + " d.x,\n", + " d.y,\n", + " d.y_err,\n", + " fmt=\"o\",\n", + " ms=3,\n", + " color=colour,\n", + " label=rf\"{label}: $\\rho$ = {settings[label]['rho']:.2f}\",\n", + " )\n", "for name in correct:\n", " colour, hatch = style[name]\n", - " lo, hi = np.percentile(\n", - " curves(problems[name], samples[name], x_fine), [5, 95], axis=0\n", - " )\n", + " lo, hi = bands[name]\n", " plotstyle.band(ax, x_fine, lo, hi, color=colour, hatch=hatch, label=name)\n", - "ax.plot(x_fine, truth(x_fine), \"--\", color=\"k\", label=\"truth\")\n", - "for d, colour in zip(datasets.values(), plotstyle.COLOURS):\n", - " ax.plot(d.x, d.y, \".\", ms=2, color=\"0.5\")\n", - "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"90 % bands: the defensible three\")\n", + "ax.set(\n", + " xlabel=\"$x$\",\n", + " ylabel=r\"$y$\",\n", + " title=\"90 % bands: the good three\",\n", + ")\n", "ax.legend(fontsize=8)\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "12a5ada8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:00.953842Z", - "iopub.status.busy": "2026-09-14T19:54:00.953574Z", - "iopub.status.idle": "2026-09-14T19:54:01.178416Z", - "shell.execute_reply": "2026-09-14T19:54:01.177281Z" - } - }, + "execution_count": 14, + "id": "816316c6", + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 704x440 with 1 Axes>" ] @@ -686,18 +528,14 @@ "fig, ax = plt.subplots()\n", "for name in wrong:\n", " colour, hatch = style[name]\n", - " lo, hi = np.percentile(\n", - " curves(problems[name], samples[name], x_fine), [5, 95], axis=0\n", - " )\n", + " lo, hi = bands[name]\n", " plotstyle.band(ax, x_fine, lo, hi, color=colour, hatch=hatch, label=name)\n", - "ax.plot(x_fine, truth(x_fine), \"--\", color=\"k\", label=\"truth\")\n", - "for d, colour in zip(datasets.values(), plotstyle.COLOURS):\n", - " ax.plot(d.x, d.y, \".\", ms=2, color=\"0.5\")\n", + "ax.axhline(0.0, ls=\"--\", color=\"k\", label=\"truth\")\n", + "ax.axvspan(1.0, 1.42, color=\"0.93\", zorder=0)\n", "ax.set(\n", " xlabel=\"$x$\",\n", - " ylabel=\"$y$\",\n", - " ylim=(-1.2, 1.2),\n", - " title=\"90 % bands: the wrong three\",\n", + " ylabel=r\"$y - y_\\mathrm{truth}$\",\n", + " title=\"90 % bands: the bad three\",\n", ")\n", "ax.legend(fontsize=8)\n", "plt.show()" @@ -705,22 +543,15 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "dbd240fa", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:01.180750Z", - "iopub.status.busy": "2026-09-14T19:54:01.180501Z", - "iopub.status.idle": "2026-09-14T19:54:01.816659Z", - "shell.execute_reply": "2026-09-14T19:54:01.815539Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 836x836 with 9 Axes>" + "<Figure size 1067x1067 with 16 Axes>" ] }, "metadata": {}, @@ -728,6 +559,15 @@ } ], "source": [ + "# zoom to the two cases that recover the curve: the quoted-systematics\n", + "# posterior is so wide that its range would flatten the others to points\n", + "tight = np.vstack(\n", + " [\n", + " samples[name][:, problems[name].columns(cubic.params)]\n", + " for name in (\"inferred normalisations\", \"inferred systematics\")\n", + " ]\n", + ")\n", + "span = list(zip(*np.percentile(tight, [0.5, 99.5], axis=0)))\n", "fig = None\n", "for name in correct:\n", " colour, _ = style[name]\n", @@ -736,8 +576,8 @@ " samples[name][:, p.columns(cubic.params)],\n", " fig=fig,\n", " labels=[f\"${c.latex}$\" for c in cubic.params],\n", - " truths=a_true[1:],\n", - " range=[(0.9, 1.1), (-0.6, -0.1), (-0.75, -0.4)],\n", + " truths=a_true,\n", + " range=span,\n", " **plotstyle.corner_kwargs(\n", " color=colour,\n", " fill_contours=False,\n", @@ -766,32 +606,26 @@ "directly. Case 3 infers the *width* $\\eta_i$ of the mode, so it does not say\n", "which way a dataset moved, only how far it is prepared to let it move.\n", "\n", - "Watch `exp 3`, which quoted 3 % and needed 15 %, and `exp 4`, which quoted 25 %\n", - "and needed none of it." + "Watch `exp R`, which quoted 5 % and is off by 20 %, four times its own\n", + "quote, and `exp Q`, which quoted 25 % and is off by 20 % — comfortably\n", + "inside what it allowed." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "a640c45a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:01.820706Z", - "iopub.status.busy": "2026-09-14T19:54:01.820341Z", - "iopub.status.idle": "2026-09-14T19:54:01.832391Z", - "shell.execute_reply": "2026-09-14T19:54:01.831151Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dataset quoted true rho inferred rho inferred eta\n", - "exp 1 8% 0.94 0.93 +/- 0.01 9% +/- 6%\n", - "exp 2 12% 1.10 1.09 +/- 0.01 14% +/- 9%\n", - "exp 3 3% 1.15 1.11 +/- 0.02 10% +/- 4%\n", - "exp 4 25% 1.02 1.01 +/- 0.01 21% +/- 16%\n" + "exp P 12% 1.10 0.98 +/- 0.05 13% +/- 8%\n", + "exp Q 25% 0.80 0.71 +/- 0.03 32% +/- 21%\n", + "exp R 5% 1.20 1.08 +/- 0.04 11% +/- 8%\n", + "exp S 10% 0.87 0.80 +/- 0.04 17% +/- 10%\n" ] } ], @@ -800,7 +634,7 @@ "p_eta, s_eta = correct[\"inferred systematics\"], samples[\"inferred systematics\"]\n", "header = f\"{'dataset':8s} {'quoted':>7s} {'true rho':>9s} {'inferred rho':>16s} {'inferred eta':>16s}\"\n", "print(header)\n", - "for label in free:\n", + "for label in comps:\n", " rho_draws = np.exp(s_rho[:, p_rho.columns(rhos[label])])\n", " eta_draws = np.exp(s_eta[:, p_eta.columns(etas[label])])\n", " print(\n", @@ -812,20 +646,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "a9e36535", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:01.835253Z", - "iopub.status.busy": "2026-09-14T19:54:01.834902Z", - "iopub.status.idle": "2026-09-14T19:54:02.019993Z", - "shell.execute_reply": "2026-09-14T19:54:02.019207Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "<Figure size 704x440 with 1 Axes>" ] @@ -836,13 +663,13 @@ ], "source": [ "fig, ax = plt.subplots()\n", - "positions = np.arange(len(free))\n", - "rho_mean = [np.exp(s_rho[:, p_rho.columns(rhos[label])]).mean() for label in free]\n", - "rho_std = [np.exp(s_rho[:, p_rho.columns(rhos[label])]).std() for label in free]\n", + "positions = np.arange(len(comps))\n", + "rho_mean = [np.exp(s_rho[:, p_rho.columns(rhos[label])]).mean() for label in comps]\n", + "rho_std = [np.exp(s_rho[:, p_rho.columns(rhos[label])]).std() for label in comps]\n", "ax.errorbar(\n", " positions,\n", - " np.ones(len(free)),\n", - " [settings[label][\"sys\"] for label in free],\n", + " np.ones(len(comps)),\n", + " [settings[label][\"sys\"] for label in comps],\n", " fmt=\"none\",\n", " ecolor=\"0.6\",\n", " capsize=8,\n", @@ -858,7 +685,7 @@ ")\n", "ax.plot(\n", " positions,\n", - " [settings[label][\"rho\"] for label in free],\n", + " [settings[label][\"rho\"] for label in comps],\n", " \"x\",\n", " ms=11,\n", " color=\"k\",\n", @@ -866,7 +693,7 @@ ")\n", "ax.set(\n", " xticks=positions,\n", - " xticklabels=free,\n", + " xticklabels=list(comps),\n", " ylabel=r\"$\\rho$\",\n", " title=\"The normalisations, recovered\",\n", ")\n", @@ -874,86 +701,6 @@ "plt.show()" ] }, - { - "cell_type": "markdown", - "id": "8307df97", - "metadata": {}, - "source": [ - "## Where the Peelle mode actually bites\n", - "\n", - "In the comparison above, building the mode from the data barely moved the\n", - "answer. That is worth understanding rather than glossing over: with an\n", - "absolutely normalised experiment holding the scale and four others overlapping,\n", - "there is very little room for the covariance to pull the fit down.\n", - "\n", - "Peelle's Pertinent Puzzle shows itself in the case it was discovered in —\n", - "fitting a *constant* to data that share one normalisation. Two points, 1.5 and\n", - "1.0, each with a 10 % statistical error and a common 20 % normalisation\n", - "uncertainty. Build the mode from the prediction and the fit lands between the\n", - "points; build it from the data and it lands **below both of them**, at exactly\n", - "the value [D'Agostini\n", - "(1994)](https://doi.org/10.1016/0168-9002(94)90719-6) derived:\n", - "\n", - "$$t = \\frac{\\bar y}{1 + (s/\\sigma)^2 \\sum_i (y_i - \\bar y)^2}.$$" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "b3cf1ec6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:02.022945Z", - "iopub.status.busy": "2026-09-14T19:54:02.022709Z", - "iopub.status.idle": "2026-09-14T19:54:02.043097Z", - "shell.execute_reply": "2026-09-14T19:54:02.042558Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mode from the prediction MAP t = 1.2035\n", - "mode from the data (PPP) MAP t = 0.8333\n", - "no normalisation at all MAP t = 1.2500\n", - "\n", - "the data are [1.5 1. ], mean 1.250\n", - "D'Agostini's closed form for the data-built mode: 0.8333\n" - ] - } - ], - "source": [ - "y_pair = np.array([1.5, 1.0])\n", - "sigma_pair, s_pair = 0.1, 0.2\n", - "d_pair = rx.Dataset(\n", - " np.array([0.0, 1.0]), y_pair, np.full(2, sigma_pair), norm_err=s_pair, label=\"pair\"\n", - ")\n", - "t = rx.Parameter(\"t\", prior=stats.uniform(0.0, 5.0), latex=\"t\")\n", - "const = rx.Model(lambda x, t: np.full(np.size(x), t), [t])\n", - "comp_pair = rx.Comparison(d_pair, const)\n", - "\n", - "panel = {\n", - " \"mode from the prediction\": rx.Constraint(\n", - " [comp_pair], terms=[T.normalization(magnitude=s_pair)]\n", - " ),\n", - " \"mode from the data (PPP)\": rx.Constraint(\n", - " [comp_pair], terms=[rx.Term(s_pair * d_pair.y, kind=\"mode\", on=comp_pair)]\n", - " ),\n", - " \"no normalisation at all\": rx.Constraint([comp_pair]),\n", - "}\n", - "for name, c in panel.items():\n", - " p = rx.Problem([c])\n", - " best = minimize_scalar(\n", - " lambda v: -p.log_posterior(np.array([v])), bounds=(0.1, 4.0), method=\"bounded\"\n", - " )\n", - " print(f\"{name:26s} MAP t = {best.x:.4f}\")\n", - "y_bar = y_pair.mean()\n", - "closed = y_bar / (1 + (s_pair / sigma_pair) ** 2 * np.sum((y_pair - y_bar) ** 2))\n", - "print(f\"\\nthe data are {y_pair}, mean {y_bar:.3f}\")\n", - "print(f\"D'Agostini's closed form for the data-built mode: {closed:.4f}\")" - ] - }, { "cell_type": "markdown", "id": "7331f252", @@ -969,14 +716,7 @@ "cell_type": "code", "execution_count": 18, "id": "1c04818c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:02.046098Z", - "iopub.status.busy": "2026-09-14T19:54:02.045860Z", - "iopub.status.idle": "2026-09-14T19:54:02.051320Z", - "shell.execute_reply": "2026-09-14T19:54:02.050611Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "def correlation(S):\n", @@ -994,7 +734,7 @@ " return im\n", "\n", "\n", - "first = comps[\"exp 0\"]\n", + "first = comps[\"exp P\"]\n", "theta_map = np.median(samples[\"quoted systematics\"], axis=0)[: len(cubic.params)]" ] }, @@ -1014,18 +754,11 @@ "cell_type": "code", "execution_count": 19, "id": "7348683c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:02.054344Z", - "iopub.status.busy": "2026-09-14T19:54:02.054094Z", - "iopub.status.idle": "2026-09-14T19:54:02.347440Z", - "shell.execute_reply": "2026-09-14T19:54:02.346358Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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5ujwBiI6Oxo4dO9CiRQsAZQmvn5+fw4VxGzVqhO+//x6vvfYaHnvsMeTn56NZs2YYN24cnnnmGWi1Wpefs1z59arsmgohkJuba/m3qOy6VbdQeEX/To6+Z0f6dubMGVy6dAn9+vWrMJGtiDuuVfmDU5MmTcLkyZNt2oeFhVl971HNExgYiCtXriA7O9tmX/kfGNu2bavwj76EhASbB7Aq+v4q98+SX97e3gBgeXCt/HybN2/G7t27KzzfP/8Yd0ZAQAAyMjKwcOFCpKWl4dNPP0VBQQEGDhyIpUuXIiwsrNI+63Q6aDQam+0ajQY6nc7yXsr/azAYbM5fvu3qtjLtZNi7xo6qqEybt7e3dLzS0lJs3boVQNkf3rIJDLmPv7+/w/WcV61ahfr161sNSIwbNw59+vTB4cOH0axZM6k2alKjywaFhIRAr9dXWIj35MmTAGw/jB15ylfWzz//jFOnTmHRokWWJAcoW2nBWZ07d8ZXX30Fk8mEjIwMLF68GElJSdDpdHj66afdck7g7+tV2TXV6XSWJ8TdqaJ/J3e9ZwC4+eabkZSUhCFDhiAhIQFr1qyp8BfS1dxxrcqfil26dCkGDx7s0LFUMyxbtgwvvPAChg8fjoULF+LBBx+07LvhhhsAAMOHD6/wD4aKKIridF/Kzzdu3DiMGTPG6TgyQkJCkJSUhKSkJJSWluLzzz/H6NGjMXPmTLz11lvVjl8+MljRz3v5tvKn6Bs1aiTVrjrX1hNMJhNGjhyJn376CSkpKXjxxRfx0UcfYezYsZ7uWo2Wk5PjcIF6s9ls8+R9YGCg1dP11XHgwAE0a9bM6nvwxhtvtNon00ZNanRhcy8vL/To0QOpqalWtxYBYPny5fD19UX37t3d3o/yv1jNZrPV9iVLljgVTwhh+X+tVov27dvj/fffR0BAAHbs2OHwOQMCAqT/+u3atSv8/PywYsUKq+35+fn4+uuv0aNHD7tlfypSUlKC8+fPV2sZMldf53/q27cvNm3ahF27dqFPnz64ePFile3dca169OiB0NDQSgstX/29QTWTVqvFkiVL8NRTT+Ghhx7CG2+8YdnXokULdO7cGfPnz69wpPn06dMu7Uvnzp3RokULzJs3DyUlJQ6f78yZM0hNTbU7zeL8+fMwmUyWr/V6PUaNGoWQkBCXrbxTv359dOvWDcuWLbMa1S8oKMCSJUvQunVrS1mgoUOHYv/+/fjhhx+sYrzzzjvQ6/UYNGgQgLJ/q6CgIFWM+pvNZvzf//2fZerCjBkz0LVrV0ybNs3p1Xxqg5ycHISHhCE6OtqhV4sWLWy2zZ0712X9unLlik2Jr/LR6/J/T5k2alKjRygBYM6cOejatSsGDhyIWbNmISgoCEuXLsXKlSsxd+7cazKa1r59ezRu3BjTpk1D3bp1ERISgs8++8wmyZW1aNEifPfddxg9erRlJG758uW4fPkyEhMTHT7nLbfcgjfffBM///wzbrzxRmi12kqvS0hICF5++WU8/fTTePbZZ/HAAw8gLy8PSUlJKCoqwpw5c5x6T6tWrcK9996L+fPnY8KECU7FcPV1rsitt96Kn376Cf369UOPHj2wcePGSqdJuONaGQwGLF68GMOHD8f999+PRx99FFFRUTh58iR++uknbNiwAd9//3113ya5maIoeOONNxASEoLJkyfj4sWLeOWVVwAAn332Gfr164e4uDg88sgjaNKkCU6fPo1ffvkFu3fvxm+//eayfmi1WqxcuRLx8fFo3bo1xo0bh+joaJw8eRJbtmzB2bNnsWXLlkqP37ZtG4YMGYI333wTTz31VKXtfv75Z0yePBnDhg1Dy5YtoSgKVq1ahfz8fDz66KMuez8LFixAr1690KVLFzz22GPQarV47733kJ+fj7Vr11raPf3000hLS8Odd96JpKQkNG7cGF999RVWrlyJ9957Dw0bNrS0vf3227Fy5Uq0bdsWderUqbAOpacJIfDwww9j1apVWLdunWUe6b/+9S/ceuutmDVrFl5++WUP97JmunLlCophxt0Ihz9sa0hXJB8mfFl4Gunp6Vaf/64anQTKbpNnZWVZbStfIrl82otMGzXxyAiln58fwsLCbG5H6PV6hIWFWRWObtOmDdLT09GgQQOMHDkSffr0wc6dO7F69WpMmjTJbkygLOOvqBhwVduvngfj5+eHjRs34sYbb8SIESMwZMgQCCHw3nvvISwszOofvqKlDv+57cEHH8TQoUOxYMEC9OvXD/Hx8fjll1+wbt063HvvvQ6f89lnn8XQoUNx//33o2XLllaT2ivqz6RJk7Bq1SrLSN2IESMQGRmJ7du3o23btnavj7+/v02hYW9vb4SFhUktQ1ZZXEfesyN9+2fbm2++Gf/9738BlD1gUdUojjuu1ZAhQ7Bjxw5otVqMGTMGnTt3xjPPPIPc3FwsW7as0r6QZ91www1ISEiwGpV+/vnnsXDhQmRmZuKrr74CUHY7NjMzEykpKTh48CCWL1+OI0eOYNCgQcjMzLQcGxUVhYSEhAp/iVV0LqDs5zkhIQENGjSwbLvpppuwf/9+TJo0CXv27MGKFStw4sQJjB49Gj/++KOlXXR0tM0czvDwcCQkJFhunVdm0KBB+P777xEQEGB50Kh169Y4cOAAevbsaWmXkJBgebjyagkJCWjZsqXN9gEDBuCmm26yfB0bG4t9+/bh/vvvx+bNm/Htt9/innvuwW+//YY2bdpY2vn4+OC7777Dv//9bxw6dAhffvklGjRogO3bt9s80LBo0SKMGzcOqampWLBgAb799lsAZfMTK5pjGhISgoSEBJui7f9U0dKL3bp1q3DOY8uWLStdHhcAvvrqK5w+fRqrV69Gr169LNs7deqEqVOn4uDBgygsLKyyP7VdoKJDkEYv9QpUysbSIiIiEBUVZXm5MqGMiYnBkSNHrO46lT8gFxMTI91GTRTBe2xERESkQllZWYiOjsaDmigEKHI3XS8LI5aYs3DixAmr6i8yxowZg6ysLMsqTOVyc3PxzjvvYNiwYbjxxhuxZ88etGnTBt98843lgZ77778fGRkZlkoPMm3UpMbf8iYiIiKqilZRoJV8CEsLxx/Wevvtt5GXl4c9e/YgLy8PL7/8MjQaDaZOnQqgbBnkF154ATfffDNuvPFGxMXFYcqUKRg+fDjuu+8+SxK6YcMGS0yZNmrChJKIiIhUTauUvaTaOhG/uLgYRUVFGDBgAICyslJa7d+RgoODMW3aNKtb1a+//joSEhKwZcsWNG/eHO+8845NrWKZNmrBW95ERESkSuW3vCfqG1rmRtqTJ4z4d+lxp255U+U4QklERESq5u4RSrKPCSURERGpmrvnUJJ9TCiJiIhI1RTI10FkOukeTCiJiIhI1ThC6XnShc2NRiOysrJgNBrd2R8iquX4WUNEjtLi73mUdl+e7ux1SnqE8vTp04iOjsYoNIChisO6hdlfKYXoeiL7V/G1NuLcfk93wSmynzXTZ8a77Jx6f/trsvuFh8kF09j/daUxBMvFkqBo5X49asOqXvkFADSB9t+j7PmEzluijf3rLkvI/hwqLlogTuu6G3xCIxlLpp3E+xMS36OyvIPruixWdWgU+c9iTc38yFY93vImIiIiVXPoKW8mlG7BhJKIiIhUzaE5lDX0rpLaMaEkIiIiVdM4MELJW97uwYSSiIiIVI0jlJ7notnJRERERFRbcYSSiIiIVI0P5XgeE0oiIiJSNc6h9DwmlERERKRqnEPpeUwoiYiISNXKV8qRbUuu53BC2THYB2FafaX7t1wolIrDFXWopuNfsZ711OReiAwyVLr/pRe+kYojs6KO2WS228ZUVCx1Po3e/seqKCmSigWNxKonkitUakpL7MeS6JeQ6BMAKDKrsZhdt7ymIrsCjiJccj7pKBLXS5G8DlLnlFlNR1x/n20aB0YoNfxsdwuOUBIREZGq8aEcz2NCSURERKrGOZSex4SSiIiIVI1PeXseE0oiIiJSNY5Qeh4TSiIiIlI1jaJIP2xTnYdy8vLy4O3tDW9v7yrb5efn4/LlyxXuq1evHjQaDcxmM86ePWuzPyQkxG78mogJJREREamaolGgSN7zVpx4yn3Xrl0YO3YsDh48CLPZjHvuuQeLFi2Cv79/he2XLl2Kl19+2WpbXl4eSktLceHCBQQGBuLUqVOIjo5GnTp1oNX+XRXhww8/RP/+/R3uo6dxLW8iIiJSNY0W0GgVyZdjsfPy8jBgwAB06dIFubm5OHLkCNLT0/H4449Xesxjjz2G06dPW71iYmKQkJCAwMBAq7ZbtmyxaqfGZBJgQklERERqp9VAkXxB61jq88UXX+DSpUt4/fXX4e3tjejoaCQnJ2PZsmXIycmRirFr1y7s3r0b48aNs9lnNBqRl5fnUJ9qIpff8pYtWC5TAJ3Fz8kZnHBdO8gULAfkCqCnvJJgt42pRK74tEyRdL3edYXNZYmiAvttvCU+c2ULm+u87LeRKcItScgWNpchEUv2U0YI+98P0MpdB5kC6DLFz6/HT0iHbnmbHbsC6enpiIuLsxpZ7N69O0pLS7F792707NnTbowlS5YgKiqqwtHHdu3aQavVIiAgAE888QSSkpKg06lvRqL6ekxERER0lfLb2VJt/0oos7OzrbYHBgba3I4GgHPnzqFOnTpW2+rWrQsAFT5U809FRUX49NNP8cQTT1jNldTpdJg1axYeeeQRBAQEIC0tDcOGDYPJZML06dOl3ktNwlveREREpGqKRuPQCwA6duyI6Ohoy2vu3LkVx1YUGI3Wo8PlX1+dIFbmyy+/RF5eHh544AGr7eHh4UhKSkJgYCAURUH//v3x5JNPYv78+c5cAo/jCCURERGpmjMjlOnp6YiIiLBsr2h0EgAaNGiAQ4cOWW07c+YMACAyMtLu+ZYsWYJ+/fqhUaNGdts2a9YMp0+fRlFREXx8fOy2r0k4QklERESqVj6HUur111I5ERERiIqKsrwqSyi7d++O3377zeoWeVpaGgwGA9q2bQsAMJlMOH36NIqLi62OPXLkCH744YcKH8YRwnbG66+//orw8HDVJZMARyiJiIhI5cqSRckHxmTXaPzLoEGD0LJlS4wePRpz5sxBVlYWZs6cicmTJ1sSvxMnTqBJkyZYvXo1Bg8ebDn2gw8+QL169ZCYmGgTd+bMmVAUBfHx8TAYDFizZg0WLlyIt956y6H+1RRMKImIiEjVFAdueTuaUOr1emzcuBFJSUkYMmQIDAYDkpOTMWXKFEsbrVaL+vXrW40sCiGQlpaGCRMmQK/X28SdPHky5s2bh4kTJ+LSpUto3rw5vv76a8THy1WwqGmYUBIREZGqKcrft7Jl2joqIiICH3/8caX7o6Ojcfr0aZvz7Nixo9Jj/Pz8kJycjOTkZIf7UxMxoSQiIiJV02g10Eje8pZtR47xWEIpU7Rcpvi5bCwqw6LfpBZ6P2/o/SufmC5TQByQK1r+4rR1dtvMmGU7B6oiMvO4zKVyRdKlzidbaFxivTlhLLEfR6JgOQCIwny7bTQBIVKxpLjwOkjx8pY7n0zBdckC70Kmndb21qptG8lf/a4sFu9m5Q/cyLYl1+MIJREREamaQyvlSN4aJ8cwoSQiIiJVK6tDKXvLmwmlOzChJCIiInVz4JY3mFC6BRNKIiIiUjWNokAjeStbw2cJ3IIJJREREamaotU4UNhcPQ8bqQkTSiIiIlI1h9by5i1vt2BCSURERKrGp7w9jwklERERqRpveXseE0oiIiJSNUUrfytbcVFte7JWoxNK2RVwZFbUqamr6XDlGqKK+dYNgV9YUKX7TUXFUnFMJfZXpZFZBWfGc19LnW/6zHi7bcwSfXI1mdV5vCVWt1G0cr+NFR9/u200V3KkYkFidRuXrYADSK26o3hVvoqTdSyJvuvlVh+SOadGpl+yfVcRRePAWt685e0WNTqhJCIiIrJHo3FgLW/JZTrJMUwoiYiISNW4lrfnMaEkIiIiVeNDOZ7HhJKIiIhUTVE0UCRvZSsKE0p3YEJJREREqlZW2FxyDiVvebsFE0oiIiJSNwdueYO3vN2CCSURERGpmqJxYA4ln/J2CyaUREREpGqKxoE5lEwo3eK6SChlipbLFD+XjUVE14BGW2VRaI1e7uPLbDLbbSMzsiFTsBwAXnrhG5fFkiH7y1FIXAeZ4ueKRBwA0HqZ7Dcyy8WSaSc0EucDpAqNw2w/lqKTK0Yu9R4li8XL9EuujWRhfRU9vFL2lLdk0X3e8naL6yKhJCIiotrL3WWDjEYjFi1ahC1btsBgMGDUqFHo0aNHpe0vXLiABx980GZ7cnIyOnXq5HTcmowJJREREamaRtFIr4CjcWLkddiwYcjMzMSUKVOQlZWFPn36YNmyZRg+fHiF7QsLC7F27Vq8++67aNCggWV7w4YNqxW3JmNCSURERKrmzhHKH3/8EatXr0ZGRgbatGkDACguLsYzzzyDoUOHVpnI9u7dGy1atHB53JpIXb0lIiIi+ofyhFL25YhvvvkGTZs2tSR9ADB06FCcPHkSe/furfLYlJQUjBw5EikpKTh+/LjL4tZETCiJiIhI3RTF8qS3vReUssLm2dnZyMrKsrzy8vIqDH306FFER0dbbSv/+ujRo5V2KTY2Fh07dkS/fv2QmZmJli1bYuvWrdWOW1PxljcRERGpmjO3vDt27Gi1PSUlBTNmzLBpX1xcDD8/P6ttBoMBAFBUVFThOerWrYtdu3bBx8cHADBmzBgMGjQITzzxBHbs2OF03JqMCSURERGpmjMJZXp6OiIiIizbAwMDK2wfFBSEQ4cOWW27cOECACAkJKTCY7y9vW22DRo0COPHj0dpaSn0er1TcWsyJpRERESkahqNRn4t778edomIiEBUVJTd9q1bt8batWstiSAA7N69GwDQqlUr6T7m5OSU9fOv87sqbk3BOZRERESkaopGfg6lolEcij106FAUFRVh0aJFAIDS0lK89dZb6N27NyIjIwEAZ8+exeDBg/Hrr78CKHvgJisryxIjKysLb7/9NgYOHAjtXwXYZeKqSa0ZoZRdAUdmRZ2edfzstiGi6lH8A6ExBFe6X5TIzTHS6+23k1khxlwit7qIzCo4MqvpyMYSkqvNGItK7LZx5Qoieol+aWVWdZGkyKyAA8itlCPDWOqaOACUEh+5dl722yneEm18/KXOpybuLBsUHR2NxYsXY8KECfj4449x5swZ6PV6pKWlWdoUFBRg7dq1GDNmDADA19cXd9xxB3x9fWEwGLBjxw707t0bCxYscCiumtSahJKIiIiuT+5eKee+++5DfHw8duzYAYPBgE6dOkGn+zuFqlevHlavXm1ZBadnz57IzMzE3r17cenSJTRv3tzmiW6ZuGqizl4TERER/UVRNNLr2itOrlEeFhaGO+64o8J9fn5+GDx4sNU2nU6Htm3bViuumjChJCIiIlVTtBpotHLTGVw5tYP+xoSSiIiIVM3dt7zJPiaUREREpGqKxoGEUmVrZKsFE0oiIiJSNcuyipJtyfWYUBIREZGq8Za35zGhJCIiIlVTNIoDt7wdK2xOcphQ/oNM0fIfzhe4JA4RVYPsbasaeHtLpmA5IFcAXTaWDGGyX4xc9aM7MsXUJYqfC5NcUXZF4sljIVng3WVpkGxBeVcVgb8GeMvb85hQEhERkaopGq30aknSqyqRQ5hQEhERkbppNPIjqhyhdAsmlERERKRuGo2qp8FcD5hQEhERkbpptFLzVcvbkusxoSQiIiJ102gduOXNhNIdmFASERGRunEOpccxoSQiIiJVY9kgz2NCSUREROqmOHDLW+Etb3dgQukEVxU/l41FVBspdibZC6Mrz+W6EQuZWMJsv4A4IFe0XKb4OQCkvJIg1c5VzBJF0rWS10GGkGwnVYNQtvC3BJkC6IpOLxdLpl8y55M6G1x6HdyOt7w9jgklERERqRpveXseE0oiIiJSNz7l7XFMKImIiEjdeMvb45hQEhERkarZm3P9z7YEmM1mXL58GUJYz0AOCgqCokjPtLVgQklERETqdg2WXszJycGuXbtgMBjQrl07aCUS2D/++APHjh1D48aN0aRJE6t9RUVF2LRpk80xHTp0QP369Z3qo4wzZ87goYcewrfffovi4mKb/dnZ2QgPD3c4LhNKIiIiUjc3z6Fcvnw5xo0bh5iYGJw9exb+/v5IS0tDw4YNK2y/bds2PPnkk7hw4QIaNmyIjIwM3HbbbVixYgUMBgMA4Pz580hMTETPnj3h7+9vObZOnTpuTSinTJmCP//8EwsWLKjwPKGhoU7FZUJJREREqlb2lLfsLW/HRihPnjyJsWPHYvbs2Xj88cdRWlqK3r1746GHHsLGjRsrPObcuXOYP38+2rVrZ/m6ffv2mDZtGubNm2fVdv78+WjRooVDfaqObdu24dNPP0WHDh1cGpcJJREREamb4sAtb8WxhPLzzz+Ht7c3xo8fDwDQ6/WYNGkS7r77bpw6dQqRkZE2x9x5551WX9etWxf9+vXD9u3bbdru3bsXp06dQvPmzREdHe1Q35wRHByMgIAAl8flo05ERESkaopG69ALKJsrmJWVZXnl5eVVGDszMxOxsbHw8vKybGvTpg2EENi7d69U/0wmE7Zt21bhSOT06dMxffp0xMTEYMiQIcjNzXXiCsiLj4/Hf/7zH5fHvS5GKLVOPI3kbrIr4MisqMPVdKg20obUhTasTqX7NaUlUnFEkf2fMZlbZeZSuaV5hMQKMcYiub7LkF0B58Vp61wWS4ZG4j2aJK+Dor22KxnJ0PlcdkkcAND4+Ei1U7zst5NpI3z97baRVWOSCCfKBnXs2NFqc0pKCmbMmGHTPCcnx2ZeYVhYmGWfjOnTp+PPP//EqlWrLNv8/PywadMm9O7dGwBw/PhxdO3aFZMmTcIHH3wg916cUFpaitdffx2//vorWrVqZfNwUUpKilMjmDXme4GIiIjIKU485Z2eno6IiAjL5sDAwAqbe3l52YwaFhQUWPbZM2/ePLzxxhtYs2YNbrzxRsv20NBQSzIJAA0bNsSTTz6Jl19+2a0J5fbt2xEXF4fz589j8+bNNvuTk5OdisuEkoiIiGqdiIgIREVF2W3XpEkTZGRkWG07ceKEZV9V3n33XSQlJeHLL79E//797Z4rJCQEOTk5KCoqgo/kyLWjKipV5AqcQ0lERESqVl7YXOrlYNmg/v374/Dhw1bzJVeuXInIyEjExcUBAAoLC5GamoozZ85Y2vz73//G5MmTsXLlSiQk2E4nuXDhgs22r7/+GrGxsW5LJv8pOzsbx44dg9lsf6qOPRyhJCIiInVzYx3KXr16ITExEXfddReSkpKQlZWFuXPn4qOPPoLmr9vnZ86cQWJiIlavXo3Bgwfjk08+weOPP44JEyYAAFJTUwGUzZu8/fbbAQCLFy/G1q1bER8fD4PBgDVr1iAtLc1qnqW7LF26FMnJyZYE2N/fH5MnT8a0adOg1+udismEkoiIiNTNzWt5r1y5Eu+//z7S0tJgMBiwYcMGq/mPfn5+SEhIsKwwk5ubiwEDBuD48eN4//33Le3q169vSSiTkpLw7bffYs2aNbh06RJuuukmvPnmm24vHfTJJ59g/PjxeOihh3D77bfD29sb27dvx1tvvYX8/HzMmTPHqbhMKImIiEjVygqbyyWKzjzp7+XlhSeeeAJPPPFEhfvr1atnGYUEgEcffRSPPvqo3bh9+/ZF3759He5PdcyfPx9z5szBk08+admWmJiI3r17484778Trr79uGXl1BOdQEhERkbop2r9ve9t7KY4vvXg9OXbsGAYMGGCzvUePHjAajdKlkP6JCSURERGpm6KUrYAj9ap5tauvpXr16mHXrl022w8cOACz2Vxp+SR7eMvbw2SKlssUP5eNRaQWmoAQaALDKt0vSoqk4ghvX/ttjPYLbHsX5kudT6YAumyhbpki6bJkipbLFD+fPjNe6nwyRctd+f5cWfxcIxFLttC91PkkC7xrvez/LtD6eNttI/uzoyrlyaJs21ps5MiRePTRR3Hp0iX06tULXl5e2L59O6ZOnYp77rkHOp1zqSETSiIiIlI1oWggJBNF2XbXq0mTJuHMmTN44oknUFpaatk+bNgwvPvuu07HZUJJRERE6sYRSmkajQazZ8/G5MmTsW/fPpSUlCA2NhaNGjWqVlwmlERERKRuiiI/N7KWz6EsV69ePUsJI1dgQklERETqplEcWMu79iWUSUlJyM3NxezZs/HKK6/YrE1+tdmzZzv1YA4TSiIiIlI1zqGs2oEDB3DhwgUYjUbL/1fGaHTugTMmlERERKRyDsyhrIUVE9euXVvh/7tS7buqREREdH2RrkHpSOJ5fVq7di2KiiouHVXVPntq91UlIiIi9WNCKW38+PGVroZT1T57eMubiIiIVE0oigNzKGvfQzkyTCYTrly5goCAAKeOZ0KpArIr4MisqMPVdEgtFI0WirbyNXeF9BOd9tspOi/7baroi1W7Grr6iwyZVXBeeuEbl8VyJdlVd2SuqVkilqJx3b+z0MrFkumXxmSy20Yx22+jOqxDadfUqVORl5eHy5cvY+rUqfDz+zsfMJvNOHDgACIjI+Hv7+9UfCaUREREpG6sQ2lXZmYmLly4gNLSUmRmZkKv11v26XQ6NGzYEHPnznU6PhNKIiIiUjeOUNq1bt06AMCgQYOwdOlShIaGujQ+E0oiIiJSNdahlOeuskFMKImIiEjdFAdWyqmlt7yvdvjwYSxbtgx//PEHSkpKrPYtWLAAQUFBDsdkQklERETqxlve0n799Vf07NkTbdu2xa5du9ClSxf873//w8mTJ9GtWzcIIZyKW7uvKhEREakf61BKe/XVV/Hcc89h69atCA4OxqeffoqjR4/i4YcfRnh4OIKDg52KyxFKIiIiUjeOUEr7/fffMXv2bACAVqtFcXEx9Ho95s6di3r16sFoNEKnczw9rN1XlYiIiFSvvLC53Kt2z6HMy8uzzJGsU6cOTp06BQDw9/eHoii4fPmyU3Fr9Ailtpb/oztKpmi5TPFz2VhE7iR0XhA670r3KxrJQuMSRctFYb79OD5yxX61XvaLRuvNriuKLVPsGgA0RSV225gk2sgWLJcpgO7K4ueK5AMZsgXQr1UcANBKfj/InFOmjc6FReBrDDePUJ45cwZTp07Fli1bYDAYcN9992HSpElQqshTZI5xJq4rdejQAW+++Sbq16+PVatWwcfHByEhIU7FqtEJJREREZFdbixsbjQacccddyA0NBSffvopsrKyMGbMGBQUFOD55593+hhn4rpCYmIifHx8AABJSUno1asXmjZtCp1Oh4ULFzodlwklERERqZsbRyi/+uor7NmzB1lZWYiMjET79u3x/PPP46WXXsLkyZMtyZmjxzgT1xUWLVpk+f/mzZvj0KFD2LdvHxo0aIDIyEin46poPJuIiIjIlvz8SfkC6OV+/PFH3HTTTVbJVv/+/XH58mXs3r3b6WOciesOvr6+6NChQ7WSSYAjlERERKR6igMjj2W3vLOzs622BgYGIjAw0KZ1VlYWwsPDrbbVr18fAHDy5MkKzyBzjDNxnZWUlITc3FyptrNnz67wOtjDhJKIiIhUrewpb7m5keXtOnbsaLU9JSUFM2bMsGlvNpttyujo9XoAgMlU8UN4Msc4E9dZBw4cwIULF6TaGo1Gp87BhJKIiIjUTQDSC7z81S49PR0RERGWzZWNytWtWxcZGRlW286fP2/Z5+wxzsR1lrvW774a51ASERGRqpkhYBaSr78yyoiICERFRVlelSWUHTt2xN69e63qM27ZsgV6vR5t2rRx+hhn4rqS0WjEH3/8gR9++MFmPW9nMKEkIiIiVRMOvhwxdOhQBAYGIjk5GaWlpTh16hRmzZqFkSNHWmo2ZmVlISoqCmlpadLHyLRxl4ULF6Jhw4Zo2rQpevXqhYsXL2Ls2LH4+uuvnY7JhJKIiIhUzSwcezkiKCgIqamp+P777xEUFITGjRujdevWePfddy1tjEYjTp48icLCQuljZNq4w8KFC5GUlISkpCTs2bPHcnt94sSJePXVV52OyzmUtYzsCjgyK+pwNR1yJ6HzhtBVUYfNLDdxXNHY/5jTBNgfDdBcyZE6HyRWPdGaXTfhXnaVFZlVcFy5+ovMKjgyq+nIxhIuXH1IhivPJ3vdNRLtNKX2fy6u9bW6FoQAhOQkSum5llfp2LEj9u/fjwsXLsDX1xd+fta//6Kjo3HixAnUqVNH+hjZNq62YMECLFy4EEOHDgUAaP5aZapNmzbIyMhAYWEhfH19HY7LhJKIiIhUzZGRR0dHKK8WFhZW4XatVouoqCiHjnG0jaucO3cOrVu3ttmu0+mg1WpRXFzsVELJW95ERESkeu6YP3k9atKkCVJTU22279u3DwEBAQgODnYqLkcoiYiISNWu1Qjl9WDcuHF44IEHcOrUKdx9990wm83Ys2cPXnjhBYwfP97puByhJCIiIlUTQjj0qs3uu+8+TJs2De+88w66dOmCc+fO4Y477kBsbCyef/55p+NyhJKIiIhUzfzXS7ZtbZaamornnnsOEydOxPbt21FcXIy4uDjccMMN1YrLhJKIiIhUTTiwUk4tH6DEhAkTsH37dkRERCA+3n4FBVm85U1ERESq5s46lNeb6OhoHD161OVxmVASERGRujkyf7KWD1FOmjQJU6ZMwfHjx10al7e8qUIyRctZ/JxqFY1Wrl0NLRqtaGve+IFMwXJArgC6bCw1kyqAXgP/na8FzqGUN2fOHOzcuRONGjVCvXr1bGpObt++3bJ6jiOYUBIREZGqCTgwh9KtPan5Ro0ahUGDBlW639nVephQEhERkaqZhYBZMqOUbXe9ys7OxrRp0xAYGOjSuLVzbJyIiIiuG7Kr5HC1HGD58uXIzc11eVwmlERERKRqfMpb3s0334zt27e7PC5veRMREZG6OfLwdi1PKMePH48JEyYgNzcX7du3t3kop0mTJtBqJR9CvAoTSiIiIlI1MwTMkpmibLvr1cMPP4wzZ87ggQceqHB/dnY2wsPDHY7LhJKIiIhUjSvlyFu/fj1KSkoq3R8WFuZUXCaUREREpGqOzI2s7XMob7nlFrfEZUJJTnNV8XPZWFS7CCgQilLpfkWRe6ZQyLTT2G+jSBY2FxqTy2JJnc9lkeSKn0sV1wagSFxTIVkEXqZouUzxc9lYamaW+PfRSP4bqglHKB2Tk5ODt956CxkZGSgpKUFsbCwef/xxNGnSxOmYfMqbiIiIVK18DqXsqzY7fPgwYmJiMG/ePOTn50Oj0WDFihVo2bIlvv32W6fjcoSSiIiI1I1PeUtLSUnBLbfcghUrVliKm5eWlmLy5MmYNGkS9u3b51RcJpRERESkalwpR94vv/yCL774wmqlHL1ej9mzZyMgIACXL19GQECAw3GZUBIREZGqmQQgOzXUdI3yya1bt+K///0vDAYDBg8ejMjIyCrb5+bmIi0tDceOHUPjxo2RmJgIHx8fy/68vDzMnTvX5rgRI0agRYsW0v1SKpmbLv5KtCvbbw/nUBIREZGqlY9Qyr7cbdq0aYiPj8cff/yB9evXIyYmBtu2bau0/RdffIHWrVvjyy+/xNmzZzFr1izExMTg2LFjljZ5eXl48cUXcf78+Wr1rUuXLpg6darV8oslJSWYPHkyYmNjYTAYnIrLEUoiIiJSNbMQMNWQW9779u3Da6+9htTUVAwYMAAAMHLkSIwfPx579uyp8JimTZsiMzMTQUFBAACj0YiOHTti2rRpWLZsmVXbxx57zKERyX968cUX0a1bNzRs2BC33HILvLy8sGfPHly+fBlff/2103E5QklERESqVlaHUnaE0r19WbVqFcLDwxEf/3eJqoceegh79+7FoUOHKjzmlltusSSTAKDT6dCuXTscPXrUpu3y5csxe/ZsrF69GsXFxQ73r0mTJvj999/x3HPPoW7duvD19cWYMWNw4MAB9OrVy+F4lj47fSQRERFRDWAyOzCH8q922dnZVtsDAwOtHlRx1sGDB9G0aVOruYjNmze37Cv//6pcvnwZ69atw8iRI622BwQE4MiRIwgLC8PixYvx7LPPIi0tDTfccINDfQwICEBycrJDx9jDhJKIiIhUzZmnvDt27Gi1PSUlBTNmzLBpf/HiRbz99ttVxmzTpg0GDx4MALhy5YrVaCMABAcHW/bZ7Z/ZjNGjR0Ov12Pq1KlWMfbv34+oqCgAwOzZs9G1a1c8+uij2LBhg9245bKyspCSkoLFixdbJb0zZ85Ez5490a1bN+lYV2NCSW4luwKOzIo6XE2nllE0Za9K97vuvpUrV66BTCzZ85ldt+qOzMo1UnEkVtMB5FfUcRXZFXBkVtS53lfTuR45M4cyPT0dERERlu2uGJ0EAIPBgOPHj1tty8nJseyrsm9mMx544AH88ssv+OGHHxAaGmoV9+rjvby8MHbsWDzzzDMwm83QSP6MT506FX379rV5mnvw4MG4//77sXv3bqk4/8SEkoiIiFTNDAfW8v7rvxEREZbRvqqEhoZWOHJZmZiYGGzevBlCCEvSVj53MiYmptLjhBAYN24cNmzYgM2bN1fZ9upjioqKYDKZpBPKrVu3Yvr06TbbW7VqhSNHjiA/Px/+/v5Ssa7Gh3KIiIhI1Uxm4dDLnYYMGYLTp0/jm2/+Hg1fvHgxWrVqZZk/mZOTgxkzZuDAgQMA/k4m169fj82bNyM2NtYmbmZmJkpKSixfl5SU4MMPP0TXrl2h1+ul++fn54c///zTZvu5c+dQWFgInc65sUaOUBIREZGqCQfmUAo3lw1q1aoVkpOTce+99+Lee+9FVlYWfvzxR6SlpVna5OTk4MUXX0SbNm3QokULzJo1C0uWLMHw4cOxYsUKS7vg4GA89dRTAMrKEY0cORKdO3eGwWDA+vXrAQBr1qxxqH99+vRBcnIy4uLiUK9ePQBAQUEBJk6ciO7du8Pb29up982EkoiIiFTNJORXwLkWK+W88sorSEhIwJYtW9CqVSssWLAADRo0sOwPDg5GSkqKpZ5kq1atkJKSUmXMUaNGoVevXkhLS8OlS5fwxhtvoH///g6NTgJlcyg7d+6Mpk2bWupQ7t69GyaTCT/88IPD77UcE0oiIiJStZq4lneXLl3QpUuXCvcFBwdbzcscOHAgBg4caDdmZGQkxo4dW61+1alTB9u3b8f8+fOxdetWGI1GPPjgg5g4cSKio6OdjsuEkoiIiFTNkbmR7p5DqQbBwcGsQ0lERER0tZo0h7K2YkJJREREqmaCA3Mo3dqT2osJJdUIMkXLZYqfy8aiWqSq4uiOkC0MLlGMXP6cEkXLXXg+jUTRcvM1LljuajJFy6918XNhdt01lSk8f62Lzl8LNXEOZW3DhJKIiIhUzWwGzJJzI12Yv9NVmFASERGRqpkdKBvEZ3LcgwklERERqRpveXseE0oiIiJSNZMQMEkmirLtyDFMKImIiEjVzGbhwBxKJpTuwISSiIiIVM0MB+ZQurUntRcTSiIiIlI1zqH0PBcVaCMiIiKi2oojlERERKRqfCjH8zyWUGoVxVOnJpWSXQFHZkUdrqajAlotoK38I0r2V4LUJ42Xt/04Xj5y59N52W9kLJWKJUyuWwVH53PZbhtzqdFuG0UjNwNNZjUWV64Q40quWk1HNpYsmetlLrH/b3g9MpsFTHwox6M4QklERESqZnIgoZRtR45hQklERESqxoTS85hQEhERkaqZhXyiyHzSPZhQEhERkapxhNLzmFASERGRqjGh9DwmlERERKRqfMrb85hQEhERkaqZhAMjlNewDmVeXh68vb3h7V11aTKz2YyzZ8/abA8JCanwWNm41xJXyiEiIiJVK7/lLftyt127dqF169aoV68eAgICMHLkSOTn51fa/tSpU4iIiECrVq3Qpk0by2vz5s3VinstcYSSrjsyRctZ/Pw6oJH7e1gI+8WgFUUilkYrdT64sFi3orV/TlcWPyd5sgXLZQqgu7L4eW1Vk2555+XlYcCAARgyZAjS09Nx9uxZ9OrVC48//jg++OCDKo/dsmULWrRo4fK41wJHKImIiEjVjGbh0MudvvjiC1y6dAmvv/46vL29ER0djeTkZCxbtgw5OTlVvw+jEXl5eS6Pey0woSQiIiJVq0m3vNPT0xEXF4fAwEDLtu7du6O0tBS7d++u8th27dohPDwc9evXxyuvvAKj8e+lNKsT91rgLW8iIiJSNWcKm2dnZ1ttDwwMtErWyplMJpw7d67KmL6+vggKCgIAnDt3DnXq1LHaX7duXQCo8MEbANDpdJg1axYeeeQRBAQEIC0tDcOGDYPJZML06dOdjnstMaEkIiIiVTMJIf30dnm7jh07Wm1PSUnBjBkzbNqfOHECt956a5Uxhw8fjnnz5gEAFEWxGlkEYPlaW8m86PDwcCQlJVm+7t+/P5588knMnz/fklA6E/daYkJJREREquZMYfP09HRERERYtlc0OgkAjRs3xunTp6X70qBBAxw6dMhq25kzZwAAkZGR0nGaNWuG06dPo6ioCD4+Pi6L6y6cQ0lERESqZnZg/mT5U94RERGIioqyvCpLKB3VvXt3/Pbbb1a31NPS0mAwGNC2bVsAZbfRT58+jeLiYgCAqGB09ddff0V4eDh8fHyk43oSRyiJiIhI1WrS0ouDBg1Cy5YtMXr0aMyZMwdZWVmYOXMmJk+ebEkOT5w4gSZNmmD16tUYPHgwZs6cCUVREB8fD4PBgDVr1mDhwoV46623HIrrSUwoiYiISNVMwgyTZA1Yk0Rt2urQ6/XYuHEjkpKSMGTIEBgMBiQnJ2PKlCmWNlqtFvXr17ckgpMnT8a8efMwceJEXLp0Cc2bN8fXX3+N+Ph4h+J6EhNKIiIiUrWaVNgcKLud/vHHH1e6Pzo62mpepp+fH5KTk5GcnFytuJ7k8oRSqyiuDknkcq5aTUc2FjlOaHQQmso/ohSzsdJ9VrQSH3NVnMdyPr2X5PnsP22plMjdnhJm+6vgKDq9VCyNxC0xTVGJ/T5p5UZ3tBKjRcLk3pEiZwkXrnYkswqOzGo6srFk+l5Tr3t11KRb3rUVRyiJiIhI1YxmSK+AY7z+8ukagQklERERqVpNu+VdGzGhJCIiIlUzCQdueUsWQCfHMKEkIiIiVeMcSs9jQklERESqxlvenseEkoiIiFSNI5Sex4SSiIiIVE0IASGZKFa0zCFVHxNKIiIiUjXzVWt0y7Ql13M4odQqCouXU60gW7BcpgA6i587QaOtsuC47K8EmQLoVRVQt8TxklwrV6YYuWQsmU9ameLnsufUetn/XjZLFsWWKZ6tcWGsa+1aFz8H5AqgSxU/r4HXs9qEkB955AilW3CEkoiIiFRNmCF/y/s6zKdrAiaUREREpGq85e15TCiJiIhI1YSQH3nkHW/3YEJJREREqiYcmEPJp7zdgwklERERqRpveXseE0oiIiJSNWF2oA4lE0q3YEJJRERE6uZAQgkmlG7BhJKIiIhUzSwEzJJzI2XbkWOYUBJVk0zRcpni57Kxag8NoGiq2C338SX1q0Ort98bVxY295aMJcPkwsLmPt5222gkzydV2LzUftF5AIC2iu+Dv8gWXJehyJyvRLLvEmSLpMsULXdV8XO14dKLnseEkoiIiFSNcyg9jwklERERqZrZLP/0tgtXzaSrMKEkIiIiVatpdShLS0vx/vvvY8uWLTAYDLjvvvtw++23V9p+5cqV+PDDDyvct2zZMgQHB+PChQv4v//7P5v9zz//PG699VZXdd1pTCiJiIhI1crW8pZv62733HMP9u/fj6SkJGRlZeGOO+7ARx99hJEjR1bYvm3btvDxsZ7nPGPGDFy5cgXBwcEAgMLCQqxbtw7z589HVFSUpV2TJk3c9j4cwYSSiIiIVE0I+cLm7h6h3Lx5M7766ivs2bMHrVq1AlCWDE6ZMgUjRoyARmP70FfTpk3RtGlTy9cXL17Evn378Morr9i07dmzJ1q0aOG+N+Ak+4+yEREREdVg5Q/lyL7cacOGDWjWrJklmQTKRixPnTqFPXv2SMVYtmwZhBAYPXq0zb7nn38ew4YNw7Rp0/Dnn3+6qtvVxoSSiIiIVM2ZhDI7OxtZWVmWV15enkv6cvToUatb0gAQHR1t2SdjyZIlGDx4MOrWrWu1/eabb0a3bt1w55134uDBg2jZsiW2bNnikn5XF295ExERkao5U9i8Y8eOVttTUlIwY8YMm/bZ2dkYN25clTH79OmDp556CgBQUlICPz/rmsLlX5eUlNjt3/bt27Fnzx688cYbVtvr1q2LHTt2wNu7rF7sfffdhyFDhuDJJ5/Erl277MZ1NyaUREREpGrO1KFMT09HRESEZXtgYGCF7YOCgjBhwoQqY5aPQAJAcHAwDh48aLX/woULln32LFmyBE2aNEHv3r2ttpcnkldLTEzE+PHjUVpaCr3e/gIN7sSEkugakF0BR2ZFndqymo7QaCE02ioaKFJxpFppJT4KpVfKsb+CiuLjLxlLYtUduUgQvvbPKUqK7J9Pok8AoJNYuUZ2hRgZGheulCOzyo8ryZ5Ppp2rVtORjVVTOLNSTkREhM2t6Yr4+flh4MCB0n1p3bo11qxZY5Xk7d69G4qiWM2rrEhBQQE+++wzJCUlQVHs/3RfunQJGo0GWm0Vn5XXCOdQEhERkaoJc9lT3jIvdz+UM2zYMBQXF+P9998HUFaT8s0330SfPn0QGRkJADh79iwGDhyIX375xerYlStXoqCgAGPHjrWJu27dOhw/ftzy9fHjx/H2229j0KBBFT45fq1xhJKIiIhUTQj5ckDurmveoEEDfPDBB3j44Yfx0Ucf4ezZs/D398eGDRssbQoKCrBu3To89NBDVscuWbIECQkJVrfiywUEBGDAgAHQ6XQwGAzIyMhA//79MX/+fPe+IUlMKImIiEjVatpa3vfeey/69++PXbt2wWAwoH379la3pevXr4+vv/4aHTp0+LtfQuDZZ5/FTTfdVGHM7t27IzMzE/v378elS5fQrFkzy4hnTcCEkoiIiFSt/Ha2bNtrISQkxObBmnK+vr428zIVRUFCQkKVMbVard15mJ7ChJKIiIhUTQgThOQDY0LItSPHMKEkIiIiVRNmBxJKyXbkGCaUREREpGrCbHYgoby2ZaFqCyaUREREpGrCZIIwSSaUku3IMUwoiWoQmaLlMsXPAWBEdTtTmygurOHmylhVFXYvV0Nv3ylaz9fFo4rJFiyXKYD+/vPV7Y2LODCHEpxD6RZMKImIiEjVeMvb85hQEhERkarxoRzPY0JJREREqsaE0vOYUBIREZGq8Za35zGhJCIiIlUzCxMUyYTSzIdy3IIJJREREakab3l7HhNKIiIiUjXe8vY8JpRERESkbg4UNgcLm7uFdEJpNBoBAJfMRrd1hojsuwK5n8GsrCyEh4dDp1PX343lnzXZp89U3VDIjTIoEvOlFGOx3Taawlyp88n0SxRckYsldT65X46mSxfttjHn5bjsfOYi+wX4S64UScWSYnLdqJPMCJap1HW/C4VZyLWTeI+uHH2T+aypKZ8zwoHC5oJzKN1C+jvg3LlzAIAXcv90V1+IyIU+iY7GiRMnEBUV5emuOKT8s6Zbnzs83BMisqemfM7wlrfnSSeUrVq1Qnp6OurWrevxv0SISE54eLinu+AwftYQqUtN+JzhQzmeJ/1p7ePjgw4dOrizL0RE/KwhIoeVjVDKjTxyhNI9+Oc/ERERqZoQDtzylpx/TY5hQklERESqxlvenseEkoiIiFTNbDYBsivlMKF0CyaUREREpG4mswN1KK/NLe/i4mL89NNPCAwMRKdOnaSOycnJwa5du2AwGNCuXTtotVqn2ngCE0oiIiJSNSHkRyjdXYeyuLgY06dPxyeffIKSkhLExcVh06ZNdo9bvnw5xo0bh5iYGJw9exb+/v5IS0tDw4YNHWrjKRpPd4CIiIioOsrnUMq+3KmwsBChoaHIyMjAgAEDpI45efIkxo4di1dffRU7duzAkSNHULduXTz00EMOtfEkJpRERESkauWFzeVe7r3lHRwcjKSkJNStW1f6mM8//xze3t4YP348AECv12PSpEnYtGkTTp06Jd3Gk3jLm4iIiFTNXHIZiuwtb2PZsqDZ2dlW2wMDAxEYGOjyvsnIzMxEbGwsvLy8LNvatGkDIQT27t2LyMhIqTaexISSiIiIVMlgMCA0NBQX/5fq0HG+vr7o2LGj1baUlBTMmDHDpm1BQQG+//77KuNFR0ejdevWDvXhajk5OQgNDbXaFhYWZtkn28aTmFASERGRKgUHB+PIkSO4cuWKQ8eZzWZoNNaz/iobnczNzcX7779fZbw+ffpUK6H08vJCbm6u1baCggLLPtk2nsSEkoiIiFQrODgYwcHBbosfERGB1FTHRkAd1aRJE2RkZFhtO3HihGWfbBtP4kM5RERERNdQYWEhUlNTcebMGQBA//79cfjwYezdu9fSZuXKlYiMjERcXJx0G0/iCCURERGRC3333XcoLCxEVlYWzp8/j9TUVGg0GksZoTNnziAxMRGrV6/G4MGD0atXLyQmJuKuu+5CUlISsrKyMHfuXHz00UeWW/MybTxJEUIIT3eCiIiI6HoxZswYnD9/3mqbVqvF2rVrAQBnz57FAw88gOeffx633norAKCkpATvv/8+tmzZAoPBgPvuuw+9e/e2iiHTxlOYUBIRERFRtXh+jJSIiIiIVI0JJRERERFVCxNKIiIiIqoWJpREREREVC1MKImIiIioWphQEhEREVG1MKEkIiIiomphQklERERE1cKEkoiIiIiqhQklEREREVULE0oiIiIiqhYmlERERERULUwoiYiIiKha/h9ppfgl00g0SwAAAABJRU5ErkJggg==", "text/plain": [ "<Figure size 836x396 with 3 Axes>" ] @@ -1068,20 +801,13 @@ "cell_type": "code", "execution_count": 20, "id": "1bedc753", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:02.350463Z", - "iopub.status.busy": "2026-09-14T19:54:02.349977Z", - "iopub.status.idle": "2026-09-14T19:54:44.924138Z", - "shell.execute_reply": "2026-09-14T19:54:44.923001Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "diagonal only: m = 0.829 +/- 0.027 full matrix: m = 0.834 +/- 0.050\n" + "diagonal only: m = 0.779 +/- 0.029 full matrix: m = 0.796 +/- 0.050\n" ] } ], @@ -1110,18 +836,11 @@ "cell_type": "code", "execution_count": 21, "id": "cc9ef059", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:44.926615Z", - "iopub.status.busy": "2026-09-14T19:54:44.926373Z", - "iopub.status.idle": "2026-09-14T19:54:45.191903Z", - "shell.execute_reply": "2026-09-14T19:54:45.191010Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "<Figure size 605x605 with 4 Axes>" ] @@ -1165,7 +884,7 @@ "source": [ "### 3. Unknown or misreported magnitudes\n", "\n", - "A `model_error` term scales with the prediction, and its fraction sets how far\n", + "A `proportional_error` term scales with the prediction, and its fraction sets how far\n", "the total error exceeds what was reported." ] }, @@ -1173,18 +892,11 @@ "cell_type": "code", "execution_count": 22, "id": "c32c89dd", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:45.195180Z", - "iopub.status.busy": "2026-09-14T19:54:45.194898Z", - "iopub.status.idle": "2026-09-14T19:54:45.448361Z", - "shell.execute_reply": "2026-09-14T19:54:45.447124Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 704x440 with 1 Axes>" ] @@ -1197,12 +909,12 @@ "gamma = rx.Parameter(\n", " \"log_gamma\", prior=stats.norm(np.log(0.1), 0.5), latex=r\"\\log\\gamma\"\n", ")\n", - "d0 = datasets[\"exp 0\"]\n", + "d0 = datasets[\"exp P\"]\n", "fig, ax = plt.subplots()\n", "ax.plot(d0.x, d0.y_err, \"--\", color=\"k\", label=\"reported\")\n", "for g, colour in zip((0.02, 0.05, 0.10), plotstyle.COLOURS):\n", " p = rx.Problem(\n", - " [rx.Constraint([first], terms=[T.model_error(gamma, averaging=True)])]\n", + " [rx.Constraint([first], terms=[T.proportional_error(gamma, averaging=True)])]\n", " )\n", " total = np.sqrt(np.diag(p.constraints[0].matrix(np.append(theta_map, np.log(g)))))\n", " ax.plot(d0.x, total, color=colour, label=rf\"$\\gamma$ = {g}\")\n", @@ -1228,18 +940,11 @@ "cell_type": "code", "execution_count": 23, "id": "2ddf9ef6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-14T19:54:45.450980Z", - "iopub.status.busy": "2026-09-14T19:54:45.450734Z", - "iopub.status.idle": "2026-09-14T19:54:45.537904Z", - "shell.execute_reply": "2026-09-14T19:54:45.536840Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 836x396 with 2 Axes>" ] @@ -1249,7 +954,7 @@ } ], "source": [ - "two = [comps[\"exp 1\"], comps[\"exp 2\"]]\n", + "two = [comps[\"exp Q\"], comps[\"exp R\"]]\n", "c_shared = rx.Constraint(two, terms=[T.normalization(magnitude=0.1, on=two)])\n", "c_each = rx.Constraint(two, terms=[T.normalization(magnitude=0.1, on=c) for c in two])\n", "fig, axes = plt.subplots(1, 2, figsize=(7.6, 3.6))\n", @@ -1262,32 +967,10 @@ " ax,\n", " rx.Problem([c]).constraints[0].matrix(theta_map),\n", " title,\n", - " dividers=(datasets[\"exp 1\"].n,),\n", + " dividers=(datasets[\"exp Q\"].n,),\n", " )\n", "plt.show()" ] - }, - { - "cell_type": "markdown", - "id": "9a97711a", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "| case | term | structure of $\\Sigma$ |\n", - "|---|---|---|\n", - "| normalisation inferred | `model \\| tf.scale(rho)` | none: the mean moves |\n", - "| quoted normalisation | `T.normalization(magnitude=)` | rank one, from the prediction |\n", - "| inferred systematic magnitude | `T.normalization(parameter=eta, on=comp)` | rank one, sampled size |\n", - "| smooth systematic in $x$ | `T.kernel(...)` | dense, decaying with distance |\n", - "| reported correlated statistics | `rx.Term(C, kind=\"matrix\")` | whatever was reported |\n", - "| unknown model error | `T.model_error(gamma)` | sampled diagonal |\n", - "| shared across datasets | one term `on=[c1, c2]` | off-diagonal blocks |\n", - "\n", - "Declaring the uncertainty *is* the modelling choice, and the inference machinery\n", - "does not change. Whatever we declare, the mode multiplies the **prediction**,\n", - "never the data (recipe 27)." - ] } ], "metadata": {